%% Genetic Programming Bibliography %%$Revision: 1.8420 $ $Date: 2025/06/29 08:17:24 $ %%Created by W.B.Langdon cs.ucl.ac.nl January 1995 %%Based on J.Koza's GP bibliography of 14 March 1994 %% To add references to your papers see %% ftp://ftp.cs.bham.ac.uk/pub/authors/W.B.Langdon/biblio/ %%optional @Proceedings{toc:2011:cec, key = "CEC 2011", title = "Proceedings of the 2011 IEEE Congress on Evolutionary Computation", year = "2011", month = jun, DOI = "doi:10.1109/CEC.2011.5949582", notes = "Also known as \cite{5949582}", } @Proceedings{cover:2010:MECHATRONIKA, key = "MECHATRONIKA, 2010", title = "13th International Symposium MECHATRONIKA, 2010", year = "2010", month = jun, URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5521207", notes = "Also known as \cite{5521207}", } @Article{tagkey1997126, title = "Genetic programming: Proceedings of the first annual conference 1996 : Edited by John R. Koza, David E. Goldberg, David B. Fogel and Rick L. Riolo. MIT Press, Cambridge, MA. (1996). 568 pages. \$75.00", journal = "Computers \& Mathematics with Applications", volume = "33", number = "5", pages = "126--127", year = "1997", ISSN = "0898-1221", DOI = "doi:10.1016/S0898-1221(97)00025-4", URL = "http://www.sciencedirect.com/science/article/B6TYJ-3SNTGM2-D/2/23afe396341b39baf74fcd29db315b46", key = "tagkey1997126", notes = "No author given. Contents listing of \cite{koza:gp96}", } @Article{tagkey1997129, title = "Advances in genetic programming, volume 2 : Edited by Peter Angeline and Kenneth Kinnear, Jr. MIT Press, Cambridge, MA. (1996). 538 pages. \$50.00", journal = "Computers \& Mathematics with Applications", volume = "33", number = "5", pages = "129", year = "1997", ISSN = "0898-1221", DOI = "doi:10.1016/S0898-1221(97)82933-1", URL = "http://www.sciencedirect.com/science/article/B6TYJ-3SNTGM2-T/2/4d3bcc2dda31e9aca679eba60ff95a3a", key = "tagkey1997129", size = "0.5 pages", notes = "Contents listing of \cite{book:1996:aigp2}. No author given. To get, try other articles on page 129", } @Article{tagkey1999291, title = "Advances in genetic programming, volume III : Edited by Lee Spector, William B. Langdon, Una-May O'Reilly and Peter J. Angeline. MIT Press, Cambridge, MA. (1999). 476 pages. \$55.00", journal = "Computers \& Mathematics with Applications", volume = "38", number = "11-12", pages = "291--291", year = "1999", ISSN = "0898-1221", DOI = "doi:10.1016/S0898-1221(99)91267-1", URL = "http://www.sciencedirect.com/science/article/B6TYJ-48778B1-3H/2/1d6f4728f10e14a24f4f28189d15f818", key = "tagkey1999291", notes = "Contents listing of \cite{spector:1999:aigp3}. No author given.", } @Article{tagkey1999132, title = "Genetic programming and data structures: Genetic programming + data STRUCTURES = automatic programming! : By W. B. Langdon. Kluwer Academic Publishers, Boston, MA. (1998). 278 pages. \$125.00. NLG 285.00, GBP 85.00", journal = "Computers \& Mathematics with Applications", volume = "37", number = "3", pages = "132--132", year = "1999", ISSN = "0898-1221", DOI = "doi:10.1016/S0898-1221(99)90375-9", DOI = "doi:10.1016/S0898-1221(99)90239-0", URL = "http://www.sciencedirect.com/science/article/B6TYJ-489YTT5-2T/2/13179f12104abafe66b36e402ef358d9", key = "tagkey1999132", notes = "Contents listing of \cite{langdon:book}. No author given.", } @Article{tagkey1995115, title = "Genetic programming II: Automatic discovery of reusable programs : By John R. Koza. MIT Press, Cambridge, MA. (1994). 746 pages. \$45.00", journal = "Computers \& Mathematics with Applications", volume = "29", number = "3", pages = "115--115", year = "1995", ISSN = "0898-1221", DOI = "doi:10.1016/0898-1221(95)90099-3", URL = "http://www.sciencedirect.com/science/article/B6TYJ-48F4PJH-H/2/bd467ac24453cb0b3f9dbbf15075bedb", key = "tagkey1995115", notes = "Contents listing of \cite{koza:gp2}. No author given.", } @Article{tagkey1999282, title = "Evolutionary algorithms in engineering and computer science: Recent advances in genetic algorithms, evolution strategies, evolutionary programming, genetic programming and industrial applications : Edited by K. Miettinen, P. Neittaanmaki, M. M. Makela and J. Periaux. John Wiley \& Sons, Ltd., Chichester. (1999). pounds60.00", journal = "Computers \& Mathematics with Applications", volume = "38", number = "11-12", pages = "282--282", year = "1999", ISSN = "0898-1221", DOI = "doi:10.1016/S0898-1221(99)91189-6", URL = "http://www.sciencedirect.com/science/article/B6TYJ-48778B1-24/2/ee28594e33abf3bd7c4a9fc997b98492", key = "tagkey1999282", } @Article{tagkey2002475, title = "Automated generation of robust error recovery logic in assembly systems using genetic programming : Cem M. Baydar, Kazuhiro Saitou, v20, n1, 2001, pp55-68", journal = "Journal of Manufacturing Systems", volume = "21", number = "6", pages = "475--476", year = "2002", ISSN = "0278-6125", DOI = "doi:10.1016/S0278-6125(02)80094-2", URL = "http://www.sciencedirect.com/science/article/B6VJD-4920DSC-1N/2/93bf79c7eb0d6ad94d169ed1b37ec77f", key = "tagkey2002475", notes = "Abstract of \cite{Baydar200155}", } @Misc{2018:MITtechreview, title = "Intelligent Machines Evolutionary algorithm outperforms deep-learning machines at video games", howpublished = "MIT Technolgy Review", key = "MIT Technolgy Review", year = "2018", month = "18 " # jul, keywords = "genetic algorithms, genetic programming", broken = "https://www.technologyreview.com/s/611568", size = "9 pages", abstract = "Neural networks have garnered all the headlines, but a much more powerful approach is waiting in the wings. by Emerging Technology from the arXiv July 18, 2018 Summary of https://arxiv.org/pdf/1806.05695 See instead \cite{Wilson:2018:GECCO}", notes = "No author given. another bit of electonic detritus?", } @Article{Sapienz:2018:sigevolution, title = "Evolutionary Algorithms for Software Testing in {Facebook}", journal = "SIGEVOlution", key = "SIGEVOlution", year = "2018", volume = "11", number = "2", pages = "7", month = "12 " # jul, keywords = "genetic algorithms, genetic programming, SBSE, mobile computing, smart phone", ISSN = "1931-8499", video_url = "https://www.youtube.com/watch?v=j3eV8NiWLg4", URL = "http://www.sigevolution.org/issues/SIGEVOlution1102.pdf", DOI = "doi:10.1145/3264700.3264702", size = "1 page", abstract = "Sapienz is an approach to Android testing that uses multi-objective evolutionary algorithms to automatically explore and optimise test sequences, minimising length, while simultaneously maximising coverage and fault revelation. It is in production now helping to improve the quality of Facebook software!", notes = "Yue Jia, Mark Harman, Ke Mao. Cites ISSTA 2016 \cite{mao:sapienz:16} https://doi.org/10.1145/2931037.2931054 https://github.com/Rhapsod/sapienz Sapienz Prototype (Out-of-date) MaJiCKe Sapienz Multiobjective Automated Android Testing", } @Misc{glasilo_1_16_ang, title = "Store Steel 165 years", howpublished = "Internal information magazine", key = "Store Steel", keywords = "genetic algorithms, genetic programming", URL = "http://www.store-steel.si/Data/InterniInformativniCasopis/glasilo_1_16_ang.pdf", size = "16 pages", notes = "a few paragraphs on Miha Kovacic. No author given.", } @Article{ababsa:2018:IJAC, author = "Tarek Ababsa and Noureddine Djedl and Yves Duthen", title = "Genetic programming-based self-reconfiguration planning for metamorphic robot", journal = "International Journal of Automation and Computing", year = "2018", volume = "15", number = "4", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s11633-016-1049-4", DOI = "doi:10.1007/s11633-016-1049-4", } @InProceedings{Ababsa:2022:ISNIB, author = "Tarek Ababsa", booktitle = "2022 International Symposium on iNnovative Informatics of Biskra (ISNIB)", title = "A {SIMD} Interpreter for Linear Genetic Programming", year = "2022", abstract = "Genetic programming (GP) has been applied as an automatic programming tool to solve various kinds of problems by genetically breeding a population of computer programs using biologically inspired operations. However, it is well known as a computationally demanding approach with a significant potential of parallelization. In this paper, we emphasize parallelizing the evaluation of genetic programs on Graphics Processing Unit (GPU). We used a compact representation for genotypes. This representation is a memory-efficient method that allows efficient evaluation of programs. Our implementation clearly distinguishes between an individual's genotype and phenotype. Thus, the individuals are represented as linear entities (arrays of 32 bits integers) that are decoded and expressed just like nonlinear entities (trees).", keywords = "genetic algorithms, genetic programming, linear genetic programming, GPU, Graphics, Automatic programming, Sociology, Graphics processing units, Arrays, Statistics, Parallel Processing, GPGPU, symbolic regression", DOI = "doi:10.1109/ISNIB57382.2022.10075819", month = dec, notes = "Also known as \cite{10075819}", } @InProceedings{Abarghouei:2009:SOCPAR, author = "Amir Atapour Abarghouei and Afshin Ghanizadeh and Saman Sinaie and Siti Mariyam Shamsuddin", title = "A Survey of Pattern Recognition Applications in Cancer Diagnosis", booktitle = "International Conference of Soft Computing and Pattern Recognition, SOCPAR '09", year = "2009", month = dec, pages = "448--453", keywords = "genetic algorithms, genetic programming, artificial neural networks, cancer diagnosis, image processing, medical images, pattern recognition applications, wavelet analysis, cancer, medical image processing, pattern recognition", DOI = "doi:10.1109/SoCPaR.2009.93", abstract = "In this paper, some of the image processing and pattern recognition methods that have been used on medical images for cancer diagnosis are reviewed. Previous studies on Artificial Neural Networks, Genetic Programming, and Wavelet Analysis are described with their working process and advantages. The definition of each method is provided in this study, and the acknowledgment is granted for previous related research activities.", notes = "Also known as \cite{5368648}", } @Article{ABBA:2020:JH, author = "S. I. Abba and Sinan Jasim Hadi and Saad Sh. Sammen and Sinan Q. Salih and R. A. Abdulkadir and Quoc Bao Pham and Zaher Mundher Yaseen", title = "Evolutionary computational intelligence algorithm coupled with self-tuning predictive model for water quality index determination", journal = "Journal of Hydrology", volume = "587", pages = "124974", year = "2020", ISSN = "0022-1694", DOI = "doi:10.1016/j.jhydrol.2020.124974", URL = "http://www.sciencedirect.com/science/article/pii/S0022169420304340", keywords = "genetic algorithms, genetic programming, Water quality index, Watershed management, Extreme Gradient Boosting, Extreme Learning Machine, Kinta River", abstract = "Anthropogenic activities affect the water bodies and result in a drastic reduction of river water quality (WQ). The development of a reliable intelligent model for evaluating the suitability of water remains a challenging task facing hydro-environmental engineers. The current study is investigated the applicability of Extreme Gradient Boosting (XGB) and Genetic Programming (GP) in obtaining feature importance, and then abstracted input variables were imposed into the predictive model (the Extreme Learning Machine (ELM)) for the prediction of water quality index (WQI). The stand-alone modeling schema is compared with the proposed hybrid models where the optimum variables are supplied into the GP, XGB, linear regression (LR), stepwise linear regression (SWLR) and ELM models. The WQ data is obtained from the Department of Environment (DoE) (Malaysia), and results are evaluated in terms of determination coefficient (R2) and root mean square error (RMSE). The results demonstrated that the hybrid GPELM and XGBELM models outperformed the standalone GP, XGB, and ELM models for the prediction of WQI at Kinta River basin. A comparison of the hybrid models showed that the predictive skill of GPELM (RMSE = 3.441 training and RMSE = 3.484 testing) over XGBELM improving the accuracy by decreasing the values of RMSE by 5percent and 9percent for training and testing, respectively with regards to XGBELM (RMSE = 3.606 training and RMSE = 3.816 testing). Although regressions are often proposed as reference models (LR and SWLR), when combined with computational intelligence, they still provide satisfactory results in this study. The proposed hybrid GPELM and XGBELM models have improved the prediction accuracy with minimum number of input variables and can therefore serve as reliable predictive tools for WQI at Kinta River basin", } @Article{journals/iajit/AbbasiSA14, author = "Almas Abbasi and Woo Chaw Seng and Imran Shafiq Ahmad", title = "Multi Block based Image Watermarking in Wavelet Domain Using Genetic Programming", journal = "The International Arab Journal of Information Technology", year = "2014", number = "6", volume = "11", pages = "582--589", keywords = "genetic algorithms, genetic programming, Robust watermark, wavelet domain, digital watermarking, HVS", ISSN = "1683-3198", bibdate = "2014-11-03", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/iajit/iajit11.html#AbbasiSA14", broken = "http://ccis2k.org/iajit/?option=com_content&task=blogcategory&id=94&Itemid=364", URL = "https://iajit.org/PDF/vol.11,no.6/6348.pdf", URL = "https://www.semanticscholar.org/paper/Multi-block-based-image-watermarking-in-wavelet-do-Abbasi-Seng/f7172a8a0b6d15ddedf81fc5a98117ff2078a89c", size = "8 pages", abstract = "The increased use of the Internet in sharing and distribution of digital data makes it is very difficult to maintain copyright and ownership of data. Digital watermarking offers a method for authentication and copyright protection. We propose a blind, still image, Genetic Programming (GP) based robust watermark scheme for copyright protection. In this scheme, pseudorandom sequence of real number is used as watermark. It is embedded into perceptually significant blocks of vertical and horizontal sub-band in wavelet domain to achieve robustness. GP is used to structure the watermark for improved imperceptibility by considering the Human Visual System (HVS) characteristics such as luminance sensitivity and self and neighbourhood contrast masking. We also present a GP function which determines the optimal watermark strength for selected coefficients irrespective of the block size. Watermark detection is performed using correlation. Our experiments show that in proposed scheme the watermark resists image processing attack, noise attack, geometric attack and cascading attack. We compare our proposed technique with other two genetic perceptual model based techniques. Comparison results show that our multiblock based technique is approximately 5percent, and 23percent more robust, then the other two compared techniques.", } @InProceedings{DBLP:conf/ssci/AbbasiAW21, author = "Muhammad Shabbir Abbasi and Harith Al-Sahaf and Ian Welch", title = "Automated Behavior-based Malice Scoring of Ransomware Using Genetic Programming", booktitle = "IEEE Symposium Series on Computational Intelligence, SSCI 2021", pages = "1--8", publisher = "IEEE", year = "2021", month = dec # " 5-7", address = "Orlando, FL, USA", keywords = "genetic algorithms, genetic programming Symbolic regression, ransomware, malice scoring", isbn13 = "978-1-7281-9049-5", timestamp = "Thu, 03 Feb 2022 09:28:31 +0100", biburl = "https://dblp.org/rec/conf/ssci/AbbasiAW21.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://doi.org/10.1109/SSCI50451.2021.9660009", DOI = "doi:10.1109/SSCI50451.2021.9660009", size = "8 pages", abstract = "Malice or severity scoring models are a technique for detection of maliciousness. A few ransom-ware detection studies use malice scoring models for detection of ransomware-like behaviour. These models rely on the weighted sum of some manually chosen features and their weights by a domain expert. To automate the modelling of malice scoring for ransomware detection, we propose a method based on Genetic Programming (GP) that automatically evolves a behavior-based malice scoring model by selecting appropriate features and functions from the input feature and operator sets. The experimental results show that the best-evolved model correctly assigned a malice score, below the threshold value to over 85percent of the unseen goodware instances, and over the threshold value to more than 99percent of the unseen ransomware instances.", } @Article{Abbaspour:2013:WSE, author = "Akram Abbaspour and Davood Farsadizadeh and Mohammad Ali Ghorbani", title = "Estimation of hydraulic jump on corrugated bed using artificial neural networks and genetic programming", journal = "Water Science and Engineering", volume = "6", number = "2", pages = "189--198", year = "2013", ISSN = "1674-2370", DOI = "doi:10.3882/j.issn.1674-2370.2013.02.007", URL = "http://www.sciencedirect.com/science/article/pii/S1674237015302362", abstract = "Artificial neural networks (ANNs) and genetic programming (GP) have recently been used for the estimation of hydraulic data. In this study, they were used as alternative tools to estimate the characteristics of hydraulic jumps, such as the free surface location and energy dissipation. The dimensionless hydraulic parameters, including jump depth, jump length, and energy dissipation, were determined as functions of the Froude number and the height and length of corrugations. The estimations of the ANN and GP models were found to be in good agreement with the measured data. The results of the ANN model were compared with those of the GP model, showing that the proposed ANN models are much more accurate than the GP models.", keywords = "genetic algorithms, genetic programming, artificial neural networks, corrugated bed, Froude number, hydraulic jump", } @InProceedings{Abbass:2002:WCCI, publisher_address = "Piscataway, NJ, USA", author = "H. Abbass and N. X. Hoai and R. I. (Bob) McKay", booktitle = "Proceedings, 2002 World Congress on Computational Intelligence", DOI = "doi:10.1109/CEC.2002.1004490", notes = "Refereed International Conference Papers", pages = "1654--1666", publisher = "IEEE Press", title = "AntTAG: A New Method to Compose Computer Programs Using Colonies of Ants", URL = "http://sc.snu.ac.kr/PAPERS/TAGACOcec02.pdf", volume = "2", year = "2002", keywords = "genetic algorithms, genetic programming", size = "6 pages", abstract = "Genetic Programming (GP) plays the primary role for the discovery of programs through evolving the program's set of parse trees. In this paper, we present a new technique for constructing programs through Ant Colony Optimisation (ACO) using the tree adjunct grammar (TAG) formalism. We call the method AntTAG and we show that the results are very promising.", } @InProceedings{abbattista:1999:SAGAACS, author = "Fabio Abbattista and Valeria Carofiglio and Mario Koppen", title = "Scout Algorithms and Genetic Algorithms: A Comparative Study", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "769", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-803.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-803.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{abbod2007, author = "Maysam F. Abbod and M. Mahfouf and D. A. Linkens and C. M. Sellars", title = "Evolutionary Computing for Metals Properties Modelling", booktitle = "THERMEC 2006", year = "2006", volume = "539", pages = "2449--2454", series = "Materials Science Forum", address = "Vancouver", publisher_address = "Switzerland", month = jul # " 4-8", publisher = "Trans Tech Publications", keywords = "genetic algorithms, genetic programming, strain, alloy materials, modeling, material property, stress", ISSN = "1662-9752", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.1011.6271", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1011.6271", URL = "http://www.scientific.net/MSF.539-543.2449.pdf", DOI = "doi:10.4028/www.scientific.net/MSF.539-543.2449", size = "6 pages", abstract = "During the last decade Genetic Programming (GP) has emerged as an efficient methodology for teaching computers how to program themselves. This paper presents research work which uses GP for developing mathematical equations for the response surfaces that have been generated using hybrid modelling techniques for predicting the properties of materials under hot deformation. Collected data from the literature and experimental work on aluminium are used as the initial training data for the GP to develop the mathematical models under different deformation conditions and compositions.", notes = "Published Feb 2007 in Materials Science Forum ?", } @InProceedings{Abbona:2020:CEC, author = "Francesca Abbona and Leonardo Vanneschi and Marco Bona and Mario Giacobini", title = "A {GP} Approach for Precision Farming", booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC 2020", year = "2020", editor = "Yaochu Jin", pages = "paper id24248", address = "internet", month = "19-24 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Cows, Precision Livestock Farming, PLF, Cattle Breeding, Piedmontese Bovines", isbn13 = "978-1-7281-6929-3", DOI = "doi:10.1109/CEC48606.2020.9185637", size = "8 pages", abstract = "Livestock is increasingly treated not just as food containers, but as animals that can be susceptible to stress and diseases, affecting, therefore, the production of offspring and the performance of the farm. The breeder needs a simple and useful tool to make the best decisions for his farm, as well as being able to objectively check whether the choices and investments made have improved or worsened its performance. The amount of data is huge but often dispersive: it is therefore essential to provide the farmer with a clear and comprehensible solution, that represents an additional investment. This research proposes a genetic programming approach to predict the yearly number of weaned calves per cow of a farm, namely the measure of its performance. To investigate the efficiency of genetic programming in such a problem, a dataset composed by observations on representative Piedmontese breedings was used. The results show that the algorithm is appropriate, and can perform an implicit feature selection, highlighting important variables and leading to simple and interpretable models.", notes = "Cow calves, north itally. ANABORAPI. perinatal mortality death during weaning (60 days). GPlab Matlab. Kruskal-Wallis stats test. Natural v. artificial insemination. 2017, 2018 data. Crossover, mutation, shrink mutaion swap mutation. mydivide Herd size. GP8 comphrensible evolved model. Time between calve birth and next calf birth. Department of Veterinary Sciences, University of Torino. ANABORAPI, Associazione Nazionale Allevatori Bovini Razza Piemontese https://wcci2020.org/ Also known as \cite{9185637}", } @Article{ABBONA:2020:LS, author = "Francesca Abbona and Leonardo Vanneschi and Marco Bona and Mario Giacobini", title = "Towards modelling beef cattle management with Genetic Programming", journal = "Livestock Science", year = "2020", volume = "241", pages = "104205", keywords = "genetic algorithms, genetic programming, Precision livestock farming, Evolutionary algorithms, Machine learning, Cattle breeding, Piemontese bovines", ISSN = "1871-1413", URL = "https://iris.unito.it/retrieve/e27ce430-63b3-2581-e053-d805fe0acbaa/Abbona2020_LS_OA.pdf", URL = "http://www.sciencedirect.com/science/article/pii/S1871141320302481", DOI = "doi:10.1016/j.livsci.2020.104205", abstract = "Among the Italian Piemontese Beef Breedings, the yearly production of calves weaned per cow, that is the calves that survive during the period of 60 days following birth, is identified as the main target expressing the performance of a farm. modeling farm dynamics in order to predict the value of this parameter is a possible solution to investigate and highlight breeding strengths, and to find alternatives to penalizing factors. The identification of such variables is a complex but solvable task, since the amount of recorded data among livestock is nowadays huge and manageable through Machine Learning techniques. Besides, the evaluation of the effectiveness of the type of management allows the breeder to consolidate the ongoing processes or, on the contrary, to adopt new management strategies. To solve this problem, we propose a Genetic Programming approach, a white-box technique suitable for big data management, and with an intrinsic ability to select important variables, providing simple models. The most frequent variables encapsulated in the models built by Genetic Programming are highlighted, and their zoological significance is investigated a posteriori, evaluating the performance of the prediction models. Moreover, two of the final expressions selected only three variables among the 48 given in input, one of which is the best performing among GP models. The expressions were then analyzed in order to propose a zootechnical interpretation of the equations. Comparisons with other common techniques, including also black-box methods, are performed, in order to evaluate the performance of different type of methods in terms of accuracy and generalization ability. The approach entailed constructive and helpful considerations to the addressed task, confirming its key-role in the zootechnical field, especially in the beef breeding management", } @Article{abbona:2022:AS, author = "Francesca Abbona and Leonardo Vanneschi and Mario Giacobini", title = "Towards a Vectorial Approach to Predict Beef Farm Performance", journal = "Applied Sciences", year = "2022", volume = "12", number = "3", keywords = "genetic algorithms, genetic programming", ISSN = "2076-3417", URL = "https://www.mdpi.com/2076-3417/12/3/1137", DOI = "doi:10.3390/app12031137", abstract = "Accurate livestock management can be achieved by means of predictive models. Critical factors affecting the welfare of intensive beef cattle husbandry systems can be difficult to be detected, and Machine Learning appears as a promising approach to investigate the hundreds of variables and temporal patterns lying in the data. In this article, we explore the use of Genetic Programming (GP) to build a predictive model for the performance of Piemontese beef cattle farms. In particular, we investigate the use of vectorial GP, a recently developed variant of GP, that is particularly suitable to manage data in a vectorial form. The experiments conducted on the data from 2014 to 2018 confirm that vectorial GP can outperform not only the standard version of GP but also a number of state-of-the-art Machine Learning methods, such as k-Nearest Neighbors, Generalized Linear Models, feed-forward Neural Networks, and long- and short-term memory Recurrent Neural Networks, both in terms of accuracy and generalizability. Moreover, the intrinsic ability of GP in performing an automatic feature selection, while generating interpretable predictive models, allows highlighting the main elements influencing the breeding performance.", notes = "also known as \cite{app12031137}", } @InProceedings{aicsu-91:abbot, author = "R. J. Abbott", title = "Niches as a GA divide-and-conquer strategy", booktitle = "Proceedings of the Second Annual AI Symposium for the California State University", year = "1991", editor = "Art Chapman and Leonard Myers", pages = "133--136", publisher = "California State University", keywords = "genetic algorithms, genetic programming", } @InProceedings{abbott:2003:OOGP, author = "Russell J. Abbott", title = "Object-Oriented Genetic Programming, An Initial Implementation", booktitle = "Proceedings of the Sixth International Conference on Computational Intelligence and Natural Computing", year = "2003", address = "Embassy Suites Hotel and Conference Center, Cary, North Carolina USA", month = sep # " 26-30", keywords = "genetic algorithms, genetic programming, object-oriented, STGP", URL = "http://abbott.calstatela.edu/PapersAndTalks/OOGP.pdf", size = "4 pages", abstract = "This paper describes oogp, an object-oriented genetic programming system. Oogp provides traditional genetic programming capabilities in an object-oriented framework. Among the advantages of object-oriented genetic programming are: (a) strong typing, (b) availability of existing class libraries for inclusion in generated programs, and (c) straightforward extensibility to include features such as iteration as object-oriented methods. Oogp is written in Java and makes extensive use of Java's reflection capabilities. Oogp includes a relatively straightforward but apparently innovative simplification capability.", notes = "http://axon.cs.byu.edu/Dan/cinc03.html/index.html/ Broken Jan 2013 http://www.ee.duke.edu/JCIS/ parity Assignment-Stmt, Block-Stmt, If-Stmt, and While-Stmt Automatic simplification {"}A limit may be placed on the number of steps allowed. When exceeded, an ExcessiveStepsException is thrown{"} Iteration cites \cite{HPL-2001-327}.", } @InProceedings{abbott:2003:MLMTA, author = "Russ Abbott and Jiang Guo and Behzad Parviz", title = "Guided Genetic Programming", booktitle = "The 2003 International Conference on Machine Learning; Models, Technologies and Applications (MLMTA'03)", year = "2003", address = "las Vegas", month = "23-26 " # jun, publisher = "CSREA Press", keywords = "genetic algorithms, genetic programming, guided genetic programming", URL = "http://abbott.calstatela.edu/PapersAndTalks/Guided%20Genetic%20Programming.pdf", size = "7 pages", abstract = "We argue that genetic programming has not made good on its promise to generate computer programs automatically. It then describes an approach that would allow that promise to be fulfilled by running a genetic programming engine under human guidance.", notes = "http://www.ashland.edu/~iajwa/conferences/2003/MLMTA/ Oogp Java. Sort (all ArrayList methods and iterate() loop and insertAsc()). Guided GP 3 populations (coevolution) {"}The user contributes...a top-down analysis{"}. {"}Allowing a user to suggest building blocks may be a reasonable compromise{"}. Cites \cite{langdon:book} \cite{HPL-2001-327} \cite{ppsn92:oReilly} \cite{icga93:kinnear}", } @InProceedings{DBLP:conf/icai/AbbottPS04, author = "Russ Abbott and Behzad Parviz and Chengyu Sun", title = "Genetic Programming Reconsidered", year = "2004", pages = "1113--1116", bibsource = "DBLP, http://dblp.uni-trier.de", editor = "Hamid R. Arabnia and Youngsong Mun", publisher = "CSREA Press", booktitle = "Proceedings of the International Conference on Artificial Intelligence, IC-AI '04, Volume 2 {\&} Proceedings of the International Conference on Machine Learning; Models, Technologies {\&} Applications, MLMTA '04", volume = "2", address = "Las Vegas, Nevada, USA", month = jun # " 21-24", keywords = "genetic algorithms, genetic programming, evolutionary pathway, fitness function, teleological evolution, adaptive evolution", ISBN = "1-932415-32-7", URL = "http://abbott.calstatela.edu/PapersAndTalks/GeneticProgrammingReconsidered.pdf", URL = "https://dblp.org/rec/conf/icai/AbbottPS04.html?view=bibtex", size = "4 pages", abstract = "Even though the Genetic Programming (GP) mechanism is capable of evolving any computable function, the means through which it does so is inherently flawed: the user must provide the GP engine with an evolutionary pathway toward a solution. Hence Genetic Programming is problematic as a mechanism for generating creative solutions to specific problems.", notes = "sort, fitness function cheat ", } @Article{abdel-kader:2022:Infrastructures, author = "Mohamed Y. Abdel-Kader and Ahmed M. Ebid and Kennedy C. Onyelowe and Ibrahim M. Mahdi and Ibrahim Abdel-Rasheed", title = "{(AI)} in Infrastructure Projects-Gap Study", journal = "Infrastructures", year = "2022", volume = "7", number = "10", pages = "Article No. 137", keywords = "genetic algorithms, genetic programming", ISSN = "2412-3811", URL = "https://www.mdpi.com/2412-3811/7/10/137", DOI = "doi:10.3390/infrastructures7100137", abstract = "Infrastructure projects are usually complicated, expensive, long-term mega projects; accordingly, they are the type of projects that most need optimisation in the design, construction and operation stages. A great deal of earlier research was carried out to optimise the performance of infrastructure projects using traditional management techniques. Recently, artificial intelligence (AI) techniques were implemented in infrastructure projects to improve their performance and efficiency due to their ability to deal with fuzzy, incomplete, inaccurate and distorted data. The aim of this research is to collect, classify, analyse and review all of the available previous research related to implementing AI techniques in infrastructure projects to figure out the gaps in the previous studies and the recent trends in this research area. A total of 159 studies were collected since the beginning of the 1990s until the end of 2021. This database was classified based on publishing date, infrastructure subject and the used AI technique. The results of this study show that implementing AI techniques in infrastructure projects is rapidly increasing. They also indicate that transportation is the first and the most AI-using project and that both artificial neural networks (ANN) and particle swarm optimisation (PSO) are the most implemented techniques in infrastructure projects. Finally, the study presented some opportunities for farther research, especially in natural gas projects.", notes = "also known as \cite{infrastructures7100137}", } @Article{ABDELALEEM:2022:tws, author = "Basem H. AbdelAleem and Mohamed K. Ismail and May Haggag and Wael El-Dakhakhni and Assem A. A. Hassan", title = "Interpretable soft computing predictions of elastic shear buckling in tapered steel plate girders", journal = "Thin-Walled Structures", volume = "176", pages = "109313", year = "2022", ISSN = "0263-8231", DOI = "doi:10.1016/j.tws.2022.109313", URL = "https://www.sciencedirect.com/science/article/pii/S026382312200235X", keywords = "genetic algorithms, genetic programming, Data-driven models, Elastic shear buckling strength, Multi-gene genetic programming, Variable importance, Partial dependence plots, Tapered end web panel", abstract = "The complexity of the shear buckling in tapered plate girders has motivated researchers to conduct experimental and numerical investigations to understand the underlying mechanisms controlling such phenomenon, and subsequently develop related design-oriented expressions. However, existing predictive models have been developed and validated using limited datasets and/or traditional regression techniques-restricting both the model utility, when considering a wider range of design parameters, and the model generalizability, due to associated uncertainties. To address these issues, the present study employed a powerful soft computing technique-multi-gene genetic programming (MGGP), to develop design expressions to predict the elastic shear buckling strength of tapered end plate girder web panels. A dataset of 427 experimental and experimentally validated numerical results was used in training, validating, and testing the developed MGGP models. Guided by mechanics and findings from previous studies, the key parameters controlling the strength were identified, and MGGP were employed to reveal the interdependence between such parameters and subsequently develop interpretable predictive models. The prediction accuracy of the developed models was evaluated against that of other existing models using various statistical measures. Several filter and embedded variable importance techniques were used to rank the model input parameters according to their significance in predicting the elastic shear buckling strength. These techniques include the variable importance random forest and the relative influence gradient boosting techniques. Moreover, partial dependence plots were employed to explore the effect of the input variables on the strength. The results obtained from this study demonstrated the robustness of the developed MGGP expression for predicting the elastic shear buckling strength of tapered plate girder end web panel. The developed model also exhibited a superior prediction accuracy and generalizability compared to currently existing ones. Furthermore, the developed partial dependence plots facilitated interpreting the influence of all input variables on the predicted elastic shear buckling strength", } @InProceedings{Abdelaziz:2012:SEMCCO, author = "Almoataz Y. Abdelaziz and S. F. Mekhamer and H. M. Khattab and M. L. A. Badr and Bijaya Ketan Panigrahi", title = "Gene Expression Programming Algorithm for Transient Security Classification", booktitle = "Proceedings of the Third International Conference on Swarm, Evolutionary, and Memetic Computing, SEMCCO 2012", year = "2012", editor = "Bijaya Ketan Panigrahi and Swagatam Das and Ponnuthurai Nagaratnam Suganthan and Pradipta Kumar Nanda", volume = "7677", series = "Lecture Notes in Computer Science", pages = "406--416", address = "Bhubaneswar, India", month = dec # " 20-22", publisher = "Springer", keywords = "genetic algorithms, genetic programming, gene expression programming", isbn13 = "978-3-642-35379-6", DOI = "doi:10.1007/978-3-642-35380-2_48", bibsource = "OAI-PMH server at works.bepress.com", oai = "oai:works.bepress.com:almoataz_abdelaziz-1083", URL = "http://works.bepress.com/almoataz_abdelaziz/42", size = "11 pages", abstract = "In this paper, a gene expression programming (GEP) based algorithm is implemented for power system transient security classification. The GEP algorithms as evolutionary algorithms for pattern classification have recently received attention for classification problems because they can perform global searches. The proposed methodology applies the GEP for the first time in transient security assessment and classification problems of power systems. The proposed algorithm is examined using different IEEE standard test systems. Power system three phase short circuit contingency has been used to test the proposed algorithm. The algorithm checks the static security status of the power system then classifies the transient security of the power system as secure or not secure. Performance of the algorithm is compared with other neural network based classification algorithms to show its superiority for transient security classification.", } @Article{Abdelbaky:2018:IJACSA, author = "Ibrahim Z. Abdelbaky and Ahmed F. Al-Sadek and Amr A. Badr", title = "Applying Machine Learning Techniques for Classifying Cyclin-Dependent Kinase Inhibitors", journal = "International Journal of Advanced Computer Science and Applications", year = "2018", number = "11", volume = "9", pages = "229--235", keywords = "genetic algorithms, genetic programming, cdk inhibitors, random forest classification", publisher = "The Science and Information (SAI) Organization", bibsource = "OAI-PMH server at thesai.org", language = "eng", oai = "oai:thesai.org:10.14569/IJACSA.2018.091132", URL = "http://thesai.org/Downloads/Volume9No11/Paper_32-Applying_Machine_Learning_Techniques.pdf", DOI = "doi:10.14569/IJACSA.2018.091132", size = "7 pages", abstract = "The importance of protein kinases made them a target for many drug design studies. They play an essential role in cell cycle development and many other biological processes. Kinases are divided into different subfamilies according to the type and mode of their enzymatic activity. Computational studies targeting kinase inhibitors identification is widely considered for modelling kinase-inhibitor. This modelling is expected to help in solving the selectivity problem arising from the high similarity between kinases and their binding profiles. In this study, we explore the ability of two machine-learning techniques in classifying compounds as inhibitors or non-inhibitors for two members of the cyclin-dependent kinases as a subfamily of protein kinases. Random forest and genetic programming were used to classify CDK5 and CDK2 kinases inhibitors. This classification is based on calculated values of chemical descriptors. In addition, the response of the classifiers to adding prior information about compounds promiscuity was investigated. The results from each classifier for the datasets were analysed by calculating different accuracy measures and metrics. Confusion matrices, accuracy, ROC curves, AUC values, F1 scores, and Matthews correlation, were obtained for the outputs. The analysis of these accuracy measures showed a better performance for the RF classifier in most of the cases. In addition, the results show that promiscuity information improves the classification accuracy, but its significant effect was notably clear with GP classifiers.", } @InProceedings{Abdelbar:aspgp03, author = "Ashraf M. Abdelbar and Sherif Ragab and Sara Mitri", title = "Applying Co-Evolutionary Particle Swam Optimization to the Egyptian Board Game Seega", booktitle = "Proceedings of The First Asian-Pacific Workshop on Genetic Programming", year = "2003", editor = "Sung-Bae Cho and Nguyen Xuan Hoai and Yin Shan", pages = "9--15", address = "Rydges (lakeside) Hotel, Canberra, Australia", month = "8 " # dec, keywords = "Particle Swarm Optimisation, Co-evolution, Game", ISBN = "0-9751724-0-9", URL = "http://infoscience.epfl.ch/record/90539/", URL = "http://infoscience.epfl.ch/record/90539/files/abdelbar03applyingParticle.pdf", abstract = "Seega is an ancient Egyptian two-phase board game that, in certain aspects, is more difficult than chess. The two-player game is played on either a 5 x 5, 7 x 7, or 9 x 9 board. In the first and more difficult phase of the game, players take turns placing one disk each on the board until the board contains only one empty cell. In the second phase players take turns moving disks of their colour; a disk that becomes surrounded by disks of the opposite color is captured and removed from the board. We have developed a Seega program that employs co-evolutionary particle swarm optimisation in the generation of feature evaluation scores. Two separate swarms are used to evolve White players and Black players, respectively; each particle represents feature weights for use in the position evaluation. Experimental results are presented and the performance of the full game engine is discussed.", notes = "PSO, Not GP \cite{aspgp03}", } @InProceedings{Abdelbari:2017:ICCMS, author = "Hassan Abdelbari and Kamran Shafi", title = "A Genetic Programming Ensemble Method for Learning Dynamical System Models", booktitle = "Proceedings of the 8th International Conference on Computer Modeling and Simulation", year = "2017", pages = "47--51", address = "Canberra, Australia", publisher = "ACM", keywords = "genetic algorithms, genetic programming, complex dynamical systems, modelling and simulation, symbolic regression", isbn13 = "978-1-4503-4816-4", URL = "http://doi.acm.org/10.1145/3036331.3036336", DOI = "doi:10.1145/3036331.3036336", acmid = "3036336", abstract = "Modelling complex dynamical systems plays a crucial role to understand several phenomena in different domains such as physics, engineering, biology and social sciences. In this paper, a genetic programming ensemble method is proposed to learn complex dynamical systems underlying mathematical models, represented as differential equations, from system time series observations. The proposed method relies on decomposing the modelling space based on given variable dependencies. An ensemble of learners is then applied in this decomposed space and their output is combined to generate the final model. Two examples of complex dynamical systems are used to test the performance of the proposed methodology where the standard genetic programming method has struggled to find matching model equations. The empirical results show the effectiveness of the proposed methodology in learning closely matching structure of almost all system equations.", notes = "conf/iccms/AbdelbariS17", } @Article{abdelbari:2019:Systems, author = "Hassan Abdelbari and Kamran Shafi", title = "A System Dynamics Modeling Support System Based on Computational Intelligence", journal = "Systems", year = "2019", volume = "7", number = "4", keywords = "genetic algorithms, genetic programming", ISSN = "2079-8954", URL = "https://www.mdpi.com/2079-8954/7/4/47", DOI = "doi:10.3390/systems7040047", abstract = "System dynamics (SD) is a complex systems modelling and simulation approach with wide ranging applications in various science and engineering disciplines. While subject matter experts lead most of the model building, recent advances have attempted to bring system dynamics closer to fast growing fields such as data sciences. This may prove promising for the development of novel support methods that augment human cognition and improve efficiencies in the model building process. A few different directions have been explored recently to support individual modelling stages, such as the generation of model structure, model calibration and policy optimisation. However, an integrated approach that supports across the board modelling process is still missing. In this paper, a prototype integrated modelling support system is presented for the purpose of supporting the modellers at each stage of the process. The proposed support system facilitates data-driven inferring of causal loop diagrams (CLDs), stock-flow diagrams (SFDs), model equations and the estimation of model parameters using computational intelligence (CI) techniques. The ultimate goal of the proposed system is to support the construction of complex models, where the human power is not enough. With this goal in mind, we demonstrate the working and utility of the proposed support system. We have used two well-known synthetic reality case studies with small models from the system dynamics literature, in order to verify the support system performance. The experimental results showed the effectiveness of the proposed support system to infer close model structures to target models directly from system time-series observations. Future work will focus on improving the support system so that it can generate complex models on a large scale.", notes = "also known as \cite{systems7040047}", } @Article{Abdelmalek:2009:JAMDS, title = "Selecting the Best Forecasting-Implied Volatility Model Using Genetic Programming", author = "Wafa Abdelmalek and Sana {Ben Hamida} and Fathi Abid", journal = "Journal of Applied Mathematics and Decision Sciences", year = "2009", publisher = "Hindawi Publishing Corporation", keywords = "genetic algorithms, genetic programming", URL = "http://downloads.hindawi.com/journals/ads/2009/179230.pdf", URL = "http://www.hindawi.com/journals/ads/2009/179230.html", DOI = "doi:10.1155/2009/179230", ISSN = "11739126", bibsource = "OAI-PMH server at www.doaj.org", language = "eng", oai = "oai:doaj-articles:b3bc3b339d2f713819080ff9b253312a", abstract = "The volatility is a crucial variable in option pricing and hedging strategies. The aim of this paper is to provide some initial evidence of the empirical relevance of genetic programming to volatility's forecasting. By using real data from S\&P500 index options, the genetic programming's ability to forecast Black and Scholes-implied volatility is compared between time series samples and moneyness-time to maturity classes. Total and out-of-sample mean squared errors are used as forecasting's performance measures. Comparisons reveal that the time series model seems to be more accurate in forecasting-implied volatility than moneyness time to maturity models. Overall, results are strongly encouraging and suggest that the genetic programming approach works well in solving financial problems.", notes = "Article ID 179230 1RU: MODESFI, Faculty of Economics and Business, Road of the Airport Km 4, 3018 Sfax, Tunisia 2Laboratory of Intelligent IT Engineering, Higher School of Technology and Computer Science, 2035 Charguia, Tunisia", } @Article{Abdelmutalab:2016:PC, author = "Ameen Abdelmutalab and Khaled Assaleh and Mohamed El-Tarhuni", title = "Automatic modulation classification based on high order cumulants and hierarchical polynomial classifiers", journal = "Physical Communication", volume = "21", pages = "10--18", year = "2016", ISSN = "1874-4907", DOI = "doi:10.1016/j.phycom.2016.08.001", URL = "http://www.sciencedirect.com/science/article/pii/S1874490716301094", abstract = "In this paper, a Hierarchical Polynomial (HP) classifier is proposed to automatically classify M-PSK and M-QAM signals in Additive White Gaussian Noise (AWGN) and slow flat fading environments. The system uses higher order cumulants (HOCs) of the received signal to distinguish between the different modulation types. The proposed system divides the overall modulation classification problem into several hierarchical binary sub-classifications. In each binary sub-classification, the HOCs are expanded into a higher dimensional space in which the two classes are linearly separable. It is shown that there is a significant improvement when using the proposed Hierarchical polynomial structure compared to the conventional polynomial classifier. Moreover, simulation results are shown for different block lengths (number of received symbols) and at different SNR values. The proposed system showed an overall improvement in the probability of correct classification that reaches 100percent using only 512 received symbols at 20 dB compared to 98percent and 98.33percent when using more complicated systems like Genetic Programming with KNN classifier (GP-KNN) and Support Vector Machines (SVM) classifiers, respectively.", keywords = "genetic algorithms, genetic programming, Modulation classification, Hierarchical polynomial classifiers, High order cumulants, Adaptive modulation", } @InProceedings{Abdelwhab:2018:SICE, author = "Mohamed Abdelwhab and A. A. Abouelsoud and Ahmed M. R. Fath Elbab", booktitle = "2018 57th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE)", title = "Tackling Dead End Scenarios by Improving Follow Gap Method with Genetic Programming", year = "2018", pages = "1566--1571", address = "Nara, Japan", abstract = "In this paper the problem of local minimum in obstacle avoidance is solved using improved follow gap method (FGM) through combination with genetic programming (GP). Two stages of controller are proposed and applied on Robotino mobile robot equipped with nine infra-red sensors. The first stage implements FGM when there is a gap between front obstacles whereas the second stage deals with the case of no front gap through the use of GP. Simulation and experimental work prove the effectiveness of the proposed method.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.23919/SICE.2018.8492687", month = sep, notes = "Egypt-Japan University of Science and Technology. Also known as \cite{8492687}", } @PhdThesis{AbdGaus:thesis, author = "Yona Falinie {Abd Gaus}", title = "Artificial Intelligence System for Continuous Affect Estimation from Naturalistic Human Expressions", school = "Brunel University", year = "2018", address = "London, UK", month = jan, keywords = "genetic algorithms, genetic programming", URL = "http://bura.brunel.ac.uk/handle/2438/16348", URL = "https://bura.brunel.ac.uk/bitstream/2438/16348/1/FulltextThesis.pdf", size = "165 pages", abstract = "The analysis and automatic affect estimation system from human expression has been acknowledged as an active research topic in computer vision community. Most reported affect recognition systems, however, only consider subjects performing well-defined acted expression, in a very controlled condition, so they are not robust enough for real-life recognition tasks with subject variation, acoustic surrounding and illumination change. In this thesis, an artificial intelligence system is proposed to continuously (represented along a continuum e.g., from -1 to +1) estimate affect behaviour in terms of latent dimensions (e.g., arousal and valence) from naturalistic human expressions. To tackle the issues, feature representation and machine learning strategies are addressed. In feature representation, human expression is represented by modalities such as audio, video, physiological signal and text modality. Hand- crafted features is extracted from each modality per frame, in order to match with consecutive affect label. However, the features extracted maybe missing information due to several factors such as background noise or lighting condition. Haar Wavelet Transform is employed to determine if noise cancellation mechanism in feature space should be considered in the design of affect estimation system. Other than hand-crafted features, deep learning features are also analysed in terms of the layer-wise; convolutional and fully connected layer. Convolutional Neural Network such as AlexNet, VGGFace and ResNet has been selected as deep learning architecture to do feature extraction on top of facial expression images. Then, multimodal fusion scheme is applied by fusing deep learning feature and hand-crafted feature together to improve the performance. In machine learning strategies, two-stage regression approach is introduced. In the first stage, baseline regression methods such as Support Vector Regression are applied to estimate each affect per time. Then in the second stage, subsequent model such as Time Delay Neural Network, Long Short-Term Memory and Kalman Filter is proposed to model the temporal relationships between consecutive estimation of each affect. In doing so, the temporal information employed by a subsequent model is not biased by high variability present in consecutive frame and at the same time, it allows the network to exploit the slow changing dynamic between emotional dynamic more efficiently. Following of two-stage regression approach for unimodal affect analysis, fusion information from different modalities is elaborated. Continuous emotion recognition in-the-wild is leveraged by investigating mathematical modelling for each emotion dimension. Linear Regression, Exponent Weighted Decision Fusion and Multi-Gene Genetic Programming are implemented to quantify the relationship between each modality. In summary, the research work presented in this thesis reveals a fundamental approach to automatically estimate affect value continuously from naturalistic human expression. The proposed system, which consists of feature smoothing, deep learning feature, two-stage regression framework and fusion using mathematical equation between modalities is demonstrated. It offers strong basis towards the development artificial intelligent system on estimation continuous affect estimation, and more broadly towards building a real-time emotion recognition system for human-computer interaction.", notes = "Supervisor: Hongying Meng", } @InProceedings{AbdGaus:2018:ieeeFG, author = "Yona Falinie A. Gaus and Hongying Meng", booktitle = "2018 13th IEEE International Conference on Automatic Face Gesture Recognition (FG 2018)", title = "Linear and Non-Linear Multimodal Fusion for Continuous Affect Estimation In-the-Wild", year = "2018", pages = "492--498", abstract = "Automatic continuous affect recognition from multiple modality in the wild is arguably one of the most challenging research areas in affective computing. In addressing this regression problem, the advantages of the each modality, such as audio, video and text, have been frequently explored but in an isolated way. Little attention has been paid so far to quantify the relationship within these modalities. Motivated to leverage the individual advantages of each modality, this study investigates behavioural modelling of continuous affect estimation, in multimodal fusion approaches, using Linear Regression, Exponent Weighted Decision Fusion and Multi-Gene Genetic Programming. The capabilities of each fusion approach are illustrated by applying it to the formulation of affect estimation generated from multiple modality using classical Support Vector Regression. The proposed fusion methods were applied in the public Sentiment Analysis in the Wild (SEWA) multi-modal dataset and the experimental results indicate that employing proper fusion can deliver a significant performance improvement for all affect estimation. The results further show that the proposed systems is competitive or outperform the other state-of-the-art approaches.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/FG.2018.00079", month = may, notes = "Also known as \cite{8373872}", } @InProceedings{4983280, author = "I. Abd Latiff and M. O. Tokhi", title = "Fast convergence strategy for Particle Swarm Optimization using spread factor", booktitle = "Evolutionary Computation, 2009. CEC '09. IEEE Congress on", year = "2009", month = may, pages = "2693--2700", keywords = "PSO velocity equation, fast convergence strategy, inertia weight, particle swarm optimization, spread factor, convergence, particle swarm optimisation", DOI = "doi:10.1109/CEC.2009.4983280", notes = "Not on GP", } @Article{journals/nca/AbdolahzareM18, title = "Nonlinear mathematical modeling of seed spacing uniformity of a pneumatic planter using genetic programming and image processing", author = "Zahra Abdolahzare and Saman Abdanan Mehdizadeh", journal = "Neural Computing and Applications", year = "2018", number = "2", volume = "29", pages = "363--375", keywords = "genetic algorithms, genetic programming", bibdate = "2018-01-25", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/nca/nca29.html#AbdolahzareM18", DOI = "doi:10.1007/s00521-016-2450-1", } @Article{Abdollahzadeh:2016:CC, author = "Gholamreza Abdollahzadeh and Ehsan Jahani and Zahra Kashir", title = "Predicting of compressive strength of recycled aggregate concrete by genetic programming", journal = "Computers and Concrete", year = "2016", volume = "18", number = "2", pages = "155--163", month = aug, keywords = "genetic algorithms, genetic programming, gene expression programming, recycled aggregate concrete, silica fume, compressive strength", ISSN = "1598-8198", DOI = "doi:10.12989/CAC.2016.18.2.155", size = "20 pages", abstract = "This paper, proposes 20 models for predicting compressive strength of recycled aggregate concrete (RAC) containing silica fume by using gene expression programming (GEP). To construct the models, experimental data of 228 specimens produced from 61 different mixtures were collected from the literature. 80% of data sets were used in the training phase and the remained 20% in testing phase. Input variables were arranged in a format of seven input parameters including age of the specimen, cement content, water content, natural aggregates content, recycled aggregates content, silica fume content and amount of superplasticizer. The training and testing showed the models have good conformity with experimental results for predicting the compressive strength of recycled aggregate concrete containing silica fume.", notes = "admin@techno-press.com Department of Civil Engineering, Babol University of Technology, Babol, Iran", } @Article{Abdou200911402, author = "Hussein A. Abdou", title = "Genetic programming for credit scoring: The case of Egyptian public sector banks", journal = "Expert Systems with Applications", volume = "36", number = "9", pages = "11402--11417", year = "2009", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2009.01.076", URL = "http://www.sciencedirect.com/science/article/B6V03-4VJSRWK-1/2/a3b8516f289c76c474c6a1eb9d26d7ec", URL = "http://results.ref.ac.uk/Submissions/Output/2691591", keywords = "genetic algorithms, genetic programming, Credit scoring, Weight of evidence, Egyptian public sector banks", abstract = "Credit scoring has been widely investigated in the area of finance, in general, and banking sectors, in particular. Recently, genetic programming (GP) has attracted attention in both academic and empirical fields, especially for credit problems. The primary aim of this paper is to investigate the ability of GP, which was proposed as an extension of genetic algorithms and was inspired by the Darwinian evolution theory, in the analysis of credit scoring models in Egyptian public sector banks. The secondary aim is to compare GP with probit analysis (PA), a successful alternative to logistic regression, and weight of evidence (WOE) measure, the later a neglected technique in published research. Two evaluation criteria are used in this paper, namely, average correct classification (ACC) rate criterion and estimated misclassification cost (EMC) criterion with different misclassification cost (MC) ratios, in order to evaluate the capabilities of the credit scoring models. Results so far revealed that GP has the highest ACC rate and the lowest EMC. However, surprisingly, there is a clear rule for the WOE measure under EMC with higher MC ratios. In addition, an analysis of the dataset using Kohonen maps is undertaken to provide additional visual insights into cluster groupings.", uk_research_excellence_2014 = "D - Journal article", } @PhdThesis{2009AbdouEthosPhD, author = "Hussein Ali Hussein Abdou", title = "Credit Scoring Models for Egyptian Banks: Neural Nets and Genetic Programming versus Conventional Techniques", school = "Plymouth Business School, University of Plymouth", year = "2009", address = "UK", month = apr, keywords = "genetic algorithms, genetic programming", URL = "https://pearl.plymouth.ac.uk/bitstream/handle/10026.1/379/2009AbdouEthosPhD.pdf", URL = "http://hdl.handle.net/10026.1/379", URL = "http://ethos.bl.uk/OrderDetails.do?did=55&uin=uk.bl.ethos.494192", size = "452 pages", abstract = "Credit scoring has been regarded as a core appraisal tool of banks during the last few decades, and has been widely investigated in the area of finance, in general, and banking sectors, in particular. In this thesis, the main aims and objectives are: to identify the currently used techniques in the Egyptian banking credit evaluation process; and to build credit scoring models to evaluate personal bank loans. In addition, the subsidiary aims are to evaluate the impact of sample proportion selection on the Predictive capability of both advanced scoring techniques and conventional scoring techniques, for both public banks and a private banking case-study; and to determine the key characteristics that affect the personal loans' quality (default risk). The stages of the research comprised: firstly, an investigative phase, including an early pilot study, structured interviews and a questionnaire; and secondly, an evaluative phase, including an analysis of two different data-sets from the Egyptian private and public banks applying average correct classification rates and estimated misclassification costs as criteria. Both advanced scoring techniques, namely, neural nets (probabilistic neural nets and multi-layer feed-forward nets) and genetic programming, and conventional techniques, namely, a weight of evidence measure, multiple discriminant analysis, probit analysis and logistic regression were used to evaluate credit default risk in Egyptian banks. In addition, an analysis of the data-sets using Kohonen maps was undertaken to provide additional visual insights into cluster groupings. From the investigative stage, it was found that all public and the vast majority of private banks in Egypt are using judgemental approaches in their credit evaluation. From the evaluative stage, clear distinctions between the conventional techniques and the advanced techniques were found for the private banking case-study; and the advanced scoring techniques (such as powerful neural nets and genetic programming) were superior to the conventional techniques for the public sector banks. Concurrent loans from other banks and guarantees by the corporate employer of the loan applicant, which have not been used in other reported studies, are identified as key variables and recommended in the specific environment chosen, namely Egypt. Other variables, such as a feasibility study and the Central Bank of Egypt report also play a contributory role in affecting the loan quality.", notes = "Supervisor John Pointon uk.bl.ethos.494192", } @InProceedings{Abdulhamid:2011:ICARA, author = "Fahmi Abdulhamid and Kourosh Neshatian and Mengjie Zhang", title = "Genetic programming for evolving programs with loop structures for classification tasks", booktitle = "5th International Conference on Automation, Robotics and Applications (ICARA 2011)", year = "2011", month = "6-8 " # dec, pages = "202--207", address = "Wellington, New Zealand", size = "6 pages", abstract = "Object recognition and classification are important tasks in robotics. Genetic Programming (GP) is a powerful technique that has been successfully used to automatically generate (evolve) classifiers. The effectiveness of GP is limited by the expressiveness of the functions used to evolve programs. It is believed that loop structures can considerably improve the quality of GP programs in terms of both performance and interpretability. This paper proposes five new loop structures using which GP can evolve compact programs that can perform sophisticated processing. The use of loop structures in GP is evaluated against GP with no loops for both image and non-image classification tasks. Evolved programs using the proposed loop structures are analysed in several problems. The results show that loop structures can increase classification accuracy compared to GP with no loops.", keywords = "genetic algorithms, genetic programming, evolving program, image classification task, nonimage classification task, object classification task, object recognition task, program loop structure, robotics, image classification, learning (artificial intelligence), object recognition, robot vision", DOI = "doi:10.1109/ICARA.2011.6144882", notes = "Also known as \cite{6144882}", } @InProceedings{Abdulhamid:2012:CEC, title = "Evolving Genetic Programming Classifiers with Loop Structures", author = "Fahmi Abdulhamid and Andy Song and Kourosh Neshatian and Mengjie Zhang", pages = "2710--2717", booktitle = "Proceedings of the 2012 IEEE Congress on Evolutionary Computation", year = "2012", editor = "Xiaodong Li", month = "10-15 " # jun, DOI = "doi:10.1109/CEC.2012.6252877", address = "Brisbane, Australia", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Conflict of Interest Papers, Classification, clustering, data analysis and data mining", abstract = "Loop structure is a fundamental flow control in programming languages for repeating certain operations. It is not widely used in Genetic Programming as it introduces extra complexity in the search. However in some circumstances, including a loop structure may enable GP to find better solutions. This study investigates the benefits of loop structures in evolving GP classifiers. Three different loop representations are proposed and compared with other GP methods and a set of traditional classification methods. The results suggest that the proposed loop structures can outperform other methods. Additionally the evolved classifiers can be small and simple to interpret. Further analysis on a few classifiers shows that they indeed have captured genuine characteristics from the data for performing classification.", notes = "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the EPS and the IET.", } @Article{abdulkarimova:2019:ajhpc, author = "Ulviya Abdulkarimova and Anna {Ouskova Leonteva} and Christian Rolando and Anne Jeannin-Girardon and Pierre Collet", title = "The {PARSEC} machine: a non-{Newtonian} supra-linear super-computer", journal = "Azerbaijan Journal of High Performance Computing", year = "2019", volume = "2", number = "2", pages = "122--140", month = dec, keywords = "genetic algorithms, genetic programming, beowulf cluster, HPC, parallel computing, relative space-time, supra-linear acceleration, qualitative acceleration, GPGPU, loosely coupled machines, artificial evolution, transfer learning, harmonic analysis, super-resolution, non-uniform sampling, Fourier transform", URL = "https://publis.icube.unistra.fr/docs/14472/easeaHPC.pdf", URL = "https://azjhpc.org/index.php/archives/15-paper/52-the-parsec-machine-a-non-newtonian-supra-linear-supercomputer", URL = "http://azjhpc.com//issue4/doi.org:10.32010:26166127.2019.2.2.122.140.pdf", DOI = "doi:10.32010/26166127.2019.2.2.122.140", size = "19 pages", abstract = "transfer-learning can turn a Beowulf cluster into a full super-computer with supra-linear qualitative acceleration. Harmonic Analysis is used as a real-world example to show the kind of result that can be achieved with the proposed super-computer architecture, that locally exploits absolute space-time parallelism on each machine (SIMD parallelism) and loosely-coupled relative space-time parallelisation between different machines (loosely coupled MIMD)", } @PhdThesis{abdulkarimova:tel-03700035, author = "Ulviya Abdulkarimova", title = "{SINUS-IT}: an evolutionary approach to harmonic analysis", title_fr = "SINUS-IT : une approche evolutionnaire de l'analyse harmonique", school = "Universite de Strasbourg", year = "2021", address = "France", month = "2 " # sep, keywords = "genetic algorithms, genetic programming, EASEA, NVIDA, CUDA, Artificial evolution, Evolution strategies, QAES, Fourier transform, FFT, Harmonic analysis, FT-ICR, Isotopic structure, GPU, GPGPU parallelisation, Island-based parallelization, Glutathione, binary radians, Brad2rad, Rad2brad, global random sampling, GRS", number = "2021STRAD018", hal_id = "tel-03700035", hal_version = "v1", URL = "https://theses.hal.science/tel-03700035/", URL = "https://theses.hal.science/tel-03700035/document", URL = "https://theses.hal.science/tel-03700035/file/ABDULKARIMOVA_Ulviya_2021_ED269.pdf", size = "185 pages", abstract = "This PhD project is about harmonic analysis of signals coming from Fourier Transform Ion Cyclotron Resonance (FT-ICR) mass spectrometer. The analysis of these signals is usually done using Fourier Transform (FT) method. However, there are several limitations of this method, one of which is not being able to find the phase parameter. Mass spectrometers are used to determine the chemical composition of compounds. It is known that if the phase component is known, it would yield an improvement in mass accuracy and mass resolving power which would help to determine the composition of a given compound more accurately. In this PhD work we use evolutionary algorithm to overcome the limitations of the FT method. We explore different sampling, speed optimization and algorithm improvement methods. We show that our proposed method outperforms the FT method as it uses short transients to resolve the peaks and it automatically yields phase values.", notes = "Some mention of GP. In English. https://icube.unistra.fr/actualites-agenda/agenda/evenement/?tx_ttnews%5Btt_news%5D=23221&cHash=58d32af8fd94c8ff397921898600e7cd MSAP Page 149--159 Appendix A, Resume en francais de la these Thesis supervisors: Pierre Collet and Christian Rolando", } @Article{Abdulkarimova:2025:GPEM, author = "Ulviya Abdulkarimova and Rodrigo Abarca-del-Rio and Pierre Collet", title = "Harnessing evolutionary algorithms for enhanced characterization of {ENSO} events", journal = "Genetic Programming and Evolvable Machines", year = "2025", volume = "26", pages = "Article no 4", note = "Online first", keywords = "genetic algorithms, genetic programming, El Nino Southern Oscillation, ENSO, Evolutionary algorithm, Symbolic regression, Stochastic optimization", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-024-09497-z", notes = "Azerbaijan State Oil and Industry University (ASOIU)/French-Azerbaijani University (UFAZ), Baku, Azerbaijan", } @PhdThesis{Abdullah:thesis, author = "Norliza Binti Abdullah", title = "Android Malware Detection System using Genetic Programming", school = "Computer Science, University of York", year = "2019", address = "UK", month = mar, keywords = "genetic algorithms, genetic programming, Supervised Learning, Multi-objective Genetic Algorithm, SPEA2, MOGP, Android Malware", URL = "https://etheses.whiterose.ac.uk/29027/", URL = "https://etheses.whiterose.ac.uk/29027/6/Abdullah_201051902.pdf", size = "165 pages", abstract = "Nowadays, smartphones and other mobile devices are playing a significant role in the way people engage in entertainment, communicate, network, work, and bank and shop online. As the number of mobile phones sold has increased dramatically worldwide, so have the security risks faced by the users, to a degree most do not realise. One of the risks is the threat from mobile malware. In this research, we investigate how supervised learning with evolutionary computation can be used to synthesise a system to detect Android mobile phone attacks. The attacks include malware, ransomware and mobile botnets. The datasets used in this research are publicly downloadable, available for use with appropriate acknowledgement. The primary source is Drebin. We also used ransomware and mobile botnet datasets from other Android mobile phone researchers. The research in this thesis uses Genetic Programming (GP) to evolve programs to distinguish malicious and non-malicious applications in Android mobile datasets. It also demonstrates the use of GP and Multi-Objective Evolutionary Algorithms (MOEAs) together to explore functional (detection rate) and non-functional (execution time and power consumption) trade-offs. Our results show that malicious and non-malicious applications can be distinguished effectively using only the permissions held by applications recorded in the application's Android Package (APK). Such a minimalist source of features can serve as the basis for highly efficient Android malware detection. Non-functional tradeoffs are also highlight.", notes = "Also known as \cite{wreo29027} uk.bl.ethos.832567 Supervisors: John Andrew Clark and Susan Stepney", } @InProceedings{Abdul-Rahim:2006:ccis, author = "A. B. {Abdul rahim} and J. Teo and A. Saudi", title = "An Empirical Comparison of Code Size Limit in Auto-Constructive Artificial Life", booktitle = "2006 IEEE Conference on Cybernetics and Intelligent Systems", year = "2006", pages = "1--6", address = "Bangkok", month = jun, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Push, Breve, ALife, PushGP", ISBN = "1-4244-0023-6", DOI = "doi:10.1109/ICCIS.2006.252308", abstract = "This paper presents an evolving swarm system of flying agents simulated as a collective intelligence within the Breve auto-constructive artificial life environment. The behaviour of each agent is governed by genetically evolved program codes expressed in the Push programming language. There are two objectives in this paper, that is to investigate the effects of firstly code size limit and secondly two different versions of the Push genetic programming language on the auto-constructive evolution of artificial life. We investigated these genetic programming code elements on reproductive competence using a measure based on the self-sustainability of the population. Self-sustainability is the point in time when the current population's agents are able to reproduce enough offspring to maintain the minimum population size without any new agents being randomly injected from the system. From the results, we found that the Push2 implementation showed slightly better evolvability than Push3 in terms of achieving self-sufficiency. In terms of code size limit, the reproductive competence of the collective swarm was affected quite significantly at certain parameter settings", notes = "Sch. of Eng. & Inf. Technol., Univ. Malaysia Sabah", } @Article{Abdulrahman:2020:IJCA, author = "Hadeel Abdulrahman and Mohamed Khatib", title = "Classification of Retina Diseases from {OCT} using Genetic Programming", journal = "International Journal of Computer Applications", year = "2020", volume = "177", number = "45", pages = "41--46", month = mar, keywords = "genetic algorithms, genetic programming, feature extraction, Optical Coherence Tomography, OCT image classification, OCT feature extraction", publisher = "Foundation of Computer Science (FCS), NY, USA", address = "New York, USA", ISSN = "0975-8887", URL = "https://www.ijcaonline.org/archives/volume177/number45/abdulrahman-2020-ijca-919973.pdf", URL = "http://www.ijcaonline.org/archives/volume177/number45/31212-2020919973", DOI = "doi:10.5120/ijca2020919973", size = "6 pages", abstract = "a fully automated method for feature extraction and classification of retina diseases is implemented. The main idea is to find a method that can extract the important features from the Optical Coherence Tomography (OCT) image, and acquire a higher classification accuracy. The using of genetic programming (GP) can achieve that aim. Genetic programming is a good way to choose the best combination of feature extraction methods from a set of feature extraction methods and determine the proper parameters for each one of the selected extraction methods. 800 OCT images are used in the proposed method, of the most three popular retinal diseases: Choroidal neovascularization (CNV), Diabetic Macular Edema (DME) and Drusen, beside the normal OCT images. While the set of the feature extraction methods that is used in this paper contains: Gabor filter, Local Binary Pattern (LBP), Gray Level Co-occurrence Matrix (GLCM), histogram of the image, and Speed Up Robust Filter (SURF). These methods are used for the both of global and local feature extraction. After that the classification process is achieved by the Support Vector Machine (SVM). The proposed method performed high accuracy as compared with the traditional methods.", notes = "Also known as \cite{10.5120/ijca2020919973,} www.ijcaonline.org Department of Artificial Intelligence, Faculty of Informatics Engineering, Aleppo University, Syria", } @InProceedings{Abednego:2011:ICEEI, author = "Luciana Abednego and Dwi Hendratmo", title = "Genetic programming hyper-heuristic for solving dynamic production scheduling problem", booktitle = "International Conference on Electrical Engineering and Informatics (ICEEI 2011)", year = "2011", month = "17-19 " # jul, pages = "K3--2", address = "Bandung, Indonesia", size = "4 pages", abstract = "This paper investigates the potential use of genetic programming hyper-heuristics for solution of the real single machine production problem. This approach operates on a search space of heuristics rather than directly on a search space of solutions. Genetic programming hyper-heuristics generate new heuristics from a set of potential heuristic components. Real data from production department of a metal industries are used in the experiments. Experimental results show genetic programming hyper-heuristics outperforms other heuristics including MRT, SPT, LPT, EDD, LDD, dan MON rules with respect to minimum tardiness and minimum flow time objectives. Further results on sensitivity to changes indicate that GPHH designs are robust. Based on experiments, GPHH outperforms six other benchmark heuristics with number of generations 50 and number of populations 50. Human designed heuristics are result of years of work by a number of experts, while GPHH automate the design of the heuristics. As the search process is automated, this would largely reduce the cost of having to create a new set of heuristics.", keywords = "genetic algorithms, genetic programming, cost reduction, dynamic production scheduling problem, genetic programming hyper heuristics, metal industries, minimum flow time, minimum tardiness, single machine production problem, cost reduction, dynamic scheduling, heuristic programming, lead time reduction, metallurgical industries, single machine scheduling", DOI = "doi:10.1109/ICEEI.2011.6021768", ISSN = "2155-6822", notes = "Also known as \cite{6021768}", } @InCollection{abernathy:2000:UGASBCRB, author = "Neil Abernathy", title = "Using a Genetic Algorithm to Select Beam Configurations for Radiosurgery of the Brain", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "1--7", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms", notes = "part of \cite{koza:2000:gagp}", } @Article{Abhishek:2014:PMS, author = "Kumar Abhishek and Biranchi Narayan Panda and Saurav Datta and Siba Sankar Mahapatra", title = "Comparing Predictability of Genetic Programming and {ANFIS} on Drilling Performance Modeling for {GFRP} Composites", journal = "Procedia Materials Science", volume = "6", pages = "544--550", year = "2014", note = "3rd International Conference on Materials Processing and Characterisation (ICMPC 2014)", ISSN = "2211-8128", DOI = "doi:10.1016/j.mspro.2014.07.069", URL = "http://www.sciencedirect.com/science/article/pii/S2211812814004349", abstract = "Drilling of glass fibre reinforced polymer (GFRP) composite material is substantially complicated from the metallic materials due to its high structural stiffness (of the composite) and low thermal conductivity of plastics. During drilling of GFRP composites, problems generally arise like fibre pull out, delamination, stress concentration, swelling, burr, splintering and micro cracking etc. which reduces overall machining performance. Now-a-days hybrid approaches have been received remarkable attention in order to model machining process behaviour and to optimise machining performance towards subsequent improvement of both quality and productivity, simultaneously. In the present research, spindle speed, feed rate, plate thickness and drill bit diameter have been considered as input parameters; and the machining yield characteristics have been considered in terms of thrust and surface roughness (output responses) of the drilled composite product. The study illustrates the applicability of genetic programming with the help of GPTIPS as well as Adaptive Neuro Fuzzy Inference System (ANFIS) towards generating prediction models for better understanding of the process behavior and for improving process performances in drilling of GFRP composites.", keywords = "genetic algorithms, genetic programming, Glass fibre reinforced polymer (GFRP), Adaptive Neuro Fuzzy Inference System (ANFIS), GPTIPS.", notes = "PhD thesis (2015) http://ethesis.nitrkl.ac.in/6916/ Experimental Investigations on Machining of CFRP Composites: Study of Parametric Influence and Machining Performance Optimization. PhD thesis. does not seem to be on GP", } @InCollection{Abid:2012:GPnew, author = "Fathi Abid and Wafa Abdelmalek and Sana {Ben Hamida}", title = "Dynamic Hedging Using Generated Genetic Programming Implied Volatility Models", booktitle = "Genetic Programming - New Approaches and Successful Applications", publisher = "InTech", year = "2012", editor = "Sebastian Ventura", chapter = "7", pages = "141--172", keywords = "genetic algorithms, genetic programming", isbn13 = "978-953-51-0809-2", DOI = "doi:10.5772/48148", size = "32 pages", notes = "Part of \cite{Ventura:2012:GPnew}. Open access book CC BY 3.0 license", } @Article{ABOELELA:2022:RE, author = "Abdelrahman E. Aboelela and Ahmed M. Ebid and Ayman L. Fayed", title = "Estimating the subgrade reaction at deep braced excavation bed in dry granular soil using genetic programming ({GP)}", journal = "Results in Engineering", volume = "13", pages = "100328", year = "2022", ISSN = "2590-1230", DOI = "doi:10.1016/j.rineng.2021.100328", URL = "https://www.sciencedirect.com/science/article/pii/S2590123021001298", keywords = "genetic algorithms, genetic programming, Deep braced excavation, Modulus of subgrade reaction", abstract = "Modulus of subgrade reaction (Ks) is a simplified and approximated approach to present the soil-structure interaction. It is widely used in designing combined and raft foundations due to its simplicity. (Ks) is not a soil propriety, its value depends on many factors including soil properties, shape, dimensions and stiffness of footing and even time (for saturated cohesive soils). Many earlier formulas were developed to estimate the (Ks) value. This research is concerned in studying the effect of de-stressing and shoring rigidity of deep excavation on the (Ks) value. A parametric study was carried out using 27 FEM models with different configurations to generate a database, then a well-known {"}Genetic Programming{"} technique was applied on the database to develop a formula to correlate the (Ks) value with the deep excavation configurations. The results indicated that (Ks) value increased with increasing the diaphragm wall stiffness and decreases with increasing the excavation depth", } @Article{Abooali:2014:JNGSE, author = "Danial Abooali and Ehsan Khamehchi", title = "Estimation of dynamic viscosity of natural gas based on genetic programming methodology", journal = "Journal of Natural Gas Science and Engineering", volume = "21", pages = "1025--1031", year = "2014", keywords = "genetic algorithms, genetic programming, Natural gas, Dynamic viscosity, Correlation", ISSN = "1875-5100", DOI = "doi:10.1016/j.jngse.2014.11.006", URL = "http://www.sciencedirect.com/science/article/pii/S1875510014003394", abstract = "Investigating the behaviour of natural gas can contribute to a detailed understanding of hydrocarbon reservoirs. Natural gas, alone or in association with oil in reservoirs, has a large impact on reservoir fluid properties. Thus, having knowledge about gas characteristics seems to be necessary for use in estimation and prediction purposes. In this project, dynamic viscosity of natural gas (mu_g), as an important quantity, was correlated with pseudo-reduced temperature (Tpr), pseudo-reduced pressure (Ppr), apparent molecular weight (Ma) and gas density (rhog) by operation of the genetic programming method on a large dataset including 1938 samples. The squared correlation coefficient (R2), average absolute relative deviation percent (AARDpercent) and average absolute error (AAE) are 0.999, 2.55percent and 0.00084 cp, respectively. The final results show that the obtained simple-to-use model can predict viscosity of natural gases with high accuracy and confidence.", notes = "GPTIPS", } @Article{ABOOALI:2019:JPSE, author = "Danial Abooali and Mohammad Amin Sobati and Shahrokh Shahhosseini and Mehdi Assareh", title = "A new empirical model for estimation of crude oil/brine interfacial tension using genetic programming approach", journal = "Journal of Petroleum Science and Engineering", volume = "173", pages = "187--196", year = "2019", keywords = "genetic algorithms, genetic programming, Interfacial tension, Correlation, Crude oil, Brine, Genetic programming (GP)", ISSN = "0920-4105", DOI = "doi:10.1016/j.petrol.2018.09.073", URL = "http://www.sciencedirect.com/science/article/pii/S0920410518308283", abstract = "Detailed understanding of the behavior of crude oils and their interactions with reservoir formations and other in-situ fluids can help the engineers to make better decisions about the future of oil reservoirs. As an important property, interfacial tension (IFT) between crude oil and brine has great impacts on the oil production efficiency in different recovery stages due to its effects on the capillary number and residual oil saturation. In the present work, a new mathematical model has been developed to estimate IFT between crude oil and brine on the basis of a number of physical properties of crude oil (i.e., specific gravity, and total acid number) and the brine (i.e., pH, NaCl equivalent salinity), temperature, and pressure. Genetic programming (GP) methodology has been implemented on a data set including 560 experimental data to develop the IFT correlation. The correlation coefficient (R2a =a 0.9745), root mean square deviation (RMSDa =a 1.8606a mN/m), and average absolute relative deviation (AARDa =a 3.3932percent) confirm the acceptable accuracy of the developed correlation for the prediction of IFT", } @Article{ABOOALI:2020:Fuel, author = "Danial Abooali and Reza Soleimani and Saeed Gholamreza-Ravi", title = "Characterization of physico-chemical properties of biodiesel components using smart data mining approaches", journal = "Fuel", volume = "266", pages = "117075", year = "2020", ISSN = "0016-2361", DOI = "doi:10.1016/j.fuel.2020.117075", URL = "http://www.sciencedirect.com/science/article/pii/S0016236120300703", keywords = "genetic algorithms, genetic programming, Fatty acid ester, Density, Speed of sound, Isentropic and isothermal compressibility, Stochastic gradient boosting", abstract = "Biodiesels are the most probable future alternatives for petroleum fuels due to their easy accessibility and extraction, comfortable transportation and storage and lower environmental pollutions. Biodiesels have wide range of molecular structures including various long chain fatty acid methyl esters (FAMEs) and fatty acid ethyl esters (FAEEs) with different thermos-physical properties. Therefore, reliable methods estimating the ester properties seems necessary to choose the appropriate one for a special diesel engine. In the present study, the effort was developing a set of novel and robust methods for estimation of four important properties of common long chain fatty acid methyl and ethyl esters including density, speed of sound, isentropic and isothermal compressibility, directly from a number of basic effective variables (i.e. temperature, pressure, molecular weight and normal melting point). Stochastic gradient boosting (SGB) and genetic programming (GP) as innovative and powerful mathematical approaches in this area were applied and implemented on large datasets including 2117, 1048, 483 and 310 samples for density, speed of sound, isentropic and isothermal compressibility, respectively. Statistical assessments revealed high applicability and accuracy of the new developed models (R2 > 0.99 and AARD < 1.7percent) and the SGB models yield more accurate and confident predictions", } @Article{DBLP:journals/nca/AbooaliK19, author = "Danial Abooali and Ehsan Khamehchi", title = "New predictive method for estimation of natural gas hydrate formation temperature using genetic programming", journal = "Neural Comput. Appl.", volume = "31", number = "7", pages = "2485--2494", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/s00521-017-3208-0", DOI = "doi:10.1007/s00521-017-3208-0", timestamp = "Thu, 10 Sep 2020 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/nca/AbooaliK19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{abraham:2003:CEC, author = "Ajith Abraham and Vitorino Ramos", title = "Web Usage Mining Using Artificial Ant Colony Clustering and Genetic Programming", booktitle = "Proceedings of the 2003 Congress on Evolutionary Computation CEC2003", editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon", pages = "1384--1391", year = "2003", publisher = "IEEE Press", address = "Canberra", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "8-12 " # dec, organisation = "IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", keywords = "genetic algorithms, genetic programming, Web Usage Mining, Ant Systems, Stigmergy, Data-Mining, Linear Genetic Programming, Adaptive control, Ant colony optimization, Artificial intelligence, Communication system traffic control, Decision support systems, Knowledge management, Marketing management, Programmable control, Traffic control, Internet, artificial life, data mining, decision support systems, electronic commerce, self-organising feature maps, statistical analysis, Web site management, Web usage mining, artificial ant colony clustering algorithm, decision support systems, distributed adaptive organisation, distributed control problems, e-commerce, intelligent marketing strategies, knowledge discovery, knowledge retrieval, network traffic flow analysis, self-organizing map", ISBN = "0-7803-7804-0", URL = "http://alfa.ist.utl.pt/~cvrm/staff/vramos/Vramos-CEC03b.pdf", URL = "http://arxiv.org/abs/cs/0412071", DOI = "doi:10.1109/CEC.2003.1299832", size = "8 pages", abstract = "The rapid e-commerce growth has made both business community and customers face a new situation. Due to intense competition on one hand and the customer's option to choose from several alternatives business community has realized the necessity of intelligent marketing strategies and relationship management. Web usage mining attempts to discover useful knowledge from the secondary data obtained from the interactions of the users with the Web. Web usage mining has become very critical for effective Web site management, creating adaptive Web sites, business and support services, personalization, network traffic flow analysis and so on. The study of ant colonies behavior and their self-organizing capabilities is of interest to knowledge retrieval/management and decision support systems sciences, because it provides models of distributed adaptive organization, which are useful to solve difficult optimization, classification, and distributed control problems, among others. In this paper, we propose an ant clustering algorithm to discover Web usage patterns (data clusters) and a linear genetic programming approach to analyze the visitor trends. Empirical results clearly shows that ant colony clustering performs well when compared to a self-organizing map (for clustering Web usage patterns) even though the performance accuracy is not that efficient when comparared to evolutionary-fuzzy clustering (i-miner) approach.", notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.", } @TechReport{abraham:2004:0405046, author = "Ajith Abraham and Ravi Jain", title = "Soft Computing Models for Network Intrusion Detection Systems", institution = "OSU", year = "2004", month = "13 " # may # " 2004", note = "Journal-ref: Soft Computing in Knowledge Discovery: Methods and Applications, Saman Halgamuge and Lipo Wang (Eds.), Studies in Fuzziness and Soft Computing, Springer Verlag Germany, Chapter 16, 20 pages, 2004", keywords = "genetic algorithms, genetic programming, Cryptography and Security", URL = "http://www.softcomputing.net/saman2.pdf", URL = "http://arxiv.org/abs/cs/0405046", abstract = "Security of computers and the networks that connect them is increasingly becoming of great significance. Computer security is defined as the protection of computing systems against threats to confidentiality, integrity, and availability. There are two types of intruders: external intruders, who are unauthorised users of the machines they attack, and internal intruders, who have permission to access the system with some restrictions. This chapter presents a soft computing approach to detect intrusions in a network. Among the several soft computing paradigms, we investigated fuzzy rule-based classifiers, decision trees, support vector machines, linear genetic programming and an ensemble method to model fast and efficient intrusion detection systems. Empirical results clearly show that soft computing approach could play a major role for intrusion detection.", notes = "ACM-class: K.6.5 cs.CR/0405046", size = "20 pages", } @Article{Abraham:2003:JIKM, author = "Ajith Abraham", title = "Business Intelligence from Web Usage Mining", journal = "Journal of Information \& Knowledge Management", year = "2003", volume = "2", number = "4", pages = "375--390", keywords = "genetic algorithms, genetic programming, Web mining, knowledge discovery, business intelligence, hybrid soft computing, neuro-fuzzy-genetic system", URL = "http://www.softcomputing.net/jikm.pdf", DOI = "doi:10.1142/S0219649203000565", size = "16 pages", abstract = "The rapid e-commerce growth has made both business community and customers face a new situation. Due to intense competition on the one hand and the customer's option to choose from several alternatives, the business community has realized the necessity of intelligent marketing strategies and relationship management. Web usage mining attempts to discover useful knowledge from the secondary data obtained from the interactions of the users with the Web. Web usage mining has become very critical for effective Web site management, creating adaptive Web sites, business and support services, personalization, network traffic flow analysis and so on. This paper presents the important concepts of Web usage mining and its various practical applications. Further a novel approach called {"}intelligent-miner{"} (i-Miner) is presented. i-Miner could optimize the concurrent architecture of a fuzzy clustering algorithm (to discover web data clusters) and a fuzzy inference system to analyze the Web site visitor trends. A hybrid evolutionary fuzzy clustering algorithm is proposed to optimally segregate similar user interests. The clustered data is then used to analyze the trends using a Takagi-Sugeno fuzzy inference system learned using a combination of evolutionary algorithm and neural network learning. Proposed approach is compared with self-organizing maps (to discover patterns) and several function approximation techniques like neural networks, linear genetic programming and Takagi?Sugeno fuzzy inference system (to analyze the clusters). The results are graphically illustrated and the practical significance is discussed in detail. Empirical results clearly show that the proposed Web usage-mining framework is efficient.", notes = "see also \cite{oai:arXiv.org:cs/0405030} http://www.worldscinet.com/jikm/jikm.shtml http://ajith.softcomputing.net Department of Computer Science, Oklahoma State University, 700 N Greenwood Avenue, Tulsa, Oklahoma 74106-0700, USA", } @Misc{oai:arXiv.org:cs/0405030, title = "Business Intelligence from Web Usage Mining", author = "Ajith Abraham", year = "2004", month = may # "~06", keywords = "genetic algorithms, genetic programming", abstract = "The rapid e-commerce growth has made both business community and customers face a new situation. Due to intense competition on one hand and the customer's option to choose from several alternatives business community has realized the necessity of intelligent marketing strategies and relationship management. Web usage mining attempts to discover useful knowledge from the secondary data obtained from the interactions of the users with the Web. Web usage mining has become very critical for effective Web site management, creating adaptive Web sites, business and support services, personalization, network traffic flow analysis and so on. In this paper, we present the important concepts of Web usage mining and its various practical applications. We further present a novel approach 'intelligent-miner' (i-Miner) to optimize the concurrent architecture of a fuzzy clustering algorithm (to discover web data clusters) and a fuzzy inference system to analyze the Web site visitor trends. A hybrid evolutionary fuzzy clustering algorithm is proposed in this paper to optimally segregate similar user interests. The clustered data is then used to analyze the trends using a Takagi-Sugeno fuzzy inference system learned using a combination of evolutionary algorithm and neural network learning. Proposed approach is compared with self-organizing maps (to discover patterns) and several function approximation techniques like neural networks, linear genetic programming and Takagi-Sugeno fuzzy inference system (to analyze the clusters). The results are graphically illustrated and the practical significance is discussed in detail. Empirical results clearly show that the proposed Web usage-mining framework is efficient.", identifier = "Journal of Information \& Knowledge Management (JIKM), World Scientific Publishing Co., Singapore, Vol. 2, No. 4, pp. 375-390, 2003", oai = "oai:arXiv.org:cs/0405030", URL = "http://arXiv.org/abs/cs/0405030", notes = "see also \cite{Abraham:2003:JIKM}", } @InCollection{abraham:2004:ECDM, author = "Ajith Abraham", title = "Evolutionary Computation in Intelligent Network Management", booktitle = "Evolutionary Computing in Data Mining", publisher = "Springer", year = "2004", editor = "Ashish Ghosh and Lakhmi C. Jain", volume = "163", series = "Studies in Fuzziness and Soft Computing", chapter = "9", pages = "189--210", keywords = "genetic algorithms, genetic programming, Linear Genetic Programming, LGP, intrusion detection, ANN, www, fuzzy clustering, fuzzy inference, computer security, RIPPER, demes (ring topology), steady state 32-bit FPU machine code GP, SVM, decision trees, i-miner", ISBN = "3-540-22370-3", URL = "http://www.softcomputing.net/ec_web-chapter.pdf", URL = "http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-175-22-33980376-0,00.html", abstract = "Data mining is an iterative and interactive process concerned with discovering patterns, associations and periodicity in real world data. This chapter presents two real world applications where evolutionary computation has been used to solve network management problems. First, we investigate the suitability of linear genetic programming (LGP) technique to model fast and efficient intrusion detection systems, while comparing its performance with artificial neural networks and classification and regression trees. Second, we use evolutionary algorithms for a Web usage-mining problem. Web usage mining attempts to discover useful knowledge from the secondary data obtained from the interactions of the users with the Web. Evolutionary algorithm is used to optimise the concurrent architecture of a fuzzy clustering algorithm (to discover data clusters) and a fuzzy inference system to analyse the trends. Empirical results clearly shows that evolutionary algorithm could play a major rule for the problems considered and hence an important data mining tool.", size = "22 pages", } @InCollection{intro:2006:GSP, author = "Ajith Abraham and Nadia Nedjah and Luiza {de Macedo Mourelle}", title = "Evolutionary Computation: from Genetic Algorithms to Genetic Programming", year = "2006", booktitle = "Genetic Systems Programming: Theory and Experiences", pages = "1--20", volume = "13", series = "Studies in Computational Intelligence", editor = "Nadia Nedjah and Ajith Abraham and Luiza {de Macedo Mourelle}", publisher = "Springer", address = "Germany", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", ISBN = "3-540-29849-5", URL = "http://www.softcomputing.net/gpsystems.pdf", DOI = "doi:10.1007/3-540-32498-4_1", abstract = "Evolutionary computation, offers practical advantages to the researcher facing difficult optimisation problems. These advantages are multi-fold, including the simplicity of the approach, its robust response to changing circumstance, its flexibility, and many other facets. The evolutionary approach can be applied to problems where heuristic solutions are not available or generally lead to unsatisfactory results. As a result, evolutionary computation have received increased interest, particularly with regards to the manner in which they may be applied for practical problem solving. we review the development of the field of evolutionary computations from standard genetic algorithms to genetic programming, passing by evolution strategies and evolutionary programming. For each of these orientations, we identify the main differences from the others. We also, describe the most popular variants of genetic programming. These include linear genetic programming (LGP), gene expression programming (GEP), multi-expression programming (MEP), Cartesian genetic programming (CGP), traceless genetic programming (TGP), gramatical evolution (GE) and genetic algorithm for deriving software (GADS).", notes = "http://www.springer.com/sgw/cda/frontpage/0,11855,5-146-22-92733168-0,00.html", size = "21 pages", } @InCollection{abraham:2006:GSP, author = "Ajith Abraham and Crina Grosan", title = "Evolving Intrusion Detection Systems", year = "2006", booktitle = "Genetic Systems Programming: Theory and Experiences", pages = "57--79", volume = "13", series = "Studies in Computational Intelligence", editor = "Nadia Nedjah and Ajith Abraham and Luiza {de Macedo Mourelle}", publisher = "Springer", address = "Germany", email = "ajith.abraham@ieee.org", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-29849-5", URL = "http://falklands.globat.com/~softcomputing.net/ids-chapter.pdf", DOI = "doi:10.1007/3-540-32498-4_3", abstract = "An Intrusion Detection System (IDS) is a program that analyses what happens or has happened during an execution and tries to find indications that the computer has been misused. An IDS does not eliminate the use of preventive mechanism but it works as the last defensive mechanism in securing the system. We evaluate the performances of two Genetic Programming techniques for IDS namely Linear Genetic Programming (LGP) and Multi-Expression Programming (MEP). Results are then compared with some machine learning techniques like Support Vector Machines (SVM) and Decision Trees (DT). Empirical results clearly show that GP techniques could play an important role in designing real time intrusion detection systems.", notes = "http://www.springer.com/sgw/cda/frontpage/0,11855,5-146-22-92733168-0,00.html", } @InProceedings{abraham:2005:CEC, author = "Ajith Abraham and Crina Grosan", title = "Genetic Programming Approach for Fault Modeling of Electronic Hardware", booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary Computation", year = "2005", editor = "David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L. Gwenn Volkert and Dan Ashlock and Marc Schoenauer", volume = "2", pages = "1563--1569", address = "Edinburgh, UK", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "2-5 " # sep, organisation = "IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, MEP, ANN, LGP", ISBN = "0-7803-9363-5", URL = "http://www.softcomputing.net/cec05.pdf", DOI = "doi:10.1109/CEC.2005.1554875", size = "7 pages", abstract = "presents two variants of Genetic Programming (GP) approaches for intelligent online performance monitoring of electronic circuits and systems. Reliability modelling of electronic circuits can be best performed by the stressor - susceptibility interaction model. A circuit or a system is deemed to be failed once the stressor has exceeded the susceptibility limits. For on-line prediction, validated stressor vectors may be obtained by direct measurements or sensors, which after preprocessing and standardisation are fed into the GP models. Empirical results are compared with artificial neural networks trained using backpropagation algorithm. The performance of the proposed method is evaluated by comparing the experiment results with the actual failure model values. The developed model reveals that GP could play an important role for future fault monitoring systems.", notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS.", } @Article{journals/jikm/AbrahamG06, author = "Ajith Abraham and Crina Grosan", title = "Decision Support Systems Using Ensemble Genetic Programming", journal = "Journal of Information \& Knowledge Management (JIKM)", year = "2006", volume = "5", number = "4", pages = "303--313", month = dec, note = "Special topic: Knowledge Discovery Using Advanced Computational Intelligence Tools", keywords = "genetic algorithms, genetic programming, gene expression programming, Decision support systems, ensemble systems, evolutionary multi-objective optimisation", ISSN = "0219-6492", DOI = "doi:10.1142/S0219649206001566", abstract = "This paper proposes a decision support system for tactical air combat environment using a combination of unsupervised learning for clustering the data and an ensemble of three well-known genetic programming techniques to classify the different decision regions accurately. The genetic programming techniques used are: Linear Genetic programming (LGP), Multi-Expression Programming (MEP) and Gene Expression Programming (GEP). The clustered data are used as the inputs to the genetic programming algorithms. Some simulation results demonstrating the difference of these techniques are also performed. Test results reveal that the proposed ensemble method performed better than the individual GP approaches and that the method is efficient.", bibdate = "2008-06-20", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/jikm/jikm5.html#AbrahamG06", } @Article{Abraham:2007:JNCS, author = "Ajith Abraham and Ravi Jain and Johnson Thomas and Sang Yong Han", title = "D-SCIDS: Distributed soft computing intrusion detection system", journal = "Journal of Network and Computer Applications", year = "2007", volume = "30", number = "1", pages = "81--98", month = jan, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1016/j.jnca.2005.06.001", abstract = "An Intrusion Detection System (IDS) is a program that analyses what happens or has happened during an execution and tries to find indications that the computer has been misused. A Distributed IDS (DIDS) consists of several IDS over a large network (s), all of which communicate with each other, or with a central server that facilitates advanced network monitoring. In a distributed environment, DIDS are implemented using co-operative intelligent agents distributed across the network(s). This paper evaluates three fuzzy rule-based classifiers to detect intrusions in a network. Results are then compared with other machine learning techniques like decision trees, support vector machines and linear genetic programming. Further, we modelled Distributed Soft Computing-based IDS (D-SCIDS) as a combination of different classifiers to model lightweight and more accurate (heavy weight) IDS. Empirical results clearly show that soft computing approach could play a major role for intrusion detection.", } @InProceedings{Abraham:2008:ieeeISI, author = "Ajith Abraham", title = "Real time intrusion prediction, detection and prevention programs", booktitle = "IEEE International Conference on Intelligence and Security Informatics, ISI 2008", year = "2008", month = jun, pages = "xli--xlii", note = "IEEE ISI 2008 Invited Talk (VI)", keywords = "genetic algorithms, genetic programming, distributed intrusion detection systems, hidden Markov model, intrusion detection program, online risk assessment, real time intrusion detection, real time intrusion prediction, real time intrusion prevention, hidden Markov models, risk management, security of data", DOI = "doi:10.1109/ISI.2008.4565018", size = "1.1 pages", abstract = "An intrusion detection program (IDP) analyzes what happens or has happened during an execution and tries to find indications that the computer has been misused. In this talk, we present some of the challenges in designing efficient intrusion detection systems (IDS) using nature inspired computation techniques, which could provide high accuracy, low false alarm rate and reduced number of features. Then we present some recent research results of developing distributed intrusion detection systems using genetic programming techniques. Further, we illustrate how intruder behavior could be captured using hidden Markov model and predict possible serious intrusions. Finally we illustrate the role of online risk assessment for intrusion prevention systems and some associated results.", notes = "Also known as \cite{4565018}", } @InProceedings{Abraham:2009:UKSIM, author = "Ajith Abraham and Crina Grosan and Vaclav Snasel", title = "Programming Risk Assessment Models for Online Security Evaluation Systems", booktitle = "11th International Conference on Computer Modelling and Simulation, UKSIM '09", year = "2009", month = "25-27 " # mar, pages = "41--46", keywords = "genetic algorithms, genetic programming, genetic programming methods, human reasoning, online security evaluation systems, perception process, programming risk assessment models, risk management, security of data", DOI = "doi:10.1109/UKSIM.2009.75", isbn13 = "978-0-7695-3593-7", abstract = "Risk assessment is often done by human experts, because there is no exact and mathematical solution to the problem.Usually the human reasoning and perception process cannot be expressed precisely. This paper propose a genetic programming approach for risk assessment. Preliminary results indicate that genetic programming methods are robust and suitable for this problem when compared to other risk assessment models.", notes = "Also known as \cite{4809735}", } @InProceedings{Abraham:2009:IAS, author = "Ajith Abraham and Crina Grosan and Hongbo Liu and Yuehui Chen", title = "Hierarchical {Takagi-Sugeno} Models for Online Security Evaluation Systems", booktitle = "Fifth International Conference on Information Assurance and Security, IAS '09", year = "2009", month = aug, volume = "1", pages = "687--692", keywords = "genetic algorithms, genetic programming, hierarchical Takagi-Sugeno models, human perception, human reasoning, intrusion detection, neuro-fuzzy programming, online security evaluation systems, risk assessment, fuzzy reasoning, hierarchical systems, human factors, interactive programming, risk management, security of data", DOI = "doi:10.1109/IAS.2009.348", abstract = "Risk assessment is often done by human experts, because there is no exact and mathematical solution to the problem. Usually the human reasoning and perception process cannot be expressed precisely. This paper propose a light weight risk assessment system based on an Hierarchical Takagi-Sugeno model designed using evolutionary algorithms. Performance comparison is done with neuro-fuzzy and genetic programming methods. Empirical results indicate that the techniques are robust and suitable for developing light weight risk assessment models, which could be integrated with intrusion detection and prevention systems.", notes = "Also known as \cite{5283215}", } @InCollection{abrams:2000:CSAMPR, author = "Zoe Abrams", title = "Complimentary Selection as an Alternative Method for Population Reproduction", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "8--15", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:2000:gagp}", } @InProceedings{abramson:1996:cccGP, author = "Myriam Abramson and Lawrence Hunter", title = "Classification using Cultural Co-Evolution and Genetic Programming", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "249--254", address = "Stanford University, CA, USA", publisher = "MIT Press", broken = "ftp://lhc.nlm.nih.gov/pub/hunter/gp96.ps", size = "6 pages", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap30.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96", } @Article{Abu-Romoh:2018:ieeeCL, author = "M. Abu-Romoh and A. Aboutaleb and Z. Rezki", journal = "IEEE Communications Letters", title = "Automatic Modulation Classification Using Moments And Likelihood Maximization", year = "2018", abstract = "Motivated by the fact that moments of the received signal are easy to compute and can provide a simple way to automatically classify the modulation of the transmitted signal, we propose a hybrid method for automatic modulation classification that lies in the intersection between likelihood-based and feature-based classifiers. Specifically, the proposed method relies on statistical moments along with a maximum likelihood engine. We show that the proposed method offers a good tradeoff between classification accuracy and complexity relative to the Maximum Likelihood (ML) classifier. Furthermore, our classifier outperforms state-of-the-art machine learning classifiers, such as genetic programming-based K-nearest neighbour (GP-KNN) classifiers, the linear support vector machine classifier (LSVM) and the fold-based Kolmogorov-Smirnov (FB-KS) algorithm.", keywords = "genetic algorithms, genetic programming, Feature extraction, Machine learning algorithms, Modulation, Probability density function, Receivers, Signal to noise ratio, Support vector machines", DOI = "doi:10.1109/LCOMM.2018.2806489", ISSN = "1089-7798", notes = "Also known as \cite{8292836}", } @InProceedings{Abubakar:2016:ICCOINS, author = "Mustapha Yusuf Abubakar and Low Tang Jung and Mohamed Nordin Zakaria and Ahmed Younesy and Abdel-Haleem Abdel-Atyz", booktitle = "2016 3rd International Conference on Computer and Information Sciences (ICCOINS)", title = "New universal gate library for synthesizing reversible logic circuit using genetic programming", year = "2016", pages = "316--321", abstract = "We newly formed universal gate library, that includes NOT, CNOT (Feyman), Toffoli, Fredkin, Swap, Peres gates and a special gate called G gate. The gate G on its own is a universal gate, but using it alone in a library will result in large circuit realization. G gate combines the operations of Generalized Toffoli gates. For example a gate called G3 combines the operations of NOT, CNOT and T3 (3 - bit Toffoli) gates all in one place. The new library was used in synthesizing reversible circuits. The experiment was done using Genetic programming algorithm that is capable of allowing the choice of any type of gate library and optimising the circuit. The results were promising because the gate complexity in the circuits were drastically reduced compared to previously attempted synthesis.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICCOINS.2016.7783234", month = aug, notes = "Also known as \cite{7783234}", } @Article{journals/qip/AbubakarJZYA17, author = "Mustapha Yusuf Abubakar and Low Tang Jung and Nordin Zakaria and Ahmed Younes and Abdel-Haleem Abdel-Aty", title = "Reversible circuit synthesis by genetic programming using dynamic gate libraries", journal = "Quantum Information Processing", year = "2017", number = "6", volume = "16", pages = "160", keywords = "genetic algorithms, genetic programming", bibdate = "2017-05-26", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/qip/qip16.html#AbubakarJZYA17", DOI = "doi:10.1007/s11128-017-1609-8", } @InProceedings{Abubakar:2018:ICCOINS, author = "Mustapha Yusuf Abubakar and Low {Tang Jung}", booktitle = "2018 4th International Conference on Computer and Information Sciences (ICCOINS)", title = "Synthesis of Reversible Logic Using Enhanced Genetic Programming Approach", year = "2018", abstract = "A new enhanced reversible logic circuit synthesis method was developed using reversible gates that include NOT, CNOT (Feynman), Toffoli, Fredkin, Swap, and Peres gates. The synthesis method was done using newly developed genetic programming. Usually previous synthesis methods that uses genetic algorithms or other similar evolutionary algorithms suffers a problem known as blotting which is a sudden uncontrolled growth of an individual (circuit), which may render the synthesis inefficient because of memory usage, making the algorithm difficult to continue running and eventually stack in a local minima, there for an optimized reversible circuit may not be generated. In this method the algorithm used was blot free, the blotting was carefully controlled by fixing a suitable length and size of the individuals in the population. Following this approach, the cost of generating circuits was greatly reduced giving the algorithm to reach the end of the last designated generation to give out optimal or near optimal results. The results of the circuits generated using this method were compared with some of the results already in the literature, and in many cases, our results appeared to be better in terms of gate count and quantum cost metrics.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICCOINS.2018.8510602", month = aug, notes = "Also known as \cite{8510602}", } @Article{Abud-Kappel:2016:Measurement, author = "Marco Andre {Abud Kappel} and Ricardo Fabbri and Roberto P. Domingos and Ivan N. Bastos", title = "Novel electrochemical impedance simulation design via stochastic algorithms for fitting equivalent circuits", journal = "Measurement", year = "2016", volume = "94", pages = "344--354", keywords = "genetic algorithms, genetic programming, Differential evolution, Electrochemical impedance, Impedance measurements, Corrosion, Optimization, Stochastic methods", ISSN = "0263-2241", URL = "https://www.sciencedirect.com/science/article/pii/S0263224116304699", DOI = "doi:10.1016/j.measurement.2016.08.008", abstract = "Electrochemical impedance spectroscopy (EIS) is of great value to corrosion studies because it is sensitive to transient changes that occur in the metal-electrolyte interface. A useful way to link the results of electrochemical impedance spectroscopy to corrosion phenomena is by simulating equivalent circuits. Equivalent circuit models are very attractive because of their relative simplicity, enabling the monitoring of electrochemical systems that have a complex physical mechanism. In this paper, the stochastic algorithm Differential Evolution is proposed to fit an equivalent circuit to the EIS results for a wide potential range. EIS is often limited to the corrosion potential despite being widely used. This greatly hinders the analysis regarding the effect of the applied potential, which strongly affects the interface, as shown, for example, in polarization curves. Moreover, the data from both the EIS and the DC values were used in the proposed scheme, allowing the best fit of the model parameters. The approach was compared to the standard Simplex square residual minimization of EIS data. In order to manage the large amount of generated data, the EIS-Mapper software package, which also plots the 2D/3D diagrams with potential, was used to fit the equivalent circuit of multiple diagrams. Furthermore, EIS-Mapper also computed all simulations. The results of 67 impedance diagrams of stainless steel in a 3.5percent NaCl medium at 25C obtained in steps of 10mV, and the respective values of the fitted parameters of the equivalent circuit are reported. The present approach conveys new insight to the use of electrochemical impedance and bridges the gap between polarization curves and equivalent electrical circuits.", } @PhdThesis{Tese_MarcoAndreAbudKappel, author = "Marco Andre Abud Kappel", title_pt = "Emprego de tecnicas computacionais estocasticas para simulacao de diagramasde espectroscopia de impedancia eletroquimica", title = "Stochastic computational techniques applied to the simulation of electrochemical impedance spectroscopy diagrams", school = "Centro de Tecnologia e Ciencias, Instituto Politecnico, Universidadedo Estado do Rio de Janeiro", year = "2016", address = "Nova Friburgo, Brazil", month = "8 " # apr, keywords = "genetic algorithms, genetic programming, Electrochemical impedance spectroscopy, Corrosion, Complex nonlinear optimization, Equivalent electrical circuit, Stochastic methods", URL = "http://www.bdtd.uerj.br/handle/1/13692", URL = "https://www.bdtd.uerj.br:8443/bitstream/1/13692/1/Tese_MarcoAndreAbudKappel.pdf", size = "169 pages", abstract = "Electrochemical impedance spectroscopy is a widely used technique in electrochemical systems characterization. With applications in several areas, the technique is very useful in the study of corrosion because it is sensitive to transient changes that occur in the metal interface. The results from the technique can be expressed and interpreted in different ways, allowing different modeling and analysis methods, such as the use of kinetic models or equivalent circuits. In corrosion, the technique is usually applied only in a few specific potentials, such as the corrosion potential, the most important. With the motivation of improving the impedance modeling and analysis process, taking into consideration that the electrochemical phenomena are strongly linked to the potential, this work introduces the possibility to express the impedance data in a wide potential range, and use them to equivalent circuits fitting. Thus, different phenomena can be modeled adequately by equivalent circuits corresponding to different potentials. For this purpose, the related inverse problem is solved for each potential through a complex nonlinear optimization process. In addition to the transient data obtained by the spectroscopy, stationary data are also used in the optimization as a regularisation factor, supporting a consistent solution to the physical phenomena involved, from the maximum experimental frequency to theoretical zero frequency. An analysis, modeling and simulation software was developed with the following features: 1) validation of experimental data, through the Kramers-Kronig relations; 2) simultaneous visualization of impedance results for a wide potential range; 3) fitting different equivalent circuits for different ranges using transient and stationary experimental data, in conjunction with deterministic or stochastic methods; 4) generation of confidence regions for the estimated parameters, making them statistically significant; 5) simulations using the fitted equivalent circuits in computer cluster; 6) parameter sensitivity analysis according to the applied potential, revealing important physical characteristics involved in the electrochemical processes. Finally, experimental fitting results and the corresponding simulations are shown and discussed. Results show that the use of a population-based stochastic optimization method not only increases the odds of finding the global optimum, but also enables the generation of confidence regions around the found values. Furthermore, only the circuit fitted with the new objective function has equivalence with both transient data and stationary data for the entire potential range involved.", resumo = "A espectroscopia de impedancia eletroquimica e uma tecnica amplamente utilizada na caracterizacao de sistemas eletroquimicos. Alem de possuir aplicacoes em diversas areas, a tecnica tem grande utilidade no estudo dacorrosao, pois e sensivel as variacoes transientes que ocorrem na interface metalica. Os resultados provenientes da tecnica podem ser expressos e interpretados de diversas formas, possibilitando diferentes metodologias de modelagem e analise, como o uso de modelos cineticos ou circuitos eletricos equivalentes. Em corrosao, a tecnica e aplicada, normalmente, em poucos potenciais especificos, como o de corrosao, o de maior importancia. Com a motivacao de aprimoraro procedimento de modelagem e analise de dados de impedancia, levando em consideracaoque os fenomenos eletroquimicos estao fortemente ligadosao potencial, este trabalho introduz a possibilidadede expressar os dados de impedancia em uma ampla faixa de potencial, e utiliza-los para ajuste de circuitos equivalentes. Assim, os diferentes fenomenos podem ser modelados, adequadamente, por circuitos eletricosequivalentes correspondentes a diferentes potenciais. Com esta finalidade, oproblema inverso associado eresolvidopara cada potencial, por meio de um processo de otimizacao complexa nao-linear. Alem dos dados transientes obtidos pela espectroscopia, dados estacionariossao utilizados na otimizacao de forma original, como uma regularizacao do problema, ajudando a garantir a obtencao de uma solucao coerente com os fenomenos fisicos envolvidos,desde a frequencia maxima do ensaio ate a frequencia nula. Um software de analise, modelagem e simulacao foi desenvolvido, com as seguintes funcionalidades: 1) validacaodos dados experimentais, por meio das relacoes de Kramers-Kronig; 2) visualizacao simultanea dos dados de impedanciapara ampla faixa de potencial; 3) ajuste dediferentes circuitos equivalentes para diferentes faixas,utilizando dados experimentais transientes e estacionarios, em conjunto com metodosdeterministicosou estocasticos; 4) geracao deregioes de confianca para os parametros ajustados, tornando-os estatisticamente significativos; 5) simulacoes utilizando os circuitos equivalentesajustadosem cluster de computador; 6) apresentacao de analise de sensibilidade dos parametros de acordo com o potencialaplicado, revelando caracteristicas fisicasimportantes envolvidas nos processos eletroquimicos. Por fim, resultados experimentaisdosajustes e das simulacoes correspondentes sao mostrados e discutidos.Os resultados obtidos mostram que a utilizacao de um metodo de otimizacao estocastico populacional nao apenas aumenta as probabilidades de se encontrar uma solucao melhor, como tambem possibilita a geracao das regioes de confianca em torno dos valores encontrados. Alem disso, apenas o circuito ajustado com a nova funcao objetivo possui equivalencia tanto com os dados transientes quanto com os dados estacionarios, para toda a faixa de potencial envolvida.", notes = "In Portuguese. Supervisors: Ivan Napoleao Bastos and Roberto Pinheiro Domingos", } @Article{Abud-Kappel:2017:ASC, author = "Marco Andre {Abud Kappel} and Fernando Cunha Peixoto and Gustavo Mendes Platt and Roberto Pinheiro Domingos and Ivan Napoleao Bastos", title = "A study of equivalent electrical circuit fitting to electrochemical impedance using a stochastic method", journal = "Applied Soft Computing", year = "2017", volume = "50", pages = "183--193", month = jan, keywords = "genetic algorithms, genetic programming, Differential evolution, Electrochemical impedance, Optimization, Stochastic method, Statistical analysis", ISSN = "1568-4946", URL = "https://www.sciencedirect.com/science/article/pii/S1568494616305993", DOI = "doi:10.1016/j.asoc.2016.11.030", size = "11 pages", abstract = "Modeling electrochemical impedance spectroscopy is usually done using equivalent electrical circuits. These circuits have parameters that need to be estimated properly in order to make possible the simulation of impedance data. Despite the fitting procedure is an optimization problem solved recurrently in the literature, rarely statistical significance of the estimated parameters is evaluated. In this work, the optimization process for the equivalent electrical circuit fitting to the impedance data is detailed. First, a mathematical development regarding the minimization of residual least squares is presented in order to obtain a statistically valid objective function of the complex nonlinear regression problem. Then, the optimization method used in this work is presented, the Differential Evolution, a global search stochastic method. Furthermore, it is shown how a population-based stochastic method like this can be used directly to obtain confidence regions to the estimated parameters. A sensitivity analysis was also conducted. Finally, the equivalent circuit fitting is done to model synthetic experimental data, in order to demonstrate the adopted procedure.", } @InProceedings{Abud-Kappel:2018:EngOpt, title = "Cartesian Genetic Programing Applied to Equivalent Electric Circuit Identification", author = "Marco Andre {Abud Kappel} and Roberto Pinheiro Domingos and Ivan Napoleao Bastos", booktitle = "Proceedings of the 6th International Conference on Engineering Optimization. EngOpt 2018", year = "2018", editor = "H. C. Rodrigues and J. Herskovits and C. M. {Mota Soares} and A. L. Araujo and J. M. Guedes and J. O. Folgado and F. Moleiro and J. F. A. Madeira", pages = "913--925", address = "Lisbon, Portugal", month = "17-19 " # sep, organisation = "Instituto Superior Tecnico", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Differential Evolution, Complex nonlinear optimization, Equivalent electric circuit identification", isbn13 = "978-3-319-97773-7", DOI = "doi:10.1007/978-3-319-97773-7_79", abstract = "Equivalent electric circuits are widely used in electrochemical impedance spectroscopy (EIS) data modeling. EIS modeling involves the identification of an electrical circuit physically equivalent to the system under analysis. This equivalence is based on the assumption that each phenomenon of the electrode interface and the electrolyte is represented by electrical components such as resistors, capacitors and inductors. This analogy allows impedance data to be used in simulations and predictions related to corrosion and electrochemistry. However, when no prior knowledge of the inner workings of the process under analysis is available, the identification of the circuit model is not a trivial task. The main objective of this work is to improve both the equivalent circuit topology identification and the parameter estimation by using a different approach than the usual Genetic Programming. In order to accomplish this goal, a methodology was developed to unify the application of Cartesian Genetic Programming to tackle system topology identification and Differential Evolution for optimization of the circuit parameters. The performance and effectiveness of this methodology were tested by performing the circuit identification on four different known systems, using numerically simulated impedance data. Results showed that the applied methodology was able to identify with satisfactory precision both the circuits and the values of the components. Results also indicated the necessity of using a stochastic method in the optimization process, since more than one electric circuit can fit the same dataset. The use of evolutionary adaptive metaheuristics such as the Cartesian Genetic Programming allows not only the estimation of the model parameters, but also the identification of its optimal topology. However, due to the possibility of multiple solutions, its application must be done with caution. Whenever possible, restrictions on the search space should be added, bearing in mind the correspondence of the model to the studied physical phenomena.", notes = "XVI Encontro de Modelagem Computacional ?", } @InProceedings{Abud-Kappel:2019:BRACIS, author = "Marco Andre {Abud Kappel}", booktitle = "2019 8th Brazilian Conference on Intelligent Systems (BRACIS)", title = "Action Scheduling Optimization using Cartesian Genetic Programming", year = "2019", pages = "293--298", month = oct, keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", ISSN = "2643-6264", DOI = "doi:10.1109/BRACIS.2019.00059", abstract = "Action scheduling optimisation is a problem that involves chronologically organizing a set of actions, jobs or commands in order to accomplish a pre-established goal. This kind of problem can be found in a number of areas, such as production planning, delivery logistic organization, robot movement planning and behavior programming for intelligent agents in games. Despite being a recurrent problem, selecting the appropriate time and order to execute each task is not trivial, and typically involves highly complex techniques. The main objective of this work is to provide a simple alternative to tackle the action scheduling problem, by using Cartesian Genetic Programming as an approach. The proposed solution involves the application of two simple main steps: defining the set of available actions and specifying an objective function to be optimized. Then, by the means of the evolutionary algorithm, an automatically generated schedule will be revealed as the most fitting to the goal. The effectiveness of this methodology was tested by performing an action schedule optimization on two different problems involving virtual agents walking in a simulated environment. In both cases, results showed that, throughout the evolutionary process, the simulated agents naturally chose the most efficient sequential and parallel combination of actions to reach greater distances. The use of evolutionary adaptive metaheuristics such as Cartesian Genetic Programming allows the identification of the best possible schedule of actions to solve a problem.", notes = "Also known as \cite{8923702}", } @InProceedings{AbuDalhoum:2005:ESM, author = "Abdel Latif {Abu Dalhoum} and Moh'd {Al Zoubi} and Marina {de la Cruz} and Alfonso Ortega and Manuel Alfonseca", title = "A Genetic Algorithm for Solving the P-Median Problem", booktitle = "European Simulation and Modeling Conference ESM'2005", year = "2005", editor = "J. Manuel Feliz Teixeira and A. E.{Carvalho Brito}", pages = "141--145", address = "Porto, Portugal", month = oct # " 24-26", organisation = "Eurosim, The European Multidisciplinary Society for Modelling and Simulation Technology", publisher = "http://www.eurosis.org", keywords = "genetic algorithms, genetic programming, grammatical evolution, Christiansen grammar, location allocation, p-median model, grammar evolution", ISBN = "90-77381-22-8", URL = "http://arantxa.ii.uam.es/~alfonsec/docs/confint/pmedian.pdf", URL = "https://www.eurosis.org/cms/files/proceedings_full/ESM2005.deel2.pdf", size = "5 pages", abstract = "One of the most popular location-allocation models among researchers is the p-median. Most of the algorithmic research on these models has been devoted to developing heuristic solution procedures. The major drawback of heuristic methods is that the time required finding solutions can become unmanageable. In this paper, we propose a new algorithm, using different variants of grammar evolution, to solve the p-median problem.", notes = "Title may be listed as 'A Genetic Algorithm for solving the P-Medium Problem'. http://www.eurosis.org/cms/files/proceedings/ESM/ESM2005contents.pdf", } @Article{ABYANI:2022:oceaneng, author = "Mohsen Abyani and Mohammad Reza Bahaari and Mohamad Zarrin and Mohsen Nasseri", title = "Predicting failure pressure of the corroded offshore pipelines using an efficient finite element based algorithm and machine learning techniques", year = "2022", journal = "Ocean Engineering", volume = "254", pages = "111382", month = "15 " # jun, keywords = "genetic algorithms, genetic programming, Offshore pipelines, Corrosion, Artificial neural network, ANN, Genetic programing, Support vector machine, SVM, Random forest, Gaussian process regression", ISSN = "0029-8018", DOI = "doi:10.1016/j.oceaneng.2022.111382", URL = "https://www.sciencedirect.com/science/article/pii/S0029801822007697", abstract = "This paper aims to predict the failure pressure of corroded offshore pipelines, employing different machine learning techniques. To this end, an efficient finite element based algorithm is programmed to numerically estimate the failure pressure of offshore pipelines, subjected to internal corrosion. In this process, since the computational effort of such numerical assessment is very high, the application of reliable machine learning methods is used as an alternative solution. Thus, 1815 realizations of four variables are generated, and each one is keyed into the numerical model of a sample pipeline. Thereafter, the machine learning models are constructed based on the results of the numerical analyses, and their performance are compared with each other. The results indicate that Gaussian Process Regression (GPR) and MultiLayer Perceptron (MLP) have the best performance among all the chosen models. Considering the testing dataset, the squared correlation coefficient and Root Mean Squared Error (RMSE) values of GPR and MLP models are 0.535, 0.545 and 0.993 and 0.992, respectively. Moreover, the Maximum Von-Mises Stress (MVMS) of the pipeline increases as the water depth grows at low levels of Internal Pressure (IP). Inversely, increase in water depth leads to reduction in the MVMS values at high IP levels", notes = "Also known as \cite{ABYANI2022111382}", } @TechReport{AcarM05tr, author = "Aybar C. Acar and Amihai Motro", title = "Intensional Encapsulations of Database Subsets by Genetic Programming", institution = "Information and Software Engineering Department, The Volgenau School of Information Technology and Engineering, George Mason University", year = "2005", number = "ISE-TR-05-01", month = feb, keywords = "genetic algorithms, genetic programming", URL = "http://ise.gmu.edu/techrep/2005/05_01.pdf", abstract = "Finding intensional encapsulations of database subsets is the inverse of query evaluation. Whereas query evaluation transforms an intensional expression (the query) to its extension (a set of data values), intensional encapsulation assigns an intensional expression to a given set of data values. We describe a method for deriving intensional representations of subsets of records in large database tables. Our method is based on the paradigm of genetic programming. It is shown to achieve high accuracy and maintain compact expression size, while requiring cost that is acceptable to all applications, but those that require instantaneous results. Intensional encapsulation has a broad range of applications including cooperative answering, information integration, security and data mining.", notes = "See \cite{conf/dexa/AcarM05}", size = "17 pages", } @InProceedings{conf/dexa/AcarM05, title = "Intensional Encapsulations of Database Subsets via Genetic Programming", author = "Aybar C. Acar and Amihai Motro", year = "2005", pages = "365--374", editor = "Kim Viborg Andersen and John K. Debenham and Roland Wagner", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3588", booktitle = "Database and Expert Systems Applications, 16th International Conference, DEXA 2005, Proceedings", address = "Copenhagen, Denmark", month = aug # " 22-26", bibdate = "2005-11-03", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/dexa/dexa2005.html#AcarM05", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-28566-0", DOI = "doi:10.1007/11546924_36", size = "10 pages", abstract = "Finding intensional encapsulations of database subsets is the inverse of query evaluation. Whereas query evaluation transforms an intensional expression (the query) to its extension (a set of data values), intensional encapsulation assigns an intensional expression to a given set of data values. We describe a method for deriving intensional representations of subsets of records in large database tables. Our method is based on the paradigm of genetic programming. It is shown to achieve high accuracy and maintain compact expression size, while requiring cost that is acceptable to all applications, but those that require instantaneous results. Intensional encapsulation has a broad range of applications including cooperative answering, information integration, security and data mining.", notes = "See also \cite{AcarM05tr}", } @PhdThesis{Acar:thesis, author = "Aybar C. Acar", title = "Query Consolidation: Interpreting Queries Sent to Independent Heterogenous Databases", school = "The Volgenau School of Information Technology and Engineering, George Mason University", year = "2008", address = "Fairfax, VA, USA", month = "23 " # jul, keywords = "genetic algorithms, genetic programming, Databases, Information Integration, Query Processing, Machine Learning", URL = "http://hdl.handle.net/1920/3223", URL = "http://digilib.gmu.edu:8080/dspace/bitstream/1920/3223/1/Acar_Aybar.pdf", size = "182 pages", abstract = "This dissertation introduces the problem of query consolidation, which seeks to interpret a set of disparate queries submitted to independent databases with a single global query. The problem has multiple applications, from improving virtual database design, to aiding users in information retrieval, to protecting against inference of sensitive data from a seemingly innocuous set of apparently unrelated queries. The problem exhibits attractive duality with the much-researched problem of query decomposition, which has been addressed intensively in the context of multidatabase environments: How to decompose a query submitted to a virtual database into a set of local queries that are evaluated in individual databases. The new problem is set in the architecture of a canonical multidatabase system, using it in the reverse direction. The reversal is built on the assumption of conjunctive queries and source descriptions. A rational and efficient query decomposition strategy is also assumed, and this decomposition is reversed to arrive at the original query by analyzing the decomposed components. The process incorporates several steps where a number of solutions must be considered, due to the fact that query decomposition is not injective. Initially, the problem of finding the most likely join plan between component queries is investigated. This is accomplished by leveraging the referential constraints available in the underlying multidatabase, or by approximating these constraints from the data when not available. This approximation is done using the information theoretic concept of conditional entropy. Furthermore, the most likely join plans are enhanced by the expansion of their projections and adding precision to their selection constraints by estimating the selection constraints that would be applied to these consolidations offline. Additionally, the extraction of a set of queries related to the same retrieval task from an ongoing sequence of incoming queries is investigated. A conditional random field model is trained to segment and label incoming query sequences. Finally, the candidate consolidations are re-encapsulated with a genetic programming approach to find simpler intentional descriptions that are extensionally equivalent to discover the original intent of the query. The dissertation explains and discusses all of the above operations and validates the methods developed with experimentation on synthesised and real-world data. The results are highly encouraging and verify that the accuracy, time performance, and scalability of the methods would make it possible to exploit query consolidation in production environments.", notes = "GP chapters 7, 8", } @Article{ACEVEDO:2020:ESA, author = "Nicolas Acevedo and Carlos Rey and Carlos Contreras-Bolton and Victor Parada", title = "Automatic design of specialized algorithms for the binary knapsack problem", journal = "Expert Systems with Applications", year = "2020", volume = "141", pages = "112908", keywords = "genetic algorithms, genetic programming, Automatic generation of algorithms, Binary knapsack problem, Hyperheuristic, Generative design of algorithms", ISSN = "0957-4174", URL = "http://www.sciencedirect.com/science/article/pii/S0957417419306268", DOI = "doi:10.1016/j.eswa.2019.112908", abstract = "Not all problem instances of a difficult combinatorial optimization problem have the same degree of difficulty for a given algorithm. Surprisingly, apparently similar problem instances may require notably different computational efforts to be solved. Few studies have explored the case that the algorithm that solves a combinatorial optimization problem is automatically designed. In consequence, the generation of the best algorithms may produce specialized algorithms according to the problem instances used during the constructive step. Following a constructive process based on genetic programming that combines heuristic components with an exact method, new algorithms for the binary knapsack problem are produced. We found that most of the automatically designed algorithms have better performance when solving instances of the same type used during construction, although the algorithms also perform well with other types of similar instances. The rest of the algorithms are partially specialized. We also found that the exact method that only solves a small knapsack problem has a key role in such results. When the algorithms are produced without considering such a method, the errors are higher. We observed this fact when the algorithms were constructed with a combination of instances from different types. These results suggest that the better the pre-classification of the instances of an optimization problem, the more specific and more efficient are the algorithms produced by the automatic generation of algorithms. Consequently, the method described in this article accelerates the search for efficient methods for NP-hard optimization problems", } @Article{ACHARYA:2020:PRL, author = "Divya Acharya and Shivani Goel and Rishi Asthana and Arpit Bhardwaj", title = "A novel fitness function in genetic programming to handle unbalanced emotion recognition data", journal = "Pattern Recognition Letters", volume = "133", pages = "272--279", year = "2020", ISSN = "0167-8655", DOI = "doi:10.1016/j.patrec.2020.03.005", URL = "http://www.sciencedirect.com/science/article/pii/S0167865520300830", keywords = "genetic algorithms, genetic programming, Emotion recognition, Fitness function, EEG, Fast Fourier transformation", abstract = "In the area of behavioral psychology, real-time emotion recognition by using physiological stimuli is an active topic of interest. This research considers the recognition of two class of emotions i.e., positive and negative emotions using EEG signals in response to happy, horror, sad, and neutral genres. In a noise-free framework for data acquisition of 50 participants, NeuroSky MindWave 2 is used. The dataset collected is unbalanced i.e., there are more instances of positive classes than negative ones. Therefore, accuracy is not a useful metric to assess the results of the unbalanced dataset because of biased results. So, the primary goal of this research is to address the issue of unbalanced emotion recognition dataset classification, for which we are proposing a novel fitness function known as Gap score (G score), which learns about both the classes by giving them equal importance and being unbiased. The genetic programming (GP) framework in which we implemented G score is named as G-score GP (GGP). The second goal is to assess how distinct genres affect human emotion recognition process and to identify an age group that is more active emotionally when their emotions are elicited. Experiments were conducted on EEG data acquired with a single-channel EEG device. We have compared the performance of GGP for the classification of emotions with state-of-the-art methods. The analysis shows that GGP provides 87.61percent classification accuracy by using EEG. In compliance with the self-reported feelings, brain signals of 26 to 35 years of age group provided the highest emotion recognition rate", } @Article{ACHARYA:2020:AA, author = "Divya Acharya and Anosh Billimoria and Neishka Srivastava and Shivani Goel and Arpit Bhardwaj", title = "Emotion recognition using fourier transform and genetic programming", journal = "Applied Acoustics", year = "2020", volume = "164", pages = "107260", month = jul, keywords = "genetic algorithms, genetic programming, Electroencephalogram, Fast Fourier Transform, Emotion recognition, Movie clips, Cinema Films", ISSN = "0003-682X", URL = "http://lrcdrs.bennett.edu.in:80/handle/123456789/1183", URL = "http://www.sciencedirect.com/science/article/pii/S0003682X19306954", DOI = "doi:10.1016/j.apacoust.2020.107260", abstract = "In cognitive science, the real-time recognition of humans emotional state is pertinent for machine emotional intelligence and human-machine interaction. Conventional emotion recognition systems use subjective feedback questionnaires, analysis of facial features from videos, and online sentiment analysis. This research proposes a system for real-time detection of emotions in response to emotional movie clips. These movie clips elicitate emotions in humans, and during that time, we have recorded their brain signals using Electroencephalogram (EEG) device and analyze their emotional state. This research work considered four class of emotions (happy, calm, fear, and sadness). This method leverages Fast Fourier Transform (FFT) for feature extraction and Genetic Programming (GP) for classification of EEG data. Experiments were conducted on EEG data acquired with a single dry electrode device NeuroSky Mind Wave 2. To collect data, a standardized database of 23 emotional Hindi film clips were used. All clips individually induce different emotions, and data collection was done based on these emotions elicited as the clips contain emotionally inductive scenes. Twenty participants took part in this study and volunteered for data collection. This system classifies four discrete emotions which are: happy, calm, fear, and sadness with an average of 89.14percent accuracy. These results demonstrated improvements in state-of-the-art methods and affirmed the potential use of our method for recognising these emotions", notes = "Also known as \cite{ACHARYA2020107260}", } @Article{ACHARYA:2021:ESA, author = "Divya Acharya and Nandana Varshney and Anindiya Vedant and Yashraj Saxena and Pradeep Tomar and Shivani Goel and Arpit Bhardwaj", title = "An enhanced fitness function to recognize unbalanced human emotions data", journal = "Expert Systems with Applications", volume = "166", pages = "114011", year = "2021", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2020.114011", URL = "https://www.sciencedirect.com/science/article/pii/S0957417420307843", keywords = "genetic algorithms, genetic programming, Emotion recognition, Fitness function, EEG, Fast Fourier Transformation, Unbalanced dataset", abstract = "In cognitive science and human-computer interaction, automatic human emotion recognition using physiological stimuli is a key technology. This research considers two-class (positive and negative) of emotions recognition using electroencephalogram (EEG) signals in response to an emotional clip from the genres happy, amusement, sad, and horror. This paper introduces an enhanced fitness function named as eD-score to recognize emotions using EEG signals. The primary goal of this research is to assess how genres affect human emotions. We also analyzed human behaviour based on age and gender responsiveness. We have compared the performance of Multilayer Perceptron (MLP), K-nearest neighbors (KNN), Support Vector Machine (SVM), D-score Genetic Programming (DGP), and enhanced D-score Genetic Programming (eDGP) for classification of emotions. The analysis shows that for two class of emotion eDGP provides classification accuracy as 83.33percent, 84.69percent, 85.88percent, and 87.61percent for 50-50, 60-40, 70-30, and 10-fold cross-validations. Generalizability and reliability of this approach is evaluated by applying the proposed approach to publicly available EEG datasets DEAP and SEED. When participants in this research are exposed to amusement genre, their reaction is positive emotion. In compliance with the self-reported feelings, brain signals of 26-35 years of age group provided the highest emotional identification. Among genders, females are more emotionally active as compared to males. These results affirmed the potential use of our method for recognizing emotions", } @Article{Acharyya:2024:KJCE, author = "Rana Acharyya and Arindam Dey", title = "Application of {MGGP} in Predicting Bearing Capacity of a Strip Footing Resting on the Crest of a Marginal Soil Hillslope", journal = "KSCE Journal of Civil Engineering", year = "2024", volume = "28", number = "10", pages = "4244--4257", keywords = "genetic algorithms, genetic programming, Strip footing on slope, Finite element analysis, Maximum bearing strength, MGGP, Sensitivity assessment", ISSN = "1226-7988", URL = "https://www.sciencedirect.com/science/article/pii/S1226798825001114", DOI = "doi:10.1007/s12205-024-1217-y", abstract = "A set of finite element investigations are performed to examine the maximum bearing strength of strip footings positioned on the crest of a cohesive-frictional marginal soil hillslope. In this regard, the influence of contributing geometrical and geotechnical parameters on the maximum bearing strength of the footing are illustrated. It is revealed that the nearness of slope face has negligible influence on the bearing strength of footing if it is located at a setback distance beyond six times the footing width. Further, using multi-gene genetic programming technique, a predictive relationship between the maximum bearing strength and the contributory factors is established and validated through relevant experimental findings. The hyper-parameters of the MGGP model are suitably optimised, as indicated by the coefficient of correlation attaining high magnitudes. A sensitivity analysis based on local perturbation is conducted to recognise the importance ranking of the contributory parameters. It is revealed that the friction angle of slope material predominantly influences the evaluation of maximum bearing strength for strip footing on slopes, followed by other contributing factors", } @InProceedings{Ackling:2011:GECCO, author = "Thomas Ackling and Bradley Alexander and Ian Grunert", title = "Evolving patches for software repair", booktitle = "GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0557-0", pages = "1427--1434", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, SBSE, Debugging, fault-repair, Python", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", URL = "https://hdl.handle.net/2440/70777", DOI = "doi:10.1145/2001576.2001768", publisher = "ACM", publisher_address = "New York, NY, USA", size = "8 pages", abstract = "Defects are a major concern in software systems. Unsurprisingly, there are many tools and techniques to facilitate the removal of defects through their detection and localisation. However, there are few tools that attempt to repair defects. To date, evolutionary tools for software repair have evolved changes directly in the program code being repaired. In this work we describe an implementation: pyEDB, that encodes changes as a series of code modifications or patches. These modifications are evolved as individuals. We show pyEDB to be effective in repairing some small errors, including variable naming errors in Python programs. We also demonstrate that evolving patches rather than whole programs simplifies the removal of spurious errors.", notes = "Sofware bugs cost 2 10**10 dollar pa. Repairs 5 relational operators and wrong name. AST. tracing program execution. mod-tables like gramatical evolution. Tarantula. 32 bit GA. Examples: middleFunc and Facebook smallworld. Also known as \cite{2001768} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)", } @Article{journals/ijprai/Acosta-MendozaMEA14, title = "Learning to Assemble Classifiers via Genetic Programming", author = "Niusvel Acosta-Mendoza and Alicia Morales-Reyes and Hugo Jair Escalante and Andres Gago Alonso", journal = "IJPRAI", year = "2014", number = "7", volume = "28", keywords = "genetic algorithms, genetic programming", bibdate = "2014-10-20", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ijprai/ijprai28.html#Acosta-MendozaMEA14", URL = "http://dx.doi.org/10.1142/S0218001414600052", } @InCollection{Adamatzky:2017:miller, author = "Andrew Adamatzky and Simon Harding and Victor Erokhin and Richard Mayne and Nina Gizzie and Frantisek Baluska and Stefano Mancuso and Georgios Ch. Sirakoulis", title = "Computers from Plants We Never Made: Speculations", booktitle = "Inspired by Nature: Essays Presented to Julian F. Miller on the Occasion of his 60th Birthday", publisher = "Springer", year = "2017", editor = "Susan Stepney and Andrew Adamatzky", volume = "28", series = "Emergence, Complexity and Computation", chapter = "17", pages = "357--387", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-67996-9", DOI = "doi:10.1007/978-3-319-67997-6_17", abstract = "Plants are highly intelligent organisms. They continuously make distributed processing of sensory information, concurrent decision making and parallel actuation. The plants are efficient green computers per se. Outside in nature, the plants are programmed and hardwired to perform a narrow range of tasks aimed to maximize the plants ecological distribution, survival and reproduction. To persuade plants to solve tasks outside their usual range of activities, we must either choose problem domains which homomorphic to the plants natural domains or modify biophysical properties of plants to make them organic electronic devices. We discuss possible designs and prototypes of computing systems that could be based on morphological development of roots, interaction of roots, and analogue electrical computation with plants, and plant-derived electronic components. In morphological plant processors data are represented by initial configuration of roots and configurations of sources of attractants and repellents; results of computation are represented by topology of the roots network. Computation is implemented by the roots following gradients of attractants and repellents, as well as interacting with each other. Problems solvable by plant roots, in principle, include shortest-path, minimum spanning tree, Voronoi diagram, alpha-shapes, convex subdivision of concave polygons. Electrical properties of plants can be modified by loading the plants with functional nanoparticles or coating parts of plants of conductive polymers. Thus, we are in position to make living variable resistors, capacitors, operational amplifiers, multipliers, potentiometers and fixed-function generators. The electrically modified plants can implement summation, integration with respect to time, inversion, multiplication, exponentiation, logarithm, division. Mathematical and engineering problems to be solved can be represented in plant root networks of resistive or reaction elements. Developments in plant-based computing architectures will trigger emergence of a unique community of biologists, electronic engineering and computer scientists working together to produce living electronic devices which future green computers will be made of.", notes = "part of \cite{miller60book} https://link.springer.com/bookseries/10624", } @InProceedings{Adami:2025:GPTP, author = "Chris Adami", title = "How evolution creates complexity: From viruses to brains", booktitle = "Genetic Programming Theory and Practice XXII", year = "2025", editor = "Wolfgang Banzhaf and Bogdan Burlacu and Stephen Kelly and Alexander Lalejini and Fabricio {Olivetti de Franca}", address = "Michigan State University, USA", month = jun # " 5-7", note = "Keynote", keywords = "genetic algorithms, genetic programming", notes = "To be published after the workshop", } @InProceedings{adamopoulos:1999:FMEPUGONN, author = "Adam V. Adamopoulos and Efstratios F. Georgopoulos and Spiridon D. Likothanassis and Photios A. Anninos", title = "Forecasting the MagnetoEncephaloGram (MEG) of Epileptic Patients Using Genetically Optimized Neural Networks", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1457--1462", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "real world applications", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-767.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-767.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InCollection{adams:2002:CSDPSAGP, author = "Thomas P. Adams", title = "Creation of Simple, Deadline, and Priority Scheduling Algorithms using Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2002", year = "2002", editor = "John R. Koza", pages = "1--10", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", URL = "http://www.genetic-programming.org/sp2002/Adams.pdf", notes = "part of \cite{koza:2002:gagp} memory, SETMO..SETM4 ECJ8, iteration (over job queue) branch and selection branch, communicate via memory. Run time used throughout (ie by functions and terminals) to identify jobs (ie scheduling tasks). 32 tasks shortest job first", } @InProceedings{Addis:2014:IACAP, author = "Mark Addis and Peter D. Sozou and Peter C. Lane and Fernand Gobet", title = "Computational Scientific Discovery and Cognitive Science Theories", booktitle = "Computing and Philosophy: Selected Papers from IACAP 2014", year = "2016", editor = "Vincent C. Mueller", pages = "83--97", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-23291-1", URL = "http://eprints.lse.ac.uk/66168/", URL = "https://doi.org/10.1007/978-3-319-23291-1_6", DOI = "doi:10.1007/978-3-319-23291-1_6", abstract = "This study is concerned with processes for discovering new theories in science. It considers a computational approach to scientific discovery, as applied to the discovery of theories in cognitive science. The approach combines two ideas. First, a process-based scientific theory can be represented as a computer program. Second, an evolutionary computational method, genetic programming, allows computer programs to be improved through a process of computational trial and error. Putting these two ideas together leads to a system that can automatically generate and improve scientific theories. The application of this method to the discovery of theories in cognitive science is examined. Theories are built up from primitive operators. These are contained in a theory language that defines the space of possible theories. An example of a theory generated by this method is described. These results support the idea that scientific discovery can be achieved through a heuristic search process, even for theories involving a sequence of steps. However, this computational approach to scientific discovery does not eliminate the need for human input. Human judgement is needed to make reasonable prior assumptions about the characteristics of operators used in the theory generation process, and to interpret and provide context for the computationally generated theories.", notes = "http://www.lse.ac.uk/philosophy/events/rethinking-theory-construction-in-social-science/", } @InProceedings{Adegboye:2017:ieeeSSCI), author = "Adesola Adegboye and Michael Kampouridis and Colin G. Johnson", booktitle = "2017 IEEE Symposium Series on Computational Intelligence (SSCI)", title = "Regression genetic programming for estimating trend end in foreign exchange market", year = "2017", address = "Honolulu, HI, USA", month = "27 " # nov # "-1 " # dec, keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-5386-2727-3", DOI = "doi:10.1109/SSCI.2017.8280833", abstract = "Most forecasting algorithms use a physical time scale for studying price movement in financial markets, making the flow of physical time discontinuous. The use of a physical time scale can make companies oblivious to significant activities in the market, which poses a risk. Directional changes is a different and newer approach, which uses an event-based time scale. This approach summarises data into alternating trends called upward directional change and downward directional change. Each of these trends are further dismembered into directional change (DC) event and overshoot (OS) event. We present a genetic programming (GP) algorithm that evolves equations that express linear and non-linear relationships between the length of DC and OS events in a given dataset. This allows us to have an expectation when a trend will reverse, which can lead to increased profitability. This novel trend reversal estimation approach is then used as part of a DC-based trading strategy. We aim to appraise whether the new knowledge can lead to greater excess return. We assess the efficiency of the modified trading strategy on 250 different directional changes datasets from five different thresholds and five different currency pairs, consisting of intraday data from the foreign exchange (Forex) spot market. Results show that our algorithm is able to return profitable trading strategies and statistically outperform state-of-the-art financial trading strategies, such as technical analysis, buy and hold and other DC-based trading strategies.", notes = "Also known as \cite{8280833} https://www.ele.uri.edu/ieee-ssci2017/", } @Article{ADEGBOYE:2021:ESA, author = "Adesola Adegboye and Michael Kampouridis", title = "Machine learning classification and regression models for predicting directional changes trend reversal in {FX} markets", journal = "Expert Systems with Applications", year = "2021", volume = "173", month = "1 " # jul, pages = "114645", keywords = "genetic algorithms, genetic programming, Directional changes, Regression, Classification, Forex/FX, Machine learning", ISSN = "0957-4174", URL = "https://kar.kent.ac.uk/89886/1/Adegboye-INT2021_preprint.pdf", URL = "https://www.sciencedirect.com/science/article/pii/S0957417421000865", DOI = "doi:10.1016/j.eswa.2021.114645", code_url = "https://github.com/adesolaadegboye/SymbolicRegression", size = "15 pages", abstract = "Most forecasting algorithms in financial markets use physical time for studying price movements, making the flow of time discontinuous. The use of physical time scale can make traders oblivious to significant activities in the market, which poses a risk. Directional changes (DC) is an alternative approach that uses event-based time to sample data. In this work, we propose a novel DC-based framework, which uses machine learning algorithms to predict when a trend will reverse. This allows traders to be in a position to take an action before this happens and thus increase their profitability. We combine our approach with a novel DC-based trading strategy and perform an in-depth investigation, by applying it to 10-min data from 20 foreign exchange markets over a 10-month period. The total number of tested datasets is 1,000, which allows us to argue that our results can be generalised and are widely applicable. We compare our results to ten benchmarks (both DC and non-DC based, such as technical analysis and buy-and-hold). Our findings show that our proposed approach is able to return a significantly higher profit, as well as reduced risk, and statistically outperform the other trading strategies in a number of different performance metrics", notes = "School of Computing, University of Kent, Medway ME4 4AG, UK", } @PhdThesis{Adegboye:thesis, author = "Adesola Tolulope Noah Adegboye", title = "Estimating Directional Changes Trend Reversal in {Forex} Using Machine Learning", school = "University of Kent", year = "2022", address = "UK", month = mar, keywords = "genetic algorithms, genetic programming", URL = "https://kar.kent.ac.uk/94107/", URL = "https://kar.kent.ac.uk/94107/1/174thesis.pdf", DOI = "doi:10.22024/UniKent/01.02.94107", size = "209 pages", abstract = "Most forecasting algorithms use a physical time scale data to study price movement in financial markets by taking snapshots in fixed schedule, making the flow of time discontinuous. The use of a physical time scale can make traders oblivious to significant activities in the market, which poses risks. For example, currency risk, the risk that exchange rate will change. Directional changes is a different and newer approach of taking snapshot of the market, which uses an event-based time scale. This approach summarises data into alternating trends called upward directional change and downward directional change according to a change in price a trader considers to be significant, which is expressed as a threshold. The trends in the summary are split into directional change (DC) and overshoot (OS) events. In this work, we propose a novel DC-based framework, which uses machine learning algorithms to forecast when the next, alternate trend is expected to begin. First, we present a genetic programming (GP) algorithm that evolves equations that express linear and non-linear relationships between the length of DC and OS events in a given dataset. Awareness of DC event and OS event lengths provide traders with an idea of when DC trends are expected to reverse and thus take appropriate action to increase profit or mitigate risk. Second, DC trends can be categorised into two distinct types: (1) trends with OS events; and (2) trends without OS events(i.e. OS event length is 0). Trends with OS events are those that continue beyond a period when they were first observed and trends without OS event are others that ends as soon as they were observed. To further improve trend reversal estimation accuracy, we identified these two categorises using classification techniques and estimated OS event length for trends that belong in the first category. We appraised whether this new knowledge could lead to an even greater excess return. Third, our novel trend reversal estimation approach was then used as part of a novel genetic algorithm (GA) based trading strategy. The strategy embedded an optimised trend reversal forecasting algorithm that was based on trend reversal point forecasted by multiple thresholds. We assessed the efficiency of our framework (i.e., a novel trend reversal approach and an optimised trading strategy) by performing an in-depth investigation. To assess our approach and evaluate the extent to which it could be generalised in Forex markets, we used five tailored thresholds to create 1000 DC datasets from 10, monthly 10 minute physical time data of 20 major Forex markets (i.e 5 thresholds * 10 months * 20 currency pairs). We compared our results to six benchmarks techniques, both DC and non-DC based, such as technical analysis and buy-and-hold. Our findings showed that our proposed approach can return a significantly higher profit at reduced risk, and statistically outperformed the other trading strategies compareds in a number of different performance metrics.", notes = "KAR id:94107 p156 'using symbolic regression GP approach ... to discover more complex relationship ... led to improvements in DC trend reversal forecasting accuracy' 'increased trading returns at reduced risk' Supervisor: Fernando Otero and Michael Kampouridis (Essex)", } @Article{ADEYI:2021:AIEPR, author = "Abiola John Adeyi and Oladayo Adeyi and Emmanuel Olusola Oke and Olusegun Abayomi Olalere and Seun Oyelami and Akinola David Ogunsola", title = "Effect of varied fiber alkali treatments on the tensile strength of Ampelocissus cavicaulis reinforced polyester composites: Prediction, optimization, uncertainty and sensitivity analysis", journal = "Advanced Industrial and Engineering Polymer Research", volume = "4", number = "1", pages = "29--40", year = "2021", ISSN = "2542-5048", DOI = "doi:10.1016/j.aiepr.2020.12.002", URL = "https://www.sciencedirect.com/science/article/pii/S2542504820300580", keywords = "genetic algorithms, genetic programming, Response surface methodology, Multigene genetic programming, Oracle crystal ball, Uncertainty and sensitivity analysis", abstract = "Studies on modeling and optimization of alkali treatment, investigation of experimental uncertainty and sensitivity analysis of alkali treatment factors of natural fibers are important to effective natural fiber reinforced polymer composite development. In this contribution, response surface methodology (RSM) was employed to investigate and optimize the effect of varied treatment factors (sodium hydroxide concentration (NaOH) and soaking time (ST)) of the alkali treatment of Ampelocissus cavicaulis natural fiber (ACNF) on the tensile strength (TS) of alkali treated ACNF reinforced polyester composite. RSM and multi gene genetic programming (MGGP) were comparatively employed to model the alkali treatment. The best model was applied in Oracle Crystal Ball (OCB) to investigate the uncertainty of the treatment results and sensitivity of the treatment factors. Results showed that increased NaOH and ST increased the TS of the alkali treated ACNF reinforced polyester composite up to 28.3500 MPa before TS decreased. The coefficient of determination (R2) and root mean square error (RMSE) of RSM model were 0.8920 and 0.6528 while that of MGGP were 0.9144 and 0.5812. The optimum alkali treatment established by RSM was 6.23percent of NaOH at 41.99 h of ST to give a TS of 28.1800 MPa with a desirability of 0.9700. The TS of the validated optimum alkali treatment condition was 28.2200 MPa. The certainty of the experimental results was 71.2580percent. TS was 13.8000percent sensitive to NaOH and 86.2000percent sensitive to ST. This work is useful for effective polymer composite materials production to reduce the enormous material and energy losses that usually accompany the process", } @Article{ADEYI:2022:AEJ, author = "Oladayo Adeyi and Abiola J. Adeyi and Emmanuel O. Oke and Bernard I. Okolo and Abayomi O. Olalere and John A. Otolorin and Samuel Okhale and Abiola E. Taiwo and Sunday O. Oladunni and Kelechi N. Akatobi", title = "Process integration for food colorant production from Hibiscus sabdariffa calyx: A case of multi-gene genetic programming ({MGGP)} model and techno-economics", journal = "Alexandria Engineering Journal", volume = "61", number = "7", pages = "5235--5252", year = "2022", ISSN = "1110-0168", DOI = "doi:10.1016/j.aej.2021.10.049", URL = "https://www.sciencedirect.com/science/article/pii/S1110016821006931", keywords = "genetic algorithms, genetic programming, Heat assisted technology based process, Multi-gene genetic programming, Annual production rate, Unit production cost, Techno-economics, calyx", abstract = "This work presents an integrated heat-assisted extraction process for the production of crude anthocyanins powder (CAnysP) from Hibiscus sabdariffa calyx using SuperPro Designer. The influence of process scale-up (0.04 -1000L) and variables (temperature, time and ethanol proportion in solvent) were investigated by adopting a circumscribed central composite design on techno-economic parameters such as annual production rate (APR) and unit production cost (UPC) CAnysP. The individual runs in the CCCD were taken as different process scenario and simulated independently. Multi-gene genetic programming (MGGP) was further used to develop robust predictive models. The robustness of the model and sensitivity analysis were ascertained using the Monte Carlo simulation. The process scenario at 30 min, 30 degreeC, 50percent and 1000 L possessed the highest CAnysP APR and lowest UPC. MGGP- models predicted R2 = 0.9984 for CAnysP APR and R2 = 0.9643 for UPC and certainty (99.98percent for CAnysP APR and 98.47percent for UPC)", } @InProceedings{adhikari:2019:SCDA, author = "Alok Adhikari and Nibedita Adhikari and K. C. Patra", title = "Shear Force Analysis and Modeling for Discharge Estimation Using Numerical and {GP} for Compound Channels", booktitle = "Soft Computing in Data Analytics", year = "2019", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-981-13-0514-6_32", DOI = "doi:10.1007/978-981-13-0514-6_32", } @Article{adhikari:JIEIa, author = "Alok Adhikari and N. Adhikari and K. C. Patra", title = "Genetic Programming: A Complementary Approach for Discharge Modelling in Smooth and Rough Compound Channels", journal = "Journal of The Institution of Engineers (India): Series A", year = "2019", volume = "100", number = "3", pages = "395--405", month = sep, keywords = "genetic algorithms, genetic programming, FIS, ANFIS, GP", ISSN = "2250-2149", URL = "http://link.springer.com/article/10.1007/s40030-019-00367-x", DOI = "doi:10.1007/s40030-019-00367-x", size = "11 pages", abstract = "Use of genetic programming (GP) in the field of river engineering is rare. During flood when the water overflows beyond its main course known as floodplain encounters various obstacles through rough materials and vegetation. Again the flow behaviour becomes more complex in a compound channel section due to shear at different regions. Discharge results from the experimental channels for varying roughness surfaces, along with data from a compound river section, are used in the GP. Model equations are derived for prediction of discharge in the compound channel using five hydraulic parameters. Derived models are tested and compared with other soft computing techniques. Few performance parameters are used to evaluate all the approaches taken for analysis. From the sensitivity analysis, the effects of parameters responsible for the flow behaviour are inferred. GP is found to give the most potential results with the highest level of accuracy. This work aims to benefit the researchers studying machine learning approaches for application in stream flow analysis.", } @Article{vu29881, author = "Sajal Kumar Adhikary and Nitin Muttil and Abdullah Yilmaz", title = "Genetic programming-based ordinary Kriging for spatial interpolation of rainfall", journal = "Journal of Hydrologic Engineering", year = "2016", volume = "21", number = "2", month = feb, keywords = "genetic algorithms, genetic programming, rainfall data, management of water resource systems, missing values, programming", publisher = "American Society of Civil Engineers", URL = "https://vuir.vu.edu.au/29881/", URL = "https://ascelibrary.org/doi/10.1061/%28ASCE%29HE.1943-5584.0001300", DOI = "doi:10.1061/(ASCE)HE.1943-5584.0001300", abstract = "Rainfall data provide an essential input for most hydrologic analyses and designs for effective management of water resource systems. However, in practice, missing values often occur in rainfall data that can ultimately influence the results of hydrologic analysis and design. Conventionally, stochastic interpolation methods such as Kriging are the most frequently used approach to estimate the missing rainfall values where the variogram model that represents spatial correlations among data points plays a vital role and significantly impacts the performance of the methods. In the past, the standard variogram models in ordinary kriging were replaced with the universal function approximator-based variogram models, such as artificial neural networks (ANN). In the current study, applicability of genetic programming (GP) to derive the variogram model and use of this GP-derived variogram model within ordinary kriging for spatial interpolation was investigated. Developed genetic programming-based ordinary kriging (GPOK) was then applied for estimating the missing rainfall data at a rain gauge station using the historical rainfall data from 19 rain gauge stations in the Middle Yarra River catchment of Victoria, Australia. The results indicated that the proposed GPOK method outperformed the traditional ordinary kriging as well as the ANN-based ordinary kriging method for spatial interpolation of rainfall. Moreover, the GP-derived variogram model is shown to have advantages over the standard and ANN-derived variogram models. Therefore, the GP-derived variogram model seems to be a potential alternative to variogram models applied in the past and the proposed GPOK method is recommended as a viable option for spatial interpolation.", } @PhdThesis{vu35054, author = "Sajal Kumar Adhikary", title = "Optimal Design of a Rain Gauge Network to Improve Streamflow Forecasting", school = "College of Engineering and Science, Victoria University", year = "2017", address = "Melbourne, Australia", month = "20 " # mar, note = "This thesis includes 1 published article for which access is restricted due to copyright (Chapters 3, 4 (first paper). Details of access to these papers have been inserted in the thesis, replacing the articles themselves.", keywords = "genetic algorithms, genetic programming, rivers, water basins, streams, stream-flow simulation, modeling, water supply, spatial interpolation, genetic programming-based ordinary kriging, thesis by publication", URL = "https://vuir.vu.edu.au/35054/", URL = "https://vuir.vu.edu.au/35054/7/ADHIKARY%20Sajal-thesis_Redacted.pdf", size = "225 pages", abstract = "Enhanced streamflow forecasting has always been an important task for researchers and water resources managers. However, streamflow forecasting is often challenging owing to the complexity of hydrologic systems. The accuracy of streamflow forecasting mainly depends on the input data, especially rainfall as it constitutes the key input in transforming rainfall into runoff. This emphasizes the need for incorporating accurate rainfall input in streamflow forecasting models in order to achieve enhanced streamflow forecasting. Based on past research, it is well-known that an optimal rain gauge network is necessary to provide high quality rainfall estimates. Therefore, this study focused on the optimal design of a rain gauge network and integration of the optimal network-based rainfall input in artificial neural network (ANN) models to enhance the accuracy of streamflow forecasting. The Middle Yarra River catchment in Victoria, Australia was selected as the case study catchment, since the management of water resources in the catchment is of great importance to the majority of Victorians. The study had three components. First, an evaluation of existing Kriging methods and universal function approximation techniques such as genetic programming (GP) and ANN were performed in terms of their potentials and suitability for the enhanced spatial estimation of rainfall. The evaluation confirmed that the fusion of GP and ordinary kriging is highly effective for the improved estimation of rainfall and the ordinary cokriging using elevation can enhance the spatial estimation of rainfall. Second, the design of an optimal rain gauge network was undertaken for the case study catchment using the kriging-based geostatistical approach based on the variance reduction framework. It is likely that an existing rain gauge network may consist of redundant stations, which have no contribution to the network performance for providing quality rainfall estimates. Therefore, the optimal network was achieved through optimal placement of additional stations (network augmentation) as well as eliminating or optimally relocating of redundant stations (network rationalization). In order to take the rainfall variability caused by climatic factors like El Nino Southern Oscillation into account, the network was designed using rainfall records for both El Nino and La Nina periods. The rain gauge network that gives the improved estimates of areal average and point rainfalls for both the El Nino and La Nina periods was selected as the optimal network. It was found that the optimal network outperformed the existing one in estimating the spatiotemporal estimates of areal average and point rainfalls. Additionally, optimal positioning of redundant stations was found to be highly effective to achieve the optimal rain gauge network. Third, an ANN-based enhanced streamflow forecasting approach was demonstrated, which incorporated the optimal rain gauge network-based input instead of using input from an existing non-optimal network to achieve the enhanced streamflow forecasting. The approach was found to be highly effective in improving the accuracy of stream-flow forecasting, particularly when the current operational rain gauge network is not an optimal one. For example, it was found that use of the optimal rain gauge network-based input results in the improvement of streamflow forecasting accuracy by 7.1percent in terms of normalised root mean square error (NRMSE) compared to the current rain gauge network based-input. Further improvement in streamflow forecasting was achieved through augmentation of the optimal network by incorporating additional fictitious rain gauge stations. The fictitious stations were added in sub-catchments that were delineated based on the digital elevation model. It was evident from the results that 18.3percent improvement in streamflow forecasting accuracy was achieved in terms of NRMSE using the augmented optimal rain gauge network-based input compared to the current rain gauge network-based input. The ANN-based input selection technique that was employed in this study for streamflow forecasting offers a viable technique for significant input variables selection as this technique is capable of learning problems involving very non-linear and complex data.", notes = "pii 'The evaluation confirmed that the fusion of GP and ordinary kriging is highly effective for the improved estimation of rainfall' Supervisor: Nitin Muttil", } @Article{Adib:2021:WRM, author = "Arash Adib and Arash Zaerpour and Ozgur Kisi and Morteza Lotfirad", title = "A Rigorous Wavelet-Packet Transform to Retrieve Snow Depth from {SSMIS} Data and Evaluation of its Reliability by Uncertainty Parameters", journal = "Water Resources Management", year = "2021", volume = "35", pages = "2723--2740", month = jul, keywords = "genetic algorithms, genetic programming, gene expression programming, passive microwave, special sensor microwave imager sounder, snow depth retrieval, discrete wavelet transform, wavelet-packet transform", publisher = "springer", bibsource = "OAI-PMH server at oai.repec.org", identifier = "RePEc:spr:waterr:v:35:y:2021:i:9:d:10.1007_s11269-021-02863-x", oai = "oai:RePEc:spr:waterr:v:35:y:2021:i:9:d:10.1007_s11269-021-02863-x", URL = "http://link.springer.com/10.1007/s11269-021-02863-x", DOI = "doi:10.1007/s11269-021-02863-x", abstract = "This study demonstrates the application of wavelet transform comprising discrete wavelet transform, maximum overlap discrete wavelet transform (MODWT), and multiresolution-based MODWT (MODWT-MRA), as well as wavelet packet transform (WP), coupled with artificial intelligence (AI)-based models including multi-layer perceptron, radial basis function, adaptive neuro-fuzzy inference system (ANFIS), and gene expression programming to retrieve snow depth (SD) from special sensor microwave imager sounder obtained from the national snow and ice data center. Different mother wavelets were applied to the passive microwave (PM) frequencies; afterward, the dominant resultant decomposed subseries comprising low frequencies (approximations) and high frequencies (details) were detected and inserted into the AI-based models. The results indicated that the WP coupled with ANFIS (WP-ANFIS) outperformed the other studied models with the determination coefficient of 0.988, root mean square error of 3.458 cm, mean absolute error of 2.682 cm, and Nash--Sutcliffe efficiency of 0.987 during testing period. The final verification also confirmed that the WP is a promising pre-processing technique to improve the accuracy of the AI-based models in SD evaluation from PM data.", } @InProceedings{Adkins:2023:evomusart, author = "Sara Adkins and Pedro Sarmento and Mathieu Barthet", title = "{LooperGP}: A Loopable Sequence Model for Live Coding Performance using GuitarPro Tablature", booktitle = "12th International Conference on Artificial Intelligence in Music, Sound, Art and Design, EvoMusArt 2023", year = "2023", month = apr # " 12-14", editor = "Colin Johnson and Nereida Rodriguez-Fernandez and Sergio M. Rebelo", series = "LNCS", volume = "13988", publisher = "Springer Verlag", address = "Brno, Czech Republic", pages = "3--19", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Controllable Music Generation, Sequence Models, Live Coding, Transformers, AI Music, Loops, Guitar Tabs", isbn13 = "978-3-031-29956-8", DOI = "doi:10.1007/978-3-031-29956-8_1", data_url = "https://github.com/dada-bots/dadaGP", size = "17 pages", abstract = "Despite their impressive offline results, deep learning models for symbolic music generation are not widely used in live performances due to a deficit of musically meaningful control parameters and a lack of structured musical form in their outputs. To address these issues we introduce LooperGP, a method for steering a Transformer-XL model towards generating loopable musical phrases of a specified number of bars and time signature, enabling a tool for live coding performances. We show that by training LooperGP on a dataset of 93681 musical loops extracted from the DadaGP dataset [Data GuitarPro], we are able to steer its generative output towards generating three times as many loopable phrases as our baseline. In a subjective listening test conducted by 31 participants, LooperGP loops achieved positive median ratings in originality, musical coherence and loop smoothness, demonstrating its potential as a performance tool.", notes = "http://www.evostar.org/2023/ EvoMusArt2023 held in conjunction with EuroGP'2023, EvoCOP2023 and EvoApplications2023", } @Misc{adler2021improving, author = "Felix Adler and Gordon Fraser and Eva Gruendinger and Nina Koerber and Simon Labrenz and Jonas Lerchenberger and Stephan Lukasczyk and Sebastian Schweikl", title = "Improving Readability of {Scratch} Programs with Search-based Refactoring", howpublished = "arXiv", year = "2021", month = "16 " # aug, keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE", eprint = "2108.07114", URL = "https://arxiv.org/abs/2108.07114", size = "13 pages", abstract = "Block-based programming languages like Scratch have become increasingly popular as introductory languages for novices. These languages are intended to be used with a tinkering approach which allows learners and teachers to quickly assemble working programs and games, but this often leads to low code quality. Such code can be hard to comprehend, changing it is error-prone, and learners may struggle and lose interest. The general solution to improve code quality is to refactor the code. However, Scratch lacks many of the common abstraction mechanisms used when refactoring programs written in higher programming languages. In order to improve Scratch code, we therefore propose a set of atomic code transformations to optimise readability by (1) rewriting control structures and (2) simplifying scripts using the inherently concurrent nature of Scratch programs. By automating these transformations it is possible to explore the space of possible variations of Scratch programs. In this paper, we describe a multi-objective search-based approach that determines sequences of code transformations which improve the readability of a given Scratch program and therefore form refactorings. Evaluation on a random sample of 1000 Scratch programs demonstrates that the generated refactorings reduce complexity and entropy in 70.4percent of the cases, and 354 projects are improved in at least one metric without making any other metric worse. The refactored programs can help both novices and their teachers to improve their code.", notes = "p4 'we need the search to evolve sequences of refactorings that explain the changes' p10 'Solutions are transformed 10.1 times on average, and the most common transformations are Swap Statements, Merge Scripts and Split Scripts' p10 'our modification operators are designed to preserve the program semantics' Published as \cite{DBLP:conf/scam/AdlerFGKLLLS21} University of Passau, Germany", } @InProceedings{DBLP:conf/scam/AdlerFGKLLLS21, author = "Felix Adler and Gordon Fraser and Eva Gruendinger and Nina Koerber and Simon Labrenz and Jonas Lerchenberger and Stephan Lukasczyk and Sebastian Schweikl", title = "Improving Readability of {Scratch} Programs with Search-based Refactoring", booktitle = "21st IEEE International Working Conference on Source Code Analysis and Manipulation, SCAM 2021", year = "2021", pages = "120--130", address = "Luxembourg", month = sep # " 27-28", note = "16000 GP entry", keywords = "genetic algorithms, genetic programming, genetic improvement, grammatical evolution, SBSE, NSGA-II, LitterBox, JSON, refactoring, Java", isbn13 = "978-1-6654-4898-7", timestamp = "Sat, 09 Apr 2022 12:45:31 +0200", biburl = "https://dblp.org/rec/conf/scam/AdlerFGKLLLS21.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://arxiv.org/abs/2108.07114", URL = "https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9610643", DOI = "doi:10.1109/SCAM52516.2021.00023", code_url = "https://github.com/se2p/artifact-scam2021", video_url = "https://youtu.be/9ndMZvgEOVg", size = "11 pages", abstract = "Block-based programming languages like SCRATCH have become increasingly popular as introductory languages for novices. These languages are intended to be used with a tinkering approach which allows learners and teachers to quickly assemble working programs and games, but this often leads to low code quality. Such code can be hard to comprehend, changing it is error-prone, and learners may struggle and lose interest. The general solution to improve code quality is to refactor the code. However, SCRATCH lacks many of the common abstraction mechanisms used when refactoring programs written in higher programming languages. In order to improve SCRATCH code, we therefore propose a set of atomic code transformations to optimise readability by (1) rewriting control structures and (2) simplifying scripts using the inherently concurrent nature of SCRATCH programs. By automating these transformations it is possible to explore the space of possible variations of SCRATCH programs. In this paper, we describe a multi-objective search-based approach that determines sequences of code transformations which improve the readability of a given SCRATCH program and therefore form refactorings. Evaluation on a random sample of 1000 SCRATCH programs demonstrates that the generated refactorings reduce complexity and entropy in 70.4% of the cases, and 354 projects are improved in at least one metric without making any other metric worse. The refactored programs can help both novices and their teachers to improve their code.", notes = "Also known as \cite{ImprovingReadabilityScratch}. See also \cite{adler2021improving} '26 atomic code transformations' [to the AST]. 'We evaluate an implementation of this approach on a random sample of 1000 learners program'. '704 are improved with respect to complexity and entropy, and 354...' size, Halstead complexity, and Shannon entropy (based on program syntax). p122 'Inverse transformation' of the AST. control dependence graph. data dependence graph. 'forward-may dataflow analysis to identify which statements are time-dependent on which other statements' p124 'integer representation inspired by grammatical evolution' p126 'verify that the code transformations preserve the semantics' 'Applicable code transformations were found for all but 28 [of 1000] projects in our dataset.' Many uses of swap. p129 'The number of code transformations that do not change the program semantics found by our search is substantial' 'neutral program space'. 'explainability central' SCAM 2021 Also known as \cite{9610643}", } @InProceedings{adorni:1998:cpapc, author = "Giovanni Adorni and Federico Bergenti and Stefano Cagnoni", title = "A cellular-programming approach to pattern classification", booktitle = "Proceedings of the First European Workshop on Genetic Programming", year = "1998", editor = "Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer and Terence C. Fogarty", volume = "1391", series = "LNCS", pages = "142--150", address = "Paris", publisher_address = "Berlin", month = "14-15 " # apr, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-64360-5", DOI = "doi:10.1007/BFb0055934", abstract = "In this paper we discuss the capability of the cellular programming approach to produce non-uniform cellular automata performing two-dimensional pattern classification. More precisely, after an introduction to the evolutionary cellular automata model, we describe a general approach suitable for designing cellular classifiers. The approach is based on a set of non-uniform cellular automata performing specific classification tasks, which have been designed by means of a cellular evolutionary algorithm. The proposed approach is discussed together with some preliminary results obtained on a benchmark data set consisting of car-plate digits.", notes = "EuroGP'98", affiliation = "University of Parma Department of Computer Engineering viale delle Scienze 43100 Parma Italy viale delle Scienze 43100 Parma Italy", } @InProceedings{adorni:1999:GPgkcsrcmsc, author = "Giovanni Adorni and Stefano Cagnoni and Monica Mordonini", title = "Genetic Programming of a Goal-Keeper Control Strategy for the RoboCup Middle Size Competition", booktitle = "Genetic Programming, Proceedings of EuroGP'99", year = "1999", editor = "Riccardo Poli and Peter Nordin and William B. Langdon and Terence C. Fogarty", volume = "1598", series = "LNCS", pages = "109--119", address = "Goteborg, Sweden", publisher_address = "Berlin", month = "26-27 " # may, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-65899-8", DOI = "doi:10.1007/3-540-48885-5_9", abstract = "In this paper we describe a genetic programming approach to the design of a motion-control strategy for a goalkeeper robot created to compete in the RoboCup99, the robot soccer world championships which have been held yearly since 1997, as part of the Italian middle size robot team (ART, Azzurra Robot Team). The evolved program sends a motion command to the robot, based on the analysis of information received from a human-coded vision sub-system. The preliminary results obtained on a simulator are encouraging. They suggest that even using very simple fitness functions and training sets including only a small sub-set of the situations that the goalkeeper is required to tackle, it is possible to evolve a complex behaviour that permits the goalkeeper to perform well also in more challenging real-world conditions.", notes = "EuroGP'99, part of \cite{poli:1999:GP} Robot goalkeeper (.4m) controlled by twin cameras using GP. Able to intercept football sometimes.", } @InProceedings{oai:CiteSeerPSU:539182, author = "Giovanni Adorni and Stefano Cagnoni and Monica Mordonini", title = "Efficient low-level vision program design using Sub-machine-code Genetic Programming", booktitle = "AIIA 2002, Workshop sulla Percezione e Visione nelle Macchine", year = "2002", editor = "Marco Gori", address = "Siena, Italy", month = "10-13 " # sep, keywords = "genetic algorithms, genetic programming", citeseer-isreferencedby = "oai:CiteSeerPSU:127480; oai:CiteSeerPSU:236266; oai:CiteSeerPSU:154480; oai:CiteSeerPSU:124779; oai:CiteSeerPSU:98673; oai:CiteSeerPSU:519310; oai:CiteSeerPSU:493558; oai:CiteSeerPSU:151056; oai:CiteSeerPSU:128026; oai:CiteSeerPSU:195245; oai:CiteSeerPSU:154216; oai:CiteSeerPSU:198970; oai:CiteSeerPSU:98326; oai:CiteSeerPSU:197064; oai:CiteSeerPSU:88615; oai:CiteSeerPSU:200149; oai:CiteSeerPSU:299112", citeseer-references = "oai:CiteSeerPSU:70349; oai:CiteSeerPSU:329358", annote = "The Pennsylvania State University CiteSeer Archives", description = "Sub-machine-code Genetic Programming (SmcGP) is a variant of GP aimed at exploiting the intrinsic parallelism of sequential CPUs.", language = "en", oai = "oai:CiteSeerPSU:539182", rights = "unrestricted", URL = "http://www-dii.ing.unisi.it/aiia2002/paper/PERCEVISIO/adorni-aiia02.pdf", URL = "http://citeseer.ist.psu.edu/539182.html", size = "8 pages", abstract = "Sub-machine-code Genetic Programming (SmcGP) is a variant of GP aimed at exploiting the intrinsic parallelism of sequential CPUs. The paper describes an approach to low-level vision algorithm design for real-time applications by means of Sub-machine-code Genetic Programming(SmcGP), a variant of GP aimed at exploiting the intrinsic parallelism of sequential CPUs. The SmcGPbased design of two processing modules of a license-plate recognition system is taken into consideration as a case study to show the potential of the approach. The paper reports results obtained in recognizing the very low-resolution binary patterns that have to be classified in such an application along with preliminary results obtained using SmcGP to design a license-plate extraction algorithm.", notes = "http://www-dii.ing.unisi.it/aiia2002/paper.htm", } @InProceedings{adorni:2001:wsc6, author = "Giovanni Adorni and Stefano Cagnoni", title = "Design of Explicitly or Implicitly Parallel Low-resolution Character Recognition Algorithms by Means of Genetic Programming", booktitle = "Soft Computing and Industry Recent Applications", year = "2001", editor = "Rajkumar Roy and Mario K{\"o}ppen and Seppo Ovaska and Takeshi Furuhashi and Frank Hoffmann", pages = "387--398", month = "10--24 " # sep, publisher = "Springer-Verlag", note = "Published 2002", keywords = "genetic algorithms, genetic programming", ISBN = "1-85233-539-4", URL = "https://link.springer.com/book/10.1007/978-1-4471-0123-9", URL = "http://www.amazon.co.uk/Soft-Computing-Industry-Recent-Applications/dp/1852335394", notes = "WSC6 March 2020 hardcover out of print, available as softcover. ", } @Misc{oai:arXiv.org:1410.0532, title = "Automated conjecturing of {Frobenius} numbers via grammatical evolution", note = "Comment: 8 pages, 2 tables; added a clear introduction, otherwise reduced text significantly", author = "Nikola Adzaga", year = "2014", month = feb # "~17", keywords = "genetic algorithms, genetic programming, grammatical evolution, mathematics, number theory, mathematics, combinatorics", bibsource = "OAI-PMH server at export.arxiv.org", oai = "oai:arXiv.org:1410.0532", URL = "http://arxiv.org/abs/1410.0532", abstract = "Conjecturing formulae and other symbolic relations occurs frequently in number theory and combinatorics. If we could automate conjecturing, we could benefit not only from speeding up, but also from finding conjectures previously out of our grasp. Grammatical evolution, a genetic programming technique, can be used for automated conjecturing in mathematics. Concretely, this work describes how one can interpret the Frobenius problem as a symbolic regression problem, and then apply grammatical evolution to it. In this manner, a few formulas for Frobenius numbers of specific quadruples were found automatically. The sketch of the proof for one conjectured formula, using lattice point enumeration method, is provided as well. Same method can easily be used on other problems to speed up and enhance the research process.", notes = "See also \cite{Adzaga:2017:EM} Faculty of Civil Engineering, University of Zagreb", } @Article{Adzaga:2017:EM, author = "Nikola Adzaga", title = "Automated Conjecturing of {Frobenius} Numbers via Grammatical Evolution", journal = "Experimental Mathematics", year = "2017", volume = "26", number = "2", pages = "247--252", keywords = "genetic algorithms, genetic programming, grammatical evolution, automated conjecturing, Frobenius problem", ISSN = "1058-6458", publisher = "Taylor \& Francis", DOI = "doi:10.1080/10586458.2016.1175393", size = "6 pages", abstract = "Conjecturing formulas and other symbolic relations occurs frequently in number theory and combinatorics. If we could automate conjecturing, we could benefit not only from faster conjecturing but also from finding conjectures previously out of our grasp. Grammatical evolution (GE), a genetic programming technique, can be used for automated conjecturing in mathematics. Concretely, this work describes how one can interpret the Frobenius problem as a symbolic regression problem, and then apply GE to it. In this manner, a few formulas for Frobenius numbers of specific quadruples were found automatically. The sketch of the proof of one conjectured formula, using lattice point enumeration method, is provided as well. The same method can easily be used on other problems to speed up and enhance the research process.", notes = "See also \cite{oai:arXiv.org:1410.0532} Department of Mathematics, Faculty of Civil Engineering, University of Zagreb, Zagreb, Croatia", } @InProceedings{Aenugu:2019:GECCO, author = "Sneha Aenugu and Lee Spector", title = "Lexicase selection in learning classifier systems", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference", year = "2019", editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and Anne Auger and Petr Posik and Leslie {Peprez Caceres} and Andrew M. Sutton and Nadarajen Veerapen and Christine Solnon and Andries Engelbrecht and Stephane Doncieux and Sebastian Risi and Penousal Machado and Vanessa Volz and Christian Blum and Francisco Chicano and Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and Jonathan Fieldsend and Jose Antonio Lozano and Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and Robin Purshouse and Thomas Baeck and Justyna Petke and Giuliano Antoniol and Johannes Lengler and Per Kristian Lehre", isbn13 = "978-1-4503-6111-8", pages = "356--364", address = "Prague, Czech Republic", DOI = "doi:10.1145/3321707.3321828", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, LCS, Learning Classifier Systems, Parent Selection, Lexicase Selection", size = "9 pages", abstract = "The lexicase parent selection method selects parents by considering performance on individual data points in random order instead of using a fitness function based on an aggregated data accuracy. While the method has demonstrated promise in genetic programming and more recently in genetic algorithms, its applications in other forms of evolutionary machine learning have not been explored. In this paper, we investigate the use of lexicase parent selection in Learning Classifier Systems (LCS) and study its effect on classification problems in a supervised setting. We further introduce a new variant of lexicase selection, called batch-lexicase selection, which allows for the tuning of selection pressure. We compare the two lexicase selection methods with tournament and fitness proportionate selection methods on binary classification problems. We show that batch-lexicase selection results in the creation of more generic rules which is favourable for generalization on future data. We further show that batch-lexicase selection results in better generalization in situations of partial or missing data.", notes = "Does not appear to be GP? Also known as \cite{3321828} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InProceedings{Affenzeller:2005:ICANNGA, author = "M. Affenzeller and S. Wagner", title = "Offspring Selection: A New Self-Adaptive Selection Scheme for Genetic Algorithms", booktitle = "Proceedings of the seventh International Conference Adaptive and Natural Computing Algorithms", year = "2005", editor = "Bernardete Ribeiro and Rudolf F. Albrecht and Andrej Dobnikar and David W. Pearson and Nigel C. Steele", pages = "218--221", address = "Coimbra, Portugal", month = "21-23 " # mar, publisher = "Springer", keywords = "genetic algorithms, genetic programming, OS-GP", isbn13 = "978-3-211-24934-5", URL = "https://link.springer.com/chapter/10.1007/3-211-27389-1_52", URL = "https://doi.org/10.1007/b138998", DOI = "doi:10.1007/3-211-27389-1_52", abstract = "In terms of goal orientedness, selection is the driving force of Genetic Algorithms (GAs). In contrast to crossover and mutation, selection is completely generic, i.e. independent of the actually employed problem and its representation. GA-selection is usually implemented as selection for reproduction (parent selection). In this paper we propose a second selection step after reproduction which is also absolutely problem independent. This self-adaptive selection mechanism, which will be referred to as offspring selection, is closely related to the general selection model of population genetics. As the problem- and representation-specific implementation of reproduction in GAs (crossover) is often critical in terms of preservation of essential genetic information, offspring selection has proven to be very suited for improving the global solution quality and robustness concerning parameter settings and operators of GAs in various fields of applications. The experimental part of the paper discusses the potential of the new selection model exemplarily on the basis of standardized real-valued test functions in high dimensions", notes = "\cite{DBLP:conf/eurocast/BurlacuAKKW17} gives this as reference for OS-GP http://icannga05.dei.uc.pt/", } @Book{Affenzeller:GAGP, author = "Michael Affenzeller and Stephan Winkler and Stefan Wagner and Andreas Beham", title = "Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications", publisher = "CRC Press", year = "2009", series = "Numerical Insights", address = "Singapore", keywords = "genetic algorithms, genetic programming", ISBN = "1-58488-629-3", URL = "http://gagp2009.heuristiclab.com/", URL = "http://www.crcpress.com/product/isbn/9781584886297", abstract = "Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications discusses algorithmic developments in the context of genetic algorithms (GAs) and genetic programming (GP). It applies the algorithms to significant combinatorial optimisation problems and describes structure identification using HeuristicLab as a platform for algorithm development. The book focuses on both theoretical and empirical aspects. The theoretical sections explore the important and characteristic properties of the basic GA as well as main characteristics of the selected algorithmic extensions developed by the authors. In the empirical parts of the text, the authors apply GAs to two combinatorial optimisation problems: the traveling salesman and capacitated vehicle routing problems. To highlight the properties of the algorithmic measures in the field of GP, they analyze GP-based nonlinear structure identification applied to time series and classification problems. Written by core members of the HeuristicLab team, this book provides a better understanding of the basic workflow of GAs and GP, encouraging readers to establish new bionic, problem-independent theoretical concepts. By comparing the results of standard GA and GP implementation with several algorithmic extensions, it also shows how to substantially increase achievable solution quality.", notes = "Reviewed in \cite{Pappa:2009:GPEM}. My copy missing pages i to vi. ", size = "379 pages", } @Article{Affenzeller:2010:IJSPM, title = "Effective allele preservation by offspring selection: an empirical study for the {TSP}", author = "Michael Affenzeller and Stefan Wagner and Stephan M. Winkler", journal = "International Journal of Simulation and Process Modelling", year = "2010", month = apr # "~11", volume = "6", number = "1", pages = "29--39", keywords = "genetic algorithms, genetic programming, soft computing, evolutionary computation, GAs selection, self adaptation, population genetics, evolution strategies, modelling, allele preservation, offspring selection, travelling salesman problem", ISSN = "1740-2131", URL = "https://pure.fh-ooe.at/en/publications/effective-allele-preservation-by-offspring-selection-an-empirical-2", URL = "http://www.inderscience.com/link.php?id=32655", DOI = "doi:10.1504/IJSPM.2010.032655", language = "eng", publisher = "Inderscience Publishers", abstract = "The basic selection ideas of the different representatives of evolutionary algorithms are sometimes quite diverse. The selection concept of Genetic Algorithms (GAs) and Genetic Programming (GP) is basically realised by the selection of above-average parents for reproduction, whereas Evolution Strategies (ES) use the fitness of newly evolved offspring as the basis for selection (survival of the fittest due to birth surplus). This contribution considers aspects of population genetics and ES in order to propose an enhanced and generic selection model for GAs which is able to preserve the alleles which are part of a high quality solution. Some selected aspects of these enhanced techniques are discussed exemplary on the basis of the Travelling Salesman Benchmark (TSP) problem instances.", bibsource = "OAI-PMH server at www.inderscience.com", notes = "GA rather than GP? Also http://www.liophant.org/i3m/i3m2008/", } @InProceedings{2453, author = "M. Affenzeller and C. Fischer and G. K. Kronberger and S. M. Winkler and S. Wagner", title = "New Genetic Programming Hypothesis Search Strategies for Improving the Interpretability in Medical Data Mining Applications", booktitle = "Proccedings of 23rd IEEE European Modeling \& Simulation Symposium EMSS 2011", year = "2011", address = "Roma, Italy", month = sep, URL = "http://research.fh-ooe.at/files/publications/2453_EMSS_2011_Affenzeller.pdf", keywords = "genetic algorithms, genetic programming", } @InProceedings{Affenzeller:2012:EMSS, author = "Michael Affenzeller and Stephan M. Winkler and Stefan Forstenlechner and Gabriel Kronberger and Michael Kommenda and Stefan Wagner and Herbert Stekel", title = "Enhanced Confidence Interpretations of GP Based Ensemble Modeling Results", booktitle = "The 24th European Modeling and Simulation Symposium, EMSS 2012", year = "2012", editor = "Emilio Jimenez and Boris Sokolov", pages = "340--345", address = "Vienna, Austria", month = sep # ", 19-21", keywords = "genetic algorithms, genetic programming, data mining, ensemble modelling, medical data analysis", URL = "http://research.fh-ooe.at/en/publication/2935", URL = "http://research.fh-ooe.at/files/publications/2935_EMSS_2012_Affenzeller.pdf", size = "6 pages", abstract = "In this paper we describe the integration of ensemble modelling into genetic programming based classification and discuss concepts how to use genetic programming specific features for achieving new confidence indicators that estimate the trustworthiness of predictions. These new concepts are tested on a real world dataset from the field of medical diagnosis for cancer prediction where the trustworthiness of modeling results is of highest importance", notes = "EMSS_76 http://www.msc-les.org/conf/emss2012/index.htm http://www.m-s-net.org/conf/i3m2012_program.pdf University of Applied Sciences Upper Austria; General Hospital Linz Central Laboratory - Austria", } @InProceedings{Affenzeller:2013:EUROCAST, author = "Michael Affenzeller and Stephan M. Winkler and Herbert Stekel and Stefan Forstenlechner and Stefan Wagner", title = "Improving the Accuracy of Cancer Prediction by Ensemble Confidence Evaluation", booktitle = "Computer Aided Systems Theory - EUROCAST 2013", year = "2013", editor = "Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia", volume = "8111", series = "Lecture Notes in Computer Science", pages = "316--323", address = "Las Palmas de Gran Canaria, Spain", month = feb # " 10-15", publisher = "Springer", note = "Revised Selected Papers, Part I", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-53855-1", language = "English", URL = "http://dx.doi.org/10.1007/978-3-642-53856-8_40", DOI = "doi:10.1007/978-3-642-53856-8_40", size = "8 pages", abstract = "This paper discusses a novel approach for the prediction of breast cancer, melanoma and cancer in the respiratory system using ensemble modelling techniques. For each type of cancer, a set of unequally complex predictors are learnt by symbolic classification based on genetic programming. In addition to standard ensemble modeling, where the prediction is based on a majority voting of the prediction models, two confidence parameters are used which aim to quantify the trustworthiness of each single prediction based on the clearness of the majority voting. Based on the calculated confidence of each ensemble prediction, predictions might be considered uncertain. The experimental part of this paper discusses the increase of accuracy that can be obtained for those samples which are considered trustable depending on the ratio of predictions that are considered trustable.", } @InCollection{Affenzeller:2013:GPTP, author = "Michael Affenzeller and Stephan M. Winkler and Gabriel Kronberger and Michael Kommenda and Bogdan Burlacu and Stefan Wagner", title = "Gaining Deeper Insights in Symbolic Regression", booktitle = "Genetic Programming Theory and Practice XI", year = "2013", series = "Genetic and Evolutionary Computation", editor = "Rick Riolo and Jason H. Moore and Mark Kotanchek", publisher = "Springer", chapter = "10", pages = "175--190", address = "Ann Arbor, USA", month = "9-11 " # may, keywords = "genetic algorithms, genetic programming, Symbolic regression, Algorithm analysis, Population diversity Building block analysis, Genealogy, Variable networks", isbn13 = "978-1-4939-0374-0", DOI = "doi:10.1007/978-1-4939-0375-7_10", abstract = "A distinguishing feature of symbolic regression using genetic programming is its ability to identify complex nonlinear white-box models. This is especially relevant in practice where models are extensively scrutinised in order to gain knowledge about underlying processes. This potential is often diluted by the ambiguity and complexity of the models produced by genetic programming. In this contribution we discuss several analysis methods with the common goal to enable better insights in the symbolic regression process and to produce models that are more understandable and show better generalisation. In order to gain more information about the process we monitor and analyse the progresses of population diversity, building block information, and even more general genealogy information. Regarding the analysis of results, several aspects such as model simplification, relevance of variables, node impacts, and variable network analysis are presented and discussed.", notes = " Part of \cite{Riolo:2013:GPTP} published after the workshop in 2013", } @InProceedings{6339, author = "Michael Affenzeller and Bogdan Burlacu and Stephan M. Winkler and Michael Kommenda and Gabriel K. Kronberger and Stefan Wagner", title = "Offspring Selection Genetic Algorithm Revisited: Improvements in Efficiency by Early Stopping Criteria in the Evaluation of Unsuccessful Individuals", booktitle = "16th International Conference on Computer Aided Systems Theory, EUROCAST 2017", year = "2017", editor = "Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia", volume = "10671", series = "Lecture Notes in Computer Science", pages = "424--431", address = "Las Palmas de Gran Canaria, Spain", month = feb, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-74718-7", DOI = "doi:10.1007/978-3-319-74718-7_51", URL = "https://link.springer.com/chapter/10.1007/978-3-319-74718-7_51", abstract = "This paper proposes some algorithmic extensions to the general concept of offspring selection which itself is an algorithmic extension of genetic algorithms and genetic programming. Offspring selection is characterized by the fact that many offspring solution candidates will not participate in the ongoing evolutionary process if they do not achieve the success criterion. The algorithmic enhancements proposed in this contribution aim to early estimate if a solution candidate will not be accepted based on partial solution evaluation. The qualitative characteristics of offspring selection are not affected by this means. The discussed variant of offspring selection is analysed for several symbolic regression problems with offspring selection genetic programming. The achievable gains in terms of efficiency are remarkable especially for large data-sets.", } @InProceedings{Affenzeller:2017:GECCO, author = "Michael Affenzeller and Stephan M. Winkler and Bogdan Burlacu and Gabriel Kronberger and Michael Kommenda and Stefan Wagner", title = "Dynamic Observation of Genotypic and Phenotypic Diversity for Different Symbolic Regression {GP} Variants", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4939-0", address = "Berlin, Germany", pages = "1553--1558", size = "6 pages", URL = "http://doi.acm.org/10.1145/3067695.3082530", DOI = "doi:10.1145/3067695.3082530", acmid = "3082530", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, genetic and phenotypic diversity, offspring selection, population dynamics, symbolic regression", month = "15-19 " # jul, abstract = "Understanding the relationship between selection, genotype-phenotype map and loss of population diversity represents an important step towards more effective genetic programming (GP) algorithms. This paper describes an approach to capture dynamic changes in this relationship. We analyse the frequency distribution of points in the diversity plane defined by structural and semantic similarity measures. We test our methodology using standard GP (SGP) on a number of test problems, as well as Offspring Selection GP (OS-GP), an algorithmic flavour where selection is explicitly focused towards adaptive change. We end with a discussion about the implications of diversity maintenance for each of the tested algorithms. We conclude that diversity needs to be considered in the context of fitness improvement, and that more diversity is not necessarily beneficial in terms of solution quality.", notes = "Also known as \cite{Affenzeller:2017:DOG:3067695.3082530} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @InProceedings{DBLP:conf/eurocast/AffenzellerBDDH19, author = "Michael Affenzeller and Bogdan Burlacu and Viktoria Dorfer and Sebastian Dorl and Gerhard Halmerbauer and Tilman Koenigswieser and Michael Kommenda and Julia Vetter and Stephan M. Winkler", title = "White Box vs. Black Box Modeling: On the Performance of Deep Learning, Random Forests, and Symbolic Regression in Solving Regression Problems", booktitle = "17th International Conference, Computer Aided Systems Theory - {EUROCAST} 2019", year = "2019", editor = "Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia", volume = "12013", series = "Lecture Notes in Computer Science", pages = "288--295", address = "Las Palmas de Gran Canaria, Spain", month = feb # " 17-22", publisher = "Springer", note = "Revised Selected Papers, Part {I}", keywords = "genetic algorithms, genetic programming", timestamp = "Mon, 05 Feb 2024 20:28:43 +0100", biburl = "https://dblp.org/rec/conf/eurocast/AffenzellerBDDH19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://doi.org/10.1007/978-3-030-45093-9_35", DOI = "doi:10.1007/978-3-030-45093-9_35", } @InProceedings{Affenzeller:2022:GPTP, author = "Bogdan Burlacu and Michael Kommenda and Gabriel Kronberger and Stephan M. Winkler and Michael Affenzeller", title = "Symbolic Regression in Materials Science: Discovering Interatomic Potentials from Data", booktitle = "Genetic Programming Theory and Practice XIX", year = "2022", editor = "Leonardo Trujillo and Stephan M. Winkler and Sara Silva and Wolfgang Banzhaf", series = "Genetic and Evolutionary Computation", pages = "1--30", address = "Ann Arbor, USA", month = jun # " 2-4", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-981-19-8459-4", URL = "https://arxiv.org/abs/2206.06422", DOI = "doi:10.1007/978-981-19-8460-0_1", abstract = "Particle-based modeling of materials at atomic scale plays an important role in the development of new materials and the understanding of their properties. The accuracy of particle simulations is determined by interatomic potentials, which allow calculating the potential energy of an atomic system as a function of atomic coordinates and potentially other properties. First-principles-based ab initio potentials can reach arbitrary levels of accuracy, however, their applicability is limited by their high computational cost. Machine learning (ML) has recently emerged as an effective way to offset the high computational costs of ab initio atomic potentials by replacing expensive models with highly efficient surrogates trained on electronic structure data. Among a plethora of current methods, symbolic regression (SR) is gaining traction as a powerful “white-box” approach for discovering functional forms of interatomic potentials. This contribution discusses the role of symbolic regression in Materials Science (MS) and offers a comprehensive overview of current methodological challenges and state-of-the-art results. A genetic programming-based approach for modeling atomic potentials from raw data (consisting of snapshots of atomic positions and associated potential energy) is presented and empirically validated on ab initio electronic structure data.", notes = "Part of \cite{Banzhaf:2022:GPTP} published after the workshop in 2023", } @InProceedings{Affenzeller:2023:GPTP, author = "Michael Affenzeller", title = "{GP} in Prescriptive Analytics", booktitle = "Genetic Programming Theory and Practice XX", year = "2023", editor = "Ting Hu and Charles Ofria and Leonardo Trujillo and Stephan Winkler", series = "Genetic and Evolutionary Computation", address = "Michigan State University, USA", month = jun # " 1-3", keywords = "genetic algorithms, genetic programming", notes = " Part of \cite{Hu:2023:GPTP} Not in published book", } @InProceedings{Affenzeller:2025:GPTP, author = "Michael Affenzeller", title = "On the Effects of Continuous Pruning on Symbolic Regression for Different Variants of Evolutionary Search", booktitle = "Genetic Programming Theory and Practice XXII", year = "2025", editor = "Wolfgang Banzhaf and Bogdan Burlacu and Stephen Kelly and Alexander Lalejini and Fabricio {Olivetti de Franca}", address = "Michigan State University, USA", month = jun # " 5-7", keywords = "genetic algorithms, genetic programming", notes = "To be published after the workshop", } @Article{Afshar:2017:RSE, author = "M. H. Afshar and M. T. Yilmaz", title = "The added utility of nonlinear methods compared to linear methods in rescaling soil moisture products", journal = "Remote Sensing of Environment", volume = "196", pages = "224--237", year = "2017", ISSN = "0034-4257", DOI = "doi:10.1016/j.rse.2017.05.017", URL = "http://www.sciencedirect.com/science/article/pii/S003442571730216X", abstract = "In this study, the added utility of nonlinear rescaling methods relative to linear methods in the framework of creating a homogenous soil moisture time series has been explored. The performances of 31 linear and nonlinear rescaling methods are evaluated by rescaling the Land Parameter Retrieval Model (LPRM) soil moisture datasets to station-based watershed average datasets obtained over four United States Department of Agriculture (USDA) Agricultural Research Service (ARS) watersheds. The linear methods include first-order linear regression, multiple linear regression, and multivariate adaptive regression splines (MARS), whereas the nonlinear methods include cumulative distribution function matching (CDF), artificial neural networks (ANN), support vector machines (SVM), Genetic Programming (GEN), and copula methods. MARS, GEN, SVM, ANN, and the copula methods are also implemented to use lagged observations to rescale the datasets. The results of a total of 31 different methods show that the nonlinear methods improve the correlation and error statistics of the rescaled product compared to the linear methods. In general, the method that yielded the best results using training data improved the validation correlations, on average, by 0.063, whereas ELMAN ANN and GEN, using lagged observations methods, yielded correlation improvements of 0.052 and 0.048, respectively. The lagged observations improved the correlations when they were incorporated into rescaling equations in linear and nonlinear fashions, with the nonlinear methods (particularly SVM and GEN but not ANN and copula) benefitting from these lagged observations more than the linear methods. The overall results show that a large majority of the similarities between the LPRM and watershed average datasets are due to linear relations; however, nonlinear relations clearly exist, and the use of nonlinear rescaling methods clearly improves the accuracy of the rescaled product.", keywords = "genetic algorithms, genetic programming, Soil moisture, Rescaling, Linear, Nonlinear, Remote sensing", } @InProceedings{Timperley:2018:GI, author = "Afsoon Afzal and Jeremy Lacomis and Claire {Le Goues} and Christopher Steven Timperley", title = "A {Turing} Test for Genetic Improvement", booktitle = "GI-2018, ICSE workshops proceedings", year = "2018", editor = "Justyna Petke and Kathryn Stolee and William B. Langdon and Westley Weimer", pages = "17--18", address = "Gothenburg, Sweden", month = "2 " # jun, publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, genetic improvement", isbn13 = "978-1-4503-5753-1", URL = "http://dx.doi.org/10.1145/3194810.3194817", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/icse2018/gi2018/papers/Timperley_2018_GI.pdf", URL = "https://afsafzal.github.io/materials/AfzalTuringTest2018.pdf", DOI = "doi:10.1145/3194810.3194817", size = "2 pages", abstract = "Genetic improvement is a research field that aims to develop searchbased techniques for improving existing code. GI has been used to automatically repair bugs, reduce energy consumption, and to improve run-time performance. In this paper, we reflect on the often-overlooked relationship between GI and developers within the context of continually evolving software systems. We introduce a distinction between transparent and opaque patches based on intended lifespan and developer interaction. Finally, we outline a Turing test for assessing the ability of a GI system to produce opaque patches that are acceptable to humans. This motivates research into the role GI systems will play in transparent development contexts.", notes = "Note author order change. GI-2018 http://geneticimprovementofsoftware.com/events/papers#icse2018 part of \cite{Petke:2018:ICSEworkshop}", } @PhdThesis{Afsoon_Afzal:thesis, author = "Afsoon Afzal", title = "Automated Testing of Robotic and Cyberphysical Systems", school = "Institute for Software Research, School of Computer Science, Carnegie Mellon University", year = "2021", address = "Pittsburgh, PA 15213, USA", month = may # " 2021", keywords = "SBSE, testing cyber-physical systems, robotics testing, automated quality assurance, simulation-based testing, challenges of testing, automated oracle inference, automated test generation", URL = "https://afsafzal.github.io/materials/thesis.pdf", size = "142 pages", abstract = "Robotics and cyberphysical systems are increasingly being deployed to settings where they are in frequent interaction with the public. Therefore, failures in these systems can be catastrophic by putting human lives in danger and causing extreme financial loss. Large-scale assessment of the quality of these systems before deployment can prevent these costly damages. Because of the complexity and other special features of these systems, testing,and more specifically automated testing, faces challenges. In this dissertation, I study the unique challenges of testing robotics and cyberphysical systems, and propose an end-to-end automated testing pipeline to provide tools and methods that can help roboticists in large-scale, automated testing of their systems. My key insight is that we can use (low-fidelity) simulation to automatically test robotic and cyber-physical systems, and identify many potentially catastrophic failures in advance at low cost. My core thesis is: Robotic and cyberphysical systems have unique features such as interacting with the physical world and integrating hardware and software components, which creates challenges for automated, large-scale testing approaches. An automated testing framework using software-in-the-loop (low-fidelity) simulation can facilitate automated testing for these systems. This framework can be offered using a clustering approach as an automated oracle, and an evolutionary-based automated test input generation with scenario coverage fitness functions. To support this thesis, I conduct a number of qualitative, quantitative, and mixed method studies that 1) identify main challenges of testing robotic and cyberphysical systems, 2) show that low-fidelity simulation can be an effective approach in detecting bugs and errors with low cost, and 3) identify challenges of using simulators in automated testing. Additionally, I propose automated techniques for creating oracles and generating test inputs to facilitate automated testing of robotic and cyberphysical systems. I present an approach to automatically generate oracles for cyberphysical systems using clustering, which can observe and identify common patterns of system behavior.These patterns can be used to distinguish erroneous behavior of the system and act as an oracle. I evaluate the quality of test inputs for robotic systems with respect to their reliability, and effectiveness in revealing faults in the system. I observe a high rate of non-determinism among test executions that complicates test input generation and evaluation, and show that coverage-based metrics are generally poor indicators of test input quality. Finally, I present an evolutionary-based automated test generation approach with a fitness function that is based on scenario coverage. The automated oracle and automated test input generation approaches contribute to a fully automated testing framework that can perform large-scale, automated testing on robotic and cyberphysical systems in simulation.", notes = "is this GP? CMU-ISR-21-105 Supervisor: Claire Le Goues", } @Article{Afzal:2021:TSE, author = "Afsoon Afzal and Manish Motwani and Kathryn T. Stolee and Yuriy Brun and Claire {Le Goues}", title = "{SOSRepair}: Expressive Semantic Search for Real-World Program Repair", journal = "IEEE Transactions on Software Engineering", year = "2021", volume = "47", number = "10", pages = "2162--2181", keywords = "genetic algorithms, genetic programming, genetic improvement, APR", ISSN = "0098-5589", URL = "https://doi.org/10.1109/TSE.2019.2944914", DOI = "doi:10.1109/TSE.2019.2944914", abstract = "Automated program repair holds the potential to significantly reduce software maintenance effort and cost. However, recent studies have shown that it often produces low-quality patches that repair some but break other functionality. We hypothesize that producing patches by replacing likely faulty regions of code with semantically-similar code fragments, and doing so at a higher level of granularity than prior approaches can better capture abstraction and the intended specification, and can improve repair quality. We create SOSRepair, an automated program repair technique that uses semantic code search to replace candidate buggy code regions with behaviorally-similar (but not identical) code written by humans. SOSRepair is the first such technique to scale to real-world defects in real-world systems. On a subset of the ManyBugs benchmark of such defects, SOSRepair produces patches for 23 (35percent) of the 65 defects, including 3, 5, and 8 defects for which previous state-of-the-art techniques Angelix, Prophet, and GenProg do not, respectively. On these 23 defects, SOSRepair produces more patches (8, 35percent) that pass all independent tests than the prior techniques. We demonstrate a relationship between patch granularity and the ability to produce patches that pass all independent tests. We then show that fault localization precision is a key factor in SOSRepair's success. Manually improving fault localisation allows SOSRepair to patch 24 (37percent) defects, of which 16 (67percent) pass all independent tests. We conclude that (1) higher-granularity, semantic-based patches can improve patch quality, (2) semantic search is promising for producing high-quality real-world defect repairs, (3) research in fault localization can significantly improve the quality of program repair techniques, and (4) semi-automated approaches in which developers suggest fix locations may produce high-quality patches.", notes = "Also known as \cite{8854217}", } @InProceedings{AfzalTF08, author = "Wasif Afzal and Richard Torkar and Robert Feldt", title = "A Systematic Mapping Study on Non-Functional Search-based Software Testing", booktitle = "Proceedings of the 20th International Conference on Software Engineering and Knowledge Engineering (SEKE '08)", year = "2008", pages = "488--493", address = "San Francisco, CA, USA", month = jul # " 1-3", publisher = "Knowledge Systems Institute Graduate School", keywords = "genetic algorithms, genetic programming", bibsource = "http://crestweb.cs.ucl.ac.uk/resources/sbse_repository/repository.html", ISBN = "1-891706-22-5", URL = "http://www.torkar.se/resources/A-systematic-mapping-study-on-non-functional-search-based-software-testing.pdf", size = "6 pages", abstract = "Automated software test generation has been applied across the spectrum of test case design methods; this includes white-box (structural), black-box (functional), grey-box (combination of structural and functional) and non-functional testing. In this paper, we undertake a systematic mapping study to present a broad review of primary studies on the application of search-based optimization techniques to non-functional testing. The motivation is to identify the evidence available on the topic and to identify gaps in the application of search-based optimization techniques to different types of non-functional testing. The study is based on a comprehensive set of 35 papers obtained after using a multi-stage selection criteria and are published in workshops, conferences and journals in the time span 1996--2007. We conclude that the search-based software testing community needs to do more and broader studies on non-functional search-based software testing (NFSBST) and the results from our systematic map can help direct such efforts.", notes = "http://www.ksi.edu/seke/seke08.html http://www.ksi.edu/seke/sk08pgm.html http://www.ksi.edu/seke/tocs/seke2008toc.pdf ", } @InProceedings{Afzal08e, author = "Wasif Afzal and Richard Torkar", title = "Suitability of Genetic Programming for Software Reliability Growth Modeling", booktitle = "The 2008 International Symposium on Computer Science and its Applications (CSA'08)", year = "2008", pages = "114--117", address = "Hobart, ACT", month = "13-15 " # oct, publisher = "IEEE Computer Society", keywords = "genetic algorithms, genetic programming, software reliability data points, software reliability growth modeling, SBSE", DOI = "doi:10.1109/CSA.2008.13", abstract = "Genetic programming (GP) has been found to be effective in finding a model that fits the given data points without making any assumptions about the model structure. This makes GP a reasonable choice for software reliability growth modeling. This paper discusses the suitability of using GP for software reliability growth modeling and highlights the mechanisms that enable GP to progressively search for fitter solutions.", notes = "Also known as \cite{4654071} ", } @InProceedings{Afzal08d, author = "Wasif Afzal and Richard Torkar", title = "A comparative evaluation of using genetic programming for predicting fault count data", booktitle = "Proceedings of the Third International Conference on Software Engineering Advances (ICSEA'08)", year = "2008", pages = "407--414", address = "Sliema, Malta", month = "26-31", keywords = "genetic algorithms, genetic programming, prediction, software reliability growth modeling, SBSE", isbn13 = "978-1-4244-3218-9", DOI = "doi:10.1109/ICSEA.2008.9", abstract = "There have been a number of software reliability growth models (SRGMs) proposed in literature. Due to several reasons, such as violation of models' assumptions and complexity of models, the practitioners face difficulties in knowing which models to apply in practice. This paper presents a comparative evaluation of traditional models and use of genetic programming (GP) for modeling software reliability growth based on weekly fault count data of three different industrial projects. The motivation of using a GP approach is its ability to evolve a model based entirely on prior data without the need of making underlying assumptions. The results show the strengths of using GP for predicting fault count data.", notes = "Also known as \cite{4668139} ", } @InProceedings{Afzal08b, author = "Wasif Afzal and Richard Torkar and Robert Feldt", title = "Prediction of fault count data using genetic programming", booktitle = "Proceedings of the 12th IEEE International Multitopic Conference (INMIC'08)", year = "2008", pages = "349--356", address = "Karachi, Pakistan", month = "23-24 " # dec, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, SBSE, fault count data, prediction", isbn13 = "978-1-4244-2823-6", URL = "http://drfeldt.googlepages.com/afzal_submitted0805icsea_prediction_.pdf", DOI = "doi:10.1109/INMIC.2008.4777762", abstract = "Software reliability growth modeling helps in deciding project release time and managing project resources. A large number of such models have been presented in the past. Due to the existence of many models, the models' inherent complexity, and their accompanying assumptions; the selection of suitable models becomes a challenging task. This paper presents empirical results of using genetic programming (GP) for modeling software reliability growth based on weekly fault count data of three different industrial projects. The goodness of fit (adaptability) and predictive accuracy of the evolved model is measured using five different measures in an attempt to present a fair evaluation. The results show that the GP evolved model has statistically significant goodness of fit and predictive accuracy.", notes = "Also known as \cite{4777762} ", } @InProceedings{Afzal:2009:SSBSE, author = "Wasif Afzal and Richard Torkar and Robert Feldt", title = "Search-Based Prediction of Fault Count Data", booktitle = "Proceedings 1st International Symposium on Search Based Software Engineering SSBSE 2009", year = "2009", editor = "Massimiliano {Di Penta} and Simon Poulding", pages = "35--38", address = "Windsor, UK", month = "13-15 " # may, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, SBSE, search-based prediction, software fault count data, software reliability growth model, symbolic regression, regression analysis, software fault tolerance", isbn13 = "978-0-7695-3675-0", DOI = "doi:10.1109/SSBSE.2009.17", abstract = "Symbolic regression, an application domain of genetic programming (GP), aims to find a function whose output has some desired property, like matching target values of a particular data set. While typical regression involves finding the coefficients of a pre-defined function, symbolic regression finds a general function, with coefficients, fitting the given set of data points. The concepts of symbolic regression using genetic programming can be used to evolve a model for fault count predictions. Such a model has the advantages that the evolution is not dependent on a particular structure of the model and is also independent of any assumptions, which are common in traditional time-domain parametric software reliability growth models. This research aims at applying experiments targeting fault predictions using genetic programming and comparing the results with traditional approaches to compare efficiency gains.", notes = "order number P3675 http://www.ssbse.info/ Also known as \cite{5033177}", } @Article{Afzal2009, author = "Wasif Afzal and Richard Torkar and Robert Feldt", title = "A systematic review of search-based testing for non-functional system properties", journal = "Information and Software Technology", year = "2009", volume = "51", number = "6", pages = "957--976", month = jun, keywords = "genetic algorithms, genetic programming, Systematic review, Non-functional system properties, Search-based software testing", ISSN = "0950-5849", URL = "http://drfeldt.googlepages.com/afzal_submitted0805ist_sysrev_nfr_sb.pdf", URL = "http://www.sciencedirect.com/science/article/B6V0B-4VHXDTD-1/2/9da989f9d874eb88d1f82d9a0878114b", DOI = "doi:10.1016/j.infsof.2008.12.005", size = "19 pages", abstract = "Search-based software testing is the application of metaheuristic search techniques to generate software tests. The test adequacy criterion is transformed into a fitness function and a set of solutions in the search space are evaluated with respect to the fitness function using a metaheuristic search technique. The application of metaheuristic search techniques for testing is promising due to the fact that exhaustive testing is infeasible considering the size and complexity of software under test. Search-based software testing has been applied across the spectrum of test case design methods; this includes white-box (structural), black-box (functional) and grey-box (combination of structural and functional) testing. In addition, metaheuristic search techniques have also been applied to test non-functional properties. The overall objective of undertaking this systematic review is to examine existing work into non-functional search-based software testing (NFSBST). We are interested in types of non-functional testing targeted using metaheuristic search techniques, different fitness functions used in different types of search-based non-functional testing and challenges in the application of these techniques. The systematic review is based on a comprehensive set of 35 articles obtained after a multi-stage selection process and have been published in the time span 1996-2007. The results of the review show that metaheuristic search techniques have been applied for non-functional testing of execution time, quality of service, security, usability and safety. A variety of metaheuristic search techniques are found to be applicable for non-functional testing including simulated annealing, tabu search, genetic algorithms, ant colony methods, grammatical evolution, genetic programming (and its variants including linear genetic programming) and swarm intelligence methods. The review reports on different fitness functions used to guide the search for each of the categories of execution time, safety, usability, quality of service and security; along with a discussion of possible challenges in the application of metaheuristic search techniques.", } @MastersThesis{Afzal:Licentiate, author = "Wasif Afzal", title = "Search-Based Approaches to Software Fault Prediction and Software Testing", school = "School of Engineering, Dept. of Systems and Software Engineering, Blekinge Institute of Technology", year = "2009", type = "Licentiate Dissertation", address = "Sweden", keywords = "genetic algorithms, genetic programming, SBSE, Software Engineering, Computer Science, Artificial Intelligence", URL = "http://www.bth.se/fou/forskinfo.nsf/all/f0738b5fc4ca0bbac12575980043def3/$file/Afzal_lic.pdf", broken = "http://www.bth.se/fou/forskinfo.nsf/all/f0738b5fc4ca0bbac12575980043def3?OpenDocument", size = "212 pages", isbn13 = "978-91-7295-163-1", language = "eng", oai = "oai:bth.se:forskinfoF0738B5FC4CA0BBAC12575980043DEF3", abstract = "Software verification and validation activities are essential for software quality but also constitute a large part of software development costs. Therefore efficient and cost-effective software verification and validation activities are both a priority and a necessity considering the pressure to decrease time-to-market and intense competition faced by many, if not all, companies today. It is then perhaps not unexpected that decisions related to software quality, when to stop testing, testing schedule and testing resource allocation needs to be as accurate as possible. This thesis investigates the application of search-based techniques within two activities of software verification and validation: Software fault prediction and software testing for non-functional system properties. Software fault prediction modeling can provide support for making important decisions as outlined above. In this thesis we empirically evaluate symbolic regression using genetic programming (a search-based technique) as a potential method for software fault predictions. Using data sets from both industrial and open-source software, the strengths and weaknesses of applying symbolic regression in genetic programming are evaluated against competitive techniques. In addition to software fault prediction this thesis also consolidates available research into predictive modeling of other attributes by applying symbolic regression in genetic programming, thus presenting a broader perspective. As an extension to the application of search-based techniques within software verification and validation this thesis further investigates the extent of application of search-based techniques for testing non-functional system properties. Based on the research findings in this thesis it can be concluded that applying symbolic regression in genetic programming may be a viable technique for software fault prediction. We additionally seek literature evidence where other search-based techniques are applied for testing of non-functional system properties, hence contributing towards the growing application of search-based techniques in diverse activities within software verification and validation.", } @InCollection{Afzal:2010:ECoaSE, author = "Wasif Afzal and Richard Torkar and Robert Feldt and Tony Gorschek", title = "Genetic Programming for Cross-Release Fault Count Predictions in Large and Complex Software Projects", booktitle = "Evolutionary Computation and Optimization Algorithms in Software Engineering: Applications and Techniques", publisher = "IGI Global", year = "2010", editor = "Monica Chis", chapter = "6", pages = "94--126", month = jun, keywords = "genetic algorithms, genetic programming, SBSE", isbn13 = "9781615208098", DOI = "doi:10.4018/978-1-61520-809-8.ch006", abstract = "Software fault prediction can play an important role in ensuring software quality through efficient resource allocation. This could, in turn, reduce the potentially high consequential costs due to faults. Predicting faults might be even more important with the emergence of short-timed and multiple software releases aimed at quick delivery of functionality. Previous research in software fault prediction has indicated that there is a need i) to improve the validity of results by having comparisons among number of data sets from a variety of software, ii) to use appropriate model evaluation measures and iii) to use statistical testing procedures. Moreover, cross-release prediction of faults has not yet achieved sufficient attention in the literature. In an attempt to address these concerns, this paper compares the quantitative and qualitative attributes of 7 traditional and machine-learning techniques for modelling the cross-release prediction of fault count data. The comparison is done using extensive data sets gathered from a total of 7 multi-release open-source and industrial software projects. These software projects together have several years of development and are from diverse application areas, ranging from a web browser to a robotic controller software. Our quantitative analysis suggests that genetic programming (GP) tends to have better consistency in terms of goodness of fit and accuracy across majority of data sets. It also has comparatively less model bias. Qualitatively, ease of configuration and complexity are less strong points for GP even though it shows generality and gives transparent models. Artificial neural networks did not perform as well as expected while linear regression gave average predictions in terms of goodness of fit and accuracy. Support vector machine regression and traditional software reliability growth models performed below average on most of the quantitative evaluation criteria while remained on average for most of the qualitative measures.", } @InProceedings{Afzal:2010:SSBSE, author = "Wasif Afzal and Richard Torkar and Robert Feldt and Greger Wikstrand", title = "Search-based Prediction of Fault-slip-through in Large Software Projects", booktitle = "Second International Symposium on Search Based Software Engineering (SSBSE 2010)", year = "2010", month = "7-9 " # sep, pages = "79--88", address = "Benevento, Italy", keywords = "genetic algorithms, genetic programming, gene expression programming, sbse, AIRS, GEP, GP, MR, PSO-ANN, artificial immune recognition system, artificial neural network, fault-slip-through, multiple regression, particle swarm optimisation, search-based prediction, software project, software testing process, artificial immune systems, fault tolerant computing, neural nets, particle swarm optimisation, program testing, regression analysis", DOI = "doi:10.1109/SSBSE.2010.19", isbn13 = "978-0-7695-4195-2", abstract = "A large percentage of the cost of rework can be avoided by finding more faults earlier in a software testing process. Therefore, determination of which software testing phases to focus improvements work on, has considerable industrial interest. This paper evaluates the use of five different techniques, namely particle swarm optimization based artificial neural networks (PSO-ANN), artificial immune recognition systems (AIRS), gene expression programming (GEP), genetic programming (GP) and multiple regression (MR), for predicting the number of faults slipping through unit, function, integration and system testing phases. The objective is to quantify improvement potential in different testing phases by striving towards finding the right faults in the right phase. We have conducted an empirical study of two large projects from a telecommunication company developing mobile platforms and wireless semiconductors. The results are compared using simple residuals, goodness of fit and absolute relative error measures. They indicate that the four search-based techniques (PSO-ANN, AIRS, GEP, GP) perform better than multiple regression for predicting the fault-slip-through for each of the four testing phases. At the unit and function testing phases, AIRS and PSO-ANN performed better while GP performed better at integration and system testing phases. The study concludes that a variety of search-based techniques are applicable for predicting the improvement potential in different testing phases with GP showing more consistent performance across two of the four test phases.", notes = "IEEE Computer Society Order Number P4195 BMS Part Number: CFP1099G-PRT Library of Congress Number 2010933544 http://ssbse.info/2010/program.php Also known as \cite{5635180}", } @InProceedings{Afzal:2010:APSEC, author = "Wasif Afzal", title = "Using Faults-Slip-Through Metric as a Predictor of Fault-Proneness", booktitle = "17th Asia Pacific Software Engineering Conference (APSEC 2010)", year = "2010", month = nov # " 30-" # dec # " 3", pages = "414--422", abstract = "Background: The majority of software faults are present in small number of modules, therefore accurate prediction of fault-prone modules helps improve software quality by focusing testing efforts on a subset of modules. Aims: This paper evaluates the use of the faults-slip-through (FST) metric as a potential predictor of fault-prone modules. Rather than predicting the fault-prone modules for the complete test phase, the prediction is done at the specific test levels of integration and system test. Method: We applied eight classification techniques, to the task of identifying fault prone modules, representing a variety of approaches, including a standard statistical technique for classification (logistic regression), tree-structured classifiers (C4.5 and random forests), a Bayesian technique (Naive Bayes), machine-learning techniques (support vector machines and back-propagation artificial neural networks) and search-based techniques (genetic programming and artificial immune recognition systems) on FST data collected from two large industrial projects from the telecommunication domain. Results: Using area under the receiver operating characteristic (ROC) curve and the location of (PF, PD) pairs in the ROC space, the faults slip-through metric showed impressive results with the majority of the techniques for predicting fault-prone modules at both integration and system test levels. There were, however, no statistically significant differences between the performance of different techniques based on AUC, even though certain techniques were more consistent in the classification performance at the two test levels. Conclusions: We can conclude that the faults-slip-through metric is a potentially strong predictor of fault-proneness at integration and system test levels. The faults-slip-through measurements interact in ways that is conveniently accounted for by majority of the data mining techniques.", keywords = "genetic algorithms, genetic programming, sbse, Bayesian technique, artificial immune recognition systems, back-propagation artificial neural networks, data mining, fault-proneness predictor, faults-slip-through metric, logistic regression, machine-learning techniques, receiver operating characteristic curve, search-based techniques, software faults, software quality, standard statistical technique, support vector machines, system test levels, tree-structured classifiers, backpropagation, data mining, neural nets, program testing, software quality, statistical analysis, support vector machines", DOI = "doi:10.1109/APSEC.2010.54", ISSN = "1530-1362", notes = "Blekinge Inst. of Technol., Ronneby, Sweden. Also known as \cite{5693218}", } @Article{Afzal201111984, author = "Wasif Afzal and Richard Torkar", title = "On the application of genetic programming for software engineering predictive modeling: A systematic review", journal = "Expert Systems with Applications", volume = "38", number = "9", pages = "11984--11997", year = "2011", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2011.03.041", URL = "http://www.sciencedirect.com/science/article/B6V03-52C8FT6-5/2/668361024e4b2bcf9a4a73195271591c", keywords = "genetic algorithms, genetic programming, Systematic review, Symbolic regression, Modelling", abstract = "The objective of this paper is to investigate the evidence for symbolic regression using genetic programming (GP) being an effective method for prediction and estimation in software engineering, when compared with regression/machine learning models and other comparison groups (including comparisons with different improvements over the standard GP algorithm). We performed a systematic review of literature that compared genetic programming models with comparative techniques based on different independent project variables. A total of 23 primary studies were obtained after searching different information sources in the time span 1995-2008. The results of the review show that symbolic regression using genetic programming has been applied in three domains within software engineering predictive modeling: (i) Software quality classification (eight primary studies). (ii) Software cost/effort/size estimation (seven primary studies). (iii) Software fault prediction/software reliability growth modelling (eight primary studies). While there is evidence in support of using genetic programming for software quality classification, software fault prediction and software reliability growth modelling; the results are inconclusive for software cost/effort/size estimation.", } @PhdThesis{Afzal:thesis, author = "Wasif Afzal", title = "Search-Based Prediction of Software Quality: Evaluations And Comparisons", school = "School of Computing, Blekinge Institute of Technology", year = "2011", address = "Sweden", month = "5 " # may, keywords = "genetic algorithms, genetic programming, SBSE", URL = "http://www.bth.se/fou/forskinfo.nsf/0/dd0dcce8cc126a52c125784500410306/$file/Dis%20Wasif%20Afzal%20thesis.pdf", isbn13 = "978-91-7295-203-4", size = "313 pages", abstract = "Software verification and validation (V&V) activities are critical for achieving software quality; however, these activities also constitute a large part of the costs when developing software. Therefore efficient and effective software V&V activities are both a priority and a necessity considering the pressure to decrease time-to-market and the intense competition faced by many, if not all, companies today. It is then perhaps not unexpected that decisions that affects software quality, e.g., how to allocate testing resources, develop testing schedules and to decide when to stop testing, needs to be as stable and accurate as possible. The objective of this thesis is to investigate how search-based techniques can support decision-making and help control variation in software V&V activities, thereby indirectly improving software quality. Several themes in providing this support are investigated: predicting reliability of future software versions based on fault history; fault prediction to improve test phase efficiency; assignment of resources to fixing faults; and distinguishing fault-prone software modules from non-faulty ones. A common element in these investigations is the use of search-based techniques, often also called metaheuristic techniques, for supporting the V&V decision-making processes. Search-based techniques are promising since, as many problems in real world, software V&V can be formulated as optimisation problems where near optimal solutions are often good enough. Moreover, these techniques are general optimization solutions that can potentially be applied across a larger variety of decision-making situations than other existing alternatives. Apart from presenting the current state of the art, in the form of a systematic literature review, and doing comparative evaluations of a variety of metaheuristic techniques on large-scale projects (both industrial and open-source), this thesis also presents methodological investigations using search-based techniques that are relevant to the task of software quality measurement and prediction. The results of applying search-based techniques in large-scale projects, while investigating a variety of research themes, show that they consistently give competitive results in comparison with existing techniques. Based on the research findings, we conclude that search-based techniques are viable techniques to use in supporting the decision-making processes within software V&V activities. The accuracy and consistency of these techniques make them important tools when developing future decision support for effective management of software V&V activities.", notes = "Advisors, Dr. Richard Torkar and Dr. Robert Feldt. http://www.bth.se/eng/calendar.nsf/allaDok/9984ce8a1e18e8ecc125782a004d4167!OpenDocument Doctoral Dissertation Series No. 2011:06", } @Article{Afzal:2013:SQJ, author = "Wasif Afzal and Richard Torkar and Robert Feldt and Tony Gorschek", title = "Prediction of faults-slip-through in large software projects: an empirical evaluation", journal = "Software Quality Journal", year = "2014", volume = "22", number = "1", pages = "51--86", month = mar, publisher = "Springer US", keywords = "genetic algorithms, genetic programming, SBSE, Prediction, Empirical, Faults-slip-through, Search-based", ISSN = "0963-9314", DOI = "doi:10.1007/s11219-013-9205-3", language = "English", oai = "oai:bth.se:forskinfo3D40224F7CBF862DC1257B7800251E66", URL = "http://www.bth.se/fou/forskinfo.nsf/all/3d40224f7cbf862dc1257b7800251e66?OpenDocument", size = "36 pages", abstract = "A large percentage of the cost of rework can be avoided by finding more faults earlier in a software test process. Therefore, determination of which software test phases to focus improvement work on has considerable industrial interest. We evaluate a number of prediction techniques for predicting the number of faults slipping through to unit, function, integration, and system test phases of a large industrial project. The objective is to quantify improvement potential in different test phases by striving toward finding the faults in the right phase. The results show that a range of techniques are found to be useful in predicting the number of faults slipping through to the four test phases; however, the group of search-based techniques (genetic programming, gene expression programming, artificial immune recognition system, and particle swarm optimisation (PSO) based artificial neural network) consistently give better predictions, having a representation at all of the test phases. Human predictions are consistently better at two of the four test phases. We conclude that the human predictions regarding the number of faults slipping through to various test phases can be well supported by the use of search-based techniques. A combination of human and an automated search mechanism (such as any of the search-based techniques) has the potential to provide improved prediction results.", } @InCollection{Afzal2016, author = "Wasif Afzal and Richard Torkar", title = "Towards Benchmarking Feature Subset Selection Methods for Software Fault Prediction", booktitle = "Computational Intelligence and Quantitative Software Engineering", publisher = "Springer", year = "2016", editor = "Witold Pedrycz and Giancarlo Succi and Alberto Sillitti", volume = "617", series = "Studies in Computational Intelligence", chapter = "3", pages = "33--58", keywords = "genetic algorithms, genetic programming, SBSE, Feature subset selection, Fault prediction, Empirical", isbn13 = "978-3-319-25964-2", DOI = "doi:10.1007/978-3-319-25964-2_3", abstract = "Despite the general acceptance that software engineering datasets often contain noisy, irrelevant or redundant variables, very few benchmark studies of feature subset selection (FSS) methods on real-life data from software projects have been conducted. This paper provides an empirical comparison of state-of-the-art FSS methods: information gain attribute ranking (IG); Relief (RLF); principal component analysis (PCA); correlation-based feature selection (CFS); consistency-based subset evaluation (CNS); wrapper subset evaluation (WRP); and an evolutionary computation method, genetic programming (GP), on five fault prediction datasets from the PROMISE data repository. For all the datasets, the area under the receiver operating characteristic curve, the AUC value averaged over 10-fold cross-validation runs, was calculated for each FSS method-dataset combination before and after FSS. Two diverse learning algorithms, C4.5 and naive Bayes (NB) are used to test the attribute sets given by each FSS method. The results show that although there are no statistically significant differences between the AUC values for the different FSS methods for both C4.5 and NB, a smaller set of FSS methods (IG, RLF, GP) consistently select fewer attributes without degrading classification accuracy. We conclude that in general, FSS is beneficial as it helps improve classification accuracy of NB and C4.5. There is no single best FSS method for all datasets but IG, RLF and GP consistently select fewer attributes without degrading classification accuracy within statistically significant boundaries.", } @InProceedings{afzali:2018:AJCAI, author = "Shima Afzali and Harith Al-Sahaf and Bing Xue and Christopher Hollitt and Mengjie Zhang", title = "A Genetic Programming Approach for Constructing Foreground and Background Saliency Features for Salient Object Detection", booktitle = "Australasian Joint Conference on Artificial Intelligence", year = "2018", editor = "Tanja Mitrovic and Bing Xue and Xiaodong Li", volume = "11320", series = "LNCS", address = "Wellington, New Zealand", month = dec # " 11-14", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-030-03990-5", URL = "http://link.springer.com/chapter/10.1007/978-3-030-03991-2_21", DOI = "doi:10.1007/978-3-030-03991-2_21", } @InProceedings{Afzali:2019:evoapplications, author = "Shima Afzali and Harith Al-Sahaf and Bing Xue and Christopher Hollitt and Mengjie Zhang", title = "Genetic Programming for Feature Selection and Feature Combination in Salient Object Detection", booktitle = "22nd International Conference, EvoApplications 2019", year = "2019", month = "24-26 " # apr, editor = "Paul Kaufmann and Pedro A. Castillo", series = "LNCS", volume = "11454", publisher = "Springer Verlag", address = "Leipzig, Germany", pages = "308--324", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Salient Object Detection, Feature combination, Feature selection", isbn13 = "978-3-030-16691-5", DOI = "doi:10.1007/978-3-030-16692-2_21", abstract = "Salient Object Detection (SOD) aims to model human visual attention system to cope with the complex natural scene which contains various objects at different scales. Over the past two decades, a wide range of saliency features have been introduced in the SOD field, however feature selection has not been widely investigated for selecting informative, non-redundant, and complementary features from the existing features. In SOD, multi-level feature extraction and feature combination are two fundamental stages to compute the final saliency map. However, designing a good feature combination framework is a challenging task and requires domain-expert intervention. In this paper, we propose a genetic programming (GP) based method that is able to automatically select the complementary saliency features and generate mathematical function to combine those features. The performance of the proposed method is evaluated using four benchmark datasets and compared to nine state-of-the-art methods. The qualitative and quantitative results show that the proposed method significantly outperformed, or achieved comparable performance to, the competitor methods.", notes = "http://www.evostar.org/2019/cfp_evoapps.php EuroGP'2019 held in conjunction with EvoCOP2019, EvoMusArt2019 and EvoApplications2019", } @PhdThesis{Afzali:thesis, author = "Shima {Afzali Vahed Moghaddam}", title = "Evolutionary Computation for Feature Manipulation in Salient Object Detection", school = "Computer Science, Victoria University of Wellington", year = "2020", address = "New Zealand", keywords = "genetic algorithms, genetic programming", URL = "http://hdl.handle.net/10063/8897", URL = "http://researcharchive.vuw.ac.nz/xmlui/handle/10063/8897?show=full", URL = "http://researcharchive.vuw.ac.nz/xmlui/bitstream/handle/10063/8897/thesis_access.pdf", size = "267 pages", abstract = "The human visual system can efficiently cope with complex natural scenes containing various objects at different scales using the visual attention mechanism. Salient object detection (SOD) aims to simulate the capability of the human visual system in prioritizing objects for high-level processing. SOD is a process of identifying and localizing the most attention grabbing object(s) of a scene and separating the whole extent of the object(s) from the scene. In SOD, significant research has been dedicated to design and introduce new features to the domain. The existing saliency feature space suffers from some difficulties such as having high dimensionality, features are not equally important, some features are irrelevant, and the original features are not informative enough. These difficulties can lead to various performance limitations. Feature manipulation is the process which improves the input feature space to enhance the learning quality and performance. Evolutionary computation (EC) techniques have been employed in a wide range of tasks due to their powerful search abilities. Genetic programming (GP) and particle swarm optimization (PSO) are well-known EC techniques which have been used for feature manipulation. The overall goal of this thesis is to develop feature manipulation methods including feature weighting, feature selection, and feature construction using EC techniques to improve the input feature set for SOD. This thesis proposes a feature weighting method using PSO to explore the relative contribution of each saliency feature in the feature combination process. Saliency features are referred to the features which are extracted from different levels (e.g., pixel, segmentation) of an image to compute the saliency values over the entire image. The experimental results show that different datasets favour different weights for the employed features. The results also reveal that by considering the importance of each feature in the combination process, the proposed method has achieved better performance than that of the competitive methods. This thesis proposes a new bottom-up SOD method to detect salient objects by constructing two new informative saliency features and designing a new feature combination framework. The proposed method aims at developing features which target to identify different regions of the image. The proposed method makes a good balance between computational time and performance. This thesis proposes a GP-based method to automatically construct foreground and background saliency features. The automatically constructed features do not require domain-knowledge and they are more informative compared to the manually constructed features. The results show that GP is robust towards the changes in the input feature set (e.g., adding more features to the input feature set) and improves the performance by introducing more informative features to the SOD domain. This thesis proposes a GP-based SOD method which automatically produces saliency maps (a 2-D map containing saliency values) for different types of images. This GP-based SOD method applies feature selection and feature combination during the learning process for SOD. GP with built-in feature selection process which selects informative features from the original set and combines the selected features to produce the final saliency map. The results show that GP can potentially explore a large search space and find a good way to combine different input features. This thesis introduces GP for the first time to construct high-level saliency features from the low-level features for SOD, which aims to improve the performance of SOD, particularly on challenging and complex SOD tasks. The proposed method constructs fewer features that achieve better saliency performance than the original full feature set.", notes = "Supervisors: Bing Xue, Mengjie Zhang, Christopher Hollitt, Harith Al-Sahaf", } @Article{Afzali:2021:ESA, author = "Shima {Afzali Vahed Moghaddam} and Harith Al-Sahaf and Bing Xue and Christopher Hollitt and Mengjie Zhang", title = "An automatic feature construction method for salient object detection: A genetic programming approach", journal = "Expert Systems with Applications", volume = "186", pages = "115726", year = "2021", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2021.115726", URL = "https://www.sciencedirect.com/science/article/pii/S0957417421011076", keywords = "genetic algorithms, genetic programming, Salient object detection, Feature construction", abstract = "Over the last two decades, salient object detection (SOD) has received increasingly more attention due to its ability to handle complex natural scenes and its various real-world applications. The performance of an SOD method mainly relies on saliency features that are extracted with different levels of information. Low-level saliency features are often effective in simple scenarios, but they are not always robust in challenging scenarios. With the recent prevalence of high-level saliency features such as deep convolutional neural networks (CNNs) features, a remarkable progress has been achieved in the SOD field. However, CNN-based constructed high-level features unavoidably drop the location information and low-level fine details (e.g., edges and corners) of salient object(s), leading to unclear/blurry boundary predictions. In addition, deep CNN methods have difficulties to generalize and accurately detect salient objects when they are trained with limited number of images (e.g. small datasets). This paper proposes a new automatic feature construction method using Genetic Programming (GP) to construct informative high-level saliency features for SOD. The proposed method takes low-level and hand-crafted saliency features as input to construct high-level features. The constructed GP-based high-level features not only detect the general objects, but they are also good at capturing details and edges/boundaries. The GP-based constructed features have better interpretability compared to CNN-based features. The proposed GP-based method can potentially cope with a small number of samples for training to obtain a good generalization as long as the given training data has enough information to represent the distribution of the data. The experiments on six datasets reveal that the new method achieves consistently high performance compared to twelve state-of-the-art SOD methods", } @InProceedings{agapie:1999:RSCC, author = "Alexandru Agapie", title = "Random Systems with Complete Connections", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "770", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-862.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{eurogp06:AgapitosLucas, author = "Alexandros Agapitos and Simon M. Lucas", title = "Learning Recursive Functions with Object Oriented Genetic Programming", editor = "Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art", booktitle = "Proceedings of the 9th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3905", year = "2006", address = "Budapest, Hungary", month = "10 - 12 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-33143-3", pages = "166--177", DOI = "doi:10.1007/11729976_15", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "This paper describes the evolution of recursive functions within an Object-Oriented Genetic Programming (OOGP) system. We evolved general solutions to factorial, Fibonacci, exponentiation, even-n-Parity, and nth-3. We report the computational effort required to evolve these methods and provide a comparison between crossover and mutation variation operators, and also undirected random search. We found that the evolutionary algorithms performed much better than undirected random search, and that mutation outperformed crossover on most problems.", notes = "Part of \cite{collet:2006:GP} EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006 Java reflection.", } @InProceedings{Agapitos:2006:CEC, author = "Alexandros Agapitos and Simon M. Lucas", title = "Evolving Efficient Recursive Sorting Algorithms", booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary Computation", year = "2006", editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas", pages = "9227--9234", address = "Vancouver", month = "16-21 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, computational complexity, evolutionary computation, object-oriented languages, object-oriented programming, OOGP, evolutionary process, fitness function, language primitives, object oriented genetic programming, recursive sorting algorithms, time complexity", ISBN = "0-7803-9487-9", URL = "http://privatewww.essex.ac.uk/~aagapi/papers/AgapitosLucasEvolvingSort.pdf", DOI = "doi:10.1109/CEC.2006.1688643", size = "8 pages", abstract = "Object Oriented Genetic Programming (OOGP) is applied to the task of evolving general recursive sorting algorithms. We studied the effects of language primitives and fitness functions on the success of the evolutionary process. For language primitives, these were the methods of a simple list processing package. Five different fitness functions based on sequence disorder were evaluated. The time complexity of the successfully evolved algorithms was measured experimentally in terms of the number of method invocations made, and for the best evolved individuals this was best approximated as O(n log(n)). This is the first time that sorting algorithms of this complexity have been evolved.", notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. Best in session. IEEE Xplore gives pages as 2677--2684", } @InProceedings{eurogp07:agapitos1, author = "Alexandros Agapitos and Simon M. Lucas", title = "Evolving a Statistics Class Using Object Oriented Evolutionary Programming", editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar", booktitle = "Proceedings of the 10th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "4445", year = "2007", address = "Valencia, Spain", month = "11-13 " # apr, pages = "291--300", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-71602-0", ISBN = "3-540-71602-5", DOI = "doi:10.1007/978-3-540-71605-1_27", abstract = "Object Oriented Evolutionary Programming is used to evolve programs that calculate some statistical measures on a set of numbers. We compared this technique with a more standard functional representation. We also studied the effects of scalar and Pareto-based multi-objective fitness functions to the induction of multi-task programs. We found that the induction of a program residing in an OO representation space is more efficient, yielding less fitness evaluations, and that scalar fitness performed better than Pareto-based fitness in this problem domain.", notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007", } @InProceedings{eurogp07:agapitos2, author = "Alexandros Agapitos and Simon M. Lucas", title = "Evolving Modular Recursive Sorting Algorithms", editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar", booktitle = "Proceedings of the 10th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "4445", year = "2007", address = "Valencia, Spain", month = "11-13 " # apr, pages = "301--310", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-71602-0", ISBN = "3-540-71602-5", DOI = "doi:10.1007/978-3-540-71605-1_28", abstract = "A fundamental issue in evolutionary learning is the definition of the solution representation language. We present the application of Object Oriented Genetic Programming to the task of coevolving general recursive sorting algorithms along with their primitive representation alphabet. We report the computational effort required to evolve target solutions and provide a comparison between crossover and mutation variation operators, and also undirected random search. We found that the induction of evolved method signatures (typed parameters and return type) can be realized through an evolutionary fitness-driven process. We also found that the evolutionary algorithm outperformed undirected random search, and that mutation performed better than crossover in this problem domain. The main result is that modular sorting algorithms can be evolved.", notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007", } @InProceedings{1277271, author = "Alexandros Agapitos and Julian Togelius and Simon Mark Lucas", title = "Evolving controllers for simulated car racing using object oriented genetic programming", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "1543--1550", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1543.pdf", DOI = "doi:10.1145/1276958.1277271", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, evolutionary computer games, evolutionary robotics, homologous uniform crossover, neural networks, object oriented, subtree macro-mutation", abstract = "The Probabilistic Adaptive Mapping Developmental Genetic Programming (PAM DGP) algorithm that cooperatively Co-evolves a population of adaptive mappings and associated genotypes is used to learn recursive solutions given a function set consisting of general (not implicitly recursive) machine-language instructions. PAM DGP using redundant encodings to model the evolution of the biological genetic code is found to more efficiently learn 2nd and 3rd order recursive Fibonacci functions than related developmental systems and traditional linear GP. PAM DGP using redundant encoding is also demonstrated to produce the semantically highest quality solutions for all three recursive functions considered (Factorial, 2nd and 3rd order Fibonacci). PAM DGP is then shown to have produced such solutions by evolving redundant mappings to select and emphasise appropriate subsets of the function set useful for producing the naturally recursive solutions.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @InProceedings{Agapitos:2007:cec, title = "Multiobjective Techniques for the Use of State in Genetic Programming Applied to Simulated Car Racing", author = "Alexandros Agapitos and Julian Togelius and Simon M. Lucas", booktitle = "2007 IEEE Congress on Evolutionary Computation", year = "2007", editor = "Dipti Srinivasan and Lipo Wang", pages = "1562--1569", address = "Singapore", month = "25-28 " # sep, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "1-4244-1340-0", file = "1977.pdf", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2007.4424659", abstract = "Multi-objective optimisation is applied to encourage the effective use of state variables in car controlling programs evolved using Genetic Programming. Three different metrics for measuring the use of state within a program are introduced. Comparisons are performed among multi- and single-objective fitness functions with respect to learning speed and final fitness of evolved individuals, and attempts are made at understanding whether there is a trade-off between good performance and stateful controllers in this problem domain.", notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C", } @InProceedings{Agapitos:2008:gecco, author = "Alexandros Agapitos and Matthew Dyson and Simon M. Lucas and Francisco Sepulveda", title = "Learning to recognise mental activities: genetic programming of stateful classifiers for brain-computer interfacing", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "1155--1162", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1155.pdf", DOI = "doi:10.1145/1389095.1389326", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, Brain computer interface, classification on Raw signal, stateful representation, statistical signal primitives", size = "8 pages", abstract = "Two families (stateful and stateless) of genetically programmed classifiers were tested on a five class brain-computer interface (BCI) data set of raw EEG signals. The ability of evolved classifiers to discriminate mental tasks from each other were analysed in terms of accuracy, precision and recall. A model describing the dynamics of state usage in stateful programs is introduced. An investigation of relationships between the model attributes and associated classification results was made. The results show that both stateful and stateless programs can be successfully evolved for this task, though stateful programs start from lower fitness and take longer to evolve", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389326}", } @InProceedings{Agapitos2:2008:gecco, author = "Alexandros Agapitos and Matthew Dyson and Jenya Kovalchuk and Simon Mark Lucas", title = "On the genetic programming of time-series predictors for supply chain management", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "1163--1170", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1163.pdf", URL = "http://privatewww.essex.ac.uk/~yvkova/Papers/GP_GECCO08.pdf", DOI = "doi:10.1145/1389095.1389327", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, Iterated single-step prediction, prediction/forecasting, single-step prediction, statistical time-series Features", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389327}", } @InProceedings{Agapitos:2008:CIG, author = "Alexandros Agapitos and Julian Togelius and Simon M. Lucas and Jurgen Schmidhuber and Andreas Konstantinidis", title = "Generating Diverse Opponents with Multiobjective Evolution", booktitle = "Proceedings of the 2008 IEEE Symposium on Computational Intelligence and Games", year = "2008", pages = "135--142", address = "Perth, Australia", month = dec # " 15-18", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Reinforcement Learning, Multiobjective Evolution, AI in Computer Games, EMOA, Car Racing, MOGA, AI game agent, computational intelligence, diverse opponent generation, game play learning, multiobjective evolutionary algorithm, nonplayer character, computer games, evolutionary computation, learning (artificial intelligence), multi-agent systems", URL = "http://julian.togelius.com/Agapitos2008Generating.pdf", DOI = "doi:10.1109/CIG.2008.5035632", abstract = "For computational intelligence to be useful in creating game agent AI, we need to focus on creating interesting and believable agents rather than just learn to play the games well. To this end, we propose a way to use multiobjective evolutionary algorithms to automatically create populations of NPCs, such as opponents and collaborators, that are interestingly diverse in behaviour space. Experiments are presented where a number of partially conflicting objectives are defined for racing game competitors, and multiobjective evolution of GP-based controllers yield Pareto fronts of interesting controllers.", notes = "Also known as \cite{5035632}", } @InProceedings{agapitos_etal:ppsn2010, author = "Alexandros Agapitos and Michael O'Neill and Anthony Brabazon", title = "Evolutionary Learning of Technical Trading Rules without Data-mining Bias", booktitle = "PPSN 2010 11th International Conference on Parallel Problem Solving From Nature", pages = "294--303", year = "2010", volume = "6238", editor = "Robert Schaefer and Carlos Cotta and Joanna Kolodziej and Guenter Rudolph", publisher = "Springer", series = "Lecture Notes in Computer Science", isbn13 = "978-3-642-15843-8", address = "Krakow, Poland", month = "11-15 " # sep, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1007/978-3-642-15844-5_30", abstract = "In this paper we investigate the profitability of evolved technical trading rules when controlling for data-mining bias. For the first time in the evolutionary computation literature, a comprehensive test for a rule's statistical significance using Hansen's Superior Predictive Ability is explicitly taken into account in the fitness function, and multi-objective evolutionary optimisation is employed to drive the search towards individual rules with better generalisation abilities. Empirical results on a spot foreign-exchange market index suggest that increased out-of-sample performance can be obtained after accounting for data-mining bias effects in a multi-objective fitness function, as compared to a single-criterion fitness measure that considers solely the average return.", } @InProceedings{Agapitos:2010:AIAI, author = "Alexandros Agapitos and Andreas Konstantinidis and Haris Haralambous and Harris Papadopoulos", title = "Evolutionary Prediction of Total Electron Content over Cyprus", booktitle = "6th IFIP Advances in Information and Communication Technology AIAI 2010", year = "2010", editor = "Harris Papadopoulos and Andreas Andreou and Max Bramer", volume = "339", series = "IFIP Advances in Information and Communication Technology", pages = "387--394", address = "Larnaca, Cyprus", month = oct # " 6-7", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Evolutionary Algorithms, Global Positioning System, Total Electron Content", DOI = "doi:10.1007/978-3-642-16239-8_50", abstract = "Total Electron Content (TEC) is an ionospheric characteristic used to derive the signal delay imposed by the ionosphere on trans-ionospheric links and subsequently overwhelm its negative impact in accurate position determination. In this paper, an Evolutionary Algorithm (EA), and particularly a Genetic Programming (GP) based model is designed. The proposed model is based on the main factors that influence the variability of the predicted parameter on a diurnal, seasonal and long-term time-scale. Experimental results show that the GP-model, which is based on TEC measurements obtained over a period of 11 years, has produced a good approximation of the modeled parameter and can be implemented as a local model to account for the ionospheric imposed error in positioning. The GP-based approach performs better than the existing Neural Network-based approach in several cases.", affiliation = "School of Computer Science and Informatics, University College Dublin, Dublin, Ireland", notes = "http://www.cs.ucy.ac.cy/aiai2010/", } @InProceedings{agapitosetal:2010:cfe, author = "Alexandros Agapitos and Michael O'Neill and Anthony Brabazon", title = "Promoting the generalisation of genetically induced trading rules", booktitle = "Proceedings of the 4th International Conference on Computational and Financial Econometrics CFE'10", year = "2010", editor = "G. Kapetanios and O. Linton and M. McAleer and E. Ruiz", pages = "E678", address = "Senate House, University of London, UK", month = "10-12 " # dec, organisation = "CSDA, LSE, Queen Mary and Westerfield College", publisher = "ERCIM", keywords = "genetic algorithms, genetic programming", URL = "http://www.cfe-csda.org/cfe10/LondonBoA.pdf", size = "Abstracts only", abstract = "The goal of Machine Learning is not to induce an exact representation of the training patterns themselves, but rather to build a model of the underlying pattern-generation process. One of the most important aspects of this computational process is how to obtain general models that are representative of the true concept, and as a result, perform efficiently when presented with novel patterns from that concept. A particular form of evolutionary machine learning, Genetic Programming, tackles learning problems by means of an evolutionary process of program discovery. In this paper we investigate the profitability of evolved technical trading rules when accounting for the problem of over-fitting. Out-of-sample rule performance deterioration is a well-known problem, and has been mainly attributed to the tendency of the evolved models to find meaningless regularities in the training dataset due to the high dimensionality of features and the rich hypothesis space. We present a review of the major established methods for promoting generalisation in conventional machine learning paradigms. Then, we report empirical results of adapting such techniques to the Genetic Programming methodology, and applying it to discover trading rules for various financial datasets.", notes = "http://www.cfe-csda.org/cfe10/", } @InProceedings{agapitos:2011:EuroGP, author = "Alexandros Agapitos and Michael O'Neill and Anthony Brabazon and Theodoros Theodoridis", title = "Maximum Margin Decision Surfaces for Increased Generalisation in Evolutionary Decision Tree Learning", booktitle = "Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011", year = "2011", month = "27-29 " # apr, editor = "Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado", series = "LNCS", volume = "6621", publisher = "Springer Verlag", address = "Turin, Italy", pages = "61--72", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-20406-7", DOI = "doi:10.1007/978-3-642-20407-4_6", abstract = "Decision tree learning is one of the most widely used and practical methods for inductive inference. We present a novel method that increases the generalisation of genetically-induced classification trees, which employ linear discriminants as the partitioning function at each internal node. Genetic Programming is employed to search the space of oblique decision trees. At the end of the evolutionary run, a (1+1) Evolution Strategy is used to geometrically optimise the boundaries in the decision space, which are represented by the linear discriminant functions. The evolutionary optimisation concerns maximising the decision-surface margin that is defined to be the smallest distance between the decision-surface and any of the samples. Initial empirical results of the application of our method to a series of datasets from the UCI repository suggest that model generalisation benefits from the margin maximisation, and that the new method is a very competent approach to pattern classification as compared to other learning algorithms.", notes = "Part of \cite{Silva:2011:GP} EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011", } @InProceedings{Agapitos:2011:GECCOcomp, author = "Alexandros Agapitos and Michael O'Neill and Anthony Brabazon", title = "Stateful program representations for evolving technical trading rules", booktitle = "GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0690-4", keywords = "genetic algorithms, genetic programming: Poster", pages = "199--200", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001858.2001969", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "A family of stateful program representations in grammar-based Genetic Programming are being compared against their stateless counterpart in the problem of binary classification of sequences of daily prices of a financial asset. Empirical results suggest that stateful classifiers learn as fast as stateless ones but generalise better to unseen data, rendering this form of program representation strongly appealing to the automatic programming of technical trading rules.", notes = "Also known as \cite{2001969} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @InProceedings{Agapitos:2011:CIG, author = "Alexandros Agapitos and Michael O'Neill and Anthony Brabazon and Theodoros Theodoridis", title = "Learning Environment Models in Car Racing Using Stateful Genetic Programming", booktitle = "Proceedings of the 2011 IEEE Conference on Computational Intelligence and Games", year = "2011", address = "Seoul, South Korea", pages = "219--226", month = "31 " # aug # " - 3 " # sep, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Reinforcement Learning, Multiobjective Evolution, AI in Computer Games, Car Racing, AI game agent, computational intelligence, diverse opponent generation, game play learning, nonplayer character, computer games, evolutionary computation, learning (artificial intelligence), multi-agent systems, 2D data structures, artificial agents, car racing games, learning environment models, model building behaviour, modular programs, non player characters, cognition, computer games, data structures, learning (artificial intelligence), multi-agent systems", isbn13 = "978-1-4577-0010-1", URL = "http://cilab.sejong.ac.kr/cig2011/proceedings/CIG2011/papers/paper54.pdf", DOI = "doi:10.1109/CIG.2011.6032010", size = "8 pages", abstract = "For computational intelligence to be useful in creating game agent AI we need to focus on methods that allow the creation and maintenance of models for the environment, which the artificial agents inhabit. Maintaining a model allows an agent to plan its actions more effectively by combining immediate sensory information along with a memories that have been acquired while operating in that environment. To this end, we propose a way to build environment models for non-player characters in car racing games using stateful Genetic Programming. A method is presented, where general-purpose 2-dimensional data-structures are used to build a model of the racing track. Results demonstrate that model-building behaviour can be cooperatively coevolved with car-controlling behaviour in modular programs that make use of these models in order to navigate successfully around a racing track.", notes = "Indexed memory. Also known as \cite{6032010}", } @InCollection{Agapitos:NCFE:2011, author = "Alexandros Agapitos and Abhinav Goyal and Cal Muckley", title = "An Evolutionary Algorithmic Investigation of {US} Corporate Payout Policy", booktitle = "Natural Computing in Computational Finance (Volume 4)", publisher = "Springer", year = "2012", editor = "Anthony Brabazon and Michael O'Neill and Dietmar Maringer", volume = "380", series = "Studies in Computational Intelligence", chapter = "7", pages = "123--139", keywords = "genetic algorithms, genetic programming, US Corporate Payout Policy, Symbolic Regression", isbn13 = "978-3-642-23335-7", URL = "http://hdl.handle.net/10197/3552", URL = "https://researchrepository.ucd.ie/bitstream/10197/3552/1/gp_bookchapter.pdf", URL = "http://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-3-642-23335-7", DOI = "doi:10.1007/978-3-642-23336-4_7", abstract = "This Chapter examines cash dividends and share repurchases in the United States during the period 1990 to 2008. In the extant literature a variety of classical statistical methodologies have been adopted, foremost among these is the method of panel regression modelling. Instead, in this Chapter, we have informed our model specifications and our coefficient estimates using a genetic program. Our model captures effects from a wide range of pertinent proxy variables related to the agency cost-based life cycle theory, the signalling theory and the catering theory of corporate payout policy determination. In line with the extant literature, our findings indicate the predominant importance of the agency-cost based life cycle theory. The adopted evolutionary algorithm approach also provides important new insights concerning the influence of firm size, the concentration of firm ownership and cash flow uncertainty with respect to corporate payout policy determination in the United States.", } @InProceedings{agapitos:evoapps12, author = "Alexandros Agapitos and Michael O'Neill and Anthony Brabazon", title = "Evolving Seasonal Forecasting Models with Genetic Programming in the Context of Pricing Weather-Derivatives", booktitle = "Applications of Evolutionary Computing, EvoApplications 2012: EvoCOMNET, EvoCOMPLEX, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoNUM, EvoPAR, EvoRISK, EvoSTIM, EvoSTOC", year = "2011", month = "11-13 " # apr, editor = "Cecilia {Di Chio} and Alexandros Agapitos and Stefano Cagnoni and Carlos Cotta and F. {Fernandez de Vega} and Gianni A. {Di Caro} and Rolf Drechsler and Aniko Ekart and Anna I Esparcia-Alcazar and Muddassar Farooq and William B. Langdon and Juan J. Merelo and Mike Preuss and Hendrik Richter and Sara Silva and Anabela Simoes and Giovanni Squillero and Ernesto Tarantino and Andrea G. B. Tettamanzi and Julian Togelius and Neil Urquhart and A. Sima Uyar and Georgios N. Yannakakis", series = "LNCS", volume = "7248", publisher = "Springer Verlag", address = "Malaga, Spain", publisher_address = "Berlin", pages = "135--144", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-29177-7", DOI = "doi:10.1007/978-3-642-29178-4_14", size = "10 pages", abstract = "In this study we evolve seasonal forecasting temperature models, using Genetic Programming (GP), in order to provide an accurate, localised, long-term forecast of a temperature profile as part of the broader process of determining appropriate pricing model for weather-derivatives, financial instruments that allow organisations to protect themselves against the commercial risks posed by weather fluctuations. Two different approaches for time-series modelling are adopted. The first is based on a simple system identification approach whereby the temporal index of the time-series is used as the sole regressor of the evolved model. The second is based on iterated single-step prediction that resembles autoregressive and moving average models in statistical time-series modelling. Empirical results suggest that GP is able to successfully induce seasonal forecasting models, and that autoregressive models compose a more stable unit of evolution in terms of generalisation performance for the three datasets investigated.", notes = "EvoFIN Part of \cite{DiChio:2012:EvoApps} EvoApplications2012 held in conjunction with EuroGP2012, EvoCOP2012, EvoBio'2012 and EvoMusArt2012", affiliation = "Financial Mathematics and Computation Research Cluster Natural Computing Research and Applications Group Complex and Adaptive Systems Laboratory, University College Dublin, Ireland", } @InCollection{Agapitos:FDMCI:2012, author = "Alexandros Agapitos and Michael O'Neill and Anthony Brabazon", title = "Genetic Programming for the Induction of Seasonal Forecasts: A Study on Weather Derivatives", booktitle = "Financial Decision Making Using Computational Intelligence", publisher = "Springer", year = "2012", editor = "Doumpos Michael and Zopounidis Constantin and Pardalos Panos", volume = "70", series = "Springer Optimization and Its Applications", chapter = "6", pages = "153--182", note = "Due: July 31, 2012", keywords = "genetic algorithms, genetic programming, Weather derivatives pricing, Seasonal temperature forecasting, Autoregressive models, Supervised ensemble learning, Generalisation", isbn13 = "978-1-4614-3772-7", URL = "http://www.springer.com/mathematics/applications/book/978-1-4614-3772-7", } @InProceedings{conf/ppsn/Agapitos12, author = "Alexandros Agapitos and Anthony Brabazon and Michael O'Neill", title = "Controlling Overfitting in Symbolic Regression Based on a Bias/Variance Error Decomposition", booktitle = "Parallel Problem Solving from Nature, PPSN XII (part 1)", year = "2012", editor = "Carlos A. {Coello Coello} and Vincenzo Cutello and Kalyanmoy Deb and Stephanie Forrest and Giuseppe Nicosia and Mario Pavone", volume = "7491", series = "Lecture Notes in Computer Science", pages = "438--447", address = "Taormina, Italy", month = sep # " 1-5", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-32936-4", DOI = "doi:10.1007/978-3-642-32937-1_44", size = "10 pages", abstract = "We consider the fundamental property of generalisation of data-driven models evolved by means of Genetic Programming (GP). The statistical treatment of decomposing the regression error into bias and variance terms provides insight into the generalisation capability of this modelling method. The error decomposition is used as a source of inspiration to design a fitness function that relaxes the sensitivity of an evolved model to a particular training dataset. Results on eight symbolic regression problems show that new method is capable on inducing better-generalising models than standard GP for most of the problems.", affiliation = "Natural Computing Research and Applications Group, University College Dublin, Ireland", } @InProceedings{agapitos:2013:EuroGP, author = "Alexandros Agapitos and Michael O'Neill and Anthony Brabazon", title = "Adaptive Distance Metrics for Nearest Neighbour Classification based on Genetic Programming", booktitle = "Proceedings of the 16th European Conference on Genetic Programming, EuroGP 2013", year = "2013", month = "3-5 " # apr, editor = "Krzysztof Krawiec and Alberto Moraglio and Ting Hu and A. Sima Uyar and Bin Hu", series = "LNCS", volume = "7831", publisher = "Springer Verlag", address = "Vienna, Austria", pages = "1--12", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-37206-3", DOI = "doi:10.1007/978-3-642-37207-0_1", abstract = "Nearest Neighbour (NN) classification is a widely-used, effective method for both binary and multi-class problems. It relies on the assumption that class conditional probabilities are locally constant. However, this assumption becomes invalid in high dimensions, and severe bias can be introduced, which degrades the performance of the method. The employment of a locally adaptive distance metric becomes crucial in order to keep class conditional probabilities approximately uniform, whereby better classification performance can be attained. This paper presents a locally adaptive distance metric for NN classification based on a supervised learning algorithm (Genetic Programming) that learns a vector of feature weights for the features composing an instance query. Using a weighted Euclidean distance metric, this has the effect of adaptive neighbourhood shapes to query locations, stretching the neighbourhood along the directions for which the class conditional probabilities don't change much. Initial empirical results on a set of real-world classification datasets showed that the proposed method enhances the generalisation performance of standard NN algorithm, and that it is a competent method for pattern classification as compared to other learning algorithms.", notes = "Part of \cite{Krawiec:2013:GP} EuroGP'2013 held in conjunction with EvoCOP2013, EvoBIO2013, EvoMusArt2013 and EvoApplications2013", } @InProceedings{agapitos:2014:EuroGP, author = "Alexandros Agapitos and James McDermott and Michael O'Neill and Ahmed Kattan and Anthony Brabazon", title = "Higher Order Functions for Kernel Regression", booktitle = "17th European Conference on Genetic Programming", year = "2014", editor = "Miguel Nicolau and Krzysztof Krawiec and Malcolm I. Heywood and Mauro Castelli and Pablo Garcia-Sanchez and Juan J. Merelo and Victor M. {Rivas Santos} and Kevin Sim", series = "LNCS", volume = "8599", publisher = "Springer", pages = "1--12", address = "Granada, Spain", month = "23-25 " # apr, organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-662-44302-6", DOI = "doi:10.1007/978-3-662-44303-3_1", abstract = "Kernel regression is a well-established nonparametric method, in which the target value of a query point is estimated using a weighted average of the surrounding training examples. The weights are typically obtained by applying a distance-based kernel function, which presupposes the existence of a distance measure. This paper investigates the use of Genetic Programming for the evolution of task-specific distance measures as an alternative to Euclidean distance. Results on seven real-world datasets show that the generalisation performance of the proposed system is superior to that of Euclidean-based kernel regression and standard GP.", notes = "Part of \cite{Nicolau:2014:GP} EuroGP'2014 held in conjunction with EvoCOP2014, EvoBIO2014, EvoMusArt2014 and EvoApplications2014", } @InProceedings{Agapitos:2014:CEC, title = "Ensemble {Bayesian} Model Averaging in Genetic Programming", author = "Alexandros Agapitos and Michael O'Neill and Anthony Brabazon", pages = "2451--2458", booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary Computation", year = "2014", month = "6-11 " # jul, editor = "Carlos A. {Coello Coello}", address = "Beijing, China", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, Genetic programming, Data mining, Classification, clustering and data analysis", DOI = "doi:10.1109/CEC.2014.6900567", abstract = "This paper considers the general problem of function estimation via Genetic Programming (GP). Data analysts typically select a model from a population of models, and then proceed as if the selected model had generated the data. This approach ignores the uncertainty in model selection, leading to over-confident inferences and lack of generalisation. We adopt a coherent method for accounting for this uncertainty through a weighted averaging of all models competing in a population of GP. It is a principled statistical method for post-processing a population of programs into an ensemble, which is based on Bayesian Model Averaging (BMA). Under two different formulations of BMA, the predictive probability density function (PDF) of a response variable is a weighted average of PDFs centred around the individual predictions of component models that take the form of either standalone programs or ensembles of programs. The weights are equal to the posterior probabilities of the models generating the predictions, and reflect the models' skill on the training dataset. The method was applied to a number of synthetic symbolic regression problems, and results demonstrate that it generalises better than standard methods for model selection, as well as methods for ensemble construction in GP.", notes = "WCCI2014", } @InProceedings{agapitos:cec2015, author = "Alexandros Agapitos and Michael O'Neill and Miguel Nicolau and David Fagan and Ahmed Kattan and Kathleen Curran", title = "Deep Evolution of Feature Representations for Handwritten Digit Recognition", booktitle = "Proceedings of 2015 IEEE Congress on Evolutionary Computation (CEC 2015)", editor = "Yadahiko Murata", pages = "2452--2459", year = "2015", address = "Sendai, Japan", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2015.7257189", abstract = "A training protocol for learning deep neural networks, called greedy layer-wise training, is applied to the evolution of a hierarchical, feed-forward Genetic Programming based system for feature construction and object recognition. Results on a popular handwritten digit recognition benchmark clearly demonstrate that two layers of feature transformations improves generalisation compared to a single layer. In addition, we show that the proposed system outperforms several standard Genetic Programming systems, which are based on hand-designed features, and use different program representations and fitness functions.", notes = "CEC2015", } @InProceedings{EvoBafin16Agapitosetal, author = "Alexandros Agapitos and Anthony Brabazon and Michael O'Neill", title = "Genetic Programming with Memory For Financial Trading", booktitle = "19th European Conference on the Applications of Evolutionary Computation", year = "2016", editor = "Giovanni Squillero and Paolo Burelli", series = "Lecture Notes in Computer Science", volume = "9597", pages = "19--34", address = "Porto, Portugal", month = mar # " 30 - " # apr # " 1", organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://dx.doi.org/10.1007/978-3-319-31204-0_2", DOI = "doi:10.1007/978-3-319-31204-0_2", abstract = "A memory-enabled program representation in strongly-typed Genetic Programming (GP) is compared against the standard representation in a number of financial time-series modelling tasks. The paper first presents a survey of GP systems that use memory. Thereafter, a number of simulations show that memory-enabled programs generalise better than their standard counterparts in most datasets of this problem domain.", notes = "EvoApplications2016 held in conjunction with EuroGP'2016, EvoCOP2016 and EvoMusArt2016", } @Article{Agapitos:2016:GPEM, author = "Alexandros Agapitos and Michael O'Neill and Ahmed Kattan and Simon M. Lucas", title = "Recursion in tree-based genetic programming", journal = "Genetic Programming and Evolvable Machines", year = "2017", volume = "18", number = "2", pages = "149--183", month = jun, keywords = "genetic algorithms, genetic programming, Evolutionary program synthesis Recursive programs, Variation operators, Fitness landscape analysis", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-016-9277-5", size = "35 pages", abstract = "Recursion is a powerful concept that enables a solution to a problem to be expressed as a relatively simple decomposition of the original problem into sub-problems of the same type. We survey previous research about the evolution of recursive programs in tree-based Genetic Programming. We then present an analysis of the fitness landscape of recursive programs, and report results on evolving solutions to a range of problems. We conclude with guidelines concerning the choice of fitness function and variation operators, as well as the handling of the halting problem. The main findings are as follows. The distribution of fitness changes initially as we look at programs of increasing size but once some threshold has been exceeded, it shows very little variation with size. Furthermore, the proportion of halting programs decreases as size increases. Recursive programs exhibit the property of weak causality; small changes in program structure may cause big changes in semantics. Nevertheless, the evolution of recursive programs is not a needle-in-a-haystack problem; the neighbourhoods of optimal programs are populated by halting individuals of intermediate fitness. Finally, mutation-based variation operators performed the best in finding recursive solutions. Evolution was also shown to outperform random search.", notes = "Factorial, Fibonacci, Exponentiation, Even-n-parity, Nth ftp://ftp.cs.ucl.ac.uk/genetic/gp-code/rand_tree.cc Random walks and error-distance correlation. Canberra distance. (hard) limit of 10000 recursive calls. '..the distribution of error is roughly independent of size' BUT '..Even-n-parity and Nth in Fig. 4d,e do not show a convergence..' 'Overall, our findings are in accordance with simulation results published in \cite{langdon:2006:eurogp}'. 'Fig. 4 Proportion of halting programs (out of 2,000,000 programs) as a function of program size' '..once programs containing recursive nodes wither away from the population, it is impossible to be introduced again.'", } @Article{Agapitos:2018:CMS, author = "Alexandros Agapitos and Anthony Brabazon and Michael O'Neill", title = "Regularised Gradient Boosting for Financial Time-series Modelling", journal = "Computational Management Science", year = "2017", volume = "14", number = "3", pages = "367--391", month = jul, keywords = "genetic algorithms, genetic programming, Boosting algorithms, Gradient boosting, Stagewise additive modelling, Regularisation, Financial time-series modelling, Financial forecasting, Feedforward neural networks, ANN, Noisy data, Ensemble learning", URL = "https://ideas.repec.org/a/spr/comgts/v14y2017i3d10.1007_s10287-017-0280-y.html", DOI = "doi:10.1007/s10287-017-0280-y", abstract = "Gradient Boosting (GB) learns an additive expansion of simple basis-models. This is accomplished by iteratively fitting an elementary model to the negative gradient of a loss function with respect to the expansion's values at each training data-point evaluated at each iteration. For the case of squared-error loss function, the negative gradient takes the form of an ordinary residual for a given training data-point. Studies have demonstrated that running GB for hundreds of iterations can lead to overfitting, while a number of authors showed that by adding noise to the training data, generalisation is impaired even with relatively few basis-models. Regularisation is realised through the shrinkage of every newly-added basis-model to the expansion. This paper demonstrates that GB with shrinkage-based regularisation is still prone to overfitting in noisy datasets. We use a transformation based on a sigmoidal function for reducing the influence of extreme values in the residuals of a GB iteration without removing them from the training set. This extension is built on top of shrinkage-based regularisation. Simulations using synthetic, noisy data show that the proposed method slows-down overfitting and reduces the generalisation error of regularised GB. The proposed method is then applied to the inherently noisy domain of financial time-series modelling. Results suggest that for the majority of datasets the method generalises better when compared against standard regularised GB, as well as against a range of other time-series modelling methods.", notes = "School of Computer Science, University College Dublin, Dublin, Ireland", } @Article{Agapitos:ieeeTEC, author = "Alexandros Agapitos and Roisin Loughran and Miguel Nicolau and Simon Lucas and Michael O'Neill and Anthony Brabazon", title = "A Survey of Statistical Machine Learning Elements in Genetic Programming", journal = "IEEE Transactions on Evolutionary Computation", year = "2019", volume = "23", number = "6", pages = "1029--1048", month = dec, keywords = "genetic algorithms, genetic programming, Statistical Machine Learning, SML, Generalisation, Overfitting, Classification, Symbolic Regression, Model selection, Regularisation, Model Averaging, Bias-Variance trade-off", ISSN = "1089-778X", URL = "http://ncra.ucd.ie/papers/08648159.pdf", DOI = "doi:10.1109/TEVC.2019.2900916", size = "20 pages", abstract = "Modern Genetic Programming operates within the Statistical Machine Learning framework. In this framework evolution needs to balance between approximation of an unknown target function on the training data and generalisation, which is the ability to predict well on new data. The article provides a survey and critical discussion of Statistical Machine Learning methods that enable Genetic Programming to generalise.", notes = "also known as \cite{8648159} School of Computer Science, University College Dublin, Ireland", } @InProceedings{agapow:1996:cbecv, author = "Paul-Michael Agapow", title = "Computational Brittleness and the Evolution of Computer Viruses", editor = "Hans-Michael Voigt and Werner Ebeling and Ingo Rechenberg and Hans-Paul Schwefel", booktitle = "Parallel Problem Solving From Nature IV. Proceedings of the International Conference on Evolutionary Computation", year = "1996", publisher = "Springer-Verlag", volume = "1141", series = "LNCS", pages = "2--11", address = "Berlin, Germany", publisher_address = "Heidelberg, Germany", month = "22-26 " # sep, keywords = "genetic algorithms, genetic programming", ISBN = "3-540-61723-X", DOI = "doi:10.1007/3-540-61723-X_964", size = "10 pages", abstract = "In recent years computer viruses have grown to be of great concern. They have also been proposed as prototypical artificial life, but the possibility of their evolution has been dismissed due to modern computer programs being computationally brittle (i.e. a random change to a functional program will almost certainly render it non-functional) and the series of steps required for the evolution of a new virus being improbable. These allegations are examined by studying homology between functional program sequences. It is concluded that programs are far less brittle than expected. While the evolution of viruses de novo is still unlikely, evolution of pre-existing viruses and programs is feasible. This has significant implications for computer security and evolutionary computation.", notes = "http://lautaro.fb10.tu-berlin.de/ppsniv.html PPSN4 (UNIX Sun RISC) {"}programs are far less brittle than expected{"}.", affiliation = "La Trobe University Computer Science V. 3083 Melbourne Australia V. 3083 Melbourne Australia", } @InCollection{agarwal:2000:GPWPPAPE, author = "Ashish Agarwal", title = "Genetic Programming for Wafer Property Prediction After Plasma Enhanced", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "16--24", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:2000:gagp}", } @InProceedings{agarwal:2023:SDECGE, author = "Ekansh Agarwal and Ajeet Kumar Verma and Anindya Pain and Shantanu Sarkar", title = "Probabilistic Analysis of a Geosynthetic Reinforced Soil Retaining Wall Under Seismic Conditions Using Genetic Programming", booktitle = "Soil Dynamics, Earthquake and Computational Geotechnical Engineering", year = "2023", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-981-19-6998-0_20", DOI = "doi:10.1007/978-981-19-6998-0_20", } @Article{Aggarwal:2011:ijcse, author = "Khushboo Aggarwal and Sunil Kumar Singh and Sakar Khattar", title = "A high Performance Algorithm for Solving large scale Travelling Salesman Problem using Distributed Memory Architectures", journal = "Indian Journal of Computer Science and Engineering", year = "2011", volume = "2", number = "4", pages = "516--521", month = aug # "-" # sep, keywords = "genetic algorithms, genetic programming, TSP, traveling salesman problem, fitness functions", ISSN = "2231-3850", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.300.6369", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.300.6369", URL = "http://www.ijcse.com/docs/INDJCSE11-02-04-175.pdf", size = "6 pages", abstract = "In this paper, we present an intelligent solution system for travelling salesman problem. The solution has three stages. The first stage uses Clustering Analysis in Data Mining to classify all customers by a number of attributes, such as distance, demand level, the density of customer, and city layout. The second stage introduces how to generate feasible routing schemes for each vehicle type. Specifically, a depth-first search algorithm with control rules is presented to generate feasible routing schemes. In the last stage, a genetic programming model is applied to find the best possible solution. Finally, we present a paradigm for using this algorithm for distributed memory architectures to gain the benefits of parallel processing.", } @Misc{Aggarwal:intern, author = "Varun Aggarwal", title = "Prediction of Protein Secondary Structure using Genetic Programming", howpublished = "Summer Internship Project Report During June-July 2003", year = "2003", keywords = "genetic algorithms, genetic programming", URL = "http://web.mit.edu/varun_ag/www/psspreport.pdf", size = "23 pages", abstract = "Project 1:Using SOM and Genetic Programming to predict Protein Secondary structure Project 2: Improving PSIPRED Predictions using Genetic Programming", notes = "Under: Dr. Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden", } @InProceedings{maccallum:2004:eurogp, author = "Varun Aggarwal and Robert MacCallum", title = "Evolved Matrix Operations for Post-Processing Protein Secondary Structure Predictions", booktitle = "Genetic Programming 7th European Conference, EuroGP 2004, Proceedings", year = "2004", editor = "Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule", volume = "3003", series = "LNCS", pages = "220--229", address = "Coimbra, Portugal", publisher_address = "Berlin", month = "5-7 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-21346-5", URL = "http://web.mit.edu/varun_ag/www/aggarwal-eurogp2004.pdf", DOI = "doi:10.1007/978-3-540-24650-3_20", abstract = "Predicting the three-dimensional structure of proteins is a hard problem, so many have opted instead to predict the secondary structural state (usually helix, strand or coil) of each amino acid residue. This should be an easier task, but it now seems that a ceiling of around 76 percent per-residue three-state accuracy has been reached. Further improvements will require the correct processing of so-called {"}long-range information{"}. We present a novel application of genetic programming to evolve high level matrix operations to post-process secondary structure prediction probabilities produced by the popular, state-of-the-art neural network based PSIPRED by David Jones. We show that global and long-range information may be used to increase three-state accuracy by at least 0.26 percentage points - a small but statistically significant difference. This is on top of the 0.14 percentage point increase already made by PSIPRED's built-in filters.", notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004", } @InCollection{Aggarwal:2006:GPTP, author = "Varun Aggarwal and Una-May O'Reilly", title = "Design of Posynomial Models for Mosfets: Symbolic Regression Using Genetic Algorithms", booktitle = "Genetic Programming Theory and Practice {IV}", year = "2006", editor = "Rick L. Riolo and Terence Soule and Bill Worzel", volume = "5", series = "Genetic and Evolutionary Computation", pages = "219--236", address = "Ann Arbor", month = "11-13 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, circuit sizing, symbolic regression, posynomial models, geometric programming", ISBN = "0-387-33375-4", URL = "http://people.csail.mit.edu/unamay/publications-dir/gptp06.pdf", DOI = "doi:10.1007/978-0-387-49650-4_14", size = "19 pages", abstract = "Starting from a broad description of analogue circuit design in terms of topology design and sizing, we discuss the difficulties of sizing and describe approaches that are manual or automatic. These approaches make use of blackbox optimisation techniques such as evolutionary algorithms or convex optimization techniques such as geometric programming. Geometric programming requires posynomial expressions for a circuit's performance measurements. We show how a genetic algorithm can be exploited to evolve a polynomial expression (i.e. model) of transistor (i.e. mosfet) behaviour more accurately than statistical techniques in the literature.", notes = "part of \cite{Riolo:2006:GPTP} Published Jan 2007 after the workshop", } @Article{Aghbashlo:2016:Energy, author = "Mortaza Aghbashlo and Shahaboddin Shamshirband and Meisam Tabatabaei and Por Lip Yee and Yaser Nabavi Larimi", title = "The use of ELM-WT (extreme learning machine with wavelet transform algorithm) to predict exergetic performance of a {DI} diesel engine running on diesel/biodiesel blends containing polymer waste", journal = "Energy", volume = "94", pages = "443--456", year = "2016", ISSN = "0360-5442", DOI = "doi:10.1016/j.energy.2015.11.008", URL = "http://www.sciencedirect.com/science/article/pii/S0360544215015327", abstract = "In this study, a novel method based on Extreme Learning Machine with wavelet transform algorithm (ELM-WT) was designed and adapted to estimate the exergetic performance of a DI diesel engine. The exergetic information was obtained by calculating mass, energy, and exergy balance equations for the experimental trials conducted at various engine speeds and loads as well as different biodiesel and expanded polystyrene contents. Furthermore, estimation capability of the ELM-WT model was compared with that of the ELM, GP (genetic programming) and ANN (artificial neural network) models. The experimental results showed that an improvement in the exergetic performance modelling of the DI diesel engine could be achieved by the ELM-WT approach in comparison with the ELM, GP, and ANN methods. Furthermore, the results showed that the applied algorithm could learn thousands of times faster than the conventional popular learning algorithms. Obviously, the developed ELM-WT model could be used with a high degree of confidence for further work on formulating novel model predictive strategy for investigating exergetic performance of DI diesel engines running on various renewable and non-renewable fuels.", keywords = "genetic algorithms, genetic programming, Biodiesel, DI diesel engine, Exergetic performance parameters, Expanded polystyrene, Cost sensitivity analysis, Extreme learning machine-wavelet (ELM-WT)", } @Article{agnelli:2002:PRL, author = "Davide Agnelli and Alessandro Bollini and Luca Lombardi", title = "Image classification: an evolutionary approach", journal = "Pattern Recognition Letters", year = "2002", number = "1-3", volume = "23", pages = "303--309", month = jan, keywords = "genetic algorithms, genetic programming, Image classification, Supervised learning", ISSN = "0167-8655", broken = "http://www.sciencedirect.com/science/article/B6V15-443K10X-6/1/7af8206767ca79f9898fec720a84c656", DOI = "doi:10.1016/S0167-8655(01)00128-3", size = "7 pages", abstract = "Evolutionary algorithms are proving viable in solving complex optimization problems such as those typical of supervised learning approaches to image understanding. This paper presents an evolutionary approach to image classification and discusses some experimental results, suggesting that genetic programming could provide a convenient alternative to standard supervised learning methods.", } @InProceedings{Agrawal:2023:ICSE, author = "Arpan Agrawal and Emily First and Zhanna Kaufman and Tom Reichel and Shizhuo Zhang and Timothy Zhou and Alex Sanchez-Stern and Talia Ringer and Yuriy Brun", title = "Proofster: Automated Formal Verification", booktitle = "Proceedings of the Demonstrations Track at the 45th International Conference on Software Engineering (ICSE)", year = "2023", pages = "26--30", address = "Melbourne", month = "14-20 " # may, keywords = "genetic algorithms, genetic programming", isbn13 = "979-8-3503-2264-4", DOI = "doi:10.1109/ICSE-Companion58688.2023.00018", video_url = "https://youtu.be/xQAi66lRfwI/", code_url = "https://proofster.cs.umass.edu/", size = "5 pages", abstract = "Formal verification is an effective but extremely work-intensive method of improving software quality. Verifying the correctness of software systems often requires significantly more effort than implementing them in the first place, despite the existence of proof assistants, such as Coq, aiding the process. Recent work has aimed to fully automate the synthesis of formal verification proofs, but little tool support exists for practitioners. This paper presents Proofster, a web-based tool aimed at assisting developers with the formal verification process via proof synthesis. Proofster inputs a Coq theorem specifying a property of a software system and attempts to automatically synthesize a formal proof of the correctness of that property. When it is unable to produce a proof, Proofster outputs the proof-space search tree its synthesis explored, which can guide the developer to provide a hint to enable Proofster to synthesize the proof. Proofster runs online at https://proofster.cs.umass.edu/ and a video demonstrating Proofster is available at https://youtu.be/xQAi66lRfwI/.", notes = "Demo Track", } @InProceedings{aguilar:1998:rcmmcfssdft, author = "Jose L. Aguilar and Mariela Cerrada", title = "Reliability-Centered Maintenance Methodology-Based Fuzzy Classifier System Design for Fault Tolerance", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "621", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, classifiers", ISBN = "1-55860-548-7", notes = "GP-98", } @InProceedings{aguilar:1999:ABGAMOP, author = "Jose Aguilar and Pablo Miranda", title = "Approaches Based on Genetic Algorithms for Multiobjective Optimization Problems", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "3--10", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-873.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-873.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{aguilar:1999:TGADRSLS, author = "Jesus Aguilar and Jose Riquelme and Miguel Toro", title = "Three Geometric Approaches for representing Decision Rules in a Supervised Learning System", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "771", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-391.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-391.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99). See also \cite{aguilar:1999:T}", } @InProceedings{aguilar:1999:T, author = "Jesus Aguilar and Jose Riquelme and Miguel Toro", title = "Three geometric approaches for representing decision rules in a supervised learning system", booktitle = "Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference", year = "1999", editor = "Scott Brave and Annie S. Wu", pages = "8--15", address = "Orlando, Florida, USA", month = "13 " # jul, keywords = "Genetic Algorithms, data mining, supervised learning, hyper rectangles, rotated hyper rectangles, hyper ellipse", abstract = "hyperrectangles, rotated hyperrectangles and hyperellipses", notes = "GECCO-99LB", } @InProceedings{aguilar3:2001:gecco, title = "Fuzzy Classifier System and Genetic Programming on System Identification Problems", author = "Jose Aguilar and Mariela Cerrada", pages = "1245--1251", year = "2001", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)", editor = "Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon and Edmund Burke", address = "San Francisco, California, USA", publisher_address = "San Francisco, CA 94104, USA", month = "7-11 " # jul, keywords = "genetic algorithms, genetic programming, real world applications", ISBN = "1-55860-774-9", URL = "http://gpbib.cs.ucl.ac.uk/gecco2001/d24.pdf", notes = "GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of \cite{spector:2001:GECCO}", } @InProceedings{WSEAS_640_Aguilar, author = "Jose Aguilar and Mariela Cerrada", title = "Genetic Programming-Based Approach for System Identification Applying Genetic Programming to obtain Separation", address = "Puerto De La Cruz, Tenerife, Spain", year = "2001", booktitle = "WSES International Conferences WSEAS NNA-FSFS-EC 2001", editor = "Nikos E. Mastorakis", pages = "6401--6406", month = feb # " 11-15", organisation = "The World Scientific and Engineering Academy and Society (WSEAS)", keywords = "genetic algorithms, genetic programming, Genetic Programming, Evolutionary Computation, Identification Systems", URL = "http://www.wseas.us/e-library/conferences/tenerife2001/papers/640.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.611.1267", size = "6 pages", abstract = "In this work, an approach based on Genetic Programming is proposed for the input-output systems identification problem. Laguerre's functions and the ARX method have been commonly used to solve the systems identification problem. Recently, approaches based on Artificial Neural Networks have been used to solve this problem. Genetic Programming is an Evolutionary Computation technique based on the evolution of mathematical symbols (constants, functions, variables, operators, etc.). To achieve the identification, a set of analysis trees is used to describe the different models (individuals) that our approach proposes during its execution. At the end of the evolutionary process, an input-output model of the system is proposed by our approach (it is the best individual).", notes = "paper ID number 640 http://www.wseas.us/e-library/conferences/tenerife2001/index.htm ", } @Misc{Aguilar:2004:sci, author = "J. Aguilar and J. Altamiranda", title = "A Data Mining Algorithm Based on the Genetic Programming", year = "2004", keywords = "genetic algorithms, genetic programming, Data Mining, Clustering", size = "10 pages", abstract = "Data Mining is composed by a set of methods to extract knowledgement from large database. One of these methods is Genetic Programming. In this work we use this method to build a Data Mining System that define a set of patterns in order to classify the data. We define a grammar, which is used by the Genetic Programming in order to define the rules that represent the patterns. In this way, we can group the data in class and simplify the information in the database according to the set of patterns.", notes = " J. Aguilar Universidad de los Andes, Facultad de Ingenieria, Departamento de Computacion, CEMISID, Merida, Venezuela, 5101 J. Altamiranda Universidad de los Andes, Facultad de Ingenieria, Postgrado de Computacion, CEMISID, Merida, Venezuela, 5101 ", } @InProceedings{Aguilar:DEU:cec2006, author = "Jose Aguilar and Gilberto Gonzalez", title = "Data Extrapolation Using Genetic Programming to Matrices Singular Values Estimation", booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary Computation", year = "2006", editor = "Gary G. Yen and Simon M. Lucas and Gary Fogel and Graham Kendall and Ralf Salomon and Byoung-Tak Zhang and Carlos A. Coello Coello and Thomas Philip Runarsson", pages = "3227--3230", address = "Vancouver, BC, Canada", month = "16-21 " # jul, publisher = "IEEE Press", ISBN = "0-7803-9487-9", URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=11108", DOI = "doi:10.1109/CEC.2006.1688718", keywords = "genetic algorithms, genetic programming", abstract = "In mathematical models where the dimensions of the matrices are very large, the use of classical methods to compute the singular values is very time consuming and requires a lot of computational resources. In this way, it is necessary to find new faster methods to compute the singular values of a very large matrix. We present a method to estimate the singular values of a matrix based on Genetic Programming (GP). GP is an approach based on the evolutionary principles of the species. GP is used to make extrapolations of data out of sample data. The extrapolations of data are achieved by irregularity functions which approximate very well the trend of the sample data. GP produces from just simple's functions, operators and a fitness function, complex mathematical expressions that adjust smoothly to a group of points of the form (xi, yi). We obtain amazing mathematical formulas that follow the behaviour of the sample data. We compare our algorithm with two techniques: the linear regression and non linear regression approaches. Our results suggest that we can predict with some percentage of error the largest singular values of a matrix without computing the singular values of the whole matrix and using only some random selected columns of the matrix.", notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D", } @Article{AguilarRivera:2015:ESA, author = "Ruben Aguilar-Rivera and Manuel Valenzuela-Rendon and J. J. Rodriguez-Ortiz", title = "Genetic algorithms and {Darwinian} approaches in financial applications: A survey", journal = "Expert Systems with Applications", year = "2015", volume = "42", number = "21", pages = "7684--7697", month = "30 " # nov, keywords = "genetic algorithms, genetic programming, Evolutionary computation, Finance, Portfolio optimization, Survey", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2015.06.001", URL = "http://www.sciencedirect.com/science/article/pii/S0957417415003954", abstract = "This article presents a review of the application of evolutionary computation methods to solving financial problems. Genetic algorithms, genetic programming, multi-objective evolutionary algorithms, learning classifier systems, co-evolutionary approaches, and estimation of distribution algorithms are the techniques considered. The novelty of our approach comes in three different manners: it covers time lapses not included in other review articles, it covers problems not considered by others, and the scope covered by past and new references is compared and analysed. The results concluded the interest about methods and problems has changed through time. Although, genetic algorithms have remained the most popular approach in the literature. There are combinations of problems and solutions methods which are yet to be investigated.", } @InProceedings{aguirre:1999:EH, author = "Arturo Hernandez Aguirre and Carlos A. Coello Coello and Bill P. Buckles", title = "A Genetic Programming Approach to Logic Function Synthesis by Means of Multiplexers", booktitle = "Proceedings of the The First NASA/DOD Workshop on Evolvable Hardware", year = "1999", editor = "Adrian Stoica and Didier Keymeulen and Jason Lohn", pages = "46--53", address = "Pasadena, California", month = "19-21 " # jul, organisation = "Jet Propulsion Laboratory, California Institute of Technology", publisher = "IEEE Computer Society", keywords = "genetic algorithms, genetic programming, evolvable hardware, 1-control line multiplexer, Boolean functions, fitness function, genetic programming approach, logic function synthesis, minimisation, multiplexers, Boolean functions, logic design, minimisation, multiplexing equipment", ISBN = "0-7695-0256-3", DOI = "doi:10.1109/EH.1999.785434", abstract = "This paper presents an approach based on the use of genetic programming to synthesize logic functions. The proposed approach uses the 1-control line multiplexer as the only design unit, defining any logic function (defined by a truth table) through the replication of this single unit. Our fitness function first explores the search space trying to find a feasible design and then concentrates in the minimization of such (fully feasible) circuit. The proposed approach is illustrated using several sample Boolean functions.", notes = "EH-1999", } @InProceedings{aguirre:1999:CCMOGA, author = "Hernan E. Aguirre and Kiyoshi Tanaka and Tatsuo Sugimura", title = "Cooperative Crossover and Mutation Operators in Genetic Algorithms", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "772", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems, poster papers", ISBN = "1-55860-611-4", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Article{Aguirre:2003:AIR, author = "Arturo Hernandez Aguirre and Carlos A. {Coello Coello}", title = "Evolutionary Synthesis of Logic Circuits Using Information Theory", journal = "Artificial Intelligence Review", year = "2003", volume = "20", number = "3-4", pages = "445--471", keywords = "genetic algorithms, genetic programming, circuit synthesis, computer-aided design, evolutionary algorithms, evolvable hardware, information theory", language = "English", publisher = "Kluwer Academic Publishers", ISSN = "0269-2821", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.378.9801", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.378.9801", URL = "http://hera.ugr.es/doi/14977278.pdf", URL = "http://dx.doi.org/10.1023/B%3AAIRE.0000006603.98023.97", DOI = "doi:10.1023/B:AIRE.0000006603.98023.97", abstract = "In this paper, we propose the use of Information Theory as the basis for designing a fitness function for Boolean circuit design using Genetic Programming. Boolean functions are implemented by replicating binary multiplexers. Entropy-based measures, such as Mutual Information and Normalised Mutual Information are investigated as tools for similarity measures between the target and evolving circuit. Three fitness functions are built over a primitive one. We show that the landscape of Normalized Mutual Information is more amenable for being used as a fitness function than simple Mutual Information. The evolutionary synthesised circuits are compared to the known optimum size. A discussion of the potential of the Information-Theoretical approach is given.", } @InProceedings{Hernandez-Aguirre:2004:MIFFfECS, title = "Mutual Information-based Fitness Functions for Evolutionary Circuit Synthesis", author = "Arturo Hernandez-Aguirre and Carlos Coello-Coello", pages = "1309--1316", booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary Computation", year = "2004", publisher = "IEEE Press", month = "20-23 " # jun, address = "Portland, Oregon", volume = "2", keywords = "genetic algorithms, genetic programming, EHW, Evolutionary Design Automation, Evolutionary design \& evolvable hardware", ISBN = "0-7803-8515-2", URL = "http://delta.cs.cinvestav.mx/~ccoello/conferences/cec04-muxmutual.pdf.gz", DOI = "doi:10.1109/CEC.2004.1331048", size = "8 pages", abstract = "Mutual Information and Normalised Mutual Information measures are investigated. The goal is the analysis of some fitness functions based in mutual information and what problems prevent them from common use. We identify and find a clear explanation to them, thereafter, we propose new fitness functions and ran several experiments to investigate their effect on the search space, convergence time, and quality of solutions.", notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.", } @Article{AGWU:2018:PT, author = "Okorie E. Agwu and Julius U. Akpabio and Sunday B. Alabi and Adewale Dosunmu", title = "Settling velocity of drill cuttings in drilling fluids: A review of experimental, numerical simulations and artificial intelligence studies", journal = "Powder Technology", volume = "339", pages = "728--746", year = "2018", keywords = "genetic algorithms, genetic programming, Artificial Intelligence, Drill cuttings, Numerical simulations, Settling velocity", ISSN = "0032-5910", DOI = "doi:10.1016/j.powtec.2018.08.064", URL = "http://www.sciencedirect.com/science/article/pii/S0032591018307022", abstract = "In this paper, a comprehensive review of experimental, numerical and artificial intelligence studies on the subject of cuttings settling velocity in drilling muds made by researchers over the last seven decades is brought to the fore. In this respect, 91 experimental, 13 numerical simulations and 7 artificial intelligence researches were isolated, reviewed, tabulated and discussed. A comparison of the three methods and the challenges facing each of these methods were also reviewed. The major outcomes of this review include: (1) the unanimity among experimental researchers that mud rheology, particle size and shape and wall effect are major parameters affecting the settling velocity of cuttings in wellbores; (2) the prevalence of cuttings settling velocity experiments done with the mud in static conditions and the wellbore in the vertical configuration; (3) the extensive use of rigid particles of spherical shape to represent drill cuttings due to their usefulness in experimental visualization, particle tracking, and numerical implementation; (4) the existence of an artificial intelligence technique - multi-gene genetic programming (MGGP) which can provide an explicit equation that can help in predicting settling velocity; (5) the limited number of experimental studies factoring in the effect of pipe rotation and well inclination effects on the settling velocity of cuttings and (6) the most applied numerical method for determining settling velocity is the finite element method. Despite these facts, there is need to perform more experiments with real drill cuttings and factor in the effects of conditions such as drillstring rotation and well inclination and use data emanating therefrom to develop explicit models that would include the effects of these. It should be noted however, that the aim of this paper is not to create an encyclopaedia of particle settling velocity research, but to provide to the researcher with a basic, theoretical, experimental and numerical overview of what has so far been achieved in the area of cuttings settling velocity in drilling muds", } @Article{AGWU:2021:UOGT, author = "Okorie Ekwe Agwu and Julius Udoh Akpabio and Adewale Dosunmu", title = "Modeling the downhole density of drilling muds using multigene genetic programming", journal = "Upstream Oil and Gas Technology", volume = "6", pages = "100030", year = "2021", ISSN = "2666-2604", DOI = "doi:10.1016/j.upstre.2020.100030", URL = "https://www.sciencedirect.com/science/article/pii/S266626042030030X", keywords = "genetic algorithms, genetic programming, Multigene genetic programming, Downhole mud density, Drilling mud, HTHP", abstract = "The main objective of this paper is to use experimental measurements of downhole pressure, temperature and initial mud density to predict downhole density using multigene genetic programming. From the results, the mean square error for the WBM density model was 0.0012, with a mean absolute error of 0.0246 and the square of correlation coefficient (R2) was 0.9998; while for the OBM, the MSE was 0.000359 with MAE of 0.01436 and R2 of 0.99995. In assessing the OBM model's generalization capability, the model had an MSE of 0.031, MAE of 0.138 and mean absolute percentage error (MAPE) of 0.95percent", } @Misc{nlin/0607029, author = "Dilip P. Ahalpara and Jitendra C. Parikh", title = "Modeling Time Series of Real Systems using Genetic Programming", howpublished = "ArXiv Nonlinear Sciences e-prints", year = "2006", month = "14 " # jul, note = "Submitted to Physical Review E", adsurl = "http://adsabs.harvard.edu/cgi-bin/nph-bib_query?bibcode=2006nlin......7029A&db_key=PRE", adsnote = "Provided by the Smithsonian/NASA Astrophysics Data System", keywords = "genetic algorithms, genetic programming", URL = "http://arxiv.org/PS_cache/nlin/pdf/0607/0607029v1.pdf", size = "10 pages", abstract = "Analytic models of two computer generated time series (Logistic map and Rossler system) and two real time series (ion saturation current in Aditya Tokamak plasma and NASDAQ composite index) are constructed using Genetic Programming (GP) framework. In each case, the optimal map that results from fitting part of the data set also provides a very good description of rest of the data. Predictions made using the map iteratively range from being very good to fair.", notes = "nlin/0607029 See also \cite{Ahalpara:2008:IJMPC}", } @Article{Ahalpara:2008:IJMPC, author = "Dilip P. Ahalpara and Jitendra C. Parikh", title = "Genetic Programming based approach for Modeling Time Series data of real systems", journal = "International Journal of Modern Physics C, Computational Physics and Physical Computation", year = "2008", volume = "19", number = "1", pages = "63--91", keywords = "genetic algorithms, genetic programming, Time series analysis, artificial neural networks", DOI = "doi:10.1142/S0129183108011942", abstract = "Analytic models of a computer generated time series (logistic map) and three real time series (ion saturation current in Aditya Tokamak plasma, NASDAQ composite index and Nifty index) are constructed using Genetic Programming (GP) framework. In each case, the optimal map that results from fitting part of the data set also provides a very good description of the rest of the data. Predictions made using the map iteratively are very good for computer generated time series but not for the data of real systems. For such cases, an extended GP model is proposed and illustrated. A comparison of these results with those obtained using Artificial Neural Network (ANN) is also carried out.", notes = "IJMPC PACS numbers: 05.45.Tp, 02.30.NW Institute for Plasma Research, Near Indira Bridge, Bhat, Gandhinagar-382428, India Physical Research Laboratory, Navrangpura, Ahmedabad-380009, India", } @Article{2008Prama..71..459A, author = "Dilip P. Ahalpara and Amit Verma and Jitendra C. Parikh and Prasanta K. Panigrahi", title = "Characterizing and modelling cyclic behaviour in non-stationary time series through multi-resolution analysis", journal = "Pramana", year = "2008", month = nov, volume = "71", pages = "459--485", publisher = "Springer India, in co-publication with Indian Academy of Sciences", keywords = "genetic algorithms, genetic programming, finance, Non-stationary time series, wavelet transform, Characterizing and modelling cyclic behaviour in non-stationary time series through multi-resolution analysis", ISSN = "0304-4289", DOI = "doi:10.1007/s12043-008-0125-x", adsurl = "http://adsabs.harvard.edu/abs/2008Prama..71..459A", adsnote = "Provided by the SAO/NASA Astrophysics Data System", abstract = "A method based on wavelet transform is developed to characterise variations at multiple scales in non-stationary time series. We consider two different financial time series, S&P CNX Nifty closing index of the National Stock Exchange (India) and Dow Jones industrial average closing values. These time series are chosen since they are known to comprise of stochastic fluctuations as well as cyclic variations at different scales. The wavelet transform isolates cyclic variations at higher scales when random fluctuations are averaged out; this corroborates correlated behaviour observed earlier in financial time series through random matrix studies. Analysis is carried out through Haar, Daubechies-4 and continuous Morlet wavelets for studying the character of fluctuations at different scales and show that cyclic variations emerge at intermediate time scales. It is found that Daubechies family of wavelets can be effectively used to capture cyclic variations since these are local in nature. To get an insight into the occurrence of cyclic variations, we then proceed to model these wavelet coefficients using genetic programming (GP) approach and using the standard embedding technique in the reconstructed phase space. It is found that the standard methods (GP as well as artificial neural networks) fail to model these variations because of poor convergence. A novel interpolation approach is developed that overcomes this difficulty. The dynamical model equations have, primarily, linear terms with additive Pade-type terms. It is seen that the emergence of cyclic variations is due to an interplay of a few important terms in the model. Very interestingly GP model captures smooth variations as well as bursty behaviour quite nicely.", notes = "(1) Institute for Plasma Research, Near Indira Bridge, Bhat, Gandhinagar, 382 428, India (2) Physical Research Laboratory, Navrangpura, Ahmedabad, 380 009, India (3) Indian Institute of Science Education and Research, Salt Lake City, Kolkata, 700 106, India", } @InProceedings{Ahalpara:2009:eurogp, author = "Dilip Ahalpara and Siddharth Arora and M Santhanam", title = "Genetic Programming Based Approach for Synchronization with Parameter Mismatches in EEG", booktitle = "Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009", year = "2009", editor = "Leonardo Vanneschi and Steven Gustafson and Alberto Moraglio and Ivanoe {De Falco} and Marc Ebner", volume = "5481", series = "LNCS", pages = "13--24", address = "Tuebingen", month = apr # " 15-17", organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-01180-1", DOI = "doi:10.1007/978-3-642-01181-8_2", notes = "Part of \cite{conf/eurogp/2009} EuroGP'2009 held in conjunction with EvoCOP2009, EvoBIO2009 and EvoWorkshops2009", } @InProceedings{Ahalpara:2010:gecco, author = "Dilip P. Ahalpara", title = "Improved forecasting of time series data of real system using genetic programming", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "977--978", keywords = "genetic algorithms, genetic programming, Poster", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830483.1830658", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "A study is made to improve short term forecasting of time series data of real system using Genetic Programming (GP) under the framework of time delayed embedding technique. GP based approach is used to make analytical model of time series data of real system using embedded vectors that help reconstruct the phase space. The map equations, involving non-linear symbolic expressions in the form of binary trees comprising of time delayed components in the immediate past, are first obtained by carrying out single-step GP fit for the training data set and usually they are found to give good fitness as well as single-step predictions. However while forecasting the time series based on multi-step predictions in the out-of-sample region in an iterative manner, these solutions often show rapid deterioration as we dynamically forward the solution in future time. With a view to improve on this limitation, it is shown that if the multi-step aspect is incorporated while making the GP fit itself, the corresponding GP solutions give multi-step predictions that are improved to a fairly good extent for around those many multi-steps as incorporated during the multi-step GP fit. Two different methods for multi-step fit are introduced, and the corresponding prediction results are presented. The modified method is shown to make better forecast for out-of-sample multi-step predictions for the time series of a real system, namely Electroencephelograph (EEG) signals.", notes = "Also known as \cite{1830658} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @InProceedings{ahalpara:2011:EuroGP, author = "Dilip Ahalpara and Abhijit Sen", title = "A Sniffer Technique for an Efficient Deduction of Model Dynamical Equations using Genetic Programming", booktitle = "Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011", year = "2011", month = "27-29 " # apr, editor = "Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado", series = "LNCS", volume = "6621", publisher = "Springer Verlag", address = "Turin, Italy", pages = "1--12", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, local search, hill climbing", isbn13 = "978-3-642-20406-7", DOI = "doi:10.1007/978-3-642-20407-4_1", abstract = "A novel heuristic technique that enhances the search facility of the standard genetic programming (GP) algorithm is presented. The method provides a dynamic sniffing facility to optimise the local search in the vicinity of the current best chromosomes that emerge during GP iterations. Such a hybrid approach, that combines the GP method with the sniffer technique, is found to be very effective in the solution of inverse problems where one is trying to construct model dynamical equations from either finite time series data or knowledge of an analytic solution function. As illustrative examples, some special function ordinary differential equations (ODEs) and integrable nonlinear partial differential equations (PDEs) are shown to be efficiently and exactly recovered from known solution data. The method can also be used effectively for solution of model equations (the direct problem) and as a tool for generating multiple dynamical systems that share the same solution space.", notes = "Mathematica. Order of partial or ordinary differential equation search in sequence starting with first order and increasing until satisfactory match found. Part of \cite{Silva:2011:GP} EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011", } @InProceedings{ahalpara:2007:EMBN, author = "Dilip P. Ahalpara and Prasanta K. Panigrahi and Jitendra C. Parikh", title = "Variations in Financial Time Series: Modelling Through Wavelets and Genetic Programming", booktitle = "Econophysics of Markets and Business Networks", year = "2007", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-88-470-0665-2_3", DOI = "doi:10.1007/978-88-470-0665-2_3", } @Article{Ahangar-Asr:2011:EC, author = "Alireza Ahangar-Asr and Asaad Faramarzi and Akbar A. Javadi and Orazio Giustolisi", title = "Modelling mechanical behaviour of rubber concrete using evolutionary polynomial regression", journal = "Engineering Computation", year = "2011", volume = "28", number = "4", pages = "492--507", keywords = "genetic algorithms, genetic programming, Mechanical \& Materials Engineering, Concretes, Mechanical behaviour of materials, Rubbers", ISSN = "0264-4401", DOI = "doi:10.1108/02644401111131902", publisher = "Emerald Group Publishing Limited", abstract = "Using discarded tyre rubber as concrete aggregate is an effective solution to the environmental problems associated with disposal of this waste material. However, adding rubber as aggregate in concrete mixture changes, the mechanical properties of concrete, depending mainly on the type and amount of rubber used. An appropriate model is required to describe the behaviour of rubber concrete in engineering applications. The purpose of this paper is to show how a new evolutionary data mining technique, evolutionary polynomial regression (EPR), is used to predict the mechanical properties of rubber concrete. Design/methodology/approach EPR is a data-driven method based on evolutionary computing, aimed to search for polynomial structures representing a system. In this technique, a combination of the genetic algorithm and the least square method is used to find feasible structures and the appropriate constants for those structures. Findings Data from 70 cases of experiments on rubber concrete are used for development and validation of the EPR models. Three models are developed relating compressive strength, splitting tensile strength, and elastic modulus to a number of physical parameters that are known to contribute to the mechanical behaviour of rubber concrete. The most outstanding characteristic of the proposed technique is that it provides a transparent, structured, and accurate representation of the behaviour of the material in the form of a polynomial function, giving insight to the user about the contributions of different parameters involved. The proposed model shows excellent agreement with experimental results, and provides an efficient method for estimation of mechanical properties of rubber concrete. Originality/value In this paper, a new evolutionary data mining approach is presented for the analysis of mechanical behaviour of rubber concrete. The new approach overcomes the shortcomings of the traditional and artificial neural network-based methods presented in the literature for the analysis of slopes. EPR provides a viable tool to find a structured representation of the system, which allows the user to gain additional information on how the system performs.", notes = "Research paper. Computational Geomechanics Group, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK Civil and Environmental Engineering Department, Faculty of Engineering, Technical University of Bari, Taranto, Italy", } @PhdThesis{Ahangar-Asr:thesis, author = "Alireza AhangarAsr", title = "Application of an Evolutionary Data Mining Technique for Constitutive Modelling of Geomaterials", school = "University of Exeter", year = "2012", address = "UK", month = "31 " # dec, keywords = "genetic algorithms, genetic programming", URL = "http://hdl.handle.net/10871/9925", URL = "https://ore.exeter.ac.uk/repository/bitstream/handle/10871/9925/AhangarasrA.pdf", size = "214 pages", abstract = "Modelling behaviour of materials involves approximating the actual behaviour with that of an idealised material that deforms in accordance with some constitutive relationships. Several constitutive models have been developed for various materials many of which involve determination of material parameters with no physical meaning. ANN is a computer-based modelling technique for computation and knowledge representation inspired by the neural architecture and operation of the human brain. It has been shown by various researchers that ANNs offer outstanding advantages in constitutive modelling of material; however, these networks have some shortcoming. In this thesis, the Evolutionary Polynomial Regression (EPR) was introduced as an alternative approach to constitutive modelling of the complex behaviour of saturated and unsaturated soils and also modelling of a number of other civil and geotechnical engineering materials and systems. EPR overcomes the shortcomings of ANN by providing a structured and transparent model representing the behaviour of the system. In this research EPR is applied to modelling of stress-strain and volume change behaviour of unsaturated soils, modelling of SWCC in unsaturated soils, hydro-thermo-mechanical modelling of unsaturated soils, identification of coupling parameters between shear strength behaviour and chemical's effects in compacted soils, modelling of permeability and compaction characteristics of soils, prediction of the stability status of soil and rock slopes and modelling the mechanical behaviour of rubber concrete. Comparisons between EPR-based material model predictions, the experimental data and the predictions from other data mining and regression modelling techniques and also the results of the parametric studies revealed the exceptional capabilities of the proposed methodology in modelling the very complicated behaviour of geotechnical and civil engineering materials.", notes = "Ahangar-Asr Supervisor: Akbar Javadi 2nd supervisor: Slobodan Djordjevic", } @Article{DBLP:journals/ewc/AhangarAsrJJ23, author = "Alireza Ahangar-Asr and A. Johari and Akbar A. Javadi", title = "An evolutionary-based polynomial regression modeling approach to predicting discharge flow rate under sheet piles", journal = "Engineering with Computers", year = "2023", volume = "39", number = "6", pages = "4093--4101", keywords = "genetic algorithms, genetic programming, EPR, Sheet piles/cut-off walls, Seepage flow rate, Evolutionary computation, Data mining", timestamp = "Sat, 08 Jun 2024 13:15:18 +0200", biburl = "https://dblp.org/rec/journals/ewc/AhangarAsrJJ23.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://rdcu.be/dPatP", DOI = "doi:10.1007/S00366-023-01872-1", size = "9 pages", notes = "Contrast EPR and GP?", } @InProceedings{Aher:2012:ICSP, author = "R. P. Aher and K. C. Jodhanle", booktitle = "Signal Processing (ICSP), 2012 IEEE 11th International Conference on", title = "Removal of Mixed Impulse noise and Gaussian noise using genetic programming", year = "2012", volume = "1", pages = "613--618", abstract = "In this paper, we have put forward a nonlinear filtering method for removing mixed Impulse and Gaussian noise, based on the two step switching scheme. The switching scheme uses two cascaded detectors for detecting the noise and two corresponding estimators which effectively and efficiently filters the noise from the image. A supervised learning algorithm, Genetic programming, is employed for building the two detectors with complementary characteristics. Most of the noisy pixels are identified by the first detector. The remaining noises are searched by the second detector, which is usually hidden in image details or with amplitudes close to its local neighbourhood. Both the detectors designed are based on the robust estimators of location and scale i.e. Median and Median Absolute Deviation (MAD). Unlike many filters which are specialised only for a particular noise model, the proposed filters in this paper are capable of effectively suppressing all kinds of Impulse and Gaussian noise. The proposed two-step Genetic Programming filters removes impulse and Gaussian noise very efficiently, and also preserves the image details.", keywords = "genetic algorithms, genetic programming, Gaussian noise, image denoising, impulse noise, learning (artificial intelligence), nonlinear filters, Gaussian noise, Median Absolute Deviation, cascaded detectors, complementary characteristics, image details, impulse noise, local neighbourhood, noisy pixels, nonlinear filtering method, second detector, supervised learning algorithm, two step switching scheme, alpha trimmed mean estimator, CWM, Gaussian Noise, Impulse noise, Median, Median Absolute Deviation (MAD), Non-Linear filters, Supervised Learning, Switching scheme", DOI = "doi:10.1109/ICoSP.2012.6491563", ISSN = "2164-5221", notes = "Also known as \cite{6491563}", } @InProceedings{Ahlgren:2020:GI, author = "John Ahlgren and Maria Eugenia Berezin and Kinga Bojarczuk and Elena Dulskyte and Inna Dvortsova and Johann George and Natalija Gucevska and Mark Harman and Ralf Laemmel and Erik Meijer and Silvia Sapora and Justin Spahr-Summers", title = "{WES}: Agent-based User Interaction Simulation on Real Infrastructure", booktitle = "GI @ ICSE 2020", year = "2020", month = "3 " # jul, editor = "Shin Yoo and Justyna Petke and Westley Weimer and Bobby R. Bruce", publisher = "ACM", address = "internet", pages = "276--284", note = "Invited Keynote", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, social testing, APR, Connectivity, Data Science, Facebook AI Research, Human Computer Interaction, UX Human, Machine Learning", isbn13 = "978-1-4503-7963-2", URL = "https://research.fb.com/wp-content/uploads/2020/04/WES-Agent-based-User-Interaction-Simulation-on-Real-Infrastructure.pdf", URL = "https://research.fb.com/publications/wes-agent-based-user-interaction-simulation-on-real-infrastructure/", video_url = "https://youtu.be/GsNKCifm44A", DOI = "doi:10.1145/3387940.3392089", size = "9 pages", abstract = "We introduce the Web-Enabled Simulation (WES) research agenda, and describe FACEBOOK WW system. We describe the application of WW to reliability, integrity and privacy at FACEBOOK, where it is used to simulate social media interactions on an infrastructure consisting of hundreds of millions of lines of code. The WES agenda draws on research from many areas of study, including Search Based Software Engineering, Machine Learning, Programming Languages, Multi Agent Systems, Graph Theory, Game AI, and AI Assisted Game Play. We conclude with a set of open problems and research challenges to motivate wider investigation.", notes = "London probable . The WES agenda draws on research from many areas of study, including (but not limited to) Search Based Software Engineering, Machine Learning, Programming Languages, Multi Agent Systems, Graph Theory, Game AI, AI Assisted Game Play. Intelligent learning/trainable Bots interact with The social graph. A/B testing. Automated Mechanism Design. Social bugs/social testing. scammer bot. Super-human bots. Big changes in machine learning classification performance, data pipeline line breakages. WES Test Oracle uses machine learning. Co-evolutionary Mechanism Learning. virtual speed humps to hinter anti-social behaviour. Video: https://youtu.be/GsNKCifm44A (13--55 minutes from start, followed by discussion, end 1:13:00) http://geneticimprovementofsoftware.com/gi2020icse.html FACEBOOK Inc", } @InProceedings{Ahlgren:2021:ICSE, author = "John Ahlgren and Maria Eugenia Berezin and Kinga Bojarczuk and Elena Dulskyte and Inna Dvortsova and Johann George and Natalija Gucevska and Mark Harman and Maria Lomeli and Erik Meijer and Silvia Sapora and Justin Spahr-Summers", title = "Testing Web Enabled Simulation at Scale Using Metamorphic Testing", booktitle = "Proceedings of the International Conference on Software Engineering, ICSE 2021", year = "2021", editor = "Arie {van Deursen} and Tao Xie and Natalia Juristo Oscar Dieste", pages = "140--149", month = "25-28 " # may, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Genetic Improvement, SBSE, Metamorphic Testing, Oracle Problem, Scalability, Testing, Test Flakiness, Web-Enabled Simulation", URL = "https://research.fb.com/publications/testing-web-enabled-simulation-at-scale-using-metamorphic-testing/", URL = "https://research.fb.com/wp-content/uploads/2021/03/Testing-Web-Enabled-Simulation-at-Scale-Using-Metamorphic-Testing.pdf", video_url = "https://www.youtube.com/watch?v=pNKqyn-90Ig", DOI = "doi:10.1109/ICSE-SEIP52600.2021.00023", size = "10 pages", abstract = "We report on Facebook deployment of MIA (Metamorphic Interaction Automaton). MIA is used to test Facebook's Web Enabled Simulation, built on a web infrastructure of hundreds of millions of lines of code. MIA tackles the twin problems of test flakiness and the unknowable oracle problem. It uses metamorphic testing to automate continuous integration and regression test execution. MIA also plays the role of a test bot, automatically commenting on all relevant changes submitted for code review. It currently uses a suite of over 40 metamorphic test cases. Even at this extreme scale, a non-trivial metamorphic test suite subset yields outcomes within 20 minutes (sufficient for continuous integration and review processes). Furthermore, our offline mode simulation reduces test flakiness from approximately 50percent (of all online tests) to 0percent (offline). Metamorphic testing has been widely-studied for 22 years. This paper is the first reported deployment into an industrial continuous integration system.", } @InProceedings{DBLP:conf/ease/AhlgrenBDDGGHLL21, author = "John Ahlgren and Kinga Bojarczuk and Sophia Drossopoulou and Inna Dvortsova and Johann George and Natalija Gucevska and Mark Harman and Maria Lomeli and Simon M. Lucas and Erik Meijer and Steve Omohundro and Rubmary Rojas and Silvia Sapora and Norm Zhou", title = "Facebook's Cyber-Cyber and Cyber-Physical Digital Twins", booktitle = "EASE 2021: Evaluation and Assessment in Software Engineering", year = "2021", editor = "Ruzanna Chitchyan and Jingyue Li and Barbara Weber and Tao Yue", address = "Trondheim, Norway", month = jun # " 21-24", publisher = "ACM", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, Web Enabled Simulation, Digital Twin, facebook, meta, social media, online, software engineering", timestamp = "Mon, 21 Jun 2021 12:29:10 +0200", biburl = "https://dblp.org/rec/conf/ease/AhlgrenBDDGGHLL21.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://research.facebook.com/publications/facebooks-cyber-cyber-and-cyber-physical-digital-twins/", URL = "https://discovery.ucl.ac.uk/id/eprint/10139789/1/EASE21.pdf", URL = "https://doi.org/10.1145/3463274.3463275", DOI = "doi:10.1145/3463274.3463275", size = "9 pages", abstract = "A cyber/cyber digital twin is a simulation of a software system. By contrast, a cyber-physical digital twin is a simulation of a non-software (physical) system. Although cyberphysical digital twins have received a lot of recent attention, their cyber--cyber counterparts have been comparatively overlooked. In this paper we show how the unique properties of cyber cyber digital twins open up exciting opportunities for research and development. Like all digital twins, the cyber--cyber digital twin is both informed by and informs the behaviour of the twin it simulates. It is therefore a software system that simulates another software system, making it conceptually truly a twin, blurring the distinction between the simulated and the simulator. Cyber-cyber digital twins can be twins of other cyber--cyber digital twins, leading to a hierarchy of twins. As we shall see, these apparently philosophical observations have practical ramifications for the design, implementation and deployment of digital twins at Meta.", } @Unpublished{ahlschwede:2000:ugppm, author = "John Ahlschwede", title = "Using Genetic Programming to Play Mancala", year = "2000", note = "http://www.corngolem.com/john/gp/index.html", keywords = "genetic algorithms, genetic programming", abstract = "This paper will explain what genetic programming is, what mancala is, how I used genetic programming to evolve mancala-playing programs, and the results I got from doing so.", } @InProceedings{ahluwalia:1996:ccpGP, author = "Manu Ahluwalia and Terence C. Fogarty", title = "Co-Evolving Hierarchical Programs Using Genetic Programming", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "419", address = "Stanford University, CA, USA", publisher = "MIT Press", size = "1 page", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap58.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96", } @InProceedings{Ahluwalia:1997:, author = "Manu Ahluwalia and Larry Bull and Terence C. Fogarty", title = "Co-evolving Functions in Genetic Programming: A Comparison in ADF Selection Strategies", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming", pages = "3--8", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1997/Ahluwalia_1997_.pdf", size = "6 pages", notes = "GP-97", } @InProceedings{ahluwalia:1997:cfGPea, author = "Manu Ahluwalia and Larry Bull and Terence C. Fogarty", title = "Co-evolving Functions in Genetic Programming: An Emergent Approach using ADFs and GLiB", booktitle = "Late Breaking Papers at the 1997 Genetic Programming Conference", year = "1997", editor = "John R. Koza", pages = "1--6", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California, 94305-3079, USA", month = "13--16 " # jul, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-206995-8", notes = "GP-97LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670", } @InProceedings{ahluwalia:1998:cfGP:ADF+GLiB, author = "M. Ahluwalia and L. Bull", title = "Co-evolving Functions in Genetic Programming: Dynamic ADF Creation using GLiB", booktitle = "Evolutionary Programming VII: Proceedings of the Seventh Annual Conference on Evolutionary Programming", year = "1998", editor = "V. William Porto and N. Saravanan and D. Waagen and A. E. Eiben", volume = "1447", series = "LNCS", pages = "809--818", address = "Mission Valley Marriott, San Diego, California, USA", publisher_address = "Berlin", month = "25-27 " # mar, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-64891-7", DOI = "doi:10.1007/BFb0040753", notes = "EP-98. University of the West of England, UK", } @InProceedings{ahluwalia:1999:AGPCS, author = "Manu Ahluwalia and Larry Bull", title = "A Genetic Programming-based Classifier System", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "11--18", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, classifier systems", ISBN = "1-55860-611-4", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco1999/ahluwalia_1999_agpcs.pdf", size = "8 pages", abstract = "john holland rules s-expressions wilson ZCS stumulus-response feature extraction KNN niche-based", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{ahluwalia:1999:CFGPCK, author = "Manu Ahluwalia and Larry Bull", title = "Coevolving Functions in Genetic Programming: Classification using K-nearest-neighbour", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "947--952", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-413.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-413.pdf", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @PhdThesis{Ahluwalia:thesis, author = "Manu Ahluwalia", title = "Co-evolving functions in genetic programming", school = "Faculty of Computer Studies and Mathematics, University of the West of England", year = "2000", address = "Bristol, UK", keywords = "genetic algorithms, genetic programming", URL = "https://uwe.primo.exlibrisgroup.com/discovery/fulldisplay?docid=alma991000187179707511&context=L&vid=44UWE_INST:44UWE_INST&lang=en&search_scope=MyInst_and_CI&adaptor=Local%20Search%20Engine&tab=Everything&query=any,contains,ahluwalia%20phd&offset=0", URL = "https://dblp.org/rec/phd/ethos/Ahluwalia00.html?view=bibtex", broken = "http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.322427", notes = "also known as \cite{alma991000187179707511} uk.bl.ethos.322427 Supervsior: Larry Bull", } @Article{Ahluwalia:2001:SA, author = "Manu Ahluwalia and Larry Bull", title = "Coevolving functions in genetic programming", journal = "Journal of Systems Architecture", volume = "47", pages = "573--585", year = "2001", number = "7", month = jul, keywords = "genetic algorithms, genetic programming, ADF, Classification, EDF, Feature selection/extraction, Hierarchical programs, Knn, Speciation", ISSN = "1383-7621", URL = "https://uwe-repository.worktribe.com/output/1090581", URL = "http://www.sciencedirect.com/science/article/B6V1F-43RV156-3/1/16dd3ab5502922479ef7bb1ca4f7b9c3", DOI = "doi:10.1016/S1383-7621(01)00016-9", size = "13 pages", abstract = "In this paper we introduce a new approach to the use of automatically defined functions (ADFs) within genetic programming. The technique consists of evolving a number of separate sub-populations of functions which can be used by a population of evolving main programs. We present and refine a set of mechanisms by which the number and constitution of the function sub-populations can be defined and compare their performance on two well-known classification tasks. A final version of the general approach, for use explicitly on classification tasks, is then presented. It is shown that in all cases the coevolutionary approach performs better than traditional genetic programming with and without ADFs.", } @InProceedings{Ahmad:2012:GECCO, author = "Arbab Masood Ahmad and Gul Muhammad Khan and Sahibzada Ali Mahmud and Julian Francis Miller", title = "Breast cancer detection using cartesian genetic programming evolved artificial neural networks", booktitle = "GECCO '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference", year = "2012", editor = "Terry Soule and Anne Auger and Jason Moore and David Pelta and Christine Solnon and Mike Preuss and Alan Dorin and Yew-Soon Ong and Christian Blum and Dario Landa Silva and Frank Neumann and Tina Yu and Aniko Ekart and Will Browne and Tim Kovacs and Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and Giovanni Squillero and Nicolas Bredeche and Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and Martin Pelikan and Silja Meyer-Nienberg and Christian Igel and Greg Hornby and Rene Doursat and Steve Gustafson and Gustavo Olague and Shin Yoo and John Clark and Gabriela Ochoa and Gisele Pappa and Fernando Lobo and Daniel Tauritz and Jurgen Branke and Kalyanmoy Deb", isbn13 = "978-1-4503-1177-9", pages = "1031--1038", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, real world applications, Algorithms, Design, Performance, Breast Cancer, Fine Needle Aspiration, FNA, ANN, Artificial Neural Network, Neuro-evolution", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330163.2330307", publisher = "ACM", publisher_address = "New York, NY, USA", size = "8 pages", abstract = "A fast learning neuro-evolutionary technique that evolves Artificial Neural Networks using Cartesian Genetic Programming (CGPANN) is used to detect the presence of breast cancer. Features from breast mass are extracted using fine needle aspiration (FNA) and are applied to the CGPANN for diagnosis of breast cancer. FNA data is obtained from the Wisconsin Diagnostic Breast Cancer website and is used for training and testing the network. The developed system produces fast and accurate results when compared to contemporary work done in the field. The error of the model comes out to be as low as 1percent for Type-I (classifying benign sample falsely as malignant) and 0.5percent for Type-II (classifying malignant sample falsely as benign).", notes = "Also known as \cite{2330307} GECCO-2012 A joint meeting of the twenty first international conference on genetic algorithms (ICGA-2012) and the seventeenth annual genetic programming conference (GP-2012)", } @InProceedings{Ahmad:2012:FIT, author = "Arbab Masood Ahmad and Gul Muhammad Khan", booktitle = "Frontiers of Information Technology (FIT), 2012 10th International Conference on", title = "Bio-signal Processing Using Cartesian Genetic Programming Evolved Artificial Neural Network (CGPANN)", year = "2012", pages = "261--268", DOI = "doi:10.1109/FIT.2012.54", abstract = "The aim of this paper is to explore the application of Neuro-Evolutionary Techniques to the diagnosis of various diseases. We applied the evolutionary technique of Cartesian Genetic programming Evolved Artificial Neural Network (CG-PANN) for the detection of three important diseases. Some cases showed excellent results while others are in the process of experimentation. In the first case we worked on diagnosing the extent of Parkinson's disease using a computer based test. Experiments in this case are in progress. In the second case, we applied the Fine Needle Aspirate (FNA) data for Breast Cancer from the WDBC website to our network to classify the samples as either benign (non-cancerous) or malignant (cancerous). The results from these experiments were highly satisfactory. In the third case, we developed a modified form of Pan-Tompkins's algorithm to detect the fiducial points from ECG signals and extracted key features from them. The features shall be applied to our network to classify the signals for the different types of Arrhythmias. Experimentation is still in progress.", keywords = "genetic algorithms, genetic programming, cardiology, diseases, electrocardiography, feature extraction, medical signal processing, neural nets, signal classification, CG-PANN, Cartesian genetic programming evolved artificial neural network, ECG signal, FNA data, Pan-Tompkins algorithm, Parkinson disease, arrhythmia, benign cancer, bio-signal processing, breast cancer, electrocardiography, experimentation process, feature extraction, fiducial point, fine needle aspirate, malignant cancer, neuro-evolutionary technique, Artificial neural networks, Cancer, Diseases, Electrocardiography, Feature extraction, Training, Breast Cancer detection, CGPANN, Cardiac Arrhythmias, FNA, Parkinson's Disease", notes = "Also known as \cite{6424333}", } @InProceedings{conf/eann/AhmadKM13, author = "Arbab Masood Ahmad and Gul Muhammad Khan and Sahibzada Ali Mahmud", title = "Classification of Arrhythmia Types Using Cartesian Genetic Programming Evolved Artificial Neural Networks", editor = "Lazaros S. Iliadis and Harris Papadopoulos and Chrisina Jayne", booktitle = "Proceedings of 14th International Conference on Engineering Applications of Neural Networks (EANN 2013), Part {I}", year = "2013", volume = "383", series = "Communications in Computer and Information Science", pages = "282--291", address = "Halkidiki, Greece", month = sep # " 13-16", publisher = "Springer", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, CGPANN, artificial neural network, neuro-evolution, CVD, cardiac arrhythmias, classification, fiducial points, LBBB beats, RBBB beats", isbn13 = "978-3-642-41012-3", bibdate = "2014-01-25", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/eann/eann2013-1.html#AhmadKM13", URL = "http://dx.doi.org/10.1007/978-3-642-41013-0", DOI = "doi:10.1007/978-3-642-41013-0_29", abstract = "Cartesian Genetic programming Evolved Artificial Neural Network (CGPANN) is explored for classification of different types of arrhythmia and presented in this paper. Electrocardiography (ECG) signal is preprocessed to acquire important parameters and then presented to the classifier. The parameters are calculated from the location and amplitudes of ECG fiducial points, determined with a new algorithm inspired by Pan-Tompkins's algorithm [14]. The classification results are satisfactory and better than contemporary methods introduced in the field.", } @InProceedings{conf/ifip12/AhmadKM14, author = "Arbab Masood Ahmad and Gul Muhammad Khan and Sahibzada Ali Mahmud", title = "Classification of Mammograms Using Cartesian Genetic Programming Evolved Artificial Neural Networks", booktitle = "Proceedings 10th IFIP WG 12.5 International Conference Artificial Intelligence Applications and Innovations, AIAI 2014", year = "2014", editor = "Lazaros S. Iliadis and Ilias Maglogiannis and Harris Papadopoulos", volume = "436", series = "IFIP Advances in Information and Communication Technology", pages = "203--213", address = "Rhodes, Greece, September 19-21, 2014", publisher = "Springer", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, mammogram image classification, GLCM, CGPANN, haralick's parameters", isbn13 = "978-3-662-44654-6", DOI = "doi:10.1007/978-3-662-44654-6_20", URL = "http://dx.doi.org/10.1007/978-3-662-44654-6_20", bibdate = "2014-09-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ifip12/aiai2014.html#AhmadKM14", URL = "http://dx.doi.org/10.1007/978-3-662-44654-6", abstract = "We developed a system that classifies masses or microcalcifications observed in a mammogram as either benign or malignant. The system assumes prior manual segmentation of the image. The image segment is then processed for its statistical parameters and applied to a computational intelligence system for classification. We used Cartesian Genetic Programming Evolved Artificial Neural Network (CGPANN) for classification. To train and test our system we selected 2000 mammogram images with equal number of benign and malignant cases from the well-known Digital Database for Screening Mammography (DDSM). To find the input parameters for our network we exploited the overlay files associated with the mammograms. These files mark the boundaries of masses or microcalcifications. A Gray Level Co-occurrence matrix (GLCM) was developed for a rectangular region enclosing each boundary and its statistical parameters computed. Five experiments were conducted in each fold of a 10-fold cross validation strategy. Testing accuracy of 100 percent was achieved in some experiments.", } @InProceedings{Ahmad:2018:GECCOcomp, author = "Hammad Ahmad and Thomas Helmuth", title = "A comparison of semantic-based initialization methods for genetic programming", booktitle = "GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2018", editor = "Carlos Cotta and Tapabrata Ray and Hisao Ishibuchi and Shigeru Obayashi and Bogdan Filipic and Thomas Bartz-Beielstein and Grant Dick and Masaharu Munetomo and Silvino {Fernandez Alzueta} and Thomas Stuetzle and Pablo Valledor Pellicer and Manuel Lopez-Ibanez and Daniel R. Tauritz and Pietro S. Oliveto and Thomas Weise and Borys Wrobel and Ales Zamuda and Anne Auger and Julien Bect and Dimo Brockhoff and Nikolaus Hansen and Rodolphe {Le Riche} and Victor Picheny and Bilel Derbel and Ke Li and Hui Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Stephane Doncieux and Richard Duro and Joshua Auerbach and Harold {de Vladar} and Antonio J. Fernandez-Leiva and JJ Merelo and Pedro A. Castillo-Valdivieso and David Camacho-Fernandez and Francisco {Chavez de la O} and Ozgur Akman and Khulood Alyahya and Juergen Branke and Kevin Doherty and Jonathan Fieldsend and Giuseppe Carlo Marano and Nikos D. Lagaros and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Boris Naujoks and Vanessa Volz and Tea Tusar and Pascal Kerschke and Riyad Alshammari and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and John R. Woodward and Shin Yoo and John McCall and Nayat Sanchez-Pi and Luis Marti and Danilo Vasconcellos and Masaya Nakata and Anthony Stein and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Ivanoe {De Falco} and Antonio {Della Cioppa} and Ernesto Tarantino and Umberto Scafuri and P. G. M. Baltus and Giovanni Iacca and Ahmed Hallawa and Anil Yaman and Alma Rahat and Handing Wang and Yaochu Jin and David Walker and Richard Everson and Akira Oyama and Koji Shimoyama and Hemant Kumar and Kazuhisa Chiba and Pramudita Satria Palar", isbn13 = "978-1-4503-5764-7", pages = "1878--1881", address = "Kyoto, Japan", DOI = "doi:10.1145/3205651.3208218", publisher = "ACM", publisher_address = "New York, NY, USA", month = "15-19 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", abstract = "During the initialization step, a genetic programming (GP) system traditionally creates a population of completely random programs to populate the initial population. These programs almost always perform poorly in terms of their total error---some might not even output the correct data type. In this paper, we present new methods for initialization that attempt to generate programs that are somewhat relevant to the problem being solved and/or increase the initial diversity (both error and behavioural diversity) of the population prior to the GP run. By seeding the population---and thereby eliminating worthless programs and increasing the initial diversity of the population---we hope to improve the performance of the GP system. Here, we present two novel techniques for initialization (Lexicase Seeding and Pareto Seeding) and compare them to a previous method (Enforced Diverse Populations) and traditional, non-seeded initialization. Surprisingly, we found that none of the initialization m", notes = "Also known as \cite{3208218} GECCO-2018 A Recombination of the 27th International Conference on Genetic Algorithms (ICGA-2018) and the 23rd Annual Genetic Programming Conference (GP-2018)", } @InProceedings{DBLP:conf/asplos/Ahmad0W22, author = "Hammad Ahmad and Yu Huang and Westley Weimer", title = "{CirFix}: automatically repairing defects in hardware design code", booktitle = "ASPLOS 2022: 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems", year = "2022", editor = "Babak Falsafi and Michael Ferdman and Shan Lu and Thomas F. Wenisch", pages = "990--1003", address = "Lausanne, Switzerland", month = "28 " # feb # "- 4 " # mar, publisher = "ACM", keywords = "genetic algorithms, genetic programming, genetic improvement, automated program repair, APR, hardware designs, HDL benchmark", timestamp = "Wed, 02 Mar 2022 18:22:59 +0100", biburl = "https://dblp.org/rec/conf/asplos/Ahmad0W22.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://doi.org/10.1145/3503222.3507763", DOI = "doi:10.1145/3503222.3507763", code_url = "https://github.com/hammad-a/verilog_repair", size = "14 pages", abstract = "CirFix, is a framework for automatically repairing defects in hardware designs implemented in languages like Verilog. We propose a novel fault localization approach based on assignments to wires and registers, and a fitness function tailored to the hardware domain to bridge the gap between software-level automated program repair and hardware descriptions. We also present a benchmark suite of 32 defect scenarios corresponding to a variety of hardware projects. Overall, CirFix produces plausible repairs for 21/32 and correct repairs for 16/32 of the defect scenarios. This repair rate is comparable to that of successful program repair approaches for software, indicating CirFix is effective at bringing over the benefits of automated program repair to the hardware domain for the first time.", } @InProceedings{DBLP:conf/ppsn/AhmadCFW22, author = "Hammad Ahmad and Padraic Cashin and Stephanie Forrest and Westley Weimer", title = "Digging into Semantics: Where Do Search-Based Software Repair Methods Search?", booktitle = "Parallel Problem Solving from Nature - PPSN XVII - 17th International Conference, PPSN 2022, Proceedings, Part II", year = "2022", editor = "Guenter Rudolph and Anna V. Kononova and Hernan E. Aguirre and Pascal Kerschke and Gabriela Ochoa and Tea Tusar", volume = "13399", series = "Lecture Notes in Computer Science", pages = "3--18", address = "Dortmund, Germany", month = sep # " 10-14", publisher = "Springer", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, SBSE, Semantic search spaces, Program repair, Patch diversity, Daikon, Defects4J", timestamp = "Tue, 16 Aug 2022 16:15:42 +0200", biburl = "https://dblp.org/rec/conf/ppsn/AhmadCFW22.bib", bibsource = "dblp computer science bibliography, https://dblp.org", isbn13 = "978-3-031-14720-3", URL = "https://web.eecs.umich.edu/~weimerw/p/weimer-asplos2022.pdf", DOI = "doi:10.1007/978-3-031-14721-0_1", abstract = "Search-based methods are a popular approach for automatically repairing software bugs, a field known as automated program repair (APR). There is increasing interest in empirical evaluation and comparison of different APR methods, typically measured as the rate of successful repairs on benchmark sets of buggy programs. Such evaluations, however, fail to explain why some approaches succeed and others fail. Because these methods typically use syntactic representations, i.e., source code, we know little about how the different methods explore their semantic spaces, which is relevant for assessing repair quality and understanding search dynamics. We propose an automated method based on program semantics, which provides quantitative and qualitative information about different APR search-based techniques. Our approach requires no manual annotation and produces both mathematical and human-understandable insights. In an empirical evaluation of 4 APR tools and 34 defects, we investigate the relationship between search-space exploration, semantic diversity and repair success, examining both the overall picture and how the tools search unfolds. Our results suggest that population diversity alone is not sufficient for finding repairs, and that searching in the right place is more important than searching broadly, highlighting future directions for the research community.", notes = "Semantic distance between code mutants estimated by comparing their invariant sets using Canberra distance. 2D visualisation. CapGem, GenProg, SimFix, TBar. Java. p12 four to twenty times more syntactic variability than (useful?) semantic variation. (ie Java syntax-to-semantics is many-to-one mapping.) PPSN2022", } @Article{Ahmad:2000:CCGc, author = "Ishfaq Ahmad", title = "Genetic Programming In Clusters", journal = "IEEE Concurrency", volume = "8", number = "3", pages = "10--11, 13", month = jul # "\slash " # sep, year = "2000", CODEN = "IECMFX", ISSN = "1092-3063", bibdate = "Tue Jan 16 11:59:57 2001", keywords = "genetic algorithms, genetic programming", ISSN = "1092-3063", publisher = "IEEE Computer Society", address = "Los Alamitos, CA, USA", URL = "http://csdl.computer.org/comp/mags/pd/2000/03/p3toc.htm", acknowledgement = ack-nhfb, DOI = "doi:10.1109/MCC.2000.10016", } @InProceedings{conf/sac/AhmadRRJ19, author = "Qadeer Ahmad and Atif Rafiq and Muhammad Adil Raja and Noman Javed", title = "Evolving {MIMO} multi-layered artificial neural networks using grammatical evolution", booktitle = "Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, SAC 2019", publisher = "ACM", year = "2019", editor = "Chih-Cheng Hung and George A. Papadopoulos", pages = "1278--1285", address = "Limassol, Cyprus", month = apr # " 8-12", keywords = "genetic algorithms, genetic programming, grammatical evolution, ANN", isbn13 = "978-1-4503-5933-7", DOI = "doi:10.1145/3297280.3297408", bibdate = "2019-05-02", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/sac/sac2019.html#AhmadRRJ19", } @Article{Ahmadi:2021:AWM, author = "Farshad Ahmadi and Saeid Mehdizadeh and Babak Mohammadi and Quoc Bao Pham and Thi Ngoc Canh Doan and Ngoc Duong Vo", title = "Application of an artificial intelligence technique enhanced with intelligent water drops for monthly reference evapotranspiration estimation", journal = "Agricultural Water Management", year = "2021", volume = "244", pages = "106622", keywords = "genetic algorithms, genetic programming, gene expression programming, empirical models, intelligent water drops, reference evapotranspiration, support vector regression", ISSN = "0378-3774", bibsource = "OAI-PMH server at oai.repec.org", oai = "oai:RePEc:eee:agiwat:v:244:y:2021:i:c:s0378377420321697", URL = "https://www.sciencedirect.com/science/article/pii/S0378377420321697", DOI = "doi:10.1016/j.agwat.2020.106622", abstract = "Reference evapotranspiration (ET0) is one of the most important parameters, which is required in many fields such as hydrological, agricultural, and climatological studies. Therefore, its estimation via reliable and accurate techniques is a necessity. The present study aims to estimate the monthly ET0 time series of six stations located in Iran. To achieve this objective, gene expression programming (GEP) and support vector regression (SVR) were used as standalone models. A novel hybrid model was then introduced through coupling the classical SVR with an optimisation algorithm, namely intelligent water drops (IWD) (i.e., SVR{$-$}IWD). Two various types of scenarios were considered, including the climatic data- and antecedent ET0 data-based patterns. In the climatic data-based models, the effective climatic parameters were recognised by using two pre-processing techniques consisting of {$\tau$} Kendall and entropy. It is worthy to mention that developing the hybrid SVR-IWD model as well as using the {$\tau$} Kendall and entropy approaches to discern the most influential weather parameters on ET0 are the innovations of current research. The results illustrated that the applied pre-processing methods introduced different climatic inputs to feed the models. The overall results of present study revealed that the proposed hybrid SVR-IWD model outperformed the standalone SVR one under both the considered scenarios when estimating the monthly ET0. In addition to the mentioned models, two types of empirical equations were also used including the Hargreaves{$-$}Samani (H{$-$}S) and Priestley{$-$}Taylor (P{$-$}T) in their original and calibrated versions. It was concluded that the calibrated versions showed superior performances compared to their original ones.", } @Article{Ahmadi:2024:swevo, author = "Bahman Ahmadi and Ali Jamali and Rammohan Mallipeddi and Nader Nariman-zadeh and Behzad Ahmadi and Hamid Khayyam", title = "Bi-level game theoretic approach for robust design: A case study of path-generating four-bar", journal = "Swarm and Evolutionary Computation", year = "2024", volume = "89", pages = "101636", keywords = "genetic algorithms, genetic programming, Bi-level optimization, Game theory, Linkage synthesis, Robust design", ISSN = "2210-6502", URL = "https://www.sciencedirect.com/science/article/pii/S2210650224001743", DOI = "doi:10.1016/j.swevo.2024.101636", abstract = "This study addresses the bi-level multi-objective optimisation problems (MOP) that raise in robust design and optimisation of engineering systems through establishing a state-of-the-art game theoretic scenario. A novel leader-follower decentralized decision-making scenario is proposed, leveraging the synergy of game theory, Robust Design Optimisation (RDO), Monte Carlo Simulation (MCS), and Artificial Intelligence (AI). The proposed algorithm can be employed for optimum robust Pareto design of a wide range of dynamical systems. In order to achieve a robust design, both the mean and variance of each objective function are considered as players in a multi-agent game setting. In this approach, both Stackelberg and cooperative games are used to model the behaviours of the players. Genetic Programming (GP) meta-models are employed to capture the Stackelberg protocol between two levels specifically for constructing the follower's rational reaction set (RRS). Additionally, the Nash bargaining function is -use to model the cooperative behaviours among players in each level. The proposed approach is applied and demonstrated through a case study involving multi-objective robust design of planar four-bar linkages. In this manner, four objective functions are assigned to four players within the system. Each player is responsible for optimising a specific objective criterion, namely the mean of tracking error (TE), variance of tracking error, mean of transmission angle and variance of transmission angle (TA) of the linkage. As a result, the four-objective optimisation problem of mechanism is transformed into a single-objective robust synthesis problem. The comparisons of the results show a significant enhancement in the robust behaviour of the linkage, while ensuring that deterministic criteria such as quality of motion and precision are preserved", } @Article{journals/eaai/AhmadizarSAT15, author = "Fardin Ahmadizar and Khabat Soltanian and Fardin AkhlaghianTab and Ioannis Tsoulos", title = "Artificial neural network development by means of a novel combination of grammatical evolution and genetic algorithm", journal = "Engineering Applications of Artificial Intelligence", year = "2015", volume = "39", bibdate = "2015-02-23", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/eaai/eaai39.html#AhmadizarSAT15", pages = "1--13", month = mar, keywords = "genetic algorithms, genetic programming, grammatical evolution, Neural networks, ANN, Adaptive penalty approach, Classification problems", ISSN = "0952-1976", URL = "http://www.sciencedirect.com/science/article/pii/S0952197614002759", URL = "http://dx.doi.org/10.1016/j.engappai.2014.11.003", abstract = "The most important problems with exploiting artificial neural networks (ANNs) are to design the network topology, which usually requires an excessive amount of expert's effort, and to train it. In this paper, a new evolutionary-based algorithm is developed to simultaneously evolve the topology and the connection weights of ANNs by means of a new combination of grammatical evolution (GE) and genetic algorithm (GA). GE is adopted to design the network topology while GA is incorporated for better weight adaptation. The proposed algorithm needs to invest a minimal expert's effort for customisation and is capable of generating any feedforward ANN with one hidden layer. Moreover, due to the fact that the generalisation ability of an ANN may decrease because of over fitting problems, the algorithm uses a novel adaptive penalty approach to simplify ANNs generated through the evolution process. As a result, it produces much simpler ANNs that have better generalization ability and are easy to implement. The proposed method is tested on some real world classification datasets, and the results are statistically compared against existing methods in the literature. The results indicate that our algorithm outperforms the other methods and provides the best overall performance in terms of the classification accuracy and the number of hidden neurons. The results also present the contribution of the proposed penalty approach in the simplicity and generalisation ability of the generated networks.", notes = "also known as \cite{AHMADIZAR20151}", } @InProceedings{Ahmed:2015:ieeeICIP, author = "Faisal Ahmed and Padma Polash Paul and Marina L. Gavrilova", booktitle = "2015 IEEE International Conference on Image Processing (ICIP)", title = "Evolutionary fusion of local texture patterns for facial expression recognition", year = "2015", pages = "1031--1035", abstract = "This paper presents a simple, yet effective facial feature descriptor based on evolutionary synthesis of different local texture patterns. Unlike the traditional face descriptors that exploit visually-meaningful facial features, the proposed method adopts a genetic programming-based feature fusion approach that uses different local texture patterns and a set of linear and nonlinear operators in order to synthesise new features. The strength of this approach lies in fusing the advantages of different state-of-the-art local texture descriptors and thus, obtaining more robust composite features. Recognition performance of the proposed method is evaluated using the Cohn-Kanade (CK) and the Japanese female facial expression (JAFFE) database. In our experiments, facial features synthesised based on the proposed approach yield an improved recognition performance, as compared to some well-known face feature descriptors.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICIP.2015.7350956", month = sep, notes = "Also known as \cite{7350956}", } @Article{ahmed:2020:IJDC, author = "Moataz Ahmed and Moustafa El-Gindy and Haoxiang Lang", title = "A novel genetic-programming based differential braking controller for an 8x8 combat vehicle", journal = "International Journal of Dynamics and Control", year = "2020", volume = "8", number = "4", keywords = "genetic algorithms, genetic programming, Stability control, Direct yaw control, Differential braking, Adaptive neuro-fuzzy, Fuzzy logic", URL = "http://link.springer.com/article/10.1007/s40435-020-00693-0", DOI = "doi:10.1007/s40435-020-00693-0", size = "15 pages", abstract = "Lateral stability of multi-axle vehicle's was not considered and studied widely despite its advantages and use in different fields such as transportation, commercial, and military applications. In this research, a novel adaptive Direct Yaw moment Control based on Genetic-Programming (GPDB) is developed and compared with an Adaptive Neuro-Fuzzy Inference System (ANFIS). In addition, a phase-plane analysis of the vehicles nonlinear model is also discussed to introduce the activation criteria to the proposed controller in order to prevent excessive control effort. The controller is evaluated through a series of severe maneuvers in the simulator. The developed GPDB resulting in comparable performance to the ANFIS controller with better implementation facility and design procedure, where a single equation replaces multiple fuzzy rules. The results show fidelity and the ability of the developed controller to stabilize the vehicle near limit-handling driving conditions", } @PhdThesis{Aboelfadl_Ahmed_Moataz, author = "Moataz Aboelfadl Ahmed", title = "Integrated Chassis Control Strategies For Multi-Wheel Combat Vehicle", school = "Department of Automotive and Mechatronics Engineering Faculty of Engineering and Applied Science, University of Ontario Institute of Technology", year = "2021", address = "Oshawa, Ontario, Canada", month = nov, keywords = "genetic algorithms, genetic programming, Chassis control, Lateral stability, Intelligent control, Multi-axle, Combat vehicles", URL = "https://hdl.handle.net/10155/1380", URL = "https://ir.library.ontariotechu.ca/handle/10155/1380", URL = "https://ir.library.ontariotechu.ca/bitstream/handle/10155/1380/Aboelfadl_Ahmed_Moataz.pdf", size = "227 pages", abstract = "Combat vehicles are exposed to high risks due to their high ground clearance and nature of operation in harsh environments. This requires robust stability controllers to cope with the rapid change and uncertainty of driving conditions on various terrains. Moreover, it is required to enhance vehicle stability and increase safety to reduce accidents fatality probability. This research focuses on investigating the effectiveness of different lateral stability controllers and their integration in enhancing the cornering performance of an 8x8 combat vehicle when driving at limited handling conditions. In this research, a new Active Rear Steering (ARS) stability controller for an 8x8 combat vehicle is introduced. This technique is extensively investigated to show its merits and effectiveness for human and autonomous operation. For human operation, the ARS is developed using Linear Quadratic Regulator (LQR) control method, which is compared with previous techniques. Furthermore, the controller is extended and tested for working in a rough and irregular road profile using a novel adaptive Integral Sliding Mode Controller (ISMC). In the case of autonomous operation, a frequency domain analysis is conducted to show the benefits of considering the steering of the rear axles in the path-following performance at different driving conditions. The study compared two different objectives for the controller; the first is including the steering of the rear axles in the path following controller, while the second is to integrate it as a stability controller with a front-steering path-following controller. In addition, this research introduces a novel Differential Braking (DB) controller. The proposed control prevents the excessive use of braking forces and consequently the longitudinal dynamics deterioration. Besides, it introduces an effective DB controller with less dependency and sensitivity to the reference yaw model. Eventually, two various Integrated Chassis Controllers (ICC) are developed and compared. The first is developed by integrating the ISMC-ARS with the DB controller using a fuzzy logic controller. The second ICC integrates the ISMC-ARS with a developed robust Torque Vectoring Controller (TVC). This integration is designed based on a performance map that shows the effective region of each controller using a new technique based on Machine Learning (ML).", notes = "Ontario Tech University Supervisor: Moustafa El-Gindy", } @InProceedings{DBLP:conf/ausai/AhmedZP12, author = "Soha Ahmed and Mengjie Zhang and Lifeng Peng", title = "Genetic Programming for Biomarker Detection in Mass Spectrometry Data", booktitle = "25th Joint Conference Australasian Conference on Artificial Intelligence, AI 2012", year = "2012", editor = "Michael Thielscher and Dongmo Zhang", volume = "7691", series = "Lecture Notes in Computer Science", pages = "266--278", address = "Sydney, Australia", month = dec # " 4-7", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-35100-6", URL = "https://rdcu.be/ee4sM", DOI = "doi:10.1007/978-3-642-35101-3_23", size = "13 pages", abstract = "Classification of mass spectrometry (MS) data is an essential step for biomarker detection which can help in diagnosis and prognosis of diseases. However, due to the high dimensionality and the small sample size, classification of MS data is very challenging. The process of biomarker detection can be referred to as feature selection and classification in terms of machine learning. Genetic programming (GP) has been widely used for classification and feature selection, but it has not been effectively applied to biomarker detection in the MS data. In this study we develop a GP based approach to feature selection, feature extraction and classification of mass spectrometry data for biomarker detection. In this approach, we firstly use GP to reduce the redundant features by selecting a small number of important features and constructing high-level features, then we use GP to classify the data based on selected features and constructed features. This approach is examined and compared with three well known machine learning methods namely decision trees, naive Bayes and support vector machines on two biomarker detection data sets. The results show that the proposed GP method can effectively select a small number of important features from thousands of original features for these problems, the constructed high-level features can further improve the classification performance, and the GP method outperforms the three existing methods, namely naive Bayes, SVMs and J48, on these problems.", } @InProceedings{Ahmed:2013:evobio, author = "Soha Ahmed and Mengjie Zhang and Lifeng Peng", title = "Feature Selection and Classification of High Dimensional Mass Spectrometry Data: A Genetic Programming Approach", booktitle = "11th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, {EvoBIO 2013}", year = "2013", editor = "Leonardo Vanneschi and William S. Bush and Mario Giacobini", month = apr # " 3-5", series = "LNCS", volume = "7833", publisher = "Springer Verlag", organisation = "EvoStar", address = "Vienna, Austria", pages = "43--55", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-37188-2", URL = "https://rdcu.be/ee4ry", DOI = "doi:10.1007/978-3-642-37189-9_5", size = "13 pages", abstract = "Biomarker discovery using mass spectrometry (MS) data is very useful in disease detection and drug discovery. The process of biomarker discovery in MS data must start with feature selection as the number of features in MS data is extremely large (e.g. thousands) while the number of samples is comparatively small. In this study, we propose the use of genetic programming (GP) for automatic feature selection and classification of MS data. This GP based approach works by using the features selected by two feature selection metrics, namely information gain (IG) and relief-f (REFS-F) in the terminal set. The feature selection performance of the proposed approach is examined and compared with IG and REFS-F alone on five MS data sets with different numbers of features and instances. Naive Bayes (NB), support vector machines (SVMs) and J48 decision trees (J48) are used in the experiments to evaluate the classification accuracy of the selected features. Meanwhile, GP is also used as a classification method in the experiments and its performance is compared with that of NB, SVMs and J48. The results show that GP as a feature selection method can select a smaller number of features with better classification performance than IG and REFS-F using NB, SVMs and J48. In addition, GP as a classification method also outperforms NB and J48 and achieves comparable or slightly better performance than SVMs on these data sets.", } @InProceedings{Ahmed:2013:CEC, author = "Soha Ahmed and Mengjie Zhang and Lifeng Peng", title = "Enhanced Feature Selection for Biomarker Discovery in LC-MS Data using GP", booktitle = "2013 IEEE Conference on Evolutionary Computation", volume = "1", year = "2013", month = jun # " 20-23", editor = "Luis Gerardo {de la Fraga}", pages = "584--591", address = "Cancun, Mexico", article_id = "1253", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-0453-2", DOI = "doi:10.1109/CEC.2013.6557621", abstract = "Biomarker detection in LC-MS data depends mainly on feature selection algorithms as the number of features is extremely high while the number of samples is very small. This makes classification of these data sets extremely challenging. In this paper we propose the use of genetic programming (GP) for subset feature selection in LC-MS data which works by maximizing the signal to noise ratio of the selected features by GP. The proposed method was applied to eight LC-MS data sets with different sample sizes and different levels of concentration of the spiked biomarkers. We evaluated the accuracy of selection from the list of biomarkers and also using the classification accuracy of the selected features via the support vector machines (SVMs) and Naive Bayes (NB) classifiers. Features selected by the proposed GP method managed to achieve perfect classification accuracy for most of the data sets. The results show that the proposed method strikes a reasonable compromise between the detection rate of the biomarkers and the classification accuracy for all data sets. The method was also compared to linear Support Vector Machine-Recursive Features Elimination (SVM-RFE) and t-test for feature selection and the results show that the biomarker detection rate of the proposed approach is higher.", notes = "Also known as \cite{6557621} CEC 2013 - A joint meeting of the IEEE, the EPS and the IET.", } @InProceedings{Ahmed:evoapps14, author = "Soha Ahmed and Mengjie Zhang and Lifeng Peng", title = "GPMS: A Genetic Programming Based Approach to Multiple Alignment of Liquid Chromatography-Mass Spectrometry Data", booktitle = "17th European Conference on the Applications of Evolutionary Computation", year = "2014", editor = "Anna Isabel Esparcia-Alcazar and Antonio Miguel Mora", series = "LNCS", volume = "8602", publisher = "Springer", pages = "915--927", address = "Granada", month = "23-25 " # apr, organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-662-45522-7", DOI = "doi:10.1007/978-3-662-45523-4_74", abstract = "Alignment of samples from Liquid chromatography-mass spectrometry (LC-MS) measurements has a significant role in the detection of biomarkers and in metabolomic studies.The machine drift causes differences between LC-MS measurements, and an accurate alignment of the shifts introduced to the same peptide or metabolite is needed. In this paper, we propose the use of genetic programming (GP) for multiple alignment of LC-MS data. The proposed approach consists of two main phases. The first phase is the peak matching where the peaks from different LC-MS maps (peak lists) are matched to allow the calculation of the retention time deviation. The second phase is to use GP for multiple alignment of the peak lists with respect to a reference. In this paper, GP is designed to perform multiple-output regression by using a special node in the tree which divides the output of the tree into multiple outputs. Finally, the peaks that show the maximum correlation after dewarping the retention times are selected to form a consensus aligned map.The proposed approach is tested on one proteomics and two metabolomics LC-MS datasets with different number of samples. The method is compared to several benchmark methods and the results show that the proposed approach outperforms these methods in three fractions of the protoemics dataset and the metabolomics dataset with a larger number of maps. Moreover, the results on the rest of the datasets are highly competitive with the other methods", notes = "EvoApplications2014 held in conjunction with EuroGP'2014, EvoCOP2014, EvoBIO2014, and EvoMusArt2014", } @InProceedings{Ahmed:2014:CEC, title = "A New {GP}-Based Wrapper Feature Construction Approach to Classification and Biomarker Identification", author = "Soha Ahmed and Mengjie Zhang and Lifeng Peng", pages = "2756--2763", booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary Computation", year = "2014", month = "6-11 " # jul, editor = "Carlos A. {Coello Coello}", address = "Beijing, China", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Evolutionary programming, Biometrics, bioinformatics and biomedical applications", DOI = "doi:10.1109/CEC.2014.6900317", abstract = "Mass spectrometry (MS) is a technology used for identification and quantification of proteins and metabolites. It helps in the discovery of proteomic or metabolomic biomarkers, which aid in diseases detection and drug discovery. The detection of biomarkers is performed through the classification of patients from healthy samples. The mass spectrometer produces high dimensional data where most of the features are irrelevant for classification. Therefore, feature reduction is needed before the classification of MS data can be done effectively. Feature construction can provide a means of dimensionality reduction and aims at improving the classification performance. In this paper, genetic programming (GP) is used for construction of multiple features. Two methods are proposed for this objective. The proposed methods work by wrapping a Random Forest (RF) classifier to GP to ensure the quality of the constructed features. Meanwhile, five other classifiers in addition to RF are used to test the impact of the constructed features on the performance of these classifiers. The results show that the proposed GP methods improved the performance of classification over using the original set of features in five MS data sets.", notes = "Also known as \cite{6900317} WCCI2014", } @InProceedings{Ahmed:2014:GECCOa, author = "Soha Ahmed and Mengjie Zhang and Lifeng Peng and Bing Xue", title = "Multiple feature construction for effective biomarker identification and classification using genetic programming", booktitle = "GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation", year = "2014", editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges", isbn13 = "978-1-4503-2662-9", pages = "249--256", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming", URL = "https://homepages.ecs.vuw.ac.nz/~xuebing/Papers/Multiple%20Feature%20Construction%20for%20Effective%20Biomarker%20Identification%20and%20Classification%20using%20Genetic%20Programming.pdf", URL = "http://doi.acm.org/10.1145/2576768.2598292", DOI = "doi:10.1145/2576768.2598292", size = "8 pages", abstract = "Biomarker identification, i.e., detecting the features that indicate differences between two or more classes, is an important task in omics sciences. Mass spectrometry (MS) provide a high throughput analysis of proteomic and metabolomic data. The number of features of the MS data sets far exceeds the number of samples, making biomarker identification extremely difficult. Feature construction can provide a means for solving this problem by transforming the original features to a smaller number of high-level features. This paper investigates the construction of multiple features using genetic programming (GP) for biomarker identification and classification of mass spectrometry data. In this paper, multiple features are constructed using GP by adopting an embedded approach in which Fisher criterion and p-values are used to measure the discriminating information between the classes. This produces nonlinear high-level features from the low-level features for both binary and multi-class mass spectrometry data sets. Meanwhile, seven different classifiers are used to test the effectiveness of the constructed features. The proposed GP method is tested on eight different mass spectrometry data sets. The results show that the high-level features constructed by the GP method are effective in improving the classification performance in most cases over the original set of features and the low-level selected features. In addition, the new method shows superior performance in terms of biomarker detection rate.", notes = "Also known as \cite{2598292} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)", } @InProceedings{Ahmed:2014:GECCOcomp, author = "Soha Ahmed and Mengjie Zhang and Lifeng Peng", title = "Prediction of detectable peptides in MS data using genetic programming", booktitle = "GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion", year = "2014", editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges", isbn13 = "978-1-4503-2881-4", keywords = "genetic algorithms, genetic programming, biological and biomedical applications: Poster", pages = "37--38", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2598394.2598421", DOI = "doi:10.1145/2598394.2598421", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "The use of mass spectrometry to verify and quantify biomarkers requires the identification of the peptides that can be detectable. In this paper, we propose the use of genetic programming (GP) to measure the detection probability of the peptides. The new GP method is tested and verified on two different yeast data sets with increasing complexity and shows improved performance over other state-of-art classification and feature selection algorithms.", notes = "Also known as \cite{2598421} Distributed at GECCO-2014.", } @Article{Ahmed:2014:CS, author = "Soha Ahmed and Mengjie Zhang and Lifeng Peng", title = "Improving Feature Ranking for Biomarker Discovery in Proteomics Mass Spectrometry Data using Genetic Programming", journal = "Connection Science", year = "2014", volume = "26", number = "3", pages = "215--243", keywords = "genetic algorithms, genetic programming, biomarker discovery, feature selection, classification", ISSN = "0954-0091", publisher = "Taylor \& Francis", DOI = "doi:10.1080/09540091.2014.906388", size = "29 pages", abstract = "Feature selection on mass spectrometry (MS) data is essential for improving classification performance and biomarker discovery. The number of MS samples is typically very small compared with the high dimensionality of the samples, which makes the problem of biomarker discovery very hard. In this paper, we propose the use of genetic programming for biomarker detection and classification of MS data. The proposed approach is composed of two phases: in the first phase, feature selection and ranking are performed. In the second phase, classification is performed. The results show that the proposed method can achieve better classification performance and biomarker detection rate than the information gain (IG) based and the RELIEF feature selection methods. Meanwhile, four classifiers, Naive Bayes, J48 decision tree, random forest and support vector machines, are also used to further test the performance of the top ranked features. The results show that the four classifiers using the top ranked features from the proposed method achieve better performance than the IG and the RELIEF methods. Furthermore, GP also outperforms a genetic algorithm approach on most of the used data sets.", notes = "Also known as \cite{Ahmed03072014}", } @InProceedings{conf/seal/AhmedZPX14, author = "Soha Ahmed and Mengjie Zhang and Lifeng Peng and Bing Xue", title = "Genetic Programming for Measuring Peptide Detectability", bibdate = "2014-11-12", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/seal/seal2014.html#AhmedZPX14", booktitle = "Simulated Evolution and Learning - 10th International Conference, {SEAL} 2014, Dunedin, New Zealand, December 15-18, 2014. Proceedings", publisher = "Springer", year = "2014", volume = "8886", editor = "Grant Dick and Will N. Browne and Peter A. Whigham and Mengjie Zhang and Lam Thu Bui and Hisao Ishibuchi and Yaochu Jin and Xiaodong Li and Yuhui Shi and Pramod Singh and Kay Chen Tan and Ke Tang", isbn13 = "978-3-319-13562-5", pages = "593--604", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming", URL = "http://dx.doi.org/10.1007/978-3-319-13563-2", } @PhdThesis{ahmed:thesis, author = "Soha Ahmed", title = "Genetic Programming for Biomarker Detection in Classification of Mass Spectrometry Data", school = "School of Engineering and Computer Science, Victoria University of Wellington", year = "2015", address = "New Zealand", keywords = "genetic algorithms, genetic programming, Biomarker detection, Mass spectrometry, Pattern Recognition and Data Mining, Bioinformatics Software, Expanding Knowledge in the Information and Computing Sciences, NS-GPMOFS, SP-GPMOFS, SPEA2, NSGAII, NB, J48, SVM, PEP-GP, GPMS, Electrospray ionisation, ESI, ensemble", URL = "https://openaccess.wgtn.ac.nz/articles/thesis/Genetic_Programming_for_Biomarker_Detection_in_Classification_of_Mass_Spectrometry_Data/17013680?file=31469105", URL = "https://openaccess.wgtn.ac.nz/ndownloader/files/31469105", DOI = "doi:10.26686/wgtn.17013680.v1", size = "246 pages", abstract = "Mass spectrometry (MS) is currently the most commonly used technology in biochemical research for proteomic analysis. The primary goal of proteomic profiling using mass spectrometry is the classification of samples from different experimental states. To classify the MS samples, the identification of protein or peptides (biomarker detection) that are expressed differently between the classes, is required. However, due to the high dimensionality of the data and the small number of samples, classification of MS data is extremely challenging. Another important aspect of biomarker detection is the verification of the detected biomarker that acts as an intermediate step before passing these biomarkers to the experimental validation stage. Biomarker detection aims at altering the input space of the learning algorithm for improving classification of proteomic or metabolomic data. This task is performed through feature manipulation. Feature manipulation consists of three aspects: feature ranking, feature selection, and feature construction. Genetic programming (GP) is an evolutionary computation algorithm that has the intrinsic capability for the three aspects of feature manipulation. The ability of GP for feature manipulation in proteomic biomarker discovery has not been fully investigated. This thesis, therefore, proposes an embedded methodology for these three aspects of feature manipulation in high dimensional MS data using GP. The thesis also presents a method for biomarker verification, using GP. The thesis investigates the use of GP for both single-objective and multi-objective feature selection and construction. In feature ranking, the thesis proposes a GP-based method for ranking subsets of features by using GP as an ensemble approach. The proposed algorithm uses GP capability to combine the advantages of different feature ranking metrics and evolve a new ranking scheme for the subset of the features selected from the top ranked features. The capability of GP as a classifier is also investigated by this method. The results show that GP can select a smaller number of features and provide a better ranking of the selected features, which can improve the classification performance of five classifiers. In feature construction, this thesis proposes a novel multiple feature construction method, which uses a single GP tree to generate a new set of high-level features from the original set of selected features. The results show that the proposed new algorithm outperforms two feature selection algorithms. In feature selection, the thesis introduces the first GP multi-objective method for biomarker detection, which simultaneously increase the classification accuracy and reduce the number of detected features. The proposed multi-objective method can obtain better subsets of features than the single-objective algorithm and two traditional multi-objective approaches for feature selection. This thesis also develops the first multi-objective multiple feature construction algorithm for MS data. The proposed method aims at both maximising the classification performance and minimizing the cardinality of the constructed new high-level features. The results show that GP can discover the complex relationships between the features and can significantly improve classification performance and reduce the cardinality. For biomarker verification, the thesis proposes the first GP biomarker verification method through measuring the peptide detectability. The method solves the imbalance problem in the data and shows improvement over the benchmark algorithms. Also, the algorithm outperforms a well-known peptide detection method. The thesis also introduces a new GP method for alignment of MS data as a preprocessing stage, which will further help in improving the biomarker detection process.", notes = "Open Access Te Herenga Waka. UW0806 ECRG Supervisors: Mengjie Zhang and Lifeng Peng", } @InProceedings{conf/evoW/AhmedZPX16, author = "Soha Ahmed and Mengjie Zhang and Lifeng Peng and Bing Xue", title = "A Multi-objective Genetic Programming Biomarker Detection Approach in Mass Spectrometry Data", booktitle = "19th European Conference on Applications of Evolutionary Computation, EvoApplications 2016", year = "2016", editor = "Giovanni Squillero and Paolo Burelli", volume = "9597", series = "Lecture Notes in Computer Science", pages = "106--122", address = "Porto, Portugal", month = mar # " 30 -- " # apr # " 1", organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", bibdate = "2016-03-23", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/evoW/evoappl2016-1.html#AhmedZPX16", isbn13 = "978-3-319-31204-0", URL = "https://homepages.ecs.vuw.ac.nz/~xuebing/Papers/Ahmed2016.pdf", URL = "https://rdcu.be/ee4d1", DOI = "doi:10.1007/978-3-319-31204-0_8", size = "17 pages", abstract = "Mass spectrometry is currently the most commonly used technology in biochemical research for proteomic analysis. The main goal of proteomic profiling using mass spectrometry is the classification of samples from different clinical states. This requires the identification of proteins or peptides (biomarkers) that are expressed differentially between different clinical states. However, due to the high dimensionality of the data and the small number of samples, classification of mass spectrometry data is a challenging task. Therefore, an effective feature manipulation algorithm either through feature selection or construction is needed to enhance the classification performance and at the same time minimise the number of features. Most of the feature manipulation methods for mass spectrometry data treat this problem as a single objective task which focuses on improving the classification performance. This paper presents two new methods for biomarker detection through multi-objective feature selection and feature construction. The results show that the proposed multi-objective feature selection method can obtain better subsets of features than the single-objective algorithm and two traditional multi-objective approaches for feature selection. Moreover, the multi-objective feature construction algorithm further improves the performance over the multi-objective feature selection algorithm. This paper is the first multi-objective genetic programming approach for biomarker detection in mass spectrometry data", notes = "EvoApplications2016 held inconjunction with EuroGP'2016, EvoCOP2016 and EvoMUSART 2016", } @Article{ahmed:2021:Materials, author = "Waleed Ahmed and Hussien Hegab and Atef Mohany and Hossam Kishawy", title = "Analysis and Optimization of Machining Hardened Steel {AISI} 4140 with {Self-Propelled} Rotary Tools", journal = "Materials", year = "2021", volume = "14", number = "20", keywords = "genetic algorithms, genetic programming, modeling, machining, optimization, rotary tools", ISSN = "1996-1944", URL = "https://www.mdpi.com/1996-1944/14/20/6106", DOI = "doi:10.3390/ma14206106", abstract = "It is necessary to improve the machinability of difficult-to-cut materials such as hardened steel, nickel-based alloys, and titanium alloys as these materials offer superior properties such as chemical stability, corrosion resistance, and high strength to weight ratio, making them indispensable for many applications. Machining with self-propelled rotary tools (SPRT) is considered one of the promising techniques used to provide proper tool life even under dry conditions. In this work, an attempt has been performed to analyse, model, and optimise the machining process of AISI 4140 hardened steel using self-propelled rotary tools. Experimental analysis has been offered to (a) compare the fixed and rotary tools performance and (b) study the effect of the inclination angle on the surface quality and tool wear. Moreover, the current study implemented some artificial intelligence-based approaches (i.e., genetic programming and NSGA-II) to model and optimise the machining process of AISI 4140 hardened steel with self-propelled rotary tools. The feed rate, cutting velocity, and inclination angle were the selected design variables, while the tool wear, surface roughness, and material removal rate (MRR) were the studied outputs. The optimal surface roughness was obtained at a cutting speed of 240 m/min, an inclination angle of 20?, and a feed rate of 0.1 mm/rev. In addition, the minimum flank tool wear was observed at a cutting speed of 70 m/min, an inclination angle of 10?, and a feed rate of 0.15 mm/rev. Moreover, different weights have been assigned for the three studied outputs to offer different optimised solutions based on the designer's interest (equal-weighted, finishing, and productivity scenarios). It should be stated that the findings of the current work offer valuable recommendations to select the optimised cutting conditions when machining hardened steel AISI 4140 within the selected ranges.", notes = "also known as \cite{ma14206106}", } @Article{AHMED:2023:isatra, author = "Umair Ahmed and Fakhre Ali and Ian Jennions", title = "Acoustic monitoring of an aircraft auxiliary power unit", journal = "ISA Transactions", year = "2023", ISSN = "0019-0578", DOI = "doi:10.1016/j.isatra.2023.01.014", URL = "https://www.sciencedirect.com/science/article/pii/S0019057823000149", keywords = "genetic algorithms, genetic programming, Aircraft, Auxiliary power unit, Condition monitoring, Acoustics, Signal processing, Machine learning, Sensors, Feature extraction, Fault detection, Microphones", abstract = "In this paper, the development and implementation of a novel approach for fault detection of an aircraft auxiliary power unit (APU) has been demonstrated. The developed approach aims to target the proactive identification of faults, in order to streamline the required maintenance and maximize the aircraft's operational availability. The existing techniques rely heavily on the installation of multiple types of intrusive sensors throughout the APU and therefore present a limited potential for deployment on an actual aircraft due to space constraints, accessibility issues as well as associated development and certification requirements. To overcome these challenges, an innovative approach based on non-intrusive sensors i.e., microphones in conjunction with appropriate feature extraction, classification, and regression techniques, has been successfully demonstrated for online fault detection of an APU. The overall approach has been implemented and validated based on the experimental test data acquired from Cranfield University's Boeing 737-400 aircraft, including the quantification of sensor location sensitivities on the efficacy of the acquired models. The findings of the overall analysis suggest that the acoustic-based models can accurately enable near real-time detection of faulty conditions i.e., Inlet Guide Vane malfunction, reduced mass flows through the Load Compressor and Bleed Valve malfunction, using only two microphones installed in the periphery of the APU. This study constitutes an enabling technology for robust, cost-effective, and efficient in-situ monitoring of an aircraft APU and potentially other associated thermal systems i.e., environmental control system, fuel system, and engines", } @Article{Ahmed:JBHI, author = "Usman Ahmed and Jerry Chun-Wei Lin and Gautam Srivastava", journal = "IEEE Journal of Biomedical and Health Informatics", title = "Towards Early Diagnosis and Intervention: An Ensemble Voting Model for Precise Vital Sign Prediction in Respiratory Disease", year = "2023", abstract = "Worldwide, cardiovascular and chronic respiratory diseases account for approximately 19 million deaths annually. Evidence indicates that the ongoing COVID-19 pandemic directly contributes to increased blood pressure, cholesterol, as well as blood glucose levels. Timely screening of critical physiological vital signs benefits both healthcare providers and individuals by detecting potential health issues. This study aims to implement a machine learning-based prediction and classification system to forecast vital signs associated with cardiovascular and chronic respiratory diseases. The system predicts patients' health status and notifies caregivers and medical professionals when necessary. Using real-world data, a linear regression model inspired by the Facebook Prophet model was developed to predict vital signs for the upcoming 180 seconds. With 180 seconds of lead time, caregivers can potentially save patients' lives through early diagnosis of their health conditions. For this purpose, a Naive Bayes classification model, a Support Vector Machine model, a Random Forest model, and genetic programming-based hyper tunning were employed. The proposed model outdoes previous attempts at vital sign prediction. Compared with alternative methods, the Facebook Prophet model has the best mean square in predicting vital signs. A hyperparameter-tuning is used to refine the model, yielding improved short- and long-term outcomes for each and every vital sign. Furthermore, the F-measure for the proposed classification model is 0.98 with an increase of 0.21. The incorporation of additional elements, such as momentum indicators, could increase the model's flexibility with calibration. The findings of this study demonstrate that the proposed model is more accurate in predicting vital signs and trends.", keywords = "genetic algorithms, genetic programming, Diseases, Medical diagnostic imaging, Medical services, Heart, Predictive models, Machine learning, Decision trees, Artificial intelligence, Sensor readings, Heart disease, Long-term lung disease", DOI = "doi:10.1109/JBHI.2023.3270888", ISSN = "2168-2208", notes = "Also known as \cite{10121013}", } @Article{AHMED:2023:suscom, author = "Usman Ahmed and Jerry Chun-Wei Lin and Gautam Srivastava", title = "Multivariate time-series sensor vital sign forecasting of cardiovascular and chronic respiratory diseases", journal = "Sustainable Computing: Informatics and Systems", volume = "38", pages = "100868", year = "2023", ISSN = "2210-5379", DOI = "doi:10.1016/j.suscom.2023.100868", URL = "https://www.sciencedirect.com/science/article/pii/S2210537923000239", keywords = "genetic algorithms, genetic programming, Machine learning, Sensor data, Cardiovascular disease, Chronic respiratory disease. TPOT", abstract = "Approximately 19 million people die each year from cardiovascular and chronic respiratory diseases. As a result of the recent Covid-19 epidemic, blood pressure, cholesterol, and blood sugar levels have risen. Not only do healthcare institutions benefit from studying physiological vital signs, but individuals also benefit from being alerted to health problems in a timely manner. This study uses machine learning to categorize and predict cardiovascular and chronic respiratory diseases. By predicting a patient's health status, caregivers and medical professionals can be alerted when needed. We predicted vital signs for 180 seconds using real-world vital sign data. A person's life can be saved if caregivers react quickly and anticipate emergencies. The tree-based pipeline optimization method (TPOT) is used instead of manually adjusting machine learning classifiers. This paper focuses on optimizing classification accuracy by combining feature pre-processors and machine learning models with TPOT genetic programming making use of linear and Prophet models to predict important indicators. The TPOT tuning parameter combines predicted values with classical classification models such as Naive Bayes, Support Vector Machines, and Random Forests. As a result of this study, we show the importance of categorizing and increasing the accuracy of predictions. The proposed model achieves its adaptive behavior by conceptually incorporating different machine learning classifiers. We compare the proposed model with several state-of-the-art algorithms using a large amount of training data. Test results at the University of Queensland using 32 patient's data showed that the proposed model outperformed existing algorithms, improving the classification of cardiovascular disease from 0.58 to 0.71 and chronic respiratory disease from 0.49 to 0.70, respectively, while minimizing the mean percent error in vital signs. Our results suggest that the Facebook Prophet prediction model in conjunction with the TPOT classification model can correctly diagnose a patient's health status based on abnormal vital signs and enables patients to receive prompt medical attention", } @InProceedings{conf/fgit/AhnOO11, author = "Chang Wook Ahn and Sanghoun Oh and Moonyoung Oh", title = "A Genetic Programming Approach to Data Clustering", booktitle = "Proceedings of the International Conference on Multimedia, Computer Graphics and Broadcasting (MulGraB 2011) Part {II}", editor = "Tai-Hoon Kim and Hojjat Adeli and William I. Grosky and Niki Pissinou and Timothy K. Shih and Edward J. Rothwell and Byeong Ho Kang and Seung-Jung Shin", year = "2011", volume = "263", series = "Communications in Computer and Information Science", pages = "123--132", address = "Jeju Island, Korea", month = dec # " 8-10", publisher = "Springer", note = "Held as Part of the Future Generation Information Technology Conference, {FGIT} 2011, in Conjunction with {GDC} 2011", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-27186-1", DOI = "doi:10.1007/978-3-642-27186-1_15", size = "10 pages", abstract = "This paper presents a genetic programming (GP) to data clustering. The aim is to accurately classify a set of input data into their genuine clusters. The idea lies in discovering a mathematical function on clustering regularities and then use the rule to make a correct decision on the entities of each cluster. To this end, GP is incorporated into the clustering procedures. Each individual is represented by a parsing tree on the program set. Fitness function evaluates the quality of clustering with regard to similarity criteria. Crossover exchanges sub-trees between parental candidates in a positionally independent fashion. Mutation introduces (in part) a new sub-tree with a low probability. The variation operators (i.e., crossover, mutation) offer an effective search capability to obtain the improved quality of solution and the enhanced speed of convergence. Experimental results demonstrate that the proposed approach outperforms a well-known reference.", affiliation = "School of Information & Communication Engineering, Sungkyunkwan University, Suwon, 440-746 Korea", bibdate = "2011-12-08", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/fgit/mulgrab2011-2.html#AhnOO11", } @Article{Aho97, author = "Hannu Ahonen and Paulo A. {de Souza Jr.} and Vijayendra Kumar Garg", title = "A genetic algorithm for fitting Lorentzian line shapes in Mossbauer spectra", journal = "Nuclear Instruments and Methods in Physics Research B", year = "1997", volume = "124", pages = "633--638", month = "5 " # may, email = "souza@iacgu7.chemie.uni-mainz.de", keywords = "genetic algorithms", ISSN = "0168583X", abstract = "A genetic algorithm was implemented for finding an approximative solution to the problem of fitting a combination of Lorentzian lines to a measured Mossbauer spectrum. This iterative algorithm exploits the idea of letting several solutions (individuals) compete with each other for the opportunity of being selected to create new solutions (reproduction). Each solution was represend as a string of binary digits (chromossome). In addition, the bits in the new solutions may be switched randomly from zero to one or conversely (mutation). The input of the program that implements the genetic algorithm consists of the measured spectrum, the maximum velocity, the peak positions and the expected number of Lorentzian lines in the spectrum. Each line is represented with the help of three variables, which correspond to its intensity, full line width at hald maxima and peak position. An additional parameter was associated to the background level in the spectrum. A chi-2 test was used for determining the quality of each parameter combination (fitness). The results obtained seem to be very promising and encourage to further development of the algorithm and its implementation.", } @InProceedings{Ahsan:2020:IBCAST, author = "Usama Ahsan and Fayyaz ul Amir Afsar Minhas", booktitle = "2020 17th International Bhurban Conference on Applied Sciences and Technology (IBCAST)", title = "{AutoQP:} Genetic Programming for Quantum Programming", year = "2020", pages = "378--382", abstract = "Quantum computing is a new era in the field of computation which makes use of quantum mechanical phenomena such as superposition, entanglement, and quantum annealing. It is a very promising field and has given a new paradigm to efficiently solve complex computational problems. However, programming quantum computers is a difficult task. In this research, we have developed a system called AutoQP which can write quantum computer code through genetic programming on a classical computer provided the input and expected output of a quantum program. We have tested AutoQP on two different quantum algorithms: Deutsch Problem and the Bernstein-Vazirani problem. In our experimental analysis, AutoQP was able to generate quantum programs for solving both problems. The code generated by AutoQP was successfully tested on actual IBM quantum computers as well. It is expected that the proposed system can be very useful for the general development of quantum programs based on the IBM gate model. The source code for the proposed system is available at the URL: https://github.com/usamaahsan93/AutoQP.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IBCAST47879.2020.9044554", ISSN = "2151-1411", month = jan, notes = "Also known as \cite{9044554}", } @Article{DBLP:journals/itiis/AhvanooeyLWW19, author = "Milad Taleby Ahvanooey and Qianmu Li and Ming Wu and Shuo Wang", title = "A Survey of Genetic Programming and Its Applications", journal = "{KSII} Trans. Internet Inf. Syst.", volume = "13", number = "4", pages = "1765--1794", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.3837/tiis.2019.04.002", DOI = "doi:10.3837/tiis.2019.04.002", timestamp = "Thu, 25 Jun 2020 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/itiis/AhvanooeyLWW19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @PhdThesis{Lifeng_Ai_Thesis, author = "Lifeng Ai", title = "{QoS-aware} web service composition using genetic algorithms", school = "Queensland University of Technology", year = "2011", address = "Australia", month = jun, keywords = "genetic algorithms, quality of service, web services, composite web services, optimisation", URL = "http://eprints.qut.edu.au/46666/1/Lifeng_Ai_Thesis.pdf", URL = "http://eprints.qut.edu.au/46666/", size = "pages", abstract = "Web service technology is increasingly being used to build various e-Applications, in domains such as e-Business and e-Science. Characteristic benefits of web service technology are its inter-operability, decoupling and just-in-time integration. Using web service technology, an e-Application can be implemented by web service composition, by composing existing individual web services in accordance with the business process of the application. This means the application is provided to customers in the form of a value-added composite web service. An important and challenging issue of web service composition, is how to meet Quality-of-Service (QoS) requirements. This includes customer focused elements such as response time, price, throughput and reliability as well as how to best provide QoS results for the composites. This in turn best fulfils customers' expectations and achieves their satisfaction. Fulfilling these QoS requirements or addressing the QoS-aware web service composition problem is the focus of this project. From a computational point of view, QoS-aware web service composition can be transformed into diverse optimisation problems. These problems are characterised as complex, large-scale, highly constrained and multi-objective problems. We therefore use genetic algorithms (GAs) to address QoS-based service composition problems. More precisely, this study addresses three important subproblems of QoS-aware web service composition; QoS-based web service selection for a composite web service accommodating constraints on inter-service dependence and conflict, QoS-based resource allocation and scheduling for multiple composite services on hybrid clouds, and performance-driven composite service partitioning for decentralised execution. Based on operations research theory, we model the three problems as a constrained optimisation problem, a resource allocation and scheduling problem, and a graph partitioning problem, respectively. Then, we present novel GAs to address these problems. We also conduct experiments to evaluate the performance of the new GAs. Finally, verification experiments are performed to show the correctness of the GAs. The major outcomes from the first problem are three novel GAs: a penaltybased GA, a min-conflict hill-climbing repairing GA, and a hybrid GA. These GAs adopt different constraint handling strategies to handle constraints on interservice dependence and conflict. This is an important factor that has been largely ignored by existing algorithms that might lead to the generation of infeasible composite services. Experimental results demonstrate the effectiveness of our GAs for handling the QoS-based web service selection problem with constraints on inter-service dependence and conflict, as well as their better scalability than the existing integer programming-based method for large scale web service selection problems. The major outcomes from the second problem has resulted in two GAs; a random-key GA and a cooperative coevolutionary GA (CCGA). Experiments demonstrate the good scalability of the two algorithms. In particular, the CCGA scales well as the number of composite services involved in a problem increases, while no other algorithms demonstrate this ability. The findings from the third problem result in a novel GA for composite service partitioning for decentralised execution. Compared with existing heuristic algorithms, the new GA is more suitable for a large-scale composite web service program partitioning problems. In addition, the GA outperforms existing heuristic algorithms, generating a better deployment topology for a composite web service for decentralised execution. These effective and scalable GAs can be integrated into QoS-based management tools to facilitate the delivery of feasible, reliable and high quality composite web services.", notes = "also know as \cite{quteprints46666} ID Code: 46666 Supervisor: Tang, Maolin & Fidge, Colin", } @InProceedings{Aichour:2007:NICSO, author = "Malek Aichour and Evelyne Lutton", title = "Cooperative Co-evolution Inspired Operators for Classical GP Schemes", booktitle = "Proceedings of International Workshop on Nature Inspired Cooperative Strategies for Optimization (NICSO '07)", year = "2007", pages = "169--178", editor = "Natalio Krasnogor and Giuseppe Nicosia and Mario Pavone and David Pelta", volume = "129", series = "Studies in Computational Intelligence", address = "Acireale, Italy", month = "8-10 " # nov, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-78986-4", DOI = "doi:10.1007/978-3-540-78987-1_16", abstract = "This work is a first step toward the design of a cooperative-coevolution GP for symbolic regression, which first output is a selective mutation operator for classical GP. Cooperative co-evolution techniques rely on the imitation of cooperative capabilities of natural populations and have been successfully applied in various domains to solve very complex optimisation problems. It has been proved on several applications that the use of two fitness measures (local and global) within an evolving population allow to design more efficient optimization schemes. We currently investigate the use of a two-level fitness measurement for the design of operators, and present in this paper a selective mutation operator. Experimental analysis on a symbolic regression problem give evidence of the efficiency of this operator in comparison to classical subtree mutation", notes = "http://www.dmi.unict.it/nicso2007/ http://www.dmi.unict.it/nicso2007/NICSO2007-program.pdf", } @InProceedings{Ain:2022:ICDMW, author = "Qurrat Ul Ain and Bing Xue and Harith Al-Sahaf and Mengjie Zhang", booktitle = "2022 IEEE International Conference on Data Mining Workshops (ICDMW)", title = "A Genetic Programming Approach to Automatically Construct Informative Attributes for Mammographic Density Classification", year = "2022", pages = "378--387", abstract = "Breast density is widely used as an initial indicator of developing breast cancer. At present, current classification methods for mammographic density usually require manual operations or expert knowledge that makes them expensive in real-time situations. Such methods achieve only moderate classification accuracy due to the limited model capacity and computational resources. In addition, most existing studies focus on improving classification accuracy using only raw images or the entire set of original attributes and remain unable to identify hidden patterns or causal information necessary to discriminate breast density classes. It is challenging to find high-quality knowledge when some attributes defining the data space are redundant or irrelevant. In this study, we present a novel attribute construction method using genetic programming (GP) for the task of breast density classification. To extract informative features from the raw mammographic images, wavelet decomposition, local binary patterns, and histogram of oriented gradients are used to include texture, local and global image properties. The study evaluates the goodness of the proposed method on two benchmark real-world mammographic image datasets and compares the results of the proposed GP method with eight conventional classification methods. The experimental results reveal that the proposed method significantly outperforms most of the commonly used classification methods in binary and multi-class classification tasks. Furthermore, the study shows the potential of G P for mammographic breast density classification by interpreting evolved attributes that highlight important breast density characteristics.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICDMW58026.2022.00057", ISSN = "2375-9259", month = nov, notes = "Also known as \cite{10031110}", } @Article{Ain:2022:ieeeTC, author = "Qurrat Ul Ain and Harith Al-Sahaf and Bing Xue and Mengjie Zhang", journal = "IEEE Transactions on Cybernetics", title = "Automatically Diagnosing Skin Cancers From Multimodality Images Using Two-Stage Genetic Programming", year = "2022", abstract = "Developing a computer-aided diagnostic system for detecting various skin malignancies from images has attracted many researchers. Unlike many machine-learning approaches, such as artificial neural networks, genetic programming (GP) automatically evolves models with flexible representation. GP successfully provides effective solutions using its intrinsic ability to select prominent features (i.e., feature selection) and build new features (i.e., feature construction). Existing approaches have used GP to construct new features from the complete set of original features and the set of operators. However, the complete set of features may contain redundant or irrelevant features that do not provide useful information for classification. This study aims to develop a two-stage GP method, where the first stage selects prominent features, and the second stage constructs new features from these selected features and operators, such as multiplication in a wrapper approach to improve the classification performance. To include local, global, texture, color, and multiscale image properties of skin images, GP selects and constructs features extracted from local binary patterns and pyramid-structured wavelet decomposition. The accuracy of this GP method is assessed using two real-world skin image datasets captured from the standard camera and specialized instruments, and compared with commonly used classification algorithms, three state of the art, and an existing embedded GP method. The results reveal that this new approach of feature selection and feature construction effectively helps improve the performance of the machine-learning classification algorithms. Unlike other black-box models, the evolved models by GP are interpretable; therefore, the proposed method can assist dermatologists to identify prominent features, which has been shown by further analysis on the evolved models.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/TCYB.2022.3182474", ISSN = "2168-2275", notes = "Also known as \cite{9819829}", } @Article{AIN:2022:eswa, author = "Qurrat Ul Ain and Harith Al-Sahaf and Bing Xue and Mengjie Zhang", title = "Genetic programming for automatic skin cancer image classification", journal = "Expert Systems with Applications", volume = "197", pages = "116680", year = "2022", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2022.116680", URL = "https://www.sciencedirect.com/science/article/pii/S0957417422001634", keywords = "genetic algorithms, genetic programming, Image classification, Dimensionality reduction, Feature selection, Feature construction", abstract = "Developing a computer-aided diagnostic system for detecting various types of skin malignancies from images has attracted many researchers. However, analyzing the behaviors of algorithms is as important as developing new systems in order to establish the effectiveness of a system in real-time situations which impacts greatly how well it can assist the dermatologist in making a diagnosis. Unlike many machine learning approaches such as Artificial Neural Networks, Genetic Programming (GP) automatically evolves models with its dynamic representation and flexibility. This study aims at analyzing recently developed GP-based approaches to skin image classification. These approaches have used the intrinsic feature selection and feature construction ability of GP to effectively construct informative features from a variety of pre-extracted features. These features encompass local, global, texture, color and multi-scale image properties of skin images. The performance of these GP methods is assessed using two real-world skin image datasets captured from standard camera and specialized instruments, and compared with six commonly used classification algorithms as well as existing GP methods. The results reveal that these constructed features greatly help improve the performance of the machine learning classification algorithms. Unlike {"}black-box{"} algorithms like deep neural networks, GP models are interpretable, therefore, our analysis shows that these methods can help dermatologists identify prominent skin image features. Further, it can help researchers identify suitable feature extraction methods for images captured from a specific instrument. Being fast, these methods can be deployed for making a quick and effective diagnosis in actual clinic situations", } @InProceedings{ain:2023:GECCOcomp, author = "Qurrat Ul Ain and Harith Al-Sahaf and Bing Xue and Mengjie Zhang", title = "A New Genetic Programming Representation for Feature Learning in Skin Cancer Detection", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "707--710", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, feature learning, feature extraction, melanoma detection, image classification: Poster", isbn13 = "9798400701191", DOI = "doi:10.1145/3583133.3590550", size = "4 pages", abstract = "The process of automatically extracting informative high-level features from skin cancer images is enhanced by integrating well-developed feature descriptors into learning algorithms. This paper develops a new genetic programming-based feature learning approach to automatically select and combine six well-developed descriptors to extract high-level features for skin cancer image classification. The new approach can automatically learn various global features for image classification. The experimental results show that the new approach achieves significantly better classification performance than the baseline approach and six commonly used feature descriptors on two real-world skin image datasets.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @InProceedings{ain:2023:AusDM, author = "Qurrat Ul Ain and Bing Xue and Harith Al-Sahaf and Mengjie Zhang", title = "Skin Cancer Detection with Multimodal Data: A Feature Selection Approach Using Genetic Programming", booktitle = "Australasian Conference on Data Science and Machine Learning, AusDM 2023", year = "2023", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-981-99-8696-5_18", DOI = "doi:10.1007/978-981-99-8696-5_18", notes = "Published in 2024", } @InProceedings{ain:2024:CEC, author = "Qurrat UI Ain and Harith Al-Sahaf and Bing Xue and Mengjie Zhang", title = "Exploring Genetic Programming Models in {Computer-Aided} Diagnosis of Skin Cancer Images", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Representation learning, Visualization, Computational modeling, Feature extraction, Skin, Lesions, Image Classification, Skin Cancer Detection", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10612105", abstract = "Extracting important information from complex skin lesion images is vital to effectively distinguish between different types of skin cancer images. In addition to providing high classification performance, such computer-aided diagnostic methods are needed where the models are interpretable and can provide knowledge about the discriminative features in skin lesion images. This underlying information can significantly assist dermatologists in identifying a particular stage or type of cancer. With its flexible representation and global search abilities, Genetic Programming (GP) is an ideal learning al-gorithm to evolve interpretable models and identify important features with significant information to discriminate between skin cancer classes. This paper provides an in-depth analysis of a recent GP-based feature learning method where different well-developed feature descriptors are integrated into the learning algorithms to extract high-level features for skin cancer image classification. The study explores the effectiveness of using feature learning for this complex task and designing program structure to suit the problem domain as it has shown promising results compared to commonly used feature descriptors and an existing GP-based feature learning method developed for general image classification. This study analyses the GP-evolved models to identify the prominent features and most effective feature descriptors important for the classification of these skin cancer images. The evolved models are interpretable, they provide knowledge that can assist dermatologists in making diagnoses in real-time clinical situations by identifying prominent skin cancer characteristics captured by the feature descriptors and learnt during the evolutionary process.", notes = "also known as \cite{10612105} WCCI 2024", } @Article{Ain:2024:CIM, author = "Qurrat Ul Ain and Harith Al-Sahaf and Bing Xue and Mengjie Zhang", title = "Automatically Evolving Interpretable Feature Vectors Using Genetic Programming for an Ensemble Classifier in Skin Cancer Detection", journal = "IEEE Computational Intelligence Magazine", year = "2024", volume = "19", number = "3", pages = "26--41", month = aug, keywords = "genetic algorithms, genetic programming, Training data, Image colour analysis, Feature extraction, Vectors, Skin cancer, Lesions, Task analysis, Medical diagnosis, Classification algorithms, Detection algorithms, Image classification, Neural networks", ISSN = "1556-6048", DOI = "doi:10.1109/MCI.2024.3401342", abstract = "Early skin cancer diagnosis saves lives as the disease can be successfully treated through complete excision. Computer-aided diagnosis methods are developed using artificial intelligence techniques to help earlier detection and identify hidden causes leading to cancers in skin lesion images. In skin cancer image classification problems, an ensemble of classifiers has demonstrated better classification ability than a single classification algorithm. Traditionally, training an ensemble uses the complete set of original features, where some of these features can be redundant or irrelevant and hence, may not provide useful information in generating good models for ensemble classification. Moreover, newly created features may help improve classification performance. To address this issue, the existing methods have used feature construction for building an ensemble classifier, which usually creates a fixed number of features that may fit the training data too well, resulting in poor test performance. This study develops a novel classification approach that combines ensemble learning, feature selection, and feature construction using genetic programming (GP) to handle the above limitations. The proposed method automatically evolves variable-length feature vectors consisting of GP-selected and GP-constructed features suitable for training an ensemble classifier. This study evaluates the effectiveness of the proposed method on two benchmark real-world skin image datasets that include dermoscopy and standard camera images. The experimental results reveal that the proposed algorithm significantly outperforms four state-of-the-art convolutional neural network methods, the existing GP approaches, and 11 commonly used machine learning methods. Furthermore, this study also includes interpreting evolved individuals that highlight important skin cancer characteristics playing a vital role in discriminating images of different cancer classes. This study shows that high classification performance can be achieved at a low cost of computational resources and inference time, and accordingly, this method is potentially suitable to be implemented in mobile devices for the automated screening of skin lesions and many other malignancies in low-resource settings.", notes = "Also known as \cite{10595537}", } @Article{Ain:TETCI, author = "Qurrat Ul Ain and Harith Al-Sahaf and Bing Xue and Mengjie Zhang", title = "Genetic Programming for Malignancy Diagnosis From Breast Cancer Histopathological Images: A Feature Learning Approach", journal = "IEEE Transactions on Emerging Topics in Computational Intelligence", keywords = "genetic algorithms, genetic programming, Feature extraction, Breast cancer, Cancer, Representation learning, Breast, Histograms, Accuracy, Image colour analysis, Vectors, Training, image classification, feature learning, histopathological images", ISSN = "2471-285X", DOI = "doi:10.1109/TETCI.2024.3523769", abstract = "Identifying breast cancer using histopathological images is crucial for early detection and treatment of breast cancer. Histopathological images suffer from a high inter-class and intra-class variability, making breast cancer identification a challenging task. Integration of well-developed feature descriptors into learning algorithms can enhance the automatic extraction of high-level features from these images. With its flexible representation and global search abilities, Genetic Programming (GP) is a good learning algorithm to potentially accomplish this goal. This paper proposes a new GP-based feature learning method for automatically selecting and combining various image descriptors to detect breast cancer from histopathological images, which is an emerging topic in computational intelligence. In the new approach, various global features can be learnt for the task of breast cancer image classification and it is capable of automatically evolving solutions. A significant improvement in the classification performance comes from the new approach compared to the existing methods on real-world histopathological image dataset. Taking a closer look at the evolved solutions helps identify the most effective feature descriptors for breast cancer image classification. Unlike the black-box models evolved in existing methods, the proposed method evolves models/solutions that can assist dermatologists in making diagnoses by identifying breast cancer characteristics captured by the feature descriptors that are automatically selected during the evolutionary process.", notes = "Also known as \cite{10843754}", } @InProceedings{aiyarak:1997:GPtootn, author = "P. Aiyarak and A. S. Saket and M. C. Sinclair", title = "Genetic Programming Approaches for Minimum Cost Topology Optimisation of Optical Telecommunication Networks", booktitle = "Second International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, GALESIA", year = "1997", editor = "Ali Zalzala", pages = "415--420", address = "University of Strathclyde, Glasgow, UK", publisher_address = "Savoy Place, London, WC2R 0BL, UK", month = "1-4 " # sep, publisher = "IEE", email = "mcs@essex.ac.uk", keywords = "genetic algorithms, genetic programming, telecommunication networks, topology", ISBN = "0-85296-693-8", URL = "http://uk.geocities.com/markcsinclair/ps/galesia97_aiy.ps.gz", DOI = "doi:10.1049/cp:19971216", size = "6 pages", abstract = "This paper compares the relative efficiency of three approaches for the minimum-cost topology optimisation of the COST 239 European Optical Network (EON) using genetic programming. The GP was run for the central nine nodes using three approaches: relational function set, decision trees, and connected nodes. Only the best two, decision trees and connected nodes, were run for the full EON. The results are also compared with earlier genetic algorithm work on the EON.", notes = "Also known as \cite{681062} GALESIA'97", } @InProceedings{Ajcevic:2013:ISPA, author = "Milos Ajcevic and Andrea {De Lorenzo} and Agostino Accardo and Alberto Bartoli and Eric Medvet", booktitle = "8th International Symposium on Image and Signal Processing and Analysis (ISPA 2013)", title = "A novel estimation methodology for tracheal pressure in mechanical ventilation control", year = "2013", month = "4-6 " # sep, address = "Trieste, Italy", pages = "695--699", keywords = "genetic algorithms, genetic programming, biomechanics, biomedical electronics, biomedical equipment, diseases, injuries, medical control systems, patient treatment, physiological models, air flow pressure, air flow properties, barotrauma, endotracheal tubes, estimation methodology, high-frequency percussive ventilation, mechanical ventilation control, nonconventional mechanical ventilatory strategy, pathological conditions, patient airway, patient treatment, state-of-the-art baseline models, tracheal pressure, ventilator circuit, volutrauma, Electron tubes, Lungs, Physiology, Pressure measurement, Testing, Ventilation", DOI = "doi:10.1109/ISPA.2013.6703827", size = "5 pages", abstract = "High-frequency percussive ventilation (HFPV) is a non-conventional mechanical ventilatory strategy which has proved useful in the treatment of a number of pathological conditions. HFPV usually involves the usage of endotracheal tubes (EET) connecting the ventilator circuit to the airway of the patient. The pressure of the air flow insufflated by HFPV must be controlled very accurately in order to avoid barotrauma and volutrauma. Since the actual tracheal pressure cannot be measured, a model for estimating such a pressure based on the EET properties and on the air flow properties that can actually be measured in clinical practice is necessary. In this work we propose a novel methodology, based on Genetic Programming, for synthesising such a model. We experimentally evaluated our models against the state-of-the-art baseline models, crafted by human experts, and found that our models for estimating tracheal pressure are significantly more accurate.", notes = "Also known as \cite{6703827}", } @PhdThesis{Ajcevic:thesis, author = "Milos Ajcevic", title = "Personalized setup of high frequency percussive ventilator by estimation of respiratory system viscoelastic parameters", school = "Universita degli studi di Trieste", year = "2013/2014", year = "2013", address = "Italy", keywords = "genetic algorithms, genetic programming, High Frequency Percussive Ventilation, Respiratory signal processing, Parameter identification", URL = "http://hdl.handle.net/10077/10976", URL = "https://www.openstarts.units.it/bitstream/10077/10976/1/Ajcevic_PhD.pdf", size = "95 pages", abstract = "High Frequency Percussive Ventilation (HFPV) is a non-conventional ventilatory modality which has proven highly effective in patients with severe gas exchange impairment. However, at the present time, HFPV ventilator provides only airway pressure measurement. The airway pressure measurements and gas exchange analysis are currently the only parameters that guide the physician during the HFPV ventilator setup and treatment monitoring. The evaluation of respiratory system resistance and compliance parameters in patients undergoing mechanical ventilation is used for lung dysfunctions detection, ventilation setup and treatment effect evaluation. Furthermore, the pressure measured by ventilator represents the sum of the endotracheal tube pressure drop and the tracheal pressure. From the clinical point of view, it is very important to take into account the real amount of pressure dissipated by endotracheal tube to avoid lung injury. HFPV is pressure controlled logic ventilation, thus hypoventilation and hyperventilation cases are possible because of tidal volume variations in function of pulmonary and endotracheal tube impedance. This thesis offers a new approach for HFPV ventilator setup in accordance with protective ventilatory strategy and optimization of alveolar recruitment using estimation of the respiratory mechanics parameters and endotracheal pressure drop. Respiratory system resistance and compliance parameters were estimated, firstly in vitro and successively in patients undergoing HFPV, applying least squares regression on Dorkin high frequency model starting from measured respiratory signals. The Blasius model was identified as the most adequate to estimate pressure drop across the endotracheal tube during HFPV. Beside measurement device was developed in order to measure respiratory parameters in patients undergoing HFPV. The possibility to tailor HFPV ventilator setup, using respiratory signals measurement and estimation of respiratory system resistance, compliance and endotracheal tube pressure drop, provided by this thesis, opens a new prospective to this particular ventilatory strategy, improving its beneficial effects and minimizing ventilator-induced lung damage.", notes = "In vitro estimation of tracheal pressure by Genetic programming Supervisor: Agostino Accardo ING-INF/06 10077_10976", } @Article{Ajibode:2020:A, author = "Adekunle Akinjobi Ajibode and Ting Shu and Zuohua Ding", title = "Evolving Suspiciousness Metrics From Hybrid Data Set for Boosting a Spectrum Based Fault Localization", journal = "IEEE Access", year = "2020", volume = "8", pages = "198451--198467", keywords = "genetic algorithms, genetic programming, Measurement, Debugging, Boosting, Debugging, fault localization, SBFL", DOI = "doi:10.1109/ACCESS.2020.3035413", ISSN = "2169-3536", abstract = "Spectrum Based Fault Localization (SBFL) uses different metrics called risk evaluation formula to guide and pinpoint faults in debugging process. The accuracy of a specific SBFL method may be limited by the used formulae and program spectra. However, it has been demonstrated recently that Genetic Programming could be used to automatically design formulae directly from the program spectra. Therefore, this article presents Genetic Programming approach for proposing risk evaluation formula with the inclusion of radicals to evolve suspiciousness metric directly from the program spectra. 92 faults from Unix utilities of SIR repository and 357 real faults from Defect4J repository were used. The approach combines these data sets, used 2percent of the total faults (113) to evolve the formulae and the remaining 7percent (336) to validate the effectiveness of the metrics generated by our approach. The proposed approach then uses Genetic Programming to run 30 evolution to produce different 30 metrics. The GP-generated metrics consistently out-performed all the classic formulae in both single and multiple faults, especially OP2 on average of 2.2percent in single faults and 3.4percent in multiple faults. The experiment results conclude that the combination of Hybrid data set and radical is a good technique to evolve effective formulae for spectra-based fault localization.", notes = "Also known as \cite{9247185}", } @InCollection{akalin:2002:DCOFSGGP, author = "Frederick R. Akalin", title = "Developing a Computer-Controller Opponent for a First-Person Simulation Game using Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2002", year = "2002", editor = "John R. Koza", pages = "11--20", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:2002:gagp}", } @Article{akbari-alashti:2015:WRM, author = "Habib Akbari-Alashti and Omid Bozorg Haddad and Miguel A. Marino", title = "Application of Fixed Length Gene Genetic Programming {(FLGGP)} in Hydropower Reservoir Operation", journal = "Water Resources Management", year = "2015", volume = "29", number = "9", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s11269-015-1003-1", DOI = "doi:10.1007/s11269-015-1003-1", } @InProceedings{Akbarzadeh:2008:fuzz, author = "Vahab Akbarzadeh and Alireza Sadeghian and Marcus V. {dos Santos}", title = "Derivation of Relational Fuzzy Classification Rules Using Evolutionary Computation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "1689--1693", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1819-0", file = "FS0398.pdf", DOI = "doi:10.1109/FUZZY.2008.4630598", ISSN = "1098-7584", keywords = "genetic algorithms, genetic programming, constrained-syntax genetic programming, evolutionary computation, knowledge-based systems, mutation-based evolutionary algorithm, relational fuzzy classification rules, fuzzy set theory, knowledge based systems", abstract = "An evolutionary system for derivation of fuzzy classification rules is presented. This system uses two populations: one of fuzzy classification rules, and one of membership function definitions. A constrained-syntax genetic programming evolves the first population and a mutation-based evolutionary algorithm evolves the second population. These two populations co-evolve to better classify the underlying dataset. Unlike other approaches that use fuzzification of continuous attributes of the dataset for discovering fuzzy classification rules, the system presented here fuzzifies the relational operators ``greater than'' and ``less than'' using evolutionary methods. For testing our system, the system is applied to the Iris dataset. Our experimental results show that our system outperforms previous evolutionary and non-evolutionary systems on accuracy of classification and derivation of interrelation between the attributes of the Iris dataset. The resulting fuzzy rules of the system can be directly used in knowledge-based systems.", notes = "Also known as \cite{4630598} WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @InProceedings{Akbarzadeh:1997:jce, author = "M.-R. Akbarzadeh-T. and E. Tunstel and M. Jamshidi", title = "Genetic Algorithms and Genetic Programming: Combining Strength in One Evolutionary Strategy", booktitle = "Proceedings of the 1997 WERC/HSRC Joint Conference on the Environment", year = "1997", pages = "373--377", address = "Albuquerque, NM, USA", month = "26-29 " # apr, organisation = "WERC Waste-management Education & Research Consortium New Mexico State University Box 30001, Department WERC Las Cruces, NM 88003-8001, USA HSRC Great Plains/Rocky Mountain Hazardous Substance Research Center Kansas State University 101 Ward Hall Manhattan, KS 66506-2502, USA", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Akbarzadeh_1997_jce.pdf", size = "5 pages", abstract = "Genetic Algorithms (GA) and Genetic Programs (GP) are two of the most widely used evolution strategies for parameter optimisation of complex systems. GAs have shown a great deal of success where the representation space is a string of binary or real-valued numbers. At the same time, GP has demonstrated success with symbolic representation spaces and where structure among symbols is explored. This paper discusses weaknesses and strengths of GA and GP in search of a combined and more evolved optimization algorithm. This combination is especially attractive for problem domains with non-homogeneous parameters. In particular, a fuzzy logic membership function is represented by numerical strings, whereas rule-sets are represented by symbols and structural connectives. Two examples are provided which exhibit how GA and GP are best used in optimising robot performance in manipulating hazardous waste. The first example involves optimisation for a fuzzy controller for a flexible robot using GA and the second example illustrates usage of GP in optimizing an intelligent navigation algorithm for a mobile robot. A novel strategy for combining GA and GP is presented.", } @InProceedings{Akbarzadeh:1998:wcci, author = "M. R. Akbarzadeh-T. and E. Tunstel and K. Kumbla and M. Jamshidi", title = "Soft computing paradigms for hybrid fuzzy controllers: experiments and applications", booktitle = "Proceedings of the 1998 IEEE World Congress on Computational Intelligence", year = "1998", pages = "1200--1205", volume = "2", address = "Anchorage, Alaska, USA", month = "5-9 " # may, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, neurocontrollers, fuzzy control, hierarchical systems, mobile robots, path planning, brushless DC motors, machine control, manipulators, soft computing paradigms, hybrid fuzzy controllers, neural networks, genetic programs, fuzzy logic-based schemes, added intelligence, adaptation, learning ability, direct drive motor, genetic algorithm-fuzzy hierarchical controller, flexible robot link, genetic programming-fuzzy behavior-based controller, mobile robot navigation task", ISBN = "0-7803-4863-X", URL = "http://www-robotics.jpl.nasa.gov/people/Edward_Tunstel/fieee98.pdf", URL = "http://ieeexplore.ieee.org/iel4/5612/15018/00686289.pdf?isNumber=15018", DOI = "doi:10.1109/FUZZY.1998.686289", size = "6 pages", abstract = "Neural networks (NN), genetic algorithms (GA), and genetic programs (GP) are often augmented with fuzzy logic-based schemes to enhance artificial intelligence of a given system. Such hybrid combinations are expected to exhibit added intelligence, adaptation, and learning ability. In the paper, implementation of three hybrid fuzzy controllers are discussed and verified by experimental results. These hybrid controllers consist of a hierarchical NN-fuzzy controller applied to a direct drive motor, a GA-fuzzy hierarchical controller applied to a flexible robot link, and a GP-fuzzy behavior-based controller applied to a mobile robot navigation task. It is experimentally shown that all three architectures are capable of significantly improving the system response.", notes = "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE World Congress on Computational Intelligence", } @Article{Akbarzadeh-T:2000:CEE, author = "M.-R. Akbarzadeh-T. and K. Kumbla and E. Tunstel and M. Jamshidi", title = "Soft computing for autonomous robotic systems", journal = "Computers and Electrical Engineering", volume = "26", pages = "5--32", year = "2000", number = "1", keywords = "genetic algorithms, genetic programming, Soft computing, Neural networks, Fuzzy logic, Robotic control, Articial intelligence", URL = "http://www.sciencedirect.com/science/article/B6V25-3Y6GXY5-2/1/6a6f9ff946815d4e95fe3884c98e74e5", URL = "http://citeseer.ist.psu.edu/373353.html", size = "28 pages", abstract = "Neural networks (NN), genetic algorithms (GA), and genetic programming (GP) are augmented with fuzzy logic-based schemes to enhance artificial intelligence of automated systems. Such hybrid combinations exhibit added reasoning, adaptation, and learning ability. In this expository article, three dominant hybrid approaches to intelligent control are experimentally applied to address various robotic control issues which are currently under investigation at the NASA Center for Autonomous Control Engineering. The hybrid controllers consist of a hierarchical NN-fuzzy controller applied to a direct drive motor, a GA-fuzzy hierarchical controller applied to position control of a flexible robot link, and a GP-fuzzy behavior based controller applied to a mobile robot navigation task. Various strong characteristics of each of these hybrid combinations are discussed and used in these control architectures. The NN-fuzzy architecture takes advantage of NN for handling complex data patterns, the GA-fuzzy architecture uses the ability of GA to optimize parameters of membership functions for improved system response, and the GP-fuzzy architecture uses the symbolic manipulation capability of GP to evolve fuzzy rule-sets.", notes = "citeseer 373353 version not identical to published version", } @InProceedings{Akbarzadeh:2003:ICNAFIPS, author = "M.-R. Akbarzadeh-T. and I. Mosavat and S. Abbasi", title = "Friendship Modeling for Cooperative Co-Evolutionary Fuzzy Systems: A Hybrid GA-GP Algorithm", booktitle = "Proceedings of the 22nd International Conference of North American Fuzzy Information Processing Society, NAFIPS 2003", year = "2003", pages = "61--66", month = "24-26 " # jul, keywords = "genetic algorithms, genetic programming, Artificial neural networks, Chaos, Computational modelling, Convergence, Evolutionary computation, Fuzzy logic, Fuzzy systems, Genetic programming, Humans, Stochastic processes, cooperative systems, fuzzy systems, groupware, modelling, table lookup, time series, chaotic time series prediction, cooperative co-evolutionary fuzzy systems, friendship modeling, function evaluations, fuzzy lookup tables, hybrid GA-GP algorithm, membership functions, rules sets", DOI = "doi:10.1109/NAFIPS.2003.1226756", size = "6 pages", abstract = "A novel approach is proposed to combine the strengths of GA and GP to optimise rule sets and membership functions of fuzzy systems in a co-evolutionary strategy in order to avoid the problem of dual representation in fuzzy systems. The novelty of proposed algorithm is twofold. One is that GP is used for the structural part (Rule sets) and GA for the string part (Membership functions). The goal is to reduce/eliminate the problem of competing conventions by co-evolving pieces of the problem separately and then in combination. Second is exploiting the synergism between rules sets and membership functions by imitating the effect of 'matching' and friendship in cooperating teams of humans, thereby significantly reducing the number of function evaluations necessary for evolution. The method is applied to a chaotic time series prediction problem and compared with the standard fuzzy table look-up scheme. demonstrate several significant improvements with the proposed approach; specifically, four times higher fitness and more steady fitness improvements as compared with epochal improvements observed in GP.", } @Misc{Akiba:2024:GGP, author = "Takuya Akiba", title = "Evolutionary Optimization of Model Merging Recipes", booktitle = "2nd GECCO workshop on Graph-based Genetic Programming", year = "2024", editor = "Dennis G. Wilson and Roman Kalkreuth and Eric Medvet and Giorgia Nadizar and Giovanni Squillero and Alberto Tonda and Yuri Lavinas", address = "Melbourne", series = "GECCO '24", month = "14 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", note = "Invited talk", keywords = "genetic algorithms, genetic programming, ANN, LLM", URL = "https://arxiv.org/abs/2403.13187", URL = "https://sakana.ai/evolutionary-model-merge/", URL = "https://graphgp.com/program/#evolutionary-optimization-of-model-merging-recipes", code_url = "https://github.com/SakanaAI/evolutionary-model-merge", notes = "Abstract only. No paper in GECCO 2024 proceedings. GECCO-2024 A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{Akilandeswari:2024:APCI, author = "A. Akilandeswari and Arasuraja G and Nagendar Yamsani and S. Radhika and N. Legapriyadharshini and S. Padmakala", title = "Enhancing Fetal Health Monitoring through {TPOT} and Optuna in Machine Learning-Driven Prenatal Care", booktitle = "2024 International Conference on Advancements in Power, Communication and Intelligent Systems (APCI)", year = "2024", month = jun, keywords = "genetic algorithms, genetic programming, Analytical models, Accuracy, Refining, Machine learning, Predictive models, Boosting, Medical diagnosis, Fetal Health Monitoring, Machine Learning in Prenatal Care, Cardiotocogram Analysis, Hyper parameter Optimisation, Automated Machine Learning (AutoML), Predictive Modelling in Healthcare", DOI = "doi:10.1109/APCI61480.2024.10617339", abstract = "This study delves into the application of advanced machine learning techniques for the classification of fetal health, a critical domain in prenatal care. Using a dataset based on cardiotocograms (CTGs), which record key fetal indicators like heart rate and uterine contractions, we compare two distinct machine learning approaches: a Random Forest Classifier optimised with the hyper parameter tuning tool Optuna, and a genetic programming-based model developed using TPOT (Tree-based Pipeline Optimisation Tool).The Random Forest Classifier, configured with specific hyper parameters, delivered an accuracy of 94.13percent and an impressive AUC of 0.9826. In contrast, the TPOT-optimised model, a Gradient Boosting Classifier with finely tuned parameters, achieved a higher accuracy of 96.01percent and an internal CV score of approximately 95.24percent. This comparison underscores the strengths and potential applications of these advanced methodologies in predicting and ensuring fetal health.", notes = "Also known as \cite{10617339}", } @InProceedings{Akilandeswari:2025:ICEARS, author = "A. Akilandeswari", title = "Evaluation of Feature Selection Techniques for Predicting {Parkinson's} Disease using Machine Learning Models", booktitle = "2025 International Conference on Electronics and Renewable Systems (ICEARS)", year = "2025", pages = "1431--1435", month = feb, keywords = "genetic algorithms, genetic programming, Logistic regression, Renewable energy sources, Accuracy, Parkinson's disease, Pipelines, Machine learning, Predictive models, Feature extraction, Random forests, Optimisation, Parkinson's Disease Prediction, Random Forest, Boruta, Recursive Feature Elimination (RFE), TPOT", DOI = "doi:10.1109/ICEARS64219.2025.10940164", abstract = "Parkinson's disease is one of the major progressive neurodegenerative disorders that majorly affect both motor and non-motor functions. The significance of this study lies in spotlighting the importance of early, accurate detection of Parkinson's disease in the betterment of patient outcomes as well as management. This paper sets out to make a case discussion about the use of machine learning models in predictive Parkinson's disease through feature selection. We apply the following methods to machine learning models, including Random Forest and Logistic Regression, in order to compare approaches, namely Boruta, Recursive Feature Elimination with Cross-Validation (RFECV), TPOT-Tree-based Pipeline Optimisation Tool, and Variance Threshold. The Random Forest model with Variance Threshold feature selection showed a high accuracy of 95percent. Our experiments also present the fact that feature selection is relevant to improve the performance of the model: indeed, different techniques show different levels of prediction accuracy. ConclusionThis paper has proven it possible to improve the prediction of disease to a considerable extent by the right selection of the feature selection technique and machine learning model.", notes = "Also known as \cite{10940164}", } @InProceedings{Akira:1999:AJ, author = "Yoshida Akira", title = "Multiple-Organisms Learning and Evolution by Genetic Programming", booktitle = "Proceedings of The Third Australia-Japan Joint Workshop on Intelligent and Evolutionary Systems", year = "1999", editor = "Bob McKay and Yasuhiro Tsujimura and Ruhul Sarker and Akira Namatame and Xin Yao and Mitsuo Gen", address = "School of Computer Science Australian Defence Force Academy, Canberra, Australia", month = "22-25 " # nov, email = "akira-yo@is.aist-nara.ac.jp", keywords = "genetic algorithms, genetic programming", notes = "Broken Nov 2011 http://www.cs.adfa.edu.au/archive/conference/aj99/programme.html Nara Advanced Institute of Science and Technology http://www.f.ait.kyushu-u.ac.jp/achievements/pub1999.html", } @InProceedings{akira:2000:moelGP, author = "Yoshida Akira", title = "Intraspecific Evolution of Learning by Genetic Programming", booktitle = "Genetic Programming, Proceedings of EuroGP'2000", year = "2000", editor = "Riccardo Poli and Wolfgang Banzhaf and William B. Langdon and Julian F. Miller and Peter Nordin and Terence C. Fogarty", volume = "1802", series = "LNCS", pages = "209--224", address = "Edinburgh", publisher_address = "Berlin", month = "15-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-67339-3", DOI = "doi:10.1007/978-3-540-46239-2_15", abstract = "Spatial dynamic pattern formations or trails can be observed in a simple square world where individuals move to look for scattered foods. They seem to show the emergence of co-operation, job separation, or division of territories when genetic programming controls the reproduction, mutation, crossing over of the organisms. We try to explain the co-operative behaviours among multiple organisms by means of density of organisms and their environment. Next, we add some interactions between organisms, and between organism and their environment to see that the more interaction make the convergence of intraspecific learning faster. At last, we study that MDL-based fitness evaluation is effective for improvement of generalisation of genetic programming.", notes = "EuroGP'2000, part of \cite{poli:2000:GP}", } @Article{journals/ijossp/AkourM17, title = "Software Defect Prediction Using Genetic Programming and Neural Networks", author = "Mohammed Akour and Wasen Yahya Melhem", journal = "International Journal of Open Source Software and Processes", year = "2017", number = "4", volume = "8", pages = "32--51", keywords = "genetic algorithms, genetic programming, ANN, SBSE", ISSN = "1942-3926", DOI = "doi:10.4018/IJOSSP.2017100102", abstract = "This article describes how classification methods on software defect prediction is widely researched due to the need to increase the software quality and decrease testing efforts. However, findings of past researches done on this issue has not shown any classifier which proves to be superior to the other. Additionally, there is a lack of research that studies the effects and accuracy of genetic programming on software defect prediction. To find solutions for this problem, a comparative software defect prediction experiment between genetic programming and neural networks are performed on four datasets from the NASA Metrics Data repository. Generally, an interesting degree of accuracy is detected, which shows how the metric-based classification is useful. Nevertheless, this article specifies that the application and usage of genetic programming is highly recommended due to the detailed analysis it provides, as well as an important feature in this classification method which allows the viewing of each attributes impact in the dataset.", notes = "Mohammed Akour (Department of Computer Information Systems, Yarmouk University, Irbid, Jordan) and Wasen Yahya Melhem (Yarmouk university, Irbid, Jordan)", } @Article{Al-Saati:2014:mosul, author = "Najla Akram Al-Saati and Taghreed Riyadh Alreffaee", title = "Software Effort Estimation Using Multi Expression Programming", journal = "AL-Rafidain Journal of Computer Sciences and Mathematics", year = "2014", volume = "11", number = "2", pages = "53--71", keywords = "genetic algorithms, genetic programming, Effort Estimation, Multi Expression Programming", publisher = "Mosul University", ISSN = "1815-4816", eissn = "2311-7990", URL = "https://csmj.mosuljournals.com/article_163756.html", URL = "https://csmj.mosuljournals.com/article__2d593a444328ad02601f0d083038e400163756.pdf", DOI = "doi:10.33899/csmj.2014.163756", size = "19 pages", abstract = "The process of finding a function that can estimate the effort of software systems is considered to be the most important and most complex process facing systems developers in the field of software engineering. The accuracy of estimating software effort forms an essential part of the software development phases. A lot of experts applied different ways to find solutions to this issue, such as the COCOMO and other methods. Recently, many questions have been put forward about the possibility of using Artificial Intelligence to solve such problems, different scientists made ​​several studies about the use of techniques such as Genetic Algorithms and Artificial Neural Networks to solve estimation problems. We use one of the Linear Genetic Programming methods (Multi Expression programming) which apply the principle of competition between equations encrypted within the chromosomes to find the best formula for resolving the issue of software effort estimation. As for to the test data, benchmark known datasets are employed taken from previous projects, the results are evaluated by comparing them with the results of Genetic Programming (GP) using different fitness functions. The gained results indicate the surpassing of the employed method in finding more efficient functions for estimating about 7 datasets each consisting of many projects.", notes = "In Arabic", } @Article{Akram:2017:ijrr, author = "Najla Akram AL-Saati and Taghreed Riyadh Alreffaee", title = "Using Multi Expression Programming in Software Effort Estimation", journal = "International Journal of Recent Research and Review", year = "2017", volume = "X", number = "2", pages = "1--10", month = jun, keywords = "genetic algorithms, genetic programming, Multi Expression Programming, SBSE, Software Effort, Estimation, Software Engineering", ISSN = "2277-8322", URL = "http://www.ijrrr.com/papers10-2/paper1-Using%20Multi%20Expression%20Programming%20in%20Software%20Effort%20Estimation.pdf", URL = "http://www.ijrrr.com/issues10-2.htm", size = "10 pages", abstract = "Estimating the effort of software systems is an essential topic in software engineering, carrying out an estimation process reliably and accurately for a software forms a vital part of the software development phases. Many researchers have used different methods and techniques hopping to find solutions to this issue, such techniques include COCOMO, SEER-SEM,SLIM and others. Recently, Artificial Intelligent techniques are being used to solve such problems; different studies have been issued focusing on techniques such as Neural Networks NN, Genetic Algorithms GA, and Genetic Programming GP. This work uses one of the linear variations of GP, namely: Multi Expression Programming (MEP) aiming to find the equation that best estimates the effort of software. Benchmark datasets (based on previous projects) are used learning and testing. Results are compared with those obtained by GP using different fitness functions. Results show that MEP is far better in discovering effective functions for the estimation of about 6 datasets each comprising several projects.", notes = "See also \cite{Akram:2018:arxiv}", } @Misc{Akram:2018:arxiv, author = "Najla Akram Al-Saati and Taghreed Riyadh Alreffaee", title = "Using Multi Expression Programming in Software Effort Estimation", howpublished = "arXiv", year = "2018", month = "30 " # apr, keywords = "genetic algorithms, genetic programming, SBSE, ANN, software effort, estimation, multi expression programming", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/corr/corr1805.html#abs-1805-00090", URL = "http://arxiv.org/abs/1805.00090", size = "10 pages", abstract = "Estimating the effort of software systems is an essential topic in software engineering, carrying out an estimation process reliably and accurately for a software forms a vital part of the software development phases. Many researchers have used different methods and techniques hopping to find solutions to this issue, such techniques include COCOMO, SEER-SEM,SLIM and others. Recently, Artificial Intelligent techniques are being used to solve such problems; different studies have been issued focusing on techniques such as Neural Networks NN, Genetic Algorithms GA, and Genetic Programming GP. This work uses one of the linear variations of GP, namely: Multi Expression Programming (MEP) aiming to find the equation that best estimates the effort of software. Benchmark datasets (based on previous projects) are used learning and testing. Results are compared with those obtained by GP using different fitness functions. Results show that MEP is far better in discovering effective functions for the estimation of about 6 datasets each comprising several projects.", notes = "Published as International Journal of Recent Research and Review, Vol. X, Issue 2, June 2017 ISSN 2277-8322 \cite{Akram:2017:ijrr}. journals/corr/abs-1805-00090", } @Article{Aksu:2012:AAPS, author = "Buket Aksu and Anant Paradkar and Marcel Matas and Ozgen Ozer and Tamer Guneri and Peter York", title = "Quality by Design Approach: Application of Artificial Intelligence Techniques of Tablets Manufactured by Direct Compression", journal = "AAPS PharmSciTech", year = "2012", volume = "13", number = "4", pages = "1138--1146", month = sep # "~06", keywords = "genetic algorithms, genetic programming, gene expression programming, artificial neural networks, ANNs, GEP, optimisation, quality by design (qbd)", DOI = "doi:10.1208/s12249-012-9836-x", URL = "http://dx.doi.org/10.1208/s12249-012-9836-x", URL = "http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3513460", URL = "http://www.ncbi.nlm.nih.gov/pubmed/22956056", language = "English", bibsource = "OAI-PMH server at www.ncbi.nlm.nih.gov", oai = "oai:pubmedcentral.nih.gov:3513460", publisher = "American Association of Pharmaceutical Scientists", abstract = "The publication of the International Conference of Harmonization (ICH) Q8, Q9, and Q10 guidelines paved the way for the standardization of quality after the Food and Drug Administration issued current Good Manufacturing Practices guidelines in 2003. Quality by Design, mentioned in the ICH Q8 guideline, offers a better scientific understanding of critical process and product qualities using knowledge obtained during the life cycle of a product. In this scope, the knowledge space is a summary of all process knowledge obtained during product development, and the design space is the area in which a product can be manufactured within acceptable limits. To create the spaces, artificial neural networks (ANNs) can be used to emphasise the multidimensional interactions of input variables and to closely bind these variables to a design space. This helps guide the experimental design process to include interactions among the input variables, along with modelling and optimisation of pharmaceutical formulations. The objective of this study was to develop an integrated multivariate approach to obtain a quality product based on an understanding of the cause--effect relationships between formulation ingredients and product properties with ANNs and genetic programming on the ramipril tablets prepared by the direct compression method. In this study, the data are generated through the systematic application of the design of experiments (DoE) principles and optimisation studies using artificial neural networks and neurofuzzy logic programs.", } @InProceedings{conf/ecsqaru/AkyolYE07, title = "A Genetic Programming Classifier Design Approach for Cell Images", author = "Aydin Akyol and Yusuf Yaslan and Osman Kaan Erol", booktitle = "Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU", bibdate = "2007-09-17", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ecsqaru/ecsqaru2007.html#AkyolYE07", publisher = "Springer", year = "2007", volume = "4724", editor = "Khaled Mellouli", isbn13 = "978-3-540-75255-4", pages = "878--888", series = "Lecture Notes in Computer Science", address = "Hammamet, Tunisia", month = oct # " 31 - " # nov # " 2", keywords = "genetic algorithms, genetic programming, cell classification, classifier design, pollen classification", DOI = "doi:10.1007/978-3-540-75256-1_76", abstract = "This paper describes an approach for the use of genetic programming (GP) in classification problems and it is evaluated on the automatic classification problem of pollen cell images. In this work, a new reproduction scheme and a new fitness evaluation scheme are proposed as advanced techniques for GP classification applications. Also an effective set of pollen cell image features is defined for cell images. Experiments were performed on Bangor/Aberystwyth Pollen Image Database and the algorithm is evaluated on challenging test configurations. We reached at 96percent success rate on the average together with significant improvement in the speed of convergence.", notes = "cf email GP mailing list Mon, 24 Dec 2007 13:42:59 +0200 You may find MATLAB codes from my Web Page below: www3.itu.edu.tr/~okerol you may refer to: O.K. Erol, I.Eksin, 2006. A New Optimization Method: Big-Bang Big-Crunch, Advences in Engineering Software, Elsevier, Vol 37, No.2, 106-111 doi:10.1016/j.advengsoft.2005.04.005", } @Article{al-afandi:2021:Algorithms, author = "Jalal Al-Afandi and Andras Horvath", title = "Adaptive Gene Level Mutation", journal = "Algorithms", year = "2021", volume = "14", number = "1", keywords = "genetic algorithms, genetic programming, evolution strategies, adaptive mutation, evolutionary programming", ISSN = "1999-4893", URL = "https://www.mdpi.com/1999-4893/14/1/16", DOI = "doi:10.3390/a14010016", size = "18 pages", abstract = "Genetic Algorithms are stochastic optimisation methods where solution candidates, complying to a specific problem representation, are evaluated according to a predefined fitness function. These approaches can provide solutions in various tasks even, where analytic solutions can not be or are too complex to be computed. In this paper we will show, how certain set of problems are partially solvable allowing us to grade segments of a solution individually, which results local and individual tuning of mutation parameters for genes. We will demonstrate the efficiency of our method on the N-Queens and travelling salesman problems where we can demonstrate that our approach always results faster convergence and in most cases a lower error than the traditional approach.", notes = "also known as \cite{a14010016} Faculty of Information Technology and Bionics, Peter Pazmany Catholic University, 1083 Budapest, Hungary", } @MastersThesis{Al-Afeef:mastersthesis, author = "Ala' S. Al-Afeef", title = "Image Reconstructing in Electrical Capacitance Tomography of Manufacturing Processes Using Genetic Programming", school = "Al-Balqa Applied University", year = "2010", address = "Al-Salt, Jordan", month = jul, email = "alaa.afeef@gmail.com", keywords = "genetic algorithms, genetic programming, Image Reconstructing, Electrical Capacitance Tomography", URL = "https://sites.google.com/site/alaaalfeef/home/Alaa_afeef_Thesis_Final.pdf", size = "137", abstract = "Electrical capacitance tomography is considered the most attractive technique for industrial process imaging because of its low construction cost, safety, fast data acquisition , non-invasiveness, non-intrusiveness, simple structure, wide application field and suitability for most kinds of flask and vessels, however, the low accuracy of the reconstructed images is the main limitation of implementing an ECT system. In order to improve the imaging accuracy, one may 1) increase the number of measurements by raising number of electrodes, 2) improve the reconstruction algorithm so that more information can be extracted from the captured data, however, increasing the number of electrodes has a limited impact on the imaging accuracy improvement. This means that, in order to improve the reconstructed image, more accurate reconstruction algorithms must be developed. In fact, ECT image reconstruction is still an inefficiently resolved problem because of many limitations, mainly the Soft-field and Ill-condition characteristic of ECT. Although there are many algorithms to solve the image reconstruction problem, these algorithms are not yet able to present a single model that can relate between image pixels and capacitance measurements in a mathematical relationship. The originality of this thesis lies in introducing a new technique for solving the non-linear inverse problem in ECT based on Genetic Programming (GP) to handle the ECT imaging for conductive materials. GP is a technique that has not been applied to ECT. GP found to be efficient in dealing with the Non-linear relation between the measured capacitance and permittivity distribution in ECT. This thesis provides new implemented software that can handle the ECT based GP problem with a user-friendly interface. The developed simulation results are promising.", } @InProceedings{Al-Afeef:2010:ISDA, author = "Alaa Al-Afeef and Alaa F. Sheta and Adnan Al-Rabea", title = "Image reconstruction of a metal fill industrial process using Genetic Programming", booktitle = "10th International Conference on Intelligent Systems Design and Applications (ISDA), 2010", year = "2010", pages = "12--17", address = "Cairo", month = "29 " # nov # "-1 " # dec, email = "alaa.afeef@gmail.com", keywords = "genetic algorithms, genetic programming, electrical capacitance tomography, ill-condition characteristic, image reconstruction, industrial process imaging, metal fill industrial process, soft-field characteristic, image reconstruction, industrial engineering, tomography, Process Tomography", isbn13 = "978-1-4244-8134-7", URL = "http://sites.google.com/site/alaaalfeef/home/8.pdf", DOI = "doi:10.1109/ISDA.2010.5687299", size = "6 pages", abstract = "Electrical Capacitance Tomography (ECT) is one of the most attractive technique for industrial process imaging because of its low construction cost, safety, non-invasiveness, non-intrusiveness, fast data acquisition, simple structure, wide application field and suitability for most kinds of flask and vessels. However, image reconstruction based ECT suffers many limitations. They include the Soft-field and Ill-condition characteristic of ECT. The basic idea of the ECT for image reconstruction for a metal fill problem is to model the image pixels as a function of the capacitance measurements. Developing this relationship represents a challenge for systems engineering community. In this paper, we presents our innovative idea on solving the non-linear inverse problem for conductive materials of the ECT using Genetic Programming (GP). GP found to be a very efficient algorithm in producing a mathematical model of image pixels in the form of Lisp expression. The reported results are promising.", notes = "Also known as \cite{5687299}", } @Book{AfeefBook2011, author = "Alaa Al-Afeef and Alaa Sheta and Adnan Rabea", title = "Image Reconstruction of a Manufacturing Process: A Genetic Programming Approach", publisher = "Lambert Academic Publishing", year = "2011", edition = "1", month = apr, keywords = "genetic algorithms, genetic programming", isbn13 = "978-3844325690", URL = "https://www.morebooks.de/store/gb/book/image-reconstruction-of-a-manufacturing-process/isbn/978-3-8443-2569-0", URL = "http://www.amazon.co.uk/Image-Reconstruction-Manufacturing-Process-Programming/dp/3844325697", abstract = "Product Description Evolutionary Computation (EC) is one of the most attractive techniques in the area of Computer Science. EC includes Genetic Algorithms (GAs), Genetic Programming (GP), Evolutionary Strategy (ES) and Evolutionary Programming (EP). GP have been widely used to solve a variety of problems in image enhancement, analysis and segmentation. This book explores the use of GP as a powerful approach to solve the image reconstruction problem for Lost Foam Casting (LFC) manufacturing process. The data set was collected using the Electrical Capacitance Tomography (ECT) technique. ECT is one of the most attractive technique for industrial process imaging because of its low construction cost, safety, non-invasiveness, non-intrusiveness, fast data acquisition, simple structure, wide application field and suitability for most kinds of flask and vessels. GP found to be a very efficient algorithm in producing a mathematical model of image pixels in a form of Lisp expression. A Graphical User Interface (GUI) Toolbox based Matlab was developed to help analysing and visualising the reconstructed images based GP problem. The reported results are promising.", size = "100 pages", } @Article{Al-Bastaki:2010:JAI, title = "{GADS} and Reusability", author = "Y. Al-Bastaki and W. Awad", year = "2010", journal = "Journal of Artificial Intelligence", volume = "3", number = "2", pages = "67--77", keywords = "genetic algorithms, genetic programming, GADS, reusability", URL = "http://docsdrive.com/pdfs/ansinet/jai/2010/67-72.pdf", broken = "http://www.doaj.org/doaj?func=openurl\&genre=article\&issn=19945450\&date=2010\&volume=3\&issue=2\&spage=67", ISSN = "19945450", bibsource = "OAI-PMH server at www.doaj.org", language = "eng", oai = "oai:doaj-articles:8a4dfe5674530875df3b83ea84856118", publisher = "Asian Network for Scientific Information", size = "6 pages", abstract = "Genetic programming is a domain-independent method that genetically breeds population of computer programs to solve problems. Genetic programming is considered to be a machine learning technique used to optimise a population of computer programs according to a fitness landscape determined by a program's ability to perform a given computational task. There are a number of representation methods to illustrate these programs, such as LISP expressions and integer lists. This study investigated the effectiveness of genetic programming in solving the symbolic regression problem where, the population programs are expressed as integer sequences rather than lisp expressions. This study also introduced the concept of reusable program to genetic algorithm for developing software.", notes = "BNF grammar, ADF, linear GP", } @InProceedings{Al-Hajj:2016:ICRERA, author = "Rami Al-Hajj and Ali Assi and Farhan Batch", booktitle = "2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA)", title = "An evolutionary computing approach for estimating global solar radiation", year = "2016", pages = "285--290", month = "20-23 " # nov, address = "Birmingham, UK", keywords = "genetic algorithms, genetic programming, Decision support systems, Evolutionary computation, Hand-held computers, climatological data, evolutionary computation, global solar radiation", DOI = "doi:10.1109/ICRERA.2016.7884553", abstract = "This paper presents a non-linear regression model based on an evolutionary computing technique namely the genetic programming for estimating solar radiation. This approach aims to estimate the best formula that represents the function for estimating the global solar radiation on horizontals with respect to the measured climatological data. First, we present a reference approach to find one global formula that models the relation among the solar radiation amount and a set of weather factors. In the second step, we present an enhanced approach that consists of multi formulas of regression in a parallel structure. The performance of the proposed approaches has been evaluated using statistical analysis measures. The obtained results were promising and comparable to those obtained by other empirical and neural models conducted by other research groups.", notes = "Also known as \cite{7884553}", } @Article{al-hajj:2021:Processes, author = "Rami Al-Hajj and Ali Assi and Mohamad Fouad and Emad Mabrouk", title = "A Hybrid {LSTM-Based} Genetic Programming Approach for {Short-Term} Prediction of Global Solar Radiation Using Weather Data", journal = "Processes", year = "2021", volume = "9", number = "7", keywords = "genetic algorithms, genetic programming", ISSN = "2227-9717", URL = "https://www.mdpi.com/2227-9717/9/7/1187", DOI = "doi:10.3390/pr9071187", abstract = "The integration of solar energy in smart grids and other utilities is continuously increasing due to its economic and environmental benefits. However, the uncertainty of available solar energy creates challenges regarding the stability of the generated power the supply-demand balance's consistency. An accurate global solar radiation (GSR) prediction model can ensure overall system reliability and power generation scheduling. This article describes a nonlinear hybrid model based on Long Short-Term Memory (LSTM) models and the Genetic Programming technique for short-term prediction of global solar radiation. The LSTMs are Recurrent Neural Network (RNN) models that are successfully used to predict time-series data. We use these models as base predictors of GSR using weather and solar radiation (SR) data. Genetic programming (GP) is an evolutionary heuristic computing technique that enables automatic search for complex solution formulas. We use the GP in a post-processing stage to combine the LSTM models' outputs to find the best prediction of the GSR. We have examined two versions of the GP in the proposed model: a standard version and a boosted version that incorporates a local search technique. We have shown an improvement in terms of performance provided by the proposed hybrid model. We have compared its performance to stacking techniques based on machine learning for combination. The results show that the suggested method provides significant improvement in terms of performance and consistency.", notes = "also known as \cite{pr9071187}", } @InProceedings{DBLP:conf/acpr/Al-HelaliCXZ19, author = "Baligh Al-Helali and Qi Chen and Bing Xue and Mengjie Zhang", editor = "Shivakumara Palaiahnakote and Gabriella Sanniti di Baja and Liang Wang and Wei Qi Yan", title = "Genetic Programming-Based Simultaneous Feature Selection and Imputation for Symbolic Regression with Incomplete Data", booktitle = "Pattern Recognition - 5th Asian Conference, {ACPR} 2019, Auckland, New Zealand, November 26-29, 2019, Revised Selected Papers, Part {II}", series = "Lecture Notes in Computer Science", volume = "12047", pages = "566--579", publisher = "Springer", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/978-3-030-41299-9_44", DOI = "doi:10.1007/978-3-030-41299-9_44", timestamp = "Mon, 24 Feb 2020 18:06:33 +0100", biburl = "https://dblp.org/rec/conf/acpr/Al-HelaliCXZ19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{DBLP:conf/ausai/Al-Helali00Z19, author = "Baligh Al-Helali and Qi Chen and Bing Xue and Mengjie Zhang", editor = "Jixue Liu and James Bailey", title = "Genetic Programming for Imputation Predictor Selection and Ranking in Symbolic Regression with High-Dimensional Incomplete Data", booktitle = "{AI} 2019: Advances in Artificial Intelligence - 32nd Australasian Joint Conference, Adelaide, SA, Australia, December 2-5, 2019, Proceedings", series = "Lecture Notes in Computer Science", volume = "11919", pages = "523--535", publisher = "Springer", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/978-3-030-35288-2_42", DOI = "doi:10.1007/978-3-030-35288-2_42", timestamp = "Mon, 25 Nov 2019 16:31:45 +0100", biburl = "https://dblp.org/rec/conf/ausai/Al-Helali00Z19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{Al-Helali:2019:SSCI, author = "Baligh Al-Helali and Qi Chen and Bing Xue and Mengjie Zhang", booktitle = "2019 IEEE Symposium Series on Computational Intelligence (SSCI)", title = "A Genetic Programming-based Wrapper Imputation Method for Symbolic Regression with Incomplete Data", year = "2019", pages = "2395--2402", month = dec, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SSCI44817.2019.9002861", abstract = "Dealing with missing values is one of the challenges in symbolic regression on many real-world data sets. One of the popular approaches to address this challenge is to use imputation. Traditional imputation methods are usually performed based on the predictive features without considering the original target variable. In this work, a genetic programming-based wrapper imputation method is proposed, which wrappers a regression method to consider the target variable when constructing imputation models for the incomplete features. In addition to the imputation performance, the regression performance is considered for evaluating the imputation models. Genetic programming (GP) is used for building the imputation models and decision tree (DT) is used for evaluating the regression performance during the GP evolutionary process. The experimental results show that the proposed method has a significant advance in enhancing the symbolic regression performance compared with some state-of- the-art imputation methods.", notes = "Also known as \cite{9002861}", } @InProceedings{Al-Helali:2020:SSCI, author = "Baligh Al-Helali and Qi Chen and Bing Xue and Mengjie Zhang", title = "Data Imputation for Symbolic Regression with Missing Values: A Comparative Study", booktitle = "2020 IEEE Symposium Series on Computational Intelligence (SSCI)", year = "2020", pages = "2093--2100", abstract = "Symbolic regression via genetic programming is considered as a crucial machine learning tool for empirical modelling. However, in reality, it is common for real-world data sets to have some data quality problems such as noise, outliers, and missing values. Although several approaches can be adopted to deal with data incompleteness in machine learning, most studies consider the classification tasks, and only a few have considered symbolic regression with missing values. In this work, the performance of symbolic regression using genetic programming on real-world data sets that have missing values is investigated. This is done by studying how different imputation methods affect symbolic regression performance. The experiments are conducted using thirteen real-world incomplete data sets with different ratios of missing values. The experimental results show that although the performance of the imputation methods differs with the data set, CART has a better effect than others. This might be due to its ability to deal with categorical and numerical variables. Moreover, the superiority of the use of imputation methods over the commonly used deletion strategy is observed.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SSCI47803.2020.9308216", month = dec, notes = "Also known as \cite{9308216}", } @InProceedings{Al-Helali:2020:EuroGP, author = "Baligh Al-Helali and Qi Chen and Bing Xue and Mengjie Zhang", title = "Hessian Complexity Measure for Genetic Programming-based Imputation Predictor Selection in Symbolic Regression with Incomplete Data", booktitle = "EuroGP 2020: Proceedings of the 23rd European Conference on Genetic Programming", year = "2020", month = "15-17 " # apr, editor = "Ting Hu and Nuno Lourenco and Eric Medvet", series = "LNCS", volume = "12101", publisher = "Springer Verlag", address = "Seville, Spain", pages = "1--17", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Symbolic regression, Incomplete data, Feature selection, Imputation, Model complexity", isbn13 = "978-3-030-44093-0", video_url = "https://www.youtube.com/watch?v=zeZvFJElkBM", DOI = "doi:10.1007/978-3-030-44094-7_1", abstract = "Missing values bring several challenges when learning from real-world data sets. Imputation is a widely adopted approach to estimating missing values. However, it has not been adequately investigated in symbolic regression. When imputing the missing values in an incomplete feature, the other features that are used in the prediction process are called imputation predictors. In this work, a method for imputation predictor selection using regularized genetic programming (GP) models is presented for symbolic regression tasks on incomplete data. A complexity measure based on the Hessian matrix of the phenotype of the evolving models is proposed. It is employed as a regularizer in the fitness function of GP for model selection and the imputation predictors are selected from the selected models. In addition to the baseline which uses all the available predictors, the proposed selection method is compared with two GP-based feature selection variations: the standard GP feature selector and GP with feature selection pressure. The trends in the results reveal that in most cases, using the predictors selected by regularized GP models could achieve a considerable reduction in the imputation error and improve the symbolic regression performance as well.", notes = "fitness = error + lamda * complexity. (Lamda fixed linear weighting) Model complexity based on second order partial derivaties (not tree size). Form n by n Hessian matrix, where n is number of inputs (terminal set size). Each element is 2nd order partial dertivative of GP tree with respect to input i and input j. Hessian is symetric matrix. Matrix C is Hessian with all constant terms set to zero. Complexity = determinant of C divided by the determinant of H. (calculated with python package SymPy). Division replaced by analytic quotiant \cite{Ni:2012:ieeeTEC}. Data missing at random. Five OpenML benchmarks. Compare python DEAP with Linear Regression, PMM, KNN (R package Simpulation). http://www.evostar.org/2020/cfp_eurogp.php Part of \cite{Hu:2020:GP} EuroGP'2020 held in conjunction with EvoCOP2020, EvoMusArt2020 and EvoApplications2020", } @InProceedings{Al-Helali:2020:CEC, author = "Baligh Al-Helali and Qi Chen and Bing Xue and Mengjie Zhang", title = "Genetic Programming with Noise Sensitivity for Imputation Predictor Selection in Symbolic Regression with Incomplete Data", booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC 2020", year = "2020", editor = "Yaochu Jin", pages = "paper id24344", address = "internet", month = "19-24 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-7281-6929-3", DOI = "doi:10.1109/CEC48606.2020.9185526", abstract = "This paper presents a feature selection method that incorporates a sensitivity-based single feature importance measure in a context-based feature selection approach. The single-wise importance is based on the sensitivity of the learning performance with respect to adding noise to the predictive features. Genetic programming is used as a context-based selection mechanism, where the selection of features is determined by the change in the performance of the evolved genetic programming models when the feature is injected with noise. Imputation is a key strategy to mitigate the data incompleteness problem. However, it has been rarely investigated for symbolic regression on incomplete data. In this work, an attempt to contribute to filling this gap is presented. The proposed method is applied to selecting imputation predictors (features/variables) in symbolic regression with missing values. The evaluation is performed on real-world data sets considering three performance measures: imputation accuracy, symbolic regression performance, and features' reduction ability. Compared with the benchmark methods, the experimental evaluation shows that the proposed method can achieve an enhanced imputation, improve the symbolic regression performance, and use smaller sets of selected predictors.", notes = "https://wcci2020.org/ Victoria University of Wellington, New Zealand. Also known as \cite{9185526}", } @InProceedings{Al-Helali:2020:CEC2, author = "Baligh Al-Helali and Qi Chen and Bing Xue and Mengjie Zhang", title = "Multi-Tree Genetic Programming-based Transformation for Transfer Learning in Symbolic Regression with Highly Incomplete Data", booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC 2020", year = "2020", editor = "Yaochu Jin", pages = "paper id24250", address = "internet", month = "19-24 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-7281-6929-3", DOI = "doi:10.1109/CEC48606.2020.9185670", abstract = "Transfer learning has been considered a key solution for the problem of learning when there is a lack of knowledge in some target domains. Its idea is to benefit from the learning on different (but related in some way) domains that have adequate knowledge and transfer what can improve the learning in the target domains. Although incompleteness is one of the main causes of knowledge shortage in many machine learning real-world tasks, it has received a little effort to be addressed by transfer learning. In particular, to the best of our knowledge, there is no single study to use transfer learning for the symbolic regression task when the underlying data are incomplete. The current work addresses this point by presenting a transfer learning method for symbolic regression on data with high ratios of missing values. A multi-tree genetic programming algorithm based feature-based transformation is proposed for transferring data from a complete source domain to a different, incomplete target domain. The experimental work has been conducted on real-world data sets considering different transfer learning scenarios each is determined based on three factors: missingness ratio, domain difference, and task similarity. In most cases, the proposed method achieved positive transductive transfer learning in both homogeneous and heterogeneous domains. Moreover, even with less significant success, the obtained results show the applicability of the proposed approach for inductive transfer learning.", notes = "https://wcci2020.org/ Victoria University of Wellington, New Zealand", } @InProceedings{Al-Helali:2020:GECCO, author = "Baligh Al-Helali and Qi Chen and Bing Xue and Mengjie Zhang", title = "Multi-Tree Genetic Programming for Feature Construction-Based Domain Adaptation in Symbolic Regression with Incomplete Data", year = "2020", editor = "Carlos Artemio {Coello Coello} and Arturo Hernandez Aguirre and Josu Ceberio Uribe and Mario Garza Fabre and Gregorio {Toscano Pulido} and Katya Rodriguez-Vazquez and Elizabeth Wanner and Nadarajen Veerapen and Efren Mezura Montes and Richard Allmendinger and Hugo Terashima Marin and Markus Wagner and Thomas Bartz-Beielstein and Bogdan Filipic and Heike Trautmann and Ke Tang and John Koza and Erik Goodman and William B. Langdon and Miguel Nicolau and Christine Zarges and Vanessa Volz and Tea Tusar and Boris Naujoks and Peter A. N. Bosman and Darrell Whitley and Christine Solnon and Marde Helbig and Stephane Doncieux and Dennis G. Wilson and Francisco {Fernandez de Vega} and Luis Paquete and Francisco Chicano and Bing Xue and Jaume Bacardit and Sanaz Mostaghim and Jonathan Fieldsend and Oliver Schuetze and Dirk Arnold and Gabriela Ochoa and Carlos Segura and Carlos Cotta and Michael Emmerich and Mengjie Zhang and Robin Purshouse and Tapabrata Ray and Justyna Petke and Fuyuki Ishikawa and Johannes Lengler and Frank Neumann", isbn13 = "9781450371285", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377930.3390160", DOI = "doi:10.1145/3377930.3390160", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference", pages = "913--921", size = "9 pages", keywords = "genetic algorithms, genetic programming, transfer tearning, incomplete data, symbolic regression", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", abstract = "Nowadays, transfer learning has gained a rapid popularity in tasks with limited data available. While traditional learning limits the learning process to knowledge available in a specific (target) domain, transfer learning can use parts of knowledge extracted from learning in a different (source) domain to help learning in the target domain. This concept is of special importance when there is a lack of knowledge in the target domain. Consequently, since data incompleteness is a serious cause of knowledge shortage in real-world learning tasks, it can be typically addressed using transfer learning. One way to achieve that is feature construction-based domain adaptation. However, although it is considered as a powerful feature construction algorithm, Genetic Programming has not been fully for domain adaptation. In this work, a multi-tree genetic programming method is proposed for feature construction-based domain adaptation. The main idea is to construct a transformation from the source feature space to the target feature space, which maps the source domain close to the target domain. This method is used for symbolic regression with missing values. The experimental work shows encouraging potential of the proposed approach when applied to real-world tasks considering different transfer learning scenarios.", notes = "Nominated for Best Paper. multi-tree GP. R packages. Missing data created randomly. Also known as \cite{10.1145/3377930.3390160} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @Article{DBLP:journals/soco/Al-Helali00021, author = "Baligh Al-Helali and Qi Chen and Bing Xue and Mengjie Zhang", title = "A new imputation method based on genetic programming and weighted {KNN} for symbolic regression with incomplete data", journal = "Soft Computing", volume = "25", number = "8", pages = "5993--6012", year = "2021", month = apr, keywords = "genetic algorithms, genetic programming, Symbolic regression, Incomplete data, KNN, Imputation", ISSN = "1432-7643", URL = "https://doi.org/10.1007/s00500-021-05590-y", DOI = "doi:10.1007/s00500-021-05590-y", timestamp = "Wed, 07 Apr 2021 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/soco/Al-Helali00021.bib", bibsource = "dblp computer science bibliography, https://dblp.org", abstract = "Incompleteness is one of the problematic data quality challenges in real-world machine learning tasks. A large number of studies have been conducted for addressing this challenge. However, most of the existing studies focus on the classification task and only a limited number of studies for symbolic regression with missing values exist. a new imputation method for symbolic regression with incomplete data is proposed. The method aims to improve both the effectiveness and efficiency of imputing missing values for symbolic regression. This method is based on genetic programming (GP) and weighted K-nearest neighbors (KNN). It constructs GP-based models using other available features to predict the missing values of incomplete features. The instances used for constructing such models are selected using weighted KNN. The experimental results on real-world data sets show that the proposed method outperforms a number of state-of-the-art methods with respect to the imputation accuracy, the symbolic regression performance, and the imputation time.", } @Article{Al-Helali:ieeeTEC, author = "Baligh Al-Helali and Qi Chen and Bing Xue and Mengjie Zhang", title = "Multi-Tree Genetic Programming with New Operators for Transfer Learning in Symbolic Regression with Incomplete Data", journal = "IEEE Transactions on Evolutionary Computation", year = "2021", volume = "25", number = "6", pages = "1049--1063", month = dec, keywords = "genetic algorithms, genetic programming, Symbolic Regression, Incomplete Data, Transfer Learning, Evolutionary Learning", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2021.3079843", size = "15 pages", abstract = "Lack of knowledge is a common consequence of data incompleteness when learning from real-world data. To deal with such a situation, this work uses transfer learning to re-use knowledge from different (yet related) but complete domains. Due to its powerful feature construction ability, genetic programming is used to construct feature-based transformations that map the feature space of the source domain to that of the target domain such that their differences are reduced. Particularly, this work proposes a new multi-tree genetic programming-based feature construction approach to transfer learning in symbolic regression with missing values. It transfers knowledge related to the importance of the features and instances in the source domain to the target domain to improve the learning performance. Moreover, new genetic operators are developed to encourage minimising the distribution discrepancy between the transformed domain and the target domain. A new probabilistic crossover is developed to make the well-constructed trees in the individuals more likely to be mated than the other trees. A new mutation operator is designed to give more probability for the poorly-constructed trees to be mutated. The experimental results show that the proposed method not only achieves better performance compared with different traditional learning methods but also advances two recent transfer learning methods on real-world data sets with various incompleteness and learning scenarios.", notes = "also known as \cite{9429709}", } @InProceedings{DBLP:conf/ausai/Al-Helali00Z20, author = "Baligh Al-Helali and Qi Chen and Bing Xue and Mengjie Zhang", editor = "Marcus Gallagher and Nour Moustafa and Erandi Lakshika", title = "Genetic Programming-Based Selection of Imputation Methods in Symbolic Regression with Missing Values", booktitle = "{AI} 2020: Advances in Artificial Intelligence - 33rd Australasian Joint Conference, {AI} 2020, Canberra, ACT, Australia, November 29-30, 2020, Proceedings", series = "Lecture Notes in Computer Science", volume = "12576", pages = "163--175", publisher = "Springer", year = "2020", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/978-3-030-64984-5_13", DOI = "doi:10.1007/978-3-030-64984-5_13", timestamp = "Mon, 15 Feb 2021 00:00:00 +0100", biburl = "https://dblp.org/rec/conf/ausai/Al-Helali00Z20.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{Al-Helali:2021:CEC, author = "Baligh Al-Helali and Qi Chen and Bing Xue and Mengjie Zhang", booktitle = "2021 IEEE Congress on Evolutionary Computation (CEC)", title = "{GP} with a Hybrid Tree-vector Representation for Instance Selection and Symbolic Regression on Incomplete Data", year = "2021", editor = "Yew-Soon Ong", pages = "604--611", address = "Krakow, Poland", month = "28 " # jun # "-1 " # jul, isbn13 = "978-1-7281-8393-0", abstract = "Data incompleteness is a pervasive problem in symbolic regression, and machine learning in general. Unfortunately, most symbolic regression methods are only applicable when the given data is complete. One common approach to handling this situation is data imputation. It works by estimating missing values based on existing data. However, which existing data should be used for imputing the missing values? The answer to this question is important when dealing with incomplete data. To address this question, this work proposes a mixed tree-vector representation for genetic programming to perform instance selection and symbolic regression on incomplete data. In this representation, each individual has two components: an expression tree and a bit vector. While the tree component constructs symbolic regression models, the vector component selects the instances that are used to impute missing values by the weighted k-nearest neighbour (WKNN) imputation method. The complete imputed instances are then used to evaluate the GP-based symbolic regression model. The obtained experimental results show the applicability of the proposed method on real-world data sets with different missingness scenarios. When compared with existing methods, the proposed method not only produces more effective symbolic regression models but also achieves more efficient imputations.", keywords = "genetic algorithms, genetic programming, Computational modeling, Machine learning, Evolutionary computation, Regression tree analysis, Symbolic Regression, Incomplete Data, Imputation, Instance Selection", DOI = "doi:10.1109/CEC45853.2021.9504767", notes = "Also known as \cite{9504767}", } @Article{Al-Helali:ETCI, author = "Baligh Al-Helali and Qi Chen and Bing Xue and Mengjie Zhang", journal = "IEEE Transactions on Emerging Topics in Computational Intelligence", title = "Genetic Programming for Feature Selection Based on Feature Removal Impact in High-Dimensional Symbolic Regression", year = "2024", volume = "8", number = "3", pages = "2269--2282", month = jun, keywords = "genetic algorithms, genetic programming, Feature extraction, Data models, Computational modelling, Task analysis, Predictive models, Machine learning, Feature selection, high dimensionality, symbolic regression", ISSN = "2471-285X", DOI = "doi:10.1109/TETCI.2024.3369407", abstract = "Symbolic regression is increasingly important for discovering mathematical models for various prediction tasks. It works by searching for the arithmetic expressions that best represent a target variable using a set of input features. However, as the number of features increases, the search process becomes more complex. To address high-dimensional symbolic regression, this work proposes a genetic programming for feature selection method based on the impact of feature removal on the performance of SR models. Unlike existing Shapely value methods that simulate feature absence at the data level, the proposed approach suggests removing features at the model level. This approach circumvents the production of unrealistic data instances, which is a major limitation of Shapely value and permutation-based methods. Moreover, after calculating the importance of the features, a cut-off strategy, which works by injecting a number of random features and using their importance to automatically set a threshold, is proposed for selecting important features. The experimental results on artificial and real-world high-dimensional data sets show that, compared with state-of-the-art feature selection methods using the permutation importance and Shapely value, the proposed method not only improves the SR accuracy but also selects smaller sets of features.", notes = "Also known as \cite{10466603}", } @Article{Al-Helali:CYB, author = "Baligh Al-Helali and Qi Chen and Bing Xue and Mengjie Zhang", journal = "IEEE Transactions on Cybernetics", title = "Multitree Genetic Programming With Feature-Based Transfer Learning for Symbolic Regression on Incomplete Data", year = "2024", volume = "54", number = "7", pages = "4014--4027", month = jul, keywords = "genetic algorithms, genetic programming, Task analysis, Feature extraction, Data models, Transfer learning, Contracts, Adaptation models, Routing, incomplete data, symbolic regression (SR), transfer learning (TL)", ISSN = "2168-2275", DOI = "doi:10.1109/TCYB.2023.3270319", abstract = "Data incompleteness is a serious challenge in real-world machine-learning tasks. Nevertheless, it has not received enough attention in symbolic regression (SR). Data missingness exacerbates data shortage, especially in domains with limited available data, which in turn limits the learning ability of SR algorithms. Transfer learning (TL), which aims to transfer knowledge across tasks, is a potential solution to solve this issue by making amends for the lack of knowledge. However, this approach has not been adequately investigated in SR. To fill this gap, a multitree genetic programming-based TL method is proposed in this work to transfer knowledge from complete source domains (SDs) to incomplete related target domains (TDs). The proposed method transforms the features from a complete SD to an incomplete TD. However, having many features complicates the transformation process. To mitigate this problem, we integrate a feature selection mechanism to eliminate unnecessary transformations. The method is examined on real-world and synthetic SR tasks with missing values to consider different learning scenarios. The obtained results not only show the effectiveness of the proposed method but also show its training efficiency compared with the existing TL methods. Compared to state-of-the-art methods, the proposed method reduced an average of more than 2.58percent and 4percent regression error on heterogeneous and homogeneous domains, respectively.", notes = "Also known as \cite{10120936}", } @Article{Al-Helali:EC, author = "Baligh Al-Helali and Qi Chen and Bing Xue and Mengjie Zhang", title = "Genetic Programming-based Feature Selection for Symbolic Regression on Incomplete Data", journal = "Evolutionary Computation", month = nov # " 21 2024", note = "Just Accepted", keywords = "genetic algorithms, genetic programming, Symbolic Regression, Incomplete Data, Feature Selection, High-dimensionality", ISSN = "1063-6560", DOI = "doi:10.1162/evco_a_00362", } @PhdThesis{WaleedAljandal2009, author = "Waleed A. Aljandal", title = "Itemset size-sensitive interestingness measures for association rule mining and link prediction", school = "Department of Computing and Information Sciences, Kansas State University", year = "2009", address = "Manhattan, Kansas, USA", month = may, keywords = "genetic algorithms, data Mining, Association Rule, Interestingness Measures, Link Prediction", URL = "https://krex.k-state.edu/dspace/handle/2097/1245", URL = "https://krex.k-state.edu/dspace/bitstream/handle/2097/1245/WaleedAljandal2009.pdf", size = "144 pages", abstract = "Association rule learning is a data mining technique that can capture relationships between pairs of entities in different domains. The goal of this research is to discover factors from data that can improve the precision, recall, and accuracy of association rules found using interestingness measures and frequent itemset mining. Such factors can be calibrated using validation data and applied to rank candidate rules in domain-dependent tasks such as link existence prediction. In addition, I use interestingness measures themselves as numerical features to improve link existence prediction. The focus of this dissertation is on developing and testing an analytical framework for association rule interestingness measures, to make them sensitive to the relative size of itemsets. I survey existing interestingness measures and then introduce adaptive parametric models for normalizing and optimizing these measures, based on the size of itemsets containing a candidate pair of co-occurring entities. The central thesis of this work is that in certain domains, the link strength between entities is related to the rarity of their shared memberships (i.e., the size of itemsets in which they co-occur), and that a data-driven approach can capture such properties by normalizing the quantitative measures used to rank associations. To test this hypothesis under different levels of variability in itemset size, I develop several test bed domains, each containing an association rule mining task and a link existence prediction task. The definitions of itemset membership and link existence in each domain depend on its local semantics. My primary goals are: to capture quantitative aspects of these local semantics in normalization factors for association rule interestingness measures; to represent these factors as quantitative features for link existence prediction, to apply them to significantly improve precision and recall in several real-world domains; and to build an experimental framework for measuring this improvement, using information theory and classification-based validation.", notes = "Not on GP Supervisor William H. Hsu", } @InProceedings{Al-Jassim:2024:EAIS, author = "Rasha S. {Al Jassim} and Shqran {Al Mansoory} and Karan Jetly and Hilal AlMaqbali", title = "Enhancing Hotel Performance Prediction in Oman's Tourism Industry: Insights from Machine Learning, Feature Analysis, and Predictive Factors", booktitle = "2024 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS)", year = "2024", month = may, keywords = "genetic algorithms, genetic programming, Accuracy, Machine learning algorithms, Tourism industry, Stability criteria, Prediction algorithms, Robustness, Decision trees, Decision Tree, Evolutionary Algorithms, XG-Boost", ISSN = "2473-4691", DOI = "doi:10.1109/EAIS58494.2024.10570014", abstract = "This paper introduces two novel fitness functions adapted to attraction attributes, extending the capabilities of the Linear Genetic Programming for Optimisation Decision Tree (LGPDT). A comparative analysis with XGBoost evaluates LGPDT's performance using both traditional and tourism datasets, examining its predictive capacity for hotel performance in Oman. Using extensive experiments and analysis, the study proved the effectiveness of LGPDT in this context, revealing promising results with a mean accuracy of 72.0percent and a standard deviation of 7.7percent. This underscores LGPDT's robustness and suitability for decision-making in the hospitality industry. Comparison with XGBoost demonstrates LGPDT's slightly higher stability in accuracy, highlighting its potential as a predictive tool. Moreover, the evaluation of new fitness functions reveals that they are computationally efficient while maintaining similar quality standards compared to previously studied fitness functions. These findings underscore LGPDT's efficacy for predicting hotel performance, offering valuable insights for industry stakeholders.", notes = "Also known as \cite{10570014}", } @InProceedings{Al-Jassim:2024:CoDIT, author = "Rasha S. {Al Jassim} and Shqran {Al Mansoory} and Karan Jetly and Hilal Ali {Abdullah AlMaqbali} and Muna {Mohammed Albalushi}", title = "Enhancing Tourism Performance in Oman: A Case Study Using Correlation-Guided Linear Genetic Programming Decision Tree (C-{LGPDT)}", booktitle = "2024 10th International Conference on Control, Decision and Information Technologies (CoDIT)", year = "2024", pages = "1655--1660", month = jul, keywords = "genetic algorithms, genetic programming, Accuracy, Evolutionary computation, Machine learning, Feature extraction, Stability analysis, Decision trees, Information technology, Optimisation, Evolutionary Algorithms, Tourism, Decision Tree, Linear Genetic Programming, Classification", ISSN = "2576-3555", DOI = "doi:10.1109/CoDIT62066.2024.10708185", abstract = "This research examines the optimisation of decision tree induction techniques by integrating evolutionary algorithms. It focuses on the Linear Genetic Programming Decision Tree (LGPDT). LGPDT employs a linear program to encode decision trees, achieving an optimal balance between accuracy and interpretability. The study introduces C-LGPDT as an extension of LGPDT, aiming to enhance its efficiency through correlation-based feature selection. This integration reduces dataset dimensionality and eliminates irrelevant or redundant features, resulting in a more accurate and interpretable decision tree model. The performance of C-LGPDT is thoroughly examined, and it is shown that it consistently outperforms older approaches, especially C4.5, and that it is more robust and accurate. A tourism dataset is also used to evaluate the C-LGPDT's performance, with an emphasis on its stability in recall and precision. Results show that C-LGPDT is effective at solving decision tree induction problems, making it a good candidate for machine learning classification tasks.", notes = "Also known as \cite{10708185}", } @Article{Al-Jeznawi:2025:jnlssr, author = "Duaa Al-Jeznawi and Laith Sadik and Saif Alzabeebee and Musab Aied Qissab Al-Janabi and Suraparb Keawsawasvong", title = "Novel explicit models for assessing the frictional resistance of pipe piles subjected to seismic effects", journal = "Journal of Safety Science and Resilience", year = "2025", volume = "6", number = "1", pages = "29--37", keywords = "genetic algorithms, genetic programming, Seismic resilience, Evolutionary polynomial regression, Multiobjective genetic algorithm, Pipe piles, Frictional resistance, Seismic excitation, Corrected SPT test blow count", ISSN = "2666-4496", URL = "https://www.sciencedirect.com/science/article/pii/S2666449624000537", DOI = "doi:10.1016/j.jnlssr.2024.06.010", abstract = "This paper introduces novel explicit models to predict the frictional resistance of open and closed-ended pipe piles subjected to seismic loading. This research employs genetic programming (GP) and multiobjective genetic algorithm-based evolutionary polynomial regression (EPR-MOGA) to develop closed-form expressions for estimating pile frictional resistance, using widely used input parameters for enhanced practicality and applicability in engineering practice. The proposed models are developed using only three input variables: the corrected standard penetration test (SPT) blow count (N1)60, the pile slenderness ratio (L/D), and the peak ground acceleration (PGA). This deliberate reduction in input complexity significantly enhances the models' applicability across a wide range of geotechnical scenarios and industries. The accuracy of the developed models was assessed via the coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE). In the case of the GP model, the evaluation metrics for the testing set for open-ended piles (R2, RMSE, and MAE values) are 0.89, 0.43, and 0.35, respectively, whereas the corresponding values for closed-ended piles are 0.93, 0.38, and 0.3, respectively. On the other hand, the EPR-MOGA approach achieves similarly encouraging results, with performance metrics of 0.92, 0.37, and 0.29 for open-ended piles and 0.91, 0.39, and 0.30 for closed-ended piles", } @Article{ThunderStormGP, author = "Ruba Al-Jundi and Mais Yasen and Nailah Al-Madi", title = "Thunderstorms Prediction using Genetic Programming", journal = "International Journal of Information Systems and Computer Sciences", year = "2018", volume = "7", number = "1", note = "Special Issue of ICSIC 2017, Held during 23-24 September 2017 in Amman Arab University, Amman, Jordan", keywords = "genetic algorithms, genetic programming, Evolutionary computation, Machine Learning, Weather Prediction.", publisher = "WARSE", ISSN = "2319-7595", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/al-madi/Thunderstorm_Prediction.pdf", URL = "http://www.warse.org/IJISCS/static/pdf/Issue/icsic2017sp14.pdf", size = "7 pages", abstract = "Thunderstorms prediction is a major challenge for efficient flight planning and air traffic management. As the inaccurate forecasting of weather poses a danger to aviation, it increases the need to build a good prediction model. Genetic Programming (GP) is one of the evolutionary computation techniques that is used for classification process. Genetic Programming has proven its efficiency especially for dynamic and nonlinear classification. This research proposes a thunderstorm prediction model that makes use of Genetic Programming and takes real data of Lake Charles Airport (LCH) as a case study. The proposed model is evaluated using different metrics such as recall, F-measure and compared with other well-known classifiers. The results show that Genetic Programming got higher recall value of predicting thunderstorms in comparison with the other classifiers.", notes = "Lake Charles Metar and SYNOP data (LCH) broken aug 2018 http://www.warse.org/IJISCS/archives", } @InProceedings{Al-Madi:2012:NaBIC, author = "N. Al-Madi and S. A. Ludwig", booktitle = "Proceedings of the Fourth World Congress on Nature and Biologically Inspired Computing, NaBIC 2012", title = "Adaptive genetic programming applied to classification in data mining", year = "2012", pages = "79--85", keywords = "genetic algorithms, genetic programming, data mining, pattern classification, adaptive GP, adaptive genetic programming, classification accuracies, crossover rates, data mining, mutation rates, Accuracy, Evolutionary computation, Sociology, Standards, Statistics, Adaptive Genetic Programming, Classification, Evolutionary Computation", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/al-madi/Adaptive_Genetic_Programming_applied_to_Classification_in_Data_Mining.pdf", DOI = "doi:10.1109/NaBIC.2012.6402243", abstract = "Classification is a data mining method that assigns items in a collection to target classes with the goal to accurately predict the target class for each item in the data. Genetic programming (GP) is one of the effective evolutionary computation techniques to solve classification problems, however, it suffers from a long run time. In addition, there are many parameters that need to be set before the GP is run. In this paper, we propose an adaptive GP that automatically determines the best parameters of a run, and executes the classification faster than standard GP. This adaptive GP has three variations. The first variant consists of an adaptive selection process ensuring that the produced solutions in the next generation are better than the solutions in the previous generation. The second variant adapts the crossover and mutation rates by modifying the probabilities ensuring that a solution with a high fitness is protected. And the third variant is an adaptive function list that automatically changes the functions used by deleting the functions that do not favourably contribute to the classification. These proposed variations were implemented and compared to the standard GP. The results show that a significant speedup can be achieved by obtaining similar classification accuracies.", notes = "Also known as \cite{6402243}", } @InProceedings{Al-Madi:2013:SSCI, author = "Nailah Al-Madi and Simone A. Ludwig", title = "Improving genetic programming classification for binary and multiclass datasets", booktitle = "IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2013", year = "2013", editor_ssci-2013 = "P. N. Suganthan", editor = "Barbara Hammer and Zhi-Hua Zhou and Lipo Wang and Nitesh Chawla", pages = "166--173", address = "Singapore", month = "16-19 " # apr, keywords = "genetic algorithms, genetic programming, Evolutionary Computation, Classification, Multiclass, Binary Classification", isbn13 = "978-1-4673-5895-8", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/al-madi/improving_GP.pdf", DOI = "doi:10.1109/CIDM.2013.6597232", size = "8 pages", abstract = "Genetic Programming (GP) is one of the evolutionary computation techniques that is used for the classification process. GP has shown that good accuracy values especially for binary classifications can be achieved, however, for multiclass classification unfortunately GP does not obtain high accuracy results. In this paper, we propose two approaches in order to improve the GP classification task. One approach (GP-K) uses the K-means clustering technique in order to transform the produced value of GP into class labels. The second approach (GP-D) uses a discretization technique to perform the transformation. A comparison of the original GP, GP-K and GP-D was conducted using binary and multiclass datasets. In addition, a comparison with other state-of-the-art classifiers was performed. The results reveal that GP-K shows good improvement in terms of accuracy compared to the original GP, however, it has a slightly longer execution time. GP-D also achieves higher accuracy values than the original GP as well as GP-K, and the comparison with the state-of-the-art classifiers reveal competitive accuracy values.", notes = "CIDM 2013, Broken March 2023 http://www.ntu.edu.sg/home/epnsugan/index_files/SSCI2013/CIDM2013.htm Also known as \cite{6597232}", } @InProceedings{AL-Madi:2013:GECCOcomp, author = "Nailah Al-Madi and Simone A. Ludwig", title = "Segment-based genetic programming", booktitle = "GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion", year = "2013", editor = "Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A. N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks", isbn13 = "978-1-4503-1964-5", keywords = "genetic algorithms, genetic programming", pages = "133--134", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/al-madi/Segment-Based_Genetic_Programming.pdf", DOI = "doi:10.1145/2464576.2464648", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Genetic Programming (GP) is one of the successful evolutionary computation techniques applied to solve classification problems, by searching for the best classification model applying the fitness evaluation. The fitness evaluation process greatly impacts the overall execution time of GP and is therefore the focus of this research study. This paper proposes a segment-based GP (SegGP) technique that reduces the execution time of GP by partitioning the dataset into segments, and using the segments in the fitness evaluation process. Experiments were done using four datasets and the results show that SegGP can obtain higher or similar accuracy results in shorter execution time compared to standard GP.", notes = "Also known as \cite{2464648} Distributed at GECCO-2013.", } @InProceedings{Al-Madi:2013:nabic, author = "Nailah Al-Madi and Simone A. Ludwig", title = "Scaling Genetic Programming for Data Classification using {MapReduce} Methodology", booktitle = "5th World Congress on Nature and Biologically Inspired Computing", year = "2013", editor = "Simone Ludwig and Patricia Melin and Ajith Abraham and Ana Maria Madureira and Kendall Nygard and Oscar Castillo and Azah Kamilah Muda and Kun Ma and Emilio Corchado", pages = "132--139", address = "Fargo, USA", month = "12-14 " # aug, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Evolutionary computation, data classification, Parallel Processing, MapReduce, Hadoop", isbn13 = "978-1-4799-1415-9", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/al-madi/MRGP.pdf", URL = "http://www.mirlabs.net/nabic13/proceedings/html/paper34.xml", DOI = "doi:10.1109/NaBIC.2013.6617851", size = "8 pages", abstract = "Genetic Programming (GP) is an optimisation method that has proved to achieve good results. It solves problems by generating programs and applying natural operations on these programs until a good solution is found. GP has been used to solve many classifications problems, however, its drawback is the long execution time. When GP is applied on the classification task, the execution time proportionally increases with the dataset size. Therefore, to manage the long execution time, the GP algorithm is parallelised in order to speed up the classification process. Our GP is implemented based on the MapReduce methodology (abbreviated as MRGP), in order to benefit from the MapReduce concept in terms of fault tolerance, load balancing, and data locality. MRGP does not only accelerate the execution time of GP for large datasets, it also provides the ability to use large population sizes, thus finding the best result in fewer numbers of generations. MRGP is evaluated using different population sizes ranging from 1,000 to 100,000 measuring the accuracy, scalability, and speedup", notes = "USB only?, IEEE Catalog Number: CFP1395H-POD Also known as \cite{6617851}", } @PhdThesis{Al-Madi:thesis, author = "Nailah Shikri Al-Madi", title = "Improved genetic programming techniques for data classification", school = "Computer Science, North Dakota State University", year = "2013", address = "Fargo, North Dakota, USA", month = dec, keywords = "genetic algorithms, genetic programming, Artificial intelligence, Computer science, Applied sciences, Data classification, Data mining, MRGP", URL = "https://library.ndsu.edu/ir/handle/10365/27097", URL = "https://library.ndsu.edu/ir/bitstream/handle/10365/27097/Improved%20Genetic%20Programming%20Techniques%20For%20Data%20Classification.pdf", broken = "http://gradworks.umi.com/36/14/3614489.html", URL = "http://search.proquest.com/docview/1518147523", size = "123 pages", abstract = "Evolutionary algorithms are one category of optimisation techniques that are inspired by processes of biological evolution. Evolutionary computation is applied to many domains and one of the most important is data mining. Data mining is a relatively broad field that deals with the automatic knowledge discovery from databases and it is one of the most developed fields in the area of artificial intelligence. Classification is a data mining method that assigns items in a collection to target classes with the goal to accurately predict the target class for each item in the data. Genetic programming (GP) is one of the effective evolutionary computation techniques to solve classification problems. GP solves classification problems as an optimization tasks, where it searches for the best solution with highest accuracy. However, GP suffers from some weaknesses such as long execution time, and the need to tune many parameters for each problem. Furthermore, GP can not obtain high accuracy for multiclass classification problems as opposed to binary problems. In this dissertation, we address these drawbacks and propose some approaches in order to overcome them. Adaptive GP variants are proposed in order to automatically adapt the parameter settings and shorten the execution time. Moreover, two approaches are proposed to improve the accuracy of GP when applied to multiclass classification problems. In addition, a Segment-based approach is proposed to accelerate the GP execution time for the data classification problem. Furthermore, a parallelisation of the GP process using the MapReduce methodology was proposed which aims to shorten the GP execution time and to provide the ability to use large population sizes leading to a faster convergence. The proposed approaches are evaluated using different measures, such as accuracy, execution time, sensitivity, specificity, and statistical tests. Comparisons between the proposed approaches with the standard GP, and with other classification techniques were performed, and the results showed that these approaches overcome the drawbacks of standard GP by successfully improving the accuracy and execution time.", notes = "Advisor: Simone A. Ludwig ProQuest, UMI Dissertations Publishing, 2014. 3614489", } @Article{Al-Madi:2016:GPEM, author = "Nailah Al-Madi", title = "Mike Preuss: Multimodal optimization by means of evolutionary algorithms", journal = "Genetic Programming and Evolvable Machines", year = "2016", volume = "17", number = "3", pages = "315--316", month = sep, note = "Book review", keywords = "genetic algorithms", ISSN = "1389-2576", URL = "https://rdcu.be/dR8cf", DOI = "doi:10.1007/s10710-016-9272-x", size = "2 pages", } @Article{Al-Mamun:2024:asoc, author = "Abdullah {Al Mamun} and Harith Al-Sahaf and Ian Welch and Masood Mansoori and Seyit Camtepe", title = "Detection of advanced persistent threat: A genetic programming approach", journal = "Applied Soft Computing", year = "2024", volume = "167", pages = "112447", keywords = "genetic algorithms, genetic programming, APT, Advanced Persistent Threat, Evolutionary computation, Machine Learning, CKC", ISSN = "1568-4946", URL = "https://www.sciencedirect.com/science/article/pii/S1568494624012213", DOI = "doi:10.1016/j.asoc.2024.112447", abstract = "Advanced Persistent Threats (APTs) are an intimidating class of cyberattacks known for their persistence, sophistication, and targeted nature. These attacks, coordinated by highly motivated adversaries, pose a grave risk to organizations and individuals, often operating stealthily and evading detection. While existing research primarily focuses on applying Machine Learning (ML) methods to analyse network traffic data for APT detection, this article introduces a novel approach that uses Genetic Programming (GP). The proposed method not only detects APT attacks but also identifies their specific life cycle stages through the evolutionary capabilities of GP. Its effectiveness lies in its ability to excel in detecting intricate patterns, even within classes with a limited number of instances, a feat that is often challenging for traditional ML techniques. The method involves evolving and optimising its models to effectively learn and adapt to complex APT behaviours. Experimentation with a publicly available dataset showcases the efficacy of the proposed method across diverse APT stages. The results demonstrate that the proposed method, GPC, achieves a 3.71percent improvement in balanced accuracy compared to the best-performing model from related works. Moreover, a thorough analysis of the best-evolved GP model uncovers valuable insights about identified features and significant patterns. This research advances the APT detection paradigm by leveraging GP's capabilities, providing a fresh and effective perspective on countering these persistent threats", } @Article{Al-Mamun:2025:cose, author = "Abdullah {Al Mamun} and Harith Al-Sahaf and Ian Welch and Seyit Camtepe", title = "Genetic programming for enhanced detection of Advanced Persistent Threats through feature construction", journal = "Computer and Security", year = "2025", volume = "149", pages = "104185", keywords = "genetic algorithms, genetic programming, APT, Advanced Persistent Threat, Evolutionary computation, Feature learning, Machine learning, Explainable AI", ISSN = "0167-4048", URL = "https://www.sciencedirect.com/science/article/pii/S0167404824004905", DOI = "doi:10.1016/j.cose.2024.104185", abstract = "Advanced Persistent Threats (APTs) pose considerable challenges in the realm of cybersecurity, characterised by their evolving tactics and complex evasion techniques. These characteristics often outsmart traditional security measures and necessitate the development of more sophisticated detection methods. This study introduces Feature Evolution using Genetic Programming (FEGP), a novel method that leverages multi-tree Genetic Programming (GP) to construct and enhance features for APT detection. While GP has been widely used for tackling various problems in different domains, our study focuses on the adaptation of GP to the multifaceted landscape of APT detection. The proposed method automatically constructs discriminative features by combining the original features using mathematical operators. By leveraging GP, the system adapts to the evolving tactics employed by APTs, enhancing the identification of APT activities with greater accuracy and reliability. To assess the efficacy of the proposed method, comprehensive experiments were conducted on widely used and publicly accessible APT datasets. Using the combination of constructed and original features on the DAPT-2020 dataset, FEGP achieved a balanced accuracy of 79.28percent, surpassing the best comparative methods by an average of 2.12percent in detecting APT stages. Additionally, using only constructed features on the Unraveled dataset, FEGP achieved a balanced accuracy of 83.14percent, demonstrating a 3.73percent improvement over the best comparative method. The findings presented in this paper underscore the importance of GP-based feature construction for APT detection, providing a pathway toward improved accuracy and efficiency in identifying APT activities. The comparative analysis of the proposed method against existing feature construction methods demonstrates FEGP's effectiveness as a state-of-the-art method for multi-class APT classification. In addition to the performance evaluation, further analysis was conducted, encompassing feature importance analysis, and a detailed time analysis", } @Article{Al-Maqaleh:2007:isi, author = "Basheer M. Al-Maqaleh and Kamal K. Bharadwaj", title = "Genetic Programming Approach to Hierarchical Production Rule Discovery", journal = "International Science Index", year = "2007", volume = "1", number = "11", pages = "531--534", keywords = "genetic algorithms, genetic programming, hierarchy, knowledge discovery in database, subsumption matrix. k", publisher = "World Academy of Science, Engineering and Technology", index = "International Science Index 11, 2007", bibsource = "http://waset.org/Publications", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.308.1481", ISSN = "1307-6892", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.308.1481", URL = "http://waset.org/publications/10022", size = "4 pages", abstract = "Automated discovery of hierarchical structures in large data sets has been an active research area in the recent past. This paper focuses on the issue of mining generalised rules with crisp hierarchical structure using Genetic Programming (GP) approach to knowledge discovery. The post-processing scheme presented in this work uses flat rules as initial individuals of GP and discovers hierarchical structure. Suitable genetic operators are proposed for the suggested encoding. Based on the Subsumption Matrix(SM), an appropriate fitness function is suggested. Finally, Hierarchical Production Rules (HPRs) are generated from the discovered hierarchy. Experimental results are presented to demonstrate the performance of the proposed algorithm.", } @InProceedings{Al-Maqaleh:2012:ACCT, author = "Basheer Mohamad Ahmad Al-Maqaleh", title = "Genetic Algorithm Approach to Automated Discovery of Comprehensible Production Rules", booktitle = "Second International Conference on Advanced Computing Communication Technologies (ACCT 2012)", year = "2012", month = jan, pages = "69--71", size = "3 pages", abstract = "In the recent past, there has been an increasing interest in applying evolutionary methods to Knowledge Discovery in Databases (KDD) and a number of successful applications of Genetic Algorithms (GA) and Genetic Programming (GP) to KDD have been demonstrated. The most predominant representation of the discovered knowledge is the standard Production Rules (PRs) in the form If P Then D. This paper presents a classification algorithm based on GA approach that discovers comprehensible rules in the form of PRs. The proposed approach has flexible chromosome encoding, where each chromosome corresponds to a PR. For the proposed scheme a suitable and effective fitness function and appropriate genetic operators are proposed for the suggested representation. Experimental results are presented to demonstrate the performance of the proposed algorithm.", keywords = "genetic algorithms, GA, KDD, PR, automated discovery, chromosome encoding, comprehensible production rules, genetic algorithm approach, genetic operators, knowledge discovery in databases, production rules, data mining, database management systems", DOI = "doi:10.1109/ACCT.2012.57", notes = "Faculty of Computer Sciences & Information Systems Thamar University, Thamar, Republic of Yemen. Also known as \cite{6168335}", } @Article{Al-Rahamneh:2011:JSEA, author = "Zainab Al-Rahamneh and Mohammad Reyalat and Alaa F. Sheta and Sulieman Bani-Ahmad and Saleh Al-Oqeili", title = "A New Software Reliability Growth Model: Genetic-Programming-Based Approach", journal = "Journal of Software Engineering and Applications", year = "2011", volume = "4", number = "8", pages = "476--481", month = aug, publisher = "Scientific Research Publishing", keywords = "genetic algorithms, genetic programming, SBSE, software reliability, modelling, software faults", ISSN = "19453116", URL = "http://www.scirp.org/journal/PaperDownload.aspx?DOI=10.4236/jsea.2011.48054", broken = "http://www.doaj.org/doaj?func=openurl\&genre=article\&issn=19453116\&date=2011\&volume=04\&issue=08\&spage=476", DOI = "doi:10.4236/jsea.2011.48054", size = "6 pages", abstract = "A variety of Software Reliability Growth Models (SRGM) have been presented in literature. These models suffer many problems when handling various types of project. The reason is; the nature of each project makes it difficult to build a model which can generalise. In this paper we propose the use of Genetic Programming (GP) as an evolutionary computation approach to handle the software reliability modelling problem. GP deals with one of the key issues in computer science which is called automatic programming. The goal of automatic programming is to create, in an automated way, a computer program that enables a computer to solve problems. GP will be used to build a SRGM which can predict accumulated faults during the software testing process. We evaluate the GP developed model and compare its performance with other common growth models from the literature. Our experiments results show that the proposed GP model is superior compared to Yamada S-Shaped, Generalised Poisson, NHPP and Schneidewind reliability models.", bibsource = "OAI-PMH server at www.doaj.org", language = "eng", oai = "oai:doaj-articles:05291f1c8d43b618b364d9e2fbc587cc", } @InProceedings{Al-Ratrout:2010:SSD, author = "Serein Al-Ratrout and Francois Siewe and Omar Al-Dabbas and Mai Al-Fawair", title = "Hybrid Multi-Agent Architecture (HMAA) for meeting scheduling", booktitle = "2010 7th International Multi- Conference on Systems, Signals and Devices", year = "2010", address = "Amman, Jordan", month = "27-30 " # jun, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, multiagent, meeting scheduling, heuristic", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.1011.3891", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1011.3891", URL = "http://www.cse.dmu.ac.uk/%7Efsiewe/papers/serein_siewe_2010.pdf", DOI = "doi:10.1109/SSD.2010.5585505", size = "6 pages", abstract = "This paper presents a novel multi-agent architecture for meeting scheduling. The proposed architecture is a new Hybrid Multi-Agent Architecture (HMAA) that generates new heuristics for solving NP-hard problems. Moreover, the paper investigates the feasibility of running computationally intensive algorithms on multi-agent architectures while preserving the ability of small agents to run on small devices, including mobile devices. Three experimental groups are conducted in order to test the feasibility of the proposed architecture. The results show that the performance of the proposed architecture is better than those of many existing meeting scheduling frameworks. Moreover, it has been proved that HMAA preserves small agents' mobility (i.e. the ability to run on small devices) while implementing evolutionary algorithms.", notes = "Serein Al-Ratrout Department of Software Engineering, Alzytoonah University, Jordan Francois Siewe Software Technology Research Laboratory, Demontfort University, UK Omar Al-Dabbas Faculty of Engineering, Al-Balqa Applied University, Jordan Mai Al-Fawair Department of Software Engineering, Alzytoonah University, Jordan Also known as \cite{5585505}", } @Article{Al-Saati:2018:IJCA, author = "Najla Akram Al-Saati and Taghreed Riyadh Al-Reffaee", title = "Employing Gene Expression Programming in Estimating Software Effort", journal = "International Journal of Computer Applications", year = "2018", volume = "182", number = "8", pages = "1--8", month = aug, keywords = "genetic algorithms, genetic programming, Gene Expression Programming, Effort Estimation, Software Engineering, Artificial Intelligence", publisher = "Foundation of Computer Science (FCS), NY, USA", address = "New York, USA", ISSN = "0975-8887", URL = "http://www.ijcaonline.org/archives/volume182/number8/29837-2018917619", URL = "https://www.ijcaonline.org/archives/volume182/number8/alsaati-2018-ijca-917619.pdf", DOI = "doi:10.5120/ijca2018917619", size = "8 pages", abstract = "The problem of estimating the effort for software packages is one of the most significant challenges encountering software designers. The precision in estimating the effort or cost can have a huge impact on software development. Various methods have been investigated in order to discover good enough solutions to this problem; lately evolutionary intelligent techniques are explored like Genetic Algorithms, Genetic Programming, Neural Networks, and Swarm Intelligence. In this work, Gene Expression Programming (GEP) is investigated to show its efficiency in acquiring equations that best estimates software effort. Datasets employed are taken from previous projects. The comparisons of learning and testing results are carried out with COCOMO, Analogy, GP and four types of Neural Networks, all show that GEP outperforms all these methods in discovering effective functions for the estimation with robustness and efficiency.", notes = "Also known as \cite{10.5120/ijca2018917619} www.ijcaonline.org Software Engineering Dept. College of Computer Sciences and Mathematics, University of Mosul, Iraq", } @Misc{journals/corr/abs-2001-09923, author = "Najla Akram Al-Saati", title = "Applying Gene Expression Programming for Solving One-Dimensional Bin-Packing Problems", howpublished = "arXiv", year = "2020", month = nov, keywords = "genetic algorithms, genetic programming, gene expression programming", volume = "abs/2001.09923", bibdate = "2020-01-30", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/corr/corr2001.html#abs-2001-09923", URL = "https://arxiv.org/abs/2001.09923", } @InProceedings{Al-Sahaf:2019:GECCOcomp, author = "Harith. Al-Sahaf and Ian Welch", title = "A genetic programming approach to feature selection and construction for ransomware, phishing and spam detection", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", isbn13 = "978-1-4503-6748-6", pages = "332--333", address = "Prague, Czech Republic", DOI = "doi:10.1145/3319619.3322083", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{3322083} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @Article{Al-Sahaf:2019:JRSNZ, author = "Harith Al-Sahaf and Ying Bi and Qi Chen and Andrew Lensen and Yi Mei and Yanan Sun and Binh Tran and Bing Xue and Mengjie Zhang", title = "A survey on evolutionary machine learning", journal = "Journal of the Royal Society of New Zealand", year = "2019", volume = "49", number = "2", pages = "205--228", note = "The 2019 Annual Collection of Reviews", keywords = "genetic algorithms, genetic programming, TPOT, AI, ANN, EML, GPU, EMO, autoML, artificial intelligence, machine learning, evolutionary computation, classification, regression, clustering, combinatorial optimisation, deep learning, transfer learning, ensemble learning", publisher = "Taylor \& Francis", URL = "https://doi.org/10.1080/03036758.2019.1609052", DOI = "doi:10.1080/03036758.2019.1609052", size = "24 pages", abstract = "Artificial intelligence (AI) emphasises the creation of intelligent machines/systems that function like humans. AI has been applied to many real-world applications. Machine learning is a branch of AI based on the idea that systems can learn from data, identify hidden patterns, and make decisions with little/minimal human intervention. Evolutionary computation is an umbrella of population-based intelligent/learning algorithms inspired by nature, where New Zealand has a good international reputation. This paper provides a review on evolutionary machine learning, i.e. evolutionary computation techniques for major machine learning tasks such as classification, regression and clustering, and emerging topics including combinatorial optimisation, computer vision, deep learning, transfer learning, and ensemble learning. The paper also provides a brief review of evolutionary learning applications, such as supply chain and manufacturing for milk/dairy, wine and seafood industries, which are important to New Zealand. Finally, the paper presents current issues with future perspectives in evolutionary machine learning.", } @InProceedings{DBLP:conf/ijcci/ShormanFCGA18, author = "Amaal R. {Al Shorman} and Hossam Faris and Pedro A. Castillo and Juan Julian Merelo Guervos and Nailah Al-Madi", editor = "Christophe Sabourin and Juan Julian Merelo Guervos and Alejandro Linares-Barranco and Kurosh Madani and Kevin Warwick", title = "The Influence of Input Data Standardization Methods on the Prediction Accuracy of Genetic Programming Generated Classifiers", booktitle = "Proceedings of the 10th International Joint Conference on Computational Intelligence, {IJCCI} 2018, Seville, Spain, September 18-20, 2018", pages = "79--85", publisher = "SciTePress", year = "2018", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.5220/0006959000790085", DOI = "doi:10.5220/0006959000790085", timestamp = "Thu, 26 Sep 2019 16:43:57 +0200", biburl = "https://dblp.org/rec/conf/ijcci/ShormanFCGA18.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @PhdThesis{Al-Zubaidi:thesis, author = "Wisam Haitham Abbood {Al-Zubaidi}", title = "Multi-objective search-based approach for software project management", school = "University of Wollongong", year = "2019", address = "Wollongong, NSW 2522, Australia", month = "31 " # mar, keywords = "genetic algorithms, genetic programming, SBSE, Iteration Planning, Agile Development, Effort Estimation, MOGP", URL = "https://ro.uow.edu.au/theses1/690/", URL = "https://ro.uow.edu.au/theses1/690/Al-Zubaidi_thesis.pdf", URL = "https://ro.uow.edu.au/cgi/viewcontent.cgi?article=1678&context=theses1", size = "254 pages", abstract = "Project management covers the entire lifecycle of software, underpinning the success or failure of many software projects. Managing modern software projects often follows the incremental and iterative process where a software product is incrementally developed through a number of iterations. In each iteration, the development team needs to complete a number of issues, each of which can be implementing a new feature for the software, modifying an existing functionality, fixing a bug or conducting some other project tasks. Although this agile approach reduces the risk of project failures, managing projects at the level of issues and iterations is still highly difficult due to the inherent dynamic nature of software, especially in large-scale software projects. Challenges in this context can be in many forms such as making accurate estimations of the resolution time and effort of resolving issues or selecting suitable issues for upcoming iterations. These integral parts of planning is highly challenging since many factors need considering such as customer business value and the team historical estimations, capability and performance. Challenges also exist at the implementation level, such as managing the reviewing of code changes made to resolve issues. There is currently a serious lack of automated support which help project managers and software development teams address those challenges. This thesis aims to fill those gaps. We leverage a huge amount of historical data in software projects to generate valuable insight for dealing with those challenges in managing iterations and issues. We reformulate those project management problems as search-based optimization problems and employ a range of evolutionary meta-heuristics search techniques to solve them. The search is simultaneously guided by a number of multiple fitness functions that express different objectives (e.g. customer business value, developer expertise and workload, and complexity of estimation models) and constraints (e.g. a team historical capability and performance) in the context of modern software projects. Using this approach, we build novel models for estimating issue resolution time and effort, suggesting appropriate issues for upcoming iterations in iteration planning and recommending suitable reviewers for code changes made to resolve issues. An extensive empirical evaluation on a range of large software projects (including Mesos, Usergrid, Aurora, Slider, Kylin, Mahout, Common, Hdfs, MapReduce, Yarn, Apstud, Mule, Dnn, Timob, Tisud, Xd, Nexus, Android, LibreOffice, Qt, and Openstack) demonstrates the highly effective performance of our approach against other alternative techniques (improvement between 1.83 to 550 percent) to show the effectiveness of our approach.", notes = "supervisor: Hoa Khanh Dam", } @InProceedings{Alagesan:2008:AHS, author = "Shri Vidhya Alagesan and Sruthi Kannan and G. Shanthi and A. P. Shanthi and Ranjani Parthasarathi", title = "Intrinsic Evolution of Large Digital Circuits Using a Modular Approach", booktitle = "NASA/ESA Conference on Adaptive Hardware and Systems, AHS '08", year = "2008", month = jun, pages = "19--26", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, Xilinx Virtex II Pro board, evolvable hardware, large digital circuits, modular approach, modular developmental Cartesian genetic programming, scalability problem, software platform, time consuming fitness evaluation, digital circuits", DOI = "doi:10.1109/AHS.2008.52", abstract = "This work pioneers a generic and flexible approach to intrinsically evolve large digital circuits. One of the popular ways of handling the scalability problem prevalent in evolvable hardware (EHW) and evolve large circuits is to partition the circuit, evolve the individual partitions and then compact them. However, as the partition sizes become larger, this method also fails. This drawback is overcome by the modular developmental Cartesian genetic programming (MDCGP) technique, which still uses partitioning, but augments it further with horizontal and vertical reuse. The results obtained are promising and show that there is 100percent evolvability for 128-bit partitions, the largest partitions evolved so far. The fitness evaluation for the evolved partitions is done by downloading them onto Xilinx Virtex II Pro board. This work is the first step towards the development of a flexible evolvable framework which harnesses the power of hardware for the time consuming fitness evaluation and at the same time provides flexibility by carrying out the other parts using the easily modifiable software platform.", notes = "Also known as \cite{4584250}", } @Article{ALAGHBARI:2023:geoen, author = "Mohammed Al-Aghbari and Ashish {M. Gujarathi}", title = "Hybrid approach of using bi-objective genetic programming in well control optimization of waterflood management", journal = "Geoenergy Science and Engineering", volume = "228", pages = "211967", year = "2023", ISSN = "2949-8910", DOI = "doi:10.1016/j.geoen.2023.211967", URL = "https://www.sciencedirect.com/science/article/pii/S2949891023005547", keywords = "genetic algorithms, genetic programming, Multi-objective optimization, Bi-objective genetic programming, BioGP, NSGA-II, Net-flow method, Waterflood optimization, Reservoir simulation", abstract = "A new hybrid optimization approach is proposed by applying bi-objective genetic programming (BioGP) algorithm along with NSGA-II algorithm to expand the diversity of the Pareto solutions and speed up the convergence. The novel methodology is used in two distinct cases: the benchmark model for the Brugge field and a Middle Eastern oil-field sector model. The Brugge field includes twenty producing wells and ten injecting wells, but the real sector model has three injectors and four producers. The two primary objectives applied are to optimize the total volume of produced oil and reduce cumulative produced water. In the optimization process, the injection rate (qwi) and the bottom-hole pressure (BHP) are the control parameters for injection and producing wells, respectively. The hybrid technique of applying BioGP guided NSGA-II in the Brugge field model demonstrated a 50percent acceleration in the convergence speed when compared to the NSGA-II solution. The calculated Pareto solutions for the Middle-Eastern sector model by the proposed methodology at various generations exhibited better diversity and convergence in comparison to the NSGA-II solutions. The highest cumulative produced oil of 550.45 times 103 m3 is obtained by the proposed hybrid methodology in comparison to the NSGA-II's highest cumulative of 522 times 103 m3. The two solution points A' and B' achieved using the BioGP guided NSGA-II have lower WOR by 17percent and 15percent, respectively, than A and B solutions established by NSGA-II alone. Pareto solution ranking is performed using the net flow method (NFM) and the best optimum solution determined for BioGP guided NSGA-II is 532.38 times 103 m3 oil using equal-based weight compared to 505.44 times 103 m3 using the entropy-based weights of 41percent oil & 59percent water. Overall, the optimal Pareto solutions achieved by the proposed methodology of using BioGP guided NSGA-II algorithm has better diversity with improvement in convergence speed in comparison to the NSGA-II", } @InProceedings{Alajas:2022:IEMTRONICS, author = "Oliver John Alajas and Ronnie Concepcion and Argel Bandala and Edwin Sybingco and Ryan Rhay Vicerra and Elmer P. Dadios and Christan Hail Mendigoria and Heinrick Aquino and Leonard Ambata and Bernardo Duarte", booktitle = "2022 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS)", title = "Detection and Quantitative Prediction of Diplocarpon earlianum Infection Rate in Strawberry Leaves using Population-based Recurrent Neural Network", year = "2022", abstract = "Fragaria ananassa, a member of the rose family's flowering plants, commonly recognized as strawberry, is prone to Diplocarpon earlianum infection that causes leaf scorch. Assessment via visual inspection of strawberries by farmers is normally ineffective, destructive, and laborious. To address this challenge, the use of integrated computer vision and machine learning techniques was done to classify a healthy from a scorch-infected strawberry leaf image and to estimate the leaf region infection rate (LRIR). A dataset made up of 204 normally healthy and 161 scorch-infected strawberry leaf images was used. Images were initially preprocessed and segmented via graph-cut segmentation to extract the region of interest for feature extraction and selection. The hybrid combination of neighborhood and principal component analysis (NCA-PCA) was used to select desirable features. Multigene genetic programming (MGGP) was used to formulate the fitness function that will be essential for determining the optimized neuron configurations of the recurrent neural network (RNN) through genetic algorithm (GA), and cuckoo search algorithm (CSA), and artificial bee colony (ABC). Four classification machine learning models were configured in which the classification tree (CTree) bested other detection models with an accuracy of 10percent and exhibited the shortest inference time of 14.746 s. The developed ABC-RNN3 model outperformed GA-RNN3 and CSA-RNN3 in performing non-invasive LRIR prediction with an R2 value of 0.948. With the use of the NCA-PCA-CTree3-ABC-RNN3 hybrid model, for crop disease detection and infection rate prediction, plant disease assessment proved to be more efficient and labor cost-effective than manual disease inspection methods.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IEMTRONICS55184.2022.9795744", month = jun, notes = "Also known as \cite{9795744}", } @InProceedings{Alajas:2022:HNICEM, author = "Oliver John Alajas and Ronnie {Concepcion II} and Argel Bandala and Edwin Sybingco and Elmer Dadios and Christan Hail Mendigoria and Heinrick Aquino", title = "Grape Phaeomoniella chlamydospora Leaf Blotch Recognition and Infected Area Approximation Using Hybrid Linear Discriminant Analysis and Genetic Programming", booktitle = "2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)", year = "2022", address = "Boracay Island, Philippines", month = "1-4 " # dec, keywords = "genetic algorithms, genetic programming, Support vector machines, SVM, Image segmentation, Visualization, Image recognition, Computational modelling, Pipelines, Process control, image processing, plant disease detection, machine learning, computer vision, soft computing, black measles", ISSN = "2770-0682", isbn13 = "978-1-6654-6493-2", DOI = "doi:10.1109/HNICEM57413.2022.10109613", size = "6 pages", abstract = "Grapes, scientifically called Vitis vinifera, are vulnerable against Phaeomoniella chlamydospora, the microorganism that causes Esca (black measles) to the leaves, trunks, cordons, and fruit of a young vineyard. Manual visual examination via the naked eye can prove to be challenging especially if done in large-scale vineyards. To address this issue, merging the use of computer vision, image processing, and machine learning was employed as a means of performing blotch identification and leaf blotch area prediction. The dataset is made up of 543 images, comprised of healthy and Esca infected leaves which were captured by an RGB camera. Images were preprocessed and segmented to isolate the diseased pixels and compute the ground truth pixel area. Desirable leaf signatures (G, B, contrast, H, R, S, a*, b*, Cb, and Cr) derived from the feature extraction process using a classification tree. The LDA12 was able to accurately distinguish the healthy from the blotch-infected leaves with a whopping 98.77percent accuracy compared to NB, KNN, and SVM. The MGSR12, with an R2 of 0.9208, topped other models such as RTree, GPR, and RLinear. The hybrid CTree-LDA12-MGSR12 algorithm proved to be ideal in performing leaf health classification and blotched area assessment of grape phenotypes which is important in plant disease identification and fungal spread prevention.", notes = "Also known as \cite{10109613}", } @TechReport{Alander:1995:ibGP, author = "Jarmo T. Alander", title = "An Indexed Bibliography of Genetic Programming", institution = "Department of Information Technology and Industrial Management, University of Vaasa", year = "1995", type = "Report Series no", number = "94-1-GP", address = "Finland", URL = "ftp://ftp.uwasa.fi/cs/report94-1/gaGPbib.ps.Z", keywords = "genetic algorithms, genetic programming", abstract = "220 references. Indexed by subject, publication type and author", notes = "http url reference not working Jan 95. ftp ok. Part of Alander's index of genetic algorithm publications (older versions, ie up to ~1993, are available via ftp, see ENCORE sites). New version dated May 18, 1995. See also Jarmo T. Alander. An indexed bibliography of genetic algorithms: Years 1957-1993. Art of CAD Ltd., Vaasa (Finland), 1994. (over 3000 GA references).", size = "46 pages", } @Book{Alander:1994:bib, author = "Jarmo T. Alander", title = "An Indexed Bibliography of Genetic Algorithms: Years 1957--1993", year = "1994", publisher = "Art of CAD ltd", address = "Vaasa, Finland", keywords = "genetic algorithms, genetic programming", URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.53.4481&rep=rep1&type=pdf", notes = "All GAs some 3000+ references", } @InProceedings{ga96fAlander, annote = "*on,*FIN,genetic programming,mathematics /algebra", author = "Jarmo T. Alander and Ghodrat Moghadampour and Jari Ylinen", title = "2nd order equation", pages = "215--218", year = "1996", editor = "Jarmo T. Alander", booktitle = "Proceedings of the Second Nordic Workshop on Genetic Algorithms and their Applications (2NWGA)", series = "Proceedings of the University of Vaasa, Nro. 13", publisher = "University of Vaasa", address = "Vaasa (Finland)", month = "19.-23.~" # aug, organisation = "Finnish Artificial Intelligence Society", keywords = "genetic algorithms, genetic programming, mathematics, algebra", URL = "ftp://ftp.uwasa.fi/cs/2NWGA/Ghodrat2.ps.Z", size = "4 pages", abstract = "In this work we have tried to use genetic programming to solve the simple second order equation", notes = "2NWGA.bib gives title as 'Solving the second order equation using genetic programming' lil-gp evolution of formular for quadratic roots. lil-gp does not seem to be robust to find the solution formula of 2nd order equation", } @Article{ALANZI:2024:jer, author = "Hamdan Alanzi and Hamoud Alenezi and Oladayo Adeyi and Abiola J. Adeyi and Emmanuel Olusola and Chee-Yuen Gan and Olusegun Abayomi Olalere", title = "Process optimization, multi-gene genetic programming modeling and reliability assessment of bioactive extracts recovery from Phyllantus emblica", journal = "Journal of Engineering Research", year = "2024", ISSN = "2307-1877", DOI = "doi:10.1016/j.jer.2024.02.020", URL = "https://www.sciencedirect.com/science/article/pii/S2307187724000476", keywords = "genetic algorithms, genetic programming, leaf, bioactive extract, Heat-assisted technology, multi gene genetic programming, reliability assessment", abstract = "This study investigates the feasibility of extracting bioactive antioxidants from Phyllantus emblica leaves using a combination of ethanol-water mixture (0-100percent) and heat-assisted extraction technology (HAE-T). Operating temperature (30-50degreeC), solid-to-liquid ratio (1:20-1:60g/mL), and extraction time (45-180min) were varied to determine their effects on extract total phenolic content (TPC), yield (EY), and antioxidant activity (AA). The Box-Behnken experimental design (BBD) within response surface methodology (RSM) was employed, with multi-objective process optimization using the desirability function algorithm to find the optimal process variables for maximizing TPC, EY, and AA simultaneously. The extraction process was modeled using BBD-RSM and multi-gene genetic programming (MGGP) algorithm, with model reliability assessed via Monte Carlo simulation. HPLC characterization identified betulinic acid, gallic acid, chlorogenic acid, caffeic acid, ellagic acid, and ferulic acid as bioactive constituents in the extract. The study found that a 50percent ethanol solution yielded the best extraction efficiency. The optimal process parameters for maximum EY (21.6565percent), TPC (67.116mg GAE/g), and AA (3.68583uM AAE/g) were determined as OT of 41.61degreeC, S:L of 1:60g/mL, and ET of 180min. Both BBD-RSM and MGGP-based models satisfactorily predicted the observed process responses, with BBD-RSM models showing slightly better performance. Reliability analysis indicated high certainty in the predictions, with BBD-RSM models achieving 99.985percent certainty for TPC, 97.569percent for EY, and 98.661percent for AA values", } @Article{ALARFAJ:2024:cscm, author = "Mohammed Alarfaj and Hisham Jahangir Qureshi and Muhammad Zubair Shahab and Muhammad Faisal Javed and Md Arifuzzaman and Yaser Gamil", title = "Machine learning based prediction models for spilt tensile strength of fiber reinforced recycled aggregate concrete", journal = "Case Studies in Construction Materials", volume = "20", pages = "e02836", year = "2024", ISSN = "2214-5095", DOI = "doi:10.1016/j.cscm.2023.e02836", URL = "https://www.sciencedirect.com/science/article/pii/S2214509523010173", keywords = "genetic algorithms, genetic programming, Gene expression programming, Fiber reinforced Recycled Aggregate Concrete, Machine Learning, Sustainability, Eco-friendly Concrete, Spilt Tensile Strength, Deep neural networks, ANN, Optimizable gaussian process regression", abstract = "The demand for concrete production has led to a significant annual requirement for raw materials, resulting in a substantial amount of waste concrete. In response, recycled aggregate concrete has emerged as a promising solution. However, it faces challenges due to the vulnerability of the hardened mortar attached to natural aggregates, leading to susceptibility to cracking and reduced strength. This study focuses on predicting the split tensile strength of fiber reinforced recycled aggregate concrete using five prediction models, including two deep neural network models DNN1 and DNN2, one optimizable Gaussian process regression (OGPR), and two genetic programming based GEP1 and GEP2 models. The models exhibited high accuracy in predicting spilt tensile strength with robust R2, RMSE, and MAE values. DNN2 has the highest R2 value of 0.94 and GEP1 has the lowest R2 value of 0.76. DNN2 model R2 was 3.3percent and 13.5percent higher than OGPR and GEP2. Similarly, DNN2 and GEP2 model performed 9.3percent and 9.21percent better than DNN1 and GEP1 respectively in terms of R2. DNN2 model performed 20.32percent and 31.5percent better than OGPR and GEP2 in terms of MAE. Similarly, GEP2 and DNN2 MAE were 13.1percent and 31.5percent better than GEP1 and DNN1. Sensitivity analysis using the relevance factor and permutation feature importance revealed that the most significant positive factors are cement, natural coarse aggregates, density of recycle aggregates, and superplasticizer while recycle aggregate concrete, max size, and water content of recycle aggregates and water content have the most negative effect on STS values. The proposed ML methods, especially DNN2 and OGPR can be effectively used in practical projects, saving time and cost for eco-friendly fiber reinforced recycled aggregate concrete mixes. However, it is required to study more input variables and use hybrid models to further enhance the accuracy and reliability of the models", } @Article{ALASKAR:2023:cscm, author = "Abdulaziz Alaskar and Ghasan Alfalah and Fadi Althoey and Mohammed Awad Abuhussain and Muhammad Faisal Javed and Ahmed Farouk Deifalla and Nivin A. Ghamry", title = "Comparative study of genetic programming-based algorithms for predicting the compressive strength of concrete at elevated temperature", journal = "Case Studies in Construction Materials", volume = "18", pages = "e02199", year = "2023", keywords = "genetic algorithms, genetic programming, gene expression programming", ISSN = "2214-5095", DOI = "doi:10.1016/j.cscm.2023.e02199", URL = "https://www.sciencedirect.com/science/article/pii/S2214509523003790", abstract = "The elevated temperature severely influences the mixed properties of concrete, causing a decrease in its strength properties. Accurate proportioning of concrete components for obtaining the required compressive strength (C-S) at elevated temperatures is a complicated and time-taking process. However, using evolutionary programming techniques such as gene expression programming (GEP) and multi-expression programming (MEP) provides the accurate prediction of concrete C-S. This article presents the genetic programming-based models (such as gene expression programming (GEP) and multi-expression programming (MEP)) for forecasting the concrete compressive strength (C-S) at elevated temperatures. In this regard, 207 C-S values at elevated temperatures were obtained from previous studies. In the model's development, C-S was considered as the output parameter with the nine most influential input parameters, including; Nano silica, cement, fly ash, water, temperature, silica fume, superplasticizer, sand, and gravels. The efficacy and accuracy of the GEP and MEP-based models were assessed by using statistical measures such as mean absolute error (MAE), correlation coefficient (R2), and root mean square error (RMSE). Moreover, models were also evaluated for external validation using different validation criteria recommended by previous studies. In comparing GEP and MEP models, GEP gave higher R2 and lower RMSE and MAE values of 0.854, 5.331 MPa, and 0.018 MPa respectively, indicating a strong correlation between actual and anticipated outputs. Thus, the GEP-based model was used further for sensitivity analysis, which revealed that cement is the most influencing factor. In addition, the proposed GEP model provides simple mathematical expression that can be easily implemented in practice", } @Article{ALATEFI:2024:cherd, author = "Saad Alatefi and Okorie Ekwe Agwu and Reda Abdel Azim and Ahmad Alkouh and Iskandar Dzulkarnain", title = "Development of multiple explicit data-driven models for accurate prediction of {CO2} minimum miscibility pressure", journal = "Chemical Engineering Research and Design", year = "2024", ISSN = "0263-8762", DOI = "doi:10.1016/j.cherd.2024.04.033", URL = "https://www.sciencedirect.com/science/article/pii/S0263876224002351", keywords = "genetic algorithms, genetic programming, Artificial intelligence, CO2, Explicit models, Gas flooding, Minimum miscibility pressure", abstract = "multiple data-driven models for predicting CO2 minimum miscibility pressure (MMP). The aim is to address the issue of existing models lacking explicit presentation. With a database of 155 data points, five models were developed using artificial neural network (ANN), multigene genetic programming (MGGP), support vector regression (SVR), multivariate adaptive regression splines (MARS), and multiple linear regression (MLR). Comparative analysis was conducted using statistical metrics (R2, MSE, MAE, RMSE), and sensitivity analysis was performed on input variables. The results showed that ANN and SVR had comparable predictive performance (ANN: R2 = 0.982, MSE = 0.00676, MAE = 0.9765, RMSE = 0.082), SVR (R2 = 0.935, MSE = 0.0041, MAE = 0.72, RMSE = 0.064) followed by MARS, MLR, and MGGP. Sensitivity analysis revealed that reservoir temperature was the most influential parameter across all models, except for the MLR algorithm where injected CO2 amount was crucial. These models can be used for a wide range of CO2 MMP ranging from 940psi to 5830psi, thus rendering them useful for any reservoir globally. These models offer improved accuracy and computational efficiency compared to existing ones, potentially reducing costs associated with laboratory experiments and providing rapid and precise CO2 MMP predictions", } @InProceedings{Alattas:2016:CT-IETA, author = "R. Alattas", booktitle = "2016 Annual Connecticut Conference on Industrial Electronics, Technology Automation (CT-IETA)", title = "Hybrid evolutionary designer of modular robots", year = "2016", abstract = "The majority of robotic design approaches start with designing morphology, then designing the robot control. Even in evolutionary robotics, the morphology tends to be fixed while evolving the robot control, which considered insufficient since the robot control and morphology are interdependent. Moreover, both control and morphology are highly interdependent with the surrounding environment, which affects the used optimisation strategies. Therefore, we propose in this paper a novel hybrid GP/GA method for designing autonomous modular robots that co-evolves the robot control and morphology and also considers the surrounding environment to allow the robot of achieving behaviour specific tasks and adapting to the environmental changes. The introduced method is automatically designing feasible robots made up of various modules. Then, our new evolutionary designer is evaluated using a benchmark problem in modular robotics, which is a walking task where the robot has to move a certain distance.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CT-IETA.2016.7868256", month = oct, notes = "Also known as \cite{7868256}", } @InProceedings{Alavi:2008:ICECT, author = "A. H. Alavi and A. A. Heshmati and H. Salehzadeh and A. H. Gandomi and A. Askarinejad", title = "Soft Computing Based Approaches for High Performance Concrete", booktitle = "Proceedings of the Sixth International Conference on Engineering Computational Technology", year = "2008", editor = "M. Papadrakakis and B. H. V. Topping", volume = "89", series = "Civil-Comp Proceedings", pages = "Paper 86", address = "Athens", publisher_address = "Stirlingshire, UK", month = "2-5 " # sep, publisher = "Civil-Comp Press", keywords = "genetic algorithms, genetic programming, linear genetic programming, high performance concrete, multilayer perceptron, compressive strength, workability, mix design", isbn13 = "978-1905088263", ISSN = "1759-3433", URL = "http://www.civil-comp.com/pubs/catalog.htm?t=contents&f=26_3", URL = "http://www.amazon.co.uk/Proceedings-International-Conference-Engineering-Computational/dp/1905088264", DOI = "doi:10.4203/ccp.89.86", abstract = "High performance concrete (HPC) is a class of concrete that provides superior performance than those of conventional types. The enhanced performance characteristics of HPC are generally achieved by the addition of various cementitious materials and chemical and mineral admixtures to conventional concrete mix designs. These parameters considerably influence the compressive strength and workability properties of HPC mixes. An extensive understanding of the relation between these parameters and properties of the resulting matrix is required for developing a standard mix design procedure for HPC mix. To avoid testing several mix proportions to generate a successful mix and also simulating the behaviour of strength and workability improvement to an arbitrary degree of accuracy that often lead to savings in cost and time, it is idealistic to develop prediction models so that the performance characteristics of HPC mixes can be evaluated from the influencing parameters. Therefore, in this paper, linear genetic programming (LGP) is used for the first time in the literature to develop mathematical models to be able to predict the strength and slump flow of HPC mixes in terms of the variables responsible. Subsequently, the LGP based prediction results are compared with the results of proposed multilayer perceptron (MLP) in terms of prediction performance. Sand-cement ratio, coarse aggregate-cement ratio, water-cement ratio, percentage of silica fume and percentage of superplasticiser are used as the input variables to the models to predict the strength and slump flow of HPC mixes. A reliable database was obtained from the previously published literature in order to develop the models. The results of the present study, based on the values of performance measures for the models, demonstrated that for the prediction of compressive strength the optimum MLP model outperforms both the best team and the best single solution that have been created by LGP. It can be seen that for the slump flow the best LGP team solution has produced better results followed by the LGP best single solution and the MLP model. It can be concluded that LGPs are able to reach a prediction performance very close to or even better than the MLP model and as promising candidates can be used for solving such complex prediction problems.", notes = "A.H. Alavi1, A.A. Heshmati1, H. Salehzadeh1, A.H. Gandomi2 and A. Askarinejad3 1College of Civil Engineering, Iran University of Science & Technology (IUST), Tehran, Iran 2College of Civil Engineering, Tafresh University, Iran 3Department of Civil, Environmental and Geomatic Engineering, Swiss Federal Institute of Technology, Zurich, Switzerland", } @InProceedings{Alavi:2008:ICECT2, author = "A. H. Alavi and A. A. Heshmati and A. H. Gandomi and A. Askarinejad and M. Mirjalili", title = "Utilisation of Computational Intelligence Techniques for Stabilised Soil", booktitle = "Proceedings of the Sixth International Conference on Engineering Computational Technology", year = "2008", editor = "M. Papadrakakis and B. H. V. Topping", volume = "89", series = "Civil-Comp Proceedings", pages = "Paper 175", address = "Athens", publisher_address = "Stirlingshire, UK", month = "2-5 " # sep, publisher = "Civil-Comp Press", keywords = "genetic algorithms, genetic programming, linear genetic programming, stabilised soil, multilayer perceptron, textural properties of soil, cement, lime, asphalt, unconfined compressive strength", isbn13 = "978-1905088263", ISSN = "1759-3433", URL = "http://www.civil-comp.com/pubs/catalog.htm?t=contents&f=26_3", URL = "http://www.amazon.co.uk/Proceedings-International-Conference-Engineering-Computational/dp/1905088264", DOI = "doi:10.4203/ccp.89.175", abstract = "In the present study, two branches of computational intelligence techniques namely, the multilayer perceptron (MLP) and linear genetic programming (LGP), are employed to simulate the complex behaviour of the strength improvement in a chemical stabilisation process. Due to a need to avoid extensive and cumbersome experimental stabilisation tests on soils on every new occasion, it was decided to develop mathematical models to be able to estimate the unconfined compressive strength (UCS) as a quality of the stabilised soil after both compaction and curing by using particle size distribution, liquid limit, plasticity index, linear shrinkage as the properties of natural soil before compaction and stabilisation and the quantities and types of stabiliser. A comprehensive and reliable set of data including 219 previously published UCS test results were used to develop the prediction models. Based on the values of performance measures for the models, it was observed that all models are able to predict the UCS value to an acceptable degree of accuracy. The results demonstrated that the optimum MLP model with one hidden layer and thirty six neurons outperforms both the best single and the best team program that have been created by LGP. It can also be concluded that the best team program evolved by LGP has a better performance than the best single evolved program. This investigation revealed that, on average, LGP is able to reach a prediction performance similar to the MLP model. Moreover, LGP as a white-box model provides the programs of an imperative language or machine language that can be inspected and evaluated to provide a better understanding of the underlying relationship between the different interrelated input and output data.", notes = "A.H. Alavi1, A.A. Heshmati1, A.H. Gandomi2, A. Askarinejad3 and M. Mirjalili4 1College of Civil Engineering, Iran University of Science and Technology, Tehran, Iran 2College of Civil Engineering, Tafresh University, Iran 3Department of Civil, Environmental and Geomatic Engineering, Swiss Federal Institute of Technology, Zurich, Switzerland 4Department of Civil & Earth Resources Engineering, Graduate School of Engineering, Kyoto University, Japan", } @Article{Alavi:2010:HP, author = "A. H. Alavi and A. H. Gandomi and M. Gandomi", title = "Comment on 'Sivapragasam C, Maheswaran R, Venkatesh V. 2008. Genetic programming approach for flood routing in natural channels. Hydrological Processes 22: 623-628'", journal = "Hydrological Processes", year = "2010", volume = "24", number = "6", pages = "798--799", month = "15 " # mar, keywords = "genetic algorithms, genetic programming, AIMGP, Discipulus", publisher = "John Wiley & Sons, Ltd.", ISSN = "1099-1085", URL = "http://onlinelibrary.wiley.com/doi/10.1002/hyp.7511/abstract", DOI = "doi:10.1002/hyp.7511", size = "1.5 pages", notes = "no abstract About \cite{Sivapragasam:2007:HP}. See also \cite{Sivapragasam:2010:HP}", } @Article{Alavi:2010:EwC, author = "Amir Hossein Alavi and Amir Hossein Gandomi and Mohammad Ghasem Sahab and Mostafa Gandomi", title = "Multi Expression Programming: A New Approach to Formulation of Soil Classification", journal = "Engineering with Computers", year = "2010", volume = "26", number = "2", pages = "111--118", month = apr, email = "ah_alavi@hotmail.com, a.h.gandomi@gmail.com", keywords = "genetic algorithms, genetic programming, Multi expression programming, Soil classification, Formulation", DOI = "doi:10.1007/s00366-009-0140-7", size = "8 pages", abstract = "This paper presents an alternative approach to formulation of soil classification by means of a promising variant of genetic programming (GP), namely multi expression programming (MEP). Properties of soil, namely plastic limit, liquid limit, colour of soil, percentages of gravel, sand, and fine-grained particles are used as input variables to predict the classification of soils. The models are developed using a reliable database obtained from the previously published literature. The results demonstrate that the MEP-based formulae are able to predict the target values to high degree of accuracy. The MEP-based formulation results are found to be more accurate compared with numerical and analytical results obtained by other researchers.", notes = "M. Gandomi School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran", } @Article{Alavi:2010:GeoMechEng, author = "Amir Hossein Alavi and Amir Hossein Gandomi and Mehdi Mousavi and Ali Mollahasani", title = "High-Precision Modeling of Uplift Capacity of Suction Caissons Using a Hybrid Computational Method", journal = "Geomechanics and Engineering", year = "2010", volume = "2", number = "4", pages = "253--280", month = dec, keywords = "genetic algorithms, genetic programming, suction caissons, uplift capacity, simulated annealing, nonlinear modelling", URL = "http://technopress.kaist.ac.kr/?page=container&journal=gae&volume=2&num=4", DOI = "doi:10.12989/gae.2010.2.4.253", abstract = "A new prediction model is derived for the uplift capacity of suction caissons using a hybrid method coupling genetic programming (GP) and simulated annealing (SA), called GP/SA. The predictor variables included in the analysis are the aspect ratio of caisson, shear strength of clayey soil, load point of application, load inclination angle, soil permeability, and loading rate. The proposed model is developed based on well established and widely dispersed experimental results gathered from the literature. To verify the applicability of the proposed model, it is employed to estimate the uplift capacity of parts of the test results that are not included in the modelling process. Traditional GP and multiple regression analyses are performed to benchmark the derived model. The external validation of the GP/SA and GP models was further verified using several statistical criteria recommended by researchers. Contributions of the parameters affecting the uplift capacity are evaluated through a sensitivity analysis. A subsequent parametric analysis is carried out and the obtained trends are confirmed with some previous studies. Based on the results, the GP/SA-based solution is effectively capable of estimating the horizontal, vertical and inclined uplift capacity of suction caissons. Furthermore, the GP/SA model provides a better prediction performance than the GP, regression and different models found in the literature. The proposed simplified formulation can reliably be employed for the pre-design of suction caissons. It may be also used as a quick check on solutions developed by more time consuming and in-depth deterministic analyses.", } @Article{Alavi:2010:ijcamieec, author = "Amir Hossein Alavi and Amir Hossein Gandomi", title = "A Robust Data Mining Approach for Formulation of Geotechnical Engineering Systems", journal = "International Journal of Computer Aided Methods in Engineering-Engineering Computations", year = "2011", volume = "28", number = "3", pages = "242--274", email = "ah_alavi@hotmail.com, a.h.gandomi@gmail.com", keywords = "genetic algorithms, genetic programming, gene expression programming, multi expression programming, Linear-based genetic programming, Data mining, Data collection, Geotechnical engineering, Programming and algorithm theory, Systems analysis, Formulation", ISSN = "0264-4401", URL = "http://www.emeraldinsight.com/journals.htm?articleid=1912293", DOI = "doi:10.1108/02644401111118132", size = "33 pages", abstract = "Purpose- The complexity of analysis of geotechnical behaviour is due to multivariable dependencies of soil and rock responses. In order to cope with this complex behavior, traditional forms of engineering design solutions are reasonably simplified. Incorporating simplifying assumptions into the development of the traditional models may lead to very large errors. In the present study, capabilities of promising variants of genetic programming (GP), namely linear genetic programming (LGP), gene expression programming (GEP) and multi expression programming (MEP) are illustrated by applying them to the formulation of several complex geotechnical engineering problems. Design/methodology/approach- LGP, GEP and MEP are new variants of GP that make a clear distinction between the genotype and the phenotype of an individual. Compared with the traditional GP, the LGP, GEP and MEP techniques are more compatible with computer architectures. This results in a significant speedup in their execution. These methods have a great ability to directly capture the knowledge contained in the experimental data without making assumptions about the underlying rules governing the system. This is one their major advantages over most of the traditional constitutive modeling methods. Findings- In order to demonstrate the simulation capabilities of LGP, GEP and MEP, they were applied to the prediction of (i) relative crest settlement of concrete-faced rockfill dams, (ii) slope stability, (iii) settlement around tunnels, and (iv) soil liquefaction. The results are compared with those obtained by other models presented in the literature and found to be more accurate. LGP has the best overall behaviour for the analysis of the considered problems in comparison with GEP and MEP. The simple and straightforward constitutive models developed using LGP, GEP and MEP provide valuable analysis tools accessible to practising engineers. Originality/value- The LGP, GEP and MEP approaches overcome the shortcomings of different methods previously presented in the literature for the analysis of geotechnical engineering systems. Contrary to artificial neural networks and many other soft computing tools, LGP, GEP and MEP provide prediction equations that can readily be used for routine design practice. The constitutive models derived using these methods can efficiently be incorporated into the finite element or finite difference analyses as material models. They may also be used as a quick check on solutions developed by more time consuming and in-depth deterministic analyses.", } @InProceedings{Alavi:2010:HBE, author = "Amir Hossein Alavi and Amir Hossein Gandomi", title = "Nonlinear Modeling of Liquefaction Behavior of Sand-Silt Mixtures in terms of Strain Energy", booktitle = "Proceedings of the 8th International Symposium on Highway and Bridge Engineering, Technology and Innovation in Transportation Infrastructure, 2010", year = "2010", editor = "Rodian Scinteie and Costel Plescan", pages = "50--69", address = "Iasi, Romania", month = "10 " # dec, organisation = "Editura Societatii Academice Matei - Teiu Botez", keywords = "genetic algorithms, genetic programming, GPLAB, Discipulus, simulated annealing, capacity energy, Matlab", isbn13 = "978-606-582-000-5", URL = "http://www.intersections.ro/Conferences/HBE2010.pdf", size = "20 pages", notes = "http://www.intersections.ro/Conferences/ HBE2010@intersections.ro", } @Article{Alavi:2010:CBM, author = "Amir Hossein Alavi and Mahmoud Ameri and Amir Hossein Gandomi and Mohammad Reza Mirzahosseini", title = "Formulation of Flow Number of Asphalt Mixes Using a Hybrid Computational Method", journal = "Construction and Building Materials", year = "2011", volume = "25", number = "3", pages = "1338--1355", month = mar, keywords = "genetic algorithms, genetic programming, Asphalt concrete mixture, Flow number, Simulated annealing, Marshall mix design, Regression analysis", ISSN = "0950-0618", DOI = "doi:10.1016/j.conbuildmat.2010.09.010", size = "18 pages", abstract = "A high-precision model was derived to predict the flow number of dense asphalt mixtures using a novel hybrid method coupling genetic programming and simulated annealing, called GP/SA. The proposed constitutive model correlates the flow number of Marshall specimens with the percentages of filler, bitumen, voids in mineral aggregate, Marshall stability and flow. The comprehensive experimental database used for the development of the model was established upon a series of uniaxial dynamic creep tests conducted in this study. Generalised regression neural network and multiple regression-based analyses were performed to benchmark the GP/SA model. The contributions of the variables affecting the flow number were evaluated through a sensitivity analysis. A subsequent parametric study was carried out and the trends of the results were confirmed with the results of the experimental study. The results indicate that the proposed GP/SA model is effectively capable of evaluating the flow number of asphalt mixtures. The derived model is remarkably straightforward and provides an analysis tool accessible to practising engineers.", notes = "a School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran b College of Civil Engineering, Tafresh University, Tafresh, Iran c Transportation Research Institute (TRI), Tehran, Iran", } @Article{Alavi20101239, author = "A. H. Alavi and A. H. Gandomi and A. A. R. Heshmati", title = "Discussion on {"}Soft computing approach for real-time estimation of missing wave heights{"} by S.N. Londhe [Ocean Engineering 35 (2008) 1080-1089]", journal = "Ocean Engineering", year = "2010", volume = "37", number = "13", pages = "1239--1240", month = sep, ISSN = "0029-8018", DOI = "doi:10.1016/j.oceaneng.2010.06.003", URL = "http://www.sciencedirect.com/science/article/B6V4F-50DXD90-1/2/b2489a1aebf49e771abca1b27d3b24b4", keywords = "genetic algorithms, genetic programming, Linear genetic programming, Tree structure, Wave forecasts", abstract = "The paper studied by Londhe (2008) \cite{Londhe20081080} uses genetic programming (GP) for estimation of missing wave heights. The paper includes some problems about the fundamental aspects and use of the GP approach. In this discussion, some controversial points of the paper are given.", } @Article{Alavi2011, author = "Amir Hossein Alavi and Pejman Aminian and Amir Hossein Gandomi and Milad {Arab Esmaeili}", title = "Genetic-based modeling of uplift capacity of suction caissons", journal = "Expert Systems with Applications", volume = "38", number = "10", pages = "12608--12618", year = "2011", month = "15 " # sep, ISSN = "0957-4174", URL = "http://www.sciencedirect.com/science/article/pii/S0957417411005653", URL = "http://www.sciencedirect.com/science/article/B6V03-52P1KNK-4/2/f33267200d0fc51ad7a086befe3a361c", DOI = "doi:10.1016/j.eswa.2011.04.049", keywords = "genetic algorithms, genetic programming, Gene expression programming, Suction caissons, Uplift capacity, Formulation", size = "11 pages", abstract = "In this study, classical tree-based genetic programming (TGP) and its recent variants, namely linear genetic programming (LGP) and gene expression programming (GEP) are used to develop new prediction equations for the uplift capacity of suction caissons. The uplift capacity is formulated in terms of several inflecting variables. An experimental database obtained from the literature is employed to develop the models. Further, a conventional statistical analysis is performed to benchmark the proposed models. Sensitivity and parametric analyses are conducted to verify the results. TGP, LGP and GEP are found to be effective methods for evaluating the horizontal, vertical, and inclined uplift capacity of suction caissons. The TGP, LGP and GEP models reach a prediction performance better than or comparable with the models found in the literature.", } @Article{Alavi:2011:JEQE, author = "Amir Hossein Alavi and Amir Hossein Gandomi and Minoo Modaresnezhad and Mehdi Mousavi", title = "New Ground-Motion Prediction Equations Using Multi Expression Programing", journal = "Journal of Earthquake Engineering", year = "2011", volume = "15", number = "4", pages = "511--536", keywords = "genetic algorithms, genetic programming, Multi-Expression Programming, Time-Domain Ground-Motion Parameters, Attenuation Relationship, Nonlinear Modelling", ISSN = "1363-2469", URL = "http://www.tandfonline.com/doi/abs/10.1080/13632469.2010.526752#.UlMR6NKc_G0", DOI = "doi:10.1080/13632469.2010.526752", size = "26 pages", abstract = "High-precision attenuation models were derived to estimate peak ground acceleration (PGA), velocity (PGV), and displacement (PGD) using a new variant of genetic programming, namely multi expression programming (MEP). The models were established based on an extensive database of ground-motion recordings released by Pacific Earthquake Engineering Research Center (PEER). For more validity verification, the models were employed to predict the ground-motion parameters of the Iranian plateau earthquakes. The results indicate that the MEP attenuation models are capable of effectively estimating the peak ground-motion parameters. The proposed models are able to reach a prediction performance comparable with the attenuation relationships found in the literature.", } @Article{Alavi2012541, author = "Amir Hossein Alavi and Amir Hossein Gandomi", title = "Energy-based numerical models for assessment of soil liquefaction", journal = "Geoscience Frontiers", volume = "3", number = "4", pages = "541--555", year = "2012", ISSN = "1674-9871", DOI = "doi:10.1016/j.gsf.2011.12.008", URL = "http://www.sciencedirect.com/science/article/pii/S167498711100137X", keywords = "genetic algorithms, genetic programming, Soil liquefaction, Capacity energy, Multi expression programming, Sand, Formulation", abstract = "This study presents promising variants of genetic programming (GP), namely linear genetic programming (LGP) and multi expression programming (MEP) to evaluate the liquefaction resistance of sandy soils. Generalised LGP and MEP-based relationships were developed between the strain energy density required to trigger liquefaction (capacity energy) and the factors affecting the liquefaction characteristics of sands. The correlations were established based on well established and widely dispersed experimental results obtained from the literature. To verify the applicability of the derived models, they were employed to estimate the capacity energy values of parts of the test results that were not included in the analysis. The external validation of the models was verified using statistical criteria recommended by researchers. Sensitivity and parametric analyses were performed for further verification of the correlations. The results indicate that the proposed correlations are effectively capable of capturing the liquefaction resistance of a number of sandy soils. The developed correlations provide a significantly better prediction performance than the models found in the literature. Furthermore, the best LGP and MEP models perform superior than the optimal traditional GP model. The verification phases confirm the efficiency of the derived correlations for their general application to the assessment of the strain energy at the onset of liquefaction.", } @InCollection{books/sp/chiong2012/AlaviGM12, author = "Amir Hossein Alavi and Amir Hossein Gandomi and Ali Mollahasani", title = "A Genetic Programming-Based Approach for the Performance Characteristics Assessment of Stabilized Soil", booktitle = "Variants of Evolutionary Algorithms for Real-World Applications", publisher = "Springer", year = "2012", editor = "Raymond Chiong and Thomas Weise and Zbigniew Michalewicz", chapter = "9", pages = "343--376", keywords = "genetic algorithms, genetic programming, Chemical stabilisation, Simulated annealing, Nonlinear modelling", isbn13 = "978-3-642-23423-1", DOI = "doi:10.1007/978-3-642-23424-8_11", abstract = "This chapter presents a variant of genetic programming, namely linear genetic programming (LGP), and a hybrid search algorithm coupling LGP and simulated annealing (SA), called LGP/SA, to predict the performance characteristics of stabilised soil. LGP and LGP/SA relate the unconfined compressive strength (UCS), maximum dry density (MDD), and optimum moisture content (OMC) metrics of stabilised soil to the properties of the natural soil as well as the types and quantities of stabilizing additives. Different sets of LGP and LGP/SA-based prediction models have been separately developed. The contributions of the parameters affecting UCS, MDD, and OMC are evaluated through a sensitivity analysis. A subsequent parametric analysis is carried out and the trends of the results are compared with previous studies. A comprehensive set of data obtained from the literature has been used for developing the models. Experimental results confirm that the accuracy of the proposed models is satisfactory. In particular, the LGP-based models are found to be more accurate than the LGP/SA-based models.", affiliation = "School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran", bibdate = "2011-11-16", bibsource = "DBLP, http://dblp.uni-trier.de/db/books/collections/Chiong2012.html#AlaviGM12", } @InCollection{Alavi:2013:MWGTE, author = "Amir Hossein Alavi and Amir Hossein Gandomi and Ali Mollahasani and Jafar {Bolouri Bazaz}", title = "Linear and Tree-Based Genetic Programming for Solving Geotechnical Engineering Problems", editor = "Xin-She Yang and Amir Hossein Gandomi and Siamak Talatahari and Amir Hossein Alavi", booktitle = "Metaheuristics in Water, Geotechnical and Transport Engineering", publisher = "Elsevier", address = "Oxford", year = "2013", pages = "289--310", chapter = "12", keywords = "genetic algorithms, genetic programming, Tree-based genetic programming, linear genetic programming, geotechnical engineering, prediction", isbn13 = "978-0-12-398296-4", DOI = "doi:10.1016/B978-0-12-398296-4.00012-X", URL = "http://www.sciencedirect.com/science/article/pii/B978012398296400012X", abstract = "This chapter presents new approaches for solving geotechnical engineering problems using classical tree-based genetic programming (TGP) and linear genetic programming (LGP). TGP and LGP are symbolic optimisation techniques that create computer programs to solve a problem using the principle of Darwinian natural selection. Generally, they are supervised, machine-learning techniques that search a program space instead of a data space. Despite remarkable prediction capabilities of the TGP and LGP approaches, the contents of reported applications indicate that the progress in their development is marginal and not moving forward. The present study introduces a state-of-the-art examination of TGP and LGP applications in solving complex geotechnical engineering problems that are beyond the computational capability of traditional methods. In order to justify the capabilities of these techniques, they are systematically employed to formulate a typical geotechnical engineering problem. For this aim, effective angle of shearing resistance (phi) of soils is formulated in terms of the physical properties of soil. The validation of the TGP and LGP models is verified using several statistical criteria. The numerical example shows the superb accuracy, efficiency, and great potential of TGP and LGP. The models obtained using TGP and LGP can be used efficiently as quick checks on solutions developed by more time consuming and in-depth deterministic analyses. The current research directions and issues that need further attention in the future are discussed. Keywords Tree-based genetic programming, linear genetic programming geotechnical engineering, prediction", notes = "Also known as \cite{Alavi2013289}", } @Article{Alavi:2014:NCA, author = "Amir Hossein Alavi and Amir Hossein Gandomi and Hadi {Chahkandi Nejad} and Ali Mollahasani and Azadeh Rashed", title = "Design equations for prediction of pressuremeter soil deformation moduli utilizing expression programming systems", journal = "Neural Computing and Applications", year = "2013", volume = "23", number = "6", pages = "1771--1786", month = nov, keywords = "genetic algorithms, genetic programming, gene expression programming, Soil deformation modulus, Expression programming techniques, Pressure meter test, Soil physical properties", publisher = "Springer-Verlag", ISSN = "0941-0643", URL = "http://link.springer.com/article/10.1007%2Fs00521-012-1144-6", DOI = "doi:10.1007/s00521-012-1144-6", language = "English", size = "16 pages", abstract = "Providing precise estimations of soil deformation modulus is very difficult due to its dependence on many factors. In this study, gene expression programming (GEP) and multi-expression programming (MEP) systems are presented to derive empirical equations for the prediction of the pressuremeter soil deformation modulus. The employed expression programming (EP) systems formulate the soil deformation modulus in terms of the soil physical properties. Selection of the best models is on the basis of developing and controlling several models with different combinations of the affecting parameters. The proposed EP-based models are established upon 114 pressure meter tests on different soil types conducted in this study. The generalisation capabilities of the models are verified using several statistical criteria. Contributions of the variables influencing the soil modulus are evaluated through a sensitivity analysis. The GEP and MEP approaches accurately characterise the soil deformation modulus resulting in a very good prediction performance. The result indicates that moisture content and soil dry unit weight can efficiently represent the initial state and consolidation history of soil for determining its modulus.", } @Article{Alavi:2014:GF, author = "Amir H. Alavi and Ehsan Sadrossadat", title = "New design equations for estimation of ultimate bearing capacity of shallow foundations resting on rock masses", journal = "Geoscience Frontiers", year = "2014", keywords = "genetic algorithms, genetic programming, Rock mass properties, Ultimate bearing capacity, Shallow foundation, Prediction, Evolutionary computation", ISSN = "1674-9871", DOI = "doi:10.1016/j.gsf.2014.12.005", URL = "http://www.sciencedirect.com/science/article/pii/S1674987114001625", abstract = "Rock masses are commonly used as the underlying layer of important structures such as bridges, dams and transportation constructions. The success of a foundation design for such structures mainly depends on the accuracy of estimating the bearing capacity of rock beneath them. Several traditional numerical approaches are proposed for the estimation of the bearing capacity of foundations resting on rock masses to avoid performing elaborate and expensive experimental studies. Despite this fact, there still exists a serious need to develop more robust predictive models. This paper proposes new nonlinear prediction models for the ultimate bearing capacity of shallow foundations resting on non-fractured rock masses using a novel evolutionary computational approach, called linear genetic programming. A comprehensive set of rock socket, centrifuge rock socket, plate load and large-scaled footing load test results is used to develop the models. In order to verify the validity of the models, the sensitivity analysis is conducted and discussed. The results indicate that the proposed models accurately characterise the bearing capacity of shallow foundations. The correlation coefficients between the experimental and predicted bearing capacity values are equal to 0.95 and 0.96 for the best LGP models. Moreover, the derived models reach a notably better prediction performance than the traditional equations.", } @Article{Alavi:2016:GSF, author = "Amir H. Alavi and Amir H. Gandomi and David J. Lary", title = "Progress of machine learning in geosciences: Preface", journal = "Geoscience Frontiers", year = "2016", volume = "7", number = "1", pages = "1--2", note = "Editorial", keywords = "genetic algorithms, genetic programming", ISSN = "1674-9871", URL = "http://www.sciencedirect.com/science/article/pii/S1674987115001243", DOI = "doi:10.1016/j.gsf.2015.10.006", size = "2 pages", notes = "Peer-review under responsibility of China University of Geosciences (Beijing)", } @Article{Alavi:2017:ACME, author = "Amir H. Alavi and Hassene Hasni and Imen Zaabar and Nizar Lajnef", title = "A new approach for modeling of flow number of asphalt mixtures", journal = "Archives of Civil and Mechanical Engineering", volume = "17", number = "2", pages = "326--335", year = "2017", ISSN = "1644-9665", DOI = "doi:10.1016/j.acme.2016.06.004", URL = "http://www.sciencedirect.com/science/article/pii/S1644966516300814", abstract = "Flow number of asphalt-aggregate mixtures is an explanatory parameter for the analysis of rutting potential of asphalt mixtures. In this study, a new model is proposed for the determination of flow number using a robust computational intelligence technique, called multi-gene genetic programming (MGGP). MGGP integrates genetic programming and classical regression to formulate the flow number of Marshall Specimens. A reliable experimental database is used to develop the proposed model. Different analyses are performed for the performance evaluation of the model. On the basis of a comparison study, the MGGP model performs superior to the models found in the literature.", keywords = "genetic algorithms, genetic programming, Asphalt mixture, Flow number, Marshall mix design", } @InProceedings{alba:1996:tGPrdflc, author = "Enrique Alba and Carlos Cotta and Jose M. Troya", title = "Type-Constrained Genetic Programming for Rule-Base Definition in Fuzzy Logic Controllers", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "255--260", address = "Stanford University, CA, USA", publisher = "MIT Press", size = "6 pages", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap31.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96", } @InProceedings{alba:1999:ERASPSPDGA, author = "Enrique Alba and Carlos Cotta and Jose M. Troya", title = "Entropic and Real-Time Analysis of the Search with Panmictic, Structured, and Parallel Distributed Genetic Algorithms", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "773", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/Ga-808.pdf", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{alba:1999:T, author = "Enrique Alba and Jose M. Troya", title = "Tackling epistasis with panmictic and structured genetic algorithms", booktitle = "Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference", year = "1999", editor = "Scott Brave and Annie S. Wu", pages = "1--7", address = "Orlando, Florida, USA", month = "13 " # jul, keywords = "Genetic Algorithms, NK", notes = "GECCO-99LB", } @Article{alba:1999:edflcSGP, author = "Enrique Alba and Carlos Cotta and Jose M. Troya", title = "Evolutionary Design of Fuzzy Logic Controllers Using Strongly-Typed GP", journal = "Mathware \& Soft Computing", year = "1999", volume = "6", number = "1", pages = "109--124", keywords = "genetic algorithms, genetic programming, Type System, Fuzzy Logic Controller, Cart-Centering Problem", URL = "http://docto-si.ugr.es/Mathware/v6n1/PS/7-alba.ps.gz", abstract = "An evolutionary approach to the design of fuzzy logic controllers is presented in this paper. We propose the use of the genetic programming paradigm to evolve fuzzy rule-bases (internally represented as type-constrained syntactic trees). This model has been applied to the cart-centering problem, although it can be readily extended to other problems. The obtained results show that a good parameterization of the algorithm, and an appropriate evaluation function, can lead to near-optimal solutions.", notes = "Mathware and softcomputing http://docto-si.ugr.es/Mathware/ENG/mathware.html", } @Book{Alba05, author = "Enrique Alba", title = "Parallel Metaheuristics: A New Class of Algorithms", publisher = "John Wiley \& Sons", month = aug, year = "2005", address = "NJ, USA", ISBN = "0-471-67806-6", keywords = "genetic algorithms, genetic programming, book, text, general computer engineering", URL = "https://www.amazon.com/Parallel-Metaheuristics-New-Class-Algorithms/dp/0471678066/ref=sr_1_1", abstract = "This single reference on parallel metaheuristic presents modern and ongoing research information on using, designing, and analysing efficient models of parallel algorithms. Table of Contents Author Information Introduction. PART I: INTRODUCTION TO METAHEURISTICS AND PARALLELISM. 1. An Introduction to Metaheuristic Techniques. 2. Measuring the Performance of Parallel Metaheuristics. 3. New Technologies in Parallelism. 4. Metaheuristics and Parallelism. PART II: PARALLEL METAHEURISTIC MODELS. 5. Parallel Genetic Algorithms. 6. Spatially Structured Genetic Programming. 7. Parallel Evolution Strategies. 8. Parallel Ant Colony Algorithms. 9. Parallel Estimation of Distribution Algorithms. 10. Parallel Scatter Search. 11. Parallel Variable Neighbourhood Search. 12. Parallel Simulated Annealing. 13. Parallel Tabu Search. 14. Parallel GRASP. 15. Parallel Hybrid Metaheuristics. 16. Parallel Multi Objective. 17. Parallel Heterogeneous Metaheuristics. PART III: THEORY AND APPLICATIONS. 18. Theory of Parallel Genetic Algorithms. 19. Parallel Metaheuristics. 20. Parallel Metaheuristics in Telecommunications. 21. Bioinformatics and Parallel Metaheuristics. Index.", notes = "US 95.", size = "00584 pages", } @InProceedings{Albalushi:2023:SWC, author = "Muna Albalushi and Rasha {Al Jassim} and Karan Jetly and Raya {Al Khayari} and Hilal {Al Maqbali}", booktitle = "2023 IEEE Smart World Congress (SWC)", title = "Optimizing Diabetes Predictive Modeling with Automated Decision Trees", year = "2023", abstract = "This paper introduces Linear Genetic Programming for Optimising Decision Tree (LGP-OptTree), a novel form of Genetic Programming (GP) aimed at enhancing diabetes detection. LGP-OptTree is designed to optimise the attributes and hyperparameters of decision trees by using a unique genotype and phenotype structure. The proposed method is evaluated on the Pima dataset and compared with other techniques. By fine-tuning the attributes and hyperparameters of decision trees using LGP-OptTree, this study aims to improve the accuracy and efficacy of diabetes detection. A performance metric is used to determine the effectiveness of the proposed method with respect to other approaches. The contribution of this research lies in providing general healthcare professionals with a new approach for enhancing diabetes detection accuracy through decision trees.", keywords = "genetic algorithms, genetic programming, Measurement, Medical services, Predictive models, Prediction algorithms, Diabetes, Decision trees, Evolutionary Algorithm", DOI = "doi:10.1109/SWC57546.2023.10449077", month = aug, notes = "Also known as \cite{10449077}", } @InProceedings{Albarracin:2016:SIBGRAPI, author = "Juan Felipe {Hernandez Albarracin} and Jefersson Alex {dos Santos} and Ricardo {da S. Torres}", booktitle = "2016 29th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)", title = "Learning to Combine Spectral Indices with Genetic Programming", year = "2016", pages = "408--415", abstract = "This paper introduces a Genetic Programming-based method for band selection and combination, aiming to support remote sensing image classification tasks. Relying on ground-truth data, our method selects spectral bands and finds the arithmetic combination of those bands (i.e., spectral index) that best separates examples of different classes. Experimental results demonstrate that the proposed method is very effective in pixel-wise binary classification problems.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SIBGRAPI.2016.063", month = oct, notes = "Also known as \cite{7813062}", } @Article{albarracin:2020:RS, author = "Juan F. H. Albarracin and Rafael S. Oliveira and Marina Hirota and Jefersson A. {dos Santos} and Ricardo da S. Torres", title = "A Soft Computing Approach for Selecting and Combining Spectral Bands", journal = "Remote Sensing", year = "2020", volume = "12", number = "14", keywords = "genetic algorithms, genetic programming", ISSN = "2072-4292", URL = "https://www.mdpi.com/2072-4292/12/14/2267", DOI = "doi:10.3390/rs12142267", abstract = "We introduce a soft computing approach for automatically selecting and combining indices from remote sensing multispectral images that can be used for classification tasks. The proposed approach is based on a Genetic-Programming (GP) framework, a technique successfully used in a wide variety of optimisation problems. Through GP, it is possible to learn indices that maximize the separability of samples from two different classes. Once the indices specialized for all the pairs of classes are obtained, they are used in pixelwise classification tasks. We used the GP-based solution to evaluate complex classification problems, such as those that are related to the discrimination of vegetation types within and between tropical biomes. Using time series defined in terms of the learnt spectral indices, we show that the GP framework leads to superior results than other indices that are used to discriminate and classify tropical biomes.", notes = "also known as \cite{rs12142267}", } @InProceedings{Albinati:2014:SMGP, author = "Julio Albinati and Gisele L. Pappa and Fernando E. B. Otero and Luiz Otavio V. B. Oliveira", title = "A Study of Semantic Geometric Crossover Operators in Regression Problems", booktitle = "Semantic Methods in Genetic Programming", year = "2014", editor = "Colin Johnson and Krzysztof Krawiec and Alberto Moraglio and Michael O'Neill", address = "Ljubljana, Slovenia", month = "13 " # sep, note = "Workshop at Parallel Problem Solving from Nature 2014 conference", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.put.poznan.pl/kkrawiec/smgp2014/uploads/Site/Albinati.pdf", size = "2 pages", notes = "SMGP 2014 http://www.cs.put.poznan.pl/kkrawiec/smgp/?n=Site.SMGP2014", } @InProceedings{Albinati:2015:EuroGP, author = "Julio Albinati and Gisele L. Pappa and Fernando E. B. Otero and Luiz Otavio V. B. Oliveira", title = "The Effect of Distinct Geometric Semantic Crossover Operators in Regression Problems", booktitle = "18th European Conference on Genetic Programming", year = "2015", editor = "Penousal Machado and Malcolm I. Heywood and James McDermott and Mauro Castelli and Pablo Garcia-Sanchez and Paolo Burelli and Sebastian Risi and Kevin Sim", series = "LNCS", volume = "9025", publisher = "Springer", pages = "3--15", address = "Copenhagen", month = "8-10 " # apr, organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, Crossover, Crossover mask optimisation", isbn13 = "978-3-319-16500-4", DOI = "doi:10.1007/978-3-319-16501-1", abstract = "This paper investigates the impact of geometric semantic crossover operators in a wide range of symbolic regression problems. First, it analyses the impact of using Manhattan and Euclidean distance geometric semantic crossovers in the learning process. Then, it proposes two strategies to numerically optimise the crossover mask based on mathematical properties of these operators, instead of simply generating them randomly. An experimental analysis comparing geometric semantic crossovers using Euclidean and Manhattan distances and the proposed strategies is performed in a test bed of twenty datasets. The results show that the use of different distance functions in the semantic geometric crossover has little impact on the test error, and that our optimized crossover masks yield slightly better results. For SGP practitioners, we suggest the use of the semantic crossover based on the Euclidean distance, as it achieved similar results to those obtained by more complex operators.", notes = "Nominated for EuroGP 2015 Best Paper. Part of \cite{Machado:2015:GP} EuroGP'2015 held in conjunction with EvoCOP2015, EvoMusArt2015 and EvoApplications2015", } @Article{albrecht:2022:Polymers, author = "Hanny Albrecht and Wolfgang Roland and Christian Fiebig and Gerald Roman Berger-Weber", title = "Multi-Dimensional Regression Models for Predicting the Wall Thickness Distribution of Corrugated Pipes", journal = "Polymers", year = "2022", volume = "14", number = "17", pages = "Article No. 3455", keywords = "genetic algorithms, genetic programming", ISSN = "2073-4360", URL = "https://www.mdpi.com/2073-4360/14/17/3455", DOI = "doi:10.3390/polym14173455", abstract = "Corrugated pipes offer both higher stiffness and higher flexibility while simultaneously requiring less material than rigid pipes. Production rates of corrugated pipes have therefore increased significantly in recent years. Due to rising commodity prices, pipe manufacturers have been driven to produce corrugated pipes of high quality with reduced material input. To the best of our knowledge, corrugated pipe geometry and wall thickness distribution significantly influence pipe properties. Essential factors in optimising wall thickness distribution include adaptation of the mold block geometry and structure optimisation. To achieve these goals, a conventional approach would typically require numerous iterations over various pipe geometries, several mold block geometries, and then fabrication of pipes to be tested experimentally—an approach which is very time-consuming and costly. To address this issue, we developed multi-dimensional mathematical models that predict the wall thickness distribution in corrugated pipes as functions of the mold geometry by using symbolic regression based on genetic programming (GP). First, the blow molding problem was transformed into a dimensionless representation. Then, a screening study was performed to identify the most significant influencing parameters, which were subsequently varied within wide ranges as a basis for a comprehensive, numerically driven parametric design study. The data set obtained was used as input for data-driven modelling to derive novel regression models for predicting wall thickness distribution. Finally, model accuracy was confirmed by means of an error analysis that evaluated various statistical metrics. With our models, wall thickness distribution can now be predicted and subsequently used for structural analysis, thus enabling digital mold block design and optimising the wall thickness distribution.", notes = "also known as \cite{polym14173455}", } @InCollection{Albuquerque:2004:EMTP, author = "Ana Claudia M. L. Albuquerque and Jorge D. Melo and Adriao D. {Doria Neto}", title = "Evolutionary Computation and Parallel Processing Applied to the Design of Multilayer Perceptrons", year = "2004", booktitle = "Evolvable Machines: Theory \& Practice", pages = "181--203", volume = "161", series = "Studies in Fuzziness and Soft Computing", chapter = "8", editor = "Nadia Nedjah and Luiza {de Macedo Mourelle}", publisher = "Springer", address = "Berlin", keywords = "genetic algorithms", ISBN = "3-540-22905-1", URL = "http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-175-22-33980449-0,00.html", notes = "Springer says published in 2005 but available Nov 2004", } @InProceedings{albuquerque:2000:irfl, author = "Paul Albuquerque and Bastien Chopard and Christian Mazza and Marco Tomassini", title = "On the Impact of the Representation on Fitness Landscapes", booktitle = "Genetic Programming, Proceedings of EuroGP'2000", year = "2000", editor = "Riccardo Poli and Wolfgang Banzhaf and William B. Langdon and Julian F. Miller and Peter Nordin and Terence C. Fogarty", volume = "1802", series = "LNCS", pages = "1--15", address = "Edinburgh", publisher_address = "Berlin", month = "15-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-67339-3", DOI = "doi:10.1007/978-3-540-46239-2_1", abstract = "In this paper we study the role of program representation on the properties of a type of Genetic Programming (GP) algorithm. In a specific case, which we believe to be generic of standard GP, we show that the way individuals are coded is an essential concept which impacts the fitness landscape. We give evidence that the ruggedness of the landscape affects the behavior of the algorithm and we find that, below a critical population, whose size is representation-dependent, premature convergence occurs.", notes = "EuroGP'2000, part of \cite{poli:2000:GP}", } @Article{alcaraz:2019:JMMP, author = "Joselito Yam II {Alcaraz} and Kunal Ahluwalia and Swee-Hock Yeo", title = "Predictive Models of {Double-Vibropolishing} in Bowl System Using Artificial Intelligence Methods", journal = "Journal of Manufacturing and Materials Processing", year = "2019", volume = "3", number = "1", keywords = "genetic algorithms, genetic programming, vibratory finishing, double vibro-polishing, artificial intelligence, regression, neural network, ANN", ISSN = "2504-4494", URL = "https://www.mdpi.com/2504-4494/3/1/27", DOI = "doi:10.3390/jmmp3010027", abstract = "Vibratory finishing is a versatile and efficient surface finishing process widely used to finish components of various functionalities. Research efforts were focused in fundamental understanding of the process through analytical solutions and simulations. On the other hand, predictive modelling of surface roughness using computational intelligence (CI) methods are emerging in recent years, though CI methods have not been extensively applied yet to a new vibratory finishing method called double-vibropolishing. In this study, multi-variable regression, artificial neural networks, and genetic programming models were designed and trained with experimental data obtained from subjecting rectangular Ti-6Al-4V test coupons to double vibropolishing in a bowl system configuration. Model selection was done by comparing the mean-absolute percentage error and r-squared values from both training and testing datasets. Exponential regression was determined as the best model for the bowl double-vibropolishing system studied with a Test MAPE score of 6.1percent and a R-squared score of 0.99. A family of curves was generated using the exponential regression model as a potential tool in predicting surface roughness with time.", notes = "also known as \cite{jmmp3010027}", } @Article{alcazar:2024:IJMS, author = "Jackson J. Alcazar", title = "Thiophene Stability in Photodynamic Therapy: A Mathematical Model Approach", journal = "International Journal of Molecular Sciences", year = "2024", volume = "25", number = "5", pages = "Article No. 2528", month = "21 " # feb, note = "Special Issue Molecular Aspects of Photodynamic Therapy", keywords = "genetic algorithms, genetic programming, safe PDT, efficient PDT, thiophene-containing photosensitiser, singlet oxygen, conceptual DFT", ISSN = "1422-0067", URL = "https://www.mdpi.com/1422-0067/25/5/2528", DOI = "doi:10.3390/ijms25052528", code_url = "https://github.com/Jacksonalcazar/Thiophene-Reactivity-toward-Singlet-Oxygen", size = "18 pages", abstract = "Thiophene-containing photosensitizers are gaining recognition for their role in photodynamic therapy (PDT). However, the inherent reactivity of the thiophene moiety toward singlet oxygen threatens the stability and efficiency of these photosensitizers. This study presents a novel mathematical model capable of predicting the reactivity of thiophene toward singlet oxygen in PDT, using Conceptual Density Functional Theory (CDFT) and genetic programming. The research combines advanced computational methods, including various DFT techniques and symbolic regression, and is validated with experimental data. The findings underscore the capacity of the model to classify photosensitizers based on their photodynamic efficiency and safety, particularly noting that photosensitizers with a constant rate 1000 times lower than that of unmodified thiophene retain their photodynamic performance without substantial singlet oxygen quenching. Additionally, the research offers insights into the impact of electronic effects on thiophene reactivity. Finally, this study significantly advances thiophene-based photosensitizer design, paving the way for therapeutic agents that achieve a desirable balance between efficiency and safety in PDT.", notes = "also known as \cite{ijms25052528} Centro de Quimica Medica, Facultad de Medicina Clinica Alemana, Universidad del Desarrollo, Santiago 7780272, Chile", } @InProceedings{Alchirch:2022:EuroGP, author = "Pantia-Marina Alchirch and Dimitrios I. Diochnos and Katia Papakonstantinopoulou", title = "Evolving Monotone Conjunctions in Regimes Beyond Proved Convergence", booktitle = "EuroGP 2022: Proceedings of the 25th European Conference on Genetic Programming", year = "2022", editor = "Eric Medvet and Gisele Pappa and Bing Xue", series = "LNCS", volume = "13223", publisher = "Springer Verlag", address = "Madrid, Spain", pages = "228--244", month = "20-22 " # apr, organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming: Poster, Evolvability, Monotone conjunctions, Distribution-specific learning, Bernoulli (p)**n distributions", isbn13 = "978-3-031-02055-1", DOI = "doi:10.1007/978-3-031-02056-8_15", abstract = "Recently it was shown, using the typical mutation mechanism that is used in evolutionary algorithms, that monotone conjunctions are provably evolvable under a specific set of Bernoulli (p)n distributions. A natural question is whether this mutation mechanism allows convergence under other distributions as well. Our experiments indicate that the answer to this question is affirmative and, at the very least, this mechanism converges under Bernoulli (p)n distributions outside of the known proved regime.", notes = "Athens University of Economics and Business (TESLAB), Athens, Greece http://www.evostar.org/2022/eurogp/ Part of \cite{Medvet:2022:GP} EuroGP'2022 held inconjunction with EvoApplications2022 EvoCOP2022 EvoMusArt2022", } @InProceedings{Aldeia:2018:CEC, author = "Guilherme Aldeia and Fabricio {de Franca}", title = "Lightweight Symbolic Regression with the Interaction-Transformation Representation", booktitle = "2018 IEEE Congress on Evolutionary Computation (CEC)", year = "2018", editor = "Marley Vellasco", address = "Rio de Janeiro, Brazil", month = "8-13 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2018.8477951", abstract = "Symbolic Regression techniques stand out from other regression analysis tools because of the possibility of generating powerful but yet simple expressions. These simple expressions may be useful in many practical situations in which the practitioner wants to interpret the obtained results, fine tune the model, or understand the generating phenomena. Despite this possibility, the current state-of-the-art algorithms for Symbolic Regression usually require a high computational budget while having little guarantees regarding the simplicity of the returned expressions. Recently, a new Data Structure representation for mathematical expressions, called Interaction-Transformation (IT), was introduced together with a search-based algorithm named SymTree that surpassed a subset of the recent Symbolic Regression algorithms and even some state-of-the-art nonlinear regression algorithms, while returning simple expressions as a result. This paper introduces a lightweight tool based on this algorithm, named Lab Assistant. This tool runs on the client-side of any compatible Internet browser with JavaScript. Alongside this tool, two algorithms using the IT representation are introduced. Some experiments are performed in order to show the potential of the Lab Assistant to help practitioners, professors, researchers and students willing to experiment with Symbolic Regression. The results showed that this tool is competent to find the correct expression for many well known Physics and Engineering relations within a reasonable average time frame of a few seconds. This tool opens up lots of possibilities in Symbolic Regression research for low-cost devices to be used in applications where a high-end computer is not available.", notes = "WCCI2018", } @InProceedings{Aldeia:2020:CEC, author = "Guilherme Aldeia and Fabricio {de Franca}", title = "A Parametric Study of Interaction-Transformation Evolutionary Algorithm for Symbolic Regression", booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC 2020", year = "2020", editor = "Yaochu Jin", pages = "paper id24027", address = "internet", month = "19-24 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-7281-6929-3", DOI = "doi:10.1109/CEC48606.2020.9185521", abstract = "The balance between approximation error and model complexity is an important trade-off for Symbolic Regression algorithms. This trade-off is achieved by means of specific operators for bloat control, modified operators, limits to the size of the generated expressions and multi-objective optimization. Recently, the representation Interaction-Transformation was introduced with the goal of limiting the search space to simpler expressions, thus avoiding bloating. This representation was used in the context of an Evolutionary Algorithm in order to find concise expressions resulting in small approximation errors competitive with the literature. Particular to this algorithm, two parameters control the complexity of the generated expression. This paper investigates the influence of those parameters w.r.t. the goodness-of-fit. Through some extensive experiments, we find that the maximum number of terms is more important to control goodness-of-fit but also that there is a limit to the extent that increasing its value renders any benefits. Second, the limit to the minimum and maximum value of the exponent has a smaller influence to the results and it can be set to a default value without impacting the final results.", notes = "https://wcci2020.org/ Federal University of ABC, Brazil", } @InProceedings{Aldeia:2021:GECCO, author = "Guilherme Seidyo Imai Aldeia and Fabricio {Olivetti de Franca}", title = "Measuring Feature Importance of Symbolic Regression Models Using Partial Effects", booktitle = "Proceedings of the 2021 Genetic and Evolutionary Computation Conference", year = "2021", editor = "Francisco Chicano and Alberto Tonda and Krzysztof Krawiec and Marde Helbig and Christopher W. Cleghorn and Dennis G. Wilson and Georgios Yannakakis and Luis Paquete and Gabriela Ochoa and Jaume Bacardit and Christian Gagne and Sanaz Mostaghim and Laetitia Jourdan and Oliver Schuetze and Petr Posik and Carlos Segura and Renato Tinos and Carlos Cotta and Malcolm Heywood and Mengjie Zhang and Leonardo Trujillo and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Fuyuki Ishikawa and Inmaculada Medina-Bulo and Frank Neumann and Andrew M. Sutton", pages = "750--758", address = "internet", series = "GECCO '21", month = jul # " 10-14", organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, XAI, explainable AI, symbolic regression, interaction-transformation, Supervised learning, SHAP, Shapley value", isbn13 = "9781450383509", DOI = "doi:10.1145/3449639.3459302", size = "9 pages", abstract = "In explainable AI, one aspect of a prediction's explanation is to measure each predictor's importance to the decision process.The importance can measure how much variation a predictor promotes locally or how much the predictor contributes to the deviation from a reference point (Shapley value). If we have the ground truth analytical model, we can calculate the former using the Partial Effect, calculated as the predictor's partial derivative. Also, we can estimate the latter by calculating the average partial effect multiplied by the difference between the predictor and the reference value. Symbolic Regression is a gray-box model for regression problems that returns an analytical model approximating the input data. Although it is often associated with interpretability, few works explore this property. We will investigate the use of Partial Effect with the analytical models generated by the Interaction-Transformation Evolutionary Algorithm symbolic regressor (ITEA). We show that the regression models returned by ITEA coupled with Partial Effect provide the closest explanations to the ground truth and a close approximation to Shapley values. These results openup new opportunities to explain symbolic regression modelscompared to the approximations provided by model agnostic approaches.", notes = "PE-ITEA AI-Feynman datasets See also \cite{Aldeia:2022:GPEM} UFABC, Santo Andre, SP, Brazil GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)", } @InProceedings{aldeia:2022:SymReg, author = "Guilherme Seidyo Imai Aldeia and Fabricio {Olivetti de Franca}", title = "{Interaction-Transformation} Evolutionary Algorithm with coefficients optimization", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion", year = "2022", editor = "Heike Trautmann and Carola Doerr and Alberto Moraglio and Thomas Bartz-Beielstein and Bogdan Filipic and Marcus Gallagher and Yew-Soon Ong and Abhishek Gupta and Anna V Kononova and Hao Wang and Michael Emmerich and Peter A. N. Bosman and Daniela Zaharie and Fabio Caraffini and Johann Dreo and Anne Auger and Konstantin Dietric and Paul Dufosse and Tobias Glasmachers and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Tea Tusar and Dimo Brockhoff and Tome Eftimov and Pascal Kerschke and Boris Naujoks and Mike Preuss and Vanessa Volz and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Mark Coletti and Catherine (Katie) Schuman and Eric ``Siggy'' Scott and Robert Patton and Paul Wiegand and Jeffrey K. Bassett and Chathika Gunaratne and Tinkle Chugh and Richard Allmendinger and Jussi Hakanen and Daniel Tauritz and John Woodward and Manuel Lopez-Ibanez and John McCall and Jaume Bacardit and Alexander Brownlee and Stefano Cagnoni and Giovanni Iacca and David Walker and Jamal Toutouh and UnaMay O'Reilly and Penousal Machado and Joao Correia and Sergio Nesmachnow and Josu Ceberio and Rafael Villanueva and Ignacio Hidalgo and Francisco {Fernandez de Vega} and Giuseppe Paolo and Alex Coninx and Antoine Cully and Adam Gaier and Stefan Wagner and Michael Affenzeller and Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and William B. Langdon and Justyna Petke and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Paetzel and Alexander Wagner and Michael Heider and Nadarajen Veerapen and Katherine Malan and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Mohammad Nabi Omidvar and Yuan Sun and Ernesto Tarantino and De Falco Ivanoe and Antonio {Della Cioppa} and Scafuri Umberto and John Rieffel and Jean-Baptiste Mouret and Stephane Doncieux and Stefanos Nikolaidis and Julian Togelius and Matthew C. Fontaine and Serban Georgescu and Francisco Chicano and Darrell Whitley and Oleksandr Kyriienko and Denny Dahl and Ofer Shir and Lee Spector and Alma Rahat and Richard Everson and Jonathan Fieldsend and Handing Wang and Yaochu Jin and Erik Hemberg and Marwa A. Elsayed and Michael Kommenda and William {La Cava} and Gabriel Kronberger and Steven Gustafson", pages = "2274--2281", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, Representation of mathematical functions, symbolic regression, coefficient optimization, benchmark, evolutionary algorithm", isbn13 = "978-1-4503-9268-6/22/07", DOI = "doi:10.1145/3520304.3533987", size = "8 pages", abstract = "Symbolic Regression is the task of finding a mathematical expression to describe the relationship between one or more independent variables with a dependent variable. The search space can be vast and include any algebraic function; thus, finding optimal values for coefficients may not be a trivial task. The Interaction-Transformation representation alleviates this problem enforcing that the coefficients of the expression is part of a linear transformation, allowing the application of least squares. But this solution also limits the search space of the expressions. This paper proposes four different strategies to optimize the coefficients of the nonlinear part of the Interaction-Transformation representation. We benchmark the proposed strategies by applying the Interaction-Transformation Evolutionary Algorithm (ITEA) to six well-known data sets to evaluate four optimization heuristics combining linear and non-linear methods. The results show that optimizing the non-linear and linear coefficients separately was the best strategy to find better-performing expressions with a higher run-time and expression size. The non-linear optimization method alone was the worst-performing method.", notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @Article{Aldeia:2022:GPEM, author = "Guilherme Seidyo Imai Aldeia and Fabricio {Olivetti de Franca}", title = "Interpretability in symbolic regression: a benchmark of explanatory methods using the {Feynman} data set", journal = "Genetic Programming and Evolvable Machines", year = "2022", volume = "23", number = "3", pages = "309--349", month = sep, note = "Special Issue: Highlights of Genetic Programming 2021 Events", keywords = "genetic algorithms, genetic programming, Symbolic regression, Explanatory methods, Feature importance attribution, Benchmark", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-022-09435-x", code_url = "https://github.com/gAldeia/iirsBenchmark", abstract = "In some situations, the interpretability of the machine learning models plays a role as important as the model accuracy. Interpretability comes from the need to trust the prediction model, verify some of its properties, or even enforce them to improve fairness. Many model-agnostic explanatory methods exists to provide explanations for black-box models. In the regression task, the practitioner can use white-boxes or gray-boxes models to achieve more interpretable results, which is the case of symbolic regression. When using an explanatory method, and since interpretability lacks a rigorous definition, there is a need to evaluate and compare the quality and different explainers. This paper proposes a benchmark scheme to evaluate explanatory methods to explain regression models, mainly symbolic regression models. Experiments were performed using 100 physics equations with different interpretable and non-interpretable regression methods and popular explanation methods, evaluating the performance of the explainers performance with several explanation measures. In addition, we further analyzed four benchmarks from the GP community. The results have shown that Symbolic Regression models can be an interesting alternative to white-box and black-box models that is capable of returning accurate models with appropriate explanations. Regarding the explainers, we observed that Partial Effects and SHAP were the most robust explanation models, with Integrated Gradients being unstable only with tree-based models. This benchmark is publicly available for further experiments.", } @InCollection{alderson:1999:TTCNDUEM, author = "David Alderson", title = "Toward a Technique for Cooperative Network Design Using Evolutionary Methods", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1999", year = "1999", editor = "John R. Koza", pages = "1--10", address = "Stanford, California, 94305-3079 USA", month = "15 " # mar, publisher = "Stanford Bookstore", keywords = "genetic algorithms", notes = "part of \cite{koza:1999:GAGPs}", } @Article{ALDREES:2024:jwpe, author = "Ali Aldrees and Majid Khan and Abubakr Taha Bakheit Taha and Mujahid Ali", title = "Evaluation of water quality indexes with novel machine learning and {SHapley} Additive {ExPlanation} ({SHAP)} approaches", journal = "Journal of Water Process Engineering", volume = "58", pages = "104789", year = "2024", ISSN = "2214-7144", DOI = "doi:10.1016/j.jwpe.2024.104789", URL = "https://www.sciencedirect.com/science/article/pii/S2214714424000199", keywords = "genetic algorithms, genetic programming, Gene expression programming, Water quality indexes, ANN, Deep neural networks, Optimizable Gaussian process regressor, SHAP", abstract = "Water quality indexes (WQI) are pivotal in assessing aquatic systems. Conventional modeling approaches rely on extensive datasets with numerous unspecified inputs, leading to time-consuming WQI assessment procedures. Numerous studies have used machine learning (ML) methods for WQI analysis but often lack model interpretability. To address this issue, this study developed five interpretable predictive models, including two gene expression programming (GEP) models, two deep neural networks (DNN) models, and one optimizable Gaussian process regressor (OGPR) model for estimating electrical conductivity (EC) and total dissolved solids (TDS). For the model development, a total of 372 records on a monthly basis were collected in the Upper Indus River at two outlet stations. The efficacy and accuracy of the models were assessed using various statistical measures, such as correlation (R), mean square error (MAE), root mean square error (RMSE), and 5-fold cross-validation. The DNN2 model demonstrated outstanding performance compared to the other five models, exhibiting R-values closer to 1.0 for both EC and TDS. However, the genetic programming-based models, GEP1 and GEP2, exhibited comparatively lower accuracy in predicting the water quality indexes. The SHapely Additive exPlanation (SHAP) analysis revealed that bicarbonate, calcium, and sulphate jointly contribute approximately 78 percent to EC, while the combined presence of sodium, bicarbonate, calcium, and magnesium accounts for around 87 percent of TDS in water. Notably, the influence of pH and chloride was minimal on both water quality indexes. In conclusion, the study highlights the cost-effective and practical potential of predictive models for EC and TDS in assessing and monitoring river water quality", } @InProceedings{Aleb:2012:GECCOcomp, author = "Nassima Aleb and Samir Kechid", title = "A new framework for scalable genetic programming", booktitle = "GECCO 2012 Symbolic regression and modeling workshop", year = "2012", editor = "Steven Gustafson and Ekaterina Vladislavleva", isbn13 = "978-1-4503-1178-6", keywords = "genetic algorithms, genetic programming", pages = "487--492", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330784.2330859", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "This paper presents a novel framework for scalable multi-objective genetic programming. We introduce a new program modeling aiming at facilitating programs' creation, execution and improvement. The proposed modeling allows making symbolic executions in such a way to reduce drastically the time of programs' executions and to allow well-founded programs recombination.", notes = "Also known as \cite{2330859} Distributed at GECCO-2012. ACM Order Number 910122.", } @InProceedings{Aleb:2013:GECCOcomp, author = "Nassima Aleb and Samir Kechid", title = "An interpolation based crossover operator for genetic programming", booktitle = "GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion", year = "2013", editor = "Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A. N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks", isbn13 = "978-1-4503-1964-5", keywords = "genetic algorithms, genetic programming", pages = "1107--1112", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2464576.2482689", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "This paper presents a new crossover operator for genetic programming. We exploit two concepts of formal methods: Weakest precondition and Craig interpolation, to perform semantically aware crossover. Weakest preconditions are used to locate faulty parts of a program and Craig interpolation is used to correct these ones.", notes = "Also known as \cite{2482689} Distributed at GECCO-2013.", } @Article{Aleksandrov:2013:JCSSI, author = "A. V. Aleksandrov and S. V. Kazakov and A. A. Sergushichev and F. N. Tsarev and A. A. Shalyto", title = "The use of evolutionary programming based on training examples for the generation of finite state machines for controlling objects with complex behavior", journal = "Journal of Computer and Systems Sciences International", year = "2013", volume = "52", number = "3", pages = "410--425", month = may, publisher = "SP MAIK Nauka/Interperiodica", language = "English", keywords = "genetic algorithms, genetic programming, FSM", ISSN = "1064-2307", DOI = "doi:10.1134/S1064230713020020", size = "16 pages", abstract = "It is proposed to use evolutionary programming to generate finite state machines (FSMs) for controlling objects with complex behaviour. The well-know approach in which the FSM performance is evaluated by simulation, which is typically time consuming, is replaced with comparison of the object's behaviour controlled by the FSM with the behaviour of this object controlled by a human. A feature of the proposed approach is that it makes it possible to deal with objects that have not only discrete but also continuous parameters. The use of this approach is illustrated by designing an FSM controlling a model aircraft executing a loop-the-loop manoeuvre.", notes = " Original Russian Text A.V. Aleksandrov, S.V. Kazakov, A.A. Sergushichev, F.N. Tsarev, A.A. Shalyto, 2013, published in Izvestiya Akademii Nauk. Teoriya i Sistemy Upravleniya, 2013, No. 3, pp. 85-100. Translated by A. Klimontovich", } @Article{Alekseeva:2018:Algorithms, author = "Natalia Alekseeva and Ivan Tanev and Katsunori Shimohara", title = "Evolving the Controller of Automated Steering of a Car in Slippery Road Conditions", journal = "Algorithms", year = "2018", volume = "11", number = "7", pages = "108", month = jul, note = "Special Issue Algorithms for PID Controller", keywords = "genetic algorithms, genetic programming, autonomous vehicles, automated steering, slippery road conditions, PID controllers", ISSN = "1999-4893", URL = "http://www.mdpi.com/1999-4893/11/7/108", URL = "http://www.mdpi.com/1999-4893/11/7/108/pdf", URL = "https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=201902252618556627", DOI = "doi:10.3390/a11070108", size = "17 pages", abstract = "The most important characteristics of autonomous vehicles are their safety and their ability to adapt to various traffic situations and road conditions. In our research, we focused on the development of controllers for automated steering of a realistically simulated car in slippery road conditions. We comparatively investigated three implementations of such controllers: a proportional-derivative (PD) controller built in accordance with the canonical servo-control model of steering, a PID controller as an extension of the servo-control, and a controller designed heuristically via the most versatile evolutionary computing paradigm: genetic programming (GP). The experimental results suggest that the controller evolved via GP offers the best quality of control of the car in all of the tested slippery (rainy, snowy, and icy) road conditions.", notes = "Department of Information Systems Design, Faculty of Engineering, Doshisha University 1-3, Tatara Miyakodani, Kyotanabe City, Kyoto 610-0394, Japan http://www.mdpi.com/journal/algorithms", } @InProceedings{Alekseeva:2019:AIM, author = "Natalia Alekseeva and Ivan Tanev and Katsunori Shimohara", booktitle = "2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)", title = "On the Emergence of Oscillations in the Evolved Autosteering of a Car on Slippery Roads", year = "2019", pages = "1371--1378", month = "8-12 " # jul, address = "Hong Kong", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-7281-2494-0", DOI = "doi:10.1109/AIM.2019.8868610", ISSN = "2159-6255", abstract = "One of the important features of autonomous vehicles is their versatility to various traffic situations and road conditions. We explore the feasibility of using genetic programming to develop an adequate auto-steering of a car in slippery road conditions. We also investigate an important emergent property of the best-evolved steering solutions - the steering oscillations - and discuss how these oscillations contribute to the better controllability of the sliding car. We present the limitations and the technical challenges of the real world implementation of steering oscillations.", notes = "Also known as \cite{8868610}", } @Article{DBLP:journals/algorithms/AlekseevaTS20, author = "Natalia Alekseeva and Ivan Tanev and Katsunori Shimohara", title = "{PD} Steering Controller Utilizing the Predicted Position on Track for Autonomous Vehicles Driven on Slippery Roads", journal = "Algorithms", year = "2020", volume = "13", number = "2", pages = "id 48", month = feb, keywords = "genetic algorithms, genetic programming, autonomous vehicles, automated steering, slippery road conditions, PD controllers, predictive model", timestamp = "Thu, 19 Mar 2020 10:23:44 +0100", biburl = "https://dblp.org/rec/journals/algorithms/AlekseevaTS20.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://www.mdpi.com/1999-4893/13/2/48/pdf", DOI = "doi:10.3390/a13020048", size = "17 pages", abstract = "Among the most important characteristics of autonomous vehicles are the safety and robustness in various traffic situations and road conditions. In this paper, we focus on the development and analysis of the extended version of the canonical proportional-derivative PD controllers that are known to provide a good quality of steering on non-slippery (dry) roads. However, on slippery roads, due to the poor yaw controllability of the vehicle (suffering from understeering and oversteering), the quality of control of such controllers deteriorates. The proposed predicted PD controller (PPD controller) overcomes the main drawback of PD controllers, namely, the reactiveness of their steering behavior. The latter implies that steering output is a direct result of the currently perceived lateral- and angular deviation of the vehicle from its intended, ideal trajectory, which is the center of the lane. This reactiveness, combined with the tardiness of the yaw control of the vehicle on slippery roads, results in a significant lag in the control loop that could not be compensated completely by the predictive (derivative) component of these controllers. In our approach, keeping the controller efforts at the same level as in PD controllers by avoiding (i) complex computations and (ii) adding additional variables, the PPD controller shows better quality of steering than that of the evolved (via genetic programming) models.", } @Article{Alemdag:2016:EG, author = "S. Alemdag and Z. Gurocak and A. Cevik and A. F. Cabalar and C. Gokceoglu", title = "Modeling deformation modulus of a stratified sedimentary rock mass using neural network, fuzzy inference and genetic programming", journal = "Engineering Geology", volume = "203", pages = "70--82", year = "2016", note = "Special Issue on Probabilistic and Soft Computing Methods for Engineering Geology", ISSN = "0013-7952", DOI = "doi:10.1016/j.enggeo.2015.12.002", URL = "http://www.sciencedirect.com/science/article/pii/S0013795215300971", abstract = "This paper investigates a series of experimental results and numerical simulations employed to estimate the deformation modulus of a stratified rock mass. The deformation modulus of rock mass has a significant importance for some applications in engineering geology and geotechnical projects including foundation, slope, and tunnel designs. Deformation modulus of a rock mass can be determined using large scale in-situ tests. This large scale sophisticated in-situ testing equipments are sometimes difficult to install, plus time consuming to be employed in the field. Therefore, this study aims to estimate indirectly the deformation modulus values via empirical methods such as the neural network, neuro fuzzy and genetic programming approaches. A series of analyses have been developed for correlating various relationships between the deformation modulus of rock mass, rock mass rating, rock quality designation, uniaxial compressive strength, and elasticity modulus of intact rock parameters. The performance capacities of proposed models are assessed and found as quite satisfactory. At the completion of a comparative study on the accuracy of models, in the results, it is seen that overall genetic programming models yielded more precise results than neural network and neuro fuzzy models.", keywords = "genetic algorithms, genetic programming, Deformation modulus, Rock mass, Neural network, Neuro fuzzy", notes = "Department of Geological Engineering, Gumushane University, Gumushane 29000, Turkey", } @InProceedings{aler:1998:5parity, author = "Ricardo Aler", title = "Immediate transference of global improvements to all individuals in a population in Genetic Programming compared to Automatically Defined Functions for the EVEN-5 PARITY problem", booktitle = "Proceedings of the First European Workshop on Genetic Programming", year = "1998", editor = "Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer and Terence C. Fogarty", volume = "1391", series = "LNCS", pages = "60--70", address = "Paris", publisher_address = "Berlin", month = "14-15 " # apr, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-64360-5", DOI = "doi:10.1007/BFb0055928", abstract = "Koza has shown how automatically defined functions (ADFs) can reduce computational effort in the GP paradigm. In Koza's ADF, as well as in standard GP, an improvement in a part of a program (an ADF or a main body) can only be transferred via crossover. In this article, we consider whether it is a good idea to transfer immediately improvements found by a single individual to the whole population. A system that implements this idea has been proposed and tested for the EVEN-5-PARITY and EVEN-6-PARITY problems. Results are very encouraging: computational effort is reduced (compared to Koza's ADFs) and the system seems to be less prone to early stagnation. Finally, our work suggests further research where less extreme approaches to our idea could be tested.", notes = "EuroGP'98", affiliation = "Universidad Carlos III de Madrid Butarque 15 28911 Leganes Madrid Espana Butarque 15 28911 Leganes Madrid Espana", } @InProceedings{aler:1998:ehp, author = "Ricardo Aler and Daniel Borrajo and Pedro Isasi", title = "Evolved Heuristics for Planning", booktitle = "Evolutionary Programming VII: Proceedings of the Seventh Annual Conference on Evolutionary Programming", year = "1998", editor = "V. William Porto and N. Saravanan and D. Waagen and A. E. Eiben", volume = "1447", series = "LNCS", pages = "745--754", address = "Mission Valley Marriott, San Diego, California, USA", publisher_address = "Berlin", month = "25-27 " # mar, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-64891-7", DOI = "doi:10.1007/BFb0040753", notes = "EP-98 http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-64891-7 EvoCK compared with PRODIGY. HAMLET. Blocksworld domain.", } @InProceedings{icml98-ricardo, author = "Ricardo Aler and Daniel Borrajo and Pedro Isasi", title = "Genetic Programming and Deductive-Inductive Learning: A Multistrategy Approach", booktitle = "Proceedings of the Fifteenth International Conference on Machine Learning, ICML'98", year = "1998", editor = "Jude Shavlik", pages = "10--18", address = "Madison, Wisconsin, USA", month = jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, Learning in Planning, Multistrategy learning", ISBN = "1-55860-556-8", URL = "http://scalab.uc3m.es/~dborrajo/papers/icml98.ps.gz", size = "9 pages", abstract = "Genetic Programming (GP) is a machine learning technique that was not conceived to use domain knowledge for generating new candidate solutions. It has been shown that GP can benefit from domain knowledge obtained by other machine learning methods with more powerful heuristics. However, it is not obvious that a combination of GP and a knowledge intensive machine learning method can work better than the knowledge intensive method alone. In this paper we present a multistrategy approach where an already multistrategy approach ({\sc hamlet} combines analytical and inductive learning) and an evolutionary technique based on GP (EvoCK) are combined for the task of learning control rules for problem solving in planning. Results show that both methods complement each other, supplying to the other method what the other method lacks and obtaining better results than using each method alone.", notes = "ICML'98 http://www.cs.wisc.edu/icml98/ blocksworld many random problems generated in order to train system. No crossover, steady state population size = 2, tournament size = 2", } @PhdThesis{aler:thesis, author = "Ricardo Aler Mur", title = "Programacion Genetica de Heuristicas para Planificacion", school = "Facultad de Informatica de la Universidad Politecnica de Madrid", year = "1999", address = "Spain", month = jul, keywords = "genetic algorithms, genetic programming, Planning, Problem Solving, Rule Based System", URL = "http://oa.upm.es/1101/1/10199907.pdf", size = "167 pages", abstract = "The aim of this thesis is to use and extend the machine learning genetic programming (GP) paradigm to learn control knowledge for domain independent planning. GP will be used as a standalone technique and as part of a multi-strategy system. Planning is the problem of finding a sequence of steps to transform an initial state in a final state. Finding a correct plan is NP-hard. A solution proposed by Artificial Intelligence is to augment a domain independent planner with control knowledge, to improve its efficiency. Machine learning techniques are used for that purpose. However, although a lot has been achieved, the domain independent planning problem has not been solved completely, therefore there is still room for research. The reason for using GP to learn planning control knowledge is twofold. First, it is intended for exploring the control knowledge space in a less biased way than other techniques. Besides, learning search control knowledge with GP will consider the planning system, the domain theory, planning search and efficiency measures in a global manner, all at the same time. Second, GP flexibility will be used to add useful biases and characteristics to another learning method that lacks them (that is, a multi-strategy GP based system). In the present work, Prodigy will be used as the base planner and Hamlet will be used as the learning system to which useful characteristics will be added through GP. In other words, GP will be used to solve some of Hamlet limitations by adding new biases/characteristics to Hamlet. In addition to the main goal, this thesis will design and experiment with methods to add background knowledge to a GP system, without modifying its basic algorithm. The first method seeds the initial population with individuals obtained by another method (Hamlet). Actually, this is the multi-strategy system discussed in the later paragraph. The second method uses a new genetic operator (instance based crossover) that is able to use instances/examples to bias its search, like other machine learning techniques. To test the validity of the methods proposed, extensive empirical and statistical validation will be carried out.", notes = "In Spanish: Genetic Programming of Heuristics for Planning School of Computer Science at Polytechnic University of Madrid Author: Ricardo Aler Mur Supervisors: Daniel Borrajo Millan and Pedro Isasi Vinuela ", } @InProceedings{aler:2000:G, author = "Ricardo Aler and Daniel Borrajo and Pedro Isasi", title = "GP fitness functions to evolve heuristics for planning", booktitle = "Evolutionary Methods for AI Planning", year = "2000", editor = "Martin Middendorf", pages = "189--195", address = "Las Vegas, Nevada, USA", month = "8 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://scalab.uc3m.es/~dborrajo/papers/gecco00.ps.gz", abstract = "There are several ways of applying Genetic Programming (GP) to STRIPS-like planning in the literature. In this paper we emphasise the use of a new one, based on learning heuristics for planning. In particular, we focus on the design of fitness functions for this task. We explore two alternatives (black and white box fitness functions) and present some empirical results", size = "5 pages", notes = "GECCO-2000WKS Part of \cite{wu:2000:GECCOWKS}", } @InProceedings{oai:CiteSeerPSU:341634, title = "Knowledge Representation Issues in Control Knowledge Learning", author = "Ricardo Aler and Daniel Borrajo and Pedro Isasi", booktitle = "Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000)", year = "2000", editor = "Pat Langley", pages = "1--8", address = "Stanford University, Standord, CA, USA", month = jun # " 29 - " # jul # " 2", publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, EBL, HAMLET, EVOCK", ISBN = "1-55860-707-2", bibsource = "DBLP, http://dblp.uni-trier.de", URL = "http://scalab.uc3m.es/~dborrajo/papers/icml00.ps.gz", URL = "http://dl.acm.org/citation.cfm?id=645529.657964", acmid = "657964", URL = "http://citeseer.ist.psu.edu/341634.html", citeseer-isreferencedby = "oai:CiteSeerPSU:42967", citeseer-references = "oai:CiteSeerPSU:104987; oai:CiteSeerPSU:15322; oai:CiteSeerPSU:554819", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:341634", rights = "unrestricted", size = "8 pages", abstract = "Knowledge representation is a key issue for any machine learning task. There have already been many comparative studies about knowledge representation with respect to machine learning in classification tasks. However, apart from some work done on reinforcement learning techniques in relation to state representation, very few studies have concentrated on the effect of knowledge representation for machine learning applied to problem solving, and more specifically, to planning. In this paper, we present an experimental comparative study of the effect of changing the input representation of planning domain knowledge on control knowledge learning. We show results in two classical domains using three different machine learning systems, that have previously shown their effectiveness on learning planning control knowledge: a pure EBL mechanism, a combination of EBL and induction (HAMLET), and a Genetic Programming based system (EVOCK).", notes = "Also known as \cite{Aler:2000:KRI:645529.657964} http://www.informatik.uni-trier.de/~ley/db/conf/icml/icml2000.html ICML 2000", } @InProceedings{aler:2001:glckg, author = "Ricardo Aler and Daniel Borrajo and Pedro Isasi", title = "Grammars for Learning Control Knowledge with GP", booktitle = "Proceedings of the 2001 Congress on Evolutionary Computation CEC2001", year = "2001", pages = "1220--1227", address = "COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "27-30 " # may, organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, computational linguistics, grammars, learning (artificial intelligence), search problems, AI planning system, EVOCK, Evolution of Control Knowledge, GP based system, PRODIGY, ad-hoc mechanisms, blocksworld domain, control knowledge learning, control rule language, control rule syntax, control rules, grammar approach flexibility, grammar specific, grammars, language restrictions, search space, standard GP, standard select type", ISBN = "0-7803-6658-1", URL = "http://scalab.uc3m.es/~dborrajo/papers/cec01.ps.gz", DOI = "doi:10.1109/CEC.2001.934330", size = "8 pages", abstract = "In standard GP there are no constraints on the structure to evolve: any combination of functions and terminals is valid. However, sometimes GP is used to evolve structures that must respect some constraints. Instead of ad-hoc mechanisms, grammars can be used to guarantee that individuals comply with the language restrictions. In addition, grammars permit great flexibility to define the search space. EVOCK (Evolution of Control Knowledge) is a GP based system that learns control rules for PRODIGY, an AI planning system. EVOCK uses a grammar to constrain individuals to PRODIGY 4.0 control rule syntax. The authors describe the grammar specific details of EVOCK. Also, the grammar approach flexibility has been used to extend the control rule language used by EVOCK in earlier work. Using this flexibility, tests were performed to determine whether using combinations of several types of control rules for planning was better than using only the standard select type. Experiments have been carried out in the blocksworld domain that show that using the combination of types of control rules does not get better individuals, but it produces good individuals more frequently", notes = "CEC-2001 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 01TH8546C, Library of Congress Number = . EVOCK, PRODIGY 4.0, blocksworld", } @Article{aler:2001:ECJ, author = "Ricardo Aler and Daniel Borrajo and Pedro Isasi", title = "Learning to Solve Planning Problems Efficiently by Means of Genetic Programming", journal = "Evolutionary Computation", year = "2001", volume = "9", number = "4", pages = "387--420", month = "Winter", keywords = "genetic algorithms, genetic programming, genetic planning, evolving heuristics, planning, search. EvoCK, STGP, blocks world, logistics, Prodigy4.0, STRIPS, PDL40.", ISSN = "1063-6560", URL = "http://www.mitpressjournals.org/doi/pdf/10.1162/10636560152642841", DOI = "doi:10.1162/10636560152642841", size = "34 pages", abstract = "Declarative problem solving, such as planning, poses interesting challenges for Genetic Programming (GP). There have been recent attempts to apply GP to planning that fit two approaches: (a) using GP to search in plan space or (b) to evolve a planner. In this article, we propose to evolve only the heuristics to make a particular planner more efficient. This approach is more feasible than (b) because it does not have to build a planner from scratch but can take advantage of already existing planning systems. It is also more efficient than (a) because once the heuristics have been evolved, they can be used to solve a whole class of different planning problems in a planning domain, instead of running GP for every new planning problem. Empirical results show that our approach (EVOCK) is able to evolve heuristics in two planning domains (the blocks world and the logistics domain) that improve PRODIGY4.0 performance. Additionally, we experiment with a new genetic operator Instance-Based Crossover that is able to use traces of the base planner as raw genetic material to be injected into the evolving population.", } @Article{aler:2002:AI, author = "Ricardo Aler and Daniel Borrajo and Pedro Isasi", title = "Using genetic programming to learn and improve control knowledge", journal = "Artificial Intelligence", year = "2002", volume = "141", number = "1-2", pages = "29--56", month = oct, keywords = "genetic algorithms, genetic programming, Speedup learning, Multi-strategy learning, Planning", URL = "http://scalab.uc3m.es/~dborrajo/papers/aij-evock.ps.gz", URL = "http://citeseer.ist.psu.edu/511810.html", DOI = "doi:10.1016/S0004-3702(02)00246-1", abstract = "The purpose of this article is to present a multi-strategy approach to learn heuristics for planning. This multi-strategy system, called HAMLET-EVOCK, combines a learning algorithm specialised in planning () and a genetic programming (GP) based system (: Evolution of Control Knowledge). Both systems are able to learn heuristics for planning on their own, but both of them have weaknesses. Based on previous experience and some experiments performed in this article, it is hypothesised that handicaps are due to its example-driven operators and not having a way to evaluate the usefulness of its control knowledge. It is also hypothesized that even if control knowledge is sometimes incorrect, it might be easily correctable. For this purpose, a GP-based stage is added, because of its complementary biases: GP genetic operators are not example-driven and it can use a fitness function to evaluate control knowledge. and are combined by seeding initial population with control knowledge. It is also useful for to start from a knowledge-rich population instead of a random one. By adding the GP stage to , the number of solved problems increases from 58% to 85% in the blocks world and from 50% to 87% in the logistics domain (0% to 38% and 0% to 42% for the hardest instances of problems considered).", notes = "Hamlet, EvoCK, PRODIGY 4.0", } @InProceedings{Aleshunas:2011:CAoUHiA, title = "Cost-benefit Analysis of Using Heuristics in {ACGP}", author = "John Aleshunas and Cezary Janikow", pages = "1177--1183", booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary Computation", year = "2011", editor = "Alice E. Smith", month = "5-8 " # jun, address = "New Orleans, USA", organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2011.5949749", abstract = "Constrained Genetic Programming (CGP) is a method of searching the Genetic Programming search space non-uniformly, giving preferences to certain subspaces according to some heuristics. Adaptable CGP (ACGP) is a method for discovery of the heuristics. CGP and ACGP have previously demonstrated their capabilities using first-order heuristics: parent-child probabilities. Recently, the same advantage has been shown for second-order heuristics: parent- children probabilities. A natural question to ask is whether we can benefit from extending ACGP with deeper-order heuristics. This paper attempts to answer this question by performing cost-benefit analysis while simulating the higher- order heuristics environment. We show that this method cannot be extended beyond the current second or possibly third-order heuristics without a new method to deal with the sheer number of such deeper-order heuristics.", notes = "CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.", } @InProceedings{Alexander:2009:cec, author = "B. J. Alexander and M. J. Gratton", title = "Constructing an Optimisation Phase Using Grammatical Evolution", booktitle = "2009 IEEE Congress on Evolutionary Computation", year = "2009", editor = "Andy Tyrrell", pages = "1209--1216", address = "Trondheim, Norway", month = "18-21 " # may, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-2959-2", file = "P395.pdf", DOI = "doi:10.1109/CEC.2009.4983083", size = "8 pages", abstract = "Optimising compilers present their authors with an intractable design space. A substantial body of work has used heuristic search techniques to search this space for the purposes of adapting optimisers to their environment. To date, most of this work has focused on sequencing, tuning and guiding the actions of atomic hand-written optimisation phases. In this paper we explore the adaption of optimisers at a deeper level by demonstrating that it is feasible to automatically build a non-trivial optimisation phase, for a simple functional language, using Grammatical Evolution. We show that the individuals evolved compare well in performance to a handwritten optimisation phase on a range of benchmarks. We conclude with proposals of how this work and its applications can be extended.", keywords = "genetic algorithms, genetic programming, grammatical evolution, SBSE, evolutionary computation, functional languages, grammars, optimising compilers, search problems, atomic hand-written optimisation phases, heuristic search techniques, intractable design space", notes = "Adl, DMO, C-MPI, FPGA, Semantics of program preserved. libGE, GAlib, effective crossover, Python, SWIG Python/C++. Canonical code. Five second time limit. Haskell. Training examples changed to 'provide traction for the evolutionary process' p1213. 'Evolution of fittest individuals highly discontinuous' p1214. Some examples where GE is competitive with hand written compiler optimisation, others less so. Evolved code _not_ like human. Compiler output _is_ correct. proof-of-concept. CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR. Also known as \cite{4983083}", } @InProceedings{alexander2014boosting, author = "Brad Alexander and Brad Zacher", title = "Boosting Search for Recursive Functions Using Partial Call-Trees", booktitle = "13th International Conference on Parallel Problem Solving from Nature", year = "2014", editor = "Thomas Bartz-Beielstein and Juergen Brank and Jim Smith", publisher = "Springer", isbn13 = "978-3-319-10761-5", pages = "384--393", series = "Lecture Notes in Computer Science", address = "Ljubljana, Slovenia", month = "13-17 " # sep, volume = "8672", keywords = "genetic algorithms, genetic programming, grammatical evolution, Recursion, Call-Tree, Adaptive Grammar", DOI = "doi:10.1007/978-3-319-10762-2_38", size = "10 pages", abstract = "Recursive functions are a compact and expressive way to solve challenging problems in terms of local processing. These properties have made recursive functions a popular target for genetic programming. Unfortunately, the evolution of substantial recursive programs has proved difficult. One cause of this problem is the difficulty in evolving both correct base and recursive cases using just information derived from running test cases. In this work we describe a framework that exploits additional information in the form of partial call-trees. Such trees - a by-product of deriving input-output cases by hand - guides the search process by allowing the separate evolution of the recursive case. We show that the speed of evolution of recursive functions is significantly enhanced by the use of partial call-trees and demonstrate application of the technique in the derivation of functions for a suite of numerical functions.", notes = "CTGGP, GE, GElib 0.26, tiny-c-compiler tcc, factorial, oddevens, log2, fib2, fib3, lucas, factorings, summands1 PPSN-XIII Cites \cite{Moraglio:2012:CEC} and \cite{yu:1998:rlaGP98} (in both cases google scholar Feb 2015 data wrong)", } @InCollection{Alexander:2014:shonan, author = "Bradley Alexander", title = "Discussion on Automatic Fault Localisation and Repair", booktitle = "Computational Intelligence for Software Engineering", publisher = "National Institute of Informatics", year = "2014", editor = "Hong Mei and Leandro Minku and Frank Neumann and Xin Yao", pages = "16--19", address = "Japan", month = oct # " 20-23", note = "NII Shonan Meeting Report: No. 2014-13", keywords = "genetic algorithms, genetic programming, genetic improvement, APR", ISSN = "2186-7437", URL = "http://shonan.nii.ac.jp/seminar/reports/wp-content/uploads/sites/56/2015/01/No.2014-13.pdf", size = "4 pages", notes = "Mention of GenProg \cite{DBLP:journals/tse/GouesNFW12} and \cite{Kim:2013:ICSE}, etc. Book also contains abstracts of talks, some on GP. National Institute of Informatics 2-1-2 Hitotsubashi, Chiyoda-Ku, Tokyo, Japan", } @InProceedings{Alexander:2016:PPSN, author = "Brad Alexander and Connie Pyromallis and George Lorenzetti and Brad Zacher", title = "Using Scaffolding with Partial Call-Trees to Improve Search", booktitle = "14th International Conference on Parallel Problem Solving from Nature", year = "2016", editor = "Julia Handl and Emma Hart and Peter R. Lewis and Manuel Lopez-Ibanez and Gabriela Ochoa and Ben Paechter", volume = "9921", series = "LNCS", pages = "324--334", address = "Edinburgh", month = "17-21 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Grammatical evolution, Recursion", isbn13 = "978-3-319-45823-6", DOI = "doi:10.1007/978-3-319-45823-6_3", size = "11 page", abstract = "Recursive functions are an attractive target for genetic programming because they can express complex computation compactly. However, the need to simultaneously discover correct recursive and base cases in these functions is a major obstacle in the evolutionary search process. To overcome these obstacles two recent remedies have been proposed. The first is Scaffolding which permits the recursive case of a function to be evaluated independently of the base case. The second is Call- Tree-Guided Genetic Programming (CTGGP) which uses a partial call tree, supplied by the user, to separately evolve the parameter expressions for recursive calls. Used in isolation, both of these approaches have been shown to offer significant advantages in terms of search performance. In this work we investigate the impact of different combinations of these approaches. We find that, on our benchmarks, CTGGP significantly outperforms Scaffolding and that a combination CTGGP and Scaffolding appears to produce further improvements in worst-case performance.", notes = "factorial, odd-evens, log2, Fibonacci and Fibonacci-3, the nth Lucas number, the nth Pell number. p331 'We ran our experiments on an AMD Opteron 6348 machine with 48 processors running at 2.8 GHz' PPSN2016", } @Misc{Alexander:2018:arxiv, author = "Brad Alexander", title = "A Preliminary Exploration of Floating Point Grammatical Evolution", howpublished = "arXiv", year = "2018", month = "9 " # jun, keywords = "genetic algorithms, genetic programming, grammatical evolution", volume = "abs/1806.03455", bibdate = "2018-08-13", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/corr/corr1806.html#abs-1806-03455", URL = "http://arxiv.org/abs/1806.03455", size = "17 pages", abstract = "Current GP frameworks are highly effective on a range of real and simulated benchmarks. However, due to the high dimensionality of the genotypes for GP, the task of visualising the fitness landscape for GP search can be difficult. This paper describes a new framework: Floating Point Grammatical Evolution (FP-GE) which uses a single floating point genotype to encode an individual program. This encoding permits easier visualisation of the fitness landscape arbitrary problems by providing a way to map fitness against a single dimension. The new framework also makes it trivially easy to apply continuous search algorithms, such as Differential Evolution, to the search problem. In this work, the FP-GE framework is tested against several regression problems, visualising the search landscape for these and comparing different search meta-heuristics.", notes = "Also known as \cite{journals/corr/abs-1806-03455}", } @InProceedings{conf/eann/AlexandirisK13, author = "Antonios K. Alexandiris and Michael Kampouridis", title = "Temperature Forecasting in the Concept of Weather Derivatives: a Comparison between Wavelet Networks and Genetic Programming", editor = "Lazaros S. Iliadis and Harris Papadopoulos and Chrisina Jayne", booktitle = "Proceedings of 14th International Conference on Engineering Applications of Neural Networks (EANN 2013), Part {I}", year = "2013", volume = "383", series = "Communications in Computer and Information Science", pages = "12--21", address = "Halkidiki, Greece", month = sep # " 13-16", publisher = "Springer", keywords = "genetic algorithms, genetic programming, weather derivatives, wavelet networks, temperature derivatives", isbn13 = "978-3-642-41012-3", bibdate = "2014-01-25", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/eann/eann2013-1.html#AlexandirisK13", URL = "http://dx.doi.org/10.1007/978-3-642-41013-0", URL = "http://dx.doi.org/10.1007/978-3-642-41013-0_2", DOI = "doi:10.1007/978-3-642-41013-0_2", abstract = "The purpose of this study is to develop a model that accurately describes the dynamics of the daily average temperature in the context of weather derivatives pricing. More precisely we compare two state of the art algorithms, namely wavelet networks and genetic programming against the classic linear approaches widely using in the contexts of temperature derivative pricing. The accuracy of the valuation process depends on the accuracy of the temperature forecasts. Our proposed models were evaluated and compared in-sample and out-of-sample in various locations. Our findings suggest that the proposed nonlinear methods significantly outperform the alternative linear models and can be used for accurate weather derivative pricing.", } @Article{Alexandridis:2017:IJF, author = "Antonis K. Alexandridis and Michael Kampouridis and Sam Cramer", title = "A comparison of wavelet networks and genetic programming in the context of temperature derivatives", journal = "International Journal of Forecasting", volume = "33", number = "1", pages = "21--47", year = "2017", ISSN = "0169-2070", DOI = "doi:10.1016/j.ijforecast.2016.07.002", URL = "http://www.sciencedirect.com/science/article/pii/S0169207016300711", abstract = "The purpose of this study is to develop a model that describes the dynamics of the daily average temperature accurately in the context of weather derivatives pricing. More precisely, we compare two state-of-the-art machine learning algorithms, namely wavelet networks and genetic programming, with the classic linear approaches that are used widely in the pricing of temperature derivatives in the financial weather market, as well as with various machine learning benchmark models such as neural networks, radial basis functions and support vector regression. The accuracy of the valuation process depends on the accuracy of the temperature forecasts. Our proposed models are evaluated and compared, both in-sample and out-of-sample, in various locations where weather derivatives are traded. Furthermore, we expand our analysis by examining the stability of the forecasting models relative to the forecasting horizon. Our findings suggest that the proposed nonlinear methods outperform the alternative linear models significantly, with wavelet networks ranking first, and that they can be used for accurate weather derivative pricing in the weather market.", keywords = "genetic algorithms, genetic programming, Weather derivatives, Wavelet networks, Temperature derivatives, Modelling, Forecasting", } @PhdThesis{Alfaro-Cid:thesis, author = "Maria Eva {Alfaro Cid}", title = "Optimisation of Time Domain Controllers for Supply Ships Using Genetic Algorithms and Genetic Programming", school = "The University of Glasgow", year = "2003", address = "Glasgow, UK", month = oct, keywords = "genetic algorithms, genetic programming", URL = "http://casnew.iti.es/papers/ThesisEva.pdf", URL = "http://ethos.bl.uk/OrderDetails.do?did=49&uin=uk.bl.ethos.398769", size = "348 pages", abstract = "The use of genetic methods for the optimisation of propulsion and heading controllers for marine vessels is presented in this thesis. The first part of this work is a study of the optimisation, using Genetic Algorithms, of controller designs based on a number of different time-domain control methodologies such as PID, Sliding Mode, H? and Pole Placement. These control methodologies are used to provide the structure for propulsion and navigation controllers for a ship. Given the variety in the number of parameters to optimise and the controller structures, the Genetic Algorithm is tested in different control optimisation problems with different search spaces. This study presents how the Genetic Algorithm solves this minimisation problem by evolving controller parameters solutions that satisfactorily perform control duties while keeping actuator usage to a minimum. A variety of genetic operators are introduced and a comparison study is conducted to find the Genetic Algorithm scheme best suited to the parameter controller optimisation problem. The performance of the four control methodologies is also compared. A variation of Genetic Algorithms, the Structured Genetic Algorithm, is also used for the optimisation of the H? controller. The H? controller optimisation presents the difficulty that the optimisation focus is not on parameters but on transfer functions. Structured Genetic Algorithm incorporates hierarchy in the representation of solutions making it very suitable for structural optimisation. The H? optimisation problem has been found to be very appropriate for comparing the performance of Genetic Algorithms versus Structured Genetic Algorithm. During the second part of this work, the use of Genetic Programming to optimise the controller structure is assessed. Genetic Programming is used to evolve control strategies that, given as inputs the current and desired state of the propulsion and heading dynamics, generate the commanded forces required to manoeuvre the ship. Two Genetic Programming algorithms are implemented. The only difference between them is how they generate the numerical constants needed for the solution of the problem. The first approach uses a random generation of constants while the second approach uses a combination of Genetic Programming with Genetic Algorithms. Finally, the controllers optimised using genetic methods are evaluated through computer simulations and real manoeuvrability tests in a laboratory water basin facility. The robustness of each controller is analysed through the simulation of environmental disturbances. Also, optimisations in presence of disturbances are carried out so that the different controllers obtained can be compared. The particular vessels used in this study are two scale models of a supply ship called CyberShip I and CyberShip II. The results obtained illustrate the benefits of using Genetic Algorithms and Genetic Programming to optimise propulsion and navigation controllers for surface ships.", notes = "uk.bl.ethos.398769", } @InProceedings{alfespshar05, title = "Clasificaci\'{o}n de Senales de Electroencefalograma Usando Programaci\'{o}n Gen\'{e}tica", author = "Eva Alfaro-Cid and Anna Esparcia-Alc\'{a}zar and Ken Sharman", booktitle = "Actas del IV Congreso Espanol sobre Metaheur\'{i}sticas, Algoritmos Evolutivos y Bioinspirados ({MAEB}'05)", month = sep, address = "Granada, Spain", year = "2005", keywords = "genetic algorithms, genetic programming", URL = "http://www.iti.upv.es/cas/nade/data/maeb05vfinal.pdf", size = "8 pages", abstract = "En este articulo presentamos una nueva manera de aplicar programacion genetica al problema de clasificacion de series temporales. Eneste caso las series de datos usadas son senalesde electroencefalograma. Se han implementado dos tipos de algoritmos de programaciongenetica: uno de ellos usa programacion distribuida mientras que el otro aplica una tecnica de muestreo aleatorio para evitar el problema de la sobreadaptacion. Los arboles resultantes obtienen porcentajes de aciertos en la clasificacion equivalentes a los que se obtienen usando metodos de clasifficacion tradicionales", notes = "in Spanish", } @InProceedings{eurogp:Alfaro-CidMM05, author = "Eva Alfaro-Cid and Euan William McGookin and David James Murray-Smith", editor = "Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. {van Hemert} and Marco Tomassini", title = "Evolution of a Strategy for Ship Guidance Using Two Implementations of Genetic Programming", booktitle = "Proceedings of the 8th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3447", year = "2005", address = "Lausanne, Switzerland", month = "30 " # mar # " - 1 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-25436-6", pages = "250--260", DOI = "doi:10.1007/978-3-540-31989-4_22", DOI = "doi:10.1007/b107383", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "In this paper the implementation of Genetic Programming (GP) to optimise a controller structure for a supply ship is assessed. GP is used to evolve control strategies that, given the current and desired state of the propulsion and heading dynamics of a supply ship as inputs, generate the commanded forces required to manoeuvre the ship. The optimised controllers are evaluated through computer simulations and real manoeuvrability tests in a water basin laboratory. In order to deal with the issue of the generation of numerical constants, two kinds of GP algorithms are implemented. The first one chooses the constants necessary to create the controller structure by random generation . The second algorithm includes a Genetic Algorithms (GAs) technique for the optimisation of such constants. The results obtained illustrate the benefits of using GP to optimise propulsion and navigation controllers for ships.", notes = "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005", } @InProceedings{conf/esann/Alfaro-CidES06, title = "Using distributed genetic programming to evolve classifiers for a brain computer interface", author = "Eva Alfaro-Cid and Anna Esparcia-Alc{\'a}zar and Ken Sharman", year = "2006", booktitle = "ESANN'2006 proceedings - European Symposium on Artificial Neural Networks", editor = "Michel Verleysen", pages = "59--66", address = "Bruges, Belgium", month = "26-28 " # apr, bibdate = "2006-08-30", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/esann/esann2006.html#Alfaro-CidES06", keywords = "genetic algorithms, genetic programming", ISBN = "2-930307-06-4", URL = "http://www.dice.ucl.ac.be/Proceedings/esann/esannpdf/es2006-44.pdf", abstract = "The objective of this paper is to illustrate the application of genetic programming to evolve classifiers for multi-channel time series data. The paper shows how high performance distributed genetic programming (GP) has been implemented for evolving classifiers. The particular application discussed herein is the classification of human electroencephalographic (EEG) signals for a brain-computer interface (BCI). The resulting classifying structures provide classification rates comparable to those obtained using traditional, human-designed, classification", notes = "http://www.dice.ucl.ac.be/Proceedings/esann/", } @InProceedings{Alfaro-Cid:2006:CEC, author = "Eva Alfaro-Cid and Ken Sharman and Anna I. Esparcia-Alcazar", title = "Evolving a Learning Machine by Genetic Programming", booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary Computation", year = "2006", editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas", pages = "958--962", address = "Vancouver", month = "16-21 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, simulated annealing, function set, learning machine, learning node, optimization algorithm, simulated annealing", ISBN = "0-7803-9487-9", DOI = "doi:10.1109/CEC.2006.1688316", size = "5 pages", abstract = "We describe a novel technique for evolving a machine that can learn. The machine is evolved using a Genetic Programming (GP) algorithm that incorporates in its function set what we have called a learning node. Such a node is tuned by a second optimisation algorithm (in this case Simulated Annealing), mimicking a natural learning process and providing the GP tree with added flexibility and adaptability. The result of the evolution is a system with a fixed structure but with some variable parameters. The system can then learn new tasks in new environments without undergoing further evolution.", notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D IEEE Xplore gives pages as 254--258", } @InProceedings{alshaes2007a, title = "Predicci\'{o}n de quiebra empresarial usando programaci\'{o}n gen\'{e}tica", author = "Eva {Alfaro Cid} and Ken Sharman and Anna I. {Esparcia Alc\'{a}zar}", booktitle = "Actas del V Congreso Espa{\~n}ol sobre Metaheur\'{i}sticas, Algoritmos Evolutivos y Bioinspirados (MAEB'07)", editor = "Francisco Almeida Rodriguez and Maria Belen Melian Batista and Jose Andres Moreno Perez and Jose Marcos Moreno Vega", publisher = "La Laguna", month = "Febrero", year = "2007", pages = "703--710", address = "Tenerife, Spain", publisher_address = "Tenerife, Spain", keywords = "genetic algorithms, genetic programming", isbn13 = "978-84-690-3470-5", URL = "https://dialnet.unirioja.es/servlet/articulo?codigo=4142085", notes = "Prediccion de quiebra empresarial usando programacion genetica in Spanish http://www.redheur.org/files/MAEBs/MAEB07.pdf", } @InProceedings{alshaescu2007a, title = "Aprendizaje autom\'{a}tico con programaci\'{o}n gen\'{e}tica", author = "Eva {Alfaro Cid} and Ken Sharman and Anna I. {Esparcia Alc\'{a}zar} and Alberto {Cuesta Ca{\~n}ada}", booktitle = "Actas del V Congreso Espa{\~n}ol sobre Metaheur\'{i}sticas, Algoritmos Evolutivos y Bioinspirados (MAEB'07)", publisher = "La Laguna", month = "Febrero", year = "2007", pages = "819--826", address = "Tenerife, Spain", publisher_address = "Tenerife, Spain", keywords = "genetic algorithms, genetic programming", isbn13 = "978-84-690-3470-5", URL = "https://dialnet.unirioja.es/servlet/articulo?codigo=4148339", notes = "Aprendizaje automatico con programacion genetica in Spanish http://www.redheur.org/files/MAEBs/MAEB07.pdf", } @InProceedings{alfaro-cid:evows07, author = "Eva Alfaro-Cid and Ken Sharman and Anna I. Esparcia-Alc\`azar", title = "A genetic programming approach for bankruptcy prediction using a highly unbalanced database", booktitle = "Applications of Evolutionary Computing, EvoWorkshops2007: {EvoCOMNET}, {EvoFIN}, {EvoIASP}, {EvoInteraction}, {EvoMUSART}, {EvoSTOC}, {EvoTransLog}", year = "2007", month = "11-13 " # apr, editor = "Mario Giacobini and Anthony Brabazon and Stefano Cagnoni and Gianni A. {Di Caro} and Rolf Drechsler and Muddassar Farooq and Andreas Fink and Evelyne Lutton and Penousal Machado and Stefan Minner and Michael O'Neill and Juan Romero and Franz Rothlauf and Giovanni Squillero and Hideyuki Takagi and A. Sima Uyar and Shengxiang Yang", series = "LNCS", volume = "4448", publisher = "Springer Verlag", address = "Valencia, Spain", pages = "169--178", keywords = "genetic algorithms, genetic programming, SVM", isbn13 = "978-3-540-71804-8", DOI = "doi:10.1007/978-3-540-71805-5_19", abstract = "in this paper we present the application of a genetic programming algorithm to the problem of bankruptcy prediction. To carry out the research we have used a database of Spanish companies. The database has two important drawbacks: the number of bankrupt companies is very small when compared with the number of healthy ones (unbalanced data) and a considerable number of companies have missing data. For comparison purposes we have solved the same problem using a support vector machine. Genetic programming has achieved very satisfactory results, improving those obtained with the support vector machine.", notes = "EvoWorkshops2007", } @InProceedings{conf/evoW/Alfaro-CidMGES08, title = "A {SOM} and {GP} Tool for Reducing the Dimensionality of a Financial Distress Prediction Problem", author = "Eva Alfaro-Cid and Antonio Miguel Mora and Juan Juli{\'a}n Merelo Guerv{\'o}s and Anna Esparcia-Alc{\'a}zar and Ken Sharman", bibdate = "2008-04-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/evoW/evoW2008.html#Alfaro-CidMGES08", booktitle = "Proceedings of Evo{COMNET}, Evo{FIN}, Evo{HOT}, Evo{IASP}, Evo{MUSART}, Evo{NUM}, Evo{STOC}, and EvoTransLog, Applications of Evolutionary Computing, EvoWorkshops", publisher = "Springer", year = "2008", volume = "4974", editor = "Mario Giacobini and Anthony Brabazon and Stefano Cagnoni and Gianni {Di Caro} and Rolf Drechsler and Anik{\'o} Ek{\'a}rt and Anna Esparcia-Alc{\'a}zar and Muddassar Farooq and Andreas Fink and Jon McCormack and Michael O'Neill and Juan Romero and Franz Rothlauf and Giovanni Squillero and Sima Uyar and Shengxiang Yang", isbn13 = "978-3-540-78760-0", pages = "123--132", series = "Lecture Notes in Computer Science", DOI = "doi:10.1007/978-3-540-78761-7_13", address = "Naples", month = "26-28 " # mar, keywords = "genetic algorithms, genetic programming", } @InProceedings{Alfaro-Cid:2008:cec, author = "E. Alfaro-Cid and P. A. Castillo and A. Esparcia and K. Sharman and J. J. Merelo and A. Prieto and J. L. J. Laredo", title = "Comparing Multiobjective Evolutionary Ensembles for Minimizing Type I and II Errors for Bankruptcy Prediction", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "2902--2908", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1823-7", file = "EC0649.pdf", DOI = "doi:10.1109/CEC.2008.4631188", abstract = "In many real world applications type I (false positive) and type II (false negative) errors have to be dealt with separately, which is a complex problem since an attempt to minimise one of them usually makes the other grow. In fact, a type of error can be more important than the other, and a trade-off that minimises the most important error type must be reached. In the case of the bankruptcy prediction problem the error type II is of greater importance, being unable to identify that a company is at risk causes problems to creditors and slows down the taking of measures that may solve the problem. Despite the importance of type II errors, most bankruptcy prediction methods take into account only the global classification error. In this paper we propose and compare two methods to optimise both error types in classification: artificial neural networks and function trees ensembles created through multiobjective Optimization. Since the multiobjective Optimization process produces a set of equally optimal results (Pareto front) the classification of the test patterns in both cases is based on the non-dominated solutions acting as an ensemble. The experiments prove that, although the best classification rates are obtained using the artificial neural network, the multiobjective genetic programming model is able to generate comparable results in the form of an analytical function.", keywords = "genetic algorithms, genetic programming", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @Article{Alfaro-Cid:2008:ieeeITS, author = "Eva Alfaro-Cid and Euan W. McGookin and David J. Murray-Smith and Thor I. Fossen", title = "Genetic Programming for the Automatic Design of Controllers for a Surface Ship", journal = "IEEE Transactions on Intelligent Transportation Systems", year = "2008", month = jun, volume = "9", number = "2", pages = "311--321", keywords = "genetic algorithms, genetic programming, control system synthesis, navigation, propulsion, ships CyberShip II, automatic design, controller structure, navigation controllers, propulsion controllers, supply ship, surface ship", ISSN = "1524-9050", DOI = "doi:10.1109/TITS.2008.922932", URL = "http://results.ref.ac.uk/Submissions/Output/2145080", abstract = "In this paper, the implementation of genetic programming (GP) to design a controller structure is assessed. GP is used to evolve control strategies that, given the current and desired state of the propulsion and heading dynamics of a supply ship as inputs, generate the commanded forces required to maneuver the ship. The controllers created using GP are evaluated through computer simulations and real maneuverability tests in a laboratory water basin facility. The robustness of each controller is analyzed through the simulation of environmental disturbances. In addition, GP runs in the presence of disturbances are carried out so that the different controllers obtained can be compared. The particular vessel used in this paper is a scale model of a supply ship called CyberShip II. The results obtained illustrate the benefits of using GP for the automatic design of propulsion and navigation controllers for surface ships.", notes = "Also known as \cite{4517335}", uk_research_excellence_2014 = "This paper represents the outcome of a Marie Curie funded collaborative project with the Norwegian University of Science and Technology (NUST), and is one of the first studies to use Genetic Programming in the design of marine vehicle control systems. The research involved the optimisation of the structure and associated gains of a control system for guiding a surface vessel. The optimised controller was used on the test vehicle in tank trials and evaluated in the Marine Cybernetics Laboratory at NUST (Contact: Thor Fossen, ), to demonstrate the power of this optimisation method for controller design.", } @InProceedings{Alfaro-Cid:2008:HIS, author = "Eva Alfaro-Cid and Anna Esparcia-Alcazar and Ken Sharman and Francisco {Fernandez de Vega} and J. J. Merelo", title = "Prune and Plant: A New Bloat Control Method for Genetic Programming", booktitle = "Eighth International Conference on Hybrid Intelligent Systems, HIS '08", year = "2008", month = sep, pages = "31--35", keywords = "genetic algorithms, genetic programming, bloat control method, genetic operator, prune and plant, time consumption, tree size reduction, mathematical operators, trees (mathematics)", DOI = "doi:10.1109/HIS.2008.127", abstract = "This paper reports a comparison of several bloat control methods and also evaluates a new proposal for limiting the size of the individuals: a genetic operator called prune and plant. The aim of this work is to prove the adequacy of this new method. Since a preliminary study of the method has already shown promising results, we have performed a thorough study in a set of benchmark problems aiming at demonstrating the utility of the new approach. Prune and plant has obtained results that maintain the quality of the final solutions in terms of fitness while achieving a substantial reduction of the mean tree size in all four problem domains considered. In addition, in one of these problem domains prune and plant has demonstrated to be better in terms of fitness, size reduction and time consumption than any of the other bloat control techniques under comparison.", notes = "Also known as \cite{4626601}", } @InCollection{series/sci/Alfaro-CidCSE08, title = "Strong Typing, Variable Reduction and Bloat Control for Solving the Bankruptcy Prediction Problem Using Genetic Programming", author = "Eva Alfaro-Cid and Alberto Cuesta-Canada and Ken Sharman and Anna Esparcia-Alcazar", bibdate = "2008-08-26", bibsource = "DBLP, http://dblp.uni-trier.de/db/series/sci/sci100.html#Alfaro-CidCSE08", booktitle = "Natural Computing in Computational Finance", publisher = "Springer", year = "2008", volume = "100", editor = "Anthony Brabazon and Michael O'Neill", isbn13 = "978-3-540-77476-1", pages = "161--185", series = "Studies in Computational Intelligence", DOI = "doi:10.1007/978-3-540-77477-8_9", chapter = "9", keywords = "genetic algorithms, genetic programming, STGP, SVM", size = "29 pages", abstract = "In this chapter we present the application of a genetic programming (GP) algorithm to the problem of bankruptcy prediction. To carry out the research we have used a database that includes extensive information (not only economic) from the companies. In order to handle the different data types we have used Strongly Typed GP and variable reduction. Also, bloat control has been implemented to obtain comprehensible classification models. For comparison purposes we have solved the same problem using a support vector machine (SVM). GP has achieved very satisfactory results, improving those obtained with the SVM.", } @InProceedings{Alfaro-Cid:2009:evonum, title = "Modeling Pheromone Dispensers Using Genetic Programming", author = "Eva Alfaro-Cid and Anna I. Esparcia-Alc\'{a}zar and Pilar Moya and Beatriu Femenia-Ferrer and Ken Sharman and J. J. Merelo", booktitle = "Applications of Evolutionary Computing, EvoWorkshops 2009: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG", editor = "Mario Giacobini and Anthony Brabazon and Stefano Cagnoni and Gianni A. Di Caro and Anik{\'o} Ek{\'a}rt and Anna Esparcia-Alc{\'a}zar and Muddassar Farooq and Andreas Fink and Penousal Machado and Jon McCormack and Michael O'Neill and Ferrante Neri and Mike Preuss and Franz Rothlauf and Ernesto Tarantino and Shengxiang Yang", volume = "5484", series = "Lecture Notes in Computer Science", address = "Tubingen, Germany", year = "2009", pages = "635--644", month = apr # " 15-17", organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-01128-3", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1007/978-3-642-01129-0_73", size = "10 pages", abstract = "Mating disruption is an agricultural technique that intends to substitute the use of insecticides for pest control. This technique consists of the diffusion of large amounts of sexual pheromone, so that the males are confused and mating is disrupted. Pheromones are released using devices called dispensers. The speed of release is, generally, a function of time and atmospheric conditions such as temperature and humidity. One of the objectives in the design of the dispensers is to minimise the effect of atmospheric conditions in the performance of the dispenser. With this objective, the Centro de Ecologia Quimica Agricola (CEQA) has designed an experimental dispenser that aims to compete with the dispensers already in the market. The hypothesis we want to validate (and which is based on experimental results) is that the performance of the CEQA dispenser is independent of the atmospheric conditions, as opposed to the most widely used commercial dispenser, Isomate CPlus. This was done using a genetic programming (GP) algorithm. GP evolved functions able to describe the performance of both dispensers and that support the initial hypothesis.", notes = "ECJ. EvoWorkshops2009 held in conjunction with EuroGP2009, EvoCOP2009, EvoBIO2009", } @InProceedings{DBLP:conf/gecco/Alfaro-CidEMMFSP09, author = "Eva Alfaro-Cid and Anna Esparcia-Alcazar and Pilar Moya and J. J. Merelo and Beatriu Femenia-Ferrer and Ken Sharman and Jaime Primo", title = "Multiobjective genetic programming approach for a smooth modeling of the release kinetics of a pheromone dispenser", booktitle = "GECCO-2009 Symbolic regression and modeling workshop (SRM)", year = "2009", editor = "Anna I. Esparcia and Ying-ping Chen and Gabriela Ochoa and Ender Ozcan and Marc Schoenauer and Anne Auger and Hans-Georg Beyer and Nikolaus Hansen and Steffen Finck and Raymond Ros and Darrell Whitley and Garnett Wilson and Simon Harding and W. B. Langdon and Man Leung Wong and Laurence D. Merkle and Frank W. Moore and Sevan G. Ficici and William Rand and Rick Riolo and Nawwaf Kharma and William R. Buckley and Julian Miller and Kenneth Stanley and Jaume Bacardit and Will Browne and Jan Drugowitsch and Nicola Beume and Mike Preuss and Stephen L. Smith and Stefano Cagnoni and Jim DeLeo and Alexandru Floares and Aaron Baughman and Steven Gustafson and Maarten Keijzer and Arthur Kordon and Clare Bates Congdon and Laurence D. Merkle and Frank W. Moore", pages = "2225--2230", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1570256.1570309", abstract = "The accurate modeling of the release kinetics of pheromone dispensers is a matter or great importance for ensuring that the dispenser field-life covers the flight period of the pest and for optimizing the layout of dispensers in the treated area. A new experimental dispenser has been recently designed by researchers at the Instituto Agroforestal del Mediterraneo - Centro de Ecologia Quimica Agricola (CEQA) of the Universidad Politecnica de Valencia (Spain). The most challenging problem for the modeling of the release kinetics of this dispensers is the difficulty in obtaining experimental measurements for building the model. The procedure for obtaining these data is very costly, both time and money wise, therefore the available data across the whole season are scarce. In prior work we demonstrated the utility of using Genetic Programming (GP) for this particular problem. However, the models evolved by the GP algorithm tend to have discontinuities in those time ranges where there are not available measurements. In this work we propose the use of a multiobjective Genetic Programming for modeling the performance of the CEQA dispenser. We take two approaches, involving two and nine objectives respectively. In the first one, one of the objectives of the GP algorithm deals with how well the model fits the experimental data, while the second objective measures how {"}smooth{"} the model behaviour is. In the second approach we have as many objectives as data points and the aim is to predict each point separately using the remaining ones. The results obtained endorse the utility of this approach for those modeling problems characterized by the lack of experimental data.", notes = "Distributed on CD-ROM at GECCO-2009. ACM Order Number 910092.", } @Article{Alfaro-Cid:2010:EC, author = "Eva Alfaro-Cid and J. J. Merelo and Francisco {Fernandez de Vega} and Anna I. Esparcia-Alcazar and Ken Sharman", title = "Bloat Control Operators and Diversity in Genetic Programming: A Comparative Study", journal = "Evolutionary Computation", year = "2010", volume = "18", number = "2", pages = "305--332", month = "Summer", keywords = "genetic algorithms, genetic programming", ISSN = "1063-6560", DOI = "doi:10.1162/evco.2010.18.2.18206", abstract = "This paper reports a comparison of several bloat control methods and also evaluates a recent proposal for limiting the size of the individuals: a genetic operator called prune and plant. The aim of this work is to test the adequacy of this method. Since a preliminary study of the method has already shown promising results, we have performed a thorough study in a set of benchmark problems aiming at demonstrating the utility of the new approach. Prune and plant has obtained results that maintain the quality of the final solutions in terms of fitness while achieving a substantial reduction of the mean tree size in all four problem domains considered. In addition, in one of these problem domains, prune and plant has demonstrated to be better in terms of fitness, size reduction, and time consumption than any of the other bloat control techniques under comparison. The experimental part of the study presents a comparison of performance in terms of phenotypic and genotypic diversity. This comparison study can provide the practitioner with some relevant clues as to which bloat control method is better suited to a particular problem and whether the advantage of a method does or does not derive from its influence on the genetic pool diversity.", } @Article{Alfaro-Cid:2014:EC, author = "Eva Alfaro-Cid and Ken Sharman and Anna I. Esparcia-Alcazar", title = "Genetic programming and serial processing for time series classification", journal = "Evolutionary Computation", year = "2014", volume = "22", number = "2", pages = "265--285", month = "Summer", keywords = "genetic algorithms, genetic programming, Classification, time series, serial data processing, real world applications", ISSN = "1063-6560", DOI = "doi:10.1162/EVCO_a_00110", size = "20 pages", abstract = "This work describes an approach devised by the authors for time series classification. In our approach genetic programming is used in combination with a serial processing of data, where the last output is the result of the classification. The use of genetic programming for classification, although still a field where more research in needed, is not new. However, the application of genetic programming to classification tasks is normally done by considering the input data as a feature vector. That is, to the best of our knowledge, there are not examples in the genetic programming literature of approaches where the time series data are processed serially and the last output is considered as the classification result. The serial processing approach presented here fills a gap in the existing literature. This approach was tested in three different problems. Two of them are real world problems whose data were gathered for on-line or conference competitions. As there are published results of these two problems this gives us the chance of comparing the performance of our approach against top performing methods. The serial processing of data in combination with genetic programming obtained competitive results in both competitions, showing its potential for solving time series classification problems. The main advantage of our serial processing approach is that it can easily handle very large data sets.", notes = "ECJ. EEG BCI competition II. Ford Classification Challenge", } @Article{alfonseca:2004:GPEM, author = "Manuel Alfonseca and Alfonso Ortega", title = "Book Review: {Grammatical Evolution}: {Evolutionary} Automatic Programming in an Arbitrary Language", journal = "Genetic Programming and Evolvable Machines", year = "2004", volume = "5", number = "4", pages = "393", month = dec, keywords = "genetic algorithms, genetic programming, grammatical evolution", ISSN = "1389-2576", URL = "https://rdcu.be/dR8co", DOI = "doi:10.1023/B:GENP.0000036057.27304.5b", size = "1 page", notes = "review of \cite{oneill:book}. Article ID: 5272973", } @Article{journals/biosystems/AlfonsecaG13, title = "Evolving an ecology of mathematical expressions with grammatical evolution", author = "Manuel Alfonseca and Francisco Jose Soler Gil", journal = "Biosystems", year = "2013", number = "2", volume = "111", keywords = "genetic algorithms, genetic programming, grammatical evolution", bibdate = "2014-12-09", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/biosystems/biosystems111.html#AlfonsecaG13", pages = "111--119", URL = "http://dx.doi.org/10.1016/j.biosystems.2012.12.004", } @Article{journals/complexity/AlfonsecaG15, author = "Manuel Alfonseca and Francisco Jose Soler Gil", title = "Evolving a predator-prey ecosystem of mathematical expressions with grammatical evolution", journal = "Complexity", year = "2015", number = "3", volume = "20", bibdate = "2015-03-04", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/complexity/complexity20.html#AlfonsecaG15", pages = "66--83", keywords = "genetic algorithms, genetic programming, grammatical evolution", URL = "http://dx.doi.org/10.1002/cplx.21507", } @Article{Algburi:2025:rineng, author = "Sameer Algburi and Aymen Mohammed and Hassan Falah Fakhruldeen and Ibrahim Abdullah and Israa Alhani and Ali Khudhair and Qusay Hassan and Michael Ssebunya and Feryal Ibrahim Jabbar", title = "Optimization of multi-energy storage in urban building clusters", journal = "Results in Engineering", year = "2025", volume = "26", pages = "105427", keywords = "genetic algorithms, genetic programming, Energy management, Storage systems, Optimization, Demand response, Efficiency", ISSN = "2590-1230", URL = "https://www.sciencedirect.com/science/article/pii/S2590123025014975", DOI = "doi:10.1016/j.rineng.2025.105427", abstract = "A data-driven optimisation framework was developed to enhance energy performance in building clusters through multi-energy storage systems, combining electrical and thermal solutions. The approach used surrogate modelling, symbolic regression, and genetic programming to simulate energy consumption, integrate weather and tariff data, and refine storage strategies across varied building types. Applied to a cluster in Bismayah city, Iraq, the methodology evaluated HVAC configurations, facade designs, and thermal mass levels to tailor storage capacity recommendations. Results revealed a 38 percent reduction in peak grid import and a 24.6 percent drop in overall energy consumption when hybrid energy storage was implemented. A thermal energy storage tank capacity of 4651 kWh, coupled with a battery storage unit of 342 kWh, demonstrated a 35 percent decrease in energy costs. Demand response participation increased by 45 percent through strategic use of pre-cooling routines and temperature reset controls. TES-only configurations achieved energy usage of 16.3 kWh/msquared, while hybrid configurations further reduced to 8.7 kWh/msquared. Budget analyses showed that investments ranging from $2.6 million to $10.4 million proportionally enhanced system performance without over-sizing. The integration of Battery and Thermal Energy Storage with Phase Change Materials further supported passive thermal control, reducing HVAC reliance during peak hours", } @InProceedings{Alghamdi:2019:GI7, author = "Mahfouth Alghamdi and Christoph Treude and Markus Wagner", title = "Toward Human-Like Summaries Generated from Heterogeneous Software Artefacts", booktitle = "7th edition of GI @ GECCO 2019", year = "2019", month = jul # " 13-17", editor = "Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and John R. Woodward", publisher = "ACM", address = "Prague, Czech Republic", pages = "1701--1702", organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, Heterogeneous software artefacts, extractive summarisation, human-like summaries", isbn13 = "978-1-4503-6748", URL = "https://arxiv.org/abs/1905.02258", URL = "https://ctreude.files.wordpress.com/2019/05/gi19.pdf", DOI = "doi:10.1145/3319619.3326814", size = "2 pages", abstract = "Automatic text summarisation has drawn considerable interest in the field of software engineering. It can improve the efficiency of software developers, enhance the quality of products, and ensure timely delivery. In this paper, we present our initial work towards automatically generating human-like multi-document summaries from heterogeneous software artefacts. Our analysis of the text properties of 545 human-written summaries from 15 software engineering projects will ultimately guide heuristics searches in the automatic generation of human-like summaries.", } @InProceedings{Alghieth:2015:INISTA, author = "Manal Alghieth and Yingjie Yang and Francisco Chiclana", booktitle = "2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA)", title = "Development of {2D} curve-fitting genetic/gene-expression programming technique for efficient time-series financial forecasting", year = "2015", abstract = "Stock market prediction is of immense interest to trading companies and buyers due to high profit margins. Therefore, precise prediction of the measure of increase or decrease of stock prices also plays an important role in buying/selling activities. This research presents a specialised extension to the genetic algorithms (GA) known as the genetic programming (GP) and gene expression programming (GEP) to explore and investigate the outcome of the GEP criteria on the stock market price prediction. The research presented in this paper aims at the modelling and prediction of short-to-medium term stock value fluctuations in the market via genetically tuned stock market parameters. The technique uses hierarchically defined GP and GEP techniques to tune algebraic functions representing the fittest equation for stock market activities. The proposed methodology is evaluated against five well-known stock market companies with each having its own trading circumstances during the past 20+ years. The proposed GEP/GP methodologies were evaluated based on variable window/population sizes, selection methods, and Elitism, Rank and Roulette selection methods. The Elitism-based approach showed promising results with a low error-rate in the resultant pattern matching with an overall accuracy of 93.46percent for short-term 5-day and 92.105 for medium-term 56-day trading periods.", keywords = "genetic algorithms, genetic programming, gene expression programming", DOI = "doi:10.1109/INISTA.2015.7276734", month = sep, notes = "Fac. of Technol., De Montfort Univ., Leicester, UK Also known as \cite{7276734}", } @InProceedings{Alghieth:2016:CEC, author = "Manal Alghieth and Yingjie Yang and Francisco Chiclana", title = "Development of a Genetic Programming-based GA Methodology for the Prediction of Short-to-Medium-term Stock Markets", booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016)", year = "2016", editor = "Yew-Soon Ong", pages = "2381--2388", address = "Vancouver", month = "24-29 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, gene expressing programming, Stock market, Time series financial forecasting", isbn13 = "978-1-5090-0623-6", URL = "https://www.dora.dmu.ac.uk/handle/2086/11896", DOI = "doi:10.1109/CEC.2016.7744083", size = "8 pages", abstract = "This research presents a specialised extension to the genetic algorithms (GA) known as the genetic programming (GP) and gene expression programming (GEP) to explore and investigate the outcome of the GEP criteria on the stock market price prediction. The aim of this research is to model and predict short-to-medium term stock value fluctuations in the market via genetically tuned stock market parameters. The technology proposes a fractional adaptive mutation rate Elitism (GEPFAMR) technique to initiate a balance between varied mutation rates and between varied-fitness chromosomes, thereby improving prediction accuracy and fitness improvement rate. The methodology is evaluated against different dataset and selection methods and showed promising results with a low error-rate in the resultant pattern matching with an overall accuracy of 95.96percent for short-term 5-day and 95.35percent for medium-term 56-day trading periods.", notes = "WCCI2016 Centre for Computational Intelligence, Faculty of Technology, De Montfort University, Leicester, UK", } @Article{ALHAMED:2022:energy, author = "Khaled H. M. Al-Hamed and Ibrahim Dincer", title = "Exergoeconomic analysis and optimization of a solar energy-based integrated system with oxy-combustion for combined power cycle and carbon capturing", journal = "Energy", volume = "250", pages = "123814", year = "2022", ISSN = "0360-5442", DOI = "doi:10.1016/j.energy.2022.123814", URL = "https://www.sciencedirect.com/science/article/pii/S0360544222007174", keywords = "genetic algorithms, genetic programming, Ammonia, Carbon capture, Energy, Exergoeconomic analysis, Gas turbine, Optimization", abstract = "This work presents a newly developed integrated system that produces multiple useful products, namely electricity, space cooling, freshwater, and ammonium bicarbonate. The two sources of energy for this integrated system are solar energy and natural gas. The natural gas is consumed in an oxy-combustion Brayton cycle to produce electricity, while the solar energy provides electric power to the carbon capturing unit to produce ammonium bicarbonate as a valuable chemical product to compensate for the operation costs of carbon capture. This integrated system is studied using the exergoeconomic analysis and the multi-objective optimization method of genetic programming and genetic algorithm to enhance the thermodynamic and economic aspects of this system. Applying such an analysis to this integrated system adds more understanding and knowledge on how effectively and efficiently this carbon capture system operates and whether or not it is financially viable to pursue this integrated system for further prototyping and concept demonstration. The results of this exergoeconomic analysis show that the production cost of ammonium bicarbonate per 1 kg in this integrated system is 0.0687 $ kg-1, and this is much lower than the market price. This means that producing ammonium bicarbonate as a way to capture carbon dioxide is feasible financially. Furthermore, the optimization results show that the overall exergy destruction rate and the overall unit cost of products are 86,000 kW and 5.19 times 10-3 $ kJ-1, respectively, when operated under optimum conditions", } @InProceedings{alharthi:2025:EuroGP, author = "Khulud Alharthi and S Zahraa Abdallah and Sabine Hauert", title = "Ghost Swarms: Learning Swarm Rules from Environmental Changes Alone", booktitle = "European Conference on Genetic Programming, EuroGP 2025", year = "2025", editor = "Bing Xue and Luca Manzoni and Illya Bakurov", volume = "15609", series = "LNCS", pages = "1--17", address = "Trieste", month = "23-25 " # apr, publisher = "Springer Nature", keywords = "genetic algorithms, genetic programming, Swarm Behaviour, Imitation Learning, Environmental Imprints", isbn13 = "978-3-031-89990-4", abstract = "Swarm behaviours emerge from agents interacting with their local environment following simple rules. While directly observing each agent can be challenging, their collective behaviour leaves detectable environmental imprints that could offer insights into the underlying swarm dynamics. However, this task is complex due to the hidden and interconnected relationships between the rules governing agent interactions, the emergent swarm behaviour, and the environmental changes generated by this behaviour. In this work, we propose a method for extracting human-readable controllers from demonstrations showing only observable environmental imprints caused by the swarm. This approach explores whether these environmental imprints can reveal the swarm's actions, even when the individual agents are challenging to track. Our approach eliminates the need for prior knowledge about the controller or its structure, enabling the successful learning of controllers from a single demonstration. We provide a novel method for understanding and managing both natural and engineered swarms by using the environmental imprints left by swarm behaviours, even when direct observation of the swarm's actions is not feasible.", notes = "also known as \cite{paper_221_environmental_changes_alone} Part of \cite{Xue:2025:GP} EuroGP'2025 held in conjunction with EvoCOP2025, EvoMusArt2025 and EvoApplications2025", } @InProceedings{Alhejali:2010:UKCI, author = "Atif M. Alhejali and Simon M. Lucas", title = "Evolving diverse {Ms. Pac-Man} playing agents using genetic programming", booktitle = "UK Workshop on Computational Intelligence (UKCI 2010)", year = "2010", month = "8-10 " # sep, pages = "1--6", abstract = "This paper uses genetic programming (GP) to evolve a variety of reactive agents for a simulated version of the classic arcade game Ms. Pac-Man. A diverse set of behaviours were evolved using the same GP setup in three different versions of the game. The results show that GP is able to evolve controllers that are well-matched to the game used for evolution and, in some cases, also generalise well to previously unseen mazes. For comparison purposes, we also designed a controller manually using the same function set as GP. GP was able to significantly outperform this hand-designed controller. The best evolved controllers are competitive with the best reactive controllers reported for this problem.", keywords = "genetic algorithms, genetic programming, Ms PacMan game, reactive agents, computer games, learning (artificial intelligence), software agents", DOI = "doi:10.1109/UKCI.2010.5625586", notes = "Also known as \cite{5625586}", } @InProceedings{Alhejali:2011:CIG, author = "Atif M. Alhejali and Simon M. Lucas", title = "Using a Training Camp with Genetic Programming to Evolve {Ms Pac-Man} Agents", booktitle = "Proceedings of the 2011 IEEE Conference on Computational Intelligence and Games", year = "2011", pages = "118--125", address = "Seoul, South Korea", month = "31 " # aug # " - 3 " # sep, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Pac-Man, Evolving Controllers, Decomposition learning, Training camp", isbn13 = "978-1-4577-0010-1", URL = "http://cilab.sejong.ac.kr/cig2011/proceedings/CIG2011/papers/paper31.pdf", DOI = "doi:10.1109/CIG.2011.6031997", size = "8 pages", abstract = "This paper investigates using a training camp in conjunction with Genetic Programming in the evolution of Ms Pac-Man playing agents. We measure the amount of effort, time and resources required to run the training camp successfully. The approach is compared with standard GP. The results indicate that better and more stable performance can be achieved using the training camp method at the expense of greater manual effort in the design of the training scenarios. However, in addition to the better results, the training camp also provides more detailed insight into the strengths and weaknesses of each controller.", notes = "Also known as \cite{6031997}", } @InProceedings{Alhejali:2013:CIG, author = "Atif M. Alhejali and Simon M. Lucas", title = "Using genetic programming to evolve heuristics for a Monte Carlo Tree Search {Ms Pac-Man} agent", booktitle = "IEEE Conference on Computational Intelligence in Games (CIG 2013)", year = "2013", month = "11-13 " # aug, address = "Niagara Falls, Canada", keywords = "genetic algorithms, genetic programming, Monte Carlo methods, artificial intelligence, computer games, tree searching, Al, MCTS, Monte Carlo tree search Ms Pac-Man agent, evolved default policy, game artificial intelligence, random agent, random default policy, Equations, Games, Mathematical model, Monte Carlo methods, Sociology, Monte Carlo Tree Search, Pac-Man", ISSN = "2325-4270", isbn13 = "978-1-4673-5311-3", DOI = "doi:10.1109/CIG.2013.6633639", abstract = "Ms Pac-Man is one of the most challenging test beds in game artificial intelligence (AI). Genetic programming and Monte Carlo Tree Search (MCTS) have already been successful applied to several games including Pac-Man. In this paper, we use Monte Carlo Tree Search to create a Ms Pac-Man playing agent before using genetic programming to enhance its performance by evolving a new default policy to replace the random agent used in the simulations. The new agent with the evolved default policy was able to achieve an 18percent increase on its average score over the agent with random default policy.", notes = "Also known as \cite{6633639}", } @PhdThesis{Alhejali:thesis, author = "Atif Mansour Alhejali", title = "Genetic Programming and the Evolution of Games Playing Agents", school = "Computing and Electronic Systems, University of Essex", year = "2013", address = "UK", keywords = "genetic algorithms, genetic programming", URL = "http://www.essex.ac.uk/csee/news_and_seminars/newsEvent.aspx?e_id=5796", notes = "Supervisor Simon M. Lucas", } @Article{ali:2004:GPEM, author = "B. Ali and A. E. A. Almaini and T. Kalganova", title = "Evolutionary Algorithms and Theirs Use in the Design of Sequential Logic Circuits", journal = "Genetic Programming and Evolvable Machines", year = "2004", volume = "5", number = "1", month = mar, keywords = "genetic algorithms, evolvable hardware, sequential circuits, state assignment", ISSN = "1389-2576", DOI = "doi:10.1023/B:GENP.0000017009.11392.e2", abstract = "design synchronous sequential logic circuits with minimum number of logic gates is suggested. The proposed method consists of four main stages. The first stage is concerned with the use of genetic algorithms (GA) for the state assignment problem to compute optimal binary codes for each symbolic state and construct the state transition table of finite state machine (FSM). The second stage defines the subcircuits required to achieve the desired functionality. The third stage evaluates the subcircuits using extrinsic Evolvable Hardware (EHW). During the fourth stage, the final circuit is assembled. The obtained results compare favourably against those produced by manual methods and other methods based on heuristic techniques.", notes = "Article ID: 5264733", } @InCollection{Ali:2008:GPTP, author = "Mostafa Z. Ali and Robert G. Reynolds and Xiangdong Che", title = "Genetic Programming for Incentive-Based Design within a Cultural Algorithms Framework", booktitle = "Genetic Programming Theory and Practice {VI}", year = "2008", editor = "Rick L. Riolo and Terence Soule and Bill Worzel", series = "Genetic and Evolutionary Computation", chapter = "16", pages = "249--269", address = "Ann Arbor", month = "15-17 " # may, publisher = "Springer", DOI = "doi:10.1007/978-0-387-87623-8_16", size = "20 pages", isbn13 = "978-0-387-87622-1", notes = "part of \cite{Riolo:2008:GPTP} published in 2009", keywords = "genetic algorithms, genetic programming", } @Article{DBLP:journals/evi/AliM20, author = "Muhammad Quamber Ali and Hammad Majeed", title = "Difficult first strategy {GP:} an inexpensive sampling technique to improve the performance of genetic programming", journal = "Evol. Intell.", volume = "13", number = "4", pages = "537--549", year = "2020", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/s12065-020-00355-2", DOI = "doi:10.1007/s12065-020-00355-2", timestamp = "Tue, 20 Oct 2020 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/evi/AliM20.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{DBLP:journals/nca/AliJAAM22, author = "Mir Masoud Ale Ali and Ali Jamali and A. Asgharnia and R. Ansari and Rammohan Mallipeddi", title = "Multi-objective Lyapunov-based controller design for nonlinear systems via genetic programming", journal = "Neural Computing and Applications", year = "2022", volume = "34", number = "2", pages = "1345--1357", month = jan, keywords = "genetic algorithms, genetic programming, Lyapunov function, Stability, Region of attraction, Pareto", ISSN = "0941-0643", URL = "https://rdcu.be/dl3Cd", URL = "https://doi.org/10.1007/s00521-021-06453-1", DOI = "doi:10.1007/s00521-021-06453-1", timestamp = "Tue, 08 Feb 2022 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/nca/AliJAAM22.bib", bibsource = "dblp computer science bibliography, https://dblp.org", size = "13 pages", abstract = "In system control, stability is considered the most important factor as unstable system is impractical or dangerous to use. Lyapunov direct method, one of the most useful tools in the stability analysis of nonlinear systems, enables the design of a controller by determining the region of attraction (ROA). However, the two main challenges posed are (1) it is hard to determine the scalar function referred to as Lyapunov function, and (2) the optimality of the designed controller is generally questionable. In this paper, multi-objective genetic programming (MOGP)-based framework is proposed to obtain both optimal Lyapunov and control functions at the same time. In other words, MOGP framework is employed to minimize several time-domain performances as well as the ROA radius to find the optimal Lyapunov and control functions. The proposed framework is tested in several nonlinear benchmark systems, and the control performance is compared with state-of-the-art algorithms.", notes = "Duffing oscillator Faculty of Mechanical Engineering, University of Guilan, Rasht, Iran", } @Article{ALI:2018:AFM, author = "Mumtaz Ali and Ravinesh C. Deo and Nathan J. Downs and Tek Maraseni", title = "Cotton yield prediction with Markov Chain Monte Carlo-based simulation model integrated with genetic programing algorithm: A new hybrid copula-driven approach", journal = "Agricultural and Forest Meteorology", volume = "263", pages = "428--448", year = "2018", keywords = "genetic algorithms, genetic programming, Crop yield prediction, Cotton yield, Climate data, Markov Chain Monte Carlo based copula model", ISSN = "0168-1923", DOI = "doi:10.1016/j.agrformet.2018.09.002", URL = "http://www.sciencedirect.com/science/article/pii/S0168192318302971", abstract = "Reliable data-driven models designed to accurately estimate cotton yield, an important agricultural commodity, can be adopted by farmers, agricultural system modelling experts and agricultural policy-makers in strategic decision-making processes. In this paper a hybrid genetic programing model integrated with the Markov Chain Monte Carlo (MCMC) based Copula technique is developed to incorporate climate-based inputs as the predictors of cotton yield, for selected study regions: Faisalabad", } @InCollection{ALI:2020:HPM, author = "Mumtaz Ali and Ravinesh C. Deo", title = "Chapter 2 - Modeling wheat yield with data-intelligent algorithms: artificial neural network versus genetic programming and minimax probability machine regression", editor = "Pijush Samui and Dieu {Tien Bui} and Subrata Chakraborty and Ravinesh C. Deo", booktitle = "Handbook of Probabilistic Models", publisher = "Butterworth-Heinemann", pages = "37--87", year = "2020", isbn13 = "978-0-12-816514-0", DOI = "doi:10.1016/B978-0-12-816514-0.00002-3", URL = "http://www.sciencedirect.com/science/article/pii/B9780128165140000023", keywords = "genetic algorithms, genetic programming, Agricultural precision, Artificial neural network, Minimax probability machine regression, Wheat yield model", abstract = "In precision agriculture, data-intelligent algorithms applied for predicting wheat yield can generate crucial information about enhancing crop production and strategic decision-making. In this chapter, artificial neural network (ANN) model is trained with three neighboring station-based wheat yields to predict the yield for two nearby objective stations that share a common geographic boundary in the agricultural belt of Pakistan. A total of 2700 ANN models (with a combination of hidden neurons, training algorithm, and hidden transfer/output functions) are developed by trial-and-error method, attaining the lowest mean square error, in which the 90 best-ranked models for 3-layered neuronal network are used for wheat prediction. Models such as learning algorithms comprised of pure linear, tangent, and logarithmic sigmoid equations in hidden transfer/output functions, executed by Levenberg-Marquardt, scaled conjugate gradient, conjugate gradient with Powell-Beale restarts, Broyden-Fletcher-Goldfarb-Shanno quasi-Newton, Fletcher-Reeves update, one-step secant, conjugate gradient with Polak-Ribiere updates, gradient descent with adaptive learning, gradient descent with momentum, and gradient descent with momentum adaptive learning method are trained. For the predicted wheat yield at objective station 1 (i.e., Toba Taik Singh), the optimal architecture was 3-14-1 (input-hidden-output neurons) trained with the Levenberg-Marquardt algorithm and logarithmic sigmoid as activation and tangent sigmoid as output function, while at objective station 2 (i.e., Bakkar), the Levenberg-Marquardt algorithm provided the best architecture (3-20-1) with pure liner as activation and tangent sigmoid as output function. The results are benchmarked with those from minimax probability machine regression (MPMR) and genetic programming (GP) in accordance with statistical analysis of predicted yield based on correlations (r), Willmott's index (WI), Nash-Sutcliffe coefficient (EV), root mean-squared error (RMSE), and mean absolute error (MAE). For objective station 1, the ANN model attained the r value of approximately 0.983, with WIapprox0.984 and EVapprox0.962, while the MPMR model attained rapprox0.957, WIapprox0.544, and EVapprox0.527, with the results attained by GP model, rapprox0.982, WIapprox0.980, and EVapprox0.955. For optimal ANN model, a relatively low value of RMSE approx 192.02kg/ha and MAE approx 162.75kg/ha was registered compared with the MPMR (RMSE approx 614.46kg/ha; MAE approx 431.29kg/ha) and GP model (RMSE approx 209.25kg/ha; MAE approx 182.84kg/ha). For both objective stations, ANN was found to be superior, as confirmed by a larger Legates-McCabe's (LM) index used in conjunction with relative RMSE and MAE. Accordingly, it is averred that ANN is considered as a useful data-intelligent contrivance for predicting wheat yield by using nearest neighbor yield", } @Article{Ali:2015:JBI, author = "Safdar Ali and Abdul Majid", title = "{Can-Evo-Ens}: Classifier stacking based evolutionary ensemble system for prediction of human breast cancer using amino acid sequences", journal = "Journal of Biomedical Informatics", volume = "54", pages = "256--269", year = "2015", month = apr, keywords = "genetic algorithms, genetic programming, Breast cancer, Amino acids, Physicochemical properties, Stacking ensemble", ISSN = "1532-0464", URL = "http://www.sciencedirect.com/science/article/pii/S1532046415000064", DOI = "doi:10.1016/j.jbi.2015.01.004", abstract = "The diagnostic of human breast cancer is an intricate process and specific indicators may produce negative results. In order to avoid misleading results, accurate and reliable diagnostic system for breast cancer is indispensable. Recently, several interesting machine-learning (ML) approaches are proposed for prediction of breast cancer. To this end, we developed a novel classifier stacking based evolutionary ensemble system Can-Evo-Ens for predicting amino acid sequences associated with breast cancer. In this paper, first, we selected four diverse-type of ML algorithms of Naive Bayes, K-Nearest Neighbour, Support Vector Machines, and Random Forest as base-level classifiers. These classifiers are trained individually in different feature spaces using physicochemical properties of amino acids. In order to exploit the decision spaces, the preliminary predictions of base-level classifiers are stacked. Genetic programming (GP) is then employed to develop a meta-classifier that optimal combine the predictions of the base classifiers. The most suitable threshold value of the best-evolved predictor is computed using Particle Swarm Optimisation technique. Our experiments have demonstrated the robustness of Can-Evo-Ens system for independent validation dataset. The proposed system has achieved the highest value of Area Under Curve (AUC) of ROC Curve of 99.95percent for cancer prediction. The comparative results revealed that proposed approach is better than individual ML approaches and conventional ensemble approaches of AdaBoostM1, Bagging, GentleBoost, and Random Subspace. It is expected that the proposed novel system would have a major impact on the fields of Biomedical, Genomics, Proteomics, Bioinformatics, and Drug Development.", } @PhdThesis{Ali:thesis, author = "Safdar Ali", title = "Intelligent Decision Making Ensemble Classification System for Breast Cancer Prediction", school = "Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences", year = "2015", address = "Nilore, Islamabad, Pakistan", month = "27 " # jul, keywords = "genetic algorithms, genetic programming, Can-Evo-Ens", URL = "http://faculty.pieas.edu.pk/abdulmajid/", URL = "http://prr.hec.gov.pk/jspui/handle/123456789//7613", URL = "https://prr.hec.gov.pk/jspui/bitstream/123456789/7613/1/Safdar-Ali_Computer_and_Information_Sciences_2015_PIEAS_ISD%20PDF.pdf", size = "159 pages", abstract = "Breast cancer is a complex and heterogeneous disease which seriously impacts women's health. The diagnostic of breast cancer is an intricate process. Therefore, an accurate and reliable prediction system for breast cancer is indispensable to avoid misleading results.In this regard, improved decision support systems are essential for breast cancer prediction. Consequently, this thesis focuses on the development of intelligent decision making systems using ensemble classification for the early prediction of breast cancer. Proteins of a breast tissue generally reflect the initial changes caused by successive genetic mutations, which may lead to cancer. In this research, such changes in protein sequences are exploited for the early diagnosis of breast cancer. It is found that substantial variation of Proline, Serine, Tyrosine, Cysteine, Arginine, and Asparagine amino acid molecules in cancerous proteins offer high discrimination for cancer diagnostic. Molecular descriptors derived from physicochemical properties of amino acids are used to transform primary protein sequences into feature spaces of amino acid composition (AAC), split amino acid composition (SAAC), pseudo amino acid composition-series (PseAAC-S), and pseudo amino acid composition-parallel (PseAAC-P). The research work in this thesis is divided in two phases. In the first phase, the basic framework is established to handle imbalanced dataset in order to enhance true prediction performance. In this phase, conventional individual learning algorithms are employed to develop different prediction systems. Firstly, in conjunction with oversampling based Mega-Trend-Diffusion (MTD) technique, individual prediction systems are developed. Secondly, homogeneous ensemble systems CanPro-IDSS and Can-CSCGnB are developed using MTD and cost-sensitive classifier (CSC) techniques, respectively. It is found that assimilation of MTD technique for the CanPro-IDSS system is superior than CSC based technique to handle imbalanced dataset of protein sequences. In this connection, a web based CanPro-IDSS cancer prediction system is also developed. Lastly, a novel heterogeneous ensemble system called IDMS-HBC is developed for breast cancer detection. The second phase of this research focuses on the exploitation of variation of amino acid molecules in cancerous protein sequences using physicochemical properties. In this phase, unlike traditional ensemble prediction approaches, the proposed IDM-PhyChm-Ens ensemble system is developed by combining the decision spaces of a specific classifier trained on different feature spaces. This intelligent ensemble system is constructed using diverse learning algorithms of Random Forest(RF), Support Vector Machines, K-Nearest Neighbor, and Naive Bayes (NB). It is observed that the combined spaces of SAAC+PseAAC-S and AAC+SAAC possess the best discrimination using ensemble-RF and ensemble-NB. Lastly, a novel classifier stacking based evolutionary ensemble system Can-Evo-Ens is also developed, whereby Genetic programming is used as the ensemble method. This study revealed that PseAAC-S feature space carries better discrimination power compared to AAC, SAAC, and PseAAC-P based feature extraction strategies. Intensive experiments are performed to evaluate the performance of the proposed intelligent decision making systems for cancer/non-cancer and breast/non-breast cancer datasets. The proposed approaches have demonstrated improvement over previous state-of-the-art approaches. The proposed systems maybe useful for academia, practitioners, and clinicians for the early diagnosis of breast cancer using protein sequences. Finally, it is expected that the findings of this research would have positive impact on diagnosis, prevention, treatment, and management of cancer", notes = "In English. PIEAS, Islamabad Supervisor: Abdul Majid Co-Supervisor: Asifullah Khan", } @Article{Ali:2010:ieeeTSE, author = "Shaukat Ali and Lionel C. Briand and Hadi Hemmati and Rajwinder K. Panesar-Walawege", title = "A Systematic Review of the Application and Empirical Investigation of Search-Based Test-Case Generation", journal = "IEEE Transactions on Software Engineering", year = "2010", volume = "36", number = "6", pages = "742--762", month = nov # "-" # dec, keywords = "genetic algorithms, genetic programming, SBSE", ISSN = "0098-5589", URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5210118&isnumber=4359463", DOI = "doi:10.1109/TSE.2009.52", size = "22 pages", abstract = "Metaheuristic search techniques have been extensively used to automate the process of generating test cases and thus providing solutions for a more cost-effective testing process. This approach to test automation, often coined as Search-based Software Testing (SBST), has been used for a wide variety of test case generation purposes. Since SBST techniques are heuristic by nature, they must be empirically investigated in terms of how costly and effective they are at reaching their test objectives and whether they scale up to realistic development artifacts. However, approaches to empirically study SBST techniques have shown wide variation in the literature. This paper presents the results of a systematic, comprehensive review that aims at characterising how empirical studies have been designed to investigate SBST cost-effectiveness and what empirical evidence is available in the literature regarding SBST cost-effectiveness and scalability. We also provide a framework that drives the data collection process of this systematic review and can be the starting point of guidelines on how SBST techniques can be empirically assessed. The intent is to aid future researchers doing empirical studies in SBST by providing an unbiased view of the body of empirical evidence and by guiding them in performing well designed empirical studies.", notes = "cites one GP paper: \cite{Wappler:2007:ASE}. TSESI-2008-09-0283", } @InProceedings{Ali:2011:ICCNIT, author = "Zulfiqar Ali and Waseem Shahzad", title = "Critical analysis of swarm intelligence based routing protocols in adhoc and sensor wireless networks", booktitle = "International Conference on Computer Networks and Information Technology (ICCNIT 2011)", year = "2011", month = "11-13 " # jul, pages = "287--292", address = "Abbottabad", size = "6 pages", abstract = "There are various bio inspired and evolutionary approaches including genetic programming (GP), Neural Network, Evolutionary programming (EP), Particle Swarm Optimisation (PSO) and Ant Colony Optimisation (ACO) used for the routing protocols in ad hoc and sensor wireless networks. There are constraints involved in these protocols due to the mobility and non infrastructure nature of an ad hoc and sensor networks. We study in this research work a probabilistic performance evaluation frameworks and Swarm Intelligence approaches (PSO, ACO) for routing protocols. The performance evaluation metrics employed for wireless and ad hoc routing algorithms is routing overhead, route optimality, and energy consumption. This survey gives critical analysis of PSO and ACO based algorithms with other approaches applied for the optimisation of an ad hoc and wireless sensor network routing protocols.", keywords = "genetic algorithms, ACO, EP, PSO, adhoc network, ant colony optimisation, bioinspired approach, critical analysis, energy consumption, evolutionary approach, evolutionary programming, mobility nature, neural network, particle swarm optimisation, probabilistic performance evaluation framework, route optimality, routing overhead, routing protocol, swarm intelligence, wireless sensor network, evolutionary computation, mobile ad hoc networks, mobility management (mobile radio), particle swarm optimisation, performance evaluation, routing protocols, wireless sensor networks", DOI = "doi:10.1109/ICCNIT.2011.6020945", ISSN = "2223-6317", notes = "not on GP Also known as \cite{6020945}", } @InProceedings{Ali:2012:SETIT, author = "Samaher Hussein Ali", booktitle = "6th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT 2012)", title = "Miner for {OACCR}: Case of medical data analysis in knowledge discovery", year = "2012", pages = "962--975", month = "21-24 " # mar, address = "Sousse, Tunisia", keywords = "genetic algorithms, genetic programming, data mining, medical administrative data processing, OACCR, TreeNet classifier, astroinformatics, bioinformatics, data mining algorithm, datasets, genetic programming data construction method, geoinformatics, hybrid techniques, knowledge discovery, medical data analysis, obtaining accurate and comprehensible classification rules, principle component analysis, scientific World Wide Web, Algorithm design and analysis, Classification algorithms, Clustering algorithms, Data mining, Databases, Training, Vegetation, Adboosting, FP-Growth, GPDCM, PCA, Random Forest", isbn13 = "978-1-4673-1657-6", DOI = "doi:10.1109/SETIT.2012.6482043", size = "14 pages", abstract = "Modern scientific data consist of huge datasets which gathered by a very large number of techniques and stored in much diversified and often incompatible data repositories as data of bioinformatics, geoinformatics, astroinformatics and Scientific World Wide Web. At the other hand, lack of reference data is very often responsible for poor performance of learning where one of the key problems in supervised learning is due to the insufficient size of the training dataset. Therefore, we try to suggest a new development a theoretically and practically valid tool for analysing small of sample data remains a critical and challenging issue for researches. This paper presents a methodology for Obtaining Accurate and Comprehensible Classification Rules (OACCR) of both small and huge datasets with the use of hybrid techniques represented by knowledge discovering. In this article the searching capability of a Genetic Programming Data Construction Method (GPDCM) has been exploited for automatically creating more visual samples from the original small dataset. Add to that, this paper attempts to developing Random Forest data mining algorithm to handle missing value problem. Then database which describes depending on their components were built by Principle Component Analysis (PCA), after that, association rule algorithm to the FP-Growth algorithm (FP-Tree) was used. At the last, TreeNet classifier determines the class under which each association rules belongs to was used. The proposed methodology provides fast, Accurate and comprehensible classification rules. Also, this methodology can be use to compression dataset in two dimensions (number of features, number of records).", notes = "Also known as \cite{6482043}", } @InProceedings{conf/icaart/Ali0NR21, author = "Muhammad Sarmad Ali and Meghana Kshirsagar and Enrique Naredo and Conor Ryan", title = "{AutoGE}: A Tool for Estimation of Grammatical Evolution Models", booktitle = "Proceedings of the 13th International Conference on Agents and Artificial Intelligence, ICAART 2021", year = "2021", editor = "Ana Paula Rocha and Luc Steels and H. Jaap {van den Herik}", volume = "2", pages = "1274--1281", address = "Online", month = feb # " 4-6", publisher = "SCITEPRESS", keywords = "genetic algorithms, genetic programming, grammatical evolution", isbn13 = "978-989-758-484-8", bibdate = "2021-03-02", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icaart/icaart2021-2.html#Ali0NR21", DOI = "doi:10.5220/0010393012741281", } @InProceedings{conf/ijcci/Ali0NR21, author = "Muhammad Sarmad Ali and Meghana Kshirsagar and Enrique Naredo and Conor Ryan", title = "Towards Automatic Grammatical Evolution for Real-world Symbolic Regression", booktitle = "Proceedings of the 13th International Joint Conference on Computational Intelligence, IJCCI", year = "2021", editor = "Thomas Baeck and Christian Wagner and Jonathan M. Garibaldi and H. K. Lam and Marie Cottrell and Juan Julian Merelo and Kevin Warwick", pages = "68--78", address = "Online", month = oct # " 25-27", organization = "INSTICC", publisher = "SCITEPRESS", keywords = "genetic algorithms, genetic programming, grammatical evolution, grammar pruning, effective genome length", isbn13 = "978-989-758-534-0", bibdate = "2021-11-19", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ijcci/ijcci2021.html#Ali0NR21", DOI = "doi:10.5220/0010691500003063", size = "11 pages", abstract = "AutoGE (Automatic Grammatical Evolution) is a tool designed to aid users of GE for the automatic estimation of Grammatical Evolution (GE) parameters, a key one being the grammar. The tool comprises of a rich suite of algorithms to assist in fine tuning a BNF (Backus-Naur Form) grammar to make it adaptable across a wide range of problems. It primarily facilitates the identification of better grammar structures and the choice of function sets to enhance existing fitness scores at a lower computational overhead. we discuss and report experimental results for our Production Rule Pruning algorithm from AutoGE which employs a simple frequency-based approach for eliminating less useful productions. It captures the relationship between production rules and function sets involved in the problem domain to identify better grammar. The experimental study incorporates an extended function set and common grammar structures for grammar definition. Preliminary results based on ten popular real-world regression datasets demonstrate that the proposed algorithm not only identifies suitable grammar structures, but also prunes the grammar which results in shorter genome length for every problem, thus optimising memory usage. Despite using a fraction of budget in pruning, AutoGE was able to significantly enhance test scores for 3 problems.", notes = "Biocomputing and Developmental Systems Lab, University of Limerick, Ireland", } @InProceedings{ali:2022:GECCO, author = "Muhammad Sarmad Ali and Meghana Kshirsagar and Enrique Naredo and Conor Ryan", title = "Automated Grammar-based Feature Selection in Symbolic Regression", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference", year = "2022", editor = "Alma Rahat and Jonathan Fieldsend and Markus Wagner and Sara Tari and Nelishia Pillay and Irene Moser and Aldeida Aleti and Ales Zamuda and Ahmed Kheiri and Erik Hemberg and Christopher Cleghorn and Chao-li Sun and Georgios Yannakakis and Nicolas Bredeche and Gabriela Ochoa and Bilel Derbel and Gisele L. Pappa and Sebastian Risi and Laetitia Jourdan and Hiroyuki Sato and Petr Posik and Ofer Shir and Renato Tinos and John Woodward and Malcolm Heywood and Elizabeth Wanner and Leonardo Trujillo and Domagoj Jakobovic and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Inmaculada Medina-Bulo and Slim Bechikh and Andrew M. Sutton and Pietro Simone Oliveto", pages = "902--910", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, feature selection, symbolic regression, production ranking, grammatical evolution, grammar pruning", isbn13 = "978-1-4503-9237-2", DOI = "doi:10.1145/3512290.3528852", abstract = "With the growing popularity of machine learning (ML), regression problems in many domains are becoming increasingly high-dimensional. Identifying relevant features from a high-dimensional dataset still remains a significant challenge for building highly accurate machine learning models.Evolutionary feature selection has been used for high-dimensional symbolic regression using Genetic Programming (GP). While grammar based GP, especially Grammatical Evolution (GE), has been extensively used for symbolic regression, no systematic grammar-based feature selection approach exists. This work presents a grammar-based feature selection method, Production Ranking based Feature Selection (PRFS), and reports on the results of its application in symbolic regression.The main contribution of our work is to demonstrate that the proposed method can not only consistently select the most relevant features, but also significantly improves the generalization performance of GE when compared with several state-of-the-art ML-based feature selection methods. Experimental results on benchmark symbolic regression problems show that the generalization performance of GE using PRFS was significantly better than that of a state-of-the-art Random Forest based feature selection in three out of four problems, while in fourth problem the performance was the same.", notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @PhdThesis{Muhammad_Sarmad_Ali:thesis, author = "Muhammad Sarmad Ali", title = "Automatic Production Selection in Grammatical Evolution", school = "Lero, Faculty of Science and Engineering, Department of Computer Science \& Information Systems, University of Limerick", year = "2023", address = "Ireland", month = "23 " # mar, keywords = "genetic algorithms, genetic programming, grammatical evolution, AutoGE", URL = "https://doi.org/10.34961/researchrepository-ul.24083970.v1", size = "152 pages", abstract = "By the very nature of its representation, symbolic regression through Grammatical Evolution (GE) stands a chance of being interpretable. However, while GE builds solutions using available building blocks (grammar productions), striving to achieve better approximation can sometimes compromise program size and hence interpretability. On the other hand, increased data dimensionality in regression problems poses a significant challenge when attempting to achieve better performance. This research addresses both issues and demonstrates that choosing the right set of grammar productions through production selection for a given problem not only lets GE perform dimensionality reduction but also reduces program sizes while maintaining the most important performance criterion, generalisation. Grammar design, especially the choice of productions, has largely been a subject of expert judgement or trial and error. We hypothesise that evolution convergence carries information which can be exploited to distinguish between worthy and less useful productions. To test this hypothesis, we devise a production ranking scheme to rank grammar productions used in solution derivations based on structural analysis. The ranking profile of productions provides rich information for production selection, and further development affirmed the effectiveness of the ranking approach. Grammar is not a static artifact in this research but rather adapts to a given problem. At different stages during evolution, productions which appear not to improve evolvability are pruned from the grammar. We develop two grammar pruning approaches: static pruning and dynamic pruning. While static pruning removes productions across subexperiments, dynamic pruning prunes the grammar across generations. The developed approaches of production ranking and grammar pruning are shown to achieve significantly smaller solutions while maintaining accuracy on a variety of synthetic as well as real-world regression problems. Algorithms developed in this research, with an extensive set of experimentation, analysis, and comparison, are integrated into an automated tool, AutoGE, which not only aids in primitive set selection but also in feature selection. Feature selection has been a challenging task, especially in high-dimensional symbolic regression. Using linear scaling to build the ranking profile of features, it is demonstrated that feature selection with AutoGE helps improve generalisation performance in high-dimensional problems compared to state-of-the-art machine learning approaches.", notes = "ir@ul.ie Supervisor: Conor Ryan and Meghana Kshirsagar", } @InProceedings{Ali:2022:ICAIoT, author = "Mohammed Sadeq Ali Ali and Mesut Cevik", booktitle = "2022 International Conference on Artificial Intelligence of Things (ICAIoT)", title = "Optimization of the Number and Placement of Routers in Wireless Mesh Networks", year = "2022", month = "29-30 " # dec, address = "Istanbul, Turkey", keywords = "genetic algorithms, genetic programming, Wireless networks, Wireless mesh networks, Evolutionary computation, Software, Reliability, Problem-solving, Internet of Things, IOT, routers, wireless network, WMNs", isbn13 = "979-8-3503-9677-5", DOI = "doi:10.1109/ICAIoT57170.2022.10121861", abstract = "Wireless Mesh Networks (WMNs) are a new type of wireless network that has been growing in popularity. These networks consist of routers and clients. The routers are called mesh routers (MRs) and the clients are called mesh clients. WMNs have several advantages over traditional wireless networks, such as more reliable coverage and faster speeds. Many different types of algorithms can be used to determine the best placement for these routers, with some algorithms being better than others depending on the environment or situation. One algorithm is called a Genetic Algorithm (GA), which uses genetic programming to find an optimal solution for router placement. GA is used to find the best placement of the router so that it can provide the most coverage possible for a specific area GA or evolutionary algorithms are based on a biological theory known as Darwin's theory. In evolutionary algorithms, it is since the information of the problem becomes chromosomes, and then the problem is solved by special problem-solving techniques in the evolutionary algorithm. The suggested method was implemented using the C++ programming environment and the NS2 software suite. Using a benchmark of produced instances, the experimental outcomes have been analysed. Variable sets of produced instances ranging in size from small to big have been explored. Consequently, several properties of WMNs, including the topological placement of mesh clients, have been recorded.", notes = "Also known as \cite{10121861}", } @Article{ALI:2023:istruc, author = "Mujahid Ali and Sai {Hin Lai}", title = "Artificial intelligent techniques for prediction of rock strength and deformation properties - A review", journal = "Structures", volume = "55", pages = "1542--1555", year = "2023", ISSN = "2352-0124", DOI = "doi:10.1016/j.istruc.2023.06.131", URL = "https://www.sciencedirect.com/science/article/pii/S2352012423008901", keywords = "genetic algorithms, genetic programming, Deformation, Unconfined Compressive Strength (UCS), Intelligent techniques, ANN, Statistical analysis", abstract = "In rock design projects, a number of mechanical properties are frequently employed, particularly unconfined compressive strength (UCS) and deformation (E). The researchers attempt to conduct an indirect investigation since direct measurement of UCS and E is time-consuming, expensive, and requires more expertise and methodologies. Recent and past studies investigate the UCS and E from rock index tests mainly P-wave velocity (Vp), slake durability index, Density, Shore hardness, Schmidt hammer Rebound number (Rn), unit weight, porosity (e) point load strength (Is(50)), and block punch strength index test as its economical and easy to use. The evaluation of these properties is the essential input into modern design methods that routinely adopt some form of numerical modeling, such as machine learning (ML), Artificial Neural Networking (ANN), finite element modeling (FEM), and finite difference methods. Besides, several researchers evaluate the correlation between the input parameters using statistical analysis tools before using them for intelligent techniques. The current study compared the results of laboratory tests, statistical analysis, and intelligent techniques for UCS and E estimation including ANN and adaptive neuro-fuzzy inference system (ANFIS), Genetic Programming (GP), Genetic Expression Programming (GEP), and hybrid models. Following the execution of the relevant models, numerous performance indicators, such as root mean squared error, coefficient of determination (R2), variance account for, and overall ranking, are reviewed to choose the best model and compare the acquired results. Based on the current review, it is concluded that the same rock types from different countries show different mechanical properties due to weathering, size, texture, mineral composition, and temperature. For instance, in the UCS of strong rock (granite) in Spain, ranges from 24 MPa to 278 MPa, whereas in Malaysian rocks, it shows 39 MPa to 212 MPa. On the other side, the coefficient of determination (R2) correlation for the UCS also varies from country to country; while using different modern techniques, the R2 values improved. Finally, recommendations on material properties and modern techniques have been suggested", } @Article{Ali:2024:jcomc, author = "Mohsin Ali and Li Chen and Qadir Bux Alias Imran Latif Qureshi and Deema Mohammed Alsekait and Adil Khan and Kiran Arif and Muhammad Luqman and Diaa Salama Abd Elminaam and Amir Hamza and Majid Khan", title = "Genetic programming-based algorithms application in modeling the compressive strength of steel fiber-reinforced concrete exposed to elevated temperatures", journal = "Composites Part C: Open Access", year = "2024", volume = "15", pages = "100529", keywords = "genetic algorithms, genetic programming, SFRC, Elevated temperature, Machine learning, Compressive strength, Predictive model, gene expression programming", ISSN = "2666-6820", URL = "https://www.sciencedirect.com/science/article/pii/S2666682024000987", DOI = "doi:10.1016/j.jcomc.2024.100529", abstract = "Steel-fiber-reinforced concrete (SFRC) has replaced traditional concrete in the construction sector, improving fracture resistance and post-cracking performance. However, extreme temperatures degrade concrete's material characteristics including stiffness and strength. The construction industry increasingly embraces machine learning (ML) to estimate concrete properties and optimise cost and time accurately. This study employs independent ML methods, gene expression programming (GEP), multi-expression programming (MEP), XGBoost, and Bayesian estimation model (BES) to predict SFRC compressive strength (CS) at high temperatures. 307 experimental data points from published studies were used to develop the models. The models were trained using 70 percent of the dataset, with 15 percent for validation and 15 percent for testing. Iterative hyperparameter adjustment and trial-and-error refining achieved optimum predictions. All the models were evaluated using correlation (R) values for training, validation, and testing datasets. MEP showed slightly lower R-values of 0.923, 0.904, and 0.949 than GEP, which performed consistently with 0.963, 0.967, and 0.961. XGBoost had the greatest training R-value of 0.997 but dropped in validation (0.918) and testing (0.896). BES model exhibited commendable performance with scores of 0.986, 0.944, and 0.897. GEP and XGBoost exhibited great accuracy, with GEP sustaining constant accuracy across all datasets, highlighting its potency in predicting CS. Interpreting model predictions using SHapley Additive exPlanation (SHAP) highlighted temperature over heating rate. CS improved significantly as the steel fiber volume fraction (Vf) reached 1.5 percent, plateauing thereafter. The proposed models are valid and accurate, providing designers and builders with a practical and adaptable method for estimating strength in SFRC structural applications, particularly under high-temperature conditions", } @InProceedings{Ali:2025:ICCAE, author = "Mohsin Ali and Maher Ali Rusho and Li Chen3 and Dany Marcelo Tasan Cruz", title = "Advancing Structural Safety: Genetic Programming Approaches to Steel Fiber-Reinforced Concrete ({SFRC)} Blast Response Prediction", booktitle = "2025 17th International Conference on Computer and Automation Engineering (ICCAE)", year = "2025", pages = "183--187", month = mar, keywords = "genetic algorithms, genetic programming, Training, Accuracy, Loading, Machine learning, Predictive models, Mathematical models, Concrete, Steel, Load modelling, Steel Fiber Reinforced Concrete (SFRC), Blast loading, gene expression programming", ISSN = "2154-4360", DOI = "doi:10.1109/ICCAE64891.2025.10980530", abstract = "Steel Fiber-Reinforced Concrete (SFRC) has emerged as a preferred material for blast-resistant structures due to its exceptional mechanical properties and energy absorption capabilities. This study introduces a machine learning-based framework to predict the maximum displacement of SFRC structural members under blast loading. Using 107 experimental data points, split into $\mathbf{7 0 \percent}$ for training and 15 percent each for validation and testing, Gene Expression Programming (GEP) and Multi-Expression Programming (MEP) were applied. The GEP model exhibited superior predictive performance with R -values of 0.964 (training), 0.968 (validation), and 0.960 (testing), while the MEP model achieved reasonable accuracy with $R$-values of 0.922, 0.905, and 0.948, respectively. Additionally, parametric analysis revealed the influence of fiber properties on SFRC behaviour. This approach not only simplifies predictive modelling but also enhances its reliability, offering valuable insights for optimising SFRC design under extreme conditions and contributing to the advancement of resilient structural systems.", notes = "Also known as \cite{10980530} School of Civil Engineering, Southeast University, Nanjing, China", } @InProceedings{Alibekov:2016:CDC, author = "Eduard Alibekov and Jiri Kubalik and Robert Babuska", booktitle = "2016 IEEE 55th Conference on Decision and Control (CDC)", title = "Symbolic method for deriving policy in reinforcement learning", year = "2016", pages = "2789--2795", abstract = "This paper addresses the problem of deriving a policy from the value function in the context of reinforcement learning in continuous state and input spaces. We propose a novel method based on genetic programming to construct a symbolic function, which serves as a proxy to the value function and from which a continuous policy is derived. The symbolic proxy function is constructed such that it maximizes the number of correct choices of the control input for a set of selected states. Maximization methods can then be used to derive a control policy that performs better than the policy derived from the original approximate value function. The method was experimentally evaluated on two control problems with continuous spaces, pendulum swing-up and magnetic manipulation, and compared to a standard policy derivation method using the value function approximation. The results show that the proposed method and its variants outperform the standard method.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CDC.2016.7798684", month = dec, notes = "Also known as \cite{7798684}", } @PhdThesis{Alibekov:thesis, author = "Eduard Alibekov", title = "Symbolic Regression for Reinforcement Learning in Continuous Spaces", school = "F3 Faculty of Electrical Engineering, Department of Cybernetics, Czech Technical University in Prague", year = "2021", address = "Czech Republic", month = aug, keywords = "genetic algorithms, genetic programming, Single Node Genetic Programming, reinforcement learning, optimal control, function approximation,evolutionary optimization, symbolic regression, robotics, autonomous systems", URL = "https://cyber.felk.cvut.cz/news/eduard-alibekov-defended-his-ph-d-thesis/", URL = "http://hdl.handle.net/10467/98283", URL = "https://dspace.cvut.cz/handle/10467/98283", URL = "https://dspace.cvut.cz/bitstream/handle/10467/98283/F3-D-2021-Alibekov-Eduard-phd_ready.pdf", size = "134 pages", abstract = "Reinforcement Learning (RL) algorithms can optimally solve dynamic decision and control problems in engineering, economics, medicine, artificial intelligence, and other disciplines.However, state-of-the-art RL methods still have not solved the transition from a small set of discrete states to fully continuous spaces. They have to rely on numerical function approximators, such as radial basis functions or neural networks, to represent the value function or policy mappings. While these numerical approximators are well-developed, the choice of a suitable architecture is a difficult step that requires significant trial-and-error tuning. Moreover, numerical approximators frequently exhibit uncontrollable surface artifacts that damage the overall performance of the controlled system. Symbolic Regression (SR) is an evolutionary optimization technique that automatically, without human intervention, generates analytical expressions to fit numerical data. The method has gained attention in the scientific community not only for its ability to recover known physical laws, but also for suggesting yet unknown but physically plausible and interpretable relationships. Additionally, the analytical nature of the result approximators allows to unleash the full power of mathematical apparatus. This thesis aims to develop methods to integrate SR into RL in a fully continuous case. To accomplish this goal, the following original contributions to the field have been developed. (i) Introduction of policy derivation methods. Their main goal is to exploit the full potential of using continuous action spaces, contrary to the state-of-the-art discretised set of actions. (ii) Quasi-symbolic policy derivation (QSPD) algorithm, specifically designed to be used with a symbolic approximation of the value function. The goal of the proposed algorithm is to efficiently derive continuous policy out of symbolic approximator. The experimental evaluation indicated the superiority of QSPD over state-of-the-art methods. (iii) Design of a symbolic proxy-function concept. Such a function is successfully used to alleviate the negative impacts of approximation artifacts on policy derivation. (iv) Study on fitness criterion in the context of SR for RL. The analysis indicated a fundamental flaw with any other symmetric error functions, including commonly used mean squared error. Instead, a new error function procedure has been proposed alongside with a novel fitting procedure. The experimental evaluation indicated dramatic improvement of the approximation quality for both numerical and symbolic approximators. (v) Robust symbolic policy derivation (RSPD) algorithm, which adds an extra level of robustness against imperfections in symbolic approximators. The experimental evaluation demonstrated significant improvements in the reachability of the goal state. All these contributions are then combined into a single,efficient SR for RL (ESRL) framework. Such a framework is able to tackle high-dimensional, fully-continuous RL problems out-of-the-box. The proposed framework has been tested on three bench-marks: pendulum swing-up, magnetic manipulation, and high-dimensional drone strike benchmark.", notes = "https://starfos.tacr.cz/en/project/GA15-22731S Supervisor: Olga Stepankova Supervisor-specialist: Robert Babuska", } @Article{ALIDOUST:2021:JCP, author = "Pourya Alidoust and Mohsen Keramati and Pouria Hamidian and Amir Tavana Amlashi and Mahsa Modiri Gharehveran and Ali Behnood", title = "Prediction of the shear modulus of municipal solid waste ({MSW):} An application of machine learning techniques", journal = "Journal of Cleaner Production", volume = "303", pages = "127053", year = "2021", ISSN = "0959-6526", DOI = "doi:10.1016/j.jclepro.2021.127053", URL = "https://www.sciencedirect.com/science/article/pii/S0959652621012725", keywords = "genetic algorithms, genetic programming, Municipal solid waste, Cyclic triaxial test, Shear modulus, Artificial neural network (ANN), Multivariate adaptive regression splines (MARS), Multi-gene genetic programming (MGGP), M5 model tree (M5Tree)", abstract = "The dynamic properties of Municipal Solid Waste (MSW) are site-specific and need to be evaluated separately in different regions. The laboratory-based evaluation of MSW has difficulties such as an unpleasant aroma or degradability of MSW, making the testing procedure unfavorable. Moreover, these evaluations are time- and cost-intensive, which may also require trained personnel to conduct the tests. To address this concern, alternatively, the shear modulus of MSW can be estimated through some predictive models. In this study, the shear modulus was evaluated using 153 cyclic triaxial tests. For this purpose, the effects of various factors, including the shear strain (ShS), age of the MSW (Age), percentage of plastic (POP), confining pressure (CP), unit weight (UW), and loading frequency (F) on the shear modulus of MSW were evaluated. The data obtained through laboratory experiments was then employed to model the dynamic response of MSW using four different machine learning techniques including Artificial Neural Networks (ANN), Multivariate Adaptive Regression Splines (MARS), Multi-Gene Genetic Programming (MGGP), and M5 model Tree (M5Tree). A comparison of the performance of developed models indicated that the ANN model outperformed the other models. More specifically, for ANN, MARS, MGGP, and M5Tree models, the corresponding values of R-squared equal to 0.9897, 0.9640, 0.9617, and 0.8482 for the training dataset, while the values for the testing dataset for ANN, MARS, MGGP, and M5Tree are 0.9812, 0.9551, 0.9574, and 0.8745. Furthermore, although the developed models using MARS and MGGP techniques resulted in more errors compared to the ANN technique, they were found to produce reliable predictions. To further compare the performance and efficiency of the developed models and study the effects of each input variable on the output variable (i.e., shear modulus), model validity, parametric study, and sensitivity analysis were performed", } @InProceedings{Aliehyaei:2014:SKIMA, author = "R. Aliehyaei and S. Khan", booktitle = "8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)", title = "Ant Colony Optimization, Genetic Programming and a hybrid approach for credit scoring: A comparative study", year = "2014", abstract = "Credit scoring is a commonly used method for evaluating the risk involved in granting credits. Both Genetic Programming (GP) and Ant Colony Optimisation (ACO) have been investigated in the past as possible tools for credit scoring. This paper reports an investigation into the relative performances of GP, ACO and a new hybrid GP-ACO approach, which relies on the ACO technique to produce the initial populations for the GP technique. Performance of the hybrid approach has been compared with both the GP and ACO approaches using two well-known benchmark data sets. Experimental results demonstrate the dependence of GP and ACO classification accuracies on the input data set. For any given data set, the hybrid approach performs better than the worse of the other two methods. Results also show that use of ACO in the hybrid approach has only a limited impact in improving GP performance.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SKIMA.2014.7083391", month = dec, notes = "Also known as \cite{7083391}", } @Article{AliGhorbani2010620, author = "Mohammad Ali Ghorbani and Rahman Khatibi and Ali Aytek and Oleg Makarynskyy and Jalal Shiri", title = "Sea water level forecasting using genetic programming and comparing the performance with Artificial Neural Networks", journal = "Computer \& Geosciences", volume = "36", number = "5", pages = "620--627", year = "2010", ISSN = "0098-3004", DOI = "doi:10.1016/j.cageo.2009.09.014", URL = "http://www.sciencedirect.com/science/article/B6V7D-4YCS020-1/2/514d629e145e62f37dbf599a1a7608a9", keywords = "genetic algorithms, genetic programming, Sea-level variations, Forecasting, Artificial Neural Networks, Comparative studies", abstract = "Water level forecasting at various time intervals using records of past time series is of importance in water resources engineering and management. In the last 20 years, emerging approaches over the conventional harmonic analysis techniques are based on using Genetic Programming (GP) and Artificial Neural Networks (ANNs). In the present study, the GP is used to forecast sea level variations, three time steps ahead, for a set of time intervals comprising 12 h, 24 h, 5 day and 10 day time intervals using observed sea levels. The measurements from a single tide gauge at Hillarys Boat Harbour, Western Australia, were used to train and validate the employed GP for the period from December 1991 to December 2002. Statistical parameters, namely, the root mean square error, correlation coefficient and scatter index, are used to measure their performances. These were compared with a corresponding set of published results using an Artificial Neural Network model. The results show that both these artificial intelligence methodologies perform satisfactorily and may be considered as alternatives to the harmonic analysis.", } @InCollection{AliGhorbani:2012:GPnew, author = "M. A. Ghorbani and R. Khatibi and H. Asadi and P. Yousefi", title = "Inter-Comparison of an Evolutionary Programming Model of Suspended Sediment Time-Series with Other Local Models", booktitle = "Genetic Programming - New Approaches and Successful Applications", publisher = "InTech", year = "2012", editor = "Sebastian Ventura", chapter = "12", pages = "255--284", keywords = "genetic algorithms, genetic programming, Gene Expression Programming, GEP, ANN, MLR, Chaos", isbn13 = "978-953-51-0809-2", DOI = "doi:10.5772/47801", size = "30 pages", notes = "Modelling Mississippi mud transport. Part of \cite{Ventura:2012:GPnew}. Open access book CC BY 3.0 license", } @Article{ALISHAH:2024:fuel, author = "Zubair {Ali Shah} and G. Marseglia and M. G. {De Giorgi}", title = "Predictive models of laminar flame speed in {NH3/H2/O3/air} mixtures using multi-gene genetic programming under varied fuelling conditions", journal = "Fuel", volume = "368", pages = "131652", year = "2024", ISSN = "0016-2361", DOI = "doi:10.1016/j.fuel.2024.131652", URL = "https://www.sciencedirect.com/science/article/pii/S0016236124008007", keywords = "genetic algorithms, genetic programming, NH, H, Laminar Flame Speed (LFS), Ignition Delay Time (IDT), Ozone (O), Multi-gene genetic programming", abstract = "The primary aim of this study is to develop and validate a novel multi-gene genetic programming approach for accurately predicting Laminar Flame Speed (LFS) in ammonia (NH3)/hydrogen (H2)/air mixtures, a key aspect in the advancement of carbon-free fuel technologies. Ammonia, particularly when blended with hydrogen, presents significant potential as a carbon-free fuel due to its enhanced reactivity. This research not only investigates the effects of hydrogen concentration, initial temperature, and pressure on LFS and Ignition Delay Time (IDT) but also explores the impact of oxidizing agents like ozone (O3) in augmenting NH3 combustion. A modified reaction mechanism was implemented and validated through parametric analysis. Main findings demonstrate that IDT decreases with higher hydrogen concentrations, increased initial temperature, and initial pressure, although the influence of pressure decreases above 10 atm. Conversely, at lower temperatures (below 1200 K) and higher hydrogen concentrations (30 percent and 50 percent), the dominance of H2 chemistry can negatively impact initial pressure. LFS increases with higher temperature and hydrogen concentration, but decreases under elevated pressure, with its effect becoming negligible above 5 atm. An optimized equivalence ratio (?) range of 1.10 - 1.15 is identified for efficient combustion. Introducing ozone into the oxidizer notably improves LFS in NH3/H2/air mixtures, with the addition of 0.01 ozone mirroring the effect of a 10 percent hydrogen addition under normal conditions. The study's fundamental contribution is the development of a multi-gene genetic algorithm, showcasing the correlation between predicted LFS values and actual values derived from chemkin simulations. The successful validation of this methodology across various case studies underscores its potential as a robust tool in zero-carbon combustion applications, marking a significant stride in the field", } @InProceedings{Alissa:2020:GECCO, author = "Mohamad Alissa and Kevin Sim and Emma Hart", title = "A Deep Learning Approach to Predicting Solutions in Streaming Optimisation Domains", year = "2020", editor = "Carlos Artemio {Coello Coello} and Arturo Hernandez Aguirre and Josu Ceberio Uribe and Mario Garza Fabre and Gregorio {Toscano Pulido} and Katya Rodriguez-Vazquez and Elizabeth Wanner and Nadarajen Veerapen and Efren Mezura Montes and Richard Allmendinger and Hugo Terashima Marin and Markus Wagner and Thomas Bartz-Beielstein and Bogdan Filipic and Heike Trautmann and Ke Tang and John Koza and Erik Goodman and William B. Langdon and Miguel Nicolau and Christine Zarges and Vanessa Volz and Tea Tusar and Boris Naujoks and Peter A. N. Bosman and Darrell Whitley and Christine Solnon and Marde Helbig and Stephane Doncieux and Dennis G. Wilson and Francisco {Fernandez de Vega} and Luis Paquete and Francisco Chicano and Bing Xue and Jaume Bacardit and Sanaz Mostaghim and Jonathan Fieldsend and Oliver Schuetze and Dirk Arnold and Gabriela Ochoa and Carlos Segura and Carlos Cotta and Michael Emmerich and Mengjie Zhang and Robin Purshouse and Tapabrata Ray and Justyna Petke and Fuyuki Ishikawa and Johannes Lengler and Frank Neumann", isbn13 = "9781450371285", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377930.3390224", DOI = "doi:10.1145/3377930.3390224", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference", pages = "157--165", size = "9 pages", keywords = "genetic algorithms, deep learning, algorithm selection problem, bin-packing", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", abstract = "In the field of combinatorial optimisation, per-instance algorithm selection still remains a challenging problem, particularly with respect to streaming problems such as packing or scheduling. Typical approaches involve training a model to predict the best algorithm based on features extracted from the data, which is well known to be a difficult task and even more challenging with streaming data. We propose a radical approach that bypasses algorithm-selection altogether by training a Deep-Learning model using solutions obtained from a set of heuristic algorithms to directly predict a solution from the instance-data. To validate the concept, we conduct experiments using a packing problem in which items arrive in batches. Experiments conducted on six large datasets using batches of varying size show the model is able to accurately predict solutions, particularly with small batch sizes, and surprisingly in a small number of cases produces better solutions than any of the algorithms used to train the model.", notes = "Also known as \cite{10.1145/3377930.3390224} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @InProceedings{Alissa:2021:CEC, author = "Mohamad Alissa and Kevin Sim and Emma Hart", booktitle = "2021 IEEE Congress on Evolutionary Computation (CEC)", title = "A Neural Approach to Generation of Constructive Heuristics", year = "2021", editor = "Yew-Soon Ong", pages = "1147--1154", address = "Krakow, Poland", month = "28 " # jun # "-1 " # jul, keywords = "genetic algorithms, genetic programming, Measurement, Navigation, Heuristic algorithms, Neural networks, ANN, Evolutionary computation, Dynamic scheduling, Automatic Heuristics Generation, Hyper-Heuristics, Encoder-Decoder LSTM, Streaming Bin-packing", isbn13 = "978-1-7281-8393-0", DOI = "doi:10.1109/CEC45853.2021.9504989", abstract = "Both algorithm-selection methods and hyper-heuristic methods rely on a pool of complementary heuristics. Improving the pool with new heuristics can improve performance, however, designing new heuristics can be challenging. Methods such as genetic programming have proved successful in automating this process in the past. Typically, these make use of problem state-information and existing heuristics as components. Here we propose a novel neural approach for generating constructive heuristics, in which a neural network acts as a heuristic by generating decisions. We evaluate two architectures, an Encoder-Decoder LSTM and a Feed-Forward Neural Network. Both are trained using the decisions output from existing heuristics on a large set of instances. We consider streaming instances of bin-packing problems in a continual stream that must be packed immediately in strict order and using a limited number of resources. We show that the new heuristics generated are capable of solving a subset of instances better than the well-known heuristics forming the original pool, and hence the overall value of the pool is improved w.r.t. both Falkenauers performance metric and the number of bins used.", notes = "Also known as \cite{9504989}", } @InProceedings{Aliwi:2020:SIU, author = "Mohamed Aliwi and Selcuk Aslan and Sercan Demirci", booktitle = "2020 28th Signal Processing and Communications Applications Conference (SIU)", title = "Firefly Programming For Symbolic Regression Problems", year = "2020", abstract = "Symbolic regression is the process of finding a mathematical formula that fits a specific set of data by searching in different mathematical expressions. This process requires great accuracy in order to reach the correct formula. In this paper, we will present a new method for solving symbolic regression problems based on the firefly algorithm. This method is called Firefly Programming (FP). The results of applying firefly programming algorithm to some symbolic regression benchmark problems will be compared to the results of Genetic Programming (GP) and Artificial Bee Colony Programming (ABCP) methods.", keywords = "genetic algorithms, genetic programming, Optimization, Statistics, Sociology, Linear programming, Brightness, firefly algorithm, symbolic regression, automatic programming", DOI = "doi:10.1109/SIU49456.2020.9302201", ISSN = "2165-0608", month = oct, notes = "Also known as \cite{9302201}", } @InProceedings{Alizadeh:2011:EAIS, author = "Mehrdad Alizadeh and Mohammad Mehdi Ebadzadeh", title = "Kernel evolution for support vector classification", booktitle = "IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS 2011)", year = "2011", month = "11-15 " # apr, pages = "93--99", address = "Paris", size = "7 pages", abstract = "Support vector machines (SVMs) have been used in a variety of classification tasks. SVMs undoubtedly are one of the most effective classifiers in several data mining applications. Determination of a kernel function and related parameters has been a bottleneck for this group of classifiers. In this paper a novel approach is proposed to use genetic programming (GP) to design domain-specific and optimal kernel functions for support vector classification (SVC) which automatically adjusts the parameters. Complex low dimensional mapping function is evolved using GP to construct an optimal linear and Gaussian kernel functions in new feature space. By using the principled kernel closure properties, these basic kernels are then used to evolve more optimal kernels. To evaluate the proposed method, benchmark datasets from UCI are applied. The result indicates that for some cases the proposed methods can find a more optimal solution than evolving known kernels.", keywords = "genetic algorithms, genetic programming, Gaussian kernel functions, automatic parameter adjustment, classification task, data mining application, domain-specific kernel functions, feature space, kernel evolution, low dimensional mapping function, optimal kernel functions, optimal linear functions, principled kernel closure properties, support vector classification, support vector machines, Gaussian processes, data mining, pattern classification, support vector machines", DOI = "doi:10.1109/EAIS.2011.5945924", notes = "Also known as \cite{5945924}", } @InProceedings{Aljahdali:2010:AICCSA, author = "Sultan Aljahdali and Alaa F. Sheta", title = "Software effort estimation by tuning COOCMO model parameters using differential evolution", booktitle = "2010 IEEE/ACS International Conference on Computer Systems and Applications (AICCSA)", year = "2010", month = "16-19 " # may, address = "Hammamet, Tunisia", abstract = "Accurate estimation of software projects costs represents a challenge for many government organisations such as the Department of Defense (DOD) and NASA. Statistical models considerably used to assist in such a computation. There is still an urgent need on finding a mathematical model which can provide an accurate relationship between the software project effort/cost and the cost drivers. A powerful algorithm which can optimise such a relationship via tuning mathematical model parameters is urgently needed. In two new model structures to estimate the effort required for software projects using Genetic Algorithms (GAs) were proposed as a modification to the famous Constructive Cost Model (COCOMO). In this paper, we follow up on our previous work and present Differential Evolution (DE) as an alternative technique to estimate the COCOMO model parameters. The performance of the developed models were tested on NASA software project dataset provided in. The developed COCOMO-DE model was able to provide good estimation capabilities.", keywords = "genetic algorithms, genetic programming, sbse, COOCMO model parameter tuning, NASA software project dataset, constructive cost model, differential evolution, mathematical model, optimisation algorithm, software effort estimation, software projects cost estimation, statistical model, optimisation, software cost estimation", DOI = "doi:10.1109/AICCSA.2010.5586985", notes = "'We suggest the use of Genetic Programming (GP) technique to build suitable model structure for the software effort estimation.' Also known as \cite{5586985}", } @Article{Aljahdali:2011:Jcomputerscience, author = "Sultan Aljahdali", title = "Development of Software Reliability Growth Models for Industrial Applications Using Fuzzy Logic", journal = "Journal of Computer Science", year = "2011", volume = "7", number = "10", pages = "1574--1580", publisher = "Science Publications", keywords = "software reliability growth models (SRGM), takagi-sugeno technique, fuzzy logic (FL), artificial neural net-works (ANN), model structure, linear regression model, NASA space", ISSN = "15493636", URL = "http://www.thescipub.com/pdf/10.3844/jcssp.2011.1574.1580", DOI = "doi:10.3844/jcssp.2011.1574.1580", size = "7 pages", abstract = "Problem statement: The use of Software Reliability Growth Models (SRGM) plays a major role in monitoring progress, accurately predicting the number of faults in the software during both development and testing processes; define the release date of a software product, helps in allocating resources and estimating the cost for software maintenance. This leads to achieving the required reliability level of a software product. Approach: We investigated the use of fuzzy logic on building SRGM to estimate the expected software faults during testing process. Results: The proposed fuzzy model consists of a collection of linear sub-models, based on the Takagi-Sugeno technique and attached efficiently using fuzzy membership functions to represent the expected software faults as a function of historical measured faults. A data set provided by John Musa of bell telephone laboratories (i.e., real time control, military and operating system applications) was used to show the potential of using fuzzy logic in solving the software reliability modelling problem. Conclusion: The developed models provided high performance modelling capabilities.", notes = "mentions GP papers but not on GP?", bibsource = "OAI-PMH server at www.doaj.org", language = "eng", oai = "oai:doaj-articles:84b6d807e851efb50b17f965f70c97d8", } @Article{Aljahdali:2013:IJARAI, author = "Sultan Aljahdali and Alaa Sheta", title = "Evolving Software Effort Estimation Models Using Multigene Symbolic Regression Genetic Programming", journal = "International Journal of Advanced Research in Artificial Intelligence", year = "2013", number = "12", volume = "2", pages = "52--57", keywords = "genetic algorithms, genetic programming, SBSE", publisher = "The Science and Information (SAI) Organization", bibsource = "OAI-PMH server at thesai.org", language = "eng", oai = "oai:thesai.org:10.14569/IJARAI.2013.021207", URL = "http://thesai.org/Downloads/IJARAI/Volume2No12/Paper_7-Evolving_Software_Effort_Estimation_Models_Using.pdf", URL = "http://dx.doi.org/10.14569/IJARAI.2013.021207", size = "6 pages", abstract = "Software has played an essential role in engineering, economic development, stock market growth and military applications. Mature software industry count on highly predictive software effort estimation models. Correct estimation of software effort lead to correct estimation of budget and development time. It also allows companies to develop appropriate time plan for marketing campaign. Now a day it became a great challenge to get these estimates due to the increasing number of attributes which affect the software development life cycle. Software cost estimation models should be able to provide sufficient confidence on its prediction capabilities. Recently, Computational Intelligence (CI) paradigms were explored to handle the software effort estimation problem with promising results. In this paper we evolve two new models for software effort estimation using Multigene Symbolic Regression Genetic Programming (GP). One model uses the Source Line Of Code (SLOC) as input variable to estimate the Effort (E); while the second model uses the Inputs, Outputs, Files, and User Enquiries to estimate the Function Point (FP). The proposed GP models show better estimation capabilities compared to other reported models in the literature. The validation results are accepted based Albrecht data set.", } @InProceedings{Aljero:2020:ICOASE, author = "Mona Khalifa A. Aljero and Nazife Dimililer", title = "Hate Speech Detection Using Genetic Programming", booktitle = "2020 International Conference on Advanced Science and Engineering (ICOASE)", year = "2020", abstract = "There has been a steep increase in the use of social media in our everyday lives in recent years. Along with this, there has been an increase in hate speech disseminated on these platforms, due to the anonymity of the users as well as the ease of use. Social media platforms need to filter and prevent the spread of hate speech to protect their users and society. Due to the high traffic, automatic detection of hate speech is necessary. Hate speech detection is one of the most difficult classification challenges in text mining. Research in this domain focuses on the use of supervised machine learning approaches, such as support vector machine, logistic regression, convolutional neural network, and random forest. Ensemble techniques have also been employed. However, the performance of these approaches has not yet reached an acceptable level. In this paper, we propose the use of the Genetic Programming (GP) approach for binary classification of hate speech on social media platforms. Each individual in the GP framework represents a classifier that is evolved to optimize Fl-score. Experimental results show the effectiveness of our GP approach; the proposed approach outperforms the state-of-the-art using the same dataset HatEval.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICOASE51841.2020.9436621", month = dec, notes = "Also known as \cite{9436621}", } @Article{Aljero:2021:A, author = "Mona Khalifa A. Aljero and Nazife Dimililer", title = "Genetic Programming Approach to Detect Hate Speech in Social Media", journal = "IEEE Access", year = "2021", volume = "9", pages = "115115--115125", abstract = "Social media sites, which became central to our everyday lives, enable users to freely express their opinions, feelings, and ideas due to a certain level of depersonalization and anonymity they provide. If there is no control, these platforms may be used to propagate hate speech. In fact, in recent years, hate speech has increased on social media. Therefore, there is a need to monitor and prevent hate speech on these platforms. However, manual control is not feasible due to the high traffic of content production on social media sites. Moreover, the language used and the length of the messages provide a challenge when using classical machine learning approaches as prediction methods. This paper presents a genetic programming (GP) model for detecting hate speech where each chromosome represents a classifier employing a universal sentence encoder as a feature. A novel mutation technique that affects only the feature values in combination with the standard one-point mutation technique improved the performance of the GP model by enriching the offspring pool with alternative solutions. The proposed GP model outperformed all state-of-the-art systems for the four publicly available hate speech datasets.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ACCESS.2021.3104535", ISSN = "2169-3536", notes = "Also known as \cite{9513275}", } @Article{DBLP:journals/elektrik/AljeroD23, author = "Mona Khalifa A. Aljero and Nazife Dimililer", title = "Binary text classification using genetic programming with crossover-based oversampling for imbalanced datasets", journal = "Turkish J. Electr. Eng. Comput. Sci.", volume = "31", number = "1", pages = "180--192", year = "2023", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.55730/1300-0632.3978", DOI = "doi:10.55730/1300-0632.3978", timestamp = "Thu, 23 Feb 2023 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/elektrik/AljeroD23.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{Alkhaldi:2022:IEEEAccess, author = "Eid Alkhaldi and Ezzatollah Salari", journal = "IEEE Access", title = "Ensemble Optimization for Invasive Ductal Carcinoma ({IDC)} Classification Using Differential Cartesian Genetic Programming", year = "2022", volume = "10", pages = "128790--128799", abstract = "The high cost of acquiring annotated histological slides for breast specimens entails exploiting an ensemble of models appropriately trained on small datasets. Histological Image Classification ensembles strive to accurately detect abnormal tissues in the breast samples by determining the correlation between the predictions of its weak learners. Nonetheless, the state-of-the-art ensemble methods, such as boosting and bagging, count merely on manipulating the dataset and lack intelligent ensemble decision making. Furthermore, the methods mentioned above are short of the diversity of the weak models of the ensemble. Likewise, other commonly used voting strategies, such as weighted averaging, are limited to how the classifiers' diversity and accuracy are balanced. Hence, In this paper, we assemble a Neural Network ensemble that integrates the models trained on small datasets by employing biologically-inspired methods. Our procedure is comprised of two stages. First, we train multiple heterogeneous pre-trained models on the benchmark Breast Histopathology Images for Invasive Ductal Carcinoma (IDC) classification dataset. In the second meta-training phase, we use the differential Cartesian Genetic Programming (dCGP) to generate a Neural Network that merges the trained models optimally. We compared our empirical outcomes with other state-of-the-art techniques. Our results demonstrate that improvising a Neural Network ensemble using Cartesian Genetic Programming transcended formerly published algorithms on slim datasets.", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming", DOI = "doi:10.1109/ACCESS.2022.3228176", ISSN = "2169-3536", notes = "Also known as \cite{9978635}", } @PhdThesis{Alkroosh:thesis, title = "Modelling pile capacity and load-settlement behaviour of piles embedded in sand \& mixed soils using artificial intelligence", author = "Iyad Salim Jabor Alkroosh", year = "2011", school = "Curtin University, Faculty of Engineering and Computing, Department of Civil Engineering", address = "Australia", month = may, keywords = "genetic algorithms, genetic programming, gene expression programming, modelling pile capacity, load-settlement behaviour of piles, artificial intelligence, (GEP) and the artificial neural networks (ANNs), numerical modelling techniques", URL = "http://espace.library.curtin.edu.au/Modelling.pdf", URL = "http://hdl.handle.net/20.500.11937/351", URL = "https://espace.curtin.edu.au/handle/20.500.11937/351", size = "338 pages", abstract = "This thesis presents the development of numerical models which are intended to be used to predict the bearing capacity and the load-settlement behaviour of pile foundations embedded in sand and mixed soils. Two artificial intelligence techniques, the gene expression programming (GEP) and the artificial neural networks (ANNs), are used to develop the models. The GEP is a developed version of genetic programming (GP). Initially, the GEP is used to model the bearing capacity of the bored piles, concrete driven piles and steel driven piles. The use of the GEP is extended to model the load-settlement behaviour of the piles but achieved limited success. Alternatively, the ANNs have been employed to model the load-settlement behaviour of the piles. The GEP and the ANNs are numerical modelling techniques that depend on input data to determine the structure of the model and its unknown parameters. The GEP tries to mimic the natural evolution of organisms and the ANNs tries to imitate the functions of human brain and nerve system. The two techniques have been applied in the field of geotechnical engineering and found successful in solving many problems. The data used for developing the GEP and ANN models are collected from the literature and comprise a total of 50 bored pile load tests and 58 driven pile load tests (28 concrete pile load tests and 30 steel pile load tests) as well as CPT data. The bored piles have different sizes and round shapes, with diameters ranging from 320 to 1800 mm and lengths from 6 to 27 m. The driven piles also have different sizes and shapes (i.e. circular, square and hexagonal), with diameters ranging from 250 to 660 mm and lengths from 8 to 36 m. All the information of case records in the data source is reviewed to ensure the reliability of used data. The variables that are believed to have significant effect on the bearing capacity of pile foundations are considered. They include pile diameter, embedded length, weighted average cone point resistance within tip influence zone and weighted average cone point resistance and weighted average sleeve friction along shaft. The sleeve friction values are not available in the bored piles data, so the weighted average sleeve friction along shaft is excluded from bored piles models. The models output is the pile capacity (interpreted failure load). Additional input variables are included for modelling the load-settlement behaviour of piles. They include settlement, settlement increment and current state of load settlement. The output is the next state of load-settlement. The data are randomly divided into two statistically consistent sets, training set for model calibration and an independent validation set for model performance verification. The predictive ability of the developed GEP model is examined via comparing the performance of the model in training and validation sets. Two performance measures are used: the mean and the coefficient of correlation. The performance of the model was also verified through conducting sensitivity analysis which aimed to determine the response of the model to the variations in the values of each input variables providing the other input variables are constant. The accuracy of the GEP model was evaluated further by comparing its performance with number of currently adopted traditional CPT-based methods. For this purpose, several ranking criteria are used and whichever method scores best is given rank 1. The GEP models, for bored and driven piles, have shown good performance in training and validation sets with high coefficient of correlation between measured and predicted values and low mean values. The results of sensitivity analysis have revealed an incremental relationship between each of the input variables and the output, pile capacity. This agrees with what is available in the geotechnical knowledge and experimental data. The results of comparison with CPT-based methods have shown that the GEP models perform well.", abstract = "The GEP technique is also used to simulate the load-settlement behaviour of the piles. Several attempts have been carried out using different input settings. The results of the favoured attempt have shown that the GEP have achieved limited success in predicting the load-settlement behaviour of the piles. Alternatively, the ANN is considered and the sequential neural network is used for modelling the load-settlement behaviour of the piles. This type of network can account for the load-settlement interdependency and has the option to feedback internally the predicted output of the current state of load settlement to be used as input for the next state of load-settlement. Three ANN models are developed: a model for bored piles and two models for driven piles (a model for steel and a model for concrete piles). The predictive ability of the models is verified by comparing their predictions in training and validation sets with experimental data. Statistical measures including the coefficient of correlation and the mean are used to assess the performance of the ANN models in training and validation sets. The results have revealed that the predicted load-settlement curves by ANN models are in agreement with experimental data for both of training and validation sets. The results also indicate that the ANN models have achieved high coefficient of correlation and low mean values. This indicates that the ANN models can predict the load-settlement of the piles accurately. To examine the performance of the developed ANN models further, the prediction of the models in the validation set are compared with number of load-transfer methods. The comparison is carried out first visually by comparing the load-settlement curve obtained by the ANN models and the load transfer methods with experimental curves. Secondly, is numerically by calculating the coefficient of correlation and the mean absolute percentage error between the experimental data and the compared methods for each case record. The visual comparison has shown that the ANN models are in better agreement with the experimental data than the load transfer methods. The numerical comparison also has shown that the ANN models scored the highest coefficient of correlation and lowest mean absolute percentage error for all compared case records. The developed ANN models are coded into a simple and easily executable computer program. The output of this study is very useful for designers and also for researchers who wish to apply this methodology on other problems in Geotechnical Engineering. Moreover, the result of this study can be considered applicable worldwide because its input data is collected from different regions.", notes = "See also \cite{Alkroosh:book} Supervisors: Hamid Nikraze and Ian Misich", bibsource = "OAI-PMH server at espace.library.curtin.edu.au", description = "The sleeve friction values are not available in the bored piles data, so the weighted average sleeve friction along shaft is excluded from bored piles models. The models output is the pile capacity (interpreted failure load). Additional input variables are included for modelling the load-settlement behaviour of piles. They include settlement, settlement increment and current state of load settlement. The output is the next state of load-settlement. The data are randomly divided into two statistically consistent sets, training set for model calibration and an independent validation set for model performance verification. This type of network can account for the load-settlement interdependency and has the option to feedback internally the predicted output of the current state of loadsettlement to be used as input for the next state of load-settlement. The developed ANN models are coded into a simple and easily executable computer program.", language = "en", oai = "oai:espace.library.curtin.edu.au:166155", rights = "unrestricted", } @Book{Alkroosh:book, author = "Iyad Alkroosh", title = "Modelling pile capacity \& load-settlement behaviour from {CPT} data: For piles in sand and mixed soils using artificial intelligence", publisher = "Lambert Academic Publishing", year = "2012", month = "23 " # may, keywords = "genetic algorithms, genetic programming, Gene Expression Programming, ANN", isbn13 = "3848436906", URL = "https://www.amazon.co.uk/Modelling-pile-capacity-load-settlement-behaviour/dp/3848436906", size = "340 pages", abstract = "This work involves the presentation of new approach attempted to predict the axial capacity and load-settlement behaviour of piles embedded in sand and mixed soils. Two artificial intelligence techniques including Gene Expression Programming (GEP) and Artificial Neural Networks (ANNs) have been used in the approach. The work begins with the definitions of the two techniques and explanation of their terminology and the theories which each of them is based on. The work also comprises extensive literature review of the proposed procedures for evaluating pile capacity and load settlement behaviour. The application of the artificial intelligence in the work begins with the use of the GEP for modelling the pile capacity. The modelling involves data collection, selection of input variables, data division, determination of setting parameters and GEP model selection and model formulation and validation. Two models are developed, a model for bored piles and two others for driven piles. In the second phase of this work, the artificial neural network used for modelling the load-settlement behaviour of the piles.", notes = "See also \cite{Alkroosh:thesis}", } @Article{Alkroosh:2014:SF, author = "I. Alkroosh and H. Nikraz", title = "Predicting pile dynamic capacity via application of an evolutionary algorithm", journal = "Soils and Foundations", volume = "54", number = "2", pages = "233--242", year = "2014", keywords = "genetic algorithms, genetic programming, gene expression programming", ISSN = "0038-0806", DOI = "doi:10.1016/j.sandf.2014.02.013", URL = "http://www.sciencedirect.com/science/article/pii/S0038080614000213", size = "10 pages", abstract = "This study presents the development of a new model obtained from the correlation of dynamic input and SPT data with pile capacity. An evolutionary algorithm, gene expression programming (GEP), was used for modelling the correlation. The data used for model development comprised 24 cases obtained from existing literature. The modelling was carried out by dividing the data into two sets: a training set for model calibration and a validation set for verifying the generalisation capability of the model. The performance of the model was evaluated by comparing its predictions of pile capacity with experimental data and with predictions of pile capacity by two commonly used traditional methods and the artificial neural networks (ANNs) model. It was found that the model performs well with a coefficient of determination, mean, standard deviation and probability density at 50percent equivalent to 0.94, 1.08, 0.14, and 1.05, respectively, for the training set, and 0.96, 0.95, 0.13, and 0.93, respectively, for the validation set. The low values of the calculated mean squared error and mean absolute error indicated that the model is accurate in predicting pile capacity. The results of comparison also showed that the model predicted pile capacity more accurately than traditional methods including the ANNs model.", notes = "The Japanese Geotechnical Society also known as \cite{Alkroosh2014233} Department of Civil Engineering", bibsource = "OAI-PMH server at espace.library.curtin.edu.au", oai = "oai:espace.library.curtin.edu.au:237657", } @Article{Allen:2003:NB, author = "Jess Allen and Hazel M. Davey and David Broadhurst and Jim K. Heald and Jem J. Rowland and Stephen G. Oliver and Douglas B. Kell", title = "High-throughput classification of yeast mutants for functional genomics using metabolic footprinting", journal = "Nature Biotechnology", year = "2003", volume = "21", number = "6", pages = "692--696", month = jun, email = "dbk@umist.ac.uk", keywords = "genetic algorithms, genetic programming", URL = "http://dbkgroup.org/Papers/NatureBiotechnology21(692-696).pdf", DOI = "doi:10.1038/nbt823", abstract = "Many technologies have been developed to help explain the function of genes discovered by systematic genome sequencing. At present, transcriptome and proteome studies dominate large-scale functional analysis strategies. Yet the metabolome, because it is 'downstream', should show greater effects of genetic or physiological changes and thus should be much closer to the phenotype of the organism. We earlier presented a functional analysis strategy that used metabolic fingerprinting to reveal the phenotype of silent mutations of yeast genes1. However, this is difficult to scale up for high-throughput screening. Here we present an alternative that has the required throughput (2 min per sample). This 'metabolic footprinting' approach recognizes the significance of 'overflow metabolism' in appropriate media. Measuring intracellular metabolites is time-consuming and subject to technical difficulties caused by the rapid turnover of intracellular metabolites and the need to quench metabolism and separate metabolites from the extracellular space. We therefore focused instead on direct, noninvasive, mass spectrometric monitoring of extracellular metabolites in spent culture medium. Metabolic footprinting can distinguish between different physiological states of wild-type yeast and between yeast single-gene deletion mutants even from related areas of metabolism. By using appropriate clustering and machine learning techniques, the latter based on genetic programming2-8, we show that metabolic footprinting is an effective method to classify 'unknown' mutants by genetic defect.", } @Article{Allen:2004:AEM, author = "Jess Allen and Hazel M. Davey and David Broadhurst and Jem J. Rowland and Stephen G. Oliver and Douglas B. Kell", title = "Discrimination of Modes of Action of Antifungal Substances by Use of Metabolic Footprinting", journal = "Applied and Environmental Microbiology", year = "2004", volume = "70", number = "10", pages = "6157--6165", month = oct, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1128/AEM.70.10.6157-6165.2004", abstract = "Diploid cells of Saccharomyces cerevisiae were grown under controlled conditions with a Bioscreen instrument, which permitted the essentially continuous registration of their growth via optical density measurements. Some cultures were exposed to concentrations of a number of antifungal substances with different targets or modes of action (sterol biosynthesis, respiratory chain, amino acid synthesis, and the uncoupler). Culture supernatants were taken and analyzed for their metabolic footprints by using direct-injection mass spectrometry. Discriminant function analysis and hierarchical cluster analysis allowed these antifungal compounds to be distinguished and classified according to their modes of action. Genetic programming, a rule-evolving machine learning strategy, allowed respiratory inhibitors to be discriminated from others by using just two masses. Metabolic footprinting thus represents a rapid, convenient, and information-rich method for classifying the modes of action of antifungal substances.", notes = "PMID:", } @InProceedings{DBLP:conf/gecco/AllenBHK09, author = "Sam Allen and Edmund K. Burke and Matthew R. Hyde and Graham Kendall", title = "Evolving reusable {3D} packing heuristics with genetic programming", booktitle = "GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation", year = "2009", editor = "Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano {van Hemert} and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique Alba", pages = "931--938", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1569901.1570029", abstract = "This paper compares the quality of reusable heuristics designed by genetic programming (GP) to those designed by human programmers. The heuristics are designed for the three dimensional knapsack packing problem. Evolutionary computation has been employed many times to search for good quality solutions to such problems. However, actually designing heuristics with GP for this problem domain has never been investigated before. In contrast, the literature shows that it has taken years of experience by human analysts to design the very effective heuristic methods that currently exist. Hyper-heuristics search a space of heuristics, rather than directly searching a solution space. GP operates as a hyper-heuristic in this paper, because it searches the space of heuristics that can be constructed from a given set of components. We show that GP can design simple, yet effective, stand-alone constructive heuristics. While these heuristics do not represent the best in the literature, the fact that they are designed by evolutionary computation, and are human competitive, provides evidence that further improvements in this GP methodology could yield heuristics superior to those designed by humans.", notes = "GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009). ACM Order Number 910092.", } @PhdThesis{Allen:thesis, author = "Sam D. Allen", title = "Algorithms and data structures for three-dimensional packing", school = "School of Computer Science, University of Nottingham", year = "2011", address = "UK", month = jul, keywords = "genetic algorithms, genetic programming, packing, shipment, business, operations research", URL = "http://etheses.nottingham.ac.uk/2779/1/thesis_nicer.pdf", size = "123 pages", abstract = "Cutting and packing problems are increasingly prevalent in industry. A well used freight vehicle will save a business money when delivering goods, as well as reducing the environmental impact, when compared to sending out two lesser-used freight vehicles. A cutting machine that generates less wasted material will have a similar effect. Industry reliance on automating these processes and improving productivity is increasing year-on-year. This thesis presents a number of methods for generating high quality solutions for these cutting and packing challenges. It does so in a number of ways. A fast, efficient framework for heuristically generating solutions to large problems is presented, and a method of incrementally improving these solutions over time is implemented and shown to produce even higher packing. The results from these findings provide the best known results for 28 out of 35 problems from the literature. This framework is analysed and its effectiveness shown over a number of datasets, along with a discussion of its theoretical suitability for higher-dimensional packing problems. A way of automatically generating new heuristics for this framework that can be problem specific, and therefore highly tuned to a given dataset, is then demonstrated and shown to perform well when compared to the expert-designed packing heuristics. Finally some mathematical models which can guarantee the optimality of packings for small datasets are given, and the (in)effectiveness of these techniques discussed. The models are then strengthened and a novel model presented which can handle much larger problems under certain conditions. The thesis finishes with a discussion about the applicability of the different approaches taken to the real-world problems that motivate them.", notes = "Supervisors: Edmund K. Burke and Graham Kendall ID Code: 2779", } @InCollection{Almal:2005:GPTP, author = "A. Almal and W. P. Worzel and E. A. Wollesen and C. D. MacLean", title = "Content Diversity in Genetic Programming and its Correlation with Fitness", booktitle = "Genetic Programming Theory and Practice {III}", year = "2005", editor = "Tina Yu and Rick L. Riolo and Bill Worzel", volume = "9", series = "Genetic Programming", chapter = "12", pages = "177--190", address = "Ann Arbor", month = "12-14 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, diversity, chaos game, fitness correlation, visualisation", ISBN = "0-387-28110-X", DOI = "doi:10.1007/0-387-28111-8_12", size = "14 pages", abstract = "A technique used to visualise DNA sequences is adapted to visualize large numbers of individuals in a genetic programming population. This is used to examine how the content diversity of a population changes during evolution and how this correlates with changes in fitness.", notes = "part of \cite{yu:2005:GPTP} Published Jan 2006 after the workshop", } @InProceedings{1144040, author = "Arpit A. Almal and Anirban P. Mitra and Ram H. Datar and Peter F. Lenehan and David W. Fry and Richard J. Cote and William P. Worzel", title = "Using genetic programming to classify node positive patients in bladder cancer", booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation", year = "2006", editor = "Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos {Coello Coello} and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens", volume = "1", ISBN = "1-59593-186-4", pages = "239--246", address = "Seattle, Washington, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p239.pdf", DOI = "doi:10.1145/1143997.1144040", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "8-12 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Biological Applications, algorithms and similarity measures, bladder cancer, classification rules, classifier design and evaluation, concept learning and induction, feature design and evaluation, feature selection, machine learning, Nodal staging, pattern analysis, program synthesis, synthesis", notes = "GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM Order Number 910060", } @InCollection{Almal:2007:GPTP, author = "A. A. Almal and C. D. MacLean and W. P. Worzel", title = "Program Structure-Fitness Disconnect and Its Impact On Evolution In GP", booktitle = "Genetic Programming Theory and Practice {V}", year = "2007", editor = "Rick L. Riolo and Terence Soule and Bill Worzel", series = "Genetic and Evolutionary Computation", chapter = "9", pages = "143--158", address = "Ann Arbor", month = "17-19" # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, phenotype, genotype, evolutionary dynamics, GP structure, GP content, speciation, population, fitness", isbn13 = "978-0-387-76308-8", DOI = "doi:10.1007/978-0-387-76308-8_9", size = "15 pages", abstract = "Simple Genetic Programming (GP) is generally considered to lack the strong separation between genotype and phenotype found in natural evolution. In many cases, the genotype and the phenotype are considered identical in GP since the program representation does not undergo any modification prior to its encounter with 'environment' in the form of inputs and a fitness function. However, this view overlooks a key fact: fitness in GP is determined without reference to the makeup of the individual programs but evolutionary changes occur in the structure and content of the individual without reference to its fitness. This creates a disconnect between 'genetic recombination' and fitness similar to that in nature that can create unexpected effects during the evolution of a population and suggests an important dynamic that has not been thoroughly considered by the GP community. This paper describes some of the observed effects of this disconnect and studies some approaches for the estimating diversity of a population which could lead to a new way of modelling the dynamics of GP. We also speculate on the similarity of these effects and some recently studied aspects of natural evolution.", notes = "part of \cite{Riolo:2007:GPTP} Published 2008", } @InCollection{Almal:2008:GPTP, author = "A. A. Almal and C. D. MacLean and W. P. Worzel", title = "A Population Based Study of Evolutionary Dynamics in Genetic Programming", booktitle = "Genetic Programming Theory and Practice {VI}", year = "2008", editor = "Rick L. Riolo and Terence Soule and Bill Worzel", series = "Genetic and Evolutionary Computation", chapter = "2", pages = "19--29", address = "Ann Arbor", month = "15-17 " # may, publisher = "Springer", DOI = "doi:10.1007/978-0-387-87623-8_2", size = "10 pages", isbn13 = "978-0-387-87622-1", notes = "part of \cite{Riolo:2008:GPTP} published in 2009", keywords = "genetic algorithms, genetic programming", } @InProceedings{almarimi2020community, author = "Nuri Almarimi and Ali Ouni and Moataz Chouchen and Islem Saidani and Mohamed Wiem Mkaouer", title = "On the Detection of Community Smells using Genetic Programming-based Ensemble Classifier Chain", booktitle = "15th IEEE/ACM International Conference on Global Software Engineering (ICGSE)", year = "2020", pages = "43--54", address = "internet", month = "26 " # jun, keywords = "genetic algorithms, genetic programming, SBSE, community smells, social debt, socio-technical factors, search-based software engineering, multi-label learning", isbn13 = "9781450370936", URL = "https://conf.researchr.org/details/icgse-2020/icgse-2020-research-papers/6/On-the-Detection-of-Community-Smells-using-Genetic-Programming-based-Ensemble-Classif", DOI = "doi:10.1145/3372787.3390439", dataset_url = "https://github.com/GP-ECC/community-smells", abstract = "Community smells are symptoms of organizational and social issues within the software development community that often increase the project costs and impact software quality. Recent studies have identified a variety of community smells and defined them as suboptimal patterns connected to organizational-social structures in the software development community such as the lack of communication, coordination and collaboration. Recognizing the advantages of the early detection of potential community smells in a software project, we introduce a novel approach that learns from various community organizational and social practices to provide an automated support for detecting community smells. In particular, our approach learns from a set of interleaving organizational-social symptoms that characterize the existence of community smell instances in a software project. We build a multi-label learning model to detect 8 common types of community smells. We use the ensemble classifier chain (ECC) model that transforms multi-label problems into several single-label problems which are solved using genetic programming (GP) to find the optimal detection rules for each smell type. To evaluate the performance of our approach, we conducted an empirical study on a benchmark of 103 open source projects and 407 community smell instances. The statistical tests of our results show that our approach can detect the eight considered smell types with an average F-measure of 89percent achieving a better performance compared to different state-of-the-art techniques. Furthermore, we found that the most influential factors that best characterize community smells include the social network density and closeness centrality as well as the standard deviation of the number of developers per time zone and per community.", notes = "Ecole de technologie superieure (ETS), Montreal, Canada. ICGSE 2020 co-located with ICSE 2020", } @InProceedings{Almarimi:2020:ICGSE, author = "Nuri Almarimi and Ali Ouni and Moataz Chouchen and Islem Saidani and Mohamed Wiem Mkaouer", booktitle = "2020 ACM/IEEE 15th International Conference on Global Software Engineering (ICGSE)", title = "On the Detection of Community Smells Using Genetic Programming-based Ensemble Classifier Chain", year = "2020", pages = "43--54", abstract = "Community smells are symptoms of organizational and social issues within the software development community that often increase the project costs and impact software quality. Recent studies have identified a variety of community smells and defined them as sub-optimal patterns connected to organizational-social structures in the software development community such as the lack of communication, coordination and collaboration. Recognizing the advantages of the early detection of potential community smells in a software project, we introduce a novel approach that learns from various community organizational and social practices to provide an auto-mated support for detecting community smells. In particular, our approach learns from a set of interleaving organizational-social symptoms that characterise the existence of community smell in-stances in a software project. We build a multi-label learning model to detect 8 common types of community smells. We use the ensemble classifier chain (ECC) model that transforms multi-label problems into several single-label problems which are solved using genetic programming (GP) to find the optimal detection rules for each smell type. To evaluate the performance of our approach, we conducted an empirical study on a benchmark of 103 open source projects and 407 community smell instances. The statistical tests of our results show that our approach can detect the eight considered smell types with an average F-measure of 89percent achieving a better performance compared to different state-of-the-art techniques. Furthermore, we found that the most influential factors that best characterise community smells include the social network density and closeness centrality as well as the standard deviation of the number of developers per time zone and per community.", keywords = "genetic algorithms, genetic programming, Search-based software engineering, SBSE, Costs, Social networking (online), Standards organizations, Collaboration, Transforms, Software quality, Community smells, Social debt, Socio-technical factors, Multi-label learning", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=10148849", month = may, notes = "Also known as \cite{10148849}", } @InProceedings{alMasalma:2022:GECCOcomp, author = "Mihyar {Al Masalma} and Malcolm Heywood", title = "Genetic Programming with External Memory in Sequence Recall Tasks", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion", year = "2022", editor = "Heike Trautmann and Carola Doerr and Alberto Moraglio and Thomas Bartz-Beielstein and Bogdan Filipic and Marcus Gallagher and Yew-Soon Ong and Abhishek Gupta and Anna V Kononova and Hao Wang and Michael Emmerich and Peter A. N. Bosman and Daniela Zaharie and Fabio Caraffini and Johann Dreo and Anne Auger and Konstantin Dietric and Paul Dufosse and Tobias Glasmachers and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Tea Tusar and Dimo Brockhoff and Tome Eftimov and Pascal Kerschke and Boris Naujoks and Mike Preuss and Vanessa Volz and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Mark Coletti and Catherine (Katie) Schuman and Eric ``Siggy'' Scott and Robert Patton and Paul Wiegand and Jeffrey K. Bassett and Chathika Gunaratne and Tinkle Chugh and Richard Allmendinger and Jussi Hakanen and Daniel Tauritz and John Woodward and Manuel Lopez-Ibanez and John McCall and Jaume Bacardit and Alexander Brownlee and Stefano Cagnoni and Giovanni Iacca and David Walker and Jamal Toutouh and UnaMay O'Reilly and Penousal Machado and Joao Correia and Sergio Nesmachnow and Josu Ceberio and Rafael Villanueva and Ignacio Hidalgo and Francisco {Fernandez de Vega} and Giuseppe Paolo and Alex Coninx and Antoine Cully and Adam Gaier and Stefan Wagner and Michael Affenzeller and Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and William B. Langdon and Justyna Petke and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Paetzel and Alexander Wagner and Michael Heider and Nadarajen Veerapen and Katherine Malan and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Mohammad Nabi Omidvar and Yuan Sun and Ernesto Tarantino and De Falco Ivanoe and Antonio {Della Cioppa} and Scafuri Umberto and John Rieffel and Jean-Baptiste Mouret and Stephane Doncieux and Stefanos Nikolaidis and Julian Togelius and Matthew C. Fontaine and Serban Georgescu and Francisco Chicano and Darrell Whitley and Oleksandr Kyriienko and Denny Dahl and Ofer Shir and Lee Spector and Alma Rahat and Richard Everson and Jonathan Fieldsend and Handing Wang and Yaochu Jin and Erik Hemberg and Marwa A. Elsayed and Michael Kommenda and William {La Cava} and Gabriel Kronberger and Steven Gustafson", pages = "518--521", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, modularity, partially observable, external memory", isbn13 = "978-1-4503-9268-6/22/07", DOI = "doi:10.1145/3520304.3528883", video_url = "https://vimeo.com/723511528", abstract = "Partially observable tasks imply that a learning agent has to recall previous state in order to make a decision in the present. Recent research with neural networks have investigated both internal and external memory mechanisms for this purpose, as well as proposing benchmarks to measure their effectiveness. These developments motivate our investigation using genetic programming and an external linked list memory model. A thorough empirical evaluation using a scalable sequence recall benchmark establishes the underlying strength of the approach. In addition, we assess the impact of decisions made regarding the instruction set and characterize the sensitivity to noise / obfuscation in the definition of the benchmarks. Compared to neural solutions to these benchmarks, GP extends the state-of-the-art to greater task depths than previously possible.", notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @Article{alMasalma:2023:GPEM, author = "Mihyar {Al Masalma} and Malcolm Heywood", title = "Benchmarking ensemble genetic programming with a linked list external memory on scalable partially observable tasks", journal = "Genetic Programming and Evolvable Machines", year = "2022", volume = "23", number = "Suppl 1", pages = "s1--s29", month = "30 " # nov, keywords = "genetic algorithms, genetic programming, External memory, Partial observability, Internal state, Ensembles, noop, pop_head, pop_tail, DIV", ISSN = "1389-2576", URL = "https://rdcu.be/daFLX", DOI = "doi:10.1007/s10710-022-09446-8", size = "29 pages", abstract = "Reactive learning agents cannot solve partially observable sequential decision-making tasks as they are limited to defining outcomes purely in terms of the observable state. However, augmenting reactive agents with external memory might provide a path for addressing this limitation. In this work, external memory takes the form of a linked list data structure that programs have to learn how to use. We identify conditions under which additional recurrent connectivity from program output to input is necessary for state disambiguation. Benchmarking against recent results from the neural network literature on three scalable partially observable sequential decision-making tasks demonstrates that the proposed approach scales much more effectively. Indeed, solutions are shown to generalize to far more difficult sequences than those experienced under training conditions. Moreover, recommendations are made regarding the instruction set and additional benchmarking is performed with input state values designed to explicitly disrupt the identification of useful states for later recall. The protected division operator appears to be particularly useful in developing simple solutions to all three tasks.", notes = "See also MSc https://dalspace.library.dal.ca/handle/10222/81503 http://hdl.handle.net/10222/81503 Faculty of Computer Science, Dalhousie University, 6050 University Avenue, Halifax, NS, Canada", } @Article{Almeida:2017:ieeeGRSL, author = "Alexandre E. Almeida and Ricardo {da S. Torres}", journal = "IEEE Geoscience and Remote Sensing Letters", title = "Remote Sensing Image Classification Using Genetic-Programming-Based Time Series Similarity Functions", year = "2017", volume = "14", number = "9", pages = "1499--1503", abstract = "In several applications, the automatic identification of regions of interest in remote sensing images is based on the assessment of the similarity of associated time series, i.e., two regions are considered as belonging to the same class if the patterns found in their spectral information observed over time are somewhat similar. In this letter, we investigate the use of a genetic programming (GP) framework to discover an effective combination of time series similarity functions to be used in remote sensing classification tasks. Performed experiments in a Forest-Savanna classification scenario demonstrated that the GP framework yields effective results when compared with the use of traditional widely used similarity functions in isolation.", keywords = "genetic algorithms, genetic programming, remote sensing, time series similarity", DOI = "doi:10.1109/LGRS.2017.2719033", ISSN = "1545-598X", month = sep, notes = "Also known as \cite{7981314}", } @Article{Almeida:2015:EI, author = "Jurandy Almeida and Jefersson A. {dos Santos} and Waner O. Miranda and Bruna Alberton and Leonor Patricia C. Morellato and Ricardo {da S. Torres}", title = "Deriving vegetation indices for phenology analysis using genetic programming", journal = "Ecological Informatics", volume = "26, Part 3", pages = "61--69", year = "2015", keywords = "genetic algorithms, genetic programming, Remote phenology, Digital cameras, Image analysis, Vegetation indices", ISSN = "1574-9541", DOI = "doi:10.1016/j.ecoinf.2015.01.003", URL = "http://www.sciencedirect.com/science/article/pii/S1574954115000114", size = "9 pages", abstract = "Plant phenology studies recurrent plant life cycle events and is a key component for understanding the impact of climate change. To increase accuracy of observations, new technologies have been applied for phenological observation, and one of the most successful strategies relies on the use of digital cameras, which are used as multi-channel imaging sensors to estimate colour changes that are related to phenological events. We monitor leaf-changing patterns of a cerrado-savanna vegetation by taking daily digital images. We extract individual plant color information and correlate with leaf phenological changes. For that, several vegetation indices associated with plant species are exploited for both pattern analysis and knowledge extraction. In this paper, we present a novel approach for deriving appropriate vegetation indices from vegetation digital images. The proposed method is based on learning phenological patterns from plant species through a genetic programming framework. A comparative analysis of different vegetation indices is conducted and discussed. Experimental results show that our approach presents higher accuracy on characterising plant species phenology.", } @InProceedings{Almeida:2016:SIBGRAPI, author = "M. A. Almeida and E. C. Pedrino and M. C. Nicoletti", booktitle = "2016 29th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)", title = "A Genetically Programmable Hybrid Virtual Reconfigurable Architecture for Image Filtering Applications", year = "2016", pages = "152--157", abstract = "A new and efficient automatic hybrid method, called Hy-EH, based on Virtual Reconfigurable Architectures (VRAs) and implemented in Field Programmable Gate Arrays (FPGAs) is proposed, for a hardware-embedded construction of image filters. The method also encompass an evolutionary software system, which represents the chromosome as a bi-dimensional grid of function elements (FEs), entirely parametrised using the Verilog-HDL (Verilog Hardware Description Language), which is reconfigured using the MATLAB toolbox GPLAB, before its download into the FPGA. In the so-called intrinsic proposals, evolutionary processes take place internally to the hardware, in a pre-defined fixed way, in extrinsic proposals evolutionary processes happen externally to the hardware. The hybrid Hy-EH method, described in this paper allows for the intrinsic creation of a flexible-sized hardware, in an extrinsic way i.e., by means of an evolutionary process that happens externally to the hardware. Hy-EH is also a convenient choice as far as extrinsic methods are considered, since it does not depend on a proprietary solution for its implementation. A comparative analysis of using the Hy-EH versus an existing intrinsic proposal, in two well-known problems, has been conducted. Results show that by using Hy-EH there was little hardware complexity due to the optimised and more flexible use of shorter chromosomes.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SIBGRAPI.2016.029", month = oct, notes = "Also known as \cite{7813028}", } @Article{Almeida:2018:ICAE, author = "M. A. Almeida and E. C. Pedrino", title = "Hybrid Evolvable Hardware for automatic generation of image filters", journal = "Integrated Computer-Aided Engineering", year = "2018", volume = "25", number = "3", pages = "289--303", keywords = "genetic algorithms, genetic programming, Evolvable Hardware, FPGA, virtual reconfigurable architecture", ISSN = "1069-2509", DOI = "doi:10.3233/ICA-180561", size = "15 pages", abstract = "In this article, a new framework is proposed and implemented for automatic generation of image filters in reconfigurable hardware (FPGA), called H-EHW (Hybrid-Evolvable Hardware). This consists basically of two modules. The first (training module) is responsible for the automatic generation of solutions (filters). The second (fusion module) converts such solutions into hardware, thus creating a virtual and reconfigurable architecture for fast image processing. Monochromatic pairs of images are used for the system training and testing. Extensive tests show that there are several benefits of the proposed system when compared to other similar systems described in the literature, such as: reduced phenotype length (generated circuit), reduced reconfiguration time, greater hardware reconfiguration flexibility and no more need for the manipulation of the bitstream of the FPGA for circuit evolution (a problem often encountered in practice by designers).", } @InCollection{almgren:2000:CADGP, author = "Magnus Almgren", title = "Communicating Agents Developed with Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "25--32", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:2000:gagp}", } @PhdThesis{AlMosawe:thesis, author = "Alaa Al-Mosawe", title = "Investigation of the performance of cracked steel members strengthened with carbon fibre reinforced polymers under impact loads", school = "Faculty of Science, Engineering and Technology, Swinburne University of Technology", year = "2016", address = "Melbourne, Australia", keywords = "genetic algorithms, genetic programming, CFRP, Fe", URL = "http://hdl.handle.net/1959.3/414765", URL = "https://researchbank.swinburne.edu.au/file/0cca0b0c-0219-4cb0-b0d3-b33bcdb159a8/1/Alaa%20Al-Mosawe%20Thesis.pdf", size = "224 pages", abstract = "Generally, steel structures are subjected to different types of loadings during their life-time. Over time these structures sustain fewer loads than those for which they were designed. The reduction in structural capacity might occur as a result of various parameters, including aging, changes in use, increases in applied loads, and as a result of environmental effects causing corrosion. These structures need to be strengthened or repaired in order to be able to carry the different applied loads. Carbon fibre reinforced polymers (CFRPs) are a new method of strengthening, the use of which has grown in the last few decades. This method of strengthening has attracted structural engineers due to its ease of application, light weight and very high tensile strength. The bond between CFRP and steel members is the main issue in understanding the bond behaviour. This thesis presents the effect of impact loading on the bond behaviour of CFRP-steel double strap joints. The results of comprehensive experimental tests are presented in this project on the basis of testing large numbers of CFRP-steel double strap joints under both static and dynamic loadings. Another series of tests was conducted to investigate the mechanical properties of the composite material itself. The mechanical properties were investigated under different loading rates, starting from quasi-static loading at 2mm/min, to impact loadings of 201000mm/minute, 258000mm/minute and 300000mm/minute. The experimental results showed that loading rate has a significant effect on the material properties, and a significant increase was shown in tensile strength and modulus of elasticity. The results of another series of tests are presented in this thesis. A number of CFRP-steel double strap joints were prepared and tested under quasi-static loads. Three different types of CFRP modulus (low modulus 165 GPa, normal modulus 205GPa and ultra-high CFRP modulus 460 GPa) were used, to study the effect of CFRP modulus on the bond behaviour between steel and CFRP laminates. In order to investigate the effect of CFRP geometry on the bond properties, two different CFRP sections were used (20 by 1.4mm and 10 by 1.4mm). The results showed a significant influence on the bond strength, strain distribution along the bond, effective bond length and failure mode for specimens with different CFRP modulus. The results also showed that a small CFRP section is sensitive to any little movement. Further tests were also conducted on CFRP-steel double strap specimens with different CFRP moduli under high impact loading rates. The load rates used in this project were 201m per minute, 258m per minute and 300m/min. The aim of this test was to find the degree of joint enhancement under dynamic loadings compared to quasi-static loads. The results showed a significant increase in load-carrying capacity, and strain distribution along the bond. However, a significant decrease in the effective bond length under impact loads was observed compared to quasi-static testing. Different failure modes were shown compared to specimens tested under quasi-static loadings. Finite element analysis was conducted in this research to model the CFRP-steel double strap joint under both quasi-static and dynamic loads. The individual components of the joint (CFRP laminate, Araldite 420 adhesive and steel plates) were first modeled and analysed under the four loading rates. The CFRP-steel double strap joints were modelled using non-linear finite element analysis using the commercial software ABAQUS 6.13. The results showed good prediction of material properties and joint behaviour using non-linear finite element analysis, and the results of tensile joint strength, strain distribution along the bond, effective bond length and failure modes were close to those tested experimentally. This thesis also shows a new formulation of CFRP-steel double strap joints using genetic programming; the data from the experimental and numerical analysis were analysed using genetic programming software. Three different parameters were used: bond length, loading rate and the CFRP modulus. The outcomes of this analysis are showing an expression tree and a new equation to express the bond strength of these types of joints. The results are assumed to be used for the range of parameters used as input data in the programming. Finally, some suggestions on future work to continue the investigation of the bond behaviour between CFRP and steel in the double strap joints are provided.", notes = "supervisor: Riadh Al-Mahaidi", } @Article{AlMosawe:2017:CS, author = "Alaa Al-Mosawe and Robin Kalfat and Riadh Al-Mahaidi", title = "Strength of Cfrp-steel double strap joints under impact loads using genetic programming", journal = "Composite Structures", volume = "160", pages = "1205--1211", year = "2017", keywords = "genetic algorithms, genetic programming, Carbon fibre, Genetic programing, Impact behaviour, Joint strength, CFRP-steel joint", ISSN = "0263-8223", URL = "http://www.sciencedirect.com/science/article/pii/S0263822316317767", DOI = "doi:10.1016/j.compstruct.2016.11.016", abstract = "Carbon fibre reinforced polymers (CFRPs) are widely used by structural engineers to increase the strength of existing structures subjected to different loading actions. Existing steel structures are subjected to impact loadings due to the presence of new types of loads, and these structures need to be strengthened to sustain the new applied loads. Design guidelines for FRP-strengthened steel structures are not yet available, due to the lack of understanding of bond properties and bond strength. This paper presents the application of genetic programming (GP) to predict the bond strength of CFRP-steel double strap joints subjected to direct tension load. Extensive data from experimental tests and finite element modelling were used to develop a new joint strength formulation. The selected parameters which have a direct impact on the joint strength were: bond length, CFRP modulus and the loading rate. A wide range of loading rates and four CFRP moduli with different bond lengths were used. The prediction of the GP model was compared with the experimental values. The model has a high value of R squared, which indicates good accuracy of results.", } @InProceedings{Al-Mulla:2009:EMBC, author = "M. R. Al-Mulla and F. Sepulveda and M. Colley and A. Kattan", title = "Classification of localized muscle fatigue with genetic programming on sEMG during isometric contraction", booktitle = "Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2009", year = "2009", month = "2-6 " # sep, address = "Minneapolis, Minnesota, USA", pages = "2633--2638", keywords = "genetic algorithms, genetic programming, GP training phase, K-means clustering, fuzzy classifier, isometric contraction, isometric sEMG signal filtering, localized muscle fatigue classification, nonfatigue classifier, rectified surface electromyography, statistical feature extraction, transition-to-fatigue classifier, two-dimensional Euclidean space, biomechanics, electromyography, fatigue, feature extraction, filtering theory, fuzzy logic, medical signal processing, neurophysiology, pattern clustering, signal classification, statistical analysis", DOI = "doi:10.1109/IEMBS.2009.5335368", ISSN = "1557-170X", abstract = "Genetic programming is used to generate a solution that can classify localized muscle fatigue from filtered and rectified surface electromyography (sEMG). The GP has two classification phases, the GP training phase and a GP testing phase. In the training phase, the program evolved with multiple components. One component analyzes statistical features extracted from sEMG to chop the signal into blocks and label them using a fuzzy classifier into three classes: non-fatigue, transition-to-fatigue and fatigue. The blocks are then projected onto a two-dimensional Euclidean space via two further (evolved) program components. K-means clustering is then applied to group similar data blocks. Each cluster is then labeled according to its dominant members. The programs that achieve good classification are evolved. In the testing phase, it tests the signal using the evolved components, however without the use of a fuzzy classifier. As the results show the evolved program achieves good classification and it can be used on any unseen isometric sEMG signals to classify fatigue without requiring any further evolution. The GP was able to classify the signal into a meaningful sequence of non-fatigue -> transition-to-fatiguer -> fatigue. By identifying a transition-to fatigue state the GP can give a prediction of an oncoming fatigue. The genetic classifier gave promising results 83.17percent correct classification on average of all signals in the test set, especially considering that the GP is classifying muscle fatigue for ten different individuals.", notes = "Also known as \cite{5335368}", } @Article{Al-Mulla:2011:MEP, author = "Mohamed R. Al-Mulla and Francisco Sepulveda and M. Colley", title = "Evolved pseudo-wavelet function to optimally decompose sEMG for automated classification of localized muscle fatigue", journal = "Medical Engineering and Physics", year = "2011", volume = "33", number = "4", pages = "411--417", month = may, keywords = "genetic algorithms, Localized muscle fatigue, sEMG, Wavelet analysis, matlab", DOI = "doi:10.1016/j.medengphy.2010.11.008", abstract = "The purpose of this study was to develop an algorithm for automated muscle fatigue detection in sports related scenarios. Surface electromyography (sEMG) of the biceps muscle was recorded from ten subjects performing semi-isometric (i.e., attempted isometric) contraction until fatigue. For training and testing purposes, the signals were labelled in two classes (Non-Fatigue and Fatigue), with the labelling being determined by a fuzzy classifier using elbow angle and its standard deviation as inputs. A genetic algorithm was used for evolving a pseudo-wavelet function for optimising the detection of muscle fatigue on any unseen sEMG signals. Tuning of the generalised evolved pseudo-wavelet function was based on the decomposition of twenty sEMG trials. After completing twenty independent pseudo-wavelet evolution runs, the best run was selected and then tested on ten previously unseen sEMG trials to measure the classification performance. Results show that an evolved pseudo-wavelet improved the classification of muscle fatigue between 7.31percent and 13.15percent when compared to other wavelet functions, giving an average correct classification of 88.41percent", } @InProceedings{AlNajar:2022:GI, author = "Mahmoud {Al Najar} and Rafael Almar and Erwin W. J. Bergsma and Jean-Marc Delvit and Dennis G. Wilson", title = "Genetic Improvement of Shoreline Evolution Forecasting Models", booktitle = "GI @ GECCO 2022", year = "2022", editor = "Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and W. B. Langdon and Justyna Petke", pages = "1916--1923", address = "Boston, USA", publisher_address = "New York, NY, USA", month = "9 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", keywords = "genetic algorithms, genetic programming, genetic improvement, Cartesian Genetic Programming, RCGP, symbolic regression, forecasting, shoreline evolution, earth observation salellite, CGP-ShoreFor, ShorefFor, physical sciences, geography, coastal erosion, Tairua New Zealand, Mielke correlation coefficient", isbn13 = "978-1-4503-9268-6/22/07", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/gecco2022/gi2022/papers/AlNajar_2022_GI.pdf", DOI = "doi:10.1145/3520304.3534041", slides_url = "https://sourcesup.renater.fr/wiki/atelieromp/_media/presentation_envia_mahmoud_al_ajar_omp_v2.pdf", slides_url = "http://geneticimprovementofsoftware.com/slides/gi2022gecco/najar-genetic-improvement-of-shoreline-evolution-forecasting-gi-gecco-22.pdf", video_url = "https://www.youtube.com/watch?v=66UiDk9lsnc&list=PLI8fiFpB7BoIHgl5CsdtjfWvHlE5N6pje&index=6", size = "8+1 pages", abstract = "Coastal development and climate change are changing the geography of our coasts, while more and more people are moving towards the coasts. Recent advances in artificial intelligence allow for automatic analysis of observational data. Cartesian Genetic Programming (CGP) is a form of Genetic Programming (GP) that has been successfully used in a large variety of tasks including data-driven symbolic regression. We formulate the problem of shoreline evolution forecasting as a Genetic Improvement (GI) problem using CGP to encode and improve upon ShoreFor, an equilibrium shoreline prediction model, to study the effectiveness of CGP in GI in forecasting tasks. This work presents an empirical study of the sensitivity of CGP to a number of evolutionary configurations and constraints and compares the performances of the evolved models to the base ShoreFor model.", notes = "Supplementary Material: Figure 8: Example evolved CGP-ShoreFor model graphs 15 years (dauly sampling). Goldman mutation \cite{goldman:2013:EuroGP} http://geneticimprovementofsoftware.com/events/gecco2022 GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @InProceedings{alnajar2023improving, author = "Mahmoud {Al Najar} and Rafael Almar and Erwin W. J. Bergsma and Jean-Marc Delvit and Dennis G. Wilson", title = "Improving a Shoreline Forecasting Model with Symbolic Regression", booktitle = "ICLR 2023 Workshop on Tackling Climate Change with Machine Learning", year = "2023", address = "Kigali Rwanda", month = "4 " # may, keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Machine Learning, Interpretable ML, XAI, Symbolic Computation, Earth Observation & Monitoring, Extreme Weather, Ocean, Atmosphere, Hybrid Physical Models, Time-series Analysis", URL = "https://www.climatechange.ai/papers/iclr2023/21", URL = "https://www.climatechange.ai/papers/iclr2023/21/paper.pdf", URL = "https://hal.science/hal-04281530", size = "12 pages", abstract = "Given the current context of climate change and the increasing population densities at coastal zones around the globe, there is an increasing need to be able to predict the development of our coasts. Recent advances in artificial intelligence allow for automatic analysis of observational data. Symbolic Regression (SR) is a type of Machine Learning algorithm that aims to find interpretable symbolic expressions that can explain relations in the data. In this work, we aim to study the problem of forecasting shoreline change using SR. We make use of Cartesian Genetic Programming (CGP) in order to encode and improve upon ShoreFor, a physical shoreline prediction model. During training, CGP individuals are evaluated and selected according to their predictive score at five different coastal sites. This work presents a comparison between a CGP-evolved model and the base ShoreFor model. In addition to evolution's ability to produce well-performing models, it demonstrates the usefulness of SR as a research tool to gain insight into the behaviors of shorelines in various geographical zones.", notes = "Published as a workshop paper at Tackling Climate Change with Machine Learning, ICLR 2023", } @PhdThesis{AlNajar:thesis, author = "Mahmoud {Al Najar}", title = "Estimating Coastal Evolution with Machine Learning", title_fr = "Estimation de l'evolution du littoral par l'apprentissage automatique", school = "University of Toulouse", year = "2023", address = "France", month = "30 " # nov, note = "forthcoming", keywords = "genetic algorithms, genetic programming, genetic improvement, Cartesian Genetic Programming, Deep Learning, Earth Observation, Shoreline forecasting, Bathymetry inversion", URL = "https://www.isae-supaero.fr/IMG/pdf/annonce_soutenance_these_m_al_najar.pdf", URL = "https://zoom.us/my/dennisgwilson", abstract = "Forecasting coastal evolution is a prerequisite for effective coastal management and has been a fundamental goal of coastal research for decades. However, coastal evolution is a complex process, and predicting its development through time remains challenging. The absence of representative datasets which accurately track the state and evolution of coastal systems greatly limits our ability to study these processes given different natural and anthropological scenarios. While traditional field surveys have been used extensively in the literature and have served as important assets in advancing our knowledge of these systems, the high operational costs of traditional field surveys limit their use to local and sparse spatio-temporal scales. Satellite-based Remote Sensing (RS) provides the opportunity for frequently monitoring the Earth at high temporal resolutions and scales, but requires the development of novel data processing methodologies for large streams of Earth Observation data. Machine Learning (ML) is a subfield of Artificial Intelligence which aims at constructing algorithms able to leverage large amounts of example data in order to automatically construct predictive models, and has been a critical component of many scientific advancements in recent years. This thesis examines the potential and capability of modern ML in two important problems in Coastal Science where ML remains unexplored. Deep Learning and Interpretable Machine Learning are applied to the problems of satellite-derived bathymetry and shoreline evolution modelling. The work demonstrates that ML is competitive with current physics-based baselines on both tasks, and shows the potential of ML in automating many of our large-scale coastal data analysis towards gaining a global understanding of coastal evolution.", resume = "a prvision de l'volution du littoral est une condition pralable a une gestion efficace des cotes et constitue un objectif fondamental de la recherche cotiere depuis des dcennies. Cependant, l'volution des cotes est un processus complexe et la prvision de son dveloppement dans le temps reste un dfi. L'absence d'ensembles de donnes reprsentatives permettant de suivre avec prcision l'tat et l'volution des systemes cotiers limite considrablement notre capacit a tudier ces processus dans le cadre de diffrents scnarios naturels et anthropologiques. Bien que les enquetes traditionnelles sur le terrain aient t largement dans la littrature et qu'elles aient permis de faire progresser notre connaissance de ces systemes, les couts oprationnels levs des enquetes sur le terrain limitent leur utilisation a des chelles spatio-temporelles locales et parses. La tldtection par satellite permet de surveiller frquemment la Terre a des rsolutions et des chelles temporelles leves, mais ncessite le dveloppement de nouvelles mthodologies de traitement des donnes pour les grands flux de donnes d'observation de la Terre. L'apprentissage automatique est un sous-domaine de l'intelligence artificielle qui vise a construire des algorithmes capables d'exploiter de grandes quantits de donnes d'entrainement afin de construire automatiquement des modeles prdictifs, et a t un lment essentiel de nombreuses avances scientifiques au cours des dernieres annes. Cette these examine le potentiel et la capacit de l'apprentissage automatique moderne dans deux problemes importants de la science cotiere o l'apprentissage automatique reste inexplor. L'apprentissage profond et l'apprentissage automatique interprtable sont appliqus aux problemes de la bathymtrie drive des satellites et de la modlisation de l'volution du trait de cote. Le travail dmontre que l'apprentissage automatique est comptitif par rapport aux bases actuelles bases sur la physique pour les deux taches, et montre le potentiel de l'apprentissage automatique dans l'automatisation d'un grand nombre de nos analyses de donnes cotieres a grande chelle afin d'obtenir une comprhension globale de l'volution du littoral.", notes = "in english? Supervisor Rafael Almar", } @Article{DBLP:journals/tsusc/AlofiBBH22, author = "Akram Alofi and Mahmoud A. Bokhari and Rami Bahsoon and Robert J. Hendley", title = "Optimizing the Energy Consumption of Blockchain-Based Systems Using Evolutionary Algorithms: A New Problem Formulation", journal = "IEEE Transactions on Sustainable Computing", year = "2022", volume = "7", number = "4", pages = "910--922", keywords = "genetic algorithms, genetic programming, genetic improvement, Search-Based Software Engineering, SBSE, Blockchain, Mining, Optimization, Evolutionary Algorithms, Sustainability", timestamp = "Sun, 12 Feb 2023 18:49:26 +0100", biburl = "https://dblp.org/rec/journals/tsusc/AlofiBBH22.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://research.birmingham.ac.uk/en/publications/optimizing-the-energy-consumption-of-blockchain-based-systems-usi", DOI = "doi:10.1109/TSUSC.2022.3160491", size = "15 pages", abstract = "... we represent the problem as a subset selection problem of miners in blockchain-based systems. We formulate the problem of blockchain energy consumption as an optimisation problem with four conflicting objectives: energy consumption, carbon emission, decentralisation and trust. We propose a model composed of different fitness functions ...", } @Article{ALOISIO:2023:ymssp, author = "Angelo Aloisio and Alessandro Contento and Rocco Alaggio and Giuseppe Quaranta", title = "Physics-based models, surrogate models and experimental assessment of the vehicle-bridge interaction in braking conditions", journal = "Mechanical Systems and Signal Processing", volume = "194", pages = "110276", year = "2023", ISSN = "0888-3270", DOI = "doi:10.1016/j.ymssp.2023.110276", URL = "https://www.sciencedirect.com/science/article/pii/S0888327023001838", keywords = "genetic algorithms, genetic programming, Bouncing, Braking, Bridge, Fragility curve, Machine learning, Moving load, Neural network, ANN, Pitching, Roughness, Surrogate model, Vehicle-bridge interaction", abstract = "The dynamics of roadway bridges crossed by vehicles moving at variable speed has attracted far less attention than that generated by vehicles travelling at constant velocity. Consequently, the role of some parameters and the combination thereof, as well as influence and accuracy of the modelling strategies, are not fully understood yet. Therefore, a large statistical analysis is performed in the present study to provide novel insights into the dynamic vehicle-bridge interaction (VBI) in braking conditions. To this end, an existing mid-span prestressed concrete bridge is selected as case study. First, several numerical simulations are performed considering alternative vehicle models (i.e., single and two degrees-of-freedom models) and different braking scenarios (i.e., soft and hard braking conditions, with both stationary and nonstationary road roughness models in case of soft braking). The statistical appraisal of the obtained results unfolds some effects of the dynamic VBI modelling in braking conditions that have not been reported in previous studies. Additionally, the use of machine learning techniques is explored for the first time to develop surrogate models able to predict the effect of the dynamic VBI in braking conditions efficiently. These surrogate models are then employed to obtain the fragility curve for the selected prestressed concrete bridge, where the attainment of the decompression moment is considered as relevant limit state. Whilst the derivation of the fragility curve using numerical simulations turned out to be almost unpractical using standard computational resources, the proposed approach that exploits surrogate models carried out via machine learning techniques was demonstrated accurate despite the dramatic reduction of the total elaboration time. Finally, the accuracy of the numerical (physics-based and surrogate) models is evaluated on a statistical basis through comparisons with experimental data", } @InProceedings{Alonso:2008:ieeeICTAI, author = "Cesar L. Alonso and Jorge Puente and Jose Luis Montana", title = "Straight Line Programs: A New Linear Genetic Programming Approach", booktitle = "20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI '08", year = "2008", month = nov, volume = "2", pages = "517--524", keywords = "genetic algorithms, genetic programming, computer programs, data structure, linear genetic programming approach, program tree encoding, straight line programs, symbolic regression problems, linear programming, regression analysis, tree data structures", DOI = "doi:10.1109/ICTAI.2008.14", ISSN = "1082-3409", abstract = "Tree encodings of programs are well known for their representative power and are used very often in Genetic Programming. In this paper we experiment with a new data structure, named straight line program (slp), to represent computer programs. The main features of this structure are described and new recombination operators for GP related to slp's are introduced. Experiments have been performed on symbolic regression problems. Results are encouraging and suggest that the GP approach based on slp's consistently outperforms conventional GP based on tree structured representations.", notes = "Also known as \cite{4669818}", } @Article{Alonso:2009:IJAIT, author = "Cesar L. Alonso and Jose Luis Montana and Jorge Puente and Cruz Enrique Borges", title = "A new Linear Genetic Programming approach based on straight line programs: some Theoretical and Experimental Aspects", journal = "International Journal on Artificial Intelligence Tools", year = "2009", volume = "18", number = "5", pages = "757--781", keywords = "genetic algorithms, genetic programming, slp, Vapnik-Chervonenkis dimension, VC", oai = "oai:CiteSeerX.psu:10.1.1.301.3133", DOI = "doi:10.1142/S0218213009000391", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.301.3133", URL = "http://paginaspersonales.deusto.es/cruz.borges/Papers/08IJAIT.pdf", abstract = "Tree encodings of programs are well known for their representative power and are used very often in Genetic Programming. In this paper we experiment with a new data structure, named straight line program (slp), to represent computer programs. The main features of this structure are described, new recombination operators for GP related to slp's are introduced and a study of the Vapnik-Chervonenkis dimension of families of slp's is done. Experiments have been performed on symbolic regression problems. Results are encouraging and suggest that the GP approach based on slp's consistently outperforms conventional GP based on tree structured representations.", notes = "IJAIT", } @InProceedings{Alonso:2009:ICTAI, author = "Cesar L. Alonso and Jose Luis Montana and Cruz Enrique Borges", title = "Evolution Strategies for Constants Optimization in Genetic Programming", booktitle = "21st International Conference on Tools with Artificial Intelligence, ICTAI '09", year = "2009", month = nov, pages = "703--707", keywords = "genetic algorithms, genetic programming, computer program, constants optimization, evolutionary computation methods, learning problems, linear genetic programming approach, symbolic regression problem, regression analysis", DOI = "doi:10.1109/ICTAI.2009.35", ISSN = "1082-3409", abstract = "Evolutionary computation methods have been used to solve several optimization and learning problems. This paper describes an application of evolutionary computation methods to constants optimization in genetic programming. A general evolution strategy technique is proposed for approximating the optimal constants in a computer program representing the solution of a symbolic regression problem. The new algorithm has been compared with a recent linear genetic programming approach based on straight-line programs. The experimental results show that the proposed algorithm improves such technique.", notes = "Also known as \cite{5366517}", } @InProceedings{conf/ijcci/AlonsoMB13, author = "Cesar Luis Alonso and Jose Luis Montana and Cruz Enrique Borges", title = "Model Complexity Control in Straight Line Program Genetic Programming", booktitle = "Proceedings of the 5th International Joint Conference on Computational Intelligence, IJCCI 2013", year = "2013", editor = "Agostinho C. Rosa and Antonio Dourado and Kurosh Madani Correia and Joaquim Filipe and Janusz Kacprzyk", pages = "25--36", address = "Vilamoura, Algarve, Portugal", month = "20-22 " # sep, publisher = "SciTePress", keywords = "genetic algorithms, genetic programming", isbn13 = "978-989-8565-77-8", bibdate = "2014-05-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ijcci/ijcci2013.html#AlonsoMB13", URL = "https://ijcci.scitevents.org/Abstract.aspx?idEvent=0fEvcjBHBM8=", URL = "https://www.scitepress.org/Link.aspx?doi=10.5220/0004554100250036", DOI = "doi:10.5220/0004554100250036", abstract = "In this paper we propose a tool for controlling the complexity of Genetic Programming models. The tool is supported by the theory of Vapnik-Chervonekis dimension (VCD) and is combined with a novel representation of models named straight line program. Experimental results, implemented on conventional algebraic structures (such as polynomials) and real problems, show that the empirical risk, penalized by suitable upper bounds for the Vapnik-Chervonenkis dimension, gives a generalisation error smaller than the use of statistical conventional techniques such as Bayesian or Akaike information criteria.", notes = "See also \cite{Alonso:2016:CI}", } @InCollection{Alonso:2016:CI, author = "Cesar L. Alonso and Jose Luis Montana and Cruz Enrique Borges", title = "Genetic Programming Model Regularization", booktitle = "Computational Intelligence", publisher = "Springer", year = "2016", editor = "Kurosh Madani and Antonio Dourado and Agostinho Rosa and Joaquim Filipe and Janusz Kacprzyk", series = "Springer Professional Technik", pages = "105--120", note = "Selected extended papers from the fifth International Joint Conference on Computational Intelligence (IJCCI 2013), held in Vilamoura, Algarve, Portugal, from 20 to 22 September 2013", keywords = "genetic algorithms, genetic programming, VC dimension", isbn13 = "978-3-319-23391-8", URL = "https://www.springerprofessional.de/en/genetic-programming-model-regularization/6856568", DOI = "doi:10.1007/978-3-319-23392-5_6", abstract = "We propose a tool for controlling the complexity of Genetic Programming models. The tool is supported by the theory of Vapnik-Chervonekis dimension (VCD) and is combined with a novel representation of models named straight line program. Experimental results, implemented on conventional algebraic structures (such as polynomials), show that the empirical risk, penalized by suitable upper bounds for the Vapnik-Chervonenkis dimension, gives a generalization error smaller than the use of statistical conventional techniques such as Bayesian or Akaike information criteria.", notes = "See also \cite{conf/ijcci/AlonsoMB13} Using incollection rather than inproceedings as the book seems more edited than a usual conference proceedings, see DBLP http://dblp.uni-trier.de/db/conf/ijcci/ijcci2013.html https://www.springerprofessional.de/en/computational-intelligence/6652954 Centro de Inteligencia Artificial, Campus de Gijon, Universidad de Oviedo, 33271, Gijon, Spain", } @InProceedings{conf/incdm/AlonsoMPSV08, title = "Modelling Medical Time Series Using Grammar-Guided Genetic Programming", author = "Fernando Alonso and Loic Martinez and Aurora Perez-Perez and Agustin Santamaria and Juan Pedro Valente", bibdate = "2010-02-01", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/incdm/incdm2008.html#AlonsoMPSV08", booktitle = "8th Industrial Conference in Data Mining, Medical Applications, E-Commerce, Marketing and Theoretical Aspects, ICDM 2008", publisher = "Springer", year = "2008", volume = "5077", editor = "Petra Perner", isbn13 = "978-3-540-70717-2", pages = "32--46", series = "Lecture Notes in Computer Science", DOI = "doi:10.1007/978-3-540-70720-2_3", address = "Leipzig, Germany", month = jul # " 16-18", keywords = "genetic algorithms, genetic programming, Time series characterization, isokinetics, symbolic distance, information extraction, reference model, text mining", size = "15 pages", abstract = "The analysis of time series is extremely important in the field of medicine, because this is the format of many medical data types. Most of the approaches that address this problem are based on numerical algorithms that calculate distances, clusters, reference models, etc. However, a symbolic rather than numerical analysis is sometimes needed to search for the characteristics of time series. Symbolic information helps users to efficiently analyse and compare time series in the same or in a similar way as a domain expert would. This paper describes the definition of the symbolic domain, the process of converting numerical into symbolic time series and a distance for comparing symbolic temporal sequences. Then, the paper focuses on a method to create the symbolic reference model for a certain population using grammar-guided genetic programming. The work is applied to the isokinetics domain within an application called I4.", notes = "Context Free Grammar", } @InProceedings{Alonso:2010:gecco, author = "Fernando Alonso and Loic Martinez and Agustin Santamaria and Aurora Perez and Juan Pedro Valente", title = "GGGP-based method for modeling time series: operator selection, parameter optimization and expert evaluation", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "989--990", keywords = "genetic algorithms, genetic programming, grammar-guided genetic programming, Poster", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830483.1830664", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "This paper describes the theoretical and experimental analysis conducted to define the best values for the various operators and parameters of a grammar-guided genetic programming process for creating isokinetic reference models for top competition athletes. Isokinetics is a medical domain that studies the strength exerted by the patient joints (knee, ankle, etc.). We also present an evaluation of the resulting reference models comparing our results with the reference models output using other methods.", notes = "Also known as \cite{1830664} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @Article{alotaibi:2023:Buildings, author = "Khalid Saqer Alotaibi and A. B. M. Saiful Islam", title = "Symbolic Regression Model for Predicting Compression Strength of Prismatic Masonry Columns Confined by {FRP}", journal = "Buildings", year = "2023", volume = "13", number = "2", pages = "Article No. 509", keywords = "genetic algorithms, genetic programming", ISSN = "2075-5309", URL = "https://www.mdpi.com/2075-5309/13/2/509", DOI = "doi:10.3390/buildings13020509", abstract = "The use of Fiber Reinforced Polymer (FRP) materials for the external confinement of existing concrete or masonry members is now an established technical solution. Several studies in the scientific literature show how FRP wrapping can improve the mechanical properties of members. Though there are numerous methods for determining the compressive strength of FRP confined concrete, no generalised formulae are available because of the greater complexity and heterogeneity of FRP-confined masonry. There are two main objectives in this analytical study: (a) proposing an entirely new mathematical expression to estimate the compressive strength of FRP confined masonry columns using symbolic regression model approach which can outperform traditional regression models, and (b) evaluating existing formulas. Over 198 tests of FRP wrapped masonry were compiled in a database and used to train the model. Several formulations from the published literature and international guidelines have been compared against experimental data. It is observed that the proposed symbolic regression model shows excellent performance compared to the existing models. The model is easier, has no restriction and thereby it can be feasibly employed to foresee the behaviour of FRP confined masonry elements. The coefficient of determination for the proposed symbolic regression model is determined as 0.91.", notes = "also known as \cite{buildings13020509}", } @PhdThesis{MoniraAloud-Ph.D.Thesis, author = "Monira Essa Aloud", title = "Modelling the High-Frequency {FX} Market: An Agent-Based Approach", school = "Department of Computing and Electronic Systems, University of Essex", year = "2013", address = "United Kingdom", month = apr, keywords = "genetic algorithms, genetic programming", URL = "http://fac.ksu.edu.sa/sites/default/files/MoniraAloud-Ph.D.Thesis.pdf", size = "183 pages", abstract = "In this thesis, we use an agent-based modelling (ABM) approach to model the trading activity in the Foreign Exchange (FX) market which is the most liquid financial market in the world. We first establish the statistical properties (stylised facts) of the trading activity in the FX market using a unique high-frequency dataset of anonymised individual traders' historical transactions on an account level, spanning 2.25 years. To the best of our knowledge, this dataset is the biggest available high-frequency dataset of individual FX market traders' historical transactions. We then construct an agentbased FX market (ABFXM) which features a number of distinguishing elements including zero-intelligence directional-change event (ZI-DCT0) trading agents and asynchronous trading-time windows. The individual agents are characterised by different levels of wealth, trading time windows, different profit objectives and risk appetites and initial activation conditions. Using the identified stylized facts as a benchmark, we evaluate the trading activity reproduced from the ABFXM and we establish that this resembles to a satisfactory level the trading activity of the real FX market. In the course of this thesis, we study in depth the constructed ABFXM. We focus on performing a systematic exploration of the constituent elements of the ABFXM and their impact on the dynamics of the FX market behaviour. In particular, our study explores and identifies the essential elements under which the stylised facts of the FX market trading activity are exhibited in the ABFXM. Our study suggests that the key elements are the ZI-DCT0 agents, heterogeneity which has been embedded in our model in different ways, asynchronous trading time windows, initial activation conditions and the generation of limit orders. We also show that the dynamics of the market trading activity depend on the number of agents one considers. We explore the emergence of the stylised facts in the trading activity when the ABFXM is populated with agents with three different strategies: a variation of the zero-intelligence with a constraint (ZI-CV) strategy; the ZI-DCT0 strategy; and a genetic programming-based (GP) strategy. Our results show that the ZI-DCT0 agents best reproduce and explain the stylised facts observed in the FX market transactions data. Our study suggests that some the observed stylised facts could be the result of introducing a threshold which triggers the agents to respond to fixed periodic patterns in the price time series.", notes = "Supervisor: Prof. Maria Fasli, Prof. Edward Tsang and Prof. Richard Olsen", } @Article{aloud:2017:coin, author = "Monira Aloud and Maria Fasli and Edward Tsang and Alexander Dupuis and Richard Olsen", title = "Modeling the High-Frequency {FX} Market: An Agent-Based Approach", journal = "Computational Intelligence", year = "2017", volume = "33", number = "4", pages = "771--825", month = nov, keywords = "genetic algorithms, genetic programming, agent-based modeling, agent-based simulation, electronic markets, FX markets, stylized facts.", ISSN = "1467-8640", URL = "http://repository.essex.ac.uk/18823/", DOI = "doi:10.1111/coin.12114", size = "51 pages", abstract = "The development of computational intelligence-based strategies for electronic markets has been the focus of intense research. To be able to design efficient and effective automated trading strategies, one first needs to understand the workings of the market, the strategies that traders use, and their interactions as well as the patterns emerging as a result of these interactions. In this article, we develop an agent-based model of the foreign exchange (FX) market, which is the market for the buying and selling of currencies. Our agent-based model of the FX market comprises heterogeneous trading agents that employ a strategy that identifies and responds to periodic patterns in the price time series. We use the agent-based model of the FX market to undertake a systematic exploration of its constituent elements and their impact on the stylised facts (statistical patterns) of transactions data. This enables us to identify a set of sufficient conditions that result in the emergence of the stylized facts similarly to the real market data, and formulate a model that closely approximates the stylized facts. We use a unique high-frequency data set of historical transactions data that enables us to run multiple simulation runs and validate our approach and draw comparisons and conclusions for each market setting.", } @Article{Alqahtani:2025:jtice, author = "Fahd Mohamad Alqahtani and Mohamed Riad Youcefi and Menad {Nait Amar} and Hakim Djema and Mohammad Ghasemi", title = "Robust group intelligent models for predicting hydrogen density and viscosity: Implication for hydrogen production, transportation, and storage", journal = "Journal of the Taiwan Institute of Chemical Engineers", year = "2025", volume = "168", pages = "105949", keywords = "genetic algorithms, genetic programming, Hydrogen, Viscosity, Density, Machine learning, Committee machine intelligent system, Multi-gene genetic programming", ISSN = "1876-1070", URL = "https://www.sciencedirect.com/science/article/pii/S1876107024006072", DOI = "doi:10.1016/j.jtice.2024.105949", abstract = "Backgrou", } @Article{Al-Rabadi:2006:EPB, author = "Anas N. Al-Rabadi", title = "Book Review: {Lee Spector $\bullet$ Automatic Quantum Computer Programming: A Genetic Programming Approach. Kluwer Academic Publishers (2004). ISBN 1-4020-7894-3. 100. 153 pp.}", journal = "The Computer Journal", volume = "49", number = "1", pages = "129--130", month = jan, year = "2006", CODEN = "CMPJA6", ISSN = "0010-4620", bibdate = "Wed Dec 21 17:38:55 MST 2005", bibsource = "http://comjnl.oxfordjournals.org/content/vol49/issue1/index.dtl", URL = "http://comjnl.oxfordjournals.org/cgi/content/full/49/1/129", URL = "http://comjnl.oxfordjournals.org/cgi/reprint/49/1/129", acknowledgement = "ack-nhfb", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1093/comjnl/bxh134", notes = "review of \cite{spector:book}", } @InProceedings{Alrefaie:2013:CIES, author = "Mohamed Taher Alrefaie and Alaa-Aldine Hamouda and Rabie Ramadan", booktitle = "IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES 2013)", title = "A smart agent to trade and predict foreign exchange market", year = "2013", month = apr, pages = "141--148", keywords = "genetic algorithms, genetic programming, foreign exchange trading, probability, US dollars daily turnover, adaptive neuro-fuzzy inference system, foreign exchange market, genetic programming approach, probability, smart agent, Companies, Fluctuations, Market research, Prediction algorithms, Predictive models, Profitability, ANFI, Agent, Forex, NSGA-II, Prediction", DOI = "doi:10.1109/CIES.2013.6611741", size = "8 pages", abstract = "Foreign Exchange market is a worldwide market to exchange currencies with 3.98 trillion US dollars daily turnover. With such a massive turnover, probability of profit is very high; however, trading in such massive market needs high knowledge, skills and full commitment in order to achieve high profit. The purpose of this work is to design a smart agent that 1) acquire Foreign Exchange market prices, 2) pre-processes it, 3) predicts future trend using Genetic Programming approach and Adaptive Neuro-fuzzy Inference System and 4) makes a buy/sell decision to maximise profitability with no human supervision.", notes = "Also known as \cite{6611741}", } @Article{Alrsai:2024:oceaneng, author = "Mahmoud Alrsai and Ala' Alsahalen and Hassan Karampour and Mohammad Alhawamdeh and Omar Alajarmeh", title = "Integrated finite element analysis and machine learning approach for propagation pressure prediction in hybrid Steel-{CFRP} subsea pipelines", journal = "Ocean Engineering", year = "2024", volume = "311", pages = "118808", keywords = "genetic algorithms, genetic programming, Hybrid steel-CFRP pipe, Buckle propagation, U-shape failure, Collapse, Machine learning", ISSN = "0029-8018", URL = "https://www.sciencedirect.com/science/article/pii/S0029801824021462", DOI = "doi:10.1016/j.oceaneng.2024.118808", abstract = "Accurate prediction of the propagation pressure (PP) in hybrid steel-CFRP pipe systems presents a substantial challenge due to intricate interactions and complex collapse failure modes. An efficient FE-based algorithm is programmed using ANSYS to numerically estimate the PP of hybrid steel-CFRP pipe, subjected to external pressure. This study employs a machine learning (ML) framework, addressing the inherent complexity with a three-phase approach: Parameter Design, Buckle Propagation Analysis, and ML Model Development. The dataset, encompassing about two thousand observations with four key features, undergoes k-fold cross-validation and min-max normalization for robust ML performance. Five ML models-Random Forest (RF), K-Nearest Neighbors (KNN), Genetic Programming (GP), Multi-layer Perceptron (MLP), and Support Vector Machine (SVM)-are developed and evaluated. The results revealed a significant influence of Ds/ts, a three-phase relationship with ts/tc, and a substantial decrease in PPh/PPs with increasing sigmays/sigmauc, predominantly exhibiting U-shaped or dog-bone failure modes in different scenarios. Proven that GP, KNN, and RF are the superior performers, ranking ahead of SVM with Gaussian Kernel (SVM-GK), MLP, and SVM with Linear Kernel (SVM-LK). Statistical metrics, Taylor Diagram analysis, and comparisons with FE results emphasize the effectiveness of GP, KNN, and RF. Additionally, normality tests and feature importance analysis provide nuanced insights", } @Article{ALSAFY:2019:CBM, author = "Rawaa Al-Safy and Alaa Al-Mosawe and Riadh Al-Mahaidi", title = "Utilization of magnetic water in cementitious adhesive for near-surface mounted {CFRP} strengthening system", journal = "Construction and Building Materials", volume = "197", pages = "474--488", year = "2019", keywords = "genetic algorithms, genetic programming, Magnetic water, Cement-based adhesive, NSM, CFRP, Concrete, GP modelling", ISSN = "0950-0618", DOI = "doi:10.1016/j.conbuildmat.2018.11.219", URL = "http://www.sciencedirect.com/science/article/pii/S0950061818329143", abstract = "Cement-based adhesive (CBA) is used as a bonding agent in Carbon Fibre Reinforced Polymer (CFRP) applications as an alternative to epoxy-based adhesive due to the drawbacks of the epoxy system under severe service conditions which negatively affect the bond between the CFRP and strengthened elements. This paper reports the results of, an investigation carried out to develop two types of CBA using magnetized water (MW) for mixing and curing. Two magnetic devices (MD-I and MD-II), with different magnetic field strengths (9000 and 6000 Gauss) respectively, were employed for water magnetization. Different water flows with different water circulation times in the magnetizer were used for each device. Compressive and splitting tensile strength tests of the magnetized CBA (MCBA) were conducted for different curing periods (3. 7, 14, 21 and 28a days) using MW. It was found that MW treatment increases the strength of CBA. The highest strength was obtained for MCBA samples when MD-I was used at a low flow rate (Fa =a 0.1a m3/hr) for 15 mins of circulation time (T). The latter was found to positively affect MCBA properties when T was increased from 15a min to 60a mins. Prediction of the compressive and tensile strength values are also studied in this paper using genetic programming, the models showed good correlation with the experimental data", } @InProceedings{Al-Sahaf:2011:ICARA, author = "Harith Al-Sahaf and Kourosh Neshatian and Mengjie Zhang", title = "Automatic feature extraction and image classification using genetic programming", booktitle = "5th International Conference on Automation, Robotics and Applications (ICARA 2011)", year = "2011", month = "6-8 " # dec, pages = "157--162", address = "Wellington, New Zealand", size = "6 pages", abstract = "In this paper, we propose a multilayer domain-independent GP-based approach to feature extraction and image classification. We propose two different structures for the system and compare the results with a baseline approach in which domain-specific pre-extracted features are used for classification. In the baseline approach, human/domain expert intervention is required to perform the task of feature extraction. The proposed approach, however, extracts (evolves) features and generates classifiers all automatically in one loop. The experiments are conducted on four image data sets. The results show that the proposed approach can achieve better performance compared to the baseline while removing the human from the loop.", keywords = "genetic algorithms, genetic programming, feature extraction, human-domain expert intervention, image classification, multilayer domain-independent GP-based approach, feature extraction, image classification", DOI = "doi:10.1109/ICARA.2011.6144874", notes = "Also known as \cite{6144874}", } @InProceedings{Al-Sahaf:2012:CEC, title = "Extracting Image Features for Classification By Two-Tier Genetic Programming", author = "Harith Al-Sahaf and Andy Song and Kourosh Neshatian and Mengjie Zhang", pages = "1630--1637", booktitle = "Proceedings of the 2012 IEEE Congress on Evolutionary Computation", year = "2012", editor = "Xiaodong Li", month = "10-15 " # jun, DOI = "doi:10.1109/CEC.2012.6256412", address = "Brisbane, Australia", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Evolutionary Computer Vision", abstract = "Image classification is a complex but important task especially in the areas of machine vision and image analysis such as remote sensing and face recognition. One of the challenges in image classification is finding an optimal set of features for a particular task because the choice of features has direct impact on the classification performance. However the goodness of a feature is highly problem dependent and often domain knowledge is required. To address these issues we introduce a Genetic Programming (GP) based image classification method, Two-Tier GP, which directly operates on raw pixels rather than features. The first tier in a classifier is for automatically defining features based on raw image input, while the second tier makes decision. Compared to conventional feature based image classification methods, Two-Tier GP achieved better accuracies on a range of different tasks. Furthermore by using the features defined by the first tier of these Two-Tier GP classifiers, conventional classification methods obtained higher accuracies than classifying on manually designed features. Analysis on evolved Two-Tier image classifiers shows that there are genuine features captured in the programs and the mechanism of achieving high accuracy can be revealed. The Two-Tier GP method has clear advantages in image classification, such as high accuracy, good interpretability and the removal of explicit feature extraction process.", notes = "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the EPS and the IET.", } @Article{AlSahaf2012, author = "Harith Al-Sahaf and Andy Song and Kourosh Neshatian and Mengjie Zhang", title = "Two-Tier genetic programming: towards raw pixel-based image classification", journal = "Expert Systems with Applications", volume = "39", number = "16", pages = "12291--12301", year = "2012", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2012.02.123", URL = "http://www.sciencedirect.com/science/article/pii/S0957417412003867", keywords = "genetic algorithms, genetic programming, Evolutionary computation, Feature extraction, Feature selection, Image classification", abstract = "Classifying images is of great importance in machine vision and image analysis applications such as object recognition and face detection. Conventional methods build classifiers based on certain types of image features instead of raw pixels because the dimensionality of raw inputs is often too large. Determining an optimal set of features for a particular task is usually the focus of conventional image classification methods. In this study we propose a Genetic Programming (GP) method by which raw images can be directly fed as the classification inputs. It is named as Two-Tier GP as every classifier evolved by it has two tiers, the other for computing features based on raw pixel input, one for making decisions. Relevant features are expected to be self-constructed by GP along the evolutionary process. This method is compared with feature based image classification by GP and another GP method which also aims to automatically extract image features. Four different classification tasks are used in the comparison, and the results show that the highest accuracies are achieved by Two-Tier GP. Further analysis on the evolved solutions reveals that there are genuine features formulated by the evolved solutions which can classify target images accurately.", } @InProceedings{Al-Sahaf:2013:CEC, article_id = "1692", author = "Harith Al-Sahaf and Andy Song and Mengjie Zhang", title = "Hybridisation of Genetic Programming and Nearest Neighbour for Classification", booktitle = "2013 IEEE Conference on Evolutionary Computation", volume = "1", year = "2013", month = jun # " 20-23", editor = "Luis Gerardo {de la Fraga}", pages = "2650--2657", address = "Cancun, Mexico", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-0453-2", DOI = "doi:10.1109/CEC.2013.6557889", notes = "CEC 2013 - A joint meeting of the IEEE, the EPS and the IET.", } @InProceedings{Al-Sahaf:2013:IVCNZ, author = "Harith Al-Sahaf and Mengjie Zhang and Mark Johnston", title = "Binary image classification using genetic programming based on local binary patterns", booktitle = "28th International Conference of Image and Vision Computing New Zealand (IVCNZ 2013)", year = "2013", pages = "220--225", address = "Wellington", month = nov, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, computer vision, image classification, learning (artificial intelligence), statistical analysis, ANOVA, GP based methods, LBP, SVM, binary image classification, computer vision, image descriptor, learning instances, local binary patterns, machine learning, nonGP methods, one-way analysis of variance, support vector machine, wrapped classifiers, Accuracy, Analysis of variance, Feature extraction, Histograms, Support vector machines, Training, Vectors", DOI = "doi:10.1109/IVCNZ.2013.6727019", size = "6 pages", abstract = "Image classification represents an important task in machine learning and computer vision. To capture features covering a diversity of different objects, it has been observed that a sufficient number of learning instances are required to efficiently estimate the models' parameter values. In this paper, we propose a genetic programming (GP) based method for the problem of binary image classification that uses a single instance per class to evolve a classifier. The method uses local binary patterns (LBP) as an image descriptor, support vector machine (SVM) as a classifier, and a one-way analysis of variance (ANOVA) as an analyser. Furthermore, a multi-objective fitness function is designed to detect distinct and informative regions of the images, and measure the goodness of the wrapped classifiers. The performance of the proposed method has been evaluated on six data sets and compared to the performances of both GP based (Two-tier GP and conventional GP) and non-GP (Naive Bayes, Support Vector Machines and hybrid Naive Bayes/Decision Trees) methods. The results show that a comparable or significantly better performance has been achieved by the proposed method over all methods on all of the data sets considered.", notes = "also known as \cite{6727019}", } @InProceedings{Al-Sahaf:2013:AI, author = "Harith Al-Sahaf and Mengjie Zhang and Mark Johnston", title = "A One-Shot Learning Approach to Image Classification Using Genetic Programming", booktitle = "Proceedings of the 26th Australasian Joint Conference on Artificial Intelligence (AI2013)", year = "2013", editor = "Stephen Cranefield and Abhaya Nayak", volume = "8272", series = "LNAI", pages = "110--122", address = "Dunedin, New Zealand", month = "1-6 " # dec, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Local Binary Patterns, Image Classification, One-shot Learning", isbn13 = "978-3-319-03679-3", URL = "http://dx.doi.org/10.1007/978-3-319-03680-9_13", DOI = "doi:10.1007/978-3-319-03680-9_13", size = "13 pages", abstract = "In machine learning, it is common to require a large number of instances to train a model for classification. In many cases, it is hard or expensive to acquire a large number of instances. In this paper, we propose a novel genetic programming (GP) based method to the problem of automatic image classification via adopting a one-shot learning approach. The proposed method relies on the combination of GP and Local Binary Patterns (LBP) techniques to detect a predefined number of informative regions that aim at maximising the between-class scatter and minimising the within-class scatter. Moreover, the proposed method uses only two instances of each class to evolve a classifier. To test the effectiveness of the proposed method, four different texture data sets are used and the performance is compared against two other GP-based methods namely Conventional GP and Two-tier GP. The experiments revealed that the proposed method outperforms these two methods on all the data sets. Moreover, a better performance has been achieved by Naive Bayes, Support Vector Machine, and Decision Trees (J48) methods when extracted features by the proposed method have been used compared to the use of domain-specific and Two-tier GP extracted features.", } @InProceedings{conf/ivcnz/Al-SahafZJ14, author = "Harith Al-Sahaf and Mengjie Zhang and Mark Johnston", title = "Genetic Programming Evolved Filters from a Small Number of Instances for Multiclass Texture Classification", booktitle = "Proceedings of the 29th International Conference on Image and Vision Computing New Zealand, {IVCNZ} 2014", publisher = "ACM", year = "2014", editor = "Michael J. Cree and Lee V. Streeter and John Perrone and Michael Mayo and Anthony M. Blake", pages = "84--89", address = "Hamilton, New Zealand", month = nov # " 19-21", keywords = "genetic algorithms, genetic programming, Multiclass classification, Textures", isbn13 = "978-1-4503-3184-5", bibdate = "2015-01-29", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ivcnz/ivcnz2014.html#Al-SahafZJ14", DOI = "doi:10.1145/2683405.2683418", acmid = "2683418", abstract = "Texture classification is an essential task in pattern recognition and computer vision. In this paper, a novel genetic programming (GP) based method is proposed for the task of multiclass texture classification. The proposed method evolves a set of filters using only two instances per class. Moreover, the evolved program operates directly on the raw pixel values and does not require human intervention to perform feature selection and extraction. Two well-known and widely used data sets are used in this study to evaluate the performance of the proposed method. The performance of the new method is compared to that of two GP-based methods using the raw pixel values, and six non-GP methods using three different sets of domain-specific features. The results show that the proposed method has significantly outperformed the other methods on both data sets.", URL = "http://dl.acm.org/citation.cfm?id=2683405", } @InProceedings{conf/seal/Al-SahafZJ14, author = "Harith Al-Sahaf and Mengjie Zhang and Mark Johnston", title = "Genetic Programming for Multiclass Texture Classification Using a Small Number of Instances", bibdate = "2014-11-12", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/seal/seal2014.html#Al-SahafZJ14", booktitle = "Simulated Evolution and Learning - 10th International Conference, {SEAL} 2014, Dunedin, New Zealand, December 15-18, 2014. Proceedings", publisher = "Springer", year = "2014", volume = "8886", editor = "Grant Dick and Will N. Browne and Peter A. Whigham and Mengjie Zhang and Lam Thu Bui and Hisao Ishibuchi and Yaochu Jin and Xiaodong Li and Yuhui Shi and Pramod Singh and Kay Chen Tan and Ke Tang", isbn13 = "978-3-319-13562-5", pages = "335--346", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming", URL = "http://dx.doi.org/10.1007/978-3-319-13563-2", } @InProceedings{Al-Sahaf:2015:CEC, author = "Harith Al-Sahaf and Mengjie Zhang and Mark Johnston and Brijesh Verma", title = "Image Descriptor: A Genetic Programming Approach to Multiclass Texture Classification", booktitle = "Proceedings of 2015 IEEE Congress on Evolutionary Computation (CEC 2015)", year = "2015", editor = "Yadahiko Murata", pages = "2460--2467", address = "Sendai, Japan", month = "25-28 " # may, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-7491-7", DOI = "doi:10.1109/CEC.2015.7257190", size = "8 pages", abstract = "Texture classification is an essential task in computer vision that aims at grouping instances that have a similar repetitive pattern into one group. Detecting texture primitives can be used to discriminate between materials of different types. The process of detecting prominent features from the texture instances represents a cornerstone step in texture classification. Moreover, building a good model using a few training instances is difficult. In this study, a genetic programming (GP) descriptor is proposed for the task of multiclass texture classification. The proposed method synthesises a set of mathematical formulas relying on the raw pixel values and a sliding window of a predetermined size. Furthermore, only two instances per class are used to automatically evolve a descriptor that has the potential to effectively discriminate between instances of different textures using a simple instance-based classifier to perform the classification task. The performance of the proposed approach is examined using two widely-used data sets, and compared with two GP-based and nine well-known non-GP methods. Furthermore, three hand-crafted domain-expert designed feature extraction methods have been used with the non-GP methods to examine the effectiveness of the proposed method. The results show that the proposed method has significantly outperformed all these other methods on both data sets, and the new method evolves a descriptor that is capable of achieving significantly better performance compared to hand-crafted features.", notes = "1340 hrs 15390 CEC2015", } @InProceedings{Al-Sahaf:2015:GECCO, author = "Harith Al-Sahaf and Mengjie Zhang and Mark Johnston", title = "Evolutionary Image Descriptor: A Dynamic Genetic Programming Representation for Feature Extraction", booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation", year = "2015", editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr", isbn13 = "978-1-4503-3472-3", pages = "975--982", keywords = "genetic algorithms, genetic programming", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739480.2754661", DOI = "doi:10.1145/2739480.2754661", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Texture classification aims at categorising instances that have a similar repetitive pattern. In computer vision, texture classification represents a fundamental element in a wide variety of applications, which can be performed by detecting texture primitives of the different classes. Using image descriptors to detect prominent features has been widely adopted in computer vision. Building an effective descriptor becomes more challenging when there are only a few labelled instances. This paper proposes a new Genetic Programming (GP) representation for evolving an image descriptor that operates directly on the raw pixel values and uses only two instances per class. The new method synthesises a set of mathematical formulas that are used to generate the feature vector, and the classification is then performed using a simple instance-based classifier. Determining the length of the feature vector is automatically handled by the new method. Two GP and nine well-known non-GP methods are compared on two texture image data sets for texture classification in order to test the effectiveness of the proposed method. The proposed method is also compared to three hand-crafted descriptors namely domain-independent features, local binary patterns, and Haralick texture features. The results show that the proposed method has superior performance over the competitive methods.", notes = "Also known as \cite{2754661} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)", } @Article{Al-Sahaf:2015:EC, author = "Harith Al-Sahaf and Mengjie Zhang and Mark Johnston", title = "Binary Image Classification: A Genetic Programming Approach to the Problem of Limited Training Instances", journal = "Evolutionary Computation", year = "2016", volume = "24", number = "1", pages = "143--182", month = "Spring", keywords = "genetic algorithms, genetic programming, Local Binary Patterns, One-shot Learning, Image Classification", ISSN = "1063-6560", DOI = "doi:10.1162/EVCO_a_00146", size = "37 pages", abstract = "In the Computer Vision and Pattern Recognition fields, image classification represents an important, yet difficult, task to perform. The remarkable ability of the human visual system, which relies on only one or a few instances to learn a completely new class or an object of a class, is a challenge to build effective computer models to replicate this ability. Recently, we have proposed two Genetic Programming (GP) based methods, One-shot GP and Compound-GP, that aim to evolve a program for the task of binary classification in images. The two methods are designed to use only one or a few instances per class to evolve the model. In this study, we investigate these two methods in terms of performance, robustness, and complexity of the evolved programs. Ten data sets that vary in difficulty have been used to evaluate these two methods. We also compare them with two other GP and six non-GP methods. The results show that One-shot GP and Compound-GP outperform or achieve comparable results to other competitor methods. Moreover, the features extracted by these two methods improve the performance of other classifiers with handcrafted features and those extracted by a recently developed GP-based method in most cases", } @Article{Al-Sahaf:2016:ieeeTEC, author = "Harith Al-Sahaf and Ausama Al-Sahaf and Bing Xue and Mark Johnston and Mengjie Zhang", title = "Automatically Evolving Rotation-invariant Texture Image Descriptors by Genetic Programming", journal = "IEEE Transactions on Evolutionary Computation", year = "2017", volume = "21", number = "1", pages = "83--101", month = feb, DOI = "doi:10.1109/TEVC.2016.2577548", notes = "May 2018 opps duplicate of \cite{Al-Sahaf:2017a:ieeeTEC}", } @Article{Al-Sahaf:2017a:ieeeTEC, author = "Harith Al-Sahaf and Ausama Al-Sahaf and Bing Xue and Mark Johnston and Mengjie Zhang", title = "Automatically Evolving Rotation-Invariant Texture Image Descriptors by Genetic Programming", journal = "IEEE Transactions on Evolutionary Computation", year = "2017", volume = "21", number = "1", pages = "83--101", month = feb, keywords = "genetic algorithms, genetic programming, Classification, feature extraction, image descriptor, keypoint detection", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2016.2577548", size = "19 pages", abstract = "In computer vision, training a model that performs classification effectively is highly dependent on the extracted features, and the number of training instances. Conventionally, feature detection and extraction are performed by a domain expert who, in many cases, is expensive to employ and hard to find. Therefore, image descriptors have emerged to automate these tasks. However, designing an image descriptor still requires domain-expert intervention. Moreover, the majority of machine learning algorithms require a large number of training examples to perform well. However, labelled data is not always available or easy to acquire, and dealing with a large dataset can dramatically slow down the training process. In this paper, we propose a novel genetic programming-based method that automatically synthesises a descriptor using only two training instances per class. The proposed method combines arithmetic operators to evolve a model that takes an image and generates a feature vector. The performance of the proposed method is assessed using six datasets for texture classification with different degrees of rotation and is compared with seven domain-expert designed descriptors. The results show that the proposed method is robust to rotation and has significantly outperformed, or achieved a comparable performance to, the baseline methods.", notes = "also known as \cite{7486119}", } @InProceedings{Al-Sahaf:2017:GECCO, author = "Harith Al-Sahaf and Bing Xue and Mengjie Zhang", title = "Evolving Texture Image Descriptors Using a Multitree Genetic Programming Representation", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4939-0", address = "Berlin, Germany", pages = "219--220", size = "2 pages", URL = "http://doi.acm.org/10.1145/3067695.3076039", DOI = "doi:10.1145/3067695.3076039", acmid = "3076039", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, multiclass classification, multitree, textures", month = "15-19 " # jul, abstract = "Image descriptors play very important roles in a wide range of applications in computer vision and pattern recognition. In this paper, a multitree genetic programming method to automatically evolve image descriptors for multiclass texture image classification task is proposed. Instead of using domain knowledge, the proposed method uses only a few instances of each class to automatically identify a set of features that are distinctive between the instances of different classes. The results on seven texture classification datasets show significant, or comparable, performance has been achieved by the proposed method compared with the baseline method and six state-of-the-art methods.", notes = "Also known as \cite{Al-Sahaf:2017:ETI:3067695.3076039} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @InProceedings{conf/seal/Al-SahafXZ17, author = "Harith Al-Sahaf and Bing Xue and Mengjie Zhang", title = "A Multitree Genetic Programming Representation for Automatically Evolving Texture Image Descriptors", booktitle = "Proceedings of the 11th International Conference on Simulated Evolution and Learning, SEAL 2017", year = "2017", editor = "Yuhui Shi and Kay Chen Tan and Mengjie Zhang and Ke Tang and Xiaodong Li and Qingfu Zhang and Ying Tan and Martin Middendorf and Yaochu Jin", volume = "10593", series = "Lecture Notes in Computer Science", pages = "499--511", address = "Shenzhen, China", month = nov # " 10-13", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Multitree, Image classification, Feature extraction", bibdate = "2017-11-03", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/seal/seal2017.html#Al-SahafXZ17", isbn13 = "978-3-319-68758-2", DOI = "doi:10.1007/978-3-319-68759-9_41", abstract = "Image descriptors are very important components in computer vision and pattern recognition that play critical roles in a wide range of applications. The main task of an image descriptor is to automatically detect micro-patterns in an image and generate a feature vector. A domain expert is often needed to undertake the process of developing an image descriptor. However, such an expert, in many cases, is difficult to find or expensive to employ. In this paper, a multitree genetic programming representation is adopted to automatically evolve image descriptors. Unlike existing hand-crafted image descriptors, the proposed method does not rely on predetermined features, instead, it automatically identifies a set of features using a few instances of each class. The performance of the proposed method is assessed using seven benchmark texture classification datasets and compared to seven state-of-the-art methods. The results show that the new method has significantly outperformed its counterpart methods in most cases.", } @Article{Al-Sahaf:2017:ieeeTEC, author = "Harith Al-Sahaf and Mengjie Zhang and Ausama Al-Sahaf and Mark Johnston", journal = "IEEE Transactions on Evolutionary Computation", title = "Keypoints Detection and Feature Extraction: A Dynamic Genetic Programming Approach for Evolving Rotation-invariant Texture Image Descriptors", year = "2017", volume = "21", number = "6", pages = "825--844", month = dec, keywords = "genetic algorithms, genetic programming", ISSN = "1089-778X", URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7885048", DOI = "doi:10.1109/TEVC.2017.2685639", abstract = "The goodness of the features extracted from the instances and the number of training instances are two key components in machine learning, and building an effective model is largely affected by these two factors. Acquiring a large number of training instances is very expensive in some situations such as in the medical domain. Designing a good feature set, on the other hand, is very hard and often requires domain expertise. In computer vision, image descriptors have emerged to automate feature detection and extraction; however, domain-expert intervention is typically needed to develop these descriptors. The aim of this paper is to use Genetic Programming to automatically construct a rotation-invariant image descriptor by synthesising a set of formulae using simple arithmetic operators and first-order statistics, and determining the length of the feature vector simultaneously using only two instances per class. Using seven texture classification image datasets, the performance of the proposed method is evaluated and compared against eight domain-expert hand-crafted image descriptors. Quantitatively, the proposed method has significantly outperformed, or achieved comparable performance to, the competitor methods. Qualitatively, the analysis shows that the descriptors evolved by the proposed method can be interpreted.", notes = "Also known as \cite{7885048}", } @PhdThesis{Al-Sahaf:thesis, author = "Harith Al-Sahaf", title = "Genetic Programming for Automatically Synthesising Robust Image Descriptors with A Small Number of Instances", school = "School of Engineering and Computer Science, Victoria University of Wellington", year = "2017", address = "New Zealand", keywords = "genetic algorithms, genetic programming", URL = "http://hdl.handle.net/10063/6177", URL = "https://researcharchive.vuw.ac.nz/xmlui/bitstream/handle/10063/6177/thesis.pdf", size = "321 pages", abstract = "Image classification is a core task in many applications of computer vision, including object detection and recognition. It aims at analysing the visual content and automatically categorising a set of images into different groups. Performing image classification can largely be affected by the features used to perform this task. Extracting features from images is a challenging task due to the large search space size and practical requirements such as domain knowledge and human intervention. Human intervention is usually needed to identify a good set of keypoints (regions of interest), design a set of features to be extracted from those keypoints such as lines and corners, and develop a way to extract those features. Automating these tasks has great potential to dramatically decrease the time and cost, and may potentially improve the performance of the classification task. There are two well-recognised approaches in the literature to automate the processes of identifying keypoints and extracting image features. Designing a set of domain-independent features is the first approach, where the focus is on dividing the image into a number of predefined regions and extracting features from those regions. The second approach is synthesising a function or a set of functions to form an image descriptor that aims at automatically detecting a set of keypoints such as lines and corners, and performing feature extraction. Although employing image descriptors is more effective and very popular in the literature, designing those descriptors is a difficult task that in most cases requires domain-expert intervention. The overall goal of this thesis is to develop a new domain independent Genetic Programming (GP) approach to image classification by using GP to evolve programs that are capable of automatically detecting diverse and informative keypoints, designing a set of features, and performing feature extraction using only a small number of training instances to facilitate image classification, and are robust to different image changes such as illumination and rotation. This thesis focuses on incorporating a variety of simple arithmetic operators and first-order statistics (mid-level features) into the evolutionary process and on representation of GP to evolve programs that are robust to image changes for image classification. This thesis proposes methods for domain-independent binary classification in images using GP to automatically identify regions within an image that have the potential to improve classification while considering the limitation of having a small training set. Experimental results show that in over 67percent of cases the new methods significantly outperform the use of existing hand-crafted features and features automatically detected by other methods. This thesis proposes the first GP approach for automatically evolving an illumination-invariant dense image descriptor that detects automatically designed keypoints, and performs feature extraction using only a few instances of each class. The experimental results show improvement of 86percent on average compared to two GP-based methods, and can significantly outperform domain-expert hand-crafted descriptors in more than 89percent of the cases. This thesis also considers rotation variation of images and proposes a method for automatically evolving rotation-invariant image descriptors through integrating a set of first-order statistics as terminals. Compared to hand-crafted descriptors, the experimental results reveal that the proposed method has significantly better performance in more than 83percent of the cases. This thesis proposes a new GP representation that allows the system to automatically choose the length of the feature vector side-by-side with evolving an image descriptor. Automatically determining the length of the feature vector helps to reduce the number of the parameters to be set. The results show that this method has evolved descriptors with a very small feature vector which yet still significantly outperform the competitive methods in more than 91percent of the cases. This thesis proposes a method for transfer learning by model in GP, where an image descriptor evolved on instances of a related problem (source domain) is applied directly to solve a problem being tackled (target domain). The results show that the new method evolves image descriptors that have better generalisability compared to hand-crafted image descriptors. Those automatically evolved descriptors show positive influence on classifying the target domain datasets in more than 56percent of the cases.", notes = "supervisors: Mengjie Zhang and Mark Johnston", } @Article{Al-Sahaf:EC, author = "Harith Al-Sahaf and Ausama Al-Sahaf and Bing Xue and Mengjie Zhang", title = "Automatically Evolving Texture Image Descriptors using the Multi-tree Representation in Genetic Programming using Few Instances", journal = "Evolutionary Computation", year = "2021", volume = "29", number = "3", pages = "331--366", month = "Fall", keywords = "genetic algorithms, genetic programming, ANN, image descriptor, multi-tree, image classification, feature extraction", ISSN = "1063-6560", URL = "https://doi.org/10.1162/evco_a_00284", DOI = "doi:10.1162/evco_a_00284", size = "36 pages", abstract = "The performance of image classification is highly dependent on the quality of the extracted features that are used to build a model. Designing such features usually requires prior knowledge of the domain and is often undertaken by a domain expert who, if available, is very costly to employ. Automating the process of designing such features can largely reduce the cost and efforts associated with this task. Image descriptors, such as local binary patterns, have emerged in computer vision, and aim at detecting keypoints, for example, corners, line-segments, and shapes, in an image and extracting features from those key points. In this article, genetic programming (GP) is used to automatically evolve an image descriptor using only two instances per class by using a multitree program representation. The automatically evolved descriptor operates directly on the raw pixel values of an image and generates the corresponding feature vector. Seven well-known datasets were adapted to the few-shot setting and used to assess the performance of the proposed method and compared against six handcrafted and one evolutionary computation-based image descriptor as well as three convolutional neural network (CNN) based methods. The experimental results show that the new method has significantly outperformed the competitor image descriptors and CNN-based methods. Furthermore, different patterns have been identified from analysing the evolved programs.", } @InProceedings{eurogp:Al-SakranKJ05, author = "Sameer H. Al-Sakran and John R. Koza and Lee W. Jones", editor = "Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. {van Hemert} and Marco Tomassini", title = "Automated Re-invention of a Previously Patented Optical Lens System Using Genetic Programming", booktitle = "Proceedings of the 8th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3447", year = "2005", address = "Lausanne, Switzerland", month = "30 " # mar # " - 1 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-25436-6", pages = "25--37", DOI = "doi:10.1007/978-3-540-31989-4_3", DOI = "doi:10.1007/b107383", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "The three dozen or so known instances of human-competitive designs produced by genetic programming for antennas, mechanical systems, circuits, and controllers raise the question of whether the genetic programming can be extended to the design of complex structures from other fields. This paper discusses efforts to apply genetic programming to the automated design of optical lens systems. The paper can be read from two different perspectives. First, broadly, it chronicles the step-by-step process by which the authors approached the problem of applying genetic programming to a domain that was new to them. Second, more narrowly, it describes the use of genetic programming to re-create the complete design for the previously patented Tackaberry-Muller optical lens system. Genetic programming accomplished this {"}from scratch{"} without starting from a pre-specified number of lens and a pre-specified layout and without starting from a pre-existing good design. The genetically evolved design for the Tackaberry-Muller lens system is an example, in the field of optical design, of a human-competitive result produced by genetic programming.", notes = "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005", } @InProceedings{Al_Sallami:2012:wce, title = "Genetic Programming Testing Model", author = "Nada M. A. {Al Sallami}", booktitle = "Proceedings of the World Congress on Engineering (WCE'12)", year = "2012", editor = "S. I. Ao and Len Gelman and David WL Hukins and Andrew Hunter and A. M. Korsunsky", series = "Lecture Notes in Engineering and Computer Science", pages = "737--741", address = "London, UK", month = jul # " 4-6", organization = "International Association of Engineers", publisher = "Newswood Limited", keywords = "genetic algorithms, genetic programming, SBSE, model-based testing, test generator, finite state machine", isbn13 = "978-988-19252-1-3", URL = "http://www.iaeng.org/publication/WCE2012/WCE2012_pp737-741.pdf", size = "5", abstract = "Software testing requires the use of a model to guide such efforts as test selection and test verification. In this case, testers are performing model-based testing. This paper introduces model-based testing and discusses its tasks in general terms with proposed finite state models. These FSMs depend on software's semantic rather than its structure, , it use input-output specification and trajectory information to evolve and test general software. Finally, we close with a discussion of how our model-based testing can be used with genetic programming test generator.", notes = "volume II: The 2012 International Conference of Computational Intelligence and Intelligent Systems", } @Article{Alsberg:2000:CILS, author = "Bjorn K. Alsberg and Nathalie Marchand-Geneste and Ross D. King", title = "A new {3D} molecular structure representation using quantum topology with application to structure-property relationships", journal = "Chemometrics and Intelligent Laboratory Systems", year = "2000", volume = "54", pages = "75--91", number = "2", month = "29 " # dec, keywords = "genetic algorithms, genetic programming, Structure representation using quantum topology, StruQT, Quantitative structure-activity relationships, QSAR, Quantitative structure-property relationships, QSPR, Atoms in molecules, AIM, Quantum chemistry, Bader theory, Multivariate analysis, Partial least squares regression, 3D structure representation, Variable selection", ISSN = "0169-7439", owner = "wlangdon", broken = "http://www.sciencedirect.com/science/article/B6TFP-426XTF7-1/2/36265a259de8f80d4918ee6612612218", DOI = "doi:10.1016/S0169-7439(00)00101-5", abstract = "We present a new 3D molecular structure representation based on Richard F.W. Bader's quantum topological atoms in molecules (AIM) theory for use in quantitative structure-property/activity relationship (QSPR/QSAR) modelling. Central to this structure representation using quantum topology (StruQT) are critical points located on the electron density distribution of the molecules. Other gradient fields such as the Laplacian of the electron density distribution can also be used. The type of critical point of particular interest is the bond critical point (BCP) which is here characterised by using the following three parameters: electron density [rho], the Laplacian [nabla]2[rho] and the ellipticity [epsi]. This representation has the advantage that there is no need to probe a large number of lattice points in 3D space to capture the important parts of the 3D electronic structure as is necessary in, e.g. comparative field analysis (CoMFA). We tested the new structure representation by predicting the wavelength of the lowest UV transition for a system of 18 anthocyanidins. Different quantitative structure-property relationship (QSPR) models are constructed using several chemometric/machine learning methods such as standard partial least squares regression (PLS), truncated PLS variable selection, genetic algorithm-based variable selection and genetic programming (GP). These models identified bonds that either take part in decreasing or increasing the dominant excitation wavelength. The models also correctly emphasised on the involvement of the conjugated [pi] system for predicting the wavelength through flagging the BCP ellipticity parameters as important for this particular data set.", } @InProceedings{Alshahwan:2018:SSBSE, author = "Nadia Alshahwan and Xinbo Gao and Mark Harman and Yue Jia and Ke Mao and Alexander Mols and Taijin Tei and Ilya Zorin", title = "Deploying Search Based Software Engineering with {Sapienz} at {Facebook}", booktitle = "SSBSE 2018", year = "2018", editor = "Thelma Elita Colanzi and Phil McMinn", volume = "11036", series = "LNCS", pages = "3--45", address = "Montpellier, France", month = "8-9 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE", isbn13 = "978-3-319-99241-9", URL = "https://discovery.ucl.ac.uk/id/eprint/10060107/", URL = "https://rdcu.be/dBszz", DOI = "doi:10.1007/978-3-319-99241-9_1", video_url = "https://developers.facebook.com/videos/f8-2018/friction-free-fault-finding-with-sapienz/", size = "22 pages", abstract = "We describe the deployment of the Sapienz Search Based Software Engineering (SBSE) testing system. Sapienz has been deployed in production at Facebook since September 2017 to design test cases, localise and triage crashes to developers and to monitor their fixes. Since then, running in fully continuous integration within Facebook's production development process, Sapienz has been testing Facebook's Android app, which consists of millions of lines of code and is used daily by hundreds of millions of people around the globe. We continue to build on the Sapienz infrastructure, extending it to provide other software engineering services, applying it to other apps and platforms, and hope this will yield further industrial interest in and uptake of SBSE (and hybridisations of SBSE) as a result.", notes = "This paper was written to accompany the keynote by Mark Harman at the 10th Symposium on Search-Based Software Engineering (SSBSE 2018), Montpellier September 8-10, 2018. The paper represents the work of all the authors in realising the deployment of search based approaches to large-scale software engineering at Facebook. Author name order is alphabetical; the order is thus not intended to denote any information about the relative contribution of each author. Ke Mao will also be giving a related talk about Sapienz deployment to developers at the @Scale developers conference in San Jose, USA on 13 September 2018 https://atscaleconference.com/events/the-2018-scale-conference/ A video of a previous talk about the initial Sapienz deployment, presented at the F8 developers conference in May 2018, is also publicly available (also as high quality video recording and with no paywall): https://developers.facebook.com/videos/f8-2018/friction-free-fault-finding-with-sapienz/ also known as \cite{ssbse18-keynote}", } @InProceedings{Alshahwan:2019:GI, author = "Nadia Alshahwan", title = "Industrial experience of Genetic Improvement in {Facebook}", booktitle = "GI-2019, ICSE workshops proceedings", year = "2019", editor = "Justyna Petke and Shin Hwei Tan and William B. Langdon and Westley Weimer", pages = "1", address = "Montreal", month = "28 " # may, publisher = "IEEE", note = "Invited Keynote", keywords = "genetic algorithms, genetic programming, genetic improvement, APR", isbn13 = "978-1-7281-2268-7", URL = "https://doi.org/10.1109/GI.2019.00010", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/icse2019/Alshahwan_2019_GI.pdf", DOI = "doi:10.1109/GI.2019.00010", acmid = "3339021", size = "1 page", abstract = "Facebook recently had their first experience with Genetic Improvement (GI) by developing and deploying the automated bug fixing tool SapFix. The experience was successful resulting in landed fixes but also very educational. This paper will briefly outline some of the challenges for GI that were highlighted by this experience as well as a look at future directions in the area of mobile apps.", notes = "GI-2019 http://geneticimprovementofsoftware.com part of \cite{Petke:2019:ICSEworkshop}", } @InProceedings{Alshahwan:2023:ICST, author = "Nadia Alshahwan and Mark Harman and Alexandru Marginean", title = "Software Testing Research Challenges: An Industrial Perspective", booktitle = "16th IEEE International Conference on Software Testing, Verification and Validation (ICST 2023)", year = "2023", editor = "Sreedevi Sampath", pages = "1--10", address = "Dublin, Ireland", month = "16-20 " # apr, note = "Keynote", keywords = "genetic algorithms, genetic programming, Genetic Improvement, SBSE, Automated Software Engineering, Software Testing, Automated Program Repair, APR, Artificial Intelligence, AI, Automated Remediation, regression testing", isbn13 = "978-1-6654-5667-8", URL = "https://research.facebook.com/file/1235985840680898/Software-Testing-Research-Challenges--An-Industrial-Perspective.pdf", URL = "https://conf.researchr.org/track/icst-2023/icst-2023-keynotes", DOI = "doi:10.1109/ICST57152.2023.00008", size = "10 pages", abstract = "There have been rapid recent developments in automated software test design, repair and program improvement. Advances in artificial intelligence also have great potential impact to tackle software testing research problems. we highlight open research problems and challenges from an industrial perspective. This perspective draws on our experience at Meta Platforms, which has been actively involved in software testing research and development for approximately a decade. As we set out here, there are many exciting opportunities for software testing research to achieve the widest and deepest impact on software practice. With this overview of the research landscape from an industrial perspective, we aim to stimulate further interest in the deployment of software testing research. We hope to be able to collaborate with the scientific community on some of these research challenges.", notes = "descretization of performance execution time may lead to performance test flakiness 'need more research ... on small performance [changes] ... in noisy ... [and better] confidence intervals' 'Technologies that significantly impede developer velocity are typically discarded' 'expect rapid uptake of automated [unit] test generation' 'including the generation of suitable test oracles' and 'appropriate mocks'. 'need techniques for constructing realistic values for complex data types' e2e = end-to-end testing. test carving. [incremental] 'Delta mutation [testing] is highly change aware' 'return on investment of test effort' 'In industrial settings, other non-functional criteria are also highly important, including memory consumption, power consumption, code footprint size, scroll performance, image rendering speed/quality, server to client latency, and CPU cycle consumption' 'fitness without requiring overall system rebuild' 'A/B testing surrogate[s]' 'to pay off technical debt' 'Automated software transplantation is an important special case of refactoring.' 'the donor system can thus be used as a test oracle' ... 'software transplantation is fully automatable.' [generative AI] 'tendency to hallucinate renders it relatively unreliable on its own. However'... [SE has ground truth]. [Large Language Model] 'AI techniques that mimic existing [human] coding styles' Codex LLM. 'Replacing execution with prediction' ChatGPT LLM. 'LLMs [like undergraduates] are prone to hallucination' 'use [LLM] AI for performance optimisation' Importance of when/where to use technology. 'it is essential to deploy into the continuous integration system at a time of maximum relevance'. 'Maximum automation is achieved when human decision time is minimised.' 'fuzzing [fuzz testing] technology has achieved widespread deployment' Testability Transformation Instagram Product Foundation, Meta Platforms Inc. Facebook", } @Misc{alshahwan2024assured, author = "Nadia Alshahwan and Mark Harman and Inna Harper and Alexandru Marginean and Shubho Sengupta and Eddy Wang", title = "Assured {LLM}-Based Software Engineering", howpublished = "arXiv", year = "2024", month = "6 " # feb, note = "InteNSE 2024 Keynote", address = "Lisbon, Portugal", keywords = "genetic algorithms, genetic programming, genetic improvement, ANN, LLMSE, SBSE, prompt search space, facebook, automatic test oracle, refactoring, APR, searchable prompting language", eprint = "2402.04380", archiveprefix = "arXiv", primaryclass = "cs.SE", URL = "https://arxiv.org/abs/2402.04380", size = "6 pages", abstract = "How can we use Large Language Models (LLMs) to improve code independently of a human, while ensuring that the improved code - does not regress the properties of the original code? - improves the original in a verifiable and measurable way? To address this question, we advocate Assured LLM-Based Software Engineering; a generate-and-test approach, inspired by Genetic Improvement. Assured LLMSE applies a series of semantic filters that discard code that fails to meet these twin guarantees. This overcomes the potential problem of LLM's propensity to hallucinate. It allows us to generate code using LLMs, independently of any human. The human plays the role only of final code reviewer, as they would do with code generated by other human engineers. This paper is an outline of the content of the keynote by Mark Harman at the International Workshop on Interpretability, Robustness, and Benchmarking in Neural Software Engineering, Monday 15th April 2024, Lisbon, Portugal", notes = "https://conf.researchr.org/home/icse-2024/intense-2024 https://intense24.github.io/", } @InProceedings{Alshahwan:2024:FSEcomp, author = "Nadia Alshahwan and Jubin Chheda and Anastasia Finogenova and Beliz Gokkaya and Mark Harman and Inna Harper and Alexandru Marginean and Shubho Sengupta and Eddy Wang", title = "Automated Unit Test Improvement using Large Language Models at Meta", year = "2024", booktitle = "Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering", series = "FSE 2024", pages = "185--196", address = "Porto de Galinhas, Brazil", month = jul # " 15-19", publisher = "ACM", keywords = "TestGen-LLM, Automated Test Generation, Genetic Improvement, LLMs, Large Language Models, ANN, SBSE, Unit Testing", isbn13 = "979-8-4007-0658-5", URL = "https://doi.org/10.1145/3663529.3663839", DOI = "doi:10.1145/3663529.3663839", size = "12 pages", abstract = "This paper describes Meta TestGen-LLM tool, which uses LLMs to automatically improve existing human-written tests. TestGen-LLM verifies that its generated test classes successfully clear a set of filters that assure measurable improvement over the original test suite, thereby eliminating problems due to LLM hallucination. We describe the deployment of TestGen-LLM at Meta test-a-thons for the Instagram and Facebook platforms. In an evaluation on Reels and Stories products for Instagram, 75percent of TestGen-LLMs test cases built correctly, 57percent passed reliably, and 25percent increased coverage. During Meta Instagram and Facebook test-a-thons, it improved 11.5percent of all classes to which it was applied, with 73percent of its recommendations being accepted for production deployment by Meta software engineers. We believe this is the first report on industrial scale deployment of LLM-generated code backed by such assurances of code improvement.", notes = "Not GP?", } @InProceedings{DBLP:conf/gecco/AlshammariLHZ09, author = "Riyad Alshammari and Peter Lichodzijewski and Malcolm I. Heywood and A. Nur Zincir-Heywood", title = "Classifying SSH encrypted traffic with minimum packet header features using genetic programming", booktitle = "GECCO-2009 Defense applications of computational intelligence workshop", year = "2009", editor = "Anna I. Esparcia and Ying-ping Chen and Gabriela Ochoa and Ender Ozcan and Marc Schoenauer and Anne Auger and Hans-Georg Beyer and Nikolaus Hansen and Steffen Finck and Raymond Ros and Darrell Whitley and Garnett Wilson and Simon Harding and W. B. Langdon and Man Leung Wong and Laurence D. Merkle and Frank W. Moore and Sevan G. Ficici and William Rand and Rick Riolo and Nawwaf Kharma and William R. Buckley and Julian Miller and Kenneth Stanley and Jaume Bacardit and Will Browne and Jan Drugowitsch and Nicola Beume and Mike Preuss and Stephen L. Smith and Stefano Cagnoni and Jim DeLeo and Alexandru Floares and Aaron Baughman and Steven Gustafson and Maarten Keijzer and Arthur Kordon and Clare Bates Congdon and Laurence D. Merkle and Frank W. Moore", pages = "2539--2546", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1570256.1570358", abstract = "The classification of Encrypted Traffic, namely Secure Shell (SSH), on the fly from network TCP traffic represents a particularly challenging application domain for machine learning. Solutions should ideally be both simple - therefore efficient to deploy - and accurate. Recent advances to team based Genetic Programming provide the opportunity to decompose the original problem into a subset of classifiers with non-overlapping behaviors, in effect providing further insight into the problem domain and increasing the throughput of solutions. Thus, in this work we have investigated the identification of SSH encrypted traffic based on packet header features without using IP addresses, port numbers and payload data. Evaluation of C4.5 and AdaBoost - representing current best practice - against the Symbiotic Bid-based (SBB) paradigm of team-based Genetic Programming (GP) under data sets common and independent from the training condition indicates that SBB based GP solutions are capable of providing simpler solutions without sacrificing accuracy. ", notes = "Distributed on CD-ROM at GECCO-2009. ACM Order Number 910092.", } @InProceedings{Alshammari:2010:cec, author = "Riyad Alshammari and A. Nur Zincir-Heywood", title = "Unveiling Skype encrypted tunnels using GP", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-6910-9", abstract = "The classification of Encrypted Traffic, namely Skype, from network traffic represents a particularly challenging problem. Solutions should ideally be both simple -therefore efficient to deploy -and accurate. Recent advances to team-based Genetic Programming provide the opportunity to decompose the original problem into a subset of classifiers with non-overlapping behaviours. Thus, in this work we have investigated the identification of Skype encrypted traffic using Symbiotic Bid-Based (SBB) paradigm of team based Genetic Programming (GP) found on flow features without using IP addresses, port numbers and payload data. Evaluation of SBB-GP against C4.5 and AdaBoost -representing current best practice -indicates that SBB-GP solutions are capable of providing simpler solutions in terms number of features used and the complexity of the solution/model without sacrificing accuracy.", DOI = "doi:10.1109/CEC.2010.5586288", notes = "WCCI 2010. Also known as \cite{5586288}", } @InProceedings{Alshammari:2010:CNSM, author = "Riyad Alshammari and A. Nur Zincir-Heywood", title = "An investigation on the identification of {VoIP} traffic: Case study on Gtalk and Skype", booktitle = "2010 International Conference on Network and Service Management (CNSM)", year = "2010", month = "25-29 " # oct, pages = "310--313", abstract = "The classification of encrypted traffic on the fly from network traces represents a particularly challenging application domain. Recent advances in machine learning provide the opportunity to decompose the original problem into a subset of classifiers with non-overlapping behaviours, in effect providing further insight into the problem domain. Thus, the objective of this work is to classify VoIP encrypted traffic, where Gtalk and Skype applications are taken as good representatives. To this end, three different machine learning based approaches, namely, C4.5, AdaBoost and Genetic Programming (GP), are evaluated under data sets common and independent from the training condition. In this case, flow based features are employed without using the IP addresses, source/destination ports and payload information. Results indicate that C4.5 based machine learning approach has the best performance.", keywords = "genetic algorithms, genetic programming, AdaBoost, C4.5, Gtalk, IP address, Skype, VoIP encrypted traffic, machine learning, source/destination port, Internet telephony, learning (artificial intelligence), telecommunication traffic", DOI = "doi:10.1109/CNSM.2010.5691210", notes = "Also known as \cite{5691210}", } @InProceedings{Alshammari:2011:IMLltbtptsaVt, title = "Is Machine Learning losing the battle to produce transportable signatures against VoIP traffic?", author = "Riyad Alshammari and A. Nur Zincir-Heywood", pages = "1542--1549", booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary Computation", year = "2011", editor = "Alice E. Smith", month = "5-8 " # jun, address = "New Orleans, USA", organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, AdaBoost, C5.0, VoIP traffic classification, consecutive sampling, machine learning, naive Bayesian, random sampling, transportable signatures, voice over IP, Bayes methods, Internet telephony, learning (artificial intelligence), telecommunication security, telecommunication traffic", DOI = "doi:10.1109/CEC.2011.5949799", abstract = "Traffic classification becomes more challenging since the traditional techniques such as port numbers or deep packet inspection are ineffective against voice over IP (VoIP) applications, which uses non-standard ports and encryption. Statistical information based on network layer with the use of machine learning (ML) can achieve high classification accuracy and produce transportable signatures. However, the ability of ML to find transportable signatures depends mainly on the training data sets. In this paper, we explore the importance of sampling training data sets for the ML algorithms, specifically Genetic Programming, C5.0, Naive Bayesian and AdaBoost, to find transportable signatures. To this end, we employed two techniques for sampling network training data sets, namely random sampling and consecutive sampling. Results show that random sampling and 90-minute consecutive sampling have the best performance in terms of accuracy using C5.0 and SBB, respectively. In terms of complexity, the size of C5.0 solutions increases as the training size increases, whereas SBB finds simpler solutions.", notes = "CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.", } @Article{Alshammari:2015:JKSUCIS, author = "Riyad Alshammari and A. Nur Zincir-Heywood", title = "Identification of {VoIP} encrypted traffic using a machine learning approach", journal = "Journal of King Saud University - Computer and Information Sciences", volume = "27", number = "1", pages = "77--92", year = "2015", keywords = "genetic algorithms, genetic programming, Machine learning, Encrypted traffic, Robustness, Network signatures", ISSN = "1319-1578", DOI = "doi:10.1016/j.jksuci.2014.03.013", URL = "http://www.sciencedirect.com/science/article/pii/S1319157814000561", abstract = "We investigate the performance of three different machine learning algorithms, namely C5.0, AdaBoost and Genetic programming (GP), to generate robust classifiers for identifying VoIP encrypted traffic. To this end, a novel approach (Alshammari and Zincir-Heywood, 2011) based on machine learning is employed to generate robust signatures for classifying VoIP encrypted traffic. We apply statistical calculation on network flows to extract a feature set without including payload information, and information based on the source and destination of ports number and IP addresses. Our results show that finding and employing the most suitable sampling and machine learning technique can improve the performance of classifying VoIP significantly.", } @Article{AlShammari:2016:Energy, author = "Eiman Tamah Al-Shammari and Afram Keivani and Shahaboddin Shamshirband and Ali Mostafaeipour and Por Lip Yee and Dalibor Petkovic and Sudheer Ch", title = "Prediction of heat load in district heating systems by Support Vector Machine with Firefly searching algorithm", journal = "Energy", volume = "95", pages = "266--273", year = "2016", ISSN = "0360-5442", DOI = "doi:10.1016/j.energy.2015.11.079", URL = "http://www.sciencedirect.com/science/article/pii/S0360544215016424", abstract = "District heating systems operation can be improved by control strategies. One of the options is the introduction of predictive control model. Predictive models of heat load can be applied to improve district heating system performances. In this article, short-term multistep-ahead predictive models of heat load for consumers connected to district heating system were developed using SVMs (Support Vector Machines) with FFA (Firefly Algorithm). Firefly algorithm was used to optimize SVM parameters. Seven SVM-FFA predictive models for different time horizons were developed. Obtained results of the SVM-FFA models were compared with GP (genetic programming), ANNs (artificial neural networks), and SVMs models with grid search algorithm. The experimental results show that the developed SVM-FFA models can be used with certainty for further work on formulating novel model predictive strategies in district heating systems.", keywords = "genetic algorithms, genetic programming, District heating systems, Heat load, Estimation, Prediction, Support Vector Machines, Firefly algorithm", } @Article{ALSHARIF:2022:JBE, author = "Rashed Alsharif and Mehrdad Arashpour and Emadaldin Mohammadi Golafshani and M. Reza Hosseini and Victor Chang and Jenny Zhou", title = "Machine learning-based analysis of occupant-centric aspects: Critical elements in the energy consumption of residential buildings", journal = "Journal of Building Engineering", volume = "46", pages = "103846", year = "2022", ISSN = "2352-7102", DOI = "doi:10.1016/j.jobe.2021.103846", URL = "https://www.sciencedirect.com/science/article/pii/S2352710221017046", keywords = "genetic algorithms, genetic programming, Artificial Intelligence, Energy simulation, Metabolic rate, Predicted mean vote (PMV), Sustainability", abstract = "The housing sector consumes a significant amount of energy worldwide, which is mainly attributed to operating energy systems for the provision of thermally comfortable indoor environments. Although the literature in this field has focused on investigating critical factors in energy consumption, only a few studies have conducted a quantitative sensitivity analysis for thermal occupant factors (TOF) (i.e., metabolic rate and clothing level). Therefore, this paper introduces a framework for testing the criticality of TOF with a cross-comparison against building-related factors, considering the constraint of occupant thermal comfort. Using a building energy simulation model, the energy consumption of a case study is simulated, and building energy model alternatives are generated. The scope includes TOF and building envelope factors, with an established orthogonal experimental design. A popular branch of machine learning (ML) called linear genetic programming (LGP) is used to analyse the generated data from the experiment. Finally, a sensitivity analysis is conducted using the developed LGP model to determine and rank the criticality of the considered factors. The findings reveal that occupants' metabolic rate and clothing level have relevancy factors of -0.48 and -0.38 respectively, which ranked them 2nd and 3rd against building envelope factors for achieving energy-efficient comfortable houses. This research contributes to the literature by introducing a framework that couples orthogonal experiment design with ML techniques to quantify the criticality of TOF and rank them against building-envelope factors", } @Article{alshayeb:2021:Energies, author = "Suhaib Alshayeb and Aleksandar Stevanovic and B. Brian Park", title = "{Field-Based} Prediction Models for Stop Penalty in Traffic Signal Timing Optimization", journal = "Energies", year = "2021", volume = "14", number = "21", keywords = "genetic algorithms, genetic programming", ISSN = "1996-1073", URL = "https://www.mdpi.com/1996-1073/14/21/7431", DOI = "doi:10.3390/en14217431", abstract = "Transportation agencies optimise signals to improve safety, mobility, and the environment. One commonly used objective function to optimise signals is the Performance Index (PI), a linear combination of delays and stops that can be balanced to minimise fuel consumption (FC). The critical component of the PI is the stop penalty K, which expresses an FC stop equivalency estimated in seconds of pure delay. This study applies vehicular trajectory and FC data collected in the field, for a large fleet of modern vehicles, to compute the K-factor. The tested vehicles were classified into seven homogenous groups by using the k-prototype algorithm. Furthermore, multigene genetic programming (MGGP) is used to develop prediction models for the K-factor. The proposed K-factor models are expressed as functions of various parameters that impact its value, including vehicle type, cruising speed, road gradient, driving behaviour, idling FC, and the deceleration duration. A parametric analysis is carried out to check the developed models' quality in capturing the individual impact of the included parameters on the K-factor. The developed models showed an excellent performance in estimating the K-factor under multiple conditions. Future research shall evaluate the findings by using field-based K-values in optimising signals to reduce FC.", notes = "also known as \cite{en14217431}", } @InProceedings{Alsheddy:2012:CEC, title = "Off-line Parameter Tuning for Guided Local Search Using Genetic Programming", author = "Abdullah Alsheddy and Michael Kampouridis", pages = "112--116", booktitle = "Proceedings of the 2012 IEEE Congress on Evolutionary Computation", year = "2012", editor = "Xiaodong Li", month = "10-15 " # jun, DOI = "doi:10.1109/CEC.2012.6256155", address = "Brisbane, Australia", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Heuristics, metaheuristics and hyper-heuristics", abstract = "Guided Local Search (GLS), which is a simple meta-heuristic with many successful applications, has lambda as the only parameter to tune. There has been no attempt to automatically tune this parameter, resulting in a parameterless GLS. Such a result is a very practical objective to facilitate the use of meta-heuristics for end-users (e.g. practitioners and researchers). In this paper, we propose a novel parameter tuning approach by using Genetic Programming (GP). GP is employed to evolve an optimal formula that GLS can use to dynamically compute lambda as a function of instance-dependent characteristics. Computational experiments on the travelling salesman problem demonstrate the feasibility and effectiveness of this approach, producing parameterless formulae with which the performance of GLS is competitive (if not better) than the standard GLS.", notes = "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the EPS and the IET.", } @InProceedings{Alsina:2015:ieeeSSCI, author = "Emanuel F. Alsina and Nicola Capodieci and Giacomo Cabri and Alberto Regattieri", booktitle = "2015 IEEE Symposium Series on Computational Intelligence", title = "The Influence of the Picking Times of the Components in Time and Space Assembly Line Balancing Problems: An Approach with Evolutionary Algorithms", year = "2015", pages = "1021--1028", abstract = "The balancing of assembly lines is one of the most studied industrial problems, both in academic and practical fields. The workable application of the solutions passes through a reliable simplification of the real-world assembly line systems. Time and space assembly line balancing problems consider a realistic versions of the assembly lines, involving the optimisation of the entire line cycle time, the number of stations to install, and the area of these stations. Components, necessary to complete the assembly tasks, have different picking times depending on the area where they are allocated. The implementation in the real world of a line balanced disregarding the distribution of the tasks which use unwieldy components can result unfeasible. The aim of this paper is to present a method which balances the line in terms of time and space, hence optimises the allocation of the components using an evolutionary approach. In particular, a method which combines the bin packing problem with a genetic algorithm and a genetic programming is presented. The proposed method can be able to find different solutions to the line balancing problem and then evolve they in order to optimise the allocation of the components in certain areas in the workstation.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SSCI.2015.148", month = dec, notes = "Dept. of Phys., Inf. & Math., Univ. of Modena & Reggio Emilia, Modena, Italy Also known as \cite{7376724}", } @PhdThesis{Alsina:thesis, author = "Emanuel Federico Alsina", title = "Models for the prediction and management of complex systems in industrial and dynamic environments", title_it = "Modelli per la previsione e la gestione di sistemi complessi in ambienti dinamici e industriali", school = "Universita degli studi di Modena e Reggio Emilia", year = "2016", address = "Italy", keywords = "genetic algorithms, genetic programming", URN = "etd-11262015-110057", URL = "https://morethesis.unimore.it/theses/available/etd-11262015-110057/", URL = "https://morethesis.unimore.it/theses/available/etd-11262015-110057/unrestricted/thesis.pdf", size = "137 pages", abstract = "The world in which we live is becoming more and more complex. Modelling the reality means to create simplifications and abstractions of that, in order to figure out what is going on in this modern and complex world in which we live. Nowadays, models have become crucial to make better decisions. Models help us to be clearer thinkers, and to understand how to transform data in useful information. There are too many data out there, models take these data and structure them into information, and then into knowledge. Two main topics are discussed in this work: (1) how to model complex systems, and (2) how to make predictions within complex systems, in industrial and dynamic environments. The purpose of this thesis is to present a series of models developed to support the decision makers in the complexity management. The first topic is addressed presenting some models concerning the balancing of assembly lines, machine degradation in production lines, operation schedule, and the positing of cranes in automated warehousing. In particular, concerning the assembly lines, two bio-inspired models which optimize the global picking time of the components considering their physical allocation are presented. Moreover, the use of a multi-agent model able to simultaneously consider different factors that affect machines in a production line is analysed. This approach takes into account the ageing and the degradation of the machines, the repairs, the replacement, and the preventive maintenance activities. Furthermore, in order to present how to manage the complexity intrinsic into the operations scheduling, a model inspired by the behaviour of an ant colony is showed. Finally, another multi-agent model is showed, which is able to find the optimal dwell point in automated storage retrieval systems exploiting an idea deriving from force-fields. After that, an entire chapter is dedicated to the prediction in complex systems. Prediction in industrial and dynamic environments is a challenge that professionals and academics have to face more and more. Some models able to capture non-linear relationships between temporal events are presented. These models are applied to different fields, from the reliability of mechanical and electrical components, to renewable energy. In the final analysis, models able to predict the users behaviors within online social communities are introduced. In these cases, various machine learning approaches (such as artificial neural networks, logistic regressions, and random trees) are detailed. This thesis want to be an inspiration for those people which have to manage the complexity in industrial and dynamic environments, showing examples and results, in order to explain how to make this world a little more understandable.", notes = "Supervisor: Giacomo Cabri", } @InProceedings{Alsulaiman:2009:ieeeCISDA, author = "Fawaz A. Alsulaiman and Nizar Sakr and Julio J. Valdes and Abdulmotaleb {El Saddik} and Nicolas D. Georganas", title = "Feature selection and classification in genetic programming: Application to haptic-based biometric data", booktitle = "IEEE Symposium on Computational Intelligence for Security and Defense Applications, CISDA 2009", year = "2009", month = jul, pages = "1--7", keywords = "genetic algorithms, genetic programming, gene expression programming, analytic function, dimensionality reducers, feature selection, haptic dataset, haptic-based biometric data, haptic-based biometrics problem, high-dimensional haptic feature space, perfect classification model, feature extraction, haptic interfaces, pattern classification", DOI = "doi:10.1109/CISDA.2009.5356540", abstract = "In this paper, a study is conducted in order to explore the use of genetic programming, in particular gene expression programming (GEP), in finding analytic functions that can behave as classifiers in high-dimensional haptic feature spaces. More importantly, the determined explicit functions are used in discovering minimal knowledge-preserving subsets of features from very high dimensional haptic datasets, thus acting as general dimensionality reducers. This approach is applied to the haptic-based biometrics problem; namely, in user identity verification. GEP models are initially generated using the original haptic biometric datatset, which is imbalanced in terms of the number of representative instances of each class. This procedure was repeated while considering an under-sampled (balanced) version of the datasets. The results demonstrated that for all datasets, whether imbalanced or under-sampled, a certain number (on average) of perfect classification models were determined. In addition, using GEP, great feature reduction was achieved as the generated analytic functions (classifiers) exploited only a small fraction of the available features.", notes = "Also known as \cite{5356540}", } @InProceedings{Alsulaiman:2012:CISDA, author = "Fawaz A. Alsulaiman and Julio J. Valdes and Abdulmotaleb {El Saddik}", booktitle = "Computational Intelligence for Security and Defence Applications (CISDA), 2012 IEEE Symposium on", title = "Identity verification based on haptic handwritten signatures: Genetic programming with unbalanced data", year = "2012", DOI = "doi:10.1109/CISDA.2012.6291531", abstract = "In this paper, haptic-based handwritten signature verification using Genetic Programming (GP) classification is presented. The relevance of different haptic data types (e.g., force, position, torque, and orientation) in user identity verification is investigated. In particular, several fitness functions are used and their comparative performance is investigated. They take into account the unbalance dataset problem (large disparities within the class distribution), which is present in identity verification scenarios. GP classifiers using such fitness functions compare favourably with classical methods. In addition, they lead to simple equations using a much smaller number of attributes. It was found that collectively, haptic features were approximately as equally important as visual features from the point of view of their contribution to the identity verification process.", keywords = "genetic algorithms, genetic programming, handwriting recognition, haptic interfaces, image classification, GP classification, GP classifiers, fitness functions, genetic programming classification, haptic data types, haptic features, haptic-based handwritten signature verification, unbalance dataset problem, user identity verification, visual features, Biological cells, Biometrics, Force, Gene expression, Haptic interfaces, Vectors", notes = "Also known as \cite{6291531}", } @Article{journals/tomccap/AlsulaimanSVE13, author = "Fawaz A. Alsulaiman and Nizar Sakr and Julio J. Valdes and Abdulmotaleb El-Saddik", title = "Identity verification based on handwritten signatures with haptic information using genetic programming", journal = "ACM Transactions on Multimedia Computing, Communications, and Applications", year = "2013", volume = "9", number = "2", pages = "11:1--11:21", articleno = "11", month = may, keywords = "genetic algorithms, genetic programming, Biometrics, Haptics, classification, user verification", acmid = "2457453", publisher = "ACM", ISSN = "1551-6857", bibdate = "2013-06-05", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/tomccap/tomccap9.html#AlsulaimanSVE13", URL = "http://doi.acm.org/http://dx.doi.org/10.1145/2457450.2457453", DOI = "doi:10.1145/2457450.2457453", size = "21 pages", abstract = "In this article, haptic-based handwritten signature verification using Genetic Programming (GP) classification is presented. A comparison of GP-based classification with classical classifiers including support vector machine, k-nearest neighbours, naive Bayes, and random forest is conducted. In addition, the use of GP in discovering small knowledge-preserving subsets of features in high-dimensional datasets of haptic-based signatures is investigated and several approaches are explored. Subsets of features extracted from GP-generated models (analytic functions) are also exploited to determine the importance and relevance of different haptic data types (e.g., force, position, torque, and orientation) in user identity verification. The results revealed that GP classifiers compare favourably with the classical methods and use a much fewer number of attributes (with simple function sets).", notes = "Also known as \cite{Alsulaiman:2013:IVB:2457450.2457453} TOMCCAP", } @InProceedings{Alsulaiman:2013:HAVE, author = "Fawaz A. Alsulaiman and Julio J. Valdes and Abdulmotaleb {El Saddik}", booktitle = "IEEE International Symposium on Haptic Audio Visual Environments and Games (HAVE 2013)", title = "Identity verification based on haptic handwritten Signature: Novel fitness functions for GP framework", year = "2013", month = oct, pages = "98--102", keywords = "genetic algorithms, genetic programming, handwriting recognition, haptic interfaces, GP framework, evolutionary processes, false rejection rate, haptic based handwritten signatures, identity verification, novel fitness functions, Accuracy, Educational institutions, Evolutionary computation, Gene expression, Haptic interfaces, Programming", DOI = "doi:10.1109/HAVE.2013.6679618", size = "5 pages", abstract = "Fitness functions are the evaluation measures driving evolutionary processes towards solutions. In this paper, three fitness functions are proposed for solving the unbalanced dataset problem in Haptic-based handwritten signatures using genetic programming (GP). The use of these specifically designed fitness functions produced simpler analytical expressions than those obtained with currently available fitness measures, while keeping comparable classification accuracy. The functions introduced in this paper capture explicitly the nature of unbalanced data, exhibit better dimensionality reduction and have better False Rejection Rate.", notes = "Also known as \cite{6679618}", } @PhdThesis{Alsulaiman_Fawaz_Abdulaziz_A_2013_thesis, author = "Fawaz Abdulaziz A. Alsulaiman", title = "Towards a Continuous User Authentication Using Haptic Information", school = "School of Electrical Engineering and Computer Science, University of Ottawa", year = "2013", address = "Canada", keywords = "genetic algorithms, genetic programming, User Authentication, Identity Verification, User Identification, Haptics, Haptic-enabled Interpersonal Communication System", URL = "https://ruor.uottawa.ca/bitstream/10393/23946/3/Alsulaiman_Fawaz_Abdulaziz_A_2013_thesis.pdf", URL = "https://www.bac-lac.gc.ca/eng/services/theses/Pages/item.aspx?idNumber=1033202147", URL = "https://ruor.uottawa.ca/handle/10393/23946", size = "129 pages", abstract = "With the advancement in multimedia systems and the increased interest in haptics to be used in interpersonal communication systems, where users can see, show, hear, tell, touch and be touched, mouse and keyboard are no longer dominant input devices. Touch, speech and vision will soon be the main methods of human computer interaction. Moreover, as interpersonal communication usage increases, the need for securing user authentication grows. In this research, we examine a user's identification and verification based on haptic information. We divide our research into three main steps. The first step is to examine a pre-defined task, namely a handwritten signature with haptic information. The user target in this task is to mimic the legitimate signature in order to be verified. As a second step, we consider the user's identification and verification based on user drawings. The user target is predefined, however there are no restrictions imposed on the order or on the level of details required for the drawing. Lastly, we examine the feasibility and possibility of distinguishing users based on their haptic interaction through an interpersonal communication system. In this third step, there are no restrictions on user movements, however a free movement to touch the remote party is expected. In order to achieve our goal, many classification and feature reduction techniques have been discovered and some new ones were proposed. Moreover, in this work we use evolutionary computing in user verification and identification. Analysis of haptic features and their significance on distinguishing users is hence examined. The results show a use of visual features by Genetic Programming (GP) towards identity verification, with a probability equal to 50percent while the remaining haptic features were used with a probability of approximately 50percent. Moreover, with a handwritten signature application, a verification success rate of 97.93percent with False Acceptance Rate (FAR) of 1.28percent and 11.54percent False Rejection Rate (FRR) is achieved with the use of genetic programming enhanced with the random over sampled data set. In addition, with a totally free user movement in a haptic-enabled interpersonal communication system, an identification success rate of 83.3percent is achieved when random forest classifier is used.", notes = "OCLC number: 1033202147 supervisor: Abdulmotaleb El Saddi", } @Article{Altamiranda:2011:ieeeLAT, author = "J. Altamiranda and J. Aguilar and C. Delamarche", title = "Similarity of Amyloid Protein Motif using an Hybrid Intelligent System", journal = "IEEE Latin America Transactions (Revista IEEE America Latina)", year = "2011", month = sep, volume = "9", number = "5", pages = "700--710", note = "In Spanish", keywords = "genetic algorithms, genetic programming, AMYPdb database, amyloid protein motif, backpropagation artificial neural network, biological problem, hybrid intelligent system, nonhomologous protein family, protein sequence, regular expression, backpropagation, biology computing, neural nets, proteins", DOI = "doi:10.1109/TLA.2011.6030978", ISSN = "1548-0992", size = "11 pages", abstract = "The main objective of this research is to define and develop a comparison method of regular expressions, and apply it to amyloid proteins. In general, the biological problem that we study is concerning the search for similarities between non-homologous protein families, using regular expressions, with the goal of discover and identify specific regions conserved in the protein sequence, and in this way determine that proteins have a common origin. From the computer point of view, the problem consists of comparison of protein motifs expressed using regular expressions. A motif is a small region in a previously characterised protein, with a functional or structural significance in the protein sequence. In this work we proposed a hybrid method of motifs comparison based on the Genetic Programming, to generate the populations derived from every regular expression under comparison, and the Backpropagation Artificial Neural Network, for the comparison between them. The method of motifs comparison is tested using the database AMYPdb, and it allows discover possible similarities between amyloid families.", notes = "Also known as \cite{6030978}", } @InProceedings{Altamiranda:2013:CLEI, author = "Junior Altamiranda and Jose Aguilar and Chistian Delamarche", booktitle = "XXXIX Latin American Computing Conference (CLEI 2013)", title = "Comparison and fusion model in protein motifs", year = "2013", month = "7-11 " # oct, address = "Naiguata", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Bioinformatics, Neural Network, ANN, ACO, Ant Colony Optimization", isbn13 = "978-1-4799-2957-3", DOI = "doi:10.1109/CLEI.2013.6670618", size = "12 pages", abstract = "Motifs are useful in biology to highlight the nucleotides/amino-acids that are involved in structure, function, regulation and evolution, or to infer homology between genes/proteins. PROSITE is a strategy to model protein motifs as Regular Expressions and Position Frequency Matrices. Multiple tools have been proposed to discover biological motifs, but not for the case of the motifs comparison problem, which is NP-Complete due to flexibility and independence at each position. In this paper we present a formal model to compare two protein motifs based on the Genetic Programming to generate the population of sequences derived from every regular expression under comparison and on a Neural Network Backpropagation to calculate a motif similarity score as fitness function. Additionally, we present a fusion formal method for two similar motifs based on the Ant Colony Optimisation technique. The comparison and fusion method was tested using amyloid protein motifs.", notes = "Chistian Delamarche = Christian Delamarche Also known as \cite{6670618}", } @InCollection{kinnear:altenberg, author = "Lee Altenberg", title = "The Evolution of Evolvability in Genetic Programming", booktitle = "Advances in Genetic Programming", publisher = "MIT Press", year = "1994", editor = "Kenneth E. {Kinnear, Jr.}", pages = "47--74", chapter = "3", keywords = "genetic algorithms, genetic programming", URL = "http://dynamics.org/~altenber/PAPERS/EEGP/", URL = "http://dynamics.org/Altenberg/FILES/LeeEEGP.pdf", URL = "http://www.amazon.co.uk/Advances-Genetic-Programming-Complex-Adaptive/dp/0262111888", URL = "http://cognet.mit.edu/sites/default/files/books/9780262277181/pdfs/9780262277181_chap3.pdf", DOI = "doi:10.7551/mitpress/1108.003.0009", size = "29 pages", abstract = "The notion of ``evolvability'' --- the ability of a population to produce variants fitter than any yet existing --- is developed as it applies to genetic algorithms. A theoretical analysis of the dynamics of genetic programming predicts the existence of a novel, emergent selection phenomenon: the evolution of evolvability. This is produced by the proliferation, within programs, of blocks of code that have a higher chance of increasing fitness when added to programs. Selection can then come to mold the {\em variational} aspects of the way evolved programs are represented. A model of code proliferation within programs is analyzed to illustrate this effect. The mathematical and conceptual framework includes: the definition of evolvability as a measure of performance for genetic algorithms; application of Price's {\em Covariance and Selection Theorem} to show how the fitness function, representation, and genetic operators must interact to produce evolvability --- namely, that genetic operators produce offspring with fitnesses specifically correlated with their parent's fitnesses; how blocks of code emerge as a new level of replicator, proliferating as a function of their ``constructional fitness'', which is distinct from their schema fitness; and how programs may change from innovative code to conservative code as the populations mature. Several new selection techniques and genetic operators are proposed in order to give better control over the evolution of evolvability and improved evolutionary performance. Copyright 1996 Lee Altenberg", notes = " Price's Covariance and Selection Theorem 1970 Nature 227 pages 520-521 Fisher's Theorem 1930 'The Genetical Theory of Natural Selection, Clarendon Press, Oxford, UK pages 30-37' Generally better theory for GP -> additional fitness (of blocks) Also known as \cite{Altenberg:1994EEGP} Part of \cite{kinnear:book}", } @InProceedings{Altenberg:1994EBR, author = "Lee Altenberg", year = "1994", pages = "182--187", title = "Evolving better representations through selective genome growth", booktitle = "Proceedings of the 1st IEEE Conference on Evolutionary Computation", publisher = "IEEE", address = "Orlando, Florida, USA", month = "27-29 " # jun, publisher_address = "Piscataway, NJ, USA", volume = "1", keywords = "genetic algorithms, genetic programming", URL = "http://dynamics.org/~altenber/PAPERS/EBR/", URL = "http://dynamics.org/Altenberg/FILES/LeeEBR.pdf", abstract = "The choice of how to represent the search space for a genetic algorithm (GA) is critical to the GA's performance. Representations are usually engineered by hand and fixed for the duration of the GA run. Here a new method is described in which the degrees of freedom of the representation --- i.e. the genes -- are increased incrementally. The phenotypic effects of the new genes are randomly drawn from a space of different functional effects. Only those genes that initially increase fitness are kept. The genotype-phenotype map that results from this selection during the constructional of the genome allows better adaptation. This effect is illustrated with the NK landscape model. The resulting genotype-phenotype maps are much less epistatic than generic maps would be. They have extremely low values of ``K'' --- the number of fitness components affected by each gene. Moreover, these maps are exquisitely tuned to the specifics of the random fitness functions, and achieve fitnesses many standard deviations above generic NK landscapes with the same \gp\ maps. The evolved maps create adaptive landscapes that are much smoother than generic NK landscapes ever are. Thus a caveat should be made when making arguments about the applicability of generic properties of complex systems to evolved systems. This method may help to solve the problem of choice of representations in genetic algorithms. Copyright 1996 Lee Altenberg", notes = " ", } @InProceedings{Altenberg:1994EPIGP, author = "Lee Altenberg", year = "1994", title = "Emergent phenomena in genetic programming", booktitle = "Evolutionary Programming --- Proceedings of the Third Annual Conference", editor = "Anthony V. Sebald and Lawrence J. Fogel", publisher = "World Scientific Publishing", pages = "233--241", address = "San Diego, CA, USA", month = "24-26 " # feb, keywords = "genetic algorithms, genetic programming", ISBN = "981-02-1810-9", URL = "http://dynamics.org/~altenber/PAPERS/EPIGP/", URL = "http://dynamics.org/Altenberg/FILES/LeeEPIGP.pdf", URL = "http://dynamics.org/~altenber/FTP/LeeEPIGP.ps", URL = "http://citeseer.ist.psu.edu/398393.html", abstract = "Evolutionary computation systems exhibit various emergent phenomena, primary of which is adaptation. In genetic programming, because of the indeterminate nature of the representation, the evolution of both recombination distributions and representations can emerge from the population dynamics. A review of ideas on these phenomena is presented, including theory on the evolution of evolvability through differential proliferation of subexpressions within programs. An analysis is given of a model of genetic programming dynamics that is supportive of the ``Soft Brood Selection'' conjecture, which was proposed as a means to counteract the emergence of highly conservative code, and instead favor highly evolvable code. Copyright 1996 Lee Altenberg", notes = " EP-94 http://www.wspc.com.sg/books/compsci/2401.html broken sep 2019 http://www.natural-selection.com/eps/EP94.html", } @InProceedings{Altenberg:1995STPT, author = "Lee Altenberg", year = "1994", title = "The {Schema} {Theorem} and {Price}'s {Theorem}", booktitle = "Foundations of Genetic Algorithms 3", editor = "L. Darrell Whitley and Michael D. Vose", publisher = "Morgan Kaufmann", publisher_address = "San Francisco, CA, USA", address = "Estes Park, Colorado, USA", pages = "23--49", month = "31 " # jul # "--2 " # aug, organisation = "International Society for Genetic Algorithms", note = "Published 1995", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-356-5", URL = "http://dynamics.org/~altenber/PAPERS/STPT/", URL = "http://dynamics.org/Altenberg/FILES/LeeSTPT.pdf", DOI = "doi:10.1016/B978-1-55860-356-1.50006-6", abstract = "Holland's Schema Theorem is widely taken to be the foundation for explanations of the power of genetic algorithms (GAs). Yet some dissent has been expressed as to its implications. Here, dissenting arguments are reviewed and elaborated upon, explaining why the Schema Theorem has no implications for how well a GA is performing. Interpretations of the Schema Theorem have implicitly assumed that a correlation exists between parent and offspring fitnesses, and this assumption is made explicit in results based on Price's Covariance and Selection Theorem. Schemata do not play a part in the performance theorems derived for representations and operators in general. However, schemata re-emerge when recombination operators are used. Using Geiringer's recombination distribution representation of recombination operators, a ``missing'' schema theorem is derived which makes explicit the intuition for when a GA should perform well. Finally, the method of ``adaptive landscape'' analysis is examined and counterexamples offered to the commonly used correlation statistic. Instead, an alternative statistic---the transmission function in the fitness domain--- is proposed as the optimal statistic for estimating GA performance from limited samples. Copyright 1996 Lee Altenberg", notes = "FOGA-3 Deals with GAs as a whole, not specifically GP.", } @InCollection{Altenberg:1995GGEGPM, author = "Lee Altenberg", year = "1992", title = "Genome growth and the evolution of the genotype-phenotype map", booktitle = "Evolution as a Computational Process", editor = "Wolfgang Banzhaf and Frank H. Eeckman", publisher = "Springer-Verlag", address = "Monterey, California, USA", publisher_address = "Berlin, Germany", pages = "205--259", volume = "899", series = "Lecture Notes in Computer Science", month = jul, keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-49176-7", URL = "http://dynamics.org/~altenber/PAPERS/GGEGPM/", URL = "http://dynamics.org/Altenberg/FILES/LeeGGEGPM.pdf", URL = "https://rdcu.be/cUkY7", DOI = "doi:10.1007/3-540-59046-3_11", size = "55 pages", abstract = "The evolution of new genes is distinct from evolution through allelic substitution in that new genes bring with them new degrees of freedom for genetic variability. Selection in the evolution of new genes can therefore act to sculpt the dimensions of variability in the genome. This ``constructional'' selection effect is an evolutionary mechanism, in addition to genetic modification, that can affect the variational properties of the genome and its evolvability. One consequence is a form of genic selection: genes with large potential for generating new useful genes when duplicated ought to proliferate in the genome, rendering it ever more capable of generating adaptive variants. A second consequence is that alleles of new genes whose creation produced a selective advantage may be more likely to also produce a selective advantage, provided that gene creation and allelic variation have correlated phenotypic effects. A fitness distribution model is analyzed which demonstrates these two effects quantitatively. These are effects that select on the nature of the genotype-phenotype map. New genes that perturb numerous functions under stabilizing selection, i.e. with high pleiotropy, are unlikely to be advantageous. Therefore, genes coming into the genome ought to exhibit low pleiotropy during their creation. If subsequent offspring genes also have low pleiotropy, then genic selection can occur. If subsequent allelic variation also has low pleiotropy, then that too should have a higher chance of not being deleterious. The effects on pleiotropy are illustrated with two model genotype-phenotype maps: Wagner's linear quantitative-genetic model with Gaussian selection, and Kauffman's ``NK'' adaptive landscape model. Constructional selection is compared with other processes and ideas about the evolution of constraints, evolvability, and the genotype-phenotype map. Empirical phenomena such as dissociability in development, morphological integration, and exon shuffling are discussed in the context of this evolutionary process. Copyright 1996 Lee Altenberg", notes = " from the preface: Lee Altenberg considers the genotype-phenotype mapping and demonstrates the advantage of his constructional selection in the process of adaptation. Specifically, he considers the variational aspect of the representation problem and pleiotropy Published 1995", } @Unpublished{Altenberg:and:Feldman:1995SGTEMG2, author = "Lee Altenberg and Marcus W. Feldman", year = "1995", title = "Selection, generalized transmission, and the evolution of modifier genes. {II}. {M}odifier polymorphisms", note = "In preparation", URL = "ftp://ftp.mhpcc.edu/pub/incoming/altenberg/LeeSGTEMG2MP.ps.Z", notes = " ", } @InCollection{Altenberg:2004:MESLLQ, title = "Modularity in Evolution: Some Low-Level Questions", author = "Lee Altenberg", booktitle = "Modularity: Understanding the Development and Evolution of Complex Natural Systems", editor = "Diego Rasskin-Gutman and Werner Callebaut", publisher = "MIT Press", address = "Cambridge, MA, USA", year = "2005", chapter = "5", pages = "99--128", month = jun, keywords = "genetic algorithms, genetic programming", ISBN = "0-262-03326-7", URL = "http://dynamics.org/Altenberg/FILES/LeeMESLLQ.pdf", abstract = "Intuitive notions about the advantages of modularity for evolvability run into the problem of how we parse the organism into traits. In order to resolve the question of multiplicity, there needs to be a way to get the human observer out of the way, and define modularity in terms of physical processes. I will offer two candidate ideas towards this resolution: the dimensionality of phenotypic variation, and the causal screening off of phenotypic variables by other phenotypic variables. With this framework, the evolutionary advantages that have been attributed to modularity do not derive from modularity per se. Rather, they require that there be an 'alignment' between the spaces of phenotypic variation, and the selection gradients that are available to the organism. Modularity may facilitate such alignment, but it is not sufficient; the appropriate phenotype-fitness map in conjunction with the genotype-phenotype map is also necessary for evolvability. Conclusion I have endeavoured in this essay to delve into some of the low-level conceptual issues associated with the idea of modularity in the genotype-phenotype map. My main proposal is that the evolutionary advantages that have been attributed to modularity do not derive from modularity per se. Rather, they require that there be an 'alignment' between the spaces of phenotypic variation, and the selection gradients that are available to the organism. Modularity in the genotype-phenotype map may make such an alignment more readily attained, but it is not sufficient; the appropriate phenotype-fitness map in conjunction with the genotype-phenotype map is also necessary for evolvability.", notes = "Quantitative mutational effects under the 'House of Cards' vs. ``random-walk'' assumptions. http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=10484&mode=toc", size = "32 pages", } @InCollection{Altenberg:2004:OPSAED, title = "Open Problems in the Spectral Analysis of Evolutionary Dynamics", author = "Lee Altenberg", booktitle = "Frontiers of Evolutionary Computation", editor = "Anil Menon", series = "Genetic Algorithms And Evolutionary Computation Series", volume = "11", chapter = "4", publisher = "Kluwer Academic Publishers", address = "Boston, MA, USA", year = "2004", pages = "73--102", keywords = "genetic algorithms, genetic programming", ISBN = "1-4020-7524-3", URL = "http://dynamics.org/Altenberg/FILES/LeeOPSAED.pdf", DOI = "doi:10.1007/1-4020-7782-3_4", abstract = "For broad classes of selection and genetic operators, the dynamics of evolution can be completely characterised by the spectra of the operators that define the dynamics, in both infinite and finite populations. These classes include generalised mutation, frequency-independent selection, uniparental inheritance. Several open questions exist regarding these spectra: 1. For a given fitness function, what genetic operators and operator intensities are optimal for finding the fittest genotype? The concept of rapid first hitting time, an analog of Sinclair's rapidly mixing Markov chains, is examined. 2. What is the relationship between the spectra of deterministic infinite population models, and the spectra of the Markov processes derived from them in the case of finite populations? 3. Karlin proved a fundamental relationship between selection, rates of transformation under genetic operators, and the consequent asymptotic mean fitness of the population. Developed to analyse the stability of polymorphisms in subdivided populations, the theorem has been applied to unify the reduction principle for self-adaptation, and has other applications as well. Many other problems could be solved if it were generalised to account for the interaction of different genetic operators. Can Karlin's theorem on operator intensity be extended to account for mixed genetic operators?", notes = "Revised 2010", size = "26 pages", } @Article{altenberg:2004:ESSFSA, author = "Lee Altenberg", year = "2005", title = "Evolvability Suppression to Stabilize Far-Sighted Adaptations", journal = "Artificial Life", volume = "11", number = "3", pages = "427--443", month = "Fall", keywords = "genetic algorithms", ISSN = "1064-5462", DOI = "doi:10.1162/106454605774270633", size = "18 pages", abstract = "The opportunistic character of adaptation through natural selection can lead to `evolutionary pathologies'---situations in which traits evolve that promote the extinction of the population. Such pathologies include imprudent predation and other forms of habitat over-exploitation or the `tragedy of the commons', adaptation to temporally unreliable resources, cheating and other antisocial behaviour, infectious pathogen carrier states, parthenogenesis, and cancer, an intra-organismal evolutionary pathology. It is known that hierarchical population dynamics can protect a population from invasion by pathological genes. Can it also alter the genotype so as to prevent the generation of such genes in the first place, i.e. suppress the evolvability of evolutionary pathologies? A model is constructed in which one locus controls the expression of the pathological trait, and a series of modifier loci exist which can prevent the expression of this trait. It is found that multiple `evolvability checkpoint' genes can evolve to prevent the generation of variants that cause evolutionary pathologies. The consequences of this finding are discussed.", } @Article{Altenberg:2014:GPEM, author = "Lee Altenberg", title = "Mathematics awaits: commentary on ''Genetic Programming and Emergence'' by Wolfgang Banzhaf", journal = "Genetic Programming and Evolvable Machines", year = "2014", volume = "15", number = "1", pages = "87--89", month = mar, keywords = "genetic algorithms, genetic programming, Evolvability, Robustness, Subtree exchange, Mathematics, Matrix theory, Lagrange distribution", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-013-9198-5", size = "3 pages", abstract = "Banzhaf provides a portal to the subject of emergence, noting contentious concepts while not getting sucked into fruitless debate. Banzhaf refutes arguments against downward causation much as Samuel Johnson kicks a stone to refute Berkeley by pointing to concrete examples in genetic programming, such as the growth of repetitive patterns within programs. Repetitive patterns are theoretically predicted to emerge from the evolution of evolvability and robustness under subtree exchange. Selection and genetic operators are co-equal creators of these emergent phenomena. GP systems entirely formal, and thus their emergent phenomena are essentially mathematical. The emergence of Lagrangian distributions for tree shapes under subtree exchange, for example, gives a glimpse of the possibilities for mathematical understanding of emergence in GP. The mathematics underlying emergence in genetic programming should be pursued with vigour.", notes = "\cite{Banzhaf:2014:GPEM}", } @Article{Altenberg:2014:GPEMb, author = "Lee Altenberg", title = "Evolvability and robustness in artificial evolving systems: three perturbations", journal = "Genetic Programming and Evolvable Machines", year = "2014", volume = "15", number = "3", pages = "275--280", month = sep, keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-014-9223-3", size = "6 pages", } @InCollection{Altenberg:2016:EC, author = "Lee Altenberg", year = "2016", title = "Evolutionary Computation", booktitle = "The Encyclopedia of Evolutionary Biology", editor = "Richard M. Kliman", publisher = "Academic Press", volume = "2", pages = "40--47", address = "Oxford, UK", keywords = "genetic algorithms, genetic programming, Crossover, Encoding, Evolutionary algorithm, Evolvability, Genetic algorithm, Genetic operator, No free lunch theorems, Objective function, Optimization, Representation, Search space, Selection operator, Simulated annealing", isbn13 = "978-0-12-800426-5", URL = "https://www.sciencedirect.com/science/article/pii/B9780128000496003073", DOI = "doi:10.1016/B978-0-12-800049-6.00307-3", abstract = "Evolutionary computation is a method of solving engineering problems using algorithms that mimic Darwinian natural selection and Mendelian genetics, applied especially to optimization problems that are difficult to solve from first principles. Earliest beginnings were in the 1950s, and by the mid-1990s it had developed as an academic field with its own journals, conferences, and faculty. Several phenomena discovered in evolutionary biology were also discovered in parallel in evolutionary computation, including the evolvability problem, genetic modification, constructive neutral evolution, and genetic robustness. The related field of artificial life focuses on computational systems in which replication, natural selection, and ecological interactions are all emergent.", } @Article{Altenberg:2017:GPEM, author = "Lee Altenberg", title = "Probing the axioms of evolutionary algorithm design: Commentary on ``On the mapping of genotype to phenotype in evolutionary algorithms'' by {Peter A. Whigham, Grant Dick, and James Maclaurin}", journal = "Genetic Programming and Evolvable Machines", year = "2017", volume = "18", number = "3", pages = "363--367", month = sep, note = "Special Peer Commentary on Mapping of Genotype to Phenotype in Evolutionary Algorithms", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-017-9290-3", size = "5 pages", abstract = "Properties such as continuity, locality, and modularity may seem necessary when designing representations and variation operators for evolutionary algorithms, but a closer look at what happens when evolutionary algorithms perform well reveals counterexamples to such schemes. Moreover, these variational properties can themselves evolve in sufficiently complex open-ended systems. These properties of evolutionary algorithms remain very much open questions.", notes = "Introduction in \cite{Spector:2017:GPEM} An author's reply to this comment is available at http://dx.doi.org/10.1007/s10710-017-9289-9 \cite{Whigham:2017:GPEM2}. This comment refers to the article available at: http://dx.doi.org/10.1007/s10710-017-9288-x \cite{Whigham:2017:GPEM}.", } @Article{Althoefer:2010:ICGA, author = "Ingo Althoefer", title = "Automatic Generation and Evaluation of Recombination Games. Doctoral Dissertation by Cameron Browne, Review", journal = "ICGA Journal", year = "2010", volume = "33", number = "4", keywords = "genetic algorithms, genetic programming", URL = "https://chessprogramming.wikispaces.com/ICGA+Journal", notes = "Review of \cite{CameronBrowne:thesis}", } @Article{ALTHOEY:2023:cscm, author = "Fadi Althoey and Muhammad Naveed Akhter and Zohaib Sattar Nagra and Hamad Hassan Awan and Fayez Alanazi and Mohsin Ali Khan and Muhammad Faisal Javed and Sayed M. Eldin and Yasin Onuralp Ozkilic", title = "Prediction models for marshall mix parameters using bio-inspired genetic programming and deep machine learning approaches: A comparative study", journal = "Case Studies in Construction Materials", volume = "18", pages = "e01774", year = "2023", ISSN = "2214-5095", DOI = "doi:10.1016/j.cscm.2022.e01774", URL = "https://www.sciencedirect.com/science/article/pii/S2214509522009068", keywords = "genetic algorithms, genetic programming, Marshall Mix Parameter, Deep Learning, Prediction models, Asphalt, Bio-Inspired models", abstract = "This research study uses four machine learning techniques, i.e., Multi Expression programming (MEP), Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Ensemble Decision Tree Bagging (DT-Bagging) for the development of new and advanced models for prediction of Marshall Stability (MS), and Marshall Flow (MF) of asphalt mixes. A comprehensive and detailed database of 343 data points was established for both MS and MF. The predicting variables were chosen among the four most influential, and easy-to-determine parameters. The models were trained, tested, validated, and the outcomes of the newly developed models were compared with actual outcomes. The root squared error (RSE), Nash-Sutcliffe efficiency (NSE), mean absolute error (MAE), root mean square error (RMSE), relative root mean square error (RRMSE), regression coefficient (R2), and correlation coefficient (R), were all used to evaluate the performance of models. The sensitivity analysis (SA) revealed that in the case of MS, the rising order of input significance was bulk specific gravity of compacted aggregate, Gmb (38.56 percent) > Percentage of Aggregates, Ps (19.84 percent) > Bulk Specific Gravity of Aggregate, Gsb (19.43 percent) > maximum specific gravity paving mix, Gmm (7.62 percent), while in case of MF the order followed was: Ps (36.93 percent) > Gsb (14.11 percent) > Gmb (10.85 percent) > Gmm (10.19 percent). The outcomes of parametric analysis (PA) consistency of results in relation to previous research findings. The DT-Bagging model outperformed all other models with values of 0.971 and 0.980 (R), 16.88 and 0.24 (MAE), 28.27 and 0.36 (RMSE), 0.069 and 0.041 (RSE), 0.020 and 0.032 (RRMSE), 0.010 and 0.016 (PI), 0.931 and 0.959 (NSE), for MS and MF, respectively. The results of the comparison analysis showed that ANN, ANFIS, MEP, and DT-Bagging are all effective and reliable approaches for the estimation of MS and MF. The MEP-derived mathematical expressions represent the novelty of MEP and are relatively simple and reliable. Roverall values for MS and MF were in the order of DT-Bagging >MEP >ANFIS >ANN with all values exceeding the permitted range of 0.80 for both MS and MF. Hence, all the modeling approaches showed higher performance, possessed high generalization and predication capabilities, and assess the relative significance of input parameters in the prediction of MS and MF. Hence, the findings of this research study would assist in the safer, faster, and sustainable prediction of MS and MF, from the standpoint of resources and time required to perform the Marshall tests", } @Article{Altomare:2013:JoH, author = "C. Altomare and X. Gironella and D. Laucelli", title = "Evolutionary data-modelling of an innovative low reflective vertical quay", journal = "Journal of Hydroinformatics", year = "2013", volume = "15", number = "3", pages = "763--779", month = "1 " # jul, keywords = "genetic algorithms, genetic programming, data-mining, evolutionary polynomial regression, low reflective vertical quay, wave reflection", URL = "https://iwaponline.com/jh/article-pdf/15/3/763/387059/763.pdf", DOI = "doi:10.2166/hydro.2012.219", size = "17 pages", abstract = "Vertical walls are commonly used as berthing structures. However, conventional vertical quays may have serious technical and environmental problems, as they reflect almost all the energy of the incident waves, thus affecting operational conditions and structural strength. These drawbacks can be overcome by the use of low reflective structures, but for some instances no theoretical equations exist to determine the relationship between the reflection coefficient and parameters that affect the structural response. Therefore, this study tries to fill this gap by examining the wave reflection of an absorbing gravity wall by means of evolutionary polynomial regression, a hybrid evolutionary modelling paradigm that combines the best features of conventional numerical regression and genetic programming. The method implements a multi-modelling approach in which a multi-objective genetic algorithm is used to get optimal models in terms of parsimony of mathematical expressions and fitting to data. A database of physical laboratory observations is used to predict the reflection as a function of a set of variables that characterize wave conditions and structure features. The proposed modelling paradigm proved to be a useful tool for data analysis and is able to find feasible explicit models featured by an appreciable generalization performance.", notes = "This content is only available as a PDF.", } @Article{altomare:2020:JMSE, author = "Corrado Altomare and Daniele B. Laucelli and Hajime Mase and Xavi Gironella", title = "Determination of {Semi-Empirical} Models for Mean Wave Overtopping Using an Evolutionary Polynomial Paradigm", journal = "Journal of Marine Science and Engineering", year = "2020", volume = "8", number = "8", keywords = "genetic algorithms, genetic programming", ISSN = "2077-1312", URL = "https://www.mdpi.com/2077-1312/8/8/570", DOI = "doi:10.3390/jmse8080570", abstract = "The present work employs the so-called Evolutionary Polynomial Regression (EPR) algorithm to build up a formula for the assessment of mean wave overtopping discharge for smooth sea dikes and vertical walls. EPR is a data-mining tool that combines and integrates numerical regression and genetic programming. This technique is here employed to dig into the relationship between the mean discharge and main hydraulic and structural parameters that characterise the problem under study. The parameters are chosen based on the existing and most used semi-empirical formulas for wave overtopping assessment. Besides the structural freeboard or local wave height, the unified models highlight the importance of local water depth and wave period in combination with foreshore slope and dike slope on the overtopping phenomena, which are combined in a unique parameter that is defined either as equivalent or imaginary slope. The obtained models aim to represent a trade-off between accuracy and parsimony. The final formula is simple but can be employed for a preliminary assessment of overtopping rates, covering the full range of dike slopes, from mild to vertical walls, and of water depths from the shoreline to deep water, including structures with emergent toes.", notes = "also known as \cite{jmse8080570}", } @InProceedings{Aluko:2014:CIFEr, author = "Babatunde Aluko and Dafni Smonou and Michael Kampouridis and Edward Tsang", booktitle = "IEEE Conference on Computational Intelligence for Financial Engineering Economics (CIFEr 2104)", title = "Combining different meta-heuristics to improve the predictability of a Financial Forecasting algorithm", year = "2014", month = "27-28 " # mar, pages = "333--340", size = "8 pages", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CIFEr.2014.6924092", abstract = "Hyper-heuristics have successfully been applied to a vast number of search and optimisation problems. One of the novelties of hyper-heuristics is the fact that they manage and automate the meta-heuristic's selection process. In this paper, we implemented and analysed a hyper-heuristic framework on three meta-heuristics namely Simulated Annealing, Tabu Search, and Guided Local Search, which had successfully been applied in the past to a Financial Forecasting algorithm called EDDIE. EDDIE uses Genetic Programming to extract and learn from historical data in order to predict future financial market movements. Results show that the algorithm's effectiveness has been improved, thus making the combination of meta-heuristics under a hyper-heuristic framework an effective Financial Forecasting approach.", notes = "Also known as \cite{6924092}", } @Article{alvarado-iniesta:JoIM, author = "Alejandro Alvarado-Iniesta and Luis Gonzalo Guillen-Anaya and Luis Alberto Rodriguez-Picon and Raul Neco-Caberta", title = "Multi-objective optimization of an engine mount design by means of memetic genetic programming and a local exploration approach", journal = "Journal of Intelligent Manufacturing", year = "2020", volume = "31", pages = "19--32", month = jan, keywords = "genetic algorithms, genetic programming, Structural optimization, Multi-objective optimization, Finite element analysis, Decision making", URL = "http://link.springer.com/article/10.1007/s10845-018-1432-9", DOI = "doi:10.1007/s10845-018-1432-9", abstract = "the optimization of an engine mount design from a multi-objective. Our methodology is divided into three phases: phase one focuses on data collection through computer simulations. The objectives considered during the analyses are: total mass, first natural frequency and maximum von Mises stress. In phase two, a surrogate model by means of genetic programming is generated for each one of the objectives. Moreover, a local search procedure is incorporated into the overall genetic programming algorithm for improving its performance. Finally, in phase three, instead of steering the search to finding the approximate Pareto front, a local exploration approach based on a change in the weight space is used to lead a search into user defined directions turning the decision making more intuitive.", } @Article{alvarado-iniesta:2017:IJAMT, author = "Alejandro Alvarado-Iniesta and Diego A. Tlapa-Mendoza and Jorge Limon-Romero and Luis C. Mendez-Gonzalez", title = "Multi-objective optimization of an aluminum torch brazing process by means of genetic programming and {R-NSGA-II}", journal = "The International Journal of Advanced Manufacturing Technology", year = "2017", volume = "91", number = "9 - 12", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s00170-017-0102-y", DOI = "doi:10.1007/s00170-017-0102-y", } @Article{Alvarez:2007:JMS, author = "A. Alvarez and Alejandro Orfila and G. Basterretxea and J. Tintore and G. Vizoso and A. Fornes", title = "Forecasting front displacements with a satellite based ocean forecasting (SOFT) system", journal = "Journal of Marine Systems", year = "2007", volume = "65", number = "1-4", pages = "299--313", month = mar, note = "Marine Environmental Monitoring and Prediction - Selected papers from the 36th International Liege Colloquium on Ocean Dynamics", keywords = "genetic algorithms, genetic programming, Satellite data, Ocean prediction, Front evolution", DOI = "doi:10.1016/j.jmarsys.2005.11.017", abstract = "Relatively long term time series of satellite data are nowadays available. These spatiotemporal time series of satellite observations can be employed to build empirical models, called satellite based ocean forecasting (SOFT) systems, to forecast certain aspects of future ocean states. The forecast skill of SOFT systems predicting the sea surface temperature (SST) at sub-basin spatial scale (from hundreds to thousand kilometres), has been extensively explored in previous works. Thus, these works were mostly focused on predicting large scale patterns spatially stationary. At spatial scales smaller than sub-basin (from tens to hundred kilometres), spatiotemporal variability is more complex and propagating structures are frequently present. In this case, traditional SOFT systems based on Empirical Orthogonal Function (EOF) decompositions could not be optimal prediction systems. Instead, SOFT systems based on Complex Empirical Orthogonal Functions (CEOFs) are, a priori, better candidates to resolve these cases. In this work we study and compare the performance of an EOF and CEOF based SOFT systems forecasting the SST at weekly time scales of a propagating mesoscale structure. The SOFT system was implemented in an area of the Northern Balearic Sea (Western Mediterranean Sea) where a moving frontal structure is recurrently observed. Predictions from both SOFT systems are compared with observations and with the predictions obtained from persistence models. Results indicate that the implemented SOFT systems are superior in terms of predictability to persistence. No substantial differences have been found between the EOF and CEOF-SOFT systems.", } @InCollection{alvarez:2003:SVMASTI, author = "Gabriel Alvarez", title = "Standard Versus Micro-Genetic Algorithms for Seismic Trace Inversion", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2003", year = "2003", editor = "John R. Koza", pages = "1--10", address = "Stanford, California, 94305-3079 USA", month = "4 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms", notes = "part of \cite{koza:2003:gagp}", } @InProceedings{Alvarez:2016:GECCO, author = "Isidro M. Alvarez and Will N. Browne and Mengjie Zhang", title = "Human-inspired Scaling in Learning Classifier Systems: Case Study on the n-bit Multiplexer Problem Set", booktitle = "GECCO '16: Proceedings of the 2016 Annual Conference on Genetic and Evolutionary Computation", year = "2016", editor = "Tobias Friedrich and Frank Neumann and Andrew M. Sutton and Martin Middendorf and Xiaodong Li and Emma Hart and Mengjie Zhang and Youhei Akimoto and Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and Daniele Loiacono and Julian Togelius and Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and Faustino Gomez and Carlos M. Fonseca and Heike Trautmann and Alberto Moraglio and William F. Punch and Krzysztof Krawiec and Zdenek Vasicek and Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and Boris Naujoks and Enrique Alba and Gabriela Ochoa and Simon Poulding and Dirk Sudholt and Timo Koetzing", pages = "429--436", keywords = "genetic algorithms, genetic programming", month = "20-24 " # jul, organisation = "SIGEVO", address = "Denver, USA", publisher = "ACM", publisher_address = "New York, NY, USA", isbn13 = "978-1-4503-4206-3", DOI = "doi:10.1145/2908812.2908813", abstract = "Learning classifier systems (LCSs) originated from artificial cognitive systems research, but migrated such that LCS became powerful classification techniques. Modern LCSs can be used to extract building blocks of knowledge in order to solve more difficult problems in the same or a related domain. The past work showed that the reuse of knowledge through the adoption of code fragments, GP-like sub-trees, into the XCS learning classifier system framework could provide advances in scaling. However, unless the pattern underlying the complete domain can be described by the selected LCS representation of the problem, a limit of scaling will eventually be reached. This is due to LCSs divide and conquer approach rule-based solutions, which entails an increasing number of rules (subclauses) to describe a problem as it scales. Inspired by human problem solving abilities, the novel work in this paper seeks to reuse learned knowledge and learned functionality to scale to complex problems by transferring them from simpler problems. Progress is demonstrated on the benchmark Multiplexer (Mux) domain, albeit the developed approach is applicable to other scalable domains. The fundamental axioms necessary for learning are proposed. The methods for transfer learning in LCSs are developed. Also, learning is recast as a decomposition into a series of sub-problems. Results show that from a conventional tabula rasa, with only a vague notion of what subordinate problems might be relevant, it is possible to learn a general solution to any n-bit Mux problem for the first time. This is verified by tests on the 264, 521 and 1034 bit Mux problems.", notes = "GECCO-2016 A Recombination of the 25th International Conference on Genetic Algorithms (ICGA-2016) and the 21st Annual Genetic Programming Conference (GP-2016)", } @InProceedings{alvarez:1998:, author = "Luis F. Alvarez and Vassili V. Toropov", title = "Application of Genetic Programming to the Choice of a Structure of Global Approximations", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "1", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "genetic algorithms, genetic programming", size = "1 page", notes = "GP-98LB", } @InProceedings{oai:CiteSeerPSU:512359, author = "Luis F. Alvarez and Vassili V. Toropov and David C. Hughes and Ashraf F. Ashour", title = "Approximation model building using genetic programming methodology: applications", booktitle = "Second ISSMO/AIAA Internet Conference on Approximations and Fast Reanalysis in Engineering Optimization", year = "2000", editor = "Thouraya Baranger and Fred van Keulen", month = "25 " # may # "-2 " # jun, keywords = "genetic algorithms, genetic programming", URL = "http://www-tm.wbmt.tudelft.nl/~wbtmavk/2aro_conf/Toropov/Fred4.pdf", broken = "http://www-tm.wbmt.tudelft.nl/~wbtmavk/2aro_conf/Toropov/fred.html", URL = "http://www-tm.wbmt.tudelft.nl/~wbtmavk/2aro_conf/Toropov/FRED4.PS", URL = "http://citeseer.ist.psu.edu/512359.html", citeseer-isreferencedby = "oai:CiteSeerPSU:81525", citeseer-references = "oai:CiteSeerPSU:60878", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:512359", rights = "unrestricted", abstract = "Genetic Programming methodology is used for the creation of approximation functions obtained by the response surface methodology. Two important aspects of the problems are addressed: the choice of the plan of experiment and the model tuning using the least-squares response surface fitting. Several examples show the applications of the technique to problems where the values of response functions are obtained either by numerical simulation or laboratory experimentation.", notes = "Multicriteria Optimization of the Manufacturing Process for Roman Cement", } @PhdThesis{Alvarez:thesis, author = "L. F. Alvarez", title = "Design Optimization based on Genetic Programming", school = "Department of Civil and Environmental Engineering, University of Bradford", year = "2000", address = "UK", keywords = "genetic algorithms, genetic programming, Design Optimization, Response Surface Methodology", URL = "http://www.brad.ac.uk/staff/vtoropov/burgeon/thesis_luis/abstract.pdf", URL = "http://www.brad.ac.uk/staff/vtoropov/burgeon/thesis_luis/contents.pdf", URL = "http://www.brad.ac.uk/staff/vtoropov/burgeon/thesis_luis/chapter1.pdf", URL = "http://www.brad.ac.uk/staff/vtoropov/burgeon/thesis_luis/chapter2.pdf", URL = "http://www.brad.ac.uk/staff/vtoropov/burgeon/thesis_luis/chapter3.pdf", URL = "http://www.brad.ac.uk/staff/vtoropov/burgeon/thesis_luis/chapter4.pdf", URL = "http://www.brad.ac.uk/staff/vtoropov/burgeon/thesis_luis/chapter5.pdf", URL = "http://www.brad.ac.uk/staff/vtoropov/burgeon/thesis_luis/chapter7.pdf", URL = "http://www.brad.ac.uk/staff/vtoropov/burgeon/thesis_luis/references.pdf", URL = "http://www.brad.ac.uk/staff/vtoropov/burgeon/thesis_luis/appendixA.pdf", URL = "http://www.brad.ac.uk/staff/vtoropov/burgeon/thesis_luis/appendixB.pdf", size = "v + 132 pages", abstract = "This thesis addresses two problems arising in many real-life design optimization applications: the high computational cost of function evaluations and the presence of numerical noise in the function values. The response surface methodology is used to construct approximations of the original model. A major difficulty in building highly accurate response surfaces is the selection of the structure of an approximation function. A methodology has been developed for the approximation model building using genetic programming. It is implemented in a computer code introducing two new features: the use of design sensitivity information when available, and the allocation and evaluation of tuning parameters in separation from the evolutionary process. A combination of a genetic algorithm and a gradient-based algorithm is used for tuning of the approximation functions. The problem of the choice of a design of experiments in the response surface methodology has been reviewed and a space-filling plan adopted. The developed methodology and software have been applied to design optimization problems with numerically simulated and experimental responses, demonstrating their considerable potential. The applications cover the approximation of a response function obtained by a finite element model for the detection of damage in steel frames, the creation of an empirical model for the prediction of the shear strength in concrete deep beams and a multicriteria optimization of the process of calcination of Roman cement.", notes = "Approximation model building for design optimization using the response surface methodology and genetic programming. Luis Francisco Alvarez Barrioluengo Supervisor V.V. Toropov", } @Article{Alvarez-Diaz:2003:ael, author = "Marcos Alvarez-Diaz and Alberto Alvarez", title = "Forecasting exchange rates using genetic algorithms", journal = "Applied Economics Letters", year = "2003", volume = "10", number = "6", pages = "319--322", month = apr, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1080/13504850210158250", abstract = "A novel approach is employed to investigate the predictability of weekly data on the euro/dollar, British pound/dollar, Deutsch mark/dollar, Japanese yen/dollar, French franc/dollar and Canadian dollar/dollar exchange rates. A functional search procedure based on the Darwinian theories of natural evolution and survival, called genetic algorithms (hereinafter GA), was used to find an analytical function that best approximates the time variability of the studied exchange rates. In all cases, the mathematical models found by the GA predict slightly better than the random walk model. The models are heavily dominated by a linear relationship with the most recent past value, while contributions from nonlinear terms to the total forecasting performance are rather small. In consequence, the results agree with previous works establishing explicitly that nonlinear nature of exchange rates cannot be exploited to substantially improve forecasting.", } @Article{Alvarez-Diaz:2005:EE, author = "Marcos Alvarez-Diaz and Alberto Alvarez", title = "Genetic multi-model composite forecast for non-linear prediction of exchange rates", journal = "Empirical Economics", year = "2005", volume = "30", number = "3", pages = "643--663", month = oct, keywords = "genetic algorithms, genetic programming, Composite-forecast or data-fusion, neural networks, ANN, exchange-rate forecasting", ISSN = "0377-7332", DOI = "doi:10.1007/s00181-005-0249-5", size = "21 pages", abstract = "The existence of non-linear deterministic structures in the dynamics of exchange rates has already been amply demonstrated. In this paper, we attempt to exploit these non-linear structures employing forecasting techniques, such as Genetic Programming and Neural Networks, in the specific case of the Yen/US$ and Pound Sterling/US$ exchange rates. Forecasts obtained from genetic programming and neural networks are then genetically fused to verify whether synergy provides an improvement in the predictions. Our analysis considers both point predictions and the anticipating of either depreciations or appreciations.", } @Article{Alvarez-Diaz:2006:jbe, author = "Marcos Alvarez-Diaz and Marcos Dominquez-Torreiro", title = "Using Genetic Algorithms to Estimate and Validate Bioeconomic Models: The Case of the Ibero-atlantic Sardine Fishery", journal = "Journal of Bioeconomics", year = "2006", volume = "8", number = "1", pages = "55--65", month = apr, keywords = "genetic algorithms, genetic programming, bioeconomic modeling, linear and non-linear forecasting", ISSN = "1387-6996", DOI = "doi:10.1007/s10818-005-0494-x", abstract = "The Neo-classical approach to fisheries management is based on designing and applying bioeconomic models. Traditionally, the basic bioeconomic models have used pre-established non-linear functional forms (logistic, Cobb-Douglas) in order to try to reflect the dynamics of the renewable resources under study. This assumption might cause misspecification problems and, in consequence, a loss of predictive ability. In this work we intend to verify if there is a bias motivated by employing the said non-linear parametric perspective. For this purpose, we employ a novel non-linear and non-parametric prediction method, called Genetic Algorithms, and we compare its results with those obtained from the traditional methods.", notes = " p 64 {"}Unlike a uni-variant analysis, DARWIN now allows us to look for functional relationships between two or more time-series.{"}", } @PhdThesis{Marcos_Alvarez-Diaz:thesis, author = "Marcos Alvarez-Diaz", title = "Exchange rates forecasting using nonparametric methods", school = "Columbia University", year = "2006", address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming", isbn13 = "978-0-542-91527-7", URL = "http://search.proquest.com/docview/305345652", size = "105 pages", abstract = "The existence of non-linear deterministic structures in the dynamics of exchange rates has already been amply demonstrated in the literature. With my research, I try to explain if we can exploit these non-linear structures in order to improve our predictive ability and, secondly, if we can use these predictions to generate profitable strategies in the Foreign Exchange Market. To this purpose, I employ different nonparametric forecasting methods such as Nearest Neighbours, Genetic Programming, Artificial Neural Networks, Data-Fusion or an Evolutionary Neural Network. My analysis will be centre on the specific case of the Yen/US$ and Pound Sterling/US$ exchange rates and it considers both point predictions and the anticipating of either depreciations or appreciations. My results reveal a slight forecasting ability for one-period-ahead which is lost when more periods ahead are considered, and my trading strategy obtains above-normal profits. However, when transaction costs are incorporated, the profits practically disappear or become negative", notes = "UMI Microform 3237194 ProQuest Dissertations Publishing, 2006. 3237194", } @Article{AlvarezDiaz2008161, author = "Marcos Alvarez-Diaz and Gonzalo {Caballero Miguez}", title = "The quality of institutions: A genetic programming approach", journal = "Economic Modelling", volume = "25", number = "1", pages = "161--169", year = "2008", ISSN = "0264-9993", DOI = "doi:10.1016/j.econmod.2007.05.001", URL = "http://www.sciencedirect.com/science/article/B6VB1-4P0VD80-1/2/c0bb8da3af64aa1ea6b0a4f90e4790b0", keywords = "genetic algorithms, genetic programming, Quality of institutions, Institutional determinants, Non-parametric perspective", abstract = "The new institutional economics has studied the determinants of the quality of institutions. Traditionally, the majority of the empirical literature has adopted a parametric and linear approach. These forms impose ad hoc functional structures, sometimes introducing relationships between variables that are forced and misleading. This paper analyses the determinants of the quality of institutions using a non-parametric and non-linear approach. Specifically, we employ a Genetic Program (GP) to study the functional relation between the quality of institutions and a set of historical, economical, geographical, religious and social variables. Besides this, we compare the obtained results with those employing a parametric perspective (Ordinary Least Square Regression). Following the empirical results of our application, we can conclude that the parametric perspective adopted in previous papers about institutional quality could be accurate.", } @TechReport{Alvarez-Diaz:funcas401, author = "Marcos Alvarez-Diaz and Gonzalo {Caballero Miguez} and Mario Solino", title = "The institutional determinants of {CO2} emissions: A computational modelling approach using Artificial Neural Networks and Genetic Programming", institution = "Fundacion de las Cajas de Ahorros", year = "2008", type = "FUNCAS Working Paper", number = "401", address = "Madrid", month = jul, keywords = "genetic algorithms, genetic programming, ANN", URL = "https://dialnet.unirioja.es/ejemplar/212749", broken = "http://www.funcas.es/Publicaciones/InformacionArticulos/Publicaciones.asp?ID=1411", notes = "see \cite{Alvarez-Diaz:2011:EM}", } @Article{Alvarez-Diaz:2009:IJCEE, title = "Forecasting tourist arrivals to {Balearic} {Islands} using genetic programming", author = "Marcos Alvarez-Diaz and Josep Mateu-Sbert and Jaume Rossello-Nadal", year = "2009", volume = "1", journal = "International Journal of Computational Economics and Econometrics", number = "1", pages = "64--75", month = nov # "~06", keywords = "genetic algorithms, genetic programming, tourism forecasting, Diebold-Mariano test, tourist arrivals, Balearic Islands, UK, United Kingdom, Germany, Spain", URL = "http://www.inderscience.com/link.php?id=29153", DOI = "doi:10.1504/IJCEE.2009.029153", publisher = "Inderscience Publishers", ISSN = "1757-1189", bibsource = "OAI-PMH server at www.inderscience.com", abstract = "Traditionally, univariate time-series models have largely dominated forecasting for international tourism demand. In this paper, the ability of a genetic program (GP) to predict monthly tourist arrivals from UK and Germany to Balearic Islands, Spain is explored. GP has already been employed satisfactorily in different scientific areas, including economics. The technique shows different advantages regarding to other forecasting methods. Firstly, it does not assume a priori a rigid functional form of the model. Secondly, it is more robust and easy-to-use than other non-parametric methods. Finally, it provides explicitly a mathematical equation which allows a simple ad hoc interpretation of the results. Comparing the performance of the proposed technique against other method commonly used in tourism forecasting (no-change model, moving average and ARIMA), the empirical results reveal that GP can be a valuable tool in this field.", } @Article{AlvarezDiaz2009, author = "Marcos {Alvarez Diaz} and Manuel Gonzalez Gomez and Angeles {Saavedra Gonzalez} and Jacobo {De Una Alvarez}", title = "On dichotomous choice contingent valuation data analysis: Semiparametric methods and Genetic Programming", journal = "Journal of Forest Economics", year = "2010", volume = "16", number = "2", pages = "145--156", month = apr, keywords = "genetic algorithms, genetic programming, Dichotomous choice contingent valuation, Genetic program, Parametric techniques, Proportional hazard model", ISSN = "1104-6899", DOI = "doi:10.1016/j.jfe.2009.02.002", broken = "http://www.sciencedirect.com/science/article/B7GJ5-4XY3F46-1/2/d98566d6ee97a4f7f2c2f1b9deb29bc1", size = "12 pages", abstract = "The aim of this paper is twofold. Firstly, we introduce a novel semi-parametric technique called Genetic Programming to estimate and explain the willingness to pay to maintain environmental conditions of a specific natural park in Spain. To the authors' knowledge, this is the first time in which Genetic Programming is employed in contingent valuation. Secondly, we investigate the existence of bias due to the functional rigidity of the traditional parametric techniques commonly employed in a contingent valuation problem. We applied standard parametric methods (logit and probit) and compared with results obtained using semi parametric methods (a proportional hazard model and a genetic program). The parametric and semiparametric methods give similar results in terms of the variables finally chosen in the model. Therefore, the results confirm the internal validity of our contingent valuation exercise.", notes = "2022 https://www.nowpublishers.com/JFE", } @Article{Alvarez-Diaz:2010:AEL, title = "Forecasting exchange rates using local regression", author = "Marcos Alvarez-Diaz and Alberto Alvarez", journal = "Applied Economics Letters", year = "2010", volume = "17", number = "5", pages = "509--514", month = mar, keywords = "genetic algorithms, genetic programming, local search", ISSN = "1350-4851", URL = "http://hdl.handle.net/10261/54902", URL = "https://ideas.repec.org/a/taf/apeclt/v17y2010i5p509-514.html", DOI = "doi:10.1080/13504850801987217", oai = "oai:RePEc:taf:apeclt:v:17:y:2010:i:5:p:509-514", size = "6 pages", abstract = "In this article we use a generalisation of the standard nearest neighbours, called local regression (LR), to study the predictability of the yen/US dollar and pound sterling/US dollar exchange rates. We also compare our results with those previously obtained with global methods such as neural networks, genetic programming, data fusion and evolutionary neural networks. We want to verify if we can generalise to the exchange rate forecasting problem the belief that local methods beat global ones.", notes = "In this letter we have used LR to verify three aspects regarding to exchange rate forecasting for the Japanese yen and the British pound against US dollar. Firstly, we analyse their predictability discovering the existence of a short-term predictable structure in the temporal evolution of both currencies. Secondly, we confirm the homogeneity behaviour in terms of forecasting for weekly exchange rates and, finally, we also verify that local methods do not always beat to the global ones in an exchange rate forecasting exercise.", } @Article{Alvarez-Diaz:2010:AFE, author = "Marcos {Alvarez Diaz}", title = "Speculative strategies in the foreign exchange market based on genetic programming predictions", journal = "Applied Financial Economics", year = "2010", volume = "20", number = "6", pages = "465--476", month = mar, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1080/09603100903459782", oai = "oai:RePEc:taf:apfiec:v:20:y:2010:i:6:p:465-476", abstract = "In this article, we investigate the out-of-sample forecasting ability of a Genetic Program (GP) to approach the dynamic evolution of the yen/US dollar and British pound/US dollar exchange rates, and verify whether the method can beat the random walk model. Later on, we use the predicted values to generate a trading rule and we check the possibility of obtaining extraordinary profits in the foreign exchange market. Our results reveal a slight forecasting ability for one-period-ahead, which is lost when more periods ahead are considered. On the other hand, our trading strategy obtains above-normal profits. However, when transaction costs are incorporated, the profits practically disappear or become negative.", notes = "Department of Economics, University of Vigo, Galicia, Spain", } @Article{Alvarez-Diaz:2011:EM, author = "Marcos Alvarez-Diaz and Gonzalo Caballero-Miguez and Mario Solino", title = "The institutional determinants of {CO2} emissions: a computational modeling approach using Artificial Neural Networks and Genetic Programming", journal = "Environmetrics", year = "2011", volume = "22", number = "1", pages = "42--49", month = feb, keywords = "genetic algorithms, genetic programming, artificial neural networks, ANN, computational methods, CO2 emissions, institutional determinants", URL = "https://doi.org/10.1002/env.1025", URL = "https://onlinelibrary.wiley.com/doi/abs/10.1002/env.1025", URL = "https://onlinelibrary.wiley.com/doi/pdf/10.1002/env.1025", DOI = "doi:10.1002/env.1025", size = "8 pages", abstract = "Understanding the complex process of climate change implies the knowledge of all possible determinants of CO2 emissions. This paper studies the influence of several institutional determinants on CO2 emissions, clarifying which variables are relevant to explain this influence. For this aim, Genetic Programming and Artificial Neural Networks are used to find an optimal functional relationship between the CO2 emissions and a set of historical, economic, geographical, religious, and social variables, which are considered as a good approximation to the institutional quality of a country. Besides this, the paper compares the results using these computational methods with that employing a more traditional parametric perspective: ordinary least squares regression (OLS). Following the empirical results of the cross-country application, this paper generates new evidence on the binomial institutions and CO2 emissions. Specifically, all methods conclude a significant influence of ethnolinguistic fractionalization (ETHF) on CO2 emissions.", notes = "Replaces \cite{Alvarez-Diaz:funcas401}?", } @Article{Alvarez-Diaz:2019:Forecasting, author = "Marcos Alvarez-Diaz and Manuel Gonzalez-Gomez and Maria Soledad Otero-Giraldez", title = "Forecasting International Tourism Demand Using a Non-Linear Autoregressive Neural Network and Genetic Programming", journal = "Forecasting", year = "2019", volume = "1", number = "1", pages = "90--106", note = "Special Issue Applications of Forecasting by Hybrid Artificial Intelligent Technologies", keywords = "genetic algorithms, genetic programming, ANN, international tourism demand forecasting, artificial neural networks, SARIMA, spain", ISSN = "2571-9394", bibsource = "OAI-PMH server at oai.repec.org", identifier = "RePEc:gam:jforec:v:1:y:2018:i:1:p:7-106:d:169666", oai = "oai:RePEc:gam:jforec:v:1:y:2018:i:1:p:7-106:d:169666", URL = "https://www.mdpi.com/2571-9394/1/1/7/", URL = "https://www.mdpi.com/2571-9394/1/1/7.pdf", DOI = "doi:10.3390/forecast1010007", abstract = "This study explores the forecasting ability of two powerful non-linear computational methods: artificial neural networks and genetic programming. We use as a case of study the monthly international tourism demand in Spain, approximated by the number of tourist arrivals and of overnight stays. The forecasting results reveal that non-linear methods achieve slightly better predictions than those obtained by a traditional forecasting technique, the seasonal autoregressive integrated moving average (SARIMA) approach. This slight forecasting improvement was close to being statistically significant. Forecasters must judge whether the high cost of implementing these computational methods is worthwhile.", } @Article{Alvarez-Diaz:2020:EE, author = "Marcos Alvarez-Diaz", title = "Is it possible to accurately forecast the evolution of Brent crude oil prices? An answer based on parametric and nonparametric forecasting methods", journal = "Empirical Economics", year = "2020", volume = "59", pages = "1285--1305", month = sep, keywords = "genetic algorithms, genetic programming, ANN, KNN, oil price, Forecasting, ARIMA, M-GARCH, Neural networks, Nearest-neighbour method", DOI = "doi:10.1007/s00181-019-01665-w", abstract = "Can we accurately predict the Brent oil price? If so, which forecasting method can provide the most accurate forecasts? To unravel these questions, we aim at predicting the weekly Brent oil price growth rate by using several forecasting methods that are based on different approaches. Basically, we assess and compare the out-of-sample performances of linear parametric models (the ARIMA, the ARFIMA and the autoregressive model), a nonlinear parametric model (the GARCH-in-Mean model) and different nonparametric data-driven methods (a nonlinear autoregressive artificial neural network, genetic programming and the nearest-neighbor method). The results obtained show that (1) all methods are capable of predicting accurately both the value and the directional change in the Brent oil price, (2) there are no significant forecasting differences among the methods and (3) the volatility of the series could be an important factor to enhance our predictive ability.", notes = "Department of Fundaments of Economic Analysis and History, and Economic Institutions, University of Vigo, Vigo, Spain", } @Misc{journals/corr/abs-2005-07669, author = "Jeovane Honorio Alves and Lucas Ferrari de Oliveira", title = "Optimizing Neural Architecture Search using Limited {GPU} Time in a Dynamic Search Space: A Gene Expression Programming Approach", howpublished = "arXiv", year = "2020", volume = "abs/2005.07669", keywords = "genetic algorithms, genetic programming, gene expression programming, GPU", URL = "https://arxiv.org/abs/2005.07669", bibdate = "2020-05-22", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/corr/corr2005.html#abs-2005-07669", } @Misc{oai:arXiv.org:1002.2012, title = "Implementing Genetic Algorithms on Arduino Micro-Controllers", author = "Nuno Alves", year = "2010", month = feb # "~09", size = "10 pages", abstract = "Since their conception in 1975, Genetic Algorithms have been an extremely popular approach to find exact or approximate solutions to optimisation and search problems. Over the last years there has been an enhanced interest in the field with related techniques, such as grammatical evolution, being developed. Unfortunately, work on developing genetic optimisations for low-end embedded architectures hasn't embraced the same enthusiasm. This short paper tackles that situation by demonstrating how genetic algorithms can be implemented in Arduino Duemilanove, a 16 MHz open-source micro-controller, with limited computation power and storage resources. As part of this short paper, the libraries used in this implementation are released into the public domain under a GPL license.", bibsource = "OAI-PMH server at export.arxiv.org", oai = "oai:arXiv.org:1002.2012", keywords = "genetic algorithms, computer science, neural and evolutionary computing", URL = "http://arxiv.org/abs/1002.2012", URL = "http://arxiv.org/pdf/1002.2012v1.pdf", notes = "not GP", } @Article{ALVISO:2020:Fuel, author = "Dario Alviso and Guillermo Artana and Thomas Duriez", title = "Prediction of biodiesel physico-chemical properties from its fatty acid composition using genetic programming", journal = "Fuel", volume = "264", pages = "116844", year = "2020", ISSN = "0016-2361", DOI = "doi:10.1016/j.fuel.2019.116844", URL = "http://www.sciencedirect.com/science/article/pii/S0016236119321982", keywords = "genetic algorithms, genetic programming, Biodiesel, Fatty acid, Properties, Regression analysis", abstract = "This paper presents regression analysis of biodiesel physico-chemical properties as a function of fatty acid composition using an experimental database. The study is done by using 48 edible and non-edible oils-based biodiesel available data. Regression equations are presented as a function of fatty acid composition (saturated and unsaturated methyl esters). The physico-chemical properties studied are kinematic viscosity, flash point, cloud point, pour point (PP), cold filter plugging point, cetane (CN) and iodine numbers. The regression technique chosen to carry out this work is genetic programming (GP). Unlike multiple linear regression (MLR) strategies available in literature, GP provides generic, non-parametric regression among variables. For all properties analyzed, the performance of the regression is systematically better for GP than MLR. Indeed, the RSME related to the experimental database is lower for GP models, from approx3percent for CN to approx55percent for PP, in comparison to the best MLR model for each property. Particularly, most GP regression models reproduce correctly the dependence of properties on the saturated and unsaturated methyl esters", } @Article{ALVISO:2021:JFCA, author = "Dario Alviso and Cristhian Zarate and Guillermo Artana and Thomas Duriez", title = "Regressions of the dielectric constant and speed of sound of vegetable oils from their composition and temperature using genetic programming", journal = "Journal of Food Composition and Analysis", volume = "104", pages = "104175", year = "2021", ISSN = "0889-1575", DOI = "doi:10.1016/j.jfca.2021.104175", URL = "https://www.sciencedirect.com/science/article/pii/S0889157521003756", keywords = "genetic algorithms, genetic programming, Vegetable oils, Regression, Dielectric constant, Speed of sound, Fatty acid", abstract = "The dielectric constant (DC) and speed of sound (SoS) have been measured in many studies on vegetable oils (VOs). These measurements can be applied for quality control, for the detection of contaminants, and in works related to heated and frying VOs. There are several hundreds of VOs with potential use in the food industry, and for most of them, the DC and SoS values are not yet available. This paper proposes regression models of the DC and SoS of VOs as a function of their composition (saturated and unsaturated fatty acids) and the temperature. A regression study was conducted using available experimental databases including a total of 57 and 56 data in the range of 20-50 degreeC for the DC and SoS, respectively. The equations are obtained using genetic programming (GP). The goal is to minimize the mean absolute error (MAE) between the values of the measured and predicted DC and SoS for several VOs. The resulting GP regression equations reproduce correctly the dependencies of the DC and SoS of VOs on the saturated and unsaturated fatty acids. The validation of these equations is carried out by comparing their results to those of the experimental databases. The MAE values of the regression equations concerning the databases for DC and SoS of VOs are 0.02 and 1.0 m/s, respectively", } @Article{ALVISO:2021:Fuel, author = "Dario Alviso and Cristhian Zarate and Thomas Duriez", title = "Modeling of vegetable oils cloud point, pour point, cetane number and iodine number from their composition using genetic programming", journal = "Fuel", volume = "284", pages = "119026", year = "2021", ISSN = "0016-2361", DOI = "doi:10.1016/j.fuel.2020.119026", URL = "https://www.sciencedirect.com/science/article/pii/S0016236120320226", keywords = "genetic algorithms, genetic programming, Vegetable oils, Fatty acid, Cetane number, Iodine number, Cloud point, Pour point", abstract = "Vegetable oils (VOs) are composed of 90-98percent of triglycerides, i.e. esters composed of three fatty acids and glycerol, and small amounts of mono- and di-glycerides. Due to their physico-chemical properties, VOs have been considered for uses especially in large ships, in stationary engines and low and medium speed diesel engines, in pure form or in blends with fuel oil, diesel, biodiesel and alcohols. There are about 350 VOs with potential as fuel sources, and for most of them, physico-chemical properties values have not yet been measured. In this context, regression models using only VOs fatty acid composition are very useful. In the present paper, regression analysis of VOs cloud point (CP), pour point (PP), cetane number (CN) and iodine number (IN) as a function of saturated and unsaturated fatty acids is conducted. The study is done by using 4 experimental databases including 88 different data of VOs. Concerning the regression technique, genetic programming (GP) has been chosen. The cost function of GP is defined to minimize the Mean Absolute Error (MAE) between experimental and predicted values of each property. The resulting GP models consisting of terms including saturated and unsaturated fatty acids reproduce correctly the dependencies of all four properties on those acids. And they are validated by showing that their results are in good agreement to the experimental databases. In fact, MAE values of the proposed models with respect to the databases for CP, PP, CN and IN are lower than 4.51 degreeC, 4.54 degreeC, 3.64 and 8.01, respectively", } @Article{Alweshah:2015:IJCA, author = "Mohammed Alweshah and Walid Ahmed and Hamza Aldabbas", title = "Evolution of Software Reliability Growth Models: A Comparison of Auto-Regression and Genetic Programming Models", journal = "International Journal of Computer Applications", year = "2015", volume = "125", number = "3", pages = "20--25", month = sep, keywords = "genetic algorithms, genetic programming", publisher = "Foundation of Computer Science (FCS), NY, USA", address = "New York, USA", ISSN = "0975-8887", URL = "https://www.ijcaonline.org/archives/volume125/number3/22413-2015905864", URL = "https://www.ijcaonline.org/research/volume125/number3/alweshah-2015-ijca-905864.pdf", DOI = "doi:10.5120/ijca2015905864", size = "6 pages", abstract = "Building reliability growth models to predict software reliability and identify and remove errors is both a necessity and a challenge for software testing engineers and project managers. Being able to predict the number of faults in software helps significantly in determining the software release date and in effectively managing project resources. Most of the growth models consider two or three parameters to estimate the accumulated faults in the testing process. Interest in using evolutionary computation to solve prediction and modeling problems has grown in recent years. In this paper, we explore the use of genetic programming (GP) as a tool to help in building growth models that can accurately predict the number of faults in software early on in the testing process. The proposed GP model is based on a recursive relation derived from the history of measured faults. The developed model is tested on real-time control, military, and operating system applications. The dataset was developed by John Musa of Bell Telephone Laboratories. The results of a comparison of the GP model with the traditional and simpler auto-regression model are presented.", notes = "www.ijcaonline.org Al-Balqa Applied University, Salt, Jordan", } @InProceedings{DBLP:conf/ijcci/AlyasiriCK18, author = "Hasanen Alyasiri and John A. Clark and Daniel Kudenko", title = "Applying Cartesian Genetic Programming to Evolve Rules for Intrusion Detection System", booktitle = "Proceedings of the 10th International Joint Conference on Computational Intelligence, IJCCI 2018", editor = "Christophe Sabourin and Juan Julian Merelo Guervos and Alejandro Linares-Barranco and Kurosh Madani and Kevin Warwick", pages = "176--183", publisher = "SciTePress", year = "2018", address = "Seville, Spain", month = sep # " 18-20", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Intrusion Detection System, Stacking Ensemble", isbn13 = "978-989-758-327-8", ISSN = "2184-2825", URL = "https://www.scitepress.org/Papers/2018/69259/69259.pdf", URL = "https://doi.org/10.5220/0006925901760183", DOI = "doi:10.5220/0006925901760183", timestamp = "Thu, 26 Sep 2019 16:43:57 +0200", biburl = "https://dblp.org/rec/conf/ijcci/AlyasiriCK18.bib", bibsource = "dblp computer science bibliography, https://dblp.org", size = "8 pages", abstract = "With cyber-attacks becoming a regular feature in daily business and attackers continuously evolving their techniques, we are witnessing ever more sophisticated and targeted threats. Various artificial intelligence algorithms have been deployed to analyse such incidents. Extracting knowledge allows the discovery of new attack methods, intrusion scenarios, and attackers objectives and strategies, all of which can help distinguish attacks from legitimate behaviour. Among those algorithms, Evolutionary Computation (EC) techniques have seen significant application. Research has shown it is possible to use EC methods to construct IDS detection rules. we show how Cartesian Genetic Programming (CGP) can construct the behaviour rule upon which an intrusion detection will be able to make decisions regarding the nature of the activity observed in the system. The CGP framework evolves human readable solutions that provide an explanation of the logic behind its evolved decisions. Experiments are conducted on up-to-date cybersecurity datasets and compared with state of the art paradigms. We also introduce ensemble learning paradigm, indicating how CGP can be used as stacking technique to improve the learning performance.", notes = "Also known as \cite{alyasiri2018applying} Department of Computer Science, University of York, UK", } @PhdThesis{Hasanen_Thesis_2018, author = "Hasanen Alyasiri", title = "Developing Efficient and Effective Intrusion Detection System using Evolutionary Computation", school = "Computer Science, University of York", year = "2018", address = "UK", month = nov, keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", URL = "http://etheses.whiterose.ac.uk/id/eprint/23699", URL = "http://etheses.whiterose.ac.uk/23699/1/Hasanen_Thesis_2018.pdf", size = "157 pages", abstract = "The internet and computer networks have become an essential tool in distributed computing organisations especially because they enable the collaboration between components of heterogeneous systems. The efficiency and flexibility of online services have attracted many applications, but as they have grown in popularity so have the numbers of attacks on them. Thus, security teams must deal with numerous threats where the threat landscape is continuously evolving. The traditional security solutions are by no means enough to create a secure environment, intrusion detection systems (IDSs), which observe system works and detect intrusions, are usually used to complement other defense techniques. However, threats are becoming more sophisticated, with attackers using new attack methods or modifying existing ones. Furthermore, building an effective and efficient IDS is a challenging research problem due to the environment resource restrictions and its constant evolution. To mitigate these problems, we propose to use machine learning techniques to assist with the IDS building effort. In this thesis, Evolutionary Computation (EC) algorithms are empirically investigated for synthesising intrusion detection programs. EC can construct programs for raising intrusion alerts automatically. One novel proposed approach, i.e. Cartesian Genetic Programming, has proved particularly effective. We also used an ensemble-learning paradigm, in which EC algorithms were used as a meta-learning method to produce detectors. The latter is more fully worked out than the former and has proved a significant success. An efficient IDS should always take into account the resource restrictions of the deployed systems. Memory usage and processing speed are critical requirements. We apply a multi-objective approach to find trade-offs among intrusion detection capability and resource consumption of programs and optimise these objectives simultaneously. High complexity and the large size of detectors are identified as general issues with the current approaches. The multi-objective approach is used to evolve Pareto fronts for detectors that aim to maintain the simplicity of the generated patterns. We also investigate the potential application of these algorithms to detect unknown attacks.", notes = "supervisor: John A. Clark Identification Number/EthosID: uk.bl.ethos.772979", } @InProceedings{alyasiri2020evolving, author = "Hasanen Alyasiri", title = "Evolving Rules for Detecting Cross-Site Scripting Attacks Using Genetic Programming", booktitle = "2nd International Conference on Advances in Cyber Security, ACeS 2020", year = "2020", editor = "Mohammed Anbar and Nibras Abdullah and Selvakumar Manickam", volume = "1347", series = "Communications in Computer and Information Science", pages = "642--656", address = "Penang, Malaysia", month = dec # " 8-9", publisher = "Springer", note = "Revised Selected Papers", keywords = "genetic algorithms, genetic programming", isbn13 = "978-981-33-6834-7", timestamp = "Mon, 15 Feb 2021 12:59:21 +0100", biburl = "https://dblp.org/rec/conf/aces/Alyasiri20.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://link.springer.com/chapter/10.1007/978-981-33-6835-4_42", DOI = "doi:10.1007/978-981-33-6835-4_42", abstract = "Web services are now a critical element of many of our day-to-day activities. Their applications are one of the fastest-growing industries around. The security issues related to these services are a major concern to their providers and are directly relevant to the everyday lives of system users. Cross-Site Scripting (XSS) is a standout amongst common web application security attacks. Protection against XSS injection attacks needs more work. Machine learning has considerable potential to provide protection in this critical domain. In this article, we show how genetic programming can be used to evolve detection rules for XSS attacks. We conducted our experiments on a publicly available and up-to-date dataset. The experimental results showed that the proposed method is an effective countermeasure against XSS attacks. We then investigated the computational cost of the detection rules. The best-evolved rule has a processing time of 177.87 ms and consumes memory of 8600 bytes.", notes = "Also known as \cite{DBLP:conf/aces/Alyasiri20} Department of Computer Science, University of Kufa, Kufa, Iraq", } @InProceedings{alyasiri2021grammatical, author = "Hasanen Alyasiri and John A Clark and Ali Malik and Ruairi {de Frein}", title = "Grammatical Evolution for Detecting Cyberattacks in Internet of Things Environments", booktitle = "2021 International Conference on Computer Communications and Networks (ICCCN)", year = "2021", address = "Athens, Greece", month = "19-22 " # jul, organization = "IEEE", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", isbn13 = "978-1-6654-4835-2", URL = "https://ieeexplore.ieee.org/abstract/document/9522283", DOI = "doi:10.1109/ICCCN52240.2021.9522283", abstract = "The Internet of Things (IoT) is revolutionising nearly every aspect of modern life, playing an ever greater role in both industrial and domestic sectors. The increasing frequency of cyber-incidents is a consequence of the pervasiveness of IoT. Threats are becoming more sophisticated, with attackers using new attacks or modifying existing ones. Security teams must deal with a diverse and complex threat landscape that is constantly evolving. Traditional security solutions cannot protect such systems adequately and so researchers have begun to use Machine Learning algorithms to discover effective defense systems. we investigate how one approach from the domain of evolutionary computation, grammatical evolution, can be used to identify cyberattacks in IoT environments. The experiments were conducted on up-to-date datasets and compared with state-of-the-art algorithms. The potential application of evolutionary computation-based approaches to detect unknown attacks is also examined and discussed.", notes = "Also known as \cite{9522283} University of Kufa, Iraq", } @Article{Amandi:2018:GPEM, author = "Analia Amandi", title = "Ryan J. Urbanowicz, and Will N. Browne: Introduction to learning classifier systems Springer, 2017, 123 pp, ISBN 978-3-662-55007-6", journal = "Genetic Programming and Evolvable Machines", year = "2018", volume = "19", number = "4", pages = "569--570", month = dec, note = "Book review", keywords = "genetic algorithms, LCS", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-018-9322-7", size = "2 pages", notes = "p570 'appropriate for anyone who wants to know and begin to use Learning Classifier Systems'", } @Article{Amar:2020:jNGSE, author = "Menad Nait Amar and Mohammed Abdelfetah Ghriga and Hocine Ouaer and Mohamed El Amine Ben Seghier and Binh Thai Pham and Pal Ostebo Andersen", title = "Modeling viscosity of {CO2} at high temperature and pressure conditions", journal = "Journal of Natural Gas Science and Engineering", year = "2020", volume = "77", pages = "103271", month = may, keywords = "genetic algorithms, genetic programming, gene expression programming, ANN, carbon dioxide, correlations, data-driven, GEP, MLP, viscosity, chemical sciences/polymers, material chemistry, physical chemistry", ISSN = "1875-5100", publisher = "HAL CCSD; Elsevier", URL = "https://hal.archives-ouvertes.fr/hal-02534736", DOI = "doi:10.1016/j.jngse.2020.103271", abstract = "The present work aims at applying Machine Learning approaches to predict CO2 viscosity at different thermodynamical conditions. Various data-driven techniques including multilayer perceptron (MLP), gene expression programming (GEP) and group method of data handling (GMDH) were implemented using 1124 experimental points covering temperature from 220 to 673 K and pressure from 0.1 to 7960 MPa. Viscosity was modelled as function of temperature and density measured at the stated conditions. Four backpropagation-based techniques were considered in the MLP training phase; Levenberg-Marquardt (LM), bayesian regularization (BR), scaled conjugate gradient (SCG) and resilient backpropagation (RB). MLP-LM was the most fit of the proposed models with an overall root mean square error (RMSE) of 0.0012 mPa s and coefficient of determination (R2) of 0.9999. A comparison showed that our MLP-LM model outperformed the best preexisting Machine Learning CO2 viscosity models, and that our GEP correlation was superior to preexisting explicit correlations.", annote = "Institut des sciences analytiques et de physico-chimie pour l'environnement et les materiaux (IPREM) ; Universite de Pau et des Pays de l'Adour (UPPA)-Centre National de la Recherche Scientifique (CNRS); Institute of Research and Development; Duy-Tan University; University of Stavanger ; University of Stavanger", bibsource = "OAI-PMH server at api.archives-ouvertes.fr", description = "International audience", identifier = "hal-02534736; DOI: 10.1016/j.jngse.2020.103271", language = "en", oai = "oai:HAL:hal-02534736v1", relation = "info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jngse.2020.103271", } @Article{Amar:2019:ChemSci, author = "Yehia Amar and Artur M. Schweidtmann and Paul Deutsch and Liwei Cao and Alexei Lapkin", title = "Machine learning and molecular descriptors enable rational solvent selection in asymmetric catalysis", journal = "Chemical Science", year = "2019", volume = "10", number = "27", pages = "6697--6706", month = jul, note = "Edge Article", keywords = "genetic algorithms, genetic programming, TPOT, gamultiobj, matlab, GP surrogate models, in silico modeling", publisher = "Royal Society of Chemistry", URL = "https://pubs.rsc.org/en/content/articlepdf/2019/sc/c9sc01844a", DOI = "doi:10.1039/C9SC01844A", size = "10 pages", abstract = "Rational solvent selection remains a significant challenge in process development. Here we describe a hybrid mechanistic-machine learning approach, geared towards automated process development workflow. A library of 459 solvents was used, for which 12 conventional molecular descriptors, two reaction-specific descriptors, and additional descriptors based on screening charge density, were calculated. Gaussian process surrogate models were trained on experimental data from a Rh(CO)2(acac)/Josiphos catalysed asymmetric hydrogenation of a chiral alpha-beta unsaturated gamma-lactam. With two simultaneous objectives: high conversion and high diastereomeric excess, the multi-objective algorithm, trained on the initial dataset of 25 solvents, has identified solvents leading to better reaction outcomes. In addition to being a powerful design of experiments (DoE) methodology, the resulting Gaussian process surrogate model for conversion is, in statistical terms, predictive, with a cross-validation correlation coefficient of 0.84. After identifying promising solvents, the composition of solvent mixtures and optimal reaction temperature were found using a black-box Bayesian optimisation. We then demonstrated the application of a new genetic programming approach to select an appropriate machine learning model for a specific physical system, which should allow the transition of the overall process development workflow into the future robotic laboratories.", notes = "p6704 'the automated machine learning workflow was successfully used for the problem of solvent selection .. supplemented .. surrogate model' Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK http://rsc.li/chemical-science", } @PhdThesis{Amar:thesis, author = "Yehia Amar", title = "Accelerating process development of complex chemical reactions", school = "Department of Chemical Engineering and Biotechnology, University of Cambridge", year = "2019", address = "UK", keywords = "molecular descriptors, design of experiments, asymmetric hydrogenation, machine learning, process development", URL = "https://www.repository.cam.ac.uk/handle/1810/288220", DOI = "doi:10.17863/CAM.35535", abstract = "Process development of new complex reactions in the pharmaceutical and fine chemicals industries is challenging, and expensive. The field is beginning to see a bridging between fundamental first-principles investigations, and use of data-driven statistical methods, such as machine learning. Nonetheless, process development and optimisation in these industries is mostly driven by trial-and-error, and experience. Approaches that move beyond these are limited to the well-developed optimisation of continuous variables, and often do not yield physical insights. This thesis describes several new methods developed to address research questions related to this challenge. First, we investigated whether using physical knowledge could aid statistics-guided self-optimisation of a C-H activation reaction, in which the optimisation variables were continuous. We then considered algorithmic treatment of the more challenging discrete variables, focusing on solvents. We parametrised a library of 459 solvents with physically meaningful molecular descriptors. Our case study was a homogeneous Rh-catalysed asymmetric hydrogenation to produce a chiral gamma-lactam, with conversion and diastereoselectivity as objectives. We adapted a state-of-the-art multi-objective machine learning algorithm, based on Gaussian processes, to use the descriptors as inputs, and to create a surrogate model for each objective. The aim of the algorithm was to determine a set of Pareto solutions with a minimum experimental budget, whilst simultaneously addressing model uncertainty. We found that descriptors are a valuable tool for Design of Experiments, and can produce predictive and interpretable surrogate models. Subsequently, a physical investigation of this reaction led to the discovery of an efficient catalyst-ligand system, which we studied by operando NMR, and identified a parameterised kinetic model. Turning the focus then to ligands for asymmetric hydrogenation, we calculated versatile empirical descriptors based on the similarity of atomic environments, for 102 chiral ligands, to predict diastereoselectivity. Whilst the model fit was good, it failed to accurately predict the performance of an unseen ligand family, due to analogue bias. Physical knowledge has then guided the selection of symmetrised physico-chemical descriptors. This produced more accurate predictive models for diastereoselectivity, including for an unseen ligand family. The contribution of this thesis is a development of novel and effective workflows and methodologies for process development. These open the door for process chemists to save time and resources, freeing them up from routine work, to focus instead on creatively designing new chemistry for future real-world applications.", notes = "Is this GP? Ie does it use TPOT ? See also DOI:10.1039/c9sc01844a Supervisor: Alexei Lapkin", } @InProceedings{amaral:2022:GECCOcomp, author = "Ryan Amaral and Alexandru Ianta and Caleidgh Bayer and Robert Smith and Malcolm Heywood", title = "Benchmarking Genetic Programming in a Multi-action Reinforcement Learning Locomotion Task", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion", year = "2022", editor = "Heike Trautmann and Carola Doerr and Alberto Moraglio and Thomas Bartz-Beielstein and Bogdan Filipic and Marcus Gallagher and Yew-Soon Ong and Abhishek Gupta and Anna V Kononova and Hao Wang and Michael Emmerich and Peter A. N. Bosman and Daniela Zaharie and Fabio Caraffini and Johann Dreo and Anne Auger and Konstantin Dietric and Paul Dufosse and Tobias Glasmachers and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Tea Tusar and Dimo Brockhoff and Tome Eftimov and Pascal Kerschke and Boris Naujoks and Mike Preuss and Vanessa Volz and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Mark Coletti and Catherine (Katie) Schuman and Eric ``Siggy'' Scott and Robert Patton and Paul Wiegand and Jeffrey K. Bassett and Chathika Gunaratne and Tinkle Chugh and Richard Allmendinger and Jussi Hakanen and Daniel Tauritz and John Woodward and Manuel Lopez-Ibanez and John McCall and Jaume Bacardit and Alexander Brownlee and Stefano Cagnoni and Giovanni Iacca and David Walker and Jamal Toutouh and UnaMay O'Reilly and Penousal Machado and Joao Correia and Sergio Nesmachnow and Josu Ceberio and Rafael Villanueva and Ignacio Hidalgo and Francisco {Fernandez de Vega} and Giuseppe Paolo and Alex Coninx and Antoine Cully and Adam Gaier and Stefan Wagner and Michael Affenzeller and Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and William B. Langdon and Justyna Petke and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Paetzel and Alexander Wagner and Michael Heider and Nadarajen Veerapen and Katherine Malan and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Mohammad Nabi Omidvar and Yuan Sun and Ernesto Tarantino and De Falco Ivanoe and Antonio {Della Cioppa} and Scafuri Umberto and John Rieffel and Jean-Baptiste Mouret and Stephane Doncieux and Stefanos Nikolaidis and Julian Togelius and Matthew C. Fontaine and Serban Georgescu and Francisco Chicano and Darrell Whitley and Oleksandr Kyriienko and Denny Dahl and Ofer Shir and Lee Spector and Alma Rahat and Richard Everson and Jonathan Fieldsend and Handing Wang and Yaochu Jin and Erik Hemberg and Marwa A. Elsayed and Michael Kommenda and William {La Cava} and Gabriel Kronberger and Steven Gustafson", pages = "522--525", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, real-valued actions, continuous control, reinforcement learning", isbn13 = "978-1-4503-9268-6/22/07", DOI = "doi:10.1145/3520304.3528766", abstract = "Reinforcement learning (RL) requires an agent to interact with an environment to maximize the cumulative rather than the immediate reward. Recently, there as been a significant growth in the availability of scalable RL tasks, e.g. OpenAI gym. However, most benchmarking studies concentrate on RL solutions based on some form of deep learning. In this work, we benchmark a family of linear genetic programming based approaches to the 2-d biped walker problem. The biped walker is an example of a RL environment described in terms of a multi-dimensional, real-valued 24-d input and 4-d action space. Specific recommendations are made regarding mechanisms to adopt that are able to consistently produce solutions, in this case using transfer from periodic restarts.", notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @InProceedings{amarteifio:2004:AL, author = "Saoirse Amarteifio and Michael O'Neill", title = "An Evolutionary Approach to Complex System Regulation Using Grammatical Evolution", booktitle = "Artificial Life {XI} Ninth International Conference on the Simulation and Synthesis of Living Systems", year = "2004", editor = "Jordan Pollack and Mark Bedau and Phil Husbands and Takashi Ikegami and Richard A. Watson", pages = "551--556", address = "Boston, Massachusetts", month = "12-15 " # sep, publisher = "The MIT Press", keywords = "genetic algorithms, genetic programming, grammatical evolution", ISBN = "0-262-66183-7", URL = "http://ncra.ucd.ie/papers/alife2004.pdf", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6278781", DOI = "doi:10.7551/mitpress/1429.003.0093", size = "6 pages", abstract = "Motivated by difficulties in engineering adaptive distributed systems, we consider a method to evolve cooperation in swarms to model dynamical systems. We consider an information processing swarm model that we find to be useful in studying control methods for adaptive distributed systems and attempt to evolve systems that form consistent patterns through the interaction of constituent agents or particles. This model considers artificial ants as walking sensors in an information-rich environment. Grammatical Evolution is combined with this swarming model as we evolve an ant's response to information. The fitness of the swarm depends on information processing by individual ants, which should lead to appropriate macroscopic spatial and/or temporal patterns. We discuss three primary issues, which are tractability, representation and fitness evaluation of dynamical systems and show how Grammatical Evolution supports a promising approach to addressing these concerns", notes = "ALIFE9", } @InProceedings{amarteifio:2005:CEC, author = "Saoirse Amarteifio and Michael O'Neill", title = "Coevolving Antibodies with a Rich Representation of Grammatical Evolution", booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary Computation", year = "2005", editor = "David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L. Gwenn Volkert and Dan Ashlock and Marc Schoenauer", volume = "1", pages = "904--911", address = "Edinburgh, UK", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "2-5 " # sep, organisation = "IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, grammatical evolution, genotype-phenotype mapping", ISBN = "0-7803-9363-5", DOI = "doi:10.1109/CEC.2005.1554779", size = "8 pages", abstract = "A number of natural anticipatory systems employ dual processes of feature definition and feature exploitation. Presented here, a coevolutionary dual process model based on the immune system, considers the effect of coevolving complementary templates to bias feature selection and recombination. This work considers the issue of module exploitation in evolutionary algorithms. Our approach is characterised by the use of rich representations in grammatical evolution.", notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS.", } @MastersThesis{amarteifio:2005:IAGPMWRRIX, title = "Interpreting a Genotype-Phenotype Map with Rich Representations in XMLGE", author = "Saoirse Amarteifio", school = "University of Limerick", year = "2005", type = "Master of Science in Computer Science", address = "University of Limerick, Ireland", keywords = "genetic algorithms, genetic programming, grammatical evolution, xml", URL = "http://ncra.ucd.ie/downloads/pub/SaoirseMScThesis.pdf", size = "177 pages", abstract = "A novel XML implementation of Grammatical Evolution is developed. This has a number of interesting features such as the use of XSLT for genetic operators and the use of reflection to build an object tree from an XML expression tree. This framework is designed to be used for remote or local evaluation of evolved program structures and provides a number of abstraction layers for program evaluation and evolution. A dynamical swarm system is evolved as a special-case function induction problem to illustrate the application of XMLGE. Particle behaviours are evolved to optimise colony performance. A dual process evolutionary algorithm based on the immune system using rich representations is developed. A dual process feature detection and feature integration model is described and the performance shown on benchmark GP problems. An adaptive feature detection method uses coevolving XPath antibodies to take selective interest in primary structures. Grammars are used to generate reciprocal binding structures (antibodies) given any primary domain grammar. A codon compression algorithm is developed which shows performance improvements on symbolic regression and multiplexer problems. The algorithm is based on questions about the information content of a genome. This also exploits information from the rich representation of XMLGE.", language = "en", } @Article{Amber:2015:EB, author = "K. P. Amber and M. W. Aslam and S. K. Hussain", title = "Electricity consumption forecasting models for administration buildings of the {UK} higher education sector", journal = "Energy and Buildings", volume = "90", pages = "127--136", year = "2015", keywords = "genetic algorithms, genetic programming, Electricity forecasting, Administration buildings, Multiple regression", ISSN = "0378-7788", DOI = "doi:10.1016/j.enbuild.2015.01.008", URL = "http://www.sciencedirect.com/science/article/pii/S0378778815000110", abstract = "Electricity consumption in the administration buildings of a typical higher education campus in the UK accounts for 26percent of the campus annual electricity consumption. A reliable forecast of electricity consumption helps energy managers in numerous ways such as in preparing future energy budgets and setting up energy consumption targets. In this paper, we developed two models, a multiple regression (MR) model and a genetic programming (GP) model to forecast daily electricity consumption of an administration building located at the Southwark campus of London South Bank University in London. Both models integrate five important independent variables, i.e. ambient temperature, solar radiation, relative humidity, wind speed and weekday index. Daily values of these variables were collected from year 2007 to year 2013. The data sets from year 2007 to 2012 are used for training the models while 2013 data set is used for testing the models. The predicted test results for both the models are analysed and compared with actual electricity consumption. At the end, some conclusions are drawn about the performance of both models regarding their forecasting capabilities. The results demonstrate that the GP model performs better with a Total Absolute Error (TAE) of 6percent compared to TAE of 7percent for MR model.", } @Article{AMBER:2018:Energy, author = "K. P. Amber and R. Ahmad and M. W. Aslam and A. Kousar and M. Usman and M. S. Khan", title = "Intelligent techniques for forecasting electricity consumption of buildings", journal = "Energy", volume = "157", pages = "886--893", year = "2018", keywords = "genetic algorithms, genetic programming, Electricity forecasting, ANN, DNN, GP, MR, SVM", ISSN = "0360-5442", DOI = "doi:10.1016/j.energy.2018.05.155", URL = "http://www.sciencedirect.com/science/article/pii/S036054421830999X", abstract = "The increasing trend in building sector's energy demand calls for reliable and robust energy consumption forecasting models. This study aims to compare prediction capabilities of five different intelligent system techniques by forecasting electricity consumption of an administration building located in London, United Kingdom. These five techniques are; Multiple Regression (MR), Genetic Programming (GP), Artificial Neural Network (ANN), Deep Neural Network (DNN) and Support Vector Machine (SVM). The prediction models are developed based on five years of observed data of five different parameters such as solar radiation, temperature, wind speed, humidity and weekday index. Weekday index is an important parameter introduced to differentiate between working and non-working days. First four years data is used for training the models and to obtain prediction data for fifth year. Finally, the predicted electricity consumption of all models is compared with actual consumption of fifth year. Results demonstrate that ANN performs better than all other four techniques with a Mean Absolute Percentage Error (MAPE) of 6percent whereas MR, GP, SVM and DNN have MAPE of 8.5percent, 8.7percent, 9percent and 11percent, respectively. The applicability of this study could span to other building categories and will help energy management teams to forecast energy consumption of various buildings", } @InProceedings{amblard:2023:GGP, author = "Julien Amblard and Robert Filman and Gabriel Kopito", title = "{GPStar4:} A Flexible Framework for Experimenting with Genetic Programming", booktitle = "Graph-based Genetic Programming", year = "2023", editor = "Roman Kalkreuth and Thomas Baeck and Dennis G. Wilson and Paul Kaufmann and Leo Francoso Dal Piccol Sotto and Timothy Aktinson", pages = "1910--1915", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, experimental framework, directed acyclic graph representations, population algorithms", isbn13 = "9798400701191", DOI = "doi:10.1145/3583133.3596369", size = "6 pages", abstract = "GPStar4 is a flexible workbench for experimenting with population algorithms. It is a framework that defines a genetic cycle, with inflection points for implementing an algorithm's specific behaviors; it also provides a variety of implementations for these inflection points. A user of the system can select from the provided implementations and customize the places where alternative behavior is desired, or even create their own implementations. Components interact through a context mechanism that enables both mutable and immutable information sharing, type checking, computed defaults and event listeners.Interesting predefined components included in GPStar4 are implementations for classical tree-based expression structures; acyclic multigraphs with named ports, type systems for flat, hierarchical and attribute types, recursively defined populations using both subpopulation and build-from-parts semantics, and numeric and multi-objective fitnesses. Key enabling technologies for this flexibility include context mechanisms, choosers, and a variety of caches.GPStar4 can be run as an API library for other applications, as a command-line application, or as a stand-alone application with its own GUI.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @Article{AMERYAN:2020:CS, author = "Ala Ameryan and Mansour Ghalehnovi and Mohsen Rashki", title = "Investigation of shear strength correlations and reliability assessments of sandwich structures by kriging method", journal = "Composite Structures", volume = "253", pages = "112782", year = "2020", ISSN = "0263-8223", DOI = "doi:10.1016/j.compstruct.2020.112782", URL = "http://www.sciencedirect.com/science/article/pii/S0263822320327082", keywords = "genetic algorithms, genetic programming, Structural reliability, Kriging, Sandwich structures, Finite element, Experimental data, Failure probability", abstract = "Steel-concrete-steel (SCS) sandwich composite structure with corrugated-strip connectors (CSC) is the promising structure which is applied in offshore and building structures. The behavior prediction of shear connections is of major importance in SCS structures. The present study evaluated the existing shear strength correlations of SCS sandwich structures exploiting experimental data and Finite Element Analysis (FEA). The considered system is a double steel skin sandwich structure with CSC (DSCS). Due to the limitation of the literature regarding CSC development, some new correlations were proposed and studied relying on several FEA results through the Genetic Programming method. The accuracy of the estimated shear strength predicted by the existing and proposed equations was evaluated using the FEA data and push-out test results. The FE models were verified through experimental data. Moreover, the correlations were investigated based on reliability assessment due to the high importance of the reliability analysis of such structures. Given that high accuracy in estimating the shear strength fails to necessarily lead to acceptable results in structural reliability analysis, the reliability of the existing and proposed equations was evaluated using the Kriging model by considering experimental data. This meta-model could predict accurate values with a limited number of initial training samples", } @Article{amin:2022:Polymers, author = "Muhammad Nasir Amin and Mudassir Iqbal and Fadi Althoey and Kaffayatullah Khan and Muhammad Iftikhar Faraz and Muhammad Ghulam Qadir and Anas Abdulalim Alabdullah and Ali Ajwad", title = "Investigating the Bond Strength of {FRP} Rebars in Concrete under High Temperature Using Gene-Expression Programming Model", journal = "Polymers", year = "2022", volume = "14", number = "15", pages = "Article No. 2992", keywords = "genetic algorithms, genetic programming, gene expression programming", ISSN = "2073-4360", URL = "https://www.mdpi.com/2073-4360/14/15/2992", DOI = "doi:10.3390/polym14152992", abstract = "In recent times, the use of fibre-reinforced plastic (FRP) has increased in reinforcing concrete structures. The bond strength of FRP rebars is one of the most significant parameters for characterising the overall efficacy of the concrete structures reinforced with FRP. However, in cases of elevated temperature, the bond of FRP-reinforced concrete can deteriorate depending on a number of factors, including the type of FRP bars used, its diameter, surface form, anchorage length, concrete strength, and cover thickness. Hence, accurate quantification of FRP rebars in concrete is of paramount importance, especially at high temperatures. In this study, an artificial intelligence (AI)-based genetic-expression programming (GEP) method was used to predict the bond strength of FRP rebars in concrete at high temperatures. In order to predict the bond strength, we used failure mode temperature, fibre type, bar surface, bar diameter, anchorage length, compressive strength, and cover-to-diameter ratio as input parameters. The experimental dataset of 146 tests at various elevated temperatures were established for training and validating the model. A total of 70percent of the data was used for training the model and remaining 30percent was used for validation. Various statistical indices such as correlation coefficient (R), the mean absolute error (MAE), and the root-mean-square error (RMSE) were used to assess the predictive veracity of the GEP model. After the trials, the optimum hyperparameters were 150, 8, and 4 as number of chromosomes, head size and number of genes, respectively. Different genetic factors, such as the number of chromosomes, the size of the head, and the number of genes, were evaluated in eleven separate trials. The results as obtained from the rigorous statistical analysis and parametric study show that the developed GEP model is robust and can predict the bond strength of FRP rebars in concrete under high temperature with reasonable accuracy (i.e., R, RMSE and MAE 0.941, 2.087, and 1.620, and 0.935, 2.370, and 2.046, respectively, for training and validation). More importantly, based on the FRP properties, the model has been translated into traceable mathematical formulation for easy calculations.", notes = "also known as \cite{polym14152992}", } @Article{amin:2022:Materials, author = "Muhammad Nasir Amin and Muhammad Raheel and Mudassir Iqbal and Kaffayatullah Khan and Muhammad Ghulam Qadir and Fazal E. Jalal and Anas Abdulalim Alabdullah and Ali Ajwad and Majdi Adel Al-Faiad and Abdullah Mohammad Abu-Arab", title = "Prediction of Rapid Chloride Penetration Resistance to Assess the Influence of Affecting Variables on Metakaolin-Based Concrete Using Gene Expression Programming", journal = "Materials", year = "2022", volume = "15", number = "19", pages = "Article No. 6959", keywords = "genetic algorithms, genetic programming, gene expression programming", ISSN = "1996-1944", URL = "https://www.mdpi.com/1996-1944/15/19/6959", DOI = "doi:10.3390/ma15196959", abstract = "The useful life of a concrete structure is highly dependent upon its durability, which enables it to withstand the harsh environmental conditions. Resistance of a concrete specimen to rapid chloride ion penetration (RCP) is one of the tests to indirectly measure its durability. The central aim of this study was to investigate the influence of different variables, such as, age, amount of binder, fine aggregate, coarse aggregate, water to binder ratio, metakaolin content and the compressive strength of concrete on the RCP resistance using a genetic programming approach. The number of chromosomes (Nc), genes (Ng) and, the head size (Hs) of the gene expression programming (GEP) model were varied to study their influence on the predicted RCP values. The performance of all the GEP models was assessed using a variety of performance indices, i.e., R2, RMSE and comparison of regression slopes. The optimal GEP model (Model T3) was obtained when the Nc = 100, Hs = 8 and Ng = 3. This model exhibits an R2 of 0.89 and 0.92 in the training and testing phases, respectively. The regression slope analysis revealed that the predicted values are in good agreement with the experimental values, as evident from their higher R2 values. Similarly, parametric analysis was also conducted for the best performing Model T3. The analysis showed that the amount of binder, compressive strength and age of the sample enhanced the RCP resistance of the concrete specimens. Among the different input variables, the RCP resistance sharply increased during initial stages of curing (28-d), thus validating the model results.", notes = "also known as \cite{ma15196959}", } @Article{DBLP:journals/apin/AminiAH20, author = "Seyed Mohammad Hossein Hosseini Amini and Mohammad Abdollahi and Maryam Amir Haeri", title = "Rule-centred genetic programming {(RCGP):} an imperialist competitive approach", journal = "Appl. Intell.", volume = "50", number = "8", pages = "2589--2609", year = "2020", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/s10489-019-01601-6", DOI = "doi:10.1007/s10489-019-01601-6", timestamp = "Thu, 06 Aug 2020 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/apin/AminiAH20.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{journals/nca/AminianJAGE11, author = "Pejman Aminian and Mohamad Reza Javid and Abazar Asghari and Amir Hossein Gandomi and Milad {Arab Esmaeili}", title = "A robust predictive model for base shear of steel frame structures using a hybrid genetic programming and simulated annealing method", journal = "Neural Computing and Applications", year = "2011", number = "8", volume = "20", pages = "1321--1332", publisher = "Springer", keywords = "genetic algorithms, genetic programming, base shear, steel frame structures, simulated annealing, nonlinear modelling", ISSN = "0941-0643", DOI = "doi:10.1007/s00521-011-0689-0", size = "12 pages", abstract = "This study presents a new empirical model to estimate the base shear of plane steel structures subjected to earthquake load using a hybrid method integrating genetic programming (GP) and simulated annealing (SA), called GP/SA. The base shear of steel frames was formulated in terms of the number of bays, number of storey, soil type, and situation of braced or unbraced. A classical GP model was developed to benchmark the GP/SA model. The comprehensive database used for the development of the correlations was obtained from finite element analysis. A parametric analysis was carried out to evaluate the sensitivity of the base shear to the variation of the influencing parameters. The GP/SA and classical GP correlations provide a better prediction performance than the widely used UBC code and a neural network-based model found in the literature. The developed correlations may be used as quick checks on solutions developed by deterministic analyses.", notes = "Special Issue: ISNN 2010", affiliation = "Department of Civil Engineering, Islamic Azad University, Shahrood Branch, Shahrood, Iran", bibdate = "2011-10-18", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/nca/nca20.html#AminianJAGE11", } @Article{Aminian:2013:NCA, author = "Pejman Aminian and Hadi Niroomand and Amir Hossein Gandomi and Amir Hossein Alavi and Milad {Arab Esmaeili}", title = "New design equations for assessment of load carrying capacity of castellated steel beams: a machine learning approach", journal = "Neural Computing and Applications", year = "2013", volume = "23", number = "1", pages = "119--131", month = jul, keywords = "genetic algorithms, genetic programming, Linear genetic programming, Castellated beam, Load carrying capacity, Simulated annealing, Formulation", publisher = "Springer", language = "English", ISSN = "0941-0643", URL = "http://link.springer.com/article/10.1007%2Fs00521-012-1138-4", DOI = "doi:10.1007/s00521-012-1138-4", size = "13 pages", abstract = "This paper presents an innovative machine learning approach for the formulation of load carrying capacity of castellated steel beams (CSB). New design equations were developed to predict the load carrying capacity of CSB using linear genetic programming (LGP), and an integrated search algorithm of genetic programming and simulated annealing, called GSA. The load capacity was formulated in terms of the geometrical and mechanical properties of the castellated beams. An extensive trial study was carried out to select the most relevant input variables for the LGP and GSA models. A comprehensive database was gathered from the literature to develop the models. The generalisation capabilities of the models were verified via several criteria. The sensitivity of the failure load of CSB to the influencing variables was examined and discussed. The employed machine learning systems were found to be effective methods for evaluating the failure load of CSB. The prediction performance of the optimal LGP model was found to be better than that of the GSA model.", } @InCollection{AmirHaeri:wsc17, author = "Maryam {Amir Haeri} and Mohammad Mehdi Ebadzadeh and Gianluigi Folino", title = "Statistical Genetic Programming: The Role of Diversity", booktitle = "Soft Computing in Industrial Applications", publisher = "Springer", year = "2014", editor = "Vaclav Snasel and Pavel Kroemer and Mario Koeppen and Gerald Schaefer", volume = "223", series = "Advances in Intelligent Systems and Computing", chapter = "4", pages = "37--48", month = "21 " # nov, note = "Proceedings of the 17th Online World Conference on Soft Computing in Industrial Applications", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-00929-2", URL = "http://dx.doi.org/10.1007/978-3-319-00930-8_4", DOI = "doi:10.1007/978-3-319-00930-8_4", language = "English", abstract = "In this chapter, a new GP-based algorithm is proposed. The algorithm, named SGP (Statistical GP), exploits statistical information, i.e. mean, variance and correlation-based operators, in order to improve the GP performance. SGP incorporates new genetic operators, i.e. Correlation Based Mutation, Correlation Based Crossover, and Variance Based Editing, to drive the search process towards fitter and shorter solutions. Furthermore, this work investigates the correlation between diversity and fitness in SGP, both in terms of phenotypic and genotypic diversity. First experiments conducted on four symbolic regression problems illustrate the goodness of the approach and permits to verify the different behaviour of SGP in comparison with standard GP from the point of view of the diversity and its correlation with the fitness.", notes = "http://dap.vsb.cz/wsc17/ WSC17 2012 Online Conference on Soft Computing in Industrial Applications Anywhere on Earth, December 10-21, 2012 Author Affiliations Department of Computer Engineering and Information Technology, Amirkabir University of Technology, Tehran, Iran ICAR-CNR, Rende, Italy", } @Article{AmirHaeri:2014:GPEM, author = "Maryam {Amir Haeri} and Mohammad Mehdi Ebadzadeh and Gianluigi Folino", title = "Improving GP generalization: a variance-based layered learning approach", journal = "Genetic Programming and Evolvable Machines", year = "2015", volume = "16", number = "1", pages = "27--55", month = mar, keywords = "genetic algorithms, genetic programming, VBLL-GP, Generalisation, Layered learning, Over fitting, Variance", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-014-9220-6", size = "29 pages", abstract = "This paper introduces a new method that improves the generalisation ability of genetic programming (GP) for symbolic regression problems, named variance-based layered learning GP. In this approach, several datasets, called primitive training sets, are derived from the original training data. They are generated from less complex to more complex, for a suitable complexity measure. The last primitive dataset is still less complex than the original training set. The approach decomposes the evolution process into several hierarchical layers. The first layer of the evolution starts using the least complex (smoothest) primitive training set. In the next layers, more complex primitive sets are given to the GP engine. Finally, the original training data is given to the algorithm. We use the variance of the output values of a function as a measure of the functional complexity. This measure is used in order to generate smoother training data, and controlling the functional complexity of the solutions to reduce the overfitting. The experiments, conducted on four real-world and three artificial symbolic regression problems, demonstrate that the approach enhances the generalization ability of the GP, and reduces the complexity of the obtained solutions.", notes = "LD50, Bioavailablity, concrete, Pollen, UBall5D, RatPol2D Author Affiliations: 1. Department of Computer Engineering and Information Technology, Amirkabir University of Technology, Tehran, Iran 2. ICAR-CNR, Rende, Italy", } @Article{journals/asc/HaeriEF17, author = "Maryam Amir Haeri and Mohammad Mehdi Ebadzadeh and Gianluigi Folino", title = "Statistical genetic programming for symbolic regression", journal = "Applied Soft Computing", year = "2017", volume = "60", pages = "447--469", month = nov, keywords = "genetic algorithms, genetic programming, Symbolic regression, Well-structured subtree, Semi-well-structured tree, Well-structuredness measure, Correlation coefficient", bibdate = "2017-11-22", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/asc/asc60.html#HaeriEF17", DOI = "doi:10.1016/j.asoc.2017.06.050", abstract = "In this paper, a new genetic programming (GP) algorithm for symbolic regression problems is proposed. The algorithm, named statistical genetic programming (SGP), uses statistical information (such as variance, mean and correlation coefficient) to improve GP. To this end, we define well-structured trees as a tree with the following property: nodes which are closer to the root have a higher correlation with the target. It is shown experimentally that on average, the trees with structures closer to well-structured trees are smaller than other trees. SGP biases the search process to find solutions whose structures are closer to a well-structured tree. For this purpose, it extends the terminal set by some small well-structured subtrees, and starts the search process in a search space that is limited to semi-well-structured trees (i.e., trees with at least one well-structured subtree). Moreover, SGP incorporates new genetic operators, i.e., correlation-based mutation and correlation-based crossover, which use the correlation between outputs of each subtree and the targets, to improve the functionality. Furthermore, we suggest a variance-based editing operator which reduces the size of the trees. SGP uses the new operators to explore the search space in a way that it obtains more accurate and smaller solutions in less time. SGP is tested on several symbolic regression benchmarks. The results show that it increases the evolution rate, the accuracy of the solutions, and the generalization ability, and decreases the rate of code growth.", notes = "Department of Computer Engineering and Information Technology, Amirkabir University of Technology, Tehran, Iran", } @Article{journals/jifs/AmiriAT14, title = "Ground motion prediction equations ({GMPE}s) for elastic response spectra in the Iranian plateau using Gene Expression Programming ({GEP})", author = "Gholamreza Ghodrati Amiri and Mohamad Shamekhi Amiri and Zahra Tabrizian", journal = "Journal of Intelligent and Fuzzy Systems", year = "2014", number = "6", volume = "26", pages = "2825--2839", keywords = "genetic algorithms, genetic programming, gene expression programming", bibdate = "2014-05-27", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/jifs/jifs26.html#AmiriAT14", URL = "http://dx.doi.org/10.3233/IFS-130950", DOI = "doi:10.3233/IFS-130950", abstract = "This paper proposes ground-motion prediction equations (GMPEs) for the horizontal component of earthquake in Iranian plateau. These equations present the velocity and acceleration response spectra at 5percent damping ratio as continuous period functions, within range of 0.1 to 4 seconds. So far many equations have been presented and the recent suggested proportions are functions of several parameters. In this research, due to easy usage and lack of information in Iran, only the magnitude of earthquake, the distance between earthquake source and the location and the ground type are used as important factors. Iranian plateau is divided into two zones: Alborz-Central Iran and Zagros, each of which is divided into rock and soil region according to the ground type. Regarding the fact that the occurred and reported earthquakes in Iran are shallow, surface wave magnitude (Ms) is used in this study. Moreover, hypocentral distance is considered as distance between the earthquake source and the location. To obtain the velocity and acceleration response spectra, a Gene Expression Programming (GEP) algorithm is used which uses no constant regression model and the model is acquired smartly as a continuous period function. The consequences show a consistency with high proportionality coefficient among the observed and anticipated results", } @Article{Amiri:2013:SI, author = "Mohammad Amiri and Mahdi Eftekhari and Maryam Dehestani and Azita Tajaddini", title = "Modeling intermolecular potential of {He-F2} dimer from symmetry-adapted perturbation theory using multi-gene genetic programming", journal = "Scientia Iranica", year = "2013", volume = "20", number = "3", pages = "543--548", keywords = "genetic algorithms, genetic programming, Potential energy, SAPT, MGGP, Lennard-Jones potential, GPTIPS, Matlab", ISSN = "1026-3098", URL = "https://core.ac.uk/download/pdf/81997689.pdf", URL = "http://www.sciencedirect.com/science/article/pii/S1026309813000758", DOI = "doi:10.1016/j.scient.2012.12.040", size = "6 pages", abstract = "Any molecular dynamical calculation requires a precise knowledge of interaction potential as an input. In an appropriate form, such that the potential, with respect to the coordinates, can be evaluated easily and accurately at arbitrary geometries (in our study parameters for geometry are R and theta), a good potential energy expression can offer the exact intermolecular behaviour of systems. There are many methods to create mathematical expressions for the potential energy. In this study for the first time, we used the Multi-gene Genetic Programming (MGGP) method to generate a potential energy model for the He-F2 system. The MGGP method is one of the most powerful methods used for non-linear regression problems. A dataset of size 714 created by the SAPT 2008 program is used to generate models of MGGP. The results obtained show the power of MGGP for producing an efficient nonlinear regression model, in terms of accuracy and complexity.", } @Article{AMIRI:2021:CSCM, author = "Mostafa Amiri and Farzad Hatami and Emadaldin Mohammadi Golafshani", title = "Evaluating the synergic effect of waste rubber powder and recycled concrete aggregate on mechanical properties and durability of concrete", journal = "Case Studies in Construction Materials", volume = "15", pages = "e00639", year = "2021", ISSN = "2214-5095", DOI = "doi:10.1016/j.cscm.2021.e00639", URL = "https://www.sciencedirect.com/science/article/pii/S2214509521001546", keywords = "genetic algorithms, genetic programming, Waste rubber powder, Recycled concrete aggregate, Green concrete, Mechanical properties, Durability", abstract = "The use of waste materials in the concrete mixture can help human beings to preserve the environment and achieve environmentally-friendly concrete. In this study, the influences of simultaneous replacements of cement by waste rubber powder (WRP) and coarse aggregate by recycled concrete aggregate (RCA) on the mechanical properties and durability of concrete were investigated experimentally. To do so, concrete specimens containing the WRP with the replacement ratios of percent, 2.5 percent, and 5 percent by weight of cement, and the RCA with the replacement levels of percent, 25 percent, and 50 percent of coarse aggregate were prepared. Moreover, different water to binder ratios and binder content were used. Mechanical properties of the concrete specimens consisting of compressive, flexural, and tensile strengths and the durability test of rapid chloride migration test (RCMT) were carried out at different ages. It was observed that the mechanical properties of concrete decrease by raising the proportions of recycled materials in all replacement ratios. Because of the negative effects of the WRP and RCA on, respectively, the cement matrix and the interfacial transition zone, the reduction of the mechanical properties are higher for the concrete specimens with both recycled materials. In the case of durability, the migration rate of chloride ions in concrete reduces by increasing the WRP rates due to the blockage of micro-pores connections. However, adding the RCA has a negative effect on the durability performance of concrete. Finally, four equations were proposed and evaluated for the compressive, tensile, flexural strength reduction and durability factors of concrete containing the WRP and RCA using the genetic programming", } @Article{Amiri:2022:JRM, author = "Mostafa Amiri and Farzad Hatami and Emadaldin Mohammadi Golafshani", title = "Evaluating Simultaneous Impact of Slag and Tire Rubber Powder on Mechanical Characteristics and Durability of Concrete", journal = "Journal of Renewable Materials", year = "2022", volume = "10", number = "8", pages = "2155--2177", keywords = "genetic algorithms, genetic programming, Micronized rubber powder, ground granulated blast furnace slag, waste materials, mechanical properties, durability", ISSN = "2164-6325", URL = "https://www.sciencedirect.com/science/article/pii/S2164632522002323", DOI = "doi:10.32604/jrm.2022.019726", abstract = "In this experimental study, the impact of Portland cement replacement by ground granulated blast furnace slag (GGBFS) and micronized rubber powder (MRP) on the compressive, flexural, tensile strengths, and rapid chloride migration test (RCMT) of concrete were assessed. In this study, samples with different binder content and water to binder ratios, including the MRP with the substitution levels of 0percent, 2.5percent and 5percent, and the GGBFS with the substitution ratios of 0percent, 20percent and 40percent by weight of Portland cement were made. According to the results, in the samples containing slag and rubber powder in the early ages, on average, a 12.2percent decrease in the mechanical characteristics of concrete was observed, nonetheless with raising the age of the samples, the impact of slag on reducing the porosity of concrete lowered the negative impact of rubber powder. Regarding durability characteristics, the RCMT results of the samples were enhanced by using rubber powder because of its insulation impact. Moreover, adding slag into the MRP-included mixtures results in a 23percent reduction in the migration rate of the chloride ion averagely. At last, four mathematical statements were derived for the mechanical and durability of concrete containing the MRP and GGBFS using the genetic programming method", } @Article{AMISH:2023:rineng, author = "Mohamed Amish and Eta Etta-Agbor", title = "Genetic programming application in predicting fluid loss severity", journal = "Results in Engineering", volume = "20", pages = "101464", year = "2023", ISSN = "2590-1230", DOI = "doi:10.1016/j.rineng.2023.101464", URL = "https://www.sciencedirect.com/science/article/pii/S2590123023005911", keywords = "genetic algorithms, genetic programming, Lost circulation, Machine learning, Multigene genetic algorithms, Drilling. non-productive time", abstract = "Numerous wells worldwide encounter significant, costly, and time-consuming lost circulation issues during drilling or while deploying tubulars across naturally fractured or induced fractured formations. This can potentially lead to formation damage, wellbore instability, and even blowouts. Effectively addressing this problem and restoring fluid circulation becomes crucial to curbing non-productive time and overall operational expenses. Although numerous methods have been introduced, a universally accepted industry solution for predicting lost circulation remains absent due to the complex interplay of various factors influencing its severity. Anticipating the onset of circulation loss is imperative to mitigate its impacts, minimise costs, and reduce risks to personnel and the environment. In this study, an innovative machine learning approach employing multigene genetic algorithms is used to analyse a dataset of 16,970 drilling datasets from 61 wells within the Marun oil field, located in Iran, where severe loss of circulation occurred. Geological characteristics, operational drilling parameters, and the properties of the drilling fluid were all considered. The dataset encompasses 19 parameters, of which seven are chosen as inputs for predicting lost circulation incidents. These inputs are then employed to construct a predictive model, employing an 85:15 training-to-test data ratio. To assess the model's performance, unseen datasets are used. The novelty of this study lies in the proposed model's consideration of a concise set of relevant input parameters, particularly real-time surface drilling parameters that are easily accessible for every well. The model attains a remarkable level of prediction accuracy for fluid loss, as indicated by various performance indices. The results indicate a mean absolute error of 1.33, a root mean square error of 2.58, and a coefficient of determination of 0.968. The suggested prediction model is optimised not only for data reduction but also for universal prediction and compatibility with other existing platforms. Moreover, it aids drilling engineers in implementing suitable mitigation strategies and designing optimal values for key operational surface parameters, both prior to and during drilling operations", } @Article{nc:Amit+Geman:1997, author = "Yali Amit and Donald Geman", title = "Shape Quantization and Recognition with Randomized Trees", journal = "Neural Computation", year = "1997", volume = "9", number = "7", pages = "1545--1588", month = oct, abstract = "We explore a new approach to shape recognition based on a virtually infinite family of binary features (queries) of the image data, designed to accommodate prior information about shape invariance and regularity. Each query corresponds to a spatial arrangement of several local topographic codes (or tags), which are in themselves too primitive and common to be informative about shape. All the discriminating power derives from relative angles and distances among the tags. The important attributes of the queries are a natural partial ordering corresponding to increasing structure and complexity; semi-invariance, meaning that most shapes of a given class will answer the same way to two queries that are successive in the ordering; and stability, since the queries are not based on distinguished points and substructures. No classifier based on the full feature set can be evaluated, and it is impossible to determine a priori which arrangements are informative. Our approach is to select informative features and build tree classifiers at the same time by inductive learning. In effect, each tree provides an approximation to the full posterior where the features chosen depend on the branch that is traversed. Due to the number and nature of the queries, standard decision tree construction based on a fixed-length feature vector is not feasible. Instead we entertain only a small random sample of queries at each node, constrain their complexity to increase with tree depth, and grow multiple trees. The terminal nodes are labelled by estimates of the corresponding posterior distribution over shape classes. An image is classified by sending it down every tree and aggregating the resulting distributions. The method is applied to classifying handwritten digits and synthetic linear and nonlinear deformations of three hundred LATeX symbols. State-of-the-art error rates are achieved on the National Institute of Standards and Technology database of digits. The principal goal of the experiments on LATeX symbols is to analyse invariance, generalisation error and related issues, and a comparison with artificial neural networks methods is presented in this context.", notes = "MIT Press Cited by \cite{MatthewGSmith:2005:GPEM}.", } @Article{Ammar:2016:Neurocomputing, author = "Marwa Ammar and Souhir Bouaziz and Adel M. Alimi and Ajith Abraham", title = "Multi-agent architecture for Multiaobjective optimization of Flexible Neural Tree", journal = "Neurocomputing", volume = "214", pages = "307--316", year = "2016", ISSN = "0925-2312", DOI = "doi:10.1016/j.neucom.2016.06.019", URL = "http://www.sciencedirect.com/science/article/pii/S0925231216306579", abstract = "In this paper, a multi-agent system is introduced to parallelize the Flexible Beta Basis Function Neural Network (FBBFNT)' training as a response to the time cost challenge. Different agents are formed; a Structure Agent is designed for the FBBFNT structure optimization and a variable set of Parameter Agents is used for the FBBFNT parameter optimization. The main objectives of the FBBFNT learning process were the accuracy and the structure complexity. With the proposed multi-agent system, the main purpose is to reach a good balance between these objectives. For that, a multi-objective context was adopted which based on Pareto dominance. The agents use two algorithms: the Pareto dominance Extended Genetic Programming (PEGP) and the Pareto Multi-Dimensional Particle Swarm Optimization ( PMD _ PSO ) algorithms for the structure and parameter optimization, respectively. The proposed system is called Pareto Multi-Agent Flexible Neural Tree ( PMA _ FNT ). To assess the effectiveness of PMA _ FNT , four benchmark real datasets of classification are tested. The results compared with some classifiers published in the literature.", keywords = "genetic algorithms, genetic programming, Flexible Neural Tree, Multi-agent architecture, Multi-objective optimization, Evolutionary Computation algorithms, Negotiation, Classification", } @Article{Amoretti:2013:GPEM, author = "Michele Amoretti", title = "Introducing artificial evolution into peer-to-peer networks with the distributed remodeling framework", journal = "Genetic Programming and Evolvable Machines", year = "2013", volume = "14", number = "2", pages = "127--153", month = jun, keywords = "genetic algorithms, Peer-to-peer network, Artificial evolution, Complex adaptive system", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-013-9182-0", size = "27 pages", abstract = "A peer-to-peer (P2P) network is a complex system whose elements (peer nodes, or simply peers) cooperate to implement scalable distributed services. From a general point of view, the activities of a P2P system are consequences of external inputs coming from the environment, and of the internal feedback among nodes. The reaction of a peer to direct or indirect inputs from the environment is dictated by its functional structure, which is usually defined in terms of static rules (protocols) shared among peers. The introduction of artificial evolution mechanisms may improve the efficiency of P2P networks, with respect to resource consumption, while preserving high performance in response to the environmental needs. In this paper, we propose the distributed remodelling framework (DRF), a general approach for the design of efficient environment-driven peer-to-peer networks. As a case study, we consider an ultra-large-scale storage and computing system whose nodes perform lookups for resources provided by other nodes, to cope with task execution requests that cannot be fulfilled locally. Thanks to the DRF, workload modifications trigger reconfigurations at the level of single peers, from which global system adaptation emerges without centralised control.", } @InProceedings{amos:1998:DNAsbc, author = "Martyn Amos and Paul E. Dunne and Alan Gibbons", title = "DNA Simulation of {Boolean} Circuits", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "679--683", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "DNA Computing", ISBN = "1-55860-548-7", notes = "GP-98", } @InProceedings{Amte:2015:ICESA, author = "A. Y. Amte and P. S. Kate", booktitle = "2015 International Conference on Energy Systems and Applications", title = "Automatic generation of Lyapunov function using Genetic programming approach", year = "2015", pages = "771--775", abstract = "The paper introduces a novel approach for the automated generation of a Lyapunov function for the analysis of a given dynamic system using genetic programming (GP). Genetic programming is a branch of Genetic algorithm. It introduces the concept of GP for the automation of Lyapunov function in MATLAB used for various optimisation techniques. A Lyapunov function method used for transient stability assessment is discussed and hence discussion followed by the establishment of domain of attraction of stable equilibrium point. The results obtained by MATLAB coding for the generation of Lyapunov function of single machine infinite bus system is related by considering a ball rolling on the inner surface of a bowl which depicted in edition of Power System Analysis and Control.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICESA.2015.7503454", month = oct, notes = "Also known as \cite{7503454}", } @InProceedings{An2017aa, title = "{PyGGI}: {Python} General framework for Genetic Improvement", author = "Gabin An and Jinhan Kim and Seongmin Lee and Shin Yoo", booktitle = "Proceedings of Korea Software Congress", year = "2017", series = "KSC 2017", pages = "536--538", address = "Busan, South Korea", month = "20-22 " # dec, keywords = "genetic algorithms, genetic programming, Genetic Improvement", URL = "https://coinse.kaist.ac.kr/publications/pdfs/An2017aa.pdf", URL = "http://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE07322214&language=en_EN#", code_url = "https://github.com/coinse/pyggi", size = "3 pages", abstract = "We present Python General Framework for Genetic Improvement (PYGGI, pronounced pigi), a lightweight general framework for Genetic Improvement (GI). It is designed to be a simple and easy to configure GI tool for multiple programming languages such as Java, C, or Python. Through two case studies, we show that PYGGI can modify source code of a given program either to improve non-functional properties or to automatically repair functional faults.", notes = "177 20A1A2-5 modification operators: deletion, copying, and replacement of source code as a list of code lines. Local search algorithm. triangle program http://www.kiise.or.kr/conference/kcc/2017/ Jan 2021 http://www.dbpia.co.kr/ in Korean only Also known as \cite{an:2017:ksc}", } @InProceedings{An:2018:GI, author = "Gabin An and Jinhan Kim and Shin Yoo", title = "Comparing Line and {AST} Granularity Level for Program Repair using {PyGGI}", booktitle = "GI-2018, ICSE workshops proceedings", year = "2018", editor = "Justyna Petke and Kathryn Stolee and William B. Langdon and Westley Weimer", pages = "19--26", address = "Gothenburg, Sweden", month = "2 " # jun, publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, SBSE", isbn13 = "978-1-4503-5753-1", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/icse2018/gi2018/papers/An_2018_GI.pdf", URL = "https://coinse.kaist.ac.kr/publications/pdfs/An2018to.pdf", DOI = "doi:10.1145/3194810.3194814", size = "8 pages", abstract = "PyGGI is a lightweight Python framework that can be used to implement generic Genetic Improvement algorithms at the API level. The original version of PyGGI only provided lexical modifications, i.e., modifications of the source code at the physical line granularity level. This paper introduces new extensions to PyGGI that enables syntactic modifications for Python code, i.e., modifications that operates at the AST granularity level. Taking advantage of the new extensions, we also present a case study that compares the lexical and syntactic search granularity level for automated program repair, using ten seeded faults in a real world open source Python project. The results show that search landscapes at the AST granularity level are more effective (i.e. eventually more likely to produce plausible patches) due to the smaller sizes of ingredient spaces (i.e., the space from which we search for the material to build a patch), but may require longer time for search because the larger number of syntactically intact candidates leads to more fitness evaluations.", notes = "Slides: http://geneticimprovementofsoftware.com/wp-content/uploads/2018/06/gi-pyggi.compressed.pdf GI-2018 http://geneticimprovementofsoftware.com/events/papers#icse2018 part of \cite{Petke:2018:ICSEworkshop}", } @Article{An:2018:sigevolution, author = "Gabin An", title = "Genetic Improvement Workshop at {ICSE 2018}", journal = "SIGEVOlution", year = "2018", volume = "11", number = "4", pages = "11--13", month = dec, keywords = "genetic algorithms, genetic programming, genetic improvement", URL = "http://www.sigevolution.org/issues/SIGEVOlution1104.pdf", DOI = "doi:10.1145/3302542.3302544", size = "2 pages", abstract = "In Gothenburg, on 2nd June 2018, the fourth edition of Genetic Improvement (GI) Workshop was co-located with this year's ICSE (International Conference on Software Engineering), the biggest and probably the most prestigious software engineering conference...", notes = "http://geneticimprovementofsoftware.com/", } @InProceedings{an:2019:fse, author = "Gabin An and Aymeric Blot and Justyna Petke and Shin Yoo", title = "{PyGGI 2.0}: Language Independent Genetic Improvement Framework", booktitle = "Proceedings of the 27th Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering {ESEC/FSE} 2019)", year = "2019", editor = "Sven Apel and Alessandra Russo", pages = "1100--1104", address = "Tallinn, Estonia", month = aug # " 26--30", publisher = "ACM", keywords = "genetic algorithms, genetic programming, Genetic Improvement, APR, SBSE, XML, srcML, Python", isbn13 = "9781450355728", URL = "http://www.cs.ucl.ac.uk/staff/a.blot/files/an_2019_fse.pdf", URL = "https://esec-fse19.ut.ee/program/tool-demos/#", DOI = "doi:10.1145/3338906.3341184", video_url = "https://youtu.be/PxRUdlRDS40", slides_url = "http://crest.cs.ucl.ac.uk/fileadmin/crest/COWphotos/Other/Petke.pdf", code_url = "https://github.com/coinse/pyggi", size = "5 pages", abstract = "PyGGI is a research tool for Genetic Improvement (GI), that is designed to be versatile and easy to use. We present version 2.0 of PyGGI, the main feature of which is an XML-based intermediate program representation. It allows users to easily define GI operators and algorithms that can be reused with multiple target languages. Using the new version of PyGGI, we present two case studies. First, we conduct an Automated Program Repair (APR) experiment with the QuixBugs benchmark, one that contains defective programs in both Python and Java. Second, we replicate an existing work on runtime improvement through program specialisation for the MiniSAT satisfiability solver. PyGGI 2.0 was able to generate a patch for a bug not previously fixed by any APR tool. It was also able to achieve 14percent runtime improvement in the case of MiniSAT. The presented results show the applicability and the expressiveness of the new version of PyGGI. A video of the tool demo is at: https://youtu.be/PxRUdlRDS40", notes = "C++, C, C-sharp, Java, Python https://youtu.be/PxRUdlRDS40 4:48 minutes:seconds https://esec-fse19.ut.ee/", } @Proceedings{an:2024:GI, title = "13th International Workshop on Genetic Improvement @ICSE 2024", year = "2024", editor = "Gabin An and Aymeric Blot and Vesna Nowack and Oliver Krauss and Justyna Petke", address = "Lisbon", month = "16 " # apr, publisher = "ACM", keywords = "genetic algorithms, genetic programming, Genetic Improvement", isbn13 = "979-8-4007-0573-1/24/04", URL = "http://geneticimprovementofsoftware.com/events/icse2024", URL = "http://gpbib.cs.ucl.ac.uk/gi2024/an_2024_GI.pdf", DOI = "doi:10.1145/3643692", video_url = "https://youtube.com/playlist?list=PLI8fiFpB7BoIRqJuY80XwmH-DFT_71y2S", size = "ix + 31", abstract = "The GI workshops continue to bring together researchers from across the world to exchange ideas about using optimisation techniques, particularly evolutionary computation, such as genetic programming, to improve existing software. Contents: \cite{Yoo:2024:GI} \cite{Blot:2024:GI} \cite{Baxter:2024:GI} \cite{callan:2024:GI} \cite{Craine:2024:GI} \cite{langdon:2024:GI} \cite{Nemeth:2024:GI} \cite{Sarmiento:2024:GI} See also \cite{langdon:2024:SEN} Published: 08 August 2024", } @PhdThesis{An:thesis, author = "Gabin An", title = "Synergizing Fault Localization and Continuous Integration to Streamline Bug Resolution in Large-Scale Software Systems", school = "Korea Advanced Institute of Science and Technology", year = "2024", address = "Daejeon, Korea", month = "4 " # jun, keywords = "SBSE, fault localization, SBFL, AutoFL, FONTE, Defects4J, BugsInPy, continuous integration, bug resolution, debugging, bug assignment, buginducing commit, large language model, LLN, ANN, GPT, SAP HANA, BIC", size = "101+ pages", abstract = "This thesis explores the synergistic interaction between Continuous Integration (CI) and Fault Localization (FL) within software development, aiming to enhance the efficiency and effectiveness of the bug resolution process. CI plays a critical role as developers frequently merge code changes into a central repository, followed by automated builds and tests to quickly detect bugs and maintain a unified code-base that supports effective collaboration for large-scale software systems. FL is an automated debugging technique designed to precisely detect the locations of bugs within the codebase, reducing the burden on developers. While CI and FL each aim to streamline software development and maintenance independently, their potential for interaction has not been fully explored. This research suggests that leveraging historical CI data can enable more effective application of FL, and that FL can improve the bug resolution process within CI systems. The thesis comprises three studies: identifying common root causes of test failures using diverse information sources available in the CI environment, efficiently identifying bug-inducing commits using FL and code change histories, and developing an explainable FL technique using large language models. Each study addresses specific challenges and provides novel solutions to simplify the debugging and maintenance stages of software development. The proposed solutions are empirically evaluated and thoroughly compared against their baselines using real-world open-source software and large-scale industry software", notes = "Not GP? Text in English. Supervisor Shin Yoo", } @InProceedings{Anand:2010:ICCSIT, author = "Deepa Anand and K. K. Bharadwaj", title = "Adaptive user similarity measures for recommender systems: A genetic programming approach", booktitle = "3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT 2010)", year = "2010", month = "9-11 " # jul, volume = "8", pages = "121--125", abstract = "Recommender systems signify the shift from the paradigm of searching for items to discovering items and have been employed by an increasing number of e-commerce sites for matching users to their preferences. Collaborative Filtering is a popular recommendation technique which exploits the past user-item interactions to determine user similarity. The preferences of such similar users are leveraged to offer suggestions to the active user. Even though several techniques for similarity assessment have been suggested in literature, no technique has been proven to be optimal under all contexts/data conditions. Hence, we propose a two-stage process to assess user similarity, the first is to learn the optimal transformation function to convert the raw ratings data to preference data by employing genetic programming, and the second is to use the preference values, so derived, to compute user similarity. The application of such learnt user bias gives rise to adaptive similarity measures, i.e. similarity estimates that are dataset dependent and hence expected to work best under any data environment. We demonstrate the superiority of our proposed technique by contrasting it to traditional similarity estimation techniques on four different datasets representing varied data environments.", keywords = "genetic algorithms, genetic programming, adaptive user similarity measure, collaborative filtering, data environment, item discovery, item searching, optimal transformation function, preference value, raw ratings data, recommender system, similarity assessment, similarity estimation, user-item interaction, groupware, information filtering, recommender systems", DOI = "doi:10.1109/ICCSIT.2010.5563737", notes = "Sch. of Comput. & Syst. Sci., Jawaharlal Nehru Univ., Delhi, India Also known as \cite{5563737}", } @PhdThesis{Anand:thesis, author = "Deepa Anand", title = "Enhancing Accuracy of Recommender Systems through various approaches to Local and Global Similarity Measures", year = "2011", school = "Computer and System Sciences, Jawaharlal Nehru University", address = "New Delhi, India", month = jul, keywords = "genetic algorithms, genetic programming, recommender systems", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Anand_thesis.pdf", size = "172 pages", abstract = "Web 2.0 represents a paradigm shift in the way that internet is consumed. Users' role has evolved from that of passive consumers of content to active prosumers, implying a plethora of information sources and an ever increasing ocean of content. Collaborative Recommender systems have thus emerged as Web 2.0 personalisation tools which aid users in grappling with the overload of information by allowing the discovery of content in contrast to plain search popularised by prior web technologies. To this end Collaborative filtering (CF) exploit the preferences of users who have liked similar items in the past to help a user to identify interesting products and services. The success of CF algorithms, however, is hugely dependent on the technique designed to determine the set of users whose opinion is sought. Traditionally user closeness is assessed by matching their preferences on a set of common experiences that both share. The challenge with this kind of computation is the overabundance of available content to be experienced, at the user's disposal, thus rendering the user-preference space very sparse. The similarity so computed is thus unstable for user pairs sharing a small set of experiences and is in fact incomputable for most user pairs due to a lack of expressed common preferences. To remedy the sparsity problems we propose methods to enrich the set of user connections obtained using measures such as Pearson Correlation Coefficient (PCC) and Cosine Similarity (COS). We achieve this by leveraging on explicit trust elicitation and trust transitivity. When interacting with anonymous users online, in the absence of physical cues apparent in our daily life, trust provides a reliable measure of quality and guides the user decision process on whether or not to interact with an entity. These trust statements in addition to identifying malicious users also enhance user connectivity by establishing links between pairs of users whose closeness cannot be determined through preference data. In addition transitivity of trust can also be leveraged to further expand the set of neighbours to collaborate with. We first explore a bifurcated view of trust: functional and referral trust i.e. trust in an entity to recommend items and the trust in an entity to recommend recommenders and propose models to quantify referral trust. Such a referral-functional trust framework leads to more meaningful derivation of trust through transitivity resulting in better quality recommendations. Though trust has been extensively used in literature to support the CF process, distrust information has been explored very little in this context. We thus propose a tri-component computation of trust and distrust using preference, functional trust and referral trust in order to densify the network of user interconnections. To maintain a balance between increased coverage and the quality of recommendations, however, we quantify risk measures for each trust and distrust relationship so derived and prune the network to retain high quality relationships thus ensuring good connections formed between users through transitivity of trust and distrust. In the absence of supplemental information such as trust/distrust to provide extra knowledge about user links the local similarity connections can be harnessed to deem a pair of users similar if they are share preferences with the same set of users thus estimating the global similarity between user pairs. We investigate the effectiveness of various graph based global or indirect similarity computation schemes in enhancing the user or item neighborhood thus bettering the quality and number of recommendations obtained.", abstract = "In addition to the inadequacy of similarity measures such as PCC and VS in forming a rich user neighbourhood they are static and may not capture user matching satisfactorily and guarantee optimal performance under diverse data situations. We propose to learn similarity measures which not only adjust to the type of data at hand but also ensure optimal performance over time. Evolutionary techniques are employed to learn such adaptive similarity measures. Finally sparsity variant fusion of predictions from local and global similarity measures have been shown to offer quality recommendations. In particular the fact that local similarity measures suffice when the preference data is dense but overtaken in performance by global similarity links when preference data is scarce can be leveraged to fuse the recommendations from the two systems. We define sparsity not only for the overall system but also at the user and user-item level. We use these measures to suggest a fusion scheme tailored for each user and/or for each item to be predicted by estimating the apportionment of influence local and global similarity measures have on each prediction. We demonstrate the effectiveness of the proposed techniques through experiments performed on real world datasets.", notes = "Supvervisor: K. K. Bharadwaj. JNU", } @Article{Anand:2012:IJCSI, author = "Deepa Anand", title = "Feature Extraction for Collaborative Filtering: A Genetic Programming Approach", journal = "International Journal of Computer Science Issues", year = "2012", volume = "9", number = "5", pages = "348--354", month = sep, keywords = "genetic algorithms, genetic programming, Recommender Systems, Collaborative Filtering, Feature Extraction", publisher = "IJCSI Press", ISSN = "16940784", bibsource = "OAI-PMH server at www.doaj.org", language = "eng", oai = "oai:doaj-articles:3fdb924cd5e50b8f275ce58daca88188", URL = "http://www.ijcsi.org/contents.php?volume=9&&issue=5", URL = "http://www.ijcsi.org/papers/IJCSI-9-5-1-348-354.pdf", size = "7 pages", abstract = "Collaborative filtering systems offer customised recommendations to users by exploiting the interrelationships between users and items. Users are assessed for their similarity in tastes and items preferred by similar users are offered as recommendations. However scalability and scarcity of data are the two major bottlenecks to effective recommendations. With web based RS typically having users in order of millions, timely recommendations pose a major challenge. Sparsity of ratings data also affects the quality of suggestions. To alleviate these problems we propose a genetic programming approach to feature extraction by employing GP to convert from user-item space to user-feature preference space where the feature space is much smaller than the item space. The advantage of this approach lies in the reduction of sparse high dimensional preference information into a compact and dense low dimensional preference data. The features are constructed using GP and the individuals are evolved to generate the most discriminative set of features. We compare our approach to content based feature extraction approach and demonstrate the effectiveness of the GP approach in generating the optimal feature set.", } @Article{ANASTASOPOULOS:2021:SoftwareX, author = "Nikolaos Anastasopoulos and Ioannis G. Tsoulos and Alexandros Tzallas", title = "{GenClass:} A parallel tool for data classification based on Grammatical Evolution", journal = "SoftwareX", volume = "16", pages = "100830", year = "2021", ISSN = "2352-7110", DOI = "doi:10.1016/j.softx.2021.100830", URL = "https://www.sciencedirect.com/science/article/pii/S2352711021001199", keywords = "genetic algorithms, genetic programming, Data classification, Grammatical evolution, Stochastic methods", abstract = "A genetic programming tool is proposed here for data classification. The tool is based on Grammatical Evolution technique and it is designed to exploit multicore computing systems using the OpenMP library. The tool constructs classification programs in a C-like programming language in order to classify the input data, producing simple if-else rules and upon termination the tool produces the classification rules in C and Python files. Also, the user can use his own Backus Normal Form (BNF) grammar through a command line option. The tool is tested on a wide range of classification problems and the produced results are compared against traditional techniques for data classification, yielding very promising results", } @Article{Andaryani:2024:jhydrol, author = "Soghra Andaryani and Amin Afkhaminia", title = "Real-time prediction of river ice breakup phenomena: A jittered genetic programming model and wavelet analysis integrating remotely sensed imagery and machine learning", journal = "Journal of Hydrology", year = "2024", volume = "644", pages = "132097", keywords = "genetic algorithms, genetic programming, Albedo & ERA5 data, Breakup date, Machin learning, River Ice, Tornio", ISSN = "0022-1694", URL = "https://www.sciencedirect.com/science/article/pii/S0022169424014938", DOI = "doi:10.1016/j.jhydrol.2024.132097", abstract = "Forecasting the timing of breakup ice jams in rivers is crucial for early flood warning and effective management in cold regions where rising river flows can lead to significant damage. However, this task is hindered by insufficient data and the complex dynamics of river ice. These obstacles pose challenges in developing precise forecasting models. This study aimed to address the data scarcity issue by introducing innovative machine learning methods, focusing on classification and jittering (J), binary genetic programming (BGP), and wavelet transform (WT) for river ice forecasting (WT-JBGP) in the Tornio River, situated between Finland and Sweden. By considering time scales ranging from 2 to 32 days and time lags of 1 to 3 days, this method was applied to enhance the predictive capabilities of predictors. The findings reveal that certain predictors, with specific time scales and time lags, significantly influence the timing of breakup events. These include the 8-day temperature and 32-day discharge, both with a 2-day lag time, as well as the 4-day precipitation, approximation of albedo, and 16-day atmospheric pressure at ground level, all with a 1-day lag obtained from ERA5 and recorded data. Additionally, we conducted a quantitative evaluation of the effectiveness of the proposed model and contrasted its efficacy with that of BGP, WT-BGP, and advanced J-BGP techniques. The WT-JBGP model attains the highest overall classification accuracy of approximately 0.91, alongside a Heidke Skill Score exceeding 0.78, and a Positive Predictive Value surpassing 0.85, thereby demonstrating its superiority over competing methodologies. In summation, this study offers a promising approach to overcoming observed data scarcity in breakup date prediction, providing valuable insights into river ice dynamics", } @Misc{DBLP:journals/corr/abs-2012-03527, author = "Nikola Andelic and Sandi {Baressi Segota} and Ivan Lorencin and Zlatan Car", title = "Estimation of Gas Turbine Shaft Torque and Fuel Flow of a {CODLAG} Propulsion System Using Genetic Programming Algorithm", howpublished = "arXiv", volume = "abs/2012.03527", year = "2020", month = "7 " # dec, keywords = "genetic algorithms, genetic programming, Artificial Intelligence, Combined Diesel-Electric and Gas Propulsion System, Genetic Programming Algorithm, Gas Turbine Shaft Torque Estimation, Fuel Flow Estimation", eprinttype = "arXiv", eprint = "2012.03527", timestamp = "Wed, 09 Dec 2020 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/corr/abs-2012-03527.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://arxiv.org/abs/2012.03527", size = "25 pages", abstract = "he publicly available dataset of condition based maintenance of combined diesel-electric and gas (CODLAG) propulsion system for ships has been used to obtain symbolic expressions which could estimate gas turbine shaft torque and fuel flow using genetic programming (GP) algorithm. The entire dataset consists of 11934 samples that was divided into training and testing portions of dataset in an 80:20 ratio. The training dataset used to train the GP algorithm to obtain symbolic expressions for gas turbine shaft torque and fuel flow estimation consisted of 9548 samples. The best symbolic expressions obtained for gas turbine shaft torque and fuel flow estimation were obtained based on their R2 score generated as a result of the application of the testing portion of the dataset on the aforementioned symbolic expressions. The testing portion of the dataset consisted of 2386 samples. The three best symbolic expressions obtained for gas turbine shaft torque estimation generated R2 scores of 0.999201, 0.999296, and 0.999374, respectively. The three best symbolic expressions obtained for fuel flow estimation generated R2 scores of 0.995495, 0.996465, and 0.996487, respectively.", notes = "Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia", } @Article{DBLP:journals/hij/AndelicSLMC21, author = "Nikola Andelic and Sandi {Baressi Segota} and Ivan Lorencin and Vedran Mrzljak and Zlatan Car", title = "Estimation of {COVID-19} epidemic curves using genetic programming algorithm", journal = "Health Informatics J.", volume = "27", number = "1", pages = "146045822097672", year = "2021", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1177/1460458220976728", DOI = "doi:10.1177/1460458220976728", timestamp = "Sun, 25 Jul 2021 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/hij/AndelicSLMC21.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{andelic:2021:IJERPH, author = "Nikola Andelic and Sandi {Baressi Segota} and Ivan Lorencin and Zdravko Jurilj and Tijana Sustersic and Andela Blagojevic and Alen Protic and Tomislav Cabov and Nenad Filipovic and Zlatan Car", title = "Estimation of {COVID-19} Epidemiology Curve of the United States Using Genetic Programming Algorithm", journal = "International Journal of Environmental Research and Public Health", year = "2021", volume = "18", number = "3", keywords = "genetic algorithms, genetic programming", ISSN = "1660-4601", URL = "https://www.mdpi.com/1660-4601/18/3/959", DOI = "doi:10.3390/ijerph18030959", abstract = "Estimation of the epidemiology curve for the COVID-19 pandemic can be a very computationally challenging task. Thus far, there have been some implementations of artificial intelligence (AI) methods applied to develop epidemiology curve for a specific country. However, most applied AI methods generated models that are almost impossible to translate into a mathematical equation. In this paper, the AI method called genetic programming (GP) algorithm is used to develop a symbolic expression (mathematical equation) which can be used for the estimation of the epidemiology curve for the entire U.S. with high accuracy. The GP algorithm is used on the publicly available dataset that contains the number of confirmed, deceased and recovered patients for each U.S. state to obtain the symbolic expression for the estimation of the number of the aforementioned patient groups. The dataset consists of the latitude and longitude of the central location for each state and the number of patients in each of the goal groups for each day in the period of 22 January 2020–3 December 2020. The obtained symbolic expressions for each state are summed up to obtain symbolic expressions for estimation of each of the patient groups (confirmed, deceased and recovered). These symbolic expressions are combined to obtain the symbolic expression for the estimation of the epidemiology curve for the entire U.S. The obtained symbolic expressions for the estimation of the number of confirmed, deceased and recovered patients for each state achieved R2 score in the ranges 0.9406–0.9992, 0.9404–0.9998 and 0.9797–0.99955, respectively. These equations are summed up to formulate symbolic expressions for the estimation of the number of confirmed, deceased and recovered patients for the entire U.S. with achieved R2 score of 0.9992, 0.9997 and 0.9996, respectively. Using these symbolic expressions, the equation for the estimation of the epidemiology curve for the entire U.S. is formulated which achieved R2 score of 0.9933. Investigation showed that GP algorithm can produce symbolic expressions for the estimation of the number of confirmed, recovered and deceased patients as well as the epidemiology curve not only for the states but for the entire U.S. with very high accuracy.", notes = "also known as \cite{ijerph18030959}", } @Article{andelic:2021:JMSE, author = "Nikola Andelic and Sandi {Baressi Segota} and Ivan Lorencin and Igor Poljak and Vedran Mrzljak and Zlatan Car", title = "Use of Genetic Programming for the Estimation of {CODLAG} Propulsion System Parameters", journal = "Journal of Marine Science and Engineering", year = "2021", volume = "9", number = "6", keywords = "genetic algorithms, genetic programming", ISSN = "2077-1312", URL = "https://www.mdpi.com/2077-1312/9/6/612", DOI = "doi:10.3390/jmse9060612", abstract = "In this paper, the publicly available dataset for the Combined Diesel-Electric and Gas (CODLAG) propulsion system was used to obtain symbolic expressions for estimation of fuel flow, ship speed, starboard propeller torque, port propeller torque, and total propeller torque using genetic programming (GP) algorithm. The dataset consists of 11,934 samples that were divided into training and testing portions in an 80:20 ratio. The training portion of the dataset which consisted of 9548 samples was used to train the GP algorithm to obtain symbolic expressions for estimation of fuel flow, ship speed, starboard propeller, port propeller, and total propeller torque, respectively. After the symbolic expressions were obtained the testing portion of the dataset which consisted of 2386 samples was used to measure estimation performance in terms of coefficient of correlation (R2) and Mean Absolute Error (MAE) metric, respectively. Based on the estimation performance in each case three best symbolic expressions were selected with and without decay state coefficients. From the conducted investigation, the highest R2 and lowest MAE values were achieved with symbolic expressions for the estimation of fuel flow, ship speed, starboard propeller torque, port propeller torque, and total propeller torque without decay state coefficients while symbolic expressions with decay state coefficients have slightly lower estimation performance.", notes = "also known as \cite{jmse9060612}", } @InProceedings{Andelic:2022:SICAAI, author = "Nikola Andelic and Sandi {Baressi Segota} and Ivan Lorencin and Matko Glucina and Jelena Musulin and Daniel Stifanic and Zlatan Car", title = "Utilization of Genetic Programming for Estimation of Molecular Structures Ground State Energies", booktitle = "1st Serbian International Conference on Applied Artificial Intelligence", year = "2022", address = "Kragujevac, Serbia", month = may # " 19-20", publisher = "Springer", keywords = "genetic algorithms, genetic programming, CHNOPS dataset, ground state energies", URL = "http://aai2022.kg.ac.rs/wp-content/uploads/upload/AAI_2022_Papers.zip", size = "4 pages", abstract = "GP to predict ground-state energies of molecules made up of C, H, N, O, P, and S (CHONPS) atoms. The GP was trained and tested on a publicly available dataset which consist of 16242 molecules where ground state energies were computed using the density functional theory (DFT). The optimal parameters of GP were chosen using the random parameter search method. After multiple GP executions, the best symbolic expression was chosen using a coefficient of determination (R), mean absolute error (MAE), and root mean square error (RMSE). The best symbolic expression achieved R, MAE, and RMSE of 0.9434, 0.48, and 0.86, respectively.", notes = "http://www.aai2022.kg.ac.rs/aai-2022-papers/ SICAAI proceedings published by Springer in 2023 doi:10.1007/978-3-031-29717-5", } @Article{Andelic:2022:FI, author = "Nikola Andelic and Sandi {Baressi Segota} and Ivan Lorencin and Matko Glucina", title = "Detection of Malicious Websites Using Symbolic Classifier", journal = "Future Internet", year = "2022", volume = "14", number = "12", pages = "Article no 358", month = nov, email = "nandelic@riteh.hr", keywords = "genetic algorithms, genetic programming, malicious websites, oversampling methods, symbolic classifier, undersampling methods", ISSN = "1999-5903", publisher = "MDPI", URL = "https://www.mdpi.com/1999-5903/14/12/358", DOI = "doi:10.3390/fi14120358", size = "30 pages", abstract = "Malicious websites are web locations that attempt to install malware, which is the general term for anything that will cause problems in computer operation, gather confidential information, or gain total control over the computer. a novel approach is proposed which consists of the implementation of the genetic programming symbolic classifier (GPSC) algorithm on a publicly available dataset to obtain a simple symbolic expression (mathematical equation) which could detect malicious websites with high classification accuracy. Due to a large imbalance of classes in the initial dataset, several data sampling methods (random under-sampling/oversampling, ADASYN, SMOTE, BorderlineSMOTE, and KmeansSMOTE) were used to balance the dataset classes. For this investigation, the hyper-parameter search method was developed to find the combination of GPSC hyperparameters with which high classification accuracy could be achieved. The first investigation was conducted using GPSC with a random hyperparameter search method and each dataset variation was divided on a train and test dataset in a ratio of 70:30. To evaluate each symbolic expression, the performance of each symbolic expression was measured on the train and test dataset and the mean and standard deviation values of accuracy (ACC), AUC, precision, recall and f1-score were obtained. The second investigation was also conducted using GPSC with the random hyperparameter search method; however, 70percent, i.e., the train dataset, was used to perform 5-fold cross-validation. If the mean accuracy, AUC, precision, recall, and f1-score values were above 0.97 then final training and testing (train/test 70:30) were performed with GPSC with the same randomly chosen hyperparameters used in a 5-fold cross-validation process and the final mean and standard deviation values of the aforementioned evaluation methods were obtained. In both investigations, the best symbolic expression was obtained in the case where the dataset balanced with the KMeansSMOTE method was used for training and testing. The best symbolic expression obtained using GPSC with the random hyperparameter search method and classic train釦est procedure (70:30) on a dataset balanced with the KMeansSMOTE method achieved values of", notes = "Faculty of Engineering, University of Rijeka, 51000 Rijeka, Croatia", } @Article{Andelic:2022:Sensors, author = "Nikola Andelic and Sandi {Baressi Segota} and Ivan Lorencin and Zlatan Car", title = "The Development of Symbolic Expressions for Fire Detection with Symbolic Classifier Using Sensor Fusion Data", journal = "Sensors", year = "2022", volume = "23", number = "1", pages = "Article no 169", month = dec, email = "nandelic@riteh.hr", keywords = "genetic algorithms, genetic programming, symbolic classifier, fire-alarm, oversampling methods, undersampling methods", publisher = "MDPI", ISSN = "1424-8220", URL = "https://www.mdpi.com/1424-8220/23/1/169", DOI = "doi:10.3390/s23010169", size = "27 pages", abstract = "Fire is usually detected with fire detection systems that are used to sense one or more products resulting from the fire such as smoke, heat, infrared, ultraviolet light radiation, or gas. Smoke detectors are mostly used in residential areas while fire alarm systems (heat, smoke, flame, and fire gas detectors) are used in commercial, industrial and municipal areas. However, in addition to smoke, heat, infrared, ultraviolet light radiation, or gas, other parameters could indicate a fire, such as air temperature, air pressure, and humidity, among others. Collecting these parameters requires the development of a sensor fusion system. However, with such a system, it is necessary to develop a simple system based on artificial intelligence (AI) that will be able to detect fire with high accuracy using the information collected from the sensor fusion system. The novelty of this paper is to show the procedure of how a simple AI system can be created in form of symbolic expression obtained with a genetic programming symbolic classifier (GPSC) algorithm and can be used as an additional tool to detect fire with high classification accuracy. Since the investigation is based on an initially imbalanced and publicly available dataset (high number of samples classified as 1-Fire Alarm and small number of samples 0-No Fire Alarm), the idea is to implement various balancing methods such as random undersampling/oversampling, Near Miss-1, ADASYN, SMOTE, and Borderline SMOTE. The obtained balanced datasets were used in GPSC with random hyperparameter search combined with 5-fold cross-validation to obtain symbolic expressions that could detect fire with high classification accuracy. For this investigation, the random hyper-parameter search method and 5-fold cross-validation had to be developed. Each obtained symbolic expression was evaluated on train and test datasets to obtain mean and standard deviation values of accuracy (ACC ), area under the receiver operating characteristic curve (AUC , respectively. The symbolic expression using which best values of classification metrics were achieved is shown, and the final evaluation was performed on the original dataset.", } @Article{Andelic:2022:applsci, author = "Nikola Andelic and Ivan Lorencin and Sandi {Baressi Segota} and Zlatan Car", title = "The Development of Symbolic Expressions for the Detection of Hepatitis C Patients and the Disease Progression from Blood Parameters Using Genetic Programming-Symbolic Classification Algorithm", journal = "Applied Sciences", year = "2022", volume = "13", number = "1", pages = "Article no 574", month = dec, email = "nandelic@riteh.hr", keywords = "genetic algorithms, genetic programming, ADASYN, borderline SMOTE, genetic programming-symbolic classifier, Hepatitis C, fibrosis, cirrhosis, SMOTE", publisher = "MDPI", ISSN = "2076-3417", URL = "https://www.mdpi.com/2076-3417/13/1/574", DOI = "doi:10.3390/app13010574", size = "33 pages", abstract = "Hepatitis C is an infectious disease which is caused by the Hepatitis C virus (HCV) and the virus primarily affects the liver. Based on the publicly available dataset used in this paper the idea is to develop a mathematical equation that could be used to detect HCV patients with high accuracy based on the enzymes, proteins, and biomarker values contained in a patient blood sample using genetic programming symbolic classification (GPSC) algorithm. Not only that, but the idea was also to obtain a mathematical equation that could detect the progress of the disease i.e., Hepatitis C, Fibrosis, and Cirrhosis using the GPSC algorithm. Since the original dataset was imbalanced (a large number of healthy patients versus a small number of Hepatitis C/Fibrosis/Cirrhosis patients) the dataset was balanced using random oversampling, SMOTE, ADSYN, and Borderline SMOTE methods. The symbolic expressions (mathematical equations) were obtained using the GPSC algorithm using a rigorous process of 5-fold cross-validation with a random hyperparameter search method which had to be developed for this problem. To evaluate each symbolic expression generated with GPSC the mean and standard deviation values of accuracy (ACC), the area under the receiver operating characteristic curve (AUC), precision, recall, and F1-score were obtained. In a simple binary case (healthy vs. Hepatitis C patients) the best case was achieved with a dataset balanced with the Borderline SMOTE method. The results are ACC For the best binary and multi-class cases, the symbolic expressions are shown and evaluated on the original dataset.", } @Article{Andelic:2023:Machines, author = "Nikola Andelic and Ivan Lorencin and Sandi {Baressi Segota} and Zlatan Car", title = "Classification of Wall Following Robot Movements Using Genetic Programming Symbolic Classifier", journal = "Machines", year = "2023", volume = "11", number = "1", pages = "Article no 105", month = jan, email = "nandelic@riteh.hr", keywords = "genetic algorithms, genetic programming, classification of robot movement, oversampling methods, symbolic classifier, ultrasound sensors", publisher = "MDPI", ISSN = "2075-1702", URL = "https://www.mdpi.com/2075-1702/11/1/105", DOI = "doi:10.3390/machines11010105", size = "35 pages", abstract = "The navigation of mobile robots throughout the surrounding environment without collisions is one of the mandatory behaviors in the field of mobile robotics. The movement of the robot through its surrounding environment is achieved using sensors and a control system. The application of artificial intelligence could potentially predict the possible movement of a mobile robot if a robot encounters potential obstacles. The data used in this paper is obtained from a wall-following robot that navigates through the room following the wall in a clockwise direction with the use of 24 ultrasound sensors. The idea of this paper is to apply genetic programming symbolic classifier (GPSC) with random hyperparameter search and 5-fold cross-validation to investigate if these methods could classify the movement in the correct category (move forward, slight right turn, sharp right turn, and slight left turn) with high accuracy. Since the original dataset is imbalanced, oversampling methods (ADASYN, SMOTE, and BorderlineSMOTE) were applied to achieve the balance between class samples. These over-sampled dataset variations were used to train the GPSC algorithm with a random hyperparameter search and 5-fold cross-validation. The mean and standard deviation of accuracy (ACC), the area under the receiver operating characteristic (AUC), precision, recall, and F1−score values were used to measure the classification performance of the obtained symbolic expressions. The investigation showed that the best symbolic expressions were obtained on a dataset balanced with the BorderlineSMOTE method with ACC. respectively. The results of the investigation showed that this simple, non-linearly separable classification task could be solved using the GPSC algorithm with high accuracy.", } @Article{Andelic:2023:applsci, author = "Nikola Andelic and Ivan Lorencin and Sandi {Baressi Segota} and Zlatan Car", title = "Classification of Faults Operation of a Robotic Manipulator Using Symbolic Classifier", journal = "Applied Sciences", year = "2023", volume = "13", number = "3", pages = "Article no 1962", month = feb, email = "nandelic@riteh.hr", keywords = "genetic algorithms, genetic programming, oversampling methods, robot fault operation, random oversampling, symbolic classifier, SMOTE", publisher = "MDPI", ISSN = "2076-3417", URL = "https://www.mdpi.com/2076-3417/13/3/1962", DOI = "doi:10.3390/app13031962", size = "23 pages", abstract = "In autonomous manufacturing lines, it is very important to detect the faulty operation of robot manipulators to prevent potential damage. In this paper, the application of a genetic programming algorithm (symbolic classifier) with a random selection of hyperparameter values and trained using a 5-fold cross-validation process is proposed to determine expressions for fault detection during robotic manipulator operation, using a dataset that was made publicly available by the original researchers. The original dataset was reduced to a binary dataset (fault vs. normal operation); however, due to the class imbalance random oversampling, and SMOTE methods were applied. The quality of best symbolic expressions (SEs) was based on the highest mean values of accuracy (ACC...are equal to 0.9978, 0.998, 1.0, 0.997, and 0.998, respectively. The investigation showed that using the described procedure, symbolically expressed models of a high classification performance are obtained for the purpose of detecting faults in the operation of robotic manipulators.", } @Article{Andelic:2023:applsci2, author = "Nikola Andelic and Ivan Lorencin and Sandi {Baressi Segota} and Zlatan Car", title = "Estimation of Interaction Locations in Super Cryogenic Dark Matter Search Detectors Using Genetic Programming-Symbolic Regression Method", journal = "Applied Sciences", year = "2023", volume = "13", number = "4", pages = "Article no 2059", month = feb, email = "nandelic@riteh.hr", keywords = "genetic algorithms, genetic programming, cross-validation, interaction location, SuperCDMS, symbolic regression", publisher = "MDPI", ISSN = "2076-3417", URL = "https://www.mdpi.com/2076-3417/13/4/2059", DOI = "doi:10.3390/app13042059", size = "23 pages", abstract = "The Super Cryogenic Dark Matter Search (SuperCDMS) experiment is used to search for Weakly Interacting Massive Particles (WIMPs) candidates for dark matter particles. In this experiment, the WIMPs interact with nuclei in the detector; however, there are many other interactions (background interactions). To separate background interactions from the signal, it is necessary to measure the interaction energy and to reconstruct the location of the interaction between WIMPs and the nuclei. In recent years, some research papers have been investigating the reconstruction of interaction locations using artificial intelligence (AI) methods. In this paper, a genetic programming-symbolic regression (GPSR), with randomly tuned hyperparameters cross-validated via a five-fold procedure, was applied to the SuperCDMS experiment to estimate the interaction locations with high accuracy. To measure the estimation accuracy of obtaining the SEs, the mean and standard deviation (σ) values of R2, the root-mean-squared error (RMSE), and finally, the mean absolute error (MAE) were used. The investigation showed that using GPSR, SEs can be obtained that estimate the interaction locations with high accuracy. To improve the solution, the five best SEs were combined from the three best cases. The results demonstrated that a very high estimation accuracy can be achieved with the proposed methodology.", } @Article{Andelic:2023:Cancers, author = "Nikola Andelic and Sandi {Baressi Segota}", title = "Development of Symbolic Expressions Ensemble for Breast Cancer Type Classification Using Genetic Programming Symbolic Classifier and Decision Tree Classifier", journal = "Cancers", year = "2023", volume = "15", number = "13", pages = "article no. 3411", month = "29 " # jun, email = "nandelic@riteh.hr", keywords = "genetic algorithms, genetic programming, PCA, breast cancer, genetic programming symbolic classifier, 5-fold cross validation, random hyperparameter value search", ISSN = "2072-6694", URL = "https://www.mdpi.com/2072-6694/15/13/3411", DOI = "doi:10.3390/cancers15133411", size = "27 pages", abstract = "Breast cancer is a type of cancer with several sub-types. It occurs when cells in breast tissue grow out of control. The accurate sub-type classification of a patient diagnosed with breast cancer is mandatory for the application of proper treatment. Breast cancer classification based on gene expression is challenging even for artificial intelligence (AI) due to the large number of gene expressions. The idea in this paper is to use genetic programming symbolic classifier (GPSC) on the publicly available dataset to obtain a set of symbolic expressions (SEs) that can classify the breast cancer sub-type using gene expressions with high classification accuracy. The initial problem with the used dataset is a large number of input variables (54676 gene expressions), a small number of dataset samples (151 samples), and six classes of breast cancer sub-types that are highly imbalanced. The large number of input variables is solved with principal component analysis (PCA), while the small number of samples and the large imbalance between class samples are solved with the application of different oversampling methods generating different dataset variations. On each oversampled dataset, the GPSC with random hyperparameter values search (RHVS) method is trained using 5-fold cross validation (5CV) to obtain a set of SEs. The best set of SEs is chosen based on mean values of accuracy (ACC), the area under the receiving operating characteristic curve (AUC), precision, recall, and F1-score values. In this case, the highest classification accuracy is equal to 0.992 across all evaluation metric methods. The best set of SEs is additionally combined with a decision tree classifier, which slightly improves ACC to 0.994.", notes = "Department of Automation and Electronics, Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia", } @Article{Andelic:2023:Computers, author = "Nikola Andelic and Sandi {Baressi Segota} and Zlatan Car", title = "Improvement of Malicious Software Detection Accuracy through Genetic Programming Symbolic Classifier with Application of Dataset Oversampling Techniques", journal = "Computers", year = "2023", volume = "12", number = "12", pages = "242", email = "nikola.andjelic@uniri.hr", keywords = "genetic algorithms, genetic programming, genetic programming symbolic classifier, 5-fold cross-validation, malware software detection, oversampling techniques, random hyperparameter value search method", ISSN = "2073-431X", URL = "https://www.mdpi.com/2073-431X/12/12/242", DOI = "doi:10.3390/computers12120242", size = "19 pages", abstract = "Malware detection using hybrid features, combining binary and hexadecimal analysis with DLL calls, is crucial for leveraging the strengths of both static and dynamic analysis methods. Artificial intelligence (AI) enhances this process by enabling automated pattern recognition, anomaly detection, and continuous learning, allowing security systems to adapt to evolving threats and identify complex, polymorphic malware that may exhibit varied behaviors. This synergy of hybrid features with AI empowers malware detection systems to efficiently and proactively identify and respond to sophisticated cyber threats in real time. In this paper, the genetic programming symbolic classifier (GPSC) algorithm was applied to the publicly available dataset to obtain symbolic expressions (SEs) that could detect the malware software with high classification performance. The initial problem with the dataset was a high imbalance between class samples, so various oversampling techniques were used to obtain balanced dataset variations on which GPSC was applied. To find the optimal combination of GPSC hyperparameter values, the random hyperparameter value search method (RHVS) was developed and applied to obtain SEs with high classification accuracy. The GPSC was trained with five-fold cross-validation (5FCV) to obtain a robust set of SEs on each dataset variation. To choose the best SEs, several evaluation metrics were used, i.e., the length and depth of SEs, accuracy score (ACC), area under receiver operating characteristic curve (AUC), precision, recall, f1-score, and confusion matrix. The best-obtained SEs are applied on the original imbalanced dataset to see if the classification performance is the same as it was on balanced dataset variations. The results of the investigation showed that the proposed method generated SEs with high classification accuracy (0.9962) in malware software detection.", notes = "Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia", } @Article{andelic:2023:Technologies, author = "Nikola Andelic and Sandi {Baressi Segota}", title = "Generating Mathematical Expressions for Estimation of Atomic Coordinates of Carbon Nanotubes Using Genetic Programming Symbolic Regression", journal = "Technologies", year = "2023", volume = "11", number = "6", pages = "Article No. 185", keywords = "genetic algorithms, genetic programming", ISSN = "2227-7080", URL = "https://www.mdpi.com/2227-7080/11/6/185", DOI = "doi:10.3390/technologies11060185", abstract = "The study addresses the formidable challenge of calculating atomic coordinates for carbon nanotubes (CNTs) using density functional theory (DFT), a process that can endure for days. To tackle this issue, the research leverages the Genetic Programming Symbolic Regression (GPSR) method on a publicly available dataset. The primary aim is to assess if the resulting Mathematical Equations (MEs) from GPSR can accurately estimate calculated atomic coordinates obtained through DFT. Given the numerous hyperparameters in GPSR, a Random Hyperparameter Value Search (RHVS) method is devised to pinpoint the optimal combination of hyperparameter values, maximizing estimation accuracy. Two distinct approaches are considered. The first involves applying GPSR to estimate calculated coordinates (uc, vc, wc) using all input variables (initial atomic coordinates u, v, w, and integers n, m specifying the chiral vector). The second approach applies GPSR to estimate each calculated atomic coordinate using integers n and m alongside the corresponding initial atomic coordinates. This results in the creation of six different dataset variations. The GPSR algorithm undergoes training via a 5-fold cross-validation process. The evaluation metrics include the coefficient of determination (R2), mean absolute error (MAE), root mean squared error (RMSE), and the depth and length of generated MEs. The findings from this approach demonstrate that GPSR can effectively estimate CNT atomic coordinates with high accuracy, as indicated by an impressive R2?1.0. This study not only contributes to the advancement of accurate estimation techniques for atomic coordinates but also introduces a systematic approach for optimising hyperparameters in GPSR, showcasing its potential for broader applications in materials science and computational chemistry.", notes = "also known as \cite{technologies11060185}", } @Article{ANDELIC:2024:engappai, author = "Nikola Anelic and Ivan Lorencin and Vedran Mrzljak and Zlatan Car", title = "On the application of symbolic regression in the energy sector: Estimation of combined cycle power plant electrical power output using genetic programming algorithm", journal = "Engineering Applications of Artificial Intelligence", volume = "133", pages = "108213", year = "2024", ISSN = "0952-1976", DOI = "doi:10.1016/j.engappai.2024.108213", URL = "https://www.sciencedirect.com/science/article/pii/S0952197624003713", keywords = "genetic algorithms, genetic programming, Averaging ensemble, Bland-Altman analysis, Combined cycle power plant, Random hyperparameter values search method", abstract = "This paper focuses on the estimation of electrical power output (Pe) in a combined cycle power plant (CCPP) using ambient temperature (AT), vacuum in the condenser (V), ambient pressure (AP), and relative humidity (RH). The study stresses accurate estimation for better CCPP performance and energy efficiency through responsive control to changing conditions. The novelty lies in applying genetic programming (GP) on a publicly available dataset to generate Symbolic Expressions (SEs) for high-accuracy Pe. To address the challenge of numerous GP hyperparameters, a random hyperparameter values search method (RHVS) is introduced to find optimal combinations, resulting in SEs with higher accuracy. SEs are created with varying input variables, and their performance is evaluated using multiple metrics (coefficient of determination (R2), mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), Kling-Gupta Efficiency (KGE), and Bland-Altman (B-A) analysis). A key innovation involves combining the best SEs through an Averaging ensemble (AE), leading to a robust estimation accuracy. Notably, the AE YVE-2 achieves the highest (Pe) accuracy, including R2=0.9368, MAE=3.3378, MSE=18.4800, RMSE=4.2985, MAPE=0.7354percent, and KGE=0.9479. The investigation highlights AT as the most influential variable, underscoring the importance of choosing inputs aligned with physical processes. This paper's outlined procedure, combining GP, hyperparameter optimization, and ensemble techniques, offers an efficient method for estimating Pe in CCPP. It promises simplicity and effectiveness in real-world applications. B-A analysis proves valuable for SE selection, enhancing the proposed methodology", } @Article{ANDELIC:2024:ascom, author = "N. Anelic", title = "Improvement of pulsars detection using dataset balancing methods and symbolic classification ensemble", journal = "Astronomy and Computing", volume = "47", pages = "100801", year = "2024", ISSN = "2213-1337", DOI = "doi:10.1016/j.ascom.2024.100801", URL = "https://www.sciencedirect.com/science/article/pii/S2213133724000167", keywords = "genetic algorithms, genetic programming, Dataset balancing methods, Genetic programming symbolic classifier, Pulsars detection", abstract = "Highly accurate detection of pulsars is mandatory. With the application of machine learning (ML) algorithms, the detection of pulsars can certainly be improved if the dataset is balanced. In this paper, the publicly available dataset (HTRU2) is highly imbalanced so various balancing methods were applied. The balanced dataset was used in genetic programming symbolic classifier (GPSC) to obtain symbolic expressions (SEs) that can detect pulsars with high classification accuracy. To find the optimal combination of GPSC hyperparameters the random hyperparameter search (RHS) method was developed and applied. The GPSC was trained using 5-fold cross-validation so after each training a total of 5 SEs were obtained. The best set of SEs are selected based on their classification performance and all of them are applied on the original dataset. The best classification accuracy (ACC), the area under receiver operating characteristic (AUC), precision, recall, and f1-score were achieved in the case of the dataset balanced with the AllKNN method i.e. all mean evaluation metric values are equal to 0.995. The ensemble consisted of 25 SEs that achieved the ACC=0.978, AUC=0.9452 , Precision=0.905, Recall=0.9963, and F1-Score=0.94877, on the original dataset", } @Article{andelic:2024:Information, author = "Nikola Andelic and Sandi {Baressi Segota}", title = "Enhancing Network Intrusion Detection: A Genetic Programming Symbolic Classifier Approach", journal = "Information", year = "2024", volume = "15", number = "3", pages = "Article No. 154", keywords = "genetic algorithms, genetic programming", ISSN = "2078-2489", URL = "https://www.mdpi.com/2078-2489/15/3/154", DOI = "doi:10.3390/info15030154", abstract = "This investigation underscores the paramount imperative of discerning network intrusions as a pivotal measure to fortify digital systems and shield sensitive data from unauthorized access, manipulation, and potential compromise. The principal aim of this study is to leverage a publicly available dataset, employing a Genetic Programming Symbolic Classifier (GPSC) to derive symbolic expressions (SEs) endowed with the capacity for exceedingly precise network intrusion detection. In order to augment the classification precision of the SEs, a pioneering Random Hyperparameter Value Search (RHVS) methodology was conceptualized and implemented to discern the optimal combination of GPSC hyperparameter values. The GPSC underwent training via a robust five-fold cross-validation regimen, mitigating class imbalances within the initial dataset through the application of diverse oversampling techniques, thereby engendering balanced dataset iterations. Subsequent to the acquisition of SEs, the identification of the optimal set ensued, predicated upon metrics inclusive of accuracy, area under the receiver operating characteristics curve, precision, recall, and F1-score. The selected SEs were subsequently subjected to rigorous testing on the original imbalanced dataset. The empirical findings of this research underscore the efficacy of the proposed methodology, with the derived symbolic expressions attaining an impressive classification accuracy of 0.9945. If the accuracy achieved in this research is compared to the average state-of-the-art accuracy, the accuracy obtained in this research represents the improvement of approximately 3.78percent. In summation, this investigation contributes salient insights into the efficacious deployment of GPSC and RHVS for the meticulous detection of network intrusions, thereby accentuating the potential for the establishment of resilient cybersecurity defenses.", notes = "also known as \cite{info15030154}", } @Article{Andelic:2024:Electronics, author = "Nikola Andelic and Sandi {Baressi Segota}", title = "An Advanced Methodology for Crystal System Detection in Li-Ion Batteries", journal = "Electronics", year = "2024", volume = "13", number = "12", pages = "article number: 2278", month = jun, keywords = "genetic algorithms, genetic programming, crystal structure, genetic programming symbolic classifier, Lithium batteries, oversampling techniques, random hyperparameter value search method", ISSN = "2079-9292", URL = "https://www.mdpi.com/2079-9292/13/12/2278", DOI = "doi:10.3390/electronics13122278", size = "32 pages", abstract = "Detecting the crystal system of lithium-ion batteries is crucial for optimising their performance and safety. Understanding the arrangement of atoms or ions within the battery electrodes and electrolyte allows for improvements in energy density, cycling stability, and safety features. This knowledge also guides material design and fabrication techniques, driving advancements in battery technology for various applications. In this paper, a publicly available dataset was used to develop mathematical equations (MEs) using a genetic programming symbolic classifier (GPSC) to determine the type of crystal structure in Li-ion batteries with a high classification performance. The dataset consists of three different classes transformed into three binary classification datasets using a one-versus-rest approach. Since the target variable of each dataset variation is imbalanced, several oversampling techniques were employed to achieve balanced dataset variations. The GPSC was trained on these balanced dataset variations using a five-fold cross-validation (5FCV) process, and the optimal GPSC hyperparameter values were searched for using a random hyperparameter value search (RHVS) method. The goal was to find the optimal combination of GPSC hyperparameter values to achieve the highest classification performance. After obtaining MEs using the GPSC with the highest classification performance, they were combined and tested on initial binary classification dataset variations. Based on the conducted investigation, the ensemble of MEs could detect the crystal system of Li-ion batteries with a high classification accuracy (1.0).", notes = "Also known as \cite{electronics13122278} ECML/PKDD2003 Discovery Challenge, Industrial Electronics Department of Automation and Electronics, Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia", } @Article{andelic:IJIS, author = "Nikola Andelic and Sandi {Baressi Segota} and Zlatan Car", title = "Robust password security: a genetic programming approach with imbalanced dataset handling", journal = "International Journal of Information Security", year = "2024", volume = "23", number = "3", pages = "1761--1786", month = jun, keywords = "genetic algorithms, genetic programming, Genetic programming symbolic classifier, Random hyperparameter value search method, Fivefold cross-validation, Oversampling and undersampling techniques, Password strength classification", ISSN = "1615-5262", URL = "http://link.springer.com/article/10.1007/s10207-024-00814-2", DOI = "doi:10.1007/s10207-024-00814-2", size = "26 pages", notes = "Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia", } @Article{Andelic:2024:Computers, author = "Nikola Andelic and Sandi {Baressi Segota}", title = "Achieving High Accuracy in Android Malware Detection through Genetic Programming Symbolic Classifier", journal = "Computers", year = "2024", volume = "13", number = "8", pages = "article number: 197", month = aug, keywords = "genetic algorithms, genetic programming, Android malware detection, genetic programming symbolic classifier, oversampling techniques, random hyperparameters values method", ISSN = "2073-431X", URL = "https://www.mdpi.com/2073-431X/13/8/197/pdf?version=1723704936", URL = "https://www.mdpi.com/2073-431X/13/8/197", DOI = "doi:10.3390/computers13080197", size = "31 pages", abstract = "The detection of Android malware is of paramount importance for safeguarding users personal and financial data from theft and misuse. It plays a critical role in ensuring the security and privacy of sensitive information on mobile devices, thereby preventing unauthorized access and potential damage. Moreover, effective malware detection is essential for maintaining device performance and reliability by mitigating the risks posed by malicious software. This paper introduces a novel approach to Android malware detection, leveraging a publicly available dataset in conjunction with a Genetic Programming Symbolic Classifier (GPSC). The primary objective is to generate symbolic expressions (SEs) that can accurately identify malware with high precision. To address the challenge of imbalanced class distribution within the dataset, various oversampling techniques are employed. Optimal hyperparameter configurations for GPSC are determined through a random hyperparameter values search (RHVS) method developed in this research. The GPSC model is trained using a 10-fold cross-validation (10FCV) technique, producing a set of 10 SEs for each dataset variation. Subsequently, the most effective SEs are integrated into a threshold-based voting ensemble (TBVE) system, which is then evaluated on the original dataset. The proposed methodology achieves a maximum accuracy of 0.956, thereby demonstrating its effectiveness for Android malware detection.", notes = "Also known as \cite{computers13080197} Department of Automation and Electronics, Faculty of Engineering, University of Rijeka, 51000 Rijeka, Croatia", } @Article{Andelic:2025:apenergy, author = "Nikola Andelic and Sandi {Baressi Segota} and Vedran Mrzljak", title = "A comprehensive study on symbolic expressions for fault detection-classification in photovoltaic farms", journal = "Applied Energy", year = "2025", volume = "383", pages = "125370", keywords = "genetic algorithms, genetic programming, Data preprocessing and oversampling, Genetic programming symbolic classifier, Random hyperparameter value search method, Photovoltaic farms fault detection and classification, Threshold based voting ensemble", ISSN = "0306-2619", URL = "https://www.sciencedirect.com/science/article/pii/S030626192500100X", DOI = "doi:10.1016/j.apenergy.2025.125370", abstract = "Large-scale photovoltaic (solar) farms play a crucial role in harnessing solar energy for electricity generation through photovoltaic (PV) technology. However, the control and management of such systems pose significant challenges, particularly in fault detection. This paper introduces the application of a genetic programming symbolic classifier (GPSC) to a publicly available dataset for fault detection in photovoltaic farms. Given the imbalanced nature of the original dataset, the study necessitated the application of oversampling techniques to achieve a balanced representation of class samples. Additionally, the impact of scaling and normalizing techniques on the performance of the GPSC was thoroughly investigated. The GPSC was systematically applied to each scaled or normalised balanced dataset variation, and its hyperparameters were fine-tuned using a random hyperparameter values search (RHVS) method. The algorithm underwent training, via a 5-fold cross-validation (5FCV) process, and the best symbolic expressions (SEs) were determined based on accuracy, area under the receiver operating characteristics curve, precision, recall, and F1-score. The research yielded many SEs, which were used to develop a threshold-based voting ensemble (TBVE). The TBVE for each class was tested on the initial dataset and the threshold was finely tuned to achieve even higher classification performance in photovoltaic detection/classification. Results demonstrated that this approach produced highly accurate TBVE for each class (accuracy in the majority of cases equal to 1.0), showcasing the effectiveness of the GPSC and TBVE in fault detection/classification for photovoltaic farms", } @Article{DBLP:journals/peerj-cs/AndersI19, author = "Torsten Anders and Benjamin Inden", title = "Machine learning of symbolic compositional rules with genetic programming: dissonance treatment in Palestrina", journal = "PeerJ Comput. Sci.", volume = "5", pages = "e244", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.7717/peerj-cs.244", DOI = "doi:10.7717/peerj-cs.244", timestamp = "Tue, 16 Jun 2020 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/peerj-cs/AndersI19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{DBLP:journals/peerjpre/AndersI19, author = "Torsten Anders and Benjamin Inden", title = "Machine learning of symbolic compositional rules with genetic programming: Dissonance treatment in Palestrina", journal = "PeerJ Prepr.", volume = "7", pages = "e27731", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.7287/peerj.preprints.27731v1", DOI = "doi:10.7287/peerj.preprints.27731v1", timestamp = "Thu, 09 Jul 2020 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/peerjpre/AndersI19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{Andersen:2021:CEC, author = "Hayden Andersen and Andrew Lensen and Bing Xue", booktitle = "2021 IEEE Congress on Evolutionary Computation (CEC)", title = "Genetic Programming for Evolving Similarity Functions Tailored to Clustering Algorithms", year = "2021", editor = "Yew-Soon Ong", pages = "688--695", address = "Krakow, Poland", month = "28 " # jun # "-1 " # jul, isbn13 = "978-1-7281-8393-0", abstract = "Clustering is the process of grouping related instances of unlabelled data into distinct subsets called clusters. While there are many different clustering methods available, almost all of them use simple distance-based (dis)similarity functions such as Euclidean Distance. However, these and most other predefined dissimilarity functions can be rather inflexible by considering each feature equally and not properly capturing feature interactions in the data. Genetic Programming is an evolutionary computation approach that evolves programs in an iterative process that naturally lends itself to the evolution of functions. This paper introduces a novel framework to automatically evolve dissimilarity measures for a provided clustering dataset and algorithm. The results show that the evolved functions create clusters exhibiting high measures of cluster quality.", keywords = "genetic algorithms, genetic programming, Measurement, Clustering methods, Clustering algorithms, Evolutionary computation, Euclidean distance, Iterative methods, Clustering, Similarity Function, Feature Selection", DOI = "doi:10.1109/CEC45853.2021.9504855", notes = "Also known as \cite{9504855}", } @InProceedings{andersen:2024:GECCOcomp, author = "Hayden Andersen and Andrew Lensen and Will Browne and Yi Mei", title = "Intepretable Local Explanations Through Genetic Programming", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion", year = "2024", editor = "Jean-Baptiste Mouret and Kai Qin", pages = "247--250", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, explainable AI, XAI, machine learning, Evolutionary Machine Learning: Poster", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3654370", size = "4 pages", abstract = "As machine learning models become increasingly prevalent in everyday life, there is a growing demand for explanation of the predictions generated by these models. However, most models used by companies are black-boxes in nature, without the capacity to provide explanations to users. This reduces public trust in these models, and exists as a barrier to adoption of machine learning. Research into providing explanations to users has shown that local explanation techniques provide more acceptable explanations to users than attempting to explain an entire model, as a user often does not need to understand the entirety of a model.This work builds on prior work in the field to produce a competitive method for high-fidelity local explanations using genetic programming. Two different data representations targeted towards both users with and without machine learning experience are evaluated.The experimental results show comparable fidelity to the state-of-the art, while exhibiting more comprehensible explanations due to including fewer features in each explanation. The method enables decomposable explanations that are easy to interpret, while still capturing non-linear relationships in the original model.", notes = "GECCO-2024 EML A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{Anderson:2022:GI, author = "Damien Anderson and Paul Harvey and Yusaku Kaneta and Petros Papadopoulos and Philip Rodgers and Marc Roper", title = "Towards evolution-based autonomy in large-scale systems", booktitle = "GI @ GECCO 2022", year = "2022", editor = "Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and W. B. Langdon and Justyna Petke", pages = "1924--1925", address = "Boston, USA", publisher_address = "New York, NY, USA", month = "9 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", keywords = "genetic algorithms, genetic programming, genetic improvement, SBFL, Internet based content delivery network, CDN, 5G, grammar, Varnish Configuration Language, VCL", isbn13 = "978-1-4503-9268-6/22/07", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/gecco2022/gi2022/papers/Anderson_2022_GI.pdf", DOI = "doi:10.1145/3520304.3533975", slides_url = "http://geneticimprovementofsoftware.com/slides/gi2022gecco/anderson-towards-evolutionary-based-autonomy-gi-gecco-22.pdf", video_url = "https://www.youtube.com/watch?v=xpKcZRsRgrQ&list=PLI8fiFpB7BoIHgl5CsdtjfWvHlE5N6pje&index=5", size = "2 pages", abstract = "To achieve truly autonomous behaviour systems need to adapt to not only previously unseen circumstances but also previously unimagined ones. A hierarchical evolutionary system is proposed which is capable of displaying the emergent behaviour necessary to achieve this goal. we report the practical challenges encountered in implementing this proposed approach in a large-scale open-source system.", notes = "http://geneticimprovementofsoftware.com/events/gecco2022 digital twin Meta-Evolutionary Controllers Varnish https://www.varnish-software.com/solutions/cdn http://paul-harvey.org/publication/towards-evolution-based-autonomy-in-large-scale-systems/ GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @InProceedings{anderson:ppsn2002:pp689, author = "Eike Falk Anderson", title = "Off-Line Evolution of Behaviour for Autonomous Agents in Real-Time Computer Games", booktitle = "Parallel Problem Solving from Nature - PPSN VII", address = "Granada, Spain", month = "7-11 " # sep, pages = "689--699", year = "2002", editor = "Juan J. Merelo-Guervos and Panagiotis Adamidis and Hans-Georg Beyer and Jose-Luis Fernandez-Villacanas and Hans-Paul Schwefel", number = "2439", series = "Lecture Notes in Computer Science, LNCS", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, Games, Machine Learning, Fitness Evaluation", ISBN = "3-540-44139-5", DOI = "doi:10.1007/3-540-45712-7_66", abstract = "This paper describes and analyses a series of experiments intended to evolve a player for a variation of the classic arcade game Asteroids TM using steady state genetic programming. The player's behaviour is defined using a LISP like scripting language. While the game interprets scripts in real-time, such scripts are evolved off-line by a second program which simulates the realtime application. This method is used, as on-line evolution of the players would be too time consuming. A successful player needs to satisfy multiple conflicting objectives. This problem is addressed by the use of an automatically defined function (ADF) for each of these objectives in combination with task specific fitness functions. The overall fitness of evolved scripts is evaluated by a conventional fitness function. In addition to that, each of the ADFs is evaluated with a separate fitness function, tailored specifically to the objective that needs to be satisfied by that ADF.", } @TechReport{anderson:1994:profile, author = "Kenneth R. Anderson", title = "Courage in Profiling", institution = "BBN", year = "1994", month = "28 " # jul, keywords = "genetic algorithms, genetic programming, CASCOR1", URL = "http://openmap.bbn.com/~kanderso/performance/postscript/courage-in-profiles.ps", notes = "Compares speed of GP systems written in C (Tackett's SGPC which uses a tree representation) and Lisp (John Koza) on a symbolic regression problem, Optimised lisp performs better than expected. Koza lisp GP code performance improved 30 fold by use of profiling. Software: ftp://openmap.bbn.com/pub/kanderson/faster94/faster94/courage/koza3.lisp 2. You can easily convert your eval into a closure compiler: Paper: http://www.iro.umontreal.ca/~feeley/papers/complang87.ps.gz", } @InProceedings{andersson:1999:rmbGPrc, author = "Bjorn Andersson and Per Svensson and Peter Nordin and Mats Nordahl", title = "Reactive and Memory-Based Genetic Programming for Robot Control", booktitle = "Genetic Programming, Proceedings of EuroGP'99", year = "1999", editor = "Riccardo Poli and Peter Nordin and William B. Langdon and Terence C. Fogarty", volume = "1598", series = "LNCS", pages = "161--172", address = "Goteborg, Sweden", publisher_address = "Berlin", month = "26-27 " # may, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-65899-8", DOI = "doi:10.1007/3-540-48885-5_13", abstract = "In this paper we introduce a new approach to genetic programming with memory in reinforcement learning situations, which selects memories in order to increase the probability of modelling the most relevant parts of memory space. We evolve maps directly from state to action, rather than maps that predict reward based on state and action, which reduces the complexity of the evolved mappings. The work is motivated by applications to the control of autonomous robots. Preliminary results in software simulations indicate an enhanced learning speed and quality.", notes = "EuroGP'99, part of \cite{poli:1999:GP} AIMGP machine code GP, memory, simulated robot", } @InProceedings{andersson:2000:4lrGP, author = "Bjorn Andersson and Per Svensson and Peter Nordin and Mats Nordahl", title = "On-line Evolution of Control for a Four-Legged Robot Using Genetic Programming", booktitle = "Real-World Applications of Evolutionary Computing", year = "2000", editor = "Stefano Cagnoni and Riccardo Poli and George D. Smith and David Corne and Martin Oates and Emma Hart and Pier Luca Lanzi and Egbert Jan Willem and Yun Li and Ben Paechter and Terence C. Fogarty", volume = "1803", series = "LNCS", pages = "319--326", address = "Edinburgh", publisher_address = "Berlin", month = "17 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, linear GP", ISBN = "3-540-67353-9", DOI = "doi:10.1007/3-540-45561-2_31", size = "8 pages", abstract = "We evolve a robotic controller for a four-legged real robot enabling it to walk dynamically. Evolution is performed on-line by a linear machine code GP system. The robot has eight degrees of freedom and is built from standard R/C servos. Different walking strategies are shown by the robot during evolution and the evolving system is robust against mechanical failures.", notes = "{"}Galloping only appears after many hours of training{"} p323. EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM, EvoRob, and EvoFlight, Edinburgh, Scotland, UK, April 17, 2000 Proceedings http://evonet.lri.fr/evoweb/resources/books_journals/record.php?id=61", } @InProceedings{Andersson:1998:ecmlc, author = "Claes Andersson and Mats G. Nordahl", title = "Evolving Coupled Map Lattices for Computation", booktitle = "Proceedings of the First European Workshop on Genetic Programming", year = "1998", editor = "Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer and Terence C. Fogarty", volume = "1391", series = "LNCS", pages = "151--162", address = "Paris", publisher_address = "Berlin", month = "14-15 " # apr, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-64360-5", DOI = "doi:10.1007/BFb0055935", abstract = "Genetic Programming is used to evolve coupled map lattices for density classification. The most successful evolved rules depending only on nearest neighbors (r=1) show better performance than existing r=3 cellular automaton rules on this task.", notes = "EuroGP'98", affiliation = "Chalmers University of Technology Institute of Theoretical Physics S-412 96 Goteborg Sweden S-412 96 Goteborg Sweden", } @Misc{oai:CiteSeerPSU:491253, title = "The Rolling Stones - Genetic Programming in {AIP}", author = "Thord Andersson and Per-Erik Forssen", year = "2000", month = mar # "~06", abstract = "This report describes the design of a soccer playing agent developed in the scope of the AI Programming course. This agent uses a variant of the subsumption architecture [2]. The primitive behaviours that dene the intelligence of the agent are evolved using genetic programming [4]. We chose the genetic-programming approach instead of designs such as decision trees etc, since we wanted the intelligence in the agents to be truly articial, and not designed", citeseer-isreferencedby = "oai:CiteSeerPSU:76065", citeseer-references = "oai:CiteSeerPSU:107311; oai:CiteSeerPSU:125377", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:491253", rights = "unrestricted", URL = "http://www.ida.liu.se/~silco/AIP/Rolling-Stones.ps", URL = "http://citeseer.ist.psu.edu/491253.html", note = "student project", keywords = "genetic algorithms, genetic programming", size = "13 pages", } @InProceedings{ando:evows07, author = "Daichi Ando and Palle Dahlsted and Mats Nordahl and Hitoshi Iba", title = "Interactive GP with Tree Representation of Classical Music Pieces", booktitle = "Applications of Evolutionary Computing, EvoWorkshops2007: {EvoCOMNET}, {EvoFIN}, {EvoIASP}, {EvoInteraction}, {EvoMUSART}, {EvoSTOC}, {EvoTransLog}", year = "2007", month = "11-13 " # apr, editor = "Mario Giacobini and Anthony Brabazon and Stefano Cagnoni and Gianni A. {Di Caro} and Rolf Drechsler and Muddassar Farooq and Andreas Fink and Evelyne Lutton and Penousal Machado and Stefan Minner and Michael O'Neill and Juan Romero and Franz Rothlauf and Giovanni Squillero and Hideyuki Takagi and A. Sima Uyar and Shengxiang Yang", series = "LNCS", volume = "4448", publisher = "Springer Verlag", address = "Valencia, Spain", pages = "577--584", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-71804-8", DOI = "doi:10.1007/978-3-540-71805-5_63", abstract = "Research on the application of Interactive Evolutionary Computation(IEC) to the field of musical computation has been improved in recent years, marking an interesting parallel to the current trend of applying human characteristics or sensitivities to computer systems. However, past techniques developed for IEC-based composition have not necessarily proven very effective for professional use. This is due to the large difference between data representation used by IEC and authored classical music composition. To solve this difficulties, we purpose a new IEC approach to music composition based on classical music theory. In this paper, we describe an established system according to the above idea, and detail of making success of composition a piece.", notes = "EvoWorkshops2007", } @InProceedings{Ando:2007:cec, author = "Daichi Ando and Hitoshi Iba", title = "Interactive Composition Aid System by Means of Tree Representation of Musical Phrase", booktitle = "2007 IEEE Congress on Evolutionary Computation", year = "2007", editor = "Dipti Srinivasan and Lipo Wang", pages = "4258--4265", address = "Singapore", month = "25-28 " # sep, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "1-4244-1340-0", file = "1814.pdf", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2007.4425027", abstract = "Research on the application of Interactive Evolutionary Computation (IEC) to the field of musical computation has been improved in recent years, marking an interesting parallel to the current trend of applying human characteristics or sensitivities to computer systems. However, past techniques developed for IEC-based composition have not necessarily proven very effective for professional use. This is due to the large difference between data representation used by IEC and authored classical music composition. To solve this difficulties, the authors purpose a new IEC approach to music composition based on classical music theory. In this paper, the authors describe an established system according to the above idea, and detail of making success of composition a piece.", notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C", } @InProceedings{conf/icmc/Ando14, author = "Daichi Ando", title = "Real-time Breeding Composition System by means of Genetic Programming and Breeding Procedure", booktitle = "Proceedings International Computer Music Conference Proceedings SMC 2014", year = "2014", address = "Athens, Greece", month = "14-20 " # sep, publisher = "Michigan Publishing", keywords = "genetic algorithms, genetic programming", bibdate = "2016-01-29", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icmc/icmc2014.html#Ando14", URL = "http://hdl.handle.net/2027/spo.bbp2372.2014.062", URL = "http://quod.lib.umich.edu/cgi/p/pod/dod-idx/real-time-breeding-composition-system.pdf", URL = "http://quod.lib.umich.edu/i/icmc/bbp2372.2014", URL = "http://quod.lib.umich.edu/i/icmc/bbp2372.2014.062/--real-time-breeding-composition-system", size = "6 pages", abstract = "The use of laptop computers to produce real-time music and multimedia performances has increased significantly in recent years. In this paper, I propose a new method of generating club-style loop music in real time by means of interactive evolutionary computation (IEC). The method includes two features. The first is the concept of breeding without any consciousness of generation. The second is a multiple-ontogeny mechanism that generates several phenotypes from one genotype, incorporating ideas of co-evolution and multi-objective optimisation. The proposed method overcomes certain limitations of IEC, namely the burden of interactive evaluation and the narrow search domain resulting from handling few individuals. A performance system that generates club-style loop music from the photo album in mobile devices is implemented by means of the proposed method. This system is then tested, and the success of performances with the implemented system indicates that the proposed methods work effectively.", notes = "ICMC 2014", } @InProceedings{Ando:2009:ieeeSMC, author = "Jun Ando and Tomoharu Nagao", title = "Image classification and processing using modified parallel-ACTIT", booktitle = "IEEE International Conference on Systems, Man and Cybernetics, SMC 2009", year = "2009", month = oct, pages = "1787--1791", keywords = "genetic algorithms, genetic programming, automatic construction of tree-structural image transformation, image classification, image recognition, modified parallel-ACTIT, training image sets, image classification, tree data structures", DOI = "doi:10.1109/ICSMC.2009.5346894", ISSN = "1062-922X", abstract = "Image processing and recognition technologies are required to solve various problems. We have already proposed the system which automatically constructs image processing with Genetic Programming (GP), Automatic Construction of Tree-structural Image Transformation (ACTIT). However, it is necessary that training image sets are properly classified in advance if they have various characteristics. In this paper, we propose Modified Parallel-ACTIT which automatically classifies training image sets into several subpopulations. And it optimizes tree-structural image transformation for each training image sets in each subpopulations. We show experimentally that Modified Parallel-ACTIT is more effective in comparison with ordinary ACTIT.", notes = "Also known as \cite{5346894}", } @InProceedings{ando:2002:mgnbhg, author = "Shin Ando and Hitoshi Iba and Erina Sakamoto", title = "Modeling Genetic Network by Hybrid GP", booktitle = "Proceedings of the 2002 Congress on Evolutionary Computation CEC2002", editor = "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and Mark Shackleton", pages = "291--296", year = "2002", publisher = "IEEE Press", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", organisation = "IEEE Neural Network Council (NNC), Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", ISBN = "0-7803-7278-6", month = "12-17 " # may, notes = "CEC 2002 - A joint meeting of the IEEE, the Evolutionary Programming Society, and the IEE. Held in connection with the World Congress on Computational Intelligence (WCCI 2002)", keywords = "genetic algorithms, genetic programming, artificial data, differential equations, evolutionary modelling method, genetic regulatory network modeling, hybrid algorithm, hybrid genetic programming, least mean square method, multiple runs, real world data, regulation, statistical analysis, time series, differential equations, least mean squares methods, statistical analysis", URL = "http://citeseer.ist.psu.edu/520794.html", URL = "http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/17336/http:zSzzSzwww.miv.t.u-tokyo.ac.jpzSz~ibazSztmpzSzando.pdf/modeling-genetic-network-by.pdf", DOI = "doi:10.1109/CEC.2002.1006249", abstract = "We present an Evolutionary Modelling method for modeling genetic regulatory networks. The method features hybrid algorithm of Genetic Programming with statistical analysis to derive systems of differential equations. Genetic Programming and Least Mean Square method were combined to identify a concise form of regulation between the variables from a given set of time series. Also, results of multiple runs were statistically analysed to indicate the term with robust and significant influence. Our approach was evaluated in artificial data and real world data.", notes = "oai:CiteSeerPSU:520794", size = "6 pages", } @Article{ando:emi, author = "Shin Ando and Erina Sakamoto and Hitoshi Iba", title = "Evolutionary modeling and inference of gene network", journal = "Information Sciences", volume = "145", number = "3-4", month = sep, year = "2002", pages = "237--259", keywords = "genetic algorithms, genetic programming, Gene network, Evolutionary modeling, Time series prediction", URL = "http://www.sciencedirect.com/science/article/B6V0C-46WWB37-3/2/963172f8c0faa12d700376b07bfc96a5", ISSN = "0020-0255", DOI = "doi:10.1016/S0020-0255(02)00235-9", abstract = "we describe an Evolutionary Modeling (EM) approach to building causal model of differential equation system from time series data. The main target of the modeling is the gene regulatory network. A hybrid method of Genetic Programming (GP) and statistical analysis is featured in our work. GP and Least Mean Square method (LMS) were combined to identify a concise form of regulation between the variables from a given set of time series. Our approach was evaluated in several real-world problems. Further, Monte Carlo analysis is applied to indicate the robust and significant influence from the results for gene network analysis purpose.", } @Article{ando:2004:GPEM, author = "Shin Ando and Hitoshi Iba", title = "Classification of Gene Expression Profile Using Combinatory Method of Evolutionary Computation and Machine Learning", journal = "Genetic Programming and Evolvable Machines", year = "2004", volume = "5", number = "2", pages = "145--156", month = jun, keywords = "genetic algorithms, genetic programming, evolutionary computation, artificial immune system, wrapper approach, gene expression classification, cancer diagnosis", ISSN = "1389-2576", DOI = "doi:10.1023/B:GENP.0000023685.83861.69", abstract = "The analysis of large amount of gene expression profiles, which became available by rapidly developed monitoring tools, is an important task in Bioinformatics. The problem we address is the discrimination of gene expression profiles of different classes, such as cancerous/benign tissues. Two subtasks in such problem, feature subset selection and inductive learning has critical effect on each other. In the wrapper approach, combinatorial search of feature subset is done with performance of inductive learning as search criteria. This paper compares few combinations of supervised learning and combinatorial search when used in the wrapper approach. Also an extended GA implementation is introduced, which uses Clonal selection, a data-driven selection method. It compares very well to standard GA. The analysis of the obtained classifier reveals synergistic effect of genes in discrimination of the profiles.", notes = "Part of \cite{banzhaf:2004:biogec} Special Issue on Biological Applications of Genetic and Evolutionary Computation Guest Editor(s): Wolfgang Banzhaf , James Foster (1) Department of Electronics, School of Engineering, University of Tokyo, Yokohama, Japan (2) Department of Frontier Informatics, School of Frontier Science, University of Tokyo, Chiba, Japan", } @InProceedings{Andrade2012CIARP, author = "Felipe S. P. Andrade and Jurandy Almeida and Helio Pedrini and Ricardo {da S. Torres}", title = "Fusion of Local and Global Descriptors for Content-Based Image and Video Retrieval", booktitle = "17th Iberoamerican Congress on Pattern Recognition", year = "2012", pages = "845--853", address = "Buenos Aires, Argentina", keywords = "genetic algorithms, genetic programming", bibsource = "DBLP, http://dblp.uni-trier.de", URL = "http://dx.doi.org/10.1007/978-3-642-33275-3_104", DOI = "doi:10.1007/978-3-642-33275-3_104", abstract = "Recently, fusion of descriptors has become a trend for improving the performance in image and video retrieval tasks. Descriptors can be global or local, depending on how they analyse visual content. Most of existing works have focused on the fusion of a single type of descriptor. Different from all of them, this paper aims to analyze the impact of combining global and local descriptors. Here, we perform a comparative study of different types of descriptors and all of their possible combinations. Extensive experiments of a rigorous experimental design show that global and local descriptors complement each other, such that, when combined, they outperform other combinations or single descriptors.", } @InProceedings{Andrade:2020:SSCI, author = "Felipe S. P. Andrade and Claus Aranha and Ricardo {da Silva Torres}", title = "On the Use of Predation to Shape Evolutionary Computation", booktitle = "2020 IEEE Symposium Series on Computational Intelligence (SSCI)", year = "2020", pages = "117--124", abstract = "Classic Evolutionary Algorithms often use elitist approaches, such as fitness functions, to select individuals for new generations. In this work, we consider an alternative strategy to simulate the selection process that relies on exploiting ecological interactions between individuals instead of explicitly using a fitness based in the search progress. To demonstrate this strategy, we present an Artificial Life system which simulates an ecosystem where different species are different bio-inspired meta-heuristics, and the main ecological relationship is the predation. Specifically, individuals from a Particle Swarm Optimization (PSO), with movement rules defined by Genetic Programming, survive by predating on individuals from an Artificial Bee Colony (ABC) system that operates on traditional optimization rules. This ecology is investigated on optimization benchmarks, and we observed the development of interesting ecological dynamics between the two species.", keywords = "genetic algorithms, genetic programming, Predator prey systems, Statistics, Sociology, Biological system modeling, Ecosystems, Particle swarm optimization, Artificial Life, Evolutionary Computation, Ecological Relationship", DOI = "doi:10.1109/SSCI47803.2020.9308209", month = dec, notes = "Also known as \cite{9308209}", } @Misc{andre:UGthesis, author = "David Andre", title = "Artificial Evolution of Intelligence: Lessons from natural evolution: An illustrative approach using Genetic Programming", school = "Stanford University, Symbolic Systems Program", year = "1994", type = "BS Honors Thesis", keywords = "genetic algorithms, genetic programming", URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.66.1367&rep=rep1&type=pdf", } @InCollection{kinnear:andre, title = "Automatically Defined Features: The Simultaneous Evolution of 2-Dimensional Feature Detectors and an Algorithm for Using Them", author = "David Andre", booktitle = "Advances in Genetic Programming", publisher = "MIT Press", editor = "Kenneth E. {Kinnear, Jr.}", year = "1994", pages = "477--494", chapter = "23", keywords = "genetic algorithms, genetic programming", URL = "http://www.amazon.co.uk/Advances-Genetic-Programming-Complex-Adaptive/dp/0262111888", URL = "http://cognet.mit.edu/sites/default/files/books/9780262277181/pdfs/9780262277181_chap23.pdf", DOI = "doi:10.7551/mitpress/1108.003.0029", size = "18 pages", abstract = "Although automatically defined functions (ADFcts) with genetic programming (GP) appear to have great utility in a wide variety of domains, their application to the automatic discovery of 2-dimensional features has been only moderately successful [Koza 1993]. Boolean functions of pixel inputs, although very general, may not be the best representation for 2-dimensional features. This chapter describes a method for the simultaneous evolution of 2-dimensional hit-miss matrices and an algorithm to use these matrices in pattern recognition. Hit-miss matrices are templates that can be moved over part of an input pattern to check for a match. These matrices are evolved using a 2-dimensional genetic algorithm, while the algorithms controlling the templates are evolved using GP. The approach is applied to the problem of digit recognition, and is found to be successful at discovering individuals which can recognize very low resolution digits. Possibilities for expansion into a full-size character recognition system are discussed.", notes = " Mixture of GP and two dee GA Part of \cite{kinnear:book}", } @InProceedings{andre:maps, author = "David Andre", title = "Evolution of Mapmaking Ability: Strategies for the evolution of learning, planning, and memory using genetic programming", booktitle = "Proceedings of the 1994 IEEE World Congress on Computational Intelligence", year = "1994", volume = "1", pages = "250--255", address = "Orlando, Florida, USA", month = "27-29 " # jun, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, evolved representations, gold collection, information encoding, intelligent agent, learning, mapmaking evolution; memory, multi-phasic fitness environment, planning, brain models, cartography, cognitive systems, learning (artificial intelligence), planning (artificial intelligence)", DOI = "doi:10.1109/ICEC.1994.350007", size = "6 pages", abstract = "An essential component of an intelligent agent is the ability to observe, encode, and use information about its environment. Traditional approaches to genetic programming have focused on evolving functional or reactive programs with only a minimal use of state. This paper presents an approach for investigating the evolution of learning, planning, and memory using genetic programming. The approach uses a multi-phasic fitness environment that enforces the use of memory and allows fairly straightforward comprehension of the evolved representations. An illustrative problem of `gold' collection is used to demonstrate the usefulness of the approach. The results indicate that the approach can evolve programs that store simple representations of their environments and use these representations to produce simple plans", } @InProceedings{ieee94:andre, author = "David Andre", title = "Learning and Upgrading Rules for an OCR System Using Genetic Programming", year = "1994", booktitle = "Proceedings of the 1994 IEEE World Congress on Computational Intelligence", address = "Orlando, Florida, USA", month = "27-29 " # jun, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", URL = "http://citeseer.ist.psu.edu/31976.html", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/802/http:zSzzSzwww.cs.berkeley.eduzSz~dandrezSzpaperszSzAndre_WCCI_94_OCR_Boundary.pdf/learning-and-upgrading-rules.pdf", DOI = "doi:10.1109/ICEC.1994.349906", size = "6 pages", notes = "Uses GP both to recognise C in various fonts and to maintain manually produced extremely high level code when a new font is added", } @InProceedings{Andre:1995:ammsp, author = "David Andre", title = "The Evolution of Agents that Build Mental Models and Create Simple Plans Using Genetic Programming", booktitle = "Genetic Algorithms: Proceedings of the Sixth International Conference (ICGA95)", year = "1995", editor = "Larry J. Eshelman", pages = "248--255", address = "Pittsburgh, PA, USA", publisher_address = "San Francisco, CA, USA", month = "15-19 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, memory", ISBN = "1-55860-370-0", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Andre_1995_ammsp.pdf", size = "8 pages", notes = "Worlds 2x2, 4x4 and 8x8. Separate program trees for mapmaker and mapuser. ADFs used by mapusers only. Uses repeat, repeati (Repeat_Index), IncMem. Torrodial memory, isomophic to world. Steady state, Tournament selection (8) but with smaller (2) tournament group size for deletion. Evolved programs subjected to analysis and explanation. Evolved general solutions from limited test cases. Suggests simple strategies dominate more complex ones. GP better than random.", } @InProceedings{andre:1995:parallel, author = "David Andre and John R. Koza", title = "Parallel Genetic Programming on a Network of Transputers", booktitle = "Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications", year = "1995", editor = "Justinian P. Rosca", pages = "111--120", address = "Tahoe City, California, USA", month = "9 " # jul, keywords = "genetic algorithms, genetic programming, even 5 parity, island model, demes, INMOS, 30MHz 32 bit 16 MByte TRAM T805", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/andre_1995_parallel.pdf", size = "10 pages", abstract = "... in the C programming language ... migration rates between 1 percent and 8 percent ... more than linear speed up.", notes = "Host microsoft windows Pentium PC (visual Basic), Debugger (tram), Boss process (tram), 64 mesh node (4 neighbours) trams. like \cite{Koza:1995:pGPnt} part of \cite{rosca:1995:ml}", } @InProceedings{andre:1995:apalmm, author = "David Andre", title = "The Automatic Programming of Agents that Learn Mental Models and Create Simple Plans of Action", booktitle = "IJCAI-95 Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence", year = "1995", editor = "Chris S. Mellish", volume = "1", pages = "741--747", address = "Montreal, Quebec, Canada", publisher_address = "San Francisco, CA, USA", month = "20-25 " # aug, organisation = "IJCAII,AAAI,CSCSI", publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, memory", ISBN = "1-55860-363-8", URL = "http://ijcai.org/Past%20Proceedings/IJCAI-95-VOL%201/pdf/097.pdf", URL = "https://dl.acm.org/citation.cfm?id=1625952", size = "7 pages", abstract = "An essential component of an intelligent agent is the ability to notice, encode, store, and use information about its environment. Traditional approaches to program induction have focused on evolving functional or reactive programs. This paper presents MAPMAKER, a method for the automatic generation of agents that discover information about their environment, encode this information for later use, and create simple plans using the stored mental models. In this method, agents are multi-part computer programs that communicate through a shared memory. Both the programs and the representation scheme are evolved using genetic programming. An illustrative problem of 'gold' collection is used to demonstrate the method in which one part of a program makes a map of the world and stores it in memory, and the other part uses this map to find the gold The results indicate that the method can evolve programs that store simple representations of their environments and use these representations to produce simple plans.", notes = "MAPMAKER searches for gold", } @InProceedings{andre:1996:GKL, author = "David Andre and Forrest H {Bennett III} and John R. Koza", title = "Evolution of Intricate Long-Distance Communication Signals in Cellular Automata using Genetic Programming", booktitle = "Artificial Life V: Proceedings of the Fifth International Workshop on the Synthesis and Simulation of Living Systems", year = "1996", volume = "1", address = "Nara, Japan", publisher_address = "Cambridge, MA, USA", month = "16--18 " # may, publisher = "MIT Press", keywords = "genetic algorithms, genetic programming", URL = "http://www.genetic-programming.com/jkpdf/alife1996gkl.pdf", size = "10 pages", abstract = "A cellular automata rule for the majority classification task was evolved using genetic programming with automatically defined functions. The genetically evolved rule has an accuracy of 82.326 percent. This level of accuracy exceeds that of the Gacs-Kurdyumov-Levin (GKL) rule, all other known human-written rules, and all other rules produced by known previous automated approaches. Our genetically evolved rule is qualitatively different from other rules in that it uses a fine-grained internal representation of density information; it employs a large number of different domains and particles; and it uses an intricate set of signals for communicating information over large distances in time and space.", notes = "Alife-5 A longer version of this paper will be presented at the GP-96 conference. GP gets best solution to GKL problem {"}The population size used to evolve the current world's record for the GKL majority classification 1-dimensionall 2-sate 7-neighbor cellular authomata problem was 51,200. I believe Melanie Mitchell at the Santa Fe Institute has been doing continuing additional work on using GAs to evolve CA rules for various other problems.{"}", } @InCollection{andre:1996:aigp2, author = "David Andre and John R. Koza", title = "Parallel Genetic Programming: A Scalable Implementation Using The Transputer Network Architecture", booktitle = "Advances in Genetic Programming 2", publisher = "MIT Press", year = "1996", editor = "Peter J. Angeline and K. E. {Kinnear, Jr.}", pages = "317--337", chapter = "16", address = "Cambridge, MA, USA", keywords = "genetic algorithms, genetic programming", ISBN = "0-262-01158-1", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6277532", URL = "https://dl.acm.org/citation.cfm?id=270224", DOI = "doi:10.7551/mitpress/1109.003.0022", size = "21 pages", abstract = "This chapter describes the parallel implementation of genetic programming in the C programming language using a PC type computer (running Windows) acting as a host and a network of processing nodes using the transputer architecture. Using this approach, researchers of genetic algorithms and genetic programming can acquire computing power that is intermediate between the power of currently available workstations and that of supercomputers at a cost that is intermediate between the two. This approach is illustrated by a comparison of the computational effort required to solve the problem of symbolic regression of the Boolean even-5-parity function with different migration rates. Genetic programming required the least computational effort with an 5% migration rate. Moreover, this computational effort was less than that required for solving the problem with a serial computer and a panmictic population of the same size. That is, apart from the nearly linear speed-up in executing a fixed amount of code inherent in the parallel implementation of genetic programming, the use of distributed sub-populations with only limited migration delivered more than linear speed-up in solving the problem.", } @InProceedings{andre:1996:camc, author = "David Andre and Forrest H {Bennett III} and John R. Koza", title = "Discovery by Genetic Programming of a Cellular Automata Rule that is Better than any Known Rule for the Majority Classification Problem", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "3--11", address = "Stanford University, CA, USA", publisher = "MIT Press", URL = "http://www.genetic-programming.com/jkpdf/gp1996gkl.pdf", size = "9 pages", abstract = "It is difficult to program cellular automata. This is especially true when the desired computation requires global communication and global integration of information across great distances in the cellular space. Various human- written algorithms have appeared in the past two decades for the vexatious majority classification task for one-dimensional two-state cellular automata. This paper describes how genetic programming with automatically defined functions evolved a rule for this task with an accuracy of 82.326%. This level of accuracy exceeds that of the original 1978 Gacs-Kurdyumov-Levin (GKL) rule, all other known human-written rules, and all other known rules produced by automated methods. The rule evolved by genetic programming is qualitatively different from all previous rules in that it employs a larger and more intricate repertoire of domains and particles to represent and communicate information across the cellular space.", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap1.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96", } @InProceedings{andre:1996:introns, author = "David Andre and Astro Teller", title = "A Study in Program Response and the Negative Effects of Introns in Genetic Programming", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "12--20", address = "Stanford University, CA, USA", publisher = "MIT Press", URL = "http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/AndreTeller.ps", URL = "http://www.cs.cmu.edu/afs/cs/usr/astro/mosaic/TellerGP96/TellerGP96.html", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap2.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", size = "9 pages", abstract = "The standard method of obtaining a response in tree-based genetic programming is to take the value returned by the root node. In non-tree representations, alternate methods have been explored. One alternative is to treat a specific location in indexed memory as the response value when the program terminates. The purpose of this paper is to explore the applicability of this technique to tree-structured programs and to explore the intron effects that these studies bring to light. This paper's experimental results support the finding that this memory-based program response technique is an improvement for some, but not all, problems. In addition, this paper's experimental results support the finding that, contrary to past research and speculation, the addition or even facilitation of introns can seriously degrade the search performance of genetic programming.", notes = "GP-96 html version available from http://www.cs.cmu.edu/~astro/", } @InProceedings{andre:1996:parGP, author = "David Andre and John R. Koza", title = "A parallel implementation of genetic programming that achieves super-linear performance", booktitle = "Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications", year = "1996", editor = "Hamid R. Arabnia", volume = "III", pages = "1163--1174", address = "Sunnyvale", month = "9-11 " # aug, publisher = "CSREA", keywords = "genetic algorithms, genetic programming", URL = "http://www.genetic-programming.com/jkpdf/pdpta1996.pdf", size = "13 pages", abstract = "This paper describes the successful parallel implementation of genetic programming on a network of processing nodes using the transputer architecture. With this approach, researchers of genetic algorithms and genetic programming can acquire computing power that is intermediate between the power of currently available workstations and that of supercomputers at intermediate cost. This approach is illustrated by a comparison of the computational effort required to solve a benchmark problem. Because of the decoupled character of genetic programming, our approach achieved a nearly linear speed up from parallelization. In addition, for the best choice of parameters tested, the use of subpopulations delivered a super linear speed-up in terms of the ability of the algorithm to solve the problem. Several examples are also presented where the parallel genetic programming system evolved solutions that are competitive with human performance on the same problem.", notes = "Awarded Best Paper Award PDPTA'96", } @InCollection{andre:1997:HEC, author = "David Andre", title = "Learning and Upgrading Rules for an Optical Character Recognition System Using Genetic Programming", booktitle = "Handbook of Evolutionary Computation", publisher = "Oxford University Press", publisher_2 = "Institute of Physics Publishing", year = "1997", editor = "Thomas Baeck and David B. Fogel and Zbigniew Michalewicz", chapter = "section G8.1", keywords = "genetic algorithms, genetic programming", ISBN = "0-7503-0392-1", URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.375.6494.pdf", broken = "doi:10.1201/9781420050387.ptg", URL = "https://www.amazon.com/Handbook-Evolutionary-Computation-Computational-Intelligence/dp/0750303921", URL = "https://www.worldcat.org/title/handbook-of-evolutionary-computation/oclc/1108947278", size = "8 pages", abstract = "Rule-based systems used for optical character recognition (OCR) are notoriously difficult to write, maintain, and upgrade. This case study describes a method for using genetic programming (GP) to automatically generate and upgrade rules for an OCR system. Sets of rules for recognizing a single character are encoded as LISP programs and are evolved using GP. The rule sets are programs that evolve to examine a set of preprocessed features using complex constructs including iteration, pointers, and memory. The system was successful at learning rules for large character sets consisting of multiple fonts and sizes, with good generalization to test sets. In addition, the method was found to be successful at updating human-coded rules written in C for new fonts. This research demonstrates the successful application of GP to a difficult, noisy, real-world problem, and introduces GP as a method for learning sets of rules.", notes = "invited chapter", } @Misc{andre:cs267, author = "David Andre", title = "Multi-level parallelism in automatically synthesizing soccer-playing programs for Robocup using genetic programming", year = "1998", keywords = "genetic algorithms, genetic programming, memory", URL = "http://citeseer.ist.psu.edu/245675.html", broken = "http://www.cs.berkeley.edu/~dandre/cs267/final/cs267_final.ps", broken = "http://www.cs.berkeley.edu/~dandre/cs267/final/project_final.htm", size = "18 pages", abstract = "Many of the various proposals for tomorrow's supercomputers have included clusters of multiprocessors as an essential component. However, when designing the systems of the future, it is important to insure that the nature of the parallelism provided matches up with some relevant and important set of algorithms. This project presents empirical program synthesis as an algorithm that can successfully exploit the multiple levels of interconnect present in an multi-SMP cluster system. When applying program synthesis techniques to difficult problems, it is often the case that two distinct levels of parallelism will emerge. First, many example programs must be tested -- and can often be tested in parallel. This matches up with the {"}slow{"} interconnect on a clump-based system. Second, the execution of a particular program can often be parallelized, especially if the program is complicated or requires interactions with a complex simulation. This level of parallelism, in contrast to the first, often requires fine-grained communication. Thus, this matches up with the {"}fast{"} level of the clump-based system. In particular, this project presents a multi-level parallel system for the automatic program synthesis of soccer-playing agents for the Robocup simulator competition using genetic programming. The system uses both the fast shared-memory communication of the SMP system as well as a much slower mechanism for the inter-SMP communication. The system is benchmarked on a variety of configurations, and speedup curves are presented. Additionally, a simple LogP analysis comparing the performance of the designed system with a single-processor based NOW system is presented. Finally, the Robocup project is reviewed and the future work outlined.", notes = "my ghostview (Jan 2002) barfs at cs267_final.ps but it prints", } @Article{AK97, author = "David Andre and John R. Koza", title = "A parallel implementation of genetic programming that achieves super-linear performance", journal = "Information Sciences", year = "1998", volume = "106", number = "3-4", pages = "201--218", keywords = "genetic algorithms, genetic programming", ISSN = "0020-0255", URL = "http://www.sciencedirect.com/science/article/B6V0C-3TKS65B-21/2/22b9842f820b08883990bbae1d889c03", URL = "http://www.davidandre.com/papers/isj97.ps", DOI = "doi:10.1016/S0020-0255(97)10011-1", size = "18 pages", abstract = "This paper describes the successful parallel implementation of genetic programming on a network of processing nodes using the transputer architecture. With this approach, researchers of genetic algorithms and genetic programming can acquire computing power that is intermediate between the power of currently available workstations and that of supercomputers at intermediate cost. This approach is illustrated by a comparison of the computational effort required to solve a benchmark problem. Because of the decoupled character of genetic programming, our approach achieved a nearly linear speed up from parallelization. In addition, for the best choice of parameters tested, the use of subpopulations delivered a super-linear speed-up in terms of the ability of the algorithm to solve the problem. Several examples are also presented where the parallel genetic programming system evolved solutions that are competitive with human performance.", notes = "Information Sciences broken Aug 2023 http://www.elsevier.com/inca/publications/store/5/0/5/7/3/0/505730.pub.htt", } @InProceedings{andre:1998:tdcGPmdk, author = "David Andre and Forrest H {Bennett III} and John Koza and Martin A. Keane", title = "On the Theory of Designing Circuits using Genetic Programming and a Minimum of Domain Knowledge", booktitle = "Proceedings of the 1998 IEEE World Congress on Computational Intelligence", year = "1998", pages = "130--135", address = "Anchorage, Alaska, USA", month = "5-9 " # may, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, amplifiers, analog circuit design, circuit evolution, computational circuits, embryonic circuit elimination, filters, knowledge representation, minimal domain knowledge, problem-specific knowledge, analogue circuits, circuit CAD, circuit optimisation, intelligent design assistants, knowledge representation, programming", ISBN = "0-7803-4869-9", file = "c023.pdf", URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=00699489", DOI = "doi:10.1109/ICEC.1998.699489", size = "6 pages", abstract = "The problem of analog circuit design is a difficult problem that is generally viewed as requiring human intelligence to solve. Considerable progress has been made in automating the design of certain categories of purely digital circuits; however, the design of analog electrical circuits and mixed analog-digital circuits has not proved to be as amenable to automation. When critical analog circuits are required for a project, skilled and highly trained experts are necessary. Previous work on applying genetic programming to the design of analog circuits has proved to be successful at evolving a wide variety of circuits, including filters, amplifiers, and computational circuits; however, previous approaches have required the specification of an appropriate embryonic circuit. This paper explores a method to eliminate even this small amount of problem specific knowledge, and, in addition, proves that the representation used is capable of producing all circuits.", notes = "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE World Congress on Computational Intelligence", } @InProceedings{Andre:1999:ETD, author = "D. Andre and A. Teller", title = "Evolving {Team Darwin United}", booktitle = "RoboCup-98: Robot Soccer World Cup II", year = "1999", editor = "M. Asada and H. Kitano", volume = "1604", series = "LNCS", pages = "346--351", address = "Paris, France", month = jul # " 1998", publisher = "Springer Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-66320-7", ISSN = "0302-9743", bibdate = "Mon Sep 13 16:57:02 MDT 1999", acknowledgement = ack-nhfb, URL = "http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/Teller_Astro.ps", DOI = "doi:10.1007/3-540-48422-1_28", URL = "http://206.210.94.135/work/pdfs/Teller_Astro.pdf", size = "7 pages", abstract = "The RoboCup simulator competition is one of the most challenging international proving grounds for contemporary AI research. Exactly because of the high level of complexity and a lack of reliable strategic guidelines, the pervasive attitude has been that the problem can most successfully be attacked by human expertise, possibly assisted by some level of machine learning. This led, in RoboCup'97, to a field of simulator teams all of whose level and style of play were heavily influenced by the human designers of those teams. It is the thesis of our work that machine learning, if given the opportunity to design (learn) ``everything'' about how the simulator team operates, can develop a competitive simulator team that solves the problem using highly successful, if largely non- human, styles of play. To this end, Darwin United is a team of eleven players that have been evolved as a team of coordinated agents in the RoboCup simulator. Each agent is given a subset of the lowest level perceptual inputs and must learn to execute series of the most basic actions (turn, kick, dash) in order to participate as a member of the team. This paper presents our motivation, our approach, and the specific construction of our team that created itself from scratch.", notes = "LNCS 1604 http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-66320-7 READ and WRITE functions, ie memory, 8 programs control the 11 players however these 8 can use 8 shared ADFs", } @Misc{Andre:2021:GPTP, author = "David Andre", title = "{GP} considered Dangerous", booktitle = "Genetic Programming Theory and Practice XVIII", year = "2021", editor = "Wolfgang Banzhaf and Leonardo Trujillo and Stephan Winkler and Bill Worzel", address = "East Lansing, USA", month = "19-21 " # may, publisher = "Springer", note = "keynote", keywords = "genetic algorithms, genetic programming", video_url = "https://mediaspace.msu.edu/media/Andre_Keynote_GPTP_2021/1_gjyr7q9g", notes = "Google-X Alphabet [X] Not part of published proceedings", } @Article{Andreae:2008:IJKBIES, author = "Peter Andreae and Huayang Xie and Mengjie Zhang", title = "Genetic Programming for detecting rhythmic stress in spoken {English}", journal = "International Journal of Knowledge-Based and Intelligent Engineering Systems", year = "2008", volume = "12", number = "1", pages = "15--28", keywords = "genetic algorithms, genetic programming", ISSN = "1327-2314", publisher = "IOS Press", broken = "http://iospress.metapress.com/content/k017m554023m5732/", URL = "http://content.iospress.com/articles/international-journal-of-knowledge-based-and-intelligent-engineering-systems/kes00139", DOI = "doi:10.3233/KES-2008-12103", size = "14 pages", abstract = "Rhythmic stress detection is an important but difficult problem in speech recognition. This paper describes an approach to the automatic detection of rhythmic stress in New Zealand spoken English using a linear genetic programming system with speaker independent prosodic features and vowel quality features as terminals to classify each vowel segment as stressed or unstressed. In addition to the four standard arithmetic operators, this approach also uses other functions such as trigonometric and conditional functions in the function set to cope with the complexity of the task. The error rate on the training set is used as the fitness function. The approach is examined and compared to a decision tree approach and a support vector machine approach on a speech data set with 703 vowels segmented from 60 female adult utterances. The genetic programming approach achieved a maximum average accuracy of 92.6percent. The results suggest that the genetic programming approach developed in this paper outperforms the decision tree approach and the support vector machine approach for stress detection on this data set in terms of the detection accuracy, the ability of handling redundant features, and the automatic feature selection capability.", notes = "KES, see also \cite{xie:evows06}", } @InCollection{kinnear:andrews, author = "Martin Andrews and Richard Prager", title = "Genetic Programming for the Acquisition of Double Auction Market Strategies", booktitle = "Advances in Genetic Programming", publisher = "MIT Press", editor = "Kenneth E. {Kinnear, Jr.}", year = "1994", chapter = "16", pages = "355--368", keywords = "genetic algorithms, genetic programming, SA", URL = "http://www.amazon.co.uk/Advances-Genetic-Programming-Complex-Adaptive/dp/0262111888", broken = "http://cognet.mit.edu/sites/default/files/books/9780262277181/pdfs/9780262277181_chap16.pdf", DOI = "doi:10.7551/mitpress/1108.003.0022", size = "14 pages", abstract = "The Double Auction (DA) is the mechanism behind the minute-by-minute trading on many futures and commodity exchanges. Since 1990, DA tournaments have been held by the Santa Fe Institute. The competitors in the tournaments are strategies embodied in computer programmes written by a variety of economists, computer scientists and mathematicians. This paper describes how Genetic Programming (GP) methods have been used to create strategies superior, in local DA playoffs, to many of the hand-coded strategies. To isolate the contribution that the evolutionary process makes to the search for good strategies, we compare GP and Simulated Annealing (SA) optimisation of programmes. To reduce the cost of learning, we also investigate an approach that uses statistical measures to maintain a uniform population pressure.", notes = "'a GP approach was very successful in learning strategies for playing a simple game with complex dynamics' Ref Knobeln Contest: (broken) Sanfrancisco.ira.uka.de [129.13.13.110] /pub/knobeln Generational GP pop=300, touranment selection? size=2? Comparison with Simulated Annealing:SA also good but GP better Best GP exceeded performance of handcode routines (on average?) 65percent of time. Check details of what exctly this means. Set number of games played so could distinquish meadian from top quartile with 95percent confidence. Claims it helps, but doesnt seem to have either speeded things at lot or made much better result. Part of \cite{kinnear:book}", } @Article{Androutsopoulos:2019:GPEM, author = "Kelly Androutsopoulos", title = "Evelyne Lutton, Nathalie Perrot, Alberto Tonda: Evolutionary algorithms for food science and technology", subtitle = "Wiley, 2016, 182 pp, ISBN: 978-1-119-13683-5", journal = "Genetic Programming and Evolvable Machines", year = "2019", volume = "20", number = "1", pages = "147--149", month = mar, note = "Book Review", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dR8cU", DOI = "doi:10.1007/s10710-018-9335-2", size = "3 pages", abstract = "...Evolutionary Algorithms for Food Science and Technology would be invaluable to anyone considering using EAs in food science...", notes = "Review of \cite{Lutton:book}", } @InCollection{androvitsaneas_intelligent_2019, title = "Intelligent {Data} {Analysis} in {Electric} {Power} {Engineering} {Applications}", isbn13 = "978-3-319-94030-4", URL = "https://doi.org/10.1007/978-3-319-94030-4_11", abstract = "This chapter presents various intelligent approaches for modelling, generalization and knowledge extraction from data, which are applied in different electric power engineering domains of the real world. Specifically, the chapter presents: (1) the application of ANNs, inductive ML, genetic programming and wavelet NNs, in the problem of ground resistance estimation, an important problem for the design of grounding systems in constructions, (2) the application of ANNs, genetic programming and nature inspired techniques such as gravitational search algorithm in the problem of estimating the value of critical flashover voltage of insulators, a well-known difficult topic of electric power systems, (3) the application of specific intelligent techniques (ANNs, fuzzy logic, etc.) in load forecasting problems and in optimization tasks in transmission lines. The presentation refers to previously conducted research related to the application domains and briefly analyses each domain of application, the data corresponding to the problem under consideration, while are also included a brief presentation of each intelligent technique and presentation and discussion of the results obtained. Intelligent approaches are proved to be handy tools for the specific applications as they succeed to generalize the operation and behaviour of specific parts of electric power systems, they manage to induce new, useful knowledge (mathematical relations, rules and rule based systems, etc.) and thus they effectively assist the proper design and operation of complex real world electric power systems.", booktitle = "Machine {Learning} {Paradigms}: {Advances} in {Data} {Analytics}", publisher = "Springer", author = "V. P. Androvitsaneas and K. Boulas and G. D. Dounias", editor = "George A. Tsihrintzis and Dionisios N. Sotiropoulos and Lakhmi C. Jain", year = "2019", DOI = "doi:10.1007/978-3-319-94030-4_11", volume = "149", series = "ISRL", keywords = "genetic algorithms, genetic programming, gene expression programming, electric power systems, Gravitational Search Algorithm, ground resistance estimation, insulators, wavelet neural nets", isbn13 = "978-3-319-94029-8", pages = "269--313", } @InProceedings{Ang:2008:cec, author = "J. H. Ang and E. J. Teoh and C. H. Tan and K. C. Goh and K. C. Tan", title = "Dimension Reduction Using Evolutionary Support Vector Machines", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "3634--3641", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1823-7", file = "EC0777.pdf", DOI = "doi:10.1109/CEC.2008.4631290", abstract = "This paper presents a novel approach of hybridising two conventional machine learning algorithms for dimension reduction. Genetic Algorithm (GA) and Support Vector Machines (SVMs) are integrated effectively based on a wrapper approach. Specifically, the GA component searches for the best attribute set using principles of evolutionary process, after which the reduced dataset is presented to the SVMs. Simulation results show that GA-SVM hybrid is able to produce good classification accuracy and a high level of consistency. In addition, improvements are made to the hybrid by using a correlation measure between attributes as a fitness measure to replace the weaker members in the population with newly formed chromosomes. This correlation measure injects greater diversity and increases the overall fitness of the population", keywords = "genetic algorithms, genetic programming", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @Article{angarita-zapata:2021:Sensors, author = "Juan S Angarita-Zapata and Gina Maestre-Gongora and Jenny Fajardo Calderin", title = "A Bibliometric Analysis and Benchmark of Machine Learning and {AutoML} in Crash Severity Prediction: The Case Study of Three Colombian Cities", journal = "Sensors (Basel, Switzerland)", year = "2021", volume = "21", number = "24", month = dec # " 16", keywords = "genetic algorithms, genetic programming, TPOT, Bayes Theorem, Benchmarking, Bibliometrics, Cities, Colombia, Machine Learning, Internet of Things, automated machine learning, crash severity prediction, intelligent transportation systems, supervised learning", ISSN = "1424-8220", DOI = "doi:10.3390/s21248401", abstract = "Traffic accidents are of worldwide concern, as they are one of the leading causes of death globally. One policy designed to cope with them is the design and deployment of road safety systems. These aim to predict crashes based on historical records, provided by new Internet of Things (IoT) technologies, to enhance traffic flow management and promote safer roads. Increasing data availability has helped machine learning (ML) to address the prediction of crashes and their severity. The literature reports numerous contributions regarding survey papers, experimental comparisons of various techniques, and the design of new methods at the point where crash severity prediction (CSP) and ML converge. Despite such progress, and as far as we know, there are no comprehensive research articles that theoretically and practically approach the model selection problem (MSP) in CSP. Thus, this paper introduces a bibliometric analysis and experimental benchmark of ML and automated machine learning (AutoML) as a suitable approach to automatically address the MSP in CSP. Firstly, 2318 bibliographic references were consulted to identify relevant authors, trending topics, keywords evolution, and the most common ML methods used in related-case studies, which revealed an opportunity for the use AutoML in the transportation field. Then, we compared AutoML (AutoGluon, Auto-sklearn, TPOT) and ML (CatBoost, Decision Tree, Extra Trees, Gradient Boosting, Gaussian Naive Bayes, Light Gradient Boosting Machine, Random Forest) methods in three case studies using open data portals belonging to the cities of Medellin, Bogota, and Bucaramanga in Colombia. Our experimentation reveals that AutoGluon and CatBoost are competitive and robust ML approaches to deal with various CSP problems. In addition, we concluded that general-purpose AutoML effectively supports the MSP in CSP without developing domain-focused AutoML methods for this supervised learning problem. Finally, based on the results obtained, we introduce challenges and research opportunities that the community should explore to enhance the contributions that ML and AutoML can bring to CSP and other transportation areas.", notes = "PMID: 34960494", } @PhdThesis{angeline:dissertation, author = "Peter John Angeline", title = "Evolutionary Algorithms and Emergent Intelligence", school = "Ohio State University", year = "1993", address = "USA", size = "180 pages", keywords = "genetic algorithms, genetic programming", broken = "ftp://nervous.cis.ohio-state.edu/pub/papers/DISS/pja", URL = "http://www.ai.uga.edu/ftplib/misc/ga/papers/ToPrint/Dissertation/chapter0.ps.Z", URL = "http://www.ai.uga.edu/ftplib/misc/ga/papers/ToPrint/Dissertation/chapter1.ps.Z", URL = "http://www.ai.uga.edu/ftplib/misc/ga/papers/ToPrint/Dissertation/chapter2.ps.Z", URL = "http://www.ai.uga.edu/ftplib/misc/ga/papers/ToPrint/Dissertation/chapter3.ps.Z", URL = "http://www.ai.uga.edu/ftplib/misc/ga/papers/ToPrint/Dissertation/chapter4.ps.Z", URL = "http://www.ai.uga.edu/ftplib/misc/ga/papers/ToPrint/Dissertation/chapter5.ps.Z", URL = "http://www.ai.uga.edu/ftplib/misc/ga/papers/ToPrint/Dissertation/chapter6.ps.Z", URL = "http://www.ai.uga.edu/ftplib/misc/ga/papers/ToPrint/Dissertation/chapter7.ps.Z", URL = "http://www.ai.uga.edu/ftplib/misc/ga/papers/ToPrint/Dissertation/chapter8.ps.Z", URL = "http://www.ai.uga.edu/ftplib/misc/ga/papers/ToPrint/Dissertation/dissrefs.ps.Z", notes = " http://citeseer.ist.psu.edu/114089.html has introduction", } @InCollection{kinnear:angeline, title = "Genetic Programming and Emergent Intelligence", author = "Peter John Angeline", booktitle = "Advances in Genetic Programming", publisher = "MIT Press", editor = "Kenneth E. {Kinnear, Jr.}", year = "1994", pages = "75--98", chapter = "4", keywords = "genetic algorithms, genetic programming", URL = "http://cognet.mit.edu/sites/default/files/books/9780262277181/pdfs/9780262277181_chap4.pdf", URL = "http://citeseer.ist.psu.edu/187189.html", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/1870/http:zSzzSzwww.natural-selection.comzSzpeoplezSzpjazSzdocszSzaigp.pdf/angeline94genetic.pdf", URL = "http://www.amazon.co.uk/Advances-Genetic-Programming-Complex-Adaptive/dp/0262111888", DOI = "doi:10.7551/mitpress/1108.003.0009", size = "23 pages", abstract = "Genetic programming is but one of several problem solving methods based on a computational analogy to natural evolution. Such algorithms, collectively titled evolutionary computations, embody dynamics that permit task specific knowledge to emerge while solving the problem. In contrast to the traditional knowledge representations of artificial intelligence, this method of problem solving is termed emergent intelligence. This chapter describes some of the basics of emergent intelligence, its implementation in evolutionary computations, and its contributions to genetic programming. Demonstrations and guidelines on how to exploit emergent intelligence to extend the problem solving capabilities of genetic programming and other evolutionary computations are also presented.", notes = "'Contrasts GP with other Weak/strong AI methods, credit assignment, USEFUL, diplodity=redundancy=good, hierarchical code/decode of subroutines better than Koza ADF Loads of references' I realized that inherent dynamics of genetic programming encouraged certain emergent properties. The most important of these is that introns emerge naturally from the process to protect the developing program from crossover. Others in the field think this extra stuff in the genetic program is a bad thing, reflected by their choice of the term 'bloating' for the effect. This chapter is the first to take a positive view on GP introns and other emergent phenomena. I think this is the first paper to associate the 'extra' code in genetic programs with the intron concept. Part of \cite{kinnear:book}", } @InProceedings{icga93:angeline, author = "Peter J. Angeline and Jordan B. Pollack", title = "Competitive Environments Evolve Better Solutions for Complex Tasks", year = "1993", booktitle = "Proceedings of the 5th International Conference on Genetic Algorithms, ICGA-93", editor = "Stephanie Forrest", publisher = "Morgan Kaufmann", pages = "264--270", month = "17-21 " # jul, address = "University of Illinois at Urbana-Champaign", publisher_address = "2929 Campus Drive, Suite 260, San Mateo, CA 94403, USA", keywords = "genetic algorithms, genetic programming", URL = "http://www.demo.cs.brandeis.edu/papers/icga5.pdf", URL = "http://www.demo.cs.brandeis.edu/papers/icga5.ps.gz", URL = "http://www.natural-selection.com/Library/1993/icga93.ps.Z", size = "7 pages", abstract = "In the typical genetic algorithm experiment, the fitness function is constructed to be independent of the contents of the population to provide a consistent objective measure. Such objectivity entails significant knowledge about the environment which suggests either the problem has previously been solved or other non-evolutionary techniques may be more efficient. Furthermore, for many complex tasks an independent fitness function is either impractical or impossible to provide. In this paper, we demonstrate that competitive fitness functions, i.e. fitness functions that are dependent on the constituents of the population, can provide a more robust training environment than independent fitness functions. We describe three differing methods for competitive fitness, and discuss their respective advantages.", ISBN = "1-55860-299-2", notes = "very like thesis One method I investigated was called competitive fitness functions which is a fitness function that compares performance between members of the population to determine a ranking of individuals for reproduction. THis obviates the need for a quantitative model of the quality of solutions and replaces it with a more simplistic measure of {"}x is better than y{"}. The paper explores this concept using GLiB and appeared in ICGA93.", } @InProceedings{Angeline:1994:GPCS, author = "P. J. Angeline", title = "Genetic programming: A current snapshot", booktitle = "Proceedings of the Third Annual Conference on Evolutionary Programming", year = "1994", editor = "D. B. Fogel and W. Atmar", publisher = "Evolutionary Programming Society", keywords = "genetic algorithms, genetic programming", broken = "http://www.natural-selection.com/Library/1994/ep94-gp.ps.Z", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/1870/http:zSzzSzwww.natural-selection.comzSzpeoplezSzpjazSzdocszSzep94-gp.pdf/angeline94genetic.pdf", URL = "http://citeseer.ist.psu.edu/147407.html", abstract = "Genetic programming is a promising variant of genetic algorithms that evolves dynamic, hierarchical structures often described as programs. Genetic programming's flexibility to tailor the representation language to the problem being solved, and its specially designed crossover operator provide a robust tool for evolving problem solutions. This paper provides an introduction to genetic programming, a short review of dynamic representations used in evolutionary systems and their relation to genetic programming, and a description of some of genetic programming's inherent properties. The paper concludes with a review of on going research and some potential future directions for the field.", } @InProceedings{Angeline:1992:EIS, author = "Peter J. Angeline and Jordan B. Pollack", title = "The evolutionary induction of subroutines", booktitle = "Proceedings of the Fourteenth Annual Conference of the Cognitive Science Society", year = "1992", pages = "236--241", address = "Bloomington, Indiana, USA", publisher = "Lawrence Erlbaum", keywords = "genetic algorithms, genetic programming", URL = "http://www.demo.cs.brandeis.edu/papers/glib92.pdf", URL = "http://www.demo.cs.brandeis.edu/papers/glib92.ps.gz", URL = "http://www.natural-selection.com/Library/1992/cogsci92.ps.Z", abstract = "we describe a genetic algorithm capable of evolving large programs by exploiting two new genetic operators which construct and deconstruct parameterized subroutines. These subroutines protect useful partial solutions and help to solve the scaling problem for a class of genetic problem solving methods. We demonstrate that our algorithm acquires useful subroutines by evolving a modular program from scratch to play and win at Tic-Tac-Toe against a flawed expert. This work also serves to amplify our previous note (Pollack, 1991) that a phase transition is the principle behind induction in dynamical cognitive models.", notes = "GLiB is an emergent method for discovering task-specific modular decompositions in genetic programs. At least this is how I used to talk about it. I know consider this an individual-level self-adaptive method for forming decompositions in genetic programs.", } @TechReport{Angeline:1993:CHLR, author = "P. J. Angeline and J. B. Pollack", title = "Coevolving High-Level Representations", institution = "Laboratory for Artificial Intelligence. The Ohio State University", year = "1993", type = "July", number = "Technical report 92-PA-COEVOLVE", keywords = "genetic algorithms, genetic programming", URL = "http://www.demo.cs.brandeis.edu/papers/alife3.pdf", abstract = "Several evolutionary simulations allow for a dynamic resizing of the genotype. This is an important alternative to constraining the genotype's maximum size and complexity. In this paper, we add an additional dynamic to simulated evolution with the description of a genetic algorithm that coevolves its representation language with the genotypes. We introduce two mutation operators that permit the acquisition of modules from the genotypes during evolution. These modules form an increasingly highlevel representation language specific to the developmental environment. Experimental results illustrating interesting properties of the acquired modules and the evolved languages are provided.", size = "15 pages", } @InProceedings{angeline:1993:ema, author = "Peter J. Angeline and Jordan Pollack", title = "Evolutionary Module Acquisition", booktitle = "Proceedings of the Second Annual Conference on Evolutionary Programming", year = "1993", editor = "D. Fogel and W. Atmar", pages = "154--163", address = "La Jolla, CA, USA", month = "25-26 " # feb, organisation = "The Evolutionary Programming Society", keywords = "genetic algorithms, genetic programming, FSM, GLiB", URL = "http://www.demo.cs.brandeis.edu/papers/ep93.pdf", URL = "http://www.demo.cs.brandeis.edu/papers/ep93.ps.gz", URL = "http://www.natural-selection.com/Library/1993/ep93.ps.Z", size = "9 pages", abstract = "Evolutionary programming and genetic algorithms share many features, not the least of which is a reliance of an analogy to natural selection over a population as a means of implementing search. With their commonalities come shared problems whose solutions can be investigated at a higher level and applied to both. One such problem is the manipulation of solution parameters whose values encode a desirable sub-solution. In this paper, we define a superset of evolutionary programming and genetic algorithms, called evolutionary algorithms, and demonstrate a method of automatic modularization that protects promising partial solutions and speeds acquisition time.", notes = "Artificial Ant (John Muir). Finite State Machines. Genetic Library Builder ", } @InProceedings{Angeline:1991:CHLR, author = "P. J. Angeline and J. B. Pollack", title = "Coevolving high-level representations", booktitle = "Artificial Life III", year = "1994", editor = "Christopher G. Langton", volume = "XVII", series = "SFI Studies in the Sciences of Complexity", pages = "55--71", address = "Santa Fe, New Mexico", month = "15-19 " # jun # " 1992", publisher = "Addison-Wesley", keywords = "genetic algorithms, genetic programming", ISBN = "0-201-62492-3", URL = "http://www.demo.cs.brandeis.edu/papers/alife3.pdf", URL = "http://www.demo.cs.brandeis.edu/papers/alife3.ps.gz", URL = "https://www.amazon.co.uk/s?k=9780201624922", abstract = "Several evolutionary simulations allow for a dynamic resizing of the genotype. This is an important alternative to constraining the genotype's maximum size and complexity. In this paper, we add an additional dynamic to simulated evolution with the description of a genetic algorithm that coevolves its representation language with the genotypes. We introduce two mutation operators that permit the acquisition of modules from the genotypes during evolution. These modules form an increasingly high-level representation language specific to the developmental environment. Experimental results illustrating interesting properties of the acquired modules and the evolved languages are provided.", notes = "ALife3 Held June 1992 in Santa Fe, New Mexico, USA GLiB, Tower of Hanoi, Tic Tac Toe. Also in thesis.", } @Article{angeline:1994:BS, author = "Peter J. Angeline", title = "Genetic programming: On the programming of computers by means of natural selection,John R. Koza, A Bradford Book, MIT Press, Cambridge MA, 1992, ISBN 0-262-11170-5, xiv + 819pp., US\$55.00", journal = "Biosystems", year = "1994", volume = "33", number = "1", pages = "69--73", note = "Book review", keywords = "genetic algorithms, genetic programming", broken = "http://www.sciencedirect.com/science/article/B6T2K-49N8PP4-23/2/021e3e016b39a87da29046c37f423f73", DOI = "doi:10.1016/0303-2647(94)90062-0", notes = "Review of \cite{koza:book}", } @Article{angeline:1995:er, author = "Peter J. Angeline", title = "Evolution Revolution: An Introduction to the Special Track on Genetic and Evolutionary Programming", journal = "IEEE Expert", year = "1995", volume = "10", number = "3", pages = "6--10", month = jun, note = "Guest editor's introduction", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/MIS.1995.10027", size = "4 pages", notes = "Dec 2011 NO PDF given with 10.1109/MIS.1995.10027 fab colour picture by Karl Sims 6 articles in special track; 2 use evolutionary programming, 2 use genetic programming ( \cite{Tackett:1995:mGP} and \cite{wong:1995:glp}) and 2 use hybrids ((GA and GP \cite{howard:1995:GA-P}) and (Riziki and Zmuda, August 1995 GA and EP morphological pattern recognition))", } @InProceedings{angeline:1995:mcc, author = "Peter J. Angeline", title = "Morphogenic Evolutionary Computations: Introduction, Issues and Examples", booktitle = "Evolutionary Programming IV: The Fourth Annual Conference on Evolutionary Programming", year = "1995", editor = "John Robert McDonnell and Robert G. Reynolds and David B. Fogel", pages = "387--401", publisher = "MIT Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-262-13317-2", broken = "http://www.natural-selection.com/Library/1995/ep95-morph.ps.Z", broken = "http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=4397", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6300850", DOI = "doi:10.7551/mitpress/2887.003.0037", size = "16 pages", abstract = "Morphogenic (or morphogenetic) evolutionary computations are evolutionary computations that distinguish between the representation that is evolved and the representation that is evaluated by the fitness function. A user defined development function provides the necessary mapping between these often very different structures. Such a separation affords important advantages for these evolutionary computations, not the least of which is modification of a relatively small structure that is expanded into a much larger one for evaluation by the fitness function. This paper provides a formal definition of morphogenic evolutionary computations along with a review and discussion of the relevant literature.", notes = "EP-95", } @InCollection{angeline:1995:asa, author = "Peter J. Angeline", title = "Adaptive and Self-Adaptive Evolutionary Computations", booktitle = "Computational Intelligence: A Dynamic Systems Perspective", publisher = "IEEE Press", year = "1995", editor = "Marimuthu Palaniswami and Yianni Attikiouzel and Robert J. {Marks, II} and David B. Fogel and Toshio Fukuda", pages = "152--163", keywords = "genetic algorithms, genetic programming", language = "English", ISBN = "0-7803-1182-5", broken = "http://www.natural-selection.com/Library/1995/icec95.ps.Z", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/1007/http:zSzzSzwww.natural-selection.comzSzpeoplezSzpjazSzdocszSzicec95.pdf/angeline95adaptive.pdf", URL = "http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.493.2479&rank=6", URL = "https://www.amazon.com/Computational-Intelligence-Dynamic-System-Perspective/dp/0780311825/", URL = "https://research-repository.uwa.edu.au/en/publications/computational-intelligence-a-dynamic-system-perspective", size = "13 pages", abstract = "This paper reviews the various studies that have introduced adaptive and selfadaptive parameters into Evolutionary Computations. A formal definition of an adaptive evolutionary computation is provided with an analysis of the types of adaptive and self-adaptive parameter update rules currently in use. Previous studies are reviewed and placed into a categorisation that helps to illustrate their similarities and differences", } @Book{book:1996:aigp2, editor = "Peter J. Angeline and K. E. {Kinnear, Jr.}", title = "Advances in Genetic Programming 2", publisher = "MIT Press", year = "1996", address = "Cambridge, MA, USA", keywords = "genetic algorithms, genetic programming", ISBN = "0-262-01158-1", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/aigp2.html", URL = "http://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267488", DOI = "doi:10.7551/mitpress/1109.001.0001", size = "538 pages", abstract = "Genetic programming, a form of genetic algorithm that evolves programs and program-like executable structures, is a new paradigm for developing reliable, time- and cost-effective applications. The second volume of Advances in Genetic Programming highlights many of the most recent technical advances in this increasingly popular field.", } @InCollection{intro:1996:aigp2, author = "Peter J. Angeline", title = "Genetic Programming's Continued Evolution", booktitle = "Advances in Genetic Programming 2", publisher = "MIT Press", year = "1996", editor = "Peter J. Angeline and K. E. {Kinnear, Jr.}", pages = "1--20", chapter = "1", address = "Cambridge, MA, USA", keywords = "genetic algorithms, genetic programming", ISBN = "0-262-01158-1", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6277539", DOI = "doi:10.7551/mitpress/1109.003.0004", size = "20 pages", abstract = "Genetic programming is a variant of genetic algorithms that evolves computer programs represented as tree structures. Genetic programming and genetic algorithms are but one technique in the larger collection of evolutionary computations that also include evolution strategies and evolutionary programming. This chapter begins with a description of the various evolutionary computations that highlights their respective differences across several important dimensions. Following this, an introduction to genetic programming and its relation to other evolutionary computations is provided. Research topics of current interest in the genetic programming field are then reviewed, demonstrating the present breadth and maturity of the field. The chapter ends with a description of the organisation of the remainder of this book and a brief synopsis of each chapter that appears.", } @InCollection{angeline:1996:aigp2, author = "Peter J. Angeline", title = "Two Self-Adaptive Crossover Operators for Genetic Programming", booktitle = "Advances in Genetic Programming 2", publisher = "MIT Press", year = "1996", editor = "Peter J. Angeline and K. E. {Kinnear, Jr.}", pages = "89--110", chapter = "5", address = "Cambridge, MA, USA", keywords = "genetic algorithms, genetic programming", ISBN = "0-262-01158-1", URL = "http://www.natural-selection.com/Library/1996/aigp2.ps.Z", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6277498", DOI = "doi:10.7551/mitpress/1109.003.0009", size = "21 pages", abstract = "Two self-adaptive crossover operations are studied in a nonstandard genetic program. It is shown that for three distinct problems the results obtained when using either of the self-adaptive crossover operations are equivalent or better than the results when using standard GP crossover. A postmortem analysis of the evolved values for the self-adaptive parameters suggests that certain heuristics commonly used in genetic programming may not be optimal.", notes = "These were called Selective Self-Adaptive Crossover and Self-adaptive Multi-Crossover.", } @InProceedings{angeline:1996:leaf, author = "Peter J. Angeline", title = "An Investigation into the Sensitivity of Genetic Programming to the Frequency of Leaf Selection During Subtree Crossover", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "21--29", address = "Stanford University, CA, USA", publisher = "MIT Press", URL = "http://www.natural-selection.com/Library/1996/gp96.zip", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap3.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", size = "9 pages", abstract = "In genetic programming, crossover swaps randomly selected subtrees between parents. Typically, the probability of selecting a leaf as the subtree to be swapped is reduced, supposedly to allow larger structures on average. This paper reports on a study to determine the effect of modifying the leaf selection frequency for subtree crossover on the performance of a non-standard genetic program. Both a variety of constant values and dynamic update methods are investigated . It is shown that the performance of the genetic program is impacted by the manipulation of the leaf selection frequency and often can be improved using a random process rather than a constant value.", notes = "GP-96 multiple types of mutation Sunspot Numbers data from http://www.ngdc.noaa.gov/stp/SOLAR/SSN/ssn.html", } @InProceedings{angeline:1996:efm, author = "Peter J. Angeline", title = "Evolving Fractal Movies", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "Evolutionary Programming", pages = "503--511", address = "Stanford University, CA, USA", publisher = "MIT Press", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap84.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96 EP paper", } @InProceedings{angeline:1997:tcbbe, author = "Peter J. Angeline", title = "Subtree Crossover: Building Block Engine or Macromutation?", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming", pages = "9--17", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", URL = "http://ncra.ucd.ie/COMP41190/SubtreeXoverBuildingBlockorMacromutation_angeline_gp97.ps", size = "9 pages", abstract = "In genetic programming, crossover swaps randomly selected subtrees between parents. Recent work in genetic algorithms (Jones 1995) demonstrates that when one of the parents selected for crossover is replaced with a randomly generated parent, the algorithm performs as well or better than crossover for some problems. Terry Jones (ICGA 1995) termed this form of macromutation headless chicken crossover. The following paper investigates two forms of headless-chicken crossover for manipulating parse trees and shows that both types of macromutation perform as well or better than standard subtree crossover. It is argued that these experiments support the hypothesis that the building block hypothesis is not descriptive of the operation of subtree crossover and that sub-tree crossover is better modelled as a macromutation restricted by population content.", notes = "spirls, sun spots GP-97", } @InProceedings{Angeline:1997:aIMepesr, author = "Peter J. Angeline", title = "An Alternative to Indexed Memory for Evolving Programs with Explicit State Representations", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "evolutionary programming and evolution strategies", pages = "423--430", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", notes = "GP-97", } @InProceedings{angeline:1997:txde, author = "Peter J. Angeline", title = "Tracking Extrema in Dynamic Environments", booktitle = "Proceedings of the 6th International Conference on Evolutionary Programming", year = "1997", editor = "P. J. Angeline and R. G. Reynolds and J. R. McDonnell and R. Eberhart", volume = "1213", series = "Lecture Notes in Computer Science", pages = "335--345", address = "Indianapolis, Indiana, USA", month = apr # " 13-16", publisher = "Springer Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-62788-X", URL = "http://www.natural-selection.com/Library/1997/ep97b.pdf", DOI = "doi:10.1007/BFb0014823", size = "11 pages", abstract = "Typical applications of evolutionary optimization involve the off-line approximation of extrema of static multi-modal functions. Methods which use a variety of techniques to self-adapt mutation parameters have been shown to be more successful than methods which do not use self-adaptation. For dynamic functions, the interest is not to obtain the extrema but to follow it as closely as possible. This paper compares the on-line extrema tracking performance of an evolutionary program without self-adaptation against an evolutionary program using a self-adaptive Gaussian update rule over a number of dynamics applied to a simple static function. The experiments demonstrate that for some dynamic functions, self-adaptation is effective while for others it is detrimental.", notes = "EP-97", } @InProceedings{angeline:1997:spie, author = "Peter J. Angeline and David B. Fogel", title = "An evolutionary program for the identification of dynamical systems", booktitle = "Application and Science of Artificial Neural Networks III", year = "1997", editor = "S. Rogers", volume = "3077", pages = "409--417", publisher_address = "Bellingham, WA, USA", organisation = "SPIE-The International Society for Optical Engineering", keywords = "genetic algorithms, genetic programming, evolutionary computation, evolutionary programming, system identification, dynamical systems, optimization", URL = "http://www.natural-selection.com/Library/1997/spie97.pdf", DOI = "doi:10.1117/12.271503", size = "9 pages", abstract = "Various forms of neural networks have been applied to the identification of non-linear dynamical systems. In most of these methods, the network architecture is set prior to training. In this paper, a method that evolves a symbolic solution for plant models is described. This method uses an evolutionary program to manipulate collections of parse trees expressed in a task specific language. Experiments performed on two unknown plants show this method is competitive with those that train neural networks for similar problems", } @InCollection{Angeline:1997:HEC, author = "Peter J. Angeline", title = "Parse Trees", booktitle = "Handbook of Evolutionary Computation", publisher = "Oxford University Press", publisher_2 = "Institute of Physics Publishing", year = "1997", editor = "Thomas Baeck and David B. Fogel and Zbigniew Michalewicz", chapter = "section C1.6", keywords = "genetic algorithms, genetic programming", ISBN = "0-7503-0392-1", URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.375.6494.pdf", DOI = "doi:10.1201/9781420050387.ptc", size = "3 pages", abstract = "This section reviews parse tree representations, a popular representation for evolving executable structures. The field of genetic programming is based entirely on the flexibility of this representation. This section describes some of the history of parse trees in evolutionary computation, the form of the representation and some special properties.", } @InCollection{Angeline:1997:HECa, author = "Peter J. Angeline", title = "Mutation: Parse Trees", booktitle = "Handbook of Evolutionary Computation", publisher = "Oxford University Press", publisher_2 = "Institute of Physics Publishing", year = "1997", editor = "Thomas Baeck and David B. Fogel and Zbigniew Michalewicz", chapter = "section C3.2.5", keywords = "genetic algorithms, genetic programming", ISBN = "0-7503-0392-1", URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.375.6494.pdf", DOI = "doi:10.1201/9781420050387.ptc", size = "2 pages", abstract = "Genetics-based evolutionary computations typically discount the role of mutation operation in the induction of evolved structures. This is especially true in genetic programming where mutation operations for parse trees are often not used. Some practitioners of genetic programming believe that mutation has an important role in evolving fit parse trees. This section describes several mutation operations for parse trees used by some genetic programming enthusiasts.", notes = "grow, shrink,switch, cycle=point", } @InCollection{Angeline:1997:HECb, author = "Peter J. Angeline", title = "Crossover: parse trees", booktitle = "Handbook of Evolutionary Computation", publisher = "Oxford University Press", publisher_2 = "Institute of Physics Publishing", year = "1997", editor = "Thomas Baeck and David B. Fogel and Zbigniew Michalewicz", chapter = "section C3.3.5", keywords = "genetic algorithms, genetic programming", ISBN = "0-7503-0392-1", URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.375.6494.pdf", DOI = "doi:10.1201/9781420050387.ptc", size = "2 pages", abstract = "Described here is the standard crossover operation for parse tree representations most often used in genetic programming. Extensions to this operator for subtrees with multiple return types and genetic programs using automatically defined functions are also described.", } @InProceedings{angeline:1998:sccb, author = "Peter J. Angeline", title = "Subtree Crossover Causes Bloat", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "745--752", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, evolutionary programming", ISBN = "1-55860-548-7", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1998/angeline_1998_sccb.pdf", size = "9 pages", notes = "GP-98, Even-5 parity, intertwined spirals, sunspot prediction. ", } @Article{angeline:1998:hpees, author = "Peter J. Angeline", title = "A Historical Perspective on the Evolution of Executable Structures", journal = "Fundamenta Informaticae", year = "1998", volume = "35", number = "1--4", pages = "179--195", month = aug, email = "angeline@natural-selection.com", keywords = "genetic algorithms, genetic programming", ISSN = "0169-2968", URL = "http://www.natural-selection.com/Library/1998/gphist.pdf", size = "16 pages", abstract = "Genetic programming (Koza 1992) is a method of inducing behaviors represented as executable programs. The generality of the approach has spawned a proliferation of work in the evolution of executable structures that is unmatched in the history of the subject. This paper describes the standard approach to genetic programming, as defined in Koza (1992), and then presents the significant studies that preceded its inception as well as the diversification of techniques evolving executable structures that is currently underway in the field.", notes = "Special volume: Evolutionary Computation Also published in book form, see \cite{angeline:1999:hpees}", } @Article{angeline:1998:mips3, author = "Peter J. Angeline", title = "Multiple Interacting Programs: A Representation for Evolving Complex Behaviors", journal = "Cybernetics and Systems", year = "1998", volume = "29", number = "8", pages = "779--803", month = nov, keywords = "genetic algorithms, genetic programming, mips", ISSN = "0196-9722", URL = "http://www.natural-selection.com/Library/1998/mips3.pdf", URL = "http://www.tandfonline.com/doi/abs/10.1080/019697298125407", DOI = "doi:10.1080/019697298125407", size = "25 pages", abstract = "This paper defines a representation for expressing complex behaviors, called multiple interacting programs (MIPs), and describes an evolutionary method for evolving solutions to difficult problems expressed as MIPs structures. The MIPs representation is a generalization of neural network architectures that can model any type of dynamic system. The evolutionary training method described is based on an evolutionary program originally used to evolve the architecture and weights of recurrent neural networks. Example experiments demonstrate the training method s ability to evolve appropriate MIPs solutions for difficult problems. An analysis of the evolved solutions shows their dynamics to be interesting and non-trivial.", notes = "Sun spots, Santa Fe trail Artifical Ant, 5-bit reverser, Tree, ANN", } @InProceedings{angeline:1998:spie, author = "Peter J. Angeline", title = "Evolving Predictors for Chaotic Time Series", booktitle = "Proceedings of SPIE: Application and Science of Computational Intelligence", year = "1998", editor = "S. Rogers and D. Fogel and J. Bezdek and B. Bosacchi", volume = "3390", pages = "170--80", publisher_address = "Bellingham, WA, USA", organisation = "SPIE", keywords = "genetic algorithms, genetic programming, evolutionary computation, evolutionary programming, neural networks, chaotic time series prediction", URL = "http://www.natural-selection.com/Library/1998/spie98.pdf", DOI = "doi:10.1117/12.304803", size = "11 pages", abstract = "Neural networks are a popular representation for inducing single-step predictors for chaotic times series. For complex time series it is often the case that a large number of hidden units must be used to reliably acquire appropriate predictors. This paper describes an evolutionary method that evolves a class of dynamic systems with a form similar to neural networks but requiring fewer computational units. Results for experiments on two popular chaotic times series are described and the current methods performance is shown to compare favorably with using larger neural networks.", } @InCollection{angeline:1999:hpees, author = "Peter J. Angeline", title = "A Historical Perspective on the Evolution of Executable Structures", booktitle = "Evolutionary Computation", publisher = "Ohmsha", year = "1999", editor = "A. E. Eiben and A. Michalewicz", address = "Tokyo", keywords = "genetic algorithms, genetic programming", ISBN = "4-274-90269-2", URL = "http://www.ohmsha.co.jp/data/books/e_contents/4-274-90269-2.htm", notes = "This is the book edition of the journal, Fundamenta Informaticae, Volume 35, Nos. 1-4, 1998. See also \cite{angeline:1998:hpees} ", size = "pages", } @InCollection{angeline:2000:EC1, author = "Peter J. Angeline", title = "Parse trees", booktitle = "Evolutionary Computation 1 Basic Algorithms and Operators", publisher = "Institute of Physics Publishing", year = "2000", editor = "Thomas Baeck and David B. Fogel and Zbigniew Michalewicz", chapter = "19", pages = "155--159", address = "Bristol", keywords = "genetic algorithms, genetic programming", ISBN = "0-7503-0664-5", URL = "http://www.crcpress.com/product/isbn/9780750306645", size = "5 pages", } @Article{Angelis:2023:ACME, author = "Dimitrios Angelis and Filippos Sofos and Theodoros E. Karakasidis", title = "Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives", journal = "Archives of Computational Methods in Engineering", year = "2023", volume = "30", pages = "3845--3865", month = jul, keywords = "genetic algorithms, genetic programming", URL = "https://rdcu.be/dmkPm", DOI = "doi:10.1007/s11831-023-09922-z", size = "21 pages", abstract = "Symbolic regression (SR) is a machine learning-based regression method based on genetic programming principles that integrates techniques and processes from heterogeneous scientific fields and is capable of providing analytical equations purely from data. This remarkable characteristic diminishes the need to incorporate prior knowledge about the investigated system. SR can spot profound and elucidate ambiguous relations that can be generalisable, applicable, explainable and span over most scientific, technological, economical, and social principles. In this review, current state of the art is documented, technical and physical characteristics of SR are presented, the available programming techniques are investigated, fields of application are explored, and future perspectives are discussed.", notes = "Condensed Matter Physics Laboratory, Department of Physics, University of Thessaly, Lamia 35100, Greece", } @Article{angelis:2023:Micromachines, author = "Dimitrios Angelis and Filippos Sofos and Konstantinos Papastamatiou and Theodoros E. Karakasidis", title = "Fluid Properties Extraction in Confined Nanochannels with Molecular Dynamics and Symbolic Regression Methods", journal = "Micromachines", year = "2023", volume = "14", number = "7", pages = "Article No. 1446", keywords = "genetic algorithms, genetic programming", ISSN = "2072-666X", URL = "https://www.mdpi.com/2072-666X/14/7/1446", DOI = "doi:10.3390/mi14071446", abstract = "In this paper, we propose an alternative road to calculate the transport coefficients of fluids and the slip length inside nano-conduits in a Poiseuille-like geometry. These are all computationally demanding properties that depend on dynamic, thermal, and geometrical characteristics of the implied fluid and the wall material. By introducing the genetic programming-based method of symbolic regression, we are able to derive interpretable data-based mathematical expressions based on previous molecular dynamics simulation data. Emphasis is placed on the physical interpretability of the symbolic expressions. The outcome is a set of mathematical equations, with reduced complexity and increased accuracy, that adhere to existing domain knowledge and can be exploited in fluid property interpolation and extrapolation, bypassing timely simulations when possible.", notes = "also known as \cite{mi14071446}", } @InProceedings{Angelov:2008:GEFS, author = "Plamen Angelov and Arthur Kordon and Xiaowei Zhou", title = "Evolving fuzzy inferential sensors for process industry", booktitle = "3rd International Workshop on Genetic and Evolving Fuzzy Systems, GEFS 2008", year = "2008", month = "4-7 " # mar, address = "Witten-Boommerholz, Germany", pages = "41--46", keywords = "genetic algorithms, genetic programming, Dow Chemical Company, Takagi-Sugeno-fuzzy system, fuzzy inferential sensor, multi-objective genetic-programming-based optimization, on-line input selection techniques, on-line learning algorithm, process industry, self-tuning inferential soft sensor, chemical industry, fuzzy set theory, fuzzy systems, sensors", DOI = "doi:10.1109/GEFS.2008.4484565", abstract = "This paper describes an approach to design self-developing and self-tuning inferential soft sensors applicable to process industries. The proposal is for a Takagi-Sugeno-fuzzy system framework that has evolving (open structure) architecture, and an on-line (possibly real-time) learning algorithm. The proposed methodology is novel and it addresses the problems of self-development and self-calibration caused by drift in the data patterns due to changes in the operating regimes, catalysts aging, industrial equipment wearing, contamination etc. The proposed computational technique is data-driven and parameter-free (it only requires a couple of parameters with clear meaning and suggested values). In this paper a case study of four problems of estimation of chemical properties is considered, however, the methodology has a much wider validity. The optimal inputs to the proposed evolving inferential sensor are determined a priori and off-line using a multi-objective genetic-programming-based optimization. Different on-line input selection techniques are under development. The methodology is validated on real data provided by the Dow Chemical Company, USA.", notes = "Also known as \cite{4484565}", } @Article{Angelucci:2024:jobe, author = "Giulia Angelucci and Giuseppe Quaranta and Fabrizio Mollaioli and Sashi K. Kunnath", title = "Interpretable machine learning models for displacement demand prediction in reinforced concrete buildings under pulse-like earthquakes", journal = "Journal of Building Engineering", year = "2024", volume = "95", pages = "110124", keywords = "genetic algorithms, genetic programming, Engineering demand parameter, Gaussian process regression, Machine learning, Pulse-like earthquake, Reinforced concrete", ISSN = "2352-7102", URL = "https://www.sciencedirect.com/science/article/pii/S2352710224016929", DOI = "doi:10.1016/j.jobe.2024.110124", abstract = "This work proposes a novel procedure to guide the development of machine learning models for estimating the seismic demand in existing reinforced concrete (RC) buildings. The proposed approach is organized across two scales. A large-scale (nonparametric) machine learning model is first obtained by means of Gaussian Process Regression (GPR) using all candidate building attributes and intensity measures. SHapley Additive exPlanations (SHAP) values are used to facilitate its interpretation and to assist the rational selection of a small subset of intensity measures, which is finally employed to develop a (symbolic) reduced-scale machine learning model by means of Genetic Programming (GP). Simplified models of archetype buildings are adopted to develop machine learning techniques at both scales, in such a way to alleviate the simulation time for preparing large datasets. Refined models representative of actual buildings are instead considered for the unbiased final assessment. The proposed approach is applied to develop predictive machine learning models for the maximum inter-storey drift in bare frames, pilotis frames and frames with infills under pulse-like seismic ground motions. Consequently, the critical examination of the SHAP values revealed the most significant intensity measures and unfolded interesting patterns depending on the occupancy rate of the infills. Moreover, the final assessment demonstrates that this approach allows the management of a non-homogeneous building stock consisting of very diverse structural systems (i.e., spanning from existing buildings designed against gravity loads only to buildings that comply with outdated seismic codes) while providing satisfactory predictions of the seismic demand with minimum computational effort", } @Article{Anicic:2017:OLE, author = "Obrad Anicic and Srdan Jovic and Hivzo Skrijelj and Bogdan Nedic", title = "Prediction of laser cutting heat affected zone by extreme learning machine", journal = "Optics and Lasers in Engineering", volume = "88", pages = "1--4", year = "2017", ISSN = "0143-8166", DOI = "doi:10.1016/j.optlaseng.2016.07.005", URL = "http://www.sciencedirect.com/science/article/pii/S0143816616301385", abstract = "Heat affected zone (HAZ) of the laser cutting process may be developed based on combination of different factors. In this investigation the HAZ forecasting, based on the different laser cutting parameters, was analyzed. The main goal was to predict the HAZ according to three inputs. The purpose of this research was to develop and apply the Extreme Learning Machine (ELM) to predict the HAZ. The ELM results were compared with genetic programming (GP) and artificial neural network (ANN). The reliability of the computational models were accessed based on simulation results and by using several statistical indicators. Based upon simulation results, it was demonstrated that ELM can be used effectively in applications of HAZ forecasting.", keywords = "genetic algorithms, genetic programming, Extreme Learning Machine, Forecasting, HAZ, Laser cutting", } @InProceedings{Anjum:2019:EuroGP, author = "Muhammad Sheraz Anjum and Conor Ryan", title = "Ariadne: Evolving test data using Grammatical Evolution", booktitle = "EuroGP 2019: Proceedings of the 22nd European Conference on Genetic Programming", year = "2019", month = "24-26 " # apr, editor = "Lukas Sekanina and Ting Hu and Nuno Lourenco", series = "LNCS", volume = "11451", publisher = "Springer Verlag", address = "Leipzig, Germany", pages = "3--18", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, SBSE, SBST", isbn13 = "978-3-030-16669-4", URL = "https://www.springer.com/us/book/9783030166694", DOI = "doi:10.1007/978-3-030-16670-0_1", size = "16 pages", abstract = "Software testing is a key component in software quality assurance; it typically involves generating test data that exercises all instructions and tested conditions in a program and, due to its complexity, can consume as much as 50percent of overall software development budget. Some evolutionary computing techniques have been successfully applied to automate the process of test data generation but no existing techniques exploit variable interdependencies in the process of test data generation, even though several studies from the software testing literature suggest that the variables examined in the branching conditions of real life programs are often interdependent on each other, for example, if (x == y), etc. We propose the Ariadne system which uses Grammatical Evolution (GE) and a simple Attribute Grammar to exploit the variable interdependencies in the process of test data generation. Our results show that Ariadne dramatically improves both effectiveness and efficiency when compared with existing techniques based upon well-established criteria, attaining coverage (the standard software testing success metric for these sorts of problems) of 100percent on all benchmarks with far fewer program evaluations (often between a third and a tenth of other systems).", notes = "http://www.evostar.org/2019/cfp_eurogp.php#abstracts Part of \cite{Sekanina:2019:GP} EuroGP'2019 held in conjunction with EvoCOP2019, EvoMusArt2019 and EvoApplications2019", } @InProceedings{Anjum:2019:SSCI, author = "Aftab Anjum and Mazharul Islam and Lin Wang", booktitle = "2019 IEEE Symposium Series on Computational Intelligence (SSCI)", title = "Gene Permutation: A new Probabilistic Genetic Operator for Improving Multi Expression Programming", year = "2019", pages = "3139--3146", month = dec, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SSCI44817.2019.9003048", abstract = "Multi-expression Programming (MEP) encodes multiple genes through linear representation and is a widely useful technique for tangible applications like classification, symbolic regression and digital circuit designing. MEP uses only two genetic operators (mutation, crossover) to explore the search space and exploit genetic materials. However, after going through multiple generations and due to its naturally inspired fitness-based selection procedure, MEP significantly reduces genetic diversity in the population and ultimately produces homogeneous individuals; hence, leading to poor convergence and an ultimate fall into the local minimum. Gene-permutation, the newly proposed Probabilistic Genetic Operator, breakouts the homogeneity by rearranging and inducing new genetic materials in the individuals which in turn maintains the healthy genetic diversity in the population. Moreover, it also assists other genetic operators to produce more effective chromosomes and fully explore the search space. The experiments point out that Gene-permutation improves training efficiency as well as reduces test errors on several well-known symbolic regression problems.", notes = "Also known as \cite{9003048}", } @InProceedings{Anjum:2020:EuroGP, author = "Muhammad Sheraz Anjum and Conor Ryan", title = "Seeding Grammars in Grammatical Evolution to Improve Search Based Software Testing", booktitle = "EuroGP 2020: Proceedings of the 23rd European Conference on Genetic Programming", year = "2020", month = "15-17 " # apr, editor = "Ting Hu and Nuno Lourenco and Eric Medvet", series = "LNCS", volume = "12101", publisher = "Springer Verlag", address = "Seville, Spain", pages = "18--34", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, SBSE, SBST, Automatic test case generation, Code coverage, Evolutionary Testing", isbn13 = "978-3-030-44093-0", video_url = "https://www.youtube.com/watch?v=e2T-NYqEvh8", DOI = "doi:10.1007/978-3-030-44094-7_2", abstract = "Software-based optimization techniques have been increasingly used to automate code coverage analysis since the nineties. Although several studies suggest that interdependencies can exist between condition constructs in branching conditions of real life programs e.g. (i<=100) or (i==j), etc., to date, only the Ariadne system, a Grammatical Evolution (GE)-based Search Based Software Testing (SBST) technique, exploits interdependencies between variables to efficiently automate code coverage analysis. Ariadne employs a simple attribute grammar to exploit these dependencies, which enables it to very efficiently evolve highly complex test cases, and has been compared favourably to other well-known techniques in the literature. However, Ariadne does not benefit from the interdependencies involving constants e.g. (i<=100), which are equally important constructs of condition predicates. Furthermore, constant creation in GE can be difficult, particularly with high precision. ...", notes = "http://www.evostar.org/2020/cfp_eurogp.php Part of \cite{Hu:2020:GP} EuroGP'2020 held in conjunction with EvoCOP2020, EvoMusArt2020 and EvoApplications2020", } @InProceedings{Anjum:2020:GECCO, author = "Muhammad Sheraz Anjum and Conor Ryan", title = "Scalability Analysis of Grammatical Evolution Based Test Data Generation", year = "2020", editor = "Carlos Artemio {Coello Coello} and Arturo Hernandez Aguirre and Josu Ceberio Uribe and Mario Garza Fabre and Gregorio {Toscano Pulido} and Katya Rodriguez-Vazquez and Elizabeth Wanner and Nadarajen Veerapen and Efren Mezura Montes and Richard Allmendinger and Hugo Terashima Marin and Markus Wagner and Thomas Bartz-Beielstein and Bogdan Filipic and Heike Trautmann and Ke Tang and John Koza and Erik Goodman and William B. Langdon and Miguel Nicolau and Christine Zarges and Vanessa Volz and Tea Tusar and Boris Naujoks and Peter A. N. Bosman and Darrell Whitley and Christine Solnon and Marde Helbig and Stephane Doncieux and Dennis G. Wilson and Francisco {Fernandez de Vega} and Luis Paquete and Francisco Chicano and Bing Xue and Jaume Bacardit and Sanaz Mostaghim and Jonathan Fieldsend and Oliver Schuetze and Dirk Arnold and Gabriela Ochoa and Carlos Segura and Carlos Cotta and Michael Emmerich and Mengjie Zhang and Robin Purshouse and Tapabrata Ray and Justyna Petke and Fuyuki Ishikawa and Johannes Lengler and Frank Neumann", isbn13 = "9781450371285", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377930.3390167", DOI = "doi:10.1145/3377930.3390167", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference", pages = "1213--1221", size = "9 pages", keywords = "genetic algorithms, genetic programming, grammatical evolution, code coverage analysis, scalability, software testing, search based software testing, automatic test data generation, evolutionary testing, variable interdependencies", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", abstract = "Heuristic-based search techniques have been increasingly used to automate different aspects of software testing. Several studies suggest that variable interdependencies may exist in branching conditions of real-life programs, and these dependencies result in the need for highly precise data values (such as of the form i=j=k) for code coverage analysis. This requirement makes it very difficult for Genetic Algorithm (GA)-based approach to successfully search for the required test data from vast search spaces of real-life programs. Ariadne is the only Grammatical Evolution (GE)-based test data generation system, proposed to date, that uses grammars to exploit variable interdependencies to improve code coverage. Ariadne has been compared favourably to other well-known test data generation techniques in the literature; however, its scalability has not yet been tested for increasingly complex programs. This paper presents the results of a rigorous analysis performed to examine Ariadne's scalability. We also designed and employed a large set of highly scalable 18 benchmark programs for our experiments. Our results suggest that Ariadne is highly scalable as it exhibited 100percent coverage across all the programs of increasing complexity with significantly smaller search costs than GA-based approaches, which failed even with huge search budgets.", notes = "Also known as \cite{10.1145/3377930.3390167} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @Article{Anjum:2021:SNCS, author = "Muhammad Sheraz Anjum and Conor Ryan", title = "Seeding Grammars in Grammatical Evolution to Improve Search-Based Software Testing", journal = "SN Computer Science", year = "2021", volume = "2", pages = "280", keywords = "genetic algorithms, genetic programming, Grammatical evolution, Automatic test data generation, Code coverage, Evolutionary testing", DOI = "doi:10.1007/s42979-021-00631-7", size = "19 pages", abstract = "Heuristic-based optimization techniques have been increasingly used to automate different types of code coverage analysis. Several studies suggest that interdependencies (in the form of comparisons) may exist between the condition constructs, of variables and constant values, in the branching conditions of real-world programs, e.g. ( i≤100 ) or ( i==j ), etc. In this work, by interdependencies we refer to the situations where, to satisfy a branching condition, there must be a certain relation-ship between the values of some specific condition constructs (which may or may not be a part of the respective condition predicates). For example, the values of variables i and j must be equal to satisfy the condition of ( i==j ), and the value of variable k must be equal to 100 for the satisfaction of the condition of ( k==100 ). To date, only the Ariadne, a Grammatical Evolution (GE)-based system, exploits these interdependencies between input variables (e.g. of the form ( i≤j ) or ( i==j ), etc.) to efficiently generate test data. Ariadne employs a simple attribute grammar to exploit these dependencies, which enables it to evolve complex test data, and has been compared favourably to other well-known techniques in the literature. However, Ariadne does not benefit from interdependencies involving constants, e.g. ( i≤100 ) or ( j==500 ), etc., due to the difficulty in evolving precise values, and these are equally important constructs of condition predicates. Furthermore, constant creation in GE can be difficult, particularly with high precision. We propose to seed the grammar with constants extracted from the source code of the program under test to enhance and extend Ariadne capability to exploit richer types of dependencies (involving all combinations of both variables and constant values). We compared our results with the original system of Ariadne against a large set of benchmark problems which include 10 numeric programs in addition to the ones originally used for Ariadne. Our results demonstrate that the seeding strategy not only dramatically improves the generality of the system, as it improves the code coverage (effectiveness) by impressive margins, but it also reduces the search budgets (efficiency) often up to an order of magnitude. Moreover, we also performed a rigorous analysis to investigate the scalability of our improved Ariadne, showing that it stays highly scalable when compared to both the original system of Ariadne and GA-based test data generation approach", } @Misc{DBLP:journals/corr/abs-1904-03368, author = "Aftab Anjum and Fengyang Sun and Lin Wang and Jeff Orchard", title = "A Novel Continuous Representation of Genetic Programmings using Recurrent Neural Networks for Symbolic Regression", howpublished = "arXiv", volume = "abs/1904.03368", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "http://arxiv.org/abs/1904.03368", archiveprefix = "arXiv", eprint = "1904.03368", timestamp = "Fri, 11 Oct 2019 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/corr/abs-1904-03368.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{ANLAUF:2022:procs, author = "Stefan Anlauf and Andreas Haghofer and Karl Dirnberger and Stephan M. Winkler", title = "Using Heterogeneous Model Ensembles to Improve the Prediction of Yeast Contamination in Peppermint", journal = "Procedia Computer Science", volume = "200", pages = "1194--1200", year = "2022", note = "3rd International Conference on Industry 4.0 and Smart Manufacturing", ISSN = "1877-0509", DOI = "doi:10.1016/j.procs.2022.01.319", URL = "https://www.sciencedirect.com/science/article/pii/S1877050922003283", keywords = "genetic algorithms, genetic programming, yeast contamination, herbs, machine learning, heterogeneous model ensembles", abstract = "In this paper, we present an heterogeneous ensemble modeling approach to learn predictors for yeast contamination in freshly harvested peppermint batches. Our research is based on data about numerous parameters of the harvesting process, such as planting, tillage, fertilization, harvesting, drying, as well as information about microbial contamination. We use several different machine learning methods, namely random forests, gradient boosting trees, symbolic regression by genetic programming, and support vector machines to learn models that predict contamination on the basis of available harvesting parameters. Using those models we form model ensembles in order to improve the accuracy as well as to reduce the false negative rate, i.e., to oversee as few contaminations as possible. As we summarize in this paper, ensemble modeling indeed helps to increase the prediction accuracy for our application, especially when using only the best models. The final prediction accuracy as well as other statistical indicators such as false negative rate and false positive rate depend on the choice of the discrimination threshold; in the optimal case, model ensembles are able to predict yeast contamination with 65.91percent accuracy and only 19.15percent of the samples are false negative, i.e., overseen contaminations", } @Article{AnnunziatoL2003:ICAE, author = "Mauro Annunziato and Carlo Bruni and Matteo Lucchetti and Stefano Pizzuti", title = "Artificial Life Approach for Continuous Optimisation of Non Stationary Dynamical Systems", journal = "Integrated Computer-Aided Engineering", year = "2003", volume = "10", number = "2", pages = "111--125", email = "lucchetti@dis.uniroma1.it", keywords = "genetic algorithms, genetic programming, artificial life", ISSN = "1069-2509", URL = "http://content.iospress.com/articles/integrated-computer-aided-engineering/ica00140", DOI = "doi:10.3233/ICA-2003-10202", size = "15 pages", abstract = "In this paper, we develop an intelligent system to approach dynamical optimisation problems emerging in control of complex systems. In particular our proposal is to exploit the adaptivity of an artificial life (alife) environment in order to achieve 'not control rules but autonomous structures able to dynamically adapt and to generate optimised-control rules'. The basic features of the proposed approach are: no intensive modelling (continuous learning directly from measurements) and capability to follow the system evolution (adaptation to environmental changes). The suggested methodology has been tested on an energy regulation problem deriving from a classical testbed in dynamical systems experimentations: the Chua's circuit. We supposed not to know the system dynamics and to be able to act only on a subset of control parameters, letting the others vary in time in a random discrete way. We let the optimisation process searching for the new best value of performance, whenever a drop due to changes in fitness landscape occurred. We present the most important results showing the effectiveness of the proposed approach in adapting to environmental non-stationary changes by recovering the optimal value of process performance.", } @InProceedings{DBLP:conf/pricai/ArdehMZ19, author = "Mazhar {Ansari Ardeh} and Yi Mei and Mengjie Zhang", editor = "Abhaya C. Nayak and Alok Sharma", title = "A Novel Genetic Programming Algorithm with Knowledge Transfer for Uncertain Capacitated Arc Routing Problem", booktitle = "{PRICAI} 2019: Trends in Artificial Intelligence - 16th Pacific Rim International Conference on Artificial Intelligence, Cuvu, Yanuca Island, Fiji, August 26-30, 2019, Proceedings, Part {I}", series = "Lecture Notes in Computer Science", volume = "11670", pages = "196--200", publisher = "Springer", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/978-3-030-29908-8_16", DOI = "doi:10.1007/978-3-030-29908-8_16", timestamp = "Mon, 15 Jun 2020 01:00:00 +0200", biburl = "https://dblp.org/rec/conf/pricai/ArdehMZ19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{AnsariArdeh:2019:CEC, author = "Mazhar {Ansari Ardeh} and Yi Mei and Mengjie Zhang", title = "Transfer Learning in Genetic Programming Hyper-heuristic for Solving Uncertain Capacitated Arc Routing Problem", booktitle = "2019 IEEE Congress on Evolutionary Computation, CEC 2019", year = "2019", editor = "Carlos A. Coello Coello", pages = "49--56", address = "Wellington, New Zealand", month = "10-13 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-7281-2152-6", DOI = "doi:10.1109/CEC.2019.8789920", size = "8 pages", abstract = "Uncertain Capacitated Arc Routing Problem (UCARP) is a combinatorial optimization problem that has many important real-world applications. Genetic programming (GP) is a powerful machine learning technique that has been successfully used to automatically evolve routing policies for UCARP. Generalisation is an open issue in the field of UCARP and in this direction, an open challenge is the case of changes in number of vehicles which currently leads to new training procedures to be initiated. Considering the expensive training cost of evolving routing policies for UCARP, a promising strategy is to learn and reuse knowledge from a previous problem solving process to improve the effectiveness and efficiency of solving a new related problem, i.e. transfer learning. Since none of the existing GP transfer methods have been used as a hyper-heuristic in solving UCARP, we conduct a comprehensive study to investigate the behaviour of the existing GP transfer methods for evolving routing policy in UCARP,", notes = "also known as \cite{8789920} IEEE Catalog Number: CFP19ICE-ART", } @InProceedings{AnsariArdeh:2019:GECCOcomp, author = "Mazhar {Ansari Ardeh} and Yi Mei and Mengjie Zhang", title = "Genetic programming hyper-heuristic with knowledge transfer for uncertain capacitated arc routing problem", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", isbn13 = "978-1-4503-6748-6", pages = "334--335", address = "Prague, Czech Republic", DOI = "doi:10.1145/3319619.3321988", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{3321988} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InProceedings{Ansel:2011:GECCO, author = "Jason Ansel and Maciej Pacula and Saman Amarasinghe and Una-May O'Reilly", title = "An efficient evolutionary algorithm for solving incrementally structured problems", booktitle = "GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0557-0", pages = "1699--1706", keywords = "genetic algorithms, genetic programming, SBSE, Real world applications", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001576.2001805", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Many real world problems have a structure where small problem instances are embedded within large problem instances, or where solution quality for large problem instances is loosely correlated to that of small problem instances. This structure can be exploited because smaller problem instances typically have smaller search spaces and are cheaper to evaluate. We present an evolutionary algorithm, INCREA, which is designed to incrementally solve a large, noisy, computationally expensive problem by deriving its initial population through recursively running itself on problem instances of smaller sizes. The INCREA algorithm also expands and shrinks its population each generation and cuts off work that doesn't appear to promise a fruitful result. For further efficiency, it addresses noisy solution quality efficiently by focusing on resolving it for small, potentially reusable solutions which have a much lower cost of evaluation. We compare INCREA to a general purpose evolutionary algorithm and find that in most cases INCREA arrives at the same solution in significantly less time.", notes = "Research compiler petabricks. GPEA. Aim: autotuning for computer when program when is actually installed on that computer. Looks at recursive sort and which chooses one of 4 types of sort (Insertion sort, quick sort, radix sort and a dummy) to use at each level of recursion. Noisy fitness evaluation (run for real, not simulation). uses T-test (trying to be too fair?). Examples: sort, matrix multiply (matmult) and eig (symmetric eigen problem). Also known as \cite{2001805} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)", } @InProceedings{DBLP:conf/IEEEpact/AnselKVRBOA14, author = "Jason Ansel and Shoaib Kamil and Kalyan Veeramachaneni and Jonathan Ragan-Kelley and Jeffrey Bosboom and Una-May O'Reilly and Saman P. Amarasinghe", title = "{OpenTuner}: an extensible framework for program autotuning", booktitle = "International Conference on Parallel Architectures and Compilation, PACT '14", year = "2014", editor = "Jose Nelson Amaral and Josep Torrellas", pages = "303--316", address = "Edmonton, Canada", month = aug # " 24-27", publisher = "ACM", keywords = "genetic algorithms, genetic programming, genetic improvement, program autotuning, multi-armed bandit problem", timestamp = "Thu, 24 Nov 2022 09:18:38 +0100", biburl = "https://dblp.org/rec/conf/IEEEpact/AnselKVRBOA14.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://doi.org/10.1145/2628071.2628092", DOI = "doi:10.1145/2628071.2628092", code_url = "http://opentuner.org/", size = "13 pages", abstract = "Program autotuning has been shown to achieve better or more portable performance in a number of domains. However, autotuners themselves are rarely portable between projects, for a number of reasons: using a domain-informed search space representation is critical to achieving good results; search spaces can be intractably large and require advanced machine learning techniques; and the landscape of search spaces can vary greatly between different problems, sometimes requiring domain specific search techniques to explore efficiently. This paper introduces OpenTuner, a new open source framework for building domain-specific multi-objective program autotuners. OpenTuner supports fully-customizable configuration representations, an extensible technique representation to allow for domain-specific techniques, and an easy to use interface for communicating with the program to be autotuned. A key capability inside OpenTuner is the use of ensembles of disparate search techniques simultaneously; techniques that perform well will dynamically be allocated a larger proportion of tests. We demonstrate the efficacy and generality of OpenTuner by building auto-tuners for 7 distinct projects and 16 total benchmarks, showing speedups over prior techniques of these projects of up to 2.8 fold with little programmer effort.", notes = "Not GP? GCC_FLAGS GCC_PARAMS. AUC Bandit Multiple simultaneous search. time, accuracy, energy, size, confidence. GCC/G++ Flags: fft.c, matrixmultiply.cpp, raytracer.cpp, tsp_ga.cpp Halide: Blur, Wavelet, Bilateral. Linpack PetaBricks: Poisson, Sort, Strassen, Tridiagonal Solver. Stencil, Laplacian, Stencil, Unitary Matrix, Mario", } @Misc{DBLP:journals/corr/abs-2305-16956, author = "Fabio Anselmi and Mauro Castelli and Alberto d'Onofrio and Luca Manzoni and Luca Mariot and Martina Saletta", title = "Local Search, Semantics, and Genetic Programming: a Global Analysis", howpublished = "arXiv", volume = "abs/2305.16956", year = "2023", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.48550/arXiv.2305.16956", DOI = "doi:10.48550/ARXIV.2305.16956", eprinttype = "arXiv", eprint = "2305.16956", timestamp = "Mon, 05 Feb 2024 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/corr/abs-2305-16956.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{Anthes:2009:ACM, author = "Gary Anthes", title = "Deep Data Dives Discover Natural Laws", journal = "Communications of the ACM", year = "2009", volume = "52", number = "11", pages = "13--14", month = nov, note = "News", keywords = "genetic algorithms, genetic programming", ISSN = "0001-0782", URL = "http://cacm.acm.org/magazines/2009/11/48443-deep-data-dives-discover-natural-laws/pdf", DOI = "doi:10.1145/1592761.1592768", size = "2 pages", abstract = "Computer scientists have found a way to bootstrap science, using evolutionary computation to find fundamental meaning in massive amounts of raw data. Mining scientific data for patterns and relationships has been a common practice for decades, and the use of self-mutating genetic algorithms is nothing new, either. But now a pair of computer scientists at Cornell University have pushed these techniques into an entirely new realm, one that could fundamentally transform the methods of science at the frontiers of research.", notes = "Report on \cite{Science09:Schmidt}", } @MastersThesis{hdl:1860/18, title = "Evolving board evaluation fuctions for a complex strategy game", author = "Lisa Patricia Anthony", year = "2002", month = dec # "~30", language = "en_US", school = "Drexel University", keywords = "genetic algorithms, genetic programming", URL = "http://dspace.library.drexel.edu/handle/1721.1/18", URL = "http://dspace.library.drexel.edu/bitstream/1860/18/1/anthony_thesis.pdf", size = "73 pages", abstract = "The development of board evaluation functions for complex strategy games has been approached in a variety of ways. The analysis of game interactions is recognized as a valid analogy to common real-world problems, which often present difficulty in designing algorithms to solve them. Genetic programming, as a branch of evolutionary computation, provides advantages over traditional algorithms in solving these complex real-world problems in speed, robustness and flexibility. This thesis attempts to address the problem of applying genetic programming techniques to the evolution of a strategy for evaluating potential moves in a one-step lookahead intelligent agent heuristic for a complex strategybased game. This is meant to continue the work in artificial intelligence which seeks to provide computer systems with the tools they need to learn how to operate within a domain, given only the basic building blocks. The issues surrounding this problem are formulated and techniques are presented within the realm of genetic programming which aim to contribute to the solution of this problem. The domain chosen is the strategy game known as Acquire, whose object is to amass wealth while investing stock in hotel chains and effecting mergers of these chains as they grow. The evolution of the board evaluation functions to be used by agent players of the game is accomplished via genetic programming. Implementation details are discussed, empirical results are presented, and the strategies of some of the best players are analyzed. Future improvements on these techniques within this domain are outlined, as well as implications for artificial intelligence and genetic programming.", notes = "format = 318461", } @Article{Anthony:2017/08/facebook, author = "Sebastian Anthony", title = "{Facebook}'s evolutionary search for crashing software bugs", journal = "ars technica UK", year = "2017", month = "22 " # aug # ", 07:52", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, SBSE, Facebook, Sapienz", URL = "https://arstechnica.co.uk/information-technology/2017/08/facebook-dynamic-analysis-software-sapienz/", video_url = "https://developers.facebook.com/videos/f8-2018/friction-free-fault-finding-with-sapienz/", size = "2 pages", abstract = "Ars gets the first look at Facebook's fancy new dynamic analysis tool.", notes = "Is this GP? Cites Ke Mao, Mark Harman, Yue Jia. Sapienz: Multi-objective automated testing for Android applications. In International Symposium on Software Testing and Analysis (ISSTA, 2016). pp. 94-105 http://www.cs.ucl.ac.uk/staff/K.Mao/archive/p_issta16_sapienz.pdf facebook video https://developers.facebook.com/videos/f8-2018/friction-free-fault-finding-with-sapienz/", } @MastersThesis{antolik:mastersthesis, author = "Jan Antolik", title = "Evolutionary Tree Genetic Programming", school = "Department of Computing and Information Sciences, College of Arts and Sciences, Kansan State University", year = "2004", type = "Master of Science", address = "Manhattan, Kansas, USA", keywords = "genetic algorithms, genetic programming", URL = "http://www.ms.mff.cuni.cz/~antoj9am/thesis.pdf", size = "49 pages", abstract = "We introduce an extension of a genetic programming (GP) algorithm we call Evolutionary Tree Genetic Programming (ETGP). The biological motivation behind this work is the observation that the natural evolution follows a tree like pattern. We want to simulate similar behaviour in artificial evolutionary systems such as GP. In this thesis we provide multiple reasons why we believe simulation of this phenomenon can be beneficial for GP systems. We present various empirical results from test runs. As the test bed for our experiments two standard benchmark problems for GP systems are used, particularly the Artificial Ant problem and the Multiplexer problem. The performance of the ETGP algorithm is compared to the performance of GP system. Unfortunately no significant speedup is found. Some unexpected behaviors of our system are also identified, and a hypothesis is formulated that addresses the question of why we observe this strange behaviour and the lack of speedup. Suggestions on how to extend the ETGP system to overcome the problems identified by this hypothesis are then presented in the end of our concluding chapter.", notes = "Approved by: Major Professor William Hsu", } @InProceedings{1068312, author = "Jan Antolik and William H. Hsu", title = "Evolutionary tree genetic programming", booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation", year = "2005", editor = "Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and Edwin D. {de Jong} and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler", volume = "2", ISBN = "1-59593-010-8", pages = "1789--1790", address = "Washington DC, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2005/docs/p1789.pdf", DOI = "doi:10.1145/1068009.1068312", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "25-29 " # jun, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Poster", abstract = "We introduce a clustering-based method of subpopulation management in genetic programming (GP) called Evolutionary Tree Genetic Programming (ETGP). The biological motivation behind this work is the observation that the natural evolution follows a tree-like phylogenetic pattern. Our goal is to simulate similar behavior in artificial evolutionary systems such as GP. To test our model we use three common GP benchmarks: the Ant Algorithm, 11-Multiplexer, and Parity problems.The performance of the ETGP system is empirically compared to those of the GP system. Code size and variance are consistently reduced by a small but statistically significant percentage, resulting in a slight speedup in the Ant and 11-Multiplexer problems, while the same comparisons on the Parity problem are inconclusive.", notes = "GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM Order Number 910052", } @InProceedings{Antonelli:2013:NAFIPS, author = "Michela Antonelli and Pietro Ducange and Francesco Marcelloni and Armando Segatori", title = "Evolutionary Fuzzy Classifiers for Imbalanced Datasets: An Experimental Comparison", booktitle = "Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS 2013)", year = "2013", month = "24-28 " # jun, pages = "13--18", address = "Edmonton, Canada", keywords = "genetic algorithms, genetic programming, database management systems, fuzzy set theory, learning (artificial intelligence), pattern classification, statistical testing, EFC, FRBC, ROC curve, complexity optimisation, embedded rule base generation, evolutionary data base learning, evolutionary fuzzy classifiers, genetic programming algorithm, genetic rule selection, hierarchical fuzzy rule-based classifier, imbalanced datasets, membership function parameters tuning, multiobjective evolutionary learning scheme, nonparametric statistical tests, rule base learning, sensitivity optimisation, specificity optimisation, Accuracy, Biological cells, Complexity theory, Genetics, Input variables, Training, Tuning, Fuzzy Rule-based Classifiers, Genetic and Evolutionary Fuzzy Systems, Imbalanced Datasets", isbn13 = "978-1-4799-0348-1", DOI = "doi:10.1109/IFSA-NAFIPS.2013.6608367", size = "6 pages", abstract = "In this paper, we compare three state-of-the-art evolutionary fuzzy classifiers (EFCs) for imbalanced datasets. The first EFC performs an evolutionary data base learning with an embedded rule base generation. The second EFC builds a hierarchical fuzzy rule-based classifier (FRBC): first, a genetic programming algorithm is used to learn the rule base and then a post-process, which includes a genetic rule selection and a membership function parameters tuning, is applied to the generated FRBC. The third EFC is an extension of a multi-objective evolutionary learning scheme we have recently proposed: the rule base and the membership function parameters of a set of FRBCs are concurrently learnt by optimising the sensitivity, the specificity and the complexity. By performing non-parametric statistical tests, we show that, without re-balancing the training set, the third EFC outperforms, in terms of area under the ROC curve, the other comparison approaches.", notes = "Also known as \cite{6608367}", } @InProceedings{conf/setn/AntoniouGTVL10, title = "A Gene Expression Programming Environment for Fatigue Modeling of Composite Materials", author = "Maria A. Antoniou and Efstratios F. Georgopoulos and Konstantinos A. Theofilatos and Anastasios P. Vassilopoulos and Spiridon D. Likothanassis", booktitle = "6th Hellenic Conference on Artificial Intelligence: Theories, Models and Applications (SETN 2010)", year = "2010", volume = "6040", editor = "Stasinos Konstantopoulos and Stavros J. Perantonis and Vangelis Karkaletsis and Constantine D. Spyropoulos and George A. Vouros", pages = "297--302", series = "Lecture Notes in Computer Science", address = "Athens, Greece", month = may # " 4-7", publisher = "Springer", isbn13 = "978-3-642-12841-7", keywords = "genetic algorithms, genetic programming", bibdate = "2010-05-11", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/setn/setn2010.html#AntoniouGTVL10", DOI = "doi:10.1007/978-3-642-12842-4", abstract = "In the current paper is presented the application of a Gene Expression Programming Environment in modeling the fatigue behavior of composite materials. The environment was developed using the JAVA programming language, and is an implementation of a variation of Gene Expression Programming. Gene Expression Programming (GEP) is a new evolutionary algorithm that evolves computer programs (they can take many forms: mathematical expressions, neural networks, decision trees, polynomial constructs, logical expressions, and so on). The computer programs of GEP, irrespective of their complexity, are all encoded in linear chromosomes. Then the linear chromosomes are expressed or translated into expression trees (branched structures). Thus, in GEP, the genotype (the linear chromosomes) and the phenotype (the expression trees) are different entities (both structurally and functionally). This is the main difference between GEP and classical tree based Genetic Programming techniques. In order to evaluate the performance of the presented environment, we tested it in fatigue modeling of composite materials.", } @InProceedings{Antoniou:2010:AIAI, author = "Maria Antoniou and Efstratios Georgopoulos and Konstantinos Theofilatos and Spiridon Likothanassis", title = "Forecasting Euro - United States Dollar Exchange Rate with Gene Expression Programming", booktitle = "6th IFIP Advances in Information and Communication Technology AIAI 2010", year = "2010", editor = "Harris Papadopoulos and Andreas Andreou and Max Bramer", volume = "339", series = "IFIP Advances in Information and Communication Technology", pages = "78--85", address = "Larnaca, Cyprus", month = oct # " 6-7", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Gene Expression Programming", DOI = "doi:10.1007/978-3-642-16239-8_13", abstract = "In the current paper we present the application of our Gene Expression Programming Environment in forecasting Euro-United States Dollar exchange rate. Specifically, using the GEP Environment we tried to forecast the value of the exchange rate using its previous values. The data for the EURO-USD exchange rate are online available from the European Central Bank (ECB). The environment was developed using the JAVA programming language, and is an implementation of a variation of Gene Expression Programming. Gene Expression Programming (GEP) is a new evolutionary algorithm that evolves computer programs (they can take many forms: mathematical expressions, neural networks, decision trees, polynomial constructs, logical expressions, and so on). The computer programs of GEP, irrespective of their complexity, are all encoded in linear chromosomes. Then the linear chromosomes are expressed or translated into expression trees (branched structures). Thus, in GEP, the genotype (the linear chromosomes) and the phenotype (the expression trees) are different entities (both structurally and functionally). This is the main difference between GEP and classical tree based Genetic Programming techniques.", affiliation = "Pattern Recognition Laboratory, Dept. of Computer Engineering & Informatics, University of Patras, 26500 Patras, Greece", notes = "http://www.cs.ucy.ac.cy/aiai2010/", } @InProceedings{foga90*193, author = "Hendrik James Antonisse", title = "A Grammar-Based Genetic Algorithm", pages = "193--204", ISBN = "1-55860-170-8", editor = "Gregory J. E. Rawlins", booktitle = "Foundations of Genetic Algorithms", month = "15--18 " # jul # " 1990", address = "Indiana University, Bloomington, USA", publisher = "Morgan Kaufmann", publisher_address = "San Mateo", keywords = "genetic algorithms, genetic programming, inductive bias, high-level representations, crossover", year = "1991", DOI = "doi:10.1016/B978-0-08-050684-5.50015-X", abstract = "High-level syntactically-based representations pose problems for applying the GA because it is hard to construct crossover operators that always result in legal offspring. This paper proposes a reformulation of the genetic algorithm that makes it appropriate to any representation that can be cast in a formal grammar. This reformulation is consistent with recent reinterpretations of GA foundations in set-theoretic terms, and concentrates on the modifications required to make the space of legal structures closed under the crossover operator. The analysis places no restriction on the form of the grammars.", notes = "FOGA-90 Published in 1991. cited by \cite{bruhn:2002:ECJ} grammar-based crossover, parity. K-armed bandit", } @InProceedings{antonov:2024:GECCO, author = "Kirill Antonov and Roman Kalkreuth and Kaifeng Yang and Thomas Baeck and Niki Stein and Anna Kononova", title = "A Functional Analysis Approach to Symbolic Regression", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference", year = "2024", editor = "Ting Hu and Aniko Ekart and Julia Handl and Xiaodong Li and Markus Wagner and Mario Garza-Fabre and Kate Smith-Miles and Richard Allmendinger and Ying Bi and Grant Dick and Amir H Gandomi and Marcella Scoczynski Ribeiro Martins and Hirad Assimi and Nadarajen Veerapen and Yuan Sun and Mario Andres Munyoz and Ahmed Kheiri and Nguyen Su and Dhananjay Thiruvady and Andy Song and Frank Neumann and Carla Silva", pages = "859--867", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, symbolic regression, functional analysis, hilbert space optimization", isbn13 = "979-8-4007-0494-9", URL = "https://arxiv.org/abs/2402.06299", DOI = "doi:10.1145/3638529.3654079", size = "9 pages", abstract = "Symbolic regression (SR) poses a significant challenge for randomized search heuristics due to its reliance on the synthesis of expressions for input-output mappings. Although traditional genetic programming (GP) algorithms have achieved success in various domains, they exhibit limited performance when tree-based representations are used for SR. To address these limitations, we introduce a novel SR approach called Fourier Tree Growing (FTG) that draws insights from functional analysis. This new perspective enables us to perform optimization directly in a different space, thus avoiding intricate symbolic expressions. Our proposed algorithm exhibits significant performance improvements over traditional GP methods on a range of classical one-dimensional benchmarking problems. To identify and explain the limiting factors of GP and FTG, we perform experiments on a large-scale polynomials benchmark with high-order polynomials up to degree 100. To the best of the authors' knowledge, this work represents the pioneering application of functional analysis in addressing SR problems. The superior performance of the proposed algorithm and insights into the limitations of GP open the way for further advancing GP for SR and related areas of explainable machine learning.", notes = "GECCO-2024 GP A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{Antony:2023:ICCCNT, author = "Divya Antony and Naseer C", booktitle = "2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)", title = "Comparison of {CNN} and {YOLOv5} For Melanoma Detection", year = "2023", abstract = "MELANOMA is one of the most dangerous forms of skin cancer that results from melanocytes, which produce the brown pigment that gives skin its colour. Sometimes it forms multi-colours based on its stage. The survival of melanoma patients depends on the early identification of the disease. But in the early stage, it becomes very small and does not meet the dermoscopic standards for cancer detection such as irregular shape, network, colour of pigments and also it is difficult to differentiate lesion to benign and melanoma. So early detection of melanoma is difficult. Therefore, we need an accurate melanoma classifier that classifies lesions at begin of the stage. In this paper, a comprehensive analysis of cnn, Alexnet and yolov5 for melanoma detection is performed using Accuracy, Precision, Recall and F1 Score", keywords = "genetic algorithms, genetic programming, Multi tree genetic programming, Location awareness, Shape, Melanoma, Colour, Pigments, Skin, Lesions, You only look once, Regional convolutional neural network", DOI = "doi:10.1109/ICCCNT56998.2023.10307675", ISSN = "2473-7674", month = jul, notes = "Also known as \cite{10307675}", } @Article{Anwar:2024:Structures, author = "Mohamed M. Anwar and Mohamed K. Ismail and Hossam A. Hodhod and Wael El-Dakhakhni and Hatem H. A. Ibrahim", title = "Mechanics guided data-driven model for seismic shear strength of exterior beam-column joints", journal = "Structures", year = "2024", volume = "69", pages = "107320", keywords = "genetic algorithms, genetic programming, Exterior beam-column joints, Seismic shear strength, Mechanics, Variables selection, Multi-gene genetic programming, Predictions, Sensitivity analyses", ISSN = "2352-0124", URL = "https://www.sciencedirect.com/science/article/pii/S2352012424014723", DOI = "doi:10.1016/j.istruc.2024.107320", abstract = "This study presents an enhanced predictive model for the seismic shear strength of exterior beam-column joints (BCJs). Initially, the principles of strut-and-tie mechanism and variable selection procedures were first used to identify the most influential parameters. Subsequently, an evolutionary algorithm, specifically multigene genetic programming (MGGP), was used to search for the near-optimal predictive model. The dataset used to develop, train, and test the proposed model was compiled from previously published tests, focusing specifically on cyclically loaded exterior BCJs that encountered shear and flexure -shear failures. The prediction performance of the developed model was assessed through various statistical measures, and then compared with that of other existing models. Additionally, sensitivity analyses were also performed to identify the influence and importance of each design parameter. The results demonstrated that the methodology employed in this study yielded an elegant model that adheres to the underlying mechanics and provides higher prediction accuracy compared to existing models. Furthermore, the sensitivity analyses showed that BCJ shear strength positively correlates with concrete compressive strength, beam reinforcement, joint transverse reinforcement, column intermediate vertical reinforcement, and axial load ratio, while it negatively correlates with the joint aspect ratio. Among these design parameters, beam reinforcement has the greatest influence on the model response, followed by concrete compressive strength. Conversely, column intermediate vertical reinforcement and axial load ratio have the least impact on the model response. The notable prediction capabilities and robustness demonstrated by the developed model render it an efficient design tool with promising potentials for adoption by practicing engineers and for consideration in design guidelines", } @InProceedings{ICIP99_Vol1*529, author = "Shinya Aoki and Tomoharu Nagao", title = "Automatic construction of tree-structural image transformation using genetic programming", pages = "529--533", booktitle = "Proceedings of the 1999 International Conference on Image Processing ({ICIP}-99)", month = oct # " ~24--28", publisher = "IEEE", year = "1999", volume = "1", address = "Kobe", publisher_address = "Los Alamitos, CA, USA", keywords = "genetic algorithms, genetic programming, automatic construction, image filters, medical image processing, tree-structural image transformation, image coding, image processing", DOI = "doi:10.1109/ICIP.1999.821685", abstract = "We previously proposed an automatic construction method of image transformations. In this method, we approximated an unknown image transformation by a series of several known image filters, and a genetic algorithm optimizes their combination to meet the processing purpose presented by sets of original and target images. In this paper, we propose an extended method named {"}Automatic Construction of Tree-structural Image Transformations (ACTIT){"}. In this new method, a tree whose interior nodes are image filters and leaf ones are input images approximates the transformation. The structures of the trees are optimized using genetic programming. ACTIT finds practical filter combinations that are too complicated to be designed by hand. It can be applied to various kinds of image processing tasks. We show examples of its applications to document and medical image processing", notes = "Also known as \cite{821685}", } @InProceedings{aparicio:1999:PM, author = "Joaquim N. Aparicio and Luis Correia and Fernando Moura-Pires", title = "Populations are Multisets-PLATO", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1845--1850", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "methodology, pedagogy and philosophy", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/MP-603.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/MP-603.pdf", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Applegate:2013:CEC, article_id = "1637", author = "Douglas Applegate and Blayne Mayfield", title = "An Analysis of Exchanging Fitness Cases with Population Size in Symbolic Regression Genetic Programming with Respect to the Computational Model", booktitle = "2013 IEEE Conference on Evolutionary Computation", volume = "1", year = "2013", month = jun # " 20-23", editor = "Luis Gerardo {de la Fraga}", pages = "3111--3116", address = "Cancun, Mexico", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-0453-2", DOI = "doi:10.1109/CEC.2013.6557949", notes = "CEC 2013 - A joint meeting of the IEEE, the EPS and the IET.", } @InProceedings{conf/lacci/AquinoRGBL17, author = "Nelson Marcelo Romero Aquino and Manasses Ribeiro and Matheus Gutoski and Cesar Manuel {Vargas Benitez} and Heitor Silverio Lopes", title = "A gene expression programming approach for evolving multi-class image classifiers", booktitle = "2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI)", year = "2017", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, gene expression programming", isbn13 = "978-1-5386-3734-0", URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8275062", DOI = "doi:10.1109/LA-CCI.2017.8285696", size = "6 pages", abstract = "This paper presents a methodology to perform multi-class image classification using Gene Expression Programming(GEP) in both balanced and unbalanced datasets. Descriptors are extracted from images and then their dimensionality are reduced by applying Principal Component Analysis. The aspects extracted from images are texture, colour and shape that are, later, concatenated in a feature vector. Finally, GEP is used to evolve trees capable of performing as classifiers using the features as terminals. The quality of the solution evolved is evaluated by the introduced Cross-Entropy-Loss-based fitness function and compared with standard fitness function (both accuracy and product of sensibility and specificity). A novel GEP function linker Softmax-based is introduced. GEP performance is compared with the obtained by classifiers with tree structure, as C4.5 and Random Forest algorithms. Results show that GEP is capable of evolving classifiers able to achieve satisfactory results for image multi-class classification.", notes = "Also known as \cite{8285696}", } @InProceedings{Aquino:2021:HNICEM, author = "Heinrick L. Aquino and Ronnie S. Concepcion and Andres Philip Mayol and Argel A. Bandala and Alvin Culaba and Joel Cuello and Elmer P. Dadios and Aristotle T. Ubando and Jayne Lois G. {San Juan}", booktitle = "2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)", title = "Prediction of Moisture Content of Chlorella vulgaris Microalgae Using Hybrid Evolutionary Computing and Neural Network Variants for Biofuel Production", year = "2021", month = "28-30 " # nov, address = "Manila, Philippines", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-6654-0168-5", DOI = "doi:10.1109/HNICEM54116.2021.9731926", abstract = "Moisture content is an imperative indicator of biofuel lipid content in microalgae. This paper developed a reliable, computationally cost-effective combination of artificial neurons and an optimization tool for moisture content concentration prediction using computational intelligence. A total of 83 data of microalgae var. Chlorella vulgaris moisture content parameter factors were used. Using feed-forward, recurrent, and deep neural networks as prediction models, their MSE and R2 values were analyzed. Genetic programming GPTIPSv2, a multigene symbolic regression genetic programming (MSRGP) tool, was used to create objective functions of the ANNs. This convergence function was the main element in developing a genetic algorithm (GA)-optimized recurrent neural network model considered to suggest the optimal quantity of neurons in each of the hidden layers in neural network architecture. The feed-forward artificial neural network with 22 neurons in its layer was recommended using the Levenberg-Marquardt training tool. The MSE (5.27e-6) and R2 (0.9999) results of this model surpassed the other neural networks models. Hence, it implies that the developed optimized Levenberg-Marquardt-based feed-forward neural network is an effective moisture content predictor as it provided highly accurate and sensitive results at a low cost.", notes = "Also known as \cite{9731926}", } @Article{vu37709, author = "Sachindra Dhanapala Arachchige and Khandakar Ahmed and S Shahid and B. J. C Perera", title = "Cautionary note on the use of genetic programming in statistical downscaling", journal = "International Journal of Climatology", year = "2018", volume = "38", number = "8", pages = "3449--3465", month = jun, note = "SHORT COMMUNICATION", keywords = "genetic algorithms, genetic programming, GP algorithm, Victoria, Pakistan, downscaling models, climate, predictor--predictand relationships, atmospheric domain", publisher = "Royal Meteorological Society", ISSN = "1097-0088", URL = "https://vuir.vu.edu.au/37709/", DOI = "doi:10.1002/joc.5508", abstract = "The selection of inputs (predictors) to downscaling models is an important task in any statistical downscaling exercise. The selection of an appropriate set of predictors to a downscaling model enhances its generalization skills as such set of predictors can reliably explain the catchment-scale hydroclimatic variable (predictand). Among the predictor selection procedures seen in the literature, the use of genetic programming (GP) can be regarded as a unique approach as it not only selects a set of predictors influential on the predictand but also simultaneously determines a linear or nonlinear regression relationship between the predictors and the predictand. In this short communication, the details of an investigation on the assessment of effectiveness of GP in identifying a unique optimum set of predictors influential on the predictand and its ability to generate a unique optimum predictor-predictand relationship are presented. In this investigation, downscaling models were evolved for relatively wet and dry precipitation stations pertaining to two study areas using two different sets of reanalysis data for each calendar month maintaining the same GP attributes. It was found that irrespective of the climate regime (i.e., wet and dry) and reanalysis data set used, the probability of identification of a unique optimum set of predictors influential on precipitation by GP is quite low. Therefore, it can be argued that the use of GP for the selection of a unique optimum set of predictors influential on a predictand is not effective. However, when run repetitively, GP algorithm selected certain predictors more frequently than others. Also, when run repetitively, the structure of the predictor-predictand relationships evolved by GP varied from one run to another, indicating that the physical interpretation of the predictor-predictand relationships evolved by GP in a downscaling exercise can be unreliable.", } @InProceedings{aragon-jurado:2024:CEC, author = "Jose Miguel Aragon-Jurado and Javier Jareno and Juan Carlos {de la Torre} and Patricia Ruiz and Bernabe Dorronsoro", title = "Two-Level Software Obfuscation with Cooperative Co-Evolutionary Algorithms", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Measurement, Source coding, Plagiarism, Software algorithms, Evolutionary computation, Software, Security, Source code obfuscation, LLVM, Intermediate Representation, IR, Tigress, Cooperative coevolution", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10612116", size = "8 pages", abstract = "Computing devices are ubiquitous nowadays and because of the rise of new paradigms as the Internet of Things, their presence is continuously growing. Software (SW) is highly exposed, and SW companies are forced to protect their products from attacks to prevent plagiarism and the detection of security flaws. Obfuscation is a widespread technique to protect SW. It consists in making the code unintelligible, so that it is very hard to learn how it works. There are numerous obfuscation techniques, but they often require expert hands. Therefore, there is a clear need for fully automatic obfuscation tools that can offer high quality outputs independently of the specific features of the considered SW. we define a novel combinatorial optimisation problem for a two-level obfuscation method that makes use of typical obfuscation transformations, those provided by Tigress framework, as well as classical optimisation ones, those from LLVM compilation framework. The problem is solved with a cooperative co-evolutionary cellular genetic algorithm, providing a tool for automatic SW obfuscation. Three different obfuscation metrics are considered as fitness function. The results show that the proposed methodology offers outstanding obfuscation results, outperforming the original programs by up to 6,152,547percent. Moreover, compared to approaches from the literature, these results are as much as 405 times better.", notes = "also known as \cite{10612116} WCCI 2024", } @Article{Arakawa:2006:CILS, author = "Masamoto Arakawa and Kiyoshi Hasegawa and Kimito Funatsu", title = "QSAR study of anti-HIV HEPT analogues based on multi-objective genetic programming and counter-propagation neural network", journal = "Chemometrics and Intelligent Laboratory Systems", year = "2006", volume = "83", number = "2", pages = "91--98", month = "15 " # sep, keywords = "genetic algorithms, genetic programming, Multi-objective optimisation, Variable selection, HEPT, quantitative structure activity relationship", DOI = "doi:10.1016/j.chemolab.2006.01.009", abstract = "Quantitative structure-activity relationship (QSAR) has been developed for a set of inhibitors of the human immunodeficiency virus 1 (HIV-1) reverse transcriptase, derivatives of 1-[(2-hydroxyethoxy)methyl]-6-(phenylthio)thymine (HEPT). Structural descriptors used in this study are Hansch constants for each substituent and topological descriptors. We have applied the variable selection method based on multi-objective genetic programming (GP) to the HEPT data and constructed the nonlinear QSAR model using counter-propagation (CP) neural network with the selected variables. The obtained network is accurate and interpretable. Moreover in order to confirm a predictive ability of the model, a validation test was performed.", } @InProceedings{Aranha:2006:ASPGP, title = "The effect of using evolutionary algorithms on ant clustering techniques", author = "Claus Aranha and Hitoshi Iba", booktitle = "Proceedings of the Third Asian-Pacific workshop on Genetic Programming", year = "2006", editor = "The Long Pham and Hai Khoi Le and Xuan Hoai Nguyen", pages = "24--34", ISSN = "18590209", address = "Military Technical Academy, Hanoi, VietNam", keywords = "genetic algorithms, genetic programming", URL = "http://gpbib.cs.ucl.ac.uk/aspgp06/Aranha_2006_ASPGP.pdf", size = "11 pages", abstract = "Ant-based clustering is a biologically inspired data clustering technique. In this technique, multiple agents carry the information to be clustered, and make local comparisons. In this work we use genetic algorithms to improve the implementation and use of ant-clustering techniques.", notes = "broken march 2020 http://www.aspgp.org", } @InProceedings{Araseki:2012:SCIS, author = "Hitoshi Araseki", booktitle = "Soft Computing and Intelligent Systems (SCIS) and 13th International Symposium on Advanced Intelligent Systems (ISIS), 2012 Joint 6th International Conference on", title = "Effectiveness of scale-free properties in genetic programming", year = "2012", pages = "285--289", address = "Kobe", month = "20-24 " # nov, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SCIS-ISIS.2012.6505204", abstract = "In this paper, we propose a new selection method, named scale-free selection, which is based on a scale-free network. Through study of the complex network, scale-free networks have been found in various fields. In recent years, it has been proposed that a scale-free property be applied to some optimisation problems. We investigate if the new selection method is an effective selection method to apply to genetic programming. Our experimental results on three benchmark problems show that performance of the scale-free selection model is similar to the usual selection methods in spite of different optimisations and may be able to resolve the bloating problem in genetic programming. Further, we show that the optimisation problem is relevant to complex network study.", notes = "Also known as \cite{6505204}", } @InProceedings{Araseki:2013:EVOLVE, author = "Hitoshi Araseki", title = "Genetic Programming with Scale-Free Dynamics", booktitle = "EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV", year = "2013", editor = "Michael Emmerich and Andre Deutz and Oliver Schuetze and Thomas Baeck and Emilia Tantar and Alexandru-Adrian and Pierre {Del Moral} and Pierrick Legrand and Pascal Bouvry and Carlos A. Coello", volume = "227", series = "Advances in Intelligent Systems and Computing", pages = "277--291", address = "Leiden, Holland", month = jul # " 10-13", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-01127-1", DOI = "doi:10.1007/978-3-319-01128-8_18", abstract = "This paper describe a new selection method, named SFSwT (Scale-Free Selection method with Tournament mechanism) which is based on a scale-free network study. A scale-free selection model was chosen in order to generate a scale-free structure. The proposed model reduces computational complexity and improves computational performance compared with a previous version of the model. Experimental results with various benchmark problems show that performance of the SFSwT is higher than with other selection methods. In various fields, scale-free structures are closely related to evolutionary computation. Further, it was found through the experiments that the distribution of node connectivity could be used as an index of search efficiency.", } @Article{agriculture13050935, author = "Adolfo Vicente Araujo and Caroline Mota and Sajid Siraj", title = "Using Genetic Programming to Identify Characteristics of Brazilian Regions in Relation to Rural Credit Allocation", journal = "Agriculture", year = "2023", volume = "13", number = "5", pages = "article--number 935", month = "24 " # apr, keywords = "genetic algorithms, genetic programming, rural credit, criteria analysis, family farming, machine learning", ISSN = "2077-0472", URL = "https://www.mdpi.com/2077-0472/13/5/935", DOI = "doi:10.3390/agriculture13050935", size = "14 pages", abstract = "Rural credit policies have a strong impact on food production and food security. The attribution of credit policies to agricultural production is one of the main problems preventing the guarantee of agricultural expansion. In this work, we conduct family typology analysis applied to a set of research data to characterize different regions. Through genetic programming, a model was developed using user-defined terms to identify the importance and priority of each criterion used for each region. Access to credit results in economic growth and provides greater income for family farmers, as observed by the results obtained in the model for the Sul region. The Nordeste region indicates that the cost criterion is relevant, and according to previous studies, the Nordeste region has the highest number of family farming households and is also the region with the lowest economic growth. An important aspect discovered by this research is that the allocation of rural credit is not ideal. Another important aspect of the research is the challenge of capturing the degree of diversity across different regions, and the typology is limited in its ability to accurately represent all variations. Therefore, it was possible to characterize how credit is distributed across the country and the main factors that can influence access to credit.", notes = "Department of Industrial Engineering, Federal University of Pernambuco, Recife 50670-901, Brazil", } @InProceedings{Die99, author = "Dieferson L. A. Araujo and Heitor S. Lopes and Alex A. Freitas", title = "A parallel genetic algorithm for rule discovery in large databases", booktitle = "Proceedings of IEEE Systems, Man and Cybernetics Conference", year = "1999", volume = "III", pages = "940--945", note = "Tokyo, Japan, 12-15/october/1999", keywords = "genetic algorithms, data mining, parallel", URL = "http://www.cpgei.cefetpr.br/publicacoes/1999/ieeesmc99.zip", notes = "http://ieeexplore.ieee.org/search/srchabstract.jsp?arnumber=823354&isnumber=17812&punumber=6569&k2dockey=823354@ieeecnfs&query=(freitas%20a.%20%20a.%3Cin%3Eau%20)&pos=7", } @InProceedings{araujo:2000:R, author = "Dieferson L. A. Araujo and Heitor S. Lopes and Alex A. Freitas", title = "Rule discovery with a parallel genetic algorithm", booktitle = "Data Mining with Evolutionary Algorithms", year = "2000", editor = "Alex A. Freitas and William Hart and Natalio Krasnogor and Jim Smith", pages = "89--94", address = "Las Vegas, Nevada, USA", month = "8 " # jul, keywords = "genetic algorithms, data mining, parallel", URL = "http://www.cpgei.cefetpr.br/~hslopes/publicacoes/2000/gecco2000b.zip", notes = "GECCO-2000WKS Part of \cite{wu:2000:GECCOWKS}", } @InProceedings{araujo:2003:ICES, author = "Sergio G. Araujo and A. Mesquita and Aloysio C. P. Pedroza", title = "Using Genetic Programming and High Level Synthesis to Design Optimized Datapath", booktitle = "Evolvable Systems: From Biology to Hardware, Fifth International Conference, ICES 2003", year = "2003", editor = "Andy M. Tyrrell and Pauline C. Haddow and Jim Torresen", volume = "2606", series = "LNCS", pages = "434--445", address = "Trondheim, Norway", month = "17-20 " # mar, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-00730-X", DOI = "doi:10.1007/3-540-36553-2_39", abstract = "a methodology to design optimised electronic digital systems from high abstraction level descriptions. The methodology uses Genetic Programming in addition to high-level synthesis tools to automatically improve design structural quality (area measure). A two-stage, multiobjective optimization algorithm is used to search for circuits with the desired functionality subjected additionally to chip area constraints. Experiment with a square-root approximation datapath design targeted to FPGA exemplifies the proposed methodology.", notes = "ICES-2003", } @InProceedings{semish2003meta007, title = "{S}{\'i}ntese de Circuitos Digitais Otimizados via Programa{\c c}{\~a}o Gen{\'e}tica", author = "Sergio Granato {de Araujo} and Antonio C. Mesquita and Aloysio C. P. Pedroza", year = "2003", abstract = "This paper presents a methodology for the design of optimized electronic digital systems from high abstraction level descriptions. The methodology uses Genetic Programming in addition to high-level synthesis tools to improve the design quality (area optimization). A two-stage, multiobjective optimization algorithm was used to search for circuits with the desired functionality subjected additionally to chip area constraints. Experiment with a square-root approximation function design targeted to FPGA illustrates the methodology.", identifier = "semish2003article007", language = "por; eng", rights = "Sociedade Brasileira de Computa{\c c}{\~a}o", source = "semish2003", URL = "http://www.lbd.dcc.ufmg.br/bdbcomp/servlet/Trabalho?id=2490", URL = "http://www.gta.ufrj.br/ftp/gta/TechReports/AMP03d.pdf", booktitle = "XXX Semin{\'a}rio Integrado de Software e Hardware", volume = "III", pages = "273--285", address = "Unicamp, Campinas, SP, Brasil", month = "2-8 " # aug, keywords = "genetic algorithms, genetic programming", size = "13 pages", resumo = "Este trabalho apresenta uma metodologia para o projeto de circuitos eletronicos digitais otimizados a partir de descricoes feitas em alto nivel de abstracao. A metodologia utiliza Programacao Genetica e ferramentas de sintese de alto nivel para melhorar a qualidade do projeto (otimizacao de area). Foi utilizado um algoritmo de otimizacao com multiplos objetivos, composto de dois estagios, para buscar por circuitos com a funcionalidade desejada e com restricoes de area. Um experimento de projeto em FPGA de uma funcao que aproxima a raiz quadrada ilustra a metodologia.", notes = "XXIII Brazilian Symposium on Computation (SBC'03) http://www.sbc.org.br/sbc2003/semish.html In Portuguese Available as technical report GTA-03-50", } @InProceedings{araujo:2004:eurogp, author = "Lourdes Araujo", title = "Genetic Programming for Natural Language Parsing", booktitle = "Genetic Programming 7th European Conference, EuroGP 2004, Proceedings", year = "2004", editor = "Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule", volume = "3003", series = "LNCS", pages = "230--239", address = "Coimbra, Portugal", publisher_address = "Berlin", month = "5-7 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-21346-5", DOI = "doi:10.1007/978-3-540-24650-3_21", abstract = "Our aim is to prove the effectiveness of the genetic programming approach in automatic parsing of sentences of real texts. Classical parsing methods are based on complete search techniques to find the different interpretations of a sentence. However, the size of the search space increases exponentially with the length of the sentence or text to be parsed and the size of the grammar, so that exhaustive search methods can fail to reach a solution in a reasonable time. This paper presents the implementation of a probabilistic bottom-up parser based on genetic programming which works with a population of partial parses, i.e. parses of sentence segments. The quality of the individuals is computed as a measure of its probability, which is obtained from the probability of the grammar rules and lexical tags involved in the parse. In the approach adopted herein, the size of the trees generated is limited by the length of the sentence. In this way, the size of the search space, determined by the size of the sentence to parse, the number of valid lexical tags for each words and specially by the size of the grammar, is also limited.", notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004", } @InProceedings{Araujo:PPSN:2006, author = "L. Araujo", title = "Multiobjective Genetic Programming for Natural Language Parsing and Tagging", booktitle = "Parallel Problem Solving from Nature - PPSN IX", year = "2006", editor = "Thomas Philip Runarsson and Hans-Georg Beyer and Edmund Burke and Juan J. Merelo-Guervos and L. Darrell Whitley and Xin Yao", volume = "4193", pages = "433--442", series = "LNCS", address = "Reykjavik, Iceland", publisher_address = "Berlin", month = "9-13 " # sep, publisher = "Springer-Verlag", ISBN = "3-540-38990-3", keywords = "genetic algorithms, genetic programming", URL = "http://ppsn2006.raunvis.hi.is/proceedings/055.pdf", DOI = "doi:10.1007/11844297_44", size = "10 pages", abstract = "Parsing and Tagging are very important tasks in Natural Language Processing. Parsing amounts to searching the correct combination of grammatical rules among those compatible with a given sentence. Tagging amounts to labelling each word in a sentence with its lexical category and, because many words belong to more than one lexical class, it turns out to be a disambiguation task. Because parsing and tagging are related tasks, its simultaneous resolution can improve the results of both of them. This work aims developing a multiobjective genetic program to perform simultaneously statistical parsing and tagging. It combines the statistical data about grammar rules and about tag sequences to guide the search of the best structure. Results show that any of the implemented multiobjective optimisation models improve on the results obtained in the resolution of each problem separately.", notes = "PPSN-IX", } @InProceedings{Araujo:2010:cec, author = "Lourdes Araujo and Jesus Santamaria", title = "Evolving natural language grammars without supervision", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-6910-9", abstract = "Unsupervised grammar induction is one of the most difficult works of language processing. Its goal is to extract a grammar representing the language structure using texts without annotations of this structure. We have devised an evolutionary algorithm which for each sentence evolves a population of trees that represent different parse trees of that sentence. Each of these trees represent a part of a grammar. The evaluation function takes into account the contexts in which each sequence of Part-Of-Speech tags (POSseq) appears in the training corpus, as well as the frequencies of those POSseqs and contexts. The grammar for the whole training corpus is constructed in an incremental manner. The algorithm has been evaluated using a well known Annotated English corpus, though the annotation have only been used for evaluation purposes. Results indicate that the proposed algorithm is able to improve the results of a classical optimisation algorithm, such as EM (Expectation Maximisation), for short grammar constituents (right side of the grammar rules), and its precision is better in general.", DOI = "doi:10.1109/CEC.2010.5586291", notes = "WCCI 2010. Also known as \cite{5586291}", } @InProceedings{Araujo:2015:GECCOcomp, author = "Lourdes Araujo and Juan Martinez-Romo and Andres Duque", title = "Grammatical Evolution for Identifying Wikipedia Taxonomies", booktitle = "GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation", year = "2015", editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr", isbn13 = "978-1-4503-3488-4", keywords = "genetic algorithms, genetic programming, grammatical evolution: Poster", pages = "1345--1346", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739482.2764629", DOI = "doi:10.1145/2739482.2764629", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "This work applies Grammatical Evolution to identify taxonomic hierarchies of concepts from Wikipedia. Each article in Wikipedia covers a concept and is cross-linked by hyperlinks that connect related concepts. Hierarchical taxonomies and their generalization to ontologies are a highly useful resource for many applications by enabling semantic search and reasoning. We have developed a system which arranges a set of Wikipedia concepts into a taxonomy.", notes = "Also known as \cite{2764629} Distributed at GECCO-2015.", } @Article{AraujoMF18, author = "Lourdes Araujo and Juan Martinez-Romo and Andres Duque Fernandez", title = "Discovering taxonomies in {Wikipedia} by means of grammatical evolution", journal = "Soft Computing", year = "2018", volume = "22", number = "9", pages = "2907--2919", keywords = "genetic algorithms, genetic programming, grammatical evolution", URL = "https://doi.org/10.1007/s00500-017-2544-4", timestamp = "Sat, 05 May 2018 23:05:31 +0200", biburl = "https://dblp.org/rec/bib/journals/soco/AraujoMF18", DOI = "doi:10.1007/s00500-017-2544-4", abstract = "This work applies grammatical evolution to identify taxonomic hierarchies of concepts from Wikipedia. Each article in Wikipedia covers a topic and is cross-linked by hyperlinks that connect related topics. Hierarchical taxonomies and their generalization to ontologies are a highly useful resource for many applications since they enable semantic search and reasoning. Thus, the automatic identification of taxonomies composed of concepts associated with linked Wikipedia pages has attracted much attention. We have developed a system which arranges a set of Wikipedia concepts into a taxonomy. This technique is based on the relationships among a set of features extracted from the contents of the Wikipedia pages. We have used a grammatical evolution algorithm to discover the best way of combining the considered features in an explicit function. Candidate functions are evaluated by applying a genetic algorithm to approximate the optimal taxonomy that the function can provide for a number of training cases. The fitness is computed as an average of the precision obtained by comparing, for the set of training cases, the taxonomy provided by the evaluated function with the reference one. Experimental results show that the proposal is able to provide valuable functions to find high-quality taxonomies.", } @Article{Araujo:GPEM20, author = "Lourdes Araujo", title = "Genetic programming for natural language processing", journal = "Genetic Programming and Evolvable Machines", year = "2020", volume = "21", number = "1-2", pages = "11--32", month = jun, note = "Twentieth Anniversary Issue", keywords = "genetic algorithms, genetic programming, Grammatical evolution, NLP, Natural language processing, Applications, Challenges", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-019-09361-5", size = "22 pages", abstract = "This work takes us through the literature on applications of genetic programming to problems of natural language processing. The purpose of natural language processing is to allow us to communicate with computers in natural language. Among the problems addressed in the area is, for example, the extraction of information, which draws relevant data from unstructured texts written in natural language. There are also domains of application of particular relevance because of the difficulty in dealing with the corresponding documents, such as opinion mining in social networks, or because of the need for high precision in the information extracted, such as the biomedical domain. There have been proposals to apply genetic programming techniques in several of these areas. This tour allows us to observe the potential (not yet fully exploited) of such applications. We also review some cases in which genetic programming can provide information that is absent from other approaches, revealing its ability to provide easy to interpret results, in form of programs or functions. Finally, we identify some important challenges in the area.", } @Article{Arbuckle:2014:IJCISTUDIES, author = "Tom Arbuckle and Damien Hogan and Conor Ryan", title = "Learning predictors for flash memory endurance: a comparative study of alternative classification methods", journal = "International Journal of Computational Intelligence Studies", year = "2014", volume = "3", number = "1", pages = "18--39", month = jan # "~14", keywords = "genetic algorithms, genetic programming, flash memory endurance, performance prediction, linear programming, support vector machines, SVMs, learning predictors, classification methods, timing data, erasure, programming, modelling", publisher = "Inderscience Publishers", language = "eng", ISSN = "1755-4985", bibsource = "OAI-PMH server at www.inderscience.com", URL = "http://www.inderscience.com/link.php?id=58644", DOI = "doi:10.1504/IJCISTUDIES.2014.058644", abstract = "Flash memory's ability to be programmed multiple times is called its endurance. Beyond being able to give more accurate chip specifications, more precise knowledge of endurance would permit manufacturers to use flash chips more effectively. Rather than physical testing to determine chip endurance, which is impractical because it takes days and destroys an area of the chip under test, this research seeks to predict whether chips will meet chosen endurance criteria. Timing data relating to erasure and programming operations is gathered as the basis for modelling. The purpose of this paper is to determine which methods can be used on this data to accurately and efficiently predict endurance. Traditional statistical classification methods, support vector machines and genetic programming are compared. Cross-validating on common datasets, the classification methods are evaluated for applicability, accuracy and efficiency and their respective advantages and disadvantages are quantified.", } @InProceedings{Arcanjo:2011:GECCO, author = "Filipe {de Lima Arcanjo} and Gisele Lobo Pappa and Paulo Viana Bicalho and {Wagner Meira, Jr.} and Altigran Soares {da Silva}", title = "Semi-supervised genetic programming for classification", booktitle = "GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0557-0", pages = "1259--1266", keywords = "genetic algorithms, genetic programming, Genetics based machine learning", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001576.2001746", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Learning from unlabeled data provides innumerable advantages to a wide range of applications where there is a huge amount of unlabeled data freely available. Semi-supervised learning, which builds models from a small set of labeled examples and a potential large set of unlabeled examples, is a paradigm that may effectively use those unlabeled data. Here we propose KGP, a semi-supervised transductive genetic programming algorithm for classification. Apart from being one of the first semi-supervised algorithms, it is transductive (instead of inductive), i.e., it requires only a training dataset with labeled and unlabeled examples, which should represent the complete data domain. The algorithm relies on the three main assumptions on which semi-supervised algorithms are built, and performs both global search on labeled instances and local search on unlabeled instances. Periodically, unlabeled examples are moved to the labeled set after a weighted voting process performed by a committee. Results on eight UCI datasets were compared with Self-Training and KNN, and showed KGP as a promising method for semi-supervised learning.", notes = "Also known as \cite{2001746} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)", } @InProceedings{Archanjo:2011:IRI, author = "Gabriel A. Archanjo and Fernando J. {Von Zuben}", title = "Induction of linear genetic programs for relational database manipulation", booktitle = "IEEE International Conference on Information Reuse and Integration (IRI 2011)", year = "2011", month = "3-5 " # aug, pages = "347--352", address = "Las Vegas, USA", size = "6 pages", abstract = "In virtually all fields of human activity, softwares are used to manage processes and manipulate information, usually stored in computer databases. In fields like Knowledge Discovery and Data Mining (KDD), different approaches have been used to extract patterns or more meaningful information from datasets, including genetic programming. Nevertheless, the induction of programs that not only query data, but also manipulate it, has not been widely explored. This work presents Linear Genetic Programming for Databases (LGPDB), a tool to induce programs manipulating entities stored in a relational database. It combines a Linear Genetic Programming (LGP) induction environment and a simple relational database management system (RDBMS). A hypothetical library system is used to show LGPDB in action. Programs were induced to provide a set of selected features for this system and results indicate that genetic programming can be used to model processes that query, delete, insert and update records in a relational database.", keywords = "genetic algorithms, genetic programming, KDD, Knowledge Discovery and Data Mining, LGPDB, Linear Genetic Programming for Databases, hypothetical library system, relational database management system, data mining, relational databases", DOI = "doi:10.1109/IRI.2011.6009572", notes = "Also known as \cite{6009572}", } @Article{Archanjo:2012:ASE, author = "Gabriel A. Archanjo and Fernando J. {Von Zuben}", title = "Genetic Programming for Automating the Development of Data Management Algorithms in Information Technology Systems", journal = "Advances in Software Engineering", year = "2012", pages = "Article ID 893701", publisher = "Hindawi Publishing Corporation", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, SQL", ISSN = "16878655", URL = "http://www.hindawi.com/journals/ase/2012/893701/", broken = "http://www.doaj.org/doaj?func=openurl\&genre=article\&issn=16878655\&date=2012\&volume=2012\&issue=\&spage=", DOI = "doi:10.1155/2012/893701", size = "14 pages", abstract = "Information technology (IT) systems are present in almost all fields of human activity, with emphasis on processing, storage, and handling of datasets. Automated methods to provide access to data stored in databases have been proposed mainly for tasks related to knowledge discovery and data mining (KDD). However, for this purpose, the database is used only to query data in order to find relevant patterns associated with the records. Processes modelled on IT systems should manipulate the records to modify the state of the system. Linear genetic programming for databases (LGPDB) is a tool proposed here for automatic generation of programs that can query, delete, insert, and update records on databases. The obtained results indicate that the LGPDB approach is able to generate programs for effectively modelling processes of IT systems, opening the possibility of automating relevant stages of data manipulation, and thus allowing human programmers to focus on more complex tasks.", notes = "Article ID 893701", bibsource = "OAI-PMH server at www.doaj.org", language = "eng", oai = "oai:doaj-articles:1999bf5251ee08492e4a5bfbdfe2af9a", source = "Advances in Software Engineering", notes = "library books. agile software development, model view controller MVC, in memory, rollback, weakly typed but STGP, elitist. Fitness based on internal state of database rather than results of queries. 14 tables. pop=1000? Is function set complete? 'toward automatic relevant and basic stages of [information technology] IT system development'. Laboratory of Bioinformatics and Bioinspired Computing, School of Electrical and Computer Engineering, University of Campinas (Unicamp), 13083-970 Campinas, SP, Brazil", } @InProceedings{1144042, author = "Francesco Archetti and Stefano Lanzeni and Enza Messina and Leonardo Vanneschi", title = "Genetic programming for human oral bioavailability of drugs", booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation", year = "2006", editor = "Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos {Coello Coello} and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens", volume = "1", ISBN = "1-59593-186-4", pages = "255--262", address = "Seattle, Washington, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p255.pdf", DOI = "doi:10.1145/1143997.1144042", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "8-12 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Biological Applications, bioavailability, bioinformatics, complexity measures, molecular descriptors, performance measures, SVM, ANN, LLSR, CFS, PCA, AIC, feature selection, SMILES", size = "8 pages", abstract = "Automatically assessing the value of bioavailability from the chemical structure of a molecule is a very important issue in biomedicine and pharmacology. In this paper, we present an empirical study of some well known Machine Learning techniques, including various versions of Genetic Programming, which have been trained to this aim using a dataset of molecules with known bioavailability. Genetic Programming has proven the most promising technique among the ones that have been considered both from the point of view of the accurateness of the solutions proposed, of the generalisation capabilities and of the correlation between predicted data and correct ones. Our work represents a first answer to the demand for quantitative bioavailability estimation methods proposed in literature, since the previous contributions focus on the classification of molecules into classes with similar bioavailability. Categories and Subject Descriptors", notes = "GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM Order Number 910060 Winner best paper.", } @InProceedings{Archetti:2007:evobio, author = "Francesco Archetti and Stefano Lanzeni and Enza Messina and Leonardo Vanneschi", title = "Genetic Programming and Other Machine Learning Approaches to Predict Median Oral Lethal Dose (LD50) and Plasma Protein Binding Levels (%PPB) of Drugs", booktitle = "EvoBIO 2007, Proceedings of the 5th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics", year = "2007", editor = "Elena Marchiori and Jason H. Moore and Jagath C. Rajapakse", volume = "4447", series = "Lecture Notes in Computer Science", pages = "11--23", address = "Valencia, Spain", publisher_address = "Berlin Heidelberg NewYork", month = apr # " 11-13", publisher = "Springer", keywords = "genetic algorithms, genetic programming", ISSN = "0302-9743", ISBN = "3-540-71782-X", ISBN-13 = "978-3-540-71782-9", DOI = "doi:10.1007/978-3-540-71783-6_2", abstract = "Computational methods allowing reliable pharmacokinetics predictions for newly synthesised compounds are critically relevant for drug discovery and development. Here we present an empirical study focusing on various versions of Genetic Programming and other well known Machine Learning techniques to predict Median Oral Lethal Dose (LD50) and Plasma Protein Binding (%PPB) levels. Since these two parameters respectively characterise the harmful effects and the distribution into human body of a drug, their accurate prediction is essential for the selection of effective molecules. The obtained results confirm that Genetic Programming is a promising technique for predicting pharmacokinetics parameters, both from the point of view of the accurateness and of the generalisation ability.", notes = "EvoBIO2007", } @Article{Archetti:2007:GPEM, author = "Francesco Archetti and Stefano Lanzeni and Enza Messina and Leonardo Vanneschi", title = "Genetic programming for computational pharmacokinetics in drug discovery and development", journal = "Genetic Programming and Evolvable Machines", year = "2007", volume = "8", number = "4", pages = "413--432", month = dec, note = "special issue on medical applications of Genetic and Evolutionary Computation", keywords = "genetic algorithms, genetic programming, Computational pharmacokinetics, Drug discovery, QSAR, SMILES", ISSN = "1389-2576", URL = "https://rdcu.be/cYj4W", DOI = "doi:10.1007/s10710-007-9040-z", size = "20 pages", abstract = "The success of a drug treatment is strongly correlated with the ability of a molecule to reach its target in the patient's organism without inducing toxic effects. Moreover the reduction of cost and time associated with drug discovery and development is becoming a crucial requirement for pharmaceutical industry. Therefore computational methods allowing reliable predictions of newly synthesised compounds properties are of outmost relevance. In this paper we discuss the role of genetic programming in predictive pharmacokinetics, considering the estimation of adsorption, distribution, metabolism, excretion and toxicity processes (ADMET) that a drug undergoes into the patient's organism. We compare genetic programming with other well known machine learning techniques according to their ability to predict oral bioavailability (%F), median oral lethal dose (LD50) and plasma-protein binding levels (%PPB). Since these parameters respectively characterise the percentage of initial drug dose that effectively reaches the systemic blood circulation, the harmful effects and the distribution into the organism of a drug, they are essential for the selection of potentially good molecules. Our results suggest that genetic programming is a valuable technique for predicting pharmacokinetics parameters, both from the point of view of the accuracy and of the generalisation ability.", notes = "GP, LS2-GP, LS2-C-GP, DF-GP, AIC, Weka ANN, MLP, SVM, Linear regression, PCFS, CorrFS p429 'We have tried to investigate the reasons why GP ...'", } @InProceedings{Archetti:2008:wivace, author = "Francesco Archetti and Mauro Castelli and Ilaria Giordani and Leonardo Vanneschi", title = "Classification of colon tumor tissues using genetic programming", booktitle = "Artificial Life and Evolutionary Computation: Proceedings of Wivace 2008", year = "2008", editor = "J. Roberto Serra and Marco Villani and Irene Poli", pages = "49--58", address = "Venice, Italy", month = "8-10 " # sep, publisher = "World Scientific Publishing Co.", keywords = "genetic algorithms, genetic programming", isbn13 = "9789814287456", URL = "ftp://ftp.ce.unipr.it/pub/cagnoni/WIV08/paper%202.pdf", broken = "http://ebooks.worldscinet.com/ISBN/9789814287456/9789814287456_0004.html", size = "10 pages", abstract = "A Genetic Programming (GP) framework for classification is presented in this paper and applied to a publicly available biomedical microarray dataset representing a collection of expression measurements from colon biopsy experiments [3]. We report experimental results obtained using two different well known fitness criteria: the area under the receiving operating curve (ROC) and the percentage of correctly classified instances (CCI). These results, and their comparison with the ones obtained by three non-evolutionary Machine Learning methods (Support Vector Machines, Voted Perceptron and Random Forests) on the same data, seem to hint that GP is a promising technique for this kind of classification both from the viewpoint of the accuracy of the proposed solutions and of the generalisation ability. These results are encouraging and should pave the way to a deeper study of GP for classification applied to biomedical microarray data sets.", notes = "Workshop Italiano di Vita Artificiale e Computazione Evolutiva http://wivace.unimore.it/ Dept. of Informatics, Systems and Communication, University of Milano-Bicocca, 20126 Milan, Italy http://microarray.princeton.edu/oncology/affydata/index.html", } @Article{Archetti2010170, title = "Genetic programming for {QSAR} investigation of docking energy", author = "Francesco Archetti and Ilaria Giordani and Leonardo Vanneschi", journal = "Applied Soft Computing", volume = "10", number = "1", pages = "170--182", year = "2010", month = jan, keywords = "genetic algorithms, genetic programming, Machine learning, Regression, Docking energy, Computational biology, Drug design, QSAR", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2009.06.013", broken = "http://www.sciencedirect.com/science/article/B6W86-4WP47KG-3/2/20419bfc47761543f509e96265d88e5d", abstract = "Statistical methods, and in particular Machine Learning, have been increasingly used in the drug development workflow to accelerate the discovery phase and to eliminate possible failures early during clinical developments. In the past, the authors of this paper have been working specifically on two problems: (i) prediction of drug induced toxicity and (ii) evaluation of the target drug chemical interaction based on chemical descriptors. Among the numerous existing Machine Learning methods and their application to drug development (see for instance [F. Yoshida, J.G. Topliss, QSAR model for drug human oral bioavailability, Journal of Medicinal Chemistry 43 (2000) 2575-2585; Frohlich, J. Wegner, F. Sieker, A. Zell, Kernel functions for attributed molecular graphs - a new similarity based approach to ADME prediction in classification and regression, QSAR and Combinatorial Science, 38(4) (2003) 427-431; C.W. Andrews, L. Bennett, L.X. Yu, Predicting human oral bioavailability of a compound: development of a novel quantitative structure-bioavailability relationship, Pharmacological Research 17 (2000) 639-644; J Feng, L. Lurati, H. Ouyang, T. Robinson, Y. Wang, S. Yuan, S.S. Young, Predictive toxicology: benchmarking molecular descriptors and statistical methods, Journal of Chemical Information Computer Science 43 (2003) 1463-1470; T.M. Martin, D.M. Young, Prediction of the acute toxicity (96-h LC50) of organic compounds to the fat head minnow (Pimephales promelas) using a group contribution method, Chemical Research in Toxicology 14(10) (2001) 1378-1385; G. Colmenarejo, A. Alvarez-Pedraglio, J.L. Lavandera, Chemoinformatic models to predict binding affinities to human serum albumin, Journal of Medicinal Chemistry 44 (2001) 4370-4378; J. Zupan, P. Gasteiger, Neural Networks in Chemistry and Drug Design: An Introduction, 2nd edition, Wiley, 1999]), we have been specifically concerned with Genetic Programming. A first paper [F. Archetti, E. Messina, S. Lanzeni, L. Vanneschi, Genetic programming for computational pharmacokinetics in drug discovery and development, Genetic Programming and Evolvable Machines 8(4) (2007) 17-26 \cite{Archetti:2007:GPEM}] has been devoted to problem (i). The present contribution aims at developing a Genetic Programming based framework on which to build specific strategies which are then shown to be a valuable tool for problem (ii). In this paper, we use target estrogen receptor molecules and genistein based drug compounds. Being able to precisely and efficiently predict their mutual interaction energy is a very important task: for example, it may have an immediate relationship with the efficacy of genistein based drugs in menopause therapy and also as a natural prevention of some tumours. We compare the experimental results obtained by Genetic Programming with the ones of a set of non-evolutionary Machine Learning methods, including Support Vector Machines, Artificial Neural Networks, Linear and Least Square Regression. Experimental results confirm that Genetic Programming is a promising technique from the viewpoint of the accuracy of the proposed solutions, of the generalization ability and of the correlation between predicted data and correct ones.", } @Article{Archetti20101395, author = "Francesco Archetti and Ilaria Giordani and Leonardo Vanneschi", title = "Genetic programming for anticancer therapeutic response prediction using the NCI-60 dataset", journal = "Computers \& Operations Research", volume = "37", number = "8", pages = "1395--1405", year = "2010", note = "Operations Research and Data Mining in Biological Systems", ISSN = "0305-0548", DOI = "doi:10.1016/j.cor.2009.02.015", URL = "http://www.sciencedirect.com/science/article/B6VC5-4VS40CF-4/2/a55e5b35bc3d30ac9057d5fb8cdcd2d0", keywords = "genetic algorithms, genetic programming, Machine learning, Regression, Microarray data, Anticancer therapy, NCI-60", abstract = "Statistical methods, and in particular machine learning, have been increasingly used in the drug development workflow. Among the existing machine learning methods, we have been specifically concerned with genetic programming. We present a genetic programming-based framework for predicting anticancer therapeutic response. We use the NCI-60 microarray dataset and we look for a relationship between gene expressions and responses to oncology drugs Fluorouracil, Fludarabine, Floxuridine and Cytarabine. We aim at identifying, from genomic measurements of biopsies, the likelihood to develop drug resistance. Experimental results, and their comparison with the ones obtained by Linear Regression and Least Square Regression, hint that genetic programming is a promising technique for this kind of application. Moreover, genetic programming output may potentially highlight some relations between genes which could support the identification of biological meaningful pathways. The structures that appear more frequently in the 'best' solutions found by genetic programming are presented.", } @InProceedings{Arcuri:2007:ASE, author = "Andrea Arcuri and Xin Yao", title = "Coevolving Programs and Unit Tests from their Specification", booktitle = "IEEE International Conference on Automated Software Engineering (ASE)", year = "2007", address = "Atlanta, Georgia, USA", month = nov # " 5-9", organisation = "IEEE", keywords = "genetic algorithms, genetic programming, Automatic Programming, Coevolution, Software Testing, Formal Specification, Sorting, SBSE", DOI = "doi:10.1145/1321631.1321693", abstract = "Writing a formal specification before implementing a program helps to find problems with the system requirements. The requirements might be for example incomplete and ambiguous. Fixing these types of errors is very difficult and expensive during the implementation phase of the software development cycle. Although writing a formal specification is usually easier than implementing the actual code, writing a specification requires time, and often it is preferred, instead, to use this time on the implementation. In this paper we introduce for the first time a framework that might evolve any possible generic program from its specification. We use the Genetic Programming to evolve the programs, and at the same time we exploit the specifications to coevolve sets of unit tests. Programs are rewarded on how many tests they do not fail, whereas the unit tests are rewarded on how many programs they make fail. We present and analyse four different problems on which this novel technique is successfully applied.", notes = "http://www.cse.msu.edu/ase2007/", } @InProceedings{Arcuri:2008:ICSEphd, author = "Andrea Arcuri", title = "On the automation of fixing software bugs", booktitle = "ICSE Companion '08: Companion of the 30th international conference on Software engineering", year = "2008", pages = "1003--1006", address = "Leipzig, Germany", publisher_address = "New York, NY, USA", publisher = "ACM", note = "Doctoral symposium session", keywords = "genetic algorithms, genetic programming, co-evolution, SuA, SBSE", isbn13 = "978-1-60558-079-1", URL = "http://delivery.acm.org/10.1145/1380000/1370223/p1003-arcuri.pdf", DOI = "doi:10.1145/1370175.1370223", size = "4 pages", abstract = "Software Testing can take up to half of the resources of the development of new software. Although there has been a lot of work on automating the testing phase, fixing a bug after its presence has been discovered is still a duty of the programmers. Techniques to help the software developers for locating bugs exist though, and they take name of Automated Debugging. However, to our best knowledge, there has been only little attempt in the past to completely automate the actual changing of the software for fixing the bugs. Therefore, in this paper we propose an evolutionary approach to automate the task of fixing bugs. The basic idea is to evolve the programs (e.g., by using Genetic Programming) with a fitness function that is based on how many unit tests they are able to pass. If a formal specification of the buggy software is given, more sophisticated fitness functions can be designed. Moreover, by using the formal specification as an oracle, we can generate as many unit tests as we want. Hence, a co-evolution between programs and unit tests might take place to give even better results. It is important to know that, to fix the bugs in a program with this novel approach, a user needs only to provide either a formal specification or a set of unit tests. No other information is required.", notes = "p1006 {"}We are building our prototype on top of our previous system for AP{"} \cite{Arcuri:2007:ASE} also known as \cite{1370223} Doctoral Symposium of the IEEE International Conference in Software Engineering", } @InProceedings{Arcuri:2008:cec, author = "Andrea Arcuri and Xin Yao", title = "A Novel Co-Evolutionary Approach to Automatic Software Bug Fixing", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "162--168", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1823-7", file = "EC0063.pdf", DOI = "doi:10.1109/CEC.2008.4630793", abstract = "Many tasks in Software Engineering are very expensive, and that has led the investigation to how to automate them. In particular, Software Testing can take up to half of the resources of the development of new software. Although there has been a lot of work on automating the testing phase, fixing a bug after its presence has been discovered is still a duty of the programmers. In this paper we propose an evolutionary approach to automate the task of fixing bugs. This novel evolutionary approach is based on Co-evolution, in which programs and test cases co-evolve, influencing each other with the aim of fixing the bugs of the programs. This competitive co-evolution is similar to what happens in nature for predators and prey. The user needs only to provide a buggy program and a formal specification of it. No other information is required. Hence, the approach may work for any implementable software. We show some preliminary experiments in which bugs in an implementation of a sorting algorithm are automatically fixed.", keywords = "genetic algorithms, genetic programming", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @InProceedings{ArcuriWCY08, author = "Andrea Arcuri and David Robert White and John Clark and Xin Yao", title = "Multi-Objective Improvement of Software using Co-evolution and Smart Seeding", booktitle = "Proceedings of the 7th International Conference on Simulated Evolution And Learning (SEAL '08)", year = "2008", editor = "Xiaodong Li and Michael Kirley and Mengjie Zhang and David G. Green and Victor Ciesielski and Hussein A. Abbass and Zbigniew Michalewicz and Tim Hendtlass and Kalyanmoy Deb and Kay Chen Tan and J{\"u}rgen Branke and Yuhui Shi", volume = "5361", series = "Lecture Notes in Computer Science", pages = "61--70", address = "Melbourne, Australia", month = dec # " 7-10", publisher = "Springer", keywords = "genetic algorithms, genetic programming, SBSE", bibsource = "http://crestweb.cs.ucl.ac.uk/resources/sbse_repository/repository.html", isbn13 = "978-3-540-89693-7", DOI = "doi:10.1007/978-3-540-89694-4_7", size = "10 pages", abstract = "Optimising non-functional properties of software is an important part of the implementation process. One such property is execution time, and compilers target a reduction in execution time using a variety of optimisation techniques. Compiler optimisation is not always able to produce semantically equivalent alternatives that improve execution times, even if such alternatives are known to exist. Often, this is due to the local nature of such optimisations. In this paper we present a novel framework for optimising existing software using a hybrid of evolutionary optimisation techniques. Given as input the implementation of a program or function, we use Genetic Programming to evolve a new semantically equivalent version, optimised to reduce execution time subject to a given probability distribution of inputs. We employ a co-evolved population of test cases to encourage the preservation of the program's semantics, and exploit the original program through seeding of the population in order to focus the search. We carry out experiments to identify the important factors in maximising efficiency gains. Although in this work we have optimised execution time, other non-functional criteria could be optimised in a similar manner.", notes = "Also known as \cite{DBLP:conf/seal/ArcuriWCY08}", bibsource = "DBLP, http://dblp.uni-trier.de", } @TechReport{Arcuri09, author = "Andrea Arcuri", title = "Evolutionary Repair of Faulty Software", institution = "University of Birmingham, School of Computer Science", year = "2009", type = "Technical Report", number = "CSR-09-02", address = "B15 2TT, UK", month = apr, keywords = "genetic algorithms, genetic programming, SBSE", URL = "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/2009/CSR-09-02.pdf", size = "34 pages", abstract = "Testing and fault localization are very expensive software engineering tasks that have been tried to be automated. Although many successful techniques have been designed, the actual change of the code for fixing the discovered faults is still a human-only task. Even in the ideal case in which automated tools could tell us exactly where the location of a fault is, it is not always trivial how to fix the code. In this paper we analyse the possibility of automating the complex task of fixing faults. We propose to model this task as a search problem, and hence to use for example evolutionary algorithms to solve it. We then discuss the potential of this approach and how its current limits can be addressed in the future. This task is extremely challenging and mainly unexplored in literature. Hence, this paper only covers an initial investigation and gives directions for future work. A research prototype called JAFF and a case study are presented to give first validation of this approach.", notes = "cited by \cite{Ackling:2011:GECCO}", } @InProceedings{Arcuri:2009:SSBSE, author = "Andrea Arcuri", title = "On Search Based Software Evolution", booktitle = "Proceedings 1st International Symposium on Search Based Software Engineering SSBSE 2009", year = "2009", editor = "Massimiliano {Di Penta} and Simon Poulding", pages = "39--42", address = "Windsor, UK", month = "13-15 " # may, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, SBSE, program coevolution, program test case, search algorithm, software engineering problem, software evolution, program testing, search problems, software engineering", isbn13 = "978-0-7695-3675-0", DOI = "doi:10.1109/SSBSE.2009.12", abstract = "Writing software is a difficult and expensive task. Its automation is hence very valuable. Search algorithms have been successfully used to tackle many software engineering problems. Unfortunately, for some problems the traditional techniques have been of only limited scope, and search algorithms have not been used yet. We hence propose a novel framework that is based on a co-evolution of programs and test cases to tackle these difficult problems.This framework can be used to tackle software engineering tasks such as automatic refinement, fault correction,improving non-functional criteria and reverse engineering.While the programs evolve to accomplish one of these tasks, test cases are co-evolved at the the same time to find new faults in the evolving programs.", notes = "order number P3675 http://www.ssbse.info/ Also known as \cite{5033178}", } @PhdThesis{Arcuri:thesis, author = "Andrea Arcuri", title = "Automatic software generation and improvement through search based techniques", school = "School of Computer Science, University of Birmingham", year = "2009", address = "UK", month = aug, keywords = "genetic algorithms, genetic programming, SBSE", URL = "http://etheses.bham.ac.uk/400/1/Arcuri09PhD.pdf", URL = "http://etheses.bham.ac.uk/400/", size = "234 pages", abstract = "Writing software is a difficult and expensive task. Its automation is hence very valuable. Search algorithms have been successfully used to tackle many software engineering problems. Unfortunately, for some problems the traditional techniques have been of only limited scope, and search algorithms have not been used yet. We hence propose a novel framework that is based on a co-evolution of programs and test cases to tackle these difficult problems. This framework can be used to tackle software engineering tasks such as Automatic Refinement, Fault Correction and Improving Non-functional Criteria. These tasks are very difficult, and their automation in literature has been limited. To get a better understanding of how search algorithms work, there is the need of a theoretical foundation. That would help to get better insight of search based software engineering. We provide first theoretical analyses for search based software testing, which is one of the main components of our co-evolutionary framework. This thesis gives the important contribution of presenting a novel framework, and we then study its application to three difficult software engineering problems. In this thesis we also give the important contribution of defining a first theoretical foundation.", } @Article{Arcuri2010, author = "Andrea Arcuri and Xin Yao", title = "Co-evolutionary automatic programming for software development", journal = "Information Sciences", year = "2014", volume = "259", pages = "412--432", ISSN = "0020-0255", DOI = "doi:10.1016/j.ins.2009.12.019", URL = "http://www.sciencedirect.com/science/article/B6V0C-4Y34WFM-2/2/6700572128cf209a061759f28c5b7020", keywords = "genetic algorithms, genetic programming, SBSE, STGP, Automatic programming, Automatic refinement, Co-evolution, Software testing", size = "21 pages", abstract = "Since the 1970s the goal of generating programs in an automatic way (i.e., Automatic Programming) has been sought. A user would just define what he expects from the program (i.e., the requirements), and it should be automatically generated by the computer without the help of any programmer. Unfortunately, this task is much harder than expected. Although transformation methods are usually employed to address this problem, they cannot be employed if the gap between the specification and the actual implementation is too wide. In this paper we introduce a novel conceptual framework for evolving programs from their specification. We use genetic programming to evolve the programs, and at the same time we exploit the specification to co-evolve sets of unit tests. Programs are rewarded by how many tests they do not fail, whereas the unit tests are rewarded by how many programs they make to fail. We present and analyse seven different problems on which this novel technique is successfully applied.", notes = "competitive co-evolution. MaxValue, AllEqual, Triangle Classification, Swap, Order, Sorting, Median. One variable called result write_result read_result. Simple first order logic specification. Java, ECJ. Random sampling SSP, ensemble N-version programming.", } @Article{Arcuri20113494, author = "Andrea Arcuri", title = "Evolutionary repair of faulty software", journal = "Applied Soft Computing", volume = "11", number = "4", pages = "3494--3514", year = "2011", ISSN = "1568-4946", URL = "http://crest.cs.ucl.ac.uk/fileadmin/crest/sebasepaper/Arcurid09d.pdf", DOI = "doi:10.1016/j.asoc.2011.01.023", URL = "http://www.sciencedirect.com/science/article/B6W86-5223XWX-1/2/5d81be4fc12644887723df167e134516", keywords = "genetic algorithms, genetic programming, Repair, Fault localisation, Automated debugging, Search Based Software Engineering, Coevolution", abstract = "Testing and fault localization are very expensive software engineering tasks that have been tried to be automated. Although many successful techniques have been designed, the actual change of the code for fixing the discovered faults is still a human-only task. Even in the ideal case in which automated tools could tell us exactly where the location of a fault is, it is not always trivial how to fix the code. In this paper we analyse the possibility of automating the complex task of fixing faults. We propose to model this task as a search problem, and hence to use for example evolutionary algorithms to solve it. We then discuss the potential of this approach and how its current limitations can be addressed in the future. This task is extremely challenging and mainly unexplored in the literature. Hence, this paper only covers an initial investigation and gives directions for future work. A research prototype called JAFF and a case study are presented to give first validation of this approach.", } @InProceedings{Ardeh:2020:SSCI, author = "Mazhar {Ansari Ardeh} and Yi Mei and Mengjie Zhang", booktitle = "2020 IEEE Symposium Series on Computational Intelligence (SSCI)", title = "A GPHH with Surrogate-assisted Knowledge Transfer for Uncertain Capacitated Arc Routing Problem", year = "2020", pages = "2786--2793", abstract = "The Uncertain Capacited Arc Routing Problem is an important and challenging problem that has many real-world applications. Genetic Programming is used to evolve routing policies for vehicles to make real-time decisions and handle uncertain environments efficiently. However, when the problem scenario changes (e.g. a new vehicle is bought or an existing vehicle breaks down), the previously trained routing policy becomes ineffective and a new routing policy needs to be retrained. The retraining process is time-consuming. On the other hand, by extraction and transfer of some knowledge learned from the previous similar problems, the efficiency and effectiveness of the retraining process can be improved. Previous studies have found that the lack of diversity in the transferred materials (e.g. sub-trees) could hurt the effectiveness of transfer learning. As a result, instead of using the genetic materials from a source domain directly, in this work, we use the knowledge from the source domain to create a surrogate model. This surrogate is used on a large number of randomly generated individuals by GP in the target domain to select the promising initial individuals. This way, the diversity of the initial population can be maintained by randomly generated individuals, but also guided by the transferred surrogate model. Our experiments demonstrate that the proposed surrogate-assisted transfer learning method is superior to existing methods and can improve training efficiency and final performance of GP in the target domain.", keywords = "genetic algorithms, genetic programming, Routing, Task analysis, Statistics, Sociology, Learning systems, Knowledge transfer, Training", DOI = "doi:10.1109/SSCI47803.2020.9308398", month = dec, notes = "Also known as \cite{9308398}", } @InProceedings{Ardeh:2020:CEC, author = "Mazhar {Ansari Ardeh} and Yi Mei and Mengjie Zhang", title = "Genetic Programming Hyper-Heuristics with Probabilistic Prototype Tree Knowledge Transfer for Uncertain Capacitated Arc Routing Problems", booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC 2020", year = "2020", editor = "Yaochu Jin", pages = "paper id24067", address = "internet", month = "19-24 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-7281-6929-3", DOI = "doi:10.1109/CEC48606.2020.9185714", abstract = "The Uncertain Capacitated Arc Routing Problem (UCARP) is an important combinatorial optimisation problem with extensive real-world applications. Genetic Programming (GP) has shown effectiveness in automatically evolving routing policies to handle the uncertain environment in UCARP. However, whenever a UCARP scenario changes, e.g. when a new vehicle is bought, the previously trained routing policy may no longer work effectively, and one has to retrain a new policy. Retraining a new policy from scratch can be time-consuming but the transfer of knowledge gained from solving the previous similar scenarios may help improve the efficiency of the retraining process. In this paper, we propose a novel transfer learning method by learning the probability distribution of good solutions from source domains and modeling it as a probabilistic prototype tree. We demonstrate that this approach is capable of capturing more information about the source domain compared to transfer learning based on (sub-)tree transfers and even create good trees that are not seen in source domains. Our experimental results showed that our method made the retraining process more efficient and one can obtain an initial state for solving difficult problems that is significantly better than existing methods. The final performance of all algorithms, were comparable, implying that there was no negative transfer.", notes = "https://wcci2020.org/ Victoria University of Wellington, New Zealand. Also known as \cite{9185714}", } @InProceedings{ardeh:2020:AI, author = "Mazhar {Ansari Ardeh} and Yi Mei and Mengjie Zhang", title = "A Parametric Framework for Genetic Programming with Transfer Learning for Uncertain Capacitated Arc Routing Problem", booktitle = "AI 2020: Advances in Artificial Intelligence", year = "2020", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-030-64984-5_12", DOI = "doi:10.1007/978-3-030-64984-5_12", } @InProceedings{Ardeh:2021:CEC, author = "Mazhar {Ansari Ardeh} and Yi Mei and Mengjie Zhang", booktitle = "2021 IEEE Congress on Evolutionary Computation (CEC)", title = "Surrogate-Assisted Genetic Programming with Diverse Transfer for the Uncertain Capacitated Arc Routing Problem", year = "2021", editor = "Yew-Soon Ong", pages = "628--635", address = "Krakow, Poland", month = "28 " # jun # "-1 " # jul, isbn13 = "978-1-7281-8393-0", abstract = "The Uncertain Capacited Arc Routing Problem (UCARP) is an important routing problem that can model uncertainties of real-world scenarios. Genetic Programming (GP) is a powerful method for evolving routing policies for vehicles to enable them make real-time decisions and handle environmental uncertainties. When facing various problem domains, knowledge transfer can improve the effectiveness of the GP training. Previous studies have demonstrated that due to the existence of duplicated GP individuals in the source domain, the existing transfer learning methods do not perform satisfactorily for UCARP. To address this issue, in this work, we propose a method for detecting duplicates in the source domain and initialising the GP population in the target domain with phenotypically unique individuals. Additionally, since the presence of duplicates can limit the number of good GP individuals, we propose a surrogate-assisted initialisation approach that is able to generate much more diversely distributed initial individuals in the target domain. Our experiments demonstrate that our proposed transfer learning method can significantly improve the effectiveness of GP for training new UCARP routing policies. Compared with the state-of-the-art GP with knowledge transfer, the proposed approach can obtain significantly better solutions on a wide range of UCRP instances, in terms of both initial and final quality.", keywords = "genetic algorithms, genetic programming, Training, Adaptation models, Uncertainty, Transfer learning, Sociology, Routing", DOI = "doi:10.1109/CEC45853.2021.9504817", notes = "Also known as \cite{9504817}", } @InProceedings{Ardeh:2021:GECCO, author = "Mazhar {Ansari Ardeh} and Yi Mei and Mengjie Zhang", title = "A Novel Multi-Task Genetic Programming Approach to Uncertain Capacitated Arc Routing Problem", booktitle = "Proceedings of the 2021 Genetic and Evolutionary Computation Conference", year = "2021", editor = "Francisco Chicano and Alberto Tonda and Krzysztof Krawiec and Marde Helbig and Christopher W. Cleghorn and Dennis G. Wilson and Georgios Yannakakis and Luis Paquete and Gabriela Ochoa and Jaume Bacardit and Christian Gagne and Sanaz Mostaghim and Laetitia Jourdan and Oliver Schuetze and Petr Posik and Carlos Segura and Renato Tinos and Carlos Cotta and Malcolm Heywood and Mengjie Zhang and Leonardo Trujillo and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Fuyuki Ishikawa and Inmaculada Medina-Bulo and Frank Neumann and Andrew M. Sutton", pages = "759--767", address = "internet", series = "GECCO '21", month = jul # " 10-14", organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, Uncertain Capacitated Arc Routing Problem, Hyper Heuristics, Multi-task Optimisation", isbn13 = "9781450383509", DOI = "doi:10.1145/3449639.3459322", size = "9 pages", abstract = "Uncertain Capacitated Arc Routing Problem (UCARP) is an NP-hard optimisation problem with many applications in logistics domains. Genetic Programming (GP) is capable of evolving routing policies to handle the uncertain environment of UCARP. There are many different but related UCARP domains in the real world to be solved (e.g. winter gritting and waste collection for different cities). Instead of training a routing policy for each of them, we can use the multi-task learning paradigm to improve the training effectiveness by sharing the common knowledge among the related UCARP domains. Previous studies showed that GP population for solving UCARP loses diversity during its evolution, which decreases the effectiveness of knowledge sharing. we propose a novel multi-task GP approach that takes the uniqueness of transferable knowledge, as well as its quality, into consideration. Additionally, the transferred knowledge in a manner that improves diversity. We investigated the performance of the proposed method with several experimental studies and demonstrated that the designed knowledge transfer mechanism can significantly improve the performance of GP for solving UCARP", notes = "Victoria University of Wellington GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)", } @PhdThesis{Ansari_Ardeh2022, author = "Mazhar {Ansari Ardeh}", title = "{Transfer Optimisation in Genetic Programming for Solving Uncertain Capacitated Arc Routing Problem}", school = "Computer Science, Victoria University of Wellington", year = "2022", address = "New Zealand", month = "15 " # jul, keywords = "genetic algorithms, genetic programming, UCARP, Evolutionary computation, Fuzzy computation, Transfer Optimisation, Uncertain Capacitated Arc Routing Problem", URL = "https://openaccess.wgtn.ac.nz/ndownloader/files/36279690", URL = "https://openaccess.wgtn.ac.nz/articles/thesis/Transfer_Optimisation_in_Genetic_Programming_for_Solving_Uncertain_Capacitated_Arc_Routing_Problem/20311185", DOI = "doi:10.26686/wgtn.20311185", size = "286 pages", abstract = "Uncertain Capacitated Arc Routing Problem (UCARP) is a combinatorial optimisation problem with many important real-world applications. Genetic programming (GP) is a powerful machine learning technique that has been successfully used to automatically evolve routing policies for UCARP. Reusability is an open issue in the field of UCARP and in this direction, an open challenge is the case of scenario changes, e.g. change in the number of vehicles and probability distributions of random demands, which typically requires new training procedures to be initiated. Considering the expensive training cost of evolving routing policies for UCARP, a promising strategy is to learn and reuse knowledge from a previous problem-solving process to improve the effectiveness and efficiency of solving a new related problem, i.e. transfer learning. The overall goal of this thesis is to develop novel knowledge transfer algorithms for GP for solving UCARP to handle environment changes more effectively and efficiently. To fulfill this goal, a plethora of machine learning techniques, i.e. surrogate models, feature selection, searching and specialised genetic operators, are used in this thesis. First, this thesis explores the effectiveness of the existing transfer optimisation methods for solving UCARP. Accordingly, one of the main directions of this thesis is towards identifying the nature of transferable knowledge, which can impact the quality of knowledge transfer for GP to solve UCARP. For this purpose, a collection of the state-of-the-art transfer optimisation GP algorithms are evaluated for UCARP. After identifying some potential gaps in the literature, a number of preliminary transfer optimisation algorithms are proposed that supplement the literature. To evaluate the algorithms, a large set of knowledge transfer scenarios with various source and target problems were designed based on real-world datasets. According to the results, none of the methods showed significant improvement in the effectiveness of the trained UCARP routing policies. These results revealed the need for more effective transfer optimisation methods specifically designed for UCARP. Furthermore, our investigations revealed that the presence of duplicates in knowledge sources is one of the main challenges for effective transfer optimisation in solving UCARP. Second, we propose approaches to handling the presence of duplicates in the transferred knowledge. The first approach increases population diversity after knowledge transfer to counteract the loss of diversity that is introduced by the presence of duplicates in the transferred knowledge. In the second approach, the duplicates are removed from the transferred knowledge. Then, the transferred knowledge is used to create a diverse initial GP population of high-quality individuals. Both approaches are investigated through detailed experimental studies. The results indicate that, while the first approach did not perform better than GP with knowledge transfer, the second can improve the effectiveness of training routing policies with GP significantly. Third, this thesis proposes a novel algorithm that transfers the phenotypic characteristics of the routing policies for solving the source problem. In the new algorithm, the most fit and unique source routing policies are used for initialising GP for solving the target problem. Then, a tabu list is placed on the source routing policies and the GP process is prohibited from recreating any of the source routing policies. The motivation for this approach is that, due to the existence of similarity between the source and target problems, source routing policies are unlikely to have a good performance for the target problem. Our experimental studies confirmed that by prohibiting GP from recreating source policies, and the computational resources will be spent on searching and evaluating new regions of the search space, which can lead to discovering better solutions. Fourth, this thesis proposes a novel knowledge transfer algorithm based on the idea of maintaining the transferred knowledge as an auxiliary population. In this approach, first, the best individuals of the duplicate-free knowledge source are used to initialise GP. Additionally, these transferred individuals are also maintained as an auxiliary population and are evolved alongside the main population. To save the computational cost, the auxiliary population is evolved with a surrogate method. Additionally, an elaborate knowledge exchange mechanism between the two populations is devised that emphasises transferring high-quality and unique individuals, the transfer of which can improve the diversity of the receiving population. This allows GP to overcome the problem of losing its population diversity during the evolutionary process. Our detailed experimental results confirmed the superior performance of the proposed algorithm and confirmed that the proposed method improved the phenotypic diversity of GP population.", notes = "Supervisors: Yi Mei and Mengjie Zhang", } @Article{Ardeh:2022:ieeeTEC, author = "Mazhar {Ansari Ardeh} and Yi Mei and Mengjie Zhang", title = "Genetic Programming With Knowledge Transfer and Guided Search for Uncertain Capacitated Arc Routing Problem", journal = "IEEE Transactions on Evolutionary Computation", year = "2022", volume = "26", number = "4", pages = "765--779", month = aug, keywords = "genetic algorithms, genetic programming", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2021.3129278", abstract = "The uncertain capacitated arc routing problem has many real-world applications in logistics domains. Genetic programming (GP) is a promising approach to training routing policies to make real-time decisions and handle uncertain events effectively. In the real world, there are various problem domains and no single routing policy can work effectively in all of them. Instead of training in isolation, we can leverage the relatedness between the problems and transfer knowledge from previously solved source problems to solve the target problem. The existing transfer methods are not effective enough due to the loss of diversity during the knowledge transfer. To increase the diversity of the transferred knowledge, in this article, we propose a novel GP method that removes phenotypic duplicates from the source individuals to initialize the target individuals. Furthermore, assuming that the transferred knowledge used in initialization already includes all the important knowledge ex", notes = "also known as \cite{9621218}", } @Article{Ardeh:ieeeTEC2, author = "Mazhar {Ansari Ardeh} and Yi Mei and Mengjie Zhang and Xin Yao", title = "Knowledge Transfer Genetic Programming with Auxiliary Population for Solving Uncertain Capacitated Arc Routing Problem", journal = "IEEE Transactions on Evolutionary Computation", year = "2023", volume = "27", number = "2", pages = "311--325", month = apr, keywords = "genetic algorithms, genetic programming, Arc routing, GP, hyper-heuristics, transfer optimization", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2022.3169289", size = "15 pages", abstract = "The uncertain capacitated arc routing problem is an NP-hard combinatorial optimisation problem with a wide range of applications in logistics domains. Genetic programming hyper-heuristic has been successfully applied to evolve routing policies to effectively handle the uncertain environment in this problem. The real world usually encounters different but related instances due to events like season change and vehicle breakdowns, and it is desirable to transfer knowledge gained from solving one instance to help solve another related one. However, the solutions found by the genetic programming process can lack diversity, and the existing methods use the transferred knowledge mainly during initialisation. Thus, they cannot sufficiently handle the change from the source to the target instance. To address this issue, we develop a novel knowledge transfer genetic programming with an auxiliary population. In addition to the main population for the target instance, we initialise a", notes = "also known as \cite{9761253}", } @InCollection{ardell:1994:TOPE, author = "David H. Ardell", title = "TOPE and Magic Squares: A Simple GA Approach to Combinatorial Optimization", booktitle = "Genetic Algorithms at Stanford 1994", year = "1994", editor = "John R. Koza", pages = "1--6", address = "Stanford, California, 94305-3079 USA", month = dec, organisation = "Stanford University", publisher = "Stanford Bookstore", keywords = "genetic algorithms", ISBN = "0-18-187263-3", notes = "Uses Genesis This volume contains 20 papers written and submitted by students describing their term projects for the course {"}Genetic Algorithms and Genetic Programming{"} (Computer Science 426) at Stanford University offered during the fall quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs426.html", } @Article{DBLP:journals/rcs/ArellanoR19, author = "Humberto Velasco Arellano and Martin Montes Rivera", title = "Forward Kinematics for 2 {DOF} Planar Robot using Linear Genetic Programming", journal = "Research in Computing Science", volume = "148", number = "6", pages = "123--133", year = "2019", keywords = "genetic algorithms, genetic programming, forward kinematics, automatic robot modeling, linear genetic programming", ISSN = "1870-4069", URL = "https://www.rcs.cic.ipn.mx/2019_148_6/Forward%20Kinematics%20for%202%20DOF%20Planar%20Robot%20using%20Linear%20Genetic%20Programming.pdf", timestamp = "Wed, 17 Feb 2021 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/rcs/ArellanoR19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", size = "11 pages", abstract = "In the field of robotics, forward kinematics is an activity that allows finding a mathematical model for the resulting position in the final effector based on the robot joints position, a popular alternative for determining this model is defined by the Denavit Hartenberg convention, nevertheless, this method requires knowledge about linear algebra and three-dimensional spatial kinematics. Machine learning uses specific computational methodologies to solving similar problems in several areas, so it could be a viable answer for automatic determining of forwarding kinematics. we propose the use of genetic programming as a machine learning algorithm for finding the forward kinematics of a 2 degrees of freedom robot, getting a satisfactory outcome obtaining a satisfactory result with blocks that describe the expected solution, validating the capacity of the genetic programming in order to validate this algorithm for later work with more complex robots.", notes = "https://www.rcs.cic.ipn.mx/", } @Article{Arganis:2009:AiCE, title = "Genetic Programming and Standardization in Water Temperature Modelling", author = "Maritza Arganis and Rafael Val and Jordi Prats and Katya Rodriguez and Ramon Dominguez and Josep Dolz", journal = "Advances in Civil Engineering", year = "2009", volume = "2009", publisher = "Hindawi Publishing Corporation", keywords = "genetic algorithms, genetic programming", ISSN = "16878086", URL = "http://downloads.hindawi.com/journals/ace/2009/353960.pdf", DOI = "doi:10.1155/2009/353960", bibsource = "OAI-PMH server at www.doaj.org", language = "eng", oai = "oai:doaj-articles:c20c6fc231b4ba552b81c9f42d58f35f", abstract = "An application of Genetic Programming (an evolutionary computational tool) without and with standardization data is presented with the aim of modeling the behavior of the water temperature in a river in terms of meteorological variables that are easily measured, to explore their explanatory power and to emphasize the utility of the standardization of variables in order to reduce the effect of those with large variance. Recorded data corresponding to the water temperature behavior at the Ebro River, Spain, are used as analysis case, showing a performance improvement on the developed model when data are standardized. This improvement is reflected in a reduction of the mean square error. Finally, the models obtained in this document were applied to estimate the water temperature in 2004, in order to provide evidence about their applicability to forecasting purposes.", notes = "Article ID 353960", } @InCollection{Arganis:2012:GPnew, author = "M. L. Arganis and R. Val and R. Dominguez and K. Rodriguez and Josep Dolz and J. M. Eaton", title = "Comparison Between Equations Obtained by Means of Multiple Linear Regression and Genetic Programming to Approach Measured Climatic Data in a River", booktitle = "Genetic Programming - New Approaches and Successful Applications", publisher = "InTech", year = "2012", editor = "Sebastian Ventura", chapter = "11", pages = "239--254", keywords = "genetic algorithms, genetic programming", isbn13 = "978-953-51-0809-2", DOI = "doi:10.5772/50556", size = "16 pages", notes = "Asco Nuclear Power Station, Ebro River, Spain. Part of \cite{Ventura:2012:GPnew}. Open access book CC BY 3.0 license", } @Article{Arganis:2015:REDIN, author = "Maritza Liliana {Arganis Juarez} and Margarita {Preciado-Jimenez} and Katya {Rodriguez-Vazquez}", title = "Daily rainfall interpolation models obtained by means of genetic programming", journal = "Revista Facultad de Ingenieria Universidad de Antioquia", year = "2015", number = "75", pages = "189--201", month = may, keywords = "genetic algorithms, genetic programming, daily rainfall, genetic programming, interpolation models, isohyet, geographic coordinates, Co Kriging, missing data, Matlab, 29 July 2006", ISSN = "0120-6230", URL = "https://revistas.udea.edu.co/index.php/ingenieria/article/view/21564/18766", DOI = "doi:10.17533/udea.redin.n75a18", size = "13 pages", abstract = "The evolutionary computing algorithm of genetic programming was applied to obtain mathematical daily rainfall interpolation models in one climatologic station, using the measured data in nearby stations in Cutzamala River basin in Mexico. The obtained models take into account both the geographical coordinates of the climatologic station and also its elevation; the answer of these models was compared against those obtained by means of multiple linear regression and a nonlinear model with parameters obtained with genetic algorithms; genetic programming models gave the best performance. Isohyets maps were then obtained to compare the spatial shapes between measured and calculated rainfall data in Cutzamala River Basin, for a maximum historic storm recorded in 2006 year, showing an adequate agreement of the results in case of rainfalls greater than 23 mm. Genetic programming represent a useful practical tool for approaching mathematical models of variables applied in engineering problems and new models could be obtained in several basins by applying these algorithms.", resumen = "Se aplico el algoritmo de computo evolutivo de programacion genetica (PG) para obtener modelos matematicos de interpolacion de precipitacion diaria en una estacion climatologica, utilizando datos medidos en las estaciones cercanas a la cuenca del Rio Cutzamala en Mexico. Los modelos obtenidos toman en cuenta tanto las coordenadas geograficas de las estaciones climatologicas como su elevacion; la respuesta de los modelos se comparo contra los resultados obtenidos con ayuda de regresiones lineales multiples, presentando un mejor desempeno programacion genetica. Adicionalmente, se construyeron mapas de isoyetas para comparar las formas espaciales entre los datos de precipitacion medidos y calculados en la cuenca del Rio Cutzamala para una tormenta maxima historica registrada en el ano 2006, observandose concordancia en los resultados en el caso de precipitaciones mayores de 23 mm. La programacion genetica representa una herramienta de utilidad practica para aproximar modelos matematicos de variables aplicadas en problemas de ingenieria y se pueden obtener nuevos modelos en distintas cuencas al aplicar estos algoritmos.", notes = "UNAM, Instituto de Ingenieria, Facultad de Ingenieria, Universidad Nacional Autonoma de Mexico p199 'Using the GP model (eq 3) also gave higher correlations than those used with the simple regression model'", } @Article{ARGANISJUAREZ:2022:jksus, author = "Maritza Liliana {Arganis Juarez} and Maria Fernanda {Hernandez Ignacio} and Sandra Lizbeth {Rosales Silvestre} and Javier {Osnaya Romero} and Eliseo {Carrizosa Elizondo}", title = "Evaluation of the capacity of {PET} bottles, water aeration, and water recirculation to reduce evaporation in containers of water", journal = "Journal of King Saud University - Science", volume = "34", number = "4", pages = "102046", year = "2022", ISSN = "1018-3647", DOI = "doi:10.1016/j.jksus.2022.102046", URL = "https://www.sciencedirect.com/science/article/pii/S1018364722002270", keywords = "genetic algorithms, genetic programming, Water evaporation, Evaporation reduction, PET plastic bottles, Principal component analysis", abstract = "Objective: To evaluate the effectiveness of an evaporation reduction method in which the greater part of the water surface was covered with PET-type plastic bottles. The capacity of this method to diminish natural evaporation was compared to water aeration, water recirculation, and the control (without intervention). With evolutionary computation, including genetic algorithms and genetic programming, equations for calculating evaporation were developed based on meteorological factors. Methods Four containers of water were placed on a flat roof in Mexico City (thus exposed to the factors of weather), and evaporation was measured daily with an evaporimeter. Each container was assigned to one of the evaporation reduction methods (PET bottles, water aeration, or water recirculation) or to the control (without intervention). Evaporation-related variables were selected according to previous reports and principal component analysis, and their values were acquired from a nearby meteorological station. The study was conducted from April of 2020 to February of 2021. Results Covering the water surface with PET bottles avoided 38.61percent (a total of 139mm) of natural evaporation, which is represented by the control. The water aeration and water recirculation methods diminished evaporation by 7.22percent (26mm) and 2.22percent (8mm), respectively. The best equations for estimating evaporation were obtained with genetic programming for the control container and a genetic algorithm for the container with PET bottles. Conclusions The PET bottle method of evaporation reduction was 7 and 17 times more effective than water aeration and water recirculation, respectively. The 38.61percent decrease in evaporation achieved by covering the water surface with PET bottles constitutes a substantial savings in water. Hence, the implementation of such a method should be considered to contribute to water conservation in reservoirs. The use of PET bottles is a practical and inexpensive method requiring only a few cleaning maneuvers to prevent the proliferation of unwanted aquatic fauna.", } @Article{Argyri2012, author = "Anthoula A. Argyri and Roger M. Jarvis and David Wedge and Yun Xu and Efstathios Z. Panagou and Royston Goodacre and George-John E. Nychas", title = "A comparison of Raman and FT-IR spectroscopy for the prediction of meat spoilage", journal = "Food Control", volume = "29", number = "2", pages = "461--470", year = "2013", note = "Predictive Modelling of Food Quality and Safety", ISSN = "0956-7135", DOI = "doi:10.1016/j.foodcont.2012.05.040", URL = "http://www.sciencedirect.com/science/article/pii/S0956713512002745", keywords = "genetic algorithms, genetic programming, Meat spoilage, Raman spectroscopy, FT-IR, Multivariate analysis, Evolutionary computing", abstract = "In this study, time series spectroscopic, microbiological and sensory analysis data were obtained from minced beef samples stored under different packaging conditions (aerobic and modified atmosphere packaging) at 5 C. These data were analysed using machine learning and evolutionary computing methods, including partial least square regression (PLS-R), genetic programming (GP), genetic algorithm (GA), artificial neural networks (ANNs) and support vector machines regression (SVR) including different kernel functions [i.e. linear (SVRL), polynomial (SVRP), radial basis (RBF) (SVRR) and sigmoid functions (SVRS)]. Models predictive of the microbiological load and sensory assessment were calculated using these methods and the relative performance compared. In general, it was observed that for both FT-IR and Raman calibration models, better predictions were obtained for TVC, LAB and Enterobacteriaceae, whilst the FT-IR models performed in general slightly better in predicting the microbial counts compared to the Raman models. Additionally, regarding the predictions of the microbial counts the multivariate methods (SVM, PLS) that had similar performances gave better predictions compared to the evolutionary ones (GA-GP, GA-ANN, GP). On the other hand, the GA-GP model performed better from the others in predicting the sensory scores using the FT-IR data, whilst the GA-ANN model performed better in predicting the sensory scores using the Raman data. The results of this study demonstrate for the first time that Raman spectroscopy as well as FT-IR spectroscopy can be used reliably and accurately for the rapid assessment of meat spoilage.", } @Article{Ari:2021:SAUJS, author = "Davut Ari and Baris Baykant Alagoz", title = "A Review of Genetic Programming: Popular Techniques, Fundamental Aspects, Software Tools and Applications", journal = "Sakarya University Journal of Science", year = "2021", volume = "25", number = "2", pages = "397--416", month = apr, keywords = "genetic algorithms, genetic programming, gp types, gp applications, gp software", publisher = "Sakarya University", ISSN = "2147-835X", URL = "https://abakus.inonu.edu.tr/items/21ec5e12-72fd-42e0-98e2-baf039ece53a", broken = "http://www.saujs.sakarya.edu.tr/en/pub/issue/60672/793333", URL = "https://dergipark.org.tr/en/download/article-file/1284370", DOI = "doi:10.16984/saufenbilder.793333", size = "20 pages", abstract = "Genetic Programming (GP) is one of the evolutionary computation (EC) methods followed with great interest by many researchers. When GP first appeared, it has become a popular computational intelligence method because of its successful applications and its potentials to find effective solutions for difficult practical problems of many different disciplines. With the use of GP in a wide variety of areas, numerous variants of GP methods have emerged to provide more effective solutions for computation problems of diverse application fields. Therefore, GP has a very rich literature that is progressively growing. Many GP software tools developed along with process of GP algorithms. There is a need for an inclusive survey of GP literature from the beginning to today of GP in order to reveal the role of GP in the computational intelligence field. This survey study aims to provide an overview of the growing GP literature in a systematic way. The researchers, who need to implement GP methods, can gain insight of potentials in GP methods, their essential drawbacks and prevalent superiorities. Accordingly, taxonomy of GP methods is given by a systematic review of popular GP methods. In this manner, GP methods are analyzed according to two main categories, which consider the discrepancies in their program (chromosome) representation styles and their methodologies. Besides, GP applications in diverse problems are summarized. This literature survey is especially useful for new researchers to gain the required broad perspective before implementing a GP method in their problems.", notes = "also known as \cite{article_793333} SAUJS http://www.saujs.sakarya.edu.tr/en/", } @InProceedings{Ari:2021:ICIT, author = "Davut Ari and Baris Baykant Alagoz", title = "A Genetic Programming Based Pollutant Concentration Predictor Design for Urban Pollution Monitoring Based on Multi-Sensor Electronic Nose", booktitle = "2021 International Conference on Information Technology (ICIT)", year = "2021", pages = "168--172", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICIT52682.2021.9491122", month = jul, abstract = "An important part of air pollution control is the pollution monitoring. Since industrial spectrometers are expensive equipment, the number of observation points to monitor air pollution over an urban area can be limited. The low-cost multi-sensors network can spread over areas and form a wide-area electronic nose to estimate pollutant concentration distributions. However, the collected multisensor data should be analysed to correctly estimate pollutant concentrations. This study demonstrates implementation of genetic programming (GP) to obtain prediction models that can estimate CO and NO2 concentrations from multisensor electronic nose data. For this purpose, to function as an electronic nose, a regression model from a training data set is obtained by using a tree-based GP algorithm. In order to improve performance of the GP based prediction models, data normalization is performed and prediction performance enhancements are demonstrated via statistical performance analyses on a test data set.", notes = "Also known as \cite{9491122}", } @InProceedings{conf/cit/AriA21a, author = "Davut Ari and Baris Baykant Alagoz", title = "Modeling Daily Financial Market Data by Using Tree-Based Genetic Programming", booktitle = "2021 International Conference on Information Technology, ICIT", year = "2021", pages = "382--386", address = "Amman, Jordan", month = "14-15 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-6654-2870-5", bibdate = "2021-10-14", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/cit/cit2021.html#AriA21a", DOI = "doi:10.1109/ICIT52682.2021.9491652", abstract = "A behavioural modelling of financial markets based on daily data is not an easy problem for machine learning algorithms. The social and physiological factors can take effect on market data and result in significant uncertainty in data. This study demonstrates an implementation of tree-based genetic programming (GP) to develop a mathematical model of stock market from the daily stock data of other stock markets to observe relations between global market trends and to consider this effect in market prediction problems. To obtain a prediction model of Istanbul Stock Exchange 100 Index (ISE100), numerical data from ISE100 and seven other international stock market indices are used to produce GP models that can estimate daily price changes in ISE100 according to daily change in other international stock market indices. To reduce negative effects of the data uncertainty on the GP modelling, ensemble average GP modelling performances are investigated and the results are reported for future research direction suggestions.", } @Article{ari:2022:NCaA, author = "Davut Ari and Baris Baykant Alagoz", title = "An effective integrated genetic programming and neural network model for electronic nose calibration of air pollution monitoring application", journal = "Neural Computing and Applications", year = "2022", volume = "34", number = "15", keywords = "genetic algorithms, genetic programming, ANN", URL = "http://link.springer.com/article/10.1007/s00521-022-07129-0", DOI = "doi:10.1007/s00521-022-07129-0", } @PhdThesis{DBLP:phd/tr/Ari23, author = "Davut Ari", title = "The genetic programming and its applications in engineering", title_tr = "Genetik programlama ve m{\"{u}}hendislikte uygulamalar{\i}", school = "{\.{I}}n{\"{o}}n{\"{u}} University, Turkey", year = "2023", month = jan, keywords = "genetic algorithms, genetic programming, Gene Expression Programming, Hava kirliligi = Air pollution, Veri analizi = Data analysis", URL = "https://tez.yok.gov.tr/UlusalTezMerkezi/tezDetay.jsp?id=5cvay5p_jJQNEtvmnO5fww&no=HNOMlxzz-uJmirw8rmMHEQ", timestamp = "Sun, 29 Oct 2023 01:00:00 +0200", biburl = "https://dblp.org/rec/phd/tr/Ari23.bib", bibsource = "dblp computer science bibliography, https://dblp.org", size = "123 pages", abstract = "Genetic Programming (GP) is an evolutionary computational method that can generate symbolic and mathematical models. In addition to being a type of evolutionary computing, the GP is also frequently used in solving symbolic regression problems in machine learning applications. Since its first appearance, it has become one of the popular evolutionary calculation methods as a result of being successfully applied for solution of modeling problems appeared in many different disciplines. Within the scope of this thesis, research studies have been carried out for development of data driven prediction models and their engineering applications by using the classical GP and its a variant, Gene Expression Programming (GEP). In order to increase the effectiveness of these GP methods in practice, data normalization, ensemble learning, hybrid model development and hyperparameter optimization techniques are studied. In addition, the chromosome structure of the GEP method has been modified and an optimal solution to the constant value determination problem has been proposed. Then, the modified GEP method was combined with popular metaheuristic optimization methods, and thus a metaheuristic optimization based GEP (MetaSezGEP) approach was developed. Contributions of these improvements to some engineering applications have been investigated.", notes = "in Turkish Tez No 774886. 36183619043 Supvervisor: Baris Baykant ALAGOZ", } @Article{ari:2023:SC, author = "Davut Ari and Baris Baykant Alagoz", title = "{DEHypGpOls:} a genetic programming with evolutionary hyperparameter optimization and its application for stock market trend prediction", journal = "Soft Computing", year = "2023", volume = "27", number = "5", pages = "2553--2574", month = mar, keywords = "genetic algorithms, genetic programming, Stock market prediction, Stock price, Hyperparameter optimization, Trend prediction", ISSN = "1432-7643", URL = "https://rdcu.be/daFKI", URL = "http://link.springer.com/article/10.1007/s00500-022-07571-1", DOI = "doi:10.1007/s00500-022-07571-1", size = "22 pages", abstract = "Stock markets are a popular kind of financial markets because of the possibility of bringing high revenues to their investors. To reduce risk factors for investors, intelligent and automated stock market forecast tools are developed by using computational intelligence techniques. This study presents a hyperparameter optimal genetic programming-based forecast model generation algorithm for a-day-ahead prediction of stock market index trends. To obtain an optimal forecast model from the modeling dataset, a differential evolution (DE) algorithm is employed to optimize hyperparameters of the genetic programming orthogonal least square (GpOls) algorithm. Thus, evolution of GpOls agents within the hyperparameter search space enables adaptation of the GpOls algorithm for the modeling dataset. This evolutionary hyperparameter optimization technique can enhance the data-driven modeling performance of the GpOls algorithm and allow the optimal autotuning of user-defined parameters. In the current study, the proposed DE-based hyper-GpOls (DEHypGpOls) algorithm is used to generate forecaster models for prediction of a-day-ahead trend prediction for the Istanbul Stock Exchange 100 (ISE100) and the Borsa Istanbul 100 (BIST100) indexes. In this experimental study, daily trend data from ISE100 and BIST100 and seven other international stock markets are used to generate a-day-ahead trend forecaster models. Experimental studies on 4 different time slots of stock market index datasets demonstrated that the forecast models of the DEHypGpOls algorithm could provide 57.87 percent average accuracy in buy-sell recommendations. The market investment simulations with these datasets showed that daily investments to the ISE100 and BIST100 indexes according to buy or sell signals of the forecast model of DEHypGpOls could provide 4.8 percent more average income compared to the average income of a long-term investment strategy.", } @Article{Ari:2023:ASOC, author = "Davut Ari and Baris Baykant Alagoz", title = "A differential evolutionary chromosomal gene expression programming technique for electronic nose applications", journal = "Applied Soft Computing", year = "2023", volume = "136", pages = "110093", month = mar, keywords = "genetic algorithms, genetic programming, Gene expression programming, Air quality electronic nose, Differential evolution, Sensor calibration", ISSN = "1568-4946", URL = "https://www.sciencedirect.com/science/article/pii/S1568494623001114", DOI = "doi:10.1016/j.asoc.2023.110093", abstract = "The intelligent system applications require automated data-driven modeling tools. The performance consistency of modeling tools is very essential to reduce the need for human intervention. Classical Gene Expression Programmings (GEPs) employ predefined genetic rules for the node-based evolution of expression trees in the absence of optimal numerical values of constant terminals, and these shortcomings can limit the search efficiency of expression trees. To alleviate negative impacts of these limitations on the data-driven GEP modeling performance, a Differential Evolutionary Chromosomal GEP (DEC-GEP) algorithm is suggested. The DEC-GEP uses the Differential Evolution (DE) algorithm for the optimization of a complete genotype of expression trees. For this purpose, a modifier gene container, which stores numerical values of constant terminals, is appended to the frame of GEP chromosome, and this modified chromosome structure enables simultaneous optimization of expression tree genotypes together with numerical values of constant terminals. Besides, the DEC-GEP algorithm can benefit from exploration and exploitation capabilities of the DE algorithm for more efficient evolution of GEP expression trees. To investigate consistency of the DEC-GEP algorithm in a data-driven modeling application, an experimental study was conducted for soft calibration of the low-cost, solid-state sensor array measurements, and results indicated that the DEC-GEP could yield dependable CO concentration estimation models for electronic nose applications.", notes = "also known as \cite{ARI2023110093}", } @InProceedings{Arif:2017:ICTAI, author = "Muhammad Hassan Arif and Jianxin Li and Muhammad Iqbal", booktitle = "2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI)", title = "Solving Social Media Text Classification Problems Using Code Fragment-Based XCSR", year = "2017", pages = "485--492", abstract = "Sentiment analysis and spam detection of social media text messages are two challenging data analysis tasks due to sparse and high-dimensional feature vectors. Learning classifier systems (LCS) are rule-based evolutionary computing systems and have limited capabilities to handle real valued sparse high-dimensional big data sets. LCS techniques use interval based representations to handle real valued feature vectors. In the work presented here, interval based representation is replaced by genetic programming based tree like structures to classify high-dimensional real valued text feature vectors. Multiple experiments are conducted on different social media text data sets, i.e. tweets, film reviews, Amazon and yelp reviews, SMS and Email spam message to evaluate the proposed scheme. Real valued feature vectors are generated from these data sets using term frequency inverse document frequency and/or sentiment lexicons-based features. Results depicts the supremacy of the new encoding scheme over interval based representations in both small and large social media text data sets.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICTAI.2017.00080", ISSN = "2375-0197", month = nov, notes = "The Institute of Advanced Computing Technology, Beihang University, Beijing, China Also known as \cite{8371983}", } @InProceedings{Aristotle-De-Leon:2022:HNICEM, author = "Joseph {Aristotle De Leon} and Mike {Louie Enriquez} and Ronnie Concepcion and Ira Valenzuela and Ryan {Rhay Vicerra} and Homer Co and Argel Bandala and Elmer Dadios", booktitle = "2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)", title = "Vertical Electrical Sounding Inversion Models Trained from Dataset using Synthetic Data and Genetic Programming", year = "2022", abstract = "The inversion process of Vertical Electrical Sounding (VES) is an important step in 1-D subsurface surface surveys to determine the true resistivities and heights of different layers of soil or rocks underground which is beneficial in geological and hydrological applications like locating potential areas for aquifers. Machine learning based algorithms is currently a trend in the inversion of vertical electrical sounding (VES) data to address the issues of the conventional methods. However, most models trained are being limited to one electrode half spacing configuration, and not being able to explain the underlying relationships of the model. Hence, the present study seeks to address these by obtaining VES inversion models for four-layer earth models through genetic programming and a synthetic dataset. The synthetic dataset covering different electrode half spacing configurations and VES curve types was generated and used to train the genetic programming model through GPTIPS software. By testing the best models on the synthetic dataset, it offered good metrics on the true resistivities of each layer, but performed poorly on estimating the layers' heights. Regardless, the models obtained can be symbolically expressed and be interpreted which has not been done in other machine learning inversion models for VES. While this study's implementation of genetic programming is not yet satisfactory, obtaining the symbolic expressions can allow future works to systematically improve the worst performing models.", keywords = "genetic algorithms, genetic programming, Measurement, Electrodes, Earth, Machine learning, Conductivity, Soil, Vertical Electrical Sounding, Inversion, Underground Imaging, Resistivity Imaging", DOI = "doi:10.1109/HNICEM57413.2022.10109565", ISSN = "2770-0682", month = dec, notes = "Also known as \cite{10109565}", } @InProceedings{Arita:1997:hamilton, author = "Masanori Arita and Akira Suyama and Masami Hagiya", title = "A Heuristic Approach for Hamiltonian Path Problem with Molecules", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "DNA Computing", pages = "457--462", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", notes = "GP-97", } @InProceedings{Arkoudas:2008:AAAIf, author = "Konstantine Arkoudas", title = "Automatically Discovering Euler's Identity via Genetic Programming", booktitle = "AAAI Fall Symposium", year = "2008", editor = "Selmer Bringsjord and Andrew Shilliday", pages = "1--7", address = "Arlington, Virginia, USA", publisher_address = "Menlo Park, California, USA", month = nov # " 7-9", publisher = "AAAI", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-57735-395-9", URL = "http://www.aaai.org/Papers/Symposia/Fall/2008/FS-08-03/FS08-03-001.pdf", size = "7 pages", abstract = "We show that by using machine learning techniques (genetic programming, in particular), Euler's famous identity (V - E + F = 2) can be automatically discovered from a limited amount of data indicating the values of V , E, and F for a small number of polyhedra the five platonic solids. This result suggests that mechanized inductive techniques have an important role to play in the process of doing creative mathematics, and that large amounts of data are not necessary for the extraction of important regularities. Genetic programming was implemented from scratch in SML-NJ.", notes = "Technical Report FS-08-03. Published by The AAAI Press http://www.aaai.org/Press/Reports/Symposia/Fall/fs-08-03.php Cube, triangular prism, pentagonal prism, square pyramid, triangular pyramid, pentagonal pyramid, octahedron, tower, truncated cube. SML. The source code can be downloaded from www.rpi.edu/~arkouk/euler/", } @Article{Arkov:2000:ARC, author = "V. Arkov and C. Evans and P. J. Fleming and D. C. Hill and J. P. Norton and I. Pratt and D. Rees and K. Rodriguez-Vazquez", title = "System Identification Strategies Applied to Aircraft Gas Turbine Engines", journal = "Annual Reviews in Control", volume = "24", pages = "67--81", year = "2000", number = "1", keywords = "genetic algorithms, genetic programming, gas turbines, system identification, frequency domain, multisine signals least-squares estimation, time-varying systems, structure selection", ISSN = "1367-5788", broken = "http://www.sciencedirect.com/science/article/B6V0H-482MDPD-8/2/dd470648e2228c84efe7e14ca3841b7e", DOI = "doi:10.1016/S1367-5788(00)90015-4", size = "15 pages", abstract = "A variety of system identification techniques are applied to the derivation of models of aircraft gas turbine dynamics. The motivation behind the study is to improve the efficiency and cost-effectiveness of system identification techniques currently used in the industry. Four system identification approaches are outlined in this paper. They are based upon: identification using ambient noise only data, multisine testing and frequency-domain identification, time-varying models estimated using extended least squares with optimal smoothing, and multiobjective genetic programming to select model structure.", notes = "Also known as \cite{Arkov200067}", } @Article{journals/ewc/ArmaghaniFMFT18, author = "Danial Jahed Armaghani and Roohollah Shirani Faradonbeh and Ehsan Momeni and Ahmad Fahimifar and Mahmood M. D. Tahir", title = "Performance prediction of tunnel boring machine through developing a gene expression programming equation", journal = "Engineering with Computers", year = "2018", number = "1", volume = "34", pages = "129--141", month = jan, keywords = "genetic algorithms, genetic programming, gene expression programming", ISSN = "0177-0667", bibdate = "2018-01-19", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ewc/ewc34.html#ArmaghaniFMFT18", DOI = "doi:10.1007/s00366-017-0526-x", abstract = "The prediction of tunnel boring machine (TBM) performance in a specified rock mass condition is crucial for any mechanical tunneling project. TBM performance prediction in accurate may reduce the risks related to high capital costs and scheduling for tunneling. This paper presents a new model/equation based on gene expression programming (GEP) to estimate performance of TBM by means of the penetration rate (PR). To achieve the aim of the study, the Pahang-Selangor Raw Water Transfer tunnel in Malaysia was investigated and the data related to field observations and laboratory tests were used in modelling of PR of TBM. A database (1286 datasets in total) comprising 7 model inputs related to rock (mass and material) properties and machine characteristics and 1 output (PR) was prepared to use in GEP modelling. To evaluate capability of the developed GEP equation, a multiple regression (MR) model was also proposed. A comparison between the obtained results has been done using several performance indices and the best equations of GEP and MR were selected. System results for the developed GEP equation based on coefficient of determination (R 2) were obtained as 0.855 and 0.829 for training and testing datasets, respectively, while these values were achieved as 0.795 and 0.789 for the developed MR equation. Concluding remark is that the GEP equation is superior in comparison with the MR equation and it can be introduced as a new equation in the field of TBM performance prediction.", } @Article{armaghani:2018:NCaA, author = "Danial Jahed Armaghani and Roohollah Shirani Faradonbeh and Hossein Rezaei and Ahmad Safuan A. Rashid and Hassan Bakhshandeh Amnieh", title = "Settlement prediction of the rock-socketed piles through a new technique based on gene expression programming", journal = "Neural Computing and Applications", year = "2018", volume = "29", number = "11", keywords = "genetic algorithms, genetic programming, gene expression programming", URL = "http://link.springer.com/article/10.1007/s00521-016-2618-8", DOI = "doi:10.1007/s00521-016-2618-8", } @InProceedings{Armani_2010, author = "Umberto Armani and Vassili V. Toropov and Andrey Polynkin and Osvaldo M. Querin and Luis Alvarez", title = "Enhancements to a hybrid genetic programming technique applied to symbolic regression", booktitle = "Proceedings of the 8th ASMO UK / ISSMO conference on Engineering Design Optimization Product and Process Improvement", year = "2010", editor = "Fabian Duddeck and Osvaldo M. Querin and Johann Sienz and Vassili V. Toropov and M. Hasan Shaheed", address = "Queen Mary University of London, UK", month = jul # " 8-9", keywords = "genetic algorithms, genetic programming", isbn13 = "978-0-85316-292-6", URL = "http://www.asmo-uk.com/8th-asmo-uk/html/menu_page.html", URL = "http://www.asmo-uk.com/8th-asmo-uk/presentations/session1_presentation5.pdf", abstract = "A major problem in genetic programming techniques is premature convergence, which emerges during evolution as a progressive loss of variability among individuals in the population. Moreover, the mechanisms according to which individuals are created, recombined and evaluated have of course strong influence on the chances of success. Increasing variability of the population and expressivity of the genotype are then major issues for genetic programming techniques. The aim of this paper is to investigate if a hybrid, tree-based GP implementation written for symbolic regression purposes can be improved in terms of reliability and precision of the results both by several modifications of the standard GP components and by pre-processing the input data set. In order to increase variability, the effect of a simple archive updating strategy and of a periodical killing of a large part of the population (with the insertion of new and composed individuals) is assessed. As a promising measure to preserve variation among individuals, a MinMax approach in the definition of the fitness function is also proposed and tested as an alternative to the plain aggregating approach. With regard to expressivity, a simple solution consisting in the definition of a unary function that introduces a translation in the argument of the function itself is put forward. Other experiments are performed to assess if the redefinition of the fitness function using a normalised error can have beneficial effects on the evolution, as an alternative to the common root mean square error. Finally, the splitting of the input data set in two different subsets, respectively for parameter tuning and fitness evaluation, is investigated.", notes = "SQP Rosenbrock KILLandFILL Kotanchek RatPol2D Hock Branin-Hoo Salustowicz session1_presentation5.pdf is slides School of Civil Engineering, University of Leeds, LS2 9JT, UK", } @InProceedings{Armani_2011_1, author = "Umberto Armani and Dirk Jan Boon and Vassili V. Toropov and Andrey Polynkin and Leslie J. Clark and Mary B. Stowe", title = "Generation of models related to aluminium surface treatment using genetic programming", booktitle = "Proceedings of the 9th world congress on structural and multidisciplinary optimization (WCSMO9),", year = "2011", address = "Shizuoka, Japan", month = jun # " 13-17", keywords = "genetic algorithms, genetic programming, hybrid genetic programming, corrosion, model", broken = "http://pbl.eng.kagawa-u.ac.jp/kani/index4.php", URL = "http://pbl.eng.kagawa-u.ac.jp/kani/p/paper246_1.pdf", size = "9 pages", abstract = "Surface treatment in aerospace industry is of paramount importance for protection of metallic structures against corrosion, in particular aluminium alloys. One of the common techniques consists of the generation of a surface coating (through chemical conversion or anodising) followed by the application of a primer paint containing a water soluble chromate salt, such as barium chromate BaCrO4 or strontium chromate SrCrO4. Such treatment allows for corrosion protection of the aluminium alloy in the presence of moisture even in the case of damage to the protective coating, through chemical and mass transfer processes involving the primer, water, the exposed alloy and the chromate salts. The availability of empirical models describing the quantity of chromate dissolving into the aqueous medium is therefore important for understanding the corrosion protection process and it could lead to improvements in the development and qualification of new corrosion protection systems. The main aim of this paper is to provide improved models to describe the quantity of dissolved chromate in water for three different chromate-based primers, considering as independent variables the time treated aluminium alloy samples are left in an aqueous solution and the acidity of the solution. To produce the models a hybrid genetic programming technique is used. Its role is to generate models through symbolic regression on experimental data provided by industry. Being a non-parametric regression technique, genetic programming is successful in finding a range of models whose mathematical structure is different from existing ones", } @InProceedings{Armani_2011_2, author = "U. Armani and Z. Khatir and Amirul Khan and V. V. Toropov and A. Polynkin and H. Thompson and N. Kapur and C. J. Noakes", title = "Control of Physical Consistency in Metamodel Building by Genetic Programming", booktitle = "Proceedings of the Second International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering (CSC2011)", year = "2011", editor = "Y. Tsompanakis and B. H. V. Topping", pages = "Paper 43", address = "Chania, Greece", publisher_address = "Stirlingshire, UK", publisher = "Civil-Comp Press", keywords = "genetic algorithms, genetic programming, high-fidelity design optimisation, metamodel, mathematical structure, non-linear system, analytical expression, engineering applications", URL = "http://www.ctresources.info/ccp/paper.html?id=6631", DOI = "doi:10.4203/ccp.97.43", size = "18 pages", abstract = "Soft computing has grown in importance in recent years, allowing engineers to handle more and more complex problems. Computer power has made different classes of computationally intensive techniques viable and successful alternatives to other established methods. Algorithms based on machine learning, data mining and genetically inspired methods are in some cases the only choice when the knowledge of the problem is scarce. Genetic programming (GP) [1] can be considered one of the latest techniques to have appeared in the range of soft computing tools. It is a genetically-inspired method able to generate from a data set global metamodels describing the relationship between a system's input and output data. Typically, genetic operators are used to recombine parts of mathematical expressions in a randomised but directed way until a high quality metamodel (i.e. a model of a model) is found. The major strength of genetic programming lies in its ability to provide explicit metamodels, making possible the use of traditional analytical methods for the subsequent analysis and optimisation. A problem arises that the stochastic nature of GP reduces the possibility of controlling the consistency of the generated metamodels. It is not uncommon in a conventional GP experiment to obtain expressions that despite showing low errors cannot be used in an application as their response is not consistent with the assumptions imposed by the problem's nature. In this paper it is described how control of the physical consistency of the generated metamodels can be improved using some basic knowledge regarding the problem at hand by imposing constraints in the problem formulation. The benefits of the new strategy are shown through a benchmark problem. Two case studies where genetic programming has been successfully applied to optimise the ventilation design of an industrial bread baking oven and of a hospital ward are also presented. In both cases data provided by computational fluid dynamics (CFD) simulations were used to generate a metamodel and genetic algorithm techniques were used to find the optimum of the modelled response. Validation of the optimal point performed using data generated by additional CFD simulations confirmed the high quality of the metamodels. In a case study the optimum found by genetic programming matches the optimum found by another metamodelling technique.", } @InProceedings{Armani_2012, author = "U. Armani and S. Coggon and V. V. Toropov", title = "Derivation of Deterministic Design Data from Stochastic Analysis in the Aircraft Design Process", booktitle = "Proceedings of the Eleventh International Conference on Computational Structures Technology (CST2012)", year = "2012", editor = "B. H. V. Topping", pages = "Paper 216", address = "Dubrovnik, Croatia", publisher_address = "Stirlingshire, UK", month = "4-7 " # sep, publisher = "Civil-Comp Press", keywords = "genetic algorithms, genetic programming, industrial optimisation, metamodel, polynomial chaos expansion, sensitivity analysis, particle swarm optimisation, dimensionality reduction", URL = "http://webapp.tudelft.nl/proceedings/cst2012/html/summary/armani.htm", URL = "http://webapp.tudelft.nl/proceedings/cst2012/pdf/armani.pdf", DOI = "doi:10.4203/ccp.99.216", size = "16 pages", abstract = "The application of uncertainty management techniques to the aircraft design process is currently a high profile research area and of key strategic interest within aerospace industry. Within the aircraft design process there is always a difficult balance between non specific and specific design steps for configuration and design maturity versus the overall project lead time. This leads to either an immature design that causes delays of the entry into service or significant re-design loops within the aircraft development project again resulting in a significant cost penalty. The ability to quantify uncertainties in the design enables the application of more robust optimisation approaches to balance the quantitative risks of design evolution against the aircraft performance implications (e.g. aircraft weight) and specific design lead time. Although the application of stochastic analysis is a powerful way of making informed design decisions, its integration into the standard design process requires the generation of deterministic design data which achieve the design targets from an uncertainty approach. In this paper the problem of retrieving deterministic design data from a collection of responses provided by aircraft structural computer models is addressed. Firstly, a framework that enables metamodel generation and dimensionality reduction is presented. The framework relies on polynomial chaos expansion (PCE) for metamodel generation [1]. The technique was chosen for its ability to ease the sensitivity analysis process, as sensitivity information in the form of Sobol indices can be extracted analytically from the PCE metamodels. Secondly, a search algorithm that can be used to explore the metamodels generated by PCE is presented. The algorithm, based on the particle swarm optimisation (PSO) paradigm [2], was developed specifically to be used in constrained search problems: it performs a search of the design configurations that produces a specified target response level. Constraints can also be defined using additional metamodels. The framework and the search algorithm have been validated on an aircraft structural analysis problem. The accuracy of the results and the reduced computational cost of the entire process make the presented methodology a valuable tool for uncertainty and sensitivity analysis in the aerospace industry.", notes = "PSO rather than GP? http://webapp.tudelft.nl/proceedings/cst2012/html/home.htm", } @PhdThesis{Armani_PhD_thesis, author = "Umberto Armani", title = "Development of a hybrid genetic programming technique for computationally expensive optimisation problems", school = "School of Civil Engineering, University of Leeds", year = "2014", address = "UK", month = feb, keywords = "genetic algorithms, genetic programming", URL = "http://etheses.whiterose.ac.uk/7281/1/Armani_PhD_thesis_resubmission_grerrors_corrected.pdf", URL = "http://etheses.whiterose.ac.uk/7281/", URL = "http://ethos.bl.uk/OrderDetails.do?did=50&uin=uk.bl.ethos.631392", size = "406 pages", abstract = "The increasing computational power of modern computers has contributed to the advance of nature-inspired algorithms in the fields of optimisation and metamodelling. Genetic programming (GP) is a genetically-inspired technique that can be used for meta modelling purposes. GP main strength is in the ability to infer the mathematical structure of the best model fitting a given data set, relying exclusively on input data and on a set of mathematical functions given by the user. Model inference is based on an iterative or evolutionary process, which returns the model as a symbolic expression (text expression). As a result, model evaluation is inexpensive and the generated expressions can be easily deployed to other users. Despite genetic programming has been used in many different branches of engineering, its diffusion on industrial scale is still limited. The aims of this thesis are to investigate the intrinsic limitations of genetic programming, to provide a comprehensive review of how researchers have tackled genetic programming main weaknesses and to improve genetic programming ability to extract accurate models from data. In particular, research has followed three main directions. The first has been the development of regularisation techniques to improve the generalisation ability of a model of a given mathematical structure, based on the use of a specific tuning algorithm in case sinusoidal functions are among the functions the model is composed of. The second has been the analysis of the influence that prior knowledge regarding the function to approximate may have on genetic programming inference process. The study has led to the introduction of a strategy that allows to use prior knowledge to improve model accuracy. Thirdly, the mathematical structure of the models returned by genetic programming has been systematically analysed and has led to the conclusion that the linear combination is the structure that is mostly returned by genetic programming runs. A strategy has been formulated to reduce the evolutionary advantage of linear combinations and to protect more complex classes of individuals throughout the evolution. The possibility to use genetic programming in industrial optimisation problems has also been assessed with the help of a new genetic programming implementation developed during the research activity. Such implementation is an open source project and is freely downloadable from http://www.personal.leeds.ac.uk/~cnua/mypage.html.", notes = "HyGP C.4 Hock function C.5 Branin-Hoo function C.6 Rosenbrock function (PCE comparison) C.7 Kotanchek function (PCE comparison) C.8 10-bar truss optimisation C.9 Hospital ward ventilation optimisation C.10 Chromate diffusion model C.11 Jet pump model C.12 Bread baking oven design optimisation C.13 Aerodynamic optimisation of NASA rotor 37 compressor rotor blade uk.bl.ethos.631392", } @InProceedings{arnaldo:2014:EuroGP, author = "Ignacio Arnaldo and Kalyan Veeramachaneni and Una-May O'Reilly", title = "Flash: A GP-GPU Ensemble Learning System for Handling Large Datasets", booktitle = "17th European Conference on Genetic Programming", year = "2014", editor = "Miguel Nicolau and Krzysztof Krawiec and Malcolm I. Heywood and Mauro Castelli and Pablo Garcia-Sanchez and Juan J. Merelo and Victor M. {Rivas Santos} and Kevin Sim", series = "LNCS", volume = "8599", publisher = "Springer", pages = "13--24", address = "Granada, Spain", month = "23-25 " # apr, organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, GPU", isbn13 = "978-3-662-44302-6", DOI = "DOI:10.1007/978-3-662-44303-3_2", abstract = "The Flash system runs ensemble-based Genetic Programming (GP) symbolic regression on a shared memory desktop. To significantly reduce the high time cost of the extensive model predictions required by symbolic regression, its fitness evaluations are tasked to the desktop's GPU. Successive GP {"}instances{"} are run on different data subsets and randomly chosen objective functions. Best models are collected after a fixed number of generations and then fused with an adaptive, output-space method. New instance launches are halted once learning is complete. We demonstrate that Flash's ensemble strategy not only makes GP more robust, but it also provides an informed online means of halting the learning process. Flash enables GP to learn from a dataset composed of 370K exemplars and 90 features, evolving a population of 1000 individuals over 100 generations in as few as 50 seconds.", notes = "Part of \cite{Nicolau:2014:GP} EuroGP'2014 held in conjunction with EvoCOP2014, EvoBIO2014, EvoMusArt2014 and EvoApplications2014", } @InProceedings{Arnaldo:2014:GECCO, author = "Ignacio Arnaldo and Krzysztof Krawiec and Una-May O'Reilly", title = "Multiple regression genetic programming", booktitle = "GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation", year = "2014", editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges", isbn13 = "978-1-4503-2662-9", pages = "879--886", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, MRGP, Multiple Regression, Artificial intelligence, Automatic Programming", URL = "https://www.cs.put.poznan.pl/kkrawiec/wiki/uploads/Site/2015GeccoMRGP.pdf", URL = "http://doi.acm.org/10.1145/2576768.2598291", DOI = "doi:10.1145/2576768.2598291", code_url = "https://flexgp.github.io/gp-learners/mrgp.html", size = "8 pages", abstract = "We propose a new means of executing a genetic program which improves its output quality. Our approach, called Multiple Regression Genetic Programming (MRGP) decouples and linearly combines a program's subexpressions via multiple regression on the target variable. The regression yields an alternate output: the prediction of the resulting multiple regression model. It is this output, over many fitness cases, that we assess for fitness, rather than the program's execution output. MRGP can be used to improve the fitness of a final evolved solution. On our experimental suite, MRGP consistently generated solutions fitter than the result of competent GP or multiple regression. When integrated into GP, inline MRGP, on the basis of equivalent computational budget, outperforms competent GP while also besting post-run MRGP. Thus MRGP's output method is shown to be superior to the output of program execution and it represents a practical, cost neutral, improvement to GP.", notes = "Also known as \cite{2598291} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)", } @InProceedings{Arnaldo:2015:GECCO, author = "Ignacio Arnaldo and Una-May O'Reilly and Kalyan Veeramachaneni", title = "Building Predictive Models via Feature Synthesis", booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation", year = "2015", editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr", isbn13 = "978-1-4503-3472-3", pages = "983--990", keywords = "genetic algorithms, genetic programming", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739480.2754693", DOI = "doi:10.1145/2739480.2754693", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "We introduce Evolutionary Feature Synthesis (EFS), a regression method that generates readable, nonlinear models of small to medium size datasets in seconds. EFS is, to the best of our knowledge, the fastest regression tool based on evolutionary computation reported to date. The feature search involved in the proposed method is composed of two main steps: feature composition and feature subset selection. EFS adopts a bottom-up feature composition strategy that eliminates the need for a symbolic representation of the features and exploits the variable selection process involved in pathwise regularized linear regression to perform the feature subset selection step. The result is a regression method that is competitive against neural networks, and outperforms both linear methods and Multiple Regression Genetic Programming, up to now the best regression tool based on evolutionary computation.", notes = "Also known as \cite{2754693} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)", } @Proceedings{Arnold:2014:GECCOcomp, title = "GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion", year = "2014", editor = "Dirk Arnold and Mengjie Zhang and Ryan Urbanowicz and Muhammad Iqbal and Kamran Shafi and Forrest Stonedahl and William Rand and Tea Tusar and Boris Naujoks and David Walker and Richard Everson and Jonathan Fieldsend and Stefan Wagner and Michael Affenzeller and Zhun Fan and Yaochu Jin and Hod Lipson and Erik Goodman and Alexandru-Adrian Tantar and Emilia Tantar and Peter A. N. Bosman and Kent McClymont and Kevin Sim and Gabriela Ochoa and Ed Keedwell and Anna I Esparcia-Alcazar and Frank W. Moore and Jaume Bacardit and Ignacio Arnaldo and Kalyan Veeramachaneni and Una-May O'Reilly and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Steven Gustafson and Ekaterina Vladislavleva and John Woodward and Jerry Swan and Earl Barr and Krzysztof Krawiec and Chris Simons and John Clark and Dirk Sudholt and Anna Esparcia and Aniko Ekart and Carola Doerr and Anne Auger", address = "Vancouver, BC, Canada", publisher_address = "New York, NY, USA", month = "12-16 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, Keynotes and invited talk, ant colony optimization and swarm intelligence, artificial immune systems, artificial life, robotics, and evolvable hardware, biological and biomedical applications, digital entertainment technologies and arts, estimation of distribution algorithms, evolution strategies and evolutionary programming, evolutionary combinatorial optimization and metaheuristics, evolutionary machine learning, evolutionary multiobjective optimization, generative and developmental systems, integrative genetic and evolutionary computation, parallel evolutionary systems, real world applications, search based software engineering, self-* search, theory, Introductory tutorials, Advanced tutorials, Specialized tutorials, 17th annual international workshop on learning classifier systems, Eighth Annual Workshop on Evolutionary Computation and Multi-Agent Systems and Simulation (ECoMASS), student workshop, VizGEC: Workshop on visualisation in genetic and evolutionary computation, Workshop on Evolutionary Computation Software Systems (EvoSoft), evolutionary synthesis of dynamical systems, Workshop on Green and Efficient Energy Applications of Genetic and Evolutionary Computation (GreenGEC), Workshop on Problem Understanding and Real-world Optimisation (PURO), Workshop on genetic and evolutionary computation in defense, security and risk management (SecDef), Workshop on Evolutionary Computation for Big Data and Big Learning, Workshop on Medical Applications of Genetic and Evolutionary Computation (MedGEC), Workshop on Symbolic Regression and Modelling, 4th workshop on evolutionary computation for the automated design of algorithms, Workshop on Metaheuristic Design Patterns (MetaDeeP), Late breaking abstracts workshop, Women@GECCO 2014", isbn13 = "978-1-4503-2881-4", URL = "http://dl.acm.org/citation.cfm?id=2598394", DOI = "doi:10.1145/2598394", abstract = "It is my pleasure to welcome you to Philadelphia for the 2012 Genetic and Evolutionary Computation Conference (GECCO-2012). This is the first time GECCO has been held in Philly. We very much you hope you enjoy this historic American city and all it has to offer. This will be my 14th year attending GECCO. I have contributed a number of papers and have enjoyed many thought-provoking presentations over the years. GECCO has played a very important role in my research program and in the training of many of my students and postdocs. I agreed to serve as General Chair of GECCO-2012 because it was time to give back to the community I have enjoyed being a part of since 1999. Terence Soule served as the editor-in-chief this year and did a very skillful job maintaining the high quality of the conference. GECCO-2012 accepted 172 full papers for oral presentation out of a total of 467 submitted. This is an acceptance rate of less than 37percent. I am very thankful to Terry, Anne Auger, our Proceedings Chair, and all the track chairs for their hard work managing the review, selection and scheduling process for the scientific papers. One of the highlights of every GECCO is the free tutorials and the free workshops held during the first two days of the conference. I found these to be incredibly helpful when I was still learning about the field.", notes = "Distributed at GECCO-2014.", } @Article{Arora:2012:IJCA, title = "Optimization of Decision Rules in Fuzzy Classification", author = "Renuka Arora and Sudesh Kumar", journal = "International Journal of Computer Applications", year = "2012", volume = "51", number = "3", pages = "13--17", month = aug, keywords = "genetic algorithms, genetic programming", ISSN = "09758887", bibsource = "OAI-PMH server at www.doaj.org", language = "eng", oai = "oai:doaj-articles:2c58ebd9b3af7344384924c400738db2", URL = "http://research.ijcaonline.org/volume51/number3/pxc3880505.pdf", DOI = "doi:10.5120/8021-0505", publisher = "Foundation of Computer Science (FCS)", size = "5 pages", abstract = "There are various advances in data collection that can intelligently and automatically analyse and mine knowledge from large amounts of data. World Wide Web as a global information system has flooded us with a tremendous amount of data and information Discovery of knowledge and decision-making directly from such huge volumes of data contents is a real challenge. The Knowledge Discovery in Databases (KDD) is the process of extracting the knowledge from huge data collection. Data mining is a step of KDD in which patterns or models are extracted from data by using some automated techniques. Discovering knowledge in the form of classification rules is one of the most important tasks of data mining. Discovery of comprehensible, concise and effective rules helps us to make right decisions. Therefore, several Machine Learning techniques are applied for discovery of classification rules. Recently there have been several applications of genetic algorithms for effective rules with high predictive accuracy.", notes = "Balloon, Poker", } @InProceedings{Arora:2010:NAACL, author = "Shilpa Arora and Elijah Mayfield and Carolyn Penstein-Rose and Eric Nyberg", title = "Sentiment Classification Using Automatically Extracted Subgraph Features", booktitle = "Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text", series = "CAAGET '10", year = "2010", address = "Los Angeles, California", pages = "131--139", month = jun, keywords = "genetic algorithms, genetic programming, GP", URL = "http://dl.acm.org/citation.cfm?id=1860631.1860647", acmid = "1860647", oai = "oai:CiteSeerX.psu:10.1.1.207.7440", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.207.7440", URL = "http://www.cs.cmu.edu/%7Eemayfiel/AroraMayfieldRoseNybergNAACL2010.pdf", publisher = "Association for Computational Linguistics", publisher_address = "Stroudsburg, PA, USA", size = "9 pages", abstract = "In this work, we propose a novel representation of text based on patterns derived from linguistic annotation graphs. We use a subgraph mining algorithm to automatically derive features as frequent subgraphs from the annotation graph. This process generates a very large number of features, many of which are highly correlated. We propose a genetic programming based approach to feature construction which creates a fixed number of strong classification predictors from these subgraphs. We evaluate the benefit gained from evolved structured features, when used in addition to the bag-of-words features, for a sentiment classification task.", } @InProceedings{Arpaia:2009:I2MTC, author = "Pasquale Arpaia and Fabrizio Clemente and Carlo Manna and Giuseppe Montenero", title = "Automatic modeling based on cultural programming for osseointegration diagnosis", booktitle = "IEEE Instrumentation and Measurement Technology Conference, I2MTC '09", year = "2009", pages = "1274--1277", address = "Singapore", month = "5-7 " # may, keywords = "genetic algorithms, genetic programming, gene expression programming, EIS data, artificial intelligence, automatic modeling, bone implant, cultural programming, electrical impedance spectroscopy, evolutionary programming approach, metallic implant, osseointegration diagnosis, prosthesis, artificial intelligence, biomedical measurement, bone, electric impedance measurement, equivalent circuits, evolutionary computation, genetics, medical computing, orthopaedics, prosthetics", isbn13 = "978-1-4244-3352-0", ISSN = "1091-5281", DOI = "doi:10.1109/IMTC.2009.5168651", size = "4 pages", abstract = "The problem of modelling equivalent circuits for interpreting Electrical Impedance Spectroscopy (EIS) data in monitoring osseointegration level of metallic implants in bone is faced by means of an evolutionary programming approach based on cultural algorithms. With respect to state-of-the-art gene expression programming, the information on search advance acquired by most promising individuals during the evolution is shared with the entire population of potential solutions and stored also for next generations. Experimental results of the application such cultural programming-based analytical modelling to in-vitro EIS measurements of bone in-growth around metallic implants during prosthesis osseointegration are presented.", notes = "Also known as \cite{5168651}", } @Article{Arroba:2015:grid, title = "Enhancing regression models for complex systems using evolutionary techniques for feature engineering", author = "Patricia Arroba and Jose Luis Risco-Martin and Marina Zapater and Jose Manuel Moya and Jose Luis Ayala", journal = "Journal of Grid Computing", year = "2015", volume = "13", number = "3", pages = "409--423", publisher = "Springer", month = sep # "~27", keywords = "genetic algorithms, genetic programming", URL = "http://eprints.ucm.es/30960/", URL = "http://eprints.ucm.es/30960/1/JGridComputing2014.pdf", URL = "http://link.springer.com/article/10.1007%2Fs10723-014-9313-8", ISSN = "1572-9184", DOI = "doi:10.1007/s10723-014-9313-8", abstract = "This work proposes an automatic methodology for modelling complex systems. Our methodology is based on the combination of Grammatical Evolution and classical regression to obtain an optimal set of features that take part of a linear and convex model. This technique provides both Feature Engineering and Symbolic Regression in order to infer accurate models with no effort or designer's expertise requirements. As advanced Cloud services are becoming mainstream, the contribution of data centers in the overall power consumption of modern cities is growing dramatically. These facilities consume from 10 to 100 times more power per square foot than typical office buildings. Modeling the power consumption for these infrastructures is crucial to anticipate the effects of aggressive optimisation policies, but accurate and fast power modelling is a complex challenge for high-end servers not yet satisfied by analytical approaches. For this case study, our methodology minimises error in power prediction. This work has been tested using real Cloud applications resulting on an average error in power estimation of 3.98percent. Our work improves the possibilities of deriving Cloud energy efficient policies in Cloud data centers being applicable to other computing environments with similar characteristics.", bibsource = "OAI-PMH server at eprints.ucm.es", language = "en", oai = "oai:www.ucm.es:30960", relation = "10.1007/s10723-014-9313-8; TIN2008-00508", rights = "info:eu-repo/semantics/openAccess", type = "PeerReviewed", } @Article{Arsalan:2017:ASC, author = "Muhammad Arsalan and Aqsa Saeed Qureshi and Asifullah Khan and Muttukrishnan Rajarajan", title = "Protection of medical images and patient related information in healthcare: Using an intelligent and reversible watermarking technique", journal = "Applied Soft Computing", volume = "51", pages = "168--179", year = "2017", month = feb, keywords = "genetic algorithms, genetic programming, Health care, Integer Wavelet Transform, Reversible watermarking, Medical images", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2016.11.044", URL = "http://www.sciencedirect.com/science/article/pii/S1568494616306135", abstract = "This work presents an intelligent technique based on reversible watermarking for protecting patient and medical related information. In the proposed technique IRW-Med, the concept of companding function is exploited for reducing embedding distortion, while Integer Wavelet Transform (IWT) is used as an embedding domain for achieving reversibility. Histogram processing is employed to avoid underflow/overflow. In addition, the learning capabilities of Genetic Programming (GP) are exploited for intelligent wavelet coefficient selection. In this context, GP is used to evolve models that not only make an optimal tradeoff between imperceptibility and capacity of the watermark, but also exploit the wavelet coefficient hidden dependencies and information related to the type of sub band. The novelty of the proposed IRW-Med technique lies in its ability to generate a model that can find optimal wavelet coefficients for embedding, and also acts as a companding factor for watermark embedding. The proposed IRW-Med is thus able to embed watermark with low distortion, take out the hidden information, and also recovers the original image. The proposed IRW-Med technique is effective with respect to capacity and imperceptibility and effectiveness is demonstrated through experimental comparisons with existing techniques using standard images as well as a publically available medical image dataset.", } @InProceedings{conf/cibb/ArseneAB13, author = "Corneliu T. C. Arsene and Denisa Ardevan and Paul Bulzu", title = "Reverse Engineering Methodology for Bioinformatics Based on Genetic Programming, Differential Expression Analysis and Other Statistical Methods", publisher = "Springer", year = "2013", volume = "8452", keywords = "genetic algorithms, genetic programming", bibdate = "2014-07-16", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/cibb/cibb2013.html#ArseneAB13", booktitle = "CIBB", editor = "Enrico Formenti and Roberto Tagliaferri and Ernst Wit", isbn13 = "978-3-319-09041-2", pages = "161--177", series = "Lecture Notes in Computer Science", URL = "http://dx.doi.org/10.1007/978-3-319-09042-9", } @InProceedings{Arshad:2014:FIT, author = "Junaid Arshad and Aneela Zameer and Asifullah Khan", booktitle = "12th International Conference on Frontiers of Information Technology (FIT)", title = "Wind Power Prediction Using Genetic Programming Based Ensemble of Artificial Neural Networks ({GPeANN})", year = "2014", pages = "257--262", abstract = "Over the past couple of years, the share of wind power in electrical power system has increased considerably. Because of the irregular characteristics of wind, the power generated by the wind turbines fluctuates continuously. The unstable nature of the wind power thus poses a serious challenge in power distribution systems. For reliable power distribution, wind power prediction system has become an essential component in power distribution systems. In this Paper, a wind power forecasting strategy composed of Artificial Neural Networks (ANN) and Genetic Programming (GP) is proposed. Five neural networks each having different structure and different learning algorithm were used as base regressors. Then the prediction of these neural networks along with the original data is used as input for GP based ensemble predictor. The proposed wind power forecasting strategy is applied to the data from five wind farms located in same region of Europe. Numerical results and comparison with existing wind power forecasting strategies demonstrates the efficiency of the proposed strategy.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/FIT.2014.55", month = dec, notes = "Also known as \cite{7118409}", } @InProceedings{Arshad:2014:IJCNN, author = "R. Arshad and G. M. Khan and S. A. Mahmud", booktitle = "International Joint Conference on Neural Networks (IJCNN 2014)", title = "Smart bandwidth management using a recurrent Neuro-Evolutionary technique", year = "2014", month = jul, pages = "2240--2247", abstract = "The requirement for correct bandwidth allocation and management in a multitude of different communication mediums has generated some exceedingly tedious challenges that need to be addressed both intelligently and with innovative solutions. Current advances in high speed broadband technologies have manifold increased the amount of bandwidth required during successful multimedia streaming. The progressive growth of Neuro-Evolutionary techniques have presented themselves as worthy options to address many of the challenges faced during multimedia streaming. In this paper a Neuro-Evolutionary technique called the Recurrent Cartesian Genetic Programming Evolved Artificial Neural Network(RCGPANN) is presented for prediction of future frame sizes. The proposed technique takes into account the traffic size trend of the historically transmitted data for future frame size prediction. The predicted frame size forms the basis for estimation of the amount of bandwidth necessary for transmission of future frame. Different linear regression and probabilistic approaches are employed to estimate the allocated bandwidth, while using the predicted frame size. Our proposed intelligent traffic size prediction along with bandwidth estimation and management results in a 98percent increased efficiency.", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", DOI = "doi:10.1109/IJCNN.2014.6889727", notes = "Also known as \cite{6889727}", } @InProceedings{Arslan:2017:BIYOMUT, author = "Sibel Arslan and Celal Ozturk", booktitle = "2017 21st National Biomedical Engineering Meeting (BIYOMUT)", title = "Feature Selected Cancer Data Classification with Genetic Programming", year = "2017", pages = "i--iv", abstract = "Classification is used to distribute data to classes defined on the dataset. Classification algorithms determine the classes in which the data in the test set is to be included by learning the distribution of classes in the training set. It is directly dependent on the choice of which properties to use in the classification. The most prominent features of cancer data in this work are selection and classification using genetic programming method. It has been seen that very successful classification results are obtained with Genetic Programming.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/BIYOMUT.2017.8478885", month = nov, notes = "Also known as \cite{8478885}", } @Article{ARSLAN:2019:ASC, author = "Sibel Arslan and Celal Ozturk", title = "Multi Hive Artificial Bee Colony Programming for high dimensional symbolic regression with feature selection", journal = "Applied Soft Computing", volume = "78", pages = "515--527", year = "2019", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2019.03.014", URL = "http://www.sciencedirect.com/science/article/pii/S1568494619301322", keywords = "genetic algorithms, genetic programming, Feature selection, Artificial bee colony programming, Multi hive artificial bee colony programming, High dimension data", abstract = "Feature selection is a process that provides model extraction by specifying necessary or related features and improves generalization. The Artificial Bee Colony (ABC) algorithm is one of the most popular optimization algorithms inspired on swarm intelligence developed by simulating the search behavior of honey bees. Artificial Bee Colony Programming (ABCP) is a recently proposed high level automatic programming technique for a Symbolic Regression (SR) problem based on the ABC algorithm. In this paper, a new feature selection method based on ABCP is proposed, Multi Hive ABCP (MHABCP) for high-dimensional SR problems. The learning ability and generalization performance of the proposed MHABCP is investigated using synthetic and real high-dimensional SR datasets and is compared with basic ABCP and GP automatic programming methods. Experimental results show that MHABCP has better performance choosing relevant features in high dimensional SR problems and generalization than other methods", } @Article{arslan:2019:AS, author = "Sibel Arslan and Celal Ozturk", title = "Artificial Bee Colony Programming Descriptor for {Multi-Class} Texture Classification", journal = "Applied Sciences", year = "2019", volume = "9", number = "9", note = "Special Issue Machine Learning and Compressed Sensing in Image Reconstruction", keywords = "genetic algorithms, genetic programming, Texture classification, artificial bee colony programming-descriptor, image descriptor, local binary pattern, genetic programming-descriptor", ISSN = "2076-3417", URL = "https://www.mdpi.com/2076-3417/9/9/1930", URL = "https://www.mdpi.com/2076-3417/9/9/1930.pdf", DOI = "doi:10.3390/app9091930", size = "18 page", abstract = "Texture classification is one of the machine learning methods that attempts to classify textures by evaluating samples. Extracting related features from the samples is necessary to successfully classify textures. It is a very difficult task to extract successful models in the texture classification problem. The Artificial Bee Colony (ABC) algorithm is one of the most popular evolutionary algorithms inspired by the search behaviour of honey bees. Artificial Bee Colony Programming (ABCP) is a recently introduced high-level automatic programming method for a Symbolic Regression (SR) problem based on the ABC algorithm. ABCP has applied in several fields to solve different problems up to date. In this paper, the Artificial Bee Colony Programming Descriptor (ABCP-Descriptor) is proposed to classify multi-class textures. The models of the descriptor are obtained with windows sliding on the textures. Each sample in the texture dataset is defined instance. For the classification of each texture, only two random selected instances are used in the training phase. The performance of the descriptor is compared standard Local Binary Pattern (LBP) and Genetic Programming-Descriptor (GP-descriptor) in two commonly used texture datasets. When the results are evaluated, the proposed method is found to be a useful method in image processing and has good performance compared to LBP and GP-descriptor.", notes = "also known as \cite{app9091930}", } @InProceedings{Arslan:2022:ASYU, author = "Sibel Arslan and Nursah Kutuk", booktitle = "2022 Innovations in Intelligent Systems and Applications Conference (ASYU)", title = "Titan Yellow Biosorption of Hemp Waste in Acidic Medium and Modeling of Experimental Conditions by Multi Gene Genetic Programming", year = "2022", abstract = "In recent years, pollutants such as dyes, drugs and heavy metals in wastewater have caused serious environmental pollution. In this study, biosorption of titan yellow (TY) using hemp waste was studied. In the biosorption of TY dye to hemp waste, 88percent biosorption was achieved with an initial dye concentration of 10 mg/L and a biosorbent ratio of 2 g/L in acidic medium. When Langmuir and Freundlich isotherms were examined, R-squared values were obtained as 0.92 and 0.95, respectively. Its maximum biosorption capacity has been calculated as 51.8 mg/g. It has also been observed that the biosorption process adapts to the pseudo second order reaction R-squared = 0.99) kinetics. We have also developed more accurate and reliable correlation models using Multi-Gene Genetic Programming (MGGP), a powerful method based on evolutionary computation. The performance of the developed models was examined using three statistical criteria. A comparison of the criteria reveals that MGGP is effective in simulating the biosorption process in the real world.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ASYU56188.2022.9925394", ISSN = "2770-7946", month = sep, notes = "Also known as \cite{9925394}", } @Article{ARSLAN:2024:asoc, author = "Sibel Arslan", title = "Immune Plasma Programming: A new evolutionary computation-based automatic programming method", journal = "Applied Soft Computing", volume = "152", pages = "111204", year = "2024", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2023.111204", URL = "https://www.sciencedirect.com/science/article/pii/S156849462301222X", keywords = "genetic algorithms, genetic programming, Automatic programming, Immune plasma programming, Immune plasma algorithm, Symbolic regression", abstract = "Immune plasma therapy, one of the treatment modalities, has proven effective in combating the now rapidly spreading COVID-19 and many other pandemics. The immune plasma algorithm (IPA), inspired by the application phases of this treatment modality, is a recently proposed metaheuristic algorithm. Since its introduction, it has achieved promising results in engineering applications. In this paper, we propose for the first time immune plasma programming (IPP) based on the structure of IPA as a new evolutionary computation-based automatic programming (AP) method. It is compared with well-known AP methods such as artificial bee colony programming, genetic programming, and cartesian ant programming using symbolic regression test problems. It is also compared with baseline methods, many of which are based on recurrent neural networks and a real-word problem is solved. The control parameters of IPP are also tuned separately. The results of the experiments and statistical tests have shown that the prediction accuracy and convergence speed of the models produced by IPP are high. Therefore, IPP has been proposed as a method that can be used to solve various problems", } @Article{ARSLAN:2023:asoc, author = "Sibel Arslan and Celal Ozturk", title = "A comprehensive review of automatic programming methods", journal = "Applied Soft Computing", volume = "143", pages = "110427", year = "2023", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2023.110427", URL = "https://www.sciencedirect.com/science/article/pii/S1568494623004453", keywords = "genetic algorithms, genetic programming, Artificial intelligence, Evolutionary computation, Automatic programming", abstract = "Automatic programming (AP) is one of the most attractive branches of artificial intelligence because it provides effective solutions to problems with limited knowledge in many different application areas. AP methods can be used to determine the effects of a system's inputs on its outputs. Although there is increasing interest in solving many problems using these methods for a variety of applications, there is a lack of reviews that address the methods. Therefore, the goal of this paper is to provide a comprehensive literature review of AP methods. At the same time, we mention the main characteristics of the methods by grouping them according to how they represent solutions. We also try to give an outlook on the future of the field by highlighting possible bottlenecks and perspectives for the benefit of the researchers involved", } @Article{ARSLAN:2023:eswa, author = "Sibel Arslan and Nursah Kutuk", title = "Symbolic regression with feature selection of dye biosorption from an aqueous solution using pumpkin seed husk using evolutionary computation-based automatic programming methods", journal = "Expert Systems with Applications", volume = "231", pages = "120676", year = "2023", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2023.120676", URL = "https://www.sciencedirect.com/science/article/pii/S0957417423011788", keywords = "genetic algorithms, genetic programming, Pumpkin seed husk, Biosorption, Titan yellow, System modeling, Artificial bee colony programming", abstract = "Industrial waste pollution is a serious and systematic problem that harms the environment and people. The development of cheap, simple, and efficient techniques to solve this problem is important for sustainability. In this study, both experimental and evolutionary computation (EC)-based automatic programming (AP) methods were used to investigate the biosorption process for water treatment. In the experiments, titan yellow (TY), an anionic dye, was biosorbed from an aqueous solution containing pumpkin seed husk (PSH). The structure of PSH was examined using a Fourier transform infrared spectroscopy (FTIR) and a scanning electron microscope (SEM). The result of the experimental studies was that TY biosorption of PSH reached a biosorption efficiency of 95percent after 120 min of contact time. The maximum biosorption capacity (qmax) was calculated to be 181.8 mg/g. It was found that the biosorption of TY better followed the Dubinin-Radushkevich isotherm (R2=0.98) and pseudo second-order reaction kinetics (R2=0.99). Based on the thermodynamic data, the biosorption process was exothermic and spontaneous. After the experiments, the process was modeled using pH, biosorbent concentration, initial dye concentration, contact time, and temperature as inputs and biosorption efficiency (percent) as output for the methods. Moreover, the success of these AP methods was compared with a newly proposed evolutionary method. The simulation results indicate that AP methods generate best models (Rtrain2=0.99 and Rtest2=0.97). At the same time, the most important parameter of the process in the feature analysis is contact time. This study shows that EC-based AP methods can effectively model applications such as the biosorption process", } @Article{ARSLAN:2023:engappai, author = "Sibel Arslan and Kemal Koca", title = "Investigating the best automatic programming method in predicting the aerodynamic characteristics of wind turbine blade", journal = "Engineering Applications of Artificial Intelligence", volume = "123", pages = "106210", year = "2023", ISSN = "0952-1976", DOI = "doi:10.1016/j.engappai.2023.106210", URL = "https://www.sciencedirect.com/science/article/pii/S0952197623003949", keywords = "genetic algorithms, genetic programming, Automatic programming, Artificial bee colony programming, Aerodynamic coefficients, Power efficiency, Wind turbine blade", abstract = "Automatic programming (AP) is a subfield of artificial intelligence (AI) that can automatically generate computer programs and solve complex engineering problems. This paper presents the accuracy of four different AP methods in predicting the aerodynamic coefficients and power efficiency of the AH 93-W-145 wind turbine blade at different Reynolds numbers and angles of attack. For the first time in the literature, Genetic Programming (GP) and Artificial Bee Colony Programming (ABCP) methods are used for such predictions. In addition, Airfoil Tools and JavaFoil are used for airfoil selection and dataset generation. The Reynolds number and angle of attack of the wind turbine airfoil are input parameters, while the coefficients CL, CD and power efficiency are output parameters. The results show that while all four methods tested in the study accurately predict the aerodynamic coefficients, Multi Gene GP (MGGP) method achieves the highest accuracy for RTrain2 and RTest2 (R2 values in CD Train: 0.997-Test: 0.994, in CL Train: 0.991-Test: 0.990, in PE Train: 0.990-Test: 0.970). By providing the most precise model for properly predicting the aerodynamic performance of higher cambered wind turbine airfoils, this innovative and comprehensive study will close a research gap. This will make a significant contribution to the field of AI and aerodynamics research without experimental cost, labor, and additional time", } @Article{arslan:2005:GPEM, author = "Tughrul Arslan", title = "Book Review: Evolvable Components--From Theory to Hardware Implementations", journal = "Genetic Programming and Evolvable Machines", year = "2005", volume = "6", number = "4", pages = "461--462", month = dec, keywords = "genetic algorithms, evolvable hardware", ISSN = "1389-2576", URL = "https://rdcu.be/dR8dB", DOI = "doi:10.1007/s10710-005-3718-x", size = "2 pages", abstract = "Book Review: Evolvable Components--From Theory to Hardware Implementations by Lukas Sekanina Springer, 2003, ISBN 3-540-40377-9", notes = "review of \cite{sekanina:2003:book}", } @InProceedings{Arvaneh:2009:ICBPE, author = "M. Arvaneh and H. Ahmadi and A. Azemi and M. Shajiee and Z. S. Dastgheib", title = "Prediction of Paroxysmal Atrial Fibrillation by dynamic modeling of the {PR} interval of {ECG}", booktitle = "International Conference on Biomedical and Pharmaceutical Engineering, ICBPE '09", year = "2009", month = "2-4 " # dec, pages = "1--5", keywords = "genetic algorithms, genetic programming, ECG signal, PR interval, Paroxysmal Atrial Fibrillation, electrocardiography, neural networks, ANN, electrocardiography, neural nets", DOI = "doi:10.1109/ICBPE.2009.5384063", size = "5 pages", abstract = "In this work, we propose a new method for prediction of Paroxysmal Atrial Fibrillation (PAF) by only using the PR interval of ECG signal. We first obtain a nonlinear structure and parameters of PR interval by a Genetic Programming (GP) based algorithm. Next, we use the neural networks for prediction of PAF. The inputs of the neural networks are the parameters of nonlinear model of the PR intervals. For the modeling and prediction we have limited ourselves to only 30 seconds of an ECG signal, which is one of the advantages of our proposed approach. For comparison purposes, we have modeled 30 seconds of ECG signals by time based modeling method and have compared prediction results of them.", notes = "Also known as \cite{5384063}", } @Article{aryafar:2019:EES, author = "Ahmad Aryafar and Vahid Khosravi and Hosniyeh Zarepourfard and Reza Rooki", title = "Evolving genetic programming and other {AI-based} models for estimating groundwater quality parameters of the Khezri plain, Eastern Iran", journal = "Environmental Earth Sciences", year = "2019", volume = "78", number = "3", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s12665-019-8092-8", DOI = "doi:10.1007/s12665-019-8092-8", } @Article{Asadi:2010:ASC, author = "Mojtaba Asadi and Mehdi Eftekhari and Mohammad Hossein Bagheripour", title = "Evaluating the strength of intact rocks through genetic programming", journal = "Applied Soft Computing", year = "2011", volume = "11", number = "2", pages = "1932--1937", month = mar, keywords = "genetic algorithms, genetic programming, Information criterion, Intact rock, Failure criteria", ISSN = "1568-4946", URL = "http://www.sciencedirect.com/science/article/B6W86-50CVPW4-2/2/863c13a5a1c7be6da7b1ea6592b11bd3", DOI = "doi:10.1016/j.asoc.2010.06.009", size = "13 pages", abstract = "Good prediction of the strength of rocks has many theoretical and practical applications. Analysis, design and construction of underground openings and tunnels, open pit mines and rock-based foundations are some examples of applications in which prediction of the strength of rocks is of great importance. The prediction might be done using mathematical expressions called failure criteria. In most cases, failure criteria of jointed rocks contain the value of strength of intact rock, i.e. the rock without joints and cracks. Therefore, the strength of intact rock can be used directly in applications and indirectly to predict the strength of jointed rock masses. On the other part, genetic programming method is one of the most powerful methods in machine learning field and could be used for non-linear regression problems. The derivation of an appropriate equation for evaluating the strength of intact rock is the common objective of many researchers in civil and mining engineering; therefore, mathematical expressions were derived in this paper to predict the strength of the rock using a genetic programming approach. The data of 51 rock types were used and the efficiency of equations obtained was illustrated graphically through figures.", notes = "a Sirjan engineering college, Department of Civil Engineering, Iran b Shahid Bahonar University of Kerman, Department of Computer Engineering, Iran c Shahid Bahonar University of Kerman, Department of Civil Engineering, Iran", } @Article{AsadiTashvigh:2015:Calphad, author = "Akbar Asadi Tashvigh and Farzin Zokaee Ashtiani and Mohammad Karimi and Ahmad Okhovat", title = "A novel approach for estimation of solvent activity in polymer solutions using genetic programming", journal = "Calphad", volume = "51", pages = "35--41", year = "2015", ISSN = "0364-5916", DOI = "doi:10.1016/j.calphad.2015.07.005", URL = "http://www.sciencedirect.com/science/article/pii/S0364591615300080", abstract = "In this paper, genetic programming (GP) as a novel approach for the explicit modelling the phase equilibria of polymer solutions is presented. The objective of this study is to develop robust model based on experimental data for prediction of solvent activity in polymer/solvent mixtures. Molecular weight, density, chemical structures of polymer and solvent, and concentration of polymer solution were considered as input parameters of the model. Activity of solvent is considered as output parameter of the model. Some statistical parameters were calculated in order to investigate the reliability of model. The results showed very well agreement with the experimental data with an average error of less than 3percent.", keywords = "genetic algorithms, genetic programming, Solvent activity, Polymer solution, Phase equilibria", } @Article{ASADZADEH:2021:AES, author = "Mohammad Zhian Asadzadeh and Hans-Peter Ganser and Manfred Mucke", title = "Symbolic regression based hybrid semiparametric modelling of processes: An example case of a bending process", journal = "Applications in Engineering Science", volume = "6", pages = "100049", year = "2021", ISSN = "2666-4968", DOI = "doi:10.1016/j.apples.2021.100049", URL = "https://www.sciencedirect.com/science/article/pii/S2666496821000157", keywords = "genetic algorithms, genetic programming, Hybrid modelling, Symbolic regression, Knowledge discovery, Metal sheet bending", abstract = "Hybrid semiparametric models integrate physics-based ({"}white-box{"}, parametric) and data-driven ({"}black-box{"}, non-parametric) submodels. Black-box models are often implemented using artificial neural networks (ANNs). In this work, we investigate the fitness of symbolic regression (SR) for black-box modelling. The main advantage of this approach is that a trained hybrid model can be expressed in closed form as an algebraic equation. We examine and test the idea on a simple example, namely the v-shape bending of a metal sheet, where an analytical solution for the stamping force is readily available. We explore unconstrained and hybrid symbolic regression modelling to show that hybrid SR models, where the regression tree is partly fixed according to a-priori knowledge, perform much better than purely data-driven SR models based on unconstrained regression trees. Furthermore, the generation of algebraic equations by this method is much more repeatable, which makes the approach applicable to process knowledge discovery", } @InCollection{ashcraft:2003:NEBCEGA, author = "Kenneth Ashcraft", title = "Nark: Evolving Bug-Finding Compiler Extensions with Genetic Algorithms", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2003", year = "2003", editor = "John R. Koza", pages = "11--20", address = "Stanford, California, 94305-3079 USA", month = "4 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms", URL = "http://www.genetic-programming.org/sp2003/Ashcraft.pdf", notes = "part of \cite{koza:2003:gagp}", } @Article{Ashiru:1998:MM, author = "I. Ashiru and C. A. Czarnecki", title = "Evolving communicating controllers for multiple mobile robot systems", journal = "Microprocessors and Microsystems", year = "1998", volume = "21", pages = "393--402", number = "6", keywords = "genetic algorithms, genetic programming, Mobile robots, Communication", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6V0X-3TB0788-6/2/445577f1e7cd0c0d531457835edf327e", ISSN = "0141-9331", DOI = "doi:10.1016/S0141-9331(98)00054-4", abstract = "Multiple mobile robot systems working together to achieve a task have many advantages over single robot systems. However, the planning and execution of a task which is to be undertaken by multiple robots is extremely difficult. To date no tools exist which allow such systems to be engineered. One of the key questions that arises when developing such systems is: does communication between the robots aid the completion of the task, and if so what information should be communicated? This paper presents the results of an investigation undertaken to address the above question. The approach adopted is to use genetic programming (GP) with the aim of evolving a controller, and letting the evolution process determine what information should be communicated and how best to use this information. A number of experiments were performed with the aim of determining the communication requirements. The results of these experiments are presented in this paper. It is shown that the GP system evolved controllers whose performance benefitted as a result of the communication process.", } @InProceedings{ashlock:1997:GPdd, author = "Dan Ashlock", title = "GP-Automata for Dividing the Dollar", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming", pages = "18--26", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1997/ashlock_1997_GPdd.pdf", size = "9 pages", notes = "GP-97", } @InProceedings{ashlock:1997:spbs, author = "Dan Ashlock and Charles Richter", title = "The Effect of Splitting Populations on Bidding Strategies", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming", pages = "27--34", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", URL = "http://dakotarichter.com/papers/AshlockRichterSplittingPopulationsGP97.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1997/ashlock_1997_spbs.pdf", size = "8 pages", abstract = "In this paper we explore the effects of splitting a single population of artificial agents engaging in a simple double auction game into two competing populations by modifying experiments reported in [Ashlock, 1997]. The original paper used a new genetic programming tool, termed GP-Automata, to induce bidding strategies with a genetic algorithm for Nash's game divide the dollar. The motivation for performing the research is the biological notion of inclusive fitness and kinship theory. The a priori hypothesis of the authors was that behaviour of the agents in the simulated market would change substantially when they were no longer forced to be similar to one another by the genetic mechanism used to induce new bidding strategies. While breeding takes place only within each population, all bidding is between agents from different populations. The agents in the original (single population) paper strongly favoured {"}fair{"} Nash equilibria of the divide the dollar game, at odds with the economic theory for egoistic agents. When controls for kinship effects are implemented by splitting the population a substantial effect is observed. When agents doing the bidding are not close genetic kin to one another the 'unfair' Nash equilbria regain a great deal of their former prominence. This result is of importance to any sort of evolutionary algorithm creating artificial agents, as kinship theory can confound game-theoretic predictions that assume egoistic agents. The current research also arguably increases the level of realism in the simulation of a double auction market.", notes = "GP-97", } @InProceedings{ashlock:1998:fctsGP, author = "Dan Ashlock and James I. Lathrop", title = "A Fully Characterized Test Suite for Genetic Programming", booktitle = "Evolutionary Programming VII: Proceedings of the Seventh Annual Conference on Evolutionary Programming", year = "1998", editor = "V. William Porto and N. Saravanan and D. Waagen and A. E. Eiben", volume = "1447", series = "LNCS", pages = "537--546", address = "Mission Valley Marriott, San Diego, California, USA", publisher_address = "Berlin", month = "25-27 " # mar, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-64891-7", URL = "https://rdcu.be/cTHTU", URL = "https://link.springer.com/chapter/10.1007/BFb0040805", DOI = "doi:10.1007/BFb0040753", size = "10 pages", abstract = "We present a family of related test problems for genetic programming. These test problems form a very simple test environment that nevertheless possesses some degree of algorithmic subtlety. We term this genetic programming environment plus-one-recall-store (PORS). This genetic programming environment has only a pair of terminals, 1 and recall, and a pair of operations, plus and store, together with a single memory location. We present an extensive mathematical characterization of the PORS environment and report experiments testing the benefits of incorporating expert knowledge into the initial population and into the operation of crossover. The experiments indicate that, in the test environment, expert knowledge is best incorporated only in the initial population. This is a welcome result as this is the computationally inexpensive choice of the two methods of incorporating expert knowledge tested.", notes = "EP-98. Iowa State University.", } @InProceedings{ashlock:1998:ISAc, author = "Dan Ashlock and Mark Joenks", title = "{ISAc} Lists, A Different Representation for Program Induction", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "3--10", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-548-7", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1998/ashlock_1998_ISAc.pdf", size = "9 pages", abstract = "The traditional method of using a genetic algorithm to perform program induction is genetic programming which operates upon parse trees. In this papers we introduce a simpler data structure for program induction, the If-Statement-Action ISAc table. We test this data structure on the Tartarus problem of Astro Teller and compare its performance with simple string genes for Tartarus, Teller's own work, and with GP-Automata. In addition to the main result we present a new baseline study for the Tartarus problem. These baseline results suggest state information alone, without reactive ability, can provide relatively high fitness of the Tartarus problem.", notes = "Tartarus GP-98", } @InProceedings{ashlock:2003:taaaogptve, author = "Daniel A. Ashlock and Kenneth M. Bryden", title = "Thermal agents: An application of genetic programming to virtual engineering", booktitle = "Proceedings of the 2003 Congress on Evolutionary Computation CEC2003", editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon", pages = "1340--1347", year = "2003", publisher = "IEEE Press", address = "Canberra", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "8-12 " # dec, organisation = "IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", keywords = "genetic algorithms, genetic programming, Boundary conditions, Genetic engineering, Geometry, Impedance, Iterative methods, System testing, Systems engineering and theory, Temperature, Thermal engineering, iterative methods, mechanical engineering computing, temperature distribution, thermal engineering, cellular decomposition, exploratory analysis, iterative method, iterative thermal solver, thermal agents, thermal boundary condition, virtual engineering", ISBN = "0-7803-7804-0", DOI = "doi:10.1109/CEC.2003.1299824", size = "8 pages", abstract = "The temperature profile across an object is easy to compute by iterative methods. The time spent waiting for iterative solutions to converge for multiple objects in a complex configuration is an impediment to exploratory analysis of engineering systems. A rapidly computed initial guess can speed convergence for an iterative thermal solver. We describe and test a system for creating thermal agents that supply such initial guesses. Thermal agents are specific to an object geometry but general across different thermal boundary conditions. During an off-line training phase, genetic programming is used to locate a thermal agent by training on one or more sets of boundary conditions. In use, thermal agents transform boundary conditions into a rapidly converged set of initial values on a cellular decomposition of an object.", notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.", } @InProceedings{Ashlock:2004:OToECP, title = "On Taxonomy of Evolutionary Computation Problems", author = "Daniel Ashlock and Kenneth M. Bryden and Steven Corns", pages = "1713--1719", booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary Computation", year = "2004", volume = "2", publisher = "IEEE Press", month = "20-23 " # jun, address = "Portland, Oregon", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, data visualisation, evolutionary computation, graph theory, pattern classification, pattern clustering, tree data structures, tree searching cladogram, classification technique, evolutionary computation problems, graph based evolutionary algorithms, hierarchical clustering, standard taxonomic technique, taxonomy, Theory of evolutionary algorithms, Combinatorial \& numerical optimization", URL = "https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.1072.3780&rep=rep1&type=pdf", DOI = "doi:10.1109/CEC.2004.1331102", size = "7 pages", abstract = "Taxonomy is the practice of classifying members of a group based on their measurable characteristics. In evolutionary computation the problem of telling when two problems are similar is both challenging and important. An accurate classification technique would yield large benefits by permitting a researcher to rationally chose algorithm and parameter setting based on past experience. This study uses a standard taxonomic technique, hierarchical clustering, on a set of taxonomic characters derived from a comparative study using graph based evolutionary algorithms.", notes = "Also known as \cite{1331102}. CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.", } @InProceedings{Ashlock:2004:CaT, title = "Coevolution and Tartarus", author = "Daniel Ashlock and Stephen Willson and Nicole Leahy", pages = "1618--1624", booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary Computation", year = "2004", publisher = "IEEE Press", month = "20-23 " # jun, address = "Portland, Oregon", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Coevolution \& collective behavior, Evolutionary intelligent agents", URL = "http://orion.math.iastate.edu/danwell/eprints/TartarusCE.pdf", URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=01331089", DOI = "doi:10.1109/CEC.2004.1331089", abstract = "This study applies coevolution to the Tartarus task. If the coevolving test cases are viewed as a form of parasite the question of virulence becomes an important feature of the algorithm. This study compares two types of parasites. The impact of coevolution in this study is at odds with intuition and statistically significant. Analysis suggests that disruptive crossover has a key effect. In the presence of disruptive crossover, coevolution may need to be modified to be effective. The key method of dealing with disruptive crossover is tracking the age of the Tartarus agents. Using only older agents to drive coevolution of test cases substantially enhances the performance of one of the two type of coevolution studied.", notes = "GP-automata. Disruptive effect of crossover and mutation important in co-evolution studies. CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.", } @InProceedings{ashlock:2005:CECd, author = "Daniel A. Ashlock and Kenneth M. Bryden and Wendy Ashlock and Stephen P. Gent", title = "Rapid Training of Thermal Agents with Single Parent Genetic Programming", booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary Computation", year = "2005", editor = "David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L. Gwenn Volkert and Dan Ashlock and Marc Schoenauer", volume = "3", pages = "2122--2129", address = "Edinburgh, UK", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "2-5 " # sep, organisation = "IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9363-5", DOI = "doi:10.1109/CEC.2005.1554957", size = "8 pages", abstract = "The temperature profile across an object can be computed by iterative methods. The time spent waiting for iterative solutions to converge for multiple objects in a complex configuration is an impediment to exploratory analysis of engineering systems. A high-quality rapidly computed initial guess can speed convergence for an iterative algorithm. A system is described and tested for creating thermal agents that supply such initial guesses. Thermal agents are specific to an object but general across different thermal boundary conditions. During an off-line training phase, genetic programming is used to locate a thermal agent by training on several sets of boundary conditions. In use, thermal agents transform boundary conditions into rapidly-converged initial values on a cellular decomposition of an object. the impact of using single parent genetic programming on thermal agents is tested. Single parent genetic programming replaces the usual sub-tree crossover in genetic programming with crossover with members of an unchanging ancestor set. The use of this ancestor set permits the incorporation of expert knowledge into the system as well as permitting the re-use of solutions derived on one object to speed training of thermal agents for another object. For three types of experiments, incorporating expert knowledge; re-using evolved solutions; and transferring knowledge between distinct configurations statistically significant improvements are obtained with single parent techniques.", notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS.", } @InProceedings{Ashlock:2006:CECtax, author = "Daniel A. Ashlock and Kenneth M. Bryden and Steven Corns and Justin Schonfeld", title = "An Updated Taxonomy of Evolutionary Computation Problems using Graph-based Evolutionary Algorithms", booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary Computation", year = "2006", editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas", pages = "403--410", address = "Vancouver", month = "16-21 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9487-9", DOI = "doi:10.1109/CEC.2006.1688295", size = "8 pages", abstract = "Graph based evolutionary algorithms use combinatorial graphs to impose a topology or geographic structure on an evolving population. It has been demonstrated that, for a fixed problem, time to solution varies substantially with the choice of graph. This variation is not simple with very different graphs yielding faster solution times for different problems. Normalised time to solution for many graphs thus forms an objective character that can be used for classifying the type of a problem, separate from its hardness measured with average time to solution. This study uses fifteen combinatorial graphs to classify 40 evolutionary computation problems. The resulting classification is done using neighbour joining, and the results are also displayed using non-linear projection. The different methods of grouping evolutionary computation problems into similar types exhibit substantial agreement. Numerical optimisation problems form a close grouping while some other groups of problems scatter across the taxonomy. This paper updates an earlier taxonomy of 23 problems and introduces new classification techniques.", notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D IEEE Xplore gives pages as 96--103", } @Book{Ashlock:2006:book, author = "Daniel Ashlock", title = "Evolutionary Computation for Modeling and Optimization", publisher = "Springer", year = "2006", keywords = "genetic algorithms, genetic programming", isbn13 = "978-0-387-22196-0", DOI = "doi:10.1007/0-387-31909-3", size = "approx 571 pages", abstract = "Evolutionary Computation for Optimisation and Modelling is an introduction to evolutionary computation, a field which includes genetic algorithms, evolutionary programming, evolution strategies, and genetic programming. The text is a survey of some application of evolutionary algorithms. It introduces mutation, crossover, design issues of selection and replacement methods, the issue of populations size, and the question of design of the fitness function. It also includes a methodological material on efficient implementation. Some of the other topics in this book include the design of simple evolutionary algorithms, applications to several types of optimization, evolutionary robotics, simple evolutionary neural computation, and several types of automatic programming including genetic programming. The book gives applications to biology and bioinformatics and introduces a number of tools that can be used in biological modelling, including evolutionary game theory. Advanced techniques such as cellular encoding, grammar based encoding, and graph based evolutionary algorithms are also covered. This book presents a large number of homework problems, projects, and experiments, with a goal of illustrating single aspects of evolutionary computation and comparing different methods. Its readership is intended for an undergraduate or first-year graduate course in evolutionary computation for computer science, engineering, or other computational science students. Engineering, computer science, and applied math students will find this book a useful guide to using evolutionary algorithms as a problem solving tool. Written for: Undergraduate and graduate students", notes = "GP in Chapter 12 ISAc, Chapter 13 graph based EA, Chapter 14 cellular encoding", size = "572 pages. 2010 available in paper back. ISBN-13: 978-1441919694", } @InProceedings{Ashlock:2006:ANNIE, author = "Daniel Ashlock and Kenneth M. Bryden and Nathan G. Johnson", title = "Evolvable Threaded Controllers for a Multi-Agent Grid Robot Task", booktitle = "ANNIE 2006, Intelligent Engineering Systems through Artificial Neural Networks", year = "2006", editor = "Cihan H. Dagli and Anna L. Buczak and David L. Enke and Mark Embrechts and Okan Ersoy", volume = "16", chapter = "22", address = "St. Louis, MO, USA", month = nov # " 5-8", keywords = "genetic algorithms, genetic programming", isbn13 = "0791802566", DOI = "doi:10.1115/1.802566.paper22", abstract = "If skip action (ISAc) lists are a linear genetic programming data structure that can be used as an evolvable grid robot controller. In this study ISAc lists are modified to run multiple control threads so that a single ISAc list can control multiple grid robots. The threaded ISAc lists are tested by evolving them to control 20--25 grid robots that all must exit a virtual room through a single door. The evolutionary algorithm used rapidly locates a variety of controllers that permit the room to be cleared efficiently.", } @InProceedings{Ashlock:2006:ANNIEa, author = "Daniel Ashlock and Kenneth M. Bryden", title = "Function Stacks, {GBEAs}, and Crossover for the Parity Problem", booktitle = "ANNIE 2006, Intelligent Engineering Systems through Artificial Neural Networks", year = "2006", editor = "Cihan H. Dagli and Anna L. Buczak and David L. Enke and Mark Embrechts and Okan Ersoy", volume = "16", chapter = "18", address = "St. Louis, MO, USA", month = nov # " 5-8", note = "Part I: Evolutionary Computation", keywords = "genetic algorithms, genetic programming", isbn13 = "0791802566", DOI = "doi:10.1115/1.802566.paper18", abstract = "Function stacks are a directed acyclic graph representation for genetic programming that subsumes the need for automatically defined functions, substantially reduces the number of operations required to solve a problem, and permits the use of a conservative crossover operator. Function stacks are a generalisation of Cartesian genetic programming. Graph based evolutionary algorithms are a method for improving evolutionary algorithm performance by imposing a connection topology on an evolutionary population to strike an efficient balance between exploration and exploration. In this study the parity problems using function stacks for parity on 3, 4, 5, and 6 variables are tested on fifteen graphical connection topologies with and without crossover. Choosing the correct graph is found to have a statistically significant impact on time to solution. The conservative crossover operator for function stacks, new in this study, is found to improve time to solution by 4 to 9 fold with more improvement in harder instances of the parity problem.", } @InProceedings{Ashlock:2008:cec, author = "Daniel Ashlock and Taika {von Konigslow}", title = "Evolution of Artificial Ring Species", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "653--659", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1823-7", file = "EC0169.pdf", DOI = "doi:10.1109/CEC.2008.4630865", size = "7 pages", abstract = "Biological ring species are a population surrounding a geographic obstruction such as a large lake or a mountain range. Adjacent sub-populations are mutually fertile, but fertility drops with distance. This study attempts to create examples of artificial ring species using evolutionary algorithms. ISAc lists, a representation with self-organised and potentially complex genetics, are used to evolve controllers for the Tartarus task. The breeding population of Tartarus controllers are arranged in a ring-shaped configuration with strictly local gene flow. Fertility is defined to be the probability that a child will have fitness at least that of its least fit parent. Fertility is found to drop steadily and significantly with distance around the ring in each of twelve replicates of the experiment. Comparison of fertility at various distances within a ring-shaped population is compared with sampled intra-population fertility. Some populations are found to have significantly higher than background fertility with other populations. This phenomena suggests the presence of aggressive genetics or dominant phenotype in which a creature has an enhanced probability of simply cloning its own phenotype during crossover. In addition to creating examples of artificial ring species this study also achieved a very high level of fitness with the Tartarus task. A comparison is made with another study that uses hybridisation to achieve record breaking Tartarus fitness.", keywords = "genetic algorithms, genetic programming", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @InProceedings{Ashlock3:2008:cec, author = "Daniel Ashlock and Elizabeth Warner", title = "The Geometry of Tartarus Fitness Cases", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "1309--1316", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1823-7", file = "EC0339.pdf", DOI = "doi:10.1109/CEC.2008.4630965", size = "8 pages", abstract = "Tartarus is a standard AI task for grid robots in which boxes must be moved to the walls of a virtual world. There are 320320 fitness cases for the standard Tartarus task of which 297040 are valid according to the original statement of the problem. This paper studies different schemes for allocating fitness trials for Tartarus using an agent-based metric on the fitness cases to aid in the design process. This agent-based metric is a tool that permits exploration of the geometry of the space of fitness cases. The information gained from this exploration demonstrates why a scheme designed to yield a superior set of training cases in fact yielded an inferior one. The information gained also suggests a new scheme for allocating fitness trials that decreases the number of trials required to achieve a given fitness of the best agent. This scheme achieves similar fitness to a standard evolutionary algorithm using fewer fitness cases. The space of fitness cases for Tartarus is found, relative to the agent-based metric, to form a hollow sphere with a nonuniform distribution of the fitness cases within the space. The tools developed in this study include a generalisable technique for placing an agent-based metric space structure on the fitness cases of any problem that has multiple fitness cases. This metric space structure can be used to better understand the distribution of fitness cases and so design more effective evolutionary algorithms.", keywords = "genetic algorithms, genetic programming", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @InProceedings{Ashlock5:2008:cec, author = "Daniel A. Ashlock and Kenneth M. Bryden and Steven Corns", title = "Small Population Effects and Hybridization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "2637--2643", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1823-7", file = "EC0599.pdf", DOI = "doi:10.1109/CEC.2008.4631152", size = "7 pages", abstract = "This paper examines the confluence of two lines of research that seek to improve the performance of evolutionary computation systems through management of information flow. The first is hybridisation; the second is using small population effects. Hybridisation consists of restarting evolutionary algorithms with copies of bestof- population individuals drawn from many populations. Small population effects occur when an evolutionary algorithm's performance, either speed or probability of premature convergence, is improved by use of a very small population. This paper presents a structure for evolutionary computation called a blender which performs hybridisation of many small populations. The blender algorithm is tested on the PORS and Tartarus tasks. Substantial and significant effects result from varying the size of the small populations used and from varying the frequency with which hybridisation is performed. The major effect results from changing the frequency of hybridization; the impact of population size is more modest. The parameter settings which yield best performance of the blender algorithm are remarkably consistent across all seven sets of experiments performed. Blender performance is found to be superior to other algorithms for six cases of the PORS problem. For Tartarus, blender performs well, but not as well as the previous hybridization experiments that motivated its development.", keywords = "genetic algorithms, genetic programming", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @InProceedings{Ashlock:2009:ANNIEa, author = "Daniel Ashlock and Adam J. Shuttleworth and Kenneth M. Bryden", title = "Induction of Virtual Sensors with Function Stacks", booktitle = "ANNIE 2009, Intelligent Engineering Systems through Artificial Neural Networks", year = "2009", editor = "Cihan H. Dagli and K. Mark Bryden and Steven M. Corns and Mitsuo Gen and Kagan Tumer and Gursel Suer", volume = "19", address = "St. Louis, MO, USA", note = "Part I", keywords = "genetic algorithms, genetic programming", isbn13 = "9780791802953", DOI = "doi:10.1115/1.802953.paper4", abstract = "Virtual sensors are mathematical models that predict the readings of a sensor in a location currently without an operational sensor. Virtual sensors can be used to compensate for a failed sensor or as a framework for supporting mathematical decomposition of a model of a complex system. This study applies a novel genetic programming representation called a function stack to the problem of virtual sensor induction in a simple thermal system. Real-valued function stacks are introduced in this study. The thermal system modelled is a heat exchanger. Function stacks are found to be able to efficiently find compact and accurate models for each often sensors using the data from the other sensors. This study serves as proof-of-concept for using function stacks as a modeling technology for virtual sensors.", } @InProceedings{Ashlock:2009:ANNIE, author = "Daniel Ashlock and Douglas McCorkle and Kenneth M. Bryden", title = "Logic Function Induction with the Blender Algorithm Using Function Stacks", booktitle = "ANNIE 2009, Intelligent Engineering Systems through Artificial Neural Networks", year = "2009", editor = "Cihan H. Dagli and K. Mark Bryden and Steven M. Corns and Mitsuo Gen and Kagan Tumer and Gursel Suer", volume = "19", pages = "189--196", chapter = "24", address = "St. Louis, MO, USA", note = "Part III Evolutionary Computation", keywords = "genetic algorithms, genetic programming", isbn13 = "9780791802953", DOI = "doi:10.1115/1.802953.paper24", abstract = "This paper applies two techniques, hybridisation and small population effects, to the problem of logic function induction. It also uses an efficient representation for genetic programming called a function stack. Function stacks are a directed acyclic graph representation used in place of the more common tree-structured representation. This study is the second exploring an algorithm for evolutionary computation called the blender algorithm which performs hybridization of many small populations. The blender algorithm is tested on the 3 and 4 variable parity problems. Confirming and sharpening earlier results on the use of small population sizes for the parity problem, it is demonstrated that subpopulation size and intervals between population mixing steps are critical parameters. The blender algorithm is found to perform well on the parity problem.", } @InProceedings{Ashlock:2010:cec, author = "Daniel Ashlock and Justin Schonfeld", title = "Evolution for automatic assessment of the difficulty of {Sokoban} boards", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-6910-9", DOI = "doi:10.1109/CEC.2010.5586239", size = "8 pages", abstract = "Many games have a collection of boards with the difficulty of an instance of the game determined by the starting configuration of the board. Correctly rating the difficulty of the boards is somewhat haphazard and required either a remarkable level of understanding of the game or a good deal of play-testing. In this study we explore evolutionary algorithms as a tool to automatically grade the difficulty of boards for a version of the game sokoban. Mean time-to-solution by an evolutionary algorithm and number of failures to solve a board are used as a surrogate for the difficulty of a board. Initial testing with a simple string-based representation, giving a sequence of moves for the Sokoban agent, provided very little signal; it usually failed. Two other representations, based on a reactive linear genetic programming structure called an ISAc list, generated useful hardness-classification information for both hardness surrogates. These two representations differ in that one uses a randomly initialised population of ISAc lists while the other initialises populations with competent agents pre-trained on random collections of sokoban boards. The study encompasses four hardness surrogates: probability-of-failure and mean time-to-solution for each of these two representations. All four are found to generate similar information about board hardness, but probability-of-failure with pre-evolved agents is found to be faster to compute and to have a clearer meaning than the other three board-hardness surrogates.", notes = "ISAc lists. bulldozer. http://sokoban.dk/science/ WCCI 2010. Also known as \cite{5586239}", } @InProceedings{Ashlock:2015:CEC, author = "Daniel Ashlock and Jeffrey Tsang", title = "Evolving Fractal Art with a Directed Acyclic Graph Genetic Programming Representation", booktitle = "Proceedings of 2015 IEEE Congress on Evolutionary Computation (CEC 2015)", year = "2015", editor = "Yadahiko Murata", pages = "2137--2144", address = "Sendai, Japan", month = "25-28 " # may, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-7491-7", URL = "http://eldar.mathstat.uoguelph.ca/dashlock/eprints/RFSfrac.pdf", DOI = "doi:10.1109/CEC.2015.7257148", abstract = "A class of fractals called orbit capture fractals are generated by iterating a function on a point until the point's trajectory enters a capture zone. This study uses a digraph based representation for genetic programming to evolve functions used to generate orbit capture fractals. Three variations on the genetic programming system are examined using two fitness functions. The first fitness function maximizes the entropy of the distribution of capture numbers, while the second places a geometric constraint on the distribution of capture numbers. Some combinations of representation and fitness function generate fractals often, while others yield interesting non-fractal images most of the time.", notes = "0950 hrs 15492 CEC2015", } @InProceedings{Ashlock:2016:CEC, author = "Daniel A. Ashlock and Joseph Alexander Brown", title = "Evolutionary Partitioning Regression with Function Stacks", booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016)", year = "2016", editor = "Yew Song Ong", pages = "1469--1476", address = "Vancouver", month = "25-29 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-5090-0623-6", DOI = "doi:10.1109/CEC.2016.7743963", size = "8 pages", abstract = "Partitioning regression is the simultaneous fitting of multiple models to a set of data and partitioning of that data into easily modelled classes. The key to partitioning regression with evolution is minimum error assignment during fitness evaluation. Assigning a point to the model for which it has the least error while using evolution to minimize total model error encourages the evolution of models that cleanly partition data. This study demonstrates the efficacy of partitioning regression with two or three models on simple bivariate data sets. Possible generalizations to the general case of clustering are outlined.", notes = "CEC2016 WCCI2016", } @InProceedings{Ashlock:2016:CECa, author = "Daniel Ashlock and Garrison Greenwood", title = "Generalized Divide the Dollar", booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016)", year = "2016", editor = "Yew-Soon Ong", pages = "343--350", address = "Vancouver", month = "24-29 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, FSM", isbn13 = "978-1-5090-0623-6", DOI = "doi:10.1109/CEC.2016.7743814", size = "8 pages", abstract = "Divide the dollar is a two-player simultaneous derived from a game invented by John Nash because its strategy space has an entire subspace of Nash equilibria. This study describes and explores a family of generalizations of divide the dollar with easily controlled properties. If we view divide the dollar as modelling the process of making a bargain, then the generalized game makes it easy to model the impact of external subsidies on bargaining. Classical divide the dollar is compared to four generalizations representing a simple subsidy in three different amounts and a more complex type of subsidy. The distribution of simple strategies that arise under replicator dynamics is compared to the bids that arise in populations of evolving, adaptive agents. Agents are encoded using a finite state representation that conditions its transitions on the result of bargains. These results fall into three categories, the first player obtains a higher amount, the second one does, or the agents fail to make a deal. The replicator dynamic results are compared to obtain the naive degree of distortion caused by the subsidies. The results for evolving agents are then examined to figure out the degree to which adaptation compensated for or amplifies this distortion.", notes = "WCCI2016", } @InProceedings{ashlock:2005:CECw, author = "Wendy Ashlock and Dan Ashlock", title = "Single Parent Genetic Programming", booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary Computation", year = "2005", editor = "David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L. Gwenn Volkert and Dan Ashlock and Marc Schoenauer", volume = "2", pages = "1172--1179", address = "Edinburgh, UK", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "2-5 " # sep, organisation = "IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9363-5", DOI = "doi:10.1109/CEC.2005.1554823", size = "8 pages", abstract = "The most controversial part of genetic programming is its highly disruptive and potentially innovative subtree crossover operator. The clearest problem with the crossover operator is its potential to induce defensive metaselection for large parse trees, a process usually termed 'bloat'. Single parent genetic programming is a form of genetic programming in which bloat is reduced by doing subtree crossover with a fixed population of ancestor trees. Analysis of mean tree size growth demonstrates that this fixed and limited set of crossover partners provides implicit, automatic control on tree size in the evolving population, reducing the need for additionally disruptive trimming of large trees. The choice of ancestor trees can also incorporate expert knowledge into the genetic programming system. The system is tested on four problems: plus-one-recall-store (PORS), odd parity, plus-times-half (PTH) and a bioinformatic model fitting problem (NIPs). The effectiveness of the technique varies with the problem and choice of ancestor set. At the extremes, improvements in time to solution in excess of 4700-fold were observed for the PORS problem, and no significant improvements for the PTH problem were observed.", notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS.", } @InProceedings{ashlock:2006:cecW, author = "Wendy Ashlock", title = "Using Very Small Population Sizes in Genetic Programming", booktitle = "2006 IEEE World Congress on Computational Intelligence, 2006 IEEE Congress on Evolutionary Computation", year = "2006", pages = "1023--1030", address = "Vancouver", month = "16-21 " # jul, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2006.1688325", size = "8 pages", abstract = "This paper examines the use of very small (4-7) population sizes in genetic programming. When using exploitive operators, this results in hillclimbing; when using exploratory operators this results in genetic drift. The end result is a different way of searching the space which gives insight into the fitness landscape and the nature of the variation operators used. This study compares the use of very small population sizes with the use of population sizes up to 1000 for three genetic programming problems: 4-parity using parse trees, Tartarus using ISAc lists, and several versions of plus-onerecall- store (PORS) using parse trees. For 4-parity and Tartarus with 60 ISAc nodes, algorithms with very small population sizes found more solutions faster. For PORS, the effect was less pronounced: more solutions were found, but the algorithm was faster only than when using slightly larger populations. For Tartarus with 30 ISAc nodes, no effect was detected.", notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D IEEE Xplore gives pages = 319--326,", } @InProceedings{Ashlock:2006:ANNIEw, author = "Wendy Ashlock", title = "Mutation vs. Crossover with Genetic Programming", booktitle = "ANNIE 2006, Intelligent Engineering Systems through Artificial Neural Networks", year = "2006", editor = "Cihan H. Dagli and Anna L. Buczak and David L. Enke and Mark Embrechts and Okan Ersoy", volume = "16", address = "St. Louis, MO, USA", month = nov # " 5-8", note = "Part I: Evolutionary Computation", keywords = "genetic algorithms, genetic programming", isbn13 = "0791802566", DOI = "doi:10.1115/1.802566.paper2", abstract = "Understanding how variation operators work leads to a better understanding both of the search space and of the problem being solved. This study examines the behaviour of mutation and crossover operators in genetic programming using parse trees to find solutions to 3-parity and 4-parity. The standard subtree crossover and subtree mutation operators are studied along with two new operators, fold mutation and fusion crossover. They are studied in terms of how often and how fast they solve the problem; how much they change the fitness on average; and what proportion of variations are neutral, harmful, and helpful. It is found that operators behave differently when used alone than when used together with another operator and that some operators behave differently when solving 3-parity and when solving 4-parity.", } @InProceedings{Ashlock:2011:CIBCB, author = "Wendy Ashlock and Daniel Ashlock", title = "Designing artificial organisms for use in biological simulations", booktitle = "IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2011)", year = "2011", month = "11-15 " # apr, address = "Paris", keywords = "genetic algorithms, genetic programming, Smith-Waterman crossover, artificial genes, artificial organisms, biological simulations, context free grammar, gene interaction, genetic programming maximum problem, genomic level, gridwalkers, horizontal gene transfer, plus-one-recall-store, rugged multimodal fitness landscapes, self-avoiding walk problem, size-neutral crossover, variable length strings, biology computing, context-free grammars, genetics", DOI = "doi:10.1109/CIBCB.2011.5948463", size = "8 pages", abstract = "In this paper we investigate two types of artificial organism which have the potential to be useful in biological simulations at the genomic level, such as simulations of speciation or gene interaction. Biological problems of this type are usually studied either with simulations using artificial genes that are merely evolving strings with no phenotype, ignoring the possibly crucial contribution of natural selection, or with real biological data involving so much complexity that it is difficult to sort out the important factors. This research provides a middle ground. The artificial organisms are: gridwalkers (GWs), a variation on the self-avoiding walk problem, and plus-one-recall-store (PORS), a simple genetic programming maximum problem implemented with a context free grammar. Both are known to have rugged multimodal fitness landscapes. We define a new variation operator, a kind of aligned crossover for variable length strings, which we call Smith-Waterman crossover. The problems, using Smith-Waterman crossover, size-neutral crossover (a kind of non-aligned crossover defined in), mutation only, and horizontal gene transfer (such as occurs in biology with retroviruses) are explored. We define a measure called fitness preservation to quantify the differences in their fitness landscapes and to provide guidance to researchers in determining which problem/variation operator set is best for their simulation.", notes = "Also known as \cite{5948463}", } @InProceedings{Ashlock:2019:CIBCB, author = "Daniel Ashlock and Wendy Ashlock and James Montgomery", title = "Implementing Phenotypic Plasticity with an Adaptive Generative Representation", booktitle = "2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)", year = "2019", address = "Siena, Italy", month = "9-11 " # jul, keywords = "genetic algorithms, genetic programming, SAW, AGR, agent", isbn13 = "978-1-7281-1463-7", URL = "https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8791496", DOI = "doi:10.1109/CIBCB.2019.8791496", size = "8 pages", abstract = "This study compares an adaptive and a non-adaptive representation for finding long walks on obstructed grids. This process models adaptation of a simple plant to an environment where the plant's ability to grow is impeded by obstructions such as resource poor areas like bare rock. The intent of the adaptive representation is to model the biological phenomenon of phenotypic plasticity in which gene regulation is at least partially in response to environmental cues, in this case the obstructions. The adaptive representation is found to have a substantial advantage, with the greatest level of advantage at intermediate levels of obstruction. Agents are asked to solve multiple problem instances simultaneously (i.e. using the same chromosome). The advantage of the adaptive representation is also found to be higher when more boards are used in fitness evaluation.", notes = "Is this GP? cites Ashlock:2016:CEC Also known as \cite{8791496}", } @Article{ashofteh:2019:EMaA, author = "Parisa-Sadat Ashofteh and Omid Bozorg-Haddad and Hugo A. Loaiciga", title = "Logical genetic programming {(LGP)} application to water resources management", journal = "Environmental Monitoring and Assessment", year = "2019", volume = "192", number = "1", pages = "Article number: 34", keywords = "genetic algorithms, genetic programming, GP algorithm, LGP approach, Standard operating procedure (SOP) rule, Logical operators, Logical functions, Multi-conditional mathematical problem", URL = "http://link.springer.com/article/10.1007/s10661-019-8014-y", DOI = "doi:10.1007/s10661-019-8014-y", size = "11 pages", abstract = "Genetic programming (GP) is a variant of evolutionary algorithms (EA). EAs are general-purpose search algorithms. Yet, GP does not solve multi-conditional problems satisfactorily. This study improves the GP predictive skill by development and integration of mathematical logical operators and functions to it. The proposed improvement is herein named logical genetic programming (LGP) whose performance is compared with that of GP using examples from the fields of mathematics and water resources. The results of the examples show the LGP superior performance in both examples, with LGP producing improvements of 74 and 42 percent in the objective functions of the mathematical and water resources examples, respectively, when compared with the GP results. The objective functions minimize the mean absolute error (MAE). The comparison of the LGP and GP results with alternative performance criteria demonstrate a better capability of the former algorithm in solving multi-conditional problems.", } @Article{Ashour:2003:CS, author = "A. F. Ashour and L. F. Alvarez and V. V. Toropov", title = "Empirical modelling of shear strength of {RC} deep beams by genetic programming", journal = "Computers and Structures", year = "2003", volume = "81", number = "5", pages = "331--338", month = mar, keywords = "genetic algorithms, genetic programming, Reinforced concrete deep beams, Empirical model building", broken = "http://www.sciencedirect.com/science/article/B6V28-47S6J5M-5/2/03211d57903fd1d7c48ac56fb32d1d36", DOI = "doi:10.1016/S0045-7949(02)00437-6", abstract = "This paper investigates the feasibility of using previous termgeneticnext term programming (GP) to create an empirical model for the complicated non-linear relationship between various input parameters associated with reinforced concrete (RC) deep beams and their ultimate shear strength. GP is a relatively new form of artificial intelligence, and is based on the ideas of Darwinian theory of evolution and previous termgenetics.next term The size and structural complexity of the empirical model are not specified in advance, but these characteristics evolve as part of the prediction. The engineering knowledge on RC deep beams is also included in the search process through the use of appropriate mathematical functions. The model produced by GP is constructed directly from a set of experimental results available in the literature. The validity of the obtained model is examined by comparing its response with the shear strength of the training and other additional datasets. The developed model is then used to study the relationships between the shear strength and different influencing parameters. The predictions obtained from GP agree well with experimental observations.", } @Article{Ashrafian:2020:Measurement, author = "Ali Ashrafian and Amir H. Gandomi and Mohammad Rezaie-Balf and Mohammad Emadi", title = "An evolutionary approach to formulate the compressive strength of roller compacted concrete pavement", journal = "Measurement", year = "2020", volume = "152", pages = "107309", month = feb, keywords = "genetic algorithms, genetic programming, Gene expression programming, Evolutionary approach, Roller compacted concrete pavement, Compressive strength, Prediction", ISSN = "0263-2241", URL = "http://www.sciencedirect.com/science/article/pii/S026322411931173X", DOI = "doi:10.1016/j.measurement.2019.107309", abstract = "The construction and maintenance of roads pavement was a critical problem in the last years. Therefore, the use of roller-compacted concrete pavement (RCCP) in road problems is widespread. The compressive strength (fc) is the key characteristic of the RCCP caused to significant impact on the cost of production. In this study, an evolutionary-based algorithm named gene expression programming (GEP) is implemented to propose novel predictive formulas for the fc of RCCP. The fc is formulated based on important factor used in mixture proportion in three different combinations of dimensional form (coarse aggregate, fine aggregate, cement, pulverized fly ash, water, and binder), non-dimensional form (water to cement ratio, water to binder ratio, coarse to fine aggregate ratio and pulverized fly ash to binder ratio) and percentage form of input variables. A comprehensive and reliable database incorporating 235 experimental cases collected from several studies. Furthermore, mean absolute error (MAE), root mean square error (RMSE), correlation coefficient (r), average absolute error (AAE), performance index (PI), and objective function (OBJ) as the internal standard statistical measures and external validation evaluated proposed GEP-based models. Uncertainty and parametric studies were carried out to verify the results. Moreover, sensitivity analysis to determine the importance of each predictor on fc of RCCP revealed that fine aggregate content and water to binder ratio is the most useful predictor in dimensional, non-dimensional and percentage forms, respectively. The proposed equation-based models are found to be simple, robustness and straightforward to use, and provide consequently new formulations for fc of RCCP.", notes = "also known as \cite{ASHRAFIAN2020107309}", } @Article{ASIF:2024:cscm, author = "Usama Asif and Muhammad Faisal Javed and Maher Abuhussain and Mujahid Ali and Waseem Akhtar Khan and Abdullah Mohamed", title = "Predicting the mechanical properties of plastic concrete: An optimization method by using genetic programming and ensemble learners", journal = "Case Studies in Construction Materials", volume = "20", pages = "e03135", year = "2024", ISSN = "2214-5095", DOI = "doi:10.1016/j.cscm.2024.e03135", URL = "https://www.sciencedirect.com/science/article/pii/S2214509524002869", keywords = "genetic algorithms, genetic programming, Plastic concrete, Machine learning, Compressive strength, Flexural strength, Sustainability, Ensemble learning algorithms, Gene expression programming", abstract = "This study presents a comparative analysis of individual and ensemble learning algorithms (ELAs) to predict the compressive strength (CS) and flexural strength (FS) of plastic concrete. Multilayer perceptron neuron network (MLPNN), Support vector machine (SVM), random forest (RF), and decision tree (DT) were used as base learners, which were then combined with bagging and Adaboost methods to improve the predictive performance. In addition, gene expression programming (GEP) was used to develop computational equations that can be used to predict the CS and FS of plastic concrete. An extensive database containing 357 and 125 data points was obtained from the literature, and the eight most impactful ingredients were used in the model's development. The accuracy of all models was assessed using several statistical measures, including an error matrix, Akaike information criterion (AIC), K-fold cross-validation, and other external validation equations. Furthermore, sensitivity and SHAP analysis were performed to evaluate input variables' relative significance and impact on the anticipated CS and FS. Based on statistical measures and other validation criteria, GEP outpaces all other individual models, whereas, in ELAs, the SVR ensemble with Adaboost and RF modified with the Bagging technique demonstrated superior performance. SHapley Additive exPlanations (SHAP) and sensitivity analysis reveal that plastic, cement, water, and the age of the specimens have the highest influence, while superplasticizer has the lowest impact, which is consistent with experimental studies. Moreover, GUI and GEP-based simple mathematical correlation can enhance the practical scope of this study and be an effective tool for the pre-mix design of plastic concrete", } @Article{Asif:2025:mtcomm, author = "Usama Asif and Waseem Akhtar Khan and Khawaja Atif Naseem and Syed Abdul Sami Rizvi", title = "Enhancing the predictive accuracy of marshall design tests using generative adversarial networks and advanced machine learning techniques", journal = "Materials Today Communications", year = "2025", volume = "45", pages = "112379", keywords = "genetic algorithms, genetic programming, Hot mixed asphalt, Generative adversarial networks, Ensemble learning with stacking, Marshall stability, Marshall flow, gene expression programming", ISSN = "2352-4928", URL = "https://www.sciencedirect.com/science/article/pii/S2352492825008918", DOI = "doi:10.1016/j.mtcomm.2025.112379", abstract = "Experimentally determining the Marshall design test results for Air voids (Va), Marshall Stability (MS), and Marshall Flow (MF) in hot mixed asphalt (HMA) is often expensive, time-consuming, and requires skilled personnel. To address these challenges, various traditional machine learning (ML) models have been employed to optimise the mix design of HMA. However, their performance is significantly limited by the size and quality of the training dataset. To address these limitations, this study employed Generative Adversarial Networks (GANs) to augment the dataset, which consisted of 184 samples gathered from four construction projects in Pakistan. The augmented dataset was then used to train two advanced ML models: Gene Expression Programming (GEP) and ensemble learning with stacking (ELS). A thorough comparison of the models trained on both original and GAN-augmented datasets was conducted using a range of statistical metrics to evaluate their predictive performance. Additionally, sensitivity and parametric analysis were performed to assess the impact of input variables on the outputs. The results demonstrate that GAN-augmented data significantly improved model accuracy, with GEP and ELS achieving Rsquared values exceeding 0.93 in all cases. Furthermore, GEP models provided interpretable equations for HMA predictions. Sensitivity analysis identified binder content (Pbpercent) as the most influential variable, contributing over 55percent to the variance in Va and MF predictions and 61.56percent in MS. In contrast, other inputs had minimal influence, which was consistent with the experimental findings. This study highlights the potential of advanced ML techniques and data augmentation in developing reliable predictive models for Marshall design test results, advancing efficient HMA design practices", } @Article{ASIM:2018:SDEE, author = "Khawaja M. Asim and Adnan Idris and Talat Iqbal and Francisco Martinez-Alvarez", title = "Seismic indicators based earthquake predictor system using Genetic Programming and AdaBoost classification", journal = "Soil Dynamics and Earthquake Engineering", volume = "111", pages = "1--7", year = "2018", keywords = "genetic algorithms, genetic programming, Earthquake predictor system, Seismic indicators, AdaBoost, Earthquake prediction", ISSN = "0267-7261", URL = "http://www.sciencedirect.com/science/article/pii/S0267726118301349", URL = "https://iranarze.ir/wp-content/uploads/2018/09/E9269-IranArze.pdf", DOI = "doi:10.1016/j.soildyn.2018.04.020", size = "7 pages", abstract = "In this study an earthquake predictor system is proposed by combining seismic indicators along with Genetic Programming (GP) and AdaBoost (GP-AdaBoost) based ensemble method. Seismic indicators are computed through a novel methodology in which, the indicators are computed to obtain maximum information regarding seismic state of the region. The computed seismic indicators are used with GP-AdaBoost algorithm to develop an Earthquake Predictor system (EP-GPBoost). The setup has been arranged to provide predictions for earthquakes of magnitude 5.0 and above, fifteen days prior to the earthquake. The regions of Hindukush, Chile and Southern California are considered for experimentation. The EP-GPBoost has produced noticeable improvement in earthquake prediction due to collaboration of strong searching and boosting capabilities of GP and AdaBoost, respectively. The earthquake predictor system shows enhanced results in terms of accuracy, precision and Matthews Correlation Coefficient for the three considered regions in comparison to contemporary results", notes = "Centre for Earthquake Studies, National Centre for Physics, Pakistan", } @Article{ASKARI:2022:renene, author = "Ighball Baniasad Askari and Amin Shahsavar and Mehdi Jamei and Francesco Calise and Masoud Karbasi", title = "A parametric assessing and intelligent forecasting of the energy and exergy performances of a dish concentrating photovoltaic/thermal collector considering six different nanofluids and applying two meticulous soft computing paradigms", journal = "Renewable Energy", volume = "193", pages = "149--166", year = "2022", ISSN = "0960-1481", DOI = "doi:10.1016/j.renene.2022.04.155", URL = "https://www.sciencedirect.com/science/article/pii/S0960148122006231", keywords = "genetic algorithms, genetic programming, Dish concentrating photovoltaic thermal system, Exergy, Multi-gene genetic optimization, Nanofluid, Thermodynamic analysis", abstract = "In the present study, the application of six engine oil-based Nano fluids (NFs) in a solar concentrating photovoltaic thermal (CPVT) collector is investigated. The calculations were performed for different values of nanoparticle volume concentration, receiver tube diameter, concentrator surface area, receiver length, receiver actual to the maximum number of channels ratio, beam radiation, and a constant volumetric flow rate. Besides, two novel soft computing paradigms namely, the cascaded forward neural network (CFNN) and Multi-gene genetic programming (MGGP) were adopted to predict the first law efficiency (?I) and second law efficiency (?II) of the system based on the influential parameters, as the input features. It was found that the increase of nanoparticle concentration leads to an increase in ?I and a decrease in ?II. Moreover, the rise of both the concentrator surface area (from 5 m2 to 20 m2) and beam irradiance (from 150 W/m2 to 1000 W/m2) entails an increase in both the ?I (by 39percent and 261percent) and ?II (by 55percent and 438percent). Furthermore, it was reported that the pattern of changes in both ?I and ?II with serpentine tube diameter, receiver plate length, and absorber tube length is increasing-decreasing. The results of modeling demonstrated that the CFNN had superior performance than the MGGP model", } @InProceedings{EUSIPCO:2010, author = "Muhammad Waqar Aslam and Asoke Kumar Nandi", title = "Detection of Diabetes Using Genetic Programming", booktitle = "18th European Signal Processing Conference, EUSIPCO 2010", year = "2010", pages = "1184--1188", month = aug # " 23-27", organization = "Eurasip", keywords = "genetic algorithms, genetic programming", URL = "http://www.eurasip.org/Proceedings/Eusipco/Eusipco2010/Contents/papers/1569291873.pdf", size = "5 pages", abstract = "Diabetes is one of the common and rapidly increasing diseases in the world. It is a major health problem in most of the countries. Due to its importance, the need for automated detection of this disease is increasing. The method proposed here uses genetic programming (GP) and a variation of genetic programming called GP with comparative partner selection (CPS) for diabetes detection. The proposed system consists of two stages. In first stage we use genetic programming to produce an individual from training data, that converts the available features to a single feature such that it has different values for healthy and patient (diabetes) data. In the next stage we use test data for testing of that individual. The proposed system was able to achieve 78.5 (pm 2.2)percent accuracy. The results showed that GP based classifier can assist in the diagnosis of diabetes disease.", owner = "waqar", timestamp = "2013.03.25", } @InProceedings{Aslam:2010:milcom, author = "Muhammad Waqar Aslam and Zhechen Zhu and Asoke K. Nandi", title = "Automatic digital modulation classification using Genetic Programming with K-Nearest Neighbor", booktitle = "MILCOM 2010", year = "2010", month = oct # " 31-" # nov # " 3", pages = "1731--1736", abstract = "Automatic modulation classification is an intrinsically interesting problem with various civil and military applications. A generalised digital modulation classification algorithm has been developed and presented in this paper. The proposed algorithm uses Genetic Programming (GP) with K-Nearest Neighbour (K-NN). The algorithm is used to identify BPSK, QPSK, 16QAM and 64QAM modulations. Higher order cumulants have been used as input features for the algorithm. A two-stage classification approach has been used to improve the classification accuracy. The high performance of the method is demonstrated using computer simulations and in comparisons with existing methods.", keywords = "genetic algorithms, genetic programming, 16QAM, 64QAM, BPSK, K-nearest neighbour, QPSK, automatic digital modulation classification, civil application, computer simulations, military application, quadrature amplitude modulation, quadrature phase shift keying, signal classification", DOI = "doi:10.1109/MILCOM.2010.5680232", ISSN = "2155-7578", notes = "Also known as \cite{5680232}", } @InProceedings{EUSIPCO:2011, author = "Muhammad Waqar Aslam and Zhechen Zhu and Asoke Kumar Nandi", title = "Robust QAM Classification Using Genetic Programming and Fisher Criterion", booktitle = "19th European Signal Processing Conference, EUSIPCO 2011", year = "2011", pages = "995--999", address = "Barcelona, Spain", month = "28 " # aug # " - 2 " # sep, organization = "Eurasip", keywords = "genetic algorithms, genetic programming", URL = "http://www.eurasip.org/Proceedings/Eusipco/Eusipco2011/papers/1569422149.pdf", size = "5 pages", abstract = "Automatic modulation recognition has seen increasing demand in recent years. It has found many applications in wireless communications, including both civilian and military applications. It is a scheme to identify automatically the modulation type of received signal by observing data samples of received signals in the presence of noise. In this paper a combination of genetic programming (GP) and Fisher criterion is proposed for classification of QAM modulation schemes for the first time. This method appears to be both efficient and robust. Due to an increase in importance of QAM modulations schemes in recent times we have used QAM for classification purpose. The modulations considered here are QAM16 and QAM64. Simulations and results show that the performance achieved using GP are better than other methods presented so far", timestamp = "2013.03.25", } @Article{Aslam:2012:ieeeTWC, author = "Muhammad Waqar Aslam and Zhechen Zhu and Asoke Kumar Nandi", title = "Automatic Modulation Classification Using Combination of Genetic Programming and {KNN}", journal = "IEEE Transactions on Wireless Communications", year = "2012", volume = "11", number = "8", pages = "2742--2750", month = aug, keywords = "genetic algorithms, genetic programming, Automatic modulation classification, K-nearest neighbour, Classification using genetic programming, Higher order cumulants", ISSN = "1536-1276", DOI = "doi:10.1109/TWC.2012.060412.110460", size = "9 pages", abstract = "Automatic Modulation Classification (AMC) is an intermediate step between signal detection and demodulation. It is a very important process for a receiver that has no, or limited, knowledge of received signals. It is important for many areas such as spectrum management, interference identification and for various other civilian and military applications. This paper explores the use of Genetic Programming (GP) in combination with K-nearest neighbour (KNN) for AMC. KNN has been used to evaluate fitness of GP individuals during the training phase. Additionally, in the testing phase, KNN has been used for deducing the classification performance of the best individual produced by GP. Four modulation types are considered here: BPSK, QPSK, QAM16 and QAM64. Cumulants have been used as input features for GP. The classification process has been divided into two-stages for improving the classification accuracy. Simulation results demonstrate that the proposed method provides better classification performance compared to other recent methods.", notes = "Also known as \cite{6213036}", } @PhdThesis{AslamMuh_Feb2013_10353, author = "Muhammad Waqar Aslam", title = "Pattern recognition using genetic programming for classification of diabetes and modulation data", school = "University of Liverpool", year = "2013", address = "UK", month = feb, keywords = "genetic algorithms, genetic programming", URL = "http://repository.liv.ac.uk/10353/1/AslamMuh_Feb2013_10353.pdf", URL = "http://repository.liv.ac.uk/10353/", URL = "http://ethos.bl.uk/OrderDetails.do?did=47&uin=uk.bl.ethos.579375", size = "220 pages", abstract = "The field of science whose goal is to assign each input object to one of the given set of categories is called pattern recognition. A standard pattern recognition system can be divided into two main components, feature extraction and pattern classification. During the process of feature extraction, the information relevant to the problem is extracted from raw data, prepared as features and passed to a classifier for assignment of a label. Generally, the extracted feature vector has fairly large number of dimensions, from the order of hundreds to thousands, increasing the computational complexity significantly. Feature generation is introduced to handle this problem which filters out the unwanted features. The functionality of feature generation has become very important in modern pattern recognition systems as it not only reduces the dimensions of the data but also increases the classification accuracy. A genetic programming (GP) based framework has been used in this thesis for feature generation. GP is a process based on the biological evolution of features in which combination of original features are evolved. The stronger features propagate in this evolution while weaker features are discarded. The process of evolution is optimised in a way to improve the discriminatory power of features in every new generation. The final features generated have more discriminatory power than the original features, making the job of classifier easier. One of the main problems in GP is a tendency towards suboptimal-convergence. In this thesis, the response of features for each input instance which gives insight into strengths and weaknesses of features is used to avoid suboptimal-convergence. The strengths and weaknesses are used to find the right partners during crossover operation which not only helps to avoid suboptimal-convergence but also makes the evolution more effective. In order to thoroughly examine the capabilities of GP for feature generation and to cover different scenarios, different combinations of GP are designed. Each combination of GP differs in the way, the capability of the features to solve the problem (the fitness function) is evaluated. In this research Fisher criterion, Support Vector Machine and Artificial Neural Network have been used to evaluate the fitness function for binary classification problems while K-nearest neighbour classifier has been used for fitness evaluation of multi-class classification problems. Two Real world classification problems (diabetes detection and modulation classification) are used to evaluate the performance of GP for feature generation. These two problems belong to two different categories; diabetes detection is a binary classification problem while modulation classification is a multi-class classification problem. The application of GP for both the problems helps to evaluate the performance of GP for both categories. A series of experiments are conducted to evaluate and compare the results obtained using GP. The results demonstrate the superiority of GP generated features compared to features generated by conventional methods.", notes = "Supervisor: Asoke Kumar Nandi uk.bl.ethos.579375", } @Article{Aslam:2013:ESA, author = "Muhammad Waqar Aslam and Zhechen Zhu and Asoke Kumar Nandi", title = "Feature generation using genetic programming with comparative partner selection for diabetes classification", journal = "Expert Systems with Applications", volume = "40", number = "13", pages = "5402--5412", year = "2013", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2013.04.003", URL = "http://www.sciencedirect.com/science/article/pii/S0957417413002406", abstract = "Abstract The ultimate aim of this research is to facilitate the diagnosis of diabetes, a rapidly increasing disease in the world. In this research a genetic programming (GP) based method has been used for diabetes classification. GP has been used to generate new features by making combinations of the existing diabetes features, without prior knowledge of the probability distribution. The proposed method has three stages: features selection is performed at the first stage using t-test, Kolmogorov-Smirnov test, Kullback-Leibler divergence test, F-score selection, and GP. The results of feature selection methods are used to prepare an ordered list of original features where features are arranged in decreasing order of importance. Different subsets of original features are prepared by adding features one by one in each subset using sequential forward selection method according to the ordered list. At the second stage, GP is used to generate new features from each subset of original diabetes features, by making non-linear combinations of the original features. A variation of GP called GP with comparative partner selection (GP-CPS), using the strengths and the weaknesses of GP generated features, has been used at the second stage. The performance of GP generated features for classification is tested using the k-nearest neighbour and support vector machine classifiers at the last stage. The results and their comparisons with other methods demonstrate that the proposed method exhibits superior performance over other recent methods.", keywords = "genetic algorithms, genetic programming, Pima Indian diabetes, Comparative partner selection", } @InProceedings{Aslam:2013:MLSP, author = "Muhammad Waqar Aslam and Zhechen Zhu and Asoke Kumar Nandi", booktitle = "IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2013)", title = "Improved comparative partner selection with brood recombination for genetic programming", year = "2013", month = "22-25 " # sep, keywords = "genetic algorithms, genetic programming, brood recombination, improved comparative partner selection", DOI = "doi:10.1109/MLSP.2013.6661901", ISSN = "1551-2541", size = "5 pages", abstract = "The aim of all evolutionary methods is to find the best solution from search space without testing every solution in search space. This study employs strengths and weaknesses of solutions for finding the best solution of any problem in genetic programming. The strengths and weaknesses are used to assist in finding the right partners (solutions) during crossover operation. The probability of crossover between two solutions is evaluated using relative strengths and weaknesses as well as overall strengths of solutions (Improved Comparative Partner Selection (ICPS)). The solutions qualifying for crossover through ICPS criteria are supposed to produce better solutions and are allowed to produce more children through brood recombination. The brood recombination helps to exploit the search space close to the optimum solution more efficiently. The proposed method is applied on different benchmarking problems and results demonstrate that the method is highly efficient in exploring the search space.", notes = "Also known as \cite{6661901}", } @InProceedings{Aslam:2015:SAI, author = "Muhammad Waqar Aslam", booktitle = "Science and Information Conference (SAI 2015)", title = "Selection of fitness function in genetic programming for binary classification", year = "2015", pages = "489--493", abstract = "Fitness function is a key parameter in genetic programming (GP) and is also known as the driving force of GP. It determines how well a solution is able to solve the given problem. The design of fitness function is instrumental in performance improvement of GP. In this study we evaluate different fitness functions for binary classification using two benchmarking datasets. Two types of fitness functions are used. One type uses statistical distribution of classes in the datasets and the other uses machine learning classifiers. A detailed analysis and comparison are given between different fitness functions in terms of performance and computational complexity.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SAI.2015.7237187", month = jul, notes = "Also known as \cite{7237187}", } @Article{ASLAM:2018:ASC, author = "Muhammad Waqar Aslam and Zhechen Zhu and Asoke Kumar Nandi", title = "Diverse partner selection with brood recombination in genetic programming", journal = "Applied Soft Computing", volume = "67", pages = "558--566", year = "2018", keywords = "genetic algorithms, genetic programming, Diversity, Partner selection, Brood recombination", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2018.03.035", URL = "http://www.sciencedirect.com/science/article/pii/S1568494618301571", abstract = "The ultimate goal of learning algorithms is to find the best solution from a search space without testing each and every solution available in the search space. During the evolution process new solutions (children) are produced from existing solutions (parents), where new solutions are expected to be better than existing solutions. This paper presents a new parent selection method for the crossover operation in genetic programming. The idea is to promote crossover between two behaviourally (phenotype) diverse parents such that the probability of children being better than their parents increases. The relative phenotype strengths and weaknesses of pairs of parents are exploited to find out if their crossover is beneficial or not (diverse partner selection (DPS)). Based on the probable improvement in children compared to their parents, crossover is either allowed or disallowed. The parents qualifying for crossover through this process are expected to produce much better children and are allowed to produce more children than normal parents through brood recombination (BR). BR helps to explore the search space around diverse parents much more efficiently. Experimental results from different benchmarking problems demonstrate that the proposed method (DPS with BR) improves the performance of genetic programming significantly", } @InProceedings{Aslan:2019:evoapplications, author = "Mehmet Aslan and Sevil Sen", title = "Evolving Trust Formula to Evaluate Data Trustworthiness in {VANETs} Using Genetic Programming", booktitle = "22nd International Conference, EvoApplications 2019", year = "2019", month = "24-26 " # apr, editor = "Paul Kaufmann and Pedro A. Castillo", series = "LNCS", volume = "11454", publisher = "Springer Verlag", address = "Leipzig, Germany", pages = "413--429", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Evolutionary computation, Trust management, Data trust, Vehicular Ad Hoc Networks, VANETs", isbn13 = "978-3-030-16691-5", URL = "https://web.cs.hacettepe.edu.tr/~ssen/files/papers/EvoStar19-1.pdf", URL = "https://rdcu.be/ekEwr", DOI = "doi:10.1007/978-3-030-16692-2_28", size = "16 pages", abstract = "Vehicular Ad Hoc Networks (VANETs) provide traffic safety, improve traffic efficiency and present infotainment by sending messages about events on the road. Trust is widely used to distinguish genuine messages from fake ones. However, trust management in VANETs is a challenging area due to their dynamically changing and decentralized topology. In this study, a genetic programming based trust management model for VANETs is proposed to properly evaluate trustworthiness of data about events. A large number of features is introduced in order to take into account VANETs complex characteristics. Simulations with bogus information attack scenarios show that the proposed trust model considerably increase the security of the network.", notes = "http://www.evostar.org/2019/cfp_evoapps.php EuroGP'2019 held in conjunction with EvoCOP2019, EvoMusArt2019 and EvoApplications2019", } @Article{ASLAN:2023:vehcom, author = "Mehmet Aslan and Sevil Sen", title = "A dynamic trust management model for vehicular ad hoc networks", journal = "Vehicular Communications", volume = "41", pages = "100608", year = "2023", ISSN = "2214-2096", DOI = "doi:10.1016/j.vehcom.2023.100608", URL = "https://www.sciencedirect.com/science/article/pii/S2214209623000384", keywords = "genetic algorithms, genetic programming, Vehicular ad hoc networks, Security, Trust management, Evolutionary computation, Evolutionary dynamic optimization", abstract = "Trust management in vehicular ad hoc networks (VANETs) is a challenging dynamic optimization problem due to their decentralized, infrastructureless, and dynamically changing topology. Evolutionary computation (EC) algorithms are good candidates for solving dynamic optimization problems (DOPs), since they are inspired from the biological evolution that is occurred as a result of changes in the environment. In this study, we explore the use of genetic programming (GP) algorithm and evolutionary dynamic optimization (EDO) techniques to build a dynamic trust management model for VANETs. The proposed dynamic trust management model properly evaluates the trustworthiness of vehicles and their messages in the simulation of experimental scenarios including bogus information attacks. The simulation results show that the evolved trust calculation formula prevents the propagation of bogus messages over VANETs successfully and the dynamic trust management model detects changes in the problem and reacts to them in a timely manner. The best evolved formula achieves 89.38percent Matthews Correlation Coefficient (MCC), 91.81percent detection rate (DR), and 1.01percent false positive rate (FPR), when approx 5percent of the network traffic is malicious. The formula obtains 87.33percent MCC, 92.01percent DR, and 4.8percent FPR when approx 40percent of the network traffic is malicious, demonstrating its robustness to increasing malicious messages. The proposed model is also run on a real-world traffic model and obtains high MCC and low FPR values. To the best of our knowledge, this is the first application of EC and EDO techniques that generate a trust formula automatically for dynamic trust management in VANETs", } @Article{Asonitis:2023:GPEM, author = "Tasos Asonitis and Richard Allmendinger and Matt Benatan and Ricardo Climent", title = "{SonOpt}: understanding the behaviour of bi-objective population-based optimisation algorithms through sound", journal = "Genetic Programming and Evolvable Machines", year = "2023", volume = "24", pages = "article no. 3", note = "Special Issue: Evolutionary Computation in Art, Music and Design", keywords = "genetic algorithms, genetic programming, Sonification, Multi-objective optimisation, Population-based optimisation algorithms, Algorithm behaviour, Hypervolume, Sound", ISSN = "1389-2576", URL = "https://rdcu.be/c7KTf", DOI = "doi:10.1007/s10710-023-09451-5", code_url = "https://github.com/tasos-a/SonOpt-2.0", size = "41 pages", abstract = "We present an extension of SonOpt, the first ever openly available tool for the sonification of bi-objective population-based optimisation algorithms. SonOpt has already introduced benefits on the understanding of algorithmic behaviour by proposing the use of sound as a medium for the process monitoring of bi-objective optimisation algorithms. The first edition of SonOpt used two different sonification paths to provide information on convergence, population diversity, recurrence of objective values across consecutive generations and the shape of the approximation set. The present extension provides further insight through the introduction of a third sonification path, which involves hypervolume contributions to facilitate the understanding of the relative importance of non-dominated solutions. Using a different sound generation approach than the existing ones, this newly proposed sonification path", } @Article{Asouti:2009:GPEM, author = "V. G. Asouti and I. C. Kampolis and K. C. Giannakoglou", title = "A grid-enabled asynchronous metamodel-assisted evolutionary algorithm for aerodynamic optimization", journal = "Genetic Programming and Evolvable Machines", year = "2009", volume = "10", number = "4", pages = "373--389", month = dec, keywords = "genetic algorithms, Asynchronous evolutionary algorithms, Metamodels, Grid computing, Aerodynamic shape optimization", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-009-9090-5", size = "17 pages", abstract = "A Grid-enabled asynchronous metamodel-assisted evolutionary algorithm is presented and assessed on a number of aerodynamic shape optimization problems. An efficient way of implementing surrogate evaluation models or metamodels (artificial neural networks) in the context of an asynchronous evolutionary algorithm is proposed. The use of metamodels relies on the inexact pre-evaluation technique already successfully applied to synchronous (i.e. generation-based) evolutionary algorithms, which needs to be revisited so as to efficiently cooperate with the asynchronous search method. The so-created asynchronous metamodel-assisted evolutionary algorithm is further enabled for Grid Computing. The Grid deployment of the algorithm relies on three middleware layers: GridWay, Globus Toolkit and Condor. Single- and multi-objective CFD-based designs of isolated airfoils and compressor cascades are handled using the proposed algorithm and the gain in CPU cost is demonstrated.", notes = "Parallel CFD & Optimization Unit, Lab. of Thermal Turbomachines, School of Mechanical Engineering, National Technical University of Athens, P.O. Box 64069, Athens, 15710, Greece", } @Article{journals/swevo/AssimiJN17, author = "Hirad Assimi and Ali Jamali and Nader Nariman-Zadeh", title = "Sizing and topology optimization of truss structures using genetic programming", journal = "Swarm and Evolutionary Computation", year = "2017", volume = "37", pages = "90--103", month = dec, keywords = "genetic algorithms, genetic programming, topology optimisation, sizing optimisation, truss structure", bibdate = "2017-12-04", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/swevo/swevo37.html#AssimiJN17", DOI = "doi:10.1016/j.swevo.2017.05.009", abstract = "This paper presents a genetic programming approach for simultaneous optimisation of sizing and topology of truss structures. It aims to find the optimal cross-sectional areas and connectivities of the joints to achieve minimum weight in the search space. The structural optimisation problem is subjected to kinematic stability, maximum allowable stress and deflection. This approach uses the variable-length representation of potential solutions in the shape of computer programs and evolves to the optimum solution. This method has the capability to identify redundant truss elements and joints in the design space. The obtained results are compared with existing popular and competent techniques in literature and its competence as a tool in the optimisation problem are demonstrated in solving some benchmark examples, the proposed approach provided lighter truss structures than the available solutions reported in the literature.", } @Article{journals/eswa/AssimiJ18, author = "Hirad Assimi and Ali Jamali", title = "A hybrid algorithm coupling genetic programming and Nelder-Mead for topology and size optimization of trusses with static and dynamic constraints", journal = "Expert Systems with Applications", year = "2018", volume = "95", pages = "127--141", ISSN = "0957-4174", keywords = "genetic algorithms, genetic programming", bibdate = "2018-01-02", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/eswa/eswa95.html#AssimiJ18", DOI = "doi:10.1016/j.eswa.2017.11.035", } @Article{assimi:NCaA, author = "Hirad Assimi and Ali Jamali and Nader Nariman-zadeh", title = "Multi-objective sizing and topology optimization of truss structures using genetic programming based on a new adaptive mutant operator", journal = "Neural Computing and Applications", year = "2019", volume = "31", number = "10", pages = "5729--5749", month = oct, keywords = "genetic algorithms, genetic programming, Multi-objective optimization, Topology, Truss, Adaptive mutant operator", ISSN = "0941-0643", URL = "http://link.springer.com/article/10.1007/s00521-018-3401-9", DOI = "doi:10.1007/s00521-018-3401-9", size = "21 pages", abstract = "Most real-world engineering problems deal with multiple conflicting objectives simultaneously. In order to address this issue in truss optimization, this paper presents a multi-objective genetic programming approach for sizing and topology optimization of trusses. It aims to find the optimal cross-sectional areas and connectivities between the nodes to achieve a set of trade-off solutions to satisfy all the optimization objective functions subjected to some constraints such as kinematic stability, maximum allowable stress in members and nodal deflections. It also uses the variable-length representation of potential solutions in the shape of computer programs and evolves to the potential final set of solutions. This approach also employs an adaptive mutant factor besides the classical genetic operators to improve the exploring capabilities of Genetic Programming in structural optimization. The intrinsic features of genetic programming help to identify redundant truss members and nodes in the design space, while no violation of constraints occurs. Our approach applied to some numerical examples and found a better non-dominated solution set in the most cases in comparison with the competent methods available in the literature.", } @InProceedings{Assis:2014:CICS, author = "Carlos A. S. Assis and Adriano C. M. Pereira and Marconi A. Pereira and Eduardo G. Carrano", booktitle = "IEEE Symposium on Computational Intelligence in Cyber Security (CICS 2014)", title = "A genetic programming approach for fraud detection in electronic transactions", year = "2014", month = dec, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CICYBS.2014.7013373", size = "9 pages", abstract = "The volume of on line transactions has increased considerably in the recent years. Consequently, the number of fraud cases has also increased, causing billion dollar losses each year worldwide. Therefore, it is mandatory to employ mechanisms that are able to assist in fraud detection. In this work, it is proposed the use of Genetic Programming (GP) to identify frauds (charge back) in electronic transactions, more specifically in online credit card operations. A case study, using a real dataset from one of the largest Latin America electronic payment systems, has been conducted in order to evaluate the proposed algorithm. The presented algorithm achieves good performance in fraud detection, obtaining gains up to 17percent with regard to the actual company baseline. Moreover, several classification problems, with considerably different datasets and domains, have been used to evaluate the performance of the algorithm. The effectiveness of the algorithm has been compared with other methods, widely employed for classification. The results show that the proposed algorithm achieved good classification effectiveness in all tested instances.", notes = "Centro Fed. de Educ., Tecnol. de Minas Gerais, Belo Horizonte, Brazil Also known as \cite{7013373}", } @InProceedings{Assuncao:2017:CEC, author = "Filipe Assuncao and Nuno Lourenco and Penousal Machado and Bernardete Ribeiro", booktitle = "2017 IEEE Congress on Evolutionary Computation (CEC)", title = "Automatic generation of neural networks with structured Grammatical Evolution", year = "2017", pages = "1557--1564", abstract = "The effectiveness of Artificial Neural Networks (ANNs) depends on a non-trivial manual crafting of their topology and parameters. Typically, practitioners resort to a time consuming methodology of trial-and-error to find and/or adjust the models to solve specific tasks. To minimise this burden one might resort to algorithms for the automatic selection of the most appropriate properties of a given ANN. A remarkable example of such methodologies is Grammar-based Genetic Programming. This work analyses and compares the use of two grammar-based methods, Grammatical Evolution (GE) and Structured Grammatical Evolution (SGE), to automatically design and configure ANNs. The evolved networks are used to tackle several classification datasets. Experimental results show that SGE is able to automatically build better models than GE, and that are competitive with the state of the art, outperforming hand-designed ANNs in all the used benchmarks.", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", DOI = "doi:10.1109/CEC.2017.7969488", month = jun, notes = "Also known as \cite{7969488}", } @InProceedings{Assuncao:2017:GECCO, author = "Filipe Assuncao and Nuno Lourenco and Penousal Machado and Bernardete Ribeiro", title = "Towards the Evolution of Multi-layered Neural Networks: A Dynamic Structured Grammatical Evolution Approach", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4920-8", address = "Berlin, Germany", pages = "393--400", size = "8 pages", URL = "http://doi.acm.org/10.1145/3071178.3071286", DOI = "doi:10.1145/3071178.3071286", acmid = "3071286", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, grammatical evolution, Artificial Neural Networks, Classification, Grammar-based Genetic Programming, NeuroEvolution", month = "15-19 " # jul, abstract = "Current grammar-based NeuroEvolution approaches have several shortcomings. On the one hand, they do not allow the generation of Artificial Neural Networks (ANNs) composed of more than one hidden-layer. On the other, there is no way to evolve networks with more than one output neuron. To properly evolve ANNs with more than one hidden-layer and multiple output nodes there is the need to know the number of neurons available in previous layers. In this paper we introduce Dynamic Structured Grammatical Evolution (DSGE): a new genotypic representation that overcomes the aforementioned limitations. By enabling the creation of dynamic rules that specify the connection possibilities of each neuron, the methodology enables the evolution of multi-layered ANNs with more than one output neuron. Results in different classification problems show that DSGE evolves effective single and multi-layered ANNs, with a varying number of output neurons.", notes = "Also known as \cite{Assun\&\#xE7;\&\#xE3;o:2017:TEM:3071178.3071286} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @InProceedings{Assuncao:2018:EuroGP, author = "Filipe Assuncao and Nuno Lourenco and Penousal Machado and Bernardete Ribeiro", title = "Using {GP} is {NEAT}: Evolving Compositional Pattern Production Functions", booktitle = "EuroGP 2018: Proceedings of the 21st European Conference on Genetic Programming", year = "2018", month = "4-6 " # apr, editor = "Mauro Castelli and Lukas Sekanina and Mengjie Zhang and Stefano Cagnoni and Pablo Garcia-Sanchez", series = "LNCS", volume = "10781", publisher = "Springer Verlag", address = "Parma, Italy", pages = "3--18", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", isbn13 = "978-3-319-77552-4", DOI = "doi:10.1007/978-3-319-77553-1_1", abstract = "The success of Artificial Neural Networks (ANNs) highly depends on their architecture and on how they are trained. However, making decisions regarding such domain specific issues is not an easy task, and is usually performed by hand, through an exhaustive trial-and-error process. Over the years, researches have developed and proposed methods to automatically train ANNs. One example is the HyperNEAT algorithm, which relies on NeuroEvolution of Augmenting Topologies (NEAT) to create Compositional Pattern Production Networks (CPPNs). CPPNs are networks that encode the mapping between neuron positions and the synaptic weight of the ANNs connection between those neurons. Although this approach has obtained some success, it requires meticulous parametrisation to work properly. In this article we present a comparison of different Evolutionary Computation methods to evolve Compositional Pattern Production Functions: structures that have the same goal as CPPNs, but that are encoded as functions instead of networks. In addition to NEAT three methods are used to evolve such functions: Genetic Programming (GP), Grammatical Evolution, and Dynamic Structured Grammatical Evolution. The results show that GP is able to obtain competitive performance, often surpassing the other methods, without requiring the fine tuning of the parameters.", notes = "Part of \cite{Castelli:2018:GP} EuroGP'2018 held in conjunction with EvoCOP2018, EvoMusArt2018 and EvoApplications2018", } @InProceedings{Assuncao:2018:EuroGPa, author = "Filipe Assuncao and Nuno Lourenco and Penousal Machado and Bernardete Ribeiro", title = "Evolving the Topology of Large Scale Deep Neural Networks", booktitle = "EuroGP 2018: Proceedings of the 21st European Conference on Genetic Programming", year = "2018", month = "4-6 " # apr, editor = "Mauro Castelli and Lukas Sekanina and Mengjie Zhang and Stefano Cagnoni and Pablo Garcia-Sanchez", series = "LNCS", volume = "10781", publisher = "Springer Verlag", address = "Parma, Italy", pages = "19--34", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Convolutional Neural Networks, Deep Neural Networks, Genetic Algorithm, Dynamic Structured Grammatical Evolution", isbn13 = "978-3-319-77552-4", URL = "http://www.human-competitive.org/sites/default/files/assuncao-paper-a.pdf", DOI = "doi:10.1007/978-3-319-77553-1_2", size = "16 pages", abstract = "In the recent years Deep Learning has attracted a lot of attention due to its success in difficult tasks such as image recognition and computer vision. Most of the success in these tasks is merit of Convolutional Neural Networks (CNNs), which allow the automatic construction of features. However, designing such networks is not an easy task, which requires expertise and insight. In this paper we introduce DENSER, a novel representation for the evolution of deep neural networks. In concrete we adapt ideas from Genetic Algorithms (GAs) and Grammatical Evolution (GE) to enable the evolution of sequences of layers and their parameters. We test our approach in the well-known image classification CIFAR-10 dataset. The results show that our method: (i) outperforms previous evolutionary approaches to the generations of CNNs; (ii) is able to create CNNs that have state-of-the-art performance while using less prior knowledge (iii) evolves CNNs with novel topologies, unlikely to be designed by hand. For instance, the best performing CNNs obtained during evolution has an unexpected structure using six consecutive dense layers. On the CIFAR-10 the best model reports an average error of 5.87percent on test data.", notes = "2018 HUMIES finalist Part of \cite{Castelli:2018:GP} EuroGP'2018 held in conjunction with EvoCOP2018, EvoMusArt2018 and EvoApplications2018", } @Misc{DBLP:journals/corr/abs-1801-01563, author = "Filipe Assuncao and Nuno Lourenco and Penousal Machado and Bernardete Ribeiro", title = "{DENSER:} Deep Evolutionary Network Structured Representation", howpublished = "arXiv", year = "2018", edition = "v3", month = "1 " # jun, volume = "abs/1801.01563", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, ANN", URL = "http://www.human-competitive.org/sites/default/files/assuncao-paper-b_0.pdf", URL = "http://arxiv.org/abs/1801.01563", size = "11 pages", abstract = "Deep Evolutionary Network Structured Representation (DENSER) is a novel approach to automatically design Artificial Neural Networks (ANNs) using Evolutionary Computation. The algorithm not only searches for the best network topology (e.g., number of layers, type of layers), but also tunes hyper-parameters, such as, learning parameters or data augmentation parameters. The automatic design is achieved using a representation with two distinct levels, where the outer level encodes the general structure of the network, i.e., the sequence of layers, and the inner level encodes the parameters associated with each layer. The allowed layers and range of the hyper-parameters values are defined by means of a human-readable Context-Free Grammar. DENSER was used to evolve ANNs for CIFAR-10, obtaining an average test accuracy of 94.13percent. The networks evolved for the CIFA--10 are tested on the MNIST, Fashion-MNIST, and CIFAR-100; the results are highly competitive, and on the CIFAR-100 we report a test accuracy of 78.75percent. our CIFAR-100 results are the highest performing models generated by methods that aim at the automatic design of Convolutional Neural Networks (CNNs), and are amongst the best for manually designed and fine-tuned CNNs.", notes = "See \cite{Assuncao:2019:GPEM}. 2018 HUMIES finalist https://github.com/fillassuncao/denser-models Python 2", } @InProceedings{Assuncao:2019:EuroGP, author = "Filipe Assuncao and Nuno Lourenco and Penousal Machado and Bernardete Ribeiro", title = "Fast {DENSER}: Efficient Deep {NeuroEvolution}", booktitle = "EuroGP 2019: Proceedings of the 22nd European Conference on Genetic Programming", year = "2019", month = "24-26 " # apr, editor = "Lukas Sekanina and Ting Hu and Nuno Lourenco", series = "LNCS", volume = "11451", publisher = "Springer Verlag", address = "Leipzig, Germany", pages = "197--212", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, ANN: Poster", isbn13 = "978-3-030-16669-4", URL = "https://www.springer.com/us/book/9783030166694", DOI = "doi:10.1007/978-3-030-16670-0_13", size = "16 pages", abstract = "The search for Artificial Neural Networks (ANNs) that are effective in solving a particular task is a long and time consuming trial-and-error process where we have to make decisions about the topology of the network, learning algorithm, and numerical parameters. To ease this process, we can resort to methods that seek to automatically optimise either the topology or simultaneously the topology and learning parameters of ANNs. The main issue of such approaches is that they require large amounts of computational resources, and take a long time to generate a solution that is considered acceptable for the problem at hand. The current paper extends Deep Evolutionary Network Structured Representation (DENSER): a general-purpose NeuroEvolution (NE) approach that combines the principles of Genetic Algorithms with Grammatical Evolution; to adapt DENSER to optimise networks of different structures, or to solve various problems the user only needs to change the grammar that is specified in a text human-readable format. The new method, Fast DENSER (F-DENSER), speeds up DENSER, and adds another representation-level that allows the connectivity of the layers to be evolved. The results demonstrate that F-DENSER has a speedup of 20 times when compared to the time DENSER takes to find the best solutions. Concerning the effectiveness of the approach, the results are highly competitive with the state-of-the-art, with the best performing network reporting an average test accuracy of 91.46percent on CIFAR-10. This is particularly remarkable since the reduction in the running time does not compromise the performance of the generated solutions.", notes = "http://www.evostar.org/2019/cfp_eurogp.php#abstracts Part of \cite{Sekanina:2019:GP} EuroGP'2019 held in conjunction with EvoCOP2019, EvoMusArt2019 and EvoApplications2019", } @Article{Assuncao:2019:GPEM, author = "Filipe Assuncao and Nuno Lourenco and Penousal Machado and Bernardete Ribeiro", title = "{DENSER}: deep evolutionary network structured representation", journal = "Genetic Programming and Evolvable Machines", year = "2019", volume = "20", number = "1", pages = "5--35", month = mar, keywords = "genetic algorithms, genetic programming, Grammatical Evolution, ANN", ISSN = "1389-2576", URL = "https://arxiv.org/abs/1801.01563", DOI = "doi:10.1007/s10710-018-9339-y", code_url = "https://github.com/fillassuncao/denser-models", size = "31 pages", abstract = "Deep evolutionary network structured representation (DENSER) is a novel evolutionary approach for the automatic generation of deep neural networks (DNNs) which combines the principles of genetic algorithms (GAs) with those of dynamic structured grammatical evolution (DSGE). The GA-level encodes the macro structure of evolution, i.e., the layers, learning, and/or data augmentation methods (among others); the DSGE-level specifies the parameters of each GA evolutionary unit and the valid range of the parameters. The use of a grammar makes DENSER a general purpose framework for generating DNNs: one just needs to adapt the grammar to be able to deal with different network and layer types, problems, or even to change the range of the parameters. DENSER is tested on the automatic generation of convolutional neural networks (CNNs) for the CIFAR-10 dataset, with the best performing networks reaching accuracies of up to 95.22percent. Furthermore, we take the fittest networks evolved on the CIFAR-10, and apply them to classify MNIST, Fashion-MNIST, SVHN, Rectangles, and CIFAR-100. The results show that the DNNs discovered by DENSER during evolution generalise, are robust, and scale. The most impressive result is the 78.75percent classification accuracy on the CIFAR-100 dataset, which, sets a new state-of-the-art on methods that seek to automatically design CNNs.", } @Misc{assunccao2019automatic, author = "Filipe Assuncao and Joao Correia and Ruben Conceicao and Mario Pimenta and Bernardo Tome and Nuno Lourenco and Penousal Machado", title = "Automatic Design of Artificial Neural Networks for Gamma-Ray Detection", howpublished = "arXiv", year = "2019", month = "9 " # may, keywords = "genetic algorithms, genetic programming, Grammatical Evolution, ANN", URL = "https://arxiv.org/abs/1905.03532", URL = "http://human-competitive.org/sites/default/files/f_denser_gamma_hadron.pdf", size = "8 pages", abstract = "The goal of this work is to investigate the possibility of improving current gamma/hadron discrimination based on their shower patterns recorded on the ground. To this end we propose the use of Convolutional Neural Networks (CNNs) for their ability to distinguish patterns based on automatically designed features. In order to promote the creation of CNNs that properly uncover the hidden patterns in the data, and at same time avoid the burden of hand-crafting the topology and learning hyper-parameters we resort to NeuroEvolution; in particular we use Fast-DENSER++, a variant of Deep Evolutionary Network Structured Representation. The results show that the best CNN generated by Fast-DENSER++ improves by a factor of 2 when compared with the results reported by classic statistical approaches. Additionally, we experiment ensembling the 10 best generated CNNs, one from each of the evolutionary runs; the ensemble leads to an improvement by a factor of 2.3. These results show that it is possible to improve the gamma/hadron discrimination based on CNNs that are automatically generated and are trained with instances of the ground impact patterns.", notes = "Entered 2019 Humies. Also known as \cite{DBLP:journals/corr/abs-1905-03532}", } @InProceedings{Assuncao:2020:EuroGP, author = "Filipe Assuncao and Nuno Lourenco and Bernardete Ribeiro and Penousal Machado", title = "Incremental Evolution and Development of Deep Artificial Neural Networks", booktitle = "EuroGP 2020: Proceedings of the 23rd European Conference on Genetic Programming", year = "2020", month = "15-17 " # apr, editor = "Ting Hu and Nuno Lourenco and Eric Medvet", series = "LNCS", volume = "12101", publisher = "Springer Verlag", address = "Seville, Spain", pages = "35--51", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, ANN, Incremental development, NeuroEvolution, Convolutional Neural Networks", isbn13 = "978-3-030-44093-0", video_url = "https://youtu.be/XuBDIgbpqZM", DOI = "doi:10.1007/978-3-030-44094-7_3", abstract = "NeuroEvolution (NE) methods are known for applying Evolutionary Computation to the optimisation of Artificial Neural Networks (ANNs). Despite aiding non-expert users to design and train ANNs, the vast majority of NE approaches disregard the knowledge that is gathered when solving other tasks, i.e., evolution starts from scratch for each problem, ultimately delaying the evolutionary process. To overcome this drawback, we extend Fast Deep Evolutionary Network Structured Representation (Fast-DENSER) to incremental development. We hypothesise that by transferring the knowledge gained from previous tasks we can attain superior results and speedup evolution. The results show that the average performance of the models generated by incremental development is statistically superior to the non-incremental average performance. In case the number of evaluations performed by incremental development is smaller than the performed by non-incremental development the attained results are similar in performance, which indicates that incremental development speeds up evolution. Lastly, the models generated using incremental development generalise better, and thus, without further evolution, report a superior performance on unseen problems.", notes = "http://www.evostar.org/2020/cfp_eurogp.php Part of \cite{Hu:2020:GP} EuroGP'2020 held in conjunction with EvoCOP2020, EvoMusArt2020 and EvoApplications2020", } @InProceedings{Assuncao:2020:evoapplications, author = "Filipe Assuncao and Nuno Lourenco and Bernardete Ribeiro and Penousal Machado", title = "Evolution of Scikit-Learn Pipelines with Dynamic Structured Grammatical Evolution", booktitle = "23rd International Conference, EvoApplications 2020", year = "2020", month = "15-17 " # apr, editor = "Pedro A. Castillo and Juan Luis {Jimenez Laredo} and Francisco {Fernandez de Vega}", series = "LNCS", volume = "12104", publisher = "Springer Verlag", address = "Seville, Spain", pages = "530--545", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Automated Machine Learning, Scikit-Learn, Dynamic Structured Grammatical", isbn13 = "978-3-030-43721-3", video_url = "https://www.youtube.com/watch?v=ZXG4MohPDXQ", DOI = "doi:10.1007/978-3-030-43722-0_34", abstract = "The deployment of Machine Learning (ML) models is a difficult and time-consuming job that comprises a series of sequential and correlated tasks that go from the data pre-processing, and the design and extraction of features, to the choice of the ML algorithm and its parameterisation. The task is even more challenging considering that the design of features is in many cases problem specific, and thus requires domain-expertise. To overcome these limitations Automated Machine Learning (AutoML) methods seek to automate, with few or no human-intervention, the design of pipelines, i.e., automate the selection of the sequence of methods that have to be applied to the raw data. These methods have the potential to enable non-expert users to use ML, and provide expert users with solutions that they would unlikely consider. In particular, this paper describes AutoML-DSGE: a novel grammar-based framework that adapts Dynamic Structured Grammatical Evolution (DSGE) to the evolution of Scikit-Learn classification pipelines. The experimental results include comparing AutoML-DSGE to another grammar-based AutoML framework, Resilient Classification Pipeline Evolution (RECIPE), and show that the average performance of the classification pipelines generated by AutoML-DSGE is always superior to the average performance of RECIPE; the differences are statistically significant in 3 out of the 10 used datasets.", notes = "CISUC, Department of Informatics Engineering, University of Coimbra, Portugal http://www.evostar.org/2020/ EvoApplications2020 held in conjunction with EuroGP'2020, EvoMusArt2020 and EvoCOP2020", } @Article{ASTERIS:2021:CBM, author = "Panagiotis G. Asteris and Athanasia D. Skentou and Abidhan Bardhan and Pijush Samui and Paulo B. Lourenco", title = "Soft computing techniques for the prediction of concrete compressive strength using Non-Destructive tests", journal = "Construction and Building Materials", volume = "303", pages = "124450", year = "2021", ISSN = "0950-0618", DOI = "doi:10.1016/j.conbuildmat.2021.124450", URL = "https://www.sciencedirect.com/science/article/pii/S0950061821022078", keywords = "genetic algorithms, genetic programming, Artificial neural networks, Compressive strength of Concrete, Non-destructive testing methods, Soft computing, Artificial Intelligence", abstract = "This study presents a comparative assessment of conventional soft computing techniques in estimating the compressive strength (CS) of concrete using two non-destructive tests, namely ultrasonic pulse velocity and rebound hammer test. In specific, six conventional soft computing models namely back-propagation neural network (BPNN), relevance vector machine, minimax probability machine regression, genetic programming, Gaussian process regression, and multivariate adaptive regression spline, were used. To construct and validate these models, a total of 629 datasets were collected from the literature. Experimental results show that the BPNN attained the most accurate prediction of concrete CS based on both ultrasonic pulse velocity and rebound number values. The results of the employed MARS and BPNN models are significantly better than those obtained in earlier studies. Thus, these two models are very potential to assist engineers in the design phase of civil engineering projects to estimate the concrete CS with a greater accuracy level", } @Article{ASTERIS:2021:ES, author = "Panagiotis G. Asteris and Paulo B. Lourenco and Mohsen Hajihassani and Chrissy-Elpida N. Adami and Minas E. Lemonis and Athanasia D. Skentou and Rui Marques and Hoang Nguyen and Hugo Rodrigues and Humberto Varum", title = "Soft computing-based models for the prediction of masonry compressive strength", journal = "Engineering Structures", volume = "248", pages = "113276", year = "2021", ISSN = "0141-0296", DOI = "doi:10.1016/j.engstruct.2021.113276", URL = "https://www.sciencedirect.com/science/article/pii/S0141029621013997", keywords = "genetic algorithms, genetic programming, Artificial neural networks, Machine learning, Masonry, Metaheuristic algorithms, Compressive strength", abstract = "Masonry is a building material that has been used in the last 10.000 years and remains competitive today for the building industry. The compressive strength of masonry is used in modern design not only for gravitational and lateral loading, but also for quality control of materials and execution. Given the large variations of geometry of units and joint thickness, materials and building practices, it is not feasible to test all possible combinations. Many researchers tried to provide relations to estimate the compressive strength of masonry from the constituents, which remains a challenge. Similarly, modern design codes provide lower bound solutions, which have been demonstrated to be weakly correlated to observed test results in many cases. The present paper adopts soft-computing techniques to address this problem and a dataset with 401 specimens is considered. The obtained results allow to identify the most relevant parameters affecting masonry compressive strength, areas in which more experimental research is needed and expressions providing better estimates when compared to formulas existing in codes or literature", } @Article{ASTERIS:2021:TG, author = "Panagiotis G. Asteris and Anna Mamou and Mohsen Hajihassani and Mahdi Hasanipanah and Mohammadreza Koopialipoor and Tien-Thinh Le and Navid Kardani and Danial J. Armaghani", title = "Soft computing based closed form equations correlating {L} and {N}-type {Schmidt} hammer rebound numbers of rocks", journal = "Transportation Geotechnics", year = "2021", volume = "29", month = jul, pages = "100588", keywords = "genetic algorithms, genetic programming, Artificial neural networks, Machine learning, Metaheuristic algorithms, Non-destructive testing, Rocks, Schmidt hammer rebound number", ISSN = "2214-3912", DOI = "doi:10.1016/j.trgeo.2021.100588", URL = "https://www.sciencedirect.com/science/article/pii/S2214391221000787", abstract = "This paper reports the results of soft computing-based models correlating L and N-type Schmidt hammer rebound numbers of rock. A data-independent database was compiled from available measurements reported in the literature, which was used to train and develop back propagating neural networks, genetic programming and least square method models for the prediction of L-type Schmidt hammer rebound numbers. The results show that the highest predictive accuracy was obtained for the neural network model, which predicts the L type Schmidt hammer rebound number, with less than plus-minus20percent deviation from the experimental data for 97.27percent of the samples. The optimum neural network is presented as a closed form equation and is also incorporated into an Excel-based graphical user interface, which directly calculates the Rn(L) number for any input Rn(N) = 12.40-75.97 and which is made available as supplementary material", notes = "Also known as \cite{ASTERIS2021100588}", } @Article{ates:2023:Algorithms, author = "Cihan Ates and Dogan Bicat and Radoslav Yankov and Joel Arweiler and Rainer Koch and Hans-Jorg Bauer", title = "Model Predictive Evolutionary Temperature Control via {Neural-Network-Based} Digital Twins", journal = "Algorithms", year = "2023", volume = "16", number = "8", pages = "Article No. 387", keywords = "genetic algorithms, genetic programming", ISSN = "1999-4893", URL = "https://www.mdpi.com/1999-4893/16/8/387", DOI = "doi:10.3390/a16080387", abstract = "In this study, we propose a population-based, data-driven intelligent controller that leverages neural-network-based digital twins for hypothesis testing. Initially, a diverse set of control laws is generated using genetic programming with the digital twin of the system, facilitating a robust response to unknown disturbances. During inference, the trained digital twin is used to virtually test alternative control actions for a multi-objective optimisation task associated with each control action. Subsequently, the best policy is applied to the system. To evaluate the proposed model predictive control pipeline, experiments are conducted on a multi-mode heat transfer test rig. The objective is to achieve homogeneous cooling over the surface, minimizing the occurrence of hot spots and energy consumption. The measured variable vector comprises high dimensional infrared camera measurements arranged as a sequence (655,360 inputs), while the control variable includes power settings for fans responsible for convective cooling (3 outputs). Disturbances are induced by randomly altering the local heat loads. The findings reveal that by using an evolutionary algorithm on measured data, a population of control laws can be effectively learnt in the virtual space. This empowers the system to deliver robust performance. Significantly, the digital twin-assisted, population-based model predictive control (MPC) pipeline emerges as a superior approach compared to individual control models, especially when facing sudden and random changes in local heat loads. Leveraging the digital twin to virtually test alternative control policies leads to substantial improvements in the controller's performance, even with limited training data.", notes = "also known as \cite{a16080387}", } @InProceedings{Athapaththu:2020:ICAC, author = "A. M. H. N Athapaththu and D. U. S Illeperumarachchi and H. M. K. U Herath and H. K Jayasinghe and W. H. Rankothge and Narmadha Gamage", title = "Supply and Demand Planning for Water: A Sustainable Water Management System", booktitle = "2020 2nd International Conference on Advancements in Computing (ICAC)", year = "2020", volume = "1", pages = "305--310", abstract = "Sustainable water management requires maintaining the balance between the demand and supply, specifically addressing water demand in urban, agricultural, and natural systems. Having an insight on water supply forecasting and water consumption forecasting, will be useful to generate an optimal water distribution plan. A platform that targets the sustainable water management concepts for domestic usage and paddy cultivation is proposed in this paper, with the following components: (1) forecasting water levels of reservoirs, (2) forecasting water consumption patterns, and (3) optimizing the water distribution. We have used Recurrent Neural Network (RNN) and, Long Short-Term Memory (LSTM) for forecasting modules and, Genetic Programming (GP) for optimizing water distribution. Our results show that, using our proposed modules, sustainable water management related services can be automated efficiently and effectively.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICAC51239.2020.9357256", month = dec, notes = "Also known as \cite{9357256}", } @Article{journals/aai/AticiE17, author = "Umit Atici and Adem Ersoy", title = "Applied Genetic Programming for Predicting Specific Cutting Energy for Cutting Natural Stones", journal = "Applied Artificial Intelligence", year = "2017", number = "5-6", volume = "31", pages = "439--452", keywords = "genetic algorithms, genetic programming, gene expression programming", bibdate = "2017-12-28", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/aai/aai31.html#AticiE17", DOI = "doi:10.1080/08839514.2017.1378140", abstract = "n the processing of marbles and other natural stones, the major cost involved in sawing with circular diamond sawblades is the energy cost. This paper reports a new and efficient approach to the formulation of SEcut using gene expression programming (GEP) based on not only rock characteristics but also design and operating parameters. Twenty-three rock types classified into four groups were cut using three types of circular diamond saws at different feed rates, depths of cut, and peripheral speeds. The input parameters used to develop the GEP-based SEcut prediction model were as follows: physico-mechanical rock characteristics (uniaxial compressive strength, Shore scleroscope hardness, Schmidt rebound hardness, and Bohme surface abrasion), operating parameters (feed rate, depth of cut, and peripheral speed), and a design variable (diamond concentration in the sawblade). The performance of the model was comprehensively evaluated on the basis of statistical criteria such as R2 (0.95).", } @InProceedings{Atiencia-Villagomez:2012:ICIEA, author = "Jose Miguel {Atiencia Villagomez} and Askhat Diveev and Elena Sofronova", title = "The network operator method for synthesis of intelligent control system", booktitle = "7th IEEE Conference on Industrial Electronics and Applications, ICIEA 2012", year = "2012", editor = "Wenxiang Xie", pages = "174--179", address = "Singapore", month = "18-20 " # jul, keywords = "genetic algorithms, genetic programming, aircraft control", isbn13 = "978-1-4577-2117-5", DOI = "doi:10.1109/ICIEA.2012.6360718", abstract = "Application of the network operator for the synthesis of intelligent control systems is considered. An example of the synthesis of automatic control on the space trajectories of unmanned helicopter is given.", notes = "Network operator matrices, NOM, logical network operator. quadrotor. http://www.network-operator.com/ www page includes demo. Entered for 2013 HUMIES GECCO 2013 Peoples' Friendship University of Russia, Moscow, Russia. Also known as \cite{6360718}", } @Article{Atiquzzaman:2016:IJHST, title = "Prediction of inflows from dam catchment using genetic programming", author = "Md Atiquzzaman and Jaya Kandasamy", journal = "International Journal of Hydrology Science and Technology", year = "2016", month = mar # "~28", volume = "6", number = "2", pages = "103--117", keywords = "genetic algorithms, genetic programming, MIKE11-NAM, hydroinformatics, climate scenarios, forecasting, hydrology, rainfall prediction, inflows, inflow prediction, catchment runoff, dam catchment, water management, water resources, Australia, flow simulation", publisher = "Inderscience Publishers", ISSN = "2042-7816", bibsource = "OAI-PMH server at www.inderscience.com", language = "eng", rights = "Inderscience Copyright", URL = "http://www.inderscience.com/link.php?id=75560", DOI = "doi:10.1504/IJHST.2016.075560", abstract = "Application of hydroinformatics tools for managing water resources is common in the water industry. Over the last few decades, several hydroinformatics tools including genetic programming (GP) have been developed and applied in hydrology. GP has been successfully applied for calibration of numerous event-based rainfall and runoff models. However, applying GP to predict long-term time series for the management of water resources is limited. This study demonstrates GP's application in long-term prediction of catchment runoff concerning a dam located in Oberon, New South Wales, Australia. The calibration showed excellent agreement between the observed and simulated flows recorded over 30 years. The model was then applied for the assessment of catchment yields for a future 100 years flows based on two assumed climatic scenarios.", } @Article{ATIQUZZAMAN:2018:CG, author = "Md Atiquzzaman and Jaya Kandasamy", title = "Robustness of Extreme Learning Machine in the prediction of hydrological flow series", journal = "Computer \& Geosciences", volume = "120", pages = "105--114", year = "2018", keywords = "genetic algorithms, genetic programming, Catchment, Flow series, Prediction, Hydrology, Modeling, Extreme learning machine", URL = "http://www.sciencedirect.com/science/article/pii/S0098300417304867", ISSN = "0098-3004", DOI = "doi:10.1016/j.cageo.2018.08.003", URL = "http://www.sciencedirect.com/science/article/pii/S0098300417304867", abstract = "Prediction of hydrological flow series generated from a catchment is an important aspect of water resources management and decision making. The underlying process underpinning catchment flow generation is complex and depends on many parameters. Determination of these parameters using a trial and error method or optimization algorithm is time consuming. Application of Artificial Intelligence (AI) based machine learning techniques including Artificial Neural Network, Genetic Programming (GP) and Support Vector Machine (SVM) replaced the complex modeling process and at the same time improved the prediction accuracy of hydrological time-series. However, they still require numerous iterations and computational time to generate optimum solutions. This study applies the Extreme Learning Machine (ELM) to hydrological flow series modeling and compares its performance with GP and Evolutionary Computation based SVM (EC-SVM). The robustness and performance of ELM were studied using the data from two different catchments located in two different climatic conditions. The robustness of ELM was evaluated by varying number of lagged input variables the number of hidden nodes and input parameter (regularization coefficient). Higher lead days prediction and extrapolation capability were also investigated. The results show that (1) ELM yields reasonable results with two or higher lagged input variables (flows) for 1-day lead prediction; (2) ELM produced satisfactory results very rapidly when the number of hidden nodes was greater than or equal to 1000; (3) ELM showed improved results when regularization coefficient was fine-tuned; (4) ELM was able to extrapolate extreme values well; (5) ELM generated reasonable results for higher number of lead days (second and third) predictions; (6) ELM was computationally much faster and capable of producing better results compared to other leading AI methods for prediction of flow series from the same catchment. ELM has the potential for forecasting real-time hydrological flow series", } @InProceedings{Atkin:1993:GPLAMS, author = "M. Atkin and P. R. Cohen", title = "Genetic programming to learn an agent's monitoring strategy", booktitle = "Proceedings of the AAAI-93 Workshop on Learning Action Models", year = "1993", editor = "Wei-Min Shen", pages = "36--41", publisher = "AAAI Press", URL = "http://www.aaai.org/Papers/Workshops/1993/WS-93-06/WS93-06-009.pdf", URL = "http://www.aaai.org/Library/Workshops/ws93-06.php", keywords = "genetic algorithms, genetic programming", size = "6 pages", abstract = "Many tasks require an agent to monitor its environment, but little is known about appropriate monitoring strategies to use in particular situations. Our approach is to learn good monitoring strategies with a genetic programming algorithm. To this end, we have developed a simple agent programming language in which we represent monitoring strategies as programs that control a simulated robot, and a simulator in which the programs can be evaluated. The effect of different environments and tasks is determined experimentally; changing features of the environment will change which strategies are learnt. The correspondence can then be analysed.", notes = "Also available as \cite{Atkin:1993:GPLAMSa}?", } @TechReport{Atkin:1993:GPLAMSa, author = "M. Atkin and P. R. Cohen", title = "Genetic programming to learn an agent's monitoring strategy", institution = "Computer Science Department, University of Massachusetts", year = "1993", type = "Technical report", number = "TR-93-26", address = "Amherst, MA, USA", URL = "http://www-eksl.cs.umass.edu/papers/93-26.ps", keywords = "genetic algorithms, genetic programming", notes = "Also available as \cite{Atkin:1993:GPLAMS}?", size = "15 pages", } @InProceedings{Atkin:1994:LMSDGP, author = "Marc S. Atkin and Paul R. Cohen", title = "Learning monitoring strategies: A difficult genetic programming application", booktitle = "Proceedings of the 1994 IEEE World Congress on Computational Intelligence", year = "1994", volume = "1", pages = "328--332a", address = "Orlando, Florida, USA", month = "27-29 " # jun, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, cupcake problem, agent control language, genetic programming application, monitoring strategy learning, optimal strategies, possible behaviour, learning (artificial intelligence), monitoring; optimisation", URL = "http://www-eksl.cs.umass.edu/papers/AtkinIEEE.pdf", URL = "http://citeseer.ist.psu.edu/94049.html", DOI = "doi:10.1109/ICEC.1994.349931", size = "6 pages", abstract = "Finding optimal or at least good monitoring strategies is an important consideration when designing an agent. We have applied genetic programming to this task, with mixed results. Since the agent control language was kept purposefully general, the set of monitoring strategies constitutes only a small part of the overall space of possible behaviours. Because of this, it was often difficult for the genetic algorithm to evolve them, even though their performance was superior. These results raise questions as to how easy it will be for genetic programming to scale up as the areas it is applied to become more complex.", notes = "Novel? chrome/program structure linear, close to assembly language, used GOTOs and interrupt handlers. Did _not_ get performance improvement on changing to parse trees. Did evolve progs to control agents which moved to the goal without colliding with an obstacle. Finally cautions about problems with GP scaling up. {"}Also tried local mating (also known as fine grain parallelism){"} Also available as Technical Report 94-52, Dept. of Computer Science, University of Massachusetts/Amherst, USA?", } @TechReport{atkin:1995:mea, author = "Marc S. Atkin and Paul R. Cohen", title = "Monitoring in Embedded Agents", institution = "Experimental Knowledge Systems Laboratory, Computer Science Department, University of Massachusetts", year = "1995", type = "Computer Science Technical Report", number = "95-66", address = "Box 34610, Lederle Graduate Research Center, Amherst. MA 01003-4610, USA", keywords = "genetic algorithms, genetic programming", URL = "http://www-eksl.cs.umass.edu/papers/ijcai95-msa_95-66.pdf", abstract = "Finding good monitoring strategies is an important process in the design of any embedded agent. We describe the nature of the monitoring problem, point out what makes it difficult, and show that while periodic monitoring strategies are often the easiest to derive, they are not always the most appropriate. We demonstrate mathematically and empirically that for a wide class of problems, the so-called 'cupcake problems', there exists a simple strategy, interval reduction, that outperforms periodic monitoring. We also show how features of the environment may influence the choice of the optimal strategy. The paper concludes with some thoughts about a monitoring strategy taxonomy, and what its defining features might be.", notes = "refs to Atkin's Masters Thesis. Simulated robot in 2 dee world, sensors, conditionals, loop. LTB, explains what the cupcake problem is. interrupt handlers. Theoretical justification for cupcake result.", size = "11 pages", } @Article{atkin:1995:AB, author = "Marc S. Atkin and Paul R. Cohen", title = "Monitoring Strategies for Embedded Agents: Experiments and Analysis", journal = "Adaptive Behavior", year = "1995", volume = "4", number = "2", pages = "125--172", month = "Fall", keywords = "genetic algorithms, genetic programming, Monitoring, embedded agents, planning", URL = "http://www-eksl.cs.umass.edu/papers/atkin96.pdf", abstract = "Monitoring is an important activity for any embedded agent. To operate effectively, agents must gather information about their environment. The policy by which they do this is called a monitoring strategy. Our work has focused on classifying different types of monitoring strategies and understanding how strategies depend on features of the task and environment. We have discovered only a few general monitoring strategies, in particular periodic and interval reduction, and speculate that there are no more. The relative advantages and generality of each strategy will be discussed in detail. The wide applicability of interval reduction will be demonstrated both empirically and analytically. We conclude with a number of general laws that state when a strategy is most appropriate.", } @InProceedings{Atkins:2010:cec, author = "Daniel L Atkins and Roman Klapaukh and Will N Browne and Mengjie Zhang", title = "Evolution of aesthetically pleasing images without human-in-the-loop", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-6910-9", abstract = "Evolutionary Art is a sub-field of Evolutionary Computing that involves creating interesting images using Evolutionary Techniques. Previously Genetic Programming has been used to create such images autonomously -that is, without a human in the loop. However, this work did not explore alternative fitness measures, consider colour in fitness or provide independent validation of results. Four fitness functions based on the concept that the pleasingness of an image is based on the ratio of image complexity to processing complexity are explored. We introduce the use of Shannon Entropy as a measure of image complexity to compare with Jpeg Compression. Similarly, we introduce Run Length Encoding to compare with Fractal Compression as a measure of processing complexity. A survey of 100 participants showed that it is possible to generate aesthetically pleasing graphics using each fitness function. Importantly, it was the introduction of colour that separated the aesthetic effects of the fitness measures.", DOI = "doi:10.1109/CEC.2010.5586283", notes = "Three 'separate' GP programs: one for each colour, per image. RMIT-GP package. Web based trial with Likert scale (1..5). Nice pictures. WCCI 2010. Also known as \cite{5586283}", } @InProceedings{Atkins:2011:ADIGPAtAFEfIC, title = "A Domain Independent Genetic Programming Approach to Automatic Feature Extraction for Image Classification", author = "Daniel Atkins and Kourosh Neshatian and Mengjie Zhang", pages = "238--245", booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary Computation", year = "2011", editor = "Alice E. Smith", month = "5-8 " # jun, address = "New Orleans, USA", organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, automatic image feature extraction, baseline system, classifier system, domain independent genetic programming, human-extracted features, image classification, feature extraction, image classification", DOI = "doi:10.1109/CEC.2011.5949624", abstract = "In this paper we explore the application of Genetic Programming (GP) to the problem of domain-independent image feature extraction and classification. We propose a new GP-based image classification system that extracts image features autonomously, and compare its performance against a baseline GP-based classifier system that uses human-extracted features. We found that the proposed system has a similar performance to the baseline system, and that GP is capable of evolving a single program that can both extract useful features and use those features to classify an image.", notes = "CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.", } @TechReport{Atkins:1998:space, author = "Diane J. Atkins", title = "Applying Space Technology to Enhance Control of an Artificial Arm for Children and Adults with Amputations", institution = "The Institute for Rehabilitation and Research (TIRR)", year = "1998", number = "IN-63 006665?", address = "USA", month = "30 " # jun, keywords = "genetic algorithms, genetic programming, myoelectric, MRI", URL = "http://hdl.handle.net/2060/19990025668", URL = "http://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19990025668.pdf", size = "4 pages", notes = "Texas medical Center, NASA Johnson Space Center. TIRR Amputee Program. Dr. Kirstin Ann Farry. Intelligenta Inc. NIH. SBIR. Dept of Education. Appears to have been written in 1999.", } @InProceedings{Atkinson:2018:EuroGP, author = "Timothy Atkinson and Detlef Plump and Susan Stepney", title = "Evolving Graphs by Graph Programming", booktitle = "EuroGP 2018: Proceedings of the 21st European Conference on Genetic Programming", year = "2018", month = "4-6 " # apr, editor = "Mauro Castelli and Lukas Sekanina and Mengjie Zhang and Stefano Cagnoni and Pablo Garcia-Sanchez", series = "LNCS", volume = "10781", publisher = "Springer Verlag", address = "Parma, Italy", pages = "35--51", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", isbn13 = "978-3-319-77552-4", URL = "http://eprints.whiterose.ac.uk/126500/1/AtkinsonPlumpStepney.EuroGP.18.pdf", DOI = "doi:10.1007/978-3-319-77553-1_3", abstract = "Rule-based graph programming is a deep and rich topic. We present an approach to exploiting the power of graph programming as a representation and as an execution medium in an evolutionary algorithm (EGGP). We demonstrate this power in comparison with Cartesian Genetic Programming (CGP), showing that it is significantly more efficient in terms of fitness evaluations on some classic benchmark problems. We hypothesise that this is due to its ability to exploit the full graph structure, leading to a richer mutation set, and outline future work to test this hypothesis, and to exploit further the power of graph programming within an EA.", notes = "Part of \cite{Castelli:2018:GP} EuroGP'2018 held in conjunction with EvoCOP2018, EvoMusArt2018 and EvoApplications2018", } @Misc{DBLP:journals/corr/abs-1810-10453, author = "Timothy Atkinson and Detlef Plump and Susan Stepney", title = "Semantic Neutral Drift", howpublished = "arXiv", year = "2018", month = "24 " # oct, keywords = "genetic algorithms, genetic programming Evolutionary Algorithms, Neutral Drift, Semantic Equivalence, Mutation Operators, Graph Programming", eprint = "1810.10453", timestamp = "Wed, 31 Oct 2018 14:24:29 +0100", biburl = "https://dblp.org/rec/bib/journals/corr/abs-1810-10453", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "http://arxiv.org/abs/1810.10453", size = "16 pages", abstract = "We introduce the concept of Semantic Neutral Drift (SND) for evolutionary algorithms, where we exploit equivalence laws to design semantics preserving mutations guaranteed to preserve individuals fitness scores. A number of digital circuit benchmark problems have been implemented with rule-based graph programs and empirically evaluated, demonstrating quantitative improvements in evolutionary performance. Analysis reveals that the benefits of the designed SND reside in more complex processes than simple growth of individuals, and that there are circumstances where it is beneficial to choose otherwise detrimental parameters for an evolutionary algorithm if that facilitates the inclusion of SND.", notes = "P-GP 2, EGGP, DeMorgan, Bit Added, Bit-Comparator, Bit-Multiplier", } @InProceedings{Atkinson:2019:EuroGP, author = "Timothy Atkinson and John Drake and Athena Karsa and Jerry Swan", title = "Quantum Program Synthesis: Swarm Algorithms and Benchmarks", booktitle = "EuroGP 2019: Proceedings of the 22nd European Conference on Genetic Programming", year = "2019", month = "24-26 " # apr, editor = "Lukas Sekanina and Ting Hu and Nuno Lourenco", series = "LNCS", volume = "11451", publisher = "Springer Verlag", address = "Leipzig, Germany", pages = "19--34", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-030-16669-4", URL = "https://www.springer.com/us/book/9783030166694", DOI = "doi:10.1007/978-3-030-16670-0_2", size = "16 pages", abstract = "In the two decades since Shor celebrated quantum algorithm for integer factorisation, manual design has failed to produce the anticipated growth in the number of quantum algorithms. Hence, there is a great deal of interest in the automatic synthesis of quantum circuits and algorithms. Here we present a set of experiments which use Ant Programming to automatically synthesise quantum circuits. In the proposed approach, ants choosing paths in high-dimensional Cartesian space are analogous to transformation of qubits in Hilbert space. In addition to the proposed algorithm, we introduce new evaluation criteria for searching the space of quantum circuits, both for classical simulation and simulation on a quantum computer. We demonstrate that the proposed approach significantly outperforms random search on a suite of benchmark problems based on these new measures.", notes = "http://www.evostar.org/2019/cfp_eurogp.php#abstracts Part of \cite{Sekanina:2019:GP} EuroGP'2019 held in conjunction with EvoCOP2019, EvoMusArt2019 and EvoApplications2019", } @InProceedings{Atkinson:2019:GECCO, author = "Timothy Atkinson and Detlef Plump and Susan Stepney", title = "Evolving graphs with horizontal gene transfer", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference", year = "2019", editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and Anne Auger and Petr Posik and Leslie {Peprez Caceres} and Andrew M. Sutton and Nadarajen Veerapen and Christine Solnon and Andries Engelbrecht and Stephane Doncieux and Sebastian Risi and Penousal Machado and Vanessa Volz and Christian Blum and Francisco Chicano and Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and Jonathan Fieldsend and Jose Antonio Lozano and Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and Robin Purshouse and Thomas Baeck and Justyna Petke and Giuliano Antoniol and Johannes Lengler and Per Kristian Lehre", isbn13 = "978-1-4503-6111-8", pages = "968--976", address = "Prague, Czech Republic", DOI = "doi:10.1145/3321707.3321788", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, Evolving Graphs, Horizontal Gene Transfer, Neutrality", size = "9 pages", abstract = "We introduce a form of neutral Horizontal Gene Transfer (HGT) to Evolving Graphs by Graph Programming (EGGP). We introduce the mu x lambda evolutionary algorithm, where u parents each produce l children who compete with only their parents. HGT events then copy the entire active component of one surviving parent into the inactive component of another parent, exchanging genetic information without reproduction. Experimental results from 14 symbolic regression benchmark problems show that the introduction of the u x l EA and HGT events improve the performance of EGGP. Comparisons with Genetic Programming and Cartesian Genetic Programming strongly favour our proposed approach.", notes = "See also \cite{Atkinson:GPEM:H2019} Also known as \cite{3321788} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @Article{Atkinson:GPEM:H2019, author = "Timothy Atkinson and Detlef Plump and Susan Stepney", title = "Horizontal gene transfer for recombining graphs", journal = "Genetic Programming and Evolvable Machines", year = "2020", volume = "21", number = "3", pages = "321--347", month = sep, note = "Special Issue: Highlights of Genetic Programming 2019 Events", keywords = "genetic algorithms, genetic programming, Graph-based genetic programming, Neuroevolution, Horizontal gene transfer, HGT, EGGP", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-020-09378-1", size = "27 pages", abstract = "We introduce a form of neutral horizontal gene transfer (HGT) to evolving graphs by graph programming (EGGP). We introduce the mu x lambda evolutionary algorithm (EA), where mu parents each produce lambda children who compete only with their parents. HGT events then copy the entire active component of one surviving parent into the inactive component of another parent, exchanging genetic information without reproduction. Experimental results from symbolic regression problems show that the introduction of the mu x lambda EA and HGT events improve the performance of EGGP. Comparisons with genetic programming and Cartesian genetic programming strongly favour our proposed approach. We also investigate the effect of using HGT events in neuroevolution tasks. We again find that the introduction of HGT improves the performance of EGGP, demonstrating that HGT is an effective cross-domain mechanism for recombining graphs.", } @PhdThesis{Atkinson:thesis, author = "Timothy Atkinson", title = "Evolving Graphs by Graph Programming", school = "University of York", year = "2019", address = "UK", keywords = "genetic algorithms, genetic programming, EGGP", URL = "https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.803685", URL = "http://etheses.whiterose.ac.uk/26524/", URL = "http://etheses.whiterose.ac.uk/26524/1/thesis_whiterose.pdf", size = "276 pages", abstract = "Graphs are a ubiquitous data structure in computer science and can be used to represent solutions to difficult problems in many distinct domains. This motivates the use of Evolutionary Algorithms to search over graphs and efficiently find approximate solutions. However, existing techniques often represent and manipulate graphs in an ad-hoc manner. In contrast, rule-based graph programming offers a formal mechanism for describing relations over graphs. This thesis proposes the use of rule-based graph programming for representing and implementing genetic operators over graphs. We present the Evolutionary Algorithm Evolving Graphs by Graph Programming and a number of its extensions which are capable of learning stateful and stateless digital circuits, symbolic expressions and Artificial Neural Networks. We demonstrate that rule-based graph programming may be used to implement new and effective constraint-respecting mutation operators and show that these operators may strictly generalise others found in the literature. Through our proposal of Semantic Neutral Drift, we accelerate the search process by building plateaus into the fitness landscape using domain knowledge of equivalence. We also present Horizontal Gene Transfer, a mechanism whereby graphs may be passively recombined without disrupting their fitness. Through rigorous evaluation and analysis of over 20,000 independent executions of Evolutionary Algorithms, we establish numerous benefits of our approach. We find that on many problems, Evolving Graphs by Graph Programming and its variants may significantly outperform other approaches from the literature. Additionally, our empirical results provide further evidence that neutral drift aids the efficiency of evolutionary search.", notes = "date also given as March 2020. Supervisors: Susan Stepney and Detlef Plump", } @InProceedings{atkinson-abutridy:1999:A, author = "John A. Atkinson-Abutridy and Julio R. Carrasco-Leon", title = "An evolutionary model for dynamically controlling a behavior-based autonomous agent", booktitle = "Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference", year = "1999", editor = "Scott Brave and Annie S. Wu", pages = "16--24", address = "Orlando, Florida, USA", month = "13 " # jul, notes = "GECCO-99LB", } @InProceedings{atlan:1994:gpjss, author = "Laurent Atlan and Jerome Bonnet and Martine Naillon", title = "Learning Distributed Reactive Strategies by Genetic Programming for the General Job Shop Problem", booktitle = "Proceedings of the 7th annual Florida Artificial Intelligence Research Symposium", year = "1994", address = "Pensacola, Florida, USA", month = may, organisation = "Dassault-Aviation, Artificial Intelligence Department", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", URL = "ftp://ftp.ens.fr/pub/reports/biologie/disgajsp.ps.Z", size = "11 pages", abstract = "proposed is a general system to infer symbolic policy functions for distributed reactive scheduling in non-stationary environments. The job shop problem is only used as a validating case study. Our system is based both on an original distributed scheduling model and on genetic programming for the inference of symbolic policy functions. The purpose is to determine heuristic policies that are local in time, long term near-optimal, and robust with respect to perturbations. Furthermore, the policies are local in state space: the global decision problem is split into as many decision problems as there are agents, i.e. machines in the job shop problem. If desired, the genetic algorithm can use expert knowledge as a priori knowledge, via implementation of the symbolic representation of the policy functions.", notes = "'To be published in the proceedings of the Seventh Annual Florida Artificial Intelligence Research Symposium' DGT/DEA/IA2 December 1993 Combination of GP and Giffler and Thompson algorithm Dassault-Aviation DGT/DEA/IA2 Artificial Intelligence Department 78, Quai Marcel Dassault, 92214 Saint-Cloud France", } @InProceedings{Atmosukarto:2010:UGP:1904935.1906046, author = "Indriyati Atmosukarto and Linda G. Shapiro and Carrie Heike", title = "The Use of Genetic Programming for Learning {3D} Craniofacial Shape Quantifications", booktitle = "Proceedings of the 2010 20th International Conference on Pattern Recognition", year = "2010", editor = "Aytul Ercil", pages = "2444--2447", address = "Istanbul, Turkey", month = "23-26 " # aug, organisation = "International Association for Pattern Recognition (IAPR)", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, 3D Shape quantification", isbn13 = "978-0-7695-4109-9", URL = "http://www.cs.washington.edu/research/VACE/Multimedia/icpr10_Atmosukarto.pdf", URL = "http://grail.cs.washington.edu/pub/papers/atmosukarto2010uog.pdf", DOI = "doi:10.1109/ICPR.2010.598", acmid = "1906046", size = "4 pages", abstract = "Craniofacial disorders commonly result in various head shape dysmorphologies. The goal of this work is to quantify the various 3D shape variations that manifest in the different facial abnormalities in individuals with a craniofacial disorder called 22q11.2 Deletion Syndrome. Genetic programming (GP) is used to learn the different 3D shape quantifications. Experimental results show that the GP method achieves a higher classification rate than those of human experts and existing computer algorithms [1], [2].", notes = "ICPR '10", } @PhdThesis{AtmosukartoPhd, author = "Indriyati Atmosukarto", title = "{3D} Shape Analysis for Quantification, Classification, and Retrieval", school = "Computer Science and Engineering, University of Washington", year = "2010", address = "USA", keywords = "genetic algorithms, genetic programming", URL = "http://grail.cs.washington.edu/theses/AtmosukartoPhd.pdf", size = "139 pages", abstract = "Three-dimensional objects are now commonly used in a large number of applications including games, mechanical engineering, archaeology, culture, and even medicine. As a result, researchers have started to investigate the use of 3D shape descriptors that aim to encapsulate the important shape properties of the 3D objects. This thesis presents new 3D shape representation methodologies for quantification, classification and retrieval tasks that are flexible enough to be used in general applications, yet detailed enough to be useful in medical craniofacial dysmorphology studies. The methodologies begin by computing low-level features at each point of the 3D mesh and aggregating the features into histograms over mesh neighbourhoods. Two different methodologies are defined. The first methodology begins by learning the characteristics of salient point histograms for each particular application, and represents the points in a 2D spatial map based on longitude-latitude transformation. The second methodology represents the 3D objects by using the global 2D histogram of the azimuth-elevation angles of the surface normals of the points on the 3D objects. Four datasets, two craniofacial datasets and two general 3D object datasets, were obtained to develop and test the different shape analysis methods developed in this thesis. Each dataset has different shape characteristics that help explore the different properties of the methodologies. Experimental results on classifying the craniofacial datasets show that our methodologies achieve higher classification accuracy than medical experts and existing state-of-the-art 3D descriptors. Retrieval and classification results using the general 3D objects show that our methodologies are comparable to existing view-based and feature-based descriptors and outperform these descriptors in some cases. Our methodology can also be used to speed up the most powerful general 3D object descriptor to date.", notes = "GPLAB, Matlab", } @Article{Atmosukarto:2011:GPEM, author = "Indriyati Atmosukarto", title = "{GPLAB}: software review", journal = "Genetic Programming and Evolvable Machines", year = "2012", volume = "12", number = "4", pages = "457--459", month = dec, note = "Software Review", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dR8d6", DOI = "doi:10.1007/s10710-011-9142-5", size = "3 pages", notes = "Matlab", } @Article{ATTIAS:2023:cej, author = "Rinat Attias and Sourav Bhowmick and Yoed Tsur", title = "Distribution function of relaxation times: An alternative to classical methods for evaluating the reaction kinetics of oxygen evolution reaction", journal = "Chemical Engineering Journal", volume = "476", pages = "146708", year = "2023", ISSN = "1385-8947", DOI = "doi:10.1016/j.cej.2023.146708", URL = "https://www.sciencedirect.com/science/article/pii/S1385894723054396", keywords = "genetic algorithms, genetic programming, Tafel slope, DFRT, DRT, ISGP, EIS, Reaction kinetics", abstract = "Reaction kinetics of RuO2 is precisely evaluated by distribution function of relaxation times (DFRT) model using impedance spectroscopy analysis by genetic programming (ISGP). Effective resistances of the Faradaic processes, measured using Electrochemical impedance spectroscopy (EIS) at various overpotentials, were determined using DFRT, by separating and associating three electrochemical phenomena occurring during the reaction. The effective resistances are used to generate a Tafel plot of potential as a function of log1Reff. The classical method, based on linear sweep voltammetry (LSV), to evaluate the Tafel slope is associated with some considerations for accurate results. For RuO2, a reference catalyst, LSV illustrates an average Tafel slope of 182 pm 8 mV/dec while the effective resistance method, estimated using DFRT, shows an average of 181 pm 3 mV/dec. A relative error of 0.3 percent between the two methodologies, and a lower standard deviation for DFRT demonstrate the higher precision and effectiveness of ISGP in determining the reaction kinetics via Tafel slope analysis. Therefore, using DFRT with the ability to separate Faradaic from non-Faradaic processes to evaluate the relevant part of the effective resistance, reaction kinetics can be estimated, avoiding shortcomings of the classical Tafel method", } @InProceedings{Atwater:2012:GECCO, author = "Aaron Atwater and Malcolm I. Heywood and Nur Zincir-Heywood", title = "GP under streaming data constraints: a case for pareto archiving?", booktitle = "GECCO '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference", year = "2012", editor = "Terry Soule and Anne Auger and Jason Moore and David Pelta and Christine Solnon and Mike Preuss and Alan Dorin and Yew-Soon Ong and Christian Blum and Dario Landa Silva and Frank Neumann and Tina Yu and Aniko Ekart and Will Browne and Tim Kovacs and Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and Giovanni Squillero and Nicolas Bredeche and Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and Martin Pelikan and Silja Meyer-Nienberg and Christian Igel and Greg Hornby and Rene Doursat and Steve Gustafson and Gustavo Olague and Shin Yoo and John Clark and Gabriela Ochoa and Gisele Pappa and Fernando Lobo and Daniel Tauritz and Jurgen Branke and Kalyanmoy Deb", isbn13 = "978-1-4503-1177-9", pages = "703--710", keywords = "genetic algorithms, genetic programming", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330163.2330262", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Classification as applied to streaming data implies that only a small number of new training instances appear at each generation and are never explicitly reintroduced by the stream. Pareto competitive coevolution provides a potential framework for archiving useful training instances between generations under an archive of finite size. Such a coevolutionary framework is defined for the online evolution of classifiers under genetic programming. Benchmarking is performed under multi-class data sets with class imbalance and training partitions with between 1,000's to 100,000's of instances. The impact of enforcing different constraints for accessing the stream are investigated. The role of online adaptation is explicitly documented and tests made on the relative impact of label error on the quality of streaming classifier results.", notes = "Also known as \cite{2330262} GECCO-2012 A joint meeting of the twenty first international conference on genetic algorithms (ICGA-2012) and the seventeenth annual genetic programming conference (GP-2012)", } @InProceedings{Atwater:2013:GECCO, author = "Aaron Atwater and Malcolm I. Heywood", title = "Benchmarking {Pareto} archiving heuristics in the presence of concept drift: diversity versus age", booktitle = "GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference", year = "2013", editor = "Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi", isbn13 = "978-1-4503-1963-8", pages = "885--892", keywords = "genetic algorithms, genetic programming", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2463372.2463489", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "A framework for coevolving genetic programming teams with Pareto archiving is benchmarked under two representative tasks for non-stationary streaming environments. The specific interest lies in determining the relative contribution of diversity and aging heuristics to the maintenance of the Pareto archive. Pareto archiving, in turn, is responsible for targeting data (and therefore champion individuals) as appropriate for retention beyond the limiting scope of the sliding window interface to the data stream. Fitness sharing alone is considered most effective under a non-stationary stream characterised by continuous (incremental) changes. Fitness sharing with an aging heuristic acts as the preferred heuristic when the stream is characterised by non-stationary stepwise changes.", notes = "Also known as \cite{2463489} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)", } @InProceedings{Auerbach:2014:ALIFE, author = "Joshua E. Auerbach and Deniz Aydin and Andrea Maesani and Przemyslaw M. Kornatowski and Titus Cieslewski and Gregoire Heitz and Pradeep R. Fernando and Ilya Loshchilov and Ludovic Daler and Dario Floreano", title = "{RoboGen}: Robot Generation through Artificial Evolution", booktitle = "Proceedings of the Fourteenth International Conference of the Synthesis and Simulation of Living Systems, ALIFE 14", year = "2014", editor = "Hiroki Sayama and John Rieffel and Sebastian Risi and Rene Doursat and Hod Lipson", series = "Complex Adaptive Systems", pages = "136--137", address = "New York", month = "30 " # jul # "-2 " # aug, organisation = "International Society for Artificial Life", publisher = "MIT Press", keywords = "genetic algorithms, genetic programming, RoboGen", isbn13 = "9780262326216 ?", URL = "http://mitpress.mit.edu/sites/default/files/titles/content/alife14/ch022.html", DOI = "doi:10.7551/978-0-262-32621-6-ch022", size = "2 pages", abstract = "Science instructors from a wide range of disciplines agree that hands-on laboratory components of courses are pedagogically necessary (Freedman, 1997). However, certain shortcomings of current laboratory exercises have been pointed out by several authors (Mataric, 2004; Hofstein and Lunetta, 2004). The overarching theme of these analyses is that hands-on components of courses tend to be formulaic, closed-ended, and at times outdated. To address these issues, we envision a novel platform that is not only a didactic tool but is also an experimental testbed for users to play with different ideas in evolutionary robotics (Nolfi and Floreano, 2000), neural networks, physical simulation, 3D printing, mechanical assembly, and embedded processing. Here, we introduce RoboGen an open-source software and hardware platform designed for the joint evolution of robot morphologies and controllers a la Sims (1994); Lipson and Pollack (2000); Bongard and Pfeifer (2003). Robo-Gen has been designed specifically to allow evolved robots to be easily manufactured via widely available desktop 3D-printers, and the use of simple, open-source, low-cost, off-the-shelf electronic components. RoboGen features an evolution engine complete with a physics simulator, as well as utilities both for generating design files of body components for 3D printing, and for compiling neural-network controllers to run on an Arduino microcontroller board. In this paper, we describe the RoboGen platform, and provide some metrics to assess the success of using it as the hands-on component of a masters-level bio-inspired artificial intelligence course.", notes = "Laboratory of Intelligent Systems Ecole Polytechnique Federale de Lausanne. ALIFE 14 http://mitpress.mit.edu/books/artificial-life-14 ALIFE14NYC@gmail.com", } @InProceedings{sbrn2000meta029, author = "Douglas A. Augusto and Helio J. C. Barbosa", title = "Symbolic Regression via Genetic Programming", booktitle = "{VI} Brazilian Symposium on Neural Networks (SBRN'00)", year = "2000", pages = "173", address = "Rio de Janeiro, RJ, Brazil", month = jan # " 22-25", publisher = "IEEE", note = "VI Simposio Brasileiro de Redes Neurais", keywords = "genetic algorithms, genetic programming", ISBN = "0-7695-0856-1", identifier = "sbrn2000article029", language = "eng", source = "sbrn2000", broken = "http://csdl.computer.org/comp/proceedings/sbrn/2000/0856/00/08560173abs.htm", DOI = "doi:10.1109/SBRN.2000.889734", abstract = "In this work, we present an implementation of symbolic regression, which is based on genetic programming (GP). Unfortunately, standard implementations of GP in compiled languages are not usually the most efficient ones. The present approach employs a simple representation for tree-like structures by making use of Read's linear code, leading to more simplicity and better performance when compared with traditional GP implementations. Creation, crossover and mutation of individuals are formalized. An extension allowing for the creation of random coefficients is presented. The efficiency of the proposed implementation was confirmed in computational experiments, which are summarized in this paper.", } @InProceedings{Augusto:2008:gecco, author = "Douglas A. Augusto and Helio J. C. Barbosa and Nelson F. F. Ebecken", title = "Coevolution of data samples and classifiers integrated with grammatically-based genetic programming for data classification", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "1171--1178", address = "Atlanta, GA, USA", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, competitive coevolution, context-free grammar, data classification", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1171.pdf", DOI = "doi:10.1145/1389095.1389328", size = "8 pages", abstract = "The present work treats the data classification task by means of evolutionary computation techniques using three ingredients: genetic programming, competitive coevolution, and context-free grammar. The robustness and symbolic/interpretative qualities of the genetic programming are employed to construct classification trees via Darwinian evolution. The flexible formal structure of the context-free grammar replaces the standard genetic programming representation and describes a language which encodes trees of varying complexity. Finally, competitive coevolution is used to promote competitions between data samples and classification trees in order to create and sustain an evolutionary arms-race for improved solutions", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389328}", } @InProceedings{Augusto:2010:gecco, author = "Douglas Adriano Augusto and Helio Jose Correa Barbosa and Nelson Francisco Favilla Ebecken", title = "Coevolutionary multi-population genetic programming for data classification", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "933--940", keywords = "genetic algorithms, genetic programming, distributed genetic programming", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830483.1830650", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "This work presents a new evolutionary ensemble method for data classification, which is inspired by the concepts of bagging and boosting, and aims at combining their good features while avoiding their weaknesses. The approach is based on a distributed multiple-population genetic programming (GP) algorithm which exploits the technique of coevolution at two levels. On the inter-population level the populations cooperate in a semi-isolated fashion, whereas on the intrapopulation level the candidate classifiers coevolve competitively with the training data samples. The final classifier is a voting committee composed by the best members of all the populations. The experiments performed in a varying number of populations show that our approach outperforms both bagging and boosting for a number of benchmark problems.", notes = "Also known as \cite{1830650} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @InProceedings{Augusto:2011:GECCOcomp, author = "Douglas A. Augusto and Helio J. C. Barbosa and Andre M. S. Barreto and Heder S. Bernardino", title = "A new approach for generating numerical constants in grammatical evolution", booktitle = "GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", pages = "193--194", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, grammatical evolution: Poster", isbn13 = "978-1-4503-0690-4", DOI = "doi:10.1145/2001858.2001966", abstract = "A new approach for numerical-constant generation in Grammatical Evolution is presented. Experiments comparing our method with the three most popular methods for constant creation are performed. By varying the number of bits to represent a constant, we can increase our methods precision to the desired level of accuracy, overcoming by a large margin the other approaches.", notes = "Also known as \cite{2001966} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @InProceedings{Augusto:2011:EPIA, author = "Douglas Adriano Augusto and Helio J. C. Barbosa and Andre {da Motta Salles Barreto} and Heder S. Bernardino", title = "Evolving Numerical Constants in Grammatical Evolution with the Ephemeral Constant Method", booktitle = "Proceedings 15th Portuguese Conference on Artificial Intelligence, {EPIA 2011}", year = "2011", editor = "Luis Antunes and Helena Sofia Pinto", volume = "7026", series = "Lecture Notes in Computer Science", pages = "110--124", address = "Lisbon, Portugal", month = oct # " 10-13", keywords = "genetic algorithms, genetic programming, grammatical evolution, constant creation", isbn13 = "978-3-642-24768-2", DOI = "doi:10.1007/978-3-642-24769-9_9", affiliation = "Laboratorio Nacional de Computacao Cientifica, Petropolis, RJ, Brazil", } @Article{Augusto2012, author = "Douglas A. Augusto and Helio J. C. Barbosa", title = "Accelerated parallel genetic programming tree evaluation with {OpenCL}", journal = "Journal of Parallel and Distributed Computing", volume = "73", number = "1", pages = "86--100", year = "2013", note = "Metaheuristics on GPUs", ISSN = "0743-7315", DOI = "doi:10.1016/j.jpdc.2012.01.012", URL = "http://www.sciencedirect.com/science/article/pii/S074373151200024X", keywords = "genetic algorithms, genetic programming, GPU, OpenCL, GP-GPU, Accelerated tree evaluation", abstract = "Inspired by the process of natural selection, genetic programming (GP) aims at automatically building arbitrarily complex computer programs. Being classified as an embarrassingly parallel technique, GP can theoretically scale up to tackle very diverse problems by increasingly adding computational power to its arsenal. With today's availability of many powerful parallel architectures, a challenge is to take advantage of all those heterogeneous compute devices in a portable and uniform way. This work proposes both (i) a transcription of existing GP parallelisation strategies into the OpenCL programming platform; and (ii) a freely available implementation to evaluate its suitability for GP, by assessing the performance of parallel strategies on the CPU and GPU processors from different vendors. Benchmarks on the symbolic regression and data classification domains were performed. On the GPU we could achieve 13 billion node evaluations per second, delivering almost 10 times the throughput of a twelve-core CPU.", notes = "Genetic Programming in OpenCL Source", } @InCollection{Augusto:2012:GPnew, author = "Douglas A. Augusto and Heder S. Bernardino and Helio J. C. Barbosa", title = "Parallel Genetic Programming on Graphics Processing Units", booktitle = "Genetic Programming - New Approaches and Successful Applications", publisher = "InTech", year = "2012", editor = "Sebastian Ventura", chapter = "5", pages = "95--114", keywords = "genetic algorithms, genetic programming, GPU, OpenCL, stack-based interpreter", isbn13 = "978-953-51-0809-2", DOI = "doi:10.5772/48364", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.374.7459", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.374.745", size = "20 pages", notes = "data level parallelism, SIMD, no performance figures. Part of \cite{Ventura:2012:GPnew}. Open access book CC BY 3.0 license", } @InProceedings{Augusto:2013:GECCOcomp, author = "Douglas A. Augusto and Heder S. Bernardino and Helio J. C. Barbosa", title = "Improving recruitment effectiveness using genetic programming techniques", booktitle = "GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion", year = "2013", editor = "Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A. N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks", isbn13 = "978-1-4503-1964-5", keywords = "genetic algorithms, genetic programming", pages = "177--178", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2464576.2464673", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "A real-world problem, namely to improve the recruitment effectiveness of a certain company, is tackled here by evolving accurate and human-readable classifiers by means of grammar-based genetic programming techniques.", notes = "Also known as \cite{2464673} Distributed at GECCO-2013.", } @InProceedings{Augusto:2013:CCI.CBIC, author = "D. A. Augusto and H. S. Bernardino and H. J. C. Barbosa", booktitle = "BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence (BRICS-CCI CBIC 2013)", title = "Predicting the Performance of Job Applicants by Means of Genetic Programming", year = "2013", month = sep, pages = "98--103", abstract = "Since their early development, genetic programming-based algorithms have been showing to be successful at challenging problems, attaining several human-competitive results and other awards. This paper will present another achievement of such algorithms by describing how our team has won an international machine-learning competition. We have solved, by means of grammar-based genetic programming techniques, a real-world problem of meritocracy in jobs by evolving classifiers that were both accurate and human-readable.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/BRICS-CCI-CBIC.2013.27", notes = "Also known as \cite{6855836}", } @InCollection{Augustoetal2013, author = "Douglas Adriano Augusto and Heder Soares Bernardino and Helio Jose Correa Barbosa", title = "Programa\c{c}\~{a}o Gen\'{e}tica", booktitle = "Meta-Heursticas em Pesquisa Operacional", publisher = "Omnipax", year = "2013", editor = "Heitor Silvrio Lopes and Luiz Carlos de Abreu Rodrigues and Maria Teresinha Arns Steiner", chapter = "5", pages = "69--86", address = "Curitiba, PR", edition = "1", keywords = "genetic algorithms, genetic programming, operations research, Optimization", isbn13 = "978-85-64619-10-4", URL = "http://omnipax.com.br/site/?page_id=387", DOI = "doi:10.7436/2013.mhpo.05", size = "18 pages", abstract = "Genetic programming is an evolutionary metaheuristic designed to automatically generate programs by means of an iterative process inspired by the theory of natural selection. In operational research, genetic programming techniques are normally used to infer heuristics for decision-making problems. In this way, genetic programming is a hyper-heuristic creating new search methods which are more efficient that those traditionally considered. This chapter describes genetic programming and presents its applications in the operations research field.", abstract = "A programacao genetica e uma meta-heuristica evolucionaria destinada a geracao automatica de programas atraves de um processo iterativo inspirado pela teoria da selecao natural. Em pesquisa operacional, tecnicas de programacao genetica sao normalmente usadas para inferir heuristicas para problemas de tomada de decisao. Desta forma, a programacao genetica assume o papel de hiper-heuristica criando novos metodos de busca mais eficientes que os tradicionalmente considerados. O presente capitulo descreve a programacao genetica e apresenta suas aplicacoes no campo da pesquisa operacional.", notes = "In Portuguese", } @InProceedings{Augustsson:2002:gecco, author = "Peter Augustsson and Krister Wolff and Peter Nordin", title = "Creation Of {A} Learning, Flying Robot By Means Of Evolution", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "1279--1285", address = "New York", publisher_address = "San Francisco, CA 94104, USA", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "genetic algorithms, genetic programming, evolutionary robotics, evolutionary algorithm, flying", ISBN = "1-55860-878-8", URL = "http://fy.chalmers.se/~wolff/Papers/ANW_gecco02.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-22.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/ROB196.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/ROB196.pdf", size = "7 pages", abstract = "We demonstrate the first instance of a real on-line robot learning to develop feasible flying (flapping) behavior, using evolution. Here we present the experiments and results of the first use of evolutionary methods for a flying robot. With nature's own method, evolution, we address the highly non-linear fluid dynamics of flying. The flying robot is constrained in a test bench where timing and movement of wing flapping is evolved to give maximal lifting force. The robot is assembled with standard off-the-shelf R/C servomotors as actuators. The implementation is a conventional steady-state linear evolutionary algorithm.", notes = "GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002) Winner of the best-paper award at GECCO-2002, in Evolutionary Robotics.", } @InProceedings{aurnhammer:evows07, author = "Melanie Aurnhammer", title = "Evolving Texture Features by Genetic Programming", booktitle = "Applications of Evolutionary Computing, EvoWorkshops2007: {EvoCOMNET}, {EvoFIN}, {EvoIASP}, {EvoInteraction}, {EvoMUSART}, {EvoSTOC}, {EvoTransLog}", year = "2007", month = "11-13 " # apr, editor = "Mario Giacobini and Anthony Brabazon and Stefano Cagnoni and Gianni A. {Di Caro} and Rolf Drechsler and Muddassar Farooq and Andreas Fink and Evelyne Lutton and Penousal Machado and Stefan Minner and Michael O'Neill and Juan Romero and Franz Rothlauf and Giovanni Squillero and Hideyuki Takagi and A. Sima Uyar and Shengxiang Yang", series = "LNCS", volume = "4448", publisher = "Springer Verlag", address = "Valencia, Spain", pages = "351--358", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-71804-8", DOI = "doi:10.1007/978-3-540-71805-5_38", abstract = "Feature extraction is a crucial step for Computer Vision applications. Finding appropriate features for an application often means hand-crafting task specific features with many parameters to tune. A generalisation to other applications or scenarios is in many cases not possible. Instead of engineering features, we describe an approach which uses Genetic Programming to generate features automatically. In addition, we do not predefine the dimension of the feature vector but pursue an iterative approach to generate an appropriate number of features. We present this approach on the problem of texture classification based on co-occurrence matrices. Our results are compared to those obtained by using seven Haralick texture features, as well as results reported in the literature on the same database. Our approach yielded a classification performance of up to 87percent which is an improvement of 30percent over the Haralick features. We achieved an improvement of 12percent over previously reported results while reducing the dimension of the feature vector from 78 to four.", notes = "EvoWorkshops2007", } @TechReport{austin:2003:WP, author = "M. P. Austin and R. G. Bates and M. A. H. Dempster and S. N. Williams", title = "Adaptive systems for foreign exchange trading", institution = "Judge Institute of Management, University of Cambridge", year = "2003", type = "Working paper", number = "WP 15/2003", address = "UK", keywords = "genetic algorithms, genetic programming", URL = "http://mahd-pc.jbs.cam.ac.uk/archive/PAPERS/WP1503.pdf", notes = "Research Papers in Management Studies. in Eclectic \cite{Austin:2004:E} See \cite{Austin:2004:QF} ", size = "12 pages", } @Article{Austin:2004:E, author = "Mark Austin and Graham Bates and Michael Dempster and Stacy Williams", title = "Adaptive systems for foreign exchange trading", journal = "Eclectic", year = "2004", volume = "18", pages = "21--26", month = "Autumn", keywords = "genetic algorithms, genetic programming", URL = "http://mahd-pc.jbs.cam.ac.uk/archive/PAPERS/adaptive.pdf", size = "6 pages", abstract = "A joint project between academics and bankers has shown how banks can improve the forecasting performance of their technical trading systems in foreign exchange markets. Professor Michael Dempster and Graham Bates, both of the Centre for Financial Research, Cambridge, and Dr Mark Austin and Dr Stacy Williams, both of HSBC Global Markets, outline the results of their research. Consistently predicting FX markets has seemed like an impossible goal but recent advances in financial research now suggest it can be done. Automated trading systems are being used successfully to predict intraday and daily exchange rates. Trading systems using only publicly available technical indicators can be profitable ? but those that also use proprietary information can be more accurate and therefore more profitable. A joint project by the Centre for Financial Research (at the Judge Institute of Management, Cambridge University) and HSBC used the bank's customer order information to show that using proprietary information in trading systems can improve their forecasting performance and profitability. The research findings also intuitively make sense. Successful traders in the FX markets apply human judgement to a range of information and techniques. In this project the researchers mimicked these traders by combining the techniques of technical analysis with the stream of public and non-public information available to them.", } @Article{Austin:2004:QF, title = "Adaptive systems for foreign exchange trading", author = "Mark P. Austin and Graham Bates and Michael A. H. Dempster and Vasco Leemans and Stacy N. Williams", journal = "Quantitative Finance", month = aug, number = "4", pages = "37--45", publisher = "Routledge, part of the Taylor and Francis Group", volume = "4", year = "2004", keywords = "genetic algorithms, genetic programming, fx trading", citeulike-article-id = "98141", ISSN = "1469-7688", URL = "http://www-cfr.jbs.cam.ac.uk/archive/PRESENTATIONS/seminars/2006/dempster2.pdf", DOI = "doi:10.1080/14697680400008593", size = "9 pages", abstract = "Foreign exchange markets are notoriously difficult to predict. For many years academics and practitioners alike have tried to build trading models, but history has not been kind to their efforts. Consistently predicting FX markets has seemed like an impossible goal but recent advances in financial research now suggest otherwise. With newly developed computational techniques and newly available data, the development of successful trading models is looking possible. The Centre for Financial Research (CFR) at Cambridge University's Judge Institute of Management has been researching trading techniques in foreign exchange markets for a number of years. Over the last 18 months a joint project with HSBC Global Markets has looked at how the bank's proprietary information on customer order flow and on the customer limit order book can be used to enhance the profitability of technical trading systems in FX markets. Here we give an overview of that research and report our results.", notes = "Also in Eclectic 18 Autumn (2004) pp21-26 \cite{Austin:2004:E} www.eclectic.co.uk and technical report WP15/2003 \cite{austin:2003:WP} ", } @InProceedings{autones:2004:eurogp, author = "Mathieu Autones and Aryel Beck and Phillippe Camacho and Nicolas Lassabe and Herve Luga and Franccois Scharffe", title = "Evaluation of chess position by modular neural network generated by genetic algorithm", booktitle = "Genetic Programming 7th European Conference, EuroGP 2004, Proceedings", year = "2004", editor = "Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule", volume = "3003", series = "LNCS", pages = "1--10", address = "Coimbra, Portugal", publisher_address = "Berlin", month = "5-7 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-21346-5", DOI = "doi:10.1007/978-3-540-24650-3_1", abstract = "Chess engine Tempo. One of the major difficulties for this type of program lies in the function for evaluating game positions. This function is composed of a large number of parameters which have to be determined and then adjusted. We propose an alternative which consists in replacing this function by an artificial neuron network (ANN). Without topological knowledge of this complex network, we use the evolutionist methods for its inception, thus enabling us to obtain, among other things, a modular network. Finally, we present our results: (i) reproduction of the XOR function which validates the method used and (ii) generation of an evaluation function", notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004", } @InProceedings{Aversano:2005:WSEC, author = "Lerina Aversano and Massimiliano {Di Penta} and Kunal Taneja", title = "A genetic programming approach to support the design of service compositions", booktitle = "Proceedings of the first International Workshop of Engineering Service Compositions, WESC'05", year = "2005", editor = "Christian Zirpins and Guadalupe Ortiz and Winfried Lamersdorf and Wolfgang Emmerich", pages = "17--24", address = "Amsterdam, The Netherlands", number = "RC23821 (W0512-008)", month = dec, series = "IBM Research Reports", keywords = "genetic algorithms, genetic programming", URL = "http://domino.research.ibm.com/library/cyberdig.nsf/papers/DE71563B7B69D362852570D000548D0D/$File/rc23821.pdf", notes = "Slides http://www.rcost.unisannio.it/mdipenta/papers/wesc05.pdf parts of \cite{WESC05}", } @Article{Aversano:2006:IJCSSE, author = "Lerina Aversano and Massimiliano {Di Penta} and Kunal Taneja", title = "A genetic programming approach to support the design of service compositions", journal = "International Journal of Computer Systems Science \& Engineering", year = "2006", volume = "21", number = "4", pages = "247--254", month = jul, organisation = "Curtin University of Technology, Australia", publisher = "CRL Publishing, admin@crlpublishing.co.uk", keywords = "genetic algorithms, genetic programming, SBSE, service compositions, distributed software, workflow", ISSN = "0267 6192", URL = "http://www.rcost.unisannio.it/mdipenta/papers/csse06.pdf", size = "8 pages", oai = "oai:CiteSeerXPSU:10.1.1.145.843", abstract = "The design of service composition is one of the most challenging research problems in service-oriented software engineering. Building composite services is concerned with identifying a suitable set of services that orchestrated in some way is able to solve a business goal which cannot be resolved using a single service amongst those available. Despite the literature reports several approaches for (semi) automatic service composition, several problems, such as the capability of determining the composition's topology, still remain open. This paper proposes a search-based approach to semi-automatically support the design of service compositions. In particular, the approach uses genetic programming to automatically generate workflows that accomplish a business goal and exhibit a given QoS level, with the aim of supporting the service integrator activities in the finalization of the workflow.", notes = "WSDL, BPEL4WS. GP tree made of sequence, switch flow, loop nodes. Pop=100, Generations=1000, initioal pop<= 5 nodes. Fitness based on precision and recall. GP compared with exhuastive search. Cited by \cite{Rodriguez-Mier:2010:EI}, cites \{1068189} GECCO 2005. SeCSEP", } @InProceedings{conf/hais/AvilaGV09, title = "Multi-label Classification with Gene Expression Programming", author = "J. L. Avila and Eva Lucrecia {Gibaja Galindo} and Sebastian Ventura", bibdate = "2009-06-24", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/hais/hais2009.html#AvilaGV09", booktitle = "Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2009", publisher = "Springer", year = "2009", volume = "5572", editor = "Emilio Corchado and Xindong Wu and Erkki Oja and Alvaro Herrero and Bruno Baruque", isbn13 = "978-3-642-02318-7", pages = "629--637", series = "Lecture Notes in Computer Science", address = "Salamanca, Spain", month = jun # " 10-12", keywords = "genetic algorithms, genetic programming, gene expression programming", URL = "http://dx.doi.org/10.1007/978-3-642-02319-4", DOI = "doi:10.1007/978-3-642-02319-4_76", abstract = "In this paper, we introduce a Gene Expression Programming algorithm for multi label classification. This algorithm encodes each individual into a discriminant function that shows whether a pattern belongs to a given class or not. The algorithm also applies a niching technique to guarantee that the population includes functions for each existing class. In order to evaluate the quality of our algorithm, its performance is compared to that of four recently published algorithms. The results show that our proposal is the best in terms of accuracy, precision and recall", } @InCollection{Avila-Jimenez:2010:HAIS, author = "Jose Luis Avila-Jimenez and Eva Gibaja and Sebastian Ventura", title = "Evolving Multi-label Classification Rules with Gene Expression Programming: A Preliminary Study", booktitle = "Hybrid Artificial Intelligence Systems", year = "2010", series = "Lecture Notes in Computer Science", editor = "Emilio Corchado and Manuel {Grana Romay} and Alexandre {Manhaes Savio}", publisher = "Springer", pages = "9--16", volume = "6077", address = "San Sebastian, Spain", month = jun # " 23-25", DOI = "doi:10.1007/978-3-642-13803-4_2", keywords = "genetic algorithms, genetic programming, gene expression programming", abstract = "The present work expounds a preliminary work of a genetic programming algorithm to deal with multi-label classification problems. The algorithm uses Gene Expression Programming and codifies a classification rule into each individual. A niching technique assures diversity in the population. The final classifier is made up by a set of rules for each label that determines if a pattern belongs or not to the label. The proposal have been tested over several domains and compared with other multi-label algorithms and the results shows that it is specially suitable to handle with nominal data sets.", affiliation = "University of Cordoba Department of Computer Sciences and Numerical Analysis", } @Article{journals/mvl/Avila-JimenezGZV11, author = "Jose Luis Avila-Jimenez and Eva Lucrecia {Gibaja Galindo} and Amelia Zafra and Sebastian Ventura", title = "A Gene Expression Programming Algorithm for Multi-Label Classification", journal = "Journal of Multiple-Valued Logic and Soft Computing", year = "2011", number = "2-3", volume = "17", pages = "183--206", keywords = "genetic algorithms, genetic programming, gene expression programming, multi-label classification, discriminant functions, machine learning", ISSN = "1542-3980", URL = "http://www.oldcitypublishing.com/journals/mvlsc-home/mvlsc-issue-contents/mvlsc-volume-17-number-2-3-2011/mvlsc-17-2-3-p-183-206/", broken = "http://www.oldcitypublishing.com/MVLSC/MVLSCcontents/MVLSCv17n2-3contents.html", broken = "http://www.oldcitypublishing.com/MVLSC/MVLSCabstracts/MVLSC17.2-3abstracts/MVLSCv17n2-3p183-206Avila.html", abstract = "This paper presents a Gene Expression Programming algorithm for multilabel classification which encodes a discriminant function into each individual to show whether a pattern belongs to a given class or not. The algorithm also applies a niching technique to guarantee that the population includes functions for each existing class. The algorithm has been compared to other recently published algorithms. The results found on several datasets demonstrate the feasibility of this approach in the tackling of multi-label problems.", notes = "SPECIAL ISSUE Soft Computing Techniques in Data Mining", bibdate = "2011-07-04", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/mvl/mvl17.html#Avila-JimenezGZV11", } @PhdThesis{Avila-Jimenez:thesis, author = "Jose Luis Avila-Jimenez", title = "Genetic Programing for multi-label classification", titulo = "Modelos de Aprendizaje Basados en Programacion Genetica para Clasificacion Multi-Etiqueta", school = "Department of Computers Science and Numerical Analysis, University of Cordoba", year = "2013", address = "Spain", month = jun, keywords = "genetic algorithms, genetic programming", broken = "http://www.uco.es/~ma1vesos/en/research/phdstudents.html", URL = "http://www.uco.es/grupos/kdis/docs/thesis/2013-JLAvila.pdf", broken = "http://www.uco.es/grupos/kdis/index.php?option=com_jresearch&view=thesis&task=show&id=4&Itemid=51&lang=en", URL = "https://dialnet.unirioja.es/servlet/tesis?codigo=70284", size = "226 pages", abstract = "El problema de clasificacion consiste en asociar una serie de etiquetas a una serie de ejemplos o patrones. En la clasificacion clasica a cada patron de entrenamiento solamente se le puede asociar una sola etiqueta de un conjunto de etiquetas. Por tanto se consideran que los conjuntos de clases objetivo en los que se agruparan los patrones son por definicion conjuntos disjuntos. En el caso de la clasificacion multi-etiqueta, los conjuntos objetivos no son disjuntos, pudiendo haber patrones a los que se les asocie mas de una etiqueta. Por tanto, los ejemplos se asocian a un conjunto de etiquetas y el resultado puede tomar varios valores dentro del conjunto de etiquetas[1]. El objetivo que se plantea en esta tesis es el desarrollo de una serie de modelos de programacion genetica para resolver problemas de clasificacion multi-etiqueta.", notes = "In Spanish. Supervisor: Sebastian Ventura Soto 2nd supervisor Eva Lucrecia Gibaja Galindo TIN2008-06681-C06-03 P08-TIC-3720", } @InProceedings{conf/lion/Awad11, author = "Wasan Shaker Awad", title = "Designing Stream Cipher Systems Using Genetic Programming", booktitle = "Selected papers from the 5th International Conference on Learning and Intelligent Optimization (LION 5) 2011", year = "2011", editor = "Carlos A. {Coello Coello}", volume = "6683", series = "Lecture Notes in Computer Science", pages = "308--320", address = "Rome, Italy", month = jan # " 17-21", note = "Selected Papers", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-25565-6", DOI = "doi:10.1007/978-3-642-25566-3_23", size = "13 pages", abstract = "Genetic programming is a good technique for finding near-global optimal solutions for complex problems, by finding the program used to solve the problems. One of these complex problems is designing stream cipher systems automatically. Steam cipher is an important encryption technique used to protect private information from an unauthorised access, and it plays an important role in the communication and storage systems. In this work, we propose a new approach for designing stream cipher systems of good properties, such as high degree of security and efficiency. The proposed approach is based on the genetic programming. Three algorithms are presented here, which are simple genetic programming, simulated annealing programming, and adaptive genetic programming. Experiments were performed to study the effectiveness of these algorithms in solving the underlying problem.", bibdate = "2011-11-16", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/lion/lion2011.html#Awad11", affiliation = "Department of Information Systems, College of Information Technology, University of Bahrain, Sakheer, Bahrain", } @InCollection{Awange2016, author = "Joseph L. Awange and Bela Palancz", title = "Symbolic Regression", booktitle = "Geospatial Algebraic Computations: Theory and Applications", publisher = "Springer", year = "2016", chapter = "11", pages = "203--216", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-25465-4", DOI = "doi:10.1007/978-3-319-25465-4_11", abstract = "Symbolic regression (SR) is the process of determining the symbolic function, which describes a data set-effectively developing an analytic model, which summarizes the data and is useful for predicting response behaviours as well as facilitating human insight and understanding. The symbolic regression approach adopted herein is based upon genetic programming wherein a population of functions are allowed to breed and mutate with the genetic propagation into subsequent generations based upon a survival-of-the-fittest criteria. Amazingly, this works and, although computationally intensive, summary solutions may be reasonably discovered using current laptop and desktop computers.", notes = "Author Affiliations Curtin University, Perth, West Australia, Australia Budapest University of Technology and Economics, Budapest, Hungary", } @Article{AWOYERA:2020:JMRT, author = "Paul O. Awoyera and Mehmet S. Kirgiz and A. Viloria and D. Ovallos-Gazabon", title = "Estimating strength properties of geopolymer self-compacting concrete using machine learning techniques", journal = "Journal of Materials Research and Technology", year = "2020", volume = "9", number = "4", pages = "9016--9028", month = jul # "--" # aug, keywords = "genetic algorithms, genetic programming, Gene expression programming, Artificial neural networks, ANN, Predictor, Response, Self-Compacting concrete, Geopolymers", ISSN = "2238-7854", DOI = "doi:10.1016/j.jmrt.2020.06.008", URL = "http://www.sciencedirect.com/science/article/pii/S2238785420314095", size = "13 pages", abstract = "There has been a persistent drive for sustainable development in the concrete industry. While there are series of encouraging experimental research outputs, yet the research field requires a standard framework for the material development. In this study, the strength characteristics of geopolymer self-compacting concrete made by addition of mineral admixtures, have been modelled with both genetic programming (GEP) and the artificial neural networks (ANN) techniques. The study adopts a 12M sodium hydroxide and sodium silicate alkaline solution of ratio to fly ash at 0.33 for geopolymer reaction. In addition to the conventional material (river sand), fly ash was partially replaced with silica fume and granulated blast furnace slag. Various properties of the concrete, filler ability and passing ability of fresh mixtures, and compressive, split-tensile and flexural strength of hardened concrete were determined. The model development involved using raw materials and fresh mix properties as predictors, and strength properties as response. Results shows that the use of the admixtures enhanced both the fresh and hardened properties of the concrete. Both GEP and ANN methods exhibited good prediction of the experimental data, with minimal errors. However, GEP models can be preferred as simple equations are developed from the process, while ANN is only a predictor", notes = "Department of civil Engineering, Covenant University, Ota, Nigeria", } @InProceedings{Awuley:2016:CEC, author = "Anthony Awuley and Brian J. Ross", title = "Feature Selection and Classification Using Age Layered Population Structure Genetic Programming", booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016)", year = "2016", editor = "Yew-Soon Ong", pages = "2417--2426", address = "Vancouver", month = "24-29 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-5090-0623-6", DOI = "doi:10.1109/CEC.2016.7744088", abstract = "This paper presents a new algorithm called Feature Selection Age Layered Population Structure (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS is a modification of Hornby's ALPS algorithm - an evolutionary algorithm renown for avoiding pre-mature convergence on difficult problems. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal-symbol selection for the construction of GP trees/sub-trees. The FSALPS meta-heuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non-converging evolutionary process that favours selection of features with high discrimination of class labels. We compared the performance of canonical GP, ALPS and FSALPS on some high-dimensional benchmark classification datasets, including a hyperspectral vision problem. Although all algorithms had similar classification accuracy, ALPS and FSALPS usually dominated canonical GP in terms of smaller and efficient trees. Furthermore, FSALPS significantly outperformed canonical GP, ALPS, and other feature selection strategies in the literature in its ability to perform dimensionality reduction", notes = "WCCI2016 MSc: (167 pages) https://core.ac.uk/download/pdf/62649745.pdf", } @MastersThesis{Aydogan:mastersthesis, author = "Emre Aydogan", title = "Automatic Generation of Mobile Malwares Using Genetic Programming", school = "Hacettepe Universitesi", year = "2014", address = "Ankara, Turkey", month = aug, keywords = "genetic algorithms, genetic programming, mobile malware, static analysis, obfuscation, evolutionary computation,", URL = "https://web.cs.hacettepe.edu.tr/~ssen/files/thesis/EmreTez.pdf", size = "82 pages", abstract = "The number of mobile devices has increased dramatically in the past few years. These smart devices provide many useful functionalities accessible from anywhere at any time, such as reading and writing e-mails, surfing on the Internet, showing facilities nearby, and the like. Hence, they become an inevitable part of our daily lives. However the popularity and adoption of mobile devices also attract virus writers in order to harm our devices. So, many security companies have already proposed new solutions in order to protect our mobile devices from such malicious attempts. However developing methodologies that detect unknown malwares is a research challenge, especially on devices with limited resources. This study presents a method that evolves automatically variants of malwares from the ones in the wild by using genetic programming. We aim to evaluate existing security solutions based on static analysis techniques against these evolved unknown malwares. The experimental results show the weaknesses of the static analysis tools available in the market, and the need of new detection techniques suitable for mobile devices.", notes = "Genetik Programlama Kullanilarak Mobil Zararli Yazilimların Otomatik Olarak Uretilmesi In Turkish Supervisor: Sevil Sen", } @InProceedings{Aydogan:2015:evoApplications, author = "Emre Aydogan and Sevil Sen", title = "Automatic Generation of Mobile Malwares Using Genetic Programming", booktitle = "18th European Conference on the Applications of Evolutionary Computation", year = "2015", editor = "Antonio M. Mora and Giovanni Squillero", series = "LNCS", volume = "9028", publisher = "Springer", pages = "745--756", address = "Copenhagen", month = "8-10 " # apr, organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, Mobile malware, Static analysis, Obfuscation, Evolutionary computation", isbn13 = "978-3-319-16548-6", URL = "http://web.cs.hacettepe.edu.tr/~ssen/files/papers/EvoStar15.pdf", DOI = "doi:10.1007/978-3-319-16549-3_60", size = "12 pages", abstract = "The number of mobile devices has increased dramatically in the past few years. These smart devices provide many useful functionalities accessible from anywhere at anytime, such as reading and writing e-mails, surfing on the Internet, showing facilities nearby, and the like. Hence, they become an inevitable part of our daily lives. However the popularity and adoption of mobile devices also attract virus writers in order to harm our devices. So, many security companies have already proposed new solutions in order to protect our mobile devices from such malicious attempts. However developing methodologies that detect unknown malwares is a research challenge, especially on devices with limited resources. This study presents a method that evolves automatically variants of malwares from the ones in the wild by using genetic programming (GP). We aim to evaluate the efficacy of current anti-virus products, using static analysis techniques, in the market. The experimental results show the weaknesses of the static analysis tools available in the market, and the need of new detection techniques suitable for mobile devices.", notes = "evoRISK EvoApplications2015 held in conjunction with EuroGP'2015, EvoCOP2015 and EvoMusArt2015 http://www.evostar.org/2015/cfp_evoapps.php", } @InProceedings{Aydogan:2019:WFCS, author = "E. Aydogan and S. Yilmaz and S. Sen and I. Butun and S. Forsstroem and M. Gidlund", booktitle = "2019 15th IEEE International Workshop on Factory Communication Systems (WFCS)", title = "A Central Intrusion Detection System for {RPL-Based} Industrial Internet of Things", year = "2019", month = may, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/WFCS.2019.8758024", abstract = "Although Internet-of-Things (IoT) is revolutionizing the IT sector, it is not mature yet as several technologies are still being offered to be candidates for supporting the backbone of this system. IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) is one of those promising candidate technologies to be adopted by IoT and Industrial IoT (IIoT). Attacks against RPL have shown to be possible, as the attackers use the unauthorized parent selection system of the RLP protocol. In this work, we are proposing a methodology and architecture to detect intrusions against IIoT. Especially, we are targeting to detect attacks against RPL by using genetic programming. Our results indicate that the developed framework can successfully (with high accuracy, along with high true positive and low false positive rates) detect routing attacks in RPL-based Industrial IoT networks.", notes = "Also known as \cite{8758024}", } @Article{AYDOGAN:2023:istruc, author = "Mehmet Safa Aydogan and Sema Alacali and Guray Arslan", title = "Prediction of moment redistribution capacity in reinforced concrete beams using gene expression programming", journal = "Structures", volume = "47", pages = "2209--2219", year = "2023", ISSN = "2352-0124", DOI = "doi:10.1016/j.istruc.2022.12.054", URL = "https://www.sciencedirect.com/science/article/pii/S2352012422012425", keywords = "genetic algorithms, genetic programming, Moment redistribution, RC continuous beams, Prediction, Design codes, Gene expression programming", abstract = "Moment redistribution can play an important role in making the design of reinforced concrete (RC) structures more realistic and economical. In this paper, a new comprehensive formula has been proposed that considers four input parameters that are thought to influence moment redistribution the most in statically indeterminate RC beams using gene expression programming (GEP). For this reason, an experimental database of 108 data points was collected from experimental studies in the literature to predict the moment redistribution of the RC beams using genetic programming. All of these collected data points belong to two-span RC continuous beams. The results of the GEP formulation were statistically compared with the experimental results obtained from the literature and the results from the equations provided by the current design code provisions. The results of the comparison revealed that the proposed GEP-based formulation has the best performance and accuracy among the proposed models. Moreover, the sensitivity analysis and parametric study were also carried out to evaluate the most critical parameters affecting on moment redistribution of RC continuous beams", } @InProceedings{Ayerdi:2021:FSE-IND, author = "Jon Ayerdi and Valerio Terragni and Aitor Arrieta and Paolo Tonella and Goiuria Sagardui and Maite Arratibel", title = "Generating Metamorphic Relations for Cyber-Physical Systems with Genetic Programming: An Industrial Case Study", booktitle = "ESEC/FSE 2021", year = "2021", editor = "Miltiadis Allamanis and Moritz Beller", pages = "1264--1274", address = "Athens, Greece", month = "23-28 " # aug, publisher = "ACM", keywords = "genetic algorithms, genetic programming, SBSE, CPS, mutation testing, metamorphic testing, evolutionary algorithm, cyber physical systems, quality of service, oracle generation, oracle improvement", isbn13 = "978-1-4503-8562-6", URL = "https://www.conference-publishing.com/list.php?Event=FSE21&Full=noabs#fse21ind-p28-p-title", DOI = "doi:10.1145/3468264.3473920", size = "11 pages", abstract = "One of the major challenges in the verification of complex industrial Cyber-Physical Systems is the difficulty of determining whether a particular system output or behaviour is correct or not, the so-called test oracle problem. Metamorphic testing alleviates the oracle problem by reasoning on the relations that are expected to hold among multiple executions of the system under test, which are known as Metamorphic Relations (MRs). However, the development of effective MRs is often challenging and requires the involvement of domain experts. In this paper, we present a case study aiming at automating this process. To this end, we implemented GAssertMRs, a tool to automatically generate MRs with genetic programming. We assess the cost-effectiveness of this tool in the context of an industrial case study from the elevation domain. Our experimental results show that in most cases GAssertMRs outperforms the other baselines, including manually generated MRs developed with the help of domain experts. We then describe the lessons learned from our experiments and we outline the future work for the adoption of this technique by industrial practitioners.", notes = "Mondragon University, Spain; University of Auckland, New Zealand; USI Lugano, Switzerland; Orona, n.n.", } @InProceedings{ayerdi:2022:GECCOhop, author = "Jon Ayerdi and Valerio Terragni and Aitor Arrieta and Paolo Tonella and Goiuria Sagardui and Maite Arratibel", title = "Evolutionary Generation of Metamorphic Relations for {Cyber-Physical} Systems", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion", year = "2022", editor = "Marcus Gallagher", pages = "15--16", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, quality of service, cyber physical systems, metamorphic testing, oracle improvement, oracle generation, genetic programming, evolutionary algorithm, mutation testing, metamorphic testing", isbn13 = "978-1-4503-9268-6/22/07", URL = "https://valerio-terragni.github.io/assets/pdf/ayerdi-gecco-2022.pdf", DOI = "doi:10.1145/3520304.3534077", size = "2 pages", abstract = "A problem when testing Cyber-Physical Systems (CPS) is the difficulty of determining whether a particular system output or behaviour is correct or not. Metamorphic testing alleviates such a problem by reasoning on the relations expected to hold among multiple executions of the system under test, which are known as Metamorphic Relations (MRs). However, the development of effective MRs is often challenging and requires the involvement of domain experts. This paper summarizes our recent publication: {"}Generating Metamorphic Relations for Cyber-Physical Systems with Genetic Programming: An Industrial Case Study{"}, presented at ESEC/FSE 2021. In that publication we presented GAssertMRs, the first technique to automatically generate MRs for CPS, leveraging GP to explore the space of candidate solutions. We evaluated GAssertMRs in an industrial case study, outperforming other baselines.", notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @Misc{DBLP:journals/corr/abs-2312-15302, author = "Jon Ayerdi and Valerio Terragni and Gunel Jahangirova and Aitor Arrieta and Paolo Tonella", title = "Automatically Generating Metamorphic Relations via Genetic Programming", howpublished = "arXiv", volume = "abs/2312.15302", year = "2023", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.48550/arXiv.2312.15302", DOI = "doi:10.48550/ARXIV.2312.15302", eprinttype = "arXiv", eprint = "2312.15302", timestamp = "Thu, 18 Jan 2024 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/corr/abs-2312-15302.bib", bibsource = "dblp computer science bibliography, https://dblp.org", notes = "See \cite{Genmorph_TSE_2024}", } @Article{Genmorph_TSE_2024, author = "Jon Ayerdi and Valerio Terragni and Gunel Jahangirova and Aitor Arrieta and Paolo Tonella", title = "GenMorph: Automatically Generating Metamorphic Relations via Genetic Programming", journal = "IEEE Transactions on Software Engineering", volume = "50", number = "7", pages = "1888--1900", month = jul, keywords = "genetic algorithms, genetic programming, Testing, Java, Generators, Space exploration, Manuals, metamorphic testing, oracle problem, metamorphic relations, mutation analysis, mutation testing, Filters, GenMorph, EvoSuite", ISSN = "0098-5589", URL = "https://kclpure.kcl.ac.uk/portal/en/publications/genmorph-automatically-generating-metamorphic-relations-via-genet", DOI = "doi:10.1109/TSE.2024.3407840", size = "11 pages", abstract = "Metamorphic testing is a popular approach that aims to alleviate the oracle problem in software testing. At the core of this approach are Metamorphic Relations (MRs), specifying properties that hold among multiple test inputs and corresponding outputs. Deriving MRs is mostly a manual activity, since their automated generation is a challenging and largely unexplored problem. GENMORPH, a technique to automatically generate MRs for Java methods that involve inputs and outputs that are Boolean, numerical, or ordered sequences. GENMORPH uses an evolutionary algorithm to search for effective test oracles, i.e., oracles that trigger no false alarms and expose software faults in the method under test. The proposed search algorithm is guided by two fitness functions that measure the number of false alarms and the number of missed faults for the generated MRs. Our results show that GENMORPH generates effective MRs for 18 out of 23 methods (mutation score >20 percent). Furthermore, it can increase RANDOOP fault detection capability in 7 out of 23 methods, and EVOSUITE in 14 out of 23 methods.", language = "English", notes = "also known as \cite{10542726} See \cite{DBLP:journals/corr/abs-2312-15302} Presented at ICSE 2025 \cite{Ayerdi:2025:ICSE} https://conf.researchr.org/track/icse-2025/icse-2025-journal-first-papers#Accepted-Papers", } @InProceedings{Ayerdi:2025:ICSE, author = "Jon Ayerdi and Valerio Terragni and Gunel Jahangirova and Aitor Arrieta and Paolo Tonella", title = "{GenMorph}: Automatically Generating Metamorphic Relations via Genetic Programming", booktitle = "ICSE 2025 Journal-first Papers", year = "2025", editor = "Arnon Sturm and Haipeng Cai", address = "Ottawa", month = "30 " # apr, note = "Presented \cite{Genmorph_TSE_2024}", keywords = "genetic algorithms, genetic programming, SBSE, AutoMR", URL = "https://conf.researchr.org/details/icse-2025/icse-2025-journal-first-papers/55/GenMorph-Automatically-Generating-Metamorphic-Relations-via-Genetic-Programming", abstract = "Metamorphic testing is a popular approach that aims to alleviate the oracle problem in software testing. At the core of this approach are Metamorphic Relations (MRs), specifying properties that hold among multiple test inputs and corresponding outputs. Deriving MRs is mostly a manual activity, since their automated generation is a challenging and largely unexplored problem. We present GenMorph, a technique to automatically generate MRs for Java methods that involve inputs and outputs that are Boolean, numerical, or ordered sequences. GenMorph uses an evolutionary algorithm to search for effective test oracles, i.e., oracles that trigger no false alarms and expose software faults in the method under test. The proposed search algorithm is guided by two fitness functions that measure the number of false alarms and the number of missed faults for the generated MRs. Our results show that GenMorph generates effective MRs for 18 out of 23 methods (mutation score > 20 percent). Furthermore, it can increase Randoop fault detection capability in 7 out of 23 methods, and Evosuite in 14 out of 23 methods. When compared with AutoMR, a state-of-the-art MR generator, GenMorph also outperformed its fault detection capability in 9 out of 10 methods.", notes = "Presented at ICSE 2025 Journal-first Papers", } @Article{journals/peerjpre/AyralA17, author = "Hakan Ayral and Songul Albayrak", title = "Parallel and in-process compilation of individuals for genetic programming on {GPU}", journal = "PeerJ PrePrints", year = "2017", volume = "5", pages = "e2936", keywords = "genetic algorithms, genetic programming, GPU", bibdate = "2017-06-08", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/peerjpre/peerjpre5.html#AyralA17", DOI = "doi:10.7287/peerj.preprints.2936v1", abstract = "Three approaches to implement genetic programming on GPU hardware are compilation, interpretation and direct generation of machine code. The compiled approach is known to have a prohibitive overhead compared to other two.", notes = "NOT PEER-REVIEWED", } @Article{journals/jsw/AyralA17, author = "Hakan Ayral and Songul Albayrak", title = "Effects of Population, Generation and Test Case Count on Grammatical Genetic Programming for Integer Lists", journal = "Journal of Software", year = "2017", number = "6", volume = "12", pages = "483--492", month = jun, keywords = "genetic algorithms, genetic programming", ISSN = "1796-217X", bibdate = "2017-06-29", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/jsw/jsw12.html#AyralA17", DOI = "doi:10.17706/jsw.12.6.483-492", abstract = "This paper investigates how grammatical genetic programming performs for evolving simple integer list manipulation functions. We propose three sub-problems which are related to, or component of integer sorting problem as defined by genetic programming literature. We further investigate the effects of modifying evolutionary parameters, such as the number of generations allowed, number of populations, and number of test cases, on the number and distribution of successful solutions. Finally, we propose an AST based dead-code removal for the intron induced non-functional codes on evolved individuals.", notes = "Yildiz Technical University, Computer Engineering Department, Istanbul, Turkey.", } @Article{Aytek:2008:JH, author = "Ali Aytek and Ozgur Kisi", title = "A genetic programming approach to suspended sediment modelling", journal = "Journal of Hydrology", year = "2008", volume = "351", number = "3-4", pages = "288--298", month = "15 " # apr, keywords = "genetic algorithms, genetic programming, Suspended sediment load, Rating curves, Soft computing", DOI = "doi:10.1016/j.jhydrol.2007.12.005", abstract = "This study proposes genetic programming (GP) as a new approach for the explicit formulation of daily suspended sediment-discharge relationship. Empirical relations such as sediment rating curves are often applied to determine the average relationship between discharge and suspended sediment load. This type of models generally underestimates or overestimates the amount of sediment. During recent decades, some black box models based on artificial neural networks have been developed to overcome this problem. But these type of models are implicit that can not be simply used by other investigators. Therefore it is still necessary to develop an explicit model for the discharge-sediment relationship. It is aimed in this study, to develop an explicit model based on genetic programming. Explicit models obtained using the GP are compared with rating curves and multi-linear regression techniques in suspended sediment load estimation. The daily streamflow and suspended sediment data from two stations on Tongue River in Montana are used as case studies. The results indicate that the proposed GP formulation performs quite well compared to sediment rating curves and multi-linear regression models and is quite practical for use.", notes = "Gaziantep University, Civil Engineering Department, Hydraulics Division, 27310 Gaziantep, Turkey Erciyes University, Civil Engineering Department, Hydraulics Division, 38039 Kayseri, Turkey", } @Article{Aytek:2008:JESS, author = "Ali Aytek and M Asce and Murat Alp", title = "An application of artificial intelligence for rainfall-runoff modeling", journal = "Journal of Earth System Science", year = "2008", volume = "117", number = "2", pages = "145--155", month = apr, email = "aytek@gantep.edu.tr", keywords = "genetic algorithms, genetic programming, Gene Expression Programming", URL = "http://www.ias.ac.in/jess/apr2008/d093.pdf", size = "11 pages", abstract = "This study proposes an application of two techniques of artificial intelligence (AI) for rainfall-runoff modelling: the artificial neural networks (ANN) and the evolutionary computation (EC). Two different ANN techniques, the feed forward back propagation (FFBP) and generalised regression neural network (GRNN) methods are compared with one EC method, Gene Expression Programming (GEP) which is a new evolutionary algorithm that evolves computer programs. The daily hydrometeorological data of three rainfall stations and one streamflow station for Juniata River Basin in Pennsylvania state of USA are taken into consideration in the model development. Statistical parameters such as average, standard deviation, coefficient of variation, skewness, minimum and maximum values, as well as criteria such as mean square error (MSE) and determination coefficient (R2) are used to measure the performance of the models. The results indicate that the proposed genetic programming (GP) formulation performs quite well compared to results obtained by ANNs and is quite practical for use. It is concluded from the results that GEP can be proposed as an alternative to ANN models.", } @InCollection{aytekin:1995:4-OPmap, author = "Tevfik Aytekin and Emin Erkan Korkmaz and Halil Altay G{\"{u}}vennir", title = "An application of genetic programming to the 4-OP problem using map-trees", booktitle = "Progress in Evolutionary Computation", publisher = "Springer-Verlag", year = "1995", editor = "Xin Yao", volume = "956", series = "Lecture Notes in Artificial Intelligence", pages = "28--40", address = "Heidelberg, Germany", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.bilkent.edu.tr/tech-reports/1994/BU-CEIS-9441.ps.z", URL = "http://citeseer.ist.psu.edu/16240.html", DOI = "doi:10.1007/3-540-60154-6_45", size = "13 pages", abstract = "In Genetic programming (GP) applications the programs are expressed as parse trees. A node of a parse tree is an element either from the function-set or terminal-set, and an element of a terminal set can be used in a parse tree more than once. However, when we attempt to use the elements in the terminal set at most once, we encounter problems in creating the initial random population and in crossover and mutation operations. 4-Op problem is an example for such a situation. We developed a technique called map-trees to overcome these anomalies. Experimental results on 4-Op using map-trees are presented.", notes = " Also technical report BU-CEIS-9441 Bilkent University Department of Computer Engineering", } @InProceedings{azad:2002:gecco, author = "R. Muhammad Atif Azad and Conor Ryan and Mark E. Burke and Ali R. Ansari", title = "A Re-examination Of The Cart Centering Problem Using The Chorus System", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "707--715", address = "New York", publisher_address = "San Francisco, CA 94104, USA", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-878-8", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/GP144.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/GP144.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf", abstract = "The cart centering problem is well known in the field of evolutionary algorithms and has often been used as a proof of concept problem for techniques such as Genetic Programming. This paper describes the application of a grammar based, position independent encoding scheme, Chorus, to the problem. It is shown that using the traditional experimental set up employed to solve the problem, Chorus is able to come up with the solutions which appear to beat the theoretically optimal solution, known and accepted for decades in the field of control theory. However, further investigation into the literature of the relevant area reveals that there is an inherent error in the standard E.C. experimental approach to this problem, leaving room for a multitude of solutions to out perform the apparent best. This argument is validated by the performance of Chorus, producing better solutions at a number of occasions.", notes = "GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002) Nominated for best at GECCO award", } @InProceedings{azad:2002:gecco:workshop, title = "A Position Independent Evolutionary Automatic Programming Algorithm - The {Chorus} System", author = "R. Muhammad Atif Azad", pages = "260--263", booktitle = "Graduate Student Workshop", editor = "Sean Luke and Conor Ryan and Una-May O'Reilly", year = "2002", month = "8 " # jul, publisher = "AAAI", address = "New York", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025", keywords = "genetic algorithms, genetic programming, grammatical evolution", notes = "Bird-of-a-feather Workshops, GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002) part of barry:2002:GECCO:workshop", } @InProceedings{azad:2003:gecco, author = "R. Muhammad Atif Azad and Conor Ryan", title = "Structural Emergence with Order Independent Representations", booktitle = "Genetic and Evolutionary Computation -- GECCO-2003", editor = "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller", year = "2003", pages = "1626--1638", address = "Chicago", publisher_address = "Berlin", month = "12-16 " # jul, volume = "2724", series = "LNCS", ISBN = "3-540-40603-4", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, grammatical evolution", DOI = "doi:10.1007/3-540-45110-2_57", abstract = "This paper compares two grammar based Evolutionary Automatic Programming methods, Grammatical Evolution (GE) and Chorus. Both systems evolve sequences of derivation rules which can be used to produce computer programs, however, Chorus employs a position independent representation, while GE uses polymorphic codons, the meaning of which depends on the context in which they are used. We consider issues such as the order in which rules appear in individuals, and demonstrate that an order always emerges with Chorus, which is similar to that of GE, but more flexible. The paper also examines the final step of evolution, that is, how perfect individuals are produced, and how they differ from their immediate neighbours. We demonstrate that, although Chorus appears to be more flexible structure-wise, GE tends to produce individuals with a higher neutrality, suggesting that its representation can, in some cases, make finding the perfect solution easier.", notes = "GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)", } @PhdThesis{Azad:thesis, author = "Raja Muhammad Atif Azad", title = "A Position Independent Representation for Evolutionary Automatic Programming Algorithms - The Chorus System", school = "University of Limerick", year = "2003", address = "Ireland", month = dec, URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/azad_thesis.ps.gz", size = "212 pages", keywords = "genetic algorithms, genetic programming, Chorus System, Grammatical Evolution", abstract = "We describe a new position independent encoding system, Chorus, for grammar based Evolutionary Algorithms. This scheme is coarsely based on the manner in nature in which genes produce proteins that regulate the metabolic pathways of the cell. The phenotype is the behaviour of the cells metabolism, which corresponds to the development of the computer program in our case. In this procedure, the actual protein encoded by a gene is the same regardless of the position of the gene within the genome. We show that the Chorus system has a very convenient Regular Expression type schema notation that can be used to describe the presence of various phenotypic traits. This notation is used to demonstrate that massive areas of neutrality can exist in the search landscape, and the system is also shown to be able to dispense with large areas of the search space that are unlikely to contain useful solutions. The searching capability of the system is exemplified by its application on a number of proof of concept problems, where the system has shown comparable performance to Genetic Programming and Grammatical Evolution and, in certain cases, it has produced superior results. We also analyse the role of the crossover in the Chorus System and conclude by showing its application on a real world problem from the blood flow domain.", notes = "Supervisor: Conor Ryan", } @Article{Azad:2004:ASC, author = "R. Muhammad Atif Azad and Ali R. Ansari and Conor Ryan and Michael Walsh and Tim McGloughlin", title = "An evolutionary approach to Wall Sheer Stress prediction in a grafted artery", journal = "Applied Soft Computing", publisher = "Elsevier", year = "2004", volume = "4", number = "2", pages = "139--148", month = may, keywords = "genetic algorithms, genetic programming, grammatical evolution, chorus system, Wall Shear Stress, Laser Doppler anemometry, Mathematical modeling, Computational Fluid Dynamics", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2003.11.001", abstract = "Restoring the blood supply to a diseased artery is achieved by using a vascular bypass graft. The surgical procedure is a well documented and successful technique. The most commonly cited hemodynamic factor implicated in the disease initiation and proliferation processes at graft/artery junctions is Wall Shear Stress (WSS). WSS distributions are predicted using numerical simulations as they can provide quick and precise results to assess the effects that alternative graft/artery junction geometries have on the WSS distributions in bypass grafts. Validation of the numerical model is required and in vitro studies, using laser Doppler anemometry (LDA), have been employed to achieve this. Numerically, the Wall Shear Stress is predicted using velocity values stored in the computational cell near the wall and assuming zero velocity at the wall. Experimentally obtained velocities require a mathematical model to describe their behavior. This study employs a grammar based evolutionary algorithm termed Chorus for this purpose and demonstrates that Chorus successfully attains this objective. It is shown that even with the lack of domain knowledge, the results produced by this automated system are comparable to the results in the literature.", notes = "http://www.elsevier.com/wps/find/journaldescription.cws_home/621920/description#description", } @InCollection{azad:2005:GPTP, author = "R. Muhammad Atif Azad and Conor Ryan", title = "An Examination of Simultaneous Evolution of Grammars and Solutions", booktitle = "Genetic Programming Theory and Practice {III}", year = "2005", editor = "Tina Yu and Rick L. Riolo and Bill Worzel", volume = "9", series = "Genetic Programming", chapter = "10", pages = "141--158", address = "Ann Arbor", month = "12-14 " # may, publisher = "Kluwer", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Evolving Grammars, Grammatical ADFs, Generative Representations", ISBN = "0-387-28110-X", DOI = "doi:10.1007/0-387-28111-8_10", size = "18 pages", abstract = "This chapter examines the notion of co-evolving grammars with a population of individuals. This idea has great promise because it is possible to dynamically reshape the solution space while evolving individuals. We compare such a system with a more standard system with fixed grammars and demonstrate that, on a selection of benchmark problems, the standard approach appears to be better. Several different context free grammars, including one inspired by Koza's GPPS system are examined, and a number of surprising results appear, which indicate that several representative GP benchmark problems are best tackled by a standard GP approach.", notes = "part of \cite{yu:2005:GPTP} Published Jan 2006 after the workshop", } @InProceedings{Azad:2008:geccocomp, author = "R. Muhammad Atif Azad and Conor Ryan", title = "Gecco 2008 grammatical evolution tutorial", year = "2008", editor = "Marc Ebner and Mike Cattolico and Jano {van Hemert} and Steven Gustafson and Laurence D. Merkle and Frank W. Moore and Clare Bates Congdon and Christopher D. Clack and Frank W. Moore and William Rand and Sevan G. Ficici and Rick Riolo and Jaume Bacardit and Ester Bernado-Mansilla and Martin V. Butz and Stephen L. Smith and Stefano Cagnoni and Mark Hauschild and Martin Pelikan and Kumara Sastry", isbn13 = "978-1-60558-131-6", booktitle = "GECCO-2008 tutorials", pages = "2339--2366", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p2339.pdf", DOI = "doi:10.1145/1388969.1389058", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, grammatical evolution, chorus, GAuGE, grammars, linear strings", notes = "Distributed on CD-ROM at GECCO-2008 ACM Order Number 910081. Also known as \cite{1389058}", } @InProceedings{Azad:2010:gecco, author = "R. Muhammad Atif Azad and Conor Ryan", title = "Abstract functions and lifetime learning in genetic programming for symbolic regression", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "893--900", keywords = "genetic algorithms, genetic programming", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830483.1830645", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Typically, an individual in Genetic Programming (GP) can not make the most of its genetic inheritance. Once it is mapped, its fitness is immediately evaluated and it survives only until the genetic operators and its competitors eliminate it. Thus, the key to survival is to be born strong. This paper proposes a simple alternative to this powerlessness by allowing an individual to tune its internal nodes and going through several evaluations before it has to compete with other individuals. We demonstrate that this system, Chameleon, outperforms standard GP over a selection of symbolic regression type problems on both training and test sets; that the system works harmoniously with two other well known extensions to GP, that is, linear scaling and a diversity promoting tournament selection method; that it can benefit dramatically from a simple cache; that adding to functions set does not always add to the tuning expense; and that tuning alone can be enough to promote smaller trees in the population. Finally, we touch upon the consequences of ignoring the effects of complexity when focusing on just the tree sizes to induce parsimony pressure in GP populations.", notes = "Also known as \cite{1830645} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @InProceedings{Azad:2011:GECCO, author = "R. Muhammad Atif Azad and Conor Ryan", title = "Variance based selection to improve test set performance in genetic programming", booktitle = "GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0557-0", pages = "1315--1322", keywords = "genetic algorithms, genetic programming", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001576.2001754", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "This paper proposes to improve the performance of Genetic Programming (GP) over unseen data by minimizing the variance of the output values of evolving models alongwith reducing error on the training data. Variance is a well understood, simple and inexpensive statistical measure; it is easy to integrate into a GP implementation and can be computed over arbitrary input values even when the target output is not known. Moreover, we propose a simple variance based selection scheme to decide between two models (individuals). The scheme is simple because, although it uses bi-objective criteria to differentiate between two competing models, it does not rely on a multi-objective optimisation algorithm. In fact, standard multi-objective algorithms can also employ this scheme to identify good trade-offs such as those located around the knee of the Pareto Front. The results indicate that, despite some limitations, these proposals significantly improve the performance of GP over a selection of high dimensional (multi-variate) problems from the domain of symbolic regression. This improvement is manifested by superior results over test sets in three out of four problems, and by the fact that performance over the test sets does not degrade as often witnessed with standard GP; neither is this performance ever inferior to that on the training set. As with some earlier studies, these results do not find a link between expressions of small sizes and their ability to generalise to unseen data.", notes = "Also known as \cite{2001754} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)", } @InProceedings{azad:2014:EuroGP, author = "R. Muhammad Atif Azad and Conor Ryan", title = "The Best Things Don't Always Come in Small Packages: Constant Creation in Grammatical Evolution", booktitle = "17th European Conference on Genetic Programming", year = "2014", editor = "Miguel Nicolau and Krzysztof Krawiec and Malcolm I. Heywood and Mauro Castelli and Pablo Garcia-Sanchez and Juan J. Merelo and Victor M. {Rivas Santos} and Kevin Sim", series = "LNCS", volume = "8599", publisher = "Springer", pages = "186--197", address = "Granada, Spain", month = "23-25 " # apr, organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, Grammatical Evolution :poster", isbn13 = "978-3-662-44302-6", DOI = "doi:10.1007/978-3-662-44303-3_16", abstract = "This paper evaluates the performance of various methods to constant creation in Grammatical Evolution (GE), and validates the results against those from Genetic Programming (GP). Constant creation in GE is an important issue due to the disruptive nature of ripple crossover, which can radically remap multiple terminals in an individual, and we investigate if more compact methods, which are more similar to the GP style of constant creation (Ephemeral Random Constants (ERCs), perform better. The results are surprising. The GE methods all perform significantly better than GP on unseen test data, and we demonstrate that the standard GE approach of digit concatenation does not produce individuals that are any larger than those from methods which are designed to use less genetic material.", notes = "Part of \cite{Nicolau:2014:GP} EuroGP'2014 held in conjunction with EvoCOP2014, EvoBIO2014, EvoMusArt2014 and EvoApplications2014", } @Article{Azad:2014:EC, author = "Raja Muhammad Atif Azad and Conor Ryan", title = "A Simple Approach to Lifetime Learning in Genetic Programming based Symbolic Regression", journal = "Evolutionary Computation", year = "2014", volume = "22", number = "2", pages = "287--317", month = "Summer", keywords = "genetic algorithms, genetic programming, hill climbing, Lamarckian, genetic repair, Memetic Algorithms, lifetime learning, local search, hybrid genetic algorithms", ISSN = "1063-6560", URL = "http://www.mitpressjournals.org/doi/abs/10.1162/EVCO_a_00111", URL = "https://pure.ul.ie/en/publications/a-simple-approach-to-lifetime-learning-in-genetic-programming-bas", URL = "https://ref2021-resultsapp-live.azurewebsites.net/outputs/6b1e8c2e-083e-4fc5-9f66-10dc5172e041", DOI = "doi:10.1162/EVCO_a_00111", size = "31 pages", abstract = "Genetic Programming (GP) coarsely models natural evolution to evolve computer programs. Unlike in nature, where individuals can often improve their fitness through lifetime experience, the fitness of GP individuals generally does not change during their lifetime, and there is usually no opportunity to pass on acquired knowledge. This paper introduces the Chameleon system to address this discrepancy and augment GP with lifetime learning by adding a simple local search that operates by tuning the internal nodes of individuals. Although not the first attempt to combine local search with GP, its simplicity means that it is easy to understand and cheap to implement. A simple cache is added which leverages the local search to reduce the tuning cost to a small fraction of the expected cost, and we provide a theoretical upper limit on the maximum tuning expense given the average tree size of the population and show that this limit grows very conservatively as the average tree size of the population increases. We show that Chameleon uses available genetic material more efficiently by exploring more actively than with standard GP, and demonstrate that not only does Chameleon outperform standard GP (on both training and test data) over a number of symbolic regression type problems, it does so by producing smaller individuals and that it works harmoniously with two other well known extensions to GP, namely, linear scaling and a diversity-promoting tournament selection method.", notes = "Chameleon, cache, UCI, depth first hill climbing, internal nodes (functions) only. Disappeared Sep 2014 http://casnew.iti.upv.es/index.php/evocompetitions/105-symregcompetition", } @InProceedings{Azad:2014:NaBIC, author = "R. Muhammad Atif Azad and David Medernach and Conor Ryan", title = "Efficient Approaches to Interleaved Sampling of training data for Symbolic Regression", booktitle = "Sixth World Congress on Nature and Biologically Inspired Computing", year = "2014", editor = "Ana Maria Madureira and Ajith Abraham and Emilio Corchado and Leonilde Varela and Azah Kamilah Muda and Choo yun Huoy", pages = "176--183", address = "Porto, Portugal", month = "30 " # jul # " - 1 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-5937-2/14", DOI = "doi:10.1109/NaBIC.2014.6921874", abstract = "The ability to generalise beyond the training set is paramount for any machine learning algorithm and Genetic Programming (GP) is no exception. This paper investigates a recently proposed technique to improve generalisation in GP, termed Interleaved Sampling where GP alternates between using the entire data set and only a single data point in alternate generations. This paper proposes two alternatives to using a single data point: the use of random search instead of a single data point, and simply minimising the tree size. Both the approaches are more efficient than the original Interleaved Sampling because they simply do not evaluate the fitness in half the number of generations. The results show that in terms of generalisation, random search and size minimisation are as effective as the original Interleaved Sampling; however, they are computationally more efficient in terms of data processing. Size minimisation is particularly interesting because it completely prevents bloat while still being competitive in terms of training results as well as generalisation. The tree sizes with size minimisation are substantially smaller reducing the computational expense substantially.", notes = "NaBIC 2014 http://www.mirlabs.net/nabic14/", } @InProceedings{Azad:2014:GECCOcomp, author = "R. Muhammad Atif Azad and David Medernach and Conor Ryan", title = "Efficient interleaved sampling of training data in genetic programming", booktitle = "GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion", year = "2014", editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges", isbn13 = "978-1-4503-2881-4", keywords = "genetic algorithms, genetic programming: Poster", pages = "127--128", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2598394.2598480", DOI = "doi:10.1145/2598394.2598480", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "The ability to generalise beyond the training set is important for Genetic Programming (GP). Interleaved Sampling is a recently proposed approach to improve generalisation in GP. In this technique, GP alternates between using the entire data set and only a single data point. Initial results showed that the technique not only produces solutions that generalise well, but that it so happens at a reduced computational expense as half the number of generations only evaluate a single data point. This paper further investigates the merit of interleaving the use of training set with two alternatives approaches. These are: the use of random search instead of a single data point, and simply minimising the tree size. Both of these alternatives are computationally even cheaper than the original setup as they simply do not invoke the fitness function half the time. We test the utility of these new methods on four, well cited, and high dimensional problems from the symbolic regression domain. The results show that the new approaches continue to produce general solutions despite taking only half the fitness evaluations. Size minimisation also prevents bloat while producing competitive results on both training and test data sets. The tree sizes with size ionisation are substantially smaller than the rest of the setups, which further brings down the training costs.", notes = "Also known as \cite{2598480} Distributed at GECCO-2014.", } @Article{Azad:2017:GPEM, author = "Raja Muhammad Atif Azad", title = "Krzysztof Krawiec: Behavioral program synthesis with genetic programming", journal = "Genetic Programming and Evolvable Machines", year = "2017", volume = "18", number = "1", pages = "111--113", month = mar, note = "Book review", keywords = "genetic algorithms, genetic programming, program synthesis, machine learning", ISSN = "1389-2576", URL = "https://rdcu.be/dR8eg", DOI = "doi:10.1007/s10710-016-9283-7", size = "3 pages", abstract = "Review of \cite{KrawiecBPS2016}", notes = "Springer 2016, ISBN: 978-3-319-27563-5", } @InCollection{Azad:2018:hbge, author = "R. Muhammad Atif Azad and Conor Ryan", title = "Comparing Methods to Creating Constants in Grammatical Evolution", booktitle = "Handbook of Grammatical Evolution", publisher = "Springer", year = "2018", editor = "Conor Ryan and Michael O'Neill and J. J. Collins", chapter = "10", pages = "245--262", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", isbn13 = "978-3-319-78716-9", DOI = "doi:10.1007/978-3-319-78717-6_10", abstract = "This chapter evaluates the performance of various methods to constant creation in Grammatical Evolution (GE), and validates the results by comparing against those from a reasonably standard Genetic Programming (GP) setup. Specifically, the chapter compares a standard GE method to constant creation termed digit concatenation with what this chapter calls compact methods to constant creation. Constant creation in GE is an important issue due to the disruptive nature of ripple crossover, which can radically remap multiple terminals in an individual, and we investigate if more compact methods, which are more similar to the GP style of constant creation (Ephemeral Random Constants (ERCs), perform better. The results are surprising. Against common wisdom, a standard GE approach of digit concatenation does not produce individuals that are any larger than those from methods which are designed to use less genetic material. In fact, while GP characteristically evolves increasingly larger individuals, GE (after an initial growth or drop in sizes) tends to keep individual sizes stable despite no explicit mechanisms to control size growth. Furthermore, various GE setups perform acceptably well on unseen test data and typically outperform GP. Overall, these results encourage a belief that standard GE methods to symbolic regression are relatively resistant to pathogenic evolutionary tendencies of code bloat and overfitting.", notes = "Part of \cite{Ryan:2018:hbge}", } @InProceedings{azam:1998:dsi:cs, author = "Farooq Azam and H. F. VanLandingham", title = "Dynamic Systems Identification: A Comparitive Study", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "2--5", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "genetic algorithms, genetic programming", size = "4 pages", notes = "GP-98LB", } @InProceedings{azam:1998:dsiGP, author = "Farooq Azam and H. F. VanLandingham", title = "Dynamic Systems Identification using Genetic Programming", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "6", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "genetic algorithms, genetic programming", size = "1 page", notes = "GP-98LB", } @Article{MDAZAMATHULLA2008477, author = "H. Md Azamathulla and A. {Ab. Ghani} and N. A. Zakaria and S. H. Lai and C. K. Chang and C. S. Leow and Z. Abuhasan", title = "Genetic programming to predict ski-jump bucket spill-way scour", journal = "Journal of Hydrodynamics, Ser. B", volume = "20", number = "4", pages = "477--484", year = "2008", ISSN = "1001-6058", DOI = "doi:10.1016/S1001-6058(08)60083-9", URL = "http://www.sciencedirect.com/science/article/B8CX5-4TCY8GV-B/2/f3004ab0cd7ed153a22b7f5d637afc89", month = aug, keywords = "genetic algorithms, genetic programming, neural networks, spillway scour, ski-jump bucket", abstract = "Researchers in the past had noticed that application of Artificial Neural Networks (ANN) in place of conventional statistics on the basis of data mining techniques predicts more accurate results in hydraulic predictions. Mostly these works pertained to applications of ANN. Recently, another tool of soft computing, namely, Genetic Programming (GP) has caught the attention of researchers in civil engineering computing. This article examines the usefulness of the GP based approach to predict the relative scour depth downstream of a common type of ski-jump bucket spillway. Actual field measurements were used to develop the GP model. The GP based estimations were found to be equally and more accurate than the ANN based ones, especially, when the underlying cause-effect relationship became more uncertain to model.", } @Article{Azamathulla:2010:JHE, author = "H. Md. Azamathulla and Aminuddin {Ab Ghani} and Nor Azazi Zakaria and Aytac Guven", title = "Genetic Programming to Predict Bridge Pier Scour", journal = "Journal of Hydraulic Engineering", year = "2010", volume = "136", number = "3", pages = "165--169", keywords = "genetic algorithms, genetic programming, Local scour, Bridge pier, Artificial neural networks, Radial basis function", DOI = "doi:10.1061/(ASCE)HY.1943-7900.0000133", size = "5 page", abstract = "Bridge pier scouring is a significant problem for the safety of bridges. Extensive laboratory and field studies have been conducted examining the effect of relevant variables. This note presents an alternative to the conventional regression-based equations (HEC-18 and regression equation developed by authors), in the form of artificial neural networks (ANNs) and genetic programming (GP). 398 data sets of field measurements were collected from published literature and used to train the network or evolve the program. The developed network and evolved programs were validated by using the observations that were not involved in training. The performance of GP was found more effective when compared to regression equations and ANNs in predicting the scour depth of bridge piers.", } @Article{Azamathulla2011, author = "H. Md. Azamathulla and Aytac Guven and Yusuf Kagan Demir", title = "Linear genetic programming to scour below submerged pipeline", journal = "Ocean Engineering", volume = "38", number = "8-9", pages = "995--1000", year = "2011", month = jun, ISSN = "0029-8018", DOI = "doi:10.1016/j.oceaneng.2011.03.005", URL = "http://www.sciencedirect.com/science/article/B6V4F-52M3TGW-1/2/279184e6554e6b6977d8b9f0180c9f53", keywords = "genetic algorithms, genetic programming, Local scour, Neuro-fuzzy, Pipelines", abstract = "Genetic programming (GP) has nowadays attracted the attention of researchers in the prediction of hydraulic data. This study presents Linear Genetic Programming (LGP), which is an extension to GP, as an alternative tool in the prediction of scour depth below a pipeline. The data sets of laboratory measurements were collected from published literature and were used to develop LGP models. The proposed LGP models were compared with adaptive neuro-fuzzy inference system (ANFIS) model results. The predictions of LGP were observed to be in good agreement with measured data, and quite better than ANFIS and regression-based equation of scour depth at submerged pipeline.", } @Article{azamathulla:2011:WRM, author = "Hazi Mohammad Azamathulla and Aminuddin Ab. Ghani", title = "Genetic Programming for Predicting Longitudinal Dispersion Coefficients in Streams", journal = "Water Resources Management", year = "2011", volume = "25", number = "6", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s11269-010-9759-9", DOI = "doi:10.1007/s11269-010-9759-9", } @Article{Azamathulla:2012:JH, author = "H. Md. Azamathulla and Z. Ahmad", title = "{GP} approach for critical submergence of intakes in open channel flows", journal = "Journal of Hydroinformatics", year = "2012", volume = "14", number = "4", pages = "937--943", month = oct, keywords = "genetic algorithms, genetic programming, critical submergence, intakes, open channel", ISSN = "1464-7141", URL = "http://www.iwaponline.com/jh/up/pdf/jh2012089.pdf", DOI = "doi:10.2166/hydro.2012.089", size = "7 pages", abstract = "This technical note presents the genetic programming (GP) approach to predict the critical submergence for horizontal intakes in open channel flow for different bottom clearances. Laboratory data from the literature for the critical submergence for a wide range of flow conditions were used for the development and testing of the proposed method. Froude number, Reynolds number, Weber number and ratio of intake velocity and channel velocity were considered dominant parameters affecting the critical submergence. The proposed GP approach produced satisfactory results compared to the existing predictors.", notes = "Vortex, water dam", } @Article{Azamathulla:2012a:JH, author = "H. Md. Azamathulla", title = "Gene-expression programming to predict scour at a bridge abutment", journal = "Journal of Hydroinformatics", year = "2012", volume = "14", number = "2", pages = "324--331", keywords = "genetic algorithms, genetic programming, gene expression programming, artificial neural networks, bridge abutments, local scour, radial basis function", ISSN = "1464-7141", URL = "http://www.iwaponline.com/jh/014/0324/0140324.pdf", DOI = "doi:10.2166/hydro.2011.135", size = "8 pages", abstract = "The process involved in the local scour at an abutment is so complex that it makes it difficult to establish a general empirical model to provide accurate estimation for scour. This study presents the use of gene-expression programming (GEP), which is an extension of genetic programming (GP), as an alternative approach to estimate the scour depth. The datasets of laboratory measurements were collected from the published literature and used to train the network or evolve the program. The developed network and evolved programs were validated by using the observations that were not involved in training. The proposed GEP approach gives satisfactory results compared with existing predictors and artificial neural network (ANN) modelling in predicting the scour depth at an abutment.", notes = "ANN, RBF. 'The overall performance of the GEP model is superior to the ANN model.' p330", } @Article{Azamathulla2012142, author = "H. Md. Azamathulla and Z. Ahmad", title = "Gene-expression programming for transverse mixing coefficient", journal = "Journal of Hydrology", volume = "434-435", pages = "142--148", year = "2012", month = apr, ISSN = "0022-1694", DOI = "doi:10.1016/j.jhydrol.2012.02.018", URL = "http://www.sciencedirect.com/science/article/pii/S0022169412001187", keywords = "genetic algorithms, genetic programming, Transverse mixing, Open channel flow, gene expression programming, River systems", abstract = "This study presents gene-expression programming (GEP), which is an extension of genetic programming (GP), as an alternative approach to predict the transverse mixing coefficient in open channel flows. Laboratory data were collected in the present study and also from the literature for the transverse mixing coefficient covering wide range of flow conditions. These data were used for the development and testing of the proposed method. A functional relation for the estimation of transverse mixing coefficient has been developed using GEP. The proposed GEP approach produced satisfactory results compared to the existing predictors for the transverse mixing coefficient.", } @Article{Azamathulla2012203, author = "H. Md. Azamathulla and A. Zahiri", title = "Flow discharge prediction in compound channels using linear genetic programming", journal = "Journal of Hydrology", volume = "454-455", pages = "203--207", year = "2012", month = "6 " # aug, ISSN = "0022-1694", DOI = "doi:10.1016/j.jhydrol.2012.05.065", URL = "http://www.sciencedirect.com/science/article/pii/S0022169412004684", keywords = "genetic algorithms, genetic programming, Stage-discharge curve, Flooded rivers, Floodplains", abstract = "Flow discharge determination in rivers is one of the key elements in mathematical modelling in the design of river engineering projects. Because of the inundation of floodplains and sudden changes in river geometry, flow resistance equations are not applicable for compound channels. Therefore, many approaches have been developed for modification of flow discharge computations. Most of these methods have satisfactory results only in laboratory flumes. Due to the ability to model complex phenomena, the artificial intelligence methods have recently been employed for wide applications in various fields of water engineering. Linear genetic programming (LGP), a branch of artificial intelligence methods, is able to optimise the model structure and its components and to derive an explicit equation based on the variables of the phenomena. In this paper, a precise dimensionless equation has been derived for prediction of flood discharge using LGP. The proposed model was developed using published data compiled for stage-discharge data sets for 394 laboratories, and field of 30 compound channels. The results indicate that the LGP model has a better performance than the existing models.", } @Article{Azamathulla:2012b:JH, author = "H. Md. Azamathulla", title = "Gene expression programming for prediction of scour depth downstream of sills", journal = "Journal of Hydrology", year = "2012", volume = "460-461", pages = "156--159", month = "16 " # aug, ISSN = "0022-1694", DOI = "doi:10.1016/j.jhydrol.2012.06.034", URL = "http://www.sciencedirect.com/science/article/pii/S0022169412005197?v=s5", keywords = "genetic algorithms, genetic programming, gene expression programming, Grade control structures, local scour, water resources engineering", size = "4 pages", abstract = "Local scour is an important issue in environmental and water resources engineering in order to prevent degradation of river bed and safe the stability of grade control structures, stilling basins, aprons, ski-jump bucket spillways, bed sills, weirs, check dams, etc. This short communication presents Gene-Expression Programming (GEP), which is an extension to Genetic Programming (GP), as an alternative approach to predict scour depth downstream of sills. Published data were compiled from the literature for the scour depth downstream of sills. The proposed GEP approach gives satisfactory results (R2=0.967 and RMSE =0.088) compared to existing predictors [Chinnarasri and Kositgittiwong, ICE Water Management, 161(5), 267-275, 2008] with R2 =0.87 and RMSE= 2.452 for relative scour depth.", notes = "also known as \cite{MdAzamathulla2012}", } @InCollection{Azamathulla:2013:MWGTE, author = "H. Md Azamathulla", title = "A Review on Application of Soft Computing Methods in Water Resources Engineering", editor = "Xin-She Yang and Amir Hossein Gandomi and Amir Hossein Alavi", booktitle = "Metaheuristics in Water, Geotechnical and Transport Engineering", publisher = "Elsevier", address = "Oxford", year = "2013", chapter = "2", pages = "27--41", keywords = "genetic algorithms, genetic programming, gene expression programming, Water resources engineering, applied soft computing, artificial neural network, adaptive neuro-fuzzy inference system, scour, river stage", isbn13 = "978-0-12-398296-4", DOI = "doi:10.1016/B978-0-12-398296-4.00002-7", URL = "http://www.sciencedirect.com/science/article/pii/B9780123982964000027", abstract = "This chapter reviews the application of soft computing techniques, namely radial basis function (RBF), adaptive neuro-fuzzy inference system (ANFIS), gene-expression programming (GEP), and linear genetic programming (LGP) in water resources engineering. The capabilities of these techniques have been illustated by applying them to the prediction of scour downstream of flip spillway/bridge pier and abutment scour/pipeline scour/culvert scour/sediment load in hydraulics, and the river stage-discharge curve in hydrology. The accuracy of the results obtained by the soft computing techniques supports their further use for the prediction of hydraulic and hydrologic variables. Availability of free and easy-to-apply software for a specified method can invite a huge number of its applications by enthusiastic investigators.", } @Article{journals/nca/Azamathulla13, title = "Gene-expression programming to predict friction factor for Southern Italian rivers", author = "H. Md. Azamathulla", journal = "Neural Computing and Applications", year = "2013", number = "5", volume = "23", pages = "1421--1426", keywords = "genetic algorithms, genetic programming, gene expression programming, GEP, Rivers, Friction factor, Streams, Gravel-bed", bibdate = "2013-09-27", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/nca/nca23.html#Azamathulla13", URL = "http://dx.doi.org/10.1007/s00521-012-1091-2", size = "6 pages", abstract = "This briefing article presents gene-expression programming (GEP), which is an extension to genetic programming, as an alternative approach to predict friction factor for Southern Italian rivers. Published data were compiled for the friction for 43 gravel-bed rivers of Calabria. The proposed GEP approach produces satisfactory results (R-squared = 0.958 and RMSE = 0.079) compared with existing predictors.", } @Article{journals/nca/AzamathullaAG13, title = "An expert system for predicting Manning's roughness coefficient in open channels by using gene expression programming", author = "H. Md. Azamathulla and Zulfequar Ahmad and Aminuddin {Ab. Ghani}", journal = "Neural Computing and Applications", year = "2013", number = "5", volume = "23", keywords = "genetic algorithms, genetic programming, gene expression programming, GEP", bibdate = "2013-11-19", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/nca/nca23.html#AzamathullaAG13", pages = "1343--1349", URL = "http://dx.doi.org/10.1007/s00521-012-1078-z", size = "7 pages", abstract = "Manning's roughness coefficient (n) has been widely used in the estimation of flood discharges or depths of flow in natural channels. Accurate estimation of Manning's roughness coefficient is essential for the computation of flow rate, velocity. Conventional formulae that are greatly based on empirical methods lack in providing high accuracy for the prediction of Manning's roughness coefficient. Consequently, new and accurate techniques are still highly demanded. In this study, gene expression programming (GEP) is used to estimate the Manning roughness coefficient. The estimated value of the roughness coefficient is used in Mannings equation to compute the flow parameters in open-channel flows in order to carry out a comparison between the proposed GEP-based approach and the conventional ones. Results show that computed discharge using estimated value of roughness coefficient by GEP is in good agreement (10percent) with the experimental results compared to the conventional formulae (R-squared = 0.97 and RMSE = 0.0034 for the training data and Rsquared = 0.94 and RMSE = 0.086 for the testing data).", } @Article{azamathulla:2018:AWS, author = "H. Md. Azamathulla and Upaka Rathnayake and Ahmad Shatnawi", title = "Gene expression programming and artificial neural network to estimate atmospheric temperature in Tabuk, Saudi Arabia", journal = "Applied Water Science", year = "2018", volume = "8", number = "6", keywords = "genetic algorithms, genetic programming, Gene expression programming", URL = "http://link.springer.com/article/10.1007/s13201-018-0831-6", DOI = "doi:10.1007/s13201-018-0831-6", } @InProceedings{Azaraien:2017:CADS, author = "Abbas Azaraien and Babak Djalaei and Mostafa E. Salehi", booktitle = "2017 19th International Symposium on Computer Architecture and Digital Systems (CADS)", title = "Evolutionary architecture design for approximate DCT", year = "2017", abstract = "Discrete Cosine Transform (DCT) which has a major role in image and video compression has also a major role in power consumption. Approximate Computing let us trade precision to save power in error resilient applications such as multimedia. Therefore, DCT is a potential candidate for approximation. In this paper, we propose a method for evolutionary design of DCT architecture exploiting the inherent behaviour of DCT. Unlike the prior works on DCT approximation, which concentrated mostly on optimizing, replacing, or removing less effective building blocks of DCT, in our proposed method we use the evolutionary method to find new structures for DCT. According to the results, the evolution methods lead to architectures with less area and acceptable accuracy.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CADS.2017.8310731", ISSN = "2325-937X", month = dec, notes = "Also known as \cite{8310731}", } @Article{Azarhoosh:2020:RMPD, author = "A. R. Azarhoosh and Z. Zojaji and F. {Moghadas Nejad}", title = "Nonlinear genetic-base models for prediction of fatigue life of modified asphalt mixtures by precipitated calcium carbonate", journal = "Road Materials and Pavement Design", year = "2020", volume = "21", number = "3", pages = "850--866", keywords = "genetic algorithms, genetic programming, fatigue life, indirect tensile fatigue test, ITF test, precipitated calcium carbonate, PCC, CaCO3", publisher = "Taylor \& Francis", DOI = "doi:10.1080/14680629.2018.1513372", size = "17 pages", abstract = "Fatigue cracking is the most important structural failure in flexible pavements. The results of a laboratory study evaluating the fatigue properties of mixtures containing precipitated calcium carbonate (PCC) using indirect tensile fatigue (ITF) test were investigated in this paper. The hot mix asphalt (HMA) samples were made with four PCC contents (0percent, 5percent, 10percent, and 15percent), and tested at three different testing temperatures (2degrees Celcius, 10degrees Celcius and 20degrees Celcius) and stress levels (100, 300, and 500 kPa). Due to the complex behaviour of asphalt pavement materials under various loading conditions, pavement structure, and environmental conditions, accurately predicting the fatigue life of asphalt pavement is difficult. In this study, genetic programming (GP) is used to predict the fatigue life of HMA. Based on the results of the ITF test, PCC improved the fatigue behaviour of studied mixes at different temperatures. But, the considerable negative effect of the increase of the temperature on the fatigue life of HMA is evident. On the other hand, the results indicate The GP-based formulas are simple, straightforward, and particularly valuable for providing an analysis tool accessible to practicing engineers.", notes = "p865 'The GP models are capable of predicting the fatigue life of asphalt mixtures with high accuracy.' Department of Civil Engineering, Faculty of Engineering, University of Bojnord, Bojnord, Iran", } @Article{AZARHOOSH:2019:US, author = "Mohammad Javad Azarhoosh and Rouein Halladj and Sima Askari and Abbas Aghaeinejad-Meybodi", title = "Performance analysis of ultrasound-assisted synthesized nano-hierarchical {SAPO-34} catalyst in the methanol-to-lights-olefins process via artificial intelligence methods", journal = "Ultrasonics Sonochemistry", volume = "58", pages = "104646", year = "2019", ISSN = "1350-4177", DOI = "doi:10.1016/j.ultsonch.2019.104646", URL = "http://www.sciencedirect.com/science/article/pii/S1350417719305103", keywords = "genetic algorithms, genetic programming, Ultrasound-assisted synthesis, Nano-hierarchical SAPO-34, MTO process, Multi-linear regression, Artificial neural network", abstract = "The present study has focused on performance analysis of ultrasound-assisted synthesized nano-hierarchical silico-alumino-phosphate-34 (SAPO-34) catalyst during methanol-to-light-olefins (MTO) process. A classical method, i.e., multiple linear regression (MLR) and two intelligent methods, i.e., genetic programming (GP) and artificial neural networks (ANN) were used for modeling of the performance of nano-hierarchical SAPO-34 catalyst. We studied the influence of basic parameters for the sonochemical synthesis of nano-hierarchical SAPO-34 catalyst such as crystallization time, ultrasonic irradiation time, ultrasonic intensity, amount of organic template (diethylamine (DEA) and carbon nanotube (CNT)) on its performance (methanol conversion and light olefins selectivity) in MTO process. The results revealed that the models achieved using the GP method had the highest accuracy for training and test data. Therefore, GP models were used in the following to predict the effect of main parameters for the sonochemical synthesis of nano-hierarchical SAPO-34 catalyst. Finally, an optimal catalyst with the highest yield into light olefins was predicted using the genetic optimization algorithm. The genetic models were employed as an evaluation function in the genetic algorithm (GA). A good agreement between the outputs of GP models for the optimal catalyst and experimental results were obtained. The optimal ultrasound-assisted synthesized nano-hierarchical SAPO-34 was accompanied by light olefins selectivity of 77percent and methanol conversion of 94percent from the onset of the process after", } @Article{azarhoosh:2020:AJSE, author = "Alireza Azarhoosh and Salman Pouresmaeil", title = "Prediction of Marshall Mix Design Parameters in Flexible Pavements Using Genetic Programming", journal = "Arabian Journal for Science and Engineering", year = "2020", volume = "45", number = "10", pages = "8427--8441", keywords = "genetic algorithms, genetic programming, Hot-mix asphalt, Marshall mix design, Index of aggregate particle shape and texture (particle index), Viscosity of bitumen, Genetic programming method", URL = "http://link.springer.com/article/10.1007/s13369-020-04776-0", DOI = "doi:10.1007/s13369-020-04776-0", size = "15 pages", abstract = "The mix design of asphalt concrete is usually accomplished in the Iranian ministry of road and transportation according to the Marshall method. Marshall mix design parameters are a function of grading and properties of aggregates, amount and type of bitumen in asphalt mixtures. Therefore, in order to determine these parameters and the optimum bitumen content, many samples with different compounds and conditions must be manufactured and tested in the laboratory, a process that requires considerable time and cost. Accordingly, the necessity of using new and advanced methods for the design and quality control of asphalt mixtures is becoming more and more evident. Therefore, in this study, a genetic programming simulation method was employed to predict the Marshall mix design parameters of asphalt mixtures. Also, multiple linear regression models were adopted as the base model to evaluate the models presented by the genetic programming method. The models proposed here predict the Marshall mix design parameters based on parameters such as the index of aggregate particle shape and texture, the amount and viscosity of the bitumen. The results demonstrated that the proposed methods are more efficient than the costly laboratory method, and genetic programming models with minimal error (identified in this study with RMSE and MAE parameters) and correlation coefficients > 0.9 can predict relatively accurate Marshall mix design parameters.", } @Article{AZARI:2020:AR, author = "Aryandokht Azari and Hossein Tavakoli and Brian D. Barkdoll and Omid Bozorg Haddad", title = "Predictive model of algal biofuel production based on experimental data", journal = "Algal Research", volume = "47", pages = "101843", year = "2020", ISSN = "2211-9264", DOI = "doi:10.1016/j.algal.2020.101843", URL = "http://www.sciencedirect.com/science/article/pii/S2211926419309087", keywords = "genetic algorithms, genetic programming, Bioenergy, Algae, Optimization, CO biofixation rate, Photobioreactors", abstract = "Algal biofuels are of growing interest in the quest to reduce carbon emissions in the atmosphere but the sensitivity of the fuel production to various factors is not well understood. Therefore, the effects of temperature, light intensity, carbon concentration, aeration rate, pH, and time on the CO2 biofixation rate of Chlorella vulgaris (ISC-23) were investigated using experimental, and Genetic Programming (GP) modeling techniques. The impacts of applying the cement industrial flue gas as a source of carbon, useful for the growth of microalgae, were also studied. Chlorella vulgaris (ISC-23) was cultivated in a laboratory photobioreactor on a BG-11 medium. The developed GP model was used to optimize the CO2 biofixation based on the studied variables and produce a predictive equation. By using statistical measurements and error analysis, the predictive equation was shown to agree with the experimentally obtained values. It was found that the optimum conditions occur at 26o C, and 3200 lx of light, in the existence of CO2. Applying 6percent CO2 as the input with the aeration rate of 0.5 vvm in 11 days was also reported as the optimum scenario for algae production with keeping the pH close to 7.5. The results indicate that the predictions determined with the proposed equation can be of practical worth for researchers and experts in the biofuel industry", } @InProceedings{Azari:2018:CEC, author = "Samaneh Azari and Mengjie Zhang and Bing Xue and Lifeng Peng", title = "Genetic Programming for Preprocessing Tandem Mass Spectra to Improve the Reliability of Peptide Identification", booktitle = "2018 IEEE Congress on Evolutionary Computation (CEC)", year = "2018", editor = "Marley Vellasco", address = "Rio de Janeiro, Brazil", month = "8-13 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2018.8477810", abstract = "Tandem mass spectrometry (MS/MS) is currently the most commonly used technology in proteomics for identifying proteins in complex biological samples. Mass spectrometers can produce a large number of MS/MS spectra each of which has hundreds of peaks. These peaks normally contain background noise, therefore a preprocessing step to filter the noise peaks can improve the accuracy and reliability of peptide identification. This paper proposes to preprocess the data by classifying peaks as noise peaks or signal peaks, i.e., a highly-imbalanced binary classification task, and uses genetic programming (GP) to address this task. The expectation is to increase the peptide identification reliability. Meanwhile, six different types of classification algorithms in addition to GP are used on various imbalance ratios and evaluated in terms of the average accuracy and recall. The GP method appears to be the best in the retention of more signal peaks as examined on a benchmark dataset containing 1, 674 MS/MS spectra. To further evaluate the effectiveness of the GP method, the preprocessed spectral data is submitted to a benchmark de novo sequencing software, PEAKS, to identify the peptides. The results show that the proposed method improves the reliability of peptide identification compared to the original un-preprocessed data and the intensity-based thresholding methods.", notes = "WCCI2018", } @InProceedings{DBLP:conf/acpr/AzariXZP19, author = "Samaneh Azari and Bing Xue and Mengjie Zhang and Lifeng Peng", editor = "Shivakumara Palaiahnakote and Gabriella Sanniti di Baja and Liang Wang and Wei Qi Yan", title = "A Decomposition Based Multi-objective Genetic Programming Algorithm for Classification of Highly Imbalanced Tandem Mass Spectrometry", booktitle = "Pattern Recognition - 5th Asian Conference, {ACPR} 2019, Auckland, New Zealand, November 26-29, 2019, Revised Selected Papers, Part {II}", series = "Lecture Notes in Computer Science", volume = "12047", pages = "449--463", publisher = "Springer", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/978-3-030-41299-9_35", DOI = "doi:10.1007/978-3-030-41299-9_35", timestamp = "Mon, 24 Feb 2020 18:06:33 +0100", biburl = "https://dblp.org/rec/conf/acpr/AzariXZP19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{DBLP:conf/pricai/Azari0ZP19, author = "Samaneh Azari and Bing Xue and Mengjie Zhang and Lifeng Peng", editor = "Abhaya C. Nayak and Alok Sharma", title = "Improving the Results of De novo Peptide Identification via Tandem Mass Spectrometry Using a Genetic Programming-Based Scoring Function for Re-ranking Peptide-Spectrum Matches", booktitle = "{PRICAI} 2019: Trends in Artificial Intelligence - 16th Pacific Rim International Conference on Artificial Intelligence, Cuvu, Yanuca Island, Fiji, August 26-30, 2019, Proceedings, Part {III}", series = "Lecture Notes in Computer Science", volume = "11672", pages = "474--487", publisher = "Springer", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/978-3-030-29894-4_38", DOI = "doi:10.1007/978-3-030-29894-4_38", URL = "http://arxiv.org/abs/1908.08010", timestamp = "Wed, 25 Sep 2019 18:21:16 +0200", biburl = "https://dblp.org/rec/conf/pricai/Azari0ZP19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", notes = "See also arXiv abs/1908.08010 \cite{DBLP:journals/corr/abs-1908-08010}", } @InProceedings{Azari:2019:CEC, author = "Samaneh Azari and Bing Xue and Mengjie Zhang and Lifeng Peng", title = "Learning to Rank Peptide-Spectrum Matches Using Genetic Programming", booktitle = "2019 IEEE Congress on Evolutionary Computation, CEC 2019", year = "2019", editor = "Carlos A. Coello Coello", pages = "3244--3251", address = "Wellington, New Zealand", month = "10-13 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, ranking function, peptide-spectrum match, tandem mass spectrometry", isbn13 = "978-1-7281-2152-6", DOI = "doi:10.1109/CEC.2019.8790049", size = "8 pages", abstract = "The analysis of tandem mass spectrometry (MS/MS) proteomics data relies on automated methods that assign peptides to observed MS/MS spectra. Typically these methods return a list of candidate peptide-spectrum matches (PSMs), ranked according to a scoring function. Normally the highest-scoring candidate peptide is considered as the best match for each spectrum. However, these best matches do not necessary always indicate the true matches. Identifying a full-length correct peptide by peptide identification tools is crucial, and we do not want to assign a spectrum to the peptide which is not expressed in the given biological sample. Therefore in this paper, we present a new approach to improving the previous ordering/ranking of the PSMs, aiming at bringing the correct PSM for spectrum ahead of all the incorrect ones for the same spectrum. We develop a new method called GP-PSM-rank, which employs genetic programming (GP) to learn a ranking function by combining different feature functions", notes = "also known as \cite{8790049} IEEE Catalog Number: CFP19ICE-ART", } @Article{azari:2019:JASMS, author = "Samaneh Azari and Bing Xue and Mengjie Zhang and Lifeng Peng", title = "Preprocessing Tandem Mass Spectra Using Genetic Programming for Peptide Identification", journal = "Journal of The American Society for Mass Spectrometry", year = "2019", volume = "30", number = "7", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s13361-019-02196-5", DOI = "doi:10.1007/s13361-019-02196-5", } @PhdThesis{Azari:thesis, author = "Samaneh Azari", title = "Evolutionary Algorithms for Improving De Novo Peptide Sequencing", school = "Victoria University of Wellington", year = "2020", address = "New Zealand", keywords = "genetic algorithms, genetic programming", URL = "http://hdl.handle.net/10063/8898", URL = "https://researcharchive.vuw.ac.nz/xmlui/bitstream/handle/10063/8898/thesis_access.pdf", size = "268 pages", abstract = "De novo peptide sequencing algorithms have been developed for peptide identification in proteomics from tandem mass spectra (MS/MS), which can be used to identify and discover novel peptides and proteins that do not have a database available. Despite improvements in MS instrumentation and de novo sequencing methods, a significant number of CID MS/MS spectra still remain unassigned with the current algorithms, often leading to low confidence of peptide assignments to the spectra. Moreover, current algorithms often fail to construct the completely matched sequences, and produce partial matches. Therefore, identification of full-length peptides remains challenging. Another major challenge is the existence of noise in MS/MS spectra which makes the data highly imbalanced. Also missing peaks, caused by incomplete MS fragmentation makes it more difficult to infer a full-length peptide sequence. In addition, the large search space of all possible amino acid sequences for each spectrum leads to a high false discovery rate. This thesis focuses on improving the performance of current methods by developing new algorithms corresponding to three steps of preprocessing, sequence optimisation and post-processing using machine learning for more comprehensive interrogation of MS/MS datasets. From the machine learning point of view, the three steps can be addressed by solving different tasks such as classification, optimisation, and symbolic regression. Since Evolutionary Algorithms (EAs), as effective global search techniques, have shown promising results in solving these problems, this thesis investigates the capability of EAs in improving the de novo peptide sequencing. In the preprocessing step, this thesis proposes an effective GP-based method for classification of signal and noise peaks in highly imbalanced MS/MS spectra with the purpose of having a positive influence on the reliability of the peptide identification. The results show that the proposed algorithm is the most stable classification method across various noise ratios, outperforming six other benchmark classification algorithms. The experimental results show a significant improvement in high confidence peptide assignments to MS/MS spectra when the data is preprocessed by the proposed GP method. Moreover, the first multi-objective GP approach for classification of peaks in MS/MS data, aiming at maximising the accuracy of the minority class (signal peaks) and the accuracy of the majority class (noise peaks) is also proposed in this thesis. The results show that the multi-objective GP method outperforms the single objective GP algorithm and a popular multi-objective approach in terms of retaining more signal peaks and removing more noise peaks. The multi-objective GP approach significantly improved the reliability of peptide identification. This thesis proposes a GA-based method to solve the complex optimisation task of de novo peptide sequencing, aiming at constructing full-length sequences. The proposed GA method benefits the GA capability of searching a large search space of potential amino acid sequences to find the most likely full-length sequence. The experimental results show that the proposed method outperforms the most commonly used de novo sequencing method at both amino acid level and peptide level. This thesis also proposes a novel method for re-scoring and re-ranking the peptide spectrum matches (PSMs) from the result of de novo peptide sequencing, aiming at minimising the false discovery rate as a post-processing approach. The proposed GP method evolves the computer programs to perform regression and classification simultaneously in order to generate an effective scoring function for finding the correct PSMs from many incorrect ones. The results show that the new GP-based PSM scoring function significantly improves the identification of full-length peptides when it is used to post-process the de novo sequencing results.", notes = "supervisors: Mengjie Zhang, Bing Xue, Lifeng Peng", } @InProceedings{Azaria:2016:GPTP, author = "Itay Azaria and Achiya Elyasaf and Moshe Sipper", title = "Evolving Artificial General Intelligence for Video Controllers", booktitle = "Genetic Programming Theory and Practice XIV", year = "2016", editor = "Rick Riolo and Bill Worzel and Brian Goldman and Bill Tozier", pages = "53--63", address = "Ann Arbor, USA", month = "19-21 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Hyper-Heuristic", isbn13 = "978-3-319-97087-5", URL = "https://www.cs.bgu.ac.il/~sipper/publications/Evolving%20Artificial%20General%20Intelligence.pdf", URL = "https://www.springer.com/us/book/9783319970875", DOI = "doi:10.1007/978-3-319-97088-2_4", size = "12 pages", abstract = "The General Video Game Playing Competition (GVGAI) defines a challenge of creating controllers for general video game playing, a testbed, as it were-for examining the issue of artificial general intelligence. We develop herein a game controller that mimics human-learning behaviour, focusing on the ability to generalize from experience and diminish learning time as new games present themselves. We use genetic programming to evolve hyper heuristic-based general players, our results showing the effectiveness of evolution in meeting the generality challenge.", notes = " Part of \cite{Tozier:2016:GPTP} published after the workshop", } @InProceedings{eurogp:AzariaS05, author = "Yaniv Azaria and Moshe Sipper", editor = "Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. {van Hemert} and Marco Tomassini", title = "{GP-Gammon}: Using Genetic Programming to Evolve Backgammon Players", booktitle = "Proceedings of the 8th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3447", year = "2005", address = "Lausanne, Switzerland", month = "30 " # mar # " - 1 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-25436-6", pages = "132--142", DOI = "doi:10.1007/978-3-540-31989-4_12", DOI = "doi:10.1007/b107383", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "We apply genetic programming to the evolution of strategies for playing the game of backgammon. Pitted in a 1000-game tournament against a standard benchmark player---Pubeval---our best evolved program wins 58\% of the games, the highest verifiable result to date. Moreover, several other evolved programs attain win percentages not far behind the champion, evidencing the repeatability of our approach.", notes = "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005", } @Article{azaria:2005:GPEM, author = "Yaniv Azaria and Moshe Sipper", title = "{GP-Gammon}: Genetically Programming Backgammon Players", journal = "Genetic Programming and Evolvable Machines", year = "2005", volume = "6", number = "3", pages = "283--300", month = sep, note = "Published online: 12 August 2005", keywords = "genetic algorithms, genetic programming, backgammon, self-learning, STGP, demes, coevolution", ISSN = "1389-2576", URL = "http://www.cs.bgu.ac.il/~sipper/papabs/gpgammon.pdf", URL = "https://rdcu.be/c7iTQ", DOI = "doi:10.1007/s10710-005-2990-0", size = "18 pages", abstract = "We apply genetic programming to the evolution of strategies for playing the game of backgammon. We explore two different strategies of learning: using a fixed external opponent as teacher, and letting the individuals play against each other. We conclude that the second approach is better and leads to excellent results: Pitted in a 1000-game tournament against a standard benchmark player Pubeval our best evolved program wins 62.4 percent of the games, the highest result to date. Moreover, several other evolved programs attain win percentages not far behind the champion, evidencing the repeatability of our approach.", notes = "ECJ", } @InProceedings{Azevedo:2016:CSCI, author = "Leonardo Azevedo and Ruben Nunes and Amilcar Soares", booktitle = "2016 International Conference on Computational Science and Computational Intelligence (CSCI)", title = "Genetic Programming in Geostatistical Reservoir Geophysics", year = "2016", pages = "1208--1213", abstract = "Hydrocarbon reservoir modelling and characterisation is a critical step for the success of oil and/or gas exploration and production projects. Reservoir modelling is frequently based on the results provided by geostatistical seismic inversion techniques. These procedures are computationally heavy and expensive even for small-to-medium size fields due to the use of stochastic sequential simulation as the model perturbation technique. This work proposes the use of machine learning techniques, specifically symbolic regression, a category from the group of genetic programming methodologies, as a proxy to surpass the need of stochastic sequential simulation without compromising the advantage of using these simulation methodologies, for example uncertainty assessment of the property of interest. The proposed methodology is illustrated with an application example to a real case study and the results compared with the traditional geostatistical seismic inversion approach.", keywords = "genetic algorithms, genetic programming, Computational modelling, Correlation coefficient, Data models, Iterative methods, Mathematical model, Reflection, Stochastic processes, genetic programming geostatistical seismic inversion seismic reservoir characterisation", DOI = "doi:10.1109/CSCI.2016.0228", month = dec, notes = "Also known as \cite{7881521}", } @InProceedings{Azimlu:2019:ICCSE, author = "Fateme Azimlu and Shahryar Rahnamayan and Masoud Makrehchi and Naveen Kalra", booktitle = "2019 14th International Conference on Computer Science Education (ICCSE)", title = "Comparing Genetic Programming with Other Data Mining Techniques on Prediction Models", year = "2019", pages = "785--791", month = aug, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICCSE.2019.8845381", ISSN = "2473-9464", abstract = "Prediction is one of the most important tasks in the machine learning field. Data scientists employ various learning methods to find the most appropriate and accurate model for each family of applications or dataset. This study compares the symbolic regression using genetic programming (GP), with conventional machine learning techniques. In cases it is required to model an unknown, poorly understood, and/or complicated system. In these cases, we use genetic programming to generate a symbolic model without using any pre-known model. In this paper, the GP is studied as a tool for prediction in different types of datasets and conducted experiments to verify the superiority of GP over conventional models in certain conditions and datasets. The accuracy of GP-based regression results are compared with other machine learning techniques, and are found to be more accurate in certain conditions.", notes = "Also known as \cite{8845381}", } @InProceedings{Azimlu:2021:RWACMO, author = "Fateme Azimlu and Shahryar Rahnamayan and Masoud Makrehchi", title = "House Price Prediction Using Clustering and Genetic Programming along with Conducting a Comparative Study", booktitle = "Real-World Applications of Continuous and Mixed-integer Optimization", year = "2021", editor = "Kazuhisa Chiba and Akira Oyama and Pramudita Satria Palar and Koji Shimoyama and Hemant K. Singh", pages = "1809--1816", address = "internet", series = "GECCO '21", month = jul # " 10-14", organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, symbolic Regression, Regression, Machine Learning, Clustering, Multi-level-model, House Price Prediction", isbn13 = "978-1-4503-8351-6", DOI = "doi:10.1145/3449726.3463141", size = "8 pages", abstract = "One of the most important tasks in machine learning is prediction. Data scientists use different regression methods to find the most appropriate and accurate model for each type of datasets. This study proposes a method to improve accuracy in regression and prediction. In common methods, different models are applied to the whole data to find the best model with higher accuracy. In our proposed approach, first, we cluster data using different methods such as K-means, DBSCAN, and agglomerative hierarchical clustering algorithms. Then, for each clustering method and for each generated cluster we apply various regression models including linear and polynomial regressions, SVR, neural network, and symbolic regression in order to find the most accurate model and study the genetic programming potential in improving the prediction accuracy. This model is a combination of clustering and regression. After clustering, the number of samples in each created cluster, compared to the number of samples in the whole dataset is reduced, and consequently by decreasing the number of samples in each group, we lose accuracy. On the other hand, specifying data and setting similar samples in one group enhances the accuracy and decreases the computational cost. As a case study, we used real estate data with 20 features to improve house price estimation; however, this approach is applicable to other large datasets.", notes = "University of Ontario Institute of Technology GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)", } @InProceedings{Aziz:2016:EuroGP, author = "Benjamin Aziz and Mohamed Bader and Cerana Hippolyte", title = "Search-Based SQL Injection Attacks Testing using Genetic Programming", booktitle = "EuroGP 2016: Proceedings of the 19th European Conference on Genetic Programming", year = "2016", month = "30 " # mar # "--1 " # apr, editor = "Malcolm I. Heywood and James McDermott and Mauro Castelli and Ernesto Costa and Kevin Sim", series = "LNCS", volume = "9594", publisher = "Springer Verlag", address = "Porto, Portugal", pages = "183--198", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, Search-Based Testing, SQL Injections", isbn13 = "978-3-319-30668-1", DOI = "doi:10.1007/978-3-319-30668-1_12", abstract = "Software testing is a key phase of many development methodologies as it provides a natural opportunity for integrating security early in the software development lifecycle. However despite the known importance of software testing, this phase is often overlooked as it is quite difficult and labour-intensive to obtain test datasets to effectively test an application. This lack of adequate automatic software testing renders software applications vulnerable to malicious attacks after they are deployed as detected software vulnerabilities start having an impact during the production phase. Among such attacks are SQL injection attacks. Exploitation of SQL injection vulnerabilities by malicious programs could result in severe consequences such as breaches of confidentiality and false authentication. We present in this paper a search-based software testing technique to detect SQL injection vulnerabilities in software applications. This approach uses genetic programming as a means of generating our test datasets, which are then used to test applications for SQL injection-based vulnerabilities.", notes = "Part of \cite{Heywood:2016:GP} EuroGP'2016 held in conjunction with EvoCOP2016, EvoMusArt2016 and EvoApplications2016", } @Article{Aziz:2016:GPEM, author = "Mahdi Aziz and Mohammad-H Tayarani-N and Mohammad R. Meybodi", title = "A two-objective memetic approach for the node localization problem in wireless sensor networks", journal = "Genetic Programming and Evolvable Machines", year = "2016", volume = "17", number = "4", pages = "321--358", month = dec, ISSN = "1389-2576", DOI = "doi:10.1007/s10710-016-9274-8", size = "38 pages", abstract = "Wireless sensor networks (WSNs) are emerging as an efficient way to sense the physical phenomenon without the need of wired links and spending huge money on sensor devices. In WSNs, finding the accurate locations of sensor nodes is essential since the location inaccuracy makes the collected data fruitless. In this paper, we propose a two-objective memetic approach called the Three Phase Memetic Approach that finds the locations of sensor nodes with high accuracy. The proposed algorithm is composed of three operators (phases). The first phase, which is a combination of three node-estimating approaches, is used to provide good starting locations for sensor nodes. The second and third phases are then used for mitigating the localization errors in the first operator. To test the proposed algorithm, we compare it with the simulated annealing-based localization algorithm, genetic algorithm-based localization, Particle Swarm Optimization-based Localization algorithm, trilateration-based simulated annealing algorithm, imperialist competitive algorithm and Pareto Archived Evolution Strategy on ten randomly created and four specific network topologies with four different values of transmission ranges. The comparisons indicate that the proposed algorithm outperforms the other algorithms in terms of the coordinate estimations of sensor nodes.", notes = "http://www.dennisweyland.net/blog/?p=12 doi:10.4018/jamc.2010040104", } @InProceedings{Azmi:2020:Morgeo, author = "Rida Azmi and Hicham Amar and Abderrahim Norelyaqine", booktitle = "2020 IEEE International conference of Moroccan Geomatics (Morgeo)", title = "Generate knowledge base from very high spatial resolution satellite image using robust classification rules and genetic programming", year = "2020", keywords = "genetic algorithms, genetic programming, Remote sensing, High resolution, Data Mining", DOI = "doi:10.1109/Morgeo49228.2020.9121914", month = may, size = "6 pages", abstract = "Object based image analysis techniques give accurate results when a good knowledge base is extracted from remote sensing imagery. Data mining algorithms and especially evolutionary process can extract useful knowledge that can be used in different fields. In this paper, object-oriented classification was used, more particularly object-based image analysis approach (OOIA) to classify a large feature space composed of a very high spatial resolution satellite image (VHR). Genetic programming (GP) concept was applied to extract classification rules with an induction form. Comparison of the performance of three GP algorithms (Bojarczuc_GP, Falco_GP and Tan_GP) was mad using JCLEC Framework. Results showed two main conclusions. 1) testing and evaluation of the generated rules allow us to discover that GP algorithms can classify and extract useful knowledge from VHR satellite data. 2) evaluation of the performance of the three Genetic programming models demonstrates that the Bojarczuk model is efficient on accuracy classification than the Falco and Tan models.", notes = "Faculty of Sciences of Rabat, Mohamed V University, Rabat, Morocco Also known as \cite{9121914}", } @InProceedings{Azzali:2019:EuroGP, author = "Irene Azzali and Leonardo Vanneschi and Sara Silva and Illya Bakurov and Mario Giacobini", title = "A Vectorial Approach to Genetic Programming", booktitle = "EuroGP 2019: Proceedings of the 22nd European Conference on Genetic Programming", year = "2019", month = "24-26 " # apr, editor = "Lukas Sekanina and Ting Hu and Nuno Lourenco", series = "LNCS", volume = "11451", publisher = "Springer Verlag", address = "Leipzig, Germany", pages = "213--227", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Vector-based representation, Panel Data regression: Poster", isbn13 = "978-3-030-16669-4", URL = "https://hdl.handle.net/2318/1725688", URL = "https://iris.unito.it/retrieve/e27ce42f-33ca-2581-e053-d805fe0acbaa/Azzali.pdf", URL = "https://www.springer.com/us/book/9783030166694", DOI = "doi:10.1007/978-3-030-16670-0_14", size = "16 pages", abstract = "Among the various typologies of problems to which Genetic Programming (GP) has been applied since its origins, symbolic regression is one of the most popular. A common situation consists in the prediction of a target time series based on scalar features and other time series variables collected from multiple subjects. To manage this problem with GP data needs a panel representation where each observation corresponds to a collection on a subject at a precise time instant. However, representing data in this form may imply a loss of information: for instance, the algorithm may not be able to recognize observations belonging to the same subject and their recording order. To maintain the source of knowledge supplied by ordered sequences as time series, we propose a new approach to GP that keeps instances of the same observation together in a vector, introducing vectorial variables as terminals. This new representation allows aggregate functions in the primitive GP set, included with the purpose of describing the behaviour of vectorial variables. In this work, we perform a comparative analysis of vectorial GP (VE-GP) against standard GP (ST-GP). Experiments are conducted on different benchmark problems to highlight the advantages of this new approach.", notes = "http://www.evostar.org/2019/cfp_eurogp.php#abstracts Part of \cite{Sekanina:2019:GP} EuroGP'2019 held in conjunction with EvoCOP2019, EvoMusArt2019 and EvoApplications2019", } @Article{Azzali:GPEM, author = "Irene Azzali and Leonardo Vanneschi and Andrea Mosca and Luigi Bertolotti and Mario Giacobini", title = "Towards the use of genetic programming in the ecological modelling of mosquito population dynamics", journal = "Genetic Programming and Evolvable Machines", year = "2020", volume = "21", number = "4", pages = "629--642", month = dec, keywords = "genetic algorithms, genetic programming, West Nile Virus, WNV, Ecological modelling, Machine learning, Regression", ISSN = "1389-2576", URL = "https://iris.unito.it/retrieve/handle/2318/1722575/562795/Manuscript.pdf", URL = "https://rdcu.be/cQCew", DOI = "doi:10.1007/s10710-019-09374-0", size = "14 pages", abstract = "Predictive algorithms are powerful tools to support infection surveillance plans based on the monitoring of vector abundance. In this article, we explore the use of genetic programming (GP) to build a predictive model of mosquito abundance based on environmental and climatic variables. We claim, in fact, that the heterogeneity and complexity of this kind of dataset demands algorithms capable of discovering complex relationships among variables. For this reason, we benchmarked GP performance with state of the art machine learning predictive algorithms. In order to provide a real exploitable model of mosquito abundance, we trained GP and the other algorithms on mosquito collections from 2002 to 2005 and we tested the predictive ability in 2006 collections. Results reveal that, among the studied methods, GP has the best performance in terms of accuracy and generalization ability. Moreover, the intrinsic feature selection and readability of the solution provided by GP offer the possibility of a biological interpretation of the model which highlights known or new behaviours responsible for mosquito abundance. GP, therefore, reveals to be a promising tool in the field of ecological modelling, opening the way to the use of a vector based GP approach (VE-GP) which may be more appropriate and beneficial for the problems in analysis.", } @InProceedings{Azzali:2020:EuroGP, author = "Irene Azzali and Leonardo Vanneschi and Mario Giacobini", title = "Investigating the Use of Geometric Semantic Operators in Vectorial Genetic Programming", booktitle = "EuroGP 2020: Proceedings of the 23rd European Conference on Genetic Programming", year = "2020", month = "15-17 " # apr, editor = "Ting Hu and Nuno Lourenco and Eric Medvet", series = "LNCS", volume = "12101", publisher = "Springer Verlag", address = "Seville, Spain", pages = "52--67", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Vector-based genetic programming, Time series, Sliding windows, Geometric semantic operators", isbn13 = "978-3-030-44093-0", DOI = "doi:10.1007/978-3-030-44094-7_4", abstract = "Vectorial Genetic Programming (VE_GP) is a new GP approach for panel data forecasting. Besides permitting the use of vectors as terminal symbols to represent time series and including aggregation functions to extract time series features, it introduces the possibility of evolving the window of aggregation. The local aggregation of data allows the identification of meaningful patterns overcoming the drawback of considering always the previous history of a series of data. In this work, we investigate the use of geometric semantic operators (GSOs) in VE_GP, comparing its performance with traditional GP with GSOs. Experiments are conducted on two real panel data forecasting problems, one allowing the aggregation on moving windows, one not. Results show that classical VE_GP is the best approach in both cases in terms of predictive accuracy, suggesting that GSOs are not able to evolve efficiently individuals when time series are involved. We discuss the possible reasons of this behaviour, to understand how we could design valuable GSOs for time series in the future.", notes = "http://www.evostar.org/2020/cfp_eurogp.php Part of \cite{Hu:2020:GP} EuroGP'2020 held in conjunction with EvoCOP2020, EvoMusArt2020 and EvoApplications2020", } @Article{AZZALI:2020:ASC, author = "Irene Azzali and Leonardo Vanneschi and Illya Bakurov and Sara Silva and Marco Ivaldi and Mario Giacobini", title = "Towards the use of vector based {GP} to predict physiological time series", journal = "Applied Soft Computing", volume = "89", pages = "106097", year = "2020", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2020.106097", URL = "http://www.sciencedirect.com/science/article/pii/S1568494620300375", keywords = "genetic algorithms, genetic programming, Ventilation, Physiological data, Machine learning, Time series", abstract = "Prediction of physiological time series is frequently approached by means of machine learning (ML) algorithms. However, most ML techniques are not able to directly manage time series, thus they do not exploit all the useful information such as patterns, peaks and regularities provided by the time dimension. Besides advanced ML methods such as recurrent neural network that preserve the ordered nature of time series, a recently developed approach of genetic programming, VE-GP, looks promising on the problem in analysis. VE-GP allows time series as terminals in the form of a vector, including new strategies to exploit this representation. In this paper we compare different ML techniques on the real problem of predicting ventilation flow from physiological variables with the aim of highlighting the potential of VE-GP. Experimental results show the advantage of applying this technique in the problem and we ascribe the good performances to the ability of properly catching meaningful information from time series", } @InProceedings{Azzali:2022:evoapplications, author = "Irene Azzali and Nicole Dalia Cilia and Claudio {De Stefano} and Francesco Fontanella and Mario Giacobini and Leonardo Vanneschi", title = "Vectorial {GP for Alzheimer's} Disease Prediction Through Handwriting Analysis", booktitle = "25th International Conference, EvoApplications 2022", year = "2022", month = "20-22 " # apr, editor = "Juan Luis Jimenez Laredo and J. Ignacio Hidalgo and Kehinde Oluwatoyin Babaagba", series = "LNCS", volume = "13224", publisher = "Springer", address = "Madrid", pages = "517--530", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Alzheimer disease, Artificial intelligence, Handwriting analysis, Vectorial genetic programming", isbn13 = "978-3-031-02461-0", DOI = "doi:10.1007/978-3-031-02462-7_33", abstract = "Alzheimer Disease (AD) is a neurodegenerative disease which causes a continuous cognitive decline. This decline has a strong impact on daily life of the people affected and on that of their relatives. Unfortunately, to date there is no cure for this disease. However, its early diagnosis helps to better manage the course of the disease with the treatments currently available. In recent years, AI researchers have become increasingly interested in developing tools for early diagnosis of AD based on handwriting analysis. In most cases, they use a feature engineering approach: domain knowledge by clinicians is used to define the set of features to extract from the raw data. In this paper, we present a novel approach based on vectorial genetic programming (VE_GP) to recognize the handwriting of AD patients. VE_GP is a recently defined method that enhances Genetic Programming (GP) and is able to directly manage time series in such a way to automatically extract informative features, without any need of human intervention. We applied VE_GP to handwriting data in the form of time series consisting of spatial coordinates and pressure. These time series represent pen movements collected from people while performing handwriting tasks. The presented experimental results indicate that the proposed approach is effective for this type of application. Furthermore, VE_GP is also able to generate rather small and simple models, that can be read and possibly interpreted. These models are reported and discussed in the Last part of the paper.", notes = "Also (?) presented at WIVACE 2022 XVI International Workshop on Artificial Life and Evolutionary Computation Gaeta (LT), Italy September 14-16, 2022 http://wivace2022.unicas.it/files/programWIVACE2022.pdf http://www.evostar.org/2022/ EvoApplications2022 held in conjunction with EuroGP'2022, EvoCOP2022 and EvoMusArt2022", } @Article{AZZALI:2024:swevo, author = "Irene Azzali and Nicole D. Cilia and Claudio {De Stefano} and Francesco Fontanella and Mario Giacobini and Leonardo Vanneschi", title = "Automatic feature extraction with Vectorial Genetic Programming for Alzheimer's Disease prediction through handwriting analysis", journal = "Swarm and Evolutionary Computation", volume = "87", pages = "101571", year = "2024", ISSN = "2210-6502", DOI = "doi:10.1016/j.swevo.2024.101571", URL = "https://www.sciencedirect.com/science/article/pii/S2210650224001093", keywords = "genetic algorithms, genetic programming, Vectorial Genetic Programming, Alzheimer's Disease, Machine learning, Healthcare applications", abstract = "Alzheimer's Disease (AD) is an incurable neurodegenerative disease that strongly impacts the lives of the people affected. Even if, to date, there is no cure for this disease, its early diagnosis helps to manage the course of the disease better with the treatments currently available. Even more importantly, an early diagnosis will also be necessary for the new treatments available in the future. Recently, machine learning (ML) based tools have demonstrated their effectiveness in recognizing people's handwriting in the early stages of AD. In most cases, they use features defined by using the domain knowledge provided by clinicians. In this paper, we present a novel approach based on vectorial genetic programming (VE_GP) to recognize the handwriting of AD patients. VE_GP is an enhanced version of GP that can manage time series directly. We applied VE_GP to data collected using an experimental protocol, which was defined to collect handwriting data to support the development of ML tools for the early diagnosis of AD based on handwriting analysis. The experimental results confirmed the effectiveness of the proposed approach in terms of classification performance, size, and simplicity", } @InProceedings{conf/adma/AzzawiHAXAA17, title = "Multiclass Lung Cancer Diagnosis by Gene Expression Programming and Microarray Datasets", author = "Hasseeb Azzawi and Jingyu Hou and Russul Alanni and Yong Xiang and Rana Abdu-Aljabar and Ali Azzawi", booktitle = "Advanced Data Mining and Applications - 13th International Conference, ADMA 2017, Singapore, November 5-6, 2017, Proceedings", editor = "Gao Cong and Wen-Chih Peng and Wei Emma Zhang and Chengliang Li and Aixin Sun", publisher = "Springer", year = "2017", volume = "10604", pages = "541--553", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming, gene expression programming", bibdate = "2017-11-03", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/adma/adma2017.html#AzzawiHAXAA17", isbn13 = "978-3-319-69178-7", DOI = "doi:10.1007/978-3-319-69179-4_38", } @Article{Azzini:2011:IA, author = "Antonia Azzini and Andrea G. B. Tettamanzi", title = "Evolutionary {ANNs}: A state of the art survey", journal = "Intelligenza Artificiale", year = "2011", volume = "5", number = "1", pages = "19--35", keywords = "genetic algorithms, genetic programming, ANN, Neural Networks, Classification", ISSN = "1724-8035", broken = "http://iospress.metapress.com/content/T144511510806253", DOI = "doi:10.3233/IA-2011-0002", size = "17 pages", abstract = "Neuro-genetic systems have become a very important topic of study in evolutionary computation in recent years. They are models that use evolutionary algorithms to optimise neural network design. This article is a survey of the state of art of evolutionary ANN systems, with a focus on the most recent developments, presented in the literature during the last decade. The main purpose of this work is to provide an update and extension of Yao's milestone survey, published back in 1999, by taking the most recent literature into account.", notes = "Special issue: Selected papers from the 15 International Conference of the Italian Association for Artificial Intelligence Marco Maratea, Giovanni Adorni, Stefano Cagnoni and Marco Gori", } @Article{azzolini:2025:ML, author = "Damiano Azzolini", title = "Learning answer set programs with aggregates via sampling and genetic programming", journal = "Machine Learning", year = "2025", keywords = "genetic algorithms, genetic programming", URL = "https://link.springer.com/article/10.1007/s10994-025-06780-7", DOI = "doi:10.1007/s10994-025-06780-7", } @InProceedings{B:2021:ICIRCA, author = "Akaramuthalvi J B and Suja Palaniswamy", title = "Comparison of Conventional and Automated Machine Learning approaches for Breast Cancer Prediction", booktitle = "2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA)", year = "2021", pages = "1533--1537", month = sep, keywords = "genetic algorithms, genetic programming, TPOT", DOI = "doi:10.1109/ICIRCA51532.2021.9544863", abstract = "Breast cancer is a type of cancer in which the breast cells grow out of control. It is one of the leading cause for the high pace of death in women. Breast cancer classification is mainly done with the help of Machine Learning (ML) algorithms. In this work, we did a comparative analysis by creating a framework using ML and Auto ML algorithms (genetic programming) to accurately classify the cells in the breast as cancerous or non-cancerous. The work focused on automating and optimizing the algorithms for better prediction of cancerous cells. In Auto ML, Tree- based Pipeline Optimization Tool (TPOT), a genetic programming approach is used for finding the suitable classifiers and to automatically select the significant features and parameter values associated with the classifiers. Wisconsin Breast cancer diagnostic dataset, which comprises of digitized images taken from fine needle aspirate of breast mass has been used in this work. Evaluation based on recall, precision and accuracy have showed good results.", notes = "Also known as \cite{9544863} Amrita School of Engineering, Bengaluru, Amrita Vishwa Vidyapeetham, India", } @Article{Baareh:2018:IJACSA, author = "Abdel Karim Baareh", title = "Evolutionary Design of a Carbon Dioxide Emission Prediction Model using Genetic Programming", journal = "International Journal of Advanced Computer Science and Applications", year = "2018", number = "3", volume = "9", pages = "298--303", keywords = "genetic algorithms, genetic programming, fossil fuels, carbon emission, forecasting", publisher = "The Science and Information (SAI) Organization", bibsource = "OAI-PMH server at thesai.org", language = "eng", oai = "oai:thesai.org:10.14569/IJACSA.2018.090341", URL = "http://thesai.org/Downloads/Volume9No3/Paper_41-Evolutionary_Design_of_a_Carbon_Dioxide_Emission.pdf", DOI = "doi:10.14569/IJACSA.2018.090341", size = "6 pages", abstract = "Weather pollution is considered as one of the most important, dangerous problem that affects our life and the society security from the different sides. The global warming problem affecting the atmosphere is related to the carbon dioxide emission (CO2) from the different fossil fuels along with temperature. In this paper, this phenomenon is studied to find a solution for preventing and reducing the poison CO2 gas emerged from affecting the society and reducing the smoke pollution. The developed model consists of four input attributes: the global oil, natural gas, coal, and primary energy consumption and one output the CO2 gas. The stochastic search algorithm Genetic Programming (GP) was used as an effective and robust tool in building the forecasting model. The model data for both training and testing cases were taken from the years of 1982 to 2000 and 2003 to 2010, respectively. According to the results obtained from the different evaluation criteria, it is nearly obvious that the performance of the GP in carbon gas emission estimation was very good and efficient in solving and dealing with the climate pollution problems.", } @InProceedings{Baars:2011:FedCSIS, author = "Arthur I. Baars and Kiran Lakhotia and Tanja E. J. Vos and Joachim Wegener", title = "Search-based testing, the underlying engine of Future Internet testing", booktitle = "Federated Conference on Computer Science and Information Systems (FedCSIS 2011)", year = "2011", pages = "917--923", address = "Szczecin", month = "18-21 " # sep, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, SBSE, Evolutionary computation, Internet, Optimisation, Search problems, Software, Testing, future Internet testing, search-based testing, software testing, time to market, evolutionary testing", isbn13 = "978-1-4577-0041-5", URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6078178", size = "7 pages", abstract = "The Future Internet will be a complex interconnection of services, applications, content and media, on which our society will become increasingly dependent. Time to market is crucial in Internet applications and hence release cycles grow ever shorter. This, coupled with the highly dynamic nature of the Future Internet will place new demands on software testing. Search-Based Testing is ideally placed to address these emerging challenges. Its techniques are highly flexible and robust to only partially observable systems. This paper presents an overview of Search-Based Testing and discusses some of the open challenges remaining to make search-based techniques applicable to the Future Internet.", notes = "Brief mention of GP on page 919, mostly GAs Also known as \cite{6078178}", } @Article{Babaelahi:2016:Energy, author = "Mojtaba Babaelahi and Hoseyn Sayyaadi", title = "Analytical closed-form model for predicting the power and efficiency of Stirling engines based on a comprehensive numerical model and the genetic programming", journal = "Energy", volume = "98", pages = "324--339", year = "2016", ISSN = "0360-5442", DOI = "doi:10.1016/j.energy.2016.01.031", URL = "http://www.sciencedirect.com/science/article/pii/S0360544216000505", abstract = "High accuracy and simplicity in use are two important required features of thermal models of Stirling engines. A new numerical second-order thermal model was presented through the improvement of our previous modified-PSVL model in order to have an elevated accuracy. The modified-PSVL model was modified by considering a non-isothermal model for heater and cooler. Then, the model called as CPMS-Comprehensive Polytropic Model of Stirling engine, was used to simulate the GPU-3 Stirling engine, and the obtained results were compared with those of the previous thermal models as well as the experimental data. For the sack of the simplicity, the combination of the CPMS model and genetic programming was employed to generate analytical closed-form correlation. In this regards, a comprehensive data bank of results of the CPMS was constructed and exported to the GP tool and analytical expressions of the power, efficiency, and polytropic indexes were obtained. It was shown that the analytical correlations not only had the same accuracy as the CPMS model, but also, it can be simply used without difficulties of numerical models. The CPMS and its out coming analytical expressions, predicted the power and efficiency of the GPU-3 Stirling with +1.13percent and +0.45 (as difference), respectively.", keywords = "genetic algorithms, genetic programming, Closed-form model, Comprehensive numerical model of Stirling engines, CPMS model, Non-isothermal heat exchangers, Polytropic model", } @Article{BABAELAHI:2017:EML, author = "Mojtaba Babaelahi and Hamed Eshraghi", title = "Optimum analytical design of medical heat sink with convex parabolic fin including variable thermal conductivity and mass transfer", journal = "Extreme Mechanics Letters", volume = "15", pages = "83--90", year = "2017", keywords = "genetic algorithms, genetic programming, Medical heat sink, Convex parabolic, Variable thermal conductivity, Fractional, Generalizes differential transformation method (GDTM)", ISSN = "2352-4316", DOI = "doi:10.1016/j.eml.2017.06.005", URL = "http://www.sciencedirect.com/science/article/pii/S2352431616302826", abstract = "Electronic medical devices have become more powerful in recent years. These medical devices contain arrays of electronic components, which required high-performance heat sinks to prevent from overheating and damaging. For the design of high-performance medical heat sinks, the temperature distribution should be evaluated. Thus, in this paper, the Generalized Differential Transformation Method (GDTM) is applied to the medical heat sink with a convex parabolic convective fin with variable thermal conductivity and mass transfer. In the first section of the current paper, the general heat balance equation related to the medical heat sink with convex parabolic fins is derived. Because of the fractional type of derivative, the concept of GDTM is employed to derive analytical solutions. The major aim of this study, which is exclusive for this article, is to find the closed-form analytical solution for the fractional differential equation in considered heat sink for the first time. In the next step, multiobjective optimization of the considerable fin is performed for minimum volume and maximum thermal efficiency. For evaluation of optimum design at various environmental conditions, the multiobjective optimizations are performed for a wide range of environmental conditions. In the final step, the results of multiobjective optimization in various environmental conditions are applied to the genetic programming tool and suitable analytical correlations are created for optimum geometrical design", } @Article{BABAELAHI:2020:SETA, author = "Mojtaba. Babaelahi and Hamed. Jafari", title = "Analytical design and optimization of a new hybrid solar-driven micro gas turbine/stirling engine, based on exergo-enviro-economic concept", journal = "Sustainable Energy Technologies and Assessments", volume = "42", pages = "100845", year = "2020", ISSN = "2213-1388", DOI = "doi:10.1016/j.seta.2020.100845", URL = "https://www.sciencedirect.com/science/article/pii/S2213138820312728", keywords = "genetic algorithms, genetic programming, Solar, Micro gas turbine, Exergoeconomic, Environmental, Particle swarm optimization", abstract = "One of the crucial problems in the power systems is the selection of energy-efficient systems with suitable efficiency, cost, and environmental performance. Accordingly, this paper introduces a new power generation system that supplies a significant part of the required energy from solar energy and uses liquefied natural gas (LNG) fuel as an auxiliary source. To evaluation of the system, exergo-enviro-economic analysis and thermohydraulic design of are performed using Matlab code. A comparison of the governed results with the base cycle (ThermoFlex simulation) shows good improvement in exergy efficiency fuel consumption. Since the preparation of an analytical model has a practical effect on the selection of optimum configuration, an analytical model for objective functions is provided based on the exergoeconomic and environmental numerical model. For this analytical model, A large data bank from the numerical simulation results is obtained, and the artificial intelligence tool known as Genetic Programming is used for multivariate fitting. Finally, to find the optimal configuration, various optimizations (using the particle swarm optimization) have been made, and the final optimal design has been selected. The results indicated that the thermal and exergetic efficiencies in the ultimate optimum point increased about 6.252 and 8.842 percent, respectively", } @Article{babaelahi:2025:JTAC, author = "Mojtaba Babaelahi and Mohammad Kazemi", title = "Numerical-based multi-objective optimization and regression analysis using genetic programming for novel microfabricated thermoresistive calorimetric flow sensor in precise low-velocity biomedical applications", journal = "Journal of Thermal Analysis and Calorimetry", year = "2025", keywords = "genetic algorithms, genetic programming", URL = "https://link.springer.com/article/10.1007/s10973-025-14208-6", DOI = "doi:10.1007/s10973-025-14208-6", } @Article{Babanajad:2013:AiC, author = "Saeed K. Babanajad and Amir H. Gandomi and Danial {Mohammadzadeh S.} and Amir H. Alavi", title = "Numerical modeling of concrete strength under multiaxial confinement pressures using linear genetic programming", journal = "Automation in Construction", year = "2013", volume = "36", pages = "136--144", month = dec, keywords = "genetic algorithms, genetic programming, Discipulus, Multiaxial compression, Compressive strength, Ultimate strength, Linear genetic programming", ISSN = "0926-5805", URL = "http://www.sciencedirect.com/science/article/pii/S0926580513001301", DOI = "doi:10.1016/j.autcon.2013.08.016", size = "9 pages", abstract = "New numerical models are developed to predict the strength of concrete under multi-axial compression using linear genetic programming (LGP). The models are established based on a comprehensive database obtained from the literature. To verify the applicability of the derived models, they are employed to estimate the strength of parts of the test results that are not included in the modelling process. The external validation of the model is further verified using several statistical criteria. The results obtained by the proposed models are much better than those provided by several models found in the literature. The LGP-based equations are remarkably straightforward and useful for pre-design applications.", } @InCollection{Babanajad:2015:hbgpa, author = "Saeed K. Babanajad", title = "Application of Genetic Programming for Uniaxial and Multiaxial Modeling of Concrete", booktitle = "Handbook of Genetic Programming Applications", publisher = "Springer", year = "2015", editor = "Amir H. Gandomi and Amir H. Alavi and Conor Ryan", chapter = "16", pages = "399--430", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-20882-4", DOI = "doi:10.1007/978-3-319-20883-1_16", abstract = "In current chapter, an overview of recently established genetic programming based techniques for strength modelling of concrete has been presented. The comprehensive uniaxial and multiaxial strengths modelling of hardened concrete have been concentrated in this chapter as one of the main area of interests in concrete modeling for structural engineers. For this engineering case the literature has been reviewed and the most applied numerical/analytical/experimental models and national building codes have been introduced. After reviewing the artificial intelligence/machine learning based models, genetic programming based models are presented, with accent on the applicability and efficiency of each model and its suitability. The advantages and weaknesses of the aforementioned models are summarized and compared with existing numerical/analytical/experimental models and national building codes, and a few illustrative examples briefly are presented. The genetic programming based techniques are remarkably straightforward and have enabled reliable, stable, and robust tools for pre-design and design applications.", } @PhdThesis{Babanajad:thesis, author = "Saeed Karim Baba Najad Mamaghani", title = "Methods for Sensing, Analysis and Computation of Loads and Distributed Damage in Bridges", school = "College of Engineering, University of Illinois at Chicago", year = "2016", address = "USA", keywords = "genetic algorithms, genetic programming, Bridge Weigh in Motion, Damage Detection, Discrete Sensing, Fibre Optic Sensors, Distributed Sensing", URL = "https://dspace-prod.lib.uic.edu/bitstream/handle/10027/20220/Karimbabanajadmamaghani_Saeed.pdf", URL = "https://indigo.uic.edu/handle/10027/20220", URL = "https://oatd.org/oatd/search?q=Methods+for+Sensing%2C+Analysis+and+Computation+of+Loads+and+Distributed+Damage+in+Bridges&form=basic", URL = "http://hdl.handle.net/10027/20220", size = "171 pages", abstract = "The worldwide ageing of the infrastructure and the development of new technologies in the construction industry provided a need for structural health monitoring (SHM). SHM provides a tool for owners and researchers to assess the condition of a structure and monitor its behaviour under real life conditions. Road transport and the related infrastructures are clearly an integral part of the economic, political, and social development of the western world. As an example, highway bridges as a major part of infrastructures can be greatly damaged by excessively heavy vehicles or severe environmental conditions. It is therefore, important to assure that such facilities are well maintained and function properly in order to avoid major failures or the need for costly repairs. In current thesis, it is attempted to innovate techniques in order to measure the vehicles loads affecting the bridge elements as well as damage detection methods to monitor the defects along the in-service bridge structural components. Bridge Weigh-in-Motion (BWIM) is using an existing bridge to weigh trucks while they are moving at full highway speeds. A new method of BWIM has been established in order to weigh the passing trucks relying on the shear strain measurements near the bridge abutments which differs from the flexural strain based traditional systems. The shear strain are measured using the rosettes sensors installed on the webs of bridge girders to directly measure the individual axle weights of trucks passing over the bridge abutments. Two concrete slab on steel girder bridges, and a box girder prestressed concrete with different structural types, span lengths, and different sizes were instrumented for the performance verification of the proposed BWIM system. A series of truck runs were implemented in the field to calibrate and evaluate the proposed BWIM system's efficiency. In addition, current research formulated a reference-free distributed damage detection method in order to locate the defects that occur in structures under in-service operating conditions. The sensing method is developed on the basis of Brillouin scattering phenomena. It employs the dynamic distributed strain measurement data in order to sense the structural perturbations under in-service operations, i.e. bridges subjected to traffic loadings, or aircrafts during flights. The advantage of the method developed in this study is that it enables the structure to be monitored at any stage during its service life without the need for prior reference data. An experimental program was designed to investigate the feasibility of the proposed approach in detecting the locations of very small defects. Laboratory experiments were designed in order to simulate the effect of ambient conditions in bridges, especially in terms of realistic displacements, i.e. deflections occurring in highway bridges. In a following effort, a theoretical model was also investigated to analysis the strain transfer mechanism from the structure surface to the distributed optical fibre components in the presence of local defects. The main objective pertained to the accurate quantification of local defects sizes based on distributed monitoring of strains in large structural systems. The theoretical formulation simulated the strain distribution within the components of an optical fiber crossing over a single crack opening. The proposed model was formulated in a manner to quantify defects in the presence of structural vibration. Both linear and nonlinear mechanical characteristics of optical fibre components were also assumed in the formulation. The spatial resolution effect was further numerically implemented within the formulation in order to simulate the measurement configurations. An experimental program was designed for calibration as well as the validation of theoretical formulation. The experiments involved dynamic tests of a 15 meter long steel I beam with two fabricated defects with small opening displacements ranging between 50 and 550 microns.", notes = "2016-02-17 Supervisor Farhad Ansari", } @Article{Babanajad:2017:AES, author = "Saeed K. Babanajad and Amir H. Gandomi and Amir H. Alavi", title = "New prediction models for concrete ultimate strength under true-triaxial stress states: An evolutionary approach", journal = "Advances in Engineering Software", year = "2017", keywords = "genetic algorithms, genetic programming, Gene expression programming, Artificial intelligence, Triaxial, Machine learning, Computer-aided, Strength model", ISSN = "0965-9978", URL = "http://www.sciencedirect.com/science/article/pii/S096599781630566X", DOI = "doi:10.1016/j.advengsoft.2017.03.011", abstract = "The complexity associated with the in-homogeneous nature of concrete suggests the necessity of conducting more in-depth behavioral analysis of this material in terms of different loading configurations. Distinctive feature of Gene Expression Programming (GEP) has been employed to derive computer-aided prediction models for the multiaxial strength of concrete under true-triaxial loading. The proposed models correlate the concrete true-triaxial strength (sigma 1) to mix design parameters and principal stresses (sigma 2, sigma 3), needless of conducting any time-consuming laboratory experiments. A comprehensive true-triaxial database is obtained from the literature to build the proposed models, subsequently implemented for the verification purposes. External validations as well as sensitivity analysis are further carried out using several statistical criteria recommended by researchers. More, they demonstrate superior performance to the other existing empirical and analytical models. The proposed design equations can readily be used for pre-design purposes or may be used as a fast check on deterministic solutions.", } @InProceedings{Babar:2013:ICDIM, author = "Zaheer Babar and Muhammad Waqas and Zahid Halim and Muhammad Arshad Islam", booktitle = "Eighth International Conference on Digital Information Management (ICDIM 2013)", title = "Genetic Programming Based Degree Constrained Spanning Tree Extraction", year = "2013", month = sep, pages = "241--246", keywords = "genetic algorithms, genetic programming, Degree Constraint Spanning Tree, Spanning Tree, Minimum Spanning Tree, Cyclic Interchange", DOI = "doi:10.1109/ICDIM.2013.6693966", size = "6 pages", abstract = "The problem of extracting a degree constraint spanning tree deals with extraction of a spanning tree from a given graph. Where, the degree of each node is greater than or equal to a thread hold value. Genetic Programming is an evolution based strategy appropriate for optimisation problems. In this paper we propose a genetic programming based solution to the degree constraint spanning tree extraction. The individuals of the population are represented as a tree and mutation is applied as the only genetic operator for evaluation to occur. We have further tested our proposed solution on different graph and found it to be suitable for degree constraint spanning tree extraction problem.", notes = "Also known as \cite{6693966}", } @InProceedings{Baber:2009:MSG, author = "C. Baber and N. Stanton and D. Howard and Robert J. Houghton", title = "Predicting the Structure of Covert Networks using Genetic Programming, Cognitive Work Analysis and Social Network Analysis", booktitle = "NATO RTO Modelling and Simulation Group Symposium", year = "2009", editor = "J. Ruiz", number = "RTO-MP-MSG-069 AC/323(MSG-069)TP/297", pages = "Paper 15", address = "Brussels, Belgium", month = "15-16 " # oct, organisation = "NATO Science and Technology Organization", keywords = "genetic algorithms, genetic programming", isbn13 = "978-92-837-0100-2", URL = "http://ftp.rta.nato.int/public//PubFullText/RTO/MP/RTO-MP-MSG-069///MP-MSG-069-15.pdf", broken = "http://ftp.rta.nato.int/public//PubFullText/RTO/MP/RTO-MP-MSG-069///MP-MSG-069-15.doc", URL = "https://apps.dtic.mil/sti/citations/ADA568007", size = "14 pages", abstract = "A significant challenge in intelligence analysis involves knowing when a social network description is complete, i.e., when sufficient connections have been found to render the network complete. In this paper, a combination of methods is used to predict covert network structures for specific missions. The intention is to support hypothesis-generation in the Social Network Analysis of covert organisations. The project employs a four phase approach to modelling social networks, working from task descriptions rather than from contacts between individual: phase one involves the collation of intelligence covering types of mission, in terms of actors and goals; phase two involves the building of task models, based on Cognitive Work Analysis, to provide both a process model of the operation and an indication of the constraints under which the operation will be performed; phase three involves the generation of alternative networks using Genetic Programming; phase four involves the analysis of the resulting networks using social network analysis. Subsequent analysis explores the resilience of the networks, in terms of their resistance to losses of agents or tasks. The project demonstrates that it is possible to define a set of structures that can be tackled using different intervention strategies, demonstrates how patterns of social network structures can be predicted on the basis of task knowledge, and how these structures can be used to guide the gathering of intelligence and to define plausible Covert Networks.", notes = "Cited by \cite{conf/ichit/HowardC12} RTO-MP-MSG-069 - Current uses of M&S Covering Support to Operations, Human Behaviour Representation, Irregular Warfare, Defence against Terrorism and Coalition Tactical Force Integration Utilisation actuelle M&S couvrant le soutien aux operations, la representation du comportement humain, la guerre asymetrique, la defense contre le terrorisme et l'integration d'une force tactique de coalition Broken Feb 2019 https://www.cso.nato.int/pubs/rdp.asp?RDP=RTO-MP-MSG-069", } @InProceedings{baber:2002:EuroGP, title = "Evolutionary Algorithm Approach to Bilateral Negotiations", author = "Vinaysheel Baber and Rema Ananthanarayanan and Krishna Kummamuru", editor = "James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi", booktitle = "Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002", volume = "2278", series = "LNCS", pages = "202--211", publisher = "Springer-Verlag", address = "Kinsale, Ireland", publisher_address = "Berlin", month = "3-5 " # apr, year = "2002", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-43378-3", DOI = "doi:10.1007/3-540-45984-7_20", abstract = "The Internet is quickly changing the way business-to-consumer and business-to-business commerce is conducted. The technology has created an opportunity to get beyond single-issue negotiation by determining sellers' and buyers' preferences across multiple issues, thereby creating possible joint gains for all parties. We develop simple multiple issue algorithms and heuristics that could be used in electronic auctions and electronic markets. In this study, we show how a genetic algorithm based technique, coupled with a simple heuristic can achieve good results in business negotiations. The negotiations' outcomes are evaluated on two dimensions: joint utility and number of ex-changes of offers to reach a deal. The results are promising and indicate possible use of such approaches in actual electronic commerce systems.", notes = "EuroGP'2002, part of \cite{lutton:2002:GP}", } @PhdThesis{Babic:thesis, author = "Matej Babic", title = "Analiza kaljenih materialov s pomocjo fraktalne geometrije", alternate_title = "Analysis of Hardened Materials Using Fractal Geometry", school = "Racunalnistvo in Informatiko, Fakulteta za Elektrotehniko, Univerza v Mariboru", year = "2014", address = "Maribor", keywords = "genetic algorithms, genetic programming, inteligentni sistemi, algoritmi, hibridni sistemi, strojno ucenje, fraktalna geometrija, teorija grafov, topografija materiala po toplotni obdelavi, intelligent system, algorithms, hybrid system, machine learning, ANN, fractal geometry, graph theory, topography of materials after heat treatment", URL = "https://dk.um.si/IzpisGradiva.php?id=46366&lang=eng&prip=rul:10960417:d1", URL = "https://dk.um.si/Dokument.php?id=67818&lang=eng", URL = "https://core.ac.uk/download/pdf/67581852.pdf", URL = "https://www.proquest.com/docview/2194811728", size = "178 pages", abstract = "In this dissertation we study intelligent systems and the search for knowledge, computing paradigms that are useful and beneficial for the heat treatment of materials. To identify the complexity of different heat-treated samples, we used the fractal geometry method. We designed an intelligent system through which we announced topographical properties of the material after heat treatment. We have also developed a new algorithm for 3D graph visibility. With the help of the topological properties density of 3D graphs, we have built an intelligent system which can predict the topographic characteristics of the samples after heat treatment. Fractal geometry can be used to analyse complex structures that occur in the heat treatment of materials. Thus, the use of fractal geometry demonstrates the advantages of laser heat treatment techniques over the inductive, classical and the hardening furnace. Fractal geometry is a new approach, based on the characterisation of irregular microstructures, and serves as an assessment tool for determining structural properties. It can be used in the analysis of different heattreated materials. Fractal geometry is based on the idea of invariant magnification, which means that the observed image is not the same regardless of how strong the microscope is. It should be noted that the fractal dimension does not fully characterise the geometry, but is rather an indication of irregularities. Fractal geometry was used here to determine the topographical properties of hardened materials . We have introduced a new method for calculating the fractal dimension of a 3D object. With the development of laser technology in the field of heat treatment of materials there is an increased need to develop new methods with which to determine (set) better resistance of material, lower friction and better heat resistance of material. We therefore aim to build intelligent systems to increase productivity in the field of heat treatment of materials. With the help of the intelligent system we intend to show which technique of heat treatment is best. In this dissertation we present four new composite hybrid methods: * composite hybrid genetic algorithms - multiple regression - neural network-multiple regression (we call it a hybrid loop). * composite hybrid genetic algorithm - neural network - multiple regression- neural network (we call it the optimal hybrid loop). * composite hybrid genetic algorithm - neural network - multiple regression-neural network - multiple regression (we call it the cyclic hybrid). * composite hybrid genetic algorithm - multiple regression - neural network -multiple regression - neural network (we call it the optimal linear hybrid). Composite hybrid performances were slightly worse than expected, because of the shortcomings of the individual basic methods. The multiple regression method is the worst method and adversely affected the composite hybrid. The new composite hybrids give better results than existing composite hybrid systems, however. We want to improofe results of new hybrid system, thus we built new composite hybrib, hyiper hybrid. At the end of the dissertation further comments are made and a two new hybrid systems proposed which we call the spiral hybrid and optimal spiral hybrid. This method are useful when a large number of basic methods are employed. We also propose combining (pooling) the six new hybrid methods presented in the new hyper hybrids.", notes = "Solvenian https://books.google.co.uk/books/about/Hybrid_System_of_Machine_Learning_Using.html?id=dBZ3zQEACAAJ Hybrid System of Machine Learning Using Genetic Programming and Multiple Regression Supervisor: Peter Kokol", } @Article{babic:2014:MT, author = "Matej Babic and Peter Kokol and Igor Belic and Peter Panjan and Miha Kovacic and Joze Balic and Timotej Verbovsek", title = "Prediction of the hardness of hardened specimens with a neural network", journal = "Materiali in tehnologije/Materials and Technology", year = "2014", volume = "48", number = "3", pages = "409--414", month = may # "-" # jun, keywords = "ANN, fractal dimension, laser, hardening, neural network", ISSN = "1580-2949", URL = "http://mit.imt.si/Revija/", URL = "http://mit.imt.si/Revija/izvodi/mit143/babic.htm", URL = "http://mit.imt.si/Revija/izvodi/mit143/babic.pdf", size = "6 pages", abstract = "In this article we describe the methods of intelligent systems to predict the hardness of hardened specimens. We use the mathematical method of fractal geometry in laser techniques. To optimise the structure and properties of tool steel, it is necessary to take into account the effect of the self-organisation of a dissipative structure with fractal properties at a load. Fractal material science researches the relation between the parameters of fractal structures and the dissipative properties of tool steel. This paper describes an application of the fractal dimension in the robot laser hardening of specimens. By using fractal dimensions, the changes in the structure can be determined because the fractal dimension is an indicator of the complexity of the sample forms. The tool steel was hardened with different speeds and at different temperatures. The effect of the parameters of robot cells on the material was better understood by researching the fractal dimensions of the microstructures of hardened specimens. With an intelligent system the productivity of the process of laser hardening was increased because the time of the process was decreased and the topographical property of the material was increased.", notes = "Is this GP? Page 412 gives a linear regression model. UDK 621.785.616: 620.178:004.032.26 MTAEC9, 48(3)409(2014)", } @Article{babic:2014:etv, author = "Matej Babic and Peter Kokol and Igor Belic and Peter Panjan and Miha Kovacic and Joze Balic", title = "Using of genetic programming in engineering", journal = "Elektrotehniski vestnik", year = "2014", volume = "81", number = "3", pages = "143--147", month = jul, keywords = "genetic algorithms, genetic programming, engineering, complex geometry structure", ISSN = "0013-5852", URL = "http://ev.fe.uni-lj.si/3-2014/Babic.pdf", size = "5 pages", abstract = "Intelligent systems are process coupled with robotics in industrial usually settings, though they may be used as diagnostic systems connected only to passive sensors. In this paper we use a new method which combines an intelligent genetic algorithm and multiple regression to predict the hardness of hardened specimens. The hardness of a material is an important mechanical property affecting mechanical properties of materials. The Microstructures of the hardened specimens are very complex and cannot be described them with the classical Euclidian geometry. Thus, we use a new method, i.e. fractal geometry. By using the method intelligent-system, genetic programming and multiple regression, improved production the process laser-hardening increases because of the decreased time of the process and, the improved increased topographical property of the used materials. The genetic-programming modelling results show a good agreement with the measured hardness of the hardened specimens.", abstract_si = "Inteligentni sistemi naj bi se po navadi povezali skupaj z robotiko v nastavitvah industrijskih procesov, ceprav so lahko sistemi za diagnostiko povezani samo za pasivne senzorje. Vtem clanku bomo uporabili metodo, ki zdruzuje inteligentne genetske algoritme in multiplo regresijo za napoved trdote kaljenih vzorcev. Trdota materiala je pomembna mehanska lastnost, ki vpliva na mehanske lastnosti materialov. Mikrostrukture kaljenih vzorcev so zelo kompleksne in jih ne moremo opisati s klasicno evklidsko geometrijo. Zato smo uporabili novo metodo, fraktalno geometrijo. Z metodo inteligentnega sistema, genetskim programiranjem in multiplo regresijo smo povecali proizvodnjo pri laserskem kaljenju, saj smo skrajšali cas procesa in povecali topografsko lastnost materiala. Rezultati modeliranja genetskega programiranja se dobro ujemajo z izmerjenimi vrednostmi trdote kaljenih vzorcev.", notes = "http://ev.fe.uni-lj.si/online.html Journal of Electrical Engineering and Computer Science", } @Article{babic:2016:EV, author = "Matej Babic", title = "A new hybrid-system method of Machine Learning using a new method of fractal geometry and a new method of graph theory", journal = "Elektrotehniski vestnik", year = "2016", volume = "83", number = "1-2", pages = "42--46", keywords = "genetic algorithms, genetic programming, image processing, intelligent system, visibility graphs, fractal dimension", ISSN = "0013-5852", URL = "https://ev.fe.uni-lj.si/1-2-2016/Babic.pdf", URL = "https://ev.fe.uni-lj.si/online.php?vol=83", URL = "https://www.proquest.com/docview/1789072317?sourcetype=Scholarly%20Journals", size = "5 pages", abstract = "a hybrid system method to predict the volume of the robot-laser-hardened specimens when one of the parameters in the existing model cannot be measured or calculated the intelligent-system is presented. Also, we have a model of the intelligent system to predict the volume of hardened specimens developed by someone, but we can not calculate one parameter in it. Thus, we develop a new method of the hybrid intelligent system to solve this problem. We develop a hybrid of genetic programming and multiple regression. To predict the volume of hardened specimens, we use teh neural network, genetic algorithm and multiple regression. The genetic programming modelling results show a good agreement with the measured volume of hardened specimens. We analyse the SEM picture of the microstructure of robot-laser-hardened specimens with a mathematical method. In this open problem we use the graph theory and fractal geometry. Fractal dimensions are calculated using image processing of a SEM micrographs in combination with a box-counting algorithm using ImageJ software.", notes = "cites \cite{Babic:thesis} Jozef Stefan Institute, Slovenia", } @Article{babic:2017:CAI, author = "Matej Babic and Ladislav Hluchy and Peter Krammer and Branko Matovic and Ravi Kumar and Pavel Kovac", title = "New Method for Constructing a Visibility Graph-Network in {3D} Space and a New Hybrid System of Modeling", journal = "Computing and Informatics", year = "2017", volume = "36", number = "5", pages = "1107--1126", month = "19 " # dec, keywords = "genetic algorithms, genetic programming, Artificial intelligence, visibility graphs, pattern recognition, modeling, hybrid system", ISSN = "1335-9150", URL = "https://www.cai.sk/ojs/index.php/cai/issue/view/179", URL = "https://www.cai.sk/ojs/index.php/cai/article/view/2017_5_1107/856", DOI = "doi:10.4149/cai_2017_5_1107", size = "20 pages", abstract = "This paper describes a new method for constructing a visibility graph in 3D space. We use a method for predicting porosity of hardened specimens...", } @Article{Babic:2021:Remote_Sensing, author = "Matej Babic and Dusan Petrovic and Jost Sodnik and Bozo Soldo and Marko Komac and Olena Chernieva and Miha Kovacic and Matjaz Mikos and Michele Cali", title = "Modeling and Classification of Alluvial Fans with {DEMs} and Machine Learning Methods: A Case Study of {Slovenian} Torrential Fans", journal = "Remote Sensing", year = "2021", volume = "13", number = "9", article-number = "1711", month = "28 " # apr, keywords = "genetic algorithms, genetic programming, random forest, RF, support vector machine, SVM, neural network, ANN, digital elevation model, torrential fan surfaces, geomorphometric parameters, graph method, debris flows", publisher = "MDPI", ISSN = "2072-4292", URL = "https://repozitorij.uni-lj.si/IzpisGradiva.php?id=127268", URL = "https://www.mdpi.com/2072-4292/13/9/1711", DOI = "doi:10.3390/rs13091711", size = "18 pages", abstract = "Alluvial (torrential) fans, especially those created from debris-flow activity, often endanger built environments and human life. It is well known that these kinds of territories where human activities are favored are characterized by increasing instability and related hydrological risk; therefore, treating the problem of its assessment and management is becoming strongly relevant. The aim of this study was to analyse and model the geomorphological aspects and the physical processes of alluvial fans in relation to the environmental characteristics of the territory for classification and prediction purposes. The main geomorphometric parameters capable of describing complex properties, such as relative fan position depending on the neighborhood, which can affect their formation or shape, or properties delineating specific parts of fans, were identified and evaluated through digital elevation model (DEM) data. Five machine learning (ML) methods, including a hybrid Euler graph ML method, were compared to analyze the geomorphometric parameters and physical characteristics of alluvial fans. The results obtained in 14 case studies of Slovenian torrential fans, validated with data of the empirical model proposed by Bertrand et al. (2013), confirm the validity of the developed method and the possibility to identify alluvial fans that can be considered as debris-flow prone.", notes = "Faculty of Information Studies, Ljubljanska cesta 31a, SI-8000 Novo Mesto, Slovenia", } @InProceedings{Babic:2021:SpliTech, author = "Matej Babic and Branko Ster and Janez Povh and Joel J. P. C. Rodrigues", title = "A New Composite Method of Modeling Bicycle Traffic using Convolutional Neural Networks and Genetic programming", booktitle = "2021 6th International Conference on Smart and Sustainable Technologies (SpliTech)", year = "2021", month = sep, keywords = "genetic algorithms, genetic programming, ANN, GoNM", DOI = "doi:10.23919/SpliTech52315.2021.9566405", abstract = "This study proposes a new composite method of modelling bicycle traffic in the town of Novo mesto, Slovenia, using Convolutional neural networks and Genetic programming. Public passenger transport (PPT) is important for every municipality, as the current transport system faces well-known problems such as congestion, environmental impact, lack of parking areas, increased safety risks and high energy consumption. The wider Novo mesto region, with about 30000 inhabitants, is an important industrial center and is heavily dependent on urban traffic. The aim of the research is to analyze and model bicycle rentals. Convolutional neural networks and genetic programming are used to predict bicycle traffic over 35 weeks.", notes = "Also known as \cite{9566405} See also \cite{babic:2022:IJQR} Faculty of Information studies, Novo mesto, Slovenia", } @Article{BABIC:2021:PM, author = "M. Babic and G. Lesiuk and D. Marinkovic and M. Cali", title = "Evaluation of microstructural complex geometry of robot laser hardened materials through a genetic programming model", journal = "Procedia Manufacturing", volume = "55", pages = "253--259", year = "2021", note = "FAIM 2021", ISSN = "2351-9789", DOI = "doi:10.1016/j.promfg.2021.10.036", URL = "https://www.sciencedirect.com/science/article/pii/S235197892100233X", keywords = "genetic algorithms, genetic programming, Microstructure geometry, Fractal geometry, Laser beam process parameters, Forecast model, Hardened steels", abstract = "Surface-hardening process of steel materials by robot laser technologies can involve the challenge of modeling the determining process parameters through non-conventional tools in order to evaluate the quality of the heat treatment. In the current study a new method based on fractal geometry, used to determine the microstructural properties of laser hardened steels manufactured by anthropomorphic robots, is presented. The assumptions were that the microstructure of laser hardened steel can be studied as a complex structural geometry and the modeling of the analyzed complex geometries can be made through genetic programming for prediction purposes. The effect of process parameters and their joint combination on the final microstructures geometry of the heat treated steel was investigated. In particular, the influence of temperature, laser beam velocity, and impact angle were studied since they were showed in a preliminary study to be the process parameters that most significantly influenced the quality of the heat treated steel. The developed model reached a precision of the prediction equal to 98.59 percent", } @Article{babic:2022:IJQR, author = "Matej Babic and Cristiano Fragassa and Dragan Marinkovic and Janez Povh", title = "Machine Learning Tools in the Analyze of a Bike Sharing", journal = "International Journal for Quality Research", year = "2022", volume = "16", number = "2", pages = "375--394", keywords = "genetic algorithms, genetic programming, GoNM, Transportation Systems Engineering, bicycle, Cycles, Bike-Sharing System (PBS), Artificial Intelligence (AI), Machine Learning (ML), Hybrid Intelligent Systems, Weather Conditions", ISSN = "1800-6450", URL = "http://ijqr.net/paper.php?id=989", URL = "http://ijqr.net/journal/v16-n2/4.pdf", DOI = "doi:10.24874/IJQR16.02-04", size = "20 pages", abstract = "Advanced models, based on artificial intelligence and machine learning, are used here to analyze a bike-sharing system. The specific target was to predict the number of rented bikes in the Nova Mesto, Slovenia public bike share scheme. For this purpose, the topological properties of the transport network were determined and related to the weather conditions. Pajek software was used and the system behavior during a 30-week period was investigated. Open questions were, for instance: how many bikes are shared in different weather conditions? How the network topology impacts the bike sharing system? By providing a reasonable answer to these and similar questions, several accurate ways of modeling the bike sharing system which account for both topological properties and weather conditions, were developed and used for its optimization.", notes = "02.06.2021 seminar: https://www.fpp.uni-lj.si/en/research/kappra-coffee-talks-on-research/2022053112194477/kappra-no-24 http://ijqr.net/", } @Article{babic:2022:AS, author = "Matej Babic and Dragan Marinkovic and Marco Bonfanti and Michele Cali", title = "Complexity Modeling of {Steel-Laser-Hardened} Surface Microstructures", journal = "Applied Sciences", year = "2022", volume = "12", number = "5", keywords = "genetic algorithms, genetic programming", ISSN = "2076-3417", URL = "https://www.mdpi.com/2076-3417/12/5/2458", DOI = "doi:10.3390/app12052458", abstract = "Nowadays, laser hardening is a consolidated process in many industrial sectors. One of the most interesting aspects to be considered when treating the surface-hardening process in steel materials by means of laser devices is undoubtedly the evaluation of the heat treatment quality and surface finish. In the present study, an innovative method based on fractal geometry was proposed to evaluate the quality of surface-steel-laser-hardened treatment. A suitable genetic programming study of SEM images (1280 × 950 pixels) was developed in order to predict the effect of the main laser process parameters on the microstructural geometry, assuming the microstructure of laser-hardened steel to be of a structurally complex geometrical nature. Specimens hardened by anthropomorphic laser robots were studied to determine an accurate measure of the process parameters investigated (surface temperature, laser beam velocity, laser beam impact angle). In the range of variation studied for these parameters, the genetic programming model obtained was in line with the complexity index calculated following the fractal theory. In particular, a percentage error less than 1percent was calculated. Finally, a preliminary study of the surface roughness was carried out, resulting in its strong correlation with complex surface microstructures. Three-dimensional voxel maps that reproduce the surface roughness were developed by automating a routine in Python virtual environment.", notes = "also known as \cite{app12052458}", } @Article{Babic:2022:FF, author = "Matej Babic and Dragan Marinkovic and Miha Kovacic and Branko Ster and Michele Cali", title = "A New Method of Quantifying the Complexity of Fractal Networks", journal = "Fractal and Fractional", year = "2022", volume = "6", number = "6", pages = "article number 282", keywords = "genetic algorithms, genetic programming", ISSN = "2504-3110", URL = "https://www.mdpi.com/2504-3110/6/6/282", DOI = "doi:10.3390/fractalfract6060282", abstract = "There is a large body of research devoted to identifying the complexity of structures in networks. In the context of network theory, a complex network is a graph with nontrivial topological features; features that do not occur in simple networks, such as lattices or random graphs, but often occur in graphs modeling real systems. The study of complex networks is a young and active area of scientific research inspired largely by the empirical study of real-world networks, such as computer networks and logistic transport networks. Transport is of great importance for the economic and cultural cooperation of any country with other countries, the strengthening and development of the economic management system, and in solving social and economic problems. Provision of the territory with a well-developed transport system is one of the factors for attracting population and production, serving as an important advantage for locating productive forces and providing an integration effect. we introduce a new method for quantifying the complexity of a network based on presenting the nodes of the network in Cartesian coordinates, converting to polar coordinates, and calculating the fractal dimension using the ReScaled ranged (R/S) method. Our results suggest that this approach can be used to determine complexity for any type of network that has fixed nodes, and it presents an application of this method in the public transport system.", notes = "Also known as \cite{fractalfract6060282}", } @Article{babic:2023:FaF, author = "Matej Babic and Dragan Marinkovic", title = "A New Approach to Determining the Network Fractality with Application to {Robot-Laser-Hardened} Surfaces of Materials", journal = "Fractal and Fractional", year = "2023", volume = "7", number = "10", pages = "Article No. 710", keywords = "genetic algorithms, genetic programming", ISSN = "2504-3110", URL = "https://www.mdpi.com/2504-3110/7/10/710", DOI = "doi:10.3390/fractalfract7100710", abstract = "A new method to determine a fractal network in chaotic systems is presented together with its application to the microstructure recognition of robot-laser-hardened (RLH) steels under various angles of a laser beam. The method is based on fractal geometry. An experimental investigation was conducted by investigating the effect of several process parameters on the final microstructures of material that has been heat-treated. The influences of the surface temperature, laser speed, and different orientation angles of the laser beam on the microstructural geometry of the treated surfaces were considered. The fractal network of the microstructures of robot-laser-hardened specimens was used to describe how the geometry was changed during the heat treatment of materials. In order to predict the fractal network of robot-laser-hardened specimens, we used a method based on intelligent systems, namely genetic programming (GP) and a convolutional neural network (CNN). The proposed GP model achieved a prediction accuracy of 98.4percent, while the proposed CNN model reached 96.5percent. The performed analyses demonstrate that the angles of the robot laser cell have a noticeable effect on the final microstructures. The specimen laser-hardened under the conditions of 4 mm/s, 1000 ?C, and an impact angle of the laser beam equal to 75? presented the maximum fractal network. The minimum fractal network was observed for the specimen before the robot-laser-hardening process.", notes = "also known as \cite{fractalfract7100710}", } @Article{Babic:2024:sv-jme, author = "Matej Babic and Miha Kovacic and Cristiano Fragassa and Roman Sturm", title = "Selective Laser Melting: A Novel Method for Surface Roughness Analysis", journal = "Strojniski vestnik - Journal of Mechanical Engineering", year = "2024", volume = "70", number = "7-8", pages = "313--324", keywords = "genetic algorithms, genetic programming, additive manufacturing, selective laser melting, surface roughness, fractal geometry, network theory, ANN, kNN, SVM", ISSN = "0039-2480", URL = "https://www.sv-jme.eu/?ns_articles_pdf=/ns_articles/files/ojs30/1009/66cc2d6717e58.pdf&id=7068", DOI = "doi:10.5545/sv-jme.2024.1009", size = "12 pages", abstract = "The present study introduces a novel approach to analyse the surface roughness of metal parts made by 3D selective laser melting (SLM). This technology, known for its ability to efficiently produce functional prototypes and limited-run series, is particularly effective when surface conditions directly meet usage requirements. Thus, the suitability of surfaces is a critical factor, emphasising the importance of new methods for predicting their quality. Here fractal geometry and network theory are integrated to delve into the complexities of SLM-produced surfaces, while machine learning and pattern recognition concepts are employed to evaluate the surface roughness. Specifically, genetic programming, artificial neural networks, support vector machine, random forest, k-nearest neighbors are compared in terms of accuracy demonstrating that only the first method provided valid estimation due to the presence of very little training data. Experimental work with EOS Maraging Steel MS1 and an EOS M 290 3D printer validates the method's practicality and effectiveness. Then, the research offers a fresh perspective in surface analysis and has significant implications for quality control in additive manufacturing, potentially enhancing the precision and efficiency of 3D metal printing.", notes = "sv-jme tel:+386 1 4771 137 Faculty of Information Studies, Slovenia", } @InProceedings{Babiker:2025:LT, author = "Safia Babiker and Jouri Jadalhaq and Jana Jadalhaq and Enfel Barkat", title = "Development of an Intelligent {GUI} with {ChatGPT} for Efficient and Accurate Transformer Fault Diagnosis Using {DGA}, Duval Triangle Method, and Machine Learning Automation", booktitle = "2025 22nd International Learning and Technology Conference (L\&T)", year = "2025", volume = "22", pages = "263--268", month = jan, keywords = "genetic algorithms, genetic programming, Gases, Accuracy, Oil insulation, Predictive models, Transformers, Chatbots, Minerals, Maintenance, Graphical user interfaces, Predictive maintenance, Transformer fault diagnosis, Dissolved Gas Analysis, Duval Triangle, Machine Learning, TPOTClassifier, Gradio, Artificial Intelligence, GUI, ChatGPT Integration", ISSN = "2996-2331", DOI = "doi:10.1109/LT64002.2025.10941322", abstract = "This study presents an intelligent graphical user interface (GUI) for transformer fault diagnosis, using Dissolved Gas Analysis (DGA), the Duval Triangle method, and automated machine learning model selection. Key gases-Methane (CH4), Ethylene (C2 H4), and Acetylene (C2 H2)-are analysed to classify fault types, including partial discharges and thermal/electrical faults, through gas concentration ratios. The system leverages TPOT's model selection capabilities, with the Multi-Layer Perceptron (MLP) emerging as the optimal classifier, achieving over 95percent accuracy. The GUI, developed using Gradio, enables CSV uploads for batch processing and real-time fault classification, complemented by ChatGPT-generated explanations and maintenance suggestions. A case study on mineral oil-based transformers demonstrates the tool's high diagnostic accuracy, user-friendly design, and potential for expanded applications in predictive maintenance.", notes = "Also known as \cite{10941322}", } @InProceedings{babovic:1994:camh, author = "Vladan Babovic and A. W. Minns", title = "Use of computational adaptive methodologies in hydroinformatics", booktitle = "Proceedings of the first international conference on hydroinformatics, Delft, Netherlands", year = "1994", editor = "A. Verwey and A. W. Minns and V. Babovic and C. Maksimovic", pages = "201--210", publisher_address = "P. O. Box 1675, Rotterdam, Netherlands", month = "19--23 " # sep, publisher = "A. A. Balkema", keywords = "genetic algorithms, genetic programming", ISBN = "90-5410-512-7", URL = "http://www.amazon.co.uk/Hydroinformatics-Proceedings-International-Conference-Netherlands/dp/9054105127", abstract = "Summaries a study of the performance of artificial neural networks and GP compared to an empirically-based method using a problem of salt intrusion as an example.", notes = "Does not present clear winner (ANN, GP or traditional) upto reader to choose approriate to their problem. IHE-Delft, The Netherlands ", } @InProceedings{babovic:1995:gmibed, author = "Vladan Babovic", title = "Genetic Model Induction Based on Experimental Data", booktitle = "Proceedings of the XXVIth Congress of International Association for Hydraulics Research", year = "1995", editor = "J. Gardiner", address = "London, UK", month = "11--15 " # sep, organisation = "International Association of Hydraulic Research", publisher = "Thomas Telford Ltd", keywords = "genetic algorithms, genetic programming", ISBN = "0-7277-2059-7", URL = "http://www.amazon.co.uk/Hydra-2000-Development-Proceedings-International/dp/0727720597/ref=sr_1_4?s=books&ie=UTF8&qid=1324144161&sr=1-4", broken = "http://www.iahr.net/e-shop/store/viewItem.asp?idProduct=91", abstract = "GP used to perform an analysis of sediment transport data and to induce relationshop between bed concentration of suspended sediment and the hydraulic conditions. GP results similar accuracy to traditional techniques. IHE-Delft, The Netherlands", notes = " ", } @PhdThesis{babovic:thesis, author = "Vladan Babovic", title = "Emergence, Evolution, Intelligence: Hydroinformatics", school = "International Institute for Infrastructural, Hydraulic and Environmental Engineering and Technical University Delft", year = "1996", address = "The Netherlands", month = "20 " # mar, note = "Published by A. A. Balkema Publishers", keywords = "genetic algorithms, genetic programming", ISBN = "90-5410-404-X", URL = "http://repository.tudelft.nl/view/ir/uuid%3A58c50efe-4a6a-40b4-8c60-2b81d629b49c/", URL = "http://repository.tudelft.nl/assets/uuid:58c50efe-4a6a-40b4-8c60-2b81d629b49c/EMERGENCE__EVOLUTION__INTELLIGENCE_HYDROINFORMATICS.PDF", size = "348 pages", abstract = "The computer-controlled operating environments of such facilities as automated factories, nuclear power plants, telecommunication centres and space stations are continually becoming more complex.The situation is similar, if not even more apparent and urgent, in the case of water. Water is not only mankind's most valuable natural resource, but one which is in increasingly limited supply. The fresh water is the vita! natural resource which supports all environmental activities, that is, natura! economy, and all human socio-economic activities, that is, the artificial economy. The pressure for a sustainable control and exploration of water and thus for the peaceful co-existence of human- and hydro-economies, is not only a human, socio-economic pressure, but it is the question of life and death! Hydroinformatics - the nascent technology concerned with the flow of information related to the flow of fluids and all that they convey - is probably the best possible answer yet proposed to the problem of the control of the waters, the very arteries and veins of the biosphere. This work addresses some of the central issues within hydroinformatics paradigm. It focuses on ttie analysis of decentralised and distributed computation, as well as the issues of design of individual computatiorial agents using evolutionary algorithms.", notes = "Promotor: Abbott, M.B. See also \cite{babovic:book}", } @Book{babovic:book, author = "Vladan Babovic", title = "Emergence, evolution, intelligence; Hydroinformatics - A study of distributed and decentralised computing using intelligent agents", publisher = "A. A. Balkema Publishers", year = "1996", address = "Rotterdam, Holland", keywords = "genetic algorithms, genetic programming", ISBN-13 = "978-90-5410-404-9", ISBN = "90-5410-404-X", URL = "https://www.amazon.com/Hydroinformatics-Emergence-Evolution-Intelligence-Thesis/dp/905410404X/ref=sr_1_1/165-1740647-7487049?s=books&ie=UTF8&qid=1477940894&sr=1-1&keywords=9789054104049", abstract = "The computer controlled operating environments of such facilities as automated factories, nuclear power plants, telecommunication centres and space stations are continually becoming more complex. The situation is similar, if not even more apparent and urgent, in the case of water. Water is not only mankind's most valuable natural resource, but one which is in increasingly limited supply. The fresh water is the vital natural resource which supports all environmental activities, that is, natural economy, and all human socio-economic activities, that is, the artificial economy. The pressure for a sustainable control and exploration of water and thus for the peaceful co-existence of human- & hydro-economies is not only a human, socio-economic pressure, but it is the question of life and death. Hydroinformatics - the nascent technology concerned with the flow of information related to the flow of fluids and all that they convey - is probably the best possible answer yet proposed to the problem of the control of the waters, the very arteries and veins of the biosphere.", notes = "publication of \cite{babovic:thesis}", size = "344 pages", } @InCollection{babovic:1996:wmbAI, author = "Vladan Babovic", title = "Can water resources management benefit from artificial intelligence?", booktitle = "Computation Fluid Dynamics: Bunte Bilder in der Praxis", publisher = "Meinz Verlag", year = "1996", editor = "J. Kongeter", pages = "337--358", address = "Aachen, Germany", keywords = "genetic algorithms, genetic programming", URL = "http://www.dwa.de/dwa/sitemapping.nsf/literaturvorschau?openform&bestandsnr=36547", notes = "26. IWASA International Wasserbau-Symposium Aachen 1995/96 ", } @Article{babovic:1997:eehd1, author = "Vladan Babovic and Michael B. Abbott", title = "The evolution of equation from hydraulic data, Part {I}: Theory", journal = "Journal of Hydraulic Research", year = "1997", volume = "35", number = "3", pages = "397--410", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1080/00221689709498420", size = "14 pages", abstract = "Even as hydroinformatics continues to elaborate more advanced operational tools, languages and environments for engineering and management practice, it necessarily also promotes a number of concepts and methodologies that are eminently applicable within the more traditional areas of hydraulic research. Among the many new possibilities thereby introduced, that of evolving equations from hydraulic data using evolutionary algorithms has a particularly wide range of applications. The present paper is in two parts, the first of which introduces the subject and outlines its theory, while the second is given over to four representative applications and to some of the most immediate lessons that may be drawn from these. The first of the applications is derived from a hydrologie model but provides equations with purely hydraulic interpretations. The second, taken from sediment transport studies, raises the question of ambiguity in the identification of 'thresholds' in physical processes. It also provides a means for analysing the significance of variables and indicates the need, or otherwise, for introducing further variables. A third example, based upon physical observations of salt water intrusion in estuaries, introduces the application of the present methods to accelerating prediction processes, while the fourth example extends this kind of application to cover numerically generated data, in this case appertaining to the case of flow resistance in the presence of vegetation.", notes = "See also \cite{babovic:1997:eehd2}", } @Article{babovic:1997:eehd2, author = "Vladan Babovic and Michael B. Abbott", title = "The evolution of equation from hydraulic data, Part II: Applications", journal = "Journal of Hydraulic Research", year = "1997", volume = "35", number = "3", pages = "411--430", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1080/00221689709498421", size = "20 pages", abstract = "This second part of the paper \cite{babovic:1997:eehd1} is given over to describing four representative applications and to some of the most immediate lessons that may be drawn from these. The first of the applications is derived from a hydrologic model but provides equations with purely hydraulic interpretations. The second, taken from sediment transport studies, raises the question of ambiguity in the identification of 'thresholds' in physical processes. It also provides a means for analysing the significance of variables and indicates the need, or otherwise, for introducing further variables. A third example, based upon physical observations of salt water intrusion in estuaries, introduces the application of the present methods to accelerating prediction processes, while the fourth example extends this kind of application to cover numerically-generated data, in this case appertaining to the case of flow resistance in the presence of vegetation. In conclusion, this work is set within the context of other developments, such as those of data mining and knowledge discovery generally", } @InCollection{babovic:1997:mfnls, author = "Vladan Babovic", title = "On the Modelling and Forecasting of Non-linear Systems", booktitle = "Operational Water Management: Proceedings of the European Water Resources Association Conference, Copenhagen, Denmark, 3-6 September 1997", publisher = "Balkema", year = "1997", editor = "J. C. Refsgaard and E. A. Karalis", pages = "195--202", address = "Rotterdam", keywords = "genetic algorithms, genetic programming", ISBN = "90-5410-897-5", URL = "http://www.amazon.co.uk/gp/search?index=books&linkCode=qs&keywords=9054108975", size = "8 pages", } @InProceedings{babovic:1998:stdlkm, author = "V. Babovic", title = "Sediment transport data - Large knowledge mine", booktitle = "Proceedings of the Third International Conference on Hydroscience and Engineering", year = "1998", address = "Cottbus, Germany", keywords = "genetic algorithms, genetic programming", } @InProceedings{babovic:1998:dmtsmf, author = "V. Babovic", title = "A data mining approach to time series modelling and forecasting", booktitle = "Proceeding of the Third International Conference on Hydroinformatics", year = "1998", editor = "Babovic and Larsen", pages = "847--856", address = "Copenhagen, Denmark", publisher_address = "Rotterdam", publisher = "Balkema", keywords = "genetic algorithms, genetic programming, Vltava River system, flood control and protection of Prague, artificial neural networks", ISBN = "90-5410-983-1", notes = "Hydroinformatics'98", } @InProceedings{babovic:1998:mstGP, author = "Vladan Babovic", title = "Mining sediment transport data with genetic programming", booktitle = "Proceedings of the First International Conference on New Information Technologies for Decision Making in Civil Engineering", year = "1998", pages = "875--886", address = "Montreal, Canada", month = "11-13 " # oct, keywords = "genetic algorithms, genetic programming", } @InProceedings{babovic:1999:cskd-veg, author = "Vladan Babovic and Maarten Keijzer", title = "Computer supported knowledge discovery - A case study in flow resistance induced by vegetation", booktitle = "Proceedings of the XXVIII Congress of International Association for Hydraulic Research", year = "1999", address = "Graz, Austria", month = "22-27 " # aug, keywords = "genetic algorithms, genetic programming", URL = "http://www.iahr.org/membersonly/grazproceedings99/pdf/C021.pdf", size = "7 pages", abstract = "Data Mining and Knowledge Discovery aims at providing tools to facilitate the conversion of data to a better understanding of processes that generated or produced those data. We call this the mining of data for knowledge. Data mining extracts patterns from data. It creates models from data, by using for example, genetic programming, polynomial or artificial neural networks, or even support vector machines. These new models, combined with the understanding of the physical processes - the theory - can result in an improved understanding and novel formulations of physical laws and an improved predictive capability. The present paper describes some of the very first efforts under the D2K (Data to Knowledge) Research Project currently conducted at Danish Hydraulic Institute with a support from the Danish Technical Research Council (STVF). The paper firstly outlines elementary data mining principles, particularly when applied to analysis of scientific data. In the second half of the contribution, results obtained through analysis of the data related to the additional resistance to the flow induced by flexible vegetation are presented. The data are analysed by the means of genetic programming (GP). Induced formulations and discussed in terms of accuracy and physical interpretability.", } @InProceedings{babovic:1999:d2k, author = "V. Babovic and M. Keijzer", title = "Data to knowledge - The new scientific paradigm", booktitle = "Water Industry Systems", year = "1999", editor = "Dragan Savic and Godfrey Walters", pages = "3--14", address = "Exeter, United Kingdom", month = "13-15 " # sep, publisher = "Research Studies Pr Ltd", keywords = "genetic algorithms, genetic programming", isbn13 = "9780863802485", URL = "http://bookweb.kinokuniya.co.jp/htmy/0863802486.html", notes = "CCWI-99", } @InProceedings{me15, author = "Vladan Babovic and Maarten Keijzer", title = "Evolutionary algorithms approach to induction of differential equations", booktitle = "Proceedings of the Fourth International Conference on Hydroinformatics", address = "Iowa City, USA", year = "2000", keywords = "genetic algorithms, genetic programming", month = jul # " 23-27", organisation = "IAHR/IWA/IAHS Committee on Hydroinformatics", publisher = "International Association for Hydro-Environment Engineering and Research", URL = "http://members.iahr.org/core/orders/product.aspx?catid=3&prodid=47", } @Article{babovic:1999:td2ksed, author = "Vladan Babovic", title = "Data Mining and Knowledge Discovery in Sediment Transport", journal = "Computer-Aided Civil and Infrastructure Engineering", year = "2000", volume = "15", number = "5", pages = "383--389", month = sep, keywords = "genetic algorithms, genetic programming", ISSN = "1093-9687", DOI = "doi:10.1111/0885-9507.00202", size = "7 pages", abstract = "The means for data collection have never been as advanced as they are today. Moreover, the numerical models we use today have never been so advanced. Feeding and calibrating models against collected measurements, however, represents only a one-way flow: from measurements to the model. The observations of the system can be analyzed further in the search for the information they encode. Such automated search for models accurately describing data constitutes a new direction that can be identified as that of data mining. It can be expected that in the years to come we shall concentrate our efforts more and more on the analysis of the data we acquire from natural or artificial sources and that we shall mine for knowledge from the data so acquired. Data mining and knowledge discovery aim at providing tools to facilitate the conversion of data into a number of forms, such as equations, that provide a better understanding of the process generating or producing these data. These new models combined with the already available understanding of the physical processes -- the theory -- result in an improved understanding and novel formulations of physical laws and improved predictive capability. This article describes the data mining process in general, as well as an application of a data mining technique in the domain of sediment transport. Data related to the concentration of suspended sediment near a bed are analyzed by the means of genetic programming. Machine-induced relationships are compared against formulations proposed by human experts and are discussed in terms of accuracy and physical interpretability.", notes = "Article first published online: 17 DEC 2002", } @Article{babovic:1999:GPmie, author = "Vladan Babovic and Maarten Keijzer", title = "Genetic programming as a model induction engine", journal = "Journal of Hydroinformatics", year = "2000", volume = "1", number = "1", pages = "35--60", month = jan, keywords = "genetic algorithms, genetic programming, data mining, knowledge discovery", ISSN = "1464-7141", URL = "http://jh.iwaponline.com/content/2/1/35", DOI = "doi:10.2166/hydro.2000.0004", broken = "http://www.iwaponline.com/jh/002/jh0020035.htm", size = "26 pages", abstract = "Present day instrumentation networks already provide immense quantities of data, very little of which provides any insights into the basic physical processes that are occurring in the measured medium. This is to say that the data by itself contributes little to the knowledge of such processes. Data mining and knowledge discovery aim to change this situation by providing technologies that will greatly facilitate the mining of data for knowledge. In this new setting the role of a human expert is to provide domain knowledge, interpret models suggested by the computer and devise further experiments that will provide even better data coverage. Clearly, there is an enormous amount of knowledge and understanding of physical processes that should not be just thrown away. Consequently, we strongly believe that the most appropriate way forward is to combine the best of the two approaches: theory-driven, understanding-rich with data-driven discovery process. This paper describes a particular knowledge discovery algorithm Genetic Programming (GP). Additionally, an augmented version of GP - dimensionally aware GP - which is arguably more useful in the process of scientific discovery is described in great detail. Finally, the paper concludes with an application of dimensionally aware GP to a problem of induction of an empirical relationship describing the additional resistance to flow induced by flexible vegetation.", notes = "dimensionally aware GP. Additional river water flow resistance caused by flexible vegetation closure and strong typing (STGP). dimensionally aware brood selection. Kutija-Hong model.", } @InCollection{Babovic:2000:IAHR, author = "Vladan Babovic and H. Bergmann", title = "On Computer-Aided Discovery of Knowledge in Hydraulic Engineering", booktitle = "Advances in Hydraulic Research and Engineering", publisher = "Technical University Graz", year = "2000", editor = "H. Bergmann", address = "Graz", keywords = "genetic algorithms, genetic programming", } @InCollection{me25, author = "Vladan Babovic and Maarten Keijzer", title = "On the introduction of declarative bias in knowledge discovery computer systems", booktitle = "New paradigms in river and estuarine management", editor = "Peter Goodwin", publisher = "Kluwer", year = "2001", keywords = "genetic algorithms, genetic programming", notes = "Peter Goodwin gives a short description of the workshop J. Hydraul. Eng. 127, 792 (2001); http://dx.doi.org/10.1061/(ASCE)0733-9429(2001)127:10(792) (2 pages) Workshop description https://www.lib.uidaho.edu/digital/uinews/item/ui-assembles-international-experts-on-river-issues-for-boise-short-course-and-nato-research-workshop.html", } @InProceedings{me27, author = "Vladan Babovic and Maarten Keijzer and David {Rodriguez Aguilera} and Joe Harrington", title = "An evolutionary approach to knowledge induction: Genetic Programming in Hydraulic Engineering", booktitle = "Proceedings of the World Water and Environmental Resources Congress", year = "2001", editor = "Don Phelps and Gerald Sehlke", volume = "111", pages = "64--64", address = "Orlando, Florida, USA", month = "20-24 " # may, publisher = "ASCE", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.vu.nl/~mkeijzer/publications/ASCE_paper.pdf", broken = "http://link.aip.org/link/?ASC/111/64/1", DOI = "doi:10.1061/40569(2001)64", size = "10 pages", abstract = "The process of scientific discovery has long been viewed as the pinnacle of creative thought. Thus, to many people, including some scientists themselves is seems unlikely candidate for automation by computer. However, over the past two decades researchers in AI have repeatedly questioned this attitude. The paper describes a specific evolutionary algorithm technique, genetic programming, within a scientific discovery framework, as well as its application on real world data.", notes = "World Water Congress 2001 number = 40569", } @Article{me24, author = "Vladan Babovic and Jean-Philippe Drecourt and Maarten Keijzer and Peter Friis Hansen", title = "Modelling of water supply assets: a data mining approach", journal = "Urban Water", year = "2002", volume = "4", number = "4", pages = "401--414", publisher = "Elsevier", URL = "http://www.sciencedirect.com/science/article/B6VR2-4718F0J-1/2/e361659261f99d438f8f2207f67eedf8", keywords = "genetic algorithms, genetic programming", } @Article{NordicHy, author = "Vladan Babovic and Maarten Keijzer", title = "Rainfall Runoff Modelling based on Genetic Programming", journal = "Nordic Hydrology", year = "2002", volume = "33", number = "5", pages = "331--346", month = oct, keywords = "genetic algorithms, genetic programming", ISSN = "0029-1277", URL = "http://www.iwaponline.com/nh/033/0331/0330331.pdf", DOI = "doi:10.2166/nh.2002.0012", size = "16 pages", abstract = "The runoff formation process is believed to be highly non-linear, time varying, spatially distributed, and not easily described by simple models. Considerable time and effort has been directed to model this process, and many hydrologic models have been built specifically for this purpose. All of them, however, require significant amounts of data for their respective calibration and validation. Using physical models raises issues of collecting the appropriate data with sufficient accuracy. In most cases it is difficult to collect all the data necessary for such a model. By using data driven models such as genetic programming (GP), one can attempt to model runoff on the basis of available hydrometeorological data. This work addresses use of genetic programming for creating rainfall-runoff models on the basis of data alone, as well as in combination with conceptual models (i.e taking advantage of knowledge about the problem domain).", notes = "2021 journal called Hydrology Research", } @Article{Babovic:2005:HP, author = "Vladan Babovic", title = "Data mining in hydrology", journal = "Hydrological Processes", year = "2005", volume = "19", number = "7", pages = "1511--1515", month = "30 " # apr, keywords = "genetic algorithms, genetic programming", ISSN = "1099-1085", DOI = "doi:10.1002/hyp.5862", size = "5 pages", abstract = "Present-day instrumentation networks already provide immense quantities of data, very little of which provide any insight into the basic physical phenomena that are occurring in the medium measured. In order to exploit fully the information contained in the data, scientists are developing a suite of techniques to 'mine the knowledge' from data.", notes = "Invited Commentary", } @InCollection{Babovic:2006:, author = "Vladan Babovic and Maarten Keijzer", title = "Rainfall-Runoff Modeling Based on Genetic Programming", booktitle = "Encyclopedia of Hydrological Sciences", publisher = "Wiley", year = "2006", editor = "Malcolm G. Anderson and Keith Beven and et al.", month = "15 " # apr, keywords = "genetic algorithms, genetic programming, Hydroinformatics, symbolic regression, empirical equations, rainfall-runoff", isbn13 = "9780470848944", URL = "http://onlinelibrary.wiley.com/doi/10.1002/0470848944.hsa017/abstract", DOI = "doi:10.1002/0470848944.hsa017", abstract = "The runoff formation process is believed to be highly nonlinear, time varying, spatially distributed, and not easily described by simple models. Considerable time and effort has been directed to model this process, and many hydrologic models have been built specifically for this purpose. All of them, however, require significant amounts of data for their respective calibration and validation. Using physical models raises issues of collecting the appropriate data with sufficient accuracy. In most cases, it is difficult to collect all the data necessary for such a model. By using data-driven models such as genetic programming (GP), one can attempt to model runoff on the basis of available hydrometeorological data. This work addresses the use of GP for creating rainfall-runoff (R-R) models both on the basis of data alone, as well as in combination with conceptual models (i.e taking advantage of knowledge about the problem domain).", } @InProceedings{Babovic:2007:NMHS, author = "Vladan Babovic", title = "Data-Driven Knowledge Discovery: Four Roads to Vegetation-Induced Roughness Formulae", booktitle = "Numerical Modelling of Hydrodynamics for Water Resources: Proceedings of the International Workshop on Numerical Modelling of Hydrodynamic Systems", year = "2007", editor = "Pilar Garcia Navarro and Enrique Playan", pages = "67--76", address = "Zaragoza, Spain", month = "18-21 " # jun, publisher = "Taylor \& Franics, Balkema", keywords = "genetic algorithms, genetic programming", ISBN = "0-415-44056-4", broken = "http://www.docstoc.com/docs/36112150/Numerical-Modelling-of-Hydrodynamics-for-Water-Resources", URL = "http://www.amazon.com/Numerical-Modelling-Hydrodynamics-Water-Resources/dp/0415440564/ref=cm_cr_pr_pb_t", notes = "http://www.unizar.es/nmhs/programme/programme.htm published 2008?", } @Article{Babovic:2009:JH, author = "Vladan Babovic", title = "Introducing knowledge into learning based on genetic programming", journal = "Journal of Hydroinformatics", year = "2009", volume = "11", number = "3-4", pages = "181--193", keywords = "genetic algorithms, genetic programming, empirical equations, hydraulics, sediment transport, strong typing, symbolic regression, units of measurement", ISSN = "1464-7141", URL = "http://www.iwaponline.com/jh/011/0181/0110181.pdf", DOI = "doi:10.2166/hydro.2009.041", size = "13 pages", abstract = "This work examines various methods for creating empirical equations on the basis of data while taking advantage of knowledge about the problem domain. It is demonstrated that the use of high level concepts aid in evolving equations that are easier to interpret by domain specialists. The application of the approach to real-world problems reveals that the use of such concepts results in equations with performance equal or superior to that of human experts. Finally, it is argued that the algorithm is best used as a hypothesis generator assisting scientists in the discovery process.", } @InCollection{Babovic:2010:ECinH, author = "Vladan Babovic and Raghuraj Rao", title = "Evolutionary Computing in Hydrology", booktitle = "Advances in Data-Based Approaches for Hydrologic Modeling and Forecasting", publisher = "World Scientific Publishing Co.", year = "2010", editor = "Bellie Sivakumar and Ronny Berndtsson", chapter = "7", pages = "347--369", address = "Singapore", keywords = "genetic algorithms, genetic programming", ISBN = "981-4307-97-1", URL = "http://ebooks.worldscinet.com/ISBN/9789814307987/9789814307987_0007.html", DOI = "doi:10.1142/9789814307987_0007", abstract = "Many hydrologic processes are believed to be highly complex, nonlinear, time-varying, and spatially distributed. Hence, the governing mechanisms are not easily described by simple models. With unprecedented growth in instrumentation technology, recent investigations in hydrology are supported with immense quantities of data. In order to take full advantage of the information contained in such data, scientists are increasingly relying on a suite of data-driven techniques to understand the complex hydrologic processes. Evolutionary computing (EC) techniques, with a host of optimisation and modelling tools, can contribute significantly to achieve the objectives of this knowledge-discovery exercise in hydrology. This chapter discusses the utility of these EC techniques in attempting data analysis and modeling problems associated with hydrologic systems. It introduces the concept and working principle of EC techniques in general and reviews their applications to different domains of hydrology. The study also illustrates different case studies of genetic programming (GP) technique as a modelling, data assimilation, and model emulation tool", notes = "http://www.worldscibooks.com/environsci/7783.html", } @Article{Babu:2007:EL, author = "B. V. Babu and S. Karthik", title = "Genetic Programming for Symbolic Regression of Chemical Process Systems", journal = "Engineering Letters", volume = "14", number = "2", year = "2007", pages = "42--55", month = jun, publisher = "International Association of Engineers", keywords = "genetic algorithms, genetic programming, GPLAB", ISSN = "1816-0948", oai = "oai:CiteSeerXPSU:10.1.1.148.8378", URL = "https://www.engineeringletters.com/issues_v14/issue_2/index.html", URL = "http://www.engineeringletters.com/issues_v14/issue_2/EL_14_2_6.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.148.8378", size = "14 pages", abstract = "The novel evolutionary artificial intelligence formalism namely, genetic programming (GP) a branch of genetic algorithms is used to develop mathematical models based on input-output data, instead of conventional regression and neural network modeling techniques which are commonly used for this purpose. This paper summarizes the available MATLAB toolboxes and their features. Glucose to gluconic acid batch bioprocess has been modeled using both GPLAB and hybrid approach of GP and Orthogonal Least Square method (GP OLS). GP OLS which is capable of pruning of trees has generated parsimonious expressions simpler to GPLAB, with high fitness values and low mean square error which is an indicative of the good prediction accuracy. The capability of GP OLS to generate non-linear input-output dynamic systems has been tested using an example of fed-batch bioreactor. The simulation and GP model prediction results indicate GP OLS is an efficient and fast method for predicting the order and structure for non-linear input and output model.", notes = "Modeling of glucose to gluconic acid bioprocess. Orthogonal Least Square OLS. GPOLS. ANOVA. http://www.engineeringletters.com/ Chemical Engineering Department, Birla Institute of Technology and Science (BITS), Pilani-333 031. India", } @InProceedings{Babu:2016:ICCES, author = "Kagana.Sarath Babu and N. Balaji", booktitle = "2016 International Conference on Communication and Electronics Systems (ICCES)", title = "Approximation of digital circuits using cartesian genetic programming", year = "2016", abstract = "Digital circuits can be approximated in which the exact functionality can be relaxed. Approximate circuits are constructed such that the logic given by the user is not implemented completely and hence their functionality can be traded for area, delay and power consumption. An evolutionary approach like Cartesian Genetic programming (CGP) is used in this paper to make automatic design process of digital circuits. The quality of approximate circuits can be improved along with the reduction of evolution time by using a heuristic population seeding method which is embedded into CGP. In particular, digital circuits like full adder, 2 bit multiplier and 2 bit adder are addressed in this paper. Experimental results are given where random seeding mechanism is compared with heuristic seeding methods.", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", DOI = "doi:10.1109/CESYS.2016.7889978", month = oct, notes = "Also known as \cite{7889978}", } @InProceedings{Babu:2023:ICAEECI, author = "Nithish Babu M and Preethi P", booktitle = "2023 First International Conference on Advances in Electrical, Electronics and Computational Intelligence (ICAEECI)", title = "{OCR-Based} Multi-class Classification of Hate Speech in Images", year = "2023", month = "19-20 " # oct, address = "Tiruchengode, India", keywords = "genetic algorithms, genetic programming, Training, Social networking (online), Hate speech, User-generated content, Speech recognition, Prediction methods, Decision trees, Random Forest, Naive Bayes, Optical Character Recognition, Multiclass classification, Binary classification", isbn13 = "979-8-3503-4280-2", DOI = "doi:10.1109/ICAEECI58247.2023.10370942", size = "6 pages", abstract = "The depersonalization and anonymity afforded by ubiquitous social media platforms facilitate open discourse, yet also create a potential avenue for hate speech dissemination. The rising incidence of hate speech on these platforms necessitates vigilant monitoring and intervention. However, the sheer volume of user-generated content renders manual oversight infeasible. Technological solutions must be developed to efficiently identify and mitigate hate speech, striking a balance between maintaining open expression and safeguarding against harmful content. Additionally, when using traditional machine learning methodologies as prediction methods, the language being used and the length of the messages provide a barrier. In this study, a Genetic Programming (GP) model for identifying hate speech is presented, where each chromosome acts as a classifier with a universal sentence encoder feature. The performance of the GP model was enhanced by enriching the offspring pool with alternative solutions using a unique mutation strategy that only modifies the feature values in addition to the conventional one-point mutation technique. For the six categories of hate-type hate speech text datasets, the suggested GP model beat all cutting-edge solutions. In contrast to the machine learning models such as Random Forest, Decision Tree, and Naive Bayes which gave the following accuracy of 79.25percent, 77.88percent and 77.33percent whereas GP model outperformed with an accuracy of 97percent. The following evaluation matrices are considered as precision, recall training, and testing accuracy.", notes = "Also known as \cite{10370942}", } @InProceedings{Babuska:2019:GECCO, author = "Robert Babuska", title = "Genetic programming methods for reinforcement learning", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference", year = "2019", editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and Anne Auger and Petr Posik and Leslie {Peprez Caceres} and Andrew M. Sutton and Nadarajen Veerapen and Christine Solnon and Andries Engelbrecht and Stephane Doncieux and Sebastian Risi and Penousal Machado and Vanessa Volz and Christian Blum and Francisco Chicano and Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and Jonathan Fieldsend and Jose Antonio Lozano and Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and Robin Purshouse and Thomas Baeck and Justyna Petke and Giuliano Antoniol and Johannes Lengler and Per Kristian Lehre", pages = "2--2", address = "Prague, Czech Republic", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", note = "Invited keynote", keywords = "genetic algorithms, genetic programming, Reinforcement learning, Symbolic regression", isbn13 = "978-1-4503-6111-8", DOI = "doi:10.1145/3321707.3326935", size = "1 page", abstract = "Reinforcement Learning (RL) algorithms can be used to optimally solve dynamic decision-making and control problems. With continuous valued state and input variables, RL algorithms must rely on function approximators to represent the value function and policy mappings. Commonly used numerical approximators, such as neural networks or basis function expansions, have two main drawbacks: they are black-box models offering no insight in the mappings learnt, and they require significant trial and error tuning of their meta-parameters. In addition, results obtained with deep neural networks suffer from the lack of reproducibility. In this talk, we discuss a family of new approaches to constructing smooth approximators for RL by means of genetic programming and more specifically by symbolic regression. We show how to construct process models and value functions represented by parsimonious analytic expressions using state-of-the-art algorithms, such as Single Node Genetic Programming and Multi-Gene Genetic Programming. We will include examples of non-linear control problems that can be successfully solved by reinforcement learning with symbolic regression and illustrate some of the challenges this exciting field of research is currently facing.", notes = "Abstract of keynote. Also known as \cite{3326935} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @Proceedings{Bacardit:2011:GECCOcomp, title = "GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation", year = "2011", editor = "Jaume Bacardit and Ivan Tanev and Joern Mehnen and Thomas Bartz-Beielstein and David Davis and Carlos Artemio Coello Coello and Dara Curran and Thomas Jansen and Daniele Loiacono and Albert Orriols-Puig and Ryan Urbanowicz and Simon Harding and W. B. Langdon and Man Leung Wong and Garnett Wilson and Tony Lewis and Stephen L. Smith and Stefano Cagnoni and Robert Patton and William Rand and Forrest Stonedahl and Gisele L. Pappa and Alex A. Freitas and Jerry Swan and John Woodward and Maria J. Blesa and Christian Blum and Steven Gustafson and Ekaterina Vladislavleva and Mark Hauschild and Martin Pelikan and Ender Ozcan and Andrew J. Parkes and Jonathan Rowe and Pascal Bouvry and Samee U. Khan and Gregoire Danoy and Alexandru-Adrian Tantar and Emilia Tantar and Bernabe Dorronsoro and Miguel Nicolau and Darrell Whitley", address = "Dublin, Ireland", publisher_address = "New York, NY, USA", month = "12-16 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, Ant colony optimization and swarm intelligence, Artificial life/robotics/evolvable hardware, Bioinformatics, computational, systems, and synthetic biology, Digital entertainment technologies and arts, Evolutionary combinatorial optimization and metaheuristics, Estimation of distribution algorithms, Evolutionary multiobjective optimization, Evolution strategies and evolutionary programming, Genetics based machine learning, Generative and developmental systems, Parallel evolutionary systems, Real world applications, Search-based software engineering, Self-* search, Theory, Evolutionary computation in practice, Evolutionary computation techniques for constraint handling, Fourteenth international workshop on learning classifier systems, Computational intelligence on consumer games and graphics hardware (CIGPU), Medical applications of genetic and evolutionary computation (MedGEC), Evolutionary computation and multi-agent systems and simulation (ECoMASS) - fifth annual workshop, 1st workshop on evolutionary computation for designing generic algorithms, Bio-inspired solutions for wireless sensor networks (GECCO BIS-WSN 2011), 3rd symbolic regression and modeling workshop for GECCO 2011, Optimization by building and using probabilistic models (OBUPM-2011), Scaling behaviours of landscapes, parameters and algorithms, GreenIT evolutionary computation, Graduate students workshop, Late breaking abstracts, Specialized techniques and applications, Tutorials", isbn13 = "978-1-4503-0690-4", URL = "http://dl.acm.org/citation.cfm?id=2001858", size = "approx 1508 pages", notes = "Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @InProceedings{Bach:2019:ICEAA, author = "Thuan Bui Bach and Linh Ho Manh and Kiem Nguyen Khac and Michele Beccaria and Andrea Massaccesi and Riccardo Zich", booktitle = "2019 International Conference on Electromagnetics in Advanced Applications (ICEAA)", title = "Evolved Design of Microstrip Patch Antenna by Genetic Programming", year = "2019", pages = "1393--1397", month = sep, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICEAA.2019.8879155", abstract = "An evolved antenna is an antenna designed fully or substantially by an automatic computer design program that uses an evolutionary algorithm. In this article, we present our work in using evolutionary algorithms to an automated antenna design system. Based on the primal individual structure of genetic programming (GP) is a tree form, a new data-structure computer program which can be represented as entire parameters of an antenna has been explored. The first experiment has been done successfully for automated design the antenna for 5G mobile device which is microstrip patch antenna (MPA) that operates at 3.5 GHz with 50-160 MHz of bandwidth. The innovative MPAs are obtained by this software. This work shows a great potential of the development of the intelligent computer program for automated synthesis antenna as well as conformal antenna.", notes = "Hanoi University of Science and Technology, Hanoi, Vietnam Also known as \cite{8879155}", } @InCollection{bachman:2000:UGAVLGA, author = "Brandon M. Bachman", title = "Using the Genetic Algorithm with a Variable Length Genome for Architectural", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "33--39", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms", notes = "part of \cite{koza:2000:gagp}", } @Article{back:1997:survey, author = "Thomas Back and U. Hammel and H.-P. Schwefel", title = "Evolutionary computation: comments on the history and current state", journal = "IEEE Transactions on Evolutionary Computation", year = "1997", volume = "1", number = "1", pages = "3--17", month = apr, keywords = "genetic algorithms, genetic programming, EA, CS, evolutionstrategies, EP", ISSN = "1089-778X", URL = "http://ls11-www.cs.uni-dortmund.de/people/schwefel/publications/BHS97.ps.gz", size = "15 pages", abstract = "Evolutionary computation has started to receive significant attention during the last decade, although the origins can be traced back to the late 1950's. This article surveys the history as well as the current state of this rapidly growing field. We describe the purpose, the general structure, and the working principles of different approaches, including genetic algorithms (GA) (with links to genetic programming (GP) and classifier systems (CS)), evolution strategies (ES), and evolutionary programming (EP) by analysis and comparison of their most important constituents (i.e. representations, variation operators, reproduction, and selection mechanism). Finally, we give a brief overview on the manifold of application domains, although this necessarily must remain incomplete", notes = "Reference Cited: 220 CODEN: ITEVF5", } @InCollection{back:2000:EC1, author = "Thomas Back and David B. Fogel and Darrell Whitley and Peter J. Angeline", title = "Mutation operators", booktitle = "Evolutionary Computation 1 Basic Algorithms and Operators", publisher = "Institute of Physics Publishing", year = "2000", editor = "Thomas Baeck and David B. Fogel and Zbigniew Michalewicz", chapter = "32", pages = "237--255", address = "Bristol", keywords = "genetic algorithms, genetic programming", ISBN = "0-7503-0664-5", URL = "http://www.crcpress.com/product/isbn/9780750306645", URL = "https://www.routledge.com/Evolutionary-Computation-1-Basic-Algorithms-and-Operators/Baeck-Fogel-Michalewicz/p/book/9780750306645", notes = "Section 32.5 Parse trees p248--250. Grow, shrink, switch, cycle, Gaussian mutation of numbers, enforce size limit, enforce type match (STGP \cite{montana:stgpEC})", size = "19 pages", } @InProceedings{Back:2004:UPP, author = "Thomas Baeck and Ron Breukelaar and Lars Willmes", title = "Inverse Design of Cellular Automata by Genetic Algorithms: An Unconventional Programming Paradigm", booktitle = "Unconventional Programming Paradigms: International Workshop UPP 2004", year = "2004", editor = "Jean-Pierre Banatre and Pascal Fradet and Jean-Louis Giavitto and Olivier Michel", volume = "3566", series = "LNCS", pages = "161--172", address = "Le Mont Saint Michel, France", month = sep # " 15-17", publisher = "Springer", note = "Revised Selected and Invited Papers, 2005", keywords = "genetic algorithms, genetic programming, CA", isbn13 = "978-3-540-31482-0", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.535.7340", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.535.7340", broken = "http://www.liacs.nl/~rbreukel/publications/UPP.pdf", URL = "https://doi.org/10.1007/11527800_13", DOI = "doi:10.1007/11527800_13", size = "12 pages", abstract = "Evolving solutions rather than computing them certainly represents an unconventional programming approach. The general methodology of evolutionary computation has already been known in computer science since more than 40 years, but their use to program other algorithms is a more recent invention. In this paper, we outline the approach by giving an example where evolutionary algorithms serve to program cellular automata by designing rules for their iteration. Three different goals of the cellular automata designed by the evolutionary algorithm are outlined, and the evolutionary algorithm indeed discovers rules for the CA which solve these problems efficiently.", } @InProceedings{Baeck:2023:GPTP, author = "Thomas Baeck", title = "Automatic Algorithm Configuration for Expensive Optimization Tasks", booktitle = "Genetic Programming Theory and Practice XX", year = "2023", editor = "Ting Hu and Charles Ofria and Leonardo Trujillo and Stephan Winkler", series = "Genetic and Evolutionary Computation", address = "Michigan State University, USA", month = jun # " 1-3", note = "Keynote", keywords = "genetic algorithms, genetic programming", notes = " Part of \cite{Hu:2023:GPTP} Not in published book", } @InProceedings{backer:1996:WSC, author = "Gerriet Backer", title = "Learning with missing data using Genetic Programming", booktitle = "The 1st Online Workshop on Soft Computing (WSC1)", year = "1996", broken = "http://www.bioele.nuee.nagoya-u.ac.jp/wsc1/", month = "19--30 " # aug, organisation = "Research Group on ECOmp of the Society of Fuzzy Theory and Systems (SOFT)", publisher = "Nagoya University, Japan", keywords = "genetic algorithms, genetic programming, Machine learning, Missing data, Strongly Typed Genetic Programming, STGP", broken = "http://www.pa.info.mie-u.ac.jp/bioele/wsc1/papers/d041.html", URL = "http://www.pa.info.mie-u.ac.jp/bioele/wsc1/papers/files/backer.ps.gz", abstract = "Learning with imprecise or missing data has been a major challenge for machine learning. A new approach using Strongly Typed Genetic Programming is proposed here, which uses extra computations based on other input data to approximate the missing values. It eliminates the need for pre-processing and makes use of correlations between the input data. The decision process itself and the handling of unknown data can be extracted from the resulting program for an analysis afterwards. Comparing it to an alternative approach on a simple example shows the usefulness of this approach.", size = "5 pages", notes = "Adds {"}unknown{"} data type to STGP. demo on iris classification problem (see discussion on WSC1 pages) email WSC1 organisers wsc@bioele.nuee.nagoya-u.ac.jp", } @InProceedings{conf/evoW/BackmanD08, title = "A Generative Representation for the Evolution of Jazz Solos", author = "Kjell Backman and Palle Dahlstedt", bibdate = "2008-04-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/evoW/evoW2008.html#BackmanD08", booktitle = "Proceedings of Evo{COMNET}, Evo{FIN}, Evo{HOT}, Evo{IASP}, Evo{MUSART}, Evo{NUM}, Evo{STOC}, and EvoTransLog, Applications of Evolutionary Computing, EvoWorkshops", publisher = "Springer", year = "2008", volume = "4974", editor = "Mario Giacobini and Anthony Brabazon and Stefano Cagnoni and Gianni {Di Caro} and Rolf Drechsler and Anik{\'o} Ek{\'a}rt and Anna Esparcia-Alc{\'a}zar and Muddassar Farooq and Andreas Fink and Jon McCormack and Michael O'Neill and Juan Romero and Franz Rothlauf and Giovanni Squillero and Sima Uyar and Shengxiang Yang", isbn13 = "978-3-540-78760-0", pages = "371--380", series = "Lecture Notes in Computer Science", DOI = "doi:10.1007/978-3-540-78761-7_40", address = "Naples", month = "26-28 " # mar, keywords = "genetic algorithms, genetic programming", abstract = "This paper describes a system developed to create computer based jazz improvisation solos. The generation of the improvisation material uses interactive evolution, based on a dual genetic representation: a basic melody line representation, with energy constraints ({"}rubber band{"}) and a hierarchic structure of operators that processes the various parts of this basic melody. To be able to listen to and evaluate the result in a fair way, the computer generated solos have been imported into a musical environment to form a complete jazz composition. The focus of this paper is on the data representations developed for this specific type of music. This is the first published part of an ongoing research project in generative jazz, based on probabilistic and evolutionary strategies.", } @InProceedings{Badan:2019:TPNC, author = "Filip Badan and Lukas Sekanina", title = "Optimizing Convolutional Neural Networks for Embedded Systems by Means of Neuroevolution", booktitle = "International Conference on Theory and Practice of Natural Computing, TPNC 2019", year = "2019", editor = "Carlos Martin-Vide and Geoffrey Pond and Miguel A. Vega-Rodriguez", volume = "11934", series = "LNCS", pages = "109--121", address = "Kingston, ON, Canada", month = "9-11 " # dec, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Evolutionary Algorithm Convolutional neural network Neuroevolution Embedded Systems Energy Efficiency", isbn13 = "978-3-030-34499-3", DOI = "doi:10.1007/978-3-030-34500-6_7", size = "13 pages", abstract = "Automated design methods for convolutional neural networks (CNNs) have recently been developed in order to increase the design productivity. We propose a neuroevolution method capable of evolving and optimizing CNNs with respect to the classification error and CNN complexity (expressed as the number of tunable CNN parameters), in which the inference phase can partly be executed using fixed point operations to further reduce power consumption. Experimental results are obtained with TinyDNN framework and presented using two common image classification benchmark problems: MNIST and CIFAR-10.", } @InProceedings{1277299, author = "Mohamed Bahy Bader-El-Den and Riccardo Poli", title = "A GP-based hyper-heuristic framework for evolving 3-SAT heuristics", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "1749--1749", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1749.pdf", DOI = "doi:10.1145/1276958.1277299", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming: Poster, heuristics, hyper heuristic, SAT", abstract = "We present, GP-HH, a framework for evolving local search 3-SAT heuristics based on GP. Evolved heuristics are compared against well-known SAT solvers with very encouraging results.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @InProceedings{bader-el-den07:_gener_sat_local_searc_heuris, author = "Mohamed Bader-El-Den and Riccardo Poli", title = "Generating {SAT} Local-Search Heuristics using a {GP} Hyper-Heuristic Framework", booktitle = "Evolution Artificielle, 8th International Conference", year = "2007", editor = "Nicolas Monmarch{\'e} and El-Ghazali Talbi and Pierre Collet and Marc Schoenauer and Evelyne Lutton", volume = "4926", series = "Lecture Notes in Computer Science", pages = "37--49", address = "Tours, France", month = "29-31 " # oct, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-79304-5", URL = "https://pure.port.ac.uk/ws/portalfiles/portal/79937/AE2007.pdf", DOI = "doi:10.1007/978-3-540-79305-2_4", size = "12 pages", abstract = "We present GP-HH, a framework for evolving local-search 3-SAT heuristics based on GP. The aim is to obtain disposable heuristics which are evolved and used for a specific subset of instances of a problem. We test the heuristics evolved by GP-HH against well-known local-search heuristics on a variety of benchmark SAT problems. Results are very encouraging.", notes = "EA'07", } @InProceedings{Bader-El-Den:2008:evocop, title = "Inc*: An Incremental Approach for Improving Local Search Heuristics", author = "Mohamed Bahy Bader-El-Den and Riccardo Poli", bibdate = "2008-04-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/evoW/evocop2008.html#Bader-El-DenP08", booktitle = "Proceedings of the 8th European Conference, Evolutionary Computation in Combinatorial Optimization, Evo{COP}", publisher = "Springer", year = "2008", volume = "4972", editor = "Jano I. van Hemert and Carlos Cotta", isbn13 = "978-3-540-78603-0", pages = "194--205", series = "Lecture Notes in Computer Science", DOI = "doi:10.1007/978-3-540-78604-7_17", address = "Naples, Italy", month = mar # " 26-28", keywords = "genetic algorithms, genetic programming", notes = "also known as \cite{conf/evoW/Bader-El-DenP08}", } @InProceedings{Bader-El-Den:2008:WCCI, author = "Mohamed Bader-El-Den and Riccardo Poli", title = "Analysis and Extension of the Inc* on the Satisfiability Testing Problem", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "3342--3349", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, SAT", isbn13 = "978-1-4244-1823-7", file = "EC0725.pdf", DOI = "doi:10.1109/CEC.2008.4631250", abstract = "Inc (star) is a general algorithm that can be used in conjunction with any local search heuristic and that has the potential to substantially improve the overall performance of the heuristic. The general idea of the algorithm is the following. Rather than attempting to directly solve a difficult problem, the algorithm dynamically chooses a smaller instance of the problem, and then increases the size of the instance only after the previous simplified instances have been solved, until the full size of the problem is reached. Genetic programming is used to discover new strategies for Inc*. Preliminary experiments on the satisfiability problem (SAT) problem have shown that Inc* is a competitive approach. In this paper we enhance Inc* and we experimentally test it on larger set of benchmarks, including big instances of SAT. Furthermore, we provide an analysis of the algorithm's behaviour.", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @InCollection{Bader-El-Den:2008:GPTP, author = "Mohamed Bader-El-Den and Riccardo Poli", title = "Evolving Effective Incremental Solvers for {SAT} with a Hyper-Heuristic Framework Based on Genetic Programming", booktitle = "Genetic Programming Theory and Practice {VI}", year = "2008", editor = "Rick L. Riolo and Terence Soule and Bill Worzel", series = "Genetic and Evolutionary Computation", chapter = "11", pages = "163--179", address = "Ann Arbor", month = "15-17 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, hyper-heuristic, HH, Inc, SAT, heuristics", isbn13 = "978-0-387-87622-1", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.206.3331.pdf", DOI = "doi:10.1007/978-0-387-87623-8_11", size = "16 pages", abstract = "Hyper-heuristics could simply be defined as heuristics to choose other heuristics. In other words, they are methods for combining existing heuristics to generate new ones. we use a grammar-based genetic programming hyper-heuristic framework. The framework is used for evolving effective incremental solvers for SAT. The evolved heuristics perform very well against well-known local search heuristics on a variety of benchmark SAT problems.", notes = "part of \cite{Riolo:2008:GPTP} published in 2009. Also known as \cite{El-den:2008:GPTP} \cite{Bader-el-den_evolvingeffective} Department of Computing and Electronic Systems, University of Essex", } @InProceedings{Bader-El-Den:2008:gecco, author = "Mohamed Bader-El-Den and Riccardo Poli", title = "Evolving Heuristics with Genetic Programming", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "601--602", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p601.pdf", DOI = "doi:10.1145/1389095.1389212", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, heuristics, hyperheuristics, Inc*, SAT, Evolutionary combinatorial optimisation: Poster", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389212}", } @InProceedings{BaderElDen:2009:cec, author = "Mohamed {Bader El Den} and Riccardo Poli", title = "Grammar-Based Genetic Programming for Timetabling", booktitle = "2009 IEEE Congress on Evolutionary Computation", year = "2009", editor = "Andy Tyrrell", pages = "2532--2539", address = "Trondheim, Norway", month = "18-21 " # may, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-2959-2", file = "P677.pdf", DOI = "doi:10.1109/CEC.2009.4983259", abstract = "We present a grammar-based genetic programming framework for the solving the timetabling problem via the evolution of constructive heuristics. The grammar used for producing new generations is based on graph colouring heuristics that have previously proved to be effective in constructing timetables as well as different slot allocation heuristics. The framework is tested on a widely used benchmarks in the field of exam time-tabling and compared with highly-tuned state-of-the- art approaches. Results shows that the framework is very competitive with other constructive techniques.", keywords = "genetic algorithms, genetic programming, hyperheuristics", notes = "graph colouring, exam timetabling. Grammar used to control mixing of existing well established heuristics by GP to evolve a population of hyperheuristic. To cope with randomness in existing low level heuristics, each GP individual is run several times. CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR", } @PhdThesis{Bader-El-Den:thesis, author = "Mohamed Bahr {Bader El Den}", title = "Investigation of the role of Genetic Programming in a Hyper-Heuristic Framework for Combinatorial Optimization Problems", school = "School of Computer Science and Electronic Engineering, University of Essex", year = "2009", address = "UK", keywords = "genetic algorithms, genetic programming", URL = "http://ethos.bl.uk/OrderDetails.do?did=25&uin=uk.bl.ethos.510512", notes = "http://www.essex.ac.uk/csee/news_and_seminars/newsEvent.aspx?e_id=5691", } @InProceedings{conf/ijcci/Bader-El-DenF09, author = "Mohamed Bahy Bader-El-Den and Shaheen Fatima", title = "Evolving Effective Bidding Functions for Auction based Resource Allocation Framework", year = "2009", booktitle = "International Conference on Evolutionary Computation (ICEC 2009)", editor = "Agostinho Rosa", address = "Madeira, Portugal", month = "5-7 " # oct, publisher = "INSTICC Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-989-674-014-6", URL = "https://www.researchgate.net/publication/221616501_Evolving_Effective_Bidding_Functions_for_Auction_based_Resource_Allocation_Framework", URL = "https://researchportal.port.ac.uk/portal/en/publications/evolving-effective-bidding-functions-for-auction-based-resource-allocation-framework(5323b5d3-0e0a-446a-91d5-b64bb53a592f)/export.html", broken = "http://eprints.port.ac.uk/id/eprint/3738", bibdate = "2010-03-03", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ijcci/ijcci2009.html#Salehi-AbariW09", abstract = "In this paper, we present an auction based resource allocation framework. This framework, called GPAuc, uses genetic programming for evolving bidding functions. We describe GPAuc in the context of the exam timetabling problem (ETTP). In the ETTP, there is a set of exams, which must be assigned to a predefined set of slots. Here, the exam time tabling system is the seller that auctions a set of slots. The exams are viewed as the bidding agents in need of slots. The problem is then to find a schedule (i.e., a slot for each exam) such that the total cost of conducting the exams as per the schedule is minimised. In order to arrive at such a schedule, we need to find the bidders' optimal bids. This is done using genetic programming. The effectiveness of GPAuc is demonstrated experimentally by comparing it with some existing benchmarks for exam time-tabling.", notes = "IJCCI broken http://www.icec.ijcci.org/Abstracts/2009/ICEC_2009_Abstracts.htm Appears not to be in the book, Computational Intelligence ISBN:978-3-642-20205-6, published by springer.", } @Article{journals/memetic/Bader-El-DenPF09, title = "Evolving timetabling heuristics using a grammar-based genetic programming hyper-heuristic framework", author = "Mohamed Bahy Bader-El-Den and Riccardo Poli and Shaheen Fatima", journal = "Memetic Computing", year = "2009", number = "3", volume = "1", pages = "205--219", keywords = "genetic algorithms, genetic programming, timetabling, Hyper-heuristics, Heuristics", DOI = "doi:10.1007/s12293-009-0022-y", bibdate = "2009-12-11", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/memetic/memetic1.html#Bader-El-DenPF09", abstract = "This paper introduces a Grammar-based Genetic Programming Hyper-Heuristic framework (GPHH) for evolving constructive heuristics for timetabling. In this application GP is used as an online learning method which evolves heuristics while solving the problem. In other words, the system keeps on evolving heuristics for a problem instance until a good solution is found. The framework is tested on some of the most widely used benchmarks in the field of exam timetabling and compared with the best state-of-the-art approaches. Results show that the framework is very competitive with other constructive techniques, and did outperform other hyper-heuristic frameworks on many occasions.", } @InProceedings{Bader-El-Den:2010:EuroGP, author = "Mohamed Bader-El-Den and Shaheen Fatima", title = "Genetic Programming for Auction Based Scheduling", booktitle = "Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010", year = "2010", editor = "Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar", volume = "6021", series = "LNCS", pages = "256--267", address = "Istanbul", month = "7-9 " # apr, organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-12147-0", DOI = "doi:10.1007/978-3-642-12148-7_22", abstract = "In this paper, we present a genetic programming (GP) framework for evolving agent's binding function (GPAuc) in a resource allocation problem. The framework is tested on the exam timetabling problem (ETP). There is a set of exams, which have to be assigned to a predefined set of slots and rooms. Here, the exam time tabling system is the seller that auctions a set of slots. The exams are viewed as the bidding agents in need of slots. The problem is then to find a schedule (i.e., a slot for each exam) such that the total cost of conducting the exams as per the schedule is minimised. In order to arrive at such a schedule, we need to find the bidders' optimal bids. This is done using genetic programming. The effectiveness of GPAuc is demonstrated experimentally by comparing it with some existing benchmarks for exam timetabling.", notes = "Part of \cite{Esparcia-Alcazar:2010:GP} EuroGP'2010 held in conjunction with EvoCOP2010 EvoBIO2010 and EvoApplications2010", } @InProceedings{1277272, author = "Khaled M. S. Badran and Peter I. Rockett", title = "The roles of diversity preservation and mutation in preventing population collapse in multiobjective genetic programming", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "1551--1558", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1551.pdf", DOI = "doi:10.1145/1276958.1277272", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, bloat, diversity preservation, multiobjective optimisation, population collapse", abstract = "It has been observed previously that genetic programming populations can collapse to all single node trees when a parsimony measure (tree node count) is used in a multiobjective setting. We have investigated the circumstances under which this can occur for both the 6-parity boolean learning task and a range of benchmark machine learning problems. We conclude that mutation is an important and we believe a hitherto unrecognised factor in preventing population collapse in multiobjective genetic programming; without mutation we routinely observe population collapse. From systematic variation of the mutation operator, we conclude that a necessary condition to avoid collapse is that mutation produces, on average, an increase in tree sizes (bloating) at each generation which is then counterbalanced by the parsimony pressure applied during selection. Finally, we conclude that the use of a genotype diversity preserving mechanism is ineffective at preventing population collapse.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @InProceedings{conf/eurogp/BadranR08, title = "Integrating Categorical Variables with Multiobjective Genetic Programming for Classifier Construction", author = "Khaled M. S. Badran and Peter Rockett", bibdate = "2008-04-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/eurogp/eurogp2008.html#BadranR08", booktitle = "Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008", address = "Naples", month = "26-28 " # mar, publisher = "Springer", year = "2008", volume = "4971", editor = "Michael O'Neill and Leonardo Vanneschi and Steven Gustafson and Anna Isabel {Esparcia Alcazar} and Ivanoe {De Falco} and Antonio {Della Cioppa} and Ernesto Tarantino", isbn13 = "978-3-540-78670-2", pages = "301--311", series = "Lecture Notes in Computer Science", DOI = "doi:10.1007/978-3-540-78671-9_26", keywords = "genetic algorithms, genetic programming", notes = "Part of \cite{conf/eurogp/2008} EuroGP'2008 held in conjunction with EvoCOP2008, EvoBIO2008 and EvoWorkshops2008", } @Article{Badran:2009:GPEM, author = "Khaled Badran and Peter I. Rockett", title = "The influence of mutation on population dynamics in multiobjective genetic programming", journal = "Genetic Programming and Evolvable Machines", year = "2010", volume = "11", number = "1", pages = "5--33", month = mar, keywords = "genetic algorithms, genetic programming, Multiobjective genetic programming, Population collapse, Mutation, Population dynamics, MOGP, bloat", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-009-9084-3", abstract = "Using multiobjective genetic programming with a complexity objective to overcome tree bloat is usually very successful but can sometimes lead to undesirable collapse of the population to all single-node trees. In this paper we report a detailed examination of why and when collapse occurs. We have used different types of crossover and mutation operators (depth-fair and sub-tree), different evolutionary approaches (generational and steady-state), and different datasets (6-parity Boolean and a range of benchmark machine learning problems) to strengthen our conclusion. We conclude that mutation has a vital role in preventing population collapse by counterbalancing parsimony pressure and preserving population diversity. Also, mutation controls the size of the generated individuals which tends to dominate the time needed for fitness evaluation and therefore the whole evolutionary process. Further, the average size of the individuals in a GP population depends on the evolutionary approach employed. We also demonstrate that mutation has a wider role than merely culling single-node individuals from the population; even within a diversity-preserving algorithm such as SPEA2 mutation has a role in preserving diversity.", notes = "Steady-state algorithm depth-fair crossover/depth-fair mutation", } @PhdThesis{Badran:thesis, author = "Khaled Badran", title = "Multi-objective genetic programming with an application to intrusion detection in computer networks", school = "University of Sheffield", year = "2009", address = "UK", keywords = "genetic algorithms, genetic programming", URL = "http://ethos.bl.uk/OrderDetails.do?did=1&uin=uk.bl.ethos.505474", abstract = "The widespread connectivity of computers all over the world has encouraged intruders to threaten the security of computing systems by targeting the confidentiality and integrity of information, and the availability of systems. Traditional techniques such as user authentication, data encryption and firewalls have been implemented to defend computer security but still have problems and weak points. Therefore the development of intrusion detection systems (EDS) has aroused much research interest with the aim of preventing both internal and external attacks. In misuse-based, network-based IDS, huge history files of computer network usage are analysed hi order to extract useful information, and rules are extracted to judge future network usage as legal or illegal. This process is considered as data mining for intrusion detection in computer networks.", notes = "uk.bl.ethos.505474", } @Article{Badran:2011:GPEM, author = "Khaled Badran and Peter Rockett", title = "Multi-class pattern classification using single, multi-dimensional feature-space feature extraction evolved by multi-objective genetic programming and its application to network intrusion detection", journal = "Genetic Programming and Evolvable Machines", year = "2012", volume = "13", number = "1", pages = "33--63", month = mar, note = "Special Section on Evolutionary Algorithms for Data Mining", keywords = "genetic algorithms, genetic programming, Multi-class pattern classification, Feature extraction, Feature selection, Multi-objective genetic programming", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-011-9143-4", size = "31 pages", abstract = "In this paper we investigate using multi-objective genetic programming to evolve a feature extraction stage for multiple-class classifiers. We find mappings which transform the input space into a new, multi-dimensional decision space to increase the discrimination between all classes; the number of dimensions of this decision space is optimised as part of the evolutionary process. A simple and fast multi-class classifier is then implemented in this multi-dimensional decision space. Mapping to a single decision space has significant computational advantages compared to k -class-to-2-class decompositions; a key design requirement in this work has been the ability to incorporate changing priors and/or costs associated with mislabelling without retraining. We have employed multi-objective optimization in a Pareto framework incorporating solution complexity as an independent objective to be minimised in addition to the main objective of the misclassification error. We thus give preference to simpler solutions which tend to generalise well on unseen data, in accordance with Occam's Razor. We obtain classification results on a series of benchmark problems which are essentially identical to previous, more complex decomposition approaches. Our solutions are much simpler and computationally attractive as well as able to readily incorporate changing priors/costs. In addition, we have also applied our approach to the KDD-99 intrusion detection dataset and obtained results which are highly competitive with the KDD-99 Cup winner but with a significantly simpler classification framework.", affiliation = "Vision and Information Engineering Research Group, Department of Electronic and Electrical Engineering, The University of Sheffield, Mappin Street, Sheffield, S1 3D UK", } @Article{Badran:2017:IJCA, author = "Khaled Badran and Alaa Rohim", title = "Genetic Programming Feature Extraction with Different Robust Classifiers for Network Intrusion Detection", journal = "International Journal of Computer Applications", year = "2017", volume = "168", number = "1", pages = "37--43", month = jun, keywords = "genetic algorithms, genetic programming, Pattern Recognition, Classification, Network Intrusion, Feature Extraction, Neural Network, ANN, Support Vector Machines, SVM, Decision Trees", publisher = "Foundation of Computer Science (FCS), NY, USA", ISSN = "0975-8887", URL = "https://www.ijcaonline.org/archives/volume168/number1/27841-2017914276", URL = "https://www.ijcaonline.org/archives/volume168/number1/badran-2017-ijca-914276.pdf", DOI = "doi:10.5120/ijca2017914276", size = "7 pages", abstract = "we compare the performance of three traditional robust classifiers (Neural Networks, Support Vector Machines, and Decision Trees) with and without using multi-objective genetic programming in the feature extraction phase. We argue that effective feature extraction can significantly enhance the performance of these classifiers. We have applied these three classifiers stand alone to real world five datasets from the UCI machine learning database and also to network intrusion KDD-99 cup dataset.Then,the experiments were repeated by adding the feature extraction phase.Theresults ofthetwo approachesare compared and conclude that the effective method is to evolve optimal feature extractors that transform input pattern space into a decision space in which the performance of traditional robust classifiers can be enhanced.", notes = "Also known as \cite{10.5120/ijca2017914276} www.ijcaonline.org Military Technical College, Cairo", } @InProceedings{Badura:2014:ELEKTRO, author = "Stefan Badura and Milan Fratrik and Ondrej Skvarek and Martin Klimo", title = "Bimodal vowel recognition using fuzzy logic networks - naive approach", booktitle = "ELEKTRO, 2014", year = "2014", month = may, pages = "22--25", keywords = "genetic algorithms, genetic programming, AVSR, bimodal, fusion, speech recognition, fuzzy logic", DOI = "doi:10.1109/ELEKTRO.2014.6847864", size = "4 pages", abstract = "We describe an audio visual speech recognition system (AVSR) based on fuzzy logic networks. Our system is able to recognise any time sequences and achieves positive results in the task of vowel recognition. Proposed design relies on new model and methods for training fuzzy logic circuits. We combine a simple combinatorial circuit, trained with genetic programming, with a fuzzy logic memory. Combinatorial structures are combined to a fuzzy logic network. This approach leads to design of a hierarchical, massive and layered structure for dynamic signal recognition. An AVSR system is effectively composed by fusion of such networks for audio and lip-reading parts.", notes = "Fac. of Manage. Sci. & Inf., Univ. of Zilina, Zilina, Slovakia Also known as \cite{6847864}", } @Article{journals/soco/BaeJKKKHM10, title = "Optimization of silicon solar cell fabrication based on neural network and genetic programming modeling", author = "Hyeon Bae and Tae-Ryong Jeon and Sungshin Kim and Hyun-Soo Kim and DongSeop Kim and Seung Soo Han and Gary S. May", journal = "Soft Computing - A Fusion of Foundations, Methodologies and Applications", year = "2010", number = "2", volume = "14", pages = "161--169", keywords = "genetic algorithms, genetic programming, Neural network, Particle swarm optimization, Silicon solar cell fabrication", ISSN = "1432-7643", DOI = "doi:10.1007/s00500-009-0438-9", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/soco/soco14.html#BaeJKKKHM10", abstract = "This study describes techniques for the cascade modeling and the optimization that are required to conduct the simulator-based process optimization of solar cell fabrication. Two modeling approaches, neural networks and genetic programming, are employed to model the crucial relation for the consecutively connected two processes in solar cell fabrication. One model (Model 1) is used to map the five inputs (time, amount of nitrogen and DI water in surface texturing and temperature and time in emitter diffusion) to the two outputs (reflectance and sheet resistance) of the first process. The other model (Model 2) is used to connect the two inputs (reflectance and sheet resistance) to the one output (efficiency) of the second process. After modeling of the two processes, genetic algorithms and particle swarm optimization were applied to search for the optimal recipe. In the first optimization stage, we searched for the optimal reflectance and sheet resistance that can provide the best efficiency in the fabrication process. The optimized reflectance and sheet resistance found by the particle swarm optimization were better than those found by the genetic algorithm. In the second optimization stage, the five input parameters were searched by using the reflectance and sheet resistance values obtained in the first stage. The found five variables such as the texturing time, amount of nitrogen, DI water, diffusion time, and temperature are used as a recipe for the solar cell fabrication. The amount of nitrogen, DI water, and diffusion time in the optimized recipes showed considerable differences according to the modeling approaches. More importantly, repeated applications of particle swarm optimization yielded process conditions with smaller variations, implying greater consistency in recipe generation.", } @InProceedings{bael:1999:TJSPSSBESE, author = "Patrick Van Bael and Dirk Devogelaere and M. Rijckaert", title = "The Job Shop Problem Solved with Simple, Basic Evolutionary Search Elements", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "665--669", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Baele:2009:cec, author = "Guy Baele and Nicolas Bredeche and Evert Haasdijk and Steven Maere and Nico Michiels and Yves {Van de Peer} and Christopher Schwarzer and Ronald Thenius", title = "Open-Ended On-Board Evolutionary Robotics for Robot Swarms", booktitle = "2009 IEEE Congress on Evolutionary Computation", year = "2009", editor = "Andy Tyrrell", pages = "1123--1130", address = "Trondheim, Norway", month = "18-21 " # may, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-2959-2", file = "P485.pdf", DOI = "doi:10.1109/CEC.2009.4983072", abstract = "The SYMBRION project stands at the crossroads of Artificial Life and Evolutionary Robotics: a swarm of real robots undergoes online evolution by exchanging information in a decentralized Evolutionary Robotics Scheme: the diffusion of each individual's genotype depends both on its ability to survive in an unknown environment as well as its ability to maximize mating opportunities during its lifetime, which suggests an implicit fitness. This paper presents early research and prospective ideas in the context of large-scale swarm robotics projects, focusing on the open-ended evolutionary approach in the SYMBRION project. One key issue of this work is to perform on-board evolution in a spatially distributed population of robots. A real-world experiment is also described which yields important considerations regarding open-ended evolution with real autonomous robots.", keywords = "genetic algorithms, genetic programming", notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR", } @InProceedings{Baeta:2021:evoapplications, author = "Francisco Baeta and Joao Correia and Tiago Martins and Penousal Machado", title = "{TensorGP} - Genetic Programming Engine in {TensorFlow}", booktitle = "24th International Conference, EvoApplications 2021", year = "2021", month = "7-9 " # apr, editor = "Pedro Castillo and Juanlu Jimenez-Laredo", series = "LNCS", volume = "12694", publisher = "Springer Verlag", address = "virtual event", pages = "763--778", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Parallelisation, Vectorisation, TensorFlow, GPU computing", isbn13 = "978-3-030-72698-0", DOI = "doi:10.1007/978-3-030-72699-7_48", abstract = "we resort to the TensorFlow framework to investigate the benefits of applying data vectorisation and fitness caching methods to domain evaluation in Genetic Programming. For this purpose, an independent engine was developed, TensorGP, along with a testing suite to extract comparative timing results across different architectures and amongst both iterative and vectorized approaches. Our performance benchmarks demonstrate that by exploiting the TensorFlow eager execution model, performance gains of up to two orders of magnitude can be achieved on a parallel approach running on dedicated hardware when compared to a standard iterative approach.", notes = "http://www.evostar.org/2021/ EvoApplications2021 held in conjunction with EuroGP'2021, EvoCOP2021 and EvoMusArt2021", } @InProceedings{Baeta:2021:GECCO, author = "Francisco Baeta and Joao Correia and Tiago Martins and Penousal Machado", title = "Speed Benchmarking of Genetic Programming Frameworks", booktitle = "Proceedings of the 2021 Genetic and Evolutionary Computation Conference", year = "2021", editor = "Francisco Chicano and Alberto Tonda and Krzysztof Krawiec and Marde Helbig and Christopher W. Cleghorn and Dennis G. Wilson and Georgios Yannakakis and Luis Paquete and Gabriela Ochoa and Jaume Bacardit and Christian Gagne and Sanaz Mostaghim and Laetitia Jourdan and Oliver Schuetze and Petr Posik and Carlos Segura and Renato Tinos and Carlos Cotta and Malcolm Heywood and Mengjie Zhang and Leonardo Trujillo and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Fuyuki Ishikawa and Inmaculada Medina-Bulo and Frank Neumann and Andrew M. Sutton", pages = "768--775", address = "internet", series = "GECCO '21", month = jul # " 10-14", organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, Parallelisation, Vectorisation, TensorFlow, GPU Computing", isbn13 = "9781450383509", DOI = "doi:10.1145/3449639.3459335", size = "8 pages", abstract = "Genetic Programming (GP) is known to suffer from the burden of being computationally expensive by design. While, over the years, many techniques have been developed to mitigate this issue, data vectorisation, in particular, is arguably still the most attractive strategy due to the parallel nature of GP. In this work,we employ a series of benchmarks meant to compare both the performance and evolution capabilities of different vectorised and iterative implementation approaches across several existing frameworks. Namely, TensorGP, a novel open source engine written in Python, is shown to greatly benefit from the TensorFlow library to accelerate the domain evaluation phasein GP. The presented performance benchmarks demonstratethat the TensorGP engine manages to pull ahead, with relativespeedups above two orders of magnitude for problems with ahigher number of fitness cases. Additionally, as a consequenceof being able to compute larger domains, we argue that TensorGP performance gains aid the discovery of more accurate candidate solutions.", notes = "University of Coimbra GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)", } @Article{baeta:2022:SN, author = "Francisco Baeta and Joao Correia and Tiago Martins and Penousal Machado", title = "Exploring Genetic Programming in {TensorFlow} with {TensorGP}", journal = "SN Computer Science", year = "2022", volume = "3", number = "2", keywords = "genetic algorithms, genetic programming, GPU", URL = "http://link.springer.com/article/10.1007/s42979-021-01006-8", DOI = "doi:10.1007/s42979-021-01006-8", } @InProceedings{baeta:2024:GECCOcomp, author = "Francisco Baeta and Joao Correia and Tiago Martins and Penousal Machado", title = "Exploring Evolutionary Generators within Generative Adversarial Networks", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion", year = "2024", editor = "Jean-Baptiste Mouret and Kai Qin", pages = "251--254", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, evolutionary computation, generative adversarial networks, TGPGAN, Evolutionary Machine Learning: Poster", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3654348", size = "4 pages", abstract = "Since their introduction, Generative Adversarial Networks (GANs) have represented the bulk of approaches used in image generation. Before GANs, such approaches used Machine Learning (ML) exclusively to tackle the training problems inherent to GANs. However, in recent years, evolutionary approaches have been making a comeback, not only across the field of ML but in generative modelling specifically. Successes in GPU-accelerated Genetic Programming (GP) led to the introduction of the TGPGAN framework, which used GP as a replacement for the deep convolutional network conventionally used as a GAN generator. In this paper, we delve further into the generative capabilities of evolutionary computation within adversarial models and extend the study performed in TGPGAN to analyse other evolutionary approaches. Similarly to TGPGAN, the presented approaches replace the generator component of a Deep Convolutional GAN (DCGAN): one with a line-drawing Genetic Algorithm (GA) and another with a Compositional Pattern Producing Network (CPPN). Our comparison of generative performance shows that the GA used manages to perform competitively with the original framework. More importantly, this work showcases the viability of other evolutionary approaches other than GP for the purpose of image generation.", notes = "GECCO-2024 EML A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{baeza-yates:2018:DESA, author = "Ricardo Baeza-Yates and Alfredo Cuzzocrea and Domenico Crea and Giovanni {Lo Bianco}", title = "Learning Ranking Functions by Genetic Programming Revisited", booktitle = "Database and Expert Systems Applications", year = "2018", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-319-98812-2_34", DOI = "doi:10.1007/978-3-319-98812-2_34", } @InProceedings{DBLP:conf/sac/Baeza-YatesCCB19, author = "Ricardo Baeza-Yates and Alfredo Cuzzocrea and Domenico Crea and Giovanni Lo Bianco", editor = "Chih-Cheng Hung and George A. Papadopoulos", title = "An effective and efficient algorithm for ranking web documents via genetic programming", booktitle = "Proceedings of the 34th {ACM/SIGAPP} Symposium on Applied Computing, {SAC} 2019, Limassol, Cyprus, April 8-12, 2019", pages = "1065--1072", publisher = "{ACM}", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1145/3297280.3297385", DOI = "doi:10.1145/3297280.3297385", timestamp = "Sun, 22 Sep 2019 01:00:00 +0200", biburl = "https://dblp.org/rec/conf/sac/Baeza-YatesCCB19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{DBLP:journals/peerj-cs/BaggioLCM23, author = "Cecilia Baggio and Carlos M. Lorenzetti and Rocio L. Cecchini and Ana Gabriela Maguitman", title = "Multi-objective genetic programming strategies for topic-based search with a focus on diversity and global recall", journal = "PeerJ Comput. Sci.", volume = "9", pages = "e1710", year = "2023", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.7717/peerj-cs.1710", DOI = "doi:10.7717/PEERJ-CS.1710", timestamp = "Sun, 31 Dec 2023 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/peerj-cs/BaggioLCM23.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{baghbani:2023:AS, author = "Abolfazl Baghbani and Minh Duc Nguyen and Ali Alnedawi and Nick Milne and Thomas Baumgartl and Hossam Abuel-Naga", title = "Improving Soil Stability with Alum Sludge: An {AI-Enabled} Approach for Accurate Prediction of California Bearing Ratio", journal = "Applied Sciences", year = "2023", volume = "13", number = "8", pages = "Article No. 4934", keywords = "genetic algorithms, genetic programming", ISSN = "2076-3417", URL = "https://www.mdpi.com/2076-3417/13/8/4934", DOI = "doi:10.3390/app13084934", abstract = "Alum sludge is a byproduct of water treatment plants, and its use as a soil stabilizer has gained increasing attention due to its economic and environmental benefits. Its application has been shown to improve the strength and stability of soil, making it suitable for various engineering applications. However, to go beyond just measuring the effects of alum sludge as a soil stabilizer, this study investigates the potential of artificial intelligence (AI) methods for predicting the California bearing ratio (CBR) of soils stabilized with alum sludge. Three AI methods, including two black box methods (artificial neural network and support vector machines) and one grey box method (genetic programming), were used to predict CBR, based on a database with nine input parameters. The results demonstrate the effectiveness of AI methods in predicting CBR with good accuracy (R2 values ranging from 0.94 to 0.99 and MAE values ranging from 0.30 to 0.51). Moreover, a novel approach, using genetic programming, produced an equation that accurately estimated CBR, incorporating seven inputs. The analysis of parameter sensitivity and importance, revealed that the number of hammer blows for compaction was the most important parameter, while the parameters for maximum dry density of soil and mixture were the least important. This study highlights the potential of AI methods as a useful tool for predicting the performance of alum sludge as a soil stabilizer.", notes = "also known as \cite{app13084934}", } @Article{baghbani:2023:Geotechnics, author = "Abolfazl Baghbani and Amin Soltani and Katayoon Kiany and Firas Daghistani", title = "Predicting the Strength Performance of {Hydrated-Lime} Activated Rice Husk {Ash-Treated} Soil Using Two {Grey-Box} Machine Learning Models", journal = "Geotechnics", year = "2023", volume = "3", number = "3", pages = "894--920", keywords = "genetic algorithms, genetic programming", ISSN = "2673-7094", URL = "https://www.mdpi.com/2673-7094/3/3/48", DOI = "doi:10.3390/geotechnics3030048", abstract = "Geotechnical engineering relies heavily on predicting soil strength to ensure safe and efficient construction projects. This paper presents a study on the accurate prediction of soil strength properties, focusing on hydrated-lime activated rice husk ash (HARHA) treated soil. To achieve precise predictions, the researchers employed two grey-box machine learning models--classification and regression trees (CART) and genetic programming (GP). These models introduce innovative equations and trees that readers can readily apply to new databases. The models were trained and tested using a comprehensive laboratory database consisting of seven input parameters and three output variables. The results indicate that both the proposed CART trees and GP equations exhibited excellent predictive capabilities across all three output variables--California bearing ratio (CBR), unconfined compressive strength (UCS), and resistance value (Rvalue) (according to the in-situ cone penetrometer test). The GP proposed equations, in particular, demonstrated a superior performance in predicting the UCS and Rvalue parameters, while remaining comparable to CART in predicting the CBR. This research highlights the potential of integrating grey-box machine learning models with geotechnical engineering, providing valuable insights to enhance decision-making processes and safety measures in future infrastructural development projects.", notes = "also known as \cite{geotechnics3030048}", } @Article{BAGHERI:2019:Measurement, author = "Ali Bagheri and Ali Nazari and Jay Sanjayan", title = "The use of machine learning in boron-based geopolymers: Function approximation of compressive strength by {ANN} and {GP}", journal = "Measurement", volume = "141", pages = "241--249", year = "2019", ISSN = "0263-2241", DOI = "doi:10.1016/j.measurement.2019.03.001", URL = "http://www.sciencedirect.com/science/article/pii/S0263224119302106", keywords = "genetic algorithms, genetic programming, Boron-activated geopolymer, Artificial intelligence, Aluminosilicate, Machine learning, Energy and resources", abstract = "This paper employs artificial intelligence methods in order to create a function for compressive strength of the boroaluminosilicate geopolymers based on mixture proportion variables. Boroaluminosilicate geopolymers (BASGs), a group of boron-based alkali-activated materials, not only minimise the carbon footprint in the construction industry but also decrease the consumption of energy and natural resources. Australian fly ash and iron making slag are activated in sodium and boron-based alkaline medium in order to produce the geopolymer binders. The current study employs artificial neural network in order to classify the collected data into train, test, and validation followed by genetic programming for developing a function to approximate the compressive strength of BASGs. The independent variables comprise the percentage of fly ash and slag as well as ratios of boron, silicon, and sodium ions in the alkaline solution. The performance of each method is assessed by the acquired regression and the error parameters. The obtained results show that the percent of silicon and boron ions, with positive direct correlation and the largest power in the function respectively, have the most significant effects on the compressive strength of BASG. The assessment factors, including R-squared 0.95 and root-mean-square error 0.07 in the testing data, indicate that the model explains all the variability of the response data around its mean. It implies a high level of accuracy and reliability for the model", } @InProceedings{DBLP:conf/iscc/BagheriD06, author = "Ebrahim Bagheri and Hossein Deldari", title = "Dejong Function Optimization by Means of a Parallel Approach to Fuzzified Genetic Algorithm", booktitle = "Proceedings of the 11th IEEE Symposium on Computers and Communications (ISCC 2006)", year = "2006", editor = "Paolo Bellavista and Chi-Ming Chen and Antonio Corradi and Mahmoud Daneshmand", pages = "675--680", address = "Cagliari, Sardinia, Italy", month = "26-29 " # jun, publisher = "IEEE Computer Society", keywords = "genetic algorithms", ISBN = "0-7695-2588-1", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1109/ISCC.2006.57", abstract = "Genetic Algorithms are very powerful search methods that are used in different optimisation problems. Parallel versions of genetic algorithms are easily implemented and usually increase algorithm performance [4]. Fuzzy control as another optimisation solution along with genetic algorithms can significantly increase algorithm performance. Two variations for genetic algorithm and fuzzy system composition exist. In the first approach Genetic algorithms are used to optimise and model the structure of fuzzy systems through knowledge base or membership function design while the second approach exploits fuzzy to dynamically supervise genetic algorithm performance by speedily reaching an optimal solution. In this paper we propose a new method for fuzzy parallel genetic algorithms, in which a parallel client-server single population fuzzy genetic algorithm is configured to optimise the performance of the first three Dejong functions in order to reach a global solution in the least possible iterations. Simulations show much improvement in genetic algorithm performance evaluation.", } @Article{journals/es/BagheriGBS13, author = "Mehdi Bagheri and Amir Hossein Gandomi and Mehrdad Bagheri and Mohcen Shahbaznezhad", title = "Multi-expression programming based model for prediction of formation enthalpies of nitro-energetic materials", journal = "Expert Systems", year = "2013", volume = "30", number = "1", pages = "66--78", month = feb, keywords = "genetic algorithms, genetic programming, nitro-energetic materials, multi-expression programming, formation enthalpy, QSPR", DOI = "doi:10.1111/j.1468-0394.2012.00623.x", size = "33 pages", abstract = "There has been considerable interest in predicting the properties of nitro-energetic materials to improve their performance. Not to mention insightful physical knowledge, computational-aided molecular studies can expedite the synthesis of novel energetic materials through cost reduction labours and risky experimental tests. In this paper, quantitative structure-property relationship based on multi-expression programming employed to correlate the formation enthalpies of frequently used nitro-energetic materials with their molecular properties. The simple yet accurate obtained model is able to correlate the formation enthalpies of nitro-energetic materials to their molecular structure with the accuracy comparable to experimental precision.", } @Article{Bagheri:2015:SAR_QSAR_ER, author = "M. Bagheri and T. N. G. Borhani and A. H. Gandomi and Z. A. Manan", title = "A simple modelling approach for prediction of standard state real gas entropy of pure materials", journal = "SAR and QSAR in Environmental Research", year = "2014", volume = "25", number = "9", pages = "695--710", keywords = "genetic algorithms, genetic programming, linear genetic programming (LGP), standard state absolute entropy of real gases (SSTD), feed forward neural network (FFNN), quantitative structure entropy relationship, exergy analysis", URL = "http://www.tandfonline.com/doi/abs/10.1080/1062936X.2014.942356", URL = "http://www.tandfonline.com/doi/full/10.1080/1062936X.2014.942356", DOI = "doi:10.1080/1062936X.2014.942356", abstract = "The performance of an energy conversion system depends on exergy analysis and entropy generation minimisation. A new simple four-parameter equation is presented in this paper to predict the standard state absolute entropy of real gases (SSTD). The model development and validation were accomplished using the Linear Genetic Programming (LGP) method and a comprehensive dataset of 1727 widely used materials. The proposed model was compared with the results obtained using a three-layer feed forward neural network model (FFNN model). The root-mean-square error (RMSE) and the coefficient of determination (r2) of all data obtained for the LGP model were 52.24 J/(mol K) and 0.885, respectively. Several statistical assessments were used to evaluate the predictive power of the model. In addition, this study provides an appropriate understanding of the most important molecular variables for exergy analysis. Compared with the LGP based model, the application of FFNN improved the r-squared to 0.914. The developed model is useful in the design of materials to achieve a desired entropy value.", } @Article{BAGHERI:2019:PSEP, author = "Majid Bagheri and Ali Akbari and Sayed Ahmad Mirbagheri", title = "Advanced control of membrane fouling in filtration systems using artificial intelligence and machine learning techniques: A critical review", journal = "Process Safety and Environmental Protection", volume = "123", pages = "229--252", year = "2019", keywords = "genetic algorithms, genetic programming, Membrane bioreactors, Membrane fouling, Artificial intelligence, Machine learning, Control system", ISSN = "0957-5820", DOI = "doi:10.1016/j.psep.2019.01.013", URL = "http://www.sciencedirect.com/science/article/pii/S0957582018310863", abstract = "This paper critically reviews all artificial intelligence (AI) and machine learning (ML) techniques for the better control of membrane fouling in filtration processes, with the focus on water and wastewater treatment systems. Artificial neural networks (ANNs), fuzzy logic, genetic programming and model trees were found to be four successfully employed modeling techniques. The results show that well-known ANNs such as multilayer perceptron and radial basis function can predict membrane fouling with an R2 equal to 0.99 and an error approaching zero. Genetic algorithm (GA) and particle swarm optimization (PSO) are optimization methods successfully applied to optimize parameters related to membrane fouling. These optimization techniques indicated high capabilities in tuning various parameters such as transmembrane pressure, crossflow velocity, feed temperature, and feed pH. The results of this survey demonstrate that hybrid intelligent models using intelligent optimization methods such as GA and PSO for adjusting their weights and functions perform better than single models. Clustering analysis, image recognition, and feature selection are other employed intelligent techniques with positive role in the control of membrane fouling. The application of AI and ML techniques in an advanced control system can reduce the costs of treatment by monitoring of membrane fouling, and taking the best action when necessary", } @InProceedings{baglioni:2000:eampaa, author = "Stefania Baglioni and Celia da Costa Pereira and Dario Sorbello and Andrea G. B. Tettamanzi", title = "An Evolutionary Approach to Multiperiod Asset Allocation", booktitle = "Genetic Programming, Proceedings of EuroGP'2000", year = "2000", editor = "Riccardo Poli and Wolfgang Banzhaf and William B. Langdon and Julian F. Miller and Peter Nordin and Terence C. Fogarty", volume = "1802", series = "LNCS", pages = "225--236", address = "Edinburgh", publisher_address = "Berlin", month = "15-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-67339-3", URL = "http://mago.crema.unimi.it/pub/BaglioniDaCostaPereiraSorbelloTettamanzi2000.ps", DOI = "doi:10.1007/978-3-540-46239-2_16", abstract = "Portfolio construction can become a very complicated problem, as regulatory constraints, individual investor's requirements, non-trivial indices of risk and subjective quality measures are taken into account, together with multiple investment horizons and cash-flow planning. This problem is approached using a tree of possible scenarios for the future, and an evolutionary algorithm is used to optimize an investment plan against the desired criteria and the possible scenarios. An application to a real defined benefit pension fund case is discussed.", notes = "EuroGP'2000, part of \cite{poli:2000:GP}", } @InProceedings{bagnall:1999:UAABSMUME, author = "A. J. Bagnall and G. D. Smith", title = "Using an Adaptive Agent to Bid in a Simplified Model of the UK Market in Electricity", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "774", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/Bagnall1999b.ps.gz", URL = "http://www.cs.bris.ac.uk/~kovacs/lcs.archive/Bagnall1999b.ps.gz", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Bagula:2005:ciS, author = "Antoine B. Bagula and Hong F. Wang", title = "On the Relevance of Using Gene Expression Programming in Destination-Based Traffic Engineering", booktitle = "Computational Intelligence and Security", year = "2005", volume = "3801", series = "Lecture Notes in Computer Science", pages = "224--229", keywords = "genetic algorithms, genetic programming, Gene Expression Programming", isbn_13 = "978-3-540-30818-8", DOI = "doi:10.1007/11596448", abstract = "This paper revisits the problem of Traffic Engineering (TE) to assess the relevance of using Gene Expression Programming (GEP) as a new fine-tuning algorithm in destination-based TE. We present a new TE scheme where link weights are computed using GEP and used as fine-tuning parameters in destination-based path selection. We apply the newly proposed TE scheme to compute the routing paths for the traffic offered to a 23- and 30-node test networks under different traffic conditions and differentiated services situations. We evaluate the performance achieved by the GEP algorithm compared to a memetic and the Open Shortest Path First (OSPF) algorithms in a simulated routing environment using the NS packet level simulator. Preliminary results reveal the relative efficiency of GEP compared to the memetic algorithm and OSPF routing.", } @InProceedings{bagula_2006_NOMS, author = "Antoine B. Bagula", title = "Traffic Engineering Next Generation {IP} Networks Using Gene Expression Programming", booktitle = "10th IEEE/IFIP Network Operations and Management Symposium, NOMS 2006", year = "2006", pages = "230--239", address = "Vancouver", organisation = "IFIP", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Gene Expression Programming", DOI = "doi:10.1109/NOMS.2006.1687554", size = "10 pages", abstract = "This paper addresses the problem of Traffic Engineering (TE) to evaluate the performance of evolutionary algorithms when used as IP routing optimisers and assess the relevance of using {"}Gene Expression Programming (GEP){"} as a new fine-tuning algorithm in destination- and flow-based TE. We consider a TE scheme where link weights are computed using GEP and used as either fine-tuning parameters in Open Shortest Path First (OSPF) routing or static routing cost in Constraint Based Rouiigg((CRR. Thh reeuutligg SPFa nd CBR algorithms are referred to as OSPFgepand CBRgep. The GEP algorithm is based on a hybrid optimisation model where local search complements the global search implemented by classical evolutionary algorithms to improve the genetic individuals fitness through hill-climbing. We apply the newly proposed TE scheme to compute the routing paths for the traffic offered to a 23-, 28- and 30-node test networks under different traffic conditions and differentiated services situations. We evaluate the performance achieved by the OSPFgep, CBRgepalgorithms and OSPFmal, a destination-based routing algorithm where OSPF path selection is driven by the link weights computed by a Memetic Algorithm (MA). We compare the performance achieved by the OSPFgepalgorithm to the performance of the OSPFmaand OSPF algorithms in a simulated routing environment using NS. We also compare the quality of the paths found by the CBRgepalgorithm to the quality of the paths computed by the Constraint Shortest Path First (CSPF) algorithm when routing bandwidth-guaranteed tunnels using connection-level simulation.", } @PhdThesis{urn_nbn_se_kth_diva-4213-2__fulltext, author = "Antoine B. Bagula", title = "Hybrid Routing in Next Generation {IP} Networks: {QoS} Routing Mechanisms and Network Control Strategies", school = "Royal Institute of Technology (KTH)", year = "2006", type = "Doctor of Technology", address = "Stockholm, Sweden", month = dec, keywords = "genetic algorithms, genetic programming, Gene Expression Programming", URL = "http://kth.diva-portal.org/smash/record.jsf?pid=diva2:11272", URL = "http://kth.diva-portal.org/smash/get/diva2:11272/FULLTEXT01.pdf", size = "78 pages", abstract = "Communication networks have evolved from circuit-switched and hop-by-hop routed systems into hybrid data/optical networks using the Internet as a common backbone carrying narrow- and broad-band traffic offered by a multitude of access networks. This data/optical backbone is built around a multi-technology/multi-protocol routing architecture which runs the IP protocols in a collapsed IP stack where ATM and SONET/SDH have been replaced by the suite of Generalised Multiprotocol Label Switching (GMPLS) protocols. A further evolution referred to as ``IP over Photons'' or ``All IP - All Optical'' is expected where ``redundant intermediate layers'' will be eliminated to run IP directly on top of optical cross-connects (OXCs) with the expectation of achieving savings on operation expenditures (OPEX) and capital expenditures (CAPEX). ``IP over Photons'' has been stalled by the immaturity in the control and data plane technologies leading to complex and time-consuming manual network planning and configurations which require a group of ``layer experts'' to operate and maintain a hybrid data/optical network. By making the status of each link and node of a data/optical network visible to a common control, GMPLS protocols have opened the way for automated operation and management allowing the different layers of an IP stack to be managed by a single network operator. GMPLS protocols provide the potential to make more efficient use of the IP backbone by having network management techniques such as Traffic Engineering (TE) and Network Engineering (NE), once the preserve of telecommunications, to be reinvented and deployed to effect different Quality of Service (QoS) requirements in the IP networks. NE moves bandwidth to where the traffic is offered to the network while TE moves traffic to where the bandwidth is available to achieve QoS agreements between the current and expected traffic and the available resources. However,several issues need to be resolved before TE and NE be effectively deployed in emerging and next generation IP networks. These include (1) the identification of QoS requirements of the different network layer interfaces of the emerging and next generation IP stack (2) the mapping of these QoS requirements into QoS routing mechanisms and network control strategies and (3) the deployment of these mechanisms and strategies within and beyond an Internet domain's boundaries to maximise the engineering and economic efficiency. Building upon different frameworks and research fields, this thesis revisits the issue of Traffic and Network Engineering (TE and NE) to present and evaluate the performance of different QoS routing mechanisms and network control strategies when deployed at different network layer interfaces of a hybrid data/optical network where an IP over MPLS network is layered above an MP lambdaS/Fibre infrastructure. These include mechanisms and strategies to be deployed at the IP/MPLS, MPLS/MP LS and MP lambdaS/Fiber network layer interfaces. The main contributions of this thesis are threefold. First we propose and compare the performance of hybrid routing approaches to be deployed in IP/MPLS networks by combining connectionless routing mechanisms used by classical IGP protocols and the connection oriented routing approach borrowed from MPLS. Second, we present QoS routing mechanisms and network control strategies to be deployed at the MPLS/MP lambdaS network layer interface with a focus on contention-aware routing and inter-layer visibility to improve multi-layer optimality and resilience. Finally, we build upon fiber transmission characteristics to propose QoS routing mechanisms where the routing in the MPLS and MP lS layers is conducted by Photonic characteristics of the fiber such as the availability of the physical link and its failure risk group probability.", notes = "Public defence: 2006-12-12, Aula, KTH-Forum, Isafjordsgatan 39, Kista, 13:00 Supervisor: Pehrson, Bjorn (KTH, School of Information and Communication Technology (ICT), Electronic, Computer and Software Systems, ECS) Opponent: Stiller, Bukhard, Professor (ETH Zurich)", } @Article{BAHADORI:2024:jco2u, author = "Mohammad Keshavarz Bahadori and Reza Golhosseini and Mohammad Shokouhi and Ali T. Zoghi", title = "Mixing gamma-{Al2O3}, silica-{ZIF-8} and activated carbon nanoparticles in aqueous N-methyldiethanolamine+sulfolane as a nanofluid for application on {CO2} absorption", journal = "Journal of CO2 Utilization", volume = "79", pages = "102650", year = "2024", ISSN = "2212-9820", DOI = "doi:10.1016/j.jcou.2023.102650", URL = "https://www.sciencedirect.com/science/article/pii/S2212982023002615", keywords = "genetic algorithms, genetic programming, Hybrid alkanolamine solution, Si-ZIF-8, gamma-AlO, Super activated carbon, Nanofluid", abstract = "Mixing of Al2O3, superactive carbon (SAC), and Si-ZIF-8 nanoparticles in alkanol amine solvents with aqueous and hybrid context were used to study the absorption of carbon dioxide. Hybrid-based nanofluids composed of aqueous N- methyldiethanolamine (MDEA) + Sulfolane (SFL) + small amount of nano particles including-gamma-Al2O3, Si-ZIF-8 and SAC in three concentrations levels, were prepared by weighting each components. The Zeta potential of suspensions was found thatgamma-Al2O3, Si-ZIF-8 and super activated carbon is stable in aqueous solvents. Absorption measurements were performed using the static method at a temperature of T = 313.15 K, gas partial pressure ranges up to about 1.2 MPa, nanoparticle concentrations of 0.02 - 0.50 by weight percent, and fixed concentration of MDEA (40 wtpercent) and SFL (30 wtpercent). It was found that in the hybrid context (i.e., MDEA 40 wtpercent + SFL 30 wtpercent), the addition of gamma-Al2O3 (0.1 wtpercent), Si-ZIF-8 (0.02 wtpercent) and SAC (0.1 wtpercent) has maximum effect on CO2 absorption. In aqueous context (i.e., MDEA 40 wtpercent), nanofluids used in the present study have no significant effect on CO2 capacity. The importance of the addition of SFL in nanofluids was discussed, and the experimental solubility data was modeled using Genetic Programming approach. Average absolute relative deviation (AARD), Coefficient of determination R2, as well as Root mean square error (RMSE) were 10.43percent & 8.94percent, 0.985 & 0.983, 0.051 & 0.054 for training and validating data, respectively. It is found that the determined parameters give satisfactory predictions in the modeling of the solubility", } @Article{BAHADORI:2024:ceja, author = "Mohammad Keshavarz Bahadori and Mohammad Shokouhi and Reza Golhosseini", title = "Measurements of density and viscosity of carbon dioxide-loaded and -unloaded nano-fluids: Experimental, genetic programming and physical interpretation approaches", journal = "Chemical Engineering Journal Advances", volume = "18", pages = "100600", year = "2024", ISSN = "2666-8211", DOI = "doi:10.1016/j.ceja.2024.100600", URL = "https://www.sciencedirect.com/science/article/pii/S2666821124000188", keywords = "genetic algorithms, genetic programming, Density, Viscosity, Si-zif-8, Super activated carbon, Nano-fluid", abstract = "In the present study, the density and viscosity of the CO2-loaded and -unloaded base solution and nano-fluid were experimentally measured and investigated from an intermolecular point of view. Nano-fluids are composed of nano-particles such as Al2O3 (0.1 wt.percent), Silica-2-methylimidazole, zinc salt (Si-ZIF-8) (0.02 wt.percent), and super activated carbon (SAC) (0.1 wt.percent) dispersed in aqueous and hybrid Methyl diethanolamine context (MDEA, 40 wt.percent) +Sulfolane (SFL), 30 wt.percent) +H2O) Experimental measurements were carried out at the low-temperature ranges 303.15-315.15 K, atmospheric pressure, and three different CO2 loadings. The results show that nanomaterials do not have a significant effect on the density and viscosity of the unloaded suspension; however, the density and viscosity of loaded suspensions and base solvent become more by increasing CO2 concentration. In the case of CO2-loaded fluids, the comparison of the results in the presence and absence of nanoparticles shows that the density of the solution is not much different in the two cases, but the viscosity of CO2-loaded in Si-ZIF-8, SAC, and gamma-Al2O3 base nano-fluids in comparison with base solvent shows an increase of 35 percent in high CO2 loading, ~0.3 mol CO2 per mol MDEA. Density and viscosity experimental data were modeled using the Genetic Programming approach. The highest values of absolute average relative deviation (AARD) and root mean square error (RMSE) parameters obtained for modeling data are 3.04 and 0.317, respectively, and the lowest value of regression coefficient (R2) is 0.995, which indicates the appropriate fitting of the results", } @InProceedings{DBLP:conf/nafips/BaharZE16, author = "Hosein Hamisheh Bahar and Mohammad Hossein Fazel Zarandi and Akbar Esfahanipour", title = "Generating ternary stock trading signals using fuzzy genetic network programming", booktitle = "2016 Annual Conference of the North American Fuzzy Information Processing Society, {NAFIPS} 2016, El Paso, TX, USA, October 31 - November 4, 2016", pages = "1--6", publisher = "{IEEE}", year = "2016", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1109/NAFIPS.2016.7851630", DOI = "doi:10.1109/NAFIPS.2016.7851630", timestamp = "Wed, 16 Oct 2019 14:14:51 +0200", biburl = "https://dblp.org/rec/conf/nafips/BaharZE16.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{Bahiraie:2009:AJAS, author = "Alireza Bahiraie and Noor {Akma bt Ibrahim} and A. K. M. Azhar", title = "On the Predictability of Risk Box Approach by Genetic Programming Method for Bankruptcy Prediction", journal = "American Journal of Applied Sciences", year = "2009", volume = "6", number = "9", pages = "1748--1757", keywords = "genetic algorithms, genetic programming, ratios analysis, risk box, bankruptcy prediction", ISSN = "1546-9239", URL = "http://www.scipub.org/fulltext/ajas/ajas691748-1757.pdf", oai = "oai:doaj-articles:dbcbc387f7a40da02a20dffdfbef123f", size = "10 pages", abstract = "{\bf Problem statement: }Theoretical based data representation is an important tool for model selection and interpretations in bankruptcy analysis since the numerical representation are much less transparent. Some methodological problems concerning financial ratios such as non-proportionality, non-asymetricity, non-scalicity are solved in this study and we presented a complementary technique for empirical analysis of financial ratios and bankruptcy risk. {\bf Approach:} This study presented new geometric technique for empirical analysis of bankruptcy risk using financial ratios. Within this framework, we proposed the use of a new ratio representation which named Risk Box measure (RB). We demonstrated the application of this geometric approach for variable representation, data visualization and financial ratios at different stages of corporate bankruptcy prediction models based on financial balance sheet ratios. These stages were the selection of variables (predictors), accuracy of each estimation model and the representation of each model for transformed and common ratios. {\bf Results: }We provided evidence of extent to which changes in values of this index were associated with changes in each axis values and how this may alter our economic interpretation of changes in the patterns and direction of risk components. Results of Genetic Programming (GP) models were compared as different classification models and results showed the classifiers outperform by modified ratios.{\bf Conclusion/Recommendations:} In this study, a new dimension to risk measurement and data representation with the advent of the Share Risk method (SR) was proposed. Genetic programming method is substantially superior to the traditional methods such as MDA or Logistic method. It was strongly suggested the use of SR methodology for ratio analysis, which provided a conceptual and complimentary methodological solution to many problems associated with the use of ratios. Respectively, GP will provide heuristic non linear regression as a tool in providing forecasting regression for studies associated with financial data. Genetic programming as one of the modern classification method out performs by the use of modified ratios. Our new method would be a general methodological guideline associated with financial data analysis.", ISSN = "15469239", bibsource = "OAI-PMH server at www.doaj.org", } @Article{Bahrami:2016:Fuel, author = "Peyman Bahrami and Pezhman Kazemi and Sedigheh Mahdavi and Hossein Ghobadi", title = "A novel approach for modeling and optimization of surfactant/polymer flooding based on Genetic Programming evolutionary algorithm", journal = "Fuel", volume = "179", pages = "289--298", year = "2016", ISSN = "0016-2361", DOI = "doi:10.1016/j.fuel.2016.03.095", URL = "http://www.sciencedirect.com/science/article/pii/S0016236116301375", abstract = "In this research, Genetic Programming (GP) as a novel method for modelling the Recovery Factor (RF) and the Net Present Value (NPV) in Surfactant-Polymer (SP) flooding is presented. The GP modelling, has the advantage that the created models did not require a fundamental description of the physical processes. The GP created mathematical functions for both outputs as a function of important parameters which involves in the SP flooding based on 202 different data. Moreover, 10-fold cross validation were employed to check the models overfitting. The Normalized Root Mean Squared Error (NRMSE) and the coefficient of determination (R2) of 4.83percent, 0.963 for the RF model, and 5.68percent, 0.946 for NPV model represented the accuracy of models. The importance and effect of variables on models were investigated, and simultaneous optimization was performed on both models to find the best results in terms of higher RF and NPV. The highest values of 55.03 and 7.3 Million US Dollars (MMUSD) for RF and NPV were achieved as a result of this optimization.", keywords = "genetic algorithms, genetic programming, RSM, Optimization, Polymer-surfactant flooding, 10-Fold cross validation", notes = "Young Researchers and Elite Club, Science and Research Branch, Islamic Azad University, Tehran, Iran Faculty of Pharmacy, Departments of Pharmaceutical Technology and Biopharmaceutics, Jagiellonian University, Krakow, Poland", } @Article{Bahreini-Toussi:2021:MCA, author = "Iman {Bahreini Toussi} and Abdolmajid Mohammadian and Reza Kianoush", title = "Prediction of Maximum Pressure at the Roofs of Rectangular Water Tanks Subjected to Harmonic Base Excitation Using the {Multi-Gene} Genetic Programming Method", journal = "Mathematical and Computational Applications", year = "2021", volume = "26", number = "1", keywords = "genetic algorithms, genetic programming", ISSN = "2297-8747", URL = "https://www.mdpi.com/2297-8747/26/1/6", DOI = "doi:10.3390/mca26010006", abstract = "Liquid storage tanks subjected to base excitation can cause large impact forces on the tank roof, which can lead to structural damage as well as economic and environmental losses. The use of artificial intelligence in solving engineering problems is becoming popular in various research fields, and the Genetic Programming (GP) method is receiving more attention in recent years as a regression tool and also as an approach for finding empirical expressions between the data. In this study, an OpenFOAM numerical model that was validated by the authors in a previous study is used to simulate various tank sizes with different liquid heights. The tanks are excited in three different orientations with harmonic sinusoidal loadings. The excitation frequencies are chosen as equal to the tanks natural frequencies so that they would be subject to a resonance condition. The maximum pressure in each case is recorded and made dimensionless; then, using Multi-Gene Genetic Programming (MGGP) methods, a relationship between the dimensionless maximum pressure and dimensionless liquid height is acquired. Finally, some error measurements are calculated, and the sensitivity and uncertainty of the proposed equation are analysed.", notes = "also known as \cite{mca26010006}", } @InProceedings{Bai:2010:ISDA, author = "Haiying Bai and Noriko Yata and Tomoharu Nagao", title = "Efficient evolutionary image processing using genetic programming: Reducing computation time for generating feature images of the Automatically Construction of Tree-Structural Image Transformation (ACTIT)", booktitle = "10th International Conference on Intelligent Systems Design and Applications (ISDA 2010)", year = "2010", month = nov # " 29-" # dec # " 1", pages = "302--307", abstract = "Using well-established techniques of Genetic Programming (GP), we automatically optimise image feature filters over several inputs and within transformation images, improving the Automatic Construction of Tree-Structural Image Transformation (ACTIT) system. Our objective is to also produce optimal solutions in substantially less computation time than require for generating features of ACTIT. We improved the algorithm feature filters in the process through GP, which are expressed by trees in Automatic Construction of Tree-Structural Image Transformation, to reduce computation time. Through our experimentation, we show that our new approach is accurate and requires less computation time by maintaining the feature images in conjunction with the original images.", keywords = "genetic algorithms, genetic programming, ACTIT, automatically construction of tree-structural image transformation, evolutionary image processing, image feature filters, transformation images, image processing", DOI = "doi:10.1109/ISDA.2010.5687249", notes = "Grad. Sch. of Environ. & Inf. Sci., Yokohama Nat. Univ., Yokohama, Japan. Also known as \cite{5687249}", } @Article{Bai:2025:GPEM, author = "Lin Bai and Mengchen Sun and Xianlin Jiang and Jingxuan Liu and Jialu Liu and Xiaoying Pan", title = "An oversampling method based on adaptive artificial immune network and {SMOTE}", journal = "Genetic Programming and Evolvable Machines", year = "2025", volume = "26", pages = "Article no: 20", note = "Online first", keywords = "genetic algorithms, genetic programming, AIS, Artificial immune network, Imbalanced data, Oversampling, Data distribution, AdaBoost, DT, SVM, XGBoost, Ablation", ISSN = "1389-2576", URL = "https://rdcu.be/eru8m", DOI = "doi:10.1007/s10710-025-09516-7", size = "71 pages", abstract = "ADAIN-SMOTE evolves a network structure capable of mapping the distribution of the original data, which is then used to augment the minority class", notes = "School of Computer Science, Xi'an University of Posts and Telecommunications, Xi'an 710121, China", } @InProceedings{Bai:2008:ieeeSMI, author = "Linge Bai and Manolya Eyiyurekli and David E. Breen", title = "Self-organizing primitives for automated shape composition", booktitle = "IEEE International Conference on Shape Modeling and Applications, SMI 2008", year = "2008", month = jun, pages = "147--154", keywords = "genetic algorithms, genetic programming, automated shape composition, cell behavior, chemical-field-driven aggregation, chemotaxis-driven aggregation behavior, cumulative chemical field, evolutionary computing process, fitness measure, macroscopic shape, mathematical function, morphogenic primitives, self-organizing primitive, shape formation, shape modeling, structure formation, computational geometry", DOI = "doi:10.1109/SMI.2008.4547962", abstract = "Motivated by the ability of living cells to form into specific shapes and structures, we present a new approach to shape modeling based on self-organizing primitives whose behaviors are derived via genetic programming. The key concept of our approach is that local interactions between the primitives direct them to come together into a macroscopic shape. The interactions of the primitives, called morphogenic primitives (MP), are based on the chemotaxis-driven aggregation behaviors exhibited by actual living cells. Here, cells emit a chemical into their environment. Each cell responds to the stimulus by moving in the direction of the gradient of the cumulative chemical field detected at its surface. MPs, though, do not attempt to completely mimic the behavior of real cells. The chemical fields are explicitly defined as mathematical functions and are not necessarily physically accurate. The explicit mathematical form of the chemical field functions are derived via genetic programming (GP), an evolutionary computing process that evolves a population of functions. A fitness measure, based on the shape that emerges from the chemical-field-driven aggregation, determines which functions will be passed along to later generations. This paper describes the cell interactions of MPs and the GP-based method used to define the chemical field functions needed to produce user- specified shapes from simple aggregating primitives.", notes = "Also known as \cite{4547962}", } @InProceedings{Bai:2008:gecco, author = "Linge Bai and Manolya Eyiyurekli and David E. Breen", title = "Automated shape composition based on cell biology and distributed genetic programming", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "1179--1186", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1179.pdf", DOI = "doi:10.1145/1389095.1389329", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, chemotaxis, distributed genetic programming, morphogenesis, self-organisation, shape composition", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389329}", } @InProceedings{Bai:2008:SASO, author = "Linge Bai and Manolya Eyiyurekli and David E. Breen", title = "An Emergent System for Self-Aligning and Self-Organizing Shape Primitives", booktitle = "Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems, SASO '08", year = "2008", month = oct, pages = "445--454", keywords = "genetic algorithms, genetic programming, direct morphogenetic primitives, emergent behavior, emergent system, evolutionary computing, living cells, local interaction rules, natural phenomenon, self-aligning shape primitives, self-organizing shape primitives, simulation system, user-defined shape, computational geometry", DOI = "doi:10.1109/SASO.2008.54", abstract = "Motivated by the natural phenomenon of living cells self-organizing into specific shapes and structures, we present an emergent system that uses evolutionary computing methods for designing and simulating self-aligning and self-organizing shape primitives.Given the complexity of the emergent behavior, genetic programming is employed to control the evolution of our emergent system. The system has two levels of description. At the macroscopic level, a user-specified, pre-defined shape is given as input to the system. The system outputs local interaction rules that direct morphogenetic primitives (MP) to aggregate into the shape. At the microscopic level, MPs follow interaction rules based only on local interactions. All MPs are identical and do not know the final shape to be formed. The aggregate is then evaluated at the macroscopic level for its similarity to the user-defined shape. In this paper, we present (1) an emergent system that discovers local interaction rules that direct MPs to form user-defined shapes, (2) the simulation system that implements these rules and causes MPs to self-align and self-organize into a user-defined shape, and (3) the robustness and scalability qualities of the overall approach.", notes = "Also known as \cite{4663447}", } @InCollection{Bai:2012:ME, author = "Linge Bai and David E. Breen", title = "Chemotaxis-Inspired Cellular Primitives for Self-Organizing Shape Formation", booktitle = "Morphogenetic Engineering", publisher = "Springer", year = "2012", editor = "Rene Doursat and Hiroki Sayama and Olivier Michel", series = "Understanding Complex Systems", chapter = "9", pages = "209--237", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-33901-1", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.306.4523", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.306.4523", URL = "http://dx.doi.org/10.1007/978-3-642-33902-8_9", DOI = "doi:10.1007/978-3-642-33902-8_9", size = "29 pages", abstract = "Motivated by the ability of living cells to form specific shapes and structures, we are investigating chemotaxis-inspired cellular primitives for self-organising shape formation. This chapter details our initial effort to create Morphogenetic Primitives (MPs), software agents that may be programmed to self-organise into user specified 2D shapes. The interactions of MPs are inspired by chemotaxis-driven aggregation behaviours exhibited by actual living cells. Cells emit a chemical into their environment. Each cell responds to the stimulus by moving in the direction of the gradient of the cumulative chemical field detected at its surface. The artificial chemical fields of individual MPs are explicitly defined as mathematical functions. Genetic programming is used to discover the chemical field functions that produce an automated shape formation capability. We describe the cell-based behaviours of MPs and a distributed genetic programming method that discovers the chemical fields needed to produce macroscopic shapes from simple aggregating primitives. Several examples of aggregating MPs demonstrate that chemotaxis is an effective paradigm for spatial self-organization algorithms.", } @PhdThesis{Bai_LingePhD, author = "Linge Bai", title = "Chemotaxis-based Spatial Self-Organization Algorithms", school = "Department of Computer Science, Drexel University", year = "2014", address = "Philadelphia, USA", month = aug, keywords = "genetic algorithms, genetic programming, Chemotaxis, Self-organizing systems", URL = "https://www.cs.drexel.edu/~david/Abstracts/bai_phd-abs.html", URL = "http://hdl.handle.net/1860/idea:6006", URL = "https://idea.library.drexel.edu/islandora/object/idea%3A6006/datastream/OBJ/download/Chemotaxis-based_spatial_self-organization_algorithms.pdf", size = "164 pages", abstract = "Self-organization is a process that increases the order of a system as a result of local interactions among low-level, simple components, without the guidance of an outside source. Spatial self-organization is a process in which shapes and structures emerge at a global level from collective movements of low level shape primitives. Spatial self-organization is a stochastic process, and the outcome of the aggregation cannot necessarily be guaranteed. Despite the inherent ambiguity, self-organizing complex systems arise everywhere in nature. Motivated by the ability of living cells to form specific shapes and structures, we develop two self-organizing systems towards the ultimate goal of directing the spatial self-organizing process. We first develop a self-sorting system composed of a mixture of cells. The system consistently produces a sorted structure. We then extend the sorting system to a general shape formation system. To do so, we introduce morphogenetic primitives (MP), defined as software agents, which enable self-organizing shape formation of user-defined structures through a chemotaxis paradigm. One challenge that arises from the shape formation process is that the process may form two or more stable final configurations. In order to direct the self-organizing process, we find a way to characterize the macroscopic configuration of the MP swarm. We demonstrate that statistical moments of the primitives locations can successfully capture the macroscopic structure of the aggregated shape. We do so by predicting the final configurations produced by our spatial self-organization system at an early stage in the process using features based on the statistical moments. At the next stage, we focus on developing a technique to control the outcome of bifurcating aggregations. We identify thresholds of the moments and generate biased initial conditions whose statistical moments meet the thresholds. By starting simulations with biased, random initial configurations, we successfully control the aggregation for a number of swarms produced by the agent-based shape formation system. This thesis demonstrates that chemotaxis can be used as a paradigm to create an agent-based spatial self-organization system. Furthermore, statistical moments of the swarm can be used to robustly predict and control the outcomes of the aggregation process.", notes = "3.5.2 Chemical Field Evolution via Genetic Programming https://www.cs.drexel.edu/~david/geom_biomed_comp.html#morph_prim Supervisor David Breen", } @Article{BAI:2024:ress, author = "Rui Bai and Khandaker Noman and Yu Yang2 and Yongbo Li and Weiguo Guo", title = "Towards trustworthy remaining useful life prediction through multi-source information fusion and a novel {LSTM-DAU} model", journal = "Reliability Engineering \& System Safety", year = "2024", volume = "245", pages = "110047", month = may, keywords = "genetic algorithms, genetic programming, Health index (HI), Trustworthy remaining useful life prediction, Multi-source fusion, LSTM, Dual attention unit", ISSN = "0951-8320", URL = "https://www.sciencedirect.com/science/article/pii/S0951832024001224", DOI = "doi:10.1016/j.ress.2024.110047", abstract = "Remaining useful life (RUL) prediction is a key part of the prognostic and health management of machines, which can effectively prevent catastrophic faults and decrease expensive unplanned maintenance. A good health indicator (HI) can ensure the accuracy and reliability of RUL prediction. However, most of the existing HI construction methods use only a single signal and rely heavily on prior knowledge, making it difficult to capture critical information about mechanical degradation, which in turn affects the performance of RUL prediction. To solve the above problems, a novel adaptive multi-source fusion method based on genetic programming is proposed for building a HI that can effectively reflect the health state of machines. Subsequently, a new LSTM model with a dual-attention mechanism is developed, which differentially handles the network input information and the recurrent information to improve the prediction performance and reduce the time complexity at the same time. Experimental validation is carried out on the real PRONOSTIA bearing dataset. The comparative results validate that the constructed fusion HI has better comprehensive performance than other fusion HIs, and the proposed prediction method is competitive with the current state-of-the-art methods", } @InProceedings{Bailey:2012:GECCO, author = "Alexander Bailey and Mario Ventresca and Beatrice Ombuki-Berman", title = "Automatic generation of graph models for complex networks by genetic programming", booktitle = "GECCO '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference", year = "2012", editor = "Terry Soule and Anne Auger and Jason Moore and David Pelta and Christine Solnon and Mike Preuss and Alan Dorin and Yew-Soon Ong and Christian Blum and Dario Landa Silva and Frank Neumann and Tina Yu and Aniko Ekart and Will Browne and Tim Kovacs and Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and Giovanni Squillero and Nicolas Bredeche and Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and Martin Pelikan and Silja Meyer-Nienberg and Christian Igel and Greg Hornby and Rene Doursat and Steve Gustafson and Gustavo Olague and Shin Yoo and John Clark and Gabriela Ochoa and Gisele Pappa and Fernando Lobo and Daniel Tauritz and Jurgen Branke and Kalyanmoy Deb", isbn13 = "978-1-4503-1177-9", pages = "711--718", keywords = "genetic algorithms, genetic programming", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", URL = "http://cs.adelaide.edu.au/~brad/papers/alexanderThielPeacock.pdf", DOI = "doi:10.1145/2330163.2330263", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Complex networks have attracted a large amount of research attention, especially over the past decade, due to their prevalence and importance in our daily lives. Numerous human-designed models have been proposed that aim to capture and model different network structures, for the purpose of improving our understanding the real-life phenomena and its dynamics in different situations. Groundbreaking work in genetics, medicine, epidemiology, neuroscience, telecommunications, social science and drug discovery, to name some examples, have directly resulted. Because the graph models are human made (a very time consuming process) using a small subset of example graphs, they often exhibit inaccuracies when used to model similar structures. This paper represents the first exploration into the use of genetic programming for automating the discovery and algorithm design of graph models, representing a totally new approach with great interdisciplinary application potential. We present exciting initial results that show the potential of GP to replicate existing complex network algorithms.", notes = "alexanderThielPeacock.pdf corrected version (fixed typo in background resistivity) Entered for 2013 HUMIES GECCO 2013 Also known as \cite{2330263} GECCO-2012 A joint meeting of the twenty first international conference on genetic algorithms (ICGA-2012) and the seventeenth annual genetic programming conference (GP-2012)", } @InProceedings{Bailey:2013:GECCO, author = "Alexander Bailey and Beatrice Ombuki-Berman and Mario Ventresca", title = "Automatic inference of hierarchical graph models using genetic programming with an application to cortical networks", booktitle = "GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference", year = "2013", editor = "Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi", isbn13 = "978-1-4503-1963-8", pages = "893--900", keywords = "genetic algorithms, genetic programming", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2463372.2463498", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "The pathways that relay sensory information within the brain form a network of connections, the precise organisation of which is unknown. Communities of neurons can be discerned within this tangled structure, with inhomogeneously distributed connections existing between cortical areas. Classification and modelling of these networks has led to advancements in the identification of unhealthy or injured brains, however, the current models used are known to have major deficiencies. Specifically, the community structure of the cortex is not accounted for in existing algorithms, and it is unclear how to properly design a more representative graph model. It has recently been demonstrated that genetic programming may be useful for inferring accurate graph models, although no study to date has investigated the ability to replicate community structure. In this paper we propose the first GP system for the automatic inference of algorithms capable of generating, to a high accuracy, networks with community structure. We use a common cat cortex data set to highlight the efficacy of our approach. Our experiments clearly show that the inferred graph model generates a more representative network than those currently used in scientific literature.", notes = "Also known as \cite{2463498} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)", } @Article{Bailey:2014:ieeeTEC, author = "Alexander Bailey and Mario Ventresca and Beatrice Ombuki-Berman", title = "Genetic Programming for the Automatic Inference of Graph Models for Complex Networks", journal = "IEEE Transactions on Evolutionary Computation", year = "2014", volume = "18", number = "3", pages = "405--419", month = jun, keywords = "genetic algorithms, genetic programming, complex networks, Evolutionary Computation", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2013.2281452", size = "15 pages", abstract = "Complex networks are becoming an integral tool for our understanding of an enormous variety of natural and artificial systems. A number of human-designed network generation procedures have been proposed that reasonably model specific real-life phenomena in structure and dynamics. Consequently, breakthroughs in genetics, medicine, epidemiology, neuroscience, telecommunications and the social sciences have recently resulted. A graph model is an algorithm capable of constructing arbitrarily sized networks, whose end structure will exhibit certain statistical and structural properties. The process of deriving an accurate graph model is very time intensive and challenging and may only yield highly accurate models for very specific phenomena. An automated approach based on Genetic Programming was recently proposed by the authors. However, this initial system suffered from a number of drawbacks, including an under-emphasis on creating hub vertices, the requirement of user intervention to determine objective weights and the arbitrary approach to selecting the most representative model from a population of candidate models. In this paper we propose solutions to these problems and show experimentally that the new system represents a significant improvement and is very capable of reproducing existing common graph models from even a single small initial network.", notes = "also known as \cite{6595618}", } @InProceedings{bain:2004:eafcs, title = "Evolving Algorithms for Constraint Satisfaction", author = "Stuart Bain and John Thornton and Abdul Sattar", pages = "265--272", booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary Computation", year = "2004", publisher = "IEEE Press", month = "20-23 " # jun, address = "Portland, Oregon", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Combinatorial \& numerical optimization", URL = "http://stuart.multics.org/publications/CEC2004.pdf", URL = "https://research-repository.griffith.edu.au/bitstream/handle/10072/2138/27736.pdf", DOI = "doi:10.1109/CEC.2004.1330866", size = "8 pages", abstract = "This paper proposes a framework for automatically evolving constraint satisfaction algorithms using genetic programming. The aim is to overcome the difficulties associated with matching algorithms to specific constraint satisfaction problems. A representation is introduced that is suitable for genetic programming and that can handle both complete and local search heuristics. In addition, the representation is shown to have considerably more flexibility than existing alternatives, being able to discover entirely new heuristics and to exploit synergies between heuristics. In a preliminary empirical study it is shown that the new framework is capable of evolving algorithms for solving the well-studied problem of boolean satisfiability testing.", notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.", } @InProceedings{bain04methods, author = "Stuart Bain and John Thornton and Abdul Sattar", title = "Methods of Automatic Algorithm Generation", booktitle = "8th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2004", year = "2004", editor = "Chengqi Zhang and Hans W. Guesgen and Wai-Kiang Yeap", volume = "3157", series = "Lecture Notes in Computer Science", pages = "144--153", address = "Auckland, New Zealand", month = aug # " 9-13", publisher = "Springer", keywords = "genetic algorithms, genetic programming, CSP", isbn13 = "978-3-540-22817-2", URL = "http://www.ict.griffith.edu.au/~johnt/publications/PRICAI2004stuart.pdf", DOI = "doi:10.1007/978-3-540-28633-2_17", size = "10 pages", abstract = "Many methods have been proposed to automatically generate algorithms for solving constraint satisfaction problems. The aim of these methods has been to overcome the difficulties associated with matching algorithms to specific constraint satisfaction problems. This paper examines three methods of generating algorithms: a randomised search, a beam search and an evolutionary method. The evolutionary method is shown to have considerably more flexibility than existing alternatives, being able to discover entirely new heuristics and to exploit synergies between heuristics.", notes = "uf100-01.cnf", } @InProceedings{bain05evolving, author = "Stuart Bain and John Thornton and Abdul Sattar", title = "Evolving variable-ordering heuristics for constrained optimisation", booktitle = "Principles and Practice of Constraint Programming: CP'05", year = "2005", editor = "Peter {van Beek}", volume = "3709", series = "Lecture Notes in Computer Science", pages = "732--736", address = "Sitges, Spain", month = oct # " 1-5", publisher = "Springer", keywords = "genetic algorithms, genetic programming, SAT, DIMACS", isbn13 = "978-3-540-29238-8", URL = "http://www.ict.griffith.edu.au/~johnt/publications/CP2005stuart.pdf", DOI = "doi:10.1007/11564751_54", size = "5 pages", abstract = "we present and evaluate an evolutionary approach for learning new constraint satisfaction algorithms, specifically for MAX-SAT optimisation problems. Our approach offers two significant advantages over existing methods: it allows the evolution of more complex combinations of heuristics, and; it can identify fruitful synergies among heuristics. Using four different classes of MAX-SAT problems, we experimentally demonstrate that algorithms evolved with this method exhibit superior performance in comparison to general purpose methods.", notes = "uuf100, jnh, quasigroup, MAX-2-SAT, GSAT An extended version of this paper is available from the authors homepage at http://stuart.multics.org", } @InProceedings{bain05comparison, author = "Stuart Bain and John Thornton and Abdul Sattar", title = "A Comparison of Evolutionary Methods for the Discovery of Local Search Heuristics", booktitle = "Australian Conference on Artificial Intelligence: AI'05", year = "2005", editor = "Shichao Zhang and Ray Jarvis", volume = "3809", series = "Lecture Notes in Computer Science", pages = "1068--1074", address = "Sydney", month = dec # " 5-9", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-30462-3", URL = "http://www.ict.griffith.edu.au/~s661641/publications/AI2005stuart.pdf", DOI = "doi:10.1007/11589990_142", size = "6 pages", abstract = "Methods of adaptive constraint satisfaction have recently become of interest to overcome the limitations imposed on black-box search algorithms by the no free lunch theorems. Two methods that each use an evolutionary algorithm to adapt to particular classes of problem are the CLASS system of Fukunaga and the evolutionary constraint algorithm work of Bain et al. We directly compare these methods, demonstrating that although special purpose methods can learn excellent algorithms, on average standard evolutionary operators perform even better, and are less susceptible to the problems of bloat and redundancy.", notes = "UBCSAT, GSAT, WALKSAT and NOVELTY. uf100-01 through uf100-050 from SATLIB An appendix is available on the author homepage at http://stuart.multics.org", } @PhdThesis{Bain:thesis, author = "Stuart Iain Bain", title = "Evolving Algorithms for Over-Constrained and Satisfaction Problems", school = "School of Information and Communication Technology, Griffith University", year = "2006", address = "Brisbane, Queensland, Australia", month = nov, keywords = "genetic algorithms, genetic programming", URL = "http://stuart.freeshell.org/pubs/bain06evolving.pdf", broken_url = "https://experts.griffith.edu.au/publication/n0c3cebffccba31781b944d6c54e6049b", URL = "http://hdl.handle.net/10072/365848", DOI = "doi:10.25904/1912/1794", size = "131 pages", abstract = "The notion that a universally effective problem solver may still exist, and is simply waiting to be found, is slowly being abandoned in the light of a growing body of work reporting on the narrow applicability of individual heuristics. As the formalism of the constraint satisfaction problem remains a popular choice for the representation of problems to be solved algorithmically, there exists an ongoing need for new algorithms to efficiently handle the disparate range of problems that have been posed in this representation. Given the costs associated with manually applying human algorithm development and problem solving expertise, methods that can automatically adapt to the particular features of a specific class of problem have begun to attract more attention. Whilst a number of authors have developed adaptive systems, the field, and particularly with respect to their application to constraint satisfaction problems, has seen only limited discussion as to what features are desirable for an adaptive constraint system. This may well have been a limiting factor with previous implementations, which have exhibited only subsets of the five features identified in this work as important to the utility of an adaptive constraint satisfaction system. Whether an adaptive system exhibits these features depends on both the chosen representation and the method of adaptation. In this thesis, a three-part representation for constraint algorithms is introduced, which defines an algorithm in terms of contention, preference and selection functions. An adaptive system based on genetic programming is presented that adapts constraint algorithms described using the mentioned three-part representation. This is believed to be the first use of standard genetic programming for learning constraint algorithms. Finally, to further demonstrate the efficacy of this adaptive system, its performance in learning specialised algorithms for hard, real-world problem instances is thoroughly evaluated. These instances include random as well as structured instances from known-hard benchmark distributions, industrial problems (specifically, SAT-translated planning and cryptographic problems) as well as over-constrained problem instances. The outcome of this evaluation is a set of new algorithms, valuable in their own right, specifically tailored to these problem classes. Partial results of this work have appeared in the following publications: [1] Stuart Bain, John Thornton, and Abdul Sattar (2004) Evolving algorithms for constraint satisfaction. In Proc. of the 2004 Congress on Evolutionary Computation, pages 265-272. [2] Stuart Bain, John Thornton, and Abdul Sattar (2004) Methods of automatic algorithm generation. In Proc. of the 9th Pacific Rim Conference on AI, pages 144-153. [3] Stuart Bain, John Thornton, and Abdul Sattar. (2005) A comparison of evolutionary methods for the discovery of local search heuristics. In Australian Conference on Artificial Intelligence: AI'05, pages 1068-1074. [4] Stuart Bain, John Thornton, and Abdul Sattar (2005) Evolving variable-ordering heuristics for constrained optimisation. In Principles and Practice of Constraint Programming: CP'05, pages 732-736.", notes = "Supervisors: Abdul Sattar and John Thornton", } @Article{bains:2002:CODDD, author = "William Bains and Richard Gilbert and Lilya Sviridenko and Jose-Miguel Gascon and Robert Scoffin and Kris Birchall and Inman Harvey and John Caldwell", title = "Evolutionary computational methods to predict oral bioavailability {QSPRs}", journal = "Current Opinion in Drug Discovery and Development", year = "2002", volume = "5", number = "1", pages = "44--51", month = jan, keywords = "genetic algorithms, genetic programming", ISSN = "1367-6733", broken = "http://www.labome.org/expert/bains/w-bains-570643.html", abstract = "This review discusses evolutionary and adaptive methods for predicting oral bioavailability (OB) from chemical structure. Genetic Programming (GP), a specific form of evolutionary computing, is compared with some other advanced computational methods for OB prediction. The results show that classifying drugs into 'high' and 'low' OB classes on the basis of their structure alone is solvable, and initial models are already producing output that would be useful for pharmaceutical research. The results also suggest that quantitative prediction of OB will be tractable. Critical aspects of the solution will involve the use of techniques that can: (i) handle problems with a very large number of variables (high dimensionality); (ii) cope with 'noisy' data; and (iii) implement binary choices to sub-classify molecules with behavior that are qualitatively different. Detailed quantitative predictions will emerge from more refined models that are hybrids derived from mechanistic models of the biology of oral absorption and the power of advanced computing techniques to predict the behavior of the components of those models in silico.", notes = "PMID: 11865672 [PubMed - indexed for MEDLINE] Amedis Pharmaceuticals. Review, Tutorial http://www.ncbi.nlm.nih.gov/nlmcatalog?term=1367-6733[ISSN] Oct 2016 Curr Opin Drug Discov Devel seems to have published from 1998 to 2010. ", } @Article{bains:2004:PBMB, author = "William Bains and Antranig Basman and Cat White", title = "{HERG} binding specificity and binding site structure: Evidence from a fragment-based evolutionary computing {SAR} study", journal = "Progress in Biophysics and Molecular Biology", year = "2004", volume = "86", pages = "205--233", number = "2", month = oct, keywords = "genetic algorithms, genetic programming, HERG, IKr, QSAR, Molecular descriptors, Prediction", DOI = "doi:10.1016/j.pbiomolbio.2003.09.001", abstract = "We describe the application of genetic programming, an evolutionary computing method, to predicting whether small molecules will block the HERG cardiac potassium channel. Models based on a molecular fragment-based descriptor set achieve an accuracy of 85-90% in predicting whether the IC50 of a 'blind' set of compounds is <1 [mu]M. Analysis of the models provides a 'meta-SAR', which predicts a pharmacophore of two hydrophobic features, one preferably aromatic and one preferably nitrogen-containing, with a protonatable nitrogen asymmetrically situated between them. Our experience of the approach suggests that it is robust, and requires limited scientist input to generate valuable predictive results and structural understanding of the target.", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6TBN-4BS4DJM-1/2/2bd8833742e401378469ee988d571705", size = "29 pages", notes = "Amedis, lilgp, Fixed weighted sum of ROC and Akaike fitness criterion (AIC) Many descriptors including not only description of compound but who and how measurements were made. GP run many times (1028+). http://www.elsevier.com/wps/find/journaldescription.cws_home/408/description#description Amedis Pharmaceuticals, Unit 162 Cambridge Science Park, Milton Road, Cambridge, UK p206 {"}...Fatal cardiac arrhythmias...{"} {"}HERG binds to many compounds.{"} {"}Testing for HERG...effectively mandatory...{"} p207/p221 empirically derived penalty for {"}over-complex models, in order to prevent over-fitting{"} p211 {"}GP can select efficiently from a large number of input variables{"}. p215 big difference between Training and validation ROC. Extracting chemically meaningful reasoning from evolved solutions. Relationship with possible mechanisms in HERG gap in cell wall. p226 {"}we belive it is efficient use of the machine{"} [ie computer time].", } @Article{Baird:2006:RNA, author = "Stephen D. Baird and Marcel Turcotte and Robert G. Korneluk and Martin Holcik", title = "Searching for IRES", journal = "RNA", year = "2006", volume = "12", number = "10", pages = "1755--1785", month = oct, publisher = "RNA Society", keywords = "genetic algorithms, genetic programming, IRES, RNA, secondary structure, prediction software", DOI = "doi:10.1261/rna.157806", abstract = "The cell has many ways to regulate the production of proteins. One mechanism is through the changes to the machinery of translation initiation. These alterations favor the translation of one subset of mRNAs over another. It was first shown that internal ribosome entry sites (IRESes) within viral RNA genomes allowed the production of viral proteins more efficiently than most of the host proteins. The RNA secondary structure of viral IRESes has sometimes been conserved between viral species even though the primary sequences differ. These structures are important for IRES function, but no similar structure conservation has yet to be shown in cellular IRES. With the advances in mathematical modeling and computational approaches to complex biological problems, is there a way to predict an IRES in a data set of unknown sequences? This review examines what is known about cellular IRES structures, as well as the data sets and tools available to examine this question. We find that the lengths, number of upstream AUGs, and %GC content of 5'-UTRs of the human transcriptome have a similar distribution to those of published IRES-containing UTRs. Although the UTRs containing IRESes are on the average longer, almost half of all 5'-UTRs are long enough to contain an IRES. Examination of the available RNA structure prediction software and RNA motif searching programs indicates that while these programs are useful tools to fine tune the empirically determined RNA secondary structure, the accuracy of de novo secondary structure prediction of large RNA molecules and subsequent identification of new IRES elements by computational approaches, is still not possible.", notes = "Paragraph on \cite{Yuh-JyhHu:2003:NAR} PMCID: PMC1581980", } @InProceedings{Bajurnow:aspgp03, author = "Andrei Bajurnow and Vic Ciesielski", title = "Function and terminal Set Selection for Evolving Goal Scoring Behaviour in Soccer Players", booktitle = "Proceedings of The First Asian-Pacific Workshop on Genetic Programming", year = "2003", editor = "Sung-Bae Cho and Nguyen Xuan Hoai and Yin Shan", pages = "38--44", address = "Rydges (lakeside) Hotel, Canberra, Australia", month = "8 " # dec, keywords = "genetic algorithms, genetic programming", ISBN = "0-9751724-0-9", notes = "\cite{aspgp03}", } @InProceedings{bajurnow:2004:llfegsbisp, title = "Layered Learning for Evolving Goal Scoring Behavior in Soccer Players", author = "Andrei Bajurnow and Vic Ciesielski", pages = "1828--1835", booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary Computation", year = "2004", publisher = "IEEE Press", month = "20-23 " # jun, address = "Portland, Oregon", ISBN = "0-7803-8515-2", URL = "http://goanna.cs.rmit.edu.au/~vc/papers/cec2004-bajurnow.pdf", DOI = "doi:10.1109/CEC.2004.1331118", size = "8 pages", keywords = "genetic algorithms, genetic programming, Evolutionary intelligent agents, Evolutionary Computation and Games", abstract = "Layered learning allows decomposition of the stages of learning in a problem domain. We apply this technique to the evolution of goal scoring behavior in soccer players and show that layered learning is able to find solutions comparable to standard genetic programs more reliably. The solutions evolved with layers have a higher accuracy but do not make as many goal attempts. We compared three variations of layered learning and find that maintaining the population between layers as the encapsulated learnt layer is introduced to be the most computationally efficient. The quality of solutions found by layered learning did not exceed those of standard genetic programming in terms of goal scoring ability.", notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.", } @InProceedings{Baker:2010:ieeeICWITS, author = "James Baker and Nuri Celik and Nobutaka Omaki and Jill Kobashigawa and Hyoung-Sun Youn and Magdy F. Iskander", title = "On the design of integrated HF radar systems for Homeland Security applications", booktitle = "2010 IEEE International Conference on Wireless Information Technology and Systems (ICWITS)", year = "2010", month = "28 " # oct # "-" # sep # " 3", abstract = "In this paper, HCAC's research and development efforts on the development of integrated and low cost HF radar for coastal surveillance and other Homeland Security applications are summarised. The proposed design incorporates electrically small antenna for rapid deployment, supports operation on floating platforms by using enhanced DSP algorithms to mitigate clutter, incorporates improved propagation modelling to more accurately select optimum frequency channels based on atmospheric conditionxs and overcome the errors due to terrain effects, uses Genetic Programming for automatic target recognition and classification, and provides for passive radar operation using existing broadcast transmitters to enable covert operation.", keywords = "genetic algorithms, genetic programming, DSP algorithm, broadcast transmitter, clutter, coastal surveillance, electrically small antenna, homeland security, integrated HF radar system, optimum frequency channel, propagation modeling, terrain effect, military radar, national security, radar antennas, radar clutter, signal processing", DOI = "doi:10.1109/ICWITS.2010.5611859", notes = "Also known as \cite{5611859}", } @InProceedings{Baker:2014:DBV:2638404.2638521, author = "Yolanda S. Baker and Rajeev Agrawal and James A. Foster and Daniel Beck and Gerry Dozier", title = "Detecting Bacterial Vaginosis Using Machine Learning", booktitle = "Proceedings of the 2014 ACM Southeast Regional Conference", year = "2014", pages = "46:1--46:4", address = "Kennesaw, Georgia, USA", month = mar # " 28-29", publisher = "ACM", keywords = "genetic algorithms, genetic programming", acmid = "2638521", isbn13 = "978-1-4503-2923-1", DOI = "doi:10.1145/2638404.2638521", size = "4 pages", abstract = "Bacterial Vaginosis (BV) is the most common of vaginal infections diagnosed among women during the years where they can bear children. Yet, there is very little insight as to how it occurs. There are a vast number of criteria that can be taken into consideration to determine the presence of BV. The purpose of this paper is two-fold; first to discover the most significant features necessary to diagnose the infection, second is to apply various classification algorithms on the selected features. It is observed that certain feature selection algorithms provide only a few features; however, the classification results are as good as using a large number of features.", notes = "ACM SE '14", } @InProceedings{Baker:2014:ICMLC, author = "Yolanda S. Baker and Rajeev Agrawal and James A. Foster and Daniel Beck and Gerry Dozier", title = "Applying machine learning techniques in detecting Bacterial Vaginosis", booktitle = "2014 International Conference on Machine Learning and Cybernetics", year = "2014", volume = "1", pages = "241--246", address = "Lanzhou, China", month = "13-16 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Bacterial Vaginosis, Machine learning, Feature selection, Classification", isbn13 = "978-1-4799-4216-9", DOI = "doi:10.1109/ICMLC.2014.7009123", size = "6 pages", abstract = "There are several diseases which arise because of changes in the microbial communities in the body. Scientists continue to conduct research in a quest to find the catalysts that provoke these changes in the naturally occurring microbiota. Bacterial Vaginosis (BY) is a disease that fits the above criteria. BV afflicts approximately 29percent of women in child bearing age. Unfortunately, its causes are unknown. This paper seeks to uncover the most important features for diagnosis and in turn employ classification algorithms on those features. In order to fulfill our purpose, we conducted two experiments on the data. We isolated the clinical and medical features from the full set of raw data, we compared the accuracy, precision, recall and F-measure and time elapsed for each feature selection and classification grouping. We noticed that classification results were as good or better after performing feature selection although there was a wide range in the number of features produced from the feature selection process. After comparing the experiments, the algorithms performed best on the medical dataset.", notes = "Weka, Java. PubMed ID: 25914861", } @PhdThesis{bakkoury:thesis, author = "Zohra Bakkoury", title = "Feasibility Assessement and Optimal Scheduling of Water Supply Projects", school = "School of Engineering and Computer Science, Exeter University", year = "2002", keywords = "genetic algorithms", } @Article{Bakshi:2012:ijetae, author = "Ankit Bakshi and Pallavi Pandit and Santosh Easo", title = "To Accomplish Amelioration Of Classifier Using Gene-Mutation Tactics In Genetic Programming", journal = "International Journal of Emerging Technology and Advanced Engineering", year = "2012", volume = "2", number = "12", pages = "319--322", month = dec, keywords = "genetic algorithms, genetic programming, elitism, double tournament, plain crossover", ISSN = "2250--2459", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.414.3468", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.414.3468", URL = "http://www.ijetae.com/Volume2Issue12.html", URL = "http://www.ijetae.com/files/Volume2Issue12/IJETAE_1212_59.pdf", size = "4 pages", abstract = "A phenomenon for designing classifier for three or more classes (Multiclass) problem using genetic programming (GP) is multiclass classifier. In this scenario we purported three methods named Double Tournament Method, Gene-Mutation Method and a Plain Crossover method. In Double Tournament Method, we pick out two idiosyncratic for the crossover operation on the basis of size and fitness. In Gene-Mutation tactic we are propagating two child from single parent and selecting one of them on the basis of fitness and also bring into play elitism on the child so that the mutation operation does not degrade the fitness of the distinct, whereas in Plain Crossover we select the two child for the succeeding generation on the basis of size, depth and fitness along with elitism on each step from the six child which is generated during crossover. To exhibit our approach we have designed a Multiclass Classifier using GP by taking some standard datasets. The results attained show that by applying Plain crossover together with Gene-Mutation refined the performance of the classifier.", notes = "Article 59.", } @InProceedings{Bakurov:2018:evoMusArt, author = "Illya Bakurov and Brian J. Ross", title = "Non-photorealistic Rendering with Cartesian Genetic Programming Using Graphics Processing Units", booktitle = "7th International Conference on Computational Intelligence in Music, Sound, Art and Design, EvoMUSART 2018", year = "2018", editor = "Juan Romero and Antonios Liapis and Aniko Ekart", series = "LNCS", volume = "10783", publisher = "Springer", pages = "34--49", address = "Parma, Italy", month = "4-6 " # apr, organisation = "Species", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, Evolutionary art, Non-photorealistic rendering, Graphics processing units, GPU", isbn13 = "978-3-319-77582-1", DOI = "doi:10.1007/978-3-319-77583-8_3", abstract = "A non-photorealistic rendering system implemented with Cartesian genetic programming (CGP) is discussed. The system is based on Baniasadi's NPR system using tree-based GP. The CGP implementation uses a more economical representation of rendering expressions compared to the tree-based system. The system borrows their many objective fitness evaluation scheme, which uses a model of aesthetics, colour testing, and image matching. GPU acceleration of the paint stroke application results in up to 6 times faster rendering times compared to CPU-based renderings. The convergence dynamics of CGP's mu+lambda evolutionary strategy was more unstable than conventional GP runs with large populations. One possible reason may be the sensitivity of the smaller mu+lambda population to the many-objective ranking scheme, especially when objectives are in conflict with each other. This instability is arguably an advantage as an exploratory tool, especially when considering the subjectivity inherent in evolutionary art.", notes = "EvoMusArt2018 held in conjunction with EuroGP'2018 EvoCOP2018 and EvoApplications2018 http://www.evostar.org/2018/cfp_evomusart.php", } @InProceedings{Bakurov:2019:evoapplications, author = "Illya Bakurov and Leonardo Vanneschi and Mauro Castelli and Maria Joao Freitas", title = "Supporting Medical Decisions for Treating Rare Diseases through Genetic Programming", booktitle = "22nd International Conference, EvoApplications 2019", year = "2019", month = "24-26 " # apr, editor = "Paul Kaufmann and Pedro A. Castillo", series = "LNCS", volume = "11454", publisher = "Springer Verlag", address = "Leipzig, Germany", pages = "187--203", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Geometric Semantic Genetic Programming, Medical decisions, Rare diseases", isbn13 = "978-3-030-16691-5", URL = "http://hdl.handle.net/10362/91519", URL = "https://run.unl.pt/bitstream/10362/91519/1/Supporting_medical_decisions_treating_rare_diseases_through_genetic_programming.pdf", DOI = "doi:10.1007/978-3-030-16692-2_13", size = "16 pages", abstract = "Casa dos Marcos is the largest specialized medical and residential center for rare diseases in the Iberian Peninsula. The large number of patients and the uniqueness of their diseases demand a considerable amount of diverse and highly personalized therapies, that are nowadays largely managed manually. This paper aims at catering for the emergent need of efficient and effective artificial intelligence systems for the support of the everyday activities of centres like Casa dos Marcos. We present six predictive data models developed with a genetic programming based system which, integrated into a web-application, enabled data-driven support for the therapists in Casa dos Marcos. The presented results clearly indicate the usefulness of the system in assisting complex therapeutic procedures for children suffering from rare diseases.", notes = "http://www.evostar.org/2019/cfp_evoapps.php EuroGP'2019 held in conjunction with EvoCOP2019, EvoMusArt2019 and EvoApplications2019", } @InProceedings{DBLP:conf/ijcci/BakurovCFV19, author = "Illya Bakurov and Mauro Castelli and Francesco Fontanella and Leonardo Vanneschi", editor = "Juan Julian Merelo Guervos and Jonathan M. Garibaldi and Alejandro Linares-Barranco and Kurosh Madani and Kevin Warwick", title = "A Regression-like Classification System for Geometric Semantic Genetic Programming", booktitle = "Proceedings of the 11th International Joint Conference on Computational Intelligence, {IJCCI} 2019, Vienna, Austria, September 17-19, 2019", pages = "40--48", publisher = "ScitePress", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.5220/0008052900400048", DOI = "doi:10.5220/0008052900400048", timestamp = "Mon, 27 Apr 2020 13:47:06 +0200", biburl = "https://dblp.org/rec/conf/ijcci/BakurovCFV19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{Bakurov:2021:AS, author = "Illya Bakurov and Marco Buzzelli and Mauro Castelli and Leonardo Vanneschi and Raimondo Schettini", title = "General Purpose Optimization Library ({GPOL}): A Flexible and Efficient Multi-Purpose Optimization Library in Python", journal = "Applied Sciences", year = "2021", volume = "11", number = "11", pages = "Article--number 4774", month = "1 " # jun, keywords = "genetic algorithms, genetic programming, optimization, evolutionary computation, swarm intelligence, local search, continuous optimisation, combinatorial optimization, inductive programming, supervised machine learning", ISSN = "2076-3417", URL = "https://www.mdpi.com/2076-3417/11/11/4774", DOI = "doi:10.3390/app11114774", code_url = "https://gitlab.com/ibakurov/general-purpose-optimization-library", size = "34 pages", abstract = "Several interesting libraries for optimisation have been proposed. Some focus on individual optimization algorithms, or limited sets of them, and others focus on limited sets of problems. Frequently, the implementation of one of them does not precisely follow the formal definition, and they are difficult to personalize and compare. This makes it difficult to perform comparative studies and propose novel approaches. we propose to solve these issues with the General Purpose Optimization Library (GPOL): a flexible and efficient multipurpose optimization library that covers a wide range of stochastic iterative search algorithms, through which flexible and modular implementation can allow for solving many different problem types from the fields of continuous and combinatorial optimisation and supervised machine learning problem solving. Moreover, the library supports full-batch and mini-batch learning and allows carrying out computations on a CPU or GPU. The package is distributed under an MIT license. Source code, installation instructions, demos and tutorials are publicly available in our code hosting platform (the reference is provided in the Introduction).", notes = "Also known as \cite{app11114774} Nova Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide,1070-312 Lisboa, Portugal", } @Article{BAKUROV:2021:SEC, author = "Illya Bakurov and Mauro Castelli and Olivier Gau and Francesco Fontanella and Leonardo Vanneschi", title = "Genetic programming for stacked generalization", journal = "Swarm and Evolutionary Computation", volume = "65", pages = "100913", year = "2021", ISSN = "2210-6502", DOI = "doi:10.1016/j.swevo.2021.100913", URL = "https://www.sciencedirect.com/science/article/pii/S2210650221000742", keywords = "genetic algorithms, genetic programming, Stacking, Ensemble Learning, Stacked Generalization", abstract = "In machine learning, ensemble techniques are widely used to improve the performance of both classification and regression systems. They combine the models generated by different learning algorithms, typically trained on different data subsets or with different parameters, to obtain more accurate models. Ensemble strategies range from simple voting rules to more complex and effective stacked approaches. They are based on adopting a meta-learner, i.e. a further learning algorithm, and are trained on the predictions provided by the single algorithms making up the ensemble. The paper aims at exploiting some of the most recent genetic programming advances in the context of stacked generalization. In particular, we investigate how the evolutionary demes despeciation initialization technique, ?-lexicase selection, geometric-semantic operators, and semantic stopping criterion, can be effectively used to improve GP-based systems' performance for stacked generalization (a.k.a. stacking). The experiments, performed on a broad set of synthetic and real-world regression problems, confirm the effectiveness of the proposed approach", } @Article{BAKUROV:2022:SEC, author = "I. Bakurov and M. Castelli and F. Fontanella and A. {Scotto di Freca} and L. Vanneschi", title = "A novel binary classification approach based on geometric semantic genetic programming", journal = "Swarm and Evolutionary Computation", volume = "69", pages = "101028", year = "2022", ISSN = "2210-6502", DOI = "doi:10.1016/j.swevo.2021.101028", URL = "https://www.sciencedirect.com/science/article/pii/S2210650221001905", keywords = "genetic algorithms, genetic programming, Binary classification, Geometric semantic genetic programming", abstract = "Geometric semantic genetic programming (GSGP) is a recent variant of genetic programming. GSGP allows the landscape of any supervised regression problem to be transformed into a unimodal error surface, thus it has been applied only to this kind of problem. In a previous paper, we presented a novel variant of GSGP for binary classification problems that, taking inspiration from perceptron neural networks, uses a logistic-based activation function to constrain the output value of a GSGP tree in the interval [0,1]. This simple approach allowed us to use the standard RMSE function to evaluate the train classification error on binary classification problems and, consequently, to preserve the intrinsic properties of the geometric semantic operators. The results encouraged us to investigate this approach further. To this aim, in this paper, we present the results from 18 test problems, which we compared with those achieved by eleven well-known and widely classification schemes. We also studied how the parameter settings affect the classification performance and the use of the F-score function to deal with imbalanced data. The results confirmed the effectiveness of the proposed approach", } @InProceedings{bakurov:2022:GECCO, author = "Illya Bakurov and Marco Buzzelli and Mauro Castelli and Raimondo Schettini and Leonardo Vanneschi", title = "Genetic Programming for Structural Similarity Design at Multiple Spatial Scales", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference", year = "2022", editor = "Alma Rahat and Jonathan Fieldsend and Markus Wagner and Sara Tari and Nelishia Pillay and Irene Moser and Aldeida Aleti and Ales Zamuda and Ahmed Kheiri and Erik Hemberg and Christopher Cleghorn and Chao-li Sun and Georgios Yannakakis and Nicolas Bredeche and Gabriela Ochoa and Bilel Derbel and Gisele L. Pappa and Sebastian Risi and Laetitia Jourdan and Hiroyuki Sato and Petr Posik and Ofer Shir and Renato Tinos and John Woodward and Malcolm Heywood and Elizabeth Wanner and Leonardo Trujillo and Domagoj Jakobovic and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Inmaculada Medina-Bulo and Slim Bechikh and Andrew M. Sutton and Pietro Simone Oliveto", pages = "911--919", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, multi-scale context, image quality assessment, image processing, spatially-varying kernels, structural similarity, multi-scale structural similarity index, multi-scale processing, evolutionary computation, dilated convolutions", isbn13 = "978-1-4503-9237-2", DOI = "doi:10.1145/3512290.3528783", size = "9 pages", abstract = "The growing production of digital content and its dissemination across the worldwide web require eficient and precise management. In this context, image quality assessment measures (IQAMs) play a pivotal role in guiding the development of numerous image processing systems for compression, enhancement, and restoration. The structural similarity index (SSIM) is one of the most common IQAMs for estimating the similarity between a pristine reference image and its corrupted variant. The multi-scale SSIM is one of its most popular variants that allows assessing image quality at multiple spatial scales. This paper proposes a two-stage genetic programming (GP) approach to evolve novel multi-scale IQAMs, that are simultaneously more effective and efficient. We use GP to perform feature selection in the first stage, while the second stage generates the final solutions. The experimental results show that the proposed approach outperforms the existing MS-SSIM. A comprehensive analysis of the feature selection indicates that, for extracting multi-scale similarities, spatially-varying convolutions are more effective than dilated convolutions. Moreover, we provide evidence that the IQAMs learned for one database can be successfully transferred to previously unseen databases. We conclude the paper by presenting a set of evolved multi-scale IQAMs and providing their interpretation.", notes = "'We use EDDA 9, \cite{vanneschi:2017:CEC} to seed the GP population' Demes. Hoist mutation. Fitness uses Spearman correlation. TID2013, VDID2014, GPOL Python \cite{Bakurov:2021:AS} See also \cite{Bakurov:2023:GPEM} GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @PhdThesis{Bakurov:thesis, author = "Illya Olegovich Bakurov", title = "Soft computing for Ill Posed Problems in Computer Vision", school = "NOVA Information Management School (NOVA IMS), NOVA University, Lisbon", year = "2022", address = "Portugal", month = "19 " # sep, keywords = "genetic algorithms, genetic programming, ANN, Evolutionary Computation, Swarm Intelligence, Ensemble Learning, Stacking, Computer Vision, Full Reference Image Quality Assessment, Semantic Segmentation", language = "English", URL = "http://hdl.handle.net/10362/144500", URL = "https://run.unl.pt/bitstream/10362/144500/1/D0071.pdf", size = "177 pages", abstract = "Soft computing (SC) includes computational techniques that are tolerant of approximations, missing information, and uncertainty, and aim at providing effective and efficient solutions to problems which may be unsolvable, or too time-consuming to solve, with exhaustive techniques. SC has found many applications in various domains of research and industry, including computer vision (CV). This dissertation focuses on tasks of full reference image quality assessment (FR-IQA) and fast scene understanding (FSU). The former consists of assessing images visual quality in regard to some pristine reference. The latter consists of classifying each pixel of a scene assuming a rapidly changing environment like, for instance, in a self-driving car. The current state-of-the-art (SOTA) in both FR-IQA and FSU rely upon convolutional neural networks (CNNs), which can be seen as a computational metaphor of the human visual cortex. Although CNNs achieved unprecedented results in many CV tasks, they also present several drawbacks: massive amounts of data and processing resources for training; the difficulty of outputs interpretation; reduced usability for compact battery-powered devices... This dissertation addresses FR-IQA and FSU using SC techniques other than CNNs. Initially, we created a flexible and efficient library to support our endeavors; it is publicly available and implements a wide range of metaheuristics to solve different problems. Then, we used swarm and evolutionary computation to optimize the parameters of several traditional FR-IQA measures (FR-IQAMs) that integrate the so called structural similarity paradigm; the novel parameters improve measures' precision without affecting their complexity. Afterward, we applied genetic programming (GP) to automatically formulate novel FR-IQAMs that are simultaneously simple, accurate, and interpretable. Lastly, we used GP as a meta-model for stacking efficient CNNs for FSU; the approach allowed us to obtain simple and interpretable models that did not exceed processing preconditions for real-time applications while achieving high levels of precision.", notes = "In English Supervisors: Mauro Castelli, Leonardo Vanneschi", } @Article{DBLP:journals/tip/BakurovBSCV23, author = "Illya Bakurov and Marco Buzzelli and Raimondo Schettini and Mauro Castelli and Leonardo Vanneschi", title = "Full-Reference Image Quality Expression via Genetic Programming", journal = "{IEEE} Trans. Image Process.", volume = "32", pages = "1458--1473", year = "2023", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1109/TIP.2023.3244662", DOI = "doi:10.1109/TIP.2023.3244662", timestamp = "Sat, 11 Mar 2023 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/tip/BakurovBSCV23.bib", bibsource = "dblp computer science bibliography, https://dblp.org", abstract = "Full-reference image quality measures are a fundamental tool to approximate the human visual system in various applications for digital data management: from retrieval to compression to detection of unauthorized uses. Inspired by both the effectiveness and the simplicity of hand-crafted Structural Similarity Index Measure (SSIM), in this work, we present a framework for the formulation of SSIM-like image quality measures through genetic programming. We explore different terminal sets, defined from the building blocks of structural similarity at different levels of abstraction, and we propose a two-stage genetic optimization that exploits hoist mutation to constrain the complexity of the solutions. Our optimized measures are selected through a cross-dataset validation procedure, which results in superior performance against different versions of structural similarity, measured as correlation with human mean opinion scores. We also demonstrate how, by tuning on specific datasets, it is possible to obtain solutions that are competitive with (or even outperform) more complex image quality measures.", } @Article{Bakurov:2023:GPEM, author = "Illya Bakurov and Marco Buzzelli and Raimondo Schettini and Mauro Castelli and Leonardo Vanneschi", title = "Semantic segmentation network stacking with genetic programming", journal = "Genetic Programming and Evolvable Machines", year = "2023", volume = "24", number = "2", pages = "Article number: 15", month = dec, note = "Special Issue on Highlights of Genetic Programming 2022 Events", keywords = "genetic algorithms, genetic programming, Stacking, Semantic segmentation, Ensemble learning, Deep learning, ANN", ISSN = "1389-2576", URL = "https://rdcu.be/drZeF", DOI = "doi:10.1007/s10710-023-09464-0", size = "37 pages", } @Article{Bakurov:2024:GPEM, author = "Illya Bakurov and Jose Manuel {Munoz Contreras} and Mauro Castelli and Nuno Rodrigues and Sara Silva and Leonardo Trujillo and Leonardo Vanneschi", title = "Geometric semantic genetic programming with normalized and standardized random programs", journal = "Genetic Programming and Evolvable Machines", year = "2024", volume = "25", pages = "Article no 6", note = "Online first", keywords = "genetic algorithms, genetic programming, Geometric semantic mutation, Internal covariate shift, Sigmoid distribution bias, Model simplification", ISSN = "1389-2576", URL = "https://rdcu.be/dysci", DOI = "doi:10.1007/s10710-024-09479-1", size = "29 pages", abstract = "Geometric semantic genetic programming (GSGP) represents one of the most promising developments in the area of evolutionary computation (EC) in the last decade. The results achieved by incorporating semantic awareness in the evolutionary process demonstrate the impact that geometric semantic operators have brought to the field of EC. An improvement to the geometric semantic mutation (GSM) operator is proposed, inspired by the results achieved by batch normalization in deep learning. While, in one of its most used versions, GSM relies on the use of the sigmoid function to constrain the semantics of two random programs responsible for perturbing the parent semantics, here a different approach is followed, which allows reducing the size of the resulting programs and overcoming the issues associated with the use of the sigmoid function, as commonly done in deep learning. The idea is to consider a single random program and use it to perturb the parent's semantics only after standardization or normalization. The experimental results demonstrate the suitability of the proposed approach: despite its simplicity, the presented GSM variants outperform standard GSGP on the studied benchmarks, with a difference in terms of performance that is statistically significant. Furthermore, the individuals generated by the new GSM variants are easier to simplify, allowing us to create accurate but significantly smaller solutions.", } @InProceedings{Bakurov:2024:GPTP, author = "Illya Bakurov and Nathan Haut and Wolfgang Banzhaf", title = "Sharpness-Aware Minimization in Genetic Programming", booktitle = "Genetic Programming Theory and Practice XXI", year = "2024", editor = "Stephan M. Winkler and Wolfgang Banzhaf and Ting Hu and Alexander Lalejini", series = "Genetic and Evolutionary Computation", pages = "151--175", address = "University of Michigan, USA", month = jun # " 6-8", publisher = "Springer", keywords = "genetic algorithms, genetic programming, SAM", isbn13 = "978-981-96-0076-2", URL = "https://arxiv.org/abs/2405.10267", DOI = "doi:10.1007/978-981-96-0077-9_8", size = "22 pages", abstract = "Sharpness-Aware Minimization (SAM) was recently introduced as a regularization procedure for training deep neural networks. It simultaneously minimizes the fitness (or loss) function and the so-called fitness sharpness. The latter serves as a measure of the nonlinear behaviour of a solution and does so by finding solutions that lie in neighborhoods having uniformly similar loss values across all fitness cases. we adapt SAM for tree Genetic Programming (TGP) by exploring the semantic neighborhoods of solutions using two simple approaches. By capitalising upon perturbing input and output of program trees, sharpness can be estimated and used as a second optimization criterion during the evolution. To better understand the impact of this variant of SAM on TGP, we collect numerous indicators of the evolutionary process, including generalization ability, complexity, diversity, and a recently proposed genotype-phenotype mapping to study the amount of redundancy in trees. The experimental results demonstrate that using any of the two proposed SAM adaptations in TGP allows (i) a significant reduction of tree sizes in the population and (ii) a decrease in redundancy of the trees. When assessed on real-world benchmarks, the generalization ability of the elite solutions does not deteriorate.", notes = "Published in 2025 after the workshop", } @InProceedings{Bakurov:2025:GPTP, author = "Illya Bakurov", title = "Analyzing Fitness Aggregation Strategies for Symbolic Regression Problem-Solving", booktitle = "Genetic Programming Theory and Practice XXII", year = "2025", editor = "Wolfgang Banzhaf and Bogdan Burlacu and Stephen Kelly and Alexander Lalejini and Fabricio {Olivetti de Franca}", address = "Michigan State University, USA", month = jun # " 5-7", note = "lightning talk", keywords = "genetic algorithms, genetic programming", notes = "To be published after the workshop", } @InProceedings{balakrishnan:1996:ser, author = "Karthik Balakrishnan and Vasant Honavar", title = "On Sensor Evolution in Robotics", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "Genetic Algorithms", pages = "455--460", address = "Stanford University, CA, USA", publisher = "MIT Press", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap75.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96 GA paper", } @InProceedings{Balakrishnan:1997:slrl, author = "Karthik Balakrishnan and Vasant Honavar", title = "Spatial Learning for Robot Localization", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "Artifical life and evolutionary robotics", pages = "389--397", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", notes = "GP-97", } @Article{Balandina:2017:PCS, author = "G. I. Balandina", title = "Control System Synthesis by Means of Cartesian Genetic Programming", journal = "Procedia Computer Science", volume = "103", pages = "176--182", year = "2017", note = "\{XII\} International Symposium Intelligent Systems 2016, \{INTELS\} 2016, 5-7 October 2016, Moscow, Russia", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Optimal control synthesis, nonlinear control systems", ISSN = "1877-0509", DOI = "doi:10.1016/j.procs.2017.01.051", URL = "http://www.sciencedirect.com/science/article/pii/S1877050917300522", abstract = "Cartesian Genetic Programming (CGP) is a type of Genetic Programming based on a program in a form of a directed graph. It also belongs to the methods of Symbolic Regression allowing to receive the optimal mathematical expression for a problem. Nowadays it becomes possible to use computers very effectively for symbolic regression calculations. CGP was developed by Julian Miller in 1999-2000. It represents a program for decoding a genotype (string of integers) into the phenotype (graph). The nodes of that graph contain references to functions from a function table, which could contain arithmetic, logical operations and/or user-defined functions. The inputs of those functions are connected to the node inputs, which itself could be connected to a node output or a graph input. As a result, it's possible to construct several mathematical expressions for the outputs and calculate them for the given inputs. This CGP implementation use point mutation to form new mathematical expressions. Steady-state genetic algorithm is chosen as a search engine. Solution solving the control system synthesis problem is presented in a form of the Pareto set, which contains a set of satisfactory control functions. Nonlinear Duffing oscillator is taken as a dynamic object.", } @Article{Balasubramaniam:2009:GPEM, author = "P. Balasubramaniam and A. Vincent Antony Kumar", title = "Solution of matrix {Riccati} differential equation for nonlinear singular system using genetic programming", journal = "Genetic Programming and Evolvable Machines", year = "2009", volume = "10", number = "1", pages = "71--89", month = mar, keywords = "genetic algorithms, genetic programming, Matrix Riccati differential equation, Nonlinear, Optimal control, Runge Kutta method, Singular system", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-008-9072-z", size = "19 pages", abstract = "In this paper, we propose a novel approach to find the solution of the matrix Riccati differential equation (MRDE) for nonlinear singular systems using genetic programming (GP). The goal is to provide optimal control with reduced calculation effort by comparing the solutions of the MRDE obtained from the well known traditional Runge Kutta (RK) method to those obtained from the GP method. We show that the GP approach to the problem is qualitatively better in terms of accuracy. Numerical examples are provided to illustrate the proposed method.", } @InProceedings{Balazs:2010:ieee-fuzz, author = "Krisztian Balazs and Janos Botzheim and Laszlo T. Koczy", title = "Hierarchical fuzzy system modeling by Genetic and Bacterial Programming approaches", booktitle = "IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, pages = "1866--1871", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-6920-8", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/botzheim/Balazs_2010_ieee-fuzz.pdf", DOI = "doi:10.1109/FUZZY.2010.5584220", size = "6 pages", abstract = "In this paper a method is proposed for constructing hierarchical fuzzy rule bases in order to model black box systems defined by input-output pairs, i.e. to solve supervised machine learning problems. The resultant hierarchical rule base is the knowledge base, which is constructed by using structure constructing evolutionary techniques, namely, Genetic and Bacterial Programming Algorithms. Applying hierarchical fuzzy rule bases is a way of reducing the complexity of the knowledge base, whereas evolutionary methods ensure a relatively efficient learning process. This is the reason of the investigation of this combination.", notes = "WCCI 2010. Also known as \cite{5584220}", } @InProceedings{Balazs:2010:WAC, author = "Krisztian Balazs and Janos Botzheim and Laszlo T. Koczy", title = "Hierarchical fuzzy system construction applying genetic and bacterial programming algorithms with expression tree building restrictions", booktitle = "World Automation Congress (WAC 2010)", year = "2010", month = "19-23 " # sep, address = "Kobe, Japan", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/botzheim/Balazs_2010_WAC.pdf", URL = "http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5665326", abstract = "In this paper various restrictions are proposed in the construction of hierarchical fuzzy rule bases by using Genetic and Bacterial Programming algorithms in order to model black box systems defined by input-output pairs, i.e. to solve supervised machine learning problems. The properties (learning speed, accuracy) of the established systems are observed based on simulation results and they are compared to each other.", keywords = "genetic algorithms, genetic programming, bacterial programming algorithm, black box system, genetic programming algorithm, hierarchical fuzzy rule system construction, input-output pairs, supervised machine learning problem, tree building restriction, fuzzy set theory, learning (artificial intelligence)", ISSN = "2154-4824", notes = "Also known as \cite{5665326}", } @InProceedings{Balazs:2011:ieeeFUZZ, author = "Krisztian Balazs and Laszlo T. Koczy", title = "Hierarchical-interpolative fuzzy system construction by Genetic and Bacterial Programming Algorithms", booktitle = "IEEE International Conference on Fuzzy Systems (FUZZ 2011)", year = "2011", month = "27-30 " # jun, pages = "2116--2122", address = "Taipei, Taiwan", size = "7 pages", abstract = "In this paper a method is proposed for constructing hierarchical-interpolative fuzzy rule bases in order to model black box systems defined by input-output pairs, i.e. to solve supervised machine learning problems. The resulting hierarchical rule base is the knowledge base, which is constructed by using evolutionary techniques, namely, Genetic and Bacterial Programming Algorithms. Applying hierarchical-interpolative fuzzy rule bases is an advanced way of reducing the complexity of the knowledge base, whereas evolutionary methods ensure a relatively efficient learning process. This is the reason of the investigation of this combination.", keywords = "genetic algorithms, genetic programming, bacterial programming, black box system, evolutionary technique, hierarchical-interpolative fuzzy rule bases construction, knowledge base, supervised machine learning problem, fuzzy logic, knowledge based systems, learning (artificial intelligence), mathematical programming", DOI = "doi:10.1109/FUZZY.2011.6007594", ISSN = "1098-7584", notes = "Also known as \cite{6007594}", } @InProceedings{balazs:1999:AE, author = "Marton E. Balazs and Daniel L. Richter", title = "A genetic algorithm with dynamic population: Experimental results", booktitle = "Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference", year = "1999", editor = "Scott Brave and Annie S. Wu", pages = "25--30", address = "Orlando, Florida, USA", month = "13 " # jul, keywords = "Genetic Algorithms", notes = "GECCO-99LB", } @InProceedings{conf/egice/BaldockS06, title = "Structural Topology Optimization of Braced Steel Frameworks Using Genetic Programming", author = "Robert Baldock and Kristina Shea", booktitle = "Intelligent Computing in Engineering and Architecture, 13th {EG}-{ICE} Workshop", publisher = "Springer", year = "2006", volume = "4200", editor = "Ian F. C. Smith", ISBN = "3-540-46246-5", pages = "54--61", series = "Lecture Notes in Computer Science", address = "Ascona, Switzerland", month = jun # " 25-30", note = "Revised Selected Papers", bibdate = "2006-12-06", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/egice/egice2006.html#BaldockS06", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1007/11888598", abstract = "This paper presents a genetic programming method for the topological optimisation of bracing systems for steel frameworks. The method aims to create novel, but practical, optimally-directed design solutions, the derivation of which can be readily understood. Designs are represented as trees with one-bay, one-story cellular bracing units, operated on by design modification functions. Genetic operators (reproduction, crossover, mutation) are applied to trees in the development of subsequent populations. The bracing design for a three-bay, 12-story steel framework provides a preliminary test problem, giving promising initial results that reduce the structural mass of the bracing in comparison to previous published benchmarks for a displacement constraint based on design codes. Further method development and investigations are discussed.", notes = "(1) Engineering Design Centre, University of Cambridge, Cambridge, CB2 1PZ, UK (2) Product Development, Technical University of Munich, Boltzmannstrasse 15, D-85748 Garching, Germany", } @InProceedings{Baldominos:2016:GECCOcomp, author = "Alejandro Baldominos and Carmen {del Barrio} and Yago Saez", title = "Exploring the Application of Hybrid Evolutionary Computation Techniques to Physical Activity Recognition", booktitle = "GECCO '16 Companion: Proceedings of the Companion Publication of the 2016 Annual Conference on Genetic and Evolutionary Computation", year = "2016", editor = "Tobias Friedrich and Frank Neumann and Andrew M. Sutton and Martin Middendorf and Xiaodong Li and Emma Hart and Mengjie Zhang and Youhei Akimoto and Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and Daniele Loiacono and Julian Togelius and Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and Faustino Gomez and Carlos M. Fonseca and Heike Trautmann and Alberto Moraglio and William F. Punch and Krzysztof Krawiec and Zdenek Vasicek and Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and Boris Naujoks and Enrique Alba and Gabriela Ochoa and Simon Poulding and Dirk Sudholt and Timo Koetzing", isbn13 = "978-1-4503-4323-7", pages = "1377--1384", address = "Denver, Colorado, USA", month = "20-24 " # jul, keywords = "genetic algorithms, genetic programming", organisation = "SIGEVO", DOI = "doi:10.1145/2908961.2931732", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "his paper focuses on the problem of physical activity recognition, i.e., the development of a system which is able to learn patterns from data in order to be able to detect which physical activity (e.g. running, walking, ascending stairs, etc.) a certain user is performing. While this field is broadly explored in the literature, there are few works that face the problem with evolutionary computation techniques. In this case, we propose a hybrid system which combines particle swarm optimization for clustering features and genetic programming combined with evolutionary strategies for evolving a population of classifiers, shaped in the form of decision trees. This system would run the segmentation, feature extraction and classification stages of the activity recognition chain. For this paper, we have used the PAMAP2 dataset with a basic preprocessing. This dataset is publicly available at UCI ML repository. Then, we have evaluated the proposed system using three different modes: a user-independent, a user-specific and a combined one. The results in terms of classification accuracy were poor for the first and the last mode, but it performed significantly well for the user-specific case. This paper aims to describe work in progress, to share early results an discuss them. There are many things that could be improved in this proposed system, but overall results were interesting especially because no manual data transformation took place.", notes = "Distributed at GECCO-2016.", } @Article{Baldominos:2018:Sensors, author = "Alejandro Baldominos and Yago Saez and Pedro Isasi", title = "Evolutionary Design of Convolutional Neural Networks for Human Activity Recognition in Sensor-Rich Environments", journal = "Sensors", year = "2018", volume = "18", number = "4", month = apr, keywords = "genetic algorithms, genetic programming, Grammatical Evolution, ANN", ISSN = "1424-8220", URL = "http://www.mdpi.com/1424-8220/18/4/1288", DOI = "doi:10.3390/s18041288", size = "24 pages", abstract = "Human activity recognition is a challenging problem for context-aware systems and applications. It is gaining interest due to the ubiquity of different sensor sources, wearable smart objects, ambient sensors, etc. This task is usually approached as a supervised machine learning problem, where a label is to be predicted given some input data, such as the signals retrieved from different sensors. For tackling the human activity recognition problem in sensor network environments, in this paper we propose the use of deep learning (convolutional neural networks) to perform activity recognition using the publicly available OPPORTUNITY dataset. Instead of manually choosing a suitable topology, we will let an evolutionary algorithm design the optimal topology in order to maximize the classification F1 score. After that, we will also explore the performance of committees of the models resulting from the evolutionary process. Results analysis indicates that the proposed model was able to perform activity recognition within a heterogeneous sensor network environment, achieving very high accuracies when tested with new sensor data. Based on all conducted experiments, the proposed neuroevolutionary system has proved to be able to systematically find a classification model which is capable of outperforming previous results reported in the state-of-the-art, showing that this approach is useful and improves upon previously manually-designed architectures.", article-number = "1288", notes = "Also known as \cite{s18041288} PMCID: PMC5948523 PMID: 29690587", } @InProceedings{Baldwin:1999:SIF, author = "James F. Baldwin and Trevor P. Martin and James G. Shanahan", title = "System Identification of Fuzzy Cartesian Granules Feature Models Using Genetic Programming", booktitle = "Fuzzy Logic in Artificial Intelligence, IJCAI'97 Workshop, Selected and Invited Papers", year = "1997", editor = "Anca L. Ralescu and James G. Shanahan", volume = "1566", series = "Lecture Notes in Artificial Intelligence", pages = "91--116", address = "Nagoya, Japan", month = aug # " 23-24", publisher = "Springer", note = "Published 1999", keywords = "genetic algorithms, genetic programming, artificial intelligence, fuzzy logic, IJCAI", CODEN = "LNCSD9", ISBN = "3-540-66374-6", ISSN = "0302-9743", bibdate = "Tue Sep 14 06:09:05 MDT 1999", acknowledgement = ack-nhfb, URL = "http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-164-22-1637718-0,00.html", notes = "DBLP, http://dblp.uni-trier.de DBLP:conf/ijcai/1997fl", } @Article{Baldwin:1999:IJAR, author = "James F. Baldwin and Trevor P. Martin and James G. Shanahan", title = "Controlling with words using automatically identified fuzzy Cartesian granule feature models", journal = "International Journal of Approximate Reasoning", volume = "22", pages = "109--148", year = "1999", number = "1-2", keywords = "genetic algorithms, genetic programming", URL = "http://www.sciencedirect.com/science/article/B6V07-3XWJVTP-K/1/fca9fc7ee54707e1f2ed9847e29c1b7e", abstract = "We present a new approach to representing and acquiring controllers based upon Cartesian granule features - multidimensional features formed over the cross product of words drawn from the linguistic partitions of the constituent input features - incorporated into additive models. Controllers expressed in terms of Cartesian granule features enable the paradigm {"}controlling with words{"} by translating process data into words that are subsequently used to interrogate a rule base, which ultimately results in a control action. The system identification of good, parsimonious additive Cartesian granule feature models is an exponential search problem. In this paper we present the G_DACG constructive induction algorithm as a means of automatically identifying additive Cartesian granule feature models from example data. G_DACG combines the powerful optimisation capabilities of genetic programming with a novel and cheap fitness function, which relies on the semantic separation of concepts expressed in terms of Cartesian granule fuzzy sets, in identifying these additive models. We illustrate the approach on a variety of problems including the modelling of a dynamical process and a chemical plant controller.", } @Book{balic:book, author = "Joze Balic", title = "Flexible Manufacturing Systems; Development - Structure - Operation - Handling - Tooling", publisher = "DAAAM International", year = "1999", series = "Manufacturing technology", series_editor = "B. Katalinic", address = "Vienna", email = "joze.balic@uni-mb.si", keywords = "genetic algorithms, genetic programming", ISBN = "3-901509-03-8", broken = "http://www.daaam.com/daaam/IMS%20Seite/DAAAMInternationalBooks.html", URL = "http://www.amazon.com/Contribution-integrated-manufacturing-Publishing-Manufacturing/dp/3901509038/ref=sr_1_1?ie=UTF8&s=books&qid=1254069037&sr=1-1", notes = "Chapter 9.4 Using genetic algorithms for modelling of manufacturing processes. Described are: - overview of GP - modelling of forming process by GP - GA approach for optimisation of cutting conditions copies can be obtained from publication@daaam.com", size = "223 pages", } @Misc{oai:CiteSeerPSU:316448, title = "Modeling Of Mechanical Parts Compositions Using Genetic Programming", author = "Joze Balic and Miran Brezocnik and Franci Cus", abstract = "The paper is a contribution to introducing biologically oriented ideas in conceiving a new and innovative idea in modern factories of the future. The intelligent system is treated as an autonomous organization structure efficiently adapting itself to the dynamic changes in the micro and macro environment. Simulation of self-organizing (genetic) composition of elementary (basic) mechanical parts into the product is presented as an example of the intelligent system. The genetic programming method is used. For conceiving the genetically based composition of parts, the parallels from the living systems are used. Composition takes place on the basis of the genetic content in the basic components and the influence of the environment. The genetically based composition takes place in a distributed way, non-deterministically, buttom-up, and in a self-organizing manner. The paper is also a contribution to the international research and development program Intelligent Manufacturing Systems which is one of the biggest projects ever introduced.", citeseer-isreferencedby = "oai:CiteSeerPSU:116960; oai:CiteSeerPSU:38303; oai:CiteSeerPSU:462740", citeseer-references = "oai:CiteSeerPSU:212034", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:316448", rights = "unrestricted", URL = "http://citeseer.ist.psu.edu/316448.html", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/13061/http:zSzzSzwww.faim2000.isr.umd.eduzSzfaimzSzexportzSz27e8am-b.pdf/modeling-of-mechanical-parts.pdf", keywords = "genetic algorithms, genetic programming", size = "9 pages", year = "2000", notes = "not verified University of Maribor, Faculty of Mechanical Engineering, Laboratory for Intelligent Manufacturing Systems, SI-2000 Maribor, Slovenia; University of Maribor, Faculty of Mechanical Engineering, Laboratory for Researches in Cutting, SI-2000 Maribor, Slovenia", } @Article{Balic:2002:EAAI, author = "J. Balic and M. Nastran", title = "An on-line predictive system for steel wire straightening using genetic programming", journal = "Engineering Applications of Artificial Intelligence", year = "2002", volume = "15", pages = "559--565", number = "6", month = dec, keywords = "genetic algorithms, genetic programming, Production, Control, Prediction, Accuracy", ISSN = "0952-1976", owner = "wlangdon", URL = "https://repozitorij.uni-lj.si/IzpisGradiva.php?id=43335&lang=slv", broken = "http://www.sciencedirect.com/science/article/B6V2M-48BKR53-2/2/4a53f22927ad32b0580540322d7c8868", DOI = "doi:10.1016/S0952-1976(03)00021-6", size = "7 pages", abstract = "Dimensional stability of forming processes is becoming more and more important in the modern production world. Especially when mass production is concerned, the technological system has to be reliable and accurate. Growing market demands are forcing production engineers towards process optimisation in order to achieve high machinery efficiency and reduce the production costs. An important precondition for improving the process chain is the prediction of process behaviour in advance. The paper is presenting the use of genetic programming to predict the wire geometry after forming. The results can be used as the basis for later optimisation of forming processes.", notes = "Miha Nastran and Joze Balic)", } @Article{Balic20021171, author = "Joze Balic and Marjan Korosec", title = "Intelligent tool path generation for milling of free surfaces using neural networks", journal = "International Journal of Machine Tools and Manufacture", volume = "42", number = "10", pages = "1171--1179", year = "2002", email = "joze.balic@uni-mb.si", keywords = "Neural network, CAD/CAM system, CAPP, ICAM, Milling strategy", ISSN = "0890-6955", DOI = "doi:10.1016/S0890-6955(02)00045-7", URL = "http://www.sciencedirect.com/science/article/B6V4B-45YG41B-6/2/09eff48a04f9b22be6b2ed2dd0e6d3b1", abstract = "The presented paper has an intention to show how with the help of Artificial Neural Network (ANN), the prediction of milling tool-path strategy could be made in order to establish which milling path strategy or their sequence will show the best results (will be the most appropriate) at free surface machining, according to set technological aim. In our case the best possible surface quality of machined surface was taken as the primary technological aim. Configuration of used Neural Network (NN) is presented, and the whole procedure is shown on an example of mould, for producing light switches. The verification of machined surface quality, according to average mean roughness, Ra, is also being done, and compared with the NN predicted results [COBISS.SI-ID 7318550]", notes = "Not on GP", } @Article{Balic:2006:JIM, author = "Joze Balic and Miha Kovacic and Bostjan Vaupotic", title = "Intelligent Programming of {CNC} Turning Operations using Genetic Algorithm", journal = "Journal of intelligent manufacturing", year = "2006", volume = "17", number = "3", pages = "331--340", month = jun, keywords = "genetic algorithms, genetic programming, CNC programming, GA, Intelligent CAM, Turning, Tool path generation", ISSN = "0956-5515", DOI = "doi:10.1007/s10845-005-0001-1", abstract = "CAD/CAM systems are nowadays tightly connected to ensure that CAD data can be used for optimal tool path determination and generation of CNC programs for machine tools. The aim of our research is the design of a computer-aided, intelligent and genetic algorithm(GA) based programming system for CNC cutting tools selection, tool sequences planning and optimisation of cutting conditions. The first step is geometrical feature recognition and classification. On the basis of recognised features the module for GA-based determination of technological data determine cutting tools, cutting parameters (according to work piece material and cutting tool material) and detailed tool sequence planning. Material, which will be removed, is split into several cuts, each consisting of a number of basic tool movements. In the next step, GA operations such as reproduction, crossover and mutation are applied. The process of GA-based optimisation runs in cycles in which new generations of individuals are created with increased average fitness of a population. During the evaluation of calculated results (generated NC programmes) several rules and constraints like rapid and cutting tool movement, collision, clamping and minimum machining time, which represent the fitness function, were taken into account. A case study was made for the turning operation of a rotational part. The results show that the GA-based programming has a higher efficiency. The total machining time was reduced by 16percent. The demand for a high skilled worker on CAD/CAM systems and CNC machine tools was also reduced.", } @Article{Balicki:2006:IJCSNS, author = "Jerzy Balicki", title = "Multicriterion Genetic Programming for Trajectory Planning of Underwater Vehicle", journal = "IJCSNS International Journal of Computer Science and Network Security", year = "2006", volume = "6", number = "12", month = dec, keywords = "genetic algorithms, genetic programming, remote operating vehicle, multi-criterion optimisation", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.385.5889", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.385.5889", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.385.5889.pdf", URL = "http://digilib.unsri.ac.id/download/200612A01.pdf", size = "6 pages", abstract = "An autonomous underwater vehicle is supposed to find its trajectory, systematically. It can be obtained by using genetic programming for multi-criterion optimisation of the set of alternative paths. For assessment of an underwater vehicle trajectory, three crucial criteria can be used: a total length of a path, a smoothness of a trajectory, and a measure of safety.", } @InProceedings{conf/icsoft/Balicki07, author = "Jerzy Marian Balicki", title = "Multi-Criterion Genetic Programming With Negative Selection for Finding {Pareto} Solutions", booktitle = "Proceedings of the Second International Conference on Software and Data Technologies, ICSOFT 2007", year = "2007", editor = "Joaquim Filipe and Boris Shishkov and Markus Helfert", pages = "120--127", address = "Barcelona, Spain", month = "22-25 " # jul, publisher = "INSTICC Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-989-8111-05-0", URL = "http://www.icsoft.org/Abstracts/2007/ICSOFT_2007_Abstracts.htm", DOI = "doi:10.5220/0001336201200127", abstract = "Multi-criterion genetic programming (MGP) is a relatively new approach for a decision making aid and it can be applied to determine the Pareto solutions. This purpose can be obtained by formulation of a multi-criterion optimization problem that can be solved by genetic programming. An improved negative selection procedure to handle constraints in the MGP has been proposed. In the test instance, both a workload of a bottleneck computer and the cost of system are minimized; in contrast, a reliability of the distributed system is maximized.", notes = "http://www.icsoft.org/ICSOFT2007/Area3.htm Distributed and Parallel Systems ICSOFT 2007 was held in conjunction with ENASE 2007 Others www.lania.mx/~ccoello/EMOObib.html ?", bibdate = "2009-02-24", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icsoft/icsoft2007-1.html#Balicki07", } @InProceedings{Balicki:2013:HSI, author = "Jerzy Balicki and Waldemar Korlub and Henryk Krawczyk and Jacek Paluszak", title = "Genetic Programming with Negative Selection for Volunteer Computing System Optimization", booktitle = "The 6th International Conference on Human System Interaction (HSI 2013)", year = "2013", month = "6-8 " # jun, pages = "271--278", keywords = "genetic algorithms, genetic programming, volenteer grid systems, Internet", DOI = "doi:10.1109/HSI.2013.6577835", ISSN = "2158-2246", size = "8 pages", abstract = "Volunteer computing systems like BOINC or Comcute are strongly supported by a great number of volunteers who contribute resources of their computers via the Web. So, the high efficiency of such grid system is required, and that is why we have formulated a multi-criterion optimisation problem for a volunteer grid system design. In that dilemma, both the cost of the host system and workload of a bottleneck host are minimised. On the other hand, a reliability of this grid structure is maximised. Moreover, genetic programming has been applied to determine the Pareto solutions. Finally, a negative selection procedure to handle constraints has been discussed.", notes = "Also known as \cite{6577835}", } @InCollection{series/sci/BalickiKKP14, author = "Jerzy Balicki and Waldemar Korlub and Henryk Krawczyk and Jacek Paluszak", title = "Genetic Programming for Interaction Efficient Supporting in Volunteer Computing Systems", booktitle = "Issues and Challenges in Artificial Intelligence", year = "2014", publisher = "Springer", editor = "Zdzislaw S. Hippe and Juliusz L. Kulikowski and Teresa Mroczek and Jerzy Wtorek", volume = "559", series = "Studies in Computational Intelligence", pages = "129--139", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-06883-1", bibdate = "2015-07-09", bibsource = "DBLP, http://dblp.uni-trier.de/db/series/sci/sci559.html#BalickiKKP14", DOI = "doi:10.1007/978-3-319-06883-1_11", URL = "http://dx.doi.org/10.1007/978-3-319-06883-1_11", URL = "http://dx.doi.org/10.1007/978-3-319-06883-1", size = "11 pages", abstract = "Volunteer computing systems provide a middleware for interaction between project owners and great number volunteers. In this chapter, a genetic programming paradigm has been proposed to a multi-objective scheduler design for efficient using some resources of volunteer computers via the web. In a studied problem, genetic scheduler can optimise both a workload of a bottleneck computer and cost of system. Genetic programming has been applied for finding the Pareto solutions by applying an immunological procedure. Finally, some numerical experiment outcomes have been discussed.", } @InProceedings{conf/icaisc/BalickiKSZ14, author = "Jerzy Balicki and Waldemar Korlub and Julian Szymanski and Marcin Zakidalski", title = "Big Data Paradigm Developed in Volunteer Grid System with Genetic Programming Scheduler", bibdate = "2014-05-23", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icaisc/icaisc2014-1.html#BalickiKSZ14", booktitle = "Artificial Intelligence and Soft Computing - 13th International Conference, {ICAISC} 2014, Zakopane, Poland, June 1-5, 2014, Proceedings, Part {I}", publisher = "Springer", year = "2014", volume = "8467", editor = "Leszek Rutkowski and Marcin Korytkowski and Rafal Scherer and Ryszard Tadeusiewicz and Lotfi A. Zadeh and Jacek M. Zurada", isbn13 = "978-3-319-07172-5", pages = "771--782", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming", URL = "http://dx.doi.org/10.1007/978-3-319-07173-2", } @InProceedings{Balicki:2015:HSI, author = "Jerzy Balicki and Michal Beringer and Waldemar Korlub and Piotr Przybylek and Maciej Tyszka and Marcin Zadroga", booktitle = "8th International Conference on Human System Interaction (HSI)", title = "Collective citizens' behavior modelling with support of the Internet of Things and Big Data", year = "2015", pages = "61--67", abstract = "In this paper, collective human behaviours are modelled by a development of Big Data mining related to the Internet of Things. Some studies under MapReduce architectures have been carried out to improve an efficiency of Big Data mining. Intelligent agents in data mining have been analysed for smart city systems, as well as data mining has been described by genetic programming. Furthermore, artificial neural networks have been discussed in data mining as well as an analysis of Tweeter's blogs for citizens has been proposed. Finally, some numerical experiments with fire spread around Tricity, Poland have been submitted.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/HSI.2015.7170644", ISSN = "2158-2246", month = jun, notes = "Also known as \cite{7170644}", } @Article{BALLANDIES:2021:MM, author = "Mark C. Ballandies and Evangelos Pournaras", title = "Mobile link prediction: Automated creation and crowdsourced validation of knowledge graphs", journal = "Microprocessors and Microsystems", volume = "87", pages = "104335", year = "2021", ISSN = "0141-9331", DOI = "doi:10.1016/j.micpro.2021.104335", URL = "https://www.sciencedirect.com/science/article/pii/S0141933121004944", keywords = "genetic algorithms, genetic programming, Knowledge graph, Ontology, Cyber-physical-social system, Link prediction, Crowdsourcing", abstract = "Building trustworthy knowledge graphs for cyber-physical social systems (CPSS) is a challenge. In particular, current approaches relying on human experts have limited scalability, while automated approaches are often not validated by users resulting in knowledge graphs of questionable quality. This paper introduces a novel pervasive knowledge graph builder for mobile devices that brings together automation, experts' and crowdsourced citizens' knowledge. The knowledge graph grows via automated link predictions using genetic programming that are validated by humans for improving transparency and calibrating accuracy. The knowledge graph builder is designed for pervasive devices such as smartphones and preserves privacy by localizing all computations. The accuracy, practicality, and usability of the knowledge graph builder is evaluated in a real-world social experiment that involves a smartphone implementation and a Smart City application scenario. The proposed methodology of knowledge graph building outperforms a baseline method in terms of accuracy while demonstrating its efficient calculations on smartphones and the feasibility of the pervasive human supervision process in terms of high interactions throughput. These findings promise new opportunities to crowdsource and operate pervasive reasoning systems for cyber-physical social systems in Smart Cities", } @Article{Ballesteros:2025:jlamp, author = "Ignacio Ballesteros and Clara Benac-Earle and Julio Marino and Lars-Ake Fredlund and Angel Herranz", title = "Generation of algebraic data type values using evolutionary algorithms", journal = "Journal of Logical and Algebraic Methods in Programming", year = "2025", volume = "143", pages = "101022", keywords = "genetic algorithms, genetic programming, Evolutionary algorithms, Software testing, Property-based testing, Red-black tree", ISSN = "2352-2208", URL = "https://www.sciencedirect.com/science/article/pii/S2352220824000762", DOI = "doi:10.1016/j.jlamp.2024.101022", abstract = "Automatic data generation is a key component of automated software testing. Random generation of test input data can uncover some bugs in software, but its effectiveness decreases when those inputs must satisfy complex properties in order to be meaningful. In this work, we study an evolutionary approach to generate values that can be encoded as algebraic data types plus additional properties. First, the approach is illustrated with the generation of sorted lists. Then, we generalise the technique to arbitrary algebraic data type definitions. Finally, we consider the problem of constrained data types where the data must satisfy some nontrivial property, using the well-known example of red-black trees for our experiments. This example will allow us to introduce the main principles of evolutionary algorithms and how these principles can be applied to obtain valid, nontrivial samples of a given data structure. Our experiments have revealed that this evolutionary approach is able to improve diversity, and increase the size of valid generated values with respect to simple random sampling techniques", } @InProceedings{Baltes:2020:GI9, author = "Sebastian Baltes and Markus Wagner", title = "An Annotated Dataset of Stack Overflow Post Edits", booktitle = "9th edition of GI @ GECCO 2020", year = "2020", month = jul # " 8-12", editor = "Brad Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and John R. Woodward", publisher = "ACM", address = "Internet", pages = "1923--1925", organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, Software documentation, software evolution, patches, mining soft-ware repositories, stack overflow, SOTorrent", isbn13 = "978-1-4503-7127-8", URL = "https://dl.acm.org/doi/abs/10.1145/3377929.3398108", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/gecco2020/companion_files/wksp144s2-file1.pdf", URL = "https://arxiv.org/abs/2004.08193", DOI = "doi:10.1145/3377929.3398108", code_url = "https://doi.org/10.5281/zenodo.3754159", size = "3 pages", abstract = "To improve software engineering, software repositories have been mined for code snippets and bug fixes. Typically, this mining takes place at the level of files or commits. To be able to dig deeper and to extract insights at a higher resolution, we hereby present an annotated dataset that contains over 7 million edits of code and text on Stack Overflow. Our preliminary study indicates that these edits might be a treasure trove for mining information about fine-grained patches, e.g., for the optimisation of non-functional properties.", notes = "Dataset https://doi.org/10.5281/zenodo.3754159 Broken Nov 2024 https://gi-gecco-20.gi-workshops.org/ Nov 2020 GECCO 2020 companion DOI not working also known as \cite{Baltes:2020:GECCOcomp}. Also known as \cite{10.1145/3377929.3398108} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @InProceedings{Baltes:2023:GI, author = "Sebastian Baltes", title = "All about the money: Cost modeling and optimization of cloud applications", booktitle = "12th International Workshop on Genetic Improvement @ICSE 2023", year = "2023", editor = "Vesna Nowack and Markus Wagner and Gabin An and Aymeric Blot and Justyna Petke", pages = "x", address = "Melbourne, Australia", month = "20 " # may, publisher = "IEEE", note = "Invited Keynote", keywords = "genetic algorithms, genetic programming, Genetic Improvement, cloud computing, IaC, IfC, Gartner, AWS, nonfunctional cost optimisation, software engineering, SBSE", isbn13 = "979-8-3503-1232-4", URL = "http://gpbib.cs.ucl.ac.uk/gi2023/keynote_2023_gi.pdf", DOI = "doi:10.1109/GI59320.2023.00008", slides_url = "http://gpbib.cs.ucl.ac.uk/gi2023/Sebastian_Baltes_Keynote_Cloud_Cost.pdf", slides_url = "https://empirical-software.engineering/talks/#2023", video_url = "http://gpbib.cs.ucl.ac.uk/gi2023/GI_ICSE2023_Keynote_Sebastian_Baltes.mp4", video_url = "http://gpbib.cs.ucl.ac.uk/gi2023/GI_ICSE2023_Keynote_Sebastian_Baltes.mov", video_url = "https://www.youtube.com/watch?v=M8gH0qPcJHQ&list=PLI8fiFpB7BoJLh6cUpGBjyeB1hM9DET1V&index=5", size = "1 page", abstract = "Cost is an essential non-functional property of cloud applications and is often a primary reason for companies to move to the cloud. One significant advantage of cloud platforms is the possibility to scale compute, storage, and networking resources up and down based on demand. However, as an application scales, so does the cost. Cost transparency of cloud applications is a common problem, and cloud providers have responded by providing means for detecting cost anomalies. However, detecting anomalies after billing is a workaround rather than a solution addressing the core problem. After introducing central cloud computing concepts and typical pricing approaches in the cloud, this talk outlines our vision of a vendor-agnostic cost model enabling reasoning about cost-optimal infrastructure and platform configurations based on expected workloads. The overall goal is to shift cost transparency left, i.e., to the developers and platform engineers who frequently provision cloud environments using web portals or Infrastructure-as-Code (IaC) files. The talk concludes by summarising the current trend towards Infrastructure-from-Code (IfC), where programming languages and cloud infrastructure descriptions converge into one paradigm, intending to automate infrastructure provisioning as much as possible. This area has huge potential for genetic improvement to optimize the IfC code and the provisioning mechanisms while balancing nonfunctional properties such as performance and cost.", notes = "GI @ ICSE 2023, part of \cite{Nowack:2023:GI} Amazon web services: you have to pay for that, internal cloud networking costs. SAP. duckbillgroup.com, stackoverflow monolithic application, blog post. IaaS Infrastructure as a Service: compute, storage and networking. PaaS Platform as a Service. SaaS Software as a Service (just applications that are hosted in the cloud). AWS lambda: pay only when used. Use lock-in, that is how they make money. Google Cloud: configure by web user interface. Terraform: file based configuration (ideal for GI to optimise?) GitOps: ArgoCD push IaC file, automatically updates your cloud configuration Mitigation (guard rails, prevent doing something really stupid) Infracost. AWS power tuning (optimisation). OpenCost (eg Microsoft). Infrastructure-from-Code IfC api.cost. puppet. Serverless, eg JSON. Evolution of workload. SAP database. (so far) little work in software engineering research. Some in cloud research computing, eg Google cloud v. Apache Finc (IC2E 2022) Future: mining GitHub. Takeaways 41:00 shifted left, always about tradeoffs 43:00 Q&A. Myra B Cohen. Need to simplify to start research. Light weight search. Bradley Alexander, spot instances. Handle interruptions Amazon EC2. Easier to model spot prices? Hyper-scalars Very complex. You pay for everything. Erik Frederics, offline testing, test out before actually run on the cloud. Serverless. Estimate cost before actually running. How frequently do prices. Scrape prices from the internet www web. Monolithic. SAP. Importance of stable compute platform. micro-services not a solution in themselves. Latency, no easy solutions.", } @Article{baluja:1994:taaecgi, author = "Shumeet Baluja and Dean Pomerleau and Todd Jochem", title = "Towards Automated Artificial Evolution for Computer-generated Images", journal = "Connection Science", year = "1994", volume = "6", number = "2 and 3", pages = "325--354", keywords = "genetic algorithms, genetic programming, artificial neural networks (ANN), simulated evolution, computer graphics", broken = "http://www.ri.cmu.edu/pubs/pub_1718.html", URL = "http://www.ri.cmu.edu/pub_files/pub3/baluja_shumeet_1994_1/baluja_shumeet_1994_1.pdf", DOI = "doi:10.1080/09540099408915729", size = "30 pages", abstract = " In 1991, Karl Sims presented work on artificial evolution in which he used genetic algorithms to evolve complex structures for use in computer generated images and animations. The evolution of the computer generated images progressed from simple, randomly generated shapes to interesting images which the users interactively created. The evolution advanced under the constant guidance and supervision of the user. This paper describes attempts to automate the process of image evolution through the use of artificial neural networks. The central objective of this study is to learn the user's preferences, and to apply this knowledge to evolve aesthetically pleasing images which are similar to those evolved through interactive sessions with the user. This paper presents a detailed analysis of both the shortcomings and successes encountered in the use of five artificial neural network architectures. Further possibilities for improving the performance of a fully automated system are also discussed.", notes = "Also known as \cite{BALUJA01011994} also CMU techical report CMU//CS-93-198 ", } @Article{Balzer:1985:ieeeTSE, author = "Robert Balzer", title = "A 15 Year Perspective on Automatic Programming", journal = "IEEE Transactions on Software Engineering", year = "1985", volume = "SE-11", number = "11", pages = "1257--1268", month = nov, keywords = "genetic algorithms, genetic programming, genetic improvement, Automatic programming, evolution, explanation, knowledge base, maintenance, prototyping, specification, transformation", ISSN = "0098-5589", DOI = "doi:10.1109/TSE.1985.231877", size = "12 pages", abstract = "Automatic programming consists not only of an automatic compiler, but also some means of acquiring the high-level specification to be compiled, some means of determining that it is the intended specification, and some (interactive) means of translating this high-level specification into a lower-level one which can be automatically compiled. We have been working on this extended automatic programming problem for nearly 15 years, and this paper presents our perspective and approach to this problem and justifies it in terms of our successes and failures. Much of our recent work centers on an operational testbed incorporating usable aspects of this technology. This testbed is being used as a prototyping vehicle for our own research and will soon be released to the research community as a framework for development and evolution of Common Lisp systems.", notes = "Cited by \cite{Balzer:2010:FoSER} Also known as \cite{1701945}", } @InProceedings{Balzer:2010:FoSER, author = "Robert Balzer", title = "Why Haven't We Automated Programming", booktitle = "Proceedings of the FSE/SDP Workshop on Future of Software Engineering Research", year = "2010", pages = "13--16", address = "Santa Fe, New Mexico, USA", publisher_address = "New York, NY, USA", publisher = "ACM", keywords = "genetic improvement, automatic programming, interactive, refinement", isbn13 = "978-1-4503-0427-6", URL = "http://doi.acm.org/10.1145/1882362.1882366", DOI = "doi:10.1145/1882362.1882366", acmid = "1882366", size = "4", notes = "Not GP, FSE18, http://fse18.cse.wustl.edu/foserprogram.html", } @Article{bamshad:2022:Machines, author = "Hamid Bamshad and Seongwon Jang and Hyemi Jeong and Jaesung Lee and Hyunseok Yang", title = "Comparison between Genetic Programming and Dynamic Models for Compact Electrohydraulic Actuators", journal = "Machines", year = "2022", volume = "10", number = "10", pages = "Article No. 961", keywords = "genetic algorithms, genetic programming", ISSN = "2075-1702", URL = "https://www.mdpi.com/2075-1702/10/10/961", DOI = "doi:10.3390/machines10100961", abstract = "A compact electrohydraulic actuator (C-EHA) is an innovative hydraulic system with a wide range of applications, particularly in automation, robotics, and aerospace. The actuator provides the benefits of hydraulics without the expense and space requirements of full-sized hydraulic systems and in a much cleaner manner. However, this actuator is associated with some disadvantages, such as a high level of nonlinearity, uncertainty, and a lack of studies. The development of a robust controller requires a thorough understanding of the system behaviour as well as an accurate dynamic model of the system; however, finding an accurate dynamic model of a system is not always straightforward, and it is considered a significant challenge for engineers, particularly for a C-EHA because the critical parameters inside cannot be accessed. Our research aims to evaluate and confirm the ability of genetic programming (GP) to model a nonlinear system for a C-EHA. In our paper, we present and develop a GP model for the C-EHA system. Furthermore, our study presents a dynamic model of the system for comparison with the GP model. As a result, by using this actuator in the 1-DOF arm system and conducting experiments, we confirmed that the GP model has a better performance with less positional error compared with the proposed dynamic model. The model can be used to conduct further studies, such as designing controllers or system simulations.", notes = "also known as \cite{machines10100961}", } @InProceedings{conf/emo/BandaruD13, author = "Sunith Bandaru and Kalyanmoy Deb", title = "A Dimensionally-Aware Genetic Programming Architecture for Automated Innovization", booktitle = "Proceedings of the 7th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2013", year = "2013", editor = "Robin C. Purshouse and Peter J. Fleming and Carlos M. Fonseca and Salvatore Greco and Jane Shaw", volume = "7811", series = "Lecture Notes in Computer Science", pages = "513--527", address = "Sheffield, UK", month = mar # " 19-22", publisher = "Springer", keywords = "genetic algorithms, genetic programming, dimensional awareness, automated innovization, multi-objective optimization, design principles, NSGA-II, Matlab, GA SmallGP", bibdate = "2013-03-13", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/emo/emo2013.html#BandaruD13", isbn13 = "978-3-642-37139-4", URL = "https://www.egr.msu.edu/~kdeb/papers/k2012015.pdf", URL = "http://dx.doi.org/10.1007/978-3-642-37140-0", DOI = "doi:10.1007/978-3-642-37140-0_39", size = "15 pages", abstract = "Automated innovization is an unsupervised machine learning technique for extracting useful design knowledge from Pareto-optimal solutions in the form of mathematical relationships of a certain structure. These relationships are known as design principles. Past studies have shown the applicability of automated innovization on a number of engineering design optimisation problems using a multiplicative form for the design principles. In this paper, we generalise the structure of the obtained principles using a tree-based genetic programming framework. While the underlying innovisation algorithm remains the same, evolving multiple trees, each representing a different design principle, is a challenging task. We also propose a method for introducing dimensionality information in the search process to produce design principles that are not just empirical in nature, but also meaningful to the user. The procedure is illustrated for three engineering design problems.", notes = "See also KanGAL Report Number 2012015 NB k2012015.pdf not identical to EMO 2013 publication", } @PhdThesis{Bandaru_thesis, author = "Sunith Bandaru", title = "Automated Innovization: Knowledge discovery through multi-objective optimization", school = "Indian Institute of Technology Kanpur", year = "2013", address = "India", keywords = "genetic algorithms, genetic programming, Innovization, MEMS", URL = "https://drive.google.com/file/d/0B8WHZC_8VuhxZ3FWenBfa19MSDQ/view", URL = "https://www.iitk.ac.in/kangal/deb_phd.shtml", size = "227 pages", abstract = "In recent years, there has been a growing interest in the field of post-optimality analysis. In a single objective scenario, this usually concerns optimality, sensitivity and robustness studies on the obtained solution. Multi-objective optimization on the other hand, poses an additional challenge in that there are a multitude of possible solutions (when the objectives are conflicting) which are all said to be Pareto-optimal. These solutions may collectively hold crucial design information. Properties that are common to all or most Pareto-optimal solutions can be considered as characteristic features that make good designs. Knowledge of such features, in addition to providing better insights into the problem at hand, enables the designer to hand craft solutions for other optimization tasks which are structurally similar to it; thus eliminating the need to actually optimize. Innovization is the process of extracting this information in the form of design principles, which are basically mathematical expressions relating various problem entities like variables, objectives and constraint functions. Manual innovization involves the visual identification of correlations between problem entities through two and three-dimensional plots. Thereafter, appropriate functions are used for regression and the design principles are obtained. Though the procedure has been applied to many engineering design problems, the human element involved in it limits its use in most practical applications. The present thesis firstly proposes automated innovization, an unsupervised machine learning technique that can identify correlations in any multi-dimensional space formed by variables, objectives, etc. specified by the user and subsequently performs a selective regression on the correlated part of the Pareto-optimal dataset to obtain a design principle. The correlations are automatically identified by a customized grid-based clustering algorithm and the design principle is evolved using a genetic algorithm. Next, the procedure is extended so that design principles hidden in all possible Euclidean spaces formed by the variables and objectives (and any other user-defined functions) can be obtained simultaneously, without any human interaction, in a single run of the algorithm. This is accomplished by introducing a niching strategy to evolve different species of design principles in the same population of a genetic algorithm. Automation in innovization is achieved at the cost of restricting the mathematical structure of the design principles to a certain form, the significance of which becomes clear by observing physical laws in nature. Later in this thesis, a tree-based genetic programming framework is integrated into automated innovization to obtain design principles of any generic mathematical structure. Dimensionality information is introduced in the search process to produce design principles that are meaningful to the designer. Next, the proposed automated innovization technique is used to obtain design principles for four real-world multi-objective design optimization problems from varied fields. They are: noise barrier design optimization, polymer extrusion process optimization, friction stir welding process optimization and MEMS (MicroElectroMechanical Systems) resonator design optimization. In each case the obtained design principles are presented to experts of the respective fields for interpretation to gain insights. Secondly, this thesis introduces two new innovization concepts, namely higher-level innovization and lower-level innovization. Multi-objective optimization problem formulations involve many settings that are not changed during the solution process. However, once the trade-off front is generated, the designer may wish to change them and rerun the optimization, thus obtaining more fronts. This happens in many real-world situations where the designer is initially unsure about problem elements such as constraints, variable bounds, parameters and even objective functions. Higher-level innovization answers questions like: Are the features of the original problem still valid for other generated fronts? If not, how do they change with the modified setting?. The name reflects the fact that higher-level design knowledge is gained in the process. Sometimes lower-level design knowledge may also be desired. Consider the situation when after obtaining a set of trade-off solutions for a multi-objective design problem, a posteriori decision-making approach is used to identify a region of preference on the trade-off front. Now the designer may be interested in knowing features that are common to solutions only in this partial set and are not seen in rest of the trade-off solutions, so that the designer is specifically aware of properties associated with the chosen solutions. In this thesis, the automated innovization technique is extended to perform both higher and lower-level innovization. Thirdly, this thesis studies the temporal evolution of design principles obtained using automated innovization during the course of optimization. Results on a few engineering design problems reveal that certain important design features start to evolve early on, whereas some detailed design features appear later during optimization. Interestingly, there exists a simile between evolution of design principles in engineering and human evolution. Such information about the hierarchy of key design principles should enable designers to have a deeper understanding of their problems.", notes = "SunithBandaru_PhDThesis.pdf", } @Article{Bandaru:2015:EJOR, author = "Sunith Bandaru and Tehseen Aslam and Amos H. C. Ng and Kalyanmoy Deb", title = "Generalized higher-level automated innovization with application to inventory management", journal = "European Journal of Operational Research", volume = "243", number = "2", pages = "480--496", year = "2015", ISSN = "0377-2217", DOI = "doi:10.1016/j.ejor.2014.11.015", URL = "http://www.sciencedirect.com/science/article/pii/S0377221714009199", abstract = "This paper generalises the automated innovization framework using genetic programming in the context of higher-level innovisation. Automated innovization is an unsupervised machine learning technique that can automatically extract significant mathematical relationships from Pareto-optimal solution sets. These resulting relationships describe the conditions for Pareto-optimality for the multi-objective problem under consideration and can be used by scientists and practitioners as thumb rules to understand the problem better and to innovate new problem solving techniques; hence the name innovization (innovation through optimisation). Higher-level innovization involves performing automated innovization on multiple Pareto-optimal solution sets obtained by varying one or more problem parameters. The automated innovization framework was recently updated using genetic programming. We extend this generalisation to perform higher-level automated innovization and demonstrate the methodology on a standard two-bar bi-objective truss design problem. The procedure is then applied to a classic case of inventory management with multi-objective optimisation performed at both system and process levels. The applicability of automated innovization to this area should motivate its use in other avenues of operational research.", keywords = "genetic algorithms, genetic programming, Automated innovization, Higher-level innovization, Inventory management, Knowledge discovery", } @InProceedings{Banerjee:2020:SSCI, author = "Subhashis Banerjee and Sushmita Mitra", title = "Evolving Optimal Convolutional Neural Networks", booktitle = "2020 IEEE Symposium Series on Computational Intelligence (SSCI)", year = "2020", pages = "2677--2683", abstract = "Among the different Deep Learning (DL) models, the deep Convolutional Neural Networks (CNNs) have demonstrated impressive performance in a variety of image recognition or classification tasks. Although CNNs do not require feature engineering or manual extraction of features at the input level, yet designing a suitable CNN architecture necessitates considerable expert knowledge involving enormous amount of trial-and-error activities. In this paper we attempt to automatically design a competitive CNN architecture for a given problem while consuming reasonable machine resource(s) based on a modified version of Cartesian Genetic Programming (CGP). As CGP uses only the mutation operator to generate offsprings it typically evolves slowly. We develop a new algorithm which introduces crossover to the standard CGP to generate an optimal CNN architecture. The genotype encoding scheme is changed from integer to floating-point representation for this purpose. The function genes in the nodes of the CGP are chosen as the highly functional modules of CNN. Typically CNNs use convolution and pooling, followed by activation. Rather than using each of them separately as a function gene for a node, we combine them in a novel way to construct highly functional modules. Five types of functions, called ConvBlock, average pooling, max pooling, summation, and concatenation, were considered. We test our method on an image classification dataset CIFAR10, since it is being used as the benchmark for many similar problems. Experiments demonstrate that the proposed scheme converges fast and automatically finds the competitive CNN architecture as compared to state-of-the-art solutions which require thousands of generations or GPUs involving huge computational burden.", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", DOI = "doi:10.1109/SSCI47803.2020.9308201", month = dec, notes = "Also known as \cite{9308201}", } @Article{Bang:2013:IJCAS, author = "Sung-Woo Bang and Jaekwang Kim and Jee-Hyong Lee", title = "An Approach of Genetic Programming for Music Emotion Classification", journal = "International Journal of Control, Automation and Systems", year = "2013", volume = "11", number = "6", pages = "1290--1299", month = dec, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Classification algorithm, emotion recognition, music information retrieval", ISSN = "1598-6446", language = "English", DOI = "doi:10.1007/s12555-012-9407-7", size = "10 pages", abstract = "In this paper, we suggest a new approach of genetic programming for music emotion classification. Our approach is based on Thayer's arousal-valence plane which is one of representative human emotion models. Thayer's plane which says human emotions is determined by the psychological arousal and valence. We map music pieces onto the arousal-valence plane, and classify the music emotion in that space. We extract 85 acoustic features from music signals, rank those by the information gain and choose the top k best features in the feature selection process. In order to map music pieces in the feature space onto the arousal-valence space, we apply genetic programming. The genetic programming is designed for finding an optimal formula which maps given music pieces to the arousal-valence space so that music emotions are effectively classified. k-NN and SVM methods which are widely used in classification are used for the classification of music emotions in the arousal-valence space. For verifying our method, we compare with other six existing methods on the same music data set. With this experiment, we confirm the proposed method is superior to others.", } @Article{Banga:2013:ijset, author = "Manu Banga", title = "Computational Hybrids Towards Software Defect Predictions", journal = "International Journal of Scientific Engineering and Technology", year = "2013", volume = "2", number = "5", pages = "311--316", ISSN = "2277-1581", bibsource = "OAI-PMH server at doaj.org", language = "English", oai = "oai:doaj.org/article:12a6cd2f16e947d7969f01df7e2544d9", rights = "CC by-nc-nd", keywords = "genetic algorithms, genetic programming, MLR, SVR, CART, MARS, MPFF, RBF", URL = "http://ijset.com/ijset/publication/v2s5/paper1.pdf", URL = "http://ijset.com/archive/v2i5", abstract = "In this paper, new computational intelligence sequential hybrid architectures involving Genetic Programming (GP) and Group Method of Data Handling (GMDH) viz. GPGMDH. Three linear ensembles based on (i) arithmetic mean (ii) geometric mean and (iii) harmonic mean are also developed. We also performed GP based feature selection. The efficacy of Multiple Linear Regression (MLR), Polynomial Regression, Support Vector Regression (SVR), Classification and Regression Tree (CART), Multivariate Adaptive Regression Splines (MARS), Multilayer FeedForward Neural Network (MLFF), Radial Basis Function Neural Network (RBF), Counter Propagation Neural Network (CPNN), Dynamic Evolving Neuro--Fuzzy Inference System (DENFIS), TreeNet, Group Method of Data Handling and Genetic Programming is tested on the NASA dataset. Ten-fold cross validation and t-test are performed to see if the performances of the hybrids developed are statistically significant.", } @MastersThesis{Brock_Baniasadi_Maryam_2013, author = "Maryam Baniasadi", title = "Genetic Programming for Non-Photorealistic Rendering", school = "Department of Computer Science, Brock University", year = "2013", address = "St. Catharines, Ontario, Canada L2S 3A1", month = mar, keywords = "genetic algorithms, genetic programming", URL = "http://hdl.handle.net/10464/4304", URL = "https://dr.library.brocku.ca/handle/10464/4304", URL = "https://dr.library.brocku.ca/bitstream/handle/10464/4304/Brock_Baniasadi_Maryam_2013.pdf", URL = "http://www.cosc.brocku.ca/archive/sites/all/files/downloads/research/cs1308.pdf", size = "193 pages", abstract = "This thesis focuses on developing an evolutionary art system using genetic programming. The main goal is to produce new forms of evolutionary art that filter existing images into new non-photorealistic (NPR) styles, by obtaining images that look like traditional media such as watercolor or pencil, as well as brand new effects. The approach permits GP to generate creative forms of NPR results. The GP language is extended with different techniques and methods inspired from NPR research such as colour mixing expressions, image processing filters and painting algorithm. Colour mixing is a major new contribution, as it enables many familiar and innovative NPR effects to arise. Another major innovation is that many GP functions process the canvas (rendered image), while is dynamically changing. Automatic fitness scoring uses aesthetic evaluation models and statistical analysis, and multi-objective fitness evaluation is used. Results showed a variety of NPR effects, as well as new, creative possibilities.", notes = "Also available as Technical Report # CS-13-08", } @Article{Baniasadi:2015:GPEM, author = "Maryam Baniasadi and Brian J. Ross", title = "Exploring non-photorealistic rendering with genetic programming", journal = "Genetic Programming and Evolvable Machines", year = "2015", volume = "16", number = "2", pages = "211--239", month = jun, keywords = "genetic algorithms, genetic programming, Evolutionary art", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-014-9234-0", size = "29 pages", abstract = "The field of evolutionary art focuses on using artificial evolution as a means for generating and exploring artistic images and designs. Here, we use evolutionary computation to generate painterly styles of images. A source image is read into the system, and a genetic program is evolved that will re-render the image with non-photorealistic effects. A main contribution of this research is that the colour mixing expression is evolved, which permits a variety of interesting NPR effects to arise. The mixing expression evaluates mathematical properties of the dynamically changing canvas, which results in the evolution of adaptive NPR procedures. Automatic fitness evaluation includes Ralph's aesthetic model, colour matching, and direct luminosity matching. A few simple techniques for economical brush stroke application on the canvas are supported, which produce different stylistic effects. Using our approach, a number of established, as well as innovative, non-photorealistic painting effects were produced.", } @InProceedings{Banik:2015:ICCIT, author = "Shipra Banik and A. F. M. Khodadad Khan", booktitle = "2015 18th International Conference on Computer and Information Technology (ICCIT)", title = "Forecasting US NASDAQ stock index values using hybrid forecasting systems", year = "2015", pages = "282--287", abstract = "Capability to predict precise future stock values is the most important factor in financial market to make profit. Because of virtual trading, now a day this market has turn into one of the hot targets where any person can earn profit. Thus, predicting the correct future value of a stock has become an area of hot interest. This paper attempt to forecast NASDAQ stock index values using novel hybrid forecasting models based on widely used soft computing models and time series models. The daily historical US NASDAQ closing stock index for the periods of 08 February 1971 to 24 July 2015 is used and is applied our proposed hybrid forecasting models to see whether considered forecasting models can closely forecast daily NASDAQ stock index values. Mean absolute error and root mean square error between observed and predicted NASDAQ stock index are considered as evaluation criteria. The result is compared on the basis of selected individual forecasting time series model and individual soft computing forecasting models and the proposed hybrid forecasting models. Our experimental evidences show that the proposed hybrid back-propagation artificial neural network and genetic algorithm forecasting model has outperformed as compare to other considered forecasting models for forecasting daily US NASDAQ stock index. We trust that daily US NASDAQ stock index forecasts will be notice for a number of spectators who wish to construct strategies about this index.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICCITechn.2015.7488083", month = dec, notes = "School of Engineering and Computer Science, Independent University, Bangladesh, Dhaka, Bangladesh Also known as \cite{7488083}", } @Article{Bankhead:2007:GPEM, author = "Armand {Bankhead III} and Robert B. Heckendorn", title = "Using evolvable genetic cellular automata to model breast cancer", journal = "Genetic Programming and Evolvable Machines", year = "2007", volume = "8", number = "4", pages = "381--393", month = dec, note = "special issue on medical applications of Genetic and Evolutionary Computation", keywords = "genetic algorithms, Genetic cellular automata, DCIS, Progenitor hierarchy, Ductal simulation, Hereditary genetic predisposition, Hereditary breast cancer, CA", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-007-9042-x", abstract = "Cancer is an evolutionary process. Mutated cells undergo selection for abnormal growth and survival creating a tumour. We model this process with cellular automata that use a simplified genetic regulatory network simulation to control cell behaviour and predict cancer etiology. Our genetic model gives us the ability to relate genetic mutation to cancerous outcomes. The simulation uses known histological morphology, cell types, and stochastic behavior to specifically model ductal carcinoma in situ (DCIS), a common form of non-invasive breast cancer. Using this model we examine the effects of hereditary predisposition on DCIS incidence and aggressiveness. Results show that we are able to reproduce in vivo pathological features to hereditary forms of breast cancer: earlier incidence and increased aggressiveness. We also show that a contributing factor to the different pathology of hereditary breast cancer results from the ability of progenitor cells to pass cancerous mutations on to offspring.", notes = "155 node beowulf cluster", } @InProceedings{banks:2004:lbp, author = "Edwin Roger Banks and James Hayes and Edwin Nunez", title = "Parametric Regression Through Genetic Programming", booktitle = "Late Breaking Papers at the 2004 Genetic and Evolutionary Computation Conference", year = "2004", editor = "Maarten Keijzer", address = "Seattle, Washington, USA", month = "26 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://gpbib.cs.ucl.ac.uk/gecco2004/LBP001.pdf", abstract = "Parametric regression in genetic programming can substantially speed up the search for solutions. Paradoxically, the same technique has difficulty finding a true optimum solution. The parametric formulation of a problem results in a fitness landscape that looks like an inverted brush with many bristles of almost equal length (individuals of high fitness), but with only one bristle that is very slightly longer than the rest, the optimum solution. As such it is easy to find very good, even outstanding solutions, but very difficult to locate the optimum solution. In this paper parametric regression is applied to a minimum-time-to-target problem. The solution is equivalent to the classical brachistochrone. Two formulations were tried: a parametric regression and the classical symbolic regression formulation. The parametric approach was superior without exception. We speculate the parametric approach is more generally applicable to other problems and suggest areas for more research.", notes = "Part of \cite{keijzer:2004:GECCO:lbp}", } @InProceedings{banks:2004:msa:erban, author = "E. R. Banks and J. C. Hayes and E. Nunez", title = "Parametric Regression Through Genetic Programming", editor = "R. Poli and S. Cagnoni and M. Keijzer and E. Costa and F. Pereira and G. Raidl and S. C. Upton and D. Goldberg and H. Lipson and E. {de Jong} and J. Koza and H. Suzuki and H. Sawai and I. Parmee and M. Pelikan and K. Sastry and D. Thierens and W. Stolzmann and P. L. Lanzi and S. W. Wilson and M. O'Neill and C. Ryan and T. Yu and J. F. Miller and I. Garibay and G. Holifield and A. S. Wu and T. Riopka and M. M. Meysenburg and A. W. Wright and N. Richter and J. H. Moore and M. D. Ritchie and L. Davis and R. Roy and M. Jakiela", booktitle = "GECCO 2004 Workshop Proceedings", year = "2004", month = "26-30 " # jun, address = "Seattle, Washington, USA", keywords = "genetic algorithms, genetic programming", URL = "http://gpbib.cs.ucl.ac.uk/gecco2004/WMSA003.pdf", notes = "GECCO-2004WKS Distributed on CD-ROM at GECCO-2004", } @InProceedings{Banks:gecco05lbp, author = "Edwin Roger Banks and Edwin Nunez and Paul Agarwal and Claudette Owens and Marshall McBride and Ron Liedel", title = "Genetic Programming for Discrimination of Buried Unexploded Ordnance (UXO)", booktitle = "Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}", year = "2005", month = "25-29 " # jun, editor = "Franz Rothlauf", address = "Washington, D.C., USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2005lbp/papers/66-banks.pdf", keywords = "genetic algorithms, genetic programming", abstract = "According to the Department of Defense, over 10 million acres of land in the US need to be cleared of buried unexploded ordnance (UXO). Worldwide, UXO injures thousands each year. Cleanup costs are prohibitively expensive due to the difficulties in discriminating buried UXO from other inert non-UXO objects. Government agencies are actively searching for improved sensor methodologies to detect and discriminate buried UXO from other objects. We describe the results of work performed on data gathered by the GeoPhex GEM-3 electromagnetic sensor during their attempts to discriminate buried UXO at the U.S. Army Jefferson Proving Ground (JPG). We used a variety of evolutionary computing (EC) approaches that included genetic programming, genetic algorithms, and decision-tree methods. All approaches were essentially formulated as regression problems whereby the EC algorithms used sensor data to evolve buried UXO discrimination chromosomes. Predictions were then compared with a ground-truth file and the number of false positives and negatives determined", notes = "Distributed on CD-ROM at GECCO-2005", } @InProceedings{Banks:gecco06lbp, author = "Edwin Roger Banks and Edwin Nunez and Paul Agarwal and Marshall McBride and Ronald Liedel and Claudette Owens", title = "A Comparison of Evolutionary Computing Techniques Used to Model Bi-Directional Reflectance Distribution Functions", booktitle = "Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2006)}", year = "2006", month = "8-12 " # jul, editor = "J{\"{o}}rn Grahl", address = "Seattle, WA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006etc/papers/lbp128.pdf", notes = "Distributed on CD-ROM at GECCO-2006", keywords = "genetic algorithms, genetic programming, evolutionary computation, hybrid genetic programming, symbolic regression, Bi-directional reflectance distribution function, BRDF, parsimony, Phong model", abstract = "Bi-Directional Reflectance Distribution Functions are used in many fields including computer animation modeling, military defense (radar, lidar, etc.), and others. This paper explores a variety of approaches to modelling BRDFs using different evolutionary computing (EC) techniques. We concentrate on genetic programming (GP) and in hybrid GP approaches, obtaining very close correspondence between models and data. The problem of obtaining parameters that make particular BRDF models fit to laboratory-measured reflectance data is a classic symbolic regression problem. The goal of this approach is to discover the equations that model laboratory-measured data according to several criteria of fitness. These criteria involve closeness of fit, simplicity or complexity of the model (parsimony), form of the result, and speed of discovery. As expected, free form, unconstrained GP gave the best results in terms of minimising measurement errors. However, it also yielded the most complex model forms. Certain constrained approaches proved to be far superior in terms of speed of discovery. Furthermore, application of mild parsimony pressure resulted in not only simpler expressions, but also improved results by yielding small differences between the models and the corresponding laboratory measurements.", } @InProceedings{DBLP:conf/gecco/BanksAMO09, author = "Edwin Roger Banks and Paul Agarwal and Marshall McBride and Claudette Owens", title = "A comparison of selection, recombination, and mutation parameter importance over a set of fifteen optimization tasks", booktitle = "GECCO-2009 Late-Breaking Papers", year = "2009", editor = "Anna I. Esparcia and Ying-ping Chen and Gabriela Ochoa and Ender Ozcan and Marc Schoenauer and Anne Auger and Hans-Georg Beyer and Nikolaus Hansen and Steffen Finck and Raymond Ros and Darrell Whitley and Garnett Wilson and Simon Harding and W. B. Langdon and Man Leung Wong and Laurence D. Merkle and Frank W. Moore and Sevan G. Ficici and William Rand and Rick Riolo and Nawwaf Kharma and William R. Buckley and Julian Miller and Kenneth Stanley and Jaume Bacardit and Will Browne and Jan Drugowitsch and Nicola Beume and Mike Preuss and Stephen L. Smith and Stefano Cagnoni and Jim DeLeo and Alexandru Floares and Aaron Baughman and Steven Gustafson and Maarten Keijzer and Arthur Kordon and Clare Bates Congdon and Laurence D. Merkle and Frank W. Moore", pages = "1971--1976", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1570256.1570261", abstract = "How does one choose an initial set of parameters for an evolutionary computing algorithm? Clearly some choices are dictated by the problem itself, such as the encoding of a problem solution, or how much time is available for running the evolution. Others, however, are frequently found by trial-and-error. These may include population sizes, number of populations, type of selection, recombination and mutation rates, and a variety of other parameters. Sometimes these parameters are allowed to co-evolve along with the solutions rather than by trial-and-error. But in both cases, an initial setting is needed for each parameter. When there are hundreds of parameters to be adjusted, as in some evolutionary computation tools, one would like to just spend time adjusting those that are believed to be most important, or sensitive, and leave the rest to start with an initial default value. Thus the primary goal of this paper is to establish the relative importance of each parameter. Establishing general guidance to assist in the determination of these initial default values is another primary goal of this paper. We propose to develop this guidance by studying the solutions resulting from variations around the default starting parameters applied across fifteen different application types.", notes = "Distributed on CD-ROM at GECCO-2009. ACM Order Number 910092.", } @InProceedings{DBLP:conf/gecco/BanksAMO09a, author = "Edwin Roger Banks and Paul Agarwal and Marshall McBride and Claudette Owens", title = "Lessons learned in application of evolutionary computation to a set of optimization tasks", booktitle = "GECCO-2009 Late-Breaking Papers", year = "2009", editor = "Anna I. Esparcia and Ying-ping Chen and Gabriela Ochoa and Ender Ozcan and Marc Schoenauer and Anne Auger and Hans-Georg Beyer and Nikolaus Hansen and Steffen Finck and Raymond Ros and Darrell Whitley and Garnett Wilson and Simon Harding and W. B. Langdon and Man Leung Wong and Laurence D. Merkle and Frank W. Moore and Sevan G. Ficici and William Rand and Rick Riolo and Nawwaf Kharma and William R. Buckley and Julian Miller and Kenneth Stanley and Jaume Bacardit and Will Browne and Jan Drugowitsch and Nicola Beume and Mike Preuss and Stephen L. Smith and Stefano Cagnoni and Jim DeLeo and Alexandru Floares and Aaron Baughman and Steven Gustafson and Maarten Keijzer and Arthur Kordon and Clare Bates Congdon and Laurence D. Merkle and Frank W. Moore", pages = "1977--1982", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1570256.1570262", abstract = "Many GECCO papers discuss lessons learned in a particular application, but few papers discuss lessons learned over an ensemble of problem areas. A scan of the tables of contents of the Proceedings from GECCO 2005 and 2006 showed no paper title stressing lessons learned although the term {"}pitfall{"} appeared occasionally in abstracts, typically applying to a particular practice. We present in this paper a set of broadly applicable {"}lessons learned{"} in the application of evolutionary computing (EC) techniques to a variety of problem areas and present advice related to encoding, running, monitoring, and managing an evolutionary computing task.", notes = "Distributed on CD-ROM at GECCO-2009. ACM Order Number 910092.", } @InProceedings{Banks:2009:HPCMP-UGC, author = "Edwin Roger Banks and Paul Agarwal and Marshall McBride and Claudette Owens", title = "Evolving Image Noise Filters through Genetic Programming", booktitle = "DoD High Performance Computing Modernization Program Users Group Conference (HPCMP-UGC), 2009", year = "2009", month = "15-18 " # jun, pages = "307--312", abstract = "A form of Evolutionary Computation (EC) called Genetic Programming (GP) was used to automatically discover sequences of image noise filters to remove two types of image noise and a type of communications noise associated with a remotely sensed imagery. Sensor noise was modelled by the addition of salt-and-pepper and grayscale noise to the image. Communication noise was modeled by inserting a series of blank pixels in selected image rows to replicate dropped pixel segments occurring during communication interruptions of sequential uncompressed image information. A known image was used for training the evolver. Heavy amounts of noise were added to the known image, and a filter was evolved. (The filtered image was compared to the original with the average image-to-image pixel error establishing the fitness function.). The evolved filter derived for the noisy image was then applied to never-before-seen imagery affected by similar noise conditions to judge the universal applicability of the evolved GP filter. Examples of all described images are included in the presentation. A variety of image filter primitives were used in this experiment. The evolved sequences of primitives were each then sequentially applied to produce the final filtered image. These filters were evolved over a typical run length of one week each on a small Linux cluster. Once evolved, the filters were then transported to a PC for application to the never-before-seen images, using an evolve-once, apply-many-times approach. The results of this image filtering experiment were quite dramatic.", keywords = "genetic algorithms, genetic programming, Linux cluster, communication interruptions, communications noise, evolutionary computation, grayscale noise, image filtering, image noise filters, remotely sensed imagery, salt-and-pepper noise, sensor noise, sequential uncompressed image information, Linux, filtering theory, image denoising, image resolution, image segmentation, image sequences", DOI = "doi:10.1109/HPCMP-UGC.2009.50", notes = "COLSA Corp., Huntsville, AL, USA Also known as \cite{5729481}", } @InProceedings{DBLP:conf/gecco/BanksAMO09b, author = "Edwin Roger Banks and Paul Agarwal and Marshall McBride and Claudette Owens", title = "Toward a universal operator encoding for genetic programming", booktitle = "GECCO-2009 Late-Breaking Papers", year = "2009", editor = "Anna I. Esparcia and Ying-ping Chen and Gabriela Ochoa and Ender Ozcan and Marc Schoenauer and Anne Auger and Hans-Georg Beyer and Nikolaus Hansen and Steffen Finck and Raymond Ros and Darrell Whitley and Garnett Wilson and Simon Harding and W. B. Langdon and Man Leung Wong and Laurence D. Merkle and Frank W. Moore and Sevan G. Ficici and William Rand and Rick Riolo and Nawwaf Kharma and William R. Buckley and Julian Miller and Kenneth Stanley and Jaume Bacardit and Will Browne and Jan Drugowitsch and Nicola Beume and Mike Preuss and Stephen L. Smith and Stefano Cagnoni and Jim DeLeo and Alexandru Floares and Aaron Baughman and Steven Gustafson and Maarten Keijzer and Arthur Kordon and Clare Bates Congdon and Laurence D. Merkle and Frank W. Moore", pages = "1983--1986", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1570256.1570263", size = "4 pages", abstract = "The 2002 CEC paper entitled {"}Genetic Programming with Smooth Operators for Arithmetic Expressions: Diviplication and Subdition{"} by Ursem and Krink \cite{ursem:2002:gpwsofaedas} proposed to blend certain arithmetic operators by interpolation to smooth the transition from one operator to another in the fitness landscape. Inspired by their idea, herein it is shown how to generalise further by using combinations of more than two operators, requiring log(N) additional parameters for each N operators so combined. Comparative results are reported for the application of this methodology to a variety of optimisation tasks including symbolic regression, an aspherical lens system design, a UAV path optimization, and a remote sensor image noise filter design.", notes = "cites \cite{page:1999:smuxspmGP}. Parsimony via fitness penalty, non-spherical lens design. Worse except for image noise filter where universal function set does best. Distributed on CD-ROM at GECCO-2009. ACM Order Number 910092.", } @Article{Bannister:2014:VH, author = "C. A. Bannister and C. J. Currie and A. Preece and I. Spasic", title = "Automatic development of clinical prediction models with genetic programming: A case study in cardiovascular disease", journal = "Value in Health", volume = "17", number = "3", pages = "A200--A201", year = "2014", note = "ISPOR 19th Annual International Meeting Research Abstracts", keywords = "genetic algorithms, genetic programming", ISSN = "1098-3015", DOI = "doi:10.1016/j.jval.2014.03.1171", URL = "http://www.sciencedirect.com/science/article/pii/S1098301514012224", size = "0.1 pages", abstract = "Genetic programming is an Evolutionary Computing technique, inspired by biological evolution, capable of discovering complex non-linear patterns in large datasets. Despite the potential advantages of genetic programming over standard statistical methods, its applications to survival analysis are at best rare, primarily because of the difficulty in handling censored data. The aim of this study was to demonstrate the utility of genetic programming for the automatic development of clinical prediction models using cardiovascular disease as a case study.", notes = "PRM115", } @PhdThesis{phd/ethos/Bannister15, title = "Automated Development of Clinical Prediction Models Using Genetic Programming", author = "Christian A. Bannister", year = "2015", school = "School of Computer Science \& Informatics, Cardiff University", address = "UK", month = sep, keywords = "genetic algorithms, genetic programming", URL = "http://orca.cf.ac.uk/90825/", URL = "http://orca.cf.ac.uk/90825/1/2016bannistercaphd.pdf", URL = "http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.685486", size = "410 pages", abstract = "Genetic programming is an Evolutionary Computing technique, inspired by biological evolution, capable of discovering complex non-linear patterns in large datasets. Genetic programming is a general methodology, the specific implementation of which requires development of several different specific elements such as problem representation, fitness, selection and genetic variation. Despite the potential advantages of genetic programming over standard statistical methods, its applications to survival analysis are at best rare, primarily because of the difficulty in handling censored data. The aim of this work was to develop a genetic programming approach for survival analysis and demonstrate its utility for the automatic development of clinical prediction models using cardiovascular disease as a case study. We developed a tree-based untyped steady-state genetic programming approach for censored longitudinal data, comparing its performance to the de facto statistical method (Cox regression) in the development of clinical prediction models for the prediction of future cardiovascular events in patients with symptomatic and asymptomatic cardiovascular disease, using large observational datasets. We also used genetic programming to examine the prognostic significance of different risk factors together with their non-linear combinations for the prognosis of health outcomes in cardiovascular disease. These experiments showed that Cox regression and the developed steady-state genetic programming approach produced similar results when evaluated in common validation datasets. Despite slight relative differences, both approaches demonstrated an acceptable level of discriminative and calibration at a range of times points. Whilst the application of genetic programming did not provide more accurate representations of factors that predict the risk of both symptomatic and asymptomatic cardiovascular disease when compared with existing methods, genetic programming did offer comparable performance. Despite generally comparable performance, albeit in slight favour of the Cox model, the predictors selected for representing their relationships with the outcome were quite different and, on average, the models developed using genetic programming used considerably fewer predictors. The results of the genetic programming confirm the prognostic significance of a small number of the most highly associated predictors in the Cox modelling; age, previous atherosclerosis, and albumin for secondary prevention; age, recorded diagnosis of other cardiovascular disease, and ethnicity for primary prevention in patients with type 2 diabetes. When considered as a whole, genetic programming did not produce better performing clinical prediction models, rather it used fewer predictors, most of which were the predictors that Cox regression estimated be most strongly associated with the outcome, whilst achieving comparable performance. This suggests that genetic programming may better represent the potentially non-linear relationship of (a smaller subset of) the strongest predictors. To our knowledge, this work is the first study to develop a genetic programming approach for censored longitudinal data and assess its value for clinical prediction in comparison with the well-known and widely applied Cox regression technique. Using empirical data this work has demonstrated that clinical prediction models developed by steady-state genetic programming have predictive ability comparable to those developed using Cox regression. The genetic programming models were more complex and thus more difficult to validate by domain experts, however these models were developed in an automated fashion, using fewer input variables, without the need for domain specific knowledge and expertise required to appropriately perform survival analysis. This work has demonstrated the strong potential of genetic programming as a methodology for automated development of clinical prediction models for diagnostic and prognostic purposes in the presence of censored data. This work compared untuned genetic programming models that were developed in an automated fashion with highly tuned Cox regression models that was developed in a very involved manner that required a certain amount of clinical and statistical expertise. Whilst the highly tuned Cox regression models performed slightly better in validation data, the performance of the automatically generated genetic programming models were generally comparable. The comparable performance demonstrates the utility of genetic programming for clinical prediction modelling and prognostic research, where the primary goal is accurate prediction. In aetiological research, where the primary goal is to examine the relative strength of association between risk factors and the outcome, then Cox regression and its variants remain as the de facto approach.", notes = "British Library, EThOS Supervisor: Irena Spasic", } @TechReport{banzhaf:mrl:tech, author = "Wolfgang Banzhaf", title = "Genetic Programming for Pedestrians", institution = "Mitsubishi Electric Research Laboratories, Inc.", year = "1993", type = "MERL Technical Report", number = "TR93-03", address = "Cambridge, MA, USA", month = dec, keywords = "genetic algorithms, genetic programming", URL = "ftp://lumpi.informatik.uni-dortmund.de/pub/biocomp/papers/pedes93.ps.gz", URL = "https://merl.com/publications/docs/TR93-03.pdf", size = "14 pages", abstract = "We propose an extension to the Genetic Programming paradigm which allows users of traditional Genetic Algorithms to evolve computer programs. To this end, we have to introduce mechanisms like transscription, editing and repairing into Genetic Programming. We demonstrate the feasibility of the approach by using it to develop programs for the prediction of sequences of integer numbers.", notes = "As \cite{banzhaf:mrl}", } @InProceedings{banzhaf:mrl, author = "Wolfgang Banzhaf", title = "Genetic Programming for Pedestrians", institution = "Mitsubishi Electrical Research Laboratories, Cambridge Research Center", year = "1993", booktitle = "Proceedings of the 5th International Conference on Genetic Algorithms, ICGA-93", editor = "Stephanie Forrest", publisher = "Morgan Kaufmann", pages = "628", address = "University of Illinois at Urbana-Champaign", month = "17-21 " # jul, keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-299-2", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/ftp.io.com/papers/GenProg_forPed.ps.Z", abstract = "We propose an extension to the Genetic Programming paradigm which allows users of traditional Genetic Algorithms to evolve computer programs. To this end, we have to introduce mechanisms like transcription, editing and repairing into Genetic Programming. We demonstrate the feasibility of the approach by using it to develop programs for the prediction of sequences of integer numbers.", notes = "Also available as MRL Technical Report 93-03 11 pages. \cite{banzhaf:mrl:tech} 225 bit GA, 5 bit grouping encode terminal or two arg function, clean up by editing and repair to produce variable length tree shaped prog. No looping, recursion or memory. Demonstrated on learning sequences of small integers, fails on primes. One-Page Summary. Also known as \cite{DBLP:conf/icga/Banzhaf93}", } @InProceedings{banzhaf:1994:ppsn3, author = "Wolfgang Banzhaf", title = "Genotype-Phenotype-Mapping and Neutral Variation -- A case study in Genetic Programming", booktitle = "Parallel Problem Solving from Nature III", year = "1994", editor = "Yuval Davidor and Hans-Paul Schwefel and Reinhard M{\"a}nner", series = "LNCS", volume = "866", pages = "322--332", address = "Jerusalem", publisher_address = "Berlin, Germany", month = "9-14 " # oct, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-58484-6", URL = "http://www.cs.mun.ca/~banzhaf/papers/ppsn94.pdf", URL = "ftp://lumpi.informatik.uni-dortmund.de/pub/biocomp/papers/ppsn94.ps.gz", broken = "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-58484-6", DOI = "doi:10.1007/3-540-58484-6_276", size = "11 pages", abstract = "We propose the application of a genotype-phenotype mapping to the solution of constrained optimization problems. The method consists of strictly separating the search space of genotypes from the solution space of phenotypes. A mapping from genotypes into phenotypes provides for the appropriate expression of information represented by the genotypes. The mapping is constructed as to guarantee feasibility of phenotypic solutions for the problem under study. This enforcing of constraints causes multiple genotypes to result in one and the same phenotype. Neutral variants are therefore frequent and play an important role in maintaining genetic diversity. As a specific example, we discuss Binary Genetic Programming (BGP), a variant of Genetic Programming that uses binary strings as genotypes and program trees as phenotypes.", notes = "PPSN3 Tested on symbolic regression of 0.5x**2 and exp(-3.0*x**2) Produces high level code (FORTRAN, C?) which is compiled, claims this gives huge speedup. ", } @InProceedings{banzhaf:1997:gabrrfr, author = "Wolfgang Banzhaf and Peter Nordin and Markus Olmer", title = "Generating Adaptive Behavior for a Real Robot using Function Regression within Genetic Programming", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming", pages = "35--43", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", URL = "http://www.cs.mun.ca/~banzhaf/papers/robot_over.pdf", size = "11 pages", abstract = "We discuss the generation of adaptive behaviour for an autonomous robot within the framework of a special kind of function regression used in compiling Genetic Programming (GP). The control strategy for the robot is derived, using an evolutionary algorithm, from a continuous improvement of machine language programs which are varied and selected against each other. We give an overview of our recent work on several fundamental behaviors like obstacle avoidance and object following adapted from programs that were originally random sequences of commands. It is argued that the method is generally applicable where there is a need for quick adaptation within real-time problem domains", notes = "GP-97", } @InCollection{Banzhaf:1997:HEC, author = "Wolfgang Banzhaf", title = "Interactive Evolution", booktitle = "Handbook of Evolutionary Computation", publisher = "Oxford University Press", publisher_2 = "Institute of Physics Publishing", year = "1997", editor = "Thomas Baeck and David B. Fogel and Zbigniew Michalewicz", chapter = "section C2.9", keywords = "genetic algorithms, genetic programming", ISBN = "0-7503-0392-1", URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.375.6494.pdf", DOI = "doi:10.1201/9781420050387.ptc", size = "6 pages", abstract = "We present a different approach to directing the evolutionary process through interactive selection of solutions by the human user. First the general context of interactive evolution is set, then the standard interactive evolution algorithm is discussed together with more complicated variants. Finally, several application areas are discussed and uses for the new method are exemplified using design from the literature.", } @Book{banzhaf:1997:book, author = "Wolfgang Banzhaf and Peter Nordin and Robert E. Keller and Frank D. Francone", title = "Genetic Programming -- An Introduction; On the Automatic Evolution of Computer Programs and its Applications", publisher = "Morgan Kaufmann", publisher2 = "dpunkt.verlag", year = "1998", address = "San Francisco, CA, USA", address2 = "Heidelberg", month = jan, keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-510-X", ISBN = "3-920993-58-6", URL = "https://www.amazon.co.uk/Genetic-Programming-Introduction-Artificial-Intelligence/dp/155860510X", broken = "http://www.elsevier.com/wps/find/bookdescription.cws_home/677869/description#description", notes = "details from banzhaf Tue, 23 Sep 1997 12:58:06 PDT updated banzhaf Fri, 23 Jun 2006 11:40:44 -0230 FROM THE FOREWORD BY J.R. KOZA Genetic programming addresses the problem of automatic programming, namely the problem of how to enable a computer to do useful things without instructing it, step by step, on how to do it. The rapid growth of the field of genetic programming reflects the growing recognition that, after half a century of research in the fields of artificial intelligence, machine learning, adaptive systems, automated logic, expert systems, and neural networks, we may finally have a way to achieve automatic programming. Genetic programming is fundamentally different from other approaches in terms of (i) its representation (namely, programs), (ii) the role of knowledge (none), (iii) the role of logic (none), and (iv) its mechanism (gleaned from nature) for getting to a solution within the space of possible solutions. FROM THE FIRST SECTION OF THE BOOK Automated programming will be one of the most important areas of computer science research over the next twenty years. Hardware speed and capability has leapt forward exponentially. Yet software consistently lags years behind the capabilities of the hardware. The gap appears to be ever increasing. Demand for computer code keeps growing but the process of writing code is still mired in the modern day equivalent of the medieval ``guild'' days. Like swords in the 15th century, muskets before the early 19th century and books before the printing press, each piece of computer code is, today, handmade by a craftsman for a particular purpose. The history of computer programming is a history of attempts to move away from the ``craftsman'' approach -- structured programming, object oriented programming, object libraries, rapid prototyping. But each of these advances leaves the code that does the real work firmly in the hands of a craftsman, the programmer. The ability to enable computers to learn to program themselves is of the utmost importance in freeing the computer industry and the computer user from code that is obsolete before it is released. ", size = "480 pages", } @Proceedings{banzhaf:1998:GP, title = "Genetic Programming", year = "1998", editor = "Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer and Terence C. Fogarty", volume = "1391", series = "LNCS", address = "Paris", publisher_address = "Berlin", month = "14-15 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-64360-5", DOI = "doi:10.1007/BFb0055923", size = "232 pages", notes = "EuroGP'98 See also \cite{Poli:1998:egplb} Late Breaking Papers at EuroGP'98: the First European Workshop on Genetic Programming", } @Article{lemonde:1998:23apr, key = "lemonde", title = "Les Robots inventeent la vie", journal = "Le Monde", year = "1998", month = "23 Avril", keywords = "genetic algorithms, genetic programming", notes = "in french, Description of EvoRobot'98 in particular: Stefanio Nolfi and Dario Floreano, Jean Arcady-Meyer, Henrik Lund, \cite{dittrich:1998:lmrrm}, Nick Jakobi", } @Proceedings{banzhaf:1999:gecco99, title = "GECCO-99: Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-611-4", URL = "http://www.amazon.com/exec/obidos/ASIN/1558606114/qid%3D977054373/105-7666192-3217523", size = "2 volumes", notes = "GECCO-99", } @Misc{oai:CiteSeerPSU:400591, title = "Artificial Intelligence: Genetic Programming", author = "Wolfgang Banzhaf", year = "2000", month = jul # "~04", note = "Contract no: 20851A2/2/102", keywords = "genetic algorithms, genetic programming", citeseer-isreferencedby = "oai:CiteSeerPSU:54843; oai:CiteSeerPSU:537988; oai:CiteSeerPSU:536890; oai:CiteSeerPSU:275725", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:400591", rights = "unrestricted", URL = "http://web.cs.mun.ca/~banzhaf/papers/ency.pdf", URL = "http://citeseer.ist.psu.edu/400591.html", size = "13 pages", abstract = "The term Genetic Programming describes a research area within the general field of Artificial Intelligence that deals with the evolution of computer code. This area is an out growth of two independent efforts in Artificial Intelligence, namely automatic programming and machine learning. Automatic programming is concerned with the induction of computer code which precisely fulfills certain functions, whereas machine learning studies improvement of computer programs through training and experience.", notes = "Survey/introduction to GP. See also \cite{Banzhaf2001789}", } @TechReport{oai:CiteSeerPSU:324880, author = "Wolfgang Banzhaf and Dirk Banscherus and Peter Dittrich", title = "Hierarchical Genetic Programming Using Local Modules", institution = "University of Dortmund", address = "Dortmund, Germany", year = "1998", type = "Technical Report", number = "50/98", keywords = "genetic algorithms, genetic programming", URL = "http://hdl.handle.net/2003/5365", URL = "https://eldorado.uni-dortmund.de/dspace/bitstream/2003/5365/1/ci56.pdf", citeseer-isreferencedby = "oai:CiteSeerPSU:39828", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:324880", rights = "unrestricted", URL = "http://citeseer.ist.psu.edu/324880.html", abstract = "This paper presents detailed experimental results for a new modular approach to Genetic Programming, hierarchical GP (hGP) based on the introduction of local modules. A module in a hGP program is context-dependent and should not be expected to improve all programs of a population but rather a very specific subset providing the same context. This new modular approach allows for a natural hierarchy in that local modules themselves may define local sub-modules.", notes = "see also \cite{banzhaf:2000:IJ}", size = "pages", } @Article{banzhaf:2000:IJ, author = "Wolfgang Banzhaf and Dirk Banscherus and Peter Dittrich", title = "Hierarchical Genetic Programming using Local Modules", journal = "InterJournal Complex Systems", year = "2000", volume = "228", keywords = "genetic algorithms, genetic programming", URL = "http://www.interjournal.org/manuscript_abstract.php?44691", broken = "http://web.cs.mun.ca/~banzhaf/papers/iccs98.html", URL = "https://eldorado.uni-dortmund.de/bitstream/2003/5365/1/ci56.pdf", size = "18 pages", abstract = "This paper presents a new modular approach to Genetic Programming, hierarchical GP (hGP) based on the introduction of local modules. A module in a hGP program is context-dependent and should not be expected to improve all programs of a population but rather a very specific subset providing the same context. This new modular approach allows for a natural recursiveness in that local modules themselves may define local sub-modules.", notes = "Category: Brief Article Status: Accepted Manuscript Number: [228] Submission Date: 981210 Revised On: 815 Subject(s): CX, CX.66 See also \cite{oai:CiteSeerPSU:324880}", } @Article{banzhaf:2000:genpletter, author = "W. Banzhaf and W. B. Langdon", title = "Some considerations on the reason for bloat", journal = "Genetic Programming and Evolvable Machines", year = "2002", volume = "3", number = "1", pages = "81--91", month = mar, email = "banzhaf@tarantoga.cs.uni-dortmund.de", keywords = "genetic algorithms, genetic programming, linear genomes, effective fitness, neutral variations", ISSN = "1389-2576", URL = "http://web.cs.mun.ca/~banzhaf/papers/genp_bloat.pdf", DOI = "doi:10.1023/A:1014548204452", abstract = "A representation-less model for genetic programming is presented. The model is intended to examine the mechanisms that lead to bloat in genetic programming (GP). We discuss two hypotheses (fitness causes bloat and neutral code is protective) and perform simulations to examine the predictions deduced from these hypotheses. Our observation is that predictions from both hypotheses are realized in the simulated model.", notes = "Article ID: 395990", } @Article{banzhaf:2000:ei, author = "Wolfgang Banzhaf", title = "Editorial Introduction", journal = "Genetic Programming and Evolvable Machines", year = "2000", volume = "1", number = "1/2", pages = "5--6", month = apr, ISSN = "1389-2576", DOI = "doi:10.1023/A:1010026829303", notes = "Article ID: 253701", } @Article{banzhaf:2000:IS, author = "Wolfgang Banzhaf", title = "The artificial evolution of computer code", journal = "IEEE Intelligent Systems", year = "2000", volume = "15", number = "3", pages = "74--76", month = may # "-" # jun, keywords = "genetic algorithms, genetic programming, machine code GP", ISSN = "1094-7167", URL = "http://ieeexplore.ieee.org/iel5/5254/18363/00846288.pdf", broken = "http://web.cs.mun.ca/~banzhaf/papers/ieee_intelligentsystems.pdf", URL = "http://citeseer.ist.psu.edu/399369.html", DOI = "doi:10.1109/5254.846288", size = "3 pages", abstract = "Over the past decade, the artificial evolution of computer code has become a rapidly spreading technology with many ramifications. Originally conceived as a means to enforce computer intelligence, it has now spread to all areas of machine learning and is starting to conquer pattern-recognition applications such as data mining and the human-computer interface.", notes = "part of \cite{hirsh:2000:GP}", } @Article{banzhaf:2000:ack, author = "W. Banzhaf", title = "Acknowledgement", journal = "Genetic Programming and Evolvable Machines", year = "2000", volume = "1", number = "4", pages = "307", month = oct, ISSN = "1389-2576", DOI = "doi:10.1023/A:1010022522223", notes = "Article ID: 273809", } @InCollection{Banzhaf2001789, author = "W. Banzhaf", title = "Artificial Intelligence: Genetic Programming", editor = "Neil J. Smelser and Paul B. Baltes", booktitle = "International Encyclopedia of the Social \& Behavioral Sciences", publisher = "Pergamon", address = "Oxford", year = "2001", pages = "789--792", keywords = "genetic algorithms, genetic programming", isbn13 = "978-0-08-043076-8", DOI = "doi:10.1016/B0-08-043076-7/00557-X", URL = "http://www.sciencedirect.com/science/article/B7MRM-4MT09VJ-403/2/fa4e06852750b95eb2734f9ca37ae6ad", abstract = "Genetic Programming is a new method to generate computer programs. It was derived from the model of biological evolution. Programs are 'bred' through continuous improvement of an initially random population of programs. Improvements are made possible by stochastic variation of programs and selection according to prespecified criteria for judging the quality of a solution. Programs of Genetic Programming systems evolve to solve predescribed automatic programming and machine learning problems. In this contribution the origins and the context of Genetic Programming are discussed. The primary mechanisms behind the working of the method are then outlined. Next is a review of the state-of-the-art of Genetic Programming, including the major achievements of the method in recent years. This leads to an overview of the application areas where GP is most frequently used to present. Among these areas is robotics and the control of behavior, both of real and virtual agents. The article will conclude with a section on methodological issues and future directions. The use of Genetic Programming for simulation in the social sciences is briefly sketched.", } @Article{banzhaf:2001:intro, author = "W. Banzhaf", title = "Editorial Introduction", journal = "Genetic Programming and Evolvable Machines", year = "2001", volume = "2", number = "1", pages = "5", month = mar, ISSN = "1389-2576", DOI = "doi:10.1023/A:1010076931170", size = "1 page", notes = "Article ID: 319810", } @Article{banzhaf:2001:ack, author = "W. Banzhaf", title = "Acknowledgement", journal = "Genetic Programming and Evolvable Machines", year = "2001", volume = "2", number = "4", pages = "315--315", month = dec, ISSN = "1389-2576", DOI = "doi:10.1023/A:1017497620393", notes = "Article ID: 386360", } @Article{banzhaf:2002:intro, author = "W. Banzhaf", title = "Editorial Introduction", journal = "Genetic Programming and Evolvable Machines", year = "2002", volume = "3", number = "1", pages = "5--6", month = mar, ISSN = "1389-2576", DOI = "doi:10.1023/A:1017427619473", size = "2 pages", notes = "Article ID: 395987", } @Article{banzhaf:2002:ack, author = "W. Banzhaf", title = "Acknowledgement", journal = "Genetic Programming and Evolvable Machines", year = "2002", volume = "3", number = "4", pages = "327", month = dec, ISSN = "1389-2576", DOI = "doi:10.1023/A:1020989508176", notes = "Article ID: 5103871", } @Article{banzhaf:2003:intro, author = "Wolfgang Banzhaf", title = "Editorial Introduction", journal = "Genetic Programming and Evolvable Machines", year = "2003", volume = "4", number = "1", pages = "5--6", month = mar, ISSN = "1389-2576", DOI = "doi:10.1023/A:1021808625350", notes = "Article ID: 5113069", } @InCollection{banzhaf:2003:GPTP, author = "Wolfgang Banzhaf", title = "Artificial Regulatory Networks and Genetic Programming", booktitle = "Genetic Programming Theory and Practice", publisher = "Kluwer", year = "2003", editor = "Rick L. Riolo and Bill Worzel", chapter = "4", pages = "43--61", keywords = "genetic algorithms, genetic programming, Regulatory Networks, Artificial Evolution, Evolutionary Algorithms, Development, Heterochrony", ISBN = "1-4020-7581-2", URL = "http://www.cs.mun.ca/~banzhaf/papers/toy_world3.pdf", URL = "http://www.springer.com/computer/ai/book/978-1-4020-7581-0", DOI = "doi:10.1007/978-1-4419-8983-3_4", abstract = "An artificial regulatory network able to reproduce a number of phenomena found in natural genetic regulatory networks (such as heterochrony, evolution, stability and variety of network behaviour) is proposed. The connection to a new genetic representation for Genetic Programming is outlined.", notes = "Part of \cite{RioloWorzel:2003}", size = "20 pages", } @InCollection{banzhaf:2003:ACI, author = "Wolfgang Banzhaf and Markus Brameier and Marc Stautner and Klaus Weinert", title = "Genetic Programming and Its Application in Machining Technology", booktitle = "Advances in Computational Intelligence: Theory and Practice", publisher = "Springer", year = "2003", editor = "Hans-Paul Schwefel and Ingo Wegener and Klaus Weinert", series = "Natural Computing Series", chapter = "7", pages = "194--241", keywords = "genetic algorithms, genetic programming, Linear Genetic Programming", ISBN = "3-540-43269-8", URL = "http://www.cs.mun.ca/~banzhaf/papers/CI-book-chapter.pdf", URL = "http://www.springer.com/computer/ai/book/978-3-540-43269-2", abstract = "Genetic Programming (GP) denotes a variants of evolutionary algorithms that breeds solutions to problems in the form of computer programs. In recent years, GP has become increasingly important for real-world applications, including engineering tasks in particular. This contribution integrates both further development of the GP paradigm and its applications to challenging problems in machining technology. Different variants of program representations are investigated.", notes = "Removal of Non-effective Code. Graph Interpretation. Linear Genetic Operators. Control of Variation Step Size. Control of Structural Diversity. Genetic Programming in Machining Technology. Tree-Based GP for Symbolic Regression. Graphical Representation. Parallelisation", } @Article{banzhaf:2004:intro, author = "Wolfgang Banzhaf", title = "Editorial Introduction", journal = "Genetic Programming and Evolvable Machines", year = "2004", volume = "5", number = "1", pages = "5", month = mar, ISSN = "1389-2576", DOI = "doi:10.1023/B:GENP.0000017050.75941.55", notes = "Article ID: 5264730", } @Article{Banzhaf:2004:SSP, author = "Wolfgang Banzhaf", title = "Artificial chemistries - Toward Constructive Dynamical Systems", journal = "Solid State Phenomena", year = "2004", volume = "97/98", pages = "43--50", month = apr, keywords = "genetic algorithms, genetic programming, artificial chemistries, Self-Organization, Self-Assembly, Constructive Dynamical Systems", ISSN = "1662-9779", DOI = "doi:10.4028/www.scientific.net/SSP.97-98.43", abstract = "we consider constructive dynamical systems, taking one particular Artificial Chemistry as an example. We argue that constructive dynamical systems are in fact widespread in combinatorial spaces of Artificial Chemistries.", } @Article{Banzhaf:2004:JBPC, author = "Wolfgang Banzhaf and P. Dwight Kuo", title = "Network motifs in natural and artificial transcriptional regulatory networks", journal = "Journal of Biological Physics and Chemistry", year = "2004", volume = "4", number = "2", pages = "85--92", month = jun, keywords = "genetic algorithms, genetic programming", ISSN = "1512-0856", URL = "https://www.cs.mun.ca/~banzhaf/papers/JBPC.pdf", URL = "http://www.amsi.ge/jbpc/20404/2040405.html", URL = "http://www.amsi.ge/jbpc/20404/jbpc20404.html", size = "8 pages", abstract = "We show that network motifs found in natural regulatory networks may also be found in an artificial regulatory network model created through a duplication/divergence process. It is shown that these network motifs exist more frequently in a genome created through the aforementioned process than in randomly generated genomes. These results are then compared with a network motif analysis of the gene expression networks of Escherichia coli and Saccharomyces cerevisiae. In addition, it is shown that certain individual network motifs may arise directly from the duplication/divergence mechanism.", } @Article{banzhaf:2004:ack, author = "W. Banzhaf", title = "Acknowledgement", journal = "Genetic Programming and Evolvable Machines", year = "2004", volume = "5", number = "1", pages = "7", month = dec, ISSN = "1389-2576", DOI = "doi:10.1023/B:GENP.0000017051.93386.43", notes = "Article ID: 5264731", } @Article{banzhaf:2004:biogec, author = "Wolfgang Banzhaf and James Foster", title = "Editorial Introduction", journal = "Genetic Programming and Evolvable Machines", year = "2004", volume = "5", number = "2", pages = "119--120", month = jun, keywords = "genetic algorithms, genetic programming, bioinformatics", ISSN = "1389-2576", DOI = "doi:10.1023/B:GENP.0000023710.47388.8b", notes = "BioGEC Special Issue on Biological Applications of Genetic and Evolutionary Computation Guest Editor(s): Wolfgang Banzhaf , James Foster", } @InCollection{banzhaf:2004:GPTP, author = "Wolfgang Banzhaf and Christian W. G. Lasarczyk", title = "Genetic Programming of an Algorithmic Chemistry", booktitle = "Genetic Programming Theory and Practice {II}", year = "2004", editor = "Una-May O'Reilly and Tina Yu and Rick L. Riolo and Bill Worzel", chapter = "11", pages = "175--190", address = "Ann Arbor", month = "13-15 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, artificial chemistry", ISBN = "0-387-23253-2", URL = "http://www.cs.mun.ca/~banzhaf/papers/algochem.pdf", DOI = "doi:10.1007/0-387-23254-0_11", size = "16 pages", abstract = "We introduce a new method of execution for GP-evolved programs consisting of register machine instructions. It is shown that this method can be considered as an artificial chemistry. It lends itself well to distributed and parallel computing schemes in which synchronisation and coordination are not an issue.", notes = "part of \cite{oreilly:2004:GPTP2} Sin, UCI thyroid", } @Article{banzhaf:2004:BPC, author = "Wolfgang Banzhaf and P. Dwight Kuo", title = "Network motifs in natural and artificial transcriptional regulatory networks", journal = "Journal of Biological Physics and Chemistry", year = "2004", volume = "4", number = "2", pages = "50--63", keywords = "artificial life", ISBN = "0-387-23253-2", URL = "http://www.cs.mun.ca/~kuo/Motifs_Numerical_journal.pdf", URL = "http://www.amsi.ge/jbpc/20404/2040405.html", size = "11 pages", abstract = "We show that network motifs found in natural regulatory networks may also be found in an artificial regulatory network model created through a duplication / divergence process. It is shown that these network motifs exist more frequently in a genome created through the aforementioned process than in randomly generated genomes. These results are then compared with a network motif analysis of the gene expression networks of Escherichia Coli and Saccharomyces cerevisiae. In addition, it is shown that certain individual network motifs may arise directly from the duplication / divergence mechanism.", } @InCollection{banzhaf:2004:cc, title = "The Challenge of Complexity", author = "Wolfgang Banzhaf and Julian Miller", booktitle = "Frontiers of Evolutionary Computation", editor = "Anil Menon", series = "Genetic Algorithms And Evolutionary Computation Series", volume = "11", chapter = "11", publisher = "Kluwer Academic Publishers", address = "Boston, MA, USA", year = "2004", pages = "243--260", keywords = "genetic algorithms, genetic programming, Evolutionary Algorithm, Complexity, Scaling Problem, Development, Heterochrony", ISBN = "1-4020-7524-3", URL = "http://www.cs.mun.ca/~banzhaf/papers/challenge_rev.pdf", DOI = "doi:10.1007/1-4020-7782-3_11", abstract = "the challenge provided by the problem of evolving large amounts of computer code via Genetic Programming. We argue that the problem is analogous to what Nature had to face when moving to multi-cellular life. We propose to look at developmental processes and there mechanisms to come up with solutions for this ``challenge of complexity'' in Genetic Programming", } @InProceedings{banzhaf:2005:cPC, author = "Wolfgang Banzhaf", title = "Challenging the Program Counter", booktitle = "The Grand Challenge in Non-Classical Computation: International Workshop", year = "2005", editor = "Susan Stepney and Stephen Emmott", address = "York, UK", month = "18-19 " # apr, organisation = "University of York and Microsoft Research", keywords = "genetic algorithms, genetic programming, artificial chemistry", URL = "http://www.cs.york.ac.uk/nature/workshop/papers/Banzhaf.pdf", size = "3 pages", notes = "http://www.cs.york.ac.uk/nature/workshop/", } @Article{banzhaf:2005:intro, author = "Wolfgang Banzhaf", title = "Editorial", journal = "Genetic Programming and Evolvable Machines", year = "2005", volume = "5", number = "2", pages = "135--136", month = jun, ISSN = "1389-2576", DOI = "doi:10.1007/s10710-005-6162-z", size = "1.1 pages", } @Article{banzhaf:2005:ack, author = "W. Banzhaf", title = "Acknowledgement", journal = "Genetic Programming and Evolvable Machines", year = "2005", volume = "5", number = "2", pages = "137--138", month = jun, ISSN = "1389-2576", DOI = "doi:10.1007/s10710-005-6163-y", size = "1.2 pages", } @InCollection{banzhaf:2005:GPTP, author = "Wolfgang Banzhaf and Andre Leier", title = "Evolution on Neutral Networks in Genetic Programming", booktitle = "Genetic Programming Theory and Practice {III}", year = "2005", editor = "Tina Yu and Rick L. Riolo and Bill Worzel", volume = "9", series = "Genetic Programming", chapter = "14", pages = "207--221", address = "Ann Arbor", month = "12-14 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Neutrality, Linear GP, Networks, Population Dynamics", ISBN = "0-387-28110-X", URL = "http://www.cs.mun.ca/~banzhaf/papers/GPTP3.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.137.7947", oai = "oai:CiteSeerXPSU:10.1.1.137.7947", DOI = "doi:10.1007/0-387-28111-8_14", size = "15 pages", abstract = "We examine the behaviour of an evolutionary search on neutral networks in a simple linear GP system of a Boolean function space problem. To this end we draw parallels between notions in RNA-folding problems and in Genetic Programming, observe parameters of neutral networks and discuss the population dynamics via the occupation probability of network nodes in runs on their way to the optimal solution.", notes = "part of \cite{yu:2005:GPTP} Published Jan 2006 after the workshop", } @Article{banzhaf:2006:intro, author = "Wolfgang Banzhaf", title = "Introduction", journal = "Genetic Programming and Evolvable Machines", year = "2006", volume = "6", number = "1", pages = "5--6", month = mar, ISSN = "1389-2576", DOI = "doi:10.1007/s10710-006-7015-0", size = "1.1 pages", } @Article{banzhaf:2006:ack, author = "W. Banzhaf", title = "Acknowledgement", journal = "Genetic Programming and Evolvable Machines", year = "2006", volume = "6", number = "1", pages = "7", month = mar, ISSN = "1389-2576", DOI = "doi:10.1007/s10710-006-7016-z", size = "1 page", notes = "list of reviewers", } @Article{Banzhaf:2006:NRG, author = "Wolfgang Banzhaf and Guillaume Beslon and Steffen Christensen and James Foster and Francois Kepes and Virginie Lefort and Julian Miller and Miroslav Radman and Jeremy J. Ramsden", title = "From Artificial Evolution to Computational Evolution: A Research Agenda", journal = "Nature Reviews Genetics", year = "2006", volume = "7", number = "9", pages = "729--735", month = sep, keywords = "genetic algorithms, genetic programming", ISSN = "1471-0056", DOI = "doi:10.1038/nrg1921", size = "7 pages", abstract = "Computational scientists have developed algorithms inspired by natural evolution for at least 50 years. These algorithms solve optimisation and design problems by building solutions that are 'more fit' relative to desired properties. However, the basic assumptions of this approach are outdated. We propose a research programme to develop a new field: computational evolution. This approach will produce algorithms that are based on current understanding of molecular and evolutionary biology and could solve previously unimaginable or intractable computational and biological problems.", notes = "We thank the people at Genopole Recherche, Evry, France, for generously sponsoring the meeting that initiated this paper.", } @Article{banzhaf:2007:intro, author = "Wolfgang Banzhaf", title = "Editorial introduction", journal = "Genetic Programming and Evolvable Machines", year = "2007", volume = "8", number = "1", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-007-9022-1", size = "2 pages", abstract = "As we have moved into the corporate sphere of Springer there were a number of changes, some subtle, some not so subtle. One change that is somewhat behind the scenes and eludes the eye of a reader is how Springer uses its distribution channels to spread the journal. Genetic Programming and Evolvable Machines is now accessible in over 5000 libraries across the globe. I think that speaks to the ability of this publisher, and its will to get the word out about our community. In the absence of an officially calculated impact factor I have taken the initiative myself to address this issue. Using the citation base of Google Scholar, we have evaluated the impact of GPEM by looking at all the papers published since its inception in 2000, up to May 2006. It turns out that authors did very well if publishing in GPEM. Their GPEM papers regularly featured prominently among their papers in terms of citations. 50% of our authors can count their GPEM paper among the first, second or third most cited paper of theirs. While this is certainly only true for half of the authors, it is indeed an achievement. So if you publish in GPEM, be prepared that your work is read, and also cited.", } @Article{Banzhaf:2007:Complexity, author = "Wolfgang Banzhaf and Nelishia Pillay", title = "Why Complex Systems Engineering needs Biological Development", journal = "Complexity", year = "2007", volume = "13", number = "2", pages = "12--21", month = nov # "/" # dec, keywords = "genetic algorithms, genetic programming", URL = "https://onlinelibrary.wiley.com/doi/pdf/10.1002/cplx.20199.pdf", DOI = "doi:10.1002/cplx.20199", size = "10 pages", abstract = "Here we shall discuss the need of Complex Systems Engineering to adopt principles from natural development of complex biological organisms, besides principles of natural evolution, to accomplish the type of performance that biology achieves regularly. We shall situate Complex Systems Engineering and discuss an example of how it could be employed.", notes = "Essay and Commentary", } @InCollection{Banzhaf:2008:GPTP, author = "Wolfgang Banzhaf and Simon Harding and William B. Langdon and Garnett Wilson", title = "Accelerating Genetic Programming through Graphics Processing Units", booktitle = "Genetic Programming Theory and Practice {VI}", year = "2008", editor = "Rick L. Riolo and Terence Soule and Bill Worzel", series = "Genetic and Evolutionary Computation", chapter = "15", pages = "229--249", address = "Ann Arbor", month = "15-17 " # may, publisher = "Springer", size = "20 pages", keywords = "genetic algorithms, genetic programming, graphics processing units, parallel processing, GPU", DOI = "doi:10.1007/978-0-387-87623-8_15", size = "19 pages", abstract = "Graphics Processing Units (GPUs) are in the process of becoming a major source of computational power for numerical applications. Originally designed for application of time-consuming graphics operations, GPUs are stream processors that implement the SIMD paradigm. The true degree of parallelism of GPUs is often hidden from the user, making programming even more flexible and convenient. In this chapter we survey Genetic Programming methods currently ported to GPUs.", isbn13 = "978-0-387-87622-1", notes = "part of \cite{Riolo:2008:GPTP} published in 2009", } @InCollection{Banzhaf:2013:EB, author = "Wolfgang Banzhaf", title = "Evolutionary Computation and Genetic Programming", editor = "Akhlesh Lakhtakia and Raul Jose Martin-Palma", booktitle = "Engineered Biomimicry", publisher = "Elsevier", address = "Boston", year = "2013", pages = "429--447", chapter = "17", isbn13 = "978-0-12-415995-2", DOI = "doi:10.1016/B978-0-12-415995-2.00017-9", URL = "http://www.sciencedirect.com/science/article/pii/B9780124159952000179", keywords = "genetic algorithms, genetic programming, Algorithms, Artificial intelligence, Automatic programming, Bioinspired computing, Breeding, Crossover, Differential evolution, Evolutionary computation, Evolutionary programming, Evolution strategies, Generation, Human-competitive, Machine learning, Mutation, Natural selection, Population, Reproduction, Search space", abstract = "This chapter focuses on evolutionary computation, in particular genetic programming, as examples of drawing inspiration from biological systems. We set the choice of evolution as a source for inspiration in context and discuss the history of evolutionary computation and its variants before looking more closely at genetic programming. After a discussion of methods and the state of the art, we review application areas of genetic programming and its strength in providing human-competitive solutions.", } @Article{Banzhaf:2014:GPEM, author = "Wolfgang Banzhaf", title = "Genetic Programming and Emergence", journal = "Genetic Programming and Evolvable Machines", year = "2014", volume = "15", number = "1", pages = "63--73", month = mar, keywords = "genetic algorithms, genetic programming, Emergence, Emergent phenomena, Top-down causation, Repetitive patterns, Modularity", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-013-9196-7", size = "11 pages", abstract = "Emergence and its accompanying phenomena are a widespread process in nature. Despite its prominence, there is no agreement in the sciences about the concept and how to define or measure emergence. One of the most contentious issues discussed is that of top-down (or downward) causation as a defining characteristic of systems with emergence. In this contribution we shall argue that emergence happens in Genetic Programming, for all the world to see", notes = "Commentaries to this article can be found at doi: 10.1007/s10710-013-9198-5, 10.1007/s10710-013-9199-4, 10.1007/s10710-013-9200-2, 10.1007/s10710-013-9201-1, 10.1007/s10710-013-9202-0, 10.1007/s10710-013-9203-z, 10.1007/s10710-013-9204-y. See \cite{Altenberg:2014:GPEM} \cite{Ekart:2014:GPEM} \cite{Leier:2014:GPEM} \cite{Krawiec:2014:GPEM} \cite{Montana:2014:GPEM} \cite{Sipper:2014:GPEM} \cite{Whigham:2014:GPEM} \cite{Banzhaf_reply:2014:GPEM}", } @Article{Banzhaf_reply:2014:GPEM, author = "Wolfgang Banzhaf", title = "Response to comments on ''Genetic Programming and Emergence''", journal = "Genetic Programming and Evolvable Machines", year = "2014", volume = "15", number = "1", pages = "103--108", month = mar, keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-013-9207-8", size = "6 pages", notes = "This response refers to the articles available at the following DOIs: 10.1007/s10710-013-9196-7; 10.1007/s10710-013-9198-5; 10.1007/s10710-013-9199-4; 10.1007/s10710-013-9200-2; 10.1007/s10710-013-9201-1; 10.1007/s10710-013-9202-0; 10.1007/s10710-013-9203-z; 10.1007/s10710-013-9204-y. See \cite{Banzhaf:2014:GPEM}", } @Article{Banzhaf:2016:TBS, author = "Wolfgang Banzhaf and Bert Baumgaertner and Guillaume Beslon and Rene Doursat and James Foster and Barry McMullin and Vinicius Veloso {de Melo} and Thomas Miconi and Lee Spector and Susan Stepney and Roger White", title = "Defining and Simulating Open-Ended Novelty: Requirements, Guidelines, and Challenges", journal = "Theory in Biosciences", year = "2016", volume = "135", number = "3", pages = "131--161", month = sep, note = "Special Issue in Commemoration of Olaf Breidbach - Part I", keywords = "genetic algorithms, genetic programming, open-ended evolution, Modeling and simulation, Open-ended evolution, Novelty, Innovation, Major transitions, Emergence", DOI = "doi:10.1007/s12064-016-0229-7", abstract = "The open-endedness of a system is often defined as a continual production of novelty. Here we pin down this concept more fully by defining several types of novelty that a system may exhibit, classified as variation, innovation, and emergence. We then provide a meta-model for including levels of structure in a system's model. From there, we define an architecture suitable for building simulations of open-ended novelty-generating systems and discuss how previously proposed systems fit into this framework. We discuss the design principles applicable to those systems and close with some challenges for the community.", } @Proceedings{Banzhaf:2017:GPTP, title = "Genetic Programming Theory and Practice {XV}", year = "2017", editor = "Wolfgang Banzhaf and Randal S. Olson and William Tozier and Rick Riolo", series = "Genetic and Evolutionary Computation", address = "Ann Arbor, USA", month = "19-21 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-90511-2", URL = "http://www.springer.com/gb/book/9783319905112", DOI = "doi:10.1007/978-3-319-90512-9", size = "200 pages", abstract = "Provides papers describing cutting-edge work on the theory and applications of genetic programming (GP) Offers large-scale, real-world applications (big data) of GP to a variety of problem domains, including commercial and scientific applications as well as bioinformatics problems Explores controlled semantics, lexicase and other selection methods, crossover techniques, diversity analysis and understanding of convergence tendencies These contributions, written by the foremost international researchers and practitioners of Genetic Programming (GP), explore the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP. Topics in this volume include: exploiting subprograms in genetic programming, schema frequencies in GP, Accessible AI, GP for Big Data, lexicase selection, symbolic regression techniques, co-evolution of GP and LCS, and applying ecological principles to GP. It also covers several chapters on best practices and lessons learned from hands-on experience. Readers will discover large-scale, real-world applications of GP to a variety of problem domains via in-depth presentations of the latest and most significant results.", notes = "Workshop 19-21 May 2016, published 2018. Also known as \cite{conf/gptp/2017}", } @InCollection{Banzhaf:2017:miller, author = "Wolfgang Banzhaf", title = "Some Remarks on Code Evolution with Genetic Programming", booktitle = "Inspired by Nature: Essays Presented to Julian F. Miller on the Occasion of his 60th Birthday", publisher = "Springer", year = "2017", editor = "Susan Stepney and Andrew Adamatzky", volume = "28", series = "Emergence, Complexity and Computation", chapter = "6", pages = "145--156", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, machine learning, ILP", isbn13 = "978-3-319-67996-9", DOI = "doi:10.1007/978-3-319-67997-6_6", abstract = "In this chapter we take a fresh look at the current status of evolving computer code using Genetic Programming methods. The emphasis is not so much on what has been achieved in detail in the past few years, but on the general research direction of code evolution and its ramifications for GP. We begin with a quick glance at the area of Search-based Software Engineering (SBSE), discuss the history of GP as applied to code evolution, consider various application scenarios, and speculate on techniques that might lead to a scaling-up of present-day approaches.", notes = "p148 'The transition between manual and automatic programming becomes continuous. Epochs. p153 evo-devo evolution and developmental approaches. p154 'big code' part of \cite{miller60book} https://link.springer.com/bookseries/10624", } @Proceedings{Banzhaf:2018:GPTP, title = "Genetic Programming Theory and Practice XVI", year = "2018", editor = "Wolfgang Banzhaf and Lee Spector and Leigh Sheneman", address = "Ann Arbor, USA", month = "17-20 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-030-04734-4", DOI = "doi:10.1007/978-3-030-04735-1", size = "xi + 234", abstract = "Contents: \cite{dolson:2018:GPTP} \cite{hintze:2018:GPTP} \cite{kelly:2018:GPTP} \cite{korns:2018:GPTP} \cite{kronberger:2018:GPTP} \cite{lalejini:2018:GPTP} \cite{metevier:2018:GPTP} \cite{miller:2018:GPTP} \cite{oneill:2018:GPTP} \cite{trujillo:2018:GPTP} \cite{yang:2018:GPTP}", notes = "Also known as \cite{DBLP:conf/gptp/2018}", } @Proceedings{Banzhaf:2019:GPTP, title = "Genetic Programming Theory and Practice XVII", year = "2019", editor = "Wolfgang Banzhaf and Erik Goodman and Leigh Sheneman and Leonardo Trujillo and Bill Worzel", address = "East Lansing, MI, USA", month = "16-19 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-030-39957-3", URL = "https://link.springer.com/book/10.1007/978-3-030-39958-0", URL = "https://doi.org/10.1007/978-3-030-39958-0", DOI = "doi:10.1007/978-3-030-39958-0", notes = "See \cite{Ofria:2019:GPTP}, \cite{Fernandez:2019:GPTP}, \cite{Freeland:2019:GPTP}, \cite{Goncalves:2019:GPTP}, \cite{Hu:2019:GPTP}, \cite{Kammerer:2019:GPTP}, \cite{Kelly:2019:GPTP}, \cite{Hintze:2019:GPTP}, \cite{Kordon:2019:GPTP}, \cite{Lehman:2019:GPTP}, \cite{Nicolau:2019:GPTP}, \cite{Olague:2019:GPTP}, \cite{Spector:2019:GPTP}, \cite{Rajapakse:2019:GPTP}, \cite{Saini:2019:GPTP}, \cite{Schmidt:2019:GPTP}, \cite{Shahrzad:2019:GPTP}, \cite{Sipper:2019:GPTP}, \cite{Sloss:2019:GPTP}, \cite{Heywood:2019:GPTP}, \cite{White:2019:GPTP}, \cite{Yuan:2019:GPTP}, Also known as \cite{DBLP:conf/gptp/2019}", } @Proceedings{Banzhaf:2021:GPTP, title = "Genetic Programming Theory and Practice XVIII", year = "2021", editor = "Wolfgang Banzhaf and Leonardo Trujillo and Stephan Winkler and Bill Worzel", series = "Genetic and Evolutionary Computation", address = "East Lansing, USA", month = "19-21 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-981-16-8112-7", URL = "https://link.springer.com/book/9789811681127", DOI = "doi:10.1007/978-981-16-8113-4", size = "XVIII + 212", abstract = "Note GPTP 2020 not held due to corvid pandemic Contents \cite{Bayer:2021:GPTP}, \cite{Dolson:2021:GPTP}, \cite{Fleck:2021:GPTP}, \cite{Fonseca:2021:GPTP}, \cite{Guadalupe-Hernandez:2021:GPTP}, \cite{Korns:2021:GPTP}, \cite{Kotanchek:2021:GPTP}, \cite{Langdon:2021:GPTP}, \cite{Miller:2021:GPTP}, \cite{Saini:2021:GPTP}, \cite{Sloss:2021:GPTP}, Index A Action program, 2 multi-action program, 6 Activity dependence, 166 Ascension, 203 Automated program repair, 46 B Bees algorithm, 117 Benchmarking, 8, 84 C Cache, 52, 161 Cambrian explosion, 199 Causality, 71 Classification, 166 Co-evolution, 202 Competition, 89, 114, 205 Context-free grammar, 48 Convergence phenotypic, 150, 199 Crossover asymmetry of GP subtree crossover, 151 fatherless crossover, 158 unbiased subtree crossover, 150 D Data balancing, 133, 141 Deep learning, 2, 109, 165 with genetic programming, 109 Diagnostics exploration diagnostics, 104 selection scheme diagnostics, 104 Discriminant analysis, 113 Diversity, 2, 53, 63, 88, 139, 199 phenotypic, 64 phenotypic diversity, 89 phylogenetic, 64 phylogenetic diversity, 84 E Eco-EA, 66 Efficiency, 84, 114, 129, 155, 203 Ensembles, 133, 138, 139 Exploration diagnostic, 67 Exponential growth, 206 F Feedback loop, 71 Fitness predicting evaluation time of, 160 Fitness sharing, 66 G General artificial intelligence, 165 Genetic learning, 182 Genetic programming BalancedGP, 133, 137 grammar-based vectorial GP, 22 networked runs genetic programming, 109 OrdinalGP, 134, 137 PushGP, 52, 102, 190 template-constrained genetic program- ming, 45, 109 vectorial GP, 22 Grammar-guided, 22 Graph, 28, 111, 183 Growing neural networks, 111, 168 H High performance, 143, 195 Homeostatis, 172 Horizontal gene transfer, 203 I Inefficient threads avoiding, 143 causes, 143 measurement, 143 prediction, 143 Information loss, 33, 40 Inplace crossover, 143 shuffle, 143 speedup, 143 Intellectual property, 202 L Lexicase selection, 65, 66, 83, 191 cohort lexicase selection, 83 down-sampled lexicase selection, 83 epsilon lexicase selection, 83 novelty lexicase selection, 83 Linear genetic programming, 3, 7, 18, 69, 184 Liquid types, 50, 51 M Memory bandwidth, 143 Memory use minimising, 143 Metrics, 70 Mitochondria, 203 Modular, 167, 194 Modularity, 2, 7, 69, 181, 194 Moore Law, 197, 206 N Novelty, 90, 199 P Panmictic, 146, 150 Parent selection, 65, 83, 191 Pareto tournament, 131 Partially observable, 1 Population diversity, 2, 66, 97, 199 Population initialization, 2, 5, 12, 55, 90, 174 Predicting success based on diversity, 63 Program dendrite program, 168 evolving modular program, 182 neuron program, 176 program graph, 2, 183 program representation, 46 program synthesis, 47, 52, 84 program synthesis benchmark suite, 190 programming languages, 48 Program synthesis, 47, 84, 190 program synthesis benchmark suite, 190 R Rampant mutation, 2 Reinforcement learning, 2, 17, 176 Resilience, 203, 207 S Selection offspring selection, 34 selection pressure, 34, 157, 161 Semantic constraints, 48 Semiconductor industry, 197 SMT solvers, 48, 58 Social evolution, 203, 205 Strongly-typed, 25 Sustainability, 202 Symbolic regression, 24, 30, 87, 88, 115, 116 T Tags, 183 Tangled program graphs, 2, 183 Team, 3, 183 Tournament selection, 65, 85, 90, 91, 103, 130, 143, 145, 150, 161 Tree-based GP, 26, 28 Tree depth, 57 Type-aware, 50", notes = "Published after the workshop in 2022", } @Proceedings{Banzhaf:2022:GPTP, title = "Genetic Programming Theory and Practice XIX", year = "2022", editor = "Leonardo Trujillo and Stephan M. Winkler and Sara Silva and Wolfgang Banzhaf", series = "Genetic and Evolutionary Computation", address = "East Lansing, USA", month = "2-4 " # jun, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-981-19-8459-4", ISSN = "1932-0167", URL = "https://link.springer.com/book/9789811984594", DOI = "doi:10.1007/978-981-19-8460-0", size = "XVI + 280 pages", abstract = "published after the workshop in 2023. http://gptp-workshop.com/contributions.html Contents \cite{Affenzeller:2022:GPTP} \cite{Banzhaf:2022:GPTP.2} \cite{gold:2022:GPTP} \cite{Hu:2022:GPTP} \cite{Kotanchek:2022:GPTP} \cite{LaCava:2022:GPTP} x, \cite{Machado:2022:GPTP} \cite{Moore:2022:GPTP} x, \cite{Olague:2022:GPTP} \cite{O'Reilly:2022:GPTP} x, \cite{Spector:2022:GPTP} x, \cite{Urbanowicz:2022:GPTP} \cite{Wright:2022:GPTP} April 2023 added: \cite{Stepney:2022:GPTP} \cite{Worzel:2022:GPTP}", } @InProceedings{Banzhaf:2022:GPTP.2, author = "Nathan Haut and Wolfgang Banzhaf and Bill Punch", title = "Correlation versus {RMSE} Loss Functions in Symbolic Regression Tasks", booktitle = "Genetic Programming Theory and Practice XIX", year = "2022", editor = "Leonardo Trujillo and Stephan M. Winkler and Sara Silva and Wolfgang Banzhaf", series = "Genetic and Evolutionary Computation", pages = "31--55", address = "Ann Arbor, USA", month = jun # " 2-4", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-981-19-8459-4", DOI = "doi:10.1007/978-981-19-8460-0_2", abstract = "The use of correlation as a fitness function is explored in symbolic regression tasks and its performance is compared against a more typical RMSE fitness function. Using correlation with an alignment step to conclude the evolution led to significant performance gains over RMSE as a fitness function. Employing correlation as a fitness function led to solutions being found in fewer generations compared to RMSE. We also found that fewer data points were needed in a training set to discover correct equations. The Feynman Symbolic Regression Benchmark as well as several other old and recent GP benchmark problems were used to evaluate performance.", notes = "Part of \cite{Banzhaf:2022:GPTP} published after the workshop in 2023", } @InProceedings{Banzhaf:2023:GPTP, author = "Wolfgang Banzhaf and Ting Hu and Gabriela Ochoa", title = "How the Combinatorics of Neutral Spaces Leads Genetic Programming to Discover Simple Solutions", old_title = "Neutrality", booktitle = "Genetic Programming Theory and Practice XX", year = "2023", editor = "Stephan Winkler and Leonardo Trujillo and Charles Ofria and Ting Hu", series = "Genetic and Evolutionary Computation", pages = "65--86", address = "Michigan State University, USA", month = jun # " 1-3", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-981-99-8412-1", DOI = "doi:10.1007/978-981-99-8413-8_4", abstract = "For various evolutionary systems it was found that the abundance of phenotypes in a search space, defined as the size of their respective neutral networks, is key to understanding the trajectory an evolutionary process takes from an initial to a target solution. we use a Linear Genetic Programming system to demonstrate that the abundance of phenotypes is determined by the combinatorics offered in its neutral components. This translates into the size of the neutral space available to a phenotype and also can explain the beautiful and rather curious observation that the abundance of phenotypes is dependent on their complexity in a negative exponential fashion.", notes = " Part of \cite{Hu:2023:GPTP} published after the workshop in 2024", } @Misc{DBLP:journals/corr/abs-2402-08011, author = "Wolfgang Banzhaf and Illya Bakurov", title = "On The Nature Of The Phenotype In Tree Genetic Programming", howpublished = "arXiv", volume = "abs/2402.08011", year = "2024", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.48550/arXiv.2402.08011", DOI = "doi:10.48550/ARXIV.2402.08011", eprinttype = "arXiv", eprint = "2402.08011", timestamp = "Mon, 19 Feb 2024 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/corr/abs-2402-08011.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{Banzhaf:2024:GPEM, author = "Wolfgang Banzhaf", title = "``The physics of evolution'' by Michael W. Roth, {CRC} press, 2023", journal = "Genetic Programming and Evolvable Machines", year = "2024", volume = "25", number = "2", pages = "Article no 16", month = dec, note = "Book review", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dK8KO", DOI = "doi:10.1007/s10710-024-09489-z", size = "3 pages", notes = "Koza Chair in Genetic Programming, Michigan State University, East Lansing, Michigan, USA", } @InProceedings{banzhaf:2024:GECCO, author = "Wolfgang Banzhaf and Illya Bakurov", title = "On the Nature of the Phenotype in Tree Genetic Programming", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference", year = "2024", editor = "Ting Hu and Aniko Ekart and Julia Handl and Xiaodong Li and Markus Wagner and Mario Garza-Fabre and Kate Smith-Miles and Richard Allmendinger and Ying Bi and Grant Dick and Amir H Gandomi and Marcella Scoczynski Ribeiro Martins and Hirad Assimi and Nadarajen Veerapen and Yuan Sun and Mario Andres Munyoz and Ahmed Kheiri and Nguyen Su and Dhananjay Thiruvady and Andy Song and Frank Neumann and Carla Silva", pages = "868--877", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, genotype-phenotype map, simplication, neutrality, explainability, symbolic regression", isbn13 = "979-8-4007-0494-9", DOI = "doi:10.1145/3638529.3654129", size = "10 pages", abstract = "In this contribution, we discuss the basic concepts of genotypes and phenotypes in tree-based GP (TGP), and then analyze their behavior using five real-world datasets. We show that TGP exhibits the same behavior that we can observe in other GP representations: At the genotypic level trees show frequently unchecked growth with seemingly ineffective code, but on the phenotypic level, much smaller trees can be observed. To generate phenotypes, we provide a unique technique for removing semantically ineffective code from GP trees. The approach extracts considerably simpler phenotypes while not being limited to local operations in the genotype. We generalize this transformation based on a problem-independent parameter that enables a further simplification of the exact phenotype by coarse-graining to produce approximate phenotypes. The concept of these phenotypes (exact and approximate) allows us to clarify what evolved solutions truly predict, making GP models considered at the phenotypic level much better interpretable.", notes = "GECCO-2024 GP A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{banzhaf:2024:GECCOcomp, author = "Wolfgang Banzhaf and Ting Hu", title = "Linear Genetic Programming", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion", year = "2024", editor = "Mengjie Zhang and Emma Hart", pages = "759--771", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", note = "Tutorial", keywords = "genetic algorithms, genetic programming", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3648422", size = "13 pages", notes = "GECCO-2024 A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{bao:2023:AILA, author = "Hailu Bao and Changsheng Zhang and Chen Zhang and Bin Zhang", title = "An Improved Genetic Programming Based Factor Construction for Stock Price Prediction", booktitle = "Third International Conference on Artificial Intelligence Logic and Applications, AILA 2023", year = "2023", editor = "Songmao Zhang and Yonggang Zhang", volume = "1917", series = "CCIS", pages = "227--240", address = "Changchun, China", month = aug # " 5-6", organisation = "Chinese Association for Artificial Intelligence", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-981-99-7869-4_18", DOI = "doi:10.1007/978-981-99-7869-4_18", abstract = "In the process of stock price forecasting, there are the following problems: how to find the more effective factors for stock price forecasting, and how to calculate the weight of the constructed stock correlation factor sets. To solve the above problems, this paper proposes a method of factor construction in the field of stock price prediction based on genetic programming. The method can automatically construct the factor by reading the original data set of the stock, and calculate the weight of each factor. In addition, this paper also proposes a new crossover operator, which can dynamically adjust the selection of crossover nodes by using the information in the execution process of genetic programming algorithm, so as to improve the quality of the constructed factor set. A lot of experiments have been carried out with this method. The results show that the factors constructed by this method can improve the accuracy of the stock price prediction algorithm in most cases.", notes = "Proceedings published as Artificial Intelligence Logic and Applications Software College, Northeastern University, Shenyang, 110169, China", } @InProceedings{Bao:2009:ICNC, author = "Yun Bao and Erbo Zhao and Xiaocong Gan and Dan Luo and Zhangang Han", title = "A Review on Cutting-Edge Techniques in Evolutionary Algorithms", booktitle = "Fifth International Conference on Natural Computation, 2009. ICNC '09", year = "2009", month = aug, volume = "5", pages = "347--351", keywords = "genetic algorithms, genetic programming, EA performance improvements, convergence speed, cutting-edge techniques, evolutionary algorithms, nuclear power plant, evolutionary computation", DOI = "doi:10.1109/ICNC.2009.459", abstract = "There are vast amount researches in Evolutionary Algorithms (EA). We need an overview of the current state of EA research every few years. This paper reviews some of the interesting researches at the current state in both theory and application of EA. Works in EA performance improvements are introduced in the sense of balancing between convergence speed and diversity in the population. The combination of EA with other methods is highlighted as a prospective area that may give fertility results. Some smart applications are reviewed in this paper, for example, application in nuclear power plant. The authors point out some research highlights and drawbacks in the conclusion. Future research suggestions are also given.", notes = "Also known as \cite{5364257}", } @PhdThesis{ceg_baptist_20050418, author = "Martin Josephus Baptist", title = "Modelling floodplain biogeomorphology", school = "Technische Universiteit Delft", year = "2005", address = "Holland", month = "18 " # apr, publisher = "Delft University Press", ISBN = "90-407-2582-9", keywords = "genetic algorithms, genetic programming, biogeomorphology, flood management, hydraulic modelling, nature management, hydraulic roughness, river morphology, bed shear stress, floodplain vegetation", URL = "https://repository.tudelft.nl/islandora/object/uuid%3Ab2739720-e2f6-40e2-b55f-1560f434cbee", URL = "http://repository.tudelft.nl/assets/uuid...e2f6.../ceg_baptist_20050418.pdf", size = "213 pages", abstract = "There is an increasing awareness that rivers need more room in order to safeguard flood safety under climate change conditions. Contemporary river management is creating room in the floodplains and allowing, within certain bounds, natural processes of sedimentation and erosion. One of the aims is to restore dynamic conditions, so as to get a sustainable and more diverse river ecosystem that can cope with floods. This new approach requires understanding of the interaction between the biotic and abiotic components of river systems. More specifically, it requires a better understanding of the interaction between flora and fauna and geomorphological factors. This is the object of investigation of the interdiscipline of biogeomorphology. Modelling biogeomorphological processes in river floodplains is the topic of this thesis. To reduce flood risks in the Netherlands, measures to increase the flood conveyance capacity of the Rhine River will be implemented. However, it is expected that floodplain sedimentation and softwood forest development in rehabilitated floodplains will gradually reduce the conveyance capacity and the biodiversity. Moreover, in regulated rivers, such as the Rhine River, erosion and sedimentation processes caused by channel migration, which periodically interrupt vegetation succession, cannot be allowed. Therefore, a floodplain management strategy was proposed that would meet both flood protection and nature rehabilitation objectives. This strategy, 'Cyclic Floodplain Rejuvenation (CFR)', aims at mimicking the effects of channel migration by removal of softwood forests, by lowering floodplains or by (re)constructing secondary channels. In chapter 2, the effects of CFR measures on reducing flood levels and enhancing biodiversity along the Waal River were assessed. A one-dimensional hydraulic modelling system, SOBEK, was applied together with rule-based models for floodplain vegetation succession and floodplain sedimentation. The model simulations demonstrated that the flood management strategy of Cyclic Floodplain Rejuvenation is able to sustain safe flood levels in the Waal River. Rejuvenation is then needed every 25 to 35 years on average, each time in an area of about 15percent of the total floodplain area. The rejuvenation strategy led to a diverse floodplain vegetation distribution that largely complies to the historical reference for the Waal River. Cyclic Floodplain Rejuvenation may be the appropriate answer to find symbiosis between flood protection and nature rehabilitation in highly regulated rivers. ...", notes = "In English. Supervisor H.J. de Vriend", } @Article{Baptist:2007:JHR, author = "M. J. Baptist and Vladan Babovic and J. {Rodriguez Uthurburu} and M. Keijzer and R. E. Uittenbogaard and A. Mynett and A. Verwey", title = "On inducing equations for vegetation resistance", journal = "Journal of Hydraulic Research", year = "2007", volume = "45", number = "4", pages = "435--450", keywords = "genetic algorithms, genetic programming, vegetation, roughness, resistance, knowledge discovery", ISSN = "0022-1686", DOI = "doi:10.1080/00221686.2007.9521778", size = "16 pages", abstract = "The paper describes the process of induction of equations for the description of vegetation-induced roughness from several angles. Firstly, it describes two approaches for obtaining theoretically well-founded analytical expressions for vegetation resistance. The first of the two is based on simplified assumptions for the vertical flow profile through and over vegetation, whereas the second is based on an analytical solution to the momentum balance for flow through and over vegetation. In addition to analytical expressions the paper also outlines a numerical 1-DV k-e turbulence model which includes several important features related to the influence plants exhibit on the flow. Last but not least, the paper presents a novel way of applying genetic programming to the results of the 1-DV model, in order to obtain an expression for roughness based on synthetic data. The resulting expressions are evaluated and compared with an independent data set of flume experiments", } @Article{BarabasiEtAl01, author = "Albert-Laszlo Barabasi and Vincent W. Freeh and Hawoong Jeong and Jay B. Brockman", title = "Parasitic Computing", journal = "Nature", volume = "412", year = "2001", pages = "894--897", month = "30 " # aug, keywords = "16-SAT", URL = "http://www.nd.edu/~alb/Publication06/082%20Parasitic%20computing/Parasitic%20computing.pdf", DOI = "doi:10.1038/35091039", size = "3 pages", abstract = "Reliable communication on the Internet is guaranteed by a standard set of protocols, used by all computers. Here we show that these protocols can be exploited to compute with the communication infrastructure, transforming the Internet into a distributed computer in which servers unwittingly perform computation on behalf of a remote node. In this model, which we call 'parasitic computing', one machine forces target computers to solve a piece of a complex computational problem merely by engaging them in standard communication. Consequently, the target computers are unaware that they have performed computation for the benefit of a commanding node. As experimental evidence of the principle of parasitic computing, we harness the power of several web servers across the globe, which unknown to them work together to solve an NP complete problem", notes = "fig1 p895 Segement (TCP packet) dropped due to invalid checksum One property of the TCP checksum function is that it forms a sufficient logical basis for implementing any Boolean logic function, and by extension, any arithmetic operation. 65536 32 bit messages sent to http servers on three continents Cited by \cite{arXiv:cs/0701115v1}", } @InProceedings{baradavka03, author = "Igor Baradavka and Tatiana Kalganova", title = "Assembling Strategies in Extrinsic Evolvable Hardware with Bidirectional Incremental Evolution", booktitle = "Genetic Programming, Proceedings of EuroGP'2003", year = "2003", editor = "Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa", volume = "2610", series = "LNCS", pages = "276--285", address = "Essex", publisher_address = "Berlin", month = "14-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, evolvable hardware: Poster", ISBN = "3-540-00971-X", DOI = "doi:10.1007/3-540-36599-0_25", abstract = "Bidirectional incremental evolution (BIE) has been proposed as a technique to overcome the ``stalling'' effect in evolvable hardware applications. However preliminary results show perceptible dependence of performance of BIE and quality of evaluated circuit on assembling strategy applied during reverse stage of incremental evolution. The purpose of this paper is to develop assembling strategy that will assist BIE to produce relatively optimal solution with minimal computational effort (e.g. the minimal number of generations).", notes = "EuroGP'2003 held in conjunction with EvoWorkshops 2003", } @Article{Barakova:2015:ieeeMMS, author = "Emilia I. Barakova and Roman Gorbunov and Matthias Rauterberg", journal = "IEEE Transactions on Human-Machine Systems", title = "Automatic Interpretation of Affective Facial Expressions in the Context of Interpersonal Interaction", year = "2015", volume = "45", number = "4", pages = "409--418", abstract = "This paper proposes a method for interpretation of the emotions detected in facial expressions in the context of the events that cause them. The method was developed to analyse the video recordings of facial expressions depicted during a collaborative game played as a part of the Mars-500 experiment. In this experiment, six astronauts were isolated for 520 days in a space station to simulate a flight to Mars. Seven time-dependent components of facial expressions were extracted from the video recordings of the experiment. To interpret these dynamic components, we proposed a mathematical model of emotional events. Genetic programming was used to find the locations, types, and intensities of the emotional events as well as the way the recorded facial expressions represented reactions to them. By classification of different statistical properties of the data, we found that there are significant relations between the facial expressions of different crew members and a memory effect between the collective emotional states of the crew members. The model of emotional events was validated on previously unseen video recordings of the astronauts. We demonstrated that both genetic search and optimisation of the parameters improve the accuracy of the proposed model. This method is a step toward automating the analysis of affective expressions in terms of the cognitive appraisal theory of emotion, which relies on the dependence of the expressed emotion on the causing event.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/THMS.2015.2419259", ISSN = "2168-2291", month = aug, notes = "Also known as \cite{7105880}", } @Article{Baral:2017:SSI, author = "Ashok Kumar Baral and Yoed Tsur", title = "Impedance spectroscopy of Gd-doped ceria analyzed by genetic programming (ISGP) method", journal = "Solid State Ionics", volume = "304", pages = "145--149", year = "2017", ISSN = "0167-2738", DOI = "doi:10.1016/j.ssi.2017.04.003", URL = "http://www.sciencedirect.com/science/article/pii/S0167273816309419", abstract = "This work presents the distribution function of relaxation time (DFRT) analysis of Gd doped ceria (GDC) and cobalt co-doped GDC prepared by precipitation method. Ionic transport properties and grain-boundary phenomena are discussed thoroughly based on the DFRT. The impedance results, especially the bulk and grain-boundary conductivities ( sigma b and sigma gb) and activation energies (Eb and Egb) obtained from the ISGP, are compared with the values obtained from the Equivalent Circuit Model. Grain boundary space charge (SC) effects discussed so far in the literature, generally do not consider the defect interaction between the oxygen vacancies and acceptor dopants in ceria and other oxide ion conductors. However, ISGP study clearly evidence the co-existence of SC effect and defect association in grain boundary regions, and both contribute to the grain boundary resistance (Rgb) at lower temperatures. The effect of sintering aid (Co) on the grain boundary activity is discussed considering both phenomena. Lower sintering temperature of the samples results in a relatively smaller grain boundary potential (Phi(0)) i.e., 0.15, 0.17 and 0.19 V at 300 degreeC in 0, 1 and 3 molpercent Co co-doped GDC, respectively.", keywords = "genetic algorithms, genetic programming, Impedance spectroscopy, DFRT, Grain boundary properties, Doped ceria", } @Article{baraldi:2021:Metals, author = "Daniele Baraldi and Stefan Holmstrom and Karl-Fredrik Nilsson and Matthias Bruchhausen and Igor Simonovski", title = "{316L(N)} Creep Modeling with Phenomenological Approach and Artificial Intelligence Based Methods", journal = "Metals", year = "2021", volume = "11", number = "5", keywords = "genetic algorithms, genetic programming", ISSN = "2075-4701", URL = "https://www.mdpi.com/2075-4701/11/5/698", DOI = "doi:10.3390/met11050698", abstract = "A model that describes creep behaviour is essential in the design or life assessment of components and systems that operate at high temperatures. Using the RCC-MRx data and the LCSP (logistic creep strain prediction) model, processed design data were generated over the whole creep regime of 316L(N) steel--i.e., primary, secondary, and tertiary creep. The processed design data were used to develop three models with different approaches for the creep rate: a phenomenological approach; an artificial neural network; and an artificial intelligence method based on symbolic regression and genetic programming. It was shown that all three models are capable of describing the true creep rate as a function of true creep strain and true stress over a wide range of engineering stresses and temperatures without the need of additional micro-structural information. Furthermore, the results of finite element simulations reproduce the trends of experimental data from the literature.", notes = "also known as \cite{met11050698}", } @InProceedings{barash:1998:mGAofsalf, author = "Danny Barash and Ann Orel and V. Rao Vemuri", title = "Micro Genetic Algorithms in Finding the Optimal Frequency for Stabilizing Atoms by High-intensity Laser Fields", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "7--13", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "genetic algorithms, genetic programming", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.52.1378", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.52.1378.pdf", size = "7 pages", abstract = "The goal of this paper is to explore the power of genetic algorithms, in particular the so called micro genetic algorithms, to solve a challenging problem in experimental physics. The problem is to find an optimum frequency to stabilise atoms by high-intensity laser fields. The standard approach to search for optimal laser parameters has been by trial and error. This is the first known application of a genetic algorithm technique to model atomic stabilisation. The micro genetic algorithm worked exceptionally well for this problem as a way to automate the search in a time efficient manner. A parallel platform is used to perform the genetic search efficiently. Locating the best frequency to achieve a suppression of ionization, which is predicted to occur at high intensities, can help design a laboratory experiment and tune to that frequency in order to identify a stabilization effect. The micro genetic algorithm did successfully identify this optimum frequency. It is indeed possible to ex...", notes = "GP-98LB", } @InProceedings{DBLP:conf/ae/BarateM07, author = "Renaud Barate and Antoine Manzanera", title = "Automatic Design of Vision-Based Obstacle Avoidance Controllers Using Genetic Programming", year = "2007", volume = "4926", bibdate = "2008-05-16", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ae/ae2007.html", booktitle = "Artificial Evolution", editor = "Nicolas Monmarch{\'e} and El-Ghazali Talbi and Pierre Collet and Marc Schoenauer and Evelyne Lutton", isbn13 = "978-3-540-79304-5", pages = "25--36", series = "Lecture Notes in Computer Science", address = "Tours, France", month = oct # " 29-31", publisher = "Springer", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1007/978-3-540-79305-2_3", abstract = "The work presented in this paper is part of the development of a robotic system able to learn context dependent visual clues to navigate in its environment. We focus on the obstacle avoidance problem as it is a necessary function for a mobile robot. As a first step, we use an off-line procedure to automatically design algorithms adapted to the visual context. This procedure is based on genetic programming and the candidate algorithms are evaluated in a simulation environment. The evolutionary process selects meaningful visual primitives in the given context and an adapted strategy to use them. The results show the emergence of several different behaviors outperforming hand-designed controllers.", notes = "EA'07", } @InProceedings{Barate:2008:gecco, author = "Renaud Barate and Antoine Manzanera", title = "Generalization performance of vision based controllers for mobile robots evolved with genetic programming", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "1331--1332", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1331.pdf", DOI = "doi:10.1145/1389095.1389349", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, generalisation, Obstacle avoidance, robotic simulation, vision, Poster", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389349}", } @InProceedings{DBLP:conf/sab/BarateM08, author = "Renaud Barate and Antoine Manzanera", title = "Evolving Vision Controllers with a Two-Phase Genetic Programming System Using Imitation", booktitle = "From Animals to Animats 10, Proceedings of the 10th International Conference on Simulation of Adaptive Behavior, SAB 2008", year = "2008", editor = "Minoru Asada and John C. T. Hallam and Jean-Arcady Meyer and Jun Tani", series = "Lecture Notes in Computer Science", volume = "5040", pages = "73--82", address = "Osaka, Japan", month = jul # " 7-12", publisher = "Springer", bibsource = "DBLP, http://dblp.uni-trier.de", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-69133-4", DOI = "doi:10.1007/978-3-540-69134-1_8", abstract = "We present a system that automatically selects and parameterises a vision based obstacle avoidance method adapted to a given visual context. This system uses genetic programming and a robotic simulation to evaluate the candidate algorithms. As the number of evaluations is restricted, we introduce a novel method using imitation to guide the evolution toward promising solutions. We show that for this problem, our two-phase evolution process performs better than other techniques.", notes = "part of \cite{DBLP:conf/sab/2008}", notes = "From Animals to Animats 10", } @InProceedings{Barate:2008:ECSIS-LAB-RS, author = "Renaud Barate and Antoine Manzanera", title = "Learning Vision Algorithms for Real Mobile Robots with Genetic Programming", booktitle = "ECSIS Symposium on Learning and Adaptive Behaviors for Robotic Systems, LAB-RS '08", year = "2008", month = aug, pages = "47--52", keywords = "genetic algorithms, genetic programming, learning vision algorithms, mobile robots, obstacle avoidance algorithms, supervised learning system, control engineering computing, learning (artificial intelligence), mobile robots, robot vision", DOI = "doi:10.1109/LAB-RS.2008.20", abstract = "We present a genetic programming system to evolve vision based obstacle avoidance algorithms. In order to develop autonomous behavior in a mobile robot, our purpose is to design automatically an obstacle avoidance controller adapted to the current context. We first record short sequences where we manually guide the robot to move away from the walls. This set of recorded video images and commands is our learning base. Genetic programming is used as a supervised learning system to generate algorithms that exhibit this corridor centering behavior. We show that the generated algorithms are efficient in the corridor that was used to build the learning base, and that they generalize to some extent when the robot is placed in a visually different corridor. More, the evolution process has produced algorithms that go past a limitation of our system, that is the lack of adequate edge extraction primitives. This is a good indication of the ability of this method to find efficient solutions for different kinds of environments.", notes = "Also known as \cite{4599426}", } @PhdThesis{Barate:thesis, author = "Renaud Barate", title = "Learning Visual Functions for a Mobile Robot with Genetic Programming", title_fr = "Apprentissage de fonctions visuelles pour un robot mobile par programmation genetique", school = "ENSTA", year = "2008", address = "32 Bd Victor, Paris 75015, France", month = nov, note = "In French", email = "Contact : Antoine.Manzanera@ensta.fr", keywords = "genetic algorithms, genetic programming, Vision, mobile robotics, obstacle avoidance", hal_id = "pastel-00004864", URL = "http://www.ensta.fr/~manzaner/Publis/these-barate.pdf", URL = "https://hal.inria.fr/tel-00811614v1", size = "149 pages", abstract = "Existing techniques used to learn artificial vision for mobile robots generally represent an image with a set of visual features that are computed with a hard-coded method. This impairs the system's adaptability to a changing visual environment. We propose a method to describe and learn vision algorithms globally, from the perceived image to the final decision. The target application is the obstacle avoidance function, which is necessary for any mobile robot. We formally describe the structure of vision-based obstacle avoidance algorithms with a grammar. Our system uses this grammar and genetic programming techniques to learn controllers adapted to a given visual context automatically. We use a simulation environment to test this approach and evaluate the performance of the evolved algorithms. We propose several techniques to speed up the evolution and improve the performance and generalization abilities of evolved controllers. In particular, we compare several methods that can be used to guide the evolution and we introduce a new one based on the imitation of a recorded behavior. Next we validate these methods on a mobile robot moving in an indoor environment. Finally, we indicate how this system can be adapted for other vision based applications and we give some hints for the online adaptation of the robot's behavior.", resume = "En robotique mobile, les techniques d'apprentissage qui utilisent la vision artificielle representent le plus souvent l'image par un ensemble de descripteurs visuels. Ces descripteurs sont extraits en utilisant une methode fixee a l'avance ce qui compromet les capacites d'adaptation du systeme a un environnement visuel changeant. Nous proposons une methode permettant de decrire et d'apprendre des algorithmes de vision de maniere globale, depuis l'image percue jusqu'a la decision finale. L'application visee est la fonction d'evitement d'obstacles, indispensable a tout robot mobile. Nous decrivons de maniere formelle la structure des algorithmes d'evitement d'obstacles bases sur la vision en utilisant une grammaire. Notre systeme utilise ensuite cette grammaire et des techniques de programmation genetique pour apprendre automatiquement des controleurs adaptes a un contexte visuel donne. Nous utilisons un environnement de simulation pour tester notre approche et mesurer les performances des algorithmes evolues. Nous proposons plusieurs techniques permettant d'accelerer l'evolution et d'ameliorer les performances et les capacites de generalisation des controleurs evolues. Nous comparons notamment plusieurs methodes d'evolution guidee et nous en presentons une nouvelle basee sur l'imitation d'un comportement enregistre. Par la suite nous validons ces methodes sur un robot reel se deplacant dans un environnement interieur. Nous indiquons finalement comment ce systeme peut etre adapte a d'autres applications utilisant la vision et nous proposons des pistes pour l'adaptation d'un comportement en temps reel sur le robot.", notes = "Francais. Universite Pierre et Marie Curie - Paris VI", } @Article{Barati:2014:PT, author = "Reza Barati and Seyed Ali Akbar {Salehi Neyshabouri} and Goodarz Ahmadi", title = "Development of empirical models with high accuracy for estimation of drag coefficient of flow around a smooth sphere: An evolutionary approach", journal = "Powder Technology", volume = "257", pages = "11--19", year = "2014", ISSN = "0032-5910", DOI = "doi:10.1016/j.powtec.2014.02.045", URL = "http://www.sciencedirect.com/science/article/pii/S003259101400182X", keywords = "genetic algorithms, genetic programming, Particle motion, Sphere drag, Reynolds number", abstract = "An accurate correlation for the smooth sphere drag coefficient with wide range of applicability is a useful tool in the field of particle technology. The present study focuses on the development of high accurate drag coefficient correlations from low to very high Reynolds numbers (up to 1000000) using a multi-gene Genetic Programming (GP) procedure. A clear superiority of GP over other methods is that GP is able to determine the structure and parameters of the model, simultaneously, while the structure of the model is imposed by the user in traditional regression analysis, and only the parameters of the model are assigned. In other words, in addition to the parameters of the model, the structure of it can be optimised using GP approach. Among two new and high accurate models of the present study, one of them is acceptable for the region before drag dip, and the other is applicable for the whole range of Reynolds numbers up to 1 million including the transient region from laminar to turbulent. The performances of the developed models are examined and compared with other reported models. The results indicate that these models respectively give 16.2percent and 69.4percent better results than the best existing correlations in terms of the sum of squared of logarithmic deviations (SSLD). On the other hand, the proposed models are validated with experimental data. The validation results show that all of the estimated drag coefficients are within the bounds of 7percent of experimental values.", } @Article{Barbosa:2011:CTR, author = "Helio J. C. Barbosa and Heder S. Bernardino", title = "Genetic Programming in Civil, Structural and Environmental Engineering", journal = "Computational Technology Reviews", year = "2011", volume = "4", pages = "115--145", keywords = "genetic algorithms, genetic programming", publisher = "Civil-Comp", ISSN = "2044-8430", URL = "http://www.ctresources.info/ctr/paper.html?id=32", DOI = "doi:10.4203/ctr.4.5", abstract = "Soft computing techniques have been receiving considerable attention in recent years due to their wide applicability and low ratio of implementation effort to succeed in producing good results. In civil and environmental engineering one is often faced with the problem of inferring a mathematical model from a set of observed data. Also, in structural engineering, nature-inspired techniques, especially evolutionary algorithms, have been extensively applied, mainly to parametric design optimization problems. This paper provides an overview of the applications of one of the most versatile soft computing tools available - genetic programming - to relevant design, optimization, and identification of problems arising in civil, structural, and environmental engineering. Genetic programming (GP) is a domain-independent sub-area of the evolutionary computation field. The candidate solutions are referred to as programs, a high-level structure able to represent a large class of computational artefacts. A program can be a standard computer program, a numerical function or a classifier in symbolic form, a candidate design (such as the structure of a building), among many other possibilities. In the following sections tree-based, linear, and graph-based GPs are discussed. Moreover, grammatical evolution (GE) is presented in some detail, a relatively recent GP technique in which candidate solution's genotypes are binary encoded and space transformations create the programs employing a user-defined grammar. The most common classes of problems in civil, structural, and environmental engineering in which GP has been applied are loosely grouped here into two large classes, namely model inference and design. Both types of problems correspond to activities traditionally assigned only to humans, as they require intelligence and creativity not (yet) available elsewhere. Some representative papers from the literature were reviewed and are summarized in nine tables. The tables indicate the reference number, the GP technique adopted, the class of problem considered, a short description of the application, and the main results and conclusions of the paper. Our survey indicated a much larger number of papers dealing with model inference than with design applications in the civil, structural, and environmental engineering literature. Also, as expected, the standard tree-based genetic programming (TGP) is by far the most often adopted technique. Contrary to our expectations, gene expression programming (GEP) seems to be more popular than GE, which is probably due to the fact that GE, although more elegant and flexible, requires the specification of a problem dependent grammar by the user. Genetic programming has been proving its versatility in many different fields. Due to its great expressiveness, GP is able to evolve complex artifacts, either when inducing understandable and communicable models or generating novel designs.", notes = "Laboratorio Nacional de Computacao Cientifica, Petropolis, RJ, Brazil", } @InProceedings{barbosa:2024:GECCO, author = "Pedro Barbosa and Rosina Savisaar and Alcides Fonseca", title = "Semantically Rich Local Dataset Generation for Explainable {AI} in Genomics", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference", year = "2024", editor = "Jean-Baptiste Mouret and Kai Qin and Julia Handl and Xiaodong Li and Markus Wagner and Mario Garza-Fabre and Kate Smith-Miles and Richard Allmendinger and Ying Bi and Grant Dick and Amir H Gandomi and Marcella Scoczynski Ribeiro Martins and Hirad Assimi and Nadarajen Veerapen and Yuan Sun and Mario Andres Munyoz and Ahmed Kheiri and Nguyen Su and Dhananjay Thiruvady and Andy Song and Frank Neumann and Carla Silva", pages = "267--276", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, XAI, evolutionary computation, instance generation, combinatorial optimization, local explainability, RNA splicing, Evolutionary Machine Learning", isbn13 = "979-8-4007-0494-9", DOI = "doi:10.1145/3638529.3653990", size = "10 pages", abstract = "Black box deep learning models trained on genomic sequences excel at predicting the outcomes of different gene regulatory mechanisms. Therefore, interpreting these models may provide novel insights into the underlying biology, supporting downstream biomedical applications. Due to their complexity, interpretable surrogate models can only be built for local explanations (e.g., a single instance). However, accomplishing this requires generating a dataset in the neighborhood of the input, which must maintain syntactic similarity to the original data while introducing semantic variability in the model's predictions. This task is challenging due to the complex sequence-to-function relationship of DNA.We propose using Genetic Programming to generate datasets by evolving perturbations in sequences that contribute to their semantic diversity. Our custom, domain-guided individual representation effectively constrains syntactic similarity, and we provide two alternative fitness functions that promote diversity with no computational effort. Applied to the RNA splicing domain, our approach quickly achieves good diversity and significantly outperforms a random baseline in exploring the search space, as shown by our proof-of-concept, short RNA sequence. Furthermore, we assess its generalizability and demonstrate scalability to larger sequences, resulting in a approx30\% improvement over the baseline.", notes = "GECCO-2024 EML A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{Barbosa-Diniz:2018:eniac, author = "Jessica {Barbosa Diniz} and Filipe R. Cordeiro and Pericles B. C. Miranda and Laura A. {Tomaz da Silva}", title = "A Grammar-based Genetic Programming Approach to Optimize Convolutional Neural Network Architectures", booktitle = "Anais do XV Encontro Nacional de Inteligencia Artificial e Computacional", year = "2018", editor = "Denis D. Maua and Murilo Naldi", pages = "82--93", address = "Sao Paulo, Brazil", publisher_address = "Porto Alegre, RS, Brasil", month = "22-25 " # oct, publisher = "Sociedade Brasileira de Computacao", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, PonyGE2, ANN, CNN, Keras, image processing", URL = "https://sol.sbc.org.br/index.php/eniac/article/view/4406", URL = "https://sol.sbc.org.br/index.php/eniac/article/view/4406/4330.pdf", DOI = "doi:10.5753/eniac.2018.4406", size = "12 pages", abstract = "Deep Learning is a research area under the spotlight in recent years due to its successful application to many domains, such as computer vision and image recognition. The most prominent technique derived from Deep Learning is Convolutional Neural Network, which allows the network to automatically learn representations needed for detection or classification tasks. However, Convolutional Neural Networks have some limitations, as designing these networks are not easy to master and require expertise and insight. In this work, we present the use of Genetic Algorithm associated to Grammar-based Genetic Programming to optimize Convolution Neural Network architectures. To evaluate our proposed approach, we adopted CIFAR-10 dataset to validate the evolution of the generated architectures, using the metric of accuracy to evaluate its classification performance in the test dataset. The results demonstrate that our method using Grammar-based Genetic Programming can easily produce optimized CNN architectures that are competitive and achieve high accuracy results.", notes = "Federal Rural University of Pernambuco (UFRPE), Brazil", } @InProceedings{Barbudo:2021:SoCPaR, author = "Rafael Barbudo and Sebastian Ventura and Jose Raul Romero", title = "Grammar-Based Evolutionary Approach for Automatic Workflow Composition with Open Preprocessing Sequence", booktitle = "Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021)", year = "2021", editor = "Ajith Abraham and Andries Engelbrecht and Fabio Scotti and Niketa Gandhi and Pooja Manghirmalani Mishra and Giancarlo Fortino and Virgilijus Sakalauskas and Sabri Pllana", volume = "417", series = "LNNS", pages = "647--656", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Evolutionary Algorithms, Grammar-Based Genetic Programming, TPOT", isbn13 = "978-3-030-96302-6", DOI = "doi:10.1007/978-3-030-96302-6_61", abstract = "Knowledge discovery is a complex process involving several phases. Some of them are repetitive and time-consuming, so they are susceptible of being automated. As an example, the large number of machine learning algorithms, together with their hyper-parameters, constitutes a vast search space to explore. In this vein, the term AutoML was coined to encompass those approaches automating such phases. The automatic workflow composition is an AutoML task that involves both the selection and the hyper-parameter optimisation of the algorithms addressing different phases, thus giving a more comprehensive assistance during the knowledge discovery process. Unlike other proposals that predetermine the structure of the preprocessing sequence, and in some cases the size of the workflow, our proposal generates workflows made up of an arbitrary number of preprocessing algorithms of any type and a classifier. This allows returning more accurate results since its avoids the oversimplification of the solution space. The optimisation is conducted by a grammar-guided genetic programming algorithm. The proposal has been validated and compared against TPOT and RECIPE generating workflows with greater predictive performance.", } @Article{BARBUDO:2021:JSS, author = "Rafael Barbudo and Aurora Ramirez and Francisco Servant and Jose Raul Romero", title = "{GEML:} A grammar-based evolutionary machine learning approach for design-pattern detection", journal = "Journal of Systems and Software", year = "2021", volume = "175", pages = "110919", month = may, keywords = "genetic algorithms, genetic programming, Design pattern detection, Reverse engineering, Machine learning, Associative classification, Grammar-guided genetic programming", ISSN = "0164-1212", URL = "https://www.sciencedirect.com/science/article/pii/S0164121221000169", DOI = "doi:10.1016/j.jss.2021.110919", abstract = "Design patterns (DPs) are recognised as a good practice in software development. However, the lack of appropriate documentation often hampers traceability, and their benefits are blurred among thousands of lines of code. Automatic methods for DP detection have become relevant but are usually based on the rigid analysis of either software metrics or specific properties of the source code. We propose GEML, a novel detection approach based on evolutionary machine learning using software properties of diverse nature. Firstly, GEML makes use of an evolutionary algorithm to extract those characteristics that better describe the DP, formulated in terms of human-readable rules, whose syntax is conformant with a context-free grammar. Secondly, a rule-based classifier is built to predict whether new code contains a hidden DP implementation. GEML has been validated over five DPs taken from a public repository recurrently adopted by machine learning studies. Then, we increase this number up to 15 diverse DPs, showing its effectiveness and robustness in terms of detection capability. An initial parameter study served to tune a parameter setup whose performance guarantees the general applicability of this approach without the need to adjust complex parameters to a specific pattern. Finally, a demonstration tool is also provided", } @Article{BARBUDO:2024:asoc, author = "Rafael Barbudo and Aurora Ramirez and Jose Raul Romero", title = "Grammar-based evolutionary approach for automated workflow composition with domain-specific operators and ensemble diversity", journal = "Applied Soft Computing", year = "2024", volume = "153", pages = "111292", keywords = "genetic algorithms, genetic programming, AutoML, Automated workflow composition, Algorithm selection, Hyper-parameter optimisation, Grammar-guided genetic programming, Ensemble learning, Classification", ISSN = "1568-4946", URL = "https://www.sciencedirect.com/science/article/pii/S1568494624000668", DOI = "doi:10.1016/j.asoc.2024.111292", size = "14 pages", abstract = "The process of extracting valuable and novel insights from raw data involves a series of complex steps. In the realm of Automated Machine Learning (AutoML), a significant research focus is on automating aspects of this process, specifically tasks like selecting algorithms and optimising their hyper-parameters. A particularly challenging task in AutoML is automatic workflow composition (AWC). AWC aims to identify the most effective sequence of data preprocessing and machine learning algorithms, coupled with their best hyper-parameters, for a specific dataset. However, existing AWC methods are limited in how many and in what ways they can combine algorithms within a workflow. Addressing this gap, this paper introduces EvoFlow, a grammar-based evolutionary approach for AWC. EvoFlow enhances the flexibility in designing workflow structures, empowering practitioners to select algorithms that best fit their specific requirements. EvoFlow stands out by integrating two innovative features. First, it employs a suite of genetic operators, designed specifically for AWC, to optimise both the structure of workflows and their hyper-parameters. Second, it implements a novel updating mechanism that enriches the variety of predictions made by different workflows. Promoting this diversity helps prevent the algorithm from overfitting. With this aim, EvoFlow builds an ensemble whose workflows differ in their misclassified instances. To evaluate EvoFlow's effectiveness, we carried out empirical validation using a set of classification benchmarks. We begin with an ablation study to demonstrate the enhanced performance attributable to EvoFlow's unique components. Then, we compare EvoFlow with other AWC approaches, encompassing both evolutionary and non-evolutionary techniques. Our findings show that EvoFlow's specialised genetic operators and updating mechanism substantially outperform current leading methods in predictive performance. Additionally, EvoFlow is capable of discovering workflow structures that other approaches in the literature have not considered", } @InProceedings{Barbulescu:2009:WSEAS, author = "Alina Barbulescu and Elena Bautu", title = "Meteorological time series modeling using an adaptive gene expression programming", booktitle = "Proceedings of the 10th WSEAS International Conference on Evolutionary Computation", year = "2009", editor = "Nikos E. Mastorakis and Anca Croitoru and Valentina Emilia Balas and Eduard Son and Valeri Mladenov", pages = "17--22", address = "Prague, Czech", publisher_address = "Stevens Point, Wisconsin, USA", month = "23-25 " # mar, publisher = "World Scientific and Engineering Academy and Society (WSEAS)", keywords = "genetic algorithms, genetic programming, Gene Expression Programming", isbn13 = "978-960-474-067-3", ISSN = "1790-5109", URL = "http://www.wseas.us/e-library/conferences/2009/prague/EVOLUTIONARY/EC02.pdf", size = "6 pages", acmid = "1561917", abstract = "The precipitations are characterised by important spatial and temporal variation. Model determination for such series is of high importance for hydrological purposes (e.g. weather forecasting, agriculture, flood areas, administrative planning), even if discovering patterns in such series is a very difficult problem. The objective of the current study is to describe the use of an adaptive evolutionary technique that give promising results for the development of non-linear time series models.", notes = "http://www.wseas.us/e-library/conferences/2009/prague/EVOLUTIONARY/EC00.pdf broken http://www.wseas.org/conferences/2009/prague/ec/index.html", } @InProceedings{Barbulescu:2009:WSEASb, author = "Alina Barbulescu and Elena Bautu", title = "ARIMA Models versus Gene Expression Programming in Precipitation Modeling", booktitle = "Proceedings of the 10th WSEAS International Conference on Evolutionary Computation", year = "2009", editor = "Nikos E. Mastorakis and Anca Croitoru and Valentina Emilia Balas and Eduard Son and Valeri Mladenov", pages = "112--117", address = "Prague, Czech", publisher_address = "Stevens Point, Wisconsin, USA", month = "23-25 " # mar, publisher = "World Scientific and Engineering Academy and Society (WSEAS)", keywords = "genetic algorithms, genetic programming, Gene Expression Programming, rain, time series modelling, statistical analysis", isbn13 = "978-960-474-067-3", ISSN = "1790-5109", URL = "http://www.wseas.us/e-library/conferences/2009/prague/EVOLUTIONARY/EC16.pdf", size = "6 pages", acmid = "1561931", abstract = "In this paper we present a case study: the application of some conceptually different approaches to the problem of identifying a model for a hydrological time series. The problem is particularly challenging, due to the size of the time series and more importantly, to the many complex phenomena that influence such time series and that reflect in the characteristics of the data. We use well established statistical methods to detect change points in the time series, and we model the subseries obtained by ARIMA, GEP and the adaptive variant and a combination of the two. The models obtained state the efficiency of combining pure statistical tests and methods with heuristic approaches.", notes = "http://www.wseas.us/e-library/conferences/2009/prague/EVOLUTIONARY/EC00.pdf broken http://www.wseas.org/conferences/2009/prague/ec/index.html", } @Article{Barbulescu20091, author = "Alina Barbulescu and Elena Bautu", title = "Alternative Models in Precipitation Analysis", journal = "Analele Stiintifice ale Universitatii Ovidius Constanta, Seria Matematica", year = "2009", volume = "XVII", number = "3", pages = "45--68", keywords = "genetic algorithms, genetic programming, Gene Expression Programming", ISSN = "1844-0835", URL = "http://www.anstuocmath.ro/mathematics/pdf19/Barbulescu_Bautu.pdf", size = "24 pages", abstract = "Precipitation time series intrinsically contain important information concerning climate variability and change. Well-fit models of such time series can shed light upon past weather related phenomena and can help to explain future events. The objective of this study is to investigate the application of some conceptually different methods to construct models for large hydrological time series. We perform a thorough statistical analysis of the time series, which covers the identification of the change points in the time series. Then, the subseries delimited by the change points are modelled with classical Box-Jenkins methods to construct ARIMA models and with a computational intelligence technique, gene expression programming, which produces non-linear symbolic models of the series. The combination of statistical techniques with computational intelligence methods, such as gene expression programming, for modelling time series, offers increased accuracy of the models obtained. This affirmation is illustrated with examples.", notes = "http://www.anstuocmath.ro/", } @Article{Barbulescu20092, author = "Alina Barbulescu and Elena Bautu", title = "Time Series Modeling Using an Adaptive Gene Expression Programming Algorithm", year = "2009", journal = "International Journal of Mathematical Models and Methods in Applied Sciences", volume = "3", pages = "85--93", number = "2", keywords = "genetic algorithms, genetic programming, Gene Expression Programming", ISSN = "1998-0140", URL = "http://www.naun.org/journals/m3as/mmmas-134.pdf", size = "9 pages", abstract = "Meteorological time series are characterised by important spatial and temporal variation. Model determination and the prediction of evolution of such series is of high importance for different practical purposes, even if discovering evolution patterns in such series is a very difficult problem. In this article we describe an adaptive evolutionary technique and we apply it for modelling the precipitation and temperatures collected in a region of Romania. The results are promising for the analysis of such time series.", notes = "http://www.naun.org/journals/m3as/", } @Article{Barbulescu201003, author = "Alina Barbulescu and Elena Bautu", title = "Mathematical models of climate evolution in {Dobrudja}", journal = "Theoretical and Applied Climatology", year = "2010", pages = "29--44", volume = "100", issue = "1", month = mar, publisher = "Springer Wien", keywords = "genetic algorithms, genetic programming, gene expression programming, ARIMA, Earth and Environmental Science", ISSN = "0177-798X", DOI = "doi:10.1007/s00704-009-0160-7", size = "16 pages", abstract = "The understanding of processes that occur in climate change evolution and their spatial and temporal variations are of major importance in environmental sciences. Modelling these processes is the first step in the prediction of weather change. In this context, this paper presents the results of statistical investigations of monthly and annual meteorological data collected between 1961 and 2007 in Dobrudja (South-East of Romania between the Black Sea and the lower Danube River) and the models obtained using time series analysis and gene expression programming. Using two fundamentally different approaches, we provide a comprehensive analysis of temperature variability in Dobrudja, which may be significant in understanding the processes that govern climate changes in the region.", affiliation = "Ovidius University of Constanta Faculty of Mathematics and Informatics Constanta Romania", } @Article{Barclay:2015:Procedia, author = "Jack Barclay and Vimal Dhokia and Aydin Nassehi", title = "Generating Milling Tool Paths for Prismatic Parts Using Genetic Programming", journal = "Procedia CIRP", volume = "33", pages = "490--495", year = "2015", note = "9th CIRP Conference on Intelligent Computation in Manufacturing Engineering - CIRP ICME 14", ISSN = "2212-8271", DOI = "doi:10.1016/j.procir.2015.06.060", URL = "http://www.sciencedirect.com/science/article/pii/S2212827115007039", abstract = "The automatic generation of milling tool paths traditionally relies on applying complex tool path generation algorithms to a geometric model of the desired part. For parts with unusual geometries or intricate intersections between sculpted surfaces, manual intervention is often required when normal tool path generation methods fail to produce efficient tool paths. In this paper, a simplified model of the machining process is used to create a domain-specific language that enables tool paths to be generated and optimised through an evolutionary process - formulated, in this case, as a genetic programming system. The driving force behind the optimisation is a fitness function that promotes tool paths whose result matches the desired part geometry and favours those that reach their goal in fewer steps. Consequently, the system is not reliant on tool path generation algorithms, but instead requires a description of the desired characteristics of a good solution, which can then be used to measure and evaluate the relative performance of the candidate solutions that are generated. The performance of the system is less sensitive to different geometries of the desired part and doesn't require any additional rules to deal with changes to the initial stock (e.g. when rest roughing). The method is initially demonstrated on a number of simple test components and the genetic programming process is shown to positively influence the outcome. Further tests and extensions to the work are presented.", keywords = "genetic algorithms, genetic programming, Computer numerical control (CNC), Milling", notes = "Edited by Roberto Teti", } @Article{BARDHAN:2021:EG, author = "Abidhan Bardhan and Candan Gokceoglu and Avijit Burman and Pijush Samui and Panagiotis G. Asteris", title = "Efficient computational techniques for predicting the California bearing ratio of soil in soaked conditions", journal = "Engineering Geology", volume = "291", pages = "106239", year = "2021", ISSN = "0013-7952", DOI = "doi:10.1016/j.enggeo.2021.106239", URL = "https://www.sciencedirect.com/science/article/pii/S0013795221002507", keywords = "genetic algorithms, genetic programming, Soaked CBR, Machine learning, MARS, GPR, Sub-grade design", abstract = "California bearing ratio (CBR) is one of the important parameters that is used to express the strength of the pavement subgrade of railways, roadways, and airport runways. CBR is usually determined in the laboratory in soaked conditions, which is an exhaustive and time-consuming process. Therefore, to sidestep the operation of conducting actual laboratory tests, this study presents the development of four efficient soft computing techniques, namely multivariate adaptive regression splines with piecewise linear models (MARS-L), multivariate adaptive regression splines with piecewise cubic models (MARS-C), Gaussian process regression, and genetic programming. For this purpose, a wide range of experimental results of soaked CBR was collected from an ongoing railway project of Indian Railways. Three explicit expressions are proposed to estimate the CBR of soils in soaked conditions. Separate laboratory experiments were performed to evaluate the generalization capabilities of the developed models. Furthermore, simulated datasets were used to validate the feasibility of the best-performing model. Experimental results reveal that the proposed MARS-L model attained the most accurate prediction (R2 = 0.9686 and RMSE = 0.0359 against separate laboratory experiments) in predicting the soaked CBR at all stages. Based on the accuracies attained, the proposed MARS-L model is very potential to be an alternate solution to estimate the CBR value in different phases of civil engineering projects", } @Article{BARDHAN:2021:ASC, author = "Abidhan Bardhan and Pijush Samui and Kuntal Ghosh and Amir H. Gandomi and Siddhartha Bhattacharyya", title = "{ELM-based} adaptive neuro swarm intelligence techniques for predicting the California bearing ratio of soils in soaked conditions", journal = "Applied Soft Computing", volume = "110", pages = "107595", year = "2021", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2021.107595", URL = "https://www.sciencedirect.com/science/article/pii/S1568494621005160", keywords = "genetic algorithms, genetic programming, Swarm intelligence, Soft computing, CBR, DFC, Indian Railways, Particle swarm optimization", abstract = "This study proposes novel integration of extreme learning machine (ELM) and adaptive neuro swarm intelligence (ANSI) techniques for the determination of California bearing ratio (CBR) of soils for the subgrade layers of railway tracks, a critical real-time problem of geotechnical engineering. Particle swarm optimization (PSO) with adaptive and time-varying acceleration coefficients (TAC) was employed to optimize the learning parameters of ELM. Three novel ELM-based ANSI models, namely ELM coupled-modified PSO (ELM-MPSO), ELM coupled-TAC PSO (ELM-TPSO), and ELM coupled-improved PSO (ELM-IPSO) were developed for predicting the CBR of soils in soaked conditions. Compared to standard PSO (SPSO), the modified and improved version of PSO are capable of converging to a high-quality solution at early iterations. A detailed comparison was made between the proposed models and other conventional soft computing techniques, such as conventional ELM, artificial neural network, genetic programming, support vector machine, group method of data handling, and three ELM-based swarm intelligence optimized models (ELM-based grey wolf optimization, ELM-based slime mould algorithm, and ELM-based Harris hawks optimization). Experimental results reveal that the proposed ELM-based ANSI models can attain the most accurate prediction and confirm the dominance of MPSO over SPSO. Considering the consequences and robustness of the proposed models, it can be concluded that the newly constructed ELM-based ANSI models, especially ELM-MPSO, can solve the difficulties in tuning the acceleration coefficients of SPSO by the trial-and-error method for predicting the CBR of soils and be further applied to other real-time problems of geotechnical engineering", } @Article{Bardhan:2021:CMES, author = "Abidhan Bardhan and Priyadip Manna and Vinay Kumar and Avijit Burman and Bojan Zlender and Pijush Samui", title = "Reliability Analysis of Piled Raft Foundation Using a Novel Hybrid Approach of {ANN} and Equilibrium Optimizer", journal = "CMES - Computer Modeling in Engineering and Sciences", year = "2021", volume = "128", number = "3", pages = "1033--1067", keywords = "genetic algorithms, genetic programming, Risk analysis, soil, meta-heuristic optimization, particle swarm optimization, PSO, ANN", ISSN = "1526-1492", URL = "https://www.sciencedirect.com/science/article/pii/S1526149221001545", DOI = "doi:10.32604/cmes.2021.015885", abstract = "In many civil engineering projects, Piled Raft Foundations (PRFs) are usually preferred where the incoming load from the superstructures is very high. In geotechnical engineering practice, the settlement of soil layers is a critical issue for the serviceability of the structures. Thus, assessment of risk associated with the structures corresponding to the maximum allowable settlement of soils needs to be carried out in the design phase. In this study, reliability analysis of PRF based on settlement criteria is performed using a high-performance hybrid soft computing model. The new approach is an integration of the artificial neural network (ANN) and a recently developed meta-heuristic algorithm called equilibrium optimiser (EO). The concept of reliability index was used to explore the feasibility of a newly constructed hybrid model of ANN and EO (i.e., ANN-EO) against the conventional approach of calculating the probability of failure of PRF. Experimental results show that the proposed ANN-EO attained the most accurate prediction with R2 = 0.9914 and RMSE = 0.0518 in the testing phase, which are significantly better than those obtained from conventional ANN, multivariate adaptive regression splines, and genetic programming, including the ANN optimised with particle swarm optimisation developed in this study. Based on the experimental results of different settlement values, the newly constructed ANN-EO is very potential to analyse the risk associated with civil engineering structures. Also, the present study would significantly contribute to the knowledge pool of reliability studies related to piled raft systems because the works of literature on reliability analysis of piled raft systems are relatively scarce", } @Article{BARDHAN:2022:TG, author = "Abidhan Bardhan and Anasua GuhaRay and Shubham Gupta and Biswajeet Pradhan and Candan Gokceoglu", title = "A novel integrated approach of {ELM} and modified equilibrium optimizer for predicting soil compression index of subgrade layer of Dedicated Freight Corridor", journal = "Transportation Geotechnics", year = "2022", volume = "32", pages = "100678", month = jan, keywords = "genetic algorithms, genetic programming, Subgrade layer design, Railway embankment, Soft computing, Consolidation parameter, Meta-heuristic optimization, Indian Railways, Swarm intelligence", ISSN = "2214-3912", URL = "https://www.sciencedirect.com/science/article/pii/S2214391221001689", DOI = "doi:10.1016/j.trgeo.2021.100678", size = "21 pages", abstract = "This study proposes a high-performance machine learning model to sidestep the time of conducting actual laboratory tests of soil compression index (Cc), one of the important criteria for determining the settlement of subgrade layers of roadways, railways, and airport runways. The suggested method combines the modified equilibrium optimizer (MEO) and the extreme learning machine (ELM) in a novel way. In this study, Gaussian mutation with an exploratory search mechanism was incorporated to construct the MEO and used to enhance the performance of conventional ELM by optimizing its learning parameters. PCA (Principal component analysis)-based results exhibit that the developed ELM-MEO attained the most precise prediction with R2 = 0.9746, MAE = 0.0184, and RMSE = 0.0284 in training, and R2 = 0.9599, MAE = 0.0232, and RMSE = 0.0357 in the testing phase. The results showed that the proposed ELM-MEO model outperformed the other developed models, confirming the ELM-MEO model's superiority over the other models, such as random forest, gradient boosting machine, genetic programming, including the ELM and artificial neural network (ANN)-based models optimized with equilibrium optimizer, particle swarm optimization, Harris hawks optimization, slime mould algorithm, and marine predators algorithm. Based on the experimental results, the proposed ELM-MEO can be used as a promising alternative to predict soil Cc in civil engineering projects, including rail and road projects", notes = "'Fig. 1. A typical cross-section of ballasted railway embankment showing different layers.' 'Table 1 Details of previous studies on soil Cc prediction.' Gujarat, India.", } @Article{BARDHAN:2024:apm, author = "Abidhan Bardhan", title = "Probabilistic assessment of heavy-haul railway track using multi-gene genetic programming", journal = "Applied Mathematical Modelling", volume = "125", pages = "687--720", year = "2024", ISSN = "0307-904X", DOI = "doi:10.1016/j.apm.2023.08.009", URL = "https://www.sciencedirect.com/science/article/pii/S0307904X2300358X", keywords = "genetic algorithms, genetic programming, Railway embankment, Reliability analysis, Bearing capacity, Heavy-haul freight corridor, Slope/W modelling, Artificial intelligence", abstract = "This study presented a probabilistic assessment of heavy-haul railway track using a high-performance computational model called multi-gene genetic programming (MGGP). A reliability analysis (RA) method based on MGGP and the first-order second-moment method (FOSM) has been proposed in this study. First, GP was used to map the implicit performance functions; therefore, arriving at GP-based explicit performance functions. Subsequently, the developed GP model was used to perform RA of a soil slope of heavy-haul railway track under both seismic and non-seismic conditions. Using the FOSM, soil uncertainties were mapped based on the concepts of probability theory and statistics, and a ready-made expression was developed. Simulated results demonstrate that the GP-based FOSM approach can predict the probability of failure (POF) of slope with rational accuracy. The probabilistic analysis against bearing capacity failure was also investigated in this study to ensure serviceability of the soil slope. Based on the outcomes, it can be deduced that the coefficient of variation of soil properties affects the POF of slope significantly. With the aid of the developed expression, the POF of the soil slope of heavy-haul railway track can be assessed rationally and efficiently", } @Article{Bardool:2016:JML, author = "Roghayeh Bardool and Jafar Javanmardi and Aliakbar Roosta and Amir H. Mohammadi", title = "Phase stability conditions of clathrate hydrates for methane + aqueous solution of water soluble organic promoter system: Modeling using a thermodynamic framework", journal = "Journal of Molecular Liquids", volume = "224, Part B", pages = "1117--1123", year = "2016", ISSN = "0167-7322", DOI = "doi:10.1016/j.molliq.2016.09.084", URL = "http://www.sciencedirect.com/science/article/pii/S016773221630335X", abstract = "A thermodynamic model is presented for predicting the phase stability conditions of clathrate hydrates for methane + water-soluble organic promoter aqueous solution. A new equation is then proposed to estimate the enthalpy of hydrate dissociation for methane + aqueous solution of water-soluble organic promoter using Genetic Programming (GP) and Teaching-Learning-Based Optimization (TLBO) evolutionary algorithm. The model reliably predicts the hydrate dissociation conditions for methane + aqueous solutions of tetrahydrofuran, 1,3-dioxolane, 1,4-dioxane and acetone. The van Laar model is used to calculate the activity coefficient of water in aqueous solution of water-soluble organic promoter. About 30percent of the reported experimental data were used for finding the empirical relationships to estimate the enthalpy of hydrate dissociation and the remaining 70percent was used to test the accuracy and the predictive capability of the correlation. The average absolute error for methane hydrate dissociation temperatures was found to be 0.33 K, which indicates the accuracy of the model.", keywords = "genetic algorithms, genetic programming, Gas hydrate, Clathrate hydrate, Methane, Water-soluble organic promoter, Thermodynamic model, Correlation", } @Article{Bardsiri:2015:IJBRA, title = "Combining classifiers generated by multi-gene genetic programming for protein fold recognition using genetic algorithm", author = "Mahshid Khatibi Bardsiri and Mahdi Eftekhari and Reza Mousavi", journal = "Int. J. of Bioinformatics Research and Applications", publisher = "Inderscience Publishers", year = "2015", month = mar # "~17", volume = "11", number = "2", pages = "171--186", keywords = "genetic algorithms, genetic programming, multi-gene genetic programming, protein fold recognition, bioinformatics, weighted voting, classifiers, classification accuracy", ISSN = "1744-5493", bibsource = "OAI-PMH server at www.inderscience.com", URL = "http://www.inderscience.com/link.php?id=68092", DOI = "doi:10.1504/IJBRA.2015.068092", abstract = "In this study the problem of protein fold recognition, that is a classification task, is solved via a hybrid of evolutionary algorithms namely multi-gene Genetic Programming (GP) and Genetic Algorithm (GA). Our proposed method consists of two main stages and is performed on three datasets taken from the literature. Each dataset contains different feature groups and classes. In the first step, multi-gene GP is used for producing binary classifiers based on various feature groups for each class. Then, different classifiers obtained for each class are combined via weighted voting so that the weights are determined through GA. At the end of the first step, there is a separate binary classifier for each class. In the second stage, the obtained binary classifiers are combined via GA weighting in order to generate the overall classifier. The final obtained classifier is superior to the previous works found in the literature in terms of classification accuracy.", notes = "PMID: 25786796 [PubMed - in process]", } @Article{Segota-Sandi:2023:Industry4.0, author = "{Baressi Segota} Sandi and Mrzljak Vedran and Prpic-Orsic Jasna and Zlatan Car", title = "Determining normalized friction torque of an industrial robotic manipulator using the symbolic regression method", journal = "Industry 4.0", year = "2023", volume = "8", number = "1", pages = "21--24", note = "Dominant technologies in", keywords = "genetic algorithms, genetic programming, friction torque prediction, industrial robotic manipulator, friction, machine learning, symbolic regression", ISSN = "2534-8582", URL = "https://stumejournals.com/journals/i4/2023/1/21.full.pdf", size = "4 pages", abstract = "The goal of the paper is estimating the normalized friction torque of a joint in an industrial robotic manipulator. For this purpose a source data, given as a figure, is digitized using a tool WebPlotDigitizer in order to obtain numeric data. The numeric data is the used within the machine learning algorithm genetic programming (GP), which performs the symbolic regression in order to obtain the equation that regresses the dataset in question. The obtained model shows a coefficient of determination equal to 0.87, which indicates that the model in question may be used for the wide approximation of the normalized friction torque using the torque load, operating temperature and joint velocity as inputs.", notes = "Published by Scientific Technical Union of Mechanical Engineering 'industry 4.0'. 108, RakovskiStr., 1000 Sofia, Bulgaria. office@stumejournals.com Faculty of Engineering, University of Rijeka, Croatia", } @Article{barge:2016:Water, author = "Jonathan T. Barge and Hatim O. Sharif", title = "An Ensemble Empirical Mode Decomposition, {Self-Organizing} Map, and Linear Genetic Programming Approach for Forecasting River Streamflow", journal = "Water", year = "2016", volume = "8", number = "6", keywords = "genetic algorithms, genetic programming", ISSN = "2073-4441", URL = "https://www.mdpi.com/2073-4441/8/6/247", DOI = "doi:10.3390/w8060247", abstract = "This study focused on employing Linear Genetic Programming (LGP), Ensemble Empirical Mode Decomposition (EEMD), and the Self-Organising Map (SOM) in modelling the rainfall-runoff relationship in a mid-size catchment. Models were assessed with regard to their ability to capture daily discharge at Lock and Dam 10 along the Kentucky River as well as the hybrid design of EEM-SOM-LGP to make predictions multiple time-steps ahead. Different model designs were implemented to demonstrate the improvements of hybrid designs compared to LGP as a standalone application. Additionally, LGP was used to gain a better understanding of the catchment in question and to assess its ability to capture different aspects of the flow hydrograph. As a standalone application, LGP was able to outperform published Artificial Neural Network (ANN) results over the same dataset, posting an average absolute relative error (AARE) of 17.118 and Nash-Sutcliff (E) of 0.937. Using EEMD derived IMF runoff subcomponents for forecasting daily discharge resulted in an AARE of 14.232 and E of 0.981. Clustering the EEMD-derived input space through an SOM before LGP application returned the strongest results, posting an AARE of 10.122 and E of 0.987. Applying LGP to the distinctive low and high flow seasons demonstrated a loss in correlation for the low flow season with an under-predictive nature signified by a normalised mean biased error (NMBE) of -2.353. Separating the rising and falling trends of the hydrograph showed that the falling trends were more easily captured with an AARE of 8.511 and E of 0.968 compared to the rising trends AARE of 38.744 and E of 0.948. Using the EEMD-SOM-LGP design to make predictions multiple-time-steps ahead resulted in a AARE of 43.365 and E of 0.902 for predicting streamflow three days ahead. The results demonstrate the effectiveness of using EEMD and an SOM in conjunction with LGP for streamflow forecasting.", notes = "also known as \cite{w8060247}", } @InProceedings{DBLP:conf/seal/BarileCT08, author = "Perry Barile and Victor Ciesielski and Karen Trist", title = "Non-photorealistic Rendering Using Genetic Programming", booktitle = "Proceedings of the 7th International Conference on Simulated Evolution And Learning (SEAL '08)", year = "2008", editor = "Xiaodong Li and Michael Kirley and Mengjie Zhang and David G. Green and Victor Ciesielski and Hussein A. Abbass and Zbigniew Michalewicz and Tim Hendtlass and Kalyanmoy Deb and Kay Chen Tan and J{\"u}rgen Branke and Yuhui Shi", volume = "5361", series = "Lecture Notes in Computer Science", pages = "299--308", address = "Melbourne, Australia", month = dec # " 7-10", publisher = "Springer", keywords = "genetic algorithms, genetic programming, non-photorealistic rendering, evolutionary computation", isbn13 = "978-3-540-89693-7", DOI = "doi:10.1007/978-3-540-89694-4_31", abstract = "We take a novel approach to Non-Photorealistic Rendering by adapting genetic programming in combination with computer graphics drawing techniques. As a GP tree is evaluated, upon encountering certain nodes referred to as Draw nodes, information contained within such nodes are sent to one of three virtual canvasses and a mark is deposited on the canvas. For two of the canvasses the user is able to define custom brushes to be applied to the canvas. Drawing functions are supplied with little localised information regarding the target image. Based on this local data, the drawing functions are enabled to apply contextualized information to the canvas. The obtained results include a Shroud of Turin effect, a Decal effect and a Starburst effect.", bibsource = "DBLP, http://dblp.uni-trier.de", } @InProceedings{DBLP:conf/gecco/BarileCBT09, author = "Perry Barile and Victor Ciesielski and Marsha Berry and Karen Trist", title = "Animated drawings rendered by genetic programming", booktitle = "GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation", year = "2009", editor = "Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano {van Hemert} and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique Alba", pages = "939--946", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1569901.1570030", abstract = "We describe an approach to generating animations of drawings that start as a random collection of strokes and gradually resolve into a recognizable subject. The strokes are represented as tree based genetic programs. An animation is generated by rendering the best individual in a generation as a frame of a movie. The resulting animations have an engaging characteristic in which the target slowly emerges from a random set of strokes. We have generated two qualitatively different kinds of animations, ones that use grey level straight line strokes and ones that use binary Bezier curve stokes. Around 100,000 generations are needed to generate engaging animations. Population sizes of 2 and 4 give the best convergence behaviour. Convergence can be accelerated by using information from the target in drawing a stroke. Our approach provides a large range of creative opportunities for artists. Artists have control over choice of target and the various stroke parameters.", notes = "GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009). ACM Order Number 910092.", } @InProceedings{Barlow:2013:CEC, article_id = "1360", author = "Brendan Barlow and Andy Song", title = "Towards Scene Text Recognition with Genetic Programming", booktitle = "2013 IEEE Conference on Evolutionary Computation", volume = "1", year = "2013", month = jun # " 20-23", editor = "Luis Gerardo {de la Fraga}", pages = "1310--1317", address = "Cancun, Mexico", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-0453-2", DOI = "doi:10.1109/CEC.2013.6557716", notes = "CEC 2013 - A joint meeting of the IEEE, the EPS and the IET.", } @MastersThesis{barlow2004-thesis, author = "Gregory J. Barlow", title = "Design of Autonomous Navigation Controllers for Unmanned Aerial Vehicles Using Multi-objective Genetic Programming", school = "North Carolina State University", year = "2004", address = "Raleigh, NC, USA", month = mar, keywords = "genetic algorithms, genetic programming, mobile robotics, evolutionary robotics, multi-objective optimization, incremental evolution, unmanned aerial vehicles", URL = "http://www.andrew.cmu.edu/user/gjb/includes/publications/thesis/barlow2004-thesis/barlow2004-thesis.pdf", size = "181 pages", abstract = "Unmanned aerial vehicles (UAVs) have become increasingly popular for many applications, including search and rescue, surveillance, and electronic warfare, but almost all UAVs are controlled remotely by humans. Methods of control must be developed before UAVs can become truly autonomous. While the field of evolutionary robotics (ER) has made strides in using evolutionary computation (EC) to develop controllers for wheeled mobile robots, little attention has been paid to applying EC to UAV control. EC is an attractive method for developing UAV controllers because it allows the human designer to specify the set of high level goals that are to be solved by artificial evolution. In this research, autonomous navigation controllers were developed using multi-objective genetic programming (GP) for fixed wing UAV applications. Four behavioral fitness functions were derived from flight simulations. Multi-objective GP used these fitness functions to evolve controllers that were able to locate an electromagnetic energy source, to navigate the UAV to that source efficiently using on-board sensor measurements, and to circle around the emitter. Controllers were evolved in simulation. To narrow the gap between simulated and real controllers, the simulation environment employed noisy radar signals and a sensor model with realistic inaccuracies. All computations were performed on a 92-processor Beowulf cluster parallel computer. To gauge the success of evolution, baseline fitness values for a successful controller were established by selecting values for a minimally successful controller. Two sets of experiments were performed, the first evolving controllers directly from random initial populations, the second using incremental evolution. In each set of experiments, autonomous navigation controllers were evolved for a variety of radar types. Both the direct evolution and incremental evolution experiments were able to evolve controllers that performed acceptably. However, incremental evolution vastly increased the success rate of incremental evolution over direct evolution. The final incremental evolution experiment on the most complex radar investigated in this research evolved controllers that were able to handle all of the radar types. Evolved UAV controllers were successfully transferred to a wheeled mobile robot. An acoustic array on-board the mobile robot replaced the radar sensor, and a speaker emitting a tone was used as the target. Using the evolved navigation controllers, the mobile robot moved to the speaker and circled around it. Future research will include testing the best evolved controllers by using them to fly real UAVs.", notes = "ADA460111", } @InProceedings{barlow:2004:lbp, author = "Gregory J. Barlow and Choong K. Oh and Edward Grant", title = "Incremental Evolution of Autonomous Controllers for Unmanned Aerial Vehicles using Multi-objective Genetic Programming", booktitle = "Late Breaking Papers at the 2004 Genetic and Evolutionary Computation Conference", year = "2004", editor = "Maarten Keijzer", address = "Seattle, Washington, USA", month = "26 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://www.andrew.cmu.edu/user/gjb/includes/publications/other/barlow2004-geccolbp/barlow2004-geccolbp.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco2004/LBP011.pdf", abstract = "Autonomous navigation controllers were developed for fixed wing unmanned aerial vehicle (UAV) applications using incremental evolution with multi-objective genetic programming (GP). We designed four fitness functions derived from flight simulations and used multi-objective GP to evolve controllers able to locate a radar source, navigate the UAV to the source efficiently using on-board sensor measurements, and circle closely around the emitter. We selected realistic flight parameters and sensor inputs to aid in the transference of evolved controllers to physical UAVs. We used both direct and environmental incremental evolution to evolve controllers for four types of radars: 1) continuously emitting, stationary radars, 2) continuously emitting, mobile radars, 3) intermittently emitting, stationary radars, and 4) intermittently emitting, mobile radars. The use of incremental evolution drastically increased evolution's chances of evolving a successful controller compared to direct evolution. This technique can also be used to develop a single controller capable of handling all four radar types. In the next stage of research, the best evolved controllers will be tested by using them to fly real UAVs.", notes = "Part of \cite{keijzer:2004:GECCO:lbp}", } @InProceedings{barlow:2004:geccogsw, author = "Gregory J. Barlow", title = "Autonomous Controller Design for Unmanned Aerial Vehicles using Multi-objective Genetic Programming", booktitle = "Proceedings of the Graduate Student Workshop at the 2004 Genetic and Evolutionary Computation Conference (GECCO-2004)", editor = "R. Poli and S. Cagnoni and M. Keijzer and E. Costa and F. Pereira and G. Raidl and S. C. Upton and D. Goldberg and H. Lipson and E. {de Jong} and J. Koza and H. Suzuki and H. Sawai and I. Parmee and M. Pelikan and K. Sastry and D. Thierens and W. Stolzmann and P. L. Lanzi and S. W. Wilson and M. O'Neill and C. Ryan and T. Yu and J. F. Miller and I. Garibay and G. Holifield and A. S. Wu and T. Riopka and M. M. Meysenburg and A. W. Wright and N. Richter and J. H. Moore and M. D. Ritchie and L. Davis and R. Roy and M. Jakiela", year = "2004", address = "Seattle, Washington, USA", month = "24-26 " # jun, keywords = "genetic algorithms, genetic programming, evolutionary robotics, multi-objective optimisation, unmanned aerial vehicles", URL = "http://gpbib.cs.ucl.ac.uk/gecco2004/WGSW001.pdf", URL = "http://www.andrew.cmu.edu/user/gjb/includes/publications/conference/barlow2004-geccogsw/barlow2004-geccogsw.pdf", abstract = "Autonomous navigation controllers were developed for fixed wing unmanned aerial vehicle (UAV) applications using multi-objective genetic programming (GP). Four fitness functions derived from flight simulations were designed and multi-objective GP was used to evolve controllers able to locate a radar source, navigate the UAV to the source efficiently using on-board sensor measurements, and circle around the emitter. Controllers were evolved for three different kinds of radars: stationary, continuously emitting radars, stationary, intermittently emitting radars, and mobile, continuously emitting radars. In this study, realistic flight parameters and sensor inputs were selected to aid in the transference of evolved controllers to physical UAVs.", notes = "Winner of Best Paper at the Graduate Student Workshop at the 2004 Genetic and Evolutionary Computation Conference (GECCO-2004). http://www-illigal.ge.uiuc.edu:8080/GECCO-2004/awards-winners.html GECCO-2004WKS Distributed on CD-ROM at GECCO-2004", } @InProceedings{barlow2004-cis, author = "Gregory J. Barlow and Choong K. Oh and Edward Grant", title = "Incremental Evolution of Autonomous Controllers for Unmanned Aerial Vehicles using Multi-objective Genetic Programming", booktitle = "Proceedings of the 2004 IEEE Conference on Cybernetics and Intelligent Systems (CIS)", year = "2004", pages = "688--693", address = "Singapore", month = "1-3 " # dec, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, incremental evolution, multi-objective optimisation", URL = "http://www.cs.cmu.edu/~gjb/includes/publications/conference/barlow2004-cis/barlow2004-cis.pdf", abstract = "Autonomous navigation controllers were developed for fixed wing unmanned aerial vehicle (UAV) applications using incremental evolution with multi-objective genetic programming (GP). We designed four fitness functions derived from flight simulations and used multi-objective GP to evolve controllers able to locate a radar source, navigate the UAV to the source efficiently using on-board sensor measurements, and circle closely around the emitter. We selected realistic flight parameters and sensor inputs to aid in the transference of evolved controllers to physical UAVs. We used both direct and environmental incremental evolution to evolve controllers for four types of radars: 1) continuously emitting, stationary radars, 2) continuously emitting, mobile radars, 3) intermittently emitting, stationary radars, and 4) intermittently emitting, mobile radars. The use of incremental evolution drastically increased evolution's chances of evolving a successful controller compared to direct evolution. This technique can also be used to develop a single controller capable of handling all four radar types. In the next stage of research, the best evolved controllers will be tested by using them to fly real UAVs.", notes = "IEEE CIS RAM 2004 http://cis-ram.nus.edu.sg/", } @InProceedings{barlow2005-icra, author = "Gregory J. Barlow and Leonardo S. Mattos and Edward Grant and Choong K. Oh", title = "Transference of Evolved Unmanned Aerial Vehicle Controllers to a Wheeled Mobile Robot", booktitle = "Proceedings of the IEEE International Conference on Robotics and Automation", year = "2005", editor = "Ruediger Dillmann", address = "Barcelona, Spain", month = "18-22 " # apr, organisation = "IEEE Robotics and Automation Society", publisher = "IEEE", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.cmu.edu/~gjb/includes/publications/conference/barlow2005-icra/barlow2005-icra.pdf", abstract = "Transference of controllers evolved in simulation to real vehicles is an important issue in evolutionary robotics (ER). We have previously evolved autonomous navigation controllers for fixed wing UAV applications using multi-objective genetic programming (GP). Controllers were evolved to locate a radar source, navigate the UAV to the source efficiently using on-board sensor measurements, and circle around the emitter. We successfully tested an evolved UAV controller on a wheeled mobile robot. A passive sonar system on the robot was used in place of the radar sensor, and a speaker emitting a tone was used as the target in place of a radar. Using the evolved navigation controller, the mobile robot moved to the speaker and circled around it. The results from this experiment demonstrate that our evolved controllers are capable of transference to real vehicles. Future research will include testing the best evolved controllers by using them to fly real UAVs.", notes = "http://www.icra2005.org/ ", } @InProceedings{1144023, author = "Gregory J. Barlow and Choong K. Oh", title = "Robustness analysis of genetic programming controllers for unmanned aerial vehicles", booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation", year = "2006", editor = "Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos {Coello Coello} and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens", volume = "1", ISBN = "1-59593-186-4", pages = "135--142", address = "Seattle, Washington, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p135.pdf", DOI = "doi:10.1145/1143997.1144023", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "8-12 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Artificial Life Evolutionary Robotics, Adaptive Behavior, autonomous vehicles, program synthesis, reliability, robustness, synthesis, transference, unmanned aerial vehicles", notes = "GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM Order Number 910060", } @InProceedings{Barlow:2008:gecco, author = "Gregory J. Barlow and Choong K. Oh and Stephen F. Smith", title = "Evolving cooperative control on sparsely distributed tasks for {UAV} teams without global communication", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "177--184", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p177.pdf", DOI = "doi:10.1145/1389095.1389125", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, evolutionary robotics, multiagent systems, multiobjective optimisation, unmanned aerial vehicles, Artificial life, adaptive behaviour, evolvable hardware", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389125} Phd thesis on classifiers with memory: http://www.ri.cmu.edu/pub_files/2011/7/barlow-dissertation.pdf Improving memory for optimization and learning in dynamic environments. Gregory Barlow doctoral dissertation, tech. report CMU-RI-TR-11-17", } @Article{Barmpalexis2011122, author = "Panagiotis Barmpalexis and Kyriakos Kachrimanis and Emanouil Georgarakis", title = "Solid dispersions in the development of a nimodipine floating tablet formulation and optimization by artificial neural networks and genetic programming", journal = "European Journal of Pharmaceutics and Biopharmaceutics", volume = "77", number = "1", pages = "122--131", year = "2011", ISSN = "0939-6411", DOI = "doi:10.1016/j.ejpb.2010.09.017", URL = "http://www.sciencedirect.com/science/article/B6T6C-51696TP-1/2/61fc7d46e9a66d451646234b5e96dedb", keywords = "genetic algorithms, genetic programming, Solid dispersions, Nimodipine, Controlled release, Effervescent floating tablets, Artificial neural networks", abstract = "The present study investigates the use of nimodipine-polyethylene glycol solid dispersions for the development of effervescent controlled release floating tablet formulations. The physical state of the dispersed nimodipine in the polymer matrix was characterised by differential scanning calorimetry, powder X-ray diffraction, FT-IR spectroscopy and polarised light microscopy, and the mixture proportions of polyethylene glycol (PEG), polyvinyl-pyrrolidone (PVP), hydroxypropylmethylcellulose (HPMC), effervescent agents (EFF) and nimodipine were optimised in relation to drug release (percent release at 60 min, and time at which the 90percent of the drug was dissolved) and floating properties (tablet's floating strength and duration), employing a 25-run D-optimal mixture design combined with artificial neural networks (ANNs) and genetic programming (GP). It was found that nimodipine exists as mod I microcrystals in the solid dispersions and is stable for at least a three-month period. The tablets showed good floating properties and controlled release profiles, with drug release proceeding via the concomitant operation of swelling and erosion of the polymer matrix. ANNs and GP both proved to be efficient tools in the optimization of the tablet formulation, and the global optimum formulation suggested by the GP equations consisted of PEG = 9percent, PVP = 30percent, HPMC = 36percent, EFF = 11percent, nimodipine = 14percent.", } @Article{Barmpalexis201175, author = "P. Barmpalexis and K. Kachrimanis and A. Tsakonas and E. Georgarakis", title = "Symbolic regression via genetic programming in the optimization of a controlled release pharmaceutical formulation", journal = "Chemometrics and Intelligent Laboratory Systems", year = "2011", volume = "107", number = "1", month = may, pages = "75--82", keywords = "genetic algorithms, genetic programming, Artificial neural networks, Controlled release, Experimental design, Optimisation", ISSN = "0169-7439", URL = "http://ikee.lib.auth.gr/record/226012", DOI = "doi:10.1016/j.chemolab.2011.01.012", broken = "http://www.sciencedirect.com/science/article/B6TFP-523CDG2-4/2/67c4e87b7f04a0e4f5f6fe07a1127ef8", abstract = "Symbolic regression via genetic programming (GP) was used in the optimisation of a pharmaceutical zero-order release matrix tablet, and its predictive performance was compared to that of artificial neural network (ANN) models. Two types of GP algorithms were employed: 1) standard GP, where a single population is used with a restricted or an extended function set, and 2) multi-population (island model) GP, where a finite number of populations is adopted. The amounts of four polymers, namely PEG4000, PVP K30, HPMC K100 and HPMC E50LV were selected as independent variables, while the percentage of nimodipine released in 2 and 8 h (Y2h, and Y8h), respectively, and the time at which 90% of the drug was dissolved (t90%), were selected as responses. Optimal models were selected by minimisation of the Euclidian distance between predicted and optimum release parameters. It was found that the prediction ability of GP on an external validation set was higher compared to that of the ANNs, with the multi population and standard GP combined with an extended function set, showing slightly better predictive performance. Similarity factor (f2) values confirmed GP's increased prediction performance for multi-population GP (f2 = 85.52) and standard GP using an extended function set (f2 = 84.47).", } @Article{BARMPALEXIS:2018:IJP, author = "Panagiotis Barmpalexis and Anna Karagianni and Grigorios Karasavvaides and Kyriakos Kachrimanis", title = "Comparison of multi-linear regression, particle swarm optimization artificial neural networks and genetic programming in the development of mini-tablets", journal = "International Journal of Pharmaceutics", volume = "551", number = "1", pages = "166--176", year = "2018", keywords = "genetic algorithms, genetic programming, Mini-tablets, Quality by design (QbD), Particle swarm optimization ANNs, Flow properties, DoE optimization", ISSN = "0378-5173", DOI = "doi:10.1016/j.ijpharm.2018.09.026", URL = "http://www.sciencedirect.com/science/article/pii/S037851731830677X", abstract = "In the present study, the preparation of pharmaceutical mini-tablets was attempted in the framework of Quality by Design (QbD) context, by comparing traditionally used multi-linear regression (MLR), with artificially-intelligence based regression techniques (such as standard artificial neural networks (ANNs), particle swarm optimization (PSO) ANNs and genetic programming (GP)) during Design of Experiment (DoE) implementation. Specifically, the effect of diluent type and particle size fraction for three commonly used direct compression diluents (lactose, pregelatinized starch and dibasic calcium phosphate dihydrate, DCPD) blended with either hydrophilic or hydrophobic flowing aids was evaluated in terms of: a) powder blend properties (such as bulk (Y1) and tapped (Y2) density, Carr's compressibility index (Y3, CCI), Kawakita's compaction fitting parameters a (Y4) and 1/b (Y5)), and b) mini-tablet's properties (such as relative density (Y6), average weight (Y7) and weight variation (Y8)). Results showed better flowing properties for pregelatinized starch and improved packing properties for lactose and DPCD. MLR analysis showed high goodness of fit for the Y1, Y2, Y4, Y6 and Y8 with RMSE values of Y1a =a 0.028, Y2a =a 0.032, Y4a =a 0.019, Y6a =a 0.015 and Y8a =a 0.130; while for rest responses, high correlation was observed from both standard ANNs and GP. PSO-ANNs fitting was the only regression technique that was able to adequately fit all responses simultaneously (RMSE values of Y1a =a 0.026, Y2a =a 0.022, Y3a =a 0.025, Y4a =a 0.010, Y5a =a 0.063, Y6a =a 0.013, Y7a =a 0.064 and Y8a =a 0.104)", } @Article{barmpalexis:2018:AAPSPST, author = "Panagiotis Barmpalexis and Agni Grypioti and Georgios K. Eleftheriadis and Dimitris G. Fatouros", title = "Development of a New Aprepitant Liquisolid Formulation with the Aid of Artificial Neural Networks and Genetic Programming", journal = "AAPS PharmSciTech", year = "2018", volume = "19", number = "2", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1208/s12249-017-0893-z", DOI = "doi:10.1208/s12249-017-0893-z", } @Misc{Barnes:2021:GPTP, author = "Elizabeth Barnes", title = "Viewing Anthropogenic Change Through an {AI} Lens", booktitle = "Genetic Programming Theory and Practice XVIII", year = "2021", editor = "Wolfgang Banzhaf and Leonardo Trujillo and Stephan Winkler and Bill Worzel", series = "Genetic and Evolutionary Computation", address = "East Lansing, USA", month = "19-21 " # may, note = "keynote", keywords = "genetic algorithms, genetic programming", video_url = "https://mediaspace.msu.edu/media/Barnes_Keynote_GPTP_2021/1_obavvcra", notes = "Department of Atmospheric Science, Colorado State University Not part of published proceedings", } @InProceedings{Barnes:2019:GECCOcomp, author = "Kenton M. Barnes and Michael B. Gale", title = "Meta-genetic programming for static quantum circuits", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", isbn13 = "978-1-4503-6748-6", pages = "2016--2019", address = "Prague, Czech Republic", URL = "http://wrap.warwick.ac.uk/119812/1/WRAP-meta-genetic-programming-static-quantum-circuits-Gale-2019.pdf", DOI = "doi:10.1145/3319619.3326907", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, quantum computing", size = "5 pages", abstract = "Quantum programs are difficult for humans to develop due to their complex semantics that are rooted in quantum physics. It is there-fore preferable to write specifications and then use techniques such as genetic programming (GP) to generate quantum programs in-stead. We present a new genetic programming system for quantumcircuits which can evolve solutions to the full-adder and quantumFourier transform problems in fewer generations than previouswork, despite using a general set of gates. This means that it is nolonger required to have any previous knowledge of the solutionand choose a specialised gate set based on it.", notes = "Also known as \cite{3326907} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @Misc{oai:arXiv.org:quant-ph/9907056, title = "A quantum circuit for {OR}", author = "Howard Barnum and Herbert J. Bernstein and Lee Spector", year = "1999", month = oct # "~08", abstract = "We give the first quantum circuit for computing $f(0)$ OR $f(1)$ more reliably than is classically possible with a single evaluation of the function. OR therefore joins XOR (i.e. parity, $f(0) \oplus f(1)$) to give the full set of logical connectives (up to relabeling of inputs and outputs) for which there is quantum speedup. The XOR algorithm is of fundamental importance in quantum computation; our OR algorithm (found with the aid of genetic programming), may represent a new quantum computational effect, also useful as a ``subroutine''.", oai = "oai:arXiv.org:quant-ph/9907056", URL = "http://arXiv.org/abs/quant-ph/9907056", URL = "http://arxiv.org/PS_cache/quant-ph/pdf/9907/9907056.pdf", howpublished = "arXiv.or", keywords = "genetic algorithms, genetic programming", size = "6 pages", notes = "Comment: 4 pages + 2 postscript figures. Version 3 restores the figures to Version 2, which changed the title, abstract, introduction and concluding paragraph, order of material, and emphasis from Version 1. No change in technical content", } @TechReport{2000-barnum-2, author = "Howard Barnum and Herbert J Bernstein and Lee Spector", title = "Quantum circuits for {OR} and {AND} of {OR}'s", year = "2000", institution = "University of Bristol", address = "UK", month = aug, keywords = "genetic algorithms, genetic programming", abstract-url = "http://www.cs.bris.ac.uk/Publications/pub_info.jsp?id=1000497", URL = "http://www.cs.bris.ac.uk/Publications/Papers/1000497.pdf", pubtype = "117", abstract = "We give the first quantum circuit, derived with the aid of genetic programming, for computing $f(0)$ OR $f(1)$ more reliably than is classically possible with a single evaluation of the function. OR therefore joins XOR (i.e. parity, $f(0) \oplus f(1)$) to give the full set of logical connectives (up to relabeling of inputs and outputs) for which there is quantum speedup.", notes = "See also \cite{barnum:2000:qc}", size = "15 pages", } @Article{barnum:2000:qc, author = "Howard Barnum and Herbert J Bernstein and Lee Spector", title = "Quantum circuits for {OR} and {AND} of {ORs}", journal = "Journal of Physics A: Mathematical and General", year = "2000", volume = "33", number = "45", pages = "8047--8057", month = "17 " # nov, keywords = "genetic algorithms, genetic programming", URL = "http://hampshire.edu/lspector/pubs/jpa.pdf", URL = "http://hampshire.edu/lspector/pubs/jpa.ps", abstract = "We give the first quantum circuit for computing f(0) or f(1) more reliably than is classically possible with a single evaluation function. Or therefor joins XOR (ie parity) to give the full set of logical connectives (up to relabelling of inputs and outputs) for which there is a quantum speedup", notes = "reports new quantum algorithms discovered by GP, with some details on the GP processes", } @InProceedings{baron:1999:S, author = "Christophe Baron and Guy Gouarderes", title = "Systemions to model alternative issues in problem solving", booktitle = "Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference", year = "1999", editor = "Scott Brave and Annie S. Wu", pages = "31--37", address = "Orlando, Florida, USA", month = "13 " # jul, notes = "GECCO-99LB", } @Article{Barone:2023:MLST, author = "Francesco Pio Barone and Daniele Dell'Aquila and Marco Russo", title = "A novel multi-layer modular approach for real-time fuzzy-identification of gravitational-wave signals", journal = "Machine Learning: Science and Technology", year = "2023", volume = "4", number = "4", pages = "045054", month = dec, keywords = "genetic algorithms, genetic programming, BP, gravitational-wave science, analysis of noisy timeseries, fuzzy-classification of signals, speech-processing, artificial neural networks, ANN", publisher = "IOP Publishing", ISSN = "2632-2153", URL = "https://iopscience.iop.org/article/10.1088/2632-2153/ad1200", DOI = "doi:10.1088/2632-2153/ad1200", size = "18 pages", abstract = "Advanced LIGO and Advanced Virgo ground-based interferometers are instruments capable to detect gravitational wave (GW) signals exploiting advanced laser interferometry techniques. The underlying data analysis task consists in identifying specific patterns in noisy timeseries, but it is made extremely complex by the incredibly small amplitude of the target signals. In this scenario, the development of effective GW detection algorithms is crucial. We propose a novel layered framework for real-time detection of GWs inspired by speech processing techniques and, in the present implementation, based on a state-of-the-art machine learning approach involving a hybridization of genetic programming and neural networks. The key aspects of the newly proposed framework are: the well structured, layered approach, and the low computational complexity. The paper describes the basic concepts of the framework and the derivation of the first three layers. Even if, in the present implementation, the layers are based on models derived using a machine learning approach, the proposed layered structure has a universal nature. Compared to more complex approaches, such as convolutional neural networks, which comprise a parameter set of several tens of MB and were tested exclusively for fixed length data samples, our framework has lower accuracy (e.g. it identifies of low signal-to-noise-ration GW signals, against of the state-of-the-art, at a false alarm probability of 10−2), but has a much lower computational complexity (it exploits only 4 numerical features in the present implementation) and a higher degree of modularity. Furthermore, the exploitation of short-term features makes the results of the new framework virtually independent against time-position of GW signals, simplifying its future exploitation in real-time multi-layer pipelines for gravitational-wave detection with new generation interferometers.", } @InProceedings{baronti:2002:gecco:lbp, title = "Enhancing Tournament Selection to Prevent Code Bloat in Genetic Programming", author = "Flavio Baronti and Antonina Starita", booktitle = "Late Breaking Papers at the Genetic and Evolutionary Computation Conference ({GECCO-2002})", editor = "Erick Cant{\'u}-Paz", year = "2002", month = jul, pages = "17--22", address = "New York, NY", publisher = "AAAI", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025", keywords = "genetic algorithms, genetic programming", notes = "Late Breaking Papers, {GECCO-2002}. A joint meeting of the eleventh International Conference on Genetic Algorithms ({ICGA-2002}) and the seventh Annual Genetic Programming Conference ({GP-2002}) part of cantu-paz:2002:GECCO:lbp", } @InProceedings{Barr:2014:FSE, author = "Earl T. Barr and Yuriy Brun and Premkumar Devanbu and Mark Harman and Federica Sarro", title = "The Plastic Surgery Hypothesis", booktitle = "22nd ACM SIGSOFT International Symposium on the Foundations of Software Engineering (FSE 2014)", year = "2014", editor = "Alessandro Orso and Margaret-Anne Storey and Shing-Chi Cheung", address = "Hong Kong", month = "16-12 " # nov, publisher = "ACM", keywords = "genetic improvement, SBSE, APR, Distribution, Maintenance, and Enhancement, Reusable Software, Experimentation, Languages, Measurement, Software graftability, code reuse, empirical software engineering, mining software repositories, automated program repair", URL = "http://earlbarr.com/publications/psh.pdf", size = "12 pages", abstract = "Recent work on genetic-programming-based approaches to automatic program patching have relied on the insight that the content of new code can often be assembled out of fragments of code that already exist in the code base. This insight has been dubbed the plastic surgery hypothesis; successful, well-known automatic repair tools such as GenProg rest on this hypothesis, but it has never been validated. We formalise and validate the plastic surgery hypothesis and empirically measure the extent to which raw material for changes actually already exists in projects. In this paper, we mount a large-scale study of several large Java projects, and examine a history of 15,723 commits to determine the extent to which these commits are graftable, i.e., can be reconstituted from existing code, and find an encouraging degree of graftability, surprisingly independent of commit size and type of commit. For example, we find that changes are 43percent graftable from the exact version of the software being changed. With a view to investigating the difficulty of finding these grafts, we study the abundance of such grafts in three possible sources: the immediately previous version, prior history, and other projects. We also examine the contiguity or chunking of these grafts, and the degree to which grafts can be found in the same file. Our results are quite promising and suggest an optimistic future for automatic program patching methods that search for raw material in already extant code in the project being patched.", notes = "http://fse22.gatech.edu/", } @InProceedings{Barr:2015:ISSTA, author = "Earl T. Barr and Mark Harman and Yue Jia and Alexandru Marginean and Justyna Petke", title = "Automated Software Transplantation", booktitle = "International Symposium on Software Testing and Analysis, ISSTA 2015", year = "2015", editor = "Tao Xie and Michal Young", pages = "257--269", address = "Baltimore, Maryland, USA", month = "14-17 " # jul, organisation = "ACM Special Interest Group on Software Engineering", publisher = "ACM", note = "{ACM SIGSOFT} Distinguished Paper Award", keywords = "genetic algorithms, genetic programming, genetic improvement, Automated software transplantation, autotransplantation, TXL", isbn13 = "978-1-4503-3620-8", URL = "http://crest.cs.ucl.ac.uk/autotransplantation/", URL = "http://crest.cs.ucl.ac.uk/autotransplantation/downloads/autotransplantation.pdf", URL = "http://www.human-competitive.org/sites/default/files/barr-harman-jia-marginean-petke-text.txt", DOI = "doi:10.1145/2771783.2771796", acmid = "2771796", size = "13 pages", abstract = "Automated transplantation would open many exciting avenues for software development: suppose we could autotransplant code from one system into another, entirely unrelated, system. This paper introduces a theory, an algorithm, and a tool that achieve this. Leveraging lightweight annotation, program analysis identifies an organ (interesting behaviour to transplant); testing validates that the organ exhibits the desired behavior during its extraction and after its implantation into a host. While we do not claim automated transplantation is now a solved problem, our results are encouraging: we report that in 12 of 15 experiments, involving 5 donors and 3 hosts (all popular real-world systems), we successfully autotransplanted new functionality and passed all regression tests. Autotransplantation is also already useful: in 26 hours computation time we successfully autotransplanted the H.264 video encoding functionality from the x264 system to the VLC media player; compare this to upgrading x264 within VLC, a task that we estimate, from VLC's version history, took human programmers an average of 20 days of elapsed, as opposed to dedicated, time", notes = "Winner 2016 HUMIES mutrans http://crest.cs.ucl.ac.uk/autotransplantation/MuScalpel.html#scripts muScalpel http://crest.cs.ucl.ac.uk/autotransplantation/downloads/muScalpel.tar.gz Pidgin Cflow SOX VLC Idct: Mytar Webserver TuxCrypt Cflow x264 cited by \cite{Lu:2018:ICSE} http://issta2015.cs.uoregon.edu/papers.php also known as \cite{Barr:2015:AST:2771783.2771796}", } @InProceedings{Barrero:2010:gecco, author = "David F. Barrero and David Camacho and Maria D. R-Moreno", title = "Confidence intervals of success rates in evolutionary computation", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "975--976", keywords = "genetic algorithms, genetic programming: Poster", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830483.1830657", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Success Rate (SR) is a statistic straightforward to use and interpret, however a number of non-trivial statistical issues arises when it is examined in detail. We address some of those issues, providing evidence that suggests that SR follows a binomial density function, therefore its statistical properties are independent of the flavour of the Evolutionary Algorithm (EA) and its domain. It is fully described by the SR and the number of runs. Moreover, the binomial distribution is a well known statistical distribution with a large corpus of tools available that can be used in the context of EC research.One of those tools, confidence intervals (CIs), is studied.", notes = "Santa Fe trail artificial ant Also known as \cite{1830657} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @InProceedings{barrero:2011:EuroGP, author = "David F. Barrero and Bonifacio Casta\~no and Maria D. R-Moreno and David Camacho", title = "Statistical Distribution of Generation-to-Success in GP: Application to Model Accumulated Success Probability", booktitle = "Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011", year = "2011", month = "27-29 " # apr, editor = "Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado", series = "LNCS", volume = "6621", publisher = "Springer Verlag", address = "Turin, Italy", pages = "154--165", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-20406-7", DOI = "doi:10.1007/978-3-642-20407-4_14", abstract = "Many different metrics have been defined in Genetic Programming. Depending on the experiment requirements and objectives, a collection of measures are selected in order to achieve an understanding of the algorithm behaviour. One of the most common metrics is the accumulated success probability, which evaluates the probability of an algorithm to achieve a solution in a certain generation. We propose a model of accumulated success probability composed by two parts, a binomial distribution that models the total number of success, and a lognormal approximation to the generation-to-success, that models the variation of the success probability with the generation.", notes = "Part of \cite{Silva:2011:GP} EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011", } @InProceedings{Barrero:2011:AESotAoCEiGP, title = "An Empirical Study on the Accuracy of Computational Effort in Genetic Programming", author = "David F. Barrero and Maria R-Moreno and Bonifacio Castano and David Camacho", pages = "1169--1176", booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary Computation", year = "2011", editor = "Alice E. Smith", month = "5-8 " # jun, address = "New Orleans, USA", organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, computational effort, estimation, variability sources, computational complexity, estimation theory", DOI = "doi:10.1109/CEC.2011.5949748", abstract = "Some commonly used performance measures in Genetic Programming are those defined by John Koza in his first book. These measures, mainly computational effort and number of individuals to be processed, estimate the performance of the algorithm as well as the difficulty of a problem. Although Koza's performance measures have been widely used in the literature, their behaviour is not well known. In this paper we try to study the accuracy of these measures and advance in the understanding of the factors that influence them. In order to achieve this goal, we report an empirical study that attempts to systematically measure the effects of two variability sources in the estimation of the number of individuals to be processed and the computational effort. The results obtained in those experiments suggests that these measures, in common experimental setups, and under certain circumstances, might have a high relative error.", notes = "CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.", } @InProceedings{Barrero:2013:CEC, article_id = "1254", author = "David F. Barrero and Maria D. R-Moreno and Bonifacio Castano and David Camacho", title = "Effects of the Lack of Selective Pressure on the Expected Run-Time Distribution in Genetic Programming", booktitle = "2013 IEEE Conference on Evolutionary Computation", volume = "1", year = "2013", month = jun # " 20-23", editor = "Luis Gerardo {de la Fraga}", pages = "1748--1755", address = "Cancun, Mexico", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-0453-2", DOI = "doi:10.1109/CEC.2013.6557772", notes = "CEC 2013 - A joint meeting of the IEEE, the EPS and the IET.", } @Article{Barrero:2015:GPEM, author = "David F. Barrero and Bonifacio Castano and Maria D. R-Moreno and David Camacho", title = "A study on Koza's performance measures", journal = "Genetic Programming and Evolvable Machines", year = "2015", volume = "16", number = "3", pages = "327--349", month = sep, keywords = "genetic algorithms, genetic programming, Computational effort, Performance measures, Experimental methods, Measurement error", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-014-9238-9", size = "23 pages", abstract = "John R. Koza defined several metrics to measure the performance of an Evolutionary Algorithm that have been widely used by the Genetic Programming community. Despite the importance of these metrics, and the doubts that they have generated in many authors, their reliability has attracted little research attention, and is still not well understood. The lack of knowledge about these metrics has likely contributed to the decline in their usage in the last years. This paper is an attempt to increase the knowledge about these measures, exploring in which circumstances they are more reliable, providing some clues to improve how they are used, and eventually making their use more justifiable. Specifically, we investigate the amount of uncertainty associated with the measures, taking an analytical and empirical approach and reaching theoretical boundaries to the error. Additionally, a new method to calculate Koza's performance measures is presented. It is shown that these metrics, under common experimental configurations, have an unacceptable error, which can be arbitrary large in certain conditions.", } @InProceedings{Barresi:2014:GECCOcomp, author = "Kevin M. Barresi", title = "Evolved nonlinear predictor functions for lossless image compression", booktitle = "GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion", year = "2014", editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges", isbn13 = "978-1-4503-2881-4", keywords = "genetic algorithms, genetic programming: Poster", pages = "129--130", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2598394.2598503", DOI = "doi:10.1145/2598394.2598503", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Due to the increased quantity of digital data, especially in the form of digital images, the need for effective image compression techniques is greater than ever. The JPEG lossless mode relies on predictive coding, in which accurate predictive models are critical. This study presents an efficient method of generating predictor models for input images via genetic programming. It is shown to always produce error images with entropy equal to or lower than those produced by the JPEG lossless mode. This method is demonstrated to have practical use as a real-time asymmetric image compression algorithm due to its ability to quickly and reliably derive prediction models.", notes = "Also known as \cite{2598503} Distributed at GECCO-2014.", } @Article{Barrett:2005:TP, author = "John Barrett and Aneta Kostadinova and Juan Antonio Raga", title = "Mining parasite data using genetic programming", journal = "Trends in Parasitology", year = "2005", volume = "21", number = "5", pages = "207--209", month = may, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1016/j.pt.2005.03.007", abstract = "Genetic programming is a technique that can be used to tackle the hugely demanding data-processing problems encountered in the natural sciences. Application of genetic programming to a problem using parasites as biological tags demonstrates its potential for developing explanatory models using data that are both complex and noisy.", notes = "PMID: 15837607", } @InProceedings{barrett:2003:dmtmb, author = "S. J. Barrett", title = "Recurring Analytical Problems within Drug Discovery and Development", booktitle = "Data Mining and Text Mining for Bioinformatics: Proceedings of the European Workshop", year = "2003", editor = "Tobias Scheffer and Ulf Leser", pages = "6--7", address = "Dubrovnik, Croatia", month = "22 " # sep, organisation = "KDnet", note = "Invited talk", keywords = "genetic algorithms, genetic programming, SVM, SNP", URL = "http://www2.informatik.hu-berlin.de/~scheffer/publications/ProceedingsWS2003.pdf", abstract = "The overall processes driving pharmaceuticals discovery and development research involve many disparate kinds of problems and problem-solving at multiple levels of generality and specificity. The discovery/pre-clinical processes are also highly technology-driven and specific aspects may be more dynamic over time compared to developmental research which is conducted in a more conservatively controlled manner, conducive to regulatory requirements.", notes = "Held in Conjunction with ECML / PKDD- 2003 https://www.cs.uni-potsdam.de/ml/publications/ws03proc/", } @InProceedings{barrett:2005:WSC, author = "S. J. Barrett and W. B. Langdon", title = "Advances in the Application of Machine Learning Techniques in Drug Discovery, Design and Development", booktitle = "Applications of Soft Computing: Recent Trends", year = "2006", editor = "Ashutosh Tiwari and Joshua Knowles and Erel Avineri and Keshav Dahal and Rajkumar Roy", series = "Advances in Soft Computing", volume = "36", pages = "99--110", address = "On the World Wide Web", month = "19 " # sep # " - 7 " # oct # " 2005", organisation = "World Federation of Soft Computing (WFSC), European Neural Network Society (ENNS), North American Fuzzy Information Processing Society (NAFIPS), European Society for Fuzzy Logic and Technology (EUSFLAT), and International Fuzzy Systems Association (IFSA)", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Pharmaceutical applications, Drug design, Particle swarm optimisation, Support vector machines", ISBN = "3-540-29123-7", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/barrett_2005_WSC.pdf", URL = "http://isxp1010c.sims.cranfield.ac.uk/Papers/paper196.pdf", URL = "https://link.springer.com/chapter/10.1007/978-3-540-36266-1_10", size = "21 pages", old_abstract = "Pharmaceutical discovery and development is a cascade of extremely complex and costly research encompassing many facets from: therapeutic target identification and bioinformatics study, candidate drug discovery and optimisation to pre-clinical organism-level evaluations and beyond to extensive clinical trials assessing effectiveness and safety of new medicines. Machine learning, in particular support vector machines SVM, particle swarm optimisation PSO and genetic programming GP, is increasingly used.", abstract = "Machine learning tools, in particular support vector machines (SVM), Particle Swarm Optimisation (PSO) and Genetic Programming (GP), are increasingly used in pharmaceuticals research and development. They are inherently suitable for use with noisy, high dimensional (many variables) data, as is commonly used in cheminformatic (i.e. In silico screening), bioinformatic (i.e. bio-marker studies, using DNA chip data) and other types of drug research studies. These aspects are demonstrated via review of their current usage and future prospects in context with drug discovery activities.", notes = "http://www.cranfield.ac.uk/wsc10/ broken Original conference title= WSC10: 10th Online World Conference on Soft Computing in Industrial Applications http://isxp1010c.sims.cranfield.ac.uk/Presentations/presentation196.pdf broken slides (1Mbyte) Revised following conference. Published 2006. See link.springer.com for published version", } @Article{Barrett:2006:GPEM, author = "Steven J. Barrett", title = "Intelligent Bioinformatics: The Application of Artificial Intelligence Techniques to Bioinformatics Problems John Wiley \& Sons Ltd., Chichester, UK, Keedwell, Edward and Narayanan, Ajit, 2005, 280 p., Hardcover, ISBN 0-470-02175-6", journal = "Genetic Programming and Evolvable Machines", year = "2006", volume = "7", number = "3", pages = "283--284", month = oct, note = "Book review", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dR8e5", DOI = "doi:10.1007/s10710-006-7003-4", } @InProceedings{Barriere:2008:PPSN, author = "Olivier Barriere and Evelyne Lutton and Cedric Baudrit and Mariette Sicard and Bruno Pinaud and Nathalie Perrot", title = "Modeling human expertise on a cheese ripening industrial process using GP", booktitle = "Parallel Problem Solving from Nature - PPSN X", year = "2008", editor = "Gunter Rudolph and Thomas Jansen and Simon Lucas and Carlo Poloni and Nicola Beume", volume = "5199", series = "LNCS", pages = "859--868", address = "Dortmund", month = "13-17 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-87699-5", url_fake = "http://metronum.futurs.inria.fr/html/Papers/files/pdf/Barriere_18-06-2008_INCALIN-PPSN2008-Final.pdf", DOI = "doi:10.1007/978-3-540-87700-4_85", size = "10 pages", abstract = "Industrial agrifood processes often strongly rely on human expertise, expressed as know-how and control procedures based on subjective measurements (colour, smell, texture), which are very difficult to capture and model. We deal in this paper with a cheese ripening process (of French Camembert), for which experimental data have been collected within a cheese ripening laboratory chain. A global and a monopopulation cooperative/coevolutive GP scheme (Parisian approach) have been developed in order to simulate phase prediction (i.e. a subjective estimation of human experts) from microbial proportions and Ph measurements. These two GP approaches are compared to Bayesian network modelling and simple multilinear learning algorithms. Preliminary results show the effectiveness and robustness of the Parisian GP approach.", notes = "GPLAB, Matlab, multi linear regression, INCALIN, Terminals: time derivatives of pH acidity, lactose and two bacteria concentrations. Gaussian random constants. Function set: arithmetics, log, exp?, Boolean ops. Fitness: parsimony, Euclidean sharing distance. tree GP. 30-40 nodes. Mutation Chi squared. 16 experiments each lasting 40 days. Missing data estimated by fitting splines. Log? distribution on floats. See also \cite{inria-00381681} PPSN X", } @TechReport{inria-00381681, title = "Modeling an agrifood industrial process using cooperative coevolution Algorithms", author = "Olivier Barriere and Evelyne Lutton and Pierre-Henri Wuillemin and Cedric Baudrit and Mariette Sicard and Bruno Pinaud and Nathalie Perrot", institution = "INRIA", year = "2009", number = "inria-00381681, version 1", address = "Parc Orsay, France", month = "6 " # may, keywords = "genetic algorithms, genetic programming, Parisian, Computer Science, Artificial Intelligence, Life Sciences/Food and Nutrition, Agrifood, Cheese ripening, Cooperative coevolution, Parisian approach, Bayesian Network", URL = "http://hal.inria.fr/inria-00381681/en/", URL = "http://hal.inria.fr/docs/00/38/16/81/PDF/RR2008.pdf", bibsource = "OAI-PMH server at oai.archives-ouvertes.fr", identifier = "HAL:inria-00381681, version 1", language = "EN", oai = "oai:hal.archives-ouvertes.fr:inria-00381681_v1", abstract = "This report presents two experiments related to the modeling of an industrial agrifood process using evolutionary techniques. Experiments have been focused on a specific problem which is the modeling of a Camembert-cheese ripening process. Two elated complex optimisation problems have been considered: -- a deterministic modeling problem, the phase prediction problem, for which a search for a closed form tree expression has been performed using genetic programming (GP), -- a Bayesian network structure estimation problem, considered as a two-stage problem, i.e. searching first for an approximation of an independence model using EA, and then deducing, via a deterministic algorithm, a Bayesian network which represents the equivalence class of the independence model found at the first stage. In both of these problems, cooperative-coevolution techniques (also called ``Parisian'' approaches) have been proved successful. These approaches actually allow to represent the searched solution as an aggregation of several individuals (or even as a whole population), as each individual only bears a part of the searched solution. This scheme allows to use the artificial Darwinism principles in a more economic way, and the gain in terms of robustness and efficiency is important.", size = "51 pages", } @InProceedings{barro:2022:MSMASF, author = "Diana Barro and Francesca Parpinel and Claudio Pizzi", title = "Pricing Rainfall Derivatives by Genetic Programming: A Case Study", booktitle = "Mathematical and Statistical Methods for Actuarial Sciences and Finance", year = "2022", editor = "Marco Corazza and Cira Perna and Claudio Pizzi and Marilena Sibillo", pages = "64--69", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-030-99638-3", URL = "http://link.springer.com/chapter/10.1007/978-3-030-99638-3_11", DOI = "doi:10.1007/978-3-030-99638-3_11", abstract = "we consider a genetic programming approach to price rainfall derivatives and we test it on a case study based on data collected from a meteorological station in a city in the northeast region of Friuli Venezia Giulia (Italy), characterized by a fairly abundant rainfall.", } @InProceedings{Barros:2011:GECCOcomp, author = "Rodrigo C. Barros and Marcio P. Basgalupp and Andre C. P. L. F. {de Carvalho} and Alex A. Freitas", title = "Towards the automatic design of decision tree induction algorithms", booktitle = "GECCO 2011 1st workshop on evolutionary computation for designing generic algorithms", year = "2011", editor = "Gisele L. Pappa and Alex A. Freitas and Jerry Swan and John Woodward", isbn13 = "978-1-4503-0690-4", keywords = "genetic algorithms, genetic programming", pages = "567--574", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001858.2002050", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Decision tree induction is one of the most employed methods to extract knowledge from data, since the representation of knowledge is very intuitive and easily understandable by humans. The most successful strategy for inducing decision trees, the greedy top-down approach, has been continuously improved by researchers over the years. This work, following recent breakthroughs in the automatic design of machine learning algorithms, proposes two different approaches for automatically generating generic decision tree induction algorithms. Both approaches are based on the evolutionary algorithms paradigm, which improves solutions based on metaphors of biological processes. We also propose guidelines to design interesting fitness functions for these evolutionary algorithms, which take into account the requirements and needs of the end-user.", notes = "Also known as \cite{2002050} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @InProceedings{Barros:2013:GECCO, author = "Rodrigo C. Barros and Marcio P. Basgalupp and Ricardo Cerri and Tiago S. {da Silva} and Andre C. P. L. F. {de Carvalho}", title = "A grammatical evolution approach for software effort estimation", booktitle = "GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference", year = "2013", editor = "Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi", isbn13 = "978-1-4503-1963-8", pages = "1413--1420", keywords = "genetic algorithms, genetic programming", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2463372.2463546", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Software effort estimation is an important task within software engineering. It is widely used for planning and monitoring software project development as a means to deliver the product on time and within budget. Several approaches for generating predictive models from collected metrics have been proposed throughout the years. Machine learning algorithms, in particular, have been widely-employed to this task, bearing in mind their capability of providing accurate predictive models for the analysis of project stakeholders. In this paper, we propose a grammatical evolution approach for software metrics estimation. Our novel algorithm, namely SEEGE, is empirically evaluated on public project data sets, and we compare its performance with state-of-the-art machine learning algorithms such as support vector machines for regression and artificial neural networks, and also to popular linear regression. Results show that SEEGE outperforms the other algorithms considering three different evaluation measures, clearly indicating its effectiveness for the effort estimation task.", notes = "Also known as \cite{2463546} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)", } @Article{Barros2011954, author = "Rodrigo C. Barros and Duncan D. Ruiz and Marcio P. Basgalupp", title = "Evolutionary model trees for handling continuous classes in machine learning", journal = "Information Sciences", year = "2011", volume = "181", number = "5", pages = "954--971", keywords = "genetic algorithms, genetic programming, Evolutionary algorithms, Model trees, Continuous classes, Machine learning", ISSN = "0020-0255", URL = "http://www.sciencedirect.com/science/article/B6V0C-51GHWYC-1/2/2ba74d92cb03abc637a4c377b47a4dbe", DOI = "doi:10.1016/j.ins.2010.11.010", size = "18 pages", abstract = "Model trees are a particular case of decision trees employed to solve regression problems. They have the advantage of presenting an interpretable output, helping the end-user to get more confidence in the prediction and providing the basis for the end-user to have new insight about the data, confirming or rejecting hypotheses previously formed. Moreover, model trees present an acceptable level of predictive performance in comparison to most techniques used for solving regression problems. Since generating the optimal model tree is an NP-Complete problem, traditional model tree induction algorithms make use of a greedy top-down divide-and-conquer strategy, which may not converge to the global optimal solution. we propose a novel algorithm based on the use of the evolutionary algorithms paradigm as an alternate heuristic to generate model trees in order to improve the convergence to globally near-optimal solutions. We call our new approach evolutionary model tree induction (E-Motion). We test its predictive performance using public UCI data sets, and we compare the results to traditional greedy regression/model trees induction algorithms, as well as to other evolutionary approaches. Results show that our method presents a good trade-off between predictive performance and model comprehensibility, which may be crucial in many machine learning applications.", } @Article{Barros:2014:ieeeTEC, author = "Rodrigo C. Barros and Marcio P. Basgalupp and Alex A. Freitas and Andre C. P. L. F. {de Carvalho}", title = "Evolutionary Design of Decision-Tree Algorithms Tailored to Microarray Gene Expression Data Sets", journal = "IEEE Transactions on Evolutionary Computation", year = "2014", month = dec, volume = "18", number = "6", pages = "873--892", keywords = "genetic algorithms, genetic programming", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2013.2291813", size = "20 pages", abstract = "Decision-tree induction algorithms are widely used in machine learning applications in which the goal is to extract knowledge from data and present it in a graphically intuitive way. The most successful strategy for inducing decision trees is the greedy top-down recursive approach, which has been continuously improved by researchers over the past 40 years. In this paper, we propose a paradigm shift in the research of decision trees: instead of proposing a new manually designed method for inducing decision trees, we propose automatically designing decision-tree induction algorithms tailored to a specific type of classification data set (or application domain). Following recent breakthroughs in the automatic design of machine learning algorithms, we propose a hyper-heuristic evolutionary algorithm called hyper-heuristic evolutionary algorithm for designing decision-tree algorithms (HEAD-DT) that evolves design components of top-down decision-tree induction algorithms. By the end of the evolution, we expect HEAD-DT to generate a new and possibly better decision-tree algorithm for a given application domain. We perform extensive experiments in 35 real-world microarray gene expression data sets to assess the performance of HEAD-DT, and compare it with very well known decision-tree algorithms such as C4.5, CART, and REPTree. Results show that HEAD-DT is capable of generating algorithms that significantly outperform the baseline manually designed decision-tree algorithms regarding predictive accuracy and F-measure.", notes = "also known as \cite{6670778}", } @Article{Barros:2015:GPEM, author = "Rodrigo C. Barros and Marcio P. Basgalupp and Andre C. P. L. F. {de Carvalho}", title = "Investigating fitness functions for a hyper-heuristic evolutionary algorithm in the context of balanced and imbalanced data classification", journal = "Genetic Programming and Evolvable Machines", year = "2015", volume = "16", number = "3", pages = "241--281", month = sep, keywords = "genetic algorithms, genetic programming, Hyper-heuristics, Decision trees, Fitness function, Imbalanced data", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-014-9235-z", size = "41 pages", abstract = "In this paper, we analyse in detail the impact of different strategies to be used as fitness function during the evolutionary cycle of a hyper-heuristic evolutionary algorithm that automatically designs decision-tree induction algorithms (HEAD-DT). We divide the experimental scheme into two distinct scenarios: (1) evolving a decision-tree induction algorithm from multiple balanced data sets; and (2) evolving a decision-tree induction algorithm from multiple imbalanced data sets. In each of these scenarios, we analyse the difference in performance of well-known classification performance measures such as accuracy, F-Measure, AUC, recall, and also a lesser-known criterion, namely the relative accuracy improvement. In addition, we analyse different schemes of aggregation, such as simple average, median, and harmonic mean. Finally, we verify whether the best-performing fitness functions are capable of providing HEAD-DT with algorithms more effective than traditional decision-tree induction algorithms like C4.5, CART, and REPTree. Experimental results indicate that HEAD-DT is a good option for generating algorithms tailored to (im)balanced data, since it outperforms state-of-the-art decision-tree induction algorithms with statistical significance.", notes = "Author Affiliations 1. Faculdade de Informatica (FACIN), Pontificia Universidade Catolica do Rio Grande do Sul (PUCRS), Porto Alegre, Brazil 2. Instituto de Ciencia e Tecnologia (ICT), Universidade Federal de Sao Paulo (UNIFESP), Sao Josedos Campos, Brazil 3. Instituto de Ciencias Matematicas e de Computacao (ICMC), Universidade de Sao Paulo (USP), Sao Carlos, Brazil ", } @InProceedings{barry:1999:AXCSPE, author = "Alwyn Barry", title = "Aliasing in XCS and the Consecutive State Problem: 1 - Effects", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "19--26", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-317.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-317.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{barry:1999:AXCSPS, author = "Alwyn Barry", title = "Aliasing in XCS and the Consecutive State Problem: 2 - Solutions", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "27--34", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-336.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-336.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Proceedings{barry:2002:gecco:workshop, title = "{GECCO 2002}: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference", editor = "Alwyn M. Barry", year = "2002", month = "8 " # jul, publisher = "AAAI", address = "New York", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025", keywords = "genetic algorithms, genetic programming, optimization, fuzzy model, design optimization, case-based reasoning, evolutionary algorithm, evolution strategies, simulated annealing, agents, evolutionary computation, co-evolution, parallel implementation, learning classifier system, time series prediction, grammatical evolution, multi-objective optimization, planning, scheduling, industrial applications, machine learning, niching, linkage learning", size = "330 pages", notes = "Bird-of-a-feather Workshops, GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)", } @Proceedings{barry:2003:gecco:workshop, title = "{GECCO 2003}: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference", editor = "Alwyn M. Barry", year = "2003", month = "11 " # jul, publisher = "AAAI", address = "Chigaco", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025", keywords = "genetic algorithms, genetic programming, optimization, fuzzy model, design optimization, case-based reasoning, evolutionary algorithm, evolution strategies, simulated annealing, agents, evolutionary computation, co-evolution, parallel implementation, learning classifier system, time series prediction, grammatical evolution, multi-objective optimization, planning, scheduling, machine learning, representations", URL = "http://gpbib.cs.ucl.ac.uk/gecco2003wks.bib", size = "330 pages", notes = "Bird-of-a-feather Workshops, GECCO-2003. A joint meeting of the twelth International Conference on Genetic Algorithms (ICGA-2003) and the eigth Annual Genetic Programming Conference (GP-2003) part of barry:2003:GECCO:workshop", } @InProceedings{Bartashevich:2018:GECCOcomp, author = "Palina Bartashevich and Illya Bakurov and Sanaz Mostaghim and Leonardo Vanneschi", title = "Evolving {PSO} algorithm design in vector fields using geometric semantic {GP}", booktitle = "GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2018", editor = "Carlos Cotta and Tapabrata Ray and Hisao Ishibuchi and Shigeru Obayashi and Bogdan Filipic and Thomas Bartz-Beielstein and Grant Dick and Masaharu Munetomo and Silvino {Fernandez Alzueta} and Thomas Stuetzle and Pablo Valledor Pellicer and Manuel Lopez-Ibanez and Daniel R. Tauritz and Pietro S. Oliveto and Thomas Weise and Borys Wrobel and Ales Zamuda and Anne Auger and Julien Bect and Dimo Brockhoff and Nikolaus Hansen and Rodolphe {Le Riche} and Victor Picheny and Bilel Derbel and Ke Li and Hui Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Stephane Doncieux and Richard Duro and Joshua Auerbach and Harold {de Vladar} and Antonio J. Fernandez-Leiva and JJ Merelo and Pedro A. Castillo-Valdivieso and David Camacho-Fernandez and Francisco {Chavez de la O} and Ozgur Akman and Khulood Alyahya and Juergen Branke and Kevin Doherty and Jonathan Fieldsend and Giuseppe Carlo Marano and Nikos D. Lagaros and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Boris Naujoks and Vanessa Volz and Tea Tusar and Pascal Kerschke and Riyad Alshammari and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and John R. Woodward and Shin Yoo and John McCall and Nayat Sanchez-Pi and Luis Marti and Danilo Vasconcellos and Masaya Nakata and Anthony Stein and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Ivanoe {De Falco} and Antonio {Della Cioppa} and Ernesto Tarantino and Umberto Scafuri and P. G. M. Baltus and Giovanni Iacca and Ahmed Hallawa and Anil Yaman and Alma Rahat and Handing Wang and Yaochu Jin and David Walker and Richard Everson and Akira Oyama and Koji Shimoyama and Hemant Kumar and Kazuhisa Chiba and Pramudita Satria Palar", isbn13 = "978-1-4503-5764-7", pages = "262--263", address = "Kyoto, Japan", DOI = "doi:10.1145/3205651.3205760", publisher = "ACM", publisher_address = "New York, NY, USA", month = "15-19 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", abstract = "This paper investigates the possibility of evolving new particle swarm equations representing a collective search mechanism, acting in environments with unknown external dynamics, using Geometric Semantic Genetic Programming (GSGP). The proposed method uses a novel initialization technique - the Evolutionary Demes Despeciation Algorithm (EDDA)- which allows to generate solutions of smaller size than using the traditional ramped half-and-half algorithm. We show that EDDA, using a mixture of both GP and GSGP mutation operators, allows us to evolve new search mechanisms with good generalization ability.", notes = "Also known as \cite{3205760} GECCO-2018 A Recombination of the 27th International Conference on Genetic Algorithms (ICGA-2018) and the 23rd Annual Genetic Programming Conference (GP-2018)", } @InProceedings{Bartashevich:2018:PPSN, author = "Palina Bartashevich and Illya Bakurov and Sanaz Mostaghim and Leonardo Vanneschi", title = "{PSO}-based Search Rules for Aerial Swarms Against Unexplored Vector Fields via Genetic Programming", booktitle = "15th International Conference on Parallel Problem Solving from Nature", year = "2018", editor = "Anne Auger and Carlos M. Fonseca and Nuno Lourenco and Penousal Machado and Luis Paquete and Darrell Whitley", volume = "11101", series = "LNCS", pages = "41--53", address = "Coimbra, Portugal", month = "8-12 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Particle swarm optimization, Vector fields, Semantics, EDDA", isbn13 = "978-3-319-99252-5", URL = "https://www.springer.com/gp/book/9783319992587", DOI = "doi:10.1007/978-3-319-99253-2_4", abstract = "In this paper, we study Particle Swarm Optimization (PSO) as a collective search mechanism for individuals (such as aerial micro-robots) which are supposed to search in environments with unknown external dynamics. In order to deal with the unknown disturbance, we present new PSO equations which are evolved using Genetic Programming (GP) with a semantically diverse starting population, seeded by the Evolutionary Demes Despeciation Algorithm (EDDA), that generalizes better than standard GP in the presence of unknown dynamics. The analysis of the evolved equations shows that with only small modifications in the velocity equation, PSO can achieve collective search behaviour while being unaware of the dynamic external environment, mimicking the zigzag upwind flights of birds towards the food source.", notes = "PPSN2018 http://ppsn2018.dei.uc.pt This two-volume set LNCS 11101 and 11102 constitutes the refereed proceedings of the 15th International Conference on Parallel Problem Solving from Nature, PPSN 2018", } @InProceedings{conf/isat/Bartczuk15, title = "Gene Expression Programming in Correction Modelling of Nonlinear Dynamic Objects", author = "Lukasz Bartczuk", booktitle = "Information Systems Architecture and Technology: Proceedings of 36th International Conference on Information Systems Architecture and Technology - ISAT 2015 - Part I", year = "2015", volume = "429", editor = "Leszek Borzemski and Adam Grzech and Jerzy Swiatek and Zofia Wilimowska", series = "Advances in Intelligent Systems and Computing", pages = "125--134", publisher = "Springer", bibdate = "2017-05-21", keywords = "genetic algorithms, genetic programming, gene expression programming, nonlinear modelling, dynamic objects", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/isat/isat2015-1.html#Bartczuk15", isbn13 = "978-3-319-28553-5", DOI = "doi:10.1007/978-3-319-28555-9_11", abstract = "In this paper we shown the applying of gene expression programming algorithm to correction modelling of non-linear dynamic objects. The correction modelling is the non-linear modelling method based on equivalent linearisation technique that allows to incorporate in modelling process the known linear model of the same or similar object or phenomenon. The usefulness of the proposed method will be shown on a practical example of the continuous stirred tank reactor modelling.", } @InProceedings{conf/icaisc/BartczukPK15, author = "Lukasz Bartczuk and Andrzej Przybyl and Petia D. Koprinkova-Hristova", title = "New Method for Non-linear Correction Modelling of Dynamic Objects with Genetic Programming", bibdate = "2015-06-08", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icaisc/icaisc2015-2.html#BartczukPK15", booktitle = "Artificial Intelligence and Soft Computing - 14th International Conference, {ICAISC} 2015, Zakopane, Poland, June 14-28, 2015, Proceedings, Part {II}", publisher = "Springer", year = "2015", volume = "9120", editor = "Leszek Rutkowski and Marcin Korytkowski and Rafal Scherer and Ryszard Tadeusiewicz and Lotfi A. Zadeh and Jacek M. Zurada", isbn13 = "978-3-319-19368-7", pages = "318--329", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming", URL = "http://dx.doi.org/10.1007/978-3-319-19369-4", DOI = "doi:10.1007/978-3-319-19369-4_29", } @InProceedings{conf/icaisc/BartczukG16, author = "Lukasz Bartczuk and Alexander I. Galushkin", title = "A New Method for Generating Nonlinear Correction Models of Dynamic Objects Based on Semantic Genetic Programming", bibdate = "2016-05-31", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icaisc/icaisc2016-2.html#BartczukG16", booktitle = "Artificial Intelligence and Soft Computing - 15th International Conference, {ICAISC} 2016, Zakopane, Poland, June 12-16, 2016, Proceedings, Part {II}", publisher = "Springer", year = "2016", volume = "9693", editor = "Leszek Rutkowski and Marcin Korytkowski and Rafal Scherer and Ryszard Tadeusiewicz and Lotfi A. Zadeh and Jacek M. Zurada", isbn13 = "978-3-319-39383-4", pages = "249--261", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming", URL = "http://dx.doi.org/10.1007/978-3-319-39384-1", } @InProceedings{conf/icaisc/BartczukLK16, author = "Lukasz Bartczuk and Krystian Lapa and Petia D. Koprinkova-Hristova", title = "A New Method for Generating of Fuzzy Rules for the Nonlinear Modelling Based on Semantic Genetic Programming", bibdate = "2016-05-31", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icaisc/icaisc2016-2.html#BartczukLK16", booktitle = "Artificial Intelligence and Soft Computing - 15th International Conference, {ICAISC} 2016, Zakopane, Poland, June 12-16, 2016, Proceedings, Part {II}", publisher = "Springer", year = "2016", volume = "9693", editor = "Leszek Rutkowski and Marcin Korytkowski and Rafal Scherer and Ryszard Tadeusiewicz and Lotfi A. Zadeh and Jacek M. Zurada", isbn13 = "978-3-319-39383-4", pages = "262--278", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming", URL = "http://dx.doi.org/10.1007/978-3-319-39384-1", } @InProceedings{conf/icaisc/BartczukDR17, author = "Lukasz Bartczuk and Piotr Dziwinski and Vladimir G. Redko", title = "The Concept on Nonlinear Modelling of Dynamic Objects Based on State Transition Algorithm and Genetic Programming", booktitle = "Artificial Intelligence and Soft Computing - 16th International Conference, {ICAISC} 2017, Zakopane, Poland, June 11-15, 2017, Proceedings, Part {II}", editor = "Leszek Rutkowski and Marcin Korytkowski and Rafal Scherer and Ryszard Tadeusiewicz and Lotfi A. Zadeh and Jacek M. Zurada", year = "2017", volume = "10246", pages = "209--220", series = "Lecture Notes in Computer Science", publisher = "Springer", keywords = "genetic algorithms, genetic programming", bibdate = "2017-06-02", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icaisc/icaisc2017-2.html#BartczukDR17", isbn13 = "978-3-319-59059-2; 978-3-319-59060-8", DOI = "doi:10.1007/978-3-319-59060-8_20", } @Article{Bartlett:TEVC, author = "Deaglan J. Bartlett and Harry Desmond and Pedro G. Ferreira", journal = "IEEE Transactions on Evolutionary Computation", title = "Exhaustive Symbolic Regression", year = "2024", volume = "28", number = "4", pages = "950--964", month = aug, keywords = "genetic algorithms, genetic programming, Mathematical models, Complexity theory, Optimisation, Numerical models, Biological system modelling, Standards, Search problems, Symbolic regression, data analysis, minimum description length, MDL, model selection, cosmology", DOI = "doi:10.1109/TEVC.2023.3280250", ISSN = "1941-0026", size = "15 pages", abstract = "Symbolic Regression (SR) algorithms attempt to learn analytic expressions which fit data accurately and in a highly interpretable manner. Conventional SR suffers from two fundamental issues which we address here. First, these methods search the space stochastically (typically using genetic programming) and hence do not necessarily find the best function. Second, the criteria used to select the equation optimally balancing accuracy with simplicity have been variable and subjective. To address these issues we introduce Exhaustive Symbolic Regression (ESR), which systematically and efficiently considers all possible equations-made with a given basis set of operators and up to a specified maximum complexity- and is therefore guaranteed to find the true optimum (if parameters are perfectly optimised) and a complete function ranking subject to these constraints. We implement the minimum description length principle as a rigorous method for combining these preferences into a single objective. To illustrate the power of ESR we apply it to a catalogue of cosmic chronometers and the Pantheon+ sample of supernovae to learn the Hubble rate as a function of redshift, finding 40 functions (out of 5.2 million trial functions) that fit the data more economically than the Friedmann equation. These low-redshift data therefore do not uniquely prefer the expansion history of the standard model of cosmology. We make our code and full equation sets publicly available.", notes = "Also known as \cite{10136815} Institute of Cosmology and Gravitation, University of Portsmouth, PO1 3FX Portsmouth, UK", } @InProceedings{bartlett:2023:cogsci, author = "Laura K. Bartlett and Angelo Pirrone and Noman Javed and Peter C. R. Lane and Fernand Gobet", title = "Genetic programming for developing simple cognitive models", booktitle = "Proceedings of the 45th Annual Meeting of the Cognitive Science Society", year = "2023", editor = "M. Goldwater and F. K. Anggoro and B. K. Hayes and D. C. Ong", pages = "2833--2839", address = "Sydney, Australia", month = jul # " 26-29", keywords = "genetic algorithms, genetic programming, delayed-match-to-sample,memory, psychology", URL = "http://hdl.handle.net/2299/27181", URL = "https://escholarship.org/uc/item/08x8m02w", URL = "https://researchprofiles.herts.ac.uk/files/48593960/qt08x8m02w.pdf", size = "7 pages", abstract = "Frequently in psychology, simple tasks that are designed to tap a particular feature of cognition are used without considering the other mechanisms that might be at play. For example, the delayed-match-to-sample (DMTS) task is often used to examine short-term memory; however, a number of cognitive mechanisms interact to produce the observed behaviour, such as decision-making and attention processes. As these simple tasks form the basis of more complex psychological experiments and theories, it is critical to understand what strategies might be producing the recorded behaviour. The current paper uses the GEMS methodology, a system that generates models of cognition using genetic programming, and applies it to differing DMTS experimental conditions. We investigate the strategies that participants might be using, while looking at similarities and differences in strategy depending on task variations; in this case, changes to the interval between study and recall affected the strategies used by the generated models.", notes = "http://bit.ly/44AJvz3 VP-Z-624-1983", } @InProceedings{Bartoli:2011:EuroGP, author = "Alberto Bartoli and Giorgio Davanzo and Andrea {De Lorenzo} and Eric Medvet", title = "GP-based Electricity Price Forecasting", booktitle = "Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011", year = "2011", month = "27-29 " # apr, editor = "Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado", series = "LNCS", volume = "6621", publisher = "Springer Verlag", address = "Turin, Italy", pages = "37--48", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-20406-7", DOI = "doi:10.1007/978-3-642-20407-4_4", abstract = "The electric power market is increasingly relying on competitive mechanisms taking the form of day-ahead auctions, in which buyers and sellers submit their bids in terms of prices and quantities for each hour of the next day. Methods for electricity price forecasting suitable for these contexts are crucial to the success of any bidding strategy. Such methods have thus become very important in practice, due to the economic relevance of electric power auctions. In this work we propose a novel forecasting method based on Genetic Programming. Key feature of our proposal is the handling of outliers, i.e., regions of the input space rarely seen during the learning. Since a predictor generated with Genetic Programming can hardly provide acceptable performance in these regions, we use a classifier that attempts to determine whether the system is shifting toward a difficult-to-learn region. In those cases, we replace the prediction made by Genetic Programming by a constant value determined during learning and tailored to the specific subregion expected. We evaluate the performance of our proposal against a challenging baseline representative of the state-of-the-art. The baseline analyses a real-world dataset by means of a number of different methods, each calibrated separately for each hour of the day and recalibrated every day on a progressively growing learning set. Our proposal exhibits smaller prediction error, even though we construct one single model, valid for each hour of the day and used unmodified across the entire testing set. We believe that our results are highly promising and may open a broad range of novel solutions.", notes = "Part of \cite{Silva:2011:GP} EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011", } @InProceedings{Bartoli:2012:GECCOcomp, author = "Alberto Bartoli and Giorgio Davanzo and Andrea {De Lorenzo} and Marco Mauri and Eric Medvet and Enrico Sorio", title = "Automatic generation of regular expressions from examples with genetic programming", booktitle = "GECCO Companion '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion", year = "2012", editor = "Terry Soule and Anne Auger and Jason Moore and David Pelta and Christine Solnon and Mike Preuss and Alan Dorin and Yew-Soon Ong and Christian Blum and Dario Landa Silva and Frank Neumann and Tina Yu and Aniko Ekart and Will Browne and Tim Kovacs and Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and Giovanni Squillero and Nicolas Bredeche and Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and Martin Pelikan and Silja Meyer-Nienberg and Christian Igel and Greg Hornby and Rene Doursat and Steve Gustafson and Gustavo Olague and Shin Yoo and John Clark and Gabriela Ochoa and Gisele Pappa and Fernando Lobo and Daniel Tauritz and Jurgen Branke and Kalyanmoy Deb", isbn13 = "978-1-4503-1178-6", keywords = "genetic algorithms, Genetic programming: Poster", pages = "1477--1478", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330784.2331000", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "We explore the practical feasibility of a system based on genetic programming (GP) for the automatic generation of regular expressions. The user describes the desired task by providing a set of labeled examples, in the form of text lines. The system uses these examples for driving the evolutionary search towards a regular expression suitable for the specified task. Usage of the system should require neither familiarity with GP nor with regular expressions syntax. In our GP implementation each individual represents a syntactically correct regular expression. We performed an experimental evaluation on two different extraction tasks applied to real-world datasets and obtained promising results in terms of precision and recall, even in comparison to an earlier state-of-the-art proposal.", notes = "Also known as \cite{2331000} Distributed at GECCO-2012. ACM Order Number 910122.", } @InProceedings{Bartoli:2014:GECCO, author = "Alberto Bartoli and Andrea {De Lorenzo} and Eric Medvet and Fabiano Tarlao", title = "Playing Regex Golf with Genetic Programming", booktitle = "GECCO '14: Proceeding of the sixteenth annual conference on genetic and evolutionary computation conference", year = "2014", editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges", pages = "1063--1070", organisation = "SIGEVO", address = "Vancouver, BC, Canada", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming", month = "12-16 " # jul, isbn13 = "978-1-4503-2662-9", URL = "http://machinelearning.inginf.units.it/publications/international-conference-publications/playingregexgolfwithgeneticprogramming", URL = "http://doi.acm.org/10.1145/2576768.2598333", DOI = "doi:10.1145/2576768.2598333", abstract = "Regex golf has recently emerged as a specific kind of code golf, i.e., unstructured and informal programming competitions aimed at writing the shortest code solving a particular problem. A problem in regex golf consists in writing the shortest regular expression which matches all the strings in a given list and does not match any of the strings in another given list. The regular expression is expected to follow the syntax of a specified programming language, e.g., Javascript or PHP. In this paper, we propose a regex golf player internally based on Genetic Programming. We generate a population of candidate regular expressions represented as trees and evolve such population based on a multi-objective fitness which minimises the errors and the length of the regular expression. We assess experimentally our player on a popular regex golf challenge consisting of 16 problems and compare our results against those of a recently proposed algorithm---the only one we are aware of. Our player obtains scores which improve over the baseline and are highly competitive also with respect to human players. The time for generating a solution is usually in the order of tens minutes, which is arguably comparable to the time required by human players.", notes = "Also known as \cite{2598333} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)", } @InProceedings{Bartoli:2014:PPSN, author = "Alberto Bartoli and Simone Cumar and Andrea {De Lorenzo} and Eric Medvet", title = "Compressing Regular Expression Sets for Deep Packet Inspection", booktitle = "13th International Conference on Parallel Problem Solving from Nature", year = "2014", editor = "Thomas Bartz-Beielstein and Juergen Branke and Bogdan Filipic and Jim Smith", publisher = "Springer", isbn13 = "978-3-319-10761-5", pages = "394--403", series = "Lecture Notes in Computer Science", address = "Ljubljana, Slovenia", month = "13-17 " # sep, volume = "8672", keywords = "genetic algorithms, genetic programming", URL = "http://machinelearning.inginf.units.it/publications/international-conference-publications/compressingregularexpressionsetsfordeeppacketinspection", DOI = "doi:10.1007/978-3-319-10762-2_39", abstract = "The ability to generate security-related alerts while analysing network traffic in real time has become a key mechanism in many networking devices. This functionality relies on the application of large sets of regular expressions describing attack signatures to each individual packet. Implementing an engine of this form capable of operating at line speed is considerably difficult and the corresponding performance problems have been attacked from several points of view. In this work we propose a novel approach complementing earlier proposals: we suggest transforming the starting set of regular expressions to another set of expressions which is much smaller yet classifies network traffic in the same categories as the starting set. Key component of the transformation is an evolutionary search based on Genetic Programming: a large population of expressions represented as abstract syntax trees evolves by means of mutation and crossover, evolution being driven by fitness indexes tailored to the desired classification needs and which minimise the length of each expression. We assessed our proposals on real datasets composed of up to 474 expressions and the outcome has been very good, resulting in compressions in the order of 74percent. Our results are highly encouraging and demonstrate the power of evolutionary techniques in an important application domain.", notes = "PPSN-XIII", } @Article{Bartoli:2014:Computer, author = "Alberto Bartoli and Giorgio Davanzo and Andrea {De Lorenzo} and Eric Medvet and Enrico Sorio", journal = "IEEE Computer", title = "Automatic Synthesis of Regular Expressions from Examples", year = "2014", month = dec, volume = "47", number = "12", pages = "72--80", keywords = "genetic algorithms, genetic programming, text extraction, NLP", ISSN = "0018-9162", DOI = "doi:10.1109/MC.2014.344", size = "9 pages", abstract = "We propose a system for the automatic generation of regular expressions for text-extraction tasks. The user describes the desired task only by means of a set of labelled examples. The generated regexes may be used with common engines such as those that are part of Java, PHP, Perl and so on. Usage of the system does not require any familiarity with regular expressions syntax. We performed an extensive experimental evaluation on 12 different extraction tasks applied to real-world datasets. We obtained very good results in terms of precision and recall, even in comparison to earlier state-of-the-art proposals. Our results are highly promising toward the achievement of a practical surrogate for the specific skills required for generating regular expressions, and significant as a demonstration of what can be achieved with GP-based approaches on modern IT technology.", notes = "http://regex.inginf.units.it Levenshtein distance = edit distance. NSGA-II. 'We transform a tree into a regular expression by means of a depth-first post order visit' Also known as \cite{6994453}", } @InProceedings{Bartoli:2015:EuroGP, author = "Alberto Bartoli and Andrea {De Lorenzo} and Eric Medvet and Fabiano Tarlao", title = "Learning Text Patterns using Separate-and-Conquer Genetic Programming", booktitle = "18th European Conference on Genetic Programming", year = "2015", editor = "Penousal Machado and Malcolm I. Heywood and James McDermott and Mauro Castelli and Pablo Garcia-Sanchez and Paolo Burelli and Sebastian Risi and Kevin Sim", series = "LNCS", volume = "9025", publisher = "Springer", pages = "16--27", address = "Copenhagen", month = "8-10 " # apr, organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, Regular expressions, Multiple pattern, Programming by example, Text extraction", isbn13 = "978-3-319-16500-4", DOI = "doi:10.1007/978-3-319-16501-1_2", abstract = "The problem of extracting knowledge from large volumes of unstructured textual information has become increasingly important. We consider the problem of extracting text slices that adhere to a syntactic pattern and propose an approach capable of generating the desired pattern automatically, from a few annotated examples. Our approach is based on Genetic Programming and generates extraction patterns in the form of regular expressions that may be input to existing engines without any post-processing. Key feature of our proposal is its ability of discovering automatically whether the extraction task may be solved by a single pattern, or rather a set of multiple patterns is required. We obtain this property by means of a separate-and-conquer strategy: once a candidate pattern provides adequate performance on a subset of the examples, the pattern is inserted into the set of final solutions and the evolutionary search continues on a smaller set of examples including only those not yet solved adequately. Our proposal outperforms an earlier state-of-the-art approach on three challenging datasets", notes = "Part of \cite{Machado:2015:GP} EuroGP'2015 held in conjunction with EvoCOP2015, EvoMusArt2015 and EvoApplications2015", } @InProceedings{Bartoli:2015:GECCO, author = "Alberto Bartoli and Andrea {De Lorenzo} and Eric Medvet and Fabiano Tarlao and Marco Virgolin", title = "Evolutionary Learning of Syntax Patterns for Genic Interaction Extraction", booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation", year = "2015", editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr", isbn13 = "978-1-4503-3472-3", pages = "1183--1190", keywords = "genetic algorithms, genetic programming, Real World Applications", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739480.2754706", DOI = "doi:10.1145/2739480.2754706", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "There is an increasing interest in the development of techniques for automatic relation extraction from unstructured text. The biomedical domain, in particular, is a sector that may greatly benefit from those techniques due to the huge and ever increasing amount of scientific publications describing observed phenomena of potential clinical interest. In this paper, we consider the problem of automatically identifying sentences that contain interactions between genes and proteins, based solely on a dictionary of genes and proteins and a small set of sample sentences in natural language. We propose an evolutionary technique for learning a classifier that is capable of detecting the desired sentences within scientific publications with high accuracy. The key feature of our proposal, that is internally based on Genetic Programming, is the construction of a model of the relevant syntax patterns in terms of standard part-of-speech annotations. The model consists of a set of regular expressions that are learned automatically despite the large alphabet size involved. We assess our approach on two realistic datasets and obtain 74percent accuracy, a value sufficiently high to be of practical interest and that is in line with significant baseline methods.", notes = "Also known as \cite{2754706} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)", } @Article{Bartoli:2016:ASC, author = "Alberto Bartoli and Andrea {De Lorenzo} and Eric Medvet and Fabiano Tarlao", title = "Predicting the effectiveness of pattern-based entity extractor inference", journal = "Applied Soft Computing", volume = "46", pages = "398--406", year = "2016", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2016.05.023", URL = "http://www.sciencedirect.com/science/article/pii/S1568494616302241", abstract = "An essential component of any workflow leveraging digital data consists in the identification and extraction of relevant patterns from a data stream. We consider a scenario in which an extraction inference engine generates an entity extractor automatically from examples of the desired behaviour, which take the form of user-provided annotations of the entities to be extracted from a dataset. We propose a methodology for predicting the accuracy of the extractor that may be inferred from the available examples. We propose several prediction techniques and analyse experimentally our proposals in great depth, with reference to extractors consisting of regular expressions. The results suggest that reliable predictions for tasks of practical complexity may indeed be obtained quickly and without actually generating the entity extractor.", keywords = "genetic algorithms, genetic programming, String similarity metrics, Information extraction, Hardness estimation", } @Article{Bartoli:2016:ieeeTKDE, author = "Alberto Bartoli and Andrea {De Lorenzo} and Eric Medvet and Fabiano Tarlao", title = "Inference of Regular Expressions for Text Extraction from Examples", journal = "IEEE Transactions on Knowledge and Data Engineering", year = "2016", volume = "28", number = "5", pages = "1217--1230", month = may, keywords = "genetic algorithms, genetic programming", ISSN = "1041-4347", URL = "http://www.human-competitive.org/sites/default/files/bartoli-delorenzo-medvet-tarlao-text.txt", DOI = "doi:10.1109/TKDE.2016.2515587", abstract = "A large class of entity extraction tasks from text that is either semistructured or fully unstructured may be addressed by regular expressions, because in many practical cases the relevant entities follow an underlying syntactical pattern and this pattern may be described by a regular expression. In this work, we consider the long-standing problem of synthesizing such expressions automatically, based solely on examples of the desired behaviour. We present the design and implementation of a system capable of addressing extraction tasks of realistic complexity. Our system is based on an evolutionary procedure carefully tailored to the specific needs of regular expression generation by examples. The procedure executes a search driven by a multiobjective optimization strategy aimed at simultaneously improving multiple performance indexes of candidate solutions while at the same time ensuring an adequate exploration of the huge solution space. We assess our proposal experimentally in great depth, on a number of challenging datasets. The accuracy of the obtained solutions seems to be adequate for practical usage and improves over earlier proposals significantly. Most importantly, our results are highly competitive even with respect to human operators. A prototype is available as a web application at regex.inginf.units.it", notes = "Entered 2016 HUMIES Department of Engineering and Architecture (DIA), University of Trieste, Italy. Also known as \cite{7374717}", } @Article{Bartoli:2016:ieeeIS, author = "Alberto Bartoli and Eric Medvet and Andrea {De Lorenzo} and Fabiano Tarlao", title = "Can A Machine Replace Humans In Building Regular Expressions? A Case Study", journal = "IEEE Intelligent Systems", year = "2016", volume = "31", number = "6", pages = "15--21", month = nov, keywords = "genetic algorithms, genetic programming, Buildings, Creativity, Intelligent systems, Object recognition, Pattern matching, Web pages", ISSN = "1541-1672", URL = "http://www.human-competitive.org/sites/default/files/bartoli-delorenzo-medvet-tarlao-text.txt", DOI = "doi:10.1109/MIS.2016.46", size = "10 pages", abstract = "Regular expressions are routinely used in a variety of different application domains. Building a regular expression involves a considerable amount of skill, expertise and creativity. In this work we investigate whether a machine may surrogate these qualities and construct automatically regular expressions for tasks of realistic complexity. We discuss a large scale experiment involving more than 1700 users on 10 challenging tasks. We compared the solutions constructed by these users to those constructed by a tool based on Genetic Programming that we have recently developed and made publicly available. The quality of automatically-constructed solutions turned out to be similar to the quality of those constructed by the most skilled user group; and, the time for automatic construction was similar to the time required by human users.", notes = "Entered 2016 HUMIES University of Trieste. cited by \cite{Bartoli:2016:GECCOcomp}. Also known as \cite{7478431}", } @InProceedings{conf/sac/BartoliLMT16, author = "Alberto Bartoli and Andrea {De Lorenzo} and Eric Medvet and Fabiano Tarlao", title = "Active learning approaches for learning regular expressions with genetic programming", bibdate = "2016-06-06", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/sac/sac2016.html#BartoliLMT16", booktitle = "Proceedings of the 31st Annual {ACM} Symposium on Applied Computing, Pisa, Italy, April 4-8, 2016", publisher = "ACM", year = "2016", editor = "Sascha Ossowski", isbn13 = "978-1-4503-3739-7", pages = "97--102", keywords = "genetic algorithms, genetic programming, entity extraction, information extraction, machine learning, programming by examples", DOI = "doi:10.1145/2851613.2851668", abstract = "We consider the long-standing problem of the automatic generation of regular expressions for text extraction, based solely on examples of the desired behaviour. We investigate several active learning approaches in which the user annotates only one desired extraction and then merely answers extraction queries generated by the system. The resulting framework is attractive because it is the system, not the user, which digs out the data in search of the samples most suitable to the specific learning task. We tailor our proposals to a state-of-the-art learner based on Genetic Programming and we assess them experimentally on a number of challenging tasks of realistic complexity. The results indicate that active learning is indeed a viable framework in this application domain and may thus significantly decrease the amount of costly annotation effort required.", notes = "See \cite{Bartoli:2016:acmACR}", } @Article{Bartoli:2016:acmACR, author = "Alberto Bartoli and Andrea {De Lorenzo} and Eric Medvet and Fabiano Tarlao", title = "Regex-based Entity Extraction with Active Learning and Genetic Programming", journal = "ACM SIGAPP Applied Computing Review", year = "2016", volume = "16", number = "2", pages = "7--15", month = jun, keywords = "genetic algorithms, genetic programming, entity extraction, information extraction, machine learning, programming by examples", publisher = "ACM", address = "New York, NY, USA", ISSN = "1559-6915", URL = "https://sites.google.com/site/machinelearningts/publications/international-journal-publications/regex-basedentityextractionwithactivelearningandgeneticprogramming/2016-ACR-RegexEntityExtractionActiveLearningGP.pdf", URL = "http://doi.acm.org/10.1145/2993231.2993232", DOI = "doi:10.1145/2993231.2993232", acmid = "2993232", size = "9 pages", abstract = "We consider the long-standing problem of the automatic generation of regular expressions for text extraction, based solely on examples of the desired behaviour. We investigate several active learning approaches in which the user annotates only one desired extraction and then merely answers extraction queries generated by the system. The resulting framework is attractive because it is the system, not the user, which digs out the data in search of the samples most suitable to the specific learning task. We tailor our proposals to a state-of-the-art learner based on Genetic Programming and we assess them experimentally on a number of challenging tasks of realistic complexity. The results indicate that active learning is indeed a viable framework in this application domain and may thus significantly decrease the amount of costly annotation effort required.", notes = "See \cite{conf/sac/BartoliLMT16}. Also known as \cite{Bartoli:2016:REE:2993231.2993232}", } @InProceedings{Bartoli:2016:GECCOcomp, author = "Alberto Bartoli and Andrea {De Lorenzo} and Eric Medvet and Fabiano Tarlao", title = "On the Automatic Construction of Regular Expressions from Examples (GP vs. Humans 1-0)", booktitle = "GECCO 2016 Hot of the Press", year = "2016", editor = "Benjamin Doerr and Tobias Friedrich and Frank Neumann and Andrew M. Sutton and Martin Middendorf and Xiaodong Li and Emma Hart and Mengjie Zhang and Youhei Akimoto and Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and Daniele Loiacono and Julian Togelius and Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and Faustino Gomez and Carlos M. Fonseca and Heike Trautmann and Alberto Moraglio and William F. Punch and Krzysztof Krawiec and Zdenek Vasicek and Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and Boris Naujoks and Enrique Alba and Gabriela Ochoa and Simon Poulding and Dirk Sudholt and Timo Koetzing", pages = "155--156", keywords = "genetic algorithms, genetic programming", month = "20-24 " # jul, organisation = "SIGEVO", address = "Denver, USA", publisher = "ACM", publisher_address = "New York, NY, USA", isbn13 = "978-1-4503-4323-7", DOI = "doi:10.1145/2908961.2930946", abstract = "Regular expressions are systematically used in a number of different application domains. Writing a regular expression for solving a specific task is usually quite difficult, requiring significant technical skills and creativity. We have developed a tool based on Genetic Programming capable of constructing regular expressions for text extraction automatically, based on examples of the text to be extracted. We have recently demonstrated that our tool is human-competitive in terms of both accuracy of the regular expressions and time required for their construction. We base this claim on a large-scale experiment involving more than 1700 users on 10 text extraction tasks of realistic complexity. The F-measure of the expressions constructed by our tool was almost always higher than the average F-measure of the expressions constructed by each of the three categories of users involved in our experiment (Novice, Intermediate, Experienced). The time required by our tool was almost always smaller than the average time required by each of the three categories of users. The experiment is described in full detail in Can a machine replace humans? A case study. IEEE Intelligent Systems, 2016 \cite{Bartoli:2016:ieeeIS}", notes = "Distributed at GECCO-2016.", } @InProceedings{Bartoli:2016:PPSN, author = "Alberto Bartoli and Andrea {De Lorenzo} and Eric Medvet and Fabiano Tarlao", title = "Syntactical Similarity Learning by means of Grammatical Evolution", booktitle = "14th International Conference on Parallel Problem Solving from Nature", year = "2016", editor = "Julia Handl and Emma Hart and Peter R. Lewis and Manuel Lopez-Ibanez and Gabriela Ochoa and Ben Paechter", volume = "9921", series = "LNCS", pages = "260--269", address = "Edinburgh", month = "17-21 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", isbn13 = "978-3-319-45823-6", DOI = "doi:10.1007/978-3-319-45823-6_24", abstract = "Several research efforts have shown that a similarity function synthesized from examples may capture an application-specific similarity criterion in a way that fits the application needs more effectively than a generic distance definition. In this work, we propose a similarity learning algorithm tailored to problems of syntax-based entity extraction from unstructured text streams. The algorithm takes in input pairs of strings along with an indication of whether they adhere or not adhere to the same syntactic pattern. Our approach is based on Grammatical Evolution and explores systematically a similarity definition space including all functions that may be expressed with a specialized, simple language that we have defined for this purpose. We assessed our proposal on patterns representative of practical applications. The results suggest that the proposed approach is indeed feasible and that the learned similarity function is more effective than the Levenshtein distance and the Jaccard similarity index.", notes = "PPSN2016", } @Article{Bartoli:2017:ieeeTC, author = "Alberto Bartoli and Andrea {De Lorenzo} and Eric Medvet and Fabiano Tarlao", journal = "IEEE Transactions on Cybernetics", title = "Active Learning of Regular Expressions for Entity Extraction", year = "2018", volume = "48", number = "3", pages = "1067--1080", month = mar, keywords = "genetic algorithms, genetic programming, automatic programming, evolutionary computation, inference mechanisms, man machine systems, semisupervised learning, text processing", ISSN = "2168-2267", DOI = "doi:10.1109/TCYB.2017.2680466", size = "14 pages", abstract = "We consider the automatic synthesis of an entity extractor, in the form of a regular expression, from examples of the desired extractions in an unstructured text stream. This is a long-standing problem for which many different approaches have been proposed, which all require the preliminary construction of a large dataset fully annotated by the user. we propose an active learning approach aimed at minimizing the user annotation effort: the user annotates only one desired extraction and then merely answers extraction queries generated by the system. During the learning process, the system digs into the input text for selecting the most appropriate extraction query to be submitted to the user in order to improve the current extractor. We construct candidate solutions with genetic programming (GP) and select queries with a form of querying-by-committee, i.e., based on a measure of disagreement within the best candidate solutions. All the components of our system are carefully tailored to the peculiarities of active learning with GP and of entity extraction from unstructured text. We evaluate our proposal in depth, on a number of challenging datasets and based on a realistic estimate of the user effort involved in answering each single query. The results demonstrate high accuracy with significant savings in terms of computational effort, annotated characters, and execution time over a state-of-the-art baseline.", notes = "Also known as \cite{7886274}", } @Article{BARTOLI:2019:ASC, author = "Alberto Bartoli and Andrea {De Lorenzo} and Eric Medvet and Giovanni Squillero", title = "Multi-level diversity promotion strategies for Grammar-guided Genetic Programming", journal = "Applied Soft Computing", volume = "83", pages = "105599", year = "2019", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2019.105599", URL = "http://www.sciencedirect.com/science/article/pii/S1568494619303795", keywords = "genetic algorithms, genetic programming, Representation, Grammatical evolution, CFGGP, SGE, WHGE", abstract = "Grammar-guided Genetic Programming (G3P) is a family of Evolutionary Algorithms that can evolve programs in any language described by a context-free grammar. The most widespread members of this family are based on an indirect representation: a sequence of bits or integers (the genotype) is transformed into a string of the language (the phenotype) by means of a mapping function, and eventually into a fitness value. Unfortunately, the flexibility brought by this mapping is also likely to introduce non-locality phenomena, reduce diversity, and hamper the effectiveness of the algorithm. In this paper, we experimentally characterize how population diversity, measured at different levels, varies for four popular G3P approaches. We then propose two strategies for promoting diversity which are general, independent both from the specific problem being tackled and from the other components of the Evolutionary Algorithm, such as genotype-phenotype mapping, selection criteria, and genetic operators. We experimentally demonstrate their efficacy in a wide range of conditions and from different points of view. The results also confirm the preponderant importance of the phenotype-level analyzes in diversity promotion", } @Article{Bartoli:2019:ieeeTCyber, author = "Alberto Bartoli and Mauro Castelli and Eric Medvet", title = "Weighted Hierarchical Grammatical Evolution", journal = "IEEE Transactions on Cybernetics", year = "2020", volume = "50", number = "2", pages = "476--488", month = feb, keywords = "genetic algorithms, genetic programming, Grammatical Evolution, genotype-phenotype, mapping, representation", ISSN = "2168-2267", URL = "https://sites.google.com/site/machinelearningts/publications/international-journal-publications/weightedhierarchicalgrammaticalevolution/2018-TCyb-WHGE.pdf", DOI = "doi:10.1109/TCYB.2018.2876563", size = "13 pages", abstract = "Grammatical Evolution (GE) is one of the most widespread techniques in evolutionary computation. Genotypes in GE are bit strings while phenotypes are strings of a language defined by a user-provided context-free grammar (CFG). In this work, we propose a novel procedure for mapping genotypes to phenotypes that we call Weighted Hierarchical GE (WHGE). WHGE imposes a form of hierarchy on the genotype and encodes grammar symbols with a varying number of bits based on the relative expressive power of those symbols. WHGE does not impose any constraint on the overall GE framework, in particular, WHGE may handle recursive grammars, uses the classical genetic operators, and does not need to define any bound in advance on the size of phenotypes. We assessed experimentally our proposal in depth on a set of challenging and carefully selected benchmarks, comparing the results to the standard GE framework as well as to two of the most significant enhancements proposed in the literature: Position-independent GE and Structured GE. Our results show that WHGE delivers very good results in terms of fitness as well as in terms of the properties of the genotype-phenotype mapping procedure.", notes = "also known as \cite{8525307}", } @Article{Bartoli:ieeeTcybernetics, author = "Alberto Bartoli and Andrea {De Lorenzo} and Eric Medvet and Fabiano Tarlao", title = "Automatic Search-and-Replace From Examples With Coevolutionary Genetic Programming", journal = "IEEE Transactions on Cybernetics", year = "2021", volume = "51", number = "5", pages = "2612--2624", month = may, keywords = "genetic algorithms, genetic programming, diversity promotion, find-and-replace, programming by examples, regular expressions", ISSN = "2168-2267", DOI = "doi:10.1109/TCYB.2019.2918337", abstract = "We describe the design and implementation of a system for executing search-and-replace text processing tasks automatically, based only on examples of the desired behaviour. The examples consist of pairs describing the original string and the desired modified string. Their construction, thus, does not require any specific technical skill. The system constructs a solution to the specified task that can be used unchanged on popular existing software for text processing. The solution consists of a search pattern coupled with a replacement expression: the former is a regular expression which describes both the strings to be replaced and their portions to be reused in the latter, which describes how to build the modified strings. Our proposed system is internally based on Genetic Programming and implements a form of cooperative coevolution in which two separate populations are evolved independently, one for search patterns and the other for replacement expressions. We assess our proposal on six tasks of realistic complexity obtaining very good results, both in terms of absolute quality of the solutions and with respect to the challenging baselines considered.", notes = "also known as \cite{8734703}", } @Article{bartoli:2023:GPEM, author = "Alberto Bartoli and Luca Manzoni and Eric Medvet", title = "Commentary on {``Jaws 30'', by W. B. Langdon}", journal = "Genetic Programming and Evolvable Machines", year = "2023", volume = "24", number = "2", pages = "Article number: 23", month = dec, note = "Special Issue: Thirtieth Anniversary of Genetic Programming: On the Programming of Computers by Means of Natural Selection", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/drZe8", DOI = "doi:10.1007/s10710-023-09471-1", size = "4 pages", abstract = "While genetic programming has had a huge impact on the research community, it is fair to say that its impact on industry and practitioners has been much smaller. In this commentary we elaborate on this claim and suggest some broad research goals aimed at greatly increasing such impact.", notes = "Response to \cite{langdon:jaws30} Peer commentary editors: Leonardo Vanneschi and Leonardo Trujillo \cite{Vanneschi:2023:GPEM} See also \cite{jaws30_reply}", } @InProceedings{DBLP:conf/rsctc/BartonV08, author = "Alan J. Barton and Julio J. Valdes", title = "Computational Intelligence Techniques Applied to Magnetic Resonance Spectroscopy Data of Human Brain Cancers", booktitle = "Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing, RSCTC 2008", editor = "Chien-Chung Chan and Jerzy W. Grzymala-Busse and Wojciech Ziarko", series = "Lecture Notes in Computer Science", volume = "5306", year = "2008", pages = "485--494", address = "Akron, OH, USA", month = oct # " 23-25", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-88423-1", DOI = "doi:10.1007/978-3-540-88425-5_50", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "Computational intelligence techniques were applied to human brain cancer magnetic resonance spectral data. In particular, two approaches, Rough Sets and a Genetic Programming-based Neural Network were investigated and then confirmed via a systematic Individual Dichotomization algorithm. Good preliminary results were obtained with 100percent training and 100percent testing accuracy that differentiate normal versus malignant samples.", } @InProceedings{Barton:2009:IJCNN, author = "Alan J. Barton and Julio J. Valdes and Robert Orchard", title = "Learning the neuron functions within a neural network via Genetic Programming: Applications to geophysics and hydrogeology", booktitle = "International Joint Conference on Neural Networks, IJCNN 2009", year = "2009", pages = "264--271", address = "Atlanta, Georgia, USA", month = jun # " 14-19", keywords = "genetic algorithms, genetic programming, gene expression programming, geophysics, geophysics computing, hydrology, neural nets, geophysics, hydrogeology, neural network classifier, neural network neurons, neuron functions", DOI = "doi:10.1109/IJCNN.2009.5178731", size = "8 pages", abstract = "A neural network classifier is sought. Classical neural network neurons are aggregations of a weight multiplied by an input value and then controlled via an activation function. This paper learns everything within the neuron using a variant of genetic programming called gene expression programming. That is, this paper does not explicitly use weights or activation functions within a neuron, nor bias nodes within a layer. Promising preliminary results are reported for a study of the detection of underground caves (a 1 class problem) and for a study of the interaction of water and minerals near a glacier in the Arctic (a 5 class problem).", notes = " one class membership. ANN variable with 0 mean 1 standard deviation. Also known as \cite{5178731} See \cite{Barton2009614}", } @Article{Barton2009614, author = "Alan J. Barton and Julio J. Valdes and Robert Orchard", title = "Neural networks with multiple general neuron models: A hybrid computational intelligence approach using Genetic Programming", journal = "Neural Networks", volume = "22", number = "5-6", pages = "614--622", year = "2009", note = "Advances in Neural Networks Research: IJCNN2009, 2009 International Joint Conference on Neural Networks", editor = "S. Bressler and R. Kozma and L. Perlovsky and Venayagamoorthy", keywords = "genetic algorithms, genetic programming, General neuron model, Evolutionary Computation, Hybrid algorithm, Machine learning, Parameter space, Visualization", ISSN = "0893-6080", DOI = "doi:10.1016/j.neunet.2009.06.043", URL = "http://www.sciencedirect.com/science/article/B6T08-4WNRK15-3/2/d8803b07859caa7efcd99475af7005ae", abstract = "Classical neural networks are composed of neurons whose nature is determined by a certain function (the neuron model), usually pre-specified. In this paper, a type of neural network (NN-GP) is presented in which: (i) each neuron may have its own neuron model in the form of a general function, (ii) any layout (i.e network interconnection) is possible, and (iii) no bias nodes or weights are associated to the connections, neurons or layers. The general functions associated to a neuron are learned by searching a function space. They are not provided a priori, but are rather built as part of an Evolutionary Computation process based on Genetic Programming. The resulting network solutions are evaluated based on a fitness measure, which may, for example, be based on classification or regression errors. Two real-world examples are presented to illustrate the promising behaviour on classification problems via construction of a low-dimensional representation of a high-dimensional parameter space associated to the set of all network solutions.", } @InProceedings{Barton:2010:ieeeCIBCB, author = "Alan J. Barton", title = "Searching for a single mathematical function to address the nonlinear retention time shifts problem in nanoLC-MS data: A fuzzy-evolutionary computational proteomics approach", booktitle = "2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)", year = "2010", month = may, abstract = "Proteomics involves collecting and analysing information about proteins within one or more complex samples in order to address a biological problem. One methodology is the use of high performance liquid chromatography coupled mass spectrometry (nanoLC-MS). In such a case, the accurate determination of non-linear peptide retention times between runs is expected to increase the number of identified peptides and hence, proteins. There are many approaches when using a computer for such a problem; including very interactive to completely non-interactive algorithms for finding global and local functions that may be either explicit or implicit. This paper extends previous work and explores finding an explicit global function for which two stages are involved: i) computation of a set of candidate functions (results) by the algorithm, and ii) searching within the set for patterns of interest. For the first stage, three classes of approximating global functions are considered: Class 1 functions that have a completely unknown structure, Class 2 functions that have a tiny amount of domain knowledge incorporated, and Class 3 functions that have a small amount of domain knowledge incorporated. For the second stage, some issues with current similarity measures for mathematical expressions are discussed and a new measure is proposed. Preliminary experimental results with an Evolutionary Computation algorithm called Gene Expression Programming (a variant of Genetic Programming) when used with a fuzzy membership within the fitness function are reported.", keywords = "genetic algorithms, genetic programming, gene expression programming, fuzzy-evolutionary computational proteomics approach, liquid chromatography coupled mass spectrometry, mathematical function, nanoLC-MS, nanoLC-MS data, nonlinear retention time shifts problem, biocomputing, evolutionary computation, fuzzy set theory, proteins, proteomics", DOI = "doi:10.1109/CIBCB.2010.5510688", notes = "Also known as \cite{5510688}", } @Article{Bartos:2013:SIGMOD, author = "Tomas Bartos and Tomas Skopal and Juraj Mosko", title = "Towards Efficient Indexing of Arbitrary Similarity", journal = "SIGMOD Record", year = "2013", volume = "42", number = "2", pages = "5--10", month = jul, note = "Vision Paper. ACM Special Interest Group on Management of Data", keywords = "genetic algorithms, genetic programming", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/sigmod.bib", acmid = "2503794", publisher = "ACM", address = "New York, NY, USA", ISSN = "0163-5808", URL = "http://doi.acm.org/10.1145/2503792.2503794", DOI = "doi:10.1145/2503792.2503794", size = "6 pages", abstract = "The popularity of similarity search expanded with the increased interest in multimedia databases, bioinformatics, or social networks, and with the growing number of users trying to find information in huge collections of unstructured data. During the exploration, the users handle database objects in different ways based on the used similarity models, ranging from simple to complex models. Efficient indexing techniques for similarity search are required especially for growing databases. In this paper, we study implementation possibilities of the recently announced theoretical framework SIMDEX, the task of which is to algorithmically explore a given similarity space and find possibilities for efficient indexing. Instead of a fixed set of indexing properties, such as metric space axioms, SIMDEX aims to seek for alternative properties that are valid in a particular similarity model (database) and, at the same time, provide efficient indexing. In particular, we propose to implement the fundamental parts of SIMDEX by means of the genetic programming (GP) which we expect will provide high-quality resulting set of expressions (axioms) useful for indexing.", acknowledgement = "Nelson H. F. Beebe, University of Utah, Department of Mathematics, 110 LCB, 155 S 1400 E RM 233, Salt Lake City, UT 84112-0090, USA, Tel: +1 801 581 5254, FAX: +1 801 581 4148, e-mail: \path|beebe@math.utah.edu|, \path|beebe@acm.org|, \path|beebe@computer.org| (Internet), URL: \path|http://www.math.utah.edu/~beebe/|", notes = "Bartos:2013:TEI:2503792.2503794", } @InProceedings{Bartovs:2013:GECCO, author = "Tomas Bartos and Tomas Skopal and Juraj Mosko", title = "Efficient indexing of similarity models with inequality symbolic regression", booktitle = "GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference", year = "2013", editor = "Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi", isbn13 = "978-1-4503-1963-8", pages = "901--908", keywords = "genetic algorithms, genetic programming", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2463372.2463487", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "The increasing amount of available unstructured content introduced a new concept of searching for information, the content-based retrieval. The principle behind is that the objects are compared based on their content which is far more complex than simple text or metadata based searching. Many indexing techniques arose to provide an efficient and effective similarity searching. However, these methods are restricted to a specific domain such as the metric space model. If this prerequisite is not fulfilled, indexing cannot be used, while each similarity search query degrades to sequential scanning which is unacceptable for large datasets. Inspired by previous successful results, we decided to apply the principles of genetic programming to the area of database indexing. We developed the GP-SIMDEX which is a universal framework that is capable of finding precise and efficient indexing methods for similarity searching for any given similarity data. For this purpose, we introduce the inequality symbolic regression principle and show how it helps the GP-SIMDEX Framework to find appropriate results that in most cases outperform the best-known indexing methods.", notes = "Also known as \cite{2463487} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)", } @InProceedings{conf/sisap/BartosS13, author = "Tomas Bartos and Tomas Skopal", title = "Designing Similarity Indexes with Parallel Genetic Programming", booktitle = "Proceedings of the 6th International Conference on Similarity Search and Applications (SISAP 2013)", year = "2013", editor = "Nieves R. Brisaboa and Oscar Pedreira and Pavel Zezula", volume = "8199", series = "Lecture Notes in Computer Science", pages = "294--299", address = "A Coruna, Spain", month = oct # " 2-4", publisher = "Springer", keywords = "genetic algorithms, genetic programming", bibdate = "2013-09-02", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/sisap/sisap2013.html#BartosS13", isbn13 = "978-3-642-41061-1", URL = "http://dx.doi.org/10.1007/978-3-642-41062-8", URL = "http://dx.doi.org/10.1007/978-3-642-41062-8_29", DOI = "doi:10.1007/978-3-642-41062-8_29", size = "6 pages", abstract = "The increasing diversity of unstructured databases leads to the development of advanced indexing techniques as the metric indexing model does not fit to the general similarity models. Once the most critical postulate, namely the triangle inequality, does not hold, the metric model produces notable errors during the query evaluation. To overcome this situation and to obtain more qualitative results, we want to discover better indexing models for databases using arbitrary similarity measures. However, each database is unique in a specific way, so we outline the automatic way of exploring the best indexing method. We introduce the exploration approach using parallel genetic programming principles in a multi-threaded environment built upon recently introduced SIMDEX Framework. Furthermore, we introduce smart pivot table which is an intelligent indexing method capable of incorporating obtained results. We supplement the theoretical background with experiments showing the achieved improvements in comparison to the single-threaded evaluations.", } @InCollection{Bartram:2008:ECP, author = "Derek Bartram and Michael Burrow and Xin Yao", title = "A Computational Intelligence Approach to Railway Track Intervention Planning", booktitle = "Evolutionary Computation in Practice", publisher = "Springer", year = "2008", editor = "Tina Yu and David Davis and Cem Baydar and Rajkumar Roy", volume = "88", series = "Studies in Computational Intelligence", chapter = "8", pages = "163--198", keywords = "genetic algorithms, genetic programming, k-means, RPCL, learning", isbn13 = "978-3-540-75770-2", DOI = "doi:10.1007/978-3-540-75771-9_8", abstract = "Railway track intervention planning is the process of specifying the location and time of required maintenance and renewal activities. To facilitate the process, decision support tools have been developed and typically use an expert system built with rules specified by track maintenance engineers. However, due to the complex interrelated nature of component deterioration, it is problematic for an engineer to consider all combinations of possible deterioration mechanisms using a rule based approach. To address this issue, this chapter describes an approach to the intervention planning using a variety of computational intelligence techniques. The proposed system learns rules for maintenance planning from historical data and incorporates future data to update the rules as they become available thus the performance of the system improves over time. To determine the failure type, historical deterioration patterns of sections of track are first analysed. A Rival Penalised Competitive Learning algorithm is then used to determine possible failure types. We have devised a generalised two stage evolutionary algorithm to produce curve functions for this purpose. The approach is illustrated using an example with real data which demonstrates that the proposed methodology is suitable and effective for the task in hand.", notes = "Part of \cite{TinaYu:2008:book} Scheduling, railroad, maintenance, planning. Missing data. Missing values. p181 function set + - * / sin cos tan power", } @Article{BartschJr:2016:TJU, author = "Georg {Bartsch Jr.} and Anirban P. Mitra and Sheetal A. Mitra and Arpit A. Almal and Kenneth E. Steven and Donald G. Skinner and David W. Fry and Peter F. Lenehan and William P. Worzel and Richard J. Cote", title = "Use of Artificial Intelligence and Machine Learning Algorithms with Gene Expression Profiling to Predict Recurrent Nonmuscle Invasive Urothelial Carcinoma of the Bladder", journal = "The Journal of Urology", volume = "195", number = "2", pages = "493--498", year = "2016", ISSN = "0022-5347", DOI = "doi:10.1016/j.juro.2015.09.090", URL = "http://www.sciencedirect.com/science/article/pii/S0022534715049629", abstract = "Purpose Due to the high recurrence risk of non-muscle invasive urothelial carcinoma it is crucial to distinguish patients at high risk from those with indolent disease. In this study we used a machine learning algorithm to identify the genes in patients with non muscle invasive urothelial carcinoma at initial presentation that were most predictive of recurrence. We used the genes in a molecular signature to predict recurrence risk within 5 years after transurethral resection of bladder tumour. Materials and Methods Whole genome profiling was performed on 112 frozen nonmuscle invasive urothelial carcinoma specimens obtained at first presentation on Human WG-6 BeadChips (Illumina). A genetic programming algorithm was applied to evolve classifier mathematical models for outcome prediction. Cross-validation based resampling and gene use frequencies were used to identify the most prognostic genes, which were combined into rules used in a voting algorithm to predict the sample target class. Key genes were validated by quantitative polymerase chain reaction. Results The classifier set included 21 genes that predicted recurrence. Quantitative polymerase chain reaction was done for these genes in a subset of 100 patients. A 5-gene combined rule incorporating a voting algorithm yielded 77percent sensitivity and 85percent specificity to predict recurrence in the training set, and 69percent and 62percent, respectively, in the test set. A singular 3-gene rule was constructed that predicted recurrence with 80percent sensitivity and 90percent specificity in the training set, and 71percent and 67percent, respectively, in the test set. Conclusions Using primary nonmuscle invasive urothelial carcinoma from initial occurrences genetic programming identified transcripts in reproducible fashion, which were predictive of recurrence. These findings could potentially impact nonmuscle invasive urothelial carcinoma management.", keywords = "genetic algorithms, genetic programming, urinary bladder neoplasms, neoplasm recurrence, local, genome, algorithms, software", } @InProceedings{basanta03, author = "David Basanta and Mark A. Miodownik and Elizabeth A. Holm", title = "Evolving Cellular Automata to Grow Microstructures", booktitle = "Genetic Programming, Proceedings of EuroGP'2003", year = "2003", editor = "Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa", volume = "2610", series = "LNCS", pages = "1--10", address = "Essex", publisher_address = "Berlin", month = "14-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-00971-X", URL = "http://rcswww.urz.tu-dresden.de/~basanta/eurogp03.pdf", DOI = "doi:10.1007/3-540-36599-0_1", abstract = "The properties of engineering structures such as cars, cell phones or bridges rely on materials and on the properties of these materials. The study of these properties, which are determined by the internal architecture of the material or microstructure, has significant importance for material scientists. One of the things needed for this study is a tool that can create microstructural patterns. In this paper we explore the use of a genetic algorithm to evolve the rules of an effector automata to recreate these microstructural patterns.", notes = "EuroGP'2003 held in conjunction with EvoWorkshops 2003", } @InProceedings{Basanta:2004:EH, author = "David Basanta and Mark A. Miodownik and Peter J. Bentley and Elizabeth A. Holm", title = "Evolving and Growing Microstructures of Materials using Biologically Inspired CA", booktitle = "2004 NASA/DoD Conference on Evolvable Hardware", year = "2004", publisher = "IEEE Computer Society", editor = "RS Zebulum", pages = "275--275", address = "Seattle, Washington, USA", month = jun # " 24-26", keywords = "genetic algorithms, genetic programming", ISBN = "0-7695-2145-2", DOI = "doi:10.1109/EH.2004.1310841", abstract = "The properties of engineering structures, such as robotic arms, aircrafts or bridges, rely on the properties of the materials used to build them. The internal architecture of the material or microstructure determines its properties and therefore, its study is of great interest for engineers and material scientists. Although there are tools that can provide 2D microstructural information, tools that can be used to obtain 3D characterisations of microstructures for routine analysis are not yet available to material scientists. In this paper we will describe Microconstructor. Microconstructor comprises a genetic algorithm that evolves populations of Cellular Automata inspired by developmental biology that self organise into 3D patterns that can be used for microstructural analysis.", } @Article{Baser:2017:Energy, author = "Furkan Baser and Haydar Demirhan", title = "A fuzzy regression with support vector machine approach to the estimation of horizontal global solar radiation", journal = "Energy", volume = "123", pages = "229--240", year = "2017", keywords = "genetic algorithms, genetic programming, Artificial intelligence, Fuzzy regression, METEONORM, Solar radiation model, Support vector machines", ISSN = "0360-5442", DOI = "doi:10.1016/j.energy.2017.02.008", URL = "http://www.sciencedirect.com/science/article/pii/S0360544217301822", abstract = "Accurate estimation of the amount of horizontal global solar radiation for a particular field is an important input for decision processes in solar radiation investments. In this article, we focus on the estimation of yearly mean daily horizontal global solar radiation by using an approach that uses fuzzy regression functions with support vector machine (FRF-SVM). This approach is not seriously affected by outlier observations and does not suffer from the over-fitting problem. To demonstrate the utility of the FRF-SVM approach in the estimation of horizontal global solar radiation, we conduct an empirical study over a dataset collected in Turkey and applied the FRF-SVM approach with several kernel functions. Then, we compare the estimation accuracy of the FRF-SVM approach to an adaptive neuro-fuzzy system and a coplot supported-genetic programming approach. We observe that the FRF-SVM approach with a Gaussian kernel function is not affected by both outliers and over-fitting problem and gives the most accurate estimates of horizontal global solar radiation among the applied approaches. Consequently, the use of hybrid fuzzy functions and support vector machine approaches is found beneficial in long-term forecasting of horizontal global solar radiation over a region with complex climatic and terrestrial characteristics.", } @InProceedings{Basgalupp:2014:GECCO, author = "Marcio Porto Basgalupp and Rodrigo Coelho Barros and Tiago Barabasz", title = "A grammatical evolution based hyper-heuristic for the automatic design of split criteria", booktitle = "GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation", year = "2014", editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges", isbn13 = "978-1-4503-2662-9", pages = "1311--1318", keywords = "genetic algorithms, genetic programming, grammatical evolution", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2576768.2598327", DOI = "doi:10.1145/2576768.2598327", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Top-down induction of decision trees (TDIDT) is a powerful method for data classification. A major issue in TDIDT is the decision on which attribute should be selected for dividing the nodes in subsets, creating the tree. For performing such a task, decision trees make use of a split criterion, which is usually an information-theory based measure. Apparently, there is no free-lunch regarding decision-tree split criteria, as is the case of most things in machine learning. Each application may benefit from a distinct split criterion, and the problem we pose here is how to identify the suitable split criterion for each possible application that may emerge. We propose in this paper a grammatical evolution algorithm for automatically generating split criteria through a context-free grammar. We name our new approach ESC-GE (Evolutionary Split Criteria with Grammatical Evolution). It is empirically evaluated on public gene expression datasets, and we compare its performance with state-of-the-art split criteria, namely the information gain and gain ratio. Results show that ESC-GE outperforms the baseline criteria in the domain of gene expression data, indicating its effectiveness for automatically designing tailor-made split criteria.", notes = "Also known as \cite{2598327} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)", } @InProceedings{Basher:2022:CEC, author = "Sheikh Faishal Basher and Brian J. Ross", booktitle = "2022 IEEE Congress on Evolutionary Computation (CEC)", title = "Managing Diversity and Many Objectives in Evolutionary Design", year = "2022", editor = "Carlos A. Coello Coello and Sanaz Mostaghim", address = "Padua, Italy", month = "18-23 " # jul, isbn13 = "978-1-6654-6708-7", abstract = "A new approach to evolving a diversity of high-quality solutions for problems having many objectives is presented. Mouret and Clunes MAP-Elites algorithm has been proposed as a way to evolve an assortment of diverse solutions to a problem. We extend MAP-Elites in a number of ways. We introduce into MAP-elites the many objective strategy called sum-of-ranks, which enables problems with many objectives (4 and more) to be considered in the MAP. We also enhance MAP-Elites by extending it with multiple solutions per cell (the original MAP-Elites saves only a single solution per cell). Different ways of selecting cell members for reproduction are also considered. We test the new MAP-Elites strategies on the evolutionary art application of image generation. Using procedural textures, genetic programming is used with upwards of 15 light weight image features to guide fitness. The goal is to evolve images that share image features with a given target image. Our experiments show that the new MAP-Elites algorithms produce a large number of diverse solutions, which can be competitive in quality to those from standard GP runs.", keywords = "genetic algorithms, genetic programming, Visualization, Image synthesis, Image color analysis, Sociology, Search problems, Entropy, Performance analysis, Diversity, Many-objective Optimization, Evolutionary Art, Procedural Texture", DOI = "doi:10.1109/CEC55065.2022.9870353", notes = "Also known as \cite{9870353}", } @Article{journals/apin/Bashir14, author = "Shariq Bashir", title = "Combining pre-retrieval query quality predictors using genetic programming", journal = "Appl. Intell", year = "2014", number = "3", volume = "40", pages = "525--535", keywords = "genetic algorithms, genetic programming", bibdate = "2014-03-10", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/apin/apin40.html#Bashir14", URL = "http://dx.doi.org/10.1007/s10489-013-0475-z", } @Article{Bashir:2016:ASC, author = "Shariq Bashir and Wasif Afzal and Abdul Rauf Baig", title = "Opinion-Based Entity Ranking using learning to rank", journal = "Applied Soft Computing", volume = "38", pages = "151--163", year = "2016", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2015.10.001", URL = "http://www.sciencedirect.com/science/article/pii/S156849461500616X", abstract = "As social media and e-commerce on the Internet continue to grow, opinions have become one of the most important sources of information for users to base their future decisions on. Unfortunately, the large quantities of opinions make it difficult for an individual to comprehend and evaluate them all in a reasonable amount of time. The users have to read a large number of opinions of different entities before making any decision. Recently a new retrieval task in information retrieval known as Opinion-Based Entity Ranking (OpER) has emerged. OpER directly ranks relevant entities based on how well opinions on them are matched with a user's preferences that are given in the form of queries. With such a capability, users do not need to read a large number of opinions available for the entities. Previous research on OpER does not take into account the importance and subjectivity of query keywords in individual opinions of an entity. Entity relevance scores are computed primarily on the basis of occurrences of query keywords match, by assuming all opinions of an entity as a single field of text. Intuitively, entities that have positive judgements and strong relevance with query keywords should be ranked higher than those entities that have poor relevance and negative judgments. This paper outlines several ranking features and develops an intuitive framework for OpER in which entities are ranked according to how well individual opinions of entities are matched with the user's query keywords. As a useful ranking model may be constructed from many ranking features, we apply learning to rank approach based on genetic programming (GP) to combine features in order to develop an effective retrieval model for OpER task. The proposed approach is evaluated on two collections and is found to be significantly more effective than the standard OpER approach.", keywords = "genetic algorithms, genetic programming, Entity Ranking, Opinion analysis, Learning to rank", } @Misc{DBLP:journals/corr/BasiosLWKLB17, author = "Michail Basios and Lingbo Li and Fan Wu and Leslie Kanthan and Donald Lawrence and Earl T. Barr", title = "Darwinian Data Structure Selection", howpublished = "arXiv", year = "2017", month = "10 " # jun, keywords = "genetic algorithms, genetic programming, genetic improvement, Search-based software engineering, SBSE, Software analysis and optimisation, Multi-objective optimisation, SBSE, Software Engineering", timestamp = "Tue, 29 Aug 2017 15:03:42 +0200", biburl = "http://dblp.uni-trier.de/rec/bib/journals/corr/BasiosLWKLB17", bibsource = "dblp computer science bibliography, http://dblp.org", URL = "http://arxiv.org/abs/1706.03232", size = "11 pages", abstract = "Data structure selection and tuning is laborious but can vastly improve application performance and memory footprint. We introduce ARTEMIS a multiobjective, cloud-based optimisation framework that automatically finds optimal, tuned data structures and rewrites applications to use them. ARTEMIS achieves substantial performance improvements for every project in a set of 29 Java programs uniformly sampled from GitHub. For execution time, CPU usage, and memory consumption, ARTEMIS finds at least one solution for each project that improves all measures. The median improvement across all these best solutions is 8.38percent for execution time, 24.27percent for memory consumption and 11.61percent for CPU usage. In detail, ARTEMIS improved the memory consumption of JUnit4, a ubiquitous Java testing framework, by 45.42percent memory, while also improving its execution time 2.29percent at the cost a 1.25percent increase in CPU usage. LinkedIn relies on the Cleo project as their autocompletion engine for search. ARTEMIS improves its execution time by 12.17percent, its CPU usage by 4.32percent and its memory consumption by 23.91percent.", } @InProceedings{Basios:2017:SSBSE, author = "Michail Basios and Lingbo Li and Fan Wu and Leslie Kanthan and Earl T. Barr", title = "Optimising Darwinian Data Structures on {Google Guava}", booktitle = "Proceedings of the 9th International Symposium on Search Based Software Engineering, SSBSE 2017", year = "2017", editor = "Tim Menzies and Justyna Petke", volume = "10452", series = "LNCS", pages = "161--167", address = "Paderborn, Germany", month = sep # " 9-11", publisher = "Springer", note = "Best Challenge Paper Award", keywords = "genetic algorithms, genetic improvement, SBSE, Software analysis and optimisation, Multi-objective optimisation", isbn13 = "978-3-319-66299-2", DOI = "doi:10.1007/978-3-319-66299-2_14", size = "7 pages", abstract = "Data structure selection and tuning is laborious but can vastly improve application performance and memory footprint. In this paper, we demonstrate how artemis, a multiobjective, cloud-based optimisation framework can automatically find optimal, tuned data structures and how it is used for optimising the Guava library. From the proposed solutions that artemis found, 27percent of them improve all measures (execution time, CPU usage, and memory consumption). More specifically, artemis managed to improve the memory consumption of Guava by up 13percent, execution time by up to 9percent, and 4percent CPU usage.", notes = "Is this GP? http://ssbse17.github.io/ Co-located with FSE/ESEC 2017", } @InProceedings{Basios:2018:FSE, author = "Michail Basios and Lingbo Li and Fan Wu and Leslie Kanthan and Earl T. Barr", title = "Darwinian Data Structure Selection", booktitle = "Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2018", year = "2018", editor = "Gary T. Leavens and Alessandro Garcia and Corina S. Pasareanu", pages = "118--128", address = "Lake Buena Vista, FL, USA", month = "4-9 " # nov, publisher = "ACM", keywords = "genetic algorithms, genetic programming, Genetic Improvement, Search-based Software Engineering, SBSE, Software Analysis and Optimisation", isbn13 = "978-1-4503-5573-5", URL = "http://human-competitive.org/sites/default/files/artemis.pdf", DOI = "doi:10.1145/3236024.3236043", acmid = "3236043", size = "11 pages", abstract = "Data structure selection and tuning is laborious but can vastly improve an applications performance and memory footprint. Some data structures share a common interface and enjoy multiple implementations. We call them Darwinian Data Structures (DDS), since we can subject their implementations to survival of the fittest. We introduce ARTEMIS a multi-objective, cloud-based search-based optimisation framework that automatically finds optimal, tuned DDS modulo a test suite, then changes an application to use that DDS. ARTEMIS achieves substantial performance improvements for every project in 5 Java projects from DaCapo benchmark, 8 popular projects and 30 uniformly sampled projects from GitHub. For execution time, CPU usage, and memory consumption, ARTEMIS finds at least one solution that improves all measures for 86percent (37/43) of the projects. The median improvement across the best solutions is 4.8percent, 10.1percent, 5.1percent for runtime, memory and CPU usage. These aggregate results understate ARTEMIS potential impact. Some of the benchmarks it improves are libraries or utility functions. Two examples are gson, a ubiquitous Java serialization framework, and xalan, Apache XML transformation tool. ARTEMIS improves gson by 16.5percent, 1percent and 2.2percent for memory, runtime, and CPU; ARTEMIS improves xalan's memory consumption by 23.5percent. Every client of these projects will benefit from these performance improvements.", notes = "Bronze winner 2019 HUMIES. Slides: http://www.human-competitive.org/sites/default/files/basiosslides.pptx Also known as \cite{Basios:2018:DDS:3236024.3236043}", } @PhdThesis{Basios_10070648_thesis, author = "Michail Basios", title = "Darwinian Code Optimisation", school = "Department of Computer Science, University College London", year = "2019", address = "UK", month = "18 " # jan, keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, APR, Blockchain, Java, JVM, Immortal bugs, Proteus grammar, solidify, ETH, MultiSig, ICO, trampoline", URL = "https://discovery.ucl.ac.uk/id/eprint/10070648", URL = "https://discovery.ucl.ac.uk/id/eprint/10070648/1/Basios_10070648_thesis.pdf", size = "173 pages", abstract = "Programming is laborious. A long-standing goal is to reduce this cost through automation. Genetic Improvement (GI) is a new direction for achieving this goal. It applies search to the task of program improvement. The research conducted in this thesis applies GI to program optimisation and to enable program optimisation. In particular, it focuses on automatic code optimisation for complex managed runtimes,such as Java and Ethereum Virtual Machines. We introduce the term Darwinian Data Structures (DDS) for the data structures of a program that share a common interface and enjoy multiple implementations. We call them Darwinian since we can subject their implementations to the survival of the fittest. We introduce ARTEMIS, a novel cloud-based multi-objective multi-language optimisation framework that automatically finds optimal, tuned data structures and rewrites the source code of applications accordingly to use them. ARTEMIS achieves substantial performance improvements for 44 diverse programs. ARTEMIS achieves 4.8percent, 10.1percent, 5.1percent median improvement for runtime, memory and CPU usage. Even though GI has been applied successfully to improve properties of programs running in different runtimes, GI has not been applied in Blockchains, such as Ethereum. The code immutability of programs running on top of Ethereum limits the application of GI. The first step of applying GI in Ethereum is to overcome the code immutability limitations. Thus, to enable optimisation, we present PROTEUS, a state of the art framework that automatically extends the functionality of smart contracts written in Solidity and makes them upgradeable. Thus, allowing developers to introduce alternative optimised versions of code (e.g., code that consumes less gas), found by GI, in newer versions.", notes = "http://www.darwinianoptimiser.com/ Supervisor: Earl T. Barr", } @InProceedings{DBLP:conf/visapp/BasiratR19, author = "Mina Basirat and Peter M. Roth", editor = "Alain Tremeau and Giovanni Maria Farinella and Jose Braz", title = "Learning Task-specific Activation Functions using Genetic Programming", booktitle = "Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, {VISIGRAPP} 2019, Volume 5: VISAPP, Prague, Czech Republic, February 25-27, 2019", pages = "533--540", publisher = "SciTePress", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.5220/0007408205330540", DOI = "doi:10.5220/0007408205330540", timestamp = "Tue, 04 Jun 2019 15:54:43 +0200", biburl = "https://dblp.org/rec/conf/visapp/BasiratR19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{Bassett:2012:GECCO, author = "Jeffrey Bassett and Uday Kamath and Kenneth {De Jong}", title = "A new methodology for the GP theory toolbox", booktitle = "GECCO '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference", year = "2012", editor = "Terry Soule and Anne Auger and Jason Moore and David Pelta and Christine Solnon and Mike Preuss and Alan Dorin and Yew-Soon Ong and Christian Blum and Dario Landa Silva and Frank Neumann and Tina Yu and Aniko Ekart and Will Browne and Tim Kovacs and Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and Giovanni Squillero and Nicolas Bredeche and Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and Martin Pelikan and Silja Meyer-Nienberg and Christian Igel and Greg Hornby and Rene Doursat and Steve Gustafson and Gustavo Olague and Shin Yoo and John Clark and Gabriela Ochoa and Gisele Pappa and Fernando Lobo and Daniel Tauritz and Jurgen Branke and Kalyanmoy Deb", isbn13 = "978-1-4503-1177-9", pages = "719--726", keywords = "genetic algorithms, genetic programming", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330163.2330264", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Recently Quantitative Genetics has been successfully employed to understand and improve operators in some Evolutionary Algorithms (EAs) implementations. This theory offers a phenotypic view of an algorithm's behavior at a population level, and suggests new ways of quantifying and measuring concepts such as exploration and exploitation. In this paper, we extend the quantitative genetics approach for use with Genetic Programming (GP), adding it to the set of GP analysis techniques. We use it in combination with some existing diversity and bloat measurement tools to measure, analyze and predict the evolutionary behavior of several GP algorithms. GP specific benchmark problems, such as ant trail and symbolic regression, are used to provide new insight into how various evolutionary forces work in combination to affect the search process. Finally, using the tools, a multivariate phenotypic crossover operator is designed to both improve performance and control bloat on the difficult ant trail problem.", notes = "Also known as \cite{2330264} GECCO-2012 A joint meeting of the twenty first international conference on genetic algorithms (ICGA-2012) and the seventeenth annual genetic programming conference (GP-2012)", } @PhdThesis{Bassett:thesis, author = "Jeffrey Kermes Bassett", title = "Methods for Improving the Design and Performance of Evolutionary Algorithms", school = "The Volgenau School of Engineering, George Mason University", year = "2012", address = "USA", month = "Fall", keywords = "genetic algorithms, genetic programming", URL = "http://hdl.handle.net/1920/8122", URL = "http://digilib.gmu.edu/dspace/bitstream/handle/1920/8122/Bassett_gmu_0883E_10215.pdf", size = "160 pages", abstract = "Evolutionary Algorithms (EAs) can be applied to almost any optimization or learning problem by making some simple customizations to the underlying representation and/or reproductive operators. This makes them an appealing choice when facing a new or unusual problem. Unfortunately, while making these changes is often easy, getting a customized EA to operate effectively (i.e. find a good solution quickly) can be much more difficult. Ideally one would hope that theory would provide some guidance here, but in these cases, evolutionary computation (EC) theories are not easily applied. They either require customization themselves, or they require information about the problem that essentially includes the solution. Consequently most practitioners rely on an ad-hoc approach, incrementally modifying and testing various customizations until they find something that works reasonably well. The drawback that most EC theories face is that they are closely associated with the underlying representation of an individual (i.e. the genetic code). There has been some success at addressing this limitation by applying a biology theory called quantitative genetics to EAs. This approach allows one to monitor the behaviour of an EA by observing distributions of an outwardly observable phenotypic trait (usually fitness), and thus avoid modelling the algorithm's internal details. Unfortunately, observing a single trait does not provide enough information to diagnose most problems within an EA. It is my hypothesis that using multiple traits will allow one to observe how the population is traversing the search space, thus making more detailed diagnosis possible. In this work, I adapt a newer multivariate form of quantitative genetics theory for use with evolutionary algorithms and derive a general equation of population variance dynamics. This provides a foundation for building a set of tools that can measure and visualize important characteristics of an algorithm, such as exploration, exploitation, and heritability, throughout an EA run. Finally I demonstrate that the tools can actually be used to identify and fix problems in two well known EA variants: Pittsburgh approach rule systems and genetic programming trees.", notes = "Supervisor Kenneth A. De Jong", } @Article{Bastian:2000:FSS, author = "Andreas Bastian", title = "Identifying fuzzy models utilizing genetic programming", journal = "Fuzzy Sets and Systems", volume = "113", pages = "333--350", year = "2000", number = "3", month = "1 " # aug, keywords = "genetic algorithms, genetic programming, System identification, Fuzzy modeling", URL = "http://www.sciencedirect.com/science/article/B6V05-4234BFC-1/1/261a04fa056f3f0dfe0fb79a773a971a", abstract = "Fuzzy models offer a convenient way to describe complex nonlinear systems. Moreover, they permit the user to deal with uncertainty and vagueness. Due to these advantages fuzzy models are employed in various fields of applications, e.g. control, forecasting, and pattern recognition. Nevertheless, it has to be emphasised that the identification of a fuzzy model is a complex optimisation task with many local minima. Genetic programming provides a way to solve such complex optimization problems. In this work, the use of genetic programming to identify the input variables, the rule base and the involved membership functions of a fuzzy model is proposed. For this purpose, several new reproduction operators are introduced.", } @Article{BastoFernandes:2014:PT, author = "Vitor Basto-Fernandes and Iryna Yevseyeva and Rafael Z. Frantz and Carlos Grilo and Noemi {Perez Diaz} and Michael Emmerich", title = "An Automatic Generation of Textual Pattern Rules for Digital Content Filters Proposal, Using Grammatical Evolution Genetic Programming", journal = "Procedia Technology", volume = "16", pages = "806--812", year = "2014", note = "CENTERIS 2014 - Conference on ENTERprise Information Systems / ProjMAN 2014 - International Conference on Project MANagement / HCIST 2014 - International Conference on Health and Social Care Information Systems and Technologies", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Spam filtering, Digital Content Filters, Classification", ISSN = "2212-0173", DOI = "doi:10.1016/j.protcy.2014.10.030", URL = "http://www.sciencedirect.com/science/article/pii/S2212017314002576", abstract = "This work presents a conceptual proposal to address the problem of intensive human specialised resources that are nowadays required for the maintenance and optimised operation of digital contents filtering in general and anti-spam filtering in particular. The huge amount of spam, malware, virus, and other illegitimate digital contents distributed through network services, represents a considerable waste of physical and technical resources, experts and end users time, in continuous maintenance of anti-spam filters and deletion of spam messages, respectively. The problem of cumbersome and continuous maintenance required to keep anti-spam filtering systems updated and running in an efficient way, is addressed in this work by the means of genetic programming grammatical evolution techniques, for automatic rules generation, having SpamAssassin anti-spam system and SpamAssassin public corpus as the references for the automatic filtering customisation.", } @Article{Batenkov:2010:HIG:1836543.1836558, author = "Dmitry Batenkov", title = "Hands-on introduction to genetic programming", journal = "XRDS Crossroads", year = "2010", volume = "17", number = "1", pages = "46--51", month = sep # " 2010", note = "The ACM Magazine for Students", keywords = "genetic algorithms, genetic programming, Coding Tools and Techniques, Expressions and Their Representation, Object-oriented Programming, Problem Solving, Control Methods, Search", ISSN = "1528-4972", acmid = "1836558", publisher = "ACM", URL = "http://xrds.acm.org/article.cfm?aid=1836558", DOI = "doi:10.1145/1836543.1836558", code_url = "https://xrds.acm.org/helloworld/2010/08/genetic-programming.cfm", size = "2.5 pages", abstract = "The idea to mimic the principles of Darwinian evolution in computing has been around at least since the 1950s, so long, in fact, that it has grown into the field called evolutionary computing (EC). In this tutorial, we'll learn the basic principles of EC and its offspring, genetic programming (GP), on a {"}toy problem{"} of symbolic regression. We'll also learn how to use OpenBeagle, a generic C++ object-oriented EC framework.", notes = "http://xrds.acm.org/ Christian Gagne's Open Beagle", } @Article{Batenkov:2011:GPEM, author = "Dmitry Batenkov", title = "Open BEAGLE: a generic framework for evolutionary computations", journal = "Genetic Programming and Evolvable Machines", year = "2011", volume = "12", number = "3", pages = "329--331", month = sep, note = "Software review", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dR8fs", DOI = "doi:10.1007/s10710-011-9135-4", publisher = "Springer", size = "3 pages", affiliation = "Weizmann Institute of Science, Rehovot, Israel", } @InProceedings{Bates:2003:ICCIFE, author = "R. G. Bates and M. A. H. Dempster and Y. S. Romahi", title = "Evolutionary reinforcement learning in {FX} order book and order flow analysis", booktitle = "IEEE International Conference on Computational Intelligence for Financial Engineering", year = "2003", pages = "355--362", address = "Hong Kong", month = "20-23 " # mar, keywords = "genetic algorithms, genetic programming", URL = "http://mahd-pc.jbs.cam.ac.uk/archive/PAPERS/2003/WP06.pdf", DOI = "doi:10.1109/CIFER.2003.1196282", abstract = "As macroeconomic fundamentals based modelling of FX time series have been shown not to fit the empirical evidence at horizons of less than one year, interest has moved towards microstructure-based approaches. Order flow data has recently been receiving an increasing amount of attention in equity market analyses and thus increasingly in foreign exchange as well. In this paper, order flow data is coupled with order book derived indicators and we explore whether pattern recognition techniques derived from computational learning can be applied to successfully infer trading strategies on the underlying timeseries. Due to the limited amount of data available the results are preliminary. However, the approach demonstrates promise and it is shown that using order flow and order book data is usually superior to trading on technical signals alone.", notes = "Final report to HSBC Investment Bank, November (2002). Location: Technical report WP06/2003 ", } @InProceedings{conf/agi/BatishchevaP15, author = "Vita Batishcheva and Alexey Potapov", title = "Genetic Programming on Program Traces as an Inference Engine for Probabilistic Languages", booktitle = "Proceedings of the 8th International Conference Artificial General Intelligence, AGI 2015", year = "2015", editor = "Jordi Bieger and Ben Goertzel and Alexey Potapov", volume = "9205", series = "Lecture Notes in Computer Science", pages = "14--24", address = "Berlin, Germany", month = jul # " 22-25", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Probabilistic programming, Program traces", isbn13 = "978-3-319-21364-4", bibdate = "2015-07-16", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/agi/agi2015.html#BatishchevaP15", URL = "http://dx.doi.org/10.1007/978-3-319-21365-1", DOI = "doi:10.1007/978-3-319-21365-1_2", abstract = "Methods of simulated annealing and genetic programming over probabilistic program traces are developed firstly. These methods combine expressiveness of Turing-complete probabilistic languages, in which arbitrary generative models can be defined, and search effectiveness of meta-heuristic methods. To use these methods, one should only specify a generative model of objects of interest and a fitness function over them without necessity to implement domain-specific genetic operators or mappings from objects to and from bit strings. On the other hand, implemented methods showed better quality than the traditional mh-query on several optimization tasks. Thus, these results can contribute to both fields of genetic programming and probabilistic programming.", } @MastersThesis{Batista:mastersthesis, author = "Joao Eduardo {Silva Pombinho Batista}", title = "Studying elements of genetic programming for multiclass classification", school = "Department of Informatics, Faculdade de Ciencias, Universidade de Lisboa", year = "2018", address = "Lisbon, Portugal", keywords = "genetic algorithms, genetic programming, M3GP, Java, Machine Learning, Classification, Multiclass, Multidimensional clustering, Programacao genetica, Aprendizagem automatica, Classificacao, Multi-classe, Aglomeracao multi-dimensional", URL = "http://hdl.handle.net/10451/35287", URL = "https://repositorio.ul.pt/handle/10451/35287", URL = "https://repositorio.ul.pt/bitstream/10451/35287/1/ulfc121857_tm_Jo%c3%a3o_Batista.pdf", size = "97 pages", abstract = "Although Genetic Programming (GP) has been very successful in both symbolic regression and binary classification by solving many difficult problems from various domains, it requires improvements in multiclass classification, which due to the high complexity of this kind of problems, requires specialised classifiers. In this project, we explored a multiclass classification GP-based algorithm, the M3GP [4]. The individuals in standard GP only have one node at their root. This means that their output space is in R. Unlike standard GP, M3GP allows each individual to have n nodes at its root. This variation changes the output space to Rn, allowing them to construct clusters of samples and use a cluster-based classification. Although M3GP is capable of creating interpretable models while having competitive results with state-of-the-art classifiers, such as Random Forests and Neural Networks, it has downsides. The focus of this project is to improve the algorithm by exploring two components, the fitness function, and the genetic operators' selection method. The original fitness function was accuracy-based. Since using this kind of functions does not allow a smooth evolution of the output space, we tried to improve the algorithm by exploring two distance-based fitness functions as an attempt to separate the clusters while bringing the samples closer to their respective centroids. Until now, the genetic operators in M3GP were selected with a fixed probability. Since some operators have a better effect on the fitness at different stages of the evolution, the fixed probabilities allow operators to be selected at the wrong stages of the evolution, slowing down the learning process. In this project, we try to evolve the probability the genetic operators have of being chosen over the generations. On a later stage, we proposed a new crossover genetic operator that uses three individuals for the M3GP algorithm. The results obtained show significantly better results in the training set in half the datasets, while improving the test accuracy in two datasets.", notes = "In English SEG WAV VOW YST HRT MCD3 MCD10 Adviser: Sara Guilherme Oliveira da Silva", } @InProceedings{Batista:2019:GECCOcomp, author = "Joao E. Batista and Nuno M. Rodrigues and Sara Silva", title = "To adapt or not to adapt, or the beauty of random settings", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", isbn13 = "978-1-4503-6748-6", pages = "336--337", address = "Prague, Czech Republic", DOI = "doi:10.1145/3319619.3321994", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{3321994} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InProceedings{Batista:2020:CEC, author = "Joao E. Batista and Sara Silva", title = "Improving the Detection of Burnt Areas in Remote Sensing using Hyper-features Evolved by {M3GP}", booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC 2020", year = "2020", editor = "Yaochu Jin", pages = "paper id24404", address = "internet", month = "19-24 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Classification, RemoteSensing, Feature Spaces, Hyper-features, Transfer Learning", isbn13 = "978-1-7281-6929-3", URL = "https://arxiv.org/abs/2002.00053", DOI = "doi:10.1109/CEC48606.2020.9185630", size = "8 pages", abstract = "One problem found when working with satellite images is the radiometric variations across the image and different images. Intending to improve remote sensing models for the classification of burnt areas, we set two objectives. The first is to understand the relationship between feature spaces and the predictive ability of the models, allowing us to explain the differences between learning and generalization when training and testing in different datasets. We find that training on datasets built from more than one image provides models that generalize better. These results are explained by visualizing the dispersion of values on the feature space. The second objective is to evolve hyper-features that improve the performance of different classifiers on a variety of test sets. We find the hyper-features to be beneficial, and obtain the best models with XGBoost, even if the hyper-features are optimized for a different method", notes = "https://wcci2020.org/ Faculdade de Ciencias, Universidade de Lisboa, Portugal. Also known as \cite{9185630}", } @Article{batista:2021:remotesensing, author = "Joao E. Batista and Ana I. R. Cabral and Maria J. P. Vasconcelos and Leonardo Vanneschi and Sara Silva", title = "Improving Land Cover Classification Using Genetic Programming for Feature Construction", journal = "Remote Sensing", year = "2021", volume = "13", number = "9", article-number = "1623", keywords = "genetic algorithms, genetic programming, evolutionary computation, machine learning, classification, multi-class classification, feature construction, hyperfeatures, spectral indices", ISSN = "2072-4292", URL = "https://www.mdpi.com/2072-4292/13/9/1623", URL = "https://www.mdpi.com/2072-4292/13/9/1623.pdf", DOI = "doi:10.3390/rs13091623", size = "25 pages", abstract = "Genetic programming (GP) is a powerful machine learning (ML) algorithm that can produce readable white-box models. Although successfully used for solving an array of problems in different scientific areas, GP is still not well known in the field of remote sensing. The M3GP algorithm, a variant of the standard GP algorithm, performs feature construction by evolving hyperfeatures from the original ones. In this work, we use the M3GP algorithm on several sets of satellite images over different countries to create hyperfeatures from satellite bands to improve the classification of land cover types. We add the evolved hyperfeatures to the reference datasets and observe a significant improvement of the performance of three state-of-the-art ML algorithms (decision trees, random forests, and XGBoost) on multiclass classifications and no significant effect on the binary classifications. We show that adding the M3GP hyperfeatures to the reference datasets brings better results than adding the well-known spectral indices NDVI, NDWI, and NBR. We also compare the performance of the M3GP hyperfeatures in the binary classification problems with those created by other feature construction methods such as FFX and EFS.", notes = "Also remotesensing-13-01623-v2 LASIGE, Faculty of Sciences, University of Lisbon, Campo Grande, 1749-016 Lisbon, Portugal", } @InProceedings{batista:2022:GECCOlba, author = "Joao Batista and Sara Silva", title = "Evolving a {Cloud-Robust} Water Index with Genetic Programming", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion", year = "2022", editor = "Yew-Soon Ong and Abhishek Gupta", pages = "55--56", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, feature construction, water indices, remote sensing, satellite imagery", isbn13 = "978-1-4503-9268-6/22/07", DOI = "doi:10.1145/3520304.3533946", abstract = "Over the years, remote sensing (RS) experts created many indices to help them study satellite imagery by highlighting characteristics like vegetation, water, or burnt areas, among others. In this work, we study water indices. Although there is a large number of water indices that work perfectly in unclouded imagery, clouds and shadows cast by clouds are often mistaken for water. This work is focused on the automatic feature construction using genetic programming (GP), in an attempt to make features that are more robust to these issues. To do this, we use a dataset containing pixels from areas where we could find these issues to evolve models that learn how to classify those pixels correctly. The results indicate improvements when comparing evolved features with indices, but further improvements are required to tackle other issues found.", notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @InProceedings{Batista:2022:CEC, author = "Joao E. Batista and Sara Silva", title = "Comparative study of classifier performance using automatic feature construction by {M3GP}", booktitle = "2022 IEEE Congress on Evolutionary Computation (CEC)", year = "2022", editor = "Carlos A. Coello Coello and Sanaz Mostaghim", address = "Padua, Italy", month = "18-23 " # jul, keywords = "genetic algorithms, genetic programming, M3GP, Python, naive Bayes, decision trees, Support vector machines, random forests, xgboost, Machine learning algorithms, Computational modeling, Evolutionary computation, Classification algorithms, Complexity theory, Feature Construction, Multiclass Classification", isbn13 = "978-1-6654-6708-7", DOI = "doi:10.1109/CEC55065.2022.9870343", code_url = "http://github.com/jespb/Python-M3GP", size = "8 pages", abstract = "The M3GP algorithm, originally designed to perform multiclass classification with genetic programming, is also a powerful feature construction method. Here we explore its ability to evolve hyper-features that are tailored not only to the problem to be solved, but also to the learning algorithm that is used to solve it. We pair M3GP with six different machine learning algorithms and study its performance in eight classification problems from different scientific domains, with substantial variety in the number of classes, features and samples. The results show that automatic feature construction with M3GP, when compared to using the standalone classifiers without feature construction, achieves statistically significant improvements in the majority of the test cases, sometimes by a very large margin, while degrading the weighted f-measure in only one out of 48 cases. We observe the differences in the number and size of the hyper-features evolved for each case, hypothesising that the simpler the classifier, the larger the amount of problem complexity is being captured in the hyperfeatures. Our results also reveal that the M3GP algorithm can be improved, both in execution time and in model quality, by replacing its default classifier with support vector machines or random forest classifiers.", notes = "Also known as \cite{9870343}", } @Article{Batista:2022:IJRS, author = "Joao E. Batista and Nuno M. Rodrigues and Ana I. R. Cabral and Maria J. P. Vasconcelos and Adriano Venturieri and Luiz G. T. Silva and Sara Silva", title = "Optical time series for the separation of land cover types with similar spectral signatures: cocoa agroforest and forest", journal = "International Journal of Remote Sensing", year = "2022", volume = "43", number = "9", pages = "3298--3319", keywords = "genetic algorithms, genetic programming, Cocoa agroforest classification, land cover mapping, machine learning, time series, tropical areas", publisher = "Taylor \& Francis", DOI = "doi:10.1080/01431161.2022.2089540", data_url = "https://github.com/jespb/Cocoa_PublicDS", size = "22 pages", abstract = "One of the main applications of machine learning (ML) in remote sensing (RS) is the pixel-level classification of satellite images into land cover types. Although classes with different spectral signatures can be easily separated, e.g. aquatic and terrestrial land cover types, others have similar spectral signatures and are hard to separate using only the information within a single pixel. This work focused on the separation of two cover types with similar spectral signatures, cocoa agroforest and forest, over an area in Para, Brazil. For this, we study the training and application of several ML algorithms on datasets obtained from a single composite image, a time-series (TS) composite obtained from the same location and by preprocessing the TS composite using simple TS preprocessing techniques. As expected, when ML algorithms are applied to a dataset obtained from a composite image, the median producer accuracy (PA) and user accuracy (UA) in those two classes are significantly lower than the median overall accuracy (OA) for all classes. The second dataset allows the ML models to learn the evolution of the spectral signatures over 5 months. Compared to the first dataset, the results indicate that ML models generalise better using TS data, even if the series are short and without any preprocessing. This generalization is further improved in the last dataset. The ML models are subsequently applied to an area with different geographical bounds. These last results indicate that, out of seven classifiers, the popular random forest (RF) algorithm ranked fourth, while XGBoost (xGB) obtained the best results. The best OA, as well as the best PA/UA balance, were obtained by performing feature construction using the M3GP algorithm and then applying XGB to the new extended dataset.", notes = "a LASIGE, Faculty of Sciences, University of Lisbon, Portugal", } @InProceedings{batista:2024:CEC, author = "Joao Eduardo Batista and Nuno Miguel Rodrigues and Leonardo Vanneschi and Sara Silva", title = "{M6GP:} Multiobjective Feature Engineering", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Measurement, Training, Machine learning algorithms, Power demand, Machine learning, Predictive models, Market research, Multiobjective Optimization, Feature Engineering, Explainable AI, Interpretability", isbn13 = "979-8-3503-0837-2", URL = "http://hdl.handle.net/10362/172920", DOI = "doi:10.1109/CEC60901.2024.10612107", abstract = "The current trend in machine learning is to use powerful algorithms to induce complex predictive models that often fall under the category of black-box models. Thanks to this, there is also a growing interest in studying model explainability and interpretability so that human experts can understand, validate, and correct those models. With the objective of promoting the creation of inherently interpretable models, we present M6GP. This wrapper-based multi-objective automatic feature engineering algorithm combines key components of the M3GP and NSGA-II algorithms. Wrapping M6GP around another machine learning algorithm evolves a set of features optimised for this algorithm while potentially increasing its robustness. We compare our results with M3GP and M4GP, two ancestors from the same algorithm family, and verify that, by using a multi-objective approach, M6GP obtains equal or better results. In addition, by using complexity metrics on the list of objectives, the M6GP models come down to one-fifth of the size of the M3GP models, making them easier to read by comparison.", notes = "also known as \cite{10612107} WCCI 2024", } @InProceedings{batista:2024:CEC2, author = "Joao Eduardo Batista and Adam Kotaro Pindur and Hitoshi Iba and Sara Silva", title = "Measuring Structural Complexity of {GP} Models for Feature Engineering over the Generations", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Measurement, Analytical models, Computational modeling, Pipelines, Predictive models, Prediction algorithms, Complexity theory, Model Complexity, Feature Engineering, Model Interpretability, Classification", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10611989", abstract = "Feature engineering is a necessary step in the machine learning pipeline. Together with other preprocessing methods, it allows the conversion of raw data into a dataset containing only the necessary features to solve the task at hand, reducing the computational complexity of inducing models and creating models that are potentially simpler, more robust, and more interpretable. We use M3GP, a wrapper-based feature engineering algorithm, to induce a set of features that are adapted in number and in shape to several classifiers with different levels of predictive power, from decision trees with depth 3 to random forests with 100 estimators and no depth limit. Intuition tells us that classifiers that are restricted in the number of features should compensate for this restriction by using features with a high degree of correlation with the target objective. By opposition, the principle behind the boosting algorithm tells us that we can create a strong classifier using a large set of weak features. This indicates that classifiers with no restrictions should prefer many but weaker features. Our results confirm this hypothesis while also revealing that M3GP induces unnecessarily complex features. We measure complexity using several structural complexity metrics found in the literature and show that, although our pipeline consistently obtains good results, the structural complexity of the induced models varies drastically across runs. Additionally, while the test performance peaks in the early stages of the evolution, the complexity of the feature engineering models continues to grow, with little to no return in test performance. This work promotes using several complexity metrics to measure model interpretability and identifies issues related to model complexity in M3GP, proposing solutions to improve the computational cost of inducing models and the complexity of the final models.", notes = "also known as \cite{10611989} WCCI 2024", } @Article{Batista:2025:swevo, author = "Joao E. Batista and Adam K. Pindur and Ana I. R. Cabral and Hitoshi Iba and Sara Silva", title = "Complexity, interpretability and robustness of {GP-based} feature engineering in remote sensing", journal = "Swarm and Evolutionary Computation", year = "2025", volume = "92", pages = "101761", keywords = "genetic algorithms, genetic programming, Remote sensing, Model complexity, Model interpretability, Feature construction, Forest degradation, Classification, Time series", ISSN = "2210-6502", URL = "https://www.sciencedirect.com/science/article/pii/S2210650224002992", DOI = "doi:10.1016/j.swevo.2024.101761", abstract = "Feature engineering is a crucial step in machine learning that provides better data for the learning algorithms to induce robust models, and this effort should be adapted to the capabilities of each algorithm. For example, classifiers that do not perform data transformations (e.g., cluster-based) perform better when the different classes are separated, typically requiring preprocessed data. Other models (e.g., decision trees) can perform several splits in the feature space, easily obtaining perfect results in training data, but have a higher risk of overfitting with unprocessed data. We use the rbd-GP and M3GP genetic programming algorithms to induce new features based on the original features, to be used by shallow and deep decision tree and random forest models. M3GP is wrapped around a learning algorithm, using its performance as fitness. This way, the induced features are adapted to the classifier, allowing us to compare the complexity of the features induced for the different classifiers. We measure the complexity of the induced features using several structural and functional complexity metrics found in the literature, also proposing a new metric that measures the separability of classes in the feature space. Like other authors, we use complexity as an interpretability metric, selecting three models to discuss and validate based on their performance and size. We apply these methods to remote sensing classification problems and solve two tasks that are hard due to the high similarity between the land cover classes: detecting cocoa agroforest and forecasting forest degradation up to one year in the future", } @InProceedings{conf/iwann/BatistaSSLR13, author = "Rayco Batista and Eduardo Segredo and Carlos Segura and Coromoto Leon and Casiano Rodriguez", title = "Solving the Unknown Complexity Formula Problem with Genetic Programming", bibdate = "2013-06-25", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/iwann/iwann2013-1.html#BatistaSSLR13", booktitle = "Advances in Computational Intelligence - 12th International Work-Conference on Artificial Neural Networks, {IWANN} 2013, Puerto de la Cruz, Tenerife, Spain, June 12-14, 2013, Proceedings, Part {I}", publisher = "Springer", year = "2013", volume = "7902", editor = "Ignacio Rojas and Gonzalo Joya Caparr{\'o}s and Joan Cabestany", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-38678-7", pages = "232--240", series = "Lecture Notes in Computer Science", URL = "http://dx.doi.org/10.1007/978-3-642-38679-4", DOI = "doi:10.1007/978-3-642-38679-4_22", abstract = "The Unknown Complexity Formula Problem (ucfp) is a particular case of the symbolic regression problem in which an analytical complexity formula that fits with data obtained by multiple executions of certain algorithm must be given. In this work, a set of modifications has been added to the standard Genetic Programming (GP) algorithm to deal with the ucfp. This algorithm has been applied to a set of well-known benchmark functions of the symbolic regression problem. Moreover, a real case of the ucfp has been tackled. Experimental evaluation has demonstrated the good behaviour of the proposed approach in obtaining high quality solutions, even for a real instance of the ucfp. Finally, it is worth pointing out that the best published results for the majority of benchmark functions have been improved.", } @InProceedings{2018_Batot_Sahraoui_SSBSE18, author = "Edouard Batot and Houari Sahraoui", title = "Injecting Social Diversity in Multi-Objective Genetic Programming: the Case of Model Well-formedness Rule Learning", booktitle = "SSBSE 2018", year = "2018", editor = "Thelma Elita Colanzi and Phil McMinn", volume = "11036", series = "LNCS", pages = "166--181", address = "Montpellier, France", month = "8-9 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, SBSE, Model Driven Engineering (MDE), Metamodel, Modelling Space, WFR, OCL, NSGA-II, SSDM, TF-IDF", isbn13 = "978-3-319-99241-9", URL = "http://www-ens.iro.umontreal.ca/~batotedo/papers/2018_Batot_Sahraoui_SSBSE18.pdf", DOI = "doi:10.1007/978-3-319-99241-9_8", size = "15 pages", abstract = "Software modelling activities typically involve a tedious and time-consuming effort by specially trained personnel. This lack of automation hampers the adoption of the Model Driven Engineering (MDE) paradigm. Nevertheless, in the recent years, much research work has been dedicated to learn MDE artefacts instead of writing them manually. In this context, mono- and multi-objective Genetic Programming (GP) has proven being an efficient and reliable method to derive automation knowledge by using, as training data, a set of input/out examples representing the expected behaviour of an artefact. Generally, the conformance to the training example set is the main objective to lead the search for a solution. Yet, single fitness peak, or local optima deadlock, one of the major drawbacks of GP, happens when adapted to MDE and hinder the results of the learning. We aim at showing in this paper that an improvement in population social diversity carried out during the evolutionary", notes = "FamilyTree, Statemachine, and Project Manager Social semantic diversity instead of crowding distance", } @PhdThesis{Batot_Edouard_2018_These, author = "Edouard Batot", title = "From examples to knowledge in model-driven engineering : a holistic and pragmatic approach", school = "Departement d'informatique et de recherche operationnelle, Universite de Montreal", year = "2018", address = "Canada", month = nov, keywords = "genetic algorithms, genetic programming, SBSE, Software Engineering, Model-Driven Engineering, Search Based Software Engineering, Learning from examples, Generalization and Representativeness, Machine Learning, Aritficial Intelligence, Genie logiciel, Apprentissage machine, Intelligence artificielle, Genie logiciel experimental, Apprentissage a partir d'exemples", URL = "https://papyrus.bib.umontreal.ca/xmlui/bitstream/1866/21737/2/Batot_Edouard_2018_These.pdf", URL = "http://hdl.handle.net/1866/21737", size = "169 pages", abstract = "Model-Driven Engineering (MDE) is a software development approach that proposes to raise the level of abstraction of languages in order to shift the design and understanding effort from a programmer point of view to the one of decision makers. However, the manipulation of these abstract representations, or models, has become so complex that traditional techniques are not enough to automate its inherent tasks. For its part, the Search-Based Software Engineering (SBSE) proposes to reformulate the automation of MDE tasks as optimization problems. Once reformulated, the problem will be solved by metaheuristic algorithms. With a plethora of studies on the subject, the power of automation of SBSE has been well established. Based on this observation, the Example-Based MDE community (EB-MDE) started using application examples to feed the reformulation into SBSE of the MDE task learning problem. In this context, the concordance of the output of the solutions with the examples becomes an effective barometer for evaluating the ability of a solution to solve a task. This measure has proved to be a semantic goal of choice to guide the metaheuristic search for solutions. However, while it is commonly accepted that the representativeness of the examples has an impact on the generalizability of the solutions, the study of this impact suffers from a flagrant lack of consideration. In this thesis, we propose a thorough formulation of the learning process in an MDE context including a complete methodology to characterize and evaluate the relation that exists between two important properties of the examples, their size and coverage, and the generalizability of the solutions. We perform an empirical analysis, and propose a detailed plan for further investigation of the concept of representativeness, or of other representativities.", abstract = "Le Model-Driven Engineering (MDE) est une approche de developpement logiciel qui propose d'elever le niveau d'abstraction des langages afin de deplacer l'effort de conception et de comprehension depuis le point de vue des programmeurs vers celui des decideurs du logiciel. Cependant, la manipulation de ces representations abstraites, ou modeles, est devenue tellement complexe que les moyens traditionnels ne suffisent plus a automatiser les differentes taches. De son cote, le Search-Based Software Engineering (SBSE) propose de reformuler l'automatisation des taches du MDE comme des problemes d'optimisation. Une fois reformule, la resolution du probleme sera effectuee par des algorithmes metaheuristiques. Face a la plethore d'etudes sur le sujet, le pouvoir d'automatisation du SBSE n'est plus a demontrer. C'est en s'appuyant sur ce constat que la communaute du Example-Based MDE (EBMDE) a commence a utiliser des exemples d'application pour alimenter la reformulation SBSE du probleme d'apprentissage de tache MDE. Dans ce contexte, la concordance de la sortie des solutions avec les exemples devient un barometre efficace pour evaluer l'aptitude d'une solution a resoudre une tache. Cette mesure a prouve etre un objectif semantique de choix pour guider la recherche metaheuristique de solutions. Cependant, s'il est communement admis que la representativite des exemples a un impact sur la generalisabilite des solutions, l'etude de cet impact souffre d'un manque de consideration flagrant. Dans cette these, nous proposons une formulation globale du processus d'apprentissage dans un contexte MDE incluant une methodologie complete pour caracteriser et evaluer la relation qui existe entre la generalisabilite des solutions et deux proprietes importantes des exemples, leur taille et leur couverture. Nous effectuons l'analyse empirique de ces deux proprietes et nous proposons un plan detaille pour une analyse plus approfondie du concept de representativite, ou d'autres representativites.", notes = "Suvervisor: Houari Sahraoui", } @Article{Batot:2022:SSM, author = "Edouard R. Batot and Houari Sahraoui", title = "Promoting social diversity for the automated learning of complex {MDE} artifacts", journal = "Software and Systems Modeling", year = "2022", volume = "21", pages = "1159--1178", month = jun, keywords = "genetic algorithms, genetic programming, Model-driven engineering, Social diversity, MOGP, NSGA-II, fitness sharing, ROUGE", ISSN = "1619-1366", URL = "https://rdcu.be/c69Rx", DOI = "doi:10.1007/s10270-021-00969-9", size = "20 pages", abstract = "Software modelling activities typically involve a tedious and time-consuming effort by specially trained personnel. This lack of automation hampers the adoption of model-driven engineering (MDE). Nevertheless, in the recent years, much research work has been dedicated to learn executable MDE artifacts instead of writing them manually. In this context, mono- and multi-objective genetic programming (GP) has proven being an efficient and reliable method to derive automation knowledge by using, as training data, a set of examples representing the expected behaviour of an artifact. Generally, conformance to the training example set is the main objective to lead the learning process. Yet, single fitness peak, or local optima deadlock, a common challenge in GP, hinders the application of GP to MDE. we propose a strategy to promote populations social diversity during the GP learning process. We evaluate our approach with an empirical study featuring the case of learning well-formedness rules in MDE with a multi-objective genetic programming algorithm. Our evaluation shows that integration of social diversity leads to more efficient search, faster convergence, and more generalizable results. Moreover, when the social diversity is used as crowding distance, this convergence is uniform through a hundred of runs despite the probabilistic nature of GP. It also shows that genotypic diversity strategies cannot achieve comparable results", notes = "p1165 'SSD increases proportionally to the number of examples solved and is offset by the frequency of which an example is solved by the population's individuals, which helps to adjust for the fact that some examples are more frequently solved in general' See also https://semla.polymtl.ca/wp-content/uploads/2022/11/SEMLA22_HSahraoui.pdf 'GP-based techniques allow to learn actionable and explainable artifacts but require precise specifications' ", } @InProceedings{battle:1999:GPFKBFLC, author = "Daryl Battle and Abdollah Homaifar and Edward Tunstel and Gerry Dozier", title = "Genetic Programming of Full Knowledge Bases for Fuzzy Logic Controllers", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1463--1468", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, real world applications", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-730.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-730.ps", size = "6 pages", abstract = "Genetic programming (GP) is applied to automatic discovery of full knowledge bases for use in fuzzy logic control applications. An extension to a rule learning GP system is presented that achieves this objective. In addition, GP is employed to handle selection of fuzzy set intersection operators (t-norms). The new GP system is applied to design a mobile robot path tracking controller and performance is shown to be comparable to that of a manually designed controller.", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Baudry:2014:ISSTA, author = "Benoit Baudry and Simon Allier and Martin Monperrus", title = "Tailored Source Code Transformations to Synthesize Computationally Diverse Program Variants", booktitle = "Proceedings of the 2014 International Symposium on Software Testing and Analysis, ISSTA 2014", year = "2014", pages = "149--159", address = "San Jose, CA, USA", month = jul # " 21-25", publisher = "ACM", keywords = "genetic algorithms, genetic programming, Genetic Improvement, SBSE, Program Transformation, Software Diversity", isbn13 = "978-1-4503-2645-2", acmid = "2610415", URL = "https://hal.archives-ouvertes.fr/hal-00938855/document", URL = "http://doi.acm.org/10.1145/2610384.2610415", DOI = "doi:10.1145/2610384.2610415", size = "11 pages", abstract = "The predictability of program execution provides attackers a rich source of knowledge who can exploit it to spy or remotely control the program. Moving target defence addresses this issue by constantly switching between many diverse variants of a program, which reduces the certainty that an attacker can have about the program execution. The effectiveness of this approach relies on the availability of a large number of software variants that exhibit different executions. However, current approaches rely on the natural diversity provided by of-the-shelf components, which is very limited. In this paper, we explore the automatic synthesis of large sets of program variants, called sosies. Sosies provide the same expected functionality as the original program, while exhibiting different executions. They are said to be computationally diverse. This work addresses two objectives: comparing different transformations for increasing the likelihood of sosie synthesis (densifying the search space for sosies); demonstrating computation diversity in synthesized sosies. We synthesized 30 184 sosies in total, for 9 large, real-world, open source applications. For all these programs we identified one type of program analysis that systematically increases the density of sosies; we measured computation diversity for sosies of 3 programs and found diversity in method calls or data in more than 40percent of sosies. This is a step towards controlled massive unpredictability of software.", notes = "Add/replace AST mutations of existing program's source code. Spoon, JUnit, Dagger, EasyMock. Grid5000 p152 'sosiefication is a search problem'. 'natural software diversity' p158 'In total, we were able to synthesize 30184 sosies' Definition 1. Sosie (noun). Given a program P , a test suite T S for P and a program transformation T , a variant P 0 =T (P ) is a sosie of P if the two following conditions hold 1) there is at least one test case in T S that executes the part of P that is modified by T 2) all test cases in T S pass on P 0 . http://diversify-project.eu/sosiefied-programs/ also known as \cite{Baudry:2014:TSC:2610384.2610415}", } @Article{baudry:2015:acmcs, author = "Benoit Baudry and Martin Monperrus", title = "The Multiple Facets of Software Diversity: Recent Developments in Year 2000 and Beyond", journal = "ACM Computer Surveys", year = "2015", volume = "48", number = "1", month = sep, pages = "16:1--16:26", keywords = "genetic algorithms, genetic programming, Software Engineering, Software diversity, design principles, program transformation", acmid = "2807593", publisher = "ACM", address = "New York, NY, USA", ISSN = "0360-0300", URL = "http://arxiv.org/abs/1409.7324", URL = "https://hal.inria.fr/hal-01182103/document", DOI = "doi:10.1145/2807593", size = "26 pages", abstract = "Early experiments with software diversity in the mid 1970s investigated N-version programming and recovery blocks to increase the reliability of embedded systems. Four decades later, the literature about software diversity has expanded in multiple directions: goals (fault tolerance, security, software engineering), means (managed or automated diversity), and analytical studies (quantification of diversity and its impact). Our article contributes to the field of software diversity as the first work that adopts an inclusive vision of the area, with an emphasis on the most recent advances in the field. This survey includes classical work about design and data diversity for fault tolerance, as well as the cybersecurity literature that investigates randomization at different system levels. It broadens this standard scope of diversity to include the study and exploitation of natural diversity and the management of diverse software products. Our survey includes the most recent works, with an emphasis from 2000 to the present. The targeted audience is researchers and practitioners in one of the surveyed fields who miss the big picture of software diversity. Assembling the multiple facets of this fascinating topic sheds a new light on the field.", notes = "Brief mention of GP Also known as \cite{Baudry:2015:MFS:2808687.2807593}", } @InProceedings{Baudry:2018:GI, author = "Benoit Baudry and Nicolas Harrand and Eric Schulte and Christopher Timperley and Shin Hwei Tan and Marija Selakovic and Emamurho Ugherughe", title = "A spoonful of {DevOps} helps the {GI} Go Down", booktitle = "GI-2018, ICSE workshops proceedings", year = "2018", editor = "Justyna Petke and Kathryn Stolee and William B. Langdon and Westley Weimer", pages = "35--37", address = "Gothenburg, Sweden", month = "2 " # jun, publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, genetic improvement, continuous integration, DevOps", isbn13 = "978-1-4503-5753-1", URL = "http://www.shinhwei.com/devop-gi.pdf", DOI = "doi:10.1145/3194810.3194818", size = "2 pages", abstract = "DevOps emphasizes a high degree of automation at all phases of the software development lifecyle. Meanwhile, Genetic Improvement (GI) focuses on the automatic improvement of software artefacts. In this paper, we discuss why we believe that DevOps offers an excellent technical context for easing the adoption of GI techniques by software developers. We also discuss A/B testing as a prominent and clear example of GI taking place in the wild today, albeit one with human-supervised fitness and mutation operators.", notes = "GI-2018 http://geneticimprovementofsoftware.com/events/papers#icse2018 part of \cite{Petke:2018:ICSEworkshop}", } @InCollection{bauer:1995:EEAGPACSS, author = "Eric T. Bauer", title = "Evolving Efficient Algorithms by Genetic Programming: A Case Study in Sorting", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1995", year = "1995", editor = "John R. Koza", pages = "1--10", address = "Stanford, California, 94305-3079 USA", month = "11 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-195720-5", notes = "part of \cite{koza:1995:gagp}", } @InProceedings{Bauer:2008:TREC, author = "Johannes M. Bauer and Kurt DeMaagd", title = "Network Management Practices and Sector Performance - A Genetic Programming Approach", booktitle = "36th Research Conference on Communications, Information, and Internet Policy", year = "2008", editor = "Elizabeth Mateja", address = "Arlington, VA, USA", month = sep # " 27", keywords = "genetic algorithms, genetic programming", URL = "http://www.tprcweb.com/images/stories/2008/Bauer-DeMaagd-Network-Management-2008-TPRC-fin.pdf", size = "27 pages", abstract = "Introduction The migration to next-generation network architectures, in which platform and application/content layers are relatively distinct, has unleashed a very important and possibly far-reaching policy debate as to the rules that should govern the interaction of players operating on one or both of these layers. Started as a discussion on network neutrality, the debate recently shifted focus to delineating reasonable from unreasonable forms of network management. Legislation to strengthen regulatory powers (Markey Bill) or antitrust enforcement (Conyers Bill) is pending in Congress. The Federal Communications Commission has reasserted its willingness to enforce an open internet in its Comcast Decision in August 2008.", notes = "http://www.tprcweb.com/index.php?option=com_content&view=article&id=29&Itemid=18", } @TechReport{baum:1998:tceaeTR, author = "Eric B. Baum and Igor Durdanovic", title = "Toward Code Evolution By Artificial Economies", institution = "NEC Research Institute", year = "1998", address = "4 Independence Way, Princeto, NJ 08540, USA", month = "10 " # jul, keywords = "genetic algorithms, genetic programming", size = "53 pages", notes = "Hayek2 blocks world 'crossover is much better than headless chicken mutation' meta-agents, inherited wealth, rent, intellectual property, strong typing STGP. See also \cite{baum:1998:tceae}, \cite{oai:CiteSeerPSU:5199}", } @InProceedings{baum:1998:tceae, author = "Eric B. Baum and Igor Durdanovic", title = "Toward Code Evolution By Artificial Economies", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "14--22", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "genetic algorithms, genetic programming", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.56.7596.pdf", size = "9 pages", abstract = "We have begun exploring code evolution by artificial economies. We implemented a reinforcement learning machine called Hayek2 consisting of agents, written in a machine language inspired by Ray's Tierra, that interact economically. The economic structure of Hayek2 addresses credit assignment at both the agent and meta levels. Hayek2 succeeds in evolving code to solve Blocks World problems, and has been more effective at this than our hillclimbing program and our genetic program. Our hill climber and our GP also performed well, learning algorithms as strong as a simple search program that incorporates hand-coded domain knowledge. We made efforts to optimise our hillclimbing program and it has features that may be of independent interest. Our genetic program using crossover performed far better than a version using other macro-mutations or our hillclimber, bearing on a controversy in the Genetic Programming literature", notes = "citeseerx abstract and PDF not identical GP-98LB. See also \cite{baum:1998:tceaeTR}", } @InProceedings{oai:CiteSeerPSU:5199, author = "Eric B. Baum and Igor Durdanovic", title = "Toward Code Evolution By Artificial Economies (Extended Abstract)", booktitle = "Evolution as Computation, DIMACS Workshop, Princeton, January 1999", year = "2001", editor = "Laura F. Landweber and Erik Winfree", series = "Natural Computing Series", pages = "314--332", address = "Princeton University", month = "11-12 " # jan, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-66709-1", URL = "http://citeseer.ist.psu.edu/5199.html", URL = "http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/2215/http:zSzzSzwww.neci.nj.nec.comzSzhomepageszSzericzSzevpap.pdf/toward-code-evolution-by.pdf", DOI = "doi:10.1007/978-3-642-55606-7_16", size = "16 pages", abstract = "We have begun exploring code evolution by artificial economies. We implemented a reinforcement learning machine called Hayek2 consisting of agents, written in a machine language inspired by Ray's Tierra, that interact economically. The economic structure of Hayek2 addresses credit assignment at both the agent and meta levels. Hayek2 succeeds in evolving code to solve Blocks World problems, and has been more effective at this than our hillclimbing program and our genetic program. Our hillclimber and our GP also performed well, learning algorithms as strong as a simple search program that incorporates hand-coded domain knowledge. We made efforts to optimize our hillclimbing program and it has features that may be of independent interest. Our genetic program using crossover performed far better than a version using other macro-mutations or our hillclimber, bearing on a controversy in the Genetic Programming literature.", notes = "see also \cite{baum:1998:tceaeTR}, http://dimacs.rutgers.edu/Workshops/Evolution/ Published Jan 2001 http://www.amazon.com/exec/obidos/ASIN/3540667091/dominantsystems/107-7663466-9560554", } @Article{baum:2000:NeurComp, author = "Eric B. Baum and Igor Durdanovic", title = "Evolution of Cooperative Problem Solving in an Artificial Economy", journal = "Neural Computation", year = "2000", volume = "12", number = "12", pages = "2743--2775", month = dec, keywords = "genetic algorithms, genetic programming, STGP", ISSN = "0899-7667", URL = "https://www.eecs.harvard.edu/cs286r/courses/spring06/papers/baum_nc00.pdf", DOI = "doi:10.1162/089976600300014700", eprint = "https://direct.mit.edu/neco/article-pdf/12/12/2743/814655/089976600300014700.pdf", size = "33 pages", abstract = "We address the problem of how to reinforce learning in ultracomplex environments, with huge state-spaces, where one must learn to exploit a compact structure of the problem domain. The approach we propose is to simulate the evolution of an artificial economy of computer programs. The economy is constructed based on two simple principles so as to assign credit to the individual programs for collaborating on problem solutions. We find empirically that starting from programs that are random computer code, we can develop systems that solve hard problems. In particular, our economy learned to solve almost all random Blocks World problems with goal stacks that are 200 blocks high. Competing methods solve such problems only up to goal stacks of at most 8 blocks. Our economy has also learned to unscramble about half a randomly scrambled Rubik's cube and to solve several commercially sold puzzles.", notes = "PMID: 11112253", } @Article{Baumes2008, author = "Laurent A. Baumes and Pierre Collet", title = "Examination of genetic programming paradigm for high-throughput experimentation and heterogeneous catalysis", journal = "Computational Materials Science", year = "2009", volume = "45", number = "1", pages = "27--40", month = mar, keywords = "genetic algorithms, genetic programming, Heterogeneous catalysis, High-throughput, Materials, Combinatorial, Representation, Data structure", ISSN = "0927-0256", DOI = "doi:10.1016/j.commatsci.2008.03.051", URL = "http://www.sciencedirect.com/science/article/B6TWM-4T4J19Y-1/2/809324138cc0b8f49634eae7f22e995f", size = "14 pages", abstract = "The strong feature dependencies that exist in catalyst description do not permit using common algorithms while not loosing crucial information. Data treatments are restricted by the form of input data making the full use of the experimental information impossible, confining the experimentation studies, and reducing one of the primary goals of HTE: to enlarge the search space. Consequently, an advanced representation of the catalytic data supporting the intrinsic complexity of heterogeneous catalyst data structure is proposed. Likewise, an optimization strategy that can manipulate efficiently such data type, permitting a valuable connection between algorithms, high-throughput (HT) apparatus, and databases, is depicted. Such a new methodology enables the integration of domain knowledge through its configuration considering the study to be investigated. For the first time in heterogeneous catalysis, a conceptual examination of genetic programming (GP) is achieved.", notes = "Selected papers from the E-MRS 2007 Fall Meeting Symposium G: Genetic Algorithms in Materials Science and Engineering - GAMS-2007", } @Article{BBSTLCC09, author = "L. A. Baumes and A. Blansche and P. Serna and A. Tchougang and N. Lachiche and P. Collet and A. Corma", title = "Using Genetic Programming for an Advanced Performance Assessment of Industrially Relevant Heterogeneous Catalysts", journal = "Materials and Manufacturing Processes", year = "2009", volume = "24", number = "3", pages = "282--292", month = mar, keywords = "genetic algorithms, genetic programming, Data mining, Heterogeneous catalysis, High-throughput, Materials science", ISSN = "1042-6914", publisher = "Taylor and Francis", URL = "http://lsiit.u-strasbg.fr/Publications/2009/BBSTLCC09", DOI = "doi:10.1080/10426910802679196", size = "11 pages", abstract = "Beside the ease and speed brought by automated synthesis stations and reactors technologies in materials science, adapted informatics tools must be further developed in order to handle the increase of throughput and data volume, and not to slow down the whole process. This article reports the use of genetic programming (GP) in heterogeneous catalysis. Despite the fact that GP has received only little attention in this domain, it is shown how such an approach can be turned into a very singular and powerful tool for solid optimization, discovery, and monitoring. Jointly with neural networks, the GP paradigm is employed in order to accurately and automatically estimate the whole curve conversion vs. time in the epoxidation of large olefins using titanosilicates, Ti-MCM-41 and Ti-ITQ-2, as catalysts. In contrast to previous studies in combinatorial materials science and high-throughput screening, it was possible to estimate the entire evolution of the catalytic reaction for unsynthesized catalysts. Consequently, the evaluation of the performance of virtual solids is not reduced to a single point (e.g., the conversion level at only one given reaction time or the initial reaction rate). The methodology is thoroughly detailed, while stressing on the comparison between the recently proposed Context Aware Crossover (CAX) and the traditional crossover operator.", notes = "Affiliations: Institute of Chemical Technology, CSIC-UPV, Valencia, Spain Louis Pasteur University, LSIIT, FDBT, Illkirch, France", } @Article{krueg11ease, author = "Laurent A. Baumes and Frederic Kruger and Pierre Collet", title = "{EASEA}: a generic optimization tool for {GPU} machines in asynchronous island model", journal = "Computer Methods in Materials Science", year = "2011", volume = "11", number = "3", pages = "489--499", keywords = "genetic algorithms, genetic programming, GPGPU, Evolutionary Algorithms, Island Model, Parallelism, Zeolite Materials", ISSN = "1641-8581", publisher = "The AGH University of Science and Technology Press, Open Access", URL = "http://icube-publis.unistra.fr/docs/7407/baumes.pdf", URL = "http://cmms-editorial.agh.edu.pl/abstract.php?p_id=373", size = "11 pages", abstract = "Very recently, we presented an efficient implementation of Evolutionary Algorithms (EAs) using Graphics Processing Units (GPU) for solving microporous crystal structures. Because of both the inherent complexity of zeolitic materials and the constant pressure to accelerate R and D solutions, an asynchronous island model running on clusters of machines equipped with GPU cards, i.e. the current trend for super-computers and cloud computing, is presented. This last improvement of the EASEA platform allows an effortless exploitation of hierarchical massively parallel systems. It is demonstrated that supra-linear speedup over one machine and linear speedup considering clusters of different sizes are obtained. Such an island implementation over several potentially heterogeneous machines opens new horizon for various domains of application where computation time for optimisation remains the principal bottleneck.", notes = "Address LSIIT , Illkirch, FRA", } @Article{Baumes:2011:PCCP, author = "Laurent A. Baumes and Frederic Kruger and Santiago Jimenez and Pierre Collet and Avelino Corma", title = "Boosting theoretical zeolitic framework generation for the determination of new materials structures using {GPU} programming", journal = "Physical Chemistry Chemical Physics", year = "2011", volume = "13", number = "10", pages = "4674--4678", keywords = "genetic algorithms, genetic programming, memetic genetic algorithm, EASEA, GPU, GPGPU, nVidia GTX 295, CUDA", ISSN = "1463-9076", publisher = "The Royal Society of Chemistry", DOI = "doi:10.1039/C0CP02833A", size = "5 pages", abstract = "Evolutionary algorithms have proved to be efficient for solving complicated optimization problems. On the other hand, the many-core architecture in graphical cards General Purpose Graphic Processing Unit (GPGPU) offers one of the most attractive cost/performance ratio. Using such hardware, the manuscript shows how an efficiently implemented genetic algorithm with a simple fitness function allows boosting the determination of zeolite structures. A case study is presented.", notes = "is this GP? Also known as \cite{C0CP02833A}", } @InProceedings{Baun:2023:ICBIR, author = "Jonah Jahara Baun and Adrian Genevie Janairo and Ronnie Concepcion and Kate Francisco and Mike Louie Enriquez and R-Jay Relano and Joseph Aristotle {de Leon} and Argel Bandala and Ryan Rhay Vicerra and Jonathan Dungca", booktitle = "2023 8th International Conference on Business and Industrial Research (ICBIR)", title = "Hybrid Stochastic Genetic Evolution-Based Prediction Model of Received Input Voltage for Underground Imaging Applications", year = "2023", pages = "549--555", abstract = "The capacitive resistivity technique in underground object detection comprises configured transmitter and receiver antennas that are capacitively coupled to the ground. However, underground imaging lacks a basis for determining the received voltage for precise data analysis. This study aimed to develop a prediction model of the received input voltage signal amplitude from the ground of a single-pair antenna underground imaging system. The receiver antenna circuit for this application is designed and simulated in Proteus Software. Genetic Programming (GP) is applied to predict the received input signal based on the shape of the received waveform signal, operating frequency, resistance of the waveform shaping circuit, and buffer amplifier output signal. The resulting fitness function of GP (4) is acceptable as it scored an R2 of 99.38percent with a negligible MSE of 0.0059 and an MAE of 12.3423. Then, the GP fitness function is optimised through Genetic Algorithm (GA), Differential Evolution (DE), and Evolutionary Strategy (ES) in which the GP-GA model outperformed the two hybrid models providing fast convergence and 2.49e-8 best fitness value. This study proved that GP can be effectively combined with stochastic genetic evolution algorithms to avoid lengthy mathematical calculations and accurately estimate the natural voltage received from the ground.", keywords = "genetic algorithms, genetic programming, Resistance, Imaging, Stochastic processes, Receiving antennas, Voltage, Predictive models, Conductivity, capacitive resistivity technique, underground imaging, voltage prediction, stochastic genetic evolution", DOI = "doi:10.1109/ICBIR57571.2023.10147464", month = may, notes = "Also known as \cite{10147464}", } @InProceedings{Baun:2023:HNICEM, author = "Jonah Jahara Baun and Adrian Genevie Janairo and R-Jay Relano and Ronnie Concepcion and Argel Bandala and Ryan Rhay Vicerra and Seyedali Mirjalili", title = "Single-pair Equatorial Dipole-Dipole Underground Imaging Antenna Capacitance Minimization Using Grey Wolf Optimization Algorithm", booktitle = "2023 IEEE 15th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)", year = "2023", month = nov, keywords = "genetic algorithms, genetic programming, Geometry, Sensitivity, Dipole antennas, Imaging, Receiving antennas, Conductivity, advanced metaheuristics, antenna geometry optimisation, bio-inspired optimisation, capacitance modelling, subsurface imaging", ISSN = "2770-0682", DOI = "doi:10.1109/HNICEM60674.2023.10589146", abstract = "Roads using capacitive resistivity underground imaging at very low frequencies are vulnerable to capacitance dynamics characteristic of the antenna, which can lead to unwanted signal reflection, coupling, and an adverse influence on the sensitivity of the reception. Conventional mathematical antenna models are time-consuming, repetitive, prone to error by humans, and produce inconsistent results. A novel method for optimising plate-wire antenna capacitance by equatorial dipole-dipole antenna geometry modelling has been proposed in this paper to tackle this new problem. It involves the use of Genetic programming (GP) optimised through the Grey Wolf Optimisation (GWO) algorithm. Based on 241 permutations of antenna wire radius and elevation along with the width, length, thickness, and elevation of dipole plates, GP was used to develop the fitness function of the capacitance of the antenna. The GP-GWO was used to minimise the capacitance (almost 1 nF) in order to achieve quasi-static conditions. The developed GP-GWO model was compared to the electrical properties of the default antenna and other improved versions through 3D modelling using Altair Feko. In terms of sensitivity, the suggested model outperformed the existing models to the point where it could detect the presence of the pipe that provided the lowest received voltage of 0.080V clearly.", notes = "Also known as \cite{10589146}", } @InProceedings{Bautu:2006:mmelsp, author = "Andrei Bautu and Elena Bautu", title = "Meteorological Data Analysis and Prediction by Means of Genetic Programming", booktitle = "Proceedings of the Fifth Workshop on Mathematical Modelling of Environmental and Life Sciences Problems", year = "2006", pages = "35--42", address = "Constanta, Romania", month = sep, keywords = "genetic algorithms, genetic programming", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.613.3355", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.613.3355", broken = "http://www.ima.ro/mmelsp_series/mmelsp_08_papers/bautu01_08.pdf", size = "8 pages", abstract = "Weather systems use extremely complex combinations of mathematical tools for analysis and forecasting. Unfortunately, due to phenomena in the world climate, such as the greenhouse effect, classical models may become inadequate mostly because they lack adaptation. Therefore, the weather prediction problem is suited for heuristic approaches, such as Evolutionary Algorithms. Experimentation with heuristic methods like Genetic Programming (GP) can lead to the development of new insights or promising models that can be fine tuned with more focused techniques. This paper describes a GP approach for analysis and prediction of data and provides experimental results of the afore mentioned method on real-world meteorological time series.", } @Article{Bautu20071q, author = "Andrei Bautu and Elena Bautu", title = "Quantum Circuit Design By Means Of Genetic Programming", journal = "Romanian Journal of Physics", year = "2007", volume = "52", number = "5-7", pages = "697--704", publisher = "Romanian Academy Publishing House", address = "Bucharest, Romania", keywords = "genetic algorithms, genetic programming, quantum gates", ISSN = "1221-146X", URL = "https://rjp.nipne.ro/2007_52_5-7.html", URL = "http://www.nipne.ro/rjp/2007_52_5-6/0697_0705.pdf", size = "8 pages", abstract = "Research in quantum technology has shown that quantum computers can provide dramatic advantages over classical computers for some problems. The efficiency of quantum computing is considered to become so significant that the study of quantum algorithms has attracted widespread interest. Development of quantum algorithms and circuits is difficult for a human researcher, so automatic induction of computer programs by means of genetic programming, which uses almost no auxiliary information on the search space, proved to be useful in generating new quantum algorithms. This approach takes advantage of the intrinsic parallelism of the genetic algorithm and quantum computing parallelism. The paper begins with a brief review on some basic concepts in genetic algorithms and quantum computation. Next, it describes an application of genetic programming for evolving quantum computing circuits.", notes = "S-expressions. Paper presented at the 7th International Balkan Workshop on Applied Physics, 5-7 July 2006, Constanta, Romania. http://www.nipne.ro/rjp/", } @InProceedings{conf:synasc:bautu2005, author = "Elena Bautu and Andrei Bautu and Henri Luchian", title = "A {GEP}-based approach for solving {Fredholm} first kind integral equations", booktitle = "Seventh International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2005", year = "2005", pages = "325", month = sep, publisher = "IEEE Computer Society", keywords = "genetic algorithms, genetic programming, gene expression programming, Fredholm integral equations, GEA, GEP approach, evolutionary techniques, first kind integral equations, gene expression algorithm, symbolic technique, Fredholm integral equations, evolutionary computation, symbol manipulation", ISBN = "0-7695-2453-2", DOI = "doi:10.1109/SYNASC.2005.6", size = "4 pages", abstract = "Evolutionary techniques have been widely accepted as an effective meta-heuristic for a wide variety of problems in different domains. The main purpose of this paper is to present a symbolic technique based on the evolutionary paradigm gene expression programming (GEP) for solving Fredholm first type integral equations. We present the main traits of the gene expression algorithm (GEA), and our implementation for solving first kind integral equations of Fredholm type. The results obtained on four model problems using the symbolic technique described in this paper prove it to be suitable to handle this class of problems.", } @InProceedings{Bautu:2005:SYNASC, author = "Elena Bautu and Andrei Bautu and Henri Luchian", title = "Symbolic Regression on Noisy Data with Genetic and Gene Expression Programming", booktitle = "Seventh International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC'05)", year = "2005", pages = "321--324", keywords = "genetic algorithms, genetic programming, Gene Expression Programming", DOI = "doi:10.1109/SYNASC.2005.70", abstract = "regression on a finite sample of noisy data. The purpose is to obtain a mathematical model for data which is both reliable and valid, yet the analytical expression is not restricted to any particular form. To obtain a statistical model of the noisy data set we use symbolic regression with pseudo-random number generators. We begin by describing symbolic regression and our implementation of this technique using genetic programming (GP) and gene expression programming (GEP). We present some results for symbolic regression on computer generated and real financial data sets in the final part of this paper.", } @InProceedings{conf/synasc/BautuBL07, author = "Elena Bautu and Andrei Bautu and Henri Luchian", title = "Ada{GEP} - An Adaptive Gene Expression Programming Algorithm", booktitle = "Proceedings of the Ninth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, {SYNASC} 2007", year = "2007", editor = "Viorel Negru and Tudor Jebelean and Dana Petcu and Daniela Zaharie", pages = "403--406", address = "Timisoara, Romania", month = sep # " 26-29", publisher = "IEEE Computer Society", keywords = "genetic algorithms, genetic programming, gene expression programming", isbn13 = "978-0-7695-3078-9", DOI = "doi:10.1109/SYNASC.2007.51", abstract = "Many papers focused on fine-tuning the gene expression programming (GEP) operators or their application rates in order to improve the performances of the algorithm. Much less work was done on optimizing the structural parameters of the chromosomes (i.e. number of genes and gene size). This is probably due to the fact that the no free lunch theorem states that no fixed values for these parameters will ever suit all problems. To counteract this fact, this paper presents a modified GEP algorithm, called AdaGEP, which automatically adapts the number of genes used by the chromosome. The adaptation process takes place at chromosome level, allowing chromosomes in the population to evolve with different number of genes.", notes = "p406 'The results presented in this paper demonstrate the superiority of AdaGEP over GEP on symbolic regression problems.'", bibdate = "2008-11-28", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/synasc/synasc2007.html#BautuBL07", } @Article{Bautu20071, author = "Elena Bautu and Elena Pelican", title = "Numerical Solution For {Fredholm} First Kind Integral Equations Occurring In Synthesis of Electromagnetic Fields", journal = "Romanian Journal of Physics", year = "2007", volume = "52", pages = "245--256", number = "3-4", publisher = "Romanian Academy Publishing House", keywords = "genetic algorithms, genetic programming, Gene Expression Programming, EM fields, Fredholm integral equations of the first kind, inverse problems", ISSN = "1221-146X", URL = "http://www.nipne.ro/rjp/2007_52_3-4.html", URL = "http://www.nipne.ro/rjp/2007_52_3-4/0245_0257.pdf", size = "12 pages", abstract = "It is known that Fredholm integral equations of the first kind with the kernel occur when solving with problems of synthesis of electrostatic and magnetic fields (m, n nonnegative rational numbers). This paper presents two approaches for solving such an equation. The first one involves discretisation by a collocation method and numerical solution using an approximate orthogonalisation algorithm. The second method is based on a nature inspired heuristic, namely genetic programming. It applies genetically-inspired operators to populations of potential solutions in the form of program trees, in an iterative fashion, creating new populations while searching for an optimal or near-optimal solution to the problem at hand. Results obtained in experiments are presented for both approaches.", notes = "Paper presented at the 7th International Balkan Workshop on Applied Physics, 5-7 July 2006, Constanta, Romania. http://www.nipne.ro/rjp/ Cited by \cite{Sammany:2011:Computing}", } @InProceedings{Bautu:2008:SYNASC, author = "Elena Bautu and Andrei Bautu and Henri Luchian", title = "An Evolutionary Approach for Modeling Time Series", booktitle = "10th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC '08", year = "2008", month = sep, pages = "507--513", keywords = "genetic algorithms, genetic programming, change point detection, data generation process, evolutionary approach, genetic operator, time series modeling, time series", DOI = "doi:10.1109/SYNASC.2008.63", abstract = "Change points in time series appear due to variations in the data generation process. We consider the problem of modeling time series generated by dynamic processes, and we focus on finding the change points using a specially tailored genetic algorithm. The algorithm employs a new representation, described in detail in the paper. Suitable genetic operators are also defined and explained. The results obtained on computer generated time series provide evidence that the approach can be used for change point detection, and has good potential for time series modeling.", notes = "Also known as \cite{5204862}", } @Article{Bautu20081, author = "Elena Bautu and Elena Pelican", title = "Symbolic approach for the generalized airfoil equation", journal = "Creative Mathematics and Informatics", year = "2008", volume = "17", number = "2", pages = "52--60", keywords = "genetic algorithms, genetic programming, Gene Expression Programming, Generalised airfoil equation, Fredholm integral equation of the first kind, airfoil equation", ISSN = "1584-286X", URL = "https://www.creative-mathematics.cunbm.utcluj.ro/article/symbolic-approach-for-the-generalized-airfoil-equation/", URL = "https://www.creative-mathematics.cunbm.utcluj.ro/wp-content/uploads/2008_vol_17_2/creative_2008_17_2_052_060.pdf", broken = "http://creative-mathematics.ubm.ro/issues/down.php?f=creative_17_2008_no2_052_060.pdf", size = "6 pages", abstract = "The generalised airfoil equation governs the pressure across an airfoil oscillating in a wind tunnel. In this paper we analyse the problem for an airfoil with a flap, by means of Gene Expression Programming (GEP). We present the main traits of the GEP metaheuristic and then we define its elements in order to be used for integral equations of the first kind. The results obtained by our symbolic approach confirm the suitability of this method for problems modelled by Fredholm first kind integral equations.", notes = " https://www.creative-mathematics.cunbm.utcluj.ro/", } @InProceedings{conf/cisis/BautuBL10, author = "Elena Bautu and Andrei Bautu and Henri Luchian", title = "Evolving Gene Expression Programming Classifiers for Ensemble Prediction of Movements on the Stock Market", booktitle = "The Fourth International Conference on Complex, Intelligent and Software Intensive Systems (CISIS 2010)", address = "Krakow, Poland", month = "15-18 " # feb, year = "2010", editor = "Leonard Barolli and Fatos Xhafa and Salvatore Vitabile and Hui-Huang Hsu", isbn13 = "978-0-7695-3967-6", pages = "108--115", keywords = "genetic algorithms, genetic programming, gene expression programming", DOI = "doi:10.1109/CISIS.2010.101", publisher = "IEEE Computer Society", bibdate = "2010-04-23", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/cisis/cisis2010.html#BautuBL10", } @PhdThesis{bautu:thesis, author = "Elena Bautu", title = "Intelligent Techniques for Data Modeling Problems", school = "Al. I. Cuza University", year = "2010", address = "Iasi, Romania", month = jun, note = "Romanian subtitle is Programare genetica pentru probleme de optimizare in Inteligenta artificiala", keywords = "genetic algorithms, genetic programming, gene expression programming, inverse problems, financial forecasting, data analysis, hypernetwork, hybridization", URL = "https://sites.google.com/site/ebautu/home/publications/thesis/thesis_elena_bautu.pdf", broken = "https://sites.google.com/site/ebautu/home/publications/thesis", size = "220 pages", abstract = "Supervised learning deals with the problem of discovering models from data as relationships between input and output attributes. Two types of models are distinguished: regression models for continuous output and classification models (classiffiers) for discrete output. This thesis addresses both regression and classiffication problems, with an emphasis on new applications and on proposing new evolutionary techniques. First, we address the regression domain. Symbolic regression by means of evolutionary techniques is recommended when there is little or no a priori information on the modelled process. It relies on a set of input-output observations to infer mathematical models, posing no constraints on the structure, the coefficients or the size of the model. We introduce inverse problems modeled by Fredholm integral equations of the first kind and the inverse problem of log synthesis to be modelled by symbolic regression by means of gene expression programming. A new genetic programming scheme is formulated for the problem of automatically designing quantum circuits. An adaptive version of the gene expression programming algorithm is presented, which automatically tunes the complexity of the model by a gene (de)activation mechanism. For modelling time series produced by dynamic processes, we propose an evolutionary approach that uses a novel representation (and suitable genetic operators) to partition the time series based on change points. Empirical results prove the approach to be promising. Research on building classifiers for a given problem is also extensive, since there exists no best classifier at all tasks. The problem of predicting the direction of change of stock price on the market can be formulated as the search for a classifier that links past evolution to an increase or decrease. We explore new techniques for classification, in the context of predicting the direction of change of stock price, formulated as a binary classification", notes = "See also \cite{Bautu:book}", } @Book{Bautu:book, author = "Elena Bautu", title = "Intelligent Techniques for Data Modeling Problems: Nature inspired meta-heuristics and learning models applied to time series modeling and forecasting", publisher = "Lambert Academic Publishing", year = "2012", address = "Moldova", month = "20 " # mar, keywords = "genetic algorithms, genetic programming, Gene Expression Programming", isbn13 = "978-3-8484-3479-4", broken = "https://www.lap-publishing.com/catalog/details/store/ru/book/978-3-8484-3479-4/intelligent-techniques-for-data-modeling-problems?search=978-3-8484-3479-4", URL = "https://www.amazon.com/Intelligent-Techniques-Data-Modeling-Problems/dp/3848434792", size = "224 pages", abstract = "Supervised learning deals with the problem of discovering models from data as relationships between input and output attributes. Two types of models are distinguished: regression models (for continuous output attributes) and classification models (for discrete output attributes). This thesis addresses both regression and classification problems with an emphasis on new applications and on presenting improved evolutionary techniques. Such techniques include Gene Expression Programming (classical and its adaptive version), Genetic Programming, and the hypernetwork model of learning (classical and its evolutionary version). Such methods can be successfully applied to many problems from various domains. This thesis presents applications for symbolic regression for inverse problems, quantum circuit design, modeling of dynamic processes, and forecasting price movement.", } @Article{Bautu2012, author = "Elena Bautu and Alina Barbulescu", title = "A Hybrid Approach for Modelling Financial Time Series", year = "2012", journal = "The International Arab Journal of Information Technology (IAJIT)", volume = "9", number = "4", pages = "327--335", month = jul, keywords = "genetic algorithms, genetic programming, Gene Expression Programming, Financial time series, forecasting, ARMA, GEP, and hybrid methodolog", ISSN = "1683-3198", URL = "http://www.ccis2k.org/iajit/PDF/vol.9,no.4/2806-5.pdf", size = "9 pages", abstract = "The problem we tackle concerns forecasting time series in financial markets. AutoRegressive Moving-Average (ARMA) methods and computational intelligence have also been used to tackle this problem. We propose a novel method for time series forecasting based on a hybrid combination of ARMA and Gene Expression Programming (GEP) induced models. Time series from financial domains often encapsulate different linear and non-linear patterns. ARMA models, although flexible, assume a linear form for the models. GEP evolves models adapting to the data without any restrictions with respect to the form of the model or its coefficients. Our approach benefits from the capability of ARMA to identify linear trends as well as GEP's ability to obtain models that capture nonlinear patterns from data. Investigations are performed on real data sets. They show a definite improvement in the accuracy of forecasts of the hybrid method over pure ARMA and GEP used separately. Experimental results are analysed and discussed. Conclusions and some directions for further research end the paper.", notes = "Zarqa Private University, Zarqa Jordan, iajit@ccis2k.org", } @InProceedings{Baxter:2024:GI, author = "Hunter Baxter and Yu Huang and Kevin Leach", title = "Genetic Improvement for {DNN} Security", booktitle = "13th International Workshop on Genetic Improvement @ICSE 2024", year = "2024", editor = "Gabin An and Aymeric Blot and Vesna Nowack and Oliver Krauss and Justyna Petke", pages = "11--12", address = "Lisbon", month = "16 " # apr, publisher = "ACM", note = "Best Presentation", keywords = "genetic algorithms, genetic programming, Genetic Improvement, Computer Security, ANN", isbn13 = "979-8-4007-0573-1/24/04", URL = "http://gpbib.cs.ucl.ac.uk/gi2024/Genetic_Improvement_for_DNN_Security.pdf", DOI = "doi:10.1145/3643692.3648261", slides_url = "http://gpbib.cs.ucl.ac.uk/gi2024/gi_2024_slides/leach-gi24.pdf", video_url = "https://www.youtube.com/watch?v=OXiFldz3b1U", video_url = "https://youtu.be/D2qLipAIAvE", video_url = "https://www.youtube.com/watch?v=D2qLipAIAvE&list=PLI8fiFpB7BoIRqJuY80XwmH-DFT_71y2S&index=4&pp=iAQB", size = "2 pages", abstract = "Genetic improvement (GI) in Deep Neural Networks (DNNs) has traditionally enhanced neural architecture and trained DNN parameters. Recently, GI has supported large language models by optimising DNN operator scheduling on accelerator clusters. However, with the rise of adversarial AI, particularly model extraction attacks, there is an unexplored potential for GI in fortifying Machine Learning as a Service (MLaaS) models. We suggest a novel application of GI, not to improve model performance, but to diversify operator parallelism for the purpose of a moving target defence against model extraction attacks. We discuss an application of GI to create a DNN model defense strategy that uses probabilistic isolation, offering unique benefits not present in current DNN defense systems.", notes = "https://youtu.be/D2qLipAIAvE recording made at live at the event in Portugal, including Q and A. GI @ ICSE 2024, part of \cite{an:2024:GI}", } @Article{DBLP:journals/remotesensing/BayatNZB19, author = "Mahmoud Bayat and Phan Thanh Noi and Rozita Zare and Dieu Tien Bui", title = "A Semi-empirical Approach Based on Genetic Programming for the Study of Biophysical Controls on Diameter-Growth of \emph{Fagus orientalis} in Northern Iran", journal = "Remote. Sens.", volume = "11", number = "14", pages = "1680", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.3390/rs11141680", DOI = "doi:10.3390/rs11141680", timestamp = "Mon, 11 May 2020 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/remotesensing/BayatNZB19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{Bayazidi:2014:MPiE, author = "Alireza Mohammadi Bayazidi and Gai-Ge Wang and Hamed Bolandi and Amir H. Alavi and Amir H. Gandomi", title = "Multigene Genetic Programming for Estimation of Elastic Modulus of Concrete", journal = "Mathematical Problems in Engineering", year = "2014", keywords = "genetic algorithms, genetic programming", publisher = "Hindawi", URL = "http://dx.doi.org/10.1155/2014/474289", DOI = "doi:10.1155/2014/474289", size = "10 pages", abstract = "This paper presents a new multigene genetic programming (MGGP) approach for estimation of elastic modulus of concrete. The MGGP technique models the elastic modulus behaviour by integrating the capabilities of standard genetic programming and classical regression. The main aim is to derive precise relationships between the tangent elastic moduli of normal and high strength concrete and the corresponding compressive strength values. Another important contribution of this study is to develop a generalised prediction model for the elastic moduli of both normal and high strength concrete. Numerous concrete compressive strength test results are obtained from the literature to develop the models. A comprehensive comparative study is conducted to verify the performance of the models. The proposed models perform superior to the existing traditional models, as well as those derived using other powerful soft computing tools.", notes = "Article ID 474289", } @InProceedings{Baydar:2000:GECCO, author = "Cem M. Baydar and Kazuhiro Saitou", title = "A Genetic Programming Framework for Error Recovery in Robotic Assembly Systems", pages = "756", year = "2000", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)", editor = "Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer", address = "Las Vegas, Nevada, USA", publisher_address = "San Francisco, CA 94104, USA", month = "10-12 " # jul, keywords = "genetic algorithms, genetic programming, Poster", ISBN = "1-55860-708-0", URL = "http://gpbib.cs.ucl.ac.uk/gecco2000/RW036.pdf", size = "1 page", abstract = "The advantages and performance of genetic programming in the use of error recovery planning in robotic assembly systems are presented. A framework is developed and coupled with a 3D robotic simulation software for the generation of error recovery logic in assembly systems to generate robust recovery programs in robot language itself. Performance of the system is evaluated with the simulations made on a three dimensionally modeled automated assembly line. The obtained results showed that the system is efficient of generating robust recovery plans for different error states.", notes = "A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of \cite{whitley:2000:GECCO}", } @InProceedings{oai:CiteSeerPSU:538284, author = "Cem M. Baydar and Kazuhiro Saitou", title = "Off-Line Error Recovery Logic Synthesis in Automated Assembly Lines by using Genetic Programming", booktitle = "Proceedings Of The 2000 Japan/USA Symposium On Flexible Automation", year = "2000", editor = "Steven Y. Liang and Tatsuo Arai", address = "Ann Arbor, MI, USA", month = "23-26 " # jul, organisation = "ASME", email = "kazu@umich.edu", keywords = "genetic algorithms, genetic programming, Error Recovery Synthesis, Off-line Programming, Automated Assembly Lines", ISBN = "0-7918-1998-1", broken = "http://www-personal.engin.umich.edu/~cbaydar/japan-usa-00.pdf", URL = "http://citeseer.ist.psu.edu/538284.html", size = "8 pages", abstract = "Unexpected failures are one of the most important problems, which cause costly shutdowns in an assembly line. Generally the recovery process is done by the experts or automated error recovery logic controllers embedded in the system. The previous work in the literature is focused on the on-line recovery of the assembly lines which makes the process, time and money consuming. Therefore a novel approach is necessary which requires less time and hardware effort for the generation of error recovery logic. The proposed approach is based on three-dimensional geometric modelling of the assembly line coupled with the evolutionary computation techniques to generate error recovery logic in an off-line manner. The scope of this work is focused on finding an error recovery algorithm from a predefined error case. An automated assembly line is virtually modeled and the validity of the recovery algorithm is evaluated in a generate and test fashion by using a commercial software package. The obtained results showed that the developed framework is capable of generating recovery algorithms from an arbitrary part positioning error case. It is aimed that this approach will be coupled with the error generation in the future, providing efficient ways for the study of error recovery in automated assembly lines.", notes = "http://www.asme.org/divisions/med/enewsletter/2000oct/JapanUSAsymp.html http://members.asme.org/catalog/ItemView.cfm?ItemNumber=I464CD ASME Order #: I464CD", } @InProceedings{oai:CiteSeerPSU:535775, author = "Cem M Baydar and Kazuhiro Saitou", title = "Generation of Robust Recovery Logic in Assembly Systems using Multi-Level Optimization and Genetic Programming", booktitle = "Proceedings of DETC-00 ASME 2000 Design Engineering Technical Conferences and Computers and Information in Engineering Conference", year = "2000", address = "Baltimore, Maryland, USA", month = "10-13 " # sep, keywords = "genetic algorithms, genetic programming", citeseer-isreferencedby = "oai:CiteSeerPSU:87724; oai:CiteSeerPSU:467824; oai:CiteSeerPSU:161643", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:535775", rights = "unrestricted", URL = "http://citeseer.ist.psu.edu/535775.html", size = "8 pages", abstract = "Automated assembly lines are subject to unexpected failures, which can cause costly shutdowns. Generally, these errors are handled by human experts or logic controllers. However, these controller codes are based on anticipated error scenarios and are deficient in dealing with unforeseen situations. In our previous work (Baydar and Saitou, 2000a), an approach for the automated generation of error recovery logic was discussed. The method is based on three-dimensional geometric modeling of the assembly line to generate error recovery logic in an {"}off-line{"} manner using Genetic Programming. The scope of our previous work was focused on finding an error recovery algorithm from a predefined error case. However due to the geometrical features of the assembly lines, there may be cases which can be detected as the same type of error by the sensors. Therefore robustness must be assured in the sense of having a common recovery algorithm for similar cases during the recovery sequence. In this paper, an extension of our previous study is presented to overcome this problera An assembly line is modeled and from the given error cases optimum way of error recovery is investigated using multi-level optimization. The obtained results showed that the infrastructure is capable of finding robust error recovery algorithms and multi-level optimization procedure improved the process. It is expected that the results of this study will be combined with the automatic error generation, resulting in efficient ways to automated error recovery logic synthesis.", notes = "not verified", } @InProceedings{Baydar:2001:ICRA, author = "Cem M. Baydar and Kazuhiro Saitou", title = "Off-line error prediction, diagnosis and recovery using virtual assembly systems", booktitle = "Proceedings of the IEEE International Conference on Robotics and Automation, ICRA 2001", year = "2001", volume = "1", pages = "818--823", address = "Seoul, Korea", month = "21-26 " # may, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, 3D model, Bayesian reasoning, Monte Carlo simulation, assembly line, automated assembly systems, error scenarios, peg-in-hole assembly, unexpected failures, virtual assembly systems, Bayes methods, Monte Carlo methods, assembling, fault diagnosis, industrial robots, inference mechanisms, robot programming", ISSN = "1050-4729", ISBN = "0-7803-6576-3", DOI = "doi:10.1109/ROBOT.2001.932651", size = "6 pages", abstract = "Automated assembly systems often stop their operation due to the unexpected failures occurred during their assembly process. Since these large-scale systems are composed of many parameters, it is difficult to anticipate all possible types of errors with their likelihood of occurrence. Several systems were developed in the literature, focusing on online diagnosing and recovering the assembly process in an intelligent manner based on the predicted error scenarios. However, these systems do not cover all of the possible errors and they are deficient in dealing with the unexpected error situations. The proposed approach uses Monte Carlo simulation of the assembly process with the 3D model of the assembly line to predict the possible errors in an offline manner. After that, these predicted errors can be diagnosed and recovered using Bayesian reasoning and genetic programming. A case study composed of a peg-in-hole assembly was performed and the results are discussed. It is expected that with this new approach, errors can be diagnosed and recovered accurately and costly downtime of robotic assembly systems will be reduced.", notes = "GP creates code in RAPID language. Also known as \cite{932651}", } @PhdThesis{Baydar:thesis, author = "Cem Mehmet Baydar", title = "Off-line Error Prediction and Recovery Logic Synthesis using Virtual Assembly Systems", school = "The University of Michigan", year = "2001", address = "USA", keywords = "genetic algorithms, genetic programming, Applied sciences, Robotic assembly, Failure diagnosis, Off-line, Error prediction, Recovery logic, Virtual assembly", URL = "http://mirlyn.lib.umich.edu/Record/004198436", URL = "http://books.google.co.uk/books?id=fZMfAQAAMAAJ", URL = "http://search.proquest.com/docview/275835638", ISBN = "0-493-27766-8", size = "141 pages", abstract = "The advent of industrial robots has enabled large-scale automation in assembly lines with high productivity and minimum human intervention. However, growing complexity of robotic assembly systems makes them vulnerable to perturbations in process parameters, causing unexpected failures . Generally, the recovery process from this type of failures is carried out in a limited way by human experts or automated error recovery logic controllers embedded in the system. It is not possible to predict all failures and previous work in the literature focused on 'on-line' recovery of assembly lines when a failure occurs. Extensive downtime of a production system is costly and a failure recovery process that requires less time and hardware effort would be valuable. This dissertation offers a new approach for error prediction, diagnosis and recovery in assembly systems. It combines three-dimensional geometric model of assembly system with statistical distributions of process parameters and uses Monte Carlo simulation to predict possible failures, which may not be foreseen by human experts. The calculation of the likelihood of occurrence of each failure for a detected sensory symptom is achieved by Bayesian Reasoning and Genetic Programming is used to generate the requisite error recovery codes in an 'off-line' manner. The proposed approach is implemented and its validity is demonstrated in several case studies. Although main disadvantage was identified as costly computation time because of Monte Carlo simulation and Genetic Programming, two major advantages are expected to be achieved by this approach: Reducing lengthy ramp-up time for new systems (since most of pre-launch testing is debugging error recovery codes), and diagnosing and recovering unexpected errors accurately so that costly downtimes are reduced. Future work is suggested on the application of this method to manufacturing systems and exploration of a sampling algorithm which reduces the costly computation time of Monte Carlo simulation.", notes = "Something odd with 1.5 pdf seems ok only in some readers. UMI microfilm 3016800. Chair: Kazuhiro Saitou broken http://me.engin.umich.edu/news/pubs/ar/200209annualreportbw.pdf OCLC Number: 68913755", } @Article{Baydar200155, author = "Cem M. Baydar and Kazuhiro Saitou", title = "Automated generation of robust error recovery logic in assembly systems using genetic programming", journal = "Journal of Manufacturing Systems", volume = "20", number = "1", pages = "55--68", year = "2001", ISSN = "0278-6125", DOI = "doi:10.1016/S0278-6125(01)80020-0", URL = "http://www.sciencedirect.com/science/article/B6VJD-441R1H8-6/2/cdebaddb30a67a67dc7cb6dd41fabf9f", keywords = "genetic algorithms, genetic programming, robotics, Automated Assembly Systems, Error Recovery, Multi-Level Optimization", abstract = "Automated assembly lines are subject to unexpected failures, which can cause costly shutdowns. Generally, the recovery process is done 'on-line' by human experts or automated error recovery logic controllers embedded in the system. However, these controller codes are programmed based on anticipated error scenarios and, due to the geometrical features of the assembly lines, there may be error cases that belong to the same anticipated type but are present in different positions, each requiring a different way to recover. Therefore, robustness must be assured in the sense of having a common recovery algorithm for similar cases during the recovery sequence. The proposed approach is based on three-dimensional geometric modeling of the assembly line coupled with the genetic programming and multi-level optimization techniques to generate robust error recovery logic in an 'off-line' manner. The approach uses genetic programming's flexibility to generate recovery plans in the robot language itself. An assembly line is modeled and from the given error cases an optimum way of error recovery is investigated using multi-level optimization in a 'generate and test' fashion. The obtained results showed that with the improved convergence gained by using multi-level optimisation, the infrastructure is capable of finding robust error recovery algorithms. It is expected that this approach will require less time for the generation of robust error recovery logic.", notes = "IRB6000 KAREL2, ROUTINE GPcode26, Move to POS, Move Relative...", } @Article{Baydar:2004:JIM, author = "Cem Baydar and Kazuhiro Saitou", title = "Off-line error prediction, diagnosis and recovery using virtual assembly systems", journal = "Journal of Intelligent Manufacturing", year = "2004", volume = "15", number = "5", pages = "679--692", month = oct, keywords = "genetic algorithms, genetic programming, Off-line programming, robotic assembly systems, virtual factories, error diagnosis and recovery", ISSN = "0956-5515", publisher = "Springer", DOI = "doi:10.1023/B:JIMS.0000037716.69868.d0", size = "14 pages", abstract = "Automated assembly systems often stop their operation due to the unexpected failures occurred during their assembly process. Since these large-scale systems are composed of many parameters, it is difficult to anticipate all possible types of errors with their likelihood of occurrence. Several systems were developed in the literature, focusing on on-line diagnosing and recovery of the assembly process in an intelligent manner based on the predicted error scenarios. However, these systems do not cover all of the possible errors and they are deficient in dealing with the unexpected error situations. The proposed approach uses Monte Carlo simulation of the assembly process with the 3-D model of the assembly line to predict the possible errors in an off-line manner. After that, these predicted errors are diagnosed and recovered using Bayesian reasoning and genetic algorithms. Several case studies are performed on single-station and multi-station assembly systems and the results are discussed. It is expected that with this new approach, errors can be diagnosed and recovered accurately and costly down times of robotic assembly systems will be reduced.", notes = "GP section 3.3. They generate error recovery code p688. linear chromosome Fig 4. Workspace Software. Pictures much better than \cite{Baydar:2001:ICRA}", } @InProceedings{Bayer:2021:GPTP, author = "Caleidgh Bayer and Ryan Amaral and Robert Smith and Alexandru Ianta and Malcolm Heywood", title = "Accelerating Tangled Program Graph Evolution under Visual Reinforcement Learning Tasks with Mutation and Multi-actions", booktitle = "Genetic Programming Theory and Practice XVIII", year = "2021", editor = "Wolfgang Banzhaf and Leonardo Trujillo and Stephan Winkler and Bill Worzel", series = "Genetic and Evolutionary Computation", pages = "1--19", address = "East Lansing, USA", month = "19-21 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-981-16-8112-7", DOI = "doi:10.1007/978-981-16-8113-4_1", abstract = "Tangled Program Graphs (TPG) represents a genetic programming framework in which emergent modularity incrementally composes programs into teams of programs into graphs of teams of programs. To date, the framework has been demonstrated on reinforcement learning tasks with stochastic partially observable state spaces or time series prediction. However, evolving solutions to reinforcement tasks often requires agents to demonstrate/ juggle multiple properties simultaneously. Hence, we are interesting in maintaining a population of diverse agents. Specifically, agent performance on a reinforcement learning task controls how much of the task they are exposed to. Premature convergence might therefore preclude solving aspects of a task that the agent only later encounters. Moreover, pointless complexity may also result in which graphs largely consist of hitchhikers. In this research we benchmark the use of rampant mutation (multiple mutations applied simultaneously for offspring creation) and action programs (multiple actions per state). Several parameterizations are also introduced that potentially penalize the introduction of hitchhikers. Benchmarking over five VizDoom tasks demonstrates that rampant mutation reduces the likelihood of encountering pathologically bad offspring while action programs appears to improve performance in four out of five tasks. Finally, use of TPG parameterizations that actively limit the complexity of solutions appears to result in very efficient low dimensional solutions that generalize best across all combinations of 3, 4 and 5 VizDoom tasks.", notes = "Part of \cite{Banzhaf:2021:GPTP} published after the workshop in 2022", } @Article{Baykasoglu:2004:CCR, author = "Adil Baykasoglu and Turkay Dereli and Serkan Tanis", title = "Prediction of cement strength using soft computing techniques", journal = "Cement and Concrete Research", year = "2004", volume = "34", pages = "2083--2090", number = "11", abstract = "we aim to propose prediction approaches for the 28-day compressive strength of Portland composite cement (PCC) by using soft computing techniques. Gene expression programming (GEP) and neural networks (NNs) are the soft computing techniques that are used for the prediction of compressive cement strength (CCS). In addition to these methods, stepwise regression analysis is also used to have an idea about the predictive power of the soft computing techniques in comparison to classical statistical approach. The application of the genetic programming (GP) technique GEP to the cement strength prediction is shown for the first time in this paper. The results obtained from the computational tests have shown that GEP is a promising technique for the prediction of cement strength.", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6TWG-4CBVDJS-1/2/46a55d4141904806cf09f3c92f56beb4", month = nov, keywords = "genetic algorithms, genetic programming, Gene expression programming, Modelling, Compressive strength, Cement manufacture", DOI = "doi:10.1016/j.cemconres.2004.03.028", notes = " ", } @InProceedings{Baykasoglu:2005:ICRM, author = "Adil Baykasoglu", title = "Soft computing approaches to production line design", booktitle = "ICRM'2005 3rd International Conference on Responsive Manufacturing", year = "2005", editor = "Nabil Gindy", pages = "273--279", address = "Guangzhou, China", month = "12-14 " # sep, organisation = "University of Nottingham, Guangdong University of Technology", keywords = "genetic algorithms, genetic programming, Gene Expression Programming, Manufacturing system design, soft computing", URL = "http://delta.cs.cinvestav.mx/~ccoello/EMOO/baykasoglu05a.pdf.gz", size = "5 pages", abstract = "Gene Expression Programming (GEP) is used to develop a meta-model for the multiobjective design of a hypothetical production line. The developed meta-model is used to optimize production line design with Multiple Objective Tabu Search algorithm (MOTS). It is found out that GEP and MOTS can be effectively used to solve production line design problems which are known as complex design problems.", notes = "http://www.icrm2005.org/ broken Nov 2005 ", } @Article{Baykasoglu2007767, author = "Adil Baykasoglu and Lale Ozbakir", title = "MEPAR-miner: Multi-expression programming for classification rule mining", journal = "European Journal of Operational Research", volume = "183", number = "2", pages = "767--784", year = "2007", ISSN = "0377-2217", DOI = "DOI:10.1016/j.ejor.2006.10.015", URL = "http://www.sciencedirect.com/science/article/B6VCT-4MJS038-M/2/f780e675b2900eb28473dcbf6cfa03fb", keywords = "genetic algorithms, genetic programming, Data mining, Classification rules, Multi-expression programming, Evolutionary programming", abstract = "Classification and rule induction are two important tasks to extract knowledge from data. In rule induction, the representation of knowledge is defined as IF-THEN rules which are easily understandable and applicable by problem-domain experts. In this paper, a new chromosome representation and solution technique based on Multi-Expression Programming (MEP) which is named as MEPAR-miner (Multi-Expression Programming for Association Rule Mining) for rule induction is proposed. Multi-Expression Programming (MEP) is a relatively new technique in evolutionary programming that is first introduced in 2002 by Oltean and Dumitrescu. MEP uses linear chromosome structure. In MEP, multiple logical expressions which have different sizes are used to represent different logical rules. MEP expressions can be encoded and implemented in a flexible and efficient manner. MEP is generally applied to prediction problems; in this paper a new algorithm is presented which enables MEP to discover classification rules. The performance of the developed algorithm is tested on nine publicly available binary and n-ary classification data sets. Extensive experiments are performed to demonstrate that MEPAR-miner can discover effective classification rules that are as good as (or better than) the ones obtained by the traditional rule induction methods. It is also shown that effective gene encoding structure directly improves the predictive accuracy of logical IF-THEN rules.", } @Article{Baykasoglu2008111, author = "Adil Baykasoglu and Hamza Gullu and Hanifi Canakci and Lale Ozbakir", title = "Prediction of compressive and tensile strength of limestone via genetic programming", journal = "Expert Systems with Applications", year = "2008", volume = "35", number = "1-2", pages = "111--123", month = jul # "-" # aug, keywords = "genetic algorithms, genetic programming, multi expression programming, gene expression programming, Prediction, Limestone, Strength of materials", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2007.06.006", broken = "http://www.sciencedirect.com/science/article/B6V03-4NYJ0NK-1/2/00b6bf799aaf3df77a5e0fd846b85f20", abstract = "Accurate determination of compressive and tensile strength of limestone is an important subject for the design of geotechnical structures. Although there are several classical approaches in the literature for strength prediction their predictive accuracy is generally not satisfactory. The trend in the literature is to apply artificial intelligence based soft computing techniques for complex prediction problems. Artificial neural networks which are a member of soft computing techniques were applied to strength prediction of several types of rocks in the literature with considerable success. Although artificial neural networks are successful in prediction, their inability to explicitly produce prediction equations can create difficulty in practical circumstances. Another member of soft computing family which is known as genetic programming can be a very useful candidate to overcome this problem. Genetic programming based approaches are not yet applied to the strength prediction of limestone. This paper makes an attempt to apply a promising set of genetic programming techniques which are known as multi expression programming (MEP), gene expression programming (GEP) and linear genetic programming (LGP) to the uniaxial compressive strength (UCS) and tensile strength prediction of chalky and clayey soft limestone. The data for strength prediction were generated experimentally in the University of Gaziantep civil engineering laboratories by using limestone samples collected from Gaziantep region of Turkey.", } @Article{Baykasoglu2008, author = "Adil Baykasoglu and Ahmet Oztas and Erdogan Ozbay", title = "Prediction and multi-objective optimization of high-strength concrete parameters via soft computing approaches", journal = "Expert Systems with Applications", year = "2009", volume = "36", number = "3", pages = "6145--6155", month = apr, keywords = "genetic algorithms, genetic programming, gene expression programming, Multiple objective optimization, Meta-heuristics, Prediction, High-strength concrete", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2008.07.017", URL = "http://www.sciencedirect.com/science/article/B6V03-4T0WJSK-G/2/2dd2cbea4bb9a919e91f3953aecaaa06", ISSN = "0957-4174", size = "11 pages", abstract = "The optimization of composite materials such as concrete deals with the problem of selecting the values of several variables which determine composition, compressive stress, workability and cost etc. This study presents multi-objective optimization (MOO) of high-strength concretes (HSCs). One of the main problems in the optimization of HSCs is to obtain mathematical equations that represents concrete characteristic in terms of its constitutions. In order to solve this problem, a two step approach is used in this study. In the first step, the prediction of HSCs parameters is performed by using regression analysis, neural networks and Gen Expression Programming (GEP). The output of the first step is the equations that can be used to predict HSCs properties (i.e. compressive stress, cost and workability). In order to derive these equations the data set which contains many different mix proportions of HSCs is gathered from the literature. In the second step, a MOO model is developed by making use of the equations developed in the first step. The resulting MOO model is solved by using a Genetic Algorithm (GA). GA employs weighted and hierarchical method in order to handle multiple objectives. The performances of the prediction and optimization methods are also compared in the paper.", } @Article{Baykasoglu:2009:ESA, author = "Adil Baykasoglu and Mustafa Gocken", title = "Gene expression programming based due date assignment in a simulated job shop", journal = "Expert Systems with Applications", year = "2009", volume = "36", pages = "12143--12150", number = "10", keywords = "genetic algorithms, genetic programming, Gene expression programming, Due date assignment", DOI = "doi:10.1016/j.eswa.2009.03.061", ISSN = "0957-4174", URL = "http://www.sciencedirect.com/science/article/B6V03-4VY2C6B-1/2/d174ebf2e7f0566d9c964be7d6f4f2ab", abstract = "In this paper, a new approach for due date assignment in a multi-stage job shop is proposed and evaluated. The proposed approach is based on a genetic programming technique which is known as gene expression programming (GEP). GEP is a relatively new member of the genetic programming family. The primary objective of this research is to compare the performance of the proposed due date assignment model with several previously proposed conventional due date assignment models. For this purpose, simulation models are developed and comparisons of the due date assignment models are made mainly in terms of the mean absolute percent error (MAPE), mean percent error (MPE) and mean tardiness (MT). Some additional performance measurements are also given. Simulation experiments revealed that for many test conditions the proposed due date assignment method dominates all other compared due date assignment methods.", } @Article{Baykasoglu:2010:S, title = "Genetic Programming Based Data Mining Approach to Dispatching Rule Selection in a Simulated Job Shop", author = "Adil Baykasoglu and Mustafa Gocken and Lale Ozbakir", journal = "Simulation", year = "2010", number = "12", volume = "86", pages = "715--728", keywords = "genetic algorithms, genetic programming, data mining, dispatching rules", DOI = "doi:10.1177/0037549709346561", size = "14 pages", bibdate = "2011-02-04", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/simulation/simulation86.html#BaykasogluGO10", abstract = "In this paper, a genetic programming based data mining approach is proposed to select dispatching rules which will result in competitive shop performance under a given set of shop parameters (e.g. interarrival times, pre-shop pool length). The main purpose is to select the most appropriate conventional dispatching rule set according to the current shop parameters. In order to achieve this, full factorial experiments are carried out to determine the effect of input parameters on predetermined performance measures. Afterwards, a genetic programming based data mining tool that is known as MEPAR-miner (multi-expression programming for classification rule mining) is employed to extract knowledge on the selection of best possible conventional dispatching rule set according to the current shop status. The obtained results have shown that the selected dispatching rules are appropriate ones according to the current shop parameters. All of the results are illustrated via numerical examples and experiments on simulated data.", } @Article{journals/jifs/BaykasogluM14, title = "Fuzzy functions via genetic programming", author = "Adil Baykasoglu and Sultan Maral", journal = "Journal of Intelligent and Fuzzy Systems", year = "2014", number = "5", volume = "27", pages = "2355--2364", keywords = "genetic algorithms, genetic programming", bibdate = "2014-11-03", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/jifs/jifs27.html#BaykasogluM14", URL = "http://dx.doi.org/10.3233/IFS-141205", } @Article{baykasoglu:2015:IJAMT, author = "Adil Baykasoglu and Lale Ozbakir", title = "Discovering task assignment rules for assembly line balancing via genetic programming", journal = "The International Journal of Advanced Manufacturing Technology", year = "2015", volume = "76", number = "1-4", pages = "417--434", keywords = "genetic algorithms, genetic programming, Assembly line balancing, Automatic rule generation, Evolutionary intelligence", URL = "http://link.springer.com/article/10.1007/s00170-014-6295-4", DOI = "doi:10.1007/s00170-014-6295-4", size = "18 pages", abstract = "Assembly line is one of the most commonly used manufacturing processes to produce final products in a flow line. Design of efficient assembly lines has considerable importance for the production of high-quantity standardized products. Several solution approaches such as exact, heuristic, and metaheuristics have been developed since the problem is first formulated. In this study, a new approach based on genetic programming so as to generate composite task assignment rules is proposed for balancing simple assembly lines. The proposed approach can also be applied to other types of line balancing problems. The present method makes use of genetic programming to discover task assignment rules which can be used within a single-pass constructive heuristic in order to balance a given assembly line quickly and effectively. Suitable parameters affecting the balance of the assembly line are evaluated and employed to discover highly efficient composite task assignment rules. Extensive computational results and comparisons proved the efficiency of the proposed approach in producing generic composite task assignment rules for balancing assembly lines.", } @Article{bayne:1997:ve, author = "Michael D. Bayne", title = "Vive l'evolution", journal = "Deep Magic", year = "1997", month = "12 " # feb, note = "www page", keywords = "genetic algorithms, genetic programming, Java, www", broken = "http://www.go2net.com/internet/deep/1997/02/12/", URL = "http://samskivert.com/internet/deep/1997/02/12/", size = "html 1 page", abstract = "Evolutionary computing is a blanket term encompassing a host of methodologies and philosophies, all based upon the premise that mother nature is darned good at solving problems. The world is literally crawling with problem solvers of infinite variety. Although Charles Darwin planted the idea in 1859 with the publication of The Origin of Species, the concept of mimicking mother nature's problem solving techniques didn't start to flower until the mid-1960s, when the computing power to actually investigate such techniques was readily available.", notes = "Quick overview of GP, ants GP java demo, http links to interesting places. Deep magic at http://www.go2net.com/internet/deep/ broken Feb 2012", } @Article{Baziar2011, author = "Mohammad H. Baziar and Yaser Jafarian and Habib Shahnazari and Vahid Movahed and Mohammad Amin Tutunchian", title = "Prediction of strain energy-based liquefaction resistance of sand-silt mixtures: An evolutionary approach", journal = "Computer \& Geosciences", volume = "37", number = "11", pages = "1883--1893", year = "2011", ISSN = "0098-3004", DOI = "doi:10.1016/j.cageo.2011.04.008", URL = "http://www.sciencedirect.com/science/article/B6V7D-52R9DF5-2/2/08fa46566f649fc2348af34aa83ebbb2", keywords = "genetic algorithms, genetic programming, Liquefaction, Capacity energy, Sand, Silt, Wildlife", abstract = "Liquefaction is a catastrophic type of ground failure, which usually occurs in loose saturated soil deposits under earthquake excitations. A new predictive model is presented in this study to estimate the amount of strain energy density, which is required for the liquefaction triggering of sand-silt mixtures. A wide-ranging database containing the results of cyclic tests on sand-silt mixtures was first gathered from previously published studies. Input variables of the model were chosen from the available understandings evolved from the previous studies on the strain energy-based liquefaction potential assessment. In order to avoid over training, two sets of validation data were employed and a particular monitoring was made on the behaviour of the evolved models. Results of a comprehensive parametric study on the proposed model are in accord with the previously published experimental observations. Accordingly, the amount of strain energy required for liquefaction onset increases with increase in initial effective overburden pressure, relative density, and mean grain size. The effect of nonplastic fines on strain energy-based liquefaction resistance shows a more complicated behavior. Accordingly, liquefaction resistance increases with increase in fines up to about 10-15percent and then starts to decline for a higher increase in fines content. Further verifications of the model were carried out using the valuable results of some down hole array data as well as centrifuge model tests. These verifications confirm that the proposed model, which was derived from laboratory data, can be successfully used under field conditions.", } @Article{beade:2023:NC, author = "Angel Beade and Manuel Rodriguez and Jose Santos", title = "Evolutionary feature selection approaches for insolvency business prediction with genetic programming", journal = "Natural Computing", year = "2023", volume = "22", number = "4", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s11047-023-09951-4", DOI = "doi:10.1007/s11047-023-09951-4", notes = "Business Department, University of A Coruna, Campus de Elvina, s/n, 15071, Spain", } @Article{BEADE:2024:knosys, author = "Angel Beade and Manuel Rodriguez and Jose Santos", title = "Variable selection in the prediction of business failure using genetic programming", journal = "Knowledge-Based Systems", volume = "289", pages = "111529", year = "2024", ISSN = "0950-7051", DOI = "doi:10.1016/j.knosys.2024.111529", URL = "https://www.sciencedirect.com/science/article/pii/S0950705124001643", keywords = "genetic algorithms, genetic programming, Business failure, Dimensionality reduction, Feature selection, Evolutionary computation", abstract = "This study focuses on dimensionality reduction by variable selection in business failure prediction models. A new method of dimensionality reduction by variable selection using Genetic Programming is proposed, which takes into account the relative frequency of occurrence of the explanatory variables in the evolved solutions, as well as the statistical relevance of that frequency. For a better evaluation of the proposed method and its comparison with other well-tested and widely used variable selection methods, the prediction of business failure in three temporal horizons (1, 5 and 9 years prior to failure) is considered. Additionally, a comparison of the sets of variables selected with different feature selection methods is performed, also considering different classifiers in the comparison, among which Genetic Programming is included as a classifier. The results indicate that the proposed method (using Genetic Programming as a variable selection method) is superior to the most tested and widely used methods analyzed, and this superiority increases if Genetic Programming is also used as a classification method", } @InProceedings{Beade:2024:INISTA, author = "Angel Beade and Manuel Rodriguez and Jose Santos", title = "Genetic programming for feature selection in business failure prediction. Comparison of the use of financial variables and economic environment variables", booktitle = "2024 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)", year = "2024", month = sep, keywords = "genetic algorithms, genetic programming, Economics, Technological innovation, Biological system modelling, Companies, Predictive models, Feature extraction, feature selection, business failure", ISSN = "2768-7295", DOI = "doi:10.1109/INISTA62901.2024.10683824", abstract = "In this work we have experimented with the use of genetic programming as a feature selection method as well as a classifier to obtain business failure prediction models with different prediction temporal horizons. In the prediction models, a wide set of explanatory variables has been used, all of them based on the annual accounts of the company. In addition, an extended set of explanatory variables incorporating variables from the economic environment has been considered. Comparison of the prediction results between these alternatives shows a trend towards better results using the feature selection process, while there is no trend towards better results using economic environment variables.", notes = "Also known as \cite{10683824}", } @Article{beade:2024:OR, author = "Angel Beade and Manuel Rodriguez and Jose Santos", title = "Business failure prediction models with high and stable predictive power over time using genetic programming", journal = "Operational Research", year = "2024", keywords = "genetic algorithms, genetic programming", URL = "https://link.springer.com/article/10.1007/s12351-024-00852-7", DOI = "doi:10.1007/s12351-024-00852-7", } @InProceedings{Beadle:2008:CEC, author = "Lawrence Beadle and Colin Johnson", title = "Semantically Driven Crossover in Genetic Programming", booktitle = "Proceedings of the IEEE World Congress on Computational Intelligence", year = "2008", pages = "111--116", editor = "Jun Wang", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Program Semantics, Crossover, Reduced Ordered Binary Decision Diagrams", isbn13 = "978-1-4244-1823-7", file = "EC0044.pdf", DOI = "doi:10.1109/CEC.2008.4630784", URL = "http://results.ref.ac.uk/Submissions/Output/1423275", abstract = "Crossover forms one of the core operations in genetic programming and has been the subject of many different investigations. We present a novel technique, based on semantic analysis of programs, which forces each crossover to make candidate programs take a new step in the behavioural search space. We demonstrate how this technique results in better performance and smaller solutions in two separate genetic programming experiments.", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", uk_research_excellence_2014 = "This paper was the first to introduce semantic methods in genetic programming. We show that by avoiding semantically-redundant crossover, the speed of learning by these algorithms can be accelerated. We provide a substantial analysis of results against other established methods, and statistical techniques are used to determine which problem types the new method works well on. We followed this with further semantic methods in papers for CEC2009 and GPEM Journal, 2009. This work has led to a number of papers by other research groups (e.g. UCD, TU Poznan) and a PhD at Dublin has extended the work.", } @Article{Beadle:2009:GPEM, author = "Lawrence Beadle and Colin G. Johnson", title = "Semantic Analysis of Program Initialisation in Genetic Programming", journal = "Genetic Programming and Evolvable Machines", year = "2009", volume = "10", number = "3", pages = "307--337", month = sep, keywords = "genetic algorithms, genetic programming, Program initialisation, Program semantics, Program structure", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-009-9082-5", abstract = "Population initialisation in genetic programming is both easy, because random combinations of syntax can be generated straightforwardly, and hard, because these random combinations of syntax do not always produce random and diverse program behaviours. In this paper we perform analyses of behavioural diversity, the size and shape of starting populations, the effects of purely semantic program initialisation and the importance of tree shape in the context of program initialisation. To achieve this, we create four different algorithms, in addition to using the traditional ramped half and half technique, applied to seven genetic programming problems. We present results to show that varying the choice and design of program initialisation can dramatically influence the performance of genetic programming. In particular, program behaviour and evolvable tree shape can have dramatic effects on the performance of genetic programming. The four algorithms we present have different rates of success on different problems.", } @InProceedings{Beadle:2009:cec, author = "Lawrence Beadle and Colin G Johnson", title = "Semantically Driven Mutation in Genetic Programming", booktitle = "2009 IEEE Congress on Evolutionary Computation", year = "2009", editor = "Andy Tyrrell", pages = "1336--1342", address = "Trondheim, Norway", month = "18-21 " # may, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-2959-2", file = "P009.pdf", DOI = "doi:10.1109/CEC.2009.4983099", abstract = "Using semantic analysis, we present a technique known as semantically driven mutation which can explicitly detect and apply behavioural changes caused by the syntactic changes in programs that result from the mutation operation. Using semantically driven mutation, we demonstrate increased performance in genetic programming on seven benchmark genetic programming problems over two different domains.", keywords = "genetic algorithms, genetic programming, Genetic programming, program semantics, semantically driven mutation, reduced ordered binary decision diagrams.", notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR", } @PhdThesis{Beadle:thesis, author = "Lawrence Charles John Beadle", title = "Semantic and Structural Analysis of Genetic Programming", school = "School of Computing, University of Kent", year = "2009", address = "Canterbury, UK", month = jul, keywords = "genetic algorithms, genetic programming, determinacy analysis, Craig interpolants", URL = "http://www.beadle.me/Me/LBeadle_PhD_Thesis.pdf", URL = "http://ethos.bl.uk/OrderDetails.do?did=24&uin=uk.bl.ethos.509628", URL = "https://kar.kent.ac.uk/id/eprint/30599", URL = "http://www.cs.kent.ac.uk/pubs/2009/3056/", URL = "http://www.cs.kent.ac.uk/pubs/2009/3056/content.pdf", size = "194 pages", abstract = "Genetic programming (GP) is a subset of evolutionary computation where candidate solutions are evaluated through execution or interpreted execution. The candidate solutions generated by GP are in the form of computer programs, which are evolved to achieve a stated objective. Darwinian evolutionary theory inspires the processes that make up GP which include crossover, mutation and selection. During a GP run, crossover, mutation and selection are performed iteratively until a program that satisfies the stated objectives is produced or a certain number of time steps have elapsed. The objectives of this thesis are to empirically analyse three different aspects of these evolved programs. These three aspects are diversity, efficient representation and the changing structure of programs during evolution. In addition to these analyses, novel algorithms are presented in order to test theories, improve the overall performance of GP and reduce program size. This thesis makes three contributions to the field of GP. Firstly, a detailed analysis is performed of the process of initialisation (generating random programs to start evolution) using four novel algorithms to empirically evaluate specific traits of starting populations of programs. It is shown how two factors simultaneously effect how strong the performance of starting population will be after a GP run. Secondly, semantically based operators are applied during evolution to encourage behavioural diversity and reduce the size of programs by removing inefficient segments of code during evolution. It is demonstrated how these specialist operators can be effective individually and when combined in a series of experiments. Finally, the role of the structure of programs is considered during evolution under different evolutionary parameters considering different problem domains. This analysis reveals some interesting effects of evolution on program structure as well as offering evidence to support the success of the specialist operators.", notes = "EpochX GP software Supervisor: Colin Johnson uk.bl.ethos.509628", } @InProceedings{beale:2002:RTIC, author = "Stuart Beale", title = "Traffic Data: Less is More", booktitle = "Road Transport Information and Control", year = "2002", address = "Savoy Place, London, UK", month = "19-21 " # mar, organisation = "IEE", email = "rtic2002@iee.org.uk", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1049/cp:20020233", abstract = "In support of the Governments 10 Year Transport Plan the Highways Agency has an ambitious programme to roll-out traffic systems on the English motorway network. The control methodologies within these systems can be further developed which will help meet the Government's targets to reduce congestion and accidents. This paper describes three innovative projects being undertaken by the Highways Agency. The approach to these projects departs from the traditional engineering approach, instead we have used mathematical techniques to evolve control functions that learn and operate on the available traffic data.", notes = "RTIC 2002 http://conferences.iee.org.uk/RTIC/ For {"}genetic algorithm{"} read {"}genetic programming{"}", } @Article{ga:Beard93a, author = "Nick Beard", title = "The joy of genetic programming", journal = "Personal Computer World", year = "1993", pages = "471--472", month = jun, keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/ga_beard93a.pdf", size = "2 pages", notes = "overview/introduction", } @InProceedings{Bearpark:2000:ACDM, author = "K. Bearpark and A. J. Keane", title = "Short term memory in genetic programming", booktitle = "Fourth International Conference on Adaptive Computing in Design and Manufacture, ACDM '00", year = "2000", editor = "I. C. Parmee", pages = "309--320", address = "University of Plymouth, Devon, UK", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-85233-300-3", URL = "http://eprints.soton.ac.uk/21399/1/bear_00.pdf", URL = "http://eprints.soton.ac.uk/21399/", URL = "http://www.springer.com/engineering/mechanical+engineering/book/978-1-85233-300-3", URL = "http://www.amazon.co.uk/Evolutionary-Design-Manufacture-Selected-Papers/dp/1852333006", DOI = "doi:10.1007/978-1-4471-0519-0_25", size = "12 pages", abstract = "The recognition of useful information, its retention in memory, and subsequent use plays an important part in the behaviour of many biological species. Information gained by experience in one generation can be propagated to subsequent generations by some form of teaching. Each generation can then supplement its taught learning by its own experience. In this paper we explore the role of memorised information in the performance of a Genetic Programming (GP) system that uses a tree structure as its representation. Memory is implemented in the form of a set of subtrees derived from successful members of each generation. The memory is used by a genetic operator similar to the mutation operator but with the following difference. In a tree-structured system the mutation operator replaces randomly selected sub-trees by new randomly-generated sub-trees. The memory operator replaces randomly selected sub-trees by sub-trees randomly randomly selected from the memory. To study the memory operator's impact a GP system is used to evolve a well-known expression from classical kinetics using fitness-based selection. The memory operator is used together with the common crossover and mutation operators. It is shown that the addition of a memory operator increases the probability of a successful evolution for this particular problem. At this stage we make no claim for its impact on other problems that have been successfully addressed by Genetic Programming", notes = "Evolutionary Design and Manufacture: Selected Papers from . (ACDM '00) One example physics integration of u*t+0.5*a*t*t t=1...10, u=20 or u=200 a=980 Reverse Polish RPN except for first (in Lisp) max length=11?? 19??, roulette wheel, crossover, mutation. Memory operator: when fitness improves over best of previous generation whole of tree and its subtrees are saved in memory. Later random choices from memory. elitism.Pop=2000, gen=20 40000 tests per minute (300 MHz).", } @PhdThesis{Bearpark:thesis, author = "Keith Bearpark", title = "Learning and memory in genetic programming", school = "School of Engineering Sciences, University of Southampton", year = "2000", address = "UK", keywords = "genetic algorithms, genetic programming", URL = "http://eprints.soton.ac.uk/45930/", URL = "http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.327359", abstract = "Genetic Programming is a form of Evolutionary Computation in which computer programs are evolved by methods based on simulating the natural evolution of biological species. A new generation of a species acquires the characteristics of previous generations through the inheritance of genes by sexual reproduction and through random changes in alleles by random mutation. The new generation may enhance its ability to survive by the acquisition of cultural knowledge through learning processes. This thesis combines the transfer of knowledge by genetic means with the transfer of knowledge by cultural means. In particular, it introduces a new evolutionary operator, memory operator. In conventional genetic programming systems, a new generation is formed from a mating pool whose members are selected from the fittest members of previous generation. The new generation is produced by the exchange of genes between members of the mating pool and the random replacement of genes in the offspring. The new generation may or may not be able to survive better than its predecessor in a given environment. The memory operator augments the evolutionary process by inserting into new chromosomes genetic material known to often result in fitness improvements. This material is acquired through a learning process in which the system is required to evolve generations that survive in a less demanding environment. The cultural knowledge acquired in this learning process is applied as an intelligent form of mutation to aid survival in a more demanding environment.", notes = "uk.bl.ethos.327359", } @InProceedings{beaulieu:2002:gecco, author = "Julie Beaulieu and Christian Gagn{\'e} and Marc Parizeau", title = "Lens System Design And Re-engineering With Evolutionary Algorithms", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "155--162", address = "New York", publisher_address = "San Francisco, CA 94104, USA", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "genetic algorithms, genetic programming, evolvable hardware, evolutionary reengineering, evolvable optics, lens system design", URL = "http://vision.gel.ulaval.ca/~parizeau/Publications/gecco02-lens.pdf", URL = "http://vision.gel.ulaval.ca/en/publications/Id_44/PublDetails.php", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/EH274.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-04.pdf", URL = "http://www.gel.ulaval.ca/~cgagne/pubs/lens-gecco02.pdf", URL = "http://citeseer.ist.psu.edu/532763.html", size = "8 pages", abstract = "presents some lens system design and re-engineering experimentations with genetic algorithms and genetic programming. These Evolutionary Algorithms (EA) were successfully applied to a design problem that was previously presented to expert participants of an international lens design conference. Comparative results demonstrate that the use of EA for lens system design is very much human-competitive.", ISBN = "1-55860-878-8", notes = "GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002) Nominated for best at GECCO award", } @InProceedings{Beaumont:2009:cec, author = "Darren Beaumont and Susan Stepney", title = "Grammatical Evolution of L-systems", booktitle = "2009 IEEE Congress on Evolutionary Computation", year = "2009", editor = "Andy Tyrrell", pages = "2446--2453", address = "Trondheim, Norway", month = "18-21 " # may, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-2959-2", file = "P007.pdf", DOI = "doi:10.1109/CEC.2009.4983247", abstract = "L-systems are parallel generative grammars that can model branching structures. Taking a graphical object and attempting to derive an L-system describing it is a hard problem. Grammatical Evolution (GE) is an evolutionary technique aimed at creating grammars describing the legal structures an object can take. We use GE to evolve L-systems, and investigate the effect of elitism, and the form of the underlying grammar.", keywords = "genetic algorithms, genetic programming, grammatical evolution", notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR", } @InProceedings{Bechmann:2010:ICES, author = "Matthias Bechmann and Angelika Sebald and Susan Stepney", title = "From Binary to Continuous Gates - and Back Again", booktitle = "Proceedings of the 9th International Conference Evolvable Systems: From Biology to Hardware, ICES 2010", year = "2010", editor = "Gianluca Tempesti and Andy M. Tyrrell and Julian F. Miller", series = "Lecture Notes in Computer Science", volume = "6274", pages = "335--347", address = "York", month = sep # " 6-8", publisher = "Springer", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", isbn13 = "978-3-642-15322-8", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.386.7390", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.386.7390", URL = "http://www-users.cs.york.ac.uk/~susan/bib/ss/nonstd/ices10.pdf", DOI = "doi:10.1007/978-3-642-15323-5_29", abstract = "We describe how nuclear magnetic resonance (NMR) spectroscopy can serve as a substrate for the implementation of classical logic gates. The approach exploits the inherently continuous nature of the NMR parameter space. We show how simple continuous NAND gates with sin/sin and sin/sinc characteristics arise from the NMR parameter space. We use these simple continuous NAND gates as starting points to obtain optimised target NAND circuits with robust, error-tolerant properties. We use Cartesian Genetic Programming (CGP) as our optimisation tool. The various evolved circuits display patterns relating to the symmetry properties of the initial simple continuous gates. Other circuits, such as a robust XOR circuit built from simple NAND gates, are obtained using similar strategies. We briefly mention the possibility to include other target objective functions, for example other continuous functions. Simple continuous NAND gates with sin/sin characteristics are a good starting point for the creation of error-tolerant circuits whereas the more complicated sin/sinc gate characteristics offer potential for the implementation of complicated functions by choosing some straightforward, experimentally controllable parameters appropriately.", affiliation = "Department of Chemistry, University of York, YO10 5DD UK", } @Article{Beck:2014:PLoSONE, author = "Daniel Beck and James A. Foster", title = "Machine Learning Techniques Accurately Classify Microbial Communities by Bacterial Vaginosis Characteristics", journal = "PLoS ONE", year = "2014", volume = "9", number = "2", pages = "e87830", month = feb # " 3", keywords = "genetic algorithms, genetic programming, Bacterial vaginosis, Microbiome, Lactobacillus, Vagina, Community ecology, Machine learning algorithms", bibsource = "OAI-PMH server at www.ncbi.nlm.nih.gov", publisher = "Public Library of Science", oai = "oai:pubmedcentral.nih.gov:3912131", URL = "http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3912131", URL = "http://dx.doi.org/10.1371/journal.pone.0087830", DOI = "doi:10.1371/journal.pone.0087830", size = "8 pages", abstract = "Microbial communities are important to human health. Bacterial vaginosis (BV) is a disease associated with the vagina microbiome. While the causes of BV are unknown, the microbial community in the vagina appears to play a role. We use three different machine-learning techniques to classify microbial communities into BV categories. These three techniques include genetic programming (GP), random forests (RF), and logistic regression (LR). We evaluate the classification accuracy of each of these techniques on two different datasets. We then deconstruct the classification models to identify important features of the microbial community. We found that the classification models produced by the machine learning techniques obtained accuracies above 90percent for Nugent score BV and above 80percent for Amsel criteria BV. While the classification models identify largely different sets of important features, the shared features often agree with past research.", notes = "16 S rRNA, Random Forests, Logistic Regression. pre-select 15 features. R package glmnet, lasso. ROC. pop15000 14 functions in function set. PMID:24498380", } @PhdThesis{Beck:thesis, author = "Daniel Beck", title = "Investigating the use of classification models to study microbial community associations with bacterial vaginosis", school = "University of Idaho", year = "2014", address = "USA", month = may, keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, grammatical evolution, Linear GP, Push, Bioinformatics, Biology", URL = "https://www.lib.uidaho.edu/digital/etd/items/beck_idaho_0089e_10212.html", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/daniel_beck_dissertation.pdf", size = "82 pages", abstract = "Microbial communities are highly complex, often composed of hundreds or thousands of different microbe types. They are found nearly everywhere; in soil, water, and in close association with other organisms. Microbial communities are difficult to study. Many microbes are not easily grown in laboratory conditions. Interactions between microbes may limit the applicability of observations collected using isolated taxa. However, new sequencing technology is allowing researchers to study microbial communities in novel ways. Among these new techniques is 16S rRNA fingerprinting, which enables researchers to estimate the relative abundance of most microbes in the community. These techniques are often used to study microbial communities living on or in the human body. These microbiomes are found at many different body sites and have been linked to the health of their human host. In particular, the vagina microbiome has been linked to bacterial vaginosis (BV). BV is highly prevalent with symptoms including odour, discharge, and irritation. While no single microbe has been shown to cause BV, the structure of the microbial community as a whole is associated with BV. In this thesis, I explore methods that may be used to discover associations between microbial communities and phenotypes of those communities. I focus on associations between the vagina microbiome and BV. The first two chapters of this thesis describe software tools used to explore and visualise ecological datasets. In the last two chapters, I explore the use of machine learning techniques to model the relationships between the vagina microbiome and BV. Machine learning techniques are able to produce complex models that classify microbial communities by BV characteristics. These models may capture interactions that simpler models miss.", notes = "Beck_idaho_0089E_10212 Supervisor James A Foster", } @Article{Beck:2015:BDM, author = "Daniel Beck and James A. Foster", title = "Machine learning classifiers provide insight into the relationship between microbial communities and bacterial vaginosis", journal = "BioData Mining", year = "2015", volume = "8", number = "23", month = "12 " # aug, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1186/s13040-015-0055-3", size = "9 pages", abstract = "Background: Bacterial vaginosis (BV) is a disease associated with the vagina microbiome. It is highly prevalent and is characterized by symptoms including odour, discharge and irritation. No single microbe has been found to cause BV. In this paper we use random forests and logistic regression classifiers to model the relationship between the microbial community and BV. We use subsets of the microbial community features in order to determine which features are important to the classification models. Results: We find that models generated using logistic regression and random forests perform nearly identically and identify largely similar important features. Only a few features are necessary to obtain high BV classification accuracy. Additionally, there appears to be substantial redundancy between the microbial community features. Conclusions: These results are in contrast to a previous study in which the important features identified by the classifiers were dissimilar. This difference appears to be the result of using different feature importance measures. It is not clear whether machine learning classifiers are capturing patterns different from simple correlations.", } @InProceedings{beck:1999:EAM, author = "M. A. Beck and I. C. Parmee", title = "Extending the bounds of the search space: A Multi-Population approach", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1469--1476", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "real world applications", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-762.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-762.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Misc{oai:arXiv.org:cs/0212019, title = "Thinking, Learning, and Autonomous Problem Solving", author = "Joerg D. Becker", year = "2002", month = dec # "~10", abstract = "Ever increasing computational power will require methods for automatic programming. We present an alternative to genetic programming, based on a general model of thinking and learning. The advantage is that evolution takes place in the space of constructs and can thus exploit the mathematical structures of this space. The model is formalized, and a macro language is presented which allows for a formal yet intuitive description of the problem under consideration. A prototype has been developed to implement the scheme in PERL. This method will lead to a concentration on the analysis of problems, to a more rapid prototyping, to the treatment of new problem classes, and to the investigation of philosophical problems. We see fields of application in nonlinear differential equations, pattern recognition, robotics, model building, and animated pictures.", note = "Comment: 9 pages, 4 figures", oai = "oai:arXiv.org:cs/0212019", URL = "http://arXiv.org/abs/cs/0212019", size = "27702 bytes", } @InProceedings{Becker:2021:GECCOcomp, author = "Kory Becker and Justin Gottschlich", title = "{AI} Programmer: Autonomously Creating Software Programs Using Genetic Algorithms", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2021", pages = "1513--1521", address = "internet", series = "GECCO '21", month = jul # " 10-14", organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, machine learning, evolutionary computation, artificial intelligence, genetic algorithm, program synthesis, code generation and optimization, programming languages, Brain-dead", isbn13 = "978-1-4503-8351-6", DOI = "doi:10.1145/3449726.3463125", size = "8 pages", abstract = "we present AI Programmer, a machine learning (ML) system that can automatically generate full software programs, while requiring only minimal human guidance. At its core, AI Programmer uses a genetic algorithm (GA), coupled with a tightly constrained programming language that minimizes the overhead of its ML search space. Part of AI Programmer's novelty stems from (i) its unique system design, including an embedded, hand-crafted interpreter for efficiency and security and (ii) its augmentation of classic GA to include instruction-gene randomization bindings and programming language-specific genome construction and elimination techniques. We provide a detailed examination of AI Programmer's system design, several examples detailing how the system works, and experimental data demonstrating its software generation capabilities and performance using only mainstream CPUs.", notes = "Bloomberg See also: BF-Programmer: A Counterintuitive Approach to Autonomously Building Simplistic Programs Using Genetic Algorithms Also known as \cite{10.1145/3449726.3463125} GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)", } @TechReport{becker:2003-09, author = "Lee A. Becker and Mukund Seshadri", title = "Comprehensibility and Overfitting Avoidance in Genetic Programming for Technical Trading Rules", institution = "Worcester Polytechnic Institute", year = "2003", month = may, email = "mukund@cs.wpi.edu", keywords = "genetic algorithms, genetic programming, comprehensibility , Occam's razor, overfitting, complexity penalising, S&P500, technical analysis, market timing", URL = "ftp://ftp.cs.wpi.edu/pub/techreports/pdf/03-09.pdf", URL = "http://citeseer.ist.psu.edu/574013.html", abstract = "This paper presents two methods for increasing comprehensibility in technical trading rules produced by Genetic Programming. For this application domain adding a complexity penalizing factor to the objective fitness function also avoids overfitting the training data. Using pre-computed derived technical indicators, although it biases the search, can express complexity while retaining comprehensibility. Several of the learned technical trading rules outperform a buy and hold strategy for the S&P500 on the testing period from 1990-2002, even taking into account transaction costs.", } @TechReport{becker:2003-15, author = "Lee A. Becker and Mukund Seshadri", title = "Cooperative Coevolution of Technical Trading Rules", institution = "Worcester Polytechnic Institute", year = "2003", month = may, email = "mukund@cs.wpi.edu", keywords = "genetic algorithms, genetic programming", URL = "ftp://ftp.cs.wpi.edu/pub/techreports/pdf/03-15.pdf", abstract = "This paper describes how cooperative coevolution can be used for GP of technical trading rules. A number of different methods of choosing collaborators for fitness evaluation are investigated. Several of the methods outperformed, at a statistically significant level, a buy-and-hold strategy for the S&P500 on the testing period from 1990-2002, even taking into account transaction costs.", } @InProceedings{becker:2003:CINC, author = "Lee A. Becker and Mukund Seshadri", title = "GP-evolved Technical Trading Rules Can Outperform Buy and Hold", booktitle = "Proceedings of the Sixth International Conference on Computational Intelligence and Natural Computing", year = "2003", address = "Embassy Suites Hotel and Conference Center, Cary, North Carolina USA", month = sep # " 26-30", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/W.Yan/gp-evolved-technical-trading.pdf", size = "4 pages", abstract = "This paper presents a number of experiments in which GP-evolved technical trading rules outperform a buy-and-hold strategy on the S&P500, even taking into account transaction costs. Several methodology changes from previous work are discussed and tested. These include a complexity-penalising factor, a fitness function that considers consistency of performance, and coevolution of a separate buy and sell rule.", notes = "http://axon.cs.byu.edu/Dan/cinc03.html/index.html/ Broken Jan 2013 http://www.ee.duke.edu/JCIS/ Worcester Polytechnic Institute", } @Misc{Becker:2020:TPOT, author = "Nick Becker and Dante Gama Dessavre and John Zedlewski", title = "{Faster AutoML with TPOT and RAPIDS}", howpublished = "www blog", year = "2020", month = nov # " 5", keywords = "genetic algorithms, genetic programming, TPOT, AutoML, GPU, Data Science, Machine Learning, Artificial Intelligence, AI, Big Data, Python, Higgs Boson, Airline delays", URL = "https://medium.com/rapids-ai/faster-automl-with-tpot-and-rapids-758455cd89e5", video_url = "https://youtu.be/7z4OJQdY_mw", size = "6 min read", notes = "youtu.be seems to have lost sound track NVIDIA dual Intel Xeon Platinum 8168 CPUs and one NVIDIA V100 GPU See also https://developer.nvidia.com/blog/advancing-the-state-of-the-art-in-automl-now-10x-faster-with-nvidia-gpus-and-rapids/ Jun 09, 2021 By Carol McDonald, Nick Becker and Nick Erickson", } @InCollection{Becker:2006:GPTP, author = "Ying Becker and Peng Fei and Anna M. Lester", title = "Stock Selection : An Innovative Application of Genetic Programming Methodology", booktitle = "Genetic Programming Theory and Practice {IV}", year = "2006", editor = "Rick L. Riolo and Terence Soule and Bill Worzel", volume = "5", series = "Genetic and Evolutionary Computation", pages = "315--334", address = "Ann Arbor", month = "11-13 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, equity market, stock selection, quantitative asset management Capital Asset Pricing Model, Arbitrage Pricing Model, Technical trading rules, S&P 500, Stock selection models, Information ratio, Information coefficient, Quantitative asset management", ISBN = "0-387-33375-4", DOI = "doi:10.1007/978-0-387-49650-4_19", size = "16 pages", abstract = "One of the major challenges in an information-rich financial market is how effectively to derive an optimum investment solution among vast amounts of available information. The most efficacious combination of factors or information signals can be found by evaluating millions of possibilities, which is a task well beyond the scope of manual efforts. Given the limitations of the manual approach, factor combinations are typically linear. However, the linear combination of factors might be too simple to reflect market complexities and thus fully capture the predictive power of the factors. A genetic programming process can easily explore both linear and non-linear formulae. In addition, the ease of evaluation facilitates the consideration of broader factor candidates for a stock selection model. Based upon SSgA's previous research on using genetic programming techniques to develop quantitative investment strategies, we extend our application to develop stock selection models in a large investable stock universe, the S&P 500 index. Two different fitness functions are designed to derive GP models that accommodate different investment objectives. First, we demonstrate that the GP process can generate a stock selection model for an low active risk investment style. Compared to a traditional model, the GP model has significantly enhanced future stock return ranking capability. Second, to suit an active investment style, we also use the GP process to generate a model that identifies the stocks with future returns lying in the fat tails of the return distribution. A portfolio constructed based on this model aims to aggressively generate the highest returns possible compared to an index following portfolio. Our tests show that the stock selection power of the GP models is statistically significant. Historical backtest results indicate that portfolios based on GP models outperform the benchmark and the portfolio based on the traditional model. Further, we demonstrate that GP models are more robust in accommodating various market regimes and have more consistent performance than the traditional model.", notes = "part of \cite{Riolo:2006:GPTP} Published Jan 2007 after the workshop Principal, Head of US Active Equity Research, Advanced Research Center, State Street Global Advisors, Boston, MA 02111", } @InCollection{Becker:2007:GPTP, author = "Ying L. Becker and Harold Fox and Peng Fei", title = "An Empirical Study of Multi-Objective Algorithms for Stock Ranking", booktitle = "Genetic Programming Theory and Practice {V}", year = "2007", editor = "Rick L. Riolo and Terence Soule and Bill Worzel", series = "Genetic and Evolutionary Computation", chapter = "14", pages = "239--259", address = "Ann Arbor", month = "17-19" # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-0-387-76308-8", DOI = "doi:10.1007/978-0-387-76308-8_14", size = "21 pages", abstract = "Quantitative models for stock selection and portfolio management face the challenge of determining the most efficacious factors, and how they interact, from large amounts of financial data. Genetic programming using simple objective fitness functions has been shown to be an effective technique for selecting factors and constructing multi-factor models for ranking stocks, but the resulting models can be somewhat unbalanced in satisfying the multiple objectives that portfolio managers seek: large excess returns that are consistent across time and the cross-sectional dimensions of the investment universe. In this study, we implement and evaluate three multi-objective algorithms to simultaneously optimise the information ratio, information coefficient, and intra-fractile hit rate of a portfolio. These algorithms the constrained fitness function, sequential algorithm, and parallel algorithm take widely different approaches to combine these different portfolio metrics. The results show that the multi-objective algorithms do produce well-balanced portfolio performance, with the constrained fitness function performing much better than the sequential and parallel multi-objective algorithms. Moreover, this algorithm generalises to the held-out test data set much better than any of the single fitness algorithms.", affiliation = "Advanced Research Center, State Street Global Advisors Boston MA 02111", notes = "part of \cite{Riolo:2007:GPTP} published Jan 2008", } @InProceedings{BeckerO:2009:GEC, author = "Ying L. Becker and Una-May O'Reilly", title = "Genetic programming for quantitative stock selection", booktitle = "GEC '09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation", year = "2009", editor = "Lihong Xu and Erik D. Goodman and Guoliang Chen and Darrell Whitley and Yongsheng Ding", bibsource = "DBLP, http://dblp.uni-trier.de", pages = "9--16", address = "Shanghai, China", organisation = "SigEvo", DOI = "doi:10.1145/1543834.1543837", publisher = "ACM", publisher_address = "New York, NY, USA", month = jun # " 12-14", isbn13 = "978-1-60558-326-6", keywords = "genetic algorithms, genetic programming", abstract = "We provide an overview of using genetic programming (GP) to model stock returns. Our models employ GP terminals (model decision variables) that are financial factors identified by experts. We describe the multi-stage training, testing and validation process that we have integrated with GP selection to be appropriate for financial panel data and how the GP solutions are situated within a portfolio selection strategy. We share our experience with the pros and cons of evolved linear and non-linear models, and outline how we have used GP extensions to balance different objectives of portfolio managers and control the complexity of evolved models.", notes = "Also known as \cite{DBLP:conf/gecco/BeckerO09} part of \cite{DBLP:conf/gec/2009}", } @InCollection{Bedner:1997:elca, author = "Ilja Bedner", title = "Evolving Light Cycle Algorithms", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1997", publisher = "Stanford Bookstore", year = "1997", editor = "John R. Koza", address = "Stanford, California, 94305-3079 USA", month = "17 " # mar, keywords = "genetic algorithms, genetic programming, games", ISBN = "0-18-205981-2", abstract = "Evolution of autonomous agents that must compete for survival in the light-cycle game as seen in the movie tron", notes = "part of \cite{koza:1997:GAGPs}", } @InProceedings{Beham:2008:ieeeIPDPS, author = "Andreas Beham and Stephan Winkler and Stefan Wagner and Michael Affenzeller", title = "A genetic programming approach to solve scheduling problems with parallel simulation", booktitle = "IEEE International Symposium on Parallel and Distributed Processing, IPDPS 2008", year = "2008", month = apr, pages = "1--5", keywords = "genetic algorithms, genetic programming, dispatching, fitness evaluation, parallel simulation, production planning, scheduling problem, dispatching, production planning, scheduling", DOI = "doi:10.1109/IPDPS.2008.4536379", DOI = "doi:10.1109/IPDPS.2008.4536362", ISSN = "1530-2075", abstract = "Scheduling and dispatching are two ways of solving production planning problems. In this work, based on preceding works, it is explained how these two approaches can be combined by the means of an automated rule generation procedure and simulation. Genetic programming is applied as the creator and optimizer of the rules. A simulator is used for the fitness evaluation and distributed over a number of machines. Some example results suggest that the approach could be successfully applied in the real world as the results are more than human competitive.", notes = "Also known as \cite{4536379} \cite{4536362}", } @InProceedings{3360, author = "Andreas Beham and Erik Pitzer and Michael Affenzeller", title = "Fitness Landscape based Parameter Estimation for Robust Taboo Search", booktitle = "Computer Aided Systems Theory, Eurocast 2013", year = "2013", editor = "Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia", volume = "8111", series = "LNCS", pages = "292--299", address = "Las Palmas, Spain", month = "10-15 " # feb, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Problem Instance, Problem Size, Fitness Landscape, Quadratic Assignment Problem, Large Problem Size", isbn13 = "978-3-642-53856-8", URL = "https://link.springer.com/chapter/10.1007/978-3-642-53856-8_37", DOI = "doi:10.1007/978-3-642-53856-8_37", abstract = "Metaheuristic optimization algorithms are general optimization strategies suited to solve a range of real-world relevant optimization problems. Many metaheuristics expose parameters that allow to tune the effort that these algorithms are allowed to make and also the strategy and search behaviour [1]. Adjusting these parameters allows to increase the algorithms performances with respect to different problem- and problem instance characteristics.", } @InProceedings{Beham:2016:OKC:2908961.2931724, author = "Andreas Beham and Stefan Wagner and Michael Affenzeller", title = "Optimization Knowledge Center: A Decision Support System for Heuristic Optimization", booktitle = "Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion", year = "2016", series = "GECCO '16 Companion", pages = "1331--1338", address = "New York, NY, USA", publisher = "ACM", acmid = "2931724", DOI = "doi:10.1145/2908961.2931724", isbn13 = "978-1-4503-4323-7", keywords = "genetic algorithms, genetic programming, decision-support-system, heuristic optimization, knowledge base", location = "Denver, Colorado, USA", numpages = "8", URL = "http://doi.acm.org/10.1145/2908961.2931724", } @Article{BEHBAHANI:2020:CBM, author = "Hamid Behbahani and Gholam Hossein Hamedi and Vahid {Najafi Moghaddam Gilani}", title = "Predictive model of modified asphalt mixtures with nano hydrated lime to increase resistance to moisture and fatigue damages by the use of deicing agents", journal = "Construction and Building Materials", volume = "265", pages = "120353", year = "2020", ISSN = "0950-0618", DOI = "doi:10.1016/j.conbuildmat.2020.120353", URL = "http://www.sciencedirect.com/science/article/pii/S0950061820323588", keywords = "genetic algorithms, genetic programming, Asphalt mixture, Moisture susceptibility, Fatigue life, Surface free energy, Deicing agents, Nano hydrated lime, Two-objective optimization", abstract = "Deicing agents are used to dissolving the frost on road surfaces in winter and cold areas. Researchers have evaluated the impact of different deicing agents on the moisture susceptibility performance of asphalt mixtures, but they have not investigated the effect of these agents on fatigue failure and thermodynamic parameters of asphalt mixtures. Therefore, in this research, by investigating the effect of two new deicing agents of calcium magnesium acetate (CMA) and potassium acetate (PA) as well as sodium chloride (NaCl) traditional agent on moisture and fatigue performances of asphalt mixtures, predictive model of the tensile strength ratio (TSR) and the fatigue life ratio (NFR) using genetic programming (GP) based on the surface free energy (SFE) components and other properties of asphalt mixtures were presented. Nano hydrated lime (NHL) was applied as an asphalt binder modifier and an anti-stripping agent to improve the strength of asphalt mixtures. The results indicated that the saturated mixtures in CMA had the highest indirect tensile strength (ITS) and fatigue life in lower freeze-thaw cycles, while the NaCl-saturated samples had more ITS and fatigue life in higher cycles. The CMA-saturated samples had the greatest TSR and NFR. Using NHL in all saturated samples resulted in increasing TSR and NFR values. Results of SFE method showed that using NHL increased the polar, non-polar and basic components of asphalt binders and decreased their acidic components. Also, using NHL increased the total SFE amount of asphalt binder, enhancing the adhesion of aggregate and asphalt binder and cohesion in asphalt binder membrane, and as a result, improving the moisture resistance in asphalt mixtures. Using 1.5percent NHL had the greatest effect on improving adhesion free energy (AFE), cohesion free energy (CFE) and detachment energy (DE). Among deicing solutions, CMA had the highest CFE, in general, and NaCl had the best DE values. PA-saturated samples had the greatest permeability of asphalt mixture (PAM) values. GP model had a high R2 96.4percent and 98.3percent for TSR and NFR, respectively. Using GP model to achieve the maximum TSR and NFR, the Pareto curve showed that 1.32percent NHL was the optimum value for simultaneously increasing moisture resistance and fatigue life", } @Article{Behbahani:2012:transMechtron, author = "Saeed Behbahani and Clarence W. {de Silva}", title = "Mechatronic Design Evolution Using Bond Graphs and Hybrid Genetic Algorithm With Genetic Programming", journal = "IEEE/ASME Transactions on Mechatronics", year = "2013", volume = "18", number = "1", pages = "190--199", month = feb, keywords = "genetic algorithms, genetic programming, Bond graphs, electrohydraulic systems", ISSN = "1083-4435", DOI = "doi:10.1109/TMECH.2011.2165958", size = "10 pages", abstract = "A typical mechatronic problem (modelling, identification, and design) entails finding the best system topology as well as the associated parameter values. The solution requires concurrent and integrated methodologies and tools based on the latest theories. The experience on natural evolution of an engineering system indicates that the system topology evolves at a much slower rate than the parametric values. This paper proposes a two-loop evolutionary tool, using a hybrid of genetic algorithm (GA) and genetic programming (GP) for design optimisation of a mechatronic system. Specifically, GP is used for topology optimization, while GA is responsible for finding the elite solution within each topology proposed by GP. A memory feature is incorporated with the GP process to avoid the generation of repeated topologies, a common drawback of GP topology exploration. The synergic integration of GA with GP, along with the memory feature, provides a powerful search ability, which has been integrated with bond graphs (BG) for mechatronic model exploration. The software developed using this approach provides a unified tool for concurrent, integrated, and autonomous topological realisation of a mechatronic problem. It finds the best solution (topology and parameters) starting from an abstract statement of the problem. It is able to carry out the process of system configuration realization, which is normally performed by human experts. The performance of the software tool is validated by applying it to mechatronic design problems.", notes = "Also known as \cite{6029337}", } @Article{Behbahani:2013:Mechatronics, author = "Saeed Behbahani and Clarence W. {de Silva}", journal = "IEEE/ASME Transactions on Mechatronics", title = "Niching Genetic Scheme With Bond Graphs for Topology and Parameter Optimization of a Mechatronic System", year = "2014", month = feb, volume = "19", number = "1", pages = "269--277", ISSN = "1083-4435", abstract = "This paper presents a novel multimodal evolutionary optimisation algorithm for the complex problem of concurrent and integrated design of a mechatronic system, with the objective of realising the best topology and the best parameters from a multicriteria viewpoint and with different preferences. The associated search space can be large and complex due to the existence of different classes of configurations, possible topologies, and the parameter values of the elements. The proposed algorithm efficiently explores the search space to find several elite configurations for different preferences, with more detailed competition by incorporating the domain knowledge of experts and considering some criteria that are not included in the course of regular evolutionary optimisation. The developed approach consists of a two-loop optimisation. For each topology, a genetic algorithm-based optimisation is performed to find an elite representative of the topology. The elites will compete with each other to become the best design. A strategy of restricted competition selection is employed in the competition of topologies, with the aim of finding alternative elites from which the one that best satisfies the customer preference may be chosen. The designer may incorporate a higher level competition between elites in order to obtain the global optimum.", keywords = "genetic algorithms, genetic programming, Bond graphs, car suspension, genetic algorithms (GAs), genetic programming (GP), niching genetic schemes, skyhook", DOI = "doi:10.1109/TMECH.2012.2230013", ISSN = "1083-4435", notes = "Also known as \cite{6389779}", } @InCollection{beheler:1995:UGACFOSGPI, author = "Joey Beheler", title = "Using Genetic Algorithms and Convolution to Find Optimal Strategies in Games without Perfect Information", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1995", year = "1995", editor = "John R. Koza", pages = "11--18", address = "Stanford, California, 94305-3079 USA", month = "11 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms", ISBN = "0-18-195720-5", notes = "part of \cite{koza:1995:gagp}", } @Article{Behera:2012:ACEEijcsi, author = "R. Behera and B. B. Pati and B. P. Panigrahi and S. Misra", title = "An Application of Genetic Programming for Power System Planning and Operation", journal = "ACEEE International Journal on Control System and Instrumentation", year = "2012", volume = "3", number = "2", pages = "15--20", month = mar, note = "Special Issue", keywords = "genetic algorithms, genetic programming Computer Aided Engineering, Mutation, Fitness Function", ISSN = "2158-0006", broken = "http://searchdl.org/index.php/journals/journalList/1", searchdl = "ID: 01.IJCSI.3.2.59", bibsource = "OAI-PMH server at hal.archives-ouvertes.fr", language = "ENG", oai = "oai:hal.archives-ouvertes.fr:hal-00741655", broken = "http://hal.archives-ouvertes.fr/hal-00741655", URL = "http://hal.archives-ouvertes.fr/docs/00/74/16/55/PDF/59.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.592.7439", size = "6 pages", abstract = "This work incorporates the identification of model in functional form using curve fitting and genetic programming technique which can forecast present and future load requirement. Approximating an unknown function with sample data is an important practical problem. In order to forecast an unknown function using a finite set of sample data, a function is constructed to fit sample data points. This process is called curve fitting. There are several methods of curve fitting. Interpolation is a special case of curve fitting where an exact fit of the existing data points is expected. Once a model is generated, acceptability of the model must be tested. There are several measures to test the goodness of a model. Sum of absolute difference, mean absolute error, mean absolute percentage error, sum of squares due to error (SSE), mean squared error and root mean squared errors can be used to evaluate models. Minimising the squares of vertical distance of the points in a curve (SSE) is one of the most widely used method .Two of the methods has been presented namely Curve fitting technique and Genetic Programming and they have been compared based on (SSE)sum of squares due to error.", notes = "broken April 2019 http://ijcsi.theaceee.org/", } @Article{Beiki20101091, author = "Morteza Beiki and Ali Bashari and Abbas Majdi", title = "Genetic programming approach for estimating the deformation modulus of rock mass using sensitivity analysis by neural network", journal = "International Journal of Rock Mechanics and Mining Sciences", volume = "47", number = "7", pages = "1091--1103", year = "2010", ISSN = "1365-1609", DOI = "doi:10.1016/j.ijrmms.2010.07.007", URL = "http://www.sciencedirect.com/science/article/B6V4W-50RFN0V-1/2/fa0de8195c17e39f39b1ecead4df4da4", keywords = "genetic algorithms, genetic programming, Deformation modulus of rock mass, Relative strength of effect (RSE), Sensitivity analysis about the mean", abstract = "We use genetic programming (GP) to determine the deformation modulus of rock masses. A database of 150 data sets, including modulus of elasticity of intact rock (Ei), uniaxial compressive strength (UCS), rock mass quality designation (RQD), the number of joint per meter (J/m), porosity, and dry density for possible input parameters, and the modulus deformation of the rock mass determined by a plate loading test for output, was established. The values of geological strength index (GSI) system were also determined for all sites and considered as another input parameter. Sensitivity analyses are considered to find out the important parameters for predicting of the deformation modulus of rock mass. Two approaches of sensitivity analyses, based on statistical analysis of RSE values and sensitivity analysis about the mean, are performed. Evolution of the sensitivity analyses results establish the fact that variable of UCS, GSI, and RQD play more prominent roles for predicting modulus of the rock mass, and so those are considered as the predictors to design the GP model. Finally, two equations were achieved by GP. The statistical measures of root mean square error (RMSE) and variance account for (VAF) have been used to compare GP models with the well-known existing empirical equations proposed for predicting the deformation modulus. These performance criteria proved that the GP models give higher predictions over existing empirical models.", } @Article{Beiki:2013:IJRMMS, author = "Morteza Beiki and Abbas Majdi and Ali Dadi Givshad", title = "Application of genetic programming to predict the uniaxial compressive strength and elastic modulus of carbonate rocks", journal = "International Journal of Rock Mechanics and Mining Sciences", volume = "63", pages = "159--169", year = "2013", keywords = "genetic algorithms, genetic programming", ISSN = "1365-1609", DOI = "doi:10.1016/j.ijrmms.2013.08.004", URL = "http://www.sciencedirect.com/science/article/pii/S1365160913001196", } @InProceedings{bekavac-vsnajder:2013:BSNLP, author = "Marko Bekavac and Jan Snajder", title = "GPKEX: Genetically Programmed Keyphrase Extraction from Croatian Texts", booktitle = "Proceedings of the 4th Biennial International Workshop on Balto-Slavic Natural Language Processing", year = "2013", editor = "Jakub Piskorski and Lidia Pivovarova and Hristo Tanev and Roman Yangarber", pages = "43--47", address = "Sofia, Bulgaria", publisher_address = "209 N. Eighth Street Stroudsburg, PA 18360 USA Tel: +1-570-476-8006 Fax: +1-570-476-0860 acl@aclweb.org", month = "8-9 " # aug, publisher = "Association for Computational Linguistics", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-937284-59-6", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.397.588", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.397.588", URL = "http://www.aclweb.org/anthology/W13-2407", URL = "http://www.aclweb.org/anthology/W13-2407.pdf", size = "5 pages", abstract = "We describe GPKEX, a key-phrase extraction method based on genetic programming. We represent Keyphrase scoring measures as syntax trees and evolve them to produce rankings for key phrase candidates extracted from text. We apply and evaluate GPKEX on Croatian newspaper articles. We show that GPKEX can evolve simple and interpretable key-phrase scoring measures that perform comparably to more complex machine learning methods previously developed for Croatian.", notes = "The annotated dataset is available under CC BY-NC-SA license from http://takelab.fer.hr/gpkex ACL 2013 http://www.aclweb.org/anthology/W/W13/#2400 ", } @Article{DBLP:journals/csjm/Bekkouche23, author = "Mohammed Bekkouche", title = "Correcting Instruction Expression Logic Errors with {GenExp}: A Genetic Programming Solution", journal = "Computer Science Journal of Moldova", volume = "31", number = "2", pages = "217--247", year = "2023", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, error correction, instruction expression, plausible patch, crossover, mutation", timestamp = "Tue, 22 Aug 2023 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/csjm/Bekkouche23.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "http://www.math.md/publications/csjm/issues/v31-n2/13785/", DOI = "doi:10.56415/csjm.v31.12", size = "31 pages", abstract = "Correcting logical errors in a program is not simple even with the availability of an error locating tool. we introduce GenExp, a genetic programming approach to automate the task of repairing instruction expressions from logical errors. Starting from an error location specified by the programmer, we search for a replacement instruction that passes all test cases. Specifically, we generate expressions that will substitute the selected instruction expression until we obtain one that corrects the input program. The search space is exponentially large, making exhaustive methods inefficient. Therefore, we use a genetic programming meta-heuristic that organises the search process into stages, with each stage producing a group of individuals. The results showed that our approach can find at least one plausible patch for almost all cases considered in experiments and outperforms a notable state-of-the-art error repair approach like ASTOR. Although our tool is slower than ASTOR, it provides greater precision in detecting plausible repairs, making it a suitable option for users who prioritise accuracy over speed.", } @Article{Beldek:2007:IS, author = "Ulas Beldek and Kemal Leblebicioglu", title = "Strategy creation, decomposition and distribution in particle navigation", journal = "Information Sciences", year = "2007", volume = "177", number = "3", pages = "755--770", month = "1 " # feb, keywords = "genetic algorithms, genetic programming, Rule-base, Strategy planning, Robot navigation, Maze solving, Optimization, Multi-agent systems", DOI = "doi:10.1016/j.ins.2006.07.008", abstract = "Strategy planning is crucial to control a group to achieve a number of tasks in a closed area full of obstacles. In this study, genetic programming has been used to evolve rule-based hierarchical structures to move the particles in a grid region to accomplish navigation tasks. Communications operations such as receiving and sending commands between particles are also provided to develop improved strategies. In order to produce more capable strategies, a task decomposition procedure is proposed. In addition, a conflict module is constructed to handle the challenging situations and conflicts such as blockage of a particle's pathway to destination by other particles.", } @Article{Belem:2014:IPM, author = "Fabiano M. Belem and Eder F. Martins and Jussara M. Almeida and Marcos A. Goncalves", title = "Personalized and object-centered tag recommendation methods for Web 2.0 applications", journal = "Information Processing \& Management", volume = "50", number = "4", pages = "524--553", year = "2014", ISSN = "0306-4573", DOI = "doi:10.1016/j.ipm.2014.03.002", URL = "http://www.sciencedirect.com/science/article/pii/S0306457314000181", keywords = "genetic algorithms, genetic programming, Tag recommendation, Relevance metrics, Personalisation", abstrct = "Several Web 2.0 applications allow users to assign keywords (or tags) to provide better organisation and description of the shared content. Tag recommendation methods may assist users in this task, improving the quality of the available information and, thus, the effectiveness of various tag-based information retrieval services, such as searching, content recommendation and classification. This work addresses the tag recommendation problem from two perspectives. The first perspective, centred at the object, aims at suggesting relevant tags to a target object, jointly exploiting the following three dimensions: (i) tag co-occurrences, (ii) terms extracted from multiple textual features (e.g., title, description), and (iii) various metrics to estimate tag relevance. The second perspective, centered at both object and user, aims at performing personalised tag recommendation to a target object-user pair, exploiting, in addition to the three aforementioned dimensions, a metric that captures user interests.", } @InProceedings{Belgasem:2002:ACDM, author = "A. Belgasem and T. Kalganova and A. Almaini", title = "Extrinsic Evolution of Finite State Machine", booktitle = "Proc. of ACDM2002", year = "2002", editor = "I. C. Parmee", pages = "157--168", publisher = "Springer", month = apr # " 16-18", keywords = "genetic algorithms, genetic programming, evolvable hardware", URL = "http://bura.brunel.ac.uk/handle/2438/2514", URL = "http://bura.brunel.ac.uk/bitstream/2438/2514/1/2002_Belgasem_ACDM.pdf", size = "12 pages", abstract = "extrinsic evolvable hardware approach to evolve finite state machines (FSM). Both the genetic algorithm (GA) and Evolvable Hardware (EHW) are combined together to produce optimal logic circuit. GA is used to optimise the state assignment problem. EHW is used to design the combinational parts of the desired circuit. The approach is tested on a number of finite state machines from MCNC benchmark set. These circuits have been evolved using different functional sets of logic gates and GA parameters. The results show promise for the use of this approach as a design method for sequential logic circuits.", } @InProceedings{Belhor:2016:AICCSA, author = "M. Belhor and F. Jemili", booktitle = "2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA)", title = "Intrusion detection based on genetic fuzzy classification system", year = "2016", abstract = "Information system is vital for any company. However, the opening to the outside world makes the computer system more vulnerable to attack. It is essential to protect it. Intrusion Detection System (IDS) is an auditing mechanism that analyses the traffic system and applications to identify normal use of the system and an intrusion attempt and also it prevent security managers. Despite the advantages of IDS, they suffer from a few problems. The major problem in the field of intrusion detection is the classification problem. Genetic Fuzzy System (GFS) are models capable of integrating accuracy and high comprehensibility in their results. They have been widely employed to solve classification problems. In this paper, we use a new GFS model called Genetic Programming Fuzzy Inference System for Classification (GPFIS-Class). It based on Multi-Gene Genetic Programming (MGGP). This model is not used in the intrusion detection area. We use an efficient feature selection method to eliminate data redundancy and irrelevant features in order to analyse the huge data namely the NSL-KDD data set.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/AICCSA.2016.7945690", month = nov, notes = "Also known as \cite{7945690}", } @InProceedings{beligiannis:1999:EMPFNS, author = "G. N. Beligiannis and E. N. Demiris and S. D. Likothanassis", title = "Evolutionary Multimodel Partitioning Filters for Nonlinear Systems", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1227", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, EHW, evolvable hardware, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-452.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-452.pdf", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Article{Beligiannis:2005:tIM, title = "Nonlinear model structure identification of complex biomedical data using a genetic-programming-based technique", author = "Grigorios N. Beligiannis and Lambros V. Skarlas and Spiridon D. Likothanassis and Katerina G. Perdikouri", journal = "IEEE Transactions on Instrumentation and Measurement", year = "2005", volume = "54", number = "6", pages = "2184--2190", month = dec, keywords = "genetic algorithms, genetic programming, medical signal processing, nonlinear dynamical systems complex biomedical data identification, evolutionary multimodel partitioning filters, nonlinear model structure", DOI = "doi:10.1109/TIM.2005.858573", ISSN = "0018-9456", size = "7 pages", abstract = "In this contribution, a genetic programming (GP)-based technique, which combines the ability of GP to explore both automatically and effectively, the whole set of candidate model structures and the robustness of evolutionary multimodel partitioning filters, is presented. The method is applied to the nonlinear system identification problem of complex biomedical data. Simulation results show that the algorithm identifies the true model and the true values of the unknown parameters for each different model structure, thus assisting the GP technique to converge more quickly to the (near) optimal model structure. The method has all the known advantages of the evolutionary multi model partitioning filters, that is, it is not restricted to the Gaussian case; it is applicable to on-line/adaptive operation and is computationally efficient. Furthermore, it can be realized in a parallel processing fashion, a fact which makes it amenable to very large scale integration implementation.", notes = "Fig. 3. Plot of the real (solid line) versus the predicted (dashed line) values for an epoch consisting of 300 samples of an epileptic MEG (MEG measured in pT = 10 T). Fig. 4. Plot of the real (solid line) versus the predicted (dashed line) values of an f-MCG in a normal pregnancy (f-MCG measured in pT = 10 T). TABLE II ABILITY OF THE ESTIMATED NONLINEAR MODEL IN PREDICTING ABNORMAL PREGNANCIES", } @InCollection{bell:1999:ESWRNNGA, author = "Matt Bell", title = "Evolving the Structure and Weights of Recurrent Neural Network though Genetic Algorithms", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1999", year = "1999", editor = "John R. Koza", pages = "11--20", address = "Stanford, California, 94305-3079 USA", month = "15 " # mar, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:1999:GAGPs}", } @InProceedings{DBLP:conf/ijcci/BellangerBCH23, author = "Thibaut Bellanger and Matthieu {Le Berre} and Manuel Clergue and Jin-Kao Hao", title = "A One-Vs-One Approach to Improve Tangled Program Graph Performance on Classification Tasks", booktitle = "Proceedings of the 15th International Joint Conference on Computational Intelligence, IJCCI 2023", year = "2023", editor = "Niki {van Stein} and Francesco Marcelloni and H. K. Lam and Marie Cottrell and Joaquim Filipe", address = "Rome, Italy", publisher = "SCITEPRESS", month = nov # " 13-15", pages = "53--63", keywords = "genetic algorithms, genetic programming, Classification, Tangled Program Graph, Ensemble Learning, Evolutionary Machine Learning, Evolutionary Search and Meta-Heuristics", timestamp = "Fri, 08 Dec 2023 12:42:26 +0100", biburl = "https://dblp.org/rec/conf/ijcci/BellangerBCH23.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://www.insticc.org/node/TechnicalProgram/ijcci/2023/presentationDetails/121677", DOI = "doi:10.5220/0012167700003595", abstract = "We propose an approach to improve the classification performance of the Tangled Programs Graph (TPG). TPG is a genetic programming method that aims to discover Directed Acyclic Graphs (DAGs) through an evolutionary process, where the edges carry programs that allow nodes to create a route from the root to a leaf, and the leaves represent actions or labels in classification. Despite notable successes in reinforcement learning tasks, TPG performance in classification appears to be limited in its basic version, as evidenced by the scores obtained on the MNIST dataset. However, the advantage of TPG compared to neural networks is to obtain, like decision trees, a global decision that is decomposable into simple atomic decisions and thus more easily explainable. Compared to decision trees, TPG has the advantage that atomic decisions benefit from the expressiveness of a pseudo register-based programming language, and the graph evolutionary construction prevents the emergence of overfitting. Our approach consists of decomposing the multi-class problem into a set of one-vs-one binary problems, training a set of TPG for each of them, and then combining the results of the TPGs to obtain a global decision, after selecting the best ones by a genetic algorithm. We test our approach on several benchmark datasets, and the results obtained are promising and tend to validate the proposed method.", notes = "ECTA23-RP-32", } @InProceedings{Bellanger:2024:GGP, author = "Thibaut Bellanger and Matthieu {Le Berre} and Manuel Clergue and Jin-Kao Hao", title = "Directed Acyclic Program Graph Applied to Supervised Classification", booktitle = "2nd GECCO workshop on Graph-based Genetic Programming", year = "2024", editor = "Dennis G. Wilson and Roman Kalkreuth and Eric Medvet and Giorgia Nadizar and Giovanni Squillero and Alberto Tonda and Yuri Lavinas", pages = "1676--1680", address = "Melbourne, Australia", series = "GECCO '24", month = "14 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, evolutionary algorithm, local search, supervised classification", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3664115", size = "5 pages", abstract = "In the realm of Machine Learning, the pursuit of simpler yet effective models has led to increased interest in decision trees due to their interpretability and efficiency. However, their inherent simplicity often limits their ability to handle intricate patterns in data. This paper introduces a novel approach termed Directed Acyclic Graphs of Programs, inspired by evolutionary strategies, to address this challenge. By iteratively constructing program graphs from binary decision makers, our method offers a balance of simplicity and performance for classification tasks. Notably, we emphasize the preservation of model interpretability and expressiveness, avoiding the use of ensemble techniques like voting. Experimental evaluations demonstrate the superiority of our approach over existing methods in terms of both effectiveness and interpretability.", notes = "wksp117s2 also known as \cite{bellanger:2024:GECCOcomp} GECCO-2024 GGP A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @PhdThesis{Bellucci:thesis, author = "Michael Anthony Bellucci", title = "Theoretical Studies of Excited State 1,3 Dipolar Cycloadditions", school = "Boston University", year = "2012", address = "USA", keywords = "genetic algorithms, genetic programming, Chemistry, Dipolar cycloaddition, Excited state, Hydroxy flavone, Methyl cinnamate, Physical chemistry, Proton transfer, Pure science, Quantum physics", URL = "http://www.bu.edu/phpbin/calendar/event.php?id=127428&cid=17", URL = "http://adsabs.harvard.edu/abs/2013PhDT........20B", abstract = "The 1,3 dipolar photocycloaddition reaction between 3-hydroxy-4',5,7-trimethoxyflavone (3-HTMF) and methyl cinnamate is investigated in this work. Since its inception in 2004 [JACS, 124, 13260 (2004)], this reaction remains at the forefront in the synthetic design of the rocaglamide natural products. The reaction is multi-faceted in that it involves multiple excited states and is contingent upon excited state intramolecular proton transfer (ESIPT) in 3-HTMF. Given the complexity of the reaction, there remain many questions regarding the underlying mechanism. Consequently, throughout this work we investigate the mechanism of the reaction along with a number of other properties that directly influence it. To investigate the photocycloaddition reaction, we began by studying the effects of different solvent environments on the ESIPT reaction in 3-hydroxyflavone since this underlying reaction is sensitive to the solvent environment and directly influences the cycloaddition. To study the ESIPT reaction, we developed a parallel multi-level genetic program to fit accurate empirical valence bond (EVB) potentials to ab initio data. We found that simulations with our EVB potentials accurately reproduced experimentally determined reaction rates, fluorescence spectra, and vibrational frequency spectra in all solvents. Furthermore, we found that the ultrafast ESIPT process results from a combination of ballistic transfer and intramolecular vibrational redistribution. To investigate the cycloaddition reaction mechanism, we used the string method to obtain minimum energy paths on the ab initio potential. These calculations demonstrated that the reaction can proceed through formation of an exciplex in the S1 state, followed by a non-adiabatic transition to the ground state. In addition, we investigated the enantioselective catalysis of the reaction using alpha,alpha,alpha',alpha'-tetraaryl-1,3-dioxolan-4,5-dimethanol alcohol (TADDOL). We found that TADDOL lowered the energy barrier by 10-12 kcal/mol through stabilizing hydrogen bond interactions. Using temperature accelerated molecular dynamics, we obtained the potential of mean force (PMF) associated with 3-HTMF attacking the TADDOL/methyl cinnamate complex. We found that the exo reaction is inhibited through steric interactions with the aryl substituents on TADDOL. Furthermore, we found that the exo configuration breaks the intramolecular hydrogen bond in TADDOL, which stabilizes the individual reactants apart from each other. The role of the T1 state is also discussed.", notes = "Broken June 2022 http://www.bu.edu/commencement/files/2013/05/Redbook_2013.pdf", } @MastersThesis{Belmadani2016-bn, title = "{MotifGP}: {DNA} Motif Discovery Using Multiobjective Evolution", author = "Manuel Belmadani", school = "School of Electrical Engineering and Computer Science, University of Ottawa", year = "2016", type = "Master degree in Computer Science Specialization in Bioinformatics", address = "Canada", keywords = "genetic algorithms, genetic programming", URL = "http://hdl.handle.net/10393/34213", URL = "https://ruor.uottawa.ca/bitstream/10393/34213/1/Belmadani_Manuel_2016_thesis.pdf", DOI = "doi:10.20381/ruor-5077", size = "119 pages", abstract = "The motif discovery problem is becoming increasingly important for molecular biologists as new sequencing technologies are producing large amounts of data, at rates which are unprecedented. The solution space for DNA motifs is too large to search with naive methods, meaning there is a need for fast and accurate motif detection tools. We propose MotifGP, a multiobjective motif discovery tool evolving regular expressions that characterize overrepresented motifs in a given input dataset. This thesis describes and evaluates a multiobjective strongly typed genetic programming algorithm for the discovery of network expressions in DNA sequences. Using 13 realistic data sets, we compare the results of our tool, MotifGP, to that of DREME, a state-of-art program. MotifGP outperforms DREME when the motifs to be sought are long, and the specificity is distributed over the length of the motif. For shorter motifs, the performance of MotifGP compares favourably with the state-of-the-art method. Finally, we discuss the advantages of multi-objective optimization in the context of this specific motif discovery problem.", notes = "supervisor Dr. Marcel Turcotte", } @InProceedings{Belmadani:2016:CIBCB, author = "Manuel Belmadani and Marcel Turcotte", booktitle = "2016 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)", title = "MotifGP: Using multi-objective evolutionary computing for mining network expressions in DNA sequences", year = "2016", abstract = "This paper describes and evaluates a multi-objective strongly typed genetic programming algorithm for the discovery of network expressions in DNA sequences. Using 13 realistic data sets, we compare the results of our tool, MotifGP, to that of DREME, a state-of-the-art program. MotifGP outperforms DREME when the motifs to be sought are long, and the specificity is distributed over the length of the motif. For shorter motifs, the performance of MotifGP compares favourably with the state-of-the-art method. Finally, we discuss the advantages of multi-objective optimisation in the context of this specific motif discovery problem.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CIBCB.2016.7758133", month = oct, notes = "Also known as \cite{7758133}", } @InProceedings{belpaeme:1999:evfd, author = "Tony Belpaeme", title = "Evolution of Visual Feature Detectors", booktitle = "Late Breaking Papers at EvoISAP'99: the First European Workshop on Evolutionary Computation in Image Analysis and Signal Processing", year = "1999", editor = "Riccardo Poli and Stefano Cagnoni and Hans-Michael Voigt and Terry Fogarty and Peter Nordin", pages = "1--10", address = "Goteborg, Sweden", month = "28 " # may, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming", URL = "http://arti.vub.ac.be/~tony/papers/EvoIASP99.ps.gz", URL = "http://citeseer.ist.psu.edu/362631.html", abstract = "This paper describes how sets of visual feature detectors are evolved starting from simple primitives. The primitives, of which some are inspired on visual processing observed in mammalian visual pathways, are combined using genetic programming to form a feed-forward feature-extraction hierarchy. Input to the feature detectors consists of a series of real-world images, containing objects or faces. The results show how each set of feature detectors self-organizes into a set which is capable of returning feature vectors for discriminating the input images. We discuss the influence of different settings on the evolution of the feature detectors and explain some phenomena.", notes = "EvoIASP'99 Available as CSRP-99-10 from the School of Computer Science, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK. STGP. Information returned by each (of 5) feature detector, entropy of the output vector p4 {"}if the outputs are weel spread, meaning the feature detectors return useful information, the fitness will be high. Explicit parsimony preasure, but not needed p8? LilGP.", } @Article{BELSCHNER199619, author = "R. Belschner", title = "Evaluation of Real-Time Requirements by Simulation Based Analysis", journal = "IFAC Proceedings Volumes", year = "1996", volume = "29", number = "6", pages = "19--26", month = nov, note = "20th IFAC/IFIP Workshop on Real Time Programming 1995 (WRTP '95), Fort Lauderdale, USA, 6-10 November", keywords = "genetic algorithms, genetic programming, SBSE, EPOSIX, event driven simulation, distributed real-time systems, timing requirements, worst case analysis, evolutionary strategies", ISSN = "1474-6670", URL = "http://www.sciencedirect.com/science/article/pii/S1474667017437414", DOI = "doi:10.1016/S1474-6670(17)43741-4", size = "8 pages", abstract = "This paper addresses issues related to the software design in distributed real-time automation systems. Beside the influence of the real-time operating system and the behaviour of the technical process, the properties of the communication network play an important part for the temporal correctness of the software design. In consideration of the enormous complexity, an attempt to improve the software correctness concerning the temporal behaviour by a three layered Simulation Based Analysis (SBA) System is presented. The SBA system automatically evaluates timing requirements, which can be specified by a problem adapted language, and estimates the system's behaviour in both the best case and especially the worst case by using evolutionary strategies. The SBA system is currently implemented into a prototype.", notes = "Network, Ethernet, messages. Empirical fitness landscape (fig 9) page 25. University of Stuttgart", } @InProceedings{Belur:1997:CORElb, author = "Sheela V. Belur", title = "CORE: Constrained Optimization by Random Evolution", booktitle = "Late Breaking Papers at the 1997 Genetic Programming Conference", year = "1997", editor = "John R. Koza", pages = "280--286", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California, 94305-3079, USA", month = "13--16 " # jul, publisher = "Stanford Bookstore", keywords = "genetic algorithms", ISBN = "0-18-206995-8", notes = "GP-97LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670 MATLAB", } @Article{benabdallah:2023:AJSE, author = "Fatma Zohra Benabdallah and Leila Djerou", title = "Active Contour Extension Basing on Haralick Texture Features, Multi-gene Genetic Programming, and Block Matching to Segment Thyroid in {3D} Ultrasound Images", journal = "Arabian Journal for Science and Engineering", year = "2023", volume = "48", number = "2", pages = "2429--2440", keywords = "genetic algorithms, genetic programming, multi-gene genetic programming (MGGP), GPTIPS 2, segmentation, Ultrasound images, Thyroid gland, Volume estimation", URL = "http://link.springer.com/article/10.1007/s13369-022-07286-3", DOI = "doi:10.1007/s13369-022-07286-3", size = "12 pages", abstract = "The segmentation and estimation of thyroid volume in 3D ultrasound images have attracted the research community's attention because of their great importance in clinical diagnosis. Usually, thyroid volume estimation is based on the segmentation of 3D ultrasound images, which is difficult due to various disorders, including non-homogeneous texture distribution within the thyroid region, artifacts, speckles, and the nature of the thyroid shape. This paper presents an approach to segmenting all individual slices and then reconstructing them into a 3D object to overcome these difficulties. The process involves four techniques. The VOI initialization encompasses the probable thyroid gland; it greatly affects the segmentation results. Multi-gene genetic programming determines the appropriate textural features. The block-matching technique estimates the thyroid gland's change in size and location from slice to slice. Finally, the ITKSNAP software reconstructs the 3D volume. The proposed method is compared with state-of-the-art methods to prove its effectiveness in medical image analysis. Sixteen 3D images from an ultrasound thyroid image dataset were used for the experiments. The analysis of the results based on performance evaluation metrics shows that the proposed method is more efficient than the state-of-the-art methods", notes = "Laboratory of LESIA, University of Biskra, 07000 Biskra, Algeria", } @InProceedings{Benbassat:2010:CIGPU, author = "Amit Benbassat and Moshe Sipper", title = "Evolving Lose-Checkers Players using Genetic Programming", booktitle = "IEEE Conference on Computational Intelligence and Game", year = "2010", pages = "30--37", address = "IT University of Copenhagen, Denmark", month = "18-21 " # aug, keywords = "genetic algorithms, genetic programming, explicitly defined intron, full knowledge board game, genetic programming tree, local mutation, lose checker player, multitree individual, state evaluator, computer games, trees (mathematics)", URL = "http://game.itu.dk/cig2010/proceedings/papers/cig10_005_011.pdf", DOI = "doi:10.1109/ITW.2010.5593376", size = "8 pages", abstract = "We present the application of genetic programming (GP) to the zero-sum, deterministic, full-knowledge board game of Lose Checkers. Our system implements strongly typed GP trees, explicitly defined introns, local mutations, and multitree individuals. Explicitly defined introns in the genome allow for information selected out of the population to be kept as a reservoir for possible future use. Multi-tree individuals are implemented by a method inspired by structural genes in living organisms, whereby we take a single tree describing a state evaluator and split it.", notes = "http://game.itu.dk/cig2010/proceedings/wp-content/acceptedpapers.html Also known as \cite{5593376}", } @InProceedings{Benbassat:2011:GECCOcomp, author = "Amit Benbassat and Moshe Sipper", title = "Evolving board-game players with genetic programming", booktitle = "GECCO 2011 Graduate students workshop", year = "2011", editor = "Miguel Nicolau", isbn13 = "978-1-4503-0690-4", keywords = "genetic algorithms, genetic programming", pages = "739--742", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001858.2002080", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "We present the application of genetic programming (GP) to zero-sum, deterministic, full-knowledge board games. Our work expands previous results in evolving board-state evaluation functions for Lose Checkers to a 10x10 variant of Checkers, as well as Reversi. Our system implements strongly typed GP trees, explicitly defined introns, and a selective directional crossover method.", notes = "Also known as \cite{2002080} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @InCollection{Benbassat:2012:GPTP, author = "Amit Benbassat and Achiya Elyasaf and Moshe Sipper", title = "More or Less? Two Approaches to Evolving Game-Playing Strategies", booktitle = "Genetic Programming Theory and Practice X", year = "2012", series = "Genetic and Evolutionary Computation", editor = "Rick Riolo and Ekaterina Vladislavleva and Marylyn D. Ritchie and Jason H. Moore", publisher = "Springer", chapter = "12", pages = "171--185", address = "Ann Arbor, USA", month = "12-14 " # may, keywords = "genetic algorithms, genetic programming, alpha-beta search, Checkers, Dodgem, Freecell, Hyper heuristic, Reversi", isbn13 = "978-1-4614-6845-5", URL = "http://dx.doi.org/10.1007/978-1-4614-6846-2_12", DOI = "doi:10.1007/978-1-4614-6846-2_12", abstract = "We present two opposing approaches to the evolution of game strategies, one wherein a minimal amount of domain expertise is injected into the process, the other infusing the evolutionary setup with expertise in the form of domain heuristics. We show that the first approach works well for several popular board games, while the second produces top-notch solvers for the hard game of FreeCell.", notes = "part of \cite{Riolo:2012:GPTP} published after the workshop in 2013", } @InProceedings{Benbassat:2012:GECCOcomp, author = "Amit Benbassat and Moshe Sipper", title = "Evolving players that use selective game-tree search with genetic programming", booktitle = "GECCO 2012 Late breaking abstracts workshop", year = "2012", editor = "Katya Rodriguez and Christian Blum", isbn13 = "978-1-4503-1178-6", keywords = "genetic algorithms, genetic programming", pages = "631--632", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330784.2330894", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "We present the application of genetic programming (GP) to evolving game-tree search in board games. Our work expands previous results in evolving board-state evaluation functions for multiple board games, now evolving a search-guiding evaluation function alongside it. Our system implements strongly typed GP trees, explicitly defined introns, and a selective directional crossover method.", notes = "Also known as \cite{2330894} Distributed at GECCO-2012. ACM Order Number 910122.", } @InProceedings{Benbassat:2012:CIG, author = "Amit Benbassat and Moshe Sipper", title = "Evolving both search and strategy for {Reversi} players using genetic programming", booktitle = "IEEE Conference on Computational Intelligence and Games, CIG 2012", year = "2012", pages = "47--54", address = "Granada", month = "11-14 " # sep, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, computer games, search problems, trees (mathematics), Reversi players, deterministic board game, full-knowledge board game, game-tree pruning, search algorithm, selective directional crossover method, zero-sum board game, Games, Humans, Receivers, Sociology, Statistics", isbn13 = "978-1-4673-1193-9", URL = "https://bibtex.github.io/CIG-2012-BenbassatS.html", DOI = "doi:10.1109/CIG.2012.6374137", size = "8 pages", abstract = "We present the application of genetic programming to the zero-sum, deterministic, full-knowledge board game of Reversi. Expanding on our previous work on evolving boardstate evaluation functions, we now evolve the search algorithm as well, by allowing evolved programs control of game-tree pruning. We use strongly typed genetic programming, explicitly defined introns, and a selective directional crossover method. We show that our system regularly churns out highly competent players and our results prove easy to scale.", notes = "Also known as \cite{6374137}", } @InProceedings{Benbassat:2013:CIG, author = "Amit Benbassat and Moshe Sipper", booktitle = "IEEE Conference on Computational Intelligence in Games (CIG 2013)", title = "{EvoMCTS:} Enhancing MCTS-based players through genetic programming", year = "2013", month = "11-13 " # aug, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CIG.2013.6633631", ISSN = "2325-4270", size = "8 pages", abstract = "We present EvoMCTS, a genetic programming method for enhancing level of play in games. Our work focuses on the zero-sum, deterministic, perfect-information board game of Reversi. Expanding on our previous work on evolving board-state evaluation functions for alpha-beta search algorithm variants, we now evolve evaluation functions that augment the MTCS algorithm. We use strongly typed genetic programming, explicitly defined introns, and a selective directional crossover method. Our system regularly evolves players that outperform MCTS players that use the same amount of search. Our results prove scalable and EvoMCTS players whose search is increased offline still outperform MCTS counterparts. To demonstrate the generality of our method we apply EvoMCTS successfully to the game of Dodgem.", notes = "Also known as \cite{6633631}", } @PhdThesis{BenbassatDissertation, author = "Amit Benbassat", title = "Finding Methods for Evolving Competent Agents in Multiple Domains", school = "Ben-Gurion University of the Negev", year = "2014", address = "Israel", month = sep, keywords = "genetic algorithms, genetic programming, MTCS", URL = "https://dl.dropboxusercontent.com/u/36726425/ThesisFinalSubmissionWithTitle.pdf", size = "124 pages", abstract = "We present the application of genetic programming (GP) to search in zero-sum, deterministic, full-knowledge board games. We use multiple board games and multiple search algorithms as test cases in order to exhibit the flexibility of our system. We conduct experiments evolving players for variants of Checkers, Reversi, Dodgem, Nine Men's Morris and Hex, evolving them in conjunction with the Alpha-Beta search and Monte Carlo Tree Search (MCTS) algorithms. Throughout our research we rely on modern neo-Darwinian theory specifically, the gene-centred view of evolution to guide the design of our setup. Our evolutionary system implements strongly typed GP trees, explicitly defined introns, various mutation operators, a novel selective crossover operator, and multi-tree individuals. Explicitly defined introns in the genome allow for information selected out of the population to be kept as a reserve for possible future use. Selective genetic operators allow us to apply additional selection pressure during the procreation stage. Multi-tree individuals allow us to evolve software components that can be integrated into existing search algorithms where they improve play level over hand-crafted baseline players. Our results demonstrate patent improvement in play level for every game, clearly showing that GP is applicable to evolving search in board games. Results show differing levels of scalability, with the best scalability shown when using the MCTS algorithm. We also present our highly scalable EvoMCTS system designed as a scalable, easy-to-use, quick learning tool to improve the play level in games without need for any expert domain knowledge. Pursuing the goal of general game playing (GGP) we present a system that can serve as a stepping stone on the way to general game learning (GGL), where a system can learn a game upon getting its rule set, and the human developer can improve the resulting players by supplying the learning system with relevant information about the game.", notes = "Supervisor: Moshe Sipper", } @Article{Benbassat:2014:ieeegames, author = "Amit Benbassat and Moshe Sipper", journal = "IEEE Transactions on Computational Intelligence and AI in Games", title = "{EvoMCTS:} A Scalable Approach for General Game Learning", year = "2014", volume = "6", number = "4", pages = "382--394", month = dec, keywords = "genetic algorithms, genetic programming, STGP, MCTS, Board Games, Monte Carlo Methods, Search", DOI = "doi:10.1109/TCIAIG.2014.2306914", ISSN = "1943-068X", size = "29 pages", abstract = "We present the application of genetic programming as a generic game learning approach to zero-sum, deterministic, full knowledge board games by evolving board-state evaluation functions to be used in conjunction with Monte Carlo Tree Search (MCTS). Our method involves evolving board-evaluation functions that are then used to guide the MCTS play out strategy. We examine several variants of Reversi, Dodgem, and Hex using strongly typed genetic programming, explicitly defined introns, and a selective directional crossover method. Our results show a proficiency that surpasses that of baseline handcrafted players using equal and in some cases a greater amount of search, with little domain knowledge and no expert domain knowledge. Moreover, our results exhibit scalability.", notes = "Also known as \cite{6744581}", } @Article{benbouras:2021:AS, author = "Mohammed {Amin Benbouras} and Alexandru-Ionut Petrisor", title = "Prediction of Swelling Index Using Advanced Machine Learning Techniques for Cohesive Soils", journal = "Applied Sciences", year = "2021", volume = "11", number = "2", keywords = "genetic algorithms, genetic programming", ISSN = "2076-3417", URL = "https://www.mdpi.com/2076-3417/11/2/536", DOI = "doi:10.3390/app11020536", abstract = "Several attempts have been made for estimating the vital swelling index parameter conducted by the expensive and time-consuming Oedometer test. However, they have only focused on the neuron network neglecting other advanced methods that could have increased the predictive capability of models. In order to overcome this limitation, the current study aims to elaborate an alternative model for estimating the swelling index from geotechnical physical parameters. The reliability of the approach is tested through several advanced machine learning methods like Extreme Learning Machine, Deep Neural Network, Support Vector Regression, Random Forest, LASSO regression, Partial Least Square Regression, Ridge Regression, Kernel Ridge, Stepwise Regression, Least Square Regression, and genetic Programing. These methods have been applied for modelling samples consisting of 875 Oedometer tests. Firstly, principal component analysis, Gamma test, and forward selection are used to reduce the input variable numbers. Afterward, the advanced techniques have been applied for modelling the proposed optimal inputs, and their accuracy models were evaluated through six statistical indicators and using K-fold cross validation approach. The comparative study shows the efficiency of FS-RF model. This elaborated model provided the most appropriate prediction, closest to the experimental values compared with other models and formulae proposed by the previous studies.", notes = "also known as \cite{app11020536}", } @Article{BENCHAABENE:2021:CBM, author = "Wassim {Ben Chaabene} and Moncef L. Nehdi", title = "Genetic programming based symbolic regression for shear capacity prediction of {SFRC} beams", journal = "Construction and Building Materials", volume = "280", pages = "122523", year = "2021", ISSN = "0950-0618", DOI = "doi:10.1016/j.conbuildmat.2021.122523", URL = "https://www.sciencedirect.com/science/article/pii/S095006182100283X", keywords = "genetic algorithms, genetic programming, Steel fiber, Concrete, Beam, Shear strength, Symbolic regression, Generative adversarial network, Synthetic data", abstract = "The complexity of shear transfer mechanisms in steel fiber-reinforced concrete (SFRC) has motivated researchers to develop diverse empirical and soft-computing models for predicting the shear capacity of SFRC beams. Yet, such existing methods have been developed based on limited experimental databases, which makes their generalization capability uncertain. To account for the limited experimental data available, this study pioneers a novel approach based on tabular generative adversarial networks (TGAN) to generate 2000 synthetic data examples. A {"}train on synthetic - test on real{"} philosophy was adopted. Accordingly, the entire 2000 synthetic data were used for training a genetic programming-based symbolic regression (GP-SR) model to develop a shear strength equation for SFRC beams without stirrups. The model accuracy was then tested on the entire set of 309 real experimental data examples, which thus far are unknown to the model. Results show that the novel GP-SR model achieved superior predictive accuracy, outperforming eleven existing equations. Sensitivity analysis revealed that the shear-span-to-depth ratio was the most influential parameter in the proposed equation. The present study provides an enhanced predictive model for the shear capacity of SFRC beams, which should motivate further research to effectively train evolutionary algorithms using synthetic data when acquiring large and comprehensive experimental datasets is not feasible", } @InProceedings{Benchaji:2018:CSNet, author = "Ibtissam Benchaji and Samira Douzi and Bouabid {El Ouahidi}", title = "Using Genetic Algorithm to Improve Classification of Imbalanced Datasets for Credit Card Fraud Detection", booktitle = "2018 2nd Cyber Security in Networking Conference (CSNet)", year = "2018", month = "24-26 " # oct, address = "Paris", keywords = "genetic algorithms, genetic programming, Credit cards, Sociology, Statistics, Biological cells, Clustering algorithms, Training, Fraud Detection, Imbalanced dataset, K-means clustering, Autoencoder", isbn13 = "978-1-5386-7046-0", DOI = "doi:10.1109/CSNET.2018.8602972", abstract = "With the growing usage of credit card transactions, financial fraud crimes have also been drastically increased leading to the loss of huge amounts in the finance industry. Having an efficient fraud detection method has become a necessity for all banks in order to minimize such losses. In fact, credit card fraud detection system involves a major challenge: the credit card fraud data sets are highly imbalanced since the number of fraudulent transactions is much smaller than the legitimate ones. Thus, many of traditional classifiers often fail to detect minority class objects for these skewed data sets. This paper aims first: to enhance classified performance of the minority of credit card fraud instances in the imbalanced data set, for that we propose a sampling method based on the K-means clustering and the genetic algorithm. We used K-means algorithm to cluster and group the minority kind of sample, and in each cluster we use the genetic algorithm to gain the new samples and construct an accurate fraud detection classifier.", notes = "Also known as \cite{8602972}", } @Article{Benes201392, author = "Radek Benes and Jan Karasek and Radim Burget and Kamil Riha", title = "Automatically designed machine vision system for the localization of CCA transverse section in ultrasound images", journal = "Computer Methods and Programs in Biomedicine", volume = "109", number = "1", pages = "92--103", year = "2013", keywords = "genetic algorithms, genetic programming, Common carotid artery, Localisation, Machine vision system", ISSN = "0169-2607", URL = "http://www.sciencedirect.com/science/article/pii/S0169260712001964", DOI = "doi:10.1016/j.cmpb.2012.08.014", size = "12 pages", abstract = "The common carotid artery (CCA) is a source of important information that doctors can use to evaluate the patients' health. The most often measured parameters are arterial stiffness, lumen diameter, wall thickness, and other parameters where variation with time is usually measured. Unfortunately, the manual measurement of dynamic parameters of the CCA is time consuming, and therefore, for practical reasons, the only alternative is automatic approach. The initial localisation of artery is important and must precede the main measurement. This article describes a novel method for the localization of CCA in the transverse section of a B-mode ultrasound image. The novel method was designed automatically by using the grammar-guided genetic programming (GGGP). The GGGP searches for the best possible combination of simple image processing tasks (independent building blocks). The best possible solution is represented with the highest detection precision. The method is tested on a validation database of CCA images that was specially created for this purpose and released for use by other scientists. The resulting success of the proposed solution was 82.7percent, which exceeded the current state of the art by 4percent while the computation time requirements were acceptable. The paper also describes an automatic method that was used in designing the proposed solution. This automatic method provides a universal approach to designing complex solutions with the support of evolutionary algorithms.", notes = "Brno University of Technology, Department of Telecommunications, Purkynova 118, Brno, Czech Republic", } @InProceedings{Bengio:1994:GPslrNN, author = "Samy Bengio and Yoshua Bengio and Jocelyn Cloutier", title = "Use of genetic programming for the search of a new learning rule for neutral networks", booktitle = "Proceedings of the 1994 IEEE World Congress on Computational Intelligence", year = "1994", volume = "1", pages = "324--327", address = "Orlando, Florida, USA", month = "27-29 " # jun, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, ANN", size = "4 pages", URL = "http://www.idiap.ch/~bengio/cv/publications/ps/bengio_1994_wcci.ps.gz", URL = "http://citeseer.ist.psu.edu/465154.html", DOI = "doi:10.1109/ICEC.1994.349932", abstract = "In previous work ([1, 2, 3]) we explained how to use standard optimization methods such as simulated annealing, gradient descent and genetic algorithms to optimize a parametric function which could be used as a learning rule for neural networks. To use these methods, we had to choose a fixed number of parameters and a rigid form for the learning rule. In this article, we propose to use genetic programming to find not only the values of rule parameters but also the optimal number of parameters and the form of the rule. Experiments on classification tasks suggest genetic programming finds better learning rules than other optimization methods. Furthermore, the best rule found with genetic programming outperformed the well-known backpropagation algorithm for a given set of tasks", notes = "Uses GP to produce a learning rule for training a neural network. Evolved rule like back-propergation but better, differential is cubed. Says neural network is fully connected, ", } @Article{bengio:1995:npl, author = "Samy Bengio and Yoshua Bengio and Jocelyn Cloutier", title = "On the Search for New Learning Rules for {ANN}s", journal = "Neural Processing Letters", year = "1995", volume = "2", number = "4", pages = "26--30", keywords = "genetic algorithms, genetic programming, ANN, Neural Network, Artificial Intelligence, Complex System, Nonlinear Dynamics, Practical Experiment", ISSN = "1370-4621", URL = "http://www.iro.umontreal.ca/~lisa/pointeurs/bengio_1995_npl.pdf", broken = "http://www.iro.umontreal.ca/~lisa/publications2/index.php/publications/show/131", DOI = "doi:10.1007/BF02279935", size = "12 pages", abstract = "we present a framework where a learning rule can be optimized within a parametric learning rule space. We define what we call parametric learning rules and present a theoretical study of their generalization properties when estimated from a set of learning tasks and tested over another set of tasks. We corroborate the results of this study with practical experiments.", } @PhdThesis{BenHamid:thesis, author = "Sana {Ben Hamida}", title = "Evolutionary Algorithms: Handling Constraints and Real-World Application", school = "Ecole Polytechnique", year = "2001", address = "Paris, France", month = mar, keywords = "genetic algorithms, genetic programming", URL = "http://www.cmap.polytechnique.fr/~sana/these.ps.gz", URL = "http://www.cmap.polytechnique.fr/~sana/indexAng.html", size = "225 pages", abstract = "The present work is a heuristic and experimental study in the evolutionary computation domain, and starts with an introduction to the artificial evolution with a synthesis of the principal approaches. The first part is a heuristic study devoted to constraint handling in evolutionary computation. It presents an extensive review of previous constraint handling methods in the literature and their limitations. Two solutions are then proposed. The first idea is to improve genetic operator exploration capacity for constrained optimisation problems. The logarithmic mutation operator is conceived to explore both locally and globally the search space. The second solution introduces the original Adaptive Segregational Constraint Handling Evolutionary Algorithm (ASCHEA), the main idea of which is to maintain population diversity. In order to achieve this goal, three main ingredients are used: An original adaptive penalty method, a constraint-driven recombination, and a segregation selection that distinguishes between feasible and infeasible individuals to enhance the chances of survival of the feasible ones. Moreover, a niching method with an adaptive radius is added to ASCHEA in order to handle multimodal functions. Finally, to complete the ASCHEA system, a new equality constraint handling strategy is introduced, that reduces progressively the feasible domain in order to approach the actual null-measured domain as close as possible at the end of the evolution. The second part is a case study tackling a real-world problem. The goal is to design the 2-dimensional profile of an optical lens (phase plate) in order to control focal-plane irradiance of some laser beam. The aim is to design the phase plate such that a small circular target on the focal plane is uniformly illuminated without energy loss.", notes = "In French. Chapter 7 GP v ES on laser. Supervisor: Marc Schoenauer", } @Misc{oai:HAL:hal-02489115v1, title = "Nested Monte Carlo Expression Discovery vs Genetic Programming for Forecasting Financial Volatility", author = "Sana {Ben Hamida} and Tristan Cazenave", howpublished = "HAL", year = "2020", month = feb # "~24", note = "working paper or preprint", publisher = "HAL CCSD", keywords = "genetic algorithms, genetic programming, computer science, artificial intelligence, AI", hal_id = "hal-02489115", hal_version = "v1", URL = "https://hal-univ-paris10.archives-ouvertes.fr/hal-02489115", abstract = "We are interested in discovering expressions for financial prediction using Nested Monte Carlo Search and Genetic Programming. Both methods are applied to learning from financial time series to generate nonlinear functions for market volatility prediction. The input data, that is a series of daily prices of European S\&P500 index, is filtered and sampled in order to improve the training process. Using some assessment metrics, the best generated models given by both approaches for each training sub-sample, are evaluated and compared. Results show that Nested Monte Carlo is able to generate better forecasting models than Genetic Programming for the majority of learning samples.", annote = "Laboratoire d'analyse et modelisation de systemes pour l'aide a la decision (LAMSADE) ; Universite Paris Dauphine-PSL-Centre National de la Recherche Scientifique (CNRS); Universite Paris Nanterre - UFR Sciences economiques, gestion, mathematiques, informatique (UPN SEGMI) ; Universite Paris Nanterre (UPN)", bibsource = "OAI-PMH server at api.archives-ouvertes.fr", contributor = "Laboratoire d'analyse et modelisation de systemes pour l'aide a la decision", identifier = "hal-02489115", language = "en", oai = "oai:HAL:hal-02489115v1", type = "info:eu-repo/semantics/preprint", notes = "Also known as \cite{benhamida:hal-02489115} See also \cite{DBLP:journals/ijait/Cazenave13}", } @InCollection{benini:1995:GFESOADF, author = "Luca Benini", title = "Genetic Fitting: Evolutionary Search of Optimal Approximations for Discrete Functions", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1995", year = "1995", editor = "John R. Koza", pages = "19--28", address = "Stanford, California, 94305-3079 USA", month = "11 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-195720-5", notes = "part of \cite{koza:1995:gagp}", } @InProceedings{conf/idc/BenitezWL14, author = "Cesar Manuel Vargas Benitez and Wagner Rodrigo Weinert and Heitor Silverio Lopes", title = "Gene Expression Programming for Evolving Two-Dimensional Cellular Automata in a Distributed Environment", publisher = "Springer", booktitle = "IDC", year = "2014", volume = "570", series = "Studies in Computational Intelligence", pages = "107--117", keywords = "genetic algorithms, genetic programming", editor = "David Camacho and Lars Braubach and Salvatore Venticinque and Costin Badica", isbn13 = "978-3-319-10421-8", bibdate = "2014-10-09", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/idc/idc2014.html#BenitezWL14", URL = "http://dx.doi.org/10.1007/978-3-319-10422-5", } @InProceedings{Benjamin:2008:cec, author = "Simon C. Benjamin", title = "Evolutionary Route to Computation in Self-Assembled Nanoarrays", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "3094--3101", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1823-7", file = "EC0685.pdf", DOI = "doi:10.1109/CEC.2008.4631216", abstract = "Ordered nanoarrays, i.e. regular patterns of quantum structures at the nanometre scale, can now be synthesised in a range of systems. In this paper I study a form of array computation where the internal dynamics are driven by intrinsic cell-cell interactions and global optical pulses addressing entire structure indiscriminately. The array would need to be ' wired' to conventional technologies only at its boundary. Any self assembled array would have a unique set of defects, therefore I employ an ab initio evolutionary process to subsume such flaws without any need to determine their location or nature. The approach succeeds for various forms of physical interaction within the array.", keywords = "genetic algorithms, genetic programming", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @InProceedings{Benkhelifa:2009:cec, author = "E. Benkhelifa and G. Dragffy and A. G. Pipe and M. Nibouche", title = "Design Innovation for Real World Applications, Using Evolutionary Algorithms", booktitle = "2009 IEEE Congress on Evolutionary Computation", year = "2009", editor = "Andy Tyrrell", pages = "918--924", address = "Trondheim, Norway", month = "18-21 " # may, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-2959-2", file = "P692.pdf", DOI = "doi:10.1109/CEC.2009.4983043", abstract = "This paper discusses two important features of electronic design through evolutionary processes; creativity and innovation. Hence, conventional design methodologies are discussed and compared with their counterparts via evolutionary processes. An evolutionary search is used as an engine for discovering new designs for a real world application. Attempts to extract some useful principles from the evolved designs are presented and results are compared to conventional design topologies for the same problems.", keywords = "genetic algorithms, genetic programming", notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR", } @InProceedings{Benkhelifa:2010:cec, author = "Elhadj Benkhelifa and Ashutosh Tiwari and Anthony Pipe", title = "Evolutionary design optimisation of a 32-Step Traffic Lights Controller", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, EHW", isbn13 = "978-1-4244-6910-9", DOI = "doi:10.1109/CEC.2010.5586108", size = "5 pages", abstract = "This paper shows a successful application of evolutionary algorithms for the design and optimisation of complex real world digital circuit that is a 32-Step Traffic Lights Controller. It discusses two important features of electronic design through evolutionary processes; creativity and innovation. Results are compared to conventional design topologies; and attempt to analyse the evolved designs is presented.", notes = "WCCI 2010. Also known as \cite{5586108} Decision Engineering Centre, School of applied Sciences, Cranfield University, Bedfordshire, MK43 0AL, UK.", } @InProceedings{Bennett:2007:SGAI, author = "Andrew Bennett and Derek Magee", title = "Learning Sets of Sub-Models for Spatio-Temporal Prediction", booktitle = "AI-2007 Twenty-seventh SGAI International Conference on Artificial Intelligence", year = "2007", editor = "Max Bramer and Richard Ellis", address = "Cambridge, UK", month = "10-12 " # dec, organisation = "British Computer Society's Specialist Group on Artificial Intelligence (SGAI)", keywords = "genetic algorithms, genetic programming, card game playing", URL = "http://www.bcs-sgai.org/ai2007/admin/papers2.php?f=techpapers", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.150.6694", URL = "http://citeseerx.ist.psu.edu/viewdoc/download/10.1.1.150.6694.pdf", size = "14 page", abstract = "In this paper we describe a novel technique which implements a spatio-temporal model as a set of sub-models based on first order logic. These sub-models model different, typically independent, parts of the dataset; for example different spatio or temporal contexts. To decide which sub-models to use in different situations a context chooser is used. By separating the sub-models from where they are applied allows greater flexibility for the overall model. The sub-models are learnt using an evolutionary technique called Genetic Programming. The method has been applied to spatio-temporal data. This includes learning the rules of snap by observation, learning the rules of a traffic light sequence, and finally predicting a person's course through a network of CCTV cameras.", notes = "University of Leeds, UK", } @InProceedings{Bennett:2008:CIMA, author = "Andrew Bennett and Derek Magee", title = "Using Genetic Programming to Learn Models Containing Temporal Relations from Spatio-Temporal Data", booktitle = "Proceedings of the 1st International Workshop on Combinations of Intelligent Methods and Applications", year = "2008", editor = "Ioannis Hatzilygeroudis and Constantinos Koutsojannis and Vasile Palade", address = "Patras, Greece", month = jul # " 22", organisation = "CEUR", keywords = "genetic algorithms, genetic programming", URL = "http://ftp.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-375/paper2.pdf", URL = "http://www.comp.leeds.ac.uk/andrewb/Publications/CIMA08.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.142.8374", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.150.6758", URN = "urn:nbn:de:0074-375-1", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", contributor = "CiteSeerX", language = "en", oai = "oai:CiteSeerXPSU:10.1.1.142.8374", oai = "oai:CiteSeerXPSU:10.1.1.150.6758", abstract = "In this paper we describe a novel technique for learning predictive models from non-deterministic spatio-temporal data. Our technique learns a set of sub-models that model different, typically independent, aspects of the data. By using temporal relations, and implicit feature selection, based on the use of 1st order logic expressions, we make the sub-models general, and robust to irrelevant variations in the data.We use Allen's intervals [1], plus a set of four novel temporal state relations, which relate temporal intervals to the current time. These are added to the system as background knowledge in the form of functions. To combine the sub-models into a single model a context chooser is used. This probabilistically picks the most appropriate set of sub-models to predict in a certain context, and allows the system to predict in non-deterministic situations. The models are learnt using an evolutionary technique called Genetic Programming. The method has been applied to learning the rules of snap, and uno by observation; and predicting a person's course through a network of CCTV cameras.", notes = "CIMA'08 Combinations of Intelligent Methods and Applications http://ftp.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-375/", } @PhdThesis{bennett_a, author = "Andrew David Bennett", title = "Using genetic programming to learn predictive models from spatio-temporal data", school = "School of Computing, University of Leeds", year = "2010", address = "UK", month = jul, keywords = "genetic algorithms, genetic programming", URL = "http://etheses.whiterose.ac.uk/1376/", URL = "http://etheses.whiterose.ac.uk/1376/1/bennett_a.pdf", URL = "http://ethos.bl.uk/OrderDetails.do?did=43&uin=uk.bl.ethos.530613", size = "211 pages", abstract = "This thesis describes a novel technique for learning predictive models from nondeterministic spatio-temporal data. The prediction models are represented as a production system, which requires two parts: a set of production rules, and a conflict resolver. The production rules model different, typically independent, aspects of the spatio-temporal data. The conflict resolver is used to decide which sub-set of enabled production rules should be fired to produce a prediction. The conflict resolver in this thesis can probabilistically decide which set of production rules to fire, and allows the system to predict in non-deterministic situations. The predictive models are learnt by a novel technique called Spatio-Temporal Genetic Programming (STGP). STGP has been compared against the following methods: an Inductive Logic Programming system (Progol), Stochastic Logic Programs, Neural Networks, Bayesian Networks and C4.5, on learning the rules of card games, and predicting a person's course through a network of CCTV cameras. This thesis also describes the incorporation of qualitative temporal relations within these methods. Allen's intervals [1], plus a set of four novel temporal state relations, which relate temporal intervals to the current time are used. The methods are evaluated on the card game Uno, and predicting a person's course through a network of CCTV cameras. This work is then extended to allow the methods to use qualitative spatial relations. The methods are evaluated on predicting a person's course through a network of CCTV cameras, aircraft turnarounds, and the game of Tic Tac Toe. Finally, an adaptive bloat control method is shown. This looks at adapting the amount of bloat control used during a run of STGP, based on the ratio of the fitness of the current best predictive model to the initial fitness of the best predictive model.", notes = "noughts and crosses. Not strongly typed GP. uk.bl.ethos.530613", } @InProceedings{bennett:1996:emaa, author = "Forrest H {Bennett III}", title = "Automatic Creation of an Efficient Multi-Agent Architecture Using Genetic Programming with Architecture-Altering Operations", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "30--38", address = "Stanford University, CA, USA", publisher = "MIT Press", size = "9 pages", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap4.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96", } @InProceedings{bennett:1996:emaant, author = "Forrest H {Bennett III}", title = "Emergence of a Multi-Agent Architecture and New Tactics For the Ant Colony Foraging Problem Using Genetic Programming", booktitle = "Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior: From animals to animats 4", year = "1996", editor = "Pattie Maes and Maja J. Mataric and Jean-Arcady Meyer and Jordan Pollack and Stewart W. Wilson", pages = "430--439", address = "Cape Code, USA", publisher_address = "Cambridge, MA, USA", month = "9-13 " # sep, publisher = "MIT Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-262-63178-4", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6291906", DOI = "doi:10.7551/mitpress/3118.003.0044", DOI = "doi:10.7551/mitpress/3118.001.0001", size = "10 pages", abstract = "Previous work in multi-agent systems has required the human designer to make up-front decisions about the multi-agent architecture, including the number of agents to employ and the specific tasks to be performed by each agent. This paper describes the automatic evolution of these decisions during a run of genetic programming using architecture-altering operations.Genetic programming is extended to the discovery of multi-agent solutions for a central-place foraging problem for an ant colony. In this problem each individual ant is controlled by a set of agents, where agent is used in the sense of Minsky's Society of Mind.Two new tactics for the central-place food foraging problem that were discovered by genetic programming are presented in this paper.Genetic programming was able to evolve time-efficient solutions to this problem by distributing the functions and terminals across successively more agents in such a way as to reduce the maximum number of functions executed per agent. The other source of time-efficiency in the evolved solution was the cooperation that emerged among the ants in the ant colony.", notes = "SAB-96 Each tree within individual treated as an {"}agent{"}. Uses koza add/delete adf genetic operations to evolve the number of agents as well as their code.", } @InProceedings{bennet:1996:ices60db, author = "Forrest H {Bennett III} and John R. Koza and David Andre and Martin A. Keane", title = "Evolution of a 60 Decibel op amp using genetic programming", booktitle = "Proceedings of International Conference on Evolvable Systems: From Biology to Hardware (ICES-96)", year = "1996", editor = "Tetsuya Higuchi and Iwata Masaya and Weixin Liu", volume = "1259", series = "Lecture Notes in Computer Science", address = "Tsukuba, Japan", publisher_address = "Berlin, Germany", month = "7-8 " # oct, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-63173-9", ISSN = "0302-9743", LCCN = "QA76.618 .I57 1996", bibdate = "Mon Nov 24 10:31:37 1997", acknowledgement = ack-nhfb, URL = "http://www.genetic-programming.com/jkpdf/ices1996fhbamplifier60.pdf", size = "18 pages", abstract = "Genetic programming was used to evolve both the topology and sizing (numerical values) for each component of a low-distortion, low-bias 60 decibel (1000-to-1) amplifier with good frequency generalization.", notes = "URL=version 1 as presented at the conference http://www.etl.go.jp:8080/etl/kikou/ICES96/", } @InProceedings{bennet:1997:msrrrdpe, author = "Forrest H {Bennett III}", title = "A Multi-Skilled Robot that Recognizes and Responds to Different Problem Environments", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming", pages = "44--51", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1997/bennet_1997_msrrrdpe.pdf", size = "8 pages", notes = "GP-97 two memory cells SET-D0 and SET-D1. Max rpb size 600, up to 2 ADFs (up to 200 each). Architecture altering operations. OBJECT-DIST OBJECT-KIND and ROOM-COLOR. Fitness includes time penalty. 4 rooms in continous (ie floating point) world. Program is repeatedly evaluated until 1000 timesteps or hits mine. Claims [page 49] code cant remember locations", } @InProceedings{bennett:1999:SCASE, author = "Forrest H {Bennett III} and John R. Koza and Martin A. Keane and David Andre", title = "Darwinian Programming and Engineering Design using Genetic Programming", booktitle = "Proceedings of the 1st International Workshop on Soft Computing Applied to Software Engineering", year = "1999", editor = "Conor Ryan and Jim Buckley", pages = "31--40", address = "University of Limerick, Ireland", month = "12-14 " # apr, organisation = "SCARE", publisher = "Limerick University Press", keywords = "genetic algorithms, genetic programming", ISBN = "1-874653-52-6", URL = "http://www.genetic-programming.com/jkpdf/scase1999.pdf", abstract = "One of the central challenges of computer science is to build a system that can automatically create computer programs that are competitive with those produced by humans. This paper presents a candidate set of criteria that identify when a machine-created solution is competitive with a human-produced result. We argue that the field of design is a useful testbed for determining whether an automated technique can produce results that are competitive with human-produced results. We present several results that are competitive with the products of human creativity and inventiveness. This claim is supported by the fact that each of the results infringe on previously issued patents.", notes = "broken http://scare.csis.ul.ie/scase99/ SCASE'99 Automatic analog electrical circuit synthesis: Campbell 1917 Ladder Filter patent, Zobel 1925 {"}M-Derived Half Section{"} patent, Cauer 1934 - 1936 Elliptic patents, Darlington 1952 Emitter-Follower patent", } @InProceedings{bennet:1999:astsaecGP, author = "Forrest H {Bennett III} and Martin A. Keane and David Andre and John R. Koza", title = "Automatic Synthesis of the Topology and Sizing for Analog Electrical Circuits Using Genetic Programming", booktitle = "Evolutionary Algorithms in Engineering and Computer Science", year = "1999", editor = "Kaisa Miettinen and Marko M. Makela and Pekka Neittaanmaki and Jacques Periaux", pages = "199--229", address = "Jyvaskyla, Finland", publisher_address = "Chichester, UK", month = "30 " # may # " - 3 " # jun, publisher = "John Wiley \& Sons", keywords = "genetic algorithms, genetic programming", ISBN = "0-471-99902-4", URL = "http://www.genetic-programming.com/jkpdf/eurogen1999circuits.pdf", size = "46 pages", abstract = "The design (synthesis) of an analog electrical circuit entails the creation of both the topology and sizing (numerical values) of all of the circuit's components. There has previously been no general automated technique for automatically creating the design for an analog electrical circuit from a high-level statement of the circuit's desired behavior. We have demonstrated how genetic programming can be used to automate the design of seven prototypical analog circuits, including a lowpass filter, a highpass filter, a passband filter, a bandpass filter, a frequency-measuring circuit, a 60 dB amplifier, a differential amplifier, a computational circuit for the square root function, and a time-optimal robot controller circuit. All seven of these genetically evolved circuits constitute instances of an evolutionary computation technique solving a problem that is usually thought to require human intelligence. The approach described herein can be directly applied to many other problems of analog circuit synthesis.", notes = "EUROGEN'99 http://eu.wiley.com/WileyCDA/WileyTitle/productCd-0471999024.html ", } @InProceedings{bennett:1999:AISB, author = "Forrest H {Bennett III} and John R. Koza and Martin A. Keane and David Andre", title = "Genetic programming: Biologically inspired computation that exhibits creativity in solving non-trivial problems", booktitle = "Proceedings of the AISB'99 Symposium on Scientific Creativity", year = "1999", pages = "29--38", address = "Edingburgh", month = "8-9 " # apr, organisation = "The Society for the Study of Artificial Intelligence and Simulation of Behaviour", keywords = "genetic algorithms, genetic programming", URL = "http://www.genetic-programming.com/jkpdf/aisb1999.pdf", abstract = "This paper describes a biologically inspired domain-independent technique, called genetic programming, that automatically creates computer programs to solve problems. We argue that the field of design is a useful testbed for determining whether an automated technique can produce results that are competitive with human-produced results. We present several results that are competitive with the products of human creativity and inventiveness. This claim is supported by the fact that each of the results infringe on previously issued patents. This paper presents a candidate set of criteria that identify when a machine-created solution to a problem is competitive with a human-produced result.", notes = "AISB-99", } @InProceedings{bennett:1999:BPCSPHPD, author = "Forrest H {Bennett III} and John R. Koza and James Shipman and Oscar Stiffelman", title = "Building a Parallel Computer System for \$18,000 that Performs a Half Peta-Flop per Day", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1484--1490", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, real world applications, parallel computing", ISBN = "1-55860-611-4", URL = "http://www.genetic-programming.com/jkpdf/gecco1999beowulf.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-788.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-788.ps", abstract = "Techniques of evolutionary computation generally require significant computational resources to solve non-trivial problems of interest. Increases in computing power can be realized either by using a faster computer or by parallelizing the application. Techniques of evolutionary computation are especially amenable to parallelization. This paper describes how to build a 10-node Beowulf-style parallel computer system for $18,000 that delivers about a half peta-flop (1015 floating-point operations) per day on runs of genetic programming. Each of the 10 nodes of the system contains a 533 MHz Alpha processor and runs with the Linux operating system. This amount of computational power is sufficient to yield solutions (within a couple of days per problem) to 14 published problems where genetic programming has produced results that are competitive with human-produced results.", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{bennett:1999:EMGPACPDF, author = "Forrest H {Bennett III} and John R. Koza and Martin A. Keane and Jessen Yu and William Mydlowec and Oscar Stiffelman", title = "Evolution by Means of Genetic Programming of Analog Circuits that Perform Digital Functions", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1477--1483", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, real world applications", ISBN = "1-55860-611-4", URL = "http://www.genetic-programming.com/jkpdf/gecco1999analog.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-787.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-787.ps", abstract = "This paper demonstrates the ability of genetic programming to evolve analog circuits that perform digital functions and mixed analog-digital circuits. The evolved circuits include two purely digital circuits (a 100 nano-second NAND circuit and a two-instruction arithmetic logic unit circuit) and one mixed-signal circuit, namely a three-input digital-to-analog converter.", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{bennett:2000:ICES, author = "Forrest H {Bennett III} and John R. Koza and Jessen Yu and William Mydlowec", title = "Automatic synthesis, placement, and routing of an amplifier circuit by means of genetic programming", booktitle = "Evolvable Systems: From Biology to Hardware Third International Conference, ICES 2000", year = "2000", editor = "Julian Miller and Adrian Thompson and Peter Thomson and Terence C. Fogarty", volume = "1801", series = "LNCS", pages = "1--10", address = "Edinburgh, Scotland, UK", month = "17-19 " # apr, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-67338-5", URL = "http://www.genetic-programming.com/jkpdf/ices2000.pdf", URL = "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-67338-5", URL = "http://citeseer.ist.psu.edu/471655.html", abstract = "The complete design of a circuit typically includes the tasks of creating the circuit's placement and routing as well as creating its topology and component sizing. Design engineers perform these four tasks sequentially. Each of these four tasks is, by itself, either vexatious or computationally intractable. This paper describes an automatic approach in which genetic programming starts with a high-level statement of the requirements for the desired circuit and simultaneously creates the circuit's topology, component sizing, placement, and routing as part of a single integrated design process. The approach is illustrated using the problem of designing a 60 decibel amplifier. The fitness measure considers the gain, bias, and distortion of the candidate circuit as well as the area occupied by the circuit after the automatic placement and routing.", notes = "ICES-2000", } @InProceedings{Bennett:2000:GECCOlb, author = "Forrest H {Bennett III} and Eleanor G. Rieffel", title = "Using Genetic Programming to Design Decentralized Controllers for Self-Reconfigurable Modular Robots", pages = "35--42", booktitle = "Late Breaking Papers at the 2000 Genetic and Evolutionary Computation Conference", year = "2000", editor = "Darrell Whitley", address = "Las Vegas, Nevada, USA", month = "8 " # jul, keywords = "genetic algorithms, genetic programming", notes = "Part of \cite{whitley:2000:GECCOlb}", } @InProceedings{bennett:2000:EH, author = "Forrest H {Bennett III} and Eleanor G. Rieffel", title = "Design of Decentralized Controllers for Self-Reconfigurable Modular Robots Using Genetic Programming", booktitle = "Proceedings of the Second NASA / DoD Workshop on Evolvable Hardware", year = "2000", pages = "43--52", address = "Palo Alto, California", month = jul # " 13-15", organisation = "Jet Propulsion Laboratory, California Institute of Technology", publisher = "IEEE Computer Society", keywords = "genetic algorithms, genetic programming, connectivity topology, control software, decentralized controllers, distributed controllers, physical adaptability, robustness, self-reconfigurable modular robots, control engineering computing, controllers, decentralised control", ISBN = "0-7695-0762-X", DOI = "doi:10.1109/EH.2000.869341", abstract = "Advantages of self-reconfigurable modular robots over conventional robots include physical adaptability, robustness in the presence of failures, and economies of scale. Creating control software for modular robots is one of the central challenges to realizing their potential advantages. Modular robots differ enough from traditional robots that new techniques must be found to create software to control them. The novel difficulties are due to the fact that modular robots are ideally controlled in a decentralized manner, dynamically change their connectivity topology, may contain hundreds or thousands of modules, and are expected to perform tasks properly even when some modules fail. We demonstrate the use of genetic programming to automatically create distributed controllers for self-reconfigurable modular robots. .", notes = "EH-2000 http://ic-www.arc.nasa.gov/ic/eh2000/index.html http://csdl.computer.org/comp/proceedings/eh/2000/0762/00/0762toc.htm", } @InProceedings{bennett:2001:EuroGP, author = "Forrest H {Bennett III} and Brad Dolin and Eleanor G. Rieffel", title = "Programmable Smart Membranes: Using Genetic Programming to Evolve Scalable Distributed Controllers for a Novel Self-Reconfigurable Modular Robotic Application", booktitle = "Genetic Programming, Proceedings of EuroGP'2001", year = "2001", editor = "Julian F. Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon", volume = "2038", series = "LNCS", pages = "234--245", address = "Lake Como, Italy", publisher_address = "Berlin", month = "18-20 " # apr, organisation = "EvoNET", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, modular robot, distributed control, smart membrane, self-reconfigurable, scalable, robust: Poster", ISBN = "3-540-41899-7", DOI = "doi:10.1007/3-540-45355-5_18", size = "12 pages", abstract = "Self-reconfigurable modular robotics represents a new approach to robotic hardware, in which the robot is composed of many simple, identical interacting modules. We propose a novel application of modular robotics: the programmable smart membrane, a device capable of actively filtering objects based on numerous measurable attributes. Creating control software for modular robotic tasks like the smart membrane is one of the central challenges to realizing their potential advantages. We use genetic programming to evolve distributed control software for a 2-dimensional smart membrane capable of distinguishing objects based on color. The evolved controllers exhibit scalability to a large number of modules and robustness to the initial configurations of the robotic filter and the particles.", notes = "EuroGP'2001, part of \cite{miller:2001:gp}", } @InProceedings{benolic:2022:SICAAI, author = "Leo Benolic and Zlatan Car and Nenad Filipovic", title = "Mathematical Modeling of {COVID-19} Spread Using Genetic Programming Algorithm", booktitle = "1st Serbian International Conference on Applied Artificial Intelligence", year = "2022", address = "Kragujevac, Serbia", month = may # " 19-20", publisher = "Springer", keywords = "genetic algorithms, genetic programming, artificial intelligence, COVID-19, mathematical prediction models, variants", URL = "http://aai2022.kg.ac.rs/wp-content/uploads/upload/AAI_2022_Papers.zip", URL = "http://link.springer.com/chapter/10.1007/978-3-031-29717-5_19", DOI = "doi:10.1007/978-3-031-29717-5_19", notes = "http://www.aai2022.kg.ac.rs/aai-2022-papers/ SICAAI proceedings published by Springer in 2023 doi:10.1007/978-3-031-29717-5", } @InProceedings{benolic:2023:SICAAI, author = "Leo Benolic", title = "Development of a Mathematical Model for Balloon Diameter Calculation in Percutaneous Transluminal Angioplasty Using Genetic Programming", booktitle = "Second Serbian International Conference on Applied Artificial Intelligence", year = "2023", editor = "Nenad Filipovic", volume = "999", series = "Lecture Notes in Networks and Systems", pages = "7--20", address = "Kragujevac, Serbia", month = may # " 19-20", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-031-60840-7", URL = "https://link.springer.com/chapter/10.1007/978-3-031-60840-7_2", DOI = "doi:10.1007/978-3-031-60840-7_2", abstract = "This paper describes the development of a mathematical model using genetic programming to calculate the diameter of a percutaneous transluminal angioplasty (PTA) balloon dilatation catheter for a given pressure and balloon size. The dataset used for the study was provided by Boston Scientific, and the genetic programming algorithm was implemented in Python using parallel computing. The results showed high levels of accuracy, with R2 values of 0.99989 and 0.99954 for the best and parsimonious models, respectively. The developed model can be useful for in-silico simulations of angioplasty surgery and can contribute to improving the effectiveness of the PTA balloon dilatation catheter procedure. This study demonstrates the potential of machine learning techniques for optimizing medical device performance and design. Further work could investigate the use of other machine learning techniques and larger datasets to enhance the accuracy and generalizability of the models.", } @Article{benouhiba:2021:FI, author = "Toufik Benouhiba", title = "A Multi-level Refinement Approach for Structural Synthesis of Optimal Probabilistic Models", journal = "Fundamenta Informaticae", year = "2021", volume = "179", number = "1", pages = "1--33", keywords = "genetic algorithms, genetic programming, model synthesis, refinement, Search-based software engineering, SBSE, constraint satisfaction, probabilistic model checking", publisher = "IOS press", URL = "https://journals.sagepub.com/doi/pdf/10.3233/FI-2021-2011", DOI = "doi:10.3233/FI-2021-2011", size = "33 pages", abstract = "Probabilistic models play an important role in many fields such as distributed systems and simulations. Like non-probabilistic systems, they can be synthesized using classical refinement-based techniques, but they also require identifying the probability distributions to be used and their parameters. Since a fully automated and blind refinement is generally undecidable, many works tried to synthesize them by looking for the parameters of the distributions. Syntax-guided synthesizing approaches are more powerful, they try to synthesize models structurally by using context-free grammars. However, many problems arise like huge search space, the complexity of generated models, and the limitation of context-free grammars to define constraints over the structure. In this paper, we propose a multi-step refinement approach, based on meta-models, offering several abstraction levels to reduce the size of the search space. More specifically, each refinement step is divided into two stages in which the desired shape of models is first described by context-sensitive constraints. In the second stage, model templates are instantiated by using global optimization techniques. We use our approach to a synthesize a set of optimal probabilistic models and show that context-sensitive constraints coupled with the multi-level abilities of the approach make the synthesis task more effective.", notes = "LISCO Laboratory - Department of computer science, Badji Mokthar Annaba University, PO BOX 12, Annaba, Algeria", } @InProceedings{benson:2000:E, author = "Karl Benson", title = "Evolving automatic target detection algorithms", booktitle = "Graduate Student Workshop", year = "2000", editor = "Conor Ryan and Una-May O'Reilly and William B. Langdon", pages = "249--252", address = "Las Vegas, Nevada, USA", month = "8 " # jul, keywords = "genetic algorithms, genetic programming", notes = "GECCO-2000WKS Part of \cite{wu:2000:GECCOWKS}", } @InProceedings{Benson:2000:GECCO, author = "Karl A Benson and David Booth and James Cubillo and Colin Reeves", title = "Automatic Detection of Ships in Spaceborne {SAR} Imagery", pages = "767", year = "2000", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)", editor = "Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer", address = "Las Vegas, Nevada, USA", publisher_address = "San Francisco, CA 94104, USA", month = "10-12 " # jul, keywords = "genetic algorithms, genetic programming, ANN, Poster", ISBN = "1-55860-708-0", URL = "http://gpbib.cs.ucl.ac.uk/gecco2000/RW002.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco2000/RW002.ps", size = "1 page", abstract = "This paper examines the evolution of automatic target detection algorithms and their application to the detection of shipping in spaceborne SAR imagery. ... . The FSM(GP) is clearly superior.", notes = "A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of \cite{whitley:2000:GECCO} ", } @InProceedings{benson:2000:efsmegpatdsi, author = "Karl A Benson", title = "Evolving Finite State Machines with Embedded Genetic Programming for Automatic Target Detection within SAR Imagery", booktitle = "Proceedings of the 2000 Congress on Evolutionary Computation CEC00", year = "2000", pages = "1543--1549", address = "La Jolla Marriott Hotel La Jolla, California, USA", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "6-9 " # jul, organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, image processing applications, Kohonen neural networks, automatic target detection, control structure, embedded genetic programming, evolving finite state machines, node cardinality, symbiotic relationship, embedded systems, finite state machines, object detection, self-organising feature maps", ISBN = "0-7803-6375-2", DOI = "doi:10.1109/CEC.2000.870838", abstract = "This paper presents a model comprising Finite State Machines (FSMs) with embedded Genetic Programs (GPs) which co-evolve to perform the task of Automatic Target Detection (ATD). The fusion of a FSM and GPs allows for a control structure (main program), the FSM, and sub-programs, the GPs, to co-evolve in a symbiotic relationship. The GP outputs along with the FSM state transition levels are used to construct confidence intervals that enable each pixel within the image to be classified as either target or non-target, or to cause a state transition to take place and further analysis of the pixel to be performed. The algorithms produced using this method consist of nominally four GPs, with a typical node cardinality of less than ten, that are executed in an order dictated by the FSM. The results of the experimentation performed are compared to those obtained in two independent studies of the same problem using Kohonen Neural Networks and a two stage Genetic Programming strategy.", notes = "CEC-2000 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 00TH8512, Library of Congress Number = 00-018644 ", } @InProceedings{benson:2000:PCEMMA, author = "Karl Benson", title = "Performing Classification with an Environment Manipulating Mutable Automata (EMMA)", booktitle = "Proceedings of the 2000 Congress on Evolutionary Computation CEC00", year = "2000", pages = "264--271", address = "La Jolla Marriott Hotel La Jolla, California, USA", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "6-9 " # jul, organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, system modelling and control, EMMA algorithm, classification, complex decision space, environment manipulating mutable automata, feature selection, finite state automata, hypersurface discriminant functions, object detection, symbiotic architecture, finite state machines, object detection, pattern classification", ISBN = "0-7803-6375-2", DOI = "doi:10.1109/CEC.2000.870305", size = "8 pages", abstract = "In this paper a novel approach to performing classification is presented. Hypersurface Discriminant functions are evolved using Genetic Programming. These discriminant functions reside in the states of a Finite State Automata, which has the ability to reason 1 and logically combine the hypersurfaces to generate a complex decision space. An object may be classified by one or many of the discriminant functions, this is decided by the automata. During the evolution of this symbiotic architecture, feature selection for each of the discriminant functions is achieved implicitly, a task which is normally performed before a classification algorithm is trained. Since each dis-criminant function has different features, and objects may be classified with one or more discriminant functions, no two objects from the same class need be classified using the same features. Instead, the most appropriate features for a given object are used.", notes = "CEC-2000 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 00TH8512, Library of Congress Number = 00-018644 ", } @InProceedings{benson4, author = "Karl A Benson and David Booth and James Cubillo and Colin Reeves", title = "On the use of evolution to construct finite state machines and mathematical functions to perform automatic target detection", booktitle = "Proceedings of the 3rd {IMA} conference on image processing: mathematical methods, algorithms and applications", year = "2000", address = "Leicester, UK", month = "13-15 " # sep, publisher = "IEE, Horwood Publishing Ltd", organisation = "The Institute of Mathematics and its Applications, The Institute of Physics, The Institute of Electrical Engineers", editor = "Martin J. Turner and Jonathan M. Blacklege", publisher_address = "Chichester, UK", keywords = "genetic algorithms, genetic programming", ISBN = "1-898563-72-1", URL = "http://www.amazon.co.uk/Image-Processing-III-Mathematical-Applications/dp/1898563721", notes = "Book derived from proceedings IMA http://www.ima.org.uk/ ", } @InProceedings{benson5, author = "Karl A Benson", title = "Evolving Automatic Target Detection Algorithms that logically Combine Decision Spaces", booktitle = "Proceedings of the 11th British Machine Vision Conference", year = "2000", pages = "685--694", address = "Bristol, UK", month = "11-14 " # sep, editor = "Majid Mirmehdi and Barry Thomas", publisher = "BMVA Press", keywords = "genetic algorithms, genetic programming, classification", ISBN = "1-901725-13-8", URL = "http://www.bmva.ac.uk/bmvc/2000/papers/p69.pdf", size = "10 pages", abstract = "In this paper a novel approach to performing classification is presented. Discriminant functions are constructed by combining selected features from the feature set with simple mathematical functions such as + - times divide max min. These discriminant functions are capable of forming nonlinear discontinuous hypersurfaces. For multimodal data more than one discriminant function maybe combined with logical operators before classification is performed. An algorithm capable of making decisions as to whether a combination of discriminant functions is needed to classify a data sample, or whether a single discriminant function will suffice, is developed. The algorithms used to perform classification are not written by a human. The algorithms are learnt, or rather evolved, using Evolutionary Computing techniques.", notes = " https://hdl.handle.net/1983/32ddefe3-ebe8-4571-b80b-4f7ad4f949d9", } @Misc{bensoussan2024acceleratingquantumeigensolveralgorithms, author = "Avner Bensoussan and Elena Chachkarova and Karine Even-Mendoza and Sophie Fortz and Connor Lenihan", title = "Accelerating Quantum Eigensolver Algorithms With Machine Learning", howpublished = "arXiv:2409.13587 v1", year = "2024", month = "20 " # sep, keywords = "genetic algorithms, genetic programming, genetic improvement, quant-ph", URL = "https://arxiv.org/abs/2409.13587", size = "19 pages", abstract = "we explore accelerating Hamiltonian ground state energy calculation on NISQ devices. We suggest using search-based methods together with machine learning to accelerate quantum algorithms, exemplified in the Quantum Eigensolver use case. We trained two small models on classically mined data from systems with up to 16 qubits, using XGBoost Python regressor. We evaluated our preliminary approach on 20-, 24- and 28-qubit systems by optimising the Eigensolver hyperparameters. These models predict hyperparameter values, leading to a 0.13-0.15 percent reduction in error when tested on 28-qubit systems. However, due to inconclusive results with 20- and 24-qubit systems, we suggest further examination of the training data based on Hamiltonian characteristics. In future work, we plan to train machine learning models to optimise other aspects or subroutines of quantum algorithm execution beyond its hyperparameters.", } @PhdThesis{Bensusan:thesis, author = "Hilan N. Bensusan", title = "Automatic bias learning: an inquiry into the inductive basis of induction", school = "University of Sussex", year = "1999", type = "D. Phil.", address = "UK", month = feb, keywords = "genetic algorithms, genetic programming, CIGA", URL = "http://www.cs.bris.ac.uk/Publications/Papers/1000410.pdf", URL = "https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.787184", size = "217 pages", abstract = "This thesis combines an epistemological concern about induction with a computational exploration of inductive mechanisms. It aims to investigate how inductive performance could be improved by using induction to select appropriate generalisation procedures. The thesis revolves around a meta-learning system, called The Entrencher, designed to investigate how inductive performances could be improved by using induction to select appropriate generalisation procedures. The performance of The Entrencher is discussed against the background of epistemological issues concerning induction, such as the role of theoretical vocabularies and the value of simplicity. After an introduction about machine learning and epistemological concerns with induction, Part I looks at learning mechanisms. It reviews some concepts and issues in machine learning and presents The Entrencher. The system is the first attempt to develop a learning system that induces over learning mechanisms through algorithmic representations of tasks. Part II deals with the need for theoretical terms in induction. Experiments where The Entrencher selects between different strategies for representation change are reported. The system is compared to other methods and some conclusions are drawn concerning how best to use the system. Part III considers the connection between simplicity and inductive success. Arguments for Occam's razor are considered and experiments are reported where The Entrencher is used to select when, how and how much a decision tree needs to be pruned. Part IV looks at some philosophical consequences of the picture of induction that emerges from the experiments with The Entrencher and goes over the motivations for meta-learning. Based on the picture of induction that emerges in the thesis, a new position in the scientific realism debate, transcendental surrealism, is proposed and defended. The thesis closes with some considerations concerning induction, justification and epistemological naturalism.", notes = "System in \cite{bensusan:1996:ciGP} called CIGA Constructive induction with a Genetic Algorithm COGS supervisor: Peter Williams", } @PhdThesis{Katie_Bentley:thesis, author = "Katie Anne Bentley", title = "Adaptive Behaviour through Morphological Plasticity in Natural and Artificial Systems", school = "Computer Science, UCL, University of London", year = "2006", address = "Gower Street, London, UK", keywords = "genetic algorithms, alife", URL = "http://discovery.ucl.ac.uk/1444539/1/U591845.pdf", size = "222 pages", notes = "Not GP. UMI U591845 Published by ProQuest Supervisor: Chris Clack", } @InProceedings{Bentley:1997:WSC2, author = "P. J. Bentley and J. P. Wakefield", title = "Generic Evolutionary Design", booktitle = "Soft Computing in Engineering Design and Manufacturing", year = "1997", editor = "Pravir K. Chawdhry and Rajkumar Roy and Raj K. Pant", publisher_address = "Godalming, GU7 3DJ, UK", month = "23-27 " # jun, publisher = "Springer-Verlag", pages = "289--298", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-76214-0", URL = "http://eprints.hud.ac.uk/4053/", URL = "http://www.springer.com/engineering/mechanical+eng/book/978-3-540-76214-0", DOI = "doi:10.1007/978-1-4471-0427-8_31", size = "10 pages", abstract = "Generic evolutionary design means the creation of a range of different designs by evolution. This paper introduces generic evolutionary design by a computer, describing a system capable of the evolution of a wide range of solid object designs from scratch, using a genetic algorithm. The paper reviews relevant literature, and outlines a number of advances necessitated by the development of the system, including: a new generic representation of solid objects, a new multiobjective fitness ranking method, and variable-length chromosomes. A library of modular evaluation software is also described, which allows a user to define new design problems quickly and easily by picking combinations of modules to guide the evolution of designs. Finally, the feasibility of generic evolutionary design by a computer is demonstrated by presenting the successful evolution of both conventional and unconventional designs for a range of different solid-object design tasks, e.g. tables, heatsinks, prisms, boat hulls, aerodynamic cars.", notes = "published 1998?", } @InProceedings{Bentley97, author = "P. J. Bentley and J. P. Wakefield", title = "Finding Acceptable Solutions in the {Pareto-Optimal} Range using Multiobjective Genetic Algorithms", booktitle = "Soft Computing in Engineering Design and Manufacturing", year = "1997", editor = "P. K. Chawdhry and R. Roy and R. K. Pant", pages = "231--240", publisher_address = "Godalming, GU7 3DJ, UK", month = "23-27 " # jun, publisher = "Springer-Verlag London", keywords = "genetic algorithms, MOGA", ISBN = "3-540-76214-0", URL = "http://eprints.hud.ac.uk/4052/", abstract = "This paper investigates the problem of using a genetic algorithm to converge on a small, user-defined subset of acceptable solutions to multiobjective problems, in the Pareto-optimal (P-O) range. The paper initially explores exactly why separate objectives can cause problems in a genetic algorithm (GA). A technique to guide the GA to converge on the subset of acceptable solutions is then introduced. The paper then describes the application of six multiobjective techniques (three established methods and three new, or less commonly used methods) to four test functions. The previously unpublished distribution of solutions produced in the P-O range(s) by each method is described. The distribution of solutions and the ability of each method to guide the GA to converge on a small, user-defined subset of P-O solutions is then assessed, with the conclusion that two of the new multiobjective ranking methods are most useful.", notes = "cited by \cite{Ross:2011:GPEM}. WSC2 Second On-line World Conference on Soft Computing in Engineering Design and Manufacturing", size = "10 pages", } @InProceedings{Bentley:1999:AVOCAAD, author = "Peter J. Bentley", title = "The Future of Evolutionary Design Research", booktitle = "AVOCAAD Second International Conference", year = "1999", pages = "349--350", address = "Brussels, Belgium", month = "8-10 " # apr, keywords = "genetic algorithms, genetic programming, Computer, design, International", URL = "http://cumincad.scix.net/cgi-bin/works/BrowseTree?field=series&separator=:&recurse=0&order=AZ&value=AVOCAAD", URL = "http://cumincad.scix.net/cgi-bin/works/Show?616c", URL = "http://papers.cumincad.org/data/works/att/616c.content.pdf", broken = "http://eprints.ucl.ac.uk/171652/", size = "2 pages", abstract = "The use of evolutionary algorithms to optimise designs is now well known, and well understood. The literature is overflowing with examples of designs that bear the hallmark of evolutionary optimisation: bridges, cranes, electricity pylons, electric motors, engine blocks, flywheels, satellite booms -the list is extensive and ever growing. But although the optimisation of engineering designs is perhaps the most practical and commercially beneficial form of evolutionary design for industry, such applications do not take advantage of the full potential of evolutionary design. Current research is now exploring how the related areas of evolutionary design such as evolutionary art, music and the evolution of artificial life can aid in the creation of new designs. By employing techniques from these fields, researchers are now moving away from straight optimisation, and are beginning to experiment with explorative approaches. Instead of using evolution as an optimiser, evolution is now beginning to be seen as an aid to creativity - providing new forms, new structures and even new concepts for designers.", notes = "Workshop on Morphogenetic Design, in the 2nd International Conference on Added Value of Computer Aided Architectural Design (AVOCAAD), Brussels, Belgium See also UCL CS technical report RN/99/12 http://www.cs.ucl.ac.uk/research/rns/rns99.html Sep 2018 616c.content.pdf suggests pages are 337--338", publicationstatus = "published", } @InProceedings{Bentley:1999:AISB, author = "P. J. Bentley", title = "Is evolution creative?", booktitle = "Proceedings of the AISB'99 Symposium on Creative Evolutionary Systems", year = "1999", editor = "P. J. Bentley and D. Corne", pages = "28--34", address = "Edinburgh", publisher = "The Society for the Study of Artificial Intelligence and Simulation of Behaviour", keywords = "genetic algorithms, genetic programming, gades, CE, sussex, System, systems", ISBN = "1-902956-03-6", URL = "http://www.cs.ucl.ac.uk/staff/P.Bentley/BEC6.pdf", publicationstatus = "published", size = "7 pages", abstract = "Can evolution demonstrate some of the properties of creativity? This paper argues that it can, and provides examples which the author feels illustrate some of the awesome power and feats of design which resemble creativity. Is evolution, then, truly creative? This is clearly a much harder question, for it requires a definition of creativity that most subjective and controversial of words. This paper explores and discusses various aspects of creativity, attempting to determine to what extent evolution satisfies each definition. The paper ends by summarising the discussion, and presenting amalgamations of four different worldviews.", notes = "The AISB'99 Convention took place in March 1999, hosted jointly by the University of Edinburgh and the Edinburgh College of Art", } @InProceedings{bentley:1999:TWGDACEEDP, author = "Peter Bentley and Sanjeev Kumar", title = "Three Ways to Grow Designs: A Comparison of Embryogenies for an Evolutionary Design Problem", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "35--43", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-611-4", URL = "http://www.cs.ucl.ac.uk/staff/p.bentley/BEKUC1.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-329.ps", URL = "http://dl.acm.org/citation.cfm?id=2933923.2933928", acmid = "2933928", size = "9 pages", abstract = "This paper explores the use of growth processes, or embryogenies, to map genotypes to phenotypes within evolutionary systems. Following a summary of the significant features of embryogenies, the three main types of embryogenies in Evolutionary Computation are then identified and explained: external, explicit and implicit. An experimental comparison between these three different embryogenies and an evolutionary algorithm with no embryogeny is performed. The problem set to the four evolutionary systems is to evolve tessellating tiles. In order to assess the scalability of the embryogenies, the problem is increased in difficulty by enlarging the size of tiles to be evolved. The results are surprising, with the implicit embryogeny outperforming all other techniques by showing no significant increase in the size of the genotypes or decrease in accuracy of evolution as the scale of the problem is increased.", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{bentley:1999:EA, author = "Peter J. Bentley", title = "Evolving fuzzy detectives: An investigation into the evolution of fuzzy rules", booktitle = "Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference", year = "1999", editor = "Scott Brave and Annie S. Wu", pages = "38--47", address = "Orlando, Florida, USA", month = "13 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/P.Bentley/BEC7.pdf", abstract = "This paper explores the use of genetic programming to evolve fuzzy rules for the purpose of fraud detection. The fuzzy rule evolver designed during this research is described in detail. Four key system evaluation criteria are identified: intelligibility, speed, handling noisy data, and accuracy. Three sets of experiments are then performed in order to assess the performance of different components of the system, in terms of these criteria. The paper concludes: 1. that many factors affect accuracy of classification, 2. intelligibility and processing speed mainly seem to be affected by the fuzzy membership functions and 3. noise can cause loss of accuracy proportionate to the square of noise.", notes = "GECCO-99LB, fraud detection, pre-GP 3-way clustering of each attribute multi-objective fitness function. variable size tree genotypes, bitstring in tree specifies input field, start small. Newer version available \cite{bentley:2000:EA} Iris and Wisconsin Breast Cancer. Perfomance falls lineraly or quadratically with noise.", } @Book{Bentley:evdes, editor = "Peter J. Bentley", title = "Evolutionary Design by Computers", publisher = "Morgan Kaufmann", year = "1999", keywords = "genetic algorithms, genetic programming, Computers", ISBN = "1-55860-605-X", isbn13 = "9781558606050", URL = "http://www.cs.ucl.ac.uk/staff/p.bentley/evdes.html", URL = "http://www.amazon.com/Evolutionary-Design-Computers-Peter-Bentley/dp/155860605X", abstract = "By bringing together the highest achievers in these fields for the first time, including a foreword by Richard Dawkins, this book provides the definitive ...", } @InCollection{Bentley:1999:intro, author = "Peter Bentley", title = "An introduction to evolutionary design by computers", booktitle = "Evolutionary Design by Computers", publisher = "Morgan Kaufman", year = "1999", editor = "Peter J. Bentley", chapter = "1", pages = "1--73", address = "San Francisco, USA", keywords = "genetic algorithms, genetic programming, Computer, Computers, design", notes = "Part of \cite{Bentley:evdes}", } @InProceedings{Bentley:1999:WSC, author = "Peter J. Bentley", booktitle = "Soft Computing in Industrial Applications", publisher = "Springer-Verlag London", title = "Evolving fuzzy detectives: an investigation into the evolution of fuzzy rules", year = "1999", editor = "Yukinori Suzuki and Seppo J. Ovaska and Takeshi Furuhashi and Rajkumar Roy and Yasuhiko Dote", pages = "89--106", month = sep, keywords = "genetic algorithms, genetic programming, evolution, fuzzy, industrial, industrial application, Rules", ISBN = "1-85233-293-X", URL = "http://www.cs.ucl.ac.uk/staff/P.Bentley/BECH4.pdf", URL = "http://www.amazon.com/Computing-Industrial-Applications-Yukinori-Suzuki/dp/185233293X", DOI = "doi:10.1007/978-1-4471-0509-1_8", size = "18 pages", abstract = "This paper explores the use of genetic programming to evolve fuzzy rules for the purpose of fraud detection. The fuzzy rule evolver designed during this research is described in detail. Four key system evaluation criteria are identified: intelligibility, speed, handling noisy data, and accuracy. Three sets of experiments are then performed in order to assess the performance of different components of the system, in terms of these criteria. The paper concludes: 1. that many factors affect accuracy of classification, 2. intelligibility and processing speed mainly seem to be affected by the fuzzy membership functions and 3. noise can cause loss of accuracy proportionate to the square of noise.", notes = "WSC4", } @InProceedings{Bentley:2000:ACDM, author = "P. J. Bentley", title = "Exploring component-based representations - the secret of creativity by evolution?", booktitle = "Evolutionary Design and Manufacture: Selected Papers from ACDM'00", year = "2000", editor = "I. C. Parmee", pages = "161--172", address = "University of Plymouth, Devon, UK", publisher_address = "Berlin/Heidelberg, Germany", month = apr, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, Adaptive, design", isbn13 = "9781852333003", URL = "http://www.cs.ucl.ac.uk/staff/P.Bentley/BEC9.pdf", URL = "http://www.springer.com/engineering/mechanical+eng/book/978-1-85233-300-3", URL = "http://www.springer.com/engineering/mechanical+engineering/book/978-1-85233-300-3", size = "12 pages", abstract = "This paper investigates one of the newest and most exciting methods in computer science to date: employing computers as creative problem solvers by using evolution to explore for new solutions. The paper introduces and discusses the new understanding that explorative evolution relies upon a representation based on components rather than a parameterisation of a known solution. Evolution explores how the components can be arranged, how many are needed, and the type or function of each. The extra freedom provided by this simple idea is remarkable. By using evolutionary computation for exploration instead of optimisation, this technique enables us to expand the capabilities of computers. The paper describes how the approach has already shown impressive results in the creation of novel designs and architecture, fraud detection, composition of music, and creation of art. A framework for explorative evolution is provided, with discussion of the significance and difficulties posed by each element. The paper ends with an example of creative problem solving for a simple application- showing how evolution can shape pieces of paper to make them fall slowly through the air, by spiraling down like sycamore seeds.", notes = "The Fourth International Conference on Adaptive Computing in Design and Manufacture (ACDM 2000) was held at the Evolutionary design and manufacture: selected papers from ACDM'00", } @InProceedings{Bentley:2000:EA, author = "Peter J. Bentley", title = "``Evolutionary, my dear Watson'' Investigating Committee-based Evolution of Fuzzy Rules for the Detection of Suspicious Insurance Claims", pages = "702--709", year = "2000", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)", editor = "Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer", address = "Las Vegas, Nevada, USA", publisher_address = "San Francisco, CA 94104, USA", month = "10-12 " # jul, keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-708-0", URL = "http://gpbib.cs.ucl.ac.uk/gecco2000/RW074.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco2000/RW074.ps", size = "8 pages", notes = "See also \cite{bentley:1999:EA} A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of \cite{whitley:2000:GECCO}", } @InProceedings{bentley:2001:NTEC, author = "Peter J. Bentley and Timothy Gordon and Jungwon Kim and Sanjeev Kumar", title = "New Trends in Evolutionary Computation", booktitle = "Proceedings of the 2001 Congress on Evolutionary Computation CEC2001", year = "2001", volume = "1", pages = "162--169", address = "COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "27-30 " # may, organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, new trends, creative evolution, computation embryology, evolvable hardware, artificial immune systems", ISBN = "0-7803-6658-1", DOI = "doi:10.1109/CEC.2001.934385", size = "8 pages", abstract = "In the last five years, the field of evolutionary computation (EC) has seen a resurgence of new ideas, many stemming from new biological inspirations. The paper outlines four of these new branches of research: creative evolutionary systems, computational embryology, evolvable hardware and artificial immune systems, showing how they aim to extend the capabilities of EC. Recent, unpublished results by researchers in each area at the Department of Computer Science, UCL are provided", notes = "gades CEC-2001 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 01TH8546C, Library of Congress Number =", } @InProceedings{Bentley:2001:geccowks, author = "Peter J. Bentley and Una-May O'Reilly", title = "Ten steps to make a perfect creative evolutionary design system", booktitle = "Non-Routine Design with Evolutionary Systems, GECCO-2001 Workshop", year = "2001", editor = "Peter Bentley and Mary Lou Maher and Josiah Poon", month = "7 " # jul, keywords = "genetic algorithms, genetic programming, Agency GP, design, evolutionary, SYSTEM, SYSTEMS, WORKSHOP", URL = "http://sydney.edu.au/engineering/it/~josiah/gecco_workshop_bentley.pdf", size = "7 pages", abstract = "A perfect creative evolutionary design system is impossible to achieve, but in this position paper we discuss 10 steps that might bring us a little closer to this dream. These important problems and requirements have been identified as a result of both authors' experiences on a number of projects in this area. While our solutions may not solve all of the problems, they illustrate what we regard as the current state of the art in creative evolutionary design.", notes = "broken Jun 2020 http://sydney.edu.au/engineering/it/~josiah/gecco2001_workshop_schedule.html", } @InCollection{bentley:2001:CES, author = "Peter J. Bentley and David W. Corne", title = "An Introduction to Creative Evolutionary Systems", booktitle = "Creative Evolutionary Systems", publisher = "Morgan Kaufmann", year = "2001", editor = "Peter J. Bentley and David W. Corne", pages = "1--75", month = jul, keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-673-4", URL = "http://www.sciencedirect.com/science/book/9781558606739", DOI = "doi:10.1016/B978-155860673-9/50035-5", abstract = "This chapter presents an overview of evolutionary algorithms explaining the main algorithms, and showing how all evolutionary algorithms are fundamentally the same. Evolutionary computation is all about search. In computer science and in artificial intelligence, when we use a search algorithm, we define a computational problem in terms of a search space, which can be viewed as a massive collection of potential solutions to the problem. Any position, or point, in the search space defines a particular solution, and the process of search can be viewed as the task of navigating that space. The chapter then defines and describes creative evolutionary systems, explaining why they were developed and how user interaction and changes of representation can expand the capabilities of evolution. The chapter also explores whether a creative evolutionary system can be said to actually work creatively. This chapter provides a mere introduction to the diversity of techniques that fall under the heading of creative evolutionary systems.", notes = "GP include amongst other EC techniques. Part of \cite{Bentley:2002:bookCES}", size = "75 pages", } @Book{Bentley:2002:bookCES, editor = "Peter Bentley and David Corne", title = "Creative evolutionary systems", year = "2002", publisher = "Morgan Kaufmann", address = "USA", keywords = "genetic algorithms, genetic programming, Computers", ISBN = "1-55860-673-4", isbn13 = "9781558606739", URL = "http://www.amazon.com/Creative-Evolutionary-Kaufmann-Artificial-Intelligence/dp/1558606734", abstract = "This book concentrates on applying important ideas in evolutionary computation to creative areas, such as art, music, architecture, and design.", notes = "Chapters on GP", } @Book{Bentley:2002:DB, author = "Peter J. Bentley", title = "Digital Biology. How Nature is Transforming Our Technology and Our Lives", publisher = "Simon and Schuster", year = "2002", address = "USA", keywords = "genetic algorithms, genetic programming, biology, digital, nature, technology", ISBN = "0-7432-0447-6", URL = "http://www.amazon.com/Digital-Biology-Peter-J-Bentley/dp/0743204476", notes = "Hardback", size = "272 pages", } @Article{bentley:2003:GPEM, author = "Peter J. Bentley and Jon Timmis", title = "Guest Editorial Special Issue on Artificial Immune Systems", journal = "Genetic Programming and Evolvable Machines", year = "2003", volume = "4", number = "4", pages = "307--309", month = dec, keywords = "artificial immune systems", ISSN = "1389-2576", DOI = "doi:10.1023/A:1026182810701", notes = "Special issue on artificial immune systems. Article ID: 5144845", } @Article{bentley:2004:GPEM, author = "Peter J. Bentley", title = "Fractal Proteins", journal = "Genetic Programming and Evolvable Machines", year = "2004", volume = "5", number = "1", pages = "71--101", month = mar, keywords = "genetic algorithms, fractal proteins, development, evolvability, scalability, complexity", ISSN = "1389-2576", DOI = "doi:10.1023/B:GENP.0000017011.51324.d2", abstract = "The fractal protein is a new concept intended to improve evolvability, scalability, exploitability and provide a rich medium for evolutionary computation. Here the idea of fractal proteins and fractal proteins with concentration levels are introduced, and a series of experiments showing how evolution can design and exploit them within gene regulatory networks is described.", notes = "Article ID: 5264735", } @Proceedings{DBLP:conf/icaris/2008, editor = "Peter J. Bentley and Doheon Lee and Sungwon Jung", title = "7th International Conference on Artificial Immune Systems, ICARIS 2008", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "5132", year = "2008", address = "Phuket, Thailand", month = aug # " 10-13", isbn13 = "978-3-540-85071-7", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "This book constitutes the refereed proceedings of the 7th International Conference on Artificial Immune Systems, ICARIS 2008, held in Phuket, Thailand, in ...", ISBN = "3-540-85071-6", isbn13 = "9783540850717", keywords = "Computers", } @InProceedings{Bentley:2017:ieeeSSCI, author = "P. J. Bentley and S. L. Lim", booktitle = "2017 IEEE Symposium Series on Computational Intelligence (SSCI)", title = "Fault tolerant fusion of office sensor data using cartesian genetic programming", year = "2017", abstract = "The Smart Grid of the future will enable a cleaner, more efficient and fault tolerant system of power distribution. Sensing power use and predicting demand is an important component in the Smart Grid. In this work, we describe a Cartesian Genetic Programming (CGP) system applied to a smart office. In the building, power usage is directly proportional to the number of people present. CGP is used to perform data fusion on the data collected from smart sensors embedded in the building in order to predict the number of people over a two-month period. This is a challenging task, as the sensors are unreliable, resulting in incomplete data. It is also challenging because in addition to normal staff, the building underwent renovation during the test period, resulting the presence of additional personnel who would not normally be present. Despite these difficult real-world issues, CGP was able to learn human-readable rules that when used in combination, provide a method for data fusion that is tolerant to the observed faults in the sensors.", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming", DOI = "doi:10.1109/SSCI.2017.8280827", month = nov, notes = "Also known as \cite{8280827}", } @Book{bentley:2024:AIchunks, author = "Peter J. Bentley", title = "Artificial Intelligence in Byte-sized Chunks", publisher = "Michael O'Mara", year = "2024", month = "20 " # jun, keywords = "genetic algorithms, genetic programming", isbn13 = "978-1789296563", URL = "https://www.amazon.co.uk/Artificial-Intelligence-Byte-sized-Chunks-Bite-Sized/dp/1789296560", size = "224 pages", abstract = "Artificial intelligence is headline news with the launch of the latest ChatGPT and Google Gemini. But when did we start making computers mimic the human mind? And what is the reality of the capabilities of AI now, and in the future?", } @InProceedings{benhahia:1997:GPvd, author = "Ilham Benyahia and J. Yves Potvin", title = "Genetic Programming for Vehicle Dispatch", booktitle = "Proceedings of the 1997 {IEEE} International Conference on Evolutionary Computation", year = "1997", pages = "547--552", address = "Indianapolis, USA", publisher_address = "Piscataway, NJ, USA", month = "13-16 " # apr, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICEC.1997.592371", abstract = "Vehicle dispatching is aimed at allocating real time service requests to a fleet of vehicles in movement. This task is modeled as a multiattribute choice problem. Namely, different attribute values are associated with each vehicle to describe its situation with respect to the current service request. Based on this attribute description, a utility function that approximates the decision process of a professional dispatcher is computed. This utility function evolves through genetic programming. Computational results are reported on requests collected from a courier service company", notes = "ICEC-97", } @Article{Benyahia:1998:SMC, author = "Ilham Benyahia and Jean-Yves Potvin", title = "Decision Support for Vehicle Dispatching Using Genetic Programming", journal = "IEEE Transactions on Systems, Man, and Cybernetics part A: systems and humans", year = "1998", volume = "28", number = "3", pages = "306--314", month = may, keywords = "genetic algorithms, genetic programming", URL = "http://ieeexplore.ieee.org/iel4/3468/14669/00668962.pdf", size = "9 pages", abstract = "Vehicle dispatching consists of allocating real-time service requests to a fleet of moving vehicles. In this paper, each vehicle is associated with a vector of attribute values that describes its current situation with respect to new incoming service requests. Using this attribute description, a utility function aimed at approximating the decision process of a professional dispatcher is constructed through genetic programming. Computational results are reported on requests collected from a courier service company and a comparison is provided with a neural network model and a simple dispatching policy.", } @InProceedings{conf/dms/BenyahiaT08, title = "Optimizing the Architecture of Adaptive Complex Applications Using Genetic Programming", author = "Ilham Benyahia and Vincent Talbot", publisher = "Knowledge Systems Institute", year = "2008", bibdate = "2009-06-06", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/dms/dms2008.html#BenyahiaT08", pages = "27--31", booktitle = "The 14th International Conference on Distributed Multimedia Systems, DMS'2008", address = "Hyatt Harborside at Logan Int'l Airport, Boston, USA", month = "4-6 " # sep, organisation = "Knowledge Systems Institute", keywords = "genetic algorithms, genetic programming", notes = "http://www.ksi.edu/seke/dms08.html See also \cite{Talbot:2010:IJSEA} http://citeseerx.ist.psu.edu/showciting?cid=11109136", } @PhdThesis{Benzeroual:thesis, author = "Karim Benzeroual", title = "Acquisition d'images steteoscopiques et calibration de cameras par algorithmes genetiques : application dans le domaine biomedical", title2 = "Stereoscopic image acquisition and camera calibration with genetic algorithm", school = "Ecole doctorale Sante, sciences, technologies, Tours", year = "2010", address = "France", month = "20 " # jul, keywords = "genetic algorithms", URL = "http://www.applis.univ-tours.fr/theses/2010/karim.benzeroual_3432.pdf", URL = "http://www.theses.fr/?q=benzeroual", URL = "http://www.theses.fr/2010TOUR4011", size = "215 pages", abstract = "This thesis Acquisition of stereoscopic images and camera calibration with genetic algorithms: application in the biomedical domain‖ consists to define a complete tool for acquiring the topography and texture of a 3D surface in order to apply it in the dermatological and more generally in the biomedical field. First, the objective is to study and to specify or design hardware devices such as professional cameras, assembly systems for stereo equipments, lighting system and trigger system for devices. Then, the thesis focuses on algorithmic and software aspects which relate to all mathematic and computational treatments needed to obtain a 3D surface. One of major issues addressed is the geometric calibration of stereo cameras. The developed approach pushes the limits of conventional methods in this field by proposing the use of more efficient and easier to implement optimization methods. We have shown that the algorithms using the principles of genetic algorithms can obtain more reliable results than their competitors and they can deal more easily the variable conditions of experiments. The real applications of our genetic algorithms for camera calibration cover many acquisition devices (industrial cameras, SLR cameras, stereo microscopes, beam splitters, pinhole and telecentric objectives), each acquisition device is adapted to a specific use following the requested study (microscopic areas, face or body parts). This thesis acquisition/calibration is a part of a global system called VirtualSkinLAB.", notes = "In French. Not GP? http://karim.benzeroual.com Supervisor: Gilles Venturini", } @Article{Berardi:2008:JH, author = "L. Berardi and Z. Kapelan and O. Giustolisi and D. A. Savic", title = "Development of pipe deterioration models for water distribution systems using {EPR}", journal = "Journal of Hydroinformatics", year = "2008", volume = "10", number = "2", pages = "113--126", keywords = "genetic algorithms, genetic programming, data-driven modelling, evolutionary polynomial regression, failure analysis, performance indicators, water systems", ISSN = "1464-7141", URL = "http://www.iwaponline.com/jh/010/0113/0100113.pdf", DOI = "doi:10.2166/hydro.2008.012", size = "14 pages", abstract = "The economic and social costs of pipe failures in water and wastewater systems are increasing, putting pressure on utility managers to develop annual replacement plans for critical pipes that balance investment with expected benefits in a risk-based management context. In addition to the need for a strategy for solving such a multi-objective problem, analysts and water system managers need reliable and robust failure models for assessing network performance. In particular, they are interested in assessing a conduit's propensity to fail and how to assign criticality to an individual pipe segment. pipe deterioration is modelled using Evolutionary Polynomial Regression. This data-driven technique yields symbolic formulae that are intuitive and easily understandable by practitioners. The case study involves a water quality zone within a distribution system and entails the collection of historical data to develop network performance indicators. Finally, an approach for incorporating such indicators into a decision support system for pipe rehabilitation/replacement planning is introduced and articulated.", notes = "Fig 5 bathtub curve Hydroinformatics Group, Technical University of Bari, via Orabona 4, I-70125, Bari, Italy", } @Article{Berardi:2009:WST, author = "L. Berardi and O. Giustolisi and D. A. Savic and Z. Kapelan", title = "An effective multi-objective approach to prioritisation of sewer pipe inspection", journal = "Water Science \& Technology", year = "2009", volume = "60", number = "4", pages = "841--850", keywords = "genetic algorithms, genetic programming, EPR, decision support, multi-objective optimisation, pipe inspection, prioritisation, sewer, CCTV", DOI = "doi:10.2166/wst.2009.432", size = "10 pages", abstract = "The first step in the decision making process for proactive sewer rehabilitation is to assess the condition of conduits. In a risk-based decision context the set of sewers to be inspected first should be identified based on the trade-off between the risk of failures and the cost of inspections. In this paper the most effective inspection works are obtained by solving a multi-objective optimisation problem where the total cost of the survey programme and the expected cost of emergency repairs subsequent to blockages and collapses are considered simultaneously. A multi-objective genetic algorithm (MOGA) is used to identify a set of Pareto-optimal inspection programmes. Regardless of the proven effectiveness of the genetic-algorithm approach, the scrutiny of MOGA-based inspection strategies shows that they can differ significantly from each other, even when having comparable costs. A post-processing of MOGA solutions is proposed herein, which allows priority to be assigned to each survey intervention. Results are of practical relevance for decision makers, as they represent the most effective sequence of inspection works to be carried out based on the available funds. The proposed approach is demonstrated on a real large sewer system in the UK.", notes = "Papers using GP related results WST, IWA Publishing Civil and Environmental Engineering Department, Technical University of Bari, via Orabona 4, 70125, Bari, Italy E-mail: l.berardi@poliba.it; o.giustolisi@poliba.it Centre for Water Systems, School of Engineering, Computing and Mathematics, University of Exeter, North Park Road, Exeter EX4 4QF, UK E-mail: d.savic@exeter.ac.uk; z.kapelan@exeter.ac.uk", } @InProceedings{DBLP:conf/maics/BerarducciJMS04, author = "Patrick Berarducci and Demetrius Jordan and David Martin and Jennifer Seitzer", title = "{GEVOSH}: Using Grammatical Evolution to Generate Hashing Functions", booktitle = "Proceedings of the Fifteenth Midwest Artificial Intelligence and Cognitive Sciences Conference, MAICS 2004", year = "2004", editor = "Eric G. Berkowitz", pages = "31--39", address = "Chicago, USA", month = apr # " 16-18", organisation = "Roosevelt University, Computer Science and Telecommunications", publisher = "Omnipress", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, genetic improvement", URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.3.4612.pdf", size = "8.5 pages", abstract = "In this paper, we present system GEVOSH, Grammatically Evolved Hashing. GEVOSH evolves hashing functions using grammatical evolution techniques. Hashing functions are used to expedite search in a wide number of domains. In our work, GEVOSH created hashing functions that, on average, perform better than many standard (human-generated) hash functions extracted from the literature. In this paper, we present the architecture of system GEVOSH, its main components and algorithms, and resultant generated hash functions along with comparisons to standard, human-generated functions.", notes = "Compares with six existing hash functions. Large variation? See \cite{berarducci:2004:ugw:pber}", bibsource = "DBLP, http://dblp.uni-trier.de", } @InProceedings{berarducci:2004:ugw:pber, author = "Patrick Berarducci and Demetrius Jordan and David Martin and Jennifer Seitzer", title = "{GEVOSH}: Using Grammatical Evolution to Generate Hashing Functions", editor = "R. Poli and S. Cagnoni and M. Keijzer and E. Costa and F. Pereira and G. Raidl and S. C. Upton and D. Goldberg and H. Lipson and E. {de Jong} and J. Koza and H. Suzuki and H. Sawai and I. Parmee and M. Pelikan and K. Sastry and D. Thierens and W. Stolzmann and P. L. Lanzi and S. W. Wilson and M. O'Neill and C. Ryan and T. Yu and J. F. Miller and I. Garibay and G. Holifield and A. S. Wu and T. Riopka and M. M. Meysenburg and A. W. Wright and N. Richter and J. H. Moore and M. D. Ritchie and L. Davis and R. Roy and M. Jakiela", booktitle = "GECCO 2004 Workshop Proceedings", year = "2004", month = "26-30 " # jun, address = "Seattle, Washington, USA", keywords = "genetic algorithms, genetic programming, grammatical evolution, genetic improvement", URL = "http://gpbib.cs.ucl.ac.uk/gecco2004/WUGW001.pdf", size = "4 pages", abstract = "In this paper, we present system GEVOSH, Grammatically Evolved Hashing. GEVOSH evolves hashing functions using grammatical evolution techniques. Hashing functions are used to expedite search in a wide number of domains. In our work, GEVOSH created hashing functions that, on average, perform better than many standard (human-generated) hash functions extracted from the literature. In this paper, we present the architecture of system GEVOSH, its main components and algorithms, and resultant generated hash functions along with comparisons to standard, human-generated functions.", notes = "see \cite{DBLP:conf/maics/BerarducciJMS04} GECCO-2004WKS Distributed on CD-ROM at GECCO-2004", } @Article{Beras:2025:Buildings, author = "Mitja Beras and Miran Brezocnik and Uros Zuperl and Miha Kovacic", title = "Developing an Alternative Calculation Method for the Smart Readiness Indicator Based on Genetic Programming and Linear Regression", journal = "Buildings", year = "2025", volume = "15", number = "10", pages = "Article no: 1675", month = "15 " # may, keywords = "genetic algorithms, genetic programming, SRI, modeling, linear regression, energy efficient buildings, smart buildings, optimisation", ISSN = "2075-5309", URL = "https://www.mdpi.com/2075-5309/15/10/1675", URL = "https://www.human-competitive.org/sites/default/files/humies_2025_entry_submission_-_beras_et_al_.txt", URL = "https://www.human-competitive.org/sites/default/files/buildings-15-01675-v2.pdf", DOI = "doi:10.3390/buildings15101675", size = "36 pages", abstract = "The European Union is planning to introduce a new tool for evaluating smart solutions in buildings—the Smart Readiness Indicator (SRI). As 54 energy efficiency categories must be evaluated, the triage process can be long and time-intensive. Altogether, 228 data points (or inputs) about the smartness of the buildings are required to complete the evaluation. The present paper proposes an alternative calculation method based on genetic programming (GP) for the calculation of Domains and linear regression (LR) for the calculation of Impact Factors and the total SRI score of the building. This novel calculation requires 20percent (Domain ventilation and dynamic building envelope) to 75percent (Domain cooling) fewer inputs than the original methodology. The present study evaluated 223 case study buildings, and 7 genetic programming models and 8 linear regression models were generated based on the results. The generated results are precise; the relative deviation from the experimental data for Domain scores (modeled with GP) ranged from 0.9percent to 2.9percent. The R2 for the LR models was 0.75 for most models (with two exceptions, with one with a value of 0.57 and the other with a value of 0.98). The developed method is scalable and could be used for preliminary and portfolio-level screening at early-stage assessments.", notes = "Entered 2025 HUMIES Also known as \cite{buildings15101675} Faculty of Mechanical Engineering, University of Maribor, 2000 Maribor, Slovenia", } @InProceedings{Beretta:2016:PDP, author = "S. Beretta and M. Castelli and Yuliana Martinez and Luis Munoz and Sara Silva and Leonardo Trujillo and Luciano Milanesi and Ivan Merelli", booktitle = "2016 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP)", title = "A Machine Learning Approach for the Integration of {miRNA}-Target Predictions", year = "2016", pages = "528--534", abstract = "Although several computational methods have been developed for predicting interactions between miRNA and target genes, there are substantial differences in the achieved results. For this reason, machine learning approaches are widely used for integrating the predictions obtained from different tools. In this work we adopt a method, called M3GP, which relies on a genetic programming approach, to classify results from three tools: miRanda, TargetScan, and RNAhybrid. Such algorithm is highly parallelisable and its adoption provides great advantages while handling problems involving big datasets, since it is independent from the implementation and from the architecture on which it is executed. More precisely, we apply this technique for the classification of the achieved miRNA target predictions and we compare its results with those obtained with other classifiers.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/PDP.2016.125", month = feb, notes = "Also known as \cite{7445385}", } @Article{Beretta:2018:complexity, author = "Stefano Beretta and Mauro Castelli and Luis Munoz and Leonardo Trujillo and Yuliana Martinez and Ales Popovic and Luciano Milanesi and Ivan Merelli", title = "A Scalable Genetic Programming Approach to Integrate {miRNA}-Target Predictions: Comparing Different Parallel Implementations of {M3GP}", journal = "Complexity", year = "2018", volume = "2018", pages = "Article ID 4963139", keywords = "genetic algorithms, genetic programming", URL = "http://downloads.hindawi.com/journals/complexity/2018/4963139.pdf", DOI = "doi:10.1155/2018/4963139", size = "13 pages", abstract = "There are many molecular biology approaches to the analysis of microRNA (miRNA) and target interactions, but the experiments are complex and expensive. For this reason, in silico computational approaches able to model these molecular interactions are highly desirable. Although several computational methods have been developed for predicting the interactions between miRNA and target genes, there are substantial differences in the results achieved since most algorithms provide a large number of false positives. Accordingly, machine learning approaches are widely used to integrate predictions obtained from different tools. In this work, we adopt a method called multidimensional multiclass GP with multidimensional populations (M3GP), which relies on a genetic programming approach, to integrate and classify results from different miRNA-target prediction tools. The results are compared with those obtained with other classifiers, showing competitive accuracy. Since we aim to provide genome-wide predictions with M3GP and, considering the high number of miRNA-target interactions to test (also in different species), a parallel implementation of this algorithm is recommended. In this paper, we discuss the theoretical aspects of this algorithm and propose three different parallel implementations. We show that M3GP is highly parallelisable, it can be used to achieve genome-wide predictions, and its adoption provides great advantages when handling big datasets.", } @InCollection{beretz:2002:EAMEABGP, author = "John P. Beretz", title = "Evolution of Algorithms for Multi-Species Emergent Assembly Behavior using Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2002", year = "2002", editor = "John R. Koza", pages = "21--30", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:2002:gagp}", } @InProceedings{Berge:2021:SBST, author = "Alexandre Bergel and Ignacio {Slater Munoz}", title = "Beacon: Automated Test Generation for Stack-Trace Reproduction using Genetic Algorithms", booktitle = "2021 IEEE/ACM 14th International Workshop on Search-Based Software Testing (SBST)", year = "2021", editor = "Jie M. Zhang and Erik Fredericks", pages = "1--7", address = "internet", month = "31 " # may, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, SBSE, Search-Based Software Testing, Automated Crash Reproduction, Dynamically Typed Languages, Python", isbn13 = "978-1-6654-4571-9/21/", URL = "https://drive.google.com/file/d/1fcL-M3GmBus2fnixe8zGNyS00crxV4a-/view", slide_url = "https://uchile-my.sharepoint.com/:p:/g/personal/ignacio_slater_uchile_cl/EUntuVTvl_1EiFVA3ubyrQwByC_FmNBs8r_8kyA-K97nKw?e=ZhU9UW", video_url = "https://drive.google.com/file/d/1FcavXIPaPcfZY4pu_y_DmgprHSZJfSzQ/view?usp=sharing", DOI = "doi:10.1109/SBST52555.2021.00007", size = "7 pages", abstract = "Software crashes are a problem all developers face eventually. Manually reproducing crashes can be very expensive and require a lot of effort. Recent studies have proposed techniques to automatically generate tests to detect and reproduce errors. But even if this topic has been widely studied, there has been little to no progress done for dynamically typed languages. This becomes important because current approaches take advantage of the type information inherent to statically typed languages to generate the sequence of instructions needed to reproduce a crash, thus making it unclear to judge if type information is necessary to reproduce errors. The lack of explicit type declarations in dynamic languages difficult the task of generating the instructions to replicate an error because the type checking can only be done during runtime, making algorithms less knowledgeable about the program and, therefore, making it more difficult to use search-based approaches because the algorithms have less information to work with.This paper presents a Genetic Algorithm approach to produce crash reproductions on Python based only on the information contained in the error stack-trace. An empirical study analysing three different experiments were evaluated giving mostly positive results, achieving a high precision while reproducing the desired crashes (over 70 percent). The study shows that the presented approach is independent of the kind of typing of the language, and provides a solid base to further develop the topic.", notes = "https://sbst21.github.io/program/", } @InCollection{Bergen:2010:GPTP, author = "Steven Bergen and Brian J. Ross", title = "Evolutionary Art Using Summed Multi-Objective Ranks", booktitle = "Genetic Programming Theory and Practice VIII", year = "2010", editor = "Rick Riolo and Trent McConaghy and Ekaterina Vladislavleva", series = "Genetic and Evolutionary Computation", volume = "8", address = "Ann Arbor, USA", month = "20-22 " # may, publisher = "Springer", chapter = "14", pages = "227--244", keywords = "genetic algorithms, genetic programming, evolutionary art, multi-objective optimization", isbn13 = "978-1-4419-7746-5", URL = "http://www.springer.com/computer/ai/book/978-1-4419-7746-5", DOI = "doi:10.1007/978-1-4419-7747-2_14", abstract = "This paper shows how a sum of ranks approach to multi-objective evaluation is effective for some low-order search problems, as it discourages the generation of outlier solutions. Outliers, which often arise with the traditional Pareto ranking strategy, tend to exhibit good scores on a minority of feature tests, while having mediocre or poor scores on the rest. They arise from the definition of Pareto dominance, in which an individual can be superlative in as little as a single objective in order to be considered undominated. The application considered in this research is evolutionary art, inwhich images are synthesized that adhere to an aesthetic model based on color gradient distribution. The genetic programming system uses 4 different fitness measurements, that perform aesthetic and color palette analyses. Outliers are usually undesirable in this application, because the color gradient distribution measurements requires 3 features to be satisfactory simultaneously. Sum of ranks scoring typically results in images that score better on the majority of features, and are therefore arguably more visually pleasing. Although the ranked sum strategy was originally inspired by highly dimensional problems having perhaps 20 objectives or more, this research shows that it is likewise practical for low-dimensional problems.", notes = "part of \cite{Riolo:2010:GPTP}", } @MastersThesis{Bergen:mastersthesis, author = "Steve Bergen", title = "Automatic Structure Generation using Genetic Programming and Fractal Geometry", school = "Brock University", year = "2011", keywords = "genetic algorithms, genetic programming", URL = "https://dr.library.brocku.ca/bitstream/handle/10464/3916/Brock_Bergen_Raphael_2011.pdf", URL = "http://hdl.handle.net/10464/3916", size = "127 pages", abstract = "Three dimensional model design is a well-known and studied field, with numerous real-world applications. However, the manual construction of these models can often be time-consuming to the average user, despite the advantages offered through computational advances. This thesis presents an approach to the design of 3D structures using evolutionary computation and L-systems, which involves the automated production of such designs using a strict set of fitness functions. These functions focus on the geometric properties of the models produced, as well as their quantifiable aesthetic value - a topic which has not been widely investigated with respect to 3D models. New extensions to existing aesthetic measures are discussed and implemented in the presented system in order to produce designs which are visually pleasing. The system itself facilitates the construction of models requiring minimal user initialization and no user-based feedback throughout the evolutionary cycle. The genetic programming evolved models are shown to satisfy multiple criteria, conveying a relationship between their assigned aesthetic value and their perceived aesthetic value. Exploration into the applicability and effectiveness of a multi-objective approach to the problem is also presented, with a focus on both performance and visual results. Although subjective, these results o er insight into future applications and study in the field of computational aesthetics and automated structure design.", } @InProceedings{Bergen:2012:EvoMUSART, author = "Steve Bergen and Brian Ross", title = "Aesthetic {3D} Model Evolution", booktitle = "Proceedings of the 1st International Conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design, EvoMUSART 2012", year = "2012", month = "11-13 " # apr, editor = "Penousal Machado and Juan Romero and Adrian Carballal", series = "LNCS", volume = "7247", publisher = "Springer Verlag", address = "Malaga, Spain", pages = "11--22", organisation = "EvoStar", isbn13 = "978-3-642-29141-8", DOI = "doi:10.1007/978-3-642-29142-5_2", keywords = "genetic algorithms, genetic programming, Aesthetics, L-systems, 3D models, multi-objective evaluation", abstract = "Recently, evolutionary art has been exploring the use of mathematical models of aesthetics, with the goal of automatically evolving aesthetically pleasing images. This paper investigates the application of similar models of aesthetics towards the evolution of 3-dimensional structures. We extend existing models of aesthetics used for image evaluation to the 3D realm, by considering quantifiable properties of surface geometry. Analyses used include entropy, complexity, deviation from normality, 1/f noise, and symmetry. A new 3D L-system implementation promotes accurate analyses of surface features, as well as productive rule sets when used with genetic programming. Multi-objective evaluation reconciles multiple aesthetic criteria. Experiments resulted in the generation of many models that satisfied multiple criteria. A human survey was conducted, and survey takers showed a clear preference for high-fitness highly-evolved models over low-fitness unevolved ones. This research shows that aesthetic evolution of 3D structures is a promising new research area for evolutionary art.", notes = "Part of \cite{Machado:2012:EvoMusArt_proc} EvoMUSART'2012 held in conjunction with EuroGP2012, EvoCOP2012, EvoBIO2012 and EvoApplications2012", } @Article{Bergen:2013:GPEM, author = "Steve Bergen and Brian J. Ross", title = "Aesthetic {3D} model evolution", journal = "Genetic Programming and Evolvable Machines", year = "2013", volume = "14", number = "3", pages = "339--367", month = sep, note = "Special issue on biologically inspired music, sound, art and design", keywords = "genetic algorithms, genetic programming, Aesthetics, L-systems, 3D models, Multi-objective evaluation", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-013-9187-8", size = "29 pages", abstract = "A new research frontier for evolutionary 2D image generation is the use of mathematical models of aesthetics, with the goal of automatically evolving aesthetically pleasing images. This paper investigates the application of similar models of aesthetics towards the evolution of 3-dimensional structures. We extend existing models of aesthetics used for image evaluation to the 3D realm, by considering quantifiable properties of surface geometry. Analyses used include entropy, complexity, deviation from normality, 1/f noise, and symmetry. A new 3D L-system implementation promotes accurate analyses of surface features, as well as productive rule sets when used with genetic programming. Multi-objective evaluation reconciles multiple aesthetic criteria. Experiments resulted in the generation of many models that satisfied multiple criteria. A human survey was conducted, and survey takers showed a statistically significant preference for high-fitness highly-evolved models over low-fitness unevolved ones. This research shows that aesthetic evolution of 3D structures is a promising new research area for evolutionary design.", } @InProceedings{berger:1999:AHGAVRPTWIC, author = "Jean Berger and Mourad Sassi and Martin Salois", title = "A Hybrid Genetic Algorithm for the Vehicle Routing Problem with Time Windows and Itinerary Constraints", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "44--51", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InCollection{berger:2002:DMILFAGP, author = "Eric Berger", title = "Development of a Minimal Information Line Following Algorithm using Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2002", year = "2002", editor = "John R. Koza", pages = "31--35", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", URL = "http://www.genetic-programming.org/sp2002/Berger.pdf", notes = "part of \cite{koza:2002:gagp}", } @InProceedings{bergstrom:2000:eiraatrGP, author = "Agneta Bergstrom and Patricija Jaksetic and Peter Nordin", title = "Enhancing Information Retrieval by Automatic Acquisition of Textual Relations using Genetic Programming", booktitle = "IUI 2000", year = "2000", publisher = "ACM Press", keywords = "genetic algorithms, genetic programming, machine learning, natural language processing, semantic networks, information retrieval", URL = "http://web.media.mit.edu/~lieber/IUI/Bergstrom/Bergstrom.pdf", size = "4 pages", abstract = "We have explored a novel method to find textual relations in electronic documents using genetic programming and semantic networks. This can be used for enhancing information retrieval and simplifying user interfaces. The automatic extraction of relations from text enables easier updating of electronic dictionaries and may reduce interface area both for search input and hit output on small screens such as cell phones and PDAs (Personal Digital Assistants).", notes = "www", } @InProceedings{bergstrom:2000:atrawGP, author = "Agneta Bergstrom and Patricija Jaksetic and Peter Nordin", title = "Acquiring Textual Relations Automatically on the Web using Genetic Programming", booktitle = "Genetic Programming, Proceedings of EuroGP'2000", year = "2000", editor = "Riccardo Poli and Wolfgang Banzhaf and William B. Langdon and Julian F. Miller and Peter Nordin and Terence C. Fogarty", volume = "1802", series = "LNCS", pages = "237--246", address = "Edinburgh", publisher_address = "Berlin", month = "15-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-67339-3", DOI = "doi:10.1007/978-3-540-46239-2_17", abstract = "The flood of electronic information is pouring over us, while the technology maintaining the information and making it available to us has not yet been able to catch up. One of the paradigms within information retrieval focuses on the use of thesauruses to analyse contextual/structural information. We have explored a method that automatically finds textual relations in electronic documents using genetic programming and semantic networks. Such textual relations can be used to extend and update thesauruses as well as semantic networks. The program is written in PROLOG and communicates with software for natural language parsing. The system is also an example of computationally expensive fitness function using a large database. The results from the experiment show feasibility for this type of automatic relation extraction.", notes = "EuroGP'2000, part of \cite{poli:2000:GP}", } @Article{beriro:2012:WRM, author = "Darren J. Beriro and Robert J. Abrahart and Nick J. Mount and C. Paul Nathanail", title = "Letter to the Editor on ``Precipitation Forecasting Using Wavelet-Genetic Programming and Wavelet-Neuro-Fuzzy Conjunction Models'' by Ozgur Kisi \& Jalal Shiri [Water Resources Management 25 (2011) 3135-3152]", journal = "Water Resources Management", year = "2012", volume = "26", number = "12", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s11269-012-0049-6", DOI = "doi:10.1007/s11269-012-0049-6", notes = "See \cite{Kisi:2011:WRM} and \cite{kisi:2012:WRM}", } @Article{Beriro2012, author = "Darren J. Beriro and Robert J. Abrahart and C. Paul Nathanail", title = "'Comparison of genetic programming with neuro-fuzzy systems for predicting short-term water table depth fluctuations' by Jalal Shir and Ozgur Kisi [Computers and Geosciences (2011) 1692-1701]", journal = "Computer \& Geosciences", volume = "56", pages = "216--220", year = "2013", note = "Letter to the Editor", ISSN = "0098-3004", DOI = "doi:10.1016/j.cageo.2012.04.014", URL = "http://www.sciencedirect.com/science/article/pii/S0098300412001379?v=s5", URL = "http://www.sciencedirect.com/science/article/pii/S0098300412001379", keywords = "genetic algorithms, genetic programming, Gene Expression Programming, Groundwater depth fluctuation, Neuro-fuzzy, Data-driven modelling, Time series analyse", abstract = "This letter is not a request for the original authors to fill gaps or clarify discrepancies noted in their research although such additional material would of course be welcomed. Rather, it highlights that GEP and perhaps data-driven modelling more generally would benefit greatly from the development of a sound application protocol incorporating checks and balances, informed by consensus, for training, testing and reporting of complex procedures and solutions. The original authors' thoughts on our general as well as specific issues would be warmly welcomed.", notes = "Shir article is \cite{Shiri2010}", } @Article{Beriro:2013:CG, author = "Darren J. Beriro and Robert J. Abrahart and C. Paul Nathanail", title = "'Comparison of genetic programming with neuro-fuzzy systems for predicting short-term water table depth fluctuations' by Jalal Shir \& Ozgur Kisi [Computers and Geosciences (2011) 1692-1701]", journal = "Computer \& Geosciences", volume = "56", pages = "216--220", year = "2013", keywords = "genetic algorithms, genetic programming", ISSN = "0098-3004", DOI = "doi:10.1016/j.cageo.2012.04.014", URL = "http://www.sciencedirect.com/science/article/pii/S0098300412001379", } @InProceedings{Berlanga:2005:GFS, title = "Learning fuzzy rules using Genetic Programming: Context-free grammar definition for high-dimensionality problems", author = "Francisco Jose Berlanga and Maria Jose {Del Jesus} and Francisco Herrera", booktitle = "International workshop on Genetic Fuzzy System, GFS 2005", year = "2005", editor = "Oscar Cordon and Francisco Herrera", keywords = "genetic algorithms, genetic programming", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.415.2932", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.415.2932", URL = "http://sci2s.ugr.es/keel/pdf/specific/congreso/gfs2005.pdf", broken = "http://sci2s.ugr.es/publications/ficheros/berlanga_deljesus_herrera_GFS05.pdf", size = "5 pages", abstract = "The inductive learning of a fuzzy rule-based classification system (FRBCS) with high interpretability is made difficult by the presence of a large number of features that increases the dimensionality of the problem being solved. The difficult comes from the exponential growth of the fuzzy rule search space with the increase in the number of features considered. In this work we tackle this problem, the FRBCS learning with high interpretability for high-dimensionality problems. We propose a genetic-programming-based method, where the evolved disjunctive normal form fuzzy rules compete in order to obtain an FRBCS with high interpretability (few rules and few antecedent conditions per rule) while maintaining a good performance.", } @InProceedings{Berlanga:2006:ICAISC, author = "F. J. Berlanga and M. J. {del Jesus} and M. J. Gacto and F. Herrera", title = "A Genetic-Programming-Based Approach for the Learning of Compact Fuzzy Rule-Based Classification Systems", booktitle = "Proceedings 8th International Conference on Artificial Intelligence and Soft Computing {ICAISC}", year = "2006", pages = "182--191", series = "Lecture Notes on Artificial Intelligence (LNAI)", volume = "4029", publisher = "Springer-Verlag", editor = "Leszek Rutkowski and Ryszard Tadeusiewicz and Lotfi A. Zadeh and Jacek Zurada", address = "Zakopane, Poland", month = jun # " 25-29", bibdate = "2006-07-05", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icaisc/icaisc2006.html#BerlangaJGH06", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-35748-3", DOI = "doi:10.1007/11785231_20", size = "10 pages", abstract = "In the design of an interpretable fuzzy rule-based classification system (FRBCS) the precision as much as the simplicity of the extracted knowledge must be considered as objectives. In any inductive learning algorithm, when we deal with problems with a large number of features, the exponential growth of the fuzzy rule search space makes the learning process more difficult. Moreover it leads to an FRBCS with a rule base with a high cardinality. In this paper, we propose a genetic-programming-based method for the learning of an FRBCS, where disjunctive normal form (DNF) rules compete and cooperate among themselves in order to obtain an understandable and compact set of fuzzy rules, which presents a good classification performance with high dimensionality problems. This proposal uses a token competition mechanism to maintain the diversity of the population. The good results obtained with several classification problems support our proposal.", } @InProceedings{Berlanga:2008:GEFS, author = "Francisco Jose Berlanga and Maria Jose {del Jesus} and Francisco Herrera", title = "A novel genetic cooperative-competitive fuzzy rule based learning method using genetic programming for high dimensional problems", booktitle = "3rd International Workshop on Genetic and Evolving Fuzzy Systems, GEFS 2008", year = "2008", month = "4-7 " # mar, address = "Witten-Boommerholz, Germany", pages = "101--106", keywords = "genetic algorithms, genetic programming, genetic cooperative-competitive fuzzy rule based learning method, high dimensional classification problems, high dimensional problems, token competition mechanism, fuzzy set theory, knowledge based systems, learning (artificial intelligence)", DOI = "doi:10.1109/GEFS.2008.4484575", abstract = "In this contribution, we present GP-COACH, a novel GFS based on the cooperative-competitive learning approach, that uses genetic programming to code fuzzy rules with a different number of variables, for getting compact and accurate rule bases for high dimensional problems. GP-COACH learns disjunctive normal form rules (generated by means of a context-free grammar) and uses a token competition mechanism to maintain the diversity of the population. It makes the rules compete and cooperate among themselves, giving out a compact set of fuzzy rules that presents a good performance. The good results obtained in an experimental study involving several high dimensional classification problems support our proposal.", notes = "Also known as \cite{4484575}", } @Article{Berlanga20101183, author = "F. J. Berlanga and A. J. Rivera and M. J. {del Jesus} and F. Herrera", title = "GP-COACH: Genetic Programming-based learning of COmpact and ACcurate fuzzy rule-based classification systems for High-dimensional problems", journal = "Information Sciences", volume = "180", number = "8", pages = "1183--1200", year = "2010", ISSN = "0020-0255", DOI = "doi:10.1016/j.ins.2009.12.020", URL = "http://www.sciencedirect.com/science/article/B6V0C-4Y34R0J-1/2/82039ab1549f5a0d0fc4d73b2a30bfa6", keywords = "genetic algorithms, genetic programming, Classification, Fuzzy rule-based systems, Genetic fuzzy systems, High-dimensional problems, Interpretability-accuracy trade-off", abstract = "In this paper we propose GP-COACH, a Genetic Programming-based method for the learning of COmpact and ACcurate fuzzy rule-based classification systems for High-dimensional problems. GP-COACH learns disjunctive normal form rules (generated by means of a context-free grammar) coded as one rule per tree. The population constitutes the rule base, so it is a genetic cooperative-competitive learning approach. GP-COACH uses a token competition mechanism to maintain the diversity of the population and this obliges the rules to compete and cooperate among themselves and allows the obtaining of a compact set of fuzzy rules. The results obtained have been validated by the use of non-parametric statistical tests, showing a good performance in terms of accuracy and interpretability.", } @InProceedings{bermejo:2024:CEC, author = "Enrique Bermejo and Oscar Cordon and Javier Irurita and Inmaculada Aleman and Angel Rubio Salvador", title = "Age-at-Death Estimation based on Symbolic Regression Ensemble Learning from Multiple Annotations", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Uncertainty, Accuracy, Annotations, Forensics, Decision making, Predictive models, Mathematical models, Age-at-death estimation, Ensemble learning, Symbolic regression", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10611921", abstract = "The present study addresses the problem of semiautomatic age-at-death estimation from pubic symphysis, a crucial yet complex task in forensic anthropology. Its accuracy directly depends on the quality of the pubic bone trait labeling developed by the forensic practitioners, affected by an inherent uncertainty in their definition. As interpretability is a mandatory requirement, we propose an approach where the model design is based on evolutionary learning, considering genetic programming to frame the problem as a symbolic regression task. Additionally, ensemble learning is considered to address the challenges posed by noise, uncertainty, and conflicting annotations inherent in data collected from multiple subjects. Ensemble learning provides an effective approach to navigate these challenges by facilitating consensus-building through decision making and information fusion. Hence, observer committees are formed, comprising multiple forensic specialists with different skills and expertise which provide alternative annotations. Several ensemble configurations combining different weak learners and aggregation operators are tested to assess their effectiveness in improving accuracy and reliability in age-at-death predictions. Their performance is compared against models trained on single annotations, revealing an improvement in predictive accuracy. The obtained results also highlight the benefits of incorporating diverse perspectives to address the complexities associated with human variability and anatomical assessments.", notes = "also known as \cite{10611921} WCCI 2024", } @InProceedings{Bernabe-Rodriguez:2020:PPSN, author = "Amin V. {Bernabe Rodriguez} and Carlos A. {Coello Coello}", title = "Generation of New Scalarizing Functions Using Genetic Programming", booktitle = "16th International Conference on Parallel Problem Solving from Nature, Part II", year = "2020", editor = "Thomas Baeck and Mike Preuss and Andre Deutz and Hao Wang2 and Carola Doerr and Michael Emmerich and Heike Trautmann", volume = "12270", series = "LNCS", pages = "3--17", address = "Leiden, Holland", month = "7-9 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Multi-objective optimization, Scalarizing functions", isbn13 = "978-3-030-58114-5", DOI = "doi:10.1007/978-3-030-58115-2_1", abstract = "In recent years, there has been a growing interest in multiobjective evolutionary algorithms (MOEAs) with a selection mechanism different from Pareto dominance. This interest has been mainly motivated by the poor performance of Pareto-based selection mechanisms when dealing with problems having more than three objectives (the so-called many-objective optimization problems). Two viable alternatives for solving many-objective optimization problems are decomposition-based and indicator-based MOEAs. However, it is well-known that the performance of decomposition-based MOEAs (and also of indicator-based MOEAs designed around R2) heavily relies on the scalarising function adopted. In this paper, we propose an approach for generating novel scalarizing functions using genetic programming. Using our proposed approach, we were able to generate two new scalarizing functions (called AGSF1 and AGSF2), which were validated using an indicator-based MOEA designed around R2 (MOMBI-II). This validation was conducted using a set of standard test problems and two performance indicators (hypervolume and s-energy). Our results indicate that AGSF1 has a similar performance to that obtained when using the well-known Achievement Scalarizing Function (ASF). However, AGSF2 provided a better performance than ASF in most of the test problems adopted. Nevertheless, our most remarkable finding is that genetic programming can indeed generate novel (and possible more competitive) scalarizing functions.", notes = "PPSN2020", } @InProceedings{bernabe-rodriguez:2023:GEWS2023, author = "Amin V. {Bernabe Rodriguez} and Carlos A. {Coello Coello}", title = "Designing Scalarizing Functions Using Grammatical Evolution", booktitle = "Grammatical Evolution Workshop - 25 years of GE", year = "2023", editor = "Conor Ryan and Mahsa Mahdinejad and Aidan Murphy", pages = "2004--2012", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, grammatical evolution, multi-objective optimization, scalarizing functions", isbn13 = "9798400701191", DOI = "doi:10.1145/3583133.3596354", size = "9 pages", abstract = "In this paper, we present a grammatical evolution-based framework to produce new scalarizing functions, which are known to have a significant impact on the performance of both decomposition-based multi-objective evolutionary algorithms (MOEAs) and indicator-based MOEAs which use R2. We perform two series of experiments using this framework. First, we produce many scalarizing functions using different benchmark problems to explore the behavior of the resulting functions according to the geometry of the problem adopted to generate them. Then, we perform a second round of experiments adopting two combinations of problems which yield better results in some test instances. We present the experimental validation of these new functions compared against the Achievement Scalarizing Function (ASF), which is known to provide a very good performance. For this comparative study, we adopt several benchmark problems and we are able to show that our proposal is able to generate new scalarizing functions that can outperform ASF in different problems.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @Article{BERNABERODRIGUEZ:2024:swevo, author = "Amin V. {Bernabe Rodriguez} and Braulio I. Alejo-Cerezo and Carlos A. {Coello Coello}", title = "Improving multi-objective evolutionary algorithms using Grammatical Evolution", journal = "Swarm and Evolutionary Computation", year = "2024", volume = "84", pages = "Article Number: 101434", keywords = "genetic algorithms, genetic programming, Grammatical evolution, Evolutionary algorithms, Multi-objective optimization", ISSN = "2210-6502", URL = "http://delta.cs.cinvestav.mx/~ccoello/journals/amin-swevo-final.pdf.gz", URL = "https://www.sciencedirect.com/science/article/pii/S2210650223002067", DOI = "doi:10.1016/j.swevo.2023.101434", size = "54 pages", abstract = "Multi-objective evolutionary algorithms (MOEAs) have become an effective choice to solve multi-objective optimization problems (MOPs). However, it is well known that Pareto dominance-based MOEAs struggle in MOPs with four or more objective functions due to a lack of selection pressure in high dimensional spaces. The main choices for dealing with such problems are decomposition-based and indicator-based MOEAs. In this work, we propose the use of Grammatical Evolution (an evolutionary computation search technique) to generate functions that can improve decomposition-based and indicator-based MOEAs. Namely, we propose a methodology to generate new scalarizing functions, which are known to have a great impact in the performance of decomposition-based MOEAs and in some indicator-based MOEAs. Additionally, we propose another methodology to generate hypervolume approximations, since the hypervolume is a popular performance indicator used not only in indicator-based MOEAs but also to assess performance of MOEAs. Using our first methodology, we generate two new scalarizing functions and provide their corresponding experimental validation to show that they exhibit a competitive behavior when compared against some well-known scalarizing functions such as ASF, PBI and the Tchebycheff scalarizing function. Using our second methodology, we produce 4 different hypervolume approximations and compare their performance against the Monte Carlo method and against two other state-of-the-art hypervolume approximations. The experimental results show that our functions exhibit a good compromise in terms of quality and execution time", } @InProceedings{Bernal-Urbina:2008:ijcnn, author = "M. Bernal-Urbina and A. Flores-Mendez", title = "Time Series Forecasting through Polynomial Artificial Neural Networks and Genetic Programming", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "3325--3330", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1821-3", file = "NN0903.pdf", DOI = "doi:10.1109/IJCNN.2008.4634270", ISSN = "1098-7576", abstract = "The Polynomial Artificial Neural Network (PANN) has shown to be a powerful Network for time series forecasting. Moreover, the PANN has the advantage that it encodes the information about the nature of the time series in its architecture. However, the problem with this type of network is that the terms needed to be analysed grow exponentially depending on the degree selected for the polynomial approximation. In this paper, a novel optimisation algorithm that determines the architecture of the PANN through Genetic Programming is presented. Some examples of non linear time series are included and the results are compared with those obtained by PANN with Genetic Algorithm.", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{4634270}. WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @InProceedings{Bernard:2020:ICTAI, author = "Jason Bernard and Ian McQuillan", title = "Inferring Temporal Parametric L-systems Using Cartesian Genetic Programming", booktitle = "2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI)", year = "2020", pages = "580--588", abstract = "Lindenmayer Systems (L-systems) are formal grammars that use rewriting rules to replace, in parallel, every symbol in a string with a replacement string. By iterating, a sequence of strings is produced whose symbols can model temporal processes by interpreting them as simulation instructions. Among the types of L-systems, parametric L-systems are considered useful for simulating mechanisms that change based on different influences as the parameters change. Typically, L-systems are found by taking precise measurements and using existing knowledge, which can be addressed by automatic inference. This paper presents the Plant Model Inference Tool for Parametric L-systems (PMIT-PARAM) that can automatically learn parametric L-systems from a sequence of strings generated, where at least one parameter represents time. PMIT-PARAM is evaluated on a test suite of 20 known parametric L-systems, and is found to be able to infer the correct rewriting rules for the 18 L-systems containing only non-erasing productions; however, it can find appropriate parametric equations for all 20 of the L-systems. Inferring L-systems algorithmically not only can automatically learn models and simulations of a process with potentially less effort than doing so by hand, but it may also help reveal the scientific principles governing how the process' mechanisms change over time.", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", DOI = "doi:10.1109/ICTAI50040.2020.00095", ISSN = "2375-0197", month = nov, notes = "Also known as \cite{9288168}", } @InProceedings{Bernard:2006:ECML, author = "Marc Bernard and Amaury Habrard and Marc Sebban", title = "Learning Stochastic Tree Edit Distance", booktitle = "Machine Learning: ECML 2006", year = "2006", series = "Lecture Notes in Computer Science", editor = "Johannes Furnkranz and Tobias Scheffer and Myra Spiliopoulou", publisher = "Springer", pages = "42--53", volume = "4212", DOI = "doi:10.1007/11871842_9", abstract = "Trees provide a suited structural representation to deal with complex tasks such as web information extraction, RNA secondary structure prediction, or conversion of tree structured documents. In this context, many applications require the calculation of similarities between tree pairs. The most studied distance is likely the tree edit distance (ED) for which improvements in terms of complexity have been achieved during the last decade. However, this classic ED usually uses a priori fixed edit costs which are often difficult to tune, that leaves little room for tackling complex problems. In this paper, we focus on the learning of a stochastic tree ED. We use an adaptation of the Expectation-Maximization algorithm for learning the primitive edit costs. We carried out series of experiments that confirm the interest to learn a tree ED rather than a priori imposing edit costs.", notes = "not GP but cited by \cite{mcdermott:2011:EuroGP}", affiliation = "EURISE, Universite Jean Monnet de Saint-Etienne, 23, rue Paul Michelon, 42023 cedex 2 Saint-Etienne, France", } @InProceedings{Bernardi:2006:CEC, author = "P. Bernardi and E. Sanchez and M. Schillaci and G. Squillero and M. {Sonza Reorda}", title = "An Evolutionary Methodology to Enhance Processor Software-Based Diagnosis", booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary Computation", year = "2006", editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas", pages = "3201--3206", address = "Vancouver", month = "16-21 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, microGP", ISBN = "0-7803-9487-9", DOI = "doi:10.1109/CEC.2006.1688401", size = "6 pages", abstract = "The widespread use of cheap processor cores requires the ability to quickly point out the manufacturing process criticalities in an effort to enhance the production yield. Fault diagnosis is an integral part of the industrial effort towards these goals. This paper describes an innovative application of evolutionary algorithms: iterative refinement of a diagnostic test set. Several enhancements in the used evolutionary core are additionally outlined, highlighting their relevance for the specific problem. Experimental results are reported in the paper showing the effectiveness of the approach for a widely-known microcontroller core.", notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D IEEE Xplore gives pages = {"}859--864{"},", } @InProceedings{Bernardino:2009:ICARIS, author = "Heder S. Bernardino and Helio J. C. Barbosa", title = "Grammar-Based Immune Programming for Symbolic Regression", booktitle = "Proceedings of the 8th International Conference on Artificial Immune Systems (ICARIS)", year = "2009", editor = "Paul S. Andrews and Jon Timmis and Nick D. L. Owens and Uwe Aickelin and Emma Hart and Andrew Hone and Andy M. Tyrrell", volume = "5666", series = "Lecture Notes in Computer Science", pages = "274--287", address = "York, UK", month = aug # " 9-12", publisher = "Springer", keywords = "genetic algorithms, genetic programming, grammatical evolution, Artificial immune system, immune programming, symbolic regression", isbn13 = "978-3-642-03245-5", language = "English", URL = "http://dx.doi.org/10.1007/978-3-642-03246-2_26", DOI = "doi:10.1007/978-3-642-03246-2_26", abstract = "This paper presents a Grammar-based Immune Programming (GIP) that can evolve programs in an arbitrary language using a clonal selection algorithm. A context-free grammar that defines this language is used to decode candidate programs (antibodies) to a valid representation. The programs are represented by tree data structures as the majority of the program evolution algorithms do. The GIP is applied to symbolic regression problems and the results found show that it is competitive when compared with other algorithms from the literature.", } @InProceedings{Bernardino:2010:CILAMCE, author = "Heder Soares Bernardino and Helio Jose Correa Barbosa", title = "Comparing two ways of inferring a differential equation model via Grammar-based Immune Programming", booktitle = "Proceedings of the Iberian Latin American Congress on Computational Methods in Engineering (CILAMCE)", year = "2010", editor = "Eduardo Dvorkin and Marcela Goldschmit and Mario Storti", pages = "9107--9124", address = "Buenos Aires", month = nov # " 15-18", organisation = "University of Buernos Aires", publisher = "Asociacion Argentina de Mecanica Computacional http://www.amcaonline.org.ar", keywords = "genetic algorithms, genetic programming, grammatical evolution, Artificial immune systems, grammar-based immune programming, symbolic regression, model inference", URL = "http://www.cimec.org.ar/ojs/index.php/mc/article/view/3656/3569", size = "18 pages", abstract = "An ordinary differential equation (ODE) is a mathematical form to describe physical or biological systems composed by time-derivatives of physical positions or chemical concentrations as a function of its current state. Given observed pairs, a relevant modelling problem is to find the symbolic expression of a differential equation which mathematically describes the concerned phenomenon. The Grammar-based Immune Programming (GIP) is a method for evolving programs in an arbitrary language by immunological inspiration. A program can be a computer program, a numerical function in symbolic form, or a candidate design, such as an analogue circuit. GIP can be used to solve symbolic regression problems in which the objective is to find an analytical expression of a function that better fits a given data set. At least two ways are available to solve model inference problems in the case of ordinary differential equations by means of symbolic regression techniques. The first one consists in taking numerical derivatives from the given data obtaining a set of approximations. Then a symbolic regression technique can be applied to these approximations. Another way is to numerically integrate the ODE corresponding to the candidate solution and compare the results with the observed data. Here, by means of numerical experiments, we compare the relative performance of these two ways to infer models using the GIP method.", notes = "paper ID-1364 https://sites.google.com/a/mecom2010.net/mecom-2010/home/Final.pdf?attredirects=0 mecom2010.net CILAMCE / ABMEC https://sites.google.com/a/mecom2010.net/mecom-2010/silamce-abmec", } @Article{Bernardino:2011:NC, author = "Heder S. Bernardino and Helio J. C. Barbosa", title = "Grammar-based immune programming", journal = "Natural Computing", year = "2011", volume = "10", number = "1", pages = "209--241", month = mar, keywords = "genetic algorithms, genetic programming, Grammatical evolution, Artificial immune system, AIS, Immune programming, Symbolic regression, Model inference", ISSN = "1567-7818", DOI = "doi:10.1007/s11047-010-9217-x", abstract = "This paper describes Grammar-based Immune Programming (GIP) for evolving programs in an arbitrary language by immunological inspiration. GIP is based on Grammatical Evolution (GE) in which a grammar is used to define a language and decode candidate solutions to a valid representation (program). However, by default, GE uses a Genetic Algorithm in the search process while GIP uses an artificial immune system. Some modifications are needed of an immune algorithm to use a grammar in order to efficiently decode antibodies into programs. Experiments are performed to analyse algorithm behaviour over different aspects and compare it with GEVA, a well known GE implementation. The methods are applied to identify a causal model (an ordinary differential equation) from an observed data set, to symbolically regress an iterated function f(f(x)) = g(x), and to find a symbolic representation of a discontinuous function", language = "English", } @InProceedings{Bernardino:2011:ICARIS, author = "Heder S. Bernardino and Helio J. C. Barbosa", title = "Inferring Systems of Ordinary Differential Equations via Grammar-Based Immune Programming", booktitle = "Proceedings of the International Conference on Artificial Immune Systems (ICARIS)", year = "2011", editor = "Pietro Lio and Giuseppe Nicosia and Thomas Stibor", volume = "6825", series = "Lecture Notes in Computer Science", pages = "198--211", address = "Cambridge, UK", month = jul # " 18-21", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-22370-9", URL = "http://dx.doi.org/10.1007/978-3-642-22371-6_19", DOI = "doi:10.1007/978-3-642-22371-6_19", abstract = "Grammar-based Immune Programming (GIP) is a method for evolving programs in an arbitrary language using an immunological inspiration. GIP is applied here to solve the relevant modelling problem of finding a system of differential equations, in analytical form, which better explains a given set of data obtained from a certain phenomenon. Computational experiments are performed to evaluate the approach, showing that GIP is an efficient technique for symbolic modelling.", language = "English", } @InProceedings{Bernardino:2011:CILAMCE, author = "Heder S. Bernardino and Eduardo S. Castro and Joao N. C. Guerreiro and Helio J. C. Barbosa", title = "Inferring strains on a locally deformed pipe via grammar-based immune programming", booktitle = "Proceedings of the Iberian Latin American Congress on Computational Methods in Engineering (CILAMCE)", year = "2011", editor = "Andrea R. D. Silva", address = "Ouro Preto, MG, Brazil", month = nov # " 13-16", keywords = "genetic algorithms, genetic programming", notes = "CILAMCE XXXII CILAMCE 2011, the 32th CILAMCE Conference, is being organized by the Graduate Programs in Civil (PROPEC) and Mineral Engineering (PPGEM), of the Federal University of Ouro Preto - Brazil (UFOP) http://www.acquacon.com.br/cilamce2011/en/index.php PAP007449 http://www.acquacon.com.br/cilamce2011/en/authorsinfo.php", } @InProceedings{Bernardino:2012:WCCM, author = "Heder S. Bernardino and Helio J. C. Barbosa", title = "Simultaneous topology, shape, and sizing optimization of Truss structures via grammatical evolution", booktitle = "Proceedings of the 10th World Congress on Computational Mechanics (WCCM)", year = "2012", editor = "Paulo M. Pimenta", address = "Sao Paulo, Brazil", month = "8-13 " # jul, organisation = "IACM", keywords = "genetic algorithms, genetic programming", notes = "http://docslide.us/documents/wccm2012.html http://www.iacm.info/vpage/1/0/Events/WCCM", } @InCollection{bernardinobarbosa2014, author = "Heder S. Bernardino and Helio J. C. Barbosa", title = "Infer\^{e}ncia de Modelos Utilizando a Programa\c{c}\~{a}o Imunol\'{o}gica Gramatical", booktitle = "T\'{e}cnicas de Intelig\^{e}ncia Computacional com Aplica\c{c}\~{o}es em Problemas Inversos de Engenharia", publisher = "Omnipax", year = "2014", editor = "Fran S\'{e}rgio Lobato and Valder {Steffen Jr.} and Antonio Jos\'{e} da {Silva Neto}", chapter = "4", pages = "37--50", address = "Curitiba, PR", edition = "1", keywords = "genetic algorithms, genetic programming, grammatical evolution, Inverse problems, Model identification, Grammar-based immune programming", isbn13 = "978-85-64619-15-9", URL = "http://omnipax.com.br/site/?page_id=549", DOI = "doi:10.7436/2014.tica.04", size = "14 pages", abstract = "In this chapter we present the grammar-based immune programming, a technique for evolving programs by combining a search mechanism, inspired by the clonal selection theory, with the grammatical evolution representation which makes a clear distinction between the search and the solution spaces, thus offering more flexibility. The technique is applied to the inverse problem of model identification - in symbolic form - from data. Examples of the inference of systems of ordinary differential equations are presented.", abstract = "Apresenta-se neste capitulo a programacao imunologica gramatical, uma tecnica para evolucao de programas que combina um mecanismo de busca inspirado pela teoria da selecao clonal com a representacao via evolucao gramatical, que faz uma distincao clara entre o espaco de busca e o espaco de solucoes, oferecendo portanto mais flexibilidade. A tecnica e aplicada ao problema inverso de identificacao de modelos - em forma simbolica - a partir de dados. Exemplos de inferencia de sistemas de equacoes diferenciais ordinarias sao apresentados.", notes = "In Portuguese", } @InProceedings{Bernardino:2015:CEC, author = "Heder Bernardino and Helio Barbosa", title = "Grammar-based Immune Programming to Assist in the Solution of Functional Equations", booktitle = "Proceedings of 2015 IEEE Congress on Evolutionary Computation (CEC 2015)", year = "2015", editor = "Yadahiko Murata", pages = "1167--1174", address = "Sendai, Japan", month = "25-28 " # may, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-7491-7", DOI = "doi:10.1109/CEC.2015.7257021", abstract = "Grammar-based immune programming is proposed here as a tool to assist the search for a general solution to a functional equation. A external archive is incorporated to the algorithm in order to store good solutions found during the search. By inspecting such particular solutions the user is able to generalize and construct a general solution to the functional equation considered. The main objective here is to provide the user with a large diverse set of particular solutions to the problem at hand. Preliminary computational experiments are performed where some functional equations from the literature are tackled.", notes = "1335 hrs 15529 CEC2015", } @InProceedings{Bernardo:2012:UKCI, author = "Dario Bernardo and Hani Hagras and Edward Tsang", title = "An interval type-2 Fuzzy Logic based system for model generation and summarization of arbitrage opportunities in stock markets", booktitle = "12th UK Workshop on Computational Intelligence (UKCI 2012)", year = "2012", DOI = "doi:10.1109/UKCI.2012.6335765", abstract = "Today stock market exchange and finance are centres of attention all over the world. In finance, arbitrage is the practice of taking advantage of a price misalignment between two or more stock markets where profit can be earned by striking a combination of matching deals that capitalise upon the misalignment. If one strikes when misalignment has been observed, such deals are practically risk-free. However, when risk-free profit is around, everyone would compete to take advantage of it. Therefore, the question is whether arbitrage opportunities can be predicted; after all, misalignment does not happen instantaneously. Furthermore, financial operators do not like black boxes in forecasting. In this paper, we will present a type-2 Fuzzy Logic System (FLS) for the modelling and prediction of financial applications. The proposed system is capable of generating summarised models from pre-specified number of linguistic rules, which enables the user to understand the generated models for arbitrage opportunities prediction. The system is able to use this summarised model for the prediction of arbitrage opportunities in stock markets. We have performed several experiments based on the arbitrage data which is used in stock markets to spot ahead of time arbitrage opportunities. The proposed type-2 FLS has outperformed the Evolving Decision Rule (EDR) procedure (which is based on Genetic Programming (GP) and decision trees). Like GP, the type-2 FLS is capable of providing a white box model which could be easily understood and analysed by the lay user.", keywords = "genetic algorithms, genetic programming, fuzzy logic, pricing, profitability, risk management, stock markets, financial application modelling, financial application prediction, financial operators, interval type-2 fuzzy logic based system, linguistic rules, model generation, model summarisation, price misalignment, risk-free profit, stock market exchange, type-2 FLS, Finance, Firing, Fuzzy logic, Fuzzy sets, Predictive models, Stock markets, Uncertainty, Financial Applications, Type-2 Fuzzy logic Systems, arbitrage, prediction", notes = "'We have compared the proposed approach with one of the most powerful white box modeling and prediction systems for spotting arbitrage opportunities which is EDR procedure [31]. The EDR method evolves a set of decision rules by using Genetic Programming (GP)' 'type-2 FLS' ' much better' Also known as \cite{6335765}", } @InProceedings{Bernardo:2013:ieeeFUZZ, author = "Dario Bernardo and Hani Hagras and Edward Tsang", title = "A Genetic Type-2 fuzzy logic based system for financial applications modelling and prediction", booktitle = "IEEE International Conference on Fuzzy Systems (FUZZ 2013)", year = "2013", month = jul, keywords = "genetic algorithms, genetic programming, type-2 fuzzy logic", DOI = "doi:10.1109/FUZZ-IEEE.2013.6622310", ISSN = "1098-7584", abstract = "Following the global economic crisis, many financial organisations around the World are seeking efficient frameworks for predicting and assessing financial risks. However, in the current economic situation, transparency became an important factor where there is a need to fully understand and analyse a given financial model. In this paper, we will present a Genetic Type-2 Fuzzy Logic System (FLS) for the modelling and prediction of financial applications. The proposed system is capable of generating summarised optimised type-2 FLSs based financial models which are easy to read and analyse by the lay user. The system is able to use the summarised model for prediction within financial applications. We have performed several evaluations in two distinctive financial domains one for the prediction of good/bad customers in a credit card approval application and the other domain was in the prediction of arbitrage opportunities in the stock markets. The proposed Genetic type-2 FLS has outperformed white box financial models like the Evolving Decision Rule (EDR) procedure (which is based on Genetic Programming (GP) and decision trees) and gave a comparable performance to black box models like neural networks while the proposed system provided a white box model which is easy to understand and analyse by the lay user.", notes = "Also known as \cite{6622310}", } @InProceedings{Berndt:2021:SBCCI, author = "Augusto Berndt and Isac S. Campos and Bryan Lima and Mateus Grellert and Jonata T. Carvalho and Cristina Meinhardt and Brunno A. {De Abreu}", title = "Accuracy and Size Trade-off of a Cartesian Genetic Programming Flow for Logic Optimization", booktitle = "2021 34th SBC/SBMicro/IEEE/ACM Symposium on Integrated Circuits and Systems Design (SBCCI)", year = "2021", month = aug, keywords = "genetic algorithms, genetic programming, cartesian genetic programming", DOI = "doi:10.1109/SBCCI53441.2021.9529968", abstract = "Logic synthesis tools face tough challenges when providing algorithms for synthesizing circuits with increased inputs and complexity. Traditional approaches for logic synthesis have been in the spotlight so far. However, due to advances in machine learning and their high performance in solving specific problems, such algorithms appear as an attractive option to improve electronic design tools. In our work, we explore Cartesian Genetic Programming for logic optimization of exact or approximate combinational circuits. The proposed CGP flow receives input from the circuit description in the format of AND-Inverter Graphs and its expected behavior as a truth-table. The CGP may improve solutions found by other techniques used for bootstrapping the evolutionary process or initialize the search from random (unbiased) individuals seeking optimal circuits. We propose two different evaluation methods for the CGP: to minimize the number of AIG nodes or optimize the circuit accuracy. We obtain at least 22.percent superior results when considering the ratio between accuracy and size for the benchmarks used, compared with the teams from the IWLS 2020 contest that obtained the best accuracy and size results. It is noteworthy that any logic synthesis approach based on AIGs can easily incorporate the proposed flow. The results obtained show that their usage may achieve improved logic circuits.", notes = "Also known as \cite{9529968} See \cite{Berndt:2022:JICS}", } @Article{Berndt:2022:JICS, author = "Augusto Andre {Souza Berndt} and Brunno Abreu and Isac S. Campos and Bryan Lima and Mateus Grellert and Jonata T. Carvalho and Cristina Meinhardt", title = "A {CGP}-based Logic Flow: Optimizing Accuracy and Size of Approximate Circuits", journal = "Journal of Integrated Circuits and Systems", year = "2022", volume = "17", number = "1", note = "Selected Papers from Symposium on Integrated Circuits and Systems Design, 2021", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Logic Minimization, Machine Learning, Multi-variable Optimization, Evolutionary Computing", URL = "https://jics.org.br/ojs/index.php/JICS/article/view/546/380", DOI = "doi:10.29292/jics.v17i1.546", size = "12 pages", abstract = "Logic synthesis tools face tough challenges when providing algorithms for synthesizing circuits with increased inputs and complexity. Machine learning techniques show high performance in solving specific problems, being an attractive option to improve electronic design tools. We explore Cartesian Genetic Programming (CGP) for logic optimization of exact or approximate Boolean functions in our work. The proposed CGP-based flow receives the expected circuit behavior as a truth-table and either performs the synthesis starting from random circuits or optimizes a circuit description provided in the format of an AND-Inverter Graph. The optimization flow improves solutions found by other techniques, using them for bootstrapping the evolutionary process. We use two metrics to evaluate our CGP-based flow: (i) the number of AIG nodes or (ii) the circuit accuracy. The results obtained showed that the CGP-based flow provided at least 22.6percent superior results when considering the trade-off between accuracy and size compared with two other methods that brought the best accuracy and size outcomes, respectively.", notes = "previous \cite{Berndt:2021:SBCCI} Federal University of Santa Catarina (UFSC), Brazil", } @Article{Bernhardt:2008:EC, author = "Knut Bernhardt", title = "Finding Alternatives and Reduced Formulations for Process-Based Models", journal = "Evolutionary Computation", year = "2008", volume = "16", number = "1", pages = "63--88", month = "Spring", keywords = "genetic algorithms, genetic programming, Model reduction, complexity, dimension reduction", ISSN = "1063-6560", DOI = "doi:10.1162/evco.2008.16.1.63", size = "26 pages", abstract = "This paper addresses the problem of model complexity commonly arising in constructing and using process-based models with intricate interactions. Apart from complex process details the dynamic behaviour of such systems is often limited to a discrete number of typical states. Thus, models reproducing the system's processes in all details are often too complex and over-parameterised. In order to reduce simulation times and to get a better impression of the important mechanisms, simplified formulations are desirable. In this work a data adaptive model reduction scheme that automatically builds simple models from complex ones is proposed. The method can be applied to the transformation and reduction of systems of ordinary differential equations. It consists of a multistep approach using a low dimensional projection of the model data followed by a Genetic Programming/Genetic Algorithm hybrid to evolve new model systems. As the resulting models again consist of differential equations, their process-based interpretation in terms of new state variables becomes possible. Transformations of two simple models with oscillatory dynamics, simulating a mathematical pendulum and predator-prey interactions respectively, serve as introductory examples of the method's application. The resulting equations of force indicate the predator-prey system's equivalence to a nonlinear oscillator. In contrast to the simple pendulum it contains driving and damping forces that produce a stable limit cycle.", notes = "CVODE, SUNDIALS", } @InProceedings{bersano-begey:1996:pici, author = "Tommaso F. Bersano-Begey and Jason M. Daida and John F. Vesecky and Frank L. Ludwig", title = "A Platform-Independent Collaborative Interface for Genetic Programming Applications: Image Analysis for Scientific Inquiry", booktitle = "Late Breaking Papers at the Genetic Programming 1996 Conference Stanford University July 28-31, 1996", year = "1996", editor = "John R. Koza", pages = "1--8", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California 94305-3079, USA", month = "28--31 " # jul, publisher = "Stanford Bookstore", ISBN = "0-18-201031-7", keywords = "genetic algorithms, genetic programming", notes = "GP-96LB java The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670", } @InProceedings{bersano-begey:1997:jcifGPa, author = "Tommaso F. Bersano-Begey and Jason M. Daida and John F. Vesecky and Frank L. Ludwig", title = "A {Java} Collaborative Interface for Genetic Programming Applications: Image Analysis and Scientific Inquiry", booktitle = "Proceedings of the 1997 {IEEE} International Conference on Evolutionary Computation", year = "1997", address = "Indianapolis", publisher_address = "Piscataway, NJ, USA", month = "13-16 " # apr, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", URL = "ftp://ftp.eecs.umich.edu/people/daida/papers/ICEC97image.pdf", notes = "ICEC-97 Collaborative Interface Demonstration http://www.sprl.umich.edu/acers/gaia/collab.html GAIA (Genetic programming Assistant for Image Analysis) slides http://www-personal.umich.edu/~tombb/gaia74/", } @InProceedings{Bersano-Begey:1997:cedslo, author = "Tommaso F. Bersano-Begey", title = "Controlling Exploration, Diversity and Escaping Local Optima in GP: Adapting Weights of Training Sets to Model Resource Consumption", booktitle = "Late Breaking Papers at the 1997 Genetic Programming Conference", year = "1997", editor = "John R. Koza", pages = "7--10", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California, 94305-3079, USA", month = "13--16 " # jul, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-206995-8", notes = "GP-97LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670", } @InProceedings{Bersano-Begey:1997:grffc, author = "Tommaso F. Bersano-Begey and Jason M. Daida", title = "A Discussion on Generality and Robustness and a Framework for Fitness Set Construction in Genetic Programming to Promote Robustness", booktitle = "Late Breaking Papers at the 1997 Genetic Programming Conference", year = "1997", editor = "John R. Koza", pages = "11--18", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California, 94305-3079, USA", month = "13--16 " # jul, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-206995-8", notes = "GP-97LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670 Fri, 05 Sep 1997 06:14:54 EDT I did some follow-up work in trying to improve generality of code in the wall-following problem, and started to look at how to gain more information about generality by recording the distribution of hits (rather than just their total), an iterative algorithm to check for and correct ambiguous training sets (one which can be solved by other solutions besides the correct one), and an account of the relationship between size and generality of solutions. The following was a very preliminary work, but I am now working on expanding each topic and writing them in a more formal way. slides http://www-personal.umich.edu/~tombb/gp973/", } @InProceedings{bersano-begey:1997:, author = "Tommaso F. Bersano-Begey and Patrick G. Kenny and Edmund H. Durfee", title = "Multi-Agent Teamwork, Adaptive Learning and Adversarial Planning in Robocup Using a PRS Architecture", booktitle = "IJCAI97", year = "1997", note = "accepted", keywords = "genetic algorithms, genetic programming", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.53.1962", URL = "http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=BC06E9197308E7FDF6E8347CECE81DC1?doi=10.1.1.53.1962&rep=rep1&type=pdf", size = "7 pages", abstract = "Our approach for the Robocup97 competition is to emphasise teamwork among agents by augmenting reactions (based on awareness of the current situation) with predictions (based on predefined multiagent manoeuvres). These predictions are accomplished by allowing agents to cooperatively accomplish predefined plans, which are elaborated reactively and hierarchically to ensure responsiveness to changing circumstances. By supporting the runtime construction of plans, our approach simplifies the introduction of new plans, strategies, and actions, and produces a framework for dynamic adaptation and plan recognition through automatically generating belief networks. Our implementation is built on top of UM-PRS, a procedural reasoning system architecture for real-time environments, which allows specifying, executing, and integrating plans based on subgoaling and preconditions", notes = "um-prs.pdf broken 5-sep-97 http://www.sonycsl.co.jp/person/kitano/RoboCup/ws97.html", } @InProceedings{Bersini:2000:GECCO, author = "Hugues Bersini", title = "Chemical Crossover", pages = "825--832", year = "2000", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)", editor = "Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer", address = "Las Vegas, Nevada, USA", publisher_address = "San Francisco, CA 94104, USA", month = "10-12 " # jul, keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-708-0", URL = "http://gpbib.cs.ucl.ac.uk/gecco2000/AA140.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco2000/AA140.ps", notes = "A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of \cite{whitley:2000:GECCO}", } @InProceedings{berstein:2004:mpefsgp, title = "Multiobjective Parsimony Enforcement for Superior Generalisation Performance", author = "Yaniv Bernstein and Xiaodong Li and Vic Ciesielski and Andy Song", pages = "83--89", booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary Computation", year = "2004", publisher = "IEEE Press", month = "20-23 " # jun, address = "Portland, Oregon", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Multiobjective evolutionary algorithms, Combinatorial \& numerical optimization", URL = "http://goanna.cs.rmit.edu.au/~ybernste/papers/Bernstein_CEC_2004.pdf", DOI = "doi:10.1109/CEC.2004.1330841", size = "7 pages", abstract = "Program Bloat - the phenomenon of ever-increasing program size during a GP run - is a recognised and widespread problem. Traditional techniques to combat program bloat are program size limitations or parsimony pressure (penalty functions). These techniques suffer from a number of problems, in particular their reliance on parameters whose optimal values it is difficult to a priori determine. In this paper we introduce POPE-GP, a system that makes use of the NSGA-II multiobjective evolutionary algorithm as an alternative, parameter-free technique for eliminating program bloat. We test it on a classification problem and find that while vastly reducing program size, it does improve generalisation performance.", notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.", } @InProceedings{Berthier:2010:LION, title = "Consistency Modifications for Automatically Tuned {Monte-Carlo} Tree Search", author = "Vincent Berthier and Hassen Doghmen and Olivier Teytaud", booktitle = "Learning and Intelligent OptimizatioN, LION 4", year = "2010", editor = "Roberto Battiti", address = "Venice", month = jan # " 18-22", keywords = "genetic algorithms, genetic programming, Game Go, Mathematics/Optimization and Control, Monte-Carlo Tree Search Consistency Ko-fights", URL = "http://hal.archives-ouvertes.fr/docs/00/43/71/46/PDF/consistency.pdf", URL = "HAL:http://hal.archives-ouvertes.fr/inria-00437146/en/", bibsource = "OAI-PMH server at hal.archives-ouvertes.fr", identifier = "HAL:inria-00437146, version 1", language = "EN", oai = "oai:hal.archives-ouvertes.fr:inria-00437146_v1", abstract = "Monte-Carlo Tree Search algorithms (MCTS [4, 6]), including upper confidence trees (UCT [9]), are known for their impressive ability in high dimensional control problems. Whilst the main test bed is the game of Go, there are increasingly many applications [13, 12, 7]; these algorithms are now widely accepted as strong candidates for high-dimensional control applications. Unfortunately, it is known that for optimal performance on a given problem, MCTS requires some tuning; this tuning is often handcrafted or automated, with in some cases a loss of consistency, i.e. a bad behavior asymptotically in the computational power. This highly undesirable property led to a stupid behavior of our main MCTS program MoGo in a real-world situation described in section 3. This is a big trouble for our several works on automatic parameter tuning [3] and the genetic programming of new features in MoGo. We will see in this paper: -- A theoretical analysis of MCTS consistency; -- Detailed examples of consistent and inconsistent known algorithms; -- How to modify a MCTS implementation in order to ensure consistency, independently of the modifications to the scoring module (the module which is automatically tuned and genetically programmed in MoGo); -- As a by product of this work, we'll see the interesting property that some heavily tuned MCTS implementations are better than UCT in the sense that they do not visit the complete tree (whereas UCT asymptotically does), whilst preserving the consistency at least if consistency modifications above have been made.", notes = "LION4 http://lion.disi.unitn.it/intelligent-optimization//LION4/program.php", } @Article{Bertolini:2018:SP, author = "Vittorio Bertolini and Carlos Rey Barra and Mauricio Sepulveda and Victor Parada", title = "Novel Methods Generated by Genetic Programming for the Guillotine-Cutting Problem", journal = "Scientific Programming", year = "2018", volume = "2018", pages = "6971827:1--6971827:13", keywords = "genetic algorithms, genetic programming", publisher = "Hindawi", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/sp/sp2018.html#BertoliniRSP18", URL = "http://downloads.hindawi.com/journals/sp/2018/6971827.pdf", DOI = "doi:10.1155/2018/6971827", size = "14 pages", abstract = "New constructive algorithms for the two-dimensional guillotine-cutting problem are presented. The algorithms were produced from elemental algorithmic components using evolutionary computation. A subset of the components was selected from a previously existing constructive algorithm. The algorithms' evolution and testing process used a set of 46 instances from the literature. The structure of three new algorithms is described, and the results are compared with those of an existing constructive algorithm for the problem. Several of the new algorithms are competitive with respect to a state-of-the-art constructive algorithm. A subset of novel instructions, which are responsible for the majority of the new algorithms' good performances, has also been found.", notes = "Informatics Engineering Department, University of Santiago of Chile, Santiago, Chile Also known as \cite{journals/sp/BertoliniRSP18}", } @InProceedings{Bertram:1997:ris, author = "Robert R. Bertram and Jason M. Daida and John F. Vesecky and Guy A. Meadows and Christian Wolf", title = "Reconstructing Incomplete Signals Using Nonlinear Interpolation and Genetic Algorithms", booktitle = "Late Breaking Papers at the 1997 Genetic Programming Conference", year = "1997", editor = "John R. Koza", pages = "19--27", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California, 94305-3079, USA", month = "13--16 " # jul, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-206995-8", notes = "GP-97LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670 see also \cite{bertram:1998:risiGA}", } @InProceedings{bertram:1998:risiGA, author = "Robert R. Bertram and Jason M. Daida and John F. Vesecky and Guy A. Meadows and Christian Wolf", title = "Reconstructing Incomplete Signals Using Nonlinear Interpolation and Genetic Algorithms", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "447--454", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms", URL = "http://citeseer.ist.psu.edu/244792.html", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/10392/ftp:zSzzSzftp.eecs.umich.eduzSzpeoplezSzdaidazSzpaperszSzsga98reconstruct.pdf/reconstructing-incomplete-signals-using.pdf", size = "8 pages", abstract = "This paper describes a general, nonanalytical method for deriving Fourier series coefficients using a genetic algorithm. Non-analytical methods are often needed in problems where lost portions of a complex signal require restoration. We discuss some of the difficulties involved in working with the associated trigonometric polynomials and propose an alternative solution for adapting genetic algorithms for this class of problems. We demonstrate the efficacy of our approach with a case study. Our particular case study features the processing of data that has been collected by a novel optical waveslope instrument, which measures the topography of water surfaces.", notes = "SGA-98 see also \cite{Bertram:1997:ris}", } @InProceedings{bertschinger:2024:GECCO, author = "Amanda Bertschinger and James Bagrow and Joshua Bongard", title = "Evolving Form and Function: Dual-Objective Optimization in Neural Symbolic Regression Networks", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference", year = "2024", editor = "Jean-Baptiste Mouret and Kai Qin and Julia Handl and Xiaodong Li and Markus Wagner and Mario Garza-Fabre and Kate Smith-Miles and Richard Allmendinger and Ying Bi and Grant Dick and Amir H Gandomi and Marcella Scoczynski Ribeiro Martins and Hirad Assimi and Nadarajen Veerapen and Yuan Sun and Mario Andres Munyoz and Ahmed Kheiri and Nguyen Su and Dhananjay Thiruvady and Andy Song and Frank Neumann and Carla Silva", pages = "277--285", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, symbolic regression, neuroevolution, multi-objective optimization, Evolutionary Machine Learning", isbn13 = "979-8-4007-0494-9", DOI = "doi:10.1145/3638529.3654030", size = "9 pages", abstract = "Data increasingly abounds, but distilling their underlying relationships down to something interpretable remains challenging. One approach is genetic programming, which 'symbolically regresses' a data set down into an equation. However, symbolic regression (SR) faces the issue of requiring training from scratch for each new dataset. To generalize across all datasets, deep learning techniques have been applied to SR. These networks, however, are only able to be trained using a symbolic objective: NN-generated and target equations are symbolically compared. But this does not consider the predictive power of these equations, which could be measured by a behavioral objective that compares the generated equation's predictions to actual data. Here we introduce a method that combines gradient descent and evolutionary computation to yield neural networks that minimize the symbolic and behavioral errors of the equations they generate from data. As a result, these evolved networks are shown to generate more symbolically and behaviorally accurate equations than those generated by networks trained by state-of-the-art gradient based neural symbolic regression methods. We hope this method suggests that evolutionary algorithms, combined with gradient descent, can improve SR results by yielding equations with more accurate form and function.", notes = "GECCO-2024 EML A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @Article{Berutich:2016:ESA, author = "Jose Manuel Berutich and Francisco Lopez and Francisco Luna and David Quintana", title = "Robust technical trading strategies using {GP} for algorithmic portfolio selection", journal = "Expert Systems with Applications", year = "2016", volume = "46", pages = "307--315", keywords = "genetic algorithms, genetic programming, Algorithmic trading, Portfolio management, Trading rule, Finance, buy and hold", ISSN = "0957-4174", URL = "https://e-archivo.uc3m.es/rest/api/core/bitstreams/169bcfb0-d6cc-4f0a-870b-dd9915924000/content", URL = "http://www.sciencedirect.com/science/article/pii/S0957417415007447", DOI = "doi:10.1016/j.eswa.2015.10.040", size = "12 pages", abstract = "This paper presents a Robust Genetic Programming approach for discovering profitable trading rules which are used to manage a portfolio of stocks from the Spanish market. The investigated method is used to determine potential buy and sell conditions for stocks, aiming to yield robust solutions able to withstand extreme market conditions, while producing high returns at a minimal risk. One of the biggest challenges GP evolved solutions face is over-fitting. GP trading rules need to have similar performance when tested with new data in order to be deployed in a real situation. We explore a random sampling method (RSFGP) which instead of calculating the fitness over the whole dataset, calculates it on randomly selected segments. This method shows improved robustness and out-of-sample results compared to standard genetic programming (SGP) and a volatility adjusted fitness (VAFGP). Trading strategies (TS) are evolved using financial metrics like the volatility, CAPM alpha and beta, and the Sharpe ratio alongside other Technical Indicators (TI) to find the best investment strategy. These strategies are evaluated using 21 of the most liquid stocks of the Spanish market. The achieved results clearly outperform Buy and Hold, SGP and VAFGP. Additionally, the solutions obtained with the training data during the experiments clearly show during testing robustness to step market declines as seen during the European sovereign debt crisis experienced recently in Spain. In this paper the solutions learned were able to operate for prolonged periods, which demonstrated the validity and robustness of the rules learned, which are able to operate continuously and with minimal human intervention. To sum up, the developed method is able to evolve TSs suitable for all market conditions with promising results, which suggests great potential in the method generalization capabilities. The use of financial metrics alongside popular TI enables the system to increase the stock return while proving resilient through time. The RSFGP system is able to cope with different types of markets achieving a portfolio return of 31.81percent for the testing period 2009-2013 in the Spanish market, having the IBEX35 index returned 2.67percent.", } @PhdThesis{Berutich:thesis, author = "Jose Manuel {Berutich Lindquist}", title = "Robust Optimization of Algorithmic Trading Systems", school = "Lenguajes y Ciencias de la Computacion, Universidad de Malaga", year = "2017", address = "Malaga, Andalucia, Spain", month = "22 " # may, keywords = "genetic algorithms, genetic programming", URL = "https://hdl.handle.net/10630/15353", URL = "https://riuma.uma.es/xmlui/bitstream/handle/10630/15353/TD_BERUTICH_%20LINDQUIST_Jose_Manuel.pdf", size = "157 pages", abstract = "GAs (Genetic Algorithms) and GP (Genetic Programming) are investigated for finding robust Technical Trading Strategies (TTSs). TTSs evolved with standard GA/GP techniques tend to suffer from over-fitting as the solutions evolved are very fragile to small disturbances in the data. The main objective of this thesis is to explore optimization techniques for GA/GP which produce robust TTSs that have a similar performance during both optimization and evaluation, and are also able to operate in all market conditions and withstand severe market shocks. In this thesis, two novel techniques that increase the robustness of TTSs and reduce over-fitting are described and compared to standard GA/GP optimization techniques and the traditional investment strategy Buy & Hold. The first technique employed is a robust multi-market optimization methodology using a GA. Robustness is incorporated via the environmental variables of the problem, i.e. variablity in the dataset is introduced by conducting the search for the optimum parameters over several market indices, in the hope of exposing the GA to differing market conditions. This technique shows an increase in the robustness of the solutions produced, with results also showing an improvement in terms of performance when compared to those offered by conducting the optimization over a single market. The second technique is a random sampling method we use to discover robust TTSs using GP. Variability is introduced in the dataset by randomly sampling segments and evaluating each individual on different random samples. This technique has shown promising results, substantially beating Buy & Hold. Overall, this thesis concludes that Evolutionary Computation techniques such as GA and GP combined with robust optimization methods are very suitable for developing trading systems, and that the systems developed using these techniques can be used to provide significant economic profits in all market conditions.", notes = "In English AUTOR: Jose Manuel Berutich Lindquist http://orcid.org/0000-0002-0918-9634 Supervisors: Francisco Luna Valero and Francico Lopez Valverde https://core.ac.uk/download/pdf/214843381.pdf", } @InProceedings{1068303, author = "Sireesha Besetti and Terence Soule", title = "Function choice, resiliency and growth in genetic programming", booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation", year = "2005", editor = "Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and Edwin D. {de Jong} and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler", volume = "2", ISBN = "1-59593-010-8", pages = "1771--1772", address = "Washington DC, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2005/docs/p1771.pdf", DOI = "doi:10.1145/1068009.1068303", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "25-29 " # jun, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Poster, function choice, growth, resilience", notes = "GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM Order Number 910052", } @InProceedings{Beshah:2012:INCoS, author = "Tibebe Beshah and Dejene Ejigu and Pavel Kromer and Vaclav Snasel and Jan Platos and Ajith Abraham", booktitle = "4th International Conference on Intelligent Networking and Collaborative Systems, INCoS 2012", title = "Learning the Classification of Traffic Accident Types", year = "2012", pages = "463--468", DOI = "doi:10.1109/iNCoS.2012.75", abstract = "This paper presents an application of evolutionary fuzzy classifier design to a road accident data analysis. A fuzzy classifier evolved by the genetic programming was used to learn the labelling of data in a real world road accident data set. The symbolic classifier was inspected in order to select important features and the relations among them. Selected features provide a feedback for traffic management authorities that can exploit the knowledge to improve road safety and mitigate the severity of traffic accidents.", keywords = "genetic algorithms, genetic programming, data analysis, fuzzy set theory, learning (artificial intelligence), pattern classification, road accidents, road traffic, traffic engineering computing, evolutionary fuzzy classifier design, feature selection, machine learning, real world road accident data set, road accident data analysis, road safety improvement, symbolic classifier, traffic accident severity mitigation, traffic accident type classification, traffic management authorities, Accidents, Biological cells, Indexes, Injuries, Labeling, Vehicles, fuzzy rules, machine learning, traffic accidents", notes = "Also known as \cite{6337959}", } @InProceedings{best:1999:CMGSE, author = "Michael L. Best", title = "Coevolving Mutualists Guide Simulated Evolution", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "941", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "evolution strategies and evolutionary programming, poster papers", ISBN = "1-55860-611-4", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{bettenhausen:1995:biox, author = "K. D. Bettenhausen and S. Gehlen and P. Marenbach and H. Tolle", title = "BioX++ -- {N}ew results and conceptions concerning the intelligent control of biotechnological processes", booktitle = "6th International Conference on Computer Applications in Biotechnology", year = "1995", editor = "A. Munack and K. Sch{\"u}gerl", pages = "324--327", organisation = "IFAC Publications", publisher = "Elsevier Science", email = "mali@rt.e-technik.tu-darmstadt.de", keywords = "genetic algorithms, genetic programming, Expert systems, neural networks, fuzzy systems, learning control, fermentation, biotechnology", URL = "http://www.rtr.tu-darmstadt.de/fileadmin/literature/rst_95_03.pdf", size = "4 pages", abstract = "BioX++ facilities the transparent generation of process control stratgies and sequences based on automatically self-organized structured process models. Experimental results showing the increased product yeild and the discussion of approach-specific problems are part of this paper as well as the new approaches actually examined.", notes = "14--17 May, Garmisch-Partenkirchen, Germany", } @InProceedings{bettenhausen:1995:sombbff, author = "Kurt Dirk Bettenhausen and Peter Marenbach", title = "Self-organizing modeling of biotechnological batch and fed-batch fermentations", booktitle = "EUROSIM'95", year = "1995", editor = "F. Breitenecker and I. Husinsky", address = "Vienna, Austria", publisher = "Elsevier", email = "kurt.dirk.bettenhausen@rt.e-technik.tu-darmstadt.de (Kurt Dirk Bettenhausen), mali@rt.e-technik.tu-darmstadt.de", keywords = "genetic algorithms, genetic programming, fermentation, biotechnology", URL = "http://www.rtr.tu-darmstadt.de/fileadmin/literature/rst_95_23.ps.gz", size = "5 pages", abstract = "An approach for the automatic generation of dynamic nonlinear process models obtained from experimantal process data and theoretical biological and chemical reflections using genetic programming for the supervision and coordination of the symbolic model structure during automatic development BioX++ includes (amongs fuzzy rule learning, expert system, NN also refered to) GP to produce process models, constants adapted using standard algorithmic techniques.", notes = "11--15 September, Vienna, Austria", } @InProceedings{bettenhausen:1995:sombbffGP, author = "K. D. Bettenhausen and P. Marenbach and Stephan Freyer and Hans Rettenmaier and Ulrich Nieken", title = "Self-organizing Structured modeling of a Biotechnological Fed-batch fermentation by Means of Genetic Programming", booktitle = "First International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, GALESIA", year = "1995", editor = "A. M. S. Zalzala", volume = "414", pages = "481--486", address = "Sheffield, UK", publisher_address = "London, UK", month = "12-14 " # sep, publisher = "IEE", email = "mali@rt.e-technik.tu-darmstadt.de", keywords = "genetic algorithms, genetic programming, symbolic modelling, system identification, biotechnology, predictive control", ISBN = "0-85296-650-4", URL = "http://www.rtr.tu-darmstadt.de/fileadmin/literature/rst_95_24.pdf", DOI = "doi:10.1049/cp:19951095", size = "6 pages", abstract = "12--14 September 1995, Halifax Hall, University of Sheffield, UK see also http://www.iee.org.uk/LSboard/Conf/program/galprog.htm The article describes an approach for the self-organizing generation of models of complex and unknown processes by means of GP and its application on a biotechnological fed-batch production. First experiments of the symbolic generation of structured models within an industrial cooperation with BASF are presented.", notes = "Deals much more than bettenhausen:1995:ssmbff and \cite{bettenhausen:1995:biox} with the idea of Genetic Programming. First results from an application of our approach for finding model of an industrial fed-batch fermentation process are presented which. This work was part of an cooperation of our Institute and the BASF AG, Ludwigshafen, Germany. This paper includes a more detailed description of how our GP system works. ", } @Article{beura:2018:AJSE, author = "Sambit Kumar Beura and Prasanta Kumar Bhuyan", title = "Operational Analysis of Signalized Street Segments Using Multi-gene Genetic Programming and Functional Network Techniques", journal = "Arabian Journal for Science and Engineering", year = "2018", volume = "43", number = "10", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s13369-018-3176-4", DOI = "doi:10.1007/s13369-018-3176-4", } @Article{BEURA:2020:JTH, author = "Sambit Kumar Beura and Kondamudi Vinod Kumar and Shakti Suman and Prasanta Kumar Bhuyan", title = "Service quality analysis of signalized intersections from the perspective of bicycling", journal = "Journal of Transport \& Health", volume = "16", pages = "100827", year = "2020", ISSN = "2214-1405", DOI = "doi:10.1016/j.jth.2020.100827", URL = "http://www.sciencedirect.com/science/article/pii/S2214140519300866", keywords = "genetic algorithms, genetic programming, Developing country, Signalized intersection, Bicycle level of service, Prediction modelling, Functional network", abstract = "Bicycling reduces the risk of various health problems associated with sedentary lifestyles. Hence, it is important to encourage bicycle commuting by enhancing the bikeability of transportation facilities. To support this process, this article proposes efficient bicycle level of service (BLOS) models for the assessment of signalized intersections under heterogeneous traffic conditions. Here, BLOS denotes bicyclists' perceived level of satisfaction. Method Extensive data sets (geometrical, traffic operational and built-environmental) are collected from 70 well-diversified intersection approaches of India. All approaches are also rated by 200 on-site bicyclists based on their perceived satisfaction levels on a Likert scale of 1-6 (excellent-worst). The attributes having significant influences on these ratings are then identified through Spearman's correlation analysis. Subsequently, three highly efficient techniques namely, associativity functional network (FN), genetic programming (GP) and step-wise regression are used to develop reliable BLOS models. Results As observed, the intersection BLOS is significantly (p < 0.001) influenced by total eight attributes of which crossing pedestrian volume, parking turn-over and average bicycle delay are the most dominating ones. Using these variables, the FN tool has produced the most efficient BLOS model with a coefficient of determination (R2) value of 0.92 with averaged observations. Further, the classification of BLOS ratings into six symmetrical levels A-F (excellent-worst) has reported that around 8percent intersection approaches in India are offering BLOS C-F. Conclusion The important measures of BLOS improvement at signalized intersections include the efficient management of crossing pedestrians, restrictions on nearby parking activities, and minimization of bicycle delay. The deficiencies in these aspects have perhaps made the intersection approaches in India to offer BLOS C-F. The BLOS models and transportation engineering solutions proposed in this study for the improvement of public health through bicycling are highly efficient for developing countries.", } @Article{BEURA:2021:JTH, author = "Sambit Kumar Beura and Haritha Chellapilla and Mahabir Panda and Prasanta Kumar Bhuyan", title = "Bicycle Comfort Level Rating ({BCLR)} model for urban street segments in mid-sized cities of India", journal = "Journal of Transpor \& Health", volume = "20", pages = "100971", year = "2021", ISSN = "2214-1405", DOI = "doi:10.1016/j.jth.2020.100971", URL = "https://www.sciencedirect.com/science/article/pii/S2214140520301754", keywords = "genetic algorithms, genetic programming, Bicycling comfort, Road segment, Environmental healthiness, Heterogeneous traffic, Genetic programming clustering", abstract = "Introduction The perceived comfort levels of on-street bicyclists are affected by both road characteristics and environmental healthiness. A thorough knowledge of these factors helps to encourage bicycle use and improve human health. This study thus aims to incorporate the parameters describing environmental healthiness in the evaluation of urban street performance. Methods For analysis purpose, extensive data are collected from sixty street segments of three Indian mid-sized cities. Variables having significant influences on bicycling comfort are identified using Spearman's correlation technique and a {"}Bicycle Comfort Level Rating{"} (BCLR) model is developed using the step-wise regression technique. A service scale is also defined using the Genetic Programming (GP) cluster technique to convert model outputs to letter-graded bicycling comfort levels A-F (excellent-worst). Results As observed, the bicycling comfort is influenced by total eight attributes. Of all, air quality index (AQI) is the most significant one (Spearman's correlation coefficient = 0.645). The BCLR model developed using all identified parameters has produced a high coefficient of determination (R2) value of 0.87 with overall observations. Results have also shown that around 97percent segments are offering average-worst levels of bicycling comfort (C-F) at their present scenario. Conclusion An unhealthy environment largely discourages the use of bicycles as a choice mode of transport (as the users are more likely to be exposed to environmental hazards). Hence, the improvement in factors like air quality is essential to encourage the bicycling activity. The roadway parameters like traffic volume, road width and roadside commercial activities, etc. should also be prioritized in the planning process to provide better bicycling comfort. The developed BCLR model is highly reliable for its applications in mid-sized cities of India and other developing countries. This model along with other outcomes of this study would be helpful to enhance the quality of bicycling and public health.", } @Article{Beura:2025:trpro, author = "Sambit Kumar Beura and K. Ramachandra Rao", title = "Estimating Vehicle Delay at Unsignalized Intersections with Gene-Expression Programming", journal = "Transportation Research Procedia", year = "2025", volume = "86", pages = "175--182", note = "VSI: TRPRO EWGT 2024", keywords = "genetic algorithms, genetic programming, Delay, Unsignalized intersection, Heterogeneous traffic, Gene-Expression Programming, gene expression programming", ISSN = "2352-1465", URL = "https://www.sciencedirect.com/science/article/pii/S2352146525002704", DOI = "doi:10.1016/j.trpro.2025.04.023", abstract = "The performance of an unsignalized intersection is typically evaluated based on the delay experienced by vehicles in low-priority movements. Several studies in the literature have examined delays at unsignalized intersections under homogeneous and lane-disciplined traffic conditions. However, it is challenging to find a standard delay model for mixed traffic conditions. To address this gap, the present study proposes mathematical models to estimate average traffic delays for the two least-priority movements: through and right-turning movements (for left-hand drive conditions) from minor streets. The required datasets for this investigation were obtained from thirty unsignalized intersections, encompassing both three-legged and four-legged configurations, with divided and undivided road intersection typologies collected from different parts of India. From data analysis, it was observed that the delay incurred by vehicles in through movements on the minor approach is significantly influenced by the degree of saturation (volume-to-capacity ratio), conflicting traffic volume, crossing pedestrian volume, and intersection crossing distance. Similarly, the delay incurred by vehicles in right-turning movements is significantly influenced by the same list of variables except for the intersection crossing distance. Subsequently, Gene-expression programming (GEP), a novel variant of conventional genetic programming, was successfully used to develop delay models for the respective movements. The resultant models demonstrated high prediction accuracies, with coefficient of determination (R2) values between the predicted and observed delays surpassing 0.93. A comparative analysis corroborated the superior predictive capabilities of the developed models when compared to existing ones", } @InProceedings{conf/icic/BevilacquaNMI16, title = "Adaptive Bi-objective Genetic Programming for Data-Driven System Modeling", author = "Vitoantonio Bevilacqua and Nicola Nuzzolese and Ernesto Mininno and Giovanni Iacca", bibdate = "2017-05-23", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icic/icic2016-3.html#BevilacquaNMI16", booktitle = "Intelligent Computing Methodologies - 12th International Conference, {ICIC} 2016, Lanzhou, China, August 2-5, 2016, Proceedings, Part {III}", publisher = "Springer", year = "2016", volume = "9773", editor = "De-Shuang Huang and Kyungsook Han and Abir Hussain", isbn13 = "978-3-319-42296-1", pages = "248--259", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming, multi-objective evolutionary algorithms, adaptive genetic programming, machine learning, home automation, energy efficiency", URL = "https://link.springer.com/chapter/10.1007%2F978-3-319-42297-8_24", DOI = "doi:10.1007/978-3-319-42297-8_24", abstract = "We propose in this paper a modification of one of the modern state-of-the-art genetic programming algorithms used for data-driven modelling, namely the Bi-objective Genetic Programming (BioGP). The original method is based on a concurrent minimization of both the training error and complexity of multiple candidate models encoded as Genetic Programming trees. Also, BioGP is empowered by a predator-prey co-evolutionary model where virtual predators are used to suppress solutions (preys) characterised by a poor trade-off error vs complexity. In this work, we incorporate in the original BioGP an adaptive mechanism that automatically tunes the mutation rate, based on a characterisation of the current population (in terms of entropy) and on the information that can be extracted from it. We show through numerical experiments on two different datasets from the energy domain that the proposed method, named BioAGP (where A stands for Adaptive), performs better than the original BioGP, allowing the search to maintain a good diversity level in the population, without affecting the convergence rate.", } @InProceedings{beyer:1999:FNLEOGQFM, author = "Hans-Georg Beyer and Dirk V. Arnold", title = "Fitness Noise and Localization Errors of the Optimum in General Quadratic Fitness Models", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "817--824", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "evolution strategies and evolutionary programming", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/beyer_GECCO99.ps.gz", URL = "http://www.cs.dal.ca/~dirk/docs/GECCO99.ps.gz", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{beyer_et_al:DSP:2006:498, author = "Hans-Georg Beyer and Thomas Jansen and Colin Reeves and Michael D. Vose", title = "04081 Abstracts Collection -- Theory of Evolutionary Algorithms", booktitle = "Theory of Evolutionary Algorithms", year = "2004", editor = "Hans-Georg Beyer and Thomas Jansen and Colin Reeves and Michael D. Vose", number = "04081", series = "Dagstuhl Seminar Proceedings", ISSN = "1862-4405", publisher = "Internationales Begegnungs- und Forschungszentrum (IBFI), Schloss Dagstuhl, Germany", address = "Dagstuhl, Germany", URL = "http://drops.dagstuhl.de/opus/volltexte/2006/498", note = "$<$http://drops.dagstuhl.de/opus/volltexte/2006/498$>$ [date of citation: 2006-01-01]", keywords = "genetic algorithms, genetic programming, Evolutionary algorithms, co-evolution, run time analysis, landscape analysis, Markov chains", abstract = "From 15.02.04 to 20.02.04, the Dagstuhl Seminar 04081 ``Theory of Evolutionary Algorithms'' was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available.", notes = "See also \cite{langdon:2003:normal} and \cite{mcphee:ots:gecco2004}", } @Article{beyer:2004:GPEM, author = "Hans-Georg Beyer and Markus Olhofer and Bernhard Sendhoff", title = "On the Impact of Systematic Noise on the Evolutionary Optimization Performance -- A Sphere Model Analysis", journal = "Genetic Programming and Evolvable Machines", year = "2004", volume = "5", number = "4", pages = "327--360", month = dec, keywords = "ES, evolution strategies, noisy optimisation, performance analysis, robust optimization", ISSN = "1389-2576", DOI = "doi:10.1023/B:GENP.0000036020.79188.a0", abstract = "Quality evaluations in optimisation processes are frequently noisy. In particular evolutionary algorithms have been shown to cope with such stochastic variations better than other optimization algorithms. So far mostly additive noise models have been assumed for the analysis. However, we will argue in this paper that this restriction must be relaxed for a large class of applied optimization problems. We suggest systematic noise as an alternative scenario, where the noise term is added to the objective parameters or to environmental parameters inside the fitness function. We thoroughly analyse the sphere function with systematic noise for the evolution strategy with global intermediate recombination. The progress rate formula and a measure for the efficiency of the evolutionary progress lead to a recommended ratio between [mu] and [lambda]. Furthermore, analysis of the dynamics identifies limited regions of convergence dependent on the normalized noise strength and the normalised mutation strength. A residual localisation error R[infin] can be quantified and a second [mu] to [lambda] ratio is derived by minimising R[infin].", notes = "Article ID: 5272968", } @Article{beyer:2005:GPEM, author = "Hans-Georg Beyer and Dirk V. Arnold and Silja Meyer-Nieberg", title = "A New Approach for Predicting the Final Outcome of Evolution Strategy Optimization Under Noise", journal = "Genetic Programming and Evolvable Machines", year = "2005", volume = "6", number = "1", pages = "7--24", month = mar, keywords = "ES, evolution strategies, final fitness error, noisy optimization, optimization quality, robust optimization", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-005-7617-y", abstract = "Differential-geometric methods are applied to derive steady state conditions for the (mgr/mgrI,lambda)-ES on the general quadratic test function disturbed by fitness noise of constant strength. A new approach for estimating the expected final fitness deviation observed under such conditions is presented. The theoretical results obtained are compared with real ES runs, showing a surprisingly excellent agreement.", } @Proceedings{GECCO2005, title = "{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation", year = "2005", editor = "Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and Edwin D. {de Jong} and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler", address = "Washington DC, USA", publisher_address = "New York, NY, 10286-1405, USA", month = "25-29 " # jun, organisation = "ACM SIGEVO (formerly ISGEC)", publisher = "ACM Press", keywords = "genetic algorithms, genetic programming, A-Life, Evolutionary Robotics and Adaptive Behaviour, Ant Colony Optimisation and Swarm Intelligence, Artificial Immune Systems, Biological Applications, Coevolution, Estimation of Distribution Algorithms, Evolutionary Combinatorial Optimisation, Evolutionary Multi-objective Optimization, Evolutionary Strategies, Evolutionary Programming, Evolvable Hardware, Meta-heuristics and Local Search, Real World Applications, Search-based Software Engineering", ISBN = "1-59593-010-8", URL = "http://portal.acm.org/citation.cfm?id=1068009&jmp=cit&coll=GUIDE&dl=GUIDE&CFID=48779769&CFTOKEN=55479664#supp", abstract = "The papers in this two volume proceedings are presented at the 7th Annual Genetic and Evolutionary Computation COnference (GECCO-2005), held in Washington, D.C., June 25-29, 2005.This year is an exceptional one for the GECCO conference series. First, the International Society for Genetic and Evolutionary Computation (ISGEC) which has always been GECCO's sponsor has changed to become a Special Interest Group of the ACM named SIGEVO. Being part of ACM reflects the evolution and integration of our very successful discipline into the main stream of computer science. As a consequence, the GECCO-2005 proceedings are an ACM publication and they are incorporated into the ACM Digital Library. This guarantees an even broader dissemination of Darwinian and other nature-inspired computation methods.Second, we had 549 regular paper submissions representing the absolute record of all conferences emphasising the field of evolutionary computation. Paper reviewing has been done by double blind assignment. On average each paper was evaluated by five independent reviewers. Finally, 253 paper (46.1%) have been accepted as full (max. 8 pages) papers. Additionally, 120 submissions were accepted as posters.A goal of GECCO is to encourage new areas and paradigms of evolutionary computation to gather momentum and flourish. This is accomplished by the establishment of new independent tracks each year. This year, as a result of a recombinative and creative process, GECCO-2005 comprises 16 tracks consisting of core tracks ({"}C{"}), tracks previously in GECCOs ({"}P{"}), not yet belonging to the core track family), {"}recombined{"} tracks from GECCO 2004 ({"}R{"}), and newly created tracks ({"}N{"}):.", notes = "GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM Order Number 910052", } @Article{Beyer:2006:GPEM, author = "Hans-Georg Beyer", title = "Special Issue: Best of GECCO 2005", journal = "Genetic Programming and Evolvable Machines", year = "2006", volume = "7", number = "2", pages = "129--130", month = aug, keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-006-9002-x", size = "2 pages", notes = "Introduction to special issue", } @Article{Beyer:2007:GPEM, author = "Hans-Georg Beyer and Silja Meyer-Nieberg", title = "Self-adaptation of evolution strategies under noisy fitness evaluations", journal = "Genetic Programming and Evolvable Machines", year = "2006", volume = "7", number = "4", pages = "295--328", month = dec, keywords = "Evolution strategies, Self-adaptation, Noisy optimisation, Noisy sphere model", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-006-9017-3", size = "34 pages", abstract = "This paper investigates the self-adaptation behaviour of (1,L)-evolution strategies (ES) on the noisy sphere model. To this end, the stochastic system dynamics is approximated on the level of the mean value dynamics. Being based on this microscopic analysis, the steady state behavior of the ES for the scaled noise scenario and the constant noise strength scenario will be theoretically analysed and compared with real ES runs. An explanation will be given for the random walk like behaviour of the mutation strength in the vicinity of the steady state. It will be shown that this is a peculiarity of the (1,L)-ES and that intermediate recombination strategies do not suffer from such behaviour.", } @InCollection{bezdek:1999:EADC, author = "Trevor Bezdek", title = "Evolution and Analysis of DNA Classifiers", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1999", year = "1999", editor = "John R. Koza", pages = "21--30", address = "Stanford, California, 94305-3079 USA", month = "15 " # mar, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:1999:GAGPs}", } @Article{Bhalla:2009:GPEM, author = "Navneet Bhalla", title = "Natalio Krasnogor, Steve Gustafson, David A. Pelta, and Jose L. Verdegay (eds): Systems self-assembly: multidisciplinary snapshots Elsevier, 2008, 310 pp, 41 colour plates, hard cover, \$160 USD list price, ISBN 978-0-444-52865-0", journal = "Genetic Programming and Evolvable Machines", year = "2009", volume = "10", number = "4", pages = "473--475", month = dec, note = "Book Review", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-009-9088-z", size = "3 pages", } @InProceedings{bhanu:2002:GECCO:workshop, title = "Coevolutionary Construction of Features for Transformation of Representation in Machine Learning", author = "Bir Bhanu and Krzysztof Krawiec", pages = "249--254", booktitle = "{GECCO 2002}: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference", editor = "Alwyn M. Barry", year = "2002", month = "8 " # jul, publisher = "AAAI", address = "New York", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025", keywords = "genetic algorithms, genetic programming", broken = "http://www-idss.cs.put.poznan.pl/~krawiec/./pubs/gecco2002.pdf", broken = "http://citeseer.ist.psu.edu/509773.html", URL = "https://www.researchgate.net/publication/2496301", size = "6 pages", abstract = "The main objective of this paper is to study the usefulness of cooperative coevolutionary algorithms (CCA) for improving the performance of classification of machine learning (ML) classifiers, in particular those following the symbolic paradigm. For this purpose, we present a genetic programming (GP) -based coevolutionary feature construction procedure. In the experimental part, we confront the coevolutionary methodology with difficult real-world ML task with unknown internal structure and complex interrelationships between solution subcomponents (features), as opposed to artificial problems considered usually in the literature.", notes = "also known as \cite{BhanuKrawiec02} Bird-of-a-feather Workshops, GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002) part of barry:2002:GECCO:workshop", } @InProceedings{bhanu:2002:gecco, author = "Bir Bhanu and Yingqiang Lin", title = "Learning Composite Operators For Object Detection", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "1003--1010", address = "New York", publisher_address = "San Francisco, CA 94104, USA", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "genetic algorithms, genetic programming, real world applications, composite operators, genetic image segmentation, object detection", ISBN = "1-55860-878-8", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/RWA165_v2.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-20.pdf", notes = "GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)", } @Article{bhanu:2004:ASC, author = "Bir Bhanu and Yingqiang Lin", title = "Object detection in multi-modal images using genetic programming", journal = "Applied Soft Computing", year = "2004", volume = "4", number = "2", pages = "175--201", month = may, keywords = "genetic algorithms, genetic programming", broken = "http://www.sciencedirect.com/science/article/B6W86-4BV444R-1/2/7540dd938c0b2f3059b1afb5382bd28a", DOI = "doi:10.1016/j.asoc.2004.01.004", abstract = "In this paper, we learn to discover composite operators and features that are synthesized from combinations of primitive image processing operations for object detection. Our approach is based on genetic programming (GP). The motivation for using GP-based learning is that we hope to automate the design of object detection system by automatically synthesizing object detection procedures from primitive operations and primitive features. There are many basic operations that can operate on images and the ways of combining these primitive operations to perform meaningful processing for object detection are almost infinite. The human expert, limited by experience, knowledge and time, can only try a very small number of conventional combinations. Genetic programming, on the other hand, attempts many unconventional combinations that may never be imagined by human experts. In some cases, these unconventional combinations yield exceptionally good results. To improve the efficiency of GP, we propose soft composite operator size limit to control the code-bloat problem while at the same time avoid severe restriction on the GP search. Our experiments, which are performed on selected regions of images to improve training efficiency, show that GP can synthesize effective composite operators consisting of pre-designed primitive operators and primitive features to effectively detect objects in images and the learned composite operators can be applied to the whole training image and other similar testing images.", } @InProceedings{bhanu:2003:gecco, author = "Krzysztof Krawiec and Bir Bhanu", title = "Coevolution and Linear Genetic Programming for Visual Learning", booktitle = "Genetic and Evolutionary Computation -- GECCO-2003", editor = "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller", year = "2003", pages = "332--343", address = "Chicago", publisher_address = "Berlin", month = "12-16 " # jul, volume = "2723", series = "LNCS", ISBN = "3-540-40602-6", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, Coevolution", DOI = "doi:10.1007/3-540-45105-6_39", abstract = "a novel genetically-inspired visual learning method is proposed. Given the training images, this general approach induces a sophisticated feature-based recognition system, by using cooperative coevolution and linear genetic programming for the procedural representation of feature extraction agents. The paper describes the learning algorithm and provides a firm rationale for its design. An extensive experimental evaluation, on the demanding real-world task of object recognition in synthetic aperture radar (SAR) imagery, shows the competitiveness of the proposed approach with human-designed recognition systems.", notes = "GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)", } @InProceedings{bhanu:fsu:gecco2004, author = "Bir Bhanu and Jiangang Yu and Xuejun Tan and Yingqiang Lin", title = "Feature Synthesis Using Genetic Programming for Face Expression Recognition", booktitle = "Genetic and Evolutionary Computation -- GECCO-2004, Part II", year = "2004", editor = "Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell", series = "Lecture Notes in Computer Science", pages = "896--907", address = "Seattle, WA, USA", publisher_address = "Heidelberg", month = "26-30 " # jun, organisation = "ISGEC", publisher = "Springer-Verlag", volume = "3103", ISBN = "3-540-22343-6", ISSN = "0302-9743", DOI = "doi:10.1007/b98645", size = "12", keywords = "genetic algorithms, genetic programming", notes = "GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)", } @Article{Bhanu:2004:PRL, author = "Bir Bhanu and Yingqiang Lin", title = "Synthesizing feature agents using evolutionary computation", journal = "Pattern Recognition Letters", year = "2004", volume = "25", pages = "1519--1531", number = "13", abstract = "genetic programming (GP) with smart crossover and smart mutation is proposed to discover integrated feature agents that are evolved from combinations of primitive image processing operations to extract regions-of-interest (ROIs) in remotely sensed images. The motivation for using genetic programming is to overcome the limitations of human experts, since GP attempts many unconventional ways of combination, in some cases, these unconventional combinations yield exceptionally good results. Smart crossover and smart mutation identify and keep the effective components of integrated operators called {"}agents{"} and significantly improve the efficiency of GP. Our experimental results show that compared to normal GP, our GP algorithm with smart crossover and smart mutation can find good agents more quickly during training to effectively extract the regions-of-interest and the learned agents can be applied to extract ROIs in other similar images.", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6V15-4CRY8J6-2/2/d245bfcfeee2d509066321e19d84a0fd", month = "1 " # oct, note = "Pattern Recognition for Remote Sensing (PRRS 2002)", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1016/j.patrec.2004.06.005", size = "13 pages", notes = "SAR", } @Book{Bhanu:book, author = "Bir Bhanu and Yingqiang Lin and Krzysztof Krawiec", title = "Evolutionary Synthesis of Pattern Recognition Systems", year = "2005", publisher = "Springer-Verlag", address = "New York", series = "Monographs in Computer Science", keywords = "genetic algorithms, genetic programming, visual learning, feature synthesis, Computer vision, Image processing, Object detection, Pattern recognition", ISBN = "0-387-21295-7", URL = "http://www.springer.com/west/home/computer/imaging?SGWID=4-149-22-39144807-detailsPage=ppmmedia|aboutThisBook", size = "296 pages", } @Article{Bharadwaj:2007:waset, author = "Kamal K. Bharadwaj and Basheer M. Al-Maqaleh", title = "Evolutionary Approach for Automated Discovery of Censored Production Rules", journal = "International Journal of Computer, Information Science and Engineering", volume = "1", number = "10", year = "2007", pages = "11--16", keywords = "genetic algorithms, genetic programming, data mining, machine learning, evolutionary algorithms", bibsource = "http://waset.org/Publications", ISSN = "1307-6892", publisher = "World Academy of Science, Engineering and Technology", index = "International Science Index 10, 2007", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.308.7101", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.308.7101", URL = "http://waset.org/publications/14169", URL = "http://waset.org/Publications?p=10", size = "6 pages", abstract = "In the recent past, there has been an increasing interest in applying evolutionary methods to Knowledge Discovery in Databases (KDD) and a number of successful applications of Genetic Algorithms (GA) and Genetic Programming (GP) to KDD have been demonstrated. The most predominant representation of the discovered knowledge is the standard Production Rules (PRs) in the form If P Then D. The PRs, however, are unable to handle exceptions and do not exhibit variable precision. The Censored Production Rules (CPRs), an extension of PRs, were proposed by Michalski & Winston that exhibit variable precision and supports an efficient mechanism for handling exceptions. A CPR is an augmented production rule of the form: If P Then D Unless C, where C (Censor) is an exception to the rule. Such rules are employed in situations, in which the conditional statement 'If P Then D' holds frequently and the assertion C holds rarely. By using a rule of this type we are free to ignore the exception conditions, when the resources needed to establish its presence are tight or there is simply no information available as to whether it holds or not. Thus, the 'If P Then D' part of the CPR expresses important information, while the Unless C part acts only as a switch and changes the polarity of D to ~D. This paper presents a classification algorithm based on evolutionary approach that discovers comprehensible rules with exceptions in the form of CPRs. The proposed approach has flexible chromosome encoding, where each chromosome corresponds to a CPR. Appropriate genetic operators are suggested and a fitness function is proposed that incorporates the basic constraints on CPRs. Experimental results are presented to demonstrate the performance of the proposed algorithm.", notes = "oai:CiteSeerX.psu:10.1.1.308.7101 http://waset.org/publications/14169", } @Article{Bharambe:2013:ijetae, author = "Dewendra Bharambe and Susheel Jain and Anurag Jain", title = "A Detection of Duplicate Records from Multiple Web Databases using pattern matching in UDD", journal = "International Journal of Emerging Technology and Advanced Engineering", year = "2013", volume = "3", number = "5", pages = "412--417", month = may, keywords = "genetic algorithms, genetic programming, data deduplication, UDD, SVM, WCSS, genetic algorithm, pattern matching", ISSN = "2250--2459", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.413.7928", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.413.7928", URL = "http://www.ijetae.com/files/Volume3Issue5/IJETAE_0513_68.pdf", URL = "http://www.ijetae.com/Volume3Issue5.html", abstract = "Record matching refers to the task of finding entries that refer to the same entity in two or more files, is a vital process in data integration. Most of the supervised record matching methods require training data provided by users. Such methods can not apply for web database scenario, where query results dynamically generated. In existing system, an unsupervised record matching method effectively identifies the duplicates from query result records of multiple web databases by identifying the duplicate and non duplicate set in the source and from that non duplicate set again searches for the existence of duplication. Then use two co-operative classifiers from the non duplicate set, they are Weighted Component Similarity Summing (WCSS) Classifier and Support Vector Machine (SVM) classifier. These two classifiers can be used to identify the query results iteratively from multiple web databases. In this paper we modify record matching algorithm with genetic algorithm. The genetic programming is time consuming so we proposed UDD with genetic programming. A performance evaluation for accuracy is done for the dataset with duplicates using UDD and UDD with Genetic algorithm.", notes = "Article 68.", } @Article{Bhardwaj:2011:ACIJ, author = "Arpit Bhardwaj and Aditi Sakalle and Harshita Chouhan and Harshit Bhardwaj", title = "Controlling The Problem Of Bloating Using Stepwise Crossover And Double Mutation Technique", year = "2011", journal = "Advanced Computing : an International Journal", volume = "2", number = "6", pages = "59--68", month = nov, publisher = "Academy \& Industry Research Collaboration Center (AIRCC)", keywords = "genetic algorithms, genetic programming, bloat, stepwise crossover, double mutation, elitism, fitness, Java, Oracle 10g", ISSN = "2229726X", URL = "http://airccse.org/journal/acij/papers/1111acij06.pdf", broken = "http://www.doaj.org/doaj?func=openurl\&genre=article\&issn=2229726X\&date=2011\&volume=2\&issue=6\&spage=59", DOI = "doi:10.5121/acij.2011.2606", size = "10 pages", abstract = "During the evolution of solutions using genetic programming (GP) there is generally an increase in average tree size without a corresponding increase in fitness---a phenomenon commonly referred to as bloat. The conception of bloat in Genetic Programming is a well naturalised phenomenon characterised by variable-length genomes gradually maturating in size during evolution. 'In a very real sense, bloating makes genetic programming a race against time, to find the best solution possible before bloat puts an effective stop to the search.' In this paper we are proposing a Stepwise crossover and double mutation operation in order to reduce the bloat. In this especial crossover operation we are using local elitism replacement in combination with depth limit and size of the trees to reduce the problem of bloat substantially without compromising the performance. The use of local elitism in crossover and mutation increases the accuracy of the operation and also reduces the problem of bloat and further improves the performance. To shew our approach we have designed a Multiclass Classifier using GP by taking few benchmark datasets.", notes = "IRIS, WBC, BUPA, WINE, ABALONE, SPOKEN ARABIC DIGIT, HILL-VALLEY", bibsource = "OAI-PMH server at www.doaj.org", oai = "oai:doaj-articles:4e07bf6e3c343d42b02de6aed48a4d17", } @InProceedings{Bhardwaj:2013:GECCOcomp, author = "Arpit Bhardwaj and Aruna Tiwari", title = "Performance improvement in genetic programming using modified crossover and node mutation", booktitle = "GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion", year = "2013", editor = "Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A. N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks", isbn13 = "978-1-4503-1964-5", keywords = "genetic algorithms, genetic programming", pages = "1721--1722", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2464576.2480787", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "During the evolution of solutions using Genetic Programming (GP) there is generally an increase in average tree size without a corresponding increase in fitness'a phenomenon commonly referred to as bloat. Bloating increases time to find the best solution. Sometimes, best solution can never be obtained. In this paper we are proposing a modified crossover and point mutation operation in GP algorithm in order to reduce the problem of bloat. To demonstrate our approach, we have designed a Multiclass Classifier using GP by taking few benchmark datasets. The results obtained show that by applying modified crossover together with modified node mutation reduces the problem of bloat substantially without compromising the performance.", notes = "Also known as \cite{2480787} Distributed at GECCO-2013.", } @InProceedings{Bhardwaj:2013:ICIC, author = "Arpit Bhardwaj and Aruna Tiwari", title = "A Novel Genetic Programming Based Classifier Design Using a New Constructive Crossover Operator with a Local Search Technique", booktitle = "International Conference on Intelligent Computing (ICIC 2013)", year = "2013", editor = "De-Shuang Huang and Vitoantonio Bevilacqua and Juan Carlos Figueroa and Prashan Premaratne", volume = "7995", series = "Lecture Notes in Computer Science", pages = "86--95", address = "Nanning, China", month = jul # " 28-31", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Crossover, Local Search Technique", isbn13 = "978-3-642-39478-2", DOI = "doi:10.1007/978-3-642-39479-9_11", size = "10 pages", abstract = "A common problem in genetic programming search algorithms is the destructive nature of the crossover operator in which the offspring of good parents generally has worse performance than the parents. Designing constructive crossover operators and integrating some local search techniques into the breeding process have been suggested as solutions. In this paper, we proposed the integration of variants of local search techniques in the breeding process, done by allowing parents to produce many off springs and applying a selection procedure to choose high performing off springs. Our approach has removed the randomness of crossover operator. To demonstrate our approach, we designed a Multiclass classifier and tested it on various benchmark datasets. Our method has shown the tremendous improvement over the other state of the art methods.", } @InProceedings{Bhardwaj:2014:GECCOcomp, author = "Arpit Bhardwaj and Aruna Tiwari and M. Vishaal Varma and M. Ramesh Krishna", title = "Classification of EEG signals using a novel genetic programming approach", booktitle = "GECCO 2014 Workshop on Medical Applications of Genetic and Evolutionary Computation (MedGEC)", year = "2014", editor = "Stephen L. Smith and Stefano Cagnoni and Robert M. Patton", isbn13 = "978-1-4503-2881-4", keywords = "genetic algorithms, genetic programming", pages = "1297--1304", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2598394.2609851", DOI = "doi:10.1145/2598394.2609851", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "In this paper, we present a new method for classification of electroencephalogram (EEG) signals using Genetic Programming (GP). The Empirical Mode Decomposition (EMD) is used to extract the features of EEG signals which served as an input for the GP. In this paper, new constructive crossover and mutation operations are also produced to improve GP. In these constructive crossover and mutation operators hill climbing search is integrated to remove the destructive nature of these operators. To improve GP, we apply constructive crossover on all the individuals which remain after reproduction. A new concept of selecting the global prime off-springs of the generation is also proposed. The constructive mutation approach is applied to poor individuals who are left after selecting globally prime off-springs. Improvement of the method is measured against classification accuracy, training time and the number of generations for EEG signal classification. As we show in the results section, the classification accuracy can be estimated to be 98.69percent on the test cases, which is better than classification accuracy of Liang and coworkers method which was published in 2010.", notes = "Also known as \cite{2609851} Distributed at GECCO-2014.", } @InProceedings{Bhardwaj:2014:BMEI, author = "Arpit Bhardwaj and Aruna Tiwari and Dharmil Chandarana and Darshil Babel", title = "A Genetically Optimized Neural Network for Classification of Breast Cancer Disease", booktitle = "7th International Conference on Biomedical Engineering and Informatics (BMEI 2014)", year = "2014", month = oct, pages = "693--698", keywords = "genetic algorithms, genetic programming, ANN, SVN", DOI = "doi:10.1109/BMEI.2014.7002862", size = "6 pages", abstract = "In this paper, we propose a new, Genetically Optimised Neural Network (GONN) algorithm, for solving classification problems. We evolve a neural network genetically to optimise its structure for classification. We introduce new crossover and mutation operations which differ from a normal Genetic programming life-cycle to reduce the destructive nature of these operations. We use the GONN algorithm to classify breast cancer tumours as benign or malignant. Accurate classification of a breast cancer tumour is an important task in medical diagnosis. Our algorithm gives better classification accuracy of almost 4percent and 2percent more than a Back Propagation neural network and a Support Vector Machine respectively.", notes = "Discipline of Comput. Sci. & Eng., Indian Inst. of Technol. Indore, Indore, India Also known as \cite{7002862}", } @Article{Bhardwaj:2015:ESA, author = "Arpit Bhardwaj and Aruna Tiwari", title = "Breast cancer diagnosis using Genetically Optimized Neural Network model", journal = "Expert Systems with Applications", volume = "42", number = "10", pages = "4611--4620", year = "2015", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2015.01.065", URL = "http://www.sciencedirect.com/science/article/pii/S0957417415000883", abstract = "One in every eight women is susceptible to breast cancer, at some point of time in her life. Early detection and effective treatment is the only rescue to reduce breast cancer mortality. Accurate classification of a breast cancer tumour is an important task in medical diagnosis. Machine learning techniques are gaining importance in medical diagnosis because of their classification capability. In this paper, we propose a new, Genetically Optimised Neural Network (GONN) algorithm, for solving classification problems. We evolve a neural network genetically to optimize its architecture (structure and weight) for classification. We introduce new crossover and mutation operators which differ from standard crossover and mutation operators to reduce the destructive nature of these operators. We use the GONN algorithm to classify breast cancer tumors as benign or malignant. To demonstrate our results, we had taken the WBCD database from UCI Machine Learning repository and compared the classification accuracy, sensitivity, specificity, confusion matrix, ROC curves and AUC under ROC curves of GONN with classical model and classical back propagation model. Our algorithm gives classification accuracy of 98.24percent, 99.63percent and 100percent for 50-50, 60-40, 70-30 training-testing partition respectively and 100percent for 10 fold cross validation. The results show that our approach works well with the breast cancer database and can be a good alternative to the well-known machine learning methods.", keywords = "genetic algorithms, genetic programming, Genetically Optimised Neural Network, Artificial Neural Network, Modified Crossover Operator", } @InProceedings{Bhardwaj:2015:GECCO, author = "Arpit Bhardwaj and Aruna Tiwari and M. Vishaal Varma and M. Ramesh Krishna", title = "An Analysis of Integration of Hill Climbing in Crossover and Mutation operation for EEG Signal Classification", booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation", year = "2015", editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr", isbn13 = "978-1-4503-3472-3", pages = "209--216", keywords = "genetic algorithms, genetic programming, Biological and Biomedical Applications", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739480.2754710", DOI = "doi:10.1145/2739480.2754710", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "A common problem in the diagnosis of epilepsy is the volatile and unpredictable nature of the epileptic seizures. Hence, it is essential to develop Automatic seizure detection methods. Genetic programming (GP) has a potential for accurately predicting a seizure in an EEG signal. However, the destructive nature of crossover operator in GP decreases the accuracy of predicting the onset of a seizure. Designing constructive crossover and mutation operators (CCM) and integrating local hill climbing search technique with the GP have been put forward as solutions. In this paper, we proposed a hybrid crossover and mutation operator, which uses both the standard GP and CCM-GP, to choose high performing individuals in the least possible time. To demonstrate our approach, we tested it on a benchmark EEG signal dataset. We also compared and analysed the proposed hybrid crossover and mutation operation with the other state of art GP methods in terms of accuracy and training time. Our method has shown remarkable classification results. These results affirm the potential use of our method for accurately predicting epileptic seizures in an EEG signal and hint on the possibility of building a real time automatic seizure detection system.", notes = "Also known as \cite{2754710} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)", } @Article{Bhardwaj:2016:CMPB, author = "Arpit Bhardwaj and Aruna Tiwari and Ramesh Krishna and Vishaal Varma", title = "A novel genetic programming approach for epileptic seizure detection", journal = "Computer Methods and Programs in Biomedicine", volume = "124", pages = "2--18", year = "2016", ISSN = "0169-2607", DOI = "doi:10.1016/j.cmpb.2015.10.001", URL = "http://www.sciencedirect.com/science/article/pii/S016926071500262X", abstract = "The human brain is a delicate mix of neurons (brain cells), electrical impulses and chemicals, known as neurotransmitters. Any damage has the potential to disrupt the workings of the brain and cause seizures. These epileptic seizures are the manifestations of epilepsy. The electroencephalograph (EEG) signals register average neuronal activity from the cerebral cortex and label changes in activity over large areas. A detailed analysis of these electroencephalograph (EEG) signals provides valuable insights into the mechanisms instigating epileptic disorders. Moreover, the detection of interictal spikes and epileptic seizures in an EEG signal plays an important role in the diagnosis of epilepsy. Automatic seizure detection methods are required, as these epileptic seizures are volatile and unpredictable. This paper deals with an automated detection of epileptic seizures in EEG signals using empirical mode decomposition (EMD) for feature extraction and proposes a novel genetic programming (GP) approach for classifying the EEG signals. Improvements in the standard GP approach are made using a Constructive Genetic Programming (CGP) in which constructive crossover and constructive subtree mutation operators are introduced. A hill climbing search is integrated in crossover and mutation operators to remove the destructive nature of these operators. A new concept of selecting the Globally Prime offspring is also presented to select the best fitness offspring generated during crossover. To decrease the time complexity of GP, a new dynamic fitness value computation (DFVC) is employed to increase the computational speed. We conducted five different sets of experiments to evaluate the performance of the proposed model in the classification of different mixtures of normal, interictal and ictal signals, and the accuracies achieved are outstandingly high. The experimental results are compared with the existing methods on same datasets, and these results affirm the potential use of our method for accurately detecting epileptic seizures in an EEG signal.", keywords = "genetic algorithms, genetic programming, Constructive crossover, Dynamic fitness value computation, Epilepsy", } @InProceedings{Bhardwaj:2015:IC4, author = "Harshit Bhardwaj and Pankaj Dashore", booktitle = "2015 International Conference on Computer, Communication and Control (IC4)", title = "A novel genetic programming approach to control bloat using crossover and mutation with intelligence technique", year = "2015", abstract = "Bloat is a problem that occurs when there is no advancement in fitness measure, but the size of the tree grows exponentially. Bloat eventually increases the time required to reach the optimal solution. To overcome this defect, Crossover and Mutation with Intelligence technique is proposed. We also used double tournament, in which we apply two tournaments on the basis of size and fitness respectively to select the individuals to perform Crossover with Intelligence. Our approach of overcoming bloat is tested experimentally on some benchmark datasets picked up from UCI repository and by some observations. The results verified that our Crossover and Mutation with Intelligence degrades the bloat phenomena.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IC4.2015.7375619", month = sep, notes = "Comput. Sci. Dept., Medicaps Inst. of Sci. & Technol., Indore, India Also known as \cite{7375619}", } @InProceedings{Bhardwaj:2018:ieeeCompIntl, author = "Harshit Bhardwaj and Aditi Sakalle and Arpit Bhardwaj and Aruna Tiwari and Madhushi Verma", booktitle = "2018 IEEE Symposium Series on Computational Intelligence (SSCI)", title = "Breast Cancer Diagnosis using Simultaneous Feature Selection and Classification: A Genetic Programming Approach", year = "2018", pages = "2186--2192", abstract = "Breast cancer is the most prevalent type of cancer found in women worldwide. It is becoming a leading cause of death among women in the whole world. Early detection and effective treatment of this disease is the only rescue to reduce breast cancer mortality. Because of the effective classification and high diagnostic capability expert systems are gaining popularity in this field. But the problem with machine learning algorithms is that if redundant and irrelevant features are available in the dataset then they are not being able to achieve desired performance. Therefore, in this paper, a simultaneous feature selection and classification technique using Genetic Programming (GPsfsc) is proposed for breast cancer diagnosis. To demonstrate our results, we had taken the Wisconsin Breast Cancer (WBC) and Wisconsin Diagnostic Breast Cancer (WDBC) databases from UCI Machine Learning repository and compared the classification accuracy, sensitivity, specificity, confusion matrix, and Mann Whitney test results of GONN with classical multi-tree GP algorithm for feature selection (GPmtfs). The experimental results on WBC and WDBC datasets show that the proposed method produces better classification accuracy with reduced features. Therefore, our proposed method is of great significance and can serve as first-rate clinical tool for the detection of breast cancer.", keywords = "genetic algorithms, genetic programming, Computational intelligence, Feature Selection, Breast Cancer Diagnosis, Classification", DOI = "doi:10.1109/SSCI.2018.8628935", month = nov, notes = "Also known as \cite{8628935}", } @Article{Bhardwaj:2019:ES, author = "Harshit Bhardwaj and Aditi Sakalle and Arpit Bhardwaj and Aruna Tiwari", title = "Classification of electroencephalogram signal for the detection of epilepsy using Innovative Genetic Programming", journal = "Expert Systems", year = "2019", volume = "36", number = "1", pages = "e12338", month = feb, keywords = "genetic algorithms, genetic programming", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/es/es36.html#BhardwajSBT19", DOI = "doi:10.1111/exsy.12338", abstract = "Epilepsy, sometimes called seizure disorder, is a neurological condition that justifies itself as a susceptibility to seizures. A seizure is a sudden burst of rhythmic discharges of electrical activity in the brain that causes an alteration in behaviour, sensation, or consciousness. It is essential to have a method for automatic detection of seizures, as these seizures are arbitrary and unpredictable. A profound study of the electroencephalogram (EEG) recordings is required for the accurate detection of these epileptic seizures. In this study, an Innovative Genetic Programming framework is proposed for classification of EEG signals into seizure and non-seizure. An empirical mode decomposition technique is used for the feature extraction followed by genetic programming for the classification. Moreover, a method for intron deletion, hybrid crossover, and mutation operation is proposed, which are responsible for the increase in classification accuracy and a decrease in time complexity. This suggests that the Innovative Genetic Programming classifier has a potential for accurately predicting the seizures in an EEG signal and hints on the possibility of building a real-time seizure detection system.", notes = "journals/es/BhardwajSBT19,", } @Book{bhardwaj:atgp1, author = "Arpit Bhardwaj and Aruna Tiwari and Jasjit S. Suri", title = "Advances and Trends in Genetic Programming: Volume 1: Classification Techniques and Life Cycles Paperback", publisher = "Academic Press", year = "2022", keywords = "genetic algorithms, genetic programming", isbn13 = "978-0128180204", URL = "https://www.amazon.co.uk/s?k=advances+and+trends+in+genetic+programming+%3A+volume+1%3A+classification+techniques+and+life+cycles", size = "220 pages", notes = " Sep 2023 Currently unavailable", } @InProceedings{Bhargava:2023:CSET, author = "Krishna Bhargava A and Deepak Kumar Sinha and Garima Sinha", booktitle = "2023 International Conference on Computer Science and Emerging Technologies (CSET)", title = "Network Optimization Using Genetic Programming", year = "2023", abstract = "Evolutionary Algorithms form the base for creating Artificial Intelligence applications and systems. Evolutionary Computation forms the basis of those algorithms that are used in day to day and future applications. Computer networks as we know, form a major contributor to those data sources. Big Data and all types of computer related data are found on the internet and the internet forms sources of data all over the world. Therefore, data modulation techniques form a basis of data transfer over the internet all over the world. Therefore, Evolutionary Networks are the type of computer networks that evolve over time due to the evolutionary nature of the algorithms involved. Our intention in the paper is to create a fault free data modulation technique used in computer networking. In the network, the timing is based on the evolutionary data of the customer. The internet history of the customer is taken into consideration and the data modulation is based on the customer history. How this is done is based on the pheromone concentration of ant colony optimisation and the probable path of the bee algorithm. These evolutionary algorithms are fed to the firewall and routing tables of the network to form the evolutionary network generation and user behaviour. When the algorithms working behind firewall and routing tables are replaced by Evolutionary Algorithms, then they start to showcase evolutionary behaviour. This is the foundation principle on which this paper is based. Once the firewall and the routing table are evolved, the computer network to which they are connected becomes Evolutionary Networks.", keywords = "genetic algorithms, genetic programming, Firewalls (computing), Soft sensors, Modulation, Evolutionary computation, Routing, Behavioural sciences, Evolutionary Computation, Evolutionary Algorithms, Evolutionary Networking, Network Fitness, Data Fitness, Evolutionary based Data Modulation", DOI = "doi:10.1109/CSET58993.2023.10346779", month = oct, notes = "Also known as \cite{10346779}", } @Article{Bhargavi:2010:IJCSIT, title = "Soil Classification Using {GATREE}", author = "P. Bhargavi and S. Jyothi", journal = "International Journal of Computer Science \& Information Technology", year = "2010", volume = "2", number = "5", pages = "184--191", keywords = "genetic algorithms, genetic programming, data mining, soil profile, soil database, classification", ISSN = "09754660", URL = "http://airccse.org/journal/jcsit/1010ijcsit14.pdf", DOI = "doi:10.5121/ijcsit.2010.2514", publisher = "Academy \& Industry Research Collaboration Centre (AIRCC)", bibsource = "OAI-PMH server at www.doaj.org", oai = "oai:doaj-articles:62c4c972981e7958ba9ff79981358355", size = "8 pages", abstract = "This paper details the application of a genetic programming framework for classification of decision tree of Soil data to classify soil texture. The database contains measurements of soil profile data. We have applied GATree for generating classification decision tree. GATree is a decision tree builder that is based on Genetic Algorithms(GAs). The idea behind it is rather simple but powerful. Instead of using statistic metrics that are biased towards specific trees we use a more flexible, global metric of tree quality that try to optimise accuracy and size. GATree offers some unique features not to be found in any other tree inducers while at the same time it can produce better results for many difficult problems. Experimental results are presented which illustrate the performance of generating best decision tree for classifying soil texture for soil data set.", } @InProceedings{Bhatt:2015:ieeeCGVIS, author = "M. S. Bhatt and T. P. Patalia", booktitle = "2015 IEEE International Conference on Computer Graphics, Vision and Information Security (CGVIS)", title = "Genetic programming evolved spatial descriptor for Indian monuments classification", year = "2015", pages = "131--136", abstract = "Travel and tourism are the largest service industries in India. Every year people visit tourist places. and upload pictures of their visit on social networking sites or share via mobile device with friends and relatives. Millions of such photographs are uploaded and it is almost impossible to manually classify these pictures as per the monuments they have visited. Classification is helpful to hoteliers for development of new hotel with state of the art amenities, to travel service providers, to restaurant owners, to government agencies for security etc. The proposed system extracts Genetic programming evolved spatial descriptor and classifies the Indian monuments visited by tourists based on linear Support Vector Machine(SVM). The proposed system is divided into 3 main phases: preprocessing, genetic programming evolution and classification. The Preprocessing phase converts images into a form suitable for processing by genetic programming system using Generalized Co-Occurrence Matrix. The second phase generates best so far spatial descriptor in the form of program based on the fitness. The Fitness is calculated using SVM. Once program is obtained as output it can be used for classification. The proposed system is implemented in MATLAB and achieves high accuracy.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CGVIS.2015.7449908", month = nov, notes = "Also known as \cite{7449908}", } @InProceedings{Bhattacharya:2001:GPR, author = "Maumita Bhattacharya and Baikunth Nath", title = "Genetic Programming: {A} Review of Some Concerns", volume = "2074", pages = "1031--1040", year = "2001", booktitle = "Proceedings of International Conference Computational Science Part~II - ICCS 2001", editor = "V. N. Alexandrov and J. J. Dongarra and B. A. Juliano and R. S. Renner and C. J. Kenneth Tan", series = "Lecture Notes in Computer Science", address = "San Francisco, CA, USA", month = may # " 28-30", publisher = "Springer", note = "Late Submissions", keywords = "genetic algorithms, genetic programming, bloat", CODEN = "LNCSD9", ISSN = "0302-9743", isbn13 = "978-3-540-42233-4", bibdate = "Sat Feb 2 13:04:30 MST 2002", DOI = "doi:10.1007/3-540-45718-6_109", acknowledgement = ack-nhfb, size = "10 pages", abstract = "Genetic Programming (GP) is gradually being accepted as a promising variant of Genetic Algorithm (GA) that evolves dynamic hierarchical structures, often described as programs. In other words GP seemingly holds the key to attain the goal of 'automated program generation'. However one of the serious problems of GP lies in the 'code growth' or 'size problem' that occurs as the structures evolve, leading to excessive pressure on system resources and unsatisfying convergence. Several researchers have addressed the problem. However, absence of a general framework and physical constraints, viz, infinitely large resource requirements have made it difficult to find any generic explanation and hence solution to the problem. This paper surveys the major research works in this direction from a critical angle. Overview of a few other major GP concerns is covered in brief. We conclude with a general discussion on code growth and other critical aspects of GP techniques, while attempting to highlight on future research directions to tackle such problems.", } @InProceedings{bhattacharya:2001:HIS, title = "A Linear Genetic Programming Approach for Modeling Electricity Demand Prediction in Victoria", author = "Maumita Bhattacharya and Ajith Abraham and Baikunth Nath", editor = "Ajith Abraham and Mario Koppen", booktitle = "2001 International Workshop on Hybrid Intelligent Systems", series = "LNCS", pages = "379--394", publisher = "Springer-Verlag", address = "Adelaide, Australia", publisher_address = "Berlin", month = "11-12 " # dec, year = "2001", email = "maumita.bhattacharya@infotech.monash.edu.au, ajith.abraham@infotech.monash.edu.au, b.nath@infotech.monash.edu.au", keywords = "genetic algorithms, genetic programming, Linear genetic programming, neuro-fuzzy, neural networks, forecasting, electricity demand", broken = "http://www-mugc.cc.monash.edu.au/~abrahamp/172.pdf", URL = "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-7908-1480-6", URL = "http://citeseer.ist.psu.edu/510872.html", ISBN = "3-7908-1480-6", abstract = "Genetic programming (GP), a relatively young and growing branch of evolutionary computation is gradually proving to be a promising method of modelling complex prediction and classification problems. This paper evaluates the suitability of a linear genetic programming (LGP) technique to predict electricity demand in the State of Victoria, Australia, while comparing its performance with two other popular soft computing techniques. The forecast accuracy is compared with the actual energy demand. To evaluate, we considered load demand patterns for ten consecutive months taken every 30 minutes for training the different prediction models. Test results show that while the linear genetic programming method delivered satisfactory results, the neuro fuzzy system performed best for this particular application problem, in terms of accuracy and computation time, as compared to LGP and neural networks.", notes = "HIS01 Possibly also of interest Applied Soft Computing Volume 1, Issue 2 , August 2001, Pages 127-138 doi:10.1016/S1568-4946(01)00013-8", } @InProceedings{bhattacharyya:1998:rsGPlhf, author = "Siddhartha Bhattacharyya and Olivier Pictet and Gilles Zumbach", title = "Representational Semantics for Genetic Programming Based Learning in High-Frequency Financial Data", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "11--16", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-548-7", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1998/bhattacharyya_1998_rsGPlhf.pdf", notes = "GP-98", } @Article{bhattacharyya:1998:DS, author = "Siddhartha Bhattacharyya and Parag C. Pendharkar", title = "Inductive, Evolutionary, and Neural Computing Techniques for Discrimination: A Comparative Study", journal = "Decision Sciences", year = "1998", volume = "29", number = "4", pages = "871--899", month = "Fall", keywords = "genetic algorithms, genetic programming, Discriminant Analysis, Inductive Learning, Machine Learning, and Neural Networks", ISSN = "00117315", URL = "http://tigger.uic.edu/~sidb/papers/DiscCompPaper_DecSci.pdf", size = "45 pages", abstract = "This paper provides a comparative study of machine learning techniques for two-group discrimination. Simulated data is used to examine how the different learning techniques perform with respect to certain data distribution characteristics. Both linear and nonlinear discrimination methods are considered. The data has been previously used in the comparative evaluation of a number of techniques and helps relate our findings across a range of discrimination techniques.", notes = "http://www.decisionsciences.org/dsj/ (USPS 884860) http://www.decisionsciences.org/dsj/Vol29_4/29_4_871.htm", } @InProceedings{347186, author = "Siddhartha Bhattacharyya", title = "Evolutionary algorithms in data mining: multi-objective performance modeling for direct marketing", booktitle = "KDD '00: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining", year = "2000", pages = "465--473", address = "Boston, Massachusetts, United States", publisher_address = "New York, NY, USA", organisation = "SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data AAAI : Am Assoc for Artifical Intelligence SIGART: ACM Special Interest Group on Artificial Intelligence SIGMOD: ACM Special Interest Group on Management of Data", publisher = "ACM Press", keywords = "genetic algorithms, genetic programming, Algorithms, Design, Experimentation, Human Factors, Management, Measurement, Performance, Theory, Pareto-optimal models, data mining, database marketing, evolutionary computation, multiple objectives", ISBN = "1-58113-233-6", URL = "http://tigger.uic.edu/~sidb/papers/MultiObj_KDD2000.pdf", URL = "http://portal.acm.org/ft_gateway.cfm?id=347186&type=pdf&coll=GUIDE&dl=GUIDE&CFID=43813975&CFTOKEN=68162530", DOI = "doi:10.1145/347090.347186", size = "9 pages", abstract = "Predictive models in direct marketing seek to identify individuals most likely to respond to promotional solicitations or other intervention programs. While standard modelling approaches embody single objectives, real-world decision problems often seek multiple performance measures. Decision-makers here desire solutions that simultaneously optimise on multiple objectives, or obtain an acceptable tradeoff amongst objectives. Multi-criteria problems often characterise a range of solutions, none of which dominate the others with respect to the multiple objectives, these specify the Pareto-frontier of nondominated solutions, each offering a different level of tradeoff. This paper proposes the use of evolutionary computation based procedures for obtaining a set of nondominated models with respect to multiple stated objectives. The targeting depth-of-file presents a crucial real-world criterion in direct marketing, and models here are tailored for specified file-depths. Decision-makers are thus able to obtain a set of models along the Pareto-frontier, for a specific file-depth. The choice of a model to implement can be thus based on observed tradeoffs in the different objectives, based on possibly subjective and problem specific judgements. Given distinct models tailored for different file-depths, the implementation decision can also consider performance tradeoffs at the different depths-offile. Empirical results from a real-world problem illustrate the benefits of the proposed approach. Both linear and nonlinear models obtained by genetic search are examined.", notes = "p470 'For the non-linear GP, results were found to be similar to those observed for the linear GA.' 'Elitism always provides improved performance'.", } @InCollection{bhattacharyya:2002:ECEF, author = "Siddhartha Bhattacharyya and Kumar Mehta", title = "Evolutionary Induction of Trading Models", booktitle = "Evolutionary Computation in Economics and Finance", publisher = "Physica Verlag", year = "2002", editor = "Shu-Heng Chen", volume = "100", series = "Studies in Fuzziness and Soft Computing", chapter = "17", pages = "311--332", month = "2002", keywords = "genetic algorithms, genetic programming", ISBN = "3-7908-1476-8", URL = "http://tigger.uic.edu/~sidb/papers/EvolInductionOfTradingModels.pdf", DOI = "doi:10.1007/978-3-7908-1784-3_17", abstract = "Financial markets data present a challenging opportunity for the learning of complex patterns not readily discernable. This paper investigates the use of genetic algorithms for the mining of financial time-series for patterns aimed at the provision of trading decision models. A simple yet flexible representation for trading rules is proposed, and issues pertaining to fitness evaluation examined. Two key issues in fitness evaluation, the design of a suitable fitness function reflecting desired trading characteristics and choice of appropriate training duration, are discussed and empirically examined. Two basic measures are also proposed for characterising rules obtained with alternate fitness criteria.", size = "22 pages", } @Article{bhattacharyya:2002:trEC, author = "Siddhartha Bhattacharyya and Olivier V. Pictet and Gilles Zumbach", title = "Knowledge-intensive genetic discovery in foreign exchange markets", journal = "IEEE Transactions on Evolutionary Computation", year = "2002", volume = "6", number = "2", pages = "169--181", month = apr, keywords = "genetic algorithms, genetic programming, Data mining, financial markets, foreign exchange markets, machine learning, semantic restrictions, trading models", ISSN = "1089-778X", URL = "http://tigger.uic.edu/~sidb/papers/KnowIntenGPForex__IEEE_EC.pdf", DOI = "doi:10.1109/4235.996016", size = "13 pages", abstract = "This paper considers the discovery of trading decision models from high-frequency foreign exchange (FX) markets data using genetic programming (GP). It presents a domain-related structuring of the representation and incorporation of semantic restrictions for GP-based searching of trading decision models. A defined symmetry property provides a basis for the semantics of FX trading models. The symmetry properties of basic indicator types useful in formulating trading models are defined, together with semantic restrictions governing their use in trading model specification. The semantics for trading model specification have been defined with respect to regular arithmetic, comparison and logical operators. This study also explores the use of two fitness criteria for optimization, showing more robust performance with a risk-adjusted measure of returns", notes = "CODEN: ITEVF5 INSPEC Accession Number:7256658", } @InProceedings{bhavita:2019:WREE, author = "K. Bhavita and D. Swathi and J. Manideep and D. Sree Sandeep and Maheswaran Rathinasamy", title = "{Regime-Wise} Genetic Programming Model for Improved Streamflow Forecasting", booktitle = "Water Resources and Environmental Engineering I", year = "2019", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-981-13-2044-6_17", DOI = "doi:10.1007/978-981-13-2044-6_17", } @InProceedings{Bhowan:2009:cec, author = "Urvesh Bhowan and Mark Johnston and Mengjie Zhang", title = "Differentiating Between Individual Class Performance in Genetic Programming Fitness for Classification with Unbalanced Data", booktitle = "2009 IEEE Congress on Evolutionary Computation", year = "2009", editor = "Andy Tyrrell", pages = "2802--2809", address = "Trondheim, Norway", month = "18-21 " # may, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-2959-2", file = "P289.pdf", DOI = "doi:10.1109/CEC.2009.4983294", abstract = "This paper investigates improvements to the fitness function in Genetic Programming to better solve binary classification problems with unbalanced data. Data sets are unbalanced when there is a majority of examples for one particular class over the other class(es). We show that using overall classification accuracy as the fitness function evolves classifiers with a performance bias toward the majority class at the expense of minority class performance. We develop four new fitness functions which consider the accuracy of majority and minority class separately to address this learning bias. Results using these fitness functions show that good accuracy for both the minority and majority classes can be achieved from evolved classifiers while keeping overall performance high and balanced across the two classes.", keywords = "genetic algorithms, genetic programming", notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR", } @InProceedings{Bhowan:2009:IVCNZ, title = "Genetic Programming for Image Classification with Unbalanced Data", author = "Urvesh Bhowan and Mengjie Zhang and Mark Johnston", booktitle = "Proceeding of the 24th International Conference Image and Vision Computing New Zealand, IVCNZ '09", year = "2009", month = "23-25 " # nov, pages = "316--321", ISSN = "2151-2205", address = "Wellington", publisher = "IEEE", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-4697-1", DOI = "doi:10.1109/IVCNZ.2009.5378388", abstract = "Image classification methods using unbalanced data can produce results with a performance bias. If the class representing important objects-of-interest is in the minority class, learning methods can produce the deceptive appearance of good looking results while recognition ability on the important minority class can be poor. This paper develops and compares two Genetic Programming (GP) methods for image classification problems with class imbalance. The first focuses on adapting the fitness function in GP to evolve classifiers with good individual class accuracy. The second uses a multi-objective approach to simultaneously evolve a set of classifiers along the trade-off surface representing minority and majority class accuracies. Evaluating our GP methods on two benchmark binary image classification problems with class imbalance, our results show that good solutions were evolved using both GP methods.", notes = "Also known as \cite{5378388}", } @InProceedings{DBLP:conf/ausai/BhowanZJ09, author = "Urvesh Bhowan and Mengjie Zhang and Mark Johnston", title = "Multi-Objective Genetic Programming for Classification with Unbalanced Data", booktitle = "Proceedings of the 22nd Australasian Joint Conference on Artificial Intelligence (AI'09)", year = "2009", editor = "Ann E. Nicholson and Xiaodong Li", volume = "5866", series = "Lecture Notes in Computer Science", pages = "370--380", bibsource = "DBLP, http://dblp.uni-trier.de", address = "Melbourne, Australia", month = dec # " 1-4", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-10438-1", DOI = "doi:10.1007/978-3-642-10439-8_38", abstract = "Existing learning and search algorithms can suffer a learning bias when dealing with unbalanced data sets. This paper proposes a Multi-Objective Genetic Programming (MOGP) approach to evolve a Pareto front of classifiers along the optimal trade-off surface representing minority and majority class accuracy for binary class imbalance problems. A major advantage of the MOGP approach is that by explicitly incorporating the learning bias into the search algorithm, a good set of well-performing classifiers can be evolved in a single experiment while canonical (single-solution) Genetic Programming (GP) requires some objective preference be a priori built into a fitness function. Our results show that a diverse set of solutions was found along the Pareto front which performed as well or better than canonical GP on four class imbalance problems.", } @InProceedings{Bhowan:2010:EuroGP, author = "Urvesh Bhowan and Mengjie Zhang and Mark Johnston", title = "Genetic Programming for Classification with Unbalanced Data", booktitle = "Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010", year = "2010", editor = "Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar", volume = "6021", series = "LNCS", pages = "1--13", address = "Istanbul", month = "7-9 " # apr, organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-12147-0", DOI = "doi:10.1007/978-3-642-12148-7_1", abstract = "Learning algorithms can suffer a performance bias when data sets only have a small number of training examples for one or more classes. In this scenario learning methods can produce the deceptive appearance of good looking results even when classification performance on the important minority class can be poor. This paper compares two Genetic Programming (GP) approaches for classification with unbalanced data. The first focuses on adapting the fitness function to evolve classifiers with good classification ability across both minority and majority classes. The second uses a multi-objective approach to simultaneously evolve a Pareto front (or set) of classifiers along the minority and majority class trade-off surface. Our results show that solutions with good classification ability were evolved across a range of binary classification tasks with unbalanced data.", notes = "Part of \cite{Esparcia-Alcazar:2010:GP} EuroGP'2010 held in conjunction with EvoCOP2010 EvoBIO2010 and EvoApplications2010", } @InProceedings{Bhowan:2010:gecco, author = "Urvesh Bhowan and Mengjie Zhang and Mark Johnston", title = "AUC analysis of the pareto-front using multi-objective GP for classification with unbalanced data", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "845--852", keywords = "genetic algorithms, genetic programming", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830483.1830639", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Learning algorithms can suffer a performance bias when data sets are unbalanced. This paper proposes a Multi-Objective Genetic Programming (MOGP) approach using the accuracy of the minority and majority class as learning objectives. We focus our analysis on the classification ability of evolved Pareto-front solutions using the Area Under the ROC Curve (AUC) and investigate which regions of the objective trade-off surface favour high-scoring AUC solutions. We show that a diverse set of well-performing classifiers is simultaneously evolved along the Pareto-front using the MOGP approach compared to canonical GP where only one solution is found along the objective trade-off surface, and that in some problems the MOGP solutions had better AUC than solutions evolved with canonical GP using hand-crafted fitness functions.", notes = "Also known as \cite{1830639} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @InProceedings{conf/ausai/BhowanZJ10, title = "A Comparison of Classification Strategies in Genetic Programming with Unbalanced Data", author = "Urvesh Bhowan and Mengjie Zhang and Mark Johnston", booktitle = "Australasian Conference on Artificial Intelligence", editor = "Jiuyong Li", year = "2010", volume = "6464", series = "Lecture Notes in Computer Science", pages = "243--252", address = "Adelaide", month = dec, publisher = "Springer", bibdate = "2010-11-30", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ausai/ausai2010.html#BhowanZJ10", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-17431-5", DOI = "doi:10.1007/978-3-642-17432-2_25", size = "10 pages", abstract = "Machine learning algorithms like Genetic Programming (GP) can evolve biased classifiers when data sets are unbalanced. In this paper we compare the effectiveness of two GP classification strategies. The first uses the standard (zero) class-threshold, while the second uses the best class-threshold determined dynamically on a solution-by-solution basis during evolution. These two strategies are evaluated using five different GP fitness across across a range of binary class imbalance problems, and the GP approaches are compared to other popular learning algorithms, namely, Naive Bayes and Support Vector Machines. Our results suggest that there is no overall difference between the two strategies, and that both strategies can evolve good solutions in binary classification when used in combination with an effective fitness function.", affiliation = "School of Engineering and Computer Science, Victoria University of Wellington, New Zealand", } @InProceedings{Bhowan:2011:GECCO, author = "Urvesh Bhowan and Mark Johnston and Mengjie Zhang", title = "Evolving ensembles in multi-objective genetic programming for classification with unbalanced data", booktitle = "GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0557-0", pages = "1331--1338", keywords = "genetic algorithms, genetic programming", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001576.2001756", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Machine learning algorithms can suffer a performance bias when data sets are unbalanced. This paper proposes a Multi-objective Genetic Programming approach using negative correlation learning to evolve accurate and diverse ensembles of non-dominated solutions where members vote on class membership. We also compare two popular Pareto-based fitness schemes on the classification tasks. We show that the evolved ensembles achieve high accuracy on both classes using six unbalanced binary data sets, and that this performance is usually better than many of its individual members.", notes = "Also known as \cite{2001756} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)", } @InProceedings{conf/ausai/BhowanJZ11, author = "Urvesh Bhowan and Mark Johnston and Mengjie Zhang", title = "Ensemble Learning and Pruning in Multi-Objective Genetic Programming for Classification with Unbalanced Data", booktitle = "Proceedings of the 24th Australasian Joint Conference Advances in Artificial Intelligence (AI 2011)", year = "2011", editor = "Dianhui Wang and Mark Reynolds", volume = "7106", series = "Lecture Notes in Computer Science", pages = "192--202", address = "Perth, Australia", month = dec # " 5-8", publisher = "Springer", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1007/978-3-642-25832-9_20", size = "11 pages", abstract = "Machine learning algorithms can suffer a performance bias when data sets are unbalanced. This paper develops a multi-objective genetic programming approach to evolving accurate and diverse ensembles of non-dominated solutions where members vote on class membership. We explore why the ensembles can also be vulnerable to the learning bias using a range of unbalanced data sets. Based on the notion that smaller ensembles can be better than larger ensembles, we develop a new evolutionary-based pruning method to find groups of highly-cooperative individuals that can improve accuracy on the important minority class.", affiliation = "Evolutionary Computation Research Group, Victoria University of Wellington, New Zealand", bibdate = "2011-12-02", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ausai/ausai2011.html#BhowanJZ11", } @Article{Bhowan:2012:ieeeTEC, author = "Urvesh Bhowan and Mark Johnston and Mengjie Zhang and Xin Yao", title = "Evolving Diverse Ensembles using Genetic Programming for Classification with Unbalanced Data", journal = "IEEE Transactions on Evolutionary Computation", year = "2013", volume = "17", number = "3", pages = "368--386", month = jun, keywords = "genetic algorithms, genetic programming", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6198882", DOI = "doi:10.1109/TEVC.2012.2199119", size = "19 pages", abstract = "In classification, machine learning algorithms can suffer a performance bias when data sets are unbalanced. Data sets are unbalanced when at least one class is represented by only a small number of training examples (called the minority class) while the other class(es) make up the majority. In this scenario, classifiers can have good accuracy on the majority class but very poor accuracy on the minority class(es). This paper proposes a Multi-objective Genetic Programming (MOGP) approach to evolving accurate and diverse ensembles of genetic program classifiers with good performance on both the minority and majority classes. The evolved ensembles comprise of nondominated solutions in the population where individual members vote on class membership. This paper evaluates the effectiveness of two popular Pareto-based fitness strategies in the MOGP algorithm (SPEA2 and NSGAII), and investigates techniques to encourage diversity between solutions in the evolved ensembles. Experimental results on six (binary) class imbalance problems show that the evolved ensembles outperform their individual members, as well as single-predictor methods such as canonical GP, Naive Bayes and Support Vector Machines, on highly unbalanced tasks. This highlights the importance of developing an effective fitness evaluation strategy in the underlying MOGP algorithm to evolve good ensemble members.", ISSN = "1089-778X", notes = "NSGA-II, SPEA2 Also known as \cite{6198882}", } @Article{Bhowan:2012:SMC, author = "Urvesh Bhowan and Mark Johnston and Mengjie Zhang", title = "Developing New Fitness Functions in Genetic Programming for Classification With Unbalanced Data", journal = "IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics", year = "2012", month = apr, volume = "42", number = "2", pages = "406--421", size = "16 pages", abstract = "Machine learning algorithms such as genetic programming (GP) can evolve biased classifiers when data sets are unbalanced. Data sets are unbalanced when at least one class is represented by only a small number of training examples (called the minority class) while other classes make up the majority. In this scenario, classifiers can have good accuracy on the majority class but very poor accuracy on the minority class(es) due to the influence that the larger majority class has on traditional training criteria in the fitness function. This paper aims to both highlight the limitations of the current GP approaches in this area and develop several new fitness functions for binary classification with unbalanced data. Using a range of real-world classification problems with class imbalance, we empirically show that these new fitness functions evolve classifiers with good performance on both the minority and majority classes. Our approaches use the original unbalanced training data in the GP learning process, without the need to artificially balance the training examples from the two classes (e.g., via sampling).", keywords = "genetic algorithms, genetic programming, GP learning process, biased classifiers, binary classification, class imbalance, data sets, fitness functions, machine learning algorithms, majority class, minority class, training criteria, unbalanced data, unbalanced training data, data handling, learning (artificial intelligence), pattern classification", DOI = "doi:10.1109/TSMCB.2011.2167144", ISSN = "1083-4419", notes = "Also known as \cite{6029340}", } @InProceedings{Bhowan:2013:GECCOcomp, author = "Urvesh Bhowan and Mark Johnston and Mengjie Zhang", title = "Comparing ensemble learning approaches in genetic programming for classification with unbalanced data", booktitle = "GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion", year = "2013", editor = "Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A. N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks", isbn13 = "978-1-4503-1964-5", keywords = "genetic algorithms, genetic programming", pages = "135--136", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2464576.2464643", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "This paper compares three approaches to evolving ensembles in Genetic Programming (GP) for binary classification with unbalanced data. The first uses bagging with sampling, while the other two use Pareto-based multi-objective GP (MOGP) for the trade-off between the two (unequal) classes. In MOGP, two ways are compared to build the ensembles: using the evolved Pareto front alone, and using the whole evolved population of dominated and non-dominated individuals alike. Experiments on several benchmark (binary) unbalanced tasks find that smaller, more diverse ensembles chosen during ensemble selection perform best due to better generalisation, particularly when the combined knowledge of the whole evolved MOGP population forms the ensemble.", notes = "Also known as \cite{2464643} Distributed at GECCO-2013.", } @Article{Bhowan:2014:ieeeTEC, author = "Urvesh Bhowan and Mark Johnston and Mengjie Zhang and Xin Yao", title = "Reusing Genetic Programming for Ensemble Selection in Classification of Unbalanced Data", journal = "IEEE Transactions on Evolutionary Computation", year = "2014", volume = "18", number = "6", pages = "893--908", month = dec, keywords = "genetic algorithms, genetic programming", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2013.2293393", size = "16 pages", abstract = "Classification algorithms can suffer from performance degradation when the class distribution is unbalanced. This paper develops a two-step approach to evolving ensembles using genetic programming (GP) for unbalanced data. The first step uses multi-objective (MO) GP to evolve a Pareto approximated front of GP classifiers to form the ensemble by trading-off the minority and the majority class against each other during learning. The MO component alleviates the reliance on sampling to artificially re-balance the data. The second step, which is the focus this paper, proposes a novel ensemble selection approach using GP to automatically find/choose the best individuals for the ensemble. This new GP approach combines multiple Pareto-approximated front members into a single composite genetic program solution to represent the (optimised) ensemble. This ensemble representation has two main advantages/novelties over traditional genetic algorithm (GA) approaches. Firstly, by limiting the depth of the composite solution trees, we use selection pressure during evolution to find small highly-cooperative groups of individuals for the ensemble. This means that ensemble sizes are not fixed a priori (as in GA), but vary depending on the strength of the base learners. Secondly, we compare different function set operators in the composite solution trees to explore new ways to aggregate the member outputs and thus, control how the ensemble computes its output. We show that the proposed GP approach evolves smaller, more diverse ensembles compared to an established ensemble selection algorithm, while still performing as well as, or better than the established approach. The evolved GP ensembles also perform well compared to other bagging and boosting approaches, particularly on tasks with high levels of class imbalance.", notes = "Also known as \cite{6677603}", } @InProceedings{Bhowan:2015:EuroGP, author = "Urvesh Bhowan and D. J. McCloskey", title = "Genetic Programming for Feature Selection and Question-Answer Ranking in {IBM Watson}", booktitle = "18th European Conference on Genetic Programming", year = "2015", editor = "Penousal Machado and Malcolm I. Heywood and James McDermott and Mauro Castelli and Pablo Garcia-Sanchez and Paolo Burelli and Sebastian Risi and Kevin Sim", series = "LNCS", volume = "9025", publisher = "Springer", pages = "153--166", address = "Copenhagen", month = "8-10 " # apr, organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, IBM Watson, Question answer ranking, Feature selection: Poster", isbn13 = "978-3-319-16500-4", DOI = "doi:10.1007/978-3-319-16501-1_13", abstract = "IBM Watson is an intelligent open-domain question answering system capable of finding correct answers to natural language questions in real-time. Watson uses machine learning over a large heterogeneous feature set derived from many distinct natural language processing algorithms to identify correct answers. This paper develops a Genetic Programming (GP) approach for feature selection in Watson by evolving ranking functions to order candidate answers generated in Watson. We leverage GP automatic feature selection mechanisms to identify Watson key features through the learning process. Our experiments show that GP can evolve relatively simple ranking functions that use much fewer features from the original Watson feature set to achieve comparable performances to Watson. This methodology can aid Watson implementers to better identify key components in an otherwise large and complex system for development, troubleshooting, and/or customer or domain-specific enhancements.", notes = "Part of \cite{Machado:2015:GP} EuroGP'2015 held in conjunction with EvoCOP2015, EvoMusArt2015 and EvoApplications2015", } @InProceedings{Bi:2017:IVCNZ, author = "Ying Bi and Mengjie Zhang and Bing Xue", booktitle = "2017 International Conference on Image and Vision Computing New Zealand (IVCNZ)", title = "An automatic region detection and processing approach in genetic programming for binary image classification", year = "2017", abstract = "In image classification, region detection is an effective approach to reducing the dimensionality of the image data but requires human intervention. Genetic Programming (GP) as an evolutionary computation technique can automatically identify important regions, and conduct feature extraction, feature construction and classification simultaneously. In this paper, an automatic region detection and processing approach in GP (GP-RDP) method is proposed for image classification. This approach is able to evolve important image operators to deal with detected regions for facilitating feature extraction and construction. To evaluate the performance of the proposed method, five recent GP methods and seven non-GP methods based on three types of image features are used for comparison on four image data sets. The results reveal that the proposed method can achieve comparable performance on easy data sets and significantly better performance on difficult data sets than the other comparable methods. To further demonstrate the interpretability and understandability of the proposed method, two evolved programs are analysed. The analysis shows the good interpretability of the GP-RDP method and proves that the GP-RDP method is able to identify prominent regions, evolve effective image operators to process these regions, extract and construct good features for efficient image classification.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IVCNZ.2017.8402469", ISSN = "2151-2205", month = dec, notes = "Also known as \cite{8402469}", } @InProceedings{Bi:2018:evoApplications, author = "Ying Bi and Bing Xue and Mengjie Zhang", title = "An Automatic Feature Extraction Approach to Image Classification Using Genetic Programming", booktitle = "21st International Conference on the Applications of Evolutionary Computation, EvoIASP 2018", year = "2018", editor = "Stefano Cagnoni and Mengjie Zhang", series = "LNCS", volume = "10784", publisher = "Springer", pages = "421--438", address = "Parma, Italy", month = "4-6 " # apr, organisation = "Species", keywords = "genetic algorithms, genetic programming, Image classification, Feature extraction, Image analysis", isbn13 = "978-3-319-77537-1", DOI = "doi:10.1007/978-3-319-77538-8_29", abstract = "Feature extraction is an essential process for image data dimensionality reduction and classification. However, feature extraction is very difficult and often requires human intervention. Genetic Programming (GP) can achieve automatic feature extraction and image classification but the majority of existing methods extract low-level features from raw images without any image-related operations. Furthermore, the work on the combination of image-related operators/descriptors in GP for feature extraction and image classification is limited. This paper proposes a multi-layer GP approach (MLGP) to performing automatic high-level feature extraction and classification. A new program structure, a new function set including a number of image operators/descriptors and two region detectors, and a new terminal set are designed in this approach. The performance of the proposed method is examined on six different data sets of varying difficulty and compared with five GP based methods and 42 traditional image classification methods. Experimental results show that the proposed method achieves better or comparable performance than these baseline methods. Further analysis on the example programs evolved by the proposed MLGP method reveals the good interpretability of MLGP and gives insight into how this method can effectively extract high-level features for image classification.", notes = "EvoApplications2018 held in conjunction with EuroGP'2018 EvoCOP2018 and EvoMusArt2018 http://www.evostar.org/2018/cfp_evoapps.php", } @InProceedings{Bi:2018:CEC, author = "Ying Bi and Mengjie Zhang and Bing Xue", title = "Genetic Programming for Automatic Global and Local Feature Extraction to Image Classification", booktitle = "2018 IEEE Congress on Evolutionary Computation (CEC)", year = "2018", editor = "Marley Vellasco", address = "Rio de Janeiro, Brazil", month = "8-13 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", URL = "https://openaccess.wgtn.ac.nz/articles/conference_contribution/Genetic_Programming_for_Automatic_Global_and_Local_Feature_Extraction_to_Image_Classification/13884998", DOI = "doi:10.1109/CEC.2018.8477911", size = "8 pages", abstract = "Feature extraction is an essential process to image classification. Existing feature extraction methods can extract important and discriminative image features but often require domain expert and human intervention. Genetic Programming (GP) can automatically extract features which are more adaptive to different image classification tasks. However, the majority GP-based methods only extract relatively simple features of one type i.e. local or global, which are not effective and efficient for complex image classification. In this paper, a new GP method (GP-GLF) is proposed to achieve automatically and simultaneously global and local feature extraction to image classification. To extract discriminative image features, several effective and well-known feature extraction methods, such as HOG, SIFT and LBP, are employed as GP functions in global and local scenarios. A novel program structure is developed to allow GP-GLF to evolve descriptors that can synthesise feature vectors from the input image and the automatically detected regions using these functions. The performance of the proposed method is evaluated on four different image classification data sets of varying difficulty and compared with seven GP based methods and a set of non-GP methods. Experimental results show that the proposed method achieves significantly better or similar performance than almost all the peer methods. Further analysis on the evolved programs shows the good interpretability of the GP-GLF method.", notes = "WCCI2018", } @InProceedings{bi:2018:AJCAI, author = "Ying Bi and Bing Xue and Mengjie Zhang", title = "A Gaussian {Filter-Based} Feature Learning Approach Using Genetic Programming to Image Classification", booktitle = "Australasian Joint Conference on Artificial Intelligence", year = "2018", editor = "Tanja Mitrovic and Bing Xue and Xiaodong Li", volume = "11320", series = "LNCS", pages = "251--257", address = "Wellington, New Zealand", month = dec # " 11-14", publisher = "Springer", keywords = "genetic algorithms, genetic programming, ANN, Feature learning, Image classification, Gaussian filter, Evolutionary computation, Feature extraction", isbn13 = "978-3-030-03990-5", URL = "http://link.springer.com/chapter/10.1007/978-3-030-03991-2_25", DOI = "doi:10.1007/978-3-030-03991-2_25", abstract = "To learn image features automatically from the problems being tackled is more effective for classification. However, it is very difficult due to image variations and the high dimensionality of image data. This paper proposes a new feature learning approach based on Gaussian filters and genetic programming (GauGP) for image classification. Genetic programming (GP) is a well-known evolutionary learning technique and has been applied to many visual tasks, showing good learning ability and interpretability. In the proposed GauGP method, a new program structure, a new function set and a new terminal set are developed, which allow it to detect small regions from the input image and to learn discriminative features using Gaussian filters for image classification. The performance of GauGP is examined on six different data sets of varying difficulty and compared with four GP methods, eight traditional approaches and convolutional neural networks. The experimental results show GauGP achieves significantly better or similar performance in most cases.", } @Article{Bi:2018:JZU, author = "Ying Bi and Bing Xue and Mengjie Zhang", title = "A Survey on Genetic Programming to Image Analysis", journal = "Journal of Zhengzhou University (Engineering Science)", year = "2018", volume = "39", number = "6", pages = "3--13", note = "In Chinese", keywords = "genetic algorithms, genetic programming, image analysis, evolutionary computation, feature extraction, image classification", URL = "https://yingbi92.github.io/homepage/2020/%E9%81%97%E4%BC%A0%E8%A7%84%E5%88%92%E5%9C%A8%E5%9B%BE%E5%83%8F%E5%88%86%E6%9E%90%E4%B8%8A%E7%9A%84%E5%BA%94%E7%94%A8%E7%BB%BC%E8%BF%B0%E2%80%94v4.pdf", URL = "http://gxb.zzu.edu.cn/oa/darticle.aspx?type=view&id=201802014", broken_doi = "doi:10.13705/j.issn.1671-6833.2013.06.01", size = "11 pages", abstract = "As an evolutionary computation (EC) technique, Genetic programming (GP) has been widely applied to image analysis in recent decades. However, there was no comprehensive and systematic literature review in this area. To provide guidelines for the state-of-the-art research, this paper presented a survey of the literature in recent years on GP for image analysis, including feature extraction, image classification, edge detection, and image segmentation. In addition, this paper summarised the current issues and challenges, such as computationally expensive, generalisation ability and transfer learning, on GP for image analysis, and pointd out promising research directions for future work", } @InProceedings{Bi:2019:CEC, author = "Ying Bi and Bing Xue and Mengjie Zhang", title = "An Evolutionary Deep Learning Approach Using Genetic Programming with Convolution Operators for Image Classification", booktitle = "2019 IEEE Congress on Evolutionary Computation, CEC 2019", year = "2019", editor = "Carlos A. Coello Coello", pages = "3197--3204", address = "Wellington, New Zealand", month = "10-13 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, ANN", isbn13 = "978-1-7281-2152-6", DOI = "doi:10.1109/CEC.2019.8790151", size = "8 pages", abstract = "Evolutionary deep learning (EDL) as a hot topic in recent years aims at using evolutionary computation (EC) techniques to address existing issues in deep learning. Most existing work focuses on employing EC methods for evolving hyper-parameters, deep structures or weights for neural networks (ANN). Genetic programming (GP) as an EC method is able to achieve deep learning due to the characteristics of its representation. However, many current GP-based EDL methods are limited to binary image classification. This paper proposed a new GP-based EDL method with convolution operators (COGP) for feature learning on binary and multi-class image classification. A novel flexible program structure is developed to allow COGP to evolve solutions with deep or shallow structures. Associated with the program structure, a new function set and a new terminal set are developed in COGP. The experimental results on six different image classification data sets of varying difficulty demonstrated that COGP ac", notes = "also known as \cite{8790151} IEEE Catalog Number: CFP19ICE-ART", } @InProceedings{Bi:2019:GECCO, author = "Ying Bi and Bing Xue and Mengjie Zhang", title = "An automated ensemble learning framework using genetic programming for image classification", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference", year = "2019", editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and Anne Auger and Petr Posik and Leslie {Peprez Caceres} and Andrew M. Sutton and Nadarajen Veerapen and Christine Solnon and Andries Engelbrecht and Stephane Doncieux and Sebastian Risi and Penousal Machado and Vanessa Volz and Christian Blum and Francisco Chicano and Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and Jonathan Fieldsend and Jose Antonio Lozano and Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and Robin Purshouse and Thomas Baeck and Justyna Petke and Giuliano Antoniol and Johannes Lengler and Per Kristian Lehre", isbn13 = "978-1-4503-6111-8", pages = "365--373", address = "Prague, Czech Republic", DOI = "doi:10.1145/3321707.3321750", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, Ensemble Learning, Image Classification, Feature Learning, Machine Learning, Computer Vision", size = "9 pages", abstract = "An ensemble consists of multiple learners and can achieve a better generalisation performance than a single learner. Genetic programming (GP) has been applied to construct ensembles using different strategies such as bagging and boosting. However, no GP-based ensemble methods focus on dealing with image classification, which is a challenging task in computer vision and machine learning. This paper proposes an automated ensemble learning framework using GP (EGP) for image classification. The new method integrates feature learning, classification function selection, classifier training, and combination into a single program tree. To achieve this, a novel program structure, a new function set and a new terminal set are developed in EGP. The performance of EGP is examined on nine different image classification data sets of varying difficulty and compared with a large number of commonly used methods including recently published methods. The results demonstrate that EGP achieves better performance than most competitive methods. Further analysis reveals that EGP evolves good ensembles simultaneously balancing diversity and accuracy. To the best of our knowledge, this study is the first work using GP to automatically generate ensembles for image classification.", notes = "Also known as \cite{3321750} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @Article{Bi:2020:CIM, author = "Ying Bi and Bing Xue and Mengjie Zhang", journal = "IEEE Computational Intelligence Magazine", title = "An Effective Feature Learning Approach Using Genetic Programming With Image Descriptors for Image Classification [Research Frontier]", year = "2020", volume = "15", number = "2", pages = "65--77", abstract = "Being able to extract effective features from different images is very important for image classification, but it is challenging due to high variations across images. By integrating existing well-developed feature descriptors into learning algorithms, it is possible to automatically extract informative high-level features for image classification. As a learning algorithm with a flexible representation and good global search ability, genetic programming can achieve this. In this paper, a new genetic programming-based feature learning approach is developed to automatically select and combine five existing well-developed descriptors to extract high-level features for image classification. The new approach can automatically learn various numbers of global and/or local features from different types of images. The results show that the new approach achieves significantly better classification performance in almost all the comparisons on eight data sets of varying difficulty. Further analysis reveals the effectiveness of the new approach to finding the most effective feature descriptors or combinations of them to extract discriminative features for different classification tasks.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/MCI.2020.2976186", ISSN = "1556-6048", month = may, notes = "Also known as \cite{9067779}", } @InProceedings{Bi:2020:GECCOcomp, author = "Ying Bi and Bing Xue and Mengjie Zhang", title = "Automatically Extracting Features for Face Classification Using Multi-Objective Genetic Programming", year = "2020", editor = "Richard Allmendinger and Hugo Terashima Marin and Efren Mezura Montes and Thomas Bartz-Beielstein and Bogdan Filipic and Ke Tang and David Howard and Emma Hart and Gusz Eiben and Tome Eftimov and William {La Cava} and Boris Naujoks and Pietro Oliveto and Vanessa Volz and Thomas Weise and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Rui Wang and Ran Cheng and Guohua Wu and Miqing Li and Hisao Ishibuchi and Jonathan Fieldsend and Ozgur Akman and Khulood Alyahya and Juergen Branke and John R. Woodward and Daniel R. Tauritz and Marco Baioletti and Josu Ceberio Uribe and John McCall and Alfredo Milani and Stefan Wagner and Michael Affenzeller and Bradley Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and Nayat Sanchez-Pi and Luis Marti and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and Matthew Johns and Nick Ross and Ed Keedwell and Herman Mahmoud and David Walker and Anthony Stein and Masaya Nakata and David Paetzel and Neil Vaughan and Stephen Smith and Stefano Cagnoni and Robert M. Patton and Ivanoe {De Falco} and Antonio {Della Cioppa} and Umberto Scafuri and Ernesto Tarantino and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Erik Hemberg and Riyad Alshammari and Tokunbo Makanju and Fuijimino-shi and Ivan Zelinka and Swagatam Das and Ponnuthurai Nagaratnam and Roman Senkerik", isbn13 = "9781450371278", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377929.3389989", DOI = "doi:10.1145/3377929.3389989", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion", pages = "117--118", size = "2 pages", keywords = "genetic algorithms, genetic programming, feature extraction, evolutionary multi-objective, face classification", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", abstract = "This paper proposes a new multi-objective feature extraction algorithm using genetic programming (GP) for face classification. The new multi-objective GP-based feature extraction algorithm with the idea of non-dominated sorting, which aims to maximise the objective of the classification accuracy and minimise the objective of the number of extracted features. The results show that the proposed algorithm achieves significantly better performance than the baseline methods on two different face classification datasets.", notes = "Also known as \cite{10.1145/3377929.3389989} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @InProceedings{Bi:2020:CEC, author = "Ying Bi and Bing Xue and Mengjie Zhang", title = "Genetic Programming-Based Feature Learning for Facial Expression Classification", booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC 2020", year = "2020", editor = "Yaochu Jin", pages = "paper id24102", address = "internet", month = "19-24 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming: Poster", isbn13 = "978-1-7281-6929-3", DOI = "doi:10.1109/CEC48606.2020.9185491", abstract = "Facia1 expression classification is an important but challenging task in artificial intelligence and computer vision. To effectively solve facial expression classification, it is necessary to detect/locate the face and extract features from the face. However, these two tasks are often conducted separately and manually in a traditional facial expression classification system. Genetic programming (GP) can automatically evolve solutions for a task without rich human intervention. However, very few GP-based methods have been specifically developed for facial expression classification. Therefore, this paper proposes a GP-based feature learning approach to facial expression classification. The proposed approach can automatically select small regions of a face and extract appearance features from the small regions. The experimental results on four different facial expression classification data sets show that the proposed approach achieves significantly better results in almost all the comparisons. To further show the effectiveness of the proposed approach, different numbers of training images are used in the experiments. The results indicate that the proposed approach achieves significantly better performance than any of the baseline methods using a small number of training images. Further analysis shows that the proposed approach not only selects informative regions of the face but also finds a good combination of various features to obtain a high classification accuracy.", notes = "https://wcci2020.org/ Victoria University of Wellington, New Zealand. Also known as \cite{9185491}", } @InProceedings{Bi:2020:PPSN, author = "Ying Bi and Bing Xue and Mengjie Zhang", title = "Evolving Deep Forest with Automatic Feature Extraction for Image Classification Using Genetic Programming", booktitle = "16th International Conference on Parallel Problem Solving from Nature, Part I", year = "2020", editor = "Thomas Baeck and Mike Preuss and Andre Deutz and Hao Wang2 and Carola Doerr and Michael Emmerich and Heike Trautmann", volume = "12269", series = "LNCS", pages = "3--18", address = "Leiden, Holland", month = "7-9 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, EvoDF, Evolutionary deep learning, Deep forest, Image classification, Feature extraction", isbn13 = "978-3-030-58111-4", URL = "https://openaccess.wgtn.ac.nz/articles/chapter/Evolving_deep_forest_with_automatic_feature_extraction_for_image_classification_using_genetic_programming/13158329", DOI = "doi:10.1007/978-3-030-58112-1_1", size = "14 pages", abstract = "Deep forest is an alternative to deep neural networks to use multiple layers of random forests without back-propagation for solving various problems. In this study, we propose a genetic programming-based approach to automatically and simultaneously evolving effective structures of deep forest connections and extracting informative features for image classification. First, in the new approach we define two types of modules: forest modules and feature extraction modules. Second, an encoding strategy is developed to integrate forest modules and feature extraction modules into a tree and the search strategy is introduced to search for the best solution. With these designs, the proposed approach can automatically extract image features and find forests with effective structures simultaneously for image classification. The parameters in the forest can be dynamically determined during the learning process of the new approach. The results show that the new approach can achieve better performance on the datasets having a small number of training instances and competitive performance on the datasets having a large number of training instances. The analysis of evolved solutions shows that the proposed approach uses a smaller number of random forests over the deep forest method.", notes = "tried ResNet, AlexNet, EvoDF, CNN, gcForest (8 forests), Random Forest, SVM (linear kernel), kNN on ORL, Extend Yale B, SCENE, KTH, MNIST and CIFAR-10. 'Fig 6 The solution found by EvoDF on the MNIST dataset' depth 8. PPSN2020", } @PhdThesis{YingBi_thesis, author = "Ying Bi", title = "Genetic Programming for Feature Learning in Image Classification", school = "Computer Science, Victoria University of Wellington", year = "2020", address = "New Zealand", keywords = "genetic algorithms, genetic programming, ANN, CNN, FLGP, IEGP, ensembles, STGP", URL = "https://openaccess.wgtn.ac.nz/ndownloader/files/34714144", URL = "https://openaccess.wgtn.ac.nz/articles/thesis/Genetic_Programming_for_Feature_Learning_in_Image_Classification/19529515", DOI = "doi:10.26686/wgtn.19529515", size = "292 pages", abstract = "Image classification is an important and fundamental task in computer vision and machine learning. The task is to classify images into one of some predefined groups based on the content in the images. However, image classification is a challenging task due to high variations across images, such as illumination, viewpoint, scale variations, deformation, and occlusion. To effectively solve image classification, it is necessary to extract or learn a set of meaningful features from raw pixels or images. The effectiveness of these features significantly affects classification performance. Feature learning aims to automatically learn effective features from images for classification. However, feature learning is difficult due to the high variations of images and the large search space. Genetic Programming (GP) as an Evolutionary Computation (EC) technique is known for its powerful global search ability and high interpretability of the evolved solutions. Compared with other EC methods, GP has a flexible representation of variable length and can search the solution space without any assumptions on the solution structure. The potential of GP in feature learning for image classification has not been comprehensively investigated due to the use of simple representations, e.g., functions and program structures. The overall goal of this thesis is to further investigate and explore the potential of GP for image classification by developing a new GP-based approach with a new representation to automatically learning effective features for different types of image classification tasks. Firstly, this thesis proposes a new GP based approach with image descriptors to learning global and/or local features for image classification by developing a new program structure, a new function set, a new terminal set, and a new fitness function. These new designs allow GP to detect small regions from the relatively large input image, extract features using image descriptors from the detected regions or the input image, and combine the extracted features for classification. The results show that the new approach significantly out performs five GP-based methods, eight traditional methods, and three convolutional neural network methods in almost all the comparisons on eight different datasets. Secondly, this thesis proposes a new GP-based approach with a flexible program structure and image-related operators for feature learning in image classification. The new approach learns effective features transformed by multiple layers, i.e., a filtering layer, a pooling layer, a feature extraction layer, and a feature concatenation layer, in a flexible way. The results show that the new approach achieves better performance than a large number of effective methods on 12 benchmark datasets. The solutions and features learned by the new approach provide high interpretability. Thirdly, this thesis proposes the first GP-based approach to automatically and simultaneously learning features and evolving ensembles for image classification. The new approach can learn high-level features through multiple transformations, select effective classification algorithms and optimise the parameters for these classification algorithms to build effective ensembles. The new approach outperforms a large number of benchmark methods on 12 different image classification datasets. Finally, this thesis proposes a multi-population GP-based approach with knowledge transfer and ensembles to improving both the generalisation performance and computational efficiency of GP-based feature learning algorithms for image classification. The new approach can achieve better generalisation performance and computational efficiency than baseline GP-based feature learning method. The new approach can achieve better performance on 11 datasets than a large number of benchmark methods, including many neural network-based methods.", notes = "Also known as \cite{Bi2022} supervisors: Mengjie Zhang and Bing Xue", } @Article{Bi:2020:TEVC, author = "Ying Bi and Bing Xue and Mengjie Zhang", journal = "IEEE Transactions on Evolutionary Computation", title = "Genetic Programming with Image-Related Operators and A Flexible Program Structure for Feature Learning in Image Classification", year = "2021", volume = "25", number = "1", pages = "87--101", month = feb, keywords = "genetic algorithms, genetic programming, Feature Learning, Image Classification, Representation, Evolutionary Computation", ISSN = "1941-0026", DOI = "doi:10.1109/TEVC.2020.3002229", size = "14 pages", abstract = "Feature extraction is essential for solving image classification by transforming low-level pixel values into high-level features. However, extracting effective features from images is challenging due to high variations across images in scale, rotation, illumination, and background. Existing methods often have a fixed model complexity and require domain expertise. Genetic programming with a flexible representation can find the best solution without the use of domain knowledge. This paper proposes a new genetic programming-based approach to automatically learning informative features for different image classification tasks. In the new approach, a number of image-related operators, including filters, pooling operators and feature extraction methods, are employed as functions. A flexible program structure is developed to integrate different functions and terminals into a single tree/solution. The new approach can evolve solutions of variable depths to extract various numbers and types of features from the images. The new approach is examined on 12 different image classification tasks of varying difficulty and compared with a large number of effective algorithms. The results show that the new approach achieves better classification performance than most benchmark methods. The analysis of the evolved programs/solutions and the visualisation of the learned features provide deep insights on the proposed approach.", notes = "School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand Also known as \cite{9117044}", } @Article{Bi:2020:CYB, author = "Ying Bi and Bing Xue and Mengjie Zhang", title = "Genetic Programming With a New Representation to Automatically Learn Features and Evolve Ensembles for Image Classification", journal = "IEEE Transactions on Cybernetics", year = "2021", volume = "51", number = "4", pages = "1769--1783", month = apr, keywords = "genetic algorithms, genetic programming, Ensemble learning, feature learning, image classification, representation", ISSN = "2168-2275", DOI = "doi:10.1109/TCYB.2020.2964566", size = "15 pages", abstract = "Image classification is a popular task in machine learning and computer vision, but it is very challenging due to high variation crossing images. Using ensemble methods for solving image classification can achieve higher classification performance than using a single classification algorithm. However, to obtain a good ensemble, the component (base) classifiers in an ensemble should be accurate and diverse. To solve image classification effectively, feature extraction is necessary to transform raw pixels into high-level informative features. However, this process often requires domain knowledge. This article proposes an evolutionary approach based on genetic programming to automatically and simultaneously learn informative features and evolve effective ensembles for image classification. The new approach takes raw images as inputs and returns predictions of class labels based on the evolved classifiers. To achieve this, a new individual representation, a new function set, and a new terminal set are developed to allow the new approach to effectively find the best solution. More important, the solutions of the new approach can extract informative features from raw images and can automatically address the diversity issue of the ensembles. In addition, the new approach can automatically select and optimize the parameters for the classification algorithms in the ensemble. The performance of the new approach is examined on 13 different image classification datasets of varying difficulty and compared with a large number of effective methods. The results show that the new approach achieves better classification accuracy on most datasets than the competitive methods. Further analysis demonstrates that the new approach can evolve solutions with high accuracy and diversity.", notes = "Also known as \cite{8976239}", } @Book{bi2021gpimage, author = "Ying Bi and Bing Xue and Mengjie Zhang", title = "Genetic Programming for Image Classification", publisher = "Springer Nature", year = "2021", volume = "24", series = "Adaptation, Learning, and Optimization book series", month = feb # " 9", note = "An Automated Approach to Feature Learning", keywords = "genetic algorithms, genetic programming, Evolutionary Computation, Feature Learning, Image Classification, Computer Vision, Machine Learning, Feature Extraction, Feature Selection, Feature Construction, Model Interpretability", isbn13 = "978-3030659264", ISSN = "1867-4534", URL = "https://link.springer.com/book/10.1007/978-3-030-65927-1", URL = "https://www.amazon.com/Genetic-Programming-Image-Classification-Optimization-dp-3030659267/dp/3030659267", code_url = "https://github.com/YingBi92/BookCode", DOI = "doi:10.1007/978-3-030-65927-1", size = "286 pages", abstract = "This book offers several new GP approaches to feature learning for image classification. Image classification is an important task in computer vision and machine learning with a wide range of applications. Feature learning is a fundamental step in image classification, but it is difficult due to the high variations of images. Genetic Programming (GP) is an evolutionary computation technique that can automatically evolve computer programs to solve any given problem. This is an important research field of GP and image classification. No book has been published in this field. This book shows how different techniques, e.g., image operators, ensembles, and surrogate, are proposed and employed to improve the accuracy and/or computational efficiency of GP for image classification. The proposed methods are applied to many different image classification tasks, and the effectiveness and interpretability of the learned models will be demonstrated. This book is suitable as a graduate and postgraduate level textbook in artificial intelligence, machine learning, computer vision, and evolutionary computation. Front Matter Pages i-xxviii. Introduction Pages 1-10. Computer Vision and Machine Learning Pages 11-48. Evolutionary Computation and Genetic Programming Pages 49-74. Multi-layer Representation for Binary Image Classification Pages 75-95. Evolutionary Deep Learning Using GP with Convolution Operators Pages 97-115. GP with Image Descriptors for Learning Global and Local Features Pages 117-143. GP with Image-Related Operators for Feature Learning Pages 145-177. GP for Simultaneous Feature Learning and Ensemble Learning Pages 179-205. Random Forest-Assisted GP for Feature Learning Pages 207-226. Conclusions and Future Directions Pages 227-237. Back Matter Pages 239-258. Evolutionary Computation Research Group, School of Engineering and Computer Science Victoria University of Wellington, Wellington, New Zealand", notes = "Reviewed in \cite{Zafra:2022:GPEM}", } @Article{Bi:2021:ASC, author = "Ying Bi and Bing Xue and Mengjie Zhang", title = "Multi-objective genetic programming for feature learning in face recognition", journal = "Applied Soft Computing", year = "2021", volume = "103", pages = "107152", month = may, keywords = "genetic algorithms, genetic programming, Multi-objective optimisation, Evolutionary computation, Feature learning, Face recognition", ISSN = "1568-4946", URL = "https://yingbi92.github.io/homepage/2021/MOGP.pdf", URL = "https://www.sciencedirect.com/science/article/pii/S1568494621000752", DOI = "doi:10.1016/j.asoc.2021.107152", size = "14 pages", abstract = "Face recognition is a challenging task due to high variations of pose, expression, ageing, and illumination. As an effective approach to face recognition, feature learning can be formulated as a multi-objective optimisation task of maximising classification accuracy and minimising the number of learned features. However, most of the existing algorithms focus on improving classification accuracy without considering the number of learned features. In this paper, we propose new multi-objective genetic programming (GP) algorithms for feature learning in face recognition. To achieve effective face feature learning, a new individual representation is developed to allow GP to select informative regions from the input image, extract features using various descriptors, and combine the extracted features for classification. Then two new multi-objective genetic programming (GP) algorithms, one with the idea of non-dominated sorting (NSGPFL) and the other with the idea of Strength Pareto (SPGPFL), are proposed to simultaneously optimise these two objectives. NSGPFL and SPGPFL are compared with a single-objective GP for feature learning (GPFL), a single-objective GP for weighting two objectives (GPFLW), and a large number of baseline methods. The experimental results show the effectiveness of the NSGPFL and SPGPFL algorithms by achieving better or comparable classification performance and learning a small number of features.", notes = "MOGP.pdf is a 40 page preprint", } @Article{Bi:TEVC2, author = "Ying Bi and Bing Xue and Mengjie Zhang", title = "A Divide-and-Conquer Genetic Programming Algorithm with Ensembles for Image Classification", journal = "IEEE Transactions on Evolutionary Computation", year = "2021", volume = "25", number = "6", pages = "1148--1162", month = dec, keywords = "genetic algorithms, genetic programming, Feature Learning, Knowledge Transfer, Ensemble Learning, Divide-and-Conquer, Image Classification", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2021.3082112", size = "15 pages", abstract = "Genetic programming (GP) has been applied to feature learning in image classification and achieved promising results. However, one major limitation of existing GP-based methods is the high computational cost, which may limit their applications on large-scale image classification tasks. To address this, this paper develops a divide-and-conquer GP algorithm with knowledge transfer and ensembles to achieve fast feature learning in image classification. In the new algorithm framework, a divideand-conquer strategy is employed to split the training data and the population into small subsets or groups to reduce computational time. A new knowledge transfer method is proposed to improve GP learning performance. A new fitness function based on log-loss and a new ensemble formulation strategy are developed to build an effective ensemble for image classification. The performance of the proposed approach has been examined on 12 image classification datasets of varying difficulty. The results show that the new approach achieves better classification performance in significantly less computation time than the baseline GP-based algorithm. The comparisons with state-of-theart algorithms show that the new approach achieves better or comparable performance in almost all the comparisons. Further analysis demonstrates the effectiveness of ensemble formulation and knowledge transfer in the proposed approach.", notes = "also known as \cite{9437306}", } @Article{Bi:2022:InformationSciences, author = "Ying Bi and Bing Xue and Mengjie Zhang", title = "Using a Small Number of Training Instances in Genetic Programming for Face Image Classification", journal = "Information Sciences", year = "2022", volume = "593", pages = "488--504", month = may, keywords = "genetic algorithms, genetic programming, MOGP, Image classification, Fitness measure, Small data, Evolutionary computation", ISSN = "0020-0255", URL = "https://www.sciencedirect.com/science/article/abs/pii/S0020025522000871", DOI = "doi:10.1016/j.ins.2022.01.055", abstract = "Classifying faces is a difficult task due to image variations in illumination, occlusion, pose, expression, etc. Typically, it is challenging to build a generalised classifier when the training data is small, which can result in poor generalisation. This paper proposes a new approach for the classification of face images based on multi-objective genetic programming (MOGP). In MOGP, image descriptors that extract effective features are automatically evolved by optimising two different objectives at the same time: the accuracy and the distance measure. The distance measure is a new measure intended to enhance generalisation of learned features and/or classifiers. The performance of MOGP is evaluated on eight face datasets. The results show that MOGP significantly outperforms 17 competitive methods.", notes = "also known as \cite{BI2022488}", } @Article{Learning_and_Sharing_A_Multitask_Genetic_Programming_Approach_to_Image_Feature_Learning, author = "Ying Bi and Bing Xue and Mengjie Zhang", title = "Learning and Sharing: A Multitask Genetic Programming Approach to Image Feature Learning", journal = "IEEE Transactions on Evolutionary Computation", year = "2022", volume = "26", number = "2", pages = "218--232", month = apr, note = "Special Issue on Multitask Evolutionary Computation", keywords = "genetic algorithms, genetic programming, Multitask Learning, Knowledge Sharing, Feature Learning, Image Classification", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2021.3097043", size = "15 pages", abstract = "Using evolutionary computation algorithms to solve multiple tasks with knowledge sharing is a promising approach. Image feature learning can be considered as a multitask learning problem because different tasks may have a similar feature space. Genetic programming (GP) has been successfully applied to image feature learning for classification. However, most of the existing GP methods solve one task, independently, using sufficient training data. No multitask GP method has been developed for image feature learning. Therefore, this paper develops a multitask GP approach to image feature learning for classification with limited training data. Owing to the flexible representation of GP, a new knowledge sharing mechanism based on a new individual representation is developed to allow GP to automatically learn what to share across two tasks and to improve its learning performance. The shared knowledge is encoded as a common tree, which can represent the common/general features of two tasks. With the new individual representation, each task is solved using the features extracted from a common tree and a task-specific tree representing task-specific features. To find the best common and task-specific trees, a new evolutionary search process and fitness functions are developed. The performance of the new approach is examined on six multitask learning problems of 12 image classification datasets with limited training data and compared with 17 competitive methods. Experimental results show that the new approach outperforms these comparison methods in almost all the comparisons. Further analysis reveals that the new approach learns simple yet effective common trees with high effectiveness and transferability.", notes = "also known as \cite{9484082}", } @Article{Dual-Tree_Genetic_Programming_for_Few-Shot_Image_Classification, author = "Ying Bi and Bing Xue and Mengjie Zhang", title = "Dual-Tree Genetic Programming for Few-Shot Image Classification", journal = "IEEE Transactions on Evolutionary Computation", year = "2022", volume = "26", number = "3", pages = "555--569", month = jun, keywords = "genetic algorithms, genetic programming, Representation, Fitness Evaluation, Few-Shot Learning, Image Classification", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2021.3100576", size = "15 pages", abstract = "Few-shot image classification is an important but challenging task due to high variations across images and a small number of training instances. A learning system often has poor generalisation performance due to the lack of sufficient training data. Genetic programming (GP) has been successfully applied to image classification and achieved promising performance. This paper proposes a GP-based approach with a dual-tree representation and a new fitness function to automatically learn image features for few-shot image classification. The dual-tree representation allows the proposed approach to have better search ability and learn richer features than a single-tree representation when the number of training instances is very small. The fitness function based on the classification accuracy and the distances of the training instances to the class centroids aims to improve the generalisation performance. The proposed approach can deal with different types of few-shot image classification tasks with various numbers of classes and different", notes = "also known as \cite{9499117} School of Engineering and Computer Science, Victoria University of Wellington, Wellington 6140, New Zealand", } @Article{Ying_Bi:cybernetics1, author = "Ying Bi and Bing Xue and Mengjie Zhang", title = "Instance Selection-Based Surrogate-Assisted Genetic Programming for Feature Learning in Image Classification", journal = "IEEE Transactions on Cybernetics", year = "2023", volume = "53", number = "2", pages = "1118--1132", month = feb, keywords = "genetic algorithms, genetic programming, evolutionary computation, EC, feature learning, instance selection, surrogate", ISSN = "2168-2275", DOI = "doi:10.1109/TCYB.2021.3105696", size = "15 pages", abstract = "Genetic programming (GP) has been applied to feature learning for image classification and achieved promising results. However, many GP-based feature learning algorithms are computationally expensive due to a large number of expensive fitness evaluations, especially when using a large number of training instances/images. Instance selection aims to select a small subset of training instances, which can reduce the computational cost. Surrogate-assisted evolutionary algorithms often replace expensive fitness evaluations by building surrogate models. This article proposes an instance selection-based surrogate-assisted GP for fast feature learning in image classification. The instance selection method selects multiple small subsets of images from the original training set to form surrogate training sets of different sizes. The proposed approach gradually uses these surrogate training sets to reduce the overall computational cost using a static or dynamic strategy. At each generation, the proposed approach evaluates the entire population on the small surrogate training sets and only evaluates ten current best individuals on the entire training set. The features learned by the proposed approach are fed into linear support vector machines for classification. Extensive experiments show that the proposed approach can not only significantly reduce the computational cost but also improve the generalisation performance over the baseline method, which uses the entire training set for fitness evaluations, on 11 different image datasets. The comparisons with other state-of-the-art GP and non-GP methods further demonstrate the effectiveness of the proposed approach. Further analysis shows that using multiple surrogate training sets in the proposed approach achieves better performance than using a single surrogate training set and using a random instance selection method.", notes = "Also known as \cite{9526355}", } @Article{Ying_Bi:Cybernetics2, author = "Ying Bi and Bing Xue and Mengjie Zhang", title = "Genetic Programming-Based Discriminative Feature Learning for Low-Quality Image Classification", journal = "IEEE Transactions on Cybernetics", year = "2022", volume = "52", number = "8", pages = "8272--8285", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/TCYB.2021.3049778", ISSN = "2168-2275", abstract = "Being able to learn discriminative features from low-quality images has raised much attention recently due to their wide applications ranging from autonomous driving to safety surveillance. However, this task is difficult due to high variations across images, such as scale, rotation, illumination, and viewpoint, and distortions in images, such as blur, low contrast, and noise. Image preprocessing could improve the quality of the images, but it often requires human intervention and domain knowledge. Genetic programming (GP) with a flexible representation can automatically perform image preprocessing and feature extraction without human intervention. Therefore, this study proposes a new evolutionary learning approach using GP (EFLGP) to learn discriminative features from images with blur, low contrast, and noise for classification. In the proposed approach, we develop a new program structure (individual representation), a new function set, and a new terminal set. With these new designs, EFLGP can detect small regions from a large input low-quality image, select image operators to process the regions or detect features from the small regions, and output a flexible number of discriminative features. A set of commonly used image preprocessing operators is employed as functions in EFLGP to allow it to search for solutions that can effectively handle low-quality image data. The performance of EFLGP is comprehensively investigated on eight datasets of varying difficulty under the original (clean), blur, low contrast, and noise scenarios, and compared with a large number of benchmark methods using handcrafted features and deep features. The experimental results show that EFLGP achieves significantly better or similar results in most comparisons. The results also reveal that EFLGP is more invariant than the benchmark methods to blur, low contrast, and noise.", notes = "Also known as \cite{9345467}", } @Article{Ying_Bi:Cybernetics3, author = "Ying Bi and Bing Xue and Mengjie Zhang", title = "Multitask Feature Learning as Multiobjective Optimization: A New Genetic Programming Approach to Image Classification", journal = "IEEE Transactions on Cybernetics", year = "2023", volume = "53", number = "5", pages = "3007--3020", month = may, keywords = "genetic algorithms, genetic programming, Evolutionary computation (EC), feature learning, GP, image classification, multiobjective optimisation, multitask learning", ISSN = "2168-2267", DOI = "doi:10.1109/TCYB.2022.3174519", size = "14 pages", abstract = "Feature learning is a promising approach to image classification. However, it is difficult due to high image variations. When the training data are small, it becomes even more challenging, due to the risk of overfitting. Multitask feature learning has shown the potential for improving generalization. However, existing methods are not effective for handling the case that multiple tasks are partially conflicting. Therefore, for the first time, this article proposes to solve a multitask feature learning problem as a multiobjective optimization problem by developing a genetic programming approach with a new representation to image classification. In the new approach, all the tasks share the same solution space and each solution is evaluated on multiple tasks so that the objectives of all the tasks can be optimized simultaneously using a single population. To learn effective features, a new and compact program representation is developed to allow the new approach to evolving solutions shared across tasks. The new approach can automatically find a diverse set of nondominated solutions that achieve good tradeoffs between different tasks. To further reduce the risk of overfitting, an ensemble is created by selecting non-dominated solutions to solve each image classification task. The results show that the new approach significantly outperforms a large number of benchmark methods on six problems consisting of 15 image classification datasets of varying difficulty. Further analysis shows that these new designs are effective for improving the performance. The detailed analysis clearly reveals the benefits of solving multitask feature learning as multi-objective optimisation in improving the generalisation.", notes = "also known as \cite{9781346}", } @Article{Bi:JRSNZ, author = "Ying Bi and Bing Xue and Dana Briscoe and Ross Vennell and Mengjie Zhang", title = "A new artificial intelligent approach to buoy detection for mussel farming", journal = "Journal of the Royal Society of New Zealand", year = "2023", volume = "53", number = "1", pages = "27--51", keywords = "genetic algorithms, genetic programming, Artificial intelligence, computer vision, aquaculture, evolutionary learning, object detection, deep learning, ANN", publisher = "Taylor \& Francis", ISSN = "0303-6758", DOI = "doi:10.1080/03036758.2022.2090966", size = "25 pages", abstract = "Aquaculture is an important industry in New Zealand (NZ). Mussel farmers often manually check the state of the buoys that are required to support the crop, which is labour-intensive. Artificial intelligence (AI) can provide automatic and intelligent solutions to many problems but has seldom been applied to mussel farming. In this paper, a new AI-based approach is developed to automatically detect buoys from mussel farm images taken from a farm in the South Island of NZ. The overall approach consists of four steps, i.e. data collection and preprocessing, image segmentation, keypoint detection and feature extraction, and classification. A convolutional neural network (CNN) method is applied to perform image segmentation. A new genetic programming (GP) method with a new representation, a new function set and a new terminal set is developed to automatically evolve descriptors for extracting features from key points. The new approach is applied to seven subsets and one full dataset containing images of buoys over different backgrounds and compared to three baseline methods. The new approach achieves better performance than the compared methods. Further analysis of the parameters and the evolved solutions provides more insights into the performance of the new approach to buoy detection.", notes = "School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand", } @Article{Ying_Bi:ieeeTEC, author = "Ying Bi and Bing Xue and Mengjie Zhang", title = "Genetic Programming-Based Evolutionary Deep Learning for Data-Efficient Image Classification", journal = "IEEE Transactions on Evolutionary Computation", year = "2024", volume = "28", number = "2", pages = "307--322", month = apr, keywords = "genetic algorithms, genetic programming, ANN, Evolutionary Deep Learning, Image Classification, Small Data, Evolutionary Computation, Deep Learning", ISSN = "1089-778X", URL = "https://ieeexplore.ieee.org/abstract/document/9919314/", DOI = "doi:10.1109/TEVC.2022.3214503", size = "15 pages", abstract = "Data-efficient image classification is a challenging task that aims to solve image classification using small training data. Neural network-based deep learning methods are effective for image classification, but they typically require large-scale training data and have major limitations such as requiring expertise to design network architectures and having poor interpretability. Evolutionary deep learning is a recent hot topic that combines evolutionary computation with deep learning. However, most evolutionary deep learning methods focus on evolving architectures of neural networks, which still suffers from limitations such as poor interpretability. We propose a new genetic programming-based evolutionary deep learning approach to data-efficient image classification. The new approach can automatically evolve variable-length models using many important operators from both image and classification domains. It can learn different types of image features from colo", notes = "Also known as \cite{9919314}", } @Article{Ying_Bi:ieeeTEC2, author = "Ying Bi and Bing Xue and Pablo Mesejo and Stefano Cagnoni and Mengjie Zhang", title = "A survey on evolutionary computation for computer vision and image analysis: Past, present, and future trends", journal = "IEEE Transactions on Evolutionary Computation", year = "2023", volume = "27", number = "1", pages = "5--25", month = feb, keywords = "genetic algorithms, genetic programming, evolutionary Computation, Image Analysis, Computer Vision, Pattern Recognition, Image Processing, Artificial Intelligence", ISSN = "1089-778X", URL = "https://arxiv.org/abs/2209.06399v1", URL = "https://ieeexplore.ieee.org/abstract/document/9943992/", DOI = "doi:10.1109/TEVC.2022.3220747", size = "21 pages", abstract = "Computer vision (CV) is a big and important field in artificial intelligence covering a wide range of applications. Image analysis is a major task in CV aiming to extract, analyse and understand the visual content of images. However, image-related tasks are very challenging due to many factors, e.g., high variations across images, high dimensionality, domain expertise requirement, and image distortions. Evolutionary computation (EC) approaches have been widely used for image analysis with significant achievement. However, there is no comprehensive survey of existing EC approaches to image analysis. To fill this gap, this paper provides a comprehensive survey covering all essential EC approaches to important image analysis tasks including edge detection, image segmentation, image feature analysis, image classification, object detection, and others. This survey aims to provide a better understanding of evolutionary computer vision (ECV) by discussing the contributions of different approaches and exploring how and why EC is used for CV and image analysis. The applications, challenges, issues, and trends associated to this research field are also discussed and summarised to provide further guidelines and opportunities for future research.", notes = "also known as \cite{9943992} School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China", } @InProceedings{Bi:2023:CEC, author = "Ying Bi and Bing Xue and Mengjie Zhang", title = "Evolutionary Deep-Learning for Image Classification: A Genetic Programming Approach", booktitle = "2023 IEEE Congress on Evolutionary Computation (CEC)", year = "2023", editor = "Gui DeSouza and Gary Yen", address = "Chicago, USA", month = "1-5 " # jul, note = "Tutorial", keywords = "genetic algorithms, genetic programming", URL = "https://2023.ieee-cec.org/program-html/", URL = "https://2023.ieee-cec.org/wp-content/uploads/sites/438/17EDL_proposal_YingBi.pdf", size = "5 pages", notes = "2 July 2023 2:00pm-3:50pm CEC2023 https://2023.ieee-cec.org/program-html/", } @Article{BiYing:ieeeTEC, author = "Ying Bi and Jing Liang and Bing Xue and Mengjie Zhang", title = "A Genetic Programming Approach with Building Block Evolving and Reusing to Image Classification", journal = "IEEE Transactions on Evolutionary Computation", year = "2024", volume = "28", number = "5", pages = "1366--1380", month = oct, keywords = "genetic algorithms, genetic programming, Image classification, Feature extraction, Task analysis, Training data, Training, Sociology, Random forests, Ensemble learning, evolutionary computation (EC), feature extraction", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2023.3284712", notes = "also known as \cite{10147342}", } @InProceedings{Bian:2016:ATS, author = "Song Bian and Michihiro Shintani and Zheng Wang and Masayuki Hiromoto and Anupam Chattopadhyay and Takashi Sato", booktitle = "2016 IEEE 25th Asian Test Symposium (ATS)", title = "Runtime NBTI Mitigation for Processor Lifespan Extension via Selective Node Control", year = "2016", pages = "234--239", abstract = "Negative bias temperature instability (NBTI) has become one of the major reliability concerns for nanoscale CMOS technology. The NBTI effect degrades pMOS transistors by stressing them with negatively biased voltage, while the transistors heal themselves as the negative bias is removed. In this paper, we propose a cross-layer mitigation technique for NBTI-induced timing degradation in processors. The NOP (No Operation) instruction is replaced by a custom NOP instruction for healing purpose. Cells that are likely to be stressed under negative bias are classified and their upstream cell will be replaced by the internal node control (INC) logics. Upon encountering a custom NOP instruction, the INC logics will force the NBTI-stressed cell to be in its healing mode. The optimal INC logic insertion through genetic programming approach achieves much greater delay mitigation of 44.3percent than prior works in a 10-year span with less than 4percent of power and negligible area overhead.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ATS.2016.31", month = nov, notes = "Also known as \cite{7796119}", } @InProceedings{bian:2021:apr_icse, author = "Zhiqiang Bian and Justyna Petke and Aymeric Blot", title = "Refining Fitness Functions for Search-Based Program Repair", booktitle = "APR @ ICSE 2021", year = "2021", month = "1 " # jun, editor = "Sergey Mechtaev and Shin Hwei Tan and Martin Monperrus and Lingming Zhang", publisher = "IEEE", address = "internet", pages = "1--8", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, Software Engineering, SBSE, Program Repair, Fitness Function, GenProg, ARJAe, 2Phase, Gin, EvoSuite, QuixBugs", isbn13 = "978-1-6654-4473-6", code_url = "https://github.com/SOLAR-group/apr2021artefact", URL = "http://www.cs.ucl.ac.uk/staff/a.blot/files/bian_apr-icse_2021.pdf", video_url = "https://www.youtube.com/watch?v=2CbHQMhkMTU", DOI = "doi:10.1109/APR52552.2021.00008", size = "8 pages", abstract = "Debugging is a time-consuming task for software engineers. Automated Program Repair (APR) has proved successful in automatically fixing bugs for many real-world applications. Search-based APR generates program variants that are then evaluated on the test suite of the original program, using a fitness function. In the vast majority of search based APR work only the Boolean test case result is taken into account when evaluating the fitness of a program variant. We pose that more fine-grained fitness functions could lead to a more diverse fitness landscape,and thus provide better guidance for the APR search algorithms. We thus present 2Phase, a fitness function that also incorporates the output of test case failures, and compare it with ARJAe,that shares the same principles, and the standard fitness, that only takes the Boolean test case result into consideration. We conduct the comparison on 16 buggy programs from the QuixBugs benchmark using the Gin genetic improvement framework. The results show no significant difference in the performance of all three fitness functions considered. However, Gin was able to find 8 correct fixes, more than any of the APR tools in the recent QuixBugs study.", notes = "'no significant difference between effectiveness and efficiency of the three fitness functions on our dataset' cf video 2CbHQMhkMTU 11:17 A: due to QuixBugs, may be different results on different benchmarks. A2 (red) Future work more benchmarks and especially bigger faulty software, with possibly larger search spaces. More formal analysis of search space, cf search landscape of SBSE testing. Q2: fitness diversity. A: different patches (of same example bug) should have different (numerical) fitness values. Low diversity means different patches have the same fitness. Without fitness diversity it is hard to guide the search for better patches. Q3: would other types of diversity be better? A1(off screen, voice only): tried new operators. a2: Future work may be need new measures of diversity. A3(grey): We concentrate upon fitness diversity between patches to guide search. a2: Smooth the landscape so EC algorithm is better guided (to acceptable bug repair) 'test-suite adequate patches for 11 programs, with 8 being correct fixes (more than any of the APR tools in the recent QuixBugs study)' checkpoints around numerical values in the source code as part of fitness distance calculation. Internal test oracles? But not used? patch over fitting. EvoSuite hold out tests. 'The mutation operator either appends a random new edit to a patch or removes one from a non-empty patch. The crossover operator combines two parent solutions by concatenating, in both orders, the two sequences of edits before each edit is removed with 0.5 probability to create the two children patches.' University College London, London, United Kingdom https://program-repair.org/workshop-2021/", } @Misc{oai:arXiv.org:1505.02921, author = "Simone Bianco and Gianluigi Ciocca and Raimondo Schettini", title = "How Far Can You Get By Combining Change Detection Algorithms?", year = "2015", month = may # "~12", note = "Comment: Submitted to IEEE Transactions on Image Processing", bibsource = "OAI-PMH server at export.arxiv.org", oai = "oai:arXiv.org:1505.02921", keywords = "genetic algorithms, genetic programming, computer science - computer vision and pattern recognition", URL = "http://arxiv.org/abs/1505.02921", abstract = "In this paper we investigate if simple change detection algorithms can be combined and used to create a more robust change detection algorithm by leveraging their individual peculiarities. We use Genetic Programming to combine the outputs (i.e. binary masks) of the detection algorithms with unary, binary and n-ary functions performing both masks' combination and post-processing. Genetic Programming allows us to automatically select the best algorithms, combine them in different ways, and perform the most suitable post-processing operations. Using different experimental settings, we created two algorithms that we named IUTIS-1 and IUTIS-2 (In Unity There Is Strength). These algorithms are compared against state-of-the-art change detection algorithms on the video sequences and ground truth annotations of the ChandeDetection.net (CDNET 2014) challenge. Results demonstrate that starting from simple algorithms we can achieve comparable results of more complex state-of-the-art change detection algorithms, while keeping the computational complexity affordable for real-time applications. Moreover, when our framework is applied to more complex algorithms, the resulting IUTIS-3 outperforms all the 33 state-of-the-art algorithms considered.", notes = "see \cite{Bianco:ieeeTEC}", } @Article{Bianco:ieeeTEC, author = "Simone Bianco and Gianluigi Ciocca and Raimondo Schettini", title = "Combination of Video Change Detection Algorithms by Genetic Programming", journal = "IEEE Transactions on Evolutionary Computation", year = "2017", volume = "21", number = "6", pages = "914--928", month = dec, keywords = "genetic algorithms, genetic programming, Change detection, algorithm combining and selection, CDNET", ISSN = "1089-778X", URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7898824", DOI = "doi:10.1109/TEVC.2017.2694160", size = "15 pages", abstract = "Within the field of Computer Vision, change detection algorithms aim at automatically detecting significant changes occurring in a scene by analysing the sequence of frames in a video stream. In this paper we investigate how state-of-the-art change detection algorithms can be combined and used to create a more robust algorithm leveraging their individual peculiarities. We exploited Genetic Programming (GP) to automatically select the best algorithms, combine them in different ways, and perform the most suitable post-processing operations on the outputs of the algorithms. In particular, algorithms combination and post-processing operations are achieved with unary, binary and n-ary functions embedded into the GP framework. Using different experimental settings for combining existing algorithms we obtained different GP solutions that we termed IUTIS (In Unity There Is Strength). These solutions are then compared against state-of-the-art change detection algorithms on the video sequences and ground truth annotations of the Change Detection. net (CDNET 2014) challenge. Results demonstrate that using GP, our solutions are able to outperform all the considered single state-of-the-art change detection algorithms, as well as other combination strategies. The performance of our algorithm are significantly different from those of the other state-of-the-art algorithms. This fact is supported by the statistical significance analysis conducted with the Friedman Test and Wilcoxon Rank Sum post-hoc tests.", notes = "also known as \cite{7898824} See also \cite{oai:arXiv.org:1505.02921}", } @Article{BIANCO:2020:IF, author = "Simone Bianco and Marco Buzzelli and Gianluigi Ciocca and Raimondo Schettini", title = "Neural architecture search for image saliency fusion", journal = "Information Fusion", volume = "57", pages = "89--101", year = "2020", ISSN = "1566-2535", DOI = "doi:10.1016/j.inffus.2019.12.007", URL = "http://www.sciencedirect.com/science/article/pii/S1566253519302374", keywords = "genetic algorithms, genetic programming, Saliency fusion, Evolutionary algorithms, Neural architecture search", abstract = "Saliency detection methods proposed in the literature exploit different rationales, visual clues, and assumptions, but there is no single best saliency detection algorithm that is able to achieve good results on all the different benchmark datasets. In this paper we show that fusing different saliency detection algorithms together by exploiting neural network architectures makes it possible to obtain better results. Designing the best architecture for a given task is still an open problem since the existing techniques have some limits with respect to the problem formulation, to the search space, and require very high computational resources. To overcome these problems, in this paper we propose a three-step fusion approach. In the first step, genetic programming techniques are exploited to combine the outputs of existing saliency algorithms using a set of provided operations. Having a discrete search space allows us a fast generation of the candidate solutions. In the second step, the obtained solutions are converted into backbone Convolutional Neural Networks (CNNs) where operations are all implemented with differentiable functions, allowing an efficient optimization of the corresponding parameters (in a continuous space) by backpropagation. In the last step, to enrich the expressiveness of the initial architectures, the networks are further extended with additional operations on intermediate levels of the processing that are once again efficiently optimized through backpropagation. Extensive experimental evaluations show that the proposed saliency fusion approach outperforms the state-of-the-art on the MSRAB dataset and it is able to generalize to unseen data of different benchmark datasets", } @InProceedings{DBLP:conf/ijcci/BiauWCL21, author = "Julien Biau and Dennis Wilson and Sylvain Cussat-Blanc and Herve Luga", editor = "Thomas B{\"{a}}ck and Christian Wagner and Jonathan M. Garibaldi and H. K. Lam and Marie Cottrell and Juan Juli{\'{a}}n Merelo and Kevin Warwick", title = "Improving Image Filters with Cartesian Genetic Programming", booktitle = "Proceedings of the 13th International Joint Conference on Computational Intelligence, {IJCCI} 2021, Online Streaming, October 25-27, 2021", pages = "17--27", publisher = "{SCITEPRESS}", year = "2021", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", URL = "https://doi.org/10.5220/0010640000003063", DOI = "doi:10.5220/0010640000003063", timestamp = "Fri, 19 Nov 2021 13:53:58 +0100", biburl = "https://dblp.org/rec/conf/ijcci/BiauWCL21.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{Biau:2024:evoapplications, author = "Julien Biau and Sylvain Cussat-Blanc and Herve Luga", title = "Improving Image Filter Efficiency: A Multi-objective Genetic Algorithm Approach to Optimize Computing Efficiency", booktitle = "27th International Conference, EvoApplications 2024", year = "2024", editor = "Stephen Smith and Joao Correia and Christian Cintrano", series = "LNCS", volume = "14634", publisher = "Springer", address = "Aberystwyth", month = "3-5 " # apr, pages = "19--34", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Genetic Improvement, Cartesian Genetic Programming, CGP-IP-GI, MOGA, NSGA-II, Island model, Python, Multi-Objective, Image processing, OpenCV, Real Time Applications, Embedded Systems", isbn13 = "978-3-031-56851-0", URL = "https://rdcu.be/dDZHh", DOI = "doi:10.1007/978-3-031-56852-7_2", size = "16 pages", abstract = "For real-time applications in embedded systems, an efficient image filter is not defined solely by its accuracy but by the delicate balance it strikes between precision and computational cost. While one approach to manage an algorithm computing demands involves evaluating its complexity, an alternative strategy employs a multi-objective algorithm to optimize both precision and computational cost. we introduce a multi-objective adaptation of Cartesian Genetic Programming aimed at enhancing image filter performance. We refine the existing Cartesian Genetic Programming framework for image processing by integrating the elite Non-dominated Sorting Genetic Algorithm into the evolutionary process, thus enabling the generation of a set of Pareto front solutions that cater to multiple objectives. To assess the effectiveness of our framework, we conduct a study using a Urban Traffic dataset and compare our results with those obtained using the standard framework employing a mono-objective evolutionary strategy. Our findings reveal two key advantages of this adaptation. Firstly, it generates individuals with nearly identical precision in one objective while achieving a substantial enhancement in the other objective. Secondly, the use of the Pareto front during the evolution process expands the research space, yielding individuals with improved fitness.", notes = "INIA SAS, Toulouse, France http://www.evostar.org/2024/ EvoApplications2024 held in conjunction with EuroGP'2024, EvoCOP2024 and EvoMusArt2024", } @InProceedings{Bickel:1989:tsrGA, author = "Authur S. Bickel and Riva Wenig Bickel", title = "Tree Structured Rules in Genetic Algorithms", booktitle = "Genetic Algorithms and their Applications: Proceedings of the second International Conference on Genetic Algorithms", year = "1987", editor = "John J. Grefenstette", pages = "77--81", address = "MIT, Cambridge, MA, USA", publisher_address = "Hillsdale, NJ, USA", month = "28-31 " # jul, publisher = "Lawrence Erlbaum Associates", keywords = "genetic algorithms, genetic programming", size = "5 pages", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/icga1987/Bickel_1989_tsrGA.pdf", URL = "http://gpbib.cs.ucl.ac.uk/icga/icga1987.pdf", bibtex_url = "http://dl.acm.org/citation.cfm?id=42523&CFID=53044018&CFTOKEN=44075976", abstract = "We apply genetic algorithm techniques to the creation and use of lists of tree- structured production rules varying in length and complexity. Actions, conditions and operators are randomly chosen from tables of possibilities. Use of the techniques of facilitated by the notions of time delay and dependency in performing tests or observations and by accounting for indeterminate test results. GENES, a general program, can adapt to a variety of systems by changing the contents of the various tables to accomodate the domain of a particular problem and by substituting a library of appropriate cases.", notes = "GA applied to variable length lists of tree structured production rules. Mutation applied within trees, eg > replaced by >=. Inversion applied by re-ordering rules, nb does change semantics of rules set because they are applied in order, not applied within trees. Crossover applied to lists NOT to contents of trees", } @InProceedings{Bidaud:2002:romansy, author = "Philippe Bidaud and Frederic Chapelle and G. Dumont", title = "Evolutionary optimization of mechanical and control design. Application to active endoscopes", booktitle = "Theory and Practice of Robots and Manipulators: Proceedings of the Fourteenth Cism-IFToMM Symposium", organization = "CISM - IFToMM", address = "Udine, Italy", month = jul, pages = "317--330", editor = "Giovanni Bianchi and Jean-Claude Guinot and Cezary Rzymkowski", publisher = "Springer Verlag", keywords = "genetic algorithms, genetic programming", year = "2002", number = "14", series = "RoManSy", publisher_address = "Wien/New York", isbn13 = "978-3-211-83691-0", URL = "http://www.springer.com/physics/classical+continuum+physics/book/978-3-211-83691-0", notes = "Also known as \cite{2002ACTI53}. http://www.isir.upmc.fr/?op=view_profil&id=6&pageid=publi&lang=fr See also http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.128.285&rep=rep1&type=pdf ROMANSY 14 Broken Dec 2012 http://www.meil.pw.edu.pl/romansy2002/html/romansy14.htm CISM International Centre for Mechanical Sciences, Number 438", } @InProceedings{Bidlo:2013:CEC, article_id = "1416", author = "Michal Bidlo and Zdenek Vasicek", title = "Evolution of Cellular Automata with Conditionally Matching Rules", booktitle = "2013 IEEE Conference on Evolutionary Computation", volume = "1", year = "2013", month = jun # " 20-23", editor = "Luis Gerardo {de la Fraga}", pages = "1178--1185", address = "Cancun, Mexico", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-0453-2", DOI = "doi:10.1109/CEC.2013.6557699", notes = "CEC 2013 - A joint meeting of the IEEE, the EPS and the IET.", } @InProceedings{Bidlo:2021:SSCI, author = "Michal Bidlo", booktitle = "2021 IEEE Symposium Series on Computational Intelligence (SSCI)", title = "Evolution of Approximate Functions for Image Thresholding", year = "2021", abstract = "This paper investigates the use of approximate addition and multiplication for designing image thresholding functions. Cartesian Genetic Programming is applied for the evolutionary design of circuits using various implementations of the approximate operations. The results are presented for various experimental setups and compared with the case when only exact addition and multiplication is considered. It will be shown that for some range of error metrics of the approximate operations the evolution provides solutions that are better than those provided by the exact operations. Moreover, approximate components allows reducing the implementation area of the resulting functions.", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming", DOI = "doi:10.1109/SSCI50451.2021.9659876", month = dec, notes = "Also known as \cite{9659876}", } @PhdThesis{MichelJanMarinusBieleveldCorr16, author = "Michel Jan Marinus Bieleveld", title = "Improving species distribution model quality with a parallel linear genetic programming-fuzzy algorithm", titletranslation = "Melhorar a qualidade de modelo de distribui{\c c}{\~a}o das esp{\'e}cies com um algoritmo paralelo de programa{\c c}{\~a}o linear gen{\'e}tico-fuzzy.", school = "Computer Engineering, Escola Politecnica", year = "2016", type = "Tese de Doutorado", address = "Brazil", month = "9 " # sep, keywords = "genetic algorithms, genetic programming, applied and specific algorithms, bioclimatologia, ecological niche models, fuzzy logic, species distribution modelling", bibsource = "OAI-PMH server at www.teses.usp.br", contributor = "Antonio Mauro Saraiva", language = "en", oai = "oai:teses.usp.br:tde-26012017-113329", rights = "Liberar o conte{\'u}do para acesso p{\'u}blico.", URL = "http://www.teses.usp.br/teses/disponiveis/3/3141/tde-26012017-113329/", URL = "http://www.teses.usp.br/teses/disponiveis/3/3141/tde-26012017-113329/en.php", URL = "http://www.teses.usp.br/teses/disponiveis/3/3141/tde-26012017-113329/publico/MichelJanMarinusBieleveldCorr16.pdf", publisher = "Biblioteca Digitais de Teses e Disserta{\c c}{\~o}es da USP", size = "142 pages", abstract = "Biodiversity, the variety of life on the planet, is declining due to climate change, population and species interactions and as the result f demographic and landscape dynamics. Integrated model-based assessments play a key role in understanding and exploring these complex dynamics and have proven use in conservation planning. Model-based assessments using Species Distribution Models constitute an efficient means of translating limited point data to distribution probability maps for current and future scenarios in support of conservation decision making. The aims of this doctoral study were to investigate; (1) the use of a hybrid genetic programming to build high quality models that handle noisy real-world presence and absence data, (2) the extension of this solution to exploit the parallelism inherent to genetic programming for fast scenario based decision making tasks, and (3) a conceptual framework to share models in the hope of enabling research synthesis. Subsequent to this, the quality of the method, evaluated with the true skill statistic, was examined with two case studies. The first with a dataset obtained by defining a virtual species, and the second with data extracted from the North American Breeding Bird Survey relating to mourning dove (Zenaida macroura). In these studies, the produced models effectively predicted the species distribution up to 30percent of error rate both presence and absence samples. The parallel implementation based on a twenty-node c3.xlarge Amazon EC2 StarCluster showed a linear speedup due to the multiple-deme coarse-grained design. The hybrid fuzzy genetic programming algorithm generated under certain consitions during the case studies significantly better transferable models.", abstract = "Biodiversidade, a variedade de vida no planeta, est{\'a} em decl{\'i}nio {\`a}s altera{\c c}{\~o}es clim{\'a}ticas, mudan{\c c}as nas intera{\c c}{\~o}es das popula{\c c}{\~o}es e esp{\'e}cies, bem como nas altera{\c c}{\~o}es demogr{\'a}ficas e na din{\^a}mica de paisagens. Avalia{\c c}{\~o}es integradas baseadas em modelo desempenham um papel fundamental na compreens{\~a}o e na explora{\c c}{\~a}o destas din{\^a}micas complexas e tem o seu uso comprovado no planejamento de conserva{\c c}{\~a}o da biodiversidade. Os objetivos deste estudo de doutorado foram investigar; (1) o uso de t{\'e}cnicas de programa{\c c}{\~a}o gen{\'e}tica e fuzzy para construir modelos de alta qualidade que lida com presen{\c c}a e aus{\^e}ncia de dados ruidosos do mundo real, (2) a extens{\~a}o desta solu{\c c}{\~a}o para explorar o paralelismo inerente {\`a} programa{\c c}{\~a}o gen{\'e}tica para acelerar tomadas de decis{\~a}o e (3) um framework conceitual para compartilhar modelos, na expectativa de permitir a s{\'i}ntese de pesquisa. Subsequentemente, a qualidade do m{\'e}todo, avaliada com a true skill statistic, foi examinado com dois estudos de caso. O primeiro utilizou um conjunto de dados fict{\'i}cios obtidos a partir da defini{\c c}{\~a}o de uma esp{\'e}cie virtual, e o segundo utilizou dados de uma esp{\'e}cie de pomba (Zenaida macroura) obtidos do North American Breeding Bird Survey. Nestes estudos, os modelos foram capazes de predizer a distribui{\c c}{\~a}o das esp{\'e}cies maneira correta mesmo utilizando bases de dados com at{\'e} 30percent de erros nas amostras de presen{\c c}a e de aus{\^e}ncia. A implementa{\c c}{\~a}o paralela utilizando um cluster de vinte n{\'o}s c3.xlarge Amazon EC2 StarCluster, mostrou uma acelera{\c c}{\~a}o linear devido ao arquitetura de m{\'u}ltiplos deme de granula{\c c}{\~a}o grossa. O algoritmo de programa{\c c}{\~a}o gen{\'e}tica e fuzzy gerada em determinadas condi{\c c}{\~o}es durante os estudos de caso, foram significativamente melhores na transfer{\^e}ncia do que os algoritmos do BIOMOD.", } @InProceedings{DBLP:conf/icml/BielikRV16, author = "Pavol Bielik and Veselin Raychev and Martin T. Vechev", title = "{PHOG:} Probabilistic Model for Code", booktitle = "Proceedings of the 33nd International Conference on Machine Learning, ICML 2016", year = "2016", editor = "Maria-Florina Balcan and Kilian Q. Weinberger", series = "JMLR Workshop and Conference Proceedings", volume = "48", pages = "2933--2942", address = "New York City, NY, USA", month = jun # " 19-24", publisher = "PMLR", keywords = "genetic algorithms, genetic programming, seeding", timestamp = "Wed, 29 May 2019 08:41:46 +0200", biburl = "https://dblp.org/rec/conf/icml/BielikRV16.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "http://proceedings.mlr.press/v48/bielik16.html", URL = "http://proceedings.mlr.press/v48/bielik16.pdf", size = "10 pages", abstract = "We introduce a new generative model for code called probabilistic higher order grammar (PHOG). PHOG generalises probabilistic context free grammars (PCFGs) by allowing conditioning of a production rule beyond the parent non-terminal, thus capturing rich contexts relevant to programs. Even though PHOG is more powerful than a PCFG, it can be learned from data just as efficiently. We trained a PHOG model on a large JavaScript code corpus and show that it is more precise than existing models, while similarly fast. As a result, PHOG can immediately benefit existing programming tools based on probabilistic models of code.", notes = "'Learning of TCOND Functions... Enumerative search is exponential we use it only on short functions with up to 5 instructions... The resulting functions serve as a starting population for a follow-up genetic programming search... We do not apply a cross-over operation in the genetic search procedure. Also known as \cite{pmlr-v48-bielik16} Cited by \cite{DBLP:conf/pldi/LeeHAN18}", } @Article{Biesheuvel:2004:JCE, author = "Cornelis J. Biesheuvel and Ivar Siccama and Diederick E. Grobbee and Karel G. M. Moons", title = "Genetic programming outperformed multivariable logistic regression in diagnosing pulmonary embolism", journal = "Journal of Clinical Epidemiology", year = "2004", volume = "57", pages = "551--560", number = "6", abstract = "Objective Genetic programming is a search method that can be used to solve complex associations between large numbers of variables. It has been used, for example, for myoelectrical signal recognition, but its value for medical prediction as in diagnostic and prognostic settings, has not been documented. Study design and setting We compared genetic programming and the commonly used logistic regression technique in the development of a prediction model using empirical data from a study on diagnosis of pulmonary embolism. Using part (67%) of the data, we developed and internally validated (using bootstrapping techniques) a diagnostic prediction model by genetic programming and by logistic regression, and compared both on their predictive ability in the remaining data (validation set). Results In the validation set, the area under the ROC curve of the genetic programming model was significantly larger (0.73; 95%CI: 0.64-0.82) than that of the logistic regression model (0.68; 0.59-0.77). The calibration of both models was similar, indicating a similar amount of overoptimism. Conclusion Although the interpretation of a genetic programming model is less intuitive and this is the first empirical study quantifying its value for medical prediction, genetic programming seems a promising technique to develop prediction rules for diagnostic and prognostic purposes.", owner = "wlangdon", URL = "http://igitur-archive.library.uu.nl/med/2006-0906-200235/grobbee_04_geneticprogrammingoutperformed.pdf", URL = "http://www.sciencedirect.com/science/article/B6T84-4CTB5RT-3/2/325f5e3699d990701839201564eff8d3", month = jun, keywords = "genetic algorithms, genetic programming, Logistic regression, Prediction, Diagnostic research, Discrimination, Reliability", DOI = "doi:10.1016/j.jclinepi.2003.10.011", size = "10 pages", notes = "PMID: 15246123 [PubMed - indexed for MEDLINE]", } @PhdThesis{biesheuvel:thesis, author = "Cornelis Jan Biesheuvel", title = "Diagnostic Research : improvements in design and analysis", school = "Universiteit Utrecht", year = "2005", address = "Holland", ISBN = "90-393-2706-8", keywords = "genetic algorithms, genetic programming, diagnosis, methodology, prediction research", URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/", URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/full.pdf", URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/title.pdf", URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/contents.pdf", URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/c1.pdf", URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/c2.pdf", URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/c3.pdf", URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/c4.pdf", URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/c5.pdf", URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/c6.pdf", URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/c7.pdf", URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/c8.pdf", URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/sum.pdf", URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/sam.pdf", URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/dank.pdf", URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/cv.pdf", size = "103 pages", abstract = "In the era of evidence-based medicine, diagnostic procedures also need to undergo critical evaluations. In contrast to guidelines for randomised trials and observational etiologic studies, principles and methods for diagnostic evaluations are still incomplete. The research described in this thesis was conducted to further improve the methods for design and analysis of diagnostic studies. In the past, most diagnostic accuracy studies followed a univariable or single test approach with the aim to quantify the sensitivity, specificity or likelihood ratio. However, single test studies and measures do not reflect a test's added value. It is not the singular association between a particular test result or predictor and the diagnostic outcome that is informative, but the test's value independent of diagnostic information. Multivariable modelling is necessary to estimate the value of a particular test conditional on other test results. However, diagnostic prediction rules are not the solution to everything. They have certain drawbacks, such as overoptimistic accuracy when applied to new patients. Recently, methods have been described to overcome some of these drawbacks. Typically, in diagnostic research one selects a cohort of patients with an indication for the diagnostic procedure at interest as defined by the patients' suspicion of having the disease of interest. The data are analysed cross-sectionally. When appropriate analyses are applied, results from nested case-control studies should be virtually identical to results based on a full cohort analysis. We showed that the nested case-control design offers investigators a valid and efficient alternative for a full cohort approach in diagnostic research. This may be particularly important when the results of the test under study are costly or difficult to collect. It is suggested that randomised controlled trials deliver the highest level of evidence to answer research questions. The paradigm of a randomised study design has also been applied to diagnostic research. We described that a randomised study design is not always necessary to evaluate the value of a diagnostic test to change patient outcome. A test's effect on patient outcome can be inferred and indeed considered as quantified -using decision analysis- 1) if the test is meant to include or exclude a disease for which an established reference is available, 2) if a cross-sectional accuracy study has shown the test's ability to adequately detect the presence or absence of that disease based on the reference, and finally 3) if proper, randomised therapeutic studies have provided evidence on efficacy of the optimal management of this disease. In such instances diagnostic research does not require an additional randomised comparison between two (or more) 'test-treatment strategies' (one with and one without the test under study) to establish the test's effect on patient outcome. Accordingly, diagnostic research -including the quantification of the effects of diagnostic testing on patient outcome- may be executed more efficiently. Diagnostic research aims to quantify a test's added contribution given other diagnostic information available to the physician in determining the presence or absence of a particular disease. Commonly, diagnostic prediction rules use dichotomous logistic regression analysis to predict the presence or absence of a disease. We showed that genetic programming and polytomous modelling are promising alternatives for the conventional dichotomous logistic regression analysis to develop diagnostic prediction rules. The main advantage of genetic programming is the possibility to create more flexible models with better discrimination. This is especially important in large data sets in which complex interactions between predictors and outcomes may be present.", abstract = "Using polytomous logistic regression, one can directly model diagnostic test results in relation to several diagnostic outcome categories. Simultaneous prediction of several diagnostic outcome probabilities particularly applies to situations in which more than two disorders are considered in the differential diagnoses. As this is commonly the case, polytomous regression analysis may serve clinical practice better than conventional dichotomous regression analysis. Both alternatives deserve closer attention in future diagnostic research. We also showed that the development of a diagnostic prediction rule is not the end of the 'research line', even when a rule is subsequently adjusted for optimism using internal validation techniques e.g. bootstrap techniques. External validation of such rules in new patients is always required before introducing a rule in daily practice. This indicates that internal validation of prediction models may not be sufficient and indicative for the model's performance in future patients. Rather than viewing a validation data set as a separate study to estimate an existing rule's performance, validation data may be combined with data of previous derivation studies to generate more robust prediction models using recently suggested methods.", notes = "* Title * Contents * Chapter 1: Introduction * Chapter 2: Test research versus diagnostic research * Chapter 3: Distraction from randomisation in diagnostic research * Chapter 4: Reappraisal of the nested case-control design in diagnostic research: updating the STARD guideline * Chapter 5: Validating and updating a prediction rule for neurological sequelae after childhood bacterial meningitis * Chapter 6: Genetic programming or multivariable logistic regression in diagnostic research * Chapter 7: Revisiting polytomous regression for diagnostic studies * Chapter 8: Concluding remarks * Summary * Samenvatting * Dankwoord * Curriculum Vitae * Volledig proefschrift (520 kB) OMEGA KiQ Ltd.", } @Article{Bin-Inqiad:2024:mtcomma, author = "Waleed {Bin Inqiad} and Muhammad Faisal Javed and Muhammad Shahid Siddique and Saad S. Alarifi and Hisham Alabduljabbar", title = "A comparative analysis of boosting and genetic programming techniques for predicting mechanical properties of soilcrete materials", journal = "Materials Today Communications", year = "2024", volume = "40", pages = "109920", keywords = "genetic algorithms, genetic programming, Machine learning, Soilcrete materials, Metakaolin, Compressive strength, Modulus of elasticity, Shapley analysis, gene expression programming", ISSN = "2352-4928", URL = "https://www.sciencedirect.com/science/article/pii/S2352492824019019", DOI = "doi:10.1016/j.mtcomm.2024.109920", abstract = "This study aims to predict compressive strength (CS) and modulus of elasticity (E) of soilcrete mixes to foster their widespread use in the industry. Soilcfigrete has the potential to promote sustainable construction practices by making use of locally available raw materials. However, the accurate determination of mechanical properties of soilcrete mixes is inevitable to foster their widespread use. Thus, this study employs different machine learning algorithms including Extreme Gradient Boosting (XGB), Gene Expression Programming (GEP), AdaBoost, and Multi Expression Programming (MEP) for this purpose. The XGB and AdaBoost algorithms were implemented using python programming language while MEP and GEP were implemented using specialized softwares. The data used for model development was obtained from previously published literature containing five input parameters and two output parameters. This data was split into two sets named training and testing sets for training and testing of the algorithms respectively. The developed models for CS and E prediction were validated using several error metrices and residual comparison. The objective function value which should be closer to zero for an accurate model is the least for XGB model for prediction of both variables (0.0036 for CS and 0.00315 for E). Moreover, shapley analysis was carried out using XGB model to get insights into the underlying model framework. The results highlighted that water-to-binder ratio (W/B), metakaolin (MK), and ultrasonic pulse velocity (UV) are the most significant variables for predicting E and CS of soilcrete materials. These insights can be used practically to optimise the mixture composition of soilcrete mixes according to different site requirements", } @Article{Bin-Inqiad:2024:mtcomm, author = "Waleed {Bin Inqiad} and Muhammad Faisal Javed and Muhammad Shahid Siddique and Naseer Muhammad Khan and Loai Alkhattabi and Maher Abuhussain and Hisham Alabduljabbar", title = "Comparison of boosting and genetic programming techniques for prediction of tensile strain capacity of Engineered Cementitious Composites ({ECC)}", journal = "Materials Today Communications", year = "2024", volume = "39", pages = "109222", keywords = "genetic algorithms, genetic programming, Machine learning, Engineered cementitious composites, Tensile strain capacity, Fibres, Shapley additive analysis, gene expression programming", ISSN = "2352-4928", URL = "https://www.sciencedirect.com/science/article/pii/S2352492824012030", DOI = "doi:10.1016/j.mtcomm.2024.109222", abstract = "Plain concrete is weak against tension and has low Tensile Strain Capacity (TSC) which significantly affects its long-term performance. To overcome this issue, Engineered Cementitious Composites (ECC) were developed by incorporating polymer fibres in the cement matrix which increases ductility and provides higher TSC than plain concrete and they have emerged as a viable alternative to brittle plain concrete. This study is conducted in an attempt to develop empirical prediction models for TSC prediction of ECC without requiring extensive experimental procedures. For this purpose, two evolutionary programming techniques known as Multi Expression Programming (MEP), Gene Expression Programming (GEP) along with two boosting-based techniques: AdaBoost and Extreme Gradient Boosting (XGB) were developed using data collected from published literature. The gathered dataset had seven input parameters including water-to-binder ratio, sand, fibre content, cement, fly ash, superplasticizer, and age etc. and only one output parameter i.e., TSC. The error assessment of developed models was done using correlation coefficient, Mean Absolute Error (MAE), and Objective Function (OF) etc. and the error comparison showed that XGB has the highest accuracy having the least OF value of 0.081 as compared to 0.11 of AdaBoost, 0.13 of GEP, and 0.16 of MEP. Shapley additive analysis was conducted on the XGB model since it proved to be the most accurate, and the results highlighted that fibre content, age, and water-to-binder ratio are the most important features to predict TSC of ECC", } @InProceedings{bin-murtaza:2024:GECCOcomp, author = "Sardar {Bin Murtaza} and Aidan Mccoy and Zhiyuan Ren and Aidan Murphy and Wolfgang Banzhaf", title = "{LLM} Fault Localisation within Evolutionary Computation Based Automated Program Repair", booktitle = "Large Language Models for and with Evolutionary Computation Workshop", year = "2024", editor = "Erik Hemberg and Roman Senkerik and Una-May O'Reilly and Michal Pluhacek and Tome Eftimov", pages = "1824--1829", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic improvement, fault localisation, large language models, ANN", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3664174", size = "6 pages", abstract = "Repairing bugs can be a daunting task for even a human experienced in debugging, so naturally, attempting to automatically repair programs with a computer system is quite challenging. The existing methods of automated program repair leave a lot of room for improvement. Fault localization, which aims to find lines of code that are potentially buggy, minimises the search space of an automated program repair system. Recent work has shown improvement in these fault localization methods, with the use of Large Language Models. Here, we propose a system where a LLM-based fault localization tool, which we call SemiAutoFL, is used within a fully automatic program repair program, ARJA-e. We show that using LLM-based fault localization with ARJA-e can significantly improve its performance on real world bugs. ARJA-e with SemiAutoFL can repair 10 bugs that ARJA-e was previously unable to so do. This finding adds to our understanding of how to improve fault localization and automated program repair, highlighting the potential for more efficient and accurate fault localisation methods being applied to automated program repair.", notes = "GECCO-2024 LLMfwEC A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{1274004, author = "Franck Binard and Amy Felty", title = "An abstraction-based genetic programming system", booktitle = "Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2007)}", year = "2007", month = "7-11 " # jul, editor = "Peter A. N. Bosman", pages = "2415--2422", address = "London, United Kingdom", publisher = "ACM Press", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, lambda calculus, languages, polymorphism, types", isbn13 = "978-1-59593-698-1", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p2415.pdf", DOI = "doi:10.1145/1274000.1274004", abstract = "We extend tree-based typed Genetic Programming (GP) representation schemes by introducing System F, an expressive l-calculus, for representing programs and types. At the level of programs, System F provides higher-order programming capabilities with functions and types as first-class objects, e.g., functions can take other functions and types as parameters. At the level of types, System F provides parametric polymorphism. The expressiveness of the system provides the potential for a genetic programming system to evolve looping, recursion, lists, trees and many other typical programming structures and behaviour. This is done without introducing additional external symbols in the set of predefined functions and terminals of the system. In fact, we actually remove programming structures such as if/then/else, which we replace by two abstraction operators. We also change the composition of parse trees so that they may directly include types.", notes = "Distributed on CD-ROM at GECCO-2007 ACM Order No. 910071", } @InProceedings{Binard:2008:gecco, author = "Franck Binard and Amy Felty", title = "Genetic Programming with Polymorphic Types and Higher-Order Functions", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", pages = "1187--1194", address = "Atlanta, GA, USA", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, lambda calculus, polymorphism, types", isbn13 = "978-1-60558-130-9", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1187.pdf", DOI = "doi:10.1145/1389095.1389330", size = "8 pages", abstract = "we introduce our new approach to program representation for genetic programming (GP). We replace the usual s-expression representation scheme by a strongly-typed abstraction-based representation scheme. This allows us to represent many typical computational structures by abstractions rather than by functions defined in the GP system terminal set. The result is a generic GP system that is able to express programming structures such as recursion and data types without explicit definitions. We demonstrate the expressive power of this approach by evolving simple Boolean programs without defining a set of terminals. We also evolve programs that exhibit recursive behaviour without explicitly defining recursion specific syntax in the terminal set. we present our approach and experimental results.", notes = "System-F GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389330}", } @PhdThesis{Binard:thesis, author = "Franck J. L. Binard", title = "Abstraction-Based Genetic Programming", school = "Ottawa-Carleton Institute for Computer Science, School of Information Technology and Engineering, Faculty of Engineering, University of Ottawa", year = "2009", address = "Ottawa, Canada", keywords = "genetic algorithms, genetic programming", URL = "http://www.site.uottawa.ca/~fbinard/Articles/FranckBinardPhDThesisLastVersion.pdf", URL = "http://hdl.handle.net/10393/29805", DOI = "doi:10.20381/ruor-13147", size = "178 pages", abstract = "This thesis describes a novel method for representing and automatically generating computer programs in an evolutionary computation context. Abstraction-Based Genetic Programming (ABGP) is a typed Genetic Programming representation system that uses System F, an expressive lambda-calculus, to represent the computational components from which the evolved programs are assembled. ABGP is based on the manipulation of closed, independent modules expressing computations with effects that have the ability to affect the whole genotype . These modules are plugged into other modules according to precisely defined rules to form complete computer programs. The use of System F allows the straightforward representation and use of many typical computational structures and behaviours (such as iteration, recursion, lists and trees) in modular form. This is done without introducing additional external symbols in the set of predefined functions and terminals of the system. In fact, programming structures typically included in GP terminal sets, such as if then else, may be removed and represented as abstractions in ABGP for the same problems. ABGP also provides a search space partitioning system based on the structure of the genotypes, similar to the species partitioning system of living organisms and derived from the Curry-Howard isomorphism. This thesis also presents the results obtained by applying this method to a set of problems.", notes = "Supervisor: Amy Felty", } @Book{Binard:book, author = "Franck Binard", title = "Abstraction-Based Genetic Programming: An Application of the polymorphically-typed lambda calculus to genetic programming", publisher = "Verlag Dr. Mueller", year = "2009", month = "6 " # oct, keywords = "genetic algorithms, genetic programming", ISBN = "3-639-19173-0", URL = "https://www.amazon.co.uk/Abstraction-Based-Genetic-Programming-Application-polymorphically-typed/dp/3639191730", size = "184 pages", abstract = "Abstraction-Based Genetic Programming (ABGP) is a novel Genetic Programming (GP) system in which the set of all possible genotypes is partitioned by the proofs to which each program is linked via the Curry-Howard isomorphism. In the context of ABGP, proofs are related to computer programs in the same way as species are related to organisms in the biological world. They can be seen as patterns into which alleles of genes may be plugged in. In this analogy, genes are types and an allele of a gene is a closed typed computational block that may be combined with other blocks to form an organism. The type of an allele is the gene to which it corresponds.", } @InProceedings{Bing:2010:ETCS, author = "Wu Bing and Zhang Wen-qiong and Liang Jia-hong", title = "A Genetic Multiple Kernel Relevance Vector Regression Approach", booktitle = "Second International Workshop on Education Technology and Computer Science (ETCS), 2010", year = "2010", month = mar, volume = "3", pages = "52--55", abstract = "Relevance vector machine (RVM) is a state-of-the-art technique for regression and classification, as a sparse Bayesian extension version of the support vector machine. The kernel function and parameter selection is a key problem in the research of RVM. The real-world application and recent researches have emphasised the requirement to multiple kernel learning. This paper proposes a novel regression technique, called Genetic Multiple Kernel Relevance Vector Regression (GMK RVR), which combines genetic programming and relevance vector regression to evolve a multiple kernel function. The proposed technique are compared with those of a standard RVR using the Polynomial, Gaussian RBF and Sigmoid kernel with various parameter settings, based on several benchmark problems. Numerical experiments show that the GMK performs better than such widely used kernels and prove the validation of the GMK.", keywords = "genetic algorithms, GMK validation, Gaussian RBF, Sigmoid kernel, benchmark problems, genetic multiple kernel relevance vector regression, kernel function, multiple kernel function, multiple kernel learning, parameter selection, relevance vector machine, sparse Bayesian extension, state-of-the-art technique, support vector machine, Bayes methods, learning (artificial intelligence), pattern classification, regression analysis, support vector machines", DOI = "doi:10.1109/ETCS.2010.154", notes = "Not a GP, fixed representation. Also known as \cite{5460012}", } @Article{bingol:2018:NCaA, author = "Sedat Bingol and Hidir Yanki Kilicgedik", title = "Application of gene expression programming in hot metal forming for intelligent manufacturing", journal = "Neural Computing and Applications", year = "2018", volume = "30", number = "3", keywords = "genetic algorithms, genetic programming, gene expression programming", URL = "http://link.springer.com/article/10.1007/s00521-016-2718-5", DOI = "doi:10.1007/s00521-016-2718-5", } @Article{emse19, author = "David Binkley and Nicolas Gold and Syed Islam and Jens Krinke and Shin Yoo", title = "A comparison of tree- and line-oriented observational slicing", journal = "Empirical Software Engineering", year = "2019", volume = "24", pages = "3077--3113", month = oct, note = "Special Section on Source Code Analysis and Manipulation", keywords = "genetic algorithms, genetic programming, ORBS, source code deletion, srcML, XML", ISSN = "1382-3256", URL = "http://www.cs.ucl.ac.uk/staff/j.krinke/publications/emse19.pdf", DOI = "doi:10.1007/s10664-018-9675-9", size = "41 pages", abstract = "Observation-based slicing and its generalization observational slicing are recently-introduced, language-independent dynamic slicing techniques. They both construct slices based on the dependencies observed during program execution, rather than static or dynamic dependence analysis. The original implementation of the observation-based slicing algorithm used lines of source code as its program representation. A recent variation, developed to slice modeling languages (such as Simulink), used an XML representation of an executable model. We ported the XML slicer to source code by constructing a tree representation of traditional source code through the use of srcML. This work compares the tree- and line-based slicers using four experiments involving twenty different programs, ranging from classic benchmarks to million-line production systems. The resulting slices are essentially the same size for the majority of the programs and are often identical. However, structural constraints imposed by the tree representation sometimes force the slicer to retain enclosing control structures. It can also bog down trying to delete single-token subtrees. This occasionally makes the tree-based slices larger and the tree-based slicer slower than a parallelised version of the line-based slicer. In addition, a Java versus C comparison finds that the two languages lead to similar slices, but Java code takes noticeably longer to slice. The initial experiments suggest two improvements to the tree-based slicer: the addition of a size threshold, for ignoring small subtrees, and subtree replacement. The former enables the slicer to run 3.4 times faster while producing slices that are only about 9percent larger. At the same time the subtree replacement reduces size by about 8 to 12 percent and allows the tree-based slicer to produce more natural slices.", notes = "Not on GP but interesting manipulation of human written high level code via tree based XML ? Section 3.1.2 Subtree Replacement, similar to Hoist mutation \cite{kinnear:kinnear} ? Section 6.2.2 X=10 over fitting? srcML used in genetic improvement PyGGI 2.0 eg \cite{an:2019:fse}", } @Article{Birchenhall:1995:EJ, author = "C. R. Birchenhall", copyright = "Copyright 1995 Royal Economic Society", ISSN = "00130133", journal = "The Economic Journal", number = "430", owner = "wlangdon", pages = "788--795", title = "Genetic Algorithms, Classifier Systems and Genetic Programming and their Use in the Models of Adaptive Behaviour and Learning", URL = "http://links.jstor.org/sici?sici=0013-0133%28199505%29105%3A430%3C788%3AGACSAG%3E2.0.CO%3B2-%23", volume = "105", year = "1995", keywords = "genetic algorithms, genetic programming", notes = "Reviewed? in The Economic Journal, vol 106 number 434, 1996 APPROX pages 271", } @InProceedings{DBLP:conf/petra/Bird22, author = "Jordan J. Bird", title = "{EEG} Wavelet Classification for Fall Detection with Genetic Programming", booktitle = "{PETRA} '22: The 15th International Conference on PErvasive Technologies Related to Assistive Environments, Corfu, Greece, 29 June 2022 - 1 July 2022", pages = "376--382", publisher = "{ACM}", year = "2022", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1145/3529190.3535339", DOI = "doi:10.1145/3529190.3535339", timestamp = "Tue, 12 Jul 2022 16:32:52 +0200", biburl = "https://dblp.org/rec/conf/petra/Bird22.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{Bird:2023:GPEM, author = "Jordan J. Bird and Ahmad Lotfi", title = "Fall compensation detection from {EEG} using neuroevolution and genetic hyperparameter optimisation", journal = "Genetic Programming and Evolvable Machines", year = "2023", volume = "24", number = "2", pages = "Article number: 6", month = jun, note = "Online first", keywords = "genetic algorithms, genetic programming, ANN, Evolutionary optimisation, Fall detection, EEG, Hyperheuristics, Signal classification", ISSN = "1389-2576", URL = "https://rdcu.be/dcJdp", DOI = "doi:10.1007/s10710-023-09453-3", size = "26 pages", abstract = "Detecting fall compensatory behaviour from large EEG datasets poses a difficult problem in big data which can be alleviated by evolutionary computation-based machine learning strategies. hyperheuristic optimisation solutions via evolutionary optimisation of deep neural network topologies and genetic programming of machine learning pipelines will be investigated. Wavelet extractions from signals recorded during physical activities present a binary problem for detecting fall compensation. The earlier results show that a Gaussian process model achieves an accuracy of 86.48percent. Following this, artificial neural networks are evolved through evolutionary algorithms and score similarly to most standard models; the hyperparameters chosen are well outside the bounds of batch or manual searches. Five iterations of genetic programming scored higher than all other approaches, at a mean 90.52percent accuracy. The best pipeline extracted polynomial features and performed Principal Components Analysis, before machine learning through a randomised set of decision trees, and passing the class prediction probabilities to a 72-nearest-neighbour algorithm. The best genetic solution could infer data in 0.02 seconds, whereas the second best genetic programming solution (89.79percent) could infer data in only 0.3 milliseconds.", } @PhdThesis{Bird:thesis, author = "Sarah Lynn Bird", title = "Optimizing Resource Allocations for Dynamic Interactive Applications", school = "University of California, Berkeley", year = "2014", address = "USA", month = may, keywords = "genetic algorithms, genetic programming, PACORA, Matlab", URL = "https://aspire.eecs.berkeley.edu/publication/optimizing-resource-allocations-for-dynamic-interactive-applications/", URL = "https://people.eecs.berkeley.edu/~krste/papers/bird-phd.pdf", URL = "https://escholarship.org/uc/item/9nf5z4pg", size = "121 pages", abstract = "Modern computing systems are under intense pressure to provide guaranteed responsiveness to their workloads. Ideally, applications with strict performance requirements should be given just enough resources to meet these requirements consistently, without unnecessarily siphoning resources from other applications. However, executing multiple parallel, real-time applications while satisfying response time requirements is a complex optimization problem and traditionally operating systems have provided little support to provide QoS to applications. As a result, client, cloud, and embedded systems have all resorted to over-provisioning and isolating applications to guarantee responsiveness. Instead, we present PACORA, a resource allocation framework designed to provide responsiveness guarantees to a simultaneous mix of high-throughput parallel, interactive, and real-time applications in an efficient, scalable manner. By measuring application behavior directly and using convex optimization techniques, PACORA is able to understand the resource requirements of applications and perform near-optimal resource allocation two percent from the best allocation in 1.4 milliseconds while only requiring a few hundred bytes of storage per application.", notes = "Advisors: Krste Asanovic and Dave Patterson and Burton Smith", } @InCollection{oai:CiteSeerPSU:397549, title = "Schemas and Genetic Programming", author = "Andreas Birk and Wolfgang J. Paul", booktitle = "Prerational Intelligence: Adaptive Behavior and Intelligent Systems Without Symbols and Logic {II}", publisher = "Kluwer", year = "2001", editor = "Holk Cruse and Jeffrey Dean and Helge Ritter", volume = "26", series = "Studies in Cognitive Systems", pages = "345--357", ebook_pages = "804--816", keywords = "genetic algorithms, genetic programming", ISBN = "0-7923-6666-2", isbn13 = "978-94-010-3792-1", URL = "http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-135-22-33673255-0,00.html", URL = "http://www.faculty.iu-bremen.de/birk/publications/schemas_genetic_programming.pdf", URL = "http://arti.vub.ac.be/~cyrano/PUBLICATIONS/schema_gp00.ps.gz", URL = "http://citeseer.ist.psu.edu/397549.html", DOI = "doi:10.1007/978-94-010-0870-9_50", citeseer-isreferencedby = "oai:CiteSeerPSU:106696; oai:CiteSeerPSU:67434; oai:CiteSeerPSU:532836; oai:CiteSeerPSU:86635; oai:CiteSeerPSU:54193; oai:CiteSeerPSU:315750; oai:CiteSeerPSU:89833; oai:CiteSeerPSU:66393; oai:CiteSeerPSU:226046; oai:CiteSeerPSU:360779; oai:CiteSeerPSU:193774", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:397549", rights = "unrestricted", size = "13 pages", abstract = "To investigate the mechanisms which enable systems to learn is among the most challenging of research activities. In computer science alone it is pursued by at least three communities (Carbonel 1990; Natarajan 1991; Ritter et al. 1991). The overwhelming majority of all studies treats situations with strong inductive bias, i.e. there is a fairly narrow class H of algorithms and the concept or algorithm to be learned is known a priori to lie in that class H. With the help of schemas and genetic programming we describe systems which: interact with the real world make theories about the consequences of their actions and dynamically adjust inductive bias. We present experimental data related to learning geometric concepts and moving a block in a microworld.", } @Article{Birky:2025:cma, author = "Donovan Birky and John Emery and Craig Hamel and Jacob Hochhalter", title = "Learning implicit yield surface models with uncertainty quantification for noisy datasets", journal = "Computer Methods in Applied Mechanics and Engineering", year = "2025", volume = "436", pages = "117738", keywords = "genetic algorithms, genetic programming, Symbolic regression, Machine learning, Uncertainty quantification", ISSN = "0045-7825", URL = "https://www.sciencedirect.com/science/article/pii/S0045782525000106", DOI = "doi:10.1016/j.cma.2025.117738", abstract = "Materials often exhibit stochastic mechanical behaviours due to their inherent intrinsic variability. Data acquisition also introduces extrinsic noise into data. To learn yield surface models under uncertainty, we present a method that uses genetic programming based symbolic regression (GPSR) and a multi-objective fitness function (MOSR). Previous works have demonstrated using an implicit fitness metric in GPSR that compares the partial derivatives of proposed models with those of the data, allowing the generation of mechanics-guided, implicit yield surface models. MOSR adds to that a Bayesian fitness metric to simultaneously quantify parameter uncertainty. We test this method on benchmark implicit and physical test problems to demonstrate MOSR's efficacy in finding implicit model forms on noisy data compared to the conventional implicit fitness metric. The results show that the MOSR algorithm prevents overfitting to noisy data, improves parameter estimates on data even with no noise present, and reduces model complexity, improving overall model interpretability. The MOSR method affords the ability to learn new and improved yield surface models while simultaneously quantifying the uncertainty in model parameters, leading to enhanced model interpretability", } @InProceedings{Birtolo:2010:ICEIS, author = "Cosimo Birtolo and Roberto Armenise and Luigi Troiano", title = "Supporting Menu Layout Design by Genetic Programming", booktitle = "Proceedings of the 12th International Conference on Enterprise Information Systems (ICEIS 2010)", year = "2010", editor = "Joaquim Filipe and Jos{\'e} Cordeiro", address = "Funchal, Madeira, Portugal", month = "8 - 12 " # jun, keywords = "genetic algorithms, genetic programming: poster, SBSE", broken = "https://www.poste.it/azienda/research_development/pubblicazioniCentroRicerca.html", URL = "https://www.poste.it/azienda/research_development/pubblicazioni/ICEIS10%20-%20SUPPORTING%20MENU%20LAYOUT%20DESIGN%20BY%20GP.pdf", size = "4 pages", abstract = "Graphical User Interfaces heavily rely on menus to access application functionalities. Therefore designing properly menus poses relevant usability issues to face. Indeed, trading off between semantic preferences and usability makes this task not so easy to be performed. Meta-heuristics represent a promising approach in assisting designers to specify menu layouts. In this paper, we propose a preliminary experience in adopting Genetic Programming as a natural means for evolving a menu hierarchy towards optimal structure.", notes = "http://www.iceis.org/iceis2010/index.htm http://www.iceis.org/Abstracts/2010/ICEIS_2010_Abstracts.htm Despite http://dblp.uni-trier.de/rec/bibtex/conf/iceis/BirtoloAT10 does not appear to be in electronic proceedings published by Springer isbn 978-989-8425-08-9, pages 248-251", } @InProceedings{bisat:1998:ussbctn, author = "Mona T. Bisat and Charles W. Richter and Gerald B. Sheble", title = "Using Adaptive Agents to Study Bilateral Contracts and Trade Networks", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "23--27", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "genetic algorithms, genetic programming, electricity transmission capacity, trade network game, double auction bid, power utility", URL = "http://dakotarichter.com/papers/gp98PosterPaperBisatRichterSheble.pdf", size = "5 pages", abstract = "This research is an extension of research done by Charles Richter, Gerald Sheble' and Dan Ashlock (1997, 1998) on double auction bidding strategies for electric utilities which trade competitively. This research considers the network topology and whether a successful bid transaction can occur given the flow constraints on the network. The ATC (Available Transmission Capacity) of the network is a flow constraint indicator that is used to provide feedback to agents attempting to engage in bilateral contracts. The aim is to develop adaptive agents that are able to recognize with whom they can enter a profitable bilateral contract. In other words, the agents develop preferential partner selection lists and bidding strategies in a simulated electric market. The idea of evolving preferred trading partner lists comes from the Trade Network Game (TNG) developed by Tesfatsion, Ashlock and Stanley (1995). The strategies being developed by the method described here are adaptive. The strategies are encoded in GP-Automata, a technique which combines genetic programming and finite state automata.", notes = "GP-98LB", } @PhdThesis{Bischl:thesis, author = "Bernd Bischl", title = "Model and Algorithm Selection in Statistical Learning and Optimization", school = "Fakultaet Statistik, Technische Universitaet Dortmund", year = "2014", address = "Germany", month = "7 " # feb, keywords = "genetic algorithms, genetic programming, SVM, model selection, algorithm selection, algorithm configuration, tuning, benchmarking, machine learning", URL = "https://eldorado.tu-dortmund.de/bitstream/2003/32861/1/phd.pdf", URL = "http://hdl.handle.net/2003/32861", URL = "https://eldorado.tu-dortmund.de/handle/2003/32861", DOI = "doi:10.17877/DE290R-7142", size = "37 pages", abstract = "Modern data-driven statistical techniques, e.g., non-linear classification and regression machine learning methods, play an increasingly important role in applied data analysis and quantitative research. For real-world we do not know a priori which methods will work best. Furthermore, most of the available models depend on so called hyper- or control parameters, which can drastically influence their performance. This leads to a vast space of potential models, which cannot be explored exhaustively. Modern optimization techniques, often either evolutionary or model-based, are employed to speed up this process. A very similar problem occurs in continuous and discrete optimization and, in general, in many other areas where problem instances are solved by algorithmic approaches: Many competing techniques exist, some of them heavily parametrized. Again, not much knowledge exists, how, given a certain application, one makes the correct choice here. These general problems are called algorithm selection and algorithm configuration. Instead of relying on tedious, manual trial-and-error, one should rather employ available computational power in a methodical fashion to obtain an appropriate algorithmic choice, while supporting this process with machine-learning techniques to discover and exploit as much of the search space structure as possible. In this cumulative dissertation I summarize nine papers that deal with the problem of model and algorithm selection in the areas of machine learning and optimization. Issues in benchmarking, resampling, efficient model tuning, feature selection and automatic algorithm selection are addressed and solved using modern techniques. I apply these methods to tasks from engineering, music data analysis and black-box optimization. The dissertation concludes by summarizing my published R packages for such tasks and specifically discusses two packages for parallelization on high performance computing clusters and parallel statistical experiments.", notes = "p14 'on the considered [SVM] benchmark problems even our improved genetic programming approach leads to disappointing results. Although good-performing common kernel functions can be recovered by the genetic search' In English", } @Article{BISHAYEE:2023:gsd, author = "Bhaskar Bishayee and Abhay Kumar and Sandip Kumar Lahiri and Susmita Dutta and Biswajit Ruj", title = "Modeling, optimization and comparative study on abatement of fluoride from synthetic solution using activated laterite soil and fly ash", journal = "Groundwater for Sustainable Development", volume = "23", pages = "101016", year = "2023", ISSN = "2352-801X", DOI = "doi:10.1016/j.gsd.2023.101016", URL = "https://www.sciencedirect.com/science/article/pii/S2352801X23001169", keywords = "genetic algorithms, genetic programming, Fluoride, Adsorption, Natural and waste material, Kinetic and equilibrium study", abstract = "The supply of potable water by proper treatment of fluoride laden ground water is a challenging task. Batch experiments were executed with activated laterite soil and fly ash individually for removal of fluoride. The process parameters such as particle size (100-620 ?m), dose of adsorbent (10-60 g/L), initial concentration of fluoride (2-12 mg/L), agitation speed (20-120 rpm), contact time (0.5-10 h) and pH (4.5-9.5) were investigated and it was observed that activated laterite soil had better fluoride removal efficiency than fly ash. At pH 4.5, contact time 10 h, particle size 100 ?m, adsorbent dose 60 g/L, initial concentration of fluoride 12 mg/L, and agitation speed 120 rpm, maximum removal efficiencies of fluoride using activated laterite soil and fly ash were found as 85.91 pm 0.62percent and 77.1 pm 0.39percent, respectively. Kinetic study was performed and Pseudo 2nd order kinetic model was found to fit the kinetic data best. The Freundlich isotherm model fit the equilibrium data fairly well. The values of adsorption equilibrium constant as used in Freundlich isotherm model vis-a-vis adsorption capacity (KF) for activated laterite soil and fly ash were obtained as 0.1331 and 0.0772 ((mgg-1)(Lmg-1)1/n). A thermodynamic analysis was conducted to examine the process behaviour. In order to determine whether the adsorbent may be used for further cases, regeneration of the adsorbents was also done. Generally, two types of adsorption mechanisms happen like electrostatic attraction and ion exchange. The electrostatic force of attraction favours the adsorption of negatively charged fluoride ions on positively charged adsorbent surfaces. In ion exchange, fluoride ions are exchanged by hydroxyl and hydronium ion. Based on experimental data, Multi-Gene Genetic Programming (MGGP) model could accurately predict the removal efficiency of fluoride under various operating situations. Finally, using Genetic Algorithm (GA) optimization the maximum output values for both adsorbents were estimated as 99.14percent and 99.02percent", } @InProceedings{conf/evoW/BishopCT14, title = "Feature Construction Using Genetic Programming for Classification of Images by Aesthetic Value", author = "Andrew Bishop and Victor Ciesielski and Karen Trist", booktitle = "Evolutionary and Biologically Inspired Music, Sound, Art and Design - Third European Conference, Evo{MUSART} 2014, Granada, Spain, April 23-25, 2014, Revised Selected Papers", publisher = "Springer", year = "2014", volume = "8601", editor = "Juan Romero and James McDermott and Joao Correia", isbn13 = "978-3-662-44334-7", pages = "62--73", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming", bibdate = "2014-09-24", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/evoW/evomusart2014.html#BishopCT14", URL = "http://dx.doi.org/10.1007/978-3-662-44335-4", } @InProceedings{bishop:2024:GECCO, author = "Jordan T Bishop and Jason Jooste and David Howard", title = "Evolutionary Exploration of Triply Periodic Minimal Surfaces via Quality Diversity", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference", year = "2024", editor = "Ruhul Sarker and Patrick Siarry and Julia Handl and Xiaodong Li and Markus Wagner and Mario Garza-Fabre and Kate Smith-Miles and Richard Allmendinger and Ying Bi and Grant Dick and Amir H Gandomi and Marcella Scoczynski Ribeiro Martins and Hirad Assimi and Nadarajen Veerapen and Yuan Sun and Mario Andres Munyoz and Ahmed Kheiri and Nguyen Su and Dhananjay Thiruvady and Andy Song and Frank Neumann and Carla Silva", pages = "1165--1173", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, grammatical evolution, triply periodic minimal surface, quality diversity, evolutionary design, constrained optimisation, Real World Applications", isbn13 = "979-8-4007-0494-9", DOI = "doi:10.1145/3638529.3654039", size = "9 pages", abstract = "Triply Periodic Minimal Surfaces (TPMSs) are a family of mathematical structures that exhibit constant zero mean curvature and 3-dimensional periodicity. They are often used to produce cellular solids with advantageous structural, thermal, and optical properties. Existing applications represent TPMSs as trigonometric approximations of a Fourier series. Due to the mathematical difficulty of determining new exact forms and their approximations, previous work has mostly evaluated metrics based on geometry, manufacturability, and mechanical performance across parameterisations of a small set of known TPMS equations. In this work, we define TPMS-like structures as having low estimated mean curvature, and apply a coupling of Grammatical Evolution and Quality Diversity to generate a diverse set of novel structures of this kind. We additionally explore the effect of being TPMS-like on the manufacturability of evolved structures. Results show that many TPMS-like designs can be found for different combinations of total surface area and Gaussian curvature, and that there is not a strong relationship between how TPMS-like a design is and its manufacturability. Our method serves as a basis for future application of novel TPMS-like structures and exploration of the pairing of evolutionary design with generative approaches from broader machine learning.", notes = "GECCO-2024 RWA A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @Article{Bishop96, author = "P. Bishop and R. Bloomfield", title = "Conservative theory for long-term reliability-growth prediction [of software]", journal = "IEEE Transactions on Reliability", volume = "45", number = "4", month = dec, pages = "550--560", notes = "Theoretical or Mathematical", address = "Adelard, London, UK", year = "1996", ISSN = "0018-9529", URL = "http://ieeexplore.ieee.org/iel1/24/12134/00556578.pdf?isNumber=12134&prod=JNL&arnumber=556578&arSt=550&ared=560&arAuthor=Bishop%2C+P.%3B+Bloomfield%2C+R.", URL = "http://www.adelard.co.uk/resources/papers/pdf/issre96m.pdf", abstract = "This paper describes a different approach to software reliability growth modeling which enables long-term predictions. Using relatively common assumptions, it is shown that the average value of the failure rate of the program, after a particular use-time, t, is bounded by N/(e/spl middot/t), where N is the initial number of faults. This is conservative since it places a worst-case bound on the reliability rather than making a best estimate. The predictions might be relatively insensitive to assumption violations over the longer term. The theory offers the potential for making long-term software reliability growth predictions based solely on prior estimates of the number of residual faults. The predicted bound appears to agree with a wide range of industrial and experimental reliability data. Less pessimistic results can be obtained if additional assumptions are made about the failure rate distribution of faults.", keywords = "software reliability, reliability theory, failure analysis, long-term reliability-growth prediction, software reliability growth modeling, program failure rate, use-time, initial fault number, worst-case bound, residual fault number, failure rate distribution", notes = "cf. \cite{brady:murphy}", } @PhdThesis{Biswas:thesis, author = "Arijit Biswas", title = "Optimization of Hydrometallurgical Processing of Lean Manganese Bearing Resources", school = "Metallurgical and Materials Engineering, Indian Institute of Technology", year = "2010", address = "Kharagpur, India", keywords = "genetic algorithms, genetic programming, Applied science, Chemical Engineering, Evolutionary Algorithm, Evolutionary neural network, Manganese ore, Materials science, Polymetallic sea nodules, Process flowsheeting, Sequential modular approach, Split fraction", URL = "http://www.idr.iitkgp.ac.in/xmlui/handle/123456789/951", abstract = "An evolutionary multi-objective optimization framework is evolved to model the extraction process of manganese from lean manganese bearing resources. The primary objective of this thesis is to develop a generic flowsheet and to come up with a data driven modelling approach for this purpose. Flowsheets developed for processing low grade manganese ores, such as Polymetallic Sea nodules, via various processing routes are optimized using an Evolutionary Multi-objective strategy. The work also aims to provide a considerable insight towards understanding of the leaching processes pertinent to manganese extraction. To analyse and optimize the process flow sheets for treatment of low grade manganese ores, two hydrometallurgical routes based upon ammoniacal and acid leaching in presence of reducing agents are taken up. The analyses suggested that of particular significance is the grade of the ore being treated, the energy consumed and the associated costs, options for by-product recovery, and the relative price of the products. A process scheme has been optimized here for simultaneously maximizing the metal throughput and minimizing the direct operating costs incurred within constraints set for the operating variables. This leads to a multi-objective optimization problem, which has been conducted during this study for the leaching of polymetallic nodules. To analyse the non-linear kinetics of the leaching reaction of lean manganese bearing ores, an analytical model is developed along with a number of data driven models. Terrestrial lean manganese ores need to be processed in acidic medium in presence of reducing agents like glucose, lactose and sucrose, in order to extract manganese values from them. In this study data driven models based on Neural Network and Genetic Programming are compared for two different categories of manganese ores leached in sulphuric acid medium. A Predator-prey Genetic Algorithm approach developed for this purpose is pitted against a number of other established evolutionary techniques, in addition to a commercial software. A leaching model is evolved using the fitted leaching parameters from different data driven models and is thoroughly tested for the goodness of fit against the experimental data. The strategy adopted, once again, is generic in nature and the framework can be extended for any kind of hydrometallurgical process flowsheeting.", } @Article{Biswas:2011:MMP, author = "Arijit Biswas and Ogier Maitre and Debanga Nandan Mondal and Syamal Kanti Das and Prodip Kumar Sen and Pierre Collet and Nirupam Chakraborti", title = "Data-Driven Multiobjective Analysis of Manganese Leaching from Low Grade Sources Using Genetic Algorithms, Genetic Programming, and Other Allied Strategies", journal = "Materials and Manufacturing Processes", year = "2011", volume = "26", number = "3", pages = "415--430", keywords = "genetic algorithms, genetic programming, Evolutionary algorithm, Leaching, Manganese, Multiobjective optimisation, Ocean nodules, Optimisation, Pareto frontier", ISSN = "1042-6914", URL = "http://www.tandfonline.com/doi/abs/10.1080/10426914.2010.544809", URL = "http://www.tandfonline.com/doi/pdf/10.1080/10426914.2010.544809", DOI = "doi:10.1080/10426914.2010.544809", size = "16 pages", abstract = "Data-driven models are constructed for leaching processes of various low grade manganese resources using various nature inspired strategies based upon genetic algorithms, neural networks, and genetic programming and subjected to a bi-objective Pareto optimization, once again using several evolutionary approaches. Both commercially available software and in-house codes were used for this purpose and were pitted against each other. The results led to an optimum trade-off between maximising the recovery, which is a profit oriented requirement, along with a minimisation of the acid consumption, which addresses the environmental concerns. The results led to a very complex scenario, often with different trends shown by the different methods, which were systematically analysed.", } @InProceedings{conf/softcomp/BittencourtSLAO10, author = "Evandro Bittencourt and Sidney Schossland and Raul Landmann and Denio {Murilo de Aguiar} and Adilson Gomes {De Oliveira}", title = "The Gene Expression Programming Applied to Demand Forecast", booktitle = "Soft Computing Models in Industrial and Environmental Applications, 5th International Workshop (SOCO 2010)", year = "2010", editor = "Emilio Corchado and Paulo Novais and Cesar Analide and Javier Sedano", volume = "73", series = "Advances in Intelligent and Soft Computing", pages = "197--200", address = "Guimaraes, Portugal", month = jun, publisher = "Springer", keywords = "genetic algorithms, genetic programming, gene expression programming", isbn13 = "978-3-642-13160-8", DOI = "doi:10.1007/978-3-642-13161-5_25", abstract = "This paper examines the use of artificial intelligence (in particular the application of Gene Expression Programming, GEP) to demand forecasting. In the world of production management, many data that are produced in function of the of economic activity characteristics in which they belong, may suffer, for example, significant impacts of seasonal behaviours, making the prediction of future conditions difficult by means of methods commonly used. The GEP is an evolution of Genetic Programming,which is part of the Genetic Algorithms. GEP seeks for mathematical functions, adjusting to a given set of solutions using a type of genetic heuristics from a population of random functions. In order to compare the GEP, we have used the others quantitatives method. Thus, from a data set of about demand of consumption of twelve products line metal fittings, we have compared the forecast data.", bibdate = "2010-11-04", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/softcomp/soco2010.html#BittencourtSLAO10", } @InProceedings{Bladek:2016:GECCOcomp, author = "Iwo Bladek and Krzysztof Krawiec", title = "Simultaneous Synthesis of Multiple Functions using Genetic Programming with Scaffolding", booktitle = "GECCO '16 Companion: Proceedings of the Companion Publication of the 2016 Annual Conference on Genetic and Evolutionary Computation", year = "2016", editor = "Tobias Friedrich and Frank Neumann and Andrew M. Sutton and Martin Middendorf and Xiaodong Li and Emma Hart and Mengjie Zhang and Youhei Akimoto and Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and Daniele Loiacono and Julian Togelius and Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and Faustino Gomez and Carlos M. Fonseca and Heike Trautmann and Alberto Moraglio and William F. Punch and Krzysztof Krawiec and Zdenek Vasicek and Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and Boris Naujoks and Enrique Alba and Gabriela Ochoa and Simon Poulding and Dirk Sudholt and Timo Koetzing", pages = "97--98", keywords = "genetic algorithms, genetic programming, scaffolding, multisynthesis, problem decomposition, Scala: Poster", month = "20-24 " # jul, organisation = "SIGEVO", address = "Denver, USA", publisher = "ACM", publisher_address = "New York, NY, USA", isbn13 = "978-1-4503-4323-7", poster_url = "https://www.cs.put.poznan.pl/ibladek/publications/conferences/gecco16_poster_simult.pdf", DOI = "doi:10.1145/2908961.2908992", size = "2 pages", abstract = "We consider simultaneous evolutionary synthesis of multiple functions, and verify whether such approach leads to computational savings compared to conventional synthesis of functions one-by-one. We also extend the proposed synthesis model with scaffolding, a technique originally intended to facilitate evolution of recursive programs \cite{Moraglio:2012:CEC}, and consisting in fetching the desired output from a test case, rather than calling a function. Experiment concerning synthesis of list manipulation programs in Scala allows us to conclude that parallel synthesis indeed pays off, and that engagement of scaffolding leads to further improvements.", notes = "NSGA-II https://github.com/kkrawiec/fuel Distributed at GECCO-2016.", } @InProceedings{Bladek:2017:EuroGP, author = "Iwo Bladek and Krzysztof Krawiec", title = "Evolutionary Program Sketching", booktitle = "EuroGP 2017: Proceedings of the 20th European Conference on Genetic Programming", year = "2017", editor = "Mauro Castelli and James McDermott and Lukas Sekanina", volume = "10196", series = "LNCS", pages = "3--18", address = "Amsterdam", month = "19-21 " # apr, organisation = "Species", publisher = "Springer Verlag", keywords = "genetic algorithms, genetic programming, program synthesis, satisfiability modulo theory, program sketching", isbn13 = "978-3-319-55695-6", URL = "http://repozytorium.put.poznan.pl/publication/495662", DOI = "doi:10.1007/978-3-319-55696-3_1", size = "16 pages", abstract = "Program synthesis can be posed as a satisfiability problem and approached with generic SAT solvers. Only short programs can be however synthesized in this way. Program sketching by Solar-Lezama assumes that a human provides a partial program (sketch), and that synthesis takes place only within the uncompleted parts of that program. This allows synthesizing programs that are overall longer, while maintaining manageable computational effort. In this paper, we propose Evolutionary Program Sketching (EPS), in which the role of sketch provider is handed over to genetic programming (GP). A GP algorithm evolves a population of partial programs, which are being completed by a solver while evaluated. We consider several variants of EPS, which vary in program terminals used for completion (constants, variables, or both) and in the way the completion outcomes are propagated to future generations. When applied to a range of benchmarks, EPS outperforms the conventional GP, also when the latter is given similar time budget.", notes = "raport z badan Part of \cite{Castelli:2017:GP} EuroGP'2017 held in conjunction with EvoCOP2017, EvoMusArt2017 and EvoApplications2017", } @Article{Bladek:EC, author = "Iwo Bladek and Krzysztof Krawiec and Jerry Swan", title = "Counterexample-Driven Genetic Programming: Heuristic Program Synthesis from Formal Specifications", journal = "Evolutionary Computation", year = "2018", volume = "26", number = "3", pages = "441--469", month = "Fall", keywords = "genetic algorithms, genetic programming, formal verification, counterexamples, SMT", ISSN = "1063-6560", DOI = "doi:10.1162/evco_a_00228", size = "27 pages", abstract = "Conventional genetic programming (GP) can only guarantee that synthesized programs pass tests given by the provided input-output examples. The alternative to such test-based approach is synthesizing programs by formal specification, typically realized with exact, non-heuristic algorithms. In this paper, we build on our earlier study on Counterexample-Based Genetic Programming (CDGP), an evolutionary heuristic that synthesizes programs from formal specifications. The candidate programs in CDGP undergo formal verification with a Satisfiability Modulo Theory (SMT) solver, which results in counterexamples that are subsequently turned into tests and used to calculate fitness. The original CDGP is extended here with a fitness threshold parameter that decides which programs should be verified, a more rigorous mechanism for turning counterexamples into tests, and other conceptual and technical improvements. We apply it to 24 benchmarks representing two domains: the linear integer arithmetic (LIA) and the string manipulation (SLIA) problems, showing that CDGP can reliably synthesize provably correct programs in both domains. We also confront it with two state-of-the art exact program synthesis methods and demonstrate that CDGP effectively trades longer synthesis time for smaller program size.", notes = "Extended Paper Invited from GECCO 2017", } @InProceedings{Bladek:2019:GECCO, author = "Iwo Bladek and Krzysztof Krawiec", title = "Solving symbolic regression problems with formal constraints", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference", year = "2019", editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and Anne Auger and Petr Posik and Leslie {Peprez Caceres} and Andrew M. Sutton and Nadarajen Veerapen and Christine Solnon and Andries Engelbrecht and Stephane Doncieux and Sebastian Risi and Penousal Machado and Vanessa Volz and Christian Blum and Francisco Chicano and Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and Jonathan Fieldsend and Jose Antonio Lozano and Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and Robin Purshouse and Thomas Baeck and Justyna Petke and Giuliano Antoniol and Johannes Lengler and Per Kristian Lehre", pages = "977--984", address = "Prague, Czech Republic", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, symbolic regression, constraints, formal verification, generalization", isbn13 = "978-1-4503-6111-8", URL = "https://www.cs.put.poznan.pl/ibladek/publications/conferences/gecco19_srfc_paper.pdf", DOI = "doi:10.1145/3321707.3321743", size = "8 pages", abstract = "In many applications of symbolic regression, domain knowledge constrains the space of admissible models by requiring them to have certain properties, like monotonicity, convexity, or symmetry. As only a handful of variants of genetic programming methods proposed to date can take such properties into account, we introduce a principled approach capable of synthesizing models that simultaneously match the provided training data (tests) and meet user-specified formal properties. To this end, we formalize the task of symbolic regression with formal constraints and present a range of formal properties that are common in practice. We also conduct a comparative experiment that confirms the feasibility of the proposed approach on a suite of realistic symbolic regression benchmarks extended with various formal properties. The study is summarized with discussion of results, properties of the method, and implications for symbolic regression.", notes = "Also known as \cite{3321743} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @PhdThesis{bladek:thesis, author = "Iwo Bladek", title = "Machine Learning and Formal Verification for Acquisition of Knowledge in Heuristic Program Synthesis", school = "Politechnika Poznanska", year = "2022", address = "Poznan, Poland", keywords = "genetic algorithms, genetic programming, Counterexample-Driven Genetic Programming, CDGP, Counterexample Driven Symbolic Regression, CDSR, Neuro-Guided Genetic Programming, ANN, Evolutionary Program Sketching, EPS, SMT, formal verification of software, machine learning, program synthesis, programowanie genetyczne, formalna weryfikacja oprogramowania, uczenie maszynowe, synteza programow", language = "en", URL = "https://sin.put.poznan.pl/dissertations/details/d2987", URL = "http://www.cs.put.poznan.pl/ibladek/publications/phdthesis.pdf", size = "159 pages", abstract = "we present and evaluate our techniques for efficient heuristic program synthesis based on genetic programming (GP). The common theme among our approaches is that various kinds of information (knowledge) are collected during runtime or a separate training phase, and then are used to guide GP search. Three of the described techniques, i.e., Evolutionary Program Sketching, Counterexample-Driven GP, and Counterexample-Driven Symbolic Regression, use formal verification/synthesis to either find locally optimal code fragments, or discover counterexamples exposing incorrect behavior of candidate programs. The fourth approach, Neuro-Guided GP, uses machine learning to learn the probability distribution of program instructions given input-output examples, and then uses it to bias variation operators of GP. The computational experiments show that all presented methods outperform or provide some advantages over existing state of the art methods.", abstract_pl = "W rozprawie przedstawiamy i analizujemy opracowane przez nas techniki efektywnej heurystycznej syntezy programow opartej o programowanie genetyczne (GP). Wspolna cecha tych technik jest zdobywanie roznych rodzajow informacji w trakcie dzialania algorytmu lub odrebnej fazie uczenia. Trzy z opisanych podejsc, Evolutionary Program Sketching, Counterexample-Driven GP i Counterexample-Driven Symbolic Regression, korzystaja z formalnej weryfikacji/syntezy w celu znajdowania lokalnie optymalnych fragmentow kodu lub odkrywania kontrprzykladow prezentujacych niepoprawne dzialanie generowanych programow. Czwarte podejscie, Neuro-Guided GP, korzysta z technik uczenia maszynowego w celu odkrycia rozkladu prawdopodobienstwa instrukcji na podstawie przykladow wejscie-wyjscie, a nastepnie wykorzystuje go do ukierunkowania operatorow przeszukiwania w GP. Eksperymenty obliczeniowe wykazaly, ze prezentowane metody sa konkurencyjne i oferuja wiele zalet w porownaniu do rozwiazan dostepnych w literaturze.", notes = "Also known as \cite{Bladek2022:d2987} On-line catalog to2022000422 Supervisor: Krzysztof Krawiec", } @Article{Bladek:ieeeTEC, author = "Iwo Bladek and Krzysztof Krawiec", title = "Counterexample-Driven Genetic Programming for Symbolic Regression with Formal Constraints", journal = "IEEE Transactions on Evolutionary Computation", year = "2023", volume = "27", number = "5", pages = "1327--1339", month = oct, keywords = "genetic algorithms, genetic programming, Satisfiability Modulo Theories, SMT, Symbolic regression, SR", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2022.3205286", size = "13 pages", abstract = "In symbolic regression with formal constraints, the conventional formulation of regression problem is extended with desired properties of the target model, like symmetry, monotonicity, or convexity. We present a genetic programming algorithm that solves such problems using a Satisfiability Modulo Theories solver to formally verify the candidate solutions. The essence of the method consists in collecting the counter examples resulting from model verification and using them to improve search guidance. The method is exact: upon successful termination, the produced model is guaranteed to meet the specified constraints. We compare the effectiveness of the proposed method with standard constraint-agnostic machine learning regression algorithms on a range of benchmarks, and demonstrate that it outperforms them on several performance indicators.", notes = "also known as \cite{9881536} Institute of Computing Science, Poznan University of Technology, Poznan, Poland", } @InProceedings{Blair:2013:CEC, author = "Alan Blair", title = "Learning the Caesar and Vigenere Cipher by Hierarchical Evolutionary Re-Combination", booktitle = "2013 IEEE Conference on Evolutionary Computation", volume = "1", year = "2013", month = jun # " 20-23", editor = "Luis Gerardo {de la Fraga}", pages = "605--612", address = "Cancun, Mexico", keywords = "genetic algorithms, genetic programming, HERCL, Evolutionary computation", isbn13 = "978-1-4799-0453-2", URL = "http://www.cse.unsw.edu.au/~blair/pubs/2013BlairCEC.pdf", DOI = "doi:10.1109/CEC.2013.6557624", article_id = "1135", size = "8 pages", abstract = "We describe a new programming language called HERCL, designed for evolutionary computation with the specific aim of allowing new programs to be created by combining patches of code from different parts of other programs, at multiple scales. Large-scale patches are followed up by smaller-scale patches or mutations, recursively, to produce a global random search strategy known as hierarchical evolutionary re-combination. We demonstrate the proposed system on the task of learning to encode with the Caesar or Vigenere Cipher, and show how the evolution of one task may fruitfully be cross-pollinated with evolved solutions from other related tasks.", notes = "Also known as \cite{6557624} CEC 2013 - A joint meeting of the IEEE, the EPS and the IET.", } @InProceedings{Blair:2014:GECCOcomp, author = "Alan Blair", title = "Incremental evolution of HERCL programs for robust control", booktitle = "GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion", year = "2014", editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges", isbn13 = "978-1-4503-2881-4", keywords = "genetic algorithms, genetic programming, artificial life, robotics, and evolvable hardware: Poster", pages = "27--28", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2598394.2598424", DOI = "doi:10.1145/2598394.2598424", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "We explore the evolution of programs for control tasks using the recently introduced Hierarchical Evolutionary Re-Combination Language (HERCL) which has been designed as an austere and general-purpose language, with a view toward modular evolutionary computation, combining elements from Linear GP with stack-based operations from FORTH. We show that HERCL programs can be evolved to robustly perform a benchmark double pole balancing task from a specified range of initial conditions, with the poles remaining balanced for up to an hour of simulated time.", notes = "Also known as \cite{2598424} Distributed at GECCO-2014.", } @InProceedings{Blair:2019:evomusart, author = "Alan Blair", title = "Adversarial Evolution and Deep Learning - How Does an Artist Play with Our Visual System?", booktitle = "8th International Conference on Computational Intelligence in Music, Sound, Art and Design, EvoMusArt 2019", year = "2019", editor = "Aniko Ekart and Antonios Liapis and Luz Castro", series = "LNCS", volume = "11453", publisher = "Springer", pages = "18--34", address = "Leipzig, Germany", month = "24-26 " # apr, organisation = "Species", keywords = "genetic algorithms, genetic programming, ANN, Evolutionary art, ai-generated art, Artist-critic coevolution, Adversarial training, Computational creativity", isbn13 = "978-3-030-16666-3", DOI = "doi:10.1007/978-3-030-16667-0_2", abstract = "We create artworks using adversarial coevolution between a genetic program (hercl) generator and a deep convolutional neural network (LeNet) critic. The resulting artificially intelligent artist, whimsically named Hercule LeNet, aims to produce images of low algorithmic complexity which nevertheless resemble a set of real photographs well enough to fool an adversarially trained deep learning critic modelled on the human visual system. Although it is not exposed to any pre-existing art, or asked to mimic the style of any human artist, nevertheless it discovers for itself many of the stylistic features associated with influential art movements of the 19th and 20th Century. A detailed analysis of its work can help us to better understand the way an artist plays with the human visual system to produce aesthetically appealing images.", notes = "Cover SIGEvolution 12(2) \cite{Ochoa:2019:sigevolution}. EvoMusArt2019 held in conjunction with EuroGP'2019 EvoCOP2019 and EvoApplications2019 http://www.evostar.org/2019/cfp_evomusart.php", } @Article{Blasco:2021:JSS, author = "Daniel Blasco and Jaime Font and Mar Zamorano and Carlos Cetina", title = "An evolutionary approach for generating software models: The case of {Kromaia} in Game Software Engineering", journal = "Journal of Systems and Software", year = "2021", volume = "171", pages = "110804", month = jan, note = "Winner Bronze HUMIES", keywords = "genetic algorithms, genetic programming, SBSE, MDE, Model-Driven Engineering, Search-based software engineering, Game Software Engineering", ISSN = "0164-1212", URL = "http://www.human-competitive.org/sites/default/files/blasco-font-zamorano-cetina-text.txt", URL = "http://www.human-competitive.org/sites/default/files/blasco_jss_2021_pre_0.pdf", URL = "https://www.sciencedirect.com/science/article/pii/S0164121220302089", DOI = "doi:10.1016/j.jss.2020.110804", video_url = "http://www.human-competitive.org/sites/default/files/font_for_humies.107mb.mp4", video_url = "https://youtu.be/2z9FcKKB70w", size = "26 pages", abstract = "In the context of Model-Driven Engineering applied to video games, software models are high-level abstractions that represent source code implementations of varied content such as the stages of the game, vehicles, or enemy entities (e.g., final bosses). we present our Evolutionary Model Generation (EMoGen) approach to generate software models that are comparable in quality to the models created by human developers. Our approach is based on an evolution (mutation and crossover) and assessment cycle to generate the software models. We evaluated the software models generated by EMoGen in the Kromaia video game, which is a commercial video game released on Steam and PlayStation 4. Each model generated by EMoGen has more than 1000 model elements. The results, which compare the software models generated by our approach and those generated by the developers, show that our approach achieves results that are comparable to the ones created manually by the developers in the retail and digital versions of the video game case study. However, our approach only takes five hours of unattended time in comparison to ten months of work by the developers. We perform a statistical analysis, and we make an implementation of EMoGen readily available.", notes = "Cyberpunk 2077: how 2020's biggest video game launch turned into a shambles. Many bugs and glitches on (delayed) release games was unplayable. Refunded unhappy players. Crunch. International game developers association. game agents Vermis, Boss 1300 model elements, 15000 model properties. Developer write software model (of agent) rather than C++, model is compilable. GP 6:00 noise, so apply GP to model boss (6:28 genetic modeling) UML. Six quality indicators. Interest from Kraken empire and Tequila works, King 2021 HUMIES prize giving video https://www.youtube.com/watch?v=jrT0sfq6WjM 43:10 -- 47:08 computer 5 hours == human 10 months", } @InProceedings{Blasco:2010:ARES, author = "Jorge Blasco and Agustin Orfila and Arturo Ribagorda", title = "Improving Network Intrusion Detection by Means of Domain-Aware Genetic Programming", booktitle = "International Conference on Availability, Reliability, and Security, ARES '10", year = "2010", month = feb, pages = "327--332", abstract = "One of the central areas in network intrusion detection is how to build effective systems that are able to distinguish normal from intrusive traffic. In this paper we explore the use of Genetic Programming (GP) for such a purpose. Although GP has already been studied for this task, the inner features of network intrusion detection have been systematically ignored. To avoid the blind use of GP shown in previous research, we guide the search by means of a fitness function based on recent advances on IDS evaluation. For the experimental work we use a well-known dataset (i.e. KDD-99) that has become a standard to compare research although its drawbacks. Results clearly show that an intelligent use of GP achieves systems that are comparable (and even better in realistic conditions) to top state-of-the-art proposals in terms of effectiveness, improving them in efficiency and simplicity.", keywords = "genetic algorithms, genetic programming, domain-aware genetic programming, fitness function, intrusive traffic, network intrusion detection, normal traffic, security of data", DOI = "doi:10.1109/ARES.2010.53", notes = "Also known as \cite{5438073}", } @InProceedings{bleuler:2001:mgprbus, author = "Stefan Bleuler and Martin Brack and Lothar Thiele and Eckart Zitzler", title = "Multiobjective Genetic Programming: Reducing Bloat Using SPEA2", booktitle = "Proceedings of the 2001 Congress on Evolutionary Computation CEC2001", year = "2001", pages = "536--543", address = "COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "27-30 " # may, organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, SPEA, SPEA2, Pareto, external set, bloating, compact programs, convergence speed, even-parity problem, multiobjective evolutionary technique, multiobjective genetic programming, program functionality, convergence, programming theory", ISBN = "0-7803-6658-1", URL = "ftp://ftp.tik.ee.ethz.ch/pub/people/zitzler/BBTZ2001b.ps.gz", URL = "http://citeseer.ist.psu.edu/443099.html", DOI = "doi:10.1109/CEC.2001.934438", size = "8 pages", abstract = "This study investigates the use of multiobjective techniques in Genetic Programming (GP) in order to evolve compact programs and to reduce the effects caused by bloating. The proposed approach considers the program size as a second, independent objective besides the program functionality. In combination with a recent multiobjective evolutionary technique, SPEA2, this method outperforms four other strategies to reduce bloat with regard to both convergence speed and size of the produced programs on a even-parity problem.", notes = "CEC-2001 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 01TH8546C, Library of Congress Number = Solutions to even-9 parity.", } @InCollection{Bleuler:2008:MPSN, author = "Stefan Bleuler and Johannes Bader and Eckart Zitzler", title = "Reducing Bloat in GP with Multiple Objectives", booktitle = "Multiobjective Problem Solving from Nature: from concepts to applications", publisher = "Springer", year = "2008", editor = "Joshua Knowles and David Corne and Kalyanmoy Deb", series = "Natural Computing", chapter = "9", pages = "177--200", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-72963-1", DOI = "doi:10.1007/978-3-540-72964-8_9", abstract = "This chapter investigates the use of multiobjective techniques in genetic programming (GP) in order to evolve compact programs and to reduce the effects caused by bloating. The underlying approach considers the program size as a second, independent objective besides program functionality, and several studies have found this concept to be successful in reducing bloat. Based on one specific algorithm, we demonstrate the principle of multiobjective GP and show how to apply Pareto-based strategies to GP. This approach outperforms four classical strategies to reduce bloat with regard to both convergence speed and size of the produced programs on an even-parity problem. Additionally, we investigate the question of why the Pareto-based strategies can be more effective in reducing bloat than alternative strategies on several test problems. The analysis falsifies the hypothesis that the small but less functional individuals that are kept in the population act as building blocks building blocks for larger correct solutions. This leads to the conclusion that the advantages are probably due to the increased diversity in the population.", notes = "http://www.springer.com/west/home/computer/artificial?SGWID=4-147-22-173745027-0", } @InProceedings{BT94, author = "Tobias Blickle and Lothar Thiele", title = "Genetic Programming and Redundancy", booktitle = "Genetic Algorithms within the Framework of Evolutionary Computation (Workshop at KI-94, Saarbr{\"u}cken)", editor = "J. Hopf", publisher = "Max-Planck-Institut f{\"u}r Informatik (MPI-I-94-241)", address = " Im Stadtwald, Building 44, D-66123 Saarbr{\"u}cken, Germany ", pages = "33--38", year = "1994", keywords = "genetic algorithms, genetic programming", URL = "http://www.tik.ee.ethz.ch/~tec/publications/bt94/GPandRedundancy.ps.gz", size = "6 pages", abstract = "The Genetic Programming optimization method (GP) elaborated by John Koza [Koza, 1992] is a variant of Genetic Algorithms. The search space of the problem domain consists of computer programs represented as parse trees, and the crossover operator is realized by an exchange of subtrees. Empirical analyses show that large parts of those trees are never used or evaluated which means that these parts of the trees are irrelevant for the solution or redundant. This paper is concerned with the identification of the redundancy occurring in GP. It starts with a mathematical description of the behaviour of GP and the conclusions drawn from that description among others explain the size problem which denotes the phenomenon that the average size of trees in the population grows with time.", notes = "From GP list Wed, 22 Mar 95 we did some work on the convergence problem and the redundancy in the trees in GP. It turned out that bloating is a property of GP that arises from the fact that more redundant trees have a higher probability to survive crossover. As a result, the redundant part of the trees grow bigger and bigger because the increased proportion of redundant cut-sites in the tree again lead to a higher probability to survive crossover. Gives a formula for tournament size related to proportion of crossover in a generational GP. Ie recommending T=10 for pc=0.9. This does not apply to steady state GA. ", } @TechReport{blickle:1995:css, author = "Tobias Blickle and Lothar Thiele", title = "A Comparison of Selection Schemes Used in Genetic Algorithms", institution = "TIK Institut fur Technische Informatik und Kommunikationsnetze, Computer Engineering and Networks Laboratory, ETH, Swiss Federal Institute of Technology", year = "1995", type = "TIK-Report", number = "11", edition = "2", address = "Gloriastrasse 35, 8092 Zurich, Switzerland", month = dec, keywords = "genetic algorithms, genetic programming", URL = "https://tik-old.ee.ethz.ch/file/6c0e384dceb283cd4301339a895b72b8/TIK-Report11.pdf", broken = "http://www.handshake.de/user/blickle/publications/TIK-Report11abstract.html", URL = "http://www.handshake.de/user/blickle/publications/tik-report11_v2.ps", size = "65 pages", abstract = " Genetic Algorithms are a common probabilistic optimization method based on the model of natural evolution. One important operator in these algorithms is the selection scheme for which a new description model is introduced in this paper. With this a mathematical analysis of tournament selection, truncation selection, linear and exponential ranking selection and proportional selection is carried out that allows an exact prediction of the fitness values after selection. The further analysis derives the selection intensity, selection variance, and the loss of diversity for all selection schemes. For completion a pseudo- code formulation of each method is included. The selection schemes are compared and evaluated according to their properties leading to an unified view of these different selection schemes. Furthermore the correspondence of binary tournament selection and ranking selection in the expected fitness distribution is proven.", notes = " Of special interest for the GP community may be the fact that in this report three analytic approximation formulas are obtained using GP for symbolic regression. The method is described in appendix A of the report. Second (extended and corrected) edition available via www and ftp Dec 1995 ", } @Article{blickle:1995:ea, author = "Tobias Blickle", title = "Optimieren nach dem Vorbild der Natur, Evolutionare Algorithmen", journal = "Bulletin SEV/VSE", year = "1995", volume = "86", number = "25", pages = "21--26", keywords = "genetic algorithms, genetic programming", URL = "http://www.handshake.de/user/blickle/publications/EA.ps", size = "6 pages", notes = "Introduction to GA and GP in German", } @TechReport{blickle:1995:YAGPLIC, author = "Tobias Blickle", title = "YAGPLIC User Manual", institution = "Computer Engineering and Communication Network Lab (TIK), Swiss Federal Institute of Technology (ETH)", year = "1995", address = "Gloriastrasse 35, CH-8092, Zurich", keywords = "genetic algorithms, genetic programming", broken = "http://www.tik.ee.ethz.ch/~blickle/YAGPLIC.html", notes = "Yet Another Genetic Programming Library In C Written in C for maximum performance. Object-oriented user-interface. Up to 32 data types possible in a tree and type-consistent crossover. Several selection schemes implemented: proportionate selection, ranking selection, tournament selection, truncation selection. Extensive output of statistical data for post processing with MATHEMATICA.", } @TechReport{blickle:1996:ecs, author = "Tobias Blickle", title = "Evolving Compact Solutions in Genetic Programming: A Case Study", institution = "TIK Institut fur Technische Informatik und Kommunikationsnetze, Computer Engineering and Networks Laboratory, ETH, Swiss Federal Institute of Technology", year = "1996", type = "TIK-Report", address = "Gloriastrasse 35, 8092 Zurich, Switzerland", keywords = "genetic algorithms, genetic programming", URL = "http://www.handshake.de/user/blickle/publications/ppsn1.ps", abstract = "Genetic programming (GP) is a variant of genetic algorithms where the data structures handled are trees. This makes GP especially useful for evolving functional relationships or computer programs, as both can be represented as trees. Symbolic regression is the determination of a function dependence $y=g({\bf x})$ that approximates a set of data points (${\bf x_i},y_i$). In this paper the feasibility of symbolic regression with GP is demonstrated on two examples taken from different domains. Furthermore several suggested methods from literature are compared that are intended to improve GP performance and the readability of solutions by taking into account introns or redundancy that occurs in the trees and keeping the size of the trees small. The experiments show that GP is an elegant and useful tool to derive complex functional dependencies on numerical data.", notes = "Presented at PPSN 4 ", size = "10 pages", } @InProceedings{blickle96, author = "Tobias Blickle", title = "Evolving Compact Solutions in Genetic Programming: A Case Study", editor = "Hans-Michael Voigt and Werner Ebeling and Ingo Rechenberg and Hans-Paul Schwefel", booktitle = "Parallel Problem Solving From Nature IV. Proceedings of the International Conference on Evolutionary Computation", year = "1996", publisher = "Springer-Verlag", volume = "1141", series = "LNCS", pages = "564--573", address = "Berlin, Germany", publisher_address = "Heidelberg, Germany", month = "22-26 " # sep, keywords = "genetic algorithms, genetic programming, bloat, deleting crossover", ISBN = "3-540-61723-X", URL = "http://www.handshake.de/user/blickle/publications/ppsn1.ps", URL = "http://citeseer.ist.psu.edu/blickle96evolving.html", DOI = "doi:10.1007/3-540-61723-X_1020", size = "10 pages", abstract = "Genetic programming (GP) is a variant of genetic algorithms where the data structures handled are trees. This makes GP especially useful for evolving functional relationships or computer programs, as both can be represented as trees. Symbolic regression is the determination of a function dependence y=g ( x ) that approximates a set of data points ( x i , y i ). In this paper the feasibility of symbolic regression with GP is demonstrated on two examples taken from different domains. Furthermore several suggested methods from literature are compared that are intended to improve GP performance and the readability of solutions by taking into account introns or redundancy that occurs in the trees and keeping the size of the trees small. The experiments show that GP is an elegant and useful tool to derive complex functional dependencies on numerical data.", notes = "http://lautaro.fb10.tu-berlin.de/ppsniv.html PPSN4 same as \cite{blickle:1996:ecs} Test of effectiveness of GP, EDI, deleting and adaptive anti-bloat techniques. Results differ continuous (symbolic regression) v. discrete 6-mux deleting crossover similar to code editing based on code interpretation during fitness evaluation.", affiliation = "Swiss Federal Institute of Technology (ETH) Computer Engineering and Communication Networks Lab (TIK) Gloriastrasse 35 8092 Zurich Switzerland Gloriastrasse 35 8092 Zurich Switzerland", } @PhdThesis{blickle:thesis, author = "Tobias Blickle", title = "Theory of Evolutionary Algorithms and Application to System Synthesis", school = "Swiss Federal Institute of Technology", year = "1996", address = "Zurich, Switzerland", publisher = "vdf Verlag", publisher_address = "CH-8092 Zurich", month = nov, keywords = "genetic algorithms, genetic programming", ISBN = "3-7281-2433-8", URL = "http://www.handshake.de/user/blickle/publications/diss.pdf", DOI = "doi:10.3929/ethz-a-001710359", size = "272 pages", abstract = "The subject of this thesis are Evolutionary Algorithms and their application to the automated synthesis and optimization of complex digital systems composed of hardware and software elements. In Part I the probabilistic optimization method of Evolutionary Algorithms (EAs) is presented. EAs apply the principles of natural evolution (selection and random variation) to a random set of points (population of individuals) in the search space. Evolutionary Algorithms are first embedded in the context of global optimization and the most important and widely used methods for constraint- handling are introduced, including a new method called IOS (individual objective switching). This is followed by a new formal description of selection schemes based on fitness distributions. This description enables an extensive and uniform examination of various selection schemes leading to new insights about the impact of the selection method parameters on the optimization process. Subsequently the variation (recombination) process of Evolutionary Algorithms is examined. As different analysis techniques are necessary depending on the representation of the problem (e.g. bit string, vector, tree, graph) only the recombination process for tree-representation (Genetic Programming) is considered. A major part of the explanation treats the problem of ``bloating'', i.e., the tree-size increase during optimization. Furthermore, a new redundancy based explanation of bloating is given and several methods to avoid bloating are compared. Part II is dedicated to the application of Evolutionary Algorithms to the optimization of complex digital systems. These systems are composed of hardware and software components and characterized by a high complexity caused by their heterogeneity (hardware/ software, electrical/mechanical, analog/digital). Computer-aided synthesis at the abstract system level is advantageous for application specific systems or embedded systems as it enables time-to-market to be reduced with a decrease in design errors and costs. The main task of system-synthesis is the transformation of a behavioral specification (for example given by an algorithm) into a structural specification, such as a composition of processors, general or dedicated hardware modules, memories and busses, while regarding various restrictions, e.g. maximum costs, data throughput rate, reaction time. Problems related to system synthesis are for example performance estimation, architecture optimization and design-space exploration. This thesis introduces a formal description of system-synthesis based on a new graph model where the specification is translated into a specification graph. The main tasks of system-synthesis (allocation, binding and scheduling) are defined for this graph and the process of system synthesis is formulated as a constrained global optimization problem. This optimization problem is solved by Evolutionary Algorithms using the results of Part I of the thesis. Finally, an example of synthesizing implementations of a video codec chip H.261 is described demonstrating the effectiveness of the proposed methodology and the capability of the EA to obtain the Pareto points of the design space in a single optimization run.", notes = "Of special interest for this community might be chapter 5 that deals with recombination and bloating in GP YAGPLIC Supervisor: Lothar Thiele", } @Article{DBLP:journals/ec/BlickleT96, author = "Tobias Blickle and Lothar Thiele", title = "A Comparison of Selection Schemes used in Evolutionary Algorithms", journal = "Evolutionary Computation", volume = "4", number = "4", year = "1996", pages = "361--394", bibsource = "DBLP, http://dblp.uni-trier.de", month = "Winter", keywords = "genetic algorithms, genetic programming, Selection, evolutionary algorithms, diversity, selection intensity, tournament selection, truncation selection, linear ranking", ISSN = "1063-6560", URL = "http://www.handshake.de/user/blickle/publications/ECfinal.ps", DOI = "doi:10.1162/evco.1996.4.4.361", size = "34 pages", abstract = "Evolutionary algorithms are a common probabilistic optimisation method based on the model of natural evolution. One important operator in these algorithms is the selection scheme, for which in this paper a new description model, based on fitness distributions, is introduced. With this, a mathematical analysis of tournament selection, truncation selection, ranking selection, and exponential ranking selection is carried out that allows an exact prediction of the fitness values after selection. The correspondence of binary tournament selection and ranking selection in the expected fitness distribution is proved. Furthermore, several properties of selection schemes are derived (selection intensity, selection variance, loss of diversity), and the three selection schemes are compared using these properties.", notes = "Brief use of GP symbolic regression to find nice formulae. Theoretical analysis. NB see \cite{DBLP:journals/ec/Motoki02} for update on loss of diversity under tournament selection", } @InProceedings{Blot:2019:GI, author = "Aymeric Blot", title = "Fuzzy Edit Sequences in Genetic Improvement", booktitle = "GI-2019, ICSE workshops proceedings", year = "2019", editor = "Justyna Petke and Shin Hwei Tan and William B. Langdon and Westley Weimer", pages = "30--31", address = "Montreal", month = "28 " # may, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, genetic improvement, GI, SBSE, search-based software engineering, fuzzy matching", isbn13 = "978-1-7281-2268-7", URL = "http://www.cs.ucl.ac.uk/staff/a.blot/files/blot_gi-icse_2019.pdf", URL = "http://geneticimprovementofsoftware.com/paper_pdfs/blot2019gi.pdf", DOI = "doi:10.1109/GI.2019.00016", size = "2 pages", abstract = "Genetic improvement uses automated search to find improved versions of existing software. Edit sequences have been proposed as a very convenient way to represent code modifications, focusing on the changes themselves rather than duplicating the entire program. However, edits are usually defined in terms of practical operations rather than in terms of semantic changes; indeed, crossover and other edit sequence mutations usually never guarantee semantic preservation. We propose several changes to usual edit sequences, specifically augmenting edits with content data and using fuzzy matching, in an attempt to improve semantic preservation.", notes = "GI-2019 http://geneticimprovementofsoftware.com Slides: http://www.cs.ucl.ac.uk/staff/a.blot/files/blot_gi-icse_2019_slides.pdf part of \cite{Petke:2019:ICSEworkshop}", } @InProceedings{Blot:2019:GI7, author = "Aymeric Blot and Justyna Petke", title = "On Adaptive Specialisation in Genetic Improvement", booktitle = "7th edition of GI @ GECCO 2019", year = "2019", month = jul # " 13-17", editor = "Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and John R. Woodward", publisher_address = "New York, NY, USA", publisher = "ACM", address = "Prague, Czech Republic", pages = "1703--1704", organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, Search-Based Software Engineering, Software Specialisation, Algorithm Selection, Algorithm Configuration", isbn13 = "978-1-4503-6748-6", URL = "http://www.cs.ucl.ac.uk/staff/a.blot/files/blot_gi-gecco_2019.pdf", DOI = "doi:10.1145/3319619.3326839", size = "2 pages", abstract = "Genetic improvement uses automated search to find improved versions of existing software. Software can either be evolved with general-purpose intentions or with a focus on a specific application (e.g., to improve its efficiency for a particular class of problems). Unfortunately, software specialisation to each problem application is generally performed independently, fragmenting and slowing down an already very time-consuming search process. We propose to incorporate specialisation as an online mechanism of the general search process, in an attempt to automatically devise application classes, by benefiting from past execution history.", notes = "MiniSAT Also known as \cite{Blot:2019:GECCOcomp} Also known as \cite{3326839} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InProceedings{Blot:2020:EuroGP, author = "Aymeric Blot and Justyna Petke", title = "Comparing Genetic Programming Approaches for Non-Functional Genetic Improvement Case Study: Improvement of {MiniSAT's} Running Time", booktitle = "EuroGP 2020: Proceedings of the 23rd European Conference on Genetic Programming", year = "2020", month = "15-17 " # apr, editor = "Ting Hu and Nuno Lourenco and Eric Medvet", series = "LNCS", volume = "12101", publisher = "Springer Verlag", address = "Seville, Spain", pages = "68--83", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Genetic improvement, GI, GP, Search based software engineering, SBSE, Boolean satisfiability, SAT, minisat-2.2.0, linux perf, AST, XML", isbn13 = "978-3-030-44093-0", URL = "http://www.cs.ucl.ac.uk/staff/a.blot/files/blot_eurogp_2020.pdf", URL = "https://discovery.ucl.ac.uk/id/eprint/10097404", video_url = "https://www.youtube.com/watch?v=D-UQr-P3zUQ", DOI = "doi:10.1007/978-3-030-44094-7_5", abstract = "Genetic improvement (GI) uses automated search to findimproved versions of existing software. While most GI work use geneticprogramming (GP) as the underlying search process, focus is usuallygiven to the target software only. As a result, specifics of GP algorithmsfor GI are not well understood and rarely compared to one another. In this work, we propose a robust experimental protocol to compare different GI search processes and investigate several variants of GP and random-based approaches. Through repeated experiments, we report acomparative analysis of these approaches, using one of the previously used GI scenarios: improvement of runtime of the MiniSAT satisfiability solver. We conclude that the test suites used have the most significant impact on the GI results. Both random and GP-based approaches are able to find improved software, even though the percentage of viable software variants is significantly smaller in the random case (14.5percent vs. 80.1percent). We also report that GI produces MiniSAT variants up to twice as fast as the original on sets of previously unseen instances from the same application domain.", notes = "slides http://www.cs.ucl.ac.uk/staff/a.blot/files/blot_eurogp_2020_slides.pdf PyGGI https://github.com/coinse/pyggi minisat 2. 80.1pecent of mutants GP compile, run, produce right answer. About 70percent faster than original, based on instruction count (slide 13). But... (slide 14) over fitting. Hetrogenous SAT dataset: very easy to hard. video 20 mins + questions Figure 2: 5-fold cross validation. CentOS-7 Linux kernel 3.10.0 and GCC 4.8.5. 130 combinatorial interaction testing (CIT) instances. SrcML AST https://www.srcml.org/ also known as \cite{DBLP:conf/eurogp/BlotP20} http://www.evostar.org/2020/cfp_eurogp.php Part of \cite{Hu:2020:GP} EuroGP'2020 held in conjunction with EvoCOP2020, EvoMusArt2020 and EvoApplications2020", } @InProceedings{Blot:2020:GIsp, author = "Aymeric Blot and Justyna Petke", title = "Stack-Based Genetic Improvement", booktitle = "GI @ ICSE 2020", year = "2020", month = "3 " # jul, editor = "Shin Yoo and Justyna Petke and Westley Weimer and Bobby R. Bruce", publisher = "ACM", address = "internet", pages = "289--290", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, GI, Automated Program Repair, APR, Search-based software engineering", isbn13 = "978-1-4503-7963-2", URL = "http://www.cs.ucl.ac.uk/staff/a.blot/files/blot_gi-icse_2020-1.pdf", video_url = "https://youtu.be/GsNKCifm44A", DOI = "doi:10.1145/3387940.3392174", size = "2 pages", abstract = "Genetic improvement (GI) uses automated search to find improved versions of existing software. If originally GI directly evolved populations of software, most GI work nowadays use a solution representation based on a list of mutations. This representation however has some limitations, notably in how genetic material can be re-combined. We introduce a novel stack-based representation and discuss its possible benefits", notes = "Push, different stacks for each data and each? code type. RIP Larry Tesler 1945 to 2020: inventor of cut/copy & paste (and more) EP/P023991/1 Video: https://youtu.be/GsNKCifm44A (start 1:30:29 end 1:45:45) Slides: http://geneticimprovementofsoftware.com/slides/gi2020icse/synthetic_benchmarks_slides.pdf http://geneticimprovementofsoftware.com/gi2020icse.html", } @InProceedings{Blot:2020:GI, author = "Aymeric Blot and Justyna Petke", title = "Synthetic Benchmarks for Genetic Improvement", booktitle = "GI @ ICSE 2020", year = "2020", month = "3 " # jul, editor = "Shin Yoo and Justyna Petke and Westley Weimer and Bobby R. Bruce", publisher = "ACM", address = "internet", pages = "287--288", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE", isbn13 = "978-1-4503-7963-2", URL = "http://www.cs.ucl.ac.uk/staff/a.blot/files/blot_gi-icse_2020-2.pdf", video_url = "https://youtu.be/GsNKCifm44A", DOI = "doi:10.1145/3387940.3392175", size = "2 pages", abstract = "Genetic improvement (GI) uses automated search to find improved versions of existing software. If over the years the potential of many GI approaches have been demonstrated, the intrinsic cost of evaluating real-world software makes comparing these approaches in large-scale meta-analyses very expensive. We propose and describe a method to construct synthetic GI benchmarks, to circumvent this bottleneck and enable much faster quality assessment of GI approaches.", notes = "fitness is single query of surogate model using: AST, run-time, testing. Model based on many mutations of the original program Video: https://youtu.be/GsNKCifm44A (start 2:12:38, 2:22:36 end 2:28:15) Slides: http://geneticimprovementofsoftware.com/slides/gi2020icse/synthetic_benchmarks_slides.pdf http://geneticimprovementofsoftware.com/gi2020icse.html", } @Article{blot:2021:tevc, author = "Aymeric Blot and Justyna Petke", title = "Empirical Comparison of Search Heuristics for Genetic Improvement of Software", journal = "IEEE Transactions on Evolutionary Computation", year = "2021", volume = "25", number = "5", pages = "1001--1011", month = oct, keywords = "genetic algorithms, genetic programming, genetic improvement, GI, Search-Based Software Engineering, SBSE, Stochastic Local Search, PyGGI, Uniform interleaved crossover", ISSN = "1089-778X", URL = "http://www.cs.ucl.ac.uk/staff/a.blot/publis/#blot:2021:tevc", URL = "http://www.cs.ucl.ac.uk/staff/a.blot/files/blot_tevc_2021.pdf", code_url = "https://github.com/bloa/tevc_2020_artefact", DOI = "doi:10.1109/TEVC.2021.3070271", size = "11 pages", abstract = "Genetic improvement uses automated search to improve existing software. It has been successfully used to optimise various program properties, such as runtime or energy consumption, as well as for the purpose of bug fixing. Genetic improvement typically navigates a space of thousands of patches in search for the program mutation that best improves the desired software property. While genetic programming has been dominantly used as the search strategy, more recently other search strategies, such as local search, have been tried. It is, however, still unclear which strategy is the most effective and efficient. In this paper, we conduct an in-depth empirical comparison of a total of 18 search processes using a set of 8 improvement scenarios. Additionally, we also provide new genetic improvement benchmarks and we report on new software patches found. Our results show that, overall, local search approaches achieve better effectiveness and efficiency than genetic programming approaches. Moreover, improvements were found in all scenarios (between 15percent and 68percent). A replication package can be found online: https://github.com/bloa/tevc_2020_artefact.", notes = "Supplement http://www.cs.ucl.ac.uk/staff/a.blot/files/blot_tevc_2021_supp.pdf computational speed: 'number of CPU instructions as measured by the Linux perf' Table 3 'at least 5 percent faster than the original software'. Over fitting: 'On average, 84 percent of the significant improvements confirmed during validation are also significant in the entire dataset' Table IV numbers less than 100 represent speed up. 'Speedups from 15 percent to 68 percent can be found for all scenarios'. 'the search space is largely neutral'. 'new benchmarks and target software for non-functional genetic improvement'. MiniSAT (two both C++), Sat4j (Java), Combinatorial Interaction Testing CIT, SATLIB. OptiPNG (three all C), MOEA/D (C++) and NSGA-II (C++). Random search. GP (pop=100) crossover mutation. Four types of GP crossover: Concatenation, Single-point, Uniform concatenation, Uniform interleaved. Mutation: deletion, append. GP: ' best individual in the last generation', 'the best individual evaluated over the entire search'. 'eassessed over a larger subset of [test] inputs. Local search (hill climbing, iterative improvement): mutation only. first improvement, best improvement, and tabu search. Neutral moved accepted. No restarts. unnecessary edits. 'reasonable fixed-size subsets of S neighbours of the neighbourhood of the current solution'. 'fixed-length tabu list'. 'two-stage validation step.' k-fold cross-validation. Centos-7 multi core 3.4GHz Intel i7-2600, 16GB ram, GCC 4.8.5. 'local search and genetic programming found identical or semantically equivalent software variants in most cases'. 'all been manually verified to be semantically valid. NSGA-II 'several algorithmic changes are found'. 'Across all repetitions the best performances have been obtained in around 12 percent of all runs.' 'Local search approaches clearly outperform all other approaches on the test data.' 'genetic programming and random search perform similarly' !!!! 'GI performance could be significantly improved if better search approaches are considered'. 'GP use most of the training budged recombining known mutations, while local search have more opportunities to explore software variants.' also known as \cite{9392013}", } @InProceedings{blot:hal-03595447, author = "Aymeric Blot and Justyna Petke", title = "Using Genetic Improvement to Optimise Optimisation Algorithm Implementations", booktitle = "23{\`e}me congr{\`e}s annuel de la Soci{\'e}t{\'e} Fran{\c c}aise de Recherche Op{\'e}rationnelle et d'Aide {\`a} la D{\'e}cision, ROADEF'2022", year = "2022", editor = "Khaled Hadj-Hamou", address = "Villeurbanne - Lyon, France", month = "23--25 " # feb, organization = "INSA Lyon", keywords = "genetic algorithms, genetic programming, Genetic improvement, SBSE, Algorithm design, Software engineering, Optimisation, MOEA/D, NSGA-II, PyGGI , perf, IGD, XML, SrcML", URL = "https://hal.archives-ouvertes.fr/hal-03595447", pdf = "https://hal.archives-ouvertes.fr/hal-03595447/file/roadef_2022.pdf", hal_id = "hal-03595447", hal_version = "v1", URL = "http://www.cs.ucl.ac.uk/staff/a.blot/files/blot_roadef_2022.pdf", size = "2 pages", abstract = "Genetic improvement (GI) \cite{Petke:gisurvey} uses automated search to improve existing software. It has been successfully used to fix software bugs or improve non-functional properties of software such as running time, memory usage, or energy consumption. Recently, it has been shown that genetic programming, the eponymous GI typical search algorithm, was outperformed by local search strategies \cite{blot:2021:tevc}. One result of that work was that GI was able to find interesting algorithmic changes in the implementation [Hui Li and Qingfu Zhang., 2009] of two state-of-the-art evolutionary algorithms, MOEA-D and NSGA-II. Here, we reproduce and extend this result with a simple local search, obtaining 10percent faster software variants with little to no impact on solution quality in 6/18 GI runs.", notes = "Slides http://www.cs.ucl.ac.uk/staff/a.blot/files/blot_roadef_2022_slides.pdf runtime limited to 3:45:00, five fold cross validation, 12percent reduction in execution time at validation CMOEAD::calc_distance(). Overfitting issues BUT 'generalised most of the time (12/18)' https://roadef2022.sciencesconf.org/ https://roadef2022.sciencesconf.org/data/pages/LivretROADEF_2023.pdf", } @Misc{blot:2022:corr_1, author = "Aymeric Blot and Justyna Petke", title = "{MAGPIE}: Machine Automated General Performance Improvement via Evolution of Software", howpublished = "arXiv", year = "2022", month = "4 " # aug, keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, parameter tuning, algorithm configuration, compiler optimisation, local search, multi-objective, running time, solution quality, metaheuristic-based search strategies, srcML, linear GP, GCC, LLVM, Clang, OpenJDK, GraalVM Java", URL = "https://arxiv.org/abs/2208.02811", DOI = "doi:10.48550/arxiv.2208.02811", code_url = "https://github.com/bloa/magpie", size = "19 pages", abstract = "Performance is one of the most important qualities of software. Several techniques have thus been proposed to improve it, such as program transformations, optimisation of software parameters, or compiler flags. Many automated software improvement approaches use similar search strategies to explore the space of possible improvements, yet available tooling only focuses on one approach at a time. This makes comparisons and exploration of interactions of the various types of improvement impractical. We propose MAGPIE, a unified software improvement framework. It provides a common edit sequence based representation that isolates the search process from the specific improvement technique, enabling a much simplified synergistic workflow. We provide a case study using a basic local search to compare compiler optimisation, algorithm configuration, and genetic improvement. We chose running time as our efficiency measure and evaluated our approach on four real-world software, written in C, C++, and Java. Our results show that, used independently, all techniques find significant running time improvements: up to 25percent for compiler optimisation, 97percent for algorithm configuration, and 61percent for evolving source code using genetic improvement. We also show that up to 10percent further increase in performance can be obtained with partial combinations of the variants found by the different techniques. Furthermore, the common representation also enables simultaneous exploration of all techniques, providing a competitive alternative to using each technique individually.", notes = "MiniSAT, LPG (v1.2 AClib), Sat4J, Weka (v3.8) random forrest, Dexter dataset. Written in Python 3.5+, MAGPIE is based on PyGGI 2.0 'Software is never done' 'Machine Automated General Performance Improvement through Evolution of software' 'Automated combination of the patches' (mutations) 'compiler and interpreter flag optimisation' Figure 3: Cross-validation with k-folding and holdout. p7 'only 10 training instances are actually used due to the very high evaluation cost' StmtDelete, StmtReplace, StmtInsert, ConstantSet, ConstantUpdate, ParamSet. fitness = CPU instructions (rather than CPU time). GraalVM disabled use of 'compressed ordinary object pointers'.", } @Misc{blot2022comprehensive, author = "Aymeric Blot and Justyna Petke", title = "A Comprehensive Survey of Benchmarks for Automated Improvement of Software's Non-Functional Properties", howpublished = "arXiv", year = "2022", month = "16 " # dec, keywords = "genetic algorithms, genetic programming, genetic improvement, software performance, non-functional properties, benchmark", eprint = "2212.08540", archiveprefix = "arXiv", primaryclass = "cs.SE", URL = "https://arxiv.org/abs/2212.08540", data_url = "https://bloa.github.io/nfunc_survey/", size = "43 pages", abstract = "Performance is a key quality of modern software. Although recent years have seen a spike in research on automated improvement of software's execution time, energy, memory consumption, etc., there is a noticeable lack of standard benchmarks for such work. It is also unclear how such benchmarks are representative of current software. Furthermore, frequently non-functional properties of software are targeted for improvement one-at-a-time, neglecting potential negative impact on other properties. In order to facilitate more research on automated improvement of non-functional properties of software, we conducted a survey gathering benchmarks used in previous work. We considered 5 major online repositories of software engineering work: ACM Digital Library, IEEE Xplore, Scopus, Google Scholar, and ArXiV. We gathered 5000 publications (3749 unique), which were systematically reviewed to identify work that empirically improves non-functional properties of software. We identified 386 relevant papers. We find that execution time is the most frequently targeted property for improvement (in 62 percent of relevant papers), while multi-objective improvement is rarely considered (5 percent). Static approaches are prevalent (in 53 percent of papers), with exploratory approaches (evolutionary in 18 percent and non-evolutionary in 14 percent of papers) increasingly popular in the last 10 years. Only 40 percent of 386 papers describe work that uses benchmark suites, rather than single software, of those SPEC is most popular (covered in 33 papers). We also provide recommendations for choice of benchmarks in future work, noting, e.g., lack of work that covers Python or JavaScript. We provide all programs found in the 386 papers on our dedicated web page https://bloa.github.io/nfunc_survey/ We hope that this effort will facilitate more research on the topic of automated improvement of software's non-functional properties.", notes = "See \cite{blot:2025:ACMsurveys}", } @InProceedings{Blot:2024:GI, author = "Aymeric Blot", title = "Automated Software Performance Improvement with {Magpie}", booktitle = "13th International Workshop on Genetic Improvement @ICSE 2024", year = "2024", editor = "Gabin An and Aymeric Blot and Vesna Nowack and Oliver Krauss and Justyna Petke", pages = "ix", address = "Lisbon", month = "16 " # apr, publisher = "ACM", note = "Invited tutorial", keywords = "genetic algorithms, genetic programming, Genetic Improvement", isbn13 = "979-8-4007-0573-1/24/04", URL = "http://gpbib.cs.ucl.ac.uk/gi2024/an_2024_GI.pdf", URL = "https://conf.researchr.org/details/icse-2024/gi-2024-papers/8/Automated-Software-Performance-Improvement-with-Magpie", DOI = "doi:10.1145/3643692", slides_url = "http://www.cs.ucl.ac.uk/staff/a.blot/files/blot_gi@icse_2024_slides.pdf", video_url = "https://youtu.be/ysKDzJMac0Q", video_url = "https://www.youtube.com/watch?v=ysKDzJMac0Q&list=PLI8fiFpB7BoIRqJuY80XwmH-DFT_71y2S&index=5&pp=iAQB", code_url = "https://github.com/bloa/magpie", size = "1 page", abstract = "we present Magpie, a powerful tool for both Genetic Improvement researchers and practitioners. Magpie stands at the forefront of software evolution, providing a streamlined approach to model, evolve, and automatically improve software systems. Addressing both functional and non-functional concerns, Magpie offers a user-friendly no-code interface that seamlessly integrates various search processes, as well as enabling easy Python code injection for advanced users to further tailor and specialise the improvement process to meet their specific needs. We will provide a concise overview of Magpie's internals before exploring diverse real-world scenarios. Aymeric Blot is a Senior Lecturer at the Universite of Rennes, France.", notes = "GI @ ICSE 2024, part of \cite{an:2024:GI}", } @Article{blot:2025:ACMsurveys, author = "Aymeric Blot and Justyna Petke", title = "A Comprehensive Survey of Benchmarks for Improvement of Software's Non-Functional Properties", journal = "ACM Computing Surveys", year = "2025", note = "in press", keywords = "genetic algorithms, genetic programming, genetic improvement, software performance, non-functional properties, benchmark", URL = "https://discovery.ucl.ac.uk/id/eprint/10203326/1/main.pdf", URL = "https://discovery.ucl.ac.uk/id/eprint/10203326/", data_url = "https://bloa.github.io/nfunc_survey", size = "35 pages", notes = "replaces \cite{blot2022comprehensive}", } @Proceedings{blot:2025:GI, title = "14th International Workshop on Genetic Improvement @ICSE 2025", year = "2025", editor = "Aymeric Blot and Vesna Nowack and Penn Faulkner Rainford and Oliver Krauss", address = "Ottawa", month = "27 " # apr, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Genetic Improvement", URL = "http://geneticimprovementofsoftware.com/events/icse2025", URL = "http://gpbib.cs.ucl.ac.uk/gi2025/blot_2025_GI.pdf", size = "vi + 42 pages", abstract = "The GI workshops continue to bring together researchers from across the world to exchange ideas about using optimisation techniques, particularly evolutionary computation, such as genetic programming, to improve existing software. Contents: \cite{Tan:2025:GI} \cite{Blot_magpie:2025:GI} \cite{chan:2025:GI} \cite{langdon:2025:GI} \cite{songpetchmongkol:2025:GI}, \cite{bose:2025:GI}, \cite{wang:2025:GI}, \cite{bouras:2025:GI}, See also \cite{langdon:2025:SEN} ", } @InProceedings{Blot_magpie:2025:GI, author = "Aymeric Blot", title = "Automated Software Performance Improvement with {Magpie}", booktitle = "14th International Workshop on Genetic Improvement @ICSE 2025", year = "2025", editor = "Aymeric Blot and Vesna Nowack and Penn Faulkner Rainford and Oliver Krauss", pages = "vi", address = "Ottawa", month = "27 " # apr, publisher = "IEEE", note = "Invited tutorial", keywords = "genetic algorithms, genetic programming, Genetic Improvement", URL = "http://gpbib.cs.ucl.ac.uk/gi2025/blot_2025_GI.pdf", slides_url = "https://gpbib.cs.ucl.ac.uk/gi2025/gi_2025_slides/blot_gi@icse_2025.pdf", code_url = "https://github.com/bloa/magpie", size = "1 page", abstract = "In this tutorial, I will present Magpie (https://github.com/bloa/magpie), a powerful tool for both Genetic Improvement researchers and practitioners. Magpie stands at the forefront of software evolution, providing a streamlined approach to model, evolve, and automatically improve software systems. Addressing both functional and non-functional concerns, Magpie offers a user-friendly no-code interface that seamlessly integrates various search processes, as well as enabling easy Python code injection for advanced users to further tailor and specialise the improvement process to meet their specific needs. We will provide a concise overview of Magpie internals before exploring diverse real-world scenarios.", notes = "GI @ ICSE 2025, part of \cite{blot:2025:GI} see also \cite{Blot:2024:GI}", } @Proceedings{Blum:2013:GECCOcomp, title = "GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion", year = "2013", editor = "Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A. N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks", address = "Amsterdam, The Netherlands", publisher_address = "New York, NY, USA", month = "6-10 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, Ant colony optimization and swarm intelligence, Artificial life/robotics/evolvable hardware, Biological and biomedical applications, Digital entertainment technologies and Arts, Estimation of distribution algorithms, Evolution strategies and evolutionary programming, Evolutionary combinatorial optimization and metaheuristics, Evolutionary multiobjective optimization, Generative and developmental systems, Genetics based machine learning, Integrative genetic and evolutionary computation, Parallel evolutionary systems, Real world applications, Search-based software engineering, Self-* search, Theory, Introductory tutorials, Advanced tutorials, Specialized techniques and applications tutorials, Workshop on problem understanding and real-world optimisation, Symbolic regression and modeling workshop, Black box optimization benchmarking 2013 (BBOB 2013), Sixteenth international workshop on learning classifier systems, Evolutionary computation software systems (EvoSoft'13), Visualisation methods in genetic and evolutionary computation (VizGEC 2013), Evolutionary computation and multi-agent systems and simulation (EcoMass) seventh annual workshop, Medical applications of genetic and evolutionary computation (MedGEC'13), 3rd workshop on evolutionary computation for the automated design of algorithms, Green and efficient energy applications of genetic and evolutionary computation workshop, International workshop on evolutionary computation in bioinformatics, Stack-based workshop, Student workshop, Late-breaking abstracts, Keynote talks", isbn13 = "978-1-4503-1964-5", URL = "http://dl.acm.org/citation.cfm?id=2464576", notes = "Distributed at GECCO-2013.", } @Proceedings{Blum:2013:GECCO, title = "GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference", year = "2013", editor = "Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi", address = "Amsterdam, The Netherlands", publisher_address = "New York, NY, USA", month = "6-10 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, Ant colony optimization and swarm intelligence, Artificial life/robotics/evolvable hardware, Biological and biomedical applications, Digital entertainment technologies and arts, Estimation of distribution algorithms, Evolution strategies and evolutionary programming, Evolutionary combinatorial optimization and metaheuristics, Evolutionary multiobjective optimization, Generative and developmental systems, Genetics based machine learning, Integrative genetic and evolutionary computation, Parallel evolutionary systems, Real world applications, Search-based software engineering, Self-* search, Theory", isbn13 = "978-1-4503-1963-8", URL = "http://dl.acm.org/citation.cfm?id=2463372", notes = "GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)", } @InProceedings{blume:2000:ocfromsgesGLEAM, author = "Christian Blume", title = "Optimized Collision Free Robot Move Statement Generation by the Evolutionary Software {GLEAM}", booktitle = "Real-World Applications of Evolutionary Computing", year = "2000", editor = "Stefano Cagnoni and Riccardo Poli and George D. Smith and David Corne and Martin Oates and Emma Hart and Pier Luca Lanzi and Egbert Jan Willem and Yun Li and Ben Paechter and Terence C. Fogarty", volume = "1803", series = "LNCS", pages = "327--338", address = "Edinburgh", publisher_address = "Berlin", month = "17 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, Industrial Machining Robots", ISBN = "3-540-67353-9", DOI = "doi:10.1007/3-540-45561-2_32", abstract = "The GLEAM algorithm and its implementation are a new evolutionary method application in the field of robotics. The GLEAM software generates control code for real industrial robots. Therefore GLEAM allows a time related description of the robot movement (not only a static description of robot arm configurations). This internal representation of primitive move commands is mapped to a representation of move statements of an industrial robot language, which can be loaded at the robot control and executed.", notes = "Robot command program is a vriable number of very high level command actions. EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM, EvoRob, and EvoFlight, Edinburgh, Scotland, UK, April 17, 2000 Proceedings http://evonet.lri.fr/evoweb/resources/books_journals/record.php?id=61", } @InCollection{bobrovnikoff:2000:SEISP, author = "Dmitri Bobrovnikoff", title = "{SoccerBots:} Evolving Intelligent Soccer Players", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "40--45", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:2000:gagp}", } @InProceedings{boden:1996:tsobGA, author = "Edward B. Boden and Gilford F. Martino", title = "Testing Software using Order-Based Genetic Algorithms", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "Genetic Algorithms", pages = "461--466", address = "Stanford University, CA, USA", publisher = "MIT Press", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap76.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96 GA paper", } @InProceedings{boettcher:1999:EOMC, author = "Stefan Boettcher and Allon G. Percus", title = "Extremal Optimization: Methods derived from Co-Evolution", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "825--832", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "evolution strategies and evolutionary programming", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/9904056.pdf", URL = "http://xxx.lanl.gov/abs/math.OC/9904056", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Boetticher:2006:IRI, author = "G. D. Boetticher and K. Kaminsky", title = "The Assessment and Application of Lineage Information in Genetic Programs for Producing Better Models", booktitle = "IEEE International Conference on Information Reuse and Integration", year = "2006", pages = "141--146", address = "Waikoloa Village, HI, USA", month = sep, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9788-6", DOI = "doi:10.1109/IRI.2006.252403", abstract = "One of the challenges in data mining, and in particular genetic programs, is to provide sufficient coverage of the search space in order to produce an acceptable model. Traditionally, genetic programs generate equations (chromosomes) and consider all chromosomes within a population for breeding purposes. Considering the enormity of the search space for complex problems, it is imperative to examine genetic programs breeding efforts in order to produce better solutions with less training. This research examines chromosome lineage within genetic programs in order to identify breeding patterns. Fitness values for chromosomes are sorted, then partitioned into five classes. Initial experiments reveal a distinct difference between upper, middle, and lower classes. Based upon initial results, a novel genetic programming process is proposed which breeds a new generation exclusively from the top 20 percent of a population. A second set of experiments statistically validate this proposed approach", notes = "Houston Univ., TX", } @InProceedings{Bogdanova:2019:GECCOcomp, author = "Anna Bogdanova and Jair Pereira Junior and Claus Aranha", title = "Franken-swarm: grammatical evolution for the automatic generation of swarm-like meta-heuristics", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", isbn13 = "978-1-4503-6748-6", pages = "411--412", address = "Prague, Czech Republic", DOI = "doi:10.1145/3319619.3321902", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, grammatical evolution", notes = "Also known as \cite{3321902} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @Article{BOHAIENKO:2021:RCO, author = "Vsevolod Bohaienko", title = "Selection of ?-Caputo derivatives' functional parameters in generalized water transport equation by genetic programming technique", journal = "Results in Control and Optimization", volume = "5", pages = "100068", year = "2021", ISSN = "2666-7207", DOI = "doi:10.1016/j.rico.2021.100068", URL = "https://www.sciencedirect.com/science/article/pii/S2666720721000394", keywords = "genetic algorithms, genetic programming, Moisture transport, Fractional differential equation, Parameters identification, -Caputo derivative", abstract = "The paper considers the usage of genetic programming technique to select an analytic form of functional parameter of the ?-Caputo fractional derivative. We study one-dimensional space-time fractional water transport equation with such derivatives with respect to both time and space variables that generalizes the classical Richards equation. Having water head values measured by Watermark sensors as inputs, the statement of parameters identification problem is performed. The forms of functional parameters are represented as trees and found using a genetic programming algorithm. We compare the accuracy of field data description by the model with fixed and variable forms of derivatives' functional parameters and obtained up to 30percent increase in accuracy for the training dataset and up to 15percent increase for the testing dataset when the considered method was used to select parameters' forms", } @InProceedings{Bohm:2019:GECCOcomp, author = "Clifford Bohm and Alexander Lalejini and Jory Schossau and Charles Ofria", title = "{MABE 2.0}: an introduction to {MABE} and a road map for the future of {MABE} development", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", isbn13 = "978-1-4503-6748-6", pages = "1349--1356", address = "Prague, Czech Republic", DOI = "doi:10.1145/3319619.3326825", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{3326825} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @Article{Bohm:2022:AlifeJ, author = "Clifford Bohm and Sarah Albani and Charles Ofria and Acacia Ackles", journal = "Artificial Life", title = "Using the Comparative Hybrid Approach to Disentangle the Role of Substrate Choice on the Evolution of Cognition", year = "2022", volume = "28", number = "4", pages = "423--439", abstract = "Understanding the structure and evolution of natural cognition is a topic of broad scientific interest, as is the development of an engineering toolkit to construct artificial cognitive systems. One open question is determining which components and techniques to use in such a toolkit. To investigate this question, we employ agent-based AI, using simple computational substrates (i.e., digital brains) undergoing rapid evolution. Such systems are an ideal choice as they are fast to process, easy to manipulate, and transparent for analysis. Even in this limited domain, however, hundreds of different computational substrates are used. While benchmarks exist to compare the quality of different substrates, little work has been done to build broader theory on how substrate features interact. We propose a technique called the Comparative Hybrid Approach and develop a proof-of-concept by systematically analysing components from three evolvable substrates: recurrent artificial neural networks, Markov brains, and Cartesian genetic programming. We study the role and interaction of individual elements of these substrates by recombining them in a piecewise manner to form new hybrid substrates that can be empirically tested. Here, we focus on network sparsity, memory discretization, and logic operators of each substrate. We test the original substrates and the hybrids across a suite of distinct environments with different logic and memory requirements. While we observe many trends, we see that discreteness of memory and the Markov brain logic gates correlate with high performance across our test conditions. Our results demonstrate that the Comparative Hybrid Approach can identify structural subcomponents that predict task performance across multiple computational substrates.", keywords = "genetic algorithms, genetic programming, Digital evolution, artificial intelligence, cognitive substrate, neuroscience, neuroevolution, Markov brain", DOI = "doi:10.1162/artl_a_00372", ISSN = "1064-5462", month = jan, notes = "Also known as \cite{10302012}", } @InProceedings{Bohm:2025:GPTP, author = "Cliff Bohm and Arend Hintze", title = "The Evolution of Heterogeneous Logic: An Analysis of the Buffet Method", booktitle = "Genetic Programming Theory and Practice XXII", year = "2025", editor = "Wolfgang Banzhaf and Bogdan Burlacu and Stephen Kelly and Alexander Lalejini and Fabricio {Olivetti de Franca}", address = "Michigan State University, USA", month = jun # " 5-7", note = "lightning talk", keywords = "genetic algorithms, genetic programming", notes = "To be published after the workshop", } @InProceedings{bohm:1996:eui, author = "Walter Bohm and Andreas Geyer-Schulz", title = "Exact Uniform Initialization for Genetic Programming", booktitle = "Foundations of Genetic Algorithms IV", year = "1996", editor = "Richard K. Belew and Michael Vose", pages = "379--407", address = "University of San Diego, CA, USA", publisher_address = "San Francisco, California, USA", month = "3--5 " # aug, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-460-X", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/bohm_1996_eui.pdf", URL = "http://cseweb.ucsd.edu/~rik/foga4/Abstracts/07-wb-abs.html", size = "29 pages", abstract = "In this paper we solve the problem of exactly uniform generation of complete derivation trees from k-bounded context-free languages. The result is applied and is used for developing an exact uniform initialization routine for a genetic programming variant based on an explicit representation of the grammar of the context-free language (simple genetic algorithm over k-bounded context-free languages) [Geyer-Schulz1996b] \cite{DBLP:books/daglib/0090318}. In this genetic programming variant the grammar is used to generate complete derivation trees which constitute the genomes for the algorithm. For the case that no a priori information about the solution is available, we prove that this (simple random sampling) algorithm is optimal in the sense of a minimax strategy. An exact uniform initialization routine for Koza's genetic programming variant [Koza1992] is derived as a special case.", notes = "FOGA4 k-bounded context-free languages May also use key Boehm96 Demonstrated on XOR problem", } @InProceedings{Boisbunon:2021:GECCO, author = "Aurelie Boisbunon and Carlo Fanara and Ingrid Grenet and Jonathan Daeden and Alexis Vighi and Marc Schoenauer", title = "Zoetrope Genetic Programming for regression", booktitle = "Proceedings of the 2021 Genetic and Evolutionary Computation Conference", year = "2021", editor = "Francisco Chicano and Alberto Tonda and Krzysztof Krawiec and Marde Helbig and Christopher W. Cleghorn and Dennis G. Wilson and Georgios Yannakakis and Luis Paquete and Gabriela Ochoa and Jaume Bacardit and Christian Gagne and Sanaz Mostaghim and Laetitia Jourdan and Oliver Schuetze and Petr Posik and Carlos Segura and Renato Tinos and Carlos Cotta and Malcolm Heywood and Mengjie Zhang and Leonardo Trujillo and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Fuyuki Ishikawa and Inmaculada Medina-Bulo and Frank Neumann and Andrew M. Sutton", pages = "776--784", address = "internet", series = "GECCO '21", month = jul # " 10-14", organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, ZGP, Symbolic regression, regression, Representation of mathematical functions, Supervised learning by regression, Learning linear models, Feature selection", isbn13 = "9781450383509", hal_id = "hal-03155694", hal_version = "v1", URL = "https://hal.archives-ouvertes.fr/hal-03155694/file/ZGP_regression_arxiv.pdf", DOI = "doi:10.1145/3449639.3459349", broken_code = "https://gitlab.devenv.mydatamodels.com/publications/bench-zgp-symbolic-regression/-/tree/master/", size = "9 pages", abstract = "The Zoetrope Genetic Programming (ZGP) algorithm is based on an original representation for mathematical expressions, targetting evolutionary symbolic regression. The zoetropic representation uses repeated fusion operations between partial expressions, starting from the terminal set. Repeated fusions within an individual gradually generate more complex expressions, ending up in what can be viewed as new features. These features are then linearly combined to best fit the training data. ZGP individuals then undergo specific crossover and mutation operators, and selection takes place between parents and offspring. ZGP is validated using a large number of public domain regression datasets, and compared to other symbolic regression algorithms, as well as to traditional machine learning algorithms. ZGP reaches state-of-the-art performances with respect to both types of algorithms, and demonstrates a low computational time compared to other symbolic regression approaches.", notes = "Supplemental Material https://dl.acm.org/action/downloadSupplement?doi=10.1145%2F3449639.3459349&file=p776-boisbunon_suppl.pdf Ingrid Grenet PhD thesis (2019) https://theses.hal.science/tel-02612815v1 Evaluation of the use of public toxicological data for chemical hazard prediction through computational methods. Not GP? MyDataModels, Sophia Antipolis, France GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)", } @InProceedings{Boisvert:2021:EuroGP, author = "Stephen Boisvert and John Sheppard", title = "Quality Diversity Genetic Programming for Learning Decision Tree Ensembles", booktitle = "EuroGP 2021: Proceedings of the 24th European Conference on Genetic Programming", year = "2021", editor = "Ting Hu and Nuno Lourenco and Eric Medvet", series = "LNCS", volume = "12691", publisher = "Springer Verlag", address = "Virtual Event", pages = "3--18", month = "7-9 " # apr, organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Decision tree ensemble, Quality diversity", isbn13 = "978-3-030-72811-3", DOI = "doi:10.1007/978-3-030-72812-0_1", abstract = "Quality Diversity (QD) algorithms are a class of population-based evolutionary algorithms designed to generate sets of solutions that are both fit and diverse. In this paper, we describe a strategy for applying QD concepts to the generation of decision tree ensembles by optimizing collections of trees for both individually accurate and collectively diverse predictive behavior. We compare three variants of this QD strategy with two existing ensemble generation strategies over several classification data sets. We then briefly highlight the effect of the evolutionary algorithm at the core of the strategy. The examined algorithms generate ensembles with distinct predictive behaviors as measured by classification accuracy and intrinsic diversity. The plotted behaviors hint at highly data-dependent relationships between these metrics. QD-based strategies are suggested as a means to optimize classifier ensembles along this performance curve along with other suggestions for future work.", notes = "Random forests, bagging, classification. Quality diversity, NSLC, MAP-Elites, Cully (TEVC 2017) archive update based both on similarity(BC) and fitness. Binary trees height = 4. quality/Diversity QD. accuracy v diversity. UCI. http://www.evostar.org/2021/eurogp/ Part of \cite{Hu:2021:GP} EuroGP'2021 held in conjunction with EvoCOP2021, EvoMusArt2021 and EvoApplications2021", } @InProceedings{bojarczuk:1999:DGP, author = "Celia C. Bojarczuk and Heitor S. Lopes and Alex A. Freitas", title = "Discovering comprehensible classification rules by using Genetic Programming: a case study in a medical domain", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "953--958", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, data mining, classification, medical applications", ISBN = "1-55860-611-4", URL = "http://www.cs.kent.ac.uk/people/staff/aaf/pub_papers.dir/gecco99.ps", URL = "https://www.cs.kent.ac.uk/people/staff/aaf/pubs.html", URL = "http://citeseer.ist.psu.edu/340269.html", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-417.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-417.ps", abstract = "This work it is intended to discover classification rules for diagnosing certain pathologies. These rules are capable of discriminating among 12 different pathologies, whose main symptom is chest pain. In order to discover these rules it was used genetic programming as well as some concepts of data mining, particularly the emphasis on the discovery of comprehensible knowledge.", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99). See also \cite{bojarczuk:2000:kdcp}", } @Article{bojarczuk:2000:kdcp, author = "Celia C. Bojarczuk and Heitor S. Lopes and Alex A. Freitas", title = "Genetic programming for knowledge discovery in chest-pain diagnosis", journal = "IEEE Engineering in Medicine and Biology Magazine", year = "2000", volume = "19", number = "4", pages = "38--44", month = jul # "-" # aug, keywords = "genetic algorithms, genetic programming, data mining, knowledge discovery, chest-pain diagnosis, predictive accuracy, rule set, comprehensible rules, background knowledge, preprocessing step, data sets, medical applications", ISSN = "0739-5175", URL = "http://ieeexplore.ieee.org/iel5/51/18543/00853480.pdf", URL = "https://www.cs.kent.ac.uk/people/staff/aaf/pubs.html", URL = "http://www.cs.kent.ac.uk/people/staff/aaf/pub_papers.dir/IEEE-EMB-2000.ps", URL = "http://citeseer.ist.psu.edu/459907.html", size = "7 pages", abstract = "Explores a promising data mining approach. Despite the small number of examples available in the authors' application domain (taking into account the large number of attributes), the results of their experiments can be considered very promising. The discovered rules had good performance concerning predictive accuracy, considering both the rule set as a whole and each individual rule. Furthermore, what is more important from a data mining viewpoint, the system discovered some comprehensible rules. It is interesting to note that the system achieved very consistent results by working from {"}tabula rasa,{"} without any background knowledge, and with a small number of examples. The authors emphasize that their system is still in an experiment in the research stage of development. Therefore, the results presented here should not be used alone for real-world diagnoses without consulting a physician. Future research includes a careful selection of attributes in a preprocessing step, so as to reduce the number of attributes (and the corresponding search space) given to the GP. Attribute selection is a very active research area in data mining. Given the results obtained so far, GP has been demonstrated to be a really useful data mining tool, but future work should also include the application of the GP system proposed here to other data sets, to further validate the results reported in this article.", notes = "lilgp", } @InProceedings{bojarczuk:2001:idamap, author = "Celia C. Bojarczuk and Heitor S. Lopes and Alex A. Freitas", title = "Data mining with constrained-syntax genetic programming: applications to medical data sets", booktitle = "Proceedings Intelligent Data Analysis in Medicine and Pharmacology (IDAMAP-2001)", year = "2001", note = "a workshop at MedInfo-2001", keywords = "genetic algorithms, genetic programming, data mining, classification, medical applications, Constrained-Syntax Genetic Programming", URL = "http://www.ailab.si/idamap/idamap2001/papers/bojarczuk.pdf", URL = "https://www.cs.kent.ac.uk/people/staff/aaf/pubs.html", URL = "https://kar.kent.ac.uk/13556/", URL = "http://www.cs.kent.ac.uk/people/staff/aaf/pub_papers.dir/IDAMAP-2001.ps", URL = "http://citeseer.ist.psu.edu/459555.html", abstract = "This work is intended to discover classification rules for diagnosing certain pathologies. In order to discover these rules we have developed a new constrained-syntax genetic programming algorithm based on some concepts of data mining, particularly with emphasis on the discovery of comprehensible knowledge. We compare the performance of the proposed GP algorithm with a genetic algorithm and with the very well-known decision-tree algorithm C4.5.", notes = "IDAMAP workshop http://www.ailab.si/idamap/idamap2001/ Evolves IFTHEN rules. GP syntax contrained similar to STGP. Size of rules used as component of fitness function (actually product of sensitivity, specificity and size releated coefficient. Demonstrated on 3 small medical datasets (2 UCI). ", } @InProceedings{bojarczuk03, author = "Celia C. Bojarczuk and Heitor S. Lopes and Alex A. Freitas", title = "An innovative application of a constrained-syntax genetic programming system to the problem of predicting survival of patients", booktitle = "Genetic Programming, Proceedings of EuroGP'2003", year = "2003", editor = "Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa", volume = "2610", series = "LNCS", pages = "11--21", address = "Essex", publisher_address = "Berlin", month = "14-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, data mining, classification, medical applications", URL = "https://www.cs.kent.ac.uk/people/staff/aaf/pubs.html", DOI = "doi:10.1007/3-540-36599-0_2", ISBN = "3-540-00971-X", abstract = "This paper proposes a constrained-syntax genetic programming (GP) algorithm for discovering classification rules in medical data sets. The proposed GP contains several syntactic constraints to be enforced by the system using a disjunctive normal form representation, so that individuals represent valid rule sets that are easy to interpret. The GP is compared with C4.5 in a real-world medical data set. This data set represents a difficult classification problem, and a new preprocessing method was devised for mining the data", notes = "EuroGP'2003 held in conjunction with EvoWorkshops 2003", } @Article{bojarczuk:2004:EMBM, author = "Celia C. Bojarczuk and Heitor S. Lopes and Alex A. Freitas and Edson L Michalkiewicz", title = "A constrained-syntax genetic programming system for discovering classification rules: application to medical data sets", journal = "Artificial Intelligence in Medicine", year = "2004", volume = "30", number = "1", pages = "27--48", month = jan, ISSN = "0933-3657", keywords = "genetic algorithms, genetic programming, data mining, classification, medical applications", URL = "https://www.cs.kent.ac.uk/people/staff/aaf/pubs.html", URL = "http://www.cpgei.cefetpr.br/~hslopes/publicacoes/2004/aim2004.pdf", URL = "http://www.sciencedirect.com/science/article/B6T4K-4B42BDH-1/2/77bc597c3188977bd9ffb40ba10802ac", URL = "http://www.harcourt-international.com/journals/aiim/", DOI = "doi:10.1016/j.artmed.2003.06.001", abstract = "We propose a constrained-syntax genetic programming (GP) algorithm for discovering classification rules in medical data sets. The proposed GP contains several syntactic constraints to be enforced by the system using a disjunctive normal form representation, so that individuals represent valid rule sets that are easy to interpret. The GP is compared with C4.5, a well-known decision-tree-building algorithm, and with another GP that uses Boolean inputs (BGP), in five medical data sets: chest pain, Ljubljana breast cancer, dermatology, Wisconsin breast cancer, and pediatric adrenocortical tumour. For this last data set a new preprocessing step was devised for survival prediction. Computational experiments show that, overall, the GP algorithm obtained good results with respect to predictive accuracy and rule comprehensibility, by comparison with C4.5 and BGP.", } @InProceedings{Bojtar:2020:CINTI, author = "Veronika Bojtar and Janos Botzheim", title = "Queen Bee Based Genetic Programming Method for a Hive Like Behavior", booktitle = "2020 IEEE 20th International Symposium on Computational Intelligence and Informatics (CINTI)", year = "2020", pages = "000127--000132", abstract = "Designing the behavioural attributes of a robot is challenging, and the complexity of this task is even more increased in the case of swarm robotics. For effectively solving such problems special types of evolutionary algorithms can be used such as Genetic Programming and Queen Bee Based Genetic Programming method. The revolutionary idea behind these algorithms is that they use tree based representation for the individuals in a population, thus being able to solve structure optimization problems. The goal of this paper is to introduce the idea of the Queen Bee Based Genetic Programming method and compare its effectiveness with Genetic Programming through the evolution of a successful hive based behavioral program.", keywords = "genetic algorithms, genetic programming, Statistics, Sociology, Task analysis, Informatics, Evolution (biology), queen bee based genetic programming, hive like behavior", DOI = "doi:10.1109/CINTI51262.2020.9305824", ISSN = "2471-9269", month = nov, notes = "Also known as \cite{9305824}", } @InProceedings{Bokhari:2016:GI, author = "Mahmoud Bokhari and Markus Wagner", title = "Optimising Energy Consumption Heuristically on {Android} Mobile Phones", booktitle = "Genetic Improvement 2016 Workshop", year = "2016", editor = "Justyna Petke and David R. White and Westley Weimer", pages = "1139--1140", address = "Denver", publisher_address = "New York, NY, USA", month = jul # " 20-24", organisation = "SIGEvo", publisher = "ACM", keywords = "genetic algorithms, genetic programming, Genetic Improvement, SBSE, Embedded systems, Computing methodologies, Heuristic function construction, Randomized search, Power Consumption Modelling, Energy Optimisation", URL = "http://cs.adelaide.edu.au/~markus/pub/2016-gecco-gi-energy.pdf", URL = "http://geneticimprovementofsoftware.com/wp-content/uploads/2016/06/Optimising_Energy_Consumption_Heuristically_on_Android_Mobile_Phones.pdf", DOI = "doi:10.1145/2908961.2931691", size = "2 pages", abstract = "In this paper we outline our proposed framework for optimising energy consumption on Android mobile phones. To model the power usage, we use an event-based modelling technique. The internal battery fuel gauge chip is used to measure the amount of energy being consumed and accordingly the model is built on. We use the model to estimate components' energy usages. In addition, we propose the use of evolutionary computations to prolong the battery life. This can be achieved by using the power consumption model as a fitness function, re-configuring the smartphone's default settings and modifying existing code of applications.", notes = "GECCO-2016 Workshop http://geneticimprovementofsoftware.com/", } @Misc{Bokhari:2017:arXiv, author = "Mahmoud A. Bokhari and Yuanzhong Xia and Bo Zhou and Brad Alexander and Markus Wagner", title = "Validation of Internal Meters of Mobile {Android} Devices", year = "2017", month = "24 " # jan, note = "arXiv:1701.07095", keywords = "Software engineering, Adaptive systems, System improvement, Computational intelligence", URL = "https://arxiv.org/abs/1701.07095", size = "2 pages", abstract = "In this paper we outline our results for validating the precision of the internal power meters of smart-phones under different workloads. We compare its results with an external power meter. This is the first step towards creating customized energy models on the fly and towards optimizing battery efficiency using genetic program improvements. Our experimental results indicate that the internal meters are sufficiently precise when large enough time windows are considered. This is part of our work on the dreaming smart-phone. For a technical demonstration please watch our videos https://www.youtube.com/watch?v=xeeFz2GLFdU and https://www.youtube.com/watch?v=C7WHoLW1KYw.", notes = "Used watt meters: Yoctopuce YoctoWatt, Maxim MAX17050. Used smart-phones: Nexus 6, Nexus 9 Not on GP but of interest to genetic improvement", } @InProceedings{Bokhari:2017:GI, author = "Mahmoud A. Bokhari and Bobby R. Bruce and Brad Alexander and Markus Wagner", title = "Deep Parameter Optimisation on {Android} Smartphones for Energy Minimisation - A Tale of Woe and a Proof-of-Concept", booktitle = "GI-2017", year = "2017", editor = "Justyna Petke and David R. White and W. B. Langdon and Westley Weimer", pages = "1501--1508", address = "Berlin", month = "15-19 " # jul, publisher = "ACM", keywords = "genetic algorithms, genetic programming, genetic improvement, non-functional properties, mobile devices, multi-objective optimisation, dreaming smartphone, Android 6", isbn13 = "978-1-4503-4939-0", URL = "http://geneticimprovementofsoftware.com/wp-content/uploads/2017/05/bokhari2017_deep_parameter_optimisation.pdf", URL = "http://cs.adelaide.edu.au/~markus/pub/2017gecco-deepandroid.pdf", DOI = "doi:10.1145/3067695.3082519", size = "8 pages", abstract = "With power demands of mobile devices rising, it is becoming increasingly important to make mobile software applications more energy efficient. Unfortunately, mobile platforms are diverse and very complex which makes energy behaviours difficult to model. This complexity presents challenges to the effectiveness of off-line optimisation of mobile applications. we demonstrate that it is possible to automatically optimise an application for energy on a mobile device by evaluating energy consumption in-vivo. In contrast to previous work, we use only the device's own internal meter. Our approach involves many technical challenges but represents a realistic path toward learning hardware specific energy models for program code features.", notes = "Rebound, Java, 44 test cases with test oracles. Sensitivity analysis of all integer and double constants before start reduces 38 parameters to 19. NSGA-II. Sample remaining energy in battery four times a second. flight mode not sufficient. Control temperature. Nexus 6, Nexus 9. Fixed CPU clock speed. Java garbage collector also run 4 times per second. Kolmogorov-Smirnov normality test. Recharge battery between generations. 55 mutants", } @InProceedings{Bokhari:2018:MobiQuitous, author = "Mahmoud A. Bokhari and Brad Alexander and Markus Wagner", title = "In-Vivo and Offline Optimisation of Energy Use in the Presence of Small Energy Signals: A Case Study on a Popular Android Library", booktitle = "Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, MobiQuitous 2018", year = "2018", editor = "Henning Schulzrinne and Pan Li", pages = "207--215", address = "New York", month = "5-7 " # nov, organisation = "EAI", publisher = "ACM", keywords = "genetic algorithms, genetic programming, genetic improvement, electrical batteries, software engineering, Search-based software engineering, SBSE, energy consumption, mobile applications, multi-objective optimisation, Android, Non-functional properties", isbn13 = "9781450360937", URL = "https://cs.adelaide.edu.au/~markus/pub/2018mobiquitous-smallEnergySignals.pdf", URL = "https://srb.sdl.edu.sa/esploro/outputs/conferenceProceeding/In-vivo-and-offline-optimisation-of-energy/9930446008331", DOI = "doi:10.1145/3286978.3287014", size = "9 pages", abstract = "Energy demands of applications on mobile platforms are increasing. As a result, there has been a growing interest in optimising their energy efficiency. As mobile platforms are fast-changing, diverse and complex, the optimisation of energy use is a non-trivial task. To date, most energy optimisation methods either use models or external meters to estimate energy use. Unfortunately, it becomes hard to build widely applicable energy models, and external meters are neither cheap nor easy to set up. To address this issue, we run application variants in-vivo on the phone and use a precise internal battery monitor to measure energy use. We describe a methodology for optimising a target application in-vivo and with application-specific models derived from the device's own internal meter based on jiffies and lines of code. We demonstrate that this process produces a significant improvement in energy efficiency with limited loss of accuracy.", notes = "Surrogate linear model Mobile phone physics library. p214 'energy savings of up to 22 percent'", } @InProceedings{Bokhari:2019:GI7, author = "Mahmoud A. Bokhari and Markus Wagner and Brad Alexander", title = "The Quest for Non-Functional Property Optimisation in Heterogeneous and Fragmented Ecosystems: a Distributed Approach", booktitle = "7th edition of GI @ GECCO 2019", year = "2019", month = jul # " 13-17", editor = "Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and John R. Woodward", publisher = "ACM", publisher_address = "New York, NY, USA", address = "Prague, Czech Republic", pages = "1705--1706", organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, genetic improvement, smartphone, Hardware, Batteries, Software maintenance tools, Non-functional properties, energy consumption, mobile applications, Android", isbn13 = "978-1-4503-6748-6", URL = "https://cs.adelaide.edu.au/~markus/pub/2019gecco-islands.pdf", DOI = "doi:10.1145/3319619.3326877", size = "2 pages", abstract = "The optimisation of non-functional properties of software is of growing importance in all scales of modern computing (from embedded systems to data-centres). In mobile computing, smart devices have complex interactions between their hardware and software components. Small changes in the environment can greatly impact the measurements of non-functional properties of software. In-vivo optimisation of applications on a platform can be used to evolve robust new solutions. However, the portability of such solutions performance across different platforms is questionable. In this paper we discuss the issue of optimising the non-functional properties of applications running in the Android ecosystem. We also propose a distributed framework that can mitigate such issues.", notes = " Also known as \cite{Bokhari:2019:GECCOcomp} Also known as \cite{3326877} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InProceedings{Bokhari:2020:GI9, author = "Mahmoud A. Bokhari and Markus Wagner and Brad Alexander", title = "Genetic Improvement of Software Efficiency: The Curse of Fitness Estimation", booktitle = "9th edition of GI @ GECCO 2020", year = "2020", month = jul # " 8-12", editor = "Brad Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and John R. Woodward", publisher = "ACM", address = "Internet", pages = "1926--1927", organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, Machine Learning, Non-Functional Properties, batteries, Energy Consumption, Mobile Applications, Android", isbn13 = "978-1-4503-7127-8", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/gecco2020/companion_files/wksp145s2-file1.pdf", DOI = "doi:10.1145/3377929.3398109", size = "2 pages", abstract = "Many challenges arise in the application of Genetic Improvement (GI) of Software to improve non-functional requirements of software such as energy use and run-time. These challenges are mainly centred around the complexity of the search space and the estimation of the desired fitness function. For example, such fitness function are expensive, noisy and estimating them is not a straight-forward task. we illustrate some of the challenges incomputing such fitness functions and propose a synergy between in-vivo evaluation and machine learning approaches to overcome such issues.", notes = "nexus6 android7 smartphone, garbage collection, surrogate fitness function. Broken Nov 2024 https://gi-gecco-20.gi-workshops.org/ Also known as \cite{Bokhari:2020:GECCOcomp}. Also known as \cite{10.1145/3377929.3398109} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @InProceedings{Bokhari:2020:GECCO, author = "Mahmoud A. Bokhari and Brad Alexander and Markus Wagner", title = "Towards Rigorous Validation of Energy Optimisation Experiments", year = "2020", editor = "Carlos Artemio {Coello Coello} and Arturo Hernandez Aguirre and Josu Ceberio Uribe and Mario Garza Fabre and Gregorio {Toscano Pulido} and Katya Rodriguez-Vazquez and Elizabeth Wanner and Nadarajen Veerapen and Efren Mezura Montes and Richard Allmendinger and Hugo Terashima Marin and Markus Wagner and Thomas Bartz-Beielstein and Bogdan Filipic and Heike Trautmann and Ke Tang and John Koza and Erik Goodman and William B. Langdon and Miguel Nicolau and Christine Zarges and Vanessa Volz and Tea Tusar and Boris Naujoks and Peter A. N. Bosman and Darrell Whitley and Christine Solnon and Marde Helbig and Stephane Doncieux and Dennis G. Wilson and Francisco {Fernandez de Vega} and Luis Paquete and Francisco Chicano and Bing Xue and Jaume Bacardit and Sanaz Mostaghim and Jonathan Fieldsend and Oliver Schuetze and Dirk Arnold and Gabriela Ochoa and Carlos Segura and Carlos Cotta and Michael Emmerich and Mengjie Zhang and Robin Purshouse and Tapabrata Ray and Justyna Petke and Fuyuki Ishikawa and Johannes Lengler and Frank Neumann", isbn13 = "9781450371285", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://arxiv.org/abs/2004.04500", URL = "https://doi.org/10.1145/3377930.3390245", DOI = "doi:10.1145/3377930.3390245", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference", pages = "1232--1240", size = "9 pages", keywords = "genetic algorithms, genetic programming, genetic improvement, Android, non-functional properties, energy consumption, mobile applications", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", abstract = "The optimisation of software energy consumption is of growing importance across all scales of modern computing, i.e., from embedded systems to data-centres. Practitioners in the field of Search-Based Software Engineering and Genetic Improvement of Software acknowledge that optimising software energy consumption is difficult due to noisy and expensive fitness evaluations. However, it is apparent from results to date that more progress needs to be made in rigorously validating optimisation results. This problem is pressing because modern computing platforms have highly complex and variable behaviour with respect to energy consumption. To compare solutions fairly we propose in this paper a new validation approach called R3-validation which exercises software variants in a rotated-round-robin order. Using a case study, we present an in-depth analysis of the impacts of changing system states on software energy usage, and we show how R3-validation mitigates these. We compare it with current validation approaches across multiple devices and operating systems, and we show that it aligns best with actual platform behaviour.", notes = "Also known as \cite{10.1145/3377930.3390245} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @PhdThesis{Bokhari2020_PhD, author = "Mahmoud Abdulwahab K. Bokhari", title = "Genetic Improvement of Software for Energy Efficiency in Noisy and Fragmented Eco-Systems", school = "School of Computer Science, University of Adelaide", year = "2020", address = "Australia", month = "7 " # dec, keywords = "genetic algorithms, genetic programming, genetic improvement, search based software engineering, SBSE, genetic improvement of software, non-functional properties, energy efficiency, battery optimisation, deep parameter optimisation, mobile computing, Android, validation approach, R-3 validation approch", URL = "http://hdl.handle.net/2440/130174", URL = "https://digital.library.adelaide.edu.au/dspace/bitstream/2440/130174/1/Bokhari2020_PhD.pdf", size = "166 pages", abstract = "Software has made its way to every aspect of our daily life. Users of smart devices expect almost continuous availability and uninterrupted service. However, such devices operate on restricted energy resources. As energy efficiency of software is relatively a new concern for software practitioners, there is a lack of knowledge and tools to support the development of energy efficient software. Optimising the energy consumption of software requires measuring or estimating its energy use and then optimising it. Generalised models of energy behaviour suffer from heterogeneous and fragmented eco-systems (i.e. diverse hardware and operating systems). The nature of such optimisation environments favours in-vivo optimisation which provides the ground-truth for energy behaviour of an application on a given platform. One key challenge in in-vivo energy optimisation is noisy energy readings. This is because complete isolation of the effects of software optimisation is simply infeasible, owing to random and systematic noise from the platform. we explore in-vivo optimisation using Genetic Improvement of Software (GI) for energy efficiency in noisy and fragmented eco-systems. First, we document expected and unexpected technical challenges and their solutions when conducting energy optimisation experiments. This can be used as guidelines for software practitioners when conducting energy related experiments. Second, we demonstrate the technical feasibility of in-vivo energy optimisation using GI on smart devices. We implement a new approach for mitigating noisy readings based on simple code rewrite. Third, we propose a new conceptual framework to determine the minimum number of samples required to show significant differences between software variants competing in tournaments. We demonstrate that the number of samples can vary drastically between different platforms as well as from one point of time to another within a single platform. It is crucial to take into consideration these observations when optimising in the wild or across several devices in a control environment. Finally, we implement a new validation approach for energy optimisation experiments. Through experiments, we demonstrate that the current validation approaches can mislead software practitioners to draw wrong conclusions. Our approach outperforms the current validation techniques in terms of specificity and sensitivity in distinguishing differences between validation solutions.", notes = "Supervisor: Markus Wagner Co-Supervisor: Bradley Alexander", } @InProceedings{DBLP:conf/icaart/BokhariT20, author = "Syed Mohtashim Abbas Bokhari and Oliver E. Theel", editor = "Ana Paula Rocha and Luc Steels and H. Jaap {van den Herik}", title = "Design of Scenario-based Application-optimized Data Replication Strategies through Genetic Programming", booktitle = "Proceedings of the 12th International Conference on Agents and Artificial Intelligence, ICAART 2020", year = "2020", volume = "2", pages = "120--129", address = "Valletta, Malta", month = feb # " 22-24", publisher = "SCITEPRESS", keywords = "genetic algorithms, genetic programming, Distributed Systems, Fault Tolerance, Data Replication, Quorum Protocols, Operation Availability, Operation Cost, Voting Structures, Optimization, Machine Leaning, Evolutionary Strategies", isbn13 = "978-989-758-395-7", URL = "https://doi.org/10.5220/0008955301200129", DOI = "doi:10.5220/0008955301200129", timestamp = "Tue, 14 Apr 2020 10:44:02 +0200", biburl = "https://dblp.org/rec/conf/icaart/BokhariT20.bib", bibsource = "dblp computer science bibliography, https://dblp.org", size = "10 pages", abstract = "A distributed system is a paradigm which is indispensable to the current world due to countless requests with every passing second. Therefore, in distributed computing, high availability is very important. In a dynamic environment due to the scalability and complexity of the resources and components, systems are fault-prone because millions of computing devices are connected to each other via communication links. Distributed systems allow many users to access shared computing resources which makes faults inevitable. Replication plays its role in masking failures in order to achieve a fault-tolerant distributed environment. Data replication is an appropriate means to provide highly available data access operations at relatively low operation costs. Although there are several contemporary data replication strategies being used, the question still stands which strategy is the best for a given scenario or application class assuming a certain workload, its distribution across a network, av ailability of the individual replicas, and cost of the access operations. In this regard, research focuses on analysis, simulation, and machine learning approaches to automatically identify and design such replication strategies that are optimized for a given application scenario based on predefined constraints and properties exploiting a so-called voting structure.", notes = "Department of Computer Science, University of Oldenburg, Germany", } @InProceedings{Bokhari:2020:CEC, author = "Syed Mohtashim Abbas Bokhari and Oliver Theel", title = "A Genetic Programming-based Multi-objective Optimization Approach to Data Replication Strategies for Distributed Systems", booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC 2020", year = "2020", editor = "Yaochu Jin", pages = "paper id24298", address = "internet", month = "19-24 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-7281-6929-3", DOI = "doi:10.1109/CEC48606.2020.9185598", abstract = "Data replication is the core of distributed systems to enhance their fault tolerance and make services highly available to the end-users. Data replication masks run-time failures and hence, makes the system more reliable. There are many contemporary data replication strategies for this purpose, but the decision to choose an appropriate strategy for a certain environment and a specific scenario is a challenge and full of compromises. There exists a potentially indefinite number of scenarios that cannot be covered entirely by contemporary strategies. It demands designing new data replication strategies optimized for the given scenarios. The constraints of such scenarios are often conflicting in a sense that an increase in one objective could be sacrificial to the others, which implies there is no best solution to the problem but what serves the purpose. In this regard, this research provides a genetic programming-based multi-objective optimization approach that endeavors to not only identify, but also design new data replication strategies and optimize their conflicting objectives as a single-valued metric. The research provides an intelligent, automatic mechanism to generate new replication strategies as well as easing up the decision making so that relevant strategies with satisfactory trade-offs of constraints can easily be picked and used from the generated solutions at run-time. Moreover, it makes the notion of hybrid strategies easier to accomplish which otherwise would have been very cumbersome to achieve, therefore, to optimize.", notes = "https://wcci2020.org/ University of Oldenburg, Germany. Also known as \cite{9185598}", } @InProceedings{Bokhari:2020:PRDC, author = "Syed Mohtashim Abbas Bokhari and Oliver Theel", title = "Introducing Novel Crossover and Mutation Operators into Data Replication Strategies for Distributed Systems", booktitle = "IEEE 25th Pacific Rim International Symposium on Dependable Computing, PRDC 2020", year = "2020", pages = "21--30", address = "Perth, Australia", month = "1-4 " # dec, keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-7281-8004-5", DOI = "doi:10.1109/PRDC50213.2020.00013", size = "10 pages", abstract = "In a distributed paradigm, data replication plays a vital role in achieving high availability and fault tolerance of a system. In a connected network, faults are inevitable, replication masks those faults at run-time while users are unaware of it and the system continues to work as expected. There are different strategies to enforce such fault-tolerant behavior on a system. However, there are numerous scenarios reflecting different trade-offs between several quality metrics and to identify a relevant strategy for a specific scenario is quite cumbersome since there could exist potentially infinite scenarios and solutions are limited. This requires designing new solutions satisfying the constraints of such scenarios, which may be left unaddressed otherwise. In this regard, this paper develops a mechanism to automatically design new replication strategies (up-to-now unknown), optimized for given scenarios. The paper uses genetic programming to explore unknown replication strategies. It evolves the population of replication strategies (representing each a computer program) gradually, but consistently to make them optimized to eventually meet the desired criteria. Furthermore, it introduces strong multi-crossover and multi-mutation operators into replication, which strengthens our machine learning framework, at the same time guaranteeing consistency of the solutions, to generate innovative hybrid replication strategies.", notes = "University of Oldenburg, Department of Computer Science, Germany Also known as \cite{9320430} \cite{DBLP:conf/prdc/BokhariT20}", } @InProceedings{Bokhari:2020:ICPADS, author = "Syed Mohtashim Abbas Bokhari and Oliver Theel", title = "Use of Genetic Programming Operators in Data Replication and Fault Tolerance", booktitle = "2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS)", year = "2020", pages = "290--299", abstract = "Distributed systems are a need of the current times to balance the workload since providing highly accessible data objects is of utmost importance. Faults hinder the availability of the data, thereby leading systems to fail. In this regard, data replication in distributed systems is a means to mask failures and mitigate any such possible hindrances in the availability of the data. This replicated behavior is then controlled by data replication strategies, but there are numerous scenarios reflecting different trade-offs between several quality metrics. It demands designing new replication strategies optimized for the given scenarios, which may be left unaddressed otherwise. This research, therefore, uses an automatic mechanism based on genetic programming to construct new optimized replication strategies (up-to-now) unknown. This mechanism uses a so-called voting structure of directed acyclic graphs (each representing a computer program) as a unified representation of replication strategies. These structures are interpreted by our general algorithm at run-time in order to derive respective quorums to manage replicated objects eventually. For this, the research particularly demonstrates the usefulness of various genetic operators through their instances, exploiting the heterogeneity between existing strategies, thereby creating innovative strategies flexibly. This mechanism creates new hybrid strategies and evolves them over several generations of evolution, to make them optimized while maintaining the consistency (validity) of the solutions. Our approach is very effective and extremely flexible to offer competitive results with respect to the contemporary strategies as well as generating novel strategies even with a slight use of relevant genetic operators.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICPADS51040.2020.00047", ISSN = "2690-5965", month = dec, notes = "Also known as \cite{9359219}", } @Article{olandi2019intelligent, author = "Hamed Bolandi and Wolfgang Banzhaf and Nizar Lajnef and Kaveh Barri and Amir H. Alavi", title = "An Intelligent Model for the Prediction of Bond Strength of {FRP} Bars in Concrete: A Soft Computing Approach", journal = "Technologies", year = "2019", volume = "7", number = "2", pages = "42", month = jun, keywords = "genetic algorithms, genetic programming, multi-gene genetic programming, data mining, bond strength, FRP-bar", ISSN = "ISSN 2227-7080", owner = "banzhaf", timestamp = "2019.06.11", URL = "https://www.mdpi.com/2227-7080/7/2/42/pdf", URL = "https://doi.org/10.3390/technologies7020042", DOI = "doi:10.3390/technologies7020042", size = "16 pages", abstract = "Accurate prediction of bond behaviour of fibre reinforcement polymer (FRP) concrete has a pivotal role in the construction industry. This paper presents a soft computing method called multi-gene genetic programming (MGGP) to develop an intelligent prediction model for the bond strength of FRP bars in concrete. The main advantage of the MGGP method over other similar methods is that it can formulate the bond strength by combining the capabilities of both standard genetic programming and classical regression. A number of parameters affecting the bond strength of FRP bars were identified and fed into the MGGP algorithm. The algorithm was trained using an experimental database including 223 test results collected from the literature. The proposed MGGP model accurately predicts the bond strength of FRP bars in concrete. The newly defined predictor variables were found to be efficient in characterizing the bond strength. The derived equation has better performance than the widely-used American Concrete Institute (ACI) model.", } @InProceedings{Bolandi:2019:GECCOcomp, author = "Hamed Bolandi and Wolfgang Banzhaf and Nizar Lajnef and Kaveh Barri and Amir. H Alavi", title = "Bond strength prediction of {FRP}-bar reinforced concrete: a multi-gene genetic programming approach", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", isbn13 = "978-1-4503-6748-6", pages = "364--364", address = "Prague, Czech Republic", DOI = "doi:10.1145/3319619.3322066", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{3322066} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @Misc{boldi2022environmentaldiscontinuityhypothesisdownsampled, author = "Ryan Boldi and Thomas Helmuth and Lee Spector", title = "The Environmental Discontinuity Hypothesis for Down-Sampled Lexicase Selection", year = "2022", howpublished = "arXiv 2205.15931", month = "31 " # may, keywords = "genetic algorithms, genetic programming", eprint = "2205.15931", archiveprefix = "arXiv", primaryclass = "cs.NE", URL = "https://arxiv.org/abs/2205.15931", size = "10 pages", } @InProceedings{boldi:2023:GECCOcomp, author = "Ryan Boldi and Ashley Bao and Martin Briesch and Thomas Helmuth and Dominik Sobania and Lee Spector and Alexander Lalejini", title = "The Problem Solving Benefits of {Down-Sampling} Vary by Selection Scheme", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "527--530", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, program synthesis, down-sampling, selection, regression: Poster", isbn13 = "9798400701191", DOI = "doi:10.1145/3583133.3590713", size = "4 pages", abstract = "Genetic programming systems often use large training sets to evaluate candidate solutions, which can be computationally expensive. Down-sampling training sets has long been used to decrease the computational cost of evaluation in a wide range of application domains. Indeed, recent studies have shown that both random and informed down-sampling can substantially improve problem-solving success for GP systems that use lexicase parent selection. We use the PushGP framework to experimentally test whether these down-sampling techniques can also improve problem-solving success in the context of two other commonly used selection methods, fitness-proportionate and tournament selection, across eight GP problems (four program synthesis and four symbolic regression). We verified that down-sampling can benefit the problem-solving success of both fitness-proportionate and tournament selection. However, the number of problems wherein down-sampling improved problem-solving success varied by selection scheme, suggesting that the impact of down-sampling depends both on the problem and choice of selection scheme. Surprisingly, we found that down-sampling was most consistently beneficial when combined with lexicase selection as compared to tournament and fitness-proportionate selection. Overall, our results suggest that down-sampling should be considered more often when solving test-based GP problems.", notes = "See \cite{Boldi:2024:ALife} GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @InProceedings{boldi:2023:GECCOcomp2, author = "Ryan Boldi and Alexander Lalejini and Thomas Helmuth and Lee Spector", title = "A Static Analysis of Informed {Down-Samples}", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "531--534", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, down-sampling, selection, program synthesis, regression: Poster", isbn13 = "9798400701191", DOI = "doi:10.1145/3583133.3590751", size = "4 pages", abstract = "We present an analysis of the loss of population-level test coverage induced by different down-sampling strategies when combined with lexicase selection. We study recorded populations from the first generation of genetic programming runs, as well as entirely synthetic populations. Our findings verify the hypothesis that informed down-sampling better maintains population-level test coverage when compared to random down-sampling. Additionally, we show that both forms of down-sampling cause greater test coverage loss than standard lexicase selection with no down-sampling. However, given more information about the population, we found that informed down-sampling can further reduce its test coverage loss. We also recommend wider adoption of the static population analyses we present in this work.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @InProceedings{boldi:2024:GECCOcomp3, author = "Ryan Boldi and Ashley Bao and Martin Briesch and Thomas Helmuth and Dominik Sobania and Lee Spector and Alexander Lalejini", title = "A Comprehensive Analysis of Down-sampling for Genetic Programming-based Program Synthesis", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion", year = "2024", editor = "Ting Hu and Aniko Ekart", pages = "487--490", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming: Poster", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3654134", size = "4 pages", abstract = "Genetic programming systems typically require large computational resource investments for training-set evaluations. Down-sampling these sets has proven to decrease costs and improve problem-solving success, particularly with the lexicase parent selection algorithm. We investigated its effectiveness when applied to three other common selection methods and across various program synthesis problems. Our findings show that down-sampling notably enhances all three methods, indicating its potential broad applicability. Additionally, we found informed down-sampling to be more successful than its random counterpart, particularly in selection schemes maintaining diversity like lexicase selection. We conclude that down-sampling is a promising strategy for test-based genetic programming problems, irrespective of selection scheme.This paper is a comprehensive extension of a previous poster paper [1].", notes = "GECCO-2024 GP A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{Boldi:2024:ALife, author = "Ryan Boldi and Ashley Bao and Martin Briesch and Thomas Helmuth and Dominik Sobania and Lee Spector and Alexander Lalejini", title = "Untangling the Effects of Down-Sampling and Selection in Genetic Programming", booktitle = "ALIFE 2024: Proceedings of the 2024 Artificial Life Conference", year = "2024", editor = "Andres Faina and Sebastian Risi and Eric Medvet and Kasper Stoy and Bert Chan and Karine Miras and Payam Zahadat and Djordje Grbic and Giorgia Nadizar", pages = "705--716", address = "Copenhagen", month = jul # " 22-26", organization = "The International Society for Artificial Life", publisher = "MIT Press", keywords = "genetic algorithms, genetic programming, lexicase, PushGP, program synthesis benchmark problems, tournament, fitness proportional selection, mplicit Fitness Sharing, IFS, diversity maintenance", URL = "https://direct.mit.edu/isal/proceedings/isal2024/36/88/123536", URL = "https://direct.mit.edu/isal/proceedings-pdf/isal2024/36/88/2461089/isal_a_00832.pdf", DOI = "doi:10.1162/isal_a_00832", code_url = "https://github.com/lspector/propeller", size = "12 pages", abstract = "the lexicase parent selection algorithm. We test whether these down-sampling techniques can also improve problem-solving success in the context of three other commonly used selection methods, fitness-proportionate, tournament, implicit fitness sharing plus tournament selection, across six program synthesis GP problems. We verified that down-sampling can significantly improve the problem-solving success for all three of these other selection schemes, demonstrating its general efficacy. We discern that the selection pressure imposed by the selection scheme does not interact with the down-sampling method. However, we find that informed down-sampling can improve problem solving success significantly over random down-sampling when the selection scheme has a mechanism for diversity maintenance like lexicase or implicit fitness sharing. Overall, our results suggest that down-sampling should be considered more often when solving test-based problems, regardless of the selection scheme in use.", notes = "'we expand on...'\cite{boldi:2023:GECCOcomp} 'tournament sizes of t= 2,5,10,30'. IFS tournament size = 30. 'farthest first traversal (Hochbaum and Shmoys, 1985)' Count Odds, Fizz Buzz, Scrabble Score, Small or Large, Fuel Cost, Middle Character. Turing complete instruction set...looping and conditional execution. Diversity maintenance. Selection Pressure. ALIFE 2024", } @Article{Boldi:ECJ, author = "Ryan Boldi and Martin Briesch and Dominik Sobania and Alexander Lalejini and Thomas Helmuth and Franz Rothlauf and Charles Ofria and Lee Spector", title = "Informed Down-Sampled Lexicase Selection: Identifying Productive Training Cases for Efficient Problem Solving", journal = "Evolutionary Computation", year = "2024", volume = "32", number = "4", pages = "307--337", month = "Winter", keywords = "genetic algorithms, genetic programming, lexicase selection, informed down-sampling", ISSN = "1063-6560", URL = "https://arxiv.org/abs/2301.01488", URL = "https://doi.org/10.1162/evco_a_00346", eprint = "https://direct.mit.edu/evco/article-pdf/doi/10.1162/evco_a_00346/2352336/evco_a_00346.pdf", DOI = "doi:10.1162/evco_a_00346", abstract = "Genetic Programming (GP) often uses large training sets and requires all individuals to be evaluated on all training cases during selection. Random down-sampled lexicase selection evaluates individuals on only a random subset of the training cases, allowing for more individuals to be explored with the same number of program executions. However, sampling randomly can exclude important cases from the down-sample for a number of generations, while cases that measure the same behavior (synonymous cases) may be overused. In this work, we introduce Informed Down-Sampled Lexicase Selection. This method leverages population statistics to build down-samples that contain more distinct and therefore informative training cases. Through an empirical investigation across two different GP systems (PushGP and Grammar-Guided GP), we find that informed down-sampling significantly outperforms random down-sampling on a set of contemporary program synthesis benchmark problems. Through an analysis of the created down-samples, we find that important training cases are included in the down-sample consistently across independent evolutionary runs and systems. We hypothesize that this improvement can be attributed to the ability of Informed Down-Sampled Lexicase Selection to maintain more specialist individuals over the course of evolution, while still benefiting from reduced per-evaluation costs.", notes = "boldi2024informeddownsampledlexicaseselection University of Massachusetts, Amherst, MA 01003, USA", } @PhdThesis{Bolelli-Broinizi:thesis, author = "Marcos Eduardo {Bolelli Broinizi}", title = "Ordenacao evolutiva de anuncios em publicidade computacional", title_en = "Evolutionary ad ranking for computational advertising", school = "Instituto de Matematica e Estatistica, Universidade de Sao Paulo, USP", year = "2015", address = "Sao Paulo, Brazil", month = aug, keywords = "genetic algorithms, genetic programming, computational advertising, contextual advertising, exploratory data analysis, learning to advertising, online advertising, principal component analysis", bibsource = "OAI-PMH server at www.teses.usp.br", contributor = "Joao Eduardo Ferreira", language = "pt", oai = "oai:teses.usp.br:tde-09112015-104805", rights = "Open access", URL = "http://www.teses.usp.br/teses/disponiveis/45/45134/tde-09112015-104805/", URL = "http://www.teses.usp.br/teses/disponiveis/45/45134/tde-09112015-104805/publico/def_mbroinizi.pdf", URL = "http://www.teses.usp.br/teses/disponiveis/45/45134/tde-09112015-104805/en.php", DOI = "doi:10.11606/T.45.2015.tde-09112015-104805", size = "121 pages", abstract = "Otimizar simultaneamente os interesses dos usu{\'a}rios, anunciantes e publicadores {\'e} um grande desafio na {\'a}rea de publicidade computacional. Mais precisamente, a ordena{\c c}{\~a}o de an{\'u}ncios, ou ad ranking, desempenha um papel central nesse desafio. Por outro lado, nem mesmo as melhores f{\'o}rmulas ou algoritmos de ordena{\c c}{\~a}o s{\~a}o capazes de manter seu status por um longo tempo em um ambiente que est{\'a} em constante mudan{\c c}a. Neste trabalho, apresentamos uma an{\'a}lise orientada a dados que mostra a import{\^a}ncia de combinar diferentes dimens{\~o}es de publicidade computacional por meio de uma abordagem evolutiva para ordena{\c c}{\~a}o de an{\'u}ncios afim de responder a mudan{\c c}as de forma mais eficaz. N{\'o}s avaliamos as dimens{\~o}es de valor comercial, desempenho hist{\'o}rico de cliques, interesses dos usu{\'a}rios e a similaridade textual entre o an{\'u}ncio e a p{\'a}gina. Nessa avalia{\c c}{\~a}o, n{\'o}s averiguamos o desempenho e a correla{\c c}{\~a}o das diferentes dimens{\~o}es. Como consequ{\^e}ncia, n{\'o}s desenvolvemos uma abordagem evolucion{\'a}ria para combinar essas dimens{\~o}es. Essa abordagem {\'e} composta por tr{\^e}s partes: um reposit{\'o}rio de configura{\c c}{\~o}es para facilitar a implanta{\c c}{\~a}o e avalia{\c c}{\~a}o de experimentos de ordena{\c c}{\~a}o; um componente evolucion{\'a}rio de avalia{\c c}{\~a}o orientado a dados; e um motor de programa{\c c}{\~a}o gen{\'e}tica para evoluir f{\'o}rmulas de ordena{\c c}{\~a}o de an{\'u}ncios. Nossa abordagem foi implementada com sucesso em um sistema real de publicidade computacional respons{\'a}vel por processar mais de quatorze bilh{\~o}es de requisi{\c c}{\~o}es de an{\'u}ncio por m{\^e}s. De acordo com nossos resultados, essas dimens{\~o}es se complementam e nenhuma delas deve ser neglicenciada. Al{\'e}m disso, n{\'o}s mostramos que a combina{\c c}{\~a}o evolucion{\'a}ria dessas dimens{\~o}es n{\~a}o s{\'o} {\'e} capaz de superar cada uma individualmente, como tamb{\'e}m conseguiu alcan{\c c}ar melhores resultados do que m{\'e}todos est{\'a}ticos de ordena{\c c}{\~a}o de an{\'u}ncios.", abstract = "Simultaneous optimisation of users, advertisers and publishers' interests has been a formidable challenge in online advertising. More concretely, ranking of advertising, or more simply ad ranking, has a central role in this challenge. However, even the best ranking formula or algorithm cannot withstand the ever-changing environment of online advertising for a long time. In this work, we present a data-driven analysis that shows the importance of combining different aspects of online advertising through an evolutionary approach for ad ranking in order to effectively respond to changes. We evaluated aspects ranging from bid values and previous click performance to user behaviour and interests, including the textual similarity between ad and page. In this evaluation, we assessed commercial performance along with the correlation between different aspects. Therefore, we proposed an evolutionary approach for combining these aspects. This approach was composed of three parts: a configuration repository to facilitate deployment and evaluation of ranking experiments; an evolutionary data-based evaluation component; and a genetic programming engine to evolve ad ranking formulae. Our approach was successfully implemented in a real online advertising system that processes more than fourteen billion ad requests per month. According to our results, these aspects complement each other and none of them should be neglected. Moreover, we showed that the evolutionary combination of these aspects not only outperformed each of them individually, but was also able to achieve better overall results than static ad ranking methods.", notes = "in Portuguese", } @InProceedings{bolis:2001:EuroGP, author = "Enzo Bolis and Christian Zerbi and Pierre Collet and Jean Louchet and Evelyne Lutton", title = "A GP Artificial Ant for image processing: preliminary experiments with EASEA", booktitle = "Genetic Programming, Proceedings of EuroGP'2001", year = "2001", editor = "Julian F. Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon", volume = "2038", series = "LNCS", pages = "246--255", address = "Lake Como, Italy", publisher_address = "Berlin", month = "18-20 " # apr, organisation = "EvoNET", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, Image processing, Contour detection, EASEA, Animat: Poster", ISBN = "3-540-41899-7", URL = "http://minimum.inria.fr/evo-lab/Publications/EuroGPFinal.ps.gz", DOI = "doi:10.1007/3-540-45355-5_19", size = "10 pages", abstract = "This paper describes how animat-based food foraging techniques may be applied to the design of low-level image processing algorithms. First, we show how we implemented the food foraging application using the EASEA software package. We then use this technique to evolve an animat and learn how to move inside images and detect high-gradient lines with a minimum exploration time. The resulting animats do not use standard scanning + filtering techniques but develop other image exploration strategies close to contour tracking. Experimental results on grey level images are presented.", notes = "EuroGP'2001, part of \cite{miller:2001:gp}", } @InProceedings{Bollegala:2011:GECCO, author = "Danushka Bollegala and Nasimul Noman and Hitoshi Iba", title = "{RankDE:} learning a ranking function for information retrieval using differential evolution", booktitle = "GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0557-0", pages = "1771--1778", keywords = "genetic algorithms, genetic programming, Real world applications", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001576.2001814", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Learning a ranking function is important for numerous tasks such as information retrieval (IR), question answering, and product recommendation. For example, in information retrieval, a Web search engine is required to rank and return a set of documents relevant to a query issued by a user. We propose RankDE, a ranking method that uses differential evolution (DE) to learn a ranking function to rank a list of documents retrieved by a Web search engine. To the best of our knowledge, the proposed method is the first DE-based approach to learn a ranking function for IR. We evaluate the proposed method using LETOR dataset, a standard benchmark dataset for training and evaluating ranking functions for IR. In our experiments, the proposed method significantly outperforms previously proposed rank learning methods that use evolutionary computation algorithms such as Particle Swam Optimization (PSO) and Genetic Programming (GP), achieving a statistically significant mean average precision (MAP) of 0.339 on TD2003 dataset and 0.430 on the TD2004 dataset. Moreover, the proposed method shows comparable results to the state-of-the-art non-evolutionary computational approaches on this benchmark dataset. We analyze the feature weights learnt by the proposed method to better understand the salient features for the task of learning to rank for information retrieval.", notes = "Also known as \cite{2001814} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)", } @Misc{oai:CiteSeerX.psu:10.1.1.36.6062, author = "Beate Bollig and Martin Sauerhoff and Ingo Wegener", title = "Approximability and Non-Approximability by Binary Decision Diagrams (Extended Abstract)", year = "1995", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.36.6062", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", keywords = "genetic algorithms, genetic programming, subject classification, computational and structural complexity", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.36.6062", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.36.6062.pdf", broken = "http://ls2-www.cs.uni-dortmund.de/~wegener/papers/Paper95a.ps", size = "22 pages", abstract = "The usual applications of BDDs (binary decision diagrams), e. g., in verification and for CAD problems, require an exact representation of the considered Boolean functions. However, if BDDs are used for learning Boolean functions f on the basis of classified examples (e. g., in the environment of genetic programming), it is sufficient to produce the representation of a function g approximating f . This motivates the investigation of the size of the smallest BDD approximating f . Here exponential lower bounds for several BDD variants are proved and the relations between the size of approximating BDDs, randomised BDDs, communication complexity and general approximation techniques are revealed.", notes = "see also doi:10.1006/inco.2002.3174", } @InProceedings{bollini:1999:dpEAdp, author = "Alessandro Bollini and Marco Piastra", title = "Distributed and Persistent Evolutionary Algorithms: a Design Pattern", booktitle = "Genetic Programming, Proceedings of EuroGP'99", year = "1999", editor = "Riccardo Poli and Peter Nordin and William B. Langdon and Terence C. Fogarty", volume = "1598", series = "LNCS", pages = "173--183", address = "Goteborg, Sweden", publisher_address = "Berlin", month = "26-27 " # may, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-65899-8", DOI = "doi:10.1007/3-540-48885-5_14", abstract = "In the scenario of distributed processing for evolutionary algorithms the adoption of object-oriented database management systems (ODBMS) may yield improvements in terms of both robustness and flexibility. Populations of evolvable individuals can be made persistent across several evolutionary runs, making it possible to devise incremental strategies. Moreover, virtually any number of evolutionary processes may be run in parallel on the same underlying population without explicit synchronisation beyond that provided by the locking mechanism of the ODBMS. This paper describes a design pattern for a genetic programming environment that allows combining existing techniques with persistent population storage and management.", notes = "EuroGP'99, part of \cite{poli:1999:GP} Java objectstore database", } @InProceedings{bollini:1999:A, author = "Alessandro Bollini and Marco Piastra", title = "A persistent blackboard for distributed evolutionary computation", booktitle = "Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference", year = "1999", editor = "Scott Brave and Annie S. Wu", pages = "48--56", address = "Orlando, Florida, USA", month = "13 " # jul, keywords = "Java", notes = "GECCO-99LB", } @Article{BOMARITO:2021:CS, author = "G. F. Bomarito and T. S. Townsend and K. M. Stewart and K. V. Esham and J. M. Emery and J. D. Hochhalter", title = "Development of interpretable, data-driven plasticity models with symbolic regression", journal = "Computer \& Structures", volume = "252", pages = "106557", year = "2021", ISSN = "0045-7949", DOI = "doi:10.1016/j.compstruc.2021.106557", URL = "https://www.sciencedirect.com/science/article/pii/S0045794921000791", keywords = "genetic algorithms, genetic programming, Plasticity, Homogenization, Symbolic regression", abstract = "In many applications, such as those which drive new material discovery, constitutive models are sought that have three characteristics: (1) the ability to be derived in automatic fashion with (2) high accuracy and (3) an interpretable nature. Traditionally developed models are usually interpretable but sacrifice development time and accuracy. Purely data-driven approaches are usually fast and accurate but lack interpretability. In the current work, a framework for the rapid development of interpretable, data-driven constitutive models is pursued. The approach is characterized by the use of symbolic regression on data generated with micromechanical finite element models. Symbolic regression is the search for equations of arbitrary functional form which match a given dataset. Specifically, an implicit symbolic regression technique is developed to identify a plastic yield potential from homogenized finite element response data. Through three controlled test cases of varying complexity, the approach is shown to successfully produce interpretable plasticity models. The controlled test cases are used to investigate the robustness and scalability of the method and provide reasonable recommendations for more complex applications. Finally, the recommendations are used in the application of the method to produce a porous plasticity model from data corresponding to a representative volume element of voids within a metal matrix", } @InProceedings{bomarito:2022:GECCOcomp, author = "Geoffrey Bomarito and Patrick Leser and Nolan Strauss and Karl Garbrecht and Jacob Hochhalter", title = "Bayesian Model Selection for Reducing Bloat and Overfitting in Genetic Programming for Symbolic Regression", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion", year = "2022", editor = "Heike Trautmann and Carola Doerr and Alberto Moraglio and Thomas Bartz-Beielstein and Bogdan Filipic and Marcus Gallagher and Yew-Soon Ong and Abhishek Gupta and Anna V Kononova and Hao Wang and Michael Emmerich and Peter A. N. Bosman and Daniela Zaharie and Fabio Caraffini and Johann Dreo and Anne Auger and Konstantin Dietric and Paul Dufosse and Tobias Glasmachers and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Tea Tusar and Dimo Brockhoff and Tome Eftimov and Pascal Kerschke and Boris Naujoks and Mike Preuss and Vanessa Volz and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Mark Coletti and Catherine (Katie) Schuman and Eric ``Siggy'' Scott and Robert Patton and Paul Wiegand and Jeffrey K. Bassett and Chathika Gunaratne and Tinkle Chugh and Richard Allmendinger and Jussi Hakanen and Daniel Tauritz and John Woodward and Manuel Lopez-Ibanez and John McCall and Jaume Bacardit and Alexander Brownlee and Stefano Cagnoni and Giovanni Iacca and David Walker and Jamal Toutouh and UnaMay O'Reilly and Penousal Machado and Joao Correia and Sergio Nesmachnow and Josu Ceberio and Rafael Villanueva and Ignacio Hidalgo and Francisco {Fernandez de Vega} and Giuseppe Paolo and Alex Coninx and Antoine Cully and Adam Gaier and Stefan Wagner and Michael Affenzeller and Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and William B. Langdon and Justyna Petke and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Paetzel and Alexander Wagner and Michael Heider and Nadarajen Veerapen and Katherine Malan and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Mohammad Nabi Omidvar and Yuan Sun and Ernesto Tarantino and De Falco Ivanoe and Antonio {Della Cioppa} and Scafuri Umberto and John Rieffel and Jean-Baptiste Mouret and Stephane Doncieux and Stefanos Nikolaidis and Julian Togelius and Matthew C. Fontaine and Serban Georgescu and Francisco Chicano and Darrell Whitley and Oleksandr Kyriienko and Denny Dahl and Ofer Shir and Lee Spector and Alma Rahat and Richard Everson and Jonathan Fieldsend and Handing Wang and Yaochu Jin and Erik Hemberg and Marwa A. Elsayed and Michael Kommenda and William {La Cava} and Gabriel Kronberger and Steven Gustafson", pages = "526--529", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4503-9268-6/22/07", DOI = "doi:10.1145/3520304.3528899", abstract = "When performing symbolic regression using genetic programming, overfitting and bloat can negatively impact generalizability and interpretability of the resulting equations as well as increase computation times. A Bayesian fitness metric is introduced and its impact on bloat and overfitting during population evolution is studied and compared to common alternatives in the literature. The proposed approach was found to be more robust to noise and data sparsity in numerical experiments, guiding evolution to a level of complexity appropriate to the dataset. Further evolution of the population resulted not in overfitting or bloat, but rather in slight simplifications in model form. The ability to identify an equation of complexity appropriate to the scale of noise in the training data was also demonstrated. In general, the Bayesian model selection algorithm was shown to be an effective means of regularization which resulted in less bloat and overfitting when any amount of noise was present in the training data.The efficacy of a Genetic Programming (GP) [1] solution is often characterized by its (1) fitness, i.e. ability to perform a training task, (2) complexity, and (3) generalizability, i.e. ability to perform its task in an unseen scenario. Bloat is a common phenomenon for GP in which continued training results in significant increases in complexity with minimal improvements in fitness. There are several theories for the prevalence of bloat in GP which postulate possible evolutionary benefits of bloat [2]; however, for most practical purposes bloat is a hindrance rather than a benefit. For example, bloated solutions are less interpretable and more computationally expensive. Overfitting is another common phenomena in GP and the broader machine learning field. Overfitting occurs when continued training results in better fitness but reduced generalizability.", notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @Article{BOMARITO:2023:cma, author = "G. F. Bomarito and P. E. Leser and N. C. M. Strauss and K. M. Garbrecht and J. D. Hochhalter", title = "Automated learning of interpretable models with quantified uncertainty", journal = "Computer Methods in Applied Mechanics and Engineering", volume = "403", pages = "115732", year = "2023", ISSN = "0045-7825", DOI = "doi:10.1016/j.cma.2022.115732", URL = "https://www.sciencedirect.com/science/article/pii/S0045782522006879", keywords = "genetic algorithms, genetic programming, Interpretable machine learning, Symbolic regression, Bayesian model selection, Fractional Bayes factor", abstract = "Interpretability and uncertainty quantification in machine learning can provide justification for decisions, promote scientific discovery and lead to a better understanding of model behavior. Symbolic regression provides inherently interpretable machine learning, but relatively little work has focused on the use of symbolic regression on noisy data and the accompanying necessity to quantify uncertainty. A new Bayesian framework for genetic-programming-based symbolic regression (GPSR) is introduced that uses model evidence (i.e., marginal likelihood) to formulate replacement probability during the selection phase of evolution. Model parameter uncertainty is automatically quantified, enabling probabilistic predictions with each equation produced by the GPSR algorithm. Model evidence is also quantified in this process, and its use is shown to increase interpretability, improve robustness to noise, and reduce overfitting when compared to a conventional GPSR implementation on both numerical and physical experiments", } @Article{Bonakdari:2016:FMI, author = "Hossein Bonakdari and Amir Hossein Zaji", title = "Open channel junction velocity prediction by using a hybrid self-neuron adjustable artificial neural network", journal = "Flow Measurement and Instrumentation", volume = "49", pages = "46--51", year = "2016", ISSN = "0955-5986", DOI = "doi:10.1016/j.flowmeasinst.2016.04.003", URL = "http://www.sciencedirect.com/science/article/pii/S0955598616300309", abstract = "Determining the appropriate hidden layers neuron number is one of the most important processes in modelling the Multi-Layer Perceptron Artificial Neural Network (MLP-ANN). Despite the significant effect of the MLP-ANN neurons number on predicting accuracy, there is no definite rule for its determination. In this study, a new self-neuron number adjustable, hybrid Genetic Algorithm-Artificial Neural Network (GA-ANN), is introduced and its application examined on the complex velocity field prediction of an open channel junction. The results of GA-ANN were compared with those got by the Genetic Programming (GP) method as two applications of the Genetic Algorithm (GA). The comparisons showed that the GA-ANN model can predict the open channel junction velocity with higher accuracy than the GP model, with Root Mean Squared Error (RMSE) of 0.086 and 0.156, respectively. Finally the equation, obtained by applying the GA-ANN model, predicting the velocity at the open channel junction is presented.", keywords = "genetic algorithms, genetic programming, Artificial neural network, Neuron number determination, Open channel junction, Velocity prediction", } @Article{BONAKDARI:2018:AMC, author = "Hossein Bonakdari and Zohreh Sheikh Khozani and Amir Hossein Zaji and Navid Asadpour", title = "Evaluating the apparent shear stress in prismatic compound channels using the Genetic Algorithm based on Multi-Layer Perceptron: A comparative study", journal = "Applied Mathematics and Computation", volume = "338", pages = "400--411", year = "2018", keywords = "genetic algorithms, genetic programming, Apparent shear stress, Artificial neural network, Compound channel, Hybrid method", ISSN = "0096-3003", DOI = "doi:10.1016/j.amc.2018.06.016", URL = "http://www.sciencedirect.com/science/article/pii/S0096300318305046", abstract = "Apparent shear stress acting on a vertical interface between the main channel and floodplain in a compound channel is used to quantify the momentum transfer between these sub-areas of a cross section. In order to simulate the apparent shear stress, two soft computing techniques, including the Genetic Algorithm-Artificial Neural Network (GA-ANN) and Genetic Programming (GP) along with Multiple Linear Regression (MLR) were used. The proposed GA-ANN is a novel self-hidden layer neuron adjustable hybrid method made by combining the Genetic Algorithm (GA) with the Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) method. In order to find the optimum condition of the methods considered in modeling apparent shear stress, various input combinations, fitness functions, transfer functions (for the GAA method), and mathematical functions (for the GP method) were investigated. Finally, the results of the optimum GAA and GP methods were compared with the MLR as a basic method. The results show that the hybrid GAA method with RMSE of 0.5326 outperformed the GP method with RMSE of 0.6651. In addition, the results indicate that both GAA and GP methods performed significantly better than MLR with RMSE of 1.5409 in simulating apparent shear stress in symmetric compound channels", } @Misc{journals/corr/abs-2002-02751, author = "Hossein Bonakdari and Isa Ebtehaj and Bahram Gharabaghi and Ali Sharifi and Amir Mosavi", title = "Prediction of Discharge Capacity of Labyrinth Weir with Gene Expression Programming", howpublished = "arXiv", year = "2020", volume = "abs/2002.02751", keywords = "genetic algorithms, genetic programming, gene expression programming", URL = "https://arxiv.org/abs/2002.02751", bibdate = "2020-02-10", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/corr/corr2002.html#abs-2002-02751", } @InProceedings{conf/sai/BonakdariGES20, author = "Hossein Bonakdari and Bahram Gharabaghi and Isa Ebtehaj and Ali Sharifi", title = "A New Approach to Estimate the Discharge Coefficient in Sharp-Crested Rectangular Side Orifices Using Gene Expression Programming", booktitle = "Intelligent Computing - Proceedings of the 2020 Computing Conference, Volume 3", year = "2020", editor = "Kohei Arai and Supriya Kapoor and Rahul Bhatia", pages = "77--96", series = "Advances in Intelligent Systems and Computing", volume = "1230", publisher = "Springer", keywords = "genetic algorithms, genetic programming, gene expression programming", isbn13 = "978-3-030-52242-1", bibdate = "2020-07-07", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/sai/sai2020-3.html#BonakdariGES20", DOI = "doi:10.1007/978-3-030-52243-8_7", } @Article{bonakdari:2020:Entropy, author = "Hossein Bonakdari and Azadeh Gholami and Amir Mosavi and Amin Kazemian-Kale-Kale and Isa Ebtehaj and Amir Hossein Azimi", title = "A Novel Comprehensive Evaluation Method for Estimating the Bank Profile Shape and Dimensions of Stable Channels Using the Maximum Entropy Principle", journal = "Entropy", year = "2020", volume = "22", number = "11", keywords = "genetic algorithms, genetic programming, gene expression programming", ISSN = "1099-4300", URL = "https://www.mdpi.com/1099-4300/22/11/1218", DOI = "doi:10.3390/e22111218", abstract = "This paper presents an extensive and practical study of the estimation of stable channel bank shape and dimensions using the maximum entropy principle. The transverse slope (St) distribution of threshold channel bank cross-sections satisfies the properties of the probability space. The entropy of St is subject to two constraint conditions, and the principle of maximum entropy must be applied to find the least biased probability distribution. Accordingly, the Lagrange multiplier (λ) as a critical parameter in the entropy equation is calculated numerically based on the maximum entropy principle. The main goal of the present paper is the investigation of the hydraulic parameters influence governing the mean transverse slope (St¯) value comprehensively using a Gene Expression Programming (GEP) by knowing the initial information (discharge (Q) and mean sediment size (d50)) related to the intended problem. An explicit and simple equation of the St¯ of banks and the geometric and hydraulic parameters of flow is introduced based on the GEP in combination with the previous shape profile equation related to previous researchers. Therefore, a reliable numerical hybrid model is designed, namely Entropy-based Design Model of Threshold Channels (EDMTC) based on entropy theory combined with the evolutionary algorithm of the GEP model, for estimating the bank profile shape and also dimensions of threshold channels. A wide range of laboratory and field data are used to verify the proposed EDMTC. The results demonstrate that the used Shannon entropy model is accurate with a lower average value of Mean Absolute Relative Error (MARE) equal to 0.317 than a previous model proposed by Cao and Knight (1997) (MARE = 0.98) in estimating the bank profile shape of threshold channels based on entropy for the first time. Furthermore, the EDMTC proposed in this paper has acceptable accuracy in predicting the shape profile and consequently, the dimensions of threshold channel banks with a wide range of laboratory and field data when only the channel hydraulic characteristics (e.g., Q and d50) are known. Thus, EDMTC can be used in threshold channel design and implementation applications in cases when the channel characteristics are unknown. Furthermore, the uncertainty analysis of the EDMTC supports the models high reliability with a Width of Uncertainty Bound (WUB) of ±0.03 and standard deviation (Sd) of 0.24.", notes = "also known as \cite{e22111218}", } @InProceedings{bonarini:1999:CRLAACFLCS, author = "Andrea Bonarini", title = "Comparing Reinforcement Learning Algorithms Applied to Crisp and Fuzzy Learning Classifier Systems", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "52--59", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-876.pdf", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{conf/icnc/BonfimC05, title = "{FranksTree:} A Genetic Programming Approach to Evolve Derived Bracketed {L-systems}", author = "Danilo Mattos Bonfim and Leandro Nunes {de Castro}", year = "2005", pages = "1275--1278", editor = "Lipo Wang and Ke Chen and Yew-Soon Ong", booktitle = "Advances in Natural Computation, First International Conference, ICNC 2005, Proceedings, Part II", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3611", address = "Changsha, China", month = aug # " 27-29", bibdate = "2005-07-29", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icnc/icnc2005-1.html#BonfimC05", keywords = "genetic algorithms, genetic programming, interactive evolution", ISBN = "3-540-28325-0", DOI = "doi:10.1007/11539087_168", size = "4 pages", abstract = "L-system is a grammar-like formalism introduced to simulate the development of organisms. The L-system grammar can be viewed as a sort of genetic information that will be used to generate a specific structure. However, throughout development, the string (genetic information) that will effectively be used to 'draw' the phenotype of an individual is a result of the derivation of the L-system grammar. This work investigates the effect of applying a genetic programming approach to evolve derived L-systems instead of evolving the Lsystem grammar. The crossing over of plants from different species results in hybrid plants resembling a 'Frankstree', i.e. plants resultant from phenotypically different parents that present unusual body structures.", notes = " Crossover based on identifying branches in pictures? No mutation. population=6", } @InProceedings{bongard:1999:ECAL, author = "Josh C. Bongard", title = "Coevolutionary Dynamics of a Multi-population Genetic Programming System", booktitle = "Advances in Artificial Life", year = "1999", editor = "D. Floreano and J.-D. Nicoud and F. Mondada", volume = "1674", series = "LNAI", pages = "154", address = "Lausanne", month = "13-17 " # sep, publisher = "Springer Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-66452-1", URL = "http://www.cs.uvm.edu/~jbongard/papers/s067.ps.gz", URL = "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-66452-1", URL = "http://citeseer.ist.psu.edu/319504.html", notes = "ECAL-99", } @InProceedings{bongard:2000:legion, author = "Josh C. Bongard", title = "The Legion System: A Novel Approach to Evolving Heterogeneity for Collective Problem Solving", booktitle = "Genetic Programming, Proceedings of EuroGP'2000", year = "2000", editor = "Riccardo Poli and Wolfgang Banzhaf and William B. Langdon and Julian F. Miller and Peter Nordin and Terence C. Fogarty", volume = "1802", series = "LNCS", pages = "16--28", address = "Edinburgh", publisher_address = "Berlin", month = "15-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-67339-3", DOI = "doi:10.1007/978-3-540-46239-2_2", abstract = "We investigate the dynamics of agent groups evolved to peform a collective task, and in which the behavioural heterogeneity of the group is under evolutionary control. Two task domains are studied: solutions are evolved for the two tasks using an evolutionary algorithm called the Legion system. A new metric of heterogeneity is also introduced, which measures the heterogeneity of evolved group behaviours. It was found that the amount of heterogeneity evolved in an agent group is dependent on the given problem domain: for the first task, the Legion system evolved heterogeneous groups; for the second task, primarily homogeneous groups evolved. We conclude that the proposed system, in conjunction with the introduced heterogeneity measure, can be used as a tool for investigating various issues concerning redundancy, robustness and division of labour in the context of evolutionary approaches to collective problem solving.", notes = "EuroGP'2000, part of \cite{poli:2000:GP}", } @Article{Bongard:2007:PNAS, author = "Josh Bongard and Hod Lipson", title = "Automated reverse engineering of nonlinear dynamical systems", journal = "PNAS, Proceedings of the National Academy of Sciences of the United States of America", year = "2007", volume = "104", number = "24", pages = "9943--9948", month = "12 " # jun, keywords = "genetic algorithms, genetic programming, Physical Sciences, Computer Sciences, coevolution, modelling, symbolic identification", DOI = "doi:10.1073/pnas.0609476104", size = "6 pages", abstract = "Complex nonlinear dynamics arise in many fields of science and engineering, but uncovering the underlying differential equations directly from observations poses a challenging task. The ability to symbolically model complex networked systems is key to understanding them, an open problem in many disciplines. Here we introduce for the first time a method that can automatically generate symbolic equations for a nonlinear coupled dynamical system directly from time series data. This method is applicable to any system that can be described using sets of ordinary nonlinear differential equations, and assumes that the (possibly noisy) time series of all variables are observable. Previous automated symbolic modeling approaches of coupled physical systems produced linear models or required a nonlinear model to be provided manually. The advance presented here is made possible by allowing the method to model each (possibly coupled) variable separately, intelligently perturbing and destabilising the system to extract its less observable characteristics, and automatically simplifying the equations during modelling. We demonstrate this method on four simulated and two real systems spanning mechanics, ecology, and systems biology. Unlike numerical models, symbolic models have explanatory value, suggesting that automated reverse engineering approaches for model-free symbolic nonlinear system identification may play an increasing role in our ability to understand progressively more complex systems in the future.", notes = "Cited by Philosophy of Science Machine Science James A. Evans and Andrey Rzhetsky Science 23 July 2010: Vol. 329 no. 5990 pp. 399-400 DOI:10.1126/science.1189416", } @Article{Bongard:2009:TEC, author = "Josh C. Bongard", title = "Accelerating Self-Modeling in Cooperative Robot Teams", journal = "IEEE Transactions on Evolutionary Computation", year = "2009", volume = "13", number = "2", pages = "321--332", month = apr, keywords = "genetic algorithms, genetic programming, Robots, Robot sensing systems, Training data, Sensors, Data models, Service robots, Computational modeling, self-modeling, Collective robotics, evolutionary robotics", DOI = "doi:10.1109/TEVC.2008.927236", abstract = "One of the major obstacles to achieving robots capable of operating in real-world environments is enabling them to cope with a continuous stream of unanticipated situations. In previous work, it was demonstrated that a robot can autonomously generate self-models, and use those self-models to diagnose unanticipated morphological change such as damage. In this paper, it is shown that multiple physical quadrupedal robots with similar morphologies can share self-models in order to accelerate modeling. Further, it is demonstrated that quadrupedal robots which maintain separate self-modeling algorithms but swap self-models perform better than quadrupedal robots that rely on a shared self-modeling algorithm. This finding points the way toward more robust robot teams: a robot can diagnose and recover from unanticipated situations faster by drawing on the previous experiences of the other robots.", } @InCollection{Bongard:2009:GPTP, author = "Josh Bongard", title = "A Functional Crossover Operator for Genetic Programming", booktitle = "Genetic Programming Theory and Practice {VII}", year = "2009", editor = "Rick L. Riolo and Una-May O'Reilly and Trent McConaghy", series = "Genetic and Evolutionary Computation", address = "Ann Arbor", month = "14-16 " # may, publisher = "Springer", chapter = "12", pages = "195--210", keywords = "genetic algorithms, genetic programming, homologous crossover, crossover operators, system identification", isbn13 = "978-1-4419-1653-2", DOI = "doi:10.1007/978-1-4419-1626-6_12", abstract = "Practitioners of evolutionary algorithms in general, and of genetic programming in particular, have long sought to develop variation operators that automatically preserve and combine useful genetic substructure. This is often pursued with crossover operators that swap genetic material between genotypes that have survived the selection process. However in genetic programming, crossover often has a large phenotypic effect, thereby drastically reducing the probability of a beneficial crossover event. In this paper we introduce a new crossover operator, Functional crossover (FXO), which swaps subtrees between parents based on the subtrees' functional rather than structural similarity. FXO is employed in a genetic programming system identification task, where it is shown that FXO often outperforms standard crossover on both simulated and physically-generated data sets.", notes = "part of \cite{Riolo:2009:GPTP}", } @InProceedings{Bongard:2010:gecco, author = "Josh C. Bongard", title = "A probabilistic functional crossover operator for genetic programming", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "925--932", keywords = "genetic algorithms, genetic programming", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830483.1830649", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "The original mechanism by which evolutionary algorithms were to solve problems was to allow for the gradual discovery of sub-solutions to sub-problems, and the automated combination of these sub-solutions into larger solutions. This latter property is particularly challenging when recombination is performed on genomes encoded as trees, as crossover events tend to greatly alter the original genomes and therefore greatly reduce the chance of the crossover event being beneficial. A number of crossover operators designed for tree-based genetic encodings have been proposed, but most consider crossing genetic components based on their structural similarity. In this work we introduce a tree-based crossover operator that probabilistically crosses branches based on the behavioural similarity between the branches. It is shown that this method outperforms genetic programming without crossover, random crossover, and a deterministic form of the crossover operator in the symbolic regression domain.", notes = "Also known as \cite{1830649} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @Article{Bongard:2012:ieeetec, author = "Josh C. Bongard", title = "Innocent Until Proven Guilty: Reducing Robot Shaping from Polynomial to Linear Time", journal = "IEEE Transactions on Evolutionary Computation", year = "2011", volume = "15", number = "4", pages = "571--585", month = aug, keywords = "genetic algorithms, genetic programming, Early stopping, Evolutionary computation, Joints, Manipulators, Neurons, Robot sensing systems, evolutionary robotics", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2010.2096540", abstract = "In evolutionary algorithms, much time is spent evaluating inferior phenotypes that produce no offspring. A common heuristic to address this inefficiency is to stop evaluations early if they hold little promise of attaining high fitness. However, the form of this heuristic is typically dependent on the fitness function used, and there is a danger of prematurely stopping evaluation of a phenotype that may have recovered in the remainder of the evaluation period. Here a stopping method is introduced that gradually reduces fitness over the phenotype's evaluation, rather than accumulating fitness. This method is independent of the fitness function used, only stops those phenotypes that are guaranteed to become inferior to the current offspring-producing phenotypes, and realises significant time savings across several evolutionary robotics tasks. It was found that for many tasks, time complexity was reduced from polynomial to sublinear time, and time savings increased with the number of training instances used to evaluate a phenotype as well as with task difficulty.", notes = "Also known as \cite{5703121}", } @Misc{Bongard:2017:ECML, author = "Joshua Bongard and Anuradha Kodali and Marcin Szubert and Kamalika Das and Sangram Ganguly", title = "Understanding Climate-Vegetation Interactions in Global Rainforests Through a GP-Tree Analysis", year = "2017", keywords = "genetic algorithms, genetic programming, earth resources and remote sensing", bibsource = "OAI-PMH server at ntrs.nasa.gov", coverage = "Unclassified, Unlimited, Publicly available", date = "20170918", identifier = "Document ID: 20170011183", oai = "oai:casi.ntrs.nasa.gov:20170011183", rights = "Copyright, Public use permitted", URL = "http://hdl.handle.net/2060/20170011183", abstract = "The tropical rainforests are the largest reserves of terrestrial carbon sink and therefore, the future of these rainforests is a question that is of immense importance in the geoscience research community. With the recent severe Amazonian droughts in 2005 and 2010 and ongoing drought since 2000 in the Congo region there is growing concern that these forests could succumb to precipitation reduction, causing extensive carbon release and feedback to the carbon cycle. Contradicting research has claimed that these forests are resilient to such extreme climatic events. A significant reason behind these diverse conclusions is the lack of a holistic spatio-temporal analysis of the remote sensing data available for these regions. Small scale studies that use statistical correlation measure and simple linear regression to model the climate-vegetation interactions have suffered from the lack of complete data representation and the use of simple (linear) models that fail to represent physical processes accurately, thereby leading to inconclusive or incorrect predictions about the future. In this paper we use a genetic programming (GP) based approach called symbolic regression for discovering equations that govern the vegetation climate dynamics in the rainforests. Expecting micro-regions within the rainforests to have unique characteristics compared to the overall general characteristics, we use a modified regression-tree based hierarchical partitioning of the space and build a nonlinear GP model for each partition. The discovery of these equations reveal very interesting characteristics about the Amazon and the Congo rainforests. Overall it shows that the rainforests exhibit tremendous resiliency in the face of severe droughts. Based on the partitioning of the observed data points, we can conclude that in the absence of adequate precipitation, the trees adopt to reach a different steady state and recover as soon as precipitation is back to normal.", notes = "Submitted to ECML PKDD 2017 ecmlpkdd2017.ijs.si ARC-E-DAA-TN42006; The European Conference on Machine Learning \& Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2017) ; 18-22 Sep. 2017; Skopje; Macedonia May 2018 This requested resource (Online NTRS full-text PDF) is no longer available from NTRS. email at help@sti.nasa.gov also known as \cite{oai:casi.ntrs.nasa.gov:20170011183}", } @Article{Bongiorno:2025:physa, author = "Christian Bongiorno and Lamia Lamrani", title = "Quantifying the information lost in optimal covariance matrix cleaning", journal = "Physica A: Statistical Mechanics and its Applications", year = "2025", volume = "657", pages = "130225", keywords = "genetic algorithms, genetic programming, Random matrix theory, Covariance matrix estimation, Genetic regressor programming, High-dimension statistics, Information theory", ISSN = "0378-4371", URL = "https://www.sciencedirect.com/science/article/pii/S0378437124007349", DOI = "doi:10.1016/j.physa.2024.130225", abstract = "Obtaining an accurate estimate of the underlying covariance matrix from finite sample size data is challenging due to sample size noise. In recent years, sophisticated covariance-cleaning techniques based on random matrix theory have been proposed to address this issue. Most of these methods aim to achieve an optimal covariance matrix estimator by minimizing the Frobenius norm distance as a measure of the discrepancy between the true covariance matrix and the estimator. However, this practice offers limited interpretability in terms of information theory. To better understand this relationship, we focus on the Kullback-Leibler divergence to quantify the information lost by the estimator. Our analysis centers on rotationally invariant estimators, which are state-of-art in random matrix theory, and we derive an analytical expression for their Kullback-Leibler divergence. Due to the intricate nature of the calculations, we use genetic programming regressors paired with human intuition. Ultimately, using this approach, we formulate a conjecture validated through extensive simulations, showing that the Frobenius distance corresponds to a first-order expansion term of the Kullback-Leibler divergence, thus establishing a more defined link between the two measures", } @InProceedings{bonham:1999:AIEEWCOGA, author = "Christopher R. Bonham and Ian C. Parmee", title = "An Investigation of Exploration and Exploitation Within Cluster Oriented Genetic Algorithms (COGAs)", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1491--1497", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "real world applications", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-765.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-765.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Article{Bonifaci:2013:GPEM, author = "Vincenzo Bonifaci", title = "Andrew Adamatzky: Physarum Machines: Computers from Slime Mould", journal = "Genetic Programming and Evolvable Machines", year = "2013", volume = "14", number = "1", pages = "123--124", month = mar, note = "Book Review", keywords = "genetic algorithms, genetic programming, evolvable life", ISSN = "1389-2576", URL = "https://rdcu.be/dR8fU", DOI = "doi:10.1007/s10710-012-9169-2", size = "2 pages", } @Article{Pedraza:2011:JSC, author = "Cesar Pedraza and Javier Castillo and Jose I. Martinez and Pablo Huerta and Jose L. Bosque and Javier Cano", title = "Genetic Algorithm for {Boolean} minimization in an {FPGA} cluster", journal = "The Journal of Supercomputing", year = "2011", volume = "58", number = "2", pages = "244--252", month = nov, note = "Special issue on HPC in computational Science and Engineering. Part I", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Performance evaluation, FPGA, Boolean synthesis, Hardware co-design", ISSN = "0920-8542", DOI = "doi:10.1007/s11227-010-0401-7", size = "9 pages", abstract = "Evolutionary algorithms are an alternative option to the Boolean synthesis due to that they allow one to create hardware structures that would not be able to be obtained with other techniques. This paper shows a parallel genetic programming (PGP) Boolean synthesis implementation based on a cluster of FPGAs that takes full advantage of parallel programming and hardware/software co-design techniques. The performance of our cluster of FPGAs implementation has been compared with an HPC implementation. The experimental results have shown an excellent behaviour in terms of speed up (up to x500) and in terms of solving the scalability problems of this algorithms present in previous works.", notes = "SMILE cluster, gigabit ethernet, ALTAMIRA JS20 VHDL. cited by \cite{Bonilla:2011:LASCAS}", affiliation = "DATCCCIA, ETSII, Universidad Rey Juan Carlos, Madrid, Spain", } @InProceedings{Bonilla:2011:LASCAS, author = "Cesar Pedraza Bonilla and Carlos Ivan Camargo", title = "Low Cost Platform for Evolvable-Based {Boolean} Synthesis", booktitle = "IEEE Second Latin American Symposium on Circuits and Systems (LASCAS), 2011", year = "2011", month = feb, abstract = "Evolutionary algorithms are another option for combinational synthesis because they allow for the generation of hardware structures that cannot be obtained with other techniques. This paper shows a parallel genetic programming (PGP) Boolean synthesis implementation based on a low cost cluster of an embedded platform called SIE, based on a 32-bit processor and a Spartan-3 FPGA. Some tasks of the PGP have been accelerated in hardware and results have been compared with an HPC implementation, resulting in speedup values up to approximately 180.", keywords = "genetic algorithms, genetic programming, 32-bit processor, HPC implementation, PGP Boolean synthesis implementation, SIE, combinational synthesis, embedded platform, evolutionary algorithms, evolvable-based Boolean synthesis, hardware structures, low cost cluster, low cost platform, parallel genetic programming, spartan-3 FPGA, speedup values, Boolean functions, combinational circuits, embedded systems, field programmable gate arrays, logic design, microprocessor chips", DOI = "doi:10.1109/LASCAS.2011.5750310", notes = "Also known as \cite{5750310}", } @Article{Pedraza_Oyaga_Gomez_2013, author = "Cesar A. Pedraza and Jaime V. Oyaga and Ricardo C. Gomez", title = "Sintesis booleanacon programacion genetica paralela en {CPU y GPU}", title_en = "Genetic parallel programing-based Boolean synthesis with CPU and GPU", journal = "Ingenium Revista de la facultad de ingenieria", year = "2013", volume = "14", pages = "117--130", month = "ene.", keywords = "genetic algorithms, genetic programming, Programacion paralela, sintesis booleana, GPU, algoritmo evolutivo, Parallel programming, Boolean synthesis, GPU, evolutionary algorithm", URL = "https://revistas.usb.edu.co/index.php/Ingenium/article/view/1325/1116", URL = "https://revistas.usb.edu.co/index.php/Ingenium/article/view/1325", DOI = "doi:10.21500/01247492.1325", size = "14 pages", resumen = "La sintesis booleana o combinacional es un proceso mediante el cual se optimiza una red de puertas logicas, con el fin de reducir su consumo, minimizar costos, minimizar area y aumentar el rendimiento a la hora de ser implementada. Por otra parte, la programacion genetica es una alternativa importante para generar estructuras de hardware interesantes y eficientes. Se ha demostrado que los algoritmos evolutivos (AE) tienen mejor rendimiento si se implementan en sistemas paralelos. Este articulo presenta la implementacion de un algoritmo genetico paralelo (PGP) para realizar sintesis booleana en una plataforma basada en CPU-GPU. Esta implementacion emplea el modelo de islas, el cual permite la evolucion paralela e independiente del PGP a traves de las multiples unidades de procesamiento de la GPU y los multiples nucleos de un procesador de ultima generacion. Se probaron diferentes alternativas de mapeo del PGP en la plataforma en orden de optimizar el tiempo de respuesta. Como resultado se muestra una aceleracion superior a 41.", abstract = "The Boolean or combinational synthesis is a process that optimizes a logic gates net-work, in order to reduce power consumption, minimize costs, minimize area and increase the performance when it is implemented. Moreover genetic programming is an important alternative to generate interesting and efficient hardware structures. It has been shown that evolvable algorithms are faster when implemented in parallel systems. This paper presents the implementation of a parallel genetic programming (PGP) for boolean synthesis on a GPU-CPU based platform. Our implementation uses the island mode which allows the parallel and independent evolution of the PGP through the multiple processing units of the GPU and the multiple cores of a new generation desktop processors. We tested multiple mapping alternatives of the PGP on the platform in order to optimize the PGP response time. As a result we show that our approach achieves a speedup up to 41.", notes = "in spanish Universidad Santo Tomas", } @Article{bonin:2024:GPEM, author = "Lorenzo Bonin and Luigi Rovito and Andrea {De Lorenzo} and Luca Manzoni", title = "Cellular geometric semantic genetic programming", journal = "Genetic Programming and Evolvable Machines", year = "2024", volume = "25", pages = "Article no: 8", note = "Online first", keywords = "genetic algorithms, genetic programming, Geometric semantic genetic programming, Cellular genetic programming", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-024-09480-8", size = "32 pages", } @InProceedings{Bonson:2016:SSCI, author = "J. P. C. Bonson and A. R. McIntyre and M. I. Heywood", booktitle = "2016 IEEE Symposium Series on Computational Intelligence (SSCI)", title = "On novelty driven evolution in Poker", year = "2016", abstract = "This work asks the question as to whether `novelty as an objective' is still beneficial under tasks with a lot of ambiguity, such as Poker. Specifically, Poker represents a task in which there is partial information (public and private cards) and stochastic changes in state (what card will be dealt next). In addition, bluffing plays a fundamental role in successful strategies for playing the game. On the face of it, it appears that multiple sources of variation already exist, making the additional provision of novelty as an objective unwarranted. Indeed, most previous work in which agent strategies are evolved with novelty appearing as an explicit objective are not rich in sources of ambiguity. Conversely, the task of learning strategies for playing Poker, even under the 2-player case of heads-up Limit Texas Hold'em, is widely considered to be particularly challenging on account of the multiple sources of uncertainty. We benchmark a form of genetic programming, both with and without (task independent) novelty objectives. It is clear that pursuing behavioural diversity, even under the heads-up Limit Texas Hold'em task is central to learning successful strategies. Benchmarking against static and Bayesian opponents illustrates the capability of the resulting Genetic Programming (GP) agents to bluff and vary their style of play.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SSCI.2016.7849968", month = dec, notes = "Also known as \cite{7849968}", } @InProceedings{Bonte:2010:AIAI, author = "Bert Bonte and Bart Wyns", title = "Automatically Designing Robot Controllers and Sensor Morphology with Genetic Programming", booktitle = "6th IFIP Advances in Information and Communication Technology AIAI 2010", year = "2010", editor = "Harris Papadopoulos and Andreas Andreou and Max Bramer", volume = "339", series = "IFIP Advances in Information and Communication Technology", pages = "86--93", address = "Larnaca, Cyprus", month = oct # " 6-7", publisher = "Springer", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1007/978-3-642-16239-8_14", abstract = "Genetic programming provides an automated design strategy to evolve complex controllers based on evolution in nature. In this contribution we use genetic programming to automatically evolve efficient robot controllers for a corridor following task. Based on tests executed in a simulation environment we show that very robust and efficient controllers can be obtained. Also, we stress that it is important to provide sufficiently diverse fitness cases, offering a sound basis for learning more complex behaviour. The evolved controller is successfully applied to real environments as well. Finally, controller and sensor morphology are co-evolved, clearly resulting in an improved sensor configuration.", affiliation = "Department of Electrical Energy, Systems and Automation, Ghent University, Technologiepark 913, 9052 Zwijnaarde, Belgium", notes = "http://www.cs.ucy.ac.cy/aiai2010/", } @InProceedings{Bonyadi:2007:ICEE, author = "M. R. Bonyadi and S. M. R. Azghadi and N. M. Rad and K. Navi and E. Afjei", title = "Logic Optimization for Majority Gate-Based Nanoelectronic Circuits Based on Genetic Algorithm", booktitle = "International Conference on Electrical Engineering, 2007. ICEE '07", year = "2007", address = "Lahore", month = "11-12 " # apr, keywords = "genetic algorithms, genetic programming, QCA", ISBN = "1-4244-0893-8", DOI = "doi:10.1109/ICEE.2007.4287307", size = "5 pages", abstract = "In this paper we propose a novel and efficient method for majority gate-based design. The basic Boolean primitive in quantum cellular automata (QCA) is the majority gate. Method for reducing the number of majority gates required for computing Boolean functions is developed to facilitate the conversion of sum of products (SOP) expression into QCA majority logic. This method is based on genetic algorithm and can reduce the hardware requirements for a QCA design. We will show that the proposed approach is very efficient in deriving the simplified majority expression in QCA design.", notes = "Also known as \cite{4287307}", } @InCollection{booker:2000:EC1, author = "Lashon B. Booker and David B. Fogel and Darrell Whitley and Peter J. Angeline and A. E. Eiben", title = "Recombination", booktitle = "Evolutionary Computation 1 Basic Algorithms and Operators", publisher = "Institute of Physics Publishing", year = "2000", editor = "Thomas Baeck and David B. Fogel and Zbigniew Michalewicz", chapter = "33", pages = "256--307", address = "Bristol", keywords = "genetic algorithms, genetic programming", ISBN = "0-7503-0664-5", URL = "http://www.crcpress.com/product/isbn/9780750306645", notes = "section 33.5 parse trees p286--289", size = "52 pages", } @Article{Boone:2017:CZ, author = "Randall B. Boone", title = "Evolutionary computation in zoology and ecology", journal = "Current Zoology", year = "2017", volume = "63", number = "6", pages = "675--686", month = dec, keywords = "genetic algorithms, genetic programming, agent-based modeling, case studies, evolutionary programming, evolutionary strategies", ISSN = "1674-5507", URL = "https://doi.org/10.1093/cz/zox057", DOI = "doi:10.1093/cz/zox057", size = "12 pages", abstract = "Evolutionary computational methods have adopted attributes of natural selection and evolution to solve problems in computer science, engineering, and other fields. The method is growing in use in zoology and ecology. Evolutionary principles may be merged with an agent-based modeling perspective to have individual animals or other agents compete. Four main categories are discussed: genetic algorithms, evolutionary programming, genetic programming, and evolutionary strategies. In evolutionary computation, a population is represented in a way that allows for an objective function to be assessed that is relevant to the problem of interest. The poorest performing members are removed from the population, and remaining members reproduce and may be mutated. The fitness of the members is again assessed, and the cycle continues until a stopping condition is met. Case studies include optimizing: egg shape given different clutch sizes, mate selection, migration of wildebeest, birds, and elk, vulture foraging behavior, algal bloom prediction, and species richness given energy constraints. Other case studies simulate the evolution of species and a means to project shifts in species ranges in response to a changing climate that includes competition and phenotypic plasticity. This introduction concludes by citing other uses of evolutionary computation and a review of the flexibility of the methods. For example, representing species niche spaces subject to selective pressure allows studies on cladistics, the taxon cycle, neutral versus niche paradigms, fundamental versus realized niches, community structure and order of colonization, invasiveness, and responses to a changing climate.", notes = "Natural Resource Ecology Laboratory and the Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins, CO 80523-1499, USA", } @InProceedings{booth:2004:eurogp, author = "Richard F. Booth and Alexandre V. Borovik", title = "Coevolution of Algorithms and Deterministic Solutions of Equations in Free Groups", booktitle = "Genetic Programming 7th European Conference, EuroGP 2004, Proceedings", year = "2004", editor = "Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule", volume = "3003", series = "LNCS", pages = "11--22", address = "Coimbra, Portugal", publisher_address = "Berlin", month = "5-7 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-21346-5", DOI = "doi:10.1007/978-3-540-24650-3_2", abstract = "We discuss the use of evolutionary algorithms for solving problems in combinatorial group theory, using a class of equations in free groups as a test bench. We find that, in this context, there seems to be a correlation between successful evolutionary algorithms and the existence of good deterministic algorithms. We also trace the convergence of co-evolution of the population of fitness functions to a deterministic solution.", notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004", } @InProceedings{conf/ic3k/BorcheninovO11, author = "Yaroslav V. Borcheninov and Yuri S. Okulovsky", title = "Genetic Programming with Embedded Features of Symbolic Computations", booktitle = "KDIR International Conference on Knowledge Discovery and Information Retrieval", year = "2011", editor = "Joaquim Filipe and Ana L. N. Fred", pages = "476--479", address = "Paris, France", month = "26-29 " # oct, publisher = "SciTePress", keywords = "genetic algorithms, genetic programming: poster", isbn13 = "978-989-8425-79-9", DOI = "doi:10.5220/0003682004760479", abstract = "Genetic programming is a methodology, widely used in data mining for obtaining the analytic form that describes a given experimental data set. In some cases, genetic programming is complemented by symbolic computations that simplify found expressions. We propose to unify the induction of genetic programming with the deduction of symbolic computations in one genetic algorithm. Our approach was implemented as the .NET library and successfully tested at various data mining problems: function approximation, invariants finding and classification.", notes = "Paper Nr: 126 http://www.ic3k.org/IC3K2011/ http://www.ic3k.org/IC3K2011/IC3K_paperList.htm http://www.kdir.ic3k.org/Abstracts/2011/KDIR_2011_Abstracts.htm", bibdate = "2012-05-04", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ic3k/kdir2011.html#BorcheninovO11", } @InProceedings{Borcheninov:2012:SYRCoSE, title = "Internal and online simplification in genetic programming: an experimental comparison", author = "Yaroslav Borcheninov and Yuri Okulovsky", booktitle = "Proceedings of the Spring/Summer Young Researchers' Colloquium on Software Engineering", year = "2012", editor = "Alexander S. Kamkin and Alexander K. Petrenko and Andrey N. Terekhov", volume = "6", pages = "134--138", address = "Perm, Russia", month = may, organisation = "Institute for System Programming of the Russian Academy of Sciences (ISPRAS) and Saint-Petersburg State University (SPbSU) jointly with NRU HSE", keywords = "genetic algorithms, genetic programming, symbolic computations, computer algebra systems", isbn13 = "978-5-91474-019-8", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.300.1191", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.300.1191", URL = "http://syrcose.ispras.ru/2012/files/submissions/22_syrcose2012_submission_1.pdf", URL = "http://syrcose.ispras.ru/?q=node/30", DOI = "doi:10.15514/SYRCOSE-2012-6-22", size = "5 pages", abstract = "Genetic programming is an evolutionary algorithm, which allows performing symbolic regression --- the important task of obtaining the analytical form of a model by the data, produced by the model. One of the known problems of genetic programming is expressions bloating that results in ineffectively long expressions. To prevent bloating, symbolic simplification of expression is used. We introduce a new approach to simplification in genetic programming, making it a uniform part of the evolutionary process. To do that, we develop a genetic programming on the basis of transformation rules, similarly to computer algebra systems. We compare our approach with existed solution, and prove its adequacy and effectiveness.", notes = "Ural Federal University, Yekaterinburg, Lenina str. 51. http://syrcose.ispras.ru/ https://socionet.ru/collection.xml?h=spz:cyberleninka:31892&page=4&s=sa", } @InProceedings{Borg:2007:CSAW, author = "Claudia Borg and Mike Rosner and Gordon Pace", title = "Towards Automatic Extraction of Definitions from Text", booktitle = "5th Computer Science Annual Workshop CSAW 2007", year = "2007", editor = "Claudia Borg and Sandro Spina and Charlie Abela", address = "Bighi, Malta", month = "5-6 " # nov, organisation = "CSAI Department - University of Malta", keywords = "genetic algorithms, genetic programming", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.296.4714", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.296.4714", URL = "http://staff.um.edu.mt/cbor7/publications/csaw_msc.pdf", size = "14 pages", abstract = "Definition Extraction can be useful for the creation of glossaries and in Question Answering Systems. It is a tedious task to extract such sentences manually, and thus an automatic system is desirable. In this work we will review some attempts at rule-based approaches and discuss their results. We will then propose a novel experiment involving Genetic Programming and Genetic Algorithms, aimed at assisting the discovery of grammar rules which can be used for the task of Definition Extraction.", notes = "http://www.cs.um.edu.mt/~csaw/programme.html", } @InProceedings{Borges:2010:gecco, author = "Cruz E. Borges and Cesar L. Alonso and Jose L. Montana", title = "Model selection in genetic programming", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "985--986", keywords = "genetic algorithms, genetic programming, Poster", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830483.1830662", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "In this paper we discuss the problem of model selection in Genetic Programming. We present empirical comparisons between classical statistical methods (AIC, BIC) adapted to Genetic Programming and the Structural Risk Minimisation method (SRM) based on Vapnik-Chervonenkis theory (VC), for symbolic regression problems with added noise. We also introduce a new model complexity measure for the SRM method that tries to measure the non-linearity of the model. The experimentation suggests practical advantages of using VC-based model selection with the new complexity measure, when using genetic training.", notes = "Also known as \cite{1830662} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @InProceedings{Borges:2010:ICEC, author = "Cruz Enrique Borges and Cesar L. Alonso and Jose Luis Montana and Marina {de la Cruz Echeandia} and Alfonso {Ortega de la Puente}", title = "Coevolutionary Architectures with Straight Line Programs for solving the Symbolic Regression Problem", booktitle = "Proceedings of the International Conference on Evolutionary Computation (ICEC 2010)", year = "2010", editor = "Joaquim Filipe and Janusz Kacprzyk", pages = "41--50", address = "Valencia, Spain", month = "24-26 " # oct, organisation = "INSTICC, AAAI, WfMC", publisher = "SciTePress", keywords = "genetic algorithms, genetic programming", isbn13 = "978-989-8425-31-7", URL = "http://paginaspersonales.deusto.es/cruz.borges/Papers/10ICEC.pdf", URL = "https://www.scitepress.org/PublishedPapers/2010/30751/", URL = "http://www.robinbye.com/files/publications/ICEC_2010.pdf", DOI = "doi:10.5220/0003075100410050", size = "10 pages", abstract = "To successfully apply evolutionary algorithms to the solution of increasingly complex problems we must develop effective techniques for evolving solutions in the form of interacting coadapted subcomponents. In this paper we present an architecture which involves cooperative coevolution of two subcomponents: a genetic program and an evolution strategy. As main difference with work previously done, our genetic program evolves straight line programs representing functional expressions, instead of tree structures. The evolution strategy searches for good values for the numerical terminal symbols used by those expressions. Experimentation has been performed over symbolic regression problem instances and the obtained results have been compared with those obtained by means of Genetic Programming strategies without coevolution. The results show that our coevolutionary architecture with straight line programs is capable to obtain better quality individuals than traditional genetic programming using the same amount of computational effort.", notes = " https://ecta.scitevents.org/ICEC2010/Program_Monday.htm Broken http://www.icec.ijcci.org/ICEC2010/home.asp Broken http://www.ijcci.org/Abstracts/2010/ICEC_2010_Abstracts.htm Also known as \cite{DBLP:conf/ijcci/BorgesAMEP10}", } @PhdThesis{BorgesHernandez:thesis, author = "Cruz Enrique {Borges Hernandez}", title = "Programacion Genetica, Algoritmos Evolutivos y Aprendizaje Inductivo: Hacia una solucion al problema xvii de Smale en el caso real", school = "Universidad de Cantabria Departamento de Matematicas, Estadistica y Computacion", year = "2010", address = "Santander, Spain", month = "25 " # nov, keywords = "genetic algorithms, genetic programming", URL = "http://paginaspersonales.deusto.es/cruz.borges/Papers/11Tesis.pdf", size = "215 pages", notes = "In Spanish Dirigida por: Jose Luis Montana Arnaiz and Luis Miguel Pardo Vasallo", } @Article{Borges2011706, author = "Eduardo N. Borges and Moises G. {de Carvalho} and Renata Galante and Marcos Andre Goncalves and Alberto H. F. Laender", title = "An unsupervised heuristic-based approach for bibliographic metadata deduplication", journal = "Information Processing \& Management", volume = "47", number = "5", pages = "706--718", year = "2011", note = "Managing and Mining Multilingual Documents", ISSN = "0306-4573", DOI = "doi:10.1016/j.ipm.2011.01.009", URL = "http://www.sciencedirect.com/science/article/pii/S0306457311000100", keywords = "genetic algorithms, genetic programming, Digital libraries, Metadata, Deduplication, Similarity", abstract = "Digital libraries of scientific articles contain collections of digital objects that are usually described by bibliographic meta data records. These records can be acquired from different sources and be represented using several metadata standards. These metadata standards may be heterogeneous in both, content and structure. All of this implies that many records may be duplicated in the repository, thus affecting the quality of services, such as searching and browsing. In this article we present an approach that identifies duplicated bibliographic metadata records in an efficient and effective way. We propose similarity functions especially designed for the digital library domain and experimentally evaluate them. Our results show that the proposed functions improve the quality of metadata de-duplication up to 188percent compared to four different baselines. We also show that our approach achieves statistical equivalent results when compared to a state-of-the-art method for replica identification based on genetic programming, without the burden and cost of any training process.", } @InProceedings{Boric:2007:cec, author = "Neven Boric and Pablo A. Estevez", title = "Genetic Programming-Based Clustering Using an Information Theoretic Fitness Measure", booktitle = "2007 IEEE Congress on Evolutionary Computation", year = "2007", editor = "Dipti Srinivasan and Lipo Wang", pages = "31--38", address = "Singapore", month = "25-28 " # sep, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "1-4244-1340-0", file = "1285.pdf", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2007.4424451", abstract = "A clustering method based on multitree genetic programming and an information theoretic fitness is proposed. A probabilistic interpretation is given to the output of trees that does not require a conflict resolution phase. The method can cluster data with irregular shapes, estimate the underlying models of the data for each class and use those models to classify unseen patterns. The proposed scheme is tested on several real and artificial data sets, outperforming k-means algorithm in all of them.", notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C", } @InProceedings{conf/evoW/BorlikovaPSO16, author = "Gilyana Borlikova and Michael Phillips and Louis Smith and Michael O'Neill", title = "Evolving Classification Models for Prediction of Patient Recruitment in Multicentre Clinical Trials Using Grammatical Evolution", booktitle = "19th European Conference on Applications of Evolutionary Computation, EvoApplications 2016", year = "2016", editor = "Giovanni Squillero and Paolo Burelli", volume = "9597", series = "Lecture Notes in Computer Science", pages = "46--57", address = "Porto, Portugal", month = mar # " 30 -- " # apr # " 1", organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Grammatical evolution, Clinical trials, Enrolment, Grammar-based genetic programming", bibdate = "2016-03-23", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/evoW/evoappl2016-1.html#BorlikovaPSO16", isbn13 = "978-3-319-31204-0", URL = "https://link.springer.com/chapter/10.1007/978-3-319-31204-0_4", DOI = "doi:10.1007/978-3-319-31204-0_4", abstract = "Successful and timely completion of prospective clinical trials depends on patient recruitment as patients are critical to delivery of the prospective trial data. There exists a pressing need to develop better tools/techniques to optimise patient recruitment in multi-centre clinical trials. In this study Grammatical Evolution (GE) is used to evolve classification models to predict future patient enrolment performance of investigators/site to be selected for the conduct of the trial. Prediction accuracy of the evolved models is compared with results of a range of machine learning algorithms widely used for classification. The results suggest that GE is able to successfully induce classification models and analysis of these models can help in our understanding of the factors providing advanced indication of a trial sites' future performance.", notes = "EvoApplications2016 held inconjunction with EuroGP'2016, EvoCOP2016 and EvoMUSART 2016", } @InProceedings{Borlikova:2017:GECCO, author = "Gilyana Borlikova and Michael O'Neill and Louis Smith and Michael Phillips", title = "Development of a Multi-model System to Accommodate Unknown Misclassification Costs in Prediction of Patient Recruitment in Multicentre Clinical Trials", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4939-0", address = "Berlin, Germany", pages = "263--264", size = "2 pages", URL = "http://doi.acm.org/10.1145/3067695.3076062", DOI = "doi:10.1145/3067695.3076062", acmid = "3076062", publisher = "ACM", publisher_address = "New York, NY, USA", month = "15-19 " # jul, keywords = "genetic algorithms, genetic programming, Grammatical evolution", abstract = "Clinical trials are an essential step in a new drug's approval process. Optimisation of patient recruitment is one of the major challenges facing pharma and contract research organisations (CRO) in conducting multicentre clinical trials. Improving the quality of selection of investigators/sites at the start of a trial can help to address this business problem. Grammatical Evolution (GE) was previously used to evolve classification models to predict the future patient enrolment performance of investigators/sites considered for a trial. However, the unknown target misclassification costs at the model development stage pose additional challenges. To address them we use a new composite fitness function to develop a multi-model system of decision-tree type classifiers that optimise a range of possible trade-offs between the correct classification and errors. The predictive power of the GE-evolved models is compared with a range of machine learning algorithms widely used for classification. The results of the study demonstrate that the GE-evolved multi-model system can help to circumvent uncertainty at the model development stage by providing a collection of customised models for rapid deployment in response to business needs of a clinical trial.", notes = "Also known as \cite{Borlikova:2017:DMS:3067695.3076062} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @InProceedings{10.1007/978-3-319-55702-1_50, author = "Gilyana Borlikova and Michael Phillips and Louis Smith and Miguel Nicolau and Michael O'Neill", editor = "Andreas Fink and Armin Fuegenschuh and Martin Josef Geiger", title = "Alternative Fitness Functions in the Development of Models for Prediction of Patient Recruitment in Multicentre Clinical Trials", booktitle = "Operations Research Proceedings 2016", year = "2018", publisher = "Springer International Publishing", pages = "375--381", keywords = "genetic algorithms, genetic programming, Grammatical evolution", URL = "https://link.springer.com/chapter/10.1007/978-3-319-55702-1_50", DOI = "doi:10.1007/978-3-319-55702-1_50", abstract = "For a drug to be approved for human use, its safety and efficacy need to be evidenced through clinical trials. At present, patient recruitment is a major bottleneck in conducting clinical trials. Pharma and contract research organisations (CRO) are actively looking into optimisation of different aspects of patient recruitment. One of the avenues to approach this business problem is to improve the quality of selection of investigators/sites at the start of a trial. This study builds upon previous work that used Grammatical Evolution (GE) to evolve classification models to predict the future patient enrolment performance of investigators/sites considered for a trial. Selection of investigators/sites, depending on the business context, could benefit from the use of either especially conservative or more liberal predictive models. To address this business need, decision-tree type classifiers were evolved using different fitness functions to drive GE. The functions compared were classical accuracy, balanced accuracy and F-measure with different values of parameter beta. The issue of models' generalisability was addressed by introduction of a validation procedure. The predictive power of the resultant GE-evolved models on the test set was compared with performance of a range of machine learning algorithms widely used for classification. The results of the study demonstrate that flexibility of GE induced classification models can be used to address business needs in the area of patient recruitment in clinical trials.", isbn13 = "978-3-319-55702-1", } @InCollection{Borlikova:2018:hbge, author = "Gilyana Borlikova and Louis Smith and Michael Phillips and Michael O'Neill", title = "Business Analytics and Grammatical Evolution for the Prediction of Patient Recruitment in Multicentre Clinical Trials", booktitle = "Handbook of Grammatical Evolution", publisher = "Springer", year = "2018", editor = "Conor Ryan and Michael O'Neill and J. J. Collins", chapter = "19", pages = "461--486", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", isbn13 = "978-3-319-78716-9", DOI = "doi:10.1007/978-3-319-78717-6_19", abstract = "For a drug to be approved for human use, its safety and efficacy need to be evidenced through clinical trials. Optimisation of patient recruitment is an active area of business interest for pharma and contract research organisations (CRO) conducting clinical trials. The healthcare industry and CROs are gradually starting to adapt business analytics techniques to improve processes and help boost performance. Development of methods able to predict at the outset which prospective investigators/sites will succeed in patient recruitment can provide powerful tools for this business problem. In this chapter we describe the application of Grammatical Evolution to the prediction of patient recruitment in multicentre clinical trials.", notes = "Part of \cite{Ryan:2018:hbge}", } @Article{Borrelli:2006:PhysicaA, author = "A. Borrelli and I. {De Falco} and A. {Della Cioppa} and M. Nicodemi and G. Trautteur", title = "Performance of genetic programming to extract the trend in noisy data series", journal = "Physica A: Statistical and Theoretical Physics", year = "2006", volume = "370", number = "1", pages = "104--108", month = "1 " # oct, note = "Econophysics Colloquium - Proceedings of the International Conference {"}Econophysics Colloquium{"}", keywords = "genetic algorithms, genetic programming, Multiobjective genetic programming, Stochastic time series", DOI = "doi:10.1016/j.physa.2006.04.025", abstract = "In this paper an approach based on genetic programming for forecasting stochastic time series is outlined. To obtain a suitable test-bed some well-known time series are dressed with noise. The GP approach is endowed with a multiobjective scheme relying on statistical properties of the faced series, i.e., on their momenta. Finally, the method is applied to the MIB30 Index series.", } @Article{Borrett:2023:GPEM, author = "Fraser Borrett and Mark Beckerleg", title = "A comparison of an evolvable hardware controller with an artificial neural network used for evolving the gait of a hexapod robot", journal = "Genetic Programming and Evolvable Machines", year = "2023", volume = "24", number = "1", pages = "Article number: 5", month = jun, note = "Online first", keywords = "genetic algorithms, evolvable hardware, Evolutionary robots, Artificial neural network, ANN, Hexapod robotic, Robot gait, MATLAB, EHW, FPGA, ARM", ISSN = "1389-2576", URL = "https://rdcu.be/c8Hyy", DOI = "doi:10.1007/s10710-023-09452-4", video_url = "https://link.springer.com/article/10.1007/s10710-023-09452-4#citeas", size = "30 pages", abstract = "investigates the implementation of a novel evolvable hardware controller used in evolutionary robotics. The evolvable hardware consists of a Cartesian based array of logic blocks comprised of multiplexers and logic elements. The logic blocks are configured by a bit stream which is evolved using a genetic algorithm. A comparison is performed between an evolvable hardware and an artificial neural network controller evolved using the same genetic algorithm to produce the gait of a hexapod robot. To compare the two controllers, differences in their evolutionary efficiency and robot performance are investigated. The evolutionary efficiency is measured by the required number of generations to achieve an optimal fitness. An optimal hexapod controller allows the robot to walk forward in a straight line maintaining a constant heading and body attitude. It was found that the evolutionary efficiency and performance of the evolvable hardware and artificial neural network were similar, however the EHW was the most evolutionary efficient requiring less generations on average to evolve. Both evolved controllers were evaluated in simulation, and on a physical robot using a softcore processor and custom hardware implemented on a FPGA. The implementation showed that the controllers performed equally well when deployed, allowing the hexapod to meet the optimal gait criteria. These findings have shown that the evolvable hardware controller is a valid option for robotic control of a multi-legged robot such as a hexapod as its evolutionary efficiency and deployed performance on a real robot is comparable to that of an artificial neural network. One future application of these evolvable controllers is in fault tolerance where the robot can dynamically adapt to a fault by evolving the controller to adjust to the fault conditions.", notes = " Auckland University of Technology", } @InProceedings{borrett:2024:CEC, author = "Fraser Borrett and Mark Beckerleg", title = "The Virtual Programmable Logic Device, a Novel Machine Learning Architecture", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Performance evaluation, Multiplexing, Legged locomotion, Evolutionary robotics, Programmable logic devices, Artificial neural networks, Machine learning, virtual programmable logic device, artificial neural network, evolvable hardware, evolutionary robots, hexapod robot, robot gait", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10612129", abstract = "This paper introduces a novel architecture for robotic control, called a Virtual Programmable Logic Device (VPLD) where the operation of the device can be dynamically configured using machine learning methods. The VPLD is based on the structure of a Programable Logic Device (PLD) comprising of a two-dimensional feed-forward array of function blocks, with each block containing multiplexers and function elements. However, the VPLD is implemented in software rather than hardware allowing the VPLD to be run on a CPU based system, such as PC's and ARM based embedded systems. The operation of the VPLD is determined by a configuration bitstream which configures the multiplexers (routing) and function elements of each block. A genetic algorithm is used to evolve the configuration bitstream to produce the walking gait of a hexapod robot. The controller performance and the evolutionary efficiency of the VPLD are compared with an evolved artificial neural network (ANN), and an evolvable hardware (EHW) device. It was found the VPLD and ANN had similar controller performance and evolutionary efficiency, while the EHW had a comparable controller performance however its evolutionary efficiency was poor. It is shown that the VPLD is a viable alternative to an ANN for evolutionary robotic control.", notes = "also known as \cite{10612129} WCCI 2024", } @InProceedings{borrett:2024:CEC2, author = "Fraser Borrett and Mark Beckerleg", title = "A Comparison of a Digital and Floating-Point Virtual Programmable Logic Device and an Artifical Neural Network Evolved for Robotic Navigation", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Program processors, Navigation, Programmable logic devices, Artificial neural networks, Evolutionary computation, Software, Hardware, Virtual Programmable Logic Device, Evolvable Hardware, Artificial Neural Network, Robotic Navigation, Genetic Algorithm", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10612180", abstract = "Two novel architectures, the digital and floatingpoint virtual programmable logic device (VPLD) are compared with an artificial neural network, evolved for the robotic navigational tasks of obstacle avoidance and light following for a two wheeled robot. The VPLD is based on a programmable logic device but is coded in software rather than in hardware. This allows the VPLD to be implemented on CPUs allowing it to be run on a wide range of platforms including PCs, mobile phones and ARM processors. The function of the VPLD is governed by a configuration bitstream which can be evolved by evolutionary computation. It was found that the digital and floating-point VPLDs performed well against the ANN in the navigational tasks, making the VPLD a viable alternative to an ANN for robotic navigation.", notes = "also known as \cite{10612180} WCCI 2024", } @Article{borunda:2020:Energies, author = "Monica Borunda and Katya Rodriguez-Vazquez and Raul Garduno-Ramirez and Javier {de la Cruz-Soto} and Javier Antunez-Estrada and Oscar A. Jaramillo", title = "{Long-Term} Estimation of Wind Power by Probabilistic Forecast Using Genetic Programming", journal = "Energies", year = "2020", volume = "13", number = "8", keywords = "genetic algorithms, genetic programming", ISSN = "1996-1073", URL = "https://www.mdpi.com/1996-1073/13/8/1885", DOI = "doi:10.3390/en13081885", abstract = "Given the imminent threats of climate change, it is urgent to boost the use of clean energy, being wind energy a potential candidate. Nowadays, deployment of wind turbines has become extremely important and long-term estimation of the produced power entails a challenge to achieve good prediction accuracy for site assessment, economic feasibility analysis, farm dispatch, and system operation. We present a method for long-term wind power forecasting using wind turbine properties, statistics, and genetic programming. First, due to the high degree of intermittency of wind speed, we characterise it with Weibull probability distributions and consider wind speed data of time intervals corresponding to prediction horizons of 30, 25, 20, 15 and 10 days ahead. Second, we perform the prediction of a wind speed distribution with genetic programming using the parameters of the Weibull distribution and other relevant meteorological variables. Third, the estimation of wind power is obtained by integrating the forecasted wind velocity distribution into the wind turbine power curve. To demonstrate the feasibility of the proposed method, we present a case study for a location in Mexico with low wind speeds. Estimation results are promising when compared against real data, as shown by MAE and MAPE forecasting metrics.", notes = "also known as \cite{en13081885}", } @InProceedings{boryczka:2002:gecco, author = "Mariusz Boryczka and Zbigniew J. Czech", title = "Solving Approximation Problems By Ant Colony Programming", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "133", address = "New York", publisher_address = "San Francisco, CA 94104, USA", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "genetic algorithms, genetic programming, artificial life, adaptive behavior, agents, ant colony optimization, poster paper, ant colony programming, approximation problems, automatic programming", ISBN = "1-55860-878-8", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/aaaa288.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/aaaa288.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-02.pdf", notes = "GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)", } @InProceedings{boryczka:2002:gecco:lbp, author = "Mariusz Boryczka and Zbigniew J. Czech", title = "Solving Approximation Problems by Ant Colony Programming", booktitle = "Late Breaking Papers at the Genetic and Evolutionary Computation Conference ({GECCO-2002})", editor = "Erick Cant{\'u}-Paz", year = "2002", month = jul, pages = "39--46", address = "New York, NY", publisher = "AAAI", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025", keywords = "genetic algorithms, genetic programming, automatic programming, ant colony programming, ACO, approximation problems", URL = "http://www-zo.iinf.polsl.gliwice.pl/pub/zjc/bc02.ps.Z", URL = "https://dl.acm.org/doi/pdf/10.5555/2955491.2955512", size = "8 pages", abstract = "A method of automatic programming, called genetic programming, assumes that the desired program is found by using a genetic algorithm. We propose an idea of ant colony programming in which instead of a genetic algorithm an ant colony algorithm is applied to search for the program. The test results demonstrate that the proposed idea can be used with success to solve the approximation problems.", notes = "10.5555/2955491.2955512 is one page summary Late Breaking Papers, GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms ({ICGA-2002}) and the seventh Annual Genetic Programming Conference ({GP-2002}) part of cantu-paz:2002:GECCO:lbp", } @InProceedings{DBLP:conf/vstte/BosamiyaGLPH20, author = "Jay Bosamiya and Sydney Gibson and Yao Li and Bryan Parno and Chris Hawblitzel", title = "Verified Transformations and Hoare Logic: Beautiful Proofs for Ugly Assembly Language", booktitle = "Software Verification - 12th International Conference, {VSTTE} 2020, and 13th International Workshop, {NSV} 2020", year = "2020", editor = "Maria Christakis and Nadia Polikarpova and Parasara Sridhar Duggirala and Peter Schrammel", volume = "12549", series = "Lecture Notes in Computer Science", pages = "106--123", address = "Los Angeles, CA, USA", month = jul # " 20-21", publisher = "Springer", note = "Revised Selected Papers", keywords = "genetic algorithms, genetic programming, genetic improvement, Hoare Logic", isbn13 = "978-3-030-63618-0", timestamp = "Mon, 04 Jan 2021 16:36:23 +0100", biburl = "https://dblp.org/rec/conf/vstte/BosamiyaGLPH20.bib", bibsource = "dblp computer science bibliography, https://dblp.org", DOI = "doi:10.1007/978-3-030-63618-0_7", size = "18 pages", abstract = "Hand-optimized assembly language code is often difficult to formally verify. This paper combines Hoare logic with verified code transformations to make it easier to verify such code. This approach greatly simplifies existing proofs of highly optimized OpenSSL-based AES-GCM cryptographic code. Furthermore, applying various verified transformations to the AES-GCM code enables additional platform-specific performance improvements.", notes = "p119 'we developed a genetic algorithm to search for faster instruction orderings on a given processor' cited by \cite{Chuengsatiansup:2022:GI}", } @InProceedings{bo-ka-09, author = "Alexander Boschmann and Paul Kaufmann and Marco Platzner and Michael Winkler", title = "Towards Multi-movement Hand Prostheses: Combining Adaptive Classification with High Precision Sockets", booktitle = "Technically Assisted Rehabilitation (TAR)", year = "2009", address = "Berlin, Germany", month = mar # " 18-19", organisation = "Technische Universitaet Berlin", keywords = "genetic algorithms, genetic programming, Electromyographic, EMG", URL = "http://www.ige.tu-berlin.de/fileadmin/fg176/IGE_Printreihe/TAR_2009/paper/05_boschmann.pdf", size = "4 pages", abstract = "The acceptance of hand prostheses strongly depends on their user-friendliness and functionality. Current prostheses are limited to a few movements and their operation is all but intuitive. The development of practically applicable multi-movement prostheses requires the combination of modern classification methods with novel techniques for manufacturing high precision sockets. In this paper, we introduce an approach for classifying EMG signals taken from forearm muscles using support vector machines. This classifier technique is used in an adaptive operation mode and customized to the amputee, which allows us to recognize eleven different hand movements with high accuracy. Then, we present a novel manufacturing technique for prosthesis sockets enabling a precise amputee-specific fitting and EMG sensor placement.", notes = "2nd Conference O.T.W. Orthop Aedietechnik Winkler, Minden, Germany", } @InProceedings{bose:2025:GI, author = "Damien Bose and Carol Hanna and Justyna Petke", title = "Enhancing Software Runtime with Reinforcement Learning-Driven Mutation Operator Selection in Genetic Improvement", booktitle = "14th International Workshop on Genetic Improvement @ICSE 2025", year = "2025", editor = "Aymeric Blot and Vesna Nowack and Penn {Faulkner Rainford} and Oliver Krauss", address = "Ottawa", month = "27 " # apr, note = "forthcoming", keywords = "genetic algorithms, genetic programming, Genetic Improvement, Reinforcement learning", URL = "https://rps.ucl.ac.uk/viewobject.html?cid=1&id=2360068", URL = "https://gpbib.cs.ucl.ac.uk/gi2025/bose_2025_GI.pdf", URL = "https://geneticimprovementofsoftware.com/events/icse2025#accepted-papers", video_url = "https://gpbib.cs.ucl.ac.uk/gi2025/gi_2025_slides/hanna_icse2025_b.mp4", slide_url = "https://gpbib.cs.ucl.ac.uk/gi2025/gi_2025_slides/hanna_icse2025_b.pdf", size = "8 pages", abstract = "Genetic Improvement employs heuristic search algorithms to explore the search space of program variants by modifying code using mutation operators. This research focuses on operators that delete, insert and replace source code statements. Traditionally, in GI, an operator is chosen uniformly at random at each search iteration. Reinforcement Learning to intelligently guide the selection of these operators specifically to improve program runtime. We propose to integrate RL into the operator selection process. Four Multi-Armed bandit RL algorithms (Epsilon Greedy, UCB, Probability Matching, and Policy Gradient) were integrated within a GI framework, and their efficacy and efficiency were bench marked against the traditional GI operator selection approach. These RL-guided operator selection strategies have demonstrated empirical superiority over the traditional GI methods of randomly selecting a search operator, with UCB emerging as the top-performing RL algorithm. On average, the UCB-guided Hill Climbing search algorithm produced variants that compiled and passed all tests 44% of the time, while only 22% of the variants produced by the traditional uniform random selection strategies compiled and passed all tests.", notes = "GI @ ICSE 2025, part of \cite{blot:2025:GI} UCL ID: 10204649", } @Article{Bose20091, author = "Indranil Bose and Xi Chen", title = "Quantitative models for direct marketing: A review from systems perspective", journal = "European Journal of Operational Research", year = "2009", volume = "195", number = "1", pages = "1--16", month = "16 " # may, keywords = "genetic algorithms, genetic programming, Marketing, Data mining, Customer profiling, Customer targeting, Statistical modelling, Performance evaluation", ISSN = "0377-2217", URL = "http://www.sciencedirect.com/science/article/B6VCT-4S7SV3H-3/2/39d97985eecf3aa2b863955e4227cbb0", DOI = "doi:10.1016/j.ejor.2008.04.006", abstract = "quantitative models for direct marketing models are reviewed from a systems perspective. A systems view consists of input, processing, and output and the six key activities of direct marketing that take place within these constituent parts. A discussion about inputs for direct marketing models is provided by describing the various types of data used, by determining the significance of the data, and by addressing the issue of selection of appropriate data. Two types of models, statistical and machine learning based, are popularly used for conducting direct marketing activities. The advantages and disadvantages of these two approaches are discussed along with enhancements to these models. The evaluation of output for direct marketing models is done on the basis of accuracy and profitability. Some challenges in conducting research in the area of quantitative direct marketing models are listed and some significant research questions are proposed.", notes = "Invited Review. Survey, EJOR School of Business, The University of Hong Kong, Pokfulam Road, Hong Kong, China", } @TechReport{SAND2005-0014, author = "Mark Boslough and Michael Peters and Arthurine Pierson", title = "Graduated Embodiment for Sophisticated Agent Evolution and Optimization", institution = "Sandia National Laboratories", year = "2005", number = "SAND2005-0014", address = "P.O. Box 5800, Albuquerque, NM 87185-0318, USA", month = jan, keywords = "genetic algorithms, genetic programming, Algorithms Design, Evaluation, Genetics, Hypothesis, Navigation, Optimization, Programming Robots, Simulation, Training, Intelligent agents (Computer software), Robots-Control systems, Evolutionary programming (Computer science)", URL = "https://www.sandia.gov/research/publications/details/graduated-embodiment-for-sophisticated-agent-evolution-and-optimization-2005-01-01/", URL = "http://www.cs.sandia.gov/web1433/pubsagent/Graduated_Embodiment.pdf", DOI = "doi:10.2172/921610", size = "53 pages", abstract = "We summarise the results of a project to develop evolutionary computing methods for the design of behaviours of embodied agents in the form of autonomous vehicles. We conceived and implemented a strategy called graduated embodiment. This method allows high-level behavior algorithms to be developed using genetic programming methods in a low-fidelity, disembodied modelling environment for migration to high-fidelity, complex embodied applications. This project applies our methods to the problem domain of robot navigation using adaptive waypoints, which allow navigation behaviors to be ported among autonomous mobile robots with different degrees of embodiment, using incremental adaptation and staged optimisation. Our approach to biomimetic behaviour engineering is a hybrid of human design and artificial evolution, with the application of evolutionary computing in stages to preserve building blocks and limit search space. The methods and tools developed for this project are directly applicable to other agent-based modeling needs, including climate-related conflict analysis, multiplayer training methods,and market-based hypothesis evaluation.", notes = "Unlimited Release Mark Boslough Michael Peters Evolutionary Computing & Agent Based Modeling Department Arthurine Pierson Intelligent Systems Principles Department", } @InProceedings{Boslough:2017:ieeeAero, author = "Mark Boslough", booktitle = "2017 IEEE Aerospace Conference", title = "Autonomous dynamic soaring", year = "2017", month = "4-11 " # mar, address = "Big Sky, MT, USA", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/AERO.2017.7943967", size = "20 pages", abstract = "This project makes use of biomimetic behavioural engineering in which adaptive strategies used by animals in the real world are applied to the development of autonomous robots. The key elements of the biomimetic approach are to observe and understand a survival behaviour exhibited in nature, to create a mathematical model and simulation capability for that behaviour, to modify and optimise the behaviour for a desired robotics application, and to implement it. The application described in this report is dynamic soaring, a behaviour that certain sea birds use to extract flight energy from laminar wind velocity gradients in the shallow atmospheric boundary layer directly above the ocean surface. Theoretical calculations, computational proof-of-principle demonstrations, and the first instrumented experimental flight test data for dynamic soaring are presented to address the feasibility of developing dynamic soaring flight control algorithms to sustain the flight of unmanned airborne vehicles (UAVs). Both hardware and software were developed for this application. Eight-foot custom foam glider were built and flown in a steep shear gradient. A logging device was designed and constructed with custom software to record flight data during dynamic soaring manoeuvres. A computational tool kit was developed to simulate dynamic soaring in special cases and with a full 6-degree of freedom flight dynamics model in a generalised time-dependent wind field. Several 3-dimensional visualization tools were built to replay the flight simulations. A realistic aerodynamics model of an eight-foot sailplane was developed using measured aerodynamic derivatives. Genetic programming methods were developed and linked to the simulations and visualization tools. These tools can now be generalised for other biomimetic behaviour applications. This work was carried out in 2000 and 2001, and until now its results have only been available in an internal Sandia report.", notes = "Wandering albatross Also known as \cite{7943967}", } @InProceedings{bosman:1999:LIPIDEA, author = "Peter A. N. Bosman and Dirk Thierens", title = "Linkage Information Processing In Distribution Estimation Algorithms", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "60--67", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-812.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-812.ps", size = "8 pages", abstract = "The last few years there has been an increasing amount of interest in the field of distribution estimation optimization algorithms. As more techniques are introduced, the variety in tested distribution structures increases. we analyze the implications of the form of such a structure. We show that learning the linkage relations alone and using them directly in a distribution estimation algorithm to generate new samples is not sufficient for building competent evolutionary algorithms. The information needs to be processed to identify and use the building blocks.", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @TechReport{UUCS2004047, author = "Peter A. N. Bosman and Edwin D. {de Jong}", year = "2004", title = "Grammar Transformations in an EDA for Genetic Programming", number = "UU-CS-2004-047", institution = "Department of Information and Computing Sciences, Utrecht University", pubcat = "techreport", address = "The Netherlands", keywords = "genetic algorithms, genetic programming, EDA, grammar", URL = "http://www.cs.uu.nl/research/techreps/repo/CS-2004/2004-047.pdf", URL = "http://www.cs.uu.nl/research/techreps/UU-CS-2004-047.html", abstract = "In this paper we present a new Estimation of Distribution Algorithm (EDA) for Genetic Programming (GP). We propose a probability distribution for the space of trees, based on a grammar. To introduce dependencies into the distribution, grammar transformations are performed that facilitate the description of specific subfunctions. We present some results from experiments on two benchmark problems and show some of the subfunctions that were introduced during optimization as a result of the transformations that were applied.", notes = "Royal Tree. See also \cite{bosman:2004:obu:panbos}", size = "13 pages", } @InProceedings{bosman:2004:obu:panbos, author = "Peter A. N. Bosman and Edwin D. {de Jong}", title = "Grammar Transformations in an EDA for Genetic Programming", editor = "R. Poli and S. Cagnoni and M. Keijzer and E. Costa and F. Pereira and G. Raidl and S. C. Upton and D. Goldberg and H. Lipson and E. {de Jong} and J. Koza and H. Suzuki and H. Sawai and I. Parmee and M. Pelikan and K. Sastry and D. Thierens and W. Stolzmann and P. L. Lanzi and S. W. Wilson and M. O'Neill and C. Ryan and T. Yu and J. F. Miller and I. Garibay and G. Holifield and A. S. Wu and T. Riopka and M. M. Meysenburg and A. W. Wright and N. Richter and J. H. Moore and M. D. Ritchie and L. Davis and R. Roy and M. Jakiela", booktitle = "GECCO 2004 Workshop Proceedings", year = "2004", month = "26-30 " # jun, address = "Seattle, Washington, USA", keywords = "genetic algorithms, genetic programming", abstract = "we present a new Estimation-of-Distribution Algorithm (EDA) for Genetic Programming (GP). We propose a probability distribution for the space of trees, based on a grammar. To introduce dependencies into the distribution, grammar transformations are performed that facilitate the description of specific subfunctions. We present some results from experiments on two benchmark problems and show some of the subfunctions that were introduced during optimisation as a result of the transformations that were applied.", URL = "http://gpbib.cs.ucl.ac.uk/gecco2004/WOBU001.pdf", notes = "See also \cite{UUCS2004047} GECCO-2004WKS Distributed on CD-ROM at GECCO-2004", } @InProceedings{Bosman:PPSN:2004, author = "Peter A. N. Bosman and Edwin D. {de Jong}", title = "Learning Probabilistic Tree Grammars for Genetic Programming", booktitle = "Parallel Problem Solving from Nature - PPSN VIII", year = "2004", editor = "Xin Yao and Edmund Burke and Jose A. Lozano and Jim Smith and Juan J. Merelo-Guerv\'os and John A. Bullinaria and Jonathan Rowe and Peter Ti\v{n}o Ata Kab\'an and Hans-Paul Schwefel", volume = "3242", pages = "192--201", series = "LNCS", address = "Birmingham, UK", publisher_address = "Berlin", month = "18-22 " # sep, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, EDA", ISBN = "3-540-23092-0", URL = "http://www.cs.uu.nl/~dejong/publications/edagpppsn.pdf", URL = "https://rdcu.be/dc0ju", DOI = "doi:10.1007/b100601", DOI = "doi:10.1007/978-3-540-30217-9_20", size = "10 pages", abstract = "Genetic Programming (GP) provides evolutionary methods for problems with tree representations. A recent development in Genetic Algorithms (GAs) has led to principled algorithms called Estimation-of-Distribution Algorithms (EDAs). EDAs identify and exploit structural features of a problems structure during optimization. Here, we investigate the use of a specific EDA for GP. We develop a probabilistic model that employs transformations of production rules in a context-free grammar to represent local structures. The results of performing experiments on two benchmark problems demonstrate the feasibility of the approach.", notes = "'The results indicate that our EDA for GP is feasible.'", } @PhdThesis{Van_den_Bossche:thesis, author = "Ruben {Van den Bossche}", title = "Cost-aware resource management in clusters and clouds", school = "Departement Wiskunde-Informatica, Universiteit Antwerpen", year = "2014", address = "Antwerp, Belgium", month = jun, keywords = "genetic algorithms, genetic programming", URL = "http://hdl.handle.net/10067/1174120151162165141", URL = "http://anet.uantwerpen.be/docman/irua/aae65f/8269.pdf", size = "163 pages", notes = "Supervisors prof. Dr. Jan Broeckhove and Dr. Kurt Vanmechelen COMP [c:irua:117412] Ruben Bossche", } @MastersThesis{bot:1999:masters, author = "Martijn Bot", title = "Application of Genetic Programming to the Induction of Linear Programming Trees", school = "Vrije Universiteit", year = "1999", address = "Amsterdam, The Netherlands", month = "1 " # jul, keywords = "genetic algorithms, genetic programming, data mining", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/martijn/verslag.ps.gz", URL = "http://citeseer.ist.psu.edu/243957.html", size = "48 pages", notes = "See also \cite{bot:1999:GPilct}, \cite{bot:2000:GPilct}", } @InProceedings{bot:1999:GPilct, author = "Martijn Bot and William B. Langdon", title = "Application of Genetic Programming to Induction of Linear Classification Trees", booktitle = "Proceedings of the Eleventh Belgium/Netherlands Conference on Artificial Intelligence (BNAIC'99)", year = "1999", editor = "Eric Postma and Marc Gyssens", pages = "107--114", address = "Kasteel Vaeshartelt, Maastricht, Holland", month = "3-4 " # nov, organisation = "BNVKI, Dutch and the Belgian AI Association", keywords = "genetic algorithms, genetic programming, data mining", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/martijn/BNAIC99.bot.18aug99.ps.gz", size = "8 pages", abstract = "A common problem in datamining is to find accurate classifiers for a dataset. For this purpose, genetic programming (GP) is applied to a benchmark of classification problems. In particular, using GP we are able to induce decision trees with a linear combination of variables in each function node. The effects of techniques as limited error fitness, fitness sharing Pareto scoring and domination Pareto scoring are evaluated. Results indicate that GP can be applied successfully to classification problems. Comparisons with current state-of-the-art algorithms in machine learning are presented and areas of future research are identified.", notes = "http://www.bnvki.org/ https://documentserver.uhasselt.be/handle/1942/4193 July 2024 broken http://www.cs.unimaas.nl/~bnvki/", } @InProceedings{bot:2000:GPilct, author = "Martijn C. J. Bot and William B. Langdon", title = "Application of Genetic Programming to Induction of Linear Classification Trees", booktitle = "Genetic Programming, Proceedings of EuroGP'2000", year = "2000", editor = "Riccardo Poli and Wolfgang Banzhaf and William B. Langdon and Julian F. Miller and Peter Nordin and Terence C. Fogarty", volume = "1802", series = "LNCS", pages = "247--258", address = "Edinburgh", publisher_address = "Berlin", month = "15-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-67339-3", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/martijn/bot.eurogp2000.19jan.ps.gz", URL = "http://citeseer.ist.psu.edu/318695.html", DOI = "doi:10.1007/978-3-540-46239-2_18", abstract = "A common problem in datamining is to find accurate classifiers for a dataset. For this purpose, genetic programming (GP) is applied to a set of benchmark classification problems. Using GP we are able to induce decision trees with a linear combination of variables in each function node. A new representation of decision trees using strong typing in GP is introduced. With this representation it is possible to let the GP classify into any number o f classes. Results indicate that GP can be applied successfully to classification problems. Comparisons with current state-of-the-art algorithms in machine learning are presented and areas of future research are identified.", notes = "See also \cite{bot:1999:GPilct} EuroGP'2000, part of \cite{poli:2000:GP}", } @InProceedings{Bot:2000:GECCO, author = "Martijn C. J. Bot", title = "Improving Induction of Linear Classification Trees with Genetic Programming", pages = "403--410", year = "2000", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)", editor = "Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer", address = "Las Vegas, Nevada, USA", publisher_address = "San Francisco, CA 94104, USA", month = "10-12 " # jul, keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-708-0", URL = "http://gpbib.cs.ucl.ac.uk/gecco2000/GP185.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/martijn/bot.gecco2000.19jan.ps.gz", URL = "http://citeseer.ist.psu.edu/316984.html", abstract = "Decision trees are a well known technique in machine learning for describing the underlying structure of a dataset. In [Bot and Langdon, 2000] a new representation of decision trees using strong typing in GP was introduced. In the function nodes, a linear combination of variables is made. The effects of techniques such as limited error fitness, fitness sharing Pareto scoring and domination Pareto scoring are evaluated on a set of benchmark classification problems. Comparisons with current state-of-the-art algorithms in machine learning are presented and areas of future research are identified. Results indicate that GP can be applied successfully to classification problems. Limited error fitness reduces runtime while maintaing equal accuracy. Pareto scoring works well against bloat. Fitness sharing Pareto works better than domination Pareto.", notes = "A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of \cite{whitley:2000:GECCO}", } @InProceedings{bot:2001:EuroGP, author = "Martijn C. J. Bot", title = "Feature Extraction for the k-Nearest Neighbour Classifier with Genetic Programming", booktitle = "Genetic Programming, Proceedings of EuroGP'2001", year = "2001", editor = "Julian F. Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon", volume = "2038", series = "LNCS", pages = "256--267", address = "Lake Como, Italy", publisher_address = "Berlin", month = "18-20 " # apr, organisation = "EvoNET", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, Feature Extraction, Machine Learning: Poster", ISBN = "3-540-41899-7", DOI = "doi:10.1007/3-540-45355-5_20", size = "12 pages", abstract = "In pattern recognition the curse of dimensionality can be handled either by reducing the number of features, e.g. with decision trees or by extraction of new features. We propose a genetic programming (GP) framework for automatic extraction of features with the express aim of dimension reduction and the additional aim of improving accuracy of the k-nearest neighbour (k-NN) classifier. We will show that our system is capable of reducing most datasets to one or two features while k-NN accuracy improves or stays the same. Such a small number of features has the great advantage of allowing visual inspection of the dataset in a two-dimensional plot. Since k-NN is a non-linear classification algorithm, we compare several linear fitness measures. We will show the a very simple one, the accuracy of the minimal distance to means (mdm) classifier outperforms all other fitness measures. We introduce a stopping criterion gleaned from numeric mathematics. New features are only added if the relative increase in training accuracy is more than a constant d, for the mdm classifier estimated to be 3.3%.", notes = "EuroGP'2001, part of \cite{miller:2001:gp}", } @InProceedings{bot:2001:fencgp, author = "Martijn C. J. Bot", title = "Feature Extraction for the k-Nearest Neighbour Classifier with Genetic Programming", booktitle = "Graduate Student Workshop", year = "2001", editor = "Conor Ryan", pages = "397--400", address = "San Francisco, California, USA", month = "7 " # jul, keywords = "genetic algorithms, genetic programming", notes = "GECCO-2001WKS Part of heckendorn:2001:GECCOWKS", } @InCollection{Botros:2004:EMTP, author = "Michael Botros", title = "Evolving Controllers for Miniature Robots", year = "2004", booktitle = "Evolvable Machines: Theory \& Practice", pages = "73--100", volume = "161", series = "Studies in Fuzziness and Soft Computing", chapter = "4", editor = "Nadia Nedjah and Luiza {de Macedo Mourelle}", publisher = "Springer", address = "Berlin", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-22905-1", URL = "http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-175-22-33980449-0,00.html", notes = "Springer says published in 2005 but available Nov 2004", } @InCollection{botros:2006:GSP, author = "Michael Botros", title = "Evolving Complex Robotic Behaviors Using Genetic Programming", year = "2006", booktitle = "Genetic Systems Programming: Theory and Experiences", pages = "175--194", volume = "13", series = "Studies in Computational Intelligence", editor = "Nadia Nedjah and Ajith Abraham and Luiza {de Macedo Mourelle}", publisher = "Springer", address = "Germany", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-29849-5", DOI = "doi:10.1007/3-540-32498-4_8", abstract = "In this chapter, two possible approaches for evolving complex behaviours were discussed. In the first approach, the GP is used to explore possible hierarchy in the solution through implementing ADF and maintaining a subroutine library or using neural networks as primitive functions. In the second approach, human programmer set the architecture of the robot controller and then the GP is used to evolve each module of this architecture. Two examples of architectures were discussed, the subsumption architecture and action selection architecture. Two experiments were presented to demonstrate this approach. The first used subsumption architecture to control a team of two robots with different capabilities to implement a cooperative behavior. The second experiment used action selection architecture to allow switching between the simpler behaviours that constitute the main behavior", notes = "http://www.springer.com/sgw/cda/frontpage/0,11855,5-146-22-92733168-0,00.html", } @InProceedings{Botwey:2014:EMBC, author = "Ransford Henry Botwey and Elena Daskalaki and Peter Diem and Stavroula G. Mougiakakou", booktitle = "36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2014)", title = "Multi-model data fusion to improve an early warning system for hypo-/hyperglycemic events", year = "2014", month = aug, pages = "4843--4846", address = "Chicago, IL, USA", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-7929-0", ISSN = "1557-170X", DOI = "doi:10.1109/EMBC.2014.6944708", size = "4 pages", abstract = "Correct predictions of future blood glucose levels in individuals with Type 1 Diabetes (T1D) can be used to provide early warning of upcoming hypo-/hyperglycemic events and thus to improve the patient's safety. To increase prediction accuracy and efficiency, various approaches have been proposed which combine multiple predictors to produce superior results compared to single predictors. Three methods for model fusion are presented and comparatively assessed. Data from 23 T1D subjects under sensor-augmented pump (SAP) therapy were used in two adaptive data-driven models (an autoregressive model with output correction, cARX, and a recurrent neural network, RNN). Data fusion techniques based on i) Dempster-Shafer Evidential Theory (DST), ii) Genetic Algorithms (GA), and iii) Genetic Programming (GP) were used to merge the complimentary performances of the prediction models. The fused output is used in a warning algorithm to issue alarms of upcoming hypo-/hyperglycemic events. The fusion schemes showed improved performance with lower root mean square errors, lower time lags, and higher correlation. In the warning algorithm, median daily false alarms (DFA) of 0.25percent, and 100percent correct alarms (CA) were obtained for both event types. The detection times (DT) before occurrence of events were 13.0 and 12.1 min respectively for hypo-/hyperglycemic events. Compared to the cARX and RNN models, and a linear fusion of the two, the proposed fusion schemes represents a significant improvement.", notes = "Diabetes Technology Research Group, University of Bern, Switzerland Also known as \cite{6944708}", } @InProceedings{Botzheim:2004:ishrCI, title = "Model Identification by Bacterial Optimization", author = "J. Botzheim and L. T. Koczy", booktitle = "Proceedings of the 5th International Symposium of Hungarian Researchers on Computational Intelligence", year = "2004", pages = "91--102", address = "Budapest, Hungary", month = nov, keywords = "genetic algorithms, genetic programming", URL = "http://www.bmf.hu/conferences/mtn/botzheim.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.135.7233", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", contributor = "CiteSeerX", language = "en", oai = "oai:CiteSeerXPSU:10.1.1.135.7233", abstract = "In the field of control systems it is common to use techniques based on model adaptation to carry out control for plants for which mathematical analysis may be intricate. Increasing interest in biologically inspired learning algorithms for control techniques such as artificial neural networks and fuzzy systems is in progress. In this paper a recent kind of evolutionary method called bacterial algorithm is introduced. This method can be used for fuzzy rule extraction and optimization. Bacterial Programming is also proposed in this paper. This approach is the combination of the bacterial algorithm and the genetic programming techniques and can be applied for the optimization of the structure of Bspline neural networks.", } @Article{BotzheimCabritaKoczyRuano07, author = "Janos Botzheim and Cristiano Cabrita and Laszlo T. Koczy and Antonio E. Ruano", title = "Genetic and Bacterial Programming for {B}-Spline Neural Networks Design", journal = "Journal of Advanced Computational Intelligence and Intelligent Informatics", year = "2007", volume = "11", number = "2", pages = "220--231", month = feb, keywords = "genetic algorithms, genetic programming, constructive algorithms, B-splines, bacterial programming", ISSN = "1343-0130", URL = "http://www.fujipress.jp/finder/xslt.php?mode=present&inputfile=JACII001100020012.xml", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/botzheim/BotzheimCabritaKoczyRuano07.pdf", DOI = "doi:10.20965/jaciii.2007.p0220", size = "12 pages", abstract = "The design phase of B-spline neural networks is a highly computationally complex task. Existent heuristics have been found to be highly dependent on the initial conditions employed. Increasing interest in biologically inspired learning algorithms for control techniques such as Artificial Neural Networks and Fuzzy Systems is in progress. In this paper, the Bacterial Programming approach is presented, which is based on the replication of the microbial evolution phenomenon. This technique produces an efficient topology search, obtaining additionally more consistent solutions.", } @PhdThesis{Botzheim:thesis, author = "Janos Botzheim", title = "Intelligens szamitastechnikai modellek identifiacioja evolucios es gradiens alapu tanulo algoritmusokkal", school = "Budapest University of Technology and Economics, Faculty of Electrical Engineering and Informatics", type = "{Ph.D.} thesis", year = "2007", address = "Hungary", month = "11 " # nov, keywords = "genetic algorithms, genetic programming", URL = "http://www.sze.hu/~botzheim/hid/disszertacio.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/botzheim/thesisbooklet.pdf", size = "124 pages", abstract = "The thesis discusses identification techniques of soft computing models. Its goal is to develop identification methods based on numerical data that can produce results better in terms of quality criteria (e.g. mean square error) relevant for the given applications than other techniques known from the literature. The first statement proposes the Bacterial Evolutionary Algorithm for the extraction of Mamdani-type fuzzy rules with trapezoidal membership functions. The second statement proposes the application of the Levenberg-Marquardt algorithm for local optimisation of fuzzy rules. The third statement introduces the Bacterial Memetic Algorithm, a combination of the Bacterial Evolutionary and the Levenberg-Marquardt algorithm. The fourth statement deals with Takagi-Sugeno-type fuzzy systems. The fifth statement proposes a new technique called Bacterial Programming for the design process of B-spline neural networks. Finally, the sixth statement presents the application of Bacterial Evolutionary Algorithm for the feature selection problem.", notes = "In Hungarian. 24 page english summary", } @Article{Bouaziz:2013:Neurocomputing, author = "Souhir Bouaziz and Habib Dhahri and Adel M. Alimi and Ajith Abraham", title = "A hybrid learning algorithm for evolving Flexible Beta Basis Function Neural Tree Model", journal = "Neurocomputing", volume = "117", pages = "107--117", year = "2013", keywords = "genetic algorithms, genetic programming, Flexible Beta Basis Function Neural Tree Model, Opposite-based particle swarm optimization algorithm, Time-series forecasting, Control system", ISSN = "0925-2312", DOI = "doi:10.1016/j.neucom.2013.01.024", URL = "http://www.sciencedirect.com/science/article/pii/S0925231213001975", abstract = "In this paper, a tree-based encoding method is introduced to represent the Beta basis function neural network. The proposed model called Flexible Beta Basis Function Neural Tree (FBBFNT) can be created and optimised based on the predefined Beta operator sets. A hybrid learning algorithm is used to evolving FBBFNT Model: the structure is developed using the Extended Genetic Programming (EGP) and the Beta parameters and connected weights are optimized by the Opposite-based Particle Swarm Optimisation algorithm (OPSO). The performance of the proposed method is evaluated for benchmark problems drawn from control system and time series prediction area and is compared with those of related methods.", } @InProceedings{Bouaziz:2014:CEC, title = "{PSO}-Based Update Memory for Improved Harmony Search Algorithm to the Evolution of {FBBFNT'} Parameters", author = "Souhir Bouaziz and Adel M. Alimi and Ajith Abraham", pages = "1951--1958", booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary Computation", year = "2014", month = "6-11 " # jul, editor = "Carlos A. {Coello Coello}", address = "Beijing, China", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Extended Genetic Programming, Memetic, multi-meme and hybrid algorithms", DOI = "doi:10.1109/CEC.2014.6900304", abstract = "In this paper, a PSO-based update memory for Improved Harmony Search (PSOUM-IHS) algorithm is proposed to learn the parameters of Flexible Beta Basis Function Neural Tree (FBBFNT) model. These parameters are the Beta parameters of each flexible node and the connected weights of the network. Furthermore, the FBBFNT's structure is generated and optimised by the Extended Genetic Programming (EGP) algorithm. The combination of the PSOUM-IHS and EGP in the same algorithm is so used to evolve the FBBFNT model. The performance of the proposed evolving neural network is evaluated for nonlinear systems of prediction and identification and then compared with those of related models.", notes = "harmony search is just evolutionary strategies http://www.dennisweyland.net/blog/?p=12 http://dl.acm.org/citation.cfm?id=2433395 WCCI2014", } @Article{Bouaziz:2016:ASC, author = "Souhir Bouaziz and Habib Dhahri and Adel M. Alimi and Ajith Abraham", title = "Evolving flexible beta basis function neural tree using extended genetic programmin \& Hybrid Artificial Bee Colony", journal = "Applied Soft Computing", year = "2016", volume = "47", pages = "653--668", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2016.03.006", URL = "http://www.sciencedirect.com/science/article/pii/S1568494616301156", keywords = "genetic algorithms, genetic programming, Flexible beta basis function neural tree model, Hybrid Artificial Bee Colony algorithm, Time-series forecasting", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/asc/asc47.html#BouazizDAA16", abstract = "In this paper, a new hybrid learning algorithm is introduced to evolve the flexible beta basis function neural tree (FBBFNT). The structure is developed using the Extended Genetic Programming (EGP) and the Beta parameters and connected weights are optimized by the Hybrid Artificial Bee Colony algorithm. This hybridization is essentially based on replacing the random Artificial Bee Colony (ABC) position with the guided Opposite-based Particle Swarm Optimization (OPSO) position. Such modification can minimize the delay which might be lead by the random position, in reaching the global solution. The performance of the proposed model is evaluated for benchmark problems drawn from time series prediction area and is compared with those of related methods.", notes = "also known as \cite{journals/asc/BouazizDAA16}", } @InProceedings{Boudardara:2018:ISMSIT, author = "Fateh Boudardara and Beyza Gorkemli", booktitle = "2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)", title = "Application of Artificial Bee Colony Programming to Two Trails of the Artificial Ant Problem", year = "2018", abstract = "Automatic navigation of mobile robots has an increasing importance in many application fields such as robotics, mining industry, underwater exploration, aerospace research, virtual environments and games. In this study, we use artificial bee colony programming (ABCP), which is a novel evolutionary computation based automatic programming method, to solve the artificial ant problem that is considered as one of the basic test problems in robotic path planning. In order to see the performance of this method, a series of experiments are carried out on Santa Fe and Los Altos Hills trails. The results are compared with those of genetic programming.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ISMSIT.2018.8567048", month = oct, notes = "Also known as \cite{8567048}", } @Article{Boukhelifa:2016:EC, author = "Nadia Boukhelifa and Anastasia Bezerianos and Waldo Cancino and Evelyne Lutton", title = "Evolutionary Visual Exploration: Evaluation of an {IEC} Framework for Guided Visual Search", journal = "Evolutionary Computation", year = "2017", volume = "25", number = "1", pages = "55--86", month = mar, keywords = "genetic algorithms, genetic programming, interactive evolutionary computation, visual analytics, information visualization, data mining, interactive evolutionary algorithms", publisher = "HAL CCSD; Massachusetts Institute of Technology Press (MIT Press)", ISSN = "1063-6560", annote = "Universit{\'e} Paris-Sud - Paris 11 (UP11); Interacting with Large Data (ILDA) ; Laboratoire de Recherche en Informatique (LRI) ; Universit{\'e} Paris-Sud - Paris 11 (UP11) - Centre National de la Recherche Scientifique (CNRS) - Universit{\'e} Paris-Sud - Paris 11 (UP11) - Centre National de la Recherche Scientifique (CNRS) - INRIA Saclay - Ile de France ; INRIA - INRIA; G{\'e}nie et Microbiologie des Proc{\'e}d{\'e}s Alimentaires (GMPA) ; AgroParisTech (AgroParisTech) - Institut national de la recherche agronomique (INRA)", bibsource = "OAI-PMH server at api.archives-ouvertes.fr", contributor = "Analysis and Visualization (AVIZ) and INRIA Saclay - Ile de France and INRIA - INRIA and Interacting with Large Data and G{\'e}nie et Microbiologie des Proc{\'e}d{\'e}s Alimentaires", identifier = "hal-01218959", language = "en", oai = "oai:HAL:hal-01218959v1", relation = "info:eu-repo/semantics/altIdentifier/doi/10.1162/EVCO_a_00161", URL = "https://hal.inria.fr/hal-01218959", URL = "https://hal.inria.fr/hal-01218959/document", URL = "https://hal.inria.fr/hal-01218959/file/boukhelifa_eve_preprint.pdf", DOI = "DOI:10.1162/EVCO_a_00161", size = "32 pages", abstract = "We evaluate and analyse a framework for Evolutionary Visual Exploration (EVE) that guides users in exploring large search spaces. EVE uses an interactive evolutionary algorithm to steer the exploration of multidimensional datasets towards two-dimensional projections that are interesting to the analyst. Our method smoothly combines automatically calculated metrics and user input in order to propose pertinent views to the user. In this paper, we revisit this framework and a prototype application that was developed as a demonstrator, and summarise our previous study with domain experts and its main findings. We then report on results from a new user study with a clear predefined task, that examines how users leverage the system and how the system evolves to match their needs. While previously we showed that using EVE, domain experts were able to formulate interesting hypothesis and reach new insights when exploring freely, our new findings indicate that users, guided by the interactive evolutionary algorithm, are able to converge quickly to an interesting view of their data when a clear task is specified. We provide a detailed analysis of how users interact with an evolutionary algorithm and how the system responds to their exploration strategies and evaluation patterns. Our work aims at building a bridge between the domains of visual analytics and interactive evolution. The benefits are numerous, in particular for evaluating Interactive Evolutionary Computation (IEC) techniques based on user study methodologies.", notes = "Also known as \cite{boukhelifa-EVCO2016}", } @Article{Boukhelifa:2018:GPEM, author = "Nadia Boukhelifa and Evelyne Lutton", title = "Guest editorial: Special issue on genetic programming, evolutionary computation and visualization", journal = "Genetic Programming and Evolvable Machines", year = "2018", volume = "19", number = "3", pages = "313--315", month = sep, keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://doi.org/10.1007/s10710-018-9333-4", DOI = "doi:10.1007/s10710-018-9333-4", size = "3 pages", } @InProceedings{boulas_ground_2016, author = "Konstantinos Boulas and Valilios P. Androvitsaneas and Ioannis F. Gonos and Georgios Dounias and Ioannis A. Stathopulos", title = "Ground {Resistance} {Estimation} using {Genetic} {Programming}", booktitle = "5th International Symposium and 27th National Conference on Operation Research", editor = "Athanasios Spyridakos and Lazaros Vryzidis", year = "2016", month = jun, address = "Aigaleo, Athens", pages = "66--71", keywords = "genetic algorithms, genetic programming, gene expression programming, symbolic regression, ground resistance", isbn13 = "978-618-80361-6-1", URL = "http://eeee2016.teipir.gr/ConferenceBookHELORS2016.pdf", abstract = "The objective of this paper is to use genetic programming methodologies for the modelling and estimation of ground resistance with the use of field measurements related to weather data. Grounding is important for the safe operation of any electrical installation and protects it against lightning and fault currents. The work uses both, conventional and intelligent data analysis techniques, for ground resistance modeling from field measurements. Experimental data consist of field measurements that have been performed in Greece during the previous four years. Five linear regression models have been applied to a properly selected dataset, as well as an intelligent approach based on Gene Expression Programming (GEP). Every model corresponds to a specific grounding system. A heuristic approach using GEP was performed in order to produce more robust and general models for grounding estimation. The results show that evolutionary techniques such as those based on Genetic Programming (GP) are promising for the estimation of the ground resistance.", } @InCollection{boulas_approximating_2017, author = "Konstantinos Boulas and Georgios Dounias and Chrissoleon Papadopoulos", title = "Approximating {Throughput} of {Small} {Production} {Lines} {Using} {Genetic} {Programming}", booktitle = "Operational {Research} in {Business} and {Economics}: 4th {International} {Symposium} and 26th {National} {Conference} on {Operational} {Research}, 2015", editor = "Evangelos Grigoroudis and Michael Doumpos", year = "2017", pages = "185--204", address = "Chania, Greece", month = "4-6 " # jun, publisher = "Springer", keywords = "genetic algorithms, genetic programming, production lines, symbolic regression, throughput", isbn13 = "978-3-319-33003-7", URL = "http://link.springer.com/10.1007/978-3-319-33003-7_9", DOI = "doi:10.1007/978-3-319-33003-7_9", abstract = "Genetic Programming (GP) has been used in a variety of fields to solve complicated problems. This paper shows that GP can be applied in the domain of serial production systems for acquiring useful measurements and line characteristics such as throughput. Extensive experimentation has been performed in order to set up the genetic programming implementation and to deal with problems like code bloat or over fitting. We improve previous work on estimation of throughput for three stages and present a formula for the estimation of throughput of production lines with four stations. Further work is needed, but so far, results are encouraging.", } @InProceedings{boulas_acquisition_2015, author = "Konstantinos Boulas and Georgios Dounias and Chrissoleon Papadopoulos and Athanasios Tsakonas", title = "Acquisition of {Accurate} or {Approximate} {Throughput} {Formulas} for {Serial} {Production} {Lines} through {Genetic} {Programming}", booktitle = "Proceedings of the 4th {International} {Symposium} \& 26th {National} {Conference} on {Operational} {Research}, HELORS-2015", year = "2015", volume = "1", pages = "128--133", address = "Chania, Greece", month = jun, publisher = "Hellenic Operational Research Society", keywords = "genetic algorithms, genetic programming, serial production lines, symbolic regression", isbn13 = "978-618-80361-4-7", URL = "http://mde-lab.aegean.gr/images/stories/docs/CC97.pdf", abstract = "Genetic Programming (GP) has been used in a variety of fields to solve complicated problems. This paper shows that GP can be applied in the domain of serial production systems for acquiring useful measurements and line characteristics as throughput. Extensive experimentation has been performed in order to set up the genetic programming implementation, and to deal with problems like code bloat or over fitting. Further work is needed, but so far, results are encouraging.", notes = "http://mde-lab.aegean.gr/research-material", } @InProceedings{boulas_acquisition_2018, address = "Rio Patras, Greece", title = "Acquisition of approximate throughput formulas for serial production lines with parallel machines using intelligent techniques", isbn13 = "978-1-4503-6433-1", URL = "http://dl.acm.org/citation.cfm?doid=3200947.3201028", DOI = "doi:10.1145/3200947.3201028", abstract = "Estimating the performance of a production line is a difficult problem because of the enormous number of states that exist when analysing such systems. In addition to the methods developed to address the problem, it is very useful to have a formula linking the characteristics of the line to its performance. Three cases of sort serial production lines with parallel and identical machines in each workstation are examined in this paper. By using a combinational method that applies genetic programming (GP) and an innovative nature inspired method, named sonar inspired optimization (SIO) to improve the results, three models are derived to obtain the throughput of the corresponding lines. Further work will take place because results derived in this paper are encouraging.", language = "en", booktitle = "Proceedings of the 10th {Hellenic} {Conference} on {Artificial} {Intelligence}", publisher = "ACM Press", author = "Konstantinos Boulas and Alexandros Tzanetos and Georgios Dounias", month = jul, year = "2018", note = "Article No 18", keywords = "genetic algorithms, genetic programming, parallel-machine stations, performance evaluation, serial production lines, Sonar Inspired Optimization", pages = "18:1--18:7", } @Article{Boumanchar:2018:WMR, author = "Imane Boumanchar and Younes Chhiti and Fatima Ezzahrae M'hamdi Alaoui and Abdelaziz Sahibed-Dine and Fouad Bentiss and Charafeddine Jama and Mohammed Bensitel", title = "Municipal solid waste higher heating value prediction from ultimate analysis using multiple regression and genetic programming techniques", journal = "Waste Management \& Research", year = "2018", volume = "37", number = "6", pages = "578--589", month = dec # "~19", keywords = "genetic algorithms, genetic programming, energy, higher heating value, multiple regression, municipal solid waste, prediction, chemical sciences/material chemistry, chemical sciences/polymers", bibsource = "OAI-PMH server at api.archives-ouvertes.fr", contributor = "Unite Materiaux et Transformations - UMR 8207", description = "International audience", identifier = "hal-02922402; DOI: 10.1177/0734242x18816797; WOS: 000474405200003", language = "en", oai = "oai:HAL:hal-02922402v1", relation = "info:eu-repo/semantics/altIdentifier/doi/10.1177/0734242x18816797", URL = "https://hal.univ-lille.fr/hal-02922402", DOI = "doi:10.1177/0734242x18816797", abstract = "Municipal solid waste (MSW) management presents an important challenge for all countries. In order to exploit them as a source of energy, a knowledge of their calorific value is essential. In fact, it can be experimentally measured by an oxygen bomb calorimeter. This process is, however, expensive. In this light, the purpose of this paper was to develop empirical models for the prediction of MSW higher heating value (HHV) from ultimate analysis. Two methods were used: multiple regression analysis and genetic programming formalism. Both techniques gave good results. Genetic programming, however, provides more accuracy compared to published works in terms of a great correlation coefficient (CC) and a low root mean square error (RMSE).", annote = "Universite Chouaib Doukkali (UCD); Unite Materiaux et Transformations - UMR 8207 (UMET) ; Centre National de la Recherche Scientifique (CNRS)-Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Superieure de Chimie de Lille (ENSCL)-Universite de Lille", } @Article{Boumanchar:2018:IJGE, author = "Imane Boumanchar and Younes Chhiti and Fatima Ezzahrae M'Hamdi Alaoui and Abdelaziz Sahibed-Dine and Fouad Bentiss and Charafeddine Jama and Mohammed Bensitel", title = "Multiple regression and genetic programming for coal higher heating value estimation", journal = "International Journal of Green Energy", year = "2018", volume = "15", number = "14-15", pages = "958--964", keywords = "genetic algorithms, genetic programming, coal, higher heating value, multiple regression, prediction, life sciences", URL = "https://hal.inrae.fr/hal-02620955", DOI = "doi:10.1080/15435075.2018.1529591", publisher = "Taylor \& Francis", ISSN = "1543-5075", eissn = "1543-5083", abstract = "The higher heating value (HHV) is an important characteristic for the determination of fuels quality. Nevertheless, its experimental measurement requires intricate technologies. In this work, the HHV of coal was predicted from ultimate composition using two methods: multiple regression and genetic programming. A dataset of 100 samples from literature was exploited (75percent for training and 25percent for testing). A comparative study was elaborated between the developed models and published ones in terms of correlation coefficient, root mean square error, and mean absolute percent error. The adopted models gave a good statistical performance. Abbreviations: C: Carbon; CC: Correlation coefficient; H: Hydrogen; HHV: Higher heating valueI; GT: Institute of gas technology; GP: Genetic programming; LHV: Lower heating value; MAPE: Mean absolute percent error; N: Nitrogen; O: Oxygen; RMSE: Root mean square error; S: sulfur; Wt: Weight percentage", ISSN = "1543-5075", annote = "Universite Chouaib Doukkali (UCD); Unite Materiaux et Transformations - UMR 8207 (UMET) ; Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Superieure de Chimie de Lille (ENSCL)-Universite de Lille-Centre National de la Recherche Scientifique (CNRS)", bibsource = "OAI-PMH server at api.archives-ouvertes.fr", contributor = "Unite Materiaux et Transformations - UMR 8207", identifier = "hal-02620955; DOI: 10.1080/15435075.2018.1529591; PRODINRA: 465078; WOS: 000450845200007", language = "en", oai = "oai:HAL:hal-02620955v1", relation = "info:eu-repo/semantics/altIdentifier/doi/10.1080/15435075.2018.1529591", } @Article{boumanchar:BCaB, author = "Imane Boumanchar and Kenza Charafeddine and Younes Chhiti and Fatima Ezzahrae M'hamdi Alaoui and Abdelaziz Sahibed-dine and Fouad Bentiss and Charafeddine Jama and Mohammed Bensitel", title = "Biomass higher heating value prediction from ultimate analysis using multiple regression and genetic programming", journal = "Biomass Conversion and Biorefinery", year = "2019", volume = "9", number = "3", pages = "499--509", month = sep, keywords = "genetic algorithms, genetic programming, Higher heating value, HHV prediction, Multiple variable regression", ISSN = "2190-6815", URL = "http://link.springer.com/article/10.1007/s13399-019-00386-5", DOI = "doi:10.1007/s13399-019-00386-5", size = "11 pages", abstract = "The higher heating value (HHV) is a significant parameter for the determination of fuel quality. However, its measurement is time-consuming and requires sophisticated equipment. For this reason, several researches have been interested to develop mathematical models for the prediction of HHV from fundamental composition. The purpose of this study is to develop new correlations to determine the biomass HHV from ultimate analysis. As a result, two models were elaborated. The first was developed using multiple variable regression analysis while the second has adopted genetic programming formalism. Data of 171 from various types of biomass samples were randomly used for the development (75percent) and the validation (25percent) of new equations. The accuracy of the established models was compared to previous literature works in terms of correlation coefficient (CC), average absolute error (AAE), and average bias error (ABE). The proposed models were more performing with the highest CC and the smallest errors.", } @InProceedings{Boumaza:2001:EvoWorks, author = "Amine M. Boumaza and Jean Louchet", title = "Dynamic Flies: Using Real-Time Parisian Evolution in Robotics", booktitle = "Applications of Evolutionary Computing", year = "2001", editor = "Egbert J. W. Boers and Stefano Cagnoni and Jens Gottlieb and Emma Hart and Pier Luca Lanzi and Gunther R. Raidl and Robert E. Smith and Harald Tijink", volume = "2037", series = "LNCS", pages = "288--297", address = "Lake Como, Italy", publisher_address = "Berlin", month = "18 " # apr, organisation = "EvoNET", publisher = "Springer-Verlag", note = "best paper award", keywords = "genetic algorithms, genetic programming, fly algorithm, robot", ISBN = "3-540-41920-9", DOI = "doi:10.1007/3-540-45365-2_30", abstract = "The Fly algorithm is a Parisian evolution strategy devised for parameter space exploration in computer vision applications, which has been applied to stereovision. The resulting scene model is a set of 3-D points which concentrate upon the surfaces of obstacles. In this paper, we present how the evolutionary scene analysis can be continuously updated and integrated into a specific real-time mobile robot navigation system. Simulation-based experimental results are presented.", notes = "EvoWorkshops2001", } @InProceedings{Boumaza:evowks03, author = "Amine M. Boumaza and Jean Louchet", title = "Mobile Robot Sensor Fusion Using Flies", booktitle = "Applications of Evolutionary Computing, EvoWorkshops2003: Evo{BIO}, Evo{COP}, Evo{IASP}, Evo{MUSART}, Evo{ROB}, Evo{STIM}", year = "2003", editor = "G{\"u}nther R. Raidl and Stefano Cagnoni and Juan Jes\'us Romero Cardalda and David W. Corne and Jens Gottlieb and Agn\`es Guillot and Emma Hart and Colin G. Johnson and Elena Marchiori and Jean-Arcady Meyer and Martin Middendorf", volume = "2611", series = "LNCS", pages = "357--367", address = "University of Essex, England, UK", publisher_address = "Berlin", month = "14-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, evolutionary computation, applications", isbn13 = "978-3-540-00976-4", DOI = "doi:10.1007/3-540-36605-9_33", abstract = "The Fly algorithm is a fast artificial evolution-based image processing technique. Previous work has shown how to process stereo image sequences and use the evolving population of 'flies' as a continuously updated representation of the scene for obstacle avoidance in a mobile robot. In this paper, we show that it is possible to use several sensors providing independent information sources on the surrounding scene and the robot's position, and fuse them through the introduction of corresponding additional terms into the fitness function. This sensor fusion technique keeps the main properties of the fly algorithm: asynchronous processing. no low-level image pre-processing or costly image segmentation, fast reaction to new events in the scene. Simulation test results are presented.", notes = "EvoWorkshops2003", } @Article{Boumaza:2012:GPEM, author = "Amine Boumaza", title = "Cameron Browne: Evolutionary game design, Springer briefs in computer science series", journal = "Genetic Programming and Evolvable Machines", year = "2012", volume = "13", number = "3", pages = "407--409", month = sep, note = "Book review", publisher = "Springer", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dR8gF", DOI = "doi:10.1007/s10710-012-9165-6", size = "3 pages", notes = "Review of \cite{CameronBrowne:book} Quote appears in SIGEVOlution 6(2) page 16. http://www.sigevolution.org/issues/pdf/SIGEVOlution0602.pdf", affiliation = "University of Lille Nord de France, 59000 Lille, France", } @InProceedings{bouras:2025:GI, author = "Dimitrios Stamatios Bouras and Justyna Petke and Sergey Mechtaev", title = "LLM-Assisted Crossover in Genetic Improvement of Software", booktitle = "14th International Workshop on Genetic Improvement @ICSE 2025", year = "2025", editor = "Aymeric Blot and Vesna Nowack and Penn {Faulkner Rainford} and Oliver Krauss", address = "Ottawa", month = "27 " # apr, note = "Best presentation", keywords = "genetic algorithms, genetic programming, Genetic Improvement, MAGPIE, Large Language Models, LLM, ANN", URL = "https://gpbib.cs.ucl.ac.uk/gi2025/bouras_2025_GI.pdf", URL = "https://geneticimprovementofsoftware.com/events/icse2025#accepted-papers", video_url = "https://gpbib.cs.ucl.ac.uk/gi2025/gi_2025_slides/bouras_icse2025.mp4", slide_url = "https://gpbib.cs.ucl.ac.uk/gi2025/gi_2025_slides/bouras_icse2025_slides.pdf", size = "8 pages", abstract = "We evaluated against five traditional crossover methods across seven benchmarks, measuring performance on four key metrics: average ranking, best variant execution time, efficiency in reaching performance milestones, and viable variant count. Results show that LLM-assisted crossover achieved an average ranking of 2.27 (on a scale where 1 is best and 6 is worst), making it the top-performing method across benchmarks based on the quality of the optimal variants produced. The LLM-based approach also improved the fitness (execution time) by an average of 8.5% over the best variant produced by the traditional methods. In terms of efficiency, the LLM-assisted crossover required on average 25.6% fewer variants to reach 25%, 50%, 75%, and 100% of the final performance improvement, compared to the traditional methods. Additionally, the LLM-assisted crossover produced 4.8% more viable variants across scenarios, including both source code modification and parameter tuning cases. These findings suggest that LLMs can significantly enhance genetic programming by guiding the crossover process toward more effective and viable solutions, providing motivation for further research in LLM-assisted evolutionary algorithms.", notes = "GI @ ICSE 2025, part of \cite{blot:2025:GI}", } @InProceedings{Bourmistrova:2007:cec, author = "A. Bourmistrova and S. Khantsis", title = "Control System Design Optimisation via Genetic Programming", booktitle = "2007 IEEE Congress on Evolutionary Computation", year = "2007", editor = "Dipti Srinivasan and Lipo Wang", pages = "1993--2000", address = "Singapore", month = "25-28 " # sep, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "1-4244-1340-0", file = "1691.pdf", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2007.4424718", abstract = "This paper describes a stochastic approach for comprehensive diagnostics and validation of control system architecture for Unmanned Aerial Vehicle (UAV). Mathematically based diagnostics of a 6 DoF system provides capability for a complex evaluation of system components behaviour, but are typically both memory and computationally expensive. Design and optimisation of the flight controllers is a demanding task which usually requires deep engineering knowledge of intrinsic aircraft behaviour. Evolutionary Algorithms (EAs) are known for their robustness for a wide range of optimising functions, when no a priori knowledge of the search space is available. Thus it makes evolutionary approach a promising technique to design the task controllers for complex dynamic systems such as an aircraft. In this study, EAs are used to design a controller for recovery (landing) of a small fixed-wing UAV on a frigate ship deck. The control laws are encoded in a way common for Evolutionary Programming. However, parameters (numeric coefficients in the control equations) are optimised independently using effective Evaluation Strategies, while structural changes occur at a slower rate. The fitness evaluation is made via test runs on a comprehensive 6 degree-of-freedom non-linear UAV model. The need of a well defined approach to the control system validation is dictated by the nature of UAV application, where the major source of mission success is based on autonomous control system architecture reliability. The results show that an effective controller can be designed with little knowledge of the aircraft dynamics using appropriate evolutionary techniques. An evolved controller is evaluated and a set of reliable algorithm parameters is validated.", notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C", } @InCollection{Bourmistrova:2009:AV, author = "Anna Bourmistrova and Sergey Khantsis", title = "Flight Control System Design Optimisation via Genetic Programming", booktitle = "Aerial Vehicles", publisher = "InTech", year = "2009", editor = "Thanh Mung Lam", chapter = "7", keywords = "genetic algorithms, genetic programming, mobile robotics", isbn13 = "978-953-7619-41-1", URL = "http://www.intechopen.com/download/pdf/pdfs_id/5969", bibsource = "OAI-PMH server at www.intechopen.com", language = "eng", oai = "oai:intechopen.com:5969", URL = "http://www.intechopen.com/articles/show/title/flight_control_system_design_optimisation_via_genetic_programming", DOI = "doi:10.5772/6470", abstract = "In this chapter, an application of the Evolutionary Design (ED) is demonstrated. The aim of the design was to develop a controller which provides recovery of a fixed-wing UAV onto a ship under the full range of disturbances and uncertainties that are present in the real world environment. The controller synthesis is a multistage process. However, the approach employed for synthesis of each block is very similar. Evolutionary algorithm is used as a tool to evolve and optimise the control laws. One of the greatest advantages of this methodology is that minimum or no a priori knowledge about the control methods is used, with the synthesis starting from the most basic proportional control or even from `null' control laws. During the evolution, more complex and capable laws emerge automatically. As the resulting control laws demonstrate, evolution does not tend to produce parsimonious solutions. The method demonstrating remarkable robustness in terms of convergence indicating that a near optimal solution can be found. In very limited cases, however, it may take too long time for the evolution to discover the core of a potentially optimal solution, and the process does not converge. More often than not, this hints at a poor choice of the algorithm parameters. The most important and difficult problem in Evolutionary Design is preparation of the fitness evaluation procedure with predefined special intermediate problems. Computational considerations are also of the utmost importance. Robustness of EAs comes at the price of computational cost, with many thousands of fitness evaluations required. The simulation testing covers the entire operational envelope and highlights several conditions under which recovery is risky. All environmental factors--sea wave, wind speed and turbulence--have been found to have a significant effect upon the probability of success. Combinations of several factors may result in very unfavourable conditions, even if each factor alone may not lead to a failure. For example, winds up to 12 m/s do not affect the recovery in a calm sea, and a severe ship motion corresponding to Sea State 5 also does not represent a serious threat in low winds. At the same time, strong winds in a high Sea State may be hazardous for the aircraft.", size = "34 pages", } @InCollection{Bourmistrova:2010:naEC, title = "Genetic Programming in Application to Flight Control System Design Optimisation", author = "Anna Bourmistrova and Sergey Khantsis", booktitle = "New Achievements in Evolutionary Computation", publisher = "InTech", year = "2010", editor = "Peter Korosec", chapter = "10", month = feb, keywords = "genetic algorithms, genetic programming, UAV", isbn13 = "978-953-307-053-7", language = "eng", oai = "oai:intechopen.com:8542", URL = "http://www.intechopen.com/articles/show/title/genetic-programming-in-application-to-flight-control-system-design-optimisation", URL = "http://www.intechopen.com/download/pdf/pdfs_id/8542", DOI = "DOI:10.5772/8055", notes = "the first seminal book to introduce GP as a solid and practical technique is John Koza's Genetic Programming, dated 1992. RMIT", size = "34 pages", } @InProceedings{Bousquet:2007:EA, author = "Aurelie Bousquet and Jean Louchet and Jean-Marie Rocchisani", title = "Fully Three-Dimensional Tomographic Evolutionary Reconstruction in Nuclear Medicine", booktitle = "Artificial Evolution, 2007", year = "2007", editor = "Nicolas Monmarche and El-Ghazali Talbi and Pierre Collet and Marc Schoenauer and Evelyne Lutton", volume = "4926", series = "Lecture Notes in Computer Science", pages = "231--242", address = "Tours, France", month = oct # " 29-31", publisher = "Springer", note = "Revised Selected Papers", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-79304-5", DOI = "doi:10.1007/978-3-540-79305-2_20", abstract = "3-D reconstruction in Nuclear Medicine imaging using complete Monte-Carlo simulation of trajectories usually requires high computing power. We are currently developing a Parisian Evolution Strategy in order to reduce the computing cost of reconstruction without degrading the quality of results. Our approach derives from the Fly algorithm which proved successful on real-time stereo image sequence processing. Flies are considered here as photon emitters. We developed the marginal fitness technique to calculate the fitness function, an approach usable in Parisian Evolution whenever each individual's fitness cannot be calculated independently of the rest of the population.", notes = "Published 2008?", } @InProceedings{Boutaib:2021:EuroGP, author = "Sofien Boutaib and Maha Elarbi and Slim Bechikh and Chih-Cheng Hung and Lamjed Ben Said", title = "Software Anti-patterns Detection Under Uncertainty Using A Possibilistic Evolutionary Approach", booktitle = "EuroGP 2021: Proceedings of the 24th European Conference on Genetic Programming", year = "2021", editor = "Ting Hu and Nuno Lourenco and Eric Medvet", series = "LNCS", volume = "12691", publisher = "Springer Verlag", address = "Virtual Event", pages = "181--197", month = "7-9 " # apr, organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, SBSE, code smells, code smells detection, Uncertain software class labels, PK-NN evolution, Possibility theory: Poster", isbn13 = "978-3-030-72811-3", DOI = "doi:10.1007/978-3-030-72812-0_12", abstract = "Code smells (a.k.a. anti-patterns) are manifestations of poor design solutions that could deteriorate the software maintainability and evolution. Despite the high number of existing detection methods, the issue of class label uncertainty is usually omitted. Indeed, two human experts may have different degrees of uncertainty about the smelliness of a particular software class not only for the smell detection task but also for the smell type identification one. Thus, this uncertainty should be taken into account and then processed by detection tools. Unfortunately, these latter usually reject and/or ignore uncertain data that correspond to software classes (i.e. dataset instances) with uncertain labels. This practice could considerably degrade the detection/identification process effectiveness. Motivated by this observation and the interesting performance of the Possibilistic K-NN (PK-NN) classifier in dealing with uncertain data, we propose a new possibilistic evolutionary detection method, named ADIPOK (Anti-patterns Detection and Identification using Possibilistic Optimized K-NNs), that is able to deal with label uncertainty using some concepts stemming from the Possibility theory. ADIPOK is validated using a possibilistic base of smell examples that simulates the subjectivity of software engineers' opinions' uncertainty. The statistical analysis of the obtained results on a set of comparative experiments with respect to four state-of-the-art methods show the merits of our proposed method.", notes = "software maintenance, including bugfix, but original developers are not available. Detecting code smells. Class imbalance, noisy labels. ADIPOK. Detect each smell separately. IAC. DECOR, BLOP, PK-NN. http://www.evostar.org/2021/eurogp/ Part of \cite{Hu:2021:GP} EuroGP'2021 held in conjunction with EvoCOP2021, EvoMusArt2021 and EvoApplications2021", } @InProceedings{conf/biostec/BoutorhG14, author = "Aicha Boutorh and Ahmed Guessoum", title = "Grammatical Evolution Association Rule Mining to Detect Gene-Gene Interaction", bibdate = "2014-09-23", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/biostec/bioinformatics2014.html#BoutorhG14", booktitle = "{BIOINFORMATICS} 2014 - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms, {ESEO}, Angers, Loire Valley, France, 3-6 March, 2014", publisher = "SciTePress", year = "2014", editor = "Oscar Pastor and Christine Sinoquet and Guy Plantier and Tanja Schultz and Ana L. N. Fred and Hugo Gamboa", isbn13 = "978-989-758-012-3", pages = "253--258", keywords = "genetic algorithms, genetic programming, grammatical evolution", URL = "http://dx.doi.org/10.5220/0004913702530258", } @Article{Bovermann:2014:CMJ, author = "T Bovermann and D Griffiths", journal = "Computer Music Journal", title = "Computation as Material in Live Coding", year = "2014", month = mar, volume = "38", number = "1", pages = "40--53", keywords = "genetic algorithms, genetic programming", abstract = "What does computation sound like, and how can computational processing be integrated into live-coding practice along with code? This article gives insights into three years of artistic research and performance practice with Betablocker, an imaginary central processing unit architecture, specifically designed and implemented for live-coding purposes. It covers the themes of algorithmic composition, sound generation, genetic programming, and autonomous coding in the light of self-manipulating code and artistic research practice.", DOI = "doi:10.1162/COMJ_a_00228", ISSN = "0148-9267", notes = "Also known as \cite{6818648}", } @PhdThesis{Boyd:thesis, author = "Thermodynamics of Correlations and Structure in Information Engines", title = "Alexander Blades Boyd", school = "Physics, University of California, Davis", year = "2018", address = "USA", URL = "https://www.proquest.com/pagepdf/2047668591", size = "223 pages", abstract = "Understanding structured information and computation in thermodynamics systems is crucial to progress in diverse fields, from biology at a molecular level to designed nano-scale information processors. Landauer's principle puts a bound on the energy cost of erasing a bit of information. This suggests that devices which exchange energy and information with the environment, which we call information engines, can use information as a thermodynamic fuel to extract work from a heat reservoir, or dissipate work to erase information. However, Landauer's Principle on its own neglects the detailed dynamics of physical information processing, the mechanics and structure between the start and end of a computation. Our work deepens our understanding of these nonequilibrium dynamics, leading to new principles of efficient thermodynamic control. We explore a particular type of information engine called an information ratchet, which processes a symbol string sequentially, transducing its input string to an output string. We derive a general energetic framework for these ratchets as they operate out of equilibrium, allowing us to exactly calculate work and heat production. We show that this very general form of computation must obey a Landauer-like bound, the Information Processing Second Law (IPSL), which shows that any form of temporal correlations are a potential thermodynamic fuel. We show that in order to leverage that fuel, the autonomous information ratchet must have internal states which match the predictive states of the information reservoir. This leads to a thermodynamic principle of requisite complexity, much like Ashby's law of requisite variety in cybernetics. This is a result of the modularity of information transducers. We derive the modularity dissipation, which is an energetic cost beyond Landauer's bound that predicts the structural energy costs of different implementations of the same computation. Applying the modularity dissipation to information ratchets establishes design principles for thermodynamically efficient autonomous information processors. They prescribe the ratchet's structure such that the computation saturates the bound set by the IPSL and, thus, achieves maximum thermodynamic efficiency.", notes = "not GP? ProQuest Number: 10689139 Supervisor: James P. Crutchfield", } @InCollection{bozarth:2000:PCVGP, author = "Bradley J. Bozarth", title = "Programmatic Compression of Video using Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "46--53", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:2000:gagp}", } @Article{bozek:2022:Sensors, author = "Andrzej Bozek", title = "Discovering {Stick-Slip-Resistant} Servo Control Algorithm Using Genetic Programming", journal = "Sensors", year = "2022", volume = "22", number = "1", keywords = "genetic algorithms, genetic programming", ISSN = "1424-8220", URL = "https://www.mdpi.com/1424-8220/22/1/383", DOI = "doi:10.3390/s22010383", abstract = "The stick-slip is one of negative phenomena caused by friction in servo systems. It is a consequence of complicated nonlinear friction characteristics, especially the so-called Stribeck effect. Much research has been done on control algorithms suppressing the stick-slip, but no simple solution has been found. In this work, a new approach is proposed based on genetic programming. The genetic programming is a machine learning technique constructing symbolic representation of programs or expressions by evolutionary process. In this way, the servo control algorithm optimally suppressing the stick-slip is discovered. The GP training is conducted on a simulated servo system, as the experiments would last too long in real-time. The feedback for the control algorithm is based on the sensors of position, velocity and acceleration. Variants with full and reduced sensor sets are considered. Ideal and quantized position measurements are also analysed. The results reveal that the genetic programming can successfully discover a control algorithm effectively suppressing the stick-slip. However, it is not an easy task and relatively large size of population and a big number of generations are required. Real measurement results in worse control quality. Acceleration feedback has no apparent impact on the algorithms performance, while velocity feedback is important.", notes = "also known as \cite{s22010383}", } @InProceedings{Bozogullarindan:2020:ASYU, author = "Elif Bozogullarindan and Ceylan Bozogullarindan and Celal Ozturk", title = "Transfer Learning in Artificial Bee Colony Programming", booktitle = "2020 Innovations in Intelligent Systems and Applications Conference (ASYU)", year = "2020", month = "15-17 " # oct, address = "Istanbul, Turkey", keywords = "genetic algorithms, genetic programming, artificial bee colony algorithm, learning, artificial intelligence, regression analysis, symbolic regression problems, machine learning, transfer learning, ABCP-T", isb13 = "978-1-7281-9137-9", DOI = "doi:10.1109/ASYU50717.2020.9259801", abstract = "Artificial Bee Colony Programming (ABCP) is a machine learning method based on Artificial Bee Colony (ABC) algorithm used for parametric and structured optimization problems. It is used for the solution of symbolic regression problems as Genetic Programming (GP). On the other hand, transfer learning is the approach of using the knowledge of a system trained for a particular problem in another problem having a similar distribution. There are a number of research studies in the literature reporting the successful applications of the transfer learning to machine learning and GP. the transfer learning approach is applied to ABCP for the first time and all of the new methods created this way are named as ABCP-T. As a result of the experiments conducted for the symbolic regression problems in the literature, it is observed that ABCP-T gives better results than the standard ABCP.", notes = "Also known as \cite{9259801}", } @Article{Bozorg-Haddad:2017:JEE, author = "Omid Bozorg-Haddad and Shima Soleimani and Hugo A. Loaiciga", title = "Modeling Water-Quality Parameters Using Genetic Algorithm-Least Squares Support Vector Regression and Genetic Programming", journal = "Journal of Environmental Engineering", year = "2017", volume = "143", number = "7", month = jul, keywords = "genetic algorithms, genetic programming, Genetic algorithm-least squares support vector regression (GA-LSSVR) algorithm, Water quality, Modeling, Sensitivity analysis, Principal component analysis, PCA", publisher = "American Society of Civil Engineers", URL = "https://ascelibrary.org/doi/abs/10.1061/%28ASCE%29EE.1943-7870.0001217?src=recsys", DOI = "doi:https://doi.org/10.1061/(ASCE)EE.1943-7870.0001217", size = "10 pages", abstract = "The modeling and monitoring of water-quality parameters is necessary because of the ever increasing use of water resources and contamination caused by sewage disposal. This study employs two data-driven methods for modeling water-quality parameters. The methods are the least-squares support vector regression (LSSVR) and genetic programming (GP). Model inputs to the LSSVR algorithm and GP were determined using principal component analysis (PCA). The coefficients of the LSSVR were selected by sensitivity analysis employing statistical criteria. The results of the sensitivity analysis of the LSSVR showed that its accuracy depends strongly on the values of its coefficients. The value of the Nash-Sutcliffe (NS) statistic was negative for 60percent of the combinations of coefficients applied in the sensitivity analysis. That is, using the mean of a time series would produce a more accurate estimate of water-quality parameters than the LSSVR method in 60percent of the combinations of parameters tried. The genetic algorithm (GA) was combined with LSSVR to produce the GA-LSSVR algorithm with which to achieve improved accuracy in modeling water-quality parameters. The GA-LSSVR algorithm and the GP method were employed in modeling Na+, K+, Mg2+, SO2-4, Cl-, pH, electric conductivity (EC), and total dissolved solids (TDS) in the Sefidrood River, Iran. The results indicate that the GA-LSSVR algorithm has better accuracy for modeling water-quality parameters than GP judged by the coefficient of determination (R2) and the NS criterion. The NS static established, however, that the GA-LSSVR and GP methods have the capacity to model water-quality parameters accurately.", notes = "Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, Univ. of Tehran, Karaj, 31587-77871 Tehran, Iran", } @InProceedings{Bozorgtabar:2010:IST, author = "Behzad Bozorgtabar and Farzad Noorian and Gholam Ali Rezai Rad", title = "Comparison of different PCA based Face Recognition algorithms using Genetic Programming", booktitle = "5th International Symposium on Telecommunications (IST 2010)", year = "2010", month = dec, pages = "801--805", abstract = "Face Recognition plays a vital role in automation of security systems; therefore many algorithms have been invented with varying degrees of effectiveness. After successful try out of principal component analyses (PCA) in eigenfaces method, many different PCA based algorithms such as Two Dimensional PCA (2DPCA) and Multilinear PCA (MLPCA), combined with several classifying algorithms were studied. This paper uses Genetic Programming (GP) as a clustering tool, to classify features extracted by PCA, 2DPCA and MLPCA. Results of different algorithms are compared with each other and also previous studies and it is shown that Genetic Programming can be used in combination with PCA for face recognition problems.", keywords = "genetic algorithms, genetic programming, eigenfaces method, face recognition algorithms, multilinear PCA, principal component analyses, security systems automation, two dimensional PCA, eigenvalues and eigenfunctions, face recognition, principal component analysis", DOI = "doi:10.1109/ISTEL.2010.5734132", notes = "Also known as \cite{5734132}", } @InProceedings{Bozorgtabar:2011:GCC, author = "Behzad Bozorgtabar and Farzad Noorian and Rezai Rad {Gholam Ali}", title = "A Genetic Programming approach to face recognition", booktitle = "IEEE GCC Conference and Exhibition (GCC), 2011", year = "2011", pages = "194--197", address = "Dubai, United Arab Emirates", month = feb # " 19-22", publisher = "IEEE", size = "4 pages", abstract = "Increasing demand for a fast and reliable face recognition technology has obliged researchers to try and examine different pattern recognition schemes. But until now, Genetic Programming (GP), an acclaimed pattern recognition, data mining and relation discovery methodology, has been neglected in face recognition literature. This paper tries to apply GP to face recognition. First Principal Component Analysis (PCA) is used to extract features, and then GP is used to classify image groups. To further improve the results, a leveraging method is also used. It is shown that although GP might not be efficient in its isolated form, a leveraged GP can offer results comparable to other Face recognition solutions.", keywords = "genetic algorithms, genetic programming, data mining, face recognition technology, feature extraction, image group classification, pattern recognition, principal component analysis, relation discovery methodology, data mining, face recognition, feature extraction, image classification, principal component analysis", DOI = "doi:10.1109/IEEEGCC.2011.5752477", notes = "Iran University of Science and Technology Also known as \cite{5752477}", } @Article{Bozorgtabar:2011:JSIP, author = "Behzad Bozorgtabar and Gholam Ali Rezai Rad", title = "A Genetic Programming-{PCA} Hybrid Face Recognition Algorithm", journal = "Journal of Signal and Information Processing", year = "2011", volume = "2", number = "3", pages = "170--174", publisher = "Scientific Research Publishing", keywords = "genetic algorithms, genetic programming, face recognition, principal component analysis, leveraging algorithm", ISSN = "21594465", broken = "http://www.doaj.org/doaj?func=openurl\&genre=article\&issn=21594465\&date=2011\&volume=02\&issue=03\&spage=170", DOI = "doi:10.4236/jsip.2011.23022", size = "5 pages", abstract = "Increasing demand for a fast and reliable face recognition technology has obliged researchers to try and examine different pattern recognition schemes. But until now, Genetic Programming (GP), acclaimed pattern recognition, data mining and relation discovery methodology, has been neglected in face recognition literature. This paper tries to apply GP to face recognition. First Principal Component Analysis (PCA) is used to extract features, and then GP is used to classify image groups. To further improve the results, a leveraging method is also used. It is shown that although GP might not be efficient in its isolated form, a leveraged GP can offer results comparable to other Face recognition solutions.", bibsource = "OAI-PMH server at www.doaj.org", language = "eng", oai = "oai:doaj-articles:99426617eefcb9ce06c76152e081e501", } @InProceedings{brabazon:2001:AAANZ, author = "Tony Brabazon and M. O'Neill and C. Ryan and J. J. Collins", title = "Uncovering Technical Trading Rules Using Evolutionary Automatic Programming", booktitle = "Proceedings of 2001 AAANZ Conference (Accounting Association of Australia and NZ)", year = "2001", address = "Auckland, New Zealand", month = "1-3 " # jul, keywords = "genetic algorithms, genetic programming, grammatical evolution, financial prediction", } @InProceedings{brabazon:2002:EuroGP, title = "Evolving classifiers to model the relationship between strategy and corporate performance using grammatical evolution", author = "Anthony Brabazon and Michael O'Neill and Conor Ryan and Robin Matthews", editor = "James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi", booktitle = "Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002", volume = "2278", series = "LNCS", pages = "103--112", address = "Kinsale, Ireland", publisher_address = "Berlin", month = "3-5 " # apr, publisher = "Springer-Verlag", year = "2002", keywords = "genetic algorithms, genetic programming, grammatical evolution", ISBN = "3-540-43378-3", DOI = "doi:10.1007/3-540-45984-7_10", abstract = "This study examines the potential of grammatical evolution to construct a linear classifier to predict whether a firm's corporate strategy will increase or decrease shareholder wealth. Shareholder wealth is measured using a relative fitness criterion, the change in a firm's market-value-added ranking in the Stern-Stewart Performance 1000 list, over a four year period, 1992-1996. Model inputs and structure are selected by means of grammatical evolution. The best classifier correctly categorised the direction of performance ranking change in 66.38percent of the firms in the training set and 65percent in the out-of-sample validation set providing support for a hypothesis that changes in corporate strategy are linked to changes in corporate performance.", notes = "EuroGP'2002, part of \cite{lutton:2002:GP}", } @InProceedings{brabazon:2002:gecco, author = "Anthony Brabazon and Michael O'Neill and Robin Matthews and Conor Ryan", title = "Grammatical Evolution And Corporate Failure Prediction", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "1011--1018", address = "New York", publisher_address = "San Francisco, CA 94104, USA", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, real world applications, corporate failure prediction, genotype to phenotype mapping, grammars", ISBN = "1-55860-878-8", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/RWA145.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/RWA145.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-20.pdf", size = "8 pages", abstract = "This study examines the potential of Grammatical Evolution to uncover a series of useful rules which can assist in predicting corporate failure using information drawn from financial statements. A sample of 178 publically quoted, failed and non-failed Us firms, drawn from the period 1991 to 2000 are used to train and test the model. The preliminary findings indicate that the methodology has much potential.", notes = "GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)", } @InProceedings{brabazon:2002:gecco:workshop, title = "Trading Foreign Exchange Markets Using Evolutionary Automatic Programming", author = "Tony Brabazon and Michael O'Neill", pages = "133--136", booktitle = "{GECCO 2002}: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference", editor = "Alwyn M. Barry", year = "2002", month = "8 " # jul, publisher = "AAAI", address = "New York", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025", keywords = "genetic algorithms, genetic programming, grammatical evolution", URL = "http://www.grammatical-evolution.org/gews2002/brabazon.ps", notes = "Bird-of-a-feather Workshops, GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002) part of barry:2002:GECCO:workshop", } @InProceedings{Brabazon:2003:ICAI, author = "Anthony Brabazon and Michael O'Neill", title = "A Grammar Model for Foreign-Exchange Trading", booktitle = "Proceedings of the International Conference on Artificial Intelligence", year = "2003", editor = "Hamid R. Arabnia and Rose Joshua and Youngsong Mun", volume = "II", pages = "492--498", month = "23-26 " # jun, publisher = "CSREA Press", address = "Las Vegas, USA", keywords = "genetic algorithms, genetic programming", ISBN = "1-932415-13-0", URL = "https://www.tib.eu/en/search/id/BLCP%3ACN050261220/A-Grammar-Model-for-Foreign-Exchange-Trading/", abstract = "This study examines the potential of Grammatical Evolution to uncover a series of useful technical trading rules which can be used to trade foreign exchange markets. In this study, each of the evolved programs represents a market trading system and implicitly, a predictive model. The form of these programs is not specified ex-ante but emerges by means of an evolutionary process. Daily US Dollar-DM exchange rates for the period 9/3/93 to 13/10/97 are used to train and test the model. The preliminary findings suggest that the developed rules earn positive returns in hold-out sample test periods after allowing for trading and slippage costs. This suggests potential for future research to determine whether further refinement of the methodology adopted in this study could improve the returns earned by the developed rules.", notes = "IC-AI 2003. Also known as \cite{DBLP:conf/icai/BrabazonO03}", } @InProceedings{Brabazon:2004:BYB, author = "Anthony Brabazon and Robin Matthews and Michael O'Neill", title = "Grammars, Representations, Mental Maps and Corporate Strategy", booktitle = "Business Research Yearbook: Global Business Perspectives. Proceedings of the Fifteenth Annual International Conference of the International Academy of Business Disciplines", year = "2004", editor = "C. Gardner and J. Biberman and A. Alkhafaji", volume = "11", pages = "1054--1058", address = "San Antonio, USA", publisher_address = "Saline, Michigan, USA", month = mar # " 24-27", printer = "McNaughton and Gunn", keywords = "genetic algorithms, genetic programming, grammatical evolution", notes = "broken Feb 2014 http://academic.scranton.edu/faculty/BIBERMANG1/pres.htm", } @InProceedings{brabazon:evows04, author = "Anthony Brabazon and Michael O'Neill", title = "Bond-Issuer Credit Rating with Grammatical Evolution", booktitle = "Applications of Evolutionary Computing, EvoWorkshops2004: {EvoBIO}, {EvoCOMNET}, {EvoHOT}, {EvoIASP}, {EvoMUSART}, {EvoSTOC}", year = "2004", month = "5-7 " # apr, editor = "Guenther R. Raidl and Stefano Cagnoni and Jurgen Branke and David W. Corne and Rolf Drechsler and Yaochu Jin and Colin R. Johnson and Penousal Machado and Elena Marchiori and Franz Rothlauf and George D. Smith and Giovanni Squillero", series = "LNCS", volume = "3005", address = "Coimbra, Portugal", publisher = "Springer Verlag", publisher_address = "Berlin", pages = "270--279", keywords = "genetic algorithms, genetic programming, grammatical evolution, evolutionary computation", ISBN = "3-540-21378-3", DOI = "doi:10.1007/978-3-540-24653-4_28", abstract = "This study examines the utility of Grammatical Evolution in modelling the corporate bond-issuer credit rating process, using information drawn from the financial statements of bond-issuing firms. Financial data, and the associated Standard & Poor's issuer-credit ratings of 791 US firms, drawn from the year 1999/2000 are used to train and test the model. The best developed model was found to be able to discriminate in-sample (out-of-sample) between investment-grade and junk bond ratings with an average accuracy of 87.59 (84.92)percent across a five-fold cross validation. The results suggest that the two classifications of credit rating can be predicted with notable accuracy from a relatively limited subset of firm-specific financial data, using Grammatical Evolution.", notes = "EvoWorkshops2004", } @Article{brabazon:2005:GMTSP, author = "Anthony Brabazon and Katrina Meagher and Edward Carty and Michael O'Neill and Peter Keenan", title = "Grammar-mediated time-series prediction", journal = "Journal of Intelligent Systems", year = "2004", volume = "14", number = "2--3", pages = "123--143", month = aug, keywords = "genetic algorithms, genetic programming, grammatical evolution, time-series, high-frequency finance, intra-day stock trading", ISSN = "2191-026X", ISSN = "0334-1860", DOI = "doi:10.1515/JISYS.2005.14.2-3.123", size = "20 pages", abstract = "Grammatical Evolution is a data-driven, model-induction tool, inspired by the biological gene-to-protein mapping process. This study examines the potential of Grammatical Evolution to uncover useful technical trading rulesets for intra-day equity trading. The form of these rule-sets is not specified ex-ante but emerges by means of an evolutionary process. High-frequency price data drawn from United States stock markets is used to train and test the model. The findings suggest that the developed rules earn positive returns in holdout test periods, and that the sizes of these returns are critically impacted by the choice of position exit-strategy.", } @Article{BrabazonONeill:2004:IJAMCSDCSuGE, author = "Anthony Brabazon and Michael O'Neill", title = "Diagnosing Corporate Stability using Grammatical Evolution", journal = "International Journal of Applied Mathematics and Computer Science", year = "2004", volume = "14", number = "3", pages = "363--374", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, corporate failure prediction", ISSN = "1641-876X", URL = "http://eudml.org/doc/207703", URL = "http://matwbn.icm.edu.pl/ksiazki/amc/amc14/amc1436.pdf", size = "12 pages", abstract = "Grammatical Evolution (GE) is a novel data-driven, model-induction tool, inspired by the biological gene-to-protein mapping process. This study provides an introduction to GE, and demonstrates the methodology by applying it to construct a series of models for the prediction of bankruptcy, employing information drawn from financial statements. Unlike prior studies in this domain, the raw financial information is not preprocessed into pre-determined financial ratios. Instead, the ratios to be incorporated into the classification rule are evolved from the raw financial data. This allows the creation and subsequent evolution of alternative ratio-based representations of the financial data. A sample of 178 publicly quoted, US firms, drawn from the period 1991 to 2000 are used to train and test the model. The best evolved model correctly classified 86 (77)percent of the firms in the in-sample training set (out-of-sample validation set), one year prior to failure.", notes = "Also known as \cite{Brabazon2004}", } @Article{BrabazonONeill:2004:CMSETTRfSFEMuGE, author = "Anthony Brabazon and Michael O'Neill", title = "Evolving Technical Trading Rules for Spot Foreign-Exchange Markets Using Grammatical Evolution", journal = "Computational Management Science", year = "2004", volume = "1", number = "3-4", pages = "311--327", month = oct, keywords = "genetic algorithms, genetic programming, Grammatical evolution, Foreign exchange prediction, Technical trading rules", publisher = "Springer-Verlag", ISSN = "1619-697X", URL = "https://rdcu.be/dO4Fe", DOI = "doi:10.1007/s10287-004-0018-5", size = "17 pages", abstract = "Grammatical Evolution (GE) is a novel, data-driven, model-induction tool, inspired by the biological gene-to-protein mapping process. This study provides an introduction to GE, and applies the methodology in an attempt to uncover useful technical trading rules which can be used to trade foreign exchange markets. In this study, each of the evolved rules (programs) represents a market trading system. The form of these programs is not specified ex-ante, but emerges by means of an evolutionary process. Daily US-DM, US-Stg and US-Yen exchange rates for the period 1992 to 1997 are used to train and test the model. The findings suggest that the developed rules earn positive returns in hold-out sample test periods, after allowing for trading and slippage costs. This suggests potential for future research to determine whether further refinement of the methodology adopted in this study could improve the returns earned by the developed rules. It is also noted that this novel methodology has general utility for rule-induction, and data mining applications.", } @Article{Brabazon:2005:JIS, author = "A. Brabazon and K. Meagher and E. Carty and M. O'Neill and P. Keenan", title = "Grammar-Mediated Time-Series Prediction", journal = "Journal of Intelligent Systems", year = "2005", volume = "14", number = "2-3", pages = "123--143", note = "Special Issue", notes = "duplicate of \cite{brabazon:2005:GMTSP}", } @InProceedings{brabazon:2005:CRWpiGE, author = "Anthony Brabazon and Michael O'Neill", title = "Credit Rating with pi Grammatical Evolution", booktitle = "Proceedings of Computer Methods and Systems Conference", year = "2005", editor = "R. Tadeusiewicz and A. Ligeza and M. Szymkat", volume = "1", pages = "253--260", address = "Krakow, Poland", publisher_address = "Krakow", month = "14-16 " # nov, publisher = "Oprogramowanie Naukowo-Techniczne Tadeusiewicz", keywords = "genetic algorithms, genetic programming, grammatical evolution", ISBN = "83-916420-3-8", abstract = "This study examines the utility of pi Grammatical Evolution in modelling the corporate bond-issuer credit rating process, using information drawn from the financial statements of bond-issuing firms. Financial data, and the associated Standard and Poor's issuer-credit ratings of 791 US firms, drawn from the year 1999/2000 are used to train and test the model. The best developed model was found to be able to discriminate in-sample (out-of-sample) between investment grade and junk bond ratings with an average accuracy of 86 (87)percent across a five-fold cross validation.", } @Book{Brabazon:2006:BIAS, author = "Anthony Brabazon and Michael O'Neill", title = "Biologically Inspired Algorithms for Financial Modelling", publisher = "Springer", year = "2006", series = "Natural Computing Series", keywords = "genetic algorithms, genetic programming, ant colony systems, artificial immune systems, biologically inspired algorithms (BIAs), computer trading, evolutionary methodologies, financial markets, financial trading, grammatical evolution, (GE), multilayer perceptrons, neural networks (NNs), particle swarm optimisation (PSO)", ISBN = "3-540-26252-0", DOI = "doi:10.1007/3-540-31307-9", notes = "reviewed by \cite{Kaboudan:2006:GPEM} also Brad G. Kyer, The Book Review Column 40(4), 2009, p11-17, William Gasarch, http://www.cs.umd.edu/~gasarch/bookrev/", size = "275 pages", } @Article{Brabazon:2006:I, author = "Anthony Brabazon and Michael O'Neill", title = "Credit Classification Using Grammatical Evolution", journal = "Informatica", year = "2006", volume = "30", number = "3", pages = "325--335", keywords = "genetic algorithms, genetic programming, grammatical evolution, Povzetek: Metoda gramaticne evolucije je uporabljena za klasificiranje kreditov.", ISSN = "0350-5596", URL = "http://ai.ijs.si/informatica/PDF/30-3/07_Brabazon_Credit%20Classification%20Using.pdf", size = "11 pages", abstract = "Grammatical Evolution (GE) is a novel data driven, model induction tool, inspired by the biological genetoprotein mapping process. This study provides an introduction to GE, and demonstrates the methodology by applying it to model the corporate bond-issuer credit rating process, using information drawn from the financial statements of bond-issuing firms. Financial data and the associated Standard & Poor's issuer credit ratings of 791 US firms, drawn from the year 1999/2000 are used to train and test the model. The best developed model was found to be able to discriminate in-sample (out-of-sample) between investment grade and junk bond ratings with an average accuracy of 87.59 (84.92)percent across a five-fold cross validation.", notes = "http://www.informatica.si/vol30.htm#No3", } @InCollection{Brabazon:2008:K-DC, author = "Anthony Brabazon and Michael O'Neill", title = "Bond Rating with {piGrammatical} Evolution", booktitle = "Knowledge Engineering and Intelligent Computations", publisher = "Springer", year = "2008", editor = "C. Cotta and S. Reich and R. Schaefer and A. Ligeza", volume = "102", series = "Studies in Computational Intelligence", chapter = "2", pages = "17--30", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", isbn13 = "978-3-540-77474-7", DOI = "doi:10.1007/978-3-540-77475-4_2", abstract = "Most large firms use both share and debt capital to provide long-term finance for their operations. The debt capital may be raised from a bank loan, or may be obtained by selling bonds directly to investors. As an example of the scale of US bond markets, the value of new bonds issued in 2004 totaled $5.48 trillion, and the total value of outstanding marketable bond debt at 31 December 2004 was $23.6 trillion [1]. In comparison, the total global market capitalisation of all companies quoted on the New York Stock Exchange (NYSE) at 31/12/04 was $19.8 trillion [2]. Hence, although company stocks attract most attention in the business press, bond markets are actually substantially larger. When a company issues traded debt (e.g. bonds), it must obtain a credit rating for the issue from at least one recognised rating agency (Standard and Poor's (S&P), Moody's and Fitches'). The credit rating represents an agency's opinion, at a specific date, of the credit worthiness of a borrower in general (a bond-issuer credit-rating), or in respect of a specific debt issue (a bond credit rating). These ratings impact on the borrowing cost, and the marketability of issued bonds. Although several studies have examined the potential of both statistical and machine-learning methodologies for credit rating prediction [3-6], many of these studies used relatively small sample sizes, making it difficult to generalise strongly from their findings. This study by contrast, uses a large dataset of 791 firms, and introduces pi GE to this domain.", } @Book{Brabazon:2008:edbook, editor = "Anthony Brabazon and Michael O'Neill", title = "Natural Computing in Computational Finance", publisher = "Springer", year = "2008", volume = "100", series = "Studies in Computational Intelligence", month = apr, keywords = "genetic algorithms, genetic programming, computational finance, evolution strategies, differential evolution, bacterial foraging, quantum-inspired evolutionary algorithms", isbn13 = "9783540774761", URL = "http://www.springer.com/engineering/book/978-3-540-77476-1", abstract = "Natural Computing in Computational Finance is a innovative volume containing fifteen chapters which illustrate cutting-edge applications of natural computing or agent-based modelling in modern computational finance. Following an introductory chapter the book is organised into three sections. The first section deals with optimisation applications of natural computing demonstrating the application of a broad range of algorithms including, genetic algorithms, differential evolution, evolution strategies, quantum-inspired evolutionary algorithms and bacterial foraging algorithms to multiple financial applications including portfolio optimization, fund allocation and asset pricing. The second section explores the use of natural computing methodologies such as genetic programming, neural network hybrids and fuzzy-evolutionary hybrids for model induction in order to construct market trading, credit scoring and market prediction systems. The final section illustrates a range of agent-based applications including the modeling of payment card and financial markets. Each chapter provides an introduction to the relevant natural computing methodology as well as providing a clear description of the financial application addressed. The book was written to be accessible to a wide audience and should be of interest to practitioners, academics and students, in the fields of both natural computing and finance.", size = "approx 300 pages", } @Article{Brabazon:2008:IEEECIM, author = "Anthony Brabazon and Michael O'Neill and Ian Dempsey", title = "An Introduction to Evolutionary Computation in Finance", journal = "IEEE Computational Intelligence Magazine", year = "2008", volume = "3", number = "4", pages = "42--55", month = nov, URL = "http://ieeexplore.ieee.org/xpl/tocresult.jsp?isYear=2008&isnumber=4625777&Submit32=Go+To+Issue", DOI = "doi:10.1109/MCI.2008.929841", ISSN = "1556-603X", keywords = "genetic algorithms, genetic programming, grammatical evolution, finance, evolutionary computation, financial data processing computational intelligence methodologies, evolutionary computation approach, finance", abstract = "The world of finance is an exciting and challenging environment. Recent years have seen an explosion in the application of computational intelligence methodologies in finance. In this article we provide an overview of some of these applications concentrating on those employing an evolutionary computation approach.", notes = "Also known as \cite{4625793}", } @Book{Brabazon:2009:book, editor = "Anthony Brabazon and Michael O'Neill", title = "Natural Computing in Computational Finance (Volume 2)", publisher = "Springer", year = "2009", volume = "185", series = "Studies in Computational Intelligence", month = mar, keywords = "genetic algorithms, genetic programming, computational Finance, Computational Intelligence", isbn13 = "978-3-540-95973-1", URL = "http://www.springer.com/engineering/book/978-3-540-95973-1", abstract = "About this book Recent years have seen the widespread application of Natural Computing algorithms (broadly defined in this context as computer algorithms whose design draws inspiration from phenomena in the natural world) for the purposes of financial modeling and optimisation. A related stream of work has also seen the application of learning mechanisms drawn from Natural Computing algorithms for the purposes of agent based modelling in finance and economics. In this book we have collected a series of chapters which illustrate these two faces of Natural Computing. The first part of the book illustrates how algorithms inspired by the natural world can be used as problem solvers to uncover and optimise financial models. The second part of the book examines a number agent-based simulations of financial systems. This book follows on from Natural Computing in Computational Finance (Volume 100 in Springer's Studies in Computational Intelligence series) which in turn arose from the success of EvoFIN 2007, the very first European Workshop on Evolutionary Computation in Finance & Economics held in Valencia, Spain in April 2007. Written for: Engineers, researchers, and graduate students in Computational Intelligence and Computer Finance", size = "approx 260 pages", } @Book{brabazon_oneill_maringer:2010:book, editor = "A. Brabazon and M. O'Neill and D. G. Maringer", title = "Natural Computing in Computational Finance (Volume 3)", publisher = "Springer", year = "2010", volume = "293", series = "Studies in Computational Intelligence", keywords = "genetic algorithms, genetic programming, natural computing, computational finance, computational intelligence", isbn13 = "978-3-642-13949-9", URL = "http://www.springer.com/engineering/book/978-3-642-13949-9", DOI = "doi:10.1007/978-3-642-13950-5", abstract = "This book consists of eleven chapters each of which was selected following a rigorous, peer-reviewed, selection process. The chapters illustrate the application of a range of cutting-edge natural computing and agent-based methodologies in computational finance and economics. While describing cutting edge applications, the chapters are written so that they are accessible to a wide audience. Hence, they should be of interest to academics, students and practitioners in the fields of computational finance and economics. The inspiration for this book was due in part to the success of EvoFIN 2009, the 3rd European Workshop on Evolutionary Computation in Finance and Economics. This book follows on from Natural Computing in Computational Finance Volumes I \cite{Brabazon:2008:edbook} and II \cite{Brabazon:2009:book}", size = "241 pages", } @Unpublished{abrabazon_moneill:ppsn2010, author = "A. Brabazon and M. O'Neill", howpublished = "PPSN 2010 11th International Conference on Parallel Problem Solving From Nature", address = "Krakow, Poland", month = "11-15 " # sep, note = "Tutorial", title = "Natural Computing and Finance", URL = "http://ncra.ucd.ie/papers/PPSN_tutorial_2010_published.pdf", year = "2010", keywords = "genetic algorithms, genetic programming, grammatical evolution, finance", size = "69 slides", } @InCollection{BrabazonDDOE:2012:HNCNCiFAR, author = "Anthony Brabazon and Jing Dang and Ian Dempsey and Michael O'Neill and David Edelman", title = "Natural Computing in Finance - A Review", booktitle = "Handbook of Natural Computing", publisher = "Springer", year = "2012", editor = "Grzegorz Rozenberg and Thomas Baeck and Joost N. Kok", volume = "2", chapter = "51", pages = "1707--1735", month = "19 " # aug, keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-92909-3", URL = "http://www.springer.com/computer/theoretical+computer+science/book/978-3-540-92911-6", DOI = "doi:10.1007/978-3-540-92910-9_51", abstract = "The field of natural computing (NC) has advanced rapidly over the past decade. One significant offshoot of this progress has been the application of NC methods in finance. This chapter provides an introduction to a wide range of financial problems to which NC methods have been usefully applied. The chapter also identifies open issues and suggests future directions for the application of NC methods in finance.", } @Book{Brabazon:book:NCA, author = "Anthony Brabazon and Michael O'Neill and Sean McGarraghy", title = "Natural Computing Algorithms", publisher = "Springer", year = "2015", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-662-43630-1", URL = "http://www.springer.com/computer/theoretical+computer+science/book/978-3-662-43630-1", size = "401 pages", abstract = "The field of natural computing has been the focus of a substantial research effort in recent decades. One particular strand of this research concerns the development of computational algorithms using metaphorical inspiration from systems and phenomena that occur in the natural world. These naturally inspired computing algorithms have proven to be successful problem-solvers across domains as diverse as management science, bioinformatics, finance, marketing, engineering, architecture and design. This book is a comprehensive introduction to natural computing algorithms, suitable for academic and industrial researchers and for undergraduate and graduate courses on natural computing in computer science, engineering and management science.", notes = "Chapters on GP \cite{Brabazon:book:NCA.7} \cite{Brabazon:book:NCA.17} \cite{Brabazon:book:NCA.18} \cite{Brabazon:book:NCA.19} \cite{Brabazon:book:NCA.20}", } @InCollection{Brabazon:book:NCA.7, author = "Anthony Brabazon and Michael O'Neill and Sean McGarraghy", title = "Genetic Programming", booktitle = "Natural Computing Algorithms", publisher = "Springer", year = "2015", series = "Natural Computing Series", chapter = "7", pages = "95--114", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-662-43630-1", DOI = "doi:10.1007/978-3-662-43631-8_7", abstract = "Genetic programming (GP) was initially developed to allow the automatic creation of a computer program from a high-level statement of a problem's requirements, by means of an evolutionary process. In GP, a computer program to solve a defined task is evolved from an initial population of random computer programs. An iterative evolutionary process is employed by GP, where better (fitter) programs for the task at hand are allowed to reproduce using recombination processes to recombine components of existing programs. The reproduction process is supplemented by incremental trial-and-error development, and both variety-generating mechanisms act to generate variants of existing good programs. Over time, the utility of the programs in the population improves as poorer solutions to the problem are replaced by better solutions. More generally, GP has been applied to evolve a wide range of structures (and their associated parameters) including electronic circuits, mathematical models, engineering designs, etc.", notes = "Part of \cite{Brabazon:book:NCA}", } @InCollection{Brabazon:book:NCA.17, author = "Anthony Brabazon and Michael O'Neill and Sean McGarraghy", title = "An Introduction to Developmental and Grammatical Computing", booktitle = "Natural Computing Algorithms", publisher = "Springer", year = "2015", series = "Natural Computing Series", chapter = "17", pages = "335--343", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-662-43630-1", DOI = "doi:10.1007/978-3-662-43631-8_17", abstract = "To say that the knowledge uncovered by developmental biologists has been under-exploited in natural computing is perhaps an understatement. Curiously, despite the relative lack of research attention that has been paid to these important biological processes, one of the fathers of Computer Science, Alan Turing, recognised the power of developmental systems and developed reaction-diffusion models to understand the mechanisms behind morphogenesis (the development of biological form) [638]. In recent years it is heartening to see researchers beginning to close this gap and start to explore the power of developmental processes such as genetic regulatory networks for problem solving, and the use of approaches such as self-modification of phenotypes and developmental evaluation. The surge in interest in developmental computing is illustrated by the creation of a new track dedicated to Generative and Developmental Systems which began in 2007 at the ACM Genetic and Evolutionary Computation Conference [72, 321, 347, 526, 586, 623] and which has run every year since. A special issue of the journal IEEE Transactions on Evolutionary Computation was also dedicated to this topic in 2011 [639]. In this chapter the concept of developmental computing is introduced with particular emphasis on grammatical computing, which uses grammars as a generative process in order to create structures of interest.", notes = "Part of \cite{Brabazon:book:NCA}", } @InCollection{Brabazon:book:NCA.18, author = "Anthony Brabazon and Michael O'Neill and Sean McGarraghy", title = "Grammar-Based and Developmental Genetic Programming", booktitle = "Natural Computing Algorithms", publisher = "Springer", year = "2015", series = "Natural Computing Series", chapter = "18", pages = "345--356", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-662-43630-1", DOI = "doi:10.1007/978-3-662-43631-8_18", abstract = "The use of grammars in genetic programming (GP) has a long tradition, and there are many examples of different approaches in the literature representing linear, tree-based and more generally graph-based forms. McKay et al. \cite{McKay:2010:GPEM} presented a survey of grammar-based GP in the 10th Anniversary issue of the journal Genetic Programming and Evolvable Machines. In this and subsequent chapters, we highlight some of the more influential forms of grammar-based and developmental GP.", notes = "Part of \cite{Brabazon:book:NCA}", } @InCollection{Brabazon:book:NCA.19, author = "Anthony Brabazon and Michael O'Neill and Sean McGarraghy", title = "Grammatical Evolution", booktitle = "Natural Computing Algorithms", publisher = "Springer", year = "2015", series = "Natural Computing Series", chapter = "19", pages = "357--373", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", isbn13 = "978-3-662-43630-1", ISSN = "1619-7127", DOI = "doi:10.1007/978-3-662-43631-8_19", abstract = "Grammatical Evolution (GE), a form of grammar-based genetic programming (Chap. 18 \cite{Brabazon:book:NCA.18} ), is an algorithm that can evolve computer programs, rulesets or, more generally, sentences in any language [150, 460, 470, 472, 547]. Rulesets could be as diverse as a regression model, a set of design instructions, or a trading system for a financial market. Rather than representing the programs as syntax trees, as in GP (Chap. 7 \cite{Brabazon:book:NCA.7} ) [340, 514], a linear genome representation is used in conjunction with a grammar.", notes = "Part of \cite{Brabazon:book:NCA}", } @InCollection{Brabazon:book:NCA.20, author = "Anthony Brabazon and Michael O'Neill and Sean McGarraghy", title = "Tree-Adjoining Grammars and Genetic Programming", booktitle = "Natural Computing Algorithms", publisher = "Springer", year = "2015", series = "Natural Computing Series", chapter = "20", pages = "375--381", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-662-43630-1", DOI = "doi:10.1007/978-3-662-43631-8_20", notes = "Part of \cite{Brabazon:book:NCA}", } @InCollection{Brabazon:2018:hbge, author = "Anthony Brabazon", title = "Grammatical Evolution in Finance and Economics: A Survey", booktitle = "Handbook of Grammatical Evolution", publisher = "Springer", year = "2018", editor = "Conor Ryan and Michael O'Neill and J. J. Collins", chapter = "11", pages = "263--288", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", isbn13 = "978-3-319-78716-9", DOI = "doi:10.1007/978-3-319-78717-6_11", abstract = "Finance was one of the earliest application domains for Grammatical Evolution (GE). Since the first such study in 2001, well in excess of 100 studies have been published employing GE for a diverse range of purposes encompassing financial trading, credit-risk modelling, supply chain management, detection of tax non-compliance, and corporate strategy modelling. This chapter surveys a sample of this work and in doing so, suggests some future directions for the application of GE in finance and economics.", notes = "Part of \cite{Ryan:2018:hbge}", } @Article{Brabazon:GPEM20, author = "Anthony Brabazon and Michael Kampouridis and Michael O'Neill", title = "Applications of genetic programming to finance and economics: past, present, future", journal = "Genetic Programming and Evolvable Machines", year = "2020", volume = "21", number = "1-2", pages = "33--53", month = jun, note = "Twentieth Anniversary Issue", keywords = "genetic algorithms, genetic programming, Finance, Economics, Quantitative trading", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-019-09359-z", size = "21 pages", abstract = "While the origins of genetic programming (GP) stretch back over 50 years, the field of GP was invigorated by John Koza popularisation of the methodology in the 1990s. A particular feature of the GP literature since then has been a strong interest in the application of GP to real-world problem domains. One application domain which has attracted significant attention is that of finance and economics, with several hundred papers from this subfield being listed in the GP bibliography. In this article we outline why finance and economics has been a popular application area for GP and briefly indicate the wide span of this work. However, despite this research effort there is relatively scant evidence of the usage of GP by the mainstream finance community in academia or industry. We speculate why this may be the case, describe what is needed to make this research more relevant from a finance perspective, and suggest some future directions for the application of GP in finance and economics.", } @InProceedings{BradburyJ10, author = "Jeremy S. Bradbury and Kevin Jalbert", title = "Automatic Repair of Concurrency Bugs", booktitle = "Proceedings of the 2nd International Symposium on Search Based Software Engineering (SSBSE '10)", year = "2010", editor = "Massimiliano {Di Penta} and Simon Poulding and Lionel Briand and John Clark", address = "Benevento, Italy", month = "7-9 " # sep, note = "Fast abstract", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, SBSE, concurrency, mutation :poster?", URL = "http://www.ssbse.org/2010/fastabstracts/ssbse2010_fastabstract_04.pdf", size = "2 pages", abstract = "Bugs in concurrent software are difficult to identify and fix since they may only exhibit abnormal behaviour on certain thread interleavings. We propose the use of genetic programming to incrementally create a solution that fixes a concurrency bug automatically. Bugs in a concurrent program are fixed by iteratively mutating the program and evaluating each mutation using a fitness function that compares the mutated program with the previous version. We propose three mutation operators that can fix concurrency bugs: synchronise an unprotected shared resource, expand synchronization regions to include unprotected source code, and interchange nested lock objects.", notes = "GenProg. Focus on deadlock and dead race bugs. Add synchronisation primitives around shared variables. Expand code region protected by existing synchronisation primitives (locks). Swap existing locks. Test based fitness. IBM ConTest. Hill climbing. Mutant chosen according to bug. Fast abstracts not in proceedings? broken http://www.ssbse.info/2010/program.php http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5633360", } @InCollection{braden:2002:AAPSPGA, author = "Katie Braden", title = "A simple Approach to Protein Structure Prediction using Genetic Algorithms", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2002", year = "2002", editor = "John R. Koza", pages = "36--44", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms", URL = "http://www.genetic-programming.org/sp2002/Braden.pdf", notes = "part of \cite{koza:2002:gagp}", } @InProceedings{bradley:2010:evofin, author = "Robert Gregory Bradley and Anthony Brabazon and Michael O'Neill", title = "Evolving Trading Rule-Based Policies", booktitle = "EvoFIN", year = "2010", editor = "Cecilia {Di Chio} and Anthony Brabazon and Gianni A. {Di Caro} and Marc Ebner and Muddassar Farooq and Andreas Fink and Jorn Grahl and Gary Greenfield and Penousal Machado and Michael O'Neill and Ernesto Tarantino and Neil Urquhart", volume = "6025", series = "LNCS", pages = "251--260", address = "Istanbul", month = "7-9 " # apr, organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming, grammatical evolution", isbn13 = "978-3-642-12241-5", DOI = "doi:10.1007/978-3-642-12242-2_26", abstract = "Trading-rule representation is an important factor to consider when designing a quantitative trading system. This study implements a trading strategy as a rule-based policy. The result is an intuitive human-readable format which allows for seamless integration of domain knowledge. The components of a policy are specified and represented as a set of rewrite rules in a context-free grammar. These rewrite rules define how the components can be legally assembled. Thus, strategies derived from the grammar are well-formed, domain-specific, solutions. A grammar-based Evolutionary Algorithm, Grammatical Evolution (GE), is then employed to automatically evolve intra-day trading strategies for the U.S. Stock Market. The GE methodology managed to discover profitable rules with realistic transaction costs included. The paper concludes with a number of suggestions for future work.", notes = "EvoFIN'2010 held in conjunction with EuroGP'2010 EvoCOP2010 EvoBIO2010", } @InProceedings{bradley_etal:cec2010, author = "Robert Bradley and Anthony Brabazon and Michael O'Neill", title = "Objective Function Design in a Grammatical Evolutionary Trading System", booktitle = "2010 IEEE World Congress on Computational Intelligence", pages = "3487--3494", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-6910-9", keywords = "genetic algorithms, genetic programming, grammatical evolution", DOI = "doi:10.1109/CEC.2010.5586020", abstract = "Designing a suitable objective function is an essential step in successfully applying an evolutionary algorithm to a problem. In this study we apply a grammar-based Genetic Programming algorithm called Grammatical Evolution to the problem of trading model induction. A number of experiments were performed to assess the effect of objective function design on the trading characteristics of the evolved trading strategies. Empirical results suggest that the choice of objective function has a significant impact. The paper concludes with in and out-of-sample results, and indicates a number of avenues of future work.", notes = "WCCI 2010. Also known as \cite{5586020}", } @Article{Brady:2014:acmTG, title = "{genBRDF}: discovering new analytic {BRDFs} with genetic programming", author = "Adam Brady and Jason Lawrence and Pieter Peers and Westley Weimer", journal = "ACM Transactions on Graphics", year = "2014", volume = "33", number = "4", pages = "114:1--114:11", month = jul, keywords = "genetic algorithms, genetic programming, GPU, BRDF, analytic, isotropic", publisher = "ACM", ISSN = "0730-0301", acmid = "2601193", URL = "https://web.eecs.umich.edu/~weimerw/p/brady_sig14.pdf", URL = "http://doi.acm.org/10.1145/2601097.2601193", DOI = "doi:10.1145/2601097.2601193", size = "11 pages", abstract = "We present a framework for learning new analytic BRDF models through Genetic Programming that we call genBRDF. This approach to reflectance modelling can be seen as an extension of traditional methods that rely either on a phenomenological or empirical process. Our technique augments the human effort involved in deriving mathematical expressions that accurately characterise complex high-dimensional reflectance functions through a large-scale optimisation. We present a number of analysis tools and data visualisation techniques that are crucial to sifting through the large result sets produced by genBRDF in order to identify fruitful expressions. Additionally, we highlight several new models found by genBRDF that have not previously appeared in the BRDF literature. These new BRDF models are compact and more accurate than current state-of-the-art alternatives.", notes = " p114:2 'we attempt to grow new' [mathematical expressions]. p114:5 'implemented the BRDF fitting procedure in CUDA and used a 24 node NVidia Tesla M2075 GPU cluster.' cited by \cite{Dorn:2015:Pacific-Graphics}", } @TechReport{brady:murphy, author = "Robert M. Brady and Ross J. Anderson and Robin C. Ball", title = "Murphy's law, the fitness of evolving species, and the limits of software reliability", institution = "Computer Laboratory, Cambridge", year = "1996?", email = "rja14@cl.cam.ac.uk", URL = "http://www.ftp.cl.cam.ac.uk/ftp/users/rja14/babtr.pdf", abstract = "We tackle two problems of interest to the software assurance community. Firstly, existing models of software development (such as the waterfall and spiral models) are oriented towards one-off software development projects, while the growth of mass market computing has led to a world in which most software consists of packages which follow an evolutionary development model. This leads us to ask whether anything interesting and useful may be said about evolutionary development. We answer in the affirmative. Secondly, existing reliability growth models emphasise the Poisson distribution of individual software bugs, while the empirically observed reliability growth for large systems is asymptotically slower than this. We provide a rigorous explanation of this phenomenon. Our reliability growth model is inspired by statistical thermodynamics, but also applies to biological evolution. It is in close agreement with experimental measurements of the fitness of an evolving species and the reliability of commercial software products. However, it shows that there are significant differences between the evolution of software and the evolution of species. In particular, we establish maximisation properties corresponding to Murphy?s law which work to the advantage of a biological species, but to the detriment of software reliability.", size = "11 pages", notes = "cf \cite{Bishop96} Takes huge liberties, dressing them in maths, {"}the number of defects which survive a selection process is maximised{"} {"}debugging removes the minimum possible number of bugs that must be removed in order to pass the test sequence{"}. {"}we have a dsitribution of deffects that e behaves statisically as if they were in thermal equilibrium at this{"} [1/t] {"}temperature{"}.", } @Article{DBLP:journals/npl/Braik21, author = "Malik Braik", title = "A Hybrid Multi-gene Genetic Programming with Capuchin Search Algorithm for Modeling a Nonlinear Challenge Problem: Modeling Industrial Winding Process, Case Study", journal = "Neural Process. Lett.", volume = "53", number = "4", pages = "2873--2916", year = "2021", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/s11063-021-10530-w", DOI = "doi:10.1007/s11063-021-10530-w", timestamp = "Thu, 12 Aug 2021 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/npl/Braik21.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Book{Braitenberg:1984, author = "Valentino Braitenberg", year = "1984", keywords = "NEURAL MOBILE SIMULATION EVOLUTION MOTOR-SCHEMA REACTIVE MODULAR", institution = "Europe?", title = "Vehicles", publisher = "MIT Press", annote = "Braitenberg describes a set of thought experiments in which increasingly complex vehicles are built from simple mechanical and electronic components. Each of these imaginary vehicles in some way mimics intelligent behavior, and each one is given a name that corresponds to the behavior it imitates: {"}Fear,{"} {"}Love,{"} {"}Values,{"} {"}Logic,{"} etc. Braitenberg uses these thought experiments to explore psychological ideas and the nature of intelligence. Progressing through the book, the reader sees very intricate behaviors emerge from the interaction of simple component parts. In a sense, Braitenberg {"}constructs{"} intelligent behavior---a process he calls {"}synthetic psychology.{"} - from [Hogg Martin \& Resnick 91]", address = "Cambridge MA, USA", ISBN = "0-262-52112-1", notes = "amazon says 1986", } @InProceedings{Brajer:2012:MIPRO, author = "Iva Brajer and Domagoj Jakobovic", title = "Automated design of combinatorial logic circuits", booktitle = "Proceedings of the 35th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO 2012)", year = "2012", month = "21-25 " # may, pages = "823--828", address = "Opatija, Croatia", size = "6 pages", abstract = "This paper deals with automated design of combinatorial circuits with the use of Cartesian Genetic Programming (CGP). The synthesis is based on user specifications of network functionality, while the network structure may be predefined. The results show that CGP approach is able to match the desired functionality while preserving other performance criteria, such as latency and number of gates. Additionally, the evolution process may use Verilog network descriptions as input files, which facilitates the design for larger number of inputs and test patterns.", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6240757", notes = "Also known as \cite{6240757}", } @InProceedings{Brajlih:2005:PMI, author = "Tomaz Brajlih and Igor Drstvensek and Miha Kovacic and Joze Balic", title = "Compensation of the size of the finished part for the PolyJet rapid prototyping procedure", booktitle = "Proceedings of the International Conference Polymers \& Moulds Innovations PMI 2005", year = "2005", address = "Gent, Belgium", month = apr # " 20-23", keywords = "genetic algorithms, genetic programming, hitra izdelava prototipov, PolyJet postopek, izravnalni faktor, prototipi, rapid prototyping, polyjet procedure, compensation factor", abstract = "The main accuracy problem of rapid prototyping procedures, which are using polymers as a building material is shrinking of a finished layer in the phase of polymerization. Different procedures are using different approaches to handle the problem but none of them can actually reach the accuracy that users of traditional cutting techniques are used to. To achieve better quality of the PolyJet procedure, which originally employs a method of size compensation to reach a desired accuracy, we decided to improve the procedure's performance by adjusting the compensation factor for every part separately. To this purpose some traditional methods of statistics were used, which were later combined with some newer, less traditional methods like genetic programming. The later enabled us to acquire a formula for compensation factor determination based upon the geometry of the actual part. It also showed the importance or unimportance of some influencing parameters respectively. The method resulted in a better compensation factor and better overall performance of the PolyJet procedure compared to other rapid prototyping techniques used nowadays.", notes = " broken Aug 2018 http://cobiss.izum.si/scripts/cobiss?command=DISPLAY&base=COBIB&RID=9636118", } @InProceedings{Brajlih:2006:DAAAM, author = "T. Brajlih and I. Drstvensek and B. Valentan and J. Balic", title = "Improving the Accuracy of Rapid Prototyping Procedures by Genetic Programming", booktitle = "Proceedings of the 5TH International conference of DAAAM Baltic -- Industrial Engineering", year = "2006", editor = "R. Kyeener", pages = "113--116", address = "Tallinn, Estonia", month = "20-22 " # apr, organisation = "BALTECH Consortium, Estonian Academy of Sciences, Federation of Estonian Engineering Industries, Association of Estonian Mechanical Engineers, Leonardo National Agency of Estonia, INNOMET", publisher = "DAAAM", keywords = "genetic algorithms, genetic programming", URL = "http://innomet.ttu.ee/daaam06/proceedings/Production%20Engineering/24brajilih.pdf", size = "4 pages", abstract = "To achieve better quality of the PolyJet Rapid Prototyping procedure, which originally employs a method of size compensation by scale factors to reach a desired accuracy, we decided to improve the procedure's performance by adjusting scale factors for every part separately. The main accuracy problem of rapid prototyping procedures that are using polymers as a building material is shrinking of a finished layer in the phase of polymerization. To this purpose we used genetic programming that enabled us to acquire a formula for scale factor's determination based upon the geometry of the actual part. The method resulted in optimized scale factors and better overall performance of the PolyJet procedure compared to other rapid prototyping techniques used nowadays.", } @Article{Brajlih:2006:AMME, author = "Tomaz Brajlih and Igor Drstvensek and Miha Kovacic and Joze Balic", title = "Optimizing scale factors of the PolyJet rapid prototyping procedure by genetic programming", journal = "Journal of achievements in materials and manufacturing engineering", year = "2006", volume = "16", number = "1-2", pages = "101--106", month = may # "-" # jun, note = "Special Issue of CAM3S'2005", keywords = "genetic algorithms, genetic programming, rapid prototyping, PolyJet", ISSN = "1734-8412", URL = "http://jamme.acmsse.h2.pl/index.php?id=69", URL = "http://157.158.19.167/papers_cams05/167.pdf", URL = "http://www.journalamme.org/papers_cams05/167.pdf", size = "6 pages", abstract = "The main problem of assuring a high dimensional accuracy of rapid prototyping procedures, that are using polymers as a building material, is shrinking of a finished layer during the phase of polymerisation. Therefore, the finished object is slightly smaller then the object's CAD three-dimensional model, that was used to build the prototype. Commonly used method to minimise this problem is to scale (enlarge) the original CAD model in order to compensate for the material's shrinkage during manufacturing. The scaling is usually done by the number factor (in percentages) that is recommended by the rapid prototyping machine's manufacturer. With a long-term use of the certain rapid prototyping machine the end-users can determine their own scale factor's values, which are more suited to their model's properties. This research has established a method that enables the user of a PolyJet RP machine to determine the optimal scale factor regardless of his previous experience. For that purpose the genetic programming methods were used to establish a mathematical model that enables the user to calculate optimal scale factor values for each axis (X,Y,Z) regarding a certain object's properties. This method was later tested on a series of prototypes that were scaled with factor values acquired with the established mathematical model.", notes = "http://www.journalamme.org/ broken 2022 http://157.158.19.167/index.php?id=69 Formerly Proceedings of Achievements in Mechanical and Materials Engineering. Faculty of mechanical engineering, University of Maribor, Smetanova ulica 17, SI-2000 Maribor, Slovenia *Corresponding author. E-mail address: brajlih@yahoo.com [COBISS.SI-ID 10526486]", } @TechReport{oai:CiteSeerPSU:323834, title = "{SYSGP} -- {A} {C}++ library of different {GP} variants", author = "Markus Brameier and Wolfgang Kantschik and Peter Dittrich and Wolfgang Banzhaf", institution = "Collaborative Research Center 531, University of Dortmund", year = "1998", type = "Technical Report", number = "CI-98/48", address = "Germany", keywords = "genetic algorithms, genetic programming", URL = "https://eldorado.uni-dortmund.de/bitstream/2003/5345/2/ci4898_doc.pdf", URL = "http://citeseer.ist.psu.edu/323834.html", citeseer-isreferencedby = "oai:CiteSeerPSU:39667", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:323834", rights = "unrestricted", abstract = "In recent years different variants of genetic programming (GP) have emerged all following the basic idea of GP, the automatic evolution of computer programs. Today, three basic forms of representation for genetic programs are used, namely tree, graph and linear structures. We introduce a multi-representation system, SYSGP, that allows researchers to experiment with different representations with only a minimum implementation overhead. The system further offers the possibility to combine modules of different representation forms into one genetic program. SYSGP has been implemented as a C++ library using templates that operate with a generic data type.", size = "13 pages", } @InProceedings{brameier:1999:PMCGP, author = "Markus Brameier and Frank Hoffmann and Peter Nordin and Wolfgang Banzhaf and Frank Francone", title = "Parallel Machine Code Genetic Programming", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1228", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-439.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-439.ps", size = "1 page", abstract = "AIMGP is a very fast linear genetic programming approach that evolves machine code programs. We report on a parallelization of AIMGP for a parallel transputer system resulting in an almost linear speedup.", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @TechReport{oai:CiteSeerPSU:488546, title = "Effective Linear Genetic Programming", author = "Markus Brameier and Wolfgang Banzhaf", citeseer-isreferencedby = "oai:CiteSeerPSU:75403", citeseer-references = "oai:CiteSeerPSU:46478; oai:CiteSeerPSU:271953; oai:CiteSeerPSU:349518", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:488546", rights = "unrestricted", institution = "Department of Computer Science, University of Dortmund", year = "2001", number = "Reihe CI 108/01, SFB 531", address = "44221 Dortmund, Germany", month = "29 " # oct, keywords = "genetic algorithms, genetic programming", broken = "http://sfbci.uni-dortmund.de/index.php?option=com_wrapper&Itemid=180&lang=en", URL = "http://hdl.handle.net/2003/5407", URL = "http://sfbci.uni-dortmund.de/Publications/Reference/Downloads/BB09052001.pdf", URL = "http://citeseer.ist.psu.edu/488546.html", DOI = "doi:10.17877/DE290R-15250", size = "15 pages", abstract = "Different variants of genetic operators are introduced and compared for linear genetic programming including program induction without crossover. Variation strength of crossover and mutations is controlled based on the genetic code. Effectivity of genetic operations improves on code level and on fitness level. Thereby algorithms for creating code efficient solutions are presented.", } @TechReport{oai:CiteSeerPSU:324837, author = "Markus Brameier and Wolfgang Banzhaf", title = "A Comparison of Genetic Programming and Neural Networks in Medical Data Analysis", institution = "Dortmund University", year = "1998", type = "Reihe", number = "CI 43/98, SFB 531", address = "Germany", keywords = "genetic algorithms, genetic programming", URL = "https://eldorado.uni-dortmund.de/dspace/bitstream/2003/5344/2/ci4398_doc.pdf", URL = "http://citeseer.ist.psu.edu/324837.html", citeseer-isreferencedby = "oai:CiteSeerPSU:39821", citeseer-references = "oai:CiteSeerPSU:212034; oai:CiteSeerPSU:186821", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:324837", rights = "unrestricted", abstract = "We apply an interpreting variant of linear genetic programming to several diagnosis problems in medicine. We compare our results to results obtained with neural networks and argue that genetic programming is able to show similar performances in classification and generalization even when using a relatively small number of generations. Finally, an efficient algorithm for the elimination of introns in linear genetic programs is presented", size = "pages", } @Article{Brameier:2001:TEC, author = "Markus Brameier and Wolfgang Banzhaf", title = "A Comparison of Linear Genetic Programming and Neural Networks in Medical Data Mining", journal = "IEEE Transactions on Evolutionary Computation", year = "2001", volume = "5", number = "1", pages = "17--26", month = feb, keywords = "genetic algorithms, genetic programming, Data mining, evolutionary computation, neural networks", URL = "http://web.cs.mun.ca/~banzhaf/papers/ieee_taec.pdf", size = "10 pages", abstract = "We apply linear genetic programming to several diagnosis problems in medicine. An efficient algorithm is presented that eliminates intron code in linear genetic programs. This results in a significant speedup which is especially interesting when operating with complex datasets as they are occuring in real-world applications like medicine. We compare our results to those obtained with neural networks and argue that genetic programming is able to show similar performance in classification and generalization even within a relatively small number of generations.", notes = "proben1/UCI LGP variable length string of C instruction. Branching. steady state tournament selection. two-point string crossover {"}high mutation rates have been experienced to produced better results{"} p19. Size<=256 {"}it is much easier for the GP system to implement structural introns [than semantic ones]{"} p20 {"}for all problems discussed, the performance of GP in generalization comes close to or even better then the results documented for NNs{"} (MLP, RPROP) p21 Ten demes of 500 connected in one direction circle. 5% mutation rate. {"}On average, the number of effective generations is reduced by a factor of three when using demes. Tests with and without conditionals. Runtime comparison. Intron removal (dead code) at run time.", } @Article{brameier:2001:GPEM, author = "Markus Brameier and Wolfgang Banzhaf", title = "Evolving Teams of Predictors with Linear Genetic Programming", journal = "Genetic Programming and Evolvable Machines", year = "2001", volume = "2", number = "4", pages = "381--407", month = dec, keywords = "genetic algorithms, genetic programming, evolution of teams, combination of multiple predictors, linear genetic programming", ISSN = "1389-2576", URL = "http://web.cs.mun.ca/~banzhaf/papers/teams.pdf", URL = "http://citeseer.ist.psu.edu/508652.html", URL = "http://citeseer.ist.psu.edu/411995.html", DOI = "doi:10.1023/A:1012978805372", size = "26 pages", abstract = "This paper applies the evolution of GP teams to different classification and regression problems and compares different methods for combining the outputs of the team programs. These include hybrid approaches where (1) a neural network is used to optimize the weights of programs in a team for a common decision and (2) a real numbered vector (the representation of evolution strategies) of weights is evolved with each term in parallel. The cooperative team approach results in an improved training and generalization performance compared to the standard GP method. The higher computational overhead of team evolution is counteracted by using a fast variant of linear GP. In particular, the processing time of linear genetic programs is reduced significantly by removing intron code before program execution.", notes = "Article ID: 386363", } @TechReport{oai:CiteSeerPSU:552561, title = "Explicit Control of Diversity and Effective Variation Distance in Linear Genetic Programming", author = "Markus Brameier and Wolfgang Banzhaf", year = "2002", month = feb # "~25", citeseer-isreferencedby = "oai:CiteSeerPSU:92442; oai:CiteSeerPSU:192628", citeseer-references = "oai:CiteSeerPSU:266665; oai:CiteSeerPSU:271953; oai:CiteSeerPSU:270103; oai:CiteSeerPSU:61421; oai:CiteSeerPSU:440305; oai:CiteSeerPSU:32228; oai:CiteSeerPSU:212034; oai:CiteSeerPSU:61877", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", language = "ENG", oai = "oai:eldorado:0x0004162d", oai = "oai:CiteSeerPSU:552561", rights = "unrestricted", URL = "http://eldorado.uni-dortmund.de/0x81d98002_0x0004162d", URL = "http://eldorado.uni-dortmund.de:8080/bitstream/2003/5419/1/123.pdf", URL = "http://citeseer.ist.psu.edu/552561.html", URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.383.9067.pdf", institution = "Dortmund University", keywords = "genetic algorithms, genetic programming", abstract = "We investigate structural and semantic distance metrics for linear genetic programs. Causal connections between changes of the genotype and fitness changes form a necessary condition for analyzing structural differences between genetic programs and for the two major objectives of this paper: (i) Distance information betweenin-dividuals is used to control structural diversity of population individuals actively by a two-level tournament selection. (ii) Variation distance of effective code is controlled for different genetic operators - including an effective variant of the mutation operator that works closely with the used distance metric. Numerous experiments have been performed for a regression problem, a classification task, and a Boolean problem", notes = "see also \cite{brameier:2002:EuroGP} 123.pdf crashes SUSE 10.0 KDE Konqueror 3.4.2b, Nov 2006", size = "25 pages", } @InProceedings{brameier:2002:EuroGP, title = "Explicit Control of Diversity and Effective Variation Distance in Linear Genetic Programming", author = "Markus Brameier and Wolfgang Banzhaf", editor = "James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi", booktitle = "Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002", publisher = "Springer-Verlag", volume = "2278", series = "LNCS", pages = "37--49", address = "Kinsale, Ireland", publisher_address = "Berlin", month = "3-5 " # apr, year = "2002", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-43378-3", URL = "http://www.cs.mun.ca/~banzhaf/papers/eurogp02_dist.pdf", DOI = "doi:10.1007/3-540-45984-7_4", abstract = "We have investigated structural distance metrics for linear genetic programs. Causal connections between changes of the genotype and changes of the phenotype form a necessary condition for analyzing structural differences between genetic programs and for the two objectives of this paper: (i) The distance information between individuals is used to control structural diversity of population individuals actively by a two-level tournament selection. (ii) Variation distance of effective code is controlled for different genetic operators - including a mutation operator that works closely with the applied distance measure. Numerous experiments have been performed for three benchmark problems.", notes = "EuroGP'2002, part of \cite{lutton:2002:GP} Best paper See also \cite{oai:CiteSeerPSU:552561}", } @InProceedings{brameier03, author = "Markus Brameier and Wolfgang Banzhaf", title = "Neutral Variations Cause Bloat in Linear GP", booktitle = "Genetic Programming, Proceedings of EuroGP'2003", year = "2003", editor = "Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa", volume = "2610", series = "LNCS", pages = "286--296", address = "Essex", publisher_address = "Berlin", month = "14-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-00971-X", DOI = "doi:10.1007/3-540-36599-0_26", abstract = "In this contribution we investigate the influence of different variation effects on the growth of code. A mutation-based variant of linear GP is applied that operates with minimum structural step sizes. Results show that neutral variations are a direct cause for (and not only a result of) the emergence and the growth of intron code. The influence of non-neutral variations has been found to be considerably smaller. Neutral variations turned out to be beneficial by solving two classification problems more successfully.", notes = "EuroGP'2003 held in conjunction with EvoWorkshops 2003 Section 2.3 PerlGP 'In PerlGP \cite{maccallum03}, evolved code is expanded from a tree-based genotype into a string before being evaluated with Perl's eval() function. The trees of each individual are built (and later, mutated) according to a grammar and are strongly typed. In this application, we want the evolved code to look like the example given in Figure 3; that is to say, the solution should be some arithmetic expression containing constants and RE matches against a protein sequence. The matches() function feeds the number of separate RE matches into the arithmetic expression. If the result of the expression for a given sequence is greater than zero, it is predicted/classified as nuclear, otherwise it is non-nuclear.' ", } @PhdThesis{B2005OLGP, title = "On Linear Genetic Programming", author = "Markus Brameier", month = feb, year = "2004", school = "Fachbereich Informatik, Universit{\"a}t Dortmund", address = "Germany", keywords = "genetic algorithms, genetic programming, Evolutionary algorithms, Machine learning", URL = "https://eldorado.uni-dortmund.de/bitstream/2003/20098/2/Brameierunt.pdf", URL = "https://eldorado.uni-dortmund.de/bitstream/2003/20098/1/Brameier.ps", DOI = "doi:10.17877/DE290R-253", size = "272 pages", abstract = "The thesis is about linear genetic programming (LGP), a machine learning approach that evolves computer programs as sequences of imperative instructions. Two fundamental differences to the more common tree-based variant (TGP) may be identified. These are the graph-based functional structure of linear genetic programs, on the one hand, and the existence of structurally noneffective code, on the other hand. The two major objectives of this work comprise(1) the development of more advanced methods and variation operators to produce better and more compact program solutions and (2) the analysis of general EA/GP phenomena in linear GP, including intron code, neutral variations, and code growth, among others. First, we introduce efficient algorithms for extracting features of the imperative and functional structure of linear genetic programs.In doing so, especially the detection and elimination of noneffective code during runtime will turn out as a powerful tool to accelerate the time-consuming step of fitness evaluation in GP. Variation operators are discussed systematically for the linear program representation. We will demonstrate that so called effective instruction mutations achieve the best performance in terms of solution quality.These mutations operate only on the (structurally) effective code and restrict the mutation step size to one instruction. One possibility to further improve their performance is to explicitly increase the probability of neutral variations. As a second, more time-efficient alternative we explicitly control the mutation step size on the effective code (effective step size).Minimum steps do not allow more than one effective instruction to change its effectiveness status. That is, only a single node may be connected to or disconnected from the effective graph component. It is an interesting phenomenon that, to some extent, the effective code becomes more robust against destructions over the generations already implicitly. A special concern of this thesis is to convince the reader that there are some serious arguments for using a linear representation.In a crossover-based comparison LGP has been found superior to TGP over a set of benchmark problems. Furthermore, linear solutions turned out to be more compact than tree solutions due to (1) multiple usage of subgraph results and (2) implicit parsimony pressure by structurally noneffective code. The phenomenon of code growth is analysed for different linear genetic operators. When applying instruction mutations exclusively almost only neutral variations may be held responsible for the emergence and propagation of intron code. It is noteworthy that linear genetic programs may not grow if all neutral variation effects are rejected and if the variation step size is minimum.For the same reasons effective instruction mutations realize an implicit complexity control in linear GP which reduces a possible negative effect of code growth to a minimum.Another noteworthy result in this context is that program size is strongly increased by crossover while it is hardly influenced by mutation even if step sizes are not explicitly restricted. Finally, we investigate program teams as one possibility to increase the dimension of genetic programs. It will be demonstrated that much more powerful solutions may be found by teams than by individuals. Moreover, the complexity of team solutions remains surprisingly small compared to individual programs. Both is the result of specialisation and cooperation of team members.", notes = "Day of Submission: 2003-05-28, Committee: Wolfgang Banzhaf and Martin Riedmiller and Peter Nordin. \onlineAvailableT{https://eldorado.uni-dortmund.de/handle/2003/20098}{http://hdl.handle.net/2003/20098}{2007-08-17} LS11", } @Article{oai:biomedcentral.com:1471-2105-7-16, author = "Markus Brameier and Josien Haan and Andrea Krings and Robert M MacCallum", title = "Automatic discovery of cross-family sequence features associated with protein function", publisher = "BioMed Central Ltd.", year = "2006", month = jan # "~12", journal = "BMC bioinformatics [electronic resource]", volume = "7", number = "16", keywords = "genetic algorithms, genetic programming", ISSN = "1471-2105", bibsource = "OAI-PMH server at www.biomedcentral.com", language = "en", oai = "oai:biomedcentral.com:1471-2105-7-16", rights = "Copyright 2006 Brameier et al; licensee BioMed Central Ltd.", URL = "http://www.biomedcentral.com/content/pdf/1471-2105-7-16.pdf", URL = "http://www.biomedcentral.com/1471-2105/7/16", DOI = "doi:10.1186/1471-2105-7-16", size = "16 pages", abstract = "Background Methods for predicting protein function directly from amino acid sequences are useful tools in the study of uncharacterised protein families and in comparative genomics. Until now, this problem has been approached using machine learning techniques that attempt to predict membership, or otherwise, to predefined functional categories or subcellular locations. A potential drawback of this approach is that the human-designated functional classes may not accurately reflect the underlying biology, and consequently important sequence-to-function relationships may be missed. Results We show that a self-supervised data mining approach is able to find relationships between sequence features and functional annotations. No preconceived ideas about functional categories are required, and the training data is simply a set of protein sequences and their UniProt/Swiss-Prot annotations. The main technical aspect of the approach is the co-evolution of amino acid-based regular expressions and keyword-based logical expressions with genetic programming. Our experiments on a strictly non-redundant set of eukaryotic proteins reveal that the strongest and most easily detected sequence-to-function relationships are concerned with targeting to various cellular compartments, which is an area already well studied both experimentally and computationally. Of more interest are a number of broad functional roles which can also be correlated with sequence features. These include inhibition, biosynthesis, transcription and defence against bacteria. Despite substantial overlaps between these functions and their corresponding cellular compartments, we find clear differences in the sequence motifs used to predict some of these functions. For example, the presence of polyglutamine repeats appears to be linked more strongly to the {"}transcription{"} function than to the general {"}nuclear{"} function/location. Conclusion We have developed a novel and useful approach for knowledge discovery in annotated sequence data. The technique is able to identify functionally important sequence features and does not require expert knowledge. By viewing protein function from a sequence perspective, the approach is also suitable for discovering unexpected links between biological processes, such as the recently discovered role of ubiquitination in transcription.", notes = "PMID: 16409628", } @Book{Brameier:2006:book, author = "Markus Brameier and Wolfgang Banzhaf", title = "Linear Genetic Programming", publisher = "Springer", year = "2007", number = "XVI", series = "Genetic and Evolutionary Computation", keywords = "genetic algorithms, genetic programming, Step Size Control, Syntax, algorithms, code growth, diversity control, evolutionary algorithm, genetic operators, learning, machine learning, neutral variations, optimisation, programming", ISSN = "1932-0167", ISBN = "0-387-31029-0", DOI = "doi:10.1007/978-0-387-31030-5", abstract = "Table of contents Preface, About the Authors, Acknowledgments, Introduction, I Fundamental Analyses: Basic Concepts, Representation Characteristics, A Comparison with Neural Networks, II Method Design: Segment Variations, Instruction Mutations, Analysis of Control Parameters, A Comparison with Tree-Based GP, III Advanced Techniques and Phenomena: Control of Diversity and Step Size, Code Growth and Neutral Variations, Evolution of Program Teams, References, Index.", size = "Approx. 320 pages", } @Article{NucPred-bioinformatics2007, author = "Markus Brameier and Andrea Krings and Robert M. MacCallum", title = "{NucPred} Predicting nuclear localization of proteins", journal = "Bioinformatics", year = "2007", volume = "23", number = "9", pages = "1159--1160", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1093/bioinformatics/btm066", size = "2 pages", abstract = "NucPred analyses patterns in eukaryotic protein sequences and predicts if a protein spends at least some time in the nucleus or no time at all. Subcellular location of proteins represents functional information, which is important for understanding protein interactions, for the diagnosis of human diseases and for drug discovery. NucPred is a novel web tool based on regular expression matching and multiple program classifiers induced by genetic programming. A likelihood score is derived from the programs for each input sequence and each residue position. Different forms of visualisation are provided to assist the detection of nuclear localisation signals (NLSs). The NucPred server also provides access to additional sources of biological information (real and predicted) for a better validation and interpretation of results. Availability: The web interface to the NucPred tool is provided at http://www.sbc.su.se/~maccallr/nucpred. In addition, the Perl code is made freely available under the GNU Public Licence (GPL) for simple incorporation into other tools and web servers.", notes = "PMID: 17332022 [PubMed - indexed for MEDLINE] A few lines in Mutlu Dogruel, Doctor of Philosophy, January 2008 ftp://ftp.sanger.ac.uk/pub/resources/theses/dogruel/chapter4.pdf", } @Article{Brameier:2007:BMCbinf, author = "Markus Brameier and Carsten Wiuf", title = "Ab initio identification of human {microRNAs} based on structure motifs", journal = "BMC Bioinformatics", year = "2007", volume = "8", pages = "478", month = "18 " # dec, keywords = "genetic algorithms, genetic programming, linear genetic programming", URL = "http://www.biomedcentral.com/content/pdf/1471-2105-8-478.pdf", DOI = "doi:10.1186/1471-2105-8-478", size = "11 pages", abstract = "BACKGROUND: MicroRNAs (miRNAs) are short, non-coding RNA molecules that are directly involved in post-transcriptional regulation of gene expression. The mature miRNA sequence binds to more or less specific target sites on the mRNA. Both their small size and sequence specificity make the detection of completely new miRNAs a challenging task. This cannot be based on sequence information alone, but requires structure information about the miRNA precursor. Unlike comparative genomics approaches, ab initio approaches are able to discover species-specific miRNAs without known sequence homology. RESULTS: MiRPred is a novel method for ab initio prediction of miRNAs by genome scanning that only relies on (predicted) secondary structure to distinguish miRNA precursors from other similar-sized segments of the human genome. We apply a machine learning technique, called linear genetic programming, to develop special classifier programs which include multiple regular expressions (motifs) matched against the secondary structure sequence. Special attention is paid to scanning issues. The classifiers are trained on fixed-length sequences as these occur when shifting a window in regular steps over a genome region. Various statistical and empirical evidence is collected to validate the correctness of and increase confidence in the predicted structures. Among other things, we propose a new criterion to select miRNA candidates with a higher stability of folding that is based on the number of matching windows around their genome location. An ensemble of 16 motif-based classifiers achieves 99.9 percent specificity with sensitivity remaining on an acceptable high level when requiring all classifiers to agree on a positive decision. A low false positive rate is considered more important than a low false negative rate, when searching larger genome regions for unknown miRNAs. 117 new miRNAs have been predicted close to known miRNAs on human chromosome 19. All candidate structures match the free energy distribution of miRNA precursors which is significantly shifted towards lower free energies. We employed a human EST library and found that around 75 percent of the candidate sequences are likely to be transcribed, with around 35 percent located in introns. CONCLUSION: Our motif finding method is at least competitive to state-of-the-art feature-based methods for ab initio miRNA discovery. In doing so, it requires less previous knowledge about miRNA precursor structures while programs and motifs allow a more straightforward interpretation and extraction of the acquired knowledge.", notes = "PMID: 18088431 [PubMed - indexed for MEDLINE]", } @Article{Bramerdorfer:2014:ieeeIE, author = "Gerd Bramerdorfer and Stephan M. Winkler and Michael Kommenda and Guenther Weidenholzer and Siegfried Silber and Gabriel Kronberger and Michael Affenzeller and Wolfgang Amrhein", title = "Using FE Calculations and Data-Based System Identification Techniques to Model the Nonlinear Behavior of PMSMs", journal = "IEEE Transactions on Industrial Electronics", year = "2014", month = nov, volume = "61", number = "11", pages = "6454--6462", keywords = "genetic algorithms, genetic programming, brushless machine, permanent magnet, cogging torque, torque ripple, modelling, field-oriented control, symbolic regression, artificial neural network, random forests", DOI = "doi:10.1109/TIE.2014.2303785", ISSN = "0278-0046", size = "9 pages", abstract = "This article investigates the modelling of brushless permanent magnet synchronous machines (PMSMs). The focus is on deriving an automatable process for obtaining dynamic motor models that take nonlinear effects, such as saturation, into account. The modelling is based on finite element (FE) simulations for different current vectors in the dq plane over a full electrical period. The parameters obtained are the stator flux in terms of the direct and quadrature component and the air gap torque, both modelled as functions of the rotor angle and the current vector. The data is preprocessed according to theoretical results on potential harmonics in the targets as functions of the rotor angle. A variety of modelling strategies were explored: linear regression, support vector machines, symbolic regression using genetic programming, random forests, and artificial neural networks. The motor models were optimised for each training technique and their accuracy was then compared on the basis of the initially available FE data and further FE simulations for additional current vectors. Artificial neural networks and symbolic regression using genetic programming achieved the highest accuracy especially with additional test data.", notes = "Also known as \cite{6729026}", } @InProceedings{Bramerdorfer:2014:IECON, author = "Gerd Bramerdorfer and Wolfgang Amrhein and Stephan M. Winkler and Michael Affenzeller", booktitle = "40th Annual Conference of the IEEE Industrial Electronics Society, IECON 2014", title = "Identification of a nonlinear PMSM model using symbolic regression and its application to current optimization scenarios", year = "2014", month = oct, pages = "628--633", abstract = "This article presents the nonlinear modelling of the torque of brushless PMSMs by using symbolic regression. It is still popular to characterise the operational behaviour of electrical machines by employing linear models. However, nowadays most PMSMs are highly used and thus a linear motor model does not give an adequate accuracy for subsequently derived analyses, e.g., for the calculation of the maximum torque per ampere (MTPA) trajectory. This article focuses on modelling PMSMs by nonlinear white-box models derived by symbolic regression methods. An optimised algebraic equation for modelling the machine behaviour is derived using genetic programming. By using a Fourier series representation of the motor torque a simple to handle model with high accuracy can be derived. A case study is provided for a given motor design and the motor model obtained is used for deriving the MTPA-trajectory for sinusoidal phase currents. The model is further applied for determining optimised phase current waveforms ensuring zero torque ripple.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IECON.2014.7048566", notes = "Also known as \cite{7048566}", } @InProceedings{Brandejsky:2012:ICCC, author = "Thomas Brandejsky", title = "Nonlinear system identification by GPA-ES", booktitle = "13th International Carpathian Control Conference (ICCC 2012)", year = "2012", month = may, pages = "58--62", size = "5 pages", abstract = "The paper discusses application of Genetic Programming Algorithm - Evolutionary Strategy (GPA-ES) algorithm to symbolic regression of chaotic systems. In the paper, Van der Pol oscillator and especially Rabinovich-Fabrikant equations are analysed and regressed. On the base of these experiments, novel improvements of GPA-ES algorithm are suggested.", keywords = "genetic algorithms, genetic programming, GPA-ES algorithm, Rabinovich-Fabrikant equations, Van der Pol oscillator, chaotic systems, genetic programming algorithm-evolutionary strategy algorithm, nonlinear system identification, regression analysis, symbolic regression, chaos, identification, nonlinear systems, oscillators, regression analysis", DOI = "doi:10.1109/CarpathianCC.2012.6228616", notes = "Also known as \cite{6228616}", } @Article{Brandejsky:2013:CMA, author = "Tomas Brandejsky", title = "Specific modification of a GPA-ES evolutionary system suitable for deterministic chaos regression", journal = "Computer \& Mathematics with Applications", year = "2013", volume = "66", number = "2", pages = "106--112", note = "Nostradamus 2012", ISSN = "0898-1221", DOI = "doi:10.1016/j.camwa.2013.01.011", URL = "http://www.sciencedirect.com/science/article/pii/S089812211300028X", keywords = "genetic algorithms, genetic programming, Evolutionary strategy, Optimisation, Symbolic regression, Deterministic chaos", abstract = "The paper deals with symbolic regression of deterministic chaos systems using a GPA-ES system. A Lorenz attractor, Roessler attractor, Rabinovich-Fabrikant equations and a van der Pol oscillator are used as examples of deterministic chaos systems to demonstrate significant differences in the efficiency of the symbolic regression of systems described by equations of similar complexity. Within the paper, the source of this behaviour is identified in presence of structures which are hard to be discovered during the evolutionary process due to the low probability of their occurrence in the initial population and by the low chance to produce them by standard evolutionary operators given by small probability to form them in a single step and low fitness function magnitudes of inter-steps when GPA tries to form them in more steps. This low magnitude of fitness function for particular solutions tends to eliminate them, thus increasing the number of needed evolutionary steps. As the solution of identified problems, modification of terminals and related crossover and mutation operators are suggested.", } @InCollection{Brandejsky:2013:HBO, author = "Tomas Brandejsky", title = "The Use of Local Models Optimized by Genetic Programming Algorithms in Biomedical-Signal Analysis", booktitle = "Handbook of Optimization", publisher = "Springer", year = "2013", editor = "Ivan Zelinka and Vaclav Snasel and Ajith Abraham", volume = "38", series = "Intelligent Systems Reference Library", chapter = "28", pages = "697--716", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-30503-0", URL = "http://dx.doi.org/10.1007/978-3-642-30504-7_28", DOI = "doi:10.1007/978-3-642-30504-7_28", bibdate = "2013-09-25", bibsource = "DBLP, http://dblp.uni-trier.de/db/series/isrl/isrl38.html#Brandejsky13", URL = "http://dx.doi.org/10.1007/978-3-642-30504-7", abstract = "Today researchers need to solve vague defined problems working with huge data sets describing signals close to chaotic ones. Common feature of such signals is missing algebraic model explaining their nature. Genetic Algorithms and Evolutionary Strategies are suitable to optimise such models and Genetic Programming Algorithms to develop them. Hierarchical GPA-ES algorithm presented herein is used to build compact models of difficult signals including signals representing deterministic chaos. Efficiency of GPA-ES is presented in the paper. Specific group of non-linearly composed functions similar to real biomedical signals is studied in the paper. On the base of these prerequisites, models applicable in complex biomedical signals like EEG modelling are formed and studied within the contribution.", notes = "CTU in Prague,", } @InProceedings{Brandejsky:2018:ICCAIRO, author = "Tomas Brandejsky", booktitle = "2018 International Conference on Control, Artificial Intelligence, Robotics Optimization (ICCAIRO)", title = "Influence of Two Different Crossover Operators Use Onto {GPA} Efficiency", year = "2018", pages = "127--132", abstract = "Increasing capabilities of today computers, especially size of memory and computational power open new application areas to Genetic Programming Algorithms [1]. Unfortunately, efficiency of these algorithms is not big and decreases with solved problem complexity. Thus, its increase is extremely important for opening of new application domains. There exists three main areas that should potentially influence GPA efficiency. They are algorithms, pseudo-random number generator behaviours and evolutionary operators. Genetic programming algorithms use two basic evolutionary operators - mutation and crossover in the sense of Darwinian evolution. Non-looking to the fact, that it is possible to define additional operators like e.g. application defined operators [2], there are many different implementations of both basic evolutionary operators [3] and each of them is sometimes useful in artificial evolutionary process. Thus, the main question solved in this paper is that it might bring some advance to use two randomly executed different crossover operators in GPA. The study is focused to symbolic regression problem and as GPA is used GPA-ES, because it is capable to eliminate influence of solution parameters (constants) identification and thus to produce more clear results.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICCAIRO.2018.00029", month = may, notes = "Also known as \cite{8698424}", } @InProceedings{brandejsky:2019:CSMMMIS, author = "Tomas Brandejsky", title = "Floating Data Window Movement Influence to Genetic Programming Algorithm Efficiency", booktitle = "Computational Statistics and Mathematical Modeling Methods in Intelligent Systems", year = "2019", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-030-31362-3_4", DOI = "doi:10.1007/978-3-030-31362-3_4", } @InProceedings{Brandejsky:2024:Informatics, author = "Tomas Brandejsky and Jan Merta", title = "Versatile Function {GPA}", booktitle = "2024 IEEE 17th International Scientific Conference on Informatics (Informatics)", year = "2024", pages = "448--452", month = nov, keywords = "genetic algorithms, genetic programming, Graphics processing units, Evolutionary computation, Big Data applications, Data models, Nonlinear dynamical systems, Informatics, Tuning, Optimisation, hybrid evolutionary algorithm, genetic programming algorithm, versatile function GPA, BigData, symbolic regression", DOI = "doi:10.1109/Informatics62280.2024.10900916", abstract = "continuous versatile function Genetic Programming Algorithm (GPA) developed with respect to BigData processing. The basic structure of this hierarchical evolutionary algorithm and examples of versatile functions are presented.On the basis of experiments with the hybrid evolutionary algorithm (hybrid EA) providing symbolic regression of precomputed Lorenz attractor system data representing hybrid EA's behaviour, a discussion of examples of an obtained solution is presented. The versatile function concept GPA is applicable, but it requires the hybrid evolutionary algorithm application, as is demonstrated in the paper.", notes = "Also known as \cite{10900916}", } @InProceedings{Brandstetter:2012:CIG, author = "Matthias F. Brandstetter and Samad Ahmadi", booktitle = "Computational Intelligence and Games (CIG), 2012 IEEE Conference on", title = "Reactive control of {Ms. Pac Man} using Information Retrieval based on Genetic Programming", year = "2012", pages = "250--256", DOI = "doi:10.1109/CIG.2012.6374163", abstract = "During the last years the well-known Ms. Pac Man video game has been - and still is - an interesting test bed for the research on various concepts from the broad area of Artificial Intelligence (AI). Among these concepts is the use of Genetic Programming (GP) to control the game from a human player's perspective. Several GP-based approaches have been examined so far, where traditionally they define two types of GP terminals: one type for information retrieval, the second type for issuing actions (commands) to the game world. However, by using these action terminals the controller has to manage actions issued to the game during their runtime and to monitor their outcome. In order to avoid the need for active task management this paper presents a novel approach for the design of a GP-based Ms. Pac Man controller: the proposed approach solely relies on information retrieval terminals in order to rate all possible directions of movement at every time step during a running game. Based on these rating values the controller can move the agent through the mazes of the the game world of Ms. Pac Man. With this design, which forms the main contribution of our work, we decrease the overall GP solution complexity by removing all action control management tasks from the system. It is demonstrated that by following the proposed approach such a system can successfully control an autonomous agent in a computer game environment on the level of an amateur human player.", keywords = "genetic algorithms, genetic programming, computer games, information retrieval, AI, GP solution complexity, GP-based approaches, Ms. Pac Man video game, action control management task removal, action terminals, artificial intelligence, autonomous agent control, game mazes, human player perspective, information retrieval, rating values, reactive control, task management, Abstracts, Computational intelligence, Games, Humans, Information retrieval, Sociology, Statistics", notes = "Also known as \cite{6374163}", } @InProceedings{branke:1999:RGDSSEA, author = "Jurgen Branke and Massimo Cutaia and Heinrich Dold", title = "Reducing Genetic Drift in Steady State Evolutionary Algorithms", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "68--74", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Branke:EEC:gecco2004, author = "Juergen Branke and Pablo Funes and Frederik Thiele", title = "Evolving En-Route Caching Strategies for the Internet", booktitle = "Genetic and Evolutionary Computation -- GECCO-2004, Part II", year = "2004", editor = "Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell", series = "Lecture Notes in Computer Science", pages = "434--446", address = "Seattle, WA, USA", publisher_address = "Heidelberg", month = "26-30 " # jun, organisation = "ISGEC", publisher = "Springer-Verlag", volume = "3103", ISBN = "3-540-22343-6", ISSN = "0302-9743", DOI = "doi:10.1007/b98645", DOI = "doi:10.1007/978-3-540-24855-2_55", size = "13", keywords = "genetic algorithms, genetic programming", abstract = "Nowadays, large distributed databases are commonplace. Client applications increasingly rely on accessing objects from multiple remote hosts. The Internet itself is a huge network of computers, sending documents point-to-point by routing packetized data over multiple intermediate relays. As hubs in the network become overladen, slowdowns and timeouts can disrupt the process. It is thus worth to think about ways to minimise these effects. Caching, i.e. storing replicas of previously-seen objects for later reuse, has the potential for generating large bandwidth savings and in turn a significant decrease in response time. En-route caching is the concept that all nodes in a network are equipped with a cache, and may opt to keep copies of some documents for future reuse [18]. The rules used for such decisions are called caching strategies. Designing such strategies is a challenging task, because the different nodes interact, resulting in a complex, dynamic system. In this paper, we use genetic programming to evolve good caching strategies, both for specific networks and network classes. An important result is a new innovative caching strategy that outperforms current state-of-the-art methods.", notes = "GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)", affiliation = "Institute AIFB, University of Karlsruhe, 76128 Karlsruhe Germany", } @Article{Branke:2006:ASC, author = "Jurgen Branke and Pablo Funes and Frederik Thiele", title = "Evolutionary design of en-route caching strategies", journal = "Applied Soft Computing", year = "2006", volume = "7", number = "3", pages = "890--898", month = jun, keywords = "genetic algorithms, genetic programming, En-route caching, Robustness", DOI = "doi:10.1016/j.asoc.2006.04.003", size = "9 pages", abstract = "Nowadays, large distributed databases are commonplace. Client applications increasingly rely on accessing objects from multiple remote hosts. The Internet itself is a huge network of computers, sending documents point-to-point by routing packeted data over multiple intermediate relays. As hubs in the network become over used, slowdowns and timeouts can disrupt the process. It is thus worth to think about ways to minimise these effects. Caching, i.e. storing replicas of previously-seen objects for later reuse, has the potential for generating large bandwidth savings and in turn a significant decrease in response time. En-route caching is the concept that all nodes in a network are equipped with a cache, and may opt to keep copies of some documents for future reuse [X. Tang, S.T. Chanson, Coordinated en-route web caching, IEEE Transact. Comput. 51 6 (2002) 595-607]. The rules used for such decisions are called caching strategies. Designing such strategies is a challenging task, because the different nodes interact, resulting in a complex, dynamic system. In this paper, we use genetic programming to evolve good caching strategies, both for specific networks and network classes. An important result is a new innovative caching strategy that outperforms current state-of-the-art methods.", notes = "cites \cite{Paterson:1997:ecacGP}, \cite{oneill:1999:AGCA}, \cite{Branke:EEC:gecco2004}. Caching Internet documents. Caching as deletion strategy. GP evolves priority (of cached www documents) strategy removes low priority doc from cache until space is available for new doc. Pop 60, 100 gens. Network simulation on cluster of 6 linux workstations. Linear network, GP almost optimal. Appealing GP solution RUDF Priority = last time accessed*(distance+access count). p896 GPfinal+RUDF 'significantly outperformed all the other strategies tested, with a slight advantage of RUDF over GPfinal'.", } @Proceedings{Branke:2010:GECCO, title = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", address = "Portland, OR, USA", publisher_address = "New York, NY, USA", month = jul # " 07-11", organisation = "ACM SIGEVO", publisher = "ACM", keywords = "genetic algorithms, genetic programming, Ant Colony Optimisation and Swarm Intelligence, Artificial Life, Evolutionary Robotics, Adaptive Behaviour, Evolvable Hardware, Bioinformatics and Computational Biology, Combinatorial Optimisation and Metaheuristics, Estimation of Distribution Algorithms, Evolution Strategies and Evolutionary Programming, Evolutionary Multiobjective, Optimisation, Generative and Developmental Systems, Genetics-Based Machine Learning, Parallel Evolutionary Systems, Real World Application, Search Based Software Engineering, Theory", isbn13 = "978-1-4503-0072-8", URL = "http://portal.acm.org/citation.cfm?id=1830483&coll=DL&dl=ACM&CFID=12039329&CFTOKEN=58660565", abstract = "These proceedings contain the papers presented at the 12th Annual Genetic and Evolutionary Computation Conference (GECCO-2010), held in Portland, USA, July 7-11, 2010. This year, we received 373 submissions, of which 168 were accepted as full eight-page publication with 25 minute presentation during the conference. This corresponds to an acceptance rate of 45percent. In addition, 110 submissions (29percent) have been accepted for poster presentation with two-page abstracts in the proceedings. GECCO works according to the motto one conference, many mini-conferences. This year, there were 15 separate tracks that operated independently from each other. Each track had its own track chair(s) and individual program committee. To ensure an unbiased reviewing process, all reviews were conducted double blind; no authors' names were revealed to the reviewers. About 560 researchers participated in the reviewing process. We want to thank them for all their work, which is highly appreciated and absolutely vital to ensure the high quality of the conference. In addition to the presentation of the papers contained in these proceedings, GECCO-2010 also included free tutorials, workshops, a series of sessions on Evolutionary Computation in Practice, various competitions, and late-breaking papers.", notes = "GECCO-2019 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010). ACM Order Number 910102.", } @Article{Branke:2015:EC, author = "Juergen Branke and Torsten Hildebrandt and Bernd Scholz-Reiter", title = "Hyper-heuristic Evolution of Dispatching Rules: A Comparison of Rule Representations", journal = "Evolutionary Computation", year = "2015", volume = "23", number = "2", pages = "249--277", month = "Summer", keywords = "genetic algorithms, genetic programming, Job Shop Scheduling, Dispatching Rule, Representation, CMA-ES, Artificial Neural Network", ISSN = "1063-6560", DOI = "doi:10.1162/EVCO_a_00131", size = "29 pages", abstract = "Dispatching rules are frequently used for real-time, on-line scheduling in complex manufacturing systems. Design of such rules is usually done by experts in a time consuming trial-and-error process. Recently, evolutionary algorithms have been proposed to automate the design process. There are several possibilities to represent rules for this hyper-heuristic search. Because the representation determines the search neighbourhood and the complexity of the rules that can be evolved, a suitable choice of representation is key for a successful evolutionary algorithm. In this paper we empirically compare three different representations, both numeric and symbolic, for automated rule design: A linear combination of attributes, a representation based on Artificial Neural Networks, and a tree representation. Using appropriate Evolutionary Algorithms (CMA-ES for the Neural Network and linear representations, Genetic Programming for the tree representation), we empirically investigate the suitability of each representation in a dynamic stochastic job shop scenario. We also examine the robustness of the evolved dispatching rules against variations in the underlying job shop scenario, and visualise what the rules do in order to get an intuitive understanding of their inner workings. Results indicate that the tree representation using an improved version of Genetic Programming gives the best results if many candidate rules can be evaluated, closely followed by the Neural Network representation that leads to good results already for small to moderate computational budgets. The linear representation is found to be competitive only for extremely small computational budgets.", notes = "Warwick Business School, University of Warwick, CV4 7AL Coventry, UK", } @Article{Branke:2015:ieeeTEC, author = "Juergen Branke and Su Nguyen and Christoph Pickardt and Mengjie Zhang", title = "Automated Design of Production Scheduling Heuristics: A Review", journal = "IEEE Transactions on Evolutionary Computation", year = "2016", volume = "20", number = "1", pages = "110--124", month = feb, keywords = "genetic algorithms, genetic programming, Evolutionary design, hyper-heuristic, scheduling.", ISSN = "1089-778X", URL = "http://wrap.warwick.ac.uk/88212/1/WRAP-automated-design-production-scheduling-heuristics-Branke-2015.pdf", URL = "http://wrap.warwick.ac.uk/88212/", DOI = "doi:10.1109/TEVC.2015.2429314", size = "15 pages", abstract = "Hyper-heuristics have recently emerged as a powerful approach to automate the design of heuristics for a number of different problems. Production scheduling is a particularly popular application area for which a number of different hyperheuristics have been developed and shown to be effective, efficient, easy to implement, and reusable in different shop conditions. In particular, they seem a promising way to tackle highly dynamic and stochastic scheduling problems, an aspect that is specifically emphasised in this survey. Despite their success and the substantial number of papers in this area, there is currently no systematic discussion of the design choices and critical issues involved in the process of developing such approaches. This review strives to fill this gap by summarising the state of the art, suggesting a taxonomy, and providing the interested researchers and practitioners with guidelines for the design of hyper-heuristics in production scheduling. This paper also identifies challenges and open questions and highlights various directions for future work.", notes = "This paper is a review mainly on GP methods for scheduling. Also known as \cite{7101236}", } @InProceedings{Branke:2016:WSC, author = "Juergen Branke and Matthew J. Groves and Torsten Hildebrandt", booktitle = "2016 Winter Simulation Conference (WSC)", title = "Evolving control rules for a dual-constrained job scheduling scenario", year = "2016", pages = "2568--2579", abstract = "Dispatching rules are often used for scheduling in semiconductor manufacturing due to the complexity and stochasticity of the problem. In the past, simulation-based Genetic Programming has been shown to be a powerful tool to automate the time-consuming and expensive process of designing such rules. However, the scheduling problems considered were usually only constrained by the capacity of the machines. In this paper, we extend this idea to dual-constrained flow shop scheduling, with machines and operators for loading and unloading to be scheduled simultaneously. We show empirically on a small test problem with parallel workstations, re-entrant flows and dynamic stochastic job arrival that the approach is able to generate dispatching rules that perform significantly better than benchmark rules from the literature.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/WSC.2016.7822295", month = dec, notes = "Also known as \cite{7822295}", } @InProceedings{Branke:2019:GECCOcomp, author = "Juergen Branke", title = "Simulation optimisation: tutorial", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", note = "Tutorial", isbn13 = "978-1-4503-6748-6", pages = "862--889", address = "Prague, Czech Republic", DOI = "doi:10.1145/3319619.3323385", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{3323385} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InProceedings{branquinho:2023:NEWK, author = "Henrique Branquinho and Nuno Lourenco and Ernesto Costa", title = "{SPENSER:} Towards a {NeuroEvolutionary} Approach for Convolutional Spiking Neural Networks", booktitle = "Neuroevolution at work", year = "2023", editor = "Ernesto Tarantino and Edgar Galvan and Ivanoe {De Falco} and Antonio {Della Cioppa} and Scafuri Umberto and Mengjie Zhang", pages = "2115--2122", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, grammatical evolution, neuroevolution, computer vision, spiking neural networks, DENSER", isbn13 = "9798400701191", DOI = "doi:10.1145/3583133.3596399", size = "8 pages", abstract = "Spiking Neural Networks (SNNs) have attracted recent interest due to their energy efficiency and biological plausibility. However, the performance of SNNs still lags behind traditional Artificial Neural Networks (ANNs), as there is no consensus on the best learning algorithm for SNNs. Best-performing SNNs are based on ANN to SNN conversion or learning with spike-based backpropagation through surrogate gradients. The focus of recent research has been on developing and testing different learning strategies, with hand-tailored architectures and parameter tuning. Neuroevolution (NE), has proven successful as a way to automatically design ANNs and tune parameters, but its applications to SNNs are still at an early stage. DENSER is a NE framework for the automatic design and parametrization of ANNs, based on the principles of Genetic Algorithms (GA) and Structured Grammatical Evolution (SGE). In this paper, we propose SPENSER, a NE framework for SNN generation based on DENSER, for image classification on the MNIST and Fashion-MNIST datasets. SPENSER generates competitive performing networks with a test accuracy of 99.42\% and 91.65\% respectively.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @InProceedings{Brar:2007:WCE, author = "Gursewak S Brar and Yadwinder S Brar and Yaduvir Singh", title = "A Fuzzy Entropy Algorithm For Data Extrapolation In Multi-Compressor System", booktitle = "Proceedings of the World Congress on Engineering, WCE 2007", year = "2007", volume = "I", pages = "105--110", address = "London", month = jul # " 2-4", keywords = "genetic algorithms, genetic programming, fuzzy entropy, incomplete data, classification, knowledge discovery, multi-compressor system", isbn13 = "978-988-98671-5-7", URL = "http://www.iaeng.org/publication/WCE2007/WCE2007_pp105-110.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.2111", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", contributor = "CiteSeerX", language = "en", oai = "oai:CiteSeerXPSU:10.1.1.149.2111", abstract = "In this paper incomplete quantitative data has been dealt by using the concept of fuzzy entropy. Fuzzy entropy has been used to extrapolate the data pertaining to the compressor current. Certain attributes related to the compressor current have been considered. Test data of compressor current used in this knowledge discovery algorithm knows the entire attribute clearly. The developed algorithm is very effective and can be used in the various application related to knowledge discovery and machine learning. The developed knowledge discovery algorithm using fuzzy entropy has been tested on a multi-compressor system for incomplete compressor current data and it is found that the error level is merely 4.40percent, which is far better than other available knowledge discovery algorithms", } @Article{DBLP:journals/jifs/BrasSW21, author = "Glender Bras and Alisson {Marques Silva} and Elizabeth Fialho Wanner", title = "Multi-gene genetic programming to building up fuzzy rule-base in Neo-Fuzzy-Neuron networks", journal = "J. Intell. Fuzzy Syst.", volume = "41", number = "1", pages = "499--516", year = "2021", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.3233/JIFS-202146", DOI = "doi:10.3233/JIFS-202146", timestamp = "Fri, 10 Sep 2021 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/jifs/BrasSW21.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{Bras:2024:GPEM, author = "Glender Bras and Alisson {Marques Silva} and Elizabeth F. Wanner", title = "A genetic algorithm for rule extraction in fuzzy adaptive learning control networks", journal = "Genetic Programming and Evolvable Machines", year = "2024", volume = "25", pages = "Article no 11", note = "Online first", keywords = "genetic algorithms, Fuzzy systems, Rule extraction, Forecasting", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-024-09486-2", abstract = "Falcon-GA, for rule extraction in a Fuzzy Adaptive Learning Control Network (FALCON) using a Genetic Algorithm (GA). The FALCON-GA combines multiple techniques to establish the relationships and connections among fuzzy rules, including the use of a GA for rule extraction and a Gradient-based method for fine-tuning the membership function parameters. The learning algorithm of FALCON-GA incorporates three key components: the ART (Adaptive Resonance Theory) clustering algorithm for initial membership function identification, the Genetic Algorithm for rule extraction, and the Gradient method for adjusting membership function parameters. Moreover, FALCON-GA offers flexibility by allowing the incorporation of different rule types within the FALCON architecture, making it flexible and expansible. The proposed model has been evaluated in various forecasting problems reported in the literature and compared to alternative models. Computational experiments demonstrate the effectiveness of FALCON-GA in forecasting tasks and reveal significant performance improvements compared to the original FALCON. These results indicate that Genetic Algorithms efficiently extract rules for Fuzzy Adaptive Learning Control Networks.", notes = "Is this GP? Graduate Program in Mathematical and Computational Modeling, Federal Center of Technological Education of Minas Gerais, Av. Amazonas, 7675, Belo Horizonte, MG, 30510-000, Brazil", } @Article{BRAUNE:2022:IJPE, author = "Roland Braune and Frank Benda and Karl F. Doerner and Richard F. Hartl", title = "A genetic programming learning approach to generate dispatching rules for flexible shop scheduling problems", journal = "International Journal of Production Economics", volume = "243", pages = "108342", year = "2022", ISSN = "0925-5273", DOI = "doi:10.1016/j.ijpe.2021.108342", URL = "https://www.sciencedirect.com/science/article/pii/S0925527321003182", keywords = "genetic algorithms, genetic programming, Flexible shop scheduling, Machine learning, Iterative dispatching rule, Multi-tree representation", abstract = "This paper deals with a Genetic Programming (GP) approach for solving flexible shop scheduling problems. The adopted approach aims to generate priority rules in the form of an expression tree for dispatching jobs. Therefore, in a list-scheduling algorithm, the available jobs can be ranked using the tree-based priority rules generated using GP. These priority rules were tested on benchmark problem settings, such as those of Brandimarte and Lawrence, in a static and dynamic environment. The GP approaches were then applied to a special case based on the problem setting of an industrial partner. The goal of these approaches was to minimize the maximum completion time of all jobs, which is known as the makespan. To reach this goal, we considered job assignment and machine sequencing decisions simultaneously in a single-tree representation and compared this single tree with a multi-tree approach, where the terminal sets (job- and machine-related) were strictly separated. This resulted in two parallel GP populations; they were used to first decide which job to choose and then which machine it should be assigned to. Furthermore, for both tree approaches, we implemented an iterative variant that stores recorded information of past schedules to achieve further improvements. Computational experiments revealed a consistent advantage compared to the existing advanced priority rules from the literature with considerably increased performance under the presence of unrelated parallel machines and larger instances in general", } @InCollection{brave:1994:recursive, author = "Scott Brave", title = "Evolution of Planning: Using recursive techniques in Genetic Planning", booktitle = "Artificial Life at Stanford 1994", year = "1994", editor = "John R. Koza", pages = "1--10", address = "Stanford, California, 94305-3079 USA", month = jun, organisation = "Stanford University", publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-182105-2", notes = "This volume contains 22 papers written and submitted by students describing their term projects for the course in artificial life (Computer Science 425) at Stanford University offered during the spring quarter quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs425.html", } @InProceedings{brave:1994:recursiveGW, author = "Scott Brave", title = "Using Genetic Programming to Evolve Recursive Programs for Tree Search", booktitle = "Fourth Golden West Conference on Intelligent Systems", year = "1995", editor = "Sushil J. Louis", pages = "60--65", publisher_address = "San Francisco, California, USA", month = "12-14 " # jun, publisher = "International Society for Computers and their Applications - ISCA", email = "isca@ipass.net", keywords = "genetic algorithms, genetic programming", ISBN = "1-880843-12-9", notes = "GWICS ISCA-GW-95 http://www.isca-hq.org/proc-lst.htm", } @InProceedings{brave:1994:mmGW, author = "Scott Brave", title = "Using Genetic Programming to Evolve Mental Models", booktitle = "Fourth Golden West Conference on Intelligent Systems", year = "1995", editor = "Sushil J. Louis", pages = "91--96", publisher_address = "San Francisco, California, USA", month = "12-14 " # jun, publisher = "International Society for Computers and their Applications - ISCA", email = "isca@ipass.net", keywords = "genetic algorithms, genetic programming, memory", ISBN = "1-880843-12-9", notes = "GWICS ISCA-GW-95 http://www.isca-hq.org/proc-lst.htm", } @InCollection{brave:1996:aigp2, author = "Scott Brave", title = "Evolving Recursive Programs for Tree Search", booktitle = "Advances in Genetic Programming 2", publisher = "MIT Press", year = "1996", editor = "Peter J. Angeline and K. E. {Kinnear, Jr.}", pages = "203--220", chapter = "10", address = "Cambridge, MA, USA", keywords = "genetic algorithms, genetic programming", ISBN = "0-262-01158-1", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.3.3005", URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.3.3005&rep=rep1&type=pdf", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6277538", DOI = "doi:10.7551/mitpress/1109.003.0015", abstract = "This article compares basic genetic programming, genetic programming with automatically defined functions (ADFs), and genetic programming with ADFs using a restricted form of recursion on a planning problem involving tree search. The results show that evolution of a recursive program is possible and further that, of the three techniques explored, genetic programming with recursive ADFs performs the best for the tree search problem. Additionally, genetic programming using ADFs (recursive and non-recursive) outperforms genetic programming without ADFs. The scalability of these techniques is also investigated. The computational effort required to reach a solution using ADFs with recursion is shown to remain essentially constant with world size, while genetic programming with non-recursive ADFs scales linearly at best, and basic genetic programming scales exponentially. Finally, many solutions were found using genetic programming with recursive ADFs which generalised to any world size.", notes = "Recursive ADFs, non-recursive ADFs and non-ADF GP compared on a tree searching problem. Tree depths 2-7 (ie up to 127 leaf nodes) containing one goal node. Problem arranged so can only be solved (by luck?) or by using memory. READ+WRITE update a single memory cell per tree node, ie no index, just access current cell. WRITE not as Teller but returns its argument. ADF1 and ADF2 syntax set up so one can search tree and one can move within it, cf. Andre. Recursive ADFs much better than ADFs much better than non-ADFs, difference increase as tree size increases. {"}random{"}? program search can find recursive ADF programs which solve problem. DGPC", size = "17 pages", } @InProceedings{brave:1996:dface, author = "Scott Brave", title = "Evolving Deterministic Finite Automata Using Cellular Encoding", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "39--44", address = "Stanford University, CA, USA", publisher = "MIT Press", size = "6 pages", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/1745/http:zSzzSzbrave.www.media.mit.eduzSzpeoplezSzbravezSzpublicationszSzautomata.pdf/brave96evolving.pdf", URL = "http://citeseer.ist.psu.edu/brave96evolving.html", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap5.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", abstract = "his paper presents a method for the evolution of deterministic finite automata that combines genetic programming and cellular encoding. Programs are evolved that specify actions for the incremental growth of a deterministic finite automata from an initial single-state zygote. The results show that, given a test bed of positive and negative samples, the proposed method is successful at inducing automata to recognise several different languages. 1. Introduction The automatic creation of finite...", notes = "GP-96 DGPC {"}inremental growth of finite automata from an initial single-state zygote{"}, {"}Induced automata to recognise several different (formal) languages{"} eg Tomita {"}applies cellular encoding to the evolution of determistic finite (state) automata.{"}", } @InProceedings{brave:1996:emmmGP, author = "Scott Brave", title = "The Evolution of Memory and Mental Models Using Genetic Programming", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming, memory", pages = "261--266", address = "Stanford University, CA, USA", publisher = "MIT Press", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/1745/http:zSzzSzbrave.www.media.mit.eduzSzpeoplezSzbravezSzpublicationszSzmodels.pdf/brave96evolution.pdf", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap32.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", size = "6 pages", abstract = "his paper applies genetic programming to the evolution of intelligent agents that gradually build internal representations of their surroundings for later use in planning. The method used allows for the creation of dynamically determined representations that are not pre-designed by the human creator of the system. In an illustrative path-planning problem, evolved programs learn a model of their world and use this internal representation to plan their successive actions. The results show that...", notes = "GP-96. cf. \cite{brave:1994:mmGW} ", } @Proceedings{brave:1999:gecco99lb, editor = "Scott Brave and Annie S. Wu", title = "Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference", year = "1999", address = "Orlando, Florida, USA", month = "13 " # jul, keywords = "genetic algorithms, genetic programming, Evolutionary Programming, fuzzy rules", size = "311 pages", notes = "GECCO-99LB", } @InProceedings{Bravi:2017:evoApplications, author = "Ivan Bravi and Ahmed Khalifa and Christoffer Holmgard and Julian Togelius", title = "Evolving Game-Specific UCB Alternatives for General Video Game Playing", booktitle = "20th European Conference on the Applications of Evolutionary Computation", year = "2017", editor = "Giovanni Squillero", series = "LNCS", volume = "10199", publisher = "Springer", pages = "393--406", address = "Amsterdam", month = "19-21 " # apr, organisation = "Species", keywords = "genetic algorithms, genetic programming, General AI, MTCS, Monte-Carlo Tree Search", DOI = "doi:10.1007/978-3-319-55849-3_26", abstract = "At the core of the most popular version of the Monte Carlo Tree Search (MCTS) algorithm is the UCB1 (Upper Confidence Bound) equation. This equation decides which node to explore next, and therefore shapes the behaviour of the search process. If the UCB1 equation is replaced with another equation, the behavior of the MCTS algorithm changes, which might increase its performance on certain problems (and decrease it on others). In this paper, we use genetic programming to evolve replacements to the UCB1 equation targeted at playing individual games in the General Video Game AI (GVGAI) Framework. Each equation is evolved to maximize playing strength in a single game, but is then also tested on all other games in our test set. For every game included in the experiments, we found a UCB replacement that performs significantly better than standard UCB1. Additionally, evolved UCB replacements also tend to improve performance in some GVGAI games for which they are not evolved, showing that improvements generalize across games to clusters of games with similar game mechanics or algorithmic performance. Such an evolved portfolio of UCB variations could be useful for a hyper-heuristic game-playing agent, allowing it to select the most appropriate heuristics for particular games or problems in general.", notes = "Dipartimento di Elettronica, Informatica e BioingegneriaPolitecnico di MilanoMilanoItaly EvoApplications2017 held in conjunction with EuroGP'2017, EvoCOP2017 and EvoMusArt2017 http://www.evostar.org/2017/cfp_evoapps.php.", } @InProceedings{DBLP:conf/ideal/BrazierRW04, author = "Karl J. Brazier and Graeme Richards and Wenjia Wang", title = "Implicit Fitness Sharing Speciation and Emergent Diversity in Tree Classifier Ensembles", booktitle = "Intelligent Data Engineering and Automated Learning - IDEAL 2004, 5th International Conference, Proceedings", year = "2004", pages = "333--338", editor = "Zheng Rong Yang and Richard M. Everson and Hujun Yin", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3177", ISBN = "3-540-22881-0", bibsource = "DBLP, http://dblp.uni-trier.de", address = "Exeter, UK", month = aug # " 25-27", organisation = "IEEE", keywords = "genetic algorithms, genetic programming, gene expression programming", DOI = "doi:10.1007/b99975", abstract = "Implicit fitness sharing is an approach to the stimulation of speciation in evolutionary computation for problems where the fitness of an individual is determined as its success rate over a number trials against a collection of succeed/fail tests. By fixing the reward available for each test, individuals succeeding in a particular test are caused to depress the size of one another's fitness gain and hence implicitly co-operate with those succeeding in other tests. An important class of problems of this form is that of attribute-value learning of classifiers. Here, it is recognised that the combination of diverse classifiers has the potential to enhance performance in comparison with the use of the best obtainable individual classifiers. However, proposed prescriptive measures of the diversity required have inherent limitations from which we would expect the diversity emergent from the self-organisation of speciating evolutionary simulation to be free. The approach was tested on a number of the popularly used real-world data sets and produced encouraging results in terms of accuracy and stability.", notes = "http://www.dcs.ex.ac.uk/ideal04/ a) Cleveland heart data b) Thyroid data c) Pima Indians diabetes data d) E. coli data ", } @InProceedings{Bredeche:2009:EA, author = "N. Bredeche and E. Haasdijk and A. E. Eiben", title = "On-Line, On-Board Evolution of Robot Controllers", booktitle = "9th International Conference, Evolution Artificielle, EA 2009", year = "2009", editor = "Pierre Collet and Nicolas Monmarche and Pierrick Legrand and Marc Schoenauer and Evelyne Lutton", volume = "5975", series = "Lecture Notes in Computer Science", pages = "110--121", address = "Strasbourg, France", month = oct # " 26-28", publisher = "Springer", note = "Revised Selected Papers", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-14155-3", URL = "http://www.cs.vu.nl/~gusz/papers/2009-bredeche09ea_final2-LNCS.pdf", DOI = "doi:10.1007/978-3-642-14156-0_10", size = "12 pages", abstract = "This paper reports on a feasibility study into the evolution of robot controllers during the actual operation of robots (on-line), using only the computational resources within the robots themselves (on-board). We identify the main challenges that these restrictions imply and propose mechanisms to handle them. The resulting algorithm is evaluated in a hybrid system, using the actual robots' processors interfaced with a simulator that represents the environment. The results show that the proposed algorithm is indeed feasible and the particular problems we encountered during this study give hints for further research.", notes = "EA'09 Published 2010", } @InProceedings{breeden:1999:UJE, author = "Joseph L. Breeden and Todd W. Allen", title = "Using an optimization toolkit for Java to evolve market strategies for European seeds", booktitle = "Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference", year = "1999", editor = "Scott Brave and Annie S. Wu", pages = "57--64", address = "Orlando, Florida, USA", month = "13 " # jul, keywords = "Genetic Algorithms", notes = "GECCO-99LB", } @InProceedings{conf/ijcci/BreenO16, author = "Aidan Breen and Colm O'Riordan", title = "Evolving Art using Aesthetic Analogies - Evolutionary Supervised Learning to Generate Art with Grammatical Evolution", booktitle = "Proceedings of the 8th International Joint Conference on Computational Intelligence, {IJCCI} 2016", year = "2016", editor = "Juan Julian Merelo Guervos and Fernando Melicio and Jose Manuel Cadenas and Antonio Dourado and Kurosh Madani and Antonio E. Ruano and Joaquim Filipe", pages = "59--68", address = "Porto, Portugal", month = nov # " 9-11", publisher = "SciTePress", keywords = "genetic algorithms, genetic programming", isbn13 = "978-989-758-201-1", bibdate = "2017-05-24", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ijcci/ijcci2016ecta.html#BreenO16", DOI = "doi:10.5220/0006048400590068", notes = "Volume 1: ECTA", } @InProceedings{RibeiroZV07a, author = "Jose Carlos Ribeiro and Mario Zenha-Rela and Francisco {Fernandez de Vega}", title = "An Evolutionary Approach for Performing Structural Unit-Testing on Third-Party Object-Oriented Java Software", booktitle = "Proceedings of International Workshop on Nature Inspired Cooperative Strategies for Optimization (NICSO '07)", year = "2007", pages = "379--388", editor = "Natalio Krasnogor and Giuseppe Nicosia and Mario Pavone and David Pelta", volume = "129", series = "Studies in Computational Intelligence", address = "Acireale, Italy", month = "8-10 " # nov, publisher = "Springer", keywords = "genetic algorithms, genetic programming, SBSE", bibsource = "http://crestweb.cs.ucl.ac.uk/resources/sbse_repository/repository.html", isbn13 = "978-3-540-78986-4", URL = "http://jcbribeiro.googlepages.com/NICSO2007-053.pdf", DOI = "doi:10.1007/978-3-540-78987-1_34", abstract = "Evolutionary Testing is an emerging methodology for automatically generating high quality test data. The focus of this paper is on presenting an approach for generating test cases for the unit-testing of object-oriented programs, with basis on the information provided by the structural analysis and interpretation of Java bytecode and on the dynamic execution of the instrumented test object. The rationale for working at the bytecode level is that even when the source code is unavailable, insight can still be obtained and used to guide the search-based test case generation process. Test cases are represented using the Strongly Typed Genetic Programming paradigm, which effectively mimics the polymorphic relationships, inheritance dependences and method argument constraints of object-oriented programs.", notes = "http://www.dmi.unict.it/nicso2007/ http://www.dmi.unict.it/nicso2007/NICSO2007-program.pdf", } @InProceedings{Bregieiro-Ribeiro:2008:JAEM, author = "Jose Carlos {Bregieiro Ribeiro} and Mario Alberto Zenha-Rela and Francisco {Fernandez de Vega}", title = "{eCrash:} a framework for performing evolutionary testing on third-party Java components", booktitle = "I Jornadas sobre Algoritmos Evolutivos y Metaheuristicas (JAEM 2007)", year = "2007", editor = "Enrique Alba and Francisco Herrera", pages = "137--144", address = "Zaragoza, Spain", month = "11-14 " # sep, keywords = "genetic algorithms, genetic programming, SBSE, STGP", isbn13 = "978-84-9732-593-6", URL = "http://jcbribeiro.googlepages.com/jribeiro_jaem07.pdf", size = "8 pages", abstract = "The focus of this paper is on presenting a tool for generating test data by employing evolutionary search techniques, with basis on the information provided by the structural analysis and interpretation of the Java bytecode of third-party Java components, and on the dynamic execution of the instrumented test object. The main objective of this approach is that of evolving a set of test cases that yields full structural code coverage of the test object. Such a test set can be used for effectively performing the testing activity, providing confidence in the quality and robustness of the test object. The rationale of working at the bytecode level is that even when the source code is unavailable structural testing requirements can still be derived, and used to assess the quality of a test set and to guide the evolutionary search towards reaching specific test goals.", notes = "http://neo.lcc.uma.es/jaem07/ With CEDI 2007", } @InProceedings{Bregieiro-Ribeiro:2008:AST, author = "Jose Carlos {Bregieiro Ribeiro} and Mario Alberto Zenha-Rela and Francisco {Fernandez de Vega}", title = "A strategy for evaluating feasible and unfeasible test cases for the evolutionary testing of object-oriented software", booktitle = "AST '08: Proceedings of the 3rd international workshop on Automation of software test", year = "2008", pages = "85--92", address = "Leipzig, Germany", publisher_address = "New York, NY, USA", publisher = "ACM", keywords = "genetic algorithms, genetic programming, SBSE, Search-Based Test Case Generation, Evolutionary Testing, Object-Orientation, Strongly-Typed Genetic Programming, Software Engineering, Testing and Debugging| Testing tools, Verification", isbn13 = "978-1-60558-030-2", URL = "http://jcbribeiro.googlepages.com/ast12-ribeiro.pdf", DOI = "doi:10.1145/1370042.1370061", size = "8 pages", abstract = "Evolutionary Testing is an emerging methodology for automatically producing high quality test data. The focus of our on-going work is precisely on generating test data for the structural unit-testing of object-oriented Java programs. The primary objective is that of efficiently guiding the search process towards the definition of a test set that achieves full structural coverage of the test object. However, the state problem of object-oriented programs requires specifying carefully ne-tuned methodologies that promote the traversal of problematic structures and difficult controlflow paths - which often involves the generation of complex and intricate test cases, that dene elaborate state scenarios. This paper proposes a methodology for evaluating the quality of both feasible and unfeasible test cases - i.e., those that are effectively completed and terminate with a call to the method under test, and those that abort prematurely because a runtime exception is thrown during test case execution. With our approach, unfeasible test cases are considered at certain stages of the evolutionary search, promoting diversity and enhancing the possibility of achieving full coverage.", notes = "also known as \cite{1370061}", } @InProceedings{Bregieiro-Ribeiro:2008:gecco, author = "Jose Carlos {Bregieiro Ribeiro} and Mario Alberto Zenha-Rela and Francisco {Fernandez de Vega}", title = "Strongly-typed genetic programming and purity analysis: input domain reduction for evolutionary testing problems", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "1783--1784", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1783.pdf", DOI = "doi:10.1145/1389095.1389439", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, Input domain reduction, search-based test case generation, strongly-Typed genetic programming, Search-based software engineering: Poster, Testing, Debugging, Testing tools, data generators, coverage testing, stack, bitset, STGP, EMCDG, IDR", abstract = "Search-based test case generation for object-oriented software is hindered by the size of the search space, which encompasses the arguments to the implicit and explicit parameters of the test object's public methods. The performance of this type of search problems can be enhanced by the definition of adequate Input Domain Reduction strategies. The focus of our on-going work is on employing evolutionary algorithms for generating test data for the structural unit-testing of Java programs. Test cases are represented and evolved using the Strongly-Typed Genetic Programming paradigm; Purity Analysis is particularly useful in this situation because it provides a means to automatically identify and remove Function Set entries that do not contribute to the definition of interesting test scenarios. Categories and Subject Descriptors", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389439}", } @InProceedings{Bregieiro-Ribeiro:2008:geccocomp, author = "Jose Carlos {Bregieiro Ribeiro}", title = "Search-based test case generation for object-oriented java software using strongly-typed genetic programming", year = "2008", editor = "Marc Ebner and Mike Cattolico and Jano {van Hemert} and Steven Gustafson and Laurence D. Merkle and Frank W. Moore and Clare Bates Congdon and Christopher D. Clack and Frank W. Moore and William Rand and Sevan G. Ficici and Rick Riolo and Jaume Bacardit and Ester Bernado-Mansilla and Martin V. Butz and Stephen L. Smith and Stefano Cagnoni and Mark Hauschild and Martin Pelikan and Kumara Sastry", isbn13 = "978-1-60558-131-6", booktitle = "GECCO-2008 Graduate Student Workshops", pages = "1819--1822", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1819.pdf", DOI = "doi:10.1145/1388969.1388979", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, dynect-orientation, evolutionary testing, search-based test case generation, strongly-Typed genetic programming", notes = "Distributed on CD-ROM at GECCO-2008 ACM Order Number 910081. Also known as \cite{1388979}", } @Article{BregieiroRibeiro2009, author = "Jose Carlos {Bregieiro Ribeiro} and Mario Alberto Zenha-Rela and Francisco {Fernandez de Vega}", title = "Test Case Evaluation and Input Domain Reduction Strategies for the Evolutionary Testing of Object-Oriented Software", journal = "Information and Software Technology", year = "2009", volume = "51", number = "11", pages = "1534--1548", month = nov, keywords = "genetic algorithms, genetic programming, Evolutionary Testing, Search-Based Software Engineering, Test Case Evaluation, Input Domain Reduction", ISSN = "0950-5849", DOI = "doi:10.1016/j.infsof.2009.06.009", URL = "http://www.sciencedirect.com/science/article/B6V0B-4WP47MR-2/2/798c73c2b9c5e1e9389b8a3491eac4f2", size = "15 pages", abstract = "In Evolutionary Testing, meta-heuristic search techniques are used for generating test data. The focus of our research is on employing evolutionary algorithms for the structural unit-testing of object-oriented programs. Relevant contributions include the introduction of novel methodologies for automation, search guidance and input domain reduction; the strategies proposed were empirically evaluated with encouraging results.Test cases are evolved using the Strongly-Typed Genetic Programming technique. Test data quality evaluation includes instrumenting the test object, executing it with the generated test cases, and tracing the structures traversed in order to derive coverage metrics. The methodology for efficiently guiding the search process towards achieving full structural coverage involves favouring test cases that exercise problematic structures. Purity Analysis is employed as a systematic strategy for reducing the search space.{"}", notes = "Third IEEE International Workshop on Automation of Software Test (AST 2008); Eighth International Conference on Quality Software (QSIC 2008)", } @InProceedings{DBLP:conf/gecco/RibeiroRV09, author = "Jose Carlos {Bregieiro Ribeiro} and Mario {Zenha Rela} and Francisco {Fernandez de Vega}", title = "An adaptive strategy for improving the performance of genetic programming-based approaches to evolutionary testing", booktitle = "GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation", year = "2009", editor = "Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano {van Hemert} and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique Alba", pages = "1949--1950", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming, Poster", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1569901.1570253", abstract = "This paper proposes an adaptive strategy for enhancing Genetic Programming-based approaches to automatic test case generation. The main contribution of this study is that of proposing an adaptive Evolutionary Testing methodology for promoting the introduction of relevant instructions into the generated test cases by means of mutation; the instructions from which the algorithm can choose are ranked, with their rankings being updated every generation in accordance to the feedback obtained from the individuals evaluated in the preceding generation. The experimental studies developed show that the adaptive strategy proposed improves the algorithm's efficiency considerably, while introducing a negligible computational overhead.", notes = "GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009). ACM Order Number 910092.", } @Article{Ribeiro20091534, author = "Jose Carlos {Bregieiro Ribeiro} and Mario Alberto Zenha-Rela and Francisco {Fernandez de Vega}", title = "Test Case Evaluation and Input Domain Reduction strategies for the Evolutionary Testing of Object-Oriented software", journal = "Information and Software Technology", volume = "51", number = "11", pages = "1534--1548", year = "2009", note = "Third IEEE International Workshop on Automation of Software Test (AST 2008); Eighth International Conference on Quality Software (QSIC 2008)", ISSN = "0950-5849", DOI = "doi:10.1016/j.infsof.2009.06.009", URL = "http://www.sciencedirect.com/science/article/B6V0B-4WP47MR-2/2/798c73c2b9c5e1e9389b8a3491eac4f2", keywords = "genetic algorithms, genetic programming, SBSE, Evolutionary Testing, Search-Based Software Engineering, Test Case Evaluation, Input Domain Reduction", abstract = "In Evolutionary Testing, meta-heuristic search techniques are used for generating test data. The focus of our research is on employing evolutionary algorithms for the structural unit-testing of Object-Oriented programs. Relevant contributions include the introduction of novel methodologies for automation, search guidance and Input Domain Reduction; the strategies proposed were empirically evaluated with encouraging results. Test cases are evolved using the Strongly-Typed Genetic Programming technique. Test data quality evaluation includes instrumenting the test object, executing it with the generated test cases, and tracing the structures traversed in order to derive coverage metrics. The methodology for efficiently guiding the search process towards achieving full structural coverage involves favouring test cases that exercise problematic structures. Purity Analysis is employed as a systematic strategy for reducing the search space.", } @InProceedings{Ribeiro:2010:EuroGP, author = "Jose Carlos {Bregieiro Ribeiro} and Mario Alberto Zenha-Rela and Francisco {Fernandez de Vega}", title = "Enabling Object Reuse on Genetic Programming-based Approaches to Object-Oriented Evolutionary Testing", booktitle = "Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010", year = "2010", editor = "Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar", volume = "6021", series = "LNCS", pages = "220--231", address = "Istanbul", month = "7-9 " # apr, organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming, SBSE", isbn13 = "978-3-642-12147-0", DOI = "doi:10.1007/978-3-642-12148-7_19", abstract = "Recent research on search-based test data generation for Object-Oriented software has relied heavily on typed Genetic Programming for representing and evolving test data. However, standard typed Genetic Programming approaches do not allow Object Reuse; this paper proposes a novel methodology to overcome this limitation. Object Reuse means that one instance can be passed to multiple methods as an argument, or multiple times to the same method as arguments. In the context of Object-Oriented Evolutionary Testing, it enables the generation of test programs that exercise structures of the software under test that would not be reachable otherwise. Additionally, the experimental studies performed show that the proposed methodology is able to effectively increase the performance of the test data generation process.", notes = "AT-nodes P-nodes \cite{lopez:2004:eurogp}. Java Red-Black tree and vector classes. pop=25. Part of \cite{Esparcia-Alcazar:2010:GP} EuroGP'2010 held in conjunction with EvoCOP2010 EvoBIO2010 and EvoApplications2010", } @PhdThesis{Bregieiro-Ribeiro:thesis, author = "Jose Carlos {Bregieiro Ribeiro}", title = "Contributions for Improving Genetic Programming-Based Approaches to the Evolutionary Testing of Object-Oriented Software", title_es = "Contribuciones a la Mejora de las Tecnicas para Test de Software Orientado a Objetos Mediante Programacion Genetica", school = "Departamento de Tecnologa de los Computadores y de las Comunicaciones, Universidad de Extremadura", year = "2010", address = "Merida, Spain", month = "12 " # nov, keywords = "genetic algorithms, genetic programming, SBSE", URL = "https://sites.google.com/site/jcbribeiro/jose.ribeiro_phdthesis_final.pdf", URL = "https://sites.google.com/site/jcbribeiro/", size = "188 pages", notes = "eCrash Examination Board: Prof. Xin Yao, Prof. Ernesto Costa, Prof. Carlos Cotta, Prof. Ignacio Hidalgo, Prof. Miguel Macias. Supervisors: Francisco Fernandez de Vega (Universidad de Extremadura, Spain) Mario Alberto Zenha-Rela (Universidade de Coimbra, Portugal)", } @InCollection{Bregieiro-Ribeiro:2015:hbgpa, author = "Jose Carlos {Bregieiro Ribeiro} and Ana Filipa Nogueira and Francisco {Fernandez de Vega} and Mario Alberto Zenha-Rela", title = "eCrash: a Genetic Programming-Based Testing Tool for Object-Oriented Software", booktitle = "Handbook of Genetic Programming Applications", publisher = "Springer", year = "2015", editor = "Amir H. Gandomi and Amir H. Alavi and Conor Ryan", chapter = "23", pages = "575--593", keywords = "genetic algorithms, genetic programming, SBSE, Evolutionary Testing, Object-Orientation, Search-Based Software Engineering, Unit Testing", isbn13 = "978-3-319-20882-4", DOI = "doi:10.1007/978-3-319-20883-1_23", abstract = "This paper describes the methodology, architecture and features of the eCrash framework, a Java-based tool which employs Strongly-Typed Genetic Programming to automate the generation of test data for the structural unit testing of Object-Oriented programs. The application of Evolutionary Algorithms to Test Data generation is often referred to as Evolutionary Testing. eCrash implements an Evolutionary Testing strategy developed with three major purposes: improving the level of performance and automation of the Software Testing process; minimising the interference of the tool's users on the Test Object analysis to a minimum; and mitigating the impact of users decisions in the Test Data generation process.", } @InProceedings{Bremer:2021:CSIS, author = "Joerg Bremer and Sebastian Lehnhoff", title = "Towards Evolutionary Emergence", booktitle = "Position and Communication Papers of the 16th Conference on Computer Science and Intelligence Systems", year = "2021", editor = "Maria Ganzha and Leszek Maciaszek and Marcin Paprzycki and Dominik Slezak", pages = "55--60", address = "Online", month = "2-5 " # sep, organisation = "Polish Information Processing Society, Polskie Towarzystwo Informatyczne, Warszawa, Poland", publisher = "FedCSIS", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, PSO", isbn13 = "78-83-962423-0-3", ISSN = "2300-5963", URL = "https://annals-csis.org/Volume_26/pliks/position.pdf", URL = "https://annals-csis.org/proceedings/2021/drp/pdf/111.pdf", DOI = "doi:10.15439/2021F111", size = "6 pages", abstract = "With the upcoming era of large-scale, complex cyber-physical systems, also the demand for decentralized and self-organising algorithms for coordination rises. Often such algorithms rely on emergent behavior; local observations and decisions aggregate to some global behavior without any apparent, explicitly programmed rule. Systematically designing these algorithms targeted for a new orchestration or optimisation task is, at best, tedious and error prone. Suitable and widely applicable design patterns are scarce so far. We opt for a machine learning based approach that learns the necessary mechanisms for targeted emergent behavior automatically. To achieve this,we use Cartesian genetic programming. As an example that demonstrates the general applicability of this idea, we trained a swarm-based optimization heuristics and present first results showing that the learned swarm behavior is significantly better than just random search. We also discuss the encountered pitfalls and remaining challenges on the research agenda.", abstract = "Cyber-physical systems demand self-organizing algorithms that rely on emergent behaviour; local observations and decision aggregate to global behavior without explicitly programmed rules. Designing these algorithms is error prone. Widely applicable design patterns are scarce. We opt for a machine learning approach that learns mechanisms for targeted emergent behavior automatically. We use Cartesian genetic programming. As an example, that demonstrates the general applicability of this idea, we trained a swarm based heuristics and present first results showing that the learned swarm behavior is significantly better than just random search. We also discuss the encountered pitfalls and remaining challenges on the research agenda.", notes = "Also {"}Annals of Computer Science and Information Systems{"}, ACSIS, Volume 26, 55-60 (2021) Sofia, Bulgaria secretariat@fedcsis.org http://annals-csis.org/", } @InProceedings{bremer:2022:PAAMS, author = "Joerg Bremer and Sebastian Lehnhoff", title = "Fully Distributed Cartesian Genetic Programming", booktitle = "Advances in Practical Applications of Agents, Multi-Agent Systems, and Complex Systems Simulation. The PAAMS Collection", year = "2022", editor = "Frank Dignum and Philippe Mathieu and Juan Manuel Corchado and Fernando {De La Prieta}", volume = "13616", series = "LNAI", pages = "36--49", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, parallel computing, Multi-agent system, COHDA, Distributed optimization", isbn13 = "978-3-031-18192-4", URL = "https://rdcu.be/c7nZL", URL = "http://link.springer.com/chapter/10.1007/978-3-031-18192-4_4", DOI = "doi:10.1007/978-3-031-18192-4_4", size = "14 pages", abstract = "Cartesian genetic programming is a popular version of genetic programming and has meanwhile proven its performance in many use cases. This paper introduces an algorithmic level decomposition of program evolution that can be solved by a multi-agent system in a fully distributed manner. A heuristic for distributed combinatorial problem solving is adapted to evolve programs. The applicability of the approach and the effectiveness of the multi-agent approach as well as of the evolved genetic programs are demonstrated using symbolic regression, n-parity, and classification problems.", } @Article{bremer:2023:Systems, author = "Joerg Bremer and Sebastian Lehnhoff", title = "Enhancing Local Decisions in Agent-Based Cartesian Genetic Programming by {CMA-ES}", journal = "Systems", year = "2023", volume = "11", number = "4", pages = "Article No. 177", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", ISSN = "2079-8954", URL = "https://www.mdpi.com/2079-8954/11/4/177", DOI = "doi:10.3390/systems11040177", abstract = "Cartesian genetic programming is a popular version of classical genetic programming, and it has now demonstrated a very good performance in solving various use cases. Originally, programs evolved by using a centralized optimisation approach. Recently, an algorithmic level decomposition of program evolution has been introduced that can be solved by a multi-agent system in a fully distributed manner. A heuristic for distributed combinatorial problem-solving was adapted to evolve these programs. The applicability of the approach and the effectiveness of the used multi-agent protocol as well as of the evolved genetic programs for the case of full enumeration in local agent decisions has already been successfully demonstrated. Symbolic regression, n-parity, and classification problems were used for this purpose. As is typical of decentralized systems, agents have to solve local sub-problems for decision-making and for determining the best local contribution to solving program evolution. So far, only a full enumeration of the solution candidates has been used, which is not sufficient for larger problem sizes. We extend this approach by using CMA-ES as an algorithm for local decisions. The superior performance of CMA-ES is demonstrated using Koza’s computational effort statistic when compared with the original approach. In addition, the distributed modality of the local optimisation is scrutinized by a fitness landscape analysis.", notes = "also known as \cite{systems11040177}", } @InProceedings{bremer:2023:UKCI, author = "Joerg Bremer and Sebastian Lehnhoff", title = "Hybridizing Levy Flights and Cartesian Genetic Programming for Learning {Swarm-Based} Optimization", booktitle = "UK Workshop on Computational Intelligence", year = "2023", pages = "299--310", address = "Birmingham", month = "6-8 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", URL = "http://link.springer.com/chapter/10.1007/978-3-031-47508-5_24", DOI = "doi:10.1007/978-3-031-47508-5_24", notes = "Published in 2024", } @InProceedings{Bremner:2010:ICES, author = "Paul Bremner and Mohammad Samie and Gabriel Dragffy and Tony Pipe and James Alfred Walker and Andy M. Tyrrell", title = "Evolving Digital Circuits Using Complex Building Blocks", booktitle = "Proceedings of the 9th International Conference Evolvable Systems: From Biology to Hardware, ICES 2010", year = "2010", editor = "Gianluca Tempesti and Andy M. Tyrrell and Julian F. Miller", series = "Lecture Notes in Computer Science", volume = "6274", pages = "37--48", address = "York", month = sep # " 6-8", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-15322-8", DOI = "doi:10.1007/978-3-642-15323-5_4", size = "12 pages", abstract = "This work is a study of the viability of using complex building blocks (termed molecules) within the evolutionary computation paradigm of CGP; extending it to MolCGP. Increasing the complexity of the building blocks increases the design space that is to be explored to find a solution; thus, experiments were undertaken to find out whether this change affects the optimum parameter settings required. It was observed that the same degree of neutrality and (greedy) 1+4 evolution strategy gave optimum performance. The Computational Effort used to solve a series of benchmark problems was calculated, and compared with that used for the standard implementation of CGP. Significantly less Computational Effort was exerted by MolCGP in 3 out of 4 of the benchmark problems tested. Additionally, one of the evolved solutions to the 2-bit multiplier problem was examined, and it was observed that functionality present in the molecules, was exploited by evolution in a way that would be highly unlikely if using standard design techniques.", affiliation = "Bristol Robotics Laboratory, University of the West of England, Bristol, BS16 1QY", } @InProceedings{Bremner:2011:EuroGP, author = "Paul Bremner and Mohammad Samie and Anthony G. Pipe and Gabriel Dragffy and Yang Liu", title = "Evolving Cell Array Configurations Using CGP", booktitle = "Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011", year = "2011", month = "27-29 " # apr, editor = "Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado", series = "LNCS", volume = "6621", publisher = "Springer Verlag", address = "Turin, Italy", pages = "73--84", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-20406-7", DOI = "doi:10.1007/978-3-642-20407-4_7", abstract = "A cell array is a proposed type of custom FPGA, where digital circuits can be formed from interconnected configurable cells. In this paper we have presented a means by which CGP might be adapted to evolve configurations of a proposed cell array. As part of doing so, we have suggested an additional genetic operator that exploits modularity by copying sections of the genome within a solution, and investigated its efficacy. Additionally, we have investigated applying selection pressure for parsimony during functional evolution, rather than in a subsequent stage as proposed in other work. Our results show that solutions to benchmark problems can be evolved with a good degree of efficiency, and that compact solutions can be found with no significant impact on the required number of circuit evaluations.", notes = "Part of \cite{Silva:2011:GP} EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011", } @InProceedings{Bremner:2011:MOoCCE, title = "Multi-Objective Optimisation of Cell-Array Circuit Evolution", author = "Paul Bremner and Mohammad Samie and Anthony Pipe and Andy Tyrrell", pages = "440--446", booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary Computation", year = "2011", editor = "Alice E. Smith", month = "5-8 " # jun, address = "New Orleans, USA", organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, cell-array circuit evolution, circuit decomposition technique, custom FPGA, digital circuit, interconnected configurable cell, multiobjective optimisation, field programmable gate arrays", DOI = "doi:10.1109/CEC.2011.5949651", abstract = "In this paper we have investigated the efficacy of applying multi-objective optimisation to Cartesian genetic programming (CGP) when used for evolution of cell-array configurations. A cell-array is a proposed type of custom FPGA, where digital circuits can be formed from interconnected configurable cells; thus, the CGP nodes are more complex than in its standard implementation. We have described modifications to a previously described optimisation algorithm that has led to significant improvements in performance; circuits close to a hand designed equivalent have been found, in terms of the optimised objectives. Additionally we have investigated the effect of circuit decomposition techniques on evolutionary performance. We found that using a hybrid of input and output decomposition techniques substantial reductions in evolution time were observed. Further, while the number of circuit inputs is the key factor for functional evolution time, the number of circuit outputs is the key factor for optimisation time.", notes = "CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.", } @Article{bressane:2023:Pollutants, author = "Adriano Bressane and Anna Isabel Silva Loureiro and Raissa Caroline Gomes and Admilson Irio Ribeiro and Regina Marcia Longo and Rogerio Galante Negri", title = "Spatiotemporal Effect of Land Use on Water Quality in a Peri-Urban Watershed in a Brazilian Metropolitan Region: An Approach Considering {GEP-Based} Artificial Intelligence", journal = "Pollutants", year = "2023", volume = "3", number = "1", pages = "1--11", keywords = "genetic algorithms, genetic programming, gene expression programming", ISSN = "2673-4672", URL = "https://www.mdpi.com/2673-4672/3/1/1", DOI = "doi:10.3390/pollutants3010001", abstract = "The suppression of natural spaces due to urban sprawl and increases in built and agricultural environments has affected water resource quality, especially in areas with high population densities. Considering the advances in the Brazilian environmental legal framework, the present study aimed to verify whether land use has still affected water quality through a case study of a peri-urban watershed in a Brazilian metropolitan region. Analyses of physical–chemical indicators, collected at several sample points with various land-use parameters at different seasons of the year, were carried out based on an approach combining variance analysis and genetic programming. As a result, some statistically significant spatiotemporal effects on water quality associated with the land use, such as urban areas and thermotolerant coliform (R = −0.82, p < 0.01), mixed vegetation and dissolved oxygen (R = 0.80, p < 0.001), agriculture/pasture and biochemical oxygen demand (R = 0.40, p < 0.001), and sugarcane and turbidity (R = 0.65, p < 0.001), were verified. In turn, gene expression programming allowed for the computing of the importance of land-use typologies based on their capability to explain the variances of the water quality parameter. In conclusion, in spite of the advances in the Brazilian law, land use has still significantly affected water quality. Public policies and decisions are required to ensure effective compliance with legal guidelines.", notes = "also known as \cite{pollutants3010001}", } @InCollection{breunig:1995:LIPRGP, author = "Markus M. Breunig", title = "Location Independent Pattern Recognition using Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1995", year = "1995", editor = "John R. Koza", pages = "29--38", address = "Stanford, California, 94305-3079 USA", month = "11 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming, ADF", ISBN = "0-18-195720-5", URL = "http://www.dbs.informatik.uni-muenchen.de/~breunig/HomepageResearch/Papers/PatternRecog.pdf", size = "10 pages", abstract = "This paper describes an application of genetic programming. Programs able of recognising a pattern independent of its location are evolved. Usually the evolution of programs is controlled primarily by the fitness evaluation function. This paper demonstrates how genetic programming can be encouraged to evolve programs with properties not being explicitly considered in the fitness measure like location independence. The measurements taken include the use of automatically defined functions allowing the problem to be decomposed into sub-functions, a special implementation of iteration and carefully chosen function and terminal sets. A main purpose was to minimise the restrictions imposed on the solution, i.e. giving the genetic programming as much freedom as possible while still encouraging the desired properties.", notes = "part of \cite{koza:1995:gagp}", } @InProceedings{Brezocnik:1997:DAAAM, author = "Miran Brezocnik and Joze Balic", title = "System for discovering and optimizung mathematical models using genetic programming and genetic algorithms", booktitle = "Proceedings of the 8th International DAAAM Symposium", year = "1997", editor = "Branko Katalinic", pages = "37--38", month = "23-25 " # oct, publisher = "DAAAM International", email = "mbrezocnik@uni-mb.si", ISBN = "3-901509-04-6", address = "Dubrovnik, Croatia", publisher_address = "Vienna", keywords = "genetic algorithms, genetic programming, adaptive systems, evolutionary computation", abstract = "In this paper, we propose a system for discovering and optimising of various mathematical models. The system consists of two parts. In the first part, we discover unknown mathematical models on the basis of empirical given data (learning data). In the second part, we optimise parameters of the discovered mathematical models. Genetic programming (GP) and genetic algorithm (GA) are used for discovering and optimizing of models, respectively. GP and GA are evolutionary optimization methods based on the Darwinian natural selection and survival of the fittest.", notes = "http://www.daaam.com/daaam/Past_Activities/DAAAM_International_Activities_1990-2005.pdf", } @InProceedings{Brezocnik:1997:ICDMMI, author = "Miran Brezocnik and Joze Balic", title = "Comparison of genetic programming with genetic algorithm", booktitle = "3rd International Conference Design to Manufacture in Modern Industry: Design to manufacture in modern industry", year = "1997", editor = "Anton Jezernik and Bojan Dolsak", pages = "150--156", month = sep, publisher = "University of Maribor, Faculty of Mechanical Engineering", publisher_address = "Slovenia", email = "mbrezocnik@uni-mb.si", ISBN = "86-435-0192-1", keywords = "genetic algorithms, genetic programming", abstract = "The paper is concerned about the conventional genetic algorithm (GA) and, particularly, the recently proposed paradigm: genetic programming (GP). The well-known basic knowledge of the conventional GA is briefly presented, but only for comparison with GP. On the contrary, the GP method is discussed in detail. The GP is an evolutionary process, where the fittest computer program in the space of possible computer programs is searched for. The fittest computer program represents a solution in the observed problem domain. One of the most powerful abilities of the GP that allows inclusion of rich information into computer programs is clearly presented and emphasised. Our personal views concerning the complementary nature of the conventional GA and GP are discussed. Finally, we briefly presented the implementation of the GP on the wide variety of different problems.", } @InProceedings{Brezocnik:1998:IAD, author = "Miran Brezocnik and Joze Balic", title = "A genetic programming approach for modelling of self-organizing assembly systems", booktitle = "Intelligent assembly and disassembly - IAD'98: A proceedings volume from the IFAC Workshop", year = "1998", editor = "Peter Kopacek and Dragica Noe", pages = "47--52", address = "Bled, Slovenia", publisher_address = "Oxford, UK", month = "21-23 " # may, organisation = "IFAC", publisher = "Pergamon", keywords = "genetic algorithms, genetic programming, self-organising systems, intelligent manufacturing systems, assembly, simulation", ISBN = "0-08-043042-2", URL = "http://www.amazon.co.uk/Intelligent-Assembly-Disassembly-IAD-Proceedings/dp/0080430422", URL = "http://trove.nla.gov.au/version/44951526", abstract = "The paper proposes a genetic programming approach to the modelling of the assembly of the basic components (cells) into an integrated (whole) organism. The concept is based on the simulation of self-organising uniting of live cells into tissues, organs and individuals. The assembly is treated as the basic and general principle, therefore, the basic cells can be very different. Assembling takes place on the basis of the genetic content in the basic components and is influenced by the environmental conditions. The genetic content can be topological, geometrical, technological, ecological, economical, etc. The simulation of the self-organizing genetic assembly of the variant product consisting of many basic components is given. The basic feature of genetic assembly is that it takes place in a distributed, nondeterministical, bottom-up, and self-organising manner.", } @PhdThesis{Brezocnik:thesis, author = "Miran Brezocnik", title = "MODELING OF TECHNOLOGICAL SYSTEMS BY THE USE OF GENETIC METHODS", school = "University of Maribor, Faculty of Mechanical Engineering", year = "1998", type = "phdthesis", address = "Smetanova ulica 17, SI-2000 Maribor, Slovenia", email = "mbrezocnik@uni-mb.si", keywords = "genetic algorithms, genetic programming, intelligent manufacturing systems, technological system, forming, assembly, robots, self-organisation, genetic methods, modelling, optimisation", size = "129 pages", abstract = "In this work we propose modelling of different technological systems by a general approach. The research starts with searching for common characteristics of the technological systems. After they have been found out, they are synthesised into uniform principle serving for conceiving a general method for their modeling. The method imitates associating of living cells into tissues, organs and organisms. The disturbances resulting from limited human knowledge, unpredictability of technological systems, and unexpected events in production environment are automatically eliminated during the evolutionary process. More and more intelligent behaviour of the individual technological system, which is expressed as an increasingly successful synchronisation of the material, energy and information, is obtained gradually with self-organization and without centralised instruction. In order to support the theoretical researches a system for genetic programming is developed. It is successfully used for genetic modeling of: 1. forming efficiency, 2. assembly and classification, and 3. trajectories of robots in the production environment. The results of modeling of forming efficiency show excellent correspondence between analytically obtained models, experimental results, and genetically developed models. In case of genetic modeling of assembly the basic components are integrated into the final product in a self-organising manner. Genetic modelling the trajectory of the robot, striving to arrive at the aim through a dynamic production environment, discovers the intelligent robot navigation formed during the evolutionary process.", } @Article{Brezocnik:2000:JTP, author = "Miran Brezocnik and Joze Balic and Leo Gusel", title = "Artificial intelligence approach to determination of flow curve", journal = "Journal for technology of plasticity", year = "2000", volume = "25", number = "1-2", pages = "1--7", keywords = "genetic algorithms, genetic programming, forming, flow curve, artificial intelligence", ISSN = "0350-2368", ISSN = "0354-3870", abstract = "For the control of the forming process it is necessary to know as precisely as possible the flow curve of the material formed. The paper presents the determination of the equation for the flow curve with the artificial intelligence approach. The genetic programming method (GP) was used. It is an evolutionary optimisation technique based on the Darwinian natural selection and the survival of the fittest organisms. The comparison between the experimental results, the analytical solution and the solution obtained genetically clearly shows that the genetic programming method is a very promising approach.", notes = "Novi Sad : Faculty of Technical Sciences, Institute for Production Engineering broken http://www.scindeks.nbs.bg.ac.yu/arhiva.php?issn=0350-2368&je=en", } @Book{Brezocnik:book, author = "Miran Brezocnik", title_en = "Using genetic programming in intelligent manufacturing systems", title = "Uporaba genetskega programiranja v inteligentnih proizvodnih sistemih", publisher = "University of Maribor, Faculty of mechanical engineering", year = "2000", address = "Maribor, Slovenia", email = "mbrezocnik@uni-mb.si", keywords = "genetic algorithms, genetic programming, manufacturing, intelligent manufacturing systems, modelling, assembly, metal forming, autonomous robot, evolutionary algorithms", ISBN = "86-435-0306-1", broken = "http://maja.uni-mb.si/slo/Knjige/2000-03-mon/index.htm", URL = "http://www.isbns.net/isbn/9788643503065/", URL = "http://www.worldcat.org/title/uporaba-genetskega-programiranja-v-inteligentnih-proizvodnih-sistemih/oclc/444489491", size = "160 pages", note = "In Slovenian", } @Article{Brezocnik:2001:MPT, author = "Miran Brezocnik and Joze Balic and Zlatko Kampus", title = "Modeling of forming efficiency using genetic programming", journal = "Journal of Materials Processing Technology", volume = "109", pages = "20--29", year = "2001", number = "1-2", month = "1 " # feb, email = "joze.balic@uni-mb.si", keywords = "genetic algorithms, genetic programming, Metal-forming, Yield stress, Forming efficiency, Modeling, Adaptation, Artificial intelligence", ISSN = "0924-0136", URL = "http://www.sciencedirect.com/science/article/B6TGJ-423HM9M-5/1/bcc93a13fbb04521236d3a8e16f8850b", DOI = "doi:10.1016/S0924-0136(00)00783-4", abstract = "This paper proposes new approach for modeling of various processes in metal-forming industry. As an example, we demonstrate the use of genetic programming (GP) for modeling of forming efficiency. The forming efficiency is a basis for determination of yield stress which is the fundamental characteristic of metallic materials. Several different genetically evolved models for forming efficiency on the basis of experimental data for learning were discovered. The obtained models (equations) differ in size, shape, complexity and precision of solutions. In one run out of many runs of our GP system the well-known equation of Siebel was obtained. This fact leads us to opinion that GP is a very powerful evolutionary optimization method appropriate not only for modeling of forming efficiency but also for modeling of many other processes in metal-forming industry. [COBISS.SI-ID 5979414]", notes = "Journal of Materials Processing Technology http://www.elsevier.com/wps/find/journaldescription.cws_home/505656/description#description", } @Article{Brezocnik:2001:RCIM, author = "Miran Brezocnik and Joze Balic", title = "A genetic-based approach to simulation of self-organizing assembly", journal = "Robotics and Computer-Integrated Manufacturing", volume = "17", pages = "113--120", year = "2001", number = "1-2", month = feb, keywords = "genetic algorithms, genetic programming, Intelligent manufacturing systems, Self-organizing assembly, Evolution", ISSN = "0736-5845", DOI = "doi:10.1016/S0736-5845(00)00044-2", URL = "http://www.sciencedirect.com/science/article/B6V4P-42DP1Y1-J/1/175033beb3ddb787b75c22253e5534c2", abstract = "The paper proposes a new and innovative biologically oriented idea in conceiving intelligent systems in modern factories of the future. The intelligent system is treated as an autonomous organization structure efficiently adapting itself to the dynamic changes in the production environment and the environment in a wider sense. Simulation of self-organizing assembly of mechanical parts (basic components) into the product is presented as an example of the intelligent system. The genetic programming method is used. The genetic-based assembly takes place on the basis of the genetic content in the basic components and the influence of the environment. The evolution of solutions happens in a distributed way, nondeterministically, bottom-up, and in a self-organizing manner. The paper is also a contribution to the international research and development program intelligent manufacturing systems, which is one of the biggest projects ever introduced.", notes = "Robotics and Computer-Integrated Manufacturing http://www.elsevier.com/wps/find/journaldescription.cws_home/704/description#description", } @InProceedings{Brezocnik:2001:RIM, author = "Miran Brezocnik and Miha Kovacic", title = "Survey of the evolutionary computation and its application in manufacturing systems", booktitle = "3rd International Conference on Revitalization and Modernization of Production RIM 2001", year = "2001", editor = "Milan Jurkovic and Isak Karabegovic", pages = "501--508", address = "University of Bihac, Bihacu, Bosnia and Herzegovina", month = sep, organisation = "Bihac, Tehnieki fakultet", keywords = "genetic algorithms, genetic programming", ISBN = "9958-624-10-9", } @Article{Brezocnik:2002:JIM, author = "Miran Brezocnik and Joze Balic and Karl Kuzman", title = "Genetic programming approach to determining of metal materials properties", journal = "Journal of Intelligent Manufacturing", year = "2002", volume = "13", number = "1", pages = "5--17", month = feb, email = "joze.balic@uni-mb.si", keywords = "genetic algorithms, genetic programming, materials properties, metal forming, modeling, self-organisation", ISSN = "0956-5515", DOI = "doi:10.1023/A:1013693828052", abstract = "The paper deals with determining metal materials properties by use of genetic programming (GP). As an example, the determination of the flow stress in bulk forming is presented. The flow stress can be calculated on the basis of known forming efficiency. The experimental data obtained during pressure test serve as an environment to which models for forming efficiency have to be adapted during simulated evolution as much as possible. By performing four experiments, several different models for forming efficiency are genetically developed. The models are not a result of the human intelligence but of intelligent evolutionary process. With regard to their precision, the successful models are more or less equivalent; they differ mainly in size, shape, and complexity of solutions. The influence of selection of different initial model components (genes) on the probability of successful solution is studied in detail. In one especially successful run of the GP system the Siebel's expression was genetically developed. In addition, redundancy of the knowledge hidden in the experimental data was detected and eliminated without the influence of human intelligence. Researches showed excellent agreement between the experimental data, existing analytical solutions, and models obtained genetically.", notes = "Journal of Intelligent Manufacturing http://www.springeronline.com/sgw/cda/frontpage/0,11855,4-40528-70-35668245-0,00.html", } @InProceedings{Brezocnik:2002:AMME, author = "Miran Brezocnik and Miha Kovacic", title = "Prediction of surface roughness with genetic programming", booktitle = "Proceedings of the 11th International Scientific Conference Achievements in Mechanical and Materials Engineering, AMME'2002", year = "2002", editor = "Leszek A. Dobrzanski", pages = "23--26", keywords = "genetic algorithms, genetic programming", ISBN = "83-914458-7-9", notes = "Broken Dec 2012 http://www.wamme.org/index.php?id=37&PHPSESSID=8b9ce9355f0dbdaebee40f5d6ddec320 See also \cite{Brezocnik:2004:JMPT}", } @InCollection{Brezocnik:2002:DAAAM, author = "Miran Brezocnik", title = "On intelligent learning systems for next-generation manufacturing", booktitle = "DAAAM International Scientific Book 2002", chapter = "6", pages = "39--48", publisher = "DAAAM International", year = "2002", volume = "1", address = "Vienna", month = oct, editor = "Branko Katalinic", email = "mbrezocnik@uni-mb.si", keywords = "genetic algorithms, genetic programming, manufacturing systems, artificial intelligence, learning, evolutionary computation, emergence", ISBN = "3-901509-30-5", URL = "http://www.daaam.com/", abstract = "In the first part of the paper we analyse the basic scientific and philosophical facts, as well as social circumstances, that have a great impact on manufacturing concepts. Then we propose a shift from the present manufacturing paradigm favouring particularly determinism, rationalism, and top-down organisational principles towards intelligent systems in next-generation manufacturing involving phenomena such as non-determination, emergence, learning, complexity, self-organization, bottom-up organisation, and co-existence with natural environment. In the second part we give two examples from metal forming industry and autonomous intelligent vehicles. Both systems are based on learning and imitate some excellent properties of living systems. The stable global order (i.e. the solution) of each presented system gradually emerges as a result of interactions between basic entities of which the system consists and the environment.", notes = "http://www.daaam.com/daaam/Publications/Publications.htm", } @InProceedings{Brezocnik:TMT2002, author = "Miran Brezocnik and Miha Kovacic", title = "Integrated evolutionary computation environment for optimizing and modeling of manufacturing processes", booktitle = "6th International Research/Expert Conference {"}Trends in the development of Machinery and Associated Technology{"}", year = "2002", editor = "Safet Brdarevia and Sabahudin Ekinovia and Ramon {Compamys Pascual} and Joan {Calvet Vivancos}", pages = "TMT02--073", address = "Neum, Bosnia and Herzegovina", month = "18-22 " # sep, organisation = "FACULTY OF MECHANICAL ENGINEERING IN ZENICA, UNIVERSITY OF SARAJEVO, BOSNIA AND HERZEGOVINA. UNIVERSITAT POLITECNICA DE CATALUNYA BARCELONA, DEP. D'ENGINYERIA MECANICA (SPAIN)", keywords = "genetic algorithms, genetic programming, Poster", ISBN = "9958-617-11-0", URL = "http://www.mf.unze.ba/tmt2002/tmt2002-1.htm", notes = "http://www.mf.unze.ba/tmt2002/", } @Article{Brezocnik:2003:RCIM, author = "Miran Brezocnik and Joze Balic and Zmago Brezocnik", title = "Emergence of intelligence in next-generation manufacturing systems", journal = "Robotics and Computer-Integrated Manufacturing", year = "2003", volume = "19", pages = "55--63", number = "1-2", abstract = "In the paper we propose a fundamental shift from the present manufacturing concepts and problem solving approaches towards new manufacturing paradigms involving phenomena such as emergence, intelligence, non-determinism, complexity, self-organisation, bottom-up organization, and coexistence with the ecosystem. In the first part of the paper we study the characteristics of the past and the present manufacturing concepts and the problems they caused. According to the analogy with the terms in cognitive psychology four types of problems occurring in complex manufacturing systems are identified. Then, appropriateness of various intelligent systems for solving of these four types of problems is analysed. In the second part of the paper, we study two completely different problems. These two problems are (1) identification of system in metal forming industry and (2) autonomous robot system in manufacturing environment. A genetic-based approach that imitates integration of living cells into tissues, organs, and organisms is used. The paper clearly shows how the state of the stable global order (i.e., the intelligence) of the overall system gradually emerges as a result of low-level interactions between entities of which the system consists and the environment.", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6V4P-47XW4VG-1/2/f88aada395a16da3031d89d272dae207", month = feb # "-" # apr, keywords = "genetic algorithms, genetic programming, Intelligent manufacturing systems, Emergence, Learning", DOI = "doi:10.1016/S0736-5845(02)00062-5", } @InCollection{Brezocnik:2003:DAAAM, author = "Miran Brezocnik and Miha Kovacic", title = "Modelling of intelligent mobility for next-generation manufacturing systems", volume = "2", booktitle = "DAAAM International Scientific Book 2003", publisher = "DAAAM International Vienna", year = "2003", editor = "B. Katalinic", pages = "95--102", address = "Vienna", month = jul, keywords = "genetic algorithms, genetic programming", ISBN = "3-901509-30-5", abstract = "We present the modelling of the intelligent mobility for next-generation manufacturing systems. The modelling took place in the simplified dynamic manufacturing environment with several loads, obstacles and one robot placed in it. Each agent is freely movable on the floor. The aim of the robot is to pick up all loads and to come to the goal point. For optimisation of the robot path between loads and for planning of the robot travel the genetic algorithm and the genetic programming were used, respectively. The research showed that intelligent behaviour of the robot results from the interactions of the robot with the dynamic environment.", notes = "publication@daaam.com broken Jan 2013 http://www.daaam.com/daaam/Sc_Book/DAAAM_International_Scientific_Book_2006.htm", } @InProceedings{Brezocnik:2003:tmt, author = "Miran Brezocnik and Miha Kovacic and Mirko Ficko", title = "Genetic-based approach to predict surface roughness in end milling", booktitle = "7th International Research/Expert Conference {"}Trends in the Development Machinery and Associated Technology{"}", year = "2003", pages = "529--532", address = "Barcelona, Spain", month = "15-16 " # sep, organisation = "UNIVERSITAT POLITECNICA DE CATALUNYA UNIVERSITY OF SARAJEVO ESCOLA TECNICA SUPERIOR D'ENGINYERIA INDUSTRIAL DE BARCELONA FACULTY OF MECHANICAL ENGINEERING IN ZENICA (Bosnia and Herzegovina) DEPARTAMENT D'ENGINYERIA MECANICA (Spain)", keywords = "genetic algorithms, genetic programming", ISBN = "9958-617-18-8", notes = "http://www.mf.unze.ba/tmt2003/papers.htm", } @Article{Brezocnik:2004:AJME, author = "Miran Brezocnik and Miha Kovacic and Mirko Ficko", title = "Intelligent systems for next-generation manufacturing", journal = "Academic Journal of Manufacturing Engineering", year = "2004", volume = "2", number = "1", pages = "34--37", keywords = "genetic algorithms, genetic programming", ISSN = "1583-7904", URL = "http://www.worldcat.org/title/intelligent-systems-for-next-generation-manufacturing/oclc/440013859", abstract = "In this paper we propose a fundamental shift from the present manufacturing paradigm favouring particularly determinism, rationalism, and top-down organizational principles towards intelligent systems in next-generation manufacturing involving phenomena such as non-determinism, emergence, learning, complexity, self-organization, and bottom-up organization. The problem types and different intelligent systems for solving of problems were studied. Two examples of the intelligent systems from the areas of metal forming industry and autonomous intelligent vehicles are given. Both systems are based on learning and imitate some excellent properties of the living systems. Genetic programming and genetic algorithms were used. The stable global order (i.e., solution) of each presented system gradually emerges as a result of interactions between entities of which the system consists and the environment", notes = "http://reviste.ulbsibiu.ro/ajme/ http://eng.upt.ro/auif/journal_vol_2_2004_no_1.html#8 Feb 2014 journal_vol_2_2004_no_1.html gives a number of alternative titles... ", } @Article{Brezocnik:2004:IJAMT, author = "Miran Brezocnik and Leo Gusel", title = "Predicting stress distribution in cold-formed material with genetic programming", journal = "International journal of advanced manufacturing technology", year = "2004", volume = "23", number = "7-8", pages = "467--474", email = "mbrezocnik@uni-mb.si", keywords = "genetic algorithms, genetic programming, metal forming, stress distribution, modelling", ISSN = "0268-3768", DOI = "doi:10.1007/s00170-003-1649-3", abstract = "In this paper we propose a genetic programming approach to predict radial stress distribution in cold-formed material. As an example, cylindrical specimens of copper alloy were forward extruded and analysed by the visioplasticity method. They were extruded with different coefficients of friction. The values of three independent variables (i.e., radial and axial position of measured stress node, and coefficient of friction) were collected after each extrusion. These variables influence the value of the dependent variable, i.e., radial stress. On the basis of training data set, various different prediction models for radial stress distribution were developed during simulated evolution. Accuracy of the best models was proved with the testing data set. The research showed that by proposed approach the precise prediction models can be developed; therefore, it is widely used also in other areas in metal-forming industry, where the experimental data on the process are known.", } @InProceedings{Brezocnik:2004:TMT, author = "Miran Brezocnik and Mirko Ficko and Miha Kovacic", title = "Genetic based approach to predict surface roughness", booktitle = "8th International Research/Expert Conference Trends in the Development Machinery and Associated Technology", year = "2004", pages = "91--94", address = "Neum, Bosnia and Herzegovina", month = "15-19 " # sep, keywords = "genetic algorithms, genetic programming, celno frezanje, povrsinska hrapavost, napoved hrapavosti, genetsko programiranje, end milling, surface roughness, prediction of surface roughness", ISBN = "9958-617-21-8", URL = "http://cobiss.izum.si/scripts/cobiss?command=DISPLAY&base=COBIB&RID=9009686", URL = "https://plus.cobiss.si/opac7/bib/9009686", abstract = "In this paper we propose genetic programming to predict surface roughness in end milling. Two independent data sets were obtained from measurements: the training data set and the testing data set. Spindle speed, feed rate, depth of cut and vibrations were used as independent input variables (parameters), while surface roughness was the output variable. Different surface roughness models were obtained with the training data set and genetic programming. The testing data set was used to prove the accuracy of the best model. The conclusion is that surface roughness is most influenced by the feed rate, while vibrations increase the prediction accuracy.", jaezik = "angleski", notes = "Paper number TMT04-237 http://www.mf.unze.ba/tmt2004/submittedabstracts2.htm (broken) COBISS.SI-ID: 9009686", } @Article{Brezocnik:2004:JMPT, author = "M. Brezocnik and M. Kovacic and M. Ficko", title = "Prediction of surface roughness with genetic programming", journal = "Journal of Materials Processing Technology", year = "2004", volume = "157-158", pages = "28--36", month = "20 " # dec # " 2004", keywords = "genetic algorithms, genetic programming, Manufacturing systems, Surface roughness; Milling, Evolutionary algorithms", ISSN = "0924-0136", DOI = "doi:10.1016/j.jmatprotec.2004.09.004", abstract = "In this paper, we propose genetic programming to predict surface roughness in end-milling. Two independent data sets were obtained on the basis of measurement: training data set and testing data set. Spindle speed, feed rate, depth of cut, and vibrations are used as independent input variables (parameters), while surface roughness as dependent output variable. On the basis of training data set, different models for surface roughness were developed by genetic programming. Accuracy of the best model was proved with the testing data. It was established that the surface roughness is most influenced by the feed rate, whereas the vibrations increase the prediction accuracy.", notes = "Originally in AMME 2000-2002 conference \cite{Brezocnik:2002:AMME}. Achievements in Mechanical and Materials Engineering Conference. Selected for publication as full paper in the Special Issue of the Journal of Materials Processing Technology (Elsevier, the Netherlands)", } @InProceedings{Mechatronics2004_Abstract_026, author = "Miha Kovacic and Miran Brezocnik and Joze Balic", title = "Genetic Programming Approach for Autonomous Vehicles", booktitle = "Mechatronics 2004 9th Mechatronics Forum International Conference", year = "2004", address = "METU, Ankara, Turkey", month = "30 " # aug # "-1 " # sep, organisation = "Atilim University", keywords = "genetic algorithms, genetic programming", URL = "http://mechatronics.atilim.edu.tr/mechatronics2004/papers/Mechatronics2004_Abstract_026.pdf", size = "1 page", abstract = "GP was used for intelligent path planning of an autonomous vehicle in 2D production environment. Robot had to find loads, to avoid all the obstacles and to reach the target point. The production environment (robot, loads and obstacles) are represented as free 2D shapes. The robot discretely rotates for 30 degrees left and right and moves forward by two different steps. Step decreases if the sensor detects the load or obstacle. The GP system tries to find gradually optimal program for robot navigation through production environment as a consequence of interactions between the robot and detected environment. Program for navigation can be randomly constructed of logical operators (IFLOAD, IF-OBSTACLE), basic commands (MOVE, RIGHT, LEFT), and connection functions (CF2, CF3). Each program is run several times until 100 time units for the robot's task are used or the target point is reached. The system for genetic programming was run 50-times. Robot travelled safely with all collected loads to the target point 2-times, which means that the probability of the finding successful navigation program is 4 percent. In future the researches will be oriented particularly towards conceiving an improved GP system with the possibility of use 3D models of the production environment. Preliminary results of the concept are encouraging.", notes = "University of Maribor, Faculty of Mechanical Engineering, Maribor, Slovenia", } @Article{brezocnik_2004_AJME, author = "Miran Brezocnik and Miha Kovacic and Joze Balic and Bogdan Sovilj", title = "Programming {CNC} measuring machines by genetic algorithms", journal = "Academic Journal of Manufacturing Engineering", year = "2004", volume = "2", number = "4", pages = "15--20", keywords = "genetic algorithms, genetic programming, optimisation, coordinate measuring machines, computer aided quality control, evolutionary computation", ISSN = "1583-7904", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/brezocnik_2004_AJME.pdf", size = "6 pages", abstract = "The need for efficient and reliable tools for programming of CNC coordinate measuring machine is rapidly increasing in modern production. The proposed concept based on genetic algorithms assures generation and optimization of NC programs for measuring machine. Therefore the structure, undergoing simulated evolution, is the population of NC programs. The NC programs control the tactile probe which performs simple elementary motions in the discretized measuring area. During the simulated evolution the probe movement becomes more and more optimized and intelligent solutions emerge gradually as a result of the low level interaction between the simple probe movements and the measuring environment. Example of CNC programming of measuring machine is given. Results show universality and inventiveness of the approach", notes = "http://www.eng.utt.ro/auif/ http://www.eng.utt.ro/auif/rev/issue/no-08/no-08.html#C2", } @Article{Brezocnik:2003:MMP, author = "Miran Brezocnik and Miha Kovacic", title = "Integrated genetic programming and genetic algorithm approach to predict surface roughness", journal = "Materials and Manufacturing Processes", year = "2003", volume = "18", number = "3", pages = "475--491", month = may, keywords = "genetic algorithms, genetic programming, Manufacturing systems, Surface roughness, Milling", DOI = "doi:10.1081/AMP-120022023", abstract = "we propose a new integrated genetic programming and genetic algorithm approach to predict surface roughness in end-milling. Four independent variables, spindle speed, feed rate, depth of cut, and vibrations, were measured. Those variables influence the dependent variable (i.e., surface roughness). On the basis of training data set, different models for surface roughness were developed by genetic programming. The floating-point constants of the best model were additionally optimised by a genetic algorithm. Accuracy of the model was proved on the testing data set. By using the proposed approach, more accurate prediction of surface roughness was reached than if only modelling by genetic programming had been carried out. It was also established that the surface roughness is most influenced by the feed rate, whereas the vibrations increase the prediction accuracy.", } @InProceedings{Brezocnik:2005:RIM, author = "Miran Brezocnik and Bostjan Vaupotic and Janez Fridrih and Ivo Pahole", title = "Cost estimation for punch dies by genetic programming", booktitle = "RIM 2005 / 5th International scientific conference on Production engineering", year = "2005", editor = "Milan Jurkovic and Vlatko Dolecek", pages = "167--172", month = "14-17 " # sep, publisher = "Faculty of Technical Engineering, Bihac, Bosnia and Hercegovina", email = "mbrezocnik@uni-mb.si", keywords = "genetic algorithms, genetic programming, punch dies, cost estimation", ISBN = "9958-9262-0-2", abstract = "The paper presents a new approach for cost estimation of punch dies used in metal-forming industry. In the modern business world fast and accurate information is the principal advantage in securing orders and establishing the company's renowned. Often, the offer for the manufacturing and supply of the tool must be sent within a short time. However, precise preparation of the offer requires much work. The paper presents an approach ensuring fast determination of the relatively precise cost estimate of the punch dies on the basis of the tool input parameters (e.g., outside dimensions, number of blades, number of directions of cutting). The proposed approach is based on the evolutionary searching for the adequate general equation describing the influence of the tool input parameters on punch die manufacturing cost. Evolutionary development of the equation was performed by the genetic programming and the base of the punch dies already made.", } @Article{brezocnik:2005:MMP, author = "Miran Brezocnik and Miha Kovacic and Leo Gusel", title = "Comparison Between Genetic Algorithm and Genetic Programming Approach for Modeling the Stress Distribution", journal = "Materials and Manufacturing Processes", year = "2005", volume = "20", number = "3", pages = "497--508", month = may, keywords = "genetic algorithms, genetic programming, Metal forming, Stress distribution, System modelling", ISSN = "1042-6914", URL = "http://journalsonline.tandf.co.uk/openurl.asp?genre=article&issn=1042-6914&volume=20&issue=3&spage=497", DOI = "doi:10.1081/AMP-200053541", abstract = "We compare genetic algorithm (GA) and genetic programming (GP) for system modelling in metal forming. As an example, the radial stress distribution in a cold-formed specimen (steel X6Cr13) was predicted by GA and GP. First, cylindrical workpieces were forward extruded and analysed by the visioplasticity method. After each extrusion, the values of independent variables (radial position of measured stress node, axial position of measured stress node, and coefficient of friction) were collected. These variables influence the value of the dependent variable, radial stress. On the basis of training data, different prediction models for radial stress distribution were developed independently by GA and GP. The obtained models were tested with the testing data. The research has shown that both approaches are suitable for system modeling. However, if the relations between input and output variables are complex, the models developed by the GP approach are much more accurate.", notes = "A1 Laboratory for Intelligent Manufacturing Systems, University of Maribor, Faculty of Mechanical Engineering, Maribor, Slovenia A2 Laboratory for Material Forming, University of Maribor, Faculty of Mechanical Engineering, Maribor, Slovenia", } @Article{Brezocnik:2006:AMME, author = "Miran Brezocnik and Miha Kovacic and Matej Psenicnik", title = "Prediction of steel machinability by genetic programming", journal = "Journal of achievements in materials and manufacturing engineering", year = "2006", volume = "16", number = "1-2", pages = "107--113", month = may # "-" # jun, note = "Special Issue of CAM3S'2005", keywords = "genetic algorithms, genetic programming, Steel machinability, Extra machinability, Modelling", issn_ = "Y505-3994 invalid checksum", URL = "http://jamme.acmsse.h2.pl/index.php?id=69", URL = "http://157.158.19.167/papers_cams05/1123.pdf", size = "7 pages", abstract = "The steels with extra machinability are made according to a special technological process. Such steels can be machined at high cutting speeds. In addition, the resistance of the tools used for machining, is higher than in the case of ordinary steels. It depends on several parameters, particularly on the steel chemical composition, whether the steel will meet the criterion of extra machinability. Special tests for each batch separately show whether the steel has extra machinability or not. In our research, the prediction of machinability of steels, depending on input parameters, was performed by genetic programming and data on the batches of steel already made. The model developed during the simulated evolution was tested also with the testing data set. The results show that the proposed concept can be successfully used in practice.", notes = "http://www.journalamme.org/ Formerly Proceedings of Achievements in Mechanical and Materials Engineering. (1.123) Intelligent Manufacturing Systems Laboratory, University of Maribor, Faculty of Mechanical Engineering, Smetanova ulica 17, SI-2000 Maribor, Slovenia", } @Article{brezocnik:2021:Metals, author = "Miran Brezocnik and Uros Zuperl", title = "Optimization of the Continuous Casting Process of Hypoeutectoid Steel Grades Using Multiple Linear Regression and Genetic {Programming--An} Industrial Study", journal = "Metals", year = "2021", volume = "11", number = "6", keywords = "genetic algorithms, genetic programming", ISSN = "2075-4701", URL = "https://www.mdpi.com/2075-4701/11/6/972", DOI = "doi:10.3390/met11060972", abstract = "Store Steel Ltd. is one of the major flat spring steel producers in Europe. Until 2016 the company used a three-strand continuous casting machine with 6 m radius, when it was replaced by a completely new two-strand continuous caster with 9 m radius. For the comparison of the tensile strength of 41 hypoeutectoid steel grades, we conducted 1847 tensile strength tests during the first period of testing using the old continuous caster, and 713 tensile strength tests during the second period of testing using the new continuous caster. It was found that for 11 steel grades the tensile strength of the rolled material was statistically significantly lower (t-test method) in the period of using the new continuous caster, whereas all other steel grades remained the same. To improve the new continuous casting process, we decided to study the process in more detail using the Multiple Linear Regression method and the Genetic Programming approach based on 713 items of empirical data obtained on the new continuous casting machine. Based on the obtained models of the new continuous casting process, we determined the most influential parameters on the tensile strength of a product. According to the model's analysis, the secondary cooling at the new continuous caster was improved with the installation of a self-cleaning filter in 2019. After implementing this modification, we performed an additional 794 tensile tests during the third period of testing. It was found out that, after installation of the self-cleaning filter, in 6 steel grades out of 19, the tensile strength in rolled condition improved statistically significantly, whereas all the other steel grades remained the same.", notes = "also known as \cite{met11060972}", } @Article{Briand:2006:GPEM, author = "Lionel C. Briand and Yvan Labiche and Marwa Shousha", title = "Using genetic algorithms for early schedulability analysis and stress testing in real-time systems", journal = "Genetic Programming and Evolvable Machines", year = "2006", volume = "7", number = "2", pages = "145--170", month = aug, note = "Special Issue: Best of GECCO 2005", keywords = "genetic algorithms, Software verification and validation, Schedulability theory", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-006-9003-9", abstract = "Reactive real-time systems have to react to external events within time constraints: Triggered tasks must execute within deadlines. It is therefore important for the designers of such systems to analyse the schedulability of tasks during the design process, as well as to test the system's response time to events in an effective manner once it is implemented. This article explores the use of genetic algorithms to provide automated support for both tasks. Our main objective is then to automate, based on the system task architecture, the derivation of test cases that maximise the chances of critical deadline misses within the system; we refer to this testing activity as stress testing. A second objective is to enable an early but realistic analysis of tasks' schedulability at design time. We have developed a specific solution based on genetic algorithms and implemented it in a tool. Case studies were run and results show that the tool (1) is effective at identifying test cases that will likely stress the system to such an extent that some tasks may miss deadlines, (2) can identify situations that were deemed to be schedulable based on standard schedulability analysis but that, nevertheless, exhibit deadline misses.", } @Article{Bricco:2023:ACSsynbio, author = "Alexander R. Bricco and Iliya Miralavy and Shaowei Bo and Or Perlman and David E. Korenchan and Christian T. Farrar and Michael T. McMahon and Wolfgang Banzhaf and Assaf A. Gilad", title = "A Genetic Programming Approach to Engineering {MRI} Reporter Genes", journal = "ACS Synthetic Biology", year = "2023", volume = "12", number = "4", pages = "1154--1163", keywords = "genetic algorithms, genetic programming, protein engineering, CEST MRI", ISSN = "2161-5063", URL = "https://www.sciencedirect.com/science/article/pii/S2161506323002140", DOI = "doi:10.1021/acssynbio.2c00648", abstract = "Here we develop a mechanism of protein optimization using a computational approach known as genetic programming. We developed an algorithm called Protein Optimisation Engineering Tool (POET). Starting from a small library of literature values, the use of this tool allowed us to develop proteins that produce four times more MRI contrast than what was previously state-of-the-art. Interestingly, many of the peptides produced using POET were dramatically different with respect to their sequence and chemical environment than existing CEST producing peptides, and challenge prior understandings of how those peptides function. While existing algorithms for protein engineering rely on divergent evolution, POET relies on convergent evolution and consequently allows discovery of peptides with completely different sequences that perform the same function with as good or even better efficiency. Thus, this novel approach can be expanded beyond developing imaging agents and can be used widely in protein engineering.", } @InProceedings{briesch:2023:GECCOcomp, author = "Martin Briesch and Dominik Sobania and Franz Rothlauf", title = "On the {Trade-Off} between Population Size and Number of Generations in {GP} for Program Synthesis", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "535--538", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, program synthesis, crossover, population size, generations, mutation: Poster", isbn13 = "9798400701191", DOI = "doi:10.1145/3583133.3590681", size = "4 pages", abstract = "When using genetic programming for program synthesis, we are usually constrained by a computational budget measured in program executions during evolution. The computational budget is influenced by the choice of population size and number of generations per run leading to a trade-off between both possibilities. To better understand this trade-off, we analyze the effects of different combinations of population sizes and number of generations on performance. Further, we analyze how the use of different variation operators affects this trade-off. We conduct experiments on a range of common program synthesis benchmarks and find that using larger population sizes lead to a better search performance. Additionally, we find that using high probabilities for crossover and mutation lead to higher success rates. Focusing on only crossover or using only mutation usually leads to lower search performance. In summary, we find that large populations combined with high mutation and crossover rates yield highest GP performance for program synthesis approaches.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @InProceedings{briesch:2024:GECCOcomp, author = "Martin Briesch and Ryan Boldi and Dominik Sobania and Alexander Lalejini and Thomas Helmuth and Franz Rothlauf and Charles Ofria and Lee Spector", title = "Improving Lexicase Selection with Informed {Down-Sampling}", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion", year = "2024", editor = "Marcus Gallagher", pages = "25--26", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, lexicase selection, informed down-sampling", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3664068", size = "2 pages", abstract = "This short paper presents the main findings of our work titled Informed Down-Sampled Lexicase Selection: Identifying Productive Training Cases for Efficient Problem Solving, which was recently published in the Evolutionary Computation Journal. In this work, we introduce informed down-sampled lexicase selection to dynamically build diverse subsets of training cases during evolution using population statistics. We evaluate our method on a set of program synthesis problems in two genetic programming systems and find that informed down-sampling improves performance in both systems compared to random down-sampling when using lexicase selection. Additionally, we investigate the constructed down-samples and find that informed down-sampling can identify important training cases and does so across different evolutionary runs and systems.", notes = "GECCO-2024 A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{Briggs:2006:ASPGP, title = "Functional genetic programming with combinators", author = "Forrest Briggs and Melissa O'Neill", booktitle = "Proceedings of the Third Asian-Pacific workshop on Genetic Programming", year = "2006", editor = "The Long Pham and Hai Khoi Le and Xuan Hoai Nguyen", pages = "110--127", ISSN = "18590209", address = "Military Technical Academy, Hanoi, VietNam", keywords = "genetic algorithms, genetic programming", URL = "http://sc.snu.ac.kr/courses/2006/fall/pg/aai/GP/forrest/fsb-meo-combs.pdf", URL = "http://gpbib.cs.ucl.ac.uk/aspgp06/fsb-meo-combs.pdf", size = "18 pages", abstract = "Prior program representations for genetic programming that incorporated features of modern programming languages solved harder problems than earlier representations, but required more complex genetic operators. We develop the idea of using combinator expressions as a program representation for genetic programming. This representation makes it possible to evolve programs with a variety of programming language constructs using simple genetic operators. We investigate the effort required to evolve combinator-expression solutions to several problems: linear regression, even parity on N inputs, and implementation of the stack and queue data structures. Genetic programming with combinator expressions compares favourably to prior approaches, namely the works of Yu [37], Kirshenbaum [18], Agapitos and Lucas [1], Wong and Leung [35], Koza [20], Langdon [21], and Katayama [17].", notes = "broken march 2020 http://www.aspgp.org", } @Article{Briggs:2008:IJKBIES, author = "Forrest Briggs and Melissa O'Neill", title = "Functional Genetic Programming and Exhaustive Program Search with Combinator Expressions", journal = "International Journal of Knowledge-Based and Intelligent Engineering Systems", year = "2008", volume = "12", number = "1", pages = "47--68", keywords = "genetic algorithms, genetic programming, Lambda-expressions", ISSN = "1327-2314", publisher = "IOS Press", URL = "http://content.iospress.com/articles/international-journal-of-knowledge-based-and-intelligent-engineering-systems/kes00140", DOI = "doi:10.3233/KES-2008-12105", size = "22 page", abstract = "Using a strongly typed functional programming language for genetic programming has many advantages, but evolving functional programs with variables requires complex genetic operators with special cases to avoid creating ill-formed programs. We introduce combinator expressions as an alternative program representation for genetic programming, providing the same expressive power as strongly typed functional programs, but in a simpler format that avoids variables and other syntactic clutter. We outline a complete genetic-programming system based on combinator expressions, including a novel generalised genetic operator, and also show how it is possible to exhaustively enumerate all well-typed combinator expressions up to a given size. Our experimental evidence shows that combinator expressions compare favourably with prior representations for functional genetic programming and also offers insight into situations where exhaustive enumeration outperforms genetic programming and vice versa.", notes = "Standard ML and Haskell mentioned a few times. even-N-parity, stacks and queues. KES", } @InProceedings{briney+karpinski:2003:gecco:workshop, title = "An Interdisciplinary Investigation of the Evolution and Maintenance of Conditional Strategies in Chthamalus anisopoma, using Genetic Programming and a Quantitative Genetic Model", author = "Kristin Briney and Tod Karpinski", pages = "258--261", booktitle = "{GECCO 2003}: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference", editor = "Alwyn M. Barry", year = "2003", month = "11 " # jul, publisher = "AAAI", address = "Chigaco", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025", notes = "Bird-of-a-feather Workshops, GECCO-2003. A joint meeting of the twelth International Conference on Genetic Algorithms (ICGA-2003) and the eigth Annual Genetic Programming Conference (GP-2003) part of barry:2003:GECCO:workshop", keywords = "genetic algorithms, genetic programming", } @InProceedings{Brinster:2012:APSURSI, author = "Irina Brinster and Philippe {De Wagter} and Jason Lohn", booktitle = "Antennas and Propagation Society International Symposium (APSURSI), 2012 IEEE", title = "Evaluation of stochastic algorithm performance on antenna optimization benchmarks", year = "2012", isbn13 = "978-1-4673-0461-0", address = "Chicago, IL, USA", size = "2 pages", abstract = "This paper evaluates performance of ten stochastic search algorithms on a benchmark suite of four antenna optimisation problems. Hill climbers (HC) serve as baseline algorithms. We implement several variants of genetic algorithms, evolution strategies, and genetic programming as examples of competitive strategy for achieving optimal solution. Ant colony and particle-swarm optimisation represent cooperative strategy. Static performance is measured in terms of success rates and mean hit time, while dynamic performance is evaluated from the development of the mean solution quality. Among the evaluated algorithms, steady-state GA provides the best trade-off between efficiency and effectiveness. PSO is recommended for noisy problems, while ACO and GP should be avoided for antenna optimisations because of their low efficiencies.", keywords = "genetic algorithms, genetic programming, ant colony optimisation, antennas, particle swarm optimisation, search problems, stochastic processes, Hill climbers, ant colony optimisation, antenna optimisation benchmark, cooperative strategy, evolution strategies, particle swarm optimisation, steady-state GA, stochastic algorithm performance, stochastic search algorithm, Antennas, Arrays, Benchmark testing, Electromagnetics, Heuristic algorithms, Optimisation", DOI = "doi:10.1109/APS.2012.6348758", ISSN = "1522-3965", notes = "Also known as \cite{6348758}", } @InProceedings{brizuela:1999:ADSGAJSSP, author = "Carlos A. Brizuela and Nobuo Sannomiya", title = "A Diversity Study in Genetic Algorithms for Job Shop Scheduling Problems", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "75--82", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-333.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-333.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InCollection{brock:1994:ers, author = "Oliver Brock", title = "Evolving Reusable Subroutines for Genetic Programming", booktitle = "Artificial Life at Stanford 1994", year = "1994", editor = "John R. Koza", pages = "11--19", address = "Stanford, California, 94305-3079 USA", month = jun, organisation = "Stanford University", publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-182105-2", URL = "http://robotics.stanford.edu/users/oli/PAPERS/a-life.ps", URL = "http://citeseer.ist.psu.edu/156902.html", abstract = "Although automatically defined functions (ADFs) are able to significantly reduce the computational effort required in genetic programming, reasonably di{\AE}cult problems still require large amounts of computation time. However, every time genetic programming evolves a program to solve a problem those ADFs have to be rediscovered from scratch. If the ADFs of a correct program contain partial solutions that are generally useful, they can be used to solve similar problems. This paper proposes a technique to make the information of successful ADFs accessible to genetic programming in order to reduce the computational costs of solving related problems with less computational effort and demonstrates its utility using the example of the even n-parity function.", notes = "This volume contains 22 papers written and submitted by students describing their term projects for the course in artificial life (Computer Science 425) at Stanford University offered during the spring quarter quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs425.html ADFS previously evolved may be used by subsequent GP runs. Ie become part of fitness set for rpb and adf of later runs.", } @InCollection{Broersma:2017:miller, author = "Hajo Broersma", title = "Evolution in Nanomaterio: The {NASCENCE} Project", booktitle = "Inspired by Nature: Essays Presented to Julian F. Miller on the Occasion of his 60th Birthday", publisher = "Springer", year = "2017", editor = "Susan Stepney and Andrew Adamatzky", volume = "28", series = "Emergence, Complexity and Computation", chapter = "4", pages = "87--111", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-67996-9", DOI = "doi:10.1007/978-3-319-67997-6_4", abstract = "This chapter describes some of the work carried out by members of the NASCENCE project, an FP7 project sponsored by the European Community. After some historical notes and background material, the chapter explains how nanoscale material systems have been configured to perform computational tasks by finding appropriate configuration signals using artificial evolution. Most of this exposition is centred around the work that has been carried out at the MESA+ Institute for Nanotechnology at the University of Twente using disordered networks of nanoparticles. The interested reader will also find many pointers to references that contain more details on work that has been carried out by other members of the NASCENCE consortium on composite materials based on single-walled carbon nanotubes.", notes = "part of \cite{miller60book} https://link.springer.com/bookseries/10624", } @Misc{journals/corr/abs-2005-13110, author = "Clifford Broni-Bediako and Yuki Murata and Luiz Henrique Mormille and Masayasu Atsumi", title = "Evolutionary {NAS} with Gene Expression Programming of Cellular Encoding", howpublished = "arXiv", year = "2020", volume = "abs/2005.13110", keywords = "genetic algorithms, genetic programming, gene expression programming", bibdate = "2020-05-28", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/corr/corr2005.html#abs-2005-13110", URL = "https://arxiv.org/abs/2005.13110", } @InProceedings{Broni-Bediako:2020:SSCI, author = "Cliford Broni-Bediako and Yuki Murata and Luiz H. B. Mormille and Masayasu Atsumi", title = "Evolutionary NAS with Gene Expression Programming of Cellular Encoding", booktitle = "2020 IEEE Symposium Series on Computational Intelligence (SSCI)", year = "2020", pages = "2670--2676", abstract = "The renaissance of neural architecture search (NAS) has seen classical methods such as genetic algorithms (GA) and genetic programming (GP) being exploited for convolutional neural network (CNN) architectures. While recent work have achieved promising performance on visual perception tasks, the direct encoding scheme of both GA and GP has functional complexity deficiency and does not scale well on large architectures like CNN. To address this, we present a new generative encoding scheme-symbolic linear generative encoding (SLGE)-simple, yet a powerful scheme which embeds local graph transformations in chromosomes of linear fixed-length string to develop CNN architectures of variant shapes and sizes via an evolutionary process of gene expression programming. In experiments, the effectiveness of SLGE is shown in discovering architectures that improve the performance of the state-of-the-art handcrafted CNN architectures on CIFAR-10 and CFAR-100 image classification tasks; and achieves a competitive classification error rate with the existing NAS methods using fewer GPU resources.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SSCI47803.2020.9308346", month = dec, notes = "Also known as \cite{9308346}", } @InProceedings{Brookhouse:2014:GECCOcomp, author = "James Brookhouse and Fernando E. B. Otero and Michael Kampouridis", title = "Working with {OpenCL} to speed up a genetic programming financial forecasting algorithm: initial results", booktitle = "GECCO 2014 Workshop on Evolutionary Computation Software Systems (EvoSoft)", year = "2014", editor = "Stefan Wagner and Michael Affenzeller", isbn13 = "978-1-4503-2881-4", keywords = "genetic algorithms, genetic programming, GPU", pages = "1117--1124", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "https://kar.kent.ac.uk/42144/", URL = "http://doi.acm.org/10.1145/2598394.2605689", DOI = "doi:10.1145/2598394.2605689", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "The genetic programming tool EDDIE has been shown to be a successful financial forecasting tool, however it has suffered from an increase in execution time as new features have been added. Speed is an important aspect in financial problems, especially in the field of algorithmic trading, where a delay in taking a decision could cost millions. To offset this performance loss, EDDIE has been modified to take advantage of multi-core CPUs and dedicated GPUs. This has been achieved by modifying the candidate solution evaluation to use an OpenCL kernel, allowing the parallel evaluation of solutions. Our computational results have shown improvements in the running time of EDDIE when the evaluation was delegated to the OpenCL kernel running on a multi-core CPU, with speed ups up to 21 times faster than the original EDDIE algorithm. While most previous works in the literature reported significantly improvements in performance when running an OpenCL kernel on a GPU device, we did not observe this in our results. Further investigation revealed that memory copying overheads and branching code in the kernel are potentially causes of the (under-)performance of the OpenCL kernel when running on the GPU device.", notes = "Also known as \cite{2605689} Distributed at GECCO-2014.", } @InProceedings{Brooks92RR9, title = "Artificial Life and Real Robots", year = "1992", pages = "3--10", author = "Rodney A. Brooks", booktitle = "Toward a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life", editor = "Francisco J. Varela and Paul Bourgine", address = "Cambridge, MA, USA", publisher = "MIT Press", keywords = "genetic algorithms, genetic programming", URL = "http://people.csail.mit.edu/brooks/papers/real-robots.pdf", size = "9 pages", abstract = "The first part of this paper explores the general issues in using Artificial Life techniques to program actual mobile robots. In particular it explores the difficulties inherent in transferring programs evolved in a simulated environment to run on an actual robot. It examines the dual evolution of organism morphology and nervous systems in biology. It proposes techniques to capture some of the search space pruning that dual evolution offers in the domain of robot programming. It explores the relationship between robot morphology and program structure, and techniques for capturing regularities across this mapping. The second part of the paper is much more specific. It proposes techniques which could allow realistic explorations concerning the evolution of programs to control physically embodied mobile robots. In particular we introduce a new abstraction for behaviour-based robot programming which is specially tailored to be used with genetic programming techniques. To compete with hand coding techniques it will be necessary to automatically evolve programs that are one to two orders of magnitude more complex than those previously reported in any domain. Considerable extensions to previously reported approaches to genetic programming are necessary in order to achieve this goal.", } @InProceedings{Brotto-Rebuli:2021:EuroGP, author = "Karina {Brotto Rebuli} and Leonardo Vanneschi", title = "Progressive Insular Cooperative {GP}", booktitle = "EuroGP 2021: Proceedings of the 24th European Conference on Genetic Programming", year = "2021", editor = "Ting Hu and Nuno Lourenco and Eric Medvet", series = "LNCS", volume = "12691", publisher = "Springer Verlag", address = "Virtual Event", pages = "19--35", month = "7-9 " # apr, organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Multiclass classification, Cooperative evolution", isbn13 = "978-3-030-72811-3", DOI = "doi:10.1007/978-3-030-72812-0_2", abstract = "This work presents a novel genetic programming system for multi-class classification, called progressively insular cooperative genetic programming (PIC GP). Based on the idea that effective multiclass classification can be obtained by appropriately joining classifiers that are highly specialized on the single classes, PIC GP evolves, at the same time, two populations. The first population contains individuals called specialists, and each specialist is optimized on one specific target class. The second population contains higher-level individuals, called teams, that join specialists to obtain the final algorithm prediction. By means of three simple parameters, PIC GP can tune the amount of cooperation between specialists of different classes. The first part of the paper is dedicated to a study of the influence of these parameters on the evolution dynamics. The obtained results indicate that PIC GP achieves the best performance when the evolution begins with a high level of cooperation between specialists of different classes, and then this type of cooperation is progressively decreased, until only specialists of the same class can cooperate between each other. The last part of the work is dedicated to an experimental comparison between PIC GP and a set of state-of-the-art classification algorithms. The presented results indicate that PIC GP outperforms the majority of its competitors on the studied test problems.", notes = "See also http://hdl.handle.net/10362/107369 Multi-class classification, team GP, multi-branch PIC GP, root is single non-evolving softmax node. Two parent selection. Non interacting island populations (insular). 3 class Iris. CIR=Cooperation Intensity Rate. http://www.evostar.org/2021/eurogp/ Part of \cite{Hu:2021:GP} EuroGP'2021 held in conjunction with EvoCOP2021, EvoMusArt2021 and EvoApplications2021", } @InProceedings{rebuli:2022:GECCOcomp, author = "Karina {Brotto Rebuli} and Mario Giacobini and Niccolo Tallone and Leonardo Vanneschi", title = "A preliminary study of Prediction Interval Methods with Genetic Programming", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion", year = "2022", editor = "Heike Trautmann and Carola Doerr and Alberto Moraglio and Thomas Bartz-Beielstein and Bogdan Filipic and Marcus Gallagher and Yew-Soon Ong and Abhishek Gupta and Anna V Kononova and Hao Wang and Michael Emmerich and Peter A. N. Bosman and Daniela Zaharie and Fabio Caraffini and Johann Dreo and Anne Auger and Konstantin Dietric and Paul Dufosse and Tobias Glasmachers and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Tea Tusar and Dimo Brockhoff and Tome Eftimov and Pascal Kerschke and Boris Naujoks and Mike Preuss and Vanessa Volz and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Mark Coletti and Catherine (Katie) Schuman and Eric ``Siggy'' Scott and Robert Patton and Paul Wiegand and Jeffrey K. Bassett and Chathika Gunaratne and Tinkle Chugh and Richard Allmendinger and Jussi Hakanen and Daniel Tauritz and John Woodward and Manuel Lopez-Ibanez and John McCall and Jaume Bacardit and Alexander Brownlee and Stefano Cagnoni and Giovanni Iacca and David Walker and Jamal Toutouh and UnaMay O'Reilly and Penousal Machado and Joao Correia and Sergio Nesmachnow and Josu Ceberio and Rafael Villanueva and Ignacio Hidalgo and Francisco {Fernandez de Vega} and Giuseppe Paolo and Alex Coninx and Antoine Cully and Adam Gaier and Stefan Wagner and Michael Affenzeller and Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and William B. Langdon and Justyna Petke and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Paetzel and Alexander Wagner and Michael Heider and Nadarajen Veerapen and Katherine Malan and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Mohammad Nabi Omidvar and Yuan Sun and Ernesto Tarantino and De Falco Ivanoe and Antonio {Della Cioppa} and Scafuri Umberto and John Rieffel and Jean-Baptiste Mouret and Stephane Doncieux and Stefanos Nikolaidis and Julian Togelius and Matthew C. Fontaine and Serban Georgescu and Francisco Chicano and Darrell Whitley and Oleksandr Kyriienko and Denny Dahl and Ofer Shir and Lee Spector and Alma Rahat and Richard Everson and Jonathan Fieldsend and Handing Wang and Yaochu Jin and Erik Hemberg and Marwa A. Elsayed and Michael Kommenda and William {La Cava} and Gabriel Kronberger and Steven Gustafson", pages = "530--533", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, modelling uncertainty, crisp prediction, prediction interval", isbn13 = "978-1-4503-9268-6/22/07", DOI = "doi:10.1145/3520304.3528806", abstract = "This article presents an exploratory study on modelling Prediction Intervals (PI) with two Genetic Programming (GP) methods. A PI is the range of values in which the real target value is expected to fall into. It should combine two contrasting properties: to be as narrow as possible and to include as many data observations as possible. One proposed GP method, called CWC-GP, evolves simultaneously the lower and upper boundaries of the PI using a single fitness measure that combines the width and the probability coverage of the PI. The other proposed GP method, called LUBE-GP, evolves independently the boundaries of the PI with a multi-objective approach, in which one fitness aims to minimise the width and the other aims to maximise the probability coverage of the PI. Both methods were applied with Direct and Sequential approaches. In the former, the PI is assessed without the crisp prediction of the model. In the latter, the method makes use of the crisp prediction to find the PI boundaries. The proposed methods showed to have good potential on assessing PIs and the presented preliminary results pave the way to further investigations. The most promising results were observed with the Sequential CWC-GP.", notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @InProceedings{Brotto-Rebuli:2022:WIVACE, author = "Karina {Brotto Rebuli} and Mario Giacobini and Niccolo Tallone and Leonardo Vanneschi", title = "Single and Multi-objective Genetic Programming Methods for Prediction Intervals", booktitle = "WIVACE 2022, XVI International Workshop on Artificial Life and Evolutionary Computation", year = "2022", editor = "Claudio {De Stefano} and Francesco Fontanella and Leonardo Vanneschi", volume = "1780", series = "Computer and Information Science", pages = "205--218", address = "Gaeta (LT), Italy", month = sep # " 14-16", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-031-31183-3", DOI = "doi:10.1007/978-3-031-31183-3_17", abstract = "A PI is the range of values in which the real target value of a supervised learning task is expected to fall into, and it should combine two contrasting properties: to be as narrow as possible, and to include as many data observations as possible. This article presents an study on modeling Prediction Intervals (PI) with two Genetic Programming(GP) methods. The first proposed GP method is called CWC-GP, and it evolves simultaneously the lower and upper boundaries of the PI using a single fitness measure. This measure is the Coverage Width-based Criterion (CWC), which combines the width and the probability coverage of the PI. The second proposed GP method is called LUBE-GP, and it evolves independently the lower and upper boundaries of the PI. This method applies a multi-objective approach, in which one fitness aims to minimise the width and the other aims to maximise the probability coverage of the PI. Both methods were applied both with the Direct and the Sequential approaches. In the former, the PI is assessed without the crisp prediction of the model. In the latter, the method makes use of the crisp prediction to find the PI boundaries. The proposed methods showed to have good potential on assessing PIs and the results pave the way to further investigations.", notes = "Published after the workshop. Use new author order. Supplementary material available at https://bit.ly/3zsRfGP http://wivace2022.unicas.it/files/programWIVACE2022.pdf", } @InProceedings{brotto-rebuli:2023:GECCOcomp, author = "Karina {Brotto Rebuli} and Mario Giacobini and Sara Silva and Leonardo Vanneschi", title = "A Comparison of Structural Complexity Metrics for Explainable Genetic Programming", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "539--542", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, complexity metrics, explainable AI, XAI, interpretable models: Poster", isbn13 = "9798400701191", URL = "https://novaresearch.unl.pt/en/publications/a-comparison-of-structural-complexity-metrics-for-explainable-gen", URL = "https://novaresearch.unl.pt/files/67865641/Comparison_Structural_Complexity_Metrics_for_Explainable_Genetic_Programming.pdf", DOI = "doi:10.1145/3583133.3590595", suppl_url = "https://dl.acm.org/doi/suppl/10.1145/3583133.3590595/suppl_file/p539-brottorebuli-suppl.pdf", size = "4 pages", abstract = "Genetic Programming (GP) has the potential to generate intrinsically explainable models. Despite that, in practice, this potential is not fully achieved because the solutions usually grow too much during the evolution. The excessive growth together with the functional and structural complexity of the solutions increase the computational cost and the risk of overfitting. Thus, many approaches have been developed to prevent the solutions to grow excessively in GP. However, it is still an open question how these approaches can be used for improving the interpretability of the models. This article presents an empirical study of eight structural complexity metrics that have been used as evaluation criteria in multi-objective optimisation. Tree depth, size, visitation length, number of unique features, a proxy for human interpretability, number of operators, number of non-linear operators and number of consecutive nonlinear operators were tested. The results show that potentially the best approach for generating good interpretable GP models is to use the combination of more than one structural complexity metric.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @TechReport{broughton:1998:e3DwlsGPwww, author = "T. Broughton and P. Coates and H. Jackson", title = "Exploring {3D} design worlds using {Lindenmayer} systems and Genetic Programming", institution = "University of East London", year = "1998", keywords = "genetic algorithms, genetic programming", broken = "http://homepages.uel.ac.uk/0483p/chapter12.html", notes = "www info only", } @InCollection{broughton:1999:e3DwlsGPwww, author = "T. Broughton and Paul S. Coates and Helen Jackson", title = "Exploring Three-dimensional design worlds using {Lindenmeyer} Systems and Genetic Programming", booktitle = "Evolutionary Design Using Computers", publisher = "Academic press", year = "1999", editor = "Peter Bentley", chapter = "14", pages = "323--341", address = "London, UK", keywords = "genetic algorithms, genetic programming", ISBN = "0-12-089070-4", URL = "http://www.cs.ucl.ac.uk/staff/P.Bentley/evdes.html", URL = "http://hdl.handle.net/10552/856", abstract = "The raw Lindenmeyer-system (L-system) generates random branching structures in the isospatial grid. Using a three dimensional L-system, early experiments (reported CAAD Futures 97, \cite{coates:1997:GPx3dw} ) showed that globally defined useful form (the flytrap) can evolve quite quickly using one fitness function This paper will describe further experiments undertaken using an improved L-system and multigoal evolution to evolve space/enclosure systems that satisfy both the requirements of space use and those of enclosure. This is implemented as symbiotic coevolution between: 1) L-system branching tree system whose goal is to surround the largest volume of empty space (defined as space which is 'invisible' to an outside observer). 2) Circulation system using walking three dimensional turtles to measure the spatial property of the enclosed space. The resulting enclosure phenotypes can be realised using the occupied isospatial grid points as nodes of a nurbs surface. The chapter covers: 1.0 Introduction to Genetic Programming, L-Systems and the Isospatial Grid 2.0 Three dimensional L-systems, production rules and s-expressions 3.0 Evolutionary Experiments in Simple Environments 4.0 Symbiotic Coevolution", notes = " ", } @Article{brown:1997:GPsoccer, author = "Janelle Brown", title = "AI, Teamwork is Goal of Robot Soccer Tourney", journal = "Wired News", year = "1997", volume = "5", number = "10", month = "3:04pm PDT 26 " # aug, keywords = "genetic algorithms, genetic programming", broken = "http://www.wired.com/culture/lifestyle/news/1997/08/6388", size = "1 page", abstract = "It's got all the excitement of real soccer, but without the bad haircuts and big egos. This week the Robot Soccer World Cup debuts at the International Joint Conferences on Artificial Intelligence in Japan. Matching robot against robot, RoboCup is making breakthroughs in artificial life and multi-agent collaboration, while providing a few kicks in the process.", notes = "Report on RoboCup robot competition (held at IJCAI 1997 in Nagoya, Japan) http://www.robocup.org/RoboCup/ see also \cite{luke:1997:csstcGP} and http://www.cs.umd.edu/users/seanl/soccerbots/", } @InProceedings{Brown:2010:ANNIE, author = "Joseph A. Brown and Daniel Ashlock", title = "Using Evolvable Regressors to Partition Data", booktitle = "ANNIE 2010, Intelligent Engineering Systems through Artificial Neural Networks", year = "2010", editor = "Cihan H. Dagli", volume = "20", pages = "187--194", address = "St. Louis, Mo, USA", month = nov # " 1-3", organisation = "Smart Engineering Systems Laboratory, Systems Engineering Graduate Programs, Missouri University of Science and Technology, 600 W. 14th St., Rolla, MO 65409 USA", publisher = "ASME", keywords = "genetic algorithms, genetic programming", isbn13 = "9780791859599", URL = "http://www.uoguelph.ca/~jbrown16/EvolRegress.pdf", URL = "https://asmedigitalcollection.asme.org/ebooks/book/149/chapter-abstract/30383/Using-Evolvable-Regressors-to-Partition-Data", DOI = "doi:10.1115/1.859599.paper24", abstract = "This manuscript examines permitting multiple populations of evolvable regressors to compete to be the best model for the largest number of data points. Competition between populations enables a natural process of specialisation that implicitly partitions the data. This partitioning technique uses function-stack based regressors and has the ability to discover the natural number of clusters in a data set via a process of sub-population collapse.", notes = "ASME Order Number: 859599", } @PhdThesis{Brown:thesis, author = "Joseph Alexander Brown", title = "Regression and Classification from Extinction", school = "School of Computer Science, The University of Guelph", year = "2014", address = "Canada", month = "10 " # jan, keywords = "genetic algorithms, genetic programming Bioinformatics", URL = "http://hdl.handle.net/10214/7793", URL = "https://atrium.lib.uoguelph.ca/xmlui/handle/10214/7793", URL = "https://atrium.lib.uoguelph.ca/xmlui/bitstream/handle/10214/7793/Brown_Joseph_201401_PhD.pdf", URL = "http://genealogy.math.ndsu.nodak.edu/id.php?id=188278", size = "176 pages", abstract = "Evolutionary Algorithms use the principles of natural selection and biological evolution to act as search and optimisation tools. Two novel Spatially Structured Evolutionary Algorithms: the Multiple Worlds Model (MWM) and Multiple Agent Genetic Networks (MAGnet) are presented. These evolutionary algorithms create evolved unsupervised classifiers for data. Both have a property of subpopulation collapse, where a population/node receives little or no fitness implying the number of classes is too large. This property has the best biological analog of extinction. MWM has a number of evolving populations of candidate solutions. The novel fitness function selects one member from each population, and fitness is divided between. Each of these populations meets with the biological definition of a separate species; each is a group of organisms which produces offspring within their type, but not outside of it. This fitness function creates an unsupervised classification by partitioning the data, based on which population is of highest fitness, and creates an evolved classifier for that partition. MAGnet involves a number of evolving agents spread about a graph, the nodes of which contain individual data members or problem instances. The agents will in turn test their fitness on each of the neighbouring nodes in the graph, moving to the one where they have the highest fitness. During this move they may choose to take one of these problem instances with them. The agent then undergoes evolutionary operations based on which neighbours are on the node. The locations of the problem instances over time are sorted by the evolving agents, and the agents on a node act as a classifier", notes = "Advisor: Ashlock, Daniel", } @Article{Brown:2017:GP_Diablo, author = "Joseph Alexander Brown and Valtchan Valtchanov", title = "Tile Based Genetic Programming Generation for Diablo-like games", journal = "Seeds", year = "2017", volume = "2", pages = "89--93", keywords = "genetic algorithms, genetic programming, game", URL = "http://www.procjam.com/seeds/issues/2.pdf", abstract = "Diablo's initial pitch document highlights the use of Procedural Content as a prominent feature of the game: The heart of Diablo is the randomly created dungeon. A new dungeon level is generated...", notes = "http://www.procjam.com/seeds/ Seeds is a zine made by you, the PROCJAM community, ... Kickstarter", } @Article{Brown:2010:JCP, author = "W. Michael Brown and Aidan P. Thompson and Peter A. Schultz", title = "Efficient hybrid evolutionary optimization of interatomic potential models", journal = "Journal of Chemical Physics", year = "2010", volume = "132", number = "2", pages = "024108", keywords = "genetic algorithms, genetic programming, potential energy functions, search problems", ISSN = "1089-7690", DOI = "doi:10.1063/1.3294562", size = "13 pages", abstract = "The lack of adequately predictive atomistic empirical models precludes meaningful simulations for many materials systems. We describe advances in the development of a hybrid, population based optimisation strategy intended for the automated development of material specific inter atomic potentials. We compare two strategies for parallel genetic programming and show that the Hierarchical Fair Competition algorithm produces better results in terms of transferability, despite a lower training set accuracy. We evaluate the use of hybrid local search and several fitness models using system energies and/or particle forces. We demonstrate a drastic reduction in the computation time with the use of a correlation-based fitness statistic. We show that the problem difficulty increases with the number of atoms present in the systems used for model development and demonstrate that vectorisation can help to address this issue. Finally, we show that with the use of this method, we are able to 'rediscover' the exact model for simple known two- and three-body interatomic potentials using only the system energies and particle forces from the supplied atomic configurations.", notes = "34.20.Cf Department of Multiscale Dynamic Material Modeling, Sandia National Laboratories, Albuquerque, New Mexico 87185-1322, USA", } @InProceedings{browncribbs:1996:nand, author = "H. {Brown Cribbs III} and Robert E. Smith", title = "Classifier System Renaissance: New Analogies, New Directions", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "Classifier Systems, Genetic Algorithms", pages = "547--552", address = "Stanford University, CA, USA", publisher = "MIT Press", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap89.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96 Classifier paper", } @PhdThesis{CameronBrowne:thesis, author = "Cameron Browne", title = "Automatic Generation and Evaluation of Recombination Games", school = "Faculty of Information Technology, Queensland University of Technology", year = "2008", address = "Australia", month = feb, keywords = "genetic algorithms, genetic programming, Combinatorial, Games, Design, Aesthetics, Evolutionary, Search, Yavalath", URL = "http://www.cameronius.com/cv/publications/thesis-2.47.zip", size = "251 pages", abstract = "Many new board games are designed each year, ranging from the unplayable to the truly exceptional. For each successful design there are untold numbers of failures; game design is something of an art. Players generally agree on some basic properties that indicate the quality and viability of a game, however these properties have remained subjective and open to interpretation. The aims of this thesis are to determine whether such quality criteria may be precisely defined and automatically measured through self-play in order to estimate the likelihood that a given game will be of interest to human players, and whether this information may be used to direct an automated search for new games of high quality. Combinatorial games provide an excellent test bed for this purpose as they are typically deep yet described by simple well defined rule sets. To test these ideas, a game description language was devised to express such games and a general game system implemented to play, measure and explore them. Key features of the system include modules for measuring statistical aspects of self-play and synthesising new games through the evolution of existing rule sets. Experiments were conducted to determine whether automated game measurements correlate with rankings of games by human players, and whether such correlations could be used to inform the automated search for new high quality games. The results support both hypotheses and demonstrate the emergence of interesting new rule combinations.", notes = "Reviewed by \cite{Althoefer:2010:ICGA}", } @Book{CameronBrowne:book, author = "Cameron Browne", title = "Evolutionary Game Design", publisher = "Springer", year = "2011", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4471-2178-7", URL = "http://www.springer.com/computer/ai/book/978-1-4471-2178-7", DOI = "doi:10.1007/978-1-4471-2179-4", size = "122 pages", abstract = "This book tells the story of Yavalath, the first computer-generated board game to be commercially released... Table of contents Introduction Games in General The Ludi System Measuring Games Evolving Games Viable Games Yavalath Conclusion", notes = "Softcover. Reviewed in \cite{Boumaza:2012:GPEM}. Winner 2012 HUMIES GECCO 2012 ", } @Article{Browne:2012:ICGA.Yavalath, author = "Cameron Browne", title = "Yavalath: Sample chapter from Evolutionary Game Design", journal = "ICGA Journal", year = "2012", volume = "35", number = "1", keywords = "genetic algorithms, genetic programming", URL = "https://chessprogramming.wikispaces.com/ICGA+Journal", notes = "Winner 2012 HUMIES GECCO 2012 http://www.genetic-programming.org/hc2012/Browne-Paper-1-Preface.pdf https://www.imperial.ac.uk/news/112191/doc-ra-wins-2012-prestigious-humies/", } @Article{Browne_2012_sigevolution, author = "Cameron Browne", title = "Evolutionary Game Design: Automated Game Design Comes of Age", journal = "SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation", year = "2012", volume = "6", number = "2", pages = "3--15", keywords = "genetic algorithms, genetic programming, LUDI, game description language GDL", ISSN = "1931-8499", URL = "http://www.sigevolution.org/issues/pdf/SIGEVOlution0602.pdf", DOI = "doi:10.1145/2597453.2597454", size = "13 pages", abstract = "The HUMIES awards are an annual competition held in conjunction with the Genetic and Evolutionary Computation Conference (GECCO), in which cash prizes totalling 10,000 USA dollars are awarded to the most human-competitive results produced by any form of evolutionary computation published in the previous year. This article describes the gold medal-winning entry from the 2012 Humies competition, based on the LUDI system for playing, evaluating and creating new board games. LUDI was able to demonstrate human competitive results in evolving novel board games that have gone on to be commercially published, one of which, Yavalath, has been ranked in the top 2.5percent of abstract board games ever invented. Further evidence of human-competitiveness was demonstrated in the evolved games implicitly capturing several principles of good game design, outperforming human designers in at least one case, and going on to inspire a new subgenre of games.", notes = "18 Feb 2014. Nestorgames, 2 and 3 player board hex game. Ndengrod=Pentalath 2009, no ko.", } @Misc{browne:1996:bsc, author = "David Browne", title = "Vision-Based Obstacle Avoidance: A Coevolutionary Approach", school = "Department of Software Development, Monash University", year = "1996", type = "Bachelor of Computing with Honours", address = "Australia", month = oct, keywords = "genetic algorithms, genetic programming", URL = "http://www.csse.monash.edu.au/hons/projects/1996/David.Browne/", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/browne/browne_thesis.ps.gz", size = "147 pages", abstract = "This thesis investigates the design of robust obstacle avoidance strategies. Specifically, simulated coevolution is used to breed steering agents and obstacle courses in a `computational arms race'. Both steering agent strategies and obstacle courses are represented by computer programs, and are coevolved according to the genetic programming paradigm. Previous research has found it difficult to evolve robust vision based obstacle avoidance agents. By independently evolving obstacle avoidance agents against a competing evolving species (ie the obstacle courses), it is hypothesised that the robustness of the agents will be increased. The simon system, an existing genetic programming tool, is modified and used to evolve both the obstacle avoidance agents and the obstacle courses. A comparison is made between the robustness of coevolved obstacle avoidance agents and traditionally evolved (non-coevolved) agents. Robustness is measured by average performance in a series of randomly generated obstacle courses. Experimental results show that the average robustness of the coevolved oa agents is greater than that of the traditionally evolved, and statistically it is shown that this data is representative of all cases. It is therefore concluded that coevolution is applicable to oa type problems, and can be used to evolve more robust, general purpose Vision-Based Obstacle Avoidance agents.", } @Article{Browne:2010:ACISC, author = "Nigel P. A. Browne and Marcus V. {dos Santos}", title = "Adaptive Representations for Improving Evolvability, Parameter Control, and Parallelization of Gene Expression Programming", journal = "Applied Computational Intelligence and Soft Computing", year = "2010", volume = "2010", pages = "Article ID 409045", keywords = "genetic algorithms, genetic programming, gene expression programming", URL = "http://downloads.hindawi.com/journals/acisc/2010/409045.pdf", DOI = "doi:10.1155/2010/409045", size = "19 pages", abstract = "Gene Expression Programming (GEP) is a genetic algorithm that evolves linear chromosomes encoding nonlinear (tree-like) structures. In the original GEP algorithm, the genome size is problem specific and is determined through trial and error. In this work, a method for adaptive control of the genome size is presented. The approach introduces mutation, transposition, and recombination operators that enable a population of heterogeneously structured chromosomes, something the original GEP algorithm does not support. This permits crossbreeding between normally incompatible individuals, speciation within a population, increases the evolvability of the representations, and enhances parallel GEP. To test our approach, an assortment of problems were used, including symbolic regression, classification, and parameter optimization. Our experimental results show that our approach provides a solution for the problem of self-adaptive control of the genome size of GEP's representation.", } @InProceedings{Browne:2016:GECCOcomp, author = "Will N. Browne", title = "Code Fragments: Past and Future use in Transfer Learning", booktitle = "GECCO '16 Companion: Proceedings of the Companion Publication of the 2016 Annual Conference on Genetic and Evolutionary Computation", year = "2016", editor = "Tobias Friedrich and Frank Neumann and Andrew M. Sutton and Martin Middendorf and Xiaodong Li and Emma Hart and Mengjie Zhang and Youhei Akimoto and Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and Daniele Loiacono and Julian Togelius and Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and Faustino Gomez and Carlos M. Fonseca and Heike Trautmann and Alberto Moraglio and William F. Punch and Krzysztof Krawiec and Zdenek Vasicek and Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and Boris Naujoks and Enrique Alba and Gabriela Ochoa and Simon Poulding and Dirk Sudholt and Timo Koetzing", isbn13 = "978-1-4503-4323-7", pages = "1405--1405", address = "Denver, Colorado, USA", month = "20-24 " # jul, keywords = "genetic algorithms, genetic programming", organisation = "SIGEVO", DOI = "doi:10.1145/2908961.2931737", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Code Fragments (CFs) have existed as an extension to Evolutionary Computation, specifically Learning Classifiers Systems (LCSs), for half a decade. Through the scaling, abstraction and reuse of both knowledge and functionality that CFs enable, interesting problems have been solved beyond the capability of any other technique. This paper traces the development of the different CF-based systems and outlines future research directions that will form the basis for advanced Transfer Learning in LCSs.", notes = "Distributed at GECCO-2016.", } @Article{Brownlee:2017:ieeeETCI, author = "Alexander Edward Ian Brownlee and Nathan Burles and Jerry Swan", title = "Search-Based Energy Optimization of Some Ubiquitous Algorithms", journal = "IEEE Transactions on Emerging Topics in Computational Intelligence", year = "2017", volume = "1", number = "3", pages = "188--201", month = jun, keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, Energy, Java", ISSN = "2471-285X", URL = "http://eprints.whiterose.ac.uk/117916/1/07935484_1.pdf", DOI = "doi:10.1109/TETCI.2017.2699193", size = "14 pages", abstract = "Reducing computational energy consumption is of growing importance, particularly at the extremes (i.e., mobile devices and datacentres). Despite the ubiquity of the Java virtual machine (JVM), very little work has been done to apply search-based software engineering (SBSE) to minimize the energy consumption of programs that run on it. We describe OPACITOR, a tool for measuring the energy consumption of JVM programs using a bytecode level model of energy cost. This has several advantages over time-based energy approximations or hardware measurements. It is 1) deterministic, 2) unaffected by the rest of the computational environment, 3) able to detect small changes in execution profile, making it highly amenable to metaheuristic search, which requires locality of representation. We show how generic SBSE approaches coupled with OPACITOR achieve substantial energy savings for three widely used software components. Multilayer perceptron implementations minimizing both energy and error were found, and energy reductions of up to 70percent and 39.85percent were obtained over the original code for Quicksort and object-oriented container classes, respectively. These highlight three important considerations for automatically reducing computational energy: tuning software to particular distributions of data; trading off energy use against functional properties; and handling internal dependencies that can exist within software that render simple sweeps over program variants sub-optimal. Against these, global search greatly simplifies the developer's job, freeing development time for other tasks.", notes = "Also known as \cite{7935484}", } @InProceedings{Brownlee:2018:GECCOcomp, author = "Alexander E. I. Brownlee and John R. Woodward and Nadarajen Veerapen", title = "Relating training instances to automatic design of algorithms for bin packing via features", booktitle = "GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2018", editor = "Carlos Cotta and Tapabrata Ray and Hisao Ishibuchi and Shigeru Obayashi and Bogdan Filipic and Thomas Bartz-Beielstein and Grant Dick and Masaharu Munetomo and Silvino {Fernandez Alzueta} and Thomas Stuetzle and Pablo Valledor Pellicer and Manuel Lopez-Ibanez and Daniel R. Tauritz and Pietro S. Oliveto and Thomas Weise and Borys Wrobel and Ales Zamuda and Anne Auger and Julien Bect and Dimo Brockhoff and Nikolaus Hansen and Rodolphe {Le Riche} and Victor Picheny and Bilel Derbel and Ke Li and Hui Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Stephane Doncieux and Richard Duro and Joshua Auerbach and Harold {de Vladar} and Antonio J. Fernandez-Leiva and JJ Merelo and Pedro A. Castillo-Valdivieso and David Camacho-Fernandez and Francisco {Chavez de la O} and Ozgur Akman and Khulood Alyahya and Juergen Branke and Kevin Doherty and Jonathan Fieldsend and Giuseppe Carlo Marano and Nikos D. Lagaros and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Boris Naujoks and Vanessa Volz and Tea Tusar and Pascal Kerschke and Riyad Alshammari and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and John R. Woodward and Shin Yoo and John McCall and Nayat Sanchez-Pi and Luis Marti and Danilo Vasconcellos and Masaya Nakata and Anthony Stein and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Ivanoe {De Falco} and Antonio {Della Cioppa} and Ernesto Tarantino and Umberto Scafuri and P. G. M. Baltus and Giovanni Iacca and Ahmed Hallawa and Anil Yaman and Alma Rahat and Handing Wang and Yaochu Jin and David Walker and Richard Everson and Akira Oyama and Koji Shimoyama and Hemant Kumar and Kazuhisa Chiba and Pramudita Satria Palar", isbn13 = "978-1-4503-5764-7", pages = "135--136", address = "Kyoto, Japan", DOI = "doi:10.1145/3205651.3205748", publisher = "ACM", publisher_address = "New York, NY, USA", month = "15-19 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, automatic design of algorithms, bin packing, features", acmid = "3205748", size = "2 pages", abstract = "Automatic Design of Algorithms (ADA) treats algorithm choice and design as a machine learning problem, with problem instances as training data. However, this paper reveals that, as with classification and regression, for ADA not all training sets are equally valuable. We apply genetic programming ADA for bin packing to several new and existing benchmark sets. Using sets with narrowly-distributed features for training results in highly specialised algorithms, whereas those with well-spread features result in very general algorithms. Variance in certain features has a strong correlation with the generality of the trained policies.", notes = "Also known as \cite{3205748} Also known as \cite{Brownlee:2018:RTI:3205651.3205748} GECCO-2018 A Recombination of the 27th International Conference on Genetic Algorithms (ICGA-2018) and the 23rd Annual Genetic Programming Conference (GP-2018)", } @InProceedings{Brownlee:2019:GECCO, author = "Alexander E. I. Brownlee and Justyna Petke and Brad Alexander and Earl T. Barr and Markus Wagner and David R. White", title = "{Gin}: genetic improvement research made easy", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference", year = "2019", editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and Anne Auger and Petr Posik and Leslie {Peprez Caceres} and Andrew M. Sutton and Nadarajen Veerapen and Christine Solnon and Andries Engelbrecht and Stephane Doncieux and Sebastian Risi and Penousal Machado and Vanessa Volz and Christian Blum and Francisco Chicano and Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and Jonathan Fieldsend and Jose Antonio Lozano and Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and Robin Purshouse and Thomas Baeck and Justyna Petke and Giuliano Antoniol and Johannes Lengler and Per Kristian Lehre", isbn13 = "978-1-4503-6111-8", pages = "985--993", address = "Prague, Czech Republic", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, genetic improvement, Search-based Software Engineering, SBSE, Software engineering, Software notations and tools, GI", URL = "https://cs.adelaide.edu.au/users/markus/pub/2019gecco-gintool.pdf", DOI = "doi:10.1145/3321707.3321841", code_url = "https://github.com/gintool/gin", slides_url = "Slides: http://crest.cs.ucl.ac.uk/fileadmin/crest/COWphotos/Other/Petke.pdf", size = "9 pages", abstract = "Genetic improvement (GI) is a young field of research on the cusp of transforming software development. GI uses search to improve existing software. Researchers have already shown that GI can improve human-written code, ranging from program repair to optimising run-time, from reducing energy-consumption to the transplantation of new functionality. Much remains to be done. The cost of re-implementing GI to investigate new approaches is hindering progress. Therefore, we present Gin, an extensible and modifiable toolbox for GI experimentation, with a novel combination of features. Instantiated in Java and targeting the Java ecosystem, Gin automatically transforms, builds, and tests Java projects. Out of the box, Gin supports automated test-generation and source code profiling. We show, through examples and a case study, how Gin facilitates experimentation and will speed innovation in GI.", notes = "Computing Science and Mathematics, University of Stirling, Stirling, UK. Also known as \cite{3321841} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InProceedings{Brownlee:2020:CEC, author = "Alexander Brownlee and Justyna Petke and Anna F. Rasburn", title = "Injecting Shortcuts for Faster Running {Java} Code", booktitle = "2020 IEEE Congress on Evolutionary Computation (CEC)", year = "2020", month = jul # " 19-24", editor = "Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Justyna Petke and John R. Woodward", publisher = "IEEE", address = "Internet", note = "Special Session on Genetic Improvement", keywords = "genetic algorithms, genetic programming, genetic improvement, GI, SBSE, GIN", isbn13 = "978-1-7281-6929-3", URL = "http://geneticimprovementofsoftware.com/paper_pdfs/E-24667.pdf", URL = "https://dspace.stir.ac.uk/bitstream/1893/30963/1/InjectingShortcutsCEC2020.pdf", URL = "http://hdl.handle.net/1893/30963", DOI = "doi:10.1109/CEC48606.2020.9185708", size = "8 pages", abstract = "Genetic Improvement of software applies search methods to existing software to improve the target program in some way. Impressive results have been achieved, including substantial speedups, using simple operations that replace, swap and delete lines or statements within the code. Often this is achieved by specialising code, removing parts that are unnecessary for particular use-cases. Previous work has shown that there is a great deal of potential in targeting more specialised operations that modify the code to achieve the same functionality in a different way. We propose six new edit types for Genetic Improvement of Java software, based on the insertion of break, continue and return statements. The idea is to add shortcuts that allow parts of the program to be skipped in order to speed it up. 10000 randomly-generated instances of each edit were applied to three open-source applications taken from GitHub. The key findings are: (1) compilation rates for inserted statements without surrounding if statements are 1.5 to 18.3percent; (2) edits where the insert statement is embedded within an if have compilation rates of 3.2 to 55.8percent; (3) of those that compiled, all 6 edits have a high rate of passing tests (Neutral Variant Rate), >60percent in all but one case, and so have the potential to be performance improving edits. Finally, a preliminary experiment based on local search shows how these edits might be used in practice.", notes = "jCodec, spark, spatial4j. Profile wtih hprof to find hot methods and their unit tests. Target plastic code in hot methods with local search. memory caching? NVR = Neutral Variant Rate. Speedup. https://github.com/gintool/gin http://geneticimprovementofsoftware.com/events/wcci2020 WCCI2020", } @InProceedings{Brownlee:2021:GI, author = "Alexander E. I. Brownlee and Jason Adair and Saemundur O. Haraldsson and John Jabbo", title = "Exploring the Accuracy -- Energy Trade-off in Machine Learning", booktitle = "GI @ ICSE 2021", year = "2021", month = "30 " # may, editor = "Justyna Petke and Bobby R. Bruce and Yu Huang and Aymeric Blot and Westley Weimer and W. B. Langdon", publisher = "IEEE", address = "internet", pages = "11--18", keywords = "genetic algorithms, genetic programming, genetic improvement, AI, ML, PyRAPL, NSGA-II jMetalPy", isbn13 = "978-1-6654-4466-8/21", URL = "https://geneticimprovementofsoftware.com/paper_pdfs/gi2021icse/brownlee_gi-icse_2021.pdf", video_url = "https://www.youtube.com/watch?v=bap72BF9vZw&list=PLI8fiFpB7BoKDaxvS7SQp0iA7fN7rrvDD&index=7", video_url = "https://www.youtube.com/watch?v=tgRV-AsVYko&list=PLI8fiFpB7BoKDaxvS7SQp0iA7fN7rrvDD&index=8", video_url = "https://www.youtube.com/watch?v=tB3IWHT4FT4&list=PLXTjhGKkSnI-se7uQneCX-pEDiDrQ7TIS&index=4", URL = "http://hdl.handle.net/1893/32312", DOI = "doi:10.1109/GI52543.2021.00011", data_url = "http://hdl.handle.net/11667/173", size = "8 pages", abstract = "Machine learning accounts for considerable global electricity demand and resulting environmental impact, as training a large deep-learning model produces 284000 kgs of the greenhouse gas carbon dioxide. In recent years, search-based approaches have begun to explore improving software to consume less energy. Machine learning is a particularly strong candidate for this because it is possible to trade off functionality (accuracy) against energy consumption, whereas with many programs functionality is simply a pass-or-fail constraint. We use a grid search to explore hyperparameter configurations for a multilayer perceptron on five classification data sets, considering trade-offs of classification accuracy against training or inference energy. On one data set, we show that 77 percent of energy consumption for inference can saved by reducing accuracy from 94.3 percent to 93.2 percent. Energy for training can also be reduced by 30-50 percent with minimal loss of accuracy. We also find that structural parameters like hidden layer size is a major driver of the energy-accuracy trade-off, but there is some evidence that non-structural hyperparameters influence the trade-off too. We also show that a search-based approach has the potential to identify these trade-offs more efficiently than the grid search.", notes = "data etc at http://hdl.handle.net/11667/173 Energy cost versus accuracy tradeoff, eg for tanh, relu, logistic regression. Hidden layer size (only on glass dataset). Solver (high impact). Model size, esp for online inference (eg for IoT). Video tB3IWHT4FT4 Alexander E.I. Brownlee. 15:14 Discussion chair: Yu Huang Q: Alexandre Bergel, A: Alexander E.I. Brownlee, pyRAPL easy. 16:01 Q: Westley Weimer, A: learning algorithm 14:34 W. B. Langdon A: pyRAPL v. nanoseconds v. Markus Wagner energy measurements in Joules. 19:00 Approximate computing 19:37 Yu Huang, A: python (SciKit Learn) v. Weka. 20:35 Yu Huang problems, A: noise. SE for AI. part of \cite{Petke:2021:ICSEworkshop} http://geneticimprovementofsoftware.com/events/icse2021.html", } @Article{Brownlee:2021:SEN, author = "Alexander E. I. Brownlee", title = "{Genetic Improvement @ ICSE 2021}: Personal reflection of a Workshop Participant", journal = "SIGSOFT Software Engineering Notes", year = "2021", volume = "46", number = "4", month = oct, pages = "28--30", keywords = "genetic algorithms, genetic programming, genetic improvement", publisher = "Association for Computing Machinery", ISSN = "0163-5948", URL = "https://doi.org/10.1145/3485952.3485960", DOI = "doi:10.1145/3485952.3485960", size = "3 pages", abstract = "Following Dr. Stephanie Forrest of Arizona State University keynote \cite{Forrest:2021:GI} presentation there was a wide ranging discussion at the tenth international Genetic Improvement workshop, GI-2021 @ ICSE (held as part of the International Conference on Software Engineering on Sunday 30th May 2021) \cite{Petke:2021:ICSEworkshop}. Topics included a growing range of target systems and applications, algorithmic improvements, wide-ranging questions about how other fields (especially evolutionary computation) can inform advances in GI, and about how GI is branded to other disciplines. We give a personal perspective on the workshop proceedings, the discussions that took place, and resulting prospective directions for future research.", notes = "GI-2021 ACM SIGSOFT Software Engineering Notes https://dl.acm.org/newsletter/sigsoft/ University of Stirling, UK", } @InProceedings{Brownlee:2023:SSBSE, author = "Alexander E. I. Brownlee and James Callan and Karine Even-Mendoza and Alina Geiger and Carol Hanna and Justyna Petke and Federica Sarro and Dominik Sobania", title = "Enhancing Genetic Improvement Mutations Using Large Language Models", booktitle = "SSBSE 2023: Challenge Track", year = "2023", editor = "Paolo Arcaini and Tao Yue and Erik Fredericks", organisers = "Erik Fredericks and Paolo Arcaini and Tao Yue and Rebecca Moussa and Thomas Vogel and Gregory Gay and Max Hort and Bobby R. Bruce and Jose Miguel Rojas and Vali Tawosi", volume = "14415", series = "LNCS", pages = "153--159", address = "San Francisco, USA", month = "8 " # dec, publisher = "Springer", keywords = "genetic algorithms, genetic programming, genetic improvement, GI, GIN, SBSE, LLM, AI, ANN, OpenAI, Langchain4J, Java, jCodec", isbn13 = "978-3-031-48795-8", URL = "https://arxiv.org/pdf/2310.19813.pdf", URL = "https://kclpure.kcl.ac.uk/portal/en/publications/enhancing-genetic-improvement-mutations-using-large-language-mode", URL = "http://www.cs.ucl.ac.uk/staff/J.Petke/papers/Brownlee_2023_SSBSEchallenge.pdf", code_url = "https://doi.org/10.5281/zenodo.8304433", code_url = "http://github.com/gintool/gin", DOI = "doi:10.1007/978-3-031-48796-5_13", size = "7 pages", abstract = "Large language models (LLMs) have been successfully applied to software engineering tasks, including program repair. However, their application in search-based techniques such as Genetic Improvement (GI) is still largely unexplored. we evaluate the use of LLMs as mutation operators for GI to improve the search process. We expand the Gin Java GI tool kit to call OpenAI API to generate edits for the JCodec tool. We randomly sample the space of edits using 5 different edit types. We find that the number of patches passing unit tests is up to 75percent higher with LLM-based edits than with standard Insert edits. Further, we observe that the patches found with LLMs are generally less diverse compared to standard edits. We ran GI with local search to find runtime improvements. Although many improving patches are found by LLM-enhanced GI, the best improving patch was found by standard GI", notes = "See also \cite{Brownlee:2024:ASE} Gin profiler co-located with ESEC/FSE 2023. https://conf.researchr.org/track/ssbse-2023/ssbse-2023-challenge#Accepted-papers%gismo", } @InProceedings{brownlee:2024:GECCOcomp, author = "Alexander Edward Ian Brownlee and Saemundur Oskar Haraldsson and John Robert Woodward and Markus Wagner", title = "Genetic Improvement: Taking real-world source code and improving it using computational search methods", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion", year = "2024", editor = "Mengjie Zhang and Emma Hart", pages = "1197--1230", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", note = "Tutorial", keywords = "genetic algorithms, genetic programming, genetic improvement", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3648418", size = "34 pages", notes = "GECCO-2024 A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @Article{Brownlee:2024:ASE, author = "Alexander Edward Ian Brownlee and James Callan and Karine Even-Mendoza and Alina Geiger and Carol Hanna and Justyna Petke and Federica Sarro and Dominik Sobania", title = "Large Language Model Based Mutations in Genetic Improvement", journal = "Automated Software Engineering", year = "2025", volume = "15", pages = "article number 15", note = "Special Issue on Advances in Search-Based Software", keywords = "genetic algorithms, genetic programming, Genetic Improvement, SBSE, GIN, LLM, AI, ANN, JavaParser, JVM, stochastic local search", ISSN = "0928-8910", URL = "https://rdcu.be/d67YW", URL = "https://doi.org/10.21203/rs.3.rs-4437272/v1", DOI = "doi:10.1007/s10515-024-00473-6", data_url = "https://zenodo.org/records/11173088", size = "25 pages", abstract = "we evaluate the use of LLMs as mutation operators for genetic improvement (GI), an SBSE approach, to improve the GI search process. In a preliminary work, we explored the feasibility of combining the Gin Java GI toolkit with OpenAI LLMs in order to generate an edit for the JCodec tool. Here we extend this investigation involving three LLMs and three types of prompt, and five real-world software projects. We sample the edits at random, as well as using local search. Our results show that, compared with conventional statement GI edits, LLMs produce fewer unique edits, but these compile and pass tests more often, with the OpenAI model finding test-passing edits 77percent of the time. The OpenAI and Mistral LLMs were roughly equal in finding the best run-time improvements. Simpler prompts were more successful than those providing more context and examples. Qualitative analysis revealed a wide variety of areas where LLMs typically failed to produce valid edits: commonly including inconsistent formatting, generating non-Java syntax, or refusing to provide a solution", notes = "Extends \cite{Brownlee:2023:SSBSE} ThreadMXBean.getThreadCPUTime, Langchain4J, TinyDolphin, Mistral, small changes prompt, structural changes prompt. JCodec, JUnit4, Gson, Commons-Net, Karate. Profiling. Maven 3.9.x. Sect 5.1 'over 800 valid patches were found' Sect 8 'up to 9 times more [than with random] patches passing the unit tests were found with LLM-based edits.' 'the prompts used for LLM requests greatly affect the results' ", } @InProceedings{bruce2015reducing, author = "Bobby R. Bruce and Justyna Petke and Mark Harman", title = "Reducing Energy Consumption Using Genetic Improvement", booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation", year = "2015", editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr", isbn13 = "978-1-4503-3472-3", pages = "1327--1334", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, Genetic Improvement, GI, SBSE, Search-Based Software Engineering and Self-* Search, Software Engineering, optimisation, energy optimisation, energy efficiency, energy consumption, Boolean satisfiability, SAT", URL = "http://www.cs.ucl.ac.uk/staff/J.Petke/papers/Bruce_2015_GECCO.pdf", URL = "http://doi.acm.org/10.1145/2739480.2754752", DOI = "doi:10.1145/2739480.2754752", size = "8 pages", abstract = "Genetic Improvement (GI) is an area of Search Based Software Engineering which seeks to improve software's non-functional properties by treating program code as if it were genetic material which is then evolved to produce more optimal solutions. Hitherto, the majority of focus has been on optimising program's execution time which, though important, is only one of many non-functional targets. The growth in mobile computing, cloud computing infrastructure, and ecological concerns are forcing developers to focus on the energy their software consumes. We report on investigations into using GI to automatically find more energy efficient versions of the MiniSAT Boolean satisfiability solver when specialising for three downstream applications. Our results find that GI can successfully be used to reduce energy consumption by up to 25percent", notes = "Also known as \cite{Bruce:2015:GECCO} \cite{2754752} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)", } @InProceedings{Bruce:2015:gi, author = "Bobby R. Bruce", title = "Energy Optimisation via Genetic Improvement A {SBSE} technique for a new era in Software Development", booktitle = "Genetic Improvement 2015 Workshop", year = "2015", editor = "William B. Langdon and Justyna Petke and David R. White", pages = "819--820", address = "Madrid", publisher_address = "New York, NY, USA", month = "11-15 " # jul, organisation = "SIGEvo", publisher = "ACM", keywords = "genetic algorithms, genetic programming, Genetic Improvement, SBSE, GI, Search Based Software En-gineering, energy efficiency, energy consumption, energy optimisation", isbn13 = "978-1-4503-3488-4", URL = "http://gpbib.cs.ucl.ac.uk/gi2015/energy_optimisation_via_genetic_improvement.pdf", URL = "http://doi.acm.org/10.1145/2739482.2768420", DOI = "doi:10.1145/2739482.2768420", size = "2 pages", abstract = "The discipline of Software Engineering has arisen during a time in which developers rarely concerned themselves with the energy efficiency of their software. Due to the growth in both mobile devices and large server clusters this period is undoubtedly coming to an end and, as such, new tools for creating energy-efficient software are required. This paper takes the position that Genetic Improvement, a Search-Based Software Engineering technique, has the potential to aid developers in refactoring their software to a more energy-efficient state; allowing focus to remain on functional requirements while leaving concerns over energy consumption to an automated process.", notes = "Slides: http://gpbib.cs.ucl.ac.uk/gi2015/gi_2015_slides/bruce/EnergyReductionViaGeneticImprovement.pdf position paper", } @Article{Bruce:2016:sigevolution, author = "Bobby Bruce", title = "A Report on the Genetic Improvement Workshop@GECCO 2016", journal = "SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation", year = "2016", volume = "2", number = "9", pages = "7", month = aug, keywords = "genetic algorithms, genetic programming, Genetic Improvement", ISSN = "1931-8499", URL = "http://www.sigevolution.org/issues/pdf/SIGEVOlution0902.pdf", DOI = "doi:10.1145/3066157.3066159", size = "1 page", } @InProceedings{Bruce:2016:SSBSE, author = "Bobby R. Bruce and Jonathan M. Aitken and Justyna Petke", title = "Deep Parameter Optimisation for Face Detection Using the {Viola-Jones} Algorithm in {OpenCV}", booktitle = "Proceedings of the 8th International Symposium on Search Based Software Engineering, SSBSE 2016", year = "2016", editor = "Federica Sarro and Kalyanmoy Deb", volume = "9962", series = "LNCS", pages = "238--243", address = "Raleigh, North Carolina, USA", month = "8-10 " # oct, publisher = "Springer", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, OpenCV, NSGA-II, Deep parameter optimisation, Automated parameter tuning, Multi-objective optimisation, GI", isbn13 = "978-3-319-47106-8", URL = "http://www.cs.ucl.ac.uk/staff/J.Petke/papers/Bruce_2016_SSBSE.pdf", DOI = "doi:10.1007/978-3-319-47106-8_18", code_url = "https://github.com/BobbyRBruce/DPT-OpenCV", size = "6 pages", abstract = "OpenCV is a commonly used computer vision library containing a wide variety of algorithms for the AI community. This paper uses deep parameter optimisation to investigate improvements to face detection using the Viola-Jones algorithm in OpenCV, allowing a trade-off between execution time and classification accuracy. Our results show that execution time can be decreased by 48 percent if a 1.80 percent classification inaccuracy is permitted (compared to 1.04 percent classification inaccuracy of the original, unmodified algorithm). Further execution time savings are possible depending on the degree of inaccuracy deemed acceptable by the user.", notes = "See correction \cite{bruce:RN1707} Tarball of repository is available http://www.cs.ucl.ac.uk/staff/R.Bruce/dpt_opencv.tar.gz", } @TechReport{bruce:RN1701, author = "Bobby R. Bruce and Justyna Petke and Mark Harman and Earl T. Barr", title = "Approximate Oracles and Synergy in Software Energy Search Spaces", institution = "University College, London", year = "2017", number = "RN/17/01", address = "London, UK", month = "25 " # jan, keywords = "genetic algorithms, genetic programming, genetic improvement", URL = "http://www.cs.ucl.ac.uk/fileadmin/UCL-CS/research/Research_Notes/RN_17_01.PDF", abstract = "There is a growing interest in using evolutionary computation to reduce software systems' energy consumption by using techniques such as genetic improvement. However, efficient and effective evolutionary optimisation of software systems requires a better understanding of the energy search landscape. One important choice practitioners have is whether to preserve the system's original output or permit approximation; each of which has its own search space characteristics. When output preservation is a hard constraint, we report that the maximum energy reduction achievable by evolutionary mutation is 2.69percent (0.76percent on average). By contrast, this figure increases dramatically to 95.60percent (33.90percent on average) when approximation is permitted, indicating the critical importance of approximate output quality assessment for effective evolutionary optimisation. We investigate synergy, a phenomenon that occurs when simultaneously applied evolutionary mutations produce a effect greater than their individual sum. Our results reveal that 12.0percent of all joint code modifications produced such a synergistic effect though 38.5percent produce an antagonistic interaction in which simultaneously applied mutations are less effective than when applied individually. This highlights the need for an evolutionary approach over more greedy alternatives.", notes = "Replaced by \cite{Bruce:TSE}", size = "21 pages", } @TechReport{bruce:RN1707, author = "Bobby R. Bruce", title = "Deep Parameter Optimisation for Face Detection Using the {Viola-Jones} Algorithm in {OpenCV} : A Correction", institution = "University College, London", year = "2017", number = "RN/17/07", address = "London, UK", month = "7 " # jun, keywords = "genetic algorithms, genetic programming, genetic improvement", URL = "http://www.cs.ucl.ac.uk/fileadmin/UCL-CS/research/Research_Notes/RN_17_07.pdf", abstract = "In our 2016 paper 'Deep Parameter Optimisation for Face Detection Using the Viola-Jones Algorithm in OpenCV' \cite{Bruce:2016:SSBSE} we reported on an evolutionary, multi-objective approach to deep parameter optimisation that we reported could reduce execution time of a face detection algorithm by 48percent if a 1.90percent classification inaccuracy were permitted (compared to the 1.04percent classification inaccuracy of the original, unmodified algorithm) and that further execution time savings were possible depending on the degree of inaccuracy permitted by the user. However, after publication we found an error in our experimental setup; instead of running the deep parameter optimisation framework using an evolutionary search-based approach we had been using a systematic one. We therefore re-ran the experiments using the intended evolutionary implementation alongside the systematic implementation for 1000 evaluations and again for 10000 evaluations. We found that the systematic setup is superior to the intended evolutionary setup in that it produces solutions which, when run on the test set, produce a richer Pareto frontier. The evolutionary approach, in the 10000 evaluation setup, produced a better Pareto frontier on the training set. However, the majority of these solutions were infeasible or not Pareto optimal when run on the test set. We suspect this may be due to the evolutionary approach over-fitting to the training set.", notes = "\cite{Bruce:2016:SSBSE} makes reference to now non-existent Github page: https://github.com/BobbyBruce1990/DPT-OpenCV see instead https://github.com/BobbyRBruce/DPT-OpenCV Tarball of repository is available http://www.cs.ucl.ac.uk/staff/R.Bruce/dpt_opencv.tar.gz", size = "7 pages", } @TechReport{Bruce:RN1804, author = "Bobby R. Bruce and Justyna Petke", title = "Towards automatic generation and insertion of {OpenACC} directives", institution = "University College, London", year = "2018", number = "RN/18/04", address = "London, UK", month = "12 " # apr, keywords = "genetic algorithms, genetic programming, genetic improvement, GPU, parallel computing", URL = "http://www.cs.ucl.ac.uk/fileadmin/UCL-CS/research/Research_Notes/RN_18_04.pdf", size = "35 pages", abstract = "While the use of hardware accelerators, like GPUs, can significantly improve software performance, developers often lack the expertise or time to properly translate source code to do so. In this paper we highlight two approaches to automatically offload computationally intensive tasks to a system's GPU by generating and inserting OpenACC directives; one using grammar-based genetic programming, and another using a bespoke four stage process. We find that the grammar-based genetic programming approach reduces execution time by 2.60percent on average, across the applications studied, while the bespoke four-stage approach reduces execution time by 2.44percent. Despite this, our investigation shows a handwritten OpenACC implementation is capable of reducing execution time by 65.68percent. Comparing the differences, we identified a promising avenue for future research: combining genetic improvement with better handling of data to and from the GPU.", notes = "NAS Parallel Benchmark suite GB-GP-Parallelisation and Four-Stage-Parallelisation 'both under-perform due to their poor handling of how program variables are transferred to and from the GPU' grammar-based GP. Greedy algorithms and evolutionary strategies", } @PhdThesis{bruce_bobby_r_thesis, author = "Bobby R. Bruce", title = "The Blind Software Engineer: Improving the Non-Functional Properties of Software by Means of Genetic Improvement", school = "Computer Science, University College, London", year = "2018", address = "UK", month = "12 " # jul, keywords = "genetic algorithms, genetic programming, Genetic Improvement, Search-based Software Engineering, OpenCV, Deep Parameter Optimisation, Android Smartphones, GB-GP-Parallelisation, OpenACC, DawnCC, NAS-NPB Suite, SNU-NPB", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/bruce_bobby_r_thesis.pdf", URL = "https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.756138", URL = "https://discovery.ucl.ac.uk/id/eprint/10052290/", size = "151 pages", abstract = "Life, even in its most basic of forms, continues to amaze mankind with the complexity of its design. When analysing this complexity it is easy to see why the idea of a grand designer has been such a prevalent idea in human history. If it is assumed intelligence is required to undertake a complex engineering feat, such as developing a modern computer system, then it is logical to assume a creature, even as basic as an earthworm, is the product of an even greater intelligence. Yet, as Darwin observed, intelligence is not a requirement for the creation of complex systems. Evolution, a phenomenon without consciousness or intellect can, over time, create systems of grand complexity and order. From this observation a question arises: is it possible to develop techniques inspired by Darwinian evolution to solve engineering problems without engineers? The first to ask such a question was Alan Turing, a person considered by many to be the father of computer science. In 1948 Turing proposed three approaches he believed could solve complex problems without the need for human intervention. The first was a purely logicdriven search. This arose a decade later in the form of general problem-solving algorithms. Though successful in solving toy problems which could be sufficiently formalised, solving real-world problems was found to be infeasible. The second approach Turing called cultural search. This approach would store libraries of information to then reference and provide solutions to particular problems in accordance to this information. This is similar to what we would now refer to as an expert system. Though the first expert system is hard to date due to differences in definition, the development is normally attributed to Feigenbaum, Bachanan, Lederberg, and Sutherland for their work, originating in the 1960s, on the DENRAL system. Turings last proposal was an iterative, evolutionary technique which he later expanded on stating: We cannot expect to find a good child-machine at the first attempt. One must experiment with teaching one machine and see how well it learns. One can then try another and see if it is better or worse. There is an obvious connection between this process and evolution. Though a primitive proposal in comparison to modern techniques, Turing clearly identified the foundation of what we now refer to as Evolutionary Computation (EC). EC borrows principles from biological evolution and adapts them for use in computer systems. Despite EC initially appearing to be an awkward melding between the two perpendicular disciplines of biology and computer science, useful ideas from evolutionary theory can be used in engineering processes. Just as man dreamt of flight from watching birds, EC researchers dream of self-improving systems from observing evolutionary processes. Despite these similarities, evolutionary inspired techniques in computer science have yet to build complex software systems from scratch. Though they have been successfully used to solve complex problems, such as classification and clustering, there is a general acceptance that, as in nature, these evolutionary processes take vast amounts of time to create complex structures from simple starting points. Even the best computer systems cannot compete with natures ability to evaluate many millions of variants in parallel over the course of millennia. It is for this reason research into modifying and optimising already existing software, a process known as Genetic Improvement, has blossomed. Genetic Improvement (commonly referred to as GI) modifies existing software using search-based techniques with respect to some objective. These search-based techniques are typically evolutionary and, if not, are based on iterative improvement which we may view as a form of evolution. GI sets out to solve the last mile problems of software development; problems that arise in software engineering close to completion, such as bugs or sub-optimal performance. It is the genetic improvement of non-functional properties, such as execution time and energy consumption, which we concern ourselves with in this thesis, as we find it to be the area of research which is the most interesting, and the most exciting. It is hoped that those referencing this thesis may share the same vision: that the genetic improvement of non-functional properties has the potential to transform software development, and that the work presented here is a step towards that goal. The thesis is divided into six chapters (inclusive of this Introduction chapter). In Chapter 2 we explain the background material necessary to understand the content discussed later in the following chapters. From this, in Chapter 3, we highlight our investigations into the novel nonfunctional property of energy consumption which, in part, includes a study in how energy may be reduced via the approximation of output. We then expand on this in Chapter 4 by discussing our investigations into the applicability of GI in the domain of approximate computing, which covers a study into optimising the non-functional properties of software running on novel hardware: in this case, Android tablet devices. We then show, in Chapter 5, early research into how GI may be used to specialise software for specific hardware targets; in particular, how GI may automatically modify sequential code to run on GPUs. Finally, in Chapter 6 we discuss what relevant work is currently being undertaken by using the area of genetic improvement, and provide the reader with clear and concise take-away messages from this thesis.", notes = "Java physics library Rebound, Solver.c, MiniSAT, LzmaEnc.c, LzFind.c ISNI: 0000 0004 7429 091X Supervisory team: Justyna Petke, Mark Harman, Earl T. Barr.", } @Article{Bruce:TSE, author = "Bobby R. Bruce and Justyna Petke and Mark Harman and Earl T. Barr", journal = "IEEE Transactions on Software Engineering", title = "Approximate Oracles and Synergy in Software Energy Search Spaces", year = "2019", volume = "45", number = "11", pages = "1150--1169", month = nov, keywords = "genetic algorithms, genetic programming, genetic improvement, search-based software engineering, SBSE, synergy, Energy consumption, Energy measurement, antagonism, oracle, approximation, MAGEEC, Raspberry Pi", ISSN = "0098-5589", URL = "http://www.bobbybruce.net/assets/pdfs/publications/bruce-2019-approximate.pdf", URL = "https://pdfs.semanticscholar.org/83d3/685a11e8f4855047dd3fba11a67b45aab935.pdf", URL = "https://ieeexplore.ieee.org/document/8338061/", DOI = "doi:10.1109/TSE.2018.2827066", size = "20 pages", abstract = "Reducing the energy consumption of software systems though optimisations techniques such as genetic improvement is gaining interest. However, efficient and effective improvement of software systems requires a better understanding of the code-change search space. One important choice practitioners have is whether to preserve the system's original output or permit approximation with each scenario having its own search space characteristics. When output preservation is a hard constraint, we report that the maximum energy reduction achievable by the modification operators is 2.69percent (0.76percent on average). By contrast, this figure increases dramatically to 95.60percent (33.90percent on average) when approximation is permitted, indicating the critical importance of approximate output quality assessment for code optimisation. We investigate synergy, a phenomenon that occurs when simultaneously applied source code modifications produce an effect greater than their individual sum. Our results reveal that 12.0percent of all joint code modifications produced such a synergistic effect though 38.5percent produce an antagonistic interaction in which simultaneously applied modifications are less effective than when applied individually. This highlights the need for more advanced search-based approaches.", notes = "Presented at ESEC/FSE 2018 Journal-First https://2018.fseconference.org/event/fse-2018-journal-first-approximate-oracles-and-synergy-in-software-energy-search-spaces also known as \cite{8338061}", } @InProceedings{Bruce:2022:GI, author = "Bobby R. Bruce", title = "Automatically Exploring Computer System Design Spaces", booktitle = "GI @ GECCO 2022", year = "2022", editor = "Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and W. B. Langdon and Justyna Petke", pages = "1926--1927", address = "Boston, USA", publisher_address = "New York, NY, USA", month = "9 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, search, computer architecture, ISA, gem5", isbn13 = "978-1-4503-9268-6/22/07", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/gecco2022/gi2022/papers/Bruce_2022_GI.pdf", DOI = "doi:10.1145/3520304.3534021", slides_url = "http://geneticimprovementofsoftware.com/slides/gi2022gecco/bruce-automatically-exploring-computer-gi-gecco-22.pdf", size = "2 pages", abstract = "While much research has focused on using search to optimize software, it is possible to use these same approaches to optimise other parts of the computer system. With decreased costs in silicon customisation, and the return of centralized systems carrying out specialized tasks, the time is right to begin thinking about tools to optimise systems for the needs of specific software targets. In the approach outlined, the standard Genetic Improvement process is flipped with source code considered static and the remainder of the computer system altered to the needs of the software. The project proposed is preliminary research into incorporating grammar-based GP with an advanced computer architecture simulator to automatically design computer systems. I argue this approach has the potential to significantly improve the design of computer systems while reducing manual effort.", notes = "http://geneticimprovementofsoftware.com/events/gecco2022 GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @InProceedings{bruce:1996:agOOpGP, author = "Wilker Shane Bruce", title = "Automatic Generation of Object-Oriented Programs Using Genetic Programming", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming, memory", pages = "267--272", address = "Stanford University, CA, USA", publisher = "MIT Press", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/12859/http:zSzzSzwww.scis.nova.eduzSz~brucewszSzPUBLICATIONSzSzgp96.pdf/bruce96automatic.pdf", URL = "http://citeseer.ist.psu.edu/bruce96automatic.html", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap33.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", size = "6 pages", abstract = "This research addresses the application of genetic programming to the generation of object-oriented programs. An extended chromosome data structure is presented where the set of methods associated with an object is stored as an array of program trees. Modified genetic operators are defined to manipulate this structure. Indexed memory is used to allow the programs generated by the system to access and modify object memory. These extensions to the standard genetic programming...", notes = "GP-96 Early version available from http://www.scis.nova.edu/~brucews/PUBLICATIONS/gp-96.ps (broken) Uses GP to induce stack, queue and P queue. Represents objects as array of trees, one per method. Mutation and crossover. {"}Strongly typed GP generally out performed untyped GP as was expected{"}. STGP. Says details in \cite{bruce:thesis}.", } @PhdThesis{bruce:thesis, author = "Wilker Shane Bruce", title = "The Application of Genetic Programming to the Automatic Generation of Object-Oriented Programs", school = "School of Computer and Information Sciences, Nova Southeastern University", year = "1995", address = "3100 SW 9th Avenue, Fort Lauderdale, Florida 33315, USA", month = Dec, keywords = "genetic algorithms, genetic programming, memory", URL = "https://nsuworks.nova.edu/gscis_etd/430/", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/bruce.thesis.ps.gz", size = "664 pages", abstract = "Genetic programming is an automatic programming method that creates computer programs to satisfy a software designer's input/output specification through the application of principles from genetics and evolutionary biology. A population of programs is maintained where each program is represented in the chromosome data structure as a tree. Programs are evaluated to determine their fitness in solving the specified task. Simulated genetic operations like crossover and mutation are probabilistically applied to the more highly fit programs in the population to generate new programs. These programs then replace existing programs in the population according to the principles of natural selection. The process repeats until a correct program is found or an iteration limit is reached. This research concerns itself with the application of genetic programming to the generation of object-oriented programs. A new chromosome data structure is presented in which the entire set of methods associated with an object are stored as a set of program trees. Modified genetic operators that manipulate this new structure are defined. Indexed memory methods are used to allow the programs generated by the system to access and modify object memory. The result of these modifications to the standard genetic programming paradigm is a system that can simultaneously generate all of the methods associated with an object. Experiments were performed to compare the sequential generation of object methods with two variants of simultaneous generation. The first variant used information about both method return values and object internal memory state in its fitness function. The second variant only used information about method return values. It was found that simultaneous generation of methods is possible in the domain of simple collection objects both with and without the availability of internal memory state in the fitness function. It was also found that this technique is up to four orders of magnitude more computationally expensive in terms of number of individuals generated in the search than the sequential generation of the same set of methods on an individual basis.", notes = "Supervisor: Phillip M Admas", } @InProceedings{bruce:1997:lprsbGPADF, author = "Wilker Shane Bruce", title = "The Lawnmower Problem Revisited: Stack-Based Genetic Programming and Automatically Defined Functions", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, pages = "52--57", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, ADF", broken = "http://www.scis.nova.edu/~brucews/PUBLICATIONS/gp-97.ps", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/12859/http:zSzzSzwww.scis.nova.eduzSz~brucewszSzPUBLICATIONSzSzgp97.pdf/bruce97lawnmower.pdf", URL = "http://citeseer.ist.psu.edu/bruce97lawnmower.html", size = "6 pages", abstract = "Stack-based genetic programming is an alternative to Koza-style tree-based genetic programming that generates linear programs that are executed on a virtual machine using a FORTH-style operand stack instead of tree-based function calls. A stack-based genetic programming system was extended to include the ability to generate programs containing automatically defined functions. Experiments were run to test the system using Koza's lawnmower problem. The stack-based system using automatically...", notes = "GP-97 Zero fitness if attempts to pop empty stack. LEFT primitive removed from population. ARG0 never in best best of run. 'SBGP required significantly more search than tree-based GP' 'comparisons ... may be problem dependant'. 'In both systems [GP and SBGP] the use of ADFs appreciably improved the ability of the GP system to quickly find a solution to the [lawn mower] problem.' failure of SBGP without ADFs to solve 8x12 'is most probably due to our limit of a maximium of 256 elements in a solution'.", } @InProceedings{brucherseifer:2001:EuroGP, author = "Eva Brucherseifer and Peter Bechtel and Stephan Freyer and Peter Marenbach", title = "An Indirect Block-Oriented Representation for Genetic Programming", booktitle = "Genetic Programming, Proceedings of EuroGP'2001", year = "2001", editor = "Julian F. Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon", volume = "2038", series = "LNCS", pages = "268--279", address = "Lake Como, Italy", publisher_address = "Berlin", month = "18-20 " # apr, organisation = "EvoNET", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, Block-oriented representation, Biotechnology, Process modelling, Controller design, Causality: Poster", ISBN = "3-540-41899-7", DOI = "doi:10.1007/3-540-45355-5_21", size = "12 pages", abstract = "When Genetic Programming (GP) is applied to system identification or controller design different codings can be used for internal representation of the individuals. One common approach is a block-oriented representation where nodes of the tree structure directly correspond to blocks in a block diagram. In this paper we present an indirect block-oriented representation, which adopts some aspects of the way humans perform the modelling in order to increase the GP system's performance. A causality measure based on an edit distance is examined to compare the direct an the indirect representation. Finally, results from a real world application of the indirect block-oriented representation are presented.", notes = "EuroGP'2001, part of \cite{miller:2001:gp}", } @PhdThesis{Brucherseifer:thesis, author = "Eva Brucherseifer", title = "Der Artbegriff in der Genetischen Programmierung", school = "Department of Electrical Engineering and Information Technology, TU Darmstadt", year = "2010", address = "Germany", month = sep, publisher = "Shaker Verlag GmbH", keywords = "genetic algorithms, genetic programming, GP theory, Genetische Programmierung, Evolutionaere Algorithmen, Art Analyse, Visualisierung, Artengraph, Clustering, Strukturoptimierung, Heuristische Optimierungsverfahren", ISBN-13 = "978-3-8322-9942-2", URL = "http://tubiblio.ulb.tu-darmstadt.de/54187/", URL = "https://www.amazon.com/Artbegriff-Genetischen-Programmierung/dp/3832299424", URL = "https://www.shaker.de/de/content/catalogue/index.asp?lang=de&ID=8&ISBN=978-3-8322-9942-2", size = "233 pages", zusammenfassung = "In der Automatisierung wird die Genetische Programmierung zur Modellbildung von komplexen Systemen und zum Reglerentwurf eingesetzt. Die Genetische Programmierung nimmt die biologische Evolution als Vorbild und ahmt sie nach. Mathematische Modelle werden dazu als Individuen kodiert und ueber eine Anzahl von Generationen hinweg evolutionaer selektiert und veraendert. Bei einem Evolutionslauf ueber mehrere Generationen faellt mit vielen Individuen eine kaum zu ueberblickende, grosse Datenmenge an. Deshalb sind die Auswirkungen einer fuer die Genetische Programmierung gewaehlten Konfiguration fuer den Anwender nur schwer zu kontrollieren. Fuer den Modellbildner ist es wichtig, den Ablauf und die Ergebnisse der Genetischen Programmierung schnell analysieren und bewerten zu koennen. Diese Arbeit bestimmt, erprobt und bewertet daher Methoden und Verfahren, die eine tiefergehende Analyse des Evolutionsvorgangs durch eine geeignete Visualisierung ermoeglichen. Das neuartige Artenkonzept fasst aehnliche Individuen zu Arten zusammen, um sie so visualisieren zu koennen. Die Arten ermoeglichen eine konzentrierte Analyse, die mit allen Individuen aufgrund deren grosser Anzahl nicht moeglich waere. Die Artenbildung basiert auf der Bestimmung der AEhnlichkeit von Individuen. Bekannte und neue Merkmale und Distanzmasse zum Vergleich von Individuen werden daher vorgestellt und bezueglich ihrer Anwendbarkeit fuer die Artenbildung neu bewertet. Zur Darstellung der evolutionaeren Abstammungslinien der Arten wurde der Artengraph entwickelt, der die aus den zwischen Individuen berechneten Distanzen, Abstammungen und Fitnesswerten aggregiert und so die Divergenz von Populationen visualisiert. Der Artengraph ist dem aus der Biologie bekannten Stammbaum nachempfunden und ermoeglicht eine konzentrierte, effiziente und zeitnahe Analyse eines Optimierungslaufs. Anhand einer Reglerentwicklung fuer einen Antennenarm konnte die Anwendung der entwickelten Visualisierungstechniken erfolgreich gezeigt werden. Die Analyse mittels Artengraph der evolutionaeren Entwicklung von Individuen waehrend eines Optimierungslaufs fuehrte zu einer verbesserten Parametrisierung der Genetischen Zusammenfassung In der Automatisierung wird die Genetische Programmierung zur Modellbildung von komplexen Systemen und zum Reglerentwurf eingesetzt. Die Genetische Programmierung nimmt die biologische Evolution als Vorbild und ahmt sie nach. Mathematische Modelle werden dazu als Individuen kodiert und ueber eine Anzahl von Generationen hinweg evolutionaer selektiert und veraendert. Bei einem Evolutionslauf ueber mehrere Generationen faellt mit vielen Individuen eine kaum zu ueberblickende, grosse Datenmenge an. Deshalb sind die Auswirkungen einer fuer die Genetische Programmierung gewaehlten Konfiguration fuer den Anwender nur schwer zu kontrollieren. Fuer den Modellbildner ist es wichtig, den Ablauf und die Ergebnisse der Genetischen Programmierung schnell analysieren und bewerten zu koennen. Diese Arbeit bestimmt, erprobt und bewertet daher Methoden und Verfahren, die eine tiefergehende Analyse des Evolutionsvorgangs durch eine geeignete Visualisierung ermoeglichen. Das neuartige Artenkonzept fasst aehnliche Individuen zu Arten zusammen, um sie so visualisieren zu koennen. Die Arten ermoeglichen eine konzentrierte Analyse, die mit allen Individuen aufgrund deren grosser Anzahl nicht moeglich waere. Die Artenbildung basiert auf der Bestimmung der AEhnlichkeit von Individuen. Bekannte und neue Merkmale und Distanzmasse zum Vergleich von Individuen werden daher vorgestellt und bezueglich ihrer Anwendbarkeit fuer die Artenbildung neu bewertet. Zur Darstellung der evolutionaeren Abstammungslinien der Arten wurde der Artengraph entwickelt, der die aus den zwischen Individuen berechneten Distanzen, Abstammungen und Fitnesswerten aggregiert und so die Divergenz von Populationen visualisiert. Der Artengraph ist dem aus der Biologie bekannten Stammbaum nachempfunden und ermoeglicht eine konzentrierte, effiziente und zeitnahe Analyse eines Optimierungslaufs. Anhand einer Reglerentwicklung fuer einen Antennenarm konnte die Anwendung der entwickelten Visualisierungstechniken erfolgreich gezeigt werden. Die Analyse mittels Artengraph der evolutionaeren Entwicklung von Individuen waehrend eines Optimierungslaufs fuehrte zu einer verbesserten Parametrisierung der Genetischen Programmierung. Dies konnte mit nur wenigen Optimierungslaeufen und damit geringem Zeitaufwand erreicht werden.", notes = "In German, Deutsch. SMOG from Dr.-Ing. Peter Marenbach also known as \cite{tubiblio54187}", } @Article{bruhn:2002:ECJ, author = "Peter Bruhn and Andreas Geyer-Schulz", title = "Genetic Programming over Context-Free Languages with Linear Constraints for the Knapsack Problem: First Results", journal = "Evolutionary Computation", year = "2002", volume = "10", number = "1", pages = "51--74", month = "Spring", keywords = "genetic algorithms, genetic programming, grammatical evolution, grammar-based genetic, programming, combinatorial, optimization, context-free grammars, with linear constraints, knapsack problems", broken = "http://www.ingentaconnect.com/content/mitpress/evco/2002/00000010/00000001/art00004", DOI = "doi:10.1162/106365602317301772", abstract = "we introduce genetic programming over context-free languages with linear constraints for combinatorial optimization, apply this method to several variants of the multidimensional knapsack problem, and discuss its performance relative to Michalewicz's genetic algorithm with penalty functions. With respect to Michalewicz's approach, we demonstrate that genetic programming over context-free languages with linear constraints improves convergence. A final result is that genetic programming over context-free languages with linear constraints is ideally suited to modeling complementarities between items in a knapsack problem: The more complementarities in the problem, the stronger the performance in comparison to its competitors.", } @Misc{Brule:2016:ArXiv, author = "Joshua Brule and Kevin Engel and Nick Fung and Isaac Julien", title = "Evolving Shepherding Behavior with Genetic Programming Algorithms", howpublished = "ArXiv", year = "2016", keywords = "genetic algorithms, genetic programming", bibdate = "2016-04-02", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/corr/corr1603.html#BruleEFJ16", URL = "http://arxiv.org/abs/1603.06141", } @InProceedings{Brum:2018:evocop, author = "Artur Brum and Marcus Ritt", title = "Automatic Algorithm Configuration for the Permutation Flow Shop Scheduling Problem Minimizing Total Completion Time", booktitle = "The 18th European Conference on Evolutionary Computation in Combinatorial Optimisation, EvoCOP 2018", year = "2018", editor = "Arnaud Liefooghe and Manuel Lopez-Ibanez", series = "LNCS", volume = "10782", publisher = "Springer", pages = "85--100", address = "Parma, Italy", month = "4-6 " # apr, organisation = "Species", keywords = "genetic algorithms, genetic programming, Automatic algorithm configuration, Iterated greedy algorithm, Iterated local search, Flow shop scheduling problem, Total completion time", isbn13 = "978-3-319-77448-0", DOI = "doi:10.1007/978-3-319-77449-7_6", abstract = "Automatic algorithm configuration aims to automate the often time-consuming task of designing and evaluating search methods. We address the permutation flow shop scheduling problem minimizing total completion time with a context-free grammar that defines how algorithmic components can be combined to form a full heuristic search method. We implement components from various works from the literature, including several local search procedures. The search space defined by the grammar is explored with a racing-based strategy and the algorithms obtained are compared to the state of the art", notes = "Is this GP? Searching grammar EvoCOP2018 held in conjunction with EuroGP'2018 EvoMusArt2018 and EvoApplications2018 http://www.evostar.org/2018/cfp_evocop.php", } @PhdThesis{Brum:thesis, author = "Artur Ferreira Brum", title = "Automatic Algorithm Configuration for Flow Shop Scheduling Problems", school = "Instituto de Informatica, Universidade Federal do Rio Grande do Sul", year = "2020", address = "Porto Alegre, Brazil", month = aug, keywords = "genetic algorithms, genetic programming, Informatica, Automatic algorithm configuration, Flow shop scheduling problem, Iterated greedy algorithm, Iterated local search, Problema de agendamento em flow shop, Configuracao automatica dealgoritmos, Busca local iterada, Algoritmo guloso iterado", URL = "https://www.inf.ufrgs.br/site/eventos/evento/tese-de-doutorado-de-artur-ferreira-brum/", URL = "http://hdl.handle.net/10183/213705", URL = "https://lume.ufrgs.br/handle/10183/213705", URL = "https://www.lume.ufrgs.br/bitstream/handle/10183/213705/001118296.pdf", size = "130 pages", abstract = "Scheduling problems have been a subject of interest to the optimization researchers for many years. Flow shop problems, in particular, are one of the most widely studied scheduling problems due to their application to many production environments. A large variety of solution methods can be found in the literature and, since many flow shop problems are NP-hard, the most frequently found approaches are heuristic methods. Heuristic search methods are often complex and hard to design, requiring a significant amount of time and manual work to perform such a task, which can be tedious and prone to human biases. Automatic algorithm configuration (AAC) comprises techniques to automate the design of algorithms by selecting and calibrating algorithmic components. It provides a more robust approach which can contribute to improving the state of the art. In this thesis we present a study on the permutation and the non-permutation flow shop scheduling problems. We follow a grammar-based AAC strategy to generate iterated local search or iterated greedy algorithms. We implement several algorithmic components from the literature in a parameterised solver, and explore the search space defined by the grammar with a racing-based strategy. New efficient algorithms are designed with minimal manual effort and are evaluated against benchmarks from the literature. The results show that the automatically designed algorithms can improve the state of the art in many cases, as evidenced by comprehensive computational and statistical testing.", notes = " Supervisor: Marcus Peter Ritt", } @InProceedings{Brumby:1999:SPIE, author = "Steven P. Brumby and James Theiler and Simon J. Perkins and Neal Harvey and John J. Szymanski and Jeffrey J. Bloch and Melanie Mitchell", title = "Investigation of image feature extraction by a genetic algorithm", booktitle = "Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation II, Proceedings of SPIE", year = "1999", editor = "Bruno Bosacchi and David B. Fogel and James C. Bezdek", volume = "3812", pages = "24--31", month = "19-20 " # jul, keywords = "genetic algorithms, genetic programming, Evolutionary computation, image analysis, multi-spectral analysis", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.12.8210", URL = "http://web.cecs.pdx.edu/~mm/spie3812.pdf", DOI = "doi:10.1117/12.367697", size = "8 pages", abstract = "We describe the implementation and performance of a genetic algorithm (GA) which generates image feature extraction algorithms for remote sensing applications. We describe our basis set of primitive image operators and present our chromosomal representation of a complete algorithm. Our initial application has been geospatial feature extraction using publicly available multi-spectral aerial-photography data sets. We present the preliminary results of our analysis of the efficiency of the classic genetic operations of crossover and mutation for our application, and discuss our choice of evolutionary control parameters. We exhibit some of our evolved algorithms, and discuss possible avenues for future progress.", notes = "Fixed length but includes NOP to give variable length http://www.spie.org/web/meetings/programs/sd99/confs/3812.html Los Alamos National Lab; Santa Fe Institute [3812-03]", } @InProceedings{Brumby:2000:SPIE, author = "Steven P. Brumby and Neal R. Harvey and Simon Perkins and Reid B. Porter and John J. Szymanski and James Theiler and Jeffrey J. Bloch", title = "A genetic algorithm for combining new and existing image processing tools for multispectral imagery", booktitle = "Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VI. Proceedings of SPIE", year = "2000", editor = "Sylvia S. Shen and Michael R. Descour", volume = "4049", pages = "480--490", keywords = "genetic algorithms, genetic programming, Evolutionary Computation, Image Processing, Remote Sensing, Multispectral Imagery, Hyperspectral Imagery", URL = "http://spiedigitallibrary.org/data/Conferences/SPIEP/35048/480_1.pdf", DOI = "doi:10.1117/12.410371", size = "11 pages", abstract = "We describe the implementation and performance of a genetic algorithm (GA) which evolves and combines image processing tools for multispectral imagery (MSI) datasets. Existing algorithms for particular features can also be 're-tuned' and combined with the newly evolved image processing tools to rapidly produce customised feature extraction tools. First results from our software system were presented previously. We now report on work extending our system to look for a range of broad-area features in MSI datasets. These features demand an integrated spatiospectral approach, which our system is designed to use. We describe our chromosomal representation of candidate image processing algorithms, and discuss our set of image operators. Our application has been geospatial feature extraction using publicly available MSI and hyperspectral imagery (HSI). We demonstrate our system on NASA/Jet Propulsion Laboratory's Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) HSI which has been processed to simulate MSI data from the Department of Energy's Multispectral Thermal Imager (MTI) instrument. We exhibit some of our evolved algorithms, and discuss their operation and performance.", notes = "Space and Remote Sensing Sciences, Los Alamos National Laboratory, Mail Stop D436, Los Alamos, New Mexico 87545, U.S.A.", } @InProceedings{Brumby:2001:SPIE, author = "S. P. Brumby and J. J. Bloch and N. R. Harvey and J. Theiler and S. Perkins and A. C. Young and J. J. Szymanski", title = "Evolving forest fire burn severity classification algorithms for multi-spectral imagery", booktitle = "In Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VII, Proceedings of SPIE", year = "2001", editor = "Sylvia S. Shen and Michael R. Descour", volume = "4381", pages = "236--245", keywords = "genetic algorithms, genetic programming, Multispectral imagery, Supervised classification, Forest fire, Wildfire, GENIE, Aladdin", URL = "http://public.lanl.gov/perkins/webdocs/brumby.aerosense01.pdf", DOI = "doi:10.1117/12.437013", size = "10 pages", abstract = "Between May 6 and May 18, 2000, the Cerro Grande/Los Alamos wildfire burned approximately 43,000 acres (17,500 ha) and 235 residences in the town of Los Alamos, NM. Initial estimates of forest damage included 17,000 acres (6,900 ha) of 70-100percent tree mortality. Restoration efforts following the fire were complicated by the large scale of the fire, and by the presence of extensive natural and man-made hazards. These conditions forced a reliance on remote sensing techniques for mapping and classifying the burn region. During and after the fire, remote-sensing data was acquired from a variety of aircraft-based and satellite-based sensors, including Landsat 7. We now report on the application of a machine learning technique, implemented in a software package called GENIE, to the classification of forest fire burn severity using Landsat 7 ETM+ multispectral imagery. The details of this automatic classification are compared to the manually produced burn classification, which was derived from field observations and manual interpretation of high-resolution aerial colour/infrared photography.", notes = "p3 Max size directed acyclic graph, not tree GP. GENIE object-oriented Perl. RSI's IDL language and image processing environment. C. UNIX Linux. Aladdin JAVA. Output is written to one of a number of scratch planes (memory) 'temporary workspaces where an image plane can be stored.' 'the gene [ADDP rD0 rS1 wS2] applies pixel-by-pixel addition to two input planes, read from data plane 0 and from scratch plane 1, and writes its output to scratch plane 2.' 'GENIE performs an analysis of chromosome graphs when they are created and only carries out those processing steps that actually affect the final result. Therefore, the fixed length of the chromosome acts as a maximum effective length.' hamming distance fitness pop=50. 30 gens. max chrome size 20. 3 scratch registers. 'The best evolved image-processing algorithm had the chromosome, [OPEN rD1 wS1 1 1][ADDS rD4 wS3 0.34][NEG rS1 wS1][MULTP rD4 rS3 wS2] [LINCOMB rS1 rD6 wS3 0.11][ADDP rS1 rS3 wS1][SUBP rS1 rD5 wS1]' 'The final values of S1, S2, and S3 are then combined in the linear sum, where the coefficients and intercept have been chosen by the Fisher discriminant, as described in Section 2.3, above, to produce our real-valued answer plane A (Figure 6): A = 0.0147*S1 - 0.0142*S2 + 0.0134*S3 + 1.554' 'Adjusting the threshold on A to fall at the between-peak minimum of the histogram at 0.7930 (a different optimisation criterion for the threshold than that used by default by GENIE) produces a new Boolean mask, Figure 9, in which almost all the false positives have been removed, and the remaining pixels marked as burn correspond very closely to the high severity burn regions in the BAER map'", } @InProceedings{Brumby:2001:FUSION, author = "Steven P. Brumby and James Theiler and Simon Perkins and Neal R. Harvey and John J. Szymanski", title = "Genetic programming approach to extracting features from remotely sensed imagery", booktitle = "FUSION 2001: Fourth International Conference on Image Fusion", year = "2001", address = "Montreal, Quebec, Canada", month = "7-10 " # aug, email = "brumby@lanl.gov", keywords = "genetic algorithms, genetic programming, Evolutionary Computation, Image Processing, Remote Sensing, Multispectral Imagery, Panchromatic imagery", URL = "http://public.lanl.gov/perkins/webdocs/brumbyFUSION2001.pdf", size = "8 pages", abstract = "Multi-instrument data sets present an interesting challenge to feature extraction algorithm developers. Beyond the immediate problems of spatial co-registration, the remote sensing scientist must explore a complex algorithm space in which both spatial and spectral signatures may be required to identify a feature of interest. We describe a genetic programming/supervised classifier software system, called Genie, which evolves and combines spatio-spectral image processing tools for remotely sensed imagery. We describe our representation of candidate image processing pipelines, and discuss our set of primitive image operators. Our primary application has been in the field of geospatial feature extraction, including wildfire scars and general land-cover classes, using publicly available multi-spectral imagery (MSI) and hyper-spectral imagery (HSI). Here, we demonstrate our system on Landsat 7 Enhanced Thematic Mapper (ETM+) MSI. We exhibit an evolved pipeline, and discuss its operation and performance.", notes = "oai:CiteSeerPSU:567526 seems to be wrong", } @InProceedings{oai:CiteSeerPSU:445835, author = "Steven P. Brumby and James Theiler and Jeffrey J. Bloch and Neal R. Harvey and Simon Perkins and John J. Szymanski and A. Cody Young", title = "Evolving land cover classification algorithms for multispectral and multitemporal imagery", booktitle = "Proc. SPIE Imaging Spectrometry VII", year = "2002", editor = "Michael R. Descour and Sylvia S. Shen", volume = "4480", pages = "120--129", organisation = "SPIE", keywords = "genetic algorithms, genetic programming, Feature Extraction, Supervised classification, K-means clustering, Multi-spectral imagery, Land cover, Wildfire", URL = "http://public.lanl.gov/jt/Papers/brumby_SPIE4480-14.pdf", URL = "http://citeseer.ist.psu.edu/445835.html", DOI = "doi:10.1117/12.453331", size = "10 pages", abstract = "The Cerro Grande/Los Alamos forest fire devastated over 43,000 acres (17,500 ha) of forested land, and destroyed over 200 structures in the town of Los Alamos and the adjoining Los Alamos National Laboratory. The need to measure the continuing impact of the fire on the local environment has led to the application of a number of remote sensing technologies. During and after the fire, remote-sensing data was acquired from a variety of aircraft- and satellite-based sensors, including Landsat 7 Enhanced Thematic Mapper (ETM+). We now report on the application of a machine learning technique to the automated classification of land cover using multi-spectral and multi-temporal imagery. We apply a hybrid genetic programming/supervised classification technique to evolve automatic feature extraction algorithms. We use a software package we have developed at Los Alamos National Laboratory, called GENIE, to carry out this evolution. We use multispectral imagery from the Landsat 7 ETM+ instrument from before, during, and after the wildfire. Using an existing land cover classification based on a 1992 Landsat 5 TM scene for our training data, we evolve algorithms that distinguish a range of land cover categories, and an algorithm to mask out clouds and cloud shadows. We report preliminary results of combining individual classification results using a K-means clustering approach. The details of our evolved classification are compared to the manually produced land-cover classification.", notes = "Los Alamos National Lab.", } @TechReport{Brun:2013:TR, author = "Yuriy Brun and Earl Barr and Ming Xiao and Claire {Le Goues} and P. Devanbu", title = "Evolution vs. Intelligent Design in Program Patching", institution = "Dept. of Computer Science, University of California, Davis", year = "2013", address = "USA", month = "Fall", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, SBSE", URL = "https://escholarship.org/uc/item/3z8926ks.pdf", size = "13 pages", abstract = "While Fixing bugs requires significant manual effort, recent research has shown that genetic programming (GP) can be used to search through a space of programs to automatically Find candidate bugfixing patches. Given a program, and a set of test cases (some of which fail), a GP-based repair technique evolves a patch or a patched program using program mutation and selection operators. We evaluate GenProg, a well-known GP-based patch generator, using a large, diverse dataset of over a thousand simple (both buggy and correct) student-written homework programs, using two different test sets: a white-box test set constructed to achieve edge coverage on an oracle program, and a black-box test set developed to exercise the desired specification. We Find that GenProg often succeeds at Finding a patch that will cause student programs to pass supplied white-box test cases; however, that the solution quite often overfits to the supplied tests and doesn't pass all the black-box tests. In contrast, when students patch their own buggy programs, these patches tend to pass the black-box tests as well. We also Find that the GenProg-generated patches lack enough diversity to benefit from a kind of bagging, in which a plurality vote over a population of GP-generated patches outperforms a randomly chosen individual patch. We report these results and additional relationships between GenProg's success and the size and complexity of the manual and automatic patches.", notes = "cited by \cite{Smith:2015:FSE}", } @Article{brunello:2021:Sensors, author = "Andrea Brunello and Andrea Urgolo and Federico Pittino and Andras Montvay and Angelo Montanari", title = "Virtual Sensing and Sensors Selection for Efficient Temperature Monitoring in Indoor Environments", journal = "Sensors", year = "2021", volume = "21", number = "8", keywords = "genetic algorithms, genetic programming", ISSN = "1424-8220", URL = "https://www.mdpi.com/1424-8220/21/8/2728", DOI = "doi:10.3390/s21082728", abstract = "Real-time estimation of temperatures in indoor environments is critical for several reasons, including the upkeep of comfort levels, the fulfillment of legal requirements, and energy efficiency. Unfortunately, setting an adequate number of sensors at the desired locations to ensure a uniform monitoring of the temperature in a given premise may be troublesome. Virtual sensing is a set of techniques to replace a subset of physical sensors by virtual ones, allowing the monitoring of unreachable locations, reducing the sensors deployment costs, and providing a fallback solution for sensor failures. In this paper, we deal with temperature monitoring in an open space office, where a set of physical sensors is deployed at uneven locations. Our main goal is to develop a black-box virtual sensing framework, completely independent of the physical characteristics of the considered scenario, that, in principle, can be adapted to any indoor environment. We first perform a systematic analysis of various distance metrics that can be used to determine the best sensors on which to base temperature monitoring. Then, following a genetic programming approach, we design a novel metric that combines and summarizes information brought by the considered distance metrics, outperforming their effectiveness. Thereafter, we propose a general and automatic approach to the problem of determining the best subset of sensors that are worth keeping in a given room. Leveraging the selected sensors, we then conduct a comprehensive assessment of different strategies for the prediction of temperatures observed by physical sensors based on other sensors' data, also evaluating the reliability of the generated outputs. The results show that, at least in the given scenario, the proposed black-box approach is capable of automatically selecting a subset of sensors and of deriving a virtual sensing model for an accurate and efficient monitoring of the environment.", notes = "also known as \cite{s21082728}", } @Article{BRUNELLO:2022:pmcj, author = "Andrea Brunello and Angelo Montanari and Nicola Saccomanno", title = "A genetic programming approach to {WiFi} fingerprint meta-distance learning", journal = "Pervasive and Mobile Computing", year = "2022", volume = "85", pages = "101681", keywords = "genetic algorithms, genetic programming, Indoor positioning, Wi-Fi fingerprinting, Metric, Machine learning", ISSN = "1574-1192", DOI = "doi:10.1016/j.pmcj.2022.101681", URL = "https://www.sciencedirect.com/science/article/pii/S1574119222000980", abstract = "Driven by the continuous growth in the number of mobile smart devices, location-based services are becoming a fundamental aspect in the ubiquitous computing domain. In this work, we focus on indoor scenarios, where positioning supports tasks such as navigation, logistics, and access management and control. Most indoor positioning solutions are based on WiFi fingerprinting, thanks to its ease of deployment. Such a technique often requires the adoption of a suitable distance metric to compare the currently observed WiFi access points with those pertaining to fingerprints contained in a database, and whose position is already known. Results from the literature make it evident that classical distance functions among WiFi fingerprints do not preserve spatial information in its entirety. Here, we explore the possibility of addressing such a shortcoming by combining a selection of fingerprint distance functions into a meta-distance, using a genetic programming approach to solve a symbolic regression problem. The outcomes of the investigation, based on 16 publicly available datasets, show that a small, but statistically relevant, improvement can be achieved in preserving spatial information, and that the developed meta-distance has a generalization capability no worse than top-performing classical fingerprint distance functions when trained on a dataset and tested on the others. In addition, when used within a k-nearest-neighbor positioning framework, the meta-distance outperforms all the contenders, despite not being expressly designed to support position estimation. This sheds a light on a significant relationship between preservation of spatial information and localization performance. The achieved results pave the way for the development of more advanced metric learning solutions, that go beyond the limitations of currently-employed fingerprint distance functions", } @Article{Brunello:2023:ACC, author = "Andrea Brunello and Dario Della Monica and Angelo Montanari and Nicola Saccomanno and Andrea Urgolo", journal = "IEEE Access", title = "Monitors That Learn From Failures: Pairing {STL} and Genetic Programming", year = "2023", volume = "11", pages = "57349--57364", abstract = "In several domains, systems generate continuous streams of data during their execution, including meaningful telemetry information, that can be used to perform tasks like preemptive failure detection. Deep learning models have been exploited for these tasks with increasing success, but they hardly provide guarantees over their execution, a problem which is exacerbated by their lack of interpretability. In many critical contexts, formal methods, which ensure the correct behaviour of a system, are thus necessary. However, specifying in advance all the relevant properties and building a complete model of the system against which to check them is often out of reach in real-world scenarios. To overcome these limitations, we design a framework that resorts to monitoring, a lightweight runtime verification technique that does not require an explicit model specification, and pairs it with machine learning. Its goal is to automatically derive relevant properties, related to a bad behaviour of the considered system, encoded by means of formulas of Signal Temporal Logic ( $\mathsf {STL}$ ). Results based on experiments performed on well-known benchmark datasets show that the proposed framework is able to effectively anticipate critical system behaviours in an online setting, providing human-interpretable results.", keywords = "genetic algorithms, genetic programming, Monitoring, Machine learning, Task analysis, Feature extraction, Runtime, Data mining, Telemetry, Failure analysis, Machine learning, formal methods, runtime verification, monitoring, failure detection, explainable AI, XAI", DOI = "doi:10.1109/ACCESS.2023.3277620", ISSN = "2169-3536", notes = "Also known as \cite{10129205}", } @Article{BRUNS:2019:ESA, author = "Ralf Bruns and Jurgen Dunkel and Norman Offel", title = "Learning of complex event processing rules with genetic programming", journal = "Expert Systems with Applications", volume = "129", pages = "186--199", year = "2019", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2019.04.007", URL = "http://www.sciencedirect.com/science/article/pii/S0957417419302386", keywords = "genetic algorithms, genetic programming, Complex event processing, Rule learning, Pattern mining", abstract = "Complex Event Processing (CEP) is an established software technology to extract relevant information from massive data streams. Currently, domain experts have to determine manually CEP rules that define a situation of interest. However, often CEP rules cannot be formulated by experts, because the relevant interdependencies and relations between the data are not explicitly known, but inherently hidden in the data streams. To cope with this problem, we present a new learning approach for CEP rules, which is based on Genetic Programming. We discuss in detail the different building blocks of Genetic Programming and how to adjust them to CEP rule learning. Extensive evaluations with synthetic and real world data demonstrate the high potential of the approach and give some hints about the choice of suitable process parameters", } @Article{Bryant:2001:ETAI, author = "C. H. Bryant and S. H. Muggleton and S. G. Oliver and D. B. Kell and P. G. K. Reiser and R. D. King", title = "Combining Inductive Logic Programming, Active Learning and Robotics to Discover the Function of Genes", journal = "Electronic Transactions in Artificial Intelligence", year = "2001", volume = "6", number = "12", month = "30 " # aug, publisher = "Linkoping University Electronic Press, Sweden", keywords = "ILP", ISSN = "1401-9841", URL = "http://www.ep.liu.se/ea/cis/2001/012/", URL = "http://www.stancomb.co.uk/~prr/Papers/bryant-ETAI.ps", URL = "http://www.stancomb.co.uk/~prr/Papers/bryant-ETAI.pdf", size = "40 pages", abstract = "We aim to partially automate some aspects of scientific work, namely the processes of forming hypotheses, devising trials to discriminate between these competing hypotheses, physically performing these trials and then using the results of these trials to converge upon an accurate hypothesis. We have developed ASE-Progol, an Active Learning system which uses Inductive Logic Programming to construct hypothesised first-order theories and uses a CART-like algorithm to select trials for eliminating ILP derived hypotheses. We have developed a novel form of learning curve, which in contrast to the form of learning curve normally used in Active Learning, allows one to compare the costs incurred by different leaning strategies. We plan to combine ASE-Progol with a standard laboratory robot to create a general automated approach to Functional Genomics. As a first step towards this goal, we are using ASE-Progol to rediscover how genes participate in the aromatic amino acid pathway of Saccharomyces cerevisiae. Our approach involves auxotrophic mutant trials. To date, ASE-Progol has conducted such trials in silico. However we describe how they will be performed automatically in vitro by a standard laboratory robot designed for these sorts of liquid handling tasks, namely the Beckman/Coulter Biomek 2000. Although our work to date has been limited to trials conducted in silico, the results have been encouraging. Parts of the model were removed and the ability of ASE-Progol to efficiently recover the performance of the model was measured. The cost of the chemicals consumed in converging upon a hypothesis with an accuracy in the range 46-88% was reduced if trials were selected by ASE-Progol rather than if they were sampled at random (without replacement). To reach an accuracy in the range 46-80%, ASE-Progol incurs five orders of magnitude less experimental costs than random sampling. ASE-Progol requires less time to converge upon a hypothesis with an accuracy in the range 74-87percent than if trials are sampled at random (without replacement) or selected using the naive strategy of always choosing the cheapest trial from the set of candidate trials. For example to reach an accuracy of 80%, ASE-Progol requires 4 days while random sampling requires 6 days and the naive strategy requires 10 days.", notes = "online only?", } @Article{1676819, author = "Randal E. Bryant", title = "Graph-Based Algorithms for Boolean Function Manipulation", journal = "IEEE Transactions on Computers", year = "1986", volume = "C-35", number = "8", pages = "677--691", month = aug, keywords = "DEC VAX, Boolean functions, binary decision diagrams, logic design verification, symbolic manipulation", ISSN = "0018-9340", DOI = "doi:10.1109/TC.1986.1676819", size = "15 pages", abstract = "In this paper we present a new data structure for representing Boolean functions and an associated set of manipulation algorithms. Functions are represented by directed, acyclic graphs in a manner similar to the representations introduced by Lee [1] and Akers [2], but with further restrictions on the ordering of decision variables in the graph. Although a function requires, in the worst case, a graph of size exponential in the number of arguments, many of the functions encountered in typical applications have a more reasonable representation. Our algorithms have time complexity proportional to the sizes of the graphs being operated on, and hence are quite efficient as long as the graphs do not grow too large. We present experimental results from applying these algorithms to problems in logic design verification that demonstrate the practicality of our approach.", notes = "NOT GP, exhaustive depth first search?", } @InProceedings{10.1162/978-0-262-31050-5-ch003, author = "David M. Bryson and Charles Ofria", title = "Digital Evolution Exhibits Surprising Robustness to Poor Design Decisions", booktitle = "ALIFE 2012: The Thirteenth International Conference on the Synthesis and Simulation of Living Systems", year = "2012", pages = "19--26", address = "East Lansing, Michigan, USA", month = jul # " 19-22", publisher = "MIT", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1162/978-0-262-31050-5-ch003", URL = "https://direct.mit.edu/isal/proceedings-pdf/alife2012/24/19/1901084/978-0-262-31050-5-ch003.pdf", DOI = "doi:10.1162/978-0-262-31050-5-ch003", size = "8 pages", abstract = "When designing an evolving software system, a researcher must set many aspects of the representation and inevitably make arbitrary decisions. Here we explore the consequences of poor design decisions in the development of a virtual instruction set in digital evolution systems. We evaluate the introduction of three different severities of poor choices. (1) functionally neutral instructions that water down mutational options, (2) actively deleterious instructions, and (3) a lethal die instruction. We further examine the impact of a high level of neutral bloat on the short term evolutionary potential of genotypes experiencing environmental change. We observed surprising robustness to these poor design decisions across all seven environments designed to analyse a wide range challenges. Analysis of the short term evolutionary potential of genotypes from the principal line of descent of case study populations demonstrated that the negative effects of neutral bloat in a static environment are compensated by retention of evolutionary potential during environmental change.", notes = "Avida http://avida.devosoft.org is this GP? Logic-9 Nine 1- and 2-input logic operations. Logic-77 Seventy-seven 1-, 2-, and 3-input logic operations. Match-12 Generate up to 12 specific numbers. Fibonacci-32 Output up to 32 numbers of the Fibonacci sequence, in order. Sort-10 Input 10 random numbers and output in correctly sorted order. Limited-9 Logic-9 environment with a limited resource associated with each task. Navigation Successfully traverse a labeled pathway", } @Article{buason:2005:GPEM, author = "Gunnar Buason and Nicklas Bergfeldt and Tom Ziemke", title = "Brains, Bodies, and Beyond: Competitive Co-Evolution of Robot Controllers, Morphologies and Environments", journal = "Genetic Programming and Evolvable Machines", year = "2005", volume = "6", number = "1", pages = "25--51", month = mar, keywords = "genetic algorithms, neuronal robot controller, CCE, khepera, YAKS simulator", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-005-7618-x", abstract = "We present a series of simulation experiments that incrementally extend previous work on neural robot controllers in a predator-prey scenario, in particular the work of Floreano and Nolfi, and integrates it with ideas from work on the co-evolution of robot morphologies and control systems. The aim of these experiments has been to further systematically investigate the tradeoffs and interdependencies between morphological parameters and behavioral strategies through a series of predator-prey experiments in which increasingly many aspects are subject to self-organization through competitive co-evolution. Motivated by the fact that, despite the emphasis of the interdependence of brain, body and environment in much recent research, the environment has actually received relatively little attention, the last set of experiments lets robots/species actively adapt their environments to their own needs, rather than just adapting themselves to a given environment. This paper is an extended version of: Buason and Ziemke. {"}Co-evolving task-dependent visual morphologies in predator-prey experiments,{"} in Genetic and Evolutionary Computation Conference, Cantu-Paz et al. (Eds.), Springer Verlag: Berlin, 2003, pp. 458-469.", notes = "20/100 rule", } @Article{BUCCHERI2021108722, author = "Enrico Buccheri and Daniele Dell'Aquila and Marco Russo", title = "Artificial intelligence in health data analysis: The {Darwinian} evolution theory suggests an extremely simple and zero-cost large-scale screening tool for prediabetes and type 2 diabetes", journal = "Diabetes Research and Clinical Practice", year = "2021", volume = "174", pages = "108722", month = "1 " # apr, keywords = "genetic algorithms, genetic programming, BP, Type 2 diabetes, Zero-cost dysglycemia screening, Artificial intelligence", ISSN = "0168-8227", URL = "https://pubmed.ncbi.nlm.nih.gov/33647331/", URL = "https://www.sciencedirect.com/science/article/pii/S0168822721000759", DOI = "doi:10.1016/j.diabres.2021.108722", abstract = "Aims The effective identification of individuals with early dysglycemia status is key to reduce the incidence of type 2 diabetes. We develop and validate a novel zero-cost tool that significantly simplifies the screening of undiagnosed dysglycemia. Methods We use NHANES cross-sectional data over 10 years (2007 to 2016) to derive an equation that links non-laboratory exposure variables to the possible presence of undetected dysglycemia. For the first time, we adopt a novel artificial intelligence approach based on the Darwinian evolutionary theory to analyze health data. We collected data for 47 variables. Results Age and waist circumference are the only variables required to use the model. To identify undetected dysglycemia, we obtain an area under the curve (AUC) of 75.3 percent. Sensitivity and specificity are 0.65 and 0.73 by using the optimal threshold value determined from external validation data. Conclusions The use of uniquely two variables allows to obtain a zero-cost screening tool of analogous precision than that of more complex tools widely adopted in the literature. The newly developed tool has clinical use as it significantly simplifies the screening of dysglycemia. Furthermore, we suggest that the definition of an age-related waist circumference cut-off might help to improve existing diabetes risk factors.", } @Article{BUCCHERI2022100398, author = "Enrico Buccheri and Daniele Dell'Aquila and Marco Russo", title = "Stratified analysis of the age-related waist circumference cut-off model for the screening of dysglycemia at zero-cost", journal = "Obesity Medicine", year = "2022", volume = "31", pages = "100398", month = may, keywords = "genetic algorithms, genetic programming, type 2 diabetes, Dysglycemia screening tool, Stratified analysis, Artificial intelligence, AI", ISSN = "2451-8476", URL = "https://www.sciencedirect.com/science/article/pii/S2451847622000100", DOI = "doi:10.1016/j.obmed.2022.100398", abstract = "Aims We perform a stratified analysis of the recently published age-related waist circumference cut-off model to validate its performance in the screening of dysglycemia in the US population. Methods We use NHANES data as representative of the US population. Data were subdivided into sex, ethnic and glycemia groups. We evaluate the performance of the model separately in each group through the Wilcox statistic area under the (ROC) curve, AUC. We also discuss the calibration of the model. Results For the sex-stratified analysis, we obtain AUC = 0.69--0.71 (95percent confidence interval) for male individuals and AUC = 0.75--0.78 (95percent C.I.) for female individuals. The stratified analysis is performed in different ethnic groups, namely Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black and Other Race -- Including Multi-Racial. We obtain, respectively, AUC = 0.74--0.75, AUC = 0.76--0.78, AUC = 0.73--0.75, AUC = 0.74--0.77 and AUC = 0.71--0.73 (95percent C.I.). The model achieves AUC = 0.70--0.73 (95percent C.I.) in the identification of individuals with prediabetes and AUC = 0.70--0.80 (95percent C.I.) in the identification of individuals with diabetes. Conclusions The accuracy of the model turns out to be similar in each group considered in the stratified analysis, indicating that the model is suitable to be used as a screening tool for dysglycemia in the US population", } @Article{Buccheri:2024:JPT, author = "Enrico Buccheri and Daniele Dell'Aquila and Marco Russo and Rita Chiaramonte and Michele Vecchio", title = "Appendicular Skeletal Muscle Mass in Older Adults Can Be Estimated With a Simple Equation Using a Few Zero-Cost Variables", journal = "Journal of Geriatric Physical Therapy", year = "2024", volume = "47", number = "4", pages = "E149--E158", month = oct # "/" # dec, keywords = "genetic algorithms, genetic programming, Brain Project, medicine, appendicular skeletal muscle mass, artificial intelligence, dual-energy X-ray absorptiometer, muscle mass loss", URL = "https://pubmed.ncbi.nlm.nih.gov/39079022/", DOI = "doi:10.1519/JPT.0000000000000420", size = "10 pages", notes = "Supplemental digital contents are available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Web site (www.jgptonline.com) Department of Biomedical and Biotechnological Sciences, Section of Pharmacology, University of Catania, Catania, Italy.", } @InProceedings{buchanan:2024:CEC, author = "Edgar Buchanan and Simon Hickinbotham and Rahul Dubey and Imelda Friel and Andrew Colligan and Mark Price and Andy M. Tyrrell", title = "A Quality Diversity Study in {EvoDevo} Processes for Engineering Design", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Measurement, Dimensionality reduction, Refining, Evolutionary computation, Calibration, evodevo, generative design, structural engineering, quality diversity, neural networks", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10612076", abstract = "For a long time engineering design has relied on human engineers manually crafting and refining designs using their expertise and experience. In Bio-inspired Evolutionary Development (EvoDevo), generative algorithms are employed to investigate a broader design space that may go beyond what human engineers have considered. Previous literature has demonstrated the use of quality and diversity (QD) algorithms in evolutionary approaches to drive the process to better quality solutions. This paper provides a study to understand the effects of using QD algorithms in EvoDevo processes for engineering design. This paper also analyses the impact of using different behavioural characterisations (BC) in the performance of the quality of the solutions found. The results demonstrate that quality and diversity algorithms can find better solutions than other EAs for engineering design problems. It was also found that the characterisation of the BC is important to get the best results.", notes = "also known as \cite{10612076} WCCI 2024", } @Article{BUCHNER:2020:IFAC-PapersOnLine, author = "Jens S. Buchner and Sebastian Boblest and Patrick Engel and Andrej Junginger and Holger Ulmer", title = "An Artificial-Intelligence-Based Method to Automatically Create Interpretable Models from Data Targeting Embedded Control Applications", journal = "IFAC-PapersOnLine", volume = "53", number = "2", pages = "13789--13796", year = "2020", note = "21st IFAC World Congress", ISSN = "2405-8963", DOI = "doi:10.1016/j.ifacol.2020.12.887", URL = "https://www.sciencedirect.com/science/article/pii/S240589632031226X", keywords = "genetic algorithms, genetic programming, Nonlinear, optimal automotive control, Automotive system identification, modeling, Modeling, supervision, control, diagnosis of automotive systems", abstract = "The development of new automotive drivetrain layouts requires modeling of the involved components to allow for ideal control strategies. The creation of these models is both costly and challenging, specifically because interpretability, accuracy, and computational effort need to be balanced. In this study, a method is put forward which supports experts in the modeling process and in making an educated choice to balance these constraints. The method is based on the artificial intelligence technique of genetic programming. By solving a symbolic regression problem, it automatically identifies equation-based models from data. To address possible system complexities, data-based expressions like curves and maps can additionally be employed for the model identification. The performance of the method is demonstrated based on two examples: 1. Identification of a pure equation based model, demonstrating the benefit of interpretability. 2. Creation of a hybrid-model, combining a base equation with data-based expressions. Possible applications of the method are model creation, system identification, structural optimization, and model reduction. The existing implementation in ETAS ASCMO-MOCA also offers a high efficiency increase by combining and automating the two procedural steps of embedded function engineering and calibration", } @InProceedings{eurogp06:BuchsbaumVossner, author = "Thomas Buchsbaum and Siegfried V{\"o}ssner", title = "Information-Dependent Switching of Identification Criteria in a Genetic Programming System for System Identification", editor = "Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art", booktitle = "Proceedings of the 9th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3905", year = "2006", address = "Budapest, Hungary", month = "10-12 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-33143-3", pages = "300--309", DOI = "doi:10.1007/11729976_27", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "Genetic Programming (GP) can be used to identify the nonlinear differential equations of dynamical systems. If, however, the fitness function is chosen in a classical way, the optimisation will not work very well. In this article, we explain the reasons for the failure of the GP approach and present a solution strategy for improving performance. Using more than one identification criterion (fitness function) and switching based on the information content of the data enable standard GP algorithms to find better solutions in shorter times. A computational example illustrates that identification criteria switching has a bigger influence on the results than the choice of the GP parameters has.", notes = "Part of \cite{collet:2006:GP} EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006", } @InProceedings{Buchsbaum:2007:cec, author = "Thomas Buchsbaum", title = "Toward a Winning GP Strategy for Continuous Nonlinear Dynamical System Identification", booktitle = "2007 IEEE Congress on Evolutionary Computation", year = "2007", editor = "Dipti Srinivasan and Lipo Wang", pages = "1269--1275", address = "Singapore", month = "25-28 " # sep, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "1-4244-1340-0", file = "1490.pdf", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2007.4424616", abstract = "System identification is the scientific art of building models from data. Good models are of essential importance in many areas of science and industry. Models are used to analyse, simulate, and predict systems and their states. Model structure selection and estimation of the model parameters with respect to a chosen criterion of fit are essential parts of the identification process. In this article, we investigate the suitability of genetic programming for creating continuous nonlinear state-space models from noisy time series data. We introduce methodologies from the field of chaotic time series estimation and present concepts for integrating them into a genetic programming system. We show that even small changes of the fitness evaluation approach may lead to a significantly improved performance. In combination with multiobjective optimisation, a multiple shooting approach is able to create powerful models from noisy data.", notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C", } @PhdThesis{Buchsbaum:thesis, author = "Thomas Buchsbaum", title = "Improvement of Evolutionary Computation Approaches for Continuous Dynamical System Identification - Robustness and Performance Improvement of Standard Genetic Programming by Approximation, Multiple Shooting Methods, and Iterative Approaches", school = "Institute of Engineering and Business Informatics, Graz University of Technology", year = "2007", address = "Kopernikusgasse 24 Graz, Austria", keywords = "genetic algorithms, genetic programming, System Identification, Evolutionary Computation, Design of Experiments, Input Signal Shaping, Multiple shooting Approximation Time series modeling, Dynamical systems, Continuous state space models, System Identifikation, Evolutionaere Algorithmen, Zeitreihenanalyse, Versuchsplanung, Eingangsgroessenoptimierung", language = "English", URL = "https://online.tugraz.at/tug_online/pl/ui/$ctx;lang=DE/wbAbs.showThesis?pThesisNr=24591&pOrgNr=13706", URL = "https://graz.elsevierpure.com/en/publications/improvement-of-evolutionary-computation-approaches-for-continuous", abstract = "The objective of a mathematical model is to describe certain aspects of a real system; the aim of system identification is to create models from, usually noisy, measurement data. Genetic Programming (GP) is a biology-inspired method for optimising structured representations in general, and dynamical model structures and their parameters in particular. It has been applied to continuous dynamical system identification, but suffered from weak performance and premature convergence behavior. This thesis investigates GP's suitability for creating nonlinear continuous state-space models from noisy time series data. Methodologies are introduced that improve GP's performance and robustness. For the considered test problem, it is shown that instead of solving a dynamical problem by an initial value method, a static problem can be approximated, which can be solved by symbolic regression. This approximation approach speeds up evolution considerably. Fitness evaluation using multiple shooting methods, known from the field of chaotic time series, simplifies the optimization problem by smoothing the fitness landscape; the GP algorithm finds useful building blocks more easily. Three concepts for integrating multiple shooting into GP systems are developed and compared. This thesis offers a concept for automatically switching the identification approach based on the information content of the training data. Computational studies showed that automatic switching combined the advantages of different identification approaches: Better models were created in shorter times. Further, multi-objective methods for regularisation were shown to improve the evolved models generalization abilities substantially. Investigations of model-based input signal optimisation by evolutionary computation methods complete this dissertation. The developed methodologies improve GPs performance and robustness on continuous dynamical system identification tasks. This makes GP a useful tool that assists human modelers in finding building blocks for model synthesis. By applying the introduced methods, the chance of finding hidden information in complex signals can be increased. Medicine, natural sciences, technology, and business could benefit from the improved prediction qualities of the resulting models and the cost savings due to data-efficient modeling procedures.", notes = "also known as \cite{711f8ca066c0490eb8e33fadbcd363a9}", } @Article{Buckingham:2015:JH, author = "David Buckingham and Christian Skalka and Josh Bongard", title = "Inductive machine learning for improved estimation of catchment-scale snow water equivalent", journal = "Journal of Hydrology", volume = "524", pages = "311--325", year = "2015", ISSN = "0022-1694", DOI = "doi:10.1016/j.jhydrol.2015.02.042", URL = "http://www.sciencedirect.com/science/article/pii/S0022169415001547", abstract = "Summary Infrastructure for the automatic collection of single-point measurements of snow water equivalent (SWE) is well-established. However, because SWE varies significantly over space, the estimation of SWE at the catchment scale based on a single-point measurement is error-prone. We propose low-cost, lightweight methods for near-real-time estimation of mean catchment-wide SWE using existing infrastructure, wireless sensor networks, and machine learning algorithms. Because snowpack distribution is highly nonlinear, we focus on Genetic Programming (GP), a nonlinear, white-box, inductive machine learning algorithm. Because we did not have access to near-real-time catchment-scale SWE data, we used available data as ground truth for machine learning in a set of experiments that are successive approximations of our goal of catchment-wide SWE estimation. First, we used a history of maritime snowpack data collected by manual snow courses. Second, we used distributed snow depth (HS) data collected automatically by wireless sensor networks. We compared the performance of GP against linear regression (LR), binary regression trees (BT), and a widely used basic method (BM) that naively assumes non-variable snowpack. In the first experiment set, GP and LR models predicted SWE with lower error than BM. In the second experiment set, GP had lower error than LR, but outperformed BT only when we applied a technique that specifically mitigated the possibility of over-fitting.", keywords = "genetic algorithms, genetic programming, Snow water equivalent, Machine learning, Wireless sensor network, Snowpack modelling", } @InCollection{Buckley:2010:Chiong, author = "Muneer Buckley and Zbigniew Michalewicz and Ralf Zurbruegg", title = "An Application of Genetic Programming to Forecasting Foreign Exchange Rates", booktitle = "Nature-Inspired Informatics for Intelligent Applications and Knowledge Discovery: Implications in Business, Science, and Engineering", publisher = "IGI Global", year = "2010", editor = "Raymond Chiong", chapter = "2", pages = "26--48", keywords = "genetic algorithms, genetic programming", isbn13 = "1605667056", URL = "http://hdl.handle.net/2440/54525", DOI = "doi:10.4018/978-1-60566-705-8", bibsource = "OAI-PMH server at digital.library.adelaide.edu.au", oai = "oai:digital.library.adelaide.edu.au:2440/54525", notes = "http://www.igi-global.com/Bookstore/TitleDetails.aspx?TitleId=794&DetailsType=Description Muneer Buckley (University of Adelaide, Australia) Zbigniew Michalewicz (University of Adelaide, Australia) Ralf Zurbruegg (Institute of Computer Science, Polish Academy of Sciences & Polish-Japanese Institute of Information Technology, Poland)", } @InCollection{Buckley:2009:niiiakd, title = "An Application of Genetic Programming to Forecasting Foreign Exchange Rates", author = "Muneer Buckley and Zbigniew Michalewicz and Ralf Zurbruegg", publisher = "IGI Global", year = "2009", booktitle = "Nature-Inspired Informatics for Intelligent Applications and Knowledge Discovery: Implications in Business, Science, and Engineering", editor = "Raymond Chiong", chapter = "2", pages = "26--48", keywords = "genetic algorithms, genetic programming", isbn13 = "1605667056", URL = "http://www.igi-global.com/bookstore/chapter.aspx?titleid=36310", URL = "http://hdl.handle.net/2440/54525", oai = "oai:digital.library.adelaide.edu.au:2440/54525", abstract = "There is a great need for accurate predictions of foreign exchange rates. Many industries participate in foreign exchange scenarios with little idea where the exchange rate is moving, and what the optimum decision to make at any given time is. Although current economic models do exist for this purpose, improvements could be made in both their flexibility and adaptability. This provides much room for models that do not suffer from such constraints. This chapter proposes the use of a genetic program (GP) to predict future foreign exchange rates. The GP is an extension of the DyFor GP tailored for forecasting in dynamic environments. The GP is tested on the Australian / US (AUD/USD) exchange rate and compared against a basic economic model. The results show that the system has potential in forecasting long term values, and may do so better than established models. Further improvements are also suggested.", } @Article{Bucur:2014:ASC, author = "Doina Bucur and Giovanni Iacca and Giovanni Squillero and Alberto Tonda", title = "The impact of topology on energy consumption for collection tree protocols: An experimental assessment through evolutionary computation", journal = "Applied Soft Computing", year = "2014", volume = "16", pages = "210--222", month = mar, keywords = "genetic algorithms, genetic programming, genetic improvement, Collection tree protocol (CTP), MultiHopLQI (MHLQI), Wireless sensor networks (WSN), Evolutionary algorithms (EA), Routing protocols, Verification, Energy consumption", ISSN = "1568-4946", URL = "http://www.sciencedirect.com/science/article/pii/S1568494613004213", DOI = "doi:10.1016/j.asoc.2013.12.002", size = "13 pages", abstract = "The analysis of worst-case behaviour in wireless sensor networks is an extremely difficult task, due to the complex interactions that characterize the dynamics of these systems. In this paper, we present a new methodology for analysing the performance of routing protocols used in such networks. The approach exploits a stochastic optimization technique, specifically an evolutionary algorithm, to generate a large, yet tractable, set of critical network topologies; such topologies are then used to infer general considerations on the behaviors under analysis. As a case study, we focused on the energy consumption of two well-known ad hoc routing protocols for sensor networks: the multi-hop link quality indicator and the collection tree protocol. The evolutionary algorithm started from a set of randomly generated topologies and iteratively enhanced them, maximizing a measure of how interesting such topologies are with respect to the analysis. In the second step, starting from the gathered evidence, we were able to define concrete, protocol-independent topological metrics which correlate well with protocols poor performances. Finally, we discovered a causal relation between the presence of cycles in a disconnected network, and abnormal network traffic. Such creative processes were made possible by the availability of a set of meaningful topology examples. Both the proposed methodology and the specific results presented here, that is, the new topological metrics and the causal explanation, can be fruitfully reused in different contexts, even beyond wireless sensor networks.", notes = "Also known as \cite{Bucur2014210}", } @InProceedings{Buffoni:2018:ICCC, author = "Federico Buffoni and Gabriele Gianini and Ernesto Damiani and Michael Granitzer", booktitle = "2018 IEEE International Conference on Cognitive Computing (ICCC)", title = "All-Implicants Neural Networks for Efficient Boolean Function Representation", year = "2018", pages = "82--86", abstract = "Boolean classifiers can be evolved by means of genetic algorithms. This can be done within an intercommunicating island system, of evolutionary niches, undergoing cycles that alternate long periods of isolation to short periods of information exchange. In these settings, the efficiency of the communication is a key requirement. In the present work, we address this requirement by providing a technique for efficiently representing and transmitting differential encodings of Boolean functions. We introduce a new class of Boolean Neural Networks (BNN), the all-implicants BNN, and show that this representation supports efficient update communication, better than the classical representation, based on truth tables.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICCC.2018.00019", month = jul, notes = "Also known as \cite{8457700}", } @InProceedings{bui:286, author = "Tai D. Bui and Alan A. Smith", editor = "Don Phelps and Gerald Sehlke", title = "Water Resource Engineers and Environmental Hydraulics", publisher = "ASCE", year = "2001", volume = "111", pages = "286--286", booktitle = "World Water Congress 2001", address = "Orlando, Florida, USA", month = "20-24 " # may, keywords = "genetic algorithms, genetic programming", broken = "http://link.aip.org/link/?ASC/111/286/1", DOI = "doi:10.1061/40569(2001)286", size = "10 pages", abstract = "In past decades, the fundamental notion of employing a multi-disciplinary approach to water resource projects was well received and promoted. According to this approach, water resource practitioners (especially engineers) should change their solution techniques and evaluation so that a solution would encompass a plethora of issues ? both structural and non-structural ? related to the project. It was recognized that solutions could not be based solely on mathematical models of flow conditions. Aspects such as ecology and non-technical issues such as recreational and societal needs should all be considered in the solution derivation process. Undoubtedly, sophisticated technical and mathematical tools (such as artificial neural network and genetic programming, and other tools related to Hydro-informatics) are essential to implement the approach. Added to this is the involvement of various professionals in certain projects. Planners, biologists, limnologists, economists, landscape architects, etc. are some of the other disciplines, besides engineers, involved in dealing with water resource projects. To address the issues, a distinct branch of engineering is imperative. The International Association of Hydraulic Engineering and Research initiated the Eco-hydraulic branch, the American Society of Civil Engineers formed the Environmental Hydraulic Technical Committee and the Canadian Society of Civil Engineers has the Hydrotechnical branch. All in all, these efforts are intended to ensure not only that levels of awareness are elevated but also those levels of engineering practice are adjusted to suit. As a result, solutions would be environmentally friendly and/or sympathetic.", notes = "number = 40569 Conference Proceeding Paper", } @InCollection{Bui:1997:s8p, author = "Thai Bui", title = "Solving the 8-Puzzle with Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1997", publisher = "Stanford Bookstore", year = "1997", editor = "John R. Koza", pages = "11--17", address = "Stanford, California, 94305-3079 USA", month = "17 " # mar, keywords = "genetic algorithms, genetic programming", ISBN = "0-18-205981-2", notes = "part of \cite{koza:1997:GAGPs}", } @InProceedings{Buk:2009:ICANNGA, author = "Zdenek Buk and Jan Koutnik and Miroslav Snorek", title = "{NEAT} in {HyperNEAT} Substituted with Genetic Programming", year = "2009", booktitle = "9th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2009", editor = "Mikko Kolehmainen and Pekka Toivanen and Bartlomiej Beliczynski", series = "Lecture Notes in Computer Science", volume = "5495", pages = "243--252", address = "Kuopio, Finland", month = "23-25 " # apr, publisher = "Springer", note = "Revised selected papers", keywords = "genetic algorithms, genetic programming, HyperGP, ANN", isbn13 = "978-3-642-04920-0", DOI = "doi:10.1007/978-3-642-04921-7_25", size = "10 pages", abstract = "In this paper we present application of genetic programming (GP) [1] to evolution of indirect encoding of neural network weights. We compare usage of original HyperNEAT algorithm with our implementation, in which we replaced the underlying NEAT with genetic programming. The algorithm was named HyperGP. The evolved neural networks were used as controllers of autonomous mobile agents (robots) in simulation. The agents were trained to drive with maximum average speed. This forces them to learn how to drive on roads and avoid collisions. The genetic programming lacking the NEAT complexification property shows better exploration ability and tends to generate more complex solutions in fewer generations. On the other hand, the basic genetic programming generates quite complex functions for weights generation. Both approaches generate neural controllers with similar abilities.", notes = "ICANNGA 2009", } @InProceedings{Bukhtoyarov:2010:cec, author = "Vladimir V. Bukhtoyarov and Olga E. Semenkina", title = "Comprehensive evolutionary approach for neural network ensemble automatic design", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-6910-9", abstract = "Neural network ensemble is an approach based on cooperative usage of many neural networks for problem solving. Often this approach enables to solve problem more efficiently than approach where only one network is used. The two major stages of the neural network ensemble construction are: design and training component networks, combining of the component networks predictions to produce the ensemble output. In this paper, a probability-based method is proposed to accomplish the first stage. Although this method is based on the genetic algorithm, it requires fewer parameters to be tuned. A method based on genetic programming is proposed for combining the predictions of component networks. This method allows us to build nonlinear combinations of component networks predictions providing more flexible and adaptive solutions. To demonstrate robustness of the proposed approach, its results are compared with the results obtained using other methods.", DOI = "doi:10.1109/CEC.2010.5586516", notes = "WCCI 2010. Also known as \cite{5586516}", } @Article{bukhtoyarov:2021:Computation, author = "Vladimir Viktorovich Bukhtoyarov and Vadim Sergeevich Tynchenko", title = "Design of Computational Models for Hydroturbine Units Based on a Nonparametric Regression Approach with Adaptation by Evolutionary Algorithms", journal = "Computation", year = "2021", volume = "9", number = "8", keywords = "genetic algorithms, genetic programming", ISSN = "2079-3197", URL = "https://www.mdpi.com/2079-3197/9/8/83", DOI = "doi:10.3390/computation9080083", abstract = "This article deals with the problem of designing regression models for evaluating the parameters of the operation of complex technological equipment--hydroturbine units. A promising approach to the construction of regression models based on nonparametric Nadaraya-Watson kernel estimates is considered. A known problem in applying this approach is to determine the effective values of kernel-smoothing coefficients. Kernel-smoothing factors significantly impact the accuracy of the regression model, especially under conditions of variability of noise and parameters of samples in the input space of models. This fully corresponds to the characteristics of the problem of estimating the parameters of hydraulic turbines. We propose to use the evolutionary genetic algorithm with an addition in the form of a local-search stage to adjust the smoothing coefficients. This ensures the local convergence of the tuning procedure, which is important given the high sensitivity of the quality criterion of the nonparametric model. On a set of test problems, the results were obtained showing a reduction in the modelling error by 20percent and 28percent for the methods of adjusting the coefficients by the standard and hybrid genetic algorithms, respectively, in comparison with the case of an arbitrary choice of the values of such coefficients. For the task of estimating the parameters of the operation of a hydroturbine unit, a number of promising approaches to constructing regression models based on artificial neural networks, multidimensional adaptive splines, and an evolutionary method of genetic programming were included in the research. The proposed nonparametric approach with a hybrid smoothing coefficient tuning scheme was found to be most effective with a reduction in modelling error of about 5percent compared with the best of the alternative approaches considered in the study, which, according to the results of numerical experiments, was the method of multivariate adaptive regression splines.", notes = "also known as \cite{computation9080083}", } @Article{bukhtoyarov:2023:Electronics, author = "Vladimir V. Bukhtoyarov and Vadim S. Tynchenko and Vladimir A. Nelyub and Igor S. Masich and Aleksey S. Borodulin and Andrei P. Gantimurov", title = "A Study on a Probabilistic Method for Designing Artificial Neural Networks for the Formation of Intelligent Technology Assemblies with High Variability", journal = "Electronics", year = "2023", volume = "12", number = "1", pages = "Article No. 215", keywords = "genetic algorithms, genetic programming", ISSN = "2079-9292", URL = "https://www.mdpi.com/2079-9292/12/1/215", DOI = "doi:10.3390/electronics12010215", abstract = "Currently, ensemble approaches based, among other things, on the use of non-network models are powerful tools for solving data analysis problems in various practical applications. An important problem in the formation of ensembles of models is ensuring the synergy of solutions by using the properties of a variety of basic individual solutions; therefore, the problem of developing an approach that ensures the maintenance of diversity in a preliminary pool of models for an ensemble is relevant for development and research. This article is devoted to the study of the possibility of using a method for the probabilistic formation of neural network structures developed by the authors. In order to form ensembles of neural networks, the influence of parameters of neural network structure generation on the quality of solving regression problems is considered. To improve the quality of the overall ensemble solution, using a flexible adjustment of the probabilistic procedure for choosing the type of activation function when filling in the layers of a neural network is proposed. In order to determine the effectiveness of this approach, a number of numerical studies on the effectiveness of using neural network ensembles on a set of generated test tasks and real datasets were conducted. The procedure of forming a common solution in ensembles of neural networks based on the application of an evolutionary method of genetic programming is also considered. This article presents the results of a numerical study that demonstrate a higher efficiency of the approach with a modified structure formation procedure compared to a basic approach of selecting the best individual neural networks from a preformed pool. These numerical studies were carried out on a set of test problems and several problems with real datasets that, in particular, describe the process of ore-thermal melting.", notes = "also known as \cite{electronics12010215}", } @InProceedings{bull:1996:SandS, author = "Lawrence Bull and Terence C. Fogarty", title = "Evolutionary Computing in Multi-Agent Environments: Speciation and Symbiogenesis", editor = "Hans-Michael Voigt and Werner Ebeling and Ingo Rechenberg and Hans-Paul Schwefel", booktitle = "Parallel Problem Solving From Nature IV. Proceedings of the International Conference on Evolutionary Computation", year = "1996", publisher = "Springer-Verlag", volume = "1141", series = "LNCS", pages = "12--21", address = "Berlin, Germany", publisher_address = "Heidelberg, Germany", month = "22-26 " # sep, keywords = "genetic algorithms", ISBN = "3-540-61723-X", DOI = "doi:10.1007/3-540-61723-X_965", size = "10 pages", abstract = "In this paper we introduce two macro-level operators to enhance the use of population-based evolutionary computing techniques in multiagent environments: speciation and symbiogenesis. We describe their use in conjunction with the genetic algorithm to evolve Pittsburgh-style classifier systems, where each classifier system represents an agent in a cooperative multi-agent system. The reasons for implementing these kinds of operators are discussed and we then examine their performance in developing a controller for the gait of a wall-climbing quadrupedal robot, where each leg of the quadruped is controlled by a classifier system. We find that the use of such operators can give improved performance over static population/agent configurations.", notes = "http://lautaro.fb10.tu-berlin.de/ppsniv.html PPSN4 Wall climbing quadruped robot simulation", } @InProceedings{Bull:1997:ecmaee, author = "Larry Bull and Owen Holland", title = "Evolutionary Computing in Multi-Agent Environments: Eusociality", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "Genetic Algorithms", pages = "347--352", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", notes = "GP-97", } @InProceedings{bull:1999:OZSCDM, author = "Larry Bull", title = "On using ZCS in a Simulated Continuous Double-Auction Market", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "83--90", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-806.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-806.pdf", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Bull:2009:eurogp, author = "Larry Bull and Richard Preen", title = "On Dynamical Genetic Programming: Random {Boolean} Networks in Learning Classifier Systems", booktitle = "Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009", year = "2009", editor = "Leonardo Vanneschi and Steven Gustafson and Alberto Moraglio and Ivanoe {De Falco} and Marc Ebner", volume = "5481", series = "LNCS", pages = "37--48", address = "Tuebingen", month = apr # " 15-17", organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-01180-1", DOI = "doi:10.1007/978-3-642-01181-8_4", notes = "Part of \cite{conf/eurogp/2009} EuroGP'2009 held in conjunction with EvoCOP2009, EvoBIO2009 and EvoWorkshops2009", } @Article{Bull:2009:IJPEDS, author = "Larry Bull", title = "On dynamical genetic programming: simple {Boolean} networks in learning classifier systems", journal = "International Journal of Parallel, Emergent and Distributed Systems", year = "2009", volume = "24", number = "5", pages = "421--442", month = oct, publisher = "Taylor \& Francis", keywords = "genetic algorithms, genetic programming, discrete, dynamical systems, evolution, multiplexer, unorganised machines", ISSN = "1744-5760", DOI = "doi:10.1080/17445760802660387", abstract = "Many representations have been presented to enable the effective evolution of computer programs. Turing was perhaps the first to present a general scheme by which to achieve this end. Significantly, Turing proposed a form of discrete dynamical system and yet dynamical representations remain almost unexplored within conventional genetic programming (GP). This paper presents results from an initial investigation into using simple dynamical GP representations within a learning classifier system. It is shown possible to evolve ensembles of dynamical Boolean function networks to solve versions of the well-known multiplexer problem. Both synchronous and asynchronous systems are considered.", notes = "a Department of Computer Science, University of the West of England, Bristol, UK Formerly Parallel Algorithms and Applications", } @InCollection{Bull:2017:miller, author = "Larry Bull and Rita Toth and Chris Stone and Ben {De Lacy Costello} and Andrew Adamatzky", title = "Chemical Computing Through Simulated Evolution", booktitle = "Inspired by Nature: Essays Presented to Julian F. Miller on the Occasion of his 60th Birthday", publisher = "Springer", year = "2017", editor = "Susan Stepney and Andrew Adamatzky", volume = "28", series = "Emergence, Complexity and Computation", chapter = "13", pages = "269--286", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-67996-9", DOI = "doi:10.1007/978-3-319-67997-6_13", abstract = "Many forms of unconventional computing, i.e., massively parallel computers which exploit the non-linear material properties of their substrate, can be realised through simulated evolution. That is, the behaviour of non-linear media can be controlled automatically and the structural design of the media optimized through the nature-inspired machine learning approach. This chapter describes work using the Belousov-Zhabotinsky reaction as a non-linear chemical medium in which to realise computation. Firstly, aspects of the basic structure of an experimental chemical computer are evolved to implement two Boolean logic functions through a collision-based scheme. Secondly, a controller is evolved to dynamically affect the rich spatio-temporal chemical wave behaviour to implement three Boolean functions, in both simulation and experimentation.", notes = "part of \cite{miller60book} https://link.springer.com/bookseries/10624", } @Article{bullard:1998:MD, author = "James B. Bullard and John Duffy", title = "learning and the Stability of Cycles", journal = "Macroeconomic Dynamics", year = "1998", volume = "2", number = "1", pages = "22--48", month = mar, keywords = "genetic algorithms, Learning, Multiple Equilibria, Coordination", size = "27 pages", abstract = "We investigate the extent to which agents can learn to coordinate on stationary perfect-foresight cycles in a general-equilibrium environment. Depending on the value of a preference parameter, the limiting backward (direction of time reversed) perfect-foresight dynamics are characterized by steady-state, periodic, or chaotic trajectories for real money balances. We relax the perfect-foresight assumption and examine how a population of artificial, heterogeneous adaptive agents might learn in such an environment. These artificial agents optimize given their forecasts of future prices, and they use forecast rules that are consistent with steady-state or periodic trajectories for prices. The agents' forecast rules are updated by a genetic algorithm. We find that the population of artificial adaptive agents is able eventually to coordinate on steady state and low-order cycles, but not on the higher-order periodic equilibria that exist under the perfect-foresight assumption.", notes = "Also available as working paper 1995-006B http://research.stlouisfed.org/wp/1995/95-006.pdf", } @Article{bullard:1998:JEDC, author = "James Bullard and John Duffy", title = "A model of learning and emulation with artificial adaptive agents", journal = "Journal of Economic Dynamics and Control", year = "1998", volume = "22", number = "2", pages = "179--207", month = feb, keywords = "genetic algorithms, Learning, Coordination, Overlapping generations", DOI = "doi:10.1016/S0165-1889(97)00072-9", abstract = "We study adaptive learning in a sequence of overlapping generations economies in which agents live for n periods. Agents initially have heterogeneous beliefs, and form multi-step-ahead forecasts using a forecast rule chosen from a vast set of candidate rules. Agents learn in every period by creating new forecast rules and by emulating the forecast rules of other agents. Computational experiments show that systems so defined can yield three qualitatively different types of long-run outcomes: (1) coordination on a low inflation, stationary perfect foresight equilibrium; (2) persistent currency collapse; and (3) coordination failure within the allotted time frame.", notes = "JEL classification codes: D83; C63 Also available as working paper 1994-014C http://research.stlouisfed.org/wp/1994/94-014.pdf", } @InProceedings{Bunjerdtaweeporn:2025:evoapplications, author = "Kritpol Bunjerdtaweeporn and Alberto Moraglio", title = "Building Cross-Sectional Trading Strategies via Geometric Semantic Genetic Programming", booktitle = "28th International Conference, EvoApplications 2025", year = "2025", editor = " Pablo Garcia-Sanchez and Emma Hart and Sarah L. Thomson", series = "LNCS", volume = "15612", pages = "20--37", publisher = "Springer", address = "Trieste, Italy", month = "3-5 " # apr, organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Geometric Semantic Genetic Programming, Portfolio Construction, Stock Returns Prediction, Stock Selection", isbn13 = "978-3-031-90062-4", timestamp = "Sat, 31 May 2025 23:09:36 +0200", biburl = "https://dblp.org/rec/conf/evoapps/BunjerdtaweepornM25.bib", bibsource = "dblp computer science bibliography, https://dblp.org", DOI = "doi:10.1007/978-3-031-90062-4_2", notes = "DBLP:conf/evoapps/BunjerdtaweepornM25, http://www.evostar.org/2025/ EvoApplications2025 held in conjunction with EuroGP'2025, EvoCOP2025 and EvoMusArt2025", } @Article{buontempo:2005:CIM, author = "Frances V. Buontempo and Xue Zhong Wang and Mulaisho Mwense and Nigel Horan and Anita Young and Daniel Osborn", title = "Genetic Programming for the Induction of Decision Trees to Model Ecotoxicity Data", journal = "Journal of Chemical Information and Modeling", year = "2005", volume = "45", pages = "904--912", note = "ASAP article. Web Release Date: May 12, 2005", keywords = "genetic algorithms, genetic programming, decision trees, model ecotoxicity, EPTree, C5.0 See5, recursive partitioning, S-Plus, SIMCA-P 8.0, QSAR", DOI = "doi:10.1021/ci049652n", size = "9 pages", abstract = "Automatic induction of decision trees and production rules from data to develop structure-activity models for toxicity prediction has recently received much attention, and the majority of methodologies reported in the literature are based upon recursive partitioning employing greedy searches to choose the best splitting attribute and value at each node. These approaches can be successful; however, the greedy search will necessarily miss regions of the search space. Recent literature has demonstrated the applicability of genetic programming to decision tree induction to overcome this problem. This paper presents a variant of this novel approach, using fewer mutation options and a simpler fitness function, demonstrating its utility in inducing decision trees for ecotoxicity data, via a case study of two data sets giving improved accuracy and generalization ability over a popular decision tree inducer.", notes = " http://pubs.acs.org/journals/jcisd8/index.html S1549-9596(04)09652-4 ACS Publications Division cites EPtree \cite{delisle:2004:CIM} y-scrambling. at least 10\% data coverage required of decision trees. Tournament size 16. No parsimony fitness preassure. Trees regrown. Lots of mutation if pop stagnated. Elitist but gives no improvement. -Log(LC50) vibrio fischeri. 1093 features. 60 training compounds. 100 generation. Pop 600. 1 second per generation. Department of Chemical Engineering and School of Civil Engineering, University of Leeds, Leeds LS2 9JT, U.K., AstraZeneca UK Ltd., Brixham Environmental Laboratory, Freshwater Quarry, Brixham, Devon TQ5 8BA, U.K., and Centre of Ecology and Hydrology, Monks Wood, Huntingdon PE28 2LS, U.K.", } @Article{Burakov:2013:IJICA, author = "Sergei V. Burakov and Eugene S. Semenkin", title = "Solving variational and Cauchy problems with self-configuring genetic programming algorithm", journal = "International Journal of Innovative Computing and Applications", year = "2013", volume = "5", number = "3", pages = "152--162", note = "Special Issue on: BIOMA 2012 Advances in Bio-inspired Computing. Guest Editors: Assistant Professor Jurij Silc and Associate Professor Bogdan Filipic", keywords = "genetic algorithms, genetic programming, Ordinary differential equations; Cauchy problem; variational problem; numeric methods; genetic programming algorithm; self-configuration.", ISSN = "1751-648X", DOI = "doi:10.1504/IJICA.2013.055931", abstract = "It is suggested to use genetic programming techniques for solving Cauchy problem and variational problem that allows getting the exact analytical solution if it does exist and an approximate analytical expression otherwise. Features of solving process with this approach are considered. Results of numerical experiments are given. The approach improvement is fulfilled by adopting the self-configuring genetic programming algorithm that does not require extra efforts for choosing its effective settings but demonstrates the competitive performance", notes = "Acceptance Date: 30 Nov 2012 IJICA http://www.inderscience.com/jhome.php?jcode=ijica ", } @InProceedings{wssec-rb-final, author = "Robert Burbidge", title = "A Contribution to the Foundations of {AI}: Genetic Programming and Support Vector Machines", booktitle = "Workshop and Summer School on Evolutionary Computing Lecture Series by Pioneers", year = "2008", editor = "T. M. McGinnity", address = "University of Ulster", month = "18-20 " # aug, organisation = "School of Computing and Intelligent Systems, University of Ulster", keywords = "genetic algorithms, genetic programming, SVM", URL = "http://users.aber.ac.uk/rvb/wssec-rb-final.pdf", size = "4 pages", abstract = "The aim of genetic programming is to automatically find computer programs that solve problems; using an algorithm inspired by biological evolution. The aim of the support vector machine is to model empirical data; using an algorithm based on statistical optimality. Fundamentally, both these techniques, and all artificial intelligence disciplines, use search; with differing representations, search operators and objective functions. We formally compare these two techniques as a contribution toward the foundations of artificial intelligence, and less grandiosely, in order to encourage transfer of knowledge between the two disciplines.", notes = "broken 2016 http://isel.infm.ulst.ac.uk/conference/wssec2008/ 'The search space for GP is hard'. 'The search space for the SVM is easy'. 'inherent capacity control in GP' VC-dimension.", } @InProceedings{Burbidge:2009:TAROS, author = "Robert Burbidge and Joanne H. Walker and Myra S. Wilson", title = "A Grammar for Evolution of a Robot Controller", booktitle = "TAROS 2009 Towards Autonomous Robotic Systems", year = "2009", editor = "Theocharis Kyriacou and Ulrich Nehmzow and Chris Melhuish and Mark Witkowski", series = "Intelligent Systems Research Centre Technical Report Series", pages = "182--189", address = "University of Ulster, Londonderry, United Kingdom", month = aug # " 31 - " # sep # " 2", keywords = "genetic algorithms, genetic programming, grammatical evolution, robot control", URL = "http://isrc.ulster.ac.uk/images/stories/publications/report-series/TAROS_2009.pdf", size = "8 pages", abstract = "An autonomous mobile robot requires an onboard controller that allows it to perform its tasks for long periods in isolation. One possibility is for the robot to adapt to its environment using some form of artificial intelligence. Evolutionary techniques such as genetic programming (GP) offer the possibility of automatically programming the controller based on the robot's experience of the world. Grammatical evolution (GE) is a recent evolutionary algorithm that has been successfully applied to various problems, particularly those for which GP has been successful. We present a method for applying GE to autonomous robot control and evaluate it in simulation for the Khepera robot.", notes = "http://www.infm.ulst.ac.uk/~ulrich/Taros09/", } @InProceedings{Burbidge:2009:IROS, author = "Robert Burbidge and Joanne H. Walker and Myra S. Wilson", title = "Grammatical evolution of a robot controller", booktitle = "IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009", year = "2009", month = "11-15 " # oct, address = "St. Louis, USA", pages = "357--362", keywords = "genetic algorithms, genetic programming, grammatical evolution, Khepera robot, artificial intelligence, autonomous mobile robot, evolutionary algorithm, evolutionary technique, onboard controller, robot controller, grammars, mobile robots", DOI = "doi:10.1109/IROS.2009.5354411", abstract = "An autonomous mobile robot requires an on board controller that allows it to perform its tasks for long periods in isolation. One possibility is for the robot to adapt to its environment using some form of artificial intelligence. Evolutionary techniques such as genetic programming (GP) offer the possibility of automatically programming the controller based on the robot's experience of the world. Grammatical evolution (GE) is a recent evolutionary algorithm that has been successfully applied to various problems, particularly those for which GP has been successful. We present a method for applying GE to autonomous robot control and evaluate it in simulation for the Khepera robot.", notes = "Department of Computer Science, Aberystwyth University, Penglais, SY23 3DB, UK Also known as \cite{5354411}", } @Article{Burbidge:2014:IS, author = "Robert Burbidge and Myra S. Wilson", title = "Vector-valued function estimation by grammatical evolution for autonomous robot control", journal = "Information Sciences", volume = "258", pages = "182--199", year = "2014", ISSN = "0020-0255", DOI = "doi:10.1016/j.ins.2013.09.044", URL = "http://www.sciencedirect.com/science/article/pii/S0020025513006920", keywords = "genetic algorithms, genetic programming, Grammatical evolution, Evolutionary robotics, Vector-valued function, Ripple crossover, Schema", size = "18 pages", abstract = "An autonomous mobile robot requires a robust onboard controller that makes intelligent responses in dynamic environments. Current solutions tend to lead to unnecessarily complex solutions that only work in niche environments. Evolutionary techniques such as genetic programming (GP) can successfully be used to automatically program the controller, minimising the limitations arising from explicit or implicit human design criteria, based on the robot's experience of the world. Grammatical evolution (GE) is a recent evolutionary algorithm that has been applied to various problems, particularly those for which GP has performed. We formulate robot control as vector-valued function estimation and present a novel generative grammar for vector valued functions. A consideration of the crossover operator leads us to propose a design criterion for the application of GE to vector-valued function estimation, along with a second novel generative grammar which meets this criterion. The suitability of these grammars for vectorvalued function estimation is assessed empirically on a simulated task for the Khepera robot", } @InProceedings{Burger:2011:SAICSIT, author = "Clayton Burger and Mathys C. {Du Plessis}", title = "Does Chomsky complexity affect genetic programming computational requirements?", booktitle = "Proceedings of the South African Institute of Computer Scientists and Information Technologists Conference on Knowledge, Innovation and Leadership in a Diverse, Multidisciplinary Environment", year = "2011", pages = "31--39", address = "Cape Town, South Africa", publisher = "ACM", keywords = "genetic algorithms, genetic programming, theory of computation, Turing machines", isbn13 = "978-1-4503-0878-6", DOI = "doi:10.1145/2072221.2072226", size = "9 page", abstract = "This paper presents an exploration into the relationship between Chomsky problem complexity, as defined by Theory of Computation, and the computational requirements to evolve solutions to these problems. Genetic programming is used to explore these computational requirements by evolving Turing machines that accept the languages posed. Quantifiable results are obtained by applying various metrics to the evolutionary success of these evolved Turing machines. The languages posed are samples out of three language classes from the Chomsky hierarchy, with each class having increasing levels of complexity based on the hierarchy. These languages are evolved on a two-tape Turing machine representation by making use of genetic operators found to be effective in the literature. By exploring the effects of the genetic programming algorithm population sizes and coupled genetic operator rates, it was found that the evolutionary success rates of the classes of Regular and Context-Sensitive problems have no statistical difference in computational requirements, while the Context-Free class was found to be more difficult than the other two Chomsky problem classes through the statistical significance discovered when compared to the other classes.", notes = "SAICSIT '11", acmid = "2072226", } @Article{Burgess:2001:IST, author = "Colin J. Burgess and Martin Lefley", title = "Can genetic programming improve software effort estimation? A comparative evaluation", year = "2001", journal = "Information and Software Technology", volume = "43", number = "14", pages = "863--873", month = "15 " # dec, keywords = "genetic algorithms, genetic programming, Case-based reasoning, Machine learning, Neural networks, Software effort estimation", URL = "http://www.sciencedirect.com/science/article/B6V0B-44D4196-7/1/20f45986fc0a4827ad09169178379d73", DOI = "doi:10.1016/S0950-5849(01)00192-6", abstract-url = "http://www.cs.bris.ac.uk/Publications/pub_info.jsp?id=1000586", size = "11 pages", abstract = "Accurate software effort estimation is an important part of the software process. Originally, estimation was performed using only human expertise, but more recently, attention has turned to a variety of machine learning (ML) methods. This paper attempts to evaluate critically the potential of genetic programming (GP) in software effort estimation when compared with previously published approaches, in terms of accuracy and ease of use. The comparison is based on the well-known Desharnais data set of 81 software projects derived from a Canadian software house in the late 1980s. The input variables are restricted to those available from the specification stage and significant effort is put into the GP and all of the other solution strategies to offer a realistic and fair comparison. There is evidence that GP can offer significant improvements in accuracy but this depends on the measure and interpretation of accuracy used. GP has the potential to be a valid additional tool for software effort estimation but set up and running effort is high and interpretation difficult, as it is for any complex meta-heuristic technique.", } @InCollection{2000240, author = "C. J. Burgess and M. Lefley", title = "Can Genetic Programming improve Software Effort Estimation? {A} Comparative Evaluation", booktitle = "Machine Learning Applications In Software Engineering: Series on Software Engineering and Knowledge Engineering", editor = "Du Zhang and Jeffrey J. P. Tsai", volume = "16", ISBN = "981-256-094-7", publisher = "World Scientific Publishing Co.", pages = "95--105", month = may, year = "2005", keywords = "genetic algorithms, genetic programming, Artificial Intelligence, Machine Learning, SBSE", pubtype = "7", broken = "http://www.cs.bris.ac.uk/Publications/pub_info.jsp?id=2000240", abstract = "Accurate software effort estimation is an important part of the software process. Originally, estimation was performed using only human expertise, but more recently attention has turned to a variety of machine learning methods. This paper attempts to critically evaluate the potential of genetic programming (GP) in software effort estimation when compared with previously published approaches. The comparison is based on the well-known Desharnais data set of 81 software projects derived from a Canadian software house in the late 1980s. It shows that GP can offer some significant improvements in accuracy and has the potential to be a valid additional tool for software effort estimation.", notes = "This paper is not on-line. Contact the author see \cite{Burgess:2001:IST}", } @TechReport{burgess:1999:faasdeGP, author = "Glenn Burgess", title = "Finding Approximate Analytic Solutions To Differential Equations Using Genetic Programming", institution = "Surveillance Systems Division, Defence Science and Technology Organisation, Australia", month = Feb, year = "1999", type = "Technical Report", number = "DSTO-TR-0838", address = "Salisbury, SA, 5108, Australia", notes = "Based on author's 1997 Dept. Phys. Honours Thesis, Flinders University of South Australia", keywords = "genetic algorithms, genetic programming, differential equations", broken = "http://203.36.224.190/cgi-bin/dsto/extract.pl?DSTO-TR-0838", URL = "http://www.dsto.defence.gov.au/corporate/reports/DSTO-TR-0838.pdf", size = "73 pages", abstract = "The computational optimisation technique, genetic programming, is applied to the analytic solution of general differential equations. The approach generates a mathematical expression that is an approximate or exact solution to the particular equation under consideration. The technique is applied to a number of differential equations of increasing complexity in one and two dimensions. Comparative results are given for varying several parameters of the algorithm such as the size of the calculation stack and the variety of available mathematical operators. Several novel approaches gave negative results. Angeline's module acquisition (MA) and Koza's automatically defined functions (ADF) are considered and the results of some modifications are presented. One result of significant theoretical interest is that the syntax-preserving crossover used in Genetic Programming may be generalised to allow the exchange of n-argument functions without adverse effects. The results show that Genetic Programming is an effective technique that can give reasonable results, given plenty of computing resources. The technique used here can be applied to higher dimensions; although in practice the algorithmic complexity may be too high.", } @InProceedings{Burgin:2010:cec, author = "Mark Burgin and Eugene Eberbach", title = "Bounded and periodic evolutionary machines", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-6910-9", abstract = "The aim of this paper is the development of foundations for evolutionary computation. We introduce and study two classes of evolutionary automata: bounded and periodic evolutionary machines.", DOI = "doi:10.1109/CEC.2010.5586271", notes = "WCCI 2010. Also known as \cite{5586271}", } @InProceedings{Burian:2013:AE, author = "Petr Burian", title = "Reduction of fitness calculations in Cartesian Genetic Programming", booktitle = "International Conference on Applied Electronics (AE 2013)", year = "2013", month = "10-12 " # sep, address = "Pilsen", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Multiplier, Evolutionary design, Evolutionary Algorithm, Multiplier", ISSN = "1803-7232", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6636478", size = "6 pages", abstract = "This paper deals with the valuation issue in Cartesian Genetic Programming. It explores the possibilities of the reduction of candidate solutions which are needed to be evaluated. This reduction may accelerate the process of the evolution - evolutionary design. The paper presents the approach that detects changes in the phenotype and, based on that, the algorithm can omit the valuation of a candidate solution. The author shows this approach on the evolutionary design of multipliers.", notes = "Also known as \cite{6636478}", } @InProceedings{Burian:2014:ICSES, author = "P. Burian", booktitle = "International Conference on Signals and Electronic Systems (ICSES 2014)", title = "Fast detection of active genes in Cartesian Genetic Programming", year = "2014", month = sep, abstract = "This paper deals with the implementation of fast active genes detector by an FPGA device. The author introduces the modular structure that determines active genes in genotypes of Cartesian Genetic Programming. The use of the detector is suitable if the l-back parameter takes value of 1 or 2. The paper also discusses timing performance. The introduced active genes detector can be used for the reduction of fitness calculations in Cartesian Genetic Programming.", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", DOI = "doi:10.1109/ICSES.2014.6948721", notes = "Also known as \cite{6948721}", } @InProceedings{Burian:2014:AE, author = "P. Burian", booktitle = "International Conference on Applied Electronics (AE 2014)", title = "Compact version of Cartesian Genetic Programming", year = "2014", month = sep, pages = "63--66", abstract = "This paper deals with the design of the compact version of Cartesian Genetic Programming. The focus is given to the search algorithm of type (1+1). The paper presents the approach that detects changes in the phenotype and, based on that, the algorithm can omit the evaluation of a candidate solution. The author uses the evolutionary design of multipliers as benchmark to present the efficiency of the algorithm.", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", DOI = "doi:10.1109/AE.2014.7011669", ISSN = "1803-7232", notes = "Also known as \cite{7011669}", } @InCollection{burjorjee:1999:GAGGS, author = "Keki M. Burjorjee", title = "Genetic Algorithms Go to Grade School", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1999", year = "1999", editor = "John R. Koza", pages = "31--40", address = "Stanford, California, 94305-3079 USA", month = "15 " # mar, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:1999:GAGPs}", } @Article{burk:1998:pmgGA, author = "Donald S. Burke and Kenneth A. {De Jong} and John J. Grefenstette and Connie Loggia Ramsey and Annie S. Wu", title = "Putting More Genetics into Genetic Algorithms", journal = "Evolutionary Computation", year = "1998", volume = "6", number = "4", pages = "387--410", month = "Winter", keywords = "genetic algorithms, Models of viral evolution, variable-length representation, length penalty functions, genome length adaptation, noncoding regions, duplicative genes", URL = "http://www.mitpressjournals.org/doi/pdfplus/10.1162/evco.1998.6.4.387", DOI = "doi:10.1162/evco.1998.6.4.387", size = "25 pages", abstract = "The majority of current genetic algorithms (GAs), while inspired by natural evolutionary systems, are seldom viewed as biologically plausible models. This is not a criticism of GAs, but rather a reflection of choices made regarding the level of abstraction at which biological mechanisms are modeled, and a reflection of the more engineering-oriented goals of the evolutionary computation community. Understanding better and reducing this gap between GAs and genetics has been a central issue in an interdisciplinary project whose goal is to build GA-based computational models of viral evolution. The result is a system called Virtual Virus (VIV). The VIV incorporates a number of more biologically plausible mechanisms, including a more flexible genotype-to-phenotype mapping. In VIV the genes are independent of position, and genomes can vary in length and may contain noncoding regions, as well as duplicative or competing genes. Initial computational studies with VIV have already revealed several emergent phenomena of both biological and computational interest. In the absence of any penalty based on genome length, VIV develops individuals with long genomes and also performs more poorly (from a problem-solving viewpoint) than when a length penalty is used. With a fixed linear length penalty, genome length tends to increase dramatically in the early phases of evolution and then decrease to a level based on the mutation rate. The plateau genome length (i.e., the average length of individuals in the final population) generally increases in response to an increase in the base mutation rate. When VIV converges, there tend to be many copies of good alternative genes within the individuals. We observed many instances of switching between active and inactive genes during the entire evolutionary process. These observations support the conclusion that noncoding regions serve a positive step in understanding how GAs might exploit more of the power and flexibility of biological evolution while simultaneously providing better tools for understanding evolving biological systems.", notes = "Evolutionary Computation (Journal) Special Issue: Variable-Length Representation and Noncoding Segments for Evolutionary Algorithms Edited by Annie S. Wu and Wolfgang Banzhaf", } @Misc{burk:1998:pmgGAx, author = "Donald S. Burke and Kenneth A. {De Jong} and John J. Grefenstette and Connie Loggia Ramsey and Annie S. Wu", title = "Putting More Genetics into Genetic Algorithms", howpublished = "preprint of \cite{burk:1998:pmgGA}", year = "1998", month = "19 " # oct, keywords = "genetic algorithms, Models of viral evolution, variable-length representation, length penalty functions, genome length adaptation, noncoding regions, duplicative genes", URL = "http://www.ib3.gmu.edu/gref/papers/burke-ec98.ps", } @InProceedings{burke:2002:gecco, author = "Edmund Burke and Steven Gustafson and Graham Kendall", title = "A Survey And Analysis Of Diversity Measures In Genetic Programming", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "716--723", address = "New York", publisher_address = "San Francisco, CA 94104, USA", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "genetic algorithms, genetic programming, diversity, population diversity, population dynamics", ISBN = "1-55860-878-8", URL = "http://www.cs.nott.ac.uk/~smg/research/publications/gecco-diversity-2002.ps", URL = "http://www.cs.nott.ac.uk/~smg/research/publications/gecco-diversity-2002.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/GP125.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/GP125.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf", notes = "GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002) Nominated for best at GECCO award", } @InProceedings{burke:ppsn2002:pp341, author = "Edmund Burke and Steven Gustafson and Graham Kendall and Natalio Krasnogor", title = "Advanced Population Diversity Measures in Genetic Programming", booktitle = "Parallel Problem Solving from Nature - PPSN VII", address = "Granada, Spain", month = "7-11 " # sep, pages = "341--350", year = "2002", editor = "Juan J. Merelo-Guervos and Panagiotis Adamidis and Hans-Georg Beyer and Jose-Luis Fernandez-Villacanas and Hans-Paul Schwefel", number = "2439", series = "Lecture Notes in Computer Science, LNCS", publisher = "Springer-Verlag", URL = "http://www.gustafsonresearch.com/research/publications/ppsn-2002.pdf", URL = "http://www.cs.nott.ac.uk/~smg/research/publications/ppsn-2002.ps", URL = "http://www.cs.nott.ac.uk/~smg/research/publications/ppsn-2002.pdf", URL = "http://slater.chem.nott.ac.uk/~natk/Public/PAPERS/gp-ppsn2002.ps.Z", URL = "http://citeseer.ist.psu.edu/529057.html", DOI = "doi:10.1007/3-540-45712-7_33", keywords = "genetic algorithms, genetic programming, Theory of EC, Evolution dynamics", ISBN = "3-540-44139-5", abstract = "This paper presents a survey and comparison of significant diversity measures in the genetic programming literature. This study builds on previous work by the authors to gain a deeper understanding of the conditions under which genetic programming evolution is successful. Three benchmark problems (Artificial Ant, Symbolic Regression and Even-5-Parity) are used to illustrate different diversity measures and to analyse their correlation with performance. Results show that measures of population diversity based on edit distances and phenotypic diversity suggest that successful evolution occurs when populations converge to a similar structure but with high fitness diversity.", } @InProceedings{burke:2003:gecco, author = "Edmund Burke and Steven Gustafson and Graham Kendall", title = "Ramped Half-n-Half Initialisation Bias in {GP}", booktitle = "Genetic and Evolutionary Computation -- GECCO-2003", editor = "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller", year = "2003", pages = "1800--1801", address = "Chicago", publisher_address = "Berlin", month = "12-16 " # jul, volume = "2724", series = "LNCS", ISBN = "3-540-40603-4", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, poster", URL = "http://www.cs.nott.ac.uk/~smg/research/publications/gecco-poster-2003.ps", URL = "http://www.cs.nott.ac.uk/~smg/research/publications/gecco-poster-2003.pdf", DOI = "doi:10.1007/3-540-45110-2_71", abstract = "Tree initialisation techniques for genetic programming (GP) are examined in [4,3], highlighting a bias in the standard implementation of the initialisation method Ramped Half-n-Half (RHH) [1]. GP trees typically evolve to random shapes, even when populations were initially full or minimal trees [2]. In canonical GP, unbalanced and sparse trees increase the probability that bigger subtrees are selected for recombination, ensuring code growth occurs faster and that subtree crossover will have more difficultly in producing trees within specified depth limits. The ability to evolve tree shapes which allow more legal crossover operations, by providing more possible crossover points (by being bushier), and control code growth is critical. The GP community often uses RHH [4]. The standard implementation of the RHH method selects either the grow or full method with 0.5 probability to produce a tree. If the tree is already in the initial population it is discarded and another is created by grow or full. As duplicates are typically not allowed, this standard implementation of RHH favours full over grow and possibly biases the evolutionary process.", notes = "GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)", } @InProceedings{1277273, author = "Edmund K. Burke and Matthew R. Hyde and Graham Kendall and John Woodward", title = "Automatic heuristic generation with genetic programming: evolving a jack-of-all-trades or a master of one", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "1559--1565", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1559.pdf", DOI = "doi:10.1145/1276958.1277273", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, bin packing, heuristics, hyper heuristic, reliability", abstract = "It is possible to argue that online bin packing heuristics should be evaluated by using metrics based on their performance over the set of all bin packing problems, such as the worst case or average case performance. However, this method of assessing a heuristic would only be relevant to a user who employs the heuristic over a set of problems which is actually representative of the set of all possible bin packing problems. On the other hand, a real world user will often only deal with packing problems that are representative of a particular sub-set. Their piece sizes will all belong to a particular distribution. The contribution of this paper is to show that a Genetic Programming system can automate the process of heuristic generation and produce heuristics that are human-competitive over a range of sets of problems, or which excel on a particular sub-set. We also show that the choice of training instances is vital in the area of automatic heuristic generation, due to the trade-off between the performance and generality of the heuristics generated and their applicability to new problems.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @InProceedings{Burke:2007:cec, author = "E. K. Burke and M. R. Hyde and G. Kendall and J. R. Woodward", title = "The Scalability of Evolved on Line Bin Packing Heuristics", booktitle = "2007 IEEE Congress on Evolutionary Computation", year = "2007", editor = "Dipti Srinivasan and Lipo Wang", pages = "2530--2537", address = "Singapore", month = "25-28 " # sep, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "1-4244-1340-0", file = "1668.pdf", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2007.4424789", size = "8 pages", abstract = "The on line bin packing problem concerns the packing of pieces into the least number of bins possible, as the pieces arrive in a sequential fashion. In previous work, we used genetic programming to evolve heuristics for this problem, which beat the human designed 'bestfit' algorithm. Here we examine the performance of the evolved heuristics on larger instances of the problem, which contain many more pieces than the problem instances used in training. In previous work, we concluded that we could confidently apply our heuristics to new instances of the same class of problem. Now we can make the additional claim that we can confidently apply our heuristics to problems of much larger size, not only without deterioration of solution quality, but also within a constant factor of the performance obtained by 'best fit'. Interestingly, our evolved heuristics respond to the number of pieces in a problem instance although they have no explicit access to that information. We also comment on the important point that, when solutions are explicitly constructed for single problem instances, the size of the search space explodes. How- ever, when working in the space of algorithmic heuristics, the distribution of functions represented in the search space reaches some limiting distribution and therefore the combinatorial explosion can be controlled.", notes = "Feb 2013 IEEE Xplore meta data gives wrong author. CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C", } @Article{Burke2013, author = "Edmund K Burke and Michel Gendreau and Matthew Hyde and Graham Kendall and Gabriela Ochoa and Ender Ozcan and Rong Qu", title = "Hyper-heuristics: a survey of the state of the art", journal = "Journal of the Operational Research Society", year = "2013", volume = "64", number = "12", pages = "1695--1724", month = dec, keywords = "genetic algorithms, genetic programming, Hyper-heuristics, evolutionary computation, metaheuristics, machine learning, combinatorial optimisation, scheduling", publisher = "Palgrave Macmillan", ISSN = "0160-5682", URL = "http://www.cs.nott.ac.uk/~rxq/files/HHSurveyJORS2013.pdf", DOI = "doi:10.1057/jors.2013.71", size = "20 pages", abstract = "Hyper-heuristics comprise a set of approaches that are motivated (at least in part) by the goal of automating the design of heuristic methods to solve hard computational search problems. An underlying strategic research challenge is to develop more generally applicable search methodologies. The term hyper-heuristic is relatively new, it was first used in 2000 to describe heuristics to choose heuristics in the context of combinatorial optimisation. However, the idea of automating the design of heuristics is not new; it can be traced back to the 1960s. The definition of hyper-heuristics has been recently extended to refer to a search method or learning mechanism for selecting or generating heuristics to solve computational search problems. Two main hyper-heuristic categories can be considered: heuristic selection and heuristic generation. The distinguishing feature of hyper-heuristics is that they operate on a search space of heuristics (or heuristic components) rather than directly on the search space of solutions to the underlying problem that is being addressed. This paper presents a critical discussion of the scientific literature on hyper-heuristics including their origin and intellectual roots, a detailed account of the main types of approaches, and an overview of some related areas. Current research trends and directions for future research are also discussed.", notes = "several listed approaches use GP", } @InProceedings{Burke:2005:WWERC, author = "John J. Burke", title = "Genetic Programming of Crops to Sustain or Increase Yields under Reduced Irrigation", booktitle = "World Water and Environmental Resources Congress 2005", year = "2005", editor = "Raymond Walton", address = "Anchorage, Alaska, USA", month = may # " 15-19", DOI = "doi:10.1061/40792(173)532", abstract = "Crop productivity is determined by the plant's capacity to convert energy, nutrients, and water into harvestable yield of high quality and high value. The challenge is to sustain or enhance the outputs with a declining land base, reduced water supplies, and a changing global environment. The process of crop adaptation to the environment is restricted by the genetic potential of the plant. Improving the capacity of crops to overcome or adapt to factors that limit growth would increase yield and quality, while reducing demand for irrigation. Research identifying the molecular and biochemical factors underlying crop productivity, adaptation to stressful environments, and production of high-value end products is providing new insights into strategies for germplasm improvement. Characterisation of existing genetic diversity within U.S germplasm collections for water-deficit and temperature stress resistance; and the use of biotechnology to enhance yield stabilisation in water limited environments will ensure farming sustainability in the future.", notes = "Perhaps not about GP? c2005 ASCE", } @InProceedings{Burke:PPSN:2006, author = "E. K. Burke and M. R. Hyde and G. Kendall", title = "Evolving Bin Packing Heuristics with Genetic Programming", booktitle = "Parallel Problem Solving from Nature - PPSN IX", year = "2006", editor = "Thomas Philip Runarsson and Hans-Georg Beyer and Edmund Burke and Juan J. Merelo-Guervos and L. Darrell Whitley and Xin Yao", volume = "4193", pages = "860--869", series = "LNCS", address = "Reykjavik, Iceland", publisher_address = "Berlin", month = "9-13 " # sep, publisher = "Springer-Verlag", ISBN = "3-540-38990-3", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.nott.ac.uk/~mvh/ppsn2006.pdf", DOI = "doi:10.1007/11844297_87", size = "10 pages", abstract = "The bin-packing problem is a well known NP-Hard optimisation problem, and, over the years, many heuristics have been developed to generate good quality solutions. This paper outlines a genetic programming system which evolves a heuristic that decides whether to put a piece in a bin when presented with the sum of the pieces already in the bin and the size of the piece that is about to be packed. This heuristic operates in a fixed framework that iterates through the open bins, applying the heuristic to each one, before deciding which bin to use. The best evolved programs emulate the functionality of the human designed first-fit heuristic. Thus, the contribution of this paper is to demonstrate that genetic programming can be employed to automatically evolve bin packing heuristics which are the same as high quality heuristics which have been designed by humans.", notes = "PPSN-IX", } @InCollection{Burke:2010:HBMH, author = "Edmund K. Burke and Matthew Hyde and Graham Kendall and Gabriela Ochoa and Ender Ozcan and John R. Woodward", title = "A Classification of Hyper-heuristics Approaches", booktitle = "Handbook of Metaheuristics", publisher = "Springer", year = "2010", editor = "Michel Gendreau and Jean-Yves Potvin", volume = "57", series = "International Series in Operations Research \& Management Science", chapter = "15", pages = "449--468", edition = "2nd", keywords = "genetic algorithms, genetic programming", isbn13 = "978-4419-1663-1", URL = "http://www.cs.nott.ac.uk/~gxo/papers/ChapterClassHH.pdf", DOI = "doi:10.1007/978-1-4419-1665-5_15", abstract = "The current state of the art in hyper-heuristic research comprises a set of approaches that share the common goal of automating the design and adaptation of heuristic methods to solve hard computational search problems. The main goal is to produce more generally applicable search methodologies. In this chapter we present and overview of previous categorisations of hyper-heuristics and provide a unified classification and definition which captures the work that is being undertaken in this field. We distinguish between two main hyper-heuristic categories: heuristic selection and heuristic generation. Some representative examples of each category are discussed in detail. Our goal is to both clarify the main features of existing techniques and to suggest new directions for hyper-heuristic research.", } @Article{Burke:2011:ieeeTEC, author = "Edmund K. Burke and Matthew R. Hyde and Graham Kendall", title = "Grammatical Evolution of Local Search Heuristics", journal = "IEEE Transactions on Evolutionary Computation", year = "2012", volume = "16", number = "3", pages = "406--417", month = jun, keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Grammar, Heuristic algorithms, Production, Search problems, Bin packing, heuristics, local search, stock cutting", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2011.2160401", size = "12 pages", abstract = "Genetic programming approaches have been employed in the literature to automatically design constructive heuristics for cutting and packing problems. These heuristics obtain results superior to human-created constructive heuristics, but they do not generally obtain results of the same quality as local search heuristics, which start from an initial solution and iteratively improve it. If local search heuristics can be successfully designed through evolution, in addition to a constructive heuristic which initialises the solution, then the quality of results which can be obtained by automatically generated algorithms can be significantly improved. This paper presents a grammatical evolution methodology which automatically designs good quality local search heuristics that maintain their performance on new problem instances.", notes = "iGE also known as \cite{6029980}", } @InCollection{Burkowski:2005:HBBAA, author = "Forbes Burkowski", title = "Optimization via Gene Expression Algorithms", booktitle = "Handbook of Bioinspired Algorithms and Applications", publisher = "Chapman and Hall/CRC", year = "2005", editor = "Stephan Olariu and Albert Y. Zomaya", series = "Computer \& Information Science Series", chapter = "8", pages = "Pages 8--121--8--134", keywords = "SVM", isbn13 = "978-1-58488-475-0", DOI = "doi:10.1201/9781420035063.ch8", notes = "Not on GP?", } @InProceedings{Burks:2015:GECCO, author = "Armand R. Burks and William F. Punch", title = "An Efficient Structural Diversity Technique for Genetic Programming", booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation", year = "2015", editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr", isbn13 = "978-1-4503-3472-3", pages = "991--998", keywords = "genetic algorithms, genetic programming", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739480.2754649", DOI = "doi:10.1145/2739480.2754649", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Genetic diversity plays an important role in avoiding premature convergence, which is a phenomenon that stifles the search effectiveness of evolutionary algorithms. However, approaches that avoid premature convergence by maintaining genetic diversity can do so at the cost of efficiency, requiring more fitness evaluations to find high quality solutions. We introduce a simple and efficient genetic diversity technique that is capable of avoiding premature convergence while maintaining a high level of search quality in tree-based genetic programming. Our method finds solutions to a set of benchmark problems in significantly fewer fitness evaluations than the algorithms that we compared against.", notes = "Also known as \cite{2754649} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)", } @InProceedings{Burks:2016:GPTP, author = "Armand R. Burks and William F. Punch", title = "An Investigation of Hybrid Structural and Behavioral Diversity Methods in Genetic Programming", booktitle = "Genetic Programming Theory and Practice XIV", year = "2016", editor = "Rick Riolo and Bill Worzel and Brian Goldman and Bill Tozier", pages = "19--34", address = "Ann Arbor, USA", month = "19-21 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, premature convergence, genetic diversity, structural diversity, behavioural diversity, semantics", isbn13 = "978-3-319-97087-5", URL = "https://www.springer.com/us/book/9783319970875", DOI = "doi:10.1007/978-3-319-97088-2_2", abstract = "Premature convergence is a serious problem that plagues genetic programming, stifling its search performance. Several genetic diversity maintenance techniques have been proposed for combating premature convergence and improving search efficiency in genetic programming. Recent research has shown that while genetic diversity is important, focusing directly on sustaining behavioural diversity may be more beneficial. These two related areas have received a lot of attention, yet they have often been developed independently. We investigated the feasibility of hybrid genetic and behavioral diversity techniques on a suite of problems.", notes = " Part of \cite{Tozier:2016:GPTP} published after the workshop", } @Article{Burks:2016:GPEM, author = "Armand R. Burks and William F. Punch", title = "An analysis of the genetic marker diversity algorithm for genetic programming", journal = "Genetic Programming and Evolvable Machines", year = "2017", volume = "18", number = "2", pages = "213--245", month = jun, keywords = "genetic algorithms, genetic programming, Genotypic diversity, Structural diversity, Premature convergence", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-016-9281-9", size = "33 pages", abstract = "Many diversity techniques have been developed for addressing premature convergence, which is a serious problem that stifles the search effectiveness of evolutionary algorithms. However, approaches that aim to avoid premature convergence can often take longer to discover a solution. The Genetic Marker Diversity algorithm is a new technique that has been shown to find solutions significantly faster than other approaches while maintaining diversity in genetic programming. This study provides a more in-depth analysis of the search behaviour of this technique compared to other state-of-the-art methods, as well as a comparison of the performance of these techniques on a larger and more modern set of test problems.", } @PhdThesis{Burks:thesis, author = "Armand Rashad Burks", title = "Hybrid Structural and Behavioral Diversity Techniques for Effective Genetic Programming", school = "Computer Science, Michigan State University", year = "2017", address = "USA", keywords = "genetic algorithms, genetic programming, GMD-GP, Tuberculosis Diagnosis", URL = "https://search.proquest.com/docview/1952843117", URL = "https://d.lib.msu.edu/etd/6744/datastream/OBJ/View/", URL = "https://doi.org/doi:10.25335/M5527Z", size = "107 pages", abstract = "Sustaining the diversity of evolving populations is a fundamental issue in genetic programming. We describe a novel measure of structural diversity for tree-based genetic programming, and we demonstrate its utility compared to other diversity techniques. We demonstrate our technique on the real-world application of tuberculosis screening from X-ray images. We then introduce a new paradigm of genetic programming that involves simultaneously maintaining structural and behavioural diversity in order to further improve the efficiency of genetic programming. Our results show that simultaneously promoting structural and behavioural diversity improves genetic programming by leveraging the benefits of both aspects of diversity while overcoming the shortcomings of either technique in isolation. The hybridization increases the behavioral diversity of our structural diversity technique, and increases the structural diversity of the behavioural diversity techniques. This increased diversity leads to performance gains compared to either technique in isolation. We found that in many cases, our structural diversity technique provides significant performance improvement compared to other state-of-the-art techniques. Our results from the experiments comparing the hybrid techniques indicate that the largest performance gain was typically attributed to our structural diversity technique. The incorporation of the behavioural diversity techniques provide additional improvement in many cases.", isbn13 = "9780355181548", notes = "ProQuest Number: 10621139 Supervisor William F. Punch Dissertation/thesis number 10621139", } @InProceedings{Burks:2018:GECCO, author = "Armand R. Burks and William F. Punch", title = "Genetic programming for tuberculosis screening from raw {X-ray} images", booktitle = "GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference", year = "2018", editor = "Hernan Aguirre and Keiki Takadama and Hisashi Handa and Arnaud Liefooghe and Tomohiro Yoshikawa and Andrew M. Sutton and Satoshi Ono and Francisco Chicano and Shinichi Shirakawa and Zdenek Vasicek and Roderich Gross and Andries Engelbrecht and Emma Hart and Sebastian Risi and Ekart Aniko and Julian Togelius and Sebastien Verel and Christian Blum and Will Browne and Yusuke Nojima and Tea Tusar and Qingfu Zhang and Nikolaus Hansen and Jose Antonio Lozano and Dirk Thierens and Tian-Li Yu and Juergen Branke and Yaochu Jin and Sara Silva and Hitoshi Iba and Anna I Esparcia-Alcazar and Thomas Bartz-Beielstein and Federica Sarro and Giuliano Antoniol and Anne Auger and Per Kristian Lehre", isbn13 = "978-1-4503-5618-3", pages = "1214--1221", address = "Kyoto, Japan", DOI = "doi:10.1145/3205455.3205461", publisher = "ACM", publisher_address = "New York, NY, USA", month = "15-19 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", abstract = "Genetic programming has been successfully applied to several real-world problem domains. One such application area is image classification, wherein genetic programming has been used for a variety of problems such as breast cancer detection, face detection, and pedestrian detection, to name a few. We present the use of genetic programming for detecting active tuberculosis in raw X-ray images. Our results demonstrate that genetic programming evolves classifiers that achieve promising accuracy results compared to that of traditional image classification techniques. Our classifiers do not require pre-processing, segmentation, or feature extraction beforehand. Furthermore, our evolved classifiers process a raw X-ray image and return a classification orders of magnitude faster than the reported times for traditional techniques.", notes = "Also known as \cite{3205461} GECCO-2018 A Recombination of the 27th International Conference on Genetic Algorithms (ICGA-2018) and the 23rd Annual Genetic Programming Conference (GP-2018)", } @InProceedings{Burlacu:2012:EMSS, author = "Bogdan Burlacu and Michael Affenzeller and Michael Kommenda and Stephan M. Winkler and Gabriel Kronberger", title = "Evolution Tracking in Genetic Programming", booktitle = "The 24th European Modeling and Simulation Symposium, EMSS 2012", year = "2012", editor = "Emilio Jimenez and Boris Sokolov", address = "Vienna, Austria", month = sep # ", 19-21", keywords = "genetic algorithms, genetic programming, tree fragments, evolutionary dynamics, schema theory, population diversity, bloat, introns", URL = "http://research.fh-ooe.at/en/publication/3444", URL = "http://research.fh-ooe.at/files/publications/3444_EMSS_2012_Burlacu.pdf", size = "4 pages", abstract = "Much effort has been put into understanding the artificial evolutionary dynamics within genetic programming (GP). However, the details are yet unclear so far, as to which elements make GP so powerful. This paper presents an attempt to study the evolution of a population of computer programs using HeuristicLab. A newly developed methodology for recording heredity information, based on a general conceptual framework of evolution, is employed for the analysis of algorithm behaviour on a symbolic regression benchmark problem. In our example, we find the complex interplay between selection and crossover to be the cause for size increase in the population, as the average amount of genetic information transmitted from parents to offspring remains constant and independent of run constraints (i.e., tree size and depth limits). Empirical results reveal many interesting details and confirm the validity and generality of our approach, as a tool for understanding the complex aspects of GP.", notes = "EMSS_80 University of Applied Sciences Upper Austria - Austria http://www.msc-les.org/conf/emss2012/index.htm http://www.m-s-net.org/conf/i3m2012_program.pdf", } @InProceedings{conf/eurocast/BurlacuAK13, author = "Bogdan Burlacu and Michael Affenzeller and Michael Kommenda", title = "On the Evolutionary Behavior of Genetic Programming with Constants Optimization", booktitle = "Computer Aided Systems Theory, EUROCAST 2013", year = "2013", editor = "Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia", volume = "8111", series = "Lecture Notes in Computer Science", pages = "284--291", address = "Las Palmas de Gran Canaria, Spain", month = feb # " 10-15", publisher = "Springer", note = "14th International Conference, Revised Selected Papers", keywords = "genetic algorithms, genetic programming, evolutionary behaviour, constant optimisation, symbolic regression, algorithm analysis", bibdate = "2013-12-16", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/eurocast/eurocast2013-1.html#BurlacuAK13", isbn13 = "978-3-642-53855-1", URL = "http://dx.doi.org/10.1007/978-3-642-53856-8_36", DOI = "doi:10.1007/978-3-642-53856-8_36", size = "8 pages", abstract = "Evolutionary systems are characterised by two seemingly contradictory properties: robustness and evolvability. Robustness is generally defined as an organism's ability to withstand genetic perturbation while maintaining its phenotype. Evolvability, as an organism's ability to produce useful variation. In genetic programming, the relationship between the two, mediated by selection and variation-producing operators (recombination and mutation), makes it difficult to understand the behaviour and evolutionary dynamics of the search process. In this paper, we show that a local gradient-based constants optimisation step can improve the overall population evolvability by inducing a beneficial structure-preserving bias on selection, which in the long term helps the process maintain diversity and produce better solutions.", } @InProceedings{Burlacu:2013:GECCOcomp, author = "Bogdan Burlacu and Michael Affenzeller and Michael Kommenda and Stephan Winkler and Gabriel Kronberger", title = "Visualization of genetic lineages and inheritance information in genetic programming", booktitle = "GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion", year = "2013", editor = "Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A. N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks", isbn13 = "978-1-4503-1964-5", keywords = "genetic algorithms, genetic programming", pages = "1351--1358", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2464576.2482714", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Many studies emphasise the importance of genetic diversity and the need for an appropriate tuning of selection pressure in genetic programming. Additional important aspects are the performance and effects of the genetic operators (crossover and mutation) on the transfer and stabilisation of inherited information blocks during the run of the algorithm. In this context, different ideas about the usage of lineage and genealogical information for improving genetic programming have taken shape in the last decade. Our work builds on those ideas by introducing an evolution tracking framework for assembling genealogical and inheritance graphs of populations. The proposed approach allows detailed investigation of phenomena related to building blocks, size evolution, ancestry and diversity. We introduce the notion of genetic fragments to represent sub-trees that are affected by reproductive operators (mutation and crossover) and present a methodology for tracking such fragments using flexible similarity measures. A fragment matching algorithm was designed to work on both structural and semantic levels, allowing us to gain insight into the exploratory and exploitative behaviour of the evolutionary process. The visualisation part which is the subject of this paper integrates with the framework and provides an easy way of exploring the population history. The paper focuses on a case study in which we investigate the evolution of a solution to a symbolic regression benchmark problem.", notes = "Also known as \cite{2482714} Distributed at GECCO-2013.", } @InProceedings{Burlacu:2015:APCASE, author = "Bogdan Burlacu and Michael Kommenda and Michael Affenzeller", booktitle = "2015 Asia-Pacific Conference on Computer Aided System Engineering (APCASE)", title = "Building Blocks Identification Based on Subtree Sample Counts for Genetic Programming", year = "2015", pages = "152--157", abstract = "Often, the performance of genetic programming (GP) is explained in terms of building blocks -- high-quality solution elements that get gradually assembled into larger and more complex patterns by the evolutionary process. However, the weak theoretical foundations of GP building blocks causes their role in GP evolutionary dynamics to remain still somewhat of a mystery. This paper presents a methodology for identifying GP building blocks based on their respective sample counts in the population (defined as the number of times they were sampled by the recombination operators and included in surviving offspring). Our approach represents a problem-independent way to identify important solution elements based on their influence on the evolutionary process.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/APCASE.2015.34", month = jul, notes = "Also known as \cite{7287011}", } @InProceedings{DBLP:conf/eurocast/BurlacuAK15, author = "Bogdan Burlacu and Michael Affenzeller and Michael Kommenda", title = "On the Effectiveness of Genetic Operations in Symbolic Regression", booktitle = "15th International Conference Computer Aided Systems Theory, EUROCAST 2015", year = "2015", editor = "Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia", volume = "9520", series = "Lecture Notes in Computer Science", pages = "367--374", address = "Las Palmas de Gran Canaria, Spain", month = feb # " 8-13", publisher = "Springer", note = "Revised Selected Papers", keywords = "genetic algorithms, genetic programming, Evolutionary dynamics, Algorithm analysis, Symbolic regression", isbn13 = "978-3-319-27339-6", URL = "https://doi.org/10.1007/978-3-319-27340-2_46", DOI = "doi:10.1007/978-3-319-27340-2_46", timestamp = "Thu, 25 May 2017 00:43:36 +0200", biburl = "https://dblp.org/rec/bib/conf/eurocast/BurlacuAK15", bibsource = "dblp computer science bibliography, https://dblp.org", abstract = "This paper describes a methodology for analysing the evolutionary dynamics of genetic programming (GP) using genealogical information, diversity measures and information about the fitness variation from parent to offspring. We introduce a new subtree tracing approach for identifying the origins of genes in the structure of individuals, and we show that only a small fraction of ancestor individuals are responsible for the evolution of the best solutions in the population.", } @InCollection{series/sci/BurlacuAWKK15, author = "Bogdan Burlacu and Michael Affenzeller and Stephan M. Winkler and Michael Kommenda and Gabriel Kronberger", title = "Methods for Genealogy and Building Block Analysis in Genetic Programming", bibdate = "2015-06-29", bibsource = "DBLP, http://dblp.uni-trier.de/db/series/sci/sci595.html#BurlacuAWKK15", booktitle = "Computational Intelligence and Efficiency in Engineering Systems", publisher = "Springer", year = "2015", volume = "595", editor = "Grzegorz Borowik and Zenon Chaczko and Witold Jacak and Tadeusz Luba", isbn13 = "978-3-319-15719-1", pages = "61--74", series = "Studies in Computational Intelligence", keywords = "genetic algorithms, genetic programming", URL = "http://dx.doi.org/10.1007/978-3-319-15720-7", DOI = "doi:10.1007/978-3-319-15720-7_5", abstract = "Genetic programming gradually assembles high-level structures from low-level entities or building blocks. This chapter describes methods for investigating emergent phenomena in genetic programming by looking at a population's collective behaviour. It details how these methods can be used to trace genotypic changes across lineages and genealogies. Part of the methodology, we present an algorithm for decomposing arbitrary subtrees from the population to their inherited parts, picking up the changes performed by either crossover or mutation across ancestries. This powerful tool creates new possibilities for future theoretical investigations on evolutionary algorithm behavior concerning building blocks and fitness landscape analysis.", } @InProceedings{DBLP:conf/eurocast/BurlacuAKKW17, author = "Bogdan Burlacu and Michael Affenzeller and Michael Kommenda and Gabriel Kronberger and Stephan M. Winkler", title = "Analysis of Schema Frequencies in Genetic Programming", booktitle = "16th International Conference on Computer Aided Systems Theory, EUROCAST 2017, Part I", year = "2017", editor = "Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia", volume = "10671", series = "Lecture Notes in Computer Science", pages = "432--438", address = "Las Palmas de Gran Canaria, Spain", month = feb # " 19-24", publisher = "Springer", note = "Revised Selected Papers", keywords = "genetic algorithms, genetic programming, building blocks, Schema analysis, Symbolic regression, Tree pattern matching, Evolutionary dynamics, Loss of diversity", isbn13 = "978-3-319-74717-0", URL = "https://doi.org/10.1007/978-3-319-74718-7_52", DOI = "doi:10.1007/978-3-319-74718-7_52", timestamp = "Fri, 26 Jan 2018 12:44:51 +0100", biburl = "https://dblp.org/rec/bib/conf/eurocast/BurlacuAKKW17", bibsource = "dblp computer science bibliography, https://dblp.org", size = "7 pages", abstract = "Genetic Programming (GP) schemas are structural templates equivalent to hyperplanes in the search space. Schema theories provide information about the properties of subsets of the population and the behaviour of genetic operators. In this paper we propose a practical methodology to identify relevant schemas and measure their frequency in the population. We demonstrate our approach on an artificial symbolic regression benchmark where the parts of the formula are already known. Experimental results reveal how solutions are assembled within GP and explain diversity loss in GP populations through the proliferation of repeated patterns.", notes = "p433 'propergation of genes in GP' p434 'one point crossover' from OS-GP \cite{Affenzeller:2005:ICANNGA} 'repeated patterns, modules and building blocks' 'to see how the main terms were assembled together by' [evolution]", } @InProceedings{burlacu2018schema, author = "Bogdan Burlacu and Michael Affenzeller and Michael Kommenda and Gabriel Kronberger and Stephan Winkler", title = "Schema Analysis in Tree-Based Genetic Programming", booktitle = "Genetic Programming Theory and Practice XV", editor = "Wolfgang Banzhaf and Randal S. Olson and William Tozier and Rick Riolo", year = "2017", pages = "17--37", address = "University of Michigan in Ann Arbor, USA", month = may # " 18--20", organisation = "the Center for the Study of Complex Systems", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-90511-2", URL = "https://link.springer.com/chapter/10.1007/978-3-319-90512-9_2", DOI = "doi:10.1007/978-3-319-90512-9_2", abstract = "In this chapter we adopt the concept of schemata from schema theory and use it to analyse population dynamics in genetic programming for symbolic regression. We define schemata as tree-based wild card patterns and we empirically measure their frequencies in the population at each generation. Our methodology consists of two steps: in the first step we generate schemata based on genealogical information about crossover parents and their offspring, according to several possible schema definitions inspired from existing literature. In the second step, we calculate the matching individuals for each schema using a tree pattern matching algorithm. We test our approach on different problem instances and algorithmic flavours and we investigate the effects of different selection mechanisms on the identified schemata and their frequencies.", notes = "GPTP 2017, published 2018", } @PhdThesis{Burlacu:thesis, author = "Bogdan Burlacu", title = "Tracing of Evolutionary Search Trajectories in Complex Hypothesis Spaces", school = "University Linz", year = "2017", address = "Austria", keywords = "genetic algorithms, genetic programming, evolutionary computation, evolutionary tracing, evolutionary dynamics, genotype-phenotype map, system identification, genetische Programmierung, evolutionare Algorithmus, evolutionaere Verfolgung, evolutionaere Dynamik, Genotyp-Phaenotyp Karte, Systemidentifikation", URL = "https://epub.jku.at/obvulihs/content/titleinfo/2246376", URL = "https://epub.jku.at/obvulihs/download/pdf/2246376", URL = "https://epub.jku.at/obvulihs/download/pdf/2246376.pdf", size = "213 pages", abstract = "Understanding the internal functioning of evolutionary algorithms is an essential requirement for improving their performance and reliability. Increased computational resources available in current mainstream computers make it possible for new previously infeasible research directions to be explored. Therefore, a comprehensive theoretical analysis of their mechanisms and dynamics using modern tools becomes possible. Recent algorithmic achievements like offspring selection in combination with linear scaling have enabled genetic programming (GP) to achieve high quality results in system identification in less than 50 generations using populations of only several hundred individuals. Therefore, the active gene pool of evolutionary search remains manageable and may be subjected to new theoretical investigations closely related to genetic programming schema theories, building block hypotheses and bloat theories. Genetic algorithms emulate emergent systems in which complex patterns are formed from an initially simple and random pool of elementary structures. In GP, complexity emerges under the influence of stabilizing selection which preserves the useful genetic variation created by recombination and mutation. The mapping between the structures used for solution representation and the ones used for the evaluation of fitness has a major influence on algorithm behavior. Population-wide effects concerning building blocks, genetic diversity and bloat can be conceptually seen as results of the complex interaction between phenotypic operators (selection) and genotypic operators (mutation and recombination). This coupling known as the variation-selection loop is the main engine for GP emergent behavior and constitutes the main topic of this research. This thesis aims to provide a unified theoretical framework which can explain GP evolution. To this end, it explores the way in which the genotype-phenotype map, in relation with the evolutionary operators (selection, recombination, mutation) determines algorithmic behaviour. As the title suggests, the main contribution consists of a novel tracing framework that makes it possible to determine the exact patterns of building block and gene propagation through the generations and the way smaller elements are gradually assembled into more complex structures by the evolutionary algorithm.", zusammenfassung = "Um die Leistung und Zuverlaessigkeit von evolutionaeren Algorithmen zu verbessern, ist es notwendig deren interne Funktionsweise zu verstehen. Die hohe Rechenleistung aktueller Mainstream-Computer erlaubt neue Forschungsrichtungen zu erkunden, welche frher, aufgrund fehlender Rechenleistung, nicht moeglich waren. Fuer evolutionaere Algorithmen wird es dadurch moeglich, deren interne Mechaniken und Dynamiken umfangreich zu analysieren. Aktuelle algorithmische Fortschritte, wie Nachkommensselektion (Offspring Selection) in Kombination mit linearer Skalierung, ermoeglicht Genetischer Programmierung (GP) hochqualitative Ergebnisse in der Systemidentifikation zu erreichen, in weniger als 50 Generationen bei einer Populationsgroesse von nur wenigen hunderten Individuen. Der dadurch ueberschaubare aktive Genpool der evolutionaeren Suche ermoeglicht neue theoretische Untersuchungen zum GP Schema Theorem, zur Baustein Hypothese und zu Bloat-Theorien. Genetische Algorithmen emulieren emergente Systeme in denen komplexe Muster geformt werden, basierend auf einer initialen, zufaellig generiertem Menge an elementaren Strukturen. In GP entsteht die Komplexitaet durch den Einfluss der stabilisierenden Selektion, welche nuetzliche genetische Variation erhaelt die von Rekombination und Mutation erzeugt werden. Die Zuordnung zwischen Strukturen zur Loesungsrepraesentation (Genotyp) und Fitnessevaluierung (Phaenotyp) beeinflusst das algorithmische Verhalten stark. Populationsweite Auswirkungen betreffend Bausteine, genetischer Diversitaet und Bloat entstehen durch das komplexe Zusammenwirken phaenotypischen Operatoren (Selektion) und genotypischen Operatoren (Rekombination und Mutation). Dieser Mechanismus, bekannt als Variation-Selektion Schleife, ist die treibende Kraft des emergente Verhalten von GP und bildet das Hauptforschungsthema dieser Arbeit. Diese Arbeit zielt darauf ab, einen einheitlichen, theoretischen Rauem zu schaffen welcher Evolution in GP erklaeren kann. Dafuer wird der Einfluss von auf das algorithmische Verhalten untersucht, basierend auf die Zuordnung von Genotyp und Phaenotyp, unter Beruecksichtigung der evolutionaerer Operatoren (Selektion, Rekombination, Mutation). Wie der Titel der Arbeit bereits andeutet, besteht der wichtigste Beitrag aus einem neuartigen System zur UEberwachung und Rueckverfolgung von Genen ueber mehrere Generationen hinweg. Dies ermoeglicht es das Verhalten von Bausteinen zu erforschen, sowie zu erkunden wie bei evolutionaeren Algorithmen aus kleinen Elementen nach und nach komplexere Strukturen gebildet werden.", notes = "In English Supervisor: Michael Affenzeller", } @InProceedings{Burlacu:2019:EUROCAST, author = "Bogdan Burlacu and Lukas Kammerer and Michael Affenzeller and Gabriel Kronberger", title = "Hash-Based Tree Similarity and Simplification in Genetic Programming for Symbolic Regression", booktitle = "International Conference on Computer Aided Systems Theory, EUROCAST 2019", year = "2019", editor = "Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia", volume = "12013", series = "Lecture Notes in Computer Science", pages = "361--369", address = "Las Palmas de Gran Canaria, Spain", month = "17-22 " # feb, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-030-45092-2", DOI = "doi:10.1007/978-3-030-45093-9_44", abstract = "We introduce in this paper a runtime-efficient tree hashing algorithm for the identification of isomorphic subtrees, with two important applications in genetic programming for symbolic regression: fast, online calculation of population diversity and algebraic simplification of symbolic expression trees. Based on this hashing approach, we propose a simple diversity-preservation mechanism with promising results on a collection of symbolic regression benchmark problems.", } @InProceedings{Burlacu:2018:GECCO, author = "Bogdan Burlacu and Michael Affenzeller", title = "Schema-based diversification in genetic programming", booktitle = "GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference", year = "2018", editor = "Hernan Aguirre and Keiki Takadama and Hisashi Handa and Arnaud Liefooghe and Tomohiro Yoshikawa and Andrew M. Sutton and Satoshi Ono and Francisco Chicano and Shinichi Shirakawa and Zdenek Vasicek and Roderich Gross and Andries Engelbrecht and Emma Hart and Sebastian Risi and Ekart Aniko and Julian Togelius and Sebastien Verel and Christian Blum and Will Browne and Yusuke Nojima and Tea Tusar and Qingfu Zhang and Nikolaus Hansen and Jose Antonio Lozano and Dirk Thierens and Tian-Li Yu and Juergen Branke and Yaochu Jin and Sara Silva and Hitoshi Iba and Anna I Esparcia-Alcazar and Thomas Bartz-Beielstein and Federica Sarro and Giuliano Antoniol and Anne Auger and Per Kristian Lehre", isbn13 = "978-1-4503-5618-3", pages = "1111--1118", address = "Kyoto, Japan", DOI = "doi:10.1145/3205455.3205594", publisher = "ACM", publisher_address = "New York, NY, USA", month = "15-19 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", abstract = "In genetic programming (GP), population diversity represents a key aspect of evolutionary search and a major factor in algorithm performance. In this paper we propose a new schema-based approach for observing and steering the loss of diversity in GP populations. We employ a well-known hyperschema definition from the literature to generate tree structural templates from the population's genealogy, and use them to guide the search via localized mutation within groups of individuals matching the same schema. The approach depends only on genealogy information and is easily integrated with existing GP variants. We demonstrate its potential in combination with Offspring Selection GP (OSGP) on a series of symbolic regression benchmark problems where our algorithmic variant called OSGP-S obtains superior results.", notes = "Also known as \cite{3205594} GECCO-2018 A Recombination of the 27th International Conference on Genetic Algorithms (ICGA-2018) and the 23rd Annual Genetic Programming Conference (GP-2018)", } @InProceedings{Burlacu:2019:CEC, author = "B. Burlacu and M. Affenzeller and G. Kronberger and M. Kommenda", booktitle = "2019 IEEE Congress on Evolutionary Computation (CEC)", title = "Online Diversity Control in Symbolic Regression via a Fast Hash-based Tree Similarity Measure", year = "2019", pages = "2175--2182", month = jun, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2019.8790162", abstract = "Diversity represents an important aspect of genetic programming, being directly correlated with search performance. When considered at the genotype level, diversity often requires expensive tree distance measures which have a negative impact on the algorithm's runtime performance. In this work we introduce a fast, hash-based tree distance measure to massively speed-up the calculation of population diversity during the algorithmic run. We combine this measure with the standard GA and the NSGA-II genetic algorithms to steer the search towards higher diversity. We validate the approach on a collection of benchmark problems for symbolic regression where our method consistently outperforms the standard GA as well as NSGA-II configurations with different secondary objectives.", notes = "Also known as \cite{8790162}", } @InProceedings{Burlacu:2019:GECCOcomp, author = "Bogdan Burlacu and Gabriel Kronberger and Michael Kommenda and Michael Affenzeller", title = "Parsimony measures in multi-objective genetic programming for symbolic regression", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", isbn13 = "978-1-4503-6748-6", pages = "338--339", address = "Prague, Czech Republic", DOI = "doi:10.1145/3319619.3322087", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{3322087} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InProceedings{Burlacu:2020:GECCOcomp, author = "Bogdan Burlacu and Gabriel Kronberger and Michael Kommenda", title = "{Operon C++}: An Efficient Genetic Programming Framework for Symbolic Regression", year = "2020", editor = "Richard Allmendinger and Hugo Terashima Marin and Efren Mezura Montes and Thomas Bartz-Beielstein and Bogdan Filipic and Ke Tang and David Howard and Emma Hart and Gusz Eiben and Tome Eftimov and William {La Cava} and Boris Naujoks and Pietro Oliveto and Vanessa Volz and Thomas Weise and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Rui Wang and Ran Cheng and Guohua Wu and Miqing Li and Hisao Ishibuchi and Jonathan Fieldsend and Ozgur Akman and Khulood Alyahya and Juergen Branke and John R. Woodward and Daniel R. Tauritz and Marco Baioletti and Josu Ceberio Uribe and John McCall and Alfredo Milani and Stefan Wagner and Michael Affenzeller and Bradley Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and Nayat Sanchez-Pi and Luis Marti and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and Matthew Johns and Nick Ross and Ed Keedwell and Herman Mahmoud and David Walker and Anthony Stein and Masaya Nakata and David Paetzel and Neil Vaughan and Stephen Smith and Stefano Cagnoni and Robert M. Patton and Ivanoe {De Falco} and Antonio {Della Cioppa} and Umberto Scafuri and Ernesto Tarantino and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Erik Hemberg and Riyad Alshammari and Tokunbo Makanju and Fuijimino-shi and Ivan Zelinka and Swagatam Das and Ponnuthurai Nagaratnam and Roman Senkerik", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion", pages = "1562--1570", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, C++, symbolic regression", isbn13 = "9781450371278", URL = "https://doi.org/10.1145/3377929.3398099", DOI = "doi:10.1145/3377929.3398099", code_url = "https://github.com/heal-research/operon", size = "9 pages", abstract = "Genetic Programming (GP) is a dynamic field of research where empirical testing plays an important role in validating new ideas and algorithms. The ability to easily prototype new algorithms by reusing key components and quickly obtain results is therefore important for the researcher. In this paper we introduce Operon, a C++ GP framework focused on performance, modularity and usability, featuring an efficient linear tree encoding and a scalable concurrency model where each logical thread is responsible for generating a new individual. We model the GP evolutionary process around the concept of an offspring generator, a streaming operator that defines how new individuals are obtained. The approach allows different algorithmic variants to be expressed using high-level constructs within the same generational basic loop. The evaluation routine supports both scalar and dual numbers, making it possible to calculate model derivatives via automatic differentiation. This functionality allows seamless integration with gradient-based local search approaches. We discuss the design choices behind the proposed framework and compare against two other popular GP frameworks, DEAP and HeuristicLab. We empirically show that Operon is competitive in terms of solution quality, while being able to generate results significantly faster.", notes = "See also \cite{LaCava:2021:NeurIPS} Also known as \cite{10.1145/3377929.3398099} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @Article{burlacu:NC, author = "Bogdan Burlacu and Kaifeng Yang and Michael Affenzeller", title = "Population diversity and inheritance in genetic programming for symbolic regression", journal = "Natural Computing", year = "2024", volume = "23", number = "3", pages = "531--566", month = sep, note = "Special Issue: Selected papers from the 27th International Conference on DNA Computing and Molecular Programming", keywords = "genetic algorithms, genetic programming, XAI, Symbolic regression, Supervised learning, Explainable AI, Optimization algorithm, Evolutionary algorithm", URL = "https://rdcu.be/c7n0f", URL = "http://link.springer.com/article/10.1007/s11047-022-09934-x", DOI = "doi:10.1007/s11047-022-09934-x", size = "36 pages", abstract = "we aim to empirically characterize two important dynamical aspects of GP search: the evolution of diversity and the propagation of inheritance patterns. Diversity is calculated at the genotypic and phenotypic levels using efficient similarity metrics. Inheritance information is obtained via a full genealogical record of evolution as a directed acyclic graph and a set of methods for extracting relevant patterns. Advances in processing power enable our approach to handle previously infeasible graph sizes of millions of arcs and vertices. To enable a more comprehensive analysis we employ three closely-related but different evolutionary models: canonical GP, offspring selection and age-layered population structure. Our analysis reveals that a relatively small number of ancestors are responsible for producing the majority of descendants in later generations, leading to diversity loss. We show empirically across a selection of five benchmark problems that each configuration is characterised by different rates of diversity loss and different inheritance patterns, in support of the idea that each new problem may require a unique approach to solve optimally.", notes = "HEAL, University of Applied Sciences Upper Austria,Software park 11, 4232 Hagenberg, Austria", } @InProceedings{Burlacu:2024:GPTP, author = "Bogdan Burlacu and Stephan M. Winkler and Michael Affenzeller", title = "Revisiting Gradient-Based Local Search in Symbolic Regression", booktitle = "Genetic Programming Theory and Practice XXI", year = "2024", editor = "Stephan M. Winkler and Wolfgang Banzhaf and Ting Hu and Alexander Lalejini", series = "Genetic and Evolutionary Computation", pages = "259--273", address = "University of Michigan, USA", month = jun # " 6-8", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-981-96-0076-2", URL = "https://heal.heuristiclab.com/news/post/gptp-xxi", DOI = "doi:10.1007/978-981-96-0077-9_13", notes = "Published in 2025 after the workshop", } @InProceedings{burlacu:2024:GECCOcomp, author = "Bogdan Burlacu", title = "Backend-agnostic Tree Evaluation for Genetic Programming", booktitle = "Open Source Software for Evolutionary Computation", year = "2024", editor = "Stefan Wagner and Michael Affenzeller", pages = "1649--1657", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, energy efficiency, symbolic regression", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3664161", size = "9 pages", abstract = "The explicit vectorization of the mathematical operations required for fitness calculation can dramatically increase the efficiency of tree-based genetic programming for symbolic regression. In this paper, we introduce a modern software design for the seamless integration of vectorized math libraries with tree evaluation, and we benchmark each library in terms of runtime, solution quality and energy efficiency. The latter, in particular, is an aspect of increasing concern given the growing carbon footprint of AI. With this in mind, we introduce metrics for measuring the energy usage and power draw of the evolutionary algorithm. Our results show that an optimized math backend can decrease energy usage by as much as 35\% (with a proportional decrease in runtime) without any negative effects in the quality of solutions.", notes = "GECCO-2024 EvoOSS A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{Swan:2015:gi, author = "Nathan Burles and Jerry Swan and Edward Bowles and Alexander E. I. Brownlee and Zoltan A. Kocsis and Nadarajen Veerapen", title = "Embedded Dynamic Improvement", booktitle = "Genetic Improvement 2015 Workshop", year = "2015", editor = "William B. Langdon and Justyna Petke and David R. White", pages = "831--832", address = "Madrid", publisher_address = "New York, NY, USA", month = "11-15 " # jul, organisation = "SIGEvo", publisher = "ACM", keywords = "genetic algorithms, genetic programming, Genetic Improvement", isbn13 = "978-1-4503-3488-4", URL = "http://gpbib.cs.ucl.ac.uk/gi2015/embedded_dynamic_improvement.pdf", URL = "http://doi.acm.org/10.1145/2739482.2768423", DOI = "doi:10.1145/2739482.2768423", size = "2 pages", abstract = "We discuss the useful role that can be played by a subtype of improvement programming, which we term Embedded Dynamic Improvement. In this approach, developer-specified variation points define the scope of improvement. A search framework is embedded at these variation points, facilitating the creation of adaptive software that can respond online to changes in its execution environment.", notes = "position paper, slides http://gpbib.cs.ucl.ac.uk/gi2015/gi_2015_slides/burles ", } @InProceedings{Burles:2015:SSBSE, author = "Nathan Burles and Edward Bowles and Alexander E. I. Brownlee and Zoltan A. Kocsis and Jerry Swan and Nadarajen Veerapen", title = "Object-Oriented Genetic Improvement for Improved Energy Consumption in {Google Guava}", booktitle = "SSBSE", year = "2015", editor = "Yvan Labiche and Marcio Barros", volume = "9275", series = "LNCS", pages = "255--261", address = "Bergamo, Italy", month = sep # " 5-7", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Genetic Improvement, SBSE, Object-oriented programming, Subclass substitution, Liskov Substitution Principle, Energy profiling", isbn13 = "978-3-319-22182-3", URL = "https://dspace.stir.ac.uk/bitstream/1893/22227/1/SSBSE15-oogiiecgg.pdf", URL = "http://hdl.handle.net/1893/22227", DOI = "doi:10.1007/978-3-319-22183-0_20", size = "6 pages", abstract = "In this work we use metaheuristic search to improve Google's Guava library, finding a semantically equivalent version of com.google.common.collect.ImmutableMultimap with reduced energy consumption. Semantics-preserving transformations are found in the source code, using the principle of subtype polymorphism. We introduce a new tool, Opacitor, to deterministically measure the energy consumption, and find that a statistically significant reduction to Guava's energy consumption is possible. We corroborate these results using Jalen, and evaluate the performance of the metaheuristic search compared to an exhaustive search-finding that the same result is achieved while requiring almost 200 times fewer fitness evaluations. Finally, we compare the metaheuristic search to an independent exhaustive search at each variation point, finding that the metaheuristic has superior performance.", notes = "Excludes Java garbage collection and JIT. Exhaustive search (80 CPU days). http://ssbse.info/2015", } @InProceedings{Burles:2015:SSBSEa, author = "Nathan Burles and Edward Bowles and Bobby R. Bruce and Komsan Srivisut", title = "Specialising {Guava's} Cache to Reduce Energy Consumption", booktitle = "SSBSE", year = "2015", editor = "Yvan Labiche and Marcio Barros", volume = "9275", series = "LNCS", pages = "276--281", address = "Bergamo, Italy", month = sep # " 5-7", publisher = "Springer", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, Parameter tuning, Library specialisation, Energy profiling, Reduced power consumption", isbn13 = "978-3-319-22182-3", URL = "http://www.cs.ucl.ac.uk/staff/R.Bruce/Burles2015Specialising.pdf", DOI = "doi:10.1007/978-3-319-22183-0_23", size = "6 pages", abstract = "In this article we use a Genetic Algorithm to perform parameter tuning on Google Guava Cache library, specialising it to OpenTripPlanner. A new tool, Opacitor, is used to deterministically measure the energy consumed, and we find that the energy consumption of OpenTripPlanner may be significantly reduced by tuning the default parameters of Guava's Cache library. Finally we use Jalen, which uses time and CPU load as a proxy to calculate energy consumption, to corroborate these results.", notes = "Not on GP? BRB suggests it is GP. Open Street Map tiles cached. http://ssbse.info/2015", } @InProceedings{Burling-Claridge:2016:CEC, author = "Francine Burling-Claridge and Muhammad Iqbal and Mengjie Zhang", title = "Evolutionary Algorithms for Classification of Mammographic Densities using Local Binary Patterns and Statistical Features", booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016)", year = "2016", editor = "Yew-Soon Ong", pages = "3847--3854", address = "Vancouver", month = "24-29 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-5090-0623-6", DOI = "doi:10.1109/CEC.2016.7744277", abstract = "Millions of women are suffering from breast cancer, which can be treated effectively if it is detected early. Breast density is one of the many factors that lead to an increased risk of breast cancer for women. However, it is difficult for radiologists to provide both accurate and uniform evaluations of different density levels in a large number of mammographic images generated in the screening process. Various computer aided diagnosis systems for digital mammograms have been reported in literature, but very few of them thoroughly investigate mammographic densities. This study presents a thorough analysis of classifying mammographic densities using different local binary patterns and statistical features of digital mammograms in two evolutionary algorithms, i.e., genetic programming and learning classifier systems; and four conventional classification methods, i.e., naive Bayes, decision trees, K-nearest neighbour, and support vector machines. The obtained results show that evolutionary algorithms have potential to solve these challenging real-world tasks. It is found that statistical features produced better results than local binary patterns for the experiments conducted in this study. Further, in genetic programming, the reuse of extracted knowledge from one feature set to another shows statistically significant improvement over the standard approach.", notes = "WCCI2016", } @InProceedings{burnett:2017:CEC, author = "Andrew W. Burnett and Andrew J. Parkes", booktitle = "2017 IEEE Congress on Evolutionary Computation (CEC)", title = "Exploring the landscape of the space of heuristics for local search in SAT", year = "2017", editor = "Jose A. Lozano", pages = "2518--2525", address = "Donostia, San Sebastian, Spain", publisher = "IEEE", isbn13 = "978-1-5090-4601-0", abstract = "Local search is a powerful technique on many combinatorial optimisation problems. However, the effectiveness of local search methods will often depend strongly on the details of the heuristics used within them. There are many potential heuristics, and so finding good ones is in itself a challenging search problem. A natural method to search for effective heuristics is to represent the heuristic as a small program and then apply evolutionary methods, such as genetic programming. However, the search within the space of heuristics is not well understood, and in particular little is known of the associated search landscapes. In this paper, we consider the domain of propositional satisfiability (SAT), and a generic class of local search methods called `WalkSAT'. We give a language for generating the heuristics; using this we generated over three million heuristics, in a systematic manner, and evaluated their associated fitness values. We then use this data set as the basis for an initial analysis of the landscape of the space of heuristics. We give evidence that the heuristic landscape exhibits clustering. We also consider local search on the space of heuristics and show that it can perform quite well, and could complement genetic programming methods on that space.", keywords = "genetic algorithms, genetic programming, computability, search problems, WalkSAT, clustering, combinatorial optimisation problems, evolutionary methods, local search, propositional satisfiability, search space landscapes, Computer science, Measurement, Reactive power, Systematics", isbn13 = "978-1-5090-4601-0", DOI = "doi:10.1109/CEC.2017.7969611", month = "5-8 " # jun, notes = "IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969611}", } @PhdThesis{Burrow:thesis, author = "Peter Richard Burrow", title = "Hybridising evolution and temporal difference learning", school = "University of Essex", year = "2011", address = "UK", keywords = "genetic algorithms, genetic programming", URL = "http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.572783", abstract = "This work investigates combinations of two different nature-inspired machine learning algorithms - Evolutionary Algorithms and Temporal Difference Learning. Both algorithms are introduced along with a survey of previous work in the field. A variety of ways of hybridising the two algorithms are considered, falling into two main categories - those where both algorithms operate on the same set of parameters, and those where evolution searches for beneficial parameters to aid Temporal Difference Learning. These potential approaches to hybridisation are explored by applying them to three different problem domains, all loosely linked by the theme of games. The Mountain Car task is a common reinforcement learning benchmark that has been shown to be potentially problematic for neural networks. Ms. Pac-Man is a classic arcade game with a complex virtual environment, and Othello is a popular two-player zero sum board game. Results show that simple hybridisation approaches often do not improve performance, which can be dependent on many factors of the individual algorithms. However, results have also shown that these factors can be successfully tuned by evolution. The main contributions of this thesis are an analysis of the factors that can affect individual algorithm performance, and demonstration of some novel approaches to hybridisation. These consist of use of Evolution Strategies to tune Temporal Difference Learning parameters on multiple problem domains, and evolution of n-tuple configurations for Othello board evaluation. In the latter case, a level of performance was achieved that was competitive with the state of the art.", notes = "Is this GP? EThOS ID: uk.bl.ethos.572783", } @InProceedings{busch:2002:EuroGP, title = "Automatic Generation of Control Programs for Walking Robots Using Genetic Programming", author = "Jens Busch and Jens Ziegler and Wolfgang Banzhaf and Andree Ross and Daniel Sawitzki and Christian Aue", editor = "James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi", booktitle = "Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002", volume = "2278", series = "LNCS", pages = "258--267", publisher = "Springer-Verlag", address = "Kinsale, Ireland", publisher_address = "Berlin", month = "3-5 " # apr, year = "2002", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-43378-3", URL = "http://ls2-www.cs.uni-dortmund.de/~sawitzki/AGoCPfWRUGP_Proc.pdf", DOI = "doi:10.1007/3-540-45984-7_25", abstract = "We present the system SIGEL that combines the simulation and visualization of robots with a Genetic Programming system for the automated evolution of walking. It is designed to automatically generate control programs for arbitrary robots without depending on detailed analytical information of the robots' kinematic structure. Different fitness functions as well as a variety of parameters allow the easy and interactive configuration and adaptation of the evolution process and the simulations.", notes = "EuroGP'2002, part of \cite{lutton:2002:GP}", } @Misc{oai:CiteSeerPSU:572931, author = "Stephen F. Bush and Amit B. Kulkarni", title = "Genetically Induced Communication Network Fault Tolerance", howpublished = "Invited Paper: SFI Workshop: Resilient and Adaptive Defence of Computing Networks 2002", year = "2002", keywords = "genetic algorithms, genetic programming", citeseer-isreferencedby = "oai:CiteSeerPSU:98543", citeseer-references = "oai:CiteSeerPSU:506477; oai:CiteSeerPSU:155080; oai:CiteSeerPSU:454895; oai:CiteSeerPSU:439074; oai:CiteSeerPSU:11748; oai:CiteSeerPSU:373663; oai:CiteSeerPSU:185401; oai:CiteSeerPSU:163938; oai:CiteSeerPSU:276739; oai:CiteSeerPSU:470039; oai:CiteSeerPSU:506805; oai:CiteSeerPSU:461659; oai:CiteSeerPSU:462512", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:572931", rights = "unrestricted", URL = "http://www.crd.ge.com/~bushsf/ftn/GE-SFI-AdaptiveSecurity.pdf", URL = "http://citeseer.ist.psu.edu/572931.html", size = "9 pages", abstract = "This paper presents the architecture and initial feasibility results of a proto-type communication network that uses genetic programming to evolve services and protocols as part of network operation. The network evolves responses to environmental conditions in a manner that could not be pre-programmed within legacy network nodes apriori. Apriori in this case means before network operation has begun. Genetic material is exchanged, loaded, and run dynamically within an active network. The transfer and execution of code in support of the evolution of network protocols and services would not be possible without the active network environment. Rapid generation of network service code occurs via a genetic programming paradigm. Complexity and Algorithmic Information Theory play a key role in understanding and guiding code evolution within the network.", notes = "No confirmation", } @Article{1005412, author = "Stephen F. Bush", title = "Genetically induced communication network fault tolerance", journal = "Complexity", volume = "9", number = "2", year = "2003", ISSN = "1076-2787", pages = "19--33", DOI = "doi:10.1002/cplx.20002", publisher = "John Wiley \& Sons, Inc.", keywords = "genetic algorithms, genetic programming, active networks, algorithmic information theory, Kolmogorov complexity, complexity theory, self-healing networks", URL = "http://www.crd.ge.com/~bushsf/pdfpapers/ComplexityJournal.pdf", abstract = "This article presents the architecture and initial feasibility results of a proto-type communication network that uses genetic programming to evolve services and protocols as part of network operation. The network evolves responses to environmental conditions in a manner that could not be pre-programmed within legacy network nodes a priori. A priori in this case means before network operation has begun. Genetic material is exchanged, loaded, and run dynamically within an active network. The transfer and execution of code in support of the evolution of network protocols and services would not be possible without the active network environment. Rapid generation of network service code occurs via a genetic programming paradigm. Complexity and algorithmic information theory play a key role in understanding and guiding code evolution within the network.", } @InProceedings{bush:evows05, author = "William S. Bush and Alison A. Motsinger and Scott M. Dudek and Marylyn D. Ritchie", title = "Can neural network constraints in GP provide power to detect genes associated with human disease?", booktitle = "Applications of Evolutionary Computing, EvoWorkshops2005: {EvoBIO}, {EvoCOMNET}, {EvoHOT}, {EvoIASP}, {EvoMUSART}, {EvoSTOC}", year = "2005", month = "30 " # mar # "-1 " # apr, editor = "Franz Rothlauf and Juergen Branke and Stefano Cagnoni and David W. Corne and Rolf Drechsler and Yaochu Jin and Penousal Machado and Elena Marchiori and Juan Romero and George D. Smith and Giovanni Squillero", series = "LNCS", volume = "3449", publisher = "Springer Verlag", address = "Lausanne, Switzerland", publisher_address = "Berlin", pages = "44--53", keywords = "genetic algorithms, genetic programming, evolutionary computation, ANN", ISBN = "3-540-25396-3", ISSN = "0302-9743", URL = "https://rdcu.be/dEt3y", DOI = "doi:10.1007/978-3-540-32003-6_5", DOI = "doi:10.1007/b106856", size = "10 pages", abstract = "A major goal of human genetics is the identification of susceptibility genes associated with common, complex diseases. Identifying gene-gene and gene-environment interactions which comprise the genetic architecture for a majority of common diseases is a difficult challenge. To this end, novel computational approaches have been applied to studies of human disease. Previously, a GP neural network (GPNN) approach was employed. Although the GPNN method has been quite successful, a clear comparison of GPNN and GP alone to detect genetic effects has not been made. In this paper, we demonstrate that using NN evolved by GP can be more powerful than GP alone. This is most likely due to the confined search space of the GPNN approach, in comparison to a free form GP. This study demonstrates the benefits of using GP to evolve NN in studies of the genetics of common, complex human disease.", notes = "EvoWorkshops2005 Center for Human Genetics Research, Department of Molecular Physiology & Biophysics, Vanderbilt University, Nashville, TN, 37232, USA", } @TechReport{butler:1995:eddie, author = "James M. Butler and Edward P. K. Tsang", title = "{EDDIE} Beats the Bookies", institution = "Computer Science, University of Essex", year = "1995", type = "Technical Report", number = "CSM-259", address = "Colchester CO4 3SQ, UK", month = "15 " # dec, keywords = "genetic algorithms, genetic programming", URL = "http://cswww.essex.ac.uk/CSP/papers/CSM-259.ps.Z", URL = "http://citeseer.ist.psu.edu/tsang98eddie.html", abstract = "Betting on a horse race is, in many ways, like investing in a financial market. You invest your money on the horse that you believe is going to win the race, in the hope of a return on your investment. Like some financial investments, horse race betting is a high risk investment, in that you can lose all of your money. As with making the right financial decision, the return on your investment, if you bet on the winning horse, can be considerable. In this paper, we present EDDIE, a genetic...", notes = " EDDIE, which stands for Evolutionary Dynamic Data Investment Evaluator, is designed as a tool to help channelling expert's knowledge into computer programs for making rules, which can then be examined by experts and used by other people. EDDIE is based on the concept of Genetic Programming, which borrows its ideas from evolution. EDDIE been applied to real horse races. We used the first 150 handicap races results in 1993 together with the expert knowledge that we could find from a text on horse racing to train EDDIE, which generates rules about betting. These rules were used to bet on the remaining 30 races in that season and obtained 88% return on investment. As scientists, we should always be cautious about experimental results. The sample size is small and luck may have a part to play in the success of EDDIE. However, the results justifies the investment of more time and effort into this research, which is what we are doing. See also \cite{tsang:1998:eddie}", } @InProceedings{conf/ai/ButlerK09a, title = "Optimizing a Pseudo Financial Factor Model with Support Vector Machines and Genetic Programming", author = "Matthew Butler and Vlado Keselj", booktitle = "22nd Canadian Conference on Artificial Intelligence, Canadian AI 2009", year = "2009", editor = "Yong Gao and Nathalie Japkowicz", volume = "5549", series = "Lecture Notes in Computer Science", pages = "191--194", address = "Kelowna, Canada", month = may # " 25-27", publisher = "Springer", keywords = "genetic algorithms, genetic programming, support vector machines, financial forecasting, principle component analysis", isbn13 = "978-3-642-01817-6", DOI = "doi:10.1007/978-3-642-01818-3_21", bibdate = "2009-05-18", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ai/ai2009.html#ButlerK09a", abstract = "We compare the effectiveness of Support Vector Machines (SVM) and Tree-based Genetic Programming (GP) to make accurate predictions on the movement of the Dow Jones Industrial Average (DJIA). The approach is facilitated though a novel representation of the data as a pseudo financial factor model, based on a linear factor model for representing correlations between the returns in different assets. To demonstrate the effectiveness of the data representation the results are compared to models developed using only the monthly returns of the inputs. Principal Component Analysis (PCA) is initially used to translate the data into PC space to remove excess noise that is inherent in financial data. The results show that the algorithms were able to achieve superior investment returns and higher classification accuracy with the aid of the pseudo financial factor model. As well, both models outperformed the market benchmark, but ultimately the SVM methodology was superior in terms of accuracy and investment returns.", } @Article{Buttler:GPEM, author = "Grace Buttler", title = "Artificial intelligence for fashion, {Leanne Luce}, {Apress} 2019, ISBN 978-1-4842-3930-8 how {AI} is revolutionizing the fashion industry", journal = "Genetic Programming and Evolvable Machines", year = "2022", volume = "23", number = "1", pages = "159--160", month = mar, note = "Book Review", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/cAfT5", DOI = "doi:10.1007/s10710-021-09422-8", size = "2 pages", } @Article{butz:2005:GPEM, author = "Martin V. Butz and Kumara Sastry and David E. Goldberg", title = "Strong, Stable, and Reliable Fitness Pressure in XCS due to Tournament Selection", journal = "Genetic Programming and Evolvable Machines", year = "2005", volume = "6", number = "1", pages = "53--77", month = mar, keywords = "genetic algorithms, classifier systems, LCS, learning classifier systems, XCS, tournament selection, genetics based machine learning", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-005-7619-9", abstract = "Recent analysis of the XCS classifier system have shown that successful genetic learning strongly depends on the amount of fitness pressure towards accurate classifiers. Since the traditionally used proportionate selection is dependent on fitness scaling and fitness distribution, the resulting evolutionary fitness pressure may be neither stable nor sufficiently strong. Thus, we apply tournament selection to XCS. In particular, we exhibit the weakness of proportionate selection and suggest tournament selection as a more reliable alternative. We show that tournament selection results in a learning classifier system that is more parameter independent, noise independent, and more efficient in exploiting fitness guidance in single-step problems as well as multistep problems. The evolving population is more focused on promising subregions of the problem space and thus finds the desired accurate, maximally general representation faster and more reliably.", } @Article{Butz:2006:GPEM, author = "Martin V. Butz and David E. Goldberg and Pier Luca Lanzi and Kumara Sastry", title = "Problem solution sustenance in {XCS}: Markov chain analysis of niche support distributions and the impact on computational complexity", journal = "Genetic Programming and Evolvable Machines", year = "2007", volume = "8", number = "1", pages = "5--37", month = mar, keywords = "genetic algorithms, classifier systems, Learning classifier systems, LCS, XCS, Niching, Markov chain analysis, Solution sustenance, Mutation", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-006-9012-8", size = "33 pages", abstract = "Michigan-style learning classifier systems iteratively evolve a distributed solution to a problem in the form of potentially overlapping subsolutions. Each problem niche is covered by subsolutions that are represented by a set of predictive rules, termed classifiers. The genetic algorithm is designed to evolve classifier structures that together cover the whole problem space and represent a complete problem solution. An obvious challenge for such an online evolving, distributed knowledge representation is to continuously sustain all problem subsolutions covering all problem niches, that is, to ensure niche support. Effective niche support depends both on the probability of reproduction and on the probability of deletion of classifiers in a niche. In XCS, reproduction is occurrence-based whereas deletion is support-based. In combination, niche support is assured effectively. we present a Markov chain analysis of the niche support in XCS, which we validate experimentally. Evaluations in diverse Boolean function settings, which require non-overlapping and overlapping solution structures, support the theoretical derivations. We also consider the effects of mutation and crossover on niche support. With respect to computational complexity, the paper shows that XCS is able to maintain (partially overlapping) niches with a computational effort that is linear in the inverse of the niche occurrence frequency.", } @Article{buxton:2001:MC, author = "B. F. Buxton and W. B. Langdon and S. J. Barrett", title = "Data Fusion by Intelligent Classifier Combination", journal = "Measurement and Control", year = "2001", editor = "Qing-Ping Yang", volume = "34", number = "8", pages = "229--234", month = oct, keywords = "genetic algorithms, genetic programming", ISSN = "0020-2940", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/mc/", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/mc/buxton_2001_MC.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/mc/buxton_2001_MC.prn.gz", URL = "https://discovery.ucl.ac.uk/id/eprint/10205622/", URL = "http://mac.sagepub.com/content/34/8/229", DOI = "doi:10.1177/002029400103400802", size = "6 pages", abstract = "The use of hybrid intelligent systems in industrial and commercial applications is briefly reviewed. The potential for application of such systems, in particular those that combine results from several constituent classifiers, to problems in drug design is discussed. It is shown that, although there are no general rules as to how a number of classifiers should best be combined, effective combinations can automatically be generated by genetic programming (GP). A robust performance measure based on the area under classifier receiver-operating-characteristic (ROC) curves is used as a fitness measure in order to facilitate evolution of multi-classifier systems that outperform their constituent individual classifiers. The approach is illustrated by application to publicly available Landsat data and to pharmaceutical data of the kind used in one stage of the drug design process.", notes = "http://www.instmc.org.uk/pubs/measandcontrol.htm 'Measurement + Control is neither a learned journal nor a commercial trade publication' feature issue of M&C on Signal Processing Awarded best paper prize by the Worshipful Company of Instrument Makers.", } @Misc{buxton:2002:rocket, author = "B. F. Buxton and S B Holden and P C Treleaven", title = "Intelligent Data Analysis and Fusion Techniques in Pharmaceuticals, Bioprocessing and Process Control", year = "2002", month = oct, keywords = "genetic algorithms, genetic programming, boosting, support vector machines", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/rocket/EPSRC-final-report.htm", notes = "End of project report. INTErSECT Faraday Partnership Flagship Project, 4 January 1999- 3 July 2002 Grant Reference GR/M43975", } @InProceedings{Buzdalov:2011:GECCOcomp, author = "Maxim Buzdalov", title = "Generation of tests for programming challenge tasks using evolution algorithms", booktitle = "GECCO 2011 Graduate students workshop", year = "2011", editor = "Miguel Nicolau", isbn13 = "978-1-4503-0690-4", keywords = "genetic algorithms, genetic programming, SBSE", pages = "763--766", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001858.2002086", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "In this paper, an automated method for generation of tests in order to detect inefficient (slow) solutions for programming challenge tasks is proposed. The method is based on genetic algorithms. The proposed method was applied to a task from the Internet problem archive - the Timus Online Judge. For this problem, none of the existed solutions passed the generated set of tests.", notes = "Also known as \cite{2002086} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @InProceedings{Buzdalov:2012:GECCOcomp, author = "Maxim Buzdalov and Andrey Sokolov", title = "Evolving EFSMs solving a path-planning problem by genetic programming", booktitle = "GECCO 2012 Graduate Students Workshop", year = "2012", editor = "Alison Motsinger-Reif", isbn13 = "978-1-4503-1178-6", keywords = "genetic algorithms, genetic programming", pages = "591--594", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330784.2330880", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "In this paper, we present an approach to evolving of an algorithm encoded as an extended finite-state machine that solves a simple path-planning problem - finding a path in an unknown area filled with obstacles using a constant amount of memory - by means of genetic programming. Experiments show that in 100percent of cases a reasonably correct EFSM with behavior similar to one of the BUG algorithms is evolved.", notes = "Also known as \cite{2330880} Distributed at GECCO-2012. ACM Order Number 910122.", } @InProceedings{Buzzanca:2016:CNA, author = "Marco Buzzanca and Vincenza Carchiolo and Alessandro Longheu and Michele Malgeri and Giuseppe Mangioni", title = "Evaluating the community partition quality of a network with a genetic programming approach", booktitle = "International Conference on Complex Networks and their Applications", year = "2016", editor = "Hocine Cherifi and Sabrina Gaito and Walter Quattrociocchi and Alessandra Sala", volume = "693", series = "Studies in Computational Intelligence", pages = "299--308", address = "Milan", month = "30 " # nov # " - 2 " # dec, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Community Detection, Normalize Mutual Information, Validation Function, Partition Quality", isbn13 = "978-3-319-50901-3", DOI = "doi:10.1007/978-3-319-50901-3_24", abstract = "Although the problem of partition quality evaluation is well-known in literature, most of the traditional approaches involve the application of a model built upon a theoretical foundation and then applied to real data. Conversely, this work presents a novel approach: it extracts a model from a network which partition in ground-truth communities is known, so that it can be used in other contexts. The extracted model takes the form of a validation function, which is a function that assigns a score to a specific partition of a network: the closer the partition is to the optimal, the better the score. In order to obtain a suitable validation function, we make use of genetic programming, an application of genetic algorithms where the individuals of a population are computer programs. In this paper we present a computationally feasible methodology to set up the genetic programming run, and show our design choices for the terminal set, function set, fitness function and control parameters.", notes = "Published 2017. Complex Networks {\&} Their Applications V", } @InProceedings{Byers:2011:GECCO, author = "Chad M. Byers and Betty H. C. Cheng and Philip K. McKinley", title = "Digital enzymes: agents of reaction inside robotic controllers for the foraging problem", booktitle = "GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0557-0", pages = "243--250", keywords = "genetic algorithms, genetic programming, Artificial life/robotics/evolvable hardware", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001576.2001610", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Over billions of years, natural selection has continued to select for a framework based on (1) parallelism and (2) cooperation across various levels of organisation within organisms to drive their behaviours and responses. We present a design for a bottom-up, reactive controller where the agent's response emerges from many parallelled, enzymatic interactions (bottom-up) within the biologically-inspired process of signal transduction (reactive). We use enzymes to explore the potential for evolving simulated robot controllers for the central-place foraging problem. The properties of the robot and stimuli present in its environment are encoded in a digital format (molecule) capable of being manipulated and altered through self-contained computational programs (enzymes) executing in parallel inside each controller to produce the robot's foraging behaviour. Evaluation of this design in unbounded worlds reveals evolved strategies employing one or more of the following complex behaviors: (1) swarming, (2) coordinated movement, (3) communication of concepts using a primitive language based on sound and colour, (4) cooperation, and (5) division of labour.", notes = "Also known as \cite{2001610} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)", } @InProceedings{4480232, author = "M. D. Byington and B. E. Bishop", title = "Cooperative Robot Swarm Locomotion Using Genetic Algorithms", booktitle = "System Theory, 2008. SSST 2008. 40th Southeastern Symposium on", year = "2008", month = mar, pages = "252--256", keywords = "genetic algorithms, cooperative robot swarm locomotion, decentralized controller design, locomotion controllers, robotic agents, control system synthesis, decentralised control, mobile robots, motion control, multi-robot systems", DOI = "doi:10.1109/SSST.2008.4480232", ISSN = "0094-2898", notes = "Not GP, real coded GA applied to ANN", } @InProceedings{Byrne:2009:cec, author = "Jonathan Byrne and Michael O'Neill and Erik Hemberg and Anthony Brabazon", title = "Analysis of Constant Creation Techniques on the Binomial-3 Problem with Grammatical Evolution", booktitle = "2009 IEEE Congress on Evolutionary Computation", year = "2009", editor = "Andy Tyrrell", pages = "568--573", address = "Trondheim, Norway", month = "18-21 " # may, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-2959-2", file = "P522.pdf", DOI = "doi:10.1109/CEC.2009.4982996", abstract = "This paper studies the difference between Persistent Random Constants (PRC) and Digit Concatenation as methods for generating constants. It has been shown that certain problems have different fitness landscapes depending on how they are represented, independent of changes to the combinatorial search space, thus changing problem difficulty. In this case we show that the method for generating the constants can also influence how hard the problem is for Genetic Programming.", keywords = "genetic algorithms, genetic programming, grammatical evolution", notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR", } @InProceedings{DBLP:conf/gecco/ByrneOB09, author = "Jonathan Byrne and Michael O'Neill and Anthony Brabazon", title = "Structural and nodal mutation in grammatical evolution", booktitle = "GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation", year = "2009", editor = "Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano {van Hemert} and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique Alba", pages = "1881--1882", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming, grammatical evolution, Poster", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1569901.1570215", abstract = "This study focuses on mutation in Grammatical Evolution and divides mutation events into those that are structural in nature and those that are nodal. A structural event being one that alters the length of the phenotype. A nodal event simply alters the value at any node of a derivation tree. We analyse and compare the effect of integer, nodal and structural mutations on fitness for randomly generated individuals before continuing this analysis to their relative problem-solving performance over full runs. The study highlights the importance of understanding how the search operators of an evolutionary algorithm behave. The result in this case being a form of mutation for Grammatical Evolution, node mutation, with a better property of locality than standard integer-based mutation, which does not discriminate between structural and nodal contexts.", notes = "GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009). ACM Order Number 910092.", } @InProceedings{Byrne:2010:EuroGP, author = "Jonathan Byrne and James McDermott and Michael O'Neill and Anthony Brabazon", title = "An Analysis of the Behaviour of Mutation in Grammatical Evolution", booktitle = "Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010", year = "2010", editor = "Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar", volume = "6021", series = "LNCS", pages = "14--25", address = "Istanbul", month = "7-9 " # apr, organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-12147-0", DOI = "doi:10.1007/978-3-642-12148-7_2", abstract = "This study attempts to decompose the behaviour of mutation in Grammatical Evolution (GE). Standard GE mutation can be divided into two types of events, those that are structural in nature and those that are nodal. A structural event can alter the length of the phenotype whereas a nodal event simply alters the value at any terminal (leaf or internal node) of a derivation tree. We analyse the behaviour of standard mutation and compare it to the behaviour of its nodal and structural components. These results are then compared with standard GP operators to see how they differ. This study increases our understanding of how the search operators of an evolutionary algorithm behave.", notes = "Part of \cite{Esparcia-Alcazar:2010:GP} EuroGP'2010 held in conjunction with EvoCOP2010 EvoBIO2010 and EvoApplications2010", } @InProceedings{byrne_oneill_brabazon:mendel2010, author = "J. Byrne and M. O'Neill and A. Brabazon", title = "Optimising Offensive Moves in Toribash", booktitle = "Proceedings of Mendel 2010 16th International Conference on Soft Computing", editor = "R. Matousek", pages = "78--85", year = "2010", address = "Brno, Czech Republic", month = "23-25 " # jun, publisher = "Brno University of Technology", isbn13 = "978-80-214-4120-0", notes = "0102 http://www.mendel-conference.org/", } @InProceedings{byrne_etal:cec2010, author = "Jonathan Byrne and James McDermott and Edgar Galvan-Lopez and Michael O'Neill", title = "Implementing an Intuitive Mutation Operator for Interactive Evolutionary {3D} Design", booktitle = "2010 IEEE World Congress on Computational Intelligence", pages = "2919--2925", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, grammatical evolution", isbn13 = "978-1-4244-6910-9", DOI = "doi:10.1109/CEC.2010.5586485", abstract = "Locality - how well neighbouring genotypes correspond to neighbouring phenotypes - has been described as a key element in Evolutionary Computation. Grammatical Evolution (GE) is a generative system as it uses grammar rules to derive a program from an integer encoded genome. The genome, upon which the evolutionary process is carried out, goes through several transformations before it produces an output. The aim of this paper is to investigate the impact of locality during the generative process using both qualitative and quantitative techniques. To explore this, we examine the effects of standard GE mutation using distance metrics and conduct a survey of the output designs. There are two different kinds of event that occur during standard GE Mutation. We investigate how each event type affects the locality on different phenotypic stages when applied to the problem of interactive design generation.", notes = "CEC 2010. WCCI 2010. Also known as \cite{5586485}", } @InProceedings{byrne:evoapps11, author = "Jonathan Byrne and Michael Fenton and Erik Hemberg and James McDermott and Michael O'Neill and Elizabeth Shotton and Ciaran Nally", title = "Combining Structural Analysis and Multi-Objective Criteria for Evolutionary Architectural Design", booktitle = "Applications of Evolutionary Computing, EvoApplications 2011: {EvoCOMNET}, {EvoFIN}, {EvoHOT}, {EvoMUSART}, {EvoSTIM}, {EvoTRANSLOG}", year = "2011", month = "27-29 " # apr, editor = "Cecilia {Di Chio} and Anthony Brabazon and Gianni {Di Caro} and Rolf Drechsler and Marc Ebner and Muddassar Farooq and Joern Grahl and Gary Greenfield and Christian Prins and Juan Romero and Giovanni Squillero and Ernesto Tarantino and Andrea G. B. Tettamanzi and Neil Urquhart and A. Sima Uyar", series = "LNCS", volume = "6625", publisher = "Springer Verlag", address = "Turin, Italy", publisher_address = "Berlin", pages = "204--213", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, grammatical evolution", isbn13 = "978-3-642-20519-4", DOI = "doi:10.1007/978-3-642-20520-0_21", size = "10 page", abstract = "This study evolves and categorises a population of conceptual designs by their ability to handle physical constraints. The design process involves a trade-off between form and function. The aesthetic considerations of the designer are constrained by physical considerations and material cost. In previous work, we developed a design grammar capable of evolving aesthetically pleasing designs through the use of an interactive evolutionary algorithm. This work implements a fitness function capable of applying engineering objectives to automatically evaluate designs and, in turn, reduce the search space that is presented to the user.", notes = "Part of \cite{DiChio:2011:evo_b} EvoApplications2011 held inconjunction with EuroGP'2011, EvoCOP2011 and EvoBIO2011", affiliation = "Natural Computing Research & Applications Group, University College Dublin, Ireland", } @TechReport{ByrneHembergONeill:TechReport052011, author = "Jonathan Byrne and Erik Hemberg and Michael O'Neill", title = "Interactive Operators for Evolutionary Architectural Design", institution = "Natural Computing Research \& Applications Group, School of Computer Science and Informatics, University College, Dublin", year = "2011", number = "UCD-CSI-2011-05", address = "Dublin, Ireland", month = apr # " 12", keywords = "genetic algorithms, genetic programming", broken = "http://www.csi.ucd.ie/biblio", URL = "http://www.csi.ucd.ie/files/UCD-CSI-2011-05.pdf", size = "17 pages", abstract = "In this paper we explore different techniques that allow the user to direct interactive evolutionary search. Broadening interaction beyond simple evaluation increases the amount of feedback and bias a user can apply to the search. Increased feedback will have the effect of directing the algorithm to more fruitful areas of the search space. This paper examines whether additional feedback from the user can be a benefit to the problem of evolutionary design. We find that the interface between the user and the search space plays a vital role in this process.", } @InProceedings{Byrne:2011:GECCOcomp, author = "Jonathan Byrne and Erik Hemberg and Michael O'Neill", title = "Interactive operators for evolutionary architectural design", booktitle = "GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0690-4", keywords = "genetic algorithms, genetic programming, grammatical evolution, Digital entertainment technologies and arts: Poster", pages = "43--44", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001858.2001884", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "In this paper we explore different techniques that allow the user to direct interactive evolutionary search. Broadening interaction beyond simple evaluation increases the amount of feedback and bias a user can apply to the search. Increased feedback will have the effect of directing the algorithm to more fruitful areas of the search space. This paper examines whether additional feedback from the user can be a benefit to the problem of evolutionary design. We find that the interface between the user and the search space plays a vital role in this process.", notes = "Also known as \cite{2001884} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @InProceedings{Byrne:2012:EvoMUSART, author = "Jonathan Byrne and Erik Hemberg and Anthony Brabazon and Michael O'Neill", title = "A Local Search Interface for Interactive Evolutionary Architectural Design", booktitle = "Proceedings of the 1st International Conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design, EvoMUSART 2012", year = "2012", month = "11-13 " # apr, editor = "Penousal Machado and Juan Romero and Adrian Carballal", series = "LNCS", volume = "7247", publisher = "Springer Verlag", address = "Malaga, Spain", pages = "23--34", organisation = "EvoStar", isbn13 = "978-3-642-29141-8", keywords = "genetic algorithms, genetic programming, grammatical evolution, architectural design", DOI = "doi:10.1007/978-3-642-29142-5_3", size = "12 pages", abstract = "A designer should be able to express their intentions with a design tool. This paper describes an evolutionary design tool that enables the architect to directly interact with the encoding of designs they find aesthetically pleasing. Broadening interaction beyond simple evaluation increases the amount of feedback and bias a user can apply to the search. Increased feedback will have the effect of directing the algorithm to more fruitful areas of the search space. We conduct user trials on an interface for making localised changes to an individual and evaluate if it is capable of directing search. Examination of the locality of changes made by the users provides an insight into how they explore the search space.", notes = "Part of \cite{Machado:2012:EvoMusArt_proc} EvoMUSART'2012 held in conjunction with EuroGP2012, EvoCOP2012 EvoBIO2012 and EvoApplications2012", } @PhdThesis{JonathanByrneThesis, author = "Jonathan Byrne", title = "Approaches to Evolutionary Architectural Design Exploration Using Grammatical Evolution", school = "School of Computer Science and Informatics, University College Dublin", year = "2012", address = "Ireland", month = aug # " 6", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", URL = "https://rms.ucd.ie/ufrs/!W_VA_PUB_BOOK.EDIT?POPUP=TRUE&object_id=368144095", URL = "http://ncra.ucd.ie/papers/JonathanByrneThesis.pdf", size = "220 pages", abstract = "The architectural design process is both subjective and objective in nature. The designer and end user judge a design not only by objective functionality but also by subjective form. Despite the ability of evolutionary algorithms to produce creative and novel designs, they have primarily been used to aid the design process by optimising the functionality of a design, once it has been instantiated. Designers should be able to express their subjective and objective intentions with a design tool. Grammatical evolution (GE) is a form of genetic programming that allows evolutionary techniques to be applied to systems that can be represented as a grammar. This thesis examines approaches that allow grammatical evolution to be used in the exploration phase of the architectural design process as well as optimising the design to maximise functionality. The primary focus of this thesis is to increase the amount of direct and indirect interaction available to the designer for evolutionary design exploration. The research gaps which this thesis investigates are the use of novel GE operators for active user intervention, the development of interfaces suitable for directing evolutionary search and the application of functional constraints for guiding aesthetic evolution. The contributions made by this thesis are the development of two component mutation operators, a novel animated interface for user-directed evolution and the implementation of a multi-objective finite element analysis fitness function in GE for the first time. An examination of fitness functions, operators and representations is carried out so that the designer's input to the evolutionary algorithm can be enhanced. An extensive review of computer-generated architecture, interactive evolution and grammatical evolution is conducted. Initial investigations explore whether the constraints placed on architectural designs can be expressed as a multi-objective fitness function. The application of this technique, as a means of reducing the search space presented to the architect, is then evaluated. Broadening interaction beyond evaluation increases the amount of feedback and bias a user can apply to the search. A study is conducted to examine how integer mutation in GE explores the search space. Two novel and distinct behavioural components in GE mutation are shown to exist, nodal and structural mutation. The locality of the operations is examined at different levels of the derivation process. It is shown that nodal and structural mutation cause different magnitudes of change at the phenotypic level. An interface is designed that enables the architect to directly mutate design encodings that they find aesthetically pleasing. User trials are then conducted on an interface for making localised changes to an individual and evaluate whether it is capable of directing search. The results show that users initially apply structural mutations to explore the search space and then apply smaller nodal mutations to fine tune a solution. The novel interface is shown to enable directed evolutionary search.", notes = "Supervisor Michael O'Neill http://ncra.ucd.ie/Site/Publications.shtml", } @Article{Byrne:2013:GPEM, author = "Jonathan Byrne and Erik Hemberg and Michael O'Neill and Anthony Brabazon", title = "A methodology for user directed search in evolutionary design", journal = "Genetic Programming and Evolvable Machines", year = "2013", volume = "24", number = "3", pages = "September", month = "287--314", keywords = "genetic algorithms, genetic programming, Grammatical evolution, Interactivity, Interrupt intervene and resume", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-013-9189-6", size = "28 pages", abstract = "A designer should be able to express their intentions with a design tool. This work describes a methodology that enables the architect to directly interact with the encoding of designs they find aesthetically pleasing. Broadening interaction beyond simple evaluation increases the amount of feedback and bias a user can apply to the search. Increased feedback will have the effect of directing the algorithm to more fruitful areas of the search space. We conduct trials on two interfaces for making localised changes to a design in order to evaluate if the user is capable of directing search. In addition, an examination of the locality of changes made by the users provides an insight into how they explore the search space. The results show that a suitably designed interface is capable of directing search and that the participants used different magnitudes of change during directed search.", } @InProceedings{byrne:eaauapds:2014, author = "Jonathan Byrne and Phillip Cardiff and Anthony Brabazon and Michael O'Neill", title = "Evolving an Aircraft Using a Parametric Design System", booktitle = "16th European Conference on the Applications of Evolutionary Computation (EvoMusArt 2014)", editor = "Juan Romero and James McDermott and Joao Correia", pages = "119--130", year = "2014", volume = "8601", series = "Lecture Notes in Computer Science", address = "Granada, Spain", month = apr, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-662-44334-7", DOI = "doi:10.1007/978-3-662-44335-4_11", abstract = "Traditional CAD tools generate a static solution to a design problem. Parametric systems allow the user to explore many variations on that design theme. Such systems make the computer a generative design tool and are already used extensively as a rapid prototyping technique in architecture and aeronautics. Combining a design generation tool with an evolutionary algorithm provides a methodology for optimising designs. This works uses NASA's parametric aircraft design tool (OpenVSP) and an evolutionary algorithm to evolve a range of aircraft that maximise lift and reduce drag while remaining within the framework of the original design. Our approach allows the designer to automatically optimise their chosen design and to generate models with improved aerodynamic efficiency.", } @InProceedings{byrne:aeosiagrn:cec2014, title = "An Examination of Synchronisation in Artificial Gene Regulatory Networks", author = "Jonathan Byrne and Miguel Nicolau and Anthony Brabazon and Michael O'Neill", pages = "2764--2769", booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary Computation", year = "2014", month = "6-11 " # jul, editor = "Carlos A. {Coello Coello}", address = "Beijing, China", publisher = "IEEE Press", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Evolutionary Developmental Systems, Complex Networks and Evolutionary Computation", DOI = "doi:10.1109/CEC.2014.6900385", abstract = "An Artificial Genetic Regulatory Network (GRN) is a model of the gene expression regulation mechanism in biological organisms. It is a dynamical system that is capable of mimicking non-linear time series. The GRN was adapted to allow for input and output so that the system's rich dynamics could be used for dynamic problem solving. In order for the GRN to be embedded in the environment, the time scale of the physical system has to be mapped to that of the GRN and so a synchronisation process was introduced. This work examines the impact of different synchronisation intervals and how they effect the overall performance of the GRN. A variable synchronisation step that stops once the system has stabilised is also explored as a mechanism for automatically choosing the interval size.", notes = "Also known as \cite{Byrne:2014:CEC}", } @Article{Byrne:2014:IS, author = "Jonathan Byrne and Michael Fenton and Erik Hemberg and James McDermott and Michael O'Neill", title = "Optimising complex pylon structures with grammatical evolution", journal = "Information Sciences", year = "2014", volume = "316", pages = "582--597", month = sep, ISSN = "0020-0255", DOI = "doi:10.1016/j.ins.2014.03.010", URL = "http://www.sciencedirect.com/science/article/pii/S0020025514002904", keywords = "genetic algorithms, genetic programming, grammatical evolution, Structural optimisation, Architecture, Grammar", abstract = "Evolutionary algorithms have proved their ability to optimise architectural designs but are limited by their representation, i.e., the structures that the algorithm is capable of generating. The representation is normally constrained to small structures, or parts of a larger structure, to prevent a preponderance of invalid designs. This work uses a grammar based representation to generate large scale pylon designs. It removes invalid designs from the search space, but still allows complex and large scale constructions. In order to show the suitability of this method to real world design problems, we apply it to the Royal Institute of British Architects pylon design competition. This work shows that a combination of a grammar representation with real world constraints is capable of exploring different design configurations while evolving viable and optimised designs.", notes = "Also known as \cite{byrne:ocpswge:2014}", } @Article{byrne:epamfdeao:2014, author = "Jonathan Byrne and Phillip Cardiff and Anthony Brabazon and Michael O'Neill", title = "Evolving Parametric Aircraft Models for Design Exploration and Optimisation", journal = "Neurocomputing", year = "2014", volume = "142", pages = "39--47", month = oct, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1016/j.neucom.2014.04.004", URL = "http://www.sciencedirect.com/science/article/pii/S092523121400530X", abstract = "Traditional CAD tools generate a static solution to a design problem. Parametric systems allow the user to explore many variations on that design theme. Such systems make the computer a generative design tool and are already used extensively as a rapid prototyping technique in architecture and aeronautics. Combining a design generation tool with an analysis software and an evolutionary algorithm provides a methodology for optimising designs. This work combines NASA's parametric aircraft design tool (OpenVSP) with a fluid dynamics solver (OpenFOAM) to create and analyse aircraft. An evolutionary algorithm is then used to generate a range of aircraft that maximise lift and reduce drag while remaining within the framework of the original design. Our approach allows the designer to automatically optimise their chosen design and to generate models with improved aerodynamic efficiency. Different components on three aircraft models are varied to highlight the ease and effectiveness of the parametric model optimisation.", } @Article{Cabalar:2010:NCA, title = "Constitutive modeling of Leighton Buzzard Sands using genetic programming", author = "Ali Firat Cabalar and Abdulkadir Cevik and Ibrahim H. Guzelbey", journal = "Neural Computing and Applications", year = "2010", number = "5", volume = "19", pages = "657--665", keywords = "genetic algorithms, genetic programming", publisher = "Springer London", ISSN = "0941-0643", DOI = "doi:10.1007/s00521-009-0317-4", size = "9 pages", abstract = "This paper investigates the results of laboratory experiments and numerical simulations conducted to examine the behaviour of mixtures composed of coarse (i.e. Leighton Buzzard Sand fraction B) and fine (i.e. Leighton Buzzard Sand fraction E) sand particles. Emphasis was placed on assessing the role of fines content in mixture and strain level on the deviatoric stress and pore water pressure generation using experimental (i.e. Triaxial testing) and numerical approaches (i.e. genetic programming, GP). The experimental database used for GP modelling is based on a laboratory study of the properties of saturated coarse and fine sand mixtures with various mix ratios under a 100 kPa effective stresses in a 100 mm diameter conventional triaxial testing apparatus. Experimental results show that coarse-fine sand mixtures exhibit clay-like behavior due to particle-particle effects with the increase in fines content. It is shown that GP modeling of coarse-fine sand mixtures is observed to be quite satisfactory. The results have implications in the design of compressible particulate systems and in the development of prediction tools for the field performance coarse-fine sands.", affiliation = "University of Gaziantep Geotechnical Engineering Division, Department of Civil Engineering Gaziantep Turkey", } @Article{Cabalar20091884, author = "Ali Firat Cabalar and Abdulkadir Cevik", title = "Genetic programming-based attenuation relationship: An application of recent earthquakes in Turkey", journal = "Computers \& Geosciences", volume = "35", number = "9", pages = "1884--1896", year = "2009", ISSN = "0098-3004", DOI = "doi:10.1016/j.cageo.2008.10.015", URL = "http://www.sciencedirect.com/science/article/B6V7D-4W99W08-1/2/aa19b6639659945b1d4e78c6209fe435", keywords = "genetic algorithms, genetic programming, Attenuation relationship", abstract = "This study investigates an application of genetic programming (GP) for the prediction of peak ground acceleration (PGA) using strong-ground-motion data from Turkey. The input variables in the developed GP model are the average shear-wave velocity, earthquake source to site distance and earthquake magnitude, and the output is the PGA values. The proposed GP model is based on the most reliable database compiled for earthquakes in Turkey. The results show that the consistency between the observed PGA values and the predicted ones by the GP model yields relatively high correlation coefficients (R2=0.75). The proposed model is also compared with an existing attenuation relationship and found to be more accurate.", } @Article{Cabalar201110358, author = "Ali Firat Cabalar and Abdulkadir Cevik", title = "Triaxial behavior of sand-mica mixtures using genetic programming", journal = "Expert Systems with Applications", volume = "38", number = "8", pages = "10358--10367", year = "2011", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2011.02.051", URL = "http://www.sciencedirect.com/science/article/B6V03-524FSB9-M/2/eb83d6182c4d3c0b1271b301c5a04e15", keywords = "genetic algorithms, genetic programming, Leighton Buzzard Sand, Mica, Triaxial testing, Modelling", abstract = "This study investigates an application of genetic programming (GP) for modelling of coarse rotund sand-mica mixtures. An empirical model equation is developed by means of GP technique. The experimental database used for GP modeling is based on a laboratory study of the properties of saturated coarse rotund sand and mica mixtures with various mix ratios under a 100 kPa effective stresses, because of its unusual behaviour. In the tests, deviatoric stress, and pore pressure generation, and strain have been measured in a 100 mm diameter conventional triaxial testing apparatus. The input variables in the developed GP models are the mica content, and strain, and the outputs are deviatoric stress, pore water pressure generation. The performance of accuracies of proposed GP based equations is observed to be quite satisfactory.", } @Article{CABRAL:2018:IJPRS, author = "Ana I. R. Cabral and Sara Silva and Pedro C. Silva and Leonardo Vanneschi and Maria J. Vasconcelos", title = "Burned area estimations derived from {Landsat ETM+ and OLI} data: Comparing Genetic Programming with Maximum Likelihood and Classification and Regression Trees", journal = "ISPRS Journal of Photogrammetry and Remote Sensing", volume = "142", pages = "94--105", year = "2018", keywords = "genetic algorithms, genetic programming, Burned area mapping, Savana woodlands, Classification and Regression Trees, Maximum Likelihood, Landsat ETM+/OLI", ISSN = "0924-2716", DOI = "doi:10.1016/j.isprsjprs.2018.05.007", URL = "http://www.sciencedirect.com/science/article/pii/S0924271618301400", abstract = "Every year, large areas of savannas and woodlands burn due to natural conditions and land management practices. Given the relevant level of greenhouse gas emissions produced by biomass burning in tropical regions, it is becoming even more important to clearly define historic fire regimes so that prospective emission reduction management strategies can be well informed, and their results Measured, Reported, and Verified (MRV). Thus, developing tools for accurately, and periodically mapping burned areas, based on cost advantageous, expedite, and repeatable rigorous approaches, is important. The main objective of this study is to investigate the potential of novel Genetic Programming (GP) methodologies for classifying burned areas in satellite imagery over savannas and tropical woodlands and to assess if they can improve over the popular and currently applied methods of Maximum Likelihood classification and Classification and Regression Tree analysis. The tests are performed using three Landsat images from Brazil (South America), Guinea-Bissau (West Africa) and the Democratic Republic of Congo (Central Africa). Burned areas were digitized on-screen to produce mapped information serving as surrogate ground-truth. Validation results show that all methods provide an overestimation of burned area, but GP achieves higher accuracy values in two of the three cases. GP is the most versatile machine learning method available today, but still largely underused in remote sensing. This study shows that standard GP can produce better results than two classical methods, and illustrates its versatility and potential in becoming a mainstream method for more difficult tasks involving the large amounts of newly available data", } @InProceedings{CabritaBotzheimRuanoKoczy04, author = "C. Cabrita and J. Botzheim and A. E. Ruano and L. T. Koczy", title = "Design of {B}-spline Neural Networks using a Bacterial Programming Approach", booktitle = "Proceedings of the International Joint Conference on Neural Networks, IJCNN 2004", address = "Budapest, Hungary", pages = "2313--2318", year = "2004", month = jul, keywords = "genetic algorithms, genetic programming, Evolution (biology), Evolutionary computation, Informatics, Intelligent networks, Intelligent systems, Microorganisms, Neural networks, Spline, Telecommunication network topology, evolution (biological), microorganisms, neural nets, splines (mathematics), B-spline neural network design, bacterial programming method, heuristics method, microbial evolution phenomenon", ISSN = "1098-7576", DOI = "doi:10.1109/IJCNN.2004.1380987", size = "6 pages", abstract = "The design phase of B-spline neural networks represents a very high computational task. For this purpose, heuristics have been developed, but have been shown to be dependent on the initial conditions employed. In this paper a new technique, Bacterial Programming, is proposed, whose principles are based on the replication of the microbial evolution phenomenon. The performance of this approach is illustrated and compared with existing alternatives.", notes = "This is the first paper on Bacterial Programming.", } @InProceedings{Cadar:2010:FoSER, author = "Cristian Cadar and Peter Pietzuch and Alexander L. Wolf", title = "Multiplicity computing: a vision of software engineering for next-generation computing platform applications", booktitle = "Proceedings of the FSE/SDP workshop on Future of software engineering research", year = "2010", editor = "Kevin Sullivan", series = "FoSER '10", pages = "81--86", address = "Santa Fe, New Mexico, USA", publisher_address = "New York, NY, USA", month = "7-11 " # nov, organisation = "ACM sigsoft", publisher = "ACM", keywords = "genetic improvement, cloud computing, data centers, multicore, virtualization, Design, Experimentation, Measurement, Performance, Reliability, Security", isbn13 = "978-1-4503-0427-6", URL = "http://www.doc.ic.ac.uk/~cristic/papers/multicomp-foser-10.pdf", DOI = "doi:10.1145/1882362.1882380", acmid = "1882380", size = "5 pages", abstract = "New technologies have recently emerged to challenge the very nature of computing: multicore processors, virtualised operating systems and networks, and data-centre clouds. One can view these technologies as forming levels within a new, global computing platform. We aim to open a new area of research, called multiplicity computing, that takes a radically different approach to the engineering of applications for this platform. Unlike other efforts, which are largely focused on innovations within specific levels, multiplicity computing embraces the platform as a virtually unlimited space of essentially redundant resources. This space is formed as a whole from the cross product of resources available at each level in the platform, offering a multiplicity of end-to-end resources. We seek to discover fundamentally new ways of exploiting the combinatorial multiplicity of computational, communication, and storage resources to obtain scalable applications exhibiting improved quality, dependability, and security that are both predictable and measurable.", notes = "Not on GP but does refer to GP work \cite{DBLP:conf/gecco/ForrestNWG09}. Also known as \cite{Cadar:2010:MCV:1882362.1882380}", } @InProceedings{Cadrik:2016:MENDEL, author = "Tomas Cadrik and Marian Mach", title = "Genetic Programming Algorithm Creating and Assembling Subtrees for Making Analytical Functions", booktitle = "Proceedings of the 22nd International Conference on Soft Computing (MENDEL 2016)", year = "2016", editor = "Radek Matousek", volume = "576", series = "AISC", pages = "55--63", address = "Brno, Czech Republic", month = jun # " 8-10", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-58087-6", ISSN = "2194-5357", DOI = "doi:10.1007/978-3-319-58088-3_6", abstract = "There are many optimization algorithms which can be used for solving different tasks. One of those is the genetic programming method, which can build an analytical function which can describe data. The function is coded in a tree structure. The problem is that when we decide to use lower maximal depth of the tree, the genetic programming is not able to compose a function which is good enough. This paper describes the way how to solve this problem. The approach is based on creating partial solutions represented by subtrees and composing them together to create the last tree. This approach was tested for finding a function which can correctly calculate the output according to the given inputs. The experiments showed that even when using a small maximal depth, the genetic programming using our approach can create functions with good results.", notes = "https://link.springer.com/book/10.1007/978-3-319-58088-3 ICSC-MENDEL 2016 Recent Advances in Soft Computing", } @InProceedings{caetano:2023:GECCO, author = "Victor Caetano and Matheus {Candido Teixeira} and Gisele Lobo Pappa", title = "Symbolic Regression Trees as Embedded Representations", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "411--419", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, embedded representations, semantics, transformers", isbn13 = "9798400701191", DOI = "doi:10.1145/3583131.3590423", size = "9 pages", abstract = "Representation learning is an area responsible for learning data representations that makes it easier for machine learning algorithms to extract useful information from them. Deep learning currently has the most effective methods for this task and can learn distributed representations - also known as embeddings - able to represent different properties of the data and their relationship. In this direction, this paper introduces a new way to look at tree-like GP individuals for symbolic regression. Given a set of predefined operators and a sufficiently large number of solutions sampled from the space, we train a transformer to learn an encoding/decoding function. By transforming a tree representation into a distributed representation, we are able to measure distances between trees in a much more efficient way and, more importantly, generate the potential for these representations to capture semantics. We show the distance accounting for embedding presents results very similar to those of a tree-edition, which reflects their syntactic similarity. Although the model as it stands is not able to capture semantics yet, we show its potential by using the generated tree-representation model in a simple task: measuring distances between trees in a fitness-sharing scenario.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @Article{Caglar:2015:EAAI, author = "Naci Caglar and Aydin Demir and Hakan Ozturk and Abdulhalim Akkaya", title = "A simple formulation for effective flexural stiffness of circular reinforced concrete columns", journal = "Engineering Applications of Artificial Intelligence", volume = "38", pages = "79--87", year = "2015", ISSN = "0952-1976", DOI = "doi:10.1016/j.engappai.2014.10.011", URL = "http://www.sciencedirect.com/science/article/pii/S0952197614002516", keywords = "genetic algorithms, genetic programming, Effective flexural stiffness, Moment-curvature, Reinforced concrete, Eurocode-8, TEC-2007", size = "9 pages", abstract = "Concrete cracking reduces flexural and shear stiffness of reinforced concrete (RC) members. Therefore analysing RC structures without considering the cracking effect may not represent actual behaviour. Effective flexural stiffness resulting from concrete cracking depends on some important parameters such as confinement, axial load level, section dimensions and material properties of concrete and reinforcing steel. In this study, a simple formula as a securer, quicker and more robust is proposed to determine the effective flexural stiffness of cracked sections of circular RC columns. This formula is generated by genetic programming (GP). The generalisation capabilities of the explicit formulations are compared by cross sectional analysis results and confirmed on a 3-D building model. Moreover the results from GP based formulation are compared with EC-8 and TEC-2007. It is demonstrated that the GP based model is highly successful to determine the effective flexural stiffness of circular RC columns.", } @InProceedings{cagnoni:2004:pre:preproc, author = "S. Cagnoni", title = "GECCO2004 Workshop Proceedings: Preface", editor = "R. Poli and S. Cagnoni and M. Keijzer and E. Costa and F. Pereira and G. Raidl and S. C. Upton and D. Goldberg and H. Lipson and E. {de Jong} and J. Koza and H. Suzuki and H. Sawai and I. Parmee and M. Pelikan and K. Sastry and D. Thierens and W. Stolzmann and P. L. Lanzi and S. W. Wilson and M. O'Neill and C. Ryan and T. Yu and J. F. Miller and I. Garibay and G. Holifield and A. S. Wu and T. Riopka and M. M. Meysenburg and A. W. Wright and N. Richter and J. H. Moore and M. D. Ritchie and L. Davis and R. Roy and M. Jakiela", booktitle = "GECCO 2004 Workshop Proceedings", year = "2004", month = "26-30 " # jun, address = "Seattle, Washington, USA", keywords = "genetic algorithms, genetic programming, grammatical evolution", URL = "http://gpbib.cs.ucl.ac.uk/gecco2004/", notes = "GECCO-2004WKS Distributed on CD-ROM at GECCO-2004", } @Article{cagnoni:2005:SMC, author = "Stefano Cagnoni and Federico Bergenti and Monica Mordonini and Giovanni Adorni", title = "Evolving Binary Classifiers Through Parallel Computation of Multiple Fitness Cases", journal = "IEEE Transactions on Systems, Man and Cybernetics - Part B", year = "2005", volume = "35", number = "3", pages = "548--555", month = jun, email = "cagnoni@ce.unipr.it", keywords = "genetic algorithms, genetic programming, cellular programming, sub-machine code genetic programming, multiple classifiers, pattern recognition", ISSN = "1083-4419", DOI = "doi:10.1109/TSMCB.2005.846671", size = "8 pages", abstract = "We describe two versions of a novel approach to developing binary classifiers, based on two evolutionary computation paradigms: cellular programming and genetic programming. Such an approach achieves high computation efficiency both during evolution and at runtime. Evolution speed is optimised by allowing multiple solutions to be computed in parallel. Runtime performance is optimized explicitly using parallel computation in the case of cellular programming or implicitly, taking advantage of the intrinsic parallelism of bitwise operators on standard sequential architectures in the case of genetic programming. The approach was tested on a digit recognition problem and compared to a reference classifier.", notes = "PMID: 15971922 [PubMed - indexed for MEDLINE]", } @Article{Cagnoni:2006:IA, author = "Stefano Cagnoni and Riccardo Poli", title = "Genetic and evolutionary Computation", journal = "Intelligenza Artificiale", year = "2006", volume = "3", number = "1/2", pages = "94--101", month = "Marzo-Giugno", keywords = "genetic algorithms, genetic programming, gec, gas, es, gsice, italian GEC, human-competitive", ISSN = "1724-8035", URL = "http://cswww.essex.ac.uk/staff/poli/papers/ai50-2006.pdf", size = "8 pages", abstract = "In this paper, we start by providing a gentle introduction to the field of genetic and evolutionary computation, particularly focusing on genetic algorithms, but also touching upon other areas. We then move on to briefly analyse the geographic distribution of research excellence in this field, focusing our attention specifically on Italian researchers. We then present our own interpretation of where and how genetic and evolutionary computation fits in the broader landscape of artificial intelligence research. We conclude by making a prediction of the future impact of this technology in the short term.", notes = "In English. Tutorial. http://ia.di.uniba.it/ Periodico trimestrale dell'Associazione Italiana per l'Intelligenza Artificiale by 2012 daily invention machine", } @Article{Cagnoni:2008:EC, author = "S. Cagnoni and E. Lutton and G. Olague", title = "Editorial Introduction to the Special Issue on Evolutionary Computer Vision", journal = "Evolutionary Computation", year = "2008", volume = "16", number = "4", pages = "437--438", month = "Winter", keywords = "genetic algorithms, genetic programming", ISSN = "1063-6560", DOI = "doi:10.1162/evco.2008.16.4.437", size = "2 pages", } @InProceedings{Cagnoni:2016:CEC, author = "Stefano Cagnoni and Mengjie Zhang", title = "Evolutionary Computer Vision and Image Processing: some {FAQs}, Current Challenges and Future Perspectives", booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016)", year = "2016", editor = "Yew-Soon Ong", pages = "1267--1271", address = "Vancouver", month = "24-29 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Evolutionary Computer Vision, Evolutionary Image Processing, GPU", isbn13 = "978-1-5090-0623-6", DOI = "doi:10.1109/CEC.2016.7743933", abstract = "Applications to image/signal processing and analysis have been studied since the very early years in the history of Evolutionary Computation up to a degree of popularity which has allowed terms like Evolutionary Computer Vision (ECV) and Evolutionary Image Processing (EIP) to become common among researchers. Within these fields, the role of EC has gone well beyond basic optimization of the parameters of traditional Computer Vision (CV) or Image Processing (IP) algorithms or mere use within those algorithms which comprise an optimization stage anyway. This paper, far from having the pretence of making an exhaustive review, tries to sketch the motivations behind the success of ECV/EIP, the present status of research in such a field, and a personal view of its possible developments in the near future, based on the authors' more than 20-year long direct experience.", notes = "WCCI2016", } @Article{Cahon:2004:JoH, author = "S. Cahon and N. Melab and E. G. Talbi", title = "{ParadisEO}: A Framework for the Reusable Design of Parallel and Distributed Metaheuristics", journal = "Journal of Heuristics", year = "2004", volume = "10", number = "3", pages = "357--380", month = may, note = "Special Issue: New Advances on Parallel Meta-Heuristics for Complex Problems", keywords = "genetic algorithms, genetic programming, metaheuristics, design and code reuse, parallel and distributed models, object-oriented frameworks, performance and robustness", ISSN = "1381-1231", URL = "https://rdcu.be/cMLxW", URL = "https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.538.8149&rep=rep1&type=pdf", DOI = "doi:10.1023/B:HEUR.0000026900.92269.ec", code_url = "https://nojhan.github.io/paradiseo/", size = "24 pages", abstract = "We present the ParadisEO white-box object-oriented framework dedicated to the reusable design of parallel and distributed metaheuristics (PDM). ParadisEO provides a broad range of features including evolutionary algorithms (EA), local searches (LS), the most common parallel and distributed models and hybridization mechanisms, etc. This high content and utility encourages its use at European level. ParadisEO is based on a clear conceptual separation of the solution methods from the problems they are intended to solve. This separation confers to the user a maximum code and design reuse. Furthermore, the fine-grained nature of the classes provided by the framework allow a higher flexibility compared to other frameworks. ParadisEO is of the rare frameworks that provide the most common parallel and distributed models. Their implementation is portable on distributed-memory machines as well as on shared-memory multiprocessors, as it uses standard libraries such as MPI, PVM and PThreads. The models can be exploited in a transparent way, one has just to instantiate their associated provided classes. Their experimentation on the radio network design real-world application demonstrate their efficiency.", notes = "Laboratoire d'Informatique Fondamentale de Lille, UMR CNRS 8022, Cite Scientifique, 59655 - Villeneuve d'scq Cedex, France", } @InProceedings{Cai:2005:HT, author = "Weihua Cai and Mihir Sen and K. T. Yang and Arturo Pacheco-Vega", title = "Genetic-Programming-Based Symbolic Regression for Heat Transfer Correlations of a Compact Heat Exchanger", booktitle = "ASME Summer Heat Transfer Conference (HT2005)", year = "2005", volume = "4", pages = "367--374", address = "San Francisco, California, USA", month = jul # " 17-22", publisher = "ASME", keywords = "genetic algorithms, genetic programming", ISBN = "0-7918-4734-9", DOI = "doi:10.1115/HT2005-72293", abstract = "We describe a symbolic regression methodology based on genetic programming to find correlations that can be used to estimate the performance of compact heat exchangers. Genetic programming is an evolutionary search technique in which functions represented as parse trees evolve as the search proceeds. An advantage of this approach is that functional forms of the correlation need not be assumed. The algorithm performs symbolic regression by seeking both the functional structure of the correlation and the coefficients therein that enable the closest fit to experimental data. This search is conducted within a functional domain constructed from sets of operators and terminals that are used to build tree-structures representing functions. A penalty function is used to prevent large correlations. The methodology is tested using first artificial data from a one-dimensional function and later a set of published heat exchanger experiments. Comparison with published results from the same data show that symbolic-regression correlations are as good or better. The effect of the penalty parameters on the best function is also analysed.", notes = "collocated with the ASME 2005 Pacific Rim Technical Conference and Exhibition on Integration and Packaging of MEMS, NEMS, and Electronic Systems (HT2005) University of Notre Dame, Notre Dame, IN", } @Article{Cai:2006:IJHMT, author = "Weihua Cai and Arturo Pacheco-Vega and Mihir Sen and K. T. Yang", title = "Heat transfer correlations by symbolic regression", journal = "International Journal of Heat and Mass Transfer", year = "2006", volume = "49", number = "23-24", pages = "4352--4359", month = nov, keywords = "genetic algorithms, genetic programming, Heat transfer, Correlations, Symbolic regression, Heat exchanger", DOI = "doi:10.1016/j.ijheatmasstransfer.2006.04.029", abstract = "We describe a methodology that uses symbolic regression to extract correlations from heat transfer measurements by searching for both the form of the correlation equation and the constants in it that enable the closest fit to experimental data. For this purpose we use genetic programming modified by a penalty procedure to prevent large correlation functions. The advantage of using this technique is that no initial assumption on the form of the correlation is needed. The procedure is tested using two sets of published experimental data, one for a compact heat exchanger and the other for liquid flow in a circular pipe. In both situations, predictive errors from correlations found from symbolic regression are smaller than their published counterparts. A parametric analysis of the penalty function is also carried out.", } @InCollection{Cai:2024:ISEEPS, author = "Xiatong Cai and Abdolmajid Mohammadian and Juan {Hiedra Cobo} and Hamidreza Shirkhani and Hanifeh Imanian and Pierre Payeur", title = "Chapter 16 - Soil temperature prediction in ordinary and extremely hot weather using genetic programming", booktitle = "Intelligence Systems for Earth, Environmental and Planetary Sciences", publisher = "Elsevier", year = "2024", editor = "Hossein Bonakdari and Silvio Jose Gumiere", pages = "441--464", keywords = "genetic algorithms, genetic programming, Soil temperature, Symbolic regression, Extreme weather, Climate prediction, Artificial intelligence, ANN", isbn13 = "978-0-443-13293-3", URL = "https://www.sciencedirect.com/science/article/pii/B9780443132933000191", DOI = "doi:10.1016/B978-0-443-13293-3.00019-1", abstract = "The prediction of soil temperature under climate change plays an important role in understanding hydrological processes. Genetic programming can create a mathematical equation that can be used for predictions with very high efficiency due to its explicit analytical form. However, it is rarely used in soil temperature prediction, especially in extremely hot weather conditions. The fitness of multigene genetic programming (MGGP) in ordinary weather was found to be R2=0.97, and R2=0.83 in extremely hot weather. We compared the performance of single-gene genetic programming (SGGP) and multigene genetic programming (MGGP) with benchmark linear and AI models. Results show that the MGGP algorithm outperforms linear models and is comparable with some distance-based and tree-based benchmark AI models in both ordinary and extremely hot weather. MGGP underperformed the artificial neural network. Using only a polynomial equation rather than executing a complicated model with a large input dataset, MGGP shows good simplification in soil temperature prediction", } @InProceedings{conf/ices/CaiST05, author = "Xinye Cai and Stephen L. Smith and Andrew M. Tyrrell", title = "Benefits of Employing an Implicit Context Representation on Hardware Geometry of {CGP}", year = "2005", pages = "143--154", editor = "Juan Manuel Moreno and Jordi Madrenas and Jordi Cosp", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3637", booktitle = "Evolvable Systems: From Biology to Hardware, 6th International Conference, ICES 2005, Proceedings", address = "Sitges, Spain", month = sep # " 12-14", bibsource = "DBLP, http://dblp.uni-trier.de", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", ISBN = "3-540-28736-1", DOI = "doi:10.1007/11549703_14", size = "12 pages", abstract = "Cartesian Genetic Programming (CGP) has successfully been applied to the evolution of simple image processing filters and implemented in intrinsic evolvable hardware by the authors. However, conventional CGP exhibits the undesirable characteristic of positional dependence in which the specific location of genes within the chromosome has a direct or indirect influence on the phenotype. An implicit context representation of CGP (IRCGP) has been implemented by the authors which is positionally independent and outperforms conventional CGP in this application. This paper describes the additional benefits of IRCGP when considering alternative geometries for the hardware components. Results presented show that smaller hardware arrays under IRCGP are more robust and outperform equivalent arrays implemented in conventional CGP.", } @InProceedings{eurogp06:CaiSmothTyrrell, author = "Xinye Cai and Stephen L. Smith and Andy M. Tyrrell", title = "Positional Independence and Recombination in Cartesian Genetic Programming", editor = "Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art", booktitle = "Proceedings of the 9th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3905", year = "2006", address = "Budapest, Hungary", month = "10 - 12 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", ISBN = "3-540-33143-3", pages = "351--360", DOI = "doi:10.1007/11729976_32", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "Previously, recombination (or crossover) has proved to be unbeneficial in Cartesian Genetic Programming (CGP). This paper describes the implementation of an implicit context representation for CGP in which the specific location of genes within the chromosome has no direct or indirect influence on the phenotype. Consequently, recombination has a beneficial effect and is shown to outperform conventional CGP in the even-3 parity problem.", notes = "Part of \cite{collet:2006:GP} EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006", } @InProceedings{1277300, author = "Xinye Cai and Stephen M. Welch and Praveen Koduru and Sanjoy Das", title = "Discovering structures in gene regulatory networks using genetic programming and particle swarms", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "1750--1750", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1750.pdf", DOI = "doi:10.1145/1276958.1277300", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming: Poster, bioinformatics, gene regulatory network, Particle Swarm Optimisation", size = "1 page", abstract = "GP + PSO for gene network discovery", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071. Synthetic data. PSO used to set 'parameters' in each network (evolved by GP). See also ICAI'07: The 2007 World Congress in Computer Science, Computer Engineering, & Applied Computing, Las Vegas, Nevada, USA. June 27 Discovering Structures in Gene Regulatory Networks Using Genetic Programming and Particle Swarms Xinye Cai, Stephen Welch, Praveen Koduru, and Sanjoy Das Kansas State University, Manhattan, Kansas, USA http://www.world-academy-of-science.org/worldcomp07/ws/program/ica27", } @Article{Cai:2009:IJBRA, title = "Simultaneous structure discovery and parameter estimation in gene networks using a multi-objective {GP}-{PSO} hybrid approach", author = "Xinye Cai and Praveen Koduru and Sanjoy Das and Stephen M. Welch", journal = "International Journal of Bioinformatics Research and Applications", year = "2009", month = "11 " # jun, volume = "5", number = "3", pages = "254--268", keywords = "genetic algorithms, genetic programming, gene regulatory networks, PSO, particle swarm optimisation, multi-objective optimisation, Bioinformatics, structure discovery, parameter estimation, gene networks, plant genes, plant flowering times, gene expressions", ISSN = "1744-5493", URL = "http://www.inderscience.com/link.php?id=26418", DOI = "doi:10.1504/IJBRA.2009.026418", publisher = "Inderscience Publishers", abstract = "This paper presents a hybrid algorithm based on Genetic Programming (GP) and Particle Swarm Optimisation (PSO) for the automated recovery of gene network structure. It uses gene expression time series data as well as phenotypic data pertaining to plant flowering time as its input data. The algorithm then attempts to discover simple structures to approximate the plant gene regulatory networks that produce model gene expressions and flowering times that closely resemble the input data. To show the efficacy of the proposed approach, simulation results applied to flowering time control in Arabidopsis thaliana are demonstrated and discussed.", } @PhdThesis{Xinye_Cai:thesis, author = "Xinye Cai", title = "A multi-objective {GP-PSO} hybrid algorithm for gene regulatory network modeling", school = "Department of Electrical and Computer Engineering, Kansas State University", year = "2009", address = "Manhattan, Kansas, USA", month = may, keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, multi-objective optimization, particle swarm optimization, gene regulatory network modelling, plant breeding simulation, Arabidopsis, NK fitness landscape models", isbn13 = "9781109178562", URL = "http://hdl.handle.net/2097/1492", URL = "http://krex.k-state.edu/dspace/handle/2097/1492", URL = "http://krex.k-state.edu/dspace/bitstream/handle/2097/1492/xinyecai2009.pdf", URL = "https://search.proquest.com/docview/304911232", size = "130 pages", abstract = "Stochastic algorithms are widely used in various modelling and optimization problems. Evolutionary algorithms are one class of population-based stochastic approaches that are inspired from Darwinian evolutionary theory. A population of candidate solutions is initialized at the first generation of the algorithm. Two variation operators, crossover and mutation, that mimic the real world evolutionary process, are applied on the population to produce new solutions from old ones. Selection based on the concept of survival of the fittest is used to preserve parent solutions for next generation. Examples of such algorithms include genetic algorithm (GA) and genetic programming (GP). Nevertheless, other stochastic algorithms may be inspired from animals behaviour such as particle swarm optimization (PSO), which imitates the cooperation of a flock of birds. In addition, stochastic algorithms are able to address multi-objective optimization problems by using the concept of dominance. Accordingly, a set of solutions that do not dominate each other will be obtained, instead of just one best solution. This thesis proposes a multi-objective GP-PSO hybrid algorithm to recover gene regulatory network models that take environmental data as stimulus input. The algorithm infers a model based on both phenotypic and gene expression data. The proposed approach is able to simultaneously infer network structures and estimate their associated parameters, instead of doing one or the other iteratively as other algorithms need to. In addition, a non-dominated sorting approach and an adaptive histogram method based on the hypergrid strategy are adopted to address convergence and diversity issues in multi-objective optimization. Gene network models obtained from the proposed algorithm are compared to a synthetic network, which mimics key features of Arabidopsis flowering control system, visually and numerically. Data predicted by the model are compared to synthetic data, to verify that they are able to closely approximate the available phenotypic and gene expression data. At the end of this thesis, a novel breeding strategy, termed network assisted selection, is proposed as an extension of our hybrid approach and application of obtained models for plant breeding. Breeding simulations based on network assisted selection are compared to one common breeding strategy, marker assisted selection. The results show that NAS is better both in terms of breeding speed and final phenotypic level", notes = "Supervisor Sanjoy Das", } @Article{Cai:1996:ASS, author = "Yu-Dong Cai", title = "Genetic programming for prediction of earthquake sequence type", journal = "Acta Seismologica Sinica", publisher = "Seismological Society of China", volume = "9", issue = "1", year = "1996", pages = "53--57", month = feb, keywords = "genetic algorithms, genetic programming, earthquake sequence, prediction", ISSN = "1000-9116", DOI = "doi:10.1007/BF02650623", abstract = "The genetic programming for the prediction of earthquake sequence type was studied, and the reliability for a group of samples was tested. The results show that the performance of the genetic programming is good, and therefore it might be referred as an effective technique for the prediction of earthquake sequence type.", notes = "Journal now called Earthquake Science (2009-2011)", affiliation = "Chinese Academy of Sciences Shanghai Institute of Metallurgy 200050 Shanghai China", } @Article{Cakiroglu:2024:enbuild, author = "Celal Cakiroglu and Yaren Aydin and Gebrail Bekdas and Umit Isikdag and Aidin Nobahar Sadeghifam and Laith Abualigah", title = "Cooling load prediction of a double-story terrace house using ensemble learning techniques and genetic programming with {SHAP} approach", journal = "Energy and Buildings", year = "2024", volume = "313", pages = "114254", keywords = "genetic algorithms, genetic programming, Cooling load, BIM, Energy efficiency, Predictive modeling", ISSN = "0378-7788", URL = "https://www.sciencedirect.com/science/article/pii/S0378778824003700", DOI = "doi:10.1016/j.enbuild.2024.114254", abstract = "Since the cooling systems used in buildings in hot climates account for a significant portion of the energy consumption, it is very important for both economy and environment to accurately predict the cooling load and consider it in building designs. This study aimed to maximize energy efficiency by appropriately selecting the features of a building that affect its cooling load. To this end, data-driven, accurate, and accessible tools were developed that enable the prediction of the cooling load of a building by practitioners. The study involves simulating the energy consumption of a mid-rise, double-story terrace house in Malaysia using building information modelling (BIM) and estimating the cooling load using ensemble machine learning models and genetic programming. Categorical Boosting (CatBoost), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Random Forest (RF) models have been developed and made available as an online interactive graphical user interface on the Streamlit platform. Furthermore, the symbolic regression technique has been used to obtain a closed-form equation that predicts the cooling load. The dataset used for training the predictive models comprised 94,310 data points with 10 input variables and the cooling load as the output variable. Performance metrics such as the coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE) were used to measure the predictive model performances. The results of the machine learning models indicated successful prediction, with the CatBoost model achieving the highest score (R2 = 0.9990) among the four ensemble models and the predictive equation. The SHAP analysis determined the aspect ratio of the building as the most impactful feature of the building", notes = "See also \cite{Cakiroglu:2024:enbuild_Corrigendum}", } @Article{Cakiroglu:2024:enbuild_Corrigendum, author = "Celal Cakiroglu and Yaren Aydin and Gebrail Bekdas and Umit Isikdag and Aidin Nobahar Sadeghifam and Laith Abualigah", title = "Corrigendum to ``Cooling load prediction of a double-story terrace house using ensemble learning techniques and genetic programming with {SHAP} approach'' [Energy Build. 313 (2024) 114254]", journal = "Energy and Buildings", year = "2024", volume = "315", pages = "114329", keywords = "genetic algorithms, genetic programming", ISSN = "0378-7788", URL = "https://www.sciencedirect.com/science/article/pii/S0378778824004456", DOI = "doi:10.1016/j.enbuild.2024.114329", notes = "See \cite{Cakiroglu:2024:enbuild}", } @Article{calabrese:2022:AS, author = "Francesca Calabrese and Alberto Regattieri and Raffaele Piscitelli and Marco Bortolini and Francesco Gabriele Galizia", title = "Genetic Programming-Based Feature Construction for System Setting Recognition and Component-Level Prognostics", journal = "Applied Sciences", year = "2022", volume = "12", number = "9", pages = "Article No. 4749", keywords = "genetic algorithms, genetic programming", ISSN = "2076-3417", URL = "https://www.mdpi.com/2076-3417/12/9/4749", DOI = "doi:10.3390/app12094749", abstract = "Extracting representative feature sets from raw signals is crucial in Prognostics and Health Management (PHM) for components’ behaviour understanding. The literature proposes various methods, including signal processing in the time, frequency, and time–frequency domains, feature selection, and unsupervised feature learning. An emerging task in data science is Feature Construction (FC), which has the advantages of both feature selection and feature learning. In particular, the constructed features address a specific objective function without requiring a label during the construction process. Genetic Programming (GP) is a powerful tool to perform FC in the PHM context, as it allows to obtain distinct feature sets depending on the analysis goal, i.e., diagnostics and prognostics. This paper adopts GP to extract system-level features for machinery setting recognition and component-level features for prognostics. Three distinct fitness functions are considered for the GP training, which requires a set of statistical time-domain features as input. The methodology is applied to vibration signals extracted from a test rig during run-to-failure tests under different settings. The performances of constructed features are evaluated through the classification accuracy and the Remaining Useful Life (RUL) prediction error. Results demonstrate that GP-based features classify known and novel machinery operating conditions better than feature selection and learning methods.", notes = "also known as \cite{app12094749}", } @InProceedings{Calapristi:2024:ICCAD, author = "Marco Calapristi and Luca Patane and Francesca Sapuppo and Maria Gabriella Xibilia", title = "Interpretability analysis of Symbolic Regression models for dynamical systems", booktitle = "2024 International Conference on Control, Automation and Diagnosis (ICCAD)", year = "2024", month = may, keywords = "genetic algorithms, genetic programming, Measurement, Visualization, Accuracy, System dynamics, Machine learning, Predictive models", ISSN = "2767-9896", DOI = "doi:10.1109/ICCAD60883.2024.10553801", abstract = "Symbolic Regression (SR) is a machine learning paradigm that focuses on the automatic discovery of mathematical equations that best describe the relationship between input and output features in experimental datasets. Such an approach leads to interpretable models that make it easy to incorporate the knowledge available in the system. This article addresses the application of SR as an interpretable modelling approach for industrial applications involving nonlinear dynamical systems. Interpretability in model identification is crucial for understanding system processes. While SR models are inherently interpretable, the inclusion of explainability techniques such as SHAP is being explored to improve model validation while facilitating model interpretation by process technologists. In this paper, the Narendra-Li system, which is commonly used as a testbed for dynamical system identification, is considered. The performance of the proposed solutions is evaluated using appropriate interpretability metrics that provide insight into their efficiency in capturing the underlying dynamics of the system.", notes = "Also known as \cite{10553801}", } @InProceedings{Calapristi:2024:MetroXRAINE, author = "Marco Calapristi and Luca Patane and Francesca Sapuppo and Riccardo Caponetto and Maria Gabriella Xibilia", title = "Symbolic Regression for Industrial Applications: An {NN-Based} Approach", booktitle = "2024 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)", year = "2024", pages = "618--623", month = oct, keywords = "genetic algorithms, genetic programming, Deep learning, Training, Recurrent neural networks, Accuracy, System dynamics, Soft sensors, Artificial neural networks, Mathematical models, Encoding, system identification, dynamical nonlinear systems, neural networks, ANN, model interpretability", DOI = "doi:10.1109/MetroXRAINE62247.2024.10797142", abstract = "Symbolic Regression (SR) is a machine learning approach developed for the automated identification of mathematical equations that accurately capture the relationships between input and output features within the experimental dataset. This method is capable of creating interpretable models while incorporating existing knowledge into the system. This paper addresses a problem in the development of interpretable Soft Sensors (SS) for industrial applications using SR. The challenge arises from the need to increase the dimensionality of the problem in order to capture the system dynamics, which often leads to a significant degradation in SR performance. Existing literature has highlighted this problem and proposed some solutions, such as employing Recurrent Neural Networks (RNN) instead of Genetic Programming (GP) in the SR procedure or applying Deep Learning (DL) techniques to reduce the input space. In this paper, we present a novel approach to develop interpretable SSs for industrial processes that involve the use of DL to encode the system dynamics. This effectively reduces the input space and supports the SR process without compromising the interpretability of the final solution.", notes = "Also known as \cite{10797142}", } @InProceedings{calderoni:1998:GPadsar, author = "Stephane Calderoni and Pierre Marcenac", title = "Genetic Programming For Automatic Design Of Self-Adaptive Robots", booktitle = "Proceedings of the First European Workshop on Genetic Programming", year = "1998", editor = "Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer and Terence C. Fogarty", volume = "1391", series = "LNCS", pages = "163--177", address = "Paris", publisher_address = "Berlin", month = "14-15 " # apr, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-64360-5", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/13194/http:zSzzSzwww.univ-reunion.frzSz~caldezSzpublicationszSzpaperszSzlncs1391.pdf/calderoni98genetic.pdf", URL = "http://citeseer.ist.psu.edu/267374.html", DOI = "doi:10.1007/BFb0055936", size = "15 pages", abstract = "The general framework tackled in this paper is the automatic generation of intelligent collective behaviors using genetic programming and reinforcement learning. We define a behavior-based system relying on automatic design process using artificial evolution to synthesize high level behaviors for autonomous agents. Behavioral strategies are described by tree-based structures, and manipulated by genetic evolving processes. Each strategy is dynamically evaluated during simulation, and weighted by an adaptative value. This value is a quality factor that reflects the relevance of a strategy as a good solution for the learning task. It is computed using heterogeneous reinforcement techniques associating immediate and delayed reinforcements as dynamic progress estimators. This work has been tested upon a canonical experimentation framework: the foraging robots problem. Simulations have been conducted and have produced some promising results.", notes = "EuroGP'98", affiliation = "Iremia - Universite de La Reunion 15, Avenue Rene Cassin BP 7151 97715 Saint-Denis messag cedex 9 15, Avenue Rene Cassin BP 7151 97715 Saint-Denis messag cedex 9", } @InProceedings{oai:CiteSeerPSU:185735, author = "Stephane Calderoni and Pierre Marcenac and Remy Courdier", title = "Genetic Encoding of Agent Behavioral Strategy", booktitle = "Proceedings of the 3rd International Conference on Multi Agent Systems", year = "1998", pages = "403", publisher = "IEEE Computer Society", keywords = "genetic algorithms, genetic programming", ISBN = "0-8186-8500-X", URL = "http://portal.acm.org/citation.cfm?id=852213&jmp=cit&dl=portal&dl=ACM", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/4918/http:zSzzSzwww.univ-reunion.frzSz~caldezSzrechzSzpublicationszSzpaperszSzicmas98a.pdf/genetic-encoding-of-agent.pdf", URL = "http://citeseer.ist.psu.edu/185735.html", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:185735", rights = "unrestricted", size = "2 pages", abstract = "The general framework tackled in this paper is the automatic generation of intelligent collective behaviors using genetic programming and reinforcement learning. We define a behavior-based system relying on automatic design process using artificial evolution to synthesize high level behaviors for autonomous agents. Behavioral strategies are described by tree-based structures, and manipulated by genetic evolving processes. Each strategy is dynamically evaluated during simulation, and is weighted by an adaptation function as a quality factor that reflects its relevance as good solution for the learning task. It is computed using heterogeneous reinforcement techniques associating immediate reinforcements and delayed reinforcements as dynamic progress estimators.", } @InProceedings{calderoni:1999:BCSMD, author = "Stephane Calderoni", title = "Behavior-Based Control System in MultiAgent Domain", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1439", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, artificial life, adaptive behavior and agents, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-048.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-048.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{oai:CiteSeerPSU:247844, author = "Stephane Calderoni", title = "Generic Control Ssystem in MultiAgent Domain", booktitle = "World Multiconference on Systemics, Cybernetics and Informatics SCI-99", year = "1999", volume = "7", keywords = "genetic algorithms, genetic programming, Multiagent Systems, Control Systems, Reinforcement Learning", URL = "http://citeseer.ist.psu.edu/247844.html", citeseer-isreferencedby = "oai:CiteSeerPSU:26950", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:247844", rights = "unrestricted", abstract = "This paper reports on-going works dealing with collective learning in autonomous agents context. We propose a methodology to design robust and flexible adaptive behavior with both genetic and reinforcement learning techniques.The originality of this contribution relies on the ability of the agents to manage themselves their learning task. Indeed, rather than coming from the environment, as it is implemented in many programs, we consider that the reinforcement must be intrinsically deduced by the agent itself, from satisfaction and disapointment indicators. We show that in such a way, the agents are capable of robustness facing with unexpected situations. A collective regulation problem is presented to help in clarify the different issues tackled in this paper. A software toolkit has been developped as a support for these works.", } @InProceedings{Callan:2021:GI, author = "James Callan and Justyna Petke", title = "Optimising {SQL} Queries Using Genetic Improvement", booktitle = "GI @ ICSE 2021", year = "2021", month = "30 " # may, editor = "Justyna Petke and Bobby R. Bruce and Yu Huang and Aymeric Blot and Westley Weimer and W. B. Langdon", publisher = "IEEE", address = "internet", pages = "9--10", keywords = "genetic algorithms, genetic programming, genetic improvement, SQL, query optimisation", isbn13 = "978-1-6654-4466-8/21", URL = "https://geneticimprovementofsoftware.com/paper_pdfs/gi2021icse/callan_gi-icse_2021.pdf", video_url = "https://www.youtube.com/watch?v=WopftjIYPgs&list=PLI8fiFpB7BoKDaxvS7SQp0iA7fN7rrvDD&index=5", video_url = "https://www.youtube.com/watch?v=A-QX5dlsKUI&list=PLI8fiFpB7BoKDaxvS7SQp0iA7fN7rrvDD&index=6", video_url = "https://www.youtube.com/watch?v=nqBWLbtq6yQ&list=PLXTjhGKkSnI-se7uQneCX-pEDiDrQ7TIS&index=7", DOI = "doi:10.1109/GI52543.2021.00010", size = "2 pages", abstract = "Structured Query Language (SQL) queries are ubiquitous in modern software engineering. These queries can be costly when run on large databases with many entries and tables to consider. We propose using Genetic Improvement (GI) to explore patches for these queries, with the aim of optimising their execution time, whilst maintaining the functionality of the program in which they are used. Specifically, we propose three ways in which SQL JOIN statements can be mutated in order to improve performance. We also discuss the requirements of software being improved in this manner and the potential challenges of our approach.", notes = "refers to https://www.microsoft.com/en-gb/sql-server/sql-server-downloads Read only queries, join. 3 mutations reorder SQL select/join statements, delete join statements, change join type. Aim minimise number of rows returned by join. Testing. Representative databases, but bigger database means heavier load on computer and network. Multiobjective. Test the program not the query. Faster query can still give same result. Video nqBWLbtq6yQ James Callan 1:47 Discussion: Chair: Bobby R. Bruce (balck). Answers: James Callan(green) and Justyna Petke (red). 2:19 Q: W. B. Langdon GI versus conventional SQL query optimisation? A: James Callan existing join order optimisation done at run time and can go wrong. Offline GI query optimisation may be more effective. Database may be over engineered, eg too many columns in table. GI has more scope to get desired result, ie freedom to not replicate query exactly. 3:40 Q: Myra B. Cohen. A: GI good as can adapt as database moves over times. 4:22 Q: Alexandre Bergel, genetic programming. A: GI applicable to long and complex queries as seen in existing human written queries 5:43 Q: Westley Weimer, fitness over fitting. A: Most useful on large table and complex queries, ideally an SQL query which is used multiple times in a program. 6:54 Q: Giovani Guizzo, swapping join with where? 7:38 Q: Westley Weimer, map SQL back into relational algebra, mutate/edit in relational algebra and then transform patches into SQL. A: James Callan A: Justyna Petke motivation integration into existing or extended GI tools. 9:44 Q: Bobby R. Bruce, multi-objective. A: James Callan query time and size of result (bandwidth usage) part of \cite{Petke:2021:ICSEworkshop} http://geneticimprovementofsoftware.com/events/icse2021.html", } @InProceedings{Callan:2021:SSBSE, author = "James Callan and Justyna Petke", title = "Improving {Android} App Responsiveness through Search-Based Frame Rate Reduction", booktitle = "SSBSE 2021", year = "2021", editor = "Una-May O'Reilly and Xavier Devroey", volume = "12914", series = "LNCS", pages = "136--150", address = "Bari", month = "11-12 " # oct, publisher = "Springer", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, Search-based software engineering, Responsiveness, Android, Mobile applications, User Interface, UI, Gin, ANR", isbn13 = "978-3-030-88105-4", URL = "https://conf.researchr.org/details/ssbse-2021/ssbse-2021-rene---replications-and-negative-results/2/Improving-Android-App-Responsiveness-through-Search-Based-Frame-Rate-Reduction", DOI = "doi:10.1007/978-3-030-88106-1_10", size = "15 pages", abstract = "Responsiveness is one of the most important properties of Android applications to both developers and users. Recent survey on automated improvement of non-functional properties of Android applications shows there is a gap in application of search-based techniques to improve responsiveness. Therefore, we explore the use of genetic improvement (GI) to achieve this task. We extend Gin, an open source GI framework, to work with Android applications. Next, we apply GI to four open source Android applications, measuring frame rate as proxy for responsiveness. We find that while there are improvements to be found in UI-implementing code (up to 43percent), often applications test suites are not strong enough to safely perform GI, leading to generation of many invalid patches. We also apply GI to areas of code which have highest test-suite coverage, but find no patches leading to consistent frame rate reductions. This shows that although GI could be successful in improvement of Android apps responsiveness, any such test-based technique is currently hindered by availability of test suites covering UI elements.", notes = "https://conf.researchr.org/track/ssbse-2021/ssbse-2021-rene---replications-and-negative-results#event-overview", } @Article{callan2022, author = "James Callan and Oliver Krauss and Justyna Petke and Federica Sarro", title = "How Do {Android} Developers Improve Non-Functional Properties of Software?", journal = "Empirical Software Engineering", year = "2022", volume = "27", pages = "Article 113", note = "Topical Collection:Software Performance", keywords = "genetic algorithms, genetic programming, genetic improvement, Non-Functional property optimisation, Android optimisation, Mining android, Execution time, Bandwidth, Frame rate, Memory, NFP", publisher = "Springer", ISSN = "1382-3256", URL = "https://discovery.ucl.ac.uk/id/eprint/10145101/", URL = "https://discovery.ucl.ac.uk/id/eprint/10145101/1/Petke_Callan2022_Article_HowDoAndroidDevelopersImproveN.pdf", URL = "https://rdcu.be/cZPhl", DOI = "doi:10.1007/s10664-022-10137-2", size = "42 pages", abstract = "Nowadays there is an increased pressure on mobile app developers to take non-functional properties into account. An app that is too slow or uses much bandwidth will decrease user satisfaction, and thus can lead to users simply abandoning the app. Although automated software improvement techniques exist for traditional software, these are not as prevalent in the mobile domain. Moreover, it is yet unknown if the same software changes would be as effective. With that in mind, we mined overall 100 Android repositories to find out how developers improve execution time, memory consumption, bandwidth usage and frame rate of mobile apps. We categorised non-functional property (NFP) improving commits related to performance to see how existing automated software improvement techniques can be improved. Our results show that although NFP improving commits related to performance are rare, such improvements appear throughout the development life-cycle. We found altogether 560 NFP commits out of a total of 74408 commits analysed. Memory consumption is sacrificed most often when improving execution time or bandwidth usage, although similar types of changes can improve multiple non-functional properties at once. Code deletion is the most frequently used strategy except for frame rate, where increase in concurrency is the dominant strategy. We find that automated software improvement techniques for mobile domain can benefit from addition of SQL query improvement, caching and asset manipulation. Moreover, we provide a classifier which can drastically reduce manual effort to analyse NFP improving commits.", notes = "Used by \cite{callan:2024:GI}", } @InProceedings{Callan:2022:SSBSE, author = "James Callan and Justyna Petke", title = "Multi-objective Genetic Improvement: A Case Study with {EvoSuite}", booktitle = "14th International Symposium on Search Based Software Engineering SSBSE 2020", year = "2022", editor = "Mike Papadakis and Silvia Regina Vergilio", series = "LNCS", volume = "13711", pages = "111--117", address = "Singapore", month = "17-18 " # nov, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Genetic Improvement, GIN, NSGA2", isbn13 = "978-3-031-21251-2", DOI = "doi:10.1007/978-3-031-21251-2_8", size = "16 pages", abstract = "Automated multi-objective software optimisation offers an attractive solution to software developers wanting to balance often conflicting objectives, such as memory consumption and execution time. Work on using multi-objective search-based approaches to optimise for such non-functional software behaviour has so far been scarce, with tooling unavailable for use. To fill this gap we extended an existing generalist, open source, genetic improvement tool, Gin, with a multi-objective search strategy, NSGA-II. We ran our implementation on a mature, large software to show its use. In particular, we chose EvoSuite, a tool for automatic test case generation for Java. We use our multi-objective extension of Gin to improve both the execution time and memory usage of EvoSuite. We find improvements to execution time of up to 77.8percent and improvements to memory of up to 9.2percent on our test set. We also release our code, providing the first open source multi-objective genetic improvement tooling for improvement of memory and runtime for Java.", notes = "Challenge Track https://conf.researchr.org/home/ssbse-2022", } @Article{DBLP:journals/ese/CallanKPS22, author = "James Callan and Oliver Krauss and Justyna Petke and Federica Sarro", title = "How do Android developers improve non-functional properties of software?", journal = "Empirical Software Engineering", year = "2022", volume = "27", number = "5", pages = "113", keywords = "genetic algorithms, genetic programming, genetic improvement, Non-Functional property optimisation, Android optimisation, Miningandroid, Execution time, Bandwidth, Framerate, Memory", timestamp = "Mon, 26 Jun 2023 20:53:18 +0200", biburl = "https://dblp.org/rec/journals/ese/CallanKPS22.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://doi.org/10.1007/s10664-022-10137-2", DOI = "doi:10.1007/S10664-022-10137-2", code_url = "https://github.com/SOLAR-group/NonFunctionalAndroidCommits", size = "42 pages", } @Misc{callan2023multiobjective, author = "James Callan and Justyna Petke", title = "Multi-Objective Improvement of Android Applications", howpublished = "arXiv", year = "2023", month = "22 " # aug, note = "arxiv, 2308.11387", keywords = "genetic algorithms, genetic programming, genetic improvement, multi-objective optimization, Android apps, search-based software engineering, SBSE, mobile computing, GIDroid, Java", eprint = "2308.11387", archiveprefix = "arXiv", primaryclass = "cs.SE", URL = "https://arxiv.org/abs/2308.11387", code_url = "https://github.com/SOLAR-group/GIDroid", size = "32 pages", abstract = ".. we use Genetic improvement, a search-based technique that navigates the space of software variants to find improved software. We use a simulation-based testing framework to greatly improve the speed of search. GIDroid contains three state-of-the-art multi-objective algorithms, and two new mutation operators, which cache the results of method calls. Genetic improvement relies on testing to validate patches. Previous work showed that tests in open-source Android applications are scarce. We thus wrote tests for 21 versions of 7 Android apps, creating a new benchmark for performance improvements. We used GIDroid to improve versions of mobile apps where developers had previously found improvements to runtime, memory, and bandwidth use. Our technique automatically re-discovers 64percent of existing improvements. We then applied our approach to current versions of software in which there were no known improvements. We were able to improve execution time by up to 35percent, and memory use by up to 33percent in these apps.", notes = "?to appear in? Automated Software Engineering ?", } @InProceedings{callan:2024:GI, author = "James Callan and William B. Langdon and Justyna Petke", title = "On Reducing Network Usage with Genetic Improvement", booktitle = "13th International Workshop on Genetic Improvement @ICSE 2024", year = "2024", editor = "Gabin An and Aymeric Blot and Vesna Nowack and Oliver Krauss and Justyna Petke", pages = "23--30", address = "Lisbon", month = "16 " # apr, publisher = "ACM", keywords = "genetic algorithms, genetic programming, Genetic Improvement, Java, Source coding, Prefetching, Memory management, Genetics, Search problems, Software, SBSE, HTTP, GIDroid, Robolectric, FDroid, bandwidth", isbn13 = "979-8-4007-0573-1/24/04", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/callan_2024_GI.pdf", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=10666380", DOI = "doi:10.1145/3643692.3648262", slides_url = "http://gpbib.cs.ucl.ac.uk/gi2024/gi_2024_slides/gi2024network.pdf", code_url = "https://github.com/SOLAR-group/NetworkGI", size = "8 pages", abstract = "Mobile applications can be very network-intensive. Mobile phone users are often on limited data plans, while network infrastructure has limited capacity. There is little work on optimising network usage of mobile applications. The most popular approach has been prefetching and caching assets. However, past work has shown that developers can improve the network usage of Android applications by making changes to Java source code. We built upon this insight and investigated the effectiveness of automated, heuristic application of software patches, a technique known as Genetic Improvement (GI), to improve network usage. Genetic improvement has already shown effective at reducing the execution time and memory usage of Android applications. We thus adapt our existing GIdroid framework with a new mutation operator and develop a new profiler to identify network-intensive methods to target. Unfortunately, our approach is unable to find improvements. We conjecture this is due to the fact source code changes affecting network might be rare in the large patch search space. We thus advocate use of more intelligent search strategies in future work.", notes = "Also known as \cite{10666380} Adaway, FDroid Client, GPS Logger, Mi Mangu Nu, Materialistic, F-Droid Build Status, Ooni Probe. GI @ ICSE 2024, part of \cite{an:2024:GI}", } @PhdThesis{callan:thesis, author = "James Callan", title = "Improving the Non-Functional Properties of Android Applications with Genetic Improvement", school = "Computer Science, University College, London", year = "2024", address = "London, UK", month = "19 " # mar, keywords = "genetic algorithms, genetic programming, genetic improvement", URL = "https://discovery.ucl.ac.uk/id/eprint/10189386/", URL = "https://discovery.ucl.ac.uk/id/eprint/10189386/2/Improving_the_Non_Functional_Properties_of_Android_Applications_with_Genetic_Improvement%20%282%29.pdf", size = "192 pages", abstract = "There are 3.5 billion Android applications on the Google Play Store. However, surprisingly little work exists on automatically improving the source code of Android apps, especially when compared to traditional software. Genetic Improvement is a technique for automatically improving software which has proven successful in the past. However, its applicability in the Android domain is yet to be explored. In this thesis, we explore how GI can be used to improve Android apps. The first contribution of this thesis is an investigation into applying GI to Android with minimal changes from the standard technique, however, we achieved limited success. Next, we mined git repositories to try to find the changes that real developers make to improve the non-functional properties of applications. With what we learned in this study, we modified and successfully applied GI to improve the responsiveness of Android applications. We then moved on to multi-objective improvement, improving execution time and memory usage, but failing to improve network usage. We also provide a benchmark of tested applications that can be used to evaluate future automated improvement tools. We developed a profiler to find the most network-intensive methods to target and a novel mutation operator. Our final contribution is an adapted version of GI framework for network usage optimisation. We found that Genetic Improvement is an effective tool for improving multiple non-functional properties of Android apps. We found that by using simulation-based testing, rather than testing variants on devices, we could make GI faster and more practical. We found that there were many opportunities for GI to more closely mimic the types of changes made by developers and that caching in particular is an effective change type. We recommend future work further explores this direction.", notes = "Supervisor: Justyna Petke", } @Article{Callan:2025:ASE, author = "James Callan and Justyna Petke", title = "Multi-objective improvement of Android applications", journal = "Automated Software Engineering", year = "2025", volume = "32", pages = "article number 2", note = "Accepted 04 Nov 2024", keywords = "genetic algorithms, genetic programming, genetic improvement, Android apps, Multi-objective optimization, Search-based software engineering", ISSN = "0928-8910", URL = "https://rdcu.be/d0UCc", DOI = "doi:10.1007/s10515-024-00472-7", code_url = "https://github.com/SOLAR-group/GIDroid", size = "36 pages", abstract = "Non-functional properties, such as runtime or memory use, are important to mobile app users and developers, as they affect user experience. We propose a practical approach and the first open-source tool, GIDroid for multi-objective automated improvement of Android apps. In particular, we use Genetic Improvement, a search-based technique that navigates the space of software variants to find improved software. We use a simulation-based testing framework to greatly improve the speed of search. GIDroid contains three state-of-the-art multi-objective algorithms, and two new mutation operators, which cache the results of method calls. Genetic Improvement relies on testing to validate patches. Previous work showed that tests in open-source Android applications are scarce. We thus wrote tests for 21 versions of 7 Android apps, creating a new benchmark for performance improvements. We used GIDroid to improve versions of mobile apps where developers had previously found improvements to run-time, memory, and bandwidth use. Our technique automatically re-discovers 64percent of existing improvements. We then applied our approach to current versions of software in which there were no known improvements. We were able to improve execution time by up to 35percent, and memory use by up to 33percent in these apps.", notes = "Computer Science Department, University College London, Gower Street, London, Greater London WC1E 6BT, UK", } @InProceedings{Calumby:2014:ICIP, author = "R. T. Calumby and R. {da Silva Torres} and M. A. Goncalves", booktitle = "IEEE International Conference on Image Processing (ICIP 2014)", title = "Diversity-driven learning for multimodal image retrieval with relevance feedback", year = "2014", month = oct, pages = "2197--2201", abstract = "We introduce a new genetic programming approach for enhancing the user search experience based on relevance feedback over results produced by a multimodal image retrieval technique with explicit diversity promotion. We have studied maximal marginal relevance re-ranking methods for result diversification and their impacts on the overall retrieval effectiveness. We show that the learning process using diverse results may improve user experience in terms of both the number of relevant items retrieved and subtopic coverage.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICIP.2014.7025445", notes = "Also known as \cite{7025445}", } @Article{journals/mta/CalumbyTG14, title = "Multimodal retrieval with relevance feedback based on genetic programming", author = "Rodrigo Tripodi Calumby and Ricardo {da Silva Torres} and Marcos Andre Goncalves", journal = "Multimedia Tools Appl", year = "2014", number = "3", volume = "69", pages = "991--1019", keywords = "genetic algorithms, genetic programming", bibdate = "2014-03-21", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/mta/mta69.html#CalumbyTG14", URL = "http://dx.doi.org/10.1007/s11042-012-1152-7", } @InProceedings{Calvo-Fracasso:2018:CEC, author = "Joao V. {Calvo Fracasso} and Fernando J. {Von Zuben}", title = "Multi-objective semantic mutation for genetic programming", booktitle = "2018 IEEE Congress on Evolutionary Computation (CEC)", year = "2018", editor = "Marley Vellasco", address = "Rio de Janeiro, Brazil", month = "8-13 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2018.8477675", abstract = "Genetic Programming is a branch of Evolutionary Computation devoted to the evolution of programs. Several genetic operators have been proposed to increase the power of the search, given that the space of admissible programs is very challenging to be properly explored toward high quality solutions. Semantically-driven genetic operators are gaining more attention lately, given that the behaviour of the search operators are more predictable, possibly leading to a more efficient evolution. Nonetheless, Semantic Genetic Programming may undergo the bloat phenomenon, characterized by an uncontrolled increase in the program size along the generations. Some attempts have been made in the literature to refrain code bloat, and here we are proposing three novel semantic-driven mutation operators for the tree structure codification. A multi-objective perspective is adopted, where the mutated subtrees correspond to nondominated instances in a previously defined library of candidate subtrees. Several conflicting objectives may be incorporated into the decision making process, such as accuracy, semantic distance to a reference behaviour, and size of the subtree. Experimental results reveal that our proposed operators are effective in restraining bloating, without a negative impact on the other performance metrics, and are competitive with other relevant approaches available in the literature.", notes = "WCCI2018", } @InCollection{series/sci/Calzada-LedesmaSDOVS17, author = "Valentin Calzada-Ledesma and Hector Jose Puga Soberanes and Alfonso Rojas Dominguez and Manuel Ornelas-Rodriguez and Juan Martin Carpio Valadez and Claudia Guadalupe Gomez Santillan", title = "Comparing Grammatical Evolution's Mapping Processes on Feature Generation for Pattern Recognition Problems", booktitle = "Nature-Inspired Design of Hybrid Intelligent Systems", editor = "Patricia Melin and Oscar Castillo and Janusz Kacprzyk", year = "2017", volume = "667", publisher = "Springer", isbn13 = "978-3-319-47053-5", pages = "775--785", series = "Studies in Computational Intelligence", keywords = "genetic algorithms, genetic programming, grammatical evolution", bibdate = "2017-05-16", bibsource = "DBLP, http://dblp.uni-trier.de/db/series/sci/sci667.html#Calzada-LedesmaSDOVS17", DOI = "doi:10.1007/978-3-319-47054-2_52", isbn13 = "978-3-319-47053-5", abstract = "Grammatical Evolution (GE) is a grammar-based form of Genetic Programming. In GE, a Mapping Process (MP) and a Backus-Naur Form grammar (defined in the problem context) are used to transform each individual's genotype into its phenotype form (functional representation). There are several MPs proposed in the state-of-the-art, each of them defines how the individuals genes are used to build its phenotype form. This paper compares two MPs: the Depth-First standard map and the Position Independent Grammatical Evolution (pi GE). The comparison was performed using as use case the problem of the selection and generation of features for pattern recognition problems. A Wilcoxon Rank-Sum test was used to compare and validate the results of the different approaches.", } @Article{Calzada-Ledesma:2018:IEEEAccess, author = "Valentin Calzada-Ledesma and Hector J. Puga-Soberanes and Manuel Ornelas-Rodriguez and Alfonso Rojas-Dominguez and Juan Martin Carpio-Valadez and Andris Espinal and Jorge A. Soria-Alcaraz and Marco A. Sotelo-Figueroa", journal = "IEEE Access", title = "Evolutionary Design of Problem-Adapted Image Descriptors for Texture Classification", year = "2018", volume = "6", pages = "40450--40462", abstract = "Effective texture classification requires image descriptors capable of efficiently detecting, extracting, and describing the most relevant information in the images, so that, for instance, different texture classes can be distinguished despite image distortions such as varying illuminations, viewpoints, scales, and rotations. Designing such an image descriptor is a challenging task that typically involves the intervention of human experts. In this paper, a general method to automatically design effective image descriptors is proposed. Our method is based on grammatical evolution and, using a set of example images from a texture classification problem and a classification algorithm as inputs, generates problem-adapted image descriptors that achieve very competitive classification results. Our method is tested on five well-known texture data sets with different number of classes and image distortions to prove its effectiveness and robustness. Our classification results are statistically compared against those obtained by means of six popular hand-crafted texture descriptors in the state of the art. This statistical analysis shows that our evolutionarily designed descriptors outperform most of those designed by human experts.", keywords = "genetic algorithms, genetic programming, grammatical evolution", DOI = "doi:10.1109/ACCESS.2018.2858660", ISSN = "2169-3536", notes = "Also known as \cite{8417408}", } @InProceedings{Camargo-Bareno:2011:GECCOcomp, author = "Carlos Ivan {Camargo Bareno} and Cesar Augusto {Pedraza Bonilla} and Luis Fernado Nino and Jose Ignacio {Martinez Torre}", title = "Intrinsic evolvable hardware for combinatorial synthesis based on SoC+FPGA and GPU platforms", booktitle = "GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0690-4", keywords = "genetic algorithms, genetic programming, GPU: Poster", pages = "189--190", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001858.2001964", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "This paper presents a novel a parallel genetic programming (PGP) Boolean synthesis implementation on a low cost cluster of an embedded open platform called SIE. Some tasks of the PGP have been accelerated through a hardware coprocessor called FCU, that allows to evaluate individuals onchip as intrinsic evolution. Results have been compared with GPU and HPC implementations, resulting in speedup values up to approximately 2 and 180 respectively.", notes = "Also known as \cite{2001964} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @PhdThesis{Cambronero:thesis, author = "Jose Pablo {Cambronero Sanchez}", title = "Mining Software Artifacts for use in Automated Machine Learning", school = "Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology", year = "2021", address = "USA", month = may # " 13, 2021", keywords = "genetic algorithms, genetic programming, SBSE, TPOT", URL = "https://dspace.mit.edu/handle/1721.1/139465", URL = "https://hdl.handle.net/1721.1/139465", URL = "https://www.csail.mit.edu/event/mining-software-artifacts-use-automated-machine-learning", URL = "https://www.josecambronero.com/pdf/JCambronero-PhD-EECS-June2021.pdf", URL = "https://dspace.mit.edu/bitstream/handle/1721.1/139465/Cambronero-jcamsan-PhD-EECS-2021-thesis.pdf?sequence=1&isAllowed=y", size = "149 pages", abstract = "Successfully implementing classical supervised machine learning pipelines requires that users have software engineering, machine learning, and domain experience. Machine learning libraries have helped along the first two dimensions by providing modular implementations of popular algorithms. However, implementing a pipeline remains an iterative, tedious, and data-dependent task as users have to experiment with different pipeline designs. To make the pipeline development process accessible to non-experts and more efficient for experts, automated techniques can be used to efficiently search for high performing pipelines with little user intervention. The collection of techniques and systems that automate this task are commonly termed automated machine learning (AutoML). Inspired by the success of software mining in areas such as code search, program synthesis, and program repair, we investigate the hypothesis that information mined from software artifacts can be used to build, improve interactions with, and address missing use cases of AutoML. In particular, I will present three systems -- AL, AMS, and Janus -- that make use of software artifacts. AL mines dynamic execution traces of a collection of programs that implement machine learning pipelines and uses these mined traces to learn to produce new pipelines. AMS mines documentation and program examples to automatically generate a search space for an AutoML tool by starting from a user-chosen set of API components. And Janus mines pipeline transformations from a collection of machine learning pipelines, which can be used to improve an input pipeline while producing a nearby variant. Jointly, these systems and their experimental results show that mining software artifacts can simplify AutoML systems, make their customization easier, and apply them to novel use cases.", notes = "Supervisor: Martin C. Rinard", } @InCollection{campbell:2000:EGPDROR, author = "Elliott Campbell", title = "Evaluation of Genetic Programming for Determining Reservoir Operating Rules", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "54--59", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:2000:gagp}", } @Article{campbell:1993:MM, author = "Paul J. Campbell", copyright = "Copyright 1993 Mathematical Association of America", ISSN = "0025570x", journal = "Mathematics Magazine", number = "2", owner = "wlangdon", pages = "136--137", title = "Reviews", URL = "http://links.jstor.org/sici?sici=0025-570X%28199304%2966%3A2%3C136%3AR%3E2.0.CO%3B2-4", volume = "66", year = "1993", keywords = "genetic algorithms, genetic programming", size = "15 lines", notes = "review of \cite{koza:book}", } @Article{CAMPOBELLO2020106488, author = "Giuseppe Campobello and Daniele Dell'Aquila and Marco Russo and Antonino Segreto", title = "Neuro-genetic programming for multigenre classification of music content", journal = "Applied Soft Computing", year = "2020", volume = "94", pages = "106488", month = sep, keywords = "genetic algorithms, genetic programming, Artificial neural networks, ANN, Music genre recognition, Multi-label classifiers, Fuzzy classification", ISSN = "1568-4946", URL = "http://www.sciencedirect.com/science/article/pii/S1568494620304270", DOI = "doi:10.1016/j.asoc.2020.106488", abstract = "A machine learning approach based on hybridization of genetic programming and neural networks is used to derive mathematical models for music genre classification. We design three multi-label classifiers with different trade-offs between complexity and accuracy, which are able to identify the degree of belonging of music content to ten different music genres. Our approach is innovative as it entirely relies on simple analytical functions and a reduced number of features. Resulting classifiers have an extremely low computational complexity and are suitable to be easily integrated in low-cost embedded systems for real-time applications. The GTZAN dataset is used for model training and to evaluate the accuracy of the proposed classifiers. Despite of the reduced number of features used in our approach, the accuracy of our models is found to be similar to that of more complex music genre classification tools previously published in the literature.", } @Article{Campos:GPEM, author = "Marcelino Campos and Jose M. Sempere", title = "Generating networks of genetic processors", journal = "Genetic Programming and Evolvable Machines", year = "2022", volume = "23", number = "1", pages = "133--155", month = mar, keywords = "genetic algorithms, genetic programming, Natural computing, Networks of bio-inspired processors, Parallel genetic algorithms, Formal languages, Descriptive complexity", ISSN = "1389-2576", URL = "https://rdcu.be/czY20", DOI = "doi:10.1007/s10710-021-09423-7", size = "23 pages", abstract = "The Networks of Genetic Processors (NGPs) are non-conventional models of computation based on genetic operations over strings, namely mutation and crossover operations as it was established in genetic algorithms. Initially, they have been proposed as acceptor machines which are decision problem solvers. In that case, it has been shown that they are universal computing models equivalent to Turing machines. we propose NGPs as enumeration devices and we analyze their computational power. First, we define the model and we propose its definition as parallel genetic algorithms. Once the correspondence between the two formalisms has been established, we carry out a study of the generation capacity of the NGPs under the research framework of the theory of formal languages. We investigate the relationships between the number of processors of the model and its generative power. Our results show that the number of processors is important to increase the generative capability of the model up to an upper bound, and that NGPs are universal models of computation if they are formulated as generation devices. This allows us to affirm that parallel genetic algorithms working under certain restrictions can be considered equivalent to Turing machines and, therefore, they are universal models of computation.", notes = "Valencian Research Institute for Artificial Intelligence, Universitat Politecnica de Valencia, Spain", } @InProceedings{Can:2010:WSC, author = "Birkan Can and Cathal Heavey", title = "Sequential metamodelling with genetic programming and particle swarms", booktitle = "Proceedings of the 2009 Winter Simulation Conference (WSC)", year = "2009", month = "13-16 " # dec, pages = "3150--3157", abstract = "This article presents an application of two main component methodologies of evolutionary algorithms in simulation-based metamodelling. We present an evolutionary framework for constructing analytical metamodels and apply it to simulations of manufacturing lines with buffer allocation problem. In this framework, a particle swarm algorithm is integrated to genetic programming to perform symbolic regression of the problem. The sampling data is sequentially generated by the particle swarm algorithm, while genetic programming evolves symbolic functions of the domain. The results are promising in terms of efficiency in design of experiments and accuracy in global metamodelling.", keywords = "genetic algorithms, genetic programming, PSO, buffer allocation, design of experiment, discrete event simulation, evolutionary algorithm, global metamodelling, manufacturing lines, particle swarm algorithm, sampling data, sequential metamodelling, simulation-based metamodelling, symbolic function, symbolic regression, design of experiments, discrete event simulation, manufacturing systems, particle swarm optimisation, regression analysis, sampling methods", DOI = "doi:10.1109/WSC.2009.5429276", notes = "Also known as \cite{5429276}", } @PhdThesis{Can:thesis, author = "Birkan Can", title = "Evolutionary Modelling of Industrial Systems with Genetic Programming", school = "University of Limerick", year = "2011", type = "Doctorate of Philosophy in Engineering", address = "Ireland", month = oct, keywords = "genetic algorithms, genetic programming", URL = "http://hdl.handle.net/10344/1693", URL = "http://ulir.ul.ie/handle/10344/1693", URL = "http://ulir.ul.ie/bitstream/handle/10344/1693/2010_Birkan%2c%20Can.pdf", size = "216 pages", abstract = "Knowledge, experience, and intuition are integral parts of decision making. However, these alone are not sufficient to manage today's industrial systems. Often predictive models are required to weigh options and determine potential changes which provide the best outcome for a system. In this respect, the dissertation develops approximate models, metamodels, of industrial systems to facilitate a means to quantify system performance when the trade-off between approximation error and efficiency (time and effort spent on model development, validation, maintenance and execution) is appropriate. Discrete-event simulation (DES) is widely used to assist decision makers in the management of systems. DES facilitates analysis with high fidelity models as a consequence of its flexibility. However, this descriptiveness introduces an overhead to model building and maintenance. Furthermore, due to stochastic elements and the size of the systems modelled, model execution times can be computationally demanding. Hence, its use in operational tasks such as design, sensitivity analysis and optimisation can be significantly undermined when efficiency is a concern. In this thesis, these shortcomings are addressed through research into the use of genetic programming for metamodelling. Genetic programming is a branch of evolutionary algorithms which emulate the natural evolution of species. It can evolve programs of a domain via symbolic regression. These programs can be interpreted as logic instructions, analytical functions etc. Furthermore, genetic programming develops the models without prior assumptions about the underlying function of the training data. This can provide significant advantage for modelling of complex systems with non-linear and multimodal response characteristics. Exploiting these properties, the dissertation presents research towards developing metamodels of manufacturing systems (or their DES models) via genetic programming in the context of symbolic regression. In particular, it contributes to; (i) exploration of an appropriate experimental design method suitable to use with genetic programming, (ii) to a comparison of the performance of genetic programming with neural networks, using three different stochastic industrial problems to identify its robustness; (iii) research into an improved genetic programming and dynamic flow time estimation.", notes = "2011 Supervisor Dr. Cathal Heavey. 'thesis is available in the University Library' ", } @Article{Can2011, author = "Birkan Can and Cathal Heavey", title = "Comparison of experimental designs for simulation-based symbolic regression of manufacturing systems", journal = "Computer \& Industrial Engineering", volume = "61", number = "3", pages = "447--462", month = oct, year = "2011", ISSN = "0360-8352", DOI = "doi:10.1016/j.cie.2011.03.012", broken = "http://www.sciencedirect.com/science/article/B6V27-52JDFD9-1/2/207e7db7ff221a11f1a808666cba277d", keywords = "genetic algorithms, genetic programming, Meta-modelling, Design of experiments, Discrete-event simulation, Decision support", abstract = "In this article, an empirical analysis of experimental design approaches in simulation-based metamodelling of manufacturing systems with genetic programming (GP) is presented. An advantage of using GP is that prior assumptions on the structure of the metamodels are not required. On the other hand, having an unknown structure necessitates an analysis of the experimental design techniques used to sample the problem domain and capture its characteristics. Therefore, the study presents an empirical analysis of experimental design methods while developing GP metamodels to predict throughput rates in a common industrial system, serial production lines. The objective is to identify a robust sampling approach suitable for GP in simulation-based meta-modelling. Experiments on different sizes of production lines are presented to demonstrate the effects of the experimental designs on the complexity and quality of approximations as well as their variance. The analysis showed that GP delivered system-wide meta-models with good predictive characteristics even with the limited sample data.", } @Article{Can2012424, author = "Birkan Can and Cathal Heavey", title = "A comparison of genetic programming and artificial neural networks in metamodeling of discrete-event simulation models", journal = "Computer \& Operations Research", volume = "39", number = "2", pages = "424--436", year = "2012", month = feb, ISSN = "0305-0548", DOI = "doi:10.1016/j.cor.2011.05.004", URL = "http://www.sciencedirect.com/science/article/pii/S0305054811001286", keywords = "genetic algorithms, genetic programming, Simulation metamodel, Symbolic regression, ANN, Neural networks, Design of experiments, Decision support tool", ISSN = "0305-0548", size = "13 pages", abstract = "Genetic programming (GP) and artificial neural networks (ANNs) can be used in the development of surrogate models of complex systems. The purpose of this paper is to provide a comparative analysis of GP and ANNs for metamodelling of discrete-event simulation (DES) models. Three stochastic industrial systems are empirically studied: an automated material handling system (AMHS) in semiconductor manufacturing, an (s,S) inventory model and a serial production line. The results of the study show that GP provides greater accuracy in validation tests, demonstrating a better generalisation capability than ANN. However, GP when compared to ANN requires more computation in metamodel development. Even given this increased computational requirement, the results presented indicate that GP is very competitive in metamodelling of DES models.", notes = "p432 'The results show that across all three systems GP provided greater extrapolation capability'", bibdate = "2011-06-20", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/cor/cor39.html#CanH12", } @InProceedings{Can:2016:WSC, author = "Birkan Can and Cathal Heavey", booktitle = "2016 Winter Simulation Conference (WSC)", title = "A demonstration of machine learning for explicit functions for cycle time prediction using MES data", year = "2016", pages = "2500--2511", abstract = "Cycle time prediction represents a challenging problem in complex manufacturing scenarios. This paper demonstrates an approach that uses genetic programming (GP) and effective process time (EPT) to predict cycle time using a discrete event simulation model of a production line, an approach that could be used in complex manufacturing systems, such as a semiconductor fab. These predictive models could be used to support control and planning of manufacturing systems. GP results in a more explicit function for cycle time prediction. The results of the proposed approach show a difference between 1-6percent on the demonstrated production line.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/WSC.2016.7822289", month = dec, notes = "Also known as \cite{7822289}", } @Article{journals/nca/CanakciBG09, title = "Prediction of compressive and tensile strength of Gaziantep basalts via neural networks and gene expression programming", author = "Hanifi Canakci and Adil Baykasoglu and Hamza Gullu", journal = "Neural Computing and Applications", year = "2009", number = "8", volume = "18", pages = "1031--1041", keywords = "genetic algorithms, genetic programming, gene expression programming, Artificial neural networks, Basalt, Tensile and compressive strength", DOI = "doi:10.1007/s00521-008-0208-0", abstract = "In this paper, two soft computing approaches, which are known as artificial neural networks and Gene Expression Programming (GEP) are used in strength prediction of basalts which are collected from Gaziantep region in Turkey. The collected basalts samples are tested in the geotechnical engineering laboratory of the University of Gaziantep. The parameters, ultrasound pulse velocity, water absorption, dry density, saturated density, and bulk density which are experimentally determined based on the procedures given in ISRM (Rock characterisation testing and monitoring. Pergamon Press, Oxford, 1981) are used to predict uniaxial compressive strength and tensile strength of Gaziantep basalts. It is found out that neural networks are quite effective in comparison to GEP and classical regression analyses in predicting the strength of the basalts. The results obtained are also useful in characterizing the Gaziantep basalts for practical applications.", notes = "Department of Civil Engineering, University of Gaziantep, Gaziantep, Turkey (2) Department of Industrial Engineering, Faculty of Engineering, University of Gaziantep, 27310 Gaziantep, Turkey", bibdate = "2009-12-11", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/nca/nca18.html#CanakciBG09", } @InProceedings{cangelosi:1999:HADNN, author = "Angelo Cangelosi", title = "Heterochrony and Adaptation in Developing Neural Networks", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1241--1248", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "artificial life, adaptive behavior and agents", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-008.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-008.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Article{canham:2003:GPEM, author = "Richard O. Canham and Andy M. Tyrrell", title = "A Hardware Artificial Immune System and Embryonic Array for Fault Tolerant Systems", journal = "Genetic Programming and Evolvable Machines", year = "2003", volume = "4", number = "4", pages = "359--382", month = dec, keywords = "artificial immune systems, embryonic array, fault tolerance", ISSN = "1389-2576", DOI = "doi:10.1023/A:1026143128448", abstract = "Nature demonstrates amazing levels of fault tolerance; animals can survive injury, damage, wear and tear, and are under continual attack from infectious pathogens. This paper details inspiration from biology to provide fault tolerant electronic circuits. An artificial immune system (AIS) is used to detect faults and an embryonic array to quickly reconfigure around them. The AIS makes use of a negative selection algorithm to detect abnormal behaviour. The embryonic array takes its inspiration from the development of multi-cellular organisms; each cell contains all the information necessary to describe the complete individual. Should an electronic cell fail, its neighbours have the configuration data to take over the failed cell's functionality. Two demonstration robot control systems have been implemented to provide a Khepera robot with fault tolerance. The first is very simple and is implemented on an embryonic array within a Virtex FPGA. An AIS is also implemented within the array which learns normal behaviour. Injected stuck-at faults were detected and accommodated. The second system uses fuzzy rules (implemented in software) to provide a more graceful functionality. A small AIS has been implemented to provide fault detection; it detected all faults that produced an error greater than 15% (or 23% off straight).", notes = "Special issue on artificial immune systems Article ID: 5144848", } @InProceedings{CanhVu:2018:KSE, author = "Van {Canh Vu} and Tuan-Hao Hoang", booktitle = "2018 10th International Conference on Knowledge and Systems Engineering (KSE)", title = "Detect Wi-Fi Network Attacks Using Parallel Genetic Programming", year = "2018", pages = "370--375", abstract = "Wi-Fi network have been widely used nowadays. However, Intrusion Detection System (IDS) researches on Wi-Fi network were few and difficult since there was no common dataset between researchers on this area. Recently, Kolias et al. [2] published a comprehensive Wi-Fi network dataset extracting from real Wi-Fi traces, which is called the AWID dataset. Gene programming has proven effective in detecting network attacks, but the processing time is quite slow. Today, the development of GPU technology for high-speed parallel processing, the study of parallel programming solutions is essential. In this paper, we examined the Parallel Genetic Programming (Karoo GP) [13] in wireless attack detection to improve detection rates and processing time. The experiments showed that the processing time of Karoo GP was significantly improved compared to standard GP.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/KSE.2018.8573378", month = nov, notes = "Le Quy Don Technical University Faculty of Information Technology, Hanoi, Vietnam Also known as \cite{8573378}", } @TechReport{Cani:2014:SACtr, author = "A. Cani and M. Gaudesi and E. Sanchez and G. Squillero and A. Tonda", title = "Towards Automated Malware Creation: Code Generation and Code Integration", institution = "Electronic CAD and Reliability Group, Department of Control and Computer Engineering (DAUIN) of Politecnico di Torino", year = "2013", address = "Corso Duca degli Abruzzi, 24, 10129 Turin, Italy", month = dec # " 3", keywords = "genetic algorithms, genetic programming, MicroGP", URL = "http://www.cad.polito.it/downloads/White_papers/Towards%20Automated%20Malware%20Creation%20-%20Code%20Generation%20&%20Code%20Integration.pdf", size = "13 pages", abstract = "The analogies between computer malware and biological viruses are more than obvious. The very idea of an artificial ecosystem where malicious software can evolve and autonomously find new, more effective ways of attacking legitimate programs and damaging sensitive information is both terrifying and fascinating. The paper proposes two different ways for exploiting an evolutionary algorithm to devise malware: the former targeting heuristic-based anti-virus scanner; the latter optimising a Trojan attack. Testing the stability of a system against attacks, or checking the reliability of the heuristic scan of anti-virus software could be interesting for the research community and advantageous to the IT industry. Experimental results shows the feasibility of the proposed approaches on simple real-world test cases.", notes = "Cites \cite{Sanchez:uGP:book} and \cite{squillero:2005:GPEM} Long version of 2 page poster at SAC 2014 \cite{Cani:2014:SAC} More recent version, \cite{Cani:2014:SACtr2} 25 Jan 2014, available.", } @TechReport{Cani:2014:SACtr2, author = "A. Cani and M. Gaudesi and E. Sanchez and G. Squillero and A. Tonda", title = "Towards Automated Malware Creation: Code Generation and Code Integration", institution = "Electronic CAD and Reliability Group, Department of Control and Computer Engineering (DAUIN) of Politecnico di Torino", year = "2014", type = "Internal Report", address = "Corso Duca degli Abruzzi, 24, 10129 Turin, Italy", month = "25 " # jan, keywords = "genetic algorithms, genetic programming, MicroGP", true_link = "http://www.cad.polito.it/downloads/White_papers/Towards%20Automated%20Malware%20Creation%20-%20Code%20Generation%20&%20Code%20Integration.pdf", URL = "http://www.cad.polito.it/2014/Cani_2014_SACtr2.pdf", size = "13 pages", abstract = "The analogies between computer malware and biological viruses are more than obvious. The very idea of an artificial ecosystem where malicious software can evolve and autonomously find new, more effective ways of attacking legitimate programs and damaging sensitive information is both terrifying and fascinating. The paper proposes two different ways for exploiting an evolutionary algorithm to devise malware: the former targeting heuristic-based anti-virus scanner; the latter optimizing a Trojan attack. Testing the stability of a system against attacks, or checking the reliability of the heuristic scan of anti-virus software could be interesting for the research community and advantageous to the IT industry. Experimental results shows the feasibility of the proposed approaches on simple real-world test cases. A short paper on the same subject appeared at the 29th Symposium On Applied Computing (SAC'14).", notes = "Cites \cite{Sanchez:uGP:book} and \cite{squillero:2005:GPEM} Long version of 2 page poster at SAC 2014 \cite{Cani:2014:SAC} Orginally 3 Dec 2013 \cite{Cani:2014:SACtr} To avoid overlap URL Cani_2014_SACtr2.pdf refers to January 25, 2014 version.", } @InProceedings{Cani:2014:SAC, author = "Andrea Cani and Marco Gaudesi and Ernesto Sanchez and Giovanni Squillero and Alberto Tonda", title = "Towards automated malware creation: code generation and code integration", booktitle = "SAC '14: Proceedings of the 29th Annual ACM Symposium on Applied Computing", year = "2014", pages = "157--160", address = "Gyeongju, Korea", month = "24-28 " # mar, organisation = "ACM SIGAPP", keywords = "genetic algorithms, genetic programming, Evolutionary Computation, Malware, virus, evolutionary algorithms, security", isbn13 = "978-1-4503-2469-4", URL = "https://doi.org/10.1145/2554850.2555157", DOI = "doi:10.1145/2554850.2555157", code_url = "https://ugp3.sourceforge.net/", size = "2 pages", abstract = "This short paper proposes two different ways for exploiting an evolutionary algorithm to devise malware: the former targeting heuristic-based anti-virus scanner; the latter optimising a Trojan attack. An extended internal technical report on the same the subject can be downloaded from http://www.cad.polito.it/downloads/ \cite{Cani:2014:SACtr2}", } @Article{Cankorur-Cetinkaya:2017:MB, author = "Ayca Cankorur-Cetinkaya and Joao M. L. Dias and Jana Kludas and Nigel K. H. Slater and Juho Rousu and Stephen G. Oliver and Duygu Dikicioglu", title = "{CamOptimus:} a tool for exploiting complex adaptive evolution to optimize experiments and processes in biotechnology", journal = "Microbiology", year = "2017", volume = "163", pages = "829--839", month = "1 " # jun, keywords = "genetic algorithms, genetic programming, Pichia pastoris, experimental design tool, recombinant protein production, evolutionary algorithms, symbolic regression", DOI = "doi:10.1099/mic.0.000477", size = "11 pages", abstract = "Multiple interacting factors affect the performance of engineered biological systems in synthetic biology projects. The complexity of these biological systems means that experimental design should often be treated as a multiparametric optimization problem. However, the available methodologies are either impractical, due to a combinatorial explosion in the number of experiments to be performed, or are inaccessible to most experimentalists due to the lack of publicly available, user-friendly software. Although evolutionary algorithms may be employed as alternative approaches to optimize experimental design, the lack of simple-to-use software again restricts their use to specialist practitioners. In addition, the lack of subsidiary approaches to further investigate critical factors and their interactions prevents the full analysis and exploitation of the biotechnological system. We have addressed these problems and, here, provide a simple-to-use and freely available graphical user interface to empower a broad range of experimental biologists to employ complex evolutionary algorithms to optimize their experimental designs. Our approach exploits a Genetic Algorithm to discover the subspace containing the optimal combination of parameters, and Symbolic Regression to construct a model to evaluate the sensitivity of the experiment to each parameter under investigation. We demonstrate the utility of this method using an example in which the culture conditions for the microbial production of a bioactive human protein are optimized. CamOptimus is available online", notes = "EDITOR'S CHOICE Erratum An erratum has been published for this content: Supplementary data is available with the online Supplementary Material. p832 'GPTIPS2, a symbolic regression platform, was employed for creating evolutionary models'", } @InProceedings{Cano:2010:HAIS, author = "Alberto Cano and Amelia Zafra and Sebastian Ventura", title = "Solving Classification Problems Using Genetic Programming Algorithms on GPUs", booktitle = "Hybrid Artificial Intelligence Systems", year = "2010", series = "Lecture Notes in Computer Science", editor = "Emilio Corchado and Manuel {Grana Romay} and Alexandre {Manhaes Savio}", publisher = "Springer", pages = "17--26", volume = "6077", address = "San Sebastian, Spain", month = jun # " 23-25", DOI = "doi:10.1007/978-3-642-13803-4_3", email = "i52caroa@uco.es", keywords = "genetic algorithms, genetic programming, gpu, gpgpu, gpgpgpu", size = "10 pages", abstract = "Genetic Programming is very efficient in problem solving compared to other proposals but its performance is very slow when the size of the data increases. This paper proposes a model for multi-threaded Genetic Programming classification evaluation using a NVIDIA CUDA GPUs programming model to parallelise the evaluation phase and reduce computational time. Three different well-known Genetic Programming classification algorithms are evaluated using the parallel evaluation model proposed. Experimental results using UCI Machine Learning data sets compare the performance of the three classification algorithms in single and multithreaded Java, C and CUDA GPU code. Results show that our proposal is much more efficient.", affiliation = "University of Cordoba Department of Computing and Numerical Analysis 14071 Cordoba Spain", notes = "JCLEC. No absolute speed measure given (cf. \cite{langdon:2008:eurogp}). confusion matrix calculated on two GTX 285. big multi-class training sets from UCI (poker and shuttle) comparison with Java and Intel i7 multi-core. Three GP fitness functions. RPN interpreter", } @Article{Cano:2011:SC, author = "Alberto Cano and Amelia Zafra and Sebastian Ventura", title = "Speeding up the evaluation phase of GP classification algorithms on GPUs", journal = "Soft Computing - A Fusion of Foundations, Methodologies and Applications", year = "2012", volume = "16", number = "2", pages = "187--202", month = feb, keywords = "genetic algorithms, genetic programming, GPU, Computer Science", publisher = "Springer Berlin / Heidelberg", ISSN = "1432-7643", DOI = "doi:10.1007/s00500-011-0713-4", size = "16 pages", abstract = "The efficiency of evolutionary algorithms has become a studied problem since it is one of the major weaknesses in these algorithms. Specifically, when these algorithms are employed for the classification task, the computational time required by them grows excessively as the problem complexity increases. This paper proposes an efficient scalable and massively parallel evaluation model using the NVIDIA CUDA GPU programming model to speed up the fitness calculation phase and greatly reduce the computational time. Experimental results show that our model significantly reduces the computational time compared to the sequential approach, reaching a speedup of up to 820 times. Moreover, the model is able to scale to multiple GPU devices and can be easily extended to any evolutionary algorithm.", notes = "No absolute speed measure given (cf. \cite{langdon:2008:eurogp}). UCI: Iris, New-thyroid, Ecoli, Contraceptive, Thyroid, Penbased, Shuttle, Connect-4, KDDcup, Poker. GTX 285, two GTX 480. 64-bit Linux Ubuntu. execution time was reduced from 30 hours to 2 minutes.", affiliation = "Department of Computing and Numerical Analysis, University of Cordoba, 14071 Cordoba, Spain", } @InProceedings{conf/hais/CanoZV11, author = "Alberto Cano and Amelia Zafra and Sebastian Ventura", title = "A Parallel Genetic Programming Algorithm for Classification", booktitle = "Proceedings of the 6th International Conference on Hybrid Artificial Intelligent Systems (HAIS 2011) Part {I}", year = "2011", editor = "Emilio Corchado and Marek Kurzynski and Michal Wozniak", volume = "6678", series = "Lecture Notes in Computer Science", pages = "172--181", address = "Wroclaw, Poland", month = may # " 23-25", publisher = "Springer", keywords = "genetic algorithms, genetic programming, KEEL, JCLEC", isbn13 = "978-3-642-21218-5", DOI = "doi:10.1007/978-3-642-21219-2_23", size = "10 pages", abstract = "In this paper a Grammar Guided Genetic Programming-based method for the learning of rule-based classification systems is proposed. The method learns disjunctive normal form rules generated by means of a context-free grammar. The individual constitutes a rule based decision list that represents the full classifier. To overcome the problem of computational time of this system, it parallelises the evaluation phase reducing significantly the computation time. Moreover, different operator genetics are designed to maintain the diversity of the population and get a compact set of rules. The results obtained have been validated by the use of non-parametric statistical tests, showing a good performance in terms of accuracy and interpretability.", notes = "UCI", affiliation = "Department of Computing and Numerical Analysis, University of Cordoba, 14071 Cordoba, Spain", bibdate = "2011-06-06", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/hais/hais2011-1.html#CanoZV11", } @InProceedings{cano:2013:EuroGP, author = "Alberto Cano and Amelia Zafra and Eva L. Gibaja and Sebastian Ventura", title = "A Grammar-Guided Genetic Programming Algorithm for Multi-Label Classification", booktitle = "Proceedings of the 16th European Conference on Genetic Programming, EuroGP 2013", year = "2013", month = "3-5 " # apr, editor = "Krzysztof Krawiec and Alberto Moraglio and Ting Hu and A. Sima Uyar and Bin Hu", series = "LNCS", volume = "7831", publisher = "Springer Verlag", address = "Vienna, Austria", pages = "217--228", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, grammar-guided genetic programming, Multi-label classification, rule learning", isbn13 = "978-3-642-37206-3", DOI = "doi:10.1007/978-3-642-37207-0_19", abstract = "Multi-label classification is a challenging problem which demands new knowledge discovery methods. This paper presents a Grammar-Guided Genetic Programming algorithm for solving multi-label classification problems using IF-THEN classification rules. This algorithm, called G3P-ML, is evaluated and compared to other multi-label classification techniques in different application domains. Computational experiments show that G3P-ML often obtains better results than other algorithms while achieving a lower number of rules than the other methods.", notes = "Part of \cite{Krawiec:2013:GP} EuroGP'2013 held in conjunction with EvoCOP2013, EvoBIO2013, EvoMusArt2013 and EvoApplications2013", } @Article{Cano:2013:JPDC, author = "Alberto Cano and Juan Luis Olmo and Sebastian Ventura", title = "Parallel multi-objective Ant Programming for classification using {GPUs}", journal = "Journal of Parallel and Distributed Computing", year = "2013", volume = "73", number = "6", pages = "713--728", month = jun, keywords = "genetic algorithms, genetic programming, GPU, Reverse Polish RPN, grammar based, Ant programming (AP), Ant colony optimisation (ACO), Parallel computing, Classification", ISSN = "0743-7315", DOI = "doi:10.1016/j.jpdc.2013.01.017", size = "16 pages", abstract = "Classification using Ant Programming is a challenging data mining task which demands a great deal of computational resources when handling data sets of high dimensionality. This paper presents a new parallelisation approach of an existing multi-objective Ant Programming model for classification, using GPUs and the nVidia CUDA programming model. The computational costs of the different steps of the algorithm are evaluated and it is discussed how best to parallelise them. The features of both the CPU parallel and GPU versions of the algorithm are presented. An experimental study is carried out to evaluate the performance and efficiency of the interpreter of the rules, and reports the execution times and speedups regarding variable population size, complexity of the rules mined and dimensionality of the data sets. Experiments measure the original single-threaded and the new multi-threaded CPU and GPU times with different number of GPU devices. The results are reported in terms of the number of Giga GP operations per second of the interpreter (up to 10 billion GPops/s) and the speedup achieved (up to 834 times vs CPU, 212 times vs 4-threaded CPU). The proposed GPU model is demonstrated to scale efficiently to larger datasets and to multiple GPU devices, which allows the expansion of its applicability to significantly more complicated data sets, previously unmanageable by the original algorithm in reasonable time.", notes = "genetic programming. GPU GPops/second given for interpreter only. Two nVidia GPUs (GTX 285, 480) per host PC. Ubuntu Linux CUDA 4.2. Occupancy. UCI poker, etc. Evolved decision rules. (One per output class?) Host parallel code uses Java threads. Rules in constant memory, stack in local (off-chip) memory (L1/L2 cache).", } @Article{Cano:2013:INS, author = "Alberto Cano and Amelia Zafra and Sebastian Ventura", title = "An Interpretable Classification Rule Mining Algorithm", journal = "Information Sciences", year = "2013", volume = "240", pages = "1--20", keywords = "genetic algorithms, genetic programming, Classification, Evolutionary Programming, Interpretability, Rule Mining", ISSN = "0020-0255", URL = "http://www.sciencedirect.com/science/article/pii/S0020025513002430", DOI = "doi:10.1016/j.ins.2013.03.038", size = "20 pages", abstract = "Obtaining comprehensible classifiers may be as important as achieving high accuracy in many real-life applications such as knowledge discovery tools and decision support systems. This paper introduces an efficient Evolutionary Programming algorithm for solving classification problems by means of very interpretable and comprehensible IF-THEN classification rules. This algorithm, called the Interpretable Classification Rule Mining (ICRM) algorithm, is designed to Maximo the comprehensibility of the classifier by minims the number of rules and the number of conditions. The evolutionary process is conducted to construct classification rules using only relevant attributes, avoiding noisy and redundant data information. The algorithm is evaluated and compared to 9 other well-known classification techniques in 35 varied application domains. Experimental results are validated using several non-parametric statistical tests applied on multiple classification and interpretability metrics. The experiments show that the proposal obtains good results, improving significantly the interpretability measures over the rest of the algorithms, while achieving competitive accuracy. This is a significant advantage over other algorithms as it allows to obtain an accurate and very comprehensible classifier quickly.", } @Article{Cano:2013:JSUP, author = "Alberto Cano and Jose Maria Luna and Sebastian Ventura", title = "High performance evaluation of evolutionary-mined association rules on GPUs", journal = "The Journal of Supercomputing", year = "2013", volume = "66", number = "3", pages = "1438--1461", month = dec, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Performance evaluation, Association rules, Parallel computing, GPU", ISSN = "0920-8542", language = "English", URL = "http://link.springer.com/article/10.1007/s11227-013-0937-4/fulltext.html", DOI = "doi:10.1007/s11227-013-0937-4", size = "24 pages", abstract = "Association rule mining is a well-known data mining task, but it requires much computational time and memory when mining large scale data sets of high dimensionality. This is mainly due to the evaluation process, where the antecedent and consequent in each rule mined are evaluated for each record. This paper presents a novel methodology for evaluating association rules on graphics processing units (GPUs). The evaluation model may be applied to any association rule mining algorithm. The use of GPUs and the compute unified device architecture (CUDA) programming model enables the rules mined to be evaluated in a massively parallel way, thus reducing the computational time required. This proposal takes advantage of concurrent kernels execution and asynchronous data transfers, which improves the efficiency of the model. In an experimental study, we evaluate interpreter performance and compare the execution time of the proposed model with regard to single-threaded, multi-threaded, and graphics processing unit implementation. The results obtained show an interpreter performance above 67 billion giga operations per second, and speed-up by a factor of up to 454 over the single-threaded CPU model, when using two NVIDIA 480 GTX GPUs. The evaluation model demonstrates its efficiency and scalability according to the problem complexity, number of instances, rules, and GPU devices.", } @PhdThesis{Thesis_Alberto_Cano, author = "Alberto {Cano Rojas}", title = "New Classification Models through Evolutionary Algorithms", title_es = "Nuevos Modelos de Clasificacion mediante Algoritmos Evolutivos", school = "University of Granada", year = "2014", address = "Spain", month = jan, keywords = "genetic algorithms, genetic programming, classification, evolutionary algorithms, graphic processing units, GPU, Nvidia CUDA, UCI, ARFF, KEEL, JCLEC, G3P-MI, DGC+", URL = "https://www.people.vcu.edu/~acano/pdf/Thesis%20Alberto%20Cano.pdf", URL = "https://www.uco.es/kdis/research/theses/thesis-acano/", URL = "http://www.uco.es/grupos/kdis/docs/thesis/2014-ACano.pdf", size = "184 pages", abstract = "The objective of this thesis is the development of classification models using evolutionary algorithms, focusing on the aspects of scalability, interpretability and accuracy in complex datasets and high dimensionality. This Ph.D. thesis presents new computational models on data classification which address new open problems and challenges in data classification by means of evolutionary algorithms. Specifically, we pursue to improve the performance, scalability, interpretability and accuracy of classification models on challenging data. The performance and scalability of evolutionary-based classification models were improved through parallel computation on GPUs, which demonstrated to achieve high efficiency on speeding up classification algorithms. The conflicting problem of the interpretability and accuracy of the classification models was addressed through a highly interpretable classification algorithm which produced very comprehensible classifiers by means of classification rules. Performance on challenging data such as the imbalanced classification was improved by means of a data gravitation classification algorithm which demonstrated to achieve better classification performance both on balanced and imbalanced data. All the methods proposed in this thesis were evaluated in a proper experimental framework, by using a large number of data sets with diverse dimensionality and by comparing their performance against other state-of-the-art and recently published methods of proved quality. The experimental results obtained have been verified by applying non-parametric statistical tests which support the better performance of the methods proposed.", resumen = "Los algoritmos evolutivos inspiran su funcionamiento en base a los procesos evolu-tivos naturales con el objetivo de resolver problemas de b ́usqueda y optimizaci ́on.En esta Tesis Doctoral se presentan nuevos modelos y algoritmos que abordanproblemas abiertos y nuevos retos en la tarea de clasificaci ́on mediante el uso dealgoritmos evolutivos. Concretamente, nos marcamos como objetivo la mejora delrendimiento, escalabilidad, interpretabilidad y exactitud de los modelos de clasifi-caci ́on en conjuntos de datos complejos. En cada uno de los trabajos presentados, seha realizado una b ́usqueda bibliogr ́afica exhaustiva de los trabajos relacionados enel estado del arte, con el objetivo de estudiar propuestas similares de otros autoresy su empleo como comparativa con nuestras propuestas. En primer lugar, hemos analizado el rendimiento y la escalabilidad de los modelosevolutivos de reglas de clasificaci ́on, que han sido mejorados mediante el uso dela programaci ́on paralela en tarjetas gr ́aficas de usuario (GPUs). El empleo deGPUs ha demostrado alcanzar una gran eficiencia y rendimiento en la aceleraci ́onde los algoritmos de clasificaci ́on. La programaci ́on de prop ́osito general en GPUpara tareas de aprendizaje autom ́atico y miner ́ıa de datos ha resultado ser unnicho de investigaci ́on con un amplio abanico de posibilidades. El gran n ́umero depublicaciones en este campo muestra el creciente inter ́es de los investigadores en laaceleraci ́on de algoritmos mediante arquitecturas masivamente paralelas. Los modelos paralelos desarrollados en esta Tesis Doctoral han acelerado algo-ritmos evolutivos poblacionales, paralelizando la evaluaci ́on de cada unos de losindividuos, adem ́as de su evaluaci ́on sobre cada uno de los casos de prueba dela funci ́on de evaluaci ́on. Los resultados experimentales derivados de los modelospropuestos han demostrado la gran eficacia de las GPUs en la aceleraci ́on de losalgoritmos, especialmente sobre grandes conjuntos de datos, donde anteriormenteera inviable la ejecuci ́on de los algoritmos en un tiempo razonable. Esto abre la puerta a la aplicaci ́on de esta tecnolog ́ıa a los nuevos retos de la d ́ecada en apren-dizaje autom ́atico, tales como Big Data y el procesamiento de streams de datos entiempo real. En segundo lugar, hemos analizado la dualidad intpretabilidad-precisi ́on de los mo-delos de clasificaci ́on. Los modelos de clasificaci ́on han buscado tradicionalmentemaximizar ́unicamente la exactitud de los modelos de predicci ́on. Sin embargo,recientemente la interpretabilidad de los modelos ha demostrado ser de gran in-ter ́es en m ́ultiples campos de aplicaci ́on tales como medicina, evaluaci ́on de riesgoscrediticios, etc. En este tipo de dominios es necesario motivar las razones de laspredicciones, justificando las caracter ́ısticas por las que los modelos ofrecen talespredicciones. No obstante, habitualmente la b ́usqueda de mejorar la interpretabili-dad y la exactitud de los modelos es un problema conflictivo donde ambos objetivosno se pueden alcanzar simult ́aneamente. El problema conflictivo de la interpretabilidad y exactitud de los modelos de clasifi-caci ́on ha sido tratado mediante la propuesta de un modelo de clasificaci ́on basadoen reglas interpretables, llamado ICRM, que proporciona reglas que producen re-sultados exactos y a la vez, son altamente comprensibles por su simplicidad. Estemodelo busca buenas combinaciones de comparaciones atributo-valor que compon-gan el antecedente de una regla de clasificaci ́on. Es responsabilidad del algoritmoevolutivo encontrar las mejores combinaciones y las mejores condiciones que com-pongan las reglas del clasificador. En tercer lugar, hemos analizado conjuntos datos no balanceados. Este tipo de con-juntos de datos se caracterizan por el alto desbalanceo entre las clases, es decir, eln ́umero de instancias que pertenecen a cada una de las clases de datos no se encuen-tra equilibrado. Bajo estas circunstancias, los modelos tradicionales de clasificaci ́onsuelen estar sesgados a predecir las clases con un mayor n ́umero de ejemplos, olvi-dando habitualmente las clases minoritarias. Precisamente, en ciertos dominios elinter ́es radica verdaderamente en las clases minoritarias, y el verdadero problema esclasificar correctamente estos ejemplos minoritarios. En esta Tesis Doctoral hemosrealizado una propuesta de un modelo evolutivo de clasificaci ́on basado en grav-itaci ́on. La idea de este algoritmo se basa en el concepto f ́ısico de gravedad y lainteracci ́on entre las part ́ıculas. El objetivo era desarrollar un modelo de predicci ́onque lograse buenos resultados tanto en conjuntos de datos balanceados como nobalanceados. La adaptaci ́on de la funci ́on de ajuste teniendo en cuenta las carac-ter ́ısticas del dominio y propiedades del conjunto de datos ha ayudado a lograr subuen funcionamiento en ambos tipos de datos. Los resultados obtenidos han de-mostrado alcanzar una gran exactitud, y a su vez una suave y buena generalizaci ́onde la predicci ́on a lo largo del dominio del conjunto de datos. Todos los modelos propuestos en esta Tesis Doctoral han sido evaluados bajo unentorno experimental apropiado, mediante el uso de un gran n ́umero de conjuntosde datos de diversa dimensionalidad, n ́umero de instancias, atributos y clases, ymediante la comparaci ́on de los resultados frente a otros algoritmos del estado delarte y recientemente publicados de probada calidad. La metodolog ́ıa experimen-tal empleada busca una comparativa justa de la eficacia, robustez, rendimientoy resultados de los algoritmos. Concretamente, los conjuntos de datos han sidoparticionados en un esquema de 10 particiones cruzadas, y los experimentos hansido repetidos al menos 10 veces con diferentes semillas, para reflejar la naturalezaestoc ́astica de los algoritmos evolutivos. Los resultados experimentales obtenidoshan sido verificados mediante la aplicaci ́on de tests estad ́ısticos no param ́etricos decomparaciones m ́ultiples y por pares, tales como el de Friedman, Bonferroni-Dunn,o Wilcoxon, que apoyan estad ́ısticamente los mejores esultados obtenidos por losmodelos propuestos", notes = "p15 'speedup of up to 450 fold' 'up to 108 billion Genetic Programming operations per second (GPops/s)' 'great scalability to two and four GPUs.' Parallel evaluation of Pittsburgh rule-based classifiers on GPUs, Neurocomputing 126 (2014) 45-57 \cite{DBLP:journals/ijon/CanoZV14}. page 112. 'the GPU interpreter achieves up to 108 billion GPops/s when distributing the computation into four GPUs.' https://canoalberto.github.io/ TIN2011-22408 AP-2010-0042 Supervisors: Sebastian Ventura Soto and Amelia Zafra Gomez", } @Article{Cano:2014:Neurocomputing, author = "Alberto Cano and Amelia Zafra and Sebastian Ventura", title = "Parallel evaluation of {Pittsburgh} rule-based classifiers on {GPUs}", journal = "Neurocomputing", year = "2014", volume = "126", pages = "45--57", month = "27 " # feb, note = "Recent trends in Intelligent Data Analysis Selected papers of the The 6th International Conference on Hybrid Artificial Intelligence Systems (HAIS 2011) Online Data Processing Including a selection of papers from the International Conference on Adaptive and Intelligent Systems 2011 (ICAIS 2011)", keywords = "genetic algorithms, Pittsburgh, Classification, Rule sets, Parallel computing, GPU, GPGPU", ISSN = "0925-2312", URL = "http://www.sciencedirect.com/science/article/pii/S0925231213006875", DOI = "doi:10.1016/j.neucom.2013.01.049", size = "13 pages", abstract = "Individuals from Pittsburgh rule-based classifiers represent a complete solution to the classification problem and each individual is a variable-length set of rules. Therefore, these systems usually demand a high level of computational resources and run-time, which increases as the complexity and the size of the data sets. It is known that this computational cost is mainly due to the recurring evaluation process of the rules and the individuals as rule sets. In this paper we propose a parallel evaluation model of rules and rule sets on GPUs based on the NVIDIA CUDA programming model which significantly allows reducing the run-time and speeding up the algorithm. The results obtained from the experimental study support the great efficiency and high performance of the GPU model, which is scalable to multiple GPU devices. The GPU model achieves a rule interpreter performance of up to 64 billion operations per second and the evaluation of the individuals is speed up of up to 3461 fold when compared to the CPU model. This provides a significant advantage of the GPU model, especially addressing large and complex problems within reasonable time, where the CPU run-time is not acceptable", notes = "not on GP. Also known as \cite{2014-NEUROCOM-GPU} \cite{BLP:journals/ijon/CanoZV14} 64 billion GPop/s, see PhD thesis \cite{Thesis_Alberto_Cano}", } @InProceedings{Cano:2014:GECCO, author = "Alberto Cano and Sebastian Ventura", title = "GPU-parallel subtree interpreter for genetic programming", booktitle = "GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation", year = "2014", editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges", isbn13 = "978-1-4503-2662-9", pages = "887--894", keywords = "genetic algorithms, genetic programming, GPU", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2576768.2598272", DOI = "doi:10.1145/2576768.2598272", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Genetic Programming (GP) is a computationally intensive technique but its nature is embarrassingly parallel. Graphic Processing Units (GPUs) are many-core architectures which have been widely employed to speed up the evaluation of GP. In recent years, many works have shown the high performance and efficiency of GPUs on evaluating both the individuals and the fitness cases in parallel. These approaches are known as population parallel and data parallel. This paper presents a parallel GP interpreter which extends these approaches and adds a new parallelisation level based on the concurrent evaluation of the individual's subtrees. A GP individual defined by a tree structure with nodes and branches comprises different depth levels in which there are independent subtrees which can be evaluated concurrently. Threads can cooperate to evaluate different subtrees and share the results via GPU's shared memory. The experimental results show the better performance of the proposal in terms of the GP operations per second (GPops/s) that the GP interpreter is capable of processing, achieving up to 21 billion GPops/s using a NVIDIA 480 GPU. However, some issues raised due to limitations of currently available hardware are to be overcome by the dynamic parallelisation capabilities of the next generation of GPUs.", notes = "Also known as \cite{2598272} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)", } @Article{2014-KAIS-Cano, author = "Alberto Cano and Amelia Zafra and Sebastian Ventura", title = "Speeding up multiple instance learning classification rules on GPUs", journal = "Knowledge and Information Systems", year = "2015", volume = "44", number = "1", pages = "127--145", month = jul, keywords = "genetic algorithms, genetic programming, Multi-instance learning, Classification, Parallel computing, GPU", ISSN = "0219-1377", DOI = "doi:10.1007/s10115-014-0752-0", size = "19 pages", abstract = "Multiple instance learning is a challenging task in supervised learning and data mining. However, algorithm performance becomes slow when learning from large-scale and high-dimensional data sets. Graphics processing units (GPUs) are being used for reducing computing time of algorithms. This paper presents an implementation of the G3P-MI algorithm on GPUs for solving multiple instance problems using classification rules. The GPU model proposed is distributable to multiple GPUs, seeking for its scalability across large-scale and high-dimensional data sets. The proposal is compared to the multi-threaded CPU algorithm with streaming SIMD extensions parallelism over a series of data sets. Experimental results report that the computation time can be significantly reduced and its scalability improved. Specifically, an speedup of up to 149 times can be achieved over the multi-threaded CPU algorithm when using four GPUs, and the rules interpreter achieves great efficiency and runs over 108 billion genetic programming operations per second.", } @Article{Cano:2015:JMLR, author = "Alberto Cano and Jose Maria Luna and Amelia Zafra and Sebastian Ventura", title = "A Classification Module for Genetic Programming Algorithms in {JCLEC}", journal = "Journal of Machine Learning Research", publisher = "Microtome Publishing", volume = "16", pages = "491--494", keywords = "genetic algorithms, genetic programming", ISSN = "1533-7928 (electronic); 1532-4435 (paper)", year = "2015", month = mar, URL = "http://www.jmlr.org/", URL = "http://www.jmlr.org/papers/v16/cano15a.html", URL = "http://www.jmlr.org/papers/volume16/cano15a/cano15a.pdf", size = "4 pages", abstract = "JCLEC-Classification is a usable and extensible open source library for genetic programming classification algorithms. It houses implementations of rule-based methods for classification based on genetic programming, supporting multiple model representations and providing to users the tools to implement any classifier easily. The software is written in Java and it is available from http://jclec.sourceforge.net/classification under the GPL license.", } @Article{Cano:2016:SC, author = "Alberto Cano and Sebastian Ventura and Krzysztof J. Cios", title = "Multi-objective genetic programming for feature extraction and data visualization", journal = "Soft Computing", year = "2017", volume = "21", number = "8", pages = "2069--2089", month = apr, keywords = "genetic algorithms, genetic programming, Classification, Feature extraction, Visualization", ISSN = "1433-7479", DOI = "doi:10.1007/s00500-015-1907-y", size = "21 pages", abstract = "Feature extraction transforms high-dimensional data into a new subspace of lower dimensionality while keeping the classification accuracy. Traditional algorithms do not consider the multi-objective nature of this task. Data transformations should improve the classification performance on the new subspace, as well as to facilitate data visualization, which has attracted increasing attention in recent years. Moreover, new challenges arising in data mining, such as the need to deal with imbalanced data sets call for new algorithms capable of handling this type of data. This paper presents a Pareto-based multi-objective genetic programming algorithm for feature extraction and data visualization. The algorithm is designed to obtain data transformations that optimize the classification and visualization performance both on balanced and imbalanced data. Six classification and visualization measures are identified as objectives to be optimized by the multi-objective algorithm. The algorithm is evaluated and compared to 11 well-known feature extraction methods, and to the performance on the original high-dimensional data. Experimental results on 22 balanced and 20 imbalanced data sets show that it performs very well on both types of data, which is its significant advantage over existing feature extraction algorithms.", } @Article{CANO:2019:PR, author = "Alberto Cano and Bartosz Krawczyk", title = "Evolving rule-based classifiers with genetic programming on GPUs for drifting data streams", journal = "Pattern Recognition", volume = "87", pages = "248--268", year = "2019", keywords = "genetic algorithms, genetic programming, Machine learning, Data streams, Concept drift, Rule-based classification, GPU, High-performance data mining", ISSN = "0031-3203", DOI = "doi:10.1016/j.patcog.2018.10.024", URL = "http://www.sciencedirect.com/science/article/pii/S0031320318303765", abstract = "Designing efficient algorithms for mining massive high-speed data streams has become one of the contemporary challenges for the machine learning community. Such models must display highest possible accuracy and ability to swiftly adapt to any kind of changes, while at the same time being characterized by low time and memory complexities. However, little attention has been paid to designing learning systems that will allow us to gain a better understanding of incoming data. There are few proposals on how to design interpretable classifiers for drifting data streams, yet most of them are characterized by a significant trade-off between accuracy and interpretability. In this paper, we show that it is possible to have all of these desirable properties in one model. We introduce ERulesD2S: evolving rule-based classifier for drifting data Streams. By using grammar-guided genetic programming, we are able to obtain accurate sets of rules per class that are able to adapt to changes in the stream without a need for an explicit drift detector. Additionally, we augment our learning model with new proposals for rule propagation and data stream sampling, in order to maintain a balance between learning and forgetting of concepts. To improve efficiency of mining massive and non-stationary data, we implement ERulesD2S parallelized on GPUs. A thorough experimental study on 30 datasets proves that ERulesD2S is able to efficiently adapt to any type of concept drift and outperform state-of-the-art rule-based classifiers, while using small number of rules. At the same time ERulesD2S is highly competitive to other single and ensemble learners in terms of accuracy and computational complexity, while offering fully interpretable classification rules. Additionally, we show that ERulesD2S can scale-up efficiently to high-dimensional data streams, while offering very fast update and classification times. Finally, we present the learning capabilities of ERulesD2S for sparsely labeled data streams", } @Article{Cano:2019:LT, author = "Alberto Cano and John D. Leonard", journal = "IEEE Transactions on Learning Technologies", title = "Interpretable Multiview Early Warning System Adapted to Underrepresented Student Populations", year = "2019", volume = "12", number = "2", pages = "198--211", month = apr, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/TLT.2019.2911079", ISSN = "1939-1382", abstract = "Early warning systems have been progressively implemented in higher education institutions to predict student performance. However, they usually fail at effectively integrating the many information sources available at universities to make more accurate and timely predictions, they often lack decision-making reasoning to motivate the reasons behind the predictions, and they are generally biased toward the general student body, ignoring the idiosyncrasies of underrepresented student populations (determined by socio-demographic factors such as race, gender, residency, or status as a freshmen, transfer, adult, or first-generation students) that traditionally have greater difficulties and performance gaps. This paper presents a multiview early warning system built with comprehensible Genetic Programming classification rules adapted to specifically target underrepresented and underperforming student populations. The system integrates many student information repositories using multiview learning to improve the accuracy and timing of the predictions. Three interfaces have been developed to provide personalized and aggregated comprehensible feedback to students, instructors, and staff to facilitate early intervention and student support. Experimental results, validated with statistical analysis, indicate that this multiview learning approach outperforms traditional classifiers. Learning outcomes will help instructors and policy-makers to deploy strategies to increase retention and improve academics.", notes = "Also known as \cite{8691619}", } @InProceedings{cano:2023:GECCOcomp, author = "Jorge Cano and J. Ignacio Hidalgo and Oscar Garnica and Juan Lanchares", title = "Hardware Design of a Model Generator Based on Grammars and Cartesian Genetic Programming for Blood Glucose Prediction", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Justyna Petke and Aniko Ekart", pages = "55--56", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", isbn13 = "9798400701191", DOI = "doi:10.1145/3583133.3596427", size = "2 pages", abstract = "People with diabetes need to control their blood glucose levels to avoid dangerous situations such as getting into hypoglycemia or hyperglycemia, which can lead to long-term and short-term complications. One of the most important daily tasks of people with diabetes is to estimate or predict the glucose in a near future as a consequence of medication, eating, or insulin administration events. We present a parameterized hardware implementation of a blood glucose level predictor generator. The design was implemented over a Field Programmable Gate Array and uses as input variables a set of data from the person (blood glucose levels, carbohydrates, and insulin units). Our implementation produces personal devices the patient can use whenever new readings of the variable are available. Moreover, it could be combined with insulin pumps and continuous glucose monitoring systems to develop an artificial pancreas. For the model generation, we designed a novel technique based on grammars, cartesian genetic programming with an evolutionary strategy (1+lambda) and a fitness function based on the Clarke Error Grid Analysis. Preliminary results show that our hardware implementation achieved higher speeds and lower power consumption than its software counterparts while preserving or even improving the accuracy of the predictions.", notes = "See also \cite{Cano:2025:GPEM} GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @Article{Cano:2025:GPEM, author = "Jorge Cano and J. Ignacio Hidalgo and Oscar Garnica", title = "Hardware real-time individualised blood glucose predictor generator based on grammars and cartesian genetic programming", journal = "Genetic Programming and Evolvable Machines", year = "2025", volume = "26", pages = "Article no 3", note = "Online first", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-024-09500-7", notes = "HUPA-UCM Diabetes Dataset Departamento de Arquitectura de Computadores y Automatica, Universidad Complutense de Madrid, Madrid, Comunidad de Madrid, Spain", } @Article{cantner:2001:JASSS, author = "Uwe Cantner and Bernd Ebersberger and Horst Hanusch and Jens J. Kruger and Andreas Pyka", title = "Empirically Based Simulation: The Case of Twin Peaks in National Income", journal = "The Journal of Artificial Societies and Social Simulation", year = "2001", month = "30-" # jun, keywords = "genetic algorithms, genetic programming, bimodal productivity structure, master equation approach", ISSN = "1460-7425", URL = "http://jasss.soc.surrey.ac.uk/4/3/9.html", size = "228 kbytes", abstract = "Only recently a new stylised fact of economic growth has been introduced, the bimodal shape of the distribution of per capita income or the twin-peaked nature of that distribution. Drawing on the Summers/Hestons Penn World Table 5.6 (1991) we determine kernel density distributions which are able to detect the aforementioned twin peaked structure and show that the world income distribution starting with an unimodal structure in 1960 evolves subsequently to a bimodal or twin-peak structure. This empirical results can be explained theoretically by a synergetic model based on the master equation approach as in Pyka/Kruger/Cantner (1999). This paper attempts to extend this discussion by taking the reverse procedure, that is to find empirical evidence for the working mechanism of the theoretical model. We determine empirically the transition rates used in the synergetic approach by applying alternatively NLS to chosen functional forms and genetic programming in order to determine the functional forms and the parameters simultaneously. Using the so determined transition rates in the synergetic model leads in both cases to the emergence of the bimodal distribution, which, however, is only in the latter case a persistent phenomenon.", notes = "JASSS", } @InProceedings{Cantu-Paz:1997:mibcpGA, author = "Erick Cantu-Paz and David E. Goldberg", title = "Modeling Idealized Bounding Cases of Parallel Genetic Algorithms", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "Genetic Algorithms", pages = "353--361", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", notes = "GP-97", } @InProceedings{cantu:1998:demsPGA, author = "Erick Cantu-Paz", title = "Designing Efficient Master-Slave Parallel Genetic Algorithms", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "455", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms", notes = "SGA-98", } @InProceedings{cantu:1998:mcabcPGA, author = "Erick Cantu-Paz", title = "Using Markov Chains to Analyze a Bounding Case of Parallel Genetic Algorithms", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "456--462", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms", notes = "SGA-98", } @InProceedings{cantu-paz:1999:MPTTGA, author = "Erick Cantu-Paz", title = "Migration Policies and Takeover Times in Genetic Algorithms", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "775", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco99-migpolicy.pdf", URL = "http://dangermouse.brynmawr.edu/ec/gecco99-migpolicy.pdf", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{cantu-paz:1999:TMRMPGA, author = "Erick Cantu-Paz", title = "Topologies, Migration Rates, and Multi-Population Parallel Genetic Algorithms", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "91--98", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco99-topologies.pdf", URL = "http://dangermouse.brynmawr.edu/ec/gecco99-topologies.pdf", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{cantu-paz:1999:M, author = "Erick Cantu-Paz", title = "Migration policies, selection pressure, and parallel evolutionary algorithms", booktitle = "Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference", year = "1999", editor = "Scott Brave and Annie S. Wu", pages = "65--73", address = "Orlando, Florida, USA", month = "13 " # jul, notes = "GECCO-99LB", } @Proceedings{cantu-paz:2002:gecco:lbp, title = "Late Breaking papers at the Genetic and Evolutionary Computation Conference ({GECCO-2002})", editor = "Erick Cant{\'u}-Paz", year = "2002", month = jul, publisher = "AAAI", address = "New York, NY", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025", keywords = "genetic algorithms, genetic programming, Evolvable Network Architecture , Dynamic Neural Net, Pattern Recognition , Evolutionary Computation, Automated Sensor, Multiagent Systems, Optimisation, Evolvable Hardware , Genetic Multi-Agent Planning, Evolutionary Testing, Evolving Neural Network Architectures, Evolving Software, Airline Fleet Assignment, Ant Colony Algorithm, Artificial Immune System , Artificial Life, Evolving Cellular Automata", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002lb.bib", notes = "Late Breaking Papers, {GECCO-2002}. A joint meeting of the eleventh International Conference on Genetic Algorithms ({ICGA-2002}) and the seventh Annual Genetic Programming Conference ({GP-2002})", } @Proceedings{GECCO2003-PartI, editor = "Erick Cant{\'u}-Paz and James A. Foster and Kalyanmoy Deb and Lawrence Davis and Rajkumar Roy and Una-May O'Reilly and Hans-Georg Beyer and Russell K. Standish and Graham Kendall and Stewart W. Wilson and Mark Harman and Joachim Wegener and Dipankar Dasgupta and Mitchell A. Potter and Alan C. Schultz and Kathryn A. Dowsland and Natasha Jonoska and Julian F. Miller", title = "Genetic and Evolutionary Computation -- {GECCO 2003}, Part {I}", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "2723", year = "2003", ISBN = "3-540-40602-6", address = "Chicago, IL, USA", month = "12-16 " # jul, bibsource = "DBLP, http://dblp.uni-trier.de", keywords = "genetic algorithms, genetic programming, A-Life, Adaptive Behaviour, Agents, Artificial Immune Systems, Coevolution, DNA computing, Evolution Strategies, Evolutionary Programming, Evolutionary Robotics, Evolutionary Scheduling Routing, Evolvable Hardware, Learning Classifier Systems, Molecular computing, Quantum Computing, Real World Applications, Search Based Software Engineering, Ant Colony Optimization, grammatical evolution", DOI = "doi:10.1007/3-540-45105-6", notes = "GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)", } @Proceedings{GECCO2003-PartII, editor = "Erick Cant{\'u}-Paz and James A. Foster and Kalyanmoy Deb and Lawrence Davis and Rajkumar Roy and Una-May O'Reilly and Hans-Georg Beyer and Russell K. Standish and Graham Kendall and Stewart W. Wilson and Mark Harman and Joachim Wegener and Dipankar Dasgupta and Mitchell A. Potter and Alan C. Schultz and Kathryn A. Dowsland and Natasha Jonoska and Julian F. Miller", title = "Genetic and Evolutionary Computation -- {GECCO 2003}, Part {II}", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "2724", year = "2003", ISBN = "3-540-40603-4", month = "12-16 " # jul, bibsource = "DBLP, http://dblp.uni-trier.de", keywords = "genetic algorithms, genetic programming, A-Life, Adaptive Behavior, Agents, Artificial Immune Systems, Coevolution, DNA computing, Evolution Strategies, Evolutionary Programming, Evolutionary Robotics, Evolutionary Scheduling Routing, Evolvable Hardware, Learning Classifier Systems, Molecular computing, Quantum Computing, Real World Applications, Search Based Software Engineering, Ant Colony Optimization, grammatical evolution", DOI = "doi:10.1007/3-540-45110-2", notes = "GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)", } @InProceedings{Cao:2016:ICISCE, author = "Bo Cao and Zongli Jiang", booktitle = "2016 3rd International Conference on Information Science and Control Engineering (ICISCE)", title = "Increasing Diversity and Controlling Bloat in Linear Genetic Programming", year = "2016", pages = "414--419", abstract = "The objective of this paper is to use the age-layered population structure model to increase diversity and control bloat of the population in linear genetic programming. Firstly, we use two level tournament selection to increase the sub-population diversity in each layer, and then apply the new model to linear genetic programming to increase the sub-population and the entire population diversity. The age-layered population structure model segregates individuals into different age-layers by their age, so it limits the quantity of the old age and long length individuals. Besides, the model regularly introduces new randomly generated short individuals into the youngest layer to reduce the average length of the population. The experimental results show that the age-layered population structure model can increase diversity and control bloat of the population effectively.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICISCE.2016.97", month = jul, notes = "Also known as \cite{7726194}", } @InProceedings{Cao:2020:ISSSR, author = "Heling Cao and YangXia Meng and Jianshu Shi and Lei Li and Tiaoli Liao and Chenyang Zhao", title = "A Survey on Automatic Bug Fixing", booktitle = "2020 6th International Symposium on System and Software Reliability (ISSSR)", year = "2020", pages = "122--131", month = oct, keywords = "genetic algorithms, genetic programming, genetic improvement, APR", DOI = "doi:10.1109/ISSSR51244.2020.00029", abstract = "To reduce the cost of software debugging, Automatic Bug Fixing (ABF) techniques have been proposed for efficiently fixing and maintaining software, aiming to rapidly correct bugs in software. In this paper, we conduct a survey, analysing the capabilities of existing ABF techniques based on the test case set. We organise knowledge in this area by surveying 133 high-quality papers from 1990 to June 2020 and supplement 57 latest high-quality papers from 2017 to June 2020. This paper shows that existing ABF approaches can be divided into three main strategies: search-based, semantic-based, and template-based. Search-based ABF considers using search strategies, such as genetic programming, context similarity, to change the programs into the correct one. Semantic-based ABF involves symbolic execution and constraint solving, such as satisfiability modulo theories solver, contracts, to fix bugs. Different from the two kinds of theories above, template-based ABF is mainly based on fixing templates, such as other programs, bug reports, to fix bugs. Besides, we provide a summary of the commonly used defect benchmarks and all the available tools that are frequently used in the field of ABF. We also discuss the empirical foundations and argumentation in the area and prospect the trend of future study.", notes = "Also known as \cite{9265903}", } @InProceedings{Cao:2021:QRS-C, author = "Heling Cao and Fangzheng Liu and Jianshu Shi and Yonghe Chu and Miaolei Deng", title = "Automated Repair of Java Programs with Random Search via Code Similarity", booktitle = "2021 IEEE 21st International Conference on Software Quality, Reliability and Security Companion (QRS-C)", year = "2021", pages = "470--477", abstract = "Automatic program repair is a cutting-edge research direction in software engineering in recent years. The existing program repair techniques based on genetic programming suffer from requiring verification of a large number of candidate patches, which consume a lot of computational resources. We instead propose Random search via Code Similarity based automate program Repair (RCSRepair). First, we use test filtering and test case prioritization techniques in fault localization to reduce and restructure test cases. Second, a combination of random search and code similarity is used to generate patches. Finally, overfitting detection is performed on the patches that pass the test cases to improve the quality of the patch. The experimental results show that our approach can successfully fix 54 bugs of 224 real-world bugs in Defects4J and has outperform the compared approaches.", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, program repair, random search, test case prioritisation, patch overfitted", DOI = "doi:10.1109/QRS-C55045.2021.00075", ISSN = "2693-9371", month = dec, notes = "Also known as \cite{9742203} Henan University of Technology, zhengzhou", } @Article{cao:1998:NPSC, author = "Hongqing Cao and Lishan Kang and Zbigniew Michalewicz and Yuping Chen", title = "A Hybrid Evolutionary Modeling Algorithm for System of Ordinary Differential Equations", journal = "Neural, Parallel \& Scientific Computations", year = "1998", volume = "6", number = "2", pages = "171--188", month = jun, address = "Atlanta, USA", publisher = "Dynamic Publishers", keywords = "genetic algorithms, genetic programming", ISSN = "1061-5369", URL = "http://www.dynamicpublishers.com/Neural/neuralv6.htm", URL = "http://dl.acm.org/citation.cfm?id=293731.293733", notes = "Sep 2018 www.dynamicpublishers.org appears to have only one issue of their journal on the web", } @InProceedings{cao:1998:2eaode, author = "Hongqing Cao and Lishan Kang and Zbigniew Michalewicz and Yuping Chen", title = "A Two-level Evolutionary Algorithm for Modeling System of Ordinary Differential Equations", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "17--22", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, ODE, HEMA", ISBN = "1-55860-548-7", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1998/cao_1998_2eaode.pdf", size = "6 pages", notes = "State Key Laboratory of Software Engineering, Wuhan University, China GP-98", } @Article{cao:1999:CC, author = "Hongqing Cao and Jingxian Yu and Lishan Kang and Yuping Chen and Yongyan Chen", title = "The Kinetic Evolutionary Modeling of Complex Systems of Chemical Reactions", journal = "Computers \& Chemistry", year = "1999", volume = "23", number = "2", pages = "143--152", month = "30 " # mar, keywords = "genetic algorithms, genetic programming, kinetic analysis, Complex systems of chemical reactions, Evolutionary modeling", DOI = "doi:10.1016/S0097-8485(99)00005-4", abstract = "To overcome the drawbacks of most available methods for kinetic analysis, this paper proposes a hybrid evolutionary modelling algorithm called HEMA to build kinetic models of systems of ordinary differential equations (ODEs) automatically for complex systems of chemical reactions. The main idea of the algorithm is to embed a genetic algorithm (GA) into genetic programming (GP) where GP is employed to optimise the structure of a model, while a GA is employed to optimize its parameters. The experimental results of two chemical reaction systems show that by running the HEMA, the computer can discover the kinetic models automatically which are appropriate for describing the kinetic characteristics of the reacting systems. Those models can not only fit the kinetic data very well, but also give good predictions.", } @InProceedings{cao:1999:EMODEDS, author = "Hongqing Cao and Lishan Kang and Yuping Chen", title = "Evolutionary Modeling of Ordinary Differential Equations for Dynamic Systems", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "959--965", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-401.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-401.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Article{cao:2000:odeGP, author = "Hongqing Cao and Lishan Kang and Yuping Chen and Jingxian Yu", title = "Evolutionary Modeling of Systems of Ordinary Differential Equations with Genetic Programming", journal = "Genetic Programming and Evolvable Machines", year = "2000", volume = "1", number = "4", pages = "309--337", month = oct, keywords = "genetic algorithms, genetic programming, evolutionary modeling, system of ordinary differential equations, higher-order ordinary differential equation", ISSN = "1389-2576", URL = "http://www.ees.adelaide.edu.au/people/enviro/cao/2000-05.pdf", DOI = "doi:10.1023/A:1010013106294", size = "29 pages", abstract = "This paper describes an approach to the evolutionary modeling problem of ordinary differential equations including systems of ordinary differential equations and higher-order differential equations. Hybrid evolutionary modeling algorithms are presented to implement the automatic modeling of one- and multi-dimensional dynamic systems respectively. The main idea of the method is to embed a genetic algorithm in genetic programming where the latter is employed to discover and optimize the structure of a model, while the former is employed to optimize its parameters. A number of practical examples are used to demonstrate the effectiveness of the approach. Experimental results show that the algorithm has some advantages over most available modeling methods.", notes = "Article ID: 273810", } @Article{cao:2000:ode2GP, author = "Hong-Qing Cao and Li-Shan Kang and Tao Guo and Yu-Ping Chen and Hugo {de Garis}", title = "A two-level hybrid evolutionary algorithm for modeling one-dimensional dynamic systems by higher-order ODE models", journal = "IEEE Transactions on Systems, Man and Cybernetics -- Part B: Cybernetics", year = "2000", volume = "40", number = "2", pages = "351--357", month = apr, keywords = "genetic algorithms, genetic programming, evolutionary computation, evolutionary algorithm, ODE models, one-dimensional dynamic systems, ordinary differential equation, two-level hybrid evolutionary modeling algorithm, THEMA, crossover operator", ISSN = "1083-4419", URL = "http://ieeexplore.ieee.org/iel5/3477/18067/00836383.pdf", size = "7 pages", abstract = "This paper presents a new algorithm for modeling one-dimensional (1-D) dynamic systems by higher-order ordinary differential equation (HODE) models instead of the ARMA models as used in traditional time series analysis. A two-level hybrid evolutionary modeling algorithm (THEMA) is used to approach the modeling problem of HODE's for dynamic systems. The main idea of this modeling algorithm is to embed a genetic algorithm (GA) into genetic programming (GP), where GP is employed to optimize the structure of a model (the upper level), while a GA is employed to optimize the parameters of the model (the lower level). In the GA, we use a novel crossover operator based on a nonconvex linear combination of multiple parents which works efficiently and quickly in parameter optimization tasks. Two practical examples of time series are used to demonstrate the THEMA's effectiveness and advantages.", } @Article{cao:2001:CC, author = "Hongqing Cao and Jingxian Yu and Lishan Kang and Hanxi Yang and Xinping Ai", title = "Modeling and prediction for discharge lifetime of battery systems using hybrid evolutionary algorithms", journal = "Computers \& Chemistry", year = "2001", volume = "25", number = "3", pages = "251--259", month = may, keywords = "genetic algorithms, genetic programming, Discharge lifetime of battery systems, Lithium-ion battery, Hybrid evolutionary modelling", ISSN = "0097-8485", DOI = "doi:10.1016/S0097-8485(00)00099-1", abstract = "A hybrid evolutionary modeling algorithm (HEMA) is proposed to build the discharge lifetime models with multiple impact factors for battery systems as well as make predictions. The main idea of the HEMA is to embed a genetic algorithm (GA) into genetic programming (GP), where GP is employed to optimise the structure of a model, while a GA is employed to optimize its parameters. The experimental results on lithium-ion batteries show that the HEMA works effectively, automatically and quickly in modelling the discharge lifetime of battery systems. The algorithm has some advantages compared with most existing modelling methods and can be applied widely to solving the automatic modelling problems in many fields.", notes = "http://www.elsevier.com/wps/find/journaldescription.cws_home/627320/description#description", } @Article{cao:2003:WUJNS, author = "Hongqing Cao and Lishan Kang and Jingxian Yu", title = "Parallel Implementations of Modeling Dynamical Systems by Using System of Ordinary Differential Equations", journal = "Wuhan University Journal of Natural Sciences", year = "2003", volume = "8", number = "IB", pages = "229--233", publisher = "Wuhan University", keywords = "genetic algorithms, genetic programming, parallel genetic programming, evolutionary modeling, system of ordinary differential equations", ISSN = "1007-1202", DOI = "doi:10.1007/BF02899484", size = "5 pages", abstract = "First, an asynchronous distributed parallel evolutionary modelling algorithm (PEMA) for building the model of system of ordinary differential equations for dynamical systems is proposed in this paper. Then a series of parallel experiments have been conducted to systematically test the influence of some important parallel control parameters on the performance of the algorithm. A lot of experimental results are obtained and we make some analysis and explanations to them.", notes = "CLC number: TP 301.6", } @Article{cao:2003:NPSC, author = "Hongqing Cao and Jingxian Yu and Lishan Kang and R I Bob McKay", title = "An Experimental Study of Some Control Parameters in Parallel Genetic Programming", journal = "Neural, Parallel and Scientific Computation", year = "2003", volume = "11", number = "4", pages = "377--393", keywords = "genetic algorithms, genetic programming, parallel genetic programming, parallel control parameters, evolutionary modelling, system of ordinary differential equations", URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.80.6377.pdf", size = "17 pages", abstract = "Using the evolutionary modeling of system of ordinary differential equations (ODEs) as the test problem, this paper primarily investigates the influences of some important parallel control parameters within parallel genetic programming (GP), including the degree of connectivity between demes, the migration rate, the migration generation interval, and the migration policy, on the performance of the parallel evolutionary modelling algorithm (PEMA), which is measured from two perspectives: the solution quality and the parallel speedup. We compare the results with previous theoretical and experimental work in parallel genetic algorithms (GAs), and try to give some plausible analysis and explanations. The results may help to offer some useful design guidelines for researchers using parallel GP.", } @Article{cao:2003:CMA, author = "Hongqing Cao and Lishan Kang and Yuping Chen and Tao Guo", title = "The Dynamic Evolutionary Modeling of HODEs for Time Series Prediction", journal = "Computers \& Mathematics with Applications", year = "2003", volume = "46", number = "8-9", pages = "1397--1411", keywords = "genetic algorithms, genetic programming, Time series, Differential equation", URL = "http://www.sciencedirect.com/science/article/B6TYJ-4BRR761-P/2/4d226ed6e682798de2e1d83d01cebd95", DOI = "doi:10.1016/S0898-1221(03)90228-8", abstract = "The prediction of future values of a time series generated by a chaotic dynamic system is an extremely challenging task. Besides some methods used in traditional time series analysis, a number of nonlinear prediction methods have been developed for time series prediction, especially the evolutionary algorithms. Many researchers have built various models by using different evolutionary techniques. Different from those available models, this paper presents a new idea for modelling time series using higher-order ordinary differential equations (HODEs) models. Accordingly, a dynamic hybrid evolutionary modeling algorithm called DHEMA is proposed to approach this task. Its main idea is to embed a genetic algorithm (GA) into genetic programming (GP) where GP is employed to optimise the structure of a model, while a GA is employed to optimize its parameters. By running the DHEMA, the modeling and predicting processes can be carried on successively and dynamically with the renewing of observed data. Two practical examples are used to examine the effectiveness of the algorithm in performing the prediction task of time series whose experimental results are compared with those of standard GP.", } @InProceedings{Cao:2003:Aeafmtecfeis, author = "Hongqing Cao and Jingxian Yu and Lishan Kang", title = "An evolutionary approach for modeling the equivalent circuit for electrochemical impedance spectroscopy", booktitle = "Proceedings of the 2003 Congress on Evolutionary Computation CEC2003", editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon", pages = "1819--1825", year = "2003", publisher = "IEEE Press", address = "Canberra", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "8-12 " # dec, organisation = "IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", ISBN = "0-7803-7804-0", notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.", keywords = "genetic algorithms, genetic programming, Gene Expression Programming, GEP, HEMA, Chemistry, Electrochemical impedance spectroscopy, Equivalent circuits, Gene expression, Laboratories, Linear programming, Programming profession, Software engineering, electrochemical impedance spectroscopy, equivalent circuits, component parameters, electrochemical impedance spectroscopy, equivalent circuit, hybrid evolutionary modelling, parameter optimisation", URL = "http://www.ees.adelaide.edu.au/people/enviro/cao/2003-05.pdf", DOI = "doi:10.1109/CEC.2003.1299893", abstract = "This paper proposes an evolutionary approach to build the equivalent circuit model for electrochemical impedance spectroscopy. It works by using a hybrid evolutionary modelling algorithm (HEMA) whose main idea is to embed a genetic algorithm (GA) in gene expression programming (GEP) where GEP is employed to discover and optimise the structure of a circuit, while the GA is employed to optimize the parameters of all the electric components contained in the circuit. By running the HEMA, the computer can automatically find suitable circuit structures as well as optimise the component parameters simultaneously. Compared with most available methods, it has the advantages of automation of modeling process, great diversity of model structures, high stability and efficiency of parameter optimisation.", size = "7 pages", } @Article{Cao:2006:EI, author = "Hongqing Cao and Friedrich Recknagel and Gea-Jae Joo and Dong-Kyun Kim", title = "Discovery of Predictive Rule Sets for Chlorophyll-a Dynamics in the Nakdong River (Korea) by Means of the Hybrid Evolutionary Algorithm HEA", journal = "Ecological Informatics", year = "2006", volume = "1", number = "1", pages = "43--53", month = jan, keywords = "genetic algorithms, genetic programming, Hybrid evolutionary algorithm, Rule sets, Chl.a, Sensitivity analysis, Nakdong River", ISSN = "1574-9541", DOI = "doi:10.1016/j.ecoinf.2005.08.001", size = "11 pages", abstract = "We present a hybrid evolutionary algorithm (HEA) to discover complex rule sets predicting the concentration of chlorophyll-a (Chl.a) based on the measured meteorological, hydrological and limnological variables in the hypertrophic Nakdong River. The HEA is designed: (1) to evolve the structure of rule sets by using genetic programming and (2) to optimise the random parameters in the rule sets by means of a genetic algorithm. Time-series of input-output data from 1995 to 1998 without and with time lags up to 7 days were used for training HEA. Independent input output data for 1994 were used for testing HEA. HEA successfully discovered rule sets for multiple nonlinear relationships between physical, chemical variables and Chl.a, which proved to be predictive for unseen data as well as explanatory. The comparison of results by HEA and previously applied recurrent artificial neural networks to the same data with input--output time lags of 3 days revealed similar good performances of both methods. The sensitivity analysis for the best performing predictive rule set revealed relationships between seasons, specific input variables and Chl.a which to some degree correspond with known properties of the Nakdong River. The statistics of numerous random runs of the HEA also allowed determining most relevant input variables without a priori knowledge.", notes = "http://www.elsevier.com/wps/find/journaldescription.cws_home/705192/description#description", } @InCollection{Cao:2006:2lakes, author = "Hongqing Cao and Friedrich Recknagel and Bomchul Kim and Noriko Takamura", title = "Hybrid Evolutionary Algorithm for Rule Set Discovery in Time-Series Data to Forecast and Explain Algal Population Dynamics in Two Lakes Different in Morphometry and Eutrophication", booktitle = "Ecological Informatics: Scope, Techniques and Applications", publisher = "Springer-Verlag", year = "2006", editor = "Friedrich Recknagel", chapter = "17", pages = "347--367", address = "Berlin, Heidelberg, New York", edition = "2nd", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-28383-8", DOI = "doi:10.1007/3-540-28426-5_17", abstract = "A hybrid evolutionary algorithm (HEA) has been developed to discover predictive rule sets in complex ecological data. It has been designed to evolve the structure of rule sets by using genetic programming and to optimise the random parameters in the rule sets by means of a genetic algorithm. HEA was successfully applied to long-term monitoring data of the shallow, eutrophic Lake Kasumigaura (Japan) and the deep, mesotrophic Lake Soyang (Korea). The results have demonstrated that HEA is able to discover rule sets, which can forecast for 7-days-ahead seasonal abundances of blue-green algae and diatom populations in the two lakes with relatively high accuracy but are also explanatory for relationships between physical, chemical variables and the abundances of algal populations. The explanations and the sensitivity analysis for the best rule sets correspond well with theoretical hypotheses and experimental findings in previous studies.", notes = "http://www.springer.com/sgw/cda/frontpage/0,11855,5-10031-22-68637391-0,00.html", } @Article{Cao2008181, author = "Hongqing Cao and Friedrich Recknagel and Lydia Cetin and Byron Zhang", title = "Process-based simulation library SALMO-OO for lake ecosystems. Part 2: Multi-objective parameter optimization by evolutionary algorithms", journal = "Ecological Informatics", volume = "3", number = "2", pages = "181--190", year = "2008", ISSN = "1574-9541", DOI = "doi:10.1016/j.ecoinf.2008.02.001", URL = "http://www.sciencedirect.com/science/article/B7W63-4S69SG8-1/2/95e920ec339c554888f67696a93f2f37", keywords = "genetic algorithms, genetic programming, Multi-objective parameter optimization, SALMO-OO, Lake categories, Evolutionary algorithms", abstract = "SALMO-OO represents an object-oriented simulation library for lake ecosystems that allows to determine generic model structures for certain lake categories. It is based on complex ordinary differential equations that can be assembled by alternative process equations for algal growth and grazing as well as zooplankton growth and mortality. It requires 128 constant parameters that are causally related to the metabolic, chemical and transport processes in lakes either estimated from laboratory and field experiments or adopted from the literature. An evolutionary algorithm (EA) was integrated into SALMO-OO in order to facilitate multi-objective optimization for selected parameters and to substitute them by optimum temperature and phosphate functions. The parameters were related to photosynthesis, respiration and grazing of the three algal groups diatoms, green algae and blue-green algae. The EA determined specific temperature and phosphate functions for same parameters for 3 lake categories that were validated by ecological data of six lakes from Germany and South Africa. The results of this study have demonstrated that: (1) the hybridization of ordinary differential equations by EA provide a sophisticated approach to fine-tune crucial parameters of complex ecological models, and (2) the multi-objective parameter optimization of SALMO-OO by EA has significantly improved the accuracy of simulation results for three different lake categories.", } @Article{Cao:2014:ieeeEC, author = "Hongqing Cao and Friedrich Recknagel and Philip T. Orr", title = "Parameter Optimization Algorithms for Evolving Rule Models Applied to Freshwater Ecosystems", journal = "IEEE Transactions on Evolutionary Computation", year = "2014", month = dec, volume = "18", number = "6", pages = "793--806", keywords = "genetic algorithms, genetic programming, Evolutionary algorithm, cyanobacterial blooms, population-based algorithms", DOI = "doi:10.1109/TEVC.2013.2286404", ISSN = "1089-778X", size = "20 pages", abstract = "Predictive rule models for early warning of cyanobacterial blooms in freshwater ecosystems were developed using a hybrid evolutionary algorithm (HEA). The HEA has been designed to evolve IF-THEN-ELSE model structures using genetic programming and to optimise the stochastical constants contained in the model using population-based algorithms. This paper intensively investigated the performances of the following six alternative population-based algorithms for parameter optimisation (PO) of rule models within this hybrid methodology: (1) Hill Climbing (HC), (2) Simulated Annealing (SA), (3) Genetic Algorithm (GA), (4) Differential Evolution (DE), (5) Covariance Matrix Adaptation Evolution Strategy (CMA-ES), and (6) Estimation of Distribution Algorithm (EDA). The comparative study was carried out by predictive modelling of chlorophyll-a concentrations and the potentially toxic cyanobacterium Cylindrospermopsis raciborskii cell concentrations based on water quality time-series data in Lake Wivenhoe in Queensland (Australia) from 1998 to 2009. The experimental results demonstrate that with these PO methods, the rule models discovered by the HEA proved to be both predictive and explanatory whose IF condition indicates threshold values for some crucial water quality parameters. When Comparing different PO algorithms, HC always performed best followed by DE, GA and EDA. Whilst CMA-ES performed worst and the performance of SA varied with different data sets.", notes = "Also known as \cite{6637056}", } @Article{Cao:2016:EM, author = "Hongqing Cao and Friedrich Recknagel and Michael Bartkow", title = "Spatially-explicit forecasting of cyanobacteria assemblages in freshwater lakes by multi-objective hybrid evolutionary algorithms", journal = "Ecological Modelling", volume = "342", pages = "97--112", year = "2016", ISSN = "0304-3800", DOI = "doi:10.1016/j.ecolmodel.2016.09.024", URL = "http://www.sciencedirect.com/science/article/pii/S0304380016304938", abstract = "This paper proposes a novel multi-objective hybrid evolutionary algorithm (MOHEA) that allows spatially-explicit modelling of local outbreaks and dispersal of population density. The MOHEA was tested for modelling at once two cyanobacteria populations at one lake site, same population in two different lakes and same population at three different sites of one lake. All experiments with MOHEA used water quality time-series and abundances of Anabaena and Cylindrospermopsis monitored in the sub-tropical Lakes Wivenhoe and Somerset in Queensland (Australia) from 1999 to 2010. Results have demonstrated the capacity of MOHEA to determine generic rules that: (1) reveal crucial thresholds for outbreaks of cyanobacteria blooms, and (2) perform spatially-explicit forecasting of timing and magnitudes 7-day-ahead of bloom events.", keywords = "genetic algorithms, genetic programming, Multi-objective optimization, Hill climbing, Multi-output rule models, Cyanobacteria blooms", } @Article{CAO:2021:AIA, author = "Hongliang Cao and Yaime Jefferson Milan and Sohrab Haghighi Mood and Michael Ayiania and Shu Zhang and Xuzhong Gong and Electo Eduardo Silva Lora and Qiaoxia Yuan and Manuel Garcia-Perez", title = "A novel elemental composition based prediction model for biochar aromaticity derived from machine learning", journal = "Artificial Intelligence in Agriculture", year = "2021", volume = "5", pages = "133--141", keywords = "genetic algorithms, genetic programming, Biochar, C aromaticity, Prediction model, Machine learning", ISSN = "2589-7217", URL = "https://www.sciencedirect.com/science/article/pii/S2589721721000210", DOI = "doi:10.1016/j.aiia.2021.06.002", abstract = "The measurement of aromaticity in biochars is generally conducted using solid state 13C nuclear magnetic resonance spectroscopy, which is expensive, time-consuming, and only accessible in a small number of research-intensive universities. Mathematical modelling could be a viable alternative to predict biochar aromaticity from other much easier accessible parameters (e.g. elemental composition). In this research, Genetic Programming (GP), an advanced machine learning method, is used to develop new prediction models. In order to identify and evaluate the performance of prediction models, an experimental data set with 98 biochar samples collected from the literature was used. Due to the benefits of the intelligence iteration and learning of GP algorithm, a kind of underlying exponential relationship between the elemental compositions and the aromaticity of biochars is disclosed clearly. The exponential relationship is clearer and simpler than the polynomial mapping relationships implicated by Maroto-Valer, Mazumdar, and Mazumdar-Wang models. In this case, a novel exponential model is proposed for the prediction of biochar aromaticity. The proposed exponential model appears better prediction accuracy and generalization ability than existing polynomial models during the statistical parameter evaluation", } @InProceedings{cao:1999:CMSUNN, author = "Lijuan Cao and Tay Eng Hock (Francis) and Ma Lawrence and Wai Cheong Yeong", title = "Classification of the Market States Using Neural Network", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "776", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems, poster papers", ISBN = "1-55860-611-4", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{cao:1999:NBMCANACS, author = "Lijuan Cao and Tay Eng Hock (Francis)", title = "Neuro-Genetic Based Method to the Classification of Acupuncture Needle: A Case Study", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "99--105", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Cao:2019:AIChE, author = "Liwei Cao and Pascal Neumann and Danilo Russo and Vassilios S. Vassiliadis and Alexei A. Lapkin", title = "Symbolic Regression for the Automated Physical Model Identification in Reaction Engineering", booktitle = "2019 AIChE Annual Meeting", year = "2019", pages = "443c", address = "Orlando, FL, USA", month = "13 " # nov, publisher = "American Institute of Chemical Engineers", keywords = "genetic algorithms, genetic programming, Process Design and Development, Chemical Reaction Engineering", isbn13 = "978-0-8169-1112-7", URL = "https://www.aiche.org/conferences/aiche-annual-meeting/2019/proceeding/paper/443c-symbolic-regression-automated-physical-model-identification-reaction-engineering", abstract = "Understanding of a complex reaction system at a fundamental level is crucial as it reduces the time and resources required for process development and implementation at scale. The two distinct paradigms in developing fundamental knowledge of a chemical system start from either experimental observations (data-driven modeling), or from mechanistic a priori knowledge (physical models). With the rise of automation and tremendous modern advancements in data science the two approaches are gradually merging, although model identification for multivariable complex systems remains challenging in practice. In this work, the identification of interpretable and generalisable physical models is targeted by means of automatable, data-driven methods without a priori knowledge. A revised mixed-integer nonlinear programming (MINLP) formulation is proposed for symbolic regression (SR) to identify physical models from noisy experimental data. The identification of interpretable and generalizable models was enabled by assessing model complexity and extrapolation capability. The method is demonstrated by successful application for the identification of a kinetic model of the 4-nitrophenyl acetate (PNPA) hydrolysis reaction.", notes = "is this GP? Applications of Data Science in Catalysis and Reaction Engineering II https://www.aiche.org/academy/conferences/aiche-annual-meeting/2019/proceeding Cambridge Centre for Advanced Research and Education in Singapore (CARES) Ltd", } @PhdThesis{PhD_Thesis_Liwei_Cao_revised_version, author = "Liwei Cao", title = "Combining artificial intelligence and robotic system in chemical product/process design", school = "Department of Chemical Engineering and Biotechnology, University of Cambridge", year = "2021", address = "UK", month = jul, keywords = "genetic algorithms, genetic programming, symbolic regression, machine learning, closed-loop optimization, artificial intelligence, formulated product design, automated experimental platform, physical model identification, feature engineering", URL = "https://www.repository.cam.ac.uk/handle/1810/329408", URL = "https://www.repository.cam.ac.uk/bitstream/handle/1810/329408/PhD_Thesis_Liwei_Cao_revised_version.pdf", DOI = "doi:10.17863/CAM.76857", size = "208 pages", abstract = "Product design for formulations is an active and challenging area of research. The new challenges of a fast-paced market, products of increasing complexity, and practical translation of sustainability paradigms require re-examination the existing theoretical frameworks to include the advantages from business and research digitalisation. This thesis is based on the hypotheses that (i) new products with desired properties can be discovered by using a robotic platform combined with an intelligent optimization algorithm, and (ii) we can the connect data-driven optimisation with physico-chemical knowledge generation, which will result in a suitable model for translation of product discovery to production, thus impacting on the process development steps towards industrial applications. This thesis focuses on two complex physico chemical systems as case studies, namely the oil-in-water shampoo system and sunscreen products. Firstly, I report the coupling of a machine-learning classification algorithm with the Thompson-Sampling Efficient Multi-Optimization (TSEMO) for the simultaneous optimisation of continuous and discrete outputs. The methodology was successfully applied to the design of a formulated liquid product of commercial interest for which no physical models are available. Experiments were carried out in a semi-automated fashion using robotic platforms triggered by the machine-learning algorithms. The proposed closed-loop optimisation frame-work allowed to find suitable recipes meeting the customer-defined criteria within 15 working days, out performing human intuition in the target performance of the formulations. The framework was then extended to co-optimization of both formulation and process conditions and ingredients selection. Secondly, I report the methods for the identification of new physical knowledge in a complex system where a prior knowledge is insufficient. The application of feature engineering methods in sun cream protection prediction was discussed. It was found that the concentration of UVA and UVB filters are key features, together with product viscosity,which match with the experts' domain knowledge in sun cream product design. It was also found that through the combination of feature engineering and machine learning, high-fidelity model could be constructed. Furthermore, a modified mixed-integer nonlinear programming (MINLP) formulation for symbolic regression method was proposed for identification of physical models from noisy experimental data. The globally optimal search was extended to identify physical models and to cope with noise in the experimental data predictor variables. The methodology was proven to be successful in identifying the correct physical models describing the relationship between shear stress and shear rate for both Newtonian and non-Newtonian fluids, and simple kinetic laws of chemical reactions. The work of this thesis shows that machine learning methods, together with automated experimental system, can speed-up the R&D process of formulated product design as well as gain new physical knowledge of the complex systems.", notes = "Also known as \cite{cao_2021} Churchill College. MINLP. BASF Shanghai and BASF Ludwigshafen. Supervisor: Alexei A. Lapkin", } @InProceedings{Cao:2016:EuroGP, author = "Van Loi Cao and Miguel Nicolau and James McDermott", title = "One-class Classification for Anomaly Detection with Kernel Density Estimation and Genetic Programming", booktitle = "EuroGP 2016: Proceedings of the 19th European Conference on Genetic Programming", year = "2016", month = "30 " # mar # "--1 " # apr, editor = "Malcolm I. Heywood and James McDermott and Mauro Castelli and Ernesto Costa and Kevin Sim", series = "LNCS", volume = "9594", publisher = "Springer Verlag", address = "Porto, Portugal", pages = "3--18", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, Anomaly detection, One-class classification, Kernel Density Estimation", isbn13 = "978-3-319-30668-1", DOI = "doi:10.1007/978-3-319-30668-1_1", abstract = "A novel approach is proposed for fast anomaly detection by one-class classification. Standard kernel density estimation is first used to obtain an estimate of the input probability density function, based on the one-class input data. This can be used for anomaly detection: query points are classed as anomalies if their density is below some threshold. The disadvantage is that kernel density estimation is lazy, that is the bulk of the computation is performed at query time. For large datasets it can be slow. Therefore it is proposed to approximate the density function using genetic programming symbolic regression, before imposing the threshold. The runtime of the resulting genetic programming trees does not depend on the size of the training data. The method is tested on datasets including in the domain of network security. Results show that the genetic programming approximation is generally very good, and hence classification accuracy approaches or equals that when using kernel density estimation to carry out one-class classification directly. Results are also generally superior to another standard approach, one-class support vector machines.", notes = "Part of \cite{Heywood:2016:GP} EuroGP'2016 held in conjunction with EvoCOP2016, EvoMusArt2016 and EvoApplications2016", } @InProceedings{conf/evoW/CaoLONM16, author = "Van Loi Cao and Nhien-An Le-Khac and Michael O'Neill and Miguel Nicolau and James McDermott", title = "Improving Fitness Functions in Genetic Programming for Classification on Unbalanced Credit Card Data", booktitle = "19th European Conference on Applications of Evolutionary Computation, EvoApplications 2016", year = "2016", editor = "Giovanni Squillero and Paolo Burelli", volume = "9597", series = "Lecture Notes in Computer Science", pages = "35--45", address = "Porto, Portugal", month = mar # " 30 -- " # apr # " 1", organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Class imbalance, Credit card data, Fitness functions", bibdate = "2016-03-23", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/evoW/evoappl2016-1.html#CaoLONM16", isbn13 = "978-3-319-31204-0", DOI = "doi:10.1007/978-3-319-31204-0_3", abstract = "Credit card classification based on machine learning has attracted considerable interest from the research community. One of the most important tasks in this area is the ability of classifiers to handle the imbalance in credit card data. In this scenario, classifiers tend to yield poor accuracy on the minority class despite realizing high overall accuracy. This is due to the influence of the majority class on traditional training criteria. In this paper, we aim to apply genetic programming to address this issue by adapting existing fitness functions. We examine two fitness functions from previous studies and develop two new fitness functions to evolve GP classifiers with superior accuracy on the minority class and overall. Two UCI credit card datasets are used to evaluate the effectiveness of the proposed fitness functions. The results demonstrate that the proposed fitness functions augment GP classifiers, encouraging fitter solutions on both the minority and the majority classes.", notes = "EvoApplications2016 held inconjunction with EuroGP'2016, EvoCOP2016 and EvoMUSART 2016", } @Misc{journals/corr/CaoLNOM17, author = "Van Loi Cao and Nhien-An Le-Khac and Miguel Nicolau and Michael O'Neill and James McDermott", title = "Improving Fitness Functions in Genetic Programming for Classification on Unbalanced Credit Card Datasets", year = "2017", volume = "abs/1704.03522", keywords = "genetic algorithms, genetic programming", bibdate = "2017-06-07", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/corr/corr1704.html#CaoLNOM17", URL = "http://arxiv.org/abs/1704.03522", } @Article{Cao:2015:ASC, author = "Yilong Cao and Peter I. Rockett", title = "The Use of Vicinal-Risk Minimization for Training Decision Trees", journal = "Applied Soft Computing", year = "2015", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2015.02.043", URL = "http://www.sciencedirect.com/science/article/pii/S1568494615001507", abstract = "We propose the use of Vapnik's vicinal risk minimisation (VRM) for training decision trees to approximately maximise decision margins. We implement VRM by propagating uncertainties in the input attributes into the labelling decisions. In this way, we perform a global regularisation over the decision tree structure. During a training phase, a decision tree is constructed to minimise the total probability of classifying the labelled training examples, a process which approximately maximises the margins of the resulting classifier. We perform the necessary minimisation using an appropriate meta-heuristic (genetic programming) and present results over a range of synthetic and benchmark real datasets. We demonstrate the statistical superiority of VRM training over conventional empirical risk minimisation (ERM) and the well-known C4.5 algorithm, for a range of synthetic and real datasets. We also conclude that there is no statistical difference between trees trained by ERM and using C4.5. Training with VRM is shown to be more stable and repeatable than by ERM.", keywords = "genetic algorithms, genetic programming, Decision trees, Vicinal-risk minimisation, Decision trees, Classification", } @Article{DBLP:journals/itc/CaoHMC22, author = "Heling Cao and Zhenghaohe He and Yangxia Meng and Yonghe Chu", title = "Automatic Repair of Java Programs Weighted Fusion Similarity via Genetic Programming", journal = "Information Technology and Control", volume = "51", number = "4", pages = "738--756", year = "2022", keywords = "genetic algorithms, genetic programming, genetic improvement, APR", ISSN = "1392-124X", URL = "https://doi.org/10.5755/j01.itc.51.4.30515", DOI = "doi:10.5755/j01.itc.51.4.30515", timestamp = "Tue, 17 Jan 2023 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/itc/CaoHMC22.bib", bibsource = "dblp computer science bibliography, https://dblp.org", size = "19 pages", abstract = "Recently, automated program repair techniques have been proven to be useful in the process of software development. However, how to reduce the large search space and the random of ingredient selection is still a challenging problem. In this paper, we propose a repair approach for buggy program based on weighted fusion similarity and genetic programming. Firstly, the list of modification points is generated by selecting modification points from the suspicious statements. Secondly, the buggy repair ingredient is selected according to the value of the weighted fusion similarity, and the repair ingredient is applied to the corresponding modification points according to the selected operator. Finally, we use the test case execution information to prioritize the test cases to improve individual verification efficiency. We have implemented our approach as a tool called WSGRepair. We evaluate WSGRepair in Defects4J and compare with other program repair techniques. Experimental results show that our approach improve the success rate of buggy program repair by 28.6percent, 64percent, 29percent, 64percent and 112percent compared with the GenProg, CapGen, SimFix, jKali and jMutRepair.", } @Article{cao:2023:AS, author = "Heling Cao and Dong Han and Fangzheng Liu and Tianli Liao and Chenyang Zhao and Jianshu Shi", title = "Code Similarity and {Location-Awareness} Automatic Program Repair", journal = "Applied Sciences", year = "2023", volume = "13", number = "14", pages = "Article No. 8519", keywords = "genetic algorithms, genetic programming, genetic improvement, APR", ISSN = "2076-3417", URL = "https://www.mdpi.com/2076-3417/13/14/8519", DOI = "doi:10.3390/app13148519", abstract = "Automatic program repair has drawn more and more attention since software quality is facing increasing challenges. In existing approaches, the unlimited search space is considered to be the main limitation in finding the correct patch. So how to reduce the search space to improve the efficiency of automatic program repair remains a problem to be solved. In this work, we represent a similarity-based and location-awareness-based automatic program repair (SLARepair). SLARepair takes the similarity between codes as important search information. The search space is further subdivided by the location-awareness strategy to improve search efficiency. In addition, to better guide the search process, a new fitness function is designed for genetic programming, which brings notable improvements. Moreover, the patch verification time is further reduced by using the test case prioritization approach combined with test case filtering. Extensive experiments demonstrate that our SLARepair outperforms the state-of-the-art approaches on the Defects4J benchmark and achieves competitive performances.", notes = "also known as \cite{app13148519}", } @InProceedings{cao:2024:ICONIP, author = "Lulu Cao and Zimo Zheng and Chenwen Ding and Jinkai Cai and Min Jiang", title = "Genetic Programming Symbolic Regression with Simplification-Pruning Operator for Solving Differential Equations", booktitle = "International Conference on Neural Information Processing", year = "2023", pages = "287--298", address = "Changsha, China", month = nov # " 20-23", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-981-99-8132-8_22", DOI = "doi:10.1007/978-981-99-8132-8_22", notes = "Published in 2024 School of Informatics, Xiamen University, Fujian, China", } @InProceedings{cao:2023:FC, author = "Pu Cao and Yan Pei and Jianqiang Li", title = "Symbolic Regression Using Genetic Programming with Chaotic {Method-Based} Probability Mappings", booktitle = "Frontier Computing on Industrial Applications Volume 4", year = "2023", volume = "1134", series = "LNEE", pages = "300--312", address = "Tokyo, Japan", month = "10-13 " # jul, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-981-99-9341-3", URL = "http://link.springer.com/chapter/10.1007/978-981-99-9342-0_32", DOI = "doi:10.1007/978-981-99-9342-0_32", abstract = "we propose a novel pre-learning approach for genetic programming (GP) that aims to investigate the effect of the probability of being selected for each operator. Furthermore, we present a technique that combines chaos theory and searches for a relatively good possibility mapping for each operator using one-dimensional chaotic mapping. We conducted several sets of comparative experiments on real-world data to test the viability of the proposal. These experiments included comparisons with conventional GP, examination of the impact of various chaotic mappings on the proposed algorithm, and implementation of different optimization strategies to find the relative optimal probability mapping. The experimental results demonstrate that the proposed method can achieve better results than conventional GP in the tested dataset, without considering the total quantitative calculation amount. Through statistical tests, it has been proven that the proposed method is significantly different from the conventional method. However, the discussion regarding the circumstances under which the proposed method can obtain better results when the total calculation amount is limited is not yet fully explored due to the small-scale nature of the experiments. Our future studies will focus on improving and fully discussing this idea.", notes = "Published 2024 Graduate School of Computer Science and Engineering, University of Aizu, Aizuwakamatsu, Fukushima, 965-8580, Japan", } @Article{Cao:2024:ijfatigue, author = "Weiwen Cao and Xingyue Sun and Xu Chen", title = "Estimation and validation for fatigue properties of steels by symbolic regression", journal = "International Journal of Fatigue", year = "2024", volume = "186", pages = "108416", keywords = "genetic algorithms, genetic programming, Fatigue properties, Symbolic regression, Fatigue life prediction, Machine learning", ISSN = "0142-1123", URL = "https://www.sciencedirect.com/science/article/pii/S0142112324002743", DOI = "doi:10.1016/j.ijfatigue.2024.108416", abstract = "Estimating fatigue properties through basic mechanical properties is an expectation for achieving fatigue life prediction in engineering applications. This work aims to employ symbolic regression (SR) methods to explore the relationship among 82 types of steel. With the help of genetic programming and multi-population evolution algorithms, two SR models are established and compared with seven semi-empirical methods and four machine learning models. By comparison, the SR method offers the best balance in terms of prediction performance and interpretability for fatigue properties. Furthermore, based on fatigue properties obtained by SR methods, the predicted fatigue life of up to 91.1percent of the samples can be located within the 2-factor band", } @Article{Caparelli:2009:waset, author = "Paulo S. Caparelli and Eduardo Costa and Alexsandro S. Soares and Hipolito Barbosa", title = "Pattern Recognition of Biological Signals", journal = "International Science Index", year = "2009", volume = "3", number = "3", pages = "824--832", keywords = "genetic algorithms, genetic programming, pattern recognition, evolutionary computation, biological signal, functional programming", URL = "http://waset.org/publications/15962", URL = "http://waset.org/Publications?p=27", bibsource = "http://waset.org/Publications", ISSN = "1307-6892", publisher = "World Academy of Science, Engineering and Technology", URL = "http://waset.org/publications/15962", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.309.1795", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.309.1795", size = "9 pages", abstract = "This paper presents an evolutionary method for designing electronic circuits and numerical methods associated with monitoring systems. The instruments described here have been used in studies of weather and climate changes due to global warming, and also in medical patient supervision. Genetic Programming systems have been used both for designing circuits and sensors, and also for determining sensor parameters. The authors advance the thesis that the software side of such a system should be written in computer languages with a strong mathematical and logic background in order to prevent software obsolescence, and achieve program correctness.", notes = "International Science Index 27, 2009", } @Article{Capcarrece:2004:BS, author = "Mathieu S. Capcarrece", title = "An evolving ontogenetic cellular system for better adaptiveness", journal = "Biosystems", year = "2004", volume = "76", pages = "177--189", number = "1-3", abstract = "we present an original cellular system named Phuon. The main motivation behind this project is to go beyond classical cellular systems, such as cellular automata (CA). CA often lack adaptability and turn out to be very brittle in uncertain environment. The idea here is to add ontogeny to cellularity, growth and development being means of adaptation and thus robustness. However, we do not wish to develop yet another cellular system for the sake of it. What we are seeking in the long term is a developmental system for problem solving. This global aim enticed us into finding a way to map a desired global behaviour of the system to the local behaviour of a cell. Quite naturally a peculiar brand of genetic programming was used for that purpose. The results are still preliminary but in our view they already validate some of the hypotheses behind this work.", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6T2K-4D1R6V6-2/2/ceb26b0139eed613393486f88bc2ac23", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1016/j.biosystems.2004.05.020", notes = "Papers presented at the Fifth International Workshop on Information Processing in Cells and Tissues PMID: 15351141 [PubMed - indexed for MEDLINE]", } @Proceedings{DBLP:conf/ecal/2005, editor = "Mathieu S. Capcarrere and Alex Alves Freitas and Peter J. Bentley and Colin G. Johnson and Jon Timmis", title = "8th European Conference on Advances in Artificial Life, ECAL 2005", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3630", year = "2005", ISBN = "3-540-28848-1", address = "Canterbury, UK", month = sep # " 5-9", bibsource = "DBLP, http://dblp.uni-trier.de", } @InCollection{caplan:2004:GPTP, author = "Michael Caplan and Ying Becker", title = "Lessons Learned Using Genetic Programming in a Stock Picking Context", booktitle = "Genetic Programming Theory and Practice {II}", year = "2004", editor = "Una-May O'Reilly and Tina Yu and Rick L. Riolo and Bill Worzel", chapter = "6", pages = "87--102", address = "Ann Arbor", month = "13-15 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, stock selection, data mining, fitness functions, quantitative portfolio management", ISBN = "0-387-23253-2", DOI = "doi:10.1007/0-387-23254-0_6", abstract = "This is a narrative describing the implementation of a genetic programming technique for stock picking in a quantitatively driven, risk-controlled, US equity portfolio. It describes, in general, the problems that the authors faced in their portfolio context when using genetic programming techniques and in gaining acceptance of the technique by a skeptical audience. We discuss in some detail the construction of the fitness function, the genetic programming system's parametrisation (including data selection and internal function choice), and the interpretation and modification of the generated programs for eventual implementation.", notes = "part of \cite{oreilly:2004:GPTP2}", } @InProceedings{car:2021:SSM, author = "Zlatan Car and Sandi {Baressi Segota} and Nikola Andelic and Ivan Lorencin and Jelena Musulin and Daniel Stifanic and Vedran Mrzljak", title = "Determining inverse kinematics of a serial robotic manipulator through the use of genetic programming algorithm", booktitle = "18th International Congress of Serbian Society of Mechanics", year = "2021", editor = "Nenad Filipovic and Milos Kojic", address = "Kragujevac, Serbia", month = jun # " 28-30", keywords = "genetic algorithms, genetic programming, industrial robotics, inverse kinematics", URL = "https://www.bib.irb.hr:8443/1133808/download/1133808.Contribution_template_2021.edited.pdf", size = "6 pages", abstract = "Inverse kinematics is one of the key parts of any industrial robotic manipulator modeling.Solving the inverse kinematics of a robotic manipulator in the classical analytical manner is fairly complex and error-prone. While previous research has shown the possibility of AI application for inverse kinematics solutions, such models have certain pitfalls which makes the probability of their use low. For this reason, the authors propose Genetic Programming (GP), which is an AI method that generates models in the shapes of equations. The application of the algorithm in question shows promise, with errors being lower than 0.5 degrees for all regressed joints when evaluated using Mean Absolute Error (MAE). This points towards the fact that GP could be used in such an approach but some details of the algorithm need to be addressed, such as the tendency to generate large and simplifiable equations, or lower precision when compared to previous AI-based solutions to the same problem.", notes = "http://www.ssm.kg.ac.rs/congress_2021/ Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia", } @InProceedings{conf/icinco/CarabaliT0CC17, author = "Carmen Alicia Carabali and Luis Tituana and Jose Aguilar and Oscar Camacho and Danilo Chavez", title = "Inverse Response Systems Identification using Genetic Programming", booktitle = "Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2017, Madrid, Spain, July 26-28, 2017, Volume 1", publisher = "SciTePress", year = "2017", editor = "Oleg Gusikhin and Kurosh Madani", pages = "238--245", keywords = "genetic algorithms, genetic programming, inverse response, system identification", bibdate = "2017-12-14", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icinco/icinco2017-1.html#CarabaliT0CC17", isbn13 = "978-989-758-263-9", URL = "http://www.scitepress.org/DigitalLibrary/ProceedingsDetails.aspx?ID=Hxr/q2f7PZ4=", DOI = "doi:10.5220/0006421602380245", abstract = "In this paper, we apply genetic programming as a tool for identifying an inverse response system. In previous works, the genetic programming has been used in the context of identification problems, where the goal is to obtain the descriptions of a given system. Identification problems have been studied much from control theory, due to their practical application in industry. In some cases, a description of a system in terms of mathematical equations is not possible, for these cases are necessary new heuristic approaches like the genetic programming. Here, we like to test the quality of the genetic programming to identify inverse response systems, which are systems where the initial response is in a direction opposite to the final outcome. The tool used to develop the model of identification is GPTIPS V2, we use our approach in two cases: in the first one, the equation that describes inverse response system is determined; and in the second case, the transfer function of the system in the frequency domain is found.", affiliations = "Escuela Politecnica Nacional and Universidad de Los Andes, Ecuador ; 2 Escuela Politecnica Nacional, Ecuador", } @InProceedings{garcia:1999:efrbcGAPga, author = "Santiago Garcia and Fermin Gonzalez and Luciano Sanchez", title = "Evolving Fuzzy Rule Based Classifiers with {GA-P}: A Grammatical Approach", booktitle = "Genetic Programming, Proceedings of EuroGP'99", year = "1999", editor = "Riccardo Poli and Peter Nordin and William B. Langdon and Terence C. Fogarty", volume = "1598", series = "LNCS", pages = "203--210", address = "Goteborg, Sweden", publisher_address = "Berlin", month = "26-27 " # may, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-65899-8", DOI = "doi:10.1007/3-540-48885-5_17", abstract = "Genetic Programming can be used to evolve Fuzzy Rulebased classifiers [7]. Fuzzy GP depends on a grammar defining valid expressions of fuzzy classifiers, and guarantees that all individuals in the population are valid instances of it all along the evolution process. This is accomplished by restricting crossover and mutation so that they only take place at points of the derivation tree representing the same non-terminal, thus generating valid subtrees [13]. In Fuzzy GP, terminal symbols are fuzzy constants and variables that are chosen beforehand. In this work we propose a method for evolving both fuzzy membership functions of the variables and the Rule Base. Our method extends the GA-P hybrid method [6] by introducing a new grammar with two functional parts, one for the Fuzzy Rule Base (GP Part), and the other for the constants that define the shapes of the fuzzy sets involved in the Fuzzy Rule Base (GA Part). We have applied this method to some classical benchmarks taken from the collection of test data at the UCI Repository of Machine Learning Databases [9].", notes = "EuroGP'99, part of \cite{poli:1999:GP} Combination of grammar based GP and GA-P with fuzzy rules. UCI machine learning databases First author is Santiago Garcia Carbajal", } @Article{carbajal:2001:GPEM, author = "Santiago {Garcia Carbajal} and Fermin Gonzalez Martinez", title = "Evolutive Introns: A Non-Costly Method of Using Introns in GP", journal = "Genetic Programming and Evolvable Machines", year = "2001", volume = "2", number = "2", pages = "111--122", month = jun, keywords = "genetic algorithms, genetic programming, bloating, introns, intertwined spirals", ISSN = "1389-2576", DOI = "doi:10.1023/A:1011548229751", abstract = "We proposed a new strategy to explicitly define introns that increases the probability of selecting good crossover points as evolution goes on. Our approach differs from existing methods in the procedure followed to adapt the probabilities of groups of code being protected. We also provide some experimental results in symbolic regression and classification that reinforced our belief in the usefulness of this procedure. Collateral effects of Evolutive Introns (EIs) are also studied to determine possible modifications in the behavior of a classical Genetic Programming (GP) system.", notes = "Article ID: 335711", } @PhdThesis{GarciaCarbajal:thesis, author = "Santiago {Garcia Carbajal}", title = "Identificacion automatica de objetivos parciales mediante logica borrosa y programacion genetica dirigida por gramatica", title2 = "Automatic Identification of Partial Goals with Grammar-Directed Genetic Programming", school = "Faculty of Informatics. GIJON, Universidad de Oviedo", year = "2002", address = "Spain", email = "santi.carbajal@gmail.com", keywords = "genetic algorithms, genetic programming, algorithms, grammar directed GP", URL = "https://dialnet.unirioja.es/servlet/tesis?codigo=8570", size = "180 pages", abstract = "Automatic Defined Functions (ADFs) concept is expanded with the use of Grammar Directed Genetic Programming. The approach is applied to classical regression problems and control systems.", resumen = "En el caso de aprendizaje por refuerzo existen dos enfoques que se han venido utilizando sobre problemas de control y planfificacion, En este tesis se aplican tecnicas de computacion evolutiva a diversos problemas clasico en este campo y a un problema de control real: la induccion de la politica de control de una central termica. La metodologia desarrolla esta basada en la utilizacion de tecnicas de programacion genetica dirigida por gramaticas que permiten la introduccion automatica de conocimiento experto en el algoritmo de aprendizaje.", notes = "In spanish. Available by email Supervisor Luciano Sanchez Ramos", } @InProceedings{garcia03, author = "Santiago Garcia and John Levine and Fermin Gonzalez", title = "Multi Niche Parallel GP with a Junk-code Migration Model", booktitle = "Genetic Programming, Proceedings of EuroGP'2003", year = "2003", editor = "Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa", volume = "2610", series = "LNCS", pages = "327--334", address = "Essex", publisher_address = "Berlin", month = "14-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-00971-X", URL = "http://www.aiai.ed.ac.uk/~johnl/papers/garcia-eurogp03.ps", URL = "http://citeseer.ist.psu.edu/575183.html", DOI = "doi:10.1007/3-540-36599-0_30", abstract = "We describe in this paper a parallel implementation of Multi Niche Genetic Programming that we use to test the performance of a modified migration model. Evolutive introns is a technique developed to accelerate the convergence of GP in classification and symbolic regression problems. Here, we will copy into a differentiated subpopulation the individuals that due to the evolution process contain longer Evolutive Introns. Additionally, the multi island model is parallelised in order to speed up convergence. These results are also analysed. Our results prove that the multi island model achieves faster convergence in the three different symbolic regression problems tested, and that the junk-coded subpopulation is not significantly worse than the others, which reinforces our belief in that the important thing is not only fitness but keeping good genetic diversity along all the evolution process. The overhead introduced in the process by the existence of various island, and the migration model is reduced using a multi-thread approach.", notes = "EuroGP'2003 held in conjunction with EvoWorkshops 2003", } @InProceedings{Carbajal:2004:AL, author = "Santiago {Garcia Carbajal} and Martin Bosque Moran and Fermin Gonzales Martinez", title = "{EvolGL:} Life in a Pond", booktitle = "Artificial Life {XI} Ninth International Conference on the Simulation and Synthesis of Living Systems", year = "2004", editor = "Jordan Pollack and Mark Bedau and Phil Husbands and Takashi Ikegami and Richard A. Watson", pages = "75--80", address = "Boston, Massachusetts", month = "12-15 " # sep, publisher = "The MIT Press", keywords = "genetic algorithms, genetic programming, GA-P, artificial Life", ISBN = "0-262-66183-7", URL = "http://mitpress.mit.edu/books/artificial-life-ix", URL = "https://www.dropbox.com/s/l6fmo6eoe7wgkgj/9780262661836_ALIFE_IX.pdf", URL = "http://ieeexplore.ieee.org/servlet/opac?bknumber=6267522", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6278868", DOI = "doi:10.7551/mitpress/1429.003.0014", size = "6 pages", abstract = "In this work we present the first version of Evolgl, an artificial environment for the development and study of 3D artificial lifeforms. In this first phase on the development of the project we have focused in setting up a virtual world governed by its own laws, whose state had direct influence upon the artificial beings that inhabit it. Starting from the definition of this virtual world, we have designed a basic type of creature (Evolworm), and the genetic coding of its main characteristics. Evolutionary techniques are then used to evolve the morphological features and behavioural aspects of Evolworms. They must learn to be unfolded inside the world, escape from their enemies, find couple, and obtain food. All of this in absence of an explicitly defined fitness function. In the future we are using this environment to study some classical techniques in the evolutionary computation field, like niche programming, and promotion of junk code (introns). GA-P techniques are used to code the external appearance of the individuals (the texture), to let evolution end up with individuals adapted to be invisible in some zones of the world. The artificial system of vision, and the implementation of the worms' behavioral mechanisms so that their actions are provoked exclusively by the sensory information are still under development. At this moment, we have obtained distinct forms of evolworms, as well as different bosses of behaviour that we describe in this article.", notes = "ALIFE9 3D artificial life forms. Evolworm. Promotion of introns (junk code). Genetic definition (GA-P \cite{howard:1995:GA-P} ) of texture of moving aquatic creatures. Brain finite state automata. Colour of worms. Kill by hitting head with your tail. GP stereo Vision. 24 node MOSIX redhat 7.2 linux cluster. Separate visualisation (microsoft) PC. Pop > 600.", } @Article{Garcia:2006:IJSC, author = "Santiago {Garcia Carbajal} and Nouhad J. Rizk", title = "Hierarchical Reinforcement Learning with Grammar-Directed GA-P", journal = "International Journal of Soft Computing", year = "2006", volume = "1", number = "1", pages = "52--60", month = mar, email = "carbajal@lsi.uniovi.es", keywords = "genetic algorithms, genetic programming, reinforcement learning, grammar, knowledge", ISSN = "1816-9503", URL = "http://medwelljournals.com/abstract/?doi=ijscomp.2006.52.60", abstract = "This article proposes a grammatical approach to hierarchical reinforcement learning.It is based on the grammatical description of a problem,a complex task,or objective.The use of a grammar to control the learning process,constraining the structure of the solutions generated with standard GP, permits the inclusion of knowledge about the problem in a straightforward manner,if this knowledge exists.When the problem to be solved involves the use of fuzzy concepts,the membership functions can be evolved simultaneously within the learning process using the advantages of the GA-P paradigm. Additionally,the inclusion of penalty factors in the evaluation function allows us to try to bias the search toward solutions that are optimal in safety or economical terms,not only taking into account control matters.We tested this approach with a real problem,obtaining three different control policies as a consequence of the different fitness functions employed.So,we conclude that the manipulation of fitness function and the use of a grammar to introduce as much knowledge as possible into the search process are useful tools when applying evolutionary techniques in industrial environments.The modified fitness functions and genetic operators are discussed in the paper,too.", notes = "http://www.medwellonline.net/ijcs/", } @InCollection{Carbajal:2007:SSCE, author = "Santiago Garcia Carbajal and David W. Corne and Alejandro Conty", title = "Parallelizing Automatic Induction of {Langton} Parameter with Genetic Programming", booktitle = "Science and Supercomputing in Europe", publisher = "Cineca, Italy", year = "2007", editor = "Giovanni Erbacci", volume = "2006", pages = "540--544", email = "sgarcia@uniovi.es", keywords = "genetic algorithms, genetic programming, cellular automata, parallel programming", isbn_13 = "978-88-86037-19-8", URL = "http://www.hpc-europa.org/CD2006/contents/112-Math-Garcia.PDF", size = "5 pages", abstract = "Many classifications for Cellular Automata have been proposed during time. One of them is based on Langton Parameter. Depending on the probability of a cell of being active at one moment, Cellular Automata are divided into four types. Experimentally, interesting Cellular Automata have been shown to have Langton parameter values close to 0.3. It is said that near this value, Artificial Life is possible. We use a Genetic Programming technique to obtain transition rules with any desired value for lambda. Exploring an environment of the theoretical chaos limit, and measuring the entropy of the resulting automata, we search for Cellular Automata with interesting behavior.", notes = "University of Oviedo, Campus de Viesques, Gijon, Spain. http://www.hpc-europa.org", } @Misc{55-NN3-Carjabal, author = "Santi {Garcia Carbajal}", title = "Time Series Prediction Using Grammar-directed Genetic Programming Methods", howpublished = "NN3 Artificial Neural Network \& Computational Intelligence 2006/07 Forecasting Competition for Neural Networks \& Computational Intelligence", year = "2007", note = "ISF-2007, IJCNN 2007, DMIN 2007", keywords = "genetic algorithms, genetic programming", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.295.1670", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.295.1670", URL = "http://www.neural-forecasting-competition.com/downloads/NN3/methods/55-NN3-Carjabal.pdf", URL = "http://www.neural-forecasting-competition.com/NN3/results.htm", size = "4 pages", abstract = "We use a modified Genetic Programming System to predict the values of the reduced set proposed as benchmark for the 2007 Neural Forecasting Contest. Genetic Programming is a well known method used in symbolic regression, and classification, based in the evolution of arithmetic expressions according to a fitness function. We introduce here a grammar into the Genetic system, to let us use conditional expressions inside the syntactic trees representing the solutions to the problem. Additionally, we employ GA-P methods to automatically obtain constants inside the expressions. Our results proof the known power of Genetic Programming as a tool for solving Symbolic Regression problems, as the obtained expressions fit acceptably the proposed series. For the predicted values, some of them seem promising while others present too flat behaviours.", } @Article{GarciaCarbajal:2007:PPL, author = "Santiago {Garcia Carbajal}", title = "Parallelizing Three Dimensional Cellular Automata With {OpenMP}", journal = "Parallel Processing Letters", year = "2007", volume = "17", number = "4", pages = "349--361", month = dec, email = "sgarcia@uniovi.es", keywords = "genetic algorithms, genetic programming, cellular automata, Parallel Programming", ISSN = "0129-6264", URL = "http://www.worldscinet.com/ppl/ppl.shtml", DOI = "doi:10.1142/S0129626407003083", abstract = "This paper describes our research on using Genetic Programming to obtain transition rules for Cellular Automata, which are one type of massively parallel computing system. Our purpose is to determine the existence of a limit of chaos for three dimensional Cellular Automata, empirically demonstrated for the two dimensional case. To do so, we must study statistical properties of 3D Cellular Automata over long simulation periods. When dealing with big three dimensional meshes, applying the transition rule to the whole structure can become a extremely slow task. In this work we decompose the Automata into pieces and use OpenMp to parallelise the process. Results show that using a decomposition procedure, and distributing the mesh between a set of processors, 3D Cellular Automata can be studied without having long execution times.", notes = "PPL", } @Article{Carbone:2012:JH, author = "M. Carbone and L. Berardi and D. Laucelli and P. Piro", title = "Data-mining approach to investigate sedimentation features in combined sewer overflows", journal = "Journal of Hydroinformatics", year = "2012", volume = "14", number = "3", pages = "613--627", keywords = "genetic algorithms, genetic programming, CSOs (combined sewer overflows), data-mining techniques, Evolutionary Polynomial Regression, urban drainage, water pollutant", ISSN = "1464-7141", DOI = "doi:10.2166/hydro.2011.003", abstract = "Sedimentation is the most common and effectively practiced method of urban drainage control in terms of operating installations and duration of service. Assessing the percentage of suspended solids removed after a given detention time is essential for both design and management purposes. In previous experimental studies by some of the authors, the expression of iso-removal curves (i.e. representing the water depth where a given percentage of suspended solids is removed after a given detention time in a sedimentation column) has been demonstrated to depend on two parameters which describe particle settling velocity and flocculation factor. This study proposes an investigation of the influence of some hydrological and pollutant aggregate information of the sampled events on both parameters. The Multi-Objective (EPR-MOGA) and Multi-Case Strategy (MCS-EPR) variants of the Evolutionary Polynomial Regression (EPR) are originally used as data-mining strategies. Results are proved to be consistent with previous findings in the field and some indications are drawn for relevant practical applicability and future studies.", notes = "IWA Publishing Department of Soil Conservation, University of Calabria, Italy E-mail: patpiro@dds.unical.it Department of Civil and Environmental Engineering, Technical University of Bari, Italy", } @Misc{card:1999:GPWNTSP, author = "Stuart Card", title = "Genetic Programming of Wavelet Networks for Time Series Prediction", booktitle = "GECCO-99 Student Workshop", year = "1999", editor = "Una-May O'Reilly", pages = "341--342", address = "Orlando, Florida, USA", month = "13 " # jul, keywords = "genetic algorithms, genetic programming, neural-nets, wavelets, time, scale, frequency, prediction, stochastic, nonlinear", URL = "http://www.borg.com/~stu/GECCO99.html", abstract = "A hybrid genetic programming / neural network / wavelet technique for time series prediction is proposed. Iterative software development and experimentation are ongoing.", notes = "GECCO-99WKS Part of wu:1999:GECCOWKS", } @InProceedings{card:2004:gsw:swcar, author = "Stuart W. Card", title = "Time Series Prediction by Genetic Programming with Relaxed Assumptions in Mathematica", editor = "R. Poli and S. Cagnoni and M. Keijzer and E. Costa and F. Pereira and G. Raidl and S. C. Upton and D. Goldberg and H. Lipson and E. {de Jong} and J. Koza and H. Suzuki and H. Sawai and I. Parmee and M. Pelikan and K. Sastry and D. Thierens and W. Stolzmann and P. L. Lanzi and S. W. Wilson and M. O'Neill and C. Ryan and T. Yu and J. F. Miller and I. Garibay and G. Holifield and A. S. Wu and T. Riopka and M. M. Meysenburg and A. W. Wright and N. Richter and J. H. Moore and M. D. Ritchie and L. Davis and R. Roy and M. Jakiela", booktitle = "GECCO 2004 Workshop Proceedings", year = "2004", month = "26-30 " # jun, address = "Seattle, Washington, USA", keywords = "genetic algorithms, genetic programming", URL = "http://gpbib.cs.ucl.ac.uk/gecco2004/WGSW002.pdf", notes = "GECCO-2004WKS Distributed on CD-ROM at GECCO-2004", } @InProceedings{card:2005:CEC, author = "Stuart W. Card and Chilukuri K. Mohan", title = "Information Theoretic Indicators of Fitness, Relevant Diversity \& Pairing Potential in Genetic Programming", booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary Computation", year = "2005", editor = "David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L. Gwenn Volkert and Dan Ashlock and Marc Schoenauer", volume = "3", pages = "2545--2552", address = "Edinburgh, UK", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "2-5 " # sep, organisation = "IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9363-5", DOI = "doi:10.1109/CEC.2005.1555013", abstract = "Commonly used fitness measures, such as mean squared error, often fail to reward individuals whose presence in the population is necessary to explain substantial portions of the data variance. Diversity indicators are often arbitrary, may reflect diversity irrelevant to solving the problem, and are incommensurate with fitness measures. By contrast, information theoretic functionals are computable general indicators of fitness and diversity without these typical failings. We propose normalised mutual information, redundancy and synergy measures for genetic programming. We also propose selection for recombination and survival by {"}pairing potential{"} and {"}pair potential{"} estimation, and offer numerical examples as empirical support for theoretical claims.", notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS. Syracuse University 7417 S. Main St. P.O. Box 61 Newport, NY 13416", } @InProceedings{1144254, author = "Stuart W. Card and Chilukuri K. Mohan", title = "Ensemble selection for evolutionary learning using information theory and {Price's} theorem", booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation", year = "2006", editor = "Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos {Coello Coello} and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens", volume = "2", ISBN = "1-59593-186-4", pages = "1587--1588", address = "Seattle, Washington, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p1587.pdf", DOI = "doi:10.1145/1143997.1144254", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "8-12 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Learning Classifier Systems and other Genetics-Based Machine Learning: Poster, evolutionary computation, ensemble models, group selection, mate selection, measurement, Price's equation, theory, machine learning", size = "2 pages", abstract = "an information theoretic perspective on design and analysis of evolutionary algorithms is presented. Indicators of solution quality are developed and applied not only to individuals but also to ensembles, thereby ensuring information diversity. Prices Theorem is extended to show how joint indicators can drive reproductive sampling rate of potential parental pairings. Heritability of mutual information is identified as a key issue", notes = "GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM Order Number 910060", } @InCollection{Card:2007:GPTP, author = "Stuart W. Card and Chilukuri K. Mohan", title = "Towards an Information Theoretic Framework for Genetic programming", booktitle = "Genetic Programming Theory and Practice {V}", year = "2007", editor = "Rick L. Riolo and Terence Soule and Bill Worzel", series = "Genetic and Evolutionary Computation", chapter = "6", pages = "87--106", address = "Ann Arbor", month = "17-19 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1007/978-0-387-76308-8_6", size = "19 pages", abstract = "An information-theoretic framework is presented for the development and analysis of the ensemble learning approach of genetic programming. As evolution proceeds, this approach suggests that the mutual information between the target and models should: (i) not decrease in the population; (ii) concentrate in fewer individuals; and (iii) be distilled from the inputs, eliminating excess entropy. Normalised information theoretic indices are developed to measure fitness and diversity of ensembles, without a priori knowledge of how the multiple constituent models might be composed into a single model. With the use of these indexes for reproductive and survival selection, building blocks are less likely to be lost and more likely to be recombined. Price's Theorem is generalised to pair selection, from which it follows that the heritability of information should be stronger than the heritability of error, improving evolvability. We support these arguments with simulations using a logic function benchmark and a time series application. For a chaotic time series prediction problem, for instance, the proposed approach avoids familiar difficulties (premature convergence, deception, poor scaling, and early loss of needed building blocks) with standard GP symbolic regression systems; information-based fitness functions showed strong intergenerational correlations as required by Price's Theorem.", notes = "http://www.cscs.umich.edu/events/gptp2007/ Card-Mohan-draft-2007-4-4.pdf part of \cite{Riolo:2007:GPTP} To be published after workshop Jan 2008?", } @InCollection{Card:2008:GPTP, author = "Stuart W. Card and Chilukuri K. Mohan", title = "An Application of Information Theoretic Selection to Evolution of Models with Continuous-valued Inputs", booktitle = "Genetic Programming Theory and Practice {VI}", year = "2008", editor = "Rick L. Riolo and Terence Soule and Bill Worzel", series = "Genetic and Evolutionary Computation", chapter = "3", pages = "29--43", address = "Ann Arbor", month = "15-17 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, genetic programming, information theory, input selection, building blocks, ensemble models, diversity, fitness, entropy, mutual information, redundancy, synergy, similarity, information distance, evolvability, heritability, sufficiency, necessity, copula", isbn13 = "978-0-387-87622-1", DOI = "doi:10.1007/978-0-387-87623-8_3", size = "14 pages", abstract = "Information theoretic functionals have significant benefits as compared with traditional error based indicators of fitness and diversity. Mutual Information (MI), various normalizations of it, and similarity and distance metrics derived from it, can be used advantageously in all phases of Genetic Programming (GP), starting with input selection. However, these functionals are based on Shannon's entropy, which is strictly defined only for discrete random variables, so their application to problems involving continuous valued data requires their generalization and development of robust and efficient algorithms for their calculation. This paper outlines such algorithms and illustrates their application to a noisy continuous valued data set synthesized to test GP symbolic regression systems (Korns, 2007). Information theoretic sufficiency outperforms linear correlation in ranking the relevance of available inputs in this data set. Similar results are obtained on inputs filtered by functions that 'fold' the data, thereby destroying information; ranking these intermediate evolutionary forms, sufficiency again outperforms correlation. Sufficiency also exhibits a distinct threshold separating irrelevant terms from terms that are indeed relevant in regression of these test problems. As a less computationally costly alternative to rankings of entire populations, tournament selection is often used; on this data set, for pairwise tournament selection, sufficiency greatly outperforms correlation. Multi-objective ranking, considering also information theoretic necessity to prefer appropriately filtered inputs (over corresponding raw inputs with excess entropies), is foreshadowed.", notes = "part of \cite{Riolo:2008:GPTP} published in 2009", } @InProceedings{Card:2010:geccocomp, author = "Stuart W. Card", title = "Information distance based fitness and diversity metrics", booktitle = "GECCO 2010 Entropy, information and complexity", year = "2010", editor = "Stuart William Card and Yossi Borenstein", isbn13 = "978-1-4503-0073-5", keywords = "genetic algorithms, genetic programming, Measurement, Theory, evolutionary computation, machin e learning, fitness, diversity, metric, distance, mutual information, interaction information, algorithmic information, complexity, entropy, Kolmogorov Complexity, Kolmogorov Distance, Shannon Distance, Rajski Distance, Normalized Compression Distance, NCD, Multi-Objective Selection", pages = "1851--1854", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830761.1830815", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Commensurate indicators of diversity and fitness with desirable metric properties are derived from information distances based on Shannon entropy and Kolmogorov complexity. These metrics measure various useful distances: from an information theoretic characterisation of the phenotypic behaviour of a candidate model in the population to that of an ideal model of the target system's input-output relationship (fitness); from behavior of one candidate model to that of another (total information diversity); from the information about the target provided by one model to that provided by another (target relevant information diversity); from the code of one model to that of another (genotypic representation diversity); etc. Algorithms are cited for calculating the Shannon entropy based metrics from discrete data and estimating analogs thereof from heuristically binned continuous data; references are cited to methods for estimating the Kolmogorov complexity based metric. Not in the paper, but at the workshop, results will be shown of applying these algorithms to several synthetic and real world data sets: the simplest known deterministic chaotic flow; symbolic regression test functions; industrial process monitoring and control variables; and international political leadership data. Ongoing work is outlined.", notes = "Also known as \cite{1830815} Distributed on CD-ROM at GECCO-2010. ACM Order Number 910102.", } @PhdThesis{Card:thesis, author = "Stuart William Card", title = "Towards an Information Theoretic Framework for Evolutionary Learning", school = "Electrical Engineering and Computer Science, Syracuse University", year = "2011", address = "USA", month = aug, email = "cards@ntcnet.com", keywords = "genetic algorithms, genetic programming, diversity, ensemble model, evolvability, fitness, information distance, mutual information", URL = "https://surface.syr.edu/eecs_etd/307", URL = "https://surface.syr.edu/cgi/viewcontent.cgi?article=1311&context=eecs_etd", size = "219 pages", abstract = "The vital essence of evolutionary learning consists of information flows between the environment and the entities differentially surviving and reproducing therein. Gain or loss of information in individuals and populations due to evolutionary steps should be considered in evolutionary algorithm theory and practice. Information theory has rarely been applied to evolutionary computation, a lacuna that this dissertation addresses, with an emphasis on objectively and explicitly evaluating the ensemble models implicit in evolutionary learning. Information theoretic functionals can provide objective, justifiable, general, computable, commensurate measures of fitness and diversity. We identify information transmission channels implicit in evolutionary learning. We define information distance metrics and indices for ensembles. We extend Price's Theorem to non-random mating, give it an effective fitness interpretation and decompose it to show the key factors influencing heritability and evolvability. We argue that heritability and evolvability of our information theoretic indicators are high. We illustrate use of our indices for reproductive and survival selection. We develop algorithms to estimate information theoretic quantities on mixed continuous and discrete data via the empirical copula and information dimension. We extend statistical resampling. We present experimental and real world application results: chaotic time series prediction; parity; complex continuous functions; industrial process control; and small sample social science data. We formalize conjectures regarding evolutionary learning and information geometry.", notes = "Information Theoretic Evaluations of Ensembles. Corollaries to Price's Theorem Supervisor: Chilukuri K. Mohan", } @InProceedings{Card:2023:GPTP, author = "Stuart W. Card", title = "Towards Information Theoretic {GP} of Causal Models", booktitle = "Genetic Programming Theory and Practice XX", year = "2023", editor = "Ting Hu and Charles Ofria and Leonardo Trujillo and Stephan Winkler", series = "Genetic and Evolutionary Computation", address = "Michigan State University, USA", month = jun # " 1-3", keywords = "genetic algorithms, genetic programming", notes = " Part of \cite{Hu:2023:GPTP} Not in published book", } @InProceedings{Card:2025:GPTP, author = "Stuart Card", title = "The Needs of the Few -- Information Theory Aided Ensemble Selection for Robustly Generalizing Causal Models", booktitle = "Genetic Programming Theory and Practice XXII", year = "2025", editor = "Wolfgang Banzhaf and Bogdan Burlacu and Stephen Kelly and Alexander Lalejini and Fabricio {Olivetti de Franca}", address = "Michigan State University, USA", month = jun # " 5-7", keywords = "genetic algorithms, genetic programming", notes = "To be published after the workshop", } @InProceedings{Cardamone:2011:DSoPIuGP, title = "Dynamic Synthesis of Program Invariants using Genetic Programming", author = "Luigi Cardamone and Andrea Mocci and Carlo Ghezzi", pages = "617--624", booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary Computation", year = "2011", editor = "Alice E. Smith", month = "5-8 " # jun, address = "New Orleans, USA", organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, SBSE, invariant formula, logic formulae, loop manipulating array, program comprehension, program in verification, statement execution, symbolic program manipulation, transformation rule, iterative methods, program verification, symbol manipulation", URL = "https://www.luigicardamone.it/bibtex.htm", URL = "https://www.luigicardamone.it/tesi-pubblicazioni/cardamone2011gp.pdf", DOI = "doi:10.1109/CEC.2011.5949677", size = "8 pages", abstract = "In the field of software engineering, invariant detection techniques have been proposed to overcome the problem of software behaviour comprehension. If the code of a program is available, combining symbolic and concrete execution has been shown to provide an effective method to derive logic formulae that describe a program's behavior. However, symbolic execution does not work very well with loops, and thus such methods are not able to derive useful descriptions of programs containing loops. we present a preliminary approach that aims to integrate genetic programming to synthesise a logic formula that describes the behaviour of a loop. Such formula could be integrated in a symbolic execution based approach for invariant detection to synthesize a complex program behaviour. We present a specific representation of formulae that works well with loops manipulating arrays. The technique has been validated with a set of relevant examples with increasing complexity. The preliminary results are promising and show the feasibility of our approach.", notes = "CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.", } @PhdThesis{cardamone2012phd, author = "Luigi Cardamone", title = "Evolutionary Learning and Search-Based Content Generation in Computer Games", school = "Dipartimento di Elettronica e Informazione, Politecnico di Milano", year = "2011", year = "2012", address = "Piazza Leonardo da Vinci 32, Milan, I 20133, Italy", keywords = "genetic algorithms, search-based, content generation, evolutionary learning, on-line learning, transfer learning, neural networks, ANN, NEAT, TORCS", owner = "luigi", timestamp = "2012.03.12", URL = "https://www.luigicardamone.it/bibtex.htm", URL = "https://www.politesi.polimi.it/bitstream/10589/56704/1/2012_03_PhD_Cardamone.PDF", size = "199 pages", notes = "Not GP? 738810 Pier Luca Lanzi and Fabrizio Ferrandi", } @Article{cardenas-florido:2024:MaCA, author = "Luis {Cardenas Florido} and Leonardo Trujillo and Daniel E. Hernandez and Jose Manuel {Munoz Contreras}", title = "{M5GP:} Parallel Multidimensional Genetic Programming with Multidimensional Populations for Symbolic Regression", journal = "Mathematical and Computational Applications", year = "2024", volume = "29", number = "2", pages = "Article No. 25", keywords = "genetic algorithms, genetic programming", ISSN = "2297-8747", URL = "https://www.mdpi.com/2297-8747/29/2/25", DOI = "doi:10.3390/mca29020025", abstract = "Machine learning and artificial intelligence are growing in popularity thanks to their ability to produce models that exhibit unprecedented performance in domains that include computer vision, natural language processing and code generation. However, such models tend to be very large and complex and impossible to understand using traditional analysis or human scrutiny. Conversely, Symbolic Regression methods attempt to produce models that are relatively small and (potentially) human-readable. In this domain, Genetic Programming (GP) has proven to be a powerful search strategy that achieves state-of-the-art performance. This paper presents a new GP-based feature transformation method called M5GP, which is hybridized with multiple linear regression to produce linear models, implemented to exploit parallel processing on graphical processing units for efficient computation. M5GP is the most recent variant from a family of feature transformation methods (M2GP, M3GP and M4GP) that have proven to be powerful tools for both classification and regression tasks applied to tabular data. The proposed method was evaluated on SRBench v2.0, the current standard benchmarking suite for Symbolic Regression. Results show that M5GP achieves performance that is competitive with the state-of-the-art, achieving a top-three rank on the most difficult subset of black-box problems. Moreover, it achieves the lowest computation time when compared to other GP-based methods that have similar accuracy scores.", notes = "also known as \cite{mca29020025}", } @InProceedings{Cardenas-Valdez:2015:NEO, author = "J. R. {Cardenas Valdez} and Emigdio Z-Flores and Jose Cruz {Nunez Perez} and Leonardo Trujillo", title = "Local Search Approach to Genetic Programming for RF-PAs Modeling Implemented in FPGA", booktitle = "NEO 2015: Results of the Numerical and Evolutionary Optimization Workshop NEO 2015 held at September 23-25 2015 in Tijuana, Mexico", year = "2015", editor = "Oliver Schuetze and Leonardo Trujillo and Pierrick Legrand and Yazmin Maldonado", volume = "663", series = "Studies in Computational Intelligence", pages = "67--88", chapter = "3", publisher = "Springer", keywords = "genetic algorithms, genetic programming, EHW, FPGA, Local search, MPM, Behavioral models, DPD", isbn13 = "978-3-319-44003-3", DOI = "doi:10.1007/978-3-319-44003-3_3", abstract = "This paper presents a genetic programming (GP) approach enhanced with a local search heuristic (GP-LS) to emulate the Doherty 7 W @ 2.11 GHz Radio Frequency (RF) Power Amplifier (PA) conversion curves. GP has been shown to be a powerful modelling tool, but can be compromised by slow convergence and computational cost. The proposal is to combine the explorative search of standard GP, which build the syntax of the solution, with numerical methods that perform an exploitative and greedy local optimization of the evolved structures. The results are compared with traditional modeling techniques, particularly the memory polynomial model (MPM). The main contribution of the paper is the design, comparison and hardware emulation of GP-LS for FPGA real applications. The experimental results show that GP-LS can outperform standard MPM, and suggest a promising new direction of future work on digital pre-distortion (DPD) that requires complex behavioural models", notes = "Published 2017", } @InProceedings{Cardoso:2019:GECCO, author = "Rui P. Cardoso and Emma Hart and Jeremy V. Pitt", title = "Evolving robust policies for community energy system management", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference", year = "2019", editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and Anne Auger and Petr Posik and Leslie {Peprez Caceres} and Andrew M. Sutton and Nadarajen Veerapen and Christine Solnon and Andries Engelbrecht and Stephane Doncieux and Sebastian Risi and Penousal Machado and Vanessa Volz and Christian Blum and Francisco Chicano and Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and Jonathan Fieldsend and Jose Antonio Lozano and Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and Robin Purshouse and Thomas Baeck and Justyna Petke and Giuliano Antoniol and Johannes Lengler and Per Kristian Lehre", isbn13 = "978-1-4503-6111-8", pages = "1120--1128", address = "Prague, Czech Republic", DOI = "doi:10.1145/3321707.3321763", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, Multi-agent system, Community energy system management", size = "9 pages", abstract = "Community energy systems (CESs) are shared energy systems in which multiple communities generate and consume energy from renewable resources. At regular time intervals, each participating community decides whether to self-supply, store, trade, or sell their energy to others in the scheme or back to the grid according to a predefined policy which all participants abide by. The objective of the policy is to maximise average satisfaction across the entire CES while minimising the number of unsatisfied participants. We propose a multi-class, multi-tree genetic programming approach to evolve a set of specialist policies that are applicable to specific conditions, relating to abundance of energy, asymmetry of generation, and system volatility. Results show that the evolved policies significantly outperform a default hand crafted policy. Additionally, we evolve a generalist policy and compare its performance to specialist ones, finding that the best generalist policy can equal the performance of specialists in many scenarios. We claim that our approach can be generalised to any multi-agent system solving a common-pool resource allocation problem that requires the design of a suitable operating policy.", notes = "Also known as \cite{3321763} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @Article{CARDOSOFERNANDEZ:2023:applthermaleng, author = "V. Cardoso-Fernandez and A. Bassam and O. {May Tzuc} and M. A. {Barrera Ch.} and Jorge {de Jesus Chan-Gonzalez} and M. A. {Escalante Soberanis} and N. Velazquez-Limon and Luis J. Ricalde", title = "Global sensitivity analysis of a generator-absorber heat exchange ({GAX)} system's thermal performance with a hybrid energy source: An approach using artificial intelligence models", journal = "Applied Thermal Engineering", volume = "218", pages = "119363", year = "2023", ISSN = "1359-4311", DOI = "doi:10.1016/j.applthermaleng.2022.119363", URL = "https://www.sciencedirect.com/science/article/pii/S1359431122012935", keywords = "genetic algorithms, genetic programming, Generator-Absorber Heat Exchange (GAX), Solar refrigeration cycle, Hybrid renewable energy system, Data-driven models, PAWN method, Decision-making process, Absorption refrigeration", abstract = "Generator-absorber heat exchange (GAX) systems represent a promising alternative to substitute environmentally harmful refrigeration devices based on conventional vapor compression, as long as a proper analysis of thermal performance and the complex interactions of heat transfer that occur into GAX cycle is taken in consideration. In this research, a cooling process based on a GAX system that uses ammonia-water working fluid and a hybrid source (natural gas-solar) is studied to analyze the variables that affect the system's thermal performance. The work's novelty is the hybridization between artificial intelligence (AI) modeling and the global sensitivity analysis (GSA) developed with the PAWN method. Experimental data was obtained from a system with a cooling capacity of 10.5 kW (3 Ton), designed to work at heat source temperatures of 200 degreeC. The measured variables were the temperatures at generator, heat at evaporator, and working fluid volumetric flow. Three AI techniques (artificial neural networks, genetic programming, and support vector machines) were evaluated for modeling the thermodynamic cycle. Results obtained from the PAWN method applied to the artificial neural network, since it was the best AI model, indicates that the operational parameters with a greater impact in the system's performance are the inlet temperature at the generator (30.7 percent) and the heat measured at the evaporator for NH3 (27.4 percent), for the first output COPNH3. For the second output COPH2O, the inlet temperature at the generator (32.5 percent) and the and heat measured at the evaporator for H2O (26.7 percent), have a greater impact for such output. The proposed IA-GSA methodology contributes to the development of operational decision-making related to instrumentation, operation performance, and corrective and/or preventive maintenance actions of GAX systems. The developed thermal performance model has potential for implementation in embedded systems (smart sensors) as a critical element in control and optimization strategies to improve the performance of these cycles", } @InProceedings{Carja:2023:GPTP, author = "Oana Carja", title = "Topological puzzles in biology: how geometry shapes evolution and applications to designing intelligent collectives", booktitle = "Genetic Programming Theory and Practice XX", year = "2023", editor = "Ting Hu and Charles Ofria and Leonardo Trujillo and Stephan Winkler", series = "Genetic and Evolutionary Computation", address = "Michigan State University, USA", month = jun # " 1-3", note = "Invited Keynote", keywords = "genetic algorithms, genetic programming", notes = " Part of \cite{Hu:2023:GPTP} Not in published book", } @InProceedings{Carlet:2021:GECCO, author = "Claude Carlet and Domagoj Jakobovic and Stjepan Picek", title = "Evolutionary Algorithms-assisted Construction of Cryptographic Boolean Functions", booktitle = "Proceedings of the 2021 Genetic and Evolutionary Computation Conference", year = "2021", editor = "Francisco Chicano and Alberto Tonda and Krzysztof Krawiec and Marde Helbig and Christopher W. Cleghorn and Dennis G. Wilson and Georgios Yannakakis and Luis Paquete and Gabriela Ochoa and Jaume Bacardit and Christian Gagne and Sanaz Mostaghim and Laetitia Jourdan and Oliver Schuetze and Petr Posik and Carlos Segura and Renato Tinos and Carlos Cotta and Malcolm Heywood and Mengjie Zhang and Leonardo Trujillo and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Fuyuki Ishikawa and Inmaculada Medina-Bulo and Frank Neumann and Andrew M. Sutton", pages = "565--573", address = "internet", series = "GECCO '21", month = jul # " 10-14", organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, evolution strategies, Boolean function, Cryptography, Secondary Construction, Hidden Weight Boolean Function", isbn13 = "9781450383509", URL = "http://www.human-competitive.org/sites/default/files/picek_humies.txt", URL = "http://www.human-competitive.org/sites/default/files/ea_for_secondary_boolean_function_construction.pdf", DOI = "doi:10.1145/3449639.3459362", size = "9 pages", abstract = "In the last few decades, evolutionary algorithms were successfully applied numerous times for creating Boolean functions with good cryptographic properties. Still, the applicability of such approaches was always limited as the cryptographic community knows how to construct suitable Boolean functions with deterministic algebraic constructions. Thus, evolutionary results so far helped to increase the confidence that evolutionary techniques have a role in cryptography, but at the same time, the results themselves were seldom used. We consider a novel problem using evolutionary algorithms to improve Boolean functions obtained through algebraic constructions. To this end, we consider a recent generalisation of Hidden Weight Boolean Function construction, and we show that evolutionary algorithms can significantly improve the cryptographic properties of the functions. Our results show that the genetic algorithm performs by far the best of all the considered algorithms and improves the non-linearity property in all Boolean function sizes. As there are no known algebraic techniques to reach the same goal, we consider this application a step forward in accepting evolutionary algorithms as a powerful tool in the cryptography domain.", notes = "Entered 2021 HUMIES University of Bergen, Norway GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)", } @InProceedings{carlet:2022:GECCO, author = "Claude Carlet and Marko Djurasevic and Domagoj Jakobovic and Luca Mariot and Stjepan Picek", title = "Evolving Constructions for Balanced, Highly Nonlinear Boolean Functions", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference", year = "2022", editor = "Alma Rahat and Jonathan Fieldsend and Markus Wagner and Sara Tari and Nelishia Pillay and Irene Moser and Aldeida Aleti and Ales Zamuda and Ahmed Kheiri and Erik Hemberg and Christopher Cleghorn and Chao-li Sun and Georgios Yannakakis and Nicolas Bredeche and Gabriela Ochoa and Bilel Derbel and Gisele L. Pappa and Sebastian Risi and Laetitia Jourdan and Hiroyuki Sato and Petr Posik and Ofer Shir and Renato Tinos and John Woodward and Malcolm Heywood and Elizabeth Wanner and Leonardo Trujillo and Domagoj Jakobovic and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Inmaculada Medina-Bulo and Slim Bechikh and Andrew M. Sutton and Pietro Simone Oliveto", pages = "1147--1155", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, Boolean functions, evolutionary algorithms, non-linearity, secondary constructions, balancedness", isbn13 = "978-1-4503-9237-2", URL = "https://doi.org/10.1145/3512290.3528871", DOI = "doi:10.1145/3512290.3528871", video_url = "https://vimeo.com/725452312", abstract = "Finding balanced, highly nonlinear Boolean functions is a difficult problem where it is not known what nonlinearity values are possible to be reached in general. At the same time, evolutionary computation is successfully used to evolve specific Boolean function instances, but the approach cannot easily scale for larger Boolean function sizes. Indeed, while evolving smaller Boolean functions is almost trivial, larger sizes become increasingly difficult, and evolutionary algorithms perform suboptimally.In this work, we ask whether genetic programming (GP) can evolve constructions resulting in balanced Boolean functions with high nonlinearity. This question is especially interesting as there are only a few known such constructions. Our results show that GP can find constructions that generalize well, i.e., result in the required functions for multiple tested sizes. Further, we show that GP evolves many equivalent constructions under different syntactic representations. Interestingly, the simplest solution found by GP is a particular case of the well-known indirect sum construction.", notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @InProceedings{carlet:2022:GECCOcomp, author = "Claude Carlet and Domagoj Jakobovic and Stjepan Picek", title = "On Generalizing the Power Function Exponent Constructions with Genetic Programming", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion", year = "2022", editor = "Heike Trautmann and Carola Doerr and Alberto Moraglio and Thomas Bartz-Beielstein and Bogdan Filipic and Marcus Gallagher and Yew-Soon Ong and Abhishek Gupta and Anna V Kononova and Hao Wang and Michael Emmerich and Peter A. N. Bosman and Daniela Zaharie and Fabio Caraffini and Johann Dreo and Anne Auger and Konstantin Dietric and Paul Dufosse and Tobias Glasmachers and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Tea Tusar and Dimo Brockhoff and Tome Eftimov and Pascal Kerschke and Boris Naujoks and Mike Preuss and Vanessa Volz and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Mark Coletti and Catherine (Katie) Schuman and Eric ``Siggy'' Scott and Robert Patton and Paul Wiegand and Jeffrey K. Bassett and Chathika Gunaratne and Tinkle Chugh and Richard Allmendinger and Jussi Hakanen and Daniel Tauritz and John Woodward and Manuel Lopez-Ibanez and John McCall and Jaume Bacardit and Alexander Brownlee and Stefano Cagnoni and Giovanni Iacca and David Walker and Jamal Toutouh and UnaMay O'Reilly and Penousal Machado and Joao Correia and Sergio Nesmachnow and Josu Ceberio and Rafael Villanueva and Ignacio Hidalgo and Francisco {Fernandez de Vega} and Giuseppe Paolo and Alex Coninx and Antoine Cully and Adam Gaier and Stefan Wagner and Michael Affenzeller and Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and William B. Langdon and Justyna Petke and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Paetzel and Alexander Wagner and Michael Heider and Nadarajen Veerapen and Katherine Malan and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Mohammad Nabi Omidvar and Yuan Sun and Ernesto Tarantino and De Falco Ivanoe and Antonio {Della Cioppa} and Scafuri Umberto and John Rieffel and Jean-Baptiste Mouret and Stephane Doncieux and Stefanos Nikolaidis and Julian Togelius and Matthew C. Fontaine and Serban Georgescu and Francisco Chicano and Darrell Whitley and Oleksandr Kyriienko and Denny Dahl and Ofer Shir and Lee Spector and Alma Rahat and Richard Everson and Jonathan Fieldsend and Handing Wang and Yaochu Jin and Erik Hemberg and Marwa A. Elsayed and Michael Kommenda and William {La Cava} and Gabriel Kronberger and Steven Gustafson", pages = "691--694", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, evolutionary algorithms, APN, power function, s-box, almost perfect nonlinear", isbn13 = "978-1-4503-9268-6/22/07", DOI = "doi:10.1145/3520304.3529081", abstract = "Many works are investigating Almost Perfect Nonlinear (APN) functions and, in particular, APN power functions. Such functions are of the form F(x) = xd, and they have practical relevance as they reach in characteristic 2 the best possible differential uniformity. This work investigates whether genetic programming (GP) can {"}reinvent{"} the known expressions used to obtain exponent values d resulting in APN functions. The ultimate goal is to find classes of exponents that would be {"}transversal{"} to the known infinite classes of APN exponents, and would contain new APN exponents (for values of n necessarily larger than those for which an exhaustive search could be made so far). This would be already a breakthrough, and our hope is to find this way new infinite classes of APN exponents. Our results show this is possible but difficult, and a careful trade-off between finding new values and skipping known values is required.", notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @InProceedings{Carlet:2024:evoapplications, author = "Claude Carlet and Marko Durasevic and Bruno Gasperov and Domagoj Jakobovic and Luca Mariot and Stjepan Picek", title = "A New Angle: On Evolving Rotation Symmetric Boolean Functions", booktitle = "27th International Conference, EvoApplications 2024", year = "2024", editor = "Stephen Smith and Joao Correia and Christian Cintrano", series = "LNCS", volume = "14634", publisher = "Springer", address = "Aberystwyth", month = "3-5 " # apr, pages = "287--302", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, rotation symmetry, Boolean functions, metaheuristics, nonlinearity", isbn13 = "978-3-031-56851-0", URL = "https://rdcu.be/dDZU2", DOI = "doi:10.1007/978-3-031-56852-7_19", abstract = "Rotation symmetric Boolean functions represent an interesting class of Boolean functions as they are relatively rare compared to general Boolean functions. At the same time, the functions in this class can have excellent cryptographic properties, making them interesting for various practical applications. The usage of metaheuristics to construct rotation symmetric Boolean functions is a direction that has been explored for almost twenty years. Despite that, there are very few results considering evolutionary computation methods. This paper uses several evolutionary algorithms to evolve rotation symmetric Boolean functions with different properties. Despite using generic metaheuristics, we obtain results that are competitive with prior work relying on customized heuristics. Surprisingly, we find that bitstring and floating point encodings work better than the tree encoding. Moreover, evolving highly nonlinear general Boolean functions is easier than rotation symmetric ones.", notes = "http://www.evostar.org/2024/ EvoApplications2024 held in conjunction with EuroGP'2024, EvoCOP2024 and EvoMusArt2024", } @InProceedings{Carlet:2024:EuroGP, author = "Claude Carlet and Marko Durasevic and Domagoj Jakobovic and Luca Mariot and Stjepan Picek", editor = "Mario Giacobini and Bing Xue and Luca Manzoni", title = "Look into the Mirror: Evolving Self-dual Bent Boolean Functions", booktitle = "EuroGP 2024: Proceedings of the 27th European Conference on Genetic Programming", year = "2024", volume = "14631", series = "LNCS", publisher = "Springer", address = "Aberystwyth", month = "3-5 " # apr, organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming", pages = "161--175", abstract = "Bent Boolean functions are important objects in cryptography and coding theory, and there are several general approaches for constructing such functions. Metaheuristics proved to be a strong choice as they can provide many bent functions, even when the size of the Boolean function is large (e.g., more than 20 inputs). While bent Boolean functions represent only a small part of all Boolean functions, there are several subclasses of bent functions providing specific properties and challenges. One of the more interesting subclasses comprises (anti-)self-dual bent Boolean functions.", isbn13 = "978-3-031-56957-9", DOI = "doi:10.1007/978-3-031-56957-9_10", notes = "Part of \cite{Giacobini:2024:GP} EuroGP'2024 held in conjunction with EvoCOP2024, EvoMusArt2024 and EvoApplications2024", } @InProceedings{carlet:2025:EuroGP, author = "Claude Carlet and Marko Durasevic and Domagoj Jakobovic and Stjepan Picek and Luca Mariot", title = "A Systematic Evaluation of Evolving Highly Nonlinear Boolean Functions in Odd Sizes", booktitle = "European Conference on Genetic Programming, EuroGP 2025", year = "2025", editor = "Bing Xue and Luca Manzoni and Illya Bakurov", volume = "15609", series = "LNCS", pages = "18--34", address = "Trieste", month = "23-25 " # apr, publisher = "Springer Nature", keywords = "genetic algorithms, genetic programming, Boolean functions, nonlinearity, evolutionary algorithms, odd dimension, encodings", isbn13 = "978-3-031-89990-4", URL = "https://www.human-competitive.org/sites/default/files/humies_entry_2025.txt", URL = "https://human-competitive.org/sites/default/files/a_systematic_evaluation_of_evolving_highly_nonlinear_boolean_functions_in_odd_sizes.pdf", URL = "https://rdcu.be/ejgAa", DOI = "doi:10.1007/978-3-031-89991-1_2", size = "17 pages", abstract = "Boolean functions are mathematical objects used in diverse applications. Different applications also have different requirements, making the research on Boolean functions very active. In the last 30 years, evolutionary algorithms have been shown to be a strong option for evolving Boolean functions in different sizes and with different properties. Still, most of those works consider similar settings and provide results that are mostly interesting from the evolutionary algorithm's perspective. This work considers the problem of evolving highly nonlinear Boolean functions in odd sizes. While the formulation sounds simple, the problem is remarkably difficult, and the related work is extremely scarce. We consider three solutions encodings and four Boolean function sizes and run a detailed experimental analysis. Our results show that GP outperforms other EA in evolving highly nonlinear functions. Nevertheless, the problem is challenging, and finding optimal solutions is impossible except for the smallest tested size. However, once we added local search to the evolutionary algorithm, we managed to find a Boolean function in nine inputs with nonlinearity 241, which, to our knowledge, had never been accomplished before with evolutionary algorithms.", notes = "Entered 2025 HUMIES also known as \cite{paper_144_nonlinear_boolean_functions_in_odd_sizes} Part of \cite{Xue:2025:GP} EuroGP'2025 held in conjunction with EvoCOP2025, EvoMusArt2025 and EvoApplications2025", } @InProceedings{Carlet:2025:evoapplications, author = "Claude Carlet and Marko Durasevic and Domagoj Jakobovic and Luca Mariot and Stjepan Picek", title = "The More the Merrier: On Evolving Five-Valued Spectra Boolean Functions", booktitle = "28th International Conference, EvoApplications 2025", year = "2025", editor = " Pablo Garcia-Sanchez and Emma Hart and Sarah L. Thomson", series = "LNCS", volume = "15612", pages = "52--67", publisher = "Springer", address = "Trieste, Italy", month = "3-5 " # apr, organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Boolean Functions, Plateaudness Algorithms, Five-valued Functions", isbn13 = "978-3-031-90062-4", timestamp = "Wed, 07 May 2025 17:49:32 +0200", biburl = "https://dblp.org/rec/conf/evoapps/CarletDJMP25.bib", bibsource = "dblp computer science bibliography, https://dblp.org", DOI = "doi:10.1007/978-3-031-90062-4_4", notes = "DBLP:conf/evoapps/CarletDJMP25, http://www.evostar.org/2025/ EvoApplications2025 held in conjunction with EuroGP'2025, EvoCOP2025 and EvoMusArt2025", } @Article{Carlos-Padierna:2020:ACC, author = "L. {Carlos Padierna} and C. Villasenor-Mora and S. A. {Lopez Juarez}", journal = "IEEE Access", title = "Biomedical Classification Problems Automatically Solved by Computational Intelligence Methods", year = "2020", volume = "8", pages = "101104--101117", keywords = "genetic algorithms, genetic programming", ISSN = "2169-3536", DOI = "doi:10.1109/ACCESS.2020.2998749", abstract = "Biomedical classification problems are of great interest to both medical practitioners and computer scientists. Due to the harmful consequences of a wrong decision in this ambit, computational methods must be carefully designed to provide a reliable tool for helping physicians to obtain accurate predictions on unseen cases. Computational Intelligence (CI) provides robust models to perform optimization, classification and regression tasks. These models have been previously designed, mainly based on the expertise of computer scientists, to solve a vast number of biomedical problems. As the number of both CI algorithms and biomedical problems continues to grow, selecting the right method to solve a given problem becomes more challenging. To deal with this complexity, a systematic methodology for selecting a suitable model for a given classification problem is required. In this work, we review the more promising classification and optimization algorithms and reformulate them into a synergistic framework to automatically design and optimize pattern classifiers. Our proposal, including state-of-the-art evolutionary algorithms and support vector machines, is tested on a variety of biomedical problems. Experimental results on benchmark datasets allow us to conclude that the automatically designed classifiers reach higher or equal performance than those designed by computer specialists.", notes = "Also known as \cite{9103544}", } @InProceedings{CarPai02, author = "Jonas Carlsson and Carlos Paiz and Krister Wolff and Peter Nordin", title = "Interactive Evolution of Speech using {VoiceXML} Speaking to your {GP} System", booktitle = "Proceedings of the 6th World Multiconference on Systemics, Cybernetics and Informatics", year = "2002", editor = "Nagib Callaos and Alexander Pisarchik and Mitsuyoshi Ueda", volume = "VI", pages = "58--62", publisher = "IIIS", keywords = "genetic algorithms, genetic programming, voice XML", ISBN = "980-07-8150-1", URL = "http://citeseer.uark.edu:8080/citeseerx/showciting;jsessionid=3ACDA5B9DB9ACC6C5ECF27C2C8BEA296?cid=5226534", URL = "http://publications.lib.chalmers.se/publication/72898-interactive-evolution-of-speech-using-voicexml-speaking-to-your-gp-system", abstract = "we describe and discuss experiments in which we try to evolve meaningful sentences in English using Genetic Programming with interactive evolution. We use VoiceXML as the user interface, through which the user hears each individual, acts as the fitness function and tells the system what individuals to select. This is the first GP-system that accepts voice as guidance for fitness calculations. We use context free grammars to define the individuals and the genetic operators make sure that the grammar is followed, avoiding destructive mutation and crossover. The results show that it is possible to evolve meaningful phrases with our approach but improvements to the system are required in order to fully achieve the goal. The wide availability of voice terminals, such as phones, enables powerful learning of, for example, natural language grammar with possible feedback even from the general public. The described work also constitutes the first GP-system written in JavaScript (ECMAScript) enabling easy distributed GP-run over the Web without any installation.", } @InProceedings{conf/eusflat/CarmonaGJ15, author = "Cristobal J. Carmona and Pedro Gonzalez and Maria Jose {del Jesus}", title = "{FuGePSD}: Fuzzy Genetic Programming-based algorithm for Subgroup Discovery", booktitle = "2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology ({IFSA}-{EUSFLAT}-15)", year = "2015", editor = "Jose M. Alonso and Humberto Bustince and Marek Reformat", pages = "448--454", address = "Gijon, Spain", month = jun # " 30-3 " # jul, publisher = "Atlantis Press", keywords = "genetic algorithms, genetic programming, subgroup discovery, evolutionary fuzzy system", bibdate = "2015-11-23", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/eusflat/eusflat2015.html#CarmonaGJ15", isbn13 = "978-94-62520-77-6", URL = "http://www.atlantis-press.com/php/download_paper.php?id=23576", DOI = "doi:10.2991/ifsa-eusflat-15.2015.65", size = "8 pages", abstract = "Evolutionary Fuzzy Systems (EFSs) are fuzzy systems augmented by a learning process based on evolutionary computation such as evolutionary algorithms (EAs). These systems contribute with several advantages in the development of algorithms, and specifically in the development of subgroup discovery (SD) approaches. SD is a descriptive data mining technique using supervised learning in order to describe data with respect to a property of interest. This paper present the main features of the FuGePSD algorithm, an EFS based on genetic programming and fuzzy logic. An experimental study with a wide number of datasets shows the quality of this algorithm with respect to the remaining EFSs for SD presented throughout the literature.", } @Article{Carmona:2015:IS, author = "C. J. Carmona and V. Ruiz-Rodado and M. J. {del Jesus} and A. Weber and M. Grootveld and P. Gonzalez and D. Elizondo", title = "A fuzzy genetic programming-based algorithm for subgroup discovery and the application to one problem of pathogenesis of acute sore throat conditions in humans", journal = "Information Sciences", volume = "298", pages = "180--197", year = "2015", ISSN = "0020-0255", DOI = "doi:10.1016/j.ins.2014.11.030", URL = "http://www.sciencedirect.com/science/article/pii/S0020025514011116", size = "18 pages", keywords = "genetic algorithms, genetic programming, Subgroup discovery, Evolutionary fuzzy system, Bioinformatics", abstract = "This paper proposes a novel algorithm for subgroup discovery task based on genetic programming and fuzzy logic called Fuzzy Genetic Programming-based for Subgroup Discovery (FuGePSD). The genetic programming allows to learn compact expressions with the main objective to obtain rules for describing simple, interesting and interpretable subgroups. This algorithm incorporates specific operators in the search process to promote the diversity between the individuals. The evolutionary scheme of FuGePSD is codified through the genetic cooperative-competitive approach promoting the competition and cooperation between the individuals of the population in order to find out the optimal solutions for the SD task. FuGePSD displays its potential with high-quality results in a wide experimental study performed with respect to others evolutionary algorithms for subgroup discovery. Moreover, the quality of this proposal is applied to a case study related to acute sore throat problems.", } @InCollection{carobus:2000:EGPBUGPCPNH, author = "Alexander P. Carobus", title = "Evolution of Game Playing Behavior: Using Genetic Programming to Create Players for Net Hack", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "60--69", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:2000:gagp}", } @InProceedings{Carpinlioglu:2024:CLEO-PR, author = "Bora Carpinlioglu and Bahrem Serhat Danis and Ugur Tegin", title = "Optical Random Neural Networks with Genetic Programming", booktitle = "2024 Conference on Lasers and Electro-Optics Pacific Rim (CLEO-PR)", year = "2024", month = aug, keywords = "genetic algorithms, genetic programming, Accuracy, Neural networks, ANN, Scattering, Optical computing, Optical fiber networks, Optical imaging", ISSN = "2997-7037", DOI = "doi:10.1109/CLEO-PR60912.2024.10676723", abstract = "We demonstrate a programmable optical random neural network by genetically optimising a scattering medium. Improvements in classification accuracy are shown for image datasets. Our results indicate the effectiveness of optical computing for future computing platforms.", notes = "Also known as \cite{10676723}", } @InProceedings{Carreno:2007:cec, author = "Emiliano Carreno and Guillermo Leguizamon and Neal Wagner", title = "Evolution of Classification Rules for Comprehensible Knowledge Discovery", booktitle = "2007 IEEE Congress on Evolutionary Computation", year = "2007", editor = "Dipti Srinivasan and Lipo Wang", pages = "1261--1268", address = "Singapore", month = "25-28 " # sep, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "1-4244-1340-0", file = "1695.pdf", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2007.4424615", abstract = "This article, which lies within the data mining framework, proposes a method to build classifiers based on the evolution of rules. The method, named REC (Rule Evolution for Classifiers), has three main features: it applies genetic programming to perform a search in the space of potential solutions; a procedure allows biasing the search towards regions of comprehensible hypothesis with high predictive quality and it includes a strategy for the selection of an optimum subset of rules (classifier) from the rules obtained as the result of the evolutionary process. A comparative study between this method and the rule induction algorithm C5.0 is carried out for two application problems (data sets). Experimental results show the advantages of using the method proposed.", notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C", } @Article{CarrenoJara:2011:GPEM, author = "Emiliano {Carreno Jara}", title = "Long memory time series forecasting by using genetic programming", journal = "Genetic Programming and Evolvable Machines", year = "2012", volume = "12", number = "4", pages = "429--456", month = dec, keywords = "genetic algorithms, genetic programming, Long memory, Time series forecasting, Multi-objective search, ARFIMA models", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-011-9140-7", size = "28 pages", abstract = "Real-world time series have certain properties, such as stationarity, seasonality, linearity, among others, which determine their underlying behaviour. There is a particular class of time series called long-memory processes, characterised by a persistent temporal dependence between distant observations, that is, the time series values depend not only on recent past values but also on observations of much prior time periods. The main purpose of this research is the development, application, and evaluation of a computational intelligence method specifically tailored for long memory time series forecasting, with emphasis on many-step-ahead prediction. The method proposed here is a hybrid combining genetic programming and the fractionally integrated (long-memory) component of autoregressive fractionally integrated moving average (ARFIMA) models. Another objective of this study is the discovery of useful comprehensible novel knowledge, represented as time series predictive models. In this respect, a new evolutionary multi-objective search method is proposed to limit complexity of evolved solutions and to improve predictive quality. Using these methods allows for obtaining lower complexity (and possibly more comprehensible) models with high predictive quality, keeping run time and memory requirements low, and avoiding bloat and over-fitting. The methods are assessed on five real-world long memory time series and their performance is compared to that of statistical models reported in the literature. Experimental results show the proposed methods' advantages in long memory time series forecasting.", notes = "River Nile flow, Radial basis function, finance UK inflation rate. FI-GP. Long-memory variables. RBF-GP. fractional Gaussian Model encapsulation, lags. GPC++ version 0.40", } @InProceedings{Carreras:2010:percomWKS, author = "Iacopo Carreras and David Linner", title = "Self-evolving applications over opportunistic communication systems", booktitle = "8th IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops, 2010)", year = "2010", month = mar # " 29-" # apr # " 2", pages = "153--158", keywords = "genetic algorithms, genetic programming, BioNets, P2P, mobile devices, opportunistic communication systems, self-evolving applications, mobile radio", abstract = "In this work, we focus on an application scenario in which services running over users mobile devices exploit opportunistic communications in order to evolve over time, as the result of a distributed and collaborative process. We propose a framework which is based on genetic programming and supports an asynchronous and distributed evolution of composite services. We implement the framework over off-the-shelf components and evaluate it through field trials in the case of a gaming scenario. Results show the ability of the proposed system to evolve over time in order to adapt to varying contexts.", DOI = "doi:10.1109/PERCOMW.2010.5470677", notes = "Mashup. telephone. GUI assumed. bionets. Ten people played unsolvable quiz. Evolve workflow graph (task schedule) and data flow graph (port connections). Fitness based on resources consumed (memory, CPU, network, electrical power) and fitting user needs against 'optimal execution profile' which gives 'optimal output values'. Deadlock prevention. W3C widget. Also known as \cite{5470677}", } @Article{CarrilloGuayacan:2024:compgeo, author = "Lina-Maria Guayacan-Carrillo and Jean Sulem", title = "Symbolic regression based prediction of anisotropic closure in deep tunnels", journal = "Computers and Geotechnics", year = "2024", volume = "171", pages = "106355", keywords = "genetic algorithms, genetic programming, Tunnel excavation, Convergence measurements, Anisotropic deformation, Symbolic regression, Machine learning approach", ISSN = "0266-352X", URL = "https://www.sciencedirect.com/science/article/pii/S0266352X2400291X", DOI = "doi:10.1016/j.compgeo.2024.106355", abstract = "This work investigates the applicability of Genetic Programming with Symbolic Regression to analyse the closure evolution of tunnels during and after excavation. Special attention is given to anisotropic closure evolution, which depends on the anisotropy of the initial stress state and the intrinsic anisotropy of the rock mass formation. A methodology is proposed that takes into account the information recorded during excavation to train the algorithm and find a free-form simple mathematical expression that captures the closure evolution over time. The proposed methodology is applied to two case studies of deep tunnels with high anisotropic convergence evolution. The results obtained show that this approach performs well with the small dataset used in this work. The proposed approach is an interesting tool to improve the understanding and prediction of the ground response", } @InProceedings{DBLP:conf/flairs/CarseP01, author = "Brian Carse and Anthony G. Pipe", title = "A Framework for Evolving Fuzzy Classifier Systems Using Genetic Programming", booktitle = "Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference", year = "2001", editor = "Ingrid Russell and John F. Kolen", pages = "465--469", address = "Key West, Florida, USA", month = may # " 21-23", publisher = "AAAI Press", bibsource = "DBLP, http://dblp.uni-trier.de", keywords = "genetic algorithms, genetic programming", ISBN = "1-57735-133-9", URL = "http://www.aaai.org/Papers/FLAIRS/2001/FLAIRS01-089.pdf", URL = "https://www.aaai.org/Library/FLAIRS/flairs01contents.php", size = "5 pages", abstract = "A fuzzy classifier system framework is proposed which employs a tree-based representation for fuzzy rule (classifier) antecedents and genetic programming for fuzzy rule discovery. Such a rule representation is employed because of the expressive power and generality it endows to individual rules. The framework proposes accuracy-based fitness for individual fuzzy classifiers and employs evolutionary competition between simultaneously matched classifiers. The evolutionary algorithm (GP) is therefore searching for compact fuzzy rule bases which are simultaneously general, accurate and co-adapted. Additional extensions to the proposed framework are suggested", } @Misc{carter2003network, title = "Network surveillance and security system", author = "Ernst B. Carter and Vasily Zolotov", year = "2003", month = mar # "~13", publisher = "Google Patents", note = "US Patent App. 09/766,560", keywords = "genetic algorithms, genetic programming", URL = "http://appft1.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PG01&p=1&u=/netahtml/PTO/srchnum.html&r=1&f=G&l=50&s1=20030051026.PGNR.", URL = "http://www.google.co.uk/patents/US20030051026", size = "many pages", abstract = "A system that monitors and protects the security of computer networks uses artificial intelligence, including learning algorithms, neural networks and genetic programming, to learn from security events. The invention maintains a knowledge base of security events that updates autonomously in real time. The invention encrypts communications to exchange changes in its knowledge base with separate security systems protecting other computer networks. The invention autonomously alters its security policies in response to ongoing events. The invention tracks network communication traffic from inception at a well-known port throughout the duration of the communication including monitoring of any port the communication is switched to. The invention is able to track and UNIX processes for monitoring, threat detection, and threat response functions. The invention is able to subdivide the network communications into identifying tags for tracking and control of the communications without incurring lags in response times.", notes = "20030051026", } @Article{Carvalho:2002:ASC, author = "D. R. Carvalho and A. A. Freitas", title = "A genetic algorithm for discovering small disjunct rules in data mining", journal = "Applied Soft Computing", year = "2002", volume = "2", number = "2", pages = "75--88", month = dec, keywords = "genetic algorithms, data mining, classification, Rule discovery, Small disjuncts", URL = "http://www.sciencedirect.com/science/article/B6W86-477FN8B-1/2/2704d983f8282d055e302ebab5471fc1", URL = "https://www.cs.kent.ac.uk/people/staff/aaf/pubs.html", DOI = "doi:10.1016/S1568-4946(02)00031-5", abstract = "This paper addresses the well-known classification task of data mining, where the goal is to discover rules predicting the class of examples (records of a given dataset). In the context of data mining, small disjuncts are rules covering a small number of examples. Hence, these rules are usually error-prone, which contributes to a decrease in predictive accuracy. At first glance, this is not a serious problem, since the impact on predictive accuracy should be small. However, although each small-disjunct covers few examples, the set of all small disjuncts can cover a large number of examples. This paper presents evidence that this is the case in several datasets. This paper also addresses the problem of small disjuncts by using a hybrid decision-tree/genetic-algorithm approach. In essence, examples belonging to large disjuncts are classified by rules produced by a decision-tree algorithm (C4.5), while examples belonging to small disjuncts are classified by a genetic-algorithm specifically designed for discovering small-disjunct rules. We present results comparing the predictive accuracy of this hybrid system with the prediction accuracy of three versions of C4.5 alone in eight public domain datasets. Overall, the results show that our hybrid system achieves better predictive accuracy than all three versions of C4.5 alone.", } @InProceedings{Carvalho:2020:GECCO, author = "Pedro Carvalho and Nuno Lourenco and Filipe Assuncao and Penousal Machado", title = "{AutoLR}: An Evolutionary Approach to Learning Rate Policies", year = "2020", editor = "Carlos Artemio {Coello Coello} and Arturo Hernandez Aguirre and Josu Ceberio Uribe and Mario Garza Fabre and Gregorio {Toscano Pulido} and Katya Rodriguez-Vazquez and Elizabeth Wanner and Nadarajen Veerapen and Efren Mezura Montes and Richard Allmendinger and Hugo Terashima Marin and Markus Wagner and Thomas Bartz-Beielstein and Bogdan Filipic and Heike Trautmann and Ke Tang and John Koza and Erik Goodman and William B. Langdon and Miguel Nicolau and Christine Zarges and Vanessa Volz and Tea Tusar and Boris Naujoks and Peter A. N. Bosman and Darrell Whitley and Christine Solnon and Marde Helbig and Stephane Doncieux and Dennis G. Wilson and Francisco {Fernandez de Vega} and Luis Paquete and Francisco Chicano and Bing Xue and Jaume Bacardit and Sanaz Mostaghim and Jonathan Fieldsend and Oliver Schuetze and Dirk Arnold and Gabriela Ochoa and Carlos Segura and Carlos Cotta and Michael Emmerich and Mengjie Zhang and Robin Purshouse and Tapabrata Ray and Justyna Petke and Fuyuki Ishikawa and Johannes Lengler and Frank Neumann", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference", pages = "672--680", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, grammatical evolution, structured grammatical evolution, learning rate schedulers", isbn13 = "9781450371285", URL = "http://www.human-competitive.org/sites/default/files/carvalho-autolr.txt", URL = "http://www.human-competitive.org/sites/default/files/autolr_-_an_evolutionary_approach_to_learning_rate_policies.pdf", URL = "https://doi.org/10.1145/3377930.3390158", DOI = "doi:10.1145/3377930.3390158", size = "9 pages", abstract = "The choice of a proper learning rate is paramount for good Artificial Neural Network training and performance. In the past, one had to rely on experience and trial-and-error to find an adequate learning rate. Presently, a plethora of state of the art automatic methods exist that make the search for a good learning rate easier. While these techniques are effective and have yielded good results over the years, they are general solutions. This means the optimization of learning rate for specific network topologies remains largely unexplored. This work presents AutoLR, a framework that evolves Learning Rate Schedulers for a specific Neural Network Architecture using Structured Grammatical Evolution. The system was used to evolve learning rate policies that were compared with a commonly used baseline value for learning rate. Results show that training performed using certain evolved policies is more efficient than the established baseline and suggest that this approach is a viable means of improving a neural network's performance.", notes = "Entered 2021 HUMIES Also known as \cite{10.1145/3377930.3390158} \cite{DBLP:conf/gecco/Carvalho0AM20} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @Misc{DBLP:journals/corr/abs-2103-12623, author = "Pedro Carvalho and Nuno Lourenco and Penousal Machado", title = "Evolving Learning Rate Optimizers for Deep Neural Networks", howpublished = "ArXiv", year = "2021", month = "23 " # mar, volume = "abs/2103.12623", keywords = "genetic algorithms, genetic programming, ANN", URL = "http://www.human-competitive.org/sites/default/files/carvalho-autolr.txt", URL = "http://www.human-competitive.org/sites/default/files/evolving_learning_rate_optimizers_for_deep_neural_networks.pdf", URL = "https://arxiv.org/abs/2103.12623", size = "10 pages", abstract = "Artificial Neural Networks (ANNs) became popular due to their successful application difficult problems such image and speech recognition. However, when practitioners want to design an ANN they need to undergo laborious process of selecting a set of parameters and topology. Currently, there are several state-of-the art methods that allow for the automatic selection of some of these aspects. Learning Rate optimisers are a set of such techniques that search for good values of learning rates. Whilst these techniques are effective and have yielded good results over the years, they are general solution i.e. they do not consider the characteristics of a specific network. We propose a framework called AutoLR to automatically design Learning Rate Optimizers. Two versions of the system are detailed. The first one, Dynamic AutoLR, evolves static and dynamic learning rate optimizers based on the current epoch and the previous learning rate. The second version, Adaptive AutoLR, evolves adaptive optimizers that can fine tune the learning rate for each network weight which makes them generally more effective. The results are competitive with the best state of the art methods, even outperforming them in some scenarios. Furthermore, the system evolved a classifier, ADES, that appears to be novel and innovative since, to the best of our knowledge, it has a structure that differs from state of the art methods.", notes = "Entered 2021 HUMIES", } @InProceedings{Carvalho:2022:EuroGP, author = "Pedro Carvalho and Nuno Lourenco and Penousal Machado", title = "Evolving Adaptive Neural Network Optimizers for Image Classification", booktitle = "EuroGP 2022: Proceedings of the 25th European Conference on Genetic Programming", year = "2022", editor = "Eric Medvet and Gisele Pappa and Bing Xue", series = "LNCS", volume = "13223", publisher = "Springer Verlag", address = "Madrid, Spain", pages = "3--18", month = "20-22 " # apr, organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, ANN, Neuroevolution, Adaptive Optimizers, Structured Grammatical Evolution", isbn13 = "978-3-031-02055-1", URL = "https://www.human-competitive.org/sites/default/files/humiessubmission_2.txt", URL = "https://www.human-competitive.org/sites/default/files/evolving_adaptive_neural_network_optimizers_for_image_classification_2.pdf", DOI = "doi:10.1007/978-3-031-02056-8_1", size = "16 pages", abstract = "The evolution of hardware has enabled Artificial Neural Networks to become a staple solution to many modern Artificial Intelligence problems such as natural language processing and computer vision. The neural network effectiveness is highly dependent on the optimizer used during training, which motivated significant research into the design of neural network optimizers. Current research focuses on creating optimizers that perform well across different topologies and network types. While there is evidence that it is desirable to fine-tune optimizer parameters for specific networks, the benefits of designing optimizers specialized for single networks remain mostly unexplored. we propose an evolutionary framework called Adaptive AutoLR (ALR) to evolve adaptive optimizers for specific neural networks in an image classification task. The evolved optimizers are then compared with state-of-the-art, human-made optimizers on two popular image classification problems. The results show that some evolved optimizers perform competitively in both tasks, even achieving the best average test accuracy in one dataset. An analysis of the best evolved optimizer also reveals that it functions differently from human-made approaches. The results suggest ALR can evolve novel, high-quality optimizers motivating further research and applications of the framework.", notes = "Entered 2022 HUMIES http://www.evostar.org/2022/eurogp/ Part of \cite{Medvet:2022:GP} EuroGP'2022 held inconjunction with EvoApplications2022 EvoCOP2022 EvoMusArt2022", } @InProceedings{Carvalho:2023:EuroGP, author = "Pedro Carvalho and Jessica Megane and Nuno Lourenco and Penousal Machado", title = "Context Matters: Adaptive Mutation for Grammars", booktitle = "EuroGP 2023: Proceedings of the 26th European Conference on Genetic Programming", year = "2023", month = "12-14 " # apr, editor = "Gisele Pappa and Mario Giacobini and Zdenek Vasicek", series = "LNCS", volume = "13986", publisher = "Springer Verlag", address = "Brno, Czech Republic", pages = "117--132", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Adaptive Mutation, Grammar-design, Grammar-based Genetic Programming", isbn13 = "978-3-031-29572-0", URL = "https://rdcu.be/c8URQ", DOI = "doi:10.1007/978-3-031-29573-7_8", size = "16 pages", abstract = "Adaptive Facilitated Mutation, a self-adaptive mutation method for Structured Grammatical Evolution (SGE), biologically inspired by the theory of facilitated variation. In SGE, the genotype of individuals contains a list for each non-terminal of the grammar that defines the search space. In our proposed mutation, each individual contains an array with a different, self-adaptive mutation rate for each non-terminal. We also propose Function Grouped Grammars, a grammar design procedure to enhance the benefits of the propose mutation. Experiments were conducted on three symbolic regression benchmarks using Probabilistic Structured Grammatical Evolution (PSGE), a variant of SGE. Results show our approach is similar or better when compared with the standard grammar and mutation.", notes = "Nominated for best paper award Part of \cite{Pappa:2023:GP} EuroGP'2023 held in conjunction with EvoCOP2023, EvoMusArt2023 and EvoApplications2023", } @InProceedings{Carvalho:2021:IAM, author = "Samuel Carvalho and Joe Sullivan and Douglas Dias and Enrique Naredo and Conor Ryan", title = "Using Grammatical Evolution for Modelling Energy Consumption on a Computer Numerical Control Machine", booktitle = "6th Workshop on Industrial Applications of Metaheuristics", year = "2021", editor = "Silvino Fernandez Alzueta and Thomas Stuetzle and Pablo Valledor", pages = "1557--1563", address = "internet", series = "GECCO '21", month = jul # " 10-14", organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Real-World Applications, CNC Machines, Energy Consumption", isbn13 = "978-1-4503-8351-6", DOI = "doi:10.1145/3449726.3463185", size = "7 pages", abstract = "Discrete manufacturing is known to be a high consumer of energy and much work has been done in continuous improvement and energy saving methods addressing this issue. Computer Numerical Control (CNC) machines, commonly used in the manufacturing of metal parts, are highly energy-demanding because of many required sub-systems, such as cooling, lubrication, logical interfaces and electric motors. For this reason, there is a large body of work focusing on modeling the energy needs of this class of machine.", notes = "Limerick Institute of Technology GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)", } @InProceedings{Casadei:2019:BRACIS, author = "Felipe Casadei and Joao Francisco B. S. Martins and Gisele L. Pappa", title = "A Multi-objective Approach for Symbolic Regression with Semantic Genetic Programming", booktitle = "2019 8th Brazilian Conference on Intelligent Systems (BRACIS)", year = "2019", pages = "66--71", month = "15-18 " # oct, address = "Salvador, Brazil", keywords = "genetic algorithms, genetic programming, Geometric Semantic Genetic Programming, Symbolic Regression, Training, Semantic search, Sociology, Statistics, Predictive models, Multi-ojective Optimization", isbn13 = "978-1-7281-4254-8", ISSN = "2643-6264", DOI = "doi:10.1109/BRACIS.2019.00021", size = "6 pages", abstract = "This paper proposes a multi-objective approach for solving symbolic regression problems using Geometric Semantic Genetic Programming (GSGP). The proposed method produces models specialized in smaller regions of the semantic search space, where the errors of the models into these different regions are the objectives being optimized. The method incorporates different ways of defining these sub-regions of the semantic space as well as a method to combine the models found intending to produce a unique prediction. Experimental results obtained over 10 real-world datasets show that the proposed method outperforms traditional GSGP in 7 out of 10 datasets.", notes = "Also known as \cite{8923924}", } @InProceedings{Casado:2024:SMC, author = "Ricardo S. Casado and Mario L. Tronco and Emerson C. Pedrino", title = "A Robust Method for Camera Calibration in Noisy Settings Based on Genetic Programming", booktitle = "2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)", year = "2024", pages = "2461--2467", month = oct, keywords = "genetic algorithms, genetic programming, Training, Accuracy, Three-dimensional displays, Databases, Noise, Cameras, Mathematical models, Calibration, Noise measurement", DOI = "doi:10.1109/SMC54092.2024.10831486", abstract = "In this article, we introduce a novel camera cali-bration method using genetic programming, to calibrate cam-eras in noisy environments. Traditional calibration methods, such as those of Tsai and Zhang, extensively use the pinhole camera model, and are less accurate in the presence of noise. In this work, high precision calibration is achieved using pseudo linear genetic programming. Instead of the pinhole camera model, pseudo linear genetic programming generates mathematical functions which allow for far greater precision in the calibration process than classical methods, regardless of the environmental conditions. The method has several challenges such as identification of suitable calibration functions and creation of an extensive training database. However, the method provides advantages in terms of better results quality and practicality, as it eliminates the necessity of the intrinsic camera parameters. The results illustrate that this methodology is far superior in comparison to the current state-of-the-art technique, Zhang's widely used method, with a 20x improvement in calibration accuracy.", notes = "Also known as \cite{10831486}", } @InProceedings{Casanova:2010:cec, author = "Isidoro J. Casanova", title = "Tradinnova-LCS: Dynamic stock portfolio decision-making assistance model with genetic based machine learning", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-6910-9", abstract = "This paper describes a decision system based on rules for the management of a stock portfolio using a mechanism of dynamic learning to select the stocks to be incorporated. This system simulates the intelligent behaviour of an investor, carrying out the buying and selling of stocks, such that during each day the best stocks will be selected to be incorporated in the portfolio by reinforcement learning with genetic programming. The system has been tested in 3 time periods (1 year, 3 years and 5 years), simulating the purchase/sale of stocks in the Spanish continuous market and the results have been compared with the revaluations obtained by the best investment funds operating in Spain.", DOI = "doi:10.1109/CEC.2010.5586067", notes = "WCCI 2010. Also known as \cite{5586067}", } @PhdThesis{Edgar_Enrique_Casasola_Murillo, author = "Edgar Enrique {Casasola Murillo}", title = "Desarrollo de un modelo computacional para la especificacion de sistemas de analisis de sentimiento con comentarios de redes sociales en espanol", school = "Universidad de Costa Rica", year = "2018", address = "San Jose, Costa Rica", keywords = "genetic algorithms, genetic programming, emociones, procesamiento del lenguaje natural (ciencias de la computacion), redes sociales en linea, mineria de datos", URL = "https://catalogosiidca.csuca.org/Record/UCR.000604145", broken = "http://hdl.handle.net/123456789/293", broken = "http://repositorio.conicit.go.cr:8080/xmlui/handle/123456789/293", URL = "http://repositorio.conicit.go.cr:8080/xmlui/bitstream/handle/123456789/293/Edgar%20Enrique%20Casasola%20Murillo.pdf", size = "151 pages", abstract = "El modelo propuesto en esta tesis permite llevar a cabo la especificacion de sistemas de analisis de sentimiento a partir de comentarios de texto en idioma espanol publicados en redes sociales. Se presenta un nuevo modelo llamado SAM que integra conceptos extraidos del analisis de sistemas existentes segun la teoria proveniente de campos como la linguistica, la inteligencia artificial, y la recuperacion de informacion. El modelo fue evaluado en terminos de su completitud, pertinencia y aplicabilidad. Se concluyo que el modelo permite formalizar conceptos comunes para la comunicacion entre grupos de investigacion, y ademas proporciona una base para descripcion de sistemas de clasificacion de opiniones. SAM cuenta con potencial para agilizar el desarrollo de sistemas al facilitar la deteccion de componentes utiles y nuevas posibles combinaciones. Asimismo, permite un analisis profundo para la comparacion de diferentes sistemas. El proceso de investigacion permitio llenar un vacio conceptual que existia en el campo y como aporte colateral permitio el desarrollo de recursos computacionales y linguisticos que incluyen: software para recoleccion y normalizacion de comentarios de texto en espanol obtenido desde redes sociales, nuevos corpus, diccionarios de terminos con polaridad y datos de prueba utilizados a nivel internacional.", notes = "In Spanish Supervisor: Gabriela Marin Raventos Google translate: system development of a computational model for the specification of sentence analysis systems with comments from social networks in spain", } @InProceedings{DBLP:conf/kbse/CashinMWF19, author = "Padraic Cashin and Carianne Martinez and Westley Weimer and Stephanie Forrest", title = "Understanding Automatically-Generated Patches Through Symbolic Invariant Differences", booktitle = "34th {IEEE/ACM} International Conference on Automated Software Engineering, ASE 2019", year = "2019", pages = "411--414", address = "San Diego, CA, USA", month = nov # " 11-15", keywords = "genetic algorithms, genetic programming, GenProg, daikon, APR", timestamp = "Sun, 19 Jan 2020 15:19:48 +0100", biburl = "https://dblp.org/rec/conf/kbse/CashinMWF19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://doi.org/10.1109/ASE.2019.00046", DOI = "doi:10.1109/ASE.2019.00046", size = "4 pages", abstract = "Developer trust is a major barrier to the deployment of automatically-generated patches. Understanding the effect of a patch is a key element of that trust. We find that differences in sets of formal invariants characterize patch differences and that implication-based distances in invariant space characterize patch similarities. When one patch is similar to another it often contains the same changes as well as additional behaviour; this pattern is well-captured by logical implication. We can measure differences using a theorem prover to verify implications between invariants implied by separate programs. Although effective, theorem provers are computationally intensive; we find that string distance is an efficient heuristic for implication-based distance measurements. We propose to use distances between patches to construct a hierarchy highlighting patch similarities. We evaluated this approach on over 300 patches and found that it correctly categorises programs into semantically similar clusters. Clustering programs reduces human effort by reducing the number of semantically distinct patches that must be considered by over 50percent, thus reducing the time required to establish trust in automatically generated repairs.", } @PhdThesis{Casjens_Dissertation, author = "Swaantje Wiarda Casjens", title = "Adaption und Vergleich evolutionaerer mehrkriterieller Algorithmen mit Hilfe von Variablenwichtigkeitsmassen", title2 = "Am Beispiel der kostensensitiven Klassifikation von Lungenkrebssubtypen", title3 = "Adaptation and comparison of evolutionary multi-criteria algorithms with the help of variable importance measures - using the example of the cost-sensitive classification of lung cancer subtypes", school = "Der Fakultaet Statistik, der Technischen Universitaet Dortmund", year = "2013", address = "Germany", month = "9 " # jul, keywords = "genetic algorithms, genetic programming, MOGP, Pareto, Baum-Repraesentation evolutionaere Algorithmen, kostensensitive Klassifikation, mehrkriterielle Optimierung, Variablenwichtigkeitsmasse", URL = "https://eldorado.tu-dortmund.de/bitstream/2003/30431/1/Casjens_Dissertation.pdf", URL = "http://hdl.handle.net/2003/30431", URL = "https://eldorado.tu-dortmund.de/handle/2003/30431", DOI = "doi:10.17877/DE290R-5588", size = "213 pages", abstract = "Bei der Herleitung eines Klassifikationsmodells ist neben der Vorhersageguete auch die Guete der Variablenauswahl ein wichtiges Kriterium. Bei Einflussvariablen mit unterschiedlichen Kosten ist eine kostensensitive Klassifikation erstrebenswert, bei der ein Kompromiss aus hoher Vorhersageguete und geringen Kosten getroffen werden kann. Werden konfliktaere Ziele, wie etwa hier die Vorhersageguete und die Kosten, gleichzeitig optimiert, entsteht ein mehrkriterielles Optimierungsproblem, fuer das keine einzelne sondern eine Menge unvergleichbarer Loesungen existieren. Fuer das Auffinden der unvergleichbaren Loesungen sind evolutionaere mehrkriterielle Optimierungsalgorithmen (EMOAs) gut geeignet, da sie unter anderem nach verschiedenen Loesungen parallel suchen koennen und unabhaengig von der zugrunde liegenden Datenverteilung sind. Haeufig werden EMOAs fuer die Loesung mehrkriterieller Klassifikationsprobleme in Form von Wrapper-Ansaetzen verwendet, wobei die EMOA-Individuen als binaere Zeichenketten (Bitstrings) codiert sind und jedes Bit die Verfuegbarkeit der entsprechenden Einflussvariable beschreibt. Basierend auf diesen Variablenteilmengen und gegebenen Daten erstellt der umhuellte (wrapped) Klassifikationsalgorithmus ein Klassifikationsmodell, mit dem Ziel die Vorhersageguete zu optimieren. Erst nach der Konstruktion des Klassifikationsmodells koennen weitere Zielkriterien, wie etwa die Kosten der selektierten Variablen, ausgewertet werden. Damit entsteht eine Hierarchie der zu optimierenden Zielkriterien mit Vorteil fuer die Vorhersageguete, sodass durch einen mehrkriteriellen Wrapper-Ansatz keine nicht-hierarchischen Loesungen gefunden werden koennen. Diese Hierarchie der Zielfunktionen wird erstmals in Rahmen dieser Arbeit beschrieben und untersucht. Als Alternative zum mehrkriteriellen Wrapper-Ansatz wird in dieser Arbeit ein nicht-hierarchischer evolutionaerer mehrkriterieller Optimierungsalgorithmus mit Baum-Repraesentation (NHEMOtree) entwickelt, um mehrkriterielle Optimierungsprobleme mit gleichberechtigten Optimierungszielen zu loesen. NHEMOtree basiert auf einem EMOA mit Baum-Repraesentation, der ohne internen Klassifikationsalgorithmus die Variablenselektion vollzieht und ohne Hierarchie in den Zielfunktionen mehrkriteriell optimierte binaere Entscheidungsbaeume erstellt. Des Weiteren werden ein auf mehrkriteriellen Variablenwichtigkeitsmassen (VIMs) basierter Rekombinationsoperator fuer NHEMOtree und eine NHEMOtree-Version mit lokaler Cutoff-Optimierung entwickelt. In dieser Arbeit werden erstmalig die Loesungen einer mehrkriteriellen Optimierung durch einen mehrkriteriellen Wrapper-Ansatz und durch einen EMOA mit Baum-Repraesentation (NHEMOtree) miteinander verglichen. Die Bewertung der Loesungen erfolgt dabei sowohl mittels der bekannten S-Metrik als auch durch den hier entwickelten Dominanzquotienten. Die Guete des VIM-basierten Rekombinationsoperators wird im Vergleich zum Standard-Rekombinationsoperator fuer EMOAs mit Baum-Repraesentation untersucht. Die mehrkriteriellen Optimierungsansaetze und Operatoren werden auf medizinische und simulierte Daten angewendet. Die Ergebnisse zeigen, dass NHEMOtree bessere Loesungen als der mehrkriterielle Wrapper-Ansatz findet. Die Verwendung des VIM-basierten Rekombinationsoperators fuehrt im Gegensatz zum Standard-Operator zu nochmals besseren Loesungen des mehrkriteriellen Optimierungsproblems und zu einer schnelleren Konvergenz des NHEMO trees.", notes = "In German", } @Article{CASTEJON20181003, author = "Federico Castejon and Enrique J. Carmona", title = "Automatic design of analog electronic circuits using grammatical evolution", journal = "Applied Soft Computing", year = "2018", volume = "62", pages = "1003--1018", month = jan, keywords = "genetic algorithms, genetic programming, Grammatical evolution, EHW, Automatic circuit design, Analog circuits, Evolutionary electronics, NGSpice", ISSN = "1568-4946", URL = "http://www.sciencedirect.com/science/article/pii/S1568494617305756", DOI = "doi:10.1016/j.asoc.2017.09.036", abstract = "A new approach for automatic synthesis of analog electronic circuits based on grammatical evolution is presented. Grammatical evolution is an evolutionary algorithm based on grammar which can generate code in any programming language and uses variable length linear binary strings. The decoding of each chromosome determines which production rules in a Backus-Naur Form grammar definition are used in a genotype-to-phenotype mapping process. In our method, decoding focuses on obtaining circuit netlists. A new grammar for generating such netlists and a variant of the XOSites-based crossover operator are also presented. A post-processing stage is needed to adapt the decoded netlist prior its evaluation using the NGSpice simulator. Our approach was applied to several case studies, comprising a total of seven benchmark circuits. A comparison with previous works in the literature shows that our method produces competitive circuits in relation to the degree of compliance with the output specifications, the number of components and the number of evaluations used in the evolutionary process.", } @Article{Castejon:2020:ACC, author = "Federico Castejon and Enrique J. Carmona", journal = "IEEE Access", title = "Introducing Modularity and Homology in Grammatical Evolution to Address the Analog Electronic Circuit Design Problem", year = "2020", volume = "8", pages = "137275--137292", keywords = "genetic algorithms, genetic programming", ISSN = "2169-3536", DOI = "doi:10.1109/ACCESS.2020.3011641", abstract = "We present a new approach based on grammatical evolution (GE) aimed at addressing the analog electronic circuit design problem. In the new approach, called multi-grammatical evolution (MGE), a chromosome is a variable-length codon string that is divided into as many partitions as subproblems result from breaking down the original optimization problem: circuit topology and component sizing in our case. This leads to a modular approach where the solution of each subproblem is encoded and evolved in a partition of the chromosome. Additionally, each partition is decoded according to a specific grammar and the final solution to the original problem emerges as an aggregation result associated with the decoding process of the different partitions. Modularity facilitates the encoding and evolution of the solution in each subproblem. On the other way, homology helps to reduce the potentially destructive effect associated with standard crossover operators normally used in GE-based approaches. Seven analog circuit designs are addressed by an MGE-based method and the obtained results are compared to those obtained by different methods based on GE and other evolutionary paradigms. A simple parsimony mechanism was also introduced to ensure compliance with design specifications and reduce the number of components of the circuits obtained. We can conclude that our method obtains competitive results in the seven circuits analyzed.", notes = "Also known as \cite{9146844}", } @InProceedings{Castellano:2021:SBST, author = "Ezequiel Castellano and Ahmet Cetinkaya and Cedric Ho Thanh and Stefan Klikovits and Xiaoyi Zhang and Paolo Arcaini", title = "{Frenetic} at the {SBST} 2021 Tool Competition", booktitle = "2021 IEEE/ACM 14th International Workshop on Search-Based Software Testing (SBST)", year = "2021", editor = "Jie M Zhang and Erik Fredericks", pages = "36--37", address = "internet", month = "31 " # may, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-6654-4571-9/21/", URL = "https://raw.githubusercontent.com/ERATOMMSD/frenetic-sbst21/main/src/frenetic-sbst21-preprint.pdf", code_url = "https://github.com/ERATOMMSD/frenetic-sbst21", video_url = "https://mmm-www-videos.s3-ap-northeast-1.amazonaws.com/MMMSeminar/etc/2021_SBST_Frenetic/frenetic_talk.mp4", DOI = "doi:10.1109/SBST52555.2021.00016", size = "2 pages", abstract = "Frenetic is a genetic approach that leverages a curvature-based road representation. Given an autonomous driving agent, the goal of Frenetic is to generate roads where the agent fails to stay within its lane. In other words, Frenetic tries to minimise the out of bound distance, which is the distance between the car and either edge of the lane if the car is within the lane, and proceeds to negative values once the car drives off. This resembles classic aspects of genetic algorithms such as mutations and crossover, but introduces some nuances aiming at improving diversity of the generated roads.", notes = "National Institute of Informatics, Tokyo, Japan https://sbst21.github.io/program/", } @InProceedings{castellanos-garzon:2019:PACBBIC, author = "Jose A. Castellanos-Garzon and Juan Ramos and Yeray Mezquita Martin and Juan F. de Paz and Ernesto Costa", title = "A Genetic Programming Approach Applied to Feature Selection from Medical Data", booktitle = "Practical Applications of Computational Biology and Bioinformatics, 12th International Conference", year = "2019", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-319-98702-6_24", DOI = "doi:10.1007/978-3-319-98702-6_24", } @Article{castellanos-garzon:2020:Processes, author = "Jose A. Castellanos-Garzon and Yeray {Mezquita Martin} and Jose Luis {Jaimes Sanchez} and Santiago Manuel {Lopez Garcia} and Ernesto Costa", title = "A Genetic Programming Strategy to Induce Logical Rules for Clinical Data Analysis", journal = "Processes", year = "2020", volume = "8", number = "12", keywords = "genetic algorithms, genetic programming", ISSN = "2227-9717", URL = "https://www.mdpi.com/2227-9717/8/12/1565", DOI = "doi:10.3390/pr8121565", abstract = "This paper proposes a machine learning approach dealing with genetic programming to build classifiers through logical rule induction. In this context, we define and test a set of mutation operators across from different clinical datasets to improve the performance of the proposal for each dataset. The use of genetic programming for rule induction has generated interesting results in machine learning problems. Hence, genetic programming represents a flexible and powerful evolutionary technique for automatic generation of classifiers. Since logical rules disclose knowledge from the analysed data, we use such knowledge to interpret the results and filter the most important features from clinical data as a process of knowledge discovery. The ultimate goal of this proposal is to provide the experts in the data domain with prior knowledge (as a guide) about the structure of the data and the rules found for each class, especially to track dichotomies and inequality. The results reached by our proposal on the involved datasets have been very promising when used in classification tasks and compared with other methods.", notes = "also known as \cite{pr8121565}", } @Article{CASTELLANOSGARZON:2019:KS, author = "Jose A. Castellanos-Garzon and Ernesto Costa and Jose Luis {Jaimes S.} and Juan M. Corchado", title = "An evolutionary framework for machine learning applied to medical data", journal = "Knowledge-Based Systems", volume = "185", pages = "104982", year = "2019", ISSN = "0950-7051", DOI = "doi:10.1016/j.knosys.2019.104982", URL = "http://www.sciencedirect.com/science/article/pii/S0950705119304046", keywords = "genetic algorithms, genetic programming, Machine learning, Logical rule induction, Data mining, Supervised learning, Evolutionary computation, Ensemble classifier, Medical data", abstract = "Supervised learning problems can be faced by using a wide variety of approaches supported in machine learning. In recent years there has been an increasing interest in using the evolutionary computation paradigm as a search method for classifiers, helping the applied machine learning technique. In this context, the knowledge representation in the form of logical rules has been one of the most accepted machine learning approaches, because of its level of expressiveness. This paper proposes an evolutionary framework for rule-based classifier induction. Our proposal introduces genetic programming to build a search method for classification-rules (IF/THEN). From this approach, we deal with problems such as, maximum rule length and rule intersection. The experiments have been carried out on our domain of interest, medical data. The achieved results define a methodology to follow in the learning method evaluation for knowledge discovery from medical data. Moreover, the results compared to other methods have shown that our proposal can be very useful in data analysis and classification coming from the medical domain", } @Article{CASTELLANOSGARZON:2020:MethodsX, author = "Jose A. Castellanos-Garzon and Ernesto Costa and Jose Luis S. Jaimes and Juan M. Corchado", title = "Determining the maximum length of logical rules in a classifier and visual comparison of results", journal = "MethodsX", volume = "7", pages = "100846", year = "2020", ISSN = "2215-0161", DOI = "doi:10.1016/j.mex.2020.100846", URL = "http://www.sciencedirect.com/science/article/pii/S2215016120300650", keywords = "genetic algorithms, genetic programming, Machine learning, Logical rule induction, Data mining, Supervised learning, Evolutionary computation", abstract = "Supervised learning problems can be faced by using a wide variety of approaches supported in machine learning. In recent years there has been an increasing interest in using the evolutionary computation paradigm as the classifier search method, helping the technique of applied machine learning. In this context, the knowledge representation in form of logical rules has been one of the most accepted machine learning approaches, because of its level of expressiveness. This paper proposes an evolutionary framework for rule-based classifier induction and is based on the idea of sequential covering. We introduce genetic programming as the search method for classification-rules. From this approach, we have given results on subjects as maximum rule length, number of rules needed in a classifier and the rule intersection problem. The experiments developed on benchmark clinical data resulted in a methodology to follow in the learning method evaluation. Moreover, the results achieved compared to other methods have shown that our proposal can be v", } @InProceedings{Castelli:2010:cec, author = "Mauro Castelli and Luca Manzoni and Sara Silva and Leonardo Vanneschi", title = "A comparison of the generalization ability of different genetic programming frameworks", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-6910-9", abstract = "Generalisation is an important issue in machine learning. In fact, in several applications good results over training data are not as important as good results over unseen data. While this problem was deeply studied in other machine learning techniques, it has become an important issue for genetic programming only in the last few years. In this paper we compare the generalization ability of several different genetic programming frameworks, including some variants of multi-objective genetic programming and operator equalisation, a recently defined bloat free genetic programming system. The test problem used is a hard regression real-life application in the field of drug discovery and development, characterised by a high number of features and where the generalisation ability of the proposed solutions is a crucial issue. The results we obtained show that, at least for the considered problem, multi-optimization is effective in improving genetic programming generalization ability, outperforming all the other methods on test data.", DOI = "doi:10.1109/CEC.2010.5585925", notes = "WCCI 2010. Also known as \cite{5585925}", } @InProceedings{castelli:2011:EuroGP, author = "Mauro Castelli and Luca Manzoni and Sara Silva and Leonardo Vanneschi", title = "A Quantitative Study of Learning and Generalization in Genetic Programming", booktitle = "Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011", year = "2011", month = "27-29 " # apr, editor = "Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado", series = "LNCS", volume = "6621", publisher = "Springer Verlag", address = "Turin, Italy", pages = "25--36", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-20406-7", DOI = "doi:10.1007/978-3-642-20407-4_3", abstract = "The relationship between generalisation and solutions functional complexity in genetic programming (GP) has been recently investigated. Three main contributions are contained in this paper: (1) a new measure of functional complexity for GP solutions, called Graph Based Complexity (GBC) is defined and we show that it has a higher correlation with GP performance on out-of-sample data than another complexity measure introduced in a recent publication. (2) A new measure is presented, called Graph Based Learning Ability (GBLA). It is inspired by the GBC and its goal is to quantify the ability of GP to learn difficult training points; we show that GBLA is negatively correlated with the performance of GP on out-of-sample data. (3) Finally, we use the ideas that have inspired the definition of GBC and GBLA to define a new fitness function, whose suitability is empirically demonstrated. The experimental results reported in this paper have been obtained using three real-life multidimensional regression problems.", notes = "Part of \cite{Silva:2011:GP} EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011", } @InProceedings{conf/lion/CastelliMV11, author = "Mauro Castelli and Luca Manzoni and Leonardo Vanneschi", title = "Multi Objective Genetic Programming for Feature Construction in Classification Problems", booktitle = "5th International Conference Learning and Intelligent Optimization (LION 2011)", year = "2011", editor = "Carlos A. {Coello Coello}", volume = "6683", series = "Lecture Notes in Computer Science", pages = "503--506", address = "Rome, Italy", month = jan # " 17-21", note = "Selected Papers", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-25565-6", DOI = "doi:10.1007/978-3-642-25566-3_39", size = "4 pages", abstract = "This work introduces a new technique for features construction in classification problems by means of multi objective genetic programming (MOGP). The final goal is to improve the generalisation ability of the final classifier. MOGP can help in finding solutions with a better generalisation ability with respect to standard genetic programming as stated in [1]. The main issue is the choice of the criteria that must be optimised by MOGP. In this work the construction of new features is guided by two criteria: the first one is the entropy of the target classes as in [7] while the second is inspired by the concept of margin used in support vector machines.", affiliation = "Dipartimento di Informatica, Sistemistica e Comunicazione (DISCo), Universita degli Studi di Milano-Bicocca, 20126 Milan, Italy", bibdate = "2011-11-16", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/lion/lion2011.html#CastelliMV11", } @PhdThesis{Castelli:thesis, author = "Mauro Castelli", title = "Measures and Methods for Robust Genetic Programming", school = "University of Milano Bicocca", year = "2012", address = "Milan, Italy", keywords = "genetic algorithms, genetic programming", URL = "http://boa.unimib.it/bitstream/10281/28571/1/Phd_unimib_055904.pdf", size = "235 pages", abstract = "Defended on February, 2012 Contents 1 Introduction 1 2 Genetic Programming: Introduction 14 3 Open Issues in Genetic Programming 35 4 Measures of Overfitting, Bloat and Functional Complexity 45 5 Multi Objective Optimisation in Genetic Programming 81 6 Generalisation 103 7 Semantic 135 8 Conclusions 172 9 Future works 188", notes = "Thesis Adviser: Leonardo VANNESCHI, Thesis Supervisor: Sara SILVA", } @InProceedings{Castelli:2012:GECCO, author = "Mauro Castelli and Luca Manzoni and Leonardo Vanneschi", title = "Parameter tuning of evolutionary reactions systems", booktitle = "GECCO '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference", year = "2012", editor = "Terry Soule and Anne Auger and Jason Moore and David Pelta and Christine Solnon and Mike Preuss and Alan Dorin and Yew-Soon Ong and Christian Blum and Dario Landa Silva and Frank Neumann and Tina Yu and Aniko Ekart and Will Browne and Tim Kovacs and Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and Giovanni Squillero and Nicolas Bredeche and Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and Martin Pelikan and Silja Meyer-Nienberg and Christian Igel and Greg Hornby and Rene Doursat and Steve Gustafson and Gustavo Olague and Shin Yoo and John Clark and Gabriela Ochoa and Gisele Pappa and Fernando Lobo and Daniel Tauritz and Jurgen Branke and Kalyanmoy Deb", isbn13 = "978-1-4503-1177-9", pages = "727--734", keywords = "genetic algorithms, genetic programming", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330163.2330265", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Reaction systems is a formalism inspired by chemical reactions introduced by Rozenberg and Ehrenfeucht. Recently, an evolutionary algorithm based on this formalism, called Evolutionary Reaction Systems, has been presented. This new algorithm proved to have comparable performances to other well-established machine learning methods, like genetic programming, neural networks and support vector machines on both artificial and real-life problems. Even if the results are encouraging, to make Evolutionary Reaction Systems an established evolutionary algorithm, an in depth analysis of the effect of its parameters on the search process is needed, with particular focus on those parameters that are typical of Evolutionary Reaction Systems and do not have a counterpart in traditional evolutionary algorithms. Here we address this problem for the first time. The results we present show that one particular parameter, between the ones tested, has a great influence on the performances of Evolutionary Reaction Systems, and thus its setting deserves practitioners' particular attention: the number of symbols used to represent the reactions that compose the system. Furthermore, this work represents a first step towards the definition of a set of default parameter values for Evolutionary Reaction Systems, that should facilitate their use for beginners or inexpert practitioners.", notes = "Also known as \cite{2330265} GECCO-2012 A joint meeting of the twenty first international conference on genetic algorithms (ICGA-2012) and the seventeenth annual genetic programming conference (GP-2012)", } @Misc{Castelli:2012:arXiv, title = "An Efficient Genetic Programming System with Geometric Semantic Operators and its Application to Human Oral Bioavailability Prediction", author = "Mauro Castelli and Luca Manzoni and Leonardo Vanneschi", howpublished = "arXiv", year = "2012", month = "12 " # aug, keywords = "genetic algorithms, genetic programming", bibdate = "2012-10-10", URL = "http://arxiv.org/abs/1208.2437", size = "10 pages", abstract = "Very recently new genetic operators, called geometric semantic operators, have been defined for genetic programming. Contrarily to standard genetic operators, which are uniquely based on the syntax of the individuals, these new operators are based on their semantics, meaning with it the set of input-output pairs on training data. Furthermore, these operators present the interesting property of inducing a unimodal fitness landscape for every problem that consists in finding a match between given input and output data (for instance regression and classification). Nevertheless, the current definition of these operators has a serious limitation: they impose an exponential growth in the size of the individuals in the population, so their use is impossible in practice. This paper is intended to overcome this limitation, presenting a new genetic programming system that implements geometric semantic operators in an extremely efficient way. To demonstrate the power of the proposed system, we use it to solve a complex real-life application in the field of pharmacokinetic: the prediction of the human oral bioavailability of potential new drugs. Besides the excellent performances on training data, which were expected because the fitness landscape is unimodal, we also report an excellent generalisation ability of the proposed system, at least for the studied application. In fact, it outperforms standard genetic programming and a wide set of other well-known machine learning methods.", } @InProceedings{Castelli:evoapps13, author = "Mauro Castelli and Sara Silva and Leonardo Vanneschi and Ana Cabral and Maria J. Vasconcelos and Luis Catarino and Joao M. B. Carreiras", title = "Land Cover/Land Use Multiclass Classification Using GP with Geometric Semantic Operators", booktitle = "Applications of Evolutionary Computing, EvoApplications 2013: EvoCOMNET, EvoCOMPLEX, EvoENERGY, EvoFIN, EvoGAMES, EvoIASP, EvoINDUSTRY, EvoNUM, EvoPAR, EvoRISK, EvoROBOT, EvoSTOC", year = "2013", month = "3-5 " # apr, editor = "Anna I. Esparcia-Alcazar and Antonio Della Cioppa and Ivanoe {De Falco} and Ernesto Tarantino and Carlos Cotta and Robert Schaefer and Konrad Diwold and Kyrre Glette and Andrea Tettamanzi and Alexandros Agapitos and Paolo Burrelli and J. J. Merelo and Stefano Cagnoni and Mengjie Zhang and Neil Urquhart and Kevin Sim and Aniko Ekart and Francisco {Fernandez de Vega} and Sara Silva and Evert Haasdijk and Gusz Eiben and Anabela Simoes and Philipp Rohlfshagen", series = "LNCS", volume = "7835", publisher = "Springer Verlag", address = "Vienna", publisher_address = "Berlin", pages = "334--343", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-37191-2", DOI = "doi:10.1007/978-3-642-37192-9_34", size = "10 pages", abstract = "Multiclass classification is a common requirement of many land cover/land use applications, one of the pillars of land science studies. Even though genetic programming has been applied with success to a large number of applications, it is not particularly suited for multi-class classification, thus limiting its use on such studies. In this paper we take a step forward towards filling this gap, investigating the performance of recently defined geometric semantic operators on two land cover/land use multiclass classification problems and also on a benchmark problem. Our results clearly indicate that genetic programming using the new geometric semantic operators outperforms standard genetic programming for all the studied problems, both on training and test data.", notes = " EvoApplications2013 held in conjunction with EuroGP2013, EvoCOP2013, EvoBio'2013 and EvoMusArt2013", } @Article{Castelli:2013:ieeeCybernetics, author = "Mauro Castelli and Leonardo Vanneschi and Sara Silva", journal = "IEEE Transactions on Cybernetics", title = "Semantic Search-Based Genetic Programming and the Effect of Intron Deletion", year = "2014", volume = "44", number = "1", pages = "103--113", abstract = "The concept of semantics (in the sense of input--output behaviour of solutions on training data) has been the subject of a noteworthy interest in the genetic programming (GP) research community over the past few years. In this paper, we present a new GP system that uses the concept of semantics to improve search effectiveness. It maintains a distribution of different semantic behaviours and biases the search toward solutions that have similar semantics to the best solutions that have been found so far. We present experimental evidence of the fact that the new semantics-based GP system outperforms the standard GP and the well-known bacterial GP on a set of test functions, showing particularly interesting results for noncontinuous (i.e., generally harder to optimise) test functions. We also observe that the solutions generated by the proposed GP system often have a larger size than the ones returned by standard GP and bacterial GP and contain an elevated number of introns, i.e., parts of code that do not have any effect on the semantics. Nevertheless, we show that the deletion of introns during the evolution does not affect the performance of the proposed method.", keywords = "genetic algorithms, genetic programming, Generalisation, genetic programming (GP), introns, semantics", DOI = "doi:10.1109/TSMCC.2013.2247754", ISSN = "2168-2267", notes = "Also known as \cite{6476653}", } @InProceedings{Castelli:2013:GECCOcomp, author = "Mauro Castelli and Davide Castaldi and Leonardo Vanneschi and Ilaria Giordani and Francesco Archetti and Daniele Maccagnola", title = "An efficient implementation of geometric semantic genetic programming for anticoagulation level prediction in pharmacogenetics", booktitle = "GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion", year = "2013", editor = "Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A. N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks", isbn13 = "978-1-4503-1964-5", keywords = "genetic algorithms, genetic programming", pages = "137--138", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2464576.2464644", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "In the last few years researchers have dedicated several efforts to the definition of Genetic Programming (GP) [?] systems based on the semantics of the solutions, where by semantics we generally intend the behaviour of a program once it is executed on a set of inputs, or more particularly the set of its output values on input training data (this definition has been used, among many others, for instance in [?, ?, ?, ?]). In particular, new genetic operators, called geometric semantic operators, have been proposed by Moraglio et al. [?]. They are defined s follows:", notes = "See \cite{Castelli:2013:EPIA} Also known as \cite{2464644} Distributed at GECCO-2013.", } @InProceedings{Castelli:2013:EPIA, author = "Mauro Castelli and Davide Castaldi and Ilaria Giordani and Sara Silva and Leonardo Vanneschi and Francesco Archetti and Daniele Maccagnola", title = "An Efficient Implementation of Geometric Semantic Genetic Programming for Anticoagulation Level Prediction in Pharmacogenetics", booktitle = "Proceedings of the 16th Portuguese Conference on Artificial Intelligence, EPIA 2013", year = "2013", editor = "Luis Correia and Luis Paulo Reis and Jose Cascalho", volume = "8154", series = "Lecture Notes in Computer Science", pages = "78--89", address = "Angra do Heroismo, Azores, Portugal", month = sep # " 9-12", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-40668-3", URL = "http://link.springer.com/chapter/10.1007/978-3-642-40669-0_8", DOI = "doi:10.1007/978-3-642-40669-0_8", size = "12 pages", abstract = "The purpose of this study is to develop an innovative system for Coumarin-derived drug dosing, suitable for elderly patients. Recent research highlights that the pharmacological response of the patient is often affected by many exogenous factors other than the dosage prescribed and these factors could form a very complex relationship with the drug dosage. For this reason, new powerful computational tools are needed for approaching this problem. The system we propose is called Geometric Semantic Genetic Programming, and it is based on the use of recently defined geometric semantic genetic operators. In this paper, we present a new implementation of this Genetic Programming system, that allow us to use it for real-life applications in an efficient way, something that was impossible using the original definition. Experimental results show the suitability of the proposed system for managing anticoagulation therapy. In particular, results obtained with Geometric Semantic Genetic Programming are significantly better than the ones produced by standard Genetic Programming both on training and on out-of-sample test data.", notes = "See \cite{Castelli:2013:GECCOcomp}", } @Article{Castelli:2013:ESA, author = "Mauro Castelli and Leonardo Vanneschi and Sara Silva", title = "Prediction of high performance concrete strength using Genetic Programming with geometric semantic genetic operators", journal = "Expert Systems with Applications", volume = "40", number = "17", pages = "6856--6862", year = "2013", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2013.06.037", URL = "http://www.sciencedirect.com/science/article/pii/S0957417413004326", keywords = "genetic algorithms, genetic programming, High performance concrete, Strength prediction, Artificial intelligence, Geometric operators, Semantics, Weka, Linear regression, Square Regression, Isotonic Regression, Radial Basis Function Network, RBF, SVM, ANN", abstract = "Concrete is a composite construction material made primarily with aggregate, cement, and water. In addition to the basic ingredients used in conventional concrete, high-performance concrete incorporates supplementary cementitious materials, such as fly ash and blast furnace slag, and chemical admixture, such as superplasticizer. Hence, high-performance concrete is a highly complex material and modelling its behaviour represents a difficult task. In this paper, we propose an intelligent system based on Genetic Programming for the prediction of high-performance concrete strength. The system we propose is called Geometric Semantic Genetic Programming, and it is based on recently defined geometric semantic genetic operators for Genetic Programming. Experimental results show the suitability of the proposed system for the prediction of concrete strength. In particular, the new method provides significantly better results than the ones produced by standard Genetic Programming and other machine learning methods, both on training and on out-of-sample data.", notes = "page6861 'better (than) other well-known machine learning techniques'", } @Article{Castelli:2014:ieeeCybernetics, author = "Mauro Castelli and Leonardo Vanneschi and Sara Silva", title = "Semantic Search-Based Genetic Programming and the Effect of Intron Deletion", journal = "IEEE Transactions on Cybernetics", year = "2014", volume = "44", number = "1", pages = "103--113", month = jan, keywords = "genetic algorithms, genetic programming, Generalisation, introns, semantics", ISSN = "2168-2267", DOI = "doi:10.1109/TSMCC.2013.2247754", size = "11 pages", abstract = "The concept of semantics (in the sense of input-output behaviour of solutions on training data) has been the subject of a noteworthy interest in the genetic programming (GP) research community over the past few years. In this paper, we present a new GP system that uses the concept of semantics to improve search effectiveness. It maintains a distribution of different semantic behaviours and biases the search toward solutions that have similar semantics to the best solutions that have been found so far. We present experimental evidence of the fact that the new semantics-based GP system outperforms the standard GP and the well-known bacterial GP on a set of test functions, showing particularly interesting results for noncontinuous (i.e., generally harder to optimise) test functions. We also observe that the solutions generated by the proposed GP system often have a larger size than the ones returned by standard GP and bacterial GP and contain an elevated number of introns, i.e., parts of code that do not have any effect on the semantics. Nevertheless, we show that the deletion of introns during the evolution does not affect the performance of the proposed method.", notes = "Author list corrected as: doi:10.1109/TCYB.2014.2303551 Also known as \cite{6476653}", } @Article{Castelli:2014:ESA, author = "Mauro Castelli and Leonardo Vanneschi and Sara Silva", title = "Prediction of the Unified Parkinson's Disease Rating Scale assessment using a genetic programming system with geometric semantic genetic operators", journal = "Expert Systems with Applications", volume = "41", number = "10", pages = "4608--4616", year = "2014", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2014.01.018", URL = "http://www.sciencedirect.com/science/article/pii/S0957417414000396", keywords = "genetic algorithms, genetic programming, Unified Parkinson's Disease Rating Scale, Geometric operators, Semantics", } @InProceedings{Castelli:2014:SMGP, author = "Mauro Castelli and Luca Manzoni and Sara Silva and Leonardo Vanneschi", title = "The Influence of Population Size on Geometric Semantic GP", booktitle = "Semantic Methods in Genetic Programming", year = "2014", editor = "Colin Johnson and Krzysztof Krawiec and Alberto Moraglio and Michael O'Neill", address = "Ljubljana, Slovenia", month = "13 " # sep, note = "Workshop at Parallel Problem Solving from Nature 2014 conference", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.put.poznan.pl/kkrawiec/smgp2014/uploads/Site/Castelli.pdf", size = "2 pages", abstract = "In this work we study the influence of the population size on the learning ability of Geometric Semantic Genetic Programming (GSGP) for the task of symbolic regression. The results show that having small populations results on a better training fitness with respect to the use of large populations after the same number of fitness evaluations. However, models obtained with large populations show a better performance on unseen data.", notes = "SMGP 2014 http://www.cs.put.poznan.pl/kkrawiec/smgp/?n=Site.SMGP2014", } @InProceedings{Castelli:2014:SMGP2, author = "Mauro Castelli and Luca Manzoni and Sara Silva and Leonardo Vanneschi", title = "Self-tuning Geometric Semantic GP", booktitle = "Semantic Methods in Genetic Programming", year = "2014", editor = "Colin Johnson and Krzysztof Krawiec and Alberto Moraglio and Michael O'Neill", address = "Ljubljana, Slovenia", month = "13 " # sep, note = "Workshop at Parallel Problem Solving from Nature 2014 conference", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.put.poznan.pl/kkrawiec/smgp2014/uploads/Site/Castelli2.pdf", size = "2 pages", abstract = "In Geometric Semantic GP (GSGP), similarly to normal GP, parameter tuning is necessary to attain good performances. Here we introduce a method for self-tuning GSGP that not only saves the user the tuning task, but it also outperforms traditional hand-tuned GSGP.", notes = "SMGP 2014 http://www.cs.put.poznan.pl/kkrawiec/smgp/?n=Site.SMGP2014", } @Article{Castelli:2014:GPEM, author = "Mauro Castelli and Sara Silva and Leonardo Vanneschi", title = "A {C++} framework for geometric semantic genetic programming", journal = "Genetic Programming and Evolvable Machines", year = "2015", volume = "16", number = "1", pages = "73--81", month = mar, keywords = "genetic algorithms, genetic programming, GSGP lib, Semantics, Geometric operators, C++", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-014-9218-0", size = "9 pages", abstract = "Geometric semantic operators are new and promising genetic operators for genetic programming. They have the property of inducing a unimodal error surface for any supervised learning problem, i.e., any problem consisting in finding the match between a set of input data and known target values (like regression and classification). Thanks to an efficient implementation of these operators, it was possible to apply them to a set of real-life problems, obtaining very encouraging results. We have now made this implementation publicly available as open source software, and here we describe how to use it. We also reveal details of the implementation and perform an investigation of its efficiency in terms of running time and memory occupation, both theoretically and experimentally. The source code and documentation are available for download at http://gsgp.sourceforge.net", notes = "GSGP lib Author Affiliations: 1. ISEGI, Universidade Nova de Lisboa, 1070-312, Lisbon, Portugal 2. INESC-ID, IST, Universidade de Lisboa, 1000-029, Lisbon, Portugal 3. LabMAg, FCUL, Universidade de Lisboa, 1749-016, Lisbon, Portugal 4. CISUC, Universidade de Coimbra, 3030-290, Coimbra, Portugal", } @Article{Castelli:2014:Cybernetics, author = "M. Castelli and L. Vanneschi and S. Silva", journal = "IEEE Transactions on Cybernetics", title = "Corrections to ``Semantic Search Based Genetic Programming and the Effect of Introns Deletion'' [Jan 14 103-113]", year = "2014", month = apr, volume = "44", number = "4", pages = "565", abstract = "The paper above (ibid., vol. 44, no. 1, pp. 103-113, Jan. 2014), was printed with the incorrect author list as follows: M. Castelli, L. Vanneschi, S. Silva, A. Agapitos, and M. O'Neill. The correct author list is: M. Castelli, L. Vanneschi and S. Silva.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/TCYB.2014.2303551", ISSN = "2168-2267", notes = "Correction to \cite{Castelli:2013:ieeeCybernetics}. Also known as \cite{6736086}", } @InProceedings{Castelli:2014:GPTP, author = "Mauro Castelli and Leonardo Vanneschi and Sara Silva and Stefano Ruberto", title = "How to Exploit Alignment in the Error Space: Two Different GP Models", booktitle = "Genetic Programming Theory and Practice XII", year = "2014", editor = "Rick Riolo and William P. Worzel and Mark Kotanchek", series = "Genetic and Evolutionary Computation", pages = "133--148", address = "Ann Arbor, USA", month = "8-10 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Semantics, Error space, Geometry", isbn13 = "978-3-319-16029-0", DOI = "doi:10.1007/978-3-319-16030-6_8", abstract = "From a recent study, we know that if we are able to find two optimally aligned individuals, then we can reconstruct a globally optimal solution analytically for any regression problem. With this knowledge in mind, the objective of this chapter is to discuss two Genetic Programming (GP) models aimed at finding pairs of optimally aligned individuals. The first one of these models, already introduced in a previous publication, is SGP-1. The second model, discussed for the first time here, is called Pair Optimisation GP (POGO). The main difference between these two models is that, while SGP-1 represents solutions in a traditional way, as single expressions (as in standard GP), in POGO individuals are pairs of expressions, that evolution should push towards the optimal alignment. The results we report for both these models are extremely encouraging. In particular, ESAGP-1 outperforms standard GP and geometric semantic GP on two complex real-life applications. At the same time, a preliminary set of results obtained on a set of symbolic regression benchmarks indicate that POGP, although rather new and still in need of improvement, is a very promising model, that deserves future developments and investigation.", notes = " Part of \cite{Riolo:2014:GPTP} published after the workshop in 2015", } @Article{Castelli:2015:Neurocomputing, author = "Mauro Castelli and Roberto Henriques and Leonardo Vanneschi", title = "A geometric semantic genetic programming system for the electoral redistricting problem", journal = "Neurocomputing", volume = "154", pages = "200--207", year = "2015", ISSN = "0925-2312", DOI = "doi:10.1016/j.neucom.2014.12.003", URL = "http://www.sciencedirect.com/science/article/pii/S0925231214016671", abstract = "Redistricting consists in dividing a geographic space or region of spatial units into smaller subregions or districts. In this paper, a Genetic Programming framework that addresses the electoral redistricting problem is proposed. The method uses new genetic operators, called geometric semantic genetic operators, that employ semantic information directly in the evolutionary search process with the objective of improving its optimisation ability. The system is compared to several different redistricting techniques, including evolutionary and non-evolutionary methods. The simulations were made on ten real data-sets and, even though the studied problem does not belong to the classes of problems for which geometric semantic operators induce a unimodal fitness landscape, the results we present demonstrate the effectiveness of the proposed technique.", keywords = "genetic algorithms, genetic programming, Electoral redistricting, Semantics, Search space", } @Article{Castelli:2015:EE, author = "Mauro Castelli and Leonardo Vanneschi and Matteo {De Felice}", title = "Forecasting short-term electricity consumption using a semantics-based genetic programming framework: The South Italy case", journal = "Energy Economics", volume = "47", pages = "37--41", year = "2015", ISSN = "0140-9883", DOI = "doi:10.1016/j.eneco.2014.10.009", URL = "http://www.sciencedirect.com/science/article/pii/S0140988314002539", abstract = "Accurate and robust short-term load forecasting plays a significant role in electric power operations. This paper proposes a variant of genetic programming, improved by incorporating semantic awareness in algorithm, to address a short term load forecasting problem. The objective is to automatically generate models that could effectively and reliably predict energy consumption. The presented results, obtained considering a particularly interesting case of the South Italy area, show that the proposed approach outperforms state of the art methods. Hence, the proposed approach reveals appropriate for the problem of forecasting electricity consumption. This study, besides providing an important contribution to the energy load forecasting, confirms the suitability of genetic programming improved with semantic methods in addressing complex real-life applications.", keywords = "genetic algorithms, genetic programming, Forecasting, Electricity demand, Semantics", } @Article{Castelli:2015:FireEcology, author = "Mauro Castelli and Leonardo Vanneschi and Ales Popovic", title = "Predicting Burned Areas of Forest Fires: an Artificial Intelligence Approach", journal = "Fire Ecology", year = "2015", volume = "11", number = "1", pages = "106--118", keywords = "genetic algorithms, genetic programming, geometric semantic genetic programming", ISSN = "1933-9747", publisher = "The Association for Fire Ecology", DOI = "doi:10.4996/fireecology.1101106", size = "13 pages", abstract = "Forest fires importantly influence our environment and lives. The ability of accurately predicting the area that may be involved in a forest fire event may help in optimizing fire management efforts. Given the complexity of the task, powerful computational tools are needed for predicting the amount of area that will be burned during a forest fire. The purpose of this study was to develop an intelligent system based on genetic programming for the prediction of burned areas, using only data related to the forest under analysis and meteorological data. We used geometric semantic genetic programming based on recently defined geometric semantic genetic operators for genetic programming. Experimental results, achieved using a database of 517 forest fire events between 2000 and 2003, showed the appropriateness of the proposed system for the prediction of the burned areas. In particular, results obtained with geometric semantic genetic programming were significantly better than those produced by standard genetic programming and other state of the art machine learning methods on both training and out-of-sample data. This study suggests that deeper investigation of genetic programming in the field of forest fires prediction may be productive.", notes = "The Journal of the Association for Fire Ecology", } @Article{Castelli:2015:CINS, author = "Mauro Castelli and Leonardo Vanneschi and Leonardo Trujillo", title = "Energy Consumption Forecasting using Semantics Based Genetic Programming with Local Search Optimizer", journal = "Computational Intelligence and Neuroscience", year = "2015", volume = "2015", month = may, keywords = "genetic algorithms, genetic programming", URL = "http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4464001/", URL = "http://www.ncbi.nlm.nih.gov/pubmed/26106410", URL = "http://downloads.hindawi.com/journals/cin/2015/971908.pdf", DOI = "doi:10.1155/2015/971908", size = "9 pages", abstract = "Energy consumption forecasting (ECF) is an important policy issue in today's economies. An accurate ECF has great benefits for electric utilities and both negative and positive errors lead to increased operating costs. The paper proposes a semantic based genetic programming framework to address the ECF problem. In particular, we propose a system that finds (quasi-)perfect solutions with high probability and that generates models able to produce near optimal predictions also on unseen data. The framework blends a recently developed version of genetic programming that integrates semantic genetic operators with a local search method. The main idea in combining semantic genetic programming and a local searcher is to couple the exploration ability of the former with the exploitation ability of the latter. Experimental results confirm the suitability of the proposed method in predicting the energy consumption. In particular, the system produces a lower error with respect to the existing state-of-the art techniques used on the same dataset. More importantly, this case study has shown that including a local searcher in the geometric semantic genetic programming system can speed up the search process and can result in fitter models that are able to produce an accurate forecasting also on unseen data.", notes = "Article ID 971908", } @InProceedings{Castelli:2015:GECCO, author = "Mauro Castelli and Leonardo Trujillo and Leonardo Vanneschi and Sara Silva and Emigdio Z-Flores and Pierrick Legrand", title = "Geometric Semantic Genetic Programming with Local Search", booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation", year = "2015", editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr", isbn13 = "978-1-4503-3472-3", pages = "999--1006", keywords = "genetic algorithms, genetic programming", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739480.2754795", DOI = "doi:10.1145/2739480.2754795", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Since its introduction, Geometric Semantic Genetic Programming (GSGP) has aroused the interest of numerous researchers and several studies have demonstrated that GSGP is able to effectively optimize training data by means of small variation steps, that also have the effect of limiting overfitting. In order to speed up the search process, in this paper we propose a system that integrates a local search strategy into GSGP (called GSGP-LS). Furthermore, we present a hybrid approach, that combines GSGP and GSGP-LS, aimed at exploiting both the optimization speed of GSGP-LS and the ability to limit overfitting of GSGP. The experimental results we present, performed on a set of complex real-life applications, show that GSGP-LS achieves the best training fitness while converging very quickly, but severely overfits. On the other hand, GSGP converges slowly relative to the other methods, but is basically not affected by overfitting. The best overall results were achieved with the hybrid approach, allowing the search to converge quickly, while also exhibiting a noteworthy ability to limit overfitting. These results are encouraging, and suggest that future GSGP algorithms should focus on finding the correct balance between the greedy optimization of a local search strategy and the more robust geometric semantic operators.", notes = "Also known as \cite{2754795} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)", } @InProceedings{conf/epia/CastelliFMV15, author = "Mauro Castelli and Matteo {De Felice} and Luca Manzoni and Leonardo Vanneschi", title = "Electricity Demand Modelling with Genetic Programming", bibdate = "2015-08-26", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/epia/epia2015.html#CastelliFMV15", booktitle = "Progress in Artificial Intelligence - 17th Portuguese Conference on Artificial Intelligence, {EPIA} 2015, Coimbra, Portugal, September 8-11, 2015. Proceedings", publisher = "Springer", year = "2015", volume = "9273", editor = "Francisco C. Pereira and Penousal Machado and Ernesto Costa and Amilcar Cardoso", isbn13 = "978-3-319-23484-7", pages = "213--225", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming", URL = "http://dx.doi.org/10.1007/978-3-319-23485-4", } @Article{Castelli:2015:EB, author = "Mauro Castelli and Leonardo Trujillo and Leonardo Vanneschi and Ales Popovic", title = "Prediction of energy performance of residential buildings: a genetic programming approach", journal = "Energy and Buildings", year = "2015", volume = "102", number = "1", pages = "67--74", month = sep, keywords = "genetic algorithms, genetic programming, Energy consumption, Heating load, Cooling load, Machine learning", ISSN = "0378-7788", URL = "http://www.sciencedirect.com/science/article/pii/S0378778815003849", DOI = "doi:10.1016/j.enbuild.2015.05.013", size = "8 pages", abstract = "Energy consumption has long been emphasized as an important policy issue in today's economies. In particular, the energy efficiency of residential buildings is considered a top priority of a country's energy policy. The paper proposes a genetic programming-based framework for estimating the energy performance of residential buildings. The objective is to build a model able to predict the heating load and the cooling load of residential buildings. An accurate prediction of these parameters facilitates a better control of energy consumption and, moreover, it helps choosing the energy supplier that better fits the energy needs, which is considered an important issue in the deregulated energy market. The proposed framework blends a recently developed version of genetic programming with a local search method and linear scaling. The resulting system enables us to build a model that produces an accurate estimation of both considered parameters. Extensive simulations on 768 diverse residential buildings confirm the suitability of the proposed method in predicting heating load and cooling load. In particular, the proposed method is more accurate than the existing state-of-the-art techniques.", } @Article{Castelli:2016:GPEM, author = "Mauro Castelli and Luca Manzoni and Leonardo Vanneschi and Sara Silva and Ales Popovic", title = "Self-tuning geometric semantic Genetic Programming", journal = "Genetic Programming and Evolvable Machines", year = "2016", volume = "17", number = "1", pages = "55--74", month = mar, keywords = "genetic algorithms, genetic programming, Semantics, Parameters Tuning", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-015-9251-7", size = "20 pages", abstract = "The process of tuning the parameters that characterize evolutionary algorithms is difficult and can be time consuming. This paper presents a self-tuning algorithm for dynamically updating the crossover and mutation probabilities during a run of genetic programming. The genetic operators that are considered in this work are the geometric semantic genetic operators introduced by Moraglio et al. Differently from other existing self-tuning algorithms, the proposed one works by assigning a (different) crossover and mutation probability to each individual of the population. The experimental results we present show the appropriateness of the proposed self-tuning algorithm: on seven different test problems, the proposed algorithm finds solutions of a quality that is better than, or comparable to, the one achieved using the best known values for the geometric semantic crossover and mutation rates for the same problems. Also, we study how the mutation and crossover probabilities change during the execution of the proposed self-tuning algorithm, pointing out an interesting insight: mutation is basically the only operator used in the exploration phase, while crossover is used for exploitation, further improving good quality solutions.", } @Article{Castelli:2015:ASC, author = "Mauro Castelli and Leonardo Trujillo and Leonardo Vanneschi and Ales Popovic", title = "Prediction of relative position of {CT} slices using a computational intelligence system", journal = "Applied Soft Computing", volume = "46", pages = "537--542", year = "2016", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2015.09.021", URL = "http://www.sciencedirect.com/science/article/pii/S1568494615005931", abstract = "One of the most common techniques in radiology is the computerized tomography (CT) scan. Automatically determining the relative position of a single CT slice within the human body can be very useful. It can allow for an efficient retrieval of slices from the same body region taken in other volume scans and provide useful information to the non-expert user. This work addresses the problem of determining which portion of the body is shown by a stack of axial CT image slices. To tackle this problem, this work proposes a computational intelligence system that combines semantics-based operators for Genetic Programming with a local search algorithm, coupling the exploration ability of the former with the exploitation ability of the latter. This allows the search process to quickly converge towards (near-)optimal solutions. Experimental results, using a large database of CT images, have confirmed the suitability of the proposed system for the prediction of the relative position of a CT slice. In particular, the new method achieves a median localization error of 3.4 cm on unseen data, outperforming standard Genetic Programming and other techniques that have been applied to the same dataset. In summary, this paper makes two contributions: (i) in the radiology domain, the proposed system outperforms current state-of-the-art techniques; (ii) from the computational intelligence perspective, the results show that including a local searcher in Geometric Semantic Genetic Programming can speed up convergence without degrading test performance.", keywords = "genetic algorithms, genetic programming, Computerized tomography, Radiology, Semantics, Local search", } @Article{Castelli:2016:SEC, author = "Mauro Castelli and Leonardo Vanneschi and Luca Manzoni and Ales Popovic", title = "Semantic genetic programming for fast and accurate data knowledge discovery", journal = "Swarm and Evolutionary Computation", volume = "26", year = "2016", ISSN = "2210-6502", DOI = "doi:10.1016/j.swevo.2015.07.001", URL = "http://www.sciencedirect.com/science/article/pii/S2210650215000516", abstract = "Big data knowledge discovery emerged as an important factor contributing to advancements in society at large. Still, researchers continuously seek to advance existing methods and provide novel ones for analysing vast data sets to make sense of the data, extract useful information, and build knowledge to inform decision making. In the last few years, a very promising variant of genetic programming was proposed: geometric semantic genetic programming. Its difference with the standard version of genetic programming consists in the fact that it uses new genetic operators, called geometric semantic operators, that, acting directly on the semantics of the candidate solutions, induce by definition a unimodal error surface on any supervised learning problem, independently from the complexity and size of the underlying data set. This property should improve the evolvability of genetic programming in presence of big data and thus makes geometric semantic genetic programming an extremely promising method for mining vast amounts of data. Nevertheless, to the best of our knowledge, no contribution has appeared so far to employ this new technology to big data problems. This paper intends to fill this gap. For the first time, in fact, we show the effectiveness of geometric semantic genetic programming on several complex real-life problems, characterized by vast amounts of data, coming from several different application domains.", keywords = "genetic algorithms, genetic programming, Semantics, Knowledge discovery", } @Article{Castelli:2016:IJBIC, title = "Parameter evaluation of geometric semantic genetic programming in pharmacokinetics", author = "Mauro Castelli and Leonardo Vanneschi and Ales Popovic", journal = "Int. J. of Bio-Inspired Computation", year = "2016", month = feb # "~10", volume = "8", number = "1", pages = "42--50", keywords = "genetic algorithms, genetic programming, semantics, geometric semantic operators, regression, parameter evaluation, pharmacokinetics, semantic crossover, semantic mutation, drug discovery", publisher = "Inderscience Publishers", ISSN = "1758-0374", bibsource = "OAI-PMH server at www.inderscience.com", language = "eng", URL = "http://www.inderscience.com/link.php?id=74634", DOI = "DOI:10.1504/IJBIC.2016.074634", abstract = "The role of crossover and mutation in genetic programming has been the subject of much debate since the emergence of the field. Recently new genetic operators, called geometric semantic operators, have been introduced. Contrary to standard genetic operators, these operators present the interesting property of inducing a unimodal fitness landscape for every problem that consists in finding a match between inputs and targets. As the definition of these operators is quite recent, their effect on the evolutionary dynamics is still in many senses unknown and deserves to be studied. This paper intends to fill this gap, with a specific focus on applications in the field of pharmacokinetic. Results show that a mixture of semantic crossover and mutation is always beneficial compared to the use of only one of these operators. Furthermore, we show that the best results are obtained using values of the semantic mutation rate which are considerably higher than the ones that are typically used when traditional subtree mutation is employed.", } @Article{Castelli:2016:CIN, author = "Mauro Castelli and Leonardo Vanneschi and Ales Popovic", title = "Controlling Individuals Growth in Semantic Genetic Programming through Elitist Replacement", journal = "Computational Intelligence and Neuroscience", year = "2016", pages = "Article ID 8326760", keywords = "genetic algorithms, genetic programming", publisher = "Hindawi Publishing Corporation", bibsource = "OAI-PMH server at www.ncbi.nlm.nih.gov", identifier = "/pmc/articles/PMC4707023/", language = "en", oai = "oai:pubmedcentral.nih.gov:4707023", rights = "Copyright 2016 Mauro Castelli et al.; This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.", URL = "http://dx.doi.org/10.1155/2016/8326760", URL = "http://downloads.hindawi.com/journals/cin/2016/8326760.pdf", size = "12 pages", abstract = "In 2012, Moraglio and coauthors introduced new genetic operators for Genetic Programming, called geometric semantic genetic operators. They have the very interesting advantage of inducing a unimodal error surface for any supervised learning problem. At the same time, they have the important drawback of generating very large data models that are usually very hard to understand and interpret. The objective of this work is to alleviate this drawback, still maintaining the advantage. More in particular, we propose an elitist version of geometric semantic operators, in which offspring are accepted in the new population only if they have better fitness than their parents. We present experimental evidence, on five complex real-life test problems, that this simple idea allows us to obtain results of a comparable quality (in terms of fitness), but with much smaller data models, compared to the standard geometric semantic operators. In the final part of the paper, we also explain the reason why we consider this a significant improvement, showing that the proposed elitist operators generate manageable models, while the models generated by the standard operators are so large in size that they can be considered unmanageable.", } @InProceedings{castelli2016analysis, author = "Mauro Castelli and Luca Manzoni and Ivo Goncalves and Leonardo Vanneschi and Leonardo Trujillo and Sara Silva", title = "An Analysis of Geometric Semantic Crossover: A Computational Geometry Approach", booktitle = "Proceedings of the 8th International Joint Conference on Computational Intelligence, IJCCI (ECTA) 2016", year = "2016", pages = "201--208", publisher = "Scitepress", keywords = "genetic algorithms, genetic programming, Semantics, Convex Hull", isbn13 = "978-989-758-201-1", DOI = "doi:10.5220/0006056402010208", abstract = "Geometric semantic operators have recently shown their ability to outperform standard genetic operators on different complex real world problems. Nonetheless, they are affected by drawbacks. In this paper, we focus on one of these drawbacks, i.e. the fact that geometric semantic crossover has often a poor impact on the evolution. Geometric semantic crossover creates an offspring whose semantics stands in the segment joining the parents (in the semantic space). So, it is intuitive that it is not able to find, nor reasonably approximate, a globally optimal solution, unless the semantics of the individuals in the population contains the target. In this paper, we introduce the concept of convex hull of a genetic programming population and we present a method to calculate the distance from the target point to the convex hull. Then, we give experimental evidence of the fact that, in four different real-life test cases, the target is always outside the convex hull. As a consequence, we show that geometric semantic crossover is not helpful in those cases, and it is not even able to approximate the population to the target. Finally, in the last part of the paper, we propose ideas for future work on how to improve geometric semantic crossover.", } @Article{castelli:2017:jaihc, author = "Mauro Castelli and Raul Sormani and Leonardo Trujillo and Ales Popovic", title = "Predicting per capita violent crimes in urban areas: an artificial intelligence approach", journal = "Journal of Ambient Intelligence and Humanized Computing", year = "2017", volume = "8", number = "1", pages = "29--36", month = feb, keywords = "genetic algorithms, genetic programming, Evolutionary Computation, CSGP, LSGP, SVM, ANN, RBF, Crime Prediction Urban Security Semantics Local Search", ISSN = "1868-5145", DOI = "doi:10.1007/s12652-015-0334-3", size = "8 pages", abstract = "A major challenge facing all law-enforcement organizations is to accurately and efficiently analyse the growing volumes of crime data in order to extract useful knowledge for decision makers. This is an increasingly important task, considering the fast growth of urban populations in most countries. In particular, to reconcile urban growth with the need for security, a fundamental goal is to optimize the allocation of law enforcement resources. Moreover, optimal allocation can only be achieved if we can predict the incidence of crime within different urban areas. To answer this call, in this paper we propose an artificial intelligence system for predicting per capita violent crimes in urban areas starting from socio-economic data, law-enforcement data and other crime-related data obtained from different sources. The proposed framework blends a recently developed version of genetic programming that uses the concept of semantics during the search process with a local search method. To analyze the appropriateness of the proposed computational method for crime prediction, different urban areas of the United States have been considered. Experimental results confirm the suitability of the proposed method for addressing the problem at hand. In particular, the proposed method produces a lower error with respect to the existing state-of-the art techniques and it is particularly suitable for analysing large amounts of data. This is an extremely important feature in a world that is currently moving towards the development of smart cities.", notes = "WEKA", } @Article{journals/ijbic/CastelliVTP17, author = "Mauro Castelli and Leonardo Vanneschi and Leonardo Trujillo and Ales Popovic", title = "Stock index return forecasting: semantics-based genetic programming with local search optimiser", journal = "International Journal of Bio-Inspired Computation", year = "2017", number = "3", volume = "10", pages = "159--171", keywords = "genetic algorithms, genetic programming", bibdate = "2017-10-10", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ijbic/ijbic10.html#CastelliVTP17", DOI = "doi:10.1504/IJBIC.2017.10004325", abstract = "Making accurate stock price predictions is the pillar of effective decisions in high-velocity environments since the successful prediction of future prices could yield significant profit and reduce operational costs. Generally, solutions for this task are based on trend predictions and are driven by various factors. To add to the existing body of knowledge, we propose a semantics-based genetic programming framework. The proposed framework blends a recently developed version of genetic programming that uses semantic genetic operators with a local search method. To analyse the appropriateness of the proposed computational method for stock market price prediction, we analysed data related to the Dow Jones index and to the Istanbul Stock Index. Experimental results confirm the suitability of the proposed method for predicting stock market prices. In fact, the system produces lower errors with respect to the existing state-of-the art techniques, such as neural networks and support vector machines. forecasting; financial markets; genetic programming; semantics; local search.", } @Proceedings{Castelli:2017:GP, title = "EuroGP 2017: Proceedings of the 20th European Conference on Genetic Programming", year = "2017", editor = "Mauro Castelli and James McDermott and Lukas Sekanina", volume = "10196", series = "LNCS", address = "Amsterdam", month = "19-21 " # apr, organisation = "Species", publisher = "Springer Verlag", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1007/978-3-319-55696-3", notes = "EuroGP'2017 held in conjunction with EvoCOP2017, EvoMusArt2017 and EvoApplications2017", } @Article{Castelli:2017:SEC, author = "Mauro Castelli and Luca Manzoni and Sara Silva and Leonardo Vanneschi and Ales Popovic", title = "The influence of population size in geometric semantic {GP}", journal = "Swarm and Evolutionary Computation", volume = "32", pages = "110--120", year = "2017", ISSN = "2210-6502", DOI = "doi:10.1016/j.swevo.2016.05.004", URL = "http://www.sciencedirect.com/science/article/pii/S2210650216300256", abstract = "In this work, we study the influence of the population size on the learning ability of Geometric Semantic Genetic Programming for the task of symbolic regression. A large set of experiments, considering different population size values on different regression problems, has been performed. Results show that, on real-life problems, having small populations results in a better training fitness with respect to the use of large populations after the same number of fitness evaluations. However, performance on the test instances varies among the different problems: in datasets with a high number of features, models obtained with large populations present a better performance on unseen data, while in datasets characterized by a relative small number of variables a better generalization ability is achieved by using small population size values. When synthetic problems are taken into account, large population size values represent the best option for achieving good quality solutions on both training and test instances.", keywords = "genetic algorithms, genetic programming, Semantics, Population size", } @Article{Castelli:2017:ESA, author = "Mauro Castelli and Luca Manzoni and Leonardo Vanneschi and Ales Popovic", title = "An expert system for extracting knowledge from customers' reviews: The case of Amazon.com, {Inc.}", journal = "Expert Systems with Applications", volume = "84", pages = "117--126", year = "2017", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2017.05.008", URL = "http://www.sciencedirect.com/science/article/pii/S0957417417303263", abstract = "E-commerce has proliferated in the daily activities of end-consumers and firms alike. For firms, consumer satisfaction is an important indicator of e-commerce success. Today, consumers' reviews and feedback are increasingly shaping consumer intentions regarding new purchases and repeated purchases, while helping to attract new customers. In our work, we use an expert system to predict the sentiment of a product considering a subset of available customers' reviews.", keywords = "genetic algorithms, genetic programming, Semantics, E-commerce, Customers' feedback", } @Article{Castelli:2017:CandC, author = "Mauro Castelli and Leonardo Trujillo and Ivo Goncalves and Ales Popovic", title = "An evolutionary system for the prediction of high performance concrete strength based on semantic genetic programming", journal = "Computers and Concrete", year = "2017", volume = "19", number = "6", pages = "651--658", month = jun, keywords = "genetic algorithms, genetic programming, high performance concrete, concrete strength, local search, semantics", URL = "http://www.techno-press.org/?page=container&journal=cac&volume=19&num=6", DOI = "doi:10.12989/cac.2017.19.6.651", abstract = "High-performance concrete, besides aggregate, cement, and water, incorporates supplementary cementitious materials, such as fly ash and blast furnace slag, and chemical admixture, such as super-plasticiser. Hence, it is a highly complex material and modelling its behaviour represents a difficult task. This paper presents an evolutionary system for the prediction of high performance concrete strength. The proposed framework blends a recently developed version of genetic programming with a local search method. The resulting system enables us to build a model that produces an accurate estimation of the considered parameter. Experimental results show the suitability of the proposed system for the prediction of concrete strength. The proposed method produces a lower error with respect to the state-of-the art technique. The paper provides two contributions: from the point of view of the high performance concrete strength prediction, a system able to outperform existing state-of-the-art techniques is defined; from the machine learning perspective, this case study shows that including a local searcher in the geometric semantic genetic programming system can speed up the convergence of the search process.", } @Article{castelli2017evolutionary, author = "Mauro Castelli and Ivo Goncalves and Leonardo Trujillo and Ales Popovic", title = "An evolutionary system for ozone concentration forecasting", journal = "Information Systems Frontiers", volume = "19", number = "5", pages = "1123--1132", year = "2017", month = "1 " # oct, keywords = "genetic algorithms, genetic programming, Evolutionary computation, Smart cities, Forecasting, Air quality", ISSN = "1572-9419", DOI = "doi:10.1007/s10796-016-9706-2", publisher = "Springer", size = "10 pages", abstract = "Nowadays, with more than half of the world's population living in urban areas, cities are facing important environmental challenges. Among them, air pollution has emerged as one of the most important concerns, taking into account the social costs related to the effect of polluted air. According to a report of the World Health Organization, approximately seven million people die each year from the effects of air pollution. Despite this fact, the same report suggests that cities could greatly improve their air quality through local measures by exploiting modern and efficient solutions for smart infrastructures. Ideally, this approach requires insights of how pollutant levels change over time in specific locations. To tackle this problem, we present an evolutionary system for the prediction of pollutants levels based on a recently proposed variant of genetic programming. This system is designed to predict the amount of ozone level, based on the concentration of other pollutants collected by sensors disposed in critical areas of a city. An analysis of data related to the region of Yuen Long (one of the most polluted areas of China), shows the suitability of the proposed system for addressing the problem at hand. In particular, the system is able to predict the ozone level with greater accuracy with respect to other techniques that are commonly used to tackle similar forecasting problems.", notes = "Also known as \cite{Castelli2017}", } @Proceedings{Castelli:2018:GP, title = "EuroGP 2018: Proceedings of the 21st European Conference on Genetic Programming", year = "2018", editor = "Mauro Castelli and Lukas Sekanina and Mengjie Zhang", volume = "10781", series = "LNCS", address = "Parma, Italy", month = "4-6 " # apr, organisation = "EvoStar, Species", publisher = "Springer Verlag", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-77552-4", URL = "https://link.springer.com/book/10.1007%2F978-3-319-77553-1", DOI = "doi:10.1007/978-3-319-77553-1", size = "xii+323 pages", notes = "EuroGP'2018 held in conjunction with EvoCOP2018, EvoMusArt2018 and EvoApplications2018", } @InProceedings{Castelli:2018:EuroGP, author = "Mauro Castelli and Ivo Goncalves and Luca Manzoni and Leonardo Vanneschi", title = "Pruning Techniques for Mixed Ensembles of Genetic Programming Models", booktitle = "EuroGP 2018: Proceedings of the 21st European Conference on Genetic Programming", year = "2018", month = "4-6 " # apr, editor = "Mauro Castelli and Lukas Sekanina and Mengjie Zhang and Stefano Cagnoni and Pablo Garcia-Sanchez", series = "LNCS", volume = "10781", publisher = "Springer Verlag", address = "Parma, Italy", pages = "52--67", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-77552-4", DOI = "doi:10.1007/978-3-319-77553-1_4", abstract = "The objective of this paper is to define an effective strategy for building an ensemble of Genetic Programming (GP) models. Ensemble methods are widely used in machine learning due to their features: they average out biases, they reduce the variance and they usually generalize better than single models. Despite these advantages, building ensemble of GP models is not a well-developed topic in the evolutionary computation community. To fill this gap, we propose a strategy that blends individuals produced by standard syntax-based GP and individuals produced by geometric semantic genetic programming, one of the newest semantics-based method developed in GP. In fact, recent literature showed that combining syntactic and semantics could improve the generalization ability of a GP model. Additionally, to improve the diversity of the GP models used to build up the ensemble, we propose different pruning criteria that are based on correlation and entropy, a commonly used measure in information theory. Experimental results, obtained over different complex problems, suggest that the pruning criteria based on correlation and entropy could be effective in improving the generalization ability of the ensemble model and in reducing the computational burden required to build it.", notes = "Part of \cite{Castelli:2018:GP} EuroGP'2018 held in conjunction with EvoCOP2018, EvoMusArt2018 and EvoApplications2018", } @InProceedings{DBLP:conf/epia/CastelliMMS19, author = "Mauro Castelli and Luca Manzoni and Luca Mariot and Martina Saletta", editor = "Paulo Moura Oliveira and Paulo Novais and Luis Paulo Reis", title = "Extending Local Search in Geometric Semantic Genetic Programming", booktitle = "Progress in Artificial Intelligence - 19th {EPIA} Conference on Artificial Intelligence, {EPIA} 2019, Vila Real, Portugal, September 3-6, 2019, Proceedings, Part {I}", series = "Lecture Notes in Computer Science", volume = "11804", pages = "775--787", publisher = "Springer", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/978-3-030-30241-2_64", DOI = "doi:10.1007/978-3-030-30241-2_64", timestamp = "Mon, 15 Jun 2020 01:00:00 +0200", biburl = "https://dblp.org/rec/conf/epia/CastelliMMS19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{CASTELLI:2019:SoftwareX, author = "Mauro Castelli and Luca Manzoni", title = "{GSGP-C++} 2.0: A geometric semantic genetic programming framework", journal = "SoftwareX", volume = "10", pages = "100313", year = "2019", ISSN = "2352-7110", DOI = "doi:10.1016/j.softx.2019.100313", URL = "http://www.sciencedirect.com/science/article/pii/S2352711019301736", keywords = "genetic algorithms, genetic programming, Semantics, Machine learning", abstract = "Geometric semantic operators (GSOs) for Genetic Programming have been widely investigated in recent years, producing competitive results with respect to standard syntax based operator as well as other well-known machine learning techniques. The usage of GSOs has been facilitated by a C++ framework that implements these operators in a very efficient manner. This work presents a description of the system and focuses on a recently implemented feature that allows the user to store the information related to the best individual and to evaluate new data in a time that is linear with respect to the number of generations used to find the optimal individual. The paper presents the main features of the system and provides a step by step guide for interested users or developers", } @Article{castelli:2020:Algorithms, author = "Mauro Castelli and Ales Groznik and Ales Popovic", title = "Forecasting Electricity Prices: A Machine Learning Approach", journal = "Algorithms", year = "2020", volume = "13", number = "5", keywords = "genetic algorithms, genetic programming", ISSN = "1999-4893", URL = "https://www.mdpi.com/1999-4893/13/5/119", DOI = "doi:10.3390/a13050119", abstract = "The electricity market is a complex, evolutionary, and dynamic environment. Forecasting electricity prices is an important issue for all electricity market participants. In this study, we shed light on how to improve electricity price forecasting accuracy through the use of a machine learning technique—namely, a novel genetic programming approach. Drawing on empirical data from the largest EU energy markets, we propose a forecasting model that considers variables related to weather conditions, oil prices, and CO2 coupons and predicts energy prices 24 h ahead. We show that the proposed model provides more accurate predictions of future electricity prices than existing prediction methods. Our important findings will assist the electricity market participants in forecasting future price movements.", notes = "also known as \cite{a13050119}", } @Article{castelli:2022:AS, author = "Mauro Castelli and Luca Manzoni and Luca Mariot and Giuliamaria Menara and Gloria Pietropolli", title = "The Effect of Multi-Generational Selection in Geometric Semantic Genetic Programming", journal = "Applied Sciences", year = "2022", volume = "12", number = "10", pages = "Article No. 4836", keywords = "genetic algorithms, genetic programming, evolutionary computation, geometric operators, geometric semantic genetic programming", ISSN = "2076-3417", URL = "https://www.mdpi.com/2076-3417/12/10/4836", DOI = "doi:10.3390/app12104836", size = "13 pages", abstract = "Among the evolutionary methods, one that is quite prominent is genetic programming. In recent years, a variant called geometric semantic genetic programming (GSGP) was successfully applied to many real-world problems. Due to a peculiarity in its implementation, GSGP needs to store all its evolutionary history, i.e., all populations from the first one. We exploit this stored information to define a multi-generational selection scheme that is able to use individuals from older populations. We show that a limited ability to use old generations is actually useful for the search process, thus showing a zero-cost way of improving the performances of GSGP.", notes = "also known as \cite{app12104836}", } @Article{castelli:2023:GPEM, author = "Mauro Castelli", title = "Commentary for the {GPEM} peer commentary special section on {W. B. Langdon's ``Jaws 30''}", journal = "Genetic Programming and Evolvable Machines", year = "2023", volume = "24", number = "2", pages = "Article number: 20", month = dec, note = "Special Issue: Thirtieth Anniversary of Genetic Programming: On the Programming of Computers by Means of Natural Selection", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/drZcv", DOI = "doi:10.1007/s10710-023-09468-w", size = "3 pages", notes = "Response to \cite{langdon:jaws30} Peer commentary editors: Leonardo Vanneschi and Leonardo Trujillo \cite{Vanneschi:2023:GPEM} See also \cite{jaws30_reply}", } @InProceedings{castillo:2002:gecco, author = "Flor A. Castillo and Ken A. Marshall and James L. Green and Arthur K. Kordon", title = "Symbolic Regression In Design Of Experiments: {A} Case Study With Linearizing Transformations", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "1043--1047", address = "New York", publisher_address = "San Francisco, CA 94104, USA", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "genetic algorithms, genetic programming, real world applications, design of experiment (DoE), lack of fit, linearizing transformations, symbolic regression", ISBN = "1-55860-878-8", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/RWA194.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-20.pdf", size = "6 pages", abstract = "The paper presents the potential of genetic programming (GP)-generated symbolic regression for linearising the response in statistical design of experiments when significant Lack of Fit is detected and no additional experimental runs are economically or technically feasible because of extreme experimental conditions. An application of this approach is presented with a case study in an industrial setting at The Dow Chemical Company.", notes = "GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)", } @InProceedings{Castillo:2003:gecco, author = "Flor Castillo and Kenric Marshall and James Green and Arthur Kordon", title = "A Methodology for Combining Symbolic Regression and Design of Experiments to Improve Empirical Model Building", booktitle = "Genetic and Evolutionary Computation -- GECCO-2003", editor = "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller", year = "2003", pages = "1975--1985", address = "Chicago", publisher_address = "Berlin", month = "12-16 " # jul, volume = "2724", series = "LNCS", ISBN = "3-540-40603-4", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, symbolic regression, design of experiments, Real World Applications", DOI = "doi:10.1007/3-540-45110-2_96", abstract = "A novel methodology for empirical model building using GP-generated symbolic regression in combination with statistical design of experiments as well as undesigned data is proposed. The main advantage of this methodology is the maximum data usage when extrapolation is necessary. The methodology offers alternative non-linear models that can either linearize the response in the presence of Lack or Fit or challenge and confirm the results from the linear regression in a cost effective and time efficient fashion. The economic benefit is the reduced number of additional experiments in the presence of Lack of Fit.", notes = "GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eights Annual Genetic Programming Conference (GP-2003)", } @InCollection{castillo:2004:GPTP, author = "Flor Castillo and Arthur Kordon and Jeff Sweeney and Wayne Zirk", title = "Using Genetic Programming in Industrial Statistical Model Building", booktitle = "Genetic Programming Theory and Practice {II}", year = "2004", editor = "Una-May O'Reilly and Tina Yu and Rick L. Riolo and Bill Worzel", chapter = "3", pages = "31--48", address = "Ann Arbor", month = "13-15 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, statistical model building, symbolic regression, undesigned data", ISBN = "0-387-23253-2", DOI = "doi:10.1007/0-387-23254-0_3", abstract = "The chapter summarises the practical experience of integrating genetic programming and statistical modelling at The Dow Chemical Company. A unique methodology for using Genetic Programming in statistical modeling of designed and undesigned data is described and illustrated with successful industrial applications. As a result of the synergistic efforts, the building technique has been improved and the model development cost and time can be significantly reduced. In case of designed data Genetic Programming reduced costs by suggesting transformations as an alternative to doing additional experimentation. In case of undesigned data Genetic Programming was instrumental in reducing the model building costs by providing alternative models for consideration.", notes = "part of \cite{oreilly:2004:GPTP2}", } @InProceedings{castillo:1999:GGOMPEA, author = "P. A. Castillo and V. Rivas and J. J. Merelo and J. Gonzalez and A. Prieto and G. Romero", title = "G-Prop-III: Global Optimization of Multilayer Perceptrons using an Evolutionary Algorithm", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "942", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "evolution strategies and evolutionary programming, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/G-Prop-III_poster.ps.gz", URL = "http://geneura.ugr.es/~pedro/gprop/G-Prop-III_poster.ps.gz", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{castillo:2004:eurogp, author = "Pedro A. Castillo and Maribel G. Arenas and J. J. Merelo and Gustavo Romero and Fatima Rateb and Alberto Prieto", title = "Comparing hybrid systems to design and optimize artificial neural networks", booktitle = "Genetic Programming 7th European Conference, EuroGP 2004, Proceedings", year = "2004", editor = "Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule", volume = "3003", series = "LNCS", pages = "240--249", address = "Coimbra, Portugal", publisher_address = "Berlin", month = "5-7 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-21346-5", DOI = "doi:10.1007/978-3-540-24650-3_22", abstract = "We conduct a comparative study between hybrid methods to optimise multi-layer perceptrons: a model that optimises the architecture and initial weights of multi layer perceptrons; a parallel approach to optimise the architecture and initial weights of multilayer perceptrons; a method that searches for the parameters of the training algorithm, and an approach for cooperative co-evolutionary optimisation of multi layer perceptrons. Obtained results show that a co-evolutionary model obtains similar or better results than specialised approaches, needing much less training epochs and thus using much less simulation time.", notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004", } @InProceedings{castillo:2004:ueatsvtilmls, title = "Using Evolutionary Algorithms to Suggest Variable Transformations in Linear Model Lack-of-Fit Situations", author = "Flor Castillo and Jeff Sweeney and Wayne Zirk", pages = "556--560", booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary Computation", year = "2004", publisher = "IEEE Press", month = "20-23 " # jun, address = "Portland, Oregon", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Evolutionary Computing in the Process Industry", DOI = "doi:10.1109/CEC.2004.1330906", abstract = "When significant model lack of fit (LOF) is present in a second-order linear regression model, it is often difficult to propose the appropriate parameter transformation that will make model LOF insignificant. This paper presents the potential of genetic programming (GP) symbolic regression for reducing or eliminating significant second-order linear model LOF. A case study in an industrial setting at The Dow Chemical Company is presented to illustrate this methodology.", notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.", } @InCollection{Castillo:2006:GPTP, author = "Flor Castillo and Arthur Kordon and Guido Smits", title = "Robust Pareto Front Genetic Programming Parameter Selection Based on Design of Experiments and Industrial Data", booktitle = "Genetic Programming Theory and Practice {IV}", year = "2006", editor = "Rick L. Riolo and Terence Soule and Bill Worzel", volume = "5", series = "Genetic and Evolutionary Computation", pages = "149--166", address = "Ann Arbor", month = "11-13 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, symbolic regression, industrial applications, design of experiments, parameter selection", ISBN = "0-387-33375-4", DOI = "doi:10.1007/978-0-387-49650-4_10", size = "18 pages", abstract = "Symbolic regression based on Pareto front GP is a very effective approach for generating high-performance parsimonious empirical models acceptable for industrial applications. The chapter addresses the issue of finding the optimal parameter settings of Pareto front GP which direct the simulated evolution toward simple models with acceptable prediction error. A generic methodology based on statistical design of experiments is proposed. It includes determination of the number of replicates by half-width confidence intervals, determination of the significant factors by fractional factorial design of experiments, approaching the optimum by steepest ascent/descent, and local exploration around the optimum by Box Behnken design of experiments. The results from implementing the proposed methodology to different types of industrial data sets show that the statistically significant factors are the number of cascades, the number of generations, and the population size. The optimal values for the three parameters have been defined based on second order regression models with R2 higher than 0.97 for small, medium, and large-sized data sets. The robustness of the optimal parameters toward the types of data sets was explored and a robust setting for the three significant parameters was obtained. It reduces the calculation time by 30per cent to 50per cent without statistically significant reduction in the mean response.", notes = "part of \cite{Riolo:2006:GPTP} Published Jan 2007 after the workshop", } @InProceedings{1144264, author = "Flor Castillo and Arthur Kordon and Guido Smits and Ben Christenson and Dee Dickerson", title = "Pareto Front Genetic Programming Parameter Selection Based on Design of Experiments and Industrial Data", booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation", year = "2006", editor = "Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos {Coello Coello} and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens", volume = "2", pages = "1613--1620", address = "Seattle, Washington, USA", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "8-12 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Real-World Applications, industrial applications, Pareto front, statistical design of experiments, symbolic regression", ISBN = "1-59593-186-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p1613.pdf", DOI = "doi:10.1145/1143997.1144264", size = "8 pages", abstract = "Symbolic regression based on Pareto Front GP is the key approach for generating high-performance parsimonious empirical models acceptable for industrial applications. The paper addresses the issue of finding the optimal parameter settings of Pareto Front GP which direct the simulated evolution toward simple models with acceptable prediction error. A generic methodology based on statistical design of experiments is proposed. It includes statistical determination of the number of replicates by half-width confidence intervals, determination of the significant inputs by fractional factorial design of experiments, approaching the optimum by steepest ascent/descent, and local exploration around the optimum by Box Behnken or by central composite design of experiments. The results from implementing the proposed methodology to a small-sized industrial data set show that the statistically significant factors for symbolic regression, based on Pareto Front GP, are the number of cascades, the number of generations, and the population size. A second order regression model with high R2 of 0.97 includes the three parameters and their optimal values have been defined. The optimal parameter settings were validated with a separate small sized industrial data set. The optimal settings are recommended for symbolic regression applications using data sets with up to 5 inputs and up to 50 data points.", notes = "GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM Order Number 910060", } @InCollection{Castillo:2010:GPTP, author = "Flor Castillo and Arthur Kordon and Carlos Villa", title = "Genetic Programming Transforms in Linear Regression Situations", booktitle = "Genetic Programming Theory and Practice VIII", year = "2010", editor = "Rick Riolo and Trent McConaghy and Ekaterina Vladislavleva", series = "Genetic and Evolutionary Computation", volume = "8", address = "Ann Arbor, USA", month = "20-22 " # may, publisher = "Springer", chapter = "11", pages = "175--194", keywords = "genetic algorithms, genetic programming, Multiple Linear Regression, multicollinearity, soft sensor", isbn13 = "978-1-4419-7746-5", URL = "http://www.springer.com/computer/ai/book/978-1-4419-7746-5", DOI = "doi:10.1007/978-1-4419-7747-2_11", abstract = "The chapter summarizes the use of Genetic Programming (GP) inMultiple Linear Regression (MLR) to address multicollinearity and Lack of Fit (LOF). The basis of the proposed method is applying appropriate input transforms (model respecification) that deal with these issues while preserving the information content of the original variables. The transforms are selected from symbolic regression models with optimal trade-off between accuracy of prediction and expressional complexity, generated by multiobjective Pareto-front GP. The chapter includes a comparative study of the GP-generated transforms with Ridge Regression, a variant of ordinary Multiple Linear Regression, which has been a useful and commonly employed approach for reducing multicollinearity. The advantages of GP-generated model respecification are clearly defined and demonstrated. Some recommendations for transforms selection are given as well. The application benefits of the proposed approach are illustrated with a real industrial application in one of the broadest empirical modeling areas in manufacturing - robust inferential sensors. The chapter contributes to increasing the awareness of the potential of GP in statistical model building by MLR.", notes = "part of \cite{Riolo:2010:GPTP}", } @InCollection{Castillo:2012:GPTP, author = "Flor A. Castillo and Carlos M. Villa and Arthur K. Kordon", title = "Symbolic Regression Model Comparison Approach Using Transmitted Variation", booktitle = "Genetic Programming Theory and Practice X", year = "2012", series = "Genetic and Evolutionary Computation", editor = "Rick Riolo and Ekaterina Vladislavleva and Marylyn D. Ritchie and Jason H. Moore", publisher = "Springer", chapter = "10", pages = "139--154", address = "Ann Arbor, USA", month = "12-14 " # may, keywords = "genetic algorithms, genetic programming, Symbolic regression, Model comparison, Transmitted variation, Pareto front, Interpolation, Monte Carlo", isbn13 = "978-1-4614-6845-5", URL = "http://dx.doi.org/10.1007/978-1-4614-6846-2_10", DOI = "doi:10.1007/978-1-4614-6846-2_10", abstract = "Model evaluation in symbolic regression generated by GP is of critical importance for successful industrial applications. Typically this model evaluation is achieved by a tradeoff between model complexity and R squared. The chapter introduces a model comparison approach based on the transmission of variation from the inputs to the output. The approach is illustrated with three different data sets from real industrial applications.", notes = "part of \cite{Riolo:2012:GPTP} published after the workshop in 2013", } @Article{Castillo:2012:JILSA, author = "Jose Luis {Castillo Sequera} and Jose Raul {Fernandez del Castillo Diez} and Leon {Gonzalez Sotos}", title = "Document Clustering with Evolutionary Systems through Straight-Line Programs {"}slp{"}", journal = "Journal of Intelligent Learning Systems and Applications", year = "2012", volume = "4", number = "4", pages = "303--318", month = nov, publisher = "Scientific Research Publishing", keywords = "genetic algorithms, genetic programming, Data Mining", ISSN = "2150-8402", bibsource = "OAI-PMH server at www.doaj.org", language = "eng", oai = "oai:doaj-articles:83efc26a67e43dbab60578f576a76c3d", URL = "http://www.scirp.org/journal/PaperDownload.aspx?DOI=10.4236/jilsa.2012.44032", DOI = "doi:10.4236/jilsa.2012.44032", size = "16 pages", abstract = "In this paper, we show a clustering method supported on evolutionary algorithms with the paradigm of linear genetic programming. The Straight-Line Programs slp, which uses a data structure which will be useful to represent collections of documents. This data structure can be seen as a linear representation of programs, as well as representations in the form of graphs. It has been used as a theoretical model in Computer Algebra, and our purpose is to reuse it in a completely different context. In this case, we apply it to the field of grouping library collections through evolutionary algorithms. We show its efficiency with experimental data we got from traditional library collections.", } @InProceedings{Castle:2010:EuroGP, author = "Tom Castle and Colin G. Johnson", title = "Positional Effect of Crossover and Mutation in Grammatical Evolution", booktitle = "Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010", year = "2010", editor = "Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar", volume = "6021", series = "LNCS", pages = "26--37", address = "Istanbul", month = "7-9 " # apr, organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, crossover, mutation, position, bias", isbn13 = "978-3-642-12147-0", DOI = "doi:10.1007/978-3-642-12148-7_3", abstract = "An often-mentioned issue with Grammatical Evolution is that a small change in the genotype, through mutation or crossover, may completely change the meaning of all of the following genes. This paper analyses the crossover and mutation operations in GE, in particular examining the constructive or destructive nature of these operations when occurring at points throughout a genotype. The results we present show some strong support for the idea that events occurring at the first positions of a genotype are indeed more destructive, but also indicate that they may be the most constructive crossover and mutation points too. We also demonstrate the sensitivity of this work to the precise definition of what is constructive/destructive.", notes = "5-parity, Santa Fe trail, 6-mux, symbolic regression Part of \cite{Esparcia-Alcazar:2010:GP} EuroGP'2010 held in conjunction with EvoCOP2010 EvoBIO2010 and EvoApplications2010", } @InProceedings{castle:2012:EuroGP, author = "Tom Castle and Colin G. Johnson", title = "Evolving High-Level Imperative Program Trees with Strongly Formed Genetic Programming", booktitle = "Proceedings of the 15th European Conference on Genetic Programming, EuroGP 2012", year = "2012", month = "11-13 " # apr, editor = "Alberto Moraglio and Sara Silva and Krzysztof Krawiec and Penousal Machado and Carlos Cotta", series = "LNCS", volume = "7244", publisher = "Springer Verlag", address = "Malaga, Spain", pages = "1--12", organisation = "EvoStar", isbn13 = "978-3-642-29138-8", URL = "http://www.cs.kent.ac.uk/pubs/2012/3202/content.pdf", DOI = "doi:10.1007/978-3-642-29139-5_1", size = "12 pages", keywords = "genetic algorithms, genetic programming, Imperative programming, Loops", abstract = "We present a set of extensions to Montana's popular Strongly Typed Genetic Programming system that introduce constraints on the structure of program trees. It is demonstrated that these constraints can be used to evolve programs with a naturally imperative structure, using common high-level imperative language constructs such as loops. A set of three problems including factorial and the general even-n-parity problem are used to test the system. Experimental results are presented which show success rates and required computational effort that compare favourably against other systems on these problems, while providing support for this imperative structure.", notes = "EpochX Part of \cite{Moraglio:2012:GP} EuroGP'2012 held in conjunction with EvoCOP2012 EvoBIO2012, EvoMusArt2012 and EvoApplications2012", } @InProceedings{Castle:2012:CEC, title = "Evolving Program Trees with Limited Scope Variable Declarations", author = "Tom Castle and Colin G. Johnson", pages = "2250--2257", booktitle = "Proceedings of the 2012 IEEE Congress on Evolutionary Computation", year = "2012", editor = "Xiaodong Li", month = "10-15 " # jun, address = "Brisbane, Australia", organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", URL = "http://www.cs.kent.ac.uk/pubs/2012/3213/index.html", DOI = "doi:10.1109/CEC.2012.6256547", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, imperative, sfgp, variables", abstract = "Variables are a fundamental component of computer programs. However, rarely has the construction of new variables been left to the evolutionary process of a tree- based Genetic Programming system. We present a series of modifications to an existing GP approach to allow the evolution of high-level imperative programs with limited scope variables. We make use of several new program constructs made possible by the modifications and experimentally compare their use. Our results suggest the impact of variable declarations is problem dependent, but can potentially improve performance. It is proposed that the use of variable declarations can reduce the degree of insight required into potential solutions.", notes = "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the EPS and the IET.", } @PhdThesis{Castle12, author = "Thomas Anthony Castle", title = "Evolving High-Level Imperative Program Trees with Genetic Programming", school = "University of Kent", year = "2012", type = "PhD Thesis", address = "UK", month = jun, keywords = "genetic algorithms, genetic programming, SBSE, STGP, Verification, local variables, loops, software metrics, computer programming", URL = "http://kar.kent.ac.uk/34799/", URL = "http://kar.kent.ac.uk/34799/1/thesis.pdf", URL = "http://ethos.bl.uk/OrderDetails.do?did=48&uin=uk.bl.ethos.580157", owner = "Yuanyuan", timestamp = "2013.10.10", category = "Software/Program Verification", country = "UK", size = "106 pages", abstract = "Genetic Programming (GP) is a technique which uses an evolutionary metaphor to automatically generate computer programs. Although GP proclaims to evolve computer programs, historically it has been used to produce code which more closely resembles mathematical formulae than the well structured programs that modern programmers aim to produce. The objective of this thesis is to explore the use of GP in generating high-level imperative programs and to present some novel techniques to progress this aim. A novel set of extensions to Montana's Strongly Typed Genetic Programming system are presented that provide a mechanism for constraining the structure of program trees. It is demonstrated that these constraints are sufficient to evolve programs with a naturally imperative structure and to support the use of many common high-level imperative language constructs such as loops. Further simple algorithm modifications are made to support additional constructs, such as variable declarations that create new limited-scope variables. Six non-trivial problems, including sorting and the general even parity problem, are used to experimentally compare the performance of the systems and configurations proposed. Software metrics are widely used in the software engineering process for many purposes, but are largely unused in GP. A detailed analysis of evolved programs is presented using seven different metrics, including cyclomatic complexity and Halstead's program effort. The relationship between these metrics and a program's fitness and evaluation time is explored. It is discovered that these metrics are poorly suited for application to improve GP performance, but other potential uses are proposed.", notes = "Yuanyuan Zhang Thu, Oct 10, 2013 at 12:34 PM 3.4.1 Factorial, 3.4.2 Fibonacci, 3.4.3 Even-n-parity, 3.4.4 Reverse List, 3.4.5 Sort List, 3.4.6 Triangle 6.4.2 Program Tree Length, 6.4.3 Program Tree Depth, 6.4.4 Number of Statements, 6.4.5 Cyclomatic Complexity, 6.4.6 Halstead's Effort, 6.4.7 Prather's Measure \mu, 6.4.8 NPATH Complexity p150-151 'Our results confirmed previous findings that many complexity metrics correlate highly with program size and there was also high correlation with each other, suggesting that they are measuring similar properties. Little consistency was seen in the trends between the complexity metrics and the fitness. It was concluded that the complexity metrics used in this study do not have qualities that would make them suitable for applications to improve fitness or evaluation time in GP.' Mention on StackOverflow https://stackoverflow.com/questions/22194786/can-any-existing-machine-learning-structures-perfectly-emulate-recursive-functio uk.bl.ethos.580157", } @Article{Castro:2015:OE, author = "A. Castro and J. L. Perez and J. R. Rabunal and G. Iglesias", title = "Genetic programming and floating boom performance", journal = "Ocean Engineering", volume = "104", pages = "310--318", year = "2015", ISSN = "0029-8018", DOI = "doi:10.1016/j.oceaneng.2015.05.023", URL = "http://www.sciencedirect.com/science/article/pii/S0029801815002073", abstract = "In this paper the performance of floating booms under waves and currents is investigated by means of genetic programming (GP). This artificial intelligence (AI) technique is used to establish a mathematical expression of the significant effective draft, an essential parameter in predicting the containment capability of floating booms, and more specifically the occurrence of drainage failure. Obtained by applying GP to a comprehensive dataset of wave-current flume experiments, the expression makes the relationships among the relevant variables explicit - an advantage relative to other AI techniques such as artificial neural networks (ANN). The expression was selected as the most adequate to represent this physical problem from various expressions generated in two different stages in which dimensional and dimensionless variables were considered as input and output variables respectively. The most representative expressions obtained in both stages are presented and compared taking into account their goodness-of-fit, physical meaning, coherence and complexity. In addition, the adjustment with the experimental data obtained with these expressions is also discussed and compared with a previously developed ANN model.", keywords = "genetic algorithms, genetic programming, Floating booms, Drainage failure, Effective draft, Physical model", } @InProceedings{Castro:2019:BigDataSE, author = "Raphael Labaca Castro and Corinna Schmitt and Gabi Dreo", title = "{AIMED}: Evolving Malware with Genetic Programming to Evade Detection", booktitle = "2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE)", year = "2019", pages = "240--247", address = "Rotorua, New Zealand", month = "5-8 " # aug, keywords = "genetic algorithms, genetic programming, AIMED, Malware, Byte-level perturbations, Adversarial learning", ISSN = "2324-898X", DOI = "doi:10.1109/TrustCom/BigDataSE.2019.00040", size = "8 pages", abstract = "Genetic Programming (GP) has previously proved to achieve valuable results on the fields of image processing and arcade learning. Similarly, it can be used as an adversarial learning approach to evolve malware samples until static learning classifiers are no longer able to detect it. While the implementation is relatively simple compared with other Machine Learning approaches, results proved that GP can be a competitive solution to find adversarial malware examples comparing with similar methods. Thus, AIMED (Automatic Intelligent Malware Modifications to Evade Detection) was designed and implemented using genetic algorithms to evade malware classifiers. Our experiments suggest that the time to achieve adversarial malware samples can be reduced up to 50percent compared to classic random approaches. Moreover, we implemented AIMED to generate adversarial examples using individual malware scanners as target and tested the evasive files against further classifiers from both research and industry. The generated examples achieved up to 82percent of cross-evasion rates among the classifiers.", notes = "VirusTotal Also known as \cite{8887384}", } @Misc{DBLP:journals/corr/abs-2211-05723, author = "Henrique {Carvalho de Castro} and Bruno Henrique {Groenner Barbosa}", title = "A Python library for nonlinear system identification using Multi-Gene Genetic Programming algorithm", howpublished = "arXiv", volume = "abs/2211.05723", year = "2022", month = "10 " # nov, keywords = "genetic algorithms, genetic programming, System identification, NARMAX, MGGP, Nonlinear systems", eprinttype = "arXiv", primaryclass = "eess.SY", eprint = "2211.05723", timestamp = "Tue, 15 Nov 2022 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/corr/abs-2211-05723.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://doi.org/10.48550/arXiv.2211.05723", DOI = "doi:10.48550/arXiv.2211.05723", code_url = "https://github.com/CastroHc/MGGP", size = "7 pages", abstract = "Models can be built directly from input and output data trough a process known as system identification. The Nonlinear AutoRegressive with eXogenous inputs (NARMAX) models are among the most used mathematical representations in the area and has many successful applications on data-driven modeling in different fields. Such models become extremely large when they have high degree of non-linearity and long-term dependencies. Hence, a structure selection process must be performed to make them parsimonious. In the present paper, it is introduced a toolbox in Python that performs the structure selection process using the evolutionary algorithm named Multi-Gene Genetic Programming (MGGP). The toolbox encapsulates basic tools for parameter estimation, simulation and validation, and it allows the users to customize their evaluation function including prior knowledge and constraints in the individual structure (gray-box identification).", notes = "Also known as \cite{decastro2022python} See also MSc 29 March 2021 (broken Dec 2024) https://sigaa.ufla.br/sigaa/public/programa/noticias_desc.jsf?lc=lc=es_ES&id=1801¬icia=137337249", } @InProceedings{Casula:2009:APSURSI, author = "G. A. Casula and G. Mazzarella and N. Sirena", title = "Genetic Programming design of wire antennas", booktitle = "IEEE Antennas and Propagation Society International Symposium, APSURSI '09", year = "2009", month = jun, pages = "1--4", keywords = "genetic algorithms, genetic programming, genetic programming design, wire antennas", DOI = "doi:10.1109/APS.2009.5171505", ISSN = "1522-3965", abstract = "Genetic optimization has been used in the last years for solving different electromagnetic problems. However, this technique assumes, and binary-codes, a fixed structure from the beginning, so it has a limited use in antenna design. On the other hand, Genetic Programming is able to determine the antenna shape as an outcome of the procedure. This work describes how to use genetic programming to design wire antennas. The performances of each antenna generated by the genetic programming during the optimization process are evaluated by a standard method of moments code, NEC-2.", notes = "VSWR, SWR, gain, 800MHz Also known as \cite{5171505}", } @InCollection{Casula:2012:GPnew, author = "Giovanni Andrea Casula and Giuseppe Mazzarella", title = "Structure-Based Evolutionary Design Applied to Wire Antennas", booktitle = "Genetic Programming - New Approaches and Successful Applications", publisher = "InTech", year = "2012", editor = "Sebastian Ventura", chapter = "6", pages = "117--140", keywords = "genetic algorithms, genetic programming", isbn13 = "978-953-51-0809-2", DOI = "doi:10.5772/48249", size = "24 pages", notes = "Part of \cite{Ventura:2012:GPnew}. Open access book CC BY 3.0 license", } @InProceedings{conf/dcai/CatoiraPR10, author = "Alba Catoira and Juan Perez and Juan Rabunal", title = "Distributed Genetic Programming for Obtaining Formulas: Application to Concrete Strength", booktitle = "7th International Symposium Distributed Computing and Artificial Intelligence", year = "2010", editor = "Andre {de Leon F. de Carvalho} and Sara Rodriguez-Gonzalez and Juan {De Paz Santana} and Juan Rodriguez", volume = "79", series = "Advances in Intelligent and Soft Computing", pages = "357--364", address = "Valencia, Spain", month = "7-10 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-14882-8", DOI = "doi:10.1007/978-3-642-14883-5_46", abstract = "This paper presents a Genetic Programming algorithm which applies a clustering algorithm. The method evolves a population of trees for a fixed number of rounds or generations and applies a clustering algorithm to the population, in a way that in the selection process of trees their structure is taken into account. The proposed method, named DistClustGP, runs in a parallel environment, according to the model master-slave , so that it can evolve simultaneously different populations, and evolve together the best individuals from each cluster. DistClustGP favours the analysis of the parameters involved in the genetic process, decreases the number of generations necessary to obtain satisfactory results through evolution of different populations, due to its parallel nature, and allows the evolution of the best individuals taking into account their structure.", affiliation = "RNASA-IMEDIR group, Information and Communication Tecnologies of Information and Communications (TIC) Department, School of Computer Engineering, University of A Coruna, Campus de Elvina, A Coruna, Spain", } @InProceedings{Cattani:2009:UKCI, title = "Typed Cartesian Genetic Programming for Image Classification", author = "Phil T. Cattani and Colin G. Johnson", booktitle = "UK workshop on Computational Intelligence", year = "2009", address = "Nottingham", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.414.9907", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.414.9907", URL = "http://www.cs.kent.ac.uk/pubs/2009/2971/content.pdf", abstract = "This paper introduces an extension to Cartesian Genetic Programming (CGP), aimed at image classification problems. Individuals in the population consist of two layers of functions: image processing functions, and traditional mathematical functions. Information can be passed between these layers, and the final result can either be an image or a numerical value. This has been applied to image classification, by using CGP to evolve image processing algorithms for feature extraction. This paper presents results which show that these automatically extracted features can substantially increase classification accuracy on a medical problem concerned with the analysis of potentially cancerous cells.", notes = "UKCI 2009 website gone", } @InProceedings{Cattani:2010:cec, author = "Phil T. Cattani and Colin G. Johnson", title = "ME-CGP: Multi Expression Cartesian Genetic Programming", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", isbn13 = "978-1-4244-6910-9", abstract = "Cartesian Genetic Programming (CGP) is a form of Genetic Programming that uses directed graphs to represent programs. In this paper we propose a way of structuring a CGP algorithm to make use of the multiple phenotypes which are implicitly encoded in a genome string. We show that this leads to a large increase in efficiency compared with standard CGP where genomes are translated into only one phenotype. We call this method Multi Expression CGP (ME-CGP), based on Mihai Oltean's work on Multi Expression Programming using linear GP.", DOI = "doi:10.1109/CEC.2010.5586478", notes = "WCCI 2010. Also known as \cite{5586478}", } @PhdThesis{Cattani:thesis, author = "Philip Thomas Cattani", title = "Extending Cartesian genetic programming : multi-expression genomes and applications in image processing and classification", school = "University of Kent", year = "2014", address = "UK", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, MEP", broken = "https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.655651", URL = "https://mepx.org/papers.html#2014", abstract = "Genetic Programming (GP) is an Evolutionary Computation technique. Genetic Programming refers to a programming strategy where an artificial population of individuals represent solutions to a problem in the form of programs, and where an iterative process of selection and reproduction is used in order to evolve increasingly better solutions. This strategy is inspired by Charles Darwin theory of evolution through the mechanism of natural selection. Genetic Programming makes use of computational procedures analogous to some of the same biological processes which occur in natural evolution, namely, crossover, mutation, selection, and reproduction. Cartesian Genetic Programming (CGP) is a form of Genetic Programming that uses directed graphs to represent programs. It is called Cartesian, because this representation uses a grid of nodes that are addressed using a Cartesian co-ordinate system. This stands in contrast to GP systems which typically use a tree-based system to represent programs. In this thesis, we will show how it is possible to enhance and extend Cartesian Genetic Programming in two ways. Firstly, we show how CGP can be made to evolve programs which make use of image manipulation functions in order to create image manipulation programs. These programs can then be applied to image classification tasks as well as other image manipulation tasks such as segmentation, the creation of image filters, and transforming an input image in to a target image. Secondly, we show how the efficiency, the time it takes to solve a problem, of a CGP program can sometimes be increased by reinterpreting the semantics of a CGP genome string. We do this by applying Multi-Expression Programming to CGP.", notes = "ISNI: 0000 0004 5366 4011", } @InProceedings{cattral:1999:RAGA, author = "Robert Cattral and Franz Oppacher and Dwight Deugo", title = "Rule Acquisition with a Genetic Algorithm", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "778", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, classifier systems, poster papers", ISBN = "1-55860-611-4", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco1999/cattral_1999_raga.pdf", abstract = "Data mining, applied to poisonous mushroom machine learning benchmark", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Cavaglia:2017:APS, author = "Marco Cavaglia and Kai Staats and Luciano Errico and Kentaro Mogushi and Hunter Gabbard", title = "{LIGO} detector characterization with genetic programming", booktitle = "APS April Meeting 2017", year = "2017", pages = "Abstract: X6.00008", address = "Washington, DC, USA", month = jan # " 28-31", note = "Bulletin of the American Physical Society", keywords = "genetic algorithms, genetic programming, Laser Interferometer Gravitational-Wave", URL = "http://absimage.aps.org/image/APR17/MWS_APR17-2016-000316.pdf", URL = "http://meetings.aps.org/link/BAPS.2017.APR.X6.8", URL = "http://meetings.aps.org/Meeting/APR17/Session/X6.8", slides_url = "http://absuploads.aps.org/presentation.cfm?pid=12567", size = "0.25 pages", abstract = "Genetic Programming (GP) is a supervised approach to Machine Learning. GP has for two decades been applied to a diversity of problems, from predictive and financial modelling to data mining, from code repair to optical character recognition and product design. GP uses a stochastic search, tournament, and fitness function to explore a solution space. GP evolves a population of individual programs, through multiple generations, following the principals of biological evolution (mutation and reproduction) to discover a model that best fits or categorizes features in a given data set. We apply GP to categorization of LIGO noise and show that it can effectively be used to characterize the detector non-astrophysical noise both in low latency and offline searches.", notes = "Abstract only, presentation online: http://absuploads.aps.org/presentation.cfm?pid=12567 Abstract ID: BAPS.2017.APR.X6.8 National Science Foundation award PHY-1404139", } @Misc{Cavaglia:2018:arXiv, author = "Marco Cavaglia and Kai Staats and Teerth Gill", title = "Finding the origin of noise transients in {LIGO} data with machine learning", howpublished = "arXiv", year = "2018", month = "13 " # dec, keywords = "genetic algorithms, genetic programming, Data Analysis, Statistics and Probability", number = "LIGO-P1800043", URL = "https://arxiv.org/abs/1812.05225", size = "24 pages", abstract = "Quality improvement of interferometric data collected by gravitational-wave detectors such as Advanced LIGO and Virgo is mission critical for the success of gravitational-wave astrophysics. Gravitational-wave detectors are sensitive to a variety of disturbances of non-astrophysical origin with characteristic frequencies in the instrument band of sensitivity. Removing non-astrophysical artefacts that corrupt the data stream is crucial for increasing the number and statistical significance of gravitational-wave detections and enabling refined astrophysical interpretations of the data. Machine learning has proved to be a powerful tool for analysis of massive quantities of complex data in astronomy and related fields of study. We present two machine learning methods, based on random forest and genetic programming algorithms, that can be used to determine the origin of non-astrophysical transients in the LIGO detectors. We use two classes of transients with known instrumental origin that were identified during the first observing run of Advanced LIGO to show that the algorithms can successfully identify the origin of non-astrophysical transients in real interferometric data and thus assist in the mitigation of instrumental and environmental disturbances in gravitational-wave searches. While the data sets described in this paper are specific to LIGO, and the exact procedures employed were unique to the same, the random forest and genetic programming code bases and means by which they were applied as a dual machine learning approach are completely portable to any number of instruments in which noise is believed to be generated through mechanical couplings, the source of which is not yet discovered.", notes = "p20 'The future of ML applied to the analysis of gravitational-wave detector data is certainly bright.' Dec 2018 gives Journal reference: Commun. Comput. Phys., 25 (2019), pp. 963-987 DOI: 10.4208/cicp.OA-2018-0092 but doi not working \cite{Cavaglia:2019:CCP}", } @Article{Cavaglia:2019:CCP, author = "Marco Cavaglia and Kai Staats and Teerth Gill", title = "Finding the origin of noise transients in {LIGO} data with machine learning", journal = "Communications in Computational Physics", year = "2019", volume = "25", number = "4", pages = "963--987", month = apr, email = "marco.cavaglia@ligo.org", keywords = "genetic algorithms, genetic programming, instrumentation, astrophysics, mechanical couplings, ligo, Machine learning, gravitational waves, noise mitigation", ISSN = "1815-2406", URL = "https://arxiv.org/abs/1812.05225", DOI = "doi:10.4208/cicp.OA-2018-0092", size = "25 pages", abstract = "Quality improvement of interferometric data collected by gravitational-wave detectors such as Advanced LIGO and Virgo is mission critical for the success of gravitational-wave astrophysics. Gravitational-wave detectors are sensitive to a variety of disturbances of non-astrophysical origin with characteristic frequencies in the instrument band of sensitivity. Removing non-astrophysical artifacts that corrupt the data stream is crucial for increasing the number and statistical significance of gravitational-wave detections and enabling refined astrophysical interpretations of the data. Machine learning has proved to be a powerful tool for analysis of massive quantities of complex data in astronomy and related fields of study. We present two machine learning methods, based on random forest and genetic programming algorithms, that can be used to determine the origin of non-astrophysical transients in the LIGO detectors. We use two classes of transients with known instrumental origin that were identified during the first observing run of Advanced LIGO to show that the algorithms can successfully identify the origin of non-astrophysical transients in real interferometric data and thus assist in the mitigation of instrumental and environmental disturbances in gravitational-wave searches. While the data sets described in this paper are specific to LIGO, and the exact procedures employed were unique to the same, the random forest and genetic programming code bases and means by which they were applied as a dual machine learning approach are completely portable to any number of instruments in which noise is believed to be generated through mechanical couplings, the source of which is not yet discovered.", notes = " See also \cite{Cavaglia:2018:arXiv}", } @InProceedings{Cavalcanti-Costa:2021:CEC, author = "Joao Guilherme {Cavalcanti Costa} and Yi Mei and Mengjie Zhang", booktitle = "2021 IEEE Congress on Evolutionary Computation (CEC)", title = "Learning Initialisation Heuristic for Large Scale Vehicle Routing Problem with Genetic Programming", year = "2021", editor = "Yew-Soon Ong", pages = "1864--1871", address = "Krakow, Poland", month = "28 " # jun # "-1 " # jul, keywords = "genetic algorithms, genetic programming, Measurement, Space vehicles, Industries, NP-hard problem, Vehicle routing, Search problems, Large Scale Vehicle Routing, Hyper-Heuristic, Initialisation", isbn13 = "978-1-7281-8393-0", DOI = "doi:10.1109/CEC45853.2021.9504938", abstract = "The Large Scale Vehicle Routing Problem is a classical NP-hard problem. It has several applications in the industry and has always been the focus of studies and development of new, ever more complex, techniques to solve it. An important group of these techniques are Local Search-based, which are sensitive to the initial solution given to them. However, finding effective initial solutions is not a trivial task, requiring domain knowledge for building them. Although some Genetic Programming Hyper-Heuristics (GPHH) have tried to build better heuristics automatically, they barely give an advantage for improving the solution afterwards. This paper aims to show that Genetic Programming can identify better regions of the search space, where the initial solutions can be improved more efficiently with optimisation steps. This is done by developing new terminals and a new fitness function, which are based on the width of the routes, a metric that was recently found to be an important feature for good solutions. The obtained results show that the proposed approach finds better final solutions than when using classical initial heuristics or other GPHH, for both time efficiency and effectiveness.", notes = "Also known as \cite{9504938}", } @InProceedings{Cavalcanti-Costa:2021:SSCI, author = "Joao Guilherme {Cavalcanti Costa} and Yi Mei and Mengjie Zhang", booktitle = "2021 IEEE Symposium Series on Computational Intelligence (SSCI)", title = "Learning Penalisation Criterion of Guided Local Search for Large Scale Vehicle Routing Problem", year = "2021", abstract = "A recent case of success, the Knowledge-Guided Local Search was able to efficiently and effectively solve several (Large-Scale) Vehicle Routing Problems. This method presents an interesting concept of route compactness in their search process and uses it to penalise the solutions instead of using the traditional distance measure. Although mostly being successful, this measure sometimes leads to underperforming solutions when compared to the distance aspect. Based on the assumption that the best Guide Local Search penalisation criterion depends on the VRP instance, we make an analysis on how the algorithm behaves across different instances and also propose a Machine Learning model to learn to predict the best penalty criterion for a given instance. Genetic Programming, Support-Vector Machines and Random Forests are used in this classification task. Additionally, we also consider a regression model in order to estimate the improvement given for each mode. Results show that it is possible to find the correct class using the selected features and, in fact, some models were able to classify the majority of instances correctly. However, this is not consistent across different instances.", keywords = "genetic algorithms, genetic programming, Machine learning algorithms, Computational modelling, Vehicle routing, Predictive models, Search problems, Prediction algorithms", DOI = "doi:10.1109/SSCI50451.2021.9659939", month = dec, notes = "Also known as \cite{9659939}", } @InProceedings{Cavaliere:2020:ISCC, author = "Federica Cavaliere and Antonio {Della Cioppa} and Angelo Marcelli and Antonio Parziale and Rosa Senatore", title = "{Parkinson's} Disease Diagnosis: Towards Grammar-based Explainable Artificial Intelligence", booktitle = "IEEE ISCC 2020: IEEE Symposium on Computers and Communications 2020", year = "2020", editor = "Nicolas Montavont and Christos Douligeris", address = "internet, Rennes, France", month = jul # " 7-10", publisher = "EasyChair", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Explainable Artificial Intelligence, XAI, Parkinsons Disease, Supervised Learning by Classification, e-Health, Sociology, Machine learning, Learning (artificial intelligence), Predictive models, Germanium, Grammar", isbn13 = "978-1-7281-8087-8", ISSN = "2642-7389", URL = "https://easychair.org/publications/preprint_download/1Sh4", URL = "https://easychair.org/publications/preprint_download/1Sh4/EasyChair-Preprint-3785.pdf", DOI = "doi:10.1109/ISCC50000.2020.9219616", size = "6 pages", abstract = "The basic technology that reinvents machines to personalize human experiences is Machine Learning (ML), a branch of Artificial Intelligence (AI) and a strong buzzword in today's digital world. Despite its success, the most significant limitation of ML is the lack of transparency behind its behavior, which leaves users with a poor understanding of how it makes decisions, such it is the case for Deep Learning models. If the final user does not trust a model, he will not use it. This is especially true in medical diagnosis practice: physicians cannot simply use the predictions of the model but must trust the results it provides. This work focuses on the automatic early detection of Parkinson's disease (PD), whose impact on both the individual's quality of life and social well-being is constantly increasing with the aging of the population. To this end, we propose an explainable approach based on Genetic Programming, called Grammar Evolution (GE). This technique uses context-free grammar to describe the language of the programs to be generated and evolved. In this case, the generated programs are the explicit classification rules for the diagnosis of the subjects. The results of the experiments obtained on the publicly available H and PD data set show GE's high expressive power and performance comparable to those of several ML models that have been proposed in the literature.", notes = "also known as \cite{9219616} PonyGE2 Also available as EasyChair Preprint 3785 \cite{Cavaliere:2020:easychair} \cite{EasyChair:3785} Broken June 2024 http://conferences.imt-atlantique.fr/iscc2020/ Also known as \cite{9219616}", } @InProceedings{cavaretta:1999:DMGPTIPGE, author = "Michael J. Cavaretta and Kumar Chellapilla", title = "Data Mining using Genetic Programming: The Implications of Parsimony on Generalization Error", booktitle = "Proceedings of the Congress on Evolutionary Computation", year = "1999", editor = "Peter J. Angeline and Zbyszek Michalewicz and Marc Schoenauer and Xin Yao and Ali Zalzala", volume = "2", pages = "1330--1337", address = "Mayflower Hotel, Washington D.C., USA", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "6-9 " # jul, organisation = "Congress on Evolutionary Computation, IEEE / Neural Networks Council, Evolutionary Programming Society, Galesia, IEE", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, data mining, GP-variant, common dataset, data mining heuristic, generalisation error, less complex models, program complexity, training error, unseen data, computational complexity, data mining, generalisation (artificial intelligence)", ISBN = "0-7803-5536-9 (softbound)", ISBN = "0-7803-5537-7 (Microfiche)", DOI = "doi:10.1109/CEC.1999.782602", size = "8 pages", abstract = "A common data mining heuristic is, 'when choosing between models with the same training error, less complex models should be preferred as they perform better on unseen data'. This heuristic may not always hold. In genetic programming a preference for less complex models is implemented as: (i) placing a limit on the size of the evolved program; (ii) penalising more complex individuals, or both. The paper presents a GP-variant with no limit on the complexity of the evolved program that generates highly accurate models on a common dataset", notes = "statlog CEC-99 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. Library of Congress Number = 99-61143", } @InCollection{caverlee:2000:AGAADOBS, author = "James B. Caverlee", title = "A Genetic Algorithm Approach to Discovering an Optimal Blackjack Strategy", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "70--79", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms", notes = "part of \cite{koza:2000:gagp}", } @InProceedings{1068300, author = "Rachel Cavill and Steve Smith and Andy Tyrrell", title = "Multi-chromosomal genetic programming", booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation", year = "2005", editor = "Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and Edwin D. {de Jong} and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler", volume = "2", ISBN = "1-59593-010-8", pages = "1753--1759", address = "Washington DC, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2005/docs/p1753.pdf", DOI = "doi:10.1145/1068009.1068300", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "25-29 " # jun, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, design, performance, representations, team evolution", notes = "GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM Order Number 910052", } @InProceedings{Cavill:Tpo:cec2005, author = "Rachel Cavill and Stephen L. Smith and Andy Tyrrell", title = "The performance of polyploid evolutionary algorithms is improved both by having many chromosomes and by having many copies of each chromosome on symbolic regression problems", booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary Computation", year = "2005", editor = "David Corne and Zbigniew Michalewicz and Bob McKay and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Gunther Raidl and Kay Chen Tan and Ali Zalzala", pages = "935--941", address = "Edinburgh, Scotland, UK", month = "2-5 " # sep, publisher = "IEEE Press", volume = "1", keywords = "genetic algorithms, genetic programming, biology, cellular biophysics, evolutionary computation, regression analysis, multiple chromosomes, polyploid evolutionary algorithm, symbolic regression problem", ISBN = "0-7803-9363-5", URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=10417&isvol=1", URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=10417", DOI = "doi:10.1109/CEC.2005.1554783", abstract = "This paper presents important new findings for a new method for evolving individual programs with multiple chromosomes. Previous results have shown that evolving individuals with multiple chromosomes produced improved results over evolving individuals with a single chromosome. The multiple chromosomes are organised along two axes; there are a number of different chromosomes and a number of copies of each chromosome. This paper investigates the effects which these two axes have on the performance of the algorithm; whether the improvement in performance comes from just one of these features or whether it is a combination of them both", notes = "Last author is NOT Terrell", } @InProceedings{1144217, author = "Rachel Cavill and Stephen L Smith and Andy M Tyrrell", title = "Variable length genetic algorithms with multiple chromosomes on a variant of the Onemax problem", booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation", year = "2006", editor = "Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos {Coello Coello} and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens", volume = "2", ISBN = "1-59593-186-4", pages = "1405--1406", address = "Seattle, Washington, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p1405.pdf", DOI = "doi:10.1145/1143997.1144217", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "8-12 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "Genetic Algorithms: Poster, algorithms performance design, representation(s), size", notes = "GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM Order Number 910060", } @PhdThesis{cavill_mcgp, author = "Rachel Cavill", title = "Multi-Chromosomal Genetic Programming", school = "Department of Electronics, University of York", type = "{PhD} Dissertation", year = "2006", address = "UK", keywords = "genetic algorithms, genetic programming", URL = "http://ethos.bl.uk/OrderDetails.do?did=7&uin=uk.bl.ethos.437617", abstract = "Typically, computational models inspired by evolution have comprised a single large structure, such as a tree, string or graph, representing a single chromosome. Through the use of evolutionary operators, as mutation and recombination, over a number of generations a satisfactory solution to the problem may be found and the evolution halted. In natural, biological systems, it is not so common to find organisms which have only a single chromosome. Indeed, it is only bacteria and other relatively simple life-forms which can survive with only a single chromosome structure. All animals and plants have a much richer and more complex chromosome space, with not only multiple chromosomes, but multiple copies of each chromosome. Within artificial systems, sometimes, often for very problem-specific reasons, multiple structures are used which each make up part of the final solution. More recently, with the area of co-evolution successfully exploring the evolution of teams, further steps along this path towards a richer representation space have been investigated. This thesis investigates the exploitation of evolution with multiple chromosomes within computational models. By studying the biological model presented to us in nature, and attempting to extract the key mechanisms of multi chromosomal evolution an artificial system which imitates these mechanisms is developed. The system is designed to allow evolution with any number of chromosomes, so that experiments comparing evolution with a single chromosome to that with many may be performed. This work is not attempting to model biological evolution but is inspired by it. As well as presenting a richer representation space, the presence of multiple chromosomes also permits more complex evolutionary operators. For instance crossover may work between any pair of chromosomes, or may be restricted to be allowed only to occur between particular pairs of chromosomes. Natural systems too, display a range of crossover operators acting in different ways; therefore it is important to study the implications of using different crossover operators and to assess their relative characteristics and advantages. To this end, this thesis presents a system which allows evolution to occur with a specified number of chromosomes, conforming to k sets of n chromosomes. Using this system, experiments are done over a range of standard genetic programming benchmark problems to ascertain the affects of increasing the number of chromosomes along each of these two axis of variation. Further experiments are conducted into the behaviour of the crossover operator with this more complex representation and various crossover operators are evaluated within the system. Overall, it was found that multiple chromosomes increase the performance of the evolutionary system, insofar as better solutions were obtained more quickly in the simulations. However, in order to attain optimal increases both the number of chromosomes and the number of copies of copies of each in the system, need to be considered. The optimal number of chromosomes is shown to be problem dependent, but initial conclusions about how many chromosomes different types of problems are likely to use are also presented. Additionally, the crossover operator is shown to work best when it is restricted only to work with the exact same chromosome from the other parent.", notes = " not online? Access from EThOS: Order from print. uk.bl.ethos.437617", } @Article{journals/bioinformatics/CavillKHLNE09, title = "Genetic algorithms for simultaneous variable and sample selection in metabonomics", author = "Rachel Cavill and Hector C. Keun and Elaine Holmes and John C. Lindon and Jeremy K. Nicholson and Timothy M. D. Ebbels", journal = "Bioinformatics", year = "2009", number = "1", volume = "25", bibdate = "2009-06-22", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/bioinformatics/bioinformatics25.html#CavillKHLNE09", pages = "112--118", DOI = "doi:10.1093/bioinformatics/btn586", } @Article{Cawley:2011:GPEM, author = "Seamus Cawley and Fearghal Morgan and Brian McGinley and Sandeep Pande and Liam McDaid and Snaider Carrillo and Jim Harkin", title = "Hardware spiking neural network prototyping and application", journal = "Genetic Programming and Evolvable Machines", year = "2011", volume = "12", number = "3", pages = "257--280", month = sep, note = "Special Issue Title: Evolvable Hardware Challenges", keywords = "genetic algorithms, evolvable hardware, EMBRACE, Spiking neural networks, Network on chip, Intrinsic evolution, FPGA", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-011-9130-9", size = "24 pages", abstract = "EMBRACE has been proposed as a scalable, reconfigurable, mixed signal, embedded hardware Spiking Neural Network (SNN) device. EMBRACE, which is yet to be realised, targets the issues of area, power and scalability through the use of a low area, low power analogue neuron/synapse cell, and a digital packet-based Network on Chip (NoC) communication architecture. The paper describes the implementation and testing of EMBRACE-FPGA, an FPGA-based hardware SNN prototype. The operation of the NoC inter-neuron communication approach and its ability to support large scale, reconfigurable, highly interconnected SNNs is illustrated. The paper describes an integrated training and configuration platform and an on-chip fitness function, which supports GA-based evolution of SNN parameters. The practicalities of using the SNN development platform and SNN configuration toolset are described. The paper considers the impact of latency jitter noise introduced by the NoC router and the EMBRACE-FPGA processor-based neuron/synapse model on SNN accuracy and evolution time. Benchmark SNN applications are described and results demonstrate the evolution of high quality and robust solutions in the presence of noise. The reconfigurable EMBRACE architecture enables future investigation of adaptive hardware applications and self repair in evolvable hardware.", } @Article{DBLP:journals/ijait/Cazenave13, author = "Tristan Cazenave", title = "Monte-Carlo Expression Discovery", journal = "International Journal on Artificial Intelligence Tools", year = "2013", volume = "22", number = "1", month = feb, keywords = "genetic algorithms, genetic programming, MCTS, Monte-Carlo tree search, expression discovery, nested Monte-Carlo search, upper confidence bounds for trees, UCT, bloat", ISSN = "0218-2130", URL = "http://www.lamsade.dauphine.fr/~cazenave/papers/MCExpression.pdf", DOI = "doi:10.1142/S0218213012500352", size = "21 pages", abstract = "Monte-Carlo Tree Search is a general search algorithm that gives good results in games. Genetic Programming evaluates and combines trees to discover expressions that maximise a given fitness function. In this paper Monte-Carlo Tree Search is used to generate expressions that are evaluated in the same way as in Genetic Programming. Monte-Carlo Tree Search is transformed in order to search expression trees rather than lists of moves. We compare Nested Monte-Carlo Search to UCT (Upper Confidence Bounds for Trees) for various problems. Monte-Carlo Tree Search achieves state of the art results on multiple benchmark problems. The proposed approach is simple to program, does not suffer from expression growth, has a natural restart strategy to avoid local optima and is extremely easy to parallelise.", notes = "Jargon heavy. Universite Paris-Dauphine, 75016, Paris, France Cited by \cite{White:2015:GECCOcompa}", } @InProceedings{Cazenave:2015:, author = "Tristan Cazenave and Sana Ben Hamida", booktitle = "2015 IEEE Symposium Series on Computational Intelligence", title = "Forecasting Financial Volatility Using Nested Monte Carlo Expression Discovery", year = "2015", pages = "726--733", month = "7-10 " # dec, address = "Cape Town, South Africa", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SSCI.2015.110", size = "8 pages", abstract = "We are interested in discovering expressions for financial prediction using Nested Monte Carlo Search and Genetic Programming. Both methods are applied to learn from financial time series to generate non linear functions for market volatility prediction. The input data, that is a series of daily prices of European S&P500 index, is filtered and sampled in order to improve the training process. Using some assessment metrics, the best generated models given by both approaches for each training sub sample, are evaluated and compared. Results show that Nested Monte Carlo is able to generate better forecasting models than Genetic Programming for the majority of learning samples.", notes = "Also known as \cite{7376684}", } @InCollection{cebrian:2004:IESANN, author = "Manuel Cebrian and Alfonso Ortega {de la Puente} and Manuel Alfonseca", title = "Acceleration of a procedure to generate fractal curves of a given dimension through the probabilistic analysis of execution time", booktitle = "Intelligent Engineering Systems Through Artificial Neural Networks", publisher = "ASME Press", year = "2004", editor = "C. H. Dagli and A. L. Buczak and D. L. Enke and M. J. Embrecht", volume = "14", pages = "265--270", address = "New York", keywords = "genetic algorithms, genetic programming", ISBN = "0-7918-0228-0", URL = "http://www.ii.uam.es/~alfonsec/docs/annie.pdf", notes = "Presented at ANNIE 2004: Smart Engineering System Design: Neural Networks, Fuzzy Logic, Evolutionary Programming, Complex Systems and Data Mining, San Louis, Missouri, Nov. 7-10, 2004", size = "6 pages", } @InProceedings{1277388, author = "Manuel Cebrian and Manuel Alfonseca and Alfonso Ortega", title = "Automatic generation of benchmarks for plagiarism detection tools using grammatical evolution", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "2253--2253", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p2253.pdf", DOI = "doi:10.1145/1276958.1277388", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, grammatical evolution, Real-World Applications: Poster, human factors, reliability, source code plagiarism detection tool assessment", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @PhdThesis{Cebrian_Ramos:thesis, author = "Manuel {Cebrian Ramos}", title = "Using Algorithmic Information Theory and Stochastic Modeling to Improve Classification and Evolutionary Computation", school = "Department of Computer Science, Universidad Autonoma de Madrid", year = "2007", type = "Sobresaliente Cum Laude", address = "Spain", month = "13 " # jul, keywords = "genetic algorithms, genetic programming, grammatical evolution", URL = "http://digitool-uam.greendata.es:1801/webclient/DeliveryManager?pid=3411.pdf", URL = "http://hdl.handle.net/10486/2301", size = "244 pages", abstract = "This thesis presents theoretical and practical contributions in Algorithmic Information Theory and (Algorithmic) Stochastic Modelling. Algorithmic Information Theory is the theory concerned with obtaining an absolute measure of the information contained in an object. Stochastic Modelling is a methodology to improve an algorithm's performance by means of the introduction of random elements in its logic. One of the most interesting advances of Algorithmic Information Theory is the development of an absolute measure of similarity between objects. This measure can only be estimated, as it is incomputable by definition. The typical estimation relies on the use of data compression algorithms, being this estimation known as the compression distance. The two theoretical contributions of this thesis analyse the quality of this estimation. The first quantifies the estimation robustness when the information contained in the objects is noise-altered, concluding that it is considerably resistant to noise. The second studies the impact of the compression algorithm implementation on the estimation, yielding some practical recipes for making this choice. We use variants of the compression distance to develop two applications for classification and one for evolutionary computation. The first application addresses the problem of detecting similarities in objects which have been generated by a predecessor common source, independently of whether they use or not the same coding scheme: this includes detecting document translation and reconstructing phylogenetic threes from genetic material. We make use of the already proved usefulness of compression based similarity distances for educational plagiarism detection to develop our second application: AC, an integrated source code plagiarism detection environment. The third application makes use of this distance as a fitness function, which is used by evolutionary algorithms to automatically generate music in a given pre-defined style. Another three new applications are derived using Stochastic Modeling, two for evolutionary computation and one for classification. Two of them are intimately related and make use of the presence of Heavy Tail probability distributions in the optimisation processes involved in the generation of fractals by an evolutionary algorithm, and in the training process of a multilayer perceptron. This discovery is used to improve the performance of both algorithms by means of restart strategies. The last application presented in this thesis is a successful story of the use of a special randomised heuristic in a simple genetic algorithm to yield a state-of-the-art evolutionary algorithm for solving Constraint Satisfaction Problems.", abstract = "Esta tesis presenta contribuciones teoricas y practicas de la Teoria de Informacion Algoritmica y del Modelado Estocastico (Algoritmico). La Teoria de Informacion Algoritmica es la teoria concerniente a la obtencion de una medida absoluta de la cantidad informacion contenida en un objeto. El Modelado Estocastico es una metodologia para la mejora del rendimiento de algoritmos mediante la introduccion de elementos aleatorios en su logica. Una de las mas interesantes aportaciones de la Teoria de Informacion Algoritmica es el desarrollo de una medida absoluta de similitud entre objetos. Esta medida solo puede ser estimada, al ser no computable por definicion. La estimacion tipica se basa en el uso de algoritmos de compresion de datos, siendo esta estimacion conocida como la distancia de compresion. Las dos aportaciones teoricas de esta tesis analizan la calidad de esta estimacion. La primera cuantifica la robustez de la estimacion cuando la informacion contenida en los objetos ha sido alterada por ruido externo, concluyendo que esta es considerablemente resistente al mismo. La segunda, estudia el impacto de la implementacion del algoritmo de compresion sobre la estimacion, obteniendose algunas recetas practicas para realizar dicha eleccion. Usamos variantes de la distancia de compresion para desarrollar dos aplicaciones para clasificacion y una para computacion evolutiva. La primera aplicacion considera el problema de la deteccion de similitudes entre documentos que han sido generados por una fuente comun predecesora, independientemente de si estos usan o no la misma codificacion: esto incluye la deteccion de traducciones de documentos y la reconstruccion de arboles filogeneticos a partir de material genetico. Hacemos uso de la ya demostrada utilidad de las distancias de similitud basadas en compresion en la deteccion de plagio (en el ambito educacional) para desarrollar nuestra segunda aplicacion: AC, un entorno integrado de deteccion de plagio en codigo fuente. La tercera aplicacion hace uso de esta distancia como una funcion de fitness, que es usada por algoritmos evolutivos para generar de forma automatica musica con un estilo predefinido. Otras tres nuevas aplicaciones derivan del uso de Modelado Estocastico, dos para computacion evolutiva y una para clasificacion. Dos de ellas estan intimamente relacionadas y hacen uso de la presencia de distribuciones de probabilidad de Cola Pesada en los procesos de optimizacion involucrados en la generacion de fractales mediante un algoritmo evolutivo, y en el proceso de entrenamiento de un perceptron multicapa. Este descubrimiento se usa para mejorar el rendimiento de ambos algoritmos mediante el uso de estrategias de recomienzo. La ultima aplicacion presentada en esta tesis es una historia exitosa del uso de una heuristica aleatoria especial en un algoritmo genetico simple, obteniendose un algoritmo que equivale al estado del arte para la resolucion de Problemas de Satisfaccion de Restricciones (CSPs).", notes = "In english. Supervised by Manuel Alfonseca Moreno / Alfonso Ortega de la Puente", } @Article{Cebrian:2009:ieeeTEC, author = "Manuel Cebrian and Manuel Alfonseca and Alfonso Ortega", title = "Towards the Validation of Plagiarism Detection Tools by Means of Grammar Evolution", journal = "IEEE Transactions on Evolutionary Computation", year = "2009", month = jun, volume = "13", number = "3", pages = "477--485", keywords = "genetic algorithms, genetic programming, Grammar Evolution, Automatic programming, Benchmark testing, Data mining, Distance measurement, Evolution (biology), Genetics, Plagiarism, Probability density function, computer science education, educational technology", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2008.2008797", size = "9 pages", abstract = "Student plagiarism is a major problem in universities worldwide. In this paper, we focus on plagiarism in answers to computer programming assignments, where students mix and/or modify one or more original solutions to obtain counterfeits. Although several software tools have been developed to help the tedious and time consuming task of detecting plagiarism, little has been done to assess their quality, because determining the real authorship of the whole submission corpus is practically impossible for markers. In this paper, we present a Grammar Evolution technique which generates benchmarks for testing plagiarism detection tools. Given a programming language, our technique generates a set of original solutions to an assignment, together with a set of plagiarisms of the former set which mimic the basic plagiarism techniques performed by students. The authorship of the submission corpus is predefined by the user, providing a base for the assessment and further comparison of copy-catching tools. We give empirical evidence of the suitability of our approach by studying the behavior of one advanced plagiarism detection tool (AC) on four benchmarks coded in APL2, generated with our technique.", notes = "also known as \cite{4781609} Not GP", } @InCollection{cederberg:2002:TCTGAATNG, author = "Scott Cederberg", title = "The evolution of Cooperation: The Genetic Algorithm Applied to Three Normal-Form Games", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2002", year = "2002", editor = "John R. Koza", pages = "45--51", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms", URL = "http://www.genetic-programming.org/sp2002/Cederberg.pdf", notes = "part of \cite{koza:2002:gagp}", } @Article{Celik:2021:PAMI, author = "Bilge Celik and Joaquin Vanschoren", title = "Adaptation Strategies for Automated Machine Learning on Evolving Data", journal = "IEEE Transactions on Pattern Analysis and Machine Intelligence", year = "2021", volume = "43", number = "9", pages = "3067--3078", abstract = "Automated Machine Learning (AutoML) systems have been shown to efficiently build good models for new datasets. However, it is often not clear how well they can adapt when the data evolves over time. The main goal of this study is to understand the effect of concept drift on the performance of AutoML methods, and which adaptation strategies can be employed to make them more robust to changes in the underlying data. To that end, we propose 6 concept drift adaptation strategies and evaluate their effectiveness on a variety of AutoML approaches for building machine learning pipelines, including Bayesian optimization, genetic programming, and random search with automated stacking. These are evaluated empirically on real-world and synthetic data streams with different types of concept drift. Based on this analysis, we propose ways to develop more sophisticated and robust AutoML techniques.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/TPAMI.2021.3062900", ISSN = "1939-3539", month = sep, notes = "Also known as \cite{9366792}", } @Article{Cellini:2004:FCT, author = "F. Cellini and A. Chesson and I. Colquhoun and A. Constable and H. V. Davies and K. H. Engel and A. M. R. Gatehouse and S. Karenlampi and E. J. Kok and J-J. Leguay and S. Lehesranta and H. P. J. M. Noteborn and J. Pedersen and M. Smith", title = "Unintended effects and their detection in genetically modified crops", journal = "Food and Chemical Toxicology", year = "2004", volume = "42", pages = "1089--1125", number = "7", month = jul, keywords = "genetic algorithms, genetic programming, Genetic modification, GM, Substantial equivalence, Comparative analysis, Targeted analysis, Non-targeted analysis, Unpredictable effects, Unexpected effects", ISSN = "0278-6915", owner = "wlangdon", broken = "http://www.sciencedirect.com/science/article/B6T6P-4C004D3-3/2/aa7645e0537a1179bdf1f50aa4c376b3", URL = "http://www.entransfood.com/products/publications/WG2_paper_rev1_19jan2004_unmarked.pdf", DOI = "doi:10.1016/j.fct.2004.02.003", size = "37 pages, Review copy runs to 103 pages", abstract = "The commercialisation of GM crops in Europe is practically non-existent at the present time. The European Commission has instigated changes to the regulatory process to address the concerns of consumers and member states and to pave the way for removing the current moratorium. With regard to the safety of GM crops and products, the current risk assessment process pays particular attention to potential adverse effects on human and animal health and the environment. This document deals with the concept of unintended effects in GM crops and products, i.e. effects that go beyond that of the original modification and that might impact primarily on health. The document first deals with the potential for unintended effects caused by the processes of transgene insertion (DNA rearrangements) and makes comparisons with genetic recombination events and DNA rearrangements in traditional breeding. The document then focuses on the potential value of evolving profiling or omics technologies as non-targeted, unbiased approaches, to detect unintended effects. These technologies include metabolomics (parallel analysis of a range of primary and secondary metabolites), proteomics (analysis of polypeptide complement) and transcriptomics (parallel analysis of gene expression). The technologies are described, together with their current limitations. Importantly, the significance of unintended effects on consumer health are discussed and conclusions and recommendations presented on the various approaches outlined.", notes = "Metapontum Agrobios, SS Jonica Km 448.2, I-75010 Metaponto Matera, Italy. Publication Types: * Multicenter Study * Review PMID: 15123383 [PubMed - indexed for MEDLINE] Brief mention of (Helen Johnson et al., 2000) \cite{Johnson:2000:eamGPsir}", } @InProceedings{Cerda:2015:CSCI, author = "Jaime Cerda and Alberto Avalos and Mario Graff", booktitle = "2015 International Conference on Computational Science and Computational Intelligence (CSCI)", title = "Limitations of Genetic Programming Applied to Incipient Fault Detection: {SFRA} as Example", year = "2015", pages = "498--503", abstract = "This document deals with the application of genetic programming to the fault detection task, specifically with the power transformer fault detection problem of incipient faults. To this end we use genetic programming to obtain an highly approximated model of the a power transformer. The sweep frequency response analysis test represents the response of the transformer to a discrete variable frequency stimuli. We have been able to obtain a highly precision model which improves the precision of a commercial PG system. This result would be good if we only needed to identify the system. However, for the fault detection task, we should be able to identify the components within the transformer to assert where the fault has taken place. This is because the SFRA test when an incipient fault is present are similar but different as the fault advance. The tree generated for the model after the fault is evolved from the tree defining the power transformer model before the fault. Both trees are similar but the evolution seems to take place in a very specific random place. There is no way we can relate such changes with the physical model of the transformer. This shows the limitations of genetic programming to deal with this task and calls for extensions to the genetic programming paradigm or the merge of paradigms in order to deal with such task.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CSCI.2015.168", month = dec, notes = "Electr. Eng. Sch., UMSNH, Morelia, Mexico Also known as \cite{7424143}", } @InProceedings{Cerny:2008:gecco, author = "Brian M. Cerny and Peter C. Nelson and Chi Zhou", title = "Using differential evolution for symbolic regression and numerical constant creation", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "1195--1202", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1195.pdf", DOI = "doi:10.1145/1389095.1389331", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, combinatorial search, constant creation, differential evolution, gene expression programming, neutral mutations, optimisation, prefix gene expression programming, Redundant representations, symbolic regression", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389331}", } @MastersThesis{Cerny:mastersthesis, author = "Jan Cerny", title = "Evolutionary Design of Robot Motion Patterns", school = "Czech Technical University in Prague", year = "2012", month = "13 " # may, keywords = "genetic algorithms, genetic programming, robotics", URL = "http://cyber.felk.cvut.cz/research/theses/detail.phtml?id=226", URL = "http://cyber.felk.cvut.cz/research/theses/papers/226.pdf", size = "62 pages", abstract = "This thesis is focused on the use and implementation of Genetic Programming for generating viable motion patterns for robotic creatures. SYMBRION and REPLICATOR are two European projects whose research is focused on application of biological knowledge in robotics. One of the robots developed as a part of these projects is used in this work as a building block for two larger four legged robotic organisms. A co-evolution algorithm has been developed to generate single leg movements and adapt them to the three remaining legs. This approach of dividing the problem into two smaller sub-problems simplifies the evolution and saves the processing time. It is shown that the implemented Evolution Algorithm is indeed capable of generating motion patterns for robots very similar to those seen in nature and that by using them the robots are able to efficiently reach their predefined targets. All the experiments are conducted in a simulated environment.", notes = "Supervisor: Ing. Jiri Kubalik, Ph.D.", } @InProceedings{Cerny:evoapps13, author = "Jan Cerny and Jiri Kubalik", title = "Co-evolutionary Approach to Design of Robotic Gait", booktitle = "Applications of Evolutionary Computing, EvoApplications 2013: EvoCOMNET, EvoCOMPLEX, EvoENERGY, EvoFIN, EvoGAMES, EvoIASP, EvoINDUSTRY, EvoNUM, EvoPAR, EvoRISK, EvoROBOT, EvoSTOC", year = "2013", month = "3-5 " # apr, editor = "Anna I. Esparcia-Alcazar and Antonio Della Cioppa and Ivanoe {De Falco} and Ernesto Tarantino and Carlos Cotta and Robert Schaefer and Konrad Diwold and Kyrre Glette and Andrea Tettamanzi and Alexandros Agapitos and Paolo Burrelli and J. J. Merelo and Stefano Cagnoni and Mengjie Zhang and Neil Urquhart and Kevin Sim and Aniko Ekart and Francisco {Fernandez de Vega} and Sara Silva and Evert Haasdijk and Gusz Eiben and Anabela Simoes and Philipp Rohlfshagen", series = "LNCS", volume = "7835", publisher = "Springer Verlag", address = "Vienna", publisher_address = "Berlin", pages = "550--559", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-37191-2", DOI = "doi:10.1007/978-3-642-37192-9_55", size = "10 pages", abstract = "Manual design of motion patterns for legged robots is difficult task often with suboptimal results. To automate this process variety of approaches have been tried including various evolutionary algorithms. In this work we present an algorithm capable of generating viable motion patterns for multi-legged robots. This algorithm consists of two evolutionary algorithms working in co-evolution. The GP is evolving motion of a single leg while the GA deploys the motion to all legs of the robot. Proof-of-concept experiments show that the co-evolutionary approach delivers significantly better results than those evolved for the same robot with simple genetic programming algorithm alone.", notes = " EvoApplications2013 held in conjunction with EuroGP2013, EvoCOP2013, EvoBio'2013 and EvoMusArt2013", } @InProceedings{Cerri:2013:CEC, article_id = "1392", author = "Ricardo Cerri and Rodrigo Barros and Andre Carvalho and Alex Freitas", title = "A Grammatical Evolution Algorithm for Generation of Hierarchical Multi-Label Classification Rules", booktitle = "2013 IEEE Conference on Evolutionary Computation", volume = "1", year = "2013", month = jun # " 20-23", editor = "Luis Gerardo {de la Fraga}", pages = "454--461", address = "Cancun, Mexico", keywords = "genetic algorithms, genetic programming, grammatical evolution", isbn13 = "978-1-4799-0453-2", DOI = "doi:10.1109/CEC.2013.6557604", notes = "CEC 2013 - A joint meeting of the IEEE, the EPS and the IET.", } @InProceedings{cervigon:2023:GECCOcomp, author = "Carlos Cervigon and J. Ignacio Hidalgo", title = "Estimation of Interstitial Glucose from Physical Activity Measures Using Grammatical Evolution", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Justyna Petke and Aniko Ekart", pages = "57--58", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, grammatical evolution, blood glucose estimation, symbolic regression", isbn13 = "9798400701191", DOI = "doi:10.1145/3583133.3596432", size = "2 pages", abstract = "People with diabetes need to have their glucose levels under control, and it is essential for them to be able to know or estimate their glucose levels at any time. Continuous glucose monitors are commonly used, which measure interstitial glucose, an approximation of blood glucose, by means of a small catheter. Although most devices are not very intrusive, they do present some discomfort, and it would be preferable if these glucose levels could be estimated non-invasively, for example, through other physiological measurements collected in a simple way. This abstract describes our research on the performance of different grammatical evolution techniques to obtain accurate estimations of actual subcutaneous glucose values from non-invasive physiological measures, steps, calories and heart rates obtained with commercial smartwatches.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @InCollection{Ceryan:2016:CEECMTA, author = "Nurcihan Ceryan", title = "A Review of Soft Computing Methods Application in Rock Mechanic Engineering", booktitle = "Civil and Environmental Engineering: Concepts, Methodologies, Tools, and Applications", publisher = "IGI Global", year = "2016", chapter = "27", pages = "606--673", month = jan, keywords = "genetic algorithms, genetic programming", ISBN = "1-4666-9619-2", URL = "https://www.igi-global.com/chapter/a-review-of-soft-computing-methods-application-in-rock-mechanic-engineering/144518", DOI = "doi:10.4018/978-1-4666-9619-8.ch027", abstract = "Engineering behavior of rock mass is controlled by many factors, related to its nature and the environmental conditions. Determining all the parameters, ranking their weights, and clarifying their relative effects are very difficult tasks to accomplish. To overcome these difficulties, many researchers have employed soft computing methods in rock mechanics engineering. The soft computing methods have taken an important role in rock mechanics, and their abilities to address uncertainties, insufficient information and ambiguous linguistic expressions stand out in treating complex natural rock mass. This chapter briefly will review the development of soft computing techniques in rock mechanics engineering, especially in predicting of rock engineering classification system and mechanical properties of rock material and rock mass, determination weathering degree of rock material, evolution of rock performance, blasting and, rock slope stability. In addition, the future of the development and application of soft computing in rock mechanics engineering is discussed.", notes = "Balikesir University, Turkey", } @InProceedings{Ceska:2017:ICCAD, author = "Milan Ceska and Jiri Matyas and Vojtech Mrazek and Lukas Sekanina and Zdenek Vasicek and Tomas Vojnar", title = "Approximating Complex Arithmetic Circuits with Formal Error Guarantees: 32-bit Multipliers Accomplished", booktitle = "Proceedings of 36th IEEE/ACM International Conference On Computer Aided Design (ICCAD)", year = "2017", editor = "Iris Bahar and Sri Parameswaran", pages = "416--423", address = "Irvine, CA, USA", month = nov # " 13-16", publisher = "Institute of Electrical and Electronics Engineers", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, approximate computing, logical synthesis,", isbn13 = "978-1-5386-3093-8", language = "english", URL = "http://www.fit.vutbr.cz/research/view_pub.php?id=11420", DOI = "doi:10.1109/ICCAD.2017.8203807", size = "8 pages", abstract = "We present a novel method allowing one to approximate complex arithmetic circuits with formal guarantees on the approximation error. The method integrates in a unique way formal techniques for approximate equivalence checking into a search-based circuit optimisation algorithm. The key idea of our approach is to employ a novel search strategy that drives the search towards promptly verifiable approximate circuits. The method was implemented within the ABC tool and extensively evaluated on functional approximation of multipliers (with up to 32-bit operands) and adders (with up to 128-bit operands). Within a few hours, we constructed a high-quality Pareto set of 32-bit multipliers providing trade-offs between the circuit error and size. This is for the first time when such complex approximate circuits with formal error guarantees have been derived, which demonstrates an outstanding performance and scalability of our approach compared with existing methods that have either been applied to the approximation of multipliers limited to 8-bit operands or statistical testing has been used only. Our approach thus significantly improves capabilities of the existing methods and paves a way towards an automated design process of provably correct circuit approximations.", notes = "Bronze Winner 2018 HUMIES https://iccad.com/2017_accepted_papers", } @InProceedings{Ceska:2018:CAV, author = "Milan Ceska and Jiri Matyas and Vojtech Mrazek and Lukas Sekanina and Zdenek Vasicek and Tomas Vojnar", title = "{ADAC}: Automated Design of Approximate Circuits", booktitle = "Computer Aided Verification", year = "2018", editor = "Hana Chockler and Georg Weissenbacher", volume = "10981", series = "LNCS", pages = "612--620", address = "Oxford", month = jul # " 14-17", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming", isbn13 = "978-3-319-96145-3", DOI = "doi:10.1007/978-3-319-96145-3_35", size = "9 pages", abstract = "Approximate circuits with relaxed requirements on functional correctness play an important role in the development of resource-efficient computer systems. Designing approximate circuits is a very complex and time-demanding process trying to find optimal trade-offs between the approximation error and resource savings. In this paper, we present ADAC, a novel framework for automated design of approximate arithmetic circuits. ADAC integrates in a unique way efficient simulation and formal methods for approximate equivalence checking into a search-based circuit optimisation. To make ADAC easily accessible, it is implemented as a module of the ABC tool: a state-of-the-art system for circuit synthesis and verification. Within several hours, ADAC is able to construct high-quality Pareto sets of complex circuits (including even 32-bit multipliers), providing useful trade-offs between the resource consumption and the error that is formally guaranteed. This demonstrates outstanding performance and scalability compared with other existing approaches.", } @Article{CESKA:2020:ASC, author = "Milan Ceska and Jiri Matyas and Vojtech Mrazek and Lukas Sekanina and Zdenek Vasicek and Tomas Vojnar", title = "Adaptive verifiability-driven strategy for evolutionary approximation of arithmetic circuits", journal = "Applied Soft Computing", volume = "95", pages = "106466", year = "2020", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2020.106466", URL = "http://www.sciencedirect.com/science/article/pii/S1568494620304063", keywords = "genetic algorithms, genetic programming, Approximate computing, Energy efficiency, Circuit optimisation", abstract = "We present a novel approach for designing complex approximate arithmetic circuits that trade correctness for power consumption and play important role in many energy-aware applications. Our approach integrates in a unique way formal methods providing formal guarantees on the approximation error into an evolutionary circuit optimisation algorithm. The key idea is to employ a novel adaptive search strategy that drives the evolution towards promptly verifiable approximate circuits. As demonstrated in an extensive evaluation including several structurally different arithmetic circuits and target precisions, the search strategy provides superior scalability and versatility with respect to various approximation scenarios. Our approach significantly improves capabilities of the existing methods and paves a way towards an automated design process of provably-correct circuit approximations", } @InProceedings{Ceska:2019:EUROCAST, author = "Milan {Ceska jr.} and Milan Ceska and Jiri Matyas and Adam Pankuch and Tomas Vojnar", title = "Approximating Complex Arithmetic Circuits with Guaranteed Worst-Case Relative Error", booktitle = "International Conference on Computer Aided Systems Theory, EUROCAST 2019", year = "2019", editor = "Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia", volume = "12013", series = "Lecture Notes in Computer Science", pages = "482--490", address = "Las Palmas de Gran Canaria, Spain", month = "17-22 " # feb, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming", isbn13 = "978-3-030-45092-2", DOI = "doi:10.1007/978-3-030-45093-9_58", abstract = "We present a novel method allowing one to approximate complex arithmetic circuits with formal guarantees on the worst-case relative error, abbreviated as WCRE. WCRE represents an important error metric relevant in many applications including, e.g., approximation of neural network HW architectures. The method integrates SAT-based error evaluation of approximate circuits into a verifiability-driven search algorithm based on Cartesian genetic programming. We implement the method in our framework ADAC that provides various techniques for automated design of arithmetic circuits. Our experimental evaluation shows that, in many cases, the method offers a superior scalability and allows us to construct, within a few hours, high-quality approximations (providing trade-offs between the WCRE and size) for circuits with up to 32-bit operands. As such, it significantly improves the capabilities of ADAC.", } @Article{CESKA:2022:SEC, author = "Milan Ceska and Jiri Matyas and Vojtech Mrazek and Lukas Sekanina and Zdenek Vasicek and Tomas Vojnar", title = "{SagTree:} Towards efficient mutation in evolutionary circuit approximation", journal = "Swarm and Evolutionary Computation", year = "2022", volume = "69", pages = "100986", month = mar, keywords = "genetic algorithms, genetic programming, Approximate computing, Arithmetic circuit design, Mutation operators", ISSN = "2210-6502", DOI = "doi:10.1016/j.swevo.2021.100986", URL = "https://www.sciencedirect.com/science/article/pii/S2210650221001486", abstract = "Approximate circuits that trade the chip area for the quality of results play a key role in the development of energy-aware systems. Designing complex approximate circuits is, however, a very difficult and computationally demanding process. Evolutionary approximation-in particular, the method of Cartesian Genetic Programming (CGP)-currently represents one of the most successful approaches for automated circuit approximation. In this paper, we thoroughly investigate mutation operators for CGP with respect to the performance of circuit approximation. We design a novel dedicated operator that combines the classical single active gene mutation with a node deactivation operation (eliminating a part of the circuit forming a tree from an active gate). We show that our new operator significantly outperforms other operators on a wide class of approximation problems (such as 16 bit multipliers and dividers) and thus improves the performance of the state-of-the-art approximation techniques. Our results are grounded on a rigorous statistical evaluation including 39 approximation scenarios and 14000 runs", notes = "Milan Ceska: Conceptualization, Methodology, Supervision, Writing original draft", } @InProceedings{1274089, author = "Ahmet Cetinkaya", title = "Regular expression generation through grammatical evolution", booktitle = "Genetic and Evolutionary Computation Conference {(GECCO2007)} workshop program", year = "2007", month = "7-11 " # jul, editor = "Tina Yu", isbn13 = "978-1-59593-698-1", pages = "2643--2646", address = "London, United Kingdom", keywords = "genetic algorithms, genetic programming, grammatical evolution, regular expressions", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p2643.pdf", DOI = "doi:10.1145/1274000.1274089", publisher = "ACM Press", publisher_address = "New York, NY, USA", abstract = "This study investigates automatic regular expression generation using Grammatical Evolution. The software implementation is based on a subset of POSIX regular expression rules. For fitness calculation, a multiline text file is supplied. Lines which are required to match with generated regular expressions are specified beforehand. Fitness is evaluated according to the successful match results. Using this fitness evaluation strategy, preliminary tests have been performed on different files. Results indicate that the Grammatical Evolution approach to automatic generation of regular expressions is promising.", notes = "Distributed on CD-ROM at GECCO-2007 ACM Order No. 910071 Haskell, match HTML anchor tags in one file (266 lines), subset of POSIX, Pop=100.", } @InProceedings{DBLP:conf/inns/CettoBXM19, author = "Tomaso Cetto and Jonathan Byrne and Xiaofan Xu and David Moloney", title = "Size/Accuracy Trade-Off in Convolutional Neural Networks: An Evolutionary Approach", booktitle = "Recent Advances in Big Data and Deep Learning, Proceedings of the {INNS} Big Data and Deep Learning Conference {INNSBDDL} 2019", year = "2019", editor = "Luca Oneto and Nicolo Navarin and Alessandro Sperduti and Davide Anguita", pages = "17--26", address = "Sestri Levante, Genova, Italy", month = "16-18 " # apr, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Grammatical evolution , ANN, CNN", DOI = "doi:10.1007/978-3-030-16841-4_3", timestamp = "Wed, 08 May 2019 11:33:25 +0200", biburl = "https://dblp.org/rec/conf/inns/CettoBXM19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", abstract = "In recent years, the shift from hand-crafted design of Convolutional Neural Networks (CNN) to an automatic approach (AutoML) has garnered much attention. However, most of this work has been concentrated on generating state of the art (SOTA) architectures that set new standards of accuracy. In this paper, we use the NSGA-II algorithm for multi-objective optimization to optimize the size/accuracy trade-off in CNNs. This approach is inspired by the need for simple, effective, and mobile-sized architectures which can easily be re-trained on any datasets. This optimization is carried out using a Grammatical Evolution approach, which, implemented alongside NSGA-II, automatically generates valid network topologies which can best optimize the size/accuracy trade-off. Furthermore, we investigate how the algorithm responds to an increase in the size of the search space, moving from strictly topology optimization (number of layers, size of filter, number of kernels,etc.) and then expanding the search space to include possible variations in other hyper-parameters such as the type of optimizer, dropout rate, batch size, or learning rate, amongst others.", } @Article{Cevik:2007:ES, author = "Abdulkadir Cevik and Ibrahim H. Guzelbey", title = "A soft computing based approach for the prediction of ultimate strength of metal plates in compression", journal = "Engineering Structures", year = "2007", volume = "29", number = "3", pages = "383--394", month = mar, keywords = "genetic algorithms, genetic programming, Soft computing, Neural networks, Buckling, Plates", DOI = "doi:10.1016/j.engstruct.2006.05.005", abstract = "This paper presents two plate strength formulations applicable to metals with nonlinear stress-strain curves, such as aluminium and stainless steel alloys, obtained by soft computing techniques, namely Neural Networks (ANN) and Genetic Programming (GP). The proposed soft computing formulations are based on well-defined FE results available in the literature. The proposed formulations enable determination of the buckling strength of rectangular plates in terms of RambergOsgood parameters. The strength curves obtained by the proposed soft computing formulations show perfect agreement with FE results. The formulations are later compared with related codes and results are found to be quite satisfactory.", } @Article{Cevik:2007:JCSR, author = "Abdulkadir Cevik", title = "A new formulation for web crippling strength of cold-formed steel sheeting using genetic programming", journal = "Journal of Constructional Steel Research", year = "2007", volume = "63", number = "7", pages = "867--883", month = jul, keywords = "genetic algorithms, genetic programming, gene expression programming, Web crippling, Cold-formed steel decks, Formulation", DOI = "doi:10.1016/j.jcsr.2006.08.012", size = "18 pages", abstract = "This study presents Genetic programming (GP) as a new tool for the formulation of web crippling strength of cold-formed steel decks for various loading cases. There is no well established analytical solution of the problem due to complex plastic behaviour. The objective of this study is to provide an alternative robust formulation to related design codes and to verify the robustness of GP for the formulation of such structural engineering problems. The training and testing patterns of the proposed GP formulation are based on well established experimental results from the literature. The GP based formulation results are compared with experimental results and current design codes and found to be more accurate.", notes = "Karva", } @Article{Cevik:2007:JCSRa, author = "Abdulkadir Cevik", title = "Genetic programming based formulation of rotation capacity of wide flange beams", journal = "Journal of Constructional Steel Research", year = "2007", volume = "63", number = "7", pages = "884--893", month = jul, keywords = "genetic algorithms, genetic programming, Rotation capacity, Beams, Formulation", DOI = "doi:10.1016/j.jcsr.2006.09.004", abstract = "This study is a pioneer work that proposes genetic programming (GP) as a new approach for the explicit formulation of available rotation capacity of wide-flange beams which is an important phenomenon that determines the plastic behaviour of steel structures. The database for the GP formulation is based on extensive experimental results from literature. The results of the GP-based formulation are compared with numerical results obtained by a specialised computer program and existing analytical equations. The results indicate that the proposed GP formulation performs quite well compared to numerical results and existing analytical equations and is quite practical for use.", } @Article{Cevik:2007:JCSRb, author = "A. Cevik", title = "A new formulation for longitudinally stiffened webs subjected to patch loading", journal = "Journal of Constructional Steel Research", year = "2007", volume = "63", pages = "1328--1340", keywords = "genetic algorithms, genetic programming, Patch loading, Formulation, Girders, Webs, Longitudinal stiffeners", DOI = "doi:10.1016/j.jcsr.2006.12.004", abstract = "This study proposes a new formulation for patch loading longitudinally stiffened webs using genetic programming (GP) for the first time in the literature. The database for the GP formulation is based on extensive experimental results from the literature. The results of the GP based formulation are compared with existing models and design codes. The results indicate that the proposed GP formulation performs quite well compared to existing models and design codes.", } @Article{Cevik2008117, author = "Abdulkadir Cevik", title = "Unified formulation for ultimate capacity of shear failure of arc spot welding using genetic programming", journal = "Journal of Materials Processing Technology", volume = "204", number = "1-3", pages = "117--124", year = "2008", ISSN = "0924-0136", DOI = "doi:10.1016/j.jmatprotec.2007.10.064", URL = "http://www.sciencedirect.com/science/article/B6TGJ-4R2H7VY-3/2/b16ece537522603ec7cc693ad17fd283", keywords = "genetic algorithms, genetic programming, Arc spot welding, Ultimate capacity, Shear failure", abstract = "This study addresses genetic programming (GP) for the formulation of ultimate capacity of shear failure of arc spot welding. The proposed GP formulation is based on experimental results. The ultimate shear capacity of arc spot welding is formulated in terms of tensile strength, average welding thickness and diameter. The results of the proposed GP model are later compared with results of existing codes and are found to more accurate. In existing design codes four different equations are used whereas the proposed GP model is a unified formulation valid for all governing shear failures at the same time", } @Article{Cevik2008:ESwA1, author = "Abdulkadir Cevik and Ali Firat Cabalar", title = "Modelling damping ratio and shear modulus of sand-mica mixtures using genetic programming", journal = "Expert Systems with Applications", year = "2009", volume = "36", number = "4", pages = "7749--7757", month = may, keywords = "genetic algorithms, genetic programming, Leighton Buzzard sand, Mica, Resonant column testing", ISSN = "0957-4174", URL = "http://www.sciencedirect.com/science/article/B6V03-4TGHN90-2/2/78164c859cf3127425aedcca7e6f7d21", DOI = "doi:10.1016/j.eswa.2008.09.010", size = "9 pages", abstract = "This study presents two Genetic Programming (GP) models for damping ratio and shear modulus of sand-mica mixtures based on experimental results. The experimental database used for GP modeling is based on a laboratory study of dynamic properties of saturated coarse rotund sand and mica mixtures with various mix ratios under different effective stresses. In the tests, shear modulus, and damping ratio of the geomaterials have been measured for a strain range of 0.001% up to 0.1% using a Stokoe resonant column testing apparatus. The input variables in the developed NN models are the mica content, effective stress and strain, and the outputs are damping ratio and shear modulus. The performance of accuracies of proposed NN models are quite satisfactory (R2=0.95 for damping ratio and R2=0.98 for shear modulus).", } @Article{Cevik:2008:ESwA2, author = "Abdulkadir Cevik and Nihat Atmaca and Talha Ekmekyapar and Ibrahim H. Guzelbey", title = "Flexural buckling load prediction of aluminium alloy columns using soft computing techniques", journal = "Expert Systems with Applications", year = "2009", volume = "36", number = "3, Part 2", pages = "6332--6342", month = apr, keywords = "genetic algorithms, genetic programming, gene expression programming, Soft computing, Neural networks, Flexural buckling, Aluminium alloy columns", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2008.08.011", URL = "http://www.sciencedirect.com/science/article/B6V03-4TB6X28-1/2/3f64ccc54bc41be648922dc688ccad4a", size = "11 pages", abstract = "This paper presents the application of soft computing techniques for strength prediction of heat-treated extruded aluminium alloy columns failing by flexural buckling. Neural networks (NN) and genetic programming (GP) are presented as soft computing techniques used in the study. Gene-expression programming (GEP) which is an extension to GP is used. The training and test sets for soft computing models are obtained from experimental results available in literature. An algorithm is also developed for the optimal NN model selection process. The proposed NN and GEP models are presented in explicit form to be used in practical applications. The accuracy of the proposed soft computing models are compared with existing codes and are found to be more accurate.", } @Article{Cevik2010527, author = "Abdulkadir Cevik and M. Tolga Gogus and Ibrahim H. Guzelbey and Huzeyin Filiz", title = "Soft computing based formulation for strength enhancement of CFRP confined concrete cylinders", journal = "Advances in Engineering Software", volume = "41", number = "4", pages = "527--536", year = "2010", ISSN = "0965-9978", DOI = "doi:10.1016/j.advengsoft.2009.10.015", URL = "http://www.sciencedirect.com/science/article/B6V1P-4XPBSMR-1/2/fce8b7ee023873cc437bf1c86ee3eb19", keywords = "genetic algorithms, genetic programming, Soft computing, Stepwise regression, FRP confinement, Concrete cylinder, Strength enhancement", abstract = "This study presents the application of soft computing techniques namely as genetic programming (GP) and stepwise regression (SR) for formulation of strength enhancement of carbon-fiber-reinforced polymer (CFRP) confined concrete cylinders. The proposed soft computing based formulations are based on experimental results collected from literature. The accuracy of the proposed GP and SR formulations are quite satisfactory as compared to experimental results. Moreover, the results of proposed soft computing based formulations are compared with 15 existing models proposed by various researchers so far and are found to be more accurate.", } @Article{Cevik20112587, author = "Abdulkadir Cevik and Ebru {Akcapinar Sezer} and Ali Firat Cabalar and Candan Gokceoglu", title = "Modeling of the uniaxial compressive strength of some clay-bearing rocks using neural network", journal = "Applied Soft Computing", volume = "11", number = "2", pages = "2587--2594", year = "2011", note = "The Impact of Soft Computing for the Progress of Artificial Intelligence", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2010.10.008", URL = "http://www.sciencedirect.com/science/article/B6W86-51F7PJN-1/2/29835a31bf86c4e457cfa3e0ae15bae5", keywords = "genetic algorithms, genetic programming, Clay-bearing rock, Uniaxial compressive strength, Neural network, Slake durability index", abstract = "Uniaxial compressive strength of intact rock is significantly important for engineering geology and geotechnics, because it is an important design parameter for tunnels, rock slopes rock foundations, and it is also used as input parameter in some rock mass classification systems. This paper documents the results of laboratory experiments and numerical simulations (i.e. neural network) conducted to estimate the uniaxial compressive strength of some clay-bearing rocks selected from Turkey. Emphasis was placed on assessing the role of slake durability indices and clay contents. The input variables in developed neural network (NN) model are the origin of rocks, two/four-cycle slake durability indices and clay contents, and the output is uniaxial compressive strength. It is shown that the performance of capacities of proposed NN model is quite satisfactory. However, the NN model including four cycle slake durability index yielded slightly more precise results than that including two cycle slake durability index as input parameter. The paper also presents a comparative study on the accuracy of NN model and genetic programming (GP) in the results.", } @Article{Cevik20115650, author = "Abdulkadir Cevik", title = "Neuro-fuzzy modeling of rotation capacity of wide flange beams", journal = "Expert Systems with Applications", volume = "38", number = "5", pages = "5650--5661", year = "2011", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2010.10.070", URL = "http://www.sciencedirect.com/science/article/B6V03-51CJ387-K/2/ce5fff4acc0b21a9cd4c1ac3c5afe7df", keywords = "genetic algorithms, genetic programming, Rotation capacity, Beams, Neuro-fuzzy, Modelling", abstract = "This study is a pioneer work that investigates the feasibility of neuro-fuzzy (NF) approach for the modeling of rotation capacity of wide flange beams. The database for the NF modeling is based on experimental studies from literature. The results of the NF model are compared with numerical results obtained by a specialised computer programme and existing analytical and genetic programming based equations. The results indicate that the proposed NF model performs better. By using the proposed NF model, a wide range of parametric studies are also performed to evaluate the main effects of each variable on rotation capacity.", } @Article{Cevik20115662, author = "Abdulkadir Cevik", title = "Modeling strength enhancement of FRP confined concrete cylinders using soft computing", journal = "Expert Systems with Applications", volume = "38", number = "5", pages = "5662--5673", year = "2011", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2010.10.069", URL = "http://www.sciencedirect.com/science/article/B6V03-51CJ387-J/2/4b0e7942a4c46980f638964d442e332a", keywords = "genetic algorithms, genetic programming, Soft computing, Neural networks, Neuro-fuzzy, Stepwise regression, FRP confinement, Concrete cylinder, Strength enhancement", abstract = "This study presents the application of soft computing techniques namely as genetic programming (GP) and stepwise regression (SR), neuro-fuzzy (NF) and neural networks (NN) for modelling of strength enhancement of FRP (fibre-reinforced polymer) confined concrete cylinders. The proposed soft computing models are based on experimental results collected from literature. The accuracy of the proposed soft computing models are quite satisfactory as compared to experimental results. Moreover the results of proposed soft computing formulations are compared with 10 models existing in the literature proposed by various researchers so far and are found to be by far more accurate.", } @InProceedings{Chadalawada:2014:HIC, author = "Jayashree Chadalawada and Vladan Babovic", title = "Induction Of Governing Differential Equations From Hydrologic Time Series Data Using Genetic Programming", booktitle = "11th International Conference on Hydroinformatics", year = "2014", address = "New York, USA", month = aug # " 17-21", organisation = "IAHR/IWA Joint Committee on Hydroinformatics", keywords = "genetic algorithms, genetic programming", isbn13 = "978-0-692-28129-1", URL = "http://www.hic2014.org/xmlui/Chadalawada_2014_HIC.pdf", size = "8 pages", abstract = "This contribution describes an evolutionary method for identifying causal model from the observed time-series data. In the present case, we use a system of ordinary differential equations (ODEs) as the causal model. Usefulness of the approach is demonstrated on real-world time series of hydrologic processes and the unknown function of governing factors are determined. To explore the evolutionary search space more effectively, the right hand sides of ODEs are inferred by genetic programming (GP). The importance of different fitness criteria, as well as introduction of background knowledge about underlying processes are also being discussed and assessed. The method is applied on several cases and empirically demonstrated how successfully GP infers the systems of ODEs.", notes = "Broken June 2021 http://www.hic2014.org/xmlui/", } @Article{Chadalawada:2016:PE, author = "Jayashree Chadalawada and Vojtech Havlicek and Vladan Babovic", title = "Genetic Programming Based Approach Towards Understanding the Dynamics of Urban Rainfall-runoff Process", journal = "Procedia Engineering", volume = "154", pages = "1093--1102", year = "2016", note = "12th International Conference on Hydroinformatics (HIC 2016) - Smart Water for the Future", ISSN = "1877-7058", DOI = "doi:10.1016/j.proeng.2016.07.601", URL = "http://www.sciencedirect.com/science/article/pii/S1877705816319907", abstract = "Genetic Programming (GP) is an evolutionary-algorithm based methodology that is the best suited to model non-linear dynamic systems. The potential of GP has not been exploited to the fullest extent in the field of hydrology to understand the complex dynamics involved. The state of the art applications of GP in hydrological modelling involve the use of GP as a short-term prediction and forecast tool rather than as a framework for the development of a better model that can handle current challenges. In today's scenario, with increasing monitoring programmes and computational power, the techniques like GP can be employed for the development and evaluation of hydrological models, balancing, prior information, model complexity, and parameter and output uncertainty. In this study, GP based data driven model in a single and multi-objective framework is trained to capture the dynamics of the urban rainfall-runoff process using a series of tanks, where each tank is a storage unit in a watershed that corresponds to varying depths below the surface. The hydro-meteorological data employed in this study belongs to the Kent Ridge catchment of National University Singapore, a small urban catchment (8.5 hectares) that receives a mean annual rainfall of 2500 mm and consists of all the major land uses of Singapore.", keywords = "genetic algorithms, genetic programming, Multi-objective optimization, System Identification, Data driven modelling in Hydrology, Urban Rainfall-Runoff modelling", } @Article{chadalawada:2017:WRM, author = "Jayashree Chadalawada and Vojtech Havlicek and Vladan Babovic", title = "A Genetic Programming Approach to System Identification of {Rainfall-Runoff} Models", journal = "Water Resources Management", year = "2017", volume = "31", number = "12", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s11269-017-1719-1", DOI = "doi:10.1007/s11269-017-1719-1", } @InCollection{chai:2000:DCCPDGP, author = "Daniel Chai", title = "Development of a Computer Controller Players for Daleks using Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "80--89", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:2000:gagp}", } @InProceedings{Chaisricharoen:2009:ASICON, author = "Roungsan Chaisricharoen and Boonruk Chipipop", title = "Practical tuning of an OTA-C bandpass biquad via recurrent geometric programming", booktitle = "IEEE 8th International Conference on ASIC, ASICON '09", year = "2009", month = "20-23 " # oct, pages = "1193--1196", abstract = "The geometric programming which can be globally solved special cases of nonlinear problems is operated recurrently with calibrated", keywords = "geometric programming, HSPICE simulations, OTA-C bandpass biquad tuning, evolutionary algorithms, heuristic algorithms, operational amplifiers, recurrent geometric programming, second-order bandpass requirement, band-pass filters, biquadratic filters, operational amplifiers", DOI = "doi:10.1109/ASICON.2009.5351182", notes = "not on GP. Sch. of Inf. Technol., Mae Fah Luang Univ., Chiang Rai, Thailand Also known as \cite{5351182}", } @InProceedings{chakraborti:1998:GAplaNLP, author = "C. Chakraborti and K. K. N. Sastry", title = "The Genetic Algorithms Approach for Proving Logical Arguments in Natural Language", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "463--470", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms", notes = "SGA-98", } @InCollection{Chakraborti:2013:IMSE, author = "Nirupam Chakraborti", title = "Evolutionary Data-Driven Modeling", booktitle = "Informatics for Materials Science and Engineering", publisher = "Butterworth-Heinemann", year = "2013", editor = "Krishna Rajan", chapter = "5", pages = "71--95", address = "Oxford", keywords = "genetic algorithms, genetic programming, Neural network, Multi-objective optimisation, Evolutionary computation", isbn13 = "978-0-12-394399-6", DOI = "doi:10.1016/B978-0-12-394399-6.00005-9", URL = "http://www.sciencedirect.com/science/article/pii/B9780123943996000059", abstract = "Artificial neural networks (ANNs) and genetic programming (GP) have already emerged as two very effective computing strategies for constructing data-driven models for systems of scientific and engineering interest. However, coming up with accurate models or meta-models from noisy real-life data is often a formidable task due to their frequent association with high degrees of random noise, which might render an ANN or GP model either over- or underfitted. This problem has recently been tackled in two emerging algorithms, Evolutionary Neural Net (EvoNN) and Bi-objective Genetic Programming (BioGP), which use Pareto tradeoff and apply a bi-objective genetic algorithm (GA) in the basic framework of both ANNs and GP.", notes = "Department of Metallurgical and Materials Engineering, Indian Institute of Technology, Kharagpur, India", } @InProceedings{Chakraborti:2015:csdc, author = "Nirupam Chakraborti", title = "Data-driven paradigms of {EvoNN} and {BioGP}", booktitle = "Complex Systems Digital Campus E-conference, CS-DC'15", year = "2015", editor = "Paul Bourgine and Pierre Collet", pages = "Paper ID: 356", month = sep # " 30-" # oct # " 1", note = "Invited talk", keywords = "genetic algorithms, genetic programming, ANN", URL = "http://cs-dc-15.org/", URL = "http://cs-dc-15.org/papers/multi-scale-dynamics/evol-comp-methods-2/data-driven-paradigms-of-evonn-and-biogp/", broken_video_url = "http://bbb.univ-paris8.fr/playback/presentation/0.9.0/playback.html?meetingId=abfae475e9e5adf03d2df42d7d34f47e8e173fdc-1443413857859", abstract = "This paper will present the operational details of two recent algorithms EvoNN (Evolutionary Neural net) and BioGP (Bi-objective Genetic Programming) which are developed for modelling and optimization tasks pertinent to noisy data. EvoNN uses a neural net architecture while BioGP is based upon a tree structure typical of Genetic Programming. A bi-objective Genetic Algorithm acts on a population of either trees or neural nets, seeking a trade-off between the accuracy and complexity of the candidate models, ultimately leading to the optimum models along a Pareto frontier. Both the paradigms are tailor-made for constructing models of right complexity, and in the process of evolution they exclude the non-essential inputs. By default, an optimum model satisfying the Corrected Akaike Information Criterion (AICc) is recommended in case of EvoNN, and for BioGP the optimum model with the minimum training error is recommended. However, a Decision Maker (DM) can select a suitable model from the Pareto frontier by appropriate one can be easily picked up by applying some external criteria, if necessary. Both the algorithms tend to avoid over fitting or under fitting of any noisy data and in case of BioGP special procedures have been implemented to avoid bloat. Any pair of mutually conflicting objectives created through this procedure can also be optimized here using a built-in evolutionary strategy, incorporated as a module.", notes = "1 October 2015 5:40 to 6:10 (UTC) Evolutionary Computing Methods session Does not appear in proceedings published by Springer 2017, see \cite{Chakraborti:2016:camdtpa} Video working Dec 2019 http://bbb.univ-paris8.fr/playback/presentation/0.9.0/playback.html?meetingId=abfae475e9e5adf03d2df42d7d34f47e8e173fdc-1443413857859", } @InCollection{Chakraborti:2016:camdtpa, author = "Nirupam Chakraborti", title = "Data-Driven Bi-Objective Genetic Algorithms EvoNN and BioGP and Their Applications in Metallurgical and Materials Domain", booktitle = "Computational Approaches to Materials Design: Theoretical and Practical Aspects", publisher = "IGI Global", year = "2016", editor = "Shubhabrata Datta and J. Paulo Davim", chapter = "12", pages = "346--368", month = jun, keywords = "genetic algorithms, genetic programming", ISBN = "9781-5225-0290-6", DOI = "doi:10.4018/978-1-5225-0290-6.ch012", abstract = "Data-driven modeling and optimization are now of utmost importance in computational materials research. This chapter presents the operational details of two recent algorithms EvoNN (Evolutionary Neural net) and BioGP (Bi-objective Genetic Programming) which are particularly suitable for modeling and optimization tasks pertinent to noisy data. In both the approaches a tradeoff between the accuracy and complexity of the candidate models are sought, ultimately leading to some optimum tradeoffs. These novel strategies are tailor-made for constructing models of right complexity, excluding the non-essential inputs. They are constructed to implement the notion of Pareto-optimality using a predator-prey type genetic algorithm, providing the user with a set of optimum models, out of which an appropriate one can be easily picked up by applying some external criteria, if necessary. Several materials related problems have been solved using these algorithms in recent times and a couple of typical examples are briefly presented", } @Book{Chakraborti:book, author = "Nirupam Chakraborti", title = "Data-Driven Evolutionary Modeling in Materials Technology", publisher = "Routledge", year = "2023", keywords = "genetic algorithms, genetic programming, BioGP, matlab", isbn13 = "9781032061733", URL = "https://www.routledge.com/Data-Driven-Evolutionary-Modeling-in-Materials-Technology/Chakraborti/p/book/9781032061733", DOI = "doi:10.1201/9781003201045", size = "318 pages", abstract = "Chapter 1: Introduction Chapter 2: Data with random noise and its modeling Chapter 3: Nature inspired non-calculus optimization Chapter 4: Single-objective evolutionary algorithms Chapter 5: Multi-objective evolutionary optimization Chapter 6: Evolutionary learning and optimization using Neural Net paradigm Chapter 7: Evolutionary learning and optimization using Genetic Programming paradigm Chapter 8: The challenge of big data and Evolutionary Deep Learning Chapter 9: Software available in public domain and the commercial software Chapter 10: Applications in Iron and Steel making Chapter 11: Applications in chemical and metallurgical unit processing Chapter 12: Applications in Materials Design Chapter 13: Applications in Atomistic Materials Design Chapter 14: Applications in Manufacturing Chapter 15: Miscellaneous Applications", notes = "Sections on GP. Review in \cite{Kabliman:2023:GPEM}", } @Article{CHAKRABORTY:2020:CCE, author = "Arijit Chakraborty and Abhishek Sivaram and Lakshminarayanan Samavedham and Venkat Venkatasubramanian", title = "Mechanism discovery and model identification using genetic feature extraction and statistical testing", journal = "Computer \& Chemical Engineering", volume = "140", pages = "106900", year = "2020", ISSN = "0098-1354", DOI = "doi:10.1016/j.compchemeng.2020.106900", URL = "http://www.sciencedirect.com/science/article/pii/S009813542030123X", keywords = "genetic algorithms, genetic programming, Mechanism discovery and model identification, Statistical testing, Feature extraction", abstract = "One main drawback of many machine learning-based regression models is that they are difficult to interpret and explain. Mechanism-based first-principles models, on the other hand, can be interpreted and hence preferable. However, as they are often quite challenging to develop, the appeal of machine learning-based black-box models is natural. Here, we report a genetic algorithm-based machine learning system that automatically discovers mechanistic models from data using limited human guidance. The advantage of this approach is that it yields simple, interpretable, features and can be used to identify model forms and fundamental mechanisms that are often seen in chemical engineering. We demonstrate our system on several case studies in reaction kinetics and transport phenomena, and discuss its strengths and limitations", } @Article{Chakraborty:2017:ieeeTC, author = "Rajat Subhra Chakraborty and Ratan Rahul Jeldi and Indrasish Saha and Jimson Mathew", title = "Binary Decision Diagram Assisted Modeling of FPGA-based Physically Unclonable Function by Genetic Programming", journal = "IEEE Transactions on Computers", year = "2017", volume = "66", number = "6", pages = "971--981", month = jun, keywords = "genetic algorithms, genetic programming, Binary Decision Diagrams, Boolean Function Learning, Physically Unclonable Functions", ISSN = "0018-9340", DOI = "doi:10.1109/TC.2016.2603498", abstract = "We present a computationally efficient technique to build concise and accurate computational models for large (60 or more inputs, 1 output) Boolean functions, only a very small fraction of whose truth table is known during model building.We use Genetic Programming with Boolean logic operators, and enhance the accuracy of the technique using Reduced Ordered Binary Decision Diagram based representations of Boolean functions, whereby we exploit their canonical forms. We demonstrate the effectiveness of the proposed technique by successfully modelling several common Boolean functions, and ultimately by accurately modelling a 63-input Physically Unclonable Function circuit design on Xilinx Field Programmable Gate Array. We achieve better accuracy (at lesser computational overhead) in predicting truth table entries not seen during model building, than a previously proposed machine learning based modelling technique for similar Physically Unclonable Function circuits using Support Vector Machines. The success of this modelling technique has important implications in determining the acceptability of Physically Unclonable Functions as useful hardware security primitives, in applications such as anti-counterfeiting of integrated circuits.", notes = "Also known as \cite{7553573}", } @Article{Chakraborty:2008:IS, author = "Uday K. Chakraborty", title = "Genetic and evolutionary computing", journal = "Information Sciences", year = "2008", volume = "178", number = "23", pages = "4419--4420", month = "1 " # dec, note = "Introduction to special section on Genetic and Evolutionary Computing", keywords = "genetic algorithms, genetic programming", ISSN = "0020-0255", DOI = "doi:10.1016/j.ins.2008.07.026", URL = "http://www.sciencedirect.com/science/article/pii/S0020025508002855", } @Article{Chakraborty:2008:IJICT, author = "Uday K. Chakraborty", title = "Genetic programming model of solid oxide fuel cell stack: first results", journal = "International Journal of Information and Communication Technology (IJICT)", year = "2008", volume = "1", number = "3/4", pages = "453--461", keywords = "genetic algorithms, genetic programming, solid oxide fuel cells, SOFC stack, modelling, nonlinear dynamics, simulation", publisher = "Inderscience Publishers", ISSN = "1741-8070", bibsource = "OAI-PMH server at www.inderscience.com", language = "eng", URL = "http://www.inderscience.com/link.php?id=24015", DOI = "doi:10.1504/IJICT.2008.024015", abstract = "Models that predict performance are important tools in understanding and designing solid oxide fuel cells (SOFCs). Modelling of SOFC stack-based systems is a powerful approach that can provide useful insights into the nonlinear dynamics of the system without the need for formulating complicated systems of equations describing the electrochemical and thermal properties. Several algorithmic approaches have already been reported for the modelling of solid oxide fuel cell stack-based systems. This paper presents a new, genetic programming approach to SOFC modelling. Initial simulation results obtained with the proposed approach outperform the state-of-the-art radial basis function neural network method for this task.", } @InProceedings{Chakraborty2:2009:cec, author = "Uday K. Chakraborty", title = "An Evolutionary Computation Approach to Predicting Output Voltage from Fuel Utilization in {SOFC} Stacks", booktitle = "2009 IEEE Congress on Evolutionary Computation", year = "2009", editor = "Andy Tyrrell", pages = "2165--2171", address = "Trondheim, Norway", month = "18-21 " # may, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-2959-2", file = "P686.pdf", DOI = "doi:10.1109/CEC.2009.4983209", size = "7 pages", abstract = "Modeling of solid oxide fuel cell (SOFC) stack based systems is a powerful approach that can provide useful insights into the nonlinear dynamics of the system without the need for formulating complicated systems of equations describing the electrochemical and thermal properties. This paper presents an efficient genetic programming approach for modeling and simulation of SOFC output voltage versus fuel burn behavior. This method is shown to outperform the state-of-the-art radial basis function neural network approach for SOFC modeling.", keywords = "genetic algorithms, genetic programming, RBFANN", notes = "Fuel cell hydrogen + oxygen = steam + 1.18volts at 1000Centigrade and 1bar. DSS \cite{ga94aGathercole} Discipulus. NeuroSolutions. CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR", } @Article{Chakraborty2009740, author = "Uday Kumar Chakraborty", title = "Static and dynamic modeling of solid oxide fuel cell using genetic programming", journal = "Energy", volume = "34", number = "6", pages = "740--751", year = "2009", ISSN = "0360-5442", DOI = "doi:10.1016/j.energy.2009.02.012", URL = "http://www.sciencedirect.com/science/article/B6V2S-4W32975-1/2/c334dcacd8fee2c381ecd788e82d33fc", keywords = "genetic algorithms, genetic programming, Solid oxide fuel cell, SOFC stack, Dynamic model, Transient response, Neural network", abstract = "Modeling of solid oxide fuel cell (SOFC) systems is a powerful approach that can provide useful insights into the nonlinear dynamics of the system without the need for formulating complicated systems of equations describing the electrochemical and thermal properties. Several algorithmic approaches have in the past been reported for the modeling of solid oxide fuel cell stacks. However, all of these models have their limitations. This paper presents an efficient genetic programming approach to SOFC modeling and simulation. This method, belonging to the computational intelligence paradigm, is shown to outperform the state-of-the-art radial basis function neural network approach for SOFC modeling. Both static (fixed load) and dynamic (load transient) analyses are provided. Statistical tests of significance are used to validate the improvement in solution quality.", } @Article{CHAMANI:2020:Desalination, author = "Hooman Chamani and Pelin Yazgan-Birgi and Takeshi Matsuura and Dipak Rana and Mohamed I. {Hassan Ali} and Hassan A. Arafat and Christopher Q. Lan", title = "{CFD-based} genetic programming model for liquid entry pressure estimation of hydrophobic membranes", journal = "Desalination", volume = "476", pages = "114231", year = "2020", ISSN = "0011-9164", DOI = "doi:10.1016/j.desal.2019.114231", URL = "http://www.sciencedirect.com/science/article/pii/S0011916419318430", keywords = "genetic algorithms, genetic programming, Membrane distillation, Liquid entry pressure, Computational fluid dynamics, Modeling", abstract = "Wetting phenomenon inside the pore is a significant obstacle hindering membrane distillation (MD) from being fully industrialized. Herein, a new equation is provided for the users, using the combination of computational fluid dynamics (CFD) and genetic programming (GP) tools for estimation of liquid entry pressure (LEP), a parameter closely related to pore wetting. CFD was applied to model the wetting process inside the pore during the gradual increase in feed pressure at different scenarios in which contact angle, pore radius and membrane thickness were changed. Afterwards, GP as an intelligent method was employed to provide a computer program estimating LEP in the whole ranges in which CFD modeling was carried out. Moreover, validation was done using experimental data and then the influence of effective parameters on LEP was studied. This work provides an explicit formula for estimation of LEP in a closer agreement with the experimental data in comparison to the Young-Laplace equation. In addition, the influence of the membrane thickness was added to the equation, providing a more realistic formula for LEP estimation", } @PhdThesis{Chamani_Hooman_2021_thesis, author = "Hooman Chamani", title = "Pore Wetting in Desalination of Brine Using Membrane Distillation Process", school = "Department of Chemical and Biological Engineering, University of Ottawa", year = "2021", address = "Canada", keywords = "genetic algorithms, genetic programming, Pore Wetting, Membrane Distillation, Desalination, Modeling", URL = "https://ruor.uottawa.ca/items/00271b9a-3d2a-4a10-b169-57afe2194750", URL = "http://hdl.handle.net/10393/42946", URL = "https://ruor.uottawa.ca/bitstreams/e313e818-3681-49d4-bf24-2458f361362e/download", DOI = "doi:10.20381/ruor-27163", size = "277 pages", abstract = "It goes without saying that water scarcity is a widespread and increasingly pressing global challenge. One of the methods which can mitigate water shortage is to increase freshwater production via desalination of saline waters. Seawater and saline aquifer sources represent 97.5% of all water on Earth. Hence, treating even a small portion of saline water could significantly reduce water shortage. Although reverse osmosis is one of the state-of-the-art pressure-driven membrane desalination technologies, it is incapable of desalinating high-salinity streams due to the very high osmotic pressure to overcome. Membrane distillation (MD) is one of the emerging methods, which has attracted much attention for desalinating highly saline brines. MD is a thermally driven process in which only vapor molecules pass through the pores of a microporous hydrophobic membrane. This process, however, has not been fully commercialized due to a number of challenges, including “pore wetting”. Pore wetting refers to the presence of liquid, instead of just water vapor, inside the membrane pores, which may cause a decrease in MD flux and/or deterioration of distillate quality. Herein, a comprehensive review on pore wetting is presented, and then this phenomenon is investigated from four aspects. In the first phase of this project, a theoretical model is presented according to which the pore size distribution of membrane, a parameter affecting pore wetting risk, is estimated by employing only a few experimental data points in accordance with the wet/dry method, reducing the number of data required to be recorded largely. In the next phase, an equation is presented for the estimation of liquid entry pressure (LEP), a membrane parameter closely related to pore wetting, using computational fluid dynamics (CFD) tools and genetic programming (GP) as an intelligent technique. This equation can estimate LEP in closer agreement to experimental values in comparison to the Young-Laplace equation. In the third phase, movement of liquid-gas interface inside the membrane pore is tracked using a well-founded model, and consequently, the pressure and velocity at the interface and the required time for replacement are studied. Finally, in the last phase, a model is developed for pore wetting in vacuum MD, considering heat and mass balances at the vapor-liquid interface. This model assumes that heat only enters the pore inlet and is removed due to liquid vaporization at the vapor-liquid interface, with heat transfer through the pore wall neglected. This model shows that partial pore wetting is possible since the vapor-liquid interface might remain within the pore at the steady-state condition. Further, this model can predict the decrease in temperature from the pore inlet to the vapor-liquid interface, a phenomenon that has been reported in the literature without any proof.", notes = "supervisors: Christopher Lan and Takeshi Matsuura", } @Article{chambers:2001:GPEM, author = "Lance D. Chambers", title = "Book Review: {Genetic} Programming and Data Structures: Genetic Programming+Data Structures=Automatic Programming", journal = "Genetic Programming and Evolvable Machines", year = "2001", volume = "2", number = "3", pages = "301--303", month = sep, keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dR8hd", DOI = "doi:10.1023/A:1011957528066", notes = "Review of \cite{langdon:book} Article ID: 357598", } @Article{2002ApOpt..41.6260C, author = "Malik Chami and Denis Robilliard", title = "Inversion of oceanic constituents in case {I} and {II} waters with genetic programming algorithms", year = "2002", month = "20 " # oct, volume = "41", pages = "6260--6275", journal = "Applied Optics", number = "30", adsurl = "http://adsabs.harvard.edu/cgi-bin/nph-bib_query?bibcode=2002ApOpt..41.6260C&db_key=INST", adsnote = "Provided by the NASA Astrophysics Data System", keywords = "genetic algorithms, genetic programming, ARTIFICIAL SATELLITES, ATMOSPHERIC OPTICS, COLOUR, INFRARED SPECTROSCOPY, LIGHT TRANSMISSION, OPTICAL PROPERTIES, RADIATIVE TRANSFER, REFLECTANCE, REMOTE SENSING, SEA WATER, SPECTROSCOPIC ANALYSIS, STOCHASTIC PROCESSES, WAVE PROPAGATION", ISSN = "1559-128X", URL = "http://ao.osa.org/ViewMedia.cfm?id=70258&seq=0", DOI = "doi:10.1364/AO.41.006260", size = "16 pages", abstract = "A stochastic inverse technique based on a genetic programming (GP) algorithm was developed to invert oceanic constituents from simulated data for case I and case II water applications. The simulations were carried out with the Ordre Successifs Ocean Atmosphere (OSOA) radiative transfer model. They include the effects of oceanic substances such as algal-related chlorophyll, nonchlorophyllous suspended matter, and dissolved organic matter. The synthetic data set also takes into account the directional effects of particles through a variation of their phase function that makes the simulated data realistic. It is shown that GP can be successfully applied to the inverse problem with acceptable stability in the presence of realistic noise in the data. GP is compared with neural network methodology for case I waters; GP exhibits similar retrieval accuracy, which is greater than for traditional techniques such as band ratio algorithms. The application of GP to real satellite data [a Sea-viewing Wide Field-of-view Sensor (SeaWiFS)] was also carried out for case I waters as a validation. Good agreement was obtained when GP results were compared with the SeaWiFS empirical algorithm. For case II waters the accuracy of GP is less than 33percent, which remains satisfactory, at the present time, for remote-sensing purposes.", notes = "http://adsabs.harvard.edu/cgi-bin/nph-bib_query?bibcode=2002ApOpt..41.6260C&data_type=BIBTEX&db_key=INST%26amp;nocookieset=1", } @InProceedings{chan:2025:GI, author = "Chi Ho Chan and Spyro Nita", title = "A Three-Stage Genetic Algorithm for Compiler Flag and Library Version Selection to Minimize Execution Time", booktitle = "14th International Workshop on Genetic Improvement @ICSE 2025", year = "2025", editor = "Aymeric Blot and Vesna Nowack and Penn {Faulkner Rainford} and Oliver Krauss", address = "Ottawa", month = "27 " # apr, note = "forthcoming", keywords = "genetic algorithms, genetic programming, Genetic Improvement", URL = "https://gpbib.cs.ucl.ac.uk/gi2025/chan_2025_GI.pdf", URL = "https://geneticimprovementofsoftware.com/events/icse2025#accepted-papers", video_url = "https://gpbib.cs.ucl.ac.uk/gi2025/gi_2025_slides/ChiHoChan_icse2025.mp4", slide_url = "https://gpbib.cs.ucl.ac.uk/gi2025/gi_2025_slides/ChiHoChan_icse2025_slides.pdf", size = "8 pages", abstract = "Existing research in compiler autotuning mainly focuses on selecting optimization flags without configurable values. However, the potential of selecting optimization flags with configurable values, alongside using directory and link flags for library version selection to improve performance, remains largely unexplored. We propose a three-stage Genetic Algorithm (GA) that incrementally selects optimization flags without configurable values, then optimization flags with configurable values, and finally library versions, to minimize software execution time. We also discuss the implementation challenges of the proposed algorithm and outline potential future work.", notes = "GI @ ICSE 2025, part of \cite{blot:2025:GI}", } @Article{chan:2007:WR, author = "Wai Sum Chan and Friedrich Recknagel and Hongqing Cao and Ho-Dong Park", title = "Elucidation and short-term forecasting of microcystin concentrations in Lake Suwa (Japan) by means of artificial neural networks and evolutionary algorithms", journal = "Water Research", year = "2007", volume = "41", number = "10", pages = "2247--2255", month = may, keywords = "genetic algorithms, genetic programming, Lake Suwa, Microcystis, Microcystin, Ordination, Clustering, Forecasting, Explanation", DOI = "doi:10.1016/j.watres.2007.02.001", abstract = "Non-supervised artificial neural networks (ANN) and hybrid evolutionary algorithms (EA) were applied to analyse and model 12 years of limnological time-series data of the shallow hypertrophic Lake Suwa in Japan. The results have improved understanding of relationships between changing microcystin concentrations, Microcystis species abundances and annual rainfall intensity. The data analysis by non-supervised ANN revealed that total Microcystis abundance and extra-cellular microcystin concentrations in typical dry years are much higher than those in typical wet years. It also showed that high microcystin concentrations in dry years coincided with the dominance of the toxic Microcystis viridis whilst in typical wet years non-toxic Microcystis ichthyoblabe were dominant. Hybrid EA were used to discover rule sets to explain and forecast the occurrence of high microcystin concentrations in relation to water quality and climate conditions. The results facilitated early warning by 3-days-ahead forecasting of microcystin concentrations based on limnological and meteorological input data, achieving an r2=0.74 for testing.", notes = "Wai Sum (Grace) Chan a School of Earth and Environmental Sciences, University of Adelaide, Adelaide 5005, Australia b Cooperative Research Centre for Water Quality and Treatment, Salisbury 5108, Australia c Department of Environmental Sciences, Shinshu University, Matsumoto 390-8621, Japan", } @PhdThesis{Grace_Chan_thesis, author = "Wai Sum Chan", title = "Spatial and temporal features of hydrodynamics and biogeochemistry in Myponga Reservoir, South Australia", school = "University of Adelaide, School of Earth and Environmental Sciences", year = "2011", address = "Australia", month = oct, URL = "https://digital.library.adelaide.edu.au/dspace/handle/2440/76100", size = "206 pages", abstract = "Understanding hydrodynamic and biogeochemical processes in lakes is fundamentally important to the management of phytoplankton population and the improvement of water quality. Physical processes such as wind-driven surface mixing, thermal stratification and differential heating and cooling can affect the distribution of water, phytoplankton and sediments and the availability of nutrients and light. These lake processes, which are highly variable in space and time, affect phytoplankton dynamics in the field. This study aims to determine the spatial and temporal variability of phytoplankton and processes that either contribute to or override the variability in the artificially mixed Myponga Reservoir, South Australia. A sediment survey showed that sediments underlying deep water were richer in organic matter, carbon, nitrogen and phosphorus than the sediments underlying shallow water. This may lead to different nutrient release rates between the shallow and deep areas. ...", notes = "No mention of GP Advisor: Brookes, Justin D. Recknagel, Friedrich Adolf Lewis, David Milton", } @InProceedings{chan:1999:MAFEMSGA, author = "Zeke S. H. Chan and H. W. Ngan and A. B. Rad", title = "Minimum-Allele-Reserve-Keeper (MARK): A Fast and Effective Mutation Scheme for Genetic Algorithm (GA)", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "106--113", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{chan:1999:AS, author = "Zeke S. H. Chan and H. W. Ngan and A. B. Rad", title = "A new method to resist premature convergence: Synchonising gene-convergence with correlated recombination", booktitle = "Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference", year = "1999", editor = "Scott Brave and Annie S. Wu", pages = "74--79", address = "Orlando, Florida, USA", month = "13 " # jul, keywords = "Genetic Algorithms", notes = "GECCO-99LB", } @InCollection{chan:1995:VEWCUGP, author = "King Choi Chan", title = "Valid English Word Classifier Using Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1995", year = "1995", editor = "John R. Koza", pages = "39--48", address = "Stanford, California, 94305-3079 USA", month = "11 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-195720-5", notes = "part of \cite{koza:1995:gagp}", } @InCollection{chan:2002:AGPFAGP, author = "David Michael Chan", title = "Automatic Generation of Prime Factorization Algorithms using Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2002", year = "2002", editor = "John R. Koza", pages = "52--57", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming, PTC2, ECJ", URL = "http://www.genetic-programming.org/sp2002/Chan.pdf", size = "6 pages", abstract = "the application of the principles of genetic programming to the field of prime factorization. Any prime factorization algorithm is given one integer and must generate a complete list of primes such that, when multiplied together in varying degrees, produces the original integer. Constructing even a limited factoring algorithm in GP turns out to be extremely challenging and potentially impossible.", notes = "part of \cite{koza:2002:gagp} GP hard p57", } @InProceedings{chan03, author = "Kit Yan Chan and M. Emin Aydin and Terence C. Fogarty", title = "New Factorial Design Theoretic Crossover Operator for Parametrical Problem", booktitle = "Genetic Programming, Proceedings of EuroGP'2003", year = "2003", editor = "Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa", volume = "2610", series = "LNCS", pages = "22--33", address = "Essex", publisher_address = "Berlin", month = "14-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-00971-X", DOI = "doi:10.1007/3-540-36599-0_3", abstract = "Recent research shows that factorial design methods improve the performance of the crossover operator in evolutionary computation. However the methods employed so far ignore the effects of interaction between genes on fitness, i.e. ``epistasis''. Here we propose the application of a systematic method for interaction effect analysis to enhance the performance of the crossover operator. It is shown empirically that the proposed method significantly outperforms existing crossover operators on benchmark problems with high interaction between the variables.", notes = "EuroGP'2003 held in conjunction with EvoWorkshops 2003", } @InProceedings{chan03b, author = "Kit Yan Chan and Terence C. Fogarty", title = "Experimental design based multi-parent crossover operator", booktitle = "Genetic Programming, Proceedings of EuroGP'2003", year = "2003", editor = "Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa", volume = "2610", series = "LNCS", pages = "297--306", address = "Essex", publisher_address = "Berlin", month = "14-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-00971-X", DOI = "doi:10.1007/3-540-36599-0_27", abstract = "Recently, the methodologies of multi-parent crossover have been developed by performing the crossover operation with multi-parent. Some studies have indicated the high performance of multi-parent crossover on some numerical optimization problems. Here a new crossover operator has been proposed by integrating multi-parent crossover with the approach of experimental design. It is based on experimental design method in exploring the solution space that compensates the random search as in traditional genetic algorithm. By replacing the inbuilt randomness of crossover operator with a more systematical method, the proposed method outperforms the classical GA strategy on several GA benchmark problems.", notes = "EuroGP'2003 held in conjunction with EvoWorkshops 2003", } @InProceedings{chan:2004:eurogp, author = "Kit Yan Chan and Terence C. Fogarty", title = "An Evolutionary Algorithm for the Input-Output Block Assignment Problem", booktitle = "Genetic Programming 7th European Conference, EuroGP 2004, Proceedings", year = "2004", editor = "Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule", volume = "3003", series = "LNCS", pages = "250--258", address = "Coimbra, Portugal", publisher_address = "Berlin", month = "5-7 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-21346-5", DOI = "doi:10.1007/978-3-540-24650-3_23", abstract = "A procedure for system decomposition is developed for decentralised multi-variable systems. Optimal input-output pairing techniques are used to rearrange a large multi variable system into a structure that is closer to the block-diagonal decentralised form. The problem is transformed into a block assignment problem. An evolutionary algorithm is developed to solve this hard IP problem. The result shows that the proposed algorithm is simple to implement and efficient to find the reasonable solution.", notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004", } @Article{Chan:2009:JED, author = "Kit Yan Chan and C. K. Kwong and T. C. Wong", title = "Modelling customer satisfaction for product development using genetic programming", journal = "Journal of Engineering Design", year = "2011", volume = "22", number = "1", pages = "55--68", month = jan, publisher = "Taylor \& Francis", keywords = "genetic algorithms, genetic programming, SBSE, SPL, interaction terms, higher-order terms, customer satisfaction, design attributes", ISSN = "0954-4828", DOI = "doi:10.1080/09544820902911374", abstract = "Product development involves several processes in which product planning is the first one. Several tasks normally are required to be conducted in the product-planning process and one of them is to determine settings of design attributes for products. Facing with fierce competition in marketplaces, companies try to determine the settings such that the best customer satisfaction of products could be obtained.To achieve this, models that relate customer satisfaction to design attributes need to be developed first. Previous research has adopted various modelling techniques to develop the models, but those models are not able to address interaction terms or higher-order terms in relating customer satisfaction to design attributes, or they are the black-box type models. In this paper, a method based on genetic programming (GP) is presented to generate models for relating customer satisfaction to design attributes. The GP is first used to construct branches of a tree representing structures of a model where interaction terms and higher-order terms can be addressed. Then an orthogonal least-squares algorithm is used to determine the coefficients of the model. The models thus developed are explicit and consist of interaction terms and higher-order terms in relating customer satisfaction to design attributes. A case study of a digital camera design is used to illustrate the proposed method.", notes = "Matlab a Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong", } @Article{Chan:2010:IJPR, author = "K. Y. Chan and C. K. Kwong and Y. C. Tsim", title = "A genetic programming based fuzzy regression approach to modelling manufacturing processes", journal = "International Journal of Production Research", year = "2010", volume = "48", number = "7", pages = "1967--1982", month = apr, keywords = "genetic algorithms, genetic programming fuzzy regression, process modelling, solder paste dispensing", URL = "http://www.tandfonline.com/doi/abs/10.1080/00207540802644845", URL = "http://www.tandfonline.com/doi/pdf/10.1080/00207540802644845", DOI = "doi:10.1080/00207540802644845", size = "16 pages", abstract = "Fuzzy regression has demonstrated its ability to model manufacturing processes in which the processes have fuzziness and the number of experimental data sets for modelling them is limited. However, previous studies only yield fuzzy linear regression based process models in which variables or higher order terms are not addressed. In fact, it is widely recognised that behaviours of manufacturing processes do often carry interactions among variables or higher order terms. In this paper, a genetic programming based fuzzy regression GP-FR, is proposed for modelling manufacturing processes. The proposed method uses the general outcome of GP to construct models the structure of which is based on a tree representation, which could carry interaction and higher order terms. Then, a fuzzy linear regression algorithm is used to estimate the contributions and the fuzziness of each branch of the tree, so as to determine the fuzzy parameters of the genetic programming based fuzzy regression model. To evaluate the effectiveness of the proposed method for process modelling, it was applied to the modelling of a solder paste dispensing process. Results were compared with those based on statistical regression and fuzzy linear regression. It was found that the proposed method can achieve better goodness-of-fitness than the other two methods. Also the prediction accuracy of the model developed based on GP-FR is better than those based on the other two methods.", } @Article{Chan2010506, author = "K. Y. Chan and C. K. Kwong and T. C. Fogarty", title = "Modeling manufacturing processes using a genetic programming-based fuzzy regression with detection of outliers", journal = "Information Sciences", volume = "180", number = "4", pages = "506--518", year = "2010", ISSN = "0020-0255", DOI = "doi:10.1016/j.ins.2009.10.007", URL = "http://www.sciencedirect.com/science/article/B6V0C-4XFPR3M-3/2/1f27ff77e40dc7d917de59d3555abf36", keywords = "genetic algorithms, genetic programming, Fuzzy regression, Outlier detection, Epoxy dispensing process", abstract = "Fuzzy regression (FR) been demonstrated as a promising technique for modeling manufacturing processes where availability of data is limited. FR can only yield linear type FR models which have a higher degree of fuzziness, but FR ignores higher order or interaction terms and the influence of outliers, all of which usually exist in the manufacturing process data. Genetic programming (GP), on the other hand, can be used to generate models with higher order and interaction terms but it cannot address the fuzziness of the manufacturing process data. In this paper, genetic programming-based fuzzy regression (GP-FR), which combines the advantages of the two approaches to overcome the deficiencies of the commonly used existing modeling methods, is proposed in order to model manufacturing processes. GP-FR uses GP to generate model structures based on tree representation which can represent interaction and higher order terms of models, and it uses an FR generator based on fuzzy regression to determine outliers in experimental data sets. It determines the contribution and fuzziness of each term in the model by using experimental data excluding the outliers. To evaluate the effectiveness of GP-FR in modeling manufacturing processes, it was used to model a non-linear system and an epoxy dispensing process. The results were compared with those based on two commonly used FR methods, Tanka's FR and Peters' FR. The prediction accuracy of the models developed based on GP-FR was shown to be better than that of models based on the other two FR methods.", } @InProceedings{Chan:2010:ieee-fuzz, author = "K. Y. Chan and T. S. Dillon and C. K. Kwong", title = "Using an evolutionary fuzzy regression for affective product design", booktitle = "IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-6920-8", abstract = "In affective product design, one of the main goals is to maximise customers' affective satisfaction by optimising design variables of a new product. To achieve this, a model in relating customers' affective responses and design variables of a new product is required to be developed based on customers' survey data. However, previous research on modelling the relationship between affective response and design variables cannot address the development of explicit models either involving nonlinearity or fuzziness, which exist in customers' survey data. In this paper, an evolutionary fuzzy regression approach is proposed to generate explicit models to represent this nonlinear and fuzzy relationship between affective responses and design variables. In the approach, genetic programming is used to construct branches of a tree representing structures of a model where the nonlinearity of the model can be addressed. Fuzzy coefficients of the model, which is represented by the tree, are determined based on a fuzzy regression algorithm. As a result, the fuzzy nonlinear regression model can be obtained to relate affective responses and design variables.", DOI = "doi:10.1109/FUZZY.2010.5584493", notes = "WCCI 2010. Also known as \cite{5584493}", } @InProceedings{Chan:2010:cec, author = "Kit Yan Chan and Sing Ho Ling and Tharam Singh Dillon and Hung Nguyen", title = "Classification of hypoglycemic episodes for Type 1 diabetes mellitus based on neural networks", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-6910-9", abstract = "Hypoglycemia is dangerous for Type 1 diabetes mellitus (T1DM) patients. Based on the physiological parameters, we have developed a classification unit with hybridising the approaches of neural networks and genetic algorithm to identify the presences of hypoglycemic episodes for TIDM patients. The proposed classification unit is built and is validated by using the real T1DM patients' data sets collected from Department of Health, Government of Western Australia. Experimental results show that the proposed neural network based classification unit can achieve more accurate results on both trained and unseen T1DM patients' data sets compared with those developed based on the commonly used classification methods for medical diagnosis including statistical regression, fuzzy regression and genetic programming.", DOI = "doi:10.1109/CEC.2010.5586320", notes = "WCCI 2010. Also known as \cite{5586320}", } @InProceedings{Chan:2010:cec2, author = "Kit Yan Chan and Tharam Singh Dillon and Che Kit Kwong", title = "Polynomial modeling for manufacturing processes using a backward elimination based genetic programming", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-6910-9", abstract = "Even if genetic programming (GP) has rich literature in development of polynomial models for manufacturing processes, the polynomial models may contain redundant terms which may cause the overfitted models. In other words, those models have good accuracy on training data sets but poor accuracy on untrained data sets. In this paper, a mechanism which aims at avoiding overfitting is proposed based on a statistical method, backward elimination, which intends to eliminate insignificant terms in polynomial models. By modeling a solder paste dispenser for electronic manufacturing, results show that the insignificant terms in the polynomial model can be eliminated by the proposed mechanism. Results also show that the polynomial model generated by the proposed GP can achieve better predictions than the existing methods.", DOI = "doi:10.1109/CEC.2010.5586309", notes = "WCCI 2010. Also known as \cite{5586309}", } @Article{Chan20111623, author = "Kit Yan Chan and Tharam S. Dillon and C. K. Kwong", title = "Polynomial modeling for time-varying systems based on a particle swarm optimization algorithm", journal = "Information Sciences", volume = "181", number = "9", pages = "1623--1640", year = "2011", ISSN = "0020-0255", DOI = "doi:10.1016/j.ins.2011.01.006", URL = "http://www.sciencedirect.com/science/article/B6V0C-51X1VSV-7/2/12b12f977248967cf70b6cfd1dc37507", keywords = "genetic algorithms, genetic programming, PSO, Particle swarm optimisation, Time-varying systems, Polynomial modelling", abstract = "In this paper, an effective particle swarm optimization (PSO) is proposed for polynomial models for time varying systems. The basic operations of the proposed PSO are similar to those of the classical PSO except that elements of particles represent arithmetic operations and variables of time-varying models. The performance of the proposed PSO is evaluated by polynomial modelling based on various sets of time-invariant and time-varying data. Results of polynomial modeling in time-varying systems show that the proposed PSO outperforms commonly used modelling methods which have been developed for solving dynamic optimisation problems including genetic programming (GP) and dynamic GP. An analysis of the diversity of individuals of populations in the proposed PSO and GP reveals why the proposed PSO obtains better results than those obtained by GP.", } @Article{Chan20111648, author = "K. Y. Chan and C. K. Kwong and T. S. Dillon and Y. C. Tsim", title = "Reducing overfitting in manufacturing process modeling using a backward elimination based genetic programming", journal = "Applied Soft Computing", volume = "11", number = "2", pages = "1648--1656", year = "2011", note = "The Impact of Soft Computing for the Progress of Artificial Intelligence", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2010.04.022", URL = "http://www.sciencedirect.com/science/article/B6W86-501FPF7-6/2/4bf5179fccc0bf3772b121aef439e062", keywords = "genetic algorithms, genetic programming, Process modelling, Polynomial modelling, Overfitting", abstract = "Genetic programming (GP) has demonstrated as an effective approach in polynomial modelling of manufacturing processes. However, polynomial models with redundant terms generated by GP may depict over fitting, while the developed models have good accuracy on trained data sets but relatively poor accuracy on testing data sets. In the literature, approaches of avoiding overfitting in GP are handled by limiting the number of terms in polynomial models. However, those approaches cannot guarantee terms in polynomial models produced by GP are statistically significant to manufacturing processes. In this paper, a statistical method, backward elimination (BE), is proposed to incorporate with GP, in order to eliminate insignificant terms in polynomial models. The performance of the proposed GP has been evaluated by modeling three real-world manufacturing processes, epoxy dispenser for electronic packaging, solder paste dispenser for electronic manufacturing, and punch press system for leadframe downset in IC packaging. Empirical results show that insignificant terms in the polynomial models can be eliminated by the proposed GP and also the polynomial models generated by the proposed GP can achieve results with better predictions than the other commonly used existent methods, which are commonly used in GP for avoiding overfitting in polynomial modeling.", } @Article{Chan20119799, author = "K. Y. Chan and S. H. Ling and T. S. Dillon and H. T. Nguyen", title = "Diagnosis of hypoglycemic episodes using a neural network based rule discovery system", journal = "Expert Systems with Applications", volume = "38", number = "8", pages = "9799--9808", year = "2011", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2011.02.020", URL = "http://www.sciencedirect.com/science/article/B6V03-524WF2N-4/2/d9f5c30581fa33cc25387714abbbc4b6", keywords = "genetic algorithms, genetic programming, Neural networks, Hypoglycemic episodes, Medical diagnosis, Type 1 diabetes mellitus", abstract = "Hypoglycemia or low blood glucose is dangerous and can result in unconsciousness, seizures and even death for Type 1 diabetes mellitus (T1DM) patients. Based on the T1DM patients' physiological parameters, corrected QT interval of the electrocardiogram (ECG) signal, change of heart rate, and the change of corrected QT interval, we have developed a neural network based rule discovery system with hybridising the approaches of neural networks and genetic algorithm to identify the presences of hypoglycemic episodes for TIDM patients. The proposed neural network based rule discovery system is built and is validated by using the real T1DM patients' data sets collected from Department of Health, Government of Western Australia. Experimental results show that the proposed neural network based rule discovery system can achieve more accurate results on both trained and unseen T1DM patients' data sets compared with those developed based on the commonly used classification methods for medical diagnosis, statistical regression, fuzzy regression and genetic programming. Apart from the achievement of these better results, the proposed neural network based rule discovery system can provide explicit information in the form of production rules which compensate for the deficiency of traditional neural network method which do not provide a clear understanding of how they work in prediction as they are in an implicit black-box structure. This explicit information provided by the product rules can convince medical doctors to use the neural networks to perform diagnosis of hypoglycemia on T1DM patients.", } @InProceedings{Chan:2011:ICIEA, author = "K. Y. Chan and T. S. Dillon and C. K. Kwong and S. H. Ling", title = "Using genetic programming for developing relationship between engineering characteristics and customer requirements in new products", booktitle = "6th IEEE Conference on Industrial Electronics and Applications (ICIEA 2011)", year = "2011", month = "21-23 " # jun, pages = "526--531", address = "Beijing, China", size = "6 pages", abstract = "In product planning, development of models of relationship between engineering characteristics and customer requirements in new products is an important process in quality function deployment (QFD), which is a widely used customer driven approach. In this paper, a methodology based on genetic programming (GP) is presented to generate a reliable model that can be used to predict the customer requirements from the engineering characteristics. The proposed GP based method, which has the capability to carry out simultaneous optimisation of model relationship structures and parameters, is used to automatically generate accurate nonlinear models relating the two requirements. A case study of the digital camera design shows that the proposed GP based method produce a more accurate and interpretable models than the other commonly used methods, which ignore nonlinear terms in the model development.", keywords = "genetic algorithms, genetic programming, GP based method, QFD, accurate nonlinear models, customer driven approach, customer requirements, digital camera design, engineering characteristics, interpretable models, model development, model relationship structures, new products, nonlinear terms, product development, product planning, quality function deployment, reliable model, simultaneous optimisation, customer services, product development, production planning, quality function deployment, reliability", DOI = "doi:10.1109/ICIEA.2011.5975642", ISSN = "pending", notes = "Also known as \cite{5975642}", } @InProceedings{Chan:2011:ieeeFUZZ, author = "K. Y. Chan and T. S. Dillon and S. H. Ling and C. K. Kwong", title = "Manufacturing modeling using an evolutionary fuzzy regression", booktitle = "IEEE International Conference on Fuzzy Systems (FUZZ 2011)", year = "2011", month = "27-30 " # jun, address = "Taipei, Taiwan", pages = "2261--2267", size = "7 pages", abstract = "Fuzzy regression is a commonly used approach for modelling manufacturing processes in which the availability of experimental data is limited. Fuzzy regression can address fuzzy nature of experimental data in which fuzziness is not avoidable while carrying experiments. However, fuzzy regression can only address linearity in manufacturing process systems, but nonlinearity, which is unavoidable in the process, cannot be addressed. In this paper, an evolutionary fuzzy regression which integrates the mechanism of a fuzzy regression and genetic programming is proposed to generate manufacturing process models. It intends to overcome the deficiency of the fuzzy regression, which cannot address nonlinearities in manufacturing processes. The evolutionary fuzzy regression uses genetic programming to generate the structural form of the manufacturing process model based on tree representation which can address both linearity and nonlinearities in manufacturing processes. Then it uses a fuzzy regression to determine outliers in experimental data sets. By using experimental data excluding the outliers, the fuzzy regression can determine fuzzy coefficients which indicate the contribution and fuzziness of each term in the structural form of the manufacturing process model. To evaluate the effectiveness of the evolutionary fuzzy regression, a case study regarding modelling of epoxy dispensing process is carried out.", keywords = "genetic algorithms, genetic programming, evolutionary fuzzy regression, fuzzy coefficients, manufacturing modelling, manufacturing process model, fuzzy set theory, manufacturing processes, regression analysis", DOI = "doi:10.1109/FUZZY.2011.6007322", ISSN = "1098-7584", notes = "Also known as \cite{6007322}", } @InCollection{chan:2012:cia, author = "Kit Yan Chan and Tharam S. Dillon", title = "Polynomial Modeling in a Dynamic Environment based on a Particle Swarm Optimization", booktitle = "Computational Intelligence and Its Applications", publisher = "World Scientific", year = "2012", editor = "H K Lam and Steve S H Ling and Hung T Nguyen", pages = "23--38", keywords = "genetic algorithms, genetic programming, particle swarm optimisation, PSO, time-varying modelling, time-varying systems, polynomial modelling, evolutionary computation", isbn13 = "978-1-84816-691-2", DOI = "doi:10.1142/9781848166929_0002", bibsource = "OAI-PMH server at espace.library.curtin.edu.au", oai = "oai:espace.library.curtin.edu.au:189166", URL = "http://espace.library.curtin.edu.au/R?func=dbin-jump-full&local_base=gen01-era02&object_id=189166", abstract = "In this chapter, a particle swarm optimisation (PSO) is proposed for polynomial modelling in a dynamic environment. The basic operations of the proposed PSO are identical to the ones of the original PSO except that elements of particles represent arithmetic operations and polynomial variables of polynomial models. The performance of the proposed PSO is evaluated by polynomial modelling based on a set of dynamic benchmark functions in which their optima are dynamically moved. Results show that the proposed PSO can find significantly better polynomial models than genetic programming (GP) which is a commonly used method for polynomial modelling.", } @Article{chan:2013:IJAMT, author = "Kit Yan Chan and C. K. Kwong", title = "Modeling of epoxy dispensing process using a hybrid fuzzy regression approach", journal = "The International Journal of Advanced Manufacturing Technology", year = "2013", volume = "65", number = "1-4", pages = "589--600", month = mar, keywords = "genetic algorithms, genetic programming, Fuzzy regression, Epoxy dispensing, Microchip encapsulation, Electronic packaging, Process modelling, Semiconductor manufacturing", language = "English", publisher = "Springer-Verlag", ISSN = "0268-3768", URL = "http://espace.library.curtin.edu.au:80/R?func=dbin-jump-full&local_base=gen01-era02&object_id=185726", DOI = "doi:10.1007/s00170-012-4202-4", size = "12 pages", abstract = "In the semiconductor manufacturing industry, epoxy dispensing is a popular process commonly used in die-bonding as well as in microchip encapsulation for electronic packaging. Modelling the epoxy dispensing process is important because it enables us to understand the process behaviour, as well as determine the optimum operating conditions of the process for a high yield, low cost, and robust operation. Previous studies of epoxy dispensing have mainly focused on the development of analytical models. However, an analytical model for epoxy dispensing is difficult to develop because of its complex behaviour and high degree of uncertainty associated with the process in a real-world environment. Previous studies of modelling the epoxy dispensing process have not addressed the development of explicit models involving high-order and interaction terms, as well as fuzziness between process parameters. In this paper, a hybrid fuzzy regression (HFR) method integrating fuzzy regression with genetic programming is proposed to make up the deficiency. Two process models are generated for the two quality characteristics of the process, encapsulation weight and encapsulation thickness based on the HFR, respectively. Validation tests are performed. The performance of the models developed based on the HFR outperforms the performance of those based on statistical regression and fuzzy regression.", bibsource = "OAI-PMH server at espace.library.curtin.edu.au", oai = "oai:espace.library.curtin.edu.au:185726", } @Article{Chan:2015:ieeeFUZZ, author = "Kit Yan Chan and Hak Keung Lam and Tharam S. Dillon and Sai Ho Ling", title = "A Stepwise-Based Fuzzy Regression Procedure for Developing Customer Preference Models in New Product Development", journal = "IEEE Transactions on Fuzzy Systems", year = "2015", volume = "23", number = "5", pages = "1728--1745", month = oct, keywords = "genetic algorithms, genetic programming", ISSN = "1063-6706", DOI = "doi:10.1109/TFUZZ.2014.2375911", size = "18 pages", abstract = "too long", notes = "'Genetic programming-based fuzzy regression (GP-FR) [2] can generate explicit models in fuzzy polynomial forms involved with high order and interaction terms.' also known as \cite{6971199}", } @Article{Chan:2017:ieeeSMCS, author = "Kit Yan Chan and Hak-Keung Lam and Cedric Ka Fai Yiu and Tharam S. Dillon", title = "A Flexible Fuzzy Regression Method for Addressing Nonlinear Uncertainty on Aesthetic Quality Assessments", journal = "IEEE Transactions on Systems, Man, and Cybernetics: Systems", year = "2017", volume = "47", number = "8", pages = "2363--2377", month = aug, keywords = "genetic algorithms, genetic programming", ISSN = "2168-2216", URL = "https://ieeexplore.ieee.org/document/7907344/", DOI = "doi:10.1109/TSMC.2017.2672997", abstract = "Development of new products or services requires knowledge and understanding of aesthetic qualities that correlate to perceptual pleasure. As it is not practical to develop a survey to assess aesthetic quality for all objective features of a new product or service, it is necessary to develop a model to predict aesthetic qualities. In this paper, a fuzzy regression method is proposed to predict aesthetic quality from a given set of objective features and to account for uncertainty in human assessment. The proposed method overcomes the shortcoming of statistical regression, which can predict only quality magnitudes but cannot predict quality uncertainty. The proposed method also attempts to improve traditional fuzzy regressions, which simulate a single characteristic with which the estimated uncertainty can only increase with the increasing magnitudes of objective features. The proposed fuzzy regression method uses genetic programming to develop nonlinear structures of the models, and model coefficients are determined by optimizing the fuzzy criteria. Hence, the developed model can be used to fit the nonlinearities of sample magnitudes and uncertainties. The effectiveness and the performance of the proposed method are evaluated by the case study of perceptual images, which are involved with different sampling natures and with different amounts of samples. This case study attempts to address different characteristics of human assessments. The outcomes demonstrate that more robust models can be developed by the proposed fuzzy regression method compared with the recently developed fuzzy regression methods, when the model characteristics and fuzzy criteria are taken into account.", notes = "Also known as \cite{7907344}", } @Article{CHAN:2020:EAAI, author = "Kit Yan Chan and C. K. Kwong and Gul E. Kremer", title = "Predicting customer satisfaction based on online reviews and hybrid ensemble genetic programming algorithms", journal = "Engineering Applications of Artificial Intelligence", volume = "95", pages = "103902", year = "2020", ISSN = "0952-1976", DOI = "doi:10.1016/j.engappai.2020.103902", URL = "http://www.sciencedirect.com/science/article/pii/S0952197620302396", keywords = "genetic algorithms, genetic programming, New product development, Social media, Online customer reviews, Machine learning, Committee member selection", abstract = "Determination of the design attribute settings of a new product is essential for maximizing customer satisfaction. A model is necessary to illustrate the relation between the design attributes and dimensions of customer satisfaction such as product performance, affection and quality. The model is commonly developed based on customer survey data collected from questionnaires or interviews which require a long deployment time; hence the developed model cannot completely reflect the current marketplace. In this paper, a framework is proposed based on online reviews in which past and current customer opinions are included to develop the model. The proposed framework overcomes the limitation of the aforementioned approaches in which the developed models are not up-to-date. Indeed, the proposed framework develops models based on machine learning technologies, namely genetic programming, which has better generalization capabilities than classical approaches, and has higher transparency capabilities than implicit modelling approaches. To further enhance the prediction capability, committee member selection is proposed. The proposed selection method improves the currently used selection method which trains several models and only selects the best one. The proposed selection method generates a hybrid model which integrates the predictions of the generated models. Each prediction is weighted by how likely the prediction is agreed by others. The proposed framework is implemented on electric hair dryer design of which online reviews in amazon.com are used. Experimental results show that models with more accurate prediction capabilities can be generated by the proposed framework", } @Article{CHAN:2021:EAAI, author = "Kit Yan Chan and C. K. Kwong and Huimin Jiang", title = "Analyzing imbalanced online consumer review data in product design using geometric semantic genetic programming", journal = "Engineering Applications of Artificial Intelligence", volume = "105", pages = "104442", year = "2021", ISSN = "0952-1976", DOI = "doi:10.1016/j.engappai.2021.104442", URL = "https://www.sciencedirect.com/science/article/pii/S0952197621002906", keywords = "genetic algorithms, genetic programming, New product development, Social media, Online customer reviews, Imbalanced data mining, Multi-objective optimization", abstract = "To develop a successful product, understanding the relationship between customer satisfaction (CS) and design attributes of a new product is essential. Nowadays IoT technologies are used to collect online review data from social media. More representative CS models are developed using online review data. However, online review data is imbalanced, since popular products receive more online consumer reviews and unpopular products receive less. When imbalanced data is used, CS models learn the characteristics of majority data while rarely learning minority data. Misleading analysis for product development is made since the CS model is biased to popular products. This paper proposes an approach to generate nondominated CS models which learn equally to imbalanced data from popular and unpopular products. A multi-objective optimization problem is formulated to learn equally in imbalanced data. This problem is proposed to be solved by the geometric semantic genetic programming (GSGP); a Pareto set of nondominated CS models is generated by the GSGP. Product designers select the most preferred models in the Pareto set. The preferred nondominated CS model attempts to tradeoff unpopular and popular products, to determine optimal design attributes and maximize the CS. The case study shows that the proposed GSGP is able to generate CS models with more accurate CS predictions compared to the commonly used methods. The proposed GSGP also generates a Pareto set of nondominated CS models which equally learn consumer reviews for those dryers. Based on the Pareto set, the design team selects the most preferred CS model", } @Article{chan:2022:NCaA, author = "Kit Yan Chan and Hak-Keung Lam and Huimin Jiang", title = "A genetic programming-based convolutional neural network for image quality evaluations", journal = "Neural Computing and Applications", year = "2022", volume = "34", number = "18", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s00521-022-07218-0", DOI = "doi:10.1007/s00521-022-07218-0", } @Article{Chan:2009:BMCbi, author = "Tak-Ming Chan and Gang Li and Kwong-Sak Leung and Kin-Hong Lee", title = "Discovering multiple realistic TFBS motifs based on a generalized model", journal = "BMC Bioinformatics 2009, 10:321 doi:10.1186/1471-2105-10-321", year = "2009", volume = "10", number = "321", keywords = "genetic algorithms", DOI = "doi:10.1186/1471-2105-10-321", size = "22 pages", abstract = "Background Identification of transcription factor binding sites (TFBSs) is a central problem in Bioinformatics on gene regulation. de novo motif discovery serves as a promising way to predict and better understand TFBSs for biological verifications. Real TFBSs of a motif may vary in their widths and their conservation degrees within a certain range. Deciding a single motif width by existing models may be biased and misleading. Additionally, multiple, possibly overlapping, candidate motifs are desired and necessary for biological verification in practice. However, current techniques either prohibit overlapping TFBSs or lack explicit control of different motifs. Results We propose a new generalised model to tackle the motif widths by considering and evaluating a width range of interest simultaneously, which should better address the width uncertainty. Moreover, a meta-convergence framework for genetic algorithms (GAs), is proposed to provide multiple overlapping optimal motifs simultaneously in an effective and flexible way. Users can easily specify the difference amongst expected motif kinds via similarity test. Incorporating Genetic Algorithm with Local Filtering (GALF) for searching, the new GALF-G (G for generalised) algorithm is proposed based on the generalized model and meta-convergence framework. Conclusion GALF-G was tested extensively on over 970 synthetic, real and benchmark datasets, and is usually better than the state-of-the-art methods. The range model shows an increase in sensitivity compared with the single-width ones, while providing competitive precisions on the E. coli benchmark. Effectiveness can be maintained even using a very small population, exhibiting very competitive efficiency. In discovering multiple overlapping motifs in a real liver-specific dataset, GALF-G outperforms MEME by up to 73percent in overall F-scores. GALF-G also helps to discover an additional motif which has probably not been annotated in the data set. http://www.cse.cuhk.edu.hk/~chan/GALFG/ web site", notes = "Not GP?", } @Article{CHAND:2018:IS, author = "Shelvin Chand and Quang Huynh and Hemant Singh and Tapabrata Ray and Markus Wagner", title = "On the use of genetic programming to evolve priority rules for resource constrained project scheduling problems", journal = "Information Sciences", volume = "432", pages = "146--163", year = "2018", keywords = "genetic algorithms, genetic programming, Resource constrained project scheduling, Heuristic evolution, Evolutionary computation, Generation hyper-heuristics", ISSN = "0020-0255", DOI = "doi:10.1016/j.ins.2017.12.013", URL = "http://www.sciencedirect.com/science/article/pii/S0020025517311350", abstract = "Resource constrained project scheduling is critical in logistic and planning operations across a range of industries. Most businesses rely on priority rules to determine the order in which the activities required for the project should be executed. However, the design of such rules is non-trivial. Even with significant knowledge and experience, human experts are understandably limited in terms of the possibilities they can consider. This paper introduces a genetic programming based hyper-heuristic (GPHH) for producing efficient priority rules targeting the resource constrained project scheduling problem (RCPSP). For performance analysis of the proposed approach, a series of experiments are conducted on the standard PSPLib instances with up to 120 activities. The evolved priority rules are then compared against the existing state-of-the-art priority rules to demonstrate the efficacy of our approach. The experimental results indicate that our GPHH is capable of producing reusable priority rules which significantly out-perform the best human designed priority rules", } @PhdThesis{Chand:thesis, author = "Shelvin Chand", title = "Automated Design of Heuristics for the Resource Constrained Project Scheduling Problem", school = "Engineering \& Information Technology, Australian Defence Force Academy, University of New South Wales", year = "2018", address = "Australia", month = "6 " # nov, keywords = "genetic algorithms, genetic programming, Resource Constrained Project Scheduling, Optimization, Scheduling, Heuristics, Hyper-Heuristics", URL = "http://unsworks.unsw.edu.au/fapi/datastream/unsworks:52846/SOURCE02?view=true", URL = "http://handle.unsw.edu.au/1959.4/60621", size = "240 pages", abstract = "Classical project scheduling problem usually involves a set of non-preempt-able and precedence related activities that need to be scheduled. The resource constrained project scheduling problem (RCPSP) extends the classical project scheduling by taking into account constraints on the resources required to complete the activities. One particular approach for solving RCPSP instances is through the use of simple priority heuristics. A priority heuristic can be defined as a function which uses certain instance characteristics to construct a solution. Design of priority heuristics, however, is a non-trivial task. Usually, the process involves problem experts who extensively study instance characteristics in order to construct new heuristics. This approach can be time-consuming as well as being restrictive in terms of the possibilities that can be considered by an expert. As a result, researchers are increasingly exploring methods to automate construction of heuristics, commonly known as hyper-heuristics. Genetic programming based hyper heuristics (GPHH) are more commonly used for this task. GPHH operates on a set of problem attributes and mathematical operators to evolve heuristics. GPHHs have been used in a number of different domains such as job shop scheduling and routing. The same, however, can not be said about RCPSP, for which, the literature is relatively scant. The work presented in this thesis is directed towards addressing the aforementioned gap in the literature. Firstly, a GPHH is presented for evolving different types of priority heuristics for RCPSP. The effect of different representations and attributes are empirically evaluated and an attempt is made to evolve priority heuristics which can out-perform existing human designed priority heuristics. Next, a GPHH framework is proposed for evolving variants of the rollout-justification procedure in order to leverage the strength of this approach in discovering heuristics which can perform on par with state-of-the-art algorithmic methods. Finally, a dynamic variant of the classical RCPSP is formulated and a multi-objective GPHH is proposed for discovering priority heuristics, with strong performance and low complexity, to deal with dynamic instances. Apart from these major contributions, other improvements in GP and RCPSP are also proposed as part of the research undertaken during this PhD.", notes = "Supervisors: Dr. Hemant Singh and Prof. Tapabrata", } @Article{CHAND:2019:SEC, author = "Shelvin Chand and Hemant Singh and Tapabrata Ray", title = "Evolving heuristics for the resource constrained project scheduling problem with dynamic resource disruptions", journal = "Swarm and Evolutionary Computation", volume = "44", pages = "897--912", year = "2019", keywords = "genetic algorithms, genetic programming, Dynamic resource constrained project scheduling, Heuristic evolution, Evolutionary computation, Generation hyper-heuristics", ISSN = "2210-6502", DOI = "doi:10.1016/j.swevo.2018.09.007", URL = "http://www.sciencedirect.com/science/article/pii/S2210650217308325", abstract = "Dynamic changes and disruptions are encountered frequently in the domain of project scheduling. The nature of these dynamic events often requires project managers to make quick decisions with regards to effectively re-scheduling the activities. Priority heuristics have a significant potential for such applications due to their simplicity, intuitiveness and low computational cost. In this research, we focus on automated evolution of priority heuristics using a genetic programming hyper-heuristic (GPHH). The proposed approach uses a multi-objective scheme (MO-GPHH) to evolve priority heuristics that can perform better than the existing rules, and at the same time have low complexity. Furthermore, unlike the existing works on evolving priority heuristics that focus on only static problems, this study covers both static and dynamic instances. The proposed approach is tested on a practical dynamic variant of the classical resource constrained project scheduling problem (RCPSP) in which the resource availability varies with time and knowledge about these changes and disruptions only become available as the project progresses. Extensive numerical experiments and benchmarking are performed to demonstrate the efficacy of the proposed approach", } @Article{CHAND:2019:swarm, author = "Shelvin Chand and Hemant Singh and Tapabrata Ray", title = "Evolving rollout-justification based heuristics for resource constrained project scheduling problems", journal = "Swarm and Evolutionary Computation", volume = "50", pages = "100556", year = "2019", ISSN = "2210-6502", DOI = "doi:10.1016/j.swevo.2019.07.002", URL = "http://www.sciencedirect.com/science/article/pii/S2210650218309672", keywords = "genetic algorithms, genetic programming, Resource constrained project scheduling problem, Hyper-heuristics, Priority rules, Rollout, Justification", abstract = "Resource constrained project scheduling is critical in logistic and planning operations across a range of industries. An interesting heuristic for solving this problem is the Rollout-Justification (RJ) procedure. This procedure, which has conceptual similarities with dynamic programming, incrementally builds a solution by identifying the next activity to schedule based on the projections made using a guiding priority rule (heuristic) coupled with forward-backward local search. A critical component that affects the performance of RJ procedure is the guiding priority rule (or a set of rules). In this study, instead of using existing rules from literature, we aim to evolve new priority rules using genetic programming, and systematically investigate their use with the RJ procedure. Apart from evolving new rules, we also investigate new ways of integrating/using the rules within RJ procedure. To this end we consider the use of both forward and backward scheduling, independent and cohesive ensemble rule approaches, limited and unlimited number of function evaluations, among others. We use data from the project scheduling library (PSPLib) to train and test the evolved rules and their integration with RJ. A comprehensive set of numerical experiments are performed to benchmark the rules evolved using the proposed approach against a range of existing rules. The results demonstrate the competence and potential of the proposed approach, both in terms of accuracy and complexity", } @Article{Chandila:2019:IJAMC, author = "Anuj Chandila and Shailesh Tiwari and K. K. Mishra and Akash Punhani", title = "Environmental Adaption Method: A Heuristic Approach for Optimization", journal = "International Journal of Applied Metaheuristic Computing", year = "2019", volume = "10", number = "1", pages = "Article: 7", keywords = "genetic algorithms, genetic programming", ISSN = "1947-8283", bibsource = "OAI-PMH server at oai.repec.org", identifier = "RePEc:igg:jamc00:v:10:y:2019:i:1:p:107-131", oai = "oai:RePEc:igg:jamc00:v:10:y:2019:i:1:p:107-131", DOI = "doi:doi=10.4018/IJAMC.2019010107", abstract = "This article describes how optimisation is a process of finding out the best solutions among all available solutions for a problem. Many randomized algorithms have been designed to identify optimal solutions in optimisation problems. Among these algorithms evolutionary programming, evolutionary strategy, genetic algorithm, particle swarm optimisation and genetic programming are widely accepted for the optimisation problems. Although a number of randomized algorithms are available in literature for solving optimisation problems yet their design objectives are same. Each algorithm has been designed to meet certain goals like minimising total number of fitness evaluations to capture nearly optimal solutions, to capture diverse optimal solutions in multimodal solutions when needed and also to avoid the local optimal solution in multi modal problems. This article discusses a novel optimisation algorithm named as Environmental Adaption Method (EAM) foable 3r solving the optimisation problems. EAM is designed to reduce the overall processing time for retrieving optimal solution of the problem, to improve the quality of solutions and particularly to avoid being trapped in local optima. The results of the proposed algorithm are compared with the latest version of existing algorithms such as particle swarm optimisation (PSO-TVAC), and differential evolution (SADE) on benchmark functions and the proposed algorithm proves its effectiveness over the existing algorithms in all the taken cases.", notes = "IEC-CET, Greater Noida, India", } @Article{billchang:2004:GPEM, author = "Bill C. H. Chang and Asanga Ratnaweera and Saman K. Halgamuge and Harry C. Watson", title = "Particle Swarm Optimisation for Protein Motif Discovery", journal = "Genetic Programming and Evolvable Machines", year = "2004", volume = "5", number = "2", pages = "203--214", month = jun, keywords = "PSO, particle swarm optimisation, protein sequence motif, motif discovery, symbolic data optimisation, HPSO-TVAC", ISSN = "1389-2576", DOI = "doi:10.1023/B:GENP.0000023688.42515.92", abstract = "a modified particle swarm optimisation algorithm is proposed for protein sequence motif discovery. Protein sequences are represented as a chain of symbols and a protein sequence motif is a short sequence that exists in most of the protein sequence families. Protein sequence symbols are converted into numbers using a one to one amino acid translation table. The simulation uses EGF protein and C2H2 Zinc Finger protein families obtained from the PROSITE database. Simulation results show that the modified particle swarm optimisation algorithm is effective in obtaining global optimum sequence patterns, achieving 96.9 and 99.5 classification accuracy respectively in EGF and C2H2 Zinc Finger protein families. A better true positive hit result is achieved when compared to the motifs published in PROSITE database.", notes = "Title: Special Issue on Biological Applications of Genetic and Evolutionary Computation Guest Editor(s): Wolfgang Banzhaf , James Foster (1) Mechatronics Research Group, Mechanical and Manufacturing Engineering, University of Melbourne, Australia (2) Thermofluids Research Group, Mechanical and Manufacturing Engineering, University of Melbourne, Australia time varying c1 and c2", } @MastersThesis{Chia-Lan.Chang:masters, author = "Chia-Lan Chang", title = "Dynamic Proportion Portfolio Insurance with Genetic Programming and Market Volatility Factors Analysis", school = "National Central University, Jungli", year = "2005", address = "Taiwan", month = "30 " # jun, keywords = "genetic algorithms, genetic programming, DPPI, CPPI, market volatility, principal component analysis, PCA", URL = "http://ir.lib.ncu.edu.tw/handle/987654321/13148", broken = "http://thesis.lib.ncu.edu.tw/ETD-db/ETD-search-c/getfile?urn=92423002&filename=92423002.pdf", broken = "http://thesis.lib.ncu.edu.tw/ETD-db/ETD-search-c/view_etd_e?URN=92423002", size = "45 pages", abstract = "This thesis proposes a dynamic proportion portfolio insurance (DPPI) strategy based on the popular constant proportion portfolio insurance (CPPI) strategy. The constant multiplier in CPPI is generally regarded as the risk multiplier. It helps investor easily to understand how to allocate the capital among risky and risk-free assets and straightforward to implement. The risk multiplier in CPPI is predetermined by the investor's view-point and fixed to the end of investment duration. However, since the market changes constantly, we think that the risk multiplier should change accordingly. When the market becomes volatile, the predetermined large risk multiplier will lead to loss of insurance and DPPI may solve this kind of problem. This research identifies factors relating to market volatility. These factors are built into equation trees by genetic programming. We collected five stocks of American companies' financial data and the market information of New York Stock Exchange as input data feeding genetic programming. Experimental results show that our DPPI strategy is more profitable than traditional CPPI strategy. Because the equation trees are all different, there is no method to analyse the factor contributions to the results of the risk multiplier. We use principal component analysis to see the effect of factors, and the experimental results show that among the market volatility factors, risk-free rate influences the variances of risk multiplier most.", } @InProceedings{chang:2023:GECCOcomp, author = "Chi-Hsien Chang and Tu-Chin Chiang and Tzu-Hao Hsu and Ting-Shuo Chuang and Wen-Zhong Fang and Tian-Li Yu", title = "Taylor Polynomial Enhancer Using Genetic Programming for Symbolic Regression", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "543--546", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, symbolic regression, taylor polynomial: Poster", isbn13 = "9798400701191", DOI = "doi:10.1145/3583133.3590591", size = "4 pages", abstract = "Unlike most research of symbolic regression with genetic programming (GP) concerning black-box optimization, this paper focuses on the scenario where the underlying function is available, but due to limited computational resources or product imperfection, the function needs to be approximated with simplicity to fit measured data. Taylor polynomial (TP) is commonly used in such scenario; however, its performance drops drastically away from the expansion point. On the other hand, solely using GP does not use the knowledge of the underlying function, even though possibly inaccurate. This paper proposes using GP as a TP enhancer, namely TPE-GP, to combine the advantages from TP and GP. Specifically, TPE-GP uses infinite-order operators to compensate the power of TP with finite order. Empirically, on functions that are expressible by TP, TP outperformed both gplearn and TPE-GP as expected, while TPE-GP outperformed gplearn due to the use of TP. On functions that are not expressible by TP but expressible by the function set (FS), TPE-GP was competitive with gplearn while both outperformed TP. Finally, on functions that are not expressible by both TP and FS, TPE-GP outperformed both TP and gplearn, indicating the hybrid did achieve the synergy effect from TP and GP.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @InProceedings{Chang:2012:IIH-MSP, author = "Feng-Cheng Chang and Hsiang-Cheh Huang", booktitle = "Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2012)", title = "A Genetic Programming Based Scheme for Combining Image Operators", year = "2012", pages = "215--218", address = "Piraeus", month = "18-20 " # jul, isbn13 = "978-1-4673-1741-2", abstract = "Sophisticated image processing is usually nonlinear and difficult to model. In addition to the conventional image processing tools, we need some alternatives to bridge the gap between low-level and semantic level computation. This paper presents an idea of image processing scheme. We transform an image into different representations; feed the representations to the proper cellular automaton (CA) components to produce the information images; use the information images as the inputs to the combination program; and finally get the processed result. To identify the needed transforms, the CA transition rules, and the combination expression, we adopt genetic programming (GP) and cellular programming (CP) to search for the configuration. The searched configuration separates the parallelisable and sequential parts of the program. We don't enforce the linearity of the program, and it is likely that the searched result matches to the nonlinear nature of human semantics.", keywords = "genetic algorithms, genetic programming, cellular automata, image representation, search problems, transforms, CA transition rules, cellular automaton, cellular programming, combination expression, combination program, configuration search, image operators, image processing, image representation, image transformation, information images, low-level computation, parallelizable program parts, semantic level computation, sequential program parts, Image edge detection, Programming, Semantics, Training, cellular programming, image processing", DOI = "doi:10.1109/IIH-MSP.2012.58", notes = "Also known as \cite{6274651}", } @InProceedings{Chang:2013:IIH-MSP, author = "Feng-Cheng Chang and Hsiang-Cheh Huang", booktitle = "Ninth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2013)", title = "Experiments on Genetic Programming Based Image Artefact Detection", year = "2013", month = oct, pages = "9--12", abstract = "One of the interesting image processing applications is to detect and/or restore a damaged image. Because image damage would vary in different ways, a straightforward method is to use a program to represent the damage. Then, the type of artefact can be searched by applying programs to the original image and comparing with the target image. The run-time environment of a program is the structure of the execution resources. In this paper, we define a cellular automaton based structure as the run-time environment and use genetic programming (GP) to find the proper program for the given image artefacts. The results show that an effective GP engine requires careful configuration. The important lesson learnt from the experiments is also discussed.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IIH-MSP.2013.11", notes = "Also known as \cite{6846567}", } @InProceedings{chang:2017:AIIHMSP, author = "Feng-Cheng Chang and Hsiang-Cheh Huang", title = "A Design of Genetic Programming Scheme with {VLIW} Concepts", booktitle = "Advances in Intelligent Information Hiding and Multimedia Signal Processing", year = "2017", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-319-50212-0_37", DOI = "doi:10.1007/978-3-319-50212-0_37", } @InProceedings{Chang:2022:LifeTech, author = "Feng-Cheng Chang and Hsiang-Cheh Huang", booktitle = "2022 IEEE 4th Global Conference on Life Sciences and Technologies (LifeTech)", title = "Conditionals Support in Binary Expression Tree Based Genetic Programming", year = "2022", pages = "310--313", month = mar, keywords = "genetic algorithms, genetic programming, Conferences, Life sciences, Encoding, binary expression tree, conditional expression, image processing", DOI = "doi:10.1109/LifeTech53646.2022.9754834", abstract = "Inspired by the genetic algorithm (GA), the genetic programming (GP) was proposed for searching a program that fits a certain behavior. There are many aspects that distinguish GP from GA a lot, though GP concepts were originating from GA. In this paper, we focus on the representation scheme for a GP program. A GP program contains both operators and operands. Without proper encoding, the GP crossover and mutation are likely to produce invalid programs. Based on our previous design experiences, we proposed an alternative approach. It is a binary expression tree based representation with conditional behavior of each node. Therefore, the scheme supports unary, binary, and ternary operators. It also reduce the probability of producing invalid programs. A feature of the scheme is that conditional operators are first-class member because each evaluation embeds conditional processing. A few image-processing experiments were conducted to show the effectiveness of the design. The experimental results are also discussed", notes = "Also known as \cite{9754834}", } @InProceedings{Chang:2025:IMCOM, author = "Feng-Cheng Chang and Hsiang-Cheh Huang", title = "Exploring Genetic Programming in Image Processing: Challenges and Enhanced Techniques", booktitle = "2025 19th International Conference on Ubiquitous Information Management and Communication (IMCOM)", year = "2025", month = jan, keywords = "genetic algorithms, genetic programming, Training, Refining, Neural networks, Transformers, Image filtering, Information management, Reliability, Optimisation, Convergence, symbolic transformer", DOI = "doi:10.1109/IMCOM64595.2025.10857586", abstract = "In recent years, the advancement of AI has been primarily driven by neural networks, which, despite their success, pose challenges in terms of explainability and high-power consumption. Genetic Programming (GP) offers an interpretable alternative, although its complexity has limited its practical application. This paper explores the potential of GP through experiments on a simple image filtering task, aiming to understand its properties and limitations. We also investigate the integration of transformer concepts into the GP process. Preliminary results suggest that while GP faces convergence challenges, the introduction of symbolic transformers may enhance its effectiveness in image processing tasks. These findings open up new possibilities for optimising GP in future applications.", notes = "Also known as \cite{10857586} Tamkang University, Taiwan, R.O.C", } @Article{Chang:2010:WSEAS, author = "Hsueh-Hsien Chang", title = "Load Identification of Non-intrusive Load-monitoring System in Smart Home", journal = "WSEAS Transactions on Systems", year = "2010", volume = "9", month = jan, keywords = "genetic algorithms, genetic programming, load identification, artificial neural networks, non-intrusive load monitoring, turn-on transient energy analysis, smart home", ISSN = "1109-2777", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.455.3952", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.455.3952", URL = "http://www.worldses.org/journals/systems/systems-2010.htm", URL = "http://www.wseas.us/e-library/transactions/systems/2010/42-415.pdf", abstract = "In response to the governmental policy of saving energy sources and reducing CO2, and carry out the resident quality of local; this paper proposes a new method for a non-intrusive load-monitoring (NILM) system in smart home to implement the load identification of electric equipments and establish the electric demand management. Non-intrusive load-monitoring techniques were often based on power signatures in the past, these techniques are necessary to be improved for the results of reliability and accuracy of recognition. By using neural network (NN) in combination with genetic programming (GP) and turn-on transient energy analysis, this study attempts to identify load demands and improve recognition accuracy of non-intrusive load-monitoring results. The turn-on transient energy signature can improve the efficiency of load identification and computational time under multiple operations.", } @InProceedings{Chang:2010:ICEBE, author = "Hsueh-Hsien Chang and Ching-Lung Lin", title = "A New Method for Load Identification of Nonintrusive Energy Management System in Smart Home", booktitle = "2010 IEEE 7th International Conference on e-Business Engineering (ICEBE)", year = "2010", month = "10-12 " # nov, pages = "351--357", abstract = "In response to the governmental policy of saving energy sources and reducing CO2, and carry out the resident quality of local; this paper proposes a new method for a non-intrusive energy management (NIEM) system in smart home to implement the load identification of electric equipments and establish the electric demand management. Non-intrusive energy management techniques were often based on power signatures in the past, these techniques are necessary to be improved for the results of reliability and accuracy of recognition. By using neural network (NN) in combination with genetic programming (GP) and turn-on transient energy analysis, this study attempts to identify load demands and improve recognition accuracy of non-intrusive energy-managing results. The turn-on transient energy signature can improve the efficiency of load identification and computational time under multiple operations.", keywords = "genetic algorithms, genetic programming, GP, NIEM system, electric demand management, electric equipments, energy sources, governmental policy, load demands, load identification, neural network, non-intrusive energy management system, non-intrusive energy management techniques, non-intrusive energy-managing results, nonintrusive energy management system, power signatures, recognition accuracy, smart home, turn-on transient energy analysis, turn-on transient energy signature, demand side management, home automation, neural nets, power engineering computing, power system transients", DOI = "doi:10.1109/ICEBE.2010.24", notes = "Also known as \cite{5704339}", } @InProceedings{Chang:2008:ICNC, author = "Jia-Ruey Chang and Shun-Hsing Chen and Dar-Hao Chen and Yao-Bin Liu", title = "Rutting Prediction Model Developed by Genetic Programming Method Through Full Scale Accelerated Pavement Testing", booktitle = "Fourth International Conference on Natural Computation, ICNC '08", year = "2008", month = oct, volume = "6", pages = "326--330", keywords = "genetic algorithms, genetic programming, accelerated pavement testing, load repetitions, model evaluation, pavement performance evaluation, pavement rutting, pavement structural number, rutting prediction model, test pavements, wheel load, structural engineering computing", DOI = "doi:10.1109/ICNC.2008.673", abstract = "The application of genetic programming (GP) to pavement performance evaluation is relatively new. This paper both describes and demonstrates how to develop a model to predict the pavement rutting by using GP method. Results from closely controlled full-scale Accelerated Pavement Testing (APT) - 7 test pavements (264 records) from CRREL's HVS and 1 test pavement (8 records) from TxDOT's MLS - were employed to establish a rutting prediction model. For model evaluation purposes, additional test pavements (94 records) from both CRREL's HVS and TxDOT's MLS were used. GP was applied successfully to develop a rutting prediction model that uses wheel load, load repetitions and the pavement Structural Number (SN) as inputs. The overall R2 for 272 records is 0.8140. The model and algorithms proposed in this study provide a good foundation for further refinement when additional data is available.", notes = "Discipulus Also known as \cite{4667854}", } @InProceedings{Chang:2010:ICNC, author = "Jia-Ruey Chang and Sao-Jeng Chao", title = "Pavement maintenance and rehabilitation decisions derived by genetic programming", booktitle = "Sixth International Conference on Natural Computation (ICNC), 2010", year = "2010", month = "10-12 " # aug, volume = "5", pages = "2439--2443", address = "Yantai, Shandong, China", abstract = "The application of genetic programming (GP) to pavement performance evaluation is relatively new. GP was first proposed by John R. Koza as an evolutionary computation technique: a stochastic search method based on the Darwinian principle of `survival of the fittest', whereby intelligible relationships in a system are automatically extracted and used to generate mathematical expressions or `programs'. Nowadays, GP has been used as an important problem-solving method for function fitting and classification. In this paper, an empirical study is performed to develop a pavement maintenance and rehabilitation (M and R) decision model by using GP. As part of the research, experienced pavement engineers from the Taiwan Highway Bureau (THB) conducted pavement distress surveys on seven county roads. For each road section, the severity and coverage of existing distresses that required M and R treatments were separately identified and collated into an analytical database containing 2,340 records. These records were then used to train, validate, and apply the M and R decision model. The finding shows that the total accuracy of the evolved M and R decision model was 0.903, 0.877, and 0.878 for the training, validation, and application data set, respectively. It proves that the GP-based M and R decision model process makes the pavement knowledge extraction process more systematic, easier to use and solvable with a higher probability of success - even for complex M and R decision problems.", keywords = "genetic algorithms, genetic programming, Darwinian principle, GP-based M amp, R decision model, Taiwan highway bureau, evolutionary computation technique, pavement distress surveys, pavement knowledge extraction process, pavement maintenance, pavement performance evaluation, problem-solving method, rehabilitation decisions, stochastic search method, maintenance engineering, road building, search problems, stochastic processes", DOI = "doi:10.1109/ICNC.2010.5583502", notes = "Dept. of Civil Eng. & Environ. Inf., MingHsin Univ. of Sci. & Technol., Hsinchu, Taiwan Also known as \cite{5583502}", } @Article{Chang:2024:tws, author = "Lei Chang and Kia Khademi and Mohamed Sharaf", title = "Nonlinear vibrations and critical angular velocity information of the high-speed rotating eccentric disk made of Gori-metamaterials: Introducing data-driven solution for solving the nonlinear problems", journal = "Thin-Walled Structures", year = "2024", volume = "202", pages = "112077", keywords = "genetic algorithms, genetic programming, Rotating eccentric disk, Nonlinear vibrations, Critical angular velocity, Origami metamaterial, Data-driven solution", ISSN = "0263-8231", URL = "https://www.sciencedirect.com/science/article/pii/S0263823124005202", DOI = "doi:10.1016/j.tws.2024.112077", abstract = "Eccentric discs in rotational motion are commonly used in various technical fields, including gas turbine engines, flywheels, gears, and brakes. So, improving its critical angular velocity and frequency characteristics is a challenging issue for engineers. So, in this work for the first time, nonlinear vibrations and buckling analysis of the high-speed rotating eccentric disks using mathematical simulation and data-driven solutions are presented. One of the suggestions for improving its mechanical properties is considering metamaterials in the construction of the eccentric disk. Metamaterial is a novel synthetic material that has distinctive physical and mechanical capabilities that are unattainable in natural materials due to its well-designed structure. The properties of the eccentric disks are controlled by the amount of graphene and the degree of folding of graphene origami (GOri) across the thickness of the eccentric disks. These properties, such as Poisson's ratio, vary depending on the position and can be estimated using micromechanical models assisted by genetic programming (GP). Using von-Karman nonlinearity, transformed differential quadrature method (TDQM), and Newton's method the nonlinear governing equations are obtained and solved, respectively. The results show that when the radius ratio of the rotating eccentric disk increases by 8percent, the critical point is decreased from 420 HZ to 370 HZ, a reduction of around 12 percent. Another suggestion for improving its mechanical properties is controlling the geometrics and physics of the presented structure according to the designer's purposes", } @InCollection{Chang:2020:AIdrug, author = "Mark Chang", title = "Swarm and Evolutionary Intelligence", booktitle = "Artificial Intelligence for Drug Development, Precision Medicine, and Healthcare", publisher = "Chapman and Hall/CRC", year = "2020", chapter = "12", address = "New York", keywords = "genetic algorithms, genetic programming", isbn13 = "9780429345159", URL = "https://www.taylorfrancis.com/books/mono/10.1201/9780429345159/artificial-intelligence-drug-development-precision-medicine-healthcare-mark-chang", URL = "https://www.amazon.co.uk/Artificial-Intelligence-Development-Healthcare-Biostatistics/dp/0367362929", DOI = "doi:10.1201/9780429345159", size = "15 pages", abstract = "... Genetic programming is a systematic, domain-independent method for getting computers to solve problems automatically starting from a high-level statement of what needs to be done. The idea of genetic programming is to evolve a population of computer programs. Genetic programming does not have to be for mathematical function fitting. Evolutionary algorithms include genetic algorithms and genetic programming. Genetic programming and genetic algorithms are very similar in that they both use evolution by comparing the fitness of each candidate in a population of potential candidates over many generations. Swarm intelligence and evolutionary artificial intelligence have found applications in drug discovery and development and in artificial general intelligence.", } @InProceedings{Chang:2013:SMC, author = "Ni-Bin Chang and Benjamin Vannah", title = "Comparative Data Fusion between Genetic Programing and Neural Network Models for Remote Sensing Images of Water Quality Monitoring", booktitle = "IEEE International Conference on Systems, Man, and Cybernetics (SMC 2013)", year = "2013", month = oct, pages = "1046--1051", keywords = "genetic algorithms, genetic programming, Data fusion, machine-learning, remote sensing, surface reflectance, microcystin, harmful algal bloom", DOI = "doi:10.1109/SMC.2013.182", size = "6 pages", abstract = "Historically, algal blooms have proliferated throughout Western Lake Erie as a result of eutrophic conditions caused by urban growth and agricultural activities. Of great concern is the blue-green algae Microcystis that thrives in eutrophic conditions and generates microcystin, a powerful hepatotoxin. Microcystin poses a threat to the delicate ecosystem of Lake Erie, and it threatens commercial fishing operations and water treatment plants using the lake as a water source. Integrated Data Fusion and Machine-learning (IDFM) is an early warning system proposed by this paper for the prediction of microcystin concentrations and distribution by measuring the surface reflectance of the water body using satellite sensors. The fine spatial resolution of Landsat is fused with the high temporal resolution of MODIS to create a synthetic image possessing both high temporal and spatial resolution. As a demonstration, the spatiotemporal distribution of microcystin within western Lake Erie is reconstructed using the band data from the fused products and applied machine-learning techniques. The performance of Artificial Neural Networks (ANN) and Genetic Programming (GP) are compared and tested against traditional two-band model regression techniques. It was found that the GP model performed slightly better at predicting microcystin with an R-squared value of 0.6020 compared to 0.5277 for ANN.", notes = "Also known as \cite{6721935}", } @InProceedings{Chang:2013:ieeeICNSCerie, author = "Ni-Bin Chang and Benjamin Vannah", booktitle = "10th IEEE International Conference on Networking, Sensing and Control (ICNSC 2013)", title = "Intercomparisons between empirical models with data fusion techniques for monitoring water quality in a large lake", year = "2013", month = apr, pages = "258--263", keywords = "genetic algorithms, genetic programming, environmental science computing, geophysical image processing, image fusion, image resolution, lakes, learning (artificial intelligence), microorganisms, statistical analysis, water pollution control, water quality, GP model, IDFM technique, Lake Erie, Landsat, blue-green algae, cell growth, cell maintenance, cyanobacteria, data fusion technique, eutrophic condition, hepatotoxin, machine learning technique, microcystin, spatial resolution, statistical index, synthetic image possessing, temporal resolution, water quality monitoring, Data fusion, harmful algal bloom, machine-learning, microcystin, remote sensing, surface reflectance", DOI = "doi:10.1109/ICNSC.2013.6548747", abstract = "Lake Erie has a history of algal blooms, due to eutrophic conditions attributed to urban and agricultural activities. Blue-green algae or cyanobacteria thrive in these eutrophic conditions, since they require little energy for cell maintenance and growth. Microcystis are a type of blue-green algae of particular concern, because they produce microcystin, a potent hepatotoxin. Microcystin not only presents a threat to the ecosystem, but it threatens commercial fishing operations and water treatment plants using the lake as a water source. In this paper, we have proposed an early warning system using Integrated Data Fusion and Machine-learning (IDFM) techniques to determine microcystin concentrations and distribution by measuring the surface reflectance of the water body using satellite sensors. The fine spatial resolution of Landsat is fused with the high temporal resolution of MODIS to create a synthetic image possessing both high temporal and spatial resolution. As a demonstration, the spatiotemporal distribution of microcystin within western Lake Erie is reconstructed using the band data from the fused products and applied machine-learning techniques. Analysis of the results through statistical indices confirmed that the Genetic Programming (GP) model has potential accurately estimating microcystin concentrations in the lake (R2 = 0.5699).", notes = "Also known as \cite{6548747}", } @InProceedings{Chang:2013:ieeeICNSCtampa, author = "Ni-Bin Chang and Zhemin Xuan", booktitle = "10th IEEE International Conference on Networking, Sensing and Control (ICNSC 2013)", title = "Monitoring nutrient concentrations in {Tampa Bay} with MODIS images and machine learning models", year = "2013", month = apr, pages = "702--707", keywords = "genetic algorithms, genetic programming, environmental science computing, geophysical image processing, learning (artificial intelligence), phosphorus, remote sensing, water treatment, GP model, MODIS image, TP, Tampa Bay, aquatic environment, coastal bay, machine learning model, moderate resolution imaging spectroradiometer, nutrient concentration monitoring, remote sensing reflectance band, short-term seasonality effect, total phosphorus, MODIS, Remote sensing, coastal bay, nutrient monitoring, wastewater treatment", DOI = "doi:10.1109/ICNSC.2013.6548824", abstract = "This paper explores the spatiotemporal nutrient patterns in Tampa Bay, Florida with the aid of Moderate Resolution Imaging Spectroradiometer (MODIS) images and Genetic Programming (GP) models that are designed to link Total Phosphorus (TP) levels and remote sensing reflectance bands in aquatic environments. In-situ data were drawn from a local database to support the calibration and validation of the GP model. The GP models show the effective capacity to demonstrating the snapshots of spatio-temporal distributions of TP across the Bay, which helps to delineate the short-term seasonality effect and the global trend of TP in the coastal bay. The model output can provide informative reference for the establishment of contingency plans in treating nutrients-rich runoff.", notes = "Also known as \cite{6548824}", } @Article{Chang:2013:RSE, author = "Ni-Bin Chang and Zhemin Xuan and Y. Jeffrey Yang", title = "Exploring spatiotemporal patterns of phosphorus concentrations in a coastal bay with {MODIS} images and machine learning models", journal = "Remote Sensing of Environment", volume = "134", pages = "100--110", year = "2013", keywords = "genetic algorithms, genetic programming, Remote sensing, Coastal bay, Nutrient monitoring, MODIS", ISSN = "0034-4257", DOI = "doi:10.1016/j.rse.2013.03.002", URL = "http://www.sciencedirect.com/science/article/pii/S0034425713000746", abstract = "This paper explores the spatiotemporal patterns of total phosphorus (TP) in Tampa Bay (Bay), Florida, with the aid of Moderate Resolution Imaging Spectroradiometer (MODIS) images and genetic programming (GP) models. The study was designed to link TP concentrations with relevant water quality parameters and remote sensing reflectance bands in aquatic environments using in-situ data from a local database to support the calibration and validation of the GP model. The GP models show the effective capacity to demonstrate snapshots of spatiotemporal distributions of TP across the Bay, which helps to delineate the short-term seasonality effects and the decadal trends of TP in an environmentally sensitive coastal bay area. In the past decade, urban development and agricultural activities in the Bay area have substantially increased the use of fertilisers. Landfall hurricanes, including Frances and Jeanne in 2004 and Wilma in 2005, followed by continuous droughts from 2006 to 2008 in South Florida, made the Bay area an ideal place for a remote sensing impact assessment. A changing hydrological cycle, triggered by climate variations, exhibited unique regional patterns of varying TP waste loads into the Bay over different time scales ranging from seasons to years. With the aid of the derived GP model in this study, we were able to explore these multiple spatiotemporal distributions of TP concentrations in the Tampa Bay area aquatic environment and to elucidate these coupled dynamic impacts induced by both natural hazards and anthropogenic perturbations. This advancement enables us to identify the hot moments and hot spots of TP concentrations in the Tampa Bay region.", } @Article{Chang:2014:ieeeSTAEORS, author = "Ni-Bin Chang and Benjamin Vannah and Y. Jeffrey Yang", journal = "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing", title = "Comparative Sensor Fusion Between Hyperspectral and Multispectral Satellite Sensors for Monitoring Microcystin Distribution in Lake Erie", year = "2014", month = jun, volume = "7", number = "6", pages = "2426--2442", abstract = "Urban growth and agricultural production have caused an influx of nutrients into Lake Erie, leading to eutrophication in the water body. These conditions result in the formation of algal blooms, some of which are toxic due to the presence of Microcystis (a cyanobacteria), which produces the hepatotoxin microcystin. The hepatotoxin microcystin threatens human health and the ecosystem, and it is a concern for water treatment plants using the lake water as a tap water source. This study demonstrates the prototype of a near real-time early warning system using integrated data fusion and mining (IDFM) techniques with the aid of both hyperspectral (MERIS) and multispectral (MODIS and Landsat) satellite sensors to determine spatiotemporal microcystin concentrations in Lake Erie. In the proposed IDFM, the MODIS images with high temporal resolution are fused with the MERIS and Landsat images with higher spatial resolution to create synthetic images on a daily basis. The spatiotemporal distributions of microcystin within western Lake Erie were then reconstructed using the band data from the fused products with machine learning or data mining techniques such as genetic programming (GP) models. The performance of the data mining models derived using fused hyperspectral and fused multispectral sensor data are quantified using four statistical indices. These data mining models were further compared with traditional two-band models in terms of microcystin prediction accuracy. This study confirmed that GP models outperformed traditional two-band models, and additional spectral reflectance data offered by hyperspectral sensors produces a noticeable increase in the prediction accuracy especially in the range of low microcystin concentrations.", keywords = "genetic algorithms, genetic programming, Harmful algal bloom, image fusion, machine learning, microcystin, remote sensing", DOI = "doi:10.1109/JSTARS.2014.2329913", ISSN = "1939-1404", notes = "Also known as \cite{6851120}", } @InProceedings{Chang:2015:ICNSC, author = "N. B. Chang and S. Imen", booktitle = "12th IEEE International Conference on Networking, Sensing and Control (ICNSC)", title = "Improving the control of water treatment plant with remote sensing-based water quality forecasting model", year = "2015", pages = "51--57", abstract = "When Total Organic Carbon (TOC) in the source water is in contact with disinfectants in a drinking water treatment process, it often times causes the formation of disinfection by-products such as Trihalomethanes which have harmful effects on human health. As a result of the potential health risk of Trihalomethanes for drinking water, proper monitoring and forecasting of high TOC episodes in the source water body can be helpful for the operators who are in charge of the decisions when they have to start the removal procedures for TOC in surface water treatment plants. This issue is of great importance in Lake Mead in the United States which provides drinking water for 25 million people, while it is considered as an important recreational area and wildlife habitat as well. In this study, artificial neural network, extreme learning machine, and genetic programming are examined using the long-term observations of TOC concentration throughout the lake. Among these models, the model with the best performance was applied in the development of a forecasting model to predict TOC values on a daily basis. The forecasting process is aided by an iterative scheme via updating the daily satellite imagery in concert with retrieving the long-term memory of the past states with nonlinear autoregressive neural network with external input (NARXNET) on a rolling basis onwards. The best input scenario of NARXNET was selected with respect to several statistical indices. Numerical outputs of the forecasting process confirm the fidelity of the iterative scheme in predicting water quality status one day ahead of the time.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICNSC.2015.7116009", month = apr, notes = "Also known as \cite{7116009}", } @Article{Chang:2015:EI, author = "Ni-Bin Chang and Golam Mohiuddin and A. James Crawford and Kaixu Bai and Kang-Ren Jin", title = "Diagnosis of the artificial intelligence-based predictions of flow regime in a constructed wetland for stormwater pollution control", journal = "Ecological Informatics", volume = "28", pages = "42--60", year = "2015", ISSN = "1574-9541", DOI = "doi:10.1016/j.ecoinf.2015.05.001", URL = "http://www.sciencedirect.com/science/article/pii/S1574954115000795", abstract = "Monitoring the velocity field and stage variations in heterogeneous aquatic environments, such as constructed wetlands, is critical for understanding hydrodynamic patterns, nutrient removal capacity, and hydrographic impact on the wetland ecosystem. Obtaining low velocity measurements representative of the entire wetland system may be challenging, expensive, and even infeasible in some cases. Data-driven modelling techniques in the computational intelligence regime may provide fast predictions of the velocity field based on a handful of local measurements. They can be a convenient tool to visualize the general spatial and temporal distribution of flow magnitude and direction with reasonable accuracy in case regular hydraulic models suffer from insufficient baseline information and longer run time. In this paper, a comparison between two types of bio-inspired computational intelligence models including genetic programming (GP) and artificial neural network (ANN) models was implemented to estimate the velocity field within a constructed wetland (i.e., the Storm-water Treatment Area in South Florida) in the Everglades, Florida. Two different ANN-based models, including back propagation algorithm and extreme learning machine, were used. Model calibration and validation were driven by data collected from a local sensor network of Acoustic Doppler Velocimeters (ADVs) and weather stations. In general, the two ANN-based models outperformed the GP model in terms of several indices. Findings may improve the design and operation strategies for similar wetland systems.", keywords = "genetic algorithms, genetic programming, Constructed wetland, Stormwater Management, Artificial neural network, Velocity Flow Field, Acoustic Doppler Velocimeter", } @Article{Chang:2006:mej, author = "Shoou-Jinn Chang and Hao-Sheng Hou and Yan-Kuin Su", title = "Automated synthesis of passive filter circuits including parasitic effects by genetic programming", journal = "Microelectronics Journal", year = "2006", volume = "37", number = "8", pages = "792--799", month = aug, keywords = "genetic algorithms, genetic programming, Parasitic effects, Passive filter synthesis", DOI = "doi:10.1016/j.mejo.2005.12.012", abstract = "In this paper, we propose a genetic programming method to synthesise passive filter circuits including parasitic effects, which are very common in high-frequency application. This approach allows circuit topology and component values to be evolved simultaneously; therefore, novel circuits different from those generated by traditional methods can be explored. Experimental results show the proposed method can effectively generate not only compliant but also efficient solutions of such problems where the traditional approaches fail.", } @Article{Chang:2005:FGCS, author = "Yun Seok Chang and Kwang Suk Park and Bo Yeon Kim", title = "Nonlinear model for ECG R-R interval variation using genetic programming approach", journal = "Future Generation Computer Systems", year = "2005", volume = "21", pages = "1117--1123", number = "7", abstract = "We propose a nonlinear system modelling method, which predicts characteristics of the ECG R-R interval variation. For determining model equation, we adopted a genetic programming method in which the chromosome represents the model equation consisting of time-delayed variables, constants, and four arithmetic operators, and determines the fitness function. By genetic programming, sequences of regressive nonlinear equations are produced and evolved until the finding of the optimal model equation, which could simulate the spectral, statistical and nonlinear behaviour of the given R-R interval dynamics. Experimental results showed that the evolutionary approach could find the equation which simulates the spectral and chaotic dynamics of the given signal. Therefore, the proposed evolutionary approach is useful for the system identification of the nonlinear biological system.", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6V06-4CVX0RT-1/2/111fea795562435e39023c448749d96a", month = jul, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1016/j.future.2004.03.011", } @Article{CHS06, title = "Automated passive filter synthesis using a novel tree representation and genetic programming", author = "Shoou-Jinn Chang and Hao-Sheng Hou and Yan-Kuin Su", journal = "IEEE Transactions on Evolutionary Computation", volume = "10", number = "1", month = feb, year = "2006", pages = "93--100", keywords = "genetic algorithms, genetic programming, RLC circuits, circuit optimisation, network topology, passive filters, GP-evolved circuits, RLC circuit analysis, automated passive filter synthesis, circuit topology, tree representation, Circuit analysis, circuit representation, passive filter synthesis", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2005.861415", abstract = "This paper proposes a novel tree representation which is suitable for the analysis of RLC (i.e., resistor, inductor, and capacitor) circuits. Genetic programming (GP) based on the tree representation is applied to passive filter synthesis problems. The GP is optimised and then incorporated into an algorithm which can automatically find parsimonious solutions without predetermining the number of the required circuit components. The experimental results show the proposed method is efficient in three aspects. First, the GP-evolved circuits are more parsimonious than those resulting from traditional design methods in many cases. Second, the proposed method is faster than previous work and can effectively generate parsimonious filters of very high order where conventional methods fail. Third, when the component values are restricted to a set of preferred values, the GP method can generate compliant solutions by means of novel circuit topology.", notes = "INSPEC Accession Number:8753451 Dept. of Electr. Eng., Nat. Cheng Kung Univ., Tainan, Taiwan", } @InProceedings{WeiChang:2011:TMEE, author = "Wei Chang and Ning Hao", title = "Prediction of dissolved gas Content in transformer oil based on Genetic Programming and DGA", booktitle = "International Conference on Transportation, Mechanical, and Electrical Engineering (TMEE 2011)", year = "2011", month = "16-18 " # dec, pages = "1133--1136", address = "Changchun, China", size = "4 pages", abstract = "Genetic Programming (GP), which is suitable for prediction, is combined with transformer oil dissolved gas analysis (DGA), and also a method of the prediction of dissolved gas Content in transformer oil based on GP classification algorithm is proposed, so as to predicting the operational status and the latent faults of a power transformer effectively. The comparative results show that GP model can improve the prediction accuracy effectively.", keywords = "genetic algorithms, genetic programming, DGA, GP classification algorithm, dissolved gas content prediction, power transformer, transformer oil dissolved gas analysis, chemical analysis, power transformer insulation, transformer oil", DOI = "doi:10.1109/TMEE.2011.6199404", notes = "Also known as \cite{6199404}", } @MastersThesis{Channon:masters, author = "Alastair D. Channon", title = "The Evolutionary Emergence route to Artificial Intelligence", school = "School of Cognitive and Computing Sciences, University of Sussex", year = "1996", address = "UK", keywords = "genetic algorithms, genetic programming, Artificial Intelligence, Emergence, Artificial Life, Neural Networks, Development, Modularity, Fractals, Lindenmayer Systems, Recurrence", URL = "http://www.channon.net/alastair/msc/adc_msc.pdf", size = "30 pages", abstract = "The artificial evolution of intelligence is discussed with respect to current methods. An argument for withdrawal of the traditional fitness function in genetic algorithms is given on the grounds that this would better enable the emergence of intelligence, necessary because we cannot specify what intelligence is. A modular developmental system is constructed to aid the evolution of neural structures and a simple virtual world with many of the properties believed beneficial is set up to test these ideas. Resulting emergent properties are given, along with a brief discussion.", } @Unpublished{ChaDam97, author = "Alastair Channon and Bob Damper", title = "The Artificial Evolution of Real Intelligence by Natural Selection", note = "Published on the web site of and poster presented at the Fourth European Conference on Artificial Life (ECAL97), Brighton", year = "1997", address = "Brighton, UK", keywords = "genetic algorithms, genetic programming", URL = "http://www.channon.net/alastair/geb/ecal1997/channon_ad_ecal97.pdf", size = "10 pages", abstract = "This paper outlines a preliminary step towards the long-term goal of intelligent artificial life. Evolutionary emergence via natural selection is proposed as the way forward, in combination with other biologically-inspired principles including the developmental modularity of neural networks. In order to develop and test the ideas, an artificial world containing autonomous organisms has been created. Its underlying theory and construction are described. Resulting emergent strategies are reported both from an observer's perspective and in terms of their neural mechanisms. The results prove that the proposed approach is viable and show it to be an exciting area for further research.", notes = "Geb World, ANN, Lindenmayer Systems, Kin Similarity and Convergence", } @InProceedings{ALIFE98*384, author = "A. D. Channon and R. I. Damper", title = "Evolving Novel Behaviors via Natural Selection", booktitle = "Proceedings of the 6th International Conference on Artificial Life ({ALIFE}-98)", editor = "Christoph Adami and Richard K. Belew and Hiroaki Kitano and Charles Taylor", month = jun # "~27--29", year = "1998", pages = "384--388", publisher = "MIT Press", keywords = "genetic algorithms, genetic programming, natural selection", URL = "http://www.channon.net/alastair/geb/alife6/channon_ad_alife6.pdf", ISBN = "0-262-51099-5", address = "Cambridge, MA, USA", } @InProceedings{Channon_sab98, author = "A. D. Channon and R. I. Damper", title = "Perpetuating evolutionary emergence", booktitle = "From Animals to Animats 5: Proceedings of the Fifth International Conference on Simulation of Adaptive Behavior", year = "1998", editor = "Rolf Pfeifer and Bruce Blumberg and Jean-Arcady Meyer and Stewart W. Wilson", pages = "534--539", address = "Zurich, Switzerland", month = aug # " 17-21", publisher = "MIT Press", keywords = "genetic algorithms, genetic programming, natural selection", ISBN = "0-262-66144-6", URL = "http://www.channon.net/alastair/geb/sab98/channon_ad_sab98_nc.pdf", URL = "http://eprints.soton.ac.uk/id/eprint/250478", URL = "https://ieeexplore.ieee.org/document/6278697", DOI = "doi:10.7551/mitpress/3119.001.0001", size = "6 pages", abstract = "Perpetuating evolutionary emergence is the key to artificially evolving increasingly complex systems. In order to generate complex entities with adaptive behaviours beyond our manual design capability, long term incremental evolution with continuing emergence is called for. Purely artificial selection models, such as traditional genetic algorithms, are argued to be fundamentally inadequate for this calling and existing natural selection systems are evaluated. Thus some requirements for perpetuating evolutionary emergence are revealed. A new environment containing simple virtual autonomous organisms has been created to satisfy these requirements. Resulting evolutionary emergent behaviors are reported alongside of their neural correlates. In one example, the collective behaviour of one species clearly provides a selective force which is overcome by another species, demonstrating the perpetuation of evolutionary emergence via naturally arising coevolution.", notes = "broken June 2021 http://www.isab.org.uk/confs/sab98.php Included in google books May 2008", } @Article{ChaDam00, author = "A. D. Channon and R. I. Damper", title = "Towards the evolutionary emergence of increasingly complex advantageous behaviours", journal = "International Journal of Systems Science", year = "2000", volume = "31", number = "7", pages = "843--860", note = "Special issue on Emergent Properties of Complex Systems", keywords = "genetic algorithms, genetic programming, geb", ISSN = "0020-7721", URL = "http://www.channon.net/alastair/geb/ijssepcs/channon_ad_ijssepcs.pdf", DOI = "doi:10.1080/002077200406570", abstract = "The generation of complex entities with advantageous behaviours beyond our manual design capability requires long-term incremental evolution with continuing emergence. In this paper, we argue that artificial selection models, such as traditional genetic algorithms, are fundamentally inadequate for this goal. Existing natural selection systems are evaluated, revealing both significant achievements and pitfalls. Thus, some requirements for the perpetuation of evolutionary emergence are established. An (artificial) environment containing simple virtual autonomous organisms with neural controllers has been created to satisfy these requirements and to aid in the development of an accompanying theory of evolutionary emergence. Resulting behaviours are reported alongside their neural correlates. In a particular example, the collective behaviour of one species provides a selective force which is overcome by another species, demonstrating the incremental evolutionary emergence of advantageous behaviours via naturally arising coevolution. While the results fall short of the ultimate goal, experience with the system has provided some useful lessons for the perpetuation of emergence towards increasingly complex advantageous behaviours.", notes = "natural selection of program code Tierra. geb ANN, L-system, saga, developmental. 'For there is probably no process other than natural selection that is capable of producing the open-ended emergence of increasingly complex systems.' p859.", } @PhdThesis{channon_ad_phdthesis, author = "Alastair Channon", title = "Evolutionary Emergence: The Struggle for Existence in Artificial Biota", school = "University of Southampton", year = "2001", keywords = "genetic algorithms, genetic programming, natural selection", URL = "http://www.channon.net/alastair/geb/phdthesis/channon_ad_phdthesis.pdf", address = "UK", month = nov, size = "111 pages", abstract = "The generation of complex entities with advantageous behaviours beyond our manual design capability requires long-term incremental evolution with continuing emergence. This thesis presents the argument that artificial selection models, such as traditional genetic algorithms, are fundamentally inadequate for this goal. Existing natural selection systems are evaluated, revealing both significant achievements and pitfalls. Thus, some requirements for the perpetuation of evolutionary emergence are established. An (artificial) environment containing simple virtual autonomous organisms with neural controllers has been created to satisfy these requirements and to aid in the development of an accompanying theory of evolutionary emergence. Resulting behaviours are reported alongside their neural correlates. In one example, the collective behaviour of one species provides a selective force which is overcome by another species, demonstrating the incremental evolutionary emergence of advantageous behaviours via naturally-arising coevolution. Further behavioural or neural analysis is infeasible in this environment, so evolutionary statistical methods are employed and extended in order to classify the evolutionary dynamics. This qualitative analysis indicates that evolution is unbounded in the system. As well as validating the theory behind it, work with the system has provided some useful lessons and directions towards the evolution of increasingly complex advantageous behaviours.", } @InProceedings{Channon:2001:PAT, author = "Alastair Channon", title = "Passing the {ALife} Test: Activity Statistics Classify Evolution in {Geb} as Unbounded", booktitle = "Advances in Artificial Life: Proceedings of the Sixth European Conference on Artificial Life (ECAL2001)", series = "Lecture Notes in Computer Science", editor = "Jozef Kelemen and Petr Sosik", volume = "2159", pages = "417--426", year = "2001", keywords = "genetic algorithms, genetic programming, natural selection", publisher = "Springer-Verlag", CODEN = "LNCSD9", ISSN = "0302-9743", isbn13 = "978-3-540-42567-0", bibdate = "Sat Feb 2 13:06:02 MST 2002", bibsource = "http://link.springer-ny.com/link/service/series/0558/tocs/t2159.htm", URL = "http://www.channon.net/alastair/geb/ecal2001/channon_ad_ecal2001.pdf", DOI = "doi:10.1007/3-540-44811-X_45", abstract = "Bedau and Packard's evolutionary activity statistics [1,2] are used to classify the evolutionary dynamics in Geb [3,4], a system designed to verify and extend theories behind the generation of evolutionary emergent systems. The result is that, according to these statistics, Geb exhibits unbounded evolutionary activity, making it the first autonomous artificial system to pass this test. However, having passed it, the most prudent course of action is to look for weaknesses in the test. Two weaknesses are identified and approaches for overcoming them are proposed.", acknowledgement = "Nelson H. F. Beebe, Center for Scientific Computing, University of Utah, Department of Mathematics, 110 LCB, 155 S 1400 E RM 233, Salt Lake City, UT 84112-0090, USA, Tel: +1 801 581 5254, FAX: +1 801 581 4148, e-mail: \path|beebe@math.utah.edu|, \path|beebe@acm.org|, \path|beebe@computer.org|, \path|beebe@ieee.org| (Internet), URL: \path|http://www.math.utah.edu/~beebe/|", } @InProceedings{Channon:2002:alife, author = "Alastair Channon", title = "Improving and still passing the {ALife} test: Component-normalised activity statistics classify evolution in {Geb} as unbounded", pages = "173--181", booktitle = "Proceedings of Artificial Life VIII, the 8th International Conference on the Simulation and Synthesis of Living Systems", year = "2002", editor = "Russell K. Standish and Mark A. Bedau and Hussein A. Abbass", address = "University of New South Wales, Sydney, NSW, Australia", publisher_address = "Cambridge, MA, USA", month = "9th-13th " # dec, publisher = "The MIT Press", keywords = "genetic algorithms, genetic programming, natural selection", URL = "http://www.channon.net/alastair/geb/alife8/channon_ad_alife8.pdf", URL = "http://www.alife.org/alife8/proceedings/sub2118.pdf", size = "10 pages", abstract = "Bedau's (1998a) classification system for long-term evolutionary dynamics provides a test for open-ended evolution. Making this ALife test more rigorous, and passing it, are two of the most important open problems in the field. Previously (Channon 2001) I presented the result that Geb, a system designed to verify and extend theories behind the generation of evolutionary emergent systems (Channon & Damper 2000), has passed this test. However I also criticised the test, most significantly with regard to its normalisation method for artificial systems. This paper details a modified normalisation method, based on component activity normalisation, that overcomes these criticisms. It then presents the results of the revised test when applied to Geb, which indicate that this system does indeed exhibit open-ended evolution.", notes = "Author claims this is a GP but {"}genetic programming{"} appears nowhere in it", } @Article{Channon:2006:GPEM, author = "Alastair Channon", title = "Unbounded evolutionary dynamics in a system of agents that actively process and transform their environment", journal = "Genetic Programming and Evolvable Machines", year = "2006", volume = "7", number = "3", pages = "253--281", month = oct, keywords = "artificial life, Evolutionary dynamics, Variable-size genomes, Coevolution, Biotic selection, Emergence", ISSN = "1389-2576", URL = "http://www.channon.net/alastair/papers/channon_ad_gpem.pdf", DOI = "doi:10.1007/s10710-006-9009-3", abstract = "Bedau et al.'s statistical classification system for long-term evolutionary dynamics provides a test for open-ended evolution. Making this test more rigorous, and passing it, are two of the most important open problems for research into systems of agents that actively process and transform their environment. This paper presents a detailed description of the application of this test to Geb, a system designed to verify and extend theories behind the generation of evolutionarily emergent systems. The result is that, according to these statistics, Geb exhibits unbounded evolutionary dynamics, making it the first autonomous artificial system to pass this test. However, having passed it, the most prudent course of action is to look for weaknesses in the test. The test is criticised, most significantly with regard to its normalisation method for artificial systems. Furthermore, this paper presents a modified normalisation method, based on component activity normalisation, that overcomes these criticisms. The results of the revised test, when applied to Geb, indicate that this system does indeed exhibit open-ended evolution.", } @Article{chao:2003:GPEM, author = "Dennis L. Chao and Stephanie Forrest", title = "Information Immune Systems", journal = "Genetic Programming and Evolvable Machines", year = "2003", volume = "4", number = "4", pages = "311--331", month = dec, keywords = "artificial immune systems, collaborative design, collaborative filtering, evolutionary art, information filtering, biomorphs, sonomorphs, muzak", ISSN = "1389-2576", DOI = "doi:10.1023/A:1026139027539", abstract = "The concept of an information immune system (IIS) is introduced, in which undesirable information is eliminated before it can reach the user. The IIS is inspired by the natural immune systems that protect us from pathogens. IISs from multiple individuals can be combined to form a group IIS which filters out information undesirable to any of the members. The relationship between our proposed IIS architecture and the natural immune system is outlined, and potential applications, including information filtering, interactive design, and collaborative design, are discussed.", notes = "Special issue on artificial immune systems Article ID: 5144846 MusicFX, PolyLens, Arrow's paradox, p315 G. L. 'Nelson (1993) found that listening to a population ... {"}taxes the memory{"}'. {"}evaluate many things at once visually; p325 {"}failure of Biomorph group IIS to scale beyond three users.{"} Adaptive Radio ", } @InProceedings{Chapelle:2000:isr, author = "Frederic Chapelle and O. Chocron and Philippe Bidaud", title = "Genetic programming for inverse kinematics approximation", booktitle = "International Symposium on Robotics (ISR'00)", organization = "International Federation of Robotics", address = "Montreal, Canada", pages = "5--11", month = "14-17 " # may, year = "2000", publisher = "Canadian Society for Robotics, 2000", keywords = "genetic algorithms, genetic programming", ISBN = "0-9687044-0-9", URL = "http://books.google.co.uk/books/about/ISR_2000.html?id=u6zpAAAAMAAJ&redir_esc=y", notes = "ISR 2000 http://www.ifr.org/events/isr/", } @InProceedings{Chapelle:2000:jjcr, author = "Frederic Chapelle and G. Dumont and O. Chocron", title = "Prototypage virtuel de micro-endoscopes par algorithmes evolutionnaires", booktitle = "Journees Jeunes Chercheurs en Robotique (JJCR 13)", address = "Rennes, France", month = sep, note = "in french", size = "12 pages", keywords = "genetic algorithms", URL = "http://www.irisa.fr/manifestations/2000/jjcr/Papiers/chapelle.pdf", year = "2000", } @InProceedings{Chapelle:2001:icra, author = "Frederic Chapelle and Philippe Bidaud", title = "A closed form for inverse kinematics approximation of general {6R} manipulators using genetic programming", booktitle = "IEEE International Conference on Robotics and Automation (ICRA'01)", publisher = "IEEE", address = "Seoul, Korea", pages = "3364--3369", month = "21-28 " # may, year = "2001", volume = "4", keywords = "genetic algorithms, genetic programming, industrial manipulators, manipulator kinematics, symbol manipulation, 6R manipulators, approximation, evolutionary algorithms, industrial manipulators, inverse kinematics, joint variables, symbolic regression, steady state, demes, ADF, parsimony preasure, subsample training data, learning base", ISSN = "1050-4729", DOI = "doi:10.1109/ROBOT.2001.933137", size = "6 pages", abstract = "We present an original use of evolutionary algorithms in order to approximate by a closed form the inverse kinematic model of analytical (non-analytical) and general manipulators. The objective is to provide a fast and general solution to the inverse kinematic problem when it is extensively evaluated in the design processes of manipulators. A mathematical function is evolved through genetic programming according to the known direct kinematic model to determine an analytical expression which approximates the joint variable solution for a given end-effector configuration. As an illustration of this evolutionary symbolic regression process, the inverse kinematic models of the PUMA and GMF Arc Mate are approximated before applying the algorithm to general 6R manipulators.", notes = "INSPEC Accession Number:7018142 p2266 using integer constants. No sign of ADF?? 50 gens in 30 mins (pop size?) on Silicon Graphics O2. p3368 {"}a tournament of 200 individuals{"} -- translation error? Cites A.P.Fraser's gpc++ and Thomas Weinbrenner's GP kernel 0.5.2 cf. \cite{weinbrenner:1997:diploma}", } @PhdThesis{Chapelle:2002:thesis, author = "Frederic Chapelle", title = "Evaluation de systemes robotiques et comportements complexes par algorithmes evolutionnaires", school = "University Pierre et Marie Curie, Paris VI", month = sep, year = "2002", address = "France", note = "in french", keywords = "genetic algorithms, genetic programming, Computer-aided design, robotic systems, simultaneous structure/control evaluation, symbolic regression, inverse models, inverse kinematic problem, programming, control, simulation, medical devices, minimally invasive surgery", URL = "http://www.sudoc.fr/069898715", abstract = "Evaluation of robotic systems and complex behaviours using evolutionary algorithms : in this thesis, an original approach for evaluation of robotic systems in the context of simultaneous structure/control design is presented. It relies on the evolutionary algorithms. The initial procedures for evaluation are usually difficult to implement and expensive in computing time. The developed method uses genetic programming within an evolutionary symbolic regression algorithm, to generate expressions with various levels of refinement which are intended to approximate the original evaluations (according to the concept of metamodels). The interest of this approach is illustrated by various applications of gradual complexity where the initial evaluation methods can be simple functions, algorithms or a value drawn from a simulation considering the globality of the system to be designed, its interactions with the environment and its tasks. Reliable and fast generic models, which are solutions of the inverse kinematic problem for any 6R manipulator geometry (analytical or not), have been produced via approximating functions. The application of these techniques to a problem with dynamics resulted in fixing restrictions to the use of our method for direct approximation of constrained behaviours. Evolutionary symbolic regression is then applied within the framework of optimisations by genetic algorithms (GA), for simple cases like when a GA seeks a solution of the 2D inverse kinematic problem, or more complex like preliminary design of smart active endoscopes for minimally invasive surgery. Additionally, an extension allowing to increase the evolutionarity of GA is deduced.", notes = "Supervisor Philippe Bidaud", } @InProceedings{Chapelle:2002:jrtpm, author = "Frederic Chapelle and Philippe Bidaud and G. Dumont", title = "Conception et evaluation de micro-endoscopes basees sur les algorithmes evolutionnaires", booktitle = "Journees du Reseau Thematique Pluri-disciplinaire Micro-robotique CNRS", address = "Rennes, France", month = "6 " # nov, note = "in french", size = "6 pages", keywords = "genetic algorithms, genetic programming", year = "2002", notes = "Also known as \cite{2002COM188}", } @Article{Chapelle:2004:MMT, author = "Frederic Chapelle and Philippe Bidaud", title = "Closed form solutions for inverse kinematics approximation of general {6R} manipulators", journal = "Mechanism and Machine Theory", year = "2004", month = mar, volume = "39", pages = "323--338", number = "3", abstract = "This paper presents an original use of Evolutionary Algorithms in order to approximate by a closed form the inverse kinematic model (IKM) of analytical, non-analytical and general (i.e. with an arbitrary geometry) manipulators. The objective is to provide a fast and general solution to the inverse kinematic problem when it is extensively evaluated as in design processes of manipulators. A mathematical function is evolved through Genetic Programming according to the known direct kinematic model to determine an analytical expression which approximates the joint variable solution for a given end-effector configuration. As an illustration of this evolutionary symbolic regression process, the inverse kinematic models of the PUMA and the GMF Arc Mate are approximated before to apply the algorithm to general 6R manipulators.", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6V46-4B1XNXT-1/2/2bf40af1f930c87f19d6fcc130f2f57a", keywords = "genetic algorithms, genetic programming, Inverse kinematics, Mechanical design, Manipulators, Genetic programming, Symbolic regression", DOI = "doi:10.1016/j.mechmachtheory.2003.09.003", } @Article{Chapelle:2006:MMT, author = "Frederic Chapelle and Philippe Bidaud", title = "Evaluation functions synthesis for optimal design of hyper-redundant robotic systems", journal = "Mechanism and Machine Theory", year = "2006", volume = "41", number = "10", pages = "1196--1212", month = oct, keywords = "genetic algorithms, genetic programming, Mechanical design, Simultaneous structure/control evaluation, Functions synthesis, Hyper-redundant micro-robotics, Minimally invasive surgery", DOI = "doi:10.1016/j.mechmachtheory.2005.11.006", abstract = "Simultaneous structure/control optimisation in a robotic system design is addressed through Genetic Algorithms. Both aspects are here evolved in the same algorithm through simulations for task oriented evaluations. Moreover, a technique based on Genetic Programming is proposed to generate approximated evaluation functions. Its aim is to significantly speed the design process up, while leading to robust evaluation. A specific adaptation of these principles is investigated for the design of hyper-redundant robotic systems such as smart active endoscopes intended for minimally invasive surgery. The design of these micro-robots is based on a serial arrangement of articulated rings with associated antagonist SMA micro-actuators, whose configuration has to be adapted to the surgical operation constraints. The control strategies for an adaptation of the system geometry to the environment are based on a multi-agent approach to minimise the inter-module communication requirements. The results obtained for the particular application of colonoscopy show the consistency of the solutions.", } @InProceedings{char:1997:caiGP, author = "K. Govinda Char", title = "Constructivist AI with GP", booktitle = "Late Breaking Papers at the 1997 Genetic Programming Conference", year = "1997", editor = "John R. Koza", pages = "28--34", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California, 94305-3079, USA", month = "13--16 " # jul, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-206995-8", notes = "GP-97LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670", } @InProceedings{char:1997:elGPcAI, author = "K. Govinda Char", title = "Evolution of Learning with Genetic Programming - Constructivist AI with Genetic Programming", booktitle = "Late Breaking Papers at the 1997 Genetic Programming Conference", year = "1997", editor = "John R. Koza", pages = "289", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California, 94305-3079, USA", month = "13--16 " # jul, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-206995-8", broken = "http://www.elec.gla.ac.uk/~kchar/gp97.ps", notes = "GP-97LB PHD Students' workshop The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670", } @InCollection{Char:1997:HEC, author = "K. Govinda Char and Walter Alden Tackett", title = "Pattern recognition", booktitle = "Handbook of Evolutionary Computation", publisher = "Oxford University Press", publisher_2 = "Institute of Physics Publishing", year = "1997", editor = "Thomas Baeck and David B. Fogel and Zbigniew Michalewicz", chapter = "section F1.6.2.5", keywords = "genetic algorithms, genetic programming", ISBN = "0-7503-0392-1", URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.375.6494.pdf", URL = "http://www.crcnetbase.com/isbn/9780750308953", size = "5 pages", abstract = "Pattern recognition is one of the most important components of any intelligent system. The traditional methodologies in pattern recognition are inadequate to provide optimal solutions to a variety of pattern recognition and classification problems that are inherently complex. In recent years, evolutionary algorithms have been successfully applied to a wide range of diverse sets of problems in the field of pattern recognition. In a number of applications, evolutionary paradigms, in hybrid with the traditional techniques or in isolation, have outperformed traditional techniques. In this section we provide an overview of various pattern recognition techniques that are currently in use, the role of evolutionary computation in adaptive pattern recognition and the future trends.", notes = "Nov 2015 Chapter not showing up at http://www.crcnetbase.com/isbn/9780750308953 whole of Part F missing", } @InProceedings{char:1998:clGP, author = "K. Govinda Char", title = "Constructive Learning with Genetic Programming", booktitle = "Late Breaking Papers at EuroGP'98: the First European Workshop on Genetic Programming", year = "1998", editor = "Riccardo Poli and W. B. Langdon and Marc Schoenauer and Terry Fogarty and Wolfgang Banzhaf", pages = "1--5", address = "Paris, France", publisher_address = "School of Computer Science", month = "14-15 " # apr, publisher = "CSRP-98-10, The University of Birmingham, UK", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/csrp-98-10.pdf", size = "5 pages", notes = "EuroGP'98LB part of \cite{Poli:1998:egplb}", } @PhdThesis{char:thesis, author = "Kalyani Govinda Char", title = "Constructivist Artificial Intelligence with Genetic Programming", school = "Department of Electronics and Electrical Engineering, University of Glasgow", year = "1998", address = "Oakfield Avenue, Glasgow G12 8LT, Scotland, UK", keywords = "genetic algorithms, genetic programming", URL = "http://ethos.bl.uk/OrderDetails.do?did=10&uin=uk.bl.ethos.265641", notes = "uk.bl.ethos.265641", } @InProceedings{Chareka:2016:PRASA, author = "Tatenda Chareka and Nelishia Pillay", booktitle = "2016 Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference (PRASA-RobMech)", title = "A study of fitness functions for data classification using grammatical evolution", year = "2016", abstract = "Data classification is a well studied area with various techniques such as support vector machines, decision trees, neural networks and evolutionary algorithms, amongst others successfully applied to this domain. The research presented in this paper forms part of an initiative aimed at evaluating grammatical evolution, a recent variation of genetic programming, for data classification. The paper reports on a study conducted to compare six different measures, namely, accuracy, true positive rate, false positive rate, precision, F-score and Matthew's correlation coefficient, as fitness functions for grammatical evolution. The performance of grammatical evolution using the six measures as a fitness function is evaluated for multi-class data classification. The study has shown that the accuracy and F-score are effective as fitness functions outperforming all other measures. In some instances accuracy produced better results than F-score. Future work will examine the correlation between the characteristics of the data set and the best performing measure.", keywords = "genetic algorithms, genetic programming, grammatical evolution", DOI = "doi:10.1109/RoboMech.2016.7813165", month = nov, notes = "Also known as \cite{7813165}", } @Article{Charhate:2007:JEME, author = "S. B. Charhate and M. C. Deo and V. Sanil Kumar", title = "Soft and hard computing approaches for real-time prediction of currents in a tide-dominated coastal area", journal = "Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment", year = "2007", volume = "221", number = "4", pages = "147--163", keywords = "genetic algorithms, genetic programming, tidal currents, neural networks, harmonic analysis, current measurements", ISSN = "1475-0902", DOI = "doi:10.1243/14750902JEME77", size = "19 pages", abstract = "The prediction of tidal currents in the coastal region on a real-time or online basis is useful in taking operation- and planning-related decisions such as towing of vessels and monitoring of oil slick movements. Currently, however, this is done in offline mode on the basis of the statistical method of harmonic analysis involving fitting of harmonic functions to measured data. Alternatively, numerical solutions of hydrodynamic models can also provide spatial and temporal information on currents. Owing to the complex real sea conditions, such methods may not always yield satisfactory results. This paper discusses a few alternative approaches based on the soft computing tools of artificial neural networks (ANNs) and genetic programming (GP), as well as the hard mathematical approaches of stochastic and statistical methods. The suggested schemes use only a univariate time series of currents to forecast their future values. The measurements of coastal currents made at two locations in the Gulf of Khambhat along the west coast of India have been analysed. The current predictions over a time step of 20 min, a few hours, and a day at the specified locations were carried out. It was found that the soft computing schemes of GP and ANN performed better than the traditional hard technique of harmonic analysis in the present application. This work should initiate more application of GP in coastal engineering. Addressing the problem of current predictions in real-time mode based on analysis of observed time series of ocean currents is a specialty of this work.", notes = "ARIMA, ANN, GP. Department of Civil Engineering, Indian Institute of Technology, Bombay", } @Article{Charhate2008120, author = "S. B. Charhate and M. C. Deo and S. N. Londhe", title = "Inverse modeling to derive wind parameters from wave measurements", journal = "Applied Ocean Research", volume = "30", number = "2", pages = "120--129", year = "2008", ISSN = "0141-1187", DOI = "doi:10.1016/j.apor.2008.08.002", URL = "http://www.sciencedirect.com/science/article/B6V1V-4TCGM50-1/2/69dcf477c9fc85235d0cc5df25e6a54a", keywords = "genetic algorithms, genetic programming, Wave buoy, Wave data, Wind data, Neural networks", abstract = "The problem of deriving wind parameters from measured waves is discussed in this paper. Such a need reportedly arises in the field when the wind sensor attached to a wave rider buoy at high elevation from the sea level gets disconnected during rough weather, or otherwise needs repairs. This task is viewed as an inverse modeling approach as against the direct and common one of evaluating the wind-wave relationship. Two purely nonlinear approaches of soft computing, namely genetic programming (GP) and artificial neural network (ANN) have been used. The study is oriented towards measurements made at five different offshore locations in the Arabian Sea and around the western Indian coastline. It is found that although the results of both soft approaches rival each other, GP has a tendency to produce more accurate results than the adopted ANN. It was also noticed that the equation-based GP model could be equally useful as the one based on computer programs, and hence for the sake of simplicity in implementation, the former can be adopted. In case the entire wave rider buoy does not function for some period, a common regional GP model prescribed in this work can still produce the desired wind parameters with the help of wave observations available from anywhere in the region. A graphical user interface is developed that puts the derived models to their actual use in the field.", } @PhdThesis{Charhate:thesis, author = "Shrikant Bhauraoji Charhate", title = "Applications of soft computing techniques to solve coastal and ocean problems", school = "Department of Civil Engineering, Indian Institute of Technology, Bombay", year = "2008", address = "India", keywords = "genetic algorithms, genetic programming", URL = "http://www.civil.iitb.ac.in/~mcdeo/thesis.html", notes = "Supervised by Dr. M. C. Deo", } @InCollection{Charhate200929, author = "S B Charhate and Y. H. Dandawat and S N Londhe", title = "Genetic Programming to Forecast Stream Flow", booktitle = "Advances in Water Resources and Hydraulic Engineering", publisher = "Springer", year = "2009", pages = "29--34", keywords = "genetic algorithms, genetic programming, stream flow, peak flow", isbn13 = "978-3-540-89464-3", URL = "http://dx.doi.org/10.1007/978-3-540-89465-0_6", DOI = "doi:10.1007/978-3-540-89465-0_6", abstract = "Prediction of stream flow plays a vital role in design, construction, operation and maintenance of many hydraulic structures. The present study aims at predicting stream flow at Rajghat in Narmada river basin of India using the technique of genetic programming (GP). The GP models are developed based on monsoon and non-monsoon seasons. The present paper describes 5 separate GP models, 4 for monsoon months and 1 for non-monsoon months for predicting stream flow at Rajghat 1 day in advance. The performance of the GP models especially at peaks is the point of interest along with general prediction accuracy of the models.", notes = "Book Subtitle: Proceedings of 16th IAHR-APD Congress and 3rd Symposium of IAHR-ISHS", language = "English", } @Article{Charhate:2009:SOS, author = "S. B. Charhate and M. C. Deo and S. N. Londhe", title = "Genetic programming for real-time prediction of offshore wind", journal = "Ships and Offshore Structures", year = "2009", volume = "4", number = "1", pages = "77--88", month = mar, keywords = "genetic algorithms, genetic programming, artificial neural networks, wind speed, wind direction, wind prediction", ISSN = "1744-5302", DOI = "doi:10.1080/17445300802492638", size = "12 pages", abstract = "Wind speed and its direction at two offshore locations along the west coast of India are predicted over future time-steps of 3 to 24 hrs based on a sequence past wind measurements made by floating buoys. This is done based on a relatively new soft computing tool using genetic programming. The attention of investigators has recently been drawn to the application of this approach that differs from the well-known technique of genetic algorithms in basic coding and application of genetic operators. Unlike most of the past works dealing with causative modelling or spatial correlations, this study explores the usefulness of genetic programming to carry out temporal regression. It is found that the resulting predictions of wind movements rival those made by an equivalent and more traditional artificial neural network and sometimes appear more attractive when multiple-error criteria were applied. The success of genetic programming as a modelling tool reported in this study may inspire similar applications in future in the problem domain of offshore engineering, and more research in the computing domain as well.", notes = "Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai, India", } @Article{chatterjee:2022:JBSMSE, author = "Suman Chatterjee and Siba Sankar Mahapatra and Luciano Lamberti and Catalin I. Pruncu", title = "Prediction of welding responses using {AI} approach: adaptive neuro-fuzzy inference system and genetic programming", journal = "Journal of the Brazilian Society of Mechanical Sciences and Engineering", year = "2022", volume = "44", number = "2", pages = "Article number: 53", keywords = "genetic algorithms, genetic programming, MGGP, Laser welding, Nd, YAG laser, ANFIS, Titanium alloy, Stainless steel", URL = "http://link.springer.com/article/10.1007/s40430-021-03294-w", DOI = "doi:10.1007/s40430-021-03294-w", size = "15 pages", abstract = "Laser welding of thin sheets has widespread application in various fields such as battery manufacturing, automobiles, aviation, electronics circuits and medical sciences. Hence, it is very essential to develop a predictive model using artificial intelligence in order to achieve high-quality weldments in an economical manner. In the present study, two advanced artificial intelligence techniques, namely adaptive neuro-fuzzy inference system (ANFIS) and multi-gene genetic programming (MGGP), were implemented to predict the welding responses such as heat-affected zone, surface roughness and welding strength during joining of thin sheets using Nd:YAG laser. The study attempts to develop an appropriate predictive model for the welding process. In the proposed methodology, 70 percent of the experimental data constitutes the training set whereas remaining 30 percent data is used as testing set. The results of this study indicated that the root-mean-square error (RMSE) of tested data set ranges between 7 and 16 percent for MGGP model, while RMSE for testing data set lies 18 to 35 percent for ANFIS model. The study indicates that the MGGP predicts the welding responses in a superior manner in laser welding process and can be applied for accurate prediction of performance measures.", notes = "Department of Mechanical Engineering, National Institute of Technology Rourkela, Rourkela, India", } @Article{chattoe:1998:uEArsp, author = "Edmund Chattoe", title = "Just How (Un)realistic are Evolutionary Algorithms as Representations of Social Processes?", journal = "The Journal of Artificial Societies and Social Simulation", year = "1998", volume = "1", number = "3", month = "30 " # jun, keywords = "genetic algorithms, genetic programming, evolutionary algorithms, social evolution, selectionist paradigm", ISSN = "1460-7425", URL = "http://jasss.soc.surrey.ac.uk/1/3/2.html", size = "158407 bytes", abstract = "This paper attempts to illustrate the importance of a coherent behavioural interpretation in applying evolutionary algorithms like Genetic Algorithms and Genetic Programming to the modelling of social processes. It summarises and draws out the implications of the Neo-Darwinian Synthesis for processes of social evolution and then discusses the extent to which evolutionary algorithms capture the aspects of biological evolution which are relevant to social processes. The paper uses several recent papers in the field as case studies, discussing more and less successful uses of evolutionary algorithms in social science. The key aspects of evolution discussed in the paper are that it is dependent on relative rather than absolute fitness, it does not require global knowledge or a system level teleology, it avoids the credit assignment problem, it does not exclude Lamarckian inheritance and it is both progressive and open ended.", notes = "JASSS", } @InCollection{Chattoe:2001:OundG, author = "Edmund Chattoe", title = "The Prospects for Artificial Intelligence Techniques in Understanding Economic Behaviour: An Overview", booktitle = "Oekonomie und Gesellschaft (Economics and Society), Jahrbuch 17: Komplexitaet und Lernen", publisher = "Metropolis-Verlag", year = "2001", editor = "Peter {de Gijsen} and Thomas Schmid-Schoenbein and Johannes Schneider", pages = "135--162", address = "Marburg, Germany", keywords = "genetic algorithms, genetic programming", ISBN = "3-89518-997-9", URL = "http://www.metropolis-publisher.com/Komplexitaet-und-Lernen/997/book.do", URL = "http://www.amazon.de/%C3%96konomie-Gesellschaft-Jahrb-17-Komplexit%C3%A4t-Lernen/dp/3895189979", notes = "'It explains GP but also other techniques'", } @PhdThesis{Chattoe:thesis, author = "Edmund Chattoe", title = "The Evolution of Expectations in Boundedly Rational Agents", school = "Department of Economics, University of Oxford", year = "2003", type = "DPhil", address = "UK", keywords = "genetic algorithms, genetic programming", URL = "https://www.academia.edu/8906840/The_Evolution_of_Expectations_in_Boundedly_Rational_Agents_Front_Material", abstract = "The thesis uses a technique called Genetic Programming to show how variation and selective retention of pricing rules of thumb can produce self-organised markets even when each firm has to learn against a very noisy background of simultaneous adaptation by other firms", notes = " https://www.nuffield.ox.ac.uk/media/1653/2002-03-annual-report.pdf Edmund Chattoe-Brown (by marriage)", } @Article{chattoe:2004:gagpf, author = "Edmund Chattoe", title = "Genetic Algorithms and Genetic Programming in Computational Finance, Chen, Shu-Heng (ed.)", journal = "Journal of Artificial Societies and Social Simulation", year = "2004", volume = "7", number = "4", month = "31-" # oct, note = "Book review", keywords = "genetic algorithms, genetic programming", ISSN = "1460-7425", URL = "http://jasss.soc.surrey.ac.uk/7/4/reviews/chattoe.html", notes = "review of \cite{chen:2002:gagpcf}", } @InCollection{Chattoe-Brown:2013:SSC, author = "Edmund Chattoe-Brown and Bruce Edmonds", title = "Modelling Evolutionary Mechanisms in Social Systems", booktitle = "Simulating Social Complexity", publisher = "Springer", year = "2003", editor = "Bruce Edmonds and Ruth Meyer", volume = "VII", series = "Understanding Complex Systems", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-93812-5", URL = "http://www.springer.com/computer/information+systems+and+applications/book/978-3-540-93812-5", notes = "Due: April 30, 2013", } @InProceedings{Chaturvedi:2020:CEC, author = "Iti Chaturvedi and Erik Cambria and Sandro Cavallari and Roy E. Welsch", title = "Genetic Programming for Domain Adaptation in Product Reviews", booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC 2020", year = "2020", editor = "Yaochu Jin", pages = "paper id24673", address = "internet", month = "19-24 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Sentiment Analysis", isbn13 = "978-1-7281-6929-3", URL = "http://vigir.missouri.edu/~gdesouza/Research/Conference_CDs/IEEE_WCCI_2020/CEC/Papers/E-24673.pdf", DOI = "doi:10.1109/CEC48606.2020.9185713", size = "8 pages", abstract = "There is a large variety of products sold online and the websites are in several languages. Hence, it is desirable to train a model that can predict sentiments in different domains simultaneously. Previous authors have used deep learning to extract features from multiple domains. Here, each word is represented by a vector that is determined using co-occurrence data. Such a model requires that all sentences have the same length resulting in low accuracy. To overcome this challenge, we model the features in each sentence using a variable length tree called a Genetic Program. The polarity of clauses can be represented using mathematical operators such as plus or minus at internal nodes in the tree. The proposed model is evaluated on Amazon product reviews for different products and in different languages. We are able to outperform the accuracy of baseline multi-domain models in the range of 5-20percent.", notes = "https://wcci2020.org/ James Cook University, Australia; MIT, United States of America; Nanyang Technological University, Singapore. Also known as \cite{9185713}", } @Article{Chaturvedi:2021:CognComput, author = "Iti Chaturvedi and Chit L Su and Roy E Welsch", title = "Fuzzy Aggregated Topology Evolution for Cognitive Multi-tasks", journal = "Cognitive Computation", year = "2021", volume = "13", pages = "96--107", keywords = "genetic algorithms, genetic programming, multi-task optimisation, fuzzy logic, neuroevolution", publisher = "Springer US", bibsource = "OAI-PMH server at dspace.mit.edu", language = "en", oai = "oai:dspace.mit.edu:1721.1/131981", URL = "https://hdl.handle.net/1721.1/131981", DOI = "doi:10.1007/s12559-020-09807-4", size = "15 pages", abstract = "Evolutionary optimisation aims to tune the hyper-parameters during learning in a computationally fast manner. For optimisation of multi-task problems, evolution is done by creating a unified search space with a dimensionality that can include all the tasks. Multi-task evolution is achieved via selective imitation where two individuals with the same type of skill are encouraged to crossover. Due to the relatedness of the tasks, the resulting offspring may have a skill for a different task. In this way, we can simultaneously evolve a population where different individuals excel in different tasks. In this paper, we consider a type of evolution called Genetic Programming (GP) where the population of genes have a tree-like structure and can be of different lengths and hence can naturally represent multiple tasks. We apply the model to multi-task neuroevolution that aims to determine the optimal hyper-parameters of a neural network such as number of nodes, learning rate, and number of training epochs using evolution. Here each gene is encoded with the hyper parameters for a single neural network. Previously, optimisation was done by enabling or disabling individual connections between neurons during evolution. This method is extremely slow and does not generalise well to new neural architectures such as Seq2Seq. To overcome this limitation, we follow a modular approach where each sub-tree in a GP can be a sub-neural architecture that is preserved during crossover across multiple tasks. Lastly, in order to leverage on the inter-task covariance for faster evolutionary search, we project the features from both tasks to common space using fuzzy membership functions. The proposed model is used to determine the optimal topology of a feed-forward neural network for classification of emotions in physiological heart signals and also a Seq2seq chatbot that can converse with kindergarten children. We can outperform baselines by over 10percent in accuracy.", } @InProceedings{DBLP:conf/icarcv/ChaudhariPT08, author = "Narendra S. Chaudhari and Anuradha Purohit and Aruna Tiwari", title = "A multiclass classifier using Genetic Programming", booktitle = "10th International Conference on Control, Automation, Robotics and Vision, ICARCV 2008", year = "2008", pages = "1884--1887", address = "Hanoi, Vietnam", month = "17-20 " # dec, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICARCV.2008.4795815", abstract = "his paper presents an approach for designing classifiers for a multiclass problem using Genetic Programming (GP). The proposed approach takes an integrated view of all classes when GP evolves. An individual of the population will be represented using multiple trees. The GP is trained with a set of N training samples in steps. A concept of unfitness of a tree is used in order to improve genetic evolution. Weak trees having poor performance are given more chance to participate in the genetic operations, and thus improve themselves. In this context, a new mutation operation called nondestructive directed point mutation is used, which reduces the destructive nature of mutation operation. The approach is being demonstrated by experimenting on some datasets.", bibsource = "DBLP, http://dblp.uni-trier.de", } @Article{Chaudhari:2012:IJHST, author = "Narhari Chaudhari and Shreenivas Londhe and Kanchan Khare", title = "Estimation of pan evaporation using soft computing tools", year = "2013", month = feb # "~28", volume = "2", journal = "International Journal of Hydrology Science and Technology", issue = "4", pages = "373--390", keywords = "genetic algorithms, genetic programming, pan evaporation, soft computing, artificial neural networks, ANNs, penman's equation, water resources management, water management, evaporation estimation.", ISSN = "2042-7816", publisher = "Inderscience Publishers", bibsource = "OAI-PMH server at www.inderscience.com", language = "eng", URL = "http://www.inderscience.com/link.php?id=52375", DOI = "DOI:10.1504/IJHST.2012.052375", abstract = "Estimation of evaporation plays a key role in managing water resources projects. Traditionally, evaporation is determined using theoretical and empirical techniques as well as by pan observations. Practically, it is difficult to install evaporation pans at every location and empirical approach like Penman's equation is data intensive. This necessitates the use of an alternative approach, which can make use of readily available data and estimate evaporation reasonably with limited data. Major objective of the present study is to estimate evaporation using the soft computing tools of artificial neural networks (ANN) and genetic programming (GP) making use of the measured climatic parameters and to compare the results with traditional empirical techniques. The results indicate that the models developed using the soft computing tools of ANN and GP worked reasonably well for estimation of evaporation compared to empirical methods. GP works slightly better for higher values of pan evaporation compared to ANN.", } @Article{Chaudhari:2015:IJHST, author = "Narhari Chaudhari and Shreenivas Londhe and Kanchan Khare", title = "Spatial mapping of pan evaporation using linear genetic programming", journal = "Int. J. of Hydrology Science and Technology", publisher = "Inderscience Publishers", year = "2015", month = mar # "~07", volume = "4", number = "3", pages = "234--244", ISSN = "2042-7816", keywords = "genetic algorithms, genetic programming, evaporimeters, linear genetic programming, LGP, pan evaporation, spatial mapping, hydrology science, water resources, water management, India", bibsource = "OAI-PMH server at www.inderscience.com", URL = "http://www.inderscience.com/link.php?id=67731", DOI = "DOI:10.1504/IJHST.2014.067731", abstract = "Daily pan evaporation is of utmost importance in planning and managing water resources. The present paper involves estimation of daily pan evaporation at a particular climatic station using daily pan evaporations of surrounding ten climatic stations covering six districts of Maharashtra state (India) with variation in elevations and weather. The surrounding stations were added one by one based on the correlation of each station with the output station. The soft computing technique of linear genetic programming was employed for this spatial mapping exercise. The models were developed for each station as output station (total 11) with the remaining stations (1 to 10) as inputs added one by one. In all 110 LGP models were developed to examine the ability of linear genetic programming to work as virtual pan as and when existing evaporimeters become inoperative. The best LGP model was for Suksale station with coefficient of correlation (r = 0.94) between observed and estimated pan evaporation. This will retrieve the missing evaporation data at one location using data at other locations.", notes = "Kanchan Khare is a Professor of Civil Engineering at the Symbiosis Institute of Technology, Pune, India.", } @InProceedings{chaudhari:2016:FICSCPS, author = "Narhari Dattatraya Chaudhari and Neha Narhari Chaudhari", title = "One Day Ahead Forecast of Pan Evaporation at {Pali} Using Genetic Programming", booktitle = "Proceedings of Fifth International Conference on Soft Computing for Problem Solving", year = "2016", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-981-10-0448-3_10", DOI = "doi:10.1007/978-981-10-0448-3_10", } @InProceedings{Chaudhary:2009:INMIC, author = "U. K. Chaudhary and M. Iqbal", title = "Determination of optimum genetic parameters for symbolic non-linear regression-like problems in genetic programming", booktitle = "IEEE 13th International Multitopic Conference, INMIC 2009", year = "2009", month = dec, pages = "1--5", keywords = "genetic algorithms, genetic programming, Matlab, elitism, halfelitism-roulette, keepbest-doubletour, optimum genetic parameters, replace-doubletour, replace-lexictour, replace-tournament, symbolic non-linear regression-like problems, mathematics computing, regression analysis", DOI = "doi:10.1109/INMIC.2009.5383162", abstract = "Parametric studies have been carried out for the quartic-polynomial regression problem demonstrated in the Genetic Programming (GP) v3 toolbox of Matlab. Many classification schemes and modeling issues are polynomial based. Every possible combination originating from all available options between the two genetic parameters namely 'elitism' and 'sampling' has been analyzed while keeping all other parameters as fixed. Three performance parameters namely, execution time of a given GP run, quickness of convergence to reach the required fitness and the most important, fitness improvement factor per generation have been studied. In terms of the last mentioned performance parameter, being an indicative of diversity, it is shown that the best particular combination is 'halfelitism-sus' if naming in the general format of 'elitism-sampling' is used. On the average, this combination went on improving the fitness value (of the best so far individual) in more than 78percent of generations as the GP simulations progressed towards the required solution. Secondly, halfelitism-roulette took, on the average, as less as 6.8 generations to complete a GP run outperforming other combinations in terms of quickness of convergence with again, halfelitism-sus as second best consuming 7.4 generations to reach at the desired quartic genre. In spite of its promising average values, this combination showed a contrasting behavior depending upon the auto-evolution process at the start of a given GP run. Either it took on a right track and converged to the solution efficiently or it de-tracked in the very beginning and lost its performance regarding the three aforementioned parameters. Furthermore, it was found that for the combinations replace-doubletour and keepbest-doubletour giving the best two execution times (in seconds) to complete a given GP run, their results were least encouraging regarding the other performance parameters. Also, in contrast to some combinations such as, replace-tournament and replace-lexictour, other combinations worked satisfactorily well in at least one of the three performances studied.", notes = "Also known as \cite{5383162}", } @InProceedings{Chaudhri:2000:GECCO, author = "Omer A. Chaudhri and Jason M. Daida and Jonathan C. Khoo and Wendell S. Richardson and Rachel B. Harrison and William J. Sloat", title = "Characterizing a Tunably Difficult Problem in Genetic Programming", pages = "395--402", year = "2000", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)", editor = "Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer", address = "Las Vegas, Nevada, USA", publisher_address = "San Francisco, CA 94104, USA", month = "10-12 " # jul, keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-708-0", URL = "http://gpbib.cs.ucl.ac.uk/gecco2000/GP206.pdf", notes = "A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of \cite{whitley:2000:GECCO}", } @PhdThesis{Chaudhry:thesis, author = "Asmatullah Chaudhry", author2 = "Asmat Ullah", title = "Image Restoration using Machine Learning", school = "Ghulam Ishaq Khan Institute of Engineering Sciences \& Technology", year = "2007", address = "Topi, NWFP, Pakistan", month = mar, email = "asmatullah.chaudhry@gmail.com", keywords = "genetic algorithms, genetic programming, Image Restoration", URL = "https://fac.ksu.edu.sa/ammirza/page/22439", URL = "http://prr.hec.gov.pk/jspui/handle/123456789/4816", URL = "http://prr.hec.gov.pk/thesis/2056.pdf", size = "112 pages", abstract = "Restoration of degraded images has become an important and effective tool for many technological applications like space imaging, medical imaging and many other post-processing techniques. Most of the image restoration techniques model the degradation phenomena, usually blur and noise, and then obtain an approximation of the image. Whereas, in realistic situation, one has to estimate both the true image and the blur from the degraded image characteristics in the absence of any a priori information about the blurring system. The objective of this thesis is to develop a new punctual kriging based image restoration approach using machine-learning techniques. To achieve this objective, the research concentrates on the restoration of images corrupted with Gaussian noise by making good tradeoffs between two contradicting properties; smoothness versus edge preservation. This thesis makes the following contributions: (1) Quantitative analysis of the at hand punctual kriging based image restoration technique is carried out, (2) Fuzzy logic, punctual kriging and fuzzy averaging are used intelligently to develop a better image restoration technique, (3) A new image quality measure is proposed in terms of the semi-variograms to judge the performance of image restoration techniques, (4) Analysis of the effect of neighbourhood size on negative weights and the subsequent improvement in punctual kriging based image restoration is performed, (5) To avoid both the problems of matrix inversion failure and the negative weights in punctual kriging, artificial neural network is used to develop a neuro-fuzzy filter for image denoising, (6) Further, using genetic programming, a hybrid technique for image restoration based on fuzzy punctual kriging is developed, the developed machine learning technique uses local statistical measures along with kriged information for subsequent pixel estimation. Main parameters considered for evaluation of the proposed technique are image quality measure and computational cost. The image quality measures used for evaluation and comparison include MSE, PSNR, SSIM, wPSNR, VMSE and VPSNR. A series of empirical investigations have been made to evaluate the performance of the proposed techniques using database of standard images that show the effectiveness of our methodology.", notes = "Author also given as Ullah, Asmat eg by http://prr.hec.gov.pk/thesis/2056.pdf (broken Sep 2022). Subjects: Engineering & Technology (e) > Engineering(e1) > Computer Sciences & related disciplines(e1.9) ID Code: 2139 Supervisor: Anwar M. Mirza", } @Article{Chaudhry:2007:IJIST, author = "Asmatullah Chaudhry and Asifullah Khan and Asad Ali and Anwar M. Mirza", title = "A hybrid image restoration approach: Using fuzzy punctual kriging and genetic programming", journal = "International Journal of Imaging Systems and Technology", year = "2007", volume = "17", number = "4", pages = "224--231", keywords = "genetic algorithms, genetic programming, image restoration, fuzzy logic, punctual kriging, structure similarity index measure, SSIM, adaptive spatial filtering", ISSN = "1098-1098", DOI = "doi:10.1002/ima.20105", abstract = "We present an intelligent technique for image denoising problem of gray level images degraded with Gaussian white noise in spatial domain. The proposed technique consists of using fuzzy logic as a mapping function to decide whether a pixel needs to be krigged or not. Genetic programming is then used to evolve an optimal pixel intensity-estimation function for restoring degraded images. The proposed system has shown considerable improvement when compared both qualitatively and quantitatively with the adaptive Wiener filter, methods based on fuzzy kriging, and a fuzzy-based averaging technique. Experimental results conducted using an image database confirms that the proposed technique offers superior performance in terms of image quality measures. This also validates the use of hybrid techniques for image restoration.", } @Article{Chaudhry:2009:murjet, author = "Asmatullah Chaudhry and Anwar M. Mirza and Nisar Ahmed Memon", title = "Fusion of Linear and Non-Linear Image Restoration Filters Using Genetic Programming", journal = "Mehran university Research Journal of Engineering and Technology", year = "2009", volume = "28", number = "4", pages = "429--436", month = oct, publisher = "Mehran University of Engineering and Technology", address = "Pakistan", email = "asmatullah.chaudhry@gmail.com", keywords = "genetic algorithms, genetic programming, Image restoration, E-median filter, Adaptive Wiener filter (AWF)", ISSN = "0254-7821", searchurl = "http://direct.bl.uk/bld/OrderDetails.do?", abstract = "In this paper, we present an intelligent technique for image de-noising of gray level still images degraded with Gaussian white noise. The proposed technique consists of using E-median filter in wavelet domain and adaptive Wiener filter to restore the noisy image. Genetic programming is then used to evolve an optimal pixel intensity estimation function used to restore the degraded images. The proposed method has shown considerable improvement in the image quality as compared to the adaptive Wiener and E-median filter approaches. Experimental results carried out on several standard images and a database consisting of 450 images confirm the superiority of the proposed technique in terms of image quality. This also validates the use of hybrid techniques for image restoration.", notes = "Unique item number RN257688172 Shelfmark 5536.314400 Oct 2015 http://publications.muet.edu.pk/ only back as far as 2010.", } @InProceedings{Chauhan:2020:ICIMIA, author = "Karansingh Chauhan and Shreena Jani and Dhrumin Thakkar and Riddham Dave and Jitendra Bhatia and Sudeep Tanwar and Mohammad S. Obaidat", title = "Automated Machine Learning: The New Wave of Machine Learning", booktitle = "2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA)", year = "2020", pages = "205--212", address = "Bangalore, India", month = "5-7 " # mar, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, TPOT", DOI = "doi:10.1109/ICIMIA48430.2020.9074859", abstract = "With the explosion in the use of machine learning in various domains, the need for an efficient pipeline for the development of machine learning models has never been more critical. However, the task of forming and training models largely remains traditional with a dependency on domain experts and time-consuming data manipulation operations, which impedes the development of machine learning models in both academia as well as industry. This demand advocates the new research era concerned with fitting machine learning models fully automatically i.e., AutoML. Automated Machine Learning(AutoML) is an end-to-end process that aims at automating this model development pipeline without any external assistance. First, we provide an insights of AutoML. Second, we delve into the individual segments in the AutoML pipeline and cover their approaches in brief. We also provide a case study on the industrial use and impact of AutoML with a focus on practical applicability in a business context. At last, we conclude with the open research issues, and future research directions.", notes = "Vishwakarma Government Engineering College, Gujarat Technological University, Ahmedabad, India INSPEC Accession Number: 19556800", } @Article{Chaumont:2016:GPEM, author = "Nicolas Chaumont and Christoph Adami", title = "Evolution of sustained foraging in three-dimensional environments with physics", journal = "Genetic Programming and Evolvable Machines", year = "2016", volume = "17", number = "4", pages = "359--390", month = dec, keywords = "genetic algorithms, genetic programming, alife, Sustainable foraging, 3D environment, Physics simulator, Body-brain co-evolution, Foraging map EVO", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-016-9270-z", size = "32 pages", abstract = "Artificially evolving foraging behavior in simulated articulated animals has proved to be a notoriously difficult task. Here, we co-evolve the morphology and controller for virtual organisms in a three-dimensional physical environment to produce goal-directed locomotion in articulated agents. We show that following and reaching multiple food sources can evolve de novo, by evaluating each organism on multiple food sources placed on a basic pattern that is gradually randomized across generations. We devised a strategy of evolutionary ``staging'', where the best organism from a set of evolutionary experiments using a particular fitness function is used to seed a new set, with a fitness function that is progressively altered to better challenge organisms as evolution improves them. We find that an organism's efficiency at reaching the first food source does not predict its ability at finding subsequent ones because foraging efficiency crucially depends on the position of the last food source reached, an effect illustrated by ``foraging maps'' that capture the organism's controller state, body position, and orientation. Our best evolved foragers are able to reach multiple food sources over 90percent of the time on average, a behavior that is key to any biologically realistic simulation where a self-sustaining population has to survive by collecting food sources in three-dimensional, physical environments.", notes = "Founder organisms 'The genome description is specified in a script-like fashion' 'The types are defined by the manner in which they process inputs: Sum, Product, Divide, SumThreshold, GreaterThan, SignOf, Min, Max, Abs, If, Interpolate, Sin, Cos, Atan, Log, Exp, Sigmoid, Integrate, Differentiate, Smooth, Memory, Wave, Saw, and constant.'", } @Article{Chavan:2013:IJERA, author = "R. S. Chavan and P. M. Pawar", title = "Shear Strength of Slender Reinforced Concrete Beams without Web Reinforcement", journal = "International Journal of Engineering Research and Applications", year = "2013", volume = "3", number = "6", pages = "554--559", month = nov # "-" # dec, keywords = "genetic algorithms, genetic programming, Shear strength, fuzzy rule, shear span to depth ratio (a/d)", ISSN = "2248-9622", URL = "https://www.ijera.com/pages/v3-no6.html", URL = "https://www.ijera.com/papers/Vol3_issue6/CQ36554559.pdf", size = "6 pages", abstract = "This paper attempt to predict the shear strength of high strength concrete beam with different shear span of depth ratio without web reinforcement. The large data base available has been clustered and a linear equation analysis has been performed . The prepared models are functions of compressive strength , percentage of flexural reinforcement and depth of beam. The proposed models have been validated with existence of popular models as well as with design code provisions.", notes = "Department of Civil Engineering, Solapur University, Solapur", } @InProceedings{chavez:2007:MAEB, author = "Francisco {Chavez de la O} and Jose Luis {Guisado Lizar} and Daniel {Lombrana Gonzalez} and Francisco {Fernandez de Vega}", title = "Una Herramienta de Programacion Genetica Paralela que Aprovecha Recursos Publicos de Computacion", booktitle = "MAEB'2007, V Congreso Espanol sobre Metaheuristicas, Algoritmos Evolutivos y Bioinspirados", year = "2007", editor = "Francisco Almeida Rodriguez and Maria Belen Melian Batista and Jose Andres Moreno Perez and Jose Marcos Moreno Vega", pages = "167--173", address = "Puerto de la Cruz, Spain", publisher_address = "Tenerife, Spain", month = feb, publisher = "La Laguna", keywords = "genetic algorithms, genetic programming, Palabras clave, Algoritmos Paralelos, Programacion Genetica", isbn13 = "978-84-690-3470-5", URL = "http://icaro.eii.us.es/~jlguisado/publicaciones/MAEB2007_preprint.pdf", URL = "https://dialnet.unirioja.es/servlet/articulo?codigo=4121159", size = "7 pages", abstract = "Eeste articulo presenta una primera implementacion de una herramienta generica de programacion genetica capaz de aprovechar recursos publicos de computacion. Dadas las altas necesidades de recursos de computacion requeridos por los algoritmos evolutivos, la aplicacion del paralelismo ha sido habitual recientemente, aunque las herramientas paralelas requieren infraestructuras costosas para su aprovechamiento. El modelo que se presenta en este articulo, permite utilizar computadores distribuidos en Internet, cuyos usuarios ceden altruistamente para colaborar en proyectos de investigacion. El proceso de donacion de recursos es simple e inmediato por parte del usuario, afectando solamente a los ciclos de CPU que no son consumidos por el propio usuario. Se estudia la mejora de las prestaciones obtenidas gracias al uso de estos recursos en Programacion Genetica Distribuida.", notes = "MAEB'07 in Spanish, BOINC", } @InProceedings{Chavez:2018:GECCOcomp, author = "F. Chavez and F. {Fdez de Vega} and J. Diaz and J. A. Garcia and F. J. Rodriguez and P. A. Castillo", title = "Energy-consumption prediction of genetic programming algorithms using a fuzzy rule-based system", booktitle = "GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2018", editor = "Carlos Cotta and Tapabrata Ray and Hisao Ishibuchi and Shigeru Obayashi and Bogdan Filipic and Thomas Bartz-Beielstein and Grant Dick and Masaharu Munetomo and Silvino {Fernandez Alzueta} and Thomas Stuetzle and Pablo Valledor Pellicer and Manuel Lopez-Ibanez and Daniel R. Tauritz and Pietro S. Oliveto and Thomas Weise and Borys Wrobel and Ales Zamuda and Anne Auger and Julien Bect and Dimo Brockhoff and Nikolaus Hansen and Rodolphe {Le Riche} and Victor Picheny and Bilel Derbel and Ke Li and Hui Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Stephane Doncieux and Richard Duro and Joshua Auerbach and Harold {de Vladar} and Antonio J. Fernandez-Leiva and JJ Merelo and Pedro A. Castillo-Valdivieso and David Camacho-Fernandez and Francisco {Chavez de la O} and Ozgur Akman and Khulood Alyahya and Juergen Branke and Kevin Doherty and Jonathan Fieldsend and Giuseppe Carlo Marano and Nikos D. Lagaros and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Boris Naujoks and Vanessa Volz and Tea Tusar and Pascal Kerschke and Riyad Alshammari and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and John R. Woodward and Shin Yoo and John McCall and Nayat Sanchez-Pi and Luis Marti and Danilo Vasconcellos and Masaya Nakata and Anthony Stein and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Ivanoe {De Falco} and Antonio {Della Cioppa} and Ernesto Tarantino and Umberto Scafuri and P. G. M. Baltus and Giovanni Iacca and Ahmed Hallawa and Anil Yaman and Alma Rahat and Handing Wang and Yaochu Jin and David Walker and Richard Everson and Akira Oyama and Koji Shimoyama and Hemant Kumar and Kazuhisa Chiba and Pramudita Satria Palar", isbn13 = "978-1-4503-5764-7", pages = "9--10", address = "Kyoto, Japan", DOI = "doi:10.1145/3205651.3208216", publisher = "ACM", publisher_address = "New York, NY, USA", month = "15-19 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, Energy Consumption, Raspberry-Pi, Laptop, Tablet", abstract = "Energy awareness has gained momentum over the last decade in the software industry, as well as in environmentally concious society. Thus, algorithm designers and programmers are paying increasing attention this issue, particularly when hand-held devices are considered, given their battery-consuming characteristics. When we focus on Evolutionary Algorithms, few works have attempted to study the relationship between the main features of the algorithm, the problem to be solved and the underlying hardware where it runs. This work presents a preliminary analysis and modelling of energy consumption of EAs. We try to predict it by means of a fuzzy rule-based system, so that different devices are considered as well as a number of problems and Genetic Programming parameters. Experimental results performed show that the proposed model can predict energy consumption with very low error values.", notes = "Also known as \cite{3208216} GECCO-2018 A Recombination of the 27th International Conference on Genetic Algorithms (ICGA-2018) and the 23rd Annual Genetic Programming Conference (GP-2018)", } @InProceedings{Chavoya:2011:ITNG, author = "Arturo Chavoya and Cuauhtemoc Lopez-Martin and M. E. Meda-Campana", title = "Applying Genetic Programming for Estimating Software Development Effort of Short-scale Projects", booktitle = "Eighth International Conference on Information Technology: New Generations (ITNG 2011)", year = "2011", month = "11-13 " # apr, pages = "174--179", address = "Las Vegas, NV, USA", size = "6 pages", abstract = "Statistical regression and neural networks have frequently been used to estimate the development effort of both short and large software projects. In this paper, a genetic programming technique is used with the goal of estimating the effort required in the development of short-scale projects. Results obtained are compared with those generated using the first two techniques. A sample of 132 short-scale projects developed by 40 programmers was used for generating the three models, whereas another sample of 77 projects developed by 24 programmers was used for validating those three models. Accuracy results from the model obtained with genetic programming suggest that it could be used to estimate software development effort of short-scale projects when these projects are developed in a disciplined manner within a development controlled environment.", keywords = "genetic algorithms, genetic programming, SBSE, development controlled environment, neural networks, short scale projects, software development effort, statistical regression, software development management", DOI = "doi:10.1109/ITNG.2011.37", notes = "Also known as \cite{5945228}", } @Article{Chavoya:2012:PLOS, author = "Arturo Chavoya and Cuauhtemoc Lopez-Martin and Irma R. Andalon-Garcia and M. E. Meda-Campana", title = "Genetic Programming as Alternative for Predicting Development Effort of Individual Software Projects", journal = "PLoS ONE", year = "2012", volume = "7", number = "11", pages = "e50531", month = nov # " 30", keywords = "genetic algorithms, genetic programming, SBSE", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.1035.6477", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1035.6477", URL = "http://europepmc.org/backend/ptpmcrender.fcgi?accid%3DPMC3511534%26blobtype%3Dpdf", URL = "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0050531.pdf", DOI = "doi:10.1371/journal.pone.0050531", size = "10 pages", abstract = "Statistical and genetic programming techniques have been used to predict the software development effort of large software projects. In this paper, a genetic programming model was used for predicting the effort required in individually developed projects. Accuracy obtained from a genetic programming model was compared against one generated from the application of a statistical regression model. A sample of 219 projects developed by 71 practitioners was used for generating the two models, whereas another sample of 130 projects developed by 38 practitioners was used for validating them. The models used two kinds of lines of code as well as programming language experience as independent variables. Accuracy results from the model obtained with genetic programming suggest that it could be used to predict the software development effort of individual projects when these projects have been developed in a disciplined manner within a development-controlled environment.", } @Article{Chavoya:2013:IJACSA, author = "Arturo Chavoya and Cuauhtemoc Lopez-Martin and M. E. Meda-Campana", title = "Software Development Effort Estimation by Means of Genetic Programming", journal = "International Journal of Advanced Computer Science and Applications", year = "2013", number = "11", volume = "4", keywords = "genetic algorithms, genetic programming, SBSE, feedforward neural network, software effort estimation, statistical regression", publisher = "The Science and Information (SAI) Organization", bibsource = "OAI-PMH server at thesai.org", description = "International Journal of Advanced Computer Science and Applications(IJACSA), 4(11), 2013", language = "eng", oai = "oai:thesai.org:10.14569/IJACSA.2013.041115", URL = "http://thesai.org/Downloads/Volume4No11/Paper_15-Software_Development_Effort_Estimation_by_Means_of_Genetic_Programming.pdf", URL = "http://dx.doi.org/10.14569/IJACSA.2013.041115", DOI = "doi:10.14569/IJACSA.2013.041115", size = "8 pages", abstract = "In this study, a genetic programming technique was used with the goal of estimating the effort required in the development of individual projects. Results obtained were compared with those generated by a statistical regression and by a neural network that have already been used to estimate the development effort of individual software projects. A sample of 132 projects developed by 40 programmers was used for generating the three models and another sample of 77 projects developed by 24 programmers was used for validating the three models. Results in the accuracy of the model obtained from genetic programming suggest that it could be used to estimate software development effort of individual projects.", } @InProceedings{cheang2:2003:gecco, author = "Sin Man Cheang and Kin Hong Lee and Kwong Sak Leung", title = "Data Classification Using Genetic Parallel Programming", booktitle = "Genetic and Evolutionary Computation -- GECCO-2003", editor = "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller", year = "2003", pages = "1918--1919", address = "Chicago", publisher_address = "Berlin", month = "12-16 " # jul, volume = "2724", series = "LNCS", ISBN = "3-540-40603-4", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, Learning Classifier Systems, poster", DOI = "doi:10.1007/3-540-45110-2_88", abstract = "A novel Linear Genetic Programming (LGP) paradigm called Genetic Parallel Programming (GPP) has been proposed to evolve parallel programs based on a Multi-ALU Processor. It is found that GPP can evolve parallel programs for Data Classification problems. In this paper, five binary-class UCI Machine Learning Repository databases are used to test the effectiveness of the proposed GPP-classifier. The main advantages of employing GPP for data classification are: 1) speeding up evolutionary process by parallel hardware fitness evaluation; and 2) discovering parallel algorithms automatically. Experimental results show that the GPP-classifier evolves simple classification programs with good generalization performance. The accuracies of these evolved classifiers are comparable to other existing classification algorithms.", notes = "GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)", } @InProceedings{cheang:2003:gecco, author = "Sin Man Cheang and Kin Hong Lee and Kwong Sak Leung", title = "Improving Evolvability of Genetic Parallel Programming Using Dynamic Sample Weighting", booktitle = "Genetic and Evolutionary Computation -- GECCO-2003", editor = "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller", year = "2003", pages = "1802--1803", address = "Chicago", publisher_address = "Berlin", month = "12-16 " # jul, volume = "2724", series = "LNCS", ISBN = "3-540-40603-4", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, poster", DOI = "doi:10.1007/3-540-45110-2_72", abstract = "sample weighting effect on Genetic Parallel Programming (GPP) that evolves parallel programs to solve the training samples captured directly from a real-world system. The distribution of these samples can be extremely biased. Standard GPP assigns equal weights to all samples. It slows down evolution because crowded regions of samples dominate the fitness evaluation and cause premature convergence. This paper compares the performance of four sample weighting (SW) methods, namely, Equal SW (ESW), Class-equal SW (CSW), Static SW (SSW) and Dynamic SW (DSW) on five training sets. Experimental results show that DSW is superior in performance on tested problems.", notes = "GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)", } @InProceedings{cheang:gecco03lbp, title = "An Empirical Study of the Accelerating Phenomenon in Genetic Parallel Programming", pages = "54--61", author = "Sin Man Cheang and Kin Hong Lee and Kwong Sak Leung", year = "2003", address = "Chicago, USA", month = "12--16 " # jul, editor = "Bart Rylander", keywords = "genetic algorithms, genetic programming", booktitle = "Genetic and Evolutionary Computation Conference Late Breaking Papers", notes = "GECCO-2003LB", } @InProceedings{cheang:2003:edcpugpp, author = "Sin Man Cheang and Kin Hong Lee and Kwong Sak Leung", title = "Evolving data classification programs using genetic parallel programming", booktitle = "Proceedings of the 2003 Congress on Evolutionary Computation CEC2003", editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon", pages = "248--255", year = "2003", publisher = "IEEE Press", address = "Canberra", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "8-12 " # dec, organisation = "IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", keywords = "genetic algorithms, genetic programming, Acceleration, Classification algorithms, Concurrent computing, Data mining, Databases, Machine learning, Machine learning algorithms, Parallel programming, Registers, data analysis, learning (artificial intelligence), parallel programming, pattern classification, tree data structures, GPP-classifier, UCI machine learning repository databases, classification algorithms, data classification problems, data classification programs, evolutionary process, generalization performance, genetic parallel programming, linear genetic programming paradigm, multiALU processor, parallel algorithms, parallel hardware fitness evaluation, parallel programs", ISBN = "0-7803-7804-0", DOI = "doi:10.1109/CEC.2003.1299582", abstract = "A novel Linear Genetic Programming (Linear GP) paradigm called Genetic Parallel Programming (GPP) has been proposed to evolve parallel programs based on a Multi-ALU Processor. The GPP Accelerating Phenomenon, i.e. parallel programs are easier to be evolved than sequential programs, opens up a new two-step approach: 1) evolves a parallel program solution; and 2) serialises the parallel program to a equivalent sequential program. In this paper, five two-class UCI Machine Learning Repository databases are used to investigate the effectiveness of GPP. The main advantages to employ GPP for data classification are: 1) speeding up evolutionary process by parallel hardware fitness evaluation; 2) discovering parallel algorithms automatically; and 3) boosting evolutionary performance by the GPP Accelerating Phenomenon. Experimental results show that GPP evolves simple classification programs with good generalisation performance. The accuracies of these evolved classification programs are comparable to other existing classification algorithms.", notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.", } @InProceedings{Man:2003:Aswmtgpp, author = "Sin Man Cheang and Kin Hong Lee and Kwong Sak Leung", title = "Applying sample weighting methods to genetic parallel programming", booktitle = "Proceedings of the 2003 Congress on Evolutionary Computation CEC2003", editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon", pages = "928--935", year = "2003", publisher = "IEEE Press", address = "Canberra", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "8-12 " # dec, organisation = "IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", keywords = "genetic algorithms, genetic programming, Boolean functions, Clocks, Computer science, Computer science education, Concurrent computing, Educational programs, Evolutionary computation, Parallel programming, Silicon compounds, Boolean functions, learning (artificial intelligence), parallel programming, Boolean function, DSW, GPP, SSW, UCI medical data classification database, class-equal SW method, dynamic SW method, equal SW method, evolutionary algorithm, genetic parallel programming, real-world system, sample weighting method, static SW method, training sample, training set", ISBN = "0-7803-7804-0", notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.", DOI = "doi:10.1109/CEC.2003.1299766", abstract = "We investigate the sample weighting effect on Genetic Parallel Programming (GPP). GPP evolves parallel programs to solve the training samples in a training set. Usually, the samples are captured directly from a real-world system. The distribution of samples in a training set can be extremely biased. Standard GPP assigns equal weights to all samples. It slows down evolution because crowded regions of samples dominate the fitness evaluation causing premature convergence. This paper presents 4 sample weighting (SW) methods, i.e. Equal SW, Class-equal SW, Static SW (SSW) and Dynamic SW (DSW). We evaluate the 4 methods on 7 training sets (3 Boolean functions and 4 UCI medical data classification databases). Experimental results show that DSW is superior in performance on all tested problems. In the 5-input Symmetry Boolean function experiment, SSW and DSW boost the evolutionary performance by 465 and 745 times respectively. Due to the simplicity and effectiveness of SSW and DSW, they can also be applied to different population-based evolutionary algorithms.", } @InProceedings{cheang:2003:CIRAS, author = "Sin Man Cheang", title = "An Empirical Study of the {GPP} Accelerating Phenomenon", booktitle = "Proceedings of the second International Conference on Computational Intelligence, Robotics and Autonomous Systems -- CIRAS-2003", year = "2003", editor = "P. Vadakkepat and T. W. Wan and T. K. Chen and L. A. Poh", pages = "PS04--4--03", address = "Singapore", month = "15-18 " # dec, organisation = "Centre for Intelligent Control, National Univ. of Singapore", publisher = "National Univ. of Singapore", keywords = "genetic algorithms, genetic programming", abstract = "The Genetic Parallel Programming (GPP) is a novel Linear-structure Genetic Programming paradigm that learns parallel programs. We discover the GPP Accelerating Phenomenon, i.e. parallel programs are evolved faster than their counterpart sequential programs of identical functions. This paper presents an empirical study of Boolean function regression based on a Multi-ALU Processor that results in the phenomenon. We performed a series of random search experiments using different numbers of ALUs (w) and instructions (l). We identify that w (the degree of parallelism of the program) is the dominant factor that affects the searching performance. In a 3-input Boolean function experiment, searching a single-ALU program requires 875 times on average of the computational effort of an 8-ALU program. An investigation on the probabilities of finding solutions to different problem instances shows that parallel representation of programs can increase the probabilities of finding solutions to hard problems.", notes = "http://ciras.nus.edu.sg/2003/Proceedings/ProgramDec17.pdf", } @InProceedings{cheang:2004:eurogp, author = "Sin Man Cheang and Kin Hong Lee and Kwong Sak Leung", title = "Designing Optimal Combinational Digital Circuits Using a Multiple Logic Unit Processor", booktitle = "Genetic Programming 7th European Conference, EuroGP 2004, Proceedings", year = "2004", editor = "Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule", volume = "3003", series = "LNCS", pages = "23--34", address = "Coimbra, Portugal", publisher_address = "Berlin", month = "5-7 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-21346-5", size = "12 pages", DOI = "doi:10.1007/978-3-540-24650-3_3", abstract = "Genetic Parallel Programming (GPP) is a novel Genetic Programming paradigm. The GPP Accelerating Phenomenon, i.e. parallel programs are easier to be evolved than sequential programs, opens up a new approach to evolve solution programs in parallel forms. Based on the GPP paradigm, we developed a combinational digital circuit learning system, the GPP+MLP system. An optimal Multiple Logic Unit Processor (MLP) is designed to evaluate genetic parallel programs. To show the effectiveness of the proposed GPP+MLP system, four multi-output Binary arithmetic circuits are used. Experimental results show that both the gate counts and the propagation gate delays of the evolved circuits are less than conventional designs. For example, in a 3-bit multiplier experiment, we obtained a combinational digital circuit with 26 two-input logic gates in 6 gate levels. It uses 4 gates less than a conventional design.", notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004", } @PhdThesis{cheang:thesis, author = "Sin Man Cheang", title = "Genetic parallel programming", school = "The Chinese University of Hong Kong", year = "2005", address = "Hong Kong", month = mar, keywords = "genetic algorithms, genetic programming, Applied sciences, Parallel computing, Sample weighting, Computer science", isbn13 = "9780542235290", language = "English", URL = "https://search.proquest.com/docview/305346245", size = "257 pages", abstract = "This thesis investigates the design and implementation of a novel linear-structured Genetic Programming (GP) paradigm, Genetic Parallel Programming (GPP), in which a parallel architecture, Multi-Arithmetic-Logic-Unit Processor (MAP) is employed. The MAP is a MIMD, general-purpose register machine that can be implemented on modern Field Programmable Gate Arrays so that genetic parallel programs can be evaluated at high speed. Based on the parallel architecture of MAP, GPP evolves genetic programs in parallel form. This thesis presents a number of benchmark problems. The evolved solution programs are precise and compact. For human programmers, writing parallel programs is more difficult than writing sequential programs. However, an accelerating phenomenon in GPP, the GPP accelerating phenomenon, is observed. Experimental results show that GPP evolves parallel programs with less computational effort than that of their sequential counterparts. This creates a new approach to evolving a feasible problem solution program in parallel form and then serializes it into a sequential form if required. Since serialization is mechanical and its processing time is linear with respect to the size of the parallel program, the total learning time can be reduced significantly. In order to evolve parallel programs effectively and efficiently, this thesis also investigates different genetic operators to assist the evolution. These operators include Dynamic Sample Weighting (DSW), dual-phase fitness functions and special types of mutation for parallel programs. Since the samples in a training set are captured directly from a real-world system, the distribution of these samples can be extremely biased. DSW adjusts the weights of training samples dynamically according to their past frequency of hits. Experimental results show that DSW boosts the evolutionary performance significantly... To demonstrate the applicability of GPP, two application systems have been developed: (1) GPP Data Classification System (GPP-Classifier); and (2) GPP Logic Circuit Synthesizer (GPPLCS). The GPP-Classifier evolves MAP programs to classify data records in a database. The GPPLCS synthesizes combinational logic circuits directly from a truth table with different logic gates or RAM-based lookup-tables. High performance logic circuits are evolved and both their gate counts and propagation gate delays are less than that of the conventional designs... ... this thesis has made four major contributions: 1) parallel ; 2) revealing the GPP accelerating phenomenon; 3) inventing DSW 4) GPP Data Classification System a n d th e GPP Logic Circuit Synthesizer.", notes = "ProQuest Dissertations and Theses 3182145. Supervisors Kwong Sak Leung and Kin Hong Lee. proquest abstract was shorter than in pdf. Fibonacci", } @Article{Cheang:2006:EC, author = "Sin Man Cheang and Kwong Sak Leung and Kin Hong Lee", title = "Genetic Parallel Programming: Design and Implementation", journal = "Evolutionary Computation", year = "2006", volume = "14", number = "2", pages = "129--156", month = "Summer", keywords = "genetic algorithms, genetic programming, linear genetic programming, parallel processor architecture, MIMD, parallel assembly program, ALU MAP, GPP, Fibonacci recursive sequence", ISSN = "1063-6560", DOI = "doi:10.1162/evco.2006.14.2.129", size = "28 pages", abstract = "This paper presents a novel Genetic Parallel Programming (GPP) paradigm for evolving parallel programs running on a Multi-Arithmetic-Logic-Unit (Multi-ALU) Processor (MAP). The MAP is a Multiple Instruction-streams, Multiple Data-streams (MIMD), general-purpose register machine that can be implemented on modern Very Large-Scale Integrated Circuits (VLSIs) in order to evaluate genetic programs at high speed. For human programmers, writing parallel programs is more difficult than writing sequential programs. However, experimental results show that GPP evolves parallel programs with less computational effort than that of their sequential counterparts. It creates a new approach to evolving a feasible problem solution in parallel program form and then serialises it into a sequential program if required. The effectiveness and efficiency of GPP are investigated using a suite of 14 well-studied benchmark problems. Experimental results show that GPP speeds up evolution substantially.", notes = "Table 6 Fibonacci (FIB) experiment 'maximum 200 clock cycles (each program)'", } @Article{Cheang:2007:tec, author = "Sin Man Cheang and Kin Hong Lee and Kwong Sak Leung", title = "Applying Genetic Parallel Programming to Synthesize Combinational Logic Circuits", journal = "IEEE Transactions on Evolutionary Computation", year = "2007", volume = "11", number = "4", pages = "503--520", month = aug, keywords = "genetic algorithms, genetic programming, FPGA, Circuit design, digital circuits, evolvable hardware, parallel programming", ISSN = "1389-2576", DOI = "doi:10.1109/TEVC.2006.884044", size = "18 pages", abstract = "Experimental results show that parallel programs can be evolved more easily than sequential programs in genetic parallel programming (GPP). GPP is a novel genetic programming paradigm which evolves parallel program solutions. With the rapid development of lookup-table-based (LUT-based) field programmable gate arrays (FPGAs), traditional circuit design and optimisation techniques cannot fully exploit the LUTs in LUT-based FPGAs. Based on the GPP paradigm, we have developed a combinational logic circuit learning system, called GPP logic circuit synthesiser (GPPLCS), in which a multilogic-unit processor is used to evaluate LUT circuits. To show the effectiveness of the GPPLCS, we have performed a series of experiments to evolve combinational logic circuits with two- and four-input LUTs. In this paper, we present eleven multi-output Boolean problems and their evolved circuits. The results show that the GPPLCS can evolve more compact four-input LUT circuits than the well-known LUT-based FPGA synthesis algorithms.", } @InProceedings{Chee:2014:ROMA, author = "Wei Shun Chee and Jason Teo", booktitle = "2014 IEEE International Symposium on Robotics and Manufacturing Automation (ROMA)", title = "Using a co-evolutionary approach to automatically generate vertical undulation and lateral rolling motions for snake-like modular robot", year = "2014", pages = "236--241", abstract = "This paper explores the use of evolutionary algorithm approach to automatically design and optimise the snake-like modular robot to acquire with the vertical undulation locomotion and lateral rolling moving behaviour. A hybridized Genetic Programming and self-adaptive Differential Evolution algorithm is implemented in this work to simultaneously co-evolve both the morphology and controller of the snake-like modular robot throughout the artificial evolutionary process. This paper also illustrates on how the overall structure and control strategy of the snake-like modular robot is being designed in order for the snake-like modular robot to perform the particular locomotion. Moreover, different fitness functions had also been modelled for each locomotion experiment in computing the performance score of the snake-like modular robot. Interestingly, it was found out that the snake-like modular robot can actually travel for longer distance using vertical undulation locomotion. It was also found out that the rolling movement of the snake-like modular robot can be achieved by motors attached only in pitch and yaw axis. In conclusion, promising results were obtained in this work showing that the co-evolving evolutionary algorithm illustrated in this work is feasible to be implemented to automatically design and optimise the modular robot to evolve with various locomotion capabilities.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ROMA.2014.7295894", month = dec, notes = "Also known as \cite{7295894}", } @InProceedings{Chee:2014:ICAIET, author = "Wei Shun Chee and Jason Teo", booktitle = "4th International Conference on Artificial Intelligence with Applications in Engineering and Technology (ICAIET)", title = "Simultaneous Evolutionary-Based Optimization of Controller and Morphology of Snake-Like Modular Robots", year = "2014", pages = "37--42", abstract = "This paper explores the use of evolutionary algorithm approach to automatically design and optimise the snake-like modular robot to automatically design and optimise the snake-like modular robot to acquire the forward moving behaviour. A hybridized Genetic Programming and self-adaptive Differential Evolution algorithm is implemented to co-evolving both the morphology and controller of the robot throughout the artificial evolutionary process. Two different artificial evolutionary experiments have been conducted in this paper by using the classic DE mutation technique (DE/rand/1/bin) and a customized DE mutation technique with different mutation differential operation. It was found out that the customized DE mutation approach is more effective in co-evolving both the morphology and controller for the snake-like modular robot to acquire forward moving behaviour. Moreover, from the analysis conducted on the results obtained throughout the evolutionary process, interesting findings were discovered on the evolved morphology and moving behaviour of the snake-like modular robot. In conclusion, promising results were shown in this work which suggests that the co-evolving evolutionary algorithm presented in this work is an alternative method and feasible to be implemented to automatically design and optimise the modular robot for the moving behaviour by co-evolving both the morphology and controller of the modular robot.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICAIET.2014.16", month = dec, notes = "Also known as \cite{7351810}", } @Article{cheema:2002:BTP, author = "Jitender Jit Singh Cheema and Narendra V. Sankpal and Sanjeev S. Tambe and Bhaskar D. Kulkarni", title = "Genetic Programming Assisted Stochastic Optimization Strategies for Optimization of Glucose to Gluconic Acid Fermentation", journal = "Biotechnology Progress", year = "2002", volume = "18", number = "6", pages = "1356--1365", keywords = "genetic algorithms, genetic programming", ISSN = "8756-7938", URL = "http://www3.interscience.wiley.com/journal/121399381/abstract", DOI = "doi:10.1021/bp015509s", abstract = "This article presents two hybrid strategies for the modeling and optimization of the glucose to gluconic acid batch bioprocess. In the hybrid approaches, first a novel artificial intelligence formalism, namely, genetic programming (GP), is used to develop a process model solely from the historic process input-output data. In the next step, the input space of the GP-based model, representing process operating conditions, is optimized using two stochastic optimization (SO) formalisms, viz., genetic algorithms (GAs) and simultaneous perturbation stochastic approximation (SPSA). These SO formalisms possess certain unique advantages over the commonly used gradient-based optimization techniques. The principal advantage of the GP-GA and GP-SPSA hybrid techniques is that process modeling and optimization can be performed exclusively from the process input-output data without invoking the detailed knowledge of the process phenomenology. The GP-GA and GP-SPSA techniques have been employed for modeling and optimization of the glucose to gluconic acid bioprocess, and the optimized process operating conditions obtained thereby have been compared with those obtained using two other hybrid modeling-optimization paradigms integrating artificial neural networks (ANNs) and GA/SPSA formalisms. Finally, the overall optimized operating conditions given by the GP-GA method, when verified experimentally resulted in a significant improvement in the gluconic acid yield. The hybrid strategies presented here are generic in nature and can be employed for modeling and optimization of a wide variety of batch and continuous bioprocesses.", notes = " PMID: 12467472 [PubMed - indexed for MEDLINE] S8756-7938(01)05509-6 ACS Publications Division, American Chemical Society and American Institute of Chemical Engineers Chemical Engineering Division, National Chemical Laboratory, Pune 411008, India", } @InProceedings{Chellapilla:1997:eptm, author = "Kumar Chellapilla", title = "Evolutionary Programming with Tree Mutations: Evolving Computer Programs without Crossover", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "evolutionary programming and evolution strategies", pages = "431--438", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", notes = "GP-97 non-standard initialisation of initial pop, 6 mutation operators, no crossover 6-bit multiplexor, simple symbolic regression x+x**2+x**3+x**4, artificial ant (Santa Fe Trail), cart centering", } @InProceedings{chellapilla:1998:enlbtatuEP, author = "Kumar Chellapilla", title = "Evolving Nonlinear Controllers for Backing up a Truck-and-Trialer Using Evolutionary Programming", booktitle = "Evolutionary Programming VII: Proceedings of the Seventh Annual Conference on Evolutionary Programming", year = "1998", editor = "V. William Porto and N. Saravanan and D. Waagen and A. E. Eiben", volume = "1447", series = "LNCS", pages = "417--426", address = "Mission Valley Marriott, San Diego, California, USA", publisher_address = "Berlin", month = "25-27 " # mar, publisher = "Springer-Verlag", keywords = "evolutionary programming", ISBN = "3-540-64891-7", DOI = "doi:10.1007/BFb0040753", notes = "EP-98. ", } @InProceedings{chellapilla:1998:agnoclbbEP, author = "Kumar Chellapilla", title = "Automatic Generation of Nonlinear Optimal Control Laws for Broom Balancing using Evolutionary Programming", booktitle = "Proceedings of the 1998 IEEE World Congress on Computational Intelligence", year = "1998", pages = "195--200", address = "Anchorage, Alaska, USA", month = "5-9 " # may, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, 3D broom balancing problem, automatic nonlinear optimal control law generation, bang-bang force direction, bang-bang type control laws, broom balancing, evolutionary computation methods, evolutionary programming, inverted pendulum problem, mutation operators, state variables, time optimal nonlinear control strategy, unseen input states, bang-bang control, nonlinear control systems, optimal control, optimisation", ISBN = "0-7803-4869-9", file = "c034.pdf", DOI = "doi:10.1109/ICEC.1998.699500", size = "6 pages", abstract = "This paper explores the use of mutation operators with evolutionary programming (EP) to automatically generate time optimal 'bang-bang' type control laws for the three dimensional broom balancing (inverted pendulum) problem. EP produces a time optimal nonlinear control strategy that takes the state variables as input and determines the direction of the 'bang-bang' force to be applied. Preliminary results indicate that the control laws generated are capable of generalising over previously unseen input states and compare well with nonlinear control laws that were generated using other evolutionary computation methods.", notes = "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE World Congress on Computational Intelligence. Comparison with \cite{koza:book} results. Also known as \cite{699500}", } @InProceedings{chellapilla:1998:piempwsx, author = "Kumar Chellapilla", title = "A Preliminary Investigation into Evolving Modular Programs without Subtree Crossover", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "23--31", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, EP, ADF, parity", ISBN = "1-55860-548-7", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1998/chellapilla_1998_piempwsx.pdf", size = "10 pages", abstract = "ADF like modularity in evolutionary programming demonstrated on parity problems of various sizes", notes = "GP-98", } @InProceedings{chellapilla:1998:elsoEP, author = "Kumar Chellapilla and Hemanth Birru and Rao Sathyanarayan", title = "Effectivenss of Local Search Operators in Evolutionary Programming", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "753--761", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "evolutionary programming", ISBN = "1-55860-548-7", notes = "GP-98", } @Article{Chellapilla:1998:eptm, author = "Kumar Chellapilla", title = "Evolving Computer Programs without Subtree Crossover", journal = "IEEE Transactions on Evolutionary Computation", year = "1997", volume = "1", number = "3", pages = "209--216", month = sep, keywords = "genetic algorithms, genetic programming, symbolic expressions, Evolutionary Programming, variation operators", ISSN = "1089-778X", DOI = "doi:10.1109/4235.661552", size = "8 pages", abstract = "An evolutionary programming procedure is used for optimising computer programs in the form of symbolic expressions. Six tree mutation operators are proposed. Recombination operators such as crossover are not included. The viability and efficiency of the method is extensively investigated on a set of well-studied problems. The evidence indicates that the technique is not only viable but is indeed capable of evolving good computer programs. The results compare well with other evolutionary methods that rely on crossover to solve the same problems", notes = "negative results on building block hypothesis, C++ code available, Compares use of EP using 6 types of tree mutation with GP on: 6-mux, 3, 4, 5, 6 parity, symbolic regression, two box, two spirals, Santa Fe trail artificial ant, cart centering, 4 variation on broom balancing. In general EP wins in terms of Effort to find the solution. Gives algorithm used to create initial random trees", } @PhdThesis{Chellapilla:thesis, author = "Kumar H. Chellapilla", title = "Designing Effective Evolutionary Computations", school = "Electrical Engineering, University of California, San Diego", year = "2005", address = "USA", keywords = "genetic algorithms, genetic programming", isbn13 = "9780542572180", URL = "http://search.proquest.com/docview/305003505", size = "606 pages", abstract = "Evolutionary algorithms offer a practical approach to solving difficult real-world problems. In many problem domains, these are the only possible approaches with potential for effectively searching through complex solution spaces. For novel problem domains wherein previous research efforts are sparse or problem domain expertise is in its infancy, evolutionary algorithms offer strong alternatives for exploring the solution spaces and also gaining insights into effectively solving the problem. The principal roadblock in conventional practice is the lack of a specific approach which permits one to simultaneously control an algorithm's representation, population variation operators and population selection operators. An approach based on mathematically sound principles is adopted in this thesis to provide asymptotic guarantees on evolutionary algorithm performance followed by useful real-time methods for improving the rate of convergence. In particular, the evolutionary algorithm is decomposed into its constituent representation, population variation, and population selection operators. The population variation operators are further broken down into solution variation operators. Each component is independently analysed without being constrained by an overall architecture for the evolutionary algorithm. Each component presents several alternatives that can be chosen independently to control desired properties of the evolutionary algorithm. A new mathematical model for analysing evolutionary algorithms is developed, and necessary and sufficient conditions on the variation and selection operators for asymptotic convergence are derived. Fitness distributions and fitness distribution feature based heuristics are presented to improve the rate of convergence of an evolutionary algorithm. This thesis also presents a wide array of empirical results to demonstrate the utility, effectiveness, and applicability of the new theory. Within the new framework, evolutionary algorithms are applied to solve real, discrete and mixed parameter optimization problems. Evolutionary algorithms that guarantee asymptotic convergence are designed to solve problems involving structures such as parse trees and finite state machines. Co-evolutionary algorithms are designed to evolve an expert checkers player that rated 2045 against human checkers players. Fitness distribution heuristics are used to tune an evolutionary algorithm for improved rate of convergence for solving the travelling salesman problem.", notes = "Supervisor Anthony Sebald UMI Microform 3208643", } @InProceedings{Chen:2014:ICNC, author = "Bili Chen and Wenhua Zeng and Yangbin Lin", booktitle = "10th International Conference on Natural Computation (ICNC 2014)", title = "Applications of artificial intelligence technologies in credit scoring: A survey of literature", year = "2014", month = aug, pages = "658--664", size = "7 pages", abstract = "We covers support vector machines, artificial neural networks, genetic algorithms, genetic programming algorithms and their hybrids.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICNC.2014.6975914", notes = "Also known as \cite{6975914}", } @InProceedings{Chen:2009:CINC, author = "Bing-Rui Chen and Xia-Ting Feng and Cheng-Xiang Yang", title = "A Self-adapting Algorithm for Identifying Rheology Model and Its Parameters of Rock Mass", booktitle = "International Conference on Computational Intelligence and Natural Computing, CINC '09", year = "2009", month = jun, volume = "2", pages = "478--481", keywords = "genetic algorithms, genetic programming, Jinping-2 hydropower station, chaos-genetic algorithm, hybrid genetic programming, optimal rheological model, rheology model identification, rock mass parameters, self-adapting system identification method, tentative model, identification, natural resources, rheology", DOI = "doi:10.1109/CINC.2009.39", abstract = "As it is difficult to previously determine rockmass rheology constitutive model using phenomena methods of mechanics, so a new self-adapting system identification method, a hybrid genetic programming (GP) with the chaos-genetic algorithm (CGA) based on self-rheological characteristic of rock mass, is proposed. Genetic programming is used for exploring the model's structure and the chaos-genetic algorithm is produced to identify parameters (coefficients) in the tentative model. The optimal rheological model is determined by mechanical and rheological characteristic, important expertise etc and can describe rheological behavior of identified rock mass perfectly. The assistant tunnel B of Jinping-2 hydropower station is used as an example for verifying the proposed method. The results show that the algorithm is feasible and has great potential in finding new rheological models.", notes = "Also known as \cite{5230917}", } @PhdThesis{Carla_Chen_Thesis, author = "Carla Chia-Ming Chen", title = "Bayesian methodology for genetics of complex diseases", school = "Past, QUT Faculties \& Divisions, Faculty of Science and Technology, Queensland University of Technology", year = "2010", address = "Australia", keywords = "genetic algorithms, genetic programming, gene expression programming, Bayesian, statistics, genetics, phenotype analysis, complex diseases, complex etiology, model comparison, latent class analysis, grade of membership, fuzzy clustering, item response theory, migraine, twin study, heritability, genome-wide linkage analysis, deviance information criteria, model averaging, MCMC, genomewide association studies, epistasis, logistic regression, stochastic search algorithm, case-control studies, Type I diabetes, single nucleotide polymorphism, logic tree, logicFS, Monte Carlo logic regression, genetic programming for association study, random forest, GENICA", URL = "http://eprints.qut.edu.au/43357/", URL = "http://eprints.qut.edu.au/43357/1/Carla_Chen_Thesis.pdf", size = "291 pages", abstract = "Genetic research of complex diseases is a challenging, but exciting, area of research. The early development of the research was limited, however, until the completion of the Human Genome and HapMap projects, along with the reduction in the cost of genotyping, which paves the way for understanding the genetic composition of complex diseases. In this thesis, we focus on the statistical methods for two aspects of genetic research: phenotype definition for diseases with complex etiology and methods for identifying potentially associated Single Nucleotide Polymorphisms (SNPs) and SNP-SNP interactions. With regard to phenotype definition for diseases with complex etiology, we firstly investigated the effects of different statistical phenotyping approaches on the subsequent analysis. In light of the findings, and the difficulties in validating the estimated phenotype, we proposed two different methods for reconciling phenotypes of different models using Bayesian model averaging as a coherent mechanism for accounting for model uncertainty. In the second part of the thesis, the focus is turned to the methods for identifying associated SNPs and SNP interactions. We review the use of Bayesian logistic regression with variable selection for SNP identification and extended the model for detecting the interaction effects for population based case-control studies. In this part of study, we also develop a machine learning algorithm to cope with the large scale data analysis, namely modified Logic Regression with Genetic Program (MLR-GEP), which is then compared with the Bayesian model, Random Forests and other variants of logic regression.", notes = "ID Code: 43357 Supervisors: Mengersen, Kerrie and Keith, Jonathan", } @Article{Chen:2011:TCBB, author = "Carla Chia-Ming Chen and Holger Schwender and Jonathan Keith and Robin Nunkesser and Kerrie Mengersen and Paula Macrossan", title = "Methods for Identifying SNP Interactions: A Review on Variations of Logic Regression, Random Forest and {Bayesian} Logistic Regression", journal = "IEEE/ACM Transactions on Computational Biology and Bioinformatics", year = "2011", volume = "8", number = "6", pages = "1580--1591", month = nov # "-" # dec, keywords = "genetic algorithms, genetic programming, Gene Expression Programming, Logic regressions, Genetic Programming for Association Studies, Modified Logic Regression-Gene Expression Programming, Random Forest, Bayesian logistic regression with stochastic search algorithm, candidate gene search", ISSN = "1545-5963", DOI = "doi:10.1109/TCBB.2011.46", size = "12 pages", abstract = "Due to advancements in computational ability, enhanced technology and a reduction i the price of genotyping, more data are being generated for understanding genetic associations with diseases and disorders. However, with the availability of large data sets comes the inherent challenges of new methods of statistical analysis and modelling. Considering a complex phenotype may be the effect of a combination of multiple loci, various statistical methods have been developed for identifying genetic epistasis effects. Among these methods, logic regression (LR) is an intriguing approach incorporating tree-like structures. Various methods have built on the original LR to improve different aspects of the model. In this study, we review four variations of LR, namely Logic Feature Selection, Monte Carlo Logic Regression, Genetic Programming for Association Studies and Modified Logic Regression-Gene Expression Programming, and investigate the performance of each method using simulated and real genotype data. We contrast these with another tree-like approach, namely Random Forests, and a Bayesian logistic regression with stochastic search variable selection.", notes = "Also known as \cite{5728791}", } @Article{Chen:2013:JH, author = "Chang-Shian Chen and You-Da Jhong and Ting-Ying Wu and Shien-Tsung Chen", title = "Typhoon event-based evolutionary fuzzy inference model for flood stage forecasting", journal = "Journal of Hydrology", volume = "490", month = "20 " # may, pages = "134--143", year = "2013", keywords = "genetic algorithms, genetic programming, Fuzzy inference, Flood stage forecasting", ISSN = "0022-1694", DOI = "doi:10.1016/j.jhydrol.2013.03.033", URL = "http://www.sciencedirect.com/science/article/pii/S0022169413002424", abstract = "This study proposes an evolutionary fuzzy inference model that combines a fuzzy inference model, genetic programming (GP), and a genetic algorithm (GA) to forecast flood stages during typhoons. The number of fuzzy inference rules in the proposed approach is based on the number of typhoon flood events. The consequent part of the rule was formed by constructing GP models that depict the rainfall-stage relationship of a specific flood event, whereas the GA was used to search the parameters of the fuzzy membership functions in the premise part of the rule. This study uses the proposed event-based evolutionary fuzzy inference model to forecast the typhoon flood stages of Wu River in Taiwan. Forecasting results based on stage hydrographs and performance indices verify the forecasting ability of the proposed model. This study also identifies the weights of triggered fuzzy rules during the fuzzy inference process, showing that a fuzzy rule is triggered according to the characteristics of the flood event that forms the rule. Moreover, physical explanation of the proposed evolutionary fuzzy inference model was discussed.", notes = "See also \cite{Ting-Ying_Wu:masters} (in Chinese)", } @InProceedings{Chen:2017:ICNC-FSKD, author = "Chen Chen and Changtong Luo and Zonglin Jiang", booktitle = "2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)", title = "Elite bases regression: A real-time algorithm for symbolic regression", year = "2017", pages = "529--535", abstract = "Symbolic regression is an important but challenging research topic in data mining. It can detect the underlying mathematical models. Genetic programming (GP) is one of the most popular methods for symbolic regression. However, its convergence speed might be too slow for large scale problems with a large number of variables. This drawback has become a bottleneck in practical applications. In this paper, a new non-evolutionary real-time algorithm for symbolic regression, Elite Bases Regression (EBR), is proposed. EBR generates a set of candidate basis functions coded with parse-matrix in specific mapping rules. Meanwhile, a certain number of elite bases are preserved and updated iteratively according to the correlation coefficients with respect to the target model. The regression model is then spanned by the elite bases. A comparative study between EBR and a recent proposed machine learning method for symbolic regression, Fast Function eXtraction (FFX), are conducted. Numerical results indicate that EBR can solve symbolic regression problems more effectively.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/FSKD.2017.8393325", month = jul, notes = "State Key Laboratory of High Temperature Gas Dynamics, Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, China Also known as \cite{8393325}", } @InProceedings{Chen:2017:ISCID, author = "Chen Chen and Changtong Luo and Zonglin Jiang", booktitle = "2017 10th International Symposium on Computational Intelligence and Design (ISCID)", title = "Fast Modeling Methods for Complex System with Separable Features", year = "2017", volume = "1", pages = "201--204", abstract = "Data-driven modelling plays an increasingly important role in different areas of engineering. For most of existing methods, such as genetic programming (GP), the convergence speed might be too slow for large scale problems with a large number of variables. Fortunately, in many applications, the target models are separable in some sense. In this paper, we analyse different types of separability and establish a generalised separable model (GSM). In order to get the structure of the GSM, a multi-level block search method is proposed, in which the target model is decomposed into a number of blocks, further into minimal blocks and factors. Compare to the conventional GP, the new method can make large reductions to the search space. The minimal blocks and factors are optimised and assembled with a global optimisation search engine, low dimensional simplex evolution (LDSE). An extensive study between the proposed method and a state-of-the-art data-driven fitting tool, Eureqa, has been presented with several man-made problems. Test results indicate that the proposed method is more effective and efficient under all the investigated cases.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ISCID.2017.144", month = dec, notes = "School of Engineering Sciences, University of Chinese Academy of Sciences, Beijing 100049, China Also known as \cite{8275752}", } @Article{CHEN20181973, author = "Chen Chen and Changtong Luo and Zonglin Jiang", title = "Block building programming for symbolic regression", journal = "Neurocomputing", year = "2018", volume = "275", pages = "1973--1980", month = "31 " # jan, keywords = "genetic algorithms, genetic programming, Symbolic regression, Separable function, Block building programming", ISSN = "0925-2312", URL = "http://www.sciencedirect.com/science/article/pii/S0925231217316983", DOI = "doi:10.1016/j.neucom.2017.10.047", abstract = "Symbolic regression that aims to detect underlying data-driven models has become increasingly important for industrial data analysis. For most existing algorithms such as genetic programming (GP), the convergence speed might be too slow for large-scale problems with a large number of variables. This situation may become even worse with increasing problem size. The aforementioned difficulty makes symbolic regression limited in practical applications. Fortunately, in many engineering problems, the independent variables in target models are separable or partially separable. This feature inspires us to develop a new approach, block building programming (BBP). BBP divides the original target function into several blocks, and further into factors. The factors are then modelled by an optimization engine (e.g. GP). Under such circumstances, BBP can make large reductions to the search space. The partition of separability is based on a special method, block and factor detection. Two different optimization engines are applied to test the performance of BBP on a set of symbolic regression problems. Numerical results show that BBP has a good capability of structure and coefficient optimization with high computational efficiency.", } @Article{CHEN:2018:ESA, author = "Chen Chen and Changtong Luo and Zonglin Jiang", title = "A multilevel block building algorithm for fast modeling generalized separable systems", journal = "Expert Systems with Applications", volume = "109", pages = "25--34", year = "2018", keywords = "genetic algorithms, genetic programming, Symbolic regression, Generalized separability, Multilevel block building", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2018.05.021", URL = "http://www.sciencedirect.com/science/article/pii/S0957417418303142", abstract = "Symbolic regression is an important application area of genetic programming (GP), aimed at finding an optimal mathematical model that can describe and predict a given system based on observed input-response data. However, GP convergence speed towards the target model can be prohibitively slow for large-scale problems containing many variables. With the development of artificial intelligence, convergence speed has become a bottleneck for practical applications. In this paper, based on observations of real-world engineering equations, generalized separability is defined to handle repeated variables that appear more than once in the target model. To identify the structure of a function with a possible generalized separability feature, a multilevel block building (MBB) algorithm is proposed in which the target model is decomposed into several blocks and then into minimal blocks and factors. The minimal factors are relatively easy to determine for most conventional GP or other non-evolutionary algorithms. The efficiency of the proposed MBB has been tested by comparing it with Eureqa, a state-of-the-art symbolic regression tool. Test results indicate MBB is more effective and efficient; it can recover all investigated cases quickly and reliably. MBB is thus a promising algorithm for modeling engineering systems with separability features", } @InProceedings{chen:2009:CRTMCDM, author = "Chin-Yi Chen and Jih-Jeng Huang and Gwo-Hshiung Tzeng", title = "Nonlinear Deterministic Frontier Model Using Genetic Programming", booktitle = "Cutting-Edge Research Topics on Multiple Criteria Decision Making", year = "2009", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-642-02298-2_111", DOI = "doi:10.1007/978-3-642-02298-2_111", } @MastersThesis{oai:NSYSU:etd-0902110-103900, author = "Chun-jen Chen", title = "A New Fitness Function for Evaluating the Quality of Predicted Protein Structures", school = "National Sun Yat-sen University", year = "2010", address = "Taiwan", month = "2 " # sep, keywords = "genetic algorithms, genetic programming, prediction, tertiary structure, protein", URL = "https://etd.lis.nsysu.edu.tw/ETD-db/ETD-search-c/view_etd?URN=etd-0902110-103900", abstract = "For understanding the function of a protein, the protein structure plays an important role. The prediction of protein structure from its primary sequence has significant assistance in bioinformatics. Generally, the real protein structures can be reconstructed by some costly techniques, but predicting the protein structures helps us guess the functional expression of a protein in advance. In this thesis, we develop three terms as the materials of the fitness function that can be successfully used in protein backbone structure prediction. In the result of this thesis, it shows that over 80 percent of good values calculated from our fitness function, which are generated by the genetic programming, are better than the average in the CASP8.", notes = "In English Supervisor: Chang-Biau Yang", } @InProceedings{chen:2009:SMC, author = "Ci Chen and Shingo Mabu and Chuan Yue and Kaoru Shimada and Kotaro Hirasawa", title = "Network intrusion detection using fuzzy class association rule mining based on genetic network programming", booktitle = "IEEE International Conference on Systems, Man and Cybernetics, SMC 2009", year = "2009", pages = "60--67", address = "San Antonio, Texas, USA", month = oct, keywords = "genetic algorithms, genetic programming, Internet, anomaly detection, computer systems, directed graph structure, evolutionary optimization, fuzzy class association rule mining, fuzzy set theory, genetic network programming, machine learning, network intrusion detection, Internet, data mining, security of data", DOI = "doi:10.1109/ICSMC.2009.5346328", ISSN = "1062-922X", abstract = "Computer systems are exposed to an increasing number and type of security threats due to the expanding of Internet in recent years. How to detect network intrusions effectively becomes an important techniques. This paper presents a novel fuzzy class association rule mining method based on Genetic Network Programming (GNP) for detecting network intrusions. GNP is an evolutionary optimization techniques, which uses directed graph structures as genes instead of strings (Genetic Algorithm) or trees (Genetic Programming), leading to creating compact programs and implicitly memorizing past action sequences. By combining fuzzy set theory with GNP, the proposed method can deal with the mixed database which contains both discrete and continuous attributes. And it can be flexibly applied to both misuse and anomaly detection in Network Intrusion Detection Problem. Experimental results with KDD99Cup and DAPRA98 databases from MIT Lincoln Laboratory show that the proposed method provides a competitively high detection rate compared with other machine learning techniques.", notes = "Also known as \cite{5346328}", } @PhdThesis{etd-0114108-184337, author = "Chih-Yung Chen", title = "The Studies of Artificial Intelligent Technology and Its Applications", school = "Graduate School of Electrical Engineering, I-Shou University", year = "2007", address = "Kaohsiung, Taiwan", month = "8 " # dec, keywords = "genetic algorithms, EHW, Image Processing, Fuzzy Control, Neural Network, Evolutionary Hardware, Artificial Intelligence", URL = "http://ethesys.isu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0114108-184337", URL = "http://ethesys.isu.edu.tw/ETD-db/ETD-search/getfile?URN=etd-0114108-184337&filename=etd-0114108-184337.pdf", size = "115 pages", abstract = "This thesis focuses on the concept of system design by using artificial intelligent (AI) techniques. Four different research topics were studied. For each topic, in order to achieve the condition and goal of the system's request, all the problems were firstly modeled and then solved based on the AI techniques. The proposed approaches could sufficiently evidence the importance of AI design methodology in modern system design area. Firstly, in the research of the evolutionary hardware design, a new digital circuit genetic coding method based on genetic algorithm was proposed. Such a coding method is more flexible in the real application. Its variable structure can make it express the floor plan and routing of digital components easier. In the studies of image processing and computer vision, the first part is about a new face detection method which consists of the fast ellipse detect algorithm and the probabilistic neural network based color classifier. Cooperated with the servo motor controllers designed by fuzzy theory, the proposed face tracking system can reach the goal for the real-time using. The second part is about the study of automatic white balancing. In this part, a hybrid neural model was developed for estimating the illuminate of an image and then performing the automatic white balancing procedure according to estimated illuminate. The third part is about the digital camera auto-focusing system. In this part, the developed passive auto-focusing system could measure the sharpness value of a capture scene, and then predict the best focused position by a self-organized map based lens controller. Such a focusing system not only can move the adjustable lens to the best position, but also can save the time of focus searching. Through the examples of real work design we proposed, AI techniques in each application could be clearly described and easily understood. These researches not only show the feasibility and superiority of AI algorithm in the real system design, but also make a great improvement in comparison with the traditional design approaches. In our experiments, all studies were implemented by the software, firmware or hardware. In addition, they were also carried out by several ways, including simulation, embedded system or integrated circuit, respectively.", } @Article{Chen2009634, author = "Chih-Yung Chen and Rey-Chue Hwang", title = "A new variable topology for evolutionary hardware design", journal = "Expert Systems with Applications", volume = "36", number = "1", pages = "634--642", year = "2009", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2007.09.017", URL = "http://www.sciencedirect.com/science/article/B6V03-4PV2RVX-6/2/6aa751f84c76e323ab6ddab36f70e63d", keywords = "genetic algorithms, genetic programming, evolvable hardware, Evolutionary hardware design, Slicing structure, Routing graph", abstract = "In this paper, a novel variable topology for evolutionary hardware design is proposed. The slicing structure and routing graph are integrated into the design of evolutionary hardware. With off-line gate-level samples, simulation results clearly demonstrate the validity of this new approach performed as superior as existing methods in the logic circuit optimization. Compare with the random circuit matrix method, our approach uses less code length for evolutionary hardware description. The method we proposed could be taken as an alternative way for possible evolutionary hardware applications in the future.", notes = "EHW, GP, graph based GA", } @Article{CHEN:2020:Measurement, author = "Cong Chen and Tianhua Xu and Guang Wang and Bo Li", title = "Railway turnout system {RUL} prediction based on feature fusion and genetic programming", journal = "Measurement", volume = "151", pages = "107162", year = "2020", ISSN = "0263-2241", DOI = "doi:10.1016/j.measurement.2019.107162", URL = "http://www.sciencedirect.com/science/article/pii/S0263224119310280", keywords = "genetic algorithms, genetic programming, RUL prediction, Railway turnout system, Feature fusion", abstract = "The remaining useful life (RUL) prediction of railway turnout systems (RTS) is very important to avoid unplanned shutdowns and reduce labor costs for the normal operation of railways. One key challenge on RUL prediction is how to construct an appropriate health indicator (HI) that can be used to infer conditions of RTS. Existing methods usually adopt some inherit merits (e.g., monotonicity, trendability, and robustness), and their prediction results lack real-world physical meaning due to their {"}black-box-like{"} property. In this paper, we present a novel feature fusion method for RUL prediction, which is able to capture the relationship between RUL and HI. A variant correlation-based feature selection method is used to extract features, which has the potential to depict the degradation process optimally, and then the selected features are fused by Auto-Associative Kernel Regression (AAKR) for prediction. To reduce the noise interference, the extracted features and the combined HI are all smoothed by using the locally weighted regression. Finally, a genetic programming (GP) algorithm is employed to predict the RUL of RTS. The proposed method is extensively tested on two turnout machine degradation datasets, and the results show that the proposed approach is effective for RUL prediction of RTS", notes = "State Key Lab of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China", } @Article{CHEN:2023:swevo, author = "Haojie Chen and Xinyu Li and Liang Gao", title = "A guided genetic programming with attribute node activation encoding for resource constrained project scheduling problem", journal = "Swarm and Evolutionary Computation", volume = "83", pages = "101418", year = "2023", ISSN = "2210-6502", DOI = "doi:10.1016/j.swevo.2023.101418", URL = "https://www.sciencedirect.com/science/article/pii/S2210650223001918", keywords = "genetic algorithms, genetic programming, Resource constrained project scheduling, Guided search, Attribute Node activation encoding, Priority rule", abstract = "The large-scale characteristic and complex logic between activities have made priority rules (PRs) are more favoured in actual project scheduling, resulting in the increasing attention of genetic programming (GP) with automatically generating more effective PRs. However, the limitations of encoding and numerous random search operators in existing GPs not only affect the effectiveness of evolved PRs, but also reduce their interpretability. This paper proposes a novel Hyper-Heuristic based Guided Genetic Programming with Attribute Node Activation Encoding for resource constrained project scheduling problem. Uniquely, the proposed method transforms existing single class feature activation encoding into attribute node activation encoding for independently controlling each attribute node, and develops an attribute importance calculation method based on the frequency of attribute occurrence and activation. Based on the importance of subtrees and attributes, four guided and two random local search operators are designed to obtain more characteristic PRs. In addition, a two-stage evolution framework that automatically switches stages through iteration number is constructed to achieve performance sampling and guided generation of PRs. Based on the PSPLIB benchmark, although with fewer attribute inputs, the proposed method can generate more effective PRs with significantly better results compared to 12 existing PRs and PRs evolved from the two latest GPs in all test subsets", } @Article{CHEN:2024:mtcomm, author = "Fenghua Chen and Xinguo Qiu and Khalid A. Alnowibet", title = "Size-dependent nonlinear vibrations of functionally graded origami-enabled auxetic metamaterial plate: Application of artificial intelligence networks for solving the engineering problem", journal = "Materials Today Communications", volume = "38", pages = "108232", year = "2024", ISSN = "2352-4928", DOI = "doi:10.1016/j.mtcomm.2024.108232", URL = "https://www.sciencedirect.com/science/article/pii/S2352492824002125", keywords = "genetic algorithms, genetic programming, Nonlinear behavior, Pseudo-arc-length continuation approach, GOEAMs, Microplate, Artificial intelligence network", abstract = "Auxetic metamaterials are a kind of advanced materials that have distinct mechanical and physical characteristics that are not seen in traditional materials. This study presents a new concept for a microplate composed of graphene origami (GOri)-enabled auxetic metamaterials (GOEAMs) with functionally graded (FG) properties. The research also examines the nonlinear free vibration behavior of the microplate, which is reinforced by the GOEAMs. The microplate is composed of many layers of GOEAMs, with the GOri content varying in a layer-wise manner across the thickness. This variation in content allows for the graded modification of the auxetic property and other material characteristics. These modifications may be accurately determined using micromechanical models helped by genetic programming (GP). The modified couple stress theory (MCST) is used to accurately represent the microstructure of the current plate, given its size. This theory incorporates a single-length scale parameter. This study uses the first-order shear deformation theory and includes von Karman type nonlinearity to establish the nonlinear kinematic equations. These equations are then solved numerically using the generalized differential quadrature (GDQ) method and pseudo-arc-length continuation approach. we use mathematical modeling to collect data on the nonlinear frequency and deflection of the FG microplate made of GOEAMs. The data is then preprocessed by normalizing the input features and splitting the dataset into training and validation sets. Subsequently, an artificial intelligence network (AIN) architecture is constructed, consisting of an input layer, hidden layers, and an output layer. Once the AIN has been used to test, train, and validate the findings, this approach may be used in future studies on the nonlinear frequency and deflection of FG microplates built of GOEAMs, with reduced computational cost. Ultimately, the findings suggest that the nonlinear free vibration characteristics of the microplate may be successfully adjusted by manipulating the GOri parameter and distribution", } @InProceedings{conf/ausai/ChenZ05, author = "Guang Chen and Mengjie Zhang", title = "Evolving While-Loop Structures in Genetic Programming for Factorial and Ant Problems", year = "2005", pages = "1079--1085", booktitle = "AI 2005: Advances in Artificial Intelligence, 18th Australian Joint Conference on Artificial Intelligence, Proceedings", editor = "Shichao Zhang and Ray Jarvis", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3809", address = "Sydney, Australia", month = dec # " 5-9", keywords = "genetic algorithms, genetic programming, STGP", ISBN = "3-540-30462-2", URL = "https://rdcu.be/dgJfN", DOI = "doi:10.1007/11589990_144", size = "7 pages", abstract = "Loop is an important structure in human written programs. However, it is seldom used in the evolved programs in genetic programming (GP). use of while-loop structure in GP for the factorial and the artificial ant problems. Two different forms of the while-loop structure, count-controlled loop and event-controlled loop, are investigated. The results suggest that both forms of the while-loop structure can be successfully evolved in GP, the system with the while-loop structure is more effective and more efficient than the standard GP system for the two problems, and the evolved genetic programs with the loop-structure are much easier to interpret.", notes = "easy (non-Santa Fe) Ant. Factorial. Proportional Selection. Ramped half and half tree mutation. For loop, limits on number of iterations. p1081 'perfect solution' in half runs.", } @Article{Chen:2020:ACC, author = "Hao Chen and Zi Yuan Guo and Hong Bai Duan and Duo Ban", journal = "IEEE Access", title = "A Genetic Programming-Driven Data Fitting Method", year = "2020", volume = "8", pages = "111448--111459", month = jun, keywords = "genetic algorithms, genetic programming, Data fitting, hybrid model, tree coding, interpretability.", DOI = "doi:10.1109/ACCESS.2020.3002563", ISSN = "2169-3536", size = "12 pages", abstract = "Data fitting is the process of constructing a curve, or a set of mathematical functions, that has the best fit to a series of data points. Different with constructing a fitting model from same type of function, such as the polynomial model, we notice that a hybrid fitting model with multiple types of function may have a better fitting result. Moreover, this also shows better interpretability. However, a perfect smooth hybrid fitting model depends on a reasonable combination of multiple functions and a set of effective parameters. That is a high-dimensional multi-objective optimization problem. This paper proposes a novel data fitting model construction approach. In this approach, the model is expressed by an improved tree coding expression and constructed through an evolution search process driven by the genetic programming. In order to verify the validity of generated hybrid fitting model, 6 prediction problems are chosen for experiment studies. The experimental results show that the proposed method is superior to 7 typical methods in terms of the prediction accuracy and interpretability.", notes = "Also known as \cite{9117117} School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an 710121, China", } @Article{CHEN:2022:asoc, author = "HaoJie Chen and Jian Zhang2 and Rong Li and Guofu Ding and Shengfeng Qin", title = "A two-stage genetic programming framework for Stochastic Resource Constrained Multi-Project Scheduling Problem under New Project Insertions", journal = "Applied Soft Computing", volume = "124", pages = "109087", year = "2022", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2022.109087", URL = "https://www.sciencedirect.com/science/article/pii/S1568494622003751", keywords = "genetic algorithms, genetic programming, Multi-state combination scheduling, Hyper-heuristic, Priority rule, Stochastic resource constrained multi-project scheduling", abstract = "This study proposes a novel hyper-heuristic based two-stage genetic programming framework (HH-TGP) to solve the Stochastic Resource Constrained Multi-Project Scheduling Problem under New Project Insertions (SRCMPSP-NPI). It divides the evolution of genetic programming into generation and selection stages, and then establishes a multi-state combination scheduling mode with multiple priority rules (PRs) for the first time to realize resource constrained project scheduling under both stochastic activity duration and new project insertion. In the generation stage, based on a modified attribute set for multi-project scheduling, NSGA-II is hybridized to evolve a non-dominated PR set for forming a selectable PR set. While in the selection stage, the whole decision-making process is divided into multiple states based on the completion activity duration, and a weighted normalized evolution process with two crossovers, two mutations and four local search operators to match the optimal PR for each state from the PR set. Under the existing benchmark, HH-TGP is compared with the existing methods to verify its effectiveness", } @Article{CHEN:2022:eswa, author = "HaoJie Chen and Guofu Ding and Jian Zhang2 and Rong Li and Lei Jiang and Shengfeng Qin", title = "A filtering genetic programming framework for stochastic resource constrained multi-project scheduling problem under new project insertions", journal = "Expert Systems with Applications", volume = "198", pages = "116911", year = "2022", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2022.116911", URL = "https://www.sciencedirect.com/science/article/pii/S0957417422003487", keywords = "genetic algorithms, genetic programming, Filtering evolution, Priority rule, Stochastic resource constrained multi-project scheduling", abstract = "Multi-project management and uncertain environment are very common factors, and they bring greater challenges to scheduling due to the increase of problem complexity and response efficiency requirements. In this paper, a novel hyper-heuristic based filtering genetic programming (HH-FGP) framework is proposed for evolving priority rules (PRs) to deal with a multi-project scheduling problem considering stochastic activity duration and new project insertion together, namely the Stochastic Resource Constrained Multi-Project Scheduling Problem under New Project Insertions (SRCMPSP-NPI), within heuristic computation time. HH-FGP is designed to divide traditional evolution into sampling and filtering evolution for simultaneously filtering two kinds of parameters constituting PRs, namely depth range and attribute, to obtain more effective PRs. Based on this, the existing genetic search and local search are improved to meet the depth constraints, and a multi-objective evaluation mechanism is designed to achieve effective filtering. Under the existing benchmark, HH-FGP is compared and analysed with the existing methods to verify its effectiveness", } @Article{CHEN:2021:ESA, author = "HaoJie Chen and Guofu Ding and Shengfeng Qin and Jian Zhang2", title = "A hyper-heuristic based ensemble genetic programming approach for stochastic resource constrained project scheduling problem", journal = "Expert Systems with Applications", volume = "167", pages = "114174", year = "2021", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2020.114174", URL = "https://www.sciencedirect.com/science/article/pii/S0957417420309118", keywords = "genetic algorithms, genetic programming, Ensemble decision, Hyper-heuristics, Priority rule, Stochastic resource constrained project scheduling", abstract = "In project scheduling studies, to the best of our knowledge, the hyper-heuristic collaborative scheduling is first-time applied to project scheduling with random activity durations. A hyper-heuristic based ensemble genetic programming (HH-EGP) method is proposed for solving stochastic resource constrained project scheduling problem (SRCPSP) by evolving an ensemble of priority rules (PRs). The proposed approach features with (1) integrating the critical path method into the resource-based policy class to generate schedules; (2) improving the existing single hyper-heuristic project scheduling research to construct a suitable solution space for solving SRCPSP; and (3) bettering genetic evolution of each subpopulation from a decision ensemble with three different local searches in corporation with discriminant mutation and discriminant population renewal. In addition, a sequence voting mechanism is designed to deal with collaborative decision-making in the scheduling process for SRCPSP. The benchmark PSPLIB is performed to verify the advantage of the HH-EGP over heuristics, meta-heuristics and the single hyper-heuristic approaches", } @MastersThesis{Chen:mastersthesis, author = "Hung-Hsin Chen", title = "Genetic Programming for the Investment of the Mutual Fund with Sortino Ratio and Mean Variance Model", school = "Computer Science and Engineering, National Sun Yat-sen University", year = "2010", address = "Kaohsiung, Taiwan", month = jul, keywords = "genetic algorithms, genetic programming, trading strategy, return, Sortino ratio, risk", URL = "http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search-c/view_etd?URN=etd-0824110-122030", URL = "http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search-c/getfile?URN=etd-0824110-122030&filename=etd-0824110-122030.pdf", size = "142 pages", abstract = "In this thesis, we propose two genetic-programming-based models that improve the trading strategies for mutual funds. These two models can help investors get returns and reduce risks. The first model increases the return by selecting funds with high Sortino ratios and allocates the capital equally, achieving the best annualized return. The second model also selects funds with high Sortino ratios, but reduces the risk by allocating the capital with the mean variance model. Most importantly, our model uses the genetic programming to generate feasible trading strategies to gain return, which is suitable for the market that changes anytime. To verify our models, we simulate the investment for mutual funds from January 1999 to December 2009 (11 years in total). The experimental results show that our first model can gain return from 2004/1/1 to 2008/12/31, achieving the best annualized return 9.11%, which is better than the annualized return 6.89% of previous approaches. In addition, our second model with smaller downside volatility can achieve almost the same return as previous results.", notes = "In english", } @Article{journals/asc/ChenYP14, title = "The trading on the mutual funds by gene expression programming with {Sortino} ratio", author = "Hung-Hsin Chen and Chang-Biau Yang and Yung-Hsing Peng", journal = "Applied Soft Computing", year = "2014", volume = "15", pages = "219--230", month = feb, keywords = "genetic algorithms, genetic programming, gene expression programming", bibdate = "2014-11-05", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/asc/asc15.html#ChenYP14", URL = "http://dx.doi.org/10.1016/j.asoc.2013.09.011", size = "12 pages", notes = "Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan", } @Article{Chen2012, author = "Hung-Ming Chen2 and Wei-Ko Kao and Hsing-Chih Tsai", title = "Genetic programming for predicting aseismic abilities of school buildings", journal = "Engineering Applications of Artificial Intelligence", volume = "25", number = "6", pages = "1103--1113", year = "2012", ISSN = "0952-1976", DOI = "doi:10.1016/j.engappai.2012.04.002", URL = "http://www.sciencedirect.com/science/article/pii/S0952197612000905", keywords = "genetic algorithms, genetic programming, Prediction, Aseismic ability, School building, Soft computing", abstract = "In general, the aseismic ability of buildings is analysed using nonlinear models. To obtain aseismic abilities of buildings, numerical models are constructed based on the structural configuration and material properties of buildings, and their stress responses and behaviours are simulated. This method is complex, time-consuming, and should only be conducted by professionals. In the past, soft computing techniques have been applied in the construction field to predict the particular stress responses and behaviors; however, only a few studies have been made to predict specific properties of entire buildings. In this study, a weighted genetic programming system is developed to construct the relation models between the aseismic capacity of school buildings, and their basic design parameters. This is based on information from the database of school buildings, as well as information regarding the aseismic capacity of school buildings analysed using complete nonlinear methods. This system can be further applied to predict the aseismic capacity of the school buildings.", } @InProceedings{Jiah-ShingChen:2003:CINC, author = "Jiah-Shing Chen and Ping-Chen Lin", title = "Multi-Valued Stock Valuation Based on Fuzzy Genetic Programming Approach", booktitle = "Proceedings of the Sixth International Conference on Computational Intelligence and Natural Computing", year = "2003", pages = "CIEF3--39", address = "Embassy Suites Hotel and Conference Center, Cary, North Carolina USA", month = sep # " 26-30", keywords = "genetic algorithms, genetic programming, Trading Strategies", URL = "http://www.fin.kuas.edu.tw/people/writing_seminar.php?Sn=127", notes = "Broken Dec 2020 http://www.aiecon.org/conference/cief2003/cief2003_7.html http://axon.cs.byu.edu/Dan/cinc03.html/index.html/ Broken Jan 2013 http://www.ee.duke.edu/JCIS/ Broken Dec 2020 CIEF3-39 http://www.aiecon.org/conference/cief2003/cief2003_7.html (1) Dept. of Information Management, National Central University, Jungli, Taiwan 320, R.O.C. (2) Dept. of Information Management, Van Nung Institute of Technology, Jungli, Taiwan 320, R.O.C. See also \cite{pingchen_lin_paper} \cite{Lin:2007:IS}", } @InProceedings{chen:2024:CEC2, author = "Jiayin Chen and Ya-Hui Jia and Ying Bi and Wei-Neng Chen", title = "Generate a Single Heuristic for Multiple Dynamic Flexible Job Shop Scheduling Tasks by Genetic Programming", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Training, Technological innovation, Job shop scheduling, Processor scheduling, Heuristic algorithms, Dynamic scheduling, hyperheuristic, dynamic job shop scheduling, multitask optimization", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10611762", abstract = "Genetic programming (GP) hyper-heuristic method has been extensively studied to solve multiple dynamic job shop scheduling tasks by generating an effective heuristic for each task simultaneously. However, a fundamental question has not been answered. Do we need to customize a specific heuristic for each task? To fill this research gap, we propose to generate a single heuristic for handling multiple tasks. Without designing complex evolution mechanisms, only during the evaluation process of GP, the fitness of a heuristic is evaluated by multiple tasks. Since there are multiple tasks, a heuristic has multiple objective values. A rank aggregation (RA) fitness evaluation strategy is designed to convert multiple objective values of multiple tasks into a fitness value for a single heuristic. To validate the effectiveness of the generated solution and the proposed RA strategy, we design multitask scenarios that encompass tasks with diverse objectives, use levels, and maximum operation times. The results demonstrate that the performance of the single heuristic generated in multitask scenarios is comparable to solutions generated by GP using the single-task learning paradigm, meaning that with an appropriate training method, GP can generate a heuristic with good generality.", notes = "also known as \cite{10611762} WCCI 2024", } @Article{Chen:2016:JPDC, author = "Jie Chen and Guru Venkataramani", title = "{enDebug}: A hardware-software framework for automated energy debugging", journal = "Journal of Parallel and Distributed Computing", year = "2016", volume = "96", pages = "121--133", month = oct, keywords = "genetic algorithms, genetic programming, Energy profiling, Energy optimization", ISSN = "0743-7315", DOI = "doi:10.1016/j.jpdc.2016.05.005", URL = "http://www.sciencedirect.com/science/article/pii/S0743731516300351", abstract = "Energy consumption by software applications is one of the critical issues that determine the future of multicore software development. Inefficient software has been often cited as a major reason for wasteful energy consumption in computing systems. Without adequate tools, programmers and compilers are often left to guess the regions of code to optimize, that results in frustrating and unfruitful attempts at improving application energy. In this paper, we propose enDebug, an energy debugging framework that aims to automate the process of energy debugging. It first profiles the application code for high energy consumption using a hardware-software cooperative approach. Based on the observed application energy profile, an automated recommendation system that uses artificial selection genetic programming is used to generate the energy optimizing program mutants while preserving functional accuracy. We demonstrate the usefulness of our framework using several Splash-2, PARSEC-1.0 and SPEC CPU2006 benchmarks, where we were able to achieve up to 7percent energy savings beyond the highest compiler optimization (including profile guided optimization) settings on real-world Intel Core i7 processors.", } @Article{Chen:2025:knosys, author = "Ke Chen and Mingyang Dao and Ying Bi and Jing Liang and Zhenlong Wu and Peng Wang", title = "A new multi-tree Genetic Programming approach to feature construction in high-dimensional classification", journal = "Knowledge-Based Systems", year = "2025", volume = "319", pages = "113643", keywords = "genetic algorithms, genetic programming, Feature construction, Feature selection, High-dimensional classification", ISSN = "0950-7051", URL = "https://www.sciencedirect.com/science/article/pii/S0950705125006896", DOI = "doi:10.1016/j.knosys.2025.113643", abstract = "Classification, as a key task in machine learning, has been widely studied. However, with the rapid advancement of information technology, features in classification tasks increasingly exhibit low discriminability and strong redundancy. And the classification data has gradually changed from low-dimensional data to high-dimensional data. These characteristics not only significantly increase the complexity of the classification model training, but also lead to a decline in the generalisation ability of the classification model. How to reduce the number of original features and improve their differentiation becomes the key to improve the classification accuracy. Feature construction stands out as a crucial data processing technique for enhancing data quality. Genetic Programming (GP) has found extensive applications in feature construction due to its flexibility and strong interpretability. However, the existing GP-based feature construction methods suffer from the interference of redundant and irrelevant features in the face of high-dimensional data. By eliminating irrelevant and redundant features, reducing the search space of GP can aid in constructing more discriminative features. Motivated by this, we propose a GP algorithm that combines feature selection with feature construction for high-dimensional classification. During the evolution of GP, a subtree archive is introduced to store promising subtrees and these subtrees assist the generation of offspring. The experimental results show that the proposed method can achieve better classification performance than single-tree and multi-tree GP methods", } @InProceedings{Chen:2024:ICNC-FSKD, author = "Kunjun Chen", title = "Genetic Programming for Feature Learning and Feature Construction in Glioma Survival Prediction", booktitle = "2024 20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)", year = "2024", month = jul, keywords = "genetic algorithms, genetic programming, Representation learning, Three-dimensional displays, Magnetic resonance imaging, Feature extraction, Prediction algorithms, Classification algorithms, Prognostics and health management, Tumours, glioma, survival prediction, feature learning, feature construction", DOI = "doi:10.1109/ICNC-FSKD64080.2024.10702275", abstract = "MRI imaging plays a vital role in the initial tumour screening process and also aids in constructing a high-dimensional feature space for survival prediction tasks through the combination of sequences and tumour subregions. Traditional GP methods applied to feature construction treat features as part of the terminal set, which limits their search capabilities. Combining feature learning with GP allows for the direct use of image data, albeit dependent on predefined extraction functions. Our goal is to design a FLFC method that integrates feature learning and construction to automatically locate ROIs, extract features, construct new features, and match classifiers in 3D MRI images. To this end, we have introduced a new GP structure with an interpretable function set. To verify the algorithm, experiments were performed using the publicly available BraTS 2020 dataset. Ours achieved an average accuracy of 94.14percent in the binary classification task (differentiating between HGG and LGG) and 72.49percent in the four-class task (within HGG subtypes). These results underscore the efficacy of the FLFC method in harnessing the complexity of MRI data for meaningful survival prediction, illustrating its potential as a robust tool in medical imaging analysis and cancer prognosis.", notes = "Also known as \cite{10702275}", } @InProceedings{Chen:2015:CECa, author = "Liang-Yu Chen and Po-Ming Lee and Tzu-Chien Hsiao", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2015)", title = "A sensor tagging approach for reusing building blocks of knowledge in learning classifier systems", year = "2015", pages = "2953--2960", isbn13 = "978-1-4799-7491-7", abstract = "During the last decade, the extraction and reuse of building blocks of knowledge for the learning process of Extended Classifier System (XCS) in Multiplexer (MUX) problem domain have been demonstrate feasible by using Code Fragment (CF) (i.e. a tree-based structure ordinarily used in the field of Genetic Programming (GP)) as the representation of classifier conditions (the resulting system was called XCSCFC). However, the use of the tree-based structure may lead to the bloating problem and increase in time complexity when the tree grows deep. Therefore, we proposed a novel representation of classifier conditions for the XCS, named Sensory Tag (ST). The XCS with the ST as the input representation is called XCSSTC. The experiments of the proposed method were conducted in the MUX problem domain. The results indicate that the XCSSTC is capable of reusing building blocks of knowledge in the MUX problems. The current study also discussed about two different aspects of reusing of building blocks of knowledge. Specifically, we proposed the attribution selection' part and the 'logical relation between the attributes' part.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2015.7257256", ISSN = "1089-778X", month = may, notes = "Also known as \cite{7257256}", } @Article{CHEN:2023:jmrt, author = "Lingling Chen and Zhiyuan Wang and Aftab Ahmad Khan and Majid Khan and Muhammad Faisal Javed and Abdulaziz Alaskar and Sayed M. Eldin", title = "Development of predictive models for sustainable concrete via genetic programming-based algorithms", journal = "Journal of Materials Research and Technology", volume = "24", pages = "6391--6410", year = "2023", ISSN = "2238-7854", DOI = "doi:10.1016/j.jmrt.2023.04.180", URL = "https://www.sciencedirect.com/science/article/pii/S223878542300875X", keywords = "genetic algorithms, genetic programming, Waste foundry sand, Gene expression programming, Multi-expression programming, Solid waste, Sustainable construction", abstract = "Waste foundry sand (WFS), a by-product of the casting industry, is a potential material that may be employed as a substitute for fine aggregate in concrete. In the present study, gene expression programming (GEP) and multi-expression programming (MEP) are used to generate predictive models for the split tensile strength (STS) and elastic modulus (E) of waste foundry sand concrete (WFSC). Therefore, a comprehensive database was collected that contains 146 and 242 values of E and STS, respectively. Seven different variables were chosen as input for the development of the ML-based models. The reliability and accuracy of the proposed model were evaluated by using various statistical indicators. Given the performance assessment, both GEP and MEP accurately predict the E with a correlation of 0.994 and 0.996, respectively. However, GEP performance was much superior in predicting STS (R = 0.987) as compared to the MEP model (R = 0.892). The integrated statistical performance (rho, OF) of both models approaches zero, indicating the excellent performance and generalization potential of the developed models. For the interpretation of machine learning (ML) models, Shapley additive explanation (SHAP) was used to know about the input variables' importance and influence on the output parameter. The SHAP analysis revealed that a higher ratio of FA/TA results in the enhancement of the elastic modulus, whereas CA/C higher ratio is favorably influencing the split tensile strength up to some extent, however, this trend changes when the ratio is further increased. These soft computing prediction techniques can incentivize the use of WFS in sustainable concrete, reducing waste disposal and promoting environment-friendly construction. Furthermore, it is recommended that the findings of this study be validated with more extensive data sets and that other ML techniques be investigated", } @InProceedings{conf/icnc/ChenC05b, title = "Dynamical Proportion Portfolio Insurance with Genetic Programming", author = "Jiah-Shing Chen and Chia-Lan Chang", year = "2005", pages = "735--743", editor = "Lipo Wang and Ke Chen and Yew-Soon Ong", booktitle = "Advances in Natural Computation, First International Conference, ICNC 2005, Proceedings, Part II", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3611", address = "Changsha, China", month = aug # " 27-29", bibdate = "2005-08-01", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icnc/icnc2005-2.html#ChenC05b", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-28325-0", DOI = "doi:10.1007/11539117_104", abstract = "a dynamic proportion portfolio insurance (DPPI) strategy based on the popular constant proportion portfolio insurance (CPPI) strategy. The constant multiplier in CPPI is generally regarded as the risk multiplier. Since the market changes constantly, we think that the risk multiplier should change accordingly. This research identifies factors relating to market volatility. These factors are built into equation trees by genetic programming. Experimental results show that our DPPI strategy is more profitable than traditional CPPI strategy.", } @Article{Chen:2007:ESA, author = "J. S. Chen and Benjamin Penyang Liao", title = "Piecewise nonlinear goal-directed CPPI strategy", journal = "Expert Systems with Applications", year = "2007", volume = "33", number = "4", pages = "857--869", month = nov, keywords = "genetic algorithms, genetic programming, Portfolio insurance strategy, Goal-directed strategy, Piecewise linear GDCPPI strategy, Piecewise nonlinear GDCPPI strategy", DOI = "doi:10.1016/j.eswa.2006.07.001", abstract = "Traditional portfolio insurance (PI) strategy, such as constant proportion portfolio insurance (CPPI), only considers the floor constraint but not the goal aspect. This paper proposes a goal-directed (GD) strategy to express an investor's goal-directed trading behaviour and combines this floor-less GD strategy with the goal-less CPPI strategy to form a piecewise linear goal-directed CPPI (GDCPPI) strategy. The piecewise linear GDCPPI strategy shows that there is a wealth position M at the intersection of the GD and CPPI strategies. This M position guides investors to apply the CPPI strategy or the GD strategy depending on whether current wealth is less than or greater than M, respectively. In addition, we extend the piecewise linear GDCPPI strategy to a piecewise nonlinear GDCPPI strategy. This paper applies genetic algorithm (GA) technique to find better piecewise linear GDCPPI strategy parameters than those under the Brownian motion assumption. This paper also applies forest genetic programming (GP) technique to generate the piecewise nonlinear GDCPPI strategy. The statistical tests show that the GP strategy outperforms the GA strategy which in turn outperforms the Brownian strategy.", } @Article{Chen2008273, author = "Jiah-Shing Chen and Chia-Lan Chang and Jia-Li Hou and Yao-Tang Lin", title = "Dynamic proportion portfolio insurance using genetic programming with principal component analysis", journal = "Expert Systems with Applications", volume = "35", number = "1-2", pages = "273--278", year = "2008", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2007.06.030", URL = "http://www.sciencedirect.com/science/article/B6V03-4P40KHS-4/2/0bbb6228d04a3a1a4d59108b17c37664", keywords = "genetic algorithms, genetic programming, Dynamic proportion portfolio insurance (DPPI), Constant proportion portfolio insurance (CPPI), Principal component analysis (PCA)", abstract = "This paper proposes a dynamic proportion portfolio insurance (DPPI) strategy based on the popular constant proportion portfolio insurance (CPPI) strategy. The constant multiplier in CPPI is generally regarded as the risk multiplier. Since the market changes constantly, we think that the risk multiplier should change according to market conditions. This research identifies risk variables relating to market conditions. These risk variables are used to build the equation tree for the risk multiplier by genetic programming. Experimental results show that our DPPI strategy is more profitable than traditional CPPI strategy. In addition, principal component analysis of the risk variables in equation trees indicates that among all the risk variables, risk-free interest rate influences the risk multiplier most.", } @InProceedings{Chen:2016:YAC, author = "Jiejie Chen and Zhigang Zeng and Ping Jiang", booktitle = "2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC)", title = "Displacement prediction model of landslide based on multi-gene genetic programming", year = "2016", pages = "481--485", abstract = "In this paper, a new approach is presented for predicting landslide displacement using multi-gene genetic programming (MGGP). For the characteristic of MGGP which does not need specific assumptions, two real cases is used to prove the new approach is feasibility and validity.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/YAC.2016.7804942", month = nov, notes = "Also known as \cite{7804942}", } @Article{journals/nca/ChenZJT16, author = "Jiejie Chen and Zhigang Zeng and Ping Jiang and Huiming Tang", title = "Application of multi-gene genetic programming based on separable functional network for landslide displacement prediction", journal = "Neural Computing and Applications", year = "2016", number = "6", volume = "27", keywords = "genetic algorithms, genetic programming", bibdate = "2017-05-26", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/nca/nca27.html#ChenZJT16", pages = "1771--1784", DOI = "doi:10.1007/s00521-015-1976-y", } @InProceedings{conf/awic/ChenLW05, title = "Distributed Service Management Based on Genetic Programming", author = "Jing Chen and Zeng-zhi Li and Yun-lan Wang", year = "2005", pages = "83--88", editor = "Piotr S. Szczepaniak and Janusz Kacprzyk and Adam Niewiadomski", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3528", ISBN = "3-540-26219-9", booktitle = "Advances in Web Intelligence Third International Atlantic Web Intelligence Conference, AWIC 2005, Proceedings", address = "Lodz, Poland", month = "6-9 " # jun, bibdate = "2005-05-30", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/awic/awic2005.html#ChenLW05", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-26219-9", DOI = "doi:10.1007/11495772_14", size = "6 pages", abstract = "An architecture for online discovery quantitative model of distributed service management based on genetic programming (GP) was proposed. The GP system was capable of constructing the quantitative models online without prior knowledge of the managed elements. The model can be updated continuously in response to the changes made in provider configurations and the evolution of business demands. The GP system chose a particular subset from the numerous metrics as the explanatory variables of the model. In order to evaluate the system, a prototype is implemented to estimate the online response times for Oracle Universal Database under a TPC-W workload. Of more than 500 Oracle performance metrics, the system model choose three most influential metrics that weight 76percent of the variability of response time, illustrating the effectiveness of quantitative model constructing system and model constructing algorithms.", } @InProceedings{Chen:2005:ICMLC, author = "Jing Chen and Zeng-Zhi Li and Zhi-Gang Liao and Yun-Lan Wang", title = "Distributed Service Performance Management Based on Linear Regression and Genetic Programming", booktitle = "Proceedings of 2005 International Conference on Machine Learning and Cybernetics", year = "2005", volume = "1", pages = "560--563", month = "18-21 " # aug, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICMLC.2005.1527007", abstract = "An architecture for online discovery quantitative models system of service performance management was proposed. The system was capable of constructing the quantitative models without prior knowledge of the managed elements. The model can be updated continuously in response to the changes made in provider configurations and the evolution of business demands. Due to the existence of strong correlation between the distributed service metrics and response times, a linear and a hyper-linear quantitative models are constructed, which respectively use the stepwise multiple linear regression and genetic programming algorithms. The simulation results show that the effectiveness of quantitative model constructing system and model constructing algorithms.", notes = "Telecommunication Engineering Institute, Air Force Engineering University, Xi'an 710077, China; Institute of Computer System Architecture & Network, Xi'an Jiaotong University, Xi'an 710049, China E-MAIL: jingchen@263.net", } @Article{chen:290, author = "Li Chen", title = "Study of Applying Macroevolutionary Genetic Programming to Concrete Strength Estimation", publisher = "ASCE", year = "2003", journal = "Journal of Computing in Civil Engineering", volume = "17", number = "4", pages = "290--294", month = oct, keywords = "genetic algorithms, genetic programming, civil engineering computing, compressive strength, mixtures, concrete", URL = "http://link.aip.org/link/?QCP/17/290/1", DOI = "doi:10.1061/(ASCE)0887-3801(2003)17:4(290)", abstract = "This technical note is aimed at demonstrating a mixture-proportioning problem, which uses the macroevolutionary algorithm (MA) combined with genetic programming (GP) to estimate the compressive strength of high-performance concrete (HPC). GP provides system identification in a transparent and structured way; a fittest function type of experimental results will be obtained automatically from this method. MA is a new concept of species evolution at the higher level. It could improve the capability of searching global optima and avoid premature convergence during the selection process of GP. In the study, two appropriate functions have been found to represent the relationships between different ingredients and the compressive strength. The results show that this new model, MAGP, is better than the traditional proportional selection GP for HPC strength estimation.", notes = "Dept. of Civil Engineering, Chung Hua Univ., Hsin Chu, Taiwan 30067, Republic of China.", } @Article{Chen2008296, author = "Li Chen and Chih-Hung Tan and Shuh-Ji Kao and Tai-Sheng Wang", title = "Improvement of remote monitoring on water quality in a subtropical reservoir by incorporating grammatical evolution with parallel genetic algorithms into satellite imagery", journal = "Water Research", volume = "42", number = "1-2", pages = "296--306", year = "2008", month = jan, keywords = "genetic algorithms, genetic programming, Grammatical evolution, Parallel genetic algorithm, Water quality monitoring, Chlorophyll-a, Remote-sensed imagery", ISSN = "0043-1354", DOI = "doi:10.1016/j.watres.2007.07.014", broken = "http://www.sciencedirect.com/science/article/B6V73-4P7FS78-1/2/1cc0a607d7b67fe51a5f0d27a2c9d0fc", size = "11 pages", abstract = "Parallel GEGA was constructed by incorporating grammatical evolution (GE) into the parallel genetic algorithm (GA) to improve reservoir water quality monitoring based on remote sensing images. A cruise was conducted to ground-truth chlorophyll-a (Chl-a) concentration longitudinally along the Feitsui Reservoir, the primary water supply for Taipei City in Taiwan. Empirical functions with multiple spectral parameters from the Landsat 7 Enhanced Thematic Mapper (ETM+) data were constructed. The GE, an evolutionary automatic programming type system, automatically discovers complex nonlinear mathematical relationships among observed Chl-a concentrations and remote-sensed imageries. A GA was used afterward with GE to optimize the appropriate function type. Various parallel subpopulations were processed to enhance search efficiency during the optimization procedure with GA. Compared with a traditional linear multiple regression (LMR), the performance of parallel GEGA was found to be better than that of the traditional LMR model with lower estimating errors.", } @Article{journals/ewc/Chen11, author = "Li Chen", title = "Macro-grammatical evolution for nonlinear time series modeling-a case study of reservoir inflow forecasting", journal = "Engineering with Computers", year = "2011", volume = "27", number = "4", pages = "393--404", publisher = "Springer", keywords = "genetic algorithms, genetic programming, grammatical evolution, streamflow forecasting, nonlinear model, macroevolutionary algorithm", ISSN = "0177-0667", DOI = "doi:10.1007/s00366-011-0212-3", size = "12 pages", abstract = "Streamflow forecasting is significantly important for planning and operating water resource systems. However, stream flow formation is a highly nonlinear, time varying, spatially distributed process and difficult to forecast. This paper proposes a nonlinear model which incorporates improved real-coded grammatical evolution (GE) with a genetic algorithm (GA) to predict the ten-day inflow of the De-Chi Reservoir in central Taiwan. The GE is a recently developed evolutionary-programming algorithm used to express complex relationships among long-term nonlinear time series. The algorithm discovers significant input variables and combines them to form mathematical equations automatically. Using GA with GE optimises an appropriate type of function and its associated coefficients. To enhance searching efficiency and genetic diversity during GA optimisation, the macro-evolutionary algorithm (MA) is processed as a selection operator. The results using an example of theoretical nonlinear time series problems indicate that the proposed GEMA yields an efficient optimal solution. GEMA has the advantages of its ability to learn relationships hidden in data and express them automatically in a mathematical manner. When applied to a real world case study, the fittest equation generated through GEMA used only a single input variable in a reasonable nonlinear form. The predicting accuracies of GEMA were better than those of the traditional linear regression (LR) model and as good as those of the back-propagation neural network (BPNN). In addition, the predicting of ten-day reservoir inflows reveals the effectives of GEMA, and standardisation is beneficial to model for seasonal time series.", affiliation = "Department of Civil Engineering and Engineering Informatics, Chung Hua University, Hsin Chu, 30012 Taiwan, R.O.C", bibdate = "2011-09-23", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ewc/ewc27.html#Chen11", URL = "http://dx.doi.org/10.1007/s00366-011-0212-3", } @Article{journals/eaai/ChenKM14, title = "Prediction of slump flow of high-performance concrete via parallel hyper-cubic gene-expression programming", author = "Li Chen and Chang-Huan Kou and Shih-Wei Ma", journal = "Eng. Appl. of AI", year = "2014", volume = "34", pages = "66--74", keywords = "genetic algorithms, genetic programming, gene expression programming", bibdate = "2014-07-09", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/eaai/eaai34.html#ChenKM14", URL = "http://dx.doi.org/10.1016/j.engappai.2014.05.005", } @InProceedings{Chen:2015:CECb, author = "Lin Chen and Hong Zheng and Dan Zheng and Dongni Li", booktitle = "2015 IEEE Congress on Evolutionary Computation (CEC)", title = "An ant colony optimization-based hyper-heuristic with genetic programming approach for a hybrid flow shop scheduling problem", year = "2015", pages = "814--821", isbn13 = "978-1-4799-7491-7", abstract = "The problem of a k-stage hybrid flow shop (HFS) with one stage composed of non-identical batch processing machines and the others consisting of non-identical single processing machines is analysed in the context of the equipment manufacturing industry. Due to the complexity of the addressed problem, a hyper-heuristic which combines heuristic generation and heuristic search is proposed to solve the problem. For each sub-problem, i.e., part assignment, part sequencing and batch formation, heuristic rules are first generated by genetic programming (GP) off-line and then selected by ant colony optimisation (ACO) correspondingly. Finally, the scheduling solutions are obtained through the above generated combinatorial heuristic rules. Aiming at minimizing the total weighted tardiness of parts, a comparison experiment with the other hyper-heuristic for the same HFS problem is conducted. The result has shown that the proposed algorithm has advantages over the other method with respect to the total weighted tardiness.", keywords = "genetic algorithms, genetic programming, ant colony optimization, ACO, scheduling, discrete event systems", DOI = "doi:10.1109/CEC.2015.7256975", ISSN = "1089-778X", month = may, notes = "Also known as \cite{7256975}", } @Article{journals/soco/ChenCCHH07, author = "Mu-Yen Chen and Kuang-Ku Chen and Heien-Kun Chiang and Hwa-Shan Huang and Mu-Jung Huang", title = "Comparing extended classifier system and genetic programming for financial forecasting: an empirical study", journal = "Soft Computing", year = "2007", volume = "11", number = "12", pages = "1173--1183", month = oct, keywords = "genetic algorithms, genetic programming, Learning classifier system, Extended classifier system, Machine learning", ISSN = "1432-7643", DOI = "doi:10.1007/s00500-007-0161-3", abstract = "As a broad subfield of artificial intelligence, machine learning is concerned with the development of algorithms and techniques that allow computers to learn. These methods such as fuzzy logic, neural networks, support vector machines, decision trees and Bayesian learning have been applied to learn meaningful rules; however, the only drawback of these methods is that it often gets trapped into a local optimal. In contrast with machine learning methods, a genetic algorithm (GA) is guaranteeing for acquiring better results based on its natural evolution and global searching. GA has given rise to two new fields of research where global optimization is of crucial importance: genetic based machine learning (GBML) and genetic programming (GP). This article adopts the GBML technique to provide a three-phase knowledge extraction methodology, which makes continues and instant learning while integrates multiple rule sets into a centralized knowledge base. Moreover, the proposed system and GP are both applied to the theoretical and empirical experiments. Results for both approaches are presented and compared. This paper makes two important contributions: (1) it uses three criteria (accuracy, coverage, and fitness) to apply the knowledge extraction process which is very effective in selecting an optimal set of rules from a large population; (2) the experiments prove that the rule sets derived by the proposed approach are more accurate than GP.", bibdate = "2008-03-11", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/soco/soco11.html#ChenCCHH07", } @InProceedings{WSEAS_466-157_Chen, author = "Peng Chen and Shinji Koyama and Shinichiro Mitutake and Takashi Isoda", title = "Automatic Running Planning for Omni-Directional Mobile Robot By Genetic Programming", year = "2003", month = aug # "~11-13", pages = "5", booktitle = "WSEAS SEPAD-AIKED-ISPRA-EHAC", editor = "Nikos Mastorakis", address = "Rethymno, Greece", organisation = "The World Scientific and Engineering Academy and Society (WSEAS)", keywords = "genetic algorithms, genetic programming, off-road running, omni-directional mobile robot, crawler-roller running system, obstacle, running planning", URL = "http://www.wseas.us/e-library/conferences/digest2003/papers/digest.htm", URL = "http://www.wseas.us/e-library/conferences/digest2003/papers/466-157.pdf", size = "5 pages", abstract = "This paper presents a omni-directional mobile robot which can run on off-road and run over a obstacle. The robot equipped with crawler-roller running system. The motion analysis is also discussed to realise the autonomic off-road running. In order to automatically control the robot to run in optional direction and an orbit. We have to decide the inputting volts of the motors according to given direction or orbit. Even though we can do this by analysis theory, it is difficult to control the robot in real time. So we propose an intelligent control method using Genetic Programming (GP) to search an optimum route leading the robot to given destination and avoiding obstacles. We have carried out many practical running tests and simulations to verify the efficiency of the mechanism and the intelligent control method. In this paper we show an example of the tests.", } @Article{Chen:2005:MSSP, author = "Peng Chen and Masatoshi Taniguchi and Toshio Toyota and Zhengja He", title = "Fault diagnosis method for machinery in unsteady operating condition by instantaneous power spectrum and genetic programming", journal = "Mechanical Systems and Signal Processing", year = "2005", volume = "19", pages = "175--194", number = "1", month = jan, keywords = "genetic algorithms, genetic programming, Machinery fault diagnosis, Unsteady operating condition, Instantaneous power spectrum, Relative crossing information, Rolling bearing", ISSN = "0888-3270", owner = "wlangdon", broken = "http://www.sciencedirect.com/science/article/B6WN1-4BKPSGD-2/2/6c68916b11c23706a7fee9f780c0e637", DOI = "doi:10.1016/j.ymssp.2003.11.004", abstract = "This paper proposes a fault diagnosis method for plant machinery in an unsteady operating condition using instantaneous power spectrum (IPS) and genetic programming (GP). IPS is used to extract feature frequencies of each machine state from measured vibration signals for distinguishing faults by relative crossing information. Excellent symptom parameters for detecting faults are automatically generated by the GP. The excellent symptom parameters generated by GP can sensitively reflect the characteristics of signals for precise diagnosis. The method proposed is verified by applying it to the fault diagnosis of a rolling bearing.", notes = "also known as \cite{CHEN2005175}", } @Article{Chen:2011:IJCIS, author = "Peng Chen2 and Yong-Zai Lu", title = "Automatic Design of Robust Optimal Controller for Interval Plants using Genetic Programming and {Kharitonov} Theorem", journal = "International Journal of Computational Intelligence Systems", year = "2011", volume = "4", number = "5", pages = "826--836", month = sep, keywords = "genetic algorithms, genetic programming, interval plant, kharitonov theorem, robust optimal controller", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.1010.701", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1010.701", URL = "http://atlantis-press.com/php/download_paper.php?id%3D2376", URL = "http://www.tandfonline.com/doi/abs/10.1080/18756891.2011.9727834", DOI = "doi:10.1080/18756891.2011.9727834", size = "11 pages", abstract = "This paper presents a novel approach to automatic design of a robust optimal controller for interval plants with Genetic Programming based on Kharitonov Theorem (KT), which provides a theoretical foundation in the design of robust controller for interval plants. The structure and parameters of the robust optimal controller for interval plants are optimised by Genetic Programming and the Generalized KT related stability criteria are integrated into the solution to guarantee the stability of the closed-loop system. Consequently, the evolved controller not only minimises time-weighted absolute error (ITAE) of the closed-loop system, but also stabilizes the whole interval plant family robustly. Finally, the simulations on a benchmark problem show that the proposed method can effectively generate a robust optimal controller for interval plants.", } @InProceedings{Chen:2015:CEC, author = "Qi Chen and Bing Xue and Mengjie Zhang", title = "Generalisation and Domain Adaptation in GP with Gradient Descent for Symbolic Regression", booktitle = "Proceedings of 2015 IEEE Congress on Evolutionary Computation (CEC 2015)", year = "2015", editor = "Yadahiko Murata", pages = "1137--1144", address = "Sendai, Japan", month = "25-28 " # may, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-7491-7", DOI = "doi:10.1109/CEC.2015.7257017", abstract = "Genetic programming (GP) has been widely applied to symbolic regression problems and achieved good success. Gradient descent has also been used in GP as a complementary search to the genetic beam search to further improve symbolic regression performance. However, most existing GP approaches with gradient descent (GPGD) to symbolic regression have only been tested on the conventional symbolic regression problems such as benchmark function approximations and engineering practical problems with a single (training) data set only and the effectiveness on unseen data sets in the same domain and in different domains has not been fully investigated. This paper designs a series of experiment objectives to investigate the effectiveness and efficiency of GPGD with various settings for a set of symbolic regression problems applied to unseen data in the same domain and adapted to other domains. The results suggest that the existing GPGD method applying gradient descent to all evolved program trees three times at every generation can perform very well on the training set itself, but cannot generalise well on the unseen data set in the same domain and cannot be adapted to unseen data in an extended domain. Applying gradient descent to the best program in the final generation of GP can also improve the performance over the standard GP method and can generalise well on unseen data for some of the tasks in the same domain, but perform poorly on the unseen data in an extended domain. Applying gradient descent to the top 20percent programs in the population can generalise reasonably well on the unseen data in not only the same domain but also in an extended domain.", notes = "1105 hrs 15342 CEC2015", } @InProceedings{Chen:2016:GECCO, author = "Qi Chen and Mengjie Zhang and Bing Cue", title = "Improving Generalisation of Genetic Programming for Symbolic Regression with Structural Risk Minimisation", booktitle = "GECCO '16: Proceedings of the 2016 Annual Conference on Genetic and Evolutionary Computation", year = "2016", editor = "Tobias Friedrich", pages = "709--716", keywords = "genetic algorithms, genetic programming", month = "20-24 " # jul, organisation = "SIGEVO", address = "Denver, USA", publisher = "ACM", publisher_address = "New York, NY, USA", isbn13 = "978-1-4503-4206-3", DOI = "doi:10.1145/2908812.2908842", abstract = "Generalisation is one of the most important performance measures for any learning algorithm, no exception to Genetic Programming (GP). A number of works have been devoted to improve the generalisation ability of GP for symbolic regression. Methods based on a reliable estimation of generalisation error of models during evolutionary process are a sensible choice to enhance the generalisation of GP. Structural risk minimisation (SRM), which is based on the VC dimension in the learning theory, provides a powerful framework for estimating the difference between the generalisation error and the empirical error. Despite its solid theoretical foundation and reliability, SRM has seldom been applied to GP. The most important reason is the difficulty in measuring the VC dimension of GP models/programs. This paper introduces SRM, which is based on an empirical method to measure the VC dimension of models, into GP to improve its generalisation performance for symbolic regression. The results of a set of experiments confirm that GP with SRM has a dramatical generalisation gain while evolving more compact/less complex models than standard GP. Further analysis also shows that in most cases, GP with SRM has better generalisation performance than GP with bias-variance decomposition, which is one of the state-of-the-art methods to control overfitting.", notes = "Victoria University of Wellington GECCO-2016 A Recombination of the 25th International Conference on Genetic Algorithms (ICGA-2016) and the 21st Annual Genetic Programming Conference (GP-2016)", } @InProceedings{Chen:2016:CEC, author = "Qi Chen and Bing Xue and Ben Niu and Mengjie Zhang", title = "Improving Generalisation of Genetic Programming for High-Dimensional Symbolic Regression with Feature Selection", booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016)", year = "2016", editor = "Yew-Soon Ong", pages = "3793--3800", address = "Vancouver", month = "24-29 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-5090-0623-6", DOI = "doi:10.1109/CEC.2016.7744270", abstract = "Feature selection is a desired process when learning from high-dimensional data. However, it is seldom considered in Genetic Programming (GP) for high-dimensional symbolic regression. This work aims to develop a new method, Genetic Programming with Feature Selection (GPWFS), to improve the generalisation ability of GP for symbolic regression. GPWFS is a two-stage method. The main task of the first stage is to select important/informative features from fittest individuals, and the second stage uses a set of selected features, which is a subset of original features, for regression. To investigate the learning/optimisation performance and generalisation capability of GPWFS, a set of experiments using standard GP as a baseline for comparison have been conducted on six real-world high-dimensional symbolic regression datasets. The experimental results show that GPWFS can have better performance both on the training sets and the test sets on most cases. Further analysis on the solution size, the number of distinguished features and total number of used features in the evolved models shows that using GPWFS can induce more compact models with better interpretability and lower computational costs than standard GP.", notes = "WCCI2016", } @Article{Chen:2017:ieeeTEC, author = "Qi Chen and Mengjie Zhang and Bing Xue", title = "Feature Selection to Improve Generalisation of Genetic Programming for High-Dimensional Symbolic Regression", journal = "IEEE Transactions on Evolutionary Computation", year = "2017", volume = "21", number = "5", pages = "792--806", month = oct, keywords = "genetic algorithms, genetic programming, Symbolic Regression", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2017.2683489", abstract = "When learning from high-dimensional data for symbolic regression, genetic programming typically could not generalise well. Feature selection, as a data preprocessing method, can potentially contribute not only to improving the efficiency of learning algorithms but also to enhancing the generalisation ability. However, in genetic programming for high-dimensional symbolic regression, feature selection before learning is seldom considered. In this work, we propose a new feature selection method based on permutation to select features for high dimensional symbolic regression using genetic programming. A set of experiments has been conducted to investigate the performance of the proposed method on the generalisation of genetic programming for high-dimensional symbolic regression. The regression results confirm the superior performance of the proposed method over the other examined feature selection methods. Further analysis indicates that the models evolved by the proposed method are more likely to contain only the truly relevant features and have better interpretability.", notes = "also known as \cite{7879832}", } @InProceedings{Chen:2017:EuroGP, author = "Qi Chen and Bing Xue and Yi Mei and Mengjie Zhang", title = "Geometric Semantic Crossover with an Angle-aware Mating Scheme in Genetic Programming for Symbolic Regression", booktitle = "EuroGP 2017: Proceedings of the 20th European Conference on Genetic Programming", year = "2017", month = "19-21 " # apr, editor = "Mauro Castelli and James McDermott and Lukas Sekanina", series = "LNCS", volume = "10196", publisher = "Springer Verlag", address = "Amsterdam", pages = "229--245", organisation = "species", keywords = "genetic algorithms, genetic programming: Poster", isbn13 = "978-3-319-55695-6", DOI = "doi:10.1007/978-3-319-55696-3_15", abstract = "Recent research shows that incorporating semantic knowledge into the genetic programming (GP) evolutionary process can improve its performance. This work proposes an angle-aware mating scheme for geometric semantic crossover in GP for symbolic regression. The angle-awareness guides the crossover operating on parents which have a large angle between their relative semantics to the target semantics. The proposed idea of angle-awareness has been incorporated into one state-of-the-art geometric crossover, the locally geometric semantic crossover. The experimental results show that, compared with locally geometric semantic crossover and the regular GP crossover, the locally geometric crossover with angle-awareness not only has a significantly better learning performance but also has a notable generalisation gain on unseen test data. Further analysis has been conducted to see the difference between the angle distribution of crossovers with and without angle-awareness, which confirms that the angle-awareness changes the original distribution of angles by decreasing the number of parents with zero degree while increasing their counterparts with large angles, leading to better performance.", notes = "Part of \cite{Castelli:2017:GP} EuroGP'2017 held inconjunction with EvoCOP2017, EvoMusArt2017 and EvoApplications2017", } @InProceedings{conf/seal/0002ZX17, author = "Qi Chen and Mengjie Zhang and Bing Xue", title = "Geometric Semantic Genetic Programming with Perpendicular Crossover and Random Segment Mutation for Symbolic Regression", booktitle = "Proceedings of the 11th International Conference on Simulated Evolution and Learning, SEAL 2017", year = "2017", editor = "Yuhui Shi and Kay Chen Tan and Mengjie Zhang and Ke Tang and Xiaodong Li and Qingfu Zhang and Ying Tan and Martin Middendorf and Yaochu Jin", volume = "10593", series = "Lecture Notes in Computer Science", pages = "422--434", address = "Shenzhen, China", month = nov # " 10-13", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Symbolic regression, Geometric semantic operators", bibdate = "2017-11-03", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/seal/seal2017.html#0002ZX17", isbn13 = "978-3-319-68758-2", DOI = "doi:10.1007/978-3-319-68759-9_35", abstract = "Geometric semantic operators have been a rising topic in genetic programming (GP). For the sake of a more effective evolutionary process, various geometric search operators have been developed to use the knowledge acquired from inspecting the behaviours of GP individuals. While the current exact geometric operators lead to over-grown children in GP, existing approximate geometric operators never consider the theoretical framework of geometric semantic GP explicitly. This work proposes two new geometric search operators, i.e. perpendicular crossover and random segment mutation, to fulfil precise semantic requirements for symbolic regression under the theoretical framework of geometric semantic GP. The two operators approximate the target semantics gradually and effectively. The results show that the new geometric operators bring a notable benefit to both the learning performance and the generalisation ability of GP. In addition, they also have significant advantages over Random Desired Operator, which is a state-of-the-art geometric semantic operator.", } @InProceedings{Chen:2017:GECCOa, author = "Qi Chen and Mengjie Zhang and Bing Xue", title = "New Geometric Semantic Operators in Genetic Programming: Perpendicular Crossover and Random Segment Mutation", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4939-0", address = "Berlin, Germany", pages = "223--224", size = "2 pages", URL = "http://doi.acm.org/10.1145/3067695.3076008", DOI = "doi:10.1145/3067695.3076008", acmid = "3076008", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, geometric semantic operators, symbolic regression", month = "15-19 " # jul, abstract = "Various geometric search operators have been developed to explore the behaviours of individuals in genetic programming (GP) for the sake of making the evolutionary process more effective. This work proposes two geometric search operators to fulfil the semantic requirements under the theoretical framework of geometric semantic GP for symbolic regression. The two operators approximate the target semantics gradually but effectively. The results show that the new geometric operators can not only lead to a notable benefit to the learning performance, but also improve the generalisation ability of GP. In addition, they also bring a significant improvement to Random Desired Operator, which is a state-of-the-art geometric semantic operator.", notes = "two benchmarks LD50 and DLBCL Also known as \cite{Chen:2017:NGS:3067695.3076008} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @InProceedings{chen:2017:IES, author = "Qi Chen and Mengjie Zhang and Bing Xue", title = "Genetic Programming with Embedded Feature Construction for High-Dimensional Symbolic Regression", booktitle = "Intelligent and Evolutionary Systems", year = "2017", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-319-49049-6_7", DOI = "doi:10.1007/978-3-319-49049-6_7", } @PhdThesis{QiChen:thesis, author = "Qi Chen", title = "Improving the Generalisation of Genetic Programming for Symbolic Regression", school = "School of Engineering and Computer Science, Victoria University of Wellington", year = "2018", address = "New Zealand", keywords = "genetic algorithms, genetic programming, GPWFS, Semantic GP, VC-Dimension, SRM, Geometric Semantic GP, GSGP, GP-C5.0, GP-RF, GP-GPPI, GPWFS, GPSRM, GPOPSRM, Angle-aware Geometric Semantic Crossover, AGSX, LASSO, RF, Keijzer14, NRMSEs, LD50, DLBCL, RSS, Artificial Intelligence, AI", URL = "http://hdl.handle.net/10063/7029", URL = "https://researcharchive.vuw.ac.nz/xmlui/bitstream/handle/10063/7029/thesis_access.pdf", size = "283 pages", abstract = "Symbolic regression (SR) is a function identification process, the task of which is to identify and express the relationship between the input and output variables in mathematical models. SR is named to emphasize its ability to find the structure and coefficients of the model simultaneously. Genetic Programming (GP) is an attractive and powerful technique for SR, since it does not require any predefined model and has a flexible representation. However, GP based SR generally has a poor generalisation ability which degrades its reliability and hampers its applications to science and real-world modeling. Therefore, this thesis aims to develop new GP approaches to SR that evolve/learn models exhibiting good generalisation ability. This thesis develops a novel feature selection method in GP for high-dimensional SR. Feature selection can potentially contribute not only to improving the efficiency of learning algorithms but also to enhancing the generalisation ability. However, feature selection is seldom considered in GP for high-dimensional SR. The proposed new feature selection method uses GPs built-in feature selection ability and relies on permutation to detect the truly relevant features and discard irrelevant/noisy features. The results confirm the superiority of the proposed method over the other examined feature selection methods including random forests and decision trees on identifying the truly relevant features. Further analysis indicates that the models evolved by GP with the proposed feature selection method are more likely to contain only the truly relevant features and have better interpretability. To address the overfitting issue of GP when learning from a relatively small number of instances, this thesis proposes a new GP approach by incorporating structural risk minimisation (SRM), which is a framework to estimate the generalisation performance of models, into GP. The effectiveness of SRM highly depends on the accuracy of the Vapnik-Chervonenkis (VC) dimension measuring model complexity. This thesis significantly extends an experimental method (instead of theoretical estimation) to measure the VC-dimension of a mixture of linear and nonlinear regression models in GP for the first time. The experimental method has been conducted using uniform and non-uniform settings and provides reliable VC-dimension values. The results show that our methods have an impressively better generalisation gain and evolve more compact model, which have a much smaller behavioural difference from the target models than standard GP and GP with bootstrap, The proposed method using the optimised non-uniform setting further improves the one using the uniform setting. This thesis employs geometric semantic GP (GSGP) to tackle the unsatisfied generalisation performance of GP for SR when no overfitting occurs. It proposes three new angle-awareness driven geometric semantic operators (GSO) including selection, crossover and mutation to further explore the geometry of the semantic space to gain a greater generalisation improvement in GP for SR. The angle-awareness brings new geometric properties to these geometric operators, which are expected to provide a greater leverage for approximating the target semantics in each operation, and more importantly, to be resistant to overfitting. The results show that compared with two kinds of state-of-the-art GSOs, the proposed new GSOs not only drive the evolutionary process fitting the target semantics more efficiently but also significantly improve the generalisation performance. A further comparison on the evolved models shows that the new method generally produces simpler models with a much smaller size and containing important building blocks of the target models.", notes = "supervisors: Prof. Mengjie Zhang and Dr. Bing Xue", } @Article{Chen:ieeeTEC:8462796, author = "Qi Chen and Bing Xue and Mengjie Zhang", title = "Improving Generalisation of Genetic Programming for Symbolic Regression with Angle-Driven Geometric Semantic Operators", journal = "IEEE Transactions on Evolutionary Computation", year = "2019", volume = "23", number = "3", pages = "488--502", month = jun, keywords = "genetic algorithms, genetic programming, Geometric Semantic Operator, Symbolic Regression, Generalisation", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2018.2869621", size = "15 pages", abstract = "Geometric semantic genetic programming has recently attracted much attention. The key innovations are inducing a unimodal fitness landscape in the semantic space and providing a theoretical framework for designing geometric semantic operators. The geometric semantic operators aim to manipulate the semantics of programs by making a bounded semantic impact and generating child programs with similar or better behaviour than their parents. These properties are shown to be highly related to a notable generalisation improvement in genetic programming. However, the potential ineffectiveness and difficulties in bounding the variations in these geometric operators still limits their positive effect on generalisation. This work attempts to further explore the geometry and search space of geometric operators to gain a greater generalisation improvement in genetic programming for symbolic regression. To this end, a new angle-driven selection operator and two new angle-driven geometric search operators are proposed. The angle-awareness brings new geometric properties to these geometric operators, which are expected to provide a greater leverage for approximating the target semantics in each operation, and more importantly, be resistant to over fitting. The experiments show that compared with two state-of-the-art geometric semantic operators, our angle-driven geometric operators not only drive the evolutionary process to fit the target semantics more efficiently but also improve the generalisation performance. A further comparison between the evolved models shows that the new method generally produces simpler models with a much smaller size and is more likely to evolve towards the correct structure of the target models.", notes = "also known as \cite{8462796}", } @Article{Chen:ieeeTEVC, author = "Qi Chen and Mengjie Zhang and Bing Xue", journal = "IEEE Transactions on Evolutionary Computation", title = "Structural Risk Minimisation-Driven Genetic Programming for Enhancing Generalisation in Symbolic Regression", year = "2019", volume = "23", number = "4", pages = "703--717", month = aug, keywords = "genetic algorithms, genetic programming, Symbolic Regression, Generalisation, Structural Risk Minimisation, Vapnik-Chervonenkis Dimension", DOI = "doi:10.1109/TEVC.2018.2881392", ISSN = "1089-778X", size = "15 pages", abstract = "Generalisation ability, which reflects the prediction ability of a learnt model, is an important property in genetic programming for symbolic regression. Structural risk minimisation is a framework providing a reliable estimation of the generalisation performance of prediction models. Introducing the framework into genetic programming has the potential to drive the evolutionary process towards models with good generalisation performance. However, this is tough due to the difficulty in obtaining the Vapnik-Chervonenkis dimension of nonlinear models. To address this difficulty, this paper proposes a structural risk minimisation-driven genetic programming approach, which uses an experimental method (instead of theoretical estimation) to measure the Vapnik-Chervonenkis dimension of a mixture of linear and nonlinear regression models for the first time. The experimental method has been conducted using uniform and non-uniform settings. The results show that our method has impressive generalisation gains over standard genetic programming and genetic programming with the 0.632 bootstrap, and that the proposed method using the non-uniform setting has further improvement than its counterpart using the uniform setting. Further analyses reveal that the proposed method can evolve more compact models, and that the behavioural difference between these compact models and the target models is much smaller than their counterparts evolved by the other genetic programming methods.", notes = "Also known as \cite{8536418}", } @InProceedings{Chen:2019:CEC, author = "Qi Chen and Bing Xue and Mengjie Zhang", title = "Instance based Transfer Learning for Genetic Programming for Symbolic Regression", booktitle = "2019 IEEE Congress on Evolutionary Computation, CEC 2019", year = "2019", editor = "Carlos A. Coello Coello", pages = "3006--3013", address = "Wellington, New Zealand", month = "10-13 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Transfer learning, Symbolic Regression", isbn13 = "978-1-7281-2152-6", DOI = "doi:10.1109/CEC.2019.8790217", size = "8 pages", abstract = "Transfer learning aims to use knowledge acquired from the source domain to improve the learning performance in the target domain. It attracts increasing interests and many transfer learning approaches have been proposed. However, studies on transfer learning for genetic programming for symbolic regression are still rare, although clearly desired, due to the difficulty to evolve models with a good cross-domain generalisation ability. This work proposes a new instance weighting framework for transfer learning in genetic programming for symbolic regression. The key idea is to use a local weight updating scheme to identify and learn from more useful source domain instances and reduce the effort on the source domain instances, which are more different from the target domain data. The experimental results show that the proposed method notably enhances the learning capacity and the generalisation performance of genetic programming on the target domain and also outperforms some state-of-the-art regression methods.", notes = "also known as \cite{8790217} Also https://ecs.wgtn.ac.nz/Groups/ECRG/Talks#26/04/2019 IEEE Catalog Number: CFP19ICE-ART", } @InProceedings{Chen:2019:GECCOcomp, author = "Qi Chen and Bing Xue and Mengjie Zhang", title = "Differential evolution for instance based transfer learning in genetic programming for symbolic regression", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", isbn13 = "978-1-4503-6748-6", pages = "161--162", address = "Prague, Czech Republic", DOI = "doi:10.1145/3319619.3321941", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{3321941} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InProceedings{Chen:2020:GECCO, author = "Qi Chen and Bing Xue and Mengjie Zhang", title = "Improving Symbolic Regression Based on Correlation between Residuals and Variables", year = "2020", editor = "Carlos Artemio {Coello Coello} and Arturo Hernandez Aguirre and Josu Ceberio Uribe and Mario Garza Fabre and Gregorio {Toscano Pulido} and Katya Rodriguez-Vazquez and Elizabeth Wanner and Nadarajen Veerapen and Efren Mezura Montes and Richard Allmendinger and Hugo Terashima Marin and Markus Wagner and Thomas Bartz-Beielstein and Bogdan Filipic and Heike Trautmann and Ke Tang and John Koza and Erik Goodman and William B. Langdon and Miguel Nicolau and Christine Zarges and Vanessa Volz and Tea Tusar and Boris Naujoks and Peter A. N. Bosman and Darrell Whitley and Christine Solnon and Marde Helbig and Stephane Doncieux and Dennis G. Wilson and Francisco {Fernandez de Vega} and Luis Paquete and Francisco Chicano and Bing Xue and Jaume Bacardit and Sanaz Mostaghim and Jonathan Fieldsend and Oliver Schuetze and Dirk Arnold and Gabriela Ochoa and Carlos Segura and Carlos Cotta and Michael Emmerich and Mengjie Zhang and Robin Purshouse and Tapabrata Ray and Justyna Petke and Fuyuki Ishikawa and Johannes Lengler and Frank Neumann", isbn13 = "9781450371285", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377930.3390161", DOI = "doi:10.1145/3377930.3390161", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference", pages = "922--930", size = "9 pages", keywords = "genetic algorithms, genetic programming, generalisation, evaluation measure, symbolic regression", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", abstract = "In traditional regression analysis, a detailed examination of the residuals can provide an important way of validating the model quality. However, it has not been used in genetic programming based symbolic regression. This work aims to fill this gap and propose a new evaluation criterion of minimising the correlation between the residuals of regression models and the independent variables. Based on a recent association detection measure, maximal information coefficient which provides an accurate estimation of the correlation, the new evaluation measure is expected to enhance the generalisation of genetic programming by driving the evolutionary process towards models that are without unnecessary complexity and less likely learning from noise in data. The experiment results show that, compared with standard genetic programming which selects model based on the training error only and two state-of-the-art multiobjective genetic programming methods with mechanisms to prefer models with adequate structures, our new multiobjective genetic programming method minimising both the correlation between residuals and variable, and the training error has a consistently better generalisation performance and evolves simpler models.", notes = "Fitness also includes correlation of error(residuals) with target variables. MIC information content. WCRV. GPSR. NSGA-II. Tikhonov regularization. AQ analytic quotiant \cite{Ni:2012:ieeeTEC}. Also known as \cite{10.1145/3377930.3390161} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @Article{Chen:2020:CYB, author = "Qi Chen and Bing Xue and Mengjie Zhang", journal = "IEEE Transactions on Cybernetics", title = "Genetic Programming for Instance Transfer Learning in Symbolic Regression", year = "2022", volume = "52", number = "1", pages = "25--38", month = jan, keywords = "genetic algorithms, genetic programming, Task analysis, Estimation, Multitasking, Machine learning, Data models, Cybernetics, instance weighting, transfer learning", ISSN = "2168-2275", DOI = "doi:10.1109/TCYB.2020.2969689", abstract = "Transfer learning has attracted more attention in the machine-learning community recently. It aims to improve the learning performance on the domain of interest with the help of the knowledge acquired from a similar domain(s). However, there is only a limited number of research on tackling transfer learning in genetic programming for symbolic regression. This article attempts to fill this gap by proposing a new instance weighting framework for transfer learning in genetic programming-based symbolic regression. In the new framework, differential evolution is employed to search for optimal weights for source-domain instances, which helps genetic programming to identify more useful source-domain instances and learn from them. Meanwhile, a density estimation method is used to provide good starting points to help the search for the optimal weights while discarding some irrelevant or less important source-domain instances before learning regression models. The experimental results show that compared with genetic programming and support vector regression that learn only from the target instances, and learning from a mixture of instances from the source and target domains without any transfer learning component, the proposed method can evolve regression models which not only achieve notably better cross-domain generalization performance in stability but also reduce the trend of overfitting effectively. Meanwhile, these models are generally much simpler than those generated by the other GP methods.", notes = "Also known as \cite{9007621}", } @Article{Qi_Chen:ieeeTEC, author = "Qi Chen and Bing Xue and Mengjie Zhang", title = "Preserving Population Diversity Based on Transformed Semantics in Genetic Programming for Symbolic Regression", journal = "IEEE Transactions on Evolutionary Computation", year = "2021", volume = "25", number = "3", pages = "433--447", month = jun, keywords = "genetic algorithms, genetic programming, Population Diversity, Symbolic Regression", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2020.3046569", size = "15 pages", abstract = "Population diversity plays an important role in avoiding premature convergence in evolutionary techniques including genetic programming. Obtaining an adequate level of diversity during the evolutionary process has became a concern of many previous researches in genetic programming. This work proposes a new novelty metric for entropy based diversity measure for genetic programming. The new novelty metric is based on the transformed semantics of models in genetic programming, where the semantics are the set of outputs of a model on the training data and principal component analysis is used for a transformation of the semantics. Based on the new novelty metric, a new diversity preserving framework, which incorporates a new fitness function and a new selection operator, is proposed to help genetic programming achieve a good balance between the exploration and the exploitation, thus enhancing its learning and generalisation performance. Compared with two stat-of-the-art diversity preserving methods, the new method can generalise better and reduce the overfitting trend more effectively in most cases. Further examinations on the properties of the search process confirm that the new framework notably enhances the evolvability and locality of genetic programming.", notes = "also known as \cite{9302659} Evolutionary Computation Research Group at the School of Engineering and Computer Science, Victoria University of Wellington, Wellington 6140, New Zealand.", } @InProceedings{chen:2022:WiCI, author = "Qi Chen and Bing Xue", title = "Generalisation in Genetic Programming for Symbolic Regression: Challenges and Future Directions", booktitle = "Women in Computational Intelligence", year = "2022", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-030-79092-9_13", DOI = "doi:10.1007/978-3-030-79092-9_13", } @InProceedings{chen:2023:GECCO, author = "Qi Chen and Bing Xue and Wolfgang Banzhaf and Mengjie Zhang", title = "Relieving Genetic Programming from Coefficient Learning for Symbolic Regression via Correlation and Linear Scaling", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "420--428", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, fitness function, correlation, linear scaling, symbolic regression", isbn13 = "9798400701191", DOI = "doi:10.1145/3583131.3595918", size = "9 pages", abstract = "The difficulty of learning optimal coefficients in regression models using only genetic operators has long been a challenge in genetic programming for symbolic regression. As a simple but effective remedy it has been proposed to perform linear scaling of model outputs prior to a fitness evaluation. Recently, the use of a correlation coefficient-based fitness function with a post-processing linear scaling step for model alignment has been shown to outperform error-based fitness functions in generating symbolic regression models. In this study, we compare the impact of four evaluation strategies on relieving genetic programming (GP) from learning coefficients in symbolic regression and focusing on learning the more crucial model structure. The results from 12 datasets, including ten real-world tasks and two synthetic datasets, confirm that all these strategies assist GP to varying degrees in learning coefficients. Among the them, correlation fitness with one-time linear scaling as post-processing, due to be the most efficient while bringing notable benefits to the performance, is the recommended strategy to relieve GP from learning coefficients.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @InCollection{chen:2023:hbeml, author = "Qi Chen and Bing Xue and Will Browne and Mengjie Zhang", title = "Evolutionary Regression and Modelling", booktitle = "Handbook of Evolutionary Machine Learning", publisher = "Springer Nature", year = "2023", editor = "Wolfgang Banzhaf and Penousal Machado and Mengjie Zhang", series = "Genetic and Evolutionary Computation (GEVO)", pages = "121--149", address = "Singapore", edition = "1", month = "2 " # nov, keywords = "genetic algorithms, genetic programming", isbn13 = "978-981-99-3813-1", ISSN = "1932-0167", DOI = "doi:10.1007/978-981-99-3814-8_5", abstract = "Regression and modelling, which identify the relationship between the dependent and independent variables, play an important role in knowledge discovery from data. Symbolic regression goes a step further by learning explicitly symbolic models from data that are potentially interpretable. This chapter provides an overview of evolutionary computation techniques for regression and modelling including coefficient learning and symbolic regression. We introduce the ideas behind various evolutionary computation methods for regression and present a review of the efforts on enhancing learning capability, generalisation, interpretability and imputation of missing data in evolutionary computation for regression.", notes = "also known as \cite{Chen2024}", } @InProceedings{chen:2007:CIDM, author = "Qing-Shan Chen and De-Fu Zhang and Li-Jun Wei and Huo-Wang Chen", title = "A Modified Genetic Programming for Behavior Scoring Problem", booktitle = "IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007", year = "2007", pages = "535--539", address = "Honolulu, HI, USA", month = mar # " 1-" # apr # " 5", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Chinese commercial bank, backpropagation neural network, behavior scoring problem, financial institutions, future credit performance forecasting, real life credit data set, risk management, backpropagation, customer relationship management, financial data processing", ISBN = "1-4244-0705-2", DOI = "doi:10.1109/CIDM.2007.368921", size = "5 pages", abstract = "Behavior scoring is an important part of risk management in financial institutions, which is used to help financial institutions make better decisions in managing existing customers by forecasting their future credit performance. In this paper, a modified genetic programming (MGP) is introduced to solve the behavior scoring problems. A real life credit data set in a Chinese commercial bank is selected as the experimental data to demonstrate the classification accuracy of this method. MGP is compared with back-propagation neural network (BPN), and another GP that uses normalized inputs (NGP), the experimental results show that the MGP has slight better classification accuracy rate than NGP, and outperforms BPN in dealing with behavior scoring problems because of less historical samples of credit data in Chinese commercial banks", bibdate = "2007-09-13", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/cidm/cidm2007.html#Qing-ShanDLH07", } @InProceedings{DBLP:conf/icetc/ChenLLW23, author = "Rui Chen and Fan Lai and Yanmei Li and Xuan Wang", title = "Genetic Programming-Enabled Prediction for Students Academic Performance in Blended Learning", booktitle = "The 15th International Conference on Education Technology and Computers, ICETC 2023", pages = "297--303", publisher = "ACM", year = "2023", month = sep # " 26-28", address = "Barcelona, Spain", keywords = "genetic algorithms, genetic programming, E-Learning, Students academic performance prediction, Blended Learning, Learning behaviours, ANN, SVR, covid-19, online quizzes, online video watching time", isbn13 = "979-8-4007-0911-1/23", URL = "https://doi.org/10.1145/3629296.3629345", DOI = "doi:10.1145/3629296.3629345", timestamp = "Fri, 26 Jan 2024 00:00:00 +0100", biburl = "https://dblp.org/rec/conf/icetc/ChenLLW23.bib", bibsource = "dblp computer science bibliography, https://dblp.org", size = "7 pages", } @InProceedings{DBLP:conf/pricai/ChenMZZ24, author = "Ruiqi Chen and Yi Mei and Fangfang Zhang and Mengjie Zhang", title = "Neural Network Surrogate based on Binary Classification for Assisting Genetic Programming in Searching Scheduling Heuristic", booktitle = "Proceedings of 21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024, Part I", year = "2024", editor = "Rafik Hadfi and Patricia Anthony and Alok Sharma and Takayuki Ito and Quan Bai", volume = "15281", series = "Lecture Notes in Computer Science", pages = "309--321", address = "Kyoto, Japan", month = nov # " 18-24", publisher = "Springer", note = "Trends in Artificial Intelligence", keywords = "genetic algorithms, genetic programming, Dynamic job shop scheduling, DJSS, Surrogate, Neural network, ANN", timestamp = "Sun, 22 Dec 2024 15:48:15 +0100", biburl = "https://dblp.org/rec/conf/pricai/ChenMZZ24.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://rdcu.be/ebPrb", URL = "https://doi.org/10.1007/978-981-96-0116-5_25", DOI = "doi:10.1007/978-981-96-0116-5_25", size = "13 page", notes = "Mar 2025 ebPrb 'Share this paper' broken", } @Article{Chen20102054, author = "Shih-Huang Chen and Jun-Nan Chen", title = "Forecasting container throughputs at ports using genetic programming", journal = "Expert Systems with Applications", volume = "37", number = "3", pages = "2054--2058", year = "2010", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2009.06.054", URL = "http://www.sciencedirect.com/science/article/B6V03-4WNXTWY-M/2/1a5e0fe084ba3ea36303bd280acecc04", keywords = "genetic algorithms, genetic programming, Container throughput, Forecasting", abstract = "To accurately forecast container throughput is crucial to the success of any port operation policy. This study attempts to create an optimal predictive model of volumes of container throughput at ports by using genetic programming (GP), decomposition approach (X-11), and seasonal auto regression integrated moving average (SARIMA). Twenty-nine years of historical data from Taiwan's major ports were collected to establish and validate a forecasting model. The Mean Absolute Percent Error levels between forecast and actual data were within 4percent for all three approaches. The GP model predictions were about 32-36percent better than those of X-11 and SARIMA. These results suggest that GP is the optimal method for this case. GP predicted that container through puts at Taiwan's major ports would slowly increase in the year 2008. Since Taiwan's government opened direct transportation with China in July 2008, the issue of container throughput in Taiwan has become even more worthy of discussion.", } @InProceedings{conf/aici/ChenDW11, author = "Shi-Ming Chen and Yun-Feng Dong and Xiao-Lei Wang", title = "Lateral Jet Interaction Model Identification Based on Genetic Programming", booktitle = "Proceedings Third International Conference on Artificial Intelligence and Computational Intelligence (AICI 2011) Part {I}", year = "2011", editor = "Hepu Deng and Duoqian Miao and Jingsheng Lei and Fu Lee Wang", volume = "7002", series = "Lecture Notes in Computer Science", pages = "484--491", address = "Taiyuan, China", month = sep # " 24-25", publisher = "Springer", keywords = "genetic algorithms, genetic programming, lateral jet, model identification, missile", isbn13 = "978-3-642-23880-2", DOI = "doi:10.1007/978-3-642-23881-9_63", size = "8 pages", abstract = "Precise lateral jet interaction models are required for missiles' blending control strategies. Because of the complicated flow field, the interaction models are multivariable, complex and coupled. Traditional aerodynamics coefficients model identification used Maximum-likelihood estimation to adjust the parameters of the postulation model, but it is not good at dealing with complex nonlinear models. A genetic programming (GP) method is proposed to identify the interaction model, which not only can optimise the parameters, but also can identify the model structure. The interaction model's inputs are altitude, mach number, attack angle and fire number of jets in wind channel experiment results, and its output is interaction force coefficient. The fitness function is root mean square error. Select suitable function set and terminal set for GP, then use GP to evolve model automatically. The identify process with different reproduced probability; crossover probability and mutation probability are compared. Results shows that GP's result error is decrease 30percent than multi-variable regression method.", bibdate = "2011-09-30", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/aici/aici2011-1.html#ChenDW11", } @InProceedings{chen:1995:psmrGP, author = "Shu-Heng Chen and Chia-Hsuan Yeh", title = "Predicting Stock Returns with Genetic Programming: Do the Short-Term Nonlinear Regularities Exist?", booktitle = "Proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics", year = "1995", editor = "Doug Fisher", pages = "95--101", address = "Ft. Lauderdale, Florida, U.S.A.", month = jan # " 4-7", organisation = "Society for Artificial Intelligence and Statistics", keywords = "genetic algorithms, genetic programming", notes = "http://web.archive.org/web/20011127035349/http://www.vuse.vanderbilt.edu/~dfisher/ai-stats/fifth-workshop/contents.html", } @InProceedings{chen:1995:cqtm, author = "Shu-Heng Chen and Chia-Hsuan Yeh", title = "On the Competitiveness of the Quantity Theory of Money: A Natural-Selection Test Based on Genetic Programming", booktitle = "11th International Conference on Advanced Science and Technology", year = "1995", address = "Chicago, Illinois, U.S.A", month = "25-27 " # mar, keywords = "genetic algorithms, genetic programming", } @InProceedings{chen:1995:cale, author = "Shu-Heng Chen and Chia-Hsuan Yeh", title = "On the Coordination and Adaptability of the Large Economy: An Application of Genetic Programming to the Cobweb Model", booktitle = "Proceedings of the First International Conference on Applications of Dynamic Models to Economics", year = "1995", number = "3", series = "The School of Management National Central University's International Conference Series", pages = "121--159", address = "ChungLi, Taiwan, R.O.C.", month = jun # " 17-18", keywords = "genetic algorithms, genetic programming", } @InProceedings{chen:1995:GPpsme, author = "Shu-Heng Chen and Chia-Hsuan Yeh", title = "Genetic Programming, Predictability and Stock Market Efficiency", booktitle = "Proceedings of 1995 IFAC/IFIP/IFORS/SEDC Symposium on Modelling and Control of National and Regional Economies", year = "1995", volume = "II", address = "Gold Coast, Australia", month = jul # " 3-5", organisation = "International Federation of Automatic Control", keywords = "genetic algorithms, genetic programming", } @InProceedings{chen:1995:pcdsGP, author = "Shu-Heng Chen and Chia-Hsuan Yeh", title = "Predicting Chaotic Dynamic Systems with Genetic Programming", booktitle = "Proceedings of the 50th International Statistical Institute Session", year = "1995", address = "Beijing", month = aug # " 21-29", keywords = "genetic algorithms, genetic programming", } @InProceedings{chen:1995:itmeeipt, author = "Shu-Heng Chen and Chia-Hsuan Yeh", title = "Information Transmission, Market Efficiency and the Evolution of Information-Processing Technology", booktitle = "Proceedings of the 1995 National Conference on Management of Technology", year = "1995", editor = "C. Houng", pages = "339--348", organisation = "Chinese Society of Management of Technology", keywords = "genetic algorithms, genetic programming", } @InProceedings{chen:1996:MAAMAW, author = "Shu-Heng Chen and John Duffy and Chia-Hsuan Yeh", title = "Modelling Coordination Game as a Multi-Agent Adaptive System by Genetic Programming", booktitle = "Position Papers of the 7th European Workshop on Modelling Autonomous Agents in a Multi-Agent World (MAAMAW'96)", year = "1996", editor = "W. {Van de Velde} and J. W. Perram", month = jan # " 22-25", organisation = "Institute for Perception Research, Eindhoven, The Netherlands", note = "Technical Report 96-1", keywords = "genetic algorithms, genetic programming", } @InProceedings{chen:1996:GPcfe, author = "Shu-Heng Chen and Chia-Hsuan Yeh", title = "Genetic Programming in Computable Financial Economics", booktitle = "Proceedings of the ISCA 11th Conference: Computers and Their Applications", year = "1996", pages = "135--138", address = "San Francisco, California, U.S.A.", month = mar # " 7-9", publisher = "ISCA Press", keywords = "genetic algorithms, genetic programming", ISBN = "1-880843-15-3", URL = "ftp://econo.nccu.edu.tw/AI-ECON/YEH/1996/ISCA96/isca96.ps", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/15814/ftp:zSzzSzecono.nccu.edu.twzSzAI-ECONzSzYEHzSz1996zSzISCA96zSzisca96.pdf/genetic-programming-in-computable.pdf", URL = "http://citeseer.ist.psu.edu/324902.html", size = "5 pages", abstract = "From a computation-theoretic standpoint, this paper formalises the notion of unpredictability in the efficient market hypothesis (EMH) by a biological-based search program, i.e., genetic programming (GP). This formalisation differs from the traditional notion based on probabilistic independence in its treatment of search. Compared with the traditional notion, a GP-based search provided an explicit and efficient search program upon which an objective measure for predictability can be formalized...", } @InProceedings{chen:1996:bgntemh, author = "Shu-Heng Chen and Chia-Hsuan Yeh", title = "Bridging the Gap between Nonlinearity Tests and the Efficient Market Hypothesis by Genetic Programming", booktitle = "Proceedings of the IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering", year = "1996", pages = "34--39", address = "Crowne Plaza Manhattan, New York City", month = mar # " 24-26", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-3236-9", } @InCollection{chen:1996:GPpsme, author = "Shu-Heng Chen", title = "Genetic Programming, Predictability, and Stock Market Efficiency", booktitle = "Modelling and Control of National and Regional Economies 1995", publisher = "Pergamon", year = "1996", editor = "L. Vlacic and T. Nguyen and D. Cecez-Kecmanovic", pages = "283--288", address = "Oxford, Great Britain", keywords = "genetic algorithms, genetic programming", ISBN = "0-08-042376-0", } @InProceedings{chen:1996:cale:GPcm, author = "Shu-Heng Chen and Chia-Hsuan Yeh", title = "On the Coordination and Adaptability of the Large Economy: An Application of Genetic Programming to the Cobweb Model", booktitle = "Preprints of 13th World Congress International Federation of Automatic Control", year = "1996", volume = "L", pages = "279--284", address = "San Francisco, CA, USA", month = jun # " 30-" # jul # " 5", keywords = "genetic algorithms, genetic programming", } @InCollection{chen:1996:aigp2, author = "Shu-Heng Chen and Chia-Hsuan Yeh", title = "Genetic Programming Learning and the Cobweb Model", booktitle = "Advances in Genetic Programming 2", publisher = "MIT Press", year = "1996", editor = "Peter J. Angeline and K. E. {Kinnear, Jr.}", pages = "443--466", chapter = "22", address = "Cambridge, MA, USA", keywords = "genetic algorithms, genetic programming", ISBN = "0-262-01158-1", URL = "http://www.aiecon.org/staff/shc/pdf/AGP2.pdf", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6277525", DOI = "doi:10.7551/mitpress/1109.003.0029", abstract = "Using genetic programming to model the cobweb model as a multiagent system, this chapter generalises the work done by Arifovic (1994), which is based on genetic algorithms. We find that the rational expectations equilibrium price which can be discovered by genetic algorithms can also be discovered by genetic programming. Furthermore, genetic programming requires much less prior knowledge than genetic algorithms. The reasonable upper limit of the price and the characteristic of the equilibrium which is assumed as the prior knowledge in genetic algorithms can all be discovered by genetic programming. In addition, GP-based markets have a self-stabilising force which is capable of bringing any deviations caused by mutation back to the rational expectations equilibrium price. All of these features show that genetic programming can be a very useful tool for economists to model learning and adaptation in multiagent systems. In particular, with respect to the understanding of the dynamics of the market process, it provides us with a visible foundation for the 'invisible hand'.", size = "18 pages", } @InProceedings{chen:1996:GPcgcbr, author = "Shu-Heng Chen and John Duffy and Chia-Hsuan Yeh", title = "Genetic Programming in the Coordination Game with a Chaotic Best-Response Function", booktitle = "Evolutionary Programming V: Proceedings of the Fifth Annual Conference on Evolutionary Programming", year = "1996", editor = "Lawrence J. Fogel and Peter J. Angeline and Thomas Baeck", pages = "277--286", address = "San Diego", publisher_address = "Cambridge, MA, USA", month = feb # " 29-" # mar # " 3", publisher = "MIT Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-262-06190-2", URL = "ftp://econo.nccu.edu.tw/AI-ECON/YEH/1996/EP96/ep96.ps", URL = "http://citeseer.ist.psu.edu/rd/6296950%2C326396%2C1%2C0.25%2CDownload/http://citeseer.ist.psu.edu/cache/papers/cs/15814/ftp:zSzzSzecono.nccu.edu.twzSzAI-ECONzSzYEHzSz1996zSzEP96zSzep96.pdf/chen96genetic.pdf", URL = "http://citeseer.ist.psu.edu/326396.html", size = "11 pages", abstract = "By modelling the coordination game as GP (Genetic Programming)-based adaptive multiagent systems, this paper analyses the coordination experiments with human subjects conducted by (Van Huyck et al. 1994). In the model on which their experiments were based, the coordination pattern in the equilibrium crucially depends on the learning schemes adopted by the interactive agents in the society. While, in general, we cannot exclude the possibility of chaotic-like coordination, such a result did not...", notes = "EP-96 http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=8383 ", } @Article{chen:1996:caemh, author = "Shu-Heng Chen and Chia-Hsuan Yeh", title = "Toward a Computable Approach to the Efficient Market Hypothesis: An Application of Genetic Programming", journal = "Journal of Economic Dynamics and Control", year = "1997", volume = "21", number = "6", pages = "1043--1063", month = "1 " # jun, keywords = "genetic algorithms, genetic programming, Evolutionary computation, Minimum description length principle, Mean absolute percentage error, Efficient market hypothesis", DOI = "doi:10.1016/S0165-1889(97)82991-0", broken = "http://www.sciencedirect.com/science/article/B6V85-3SWYBJD-P/2/d1bb80ffce780c45697f44001e20f672", abstract = "From a computation-theoretic standpoint, this paper formalises the notion of unpredictability in the efficient market hypothesis (EMH) by a biological-based search program, i.e., genetic programming (GP). This formalization differs from the traditional notion based on probabilistic independence in its treatment of search. Compared with the traditional notion, a GP-based search provides an explicit and efficient search program upon which an objective measure for predictability can be formalized in terms of search intensity and chance of success in the search. This will be illustrated by an example of applying GP to predict chaotic time series. Then the EMH based on this notion will be exemplified by an application to the Taiwan and US stock market. A short-term sample of TAIEX and S&P 500 with the highest complexity defined by Rissanen's minimum description length principle (MDLP) is chosen and tested. It is found that, while linear models cannot predict better than the random walk, a GP-based search can beat random walk by 50%. It, therefore, confirms the belief that while the short-term nonlinear regularities might still exist, the search costs of discovering them might be too high to make the exploitation of these regularities profitable, hence the efficient market hypothesis is sustained.", notes = "Society of Computational Economics Conference JEL classification codes: C63; G14", } @InProceedings{chen:1996:esGP, author = "Shu-Heng Chen and John Duffy and Chia-Hsuan Yeh", title = "Equilibrium Selection Using Genetic Programming", booktitle = "Progress in Neural Information Precessing: Proceedings of the International Conference on Neural Information Processing (ICONIP'96)", year = "1996", editor = "S. Amari and L. Xu and L. Chan and I. King and K. Leung", volume = "2", pages = "1341--1346", address = "Hong Kong Convention and Exhibition Center, Hong Kong", publisher_address = "Singapore", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "981-3083-04-2", URL = "ftp://econo.nccu.edu.tw/AI-ECON/YEH/1996/ICONIP96/iconip96.ps", URL = "http://citeseer.ist.psu.edu/rd/6296950%2C323448%2C1%2C0.25%2CDownload/http://citeseer.ist.psu.edu/cache/papers/cs/15814/ftp:zSzzSzecono.nccu.edu.twzSzAI-ECONzSzYEHzSz1996zSzICONIP96zSziconip96.pdf/equilibrium-selection-using-genetic.pdf", URL = "http://citeseer.ist.psu.edu/323448.html", size = "8 pages", abstract = "We use genetic programming techniques developed by Koza (1992) to model the behaviour of a population of heterogeneous agents playing a simple coordination game with multiple equilibria. We compare the results from our computational experiments with results obtained from a number of controlled laboratory experiments conducted by Van Huyck et al. (1994) where human subjects played the same coordination game. We nd that the behavior exhibited by our population of artificially intelligent...", notes = " ", } @InProceedings{chen:1996:GPlcms, author = "Shu-Heng Chen and Chia-Hsuan Yeh", title = "Genetic Programming Learning in the Cobweb Model with Speculators", booktitle = "Proceedings of 3rd Conference on Business Education", year = "1996", pages = "155--176", address = "Department of Business Education, National Changhua University of Education, Chunghua, Taiwan", month = dec # " 5", keywords = "genetic algorithms, genetic programming", } @InProceedings{chen:1996:GPlcmsICS, author = "Shu-Heng Chen and Chia-Hsuan Yeh", title = "Genetic Programming Learning in the Cobweb Model with Speculators", booktitle = "International Computer Symposium (ICS'96). Proceedings of International Conference on Artificial Intelligence", year = "1996", pages = "39--46", address = "National Sun Yat-Sen University, Kaohsiung, Taiwan, R.O.C.", month = dec # " 19-21", keywords = "genetic algorithms, genetic programming", } @Article{chen:1996:itmeeipt, author = "Shu-Heng Chen and Chia-Hsuan Yeh", title = "Information Transmission, Market Efficiency and the Evolution of Information-Processing Technology", journal = "Journal of Technology Management", year = "1996", volume = "1", number = "1", pages = "23--41", keywords = "genetic algorithms, genetic programming", } @InProceedings{chen:1996:cfaGPothers, author = "Shu-Heng Chen and Chia-Hsuan Yeh", title = "A Comparison of Forcast Accuracy between Genetic Programming and Other Forcasters: A loss-Differential Approach", booktitle = "The First International Workshop on Machine Learning, Forecasting, and Optimization (MALFO96)", year = "1996", editor = "Daniel Borrajo and Pedro Isasi", pages = "39--51", address = "Gatafe, Spain", month = "10--12 " # jul, organisation = "Universidad Carlos III de Madrid", keywords = "genetic algorithms, genetic programming", ISBN = "84-89315-04-3", broken = "http://grial.uc3m.es/~dborrajo/malfo96.html", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/chen_1996_cfaGPothers.pdf", size = "13 pages", } @InProceedings{chen:1996:gpemh, author = "Shu-Heng Chen and Chia-Hsuan Yeh", title = "Genetic Programming and the Efficient Market Hypothesis", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "45--53", address = "Stanford University, CA, USA", publisher = "MIT Press", URL = "ftp://econo.nccu.edu.tw/AI-ECON/YEH/1996/GP96/gp96.ps", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/15814/ftp:zSzzSzecono.nccu.edu.twzSzAI-ECONzSzYEHzSz1996zSzGP96zSzgp96.pdf/chen96genetic.pdf", URL = "http://citeseer.ist.psu.edu/chen96genetic.html", size = "9 pages", abstract = "While search plays an important role in the efficient market hypothesis (EMH), the traditional formalisation of the EMH, based on probabilistic independence, fails to capture it. Due to this failure, recent findings of nonlinear tests misled us into concluding that the EMH is rejected. Even though most economists are reluctant to make this conclusion, the traditional formalization leaves us no other choice. This paper reformalizes the EMH with a biologically-based search program, i.e., genetic...", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap6.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96", } @InProceedings{chen:1997:stfr, author = "Shu-Heng Chen and Chia-Hsuan Yeh", title = "Speculative Trades and Financial Regulations: Simulations Based on Genetic Programming", booktitle = "Proceedings of the IEEE/IAFE 1997 Computational Intelligence for Financial Engineering (CIFEr'97)", year = "1997", pages = "123--129", address = "New York City, U.S.A.", month = mar # " 24-25", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, 2D parameter space, cobweb markets, financial regulations, market efficiency, price volatility reduction, simulations, speculative trades, unstable economy, economics, financial data processing, mathematical programming, simulation, stock markets", DOI = "doi:10.1109/CIFER.1997.618924", size = "7 pages", abstract = "By exploring a two-dimensional parameter space, the paper pinpoints the area where speculative trades can contribute to the reduction of price volatility and are hence imperative for market efficiency. This area is delimited by a rather restrictive financial regulations imposed on an inherently unstable economy. Specifically, depending on the associated financial regulations, the authors' GP-based simulations of cobweb markets show that speculative trades may reduce price volatility by 20percent to 50percent in an inherently unstable economy; on the other hand they may also increase price volatility by 300percent to 3000percent. The paper generalises the earlier finding by Chen and Yeh (1997), which basically shows that in an inherently stable economy, speculative trades can only be destabilising", } @InProceedings{chen:1997:setpGP, author = "Shu-Heng Chen and Chia-Hsuan Yeh", title = "Simulating Economic Transition Processes by Genetic Programming", booktitle = "Proceedings of the International Conference on Transition to Advanced Market Institutions and Economies: Systems and Operations Research Challenges (Transition'97)", year = "1997", editor = "R. Kulikowski and Z. Nahorski and J. W. Owsinski", pages = "87--93", address = "Warsaw, Poland", month = jun # " 18-21", organisation = "System Research Institute and Polish Academy of Sciences", keywords = "genetic algorithms, genetic programming", ISBN = "83-85847-81-2", } @InProceedings{chen:1997:trstpv, author = "Shu-Heng Chen and Chia-Hsuan Yeh", title = "Trading Restrictions, Speculative Trades and Price Volatility: An Application of Genetic Programming", booktitle = "Proceedings of the 3rd International Mendel Conference on Genetic Algorithms, Optimization Problems, Fuzzy Logic, Neural Networks, Rough Sets (Mendel'97)", year = "1997", pages = "31--37.", address = "Brno, Czech Republic", publisher_address = "Brno", month = jun # " 25-27", publisher = "PC-DIR", keywords = "genetic algorithms, genetic programming", ISBN = "80-214-0884-7", URL = "ftp://econo.nccu.edu.tw/AI-ECON/YEH/1997/MENDEL97/mendel97.ps", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/15814/ftp:zSzzSzecono.nccu.edu.twzSzAI-ECONzSzYEHzSz1997zSzMENDEL97zSzmendel97.pdf/chen97trading.pdf", URL = "http://citeseer.ist.psu.edu/chen97trading.html", size = "8 pages", abstract = "n this paper, genetic programming is employed to explore the significance of speculative activities in economic theory. Unlike most previous studies, this paper explicitly take interaction of speculators into account. Through genetic programming, this interaction processes is modelled as a competitive process which applies the survival-of-the-fittest principle to the selection of trading strategies. There are two interesting findings which make this paper distinctive. Firstly, while markets...", } @InProceedings{chen:1997:eannGPfd, author = "Shu-Heng Chen and Chih-Chi Ni", title = "Evolutionary Artificial Neural Networks and Genetic Programming: A Comparative Study Based on Financial Data", booktitle = "Artificial Neural Nets and Genetic Algorithms: Proceedings of the International Conference, ICANNGA97", year = "1997", editor = "George D. Smith and Nigel C. Steele and Rudolf F. Albrecht", pages = "397--400", address = "University of East Anglia, Norwich, UK", publisher = "Springer-Verlag", note = "published in 1998", keywords = "genetic algorithms, genetic programming", ISBN = "3-211-83087-1", DOI = "doi:10.1007/978-3-7091-6492-1_87", abstract = "In this paper, the stock index S&P 500 is used to test the predicting performance of genetic programming (GP) and genetic programming neural networks (GPNN). While both GP and GPNN are considered universal approximators, in this practical financial application, they perform differently. GPNN seemed to suffer the overlearning (over fitting) problem more seriously than GP; the latter outdid the former in all the simulations.", notes = "http://www.sys.uea.ac.uk/Research/ResGroups/MAG/ICANNGA97/papers_frame.html Opps duplicates chen:1997:eANNGP Note: 22 Aug 2004 chen:1997:eANNGP combined with \cite{chen:1997:eannGPfd}", } @InProceedings{chen:1997:msGP, author = "Shu-Heng Chen and Chia-Hsuan Yeh", title = "Modeling Speculators with Genetic Programming", booktitle = "Proceedings of the Sixth Conference on Evolutionary Programming", year = "1997", editor = "Peter J. Angeline and Robert G. Reynolds and John R. McDonnell and Russ Eberhart", volume = "1213", series = "Lecture Notes in Computer Science", pages = "137--147", address = "Indianapolis, Indiana, USA", publisher_address = "Berlin", month = apr # " 13-16", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, no-trade theorems", isbn13 = "978-3-540-62788-3", URL = "ftp://econo.nccu.edu.tw/AI-ECON/YEH/1997/EP97/ep97.ps", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/15814/ftp:zSzzSzecono.nccu.edu.twzSzAI-ECONzSzYEHzSz1997zSzEP97zSzep97.pdf/chen96modeling.pdf", URL = "http://citeseer.ist.psu.edu/chen96modeling.html", DOI = "doi:10.1007/BFb0014807", size = "11 pages", abstract = "In spirit of the earlier works done by Arthur (1992) and Palmer et al. (1993), this paper models speculators with genetic programming (GP) in a production economy (Muthian Economy). Through genetic programming, we approximate the consequences of speculating about the speculations of others, including the price volatility and the resulting welfare loss. Some of the patterns observed in our simulations are consistent with findings in experimental markets with human subjects. For example, we show that GP-based speculators can be noisy by nature. However, when appropriate financial regulations are imposed, GP-based speculators can also be more informative than noisy.", notes = "EP-97", } @InProceedings{chen:1997:GPmvfts, author = "Shu-Heng Chen and Chia-Hsuan Yeh", title = "Using Genetic Programming to Model Volatility in Financial Time Series", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming", pages = "58--63", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", ISBN = "1-55860-483-9", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1997/chen_1997_GPmvfts.pdf", size = "6 pages", abstract = "RGP tested by using Nikkei 255 and S&P 500 as an example", notes = "GP-97 Fixed size sliding window of the original time series. BGP used to learn first window, then whole pop used with second window (ie as population seed). Fitness = sum of errors squared also serves to give estimate of volatility.", } @InProceedings{chen:1997:GPmvfts:NS+P, author = "Shu-Heng Chen and Chia-Hsuan Yeh", title = "Using Genetic Programming to Model Volatility in Financial Time Series: The Case of {Nikkei 225} and {S\&P 500}", booktitle = "Proceedings of the 4th JAFEE International Conference on Investments and Derivatives (JIC'97)", year = "1997", pages = "288--306", address = "Aoyoma Gakuin University, Tokyo, Japan", month = jul # " 29-31", keywords = "genetic algorithms, genetic programming, recursive genetic programming, structural changes, model-specific structural changes, model-free structural changes, improvement sequence", URL = "ftp://econo.nccu.edu.tw/AI-ECON/YEH/1997/JIC97/jic97.ps", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/15814/ftp:zSzzSzecono.nccu.edu.twzSzAI-ECONzSzYEHzSz1997zSzJIC97zSzjic97.pdf/chen97using.pdf", URL = "http://citeseer.ist.psu.edu/322892.html", size = "16 pages", abstract = "In this paper we propose a time-variant and non-parametric approach to estimating volatility. This approach is based on recursive genetic programming (RGP). Here, volatility is estimated by a class of non-parametric models which are generated through a recursive competitive process. The essential feature of this approach is that it can estimate volatility by simultaneously detecting and adapting to structural changes. Thus, volatility is estimated by taking possible structural changes into account. When RGP discovers structural changes, it will quickly suggest a new class of models so that overestimation of volatility due to ignorance of structural changes can be avoided. The idea of this work is motivated by two lines of research in two different fields; one is the volatility estimation through articial neural nets in nancial engineering, and the other the design of robust adaptive systems under uncertain circumstances in articial intelligence. In this paper, the idea is ...", notes = "https://ci.nii.ac.jp/ncid/AA12420031", } @InProceedings{chen:1997:stfr:ICJAI, author = "Shu-Heng Chen and Chia-Hsuan Yeh", title = "Speculative Trades and Financial Regulations: Simulation Bassed on Genetic Programming", booktitle = "Working Notes of The IJCAI-97: Workshop on Business Applications of AI. Fifteenth International Joint Conference on Artificial Intelligence (IJCAI'97)", year = "1997", editor = "A. Ghose", pages = "1--8", address = "Nagoya, Japan", month = aug # " 23-29", keywords = "genetic algorithms, genetic programming", } @InProceedings{chen:1997:mscGPo, author = "Shu-Heng Chen and Chia-Hsuan Yeh", title = "Modelling Structural Changes with Genetic Programming: An Outline", booktitle = "Proceedings of 15th IMACS World Congress on Scientific Computation, Moldelling and Applied Mathematics", year = "1997", editor = "A. Sydow", volume = "2", pages = "621--626", address = "Berlin", month = aug # " 24-29", publisher = "Numerical Mathematics, Wissenschaft \& Technik Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-89685-552-2", } @InCollection{Chen:1997:SunYatSen, author = "Shuheng Chen and Jiaxuan Ye", title = "Competition in {"}Quantity theory of money{"} : Genetic Programming Application in Knowledge Discovery", booktitle = "Development(s) and Application(s) of Measurement Method(s) in Social Science", publisher = "Sun Yat-Sen Institute for Social Sciences and Philosophy", year = "1997", editor = "Wenshan Yang", number = "41", series = "Literature of Sun Yat-Sen Institute for Social Sciences and Philosophy", chapter = "7", pages = "139--183", address = "Taipei, Taiwan", month = sep, keywords = "genetic algorithms, genetic programming", URL = "http://www.issp.sinica.edu.tw/chinese/book/ebook/pdf1/bk41/charp-7.pdf", notes = "In Chinese. Description of GP being used for economic modeling of GDP based on \cite{koza:book}. Tests GP's ability to {"}discover{"} money supply equation M2-GNP in USA and in Taiwanese datasets. Also known as \cite{bk41/charp-7}", size = "52 pages", } @InProceedings{chen:1998:GPogmidir, author = "Shu-Heng Chen and Chia-Hsuan Yeh", title = "Genetic programming in the overlapping generations model: An illustration with the dynamics of the inflation rate", booktitle = "Evolutionary Programming VII: Proceedings of the Seventh Annual Conference on Evolutionary Programming", year = "1998", editor = "V. William Porto and N. Saravanan and D. Waagen and A. E. Eiben", volume = "1447", series = "LNCS", pages = "829--837", address = "Mission Valley Marriott, San Diego, California, USA", publisher_address = "Berlin", month = "25-27 " # mar, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-64891-7", URL = "http://link.springer.com/chapter/10.1007/BFb0040833", DOI = "doi:10.1007/BFb0040833", abstract = "In this paper, genetic programming (GP) is employed to model learning and adaptation in the overlapping generations model, one of the most popular dynamic economic models. Using a model of inflation with multiple equilibria as an illustrative example, we show that our GP-based agents are able to coordinate their actions to achieve the Pareto-superior equilibrium (the low-inflation steady state) rather than the Pareto-inferior equilibrium (the high-inflation steady state). We also test the robustness of this result with different initial conditions, economic parameters, and GP control parameters.", notes = "EP-98. National Chengchi University", } @InProceedings{chen:1998:opGP, author = "Shu-Heng Chen and Chia-Hsuan Yeh and Woh-Chiang Lee", title = "Option Pricing with Genetic Programming", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "32--37", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-548-7", URL = "ftp://econo.nccu.edu.tw/AI-ECON/YEH/1998/GP98/gp98.ps", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/15815/ftp:zSzzSzecono.nccu.edu.twzSzAI-ECONzSzYEHzSz1998zSzGP98zSzgp98.pdf/option-pricing-with-genetic.pdf", URL = "http://citeseer.ist.psu.edu/324313.html", size = "7 pages", abstract = "One of the most recent applications of GP to finance is to use genetic programming to derive option pricing formulas. Earlier studies take the Black-Scholes model as the true model and use the artificial data generated by it to train and to test GP. This paper may be regarded as the first attempt to provide some initial evidence of the empirical relevance of GP to option pricing. By using the real data from S&P 500 index options, we train and test two styles of GP, one-stage GP which does not...", notes = "GP-98", } @InProceedings{chen:1998:hdsGP, author = "Shu-Heng Chen and W.-C. Lee and C.-H. Yeh", title = "Hedging Derivative Securities with Genetic Programming", booktitle = "Application of Machine Learning and Data Mining in Finance: Workshop at ECML-98", year = "1998", editor = "G. Nakhaeizadeh and E. Steurer", pages = "140--151", address = "Dorint-Parkhotel, Chemnitz, Germany", month = "24 " # apr, keywords = "genetic algorithms, genetic programming", ISSN = "0947-5125", notes = "ECML-98 workshop 6 Broken Sep 2018 http://www.tu-chemnitz.de/informatik/ecml98/ws6_ag.txt See also \cite{Chen:1999:ISAFM}", } @InProceedings{oai:CiteSeerPSU:454950, author = "Shu-Heng Chen and Hung-Shuo Wang and Byoung-Tak Zhang", title = "Forecasting High-Frequency Financial Time Series with Evolutionary Neural Trees: The Case of {Hang-Seng} Stock Index", booktitle = "Proceedings of the International Conference on Artificial Intelligence, IC-AI '99", year = "1999", editor = "Hamid R. Arabnia", volume = "2", pages = "437--443", address = "Las Vegas, Nevada, USA", month = "28 " # jun # "-1 " # jul, publisher = "CSREA Press", keywords = "genetic algorithms, genetic programming, Evolutionary Artificial Neural Networks, Neural Trees, Sigma-Pi Neural Trees, Breeder Genetic Algorithm", ISBN = "1-892512-17-3", bibsource = "DBLP, http://dblp.uni-trier.de", URL = "http://bi.snu.ac.kr/Publications/Conferences/International/ICAI99.ps", URL = "http://citeseer.ist.psu.edu/454950.html", citeseer-isreferencedby = "oai:CiteSeerPSU:407872; oai:CiteSeerPSU:67015", citeseer-references = "oai:CiteSeerPSU:4642; oai:CiteSeerPSU:185401; oai:CiteSeerPSU:103144", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:454950", rights = "unrestricted", size = "7 pages", abstract = "In this paper, the evolutionary neural trees (ENT) are applied to forecasing the highfrequency stock returns of Heng-Sheng stock index on December, 1998. To understand what may consistute an effective implementation, six experiments are conducted. These experiments are different in data-preprocessing procedures, sample sizes, search intensity and complexity regularization. Our results shows that ENT can perform more efficiently if we can associate ENT with a linear filter so that it can concentrate on searching in the space of nonlinear signals. Also, as well demonstarted in this study, the infrequent bursts (outliers) appearing in the high-frequency data can be very disturbing for the normal operation of ENT.", notes = "broken 2022 http://www.sigmod.org/sigmod/dblp/db/conf/icai/icai1999-2.html This dataset (HSIX.HF) was downloaded from the Bridge company. The Hong Kong stock market opens 4 hours a day and five days a week. The specific period considered by us has 22 working days and 4586 observations.", } @InProceedings{chen:1999:GATSSPSNEMCS, author = "Shu-Heng Chen and Wei-Yuan Lin and Chueh-Iong Tsao", title = "Genetic Algorithms, Trading Strategies and Stochastic Processes: Some New Evidence from Monte Carlo Simulations", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "114--121", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-397.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-397.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{SHChen:1999:gpabmsm, author = "Shu-Heng Chen and Chia-Hsuan Yeh", title = "Genetic Programming in the Agent-Based Modeling of Stock Markets", booktitle = "Fifth International Conference: Computing in Economics and Finance", year = "1999", editor = "David A. Belsley and Christopher F. Baum", pages = "77", address = "Boston College, MA, USA", month = "24-26 " # jun, note = "Book of Abstracts", keywords = "genetic algorithms, genetic programming, Agent-Based Computational Economics, Social Learning, Business School, Artificial Stock Markets, Simulated Annealing, Peer Pressure", URL = "http://fmwww.bc.edu/cef99/papers/ChenYeh.pdf", size = "22 pages", abstract = "In this paper, we propose a new architecture to study artificial stock markets. This architecture rests on a mechanism called school which is a procedure to map the phenotype to the genotype or, in plain English, to uncover the secret of success. We propose an agent-based model of school, and consider school as an evolving population driven by single-population GP (SGP). The architecture also takes into consideration traders' search behavior. By simulated annealing, traders' search density can be connected to psychological factors, such as peer pressure or economic factors such as the standard of living. This market architecture was then implemented in a standard artificial stock market. Our econometric study of the resultant artificial time series evidences that the return series is independently and identically distributed (iid), and hence supports the efficient market hypothesis (EMH). What is interesting though is that this iid series was generated by traders, who do not believe in the EMH at all. In fact, our study indicates that many of our traders were able to find useful signals quite often from business school, even though these signals were short-lived.", notes = "PDF and abstract on paper differ in detail. Using PDF info", } @InProceedings{chen:1999:TAFFEAABGAM, author = "Shu-Heng Chen and Tzu-Wen Kuo", title = "Towards an Agent-Based Foundation of Financial Econometrics: An Approach Based on Genetic-Programming Artificial Markets", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "966", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-425c.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-425c.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{chen:1999:GPAASM, author = "Shu-Heng Chen and Chia-Hsuan Yeh", title = "Genetic Programming in the Agent-Based Artificial Stock Market", booktitle = "Proceedings of the Congress on Evolutionary Computation", year = "1999", editor = "Peter J. Angeline and Zbyszek Michalewicz and Marc Schoenauer and Xin Yao and Ali Zalzala", volume = "2", pages = "834--841", address = "Mayflower Hotel, Washington D.C., USA", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "6-9 " # jul, organisation = "Congress on Evolutionary Computation, IEEE / Neural Networks Council, Evolutionary Programming Society, Galesia, IEE", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, algorithms, agent-based, agent-based computational economics, artificial stock markets, business school, peer pressure, simulated annealing, social learning, time series, economics, simulated annealing, software agents, stock markets", ISBN = "0-7803-5536-9 (softbound)", ISBN = "0-7803-5537-7 (Microfiche)", DOI = "doi:10.1109/CEC.1999.782509", abstract = "In this paper, we propose a new architecture to study artificial stock markets. This architecture rests on a mechanism called 'school' which is a procedure to map the phenotype to the genotype or, in plain English, to uncover the secret of success. We propose an agent-based model of school, and consider school as an evolving population driven by single-population GP (SGP). The architecture also takes into consideration traders' search behaviour. By simulated annealing, traders' search density can be connected to psychological factors, such as peer pressure or economic factors such as the standard of living. This market architecture was then implemented in a standard artificial stock market. Our econometric study of the resultant artificial time series evidences that the return series is independently and identically distributed (iid), and hence supports the efficient market hypothesis (EMH). What is interesting though is that this lid series was generated by 'traders' who do not believe in the EMH at all. In fact, our study indicates that many of our traders were able to find useful signals quite often from business school, even though these signals were short-lived", notes = "CEC-99 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. Library of Congress Number = 99-61143", } @Article{chen:1999:SC, author = "Shu-Heng Chen and Chia-Hsuan Yeh", title = "Modeling the expectations of inflation in the OLG model with genetic programming", journal = "Soft Computing - A Fusion of Foundations, Methodologies and Applications", year = "1999", volume = "3", number = "3", pages = "53--62", month = sep, keywords = "genetic algorithms, genetic programming, overlapping generations models, bounded rationality, agent-based computational economics, Pareto-superior equilibrium", ISSN = "1432-7643", DOI = "doi:10.1007/s005000050053", abstract = "genetic programming (GP) is employed to model learning and adaptation in the overlapping generations model, one of the most popular dynamic economic models. Using a model of inflation with multiple equilibria as an illustrative example, we show that our GP-based agents are able to coordinate their actions to achieve the Pareto-superior equilibrium (the low-inflation steady state) rather than the Pareto inferior equilibrium (the high-inflation steady state). We also test the robustness of this result with different initial conditions, economic parameters, GP control parameters, and the selection mechanism. We find that as long as the survival-of-the-fittest principle is maintained, the evolutionary operators are only secondarily important. However, once the survival-of-the-fittest principle is absent, the well-coordinated economy is also gone and the inflation rate can jump quite wildly. To some extent, these results shed light on the biological foundations of economics.", } @Article{Chen:1999:ISAFM, author = "Shu-Heng Chen and Wo-Chiang Lee and Chia-Hsuan Yeh", title = "Hedging derivative securities with genetic programming", journal = "Intelligent Systems in Accounting, Finance and Management", year = "1999", volume = "8", number = "4", pages = "237--251", month = dec, note = "Special Issue: Machine Learning and Data Mining in Finance", keywords = "genetic algorithms, genetic programming, option pricing, Black-Scholes model, tracking error", ISSN = "1099-1174", DOI = "doi:10.1002/(SICI)1099-1174(199912)8:4%3C237::AID-ISAF174%3E3.0.CO%3B2-J", size = "15 pages", abstract = "One of the most recent applications of GP to finance is to use genetic programming to derive option pricing formulae. Earlier studies take the BlackScholes model as the true model and use the artificial data generated by it to train and to test GP. The aim of this paper is to provide some initial evidence of the empirical relevance of GP to option pricing. By using the real data from S&P 500 index options, we train and test our GP by distinguishing the case in-the-money from the case out-of-the-money. Unlike most empirical studies, we do not evaluate the performance of GP in terms of its pricing accuracy. Instead, the derived GP tree is compared with the Black-Scholes model in its capability to hedge. To do so, a notion of tracking error is taken as the performance measure. Based on the post-sample performance, it is found that in approximately 20percent of the 97 test paths GP has a lower tracking error than the Black--Scholes formula. We further compare our result with the ones obtained by radial basis functions and multilayer perceptrons and one-stage GP", notes = "See also \cite{chen:1998:hdsGP}", } @InProceedings{RePEc:sce:scecf0:328, author = "Shu-Heng Chen and Chung-Chi Liao and Chi-Hsuan Yeh", title = "On The Emergent Properties Of Artificial Stock Markets: Some Initial Evidences", booktitle = "Computing in Economics and Finance", year = "2000", address = "Universitat Pompeu Fabra, Barcelona, Spain", month = "6-8 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://econpapers.repec.org/paper/scescecf0/328.htm", abstract = "Using the framework of agent-based artificial stock markets, this paper addresses the two well-known properties frequently observed in financial markets, namely, price-volume relation and sunspots, from a bottom-up perspective. In spirit of ``bottom-up'', these two phenomena are pursued in a more fundamental level, i.e., we are asking: is it possible to observed the emergence of these phenomena without explicit references to the assumptions frequently used by the studies in a ``top-down'' style? Posing it slightly different, would it be enough to generate these phenomena once we model the market as an evolving decentralised system of autonomous interacting agents? Or, can these two phenomenon be coined as ``emergent phenomena'', a terminology from complex adaptive systems.To do so, simulation based on AIE-ASM Version 3 (Chen and Yeh, 2000) are conducted for multiple runs. Within the genetic programming framework, we include trading volume and some irrelevant exogenous variables into the terminal sets. This make it possible that trader can choose to believe that trading volume or sunspots can help forecast the future movement of stock returns if they are convinced so from the market behaviour endogenously generated by themselves. To have a further examination on the emergence of sunspot effects, sunspots are generated by deterministic cyclic processes, such as sin curve, and the purely iid random processes. We then test the emergent of these two phenomena by using a new version of the Granger causality test, which does not require an ad-hoc procedure of filtering.", notes = "http://ideas.repec.org/p/sce/scecf0/328.html CEF 2000 number 328", } @InProceedings{Shu-HengChen:2000:CEF, author = "Shu-Heng Chen", title = "On Bargaining Strategies in the SFI Double Auction Tournaments: Is Genetic Programming the Answer?", booktitle = "Computing in Economics and Finance", year = "2000", address = "Universitat Pompeu Fabra, Barcelona, Spain", month = "6-8 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://EconPapers.repec.org/RePEc:sce:scecf0:329", abstract = "While early computational studies of bargaining strategies, such as Rust, Miller and Palmer (1993, 1994) and Andrew and Prager (1996) all indicates the significance of agent-based modeling in the follow-up research, a real agent-based model of bargaining strategies in DA markets has never been taken. This paper attempts to take the fisrt step toward it. In this paper, genetic programming is employed to evolve bargaining strategies within the context of SFI double auction tournaments. We are interested in knowing that given a set of traders, each with a fixed trading strategies, can the automated trader driven by genetic programming eventually develop bargaining strategies which can outperform its competitors' strategies? To see how GP trader can survive in various environments, different sets of traders characterized by different compositions of bargaining strategies are chosen to compete with the single GP trader. To give a measure of the difficult level of the DA auction markets facing the GP trader, the program length is used to define the intelligence of chosen traders. In one experiment, the chosen traders are all naive; in another experiment, the traders are all sophisticated. Other experiments are placed in the middle of these two extremes.", notes = "http://enginy.upf.es/SCE/index2.html 22 Aug 2004 http://ideas.repec.org/p/sce/scecf0/329.html", } @InProceedings{Chen:2000:TAB, author = "Shu-Heng Chen", title = "Toward an Agent-Based Computational Modeling of Bargaining Strategies in Double Auction Markets with Genetic Programming", booktitle = "Intelligent Data Engineering and Automated Learning - IDEAL 2000: Data Mining, Financial Engineering, and Intelligent Agents", editor = "Kwong Sak Leung and Lai-Wan Chan and Helen Meng", year = "2000", series = "Lecture Notes in Computer Science", volume = "1983", pages = "517--531", address = "Shatin, N.T., Hong Kong, China", month = "13-15 " # dec, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-41450-9", CODEN = "LNCSD9", ISSN = "0302-9743", bibdate = "Tue Sep 10 19:08:58 MDT 2002", URL = "http://www.aiecon.org/staff/shc/pdf/toward_an_agent.pdf", URL = "http://citeseer.ist.psu.edu/463839.html", DOI = "doi:10.1007/3-540-44491-2_76", acknowledgement = ack-nhfb, size = "15 pages", abstract = "Using genetic programming, this paper proposes an agent- based computational modelling of double auction (DA) markets in the sense that a DA market is modeled as an evolving market of autonomous interacting traders (automated software agents). The specific DA market on which our modeling is based is the Santa Fe DA market ([12], [13]), which in structure, is a discrete-time version of the Arizona continuous- time experimental DA market ([14], [15]).", } @Article{Chen:2000:AOR, author = "Shu-Heng Chen and Chia-Hsuan Yeh", title = "Simulating economic transition processes by genetic programming", journal = "Annals of Operations Research", year = "2000", volume = "97", number = "1-4", pages = "265--286", month = dec, keywords = "genetic algorithms, genetic programming, Kolmogorov complexity, minimum description length principle, bounded rationality, short selling", ISSN = "0254-5330", DOI = "doi:10.1023/A:1018972006990", abstract = "Recently, genetic programming has been proposed to model agents' adaptive behaviour in a complex transition process where uncertainty cannot be formalised within the usual probabilistic framework. However, this approach has not been widely accepted by economists. One of the main reasons is the lack of the theoretical foundation of using genetic programming to model transition dynamics. Therefore, the purpose of this paper is two-fold. First, motivated by the recent applications of algorithmic information theory in economics, we would like to show the relevance of genetic programming to transition dynamics given this background. Second, we would like to supply two concrete applications to transition dynamics. The first application, which is designed for the pedagogic purpose, shows that genetic programming can simulate the non-smooth transition, which is difficult to be captured by conventional toolkits, such as differential equations and difference equations. In the second application, genetic programming is applied to simulate the adaptive behavior of speculators. This simulation shows that genetic programming can generate artificial time series with the statistical properties frequently observed in real financial time series.", } @InProceedings{oai:CiteSeerPSU:475338, author = "Shu-Heng Chen and Bin-Tzong Chie", title = "The Schema Analysis of Emergent Bargaining Strategies in Agent-Based Double Auction Markets", booktitle = "Fourth International Conference on Computational Intelligence and Multimedia Applications (ICCIMA'01)", year = "2001", pages = "61", address = "Yokusike City, Japan", month = "30 " # oct # "-1 " # nov, keywords = "genetic algorithms, genetic programming, Double Auctions, Bargaining Strategies, Predatory Pricing, Truth-Tellers", URL = "http://www.aiecon.org/staff/shc/pdf/iccima3.pdf", URL = "http://csdl.computer.org/comp/proceedings/iccima/2001/1312/00/13120061abs.htm", URL = "http://citeseer.ist.psu.edu/475338.html", citeseer-isreferencedby = "oai:CiteSeerPSU:72003", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:475338", rights = "unrestricted", abstract = "In this paper, we simulate the double auction markets with AIE-DA Ver.2. Given that all traders are truth tellers and non-adaptive, we find that the GP trader can always find the most profitable trading strategies. Furthermore, the analysis shows that the trading strategies discovered by GP are very market-specific, which makes our artificial bargaining agent behave quite intelligently.", } @InProceedings{Chen:2001:ICCIMA1, author = "Shu-Heng Chen", title = "Evolving Bargaining Strategies with Genetic Programming: An Overview of {AIE-DA}, Ver. 2, Part 1", booktitle = "Fourth International Conference on Computational Intelligence and Multimedia Applications, ICCIMA 2001", year = "2001", pages = "48--54", address = "Yokusika City, Japan", month = "30 " # oct # "-1 " # nov, keywords = "genetic algorithms, genetic programming", isbn13 = "0-7695-1312-3", URL = "http://www.aiecon.org/staff/shc/pdf/iccima1.pdf", size = "7 pages", abstract = "The purpose of this paper is to introduce the software system A IE-DA, which is designed for the implementation of an agent-based modelling of double auction markets. We shall start this introduction with the current version, Version 2, and then indicate what can be expected from the future of it.", notes = "July 2016 cannot be found in IEEE xplor See also \cite{Chen:2001:ICCIMA2}", } @InProceedings{Chen:2001:ICCIMA2, author = "Shu-Heng Chen and Bin-Tzong Chie and Chung-Ching Tai", title = "Evolving Bargaining Strategies with Genetic Programming: An Overview of {AIE-DA}, Ver. 2, Part 2", booktitle = "Fourth International Conference on Computational Intelligence and Multimedia Applications, ICCIMA 2001", year = "2001", pages = "55--60", address = "Yokusika City, Japan", month = "30 " # oct # "-1 " # nov, keywords = "genetic algorithms, genetic programming", isbn13 = "0-7695-1312-3", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", oai = "oai:CiteSeerX.psu:10.1.1.485.2866", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.485.2866", URL = "http://www.aiecon.org/staff/shc/pdf/iccima2.pdf", size = "7 pages", abstract = "The purpose of this paper is to introduce the software system AIE-DA, which is designed for the implementation of an agent-based modelling of double auction markets. We shall start this introduction with the current version, Version 2, and then indicate what can be expected from the future of it.", notes = "July 2016 cannot be found in IEEE xplor See also \cite{Chen:2001:ICCIMA1}", } @Article{Shu-HengChen:2001:JEDC, author = "Shu-Heng Chen and Chia-Hsuan Yeh", title = "Evolving traders and the business school with genetic programming: A new architecture of the agent-based artificial stock market", journal = "Journal of Economic Dynamics and Control", year = "2001", volume = "25", number = "3-4", pages = "363--393", month = mar, keywords = "genetic algorithms, genetic programming, Agent-based computational economics, Social learning, Business school, Artificial stock markets", DOI = "doi:10.1016/S0165-1889(00)00030-0", abstract = "we propose a new architecture to study artificial stock markets. This architecture rests on a mechanism called `school' which is a procedure to map the phenotype to the genotype or, in plain English, to uncover the secret of success. We propose an agent-based model of `school', and consider school as an evolving population driven by single-population GP (SGP). The architecture also takes into consideration traders' search behavior. By simulated annealing, traders' search density can be connected to psychological factors, such as peer pressure or economic factors such as the standard of living. This market architecture was then implemented in a standard artificial stock market. Our econometric study of the resultant artificial time series evidences that the return series is independently and identically distributed (iid), and hence supports the efficient market hypothesis (EMH). What is interesting though is that this iid series was generated by traders, who do not believe in the EMH at all. In fact, our study indicates that many of our traders were able to find useful signals quite often from business school, even though these signals were short-lived.", notes = "JEL classification codes: G12; G14; D83 a AI-ECON Research Group, Department of Economics, National Chengchi University, Taipei, 11623 Taiwan b AI-ECON Research Group, Department of Finance I-Shou University, Kaohsiung County, 84008 Taiwan", } @Article{Chen:2002:EJEMED, author = "Shu-Heng Chen and John Duffy and Chia-Hsuan Yeh", title = "Equilibrium Selection via Adaptation: Using Genetic Programming to Model Learning in a Coordination Game", journal = "The Electronic Journal of Evolutionary Modeling and Economic Dynamics", year = "2002", month = "15 " # jan, keywords = "genetic algorithms, genetic programming, Adaptation, Coordination Game, Equilibrium Selection, Survival of the Fittest", ISSN = "1298-0137", URL = "http://sclab.mis.yzu.edu.tw/faculty/yeh/paper/2002/e-jemed2002.pdf", URL = "https://ideas.repec.org/a/jem/ejemed/1002.html", broken = "http://beagle.montesquieu.u-bordeaux.fr/jemed/1002/", size = "44 pages", abstract = "This paper models adaptive learning behavior in a simple coordination game that Van Huyck, Cook and Battalio (1994) have investigated in a controlled laboratory setting with human subjects. We consider how populations of artificially intelligent players behave when playing the same game. We use the genetic programming paradigm, as developed by Koza (1992, 1994), to model how a population of players might learn over time. In genetic programming one seeks to breed and evolve highly fit computer programs that are capable of solving a given problem. In our application, each computer program in the population can be viewed as an individual agent's forecast rule. The various forecast rules (programs) then repeatedly take part in the coordination game evolving and adapting over time according to principles of natural selection and population genetics. We argue that the genetic programming paradigm that we use has certain advantages over other models of adaptive learning behavior in the context of the coordination game that we consider. We find that the pattern of behavior generated by our population of artificially intelligent players is remarkably similar to that followed by the human subjects who played the same game. In particular, we find that a steady state that is theoretically unstable under a myopic, bestresponse learning dynamic turns out to be stable under our genetic programming based learning system, in accordance with Van Huyck et al.'s (1994) finding using human subjects. We conclude that genetic programming techniques may serve as a plausible mechanism for modelling human behavior, and may also serve as a useful selection criterion in environments with multiple equilibria.", notes = "Also known as \cite{RePEc:jem:ejemed:1002}", } @Article{Chen:2002:JEBO, author = "Shu-Heng Chen and Chia-Hsuan Yeh", title = "On the emergent properties of artificial stock markets: the efficient market hypothesis and the rational expectations hypothesis", journal = "Journal of Economic Behavior \& Organization", year = "2002", volume = "49", pages = "217--239", number = "2", keywords = "genetic algorithms, genetic programming, Artificial stock markets, Emergent properties, Efficient market hypothesis, Rational expectations hypothesis", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6V8F-45F900X-8/2/c034ae35c111ca061a11cae1df4b2cd5", ISSN = "0167-2681", DOI = "doi:10.1016/S0167-2681(02)00068-9", abstract = "By studying two well known hypotheses in economics, this paper illustrates how emergent properties can be shown in an agent-based artificial stock market. The two hypotheses considered are the efficient market hypothesis and the rational expectations hypothesis. We inquire whether the macrobehavior depicted by these two hypotheses is consistent with our understanding of the micro-behaviour. In this agent-based model, genetic programming is applied to evolving a population of traders learning over time. We first apply a series of econometric tests to show that the EMH and the REH can be satisfied with some portions of the artificial time series. Then, by analysing traders' behavior, we show that these aggregate results cannot be interpreted as a simple scaling-up of individual behaviour. A conjecture based on sunspot-like signals is proposed to explain why macrobehavior can be very different from micro-behaviour. We assert that the huge search space attributable to genetic programming can induce sunspot-like signals, and we use simulated evolved complexity of forecasting rules and Granger causality tests to examine this assertion.", } @Book{chen:2002:gagpcf, editor = "Shu-Heng Chen", title = "Genetic Algorithms and Genetic Programming in Computational Finance", publisher = "Kluwer Academic Publishers", year = "2002", address = "Dordrecht", month = jul, keywords = "genetic algorithms, genetic programming", ISBN = "0-7923-7601-3", URL = "http://www.springer.com/economics/economic+theory/book/978-0-7923-7601-9", notes = "Sometimes refered to as Genetic Algorithms and Programming in Computational Finance", size = "512 pages", } @InCollection{ChenAO:2002:gagpcf, author = "Shu-Heng Chen", title = "An Overview", booktitle = "Genetic Algorithms and Genetic Programming in Computational Finance", publisher = "Kluwer Academic Press", year = "2002", editor = "Shu-Heng Chen", chapter = "1", pages = "1--26", keywords = "genetic algorithms, genetic programming, Agent-based Computational Finance, Financial Engineering", ISBN = "0-7923-7601-3", URL = "http://www.springer.com/economics/economic+theory/book/978-0-7923-7601-9", DOI = "doi:10.1007/978-1-4615-0835-9_1", abstract = "This chapter reviews some recent advancements in financial applications of genetic algorithms and genetic programming. We start with the more familiar applications, such as forecasting, trading, and portfolio management. We then trace the recent extensions to cash flow management, option pricing, volatility forecasting, and arbitrage. The direction then turns to agent-based computational finance, a bottom-up approach to the study of financial markets. The review also sheds light on a few technical aspects of GAs and GP, which may play a vital role in financial applications.", notes = "part of \cite{chen:2002:gagpcf}", } @InCollection{ChenKuoShieh:2002:gagpcf, author = "Shu-Heng Chen and Tzu-Wen Kuo and Yuh-Pyng Shieh", title = "Genetic Programming: A Tutorial With The Software Simple GP", booktitle = "Genetic Algorithms and Genetic Programming in Computational Finance", publisher = "Kluwer Academic Press", year = "2002", editor = "Shu-Heng Chen", chapter = "3", pages = "55--77", keywords = "genetic algorithms, genetic programming, Simple GP, Symbolic Regression, Data Generating Mechanisms, Chaotic Dynamic Series, Production Function", ISBN = "0-7923-7601-3", URL = "http://www.springer.com/economics/economic+theory/book/978-0-7923-7601-9", DOI = "doi:10.1007/978-1-4615-0835-9_3", abstract = "This chapter demonstrates a computer program for tutoring genetic programming (GP). The software, called Simple GP, is developed by the AI-ECON Research Center at National Chengchi University, Taiwan. Using this software, the instructor can help students without programming background to quickly grasp some essential elements of GP. Along with the demonstration of the software is a list of key issues regarding the effective design of the implementation of GP. Some of the issues are already well noticed and studied by financial users of GP, but some are not. While many of the issues do not have a clear-cut answer, the attached software can help beginners to tackle those issues on their own. Once they have a general grasp of how to implement GP effectively, many advanced materials prepared in this volume are there for further exploration.", notes = "part of \cite{chen:2002:gagpcf}", } @InCollection{ChenLiao:2002:gagpcf, author = "Shu-Heng Chen and Chung-Chih Liao", title = "Price Discovery in Agent-Based Computational Modeling of the Artificial Stock Market", booktitle = "Genetic Algorithms and Genetic Programming in Computational Finance", publisher = "Kluwer Academic Press", year = "2002", editor = "Shu-Heng Chen", chapter = "16", pages = "335--356", keywords = "genetic algorithms, genetic programming, Price Discovery, Homogeneous Rational Expectation Equilibrium, Agent-Based Computational Finance, Excessive Volatility", ISBN = "0-7923-7601-3", URL = "http://www.aiecon.org/staff/shc/pdf/apga002.pdf", URL = "http://www.springer.com/economics/economic+theory/book/978-0-7923-7601-9", DOI = "doi:10.1007/978-1-4615-0835-9_16", size = "8 pages", abstract = "the behaviour of price discovery within a context of an agent based stock market, in which the twin assumptions, namely, rational expectations and the representative agents normally made in mainstream economics, are removed. In this model, traders stochastically update their forecasts by searching the business school whose evolution is driven by genetic programming. Via these agent based simulations, it is found that, except for some extreme cases, the mean prices generated from these artificial markets deviate from the homogeneous rational expectation equilibrium (HREE) prices no more than by 20per cent. This figure provides us a rough idea on how different we can possibly be when the twin assumptions are not taken. Furthermore, while the HREE price should be a deterministic constant in all of our simulations, the artificial price series generated exhibit quite wild fluctuation, which may be coined as the well-known excessive volatility in finance.", notes = "part of \cite{chen:2002:gagpcf}", } @InCollection{ChenTaiChie:2002:gagpcf, author = "Shu-Heng Chen and Chung-Ching Tai and Bin-Tzong Chie", title = "Individual Rationality as a Partial Impediment to Market Efficiency: Allocative Efficiency of Markets with Smart Traders", booktitle = "Genetic Algorithms and Genetic Programming in Computational Finance", publisher = "Kluwer Academic Press", year = "2002", editor = "Shu-Heng Chen", chapter = "17", pages = "357--377", keywords = "genetic algorithms, genetic programming, Agent-Based Double Auction Markets, Quote Limits, Alpha Value, Allocative Efficiency", ISBN = "0-7923-7601-3", URL = "http://www.econ.iastate.edu/tesfatsi/shusmart.ps", URL = "http://www.springer.com/economics/economic+theory/book/978-0-7923-7601-9", DOI = "doi:10.1007/978-1-4615-0835-9_17", size = "21 pages", abstract = "we conduct two experiments within an agent-based double auction market. These two experiments allow us to see the effect of learning and smartness on price dynamics and allocative efficiency. Our results are largely consistent with the stylized facts observed in experimental economics with human subjects. From the amelioration of price deviation and allocative efficiency, the effect of learning is vividly seen. However, smartness does not enhance market performance. In fact, the experiment with smarter agents (agents without a quote limit) results in a less stable price dynamics and lower allocative efficiency.", notes = "part of \cite{chen:2002:gagpcf}", } @InProceedings{Chen:2003:AAAIs, author = "Shu-heng Chen and Bin-tzong Chie", title = "Economic Models of Innovations: Why {GP} Can Be a Possible Way Out?", booktitle = "AAAI Spring Symposium, Computational Synthesis: From Basic Building Blocks to High Level Functionality", year = "2003", editor = "Hod Lipson and Erik K. Antonsson and John R. Koza", number = "SS-03-02", series = "AAAI Technical Report", publisher_address = "Menlo Park, California, USA", publisher = "The AAAI Press", keywords = "genetic algorithms, genetic programming", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.522.5910", URL = "https://aaai.org/Library/Symposia/Spring/2003/ss03-02-007.php", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.522.5910", URL = "http://www.aaai.org/Papers/Symposia/Spring/2003/SS-03-02/SS03-02-007.pdf", abstract = "No matter how commonly the term innovation has been used in economics, a concrete analytical or computational model of innovation is not yet available. This paper argues that a breakthrough can be made with genetic programming, and proposes a functional-modularity approach to an agent-based computational economic model of innovation.", } @InProceedings{chen03, author = "Shu-Heng Chen and Tzu-Wen Kuo", title = "Overfitting or Poor Learning: A Critique of Current Financial Applications of GP", booktitle = "Genetic Programming, Proceedings of EuroGP'2003", year = "2003", editor = "Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa", volume = "2610", series = "LNCS", pages = "34--46", address = "Essex", publisher_address = "Berlin", month = "14-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-00971-X", DOI = "doi:10.1007/3-540-36599-0_4", abstract = "Motivated by a measure of predictability, this paper uses the extracted signal ratio as a measure of the degree of overfitting. With this measure, we examine the performance of one type of overfitting-avoidance design frequently used in financial applications of GP. Based on the simulation results run with the software Simple GP, we find that this design is not effective in avoiding overfitting. Furthermore, within the range of search intensity typically considered by these applications, we find that underfitting, instead of overfitting, is the more prevalent problem. This problem becomes more serious when the data is generated by a process that has a high degree of algorithmic complexity. This paper, therefore, casts doubt on the conclusions made by those early applications regarding the poor performance of GP, and recommends that changes be made to ensure progress.", notes = "EuroGP'2003 held in conjunction with EvoWorkshops 2003", } @InProceedings{Shu-HengChen:2003:CINC, author = "Shu-Heng Chen and Tzu-Wen Kuo", title = "Modeling International Short-Term Capital Flow with Genetic Programming", booktitle = "Proceedings of the Sixth International Conference on Computational Intelligence and Natural Computing", year = "2003", address = "Embassy Suites Hotel and Conference Center, Cary, North Carolina USA", month = sep # " 26-30", keywords = "genetic algorithms, genetic programming", URL = "http://nccur.lib.nccu.edu.tw/handle/140.119/23210", URL = "http://nccur.lib.nccu.edu.tw/bitstream/140.119/23210/1/Ac92092%5B1%5D.pdf", abstract = "In this paper, a non-deterministic (portfolio-based) finite-state automaton is proposed to generalise the current financial trading applications of genetic programming from single risky asset to multi risky assets. The GP-evolved trading rules are tested under various settings with respect to search intensity, genetic portfolios, and validating parameters. The rules are compared with performance of a buy and hold strategy in a context of international capital flow using data from Taiwan, the U.S., Hong Kong, Japan and the U.K. The GP are evaluated by using both the mean rule and the majority rule. However, by and large, it is found that GP was outperformed by the buy-and-hold strategy in both cases.", notes = "http://axon.cs.byu.edu/Dan/cinc03.html/index.html/ Broken Jan 2013 http://www.ee.duke.edu/JCIS/ National Chengchi University, Taiwan", } @Misc{oai:CiteSeerX.psu:10.1.1.483.8279, title = "A Functional Modularity Approach to Agent-based Modeling of the Evolution of Technology,{"} (with {B}.-{T}", author = "Shu-heng Chen and Bin-tzong Chie", publisher = "Springer", year = "2014", month = dec # "~17", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.483.8279", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", keywords = "genetic algorithms, genetic programming, agent-based computational economics, innovation, functional modularity", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.483.8279", URL = "http://www.aiecon.org/staff/shc/pdf/being.pdf", abstract = "No matter how commonly the term innovation has been used in economics, a concrete analytical or computational model of innovation is not yet avail-able. This paper argues that a breakthrough can be made with genetic programming, and proposes a functional-modularity approach to an agent-based computational economic model of innovation.", } @InProceedings{chen:2004:lbp, author = "Shu-Heng Chen and Bin-Tzong Chie", title = "Functional Modularity in the Test Bed of Economic Theory -- Using Genetic Programming", booktitle = "Late Breaking Papers at the 2004 Genetic and Evolutionary Computation Conference", year = "2004", editor = "Maarten Keijzer", address = "Seattle, Washington, USA", month = "26 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://gpbib.cs.ucl.ac.uk/gecco2004/LBP062.pdf", abstract = "In this paper, we follow the model of Chen and Chie (2004), but start with the primeval setup. The implementation of computer simulations show mutation did play an important role in the technology evolution. In a well define simulation world, the producer will exert all of his effort to make the life get better. The parameter of mutation rate is just like the frequency of innovation in the real world. Different mutation rate will shift the model to the different path of history. The path of real world might be represented by one of the mutation rate, but it must be emergent from the different behaviours of the bottom actors.", notes = "Part of keijzer:2004:GECCO:lbp", } @Article{Shu-HengChen:2004:IJMPB, author = "Shu-Heng Chen and Bin-Tzong Chie", title = "Functional Modularity in the Fundamentals of Economic Theory: Toward an Agent-Based Economic Modeling of the Evolution of Technology", journal = "International Journal of Modern Physics B", year = "2004", volume = "18", number = "17-19", pages = "2376--2386", month = jul # " 30", keywords = "genetic algorithms, genetic programming, Agent-based computational economics, innovation, functional modularity", DOI = "doi:10.1142/S0217979204025403", abstract = "No matter how commonly the term innovation has been used in economics, a concrete analytical or computational model of innovation is not yet available. This paper argues that a breakthrough can be made with genetic programming, and proposes a functional-modularity approach to an agent-based computational economic model of innovation.", notes = "A1 AI-ECON Research Center, Department of Economics, National Chengchi University, Taipei, 116, Taiwan ROC", } @Article{Chen:2005:IS, author = "Shu-Heng Chen and Chung-Chih Liao", title = "Agent-based computational modeling of the stock price-volume relation", journal = "Information Sciences", year = "2005", volume = "170", pages = "75--100", number = "1", abstract = "From the perspective of the agent-based model of stock markets, this paper examines the possible explanations for the presence of the causal relation between stock returns and trading volume. Using the agent-based approach, we find that the explanation for the presence of the stock price-volume relation may be more fundamental. Conventional devices such as information asymmetry, reaction asymmetry, noise traders or tax motives are not explicitly required. In fact, our simulation results show that the stock price-volume relation may be regarded as a generic property of a financial market, when it is correctly represented as an evolving decentralised system of autonomous interacting agents. One striking feature of agent-based models is the rich profile of agents' behaviour. This paper makes use of the advantage and investigates the micro-macro relations within the market. In particular, we trace the evolution of agents' beliefs and examine their consistency with the observed aggregate market behavior. We argue that a full understanding of the price-volume relation cannot be accomplished unless the feedback relation between individual behaviour at the bottom and aggregate phenomena at the top is well understood.", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6V0C-4B3JHTS-6/2/9e023835b1c70f176d1903dd3a8b638e", month = "18 " # feb, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1016/j.ins.2003.03.026", } @InProceedings{DBLP:conf/icnc/ChenH05a, author = "Shu-Heng Chen and Ya-Chi Huang", title = "On the Role of Risk Preference in Survivability", booktitle = "Advances in Natural Computation, Proceedings of First International Conference, ICNC 2005, Part III", year = "2005", editor = "Lipo Wang and Ke Chen and Yew-Soon Ong", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3612", pages = "612--621", address = "Changsha, China", month = aug # " 27-29", keywords = "genetic algorithms", ISBN = "3-540-28320-X", bibsource = "DBLP, http://dblp.uni-trier.de", URL = "http://www4.nccu.edu.tw/ezkm11/ezcatfiles/cust/img/img/29.pdf", DOI = "doi:10.1007/11539902_74", abstract = "Using an agent-based multi-asset artificial stock market, we simulate the survival dynamics of investors with different risk preferences. It is found that the survivability of investors is closely related to their risk preferences. Among the eight types of investors considered in this paper, only the CRRA investors with RRA coefficients close to one can survive in the long run. Other types of agents are eventually driven out of the market, including the famous CARA agents and agents who base their decision on the capital asset pricing model.", notes = "ICNC (3)", } @InCollection{Chen:2006:CNEI, author = "Shu-Heng Chen and Bin-Tzong Chie", title = "A Functional Modularity Approach to Agent-based Modeling of the Evolution of Technology", booktitle = "The Complex Networks of Economic Interactions: Essays in Agent-Based Economics and Econophysics", publisher = "Springer", year = "2006", editor = "Akira Namatame and Yuuji Aruka and Taisei Kaizouji", volume = "567", series = "Lecture Notes in Economics and Mathematical Systems", pages = "165--178", month = jan, keywords = "genetic algorithms, genetic programming, agent-based computational economics, innovation, functional modularity", ISBN = "3-540-28726-4", DOI = "doi:10.1007/3-540-28727-2_11", abstract = "No matter how commonly the term innovation has been used in economics, a concrete analytical or computational model of innovation is not yet available. This paper argues that a breakthrough can be made with genetic programming, and proposes a functional-modularity approach to an agent-based computational economic model of innovation.", } @Article{Chen:2006:IS, author = "Shu-Heng Chen", title = "Computationally intelligent agents in economics and Finance", journal = "Information Sciences", year = "2007", volume = "177", number = "5", pages = "1153--1168", month = "1 " # mar, keywords = "genetic algorithms, genetic programming, Computational intelligence, Agent-based computational economics", URL = "http://www.aiecon.org/staff/shc/pdf/INS_7416.pdf", DOI = "doi:10.1016/j.ins.2006.08.001", size = "16 pages", abstract = "This paper is an editorial guide for the second special issue on Computational Intelligence in economics and finance, which is a continuation of the special issue of Information Sciences, Vol. 170, No. 1. This second issue appears as a part of the outcome from the 3rd International Workshop on Computational Intelligence in Economics and Finance, which was held in Cary, North Carolina, September 26-30, 2003. This paper offers some main highlights of this event, with a particular emphasis on some of the observed progress made in this research field, and a brief introduction to the papers included in this special issue.", notes = "The 3rd International Workshop on Computational Intelligence in Economics and Finance (CIEF'2003)", } @InProceedings{conf/iconip/ChenN06, title = "Pretests for Genetic-Programming Evolved Trading Programs: zero-intelligence Strategies and Lottery Trading", author = "Shu-Heng Chen and Nicolas Navet", booktitle = "Neural Information Processing, 13th International Conference, {ICONIP} 2006, Proceedings, Part {III}", publisher = "Springer", year = "2006", volume = "4234", editor = "Irwin King and Jun Wang and Laiwan Chan and DeLiang L. Wang", pages = "450--460", series = "Lecture Notes in Computer Science", address = "Hong Kong, China", month = oct # " 3-6", bibdate = "2006-10-23", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/iconip/iconip2006-3.html#ChenN06", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-46484-0", DOI = "doi:10.1007/11893295_50", abstract = "Over the last decade, numerous papers have investigated the use of GP for creating financial trading strategies. Typically in the literature results are inconclusive but the investigators always suggest the possibility of further improvements, leaving the conclusion regarding the effectiveness of GP undecided. In this paper, we discuss a series of pretests, based on several variants of random search, aiming at giving more clear-cut answers on whether a GP scheme, or any other machine-learning technique, can be effective with the training data at hand. The analysis is illustrated with GP-evolved strategies for three stock exchanges exhibiting different trends.", } @InProceedings{Chen:2007:IDEAL, author = "Shu-heng Chen and Bin-tzong Chie", title = "Modularity, Product Innovation, and Consumer Satisfaction: An Agent-Based Approach", booktitle = "8th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2007", year = "2007", editor = "Hujun Yin and Peter Tino and Emilio Corchado and Will Byrne and Xin Yao", volume = "4881", series = "LNCS", pages = "1053--1062", address = "Birmingham, UK", month = dec # " 16-19", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-77226-2", annote = "The Pennsylvania State University CiteSeerX Archives", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.610.9050", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.610.9050", URL = "http://andromeda.rutgers.edu/~jmbarr/EEA2009/chen1.pdf", DOI = "doi:10.1007/978-3-540-77226-2_105", abstract = "The importance of modularity in product innovation is analysed in this paper. Through simulations with an agent-based modular economic model, we examine the significance of the use of a modular structure in new product designs in terms of its impacts upon customer satisfaction and firms' competitiveness. To achieve the above purpose, the automatically defined terminal is proposed and is used to modify the simple genetic programming.", notes = "IDEAL 2007", } @InCollection{Chen:2007:chen, title = "Failure of Genetic-Programming Induced Trading Strategies: Distinguishing between Efficient Markets and Inefficient Algorithms", author = "Shu-heng Chen and Nicolas Navet", booktitle = "Computational Intelligence in Economics and Finance: Volume II", publisher = "Springer", year = "2007", editor = "Shu-Heng Chen and Paul P. Wang and Tzu-Wen Kuo", pages = "169--182", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-72820-7", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", contributor = "CiteSeerX", language = "en", oai = "oai:CiteSeerXPSU:10.1.1.144.5068", URL = "http://www.loria.fr/~nnavet/publi/SHC_NN_Springer2007.pdf", URL = "http://www.springer.com/computer/ai/book/978-3-540-72820-7", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.144.5068", DOI = "doi:10.1007/978-3-540-72821-4_11", abstract = "Over the last decade, numerous papers have investigated the use of Genetic Programming (GP) for creating financial trading strategies. Typically, in the literature, the results are inconclusive but the investigators always suggest the possibility of further improvements, leaving the conclusion regarding the effectiveness of GP undecided. In this paper, we discuss a series of pretests aimed at giving more clear-cut answers as to whether GP can be effective with the training data at hand. Precisely, pretesting allows us to distinguish between a failure due to the market being efficient or due to GP being inefficient. The basic idea here is to compare GP with several variants of random searches and random trading behaviors having well-defined characteristics. In particular, if the outcomes of the pretests reveal no statistical evidence that GP possesses a predictive ability superior to a random search or a random trading behavior, then this suggests to us that there is no point in investing further resources in GP. The analysis is illustrated with GP-evolved strategies for nine markets exhibiting various trends.", } @InCollection{Chen:2008:GPTP, author = "Shu-Heng Chen and Ren-Jie Zeng and Tina Yu", title = "Co-Evolving Trading Strategies to Analyze Bounded Rationality in Double Auction Markets", booktitle = "Genetic Programming Theory and Practice {VI}", year = "2008", editor = "Rick L. Riolo and Terence Soule and Bill Worzel", series = "Genetic and Evolutionary Computation", chapter = "13", pages = "195--215", address = "Ann Arbor", month = "15-17 " # may, publisher = "Springer", URL = "http://www.cs.mun.ca/~tinayu/Publications_files/gptp2008.pdf", DOI = "doi:10.1007/978-0-387-87623-8_13", size = "20 pages", isbn13 = "978-0-387-87622-1", notes = "part of \cite{Riolo:2008:GPTP} published in 2009", keywords = "genetic algorithms, genetic programming, bounded rationality, zero-intelligence, agent-based modelling, human subject experiments, auction markets design, double-auction markets, macroeconomics, trading strategies, software agents, market simulation, market efficiency", size = "19 pages", abstract = "We investigate double-auction (DA) market behaviour under traders with different degrees of rationality (intelligence or cognitive ability). The rationality of decision making is implemented using genetic programming (GP), where each trader evolves a population of strategies to conduct an auction. By assigning the GP traders different population sizes to differentiate their cognitive ability, through a series of simulations, we find that increasing the traders intelligence improves the markets efficiency. However, increasing the number of intelligent traders in the market leads to a decline in the markets efficiency. By analysing the individual GP traders strategies and their co-evolution dynamics, we provide explanations to these emerging market phenomena. While auction markets are gaining popularity on the Internet, the insights can help market designers devise robust and efficient auction e-markets.", } @InProceedings{Chen:2009:CIFEr, author = "Shu-Heng Chen and Chung-Ching Tai", title = "Modeling intelligence of learning agents in an artificial double auction market", booktitle = "IEEE Symposium on Computational Intelligence for Financial Engineering, CIFEr '09", year = "2009", month = "30 " # mar # "-" # apr # " 2", pages = "36--42", keywords = "genetic algorithms, genetic programming, artificial double auction market, individual intelligence modeling, learning agents, psychological, socioeconomic, software agents, commerce, psychology, socio-economic effects, software agents", DOI = "doi:10.1109/CIFER.2009.4937500", abstract = "In psychological as well as socioeconomic studies, individual intelligence has been found decisive in many domains. In this paper, we employ genetic programming as the algorithm of our learning agents who compete with other designed strategies extracted from the literature.We then discuss the possibility of using population size as a proxy parameter of individual intelligence of software agents. By modeling individual intelligence in this way, we demonstrate not only a nearly positive relation between individual intelligence and performance, but more interestingly the effect of decreasing marginal contribution of IQ to performance found in psychological literature.", notes = "Also known as \cite{4937500}", } @InProceedings{Chen:2009:eurogp, author = "Shu-Heng Chen and Chung-Ching Tai", title = "Modeling Intelligence of Learning Agents in An Artificial Double Auction Market", booktitle = "Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009", year = "2009", editor = "Leonardo Vanneschi and Steven Gustafson and Alberto Moraglio and Ivanoe {De Falco} and Marc Ebner", volume = "5481", series = "LNCS", pages = "171--182", address = "Tuebingen", month = apr # " 15-17", organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-01180-1", DOI = "doi:10.1007/978-3-642-01181-8_15", abstract = "Individual differences in intellectual abilities can be observed across time and everywhere in the world, and this fact has been well studied by psychologists for a long time. To capture the innate heterogeneity of human intellectual abilities, this paper employs genetic programming as the algorithm of the learning agents, and then proposes the possibility of using population size as a proxy parameter of individual intelligence. By modeling individual intelligence in this way, we demonstrate not only a nearly positive relation between individual intelligence and performance, but more interestingly the effect of decreasing marginal contribution of IQ to performance found in psychological literature.", notes = "Part of \cite{conf/eurogp/2009} EuroGP'2009 held in conjunction with EvoCOP2009, EvoBIO2009 and EvoWorkshops2009", } @InProceedings{ChenZY:2009:GEC, author = "Shu-Heng Chen and Ren-Jie Zeng and Tina Yu", title = "Analysis of micro-behavior and bounded rationality in double auction markets using co-evolutionary {GP}", booktitle = "GEC '09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation", year = "2009", editor = "Lihong Xu and Erik D. Goodman and Guoliang Chen and Darrell Whitley and Yongsheng Ding", bibsource = "DBLP, http://dblp.uni-trier.de", pages = "807--810", address = "Shanghai, China", organisation = "SigEvo", URL = "http://www.cs.mun.ca/~tinayu/Publications_files/p807.pdf", DOI = "doi:10.1145/1543834.1543948", publisher = "ACM", publisher_address = "New York, NY, USA", month = jun # " 12-14", isbn13 = "978-1-60558-326-6", keywords = "genetic algorithms, genetic programming, Poster", abstract = "We investigate the dynamics of trader behaviors using a co-evolutionary genetic programming system to simulate a double-auction market. The objective of this study is two-fold. First, we seek to evaluate how, if any, the difference in trader rationality/intelligence influences trading behavior. Second, besides rationality, we also analyze how, if any, the co-evolution between two learnable traders impacts their trading behaviors. We have found that traders with different degrees of rationality may exhibit different behavior depending on the type of market they are in. When the market has a profit zone to explore, the more intelligent trader demonstrate more intelligent behaviors. Also, when the market has two learnable buyers, their co-evolution produced more profitable transactions than when there was only one learnable buyer in the market. We have analyzed the learnable traders' strategies and found their behavior are very similar to humans in decision making. We will conduct human subject experiments to validate these results in the near future.", notes = "Also known as \cite{DBLP:conf/gecco/ChenZY09} part of \cite{DBLP:conf/gec/2009}", } @InProceedings{conf/mabs/ChenTW09, title = "Does Cognitive Capacity Matter When Learning Using Genetic Programming in Double Auction Markets?", author = "Shu-Heng Chen and Chung-Ching Tai and Shu G. Wang", booktitle = "Multi-Agent-Based Simulation X", editor = "Gennaro {di Tosto} and H. {Van Dyke Parunak}", publisher = "Springer", year = "2009", volume = "5683", bibdate = "2010-09-03", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/mabs/mabs2009.html#ChenTW09", isbn13 = "978-3-642-13552-1", pages = "37--48", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1007/978-3-642-13553-8_4", abstract = "The relationship between human subjects' cognitive capacity and their economic performances has been noticed in recent years due to the evidence found in a series of cognitive economic experiments. However, there are few agent-based models aiming to characterise such relationship. This paper attempts to bridge this gap and serve as an agent-based model with a focus on agents' cognitive capacity. To capture the heterogeneity of human cognitive capacity, this paper employs genetic programming as the algorithm of the learning agents, and then uses population size as a proxy parameter of individual cognitive capacity. By modelling agents in this way, we demonstrate a nearly positive relationship between cognitive abilities and economic performance.", notes = "MABS", } @InProceedings{chen:2009:AAESCS, author = "Shu-Heng Chen", title = "Genetic Programming and Agent-Based Computational Economics: From Autonomous Agents to Product Innovation", booktitle = "Agent-Based Approaches in Economic and Social Complex Systems V", year = "2009", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-4-431-87435-5_1", DOI = "doi:10.1007/978-4-431-87435-5_1", } @InCollection{Chen:2010:maaECbit, author = "Shu-Heng Chen and Ren-Jie Zeng and Tina Yu and Shu G. Wang", title = "Bounded Rationality and Market Micro-Behaviors: Case Studies Based on Agent-Based Double Auction Markets", booktitle = "Multi-Agent Applications with Evolutionary Computation and Biologically Inspired Technologies: Intelligent Techniques for Ubiquity and Optimization", publisher = "IGI Global", year = "2010", editor = "Shu-Heng Chen and Yasushi Kambayashi and Hiroshi Sato", chapter = "5", pages = "78--94", keywords = "genetic algorithms, genetic programming", isbn13 = "9781605668987", DOI = "DOI:10.4018/978-1-60566-898-7.ch005", size = "17 pages", abstract = "We investigate the dynamics of trader behaviours using an agent-based genetic programming system to simulate double-auction markets. The objective of this study is two-fold. First, we seek to evaluate how, if any, the difference in trader rationality/intelligence influences trading behaviour. Second, besides rationality, we also analyse how, if any, the co-evolution between two learnable traders impacts their trading behaviors. We have found that traders with different degrees of rationality may exhibit different behavior depending on the type of market they are in. When the market has a profit zone to explore, the more intelligent trader demonstrates more intelligent behaviors. Also, when the market has two learnable buyers, their co-evolution produced more profitable transactions than when there was only one learnable buyer in the market. We have analyzed the trading strategies and found the learning behaviors are very similar to humans in decision-making. We plan to conduct human subject experiments to validate these results in the near future.", notes = "Shu-Heng Chen (National Chengchi University, Taiwan), Ren-Jie Zeng (Taiwan Institute of Economic Research, Taiwan), Tina Yu (Memorial University of Newfoundland, Canada) and Shu G. Wang (National Chengchi University, Taiwan)", } @Article{Chen:2011:frontierEE, author = "Shu-Heng Chen and Tina Yu", title = "Agents learned, but do we? Knowledge discovery using the agent-based double auction markets", journal = "Frontiers of Electrical and Electronic Engineering in China", year = "2011", volume = "6", number = "1", pages = "159--170", month = mar, keywords = "genetic algorithms, genetic programming, agent-based double auction markets, autonomous agents, bargaining strategies, monopsony, procrastination strategy", ISSN = "1673-3584", URL = "http://www.cs.mun.ca/~tinayu/Publications_files/frontierEE.pdf", URL = "http://dx.doi.org/10.1007/s11460-011-0132-4", DOI = "doi:10.1007/s11460-011-0132-4", size = "12 pages", abstract = "This paper demonstrates the potential role of autonomous agents in economic theory. We first dispatch autonomous agents, built by genetic programming, to double auction markets. We then study the bargaining strategies, discovered by them, and from there, an autonomous-agent-inspired economic theory with regard to the optimal procrastination is derived.", notes = "Department of Economics, Chengchi University, Taipei", } @Misc{DP_6_2011_II, author = "Shu-Heng Chen and Tina Yu", title = "Toward an Autonomous-Agents Inspired Economic Analysis", howpublished = "Discussion paper", year = "2011", month = "18 " # may, number = "6-2011/II", keywords = "genetic algorithms, genetic programming, agent-based double auction markets, autonomous agents, bargaining strategies, monopsony, procrastination strategy", URL = "http://www.assru.economia.unitn.it/files/DP_6_2011_II.pdf", size = "16 pages", abstract = "This paper demonstrates the potential role of autonomous agents in economic theory. We first dispatch autonomous agents, built by genetic programming, to double auction markets. We then study the bargaining strategies discovered by them, and from there an autonomous-agent-inspired economic theory with regard to the optimal procrastination is derived.", notes = "Text of the talk given at the ASSRU/Department of Economics Seminar, University of Turin, 18 May 2011", bibsource = "OAI-PMH server at oai.repec.openlib.org", identifier = "RePEc:trn:utwpas:1118", oai = "oai:RePEc:trn:utwpas:1118", } @InProceedings{conf/ideal/ChenS11, author = "Shu-Heng Chen and Kuo-Chuan Shik", title = "Is Genetic Programming ``Human-Competitive''? The Case of Experimental Double Auction Markets", booktitle = "Proceedings of the 12th International Conference on Intelligent Data Engineering and Automated Learning, {IDEAL} 2011", year = "2011", editor = "Hujun Yin and Wenjia Wang and Victor J. Rayward-Smith", volume = "6936", series = "Lecture Notes in Computer Science", pages = "116--126", address = "Norwich, UK", month = sep # " 7-9", publisher = "Springer", keywords = "genetic algorithms, genetic programming, experimental markets, double auctions, working memory capacity", isbn13 = "978-3-642-23877-2", DOI = "doi:10.1007/978-3-642-23878-9_15", size = "11 pages", abstract = "In this paper, the performance of human subjects is compared with genetic programming in trading. Within a kind of double auction market, we compare the learning performance between human subjects and autonomous agents whose trading behaviour is driven by genetic programming (GP). To this end, a learning index based upon the optimal solution to a double auction market problem, characterised as integer programming, is developed, and criteria tailor-made for humans are proposed to evaluate the performance of both human subjects and software agents. It is found that GP robots generally fail to discover the best strategy, which is a two-stage procrastination strategy, but some human subjects are able to do so. An analysis from the point of view of cognitive psychology further shows that the minority who were able to find this best strategy tend to have higher working memory capacities than the majority who failed to do so. Therefore, even though GP can outperform most human subjects, it is not human-competitive from a higher standard.", affiliation = "AIECON research center, Department of Economics, National Chengchi University, Taipei, Taiwan", bibdate = "2011-08-25", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ideal/ideal2011.html#ChenS11", } @Article{Chen20121, author = "Shu-Heng Chen", title = "Varieties of agents in agent-based computational economics: A historical and an interdisciplinary perspective", journal = "Journal of Economic Dynamics and Control", volume = "36", number = "1", pages = "1--25", year = "2012", ISSN = "0165-1889", DOI = "doi:10.1016/j.jedc.2011.09.003", URL = "http://www.sciencedirect.com/science/article/pii/S0165188911001692", keywords = "genetic algorithms, genetic programming, Cellular automata, Autonomous agents, Tournaments, Cognitive capacity", abstract = "In this paper, we trace four origins of agent-based computational economics (ACE), namely, the markets origin, the cellular-automata origin, the tournaments origin, and the experiments origin. Along with this trace, we examine how these origins have motivated different concepts and designs of agents in ACE, which starts from the early work on simple programmed agents, randomly behaving agents, zero-intelligence agents, human-written programmed agents, autonomous agents, and empirically calibrated agents, and extends to the newly developing cognitive agents, psychological agents, and culturally sensitive agents. The review also shows that the intellectual ideas underlying these varieties of agents cross several disciplines, which may be considered as a part of a general attempt to study humans (and their behaviour) with an integrated interdisciplinary foundation.", } @Article{BMSP:BMSP255, author = "Shu-Ying Chen and Shing-Hwang Doong", title = "Predicting item exposure parameters in computerized adaptive testing", journal = "British Journal of Mathematical and Statistical Psychology", volume = "61", number = "1", year = "2008", pages = "75--91", month = may, keywords = "genetic algorithms, genetic programming", publisher = "Blackwell Publishing Ltd", ISSN = "2044-8317", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.624.6855", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.624.6855", broken = "http://www.psych.umn.edu/psylabs/catcentral/pdf files/ch03-01.pdf", DOI = "doi:10.1348/000711006X129553", abstract = "The purpose of this study is to find a formula that describes the relationship between item exposure parameters and item parameters in computerized adaptive tests by using genetic programming (GP) - a biologically inspired artificial intelligence technique. Based on the formula, item exposure parameters for new parallel item pools can be predicted without conducting additional iterative simulations. Results show that an interesting formula between item exposure parameters and item parameters in a pool can be found by using GP. The item exposure parameters predicted based on the found formula were close to those observed from the Sympson and Hetter (1985) procedure and performed well in controlling item exposure rates. Similar results were observed for the Stocking and Lewis (1998) multinomial model for item selection and the Sympson and Hetter procedure with content balancing. The proposed GP approach has provided a knowledge-based solution for finding item exposure parameters.", notes = "PMID: 18482476", } @InProceedings{chen:1998:ecso, author = "Stephen Chen and Stephen F. Smith", title = "Experiments on Commonality in Sequencing Operators", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "471--478", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms", notes = "SGA-98", } @InProceedings{chen:1999:IGASSRAFSS, author = "Stephen Chen and Stephen F. Smith", title = "Improving Genetic Algorithms by Search Space Reductions (with Applications to Flow Shop Scheduling)", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "135--140", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-829.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-829.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{chen:1999:INACCS, author = "Stephen Chen and Stephen F. Smith", title = "Introducing a New Advantage of Crossover: Commonality-Based Selection", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "122--128", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-827.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-827.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{chen:1999:NCSRCFSS, author = "Stephen Chen and Cesar Guerra-Salcedo and Stephen F. Smith", title = "Non-Standard Crossover for a Standard Representation -- Commonality-Based Feature Subset Selection", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "129--134", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-828.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-828.pdf", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{chen:2022:FC, author = "Wei Chen2", title = "Orientation Design and Research of Heavy Bamboo Substrate Considering Genetic Programming and Artificial Intelligence Algorithm", booktitle = "Frontier Computing", year = "2022", editor = "Jason C. Hung and Neil Y. Yen and Jia-Wei Chang", volume = "827", series = "LNEE", pages = "1643--1648", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-981-16-8052-6", URL = "http://link.springer.com/chapter/10.1007/978-981-16-8052-6_241", DOI = "doi:10.1007/978-981-16-8052-6_241", abstract = "As a kind of cheap, easily available and renewable natural material, bamboo is an advantageous resource for sustainable design in today's circular economy era. a kind of genetic artificial intelligence algorithm is studied for the development of heavy bamboo substrate oriented products. Genetic algorithm is a widely used intelligent optimization algorithm. The selection optimization problem in heavy bamboo product development system is studied. The technology platform for manufacturing bamboo based fiber composites has been built, and a variety of bamboo based fiber composites used in landscape, building structural materials, packaging and transportation materials, interior high-end decoration and other fields have been successfully developed and industrialized.", notes = "This thesis is a PhD in scientific research Nanchang Institute of Technology, Nanchang, 330029, Jiangxi, China", } @InProceedings{Chen:2024:ICEPT, author = "Wei Chen3 and Xu He and Xiaowei Xu and Lu Liu", title = "Optimization of Creep Constitutive Modeling of Lead-Free Solder Using Gene Expression Programming Methods", booktitle = "2024 25th International Conference on Electronic Packaging Technology (ICEPT)", year = "2024", month = aug, keywords = "genetic algorithms, genetic programming, Adaptation models, Analytical models, Creep, Lead, Predictive models, Prediction algorithms, creep constitutive modelling, gene expression programming methods, machine learning, gene expression programming", ISSN = "2836-9734", DOI = "doi:10.1109/ICEPT63120.2024.10668800", abstract = "In this paper, the GEP method is used to derive an ontological model for creep data, focusing on the mechanical properties and structural deformation mechanisms of solder at different scales. By analysing the effect of micro-and meso-structural morphology on the material, experimental validation is provided for the construction of an ontological model that depicts the viscoplasticity of Pb- Free solder in detail. Iterative optimisation of a larger population of experimental samples will drive the development of a fine-grained intrinsic model that will be supported by solid theoretical and empirical evidence, advancing the field towards deeper understanding and innovation. In this study, the GEP algorithm is used to fit the intrinsic model of solder creep and is shown to have good fitting results. The advantages of GEP in equation model fitting are the simplicity of its heuristic algorithm, the ease of its implementation and its good optimisation results. In addition, GEP is highly flexible, adaptable and robust to noisy data. After comparing the experimental data simulations, it is found that GEP is able to accurately analyse and predict in the establishment and optimisation of creep model, which brings an important contribution to the field of solder research.", notes = "Also known as \cite{10668800} Department of physics, Nanjing University of science and technology, Nanjing, China", } @Article{CHEN:2021:CS, author = "Wenguang Chen and Jinjun Xu and Minhao Dong and Yong Yu and Mohamed Elchalakani and Fengliang Zhang", title = "Data-driven analysis on ultimate axial strain of {FRP-confined} concrete cylinders based on explicit and implicit algorithms", journal = "Composite Structures", year = "2021", volume = "268", pages = "113904", keywords = "genetic algorithms, genetic programming, FRP-confined concrete, Ultimate axial strain, Bayesian theory, Machine learning, Back-propagation artificial neural network, Multi-gene genetic programming, Support vector machine", ISSN = "0263-8223", URL = "https://research-repository.uwa.edu.au/en/publications/data-driven-analysis-on-ultimate-axial-strain-of-frp-confined-con", URL = "https://www.sciencedirect.com/science/article/pii/S0263822321003640", DOI = "doi:10.1016/j.compstruct.2021.113904", abstract = "The existing models for predicting the ultimate axial strain of FRP-confined concrete cylinders are mainly derived from the regression analyses on small datasets. Such models usually targeted more specific use cases and could give inaccurate outcomes when generalized. To this end, this paper presents the data-driven Bayesian probabilistic and machine learning prediction models (i.e., back-propagation artificial neural network, multi-gene genetic programming and support vector machine) with high accuracy. First, a comprehensive database containing 471 test results on the ultimate conditions of FRP-confined concrete cylinders was elaborately compiled from the open literature, and the quality of the database was examined and evaluated in detail. Then, an updating procedure characterized by the Bayesian parameter estimation technique was developed to evaluate the critical parameters in the existing models and refine the selected existing models accordingly. The database was also employed for deriving machine learning models. The computational efficiency, transferability and precision of the proposed models are verified. Results show that the proposed Bayesian posterior models, back-propagation artificial neural network, multi-gene genetic programming and support vector machine models achieved outstanding predictive performance, with the support vector machine yielding the highest prediction accuracy. The superior accuracy of the proposed models should assist various stakeholders in optimal use of FRP-confined concrete columns in diverse construction applications", } @Article{Chen:2025:jechem, author = "Wenning Chen and Jungchul Yun and Doyun Im and Sijia Li and Kelvian T. Mularso and Jihun Nam and Bonghyun Jo and Sangwook Lee and Hyun Suk Jung", title = "Artificial intelligence high-throughput prediction building dataset to enhance the interpretability of hybrid halide perovskite bandgap", journal = "Journal of Energy Chemistry", year = "2025", keywords = "genetic algorithms, genetic programming, Artificial intelligence, High-throughput, Perovskite bandgap, Partial dependence analysis, Model interpretability", ISSN = "2095-4956", URL = "https://www.sciencedirect.com/science/article/pii/S2095495625004632", DOI = "doi:10.1016/j.jechem.2025.05.059", abstract = "The bandgap is a key parameter for understanding and designing hybrid perovskite material properties, as well as developing photovoltaic devices. Traditional bandgap calculation methods like ultraviolet-visible spectroscopy and first-principles calculations are time- and power-consuming, not to mention capturing bandgap change mechanisms for hybrid perovskite materials across a wide range of unknown space. In the present work, an artificial intelligence ensemble comprising two classifiers (with F1 scores of 0.9125 and 0.925) and a regressor (with mean squared error of 0.0014 eV) is constructed to achieve high-precision prediction of the bandgap. The bandgap perovskite dataset is established through high-throughput prediction of bandgaps by the ensemble. Based on the self-built dataset, partial dependence analysis (PDA) is developed to interpret the bandgap influential mechanism. Meanwhile, an interpretable mathematical model with an R2 of 0.8417 is generated using the genetic programming symbolic regression (GPSR) technique. The constructed PDA maps agree well with the Shapley Additive exPlanations, the GPSR model, and experiment verification. Through PDA, we reveal the boundary effect, the bowing effect, and their evolution trends with key descriptors", } @InCollection{Chen:2010:BIEF, author = "Xi Chen and Ye Pang and Guihuan Zheng", title = "Macroeconomic Forecasting Using Genetic Programming Based Vector Error Correction Model", booktitle = "Buisness Intelligence in Economic Forcasting", publisher = "IGI Global", year = "2010", editor = "Jue Wang and Shouyang Wang", chapter = "1", pages = "1--15", keywords = "genetic algorithms, genetic programming", isbn13 = "9781615206292", DOI = "doi:10.4018/978-1-61520-629-2", abstract = "Vector autoregressions are widely used in macroeconomic forecasting since they became known in the 1970s. Extensions including vector error correction models, co-integration and dynamic factor models are all rooted in the framework of vector autoregression. The three important extensions are demonstrated to have formal equivalence between each other. Above all, they all emphasise the importance of common trends or common factors. Many researches, including a series of work of Stock and Watson, find that common factor models significantly improve accuracy in forecasting macroeconomic time series. This study follows the work of Stock and Watson. The authors propose a hybrid framework called genetic programming based vector error correction model (GPVECM), introducing genetic programming to traditional econometric models. This new method could construct common factors directly from nonstationary data set, avoiding differencing the original data and thus preserving more information. The authors' model guarantees that the constructed common factors satisfy the requirements of econometric models such as co-integration, in contrast to the traditional approach. Finally but not trivially, their model could save lots of time and energy from repeated work of unit root tests and differencing, which they believe is convenient for practitioners. An empirical study of forecasting US import from China is reported. The results of the new method dominates those of the plain vector error correction model and the ARIMA model.", notes = "http://www.igi-global.com/bookstore/titledetails.aspx?titleid=37325&detailstype=chapters Xi Chen (Deloitte Financial Advisory Services, China), Ye Pang (The People's Insurance Company (Group) of China Limited, China), and Guihuan Zheng (The People's Bank of China, China)", size = "15 pages", } @Article{Chen:2004:EAAI, author = "Xiaofang Chen and Weihua Gui and Yalin Wang and Lihui Cen", title = "Multi-step optimal control of complex process: a genetic programming strategy and its application", journal = "Engineering Applications of Artificial Intelligence", year = "2004", volume = "17", pages = "491--500", number = "5", keywords = "genetic algorithms, genetic programming, Multi-step comprehensive evaluation, Fitness function, Process optimal control", ISSN = "0952-1976", URL = "http://www.sciencedirect.com/science/article/B6V2M-4CMHSNB-1/2/5c02b126719099d090f4dba0eaaa5cea", DOI = "doi:10.1016/j.engappai.2004.04.018", owner = "wlangdon", abstract = "In many industrial processes, especially chemistry and metallurgy industry, the plant is slow for feedback and data test because of complex and varying factors. Considering the multi-objective feature and the complex problem of production stability in optimal control, this paper proposed an optimal control strategy based on genetic programming (GP), used as a multi-step state transferring procedure. The fitness function is computed by multi-step comprehensive evaluation algorithm, which provides a synthetic evaluation of multi-objective in process state based on single objective models. The punishment to process state variance is also introduced for the balance between optimal performance and stability of production. The individuals in GP are constructed as a chain linked by a few relation operators of time sequence for a facilitated evolution in GP with compact individuals. The optimal solution gained by evolution is a multi-step command program of process control, which not only ensures the optimisation tendency but also avoids violent process variation by adjusting control parameters step by step. An optimal control system for operation direction is developed based on this strategy for imperial smelting process in Shaoguan. The simulation and application results showed its effectiveness for production objects optimisation in complex process control.", } @InCollection{Chen:2014:OCMIEC, author = "Xiaofang Chen and Honglei Xu", title = "Engineering Optimization Approaches of Nonferrous Metallurgical Processes", booktitle = "Optimization and Control Methods in Industrial Engineering and Construction", publisher = "Springer", year = "2014", editor = "Honglei Xu and Xiangyu Wang", volume = "72", series = "Intelligent Systems, Control and Automation: Science and Engineering", pages = "107--124", keywords = "genetic algorithms, genetic programming, engineering optimisation, nonferrous metallurgical processes, sequential operating, imperial smelting furnace", isbn13 = "978-94-017-8043-8", language = "English", DOI = "doi:10.1007/978-94-017-8044-5_7", abstract = "The engineering optimisation approaches arising in nonferrous metallurgical processes are developed to deal with the challenges in current nonferrous metallurgical industry including resource shortage, energy crisis and environmental pollution. The great difficulties in engineering optimisation for nonferrous metallurgical process operation lie in variety of mineral resources, complexity of reactions, strong coupling and measurement disadvantages. Some engineering optimisation approaches are discussed, including operational-pattern optimisation, satisfactory optimisation with soft constraints adjustment and multi-objective intelligent satisfactory optimisation. As an engineering optimisation case, an intelligent sequential operating method for a practical Imperial Smelting Process is illustrated. Considering the complex operating optimisation for the Imperial Smelting Process, with the operating stability concerned, an intelligent sequential operating strategy is proposed on the basis of genetic programming (GP) adaptively designed, implemented as a multi-step state transferring procedure. The individuals in GP are constructed as a chain linked by a few relation operators of time sequence for a facilitated evolution with compact individuals. The optimal solution gained by evolution is a sequential operating program of process control, which not only ensures the tendency to optimisation but also avoids violent variation by operating the parameters in ordered sequences. Industrial application data are given as verifications.", } @Article{Chen:2025:asoc, author = "Xiaolong Chen and Junqing Li and Zunxun Wang and Jiake Li and Kaizhou Gao", title = "A genetic programming based cooperative evolutionary algorithm for flexible job shop with crane transportation and setup times", journal = "Applied Soft Computing", year = "2025", volume = "169", pages = "112614", keywords = "genetic algorithms, genetic programming, Genetic programming hyper heuristic, Dispatching rules, Cooperative evolutionary algorithm, Flexible job shop scheduling problem, Crane transportation", ISSN = "1568-4946", URL = "https://www.sciencedirect.com/science/article/pii/S1568494624013887", DOI = "doi:10.1016/j.asoc.2024.112614", abstract = "Confronted with increasingly complex industrial scenarios, limited transportation resources and complicated time constraints introduce significant challenges to production efficiency, requiring more robust and adaptive scheduling heuristics. In this study, a flexible job shop scheduling problem with single crane transportation and sequence-dependent setup time is considered. To address the problem, a mixed integer linear programming model is established, where two objectives, including the maximum completion time and total energy consumption, are determined simultaneously. Additionally, a genetic programming (GP) based cooperative evolutionary algorithm is developed to address the problem, in which GP is investigated as a hyper-heuristic to construct a set of problem-specific dispatching rules (DRs). The GP-based hyper-heuristic (GPHH) first evolves a set of DRs during iterations and then applies these DRs to the initialization of the population. Next, four critical path-based neighbourhood structures combined with an adaptive local search mechanism are used to enhance the exploitation capability of the algorithm. The simulation results demonstrate that the GPHH used for initialization significantly outperforms other classical heuristics in convergence capability, while the proposed GP-CEA algorithm also surpasses six state-of-the-art algorithms in exploration and exploitation, achieving superior performance on the HV, IGD, and SC metrics in approximately 59.4percent, 46.9percent, and 50.0percent of instances, respectively", } @Article{Xiaolong_Chen:TEVC, author = "Xiaolong Chen and Junqing Li and Zunxun Wang and Qingda Chen and Kaizhou Gao and Quanke Pan", title = "Optimizing Dynamic Flexible Job Shop Scheduling Using an Evolutionary Multi-Task Optimization Framework and Genetic Programming", journal = "IEEE Transactions on Evolutionary Computation", keywords = "genetic algorithms, genetic programming, Dynamic scheduling, Job shop scheduling, Heuristic algorithms, Optimisation, Real-time systems, Multitasking, Production, Evolutionary computation, Vehicle dynamics, Cranes, dynamic flexible job shop scheduling problem, evolutionary multi-task optimisation, hyperheuristics", ISSN = "1941-0026", DOI = "doi:10.1109/TEVC.2025.3543770", abstract = "Driven by the evolution of smart and sustainable manufacturing paradigms under Industry 5.0, which emphasize adaptability, connectivity, and data-driven decision-making, the dynamic flexible job shop scheduling problem (DFJSSP) has emerged as a critical area of research. The DFJSSP involves scheduling jobs in a highly dynamic and uncertain manufacturing environment where new tasks are continually introduced, further complicating the scheduling process. In this study, the DFJSSP is extended to incorporate single crane transportation and sequence-dependent setup times, reflecting real-world manufacturing constraints. To tackle this multifaceted problem, we introduce a novel approach, i.e., a multipopulation-based evolutionary multi-task optimisation(EMTO) framework. Additionally, the genetic programming algorithm is employed as a generative hyperheuristic to deal with the dynamic uncertainties in the shop floor. Two components are collaborated to optimise two objectives, i.e., minimizing the maximum completion time and the total tardiness. Furthermore, a dynamic transfer ratio is proposed, allowing the proportion of knowledge transfer to adapt throughout the iteration process, balancing convergence speed with population diversity. The results demonstrate that both the EMTO framework and the dynamic transfer ratio significantly enhance the performance of the algorithm. Compared to well-known constructive heuristics and reinforcement learning algorithm, the proposed approach enables parallel resolution of multiple optimisation objectives, leading to enhanced scheduling efficiency and adaptability in dynamic manufacturing environments.", notes = "Also known as \cite{10896746} Department of Mathematics, Yunnan Normal University, Kunming, Yunnan, China School of Information Science and Engineering, Shandong Normal University, Jinan, China", } @InProceedings{Chen:2008:ICNC, author = "Xiao-nan Chen and Hai-tao Chen and Lin Qiu and Chun-qing Duan", title = "Model of Water Production Function with Genetic Programming", booktitle = "Fourth International Conference on Natural Computation, ICNC '08", year = "2008", month = oct, volume = "6", pages = "311--314", keywords = "genetic algorithms, genetic programming, evolution calculation, optimal structure searching, water production function, water stress, irrigation, search problems", DOI = "doi:10.1109/ICNC.2008.118", abstract = "A new model for analyzing the relation between production and water stress is proposed. A model of genetic programming is established to describe water production function with evolution calculation, which can find the optimal model structure by samples. Simulation and experimental results indicated that water production function based on genetic programming is good at searching optimal structure automatically, and intelligent, accurate.", notes = "Also known as \cite{4667851}", } @InProceedings{chen:2023:BIC-TA, author = "Xinan Chen and Jing Dong and Rong Qu and Ruibin Bai", title = "Transformer Surrogate Genetic Programming for Dynamic Container Port Truck Dispatching", booktitle = "Bio-Inspired Computing: Theories and Applications", year = "2023", pages = "276--290", address = "Changsha, China", month = "15-17 " # dec, publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-981-97-2272-3_21", DOI = "doi:10.1007/978-981-97-2272-3_21", notes = "Published 2024", } @Article{Chen:2024:asoc, author = "Xinan Chen and Rong Qu and Jing Dong and Haibo Dong and Ruibin Bai", title = "Advancing container port traffic simulation: A data-driven machine learning approach in sparse data environments", journal = "Applied Soft Computing", year = "2024", volume = "166", pages = "112190", keywords = "genetic algorithms, genetic programming, Intelligent intersection, Transport simulation, Reinforcement learning, Port optimization", ISSN = "1568-4946", URL = "https://www.sciencedirect.com/science/article/pii/S1568494624009645", DOI = "doi:10.1016/j.asoc.2024.112190", abstract = "Efficient truck dispatching strategies are paramount in container terminal operations. The quality of these strategies heavily relies on accurate and expedient simulations, which provide a crucial platform for training and evaluating dispatching algorithms. In this study, we introduce data-driven machine learning methods to enhance container port truck dispatching simulation accuracy. These methods effectively surrogate the intersections within the simulation, thereby increasing the accuracy of simulated outcomes without imposing significant computational overhead in sparse data environments. We incorporate three data-driven learning methods: genetic programming (GP), reinforcement learning (RL), and a GP and RL hybrid heuristic (GPRL-H) approach. The GPRL-H method proved the most efficacious through a detailed comparative study, striking an effective balance between simulation accuracy and computational efficiency. It reduced the error rate of simulation from approximately 35percent to about 7percent, while also halving the simulation time compared to the RL-based method. Our proposed method also does not rely on precise Global Positioning System (GPS) data to simulate truck operations within a port accurately. Demonstrating robustness and adaptability, this approach holds promise for extending beyond port operations to improve the simulation accuracy of vehicle operations in various scenarios characterised by sparse data", } @InProceedings{Chen:2018:ICSESS, author = "Xianyi Chen and Guolan Lin", booktitle = "2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS)", title = "{TEA-MAC:} Traffic Estimation Adaptive {MAC} Protocol for Underwater Acoustic Networks", year = "2018", month = nov, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICSESS.2018.8663928", ISSN = "2327-0594", abstract = "Underwater acoustic sensor networks (UASNs) have been applied dramatically in many activities, such as ocean exploration and tsunami warning. However, due to the characteristics of underwater acoustic channel is quite difference from the radio and optical channel, media access control (MAC) is a crucial issue in underwater acoustic sensor networks. In this paper, we propose a Traffic Estimation Adaptive :MAC Protocol (TEA-MAC) based on a changeable duty cycle according to the traffic load. As the better the duty cycle matches the traffic of UASNs, the less energy and delay the nodes consume for data transmission, it is very import to sense the network load correctly. To address this issue, a traffic estimation algorithm based on nodes clustering and Genetic Programming is proposed in TEA-MAC, which can predict the network load successfully and set the duty cycle desirably. The Simulation results show that TEA-MAC performs better than the existing representative MAC protocols in terms of network throughput, end-to-end delay and energy efficiency.", notes = "Also known as \cite{8663928}", } @InProceedings{Chen:2007:cec, author = "Yan Chen and Shingo Mabu and Kotaro Hirasawa and Jinglu Hu", title = "Genetic Network Programming with Sarsa Learning and Its Application to Creating Stock Trading Rules", booktitle = "2007 IEEE Congress on Evolutionary Computation", year = "2007", editor = "Dipti Srinivasan and Lipo Wang", pages = "220--227", address = "Singapore", month = "25-28 " # sep, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "1-4244-1340-0", file = "1636.pdf", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2007.4424475", abstract = "In this paper, trading rules on stock market using the Genetic Network Programming (GNP) with Sarsa learning is described. GNP is an evolutionary computation, which represents its solutions using graph structures and has some useful features inherently. It has been clarified that GNP works well especially in dynamic environments since GNP can create quite compact programs and has an implicit memory function. In this paper, GNP is applied to creating a stock trading model. There are three important points: The first important point is to combine GNP with Sarsa Learning which is one of the reinforcement learning algorithms. Evolution-based methods evolve their programs after task execution because they must calculate fitness values, while reinforcement learning can change programs during task execution, therefore the programs can be created efficiently. The second important point is that GNP uses candlestick chart and selects appropriate technical indices to judge the timing of the buying and selling stocks. The third important point is that sub-nodes are used in each node to determine appropriate actions (buying/selling) and to select appropriate stock price information depending on the situation. In the simulations, the trading model is trained using the stock prices of 16 brands in 2001, 2002 and 2003. Then the generalisation ability is tested using the stock prices in 2004. From the simulation results, it is clarified that the trading rules of the proposed method obtain much higher profits than Buy&Hold method and its effectiveness has been confirmed.", notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C", } @InProceedings{Chen2:2008:cec, author = "Yan Chen and Shingo Mabu and Kaoru Shimada and Kotaro Hirasawa", title = "Real Time Updating Genetic Network Programming for Adapting to the Change of Stock Prices", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "370--377", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1823-7", file = "EC0109.pdf", DOI = "doi:10.1109/CEC.2008.4630824", abstract = "The key in stock trading model is to take the right actions for trading at the right time, primarily based on accurate forecast of future stock trends. Since an effective trading with given information of stock prices needs an intelligent strategy for the decision making, we applied Genetic Network Programming (GNP) to create a stock trading model. In this paper, we present a new method called Real Time Updating Genetic Network Programming (RTU-GNP) for adapting to the change of stock prices. There are two important points in this paper: First, the RTU-GNP method makes a stock trading decision considering both the recommendable information of technical indices and the change of stock prices according to the real time updating. Second, we combine RTU-GNP with a reinforcement learning algorithm to create the programs efficiently. The experimental results on the Japanese stock market show that the trading model with the proposed RTU-GNP method outperforms other models without time updating method. It yielded significantly higher profits than the traditional trading model without time updating. We also compare the experimental results using the proposed method with Buy&Hold method to confirm its effectiveness, and it is clarified that the proposed trading model can obtain much higher profits than Buy&Hold method.", keywords = "genetic algorithms, genetic programming", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @InProceedings{Chen:2008:gecco, author = "Yan Chen and Shingo Mabu and Kaoru Shimada and Kotaro Hirasawa", title = "Construction of portfolio optimization system using genetic network programming with control nodes", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "1693--1694", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1693.pdf", DOI = "doi:10.1145/1389095.1389413", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, control node, genetic network programming, portfolio optimisation, reinforcement learning, Real-World application: Poster", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389413}", } @InProceedings{Chen2:2009:cec, author = "Yan Chen and Shingo Mabu and Etsushi Ohkawa and Kotaro Hirasawa", title = "Constructing Portfolio Investment Strategy Based on Time Adapting Genetic Network Programming", booktitle = "2009 IEEE Congress on Evolutionary Computation", year = "2009", editor = "Andy Tyrrell", pages = "2379--2386", address = "Trondheim, Norway", month = "18-21 " # may, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-2959-2", file = "P026.pdf", DOI = "doi:10.1109/CEC.2009.4983238", keywords = "genetic algorithms, genetic programming, genetic network programming, Japanese stock market, candlestick chart, evolutionary method, investment advice, portfolio investment strategy, portfolio model, portfolio optimisation problem, portfolio problem, stock prices, technical analysis rules, technical indices, time adapting genetic network programming, investment, stock markets", abstract = "The classical portfolio problem is a problem of distributing capital to a set of stocks. By adapting to the change of stock prices, this study proposes an portfolio investment strategy based on an evolutionary method named {"}Genetic Network Programming{"} (GNP). This method makes use of the information from Technical Indices and Candlestick Chart. The proposed portfolio model, consisting of technical analysis rules, are trained to generate investment advice. Experimental results on the Japanese stock market show that the proposed investment strategy using Time Adapting GNP (TA-GNP) method outperforms other traditional models in terms of both accuracy and efficiency. We also compared the experimental results of the proposed model with the conventional GNP based methods, GA and Buy&Hold method to confirm its effectiveness, and it is clarified that the proposed investment strategy is effective on the portfolio optimization problem.", notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR. Also known as \cite{4983238}", } @InProceedings{Chen:2009:ieeeSMC, author = "Yan Chen and Kotaro Hirasawa and Shingo Mabu", title = "A portfolio selection model using genetic relation algorithm and genetic network programming", booktitle = "IEEE International Conference on Systems, Man and Cybernetics, SMC 2009", year = "2009", month = "11-14 " # oct, pages = "4378--4383", abstract = "In this paper, a new evolutionary method named genetic relation algorithm (GRA) has been proposed and applied to the portfolio selection problem. The number of brands in the stock market is generally very large, therefore, techniques for selecting the effective portfolio are likely to be of interest in the financial field. In order to pick up a fixed number of the most efficient portfolio, the proposed model considers the correlation coefficient between stocks as strength, which indicates the relationship between nodes in GRA. The algorithm evaluates the relationships between stock brands using a specific measure of strength and generates the optimal portfolio in the final generation. The efficiency of GRA method is confirmed by the stock trading model using genetic network programming (GNP) that has been proposed in the previous study. We present the experimental results obtained by GRA and compare them with those obtained by traditional method, and it is clarified that the proposed model can obtain much higher profits than the traditional one.", keywords = "genetic algorithms, genetic programming, genetic network programming, correlation coefficient, evolutionary method, genetic relation algorithm, portfolio selection model, stock market, stock markets", DOI = "doi:10.1109/ICSMC.2009.5346940", ISSN = "1062-922X", notes = "Also known as \cite{5346940}", } @Article{Chen200910735, author = "Yan Chen and Etsushi Ohkawa and Shingo Mabu and Kaoru Shimada and Kotaro Hirasawa", title = "A portfolio optimization model using Genetic Network Programming with control nodes", journal = "Expert Systems with Applications", volume = "36", number = "7", pages = "10735--10745", year = "2009", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2009.02.049", URL = "http://www.sciencedirect.com/science/article/B6V03-4VPD6KS-2/2/3cf6750a5518ab6e7d6cf817197d96bd", keywords = "genetic algorithms, genetic programming, Portfolio optimization, Genetic Network Programming, Control node, Reinforcement learning", abstract = "Many evolutionary computation methods applied to the financial field have been reported. A new evolutionary method named 'Genetic Network Programming' (GNP) has been developed and applied to the stock market recently. The efficient trading rules created by GNP has been confirmed in our previous research. In this paper a multi-brands portfolio optimisation model based on Genetic Network Programming with control nodes is presented. This method makes use of the information from technical indices and candlestick chart. The proposed optimization model, consisting of technical analysis rules, are trained to generate trading advice. The experimental results on the Japanese stock market show that the proposed optimization system using GNP with control nodes method outperforms other traditional models in terms of both accuracy and efficiency. We also compared the experimental results of the proposed model with the conventional GNP based methods, GA and Buy&Hold method to confirm its effectiveness, and it is clarified that the proposed trading model can obtain much higher profits than these methods.", } @Article{Chen200912537, author = "Yan Chen and Shingo Mabu and Kaoru Shimada and Kotaro Hirasawa", title = "A genetic network programming with learning approach for enhanced stock trading model", journal = "Expert Systems with Applications", volume = "36", number = "10", pages = "12537--12546", year = "2009", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2009.05.054", URL = "http://www.sciencedirect.com/science/article/B6V03-4WC113D-2/2/a6c6277183e3b22cc3cc50ba71d7062f", keywords = "genetic algorithms, genetic programming, Genetic Network Programming, Sarsa Learning, Stock trading model, Technical Index, Candlestick Chart", abstract = "In this paper, an enhancement of stock trading model using Genetic Network Programming (GNP) with Sarsa Learning is described. There are three important points in this paper: First, we use GNP with Sarsa Learning as the basic algorithm while both Technical Indices and Candlestick Charts are introduced for efficient stock trading decision-making. In order to create more efficient judgement functions to judge the current stock price appropriately, Importance Index (IMX) has been proposed to tell GNP the timing of buying and selling stocks. Second, to improve the performance of the proposed GNP-Sarsa algorithm, we proposed a new method that can learn the appropriate function describing the relation between the value of each technical index and the value of the IMX. This is an important point that devotes to the enhancement of the GNP-Sarsa algorithm. The third point is that in order to create more efficient judgment functions, sub-nodes are introduced in each node to select appropriate stock price information depending on the situations and to determine appropriate actions (buying/selling). To confirm the effectiveness of the proposed method, we carried out the simulation and compared the results of GNP-Sarsa with other methods like GNP with Actor Critic, GNP with Candlestick Chart, GA and Buy&Hold method. The results shows that the stock trading model using GNP-Sarsa outperforms all the other methods.", } @Article{Chen2009, author = "Yan Chen and Shingo Mabu and Kotaro Hirasawa", title = "A model of portfolio optimization using time adapting genetic network programming", journal = "Computers \& Operations Research", year = "2010", volume = "37", number = "10", pages = "1697--1707", month = oct, ISSN = "0305-0548", DOI = "doi:10.1016/j.cor.2009.12.003", URL = "http://www.sciencedirect.com/science/article/B6VC5-4Y0D6CX-1/2/2b2154c00eb0c11cef64666b20be06e1", keywords = "genetic algorithms, genetic programming, Genetic network programming, Portfolio optimisation, Reinforcement learning, Technical indices, Candlestick chart", abstract = "This paper describes a decision-making model of dynamic portfolio optimisation for adapting to the change of stock prices based on an evolutionary computation method named genetic network programming (GNP). The proposed model, making use of the information from technical indices and candlestick chart, is trained to generate portfolio investment advice. Experimental results on the Japanese stock market show that the decision-making model using time adapting genetic network programming (TA-GNP) method outperforms other traditional models in terms of both accuracy and efficiency. A comprehensive analysis of the results is provided, and it is clarified that the TA-GNP method is effective on the portfolio optimization problem.", } @InProceedings{Chen:2010:cec, author = "Yan Chen and Shingo Mabu and Kotaro Hirasawa", title = "A portfolio selection strategy using Genetic Relation Algorithm", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-6910-9", abstract = "This paper proposes a new strategy #x03B2;-GRA for portfolio selection in which the return and risk are considered as measures of strength in Genetic Relation Algorithm (GRA). Since the portfolio beta #x03B2; efficiently measures the volatility relative to the benchmark index or the capital market, #x03B2; is usually employed for portfolio evaluation or prediction, but scarcely for portfolio construction process. The main objective of this paper is to propose an integrated portfolio selection strategy, which selects stocks based on #x03B2; using GRA. GRA is a new evolutionary algorithm designed to solve the optimisation problem due to its special structure. We illustrate the proposed strategy by experiments and compare the results with those derived from the traditional models.", DOI = "doi:10.1109/CEC.2010.5586430", notes = "WCCI 2010. Also known as \cite{5586430}", } @InProceedings{conf/cnhpca/ChenLC15, author = "Yan Chen and Kangshun Li and Zhangxin Chen", title = "Parameter Identification Inverse Problems of Partial Differential Equations Based on the Improved Gene Expression Programming", booktitle = "High Performance Computing and Applications: Third International Conference, HPCA 2015", year = "2015", editor = "Jiang Xie and Zhangxin Chen and Craig C. Douglas and Wu Zhang and Yan Chen", volume = "9576", series = "Lecture Notes in Computer Science", pages = "218--227", address = "Shanghai, China", month = jul # " 26-30", publisher = "Springer", note = "Revised Selected Papers", keywords = "genetic algorithms, genetic programming, gene expression programming, partial differential equation, inverse problems, thomas algorithm", bibdate = "2017-05-23", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/cnhpca/cnhpca2015.html#ChenLC15", DOI = "doi:10.1007/978-3-319-32557-6_24", abstract = "Traditionally, solving the parameter identification inverse problems of partial differential equations encountered many difficulties and insufficiency. In this paper, we propose an improved GEP (Gene Expression Programming) to identify the parameters in the reverse problems of partial differential equations based on the self-adaptation, self-organization and self-learning characters of GEP. This algorithm simulates a parametric function itself of a partial differential equation directly through the observed values by fully taking into account inverse results caused by noises of a measured value. Modelling is unnecessary to add regularization in the modeling process aiming at special problems again. The experiment results show that the algorithm has good noise-immunity. In case there is no noise or noise is very low, the identified parametric function is almost the same as the original accurate value; when noise is very high, good results can still be obtained, which successfully realizes automation of the parameter modeling process for partial differential equations.", } @Article{DBLP:journals/jaciii/ChenS16, author = "Yan Chen and Zhihui Shi", title = "Generating Trading Rules for Stock Markets Using Robust Genetic Network Programming and Portfolio Beta", journal = "Journal of Advanced Computational Intelligence and Intelligent Informatics", year = "2016", volume = "20", number = "3", pages = "484--491", month = may, keywords = "genetic algorithms, genetic programming, portfolio beta, genetic relation algorithm, robust genetic network programming, stock trading", ISSN = "1343-0130", timestamp = "Fri, 18 Sep 2020 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/jaciii/ChenS16.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://doi.org/10.20965/jaciii.2016.p0484", DOI = "doi:10.20965/jaciii.2016.p0484", size = "8 pages", abstract = "Robust Genetic Network Programming (R-GNP) for generating trading rules for stocks is described. R-GNP is a new evolutionary algorithm, where solutions are represented using graph structures. It has been clarified that R-GNP works well especially in dynamic environments. In the proposed hybrid model, R-GNP is applied to generating stock trading rules with variance of fitness values. The unique point is that the generalization ability of R-GNP is improved by using the robust fitness function, which consists of the fitness functions with the original data and a good number of correlated data. Generally speaking, the hybrid intelligent system consists of three steps: priority selection by the portfolio beta, optimization by the Genetic Relation Algorithm (GRA), and stock trading by R-GNP. In the simulations, the trading model is trained using the stock prices of 10 brands on the Tokyo Stock Exchange, and then the generalization ability is tested. From the simulation results, it is clarified that the trading rules created by the proposed R-GNP model obtain much higher profits than the traditional methods even in the world-wide financial crisis of 2007. Hence, its effectiveness has been confirmed.", notes = "also known as \cite{Chen_2016jaciii} School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai 200433, China", } @Article{journals/soco/ChenLCW17, author = "Yan Chen and Kangshun Li and Zhangxing Chen and Jinfeng Wang", title = "Restricted gene expression programming: a new approach for parameter identification inverse problems of partial differential equation", journal = "Soft Computing", year = "2017", volume = "21", number = "10", pages = "2651--2663", keywords = "genetic algorithms, genetic programming, gene expression programming", bibdate = "2017-04-28", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/soco/soco21.html#ChenLCW17", DOI = "doi:10.1007/s00500-015-1965-1", } @InProceedings{Chen:2018:ICCS, title = "Comprehensive Learning Gene Expression Programming for Automatic Implicit Equation Discovery", author = "Yongliang Chen and Jinghui Zhong and Mingkui Tan", booktitle = "Computational Science - ICCS 2018 - 18th International Conference, Wuxi, China, June 11-13, 2018, Proceedings, Part I", publisher = "Springer", year = "2018", volume = "10860", editor = "Yong Shi and Haohuan Fu and Yingjie Tian and Valeria V. Krzhizhanovskaya and Michael Harold Lees and Jack J. Dongarra and Peter M. A. Sloot", pages = "114--128", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming, gene expression programming", isbn13 = "978-3-319-93697-0", bibdate = "2018-06-13", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/iccS/iccS2018-1.html#ChenZT18", DOI = "doi:10.1007/978-3-319-93698-7_9", notes = "conf/iccS/ChenZT18", } @PhdThesis{YuehuiChen:thesis, author = "Yuehui Chen", title = "Hybrid Soft Computing Approach to Identification and Control of Nonlinear Systems", school = "Department of Computer Science, Kumamoto University", year = "2001", address = "Japan", month = mar, email = "CHEN Yuehui ", keywords = "genetic algorithms, genetic programming, PIPE Algorithm", URL = "http://www31.freeweb.ne.jp/computer/chen_yh/thesis.pdf.001", URL = "http://www31.freeweb.ne.jp/computer/chen_yh/thesis.pdf.002", URL = "http://www31.freeweb.ne.jp/computer/chen_yh/thesis.pdf.003", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/yuehui.chen/YuehuiChenThesis.pdf", size = "182 pages", abstract = " Recently, complex industrial plants such as mobile robots, flexible manufacturing system etc., are often required to perform complex tasks with high precision under ill-defined conditions, and conventional control techniques may not be quite effective in these systems. Soft computing approaches are some computational models inspired by the simulated human and/or natural intelligence, and includes fuzzy logic, artificial neural networks, genetic and evolutionary algorithms. There have been many successful researches for the identification and control of nonlinear systems by using various soft computing techniques with different computational architectures. The experiences gained over the past decade indicate that it can be more effective to use the various soft computing approaches in a combined manner. But there is no common recognition about how to combine them in an effective way, and a unified framework of hybrid soft computing models in which various soft computing models can be developed, evolved and evaluated has not been established.", abstract = "In this research, a unified framework of hybrid soft computing models is proposed and it is applied to the identification and control of industrial plants. First, a scheme for identification and control of nonlinear systems using probabilistic incremental program evolution algorithm (PIPE) is proposed. Based on the modified PIPE (MPIPE) and some parameter tuning strategies, a unified framework of hybrid soft computing models is constructed for the identification of nonlinear systems, and then the hybrid soft computing based controller design principles and methods are developed. As an application, the proposed methods are applied to the identification and control of the thrust force (cutting torque) in the drilling system. This dissertation consists of six chapters as follows: In Chapter 1, the background and the current state of soft computing researches, and the purpose of the thesis are described briefly. In chapter 2, the basic elements of soft computing technique are discussed, including the evolutionary algorithms and random search algorithm, neural networks and fuzzy logic systems. The problems and disadvantages of the soft computing approaches are pointed out and their modification and improvements are given.", abstract = "In chapter 3, in order to cope with the problems of architecture selection and parameter optimization of soft computing models simultaneously, a unified framework is constructed in which various hybrid soft computing models can be developed, evolved and evaluated. In the proposed method, the architecture of the hybrid soft computing models is evolved by MPIPE and the parameters used in soft computing models are optimized by hybrid or non-hybrid parameter optimization strategy, respectively. The effectiveness of the proposed method has been confirmed by simulation studies. In chapter 4, some common soft computing based controller design principles are discussed briefly. Then a new control scheme for nonlinear systems based on PIPE algorithm is proposed. Finally, based on the basis function networks a unified framework for control of affine and non-affine nonlinear systems is presented with guaranteed stability analysis. The simulation and experimental results show the effectiveness of the proposed controller.", abstract = "In chapter 5, the soft computing based identification and control schemes developed in chapter 3 and 4 are applied to the drilling system. In order to control thrust force (cutting torque) in the process of drill, a number of thrust force (cutting torque) identification methods are developed, and then thrust force (cutting torque) soft model based neural control scheme are presented. Real time implementations show that the soft computing approaches based control schemes are efficient and effective. Finally in chapter 6, the results obtained in previous chapter are summarized, and some topics for future research in this direction are given. In this research, the applicability of PIPE algorithm to identification and control of nonlinear systems is confirmed. Based on the MPIPE and some parameter tuning strategies, a unified framework of hybrid soft computing models is constructed. Simulation and experiments results for the identification and control of nonlinear systems show the effectiveness of the proposed methods. The key point of the research is that various soft computing based identification and control schemes can be re-evaluated in a unified framework and then it is valuable for the proposed approach in order to construct the unified soft computing theories and applications.", notes = "my PDF reader barfed 20 July 2001. url_2 ok", } @InProceedings{Chen:2006:ESANN, author = "Yuehui Chen and Bo Yang and Ajith Abraham", title = "Optimal design of hierarchical wavelet networks for time-series forecasting", booktitle = "14th European Symposium on Artificial Neural Networks (ESANN 2006)", year = "2006", pages = "155--160", address = "Bruges, Belgium", month = apr # " 26-28", keywords = "genetic algorithms, genetic programming, ECGP", isbn13 = "2-930307-06-4", URL = "http://www.dice.ucl.ac.be/Proceedings/esann/esannpdf/es2006-57.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.136.9044", size = "6 pages", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", bibsource = "DBLP, http://dblp.uni-trier.de", language = "en", oai = "oai:CiteSeerXPSU:10.1.1.136.9044", abstract = "The purpose of this study is to identify the Hierarchical Wavelet Neural Networks (HWNN) and select important input features for each sub-wavelet neural network automatically. Based on the predefined instruction/operator sets, a HWNN is created and evolved using tree-structure based Extended Compact Genetic Programming (ECGP), and the parameters are optimised by Differential Evolution (DE) algorithm. This framework also allows input variables selection. Empirical results on benchmark time-series approximation problems indicate that the proposed method is effective and efficient.", } @InProceedings{Chen:2006:IDEAL, author = "Yuehui Chen and Yaou Zhao", title = "Face Recognition Using DCT and Hierarchical RBF Model", booktitle = "Intelligent Data Engineering and Automated Learning, IDEAL 2006", year = "2009", editor = "Emilio Corchado and Hujun Yin and Vicente Botti and Colin Fyfe", volume = "4224", series = "Lecture Notes in Computer Science", pages = "355--362", address = "Burgos, Spain", month = sep # " 20-23", publisher = "Springer", keywords = "genetic algorithms, genetic programming, DE, ECGP", isbn13 = "978-3-540-45485-4", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.482.9685", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.482.9685", URL = "http://cilab.ujn.edu.cn/paper/ideal1.pdf", DOI = "doi:10.1007/11875581_43", abstract = "This paper proposes a new face recognition approach by using the Discrete Cosine Transform (DCT) and Hierarchical Radial Basis Function Network (HRBF) classification model. The DCT is employed to extract the input features to build a face recognition system, and the HRBF is used to identify the faces. Based on the pre-defined instruction/operator sets, a HRBF model can be created and evolved. This framework allows input features selection. The HRBF structure is developed using Extended Compact Genetic Programming (ECGP) and the parameters are optimised by Differential Evolution (DE). Empirical results indicate that the proposed framework is efficient for face recognition.", } @Article{Chen:2006:N, author = "Yuehui Chen and Ajith Abraham and Bo Yang", title = "Feature selection and classification using flexible neural tree", journal = "Neurocomputing", year = "2006", volume = "70", number = "1-3", pages = "305--313", month = dec, note = "Selected Papers from the 7th Brazilian Symposium on Neural Networks (SBRN '04), 7th Brazilian Symposium on Neural Networks", keywords = "genetic algorithms, genetic programming, Flexible neural tree model, Memetic algorithm, Intrusion detection system, Breast cancer classification", ISSN = "0925-2312", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.1041.7313", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1041.7313", URL = "http://www.softcomputing.net/neucom1.pdf", URL = "http://www.sciencedirect.com/science/article/pii/S0925231206001111", DOI = "doi:10.1016/j.neucom.2006.01.022", abstract = "The purpose of this research is to develop effective machine learning or data mining techniques based on flexible neural tree FNT. Based on the pre-defined instruction/operator sets, a flexible neural tree model can be created and evolved. This framework allows input variables selection, over-layer connections and different activation functions for the various nodes involved. The FNT structure is developed using genetic programming (GP) and the parameters are optimised by a memetic algorithm (MA). The proposed approach was applied for two real-world problems involving designing intrusion detection system (IDS) and for breast cancer classification. The IDS data has 41 inputs/features and the breast cancer classification problem has 30 inputs/features. Empirical results indicate that the proposed method is efficient for both input feature selection and improved classification rate.", } @InProceedings{Chen:2007:ISNN, author = "Yuehui Chen and Qiang Wu and Feng Chen2", title = "An {IP} and {GEP} Based Dynamic Decision Model for Stock Market Forecasting", booktitle = "4th International Symposium on Neural Networks Advances in Neural Networks, ISNN 2007, Part I", year = "2007", editor = "Derong Liu and Shumin Fei and Zeng-Guang Hou and Huaguang Zhang and Changyin Sun", volume = "4491", series = "LNCS", pages = "473--479", address = "Nanjing, China", month = jun # " 3-7", publisher = "Springer", keywords = "genetic algorithms, genetic programming, gene expression programming, stock market forecasting, dynamic decision model, application result, forecasting model, favourable result, new method, static model, hybrid immune programming, generalisation capacity, artificial neural network, computational intelligence, static environment, stock market index, new dynamic decision forecasting model", isbn13 = "978-3-540-72383-7", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.626.3509", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.626.3509", broken = "http://cilab.ujn.edu.cn/paper/an%20ip%20and%20gep%20based%20dynamic%20decision%20model%20for%20stock%20market%20forecasting.pdf", DOI = "doi:10.1007/978-3-540-72383-7_56", abstract = "The forecasting models for stock market index using computational intelligence such as Artificial Neural networks (ANNs) and Genetic programming(GP), especially hybrid Immune Programming (IP) Algorithm and Gene Expression Programming (GEP) have achieved favourable results. However, these studies, have assumed a static environment. This study investigates the development of a new dynamic decision forecasting model. Application results prove the higher precision and generalisation capacity of the predicting model obtained by the new method than static models.", } @Article{Chen:2007:N, author = "Yuehui Chen and Bo Yang and Ajith Abraham", title = "Flexible neural trees ensemble for stock index modeling", journal = "Neurocomputing", year = "2007", volume = "70", number = "4-6", pages = "697--703", month = jan, note = "Advanced Neurocomputing Theory and Methodology - Selected papers from the International Conference on Intelligent Computing 2005 (ICIC 2005), International Conference on Intelligent Computing 2005", keywords = "genetic algorithms, genetic programming, Flexible neural tree, GP-like tree structure-based evolutionary algorithm, Particle swarm optimisation, Ensemble learning, Stock index", DOI = "doi:10.1016/j.neucom.2006.10.005", abstract = "The use of intelligent systems for stock market predictions has been widely established. In this paper, we investigate how the seemingly chaotic behaviour of stock markets could be well represented using flexible neural tree (FNT) ensemble technique. We considered the Nasdaq-100 index of Nasdaq Stock MarketSM and the S&P CNX NIFTY stock index. We analysed 7-year Nasdaq-100 main index values and 4-year NIFTY index values. This paper investigates the development of novel reliable and efficient techniques to model the seemingly chaotic behaviour of stock markets. The structure and parameters of FNT are optimised using genetic programming (GP) like tree structure-based evolutionary algorithm and particle swarm optimization (PSO) algorithms, respectively. A good ensemble model is formulated by the local weighted polynomial regression (LWPR). This paper investigates whether the proposed method can provide the required level of performance, which is sufficiently good and robust so as to provide a reliable forecast model for stock market indices. Experimental results show that the model considered could represent the stock indexes behaviour very accurately.", } @Book{Chen:2010:book, author = "Yuehui Chen and Ajith Abraham", title = "Tree-Structure based Hybrid Computational Intelligence", publisher = "Springer", year = "2010", volume = "2", series = "Intelligent Systems Reference Library", keywords = "genetic algorithms, genetic programming, Computational Intelligence, flexible neural trees, flexible neural trees networks, neural networks", isbn13 = "978-3-642-04738-1", URL = "http://www.springer.com/engineering/book/978-3-642-04738-1", DOI = "doi:10.1007/978-3-642-04739-8", abstract = "Research in computational intelligence is directed toward building thinking machines and improving our understanding of intelligence. As evident, the ultimate achievement in this field would be to mimic or exceed human cognitive capabilities including reasoning, recognition, creativity, emotions, understanding, learning and so on. In this book, the authors illustrate an hybrid computational intelligence framework and it applications for various problem solving tasks. Based on tree-structure based encoding and the specific function operators, the models can be flexibly constructed and evolved by using simple computational intelligence techniques. The main idea behind this model is the flexible neural tree, which is very adaptive, accurate and efficient. Based on the pre-defined instruction/operator sets, a flexible neural tree model can be created and evolved. This volume comprises of 6 chapters including an introductory chapter giving the fundamental definitions and the last Chapter provides some important research challenges. Academics, scientists as well as engineers engaged in research, development and application of computational intelligence techniques and data mining will find the comprehensive coverage of this book invaluable.", notes = "Theoretical Foundations and Applications ECGP, fuzzy, iris dataset", size = "210 pages", } @Article{Chen2011106, author = "Yuehui Chen and Bin Yang and Qingfang Meng and Yaou Zhao and Ajith Abraham", title = "Time-series forecasting using a system of ordinary differential equations", journal = "Information Sciences", volume = "181", number = "1", pages = "106--114", year = "2011", ISSN = "0020-0255", DOI = "doi:10.1016/j.ins.2010.09.006", URL = "http://www.sciencedirect.com/science/article/B6V0C-5100HS4-3/2/c9722759c9e35e7dba49e35c559ae617", keywords = "genetic algorithms, genetic programming, PSO, Hybrid evolutionary method, Network traffic, Small-time scale, The additive tree models, Ordinary differential equations, Particle swarm optimisation", abstract = "This paper presents a hybrid evolutionary method for identifying a system of ordinary differential equations (ODEs) to predict the small-time scale traffic measurements data. We used the tree-structure based evolutionary algorithm to evolve the architecture and a particle swarm optimization (PSO) algorithm to fine tune the parameters of the additive tree models for the system of ordinary differential equations. We also illustrate some experimental comparisons with genetic programming, gene expression programming and a feedforward neural network optimised using PSO algorithm. Experimental results reveal that the proposed method is feasible and efficient for forecasting the small-scale traffic measurements data.", } @Article{Chen2012274, author = "Yuehui Chen and Bin Yang and Qingfang Meng", title = "Small-time scale network traffic prediction based on flexible neural tree", journal = "Applied Soft Computing", volume = "12", number = "1", pages = "274--279", year = "2012", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2011.08.045", URL = "http://www.sciencedirect.com/science/article/pii/S1568494611003280", keywords = "genetic algorithms, genetic programming, Flexible neural tree model, Particle Swarm Optimization, Network traffic, Small-time scale", abstract = "In this paper, the flexible neural tree (FNT) model is employed to predict the small-time scale traffic measurements data. Based on the pre-defined instruction/operator sets, the FNT model can be created and evolved. This framework allows input variables selection, over-layer connections and different activation functions for the various nodes involved. The FNT structure is developed using the Genetic Programming (GP) and the parameters are optimised by the Particle Swarm Optimisation algorithm (PSO). The experimental results indicate that the proposed method is efficient for forecasting small-time scale traffic measurements and can reproduce the statistical features of real traffic measurements. We also compare the performance of the FNT model with the feed-forward neural network optimised by PSO for the same problem.", } @InProceedings{Chen:2023:evoapplications, author = "Yuheng Chen and Tao Shi and Hui Ma and Gang Chen2", title = "Multi-objective Location-Aware Service Brokering in Multi-cloud - A GPHH Approach with Transfer Learning", booktitle = "26th International Conference, EvoApplications 2023", year = "2023", month = apr # " 12-14", editor = "Joao Correia and Stephen Smith and Raneem Qaddoura", series = "LNCS", volume = "13989", publisher = "Springer Verlag", address = "Brno, Czech Republic", pages = "573--587", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Multi-objective optimisation, Multi-cloud, Service brokering, GPHH, Transfer learning", isbn13 = "978-3-031-30229-9", DOI = "doi:10.1007/978-3-031-30229-9_37", size = "15 pages", notes = "http://www.evostar.org/2023/ EvoApplications2023 held in conjunction with EuroGP'2023, EvoCOP2023 and EvoMusArt2023", } @Article{journals/tjs/ChenCKHX13, title = "Solving symbolic regression problems with uniform design-aided gene expression programming", author = "Yunliang Chen and Dan Chen and Samee Ullah Khan and Jianzhong Huang and Changsheng Xie", journal = "The Journal of Supercomputing", year = "2013", number = "3", volume = "66", keywords = "genetic algorithms, genetic programming, gene expression programming, GEP", bibdate = "2013-11-11", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/tjs/tjs66.html#ChenCKHX13", pages = "1553--1575", URL = "http://dx.doi.org/10.1007/s11227-013-0943-6", size = "23 pages", abstract = "Gene Expression Programming (GEP) significantly surpasses traditional evolutionary approaches to solving symbolic regression problems. However, existing GEP algorithms still suffer from premature convergence and slow evolution in anaphase. Aiming at these pitfalls, we designed a novel evolutionary algorithm, namely Uniform Design-Aided Gene Expression Programming (UGEP). UGEP uses (1) a mixed-level uniform table for generating initial population and (2) multiparent crossover operators by taking advantages of the dispersibility of uniform design. In addition to a theoretic analysis, we compared UGEP to existing GEP variants via a number of experiments in dealing with symbolic regression problems including function fitting and chaotic time series prediction. Experimental results indicate that UGEP excels in terms of both the capability of achieving the global optimum and the convergence speed in solving symbolic regression problems.", } @PhdThesis{Yuxin_Chen:thesis, author = "Yuxin Chen", title = "A Novel Hybrid Focused Crawling Algorithm to Build Domain-Specific Collections", school = "Virginia Polytechnic Institute and State University", year = "2007", address = "Blacksburg, Virginia, USA", month = feb # " 5", keywords = "genetic algorithms, genetic programming, digital libraries, focused crawler, classification, meta-search", URL = "http://scholar.lib.vt.edu/theses/available/etd-02162007-005107/", URL = "http://scholar.lib.vt.edu/theses/available/etd-02162007-005107/unrestricted/YuxinDissertation_etd_final1.pdf", URN = "etd-02162007-005107", size = "85 pages", abstract = "The Web, containing a large amount of useful information and resources, is expanding rapidly. Collecting domain-specific documents/information from the Web is one of the most important methods to build digital libraries for the scientific community. Focused Crawlers can selectively retrieve Web documents relevant to a specific domain to build collections for domain-specific search engines or digital libraries. Traditional focused crawlers normally adopting the simple Vector Space Model and local Web search algorithms typically only find relevant Web pages with low precision. Recall also often is low, since they explore a limited sub-graph of the Web that surrounds the starting URL set, and will ignore relevant pages outside this sub-graph. In this work, we investigated how to apply an inductive machine learning algorithm and meta-search technique, to the traditional focused crawling process, to overcome the above mentioned problems and to improve performance. We proposed a novel hybrid focused crawling framework based on Genetic Programming (GP) and meta-search. We showed that our novel hybrid framework can be applied to traditional focused crawlers to accurately find more relevant Web documents for the use of digital libraries and domain-specific search engines. The framework is validated through experiments performed on test documents from the Open Directory Project. Our studies have shown that improvement can be achieved relative to the traditional focused crawler if genetic programming and meta-search methods are introduced into the focused crawling process.", } @InProceedings{Chen:2007:WISP, author = "Zheng Chen and Siwei Lu", title = "A Genetic Programming Approach for Classification of Textures Based on Wavelet Analysis", booktitle = "IEEE International Symposium on Intelligent Signal Processing, WISP 2007", year = "2007", month = oct, pages = "1--6", keywords = "genetic algorithms, genetic programming, feature extraction, texture classification, wavelet analysis, wavelet decomposition, feature extraction, image classification, image texture, wavelet transforms", DOI = "doi:10.1109/WISP.2007.4447575", abstract = "In this paper, we propose a method for classifying textures using Genetic Programming (GP). Texture features are extracted from the energy of subimages of the wavelet decomposition. The GP is then used to evolve rules, which are arithmetic combinations of energy features, to identify whether a texture image belongs to certain class. Instead of using only one rule to discriminate the samples, a set of rules are used to perform the prediction by applying the majority voting technique. In our experiment results based on Brodatz dataset, the proposed method has achieved 99.6percent test accuracy on an average. In addition, the experiment results also show that classification rules generated by this approach are robust to some noises on textures.", notes = "Also known as \cite{4447575}", } @InProceedings{Chen:2020:CEC, author = "Xinan Chen and Ruibin Bai and Rong Qu and Haibo Dong and Jianjun Chen", title = "A Data-Driven Genetic Programming Heuristic for Real-World Dynamic Seaport Container Terminal Truck Dispatching", booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC 2020", year = "2020", editor = "Yaochu Jin", pages = "paper id24651", address = "internet", month = "19-24 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, container terminal, lorry, truck dispatching, dynamic", isbn13 = "978-1-7281-6929-3", URL = "http://www.cs.nott.ac.uk/~pszrq/files/CEC2020HGP.pdf", DOI = "doi:10.1109/CEC48606.2020.9185659", size = "8 pages", abstract = "International and domestic maritime trade has been expanding dramatically in last few decades, seaborne container transportation has become an indispensable part of maritime trade efficient and easy-to-use containers. As an important hub of container transport, container terminals use a range of metrics to measure their efficiency, among which the hourly container throughput (i.e., the number of twenty-foot equivalent unit containers, or TEUs) is the most important objective to improve. We propose a genetic programming approach to build adynamic truck dispatching system trained on real-world stochastic operations data. The experimental results demonstrated the superiority of this dynamic approach and the potential for practical applications.", notes = "https://wcci2020.org/ University of Nottingham Ningbo China, China; University of Nottingham, United Kingdom; Xi'an Jiaotong-Liverpool University, China", } @Article{Xinan_Chen:ieeeTEC, author = "Xinan Chen and Ruibin Bai and Rong Qu and Haibo Dong", title = "Cooperative Double-Layer Genetic Programming Hyper-Heuristic for Online Container Terminal Truck Dispatching", journal = "IEEE Transactions on Evolutionary Computation", year = "2023", volume = "27", number = "5", pages = "1220--1234", month = oct, keywords = "genetic algorithms, genetic programming", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2022.3209985", abstract = "In a marine container terminal, truck dispatching is a crucial problem that impacts on the operation efficiency of the whole port. Traditionally, this problem is formulated as an offline optimisation problem, whose solutions are, however, impractical for most real-world scenarios primarily because of the uncertainties of dynamic events in both yard operations and seaside loading-unloading operations. These solutions are either unattractive or infeasible to execute. Herein, for more intelligent handling of these uncertainties and dynamics, a novel cooperative double-layer genetic programming hyper-heuristic (CD-GPHH) is proposed to tackle this challenging online optimisation problem. In this new CD-GPHH, a novel scenario genetic programming (GP) approach is added on top of a traditional GP method that chooses among different GP heuristics for different scenarios to facilitate optimised truck dispatching. In contrast to traditional arithmetic GP (AGP) and GP with logic operators (LGP) which only evolve on one population, our CD-GPHH method separates the scenario and the calculation into two populations, which improved the quality of solutions in multi-scenario problems while reducing the search space. Experimental results show that our CD-GPHH dominates AGP and LGP in solving a multi-scenario function fitting problem as well as a truck dispatching problem in container terminal.", notes = "also known as \cite{9903916}", } @Article{Chen:TEVC, author = "Xinan Chen and Ruibin Bai and Rong Qu and Jing Dong and Yaochu Jin", journal = "IEEE Transactions on Evolutionary Computation", title = "Deep Reinforcement Learning Assisted Genetic Programming Ensemble Hyper-Heuristics for Dynamic Scheduling of Container Port Trucks", note = "Early access", abstract = "Efficient truck dispatching is crucial for optimising container terminal operations within dynamic and complex scenarios. Despite good progress being made recently with more advanced uncertainty-handling techniques, existing approaches still have generalisation issues and require considerable expertise and manual interventions in algorithm design. In this work, we present deep reinforcement learning-assisted genetic programming hyper-heuristics (DRL-GPHH) and their ensemble variant (DRL-GPEHH). These frameworks use a reinforcement learning agent to orchestrate a set of auto-generated genetic programming (GP) low-level heuristics, leveraging the collective intelligence, ensuring advanced robustness and an increased level of automation of the algorithm development. DRL-GPEHH, notably, excels through its concurrent integration of a GP heuristic ensemble, achieving enhanced adaptability and performance in complex, dynamic optimisation tasks. This method effectively navigates traditional convergence issues of deep reinforcement learning (DRL) in sparse reward and vast action spaces, while avoiding the reliance on expert-designed heuristics. It also addresses the inadequate performance of the single GP individual in varying and complex environments and preserves the inherent interpretability of the GP approach. Evaluations across various real port operational instances highlight the adaptability and efficacy of our frameworks. Essentially, innovations in DRL-GPHH and DRL-GPEHH reveal the synergistic potential of reinforcement learning and GP in dynamic truck dispatching, yielding transformative impacts on algorithm design and significantly advancing solutions to complex real-world optimisation problems.", keywords = "genetic algorithms, genetic programming, Containers, Dispatching, Seaports, Optimisation, Heuristic algorithms, Reinforcement learning, Marine vehicles, automatic truck dispatching, dynamic task scheduling, reinforcement learning", DOI = "doi:10.1109/TEVC.2024.3381042", ISSN = "1941-0026", notes = "Also known as \cite{10478109}", } @InProceedings{Chen:2020:ICNC, author = "Xiao Chen", booktitle = "2020 International Conference on Computing, Networking and Communications (ICNC)", title = "Energy Efficient {NFV} Resource Allocation in Edge Computing Environment", year = "2020", pages = "477--481", abstract = "With the development of IoT and 5G communication, a recent trend is to shift the Network Function Virtualisation (NFV) from the centralized cloud computing to edge computing. In this paper, we study the energy efficient NFV-Resource Allocation problem in the edge computing environment. We define two problems. In the first problem, we assume that the physical resources (PRs) on the edge do not have energy constraint. Our objective is to find an optimal deployment so that the maximum energy consumption on the PRs is minimized. In the second problem, we assume that the PRs have energy constraint and aim to find an optimal deployment to reduce the number of PRs. We prove both problems NP-complete and propose heuristic algorithms to solve them. We also design baseline algorithms using genetic programming to find approximate optimal solutions to these problems. We conduct simulations to evaluate the performance of our proposed algorithms. Simulation results show that our algorithms produce results very close to those of the baseline algorithms in a much shorter time.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICNC47757.2020.9049765", ISSN = "2325-2626", month = feb, notes = "Also known as \cite{9049765}", } @Article{chen:2021:Mathematics, author = "Xiaowu Chen and Guozhang Jiang and Yongmao Xiao and Gongfa Li and Feng Xiang", title = "A Hyper Heuristic Algorithm Based Genetic Programming for Steel Production Scheduling of {Cyber-Physical} {System-ORIENTED}", journal = "Mathematics", year = "2021", volume = "9", number = "18", keywords = "genetic algorithms, genetic programming, teel production scheduling, cyber-physical system, hyper-heuristic algorithm, heuristic scheduling rule", ISSN = "2227-7390", URL = "https://www.mdpi.com/2227-7390/9/18/2256", DOI = "doi:10.3390/math9182256", abstract = "Intelligent manufacturing is the trend of the steel industry. A cyber-physical system oriented steel production scheduling system framework is proposed. To make up for the difficulty of dynamic scheduling of steel production in a complex environment and provide an idea for developing steel production to intelligent manufacturing. The dynamic steel production scheduling model characteristics are studied, and an ontology-based steel cyber-physical system production scheduling knowledge model and its ontology attribute knowledge representation method are proposed. For the dynamic scheduling, the heuristic scheduling rules were established. With the method, a hyper-heuristic algorithm based on genetic programming is presented. The learning-based high-level selection strategy method was adopted to manage the low-level heuristic. An automatic scheduling rule generation framework based on genetic programming is designed to manage and generate excellent heuristic rules and solve scheduling problems based on different production disturbances. Finally, the performance of the algorithm is verified by a simulation case.", notes = "also known as \cite{math9182256}", } @InProceedings{Chen:2021:CEC, author = "Xuhao Eric Chen and Brian J. Ross", booktitle = "2021 IEEE Congress on Evolutionary Computation (CEC)", title = "Deep Neural Network Guided Evolution of {L}-System Trees", year = "2021", editor = "Yew-Soon Ong", pages = "2507--2514", address = "Krakow, Poland", month = "28 " # jun # "-1 " # jul, isbn13 = "978-1-7281-8393-0", abstract = "Lindenmayer systems (L-systems) are mathematical formalisms used for generating recursive structures. They are particularly effective for defining realistic tree and plant models. It takes experience to use L-systems effectively, however, as the final rendered results are often difficult to predict. This research explores the use of genetic programming (GP) and deep learning towards the automatic evolution of L-system expressions that render 2D tree designs. As done before by other researchers, the L-system language is easily defined and manipulated by the GP system. It is challenging, however, to determine a fitness function to evaluate the suitability of evolved expressions. We train a deep convolutional neural network (CNN) to recognize suitable trees rendered in the style of the L-system language. Experiments explore a number of deep CNN strategies. Results in some experiments are very promising, as images conforming to specified styles of tree species were often produced. We found that underspecifying or over-complicating the training requirements can arise, and the results become unsatisfactory in such cases. Our results also confirm that of other researchers, in that deep learning can be fooled by evolutionary algorithms, and the criteria for success learned by deep neural networks might not conform with those of human users.", keywords = "genetic algorithms, genetic programming, Deep learning, Training, Solid modeling, Three-dimensional displays, Architecture, Vegetation, Evolutionary computation, convolutional neural networks, L-systems", DOI = "doi:10.1109/CEC45853.2021.9504827", notes = "Also known as \cite{9504827}", } @Article{CHEN:2023:jmst, author = "Yimian Chen and Shuize Wang and Jie Xiong and Guilin Wu and Junheng Gao and Yuan Wu and Guoqiang Ma and Hong-Hui Wu and Xinping Mao", title = "Identifying facile material descriptors for Charpy impact toughness in low-alloy steel via machine learning", journal = "Journal of Materials Science \& Technology", volume = "132", pages = "213--222", year = "2023", ISSN = "1005-0302", DOI = "doi:10.1016/j.jmst.2022.05.051", URL = "https://www.sciencedirect.com/science/article/pii/S100503022200545X", keywords = "genetic algorithms, genetic programming, Machine learning, Symbolic regression, Low-alloy steel, Charpy impact toughness", abstract = "High toughness is highly desired for low-alloy steel in engineering structure applications, wherein Charpy impact toughness (CIT) is a critical factor determining the toughness performance. In the current work, CIT data of low-alloy steel were collected, and then CIT prediction models based on machine learning (ML) algorithms were established. Three feature construction strategies were proposed. One is solely based on alloy composition, another is based on alloy composition and heat treatment parameters, and the last one is based on alloy composition, heat treatment parameters, and physical features. A series of ML methods were used to effectively select models and material descriptors from a large number of alternatives. Compared with the strategy solely based on the alloy composition, the strategy based on alloy composition, heat treatment parameters together with physical features perform much better. Finally, a genetic programming (GP) based symbolic regression (SR) approach was developed to establish a physical meaningful formula between the selected features and targeted CIT data", } @InProceedings{Chen:2022:SCC, author = "Yuheng Chen and Tao Shi and Hui Ma and Gang Chen2", title = "Automatically Design Heuristics for Multi-Objective Location-Aware Service Brokering in Multi-Cloud", booktitle = "2022 IEEE International Conference on Services Computing (SCC)", year = "2022", pages = "206--214", month = "10-16 " # jul, address = "Barcelona, Spain", keywords = "genetic algorithms, genetic programming, Costs, Service computing, Quality of service, QoS, Dynamic scheduling, Dynamic programming, Task analysis, Multi-objective optimization, multi-cloud, location-aware, service brokering, GPHH", ISSN = "2474-2473", DOI = "doi:10.1109/SCC55611.2022.00039", size = "9 pages", abstract = "Multi-cloud provides cloud services at distributed locations. As the number of cloud services from multi-cloud providers growing, how to select proper cloud services to optimize multiple potentially conflicting objectives simultaneously has become a challenging task. Multi-objective location-aware service brokering (MOLSB) aims to provide a set of trade-off solutions to minimize cost and latency. To handle dynamic resource requirements, various heuristics have been proposed to efficiently find suitable cloud services. However, these heuristics cannot achieve consistently good performance on a wide range of problem instances. Additionally, instead of replying on a single heuristic, it is desirable to design a set of effective heuristics that can balance different objectives with varied trade-offs. Genetic Programming hyper-heuristics (GPHH) have been applied to automatically design heuristics for many multi-objective dynamic optimization problems, e.g., workflow scheduling. In this pa-per, we propose a new GPHH-based approach, named GPHH-MOLSB, to automatically generate a group of Pareto-optimal heuristics that can be used to satisfy varied QoS preferences. GPHH-MOLSB can significantly outperform several existing approaches based on evaluation on real-world datasets.", notes = "Also known as \cite{9860215}", } @InProceedings{Chen:2023:ITSC, author = "Xinan Chen and Feiyang Bao and Rong Qu and Jing Dong and Ruibin Bai", booktitle = "2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)", title = "Neural Network Assisted Genetic Programming in Dynamic Container Port Truck Dispatching", year = "2023", pages = "2246--2251", abstract = "Efficient truck dispatching is crucial for container port operations. Dynamic container port truck dispatching, a complex online optimisation problem, poses significant challenges due to its uncertain and non-linear nature. This paper presents a novel neural network assisted genetic programming (NN-GP) approach, which combines the global search of genetic programming (GP) and the local search of recurrent neural network (RNN). In this framework, the RNN further refines GP individuals after genetic operations (crossovers and mutations), enhancing solution adaptability and precision in response to dynamic and uncertain scenarios. The proposed method leverages RNN's understanding of temporal dynamics and GP's robust exploration of the solution space, effectively addressing the dynamic container truck dispatching problem. Experiments using real-world container port data demonstrate that the RNN-GP model outperforms traditional heuristic methods and standalone GP algorithms, reducing dispatching time and increasing port efficiency. This research highlights the potential of hybridizing machine learning techniques with GP in solving complex real-world optimisation problems.", keywords = "genetic algorithms, genetic programming, Adaptation models, Recurrent neural networks, Artificial neural networks, ANN, Containers, Dispatching, Optimisation", DOI = "doi:10.1109/ITSC57777.2023.10422513", ISSN = "2153-0017", month = sep, notes = "Also known as \cite{10422513}", } @InProceedings{Chen:2023:SMC, author = "Xiang-Ling Chen and Xiao-Cheng Liao and Wei-Neng Chen", booktitle = "2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)", title = "Heuristic Navigation Model Based on Genetic Programming for Multi-{UAV} Power Inspection Problem with Charging Stations", year = "2023", pages = "363--370", address = "Honolulu, Oahu, HI, USA", month = "1-4 " # oct, keywords = "genetic algorithms, genetic programming, gene expression programming, Adaptation models, Navigation, Inspection, Charging stations, Autonomous aerial vehicles, Mathematical models, Task analysis, unmanned aerial vehicles (UAVs), task assignment problem, charging problem, heuristic navigation model", ISSN = "2577-1655", isbn13 = "979-8-3503-3703-7", URL = "https://human-competitive.org/sites/default/files/entryform_6.txt", URL = "https://human-competitive.org/sites/default/files/paper_5.pdf", DOI = "doi:10.1109/SMC53992.2023.10394169", size = "8 pages", abstract = "Efficient power inspection is crucial for maintaining a stable power system. During an inspection, unmanned aerial vehicles (UAVs) usually need to be recharged due to the wide geographical range of inspection and the limited battery capacity of UAVs. This limitation makes the problem more challenging that requires not only optimising the task execution order, but also taking the chargings of UAVs into consideration. In order to address this complex problem, this work first formulates the UAV power inspection planning problem with charging stations. After that, we propose a new heuristic navigation model, in which UAVs can follow a heuristic rule to decide where to go next based on both its own information and task-related information. To obtain the heuristic rule, we design a set of features to describe the status of the UAVs and task completion. Then a genetic programming (GP) algorithm is introduced to evolve and get the heuristic rule. Finally, by applying heuristic navigation rule, the UAV navigation model can automatically prioritize task and charging order, and generate UAV flight routes that satisfy all constraints. The experiment results show that our method significantly outperforms the state-of-the-art algorithms.", notes = "Entered 2024 HUMIES Also known as \cite{10394169}", } @InProceedings{chen:2024:CEC, author = "Xinan Chen and Wenjie Yi and Ruibin Bai and Rong Qu and Yaochu Jin", title = "A Hierarchical Cooperative Genetic Programming for Complex Piecewise Symbolic Regression", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Adaptation models, Computational modeling, Sociology, Evolutionary computation, Data models, Regression analysis, symbolic regression, hierarchical structure, evolutionary algorithm", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10611754", abstract = "In regression analysis, methodologies range from black-box approaches like artificial neural networks to white-box techniques like symbolic regression. Renowned for its trans-parency and interpretability, symbolic regression has become increasingly prominent in elucidating complex data relationships. Nevertheless, its effectiveness in managing complex piecewise symbolic regression tasks poses significant challenges. This paper introduces a novel Hierarchical Cooperative Genetic Program-ming (HCGP) framework to address this issue. The HCGP model uses a unique hierarchical structure, incorporating dual cooperative genetic programming (GP) populations. This innovative design significantly enhances the capability to solve complex piecewise symbolic regression problems. Implementing a scenario-based GP is central to the HCGP framework, which strategically selects the appropriate underlying calculation GP. This feature enables the system to autonomously learn and adapt to complex scenarios, selecting the most suitable calculation GPs for each case. Our HCGP approach distinguishes itself from traditional and state-of-the-art methods. It demonstrates particular proficiency in modelling piecewise expressions within complex scenarios. The empirical evaluation of our model, conducted using benchmark datasets, has exhibited its superior accuracy and computational efficiency. This progress emphasizes the potential of HCGP in sophisticated data modelling and marks a substantial advancement in a hierarchical structure in complex piecewise symbolic regression.", notes = "also known as \cite{10611754} WCCI 2024", } @Article{CHENAR:2018:WR, author = "Shima Shamkhali Chenar and Zhiqiang Deng", title = "Development of genetic programming-based model for predicting oyster norovirus outbreak risks", journal = "Water Research", volume = "128", pages = "20--37", year = "2018", keywords = "genetic algorithms, genetic programming, Oyster norovirus outbreaks, Predictive model, Sensitivity analysis", ISSN = "0043-1354", DOI = "doi:10.1016/j.watres.2017.10.032", URL = "http://www.sciencedirect.com/science/article/pii/S0043135417308692", abstract = "Oyster norovirus outbreaks pose increasing risks to human health and seafood industry worldwide but exact causes of the outbreaks are rarely identified, making it highly unlikely to reduce the risks. This paper presents a genetic programming (GP) based approach to identifying the primary cause of oyster norovirus outbreaks and predicting oyster norovirus outbreaks in order to reduce the risks. In terms of the primary cause, it was found that oyster norovirus outbreaks were controlled by cumulative effects of antecedent environmental conditions characterized by low solar radiation, low water temperature, low gage height (the height of water above a gage datum), low salinity, heavy rainfall, and strong offshore wind. The six environmental variables were determined by using Random Forest (RF) and Binary Logistic Regression (BLR) methods within the framework of the GP approach. In terms of predicting norovirus outbreaks, a risk-based GP model was developed using the six environmental variables and various combinations of the variables with different time lags. The results of local and global sensitivity analyses showed that gage height, temperature, and solar radiation were by far the three most important environmental predictors for oyster norovirus outbreaks, though other variables were also important. Specifically, very low temperature and gage height significantly increased the risk of norovirus outbreaks while high solar radiation markedly reduced the risk, suggesting that low temperature and gage height were associated with the norovirus source while solar radiation was the primary sink of norovirus. The GP model was used to hindcast daily risks of oyster norovirus outbreaks along the Northern Gulf of Mexico coast. The daily hindcasting results indicated that the GP model was capable of hindcasting all historical oyster norovirus outbreaks from January 2002 to June 2014 in the Gulf of Mexico with only two false positive outbreaks for the 12.5-year period. The performance of the GP model was characterized with the area under the Receiver Operating Characteristic curve of 0.86, the true positive rate (sensitivity) of 78.53percent and the true negative rate (specificity) of 88.82percent, respectively, demonstrating the efficacy of the GP model. The findings and results offered new insights into the oyster norovirus outbreaks in terms of source, sink, cause, and predictors. The GP model provided an efficient and effective tool for predicting potential oyster norovirus outbreaks and implementing management interventions to prevent or at least reduce norovirus risks to both the human health and the seafood industry", } @InProceedings{Cheng:2014:GPTP, author = "Chao Cheng and William P. Worzel", title = "Application of Machine-Learning Methods to Understand Gene Expression Regulation", booktitle = "Genetic Programming Theory and Practice XII", year = "2014", editor = "Rick Riolo and William P. Worzel and Mark Kotanchek", series = "Genetic and Evolutionary Computation", pages = "1--15", address = "Ann Arbor, USA", month = "8-10 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Support Vector Machine, SVM, Random forest, GP, ENCODE, modENCODE, Transcription Factor (TF), Histone modification, ChIP-Chip, ChIP-seq, RNA-seq", isbn13 = "978-3-319-16029-0", DOI = "doi:10.1007/978-3-319-16030-6_1", abstract = "With the development and application of high-throughput technologies, an enormous amount of biological data has been produced in the past few years. These large-scale datasets make it possible and necessary to implement machine learning techniques for mining biological insights. In this chapter, we describe several examples to show how machine learning approaches are used to elucidate the mechanism of transcriptional regulation mediated by transcription factors and histone modifications. We demonstrate that machine learning provides powerful tools to quantitatively relate gene expression with transcription factor binding and histone modifications, to identify novel regulatory DNA elements in the genomes, and to predict gene functions. We also discuss the advantages and limitations of genetic programming in analysing and processing biological data.", notes = " Part of \cite{Riolo:2014:GPTP} published after the workshop in 2015", } @InCollection{Cheng:1997:rphGPri, author = "Cleve Cheng", title = "Recognizing Poker Hands with Genetic Programming and Restricted Iteration", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1997", publisher = "Stanford Bookstore", year = "1997", editor = "John R. Koza", pages = "18--27", address = "Stanford, California, 94305-3079 USA", month = "17 " # mar, keywords = "genetic algorithms, genetic programming", ISBN = "0-18-205981-2", notes = "part of \cite{koza:1997:GAGPs}", } @Article{Cheng:2018:cin, author = "Ching-Hsue Cheng and Chia-Pang Chan and Jun-He Yang", title = "A Seasonal Time-Series Model Based on Gene Expression Programming for Predicting Financial Distress", journal = "Computational Intelligence and Neuroscience", year = "2018", volume = "2018", bibdate = "2018-11-14", pages = "1067350:1--1067350:14", keywords = "genetic algorithms, genetic programming, gene expression programming", bibsource = "DBLP, http://dblp.uni-trier.de/https://www.wikidata.org/entity/Q55537117; DBLP, http://dblp.uni-trier.de/db/journals/cin/cin2018.html#ChengCY18", DOI = "doi:10.1155/2018/1067350", notes = "journals/cin/ChengCY18", } @InProceedings{Cheng:2017:BIGCOM, author = "Guang Cheng and Yang Yan", booktitle = "2017 3rd International Conference on Big Data Computing and Communications (BIGCOM)", title = "Evaluation and Design of Non-cryptographic Hash Functions for Network Data Stream Algorithms", year = "2017", pages = "239--244", abstract = "Non-cryptographic hash function is the core algorithm in network data stream technologies, its performance plays a crucial role in data stream algorithms. In this paper, two new quality criteria active flow metric and homology hash value correlation metric are firstly proposed for evaluating hash functions used in data stream algorithms. Experiments towards the metrics defined on 15 representative hash functions are performed using the real IPv6 network data captured from CERNET backbone. Bitwise operators are common candidates for implementing hash functions. We experimentally prove that XOR can introduce the most entropy to hash values compared with other 3 operators. On the basis of operator analysis, we design a novel hash function using Genetic Programming for data stream algorithm and network measurement. It can compete with the state of the art hash functions.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/BIGCOM.2017.38", month = aug, notes = "College of Cybersecurity, School of Computer Science and Engineering, Key Laboratory of Computer Network and Information, Integration (Southeast University), Ministry of Education, Southeast University, Nanjing, China. Also known as \cite{8113072}", } @InProceedings{Cheng:2009:ASIA, author = "Huifang Cheng and Yongqiang Zhang and Jing Zhao", title = "Improved Genetic Programming Model for Software Reliability", booktitle = "International Asia Symposium on Intelligent Interaction and Affective Computing, ASIA '09", year = "2009", month = dec, pages = "164--167", keywords = "genetic algorithms, genetic programming, SBSE, IGP algorithm, improved genetic programming model, software failure mechanism, software reliability, software reliability", DOI = "doi:10.1109/ASIA.2009.38", abstract = "Many existing software reliability models are based on some subjective assumptions those could be easily impractical in reality. Genetic Programming(GP for short) does not need some subjective assumption due to the basic characteristic of the data. Also, this method doesn't require to understand the inherent processes for failures, but to create models based on the given data for a {"}true{"} process during the specific modeling course, which can describe the software failure mechanisms more effectually and predict for the next failure times more exactly. This paper adopts improved GP(IGP for short) algorithm to hunting model, which can possibly reflect system behaviors, in the function spaces are compoundly constituted by the authorized function operators. Meanwhile, we have proved that IGP can obtain the best solution for failure behavior's variation rules from the convergence character of itself. Moreover, this paper makes use of Orthogonal experimental to adjust the parameters.", notes = "Also known as \cite{5376005}", } @InProceedings{Cheng:2009:ASIA2, author = "Huifang Cheng and Yongqiang Zhang and Fangping Li", title = "Improved Genetic Programming Algorithm", booktitle = "International Asia Symposium on Intelligent Interaction and Affective Computing, ASIA '09", year = "2009", month = dec, pages = "168--171", abstract = "The present study aims at improving the problem solving ability of the canonical genetic programming algorithm. The proposed method can be described as follows. The first investigates initialising population, the second investigates reproduction operator, the third investigates crossover operator, the fourth investigates mutation operation. This approach is examined on two experiments about symbolic regression. The results suggest that the new approach is more effective and more efficient than the canonical one.", keywords = "genetic algorithms, genetic programming, canonical genetic programming algorithm, crossover operator, mutation operation, problem solving, reproduction operator, symbolic regression, regression analysis", DOI = "doi:10.1109/ASIA.2009.39", notes = "Also known as \cite{5376006}", } @Article{Cheng:2021:JIM, author = "Lixin Cheng and Qiuhua Tang and Zikai Zhang and Shiqian Wu", title = "Data mining for fast and accurate makespan estimation in machining workshops", journal = "Journal of Intelligent Manufacturing", year = "2021", volume = "32", pages = "483--500", keywords = "genetic algorithms, genetic programming, gene expression programming, makespan estimation, ensemble of bpnn, clustering", publisher = "springer", bibsource = "OAI-PMH server at oai.repec.org", identifier = "RePEc:spr:joinma:v:32:y:2021:i:2:d:10.1007_s10845-020-01585-y", oai = "oai:RePEc:spr:joinma:v:32:y:2021:i:2:d:10.1007_s10845-020-01585-y", URL = "http://link.springer.com/10.1007/s10845-020-01585-y", DOI = "doi:10.1007/s10845-020-01585-y", abstract = "The fast and accurate estimation of makespan is essential for the determination of the delivery date and the sustainable development of the enterprise. In this paper, a high-quality training dataset is constructed and an adaptive ensemble model is proposed to achieve fast and accurate makespan estimation. First, both the logistics features extracted by the Pearson correlation coefficient and the new meaningful nonlinear combination features dug out by gene expression programming are first involved in this paper for constructing a high-quality dataset. Secondly, an improved clustering with elbow criterion and a resampling operation are applied simultaneously to generate representative subsets; and correspondingly, several back propagation neural network (BPNN) with the architecture optimised by genetic algorithm are trained by these subsets respectively to generate effective diverse learners; and then, a K-nearest neighbour based dynamic weight combination strategy which is sensitive to current testing sample is proposed to make full use of the learners positive effects and avoid its negative effects. Finally, the results of effective experiments prove that both the newly involved features and the improvements in the proposed ensemble are effective. In addition, comparison experiments confirm that the proposed enhanced ensemble of BPNNs outperforms significantly the prevailing approaches, including single, ensemble and hybrid models. And hence, the proposed model can be used as a convenient and reliable tool to support customer order acceptance.", } @InProceedings{Cheng:2018:SmartWorld, author = "Tiantian Cheng and Jinghui Zhong", booktitle = "2018 IEEE SmartWorld, Ubiquitous Intelligence Computing, Advanced Trusted Computing, Scalable Computing Communications, Cloud Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)", title = "An Efficient Cooperative Co-Evolutionary Gene Expression Programming", year = "2018", pages = "1422--1427", abstract = "Gene Expression Programming (GEP) is a popular and powerful evolutionary optimization technique for automatic generation of computer programs. In this paper, a Cooperative Co-evolutionary framework is proposed to improve the performance of GEP. The proposed framework consists of three components to find high-quality computer programs. One component focusing on searches for both structures and coefficients of computer programs, while the other two components focus on optimizing the structures and coefficients, respectively. The three components are working cooperatively during the evolution process. The proposed framework is tested on twelve symbolic regression problems and two real-world regression problems. Experimental results demonstrated that the proposed method can offer enhanced performances over two state-of-the-art algorithms in terms of solution accuracy and search efficiency.", keywords = "genetic algorithms, genetic programming, Gene Expression Programming, GEP, high-quality computer programs, coevolutionary gene expression, coevolutionary framework, evolutionary optimization technique, Cooperative Co-evolution", DOI = "doi:10.1109/SmartWorld.2018.00246", month = oct, notes = "Also known as \cite{8560224}", } @Article{DBLP:journals/memetic/ChengZ20, author = "Tiantian Cheng and Jinghui Zhong", title = "An efficient memetic genetic programming framework for symbolic regression", journal = "Memetic Comput.", volume = "12", number = "4", pages = "299--315", year = "2020", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/s12293-020-00311-8", DOI = "doi:10.1007/s12293-020-00311-8", timestamp = "Sat, 14 Nov 2020 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/memetic/ChengZ20.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{oai:CiteSeerPSU:521419, author = "V. H. L. Cheng and L. S. Crawford and P. K. Menon", title = "Air Traffic Control Using Genetic Search Techniques", booktitle = "1999 IEEE International Conference on Control Applications", year = "1999", volume = "1", pages = "249--254", address = "Hawai'i, HA, USA", month = aug # " 22-27", publisher = "IEEE", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-5446-X", URL = "http://www.optisyn.com/research/papers/papers/1999/traffic_99.pdf", URL = "http://citeseer.ist.psu.edu/521419.html", DOI = "doi:10.1109/CCA.1999.806209", citeseer-references = "oai:CiteSeerPSU:212034", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:521419", rights = "unrestricted", size = "7 pages", abstract = "Genetic search techniques constitute an optimisation methodology effective for solving discontinuous, non-convex, nonlinear, or non-analytic problems. This paper explores the application of such techniques to a non-analytic event-related air traffic control problem, that of runway assignment, sequencing, and scheduling of arrival flights at an airport with multiple runways. Several genetic search formulations are developed and evaluated with a representative arrival traffic scenario. The results exemplify the importance of the selection of the chromosomal representation for a genetic-search problem.", } @Article{CHENG:2020:CIE, author = "Yijun Cheng and Jun Peng and Xin Gu and Xiaoyong Zhang and Weirong Liu and Zhuofu Zhou and Yingze Yang and Zhiwu Huang", title = "An intelligent supplier evaluation model based on data-driven support vector regression in global supply chain", journal = "Computer \& Industrial Engineering", volume = "139", pages = "105834", year = "2020", ISSN = "0360-8352", DOI = "doi:10.1016/j.cie.2019.04.047", URL = "http://www.sciencedirect.com/science/article/pii/S036083521930258X", keywords = "genetic algorithms, genetic programming, SVM, Supply chain management, Supplier evaluation, Support vector regression, Multiple criteria decision making", abstract = "Supplier evaluation is an important issue in supply chain management. Most existing studies rely on expert experience to evaluate supplier performance. In order to alleviate the pressure on experts in global supply chain, an intelligent supplier evaluation model based on data-driven support vector regression (SVR) is proposed in this paper. Two methods are used in the construction process of the proposed intelligent model for supplier evaluation. The integrated multiple criteria decision making (MCDM) is employed to obtain the label of each supplier instead of the manual label. Then the obtained labels are used to train the SVR. Genetic programming (GP) is adopted to set three critical parameters of SVR without prior knowledge, which are kernel function k(.), the penalty parameter C, and the tolerable deviation epsilon. The performance of the proposed intelligent model is evaluated with the commercially available ARCIC data set. Simulation results show that the accuracy and robustness of proposed intelligent model are superior when compared with existing models", notes = "School of Information Science and Engineering, Central South University, Changsha, China", } @Article{CHENG:2020:EG, author = "Zhi-Liang Cheng and Wan-Huan Zhou and Ankit Garg", title = "Genetic programming model for estimating soil suction in shallow soil layers in the vicinity of a tree", journal = "Engineering Geology", volume = "268", pages = "105506", year = "2020", ISSN = "0013-7952", DOI = "doi:10.1016/j.enggeo.2020.105506", URL = "http://www.sciencedirect.com/science/article/pii/S0013795219308154", keywords = "genetic algorithms, genetic programming, Drying cycle, Global sensitivity analysis, Performance analysis, Soil suction, Wetting cycle", abstract = "Soil suction, an important parameter in the safety and risk assessment of geotechnical and green infrastructures, is greatly affected by plants and weather in the shallow soil layers of urban landscapes/green infrastructure. In this study, a computational model consisting of a drying-cycle model and wetting-cycle model was developed by means of a genetic programming method to depict variations in soil suction using select influential parameters. The input data in the model development were measured in a field monitoring test on the campus of the University of Macau. Soil suction was quantified by field monitoring at different distances (0.5 m, 1.5 m, and 3.0 m) from a tree, at a constant depth of 20 cm, with selected influential parameters including initial soil suction, air humidity, rainfall amount, cycle duration, and ratio of distance from tree to tree canopy. Based on the performance analysis, the efficiency and reliability of the proposed computational model are validated. The importance of each input and the coupled effect of each two input variables on the output were investigated using global sensitivity analysis. It can be concluded that the proposed computational model based on the artificial intelligence simulation method describes the relationship between field soil suction in drying-wetting cycles and select input variables within an acceptable degree of error. Accordingly, it can serve as a tool for supporting geotechnical construction design and for assessing the safety and risk of geotechnical green infrastructures", } @Article{CHENG:2023:enggeo, author = "Zhi-Liang Cheng and K. K. Pabodha M. Kannangara and Li-Jun Su and Wan-Huan Zhou and Chen Tian", title = "Physics-guided genetic programming for predicting field-monitored suction variation with effects of vegetation and atmosphere", journal = "Engineering Geology", volume = "315", pages = "107031", year = "2023", ISSN = "0013-7952", DOI = "doi:10.1016/j.enggeo.2023.107031", URL = "https://www.sciencedirect.com/science/article/pii/S0013795223000480", keywords = "genetic algorithms, genetic programming, Field-monitored soil suction, Physics-guided genetic programming, Performance evaluation, Global sensitivity analysis, Uncertainty analysis", abstract = "The complicated interactions among shallow soil, vegetation, and atmospheric parameters make the precise prediction of field-monitored soil suction under natural conditions challenging. This study integrated an analytical solution with a genetic programming (GP) model in proposing a physics-guided GP method for better calculation and prediction of field-monitored matric suction in a shallow soil layer. Model development and analysis involved 3987 collected data values for soil suction as well as atmospheric and tree-related parameters from a field monitoring site. Natural algorithm values of transpiration rates obtained by back-calculation were simulated with GP using easily obtained parameters. Global sensitivity analysis demonstrated that the tree canopy-related parameter was the most important for transpiration rate. It was indicated that the proposed physics-guided GP method greatly improved calculation accuracy and, as a result, demonstrated a better performance and was more reliable than the individual GP method in calculating field-monitored suction. The proposed physics-guided GP method was also validated as more stable and reliable due to its smaller uncertainty and higher confidence level compared to the individual GP method based on quantile regression uncertainty analysis", } @Article{CHENG:2022:jrmge, author = "Zhiliang Cheng and Wanhuan Zhou and Chen Tian", title = "Multi-perspective analysis on rainfall-induced spatial response of soil suction in a vegetated soil", journal = "Journal of Rock Mechanics and Geotechnical Engineering", volume = "14", number = "4", pages = "1280--1291", year = "2022", ISSN = "1674-7755", DOI = "doi:10.1016/j.jrmge.2022.02.009", URL = "https://www.sciencedirect.com/science/article/pii/S1674775522000622", keywords = "genetic algorithms, genetic programming, Global sensitivity analysis (GSA), Multi-gene genetic programming (MGGP), Soil suction response, Spatial variation of suction response, Uncertainty assessment", abstract = "In this study, an intelligent monitoring platform is established for continuous quantification of soil, vegetation, and atmosphere parameters (e.g. soil suction, rainfall, tree canopy, air temperature, and wind speed) to provide an efficient dataset for modeling suction response through machine learning. Two characteristic parameters representing suction response during wetting processes, i.e. response time and mean reduction rate of suction, are formulated through multi-gene genetic programming (MGGP) using eight selected influential parameters including depth, initial soil suction, vegetation- and atmosphere-related parameters. An error standard-based performance evaluation indicated that MGGP has appreciable potential for model development when working with even fewer than 100 data. Global sensitivity analysis revealed the importance of tree canopy and mean wind speed to estimation of response time and indicated that initial soil suction and rainfall amount have an important effect on the estimated suction reduction rate during a wetting process. Uncertainty assessment indicated that the two MGGP models describing suction response after rainfall are reliable and robust under uncertain conditions. In-depth analysis of spatial variations in suction response validated the robustness of two obtained MGGP models in prediction of suction variation characteristics under natural conditions", } @Article{cheng:2023:AG, author = "Zhi-Liang Cheng and K. K. Pabodha M. Kannangara and Li-Jun Su and Wan-Huan Zhou", title = "Mathematical model for approximating shield tunneling-induced surface settlement via multi-gene genetic programming", journal = "Acta Geotechnica", year = "2023", volume = "18", number = "9", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s11440-023-01847-y", DOI = "doi:10.1007/s11440-023-01847-y", } @InProceedings{Chennupati:2013:mendel, author = "Gopinath Chennupati and Conor Ryan and Raja Muhammad Atif Azad", title = "An Empirical Analysis Through the Time Complexity of {GE} Problems", booktitle = "19th International Conference on Soft Computing, MENDEL 2013", year = "2013", editor = "Radomil Matousek", pages = "37--44", address = "Brno, Czech Republic", month = jun # " 26-28, Brno", organisation = "Brno University of Technology", keywords = "genetic algorithms, genetic programming, grammatical evolution", isbn13 = "978-80-214-4755-4", URL = "https://www.researchgate.net/publication/264464282_An_empirical_analysis_through_the_time_complexity_of_GE_problems", abstract = "Computational complexity analysis on Evolutionary Algorithms can provide crucial insight into how they work. While relatively straight forward for fixed length structures, it is less so for variable length structures, although initial work has already been conducted on tree based Genetic Programming (GP) algorithms. Grammatical Evolution (GE) is a variable length string based Evolutionary Algorithm (EA) that evolves arbitrarily complex programs through complex gene interactions, but thus far, no such analysis has been conducted. We empirically analyse the time complexity of GE on two well known GP problems: the Santa Fe Ant Trail and a Symbolic Regression problem. Using a power law, we analyse the time complexity of GE in terms of population size. As a result of this, several observations are made estimating the average terminating generation, actual length and effective lengths of individuals based on the quality of the solution. We show that even with the extra layer of complexity of GE, time increases linearly for GE on Santa Fe Ant Trail problem and quadratic in nature on a symbolic regression problem as the size of simulations (i.e. population size) increase. To the best of our knowledge, this is the first attempt measuring the run-time complexity of GE. This analysis provides a way to produce a reasonably good prediction system of how a particular run will perform, and we provide details of how one can leverage this data to predict the success or otherwise of a GE run in the early generations. with the amount of data collected.", notes = "http://www.mendel-conference.org/", } @InProceedings{Chennupati:2014:NaBIC, author = "Gopinath Chennupati and Jeannie Fitzgerald and Conor Ryan", title = "On The Efficiency of Multi-core Grammatical Evolution (MCGE) Evolving Multi-Core Parallel Programs", booktitle = "Sixth World Congress on Nature and Biologically Inspired Computing", year = "2014", editor = "Ana Maria Madureira and Ajith Abraham and Emilio Corchado and Leonilde Varela and Azah Kamilah Muda and Choo yun Huoy", pages = "238--243", address = "Porto, Portugal", month = "30 " # jul # " - 1 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, OpenMP, Parallel programming, GPU", isbn13 = "978-1-4799-5937-2/14", DOI = "doi:10.1109/NaBIC.2014.6921885", size = "6 pages", abstract = "In this paper we investigate a novel technique that optimises the execution time of Grammatical Evolution through the usage of on-chip multiple processors. This technique, Multi-core Grammatical Evolution (MCGE) evolves natively parallel programs with the help of OpenMP primitives through the grammars, such that not only can we exploit parallelism while evolving individuals, but the final individuals produced can also be executed on parallel architectures even outside the evolutionary system. We test MCGE on two difficult benchmark GP problems and show its efficiency in exploiting the power of the multi-core architectures. We further discuss that, on these problems, the system evolves longer individuals while they are evaluated quicker than their serial implementation.", notes = "cites \cite{williams98} NaBIC 2014 http://www.mirlabs.net/nabic14/", } @InProceedings{Chennupati:2014:GECCOcomp, author = "Gopinath Chennupati and Conor Ryan and R. Muhammad Atif Azad", title = "Predict the success or failure of an evolutionary algorithm run", booktitle = "GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion", year = "2014", editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges", isbn13 = "978-1-4503-2881-4", keywords = "genetic algorithms, genetic programming, grammatical evolution: Poster", pages = "131--132", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2598394.2598471", DOI = "doi:10.1145/2598394.2598471", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "The quality of candidate solutions in evolutionary computation (EC) depend on multiple independent runs and a large number of them fail to guarantee optimal result. These runs consume more or less equal or sometimes higher amount of computational resources on par the runs that produce desirable results. This research work addresses these two issues (run quality, execution time), Run Prediction Model (RPM), in which undesirable quality evolutionary runs are identified to discontinue from their execution. An Ant Colony Optimisation (ACO) based classifier that learns to discover a prediction model from the early generations of an EC run. We consider Grammatical Evolution (GE) as our EC technique to apply RPM that is evaluated on four symbolic regression problems. We establish that the RPM applied GE produces a significant improvement in the success rate while reducing the execution time.", notes = "Also known as \cite{2598471} Distributed at GECCO-2014.", } @InProceedings{Chennupati:2014:GECCOcompa, author = "Gopinath Chennupati and R. Muhammad Atif Azad and Conor Ryan", title = "Multi-core GE: automatic evolution of CPU based multi-core parallel programs", booktitle = "GECCO 2014 student workshop", year = "2014", editor = "Tea Tusar and Boris Naujoks", isbn13 = "978-1-4503-2881-4", keywords = "genetic algorithms, genetic programming, grammatical evolution", pages = "1041--1044", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2598394.2605670", DOI = "doi:10.1145/2598394.2605670", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "We describe the use of on-chip multiple CPU architectures to automatically evolve parallel computer programs. These programs have the capability of exploiting the computational efficiency of the modern multi-core machines. This is significantly different from other parallel EC approaches because not only do we produce individuals that, in their final form, can exploit parallel architectures, we can also exploit the same parallel architecture during evolution to reduce evolution time. We use Grammatical Evolution along with OpenMP specific grammars to produce natively parallel code, and demonstrate that not only do we enjoy the benefit of final individuals that can run in parallel, but that our system scales effectively with the number of cores.", notes = "Also known as \cite{2605670} Distributed at GECCO-2014.", } @InProceedings{Chennupati:2014:GECCOcompb, author = "Gopinath Chennupati and R. Muhammad Atif Azad and Conor Ryan", title = "Predict the performance of GE with an ACO based machine learning algorithm", booktitle = "GECCO 2014 Workshop on Symbolic Regression and Modelling", year = "2014", editor = "Steven Gustafson and Ekaterina Vladislavleva", isbn13 = "978-1-4503-2881-4", keywords = "genetic algorithms, genetic programming, grammatical evolution", pages = "1353--1360", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2598394.2609860", DOI = "doi:10.1145/2598394.2609860", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "The quality of the evolved solutions of an evolutionary algorithm (EA) varies across different runs and a significant percentage of runs can produce solutions of undesirable quality. These runs are a waste of computational resources, particularly in difficult problems where practitioners have time bound limitations in repeating runs. This paper proposes a completely novel approach, that of a Run Prediction Model (RPM) in which we identify and terminate evolutionary runs that are likely to produce low-quality solutions. This is justified with an Ant Colony Optimization (ACO) based classifier that learns from the early generations of a run and decides whether to continue or not. We apply RPM to Grammatical Evolution (GE) applied to four benchmark symbolic regression problems and consider several contemporary machine learning algorithms to train the predictive models and find that ACO produces the best results and acceptable predictive accuracy for this first investigation. The ACO discovered prediction models are in the form of a list of simple rules. We further analyse that list manually to tune them in order to predict poor GE runs. We then apply the analysed model to GE runs on the regression problems and terminate the runs identified by the model likely to be poor, thus increasing the rate of production of successful runs while reducing the computational effort required. We demonstrate that, although there is a high bootstrapping cost for RPM, further investigation is warranted as the mean success rate and the total execution time enjoys a statistically significant boost on all the four benchmark problems.", notes = "Also known as \cite{2609860} Distributed at GECCO-2014.", } @InProceedings{Chennupati:2015:EuroGP, author = "Gopinath Chennupati and R. Muhammad Atif Azad and Conor Ryan", title = "Automatic Evolution of Parallel Recursive Programs", booktitle = "18th European Conference on Genetic Programming", year = "2015", editor = "Penousal Machado and Malcolm I. Heywood and James McDermott and Mauro Castelli and Pablo Garcia-Sanchez and Paolo Burelli and Sebastian Risi and Kevin Sim", series = "LNCS", volume = "9025", publisher = "Springer", pages = "167--178", address = "Copenhagen", month = "8-10 " # apr, organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, GPU, Automatic Parallelization, Recursion, Program Synthesis, OpenMP, Evolutionary Parallelization: Poster", isbn13 = "978-3-319-16500-4", DOI = "doi:10.1007/978-3-319-16501-1_14", size = "12 pages", abstract = "Writing recursive programs for fine-grained task-level execution on parallel architectures, such as the current generation of multi-core machines, often require the application of skilled parallelization knowledge to fully realize the potential of the hardware. This paper automates the process by using Grammatical Evolution (GE) to exploit the multi-cores through the evolution of natively parallel programs. We present Multi-core Grammatical Evolution (MCGE-II), which employs GE and OpenMP specific pragmatic information to automatically evolve task-level parallel recursive programs. MCGE-II is evaluated on six recursive C programs, and we show that it solves each of them using parallel code. We further show that MCGE-II significantly decreases the parallel computational effort as the number of cores increase, when tested on an Intel processor.", notes = "Part of \cite{Machado:2015:GP} EuroGP'2015 held in conjunction with EvoCOP2015, EvoMusArt2015 and EvoApplications2015", } @InProceedings{Chennupati:2015:evoApplications, author = "Gopinath Chennupati and R. Muhammad Atif Azad and Conor Ryan", title = "Automatic Evolution of Parallel Sorting Programs on Multi-cores", booktitle = "18th European Conference on the Applications of Evolutionary Computation", year = "2015", editor = "Antonio M. Mora and Giovanni Squillero", series = "LNCS", volume = "9028", publisher = "Springer", pages = "706--717", address = "Copenhagen", month = "8-10 " # apr, organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, Grammatical evolution, Automatic parallelisation, Recursion, Program synthesis, OpenMP, Evolutionary parallelization: Poster", isbn13 = "978-3-319-16548-6", DOI = "doi:10.1007/978-3-319-16549-3_57", abstract = "Sorting algorithms that offer the potential for data-parallel execution on parallel architectures are an excellent tool for the current generation of multi-core processors that often require skilled parallelisation knowledge to fully realize the potential of the hardware. We propose to automate the evolution of natively parallel programs using the Grammatical Evolution (GE) approach to use the computational potential of multi-cores. The proposed system, Multi-core Grammatical Evolution for Parallel Sorting (MCGE-PS), applies GE mapping along with explicit OpenMP #pragma compiler directives to automatically evolve data-level parallel iterative sorting algorithms. MCGE-PS is assessed on the generation of four non-recursive sorting programs in C. We show that it generated programs that can solve the problem that are also parallel. On a high performance Intel processor, MCGE-PS significantly reduced the execution time of the evolved programs for all the benchmark problems.", notes = "EvoPAR EvoApplications2015 held in conjunction with EuroGP'2015, EvoCOP2015 and EvoMusArt2015 http://www.evostar.org/2015/cfp_evoapps.php", } @InProceedings{Chennupati:2015:GECCO, author = "Gopinath Chennupati and R. Muhammad Atif Azad and Conor Ryan", title = "Performance Optimization of Multi-Core Grammatical Evolution Generated Parallel Recursive Programs", booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation", year = "2015", editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr", isbn13 = "978-1-4503-3472-3", pages = "1007--1014", keywords = "genetic algorithms, genetic programming, grammatical evolution", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739480.2754746", DOI = "doi:10.1145/2739480.2754746", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Although Evolutionary Computation (EC) has been used with considerable success to evolve computer programs, the majority of this work has targeted the production of serial code. Recent work with Grammatical Evolution (GE) produced Multi-core Grammatical Evolution (MCGE-II), a system that natively produces parallel code, including the ability to execute recursive calls in parallel. This paper extends this work by including practical constraints into the grammars and fitness functions, such as increased control over the level of parallelism for each individual. These changes execute the best-of-generation programs faster than the original MCGE-II with an average factor of 8.13 across a selection of hard problems from the literature. We analyze the time complexity of these programs and identify avoiding excessive parallelism as a key for further performance scaling. We amend the grammars to evolve a mix of serial and parallel code, which spawns only as many threads as is efficient given the underlying OS and hardware; this speeds up execution by a factor of 9.97.", notes = "Also known as \cite{2754746} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)", } @InProceedings{Chennupati:2015:GECCOcomp, author = "Gopinath Chennupati and R. Muhammad Atif Azad and Conor Ryan", title = "Synthesis of Parallel Iterative Sorts with Multi-Core Grammatical Evolution", booktitle = "GECCO 2015 5th Workshop on Evolutionary Computation for the Automated Design of Algorithms (ECADA'15)", year = "2015", editor = "John Woodward and Daniel Tauritz and Manuel Lopez-Ibanez", isbn13 = "978-1-4503-3488-4", keywords = "genetic algorithms, genetic programming, grammatical evolution", pages = "1059--1066", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739482.2768458", DOI = "doi:10.1145/2739482.2768458", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Writing parallel programs is a challenging but unavoidable proposition to take true advantage of multi-core processors. In this paper, we extend Multi-core Grammatical Evolution for Parallel Sorting (MCGE-PS) to evolve parallel iterative sorting algorithms while also optimizing their degree of parallelism. We use evolution to optimize the performance of these parallel programs in terms of their execution time, and our results demonstrate a significant optimization of 11.03 in performance when compared with various MCGE-PS variations as well as the GNU GCC compiler optimizations that reduce the execution time through code minimization. We then analyse the evolutionary (code growth) and non-evolutionary (thread scheduling) factors that cause performance implications. We address them to further optimize the performance and report it as 12.52.", notes = "Also known as \cite{2768458} Distributed at GECCO-2015.", } @InProceedings{Chennupati:2015:GECCOcompa, author = "Gopinath Chennupati and R. Muhammad Atif Azad and Conor Ryan", title = "On the Automatic Generation of Efficient Parallel Iterative Sorting Algorithms", booktitle = "GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation", year = "2015", editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr", isbn13 = "978-1-4503-3488-4", keywords = "genetic algorithms, genetic programming, grammatical evolution: Poster", pages = "1369--1370", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739482.2764695", DOI = "doi:10.1145/2739482.2764695", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Increasing availability of multiple processing elements on the recent desktop and personal computers poses unavoidable challenges in realizing their processing power. The challenges include programming these high processing elements. Parallel programming is an apt solution for such a realization of the computational capacity. However, it has many difficulties in developing the parallel programs. We present Multi-core Grammatical Evolution for Parallel Sorting (MCGE-PS) that automatically produces native parallel sorting programs. These programs are of iterative nature that also exploit the processing power of the multi-core processors efficiently. The performance of the resultant programs is measured in terms of the execution time. The results indicate a significant improvement over the state-of-the-art implementations. Finally, we conduct an empirical analysis on computational complexity of the evolving parallel programs. The results are competitive with that of the state-of-the-art evolutionary attempts.", notes = "Also known as \cite{2764695} Distributed at GECCO-2015.", } @PhdThesis{Chennupati:thesis, author = "Gopinath Chennupati", title = "Grammatical Evolution + Multi-Cores = Automatic Parallel Programming!", school = "CSIS Department, University of Limerick", year = "2015", month = oct, address = "Ireland", month = sep, keywords = "genetic algorithms, genetic programming, Grammatical Evolution, grammatical automatic parallel programming, GAPP", URL = "https://hdl.handle.net/10344/4828", URL = "https://researchrepository.ul.ie/articles/thesis/Grammatical_evolution_multi-cores_automatic_parallel_programming_/19811191?file=35258332", URL = "http://www.skynet.ie/~cgnath/docs/thesis.pdf", size = "311 pages", abstract = "Multi-core processors are shared memory multiprocessors integrated on a single chip which offer significantly higher processing power than traditional, single core processors. However, as the number of cores available on a single processor increases, efficiently programming them becomes increasingly more complex, often to the point where the limiting factor in speeding up tasks is the software. This thesis presents Grammatical Automatic Parallel Programming (GAPP) which uses Grammatical Evolution to automatically generate natively parallel code on multi-core processors by directly embedding GAPP OpenMP parallelization directives in problem-specific Context Free Grammars. As a result, it obviates the need for programmers to think in a parallel manner while still letting them produce parallel code. We first perform a thorough analysis on the computational complexity of Grammatical Evolution using standard benchmark problems. This analysis results in an interesting experiment which produces a system capable of predicting on-the-fly the likelihood of a particular GE run being successful. A number of difficult proof of concept problems are examined in evaluating GAPP. The performance of the system on these informs the further optimization of both the design of grammars and fitness function to extract further parallelism. We demonstrate a surprising side effect of uncontrolled parallelism, which leads to the under-use of the cores. This is addressed through the automatic generation of programs with controlled degree of parallelism. In this case, the automatically generated programs adapt to the number of cores on which they are scheduled to execute. Finally, GAPP is applied to Automatic Lockless Programming, an enormously difficult design problem, resulting in parallel code guaranteed to avoid locks on shared resources, thereby further optimizing the execution time. We then draw conclusions and make future recommendations on the use of evolutionary systems in the generation of highly constrained parallel code.", notes = "Supervisor: Conor Ryan ", } @InProceedings{Chennupati:2016:CEC, author = "Gopinath Chennupati and R. Muhammad Atif Azad and Conor Ryan", title = "Automatic Lock-free Parallel Programming on Multi-core Processors", booktitle = "CEC 2016", year = "2016", editor = "Yew Soon Ong", pages = "4143--4150", address = "Vancouver", month = "25-29 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Multi-cores, Lock-Free Programming, Evolutionary Computation, Program Synthesis, OpenMP", isbn13 = "978-1-5090-0623-6", video_url = "https://www.youtube.com/watch?v=SII66L9jKbc", video_url = "http://www.cs.ucl.ac.uk/staff/W.Langdon/cec2016/PID_16921_Chennupati_Presentation.mp4", DOI = "doi:10.1109/CEC.2016.7744316", size = "8 pages", abstract = "Writing correct and efficient parallel programs is an unavoidable challenge; the challenge becomes arduous with lock-free programming. This paper presents an automated approach, Automatic Lock-free Programming (ALP) that avoids the programming difficulties via locks for an average programmer. ALP synthesizes parallel lock-free recursive programs that are directly compilable on multi-core processors. ALP attains the dual objective of evolving parallel lock-free programs and optimizing their performance. These programs perform (in terms of execution time) significantly better than that of the parallel programs with locks, while they are competitive with the human developed programs.", notes = "http://www.cs.ucl.ac.uk/staff/W.Langdon/cec2016/ WCCI2016", } @InCollection{Chennupati:2018:hbge, author = "Gopinath Chennupati and R. Muhammad Atif Azad and Conor Ryan and Stephan Eidenbenz and Nandakishore Santhi", title = "Synthesis of Parallel Programs on Multi-Cores", booktitle = "Handbook of Grammatical Evolution", publisher = "Springer", year = "2018", editor = "Conor Ryan and Michael O'Neill and J. J. Collins", chapter = "12", pages = "289--315", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", isbn13 = "978-3-319-78716-9", DOI = "doi:10.1007/978-3-319-78717-6_12", abstract = "Multi-cores offer higher processing power than single core processors. However, as the number of cores available on a single processor increases, efficiently programming them becomes increasingly more complex, often to the point where the limiting factor in speeding up tasks is the software. We present Grammatical Automatic Parallel Programming (GAPP), a system that synthesizes parallel code on multi-cores using OpenMP parallelization primitives in problem-specific grammars. As a result, GAPP obviates the need for programmers to think parallel while still letting them produce parallel code. The performance of GAPP on a number of difficult proof of concept benchmarks informs further optimization of both the design of grammars and fitness function to extract further parallelism. We demonstrate an improved performance of evolving programs with controlled degree of parallelism. These programs adapt to the number of cores on which they are scheduled to execute.", notes = "Part of \cite{Ryan:2018:hbge}", } @InProceedings{Cherrier:2019:CEC, author = "Noelie Cherrier and Jean-Philippe Poli and Maxime Defurne and Franck Sabatie", title = "Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics", booktitle = "2019 IEEE Congress on Evolutionary Computation, CEC 2019", year = "2019", editor = "Carlos A. Coello Coello", pages = "1650--1658", address = "Wellington, New Zealand", month = "10-13 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, construction, grammar-guided genetic programming, high-energy physics, interpretability", isbn13 = "978-1-7281-2152-6", DOI = "doi:10.1109/CEC.2019.8789937", size = "9 pages", abstract = "A good feature representation is a determinant factor to achieve high performance for many machine learning algorithms in terms of classification. This is especially true for techniques that do not build complex internal representations of data (e.g. decision trees, in contrast to deep neural networks). To transform the feature space, feature construction techniques build new high-level features from the original ones. Among these techniques, Genetic Programming is a good candidate to provide interpretable features required for data analysis in high energy physics. Classically, original features or higher-level features based on physics first principles are used as inputs for training. However, physicists would benefit from an automatic and interpretable feature construction for the classification of particle collision events.Our main contribution consists in combining different aspects of Genetic Programming and applying them to feature construction for experimental physics. In parti", notes = "also known as \cite{8789937}, IEEE Catalog Number: CFP19ICE-ART", } @Article{Chesney:2024:simpa, author = "Thomas Chesney and Robert Pasley and Muhammad Asif Jaffer", title = "{RIGSS} - Inverse Generative Social Science using {R}", journal = "Software Impacts", year = "2024", volume = "21", pages = "100689", keywords = "genetic algorithms, genetic programming, Agent-based modelling, Social theory", ISSN = "2665-9638", URL = "https://www.sciencedirect.com/science/article/pii/S2665963824000770", DOI = "doi:10.1016/j.simpa.2024.100689", abstract = "We present RIGSS, software that can be used to run an Inverse Generative Social Science study in R. We give a brief overview of this research method and explain how our software can be used. We implement a Hawk Dove game as an executable example. We then discuss the potential that Inverse Generative Social Science has", } @InProceedings{Cheung:2012:CEC, title = "Use of evolutionary computation techniques for exploration and prediction of helicopter loads", author = "Catherine Cheung and Julio J. Valdes and Matthew Li", pages = "1130--1137", booktitle = "Proceedings of the 2012 IEEE Congress on Evolutionary Computation", year = "2012", editor = "Xiaodong Li", month = "10-15 " # jun, DOI = "doi:10.1109/CEC.2012.6252905", address = "Brisbane, Australia", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Ensemble Methods in Computational Intelligence (IEEE-CEC), Defence and cyber security, Classification, clustering, data analysis and data mining", abstract = "The development of accurate load spectra for helicopters is necessary for life cycle management and life extension efforts. This paper explores continued efforts to use evolutionary computation (EC) methods and machine learning techniques to estimate several helicopter dynamic loads. Estimates for the main rotor normal bending (MRNBX) on the Australian Black Hawk helicopter were generated from an input set that included thirty standard flight state and control system parameters under several flight conditions (full speed forward level flight, rolling left pullout at 1.5g, and steady 45deg left turn at full speed). Multiobjective genetic algorithms (MOGA) used in combination with the Gamma test found reduced subsets of predictor variables with Madelin potential. These subsets were used to estimate MRNBX using Cartesian genetic programming and neural network models trained by deterministic and evolutionary computation techniques, including particle swarm optimization (PSO), differential evolution (DE), and MOGA. PSO and DE were used alone or in combination with deterministic methods. Different error measures were explored including a fuzzy-based asymmetric error function. EC techniques played an important role in both the exploratory and Madelin phase of the investigation. The results of this work show that the addition of EC techniques in the modelling stage generated more accurate and correlated models than could be obtained using only deterministic optimization.", notes = "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the EPS and the IET.", } @InProceedings{Chia:2020:SSCI, author = "Hao-Cheng Chia and Tsung-Su Yeh and Tsung-Che Chiang", title = "Designing Card Game Strategies with Genetic Programming and Monte-Carlo Tree Search: A Case Study of Hearthstone", booktitle = "2020 IEEE Symposium Series on Computational Intelligence (SSCI)", year = "2020", pages = "2351--2358", month = dec, keywords = "genetic algorithms, genetic programming, collectible card games, Hearthstone: Heroes of Warcraft, Monte-Carlo tree search", DOI = "doi:10.1109/SSCI47803.2020.9308459", URL = "https://scholar.lib.ntnu.edu.tw/en/publications/designing-card-game-strategies-with-genetic-programming-and-monte-2", abstract = "This paper addresses an agent design problem of a digital collectible card game, Hearthstone, which is a two-player turn-based game. The agent has to play cards based on the game state, the hand cards, and the deck of cards to defeat the opponent. First, we design a rule-based agent by searching for the board evaluation criterion through genetic programming (GP). Then, we integrate the rule-based agent into the Monte-Carlo tree search (MCTS) framework to generate an advanced agent. Performance of the proposed agents are verified by playing against three participants in two recent Hearthstone competitions. Experimental results showed that the GP-agent can beat a simple MCTS agent and the mid-level agent in the competition. The MCTS-GP agent showed competitive performance against the best agents in the competition. We also examine the rule found by GP and observed that GP is able to identify key attributes of game states and to combine them into a useful rule automatically.", notes = "Also known as \cite{9308459}", } @Article{DBLP:journals/ijcia/ChiaT01, author = "Henry Wai Kit Chia and Chew Lim Tan", title = "Neural Logic Network Learning Using Genetic Programming", journal = "International Journal of Computational Intelligence and Applications", volume = "1", number = "4", year = "2001", bibsource = "DBLP, http://dblp.uni-trier.de", pages = "357--368", keywords = "genetic algorithms, genetic programming, Neural network, rule-based learning, data mining", DOI = "doi:10.1142/S1469026801000299", abstract = "Neural Logic Networks or Neulonets are hybrids of neural networks and expert systems capable of representing complex human logic in decision making. Each neulonet is composed of rudimentary net rules which themselves depict a wide variety of fundamental human logic rules. An early methodology employed in neulonet learning for pattern classification involved weight adjustments during back-propagation training which ultimately rendered the net rules incomprehensible. A new technique is now developed that allows the neulonet to learn by composing the net rules using genetic programming without the need to impose weight modifications, thereby maintaining the inherent logic of the net rules. Experimental results are presented to illustrate this new and exciting capability in capturing human decision logic from examples. The extraction and analysis of human logic net rules from an evolved neulonet will be discussed. These extracted net rules will be shown to provide an alternate perspective to the greater extent of knowledge that can be expressed and discovered. Comparisons will also be made to demonstrate the added advantage of using net rules, against the use of standard boolean logic of negation, disjunction and conjunction, in the realm of evolutionary computation.", } @InProceedings{chia:2004:lbp, author = "Henry Wai-Kit Chia and Chew-Lim Tan", title = "Association-Based Evolution of Comprehensible Neural Logic Networks", booktitle = "Late Breaking Papers at the 2004 Genetic and Evolutionary Computation Conference", year = "2004", editor = "Maarten Keijzer", address = "Seattle, Washington, USA", month = "26 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://gpbib.cs.ucl.ac.uk/gecco2004/LBP061.pdf", abstract = "Neural Logic Network (Neulonet) learning has been successfully used in emulating complex human reasoning processes. One recent implementation generates a single large neulonet via genetic programming using an accuracy-based fitness measure. However, in terms of human comprehensibility and amenability during logic inference, evolving multiple compact neulonets are preferred. The present work realizes this by adopting associative-classification measures of confidence and support as part of the fitness computation. The evolved neulonets are combined together to form an eventual macro-classier. Empirical study shows that associative classification integrated with neulonet learning performs better than general association-based classifiers in terms of higher accuracies and smaller rule sets. This is primarily due to the richness in logic expression inherent in the neulonet learning paradigm.", notes = "Part of \cite{keijzer:2004:GECCO:lbp}", } @InProceedings{chia:cas:gecco2004, author = "Henry Wai-Kit Chia and Chew-Lim Tan", title = "Confidence and Support Classification Using Genetically Programmed Neural Logic Networks", booktitle = "Genetic and Evolutionary Computation -- GECCO-2004, Part II", year = "2004", editor = "Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell", series = "Lecture Notes in Computer Science", pages = "836--837", address = "Seattle, WA, USA", publisher_address = "Heidelberg", month = "26-30 " # jun, organisation = "ISGEC", publisher = "Springer-Verlag", volume = "3103", ISBN = "3-540-22343-6", ISSN = "0302-9743", DOI = "doi:10.1007/b98645", URL = "http://www.comp.nus.edu.sg/~tancl/Papers/GECCO2004/gecco04post.pdf", size = "2", keywords = "genetic algorithms, genetic programming, Poster", abstract = "Typical learning classifier systems employ conjunctive logic rules for representing domain knowledge. The classifier XCS is an extension of LCS with the ability to learn boolean logic functions for data mining. However, most data mining problems cannot be expressed simply with boolean logic. Neural Logic Network (Neulonet) learning is a technique that emulates the complex human reasoning processes through the use of net rules. Each neulonet is analogous to a learning classifier that is rewarded using support and confidence measures which are often used in association-based classification. Empirical results shows promise in terms of generalisation ability and the comprehensibility of rules.", notes = "GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)", } @Article{10.1109/TKDE.2006.111, author = "Henry W. K. Chia and Chew Lim Tan and Sam Y. Sung", title = "Enhancing Knowledge Discovery via Association-Based Evolution of Neural Logic Networks", journal = "IEEE Transactions on Knowledge and Data Engineering", volume = "18", number = "7", year = "2006", publisher = "IEEE Computer Society", address = "Los Alamitos, CA, USA", pages = "889--901", keywords = "genetic algorithms, genetic programming, Data mining, knowledge acquisition, connectionism and neural nets, rule-based knowledge representation", ISSN = "1041-4347", DOI = "doi:10.1109/TKDE.2006.111", abstract = "The comprehensibility aspect of rule discovery is of emerging interest in the realm of knowledge discovery in databases. Of the many cognitive and psychological factors relating the comprehensibility of knowledge, we focus on the use of human amenable concepts as a representation language in expressing classification rules. Existing work in neural logic networks (or neulonets) provides impetus for our research; its strength lies in its ability to learn and represent complex human logic in decision-making using symbolic-interpretable net rules. A novel technique is developed for neulonet learning by composing net rules using genetic programming. Coupled with a sequential covering approach for generating a list of neulonets, the straightforward extraction of human-like logic rules from each neulonet provides an alternate perspective to the greater extent of knowledge that can potentially be expressed and discovered, while the entire list of neulonets together constitute an effective classifier. We show how the sequential covering approach is analogous to association-based classification, leading to the development of an association-based neulonet classifier. Empirical study shows that associative classification integrated with the genetic construction of neulonets performs better than general association-based classifiers in terms of higher accuracies and smaller rule sets. This is due to the richness in logic expression inherent in the neulonet learning paradigm.", } @InProceedings{chia-hsuanyeh:2001:gecco, title = "The Differences between Social and Individual Learning on the Time Series Properties: The Approach Based on Genetic Programming", author = "Chia-Hsuan Yeh and Shu-Heng Chen", pages = "191", year = "2001", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)", editor = "Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon and Edmund Burke", address = "San Francisco, California, USA", publisher_address = "San Francisco, CA 94104, USA", month = "7-11 " # jul, keywords = "genetic algorithms, genetic programming: Poster, Social Learning, Individual Learning, Artificial Stock Market, Agent-Based Modeling", ISBN = "1-55860-774-9", URL = "http://gpbib.cs.ucl.ac.uk/gecco2001/d02.pdf", size = "1 page", notes = "GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of \cite{spector:2001:GECCO}", } @InProceedings{Chiang:2010:3CA, author = "Cheng-Hsiung Chiang", title = "A genetic programming based rule generation approach for intelligent control systems", booktitle = "2010 International Symposium on Computer Communication Control and Automation (3CA)", year = "2010", month = may, volume = "1", pages = "104--107", abstract = "This paper presents an intelligent control system (namely GPICS). The GPICS consists of a Symbolic Rule Controller, a Percepter and a rAdaptor. The Percepter judges whether the control system can adapt the environment. If the system is inadaptable, the rAdaptor will be activated to search the new rule to adapt the environment; otherwise, the controller will keeps on its controlling assignments. Once the rAdaptor is activated, the flexible genetic programming will be employed for searching the new rule. Simulation results of the robotic path planning showed that the GPICS method can successfully find a satisfactory path.", keywords = "genetic algorithms, genetic programming, genetic programming intelligent control system, percepter, radaptor, rule generation approach, symbolic rule controller, intelligent control, learning (artificial intelligence), path planning", DOI = "doi:10.1109/3CA.2010.5533882", notes = "Also known as \cite{5533882}", } @InProceedings{chiang:2024:CEC, author = "Tu-Chin Chiang and Chi-Hsien Chang and Tian-Li Yu", title = "A Novel Symbolic Regressor Enhancer Using Genetic Programming", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Databases, Evolutionary computation, Benchmark testing, Syntactics, Programming, Polynomials, Symbolic regression", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10612124", abstract = "This paper proposes a framework combining genetic programming (GP) with other symbolic regression (SR) methods, called the symbolic regressor enhancer (SRE). The basic idea is to use the syntax tree of the expression obtained from other SR methods to improve both the efficiency and the quality of the evolutionary procedure. Specifically, this paper investigates on the different ways of hybridization, selection, and crossover to assemble the proposed SRE. The effectiveness of SRE is demonstrated with the Taylor polynomial, the fast function extraction, and the GP-based SR methods, including Operon, the GP variant of gene-pool optimal mixing evolutionary algorithm, the epsilon-Iexicase selection, and gplearn. Out of 28 benchmarks from the SR benchmark and the Feynman SR database, the statistical test indicates that SRE applied to each selected SR method significantly outperforms the respective SR method in at least 8 and at most 24 benchmarks.", notes = "also known as \cite{10612124} WCCI 2024", } @InProceedings{Chicotay:2014:CVPRW, author = "Sarit Chicotay and Omid E. David and Nathan S. Netanyahu", booktitle = "IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2014)", title = "Image Registration of Very Large Images via Genetic Programming", year = "2014", month = jun, pages = "329--334", abstract = "Image registration (IR) is a fundamental task in image processing for matching two or more images of the same scene taken at different times, from different viewpoints and/or by different sensors. Due to the enormous diversity of IR applications, automatic IR remains a challenging problem to this day. A wide range of techniques has been developed for various data types and problems. These techniques might not handle effectively very large images, which give rise usually to more complex transformations, e.g., deformations and various other distortions. In this paper we present a genetic programming (GP) based approach for IR, which could offer a significant advantage in dealing with very large images, as it does not make any prior assumptions about the transformation model. Thus, by incorporating certain generic building blocks into the proposed GP framework, we hope to realize a large set of specialised transformations that should yield accurate registration of very large images.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CVPRW.2014.56", notes = "Also known as \cite{6910002}", } @TechReport{wpa98086, author = "N. K. Chidambaran and Chi-Wen {Jevons Lee} and Joaquin R. Trigueros", title = "An Adaptive Evolutionary Approach to Option Pricing via Genetic Programming", institution = "Leonard N. Stern School of Buisness, New York University", year = "1998", type = "Working paper", number = "FIN-98-086", month = nov, keywords = "genetic algorithms, genetic programming", URL = "http://www.stern.nyu.edu/fin/workpapers/wpa98086.pdf", abstract = "We propose a methodology of Genetic Programming to approximate the relationship between the option price, its contract terms and the properties of the underlying stock price. An important advantage of the Genetic Programming approach is that we can incorporate currently known formulas, such as the Black-Scholes model, in the search for the best approximation to the true pricing formula. Using Monte Carlo simulations, we show that the Genetic Programming model approximates the true solution better than the Black-Scholes model when stock prices folow a jump-diffusion process. We also show that the Genetic Programming model outperforms various other models in many different settings. Other advantages of the Genetic Programming approach include its robustness to changing environment, its low demand for data, and its computational speed. Since genetic programs are flexible, self-learning and sefl-improving, they are an ideal tool for practitioners.", notes = "see also \cite{chidambaran:1998:aeaopGP} and \cite{chidambaran:2002:ECEF}", size = "48 pages", } @InProceedings{chidambaran:1998:aeaopGP, author = "N. K. Chidambaran and C. H. Jevons Lee and Joaquin R. Trigueros", title = "An Adaptive Evolutionary Approach to Option Pricing via Genetic Programming", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "38--41", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-548-7", notes = "GP-98 See also \cite{wpa98086}", } @InCollection{chidambaran:2002:ECEF, author = "N. K. Chidambaran and Joaquin Triqueros and Chi-Wen Jevons Lee", title = "Option Pricing via Genetic Programming", booktitle = "Evolutionary Computation in Economics and Finance", publisher = "Physica Verlag", year = "2002", editor = "Shu-Heng Chen", volume = "100", series = "Studies in Fuzziness and Soft Computing", chapter = "20", pages = "383--397", month = "2002", keywords = "genetic algorithms, genetic programming", ISBN = "3-7908-1476-8", DOI = "doi:10.1007/978-3-7908-1784-3_20", abstract = "We propose a methodology of Genetic Programming to approximate the relationship between the option price, its contract terms and the properties of the underlying stock price. An important advantage of the Genetic Programming approach is that we can incorporate currently known formulas, such as the Black-Scholes model, in the search for the best approximation to the true pricing formula. Using Monte Carlo simulations, we show that the Genetic Programming model approximates the true solution better than the Black-Scholes model when stock prices follow a jump-diffusion process. We also show that the Genetic Programming model outperforms various other models when pricing options in the real world. Other advantages of the Genetic Programming approach include its low demand for data, and its computational speed. Published previously in: Computational Finance Proceedings of the Sixth International Conference, Leonard N. Stern School of Business, January 1999. MIT Press, Cambridge, MA", notes = "bron Dec 2012 http://evonet.lri.fr/evoweb/resources/books_journals/record.php?id=452 See also \cite{wpa98086}", } @InProceedings{Chidambaran:2003:WSC, author = "N. K. Chidambaran", title = "Genetic programming with Monte Carlo simulation for option pricing", booktitle = "Proceedings of the 2003 Winter Simulation Conference", year = "2003", editor = "S. Chick and P. J. Sanchez and D. Ferrin and D. J. Morrice", volume = "1", pages = "285--292", address = "New Orleans, USA", month = "7-10 " # dec, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-8132-7", URL = "http://www.informs-sim.org/wsc03papers/035.pdf", size = "8 pages", abstract = "I examine the role of programming parameters in determining the accuracy of genetic programming for option pricing. I use Monte Carlo simulations to generate stock and option price data needed to develop a genetic option pricing program. I simulate data for two different stock price processes - a geometric Brownian process and a jump-diffusion process. In the jump-diffusion setting, I seed the genetic program with the Black-Scholes equation as a starting approximation. I find that population size, fitness criteria, and the ability to seed the program with known analytical equations, are important determinants of the efficiency of genetic programming.", notes = "details from ieee", } @InProceedings{Chie:gecco06lbp, author = "Bin-Tzong Chie and Chih-Chien Wang", title = "Model for Evolutionary Technology - An Automatically Defined Terminal Approach", booktitle = "Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2006)}", year = "2006", month = "8-12 " # jul, editor = "J{\"{o}}rn Grahl", address = "Seattle, WA, USA", notes = "Distributed on CD-ROM at GECCO-2006", keywords = "genetic algorithms, genetic programming, Automatically Defined Terminal, Agent-Based Modeling", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006etc/papers/lbp129.pdf", size = "7 pages", abstract = "automatically defined terminal (ADT) to keep ready and stable building blocks growing into complex structure. The idea is originated from the functional modularity approach. ADT is tested in an agent-based innovation model to see how it works and whether there is any improvement in searching new commodities for commercialising in the market; hence the market represents an environment for nourishing the development during innovative process. This paper will not only show how the capable producers with ADT work, but also how market selection plays an important role in the evolution of innovation. In other word, the agent-based modelling approach will present the evolutionary dynamic of interaction between producers and consumers in a commodity market.", } @PhdThesis{Bin-Tzong_Chie:thesis, author = "Bin-Tzong Chie", title = "Innovation in Economics: Agent-Based Computational Modelling", school = "National Chengchi University", year = "2007", address = "Taiwan", note = "in Chinese", keywords = "genetic algorithms, genetic programming, innovation, software agent, learning, quality-oriented, quantity-oriented", URL = "http://www.aiecon.org/whoweare/~btc/", URL = "https://hdl.handle.net/11296/259n33", notes = "in Chinese. Author also given as Ping Tsung,Chih Supervisor Prof. Shu-Heng Chen", } @Article{chie:2014:AS, author = "Bin-Tzong Chie and Shu-Heng Chen", title = "Competition in a New Industrial Economy: Toward an {Agent-Based} Economic Model of Modularity", journal = "Administrative Sciences", year = "2014", volume = "4", number = "3", keywords = "genetic algorithms, genetic programming, modularity, modular economy, hierarchy, markups", ISSN = "2076-3387", URL = "https://www.mdpi.com/2076-3387/4/3/192", DOI = "doi:10.3390/admsci4030192", abstract = "When firms (conglomerates) are competing, not only for the present, with a given population of customers and a fixed set of commodities or service, but also for the future, in which products are constantly evolving, what will be their competitive strategies and what will be the emerging ecology of the market? In this paper, we use the agent-based modelling of a modular economy to study the markup rate dynamics in a duopolistic setting. We find that there are multiple equilibria in the market, characterised by either a fixed point or a limit cycle. In the former case, both firms compete with the same markup rate, which is a situation similar to the familiar classic Bertrand model, except that the rate is not necessarily zero. In the latter case, both firms survive by maintaining different markup rates and different market shares.", notes = "also known as \cite{admsci4030192}", } @Article{Chien:2002:ESA, author = "Been-Chian Chien and Jung Yi Lin and Tzung-Pei Hong", title = "Learning discriminant functions with fuzzy attributes for classification using genetic programming", journal = "Expert Systems with Applications", year = "2002", volume = "23", pages = "31--37", number = "1", owner = "wlangdon", keywords = "genetic algorithms, genetic programming, Classification, Knowledge discovery, Fuzzy sets", ISSN = "0957-4174", URL = "http://www.sciencedirect.com/science/article/B6V03-45C00T2-1/2/e7d49cc18dd12961ac2e5c114c41f667", DOI = "doi:10.1016/S0957-4174(02)00025-8", abstract = "Classification is one of the important tasks in developing expert systems. Most of the previous approaches for classification problem are based on classification rules generated by decision trees. we propose a new learning approach based on genetic programming to generate discriminant functions for classifying data. An adaptable incremental learning strategy and a distance-based fitness function are developed to improve the efficiency of genetic programming-based learning process. We first transform attributes of objects into fuzzy attributes and then a set of discriminant functions is generated based on the proposed learning procedure. The set of derived functions with fuzzy attributes gives high accuracy of classification and presents a linear form. Hence, the functions can be transformed into inference rules easily and we can use the rules to provide the building of rule base in an expert system.", } @InProceedings{Chien:2002:KES, author = "Been-Chian Chien and Jung-Yi Lin", title = "A Classifier with the Function-based Decision Tree", booktitle = "Proceedings of KES'2002 the Sixth International Conference on Knowledge-Based Intelligent Information Engineering Systems", year = "2002", editor = "E. Damiani and L. C. Jain and R. J. Howlett and N. Ichalkaranje", volume = "82", series = "Frontiers in Artificial Intelligence and Applications", pages = "648--652", address = "Podere d'Ombriano, Crema, Italy", publisher_address = "Amsterdam", month = "19-19 " # sep, publisher = "IOS Press", keywords = "genetic algorithms, genetic programming, classification, decision tree, Knowledge discovery, Machine learning", ISBN = "1-58603-280-1", URL = "http://myweb.nutn.edu.tw/~bcchien/Papers/C_KES2002.pdf", size = "5 pages", abstract = "Classification is one of the important problems in the research area of knowledge discovery and machine learning. In this paper, an accurate classifier with multi-category based on the genetic programming is proposed. The classifier consists of the discriminant functions that are generated by genetic programming. We propose the function-based decision tree (FDT) to resolve the problem of ambiguity between discriminant functions, and the experiments show that the proposed methods are accurate.", notes = "Broken Dec 2012 http://www.iospress.nl/loadtop/load.php?isbn=1586032801", } @InProceedings{Chien:2003:DaWaK, author = "Been-Chian Chien and Jui-Hsiang Yang and Wen-Yang Lin", title = "Generating Effective Classifiers with Supervised Learning of Genetic Programming", booktitle = "Data Warehousing and Knowledge Discovery: 5th International Conference, DaWaK 2003", year = "2003", volume = "2737", series = "Lecture Notes in Computer Science", pages = "192--201", address = "Prague, Czech Republic", month = "3-5 " # sep, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1007/b11825", ISBN = "3-540-40807-X", abstract = "A new approach of learning classifiers using genetic programming has been developed recently. Most of the previous researches generate classification rules to classify data. However, the generation of rules is time consuming and the recognition accuracy is limited. In this paper, an approach of learning classification functions by genetic programming is proposed for classification. Since a classification function deals with numerical attributes only, the proposed scheme first transforms the nominal data into numerical values by rough membership functions. Then, the learning technique of genetic programming is used to generate classification functions. For the purpose of improving the accuracy of classification, we proposed an adaptive interval fitness function. Combining the learned classification functions with training samples, an effective classification method is presented. Numbers of data sets selected from UCI Machine Learning repository are used to show the effectiveness of the proposed method and compare with other classifiers.", } @Article{Chien:2004:PR, author = "Been-Chian Chien and Jung-Yi Lin and Wei-Pang Yang", title = "Learning effective classifiers with Z-value measure based on genetic programming", journal = "Pattern Recognition", year = "2004", volume = "37", pages = "1957--1972", number = "10", abstract = "This paper presents a learning scheme for data classification based on genetic programming. The proposed learning approach consists of an adaptive incremental learning strategy and distance-based fitness functions for generating the discriminant functions using genetic programming. To classify data using the discriminant functions effectively, the mechanism called Z-value measure is developed. Based on the Z-value measure, we give two classification algorithms to resolve ambiguity among the discriminant functions. The experiments show that the proposed approach has less training time than previous GP learning methods. The learned classifiers also have high accuracy of classification in comparison with the previous classifiers.", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6V14-4CPVJFT-3/2/51f0ecbd7d198da15f4ae094e378c5d0", month = oct, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1016/j.patcog.2004.03.016", } @InProceedings{Chien:2006:ICSMC, author = "Been-Chian Chien and Jui-Hsiang Yang", title = "Features Selection based on Rough Membership and Genetic Programming", booktitle = "IEEE International Conference on Systems, Man and Cybernetics, ICSMC '06", year = "2006", volume = "5", pages = "4124--4129", address = "Taipei, Taiwan", month = "8-11 " # oct, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", ISBN = "1-4244-0100-3", DOI = "doi:10.1109/ICSMC.2006.384780", abstract = "This paper discusses the feature selection problem upon supervised learning. A learning method based on rough sets and genetic programming is proposed to select significant features and classify numerical data. The proposed method uses rough membership to transform nominal data into numerical values, then selects important features and learns classification functions using genetic programming. We use several UCI data sets to show the performance of the proposed scheme and make comparisons with three different features selection approaches: distance measure, information measure and dependence measure. The results demonstrate that the proposed method is effective both in features selection and classification.", notes = "Member, IEEE, National University of Tainan, Tainan 700, Taiwan, R. O. C. Tel: +886-6-2606123 ext. 7707, fax:+886-6-2606125;", } @Article{DBLP:journals/mvl/ChienYH11, author = "Been-Chian Chien and Jui-Hsiang Yang and Tzung-Pei Hong", title = "Learning Discriminant Functions based on Genetic Programming and Rough Sets", journal = "Multiple-Valued Logic and Soft Computing", year = "2011", volume = "17", number = "2-3", pages = "135--155", keywords = "genetic algorithms, genetic programming, Machine learning, discriminant function, classification, rough sets.", ISSN = "1542-3980", URL = "http://www.oldcitypublishing.com/journals/mvlsc-home/mvlsc-issue-contents/mvlsc-volume-17-number-2-3-2011/mvlsc-17-2-3-p-135-155/", broken = "http://www.oldcitypublishing.com/MVLSC/MVLSCabstracts/MVLSC17.2-3abstracts/MVLSCv17n2-3p135-155Chien.html", broken = "http://www.oldcitypublishing.com/MVLSC/MVLSCcontents/MVLSCv17n2-3contents.html", size = "21 pages", abstract = "Supervised learning based on genetic programming can find different classification models including decision trees, classification rules and discriminant functions. The previous researches have shown that the classifiers learnt by GP have high precision in many application domains. However, nominal data cannot be handled and calculated by the model of using discriminant functions. In this paper, we present a scheme based on rough set theory and genetic programming to learn discriminant functions from general data containing both nominal and numerical attributes. The proposed scheme first transforms the nominal data into numerical values by applying the technique of rough sets. Then, genetic programming is used to learn discriminant functions. The conflict problem among discriminant functions is solved by an effective conflict resolution method based on the distance-based fitness function. The experimental results show that the classifiers generated by the proposed scheme using GP are effective on nominal data in comparison with C4.5, CBA, and NB-based classifiers.", notes = "Oct 2016 oldcitypublishing.com/MVLSC/ in a mess but article on there somewhere...", bibsource = "DBLP, http://dblp.uni-trier.de", } @InCollection{chien:2000:GTRUEM, author = "Edward K. Chien", title = "Grid-Based Trace Routing Using Evolutionary Methods", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "90--97", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms", notes = "part of \cite{koza:2000:gagp}", } @InProceedings{chikara:1999:CLASCS, author = "Maezawa Chikara and Atsumi Masayasu", title = "Collaborative Learning Agents with Structural Classifier Systems", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "777", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-859.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-859.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Article{Chikumbo:2015:JMCDA, author = "Oliver Chikumbo and Erik Goodman and Kalyanmoy Deb", title = "Triple Bottomline Many-Objective-Based Decision Making for a Land Use Management Problem", journal = "Journal of Multi-Criteria Decision Analysis", year = "2015", volume = "22", number = "3-4", pages = "133--159", keywords = "genetic algorithms, genetic programming, GPTIPS, evolutionary algorithms, epigenetics, Reference-Point-Based Non-dominated Sorting Genetic Algorithm II (R-NSGA II), Hyperspace Pareto Frontier (HPF), triple bottomline, Hyper-radial visualization (HRV), visual steering, multiplicative analytic hierarchy process (MAHP)", URL = "https://onlinelibrary.wiley.com/doi/abs/10.1002/mcda.1536", DOI = "doi:10.1002/mcda.1536", eprint = "https://onlinelibrary.wiley.com/doi/pdf/10.1002/mcda.1536", abstract = "A land use many-objective optimization problem for a 1500-ha farm with 315 paddocks was formulated with 14 objectives (maximizing sawlog production, pulpwood production, milk solids, beef, sheep meat, wool, carbon sequestration, water production, income and Earnings Before Interest and Tax; and minimizing costs, nitrate leaching, phosphorus loss and sedimentation). This was solved using a modified Reference-point-based Non-dominated Sorting Genetic Algorithm II augmented by simulated epigenetic operations. The search space had complex variable interactions and was based on economic data and several interoperating simulation models. The solution was an approximation of a Hyperspace Pareto Frontier (HPF), where each non-dominated trade-off point represented a set of land-use management actions taken within a 10-year period and their related management options, spanning a planning period of 50 years. A trade-off analysis was achieved using Hyper-Radial Visualization (HRV) by collapsing the HPF into a 2-D visualization capability through an interactive virtual reality (VR)-based method, thereby facilitating intuitive selection of a sound compromise solution dictated by the decision makers preferences under uncertainty conditions. Four scenarios of the HRV were considered emphasizing economic, sedimentation and nitrate leaching aspects, giving rise to a triple bottomline (i.e. the economic, environmental and social complex, where the social aspect is represented by the preferences of the various stakeholders). Highlights of the proposed approach are the development of an innovative epigenetics-based multi-objective optimizer, uncertainty incorporation in the search space data and decision making on a multi-dimensional space through a VR-simulation-based visual steering process controlled at its core by a multi-criterion decision making-based process. This approach has widespread applicability to many other wicked societal problem-solving tasks", notes = "This paper was the winner of the 2013 Wiley Practice prize https://beacon-center.org/blog/2013/06/21/wiley-practice-prize-awarded-to-beacons-multi-criterion-decision-making-team/ https://orcid.org/0000-0002-2917-9089", } @InProceedings{Chimisliu:2012:AST, author = "Valentin Chimisliu and Franz Wotawa", title = "Category Partition Method and Satisfiability Modulo Theories for test case generation", booktitle = "7th International Workshop on Automation of Software Test (AST 2012)", year = "2012", month = jun, address = "Zurich", pages = "64--70", size = "7 pages", abstract = "In this paper we focus on test case generation for large database applications in the telecommunication industry domain. In particular, we present an approach that is based on the Category Partition Method and uses the SMT solver Z3 for automatically generating input test data values for the obtained test cases. For the generation process, we make use of different test case generation strategies. First initial results show that the one based on genetic programming delivers the fewest number of test cases while retaining choice coverage. Moreover, the obtained results indicate that the presented approach is feasible for the intended application domain.", keywords = "genetic algorithms, genetic programming, SBSE, SMT solver Z3, automatic test data values generation, category partition method, intended application domain, large database applications, satisfiability modulo theories, telecommunication industry domain, test case generation strategies, automatic test pattern generation, computability, database management systems, telecommunication computing, telecommunication industry", DOI = "doi:10.1109/IWAST.2012.6228992", notes = "Also known as \cite{6228992}", } @TechReport{CS-TR-07-3, author = "Barret Chin and Mengjie Zhang", title = "Object Detection using Neural Networks and Genetic Programming", institution = "Computer Science, Victoria University of Wellington", year = "2007", type = "Technical report", number = "CS-TR-07-3", address = "New Zealand", month = nov, keywords = "genetic algorithms, genetic programming, object detection, neural networks, region refinement, feature selection", URL = "http://www.mcs.vuw.ac.nz/comp/Publications/archive/CS-TR-07/CS-TR-07-3.pdf", URL = "http://www.mcs.vuw.ac.nz/comp/Publications/CS-TR-07-3.abs.html", abstract = "This paper describes a domain independent approach to the use of neural networks (NNs) and genetic programming (GP) for object detection problems. Instead of using high level features for a particular task, this approach uses domain independent pixel statistics for object detection. The paper first compares an NN method and a GP method on four image data sets providing object detection problems of increasing difficulty. The results show that the GP method performs better than the NN method on these problems but still produces a large number of false alarms on the difficult problem and computation cost is still high. To deal with these problems, we develop a new method called GP-refine that uses a two stage learning process. The results suggest that the new GP method further improves object detection performance on the difficult object detection task.", size = "pages 13", } @InProceedings{conf/evoW/ChinZ08, title = "Object Detection Using Neural Networks and Genetic Programming", author = "Barret Chin and Mengjie Zhang", bibdate = "2008-04-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/evoW/evoW2008.html#ChinZ08", booktitle = "Proceedings of Evo{COMNET}, Evo{FIN}, Evo{HOT}, Evo{IASP}, Evo{MUSART}, Evo{NUM}, Evo{STOC}, and EvoTransLog, Applications of Evolutionary Computing, EvoWorkshops", publisher = "Springer", year = "2008", volume = "4974", editor = "Mario Giacobini and Anthony Brabazon and Stefano Cagnoni and Gianni {Di Caro} and Rolf Drechsler and Anik{\'o} Ek{\'a}rt and Anna Esparcia-Alc{\'a}zar and Muddassar Farooq and Andreas Fink and Jon McCormack and Michael O'Neill and Juan Romero and Franz Rothlauf and Giovanni Squillero and Sima Uyar and Shengxiang Yang", isbn13 = "978-3-540-78760-0", pages = "335--340", series = "Lecture Notes in Computer Science", DOI = "doi:10.1007/978-3-540-78761-7_34", address = "Naples", month = "26-28 " # mar, keywords = "genetic algorithms, genetic programming", } @PhdThesis{Chinthalapati:thesis, author = "Venkata Lakshmipathi Raju Chinthalapati", title = "Probabilistic Learning and Optimization Applied to Quantitative Finance", school = "Dept. of Mathematics, London School of Economics and Political Science", year = "2011", address = "UK", month = sep, keywords = "genetic algorithms, genetic programming, computational, information-theoretic learning with statistics, learning/statistics and optimisation, theory and algorithms, Markov processes", bibsource = "OAI-PMH server at eprints.pascal-network.org", oai = "oai:eprints.pascal-network.org:8629", URL = "http://www.lse.ac.uk/Mathematics/Research-Students/PhD-Roll-of-Honour", URL = "https://www.genealogy.math.ndsu.nodak.edu/id.php?id=192002", URL = "https://librarysearch.lse.ac.uk/permalink/f/1jad15a/44LSE_ALMA_DS21136217410002021", broken = "http://eprints.pascal-network.org/archive/00008629/", abstract = "his thesis concerns probabilistic learning theory and stochastic optimisation and investigates applications to a variety of problems arising in finance. In many sequential decision tasks, the consequences of an action emerge at a multitude of times after the action is taken. A key problem is to find good strategies for selecting actions based on both their short and long term consequences. We develop a simulation-based, two-timescale actor-critic algorithm for infinite horizon Markov decision processes with finite state and action spaces, with a discounted reward criterion. The algorithm is of the gradient descent type, searching the space of stationary randomised policies and using certain simultaneous deterministic perturbation stochastic approximation (SDPSA) gradient estimates for enhanced performance. We apply our algorithm to a mortgage refinancing problem and find that it obtains the optimal refinancing strategies in a computationally efficient manner. The problem of identifying pairs of similar time series is an important one with several applications in finance, especially to directional trading, where traders try to spot arbitrage opportunities. We use a variant of the Optimal Thermal Causal Path method (obtained by adding a curvature term and by using an approximation technique to increase the efficiency) to determine the lead-lag structure between a given pair of time-series. We apply the method to various market sectors of NYSE data and extract highly correlated pairs of time series. Because Genetic Programming (GP) is known for its ability to detect patterns such as the conditional mean and conditional variance of a time series, it is potentially well-suited to volatility forecasting. We introduce a technique for forecasting 5-day annualised volatility in exchange rates. The technique employs a series of standard methods (such as MA, EWMA, GARCH and its variants) alongside Genetic Programming forecasting methods, dynamically opting for the most appropriate technique at a given time, determined through out-of-sample tests. A particular challenge with volatility forecasting using GP is that, during learning, the GP is presented with training data generated by a noisy Markovian process, not something that is modelled in the standard probabilistic learning frameworks. We analyse, in a probabilistic model of learning, how much such training data should be presented to the GP in the learning phase for the learning to be successful.", notes = "Supervisor: Prof Martin Anthony", } @InProceedings{Chinthalapati:2012:CIFEr, author = "V. L. Raju Chinthalapati", title = "Volatility Forecast in {FX} Markets using Evolutionary Computing and Heuristic Technique", booktitle = "IEEE Computational Intelligence for Financial Engineering and Economic (CIFEr 2012)", year = "2012", editor = "Robert Golan", address = "New York, USA", month = "29-30 " # mar, organisation = "IEEE Computational Intelligence Society", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, autoregressive processes, economic forecasting, foreign exchange trading, learning (artificial intelligence), time series, 5-day annualised volatility forecasting, EUR-USD, EWMA, FX markets, GARCH(1,1), GBP-USD, GP, USD-JPY, evolutionary computing, exponentially weighted moving average, financial asset volatility, heuristic techniques, machine learning applications, mean-reversion, optimisation, time-series, volatility autocorrelation, volatility forecast, Biological system modelling, Correlation, Forecasting, Sociology, Standards", isbn13 = "978-1-4673-1802-0", type = "Conference or Workshop Item; PeerReviewed", bibsource = "OAI-PMH server at eprints.pascal-network.org", oai = "oai:eprints.pascal-network.org:8630", broken = "http://eprints.pascal-network.org/archive/00008630/", DOI = "doi:10.1109/CIFEr.2012.6327813", size = "8 pages", abstract = "A financial asset's volatility exhibits key characteristics, such as mean-reversion and high autocorrelation [1], [2]. Empirical evidence suggests that this volatility autocorrelation exponentially decays (or exhibits long-range memory) [3]. We employ Genetic Programming (GP) for volatility forecasting because of its ability to detect patterns such as the conditional mean and conditional variance of a time-series. Genetic Programming is typically applied to optimisation, searching, and machine learning applications like classification, prediction etc. From our experiments, we see that Genetic Programming is a good competitor to the standard forecasting techniques like GARCH(1,1), Moving Average (MA), Exponentially Weighted Moving Average (EWMA). However it is not a silver bullet: we observe that different forecasting methods would perform better in different market conditions. In addition to Genetic Programming, we consider a heuristic technique that employs a series of standard forecasting methods and dynamically opts for the most appropriate technique at a given time. Using a heuristic technique, we try to identify the best forecasting method that would perform better than the rest of the methods in the near out-of-sample horizon. Our work introduces a preliminary framework for forecasting 5-day annualised volatility in GBP/USD, USD/JPY, and EUR/USD.", notes = "broken 2022 http://www.ieee-cifer.org/program.html Also known as \cite{6327813}", } @InProceedings{1144138, author = "Clement Chion and Luis E. {Da Costa} and Jacques-Andre Landry", title = "Genetic programming for agricultural purposes", booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation", year = "2006", editor = "Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos {Coello Coello} and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens", volume = "1", ISBN = "1-59593-186-4", pages = "783--790", address = "Seattle, Washington, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p783.pdf", DOI = "doi:10.1145/1143997.1144138", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "8-12 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, crop nitrogen content, GP, hyperspectral imagery, management, precision farming, remote sensing, site-specific management, spectral vegetation indices (SVI), vegetation indices", notes = "GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM Order Number 910060", } @Article{Chion:2008:ieeeTGRS, author = "Clement Chion and Jacques-Andre Landry and Luis {Da Costa}", title = "A Genetic-Programming-Based Method for Hyperspectral Data Information Extraction: Agricultural Applications", journal = "IEEE Transactions on Geoscience and Remote Sensing", year = "2008", month = aug, volume = "46", number = "8", pages = "2446--2457", keywords = "genetic algorithms, genetic programming, CASI sensor, agricultural application, band selection, canopy nitrogen content, crop biophysical variable, feature selection, genetic programming-spectral vegetation index, hyperspectral data information extraction, hyperspectral remote sensing, pixel reflectance, precision farming, crops, farming, feature extraction, geophysical signal processing, vegetation mapping", DOI = "doi:10.1109/TGRS.2008.922061", ISSN = "0196-2892", abstract = "A new method, called genetic programming-spectral vegetation index (GP-SVI), for the extraction of information from hyperspectral data is presented. This method is introduced in the context of precision farming. GP-SVI derives a regression model describing a specific crop biophysical variable from hyperspectral images (verified with in situ observations). GP-SVI performed better than other methods [multiple regression, tree-based modeling, and genetic algorithm-partial least squares (GA-PLS)] on the task of correlating canopy nitrogen content in a cornfield with pixel reflectance. It is also shown that the band selection performed by GP-SVI is comparable with the selection performed by GA-PLS, a method that is specifically designed to deal with hyperspectral data.", notes = "Also known as \cite{4559746}", } @InProceedings{Chittilappilly:2023:ACCAI, author = "Rose Mary Chittilappilly and Sanjana Suresh and Shanthini Shanmugam", booktitle = "2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)", title = "A Comparative Analysis of Optimizing Medical Insurance Prediction Using Genetic Algorithm and Other Machine Learning Algorithms", year = "2023", month = may, keywords = "genetic algorithms, genetic programming, TPOT, Machine learning algorithms, Metaheuristics, Linear regression, Insurance, Manuals, Predictive models, Prediction algorithms, machine learning, medical insurance, random forest, gradient boosting, LassoLarsCV, Linear Regression, Metaheuristic Algorithm", DOI = "doi:10.1109/ACCAI58221.2023.10199979", abstract = "an analysis is conducted on different machine learning strategies to predict medical insurance charges using demographic and health-related information on individuals. Each algorithm was trained and tested on a preprocessed dataset, and the performance of various models is compared, including linear regression, random forest, gradient boosting, LassoLarsCV, and a model created with automated machine learning using TPOT, which makes use of genetic programming for optimisation. Conclusively, the results bring to light that the TPOT-generated model, which is a combination of LassoLarsCV and GradientBoostingRegressor, performed better than the other models, attaining a root mean square error of 0.0686 and an accuracy of 87.45percent on the test set. These findings suggest that automated machine learning techniques and metaheuristic optimisation, as was used in TPOT, can bring improvement to the performance of existing medical insurance cost prediction models.", notes = "Also known as \cite{10199979}", } @InProceedings{1277274, author = "Darren M. Chitty", title = "A data parallel approach to genetic programming using programmable graphics hardware", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "1566--1573", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1566.pdf", DOI = "doi:10.1145/1276958.1277274", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, data parallelism, GPU, graphics cards, OpenGL, Cg", size = "8 pages", abstract = "In recent years the computing power of graphics cards has increased significantly. Indeed, the growth in the computing power of these graphics cards is now several orders of magnitude greater than the growth in the power of computer processor units. Thus these graphics cards are now beginning to be used by the scientific community as low cost, high performance computing platforms. Traditional genetic programming is a highly computer intensive algorithm but due to its parallel nature it can be distributed over multiple processors to increase the speed of the algorithm considerably. This is not applicable for single processor architectures but graphics cards provide a mechanism for developing a data parallel implementation of genetic programming. In this paper we will describe the technique of general purpose computing using graphics cards and how to extend this technique to genetic programming. We will demonstrate the improvement in the performance of genetic programming on single processor architectures which can be achieved by harnessing the computing power of these next generation graphics cards.", notes = "NVidia GeForce 6400 GO. Fisher Iris, x^4+x^3+x^2+x, 11-mux. Cg. Cg toolkit used to compile each GP tree (fragment program) individually before transferring each separately to nVidia GeForce 6400 GO GPU. p1571 claims {"}changing the GPU program ... is relatively fast{"}. Iris, 11-Mux. Symbolic regression. Pop 500, 50 gens. Fig 11 10000 to 15000 fitness cases GPU big win over CPU. sec 3.6 'only one [GP] can be ready to be executed at any one time' GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @Article{Chitty:2012:SC, author = "Darren M. Chitty", title = "Fast parallel genetic programming: multi-core {CPU} versus many-core {GPU}", journal = "Soft Computing", year = "2012", volume = "16", number = "10", pages = "1795--1814", month = oct, keywords = "genetic algorithms, genetic programming, GPU", language = "English", publisher = "Springer-Verlag", ISSN = "1432-7643", DOI = "doi:10.1007/s00500-012-0862-0", size = "20 pages", abstract = "Genetic Programming (GP) is a computationally intensive technique which is also highly parallel in nature. In recent years, significant performance improvements have been achieved over a standard GP CPU-based approach by harnessing the parallel computational power of many-core graphics cards which have hundreds of processing cores. This enables both fitness cases and candidate solutions to be evaluated in parallel. However, this paper will demonstrate that by fully exploiting a multi-core CPU, similar performance gains can also be achieved. This paper will present a new GP model which demonstrates greater efficiency whilst also exploiting the cache memory. Furthermore, the model presented in this paper will use Streaming SIMD Extensions to gain further performance improvements. A parallel version of the GP model is also presented which optimises multiple thread execution and cache memory. The results presented will demonstrate that a multi-core CPU implementation of GP can yield performance levels that match and exceed those of the latest graphics card implementations of GP. Indeed, a performance gain of up to 420-fold over standard GP is demonstrated and a threefold gain over a graphics card implementation.", notes = "Intel i7, SIMD (SSE) GP, CPU cache, KDD cup 1999, 20 Mux, sextic, Shuttle 0.42 to 18 billion GP operations per sec = 420 to 18480 MGPops up to 8200 to 555410 MGPops (2DStackGP multi-core table 12). p1813 'at least a twofold gain in speed over the best graphics card approaches from the literature'", URL = "http://www.cs.bris.ac.uk/Publications/Papers/2001629.pdf", } @PhdThesis{Chitty:thesis, author = "Darren M. Chitty", title = "Improving the computational speed of genetic programming", school = "University of Bristol", year = "2015", address = "UK", keywords = "genetic algorithms, genetic programming, GPU", broken = "http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.686812", biburl = "https://dblp.org/rec/phd/ethos/Chitty15.bib", URL = "https://books.google.co.uk/books?id=-IqF0AEACAAJ", abstract = "Genetic Programming (GP) is well known as a computationally intensive technique especially when considering regression or classification tasks with large datasets. Consequently, there has been considerable work conducted into improving the computational speed of GP. Recently, this has concentrated on exploiting highly parallel architectures in the form of Graphics Processing Units (GPUs). However, the reported speeds fall considerably short of the computational capabilities of these GPUs. This thesis investigates this issue, seeking to considerably improve the computational speed of GP. Indeed, this thesis will demonstrate that considerable improvements in the speed of GP can be achieved when fully exploiting a parallel Central Processing Unit (CPU) exceeding the performance of the latest GPU implementations. This is achieved by recognising that GP is as much a memory bound technique as a compute bound technique. By adopting a two dimensional stack approach, better exploitation of memory resources is achieved in addition to reducing interpreter overheads. This approach is applied to CPU and GPU implementations and compares favourably with compiled versions of GP. The second aspect of this thesis demonstrates that although considerable performance gains can be achieved using parallel hardware, the role of efficiency within GP should not be forgotten. Efficiency saving can boost the computational speed of parallel GP significantly. Two methods are considered, parsimony pressure measures and efficient tournament selection. The second efficiency technique enables a CPU implementation of GP to outperform a GPU implementation for classification type tasks even though the CPU has only a tenth of the computational power. Finally both CPU and GPU are combined for ultimate performance. Speedups of more than a thousand fold over a basic sequential version of GP are achieved and three fold over the best GPU implementation from the literature. Consequently, this speedup increases the usefulness of GP as a machine learning technique.", notes = "uk.bl.ethos.686812 ISNI: 0000 0004 5920 3662 Also known as \cite{DBLP:phd/ethos/Chitty15}", } @Misc{Chitty:2016:ArXiv, title = "Faster {GPU} Based Genetic Programming Using {A} Two Dimensional Stack", author = "Darren M. Chitty", howpublished = "ArXiv", year = "2016", keywords = "genetic algorithms, genetic programming", bibdate = "2016-02-01", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/corr/corr1601.html#Chitty16", URL = "http://arxiv.org/abs/1601.00221", abstract = "Genetic Programming (GP) is a computationally intensive technique which also has a high degree of natural parallelism. Parallel computing architectures have become commonplace especially with regards Graphics Processing Units (GPU). Hence, versions of GP have been implemented that use these highly parallel computing platforms enabling significant gains in the computational speed of GP to be achieved. However, recently a two dimensional stack approach to GP using a multi-core CPU also demonstrated considerable performance gains. Indeed, performances equivalent to or exceeding that achieved by a GPU were demonstrated. This paper will demonstrate that a similar two dimensional stack approach can also be applied to a GPU based approach to GP to better exploit the underlying technology. Performance gains are achieved over a standard single dimensional stack approach when using a GPU. Overall, a peak computational speed of over 55 billion Genetic Programming Operations per Second are observed, a two fold improvement over the best GPU based single dimensional stack approach from the literature", } @Article{journals/soco/Chitty16, author = "Darren M. Chitty", title = "Improving the performance of {GPU}-based genetic programming through exploitation of on-chip memory", journal = "Soft Computing", year = "2016", number = "2", volume = "20", pages = "661--680", month = feb, keywords = "genetic algorithms, genetic programming, GPU, GPGPU, Many-core GPU, Parallel programming", ISSN = "1432-7643", bibdate = "2016-01-19", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/soco/soco20.html#Chitty16", URL = "http://dx.doi.org/10.1007/s00500-014-1530-3", DOI = "doi:10.1007/s00500-014-1530-3", size = "20 pages", abstract = "Genetic Programming (GP) (Koza, Genetic programming, MIT Press, Cambridge, 1992) is well-known as a computationally intensive technique. Subsequently, faster parallel versions have been implemented that harness the highly parallel hardware provided by graphics cards enabling significant gains in the performance of GP to be achieved. However, extracting the maximum performance from a graphics card for the purposes of GP is difficult. A key reason for this is that in addition to the processor resources, the fast on-chip memory of graphics cards needs to be fully exploited. Techniques will be presented that will improve the performance of a graphics card implementation of tree-based GP by better exploiting this faster memory. It will be demonstrated that both L1 cache and shared memory need to be considered for extracting the maximum performance. Better GP program representation and use of the register file is also explored to further boost performance. Using an NVidia Kepler 670GTX GPU, a maximum performance of 36 billion Genetic Programming Operations per Second is demonstrated.", } @InProceedings{Chitty:2016:SGAI, author = "Darren M. Chitty", title = "Experiments with High Performance Genetic Programming for Classification Problems", booktitle = "Proceedings of AI-2016, The Thirty-Sixth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence", year = "2016", editor = "Max Bramer and Miltos Petridis", pages = "221--227", address = "Cambridge", month = dec, publisher = "Springer", keywords = "genetic algorithms, genetic programming, GPU, Classification Parallel Processing", isbn13 = "978-3-319-47175-4", URL = "https://link.springer.com/chapter/10.1007/978-3-319-47175-4_15", DOI = "doi:10.1007/978-3-319-47175-4_15", abstract = "In recent years there have been many papers concerned with significantly improving the computational speed of Genetic Programming (GP) through exploitation of parallel hardware. The benefits of timeliness or being able to consider larger datasets are obvious. However, a question remains in whether there are wider benefits of this high performance GP approach. Consequently, this paper will investigate leveraging this performance by using a higher degree of evolution and ensemble approaches in order to discern if any improvement in classification accuracies can be achieved from high performance GP thereby advancing the technique itself.", } @Article{chitty2017faster, author = "Darren M. Chitty", title = "Faster {GPU}-based genetic programming using a two-dimensional stack", journal = "Soft Computing", year = "2017", volume = "21", number = "14", pages = "3859--3878", month = jul, keywords = "genetic algorithms, genetic programming, GPU, Many-core GPU Parallel programming", publisher = "Springer", URL = "https://link.springer.com/article/10.1007/s00500-016-2034-0", DOI = "doi:10.1007/s00500-016-2034-0", abstract = "Genetic programming (GP) is a computationally intensive technique which also has a high degree of natural parallelism. Parallel computing architectures have become commonplace especially with regards to Graphics Processing Units (GPU). Hence, versions of GP have been implemented that use these highly parallel computing platforms enabling significant gains in the computational speed of GP to be achieved. However, recently a two-dimensional stack approach to GP using a multi-core CPU also demonstrated considerable performance gains. Indeed, performances equivalent to or exceeding that achieved by a GPU were demonstrated. This paper will demonstrate that a similar two-dimensional stack approach can also be applied to a GPU-based approach to GP to better exploit the underlying technology. Performance gains are achieved over a standard single-dimensional stack approach when using a GPU. Overall, a peak computational speed of over 55 billion Genetic Programming Operations per Second are observed, a twofold improvement over the best GPU-based single-dimensional stack approach from the literature.", } @InProceedings{chitty:2018:ukci, author = "Darren Michael Chitty", title = "Exploiting Tournament Selection for Efficient Parallel Genetic Programming", booktitle = "18th Annual UK Workshop on Computational Intelligence, UKCI 2018", year = "2018", editor = "Ahmad Lotfi and Hamid Bouchachia and Alexander Gegov and Caroline Langensiepen and Martin McGinnity", volume = "840", series = "AISC", pages = "41--53", address = "Nottingham Trent University, UK", month = "5-7 " # sep # " 2018", publisher = "Springer", keywords = "genetic algorithms, genetic programming, HPC, Computational Efficiency", isbn13 = "978-3-319-97981-6", DOI = "doi:10.1007/978-3-319-97982-3_4", abstract = "Genetic Programming (GP) is a computationally intensive technique which is naturally parallel in nature. Consequently, many attempts have been made to improve its run-time from exploiting highly parallel hardware such as GPUs. However, a second methodology of improving the speed of GP is through efficiency techniques such as subtree caching. However achieving parallel performance and efficiency is a difficult task. This paper will demonstrate an efficiency saving for GP compatible with the harnessing of parallel CPU hardware by exploiting tournament selection. Significant efficiency savings are demonstrated whilst retaining the capability of a high performance parallel implementation of GP. Indeed, a 74percent improvement in the speed of GP is achieved with a peak rate of 96 billion GPop/s for classification type problems.", notes = "http://ukci2018.uk/accepted-papers/", } @InProceedings{chitty:2023:GECCOcomp, author = "Darren Chitty and Ed Keedwell", title = "Phased Genetic Programming for Application to the Traveling Salesman Problem", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "547--550", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, traveling salesman problem: Poster", isbn13 = "9798400701191", DOI = "doi:10.1145/3583133.3590673", size = "4 pages", abstract = "The Traveling Salesman Problem (TSP) is a difficult permutation-based optimisation problem typically solved using heuristics or meta-heuristics which search the solution problem space. An alternative is to find sets of manipulations to a solution which lead to optimality. Hyper-heuristics search this space applying heuristics sequentially, similar to a program. Genetic Programming (GP) evolves programs typically for classification or regression problems. This paper hypothesizes that GP can be used to evolve heuristic programs to directly solve the TSP. However, evolving a full program to solve the TSP is likely difficult due to required length and complexity. Consequently, a phased GP method is proposed whereby after a phase of generations the best program is saved and executed. The subsequent generation phase restarts operating on this saved program output. A full program is evolved piecemeal. Experiments demonstrate that whilst pure GP cannot solve TSP instances when using simple operators, Phased-GP can obtain solutions within 4\% of optimal for TSPs of several hundred cities. Moreover, Phased-GP operates up to nine times faster than pure GP.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @InProceedings{chitty:2023:UKCI, author = "Darren M. Chitty", title = "Strategies to Apply Genetic Programming Directly to the Traveling Salesman Problem", booktitle = "UK Workshop on Computational Intelligence", year = "2023", address = "Birmingham", month = "6-8 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-031-47508-5_25", DOI = "doi:10.1007/978-3-031-47508-5_25", notes = "Published in 2024", } @InProceedings{chitty:2024:GECCOcomp, author = "Darren M. Chitty and Ed Keedwell", title = "Greedy Strategies to Improve Phased Genetic Programming When Applied Directly to the Traveling Salesman Problem", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion", year = "2024", editor = "Ting Hu and Aniko Ekart", pages = "491--494", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, optimisation, greedy methods: Poster", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3654358", size = "4 pages", abstract = "Genetic Programming (GP) can be applied directly to combinatorial optimisation problems such as the Traveling Salesman Problem (TSP) using a phased approach. Similar to hyper-heuristics, Phased-GP evolves a program of simple operators to apply to a solution to improve it whilst operating in phases to facilitate hill-climbing. However, as optimality is approached, evolving a program of multiple operations that are not detrimental to solution quality is unlikely. Although, it can be hypothesized that if Phased-GP operates in a greedy manner, the probability of improving a near optimal solution is much greater. Two greedy Phased-GP strategies are proposed. First, using greedy GP operators which can only improve current solution quality. Second, a greedy program strategy whereby only the aspect of a GP program that provides best solution quality is retained. Combining both strategies reduced relative errors by up to a further 6\% obtaining solutions within 7\% of optimal when applied to TSPs of several thousand cities.", notes = "GECCO-2024 GP A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{chitty:2024:GECCOcomp2, author = "Darren Chitty", title = "Improving the Efficiency Of Genetic Programming for Classification Tasks Using a Phased Approach", booktitle = "9th Workshop on Industrial Applications of Metaheuristics (IAM 2024)", year = "2024", editor = "Silvino Fernandez Alzueta and Thomas Stuetzle", pages = "1702--1705", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, high performance computing, efficiency", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3664184", size = "4 pages", abstract = "Genetic Programming (GP) uses Darwinian evolution to generate algorithms for tasks such as classification and symbolic regression. However, a drawback is the interpreter used to evaluate candidate programs adding significant computational cost. Hence, many studies have sought to improve the speed of GP primarily via parallelism. However, efficiency can also offer considerable performance gains. GP has recently been applied to combinatorial optimisation using a phased approach (Phased-GP) whereby programs are evolved piecemeal avoiding reinterpretation of sub-programs. This method was found to be highly effective and efficient compared to standard GP. This paper investigates if a similar effect is observed when using phased GP to incrementally build classifiers. Tested upon known real-world classification problems, an efficiency saving of up to 98\% can be achieved with a speedup of 70 fold and no significant loss of classification accuracy. Moreover, the method can be easily used within any existing high performance parallel GP models.", notes = "GECCO-2024 IAM A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{Chiu:2001:AGP, author = "Chaochang Chiu and Jih-Tay Hsu and Chih-Yung Lin", title = "The Application of Genetic Programming in Milk Yield Prediction for Dairy Cows", booktitle = "Rough Sets and Current Trends in Computing : Second International Conference, RSCTC 2000. Revised Papers", editor = "W. Ziarko and Y. Yao", volume = "2005", pages = "598--602", series = "Lecture Notes in Computer Science", address = "Banff, Canada", publisher_address = "Heidelberg", month = oct # " 16-19", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, dynamic mutation, milk yield prediction", year = "2001", CODEN = "LNCSD9", ISSN = "0302-9743", bibdate = "Sat Feb 2 13:03:23 MST 2002", DOI = "doi:10.1007/3-540-45554-X_75", acknowledgement = ack-nhfb, abstract = "Milk yield forecasting can help dairy farmers to deal with the continuously changing condition all year round and to reduce the unnecessary overheads. Several variables (somatic cell count, pariety, day in milk, milk protein content, milk fat content, season) related to milk yield are collected as the parameters of the forecasting model. The use of an improved Genetic Programming (GP) technique with dynamic learning operators is proposed and achieved with acceptable prediction results.", } @InProceedings{Chivilikhin:2013:GECCO, author = "Daniil Chivilikhin and Vladimir Ulyantsev", title = "{MuACOsm}: a new mutation-based ant colony optimization algorithm for learning finite-state machines", booktitle = "GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference", year = "2013", editor = "Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi", isbn13 = "978-1-4503-1963-8", pages = "511--518", keywords = "genetic algorithms, genetic programming", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2463372.2463440", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "In this paper we present MuACOsm, a new method of learning Finite-State Machines (FSM) based on Ant Colony Optimisation (ACO) and a graph representation of the search space. The input data is a set of events, a set of actions and the number of states in the target FSM. The goal is to maximise the given fitness function, which is defined on the set of all FSMs with given parameters. The new algorithm is compared with evolutionary algorithms and a genetic programming related approach on the well-known Artificial Ant problem.", notes = "Also known as \cite{2463440} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)", } @InProceedings{Chivilikhin:2015:GECCOcomp, author = "Daniil Chivilikhin and Ilya Ivanov and Anatoly Shalyto", title = "Inferring Temporal Properties of Finite-State Machine Models with Genetic Programming", booktitle = "GECCO'15 Student Workshop", year = "2015", editor = "Tea Tusar and Boris Naujoks", isbn13 = "978-1-4503-3488-4", keywords = "genetic algorithms, genetic programming", pages = "1185--1188", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739482.2768475", DOI = "doi:10.1145/2739482.2768475", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "The paper presents a genetic programming based approach for inferring general form Linear Temporal Logic properties of finite-state machine models. Candidate properties are evaluated using several fitness functions, therefore multiobjective evolutionary algorithms are used. The feasibility of the approach is demonstrated by two examples.", notes = "Also known as \cite{2768475} Distributed at GECCO-2015.", } @Article{Chivilikhin:2013:PV, author = "Daniil S. Chivilikhin and Vladimir I. Ulyantsev and Anatoly A. Shalyto", title = "Solving Five Instances of the Artificial Ant Problem with Ant Colony Optimization", journal = "IFAC Proceedings Volumes", volume = "46", number = "9", pages = "1043--1048", year = "2013", note = "7th IFAC Conference on Manufacturing Modelling, Management, and Control", ISSN = "1474-6670", DOI = "doi:10.3182/20130619-3-RU-3018.00436", URL = "http://www.sciencedirect.com/science/article/pii/S1474667016344275", abstract = "The Artificial Ant problem is a common benchmark problem often used for metaheuristic algorithm performance evaluation. The problem is to find a strategy controlling an agent (called an Artificial Ant) in a game performed on a square toroidal field. Some cells of the field contain {"}food{"} pellets, which are distributed along a certain trail. In this paper we use Finite-State Machines (FSM) for strategy representation and present a new algorithm -MuACOsm - for learning finite-state machines. The new algorithm is based on an Ant Colony Optimization algorithm (ACO) and a graph representation of the search space. We compare the new algorithm with a genetic algorithm (GA), evolutionary strategies (ES), a genetic programming related approach and reinforcement learning on five instances of the Artificial Ant Problem.", keywords = "genetic algorithms, genetic programming, ant colony optimization, automata-based programming, finite-state machine, learning, induction, artificial ant problem", } @InProceedings{chlebik:2023:GECCOcomp, author = "Jakub Chlebik and Jiri Jaros", title = "Evolutionary Optimization of a Focused Ultrasound Propagation Predictor Neural Network", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "635--638", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, ultrasound propagation predictor, evolutionary design, evolutionary optimisation: Poster", isbn13 = "9798400701191", DOI = "doi:10.1145/3583133.3590661", size = "4 pages", abstract = "The search for the optimal treatment plan of a focused ultrasound-based procedure is a complex multi-modal problem, trying to deliver the solution in clinically relevant time while not sacrificing the precision below a critical threshold. To test a solution, many computationally expensive simulations must be evaluated, often thousands of times. The recent renaissance of machine learning could provide an answer to this. Indeed, a state-of-the-art neural predictor of Acoustic Propagation through a human skull was published recently, speeding up the simulation significantly. The architecture, however, could use some improvements in precision. To explore the design more deeply, we made an attempt to improve the solver by use of an evolutionary algorithm, challenging the importance of different building blocks. Using Genetic Programming, we improved their solution significantly, resulting in a solver with approximately an order of magnitude better RMSE of the predictor, while still delivering solutions in a reasonable time frame. Furthermore, a second study was conducted to gauge the effects of the multi-resolution encoding on the precision of the network, providing interesting topics for further research on the effects of the memory blocks and convolution kernel sizes for PDE RCNN solvers.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @InProceedings{cho:1996:mNNeGP, author = "Sung-Bae Cho and Katsunori Shimohara", title = "Modular Neural Networks Evolved by Genetic Programming", booktitle = "Proceedings of the 1996 {IEEE} International Conference on Evolutionary Computation", year = "1996", volume = "1", pages = "681--684", address = "Nagoya, Japan", month = "20-22 " # may, organisation = "IEEE Neural Network Council", keywords = "genetic algorithms, genetic programming, Khepera, artificial life, artificial neural network, behavior based robots, control system design, evolutionary mechanism, evolvable model, genetic programming, handwritten digits, modular neural networks, network architectures, randomly connected networks, visual categorization task, intelligent control, neural net architecture, neurocontrollers, systems analysis", ISBN = "0-7803-2902-3", DOI = "doi:10.1109/ICEC.1996.542683", size = "4 pages", abstract = "In this paper we present an evolvable model of modular neural networks which are rich in autonomy and creativity. In order to build an artificial neural network which is rich in autonomy and creativity, we have adopted the ideas and methodologies of Artificial Life. This paper describes the concepts and methodologies for the evolvable model of modular neural networks, which will be able not only to develop new functionality spontaneously but also to grow and evolve its own structure autonomously. Although the ultimate goal of this model is to design the control system for such behaviour-based robots as Khepera, we have attempted to apply the mechanism to a visual categorisation task with handwritten digits. The evolutionary mechanism has shown a strong possibility to generate useful network architectures from an initial set of randomly-connected networks.", notes = "ICEC-96 Evolves ANN network for recognising human written characters", } @Article{cho:1998:mNNeGP, author = "Sung-Bae Cho and Katsunori Shimohara", title = "Evolutionary Learning of Modular Neural Networks with Genetic Programming", journal = "Applied Intelligence", year = "1998", volume = "9", number = "3", pages = "191--200", month = nov # "/" # dec, keywords = "genetic algorithms, genetic programming, neural networks, evolutionary computation, modules, emergence, handwritten digits, OCR", ISSN = "0924-669X", DOI = "doi:10.1023/A:1008388118869", size = "10 pages", abstract = "Evolutionary design of neural networks has shown a great potential as a powerful optimisation tool. However, most evolutionary neural networks have not taken advantage of the fact that they can evolve from modules. This paper presents a hybrid method of modular neural networks and genetic programming as a promising model for evolutionary learning. This paper describes the concepts and methodologies for the evolvable model of modular neural networks, which might not only develop new functionality spontaneously, but also grow and evolve its own structure autonomously. We show the potential of the method by applying an evolved modular network to a visual categorisation task with handwritten digits. Sophisticated network architectures as well as functional subsystems emerge from an initial set of randomly-connected networks. Moreover, the evolved neural network has reproduced some of the characteristics of natural visual system, such as the organisation of coarse and fine processing of stimuli in separate pathways.", notes = "Evolves ANN network for categorizing human written characters. USA Federal post office dataset online? ", } @Proceedings{aspgp03, title = "Proceedings of The First Asian-Pacific Workshop on Genetic Programming", year = "2003", editor = "Sung-Bae Cho and Nguyen Xuan Hoai and Yin Shan", address = "Rydges (lakeside) Hotel, Canberra, Australia", month = "8 " # dec, organisation = "School of Information Technology and Electrical Engineering, Australian Defence Force Academy, University College, University of New South Wales, Australia", keywords = "genetic algorithms, genetic programming", ISBN = "0-9751724-0-9", URL = "http://sc.snu.ac.kr/~aspgp/aspgp03/aspgp03.html", size = "62 pages", } @InProceedings{D.Y.Cho:1998:GPmacstt, author = "Dong-Yeon Cho and Byoung-Tak Zhang", title = "Genetic programming of multi-agent cooperation strategies for table transport", booktitle = "The Third Asian Fuzzy Systems Symposium", year = "1998", editor = "K. C. Min", pages = "170--175", address = "Kyungnam University, Masan, Korea", month = "18-21 " # jun, organisation = "Korea Fuzzy Logic and Intelligent Systems Society (KFIS)", keywords = "genetic algorithms, genetic programming, multiagent learning, artificial life, alfife, fitness switching", URL = "http://bi.snu.ac.kr/Publications/Conferences/International/AFSS98_ChoDY.pdf", size = "6 pages", abstract = "transporting a large table using multiple...", notes = "AFSS'98", } @InProceedings{cho:1999:GPalecri, author = "D. Y. Cho and B. T. Zhang", title = "Genetic programming-based Alife techniques for evolving collective robotic intelligence", booktitle = "Proceedings 4th International Symposium on Artificial Life and Robotics", year = "1999", editor = "M. Sugisaka", pages = "236--239", address = "B-Con Plaza, Beppu, Oita, Japan", month = "19-22 " # jan, keywords = "genetic algorithms, genetic programming, artificial life, multiagent learning, fitness switching, training data selection", URL = "http://bi.snu.ac.kr/Publications/Conferences/International/AROB99.ps", URL = "http://citeseer.ist.psu.edu/455064.html", abstract = "Control strategies for a multiple robot system should be adaptive and decentralized like those of social insects. To evolve this kind of control programs, we use genetic programming (GP). However, conventional GP methods are difficult to evolve complex coordinated behaviors and not powerful enough to solve the class of problems which require some emergent behaviors to be achieved in sequence. In a previous work, we presented a novel method called fitness switching. Here we extend the fitness switching method by introducing the concept of active data selection to further accelerate evolution speed of GP. Experimental results are reported on a table transport problem in which multiple autonomous mobile robots should cooperate to transport a large and heavy table.", notes = "AROB'99 Details from www site etc", } @InProceedings{cho:2000:BEAENTMTSD, author = "Dong-Yeon Cho and Byoung-Tak Zhang", title = "Bayesian Evolutionary Algorithms for Evolving Neural Tree Models of Time Series Data", booktitle = "Proceedings of the 2000 Congress on Evolutionary Computation CEC00", volume = "2", year = "2000", pages = "1451--1458", address = "La Jolla Marriott Hotel La Jolla, California, USA", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "6-9 " # jul, organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", publisher = "IEEE Press", keywords = "Bayesian evolutionary algorithms, automatic model induction, evolving neural tree models, model induction, parallelised individual based BEAs, population based BEAs, population size, time series data, time series prediction, time series prediction problems, unlimited crossover, variation operations, Bayes methods, data analysis, evolutionary computation, neural nets, time series, trees (mathematics)", ISBN = "0-7803-6375-2", DOI = "doi:10.1109/CEC.2000.870825", abstract = "Model induction plays an important role in many fields of science and engineering to analyse data. Specifically, the performance of time series prediction whose objectives are to find out the dynamics of the underlying process in given data is greatly affected by the model. Bayesian evolutionary algorithms have been proposed as a method for automatic model induction from data. We apply Bayesian evolutionary algorithms (BEAs) to evolving neural tree models of time series data. The performances of various BEAs are compared on two time series prediction problems by varying the population size and the type of variation operations. Our experimental results support that population based BEAs with unlimited crossover find good models more efficiently than single individual BEAs, parallelised individual based BEAs, and population based BEAs with limited crossover.", notes = "CEC-2000 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 00TH8512, Library of Congress Number = 00-018644", } @Article{Cho:2006:B, author = "Dong-Yeon Cho and Kwang-Hyun Cho and Byoung-Tak Zhang", title = "Identification of biochemical networks by S-tree based genetic programming", journal = "Bioinformatics", year = "2006", volume = "22", number = "13", pages = "1631--1640", month = jul, keywords = "genetic algorithms, genetic programming", ISSN = "1367-4803", DOI = "doi:10.1093/bioinformatics/btl122", abstract = "Motivation: Most previous approaches to model biochemical networks have focused either on the characterisation of a network structure with a number of components or on the estimation of kinetic parameters of a network with a relatively small number of components. For system-level understanding, however, we should examine both the interactions among the components and the dynamic behaviours of the components. A key obstacle to this simultaneous identification of the structure and parameters is the lack of data compared with the relatively large number of parameters to be estimated. Hence, there are many plausible networks for the given data, but most of them are not likely to exist in the real system. Results: We propose a new representation named S-trees for both the structural and dynamical modelling of a biochemical network within a unified scheme. We further present S-tree based genetic programming to identify the structure of a biochemical network and to estimate the corresponding parameter values at the same time. While other evolutionary algorithms require additional techniques for sparse structure identification, our approach can automatically assemble the sparse primitives of a biochemical network in an efficient way. We evaluate our algorithm on the dynamic profiles of an artificial genetic network. In 20 trials for four settings, we obtain the true structure and their relative squared errors are less than 5percent regardless of releasing constraints about structural sparseness. In addition, we confirm that the proposed algorithm is robust within 10percent noise ratio. Furthermore, the proposed approach ensures a reasonable estimate of a real yeast fermentation pathway. The comparatively less important connections with non-zero parameters can be detected even though their orders are below 10**2 (??). To demonstrate the usefulness of the proposed algorithm for real experimental biological data, we provide an additional example on the transcriptional network of SOS response to DNA damage in Escherichia coli. We confirm that the proposed algorithm can successfully identify the true structure except only one relation. Availability: The executable program and data are available from the authors upon request.", notes = "C The Author 2006", } @Article{Cho:2025:engappai, author = "Young-in Cho and Seung-heon Oh and Jae-ho Choi and Jong Hun Woo", title = "Solving quay wall allocation problems based on deep reinforcement learning", journal = "Engineering Applications of Artificial Intelligence", year = "2025", volume = "150", pages = "110598", keywords = "genetic algorithms, genetic programming, Deep reinforcement learning, Heterogeneous graph, Flexible job-shop scheduling problem, Post-stage outfitting process, Shipbuilding", ISSN = "0952-1976", URL = "https://www.sciencedirect.com/science/article/pii/S0952197625005986", DOI = "doi:10.1016/j.engappai.2025.110598", abstract = "Quay walls and graving docks are critical production resources in shipyards. Traditionally, quay walls have not been a bottleneck resource for constructing conventional vessels, such as oil carriers and container ships. However, the growing demand for high value-added vessels requiring more complex post-stage outfitting operations has increased workloads at quay walls. Accordingly, the importance of efficient quay wall allocation has grown significantly to improve overall production efficiency and ensure timely vessel delivery. In this study, the quay wall allocation problem is modelled as a flexible job shop scheduling problem, incorporating machine preferences and preemption conditions. Notably, the uncertainty in vessel launching dates caused by delays in the erection process at graving docks is considered in the scheduling problems. To address the dynamic quay wall allocation problems, this study develops a dynamic quay wall allocation algorithm based on deep reinforcement learning, which adaptively allocates vessels to quay walls based on the working status of quay walls and the progress of outfitting operations. For this purpose, a novel Markov decision process is proposed, where a compound state representation composed of heterogeneous graphs and auxiliary matrices is devised to capture the complex relationships between quay walls and outfitting operations. In addition, an extended scheduling action space incorporating operation interruptions is defined, which can effectively use preemption conditions to enhance the scheduling performance. The performance of the proposed algorithm is evaluated through extensive numerical experiments based on test instances generated from real-world shipyard data under various environmental conditions. Experimental results demonstrate that the proposed algorithm consistently outperforms traditional rule-based heuristics and exhibits superior scalability compared to genetic programming, making it a promising solution for large-scale quay wall allocation problems", } @InProceedings{Choenni:1999:SGB, author = "Sunil Choenni", title = "On the Suitability of Genetic-Based Algorithms for Data Mining", booktitle = "Advances in Database Technologies", editor = "Yahiko Kambayashi and Dik Lun Lee and Ee-Peng Lim and Mukesh Kumar Mohania and Yoshifumi Masunaga", series = "LNCS", volume = "1552", pages = "55--67", year = "1999", CODEN = "LNCSD9", ISSN = "0302-9743", bibdate = "Tue Sep 14 06:09:05 MDT 1999", acknowledgement = ack-nhfb, keywords = "genetic algorithms, genetic programming, ADT, conceptual modelling, database technologies, mobile data access, spatio-temporal data management", address = "Singapore", month = "19-20 " # nov # " 1998", publisher = "Springer-Verlag", email = "choenni@nrl.nl", ISBN = "3-540-65690-1", DOI = "doi:10.1007/978-3-540-49121-7_5", abstract = "Data mining has as goal to extract knowledge from large databases. A database may be considered as a search space consisting of an enormous number of elements, and a mining algorithm as a search strategy. In general, an exhaustive search of the space is infeasible. Therefore, efficient search strategies are of vital importance. Search strategies on genetic-based algorithms have been applied successfully in a wide range of applications. We focus on the suitability of genetic-based algorithms for data mining. We discuss the design and implementation of a genetic-based algorithm for data mining and illustrate its potentials.", notes = "DWDM98 ER'98 Workshop on Data Warehousing and Data Mining, Mobile Data Access, and Collaborative Work Support and Spatio-Temporal Data Management Also available as Dutch military {"}National Aerospace Laboratory{"} NLR tech report. \cite{choenni:1998:SGADM} NLR Technical Publications 98484-tp.pdf NLR-TP-98484 Also {"}University of Twente{"}. Fixed length representation, one locus per database attribute. Attributes either 1) not used 2) actual value (categorical data) or 3) range, eg [3,34]. All attributes anded together to give query. Mutation and crossover a little bit smart. Microsoft Access interface. Interactive. User specifies initial topic to be mind and can interactively update this. http://wwwhome.cs.utwente.nl/~choenni/ http://www.nlr.nl/public/library/index.html#diagram", } @TechReport{choenni:1998:SGADM, author = "Sunil Choenni", title = "On the Suitability of Genetic-Based Algorithms for Data Mining", institution = "National Aerospace Laboratory", year = "1998", number = "NLR-TP-98484", address = "Amsterdam", month = nov, keywords = "genetic algorithms, genetic programming", URL = "http://www.nlr.nl/NLR-TP-98484.pdf", URL = "http://citeseer.ist.psu.edu/271039.html", abstract = "Data mining has as goal to extract knowledge from large databases. To extract this knowledge, a database may be considered as a large search space, and a mining algorithm as a search strategy. In general, a search space consists of an enormous number of elements, making an exhaustive search infeasible. Therefore, efficient search strategies are of vital importance. Search strategies based on genetic-based algorithms have been applied successfully in a wide range of applications. In this paper, we discuss the suitability of genetic-based algorithms for data mining. We discuss the design and implementation of a genetic-based algorithm for data mining and illustrate its potentials.", notes = "shorter version published as \cite{Choenni:1999:SGB} page 22 'real-life database, FAA incident database, contains aircraft incident data 1978-95'", size = "26 pages", } @TechReport{choenni:1999:ieGDMa, author = "Sunil Choenni", title = "Implementation and Evaluation of a Genetic-Based Data Mining Algorithm", institution = "National Aerospace Laboratory", year = "1999", number = "NLR-TR-99281", address = "Amsterdam", month = jul, keywords = "genetic algorithms, genetic programming", size = "13 pages", abstract = "GA can be rapidly implemented for DM yielding reasonable results. However, building an operational tool requires more effort", notes = "Jan 2000 not (yet) published. SQL queries generated. Implemented in Visual Basic. Individuals are conjenctions of predicates over database attributes implemented as binary tables. p8 DM specific limits on mutation of table rows. data mining of FAA Aircraft incident records (cleaned up, normalised) broken Sep 2018 http://www.asy.faa.gov/asp/asy_fids.asp p9 User must specify mining question, beta fraction coressponding to maximum fitness p10 individual must contain at least two elementary expressions ad-hoc rule no expression to cover more than 10percent of a domian. profiles of risky flights.", } @InProceedings{choi:1996:LANGA, author = "Andy Choi", title = "Optimizing Local Area Networks Using Genetic Algorithms", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "Genetic Algorithms", pages = "467--472", address = "Stanford University, CA, USA", publisher = "MIT Press", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap77.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96 GA paper", } @InCollection{choi:1995:OLANUGA, author = "Andy Choi", title = "Optimizing Local Area Networks Using Genetic Algorithms", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1995", year = "1995", editor = "John R. Koza", pages = "49--58", address = "Stanford, California, 94305-3079 USA", month = "11 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-195720-5", notes = "part of \cite{koza:1995:gagp}", } @Article{Choi:2015:EE, author = "Byungwoong Choi and Sung-Uk Choi", title = "Physical habitat simulations of the Dal River in Korea using the {GEP} Model", journal = "Ecological Engineering", volume = "83", pages = "456--465", year = "2015", ISSN = "0925-8574", DOI = "doi:10.1016/j.ecoleng.2015.06.042", URL = "http://www.sciencedirect.com/science/article/pii/S0925857415301038", abstract = "The GEP model, a recently-developed robust artificial intelligence technique, captures the benefits of both genetic algorithm and genetic programming by using chromosomes and expression trees. This paper presents a physical habitat simulation using the GEP model. The study area is a 2.5 km long reach of a stream, located downstream from a dam in the Dal River in Korea. Field monitoring revealed that Zacco platypus is the dominant species in the study area. The CCHE2D model and the GEP model were used for hydraulic and habitat simulations, respectively. Since the GEP model belongs to the data-driven approach, the model directly predicts the composite suitability index using the monitoring data. The GEP model is capable of considering correlations between all physical habitat variables, which is a clear advantage over knowledge-based models, such as the habitat suitability index model. The model was first validated using measured data. Distributions of the composite suitability index were then predicted using the GEP model for various flows. The predicted results were compared with those obtained using the habitat suitability index model. A sensitivity study of the GEP model was also carried out. Finally, the GEP model was used to construct habitat suitability curves for each physical habitat variable. The resulting habitat suitability curves were found to be very similar to those constructed by the method of Gosse (1982). The findings indicate that the conventional multiplicative aggregation method consistently underestimates the composite suitability index. Thus, the geometric mean method is proposed for use with calibrated coefficients.", keywords = "genetic algorithms, genetic programming, Physical habitat simulation, GEP model, Habitat suitability index, Composite suitability index, The Dal River", } @Article{Choi:2021:AbdomRadiol, author = "Ji Whae Choi and Rong Hu and Yijun Zhao and Subhanik Purkayastha and Jing Wu and Aidan J. McGirr and S. William Stavropoulos and Alvin C. Silva and Michael C. Soulen and Matthew B. Palmer and Paul J. L. Zhang and Chengzhang Zhu and Sun Ho Ahn and Harrison X. Bai", title = "Preoperative prediction of the stage, size, grade, and necrosis score in clear cell renal cell carcinoma using {MRI}-based radiomics", journal = "Abdominal Radiology", year = "2021", volume = "46", number = "6", pages = "2656--2664", month = jun, note = "Special Section on Ovarian Cancer", keywords = "genetic algorithms, genetic programming, TPOT, Renal cancer, Neoplasm progression, Imaging analysis, Medical imaging, Kidneys, Ureters, Bladder, Retroperitoneum", ISSN = "2366-004X", DOI = "doi:10.1007/s00261-020-02876-x", abstract = "Purpose Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma. Currently, there is a lack of noninvasive methods to stratify ccRCC prognosis prior to any invasive therapies. The purpose of this study was to preoperatively predict the tumor stage, size, grade, and necrosis (SSIGN) score of ccRCC using MRI-based radiomics. Methods A multicenter cohort of 364 histopathologically confirmed ccRCC patients (272 low [< 4] and 92 high [ge 4] SSIGN score) with preoperative T2-weighted and T1-contrast-enhanced MRI were retrospectively identified and divided into training (254 patients) and testing sets (110 patients). The performance of a manually optimized radiomics model was assessed by measuring accuracy, sensitivity, specificity, area under receiver operating characteristic curve (AUROC), and area under precision-recall curve (AUPRC) on an independent test set, which was not included in model training. Lastly, its performance was compared to that of a machine learning pipeline, Tree-Based Pipeline Optimization Tool (TPOT). Results The manually optimized radiomics model using Random Forest classification and Analysis of Variance feature selection methods achieved an AUROC of 0.89, AUPRC of 0.81, accuracy of 0.89 (95percent CI 0.816-0.937), specificity of 0.95 (95percent CI 0.875-0.984), and sensitivity of 0.72 (95percent CI 0.537-0.852) on the test set. The TPOT using Extra Trees Classifier achieved an AUROC of 0.94, AUPRC of 0.83, accuracy of 0.89 (95percent CI 0.816-0.937), specificity of 0.95 (95percent CI 0.875-0.984), and sensitivity of 0.72 (95percent CI 0.537-0.852) on the test set. Conclusion Preoperative MR radiomics can accurately predict SSIGN score of ccRCC, suggesting its promise as a prognostic tool that can be used in conjunction with diagnostic markers. ", notes = "Warren Alpert Medical School, Brown University, Providence, RI, 02903, USA PMID: 33386910", } @InProceedings{Choi:2018:SSBSE, author = "Kabdo Choi and Jeongju Sohn and Shin Yoo", title = "Learning Fault Localisation for Both Humans and Machines using Multi-Objective GP", booktitle = "SSBSE 2018", year = "2018", editor = "Thelma Elita Colanzi and Phil McMinn", volume = "11036", series = "LNCS", pages = "349--355", address = "Montpellier, France", month = "8-9 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, SBSE, MOGP, Fault localisation, FLUCCS, Multi-objective evolutionary algorithm, NSGA-II", isbn13 = "978-3-319-99241-9", URL = "https://coinse.kaist.ac.kr/publications/pdfs/Choi2018aa.pdf", DOI = "doi:10.1007/978-3-319-99241-9_20", size = "6 pages", abstract = "Genetic Programming has been successfully applied to fault localisation to learn ranking models that place the faulty program element as near the top as possible. However, it is also known that, when localisation results are used by Automatic Program Repair (APR) techniques, higher rankings of faulty program elements do not necessarily result in better repair effectiveness. Since APR techniques tend to use localisation scores as weights for program mutation, lower scores for non-faulty program elements are as important as high scores for faulty program elements. We formulate a multi-objective version of GP based fault localisation to learn ranking models that not only aim to place the faulty program element higher in the ranking, but also aim to assign as low scores as possible to non-faulty program elements. The results show minor improvements in the suspiciousness score distribution. However, surprisingly, the multi-objective formulation also results in more accurate fault localisation ranking-wise, placing 155 out of 386 faulty methods at the top, compared to 135 placed at the top by the single objective formulation.", } @InCollection{choi:2003:SEGADOLSJUPGMEM, author = "Seongim Choi", title = "Speedups for Efficient Genetic Algorithms: Design Optimization of Low-Boom Supersonic Jet Using Parallel GA and Micro-GA with External Memory", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2003", year = "2003", editor = "John R. Koza", pages = "21--30", address = "Stanford, California, 94305-3079 USA", month = "4 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms", URL = "http://www.genetic-programming.org/sp2003/Choi.pdf", notes = "part of \cite{koza:2003:gagp}", } @InProceedings{choi:pao:gecco2004, author = "Sung-Soon Choi and Byung-Ro Moon", title = "Polynomial Approximation of Survival Probabilities Under Multi-point Crossover", booktitle = "Genetic and Evolutionary Computation -- GECCO-2004, Part I", year = "2004", editor = "Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell", series = "Lecture Notes in Computer Science", pages = "994--1005", address = "Seattle, WA, USA", publisher_address = "Heidelberg", month = "26-30 " # jun, organisation = "ISGEC", publisher = "Springer-Verlag", volume = "3102", ISBN = "3-540-22344-4", ISSN = "0302-9743", DOI = "doi:10.1007/b98643", size = "12", keywords = "genetic algorithms, genetic programming", notes = "GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)", } @Article{CHOI:2017:SEC, author = "Tae Jong Choi and Chang Wook Ahn", title = "Artificial life based on boids model and evolutionary chaotic neural networks for creating artworks", journal = "Swarm and Evolutionary Computation", year = "2017", keywords = "genetic algorithms, genetic programming, Artificial life, Boids model, Chaotic neural networks, ANN, Differential evolution, Glitch art", ISSN = "2210-6502", DOI = "doi:10.1016/j.swevo.2017.09.003", URL = "http://www.sciencedirect.com/science/article/pii/S2210650217301700", abstract = "In this paper, we propose a multi-agent based art production framework. In existing artwork creation systems, images were generated using artificial life and evolutionary computation approaches. In artificial life, swarm intelligence or Boids model, and in evolutionary computation, genetic algorithm or genetic programming are commonly used to create images. These automated artwork creation systems make it easy to create artistic images even if the users are not professional artists. Despite the high possibility of these creation systems, however, much research has not been done so far. In this paper, we propose an art production framework that generates images using multi-agents with chaotic dynamics features. Agents act on the canvas following the three rules of Boids model. In addition, each agent possesses a chaotic neural network which trained by differential evolution algorithm, so that colors can be evolved to represent a better style. As a result, we propose an art production framework for generating processing artworks that contain highly complex dynamics. Finally, we created the glitch artworks using the proposed framework, which shows a new glitch style", } @InProceedings{Choi:2010:ICIP, author = "Wook-Jin Choi and Tae-Sun Choi", title = "Computer-aided detection of pulmonary nodules using genetic programming", booktitle = "17th IEEE International Conference on Image Processing (ICIP 2010 )", year = "2010", month = "26-29 " # sep, pages = "4353--4356", abstract = "This paper describes a novel nodule detection method that enhances false positive reduction. Lung region is extracted from CT image sequence using adaptive thresholding and 18-connectedness voxel labelling. In the extracted lung region, nodule candidates are detected using adaptive multiple thresholding and rule based classifier. After that, we extract the 3D and 2D features from nodule candidates. The nodule candidates are then classified using genetic programming (GP) based classifier. In this work, a new fitness function is proposed to generate optimal adaptive classifier. We tested the proposed algorithm by using Lung Imaging Database Consortium (LIDC) database of National Cancer Institute (NCI). The classifier was trained and evaluated using two independent dataset and whole dataset. The proposed method reduced the false positives in nodule candidates and achieved 92percent detection rate with 6.5 false positives per scan.", keywords = "genetic algorithms, genetic programming, CT image sequence, adaptive thresholding, computer-aided detection, false positive reduction, feature extraction, fitness function, lung imaging database consortium, lung region, nodule detection, pulmonary nodules, rule based classifier, voxel labelling, computerised tomography, feature extraction, image classification, image segmentation, image sequences, lung, medical image processing", DOI = "doi:10.1109/ICIP.2010.5652369", ISSN = "1522-4880", notes = "Sch. of Inf. & Mechatron., Gwangju Inst. of Sci. & Technol. (GIST), Gwangju, South Korea. Also known as \cite{5652369}", } @Article{Choi201257, author = "Wook-Jin Choi and Tae-Sun Choi", title = "Genetic programming-based feature transform and classification for the automatic detection of pulmonary nodules on computed tomography images", journal = "Information Sciences", volume = "212", pages = "57--78", year = "2012", month = "1 " # dec, ISSN = "0020-0255", DOI = "doi:10.1016/j.ins.2012.05.008", URL = "http://www.sciencedirect.com/science/article/pii/S0020025512003362", keywords = "genetic algorithms, genetic programming, CT, Pulmonary nodule detection, CAD", abstract = "An effective automated pulmonary nodule detection system can assist radiologists in detecting lung abnormalities at an early stage. In this paper, we propose a novel pulmonary nodule detection system based on a genetic programming (GP)-based classifier. The proposed system consists of three steps. In the first step, the lung volume is segmented using thresholding and 3D-connected component labelling. In the second step, optimal multiple thresholding and rule-based pruning are applied to detect and segment nodule candidates. In this step, a set of features is extracted from the detected nodule candidates, and essential 3D and 2D features are subsequently selected. In the final step, a GP-based classifier (GPC) is trained and used to classify nodules and non-nodules. GP is suitable for detecting nodules because it is a flexible and powerful technique; as such, the GPC can optimally combine the selected features, mathematical functions, and random constants. Performance of the proposed system is then evaluated using the Lung Image Database Consortium (LIDC) database. As a result, it was found that the proposed method could significantly reduce the number of false positives in the nodule candidates, ultimately achieving a 94.1percent sensitivity at 5.45 false positives per scan.", } @InProceedings{Chong:2013:SMC, author = "Chee Seng Chong and Tianyou Zhang and Kee Khoon Lee and Gih Guang Hung and Bu-Sung Lee", title = "Collaborative Analytics with Genetic Programming for Workflow Recommendation", booktitle = "IEEE International Conference on Systems, Man, and Cybernetics (SMC 2013)", year = "2013", month = oct, pages = "657--662", keywords = "genetic algorithms, genetic programming, Work flow recommendation, collaborative analytics", DOI = "doi:10.1109/SMC.2013.117", size = "6 pages", abstract = "Formulation of appropriate data analytics workflows requires intricate knowledge and rich experiences of data analytics experts. This problem is further compounded by continuous advancement and improvement in analytical algorithms. In this paper, a generic non-domain specific solution for the creation of appropriate work-flows targeted at supervised learning problems is proposed. Our adaptive work flow recommendation engine based on collaborative analytics matches analytics needs with relevant work flows in repository. It is capable of picking workflows with better performance as compared to randomly selected work-flows. The recommendation engine is now augmented by a work-flow optimiser that applies genetic programming to further improve the recommended workflows through iterative evolution, leading to better alternative workflows. This unique Collaborative Analytics Recommender System is tested on seven UCI benchmark datasets. It is shown that the final workflows produced by the system could closely approximate, in terms of accuracy, the best workflows that analytics experts could possibly design.", notes = "Terence Hung = Gih Guang Hung. Bu-Sung Lee = Francis Lee. Also known as \cite{6721870}", } @InProceedings{Chong:2022:AP-S, author = "Edmond Chong and Scott Clemens and Magdy F. Iskander and Zhengqing Yun and Joseph J. Brown and Matthew Nakamura", booktitle = "2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI)", title = "Using Genetic Programming to Achieve High Broadband Absorptivity Metamaterial in Compact Radar Band (1-11 GHz) without Lossy Materials", year = "2022", pages = "1364--1365", abstract = "In the modern age of metamaterial absorbers (MA), many implementations are generally in the C and X microwave bands. Genetic programming (GP) software is used to generate new 3D designs for metamaterial absorbers with multilayer dielectrics to achieve high broadband absorptivity in the compact radar band (CRB) (1-11 GHz) without lossy materials. GP was previously used to create an artificial magnetic conductor (AMC) with broadband capabilities in 225-450 MHz without magnetic or absorbing materials. Using the capabilities of GP to create broadband structures, the GP software is modified to create broadband MAs. Two 2D patterned structures with multilayer dielectrics were generated with above 9percent absorptivity peaks around 3 and 5 GHz. The preliminary use of GP to create broadband MA structures shows excellent potential for creating a structure that broadens the whole CRB.", keywords = "genetic algorithms, genetic programming, Three-dimensional displays, Magnetic multilayers, Radar, Nonhomogeneous media, Software, Metamaterials", DOI = "doi:10.1109/AP-S/USNC-URSI47032.2022.9886220", ISSN = "1947-1491", month = jul, notes = "Also known as \cite{9886220}", } @InProceedings{Chong:2023:USNC-URSI, author = "Edmond Chong and Sunny Zhang and Magdy F. Iskander and Zhengqing Yun", booktitle = "2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (USNC-URSI)", title = "Hybrid Genetic Programming-Based Comparative Design of Broadband Metamaterial Absorbers Using Graphene, Resistive Sheets, and Carbon Fiber", year = "2023", pages = "1249--1250", abstract = "Hybrid genetic programming (HGP) is proposed to create new design topologies in the lower gigahertz frequency with new materials such as graphene, resistive sheet, and carbon fiber. HGP can create new topologies optimised per input parameters, such as low frequency and high broadband absorptivity. These designs are built and simulated in Ansys High-Frequency Simulation Software (HFSS) and evaluated by HGP. Graphene, resistive sheet, and carbon fiber patterning are explored and implemented with HGP to create low-gigahertz frequency and high-absorptivity MMAs. The graphene, resistive sheet, and carbon fiber-based patterned designs achieved 80percent bandwidth above 80percent absorptivity from 4.6 to 11 GHz, up to 15 GHz, from 3.83 to 9.13 GHz, and from 3.77 to 10.28 GHz, respectively.", keywords = "genetic algorithms, genetic programming, Conferences, Graphene, Bandwidth, Software, Metamaterials, Broadband communication", DOI = "doi:10.1109/USNC-URSI52151.2023.10237681", ISSN = "1947-1491", month = jul, notes = "Also known as \cite{10237681}", } @MastersThesis{p.chong:mastersthesis, author = "Fuey Sian Chong", title = "A Java based Distributed Approach to Genetic Programming on the Internet", school = "Computer Science, University of Birmingham", year = "1998", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/p.chong/p.chong.msc.25-sep-98.ps.gz", size = "103 pages", abstract = "This paper presents a distributed approach to parallelise Genetic Programming on the Internet. The motivation for the approach is to harness the wealth of computing resources available on the Internet to provide the computing power required for solving difficult problems. A distributed genetic programming system termed DGP is developed in the Java programming language to demonstrate the feasibility of distributing genetic programming on the Internet. Unique features of the DGP system include the use of Java Servlets to handle the communication between DGP clients, the use of a population pool to neutralise differences in speeds of hosts, the interactive user interface and graphical displays of the evolution process. The DGP system has been implemented over the Internet and the results are favourable. Experiments were conducted to determine the performance of the DGP system. Results showed that the DGP system has a much higher probability of finding solutions as compared to the distributed approaches taken in our previous studies and the single population Genetic Programming.", notes = "Code available at http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/gp-code/DGP Phyllis Chong Awarded a distinction in MSc in Advanced Computer Science", } @TechReport{chong:1999:jDGPiTR, author = "Fuey Sian Chong", title = "Java based Distributed Genetic Programming on the Internet", institution = "University of Birmingham, School of Computer Science", year = "1999", number = "CSRP-99-7", month = apr, keywords = "genetic algorithms, genetic programming, DGP", URL = "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1999/CSRP-99-07.ps.gz", abstract = "We proposed a distributed approach for parallelising Genetic Programming on the Internet. The approach harnesses the wealth of computing resources available on the Internet to provide the computing power required by Genetic Programming to solve hard problems. A distributed genetic programming system termed DGP is developed in the Java progamming language to demonstrate the feasibility of our approach. Features of the DGP system include the use of Java Servlets to handle communication between distributed machines and the use of a population pool to facilitate migrations. In addition, the DGP system has an interactive user interface for controlling the run and graphical displays of the evolution process. The DGP system has been implemented live over the Internet and the results prove that the approach is feasible. An experiment was conducted to determine the performance of the DGP system and results showed that the DGP system has a much higher probability of finding solutions than the distributed approaches taken in our previous work and the conventional single population Genetic Programming approach.", notes = "long version of \cite{chong:1999:jDGPi} Phyllis Chong", size = "8 pages", } @InProceedings{chong:1999:jDGPi, author = "Fuey Sian Chong and W. B. Langdon", title = "Java based Distributed Genetic Programming on the Internet", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1229", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", note = "Full text in technical report CSRP-99-7", keywords = "genetic algorithms, genetic programming, DGP, Distributed Computing, Java Applet / Application, World Wide Computing, Internet, Servlets, poster", ISBN = "1-55860-611-4", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/p.chong/DGPposter.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/p.chong/DGPposter.ps.gz", code_url = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/gp-code/DGP/DGPsrc.tar.gz", size = "1 page", abstract = "A distributed approach for parallelising Genetic Programming (GP) on the Internet is proposed and its feasibility demonstrated with a distributed GP system termed DGP developed in Java. DGP is run successfully across the world over the Internet on heterogeneous platforms without any central co-ordination. The run results and the outcome of an experiment to determine DGP's performance are reported together with a description of DGP.", notes = "GECCO-99, part of \cite{banzhaf:1999:gecco99}, A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99) see also \cite{chong:1999:jDGPis} Phyllis Chong Note (2021) most URLs no longer working Code available at http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/gp-code/DGP/DGPsrc.tar.gz", } @InProceedings{chong:1999:parGA, author = "Fuey Sian Chong", title = "Java based Distributed Genetic Programming on the Internet", booktitle = "Evolutionary computation and parallel processing", year = "1999", editor = "Erick Cantu-Paz and Bill Punch", pages = "163--166", address = "Orlando, Florida, USA", month = "13 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/p.chong/GeccoWkShop.ps.gz", size = "4 pages", notes = "GECCO'99 WKSHOP Phyllis Chong", } @Misc{chong:1999:jDGPis, author = "Fuey Sian Chong and W. B. Langdon", title = "Java based Distributed Genetic Programming on the Internet", booktitle = "GECCO-99 Student Workshop", year = "1999", editor = "Una-May O'Reilly", pages = "345", address = "Orlando, Florida, USA", month = "13 " # jul, keywords = "genetic algorithms, genetic programming, distributed, evolutionary programming, Internet, java, parallel", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/p.chong/DGPposter.ps.gz", abstract = "GECCO'99 graduate WKSHOP Phyllis Chong", } @InCollection{chong:2002:GAACG, author = "Sanders Chong", title = "Genetic Algorithms Applied to Computational Genomics", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2002", year = "2002", editor = "John R. Koza", pages = "58--64", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms", URL = "http://www.genetic-programming.org/sp2002/Chong.pdf", notes = "part of \cite{koza:2002:gagp}", } @InProceedings{Chong-cun:2018:ICCCAS, author = "Chong-cun Li and Li-zhi Xu and Xue-jun Song and Zhen-xing Guo and Xiong Liu", booktitle = "2018 10th International Conference on Communications, Circuits and Systems (ICCCAS)", title = "Hardware Evolution Platform Reasearch Based on Matrix Coding {CGP}", year = "2018", pages = "466--470", abstract = "Evolvable hardware is a combination of evolutionary algorithms and reconfigurable hardware, which can change itself structure to adapt to the living environment. Evolvable hardware possessed the characteristics of self-organization, self-adaptation and self-repair. The off-line evolution of digital circuits is similar to a simulation process, which lacks real-time capability and cannot generate actual circuits for every evolutionary digital circuit. The hardware online evolution platform is designed for evolution digital circuit based on Field Programmer Gate Array. Compared with off-line evolution, the platform can monitor the status of the designed circuit in real time, and it is easily to evolve a digital circuit for practical products directly. The multiplier circuit has obtained using the on online evolution platform combining based on matrix coded Cartesian Genetic Programming.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICCCAS.2018.8768964", month = dec, notes = "Also known as \cite{8768964}", } @Article{oai:CiteSeerPSU:421006, title = "Using Perturbation To Improve Robustness Of Solutions Generated By Genetic Programming For Robot Learning", author = "Prabhas Chongstitvatana", journal = "Journal of Circuits, Systems and Computers", year = "1999", volume = "9", number = "1-2", pages = "133--143", publisher = "World Scientific Publishing Company", keywords = "genetic algorithms, genetic programming", URL = "http://www.worldscinet.com/123/09/0901n02/S0218126699000128.html", DOI = "doi:10.1142/S0218126699000128", citeseer-isreferencedby = "oai:CiteSeerPSU:397249; oai:CiteSeerPSU:59033", citeseer-references = "oai:CiteSeerPSU:212034; oai:CiteSeerPSU:51923; oai:CiteSeerPSU:70404; oai:CiteSeerPSU:23925; oai:CiteSeerPSU:61708; oai:CiteSeerPSU:14506; oai:CiteSeerPSU:160348; oai:CiteSeerPSU:115106", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:421006", rights = "unrestricted", URL = "http://citeseer.ist.psu.edu/421006.html", abstract = "This paper proposes a method to improve robustness of the robot programs generated by genetic programming. The main idea is to inject perturbation into the simulation during the evolution of the solutions. The resulting robot programs are more robust because they have evolved to tolerate the changes in their environment. We set out to test this idea using the problem of navigating a mobile robot from a starting point to a target in an unknown cluttered environment. The result of the experiments shows the effectiveness of this scheme. The analysis of the result shows that the robustness depends on the {"}experience{"} that a robot program acquired during evolution. To improve robustness, the size of the set of {"}experience{"} should be increased and/or the amount of reusing the {"}experience{"} should be increased.", notes = "discrete 2D 500x750 simulation, smellLeft,smellRight", } @InCollection{choo:2000:EDLBC, author = "Shou-yen Choo", title = "Emergence of a Division of Labor in a Bee Colony", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "98--107", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:2000:gagp}", } @Article{BC-telepar:97, author = "B. Chopard and Y. Baggi and P. Luthi and J. F. Wagen", title = "Wave Propagation and Optimal Antenna Layout using a Genetic Algorithm", journal = "Speedup", year = "1997", volume = "11", number = "2", pages = "42--47", month = nov, note = "TelePar Conference, EPFL, 1997", notes = "SPEEDUP Journal speedup@cscs.ch ", } @Article{chopard2000, author = "Bastien Chopard and Olivier Pictet and Marco Tomassini", title = "Parallel and distributed evolutionary computation for financial applications", journal = "Parallel Algorithms and Applications", year = "2000", volume = "15", pages = "15--36", keywords = "genetic algorithms, genetic programming, Evolutionary algorithms, Parallel computing, Financial application, Trading model induction", ISSN = "1063-7192", DOI = "doi:10.1080/01495730008947348", size = "22 pages", abstract = "A survey of two parallel evolutionary computation techniques is presented: the genetic algorithms and genetic programming methods. An application of this approach to the induction of trading models is presented for financial assets, which is known as a hard problem. This study analyses the potential of this approach and the benefit of parallelisation.", notes = "On Saturday, January 01, 2005 this journal was renamed International Journal of Parallel, Emergent and Distributed Systems.", } @Misc{BONDAN-CHORISMA-120155201062, author = "Bondan Chorisma and Nerfita Nikentari and Muhamad Radzi Rathomi", title = "Implementesi Algoritma Grammatical Evolution Menggunakan Steady State Untuk Prediksi Ketinggian Gelombang Laut", howpublished = "Fakultas Teknik", year = "2017", keywords = "genetic algorithms, genetic programming, grammatical evolution, Prediksi, Probabilitas, Crossover, Mutasi, Prediction, Probability, Crossover, Mutation, Steady State, Generational Replacement", URL = "http://jurnal.umrah.ac.id/wp-content/uploads/gravity_forms/1-ec61c9cb232a03a96d0947c6478e525e/2017/08/BONDAN-CHORISMA-120155201062.pdf", size = "10 pages", abstrak = "Kepulauan Riau memiliki pulau-pulau kecil diantaranya ialah pulau Bintan. Mayoritas penduduk di Pulau Bintan bermata pencahariansebagai nelayan, selain itu kegiatan perairannya juga sebagai sarana transportasi untuk menyebrangi pulau-pulau disekitarnya termasuk juga Negara yang berbatasan langsung dengan pulau Bintanyaitu Malaysia dan Singapura. Kondisi cuaca faktor terpenting yang sangat mempengaruhi kelancaran dalam kegiatan di perairan salah satunya adalah ketinggian gelombang air laut.Ketinggian gelombang dapat di prediksi berdasarkan data masa lampau yang telah didapatkan yang nantinya dijadikan sebagai pola untuk menentukanketinggian gelombang dimasa yang akan datang. Berdasarkan hasil observasi yang dilakukan dengan memanfaatkan dua metode seleksi survivor didapatkan persentase rata-rata kesalahan metode steady statesebesar 4.243 sedangkan generational replacement sebesar 4.897percent.Dengan kombinasi pada metode steady statejumlah generasi 30, ukuran populasi 100, Probabilitas crossover (Pc) 0.8 Probabilitas mutasi (Pm) 0.2 sedangkan metode generational replacementjumlah generasi 50, ukuran populasi 100, Probabilitas crossover (Pc) 0.7 Probabilitas mutasi Pm) 0.2", abstract = "Riau islands have small islands of which is the Bintan island. The majority of people in Bintan island fishermen, addition to his activities in sea as means of transport to cross the nearby islands including the state directly neighbour to Bintan island specifically Malaysia and Singapore. The weather conditions were very important factors influencing the smooth running of activities at sea one of them is the height of sea waves. The wave height can be predicted based on the data time series that have been obtained which will serve as a pattern to determine the height of a wave of the future. Based on the results of observations made by using two methods of survivor selection obtained the average percentage error steady state method of 4.243percent while the generational replacement of 4.897percent.The combination with the methods of steady state with 30 generations, the population size of 100, crossover probability (Pc) 0.8,Probability of mutation (Pm) 0.2,whereas the method of generational replacement using 50 generations, the population size of 100, crossover probability (Pc) 0.7 The probability of mutation (Pm) 0.2.", notes = "Jurusan Teknik Informatika, Fakultas Teknik Universitas Maritim Raja Ali Haji. in Indonesian", } @Article{Chou200957, author = "I-Chun Chou and Eberhard O. Voit", title = "Recent developments in parameter estimation and structure identification of biochemical and genomic systems", journal = "Mathematical Biosciences", year = "2009", volume = "219", number = "2", pages = "57--83", month = jun, keywords = "genetic algorithms, genetic programming, Parameter estimation, Network identification, Inverse modelling, Biochemical Systems Theory", ISSN = "0025-5564", broken = "http://www.sciencedirect.com/science/article/B6VHX-4VXDV4R-2/2/f7f1904f15cf7aa7404c664ae4658ce8", DOI = "doi:10.1016/j.mbs.2009.03.002", abstract = "The organisation, regulation and dynamical responses of biological systems are in many cases too complex to allow intuitive predictions and require the support of mathematical modeling for quantitative assessments and a reliable understanding of system functioning. All steps of constructing mathematical models for biological systems are challenging, but arguably the most difficult task among them is the estimation of model parameters and the identification of the structure and regulation of the underlying biological networks. Recent advancements in modern high-throughput techniques have been allowing the generation of time series data that characterise the dynamics of genomic, proteomic, metabolic, and physiological responses and enable us, at least in principle, to tackle estimation and identification tasks using top-down or inverse approaches. While the rewards of a successful inverse estimation or identification are great, the process of extracting structural and regulatory information is technically difficult. The challenges can generally be categorised into four areas, namely, issues related to the data, the model, the mathematical structure of the system, and the optimisation and support algorithms. Many recent articles have addressed inverse problems within the modelling framework of Biochemical Systems Theory (BST). BST was chosen for these tasks because of its unique structural flexibility and the fact that the structure and regulation of a biological system are mapped essentially one-to-one onto the parameters of the describing model. The proposed methods mainly focused on various optimization algorithms, but also on support techniques, including methods for circumventing the time consuming numerical integration of systems of differential equations, smoothing overly noisy data, estimating slopes of time series, reducing the complexity of the inference task, and constraining the parameter search space. Other methods targeted issues of data preprocessing, detection and amelioration of model redundancy, and model-free or model-based structure identification. The total number of proposed methods and their applications has by now exceeded one hundred, which makes it difficult for the newcomer, as well as the expert, to gain a comprehensive overview of available algorithmic options and limitations. To facilitate the entry into the field of inverse modeling within BST and related modeling areas, the article presented here reviews the field and proposes an operational work-flow that guides the user through the estimation process, identifies possibly problematic steps, and suggests corresponding solutions based on the specific characteristics of the various available algorithms. The article concludes with a discussion of the present state of the art and with a description of open questions.", notes = "GP included in Survey", } @InProceedings{Chou:1998:GC, author = "Li-Der Chou and Shao-Chi Wang", title = "Channel assignment using genetic programming in wireless networks", booktitle = "Global Telecommunications Conference, 1998. GLOBECOM 98. The Bridge to Global Integration. IEEE", year = "1998", volume = "5", pages = "2664--2668", address = "Sydney, NSW, Australia", month = "8-12 " # nov, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-4984-9", DOI = "doi:10.1109/GLOCOM.1998.776469", abstract = "It has become an important issue to design a control scheme to assign efficiently channel resources, according to the changes in network environment, in wireless networks. In the paper, a control scheme based on genetic programming is proposed and applied to assign channels in wireless networks. Compared to traditional schemes, simulation results demonstrate the superiority of the proposed control scheme", notes = "INSPEC Accession Number: 6430014 Dept. of Comput. Sci. & Inf. Eng., Nat. Central Univ., Chung-Li;", } @InProceedings{Chou:2014:ieeeSMC, author = "Yao-Hsin Chou and Shu-Yu Kuo and Chun Kuo", booktitle = "2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC)", title = "A dynamic stock trading system based on a Multi-objective Quantum-Inspired Tabu Search algorithm", year = "2014", pages = "112--119", abstract = "Recently evolutionary algorithms, such as the Genetic Algorithm (GA), Genetic Programming (GP) and Particle Swarm Optimisation (PSO), have become common approaches used in financial applications to address stock trading problems. In this paper, we propose a novel method called the Multi-objective Quantum-inspired Tabu Search (MOQTS) algorithm, which can be applied in a stock trading system. Determining the best time to buy and sell in the stock market and maximizing profits while incurring fewer risks are important issues in financial research. In order to identify ideal trading points, the proposed trading system uses various kinds of technical indicators as trading rules in order to cope with different stock situations. The proposed algorithm is used to identify the optimal combination of trading rules as our trading strategy. Moreover, it makes use of a sliding window in order to avoid the major problem of over-fitting. In the experiment, the algorithm uses both profit earned and other aspects, such as successful transaction rate and standard deviation, to analyse this system. The experimental results, in relation to profit earned and successful transaction rates in the U.S.A stock market, outperform both the traditional method and the Buy & Hold method which are common benchmarks in the field.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SMC.2014.6973893", ISSN = "1062-922X", month = oct, notes = "Also known as \cite{6973893}", } @InProceedings{chouza09:_passiv_analog_filter_desig_using, author = "Mariano Chouza and Claudio Rancan and Osvaldo Clua and Ramon Garcia-Martinez", title = "Passive Analog Filter Design Using GP Population Control Strategies", booktitle = "Opportunities and Challenges for Next-Generation Applied Intelligence: Proceedings of the International Conference on Industrial, Engineering \& Other Applications of Applied Intelligent Systems (IEA-AIE) 2009", pages = "153--158", year = "2009", editor = "Been-Chian Chien and Tzung-Pei Hong", volume = "214", series = "Studies in Computational Intelligence", publisher = "Springer-Verlag", publisher_address = "Berlin", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-92813-3", URL = "http://www.iidia.com.ar/rgm/articulos/CIS-214-153-158.pdf", DOI = "doi:10.1007/978-3-540-92814-0_24", abstract = "This paper presents the use of two different strategies for genetic programming (GP) population growth control: decreasing the computational effort by plagues and dynamic adjustment of fitness; applied to passive analog filters design based on general topologies. Obtained experimental results show that proposed strategies improve the design process performance.", } @Article{CHOVET:2017:IFAC-PapersOnLine, author = "C. Chovet and L. Keirsbulck and B. R. Noack and M. Lippert and J-M. Foucaut", title = "Machine learning control for experimental shear flows targeting the reduction of a recirculation bubble", journal = "IFAC-PapersOnLine", volume = "50", number = "1", pages = "12307--12311", year = "2017", note = "20th IFAC World Congress", keywords = "genetic algorithms, genetic programming, Machine learning control, experimental flow control, recirculation zone", ISSN = "2405-8963", DOI = "doi:10.1016/j.ifacol.2017.08.2157", URL = "http://www.sciencedirect.com/science/article/pii/S2405896317328264", abstract = "The goal is to experimentally reduce the recirculation zone of a turbulent flow (ReH = 31500). The flow is manipulated by a row of micro-blowers (pulsed jets) that are able to generate unsteady jets proportional to any variable DC. Already, periodic jet injection at a forcing frequency of StH = 0.226 can effectively reduce the reattachment length and thus the recirculation zone. A model-free machine learning control (MLC) is used to improve performance. MLC optimizes a control law with respect to a cost function and applies genetic programming as regression technique. The cost function is based on the recirculation length and penalizes actuation. MLC is shown to outperform periodic forcing. The current study demonstrates the efficacy of MLC to reduce the recirculation zone in a turbulent flow regime. Given current and past successes, we anticipate numerous experimental MLC applications", } @PhdThesis{Chowdhury:thesis, author = "Sadat U. Chowdhury", title = "A Study of the Impact of Interaction Mechanisms and Population Diversity in Evolutionary Multiagent Systems", school = "City University of New York", year = "2016", address = "USA", month = sep, keywords = "genetic algorithms, genetic programming, Artificial Intelligence and Robotics, Computer Sciences, machine learning, artificial life, multiagent systems, robotics, evolutionary systems", URL = "https://academicworks.cuny.edu/gc_etds/1607", size = "xxiv + 257 pages", abstract = "In the Evolutionary Computation (EC) research community, a major concern is maintaining optimal levels of population diversity. In the Multiagent Systems (MAS) research community, a major concern is implementing effective agent coordination through various interaction mechanisms. These two concerns coincide when one is faced with Evolutionary Multiagent Systems (EMAS). This thesis demonstrates a methodology to study the relationship between interaction mechanisms, population diversity, and performance of an evolving multiagent system in a dynamic, real-time, and asynchronous environment. An open sourced extensible experimentation platform is developed that allows plug-ins for evolutionary models, interaction mechanisms, and genotypical encoding schemes beyond the one used to run experiments. Moreover, the platform is designed to scale arbitrarily large number of parallel experiments in multi-core clustered environments. The main contribution of this thesis is better understanding of the role played by population diversity and interaction mechanisms in the evolution of multiagent systems. First, it is shown, through carefully planned experiments in three different evolutionary models, that both interaction mechanisms and population diversity have a statistically significant impact on performance in a system of evolutionary agents coordinating to achieve a shared goal of completing problems in sequential task domains. Second, it is experimentally verified that, in the sequential task domain, a larger heterogeneous population of limited-capability agents will evolve to perform better than a smaller homogeneous population of full-capability agents, and performance is influenced by the ways in which the agents interact. Finally, two novel trait-based population diversity levels are described and are shown to be effective in their applicability.", notes = "9-2016 Supervisor: Elizabeth Sklar", } @InProceedings{christensen:2002:EuroGP, title = "An Analysis of {Koza}'s Computational Effort Statistic for Genetic Programming", author = "Steffen Christensen and Franz Oppacher", editor = "James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi", booktitle = "Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002", year = "2002", volume = "2278", series = "LNCS", pages = "182--191", publisher = "Springer-Verlag", address = "Kinsale, Ireland", publisher_address = "Berlin", month = "3-5 " # apr, keywords = "genetic algorithms, genetic programming", ISBN = "3-540-43378-3", URL = "https://rdcu.be/eg43v", DOI = "doi:10.1007/3-540-45984-7_18", size = "10 pages", abstract = "As research into the theory of genetic programming progresses, more effort is being placed on systematically comparing results to give an indication of the effectiveness of sundry modifications to traditional GP. The statistic that is commonly used to report the amount of computational effort to solve a particular problem with 99percent probability is Koza's I(M, i, z) statistic. This paper analyzes this measure from a statistical perspective. In particular, Koza's I tends to underestimate the true computational effort, by 25percent or more for commonly used GP parameters and run sizes. The magnitude of this underestimate is nonlinearly decreasing with increasing run count, leading to the possibility that published results based on few runs may in fact be unmatchable when replicated at higher resolution. Additional analysis shows that this statistic also under reports the generation at which optimal results are achieved.", notes = "EuroGP'2002, part of \cite{lutton:2002:GP}", } @InProceedings{Christensen:2006:CEC, author = "Steffen Christensen and Franz Oppacher", title = "The Y-Test: Fairly Comparing Experimental Setups with Unequal Effort", booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary Computation", year = "2006", editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas", pages = "1060--1065", address = "Vancouver", month = "16-21 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9487-9", DOI = "doi:10.1109/CEC.2006.1688330", size = "6 pages", abstract = "Evolutionary Computation has been dogged by a central statistical issue: how does one fairly compare the performance of two techniques which differ in the amount of work required? While Koza's computational effort statistic attempts to answer this problem, it is a point statistic and has other statistical problems. We present the y-test, a statistical test which takes as input a set of outcomes from the observed runs of two processes A and B. The y-test synthetically performs a work-balanced comparison between k runs of A and l runs of B. We show that by choosing k and l appropriately, we can compensate for the fact that one of the processes is computationally more efficient than the other.", notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D IEEE Xplore gives pages = {"}356--361{"},", } @InProceedings{1277275, author = "Steffen Christensen and Franz Oppacher", title = "Solving the artificial ant on the Santa Fe trail problem in 20,696 fitness evaluations", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "1574--1579", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1574.pdf", DOI = "doi:10.1145/1276958.1277275", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, representation, runtime analysis, speedup technique", abstract = "In this paper, we provide an algorithm that systematically considers all small trees in the search space of genetic programming. These small trees are used to generate useful subroutines for genetic programming. This algorithm is tested on the Artificial Ant on the Santa Fe Trail problem, a venerable problem for genetic programming systems. When four levels of iteration are used, the algorithm presented here generates better results than any known published result by a factor of 7.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @PhdThesis{Christensen:thesis, author = "Steffen Moffatt Christensen", title = "Towards scalable genetic programming", year = "2007", school = "Carleton University", address = "Ottawa, Canada", month = "14 " # nov, keywords = "genetic algorithms, genetic programming", URL = "https://curve.carleton.ca/1ecedf3e-b559-41e6-aede-eac9b2209694", URL = "https://curve.carleton.ca/system/files/etd/1ecedf3e-b559-41e6-aede-eac9b2209694/etd_pdf/0ea5e5b5ae68353e6ad775c71c4ee1e4/christensen-towardsscalablegeneticprogramming.pdf", URL = "http://search.proquest.com/docview/304884668", URL = "http://www.tamale.uottawa.ca/winter2007/300107.html", DOI = "doi:10.22215/etd/2007-06411", isbn13 = "978-0-494-23290-3", order_no = "AAINR23290", size = "282 pages", abstract = "Genetic programming (GP) is a technique for automatically solving optimisation problems where candidate solutions are expressible as trees with no human intervention. We propose an extension of GP, termed scalable genetic programming, which solves problems parametrised by a scalable difficulty parameter. We first define a taxonomy of evolutionary computation (EC) systems that identifies variability dimensions and levels for EC systems. We define an algorithm, the scientist algorithm, which uses genetic programming as a subroutine to reliably make progress on scalable problems. The scientist algorithm uses a toolkit of provided routines to progress, by carrying out experiments to determine the value of different methods. We define several of the tools for this toolkit. We define and implement an algorithm for systematically considering all small trees for a problem. We then use these small trees in an iterative algorithm to define subroutines that improve performance on a problem under study. Using this algorithm, we beat the best known performance on the artificial ant on the Santa Fe trail problem by a factor of 7. As science depends on accurate hypothesis testing to make progress, we perform a comparison and evaluation of statistical techniques used to evaluate evolutionary computation systems. Finding many of these wanting, with the exception of computational effort, we introduce two additional techniques, effective mean best fitness and the y-test. We also perform an extensive analysis of the computational effort, and identify some statistical cautions around the use of this key statistic. We provide an algorithm that carefully uses computational effort to determine the best values of population size and generation number for an EC treatment. Finally, we identify several components that are of use with the scientist algorithm. We treat the use of multiobjective algorithms in GP, principal components analysis, and their combination. We demonstrate this by providing and testing an algorithm that makes evolved trees parsimonious. We introduce the notion of incremental evolution, and use it to make useful subroutines automatically from successful solutions to easy problems. We then use this to demonstrate scalable genetic programming on an integer sorting problem.", notes = "http://portal.acm.org/citation.cfm?id=1292850 http://www.tamale.uottawa.ca/winter2007/300107.html Tuesday, Jan. 30, 2007 Also known as \cite{1292850}, NR23290 supervisor Franz Oppacher", } @InProceedings{Christmas:2015:GECCOcomp, author = "Jacqueline Christmas", title = "Genetic C Programming with Probabilistic Evaluation", booktitle = "GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation", year = "2015", editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr", isbn13 = "978-1-4503-3488-4", keywords = "genetic algorithms, genetic programming: Poster", pages = "1371--1372", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739482.2764642", DOI = "doi:10.1145/2739482.2764642", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "We introduce the concept of probabilistic program evaluation, whereby the order in which the statements of a proposed program are executed, and whether individual statements are executed at all, are controlled by probability distributions associated with each statement. The sufficient statistics of these probability distributions are mutated as part of the GP scheme. We demonstrate the method on the simple problems of swapping two array elements and identifying the maximum value in an array.", notes = "EDA? Also known as \cite{2764642} Distributed at GECCO-2015.", } @InProceedings{Christodoulaki:2022:CEC, author = "Eva Christodoulaki and Michael Kampouridis", title = "Using strongly typed genetic programming to combine technical and sentiment analysis for algorithmic trading", booktitle = "2022 IEEE Congress on Evolutionary Computation (CEC)", year = "2022", editor = "Carlos A. Coello Coello and Sanaz Mostaghim", address = "Padua, Italy", month = "18-23 " # jul, isbn13 = "978-1-6654-6708-7", size = "8 pages", abstract = "Algorithmic trading has become an increasingly thriving research area and a lot of focus has been given on indicators from technical and sentiment analysis. In this paper, we examine the advantages of combining features from both analyses. To do this, we use two different genetic programming algorithms (GP). The first algorithm allows trees to contain technical and/or sentiment analysis indicators without any constraints. The second algorithm introduces technical and sentiment analysis types through a strongly typed GP, whereby one branch of a given tree contains only technical analysis indicators and another branch of the same tree contains only sentiment analysis features. This allows for better exploration and exploitation of the search space of the indicators. We perform experiments on 10 international stocks and compare the above two GPs performances. Our goal is to demonstrate that the combination of the indicators leads to improved financial performance. Our results show that the strongly typed GP is able to rank first in terms of Sharpe ratio and statistically outperform all other algorithms in terms of rate of return.", keywords = "genetic algorithms, genetic programming, Sentiment analysis, Evolutionary computation, Technical Analysis, Sentiment Analysis, Algorithmic Trading", DOI = "doi:10.1109/CEC55065.2022.9870240", notes = "Also known as \cite{9870240}", } @InProceedings{Christodoulaki:2022:CIFEr, author = "Eva Christodoulaki and Michael Kampouridis and Panagiotis Kanellopoulos", booktitle = "2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr)", title = "Technical and Sentiment Analysis in Financial Forecasting with Genetic Programming", year = "2022", abstract = "Financial Forecasting is a popular and thriving research area that relies on indicators derived from technical and sentiment analysis. In this paper, we investigate the advantages that sentiment analysis indicators provide, by comparing their performance to that of technical indicators, when both are used individually as features into a genetic programming algorithm focusing on the maximization of the Sharpe ratio. Moreover, while previous sentiment analysis research has focused mostly on the titles of articles, in this paper we use the text of the articles and their summaries. Our goal is to explore further on all possible sentiment features and identify which features contribute the most. We perform experiments on 26 different datasets and show that sentiment analysis produces better, and statistically significant, average results than technical analysis in terms of Sharpe ratio and risk.", keywords = "genetic algorithms, genetic programming, Economics, Sentiment analysis, Focusing, Forecasting, Computational intelligence, Technical Analysis, Sentiment Analysis, Financial Forecasting", DOI = "doi:10.1109/CIFEr52523.2022.9776186", ISSN = "2640-7701", month = may, notes = "Also known as \cite{9776186}", } @InProceedings{christodoulaki:2023:GECCO, author = "Evangelia Christodoulaki and Michael Kampouridis and Maria Kyropoulou", title = "Enhanced Strongly Typed Genetic Programming for Algorithmic Trading", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "1055--1063", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, algorithmic trading, technical analysis, sentiment analysis", isbn13 = "9798400701191", DOI = "doi:10.1145/3583131.3590359", size = "9 pages", abstract = "This paper proposes a novel strongly typed Genetic Programming (STGP) algorithm that combines Technical (TA) and Sentiment analysis (SA) indicators to produce trading strategies. While TA and SA have been successful when used individually, their combination has not been considered extensively. Our proposed STGP algorithm has a novel fitness function, which rewards not only a tree's trading performance, but also the trading performance of its TA and SA subtrees. To achieve this, the fitness function is equal to the sum of three components: the fitness function for the complete tree, the fitness function of the TA subtree, and the fitness function of the SA subtree. In doing so, we ensure that the evolved trees contain profitable trading strategies that take full advantage of both technical and sentiment analysis. We run experiments on 35 international stocks and compare the STGP's performance to four other GP algorithms, as well as multilayer perceptron, support vector machines, and buy and hold. Results show that the proposed GP algorithm statistically and significantly outperforms all benchmarks and it improves the financial performance of the trading strategies produced by other GP algorithms by up to a factor of two for the median rate of return.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @InProceedings{Christodoulaki:2023:SSCI, author = "Eva Christodoulaki and Michael Kampouridis", booktitle = "2023 IEEE Symposium Series on Computational Intelligence (SSCI)", title = "Fundamental, Technical and Sentiment Analysis for Algorithmic Trading with Genetic Programming", year = "2023", pages = "83--89", month = dec, keywords = "genetic algorithms, genetic programming, Support vector machines, Measurement, Sentiment analysis, Profitability, Companies, Algorithmic Trading", ISSN = "2472-8322", DOI = "doi:10.1109/SSCI52147.2023.10372070", abstract = "Algorithmic trading is a topic with major developments in the last years. Investors rely mostly on indicators derived from fundamental (FA) or technical analysis (TA), while sentiment analysis (SA) has also received attention in the last decade. This has led to great financial advantages with algorithms being the main tool to create pre-programmed trading strategies. Although the three analysis types have been mainly considered individually, their combination has not been studied as much. Given the ability of each individual analysis type in identifying profitable trading strategies, we are motivated to investigate if we can increase the profitability of such strategies by combining their indicators. we propose a novel Genetic Programming (GP) algorithm that combines the three analysis types and we showcase the advantages of their combination in terms of three financial metrics, namely Sharpe ratio, rate of return and risk. We conduct experiments on 30 companies and based on the results, the combination of the three analysis types statistically and significantly outperforms their individual results, as well as their pairwise combinations. More specifically, the proposed GP algorithm has the highest mean and median values for Sharpe ratio and rate of return, and the lowest (best) mean value for risk. Moreover, we benchmark our GP algorithm against multilayer perceptron and support vector machine, and show that it statistically outperforms both algorithms in terms of Sharpe ratio and risk.", notes = "Also known as \cite{10372070}", } @PhdThesis{Christodoulaki:thesis, author = "Evangelia Paraskevi Christodoulaki", title = "Fundamental, Sentiment and Technical analysis for Algorithmic Trading using Novel Genetic Programming algorithms", school = "School of Computer Science and Electronic Engineering, University of Essex", year = "2024", address = "UK", month = jan, keywords = "genetic algorithms, genetic programming, Financial Forcasting, strongly-typed genetic programming, FinTech", URL = "http://kampouridis.net/papers/Eva_PhdThesis_with_template.pdf", size = "231 pages", abstract = "This thesis explores genetic programming (GP) applications in algorithmic trading, addressing significant advancements in the field. Investors typically rely on fundamental analysis (FA) or technical analysis (TA) indicators, with sentiment analysis (SA) gaining recent attention. Consequently, algorithms have become the primary method for developing pre-programmed trading strategies, leading to substantial financial benefits. While each analysis type has been studied individually, their combined exploration remains limited. Our motivation is to assess if integrating FA, SA, and TA indicators can improve financial profitability. Thus, in Chapter 5, we introduce a novel GP algorithm which combines the three analysis types within the same GP structure, wanting to understand the advantages of their combination. Chapter 6 presents a strongly-typed GP architecture, where each branch of the algorithm represents one analysis type, facilitating improved exploration and exploitation. Furthermore, we showcase a novel fitness function that rewards a tree trading performance and the performance of its FA, SA,and TA subtrees. Chapter 7 aims to enhance the GP algorithm performance and increase the individuals financial advantages. Therefore, we propose a novel GP operator that encourages active trading by injecting trees into the GP population that perform a high number of trades while achieving high profitability at low risk. To evaluate our GP variants performance, we conduct experiments on stocks of 42 international companies, comparing the novel algorithm with the GP variants introduced in the same chapter. Moreover, in Chapters 5 and 6, we compare the proposed GP algorithm against four machine learning benchmarks and a financial trading strategy, while Chapter 7 focuses on comparing the novel GP algorithm exclusively with GP benchmarks. The evaluation employs three financial metrics: Sharpe ratio, rate of return, and risk. Results consistently show that the proposed GP algorithms in each chapter enhance the financial performance of trading strategies, surpassing the benchmarks", notes = "supervisor: Michael Kampouridis", } @InProceedings{christodoulaki:2022:UKCI, author = "Eva Christodoulaki and Michael Kampouridis", title = "Combining Technical and Sentiment Analysis Under a Genetic Programming Algorithm", booktitle = "21st UK Workshop on Computational Intelligence", year = "2022", editor = "George Panoutsos and Mahdi Mahfouf and Lyudmila S Mihaylova", volume = "1454", series = "Advances in Intelligent Systems and Computing", pages = "502--513", address = "Sheffield, UK", month = sep # " 7-9", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "https://link.springer.com/chapter/10.1007/978-3-031-55568-8_42", DOI = "doi:10.1007/978-3-031-55568-8_42", } @Article{Christodoulaki:2025:knosys, author = "Eva Christodoulaki and Michael Kampouridis and Maria Kyropoulou", title = "A novel strongly-typed Genetic Programming algorithm for combining sentiment and technical analysis for algorithmic trading", journal = "Knowledge-Based Systems", year = "2025", volume = "311", pages = "113054", keywords = "genetic algorithms, genetic programming, Sentiment analysis, Technical analysis, Algorithmic trading", ISSN = "0950-7051", URL = "https://www.sciencedirect.com/science/article/pii/S0950705125001017", DOI = "doi:10.1016/j.knosys.2025.113054", abstract = "The use of algorithms in finance and trading has become an increasingly thriving research area, with researchers creating automated and pre programmed trading instructions using indicators from technical and sentiment analysis. The indicators of the two analyses have been used mostly individually, despite evidence that their combination can be profitable and financially advantageous. In this paper, we examine the advantages of combining indicators from both technical and sentiment analysis through a novel genetic programming algorithm, named STGP-SATA. Our algorithm introduces technical and sentiment analysis types, through a strongly-typed architecture, whereby the associated tree contains one branch with only technical indicators and another branch with only sentiment analysis indicators. This approach allows for better exploration and exploitation of the search space of the indicators. To evaluate the performance of STGP-SATA we compare it with three other GP variants on three financial metrics, namely Sharpe ratio, rate of return and risk. We furthermore compare STGP-SATA against two financial and four algorithmic benchmarks, namely, multilayer perceptron, support vector machine, extreme gradient boosting, and long short term memory network. Our study shows that the combination of technical and sentiment analysis indicators through STGP-SATA improves the financial performance of the trading strategies and statistically and significantly outperforms the other benchmarks across the three financial metrics", } @PhdThesis{Chrosny:thesis, author = "Wojciech M. Chrosny", title = "Application of genetic programming to text categorization", school = "Computer Science, Polytechnic University", year = "2000", address = "USA?", month = jan, keywords = "genetic algorithms, genetic programming", URL = "http://search.proquest.com/docview/85536142/", size = "155 pages", abstract = "This dissertation uses genetic programming in text categorization problems. Genetic programming algorithms are applied to a set of news articles to evolve programs that determine whether the article belongs to a particular category. The programs are randomly generated from the set of initial functions and constants. Programs with the fewest amount of false assignments are favoured in the selection for recombination in the subsequent iterations of the genetic programming algorithm. The form of the solution is not determined a priori as in other text categorization methods. The basis set of functions and constants used by the genetic analysis program are specified in advance and may include the three basic logical functions and a set of vocabulary words. Other sets of basis functions can be supplied to the genetic algorithm to obtain different programs. The form in which these functions and constants are combined is determined randomly by the genetic algorithm. The results indicate that genetic programming methods are in the cases examined as good and slightly better than other decision tree or rule induction methods described by Apte et. al. [Apte 1994]. The Genetic Programming methods used a simpler set of features and functions: no word stemming no explicit stop word removal, local dictionary, Boolean functions. The F1-measure of categorization performance of 80.percent achieved by Genetic Programming compares favorably with 78.5percent break even performance of traditional Boolean rule induction methods. It is comparable with 80.5percent Breakeven performance of the rule induction methods with a more complex feature set such as word frequency [Apte 1994]. Characteristics of Genetic Programming text categorization were studied to understand the sensitivity of Genetic Programming methods to vocabulary size, population size, training and testing set selection methods. Temporal characteristics of the Reuters Article Corpus [Lewis-21578) were studied. The results are of interest to both Genetic Programming as well as Traditional categorization methods and may point to significant future performance improvements in both domains. In some cases these results were better than Apte's.", notes = "Supervisor: Robert J. Flynn Wojciech Marek Chrosny UMI Microform 9949161", } @InProceedings{chu:1999:DDCNDAGAA, author = "Chao-Hsien Chu and G. Premkumar and Carey Chou and Jianzhong Sun", title = "Dynamic Degree Constrained Network Design: A Genetic Algorithm Approach", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "141--148", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-846.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-846.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Article{chu:2022:Forests, author = "Demiao Chu and Redzo Hasanagic and Atif Hodzic and Davor Krzisnik and Damir Hodzic and Mohsen Bahmani and Marko Petric and Miha Humar", title = "Application of Temperature and Process Duration as a Method for Predicting the Mechanical Properties of Thermally Modified Timber", journal = "Forests", year = "2022", volume = "13", number = "2", keywords = "genetic algorithms, genetic programming, thermal modification, mathematical modeling, optimization", ISSN = "1999-4907", URL = "https://www.mdpi.com/1999-4907/13/2/217", DOI = "doi:10.3390/f13020217", abstract = "This study aims to investigate the influence of thermal modification (TM) on the physical and mechanical properties of wood. For this purpose, the experimental part focused on selected influential parameters, namely temperature, residence time, and density, while the four-point bending strength is obtained as the output parameter. The obtained experimental data are stochastically modelled and compared with the model created by genetic programming (GP). The classical mathematical analysis obtained treatment parameters in relation to the maximum bending strength (T = 187 °C, t = 125 min ρ = 0.780 g/cm3) and compared with the results obtained by genetic algorithm (GA) (T = 208 °C, t = 122 min, and ρ = 0.728 g/cm3). It is possible to obtain models that describe experimental results well with stochastic modelling and evolutionary algorithms.", notes = "also known as \cite{f13020217}", } @InProceedings{Chu:2008:cec, author = "Dominique Chu and Jonathan E. Rowe", title = "Crossover Operators to Control Size Growth in Linear GP and Variable Length GAs", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "336--343", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1823-7", file = "EC0096.pdf", DOI = "doi:10.1109/CEC.2008.4630819", abstract = "In various nuances of evolutionary algorithms it has been observed that variable sized genomes exhibit large degrees of redundancy and corresponding undue growth. This phenomenon is commonly referred to as ``bloat.'' The present contribution investigates the role of crossover operators as the cause for length changes in variable length genetic algorithms and linear GP. Three crossover operators are defined; each is tested with three different fitness functions. The aim of this article is to indicate suitable designs of crossover operators that allow efficient exploration of designs of solutions of a wide variety of sizes, while at the same time avoiding bloat.", keywords = "genetic algorithms, genetic programming", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @InProceedings{Chu:2015:RIVF, author = "Thi Huong Chu and Quang Uy Nguyen", booktitle = "IEEE RIVF International Conference on Computing Communication Technologies - Research, Innovation, and Vision for the Future (2015 RIVF)", title = "A new implementation to speed up Genetic Programming", year = "2015", month = jan, pages = "35--40", abstract = "Genetic Programming (GP) is an evolutionary algorithm inspired by the evolutionary process in biology. Although, GP has successfully applied to various problems, its major weakness lies in the slowness of the evolutionary process. This drawback may limit GP applications particularly in complex problems where the computational time required by GP often grows excessively as the problem complexity increases. In this paper, we propose a novel method to speed up GP based on a new implementation that can be implemented on the normal hardware of personal computers. The experiments were conducted on numerous regression problems drawn from UCI machine learning data set. The results were compared with standard GP (the traditional implementation) and an implementation based on subtree caching showing that the proposed method significantly reduces the computational time compared to the previous approaches, reaching a speedup of up to nearly 200 times.", keywords = "genetic algorithms, genetic programming, Clustering algorithms, Hardware, Sociology, Standards, Statistics, Training data, Fitness Evaluation, Speed up", DOI = "doi:10.1109/RIVF.2015.7049871", notes = "Faculty of IT, Le Quy Don University, Hanoi, Vietnam. Also known as \cite{7049871}", } @InProceedings{Chu:2016:PPSN, author = "Thi Huong Chu and Quang Uy Nguyen and Michael O'Neill", title = "Tournament Selection based on Statistical Test in Genetic Programming", booktitle = "14th International Conference on Parallel Problem Solving from Nature", year = "2016", editor = "Julia Handl and Emma Hart and Peter R. Lewis and Manuel Lopez-Ibanez and Gabriela Ochoa and Ben Paechter", volume = "9921", series = "LNCS", pages = "303--312", address = "Edinburgh", month = "17-21 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-45823-6", DOI = "doi:10.1007/978-3-319-45823-6_28", abstract = "Selection plays a critical role in the performance of evolutionary algorithms. Tournament selection is often considered the most popular techniques among several selection methods. Standard tournament selection randomly selects several individuals from the population and the individual with the best fitness value is chosen as the winner. In the context of Genetic Programming, this approach ignores the error value on the fitness cases of the problem emphasising relative fitness quality rather than detailed quantitative comparison. Subsequently, potentially useful information from the error vector may be lost. In this paper, we introduce the use of a statistical test into selection that uses information from the individual's error vector. Two variants of tournament selection are proposed, and tested on Genetic Programming for symbolic regression problems. On the benchmark problems examined we observe a benefit of the proposed methods in reducing code growth and generalisation error.", notes = "PPSN2016 Faculty of IT, Le Quy Don Technical University, Hanoi, Vietnam", } @InProceedings{Chu:2017:APSIES, author = "Thi Huong Chu and Quang Uy Nguyen", booktitle = "2017 21st Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES)", title = "Reducing code bloat in Genetic Programming based on subtree substituting technique", year = "2017", pages = "25--30", abstract = "Code bloat is a phenomenon in Genetic Programming (GP) that increases the size of individuals during the evolutionary process. Over the years, there has been a large number of research that attempted to address this problem. In this paper, we propose a new method to control code bloat and reduce the complexity of the solutions in GP. The proposed method is called Substituting a subtree with an Approximate Terminal (SAT-GP). The idea of SAT-GP is to select a portion of the largest individuals in each generation and then replace a random subtree in every individual in this portion by an approximate terminal of the similar semantics. SAT-GP is tested on twelve regression problems and its performance is compared to standard GP and the latest bloat control method (neat-GP). The experimental results are encouraging, SAT-GP achieved good performance on all tested problems regarding to the four popular performance metrics in GP research.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IESYS.2017.8233556", month = nov, notes = "Le Quy Don Technical University, Hanoi, Vietnam Also known as \cite{8233556}", } @Article{Chu:2018:IS, author = "Thi Houng Chu and Quang Uy Nguyen and Michael O'Neill", title = "Semantic tournament selection for genetic programming based on statistical analysis of error vectors", journal = "Information Sciences", year = "2018", volume = "436-437", pages = "352--366", month = apr, keywords = "genetic algorithms, genetic programming Tournament selection, Statistical test, Code bloat, Semantics", DOI = "doi:10.1016/j.ins.2018.01.030", size = "15 pages", abstract = "The selection mechanism plays a very important role in the performance of Genetic Programming (GP). Among several selection techniques, tournament selection is often considered the most popular. Standard tournament selection randomly selects a set of individuals from the population and the individual with the best fitness value is chosen as the winner. However, an opportunity exists to enhance tournament selection as the standard approach ignores finer-grained semantics which can be collected during GP program execution. In the case of symbolic regression problems, the error vectors on the training fitness cases can be used in a more detailed quantitative comparison. In this paper we introduce the use of a statistical test into GP tournament selection that uses information from the individual's error vector, and three variants of the selection strategy are proposed. We tested these methods on twenty five regression problems and their noisy variants. The experimental results demonstrate the benefit of the proposed methods in reducing GP code growth and improving the generalisation behaviour of GP solutions when compared to standard tournament selection, a similar selection technique and a state of the art bloat control approach.", } @InProceedings{Chu:2021:RIVF, author = "Thi Huong Chu and Quang {Uy Nguyen}", title = "Network Anomaly Detection Using Genetic Programming with Semantic Approximation Techniques", booktitle = "2021 RIVF International Conference on Computing and Communication Technologies (RIVF)", year = "2021", abstract = "Network anomaly detection aims at detecting malicious behaviors to the network systems. This problem is of great importance in developing intrusion detection systems to protect networks from intrusive activities. Recently, machine learning-based methods for anomaly detection have become more popular in the research community thanks to their capability in discovering unknown attacks. In the paper, we propose an application of Genetic Programming (GP) with the semantics approximation technique to network anomaly detection. Specifically, two recently proposed techniques for reducing GP code bloat, i.e. Subtree Approximation (SA) and Desired Approximation (DA) are applied for detecting network anomalies. SA and DA are evaluated on 6 datasets in the field of anomaly detection and compared with standard GP and five common machine learning methods. Experimental results show that SA and DA have achieved better results than that of standard GP and the performance of GP is competitive with other machine learning algorithms.", keywords = "genetic algorithms, genetic programming, Learning systems, Machine learning algorithms, Semantics, Intrusion detection, Machine learning, Communications technology, Semantic Approximation, Network Anomaly Detection", DOI = "doi:10.1109/RIVF51545.2021.9642140", ISSN = "2162-786X", month = aug, notes = "Also known as \cite{9642140}", } @InProceedings{Chu:2018:SoICT, author = "Thi Huong Chu and Quang Uy Nguyen and Van Loi Cao", title = "Semantics Based Substituting Technique for Reducing Code Bloat in Genetic Programming", booktitle = "Proceedings of the Ninth International Symposium on Information and Communication Technology, SoICT 2018", year = "2018", pages = "77--83", address = "Danang City, Viet Nam", month = dec # " 6-7", publisher = "ACM", keywords = "genetic algorithms, genetic programming, code growth, code bloat, semantics, time series", isbn13 = "978-1-4503-6539-0", acmid = "3287948", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/soict/soict2018.html#ChuNC18", URL = "http://doi.acm.org/10.1145/3287921.3287948", DOI = "doi:10.1145/3287921.3287948", abstract = "Genetic Programming (GP) is a technique that allows computer programs encoded as a set of tree structures to be evolved using an evolutionary algorithm. In GP, code bloat is a common phenomenon characterised by the size of individuals gradually increasing during the evolution. This phenomenon has a negative impact on GP performance in solving problems. In order to address this problem, we have recently introduced a code bloat control method based on semantics: Substituting a subtree with an Approximate Terminal (SAT-GP). In this paper, we propose an extension of SAT-GP, namely Substituting a subtree with an Approximate Subprogram (SAS-GP). We tested this method with different GP parameter settings on a real-world time series forecasting problem. The experimental results demonstrate the benefit of the proposed method in reducing the code bloat phenomenon and improving GP performance. Particularly, SAS-GP often achieves the best performance compared to other tested GP systems using four popular performance metrics in GP.", notes = "Le Quy Don Technical University, Hanoi, Vietnam also known as \cite{conf/soict/ChuNC18}", } @InProceedings{chu:2019:MMM, author = "Wei-Ta Chu and Hao-An Chu", title = "A Genetic Programming Approach to Integrate Multilayer {CNN} Features for Image Classification", booktitle = "MultiMedia Modeling", year = "2019", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-030-05710-7_53", DOI = "doi:10.1007/978-3-030-05710-7_53", } @Article{Chuang:2003:UMB, author = "Louise L. Chuang and Jeng-Yang Hwang and Been Chian Chien and Jung Yi Lin and Chiung Hsin Chang and Chen Hsiang Yu and Fong Ming Chang", title = "Predicting fetal birth weight by ultrasound with the use of genetic programming", journal = "Ultrasound in Medicine \& Biology", year = "2003", volume = "29", pages = "S163--S163", number = "5, Supplement 1", owner = "wlangdon", broken = "http://www.sciencedirect.com/science/article/B6TD2-48KKXMV-R0/2/b03751f18c26cc039779c29a58106151", month = may, keywords = "genetic algorithms, genetic programming", ISSN = "0301-5629", URL = "https://www.umbjournal.org/action/showCitFormats?pii=S0301-5629%2803%2900653-7", DOI = "doi:10.1016/S0301-5629(03)00653-7", notes = "Sep 2023 doi fails 1 Obstetrics and Gynecology, National Cheng Kung University Hospital, Tainan, Taiwan 2 Technology Research, ASN Technology Corp. (Taiwan), Tainan, Taiwan 3 Information Engineering, I, Shou University, Kaohsiung, Taiwan 4 Computer & Information Science, National Chiao Tung University, Hsinchu, Taiwan", } @InProceedings{Chuengsatiansup:2022:GI, author = "Chitchanok Chuengsatiansup and Markus Wagner and Yuval Yarom", title = "Opportunities for Genetic Improvement of Cryptographic Code", booktitle = "GI @ GECCO 2022", year = "2022", editor = "Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and W. B. Langdon and Justyna Petke", pages = "1928--1929", address = "Boston, USA", publisher_address = "New York, NY, USA", month = "9 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", keywords = "genetic algorithms, genetic programming, genetic improvement, intermediate representation, LLVM IR, CryptOpt", isbn13 = "978-1-4503-9268-6/22/07", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/gecco2022/gi2022/papers/Chuengsatiansup_2022_GI.pdf", DOI = "doi:10.1145/3520304.3534049", slides_url = "http://geneticimprovementofsoftware.com/slides/gi2022gecco/chuengsatiansup-opportunities-for-genetic-gi-gecco-22.pdf", video_url = "https://www.youtube.com/watch?v=3xD2zgucpug&list=PLI8fiFpB7BoIHgl5CsdtjfWvHlE5N6pje&index=4", size = "2 pages", abstract = "Cryptography is one of the main tools underlying the security of our connected world. Cryptographic code must meet not only high security requirements, but also exhibit excellent non-functional properties, such as high performance and unique security requirements. As both automatic code generation and genetic improvement of such code are under explored, we motivate here what makes cryptographic code a prime target for future research.", notes = "See also \cite{Kuepper:2023:PLDI} http://geneticimprovementofsoftware.com/events/gecco2022 Constant-time programming, masking. NIST P-256-mul cites \cite{KriFeeleyQEST04}, Rosita \cite{Shelton:2021:NDSS} and \cite{DBLP:conf/vstte/BosamiyaGLPH20}. 'We start with an abstract representation of the code as a data flow graph'. 'register to spill to memory'. speed up upto 2.5 times faster. Hillclimb. Mutation takes advantage of reordering x86 instructions. GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @Article{ciccolella:2023:Algorithms, author = "Simone Ciccolella and Gianluca {Della Vedova} and Vladimir Filipovic and Mauricio {Soto Gomez}", title = "Three Metaheuristic Approaches for Tumor Phylogeny Inference: An Experimental Comparison", journal = "Algorithms", year = "2023", volume = "16", number = "7", pages = "Article No. 333", keywords = "genetic algorithms, genetic programming", ISSN = "1999-4893", URL = "https://www.mdpi.com/1999-4893/16/7/333", DOI = "doi:10.3390/a16070333", abstract = "Being able to infer the clonal evolution and progression of cancer makes it possible to devise targeted therapies to treat the disease. As discussed in several studies, understanding the history of accumulation and the evolution of mutations during cancer progression is of key importance when devising treatment strategies. Given the importance of the task, many methods for phylogeny reconstructions have been developed over the years, mostly employing probabilistic frameworks. Our goal was to explore different methods to take on this phylogeny inference problem; therefore, we devised and implemented three different metaheuristic approaches--Particle Swarm Optimisation (PSO), Genetic Programming (GP) and Variable Neighbourhood Search (VNS)--under the Perfect Phylogeny and the Dollo-k evolutionary models. We adapted the algorithms to be applied to this specific context, specifically to a tree-based search space, and proposed six different experimental settings, in increasing order of difficulty, to test the novel methods amongst themselves and against a state-of-the-art method. Of the three, the PSO shows particularly promising results and is comparable to published tools, even at this exploratory stage. Thus, we foresee great improvements if alternative definitions of distance and velocity in a tree space, capable of better handling such non-Euclidean search spaces, are devised in future works.", notes = "also known as \cite{a16070333}", } @InProceedings{Ciesielski:1999:AJ, author = "Victor Ciesielski and Peter Wilson", title = "Developing a team of soccer playing robots by genetic programming", booktitle = "Proceedings of The Third Australia-Japan Joint Workshop on Intelligent and Evolutionary Systems", year = "1999", editor = "Bob McKay and Yasuhiro Tsujimura and Ruhul Sarker and Akira Namatame and Xin Yao and Mitsuo Gen", pages = "101--108", address = "School of Computer Science Australian Defence Force Academy, Canberra, Australia", month = "22-25 " # nov, keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.rmit.edu.au/~vc/papers/aus-jap-ec99.ps.gz", size = "8 pages", notes = "http://www.cs.adfa.edu.au/archive/conference/aj99/programme.html", } @InProceedings{ciesielski:2002:poecigpbrosp, author = "Vic Ciesielski and Dylan Mawhinney", title = "Prevention of Early Convergence in Genetic Programming by Replacement of Similar Programs", booktitle = "Proceedings of the 2002 Congress on Evolutionary Computation CEC2002", editor = "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and Mark Shackleton", pages = "67--72", year = "2002", publisher = "IEEE Press", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", organisation = "IEEE Neural Network Council (NNC), Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", ISBN = "0-7803-7278-6", month = "12-17 " # may, notes = "CEC 2002 - A joint meeting of the IEEE, the Evolutionary Programming Society, and the IEE. Held in connection with the World Congress on Computational Intelligence (WCCI 2002) See also \cite{oai:CiteSeerPSU:451316}", keywords = "genetic algorithms, genetic programming, CPU time, MAX problem, early convergence prevention, experimental work, fitness function, mutation, mutation rate, premature convergence, randomly generated programs, similar program replacement, similarity matching, soccer playing programs, convergence, programming", DOI = "doi:10.1109/CEC.2002.1006211", abstract = "We have investigated an approach to preventing or minimising the occurrence of premature convergence by measuring the similarity between the programs in the population and replacing the most similar ones with randomly generated programs. On a problem with known premature convergence behaviour, the MAX problem, similarity replacement significantly decreased the rate of premature convergence over the best that could be achieved by manipulation of the mutation rate. The expected CPU time for a successful run was increased due to the additional cost of the similarity matching. On a problem which has a very expensive fitness function, the evolution of a team of soccer playing programs, the degree of premature convergence rate was also significantly reduced. However, in this case the expected time for a successful run was significantly decreased indicating that similarity replacement can be worthwhile for problems with expensive evaluation functions. A significant discovery from our experimental work is that a small change to the way mutation is carried out can result in significant reductions in premature convergence", } @InProceedings{Ciesielski:2002:GPR, author = "Vic Ciesielski and Dylan Mawhinney and Peter Wilson", title = "Genetic Programming for Robot Soccer", booktitle = "RoboCup 2001: Robot Soccer World Cup V", year = "2002", volume = "2377", pages = "319--324", editor = "Andreas Birk and Silvia Coradeschi and Satoshi Tadokoro", series = "Lecture Notes in Computer Science", address = "Seattle, Washington, USA", month = aug # " 2001", publisher = "Springer", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-43912-9", ISSN = "0302-9743", CODEN = "LNCSD9", ISSN = "0302-9743", bibdate = "Tue Sep 10 19:09:58 MDT 2002", DOI = "doi:10.1007/3-540-45603-1_37", acknowledgement = ack-nhfb, size = "6 pages", abstract = "RoboCup is a complex simulated environment in which a team of players must cooperate to overcome their opposition in a game of soccer. This paper describes three experiments in the use of genetic programming to develop teams for RoboCup. The experiments used different combinations of low level and high level functions. The teams generated in experiment 2 were clearly better than the teams in experiment 1, and reached the level of `school boy soccer' where the players follow the ball and try to kick it. The teams generated in experiment 3 were quite good, however they were not as good as the teams evolved in experiment 2. The results suggest that genetic programming could be used to develop viable teams for the competition, however, much more work is needed on the higher level functions, fitness measures and fitness evaluation.", } @InProceedings{ciesielski:2003:psfsfdpqigp, author = "Vic Ciesielski and Xiang Li", title = "Pyramid search: Finding solutions for deceptive problems quickly in genetic programming", booktitle = "Proceedings of the 2003 Congress on Evolutionary Computation CEC2003", editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon", pages = "936--943", year = "2003", publisher = "IEEE Press", address = "Canberra", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "8-12 " # dec, organisation = "IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", keywords = "genetic algorithms, genetic programming, Australia, Computer science, Information technology, Parallel processing, probability, search problems, deceptive problem, discard process, evolve process, probability, pyramid search strategy, standard deviation", ISBN = "0-7803-7804-0", DOI = "doi:10.1109/CEC.2003.1299767", abstract = "In deceptive problems many runs lead to suboptimal solutions and it can be difficult to escape from these local optima and find the global best solution. We propose a pyramid search strategy for these kinds of problems. In the pyramid strategy a number of populations are initialised and independently evolved for a number of generations at which point the worst performing populations are discarded. This evolve/discard process is continued until the problem is solved or one population remains. We show that for a number of deceptive problems the pyramid strategy results in a higher probability of success with fewer evaluations and a lower standard deviation of the number evaluations to success than the conventional approach of running to a maximum number of generations and then restarting.", notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.", } @InProceedings{ciesielski:2004:ewefigp, title = "Experiments with Explicit For-loops in Genetic Programming", author = "Vic Ciesielski and Xiang Li", pages = "494--501", booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary Computation", year = "2004", publisher = "IEEE Press", month = "20-23 " # jun, address = "Portland, Oregon", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Theory of evolutionary algorithms", DOI = "doi:10.1109/CEC.2004.1330897", abstract = "Evolving programs with explicit loops presents major difficulties, primarily due to the massive increase in the size of the search space. Fitness evaluation becomes computationally expensive. We have investigated ways of dealing with these poblems by the evolution of for-loops of increasing semantic complexity. We have chosen two problems -- a modified Santa Fe ant problem and a sorting problem -- which have natural looping constructs in their solution and a solution without loops is not possible unless the tree depth is very large. We have shown that by conrolling the complexity of the loop structures it is possible to evolve smaller and more understandable programs for these problems.", notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.", } @InProceedings{eurogp:CiesielskiIJM05, author = "Victor Ciesielski and Andrew Innes and Sabu John and John Mamutil", editor = "Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. {van Hemert} and Marco Tomassini", title = "Understanding Evolved Genetic Programs for a Real World Object Detection Problem", booktitle = "Proceedings of the 8th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3447", year = "2005", address = "Lausanne, Switzerland", month = "30 " # mar # " - 1 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-25436-6", pages = "351--360", DOI = "doi:10.1007/978-3-540-31989-4_32", DOI = "doi:10.1007/b107383", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "We describe an approach to understanding evolved programs for a real world object detection problem, that of finding orthodontic landmarks in cranio-facial X-Rays. The approach involves modifying the fitness function to encourage the evolution of small programs, limiting the function set to a minimal number of operators and limiting the number of terminals (features). When this was done for two landmarks, an easy one and a difficult one, the evolved programs implemented a linear function of the features. Analysis of these linear functions revealed that underlying regularities were being captured and that successful evolutionary runs usually terminated with the best programs implementing one of a small number of underlying algorithms. Analysis of these algorithms revealed that they are a realistic solution to the object detection problem, given the features and operators available.", notes = "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005", } @InProceedings{Ciesielski:2006:CEC, author = "Vic Ciesielski and Gayan Wijesinghe and Andrew Innes and Sabu John", title = "Analysis of the Superiority of Parameter Optimization over Genetic Programming for a Difficult Object Problem", booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary Computation", year = "2006", editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas", pages = "4407--4414", address = "Vancouver", month = "16-21 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9487-9", DOI = "doi:10.1109/CEC.2006.1688454", size = "8 pages", abstract = "We describe a progression of solutions to a difficult object detection problem, that of locating landmarks in X-Rays used in orthodontic treatment planning. In our first formulation an object detector was a genetic program whose inputs were a number of attributes computed from a scanning window. We used a rich function set comprising + - times divide min; max; ifthenelse. Experimentation with different function sets revealed that using the function set + - gave detectors that were almost as accurate. Such detectors are essentially a linear combination of attributes so we also implemented a parameter optimisation solution with a particle swarm optimiser. Contrary to expectation, the PSO detectors are more accurate and smaller than the GP ones. Our analysis of the reasons for this reveals that (1) the PSO approach involves a considerably smaller search space than the GP approach, (2) in the PSO approach there is a 1-1 mapping between genotype and phenotype while in the GP approach this mapping is many-1 and many semantically equivalent potential solutions are evaluated, (3) the fitness landscape for PSO is a good one for search in that solutions are distributed in areas of high fitness that are easy to locate while the GP landscape is much more difficult to characterise and areas of high fitness more difficult to find.", notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D IEEE Xplore gives pages = {"}1264--1271{"},", } @InProceedings{eurogp07:ciesielski, author = "Vic Ciesielski and Xiang Li", title = "Data Mining of Genetic Programming Run Logs", editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar", booktitle = "Proceedings of the 10th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "4445", year = "2007", address = "Valencia, Spain", month = "11-13 " # apr, pages = "281--290", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-71602-5", isbn13 = "978-3-540-71602-0", DOI = "doi:10.1007/978-3-540-71605-1_26", abstract = "We have applied a range of data mining techniques to a data base of log file records created from genetic programming runs on twelve different problems. We have looked for unexpected patterns, or golden nuggets in the data. Six were found. The main discoveries were a surprising amount of evaluation of duplicate programs across the twelve problems and one case of pathological behaviour which suggested a review of the genetic programming configuration. For problems with expensive fitness evaluation, the results suggest that there would be considerable speedup by caching evolved programs and fitness values. A data mining analysis performed routinely in a GP application could identify problems early and lead to more effective genetic programming applications.", notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007", } @Article{Ciesielski:2008:GPEM, author = "Vic Ciesielski", title = "Linear genetic programming, Springer Science+Business Media, Markus Brameier and Wolfgang Banzhaf, 2007, 315 pp, Book Series: Genetic Programming, Hard Cover, 62.95, ISBN 0-387-31029-0", journal = "Genetic Programming and Evolvable Machines", year = "2008", volume = "9", number = "1", pages = "105--106", month = mar, keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dR8iD", DOI = "doi:10.1007/s10710-007-9036-8", size = "2 pages", notes = "book review of \cite{Brameier:2006:book}", } @Article{Ciftci:2009:EAEI, author = "Ozan Nazim Ciftci and Sibel Fadiloglu and Fahrettin Gogus and Aytac Guven", title = "Genetic programming approach to predict a model acidolysis system", journal = "Engineering Applications of Artificial Intelligence", year = "2009", volume = "22", pages = "759--766", number = "4-5", keywords = "genetic algorithms, genetic programming, gene expression programming, Acidolysis", DOI = "doi:10.1016/j.engappai.2009.01.010", ISSN = "0952-1976", URL = "http://www.sciencedirect.com/science/article/B6V2M-4VTVJNC-2/2/5894a9c11ade2e94a1ff09a18b63a062", abstract = "This paper models acidolysis of triolein and palmitic acid under the catalysis of immobilized sn-1,3 specific lipase. A gene-expression programming (GEP), which is an extension to genetic programming (GP)-based model was developed for the prediction of the concentration of major reaction products of this reaction (1-palmitoyl-2,3-oleoyl-glycerol (POO), 1,3-dipalmitoyl-2-oleoyl-glycerol (POP) and triolein (OOO). Substrate ratio (SR), reaction temperature (T) and reaction time (t) were used as input parameters. The predicted models were able to predict the progress of the reactions with a mean standard error (MSE) of less than 1.0 and R of 0.978. Explicit formulation of proposed GEP models was also presented. Considerable good performance was achieved in modeling acidolysis reaction by using GEP. The predictions of proposed GEP models were compared to those of neural network (NN) modeling, and strictly good agreement was observed between the two predictions. Statistics and scatter plots indicate that the new GEP formulations can be an alternative to experimental models.", } @Article{ciglaric:2006:SMO, author = "I. Ciglaric and A. Kidric", title = "Computer-aided derivation of the optimal mathematical models to study gear-pair dynamic by using genetic programming", journal = "Structural and Multidisciplinary Optimization", year = "2006", volume = "32", number = "2", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s00158-006-0004-3", DOI = "doi:10.1007/s00158-006-0004-3", } @Article{Cilibrasi:2004:CMJ, author = "Rudi Cilibrasi and Paul Vitanyi and Ronald {de Wolf}", title = "Algorithmic Clustering of Music Based on String Compression", journal = "Computer Music Journal", year = "2004", volume = "28", number = "4", pages = "49--67", month = "Winter", keywords = "genetic algorithms, genetic programming, complearn", URL = "http://homepages.cwi.nl/~paulv/papers/music.pdf", size = "19 pages", abstract = "All musical pieces are similar, but some are more similar than others. Apart from serving as an infinite source of discussion (''Haydn is just like Mozart No, he's not!''), such similarities are also crucial for the design of efficient music information retrieval systems. The amount of digitised music available on the Internet has grown dramatically in recent years, both in the public domain and on commercial sites; Napster and its clones are prime examples.", notes = "C 2004 Massachusetts Institute of Technology Earlier version at cs.SD/0303025 http://arxiv.org/abs/cs.SD/0303025", } @Misc{cs.CL/0412098, author = "Rudi Cilibrasi and Paul M. B. Vitanyi", title = "Automatic Meaning Discovery Using {Google}", year = "2005", number = "cs.CL/0412098", month = "15 " # mar, note = "v2", keywords = "genetic algorithms, genetic programming, randomised hill-climbing, SVM, support vector machines, complearn, Computation and Language, Artificial Intelligence, Databases, Information Retrieval, Learning", URL = "http://www.arxiv.org/abs/cs.CL/0412098", URL = "http://homepages.cwi.nl/~paulv/papers/amdug.pdf", abstract = "We have found a method to automatically extract the meaning of words and phrases from the world-wide-web using Google page counts. The approach is novel in its unrestricted problem domain, simplicity of implementation, and manifestly ontological underpinnings. The world-wide-web is the largest database on earth, and the latent semantic context information entered by millions of independent users averages out to provide automatic meaning of useful quality. We demonstrate positive correlations, evidencing an underlying semantic structure, in both numerical symbol notations and number-name words in a variety of natural languages and contexts. Next, we demonstrate the ability to distinguish between colours and numbers, and to distinguish between 17th century Dutch painters; the ability to understand electrical terms, religious terms, and emergency incidents; we conduct a massive experiment in understanding WordNet categories; and finally we demonstrate the ability to do a simple automatic English-Spanish translation.", notes = "ACM-class: I.2.4; I.2.7 Date (v1): Tue, 21 Dec 2004 16:05:36 GMT (127kb,S) Date (revised v2): Tue, 15 Mar 2005 16:53:43 GMT (58kb) cited by \cite{graham-rowe:2005:complearn} Code http://www.complearn.org/", size = "31 pages", } @Article{Cilibrasi:2005:ITIT, author = "Rudi Cilibrasi and Paul M. B. Vitanyi", title = "Clustering by Compression", journal = "IEEE Transactions on Information Theory", year = "2005", volume = "51", number = "4", pages = "1523--1545", month = apr, keywords = "genetic algorithms, genetic programming, complearn, universal dissimilarity distance, normalised compression distance, hierarchical unsupervised clustering, quartet tree method, parameter-free data-mining, heterogenous data analysis, Kolmogorov complexity", ISSN = "0018-9448", URL = "http://homepages.cwi.nl/~paulv/papers/cluster.pdf", DOI = "doi:10.1109/TIT.2005.844059", size = "21 pages", abstract = "We present a new method for clustering based on compression. The method doesn't use subject-specific features or background knowledge, and works as follows: First, we determine a parameter-free, universal, similarity distance, the normalized compression distance or NCD , computed from the lengths of compressed data files (singly and in pairwise concatenation). Second, we apply a hierarchical clustering method. The NCD is not restricted to a specific application area, and works across application area boundaries. A theoretical precursor, the normalised information distance, co-developed by one of the authors, is provably optimal. However, the optimality comes at the price of using the non-computable notion of Kolmogorov complexity. We propose axioms to capture the real-world setting, and show that the NCD approximates optimality. To extract a hierarchy of clusters from the distance matrix, we determine a dendrogram (binary tree) by a new quartet method and a fast heuristic to implement it. The method is implemented and available as public software, and is robust under choice of different compressors. To substantiate our claims of universality and robustness, we report evidence of successful application in areas as diverse as genomics, virology, languages, literature, music, handwritten digits, astronomy, and combinations of objects from completely different domains, using statistical, dictionary, and block sorting compressors. In genomics we presented new evidence for major questions in Mammalian evolution, based on whole-mitochondrial genomic analysis: the Eutherian orders and the Marsupionta hypothesis against the Theria hypothesis.", notes = "With respect to the version published in the IEEE Trans. Inform. Th., 51:4(2005), 1523-1545, we have changed Definition 2.1 of 'admissible distance' making it more general and Definitions 2.4 and 2.5 of 'normalized admissible distance,' consequently adapted Lemma 2.6 (II.2) and in its proof (II.3) and the displayed inequalities. This left Theorem 6.3 unchanged except for changing 'such that d(x; y) \le e' to 'such that d(x; y) \le e and C(v) \le C(x).'", } @InProceedings{Cilibrasi:2005:pascal, author = "Rudi Cilibrasi and Paul Vitanyi", title = "A New Quartet Tree Heuristic for Hierarchical Clustering", booktitle = "Principled methods of trading exploration and exploitation Workshop", year = "2005", address = "London", month = "6-7 " # jul, organisation = "PASCAL", keywords = "genetic algorithms, genetic programming, Computational, Information-Theoretic Learning with Statistics, Learning/Statistics, Optimisation, Theory, Algorithms", URL = "http://www.cwi.nl/~paulv/papers/quartet.pdf", size = "22 pages", abstract = "We consider the problem of constructing an an optimal-weight tree from the 3Chose(n,4) weighted quartet topologies on n objects, where optimality means that the summed weight of the embedded quartet topologies is optimal (so it can be the case that the optimal tree embeds all quartets as non-optimal topologies). We present a heuristic for reconstructing the optimal-weight tree, and a canonical manner to derive the quartet-topology weights from a given distance matrix. The method repeatedly transforms a bifurcating tree, with all objects involved as leaves, achieving a monotonic approximation to the exact single globally optimal tree. This contrasts to other heuristic search methods from biological phylogeny, like DNAML or quartet puzzling, which, repeatedly, incrementally construct a solution from a random order of objects, and subsequently add agreement values. We do not assume that there exists a true bifurcating supertree that embeds each quartet in the optimal topology, or represents the distance matrix faithfully|not even under the assumption that the weights or distances are corrupted by a measuring process. Our aim is to hierarchically cluster the input data as faithfully as possible, both phylogenetic data and data of completely different types. In our experiments with natural data, like genomic data, texts or music, the global optimum appears to be reached. Our method is capable of handling over 100 objects, possibly up to 1000 objects, while no existing quartet heuristic can computionally approximate the exact optimal solution of a quartet tree of more than about 20-30 objects without running for years. The method is implemented and available as public software.", notes = "http://eprints.pascal-network.org/archive/00001821/ paper improved after workshop?", } @InProceedings{cilibrasi_et_al:DSP:2006:598, author = "Rudi Cilibrasi and Paul M. B. Vitanyi", title = "A New Quartet Tree Heuristic for Hierarchical Clustering", booktitle = "Theory of Evolutionary Algorithms", year = "2006", editor = "Dirk V. Arnold and Thomas Jansen and Michael D. Vose and Jonathan E. Rowe", number = "06061", series = "Dagstuhl Seminar Proceedings", ISSN = "1862-4405", publisher = "Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany", address = "Dagstuhl, Germany", URL = "http://drops.dagstuhl.de/opus/volltexte/2006/598/pdf/06061.VitanyiPaulB.Paper.598.pdf", URL = "http://drops.dagstuhl.de/opus/volltexte/2006/598", note = "$<$http://drops.dagstuhl.de/opus/volltexte/2006/598$>$ [date of citation: 2006-01-01]", month = "5-10 " # feb, keywords = "genetic algorithms, genetic programming, hierarchical clustering, quartet tree method", size = "13 pages", abstract = "We present a new quartet heuristic for hierarchical clustering from a given distance matrix. We determine a dendrogram (ternary tree) by a new quartet method and a fast heuristic to implement it. We do not assume that there is a true ternary tree that generated the distances and which we with to recover as closely as possible. Our aim is to model the distance matrix as faithfully as possible by the dendrogram. Our algorithm is essentially randomised hill-climbing, using parallelised Genetic Programming, where undirected trees evolve in a random walk driven by a prescribed fitness function. Our method is capable of handling up to 60--80 objects in a matter of hours, while no existing quartet heuristic can directly compute a quartet tree of more than about 20--30 objects without running for years. The method is implemented and available as public software at www.complearn.org. We present applications in many areas like music, literature, bird-flu (H5N1) virus clustering, and automatic meaning discovery using Google.", } @PhdThesis{Cilibrasi:thesis, author = "Rudi Langston Cilibrasi", title = "Statistical Inference Through Data Compression", school = "Institute for Logic, Language and Computation, Universiteit van Amsterdam", year = "2007", address = "Plantage Muidergracht 24, 1018 TV, Amsterdam, Holland", month = "23 " # feb, keywords = "genetic algorithms, genetic programming", URL = "https://eprints.illc.uva.nl/id/eprint/2056/", URL = "http://www.illc.uva.nl/Research/Dissertations/DS-2007-01.text.pdf", broken = "http://www.lulu.com/shop/search.ep?contributorId=254359", URL = "https://books.google.co.uk/books/about/Statistical_Inference_Through_Data_Compr.html?id=PgR1DmReI30C", code_url = "https://github.com/rudi-cilibrasi/libcomplearn", size = "225 pages", ISBN = "90-6196-540-3", abstract = "This thesis provides a breadth-first tour of artificial intelligence techniques using ordinary data compression programs like zip. Using mathematical theory such as Kolmogorov Complexity and Shannon's Coding Theory, we arrive at a unique and generic perspective on universal learning with a plethora of real examples. Included are results from literature, astronomy, animal and virus evolution, linguistics, semantics, and music. An open source software package, CompLearn, is available for download so that interested readers may continue the research themselves in their own applications.", notes = "p51 'Our algorithm is essentially randomized hill-climbing, using parallellized Genetic Programming,' Supervisor: Paul M.B. Vitanyi (CWI)", } @Article{Cilibrasi:2007:ieeeTKDE, author = "Rudi L. Cilibrasi and Paul M. B. Vitanyi", title = "The Google Similarity Distance", journal = "IEEE Transactions on Knowledge and Data Engineering", year = "2007", volume = "19", number = "3", pages = "370--383", month = mar, keywords = "genetic algorithms, genetic programming, Kolmogorov complexity, wordnet, artificial common sense, Accuracy comparison with WordNet categories, automatic classification and clustering, automatic meaning discovery using Google, automatic relative semantics, automatic translation, dissimilarity semantic distance, Google search, Google distribution via page hit counts, Google code, Kolmogorov complexity, normalized compression distance (NCD ), normalized information distance (NID), normalized Google distance (NGD), meaning of words and phrases extracted from the Web, parameter-free data mining, universal similarity metric", ISSN = "1041-4347", DOI = "doi:10.1109/TKDE.2007.48", size = "14 pages", abstract = "Words and phrases acquire meaning from the way they are used in society, from their relative semantics to other words and phrases. For computers, the equivalent of {"}society{"} is {"}database,{"} and the equivalent of {"}use{"} is {"}a way to search the database{"}. We present a new theory of similarity between words and phrases based on information distance and Kolmogorov complexity. To fix thoughts, we use the World Wide Web (WWW) as the database, and Google as the search engine. The method is also applicable to other search engines and databases. This theory is then applied to construct a method to automatically extract similarity, the Google similarity distance, of words and phrases from the WWW using Google page counts. The WWW is the largest database on earth, and the context information entered by millions of independent users averages out to provide automatic semantics of useful quality. We give applications in hierarchical clustering, classification, and language translation. We give examples to distinguish between colours and numbers, cluster names of paintings by 17th century Dutch masters and names of books by English novelists, the ability to understand emergencies and primes, and we demonstrate the ability to do a simple automatic English-Spanish translation. Finally, we use the WordNet database as an objective baseline against which to judge the performance of our method. We conduct a massive randomized trial in binary classification using support vector machines to learn categories based on our Google distance, resulting in an a mean agreement of 87 percent with the expert crafted WordNet categories", notes = "Also known as \cite{4072748}", } @PhdThesis{cilliers:thesis, author = "Michael Cilliers", title = "Maintaining population diversity in evolutionary algorithms via epigenetics and speciation", school = "Academy Computer Science and Software Engineering, University of Johannesburg", year = "2022", address = "South Africa", month = nov, keywords = "genetic algorithms, genetic programming, Evolutionary computation, Species", language = "eng", URL = "https://hdl.handle.net/10210/504017", URL = "https://www.proquest.com/dissertations-theses/maintaining-population-diversity-evolutionary/docview/3132878141/se-2", size = "144 pages", abstract = "When working with evolutionary algorithms, a balance between exploration and exploitation has to be maintained. The main driver of exploration is the reproduction operation, which is dependent on population diversity to enable effective exploration. The lack of population diversity can thus become a problem if exploration is required during the later stages of the algorithm. One example where this is relevant is when working with dynamic environments. If a change occurs in the environment, the algorithm might require some exploration to adapt to the change. This research aims to demonstrate that if an algorithm is able to maintain population diversity during the latter of its execution, it will be able to adapt to changes more effectively. This research produced a set of novel algorithms to explore different methods of maintaining population diversity. The first group of developed algorithms draws inspiration from gene methylation by adding non-coding genes to the chromosome. The non-coding genes can then act as a reservoir of genetic diversity after the algorithm converges and the diversity in the expressed genes diminishes. The second algorithm implemented parapatric speciation in an evolutionary algorithm. The algorithm attempts to divide the population into separate species, which then populate different areas of the search space. Testing of the developed algorithm showed that it is possible to maintain more diversity in the latter generations of the algorithm. The developed algorithms were also able to adapt more effectively to change in the environment, indicating that the algorithms were able to use the increased diversity when required. Finally, the research showed that the maintaining of diversity is not the only option for allowing exploration in the latter stages of an algorithm. Algorithms that are less effective at maintaining diversity, but are able to rapidly produce diversity when required are also able to shift focus to exploration when adapting to change in the environment.", notes = "Also known as \cite{CilliersMichael2022Mpdi} Cilliers_Michael_Wtm.pdf Identifiers 9927009507691 Order No. 31715765 Available from ProQuest Dissertations Theses Global. (3132878141) Promotor: DA Coulter", } @PhdThesis{Cimmino:thesis, author = "Andrea Jesus {Cimmino Arriaga}", title = "Enterprise information integration: on discovering links using genetic programming", school = "Departamento de Lenguajes y Sistemas Informaticos, Universidad de Sevilla", year = "2019", address = "Sevilla, Spain", month = sep, keywords = "genetic algorithms, genetic programming, Eva4LD, Web of Data, Sorbas, Teide", URL = "https://idus.us.es/handle/11441/92456", URL = "https://idus.us.es/bitstream/handle/11441/92456/PhD-Report-1.pdf", size = "113 pages", abstract = "Both established and emergent business rely heavily on data, chiefly those that wish to become game changers. The current biggest source of data is the Web, where there is a large amount of sparse data. The Web of Data aims at providing a unified view of these islands of data. To realise this vision, it is required that the resources in different data sources that refer to the same real-world entities must be linked, which is they key factor for such a unified view. Link discovery is a trending task that aims at finding link rules that specify whether these links must be established or not. Currently there are many proposals in the literature to produce these links, especially based on meta-heuristics. Unfortunately, creating proposals based on meta-heuristics is not a trivial task, which has led to a lack of comparison between some well-established proposals. On the other hand, it has been proved that these link rules fall short in cases in which resources that refer to different real-world entities are very similar or vice versa. In this dissertation, we introduce several proposals to address the previous lacks in the literature. On the one hand we, introduce Eva4LD, which is a generic framework to build genetic programming proposals for link discovery; which are a kind of meta-heuristics proposals. Furthermore, our framework allows to implement many proposals in the literature and compare their results fairly. On the other hand, we introduce Teide, which applies effectively the link rules increasing significantly their precision without dropping their recall significantly. Unfortunately, Teide does not learn link rules, and applying all the provided link rules is computationally expensive. Due to this reason we introduce Sorbas, which learns what we call contextual link rules.", resumen = "Las empresas que desean establecer un precedente en el panorama actual tienden a recurrir al uso de datos para mejorar sus modelos de negocio. La mayor fuente de datos disponible es la Web, donde una gran cantidad es accesible aunque se encuentre fragmentada en islas de datos. La Web de los Datos tiene como objetivo dar una vision unificada de dichas islas, aunque el almacenamiento de los mismos siga siendo distribuido. Para ofrecer esta vision es necesario enlazar los recursos presentes en las islas de datos que hacen referencia a las mismas entidades del mundo real. Link discovery es el nombre atribuido a esta tarea, la cual se basa en generar reglas de enlazado que permiten establecer bajo que circunstancias dos recursos deben ser enlazados. Se pueden encontrar diferentes propuestas en la literatura de link discovery, especialmente basadas en meta-heuristicas. Por desgracia comparar propuestas basadas en meta-heuristicas no es trivial. Por otro lado, se ha probado que estas reglas de enlazado no funcionan bien cuando los recursos que hacen referencia a dos entidades distintas del mundo real son muy parecidos, o por el contrario, cuando dos recursos muy distintos hacen referencia a la misma entidad. En esta tesis presentamos varias propuestas. Por un lado, Eva4LD es un framework generico para desarrollar propuestas de link discovery basadas en programacion genetica, que es un tipo de meta-heuristica. Gracias a nuestro framework, hemos podido implementar distintas propuestas de la literatura y comprar justamente sus resultados. Por otro lado, en la tesis presentamos Teide, una propuesta que recibiendo varias reglas de enlazado las aplica de tal modo que mejora significativamente la precision de las mismas sin reducir significativamente su cobertura. Por desgracia, Teide es computacionalmente costoso debido a que no aprende reglas. Debido a este motivo, presentamos Sorbas que aprende un tipo de reglas de enlazado que denominamos reglas de enlazado con contexto.", notes = "In English. First published in July 2019 by The Distributed Group, ETSI Informatica, Avda. de la Reina Mercedes, s/n Sevilla, E-41012. SPAIN Supervisor: Rafael Corchuelo Gil", } @Article{DBLP:journals/jei/CioccaCG16, author = "Gianluigi Ciocca and Silvia Corchs and Francesca Gasparini", title = "Genetic programming approach to evaluate complexity of texture images", journal = "J. Electronic Imaging", volume = "25", number = "6", pages = "061408", year = "2016", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1117/1.JEI.25.6.061408", DOI = "doi:10.1117/1.JEI.25.6.061408", timestamp = "Wed, 29 May 2019 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/jei/CioccaCG16.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{Cirillo:2014:GECCOcomp, author = "Simone Cirillo and Stefan Lloyd", title = "A scalable symbolic expression tree interpreter for the heuristiclab optimization framework", booktitle = "GECCO 2014 Workshop on Evolutionary Computation Software Systems (EvoSoft)", year = "2014", editor = "Stefan Wagner and Michael Affenzeller", isbn13 = "978-1-4503-2881-4", keywords = "genetic algorithms, genetic programming", pages = "1141--1148", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2598394.2605692", DOI = "doi:10.1145/2598394.2605692", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "In this paper we describe a novel implementation of the Interpreter class for the metaheuristic optimisation framework HeuristicLab, comparing it with the three existing interpreters provided with the framework. The Interpreter class is an internal software component used by HeuristicLab for the evaluation of the symbolic expression trees on which its Genetic Programming (GP) implementation relies. The proposed implementation is based on the creation and compilation of a .NET Expression Tree. We also analyse the Interpreters' performance, evaluating the algorithm execution times on GP Symbolic Regression problems for different run settings. Our implementation results to be the fastest on all evaluations, with comparatively better performance the larger the run population size, dataset length and tree size are, increasing HeuristicLab's computational efficiency for large problem setups.", notes = "Also known as \cite{2605692} Distributed at GECCO-2014.", } @Misc{DBLP:journals/corr/CirilloLN14, author = "Simone Cirillo and Stefan Lloyd and Peter Nordin", title = "Evolving intraday foreign exchange trading strategies utilizing multiple instruments price series", howpublished = "ArXiv", year = "2014", volume = "abs/1411.2153", month = "8 " # nov, keywords = "genetic algorithms, genetic programming", URL = "http://arxiv.org/abs/1411.2153", timestamp = "Fri, 03 Jun 4433701 16:53:52 +", biburl = "http://dblp.uni-trier.de/rec/bib/journals/corr/CirilloLN14", bibsource = "dblp computer science bibliography, http://dblp.org", size = "15 pages", abstract = "We propose a Genetic Programming architecture for the generation of foreign exchange trading strategies. The system's principal features are the evolution of free-form strategies which do not rely on any prior models and the use of price series from multiple instruments as input data. This latter feature constitutes an innovation with respect to previous works documented in literature. In this article we use Open, High, Low, Close bar data at a 5 minutes frequency for the AUD.USD, EUR.USD, GBP.USD and USD.JPY currency pairs. We will test the implementation analysing the in-sample and out-of-sample performance of strategies for trading the USD.JPY obtained across multiple algorithm runs. We will also evaluate the differences between strategies selected according to two different criteria: one relies on the fitness obtained on the training set only, the second one makes use of an additional validation dataset. Strategy activity and trade accuracy are remarkably stable between in and out of sample results. From a profitability aspect, the two criteria both result in strategies successful on out-of-sample data but exhibiting different characteristics. The overall best performing out-of-sample strategy achieves a yearly return of 19percent.", } @Article{Citakoglu:2020:TAC, author = "Hatice Citakoglu and Bilal Babayigit and Nese Acanal Haktanir", title = "Solar radiation prediction using multi-gene genetic programming approach", journal = "Theoretical and Applied Climatology", year = "2020", volume = "142", pages = "885--897", keywords = "genetic algorithms, genetic programming, Multi-gene genetic programming, Solar radiation, Empirical equations, Turkey", URL = "https://avesis.erciyes.edu.tr/publication/details/2c40546c-794d-4d59-98b3-87a478e63ac9/solar-radiation-prediction-using-multi-gene-genetic-programming-approach", URL = "https://rdcu.be/c2XJM", DOI = "doi:10.1007/s00704-020-03356-4", size = "13 pages", abstract = "Accurate estimation of solar radiation both spatially and temporally is important for engineering studies related to climate and energy. The multi-gene genetic programming (MGGP) is proposed as a new compact method for this purpose, which is verified to yield more accurate solar radiation estimations in Turkey. Meteorological data such as extraterrestrial solar radiation, sunshine duration, mean of monthly maximum sunny hours, long-term mean of monthly maximum air temperatures, long-term mean of monthly minimum air temperatures, monthly mean air temperature, and monthly mean moisture data are selected as the MGGP model inputs. In the prediction models, the meteorological data measured from 163 stations in seven climate areas of Turkey over the period 1975 to 2015 are used. The MGGP model results for solar radiation prediction are found to be more accurate than the values given by some conventional empirical equations such as Abdalla, Angstrom, and Hargreaves-Samani. The performance of MGGP is also assessed for Turkey by single-data and multi-data models. The multi-data models of MGGP and the calibrated empirical equations are found to be more successful than the single-data models for solar radiation prediction.", notes = "Department of Civil Engineering, Erciyes University, Kayseri, Turkey", } @Article{citakoglu:2023:AG, author = "Hatice Citakoglu and Vahdettin Demir", title = "Developing numerical equality to regional intensity-duration-frequency curves using evolutionary algorithms and multi-gene genetic programming", journal = "Acta Geophysica", year = "2023", volume = "71", number = "1", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s11600-022-00883-8", DOI = "doi:10.1007/s11600-022-00883-8", } @InCollection{Citi:2009:GPTP, author = "Luca Citi and Riccardo Poli and Caterina Cinel", title = "High-significance Averages of Event-Related Potential via Genetic Programming", booktitle = "Genetic Programming Theory and Practice {VII}", year = "2009", editor = "Rick L. Riolo and Una-May O'Reilly and Trent McConaghy", series = "Genetic and Evolutionary Computation", address = "Ann Arbor", month = "14-16 " # may, publisher = "Springer", chapter = "9", pages = "135--157", keywords = "genetic algorithms, genetic programming, Event-related potentials, Register-based GP, Memory-with-Memory", isbn13 = "978-1-4419-1653-2", DOI = "doi:10.1007/978-1-4419-1626-6_9", abstract = "In this paper we use register-based genetic programming with memory-with memory to discover probabilistic membership functions that are used to divide up data-sets of event-related potentials recorded via EEG in psycho-physiological experiments based on the corresponding response times. The objective is to evolve membership functions which lead to maximising the statistical significance with which true brain waves can be reconstructed when averaging the trials in each bin. Results show that GP can significantly improve the fidelity with which ERP components can be recovered.", notes = "part of \cite{Riolo:2009:GPTP}", } @TechReport{clack:1996:adca, author = "Chris Clack and Jonny Farringdon and Peter Lidwell and Tina Yu", title = "An Adaptive Document Classification Agent", institution = "University College London", year = "1996", type = "Research Note", number = "RN/96/45", address = "Computer Science, Gower Street, London, WC1E 6BT, UK", month = "21 " # jun, note = "Submitted to BCS-ES96", keywords = "genetic algorithms, genetic programming", URL = "https://web.archive.org/web/19970817124736/http://www.cs.ucl.ac.uk:80/staff/J.Farringdon/GP/Papers-es96/paper02.html", URL = "http://www.cs.ucl.ac.uk/research/rns/rns96.html", abstract = "The development of an intelligent text classification application is discussed which uses genetic programming methods. Learning capabilities are used to effect a adaptive system in order to meet the needs of dynamic-information users. Deriving structure and priority from text, target environments are discussed where large volumes of (on-line) textual documents are manipulated.", notes = "3 figures as separte ps files in the same directory", } @TechReport{clack:1996:rn38, author = "Chris D. Clack and S. J. Gould and Peter R. Lidwell and Janet T. McDonnell", title = "Advanced Technology Support for Information Management at Friends of the Earth", institution = "University College London", year = "1996", type = "Research Note", number = "RN/96/48", address = "Computer Science, Gower Street, London, WC1E 6BT, UK", keywords = "genetic algorithms, genetic programming", URL = "ftp://bells.cs.ucl.ac.uk/functional/papers/Published/rn_96_38pagenums.pdf.gz", URL = "http://citeseer.ist.psu.edu/92899.html", abstract = "INTRODUCTION We report early results from a project to study the application of advanced technology to enhance information management in a medium sized enterprise where the collection, analysis and dissemination of information are key business processes. Our two-year TCD-funded project is a collaboration between University College London (UCL) and Friends of the Earth (FOE), a research and campaigning organisation with 65 full time employees and a turnover of about 3.5 million pounds. We explain our strategy for re-engineering information management at Foe and present three example projects which demonstrate the application of innovative IT solutions to problems associated with fundamental working practices.", size = "6 pages", } @TechReport{clack:1996:adcb, author = "Chris Clack and Jonny Farringdon and Peter Lidwell and Tina Yu", title = "Autonomous Document Classification for Business", institution = "University College London", year = "1996", type = "Research Note", number = "RN/96/48", address = "Computer Science, Gower Street, London, WC1E 6BT, UK", month = jun, note = "Appears in Autonomous Agents '97", keywords = "genetic algorithms, genetic programming, Softbot, agent architecture, pattern recognition, long term adaptation and learning", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/clack_1997_adcb.pdf", abstract = "With the continuing exponential growth of the Internet and the more recent growth of business Intranets, the commercial world is becoming increasingly aware of the problem of electronic information overload. This has encouraged interest in developing agents/softbots that can act as electronic personal assistants and can develop and adapt representations of users information needs, commonly known as profiles. As the result of collaborative research with Friends of the Earth, an environmental issues campaigning organisation, we have developed a general purpose information classification agent architecture and have applied it to the problem of document classification and routing. Collaboration with Friends of the Earth allows us to test our ideas in a non-academic context involving high volumes of documents. We use the technique of genetic programming (GP), (Koza and Rice 1992), to evolve classifying agents. This is a novel approach for document classification, where each agent evolves a parse-tree representation of a user's particular information need. The other unusual feature of our research is the longevity of our agents and the fact that they undergo a continual training process; feedback from the user enables the agent to adapt to the user's long-term information requirements.", notes = "see also \cite{clack:1997:adcb}", size = "8 pages", } @InProceedings{clack:1997:adcb, author = "Chris Clack and Jonny Farringdon and Peter Lidwell and Tina Yu", title = "Autonomous Document Classification for Business", booktitle = "The First International Conference on Autonomous Agents (Agents '97)", year = "1997", editor = "W. Lewis Johnson", pages = "201--208", address = "Marina del Rey, California, USA", publisher_address = "1515 Broadway, New York, NY 10036, USA", month = feb # " 5-8", organisation = "ACM SIGART", publisher = "ACM Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-89791-877-0", URL = "ftp://ftp.cs.ucl.ac.uk/functional/papers/Published/AA97.pdf.gz", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/clack_1997_adcb.pdf", DOI = "doi:10.1145/267658.267716", size = "11 pages", abstract = "With the continuing exponential growth of the Internet and the more recent growth of business Intranets, the commercial world is becoming increasingly aware of the problem of electronic information overload. This has encouraged interest in developing agents/softbots that can act as electronic personal assistants and can develop and adapt representations of users information needs, commonly known as profiles. As the result of collaborative research with Friends of the Earth, an environmental issues campaigning organisation, we have developed a general purpose information classification agent architecture and have applied it to the problem of document classification and routing. Collaboration with Friends of the Earth allows us to test our ideas in a non-academic context involving high volumes of documents. We use the technique of genetic programming (GP), (Koza and Rice 1992), to evolve classifying agents. This is a novel approach for document classification, where each agent evolves a parse-tree representation of a user's particular information need. The other unusual features of our research are the longevity of our agents and the fact that they undergo a continual training process; feedback from the user enables the agent to adapt to the user's long-term information requirements.", notes = "http://www.isi.edu/isd/AA97/info.html see also \cite{clack:1996:adcb}", } @TechReport{clack:1997:edc, author = "Chris Clack", title = "Software -- The Next Generation: Evolving Document Classification", institution = "UCL, Andersen Consulting", year = "1997", type = "white paper", address = "University College London, Gower Street, London", month = apr, keywords = "genetic algorithms, genetic programming", pages = "55--67", notes = "Part of {"}Emerging Technologies White Papers: Software -- The Next Generation{"} which reports the 1996 workshop on Emerging technologies held in UCL Computer Science dept. for Andersen Consulting's Emerging Technologies Group and others.", size = "13 pages", } @InCollection{clark:1995:PISW, author = "Adam Clark", title = "Predator-Prey Interactions in a Simulated World", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1995", year = "1995", editor = "John R. Koza", pages = "59--64", address = "Stanford, California, 94305-3079 USA", month = "11 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-195720-5", notes = "part of \cite{koza:1995:gagp}", } @Article{doi:10.1142/S0218196713500227, author = "David M. Clark", title = "Evolution of Algebraic Terms 1: Term to Term Operation Continuity", journal = "International Journal of Algebra and Computation", year = "2013", volume = "23", number = "05", pages = "1175--1205", month = aug, keywords = "genetic algorithms, genetic programming, nand, theory, Evolutionary computation, term generation, term operation, primal algebras", ISSN = "0218-1967", publisher = "World Scientific Publishing Company", URL = "http://www.worldscientific.com/doi/abs/10.1142/S0218196713500227", eprint = "http://www.worldscientific.com/doi/pdf/10.1142/S0218196713500227", DOI = "doi:10.1142/S0218196713500227", URL = "http://groups.yahoo.com/neo/groups/genetic_programming/conversations/messages/6284", size = "31 pages", abstract = "This study was inspired by recent successful applications of evolutionary computation to the problem of finding terms to represent arbitrarily given operations on a primal groupoid. Evolution requires that small changes in a term result in small changes in the associated term operation. We prove a theorem giving two readily testable conditions under which a groupoid must have this continuity property, and offer evidence that most primal groupoids satisfy these conditions.", notes = "Mathematics Subject Classification 2010: 08A40, 68Q05, 92D15 See also \cite{Evolution_Algebraic_Terrms_2} \cite{Evolution_Algebraic_Terrms_3} Mathematics Department State University of New York at New Paltz FOB E1, New Paltz, New York 12561, USA", } @Article{Evolution_Algebraic_Terrms_2, author = "David M. Clark and Maarten Keijzer and Lee Spector", title = "Evolution of algebraic terms 2: Deep drilling algorithm", journal = "International Journal of Algebra and Computation", year = "2016", volume = "26", number = "6", pages = "1141--1176", keywords = "genetic algorithms, genetic programming, theory, Evolutionary computation, term operation, idemprimality, primal algebras", ISSN = "0218-1967", publisher = "World Scientific Publishing Company", DOI = "doi:10.1142/S021819671650048X", size = "36 pages", abstract = "The Deep Drilling Algorithm (DDA) is an efficient non-evolutionary algorithm, extracted from previous work with evolutionary algorithms, that takes as input a finite groupoid and an operation over its universe, and searches for a term representing that operation. We give theoretical and experimental evidence that this algorithm is successful for all idemprimal term continuous groupoids, which appear to be almost all finite groupoids, and that the DDA is seriously compromised or fails for most finite groupoids not meeting both of these conditions. See our online version of the DDA at http://hampshire.edu/lspector/dda", notes = "See also \cite{doi:10.1142/S0218196713500227} \cite{Evolution_Algebraic_Terrms_3} Mathematics Department, SUNY New Paltz New Paltz, New York 12561, USA ", } @Article{Evolution_Algebraic_Terrms_3, author = "David M. Clark and Lee Spector", title = "Evolution of algebraic terms 3: Term continuity and beam algorithms", journal = "International Journal of Algebra and Computation", year = "2018", volume = "5", number = "28", pages = "759--790", keywords = "genetic algorithms, genetic programming, theory, Evolutionary computation, term operation, idemprimality, term continuity, randomizing algorithms", ISSN = "0218-1967", publisher = "World Scientific Publishing Company", DOI = "doi:10.1142/S0218196718500352", size = "32 pages", abstract = "The first paper in this series introduced the notion of term to term operation continuity for finite groupoids, and proved that two testable conditions on a finite groupoid imply that it is term continuous (TC). The second presented an evolution inspired algorithm for finding terms for operations, and gave experimental evidence that, in general, it was successful exactly when the groupoid was both idemprimal and TC. Here we describe a new class of algorithms for finding terms which brings these results together. Theorems about idemprimality and term continuity show how each of these two properties impact our algorithms. They lead to a final explanation for the success of our algorithms when the groupoid is both idemprimal and TC.", notes = "Mathematics Subject Classification 2010: 08A40, 08A70 See also \cite{doi:10.1142/S0218196713500227} \cite{Evolution_Algebraic_Terrms_2} Mathematics Department, SUNY New Paltz New Paltz, NY 12561, USA ", } @InCollection{Clarke:2017:miller, author = "Tim Clarke", title = "Cartesian Genetic Programming for Control Engineering", booktitle = "Inspired by Nature: Essays Presented to Julian F. Miller on the Occasion of his 60th Birthday", publisher = "Springer", year = "2017", editor = "Susan Stepney and Andrew Adamatzky", volume = "28", series = "Emergence, Complexity and Computation", chapter = "7", pages = "157--173", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", isbn13 = "978-3-319-67996-9", DOI = "doi:10.1007/978-3-319-67997-6_7", abstract = "Genetic programming has a proven ability to discover novel solutions to engineering problems. The author has worked with Julian F. Miller, together with some undergraduate and postgraduate students, over the last ten or so years in exploring innovation through evolution, using Cartesian Genetic Programming (CGP). Our co-supervisions and private meetings stimulated many discussions about its application to a specific problem domain: control engineering. Initially, we explored the design of a flight control system for a single rotor helicopter, where the author has considerable theoretical and practical experience. The challenge of taming helicopter dynamics (which are non-linear, highly cross-coupled and unstable) seemed ideally suited to the application of CGP. However, our combined energies drew us towards the more fundamental issues of how best to generalise the problem with the objective of freeing up the innovation process from constrictions imposed by conventional engineering thinking. This chapter provides an outline of our thoughts and hopefully may motivate a reader out there to progress this still embryonic research. The scene is set by considering a simple class of problems: the single-input, single-output, linear, time-invariant system.", notes = "part of \cite{miller60book} https://link.springer.com/bookseries/10624", } @Article{Claveria:2016:EEE, author = "Oscar Claveria and Enric Monte and Salvador Torra", title = "Quantification of Survey Expectations by Means of Symbolic Regression via Genetic Programming to Estimate Economic Growth in Central and Eastern European Economies", journal = "Eastern European Economics", year = "2016", volume = "54", number = "2", pages = "171--189", keywords = "genetic algorithms, genetic programming, Economic Climate Indicators, evolutionary algorithms, forecasting, symbolic regression, survey-based expectations, tendency surveys", ISSN = "0012-8775", URL = "https://doi.org/10.1080/00128775.2015.1136564", size = "19 pages", abstract = "Tendency surveys are the main source of agents expectations. This study has a twofold aim. First, it proposes a new method to quantify survey-based expectations by means of symbolic regression (SR) via genetic programming. Second, it combines the main SR-generated indicators to estimate the evolution of GDP, obtaining the best results for the Czech Republic and Hungary. Finally, it assesses the impact of the 2008 financial crisis, finding that the capacity of agents expectations to anticipate economic growth in most Central and Eastern European economies improved after the crisis.", notes = "Institute of Applied Economics Research (AQR-IREA), University of Barcelona, Barcelona, Spain", } @Article{CLAVERIA:2017:JAE, author = "Oscar Claveria and Enric Monte and Salvador Torra", title = "Using survey data to forecast real activity with evolutionary algorithms. A cross-country analysis", journal = "Journal of Applied Economics", volume = "20", number = "2", pages = "329--349", year = "2017", keywords = "genetic algorithms, genetic programming, C51, C55, C63, C83, C93, business and consumer surveys, forecasting, economic growth, symbolic regression, evolutionary algorithms", ISSN = "1514-0326", DOI = "doi:10.1016/S1514-0326(17)30015-6", URL = "http://www.sciencedirect.com/science/article/pii/S1514032617300156", abstract = "In this study we use survey expectations about a wide range of economic variables to forecast real activity. We propose an empirical approach to derive mathematical functional forms that link survey expectations to economic growth. Combining symbolic regression with genetic programming we generate two survey-based indicators: a perceptions index, using agents' assessments about the present, and an expectations index with their expectations about the future. In order to find the optimal combination of both indexes that best replicates the evolution of economic activity in each country we use a portfolio management procedure known as index tracking. By means of a generalized reduced gradient algorithm we derive the relative weights of both indexes. In most economies, the survey-based predictions generated with the composite indicator outperform the benchmark model for one-quarter ahead forecasts, although these improvements are only significant in Austria, Belgium and Portugal", } @Article{Claveria:2017:AEL, author = "Oscar Claveria and Enric Monte and Salvador Torra", title = "Assessment of the effect of the financial crisis on agents expectations through symbolic regression", journal = "Applied Economics Letters", year = "2017", volume = "24", number = "9", pages = "648--652", keywords = "genetic algorithms, genetic programming, Symbolic regression, evolutionary algorithms, tendency surveys, expectations, forecasting", ISSN = "1350-4851", URL = "https://doi.org/10.1080/13504851.2016.1218419", size = "6 pages", abstract = "Agents perceptions on the state of the economy can be affected during economic crises. Tendency surveys are the main source of agents expectations. The main objective of this study is to assess the impact of the 2008 financial crisis on agents expectations. With this aim, we evaluate the capacity of survey-based expectations to anticipate economic growth in the United States, Japan, Germany and the United Kingdom. We propose a symbolic regression (SR) via genetic programming approach to derive mathematical functional forms that link survey-based expectations to GDP growth. By combining the main SR-generated indicators, we generate estimates of the evolution of GDP. Finally, we analyse the effect of the crisis on the formation of expectations, and we find an improvement in the capacity of agents expectations to anticipate economic growth after the crisis in all countries except Germany.", notes = "https://www.tandfonline.com/loi/rael20 AQR-IREA (Institute of Applied Economics Research), University of Barcelona (UB), Barcelona, Spain", } @Article{Claveria:2017:QQ, author = "Oscar Claveria and Enric Monte and Salvador Torra", title = "A new approach for the quantification of qualitative measures of economic expectations", journal = "Quality \& Quantity", year = "2017", volume = "51", number = "6", pages = "2685--2706", month = nov, keywords = "genetic algorithms, genetic programming, Economic growth, Qualitative survey data, Expectations, Symbolic regression, Evolutionary algorithms", ISSN = "0033-5177", sharedit_url = "https://rdcu.be/bfEKz", DOI = "doi:10.1007/s11135-016-0416-0", size = "22 pages", abstract = "In this study a new approach to quantify qualitative survey data about the direction of change is presented. We propose a data-driven procedure based on evolutionary computation that avoids making any assumption about agents expectations. The research focuses on experts expectations about the state of the economy from the World Economic Survey in twenty eight countries of the Organisation for Economic Co-operation and Development. The proposed method is used to transform qualitative responses into estimates of economic growth. In a first experiment, we combine agents expectations about the future to construct a leading indicator of economic activity. In a second experiment, agents judgements about the present are combined to generate a coincident indicator. Then, we use index tracking to derive the optimal combination of weights for both indicators that best replicates the evolution of economic activity in each country. Finally, we compute several accuracy measures to assess the performance of these estimates in tracking economic growth. The different results across countries have led us to use multidimensional scaling analysis in order to group all economies in four clusters according to their performance.", notes = "AQR-IREA (Institute of Applied Economics Research)University of Barcelona (UB)Barcelona Spain", } @Article{Claveria:2018:SIR, author = "Oscar Claveria and Enric Monte and Salvador Torra", title = "A Data-Driven Approach to Construct Survey-Based Indicators by Means of Evolutionary Algorithms", journal = "Social Indicators Research", year = "2018", volume = "135", number = "1", pages = "1--14", month = jan, keywords = "genetic algorithms, genetic programming, Economic indicators, Survey-based indicators, Tendency surveys, Symbolic regression, Evolutionary algorithms,", ISSN = "0303-8300", sharedit_url = "https://rdcu.be/bfELF", DOI = "doi:10.1007/s11205-016-1490-3", size = "14 pages", abstract = "we propose a data-driven approach for the construction of survey-based indicators using large data sets. We make use of agents expectations about a wide range of economic variables contained in the World Economic Survey, which is a tendency survey conducted by the Ifo Institute for Economic Research. By means of genetic programming we estimate a symbolic regression that links survey-based expectations to a quantitative variable used as a yardstick, deriving mathematical functional forms that approximate the target variable. We use the evolution of GDP as a target. This set of empirically-generated indicators of economic growth, are used as building blocks to construct an economic indicator. We compare the proposed indicator to the Economic Climate Index, and we evaluate its predictive performance to track the evolution of the GDP in ten European economies. We find that in most countries the proposed indicator outperforms forecasts generated by a benchmark model.", notes = "AQR-IREA (Regional Quantitative Analysis Group)University of Barcelona (UB)Barcelona Spain", } @TechReport{RePEc:xrp:wpaper:xreap2018-4, author = "Oscar Claveria and Enric Monte and Salvador Torra", title = "Tracking economic growth by evolving expectations via genetic programming: A two-step approach", institution = "Research Institute of Applied Economics, Universitat de Barcelona", year = "2018", type = "Working Paper", number = "2018/01", address = "Av. Diagonal, 690, 08034 Barcelona, Spain", keywords = "genetic algorithms, genetic programming, Evolutionary algorithms, Symbolic regression, Business and consumer surveys, Expectations, Forecasting", URL = "http://www.ub.edu/irea/working_papers/2018/201801.pdf", URL = "https://ideas.repec.org/p/xrp/wpaper/xreap2018-4.html", size = "24 pages", abstract = "The main objective of this study is to present a two-step approach to generate estimates of economic growth based on agents expectations from tendency surveys. First, we design a genetic programming experiment to derive mathematical functional forms that approximate the target variable by combining survey data on expectations about different economic variables. We use evolutionary algorithms to estimate a symbolic regression that links survey-based expectations to a quantitative variable used as a yardstick (economic growth). In a second step, this set of empirically-generated proxies of economic growth are linearly combined to track the evolution of GDP. To evaluate the forecasting performance of the generated estimates of GDP, we use them to assess the impact of the 2008 financial crisis on the accuracy of agents' expectations about the evolution of the economic activity in 28 countries of the OECD. While in most economies we find an improvement in the capacity of agents' to anticipate the evolution of GDP after the crisis, predictive accuracy worsens in relation to the period prior to the crisis. The most accurate GDP forecasts are obtained for Sweden, Austria and Finland.", notes = "XREAP2018-4", } @Article{Claveria:2019:CE, author = "Oscar Claveria and Enric Monte and Salvador Torra", title = "Evolutionary Computation for Macroeconomic Forecasting", journal = "Computational Economics", year = "2019", volume = "53", number = "2", pages = "833--849", month = feb, keywords = "genetic algorithms, genetic programming, Evolutionary algorithms, Symbolic regression, Business and consumer surveys, Expectations, Forecasting", ISSN = "0927-7099", sharedit_url = "https://rdcu.be/bfEK5", DOI = "doi:10.1007/s10614-017-9767-4", abstract = "The main objective of this study is twofold. First, we propose an empirical modelling approach based on genetic programming to forecast economic growth by means of survey data on expectations. We use evolutionary algorithms to estimate a symbolic regression that links survey-based expectations to a quantitative variable used as a yardstick, deriving mathematical functional forms that approximate the target variable. The set of empirically-generated proxies of economic growth are used as building blocks to forecast the evolution of GDP. Second, we use these estimates of GDP to assess the impact of the 2008 financial crisis on the accuracy of agents expectations about the evolution of the economic activity in four Scandinavian economies. While we find an improvement in the capacity of agents to anticipate economic growth after the crisis, predictive accuracy worsens in relation to the period prior to the crisis. The most accurate GDP forecasts are obtained for Sweden.", notes = "AQR-IREA (Institute of Applied Economics Research)University of Barcelona (UB)Barcelona Spain", } @Article{Claveria:2019:Empirica, author = "Oscar Claveria and Enric Monte and Salvador Torra", title = "Empirical modelling of survey-based expectations for the design of economic indicators in five European regions", journal = "Empirica", year = "2019", volume = "46", number = "2", pages = "205--227", month = may, keywords = "genetic algorithms, genetic programming, Economic indicators, Qualitative survey data, Expectations, Symbolic regression, Evolutionary algorithms,", ISSN = "0340-8744", sharedit_url = "https://rdcu.be/bfELk", DOI = "doi:10.1007/s10663-017-9395-1", size = "23 pages", abstract = "we use agents expectations about the state of the economy to generate indicators of economic activity in twenty-six European countries grouped in five regions (Western, Eastern, and Southern Europe, and Baltic and Scandinavian countries). We apply a data-driven procedure based on evolutionary computation to transform survey variables in economic growth rates. In a first step, we design five independent experiments to derive a formula using survey variables that best replicates the evolution of economic growth in each region by means of genetic programming, limiting the integration schemes to the main mathematical operations. We then rank survey variables according to their performance in tracking economic activity, finding that agents perception about the overall economy compared to last year is the survey variable with the highest predictive power. In a second step, we assess the out-of-sample forecast accuracy of the evolved indicators. Although we obtain different results across regions, Austria, Slovakia, Portugal, Lithuania and Sweden are the economies of each region that show the best forecast results. We also find evidence that the forecasting performance of the survey-based indicators improves during periods of higher growth", notes = "The online version of this article ( https://doi.org/10.1007/s10663-017-9395-1) contains supplementary material, which is available to authorized users. AQR-IREA University of Barcelona, Barcelona, Spain", } @Article{CLAVERIA:2020:EM, author = "Oscar Claveria and Enric Monte and Salvador Torra", title = "Economic forecasting with evolved confidence indicators", journal = "Economic Modelling", year = "2020", ISSN = "0264-9993", DOI = "doi:10.1016/j.econmod.2020.09.015", URL = "http://www.sciencedirect.com/science/article/pii/S0264999320311998", keywords = "genetic algorithms, genetic programming, forecasting, economic growth, qualitative survey data, business and consumer expectations, symbolic regression, evolutionary algorithms", abstract = "We present a machine-learning method for sentiment indicators construction that allows an automated variable selection procedure. By means of genetic programming, we generate country-specific business and consumer confidence indicators for thirteen European economies. The algorithm finds non-linear combinations of qualitative survey expectations that yield estimates of the expected rate of economic growth. Firms' production expectations and consumers' expectations to spend on home improvements are the most frequently selected variables - both lagged and contemporaneous. To assess the performance of the proposed approach, we have designed an out-of-sample iterative predictive experiment. We found that forecasts generated with the evolved indicators outperform those obtained with time series models. These results show the potential of the methodology as a predictive tool. Furthermore, the proposed indicators are easy to implement and help to monitor the evolution of the economy, both from demand and supply sides.", } @TechReport{IREA202001, author = "Oscar Claveria and Ivana Lolic and Enric Monte and Salvador Torra and Petar Soric", title = "Economic determinants of employment sentiment: A socio-demographic analysis for the euro area", institution = "Research Institute of Applied Economics, University of Barcelona", year = "2020", type = "Working Paper", number = "2020/01", address = "Spain", month = jan # " 28", keywords = "genetic algorithms, genetic programming, unemployment, expectations, consumer behaviour, forecasting, state-space models yield", URL = "https://ideas.repec.org/p/ira/wpaper/202001.html", URL = "http://www.ub.edu/irea/working_papers/2020/202001.pdf", URL = "https://ssrn.com/abstract=3526768", DOI = "doi:10.2139/ssrn.3526768", size = "29 pages", abstract = "In this study we construct quarterly consumer confidence indicators of unemployment for the euro area using as input the consumer expectations for sixteen socio-demographic groups elicited from the Joint Harmonised EU Consumer Survey. First, we use symbolic regressions to link unemployment rates to qualitative expectations about a wide range of economic variables. By means of genetic programming we obtain the combination of expectations that best tracks the evolution of unemployment for each group of consumers. Second, we test the out-of-sample forecasting performance of the evolved expressions. Third, we use a state-space model with time-varying parameters to identify the main macroeconomic drivers of unemployment confidence and to evaluate whether the strength of the interplay between variables varies across the economic cycle. We analyse the differences across groups, obtaining better forecasts for respondents comprised in the first quartile with regards to the income of the household and respondents with at least secondary education. We also find that the questions regarding expected major purchases over the next 12 months and savings at present are by far, the variables that most frequently appear in the evolved expressions, hinting at their predictive potential to track the evolution of unemployment. For the economically deprived consumers, the confidence indicator seems to evolve independently of the macroeconomy. This finding is rather consistent throughout the economic cycle, with the exception of stock market returns, which governed unemployment confidence in the pre-crisis period.", bibsource = "OAI-PMH server at oai.repec.org", description = "JEL classification: C51, C53, C55, D12, E24, E27, J10", identifier = "RePEc:aqr:wpaper:202001", oai = "oai:RePEc:aqr:wpaper:202001", notes = "type = preprint", } @Article{Claveria:2022:AS, author = "Oscar Claveria and Enric Monte and Salvador Torra", title = "A Genetic Programming Approach for Economic Forecasting with Survey Expectations", journal = "Applied Sciences", year = "2022", volume = "12", number = "13", pages = "article no 6661", keywords = "genetic algorithms, genetic programming, forecasting, economic growth, expectations, business and consumer surveys, symbolic regression, evolutionary algorithms", ISSN = "2076-3417", URL = "https://www.mdpi.com/2076-3417/12/13/6661", DOI = "doi:10.3390/app12136661", size = "19 pages", abstract = "We apply a soft computing method to generate country-specific economic sentiment indicators that provide estimates of year-on-year GDP growth rates for 19 European economies. First, genetic programming is used to evolve business and consumer economic expectations to derive sentiment indicators for each country. To assess the performance of the proposed indicators, we first design a now-casting experiment in which we recursively generate estimates of GDP at the end of each quarter, using the latest business and consumer survey data available. Second, we design a forecasting exercise in which we iteratively re-compute the sentiment indicators in each out-of-sample period. When evaluating the accuracy of the predictions obtained for different forecast horizons, we find that the evolved sentiment indicators outperform the time-series models used as a benchmark. These results show the potential of the proposed approach for prediction purposes.", notes = "https://www.mdpi.com/journal/applsci", } @MastersThesis{cleary:2005:EGEWAGAATKP, title = "Extending Grammatical Evolution with Attribute Grammars: An Application to Knapsack Problems", author = "Robert Cleary", school = "University of Limerick", year = "2005", type = "Master of Science in Computer Science", address = "University of Limerick, Ireland", keywords = "genetic algorithms, genetic programming, grammatical evolution, grammatical swarm, attribute grammars", URL = "http://ncra.ucd.ie/Site/evoilp.html", URL = "http://ncra.ucd.ie/downloads/pub/thesisExtGEwithAGs-CRC.pdf", size = "197 pages", abstract = "Research extending the capabilities of the well-known evolutionary-algorithm (EA) of Grammatical Evolution (GE) is presented. GE essentially describes a software component for (potentially) any search algorithm (more prominently an EA) - whereby it serves to facilitate the generation of viable solutions to the problem at hand. In this way, GE can be thought of as a generally applicable, robust and pluggable component to any search-algorithm. Facilitating this plug- ability - is the ability to hand-describe the structure of solutions to a particular problem; this, under the guise of the concise and effective notation of a grammar definition. This grammar may be thought of, as the rules for the generation of solutions to a problem. Recent research has shown, that for static-problems - (problems whose optimum-solution resides within a finitely-describable set, for the set of all possible solutions), the ability to focus the search (for the optimum) on the more promising regions of this set, has provided the best-performing approaches to-date. As such, it is suggested that search be biased toward more promising areas of the set of all possible solutions. In it's use of a grammar, GE provides such a bias (as a language-bias), yet remains unable, to effectively bias the search for problems of constrained optimisation. As such, and as detailed in this thesis - the mechanism of an attribute grammar is proposed to maintain GE as a pluggable component for problems of this type also; thus extending it's robustness and general applicability. A family of academically recognised (hard) knapsack problems, are used as a testing-ground for the extended-system and the results presented are encouraging. As a side-effect of this study (and possibly more importantly) we observe some interesting behavioural findings about the GE system itself. The standard GE one-point crossover operator, emerges as exhibiting a mid evolutionary change-of-role from a constructive to destructive operator; GE's ripple-crossover is found to be heavily dependent on the presence of a GE-tail (of residual-introns) in order to function effectively; and the propagation of individuals - characterised by large-proportions of such residual-introns - is found to be an evolutionary self- adaptive response to the destructive change of role found in the one-point crossover: all of these findings are found with respect to the problems examined.", language = "en", } @InProceedings{cleary:2005:AAGDFR0MKP, author = "Robert Cleary and Michael O'Neill", title = "An Attribute Grammar Decoder for the 01 MultiConstrained Knapsack Problem", booktitle = "Evolutionary Computation in Combinatorial Optimization -- {EvoCOP}~2005", year = "2005", month = "30 " # mar # "-1 " # apr, editor = "G{\"{u}}nther R. Raidl and Jens Gottlieb", series = "LNCS", volume = "3448", publisher = "Springer Verlag", address = "Lausanne, Switzerland", publisher_address = "Berlin", pages = "34--45", keywords = "genetic algorithms, genetic programming, grammatical evolution, evolutionary computation, attribute grammar", isbn13 = "978-3-540-25337-2", ISSN = "0302-9743", DOI = "doi:10.1007/978-3-540-31996-2_4", abstract = "We describe how the standard genotype-phenotype mapping process of Grammatical Evolution (GE) can be enhanced with an attribute grammar to allow GE to operate as a decoder-based Evolutionary Algorithm (EA). Use of an attribute grammar allows GE to maintain context-sensitive and semantic information pertinent to the capacity constraints of the 01 Multi-constrained Knapsack Problem (MKP). An attribute grammar specification is used to perform decoding similar to a first-fit heuristic. The results presented are encouraging, demonstrating that GE in conjunction with attribute grammars can provide an improvement over the standard context-free mapping process for problems in this domain.", notes = "EvoCOP2005 Also known as \cite{cleary:evocop05}", } @InProceedings{1277276, author = "Janet Clegg and James Alfred Walker and Julian Francis Miller", title = "A new crossover technique for Cartesian genetic programming", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "1580--1587", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1580.pdf", DOI = "doi:10.1145/1276958.1277276", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, crossover techniques, optimisation", abstract = "Genetic Programming was first introduced by Koza using tree representation together with a crossover technique in which random sub-branches of the parents' trees are swapped to create the offspring. Later Miller and Thomson introduced Cartesian Genetic Programming, which uses directed graphs as a representation to replace the tree structures originally introduced by Koza. Cartesian Genetic Programming has been shown to perform better than the traditional Genetic Programming; but it does not use crossover to create offspring, it is implemented using mutation only. In this paper a new crossover method in Genetic Programming is introduced. The new technique is based on an adaptation of the Cartesian Genetic Programming representation and is tested on two simple regression problems. It is shown that by implementing the new crossover technique, convergence is faster than that of using mutation only in the Cartesian Genetic Programming method.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @InProceedings{Clegg:2008:gecco, author = "Janet Clegg", title = "Combining cartesian genetic programming with an estimation of distribution algorithm", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "1333--1334", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1333.pdf", DOI = "doi:10.1145/1389095.1389350", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, cartesian genetic programming, crossover techniques, optimisation: Poster", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389350}", } @InProceedings{conf/evoW/CleggS08, title = "Analogue Circuit Control through Gene Expression", author = "Kester Clegg and Susan Stepney", bibdate = "2008-04-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/evoW/evoW2008.html#CleggS08", booktitle = "Proceedings of Evo{COMNET}, Evo{FIN}, Evo{HOT}, Evo{IASP}, Evo{MUSART}, Evo{NUM}, Evo{STOC}, and EvoTransLog, Applications of Evolutionary Computing, EvoWorkshops", publisher = "Springer", year = "2008", volume = "4974", editor = "Mario Giacobini and Anthony Brabazon and Stefano Cagnoni and Gianni {Di Caro} and Rolf Drechsler and Anik{\'o} Ek{\'a}rt and Anna Esparcia-Alc{\'a}zar and Muddassar Farooq and Andreas Fink and Jon McCormack and Michael O'Neill and Juan Romero and Franz Rothlauf and Giovanni Squillero and Sima Uyar and Shengxiang Yang", isbn13 = "978-3-540-78760-0", pages = "154--163", series = "Lecture Notes in Computer Science", DOI = "doi:10.1007/978-3-540-78761-7_16", address = "Naples", month = "26-28 " # mar, keywords = "genetic algorithms, genetic programming, Cartesian genetic programming", abstract = "Software configurable analogue arrays offer an intriguing platform for automated design by evolutionary algorithms. Like previous evolvable hardware experiments, these platforms are subject to noise during physical interaction with their environment. We report preliminary results of an evolutionary system that uses concepts from gene expression to both discover and decide when to deploy analogue circuits. The output of a circuit is used to trigger its reconfiguration to meet changing conditions. We examine the issues of noise during our evolutionary runs, show how this was overcome and illustrate our system with a simple proof-of-concept task that shows how the same mechanism of control works for progressive developmental stages (canalisation) or adaptable control (homoeostasis).", } @PhdThesis{Clegg:thesis, author = "Kester Dean Clegg", title = "Evolving gene expression to reconfigure analogue devices", school = "University of York", year = "2008", address = "UK", month = May, keywords = "genetic algorithms, genetic programming, Cartesian genetic programming", URL = "http://www-users.cs.york.ac.uk/susan/teach/theses/clegg.htm", URL = "http://www.cs.york.ac.uk/ftpdir/reports/2008/YCST/05/YCST-2008-05.pdf", URL = "https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.479503", size = "203 pages", abstract = "Repeated, morphological functionality, from limbs to leaves, is widespread in nature. Pattern formation in early embryo development has shed light on how and why the same genes are expressed in different locations or at different times. Practitioners working in evolutionary computation have long regarded nature's reuse of modular functionality with admiration. But repeating nature's trick has proven difficult. To date, no one has managed to evolve the design for a car, a house or a plane. Or indeed anything where the number of interdependent parts exposed to random mutation is large. It seems that while we can use evolutionary algorithms for search-based optimisation with great success, we cannot use them to tackle large, complex designs where functional reuse is essential. This thesis argues that the modular functionality provided by gene reuse could play an important part in evolutionary computation being able to scale, and that by expressing subsets of genes in specific contexts, successive stages of phenotype configuration can be controlled by evolutionary search. We present a conceptual model of context-specific gene expression and show how a genome representation can hold many genes, only a few of which need be expressed in a solution. As genes are expressed in different contexts, their functional role in a solution changes. By allowing gene expression to discover phenotype solutions, evolutionary search can guide itself across multiple search domains. The work here describes the design and implementation of a prototype system to demonstrates the above features and evolve genomes that are able to use gene expression to find and deploy solutions, permitting mechanisms of dynamic control to be discovered by evolutionary computation.", notes = "ISNI: 0000 0000 4342 017X", } @InProceedings{Clegg:2014:PPSN, author = "Kester Clegg and Julian Miller and Kieran Massey and Mike Petty", title = "Travelling Salesman Problem solved 'in materio' by evolved carbon nanotube device", booktitle = "13th International Conference on Parallel Problem Solving from Nature", year = "2014", editor = "Thomas Bartz-Beielstein and Juergen Branke and Bogdan Filipic and Jim Smith", publisher = "Springer", isbn13 = "978-3-319-10761-5", pages = "692--701", series = "Lecture Notes in Computer Science", address = "Ljubljana, Slovenia", month = "13-17 " # sep, volume = "8672", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming", DOI = "doi:10.1007/978-3-319-10762-2_68", abstract = "We report for the first time on finding shortest path solutions for the travelling salesman problem (TSP) using hybrid in materio computation: a technique that uses search algorithms to configure materials for computation. A single-walled carbon nanotube (SWCNT) / polymer composite material deposited on a micro-electrode array is configured using static voltages so that voltage output readings determine the path order in which to visit cities in a TSP. Our initial results suggest that the hybrid computation with the SWCNT material is able to solve small instances of the TSP as efficiently as a comparable evolutionary search algorithm performing the same computation in software. Interestingly the results indicate that the hybrid system's search performance on TSPs scales linearly rather than exponentially on these smaller instances. This exploratory work represents the first step towards building SWCNT-based electrode arrays in parallel so that they can solve much larger problems.", notes = "up to 12 cities. 1+4-EA. TSP path approx 20th of an inch. SWCNT-PMMA carbon needles mixed with PMMA in Anisole. Comparison with Cartesian genetic programming. Ten fold improvement in CGP. PPSN-XIII", } @Article{Clemens:2018:ieeeAWPL, author = "Scott Clemens and Magdy F. Iskander and Zhengqing Yun and Jennifer Rayno", journal = "IEEE Antennas and Wireless Propagation Letters", title = "Hybrid Genetic Programming for the Development of Metamaterials Designs With Improved Characteristics", year = "2018", volume = "17", number = "3", pages = "513--516", keywords = "genetic algorithms, genetic programming, Artificial magnetic conductor (AMC), terahertz (THz) absorber", DOI = "doi:10.1109/LAWP.2018.2800057", ISSN = "1536-1225", month = mar, abstract = "The expansion of a hybrid genetic program's (HGP) functionality to allow for the development of broadband two-dimensional metamaterials designs with advanced characteristics is presented. Artificial magnetic conductor (AMC) ground planes and a tunable terahertz absorber generated by the HGP are compared with designs available in the literature. The HGP is shown to produce human-competitive results. The AMC ground planes synthesised by the HGP were found to produce improved results including wider bandwidths and larger reflection coefficient magnitudes than that of the human designs. For one of the designed AMCs, the HGP's bandwidth is 73.3percent larger and the minimum reflection magnitude is 1.0percent larger than the reference AMC. Similarly, the absorber synthesised by the HGP has larger bandwidths than that of a recently published absorber optimised by random hill climbing. Three bias voltages were tested with the tunable absorber. The bandwidths of the HGP absorber are 23.1percent, 37.6percent, and 400percent larger than the reference absorber, for biases of 4, 2, and 0.5 V/nm, respectively. Four example designs are discussed together with comparative results to illustrate the advantages of the developed HGP-enabled design method.", notes = "Also known as \cite{8276236}", } @InProceedings{Clemens:2019:Meeting, author = "Scott Clemens and Zhengqing Yun and Magdy F. Iskander", booktitle = "2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting", title = "Optimization of Rural Cellular Coverage on the Islands of Hawaii", year = "2019", pages = "2111--2112", month = jul, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/APUSNCURSINRSM.2019.8888613", ISSN = "1947-1491", abstract = "In this communication genetic programming (GP) is applied to optimize the coverage of wireless networks in rural areas. Three locations in the Hawaiian Islands were studied: Kohala area on Hawaii, the North Shore on Oahu, and the populated area on Maui between West Maui Mountains and Haleakala. Omnidirectional and directional base station scenarios were optimized and simulated. For all three locations studied directional base stations provided better coverage than omnidirectional base stations. For the Maui and Oahu locations directional base stations provided significantly more coverage.", notes = "Also known as \cite{8888613}", } @InProceedings{Clemens:2020:Meeting, title = "Genetic Programming for Coplanar Waveguide Continuous Transverse Stub Antenna Array Design", author = "Scott Clemens and Magdy F. Iskander and Zhengqing Yun and Gui Chao", booktitle = "2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting", year = "2020", pages = "1949--1950", abstract = "Genetic programming (GP) is developed to design an antenna array with new topologies resulting in improved performance. Three elements of a coplanar waveguide continuous transverse stub (CPW-CTS) antenna are synthesized and optimized by GP. These antenna elements are placed in series to create a 3-element frequency scanning linear array. The fitness function used to design the CPW-CTS elements accounts for impedance bandwidth, radiation pattern, cross polarization, and transmission coefficient. Simulation results are verified experimentally. The GP designed CPW-CTS antenna array was able to achieve nearly 4 times the bandwidth of a conventional CPW-CTS array design. The GP designed array maintains the array's gain when compared with the published CPW-CTS antenna array design.", keywords = "genetic algorithms, genetic programming, Weapons, Simulation, Bandwidth, Coplanar waveguides, Topology, Linear antenna arrays, Continuous transverse stub", DOI = "doi:10.1109/IEEECONF35879.2020.9329544", ISSN = "1947-1491", month = jul, notes = "Also known as \cite{9329544}", } @InProceedings{Clemens:2022:AP-S, author = "Scott Clemens and Edmond Chong and Magdy F. Iskander and Zhengqing Yun and Joseph Brown and Tyler Ray and Matthew Nakamura and Deylen Nekoba", title = "Hybrid Genetic Programming Designed Laser-Induced Graphene Based Absorber", booktitle = "2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI)", year = "2022", pages = "1084--1085", month = "10-15 " # jul, address = "Denver, CO, USA", keywords = "genetic algorithms, genetic programming, Fabrication, Graphene, Polyimides, Bandwidth, Metasurfaces, Electromagnetic absorbers", isbn13 = "978-1-6654-9659", ISSN = "1947-1491", URL = "https://2022apsursi.org/view_paper.php?PaperNum=2459", DOI = "doi:10.1109/AP-S/USNC-URSI47032.2022.9887152", size = "2 pages", abstract = "Hybrid genetic programming (HGP) is applied to the design and optimization of a laser-induced graphene (LIG) based metasurface (MS) electromagnetic absorber. The HGP designed absorber has bandwidths of 115.percent and 56.percent for absorptivity above 7percent and 8percent, respectively. It is 5.1 mm thick, with a unit cell (UC) periodicity of 8.7 mm. The LIG is generated on a polyimide substrate. The MS absorber has copper ground plane backing.", notes = "Also known as \cite{9887152} See also Edmond C.M. Chong, MSc https://scholarspace.manoa.hawaii.edu/server/api/core/bitstreams/4ba649a9-5e8d-464e-94b6-a3a808d6074d/content University of Hawaii at Manoa, Honolulu, Hawaii, USA", } @InProceedings{Clemente:evoapps12, author = "Eddie Clemente and Gustavo Olague and Leon Dozal and Martin Mancilla", title = "Object Recognition with an Optimized Ventral Stream Model Using Genetic Programming", booktitle = "Applications of Evolutionary Computing, EvoApplications 2012: EvoCOMNET, EvoCOMPLEX, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoNUM, EvoPAR, EvoRISK, EvoSTIM, EvoSTOC", year = "2011", month = "11-13 " # apr, editor = "Cecilia {Di Chio} and Alexandros Agapitos and Stefano Cagnoni and Carlos Cotta and F. {Fernandez de Vega} and Gianni A. {Di Caro} and Rolf Drechsler and Aniko Ekart and Anna I Esparcia-Alcazar and Muddassar Farooq and William B. Langdon and Juan J. Merelo and Mike Preuss and Hendrik Richter and Sara Silva and Anabela Simoes and Giovanni Squillero and Ernesto Tarantino and Andrea G. B. Tettamanzi and Julian Togelius and Neil Urquhart and A. Sima Uyar and Georgios N. Yannakakis", series = "LNCS", volume = "7248", publisher = "Springer Verlag", address = "Malaga, Spain", publisher_address = "Berlin", pages = "315--325", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-29177-7", DOI = "doi:10.1007/978-3-642-29178-4_32", size = "11 pages", abstract = "Computational neuroscience is a discipline devoted to the study of brain function from an information processing standpoint. The ventral stream, also known as the 'what' pathway, is widely accepted as the model for processing the visual information related to object identification. This paper proposes to evolve a mathematical description of the ventral stream where key features are identified in order to simplify the whole information processing. The idea is to create an artificial ventral stream by evolving the structure through an evolutionary computing approach. In previous research, the 'what' pathway is described as being composed of two main stages: the interest region detection and feature description. For both stages a set of operations were identified with the aim of simplifying the total computational cost by avoiding a number of costly operations that are normally executed in the template matching and bag of feature approaches. Therefore, instead of applying a set of previously learnt patches, product of an off-line training process, the idea is to enforce a functional approach. Experiments were carried out with a standard database and the results show that instead of 1200 operations, the new model needs about 200 operations.", notes = "EvoIASP Part of \cite{DiChio:2012:EvoApps} EvoApplications2012 held in conjunction with EuroGP2012, EvoCOP2012, EvoBio'2012 and EvoMusArt2012", affiliation = "Proyecto EvoVision, Departamento de Ciencias de la Computacion, Division de Fisica Aplicada, Centro de Investigacion Cientifica y de Estudios Superiores de Ensenada, Carretera Ensenada-Tijuana No. 3918, Zona Playitas, Ensenada, 22860 B.C., Mexico", } @InProceedings{Clemente:2013:GECCO, author = "Eddie Clemente and Francisco Chavez and Leon Dozal and Francisco {Fernandez de Vega} and Gustavo Olague", title = "Self-adjusting focus of attention by means of GP for improving a laser point detection system", booktitle = "GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference", year = "2013", editor = "Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi", isbn13 = "978-1-4503-1963-8", pages = "1237--1244", keywords = "genetic algorithms, genetic programming", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2463372.2463530", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "This paper introduces the application of a new GP based Focus of Attention technique capable of improving the accuracy level when using a Laser Pointer as an interactive device. Laser Pointers have been previously employed in combination with environment control systems as interaction devices, allowing users to send orders to devices. Accurate detection of laser spots is required for sending correct orders; moreover, false offs must be eradicated, thus preventing devices to autonomously activate/deactivate when orders have not been sent by users. The idea here is to apply a self-adjusting process to a GP based algorithm capable of focusing the attention of a visual recognition system on a narrow area of an image, where laser spots will be then located. Images are taken by video cameras working on users' environment. The results show that the new approach improves significantly the accuracy level when laser spots are present, users sending orders while maintains the extremely low values of false offs provided by previous techniques.", notes = "Also known as \cite{2463530} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)", } @Article{Clemente:2015:ASC, author = "Eddie Clemente and Francisco Chavez and Francisco {Fernandez de Vega} and Gustavo Olague", title = "Self-adjusting focus of attention in combination with a genetic fuzzy system for improving a laser environment control device system", journal = "Applied Soft Computing", volume = "32", pages = "250--265", year = "2015", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2015.03.011", URL = "http://www.sciencedirect.com/science/article/pii/S1568494615001647", abstract = "This paper presents a new algorithm capable of improving the accuracy level of a laser pointer detector used within an interactive control device system. A genetic programming based approach has been employed to develop a focus of attention algorithm, which works cooperatively with a genetic fuzzy system. The idea is to improve the detection of laser-spots depicted on images captured by video cameras working on home environments. The new and more accurate detection system, in combination with an environment control system, allows to send correct orders to home devices. The algorithm is capable of eradicating false offs, thus preventing devices to autonomously activate/deactivate appliances when orders have not been really signalled by users. Moreover, by adding self-adjusting capabilities with a genetic fuzzy system the computer vision algorithm focuses its attention on a narrower area of the image. Extensive experimental results show that the combination of the focus of attention technique with dynamic thresholding and genetic fuzzy systems improves significantly the accuracy of the laser-spot detection system while maintaining extremely low false off rates in comparison with previous approaches.", keywords = "genetic algorithms, genetic programming, Self-adjusting, Focus of attention, Laser pointer, Environment control systems, Genetic fuzzy systems", } @Article{Clemente:2018:JIRS, author = "Eddie Clemente and Marlen Meza-Sanchez and Eusebio Bugarin and Ana Yaveni Aguilar-Bustos", title = "Adaptive Behaviors in Autonomous Navigation with Collision Avoidance and Bounded Velocity of an Omnidirectional Mobile Robot: A Control Theory with Genetic Programming Approach", journal = "Journal of Intelligent and Robotic Systems", year = "2018", volume = "92", number = "2", pages = "359--380", month = oct, keywords = "genetic algorithms, genetic programming, evolutionary robotics, behaviours, collision avoidance, autonomous navigation, wheeled mobile robots, velocity constraint, lyapunov stability", ISSN = "0921-0296", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/jirs/jirs92.html#ClementeMBA18", DOI = "doi:10.1007/s10846-017-0751-y", size = "22 pages", abstract = "Integration of Control Theory and Genetic Programming paradigm toward development a family of controllers is addressed in this paper. These controllers are applied for autonomous navigation with collision avoidance and bounded velocity of an omnidirectional mobile robot. We introduce the concepts of natural and adaptive behaviours to relate each control objective with a desired behaviour for the mobile robot. Natural behaviours lead the system to fulfil a task inherently. In this work, the motion of the mobile robot to achieve desired position, ensured by applying a Control-Theory-based controller, defines the natural behaviour. The adaptive behaviour, learnt through Genetic-Programming, fits the robot motion in order to avoid collision with an obstacle while fulfilling velocity constraints. Hence, the behaviour of the mobile robot is the addition of the natural and the adaptive behaviours. Our proposed methodology achieves the discovery of 9402 behaviours without collisions where asymptotic convergence to desired goal position is demonstrated by Lyapunov stability theory. Effectiveness of proposed framework is illustrated through a comparison between experiments and numerical simulations for a real mobile robot.", notes = "Also known as \cite{journals/jirs/ClementeMBA18}", } @InProceedings{clergue:2002:gecco, author = "Manuel Clergue and Philippe Collard and Marco Tomassini and Leonardo Vanneschi", title = "Fitness Distance Correlation And Problem Difficulty For Genetic Programming", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "724--732", address = "New York", publisher_address = "San Francisco, CA 94104, USA", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "genetic algorithms, genetic programming, distance between genotypes, fitness distance correlation, problem difficulty, royal trees, trap functions", ISBN = "1-55860-878-8", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/GP072.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/GP072.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf", notes = "GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002) Nominated for best at GECCO award. Fitness landscape.", } @Article{Clune:2013:PRSB, author = "Jeff Clune and Jean-Baptiste Mouret and Hod Lipson", title = "The evolutionary origins of modularity", journal = "Proceedings of the Royal Society B", year = "2013", volume = "280", number = "1755", pages = "20122863", month = "22 " # mar, keywords = "genetic algorithms, NSGA-II, modularity, evolution, networks, evolvability, systems biology", ISSN = "1471-2954", DOI = "doi:10.1098/rspb.2012.2863", size = "9 pages", abstract = "A central biological question is how natural organisms are so evolvable (capable of quickly adapting to new environments). A key driver of evolvability is the widespread modularity of biological networks, their organisation as functional, sparsely connected subunits, but there is no consensus regarding why modularity itself evolved. Although most hypotheses assume indirect selection for evolvability, here we demonstrate that the ubiquitous, direct selection pressure to reduce the cost of connections between network nodes causes the emergence of modular networks. Computational evolution experiments with selection pressures to maximise network performance and minimise connection costs yield networks that are significantly more modular and more evolvable than control experiments that only select for performance. These results will catalyse research in numerous disciplines, such as neuroscience and genetics, and enhance our ability to harness evolution for engineering purposes.", notes = "6 Feb 2013 Broken March 2021 http://tech.groups.yahoo.com/group/genetic_programming/message/6066 1) sheds light on why modularity may evolve in biological networks (e.g. neural, genetic, metabolic, protein-protein, etc.) 2) provides a simple technique for evolving neural networks that are modular and have increased evolvability, in that they adapt faster to new environments. The modules that formed solved subproblems in the domain. Published online January 30, 2013 This article is free to access Video: http://www.youtube.com/watch?feature=player_embedded&v=SG4_aW8LMng eight pixel retina, ANN", } @InProceedings{coates:1997:GPx3dw, author = "T. Broughton and A. Tan and Paul S. Coates", title = "The use of Genetic programing in Exploring {3D} Design Worlds", booktitle = "CAAD Futures 97", year = "1997", editor = "Richard Junge", pages = "885--917", address = "Technical University Munich, Germany", month = "4-6 " # aug, publisher = "Kluwer Academic Publishers", keywords = "genetic algorithms, genetic programming", ISBN = "0-7923-4726-9", URL = "https://repository.uel.ac.uk/item/86q5y", broken = "http://hdl.handle.net/10552/854", broken = "http://www.caadfutures.org/proceedings_97.htm", URL = "http://roar.uel.ac.uk/jspui/bitstream/10552/854/1/Broughton%2c%20T%20%281997%29%20CAAD%20Futures%20pp.%20885.pdf", size = "32 pages", abstract = "Genetic algorithms are used to evolve rule systems for a generative process, in one case a shape grammar,which uses the 'Dawkins Biomorph' paradigm of user driven choices to perform artificial selection, in the other a CA/Lindenmeyer system using the Hausdorff dimension of the resultant configuration to drive natural selection. 1) Using Genetic Programming in an interactive 3d shape grammar (Amy Tan and P S Coates) A report of a generative system combining genetic programming(GP) and 3D shape grammars. The reasoning that backs up the basis for this work depends on the interpretation of design as search In this system, a 3D form is a computer program made up of functions (transformations and terminals (building blocks). Each program evaluates into a structure. Hence, in this instance a program is synonymous with form. Building blocks of form are platonic solids (box, cylinder....etc.). A Variety of combinations of the simple affine transformations of translation, scaling, rotation together with Boolean operations of union, subtraction and intersection performed on the building blocks generate different configurations of 3D forms. Using to the methodology of genetic programming, an initial population of such programs are randomly generated,subjected to a test for fitness (the eyeball test). Individual programs that have passed the test are selected to be parents for reproducing the next generation of programs via the process of recombination. 2) Using a GA to evolve rule sets to achieve a goal configuration (T.Broughton and P.Coates). The aim of these experiments was to build a framework in which a structure's form could be defined by a set of instructions encoded into its genetic make-up. This was achieved by combining a generative rule system commonly used to model biological growth with a genetic algorithm simulating the evolutionary process of selection to evolve an adaptive rule system capable of replicating any preselected 3-D shape. The generative modelling technique used is a string rewriting Lindenmayer system the genes of the emergent structures are the production rules of the L-system, and the spatial representation of the structures uses the geometry of iso-spatial dense-packed spheres.", notes = "University of East London, GB", } @InProceedings{Coates:1999:AISBces, author = "Paul Coates and Dimitrios Makris", title = "Genetic Programming and Spatial Morphogenesis", booktitle = "AISB Symposium on Creative Evolutionary Systems", year = "1999", editor = "Andrew Patrizio and Geraint A. Wiggins and Helen Pain", pages = "105--114", address = "Edinburgh College of Art and Division of Informatics, University of Edinburgh", publisher_address = "COGS, University of Sussex", month = "6-9 " # apr, organisation = "AISB", keywords = "genetic algorithms, genetic programming", ISBN = "1-902956-03-6", URL = "http://www.aisb.org.uk/publications/proceedings/proc1999/aisb1999/AISB99_Evolutionary.pdf", URL = "https://repository.uel.ac.uk/item/86q31", size = "10 pages", abstract = "This paper discusses the use of genetic programming (G.P.) for applications in the field of spatial composition. The G.P. was used to generate three-dimensional spatial forms from a set of geometrical structures. The approach uses genetic programming with a Genetic Library (G.Lib). G.P. provides a way to genetically breed a computer program to solve a problem. G. Lib enables genetic programming to define potentially useful subroutines dynamically during a run.", notes = "PDF is scanned image. Some pictures very poor. personal shape grammar, domino house. Abstract of Makris masters thesis? Dimitrios Makris Phd thesis at: DOCTEUR DE L'UNIVERSITE DE LIMOGES Discipline : Informatique 18 Octobre 2005 Etude et realisation d'un systeme declaratif de modelisation et de generation de styles par algorithmes genetiques. Application a la creation architecturale. In english STUDY AND REALISATION OF A DECLARATIVE SYSTEM FOR MODELLING AND GENERATION OF STYLE WITH GENETIC ALGORITHMS. APPLICATION IN ARCHITECTURAL DESIGN 261 pages. Not on GP. broken Aug 2020 http://epublications.unilim.fr/theses/index.php?id=4836 June 2025 perhaps https://aurore.unilim.fr/ori-oai-search/index.html AISB99_Evolutionary.pdf is whole proceedings", } @Book{Coates:2010:PA, author = "Paul Coates", title = "Programming.Architecture", publisher = "Routledge", year = "2010", month = jan # " 29th", keywords = "genetic algorithms, genetic programming", isbn13 = "978-0-415-45188-8", URL = "http://www.routledge.com/books/details/9780415451888/", abstract = "Programming.Architecture is a simple and concise introduction to the history of computing and computational design, explaining the basics of algorithmic thinking and the use of the computer as a tool for design and architecture. Introduction 1. Falling Between Two Stools 2. Rethinking Representation 3. In the Beginning was the Word 4. The Mystery of the Machine that Invents Itself 5. Evolving the Text - Being even Lazier 6. The Text of the Vernacular. Epilogue. Glossary", notes = "Reviewed by \cite{Medjdoub:2011:GPEM}", size = "200 pages", } @InProceedings{conf/iberamia/CobosMMLH12, author = "Carlos Cobos and Leydy Munoz and Martha Mendoza and Elizabeth {Leon Guzman} and Enrique Herrera-Viedma", title = "Fitness Function Obtained from a Genetic Programming Approach for Web Document Clustering Using Evolutionary Algorithms", booktitle = "Proceedings of the 13th Ibero-American Conference on {AI}, {IBERAMIA} 2012", year = "2012", editor = "Juan Pavon and Nestor D. Duque-Mendez and Ruben Fuentes-Fernandez", volume = "7637", series = "Lecture Notes in Computer Science", pages = "179--188", address = "Cartagena de Indias, Colombia", month = nov # " 13-16", publisher = "Springer", keywords = "genetic algorithms, genetic programming, web document clustering, clustering of web results, Bayesian information criteria", isbn13 = "978-3-642-34653-8", URL = "http://dx.doi.org/10.1007/978-3-642-34654-5", DOI = "doi:10.1007/978-3-642-34654-5_19", bibdate = "2012-11-17", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/iberamia/iberamia2012.html#CobosMMLH12", size = "10 pages", abstract = "Web document clustering (WDC) is an alternative means of searching the web and has become a rewarding research area. Algorithms for WDC still present some problems, in particular: inconsistencies in the content and description of clusters. The use of evolutionary algorithms is one approach for improving results. It uses standard index to evaluate the quality (as a fitness function) of different solutions of clustering. Indexes such as Bayesian Information Criteria (BIC), Davies-Bouldin, and others show good performance, but with much room for improvement. In this paper, a modified BIC fitness function for WDC based on evolutionary algorithms is presented. This function was discovered using a genetic program (from a reverse engineering view). Experiments on datasets based on DMOZ show promising results.", notes = "Advances in Artificial Intelligence", } @InProceedings{Cochran:2015:POPL, author = "Robert A. Cochran and Loris D'Antoni and Benjamin Livshits and David Molnar and Margus Veanes", title = "Program Boosting: Program Synthesis via Crowd-Sourcing", booktitle = "Proceedings of the 42nd Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages, POPL 2015", year = "2015", editor = "Andy Gill", pages = "677--688", address = "Mumbai, India", publisher_address = "New York, NY, USA", month = "15-17 " # jan, publisher = "ACM", keywords = "genetic algorithms, genetic programming, crowd-sourcing, program synthesis, regular expressions, symbolic automata SFA", isbn13 = "978-1-4503-3300-9", acmid = "2676973", URL = "https://www.cs.unc.edu/~rac/pdf/POPL15.pdf", URL = "http://doi.acm.org/10.1145/2676726.2676973", DOI = "doi:10.1145/2676726.2676973", size = "12", abstract = "In this paper, we investigate an approach to program synthesis that is based on crowd-sourcing. With the help of crowd-sourcing, we aim to capture the wisdom of the crowds to find good if not perfect solutions to inherently tricky programming tasks, which elude even expert developers and lack an easy-to-formalize specification. We propose an approach we call program boosting, which involves crowd-sourcing imperfect solutions to a difficult programming problem from developers and then blending these programs together in a way that improves their correctness. We implement this approach in a system called CROWDBOOST and show in our experiments that interesting and highly non-trivial tasks such as writing regular expressions for URLs or email addresses can be effectively crowd-sourced. We demonstrate that carefully blending the crowd-sourced results together consistently produces a boost, yielding results that are better than any of the starting programs. Our experiments on 465 program pairs show consistent boosts in accuracy and demonstrate that program boosting can be performed at a relatively modest monetary cost.", notes = "Amazon mechanical turk mturk. UTF-16, email addresses, URLs, dates, USA telephone numbers. Also known as \cite{Cochran:2015:PBP:2676726.2676973} Also available as ACM Sigplan Notices 50(1) Jan 2015 677-688, ISSN:0362-1340", } @Misc{DBLP:journals/corr/abs-1211-5098, author = "Brendan Cody-Kenny and Stephen Barrett", title = "Scaling Genetic Programming for Source Code Modification", howpublished = "arXiv", year = "2012", month = "21 " # nov, keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE", URL = "http://arxiv.org/abs/1211.5098", size = "4 pages", abstract = "In Search Based Software Engineering, Genetic Programming has been used for bug fixing, performance improvement and parallelisation of programs through the modification of source code. Where an evolutionary computation algorithm, such as Genetic Programming, is to be applied to similar code manipulation tasks, the complexity and size of source code for real-world software poses a scalability problem. To address this, we intend to inspect how the Software Engineering concepts of modularity, granularity and localisation of change can be reformulated as additional mechanisms within a Genetic Programming algorithm.", notes = "Accepted for Graduate Student Workshop, GECCO 2012, Retracted by Authors", } @InProceedings{Cody-Kenny:2013:GECCOcomp, author = "Brendan Cody-Kenny and Stephen Barrett", title = "Self-focusing genetic programming for software optimisation", booktitle = "GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion", year = "2013", editor = "Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A. N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks", isbn13 = "978-1-4503-1964-5", keywords = "genetic algorithms, genetic programming", pages = "203--204", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2464576.2464681", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Approaches in the area of Search Based Software Engineering (SBSE) have seen Genetic Programming (GP) algorithms applied to the optimisation of software. While the potential of GP for this task has been demonstrated, the complexity of real-world software code bases poses a scalability problem for its serious application. To address this scalability problem, we inspect a form of GP which incorporates a mechanism to focus operators to relevant locations within a program code base. When creating offspring individuals, we introduce operator node selection bias by allocating values to nodes within an individual. Offspring values are inherited and updated when a difference in behaviour between offspring and parent is found. We argue that this approach may scale to find optimal solutions in more complex code bases under further development.", notes = "Also known as \cite{2464681} Distributed at GECCO-2013.", } @InProceedings{Cody-Kenny:2013:SSBSE, author = "Brendan Cody-Kenny and Stephen Barrett", title = "The Emergence of Useful Bias in Self-focusing Genetic Programming for Software Optimisation", booktitle = "Symposium on Search-Based Software Engineering", year = "2013", editor = "Guenther Ruhe and Yuanyuan Zhang", volume = "8084", series = "Lecture Notes in Computer Science", pages = "306--311", address = "Leningrad", month = aug # " 24-26", publisher = "Springer", note = "Graduate Student Track", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE", isbn13 = "978-3-642-39741-7", DOI = "doi:10.1007/978-3-642-39742-4_29", size = "6 pages", abstract = "The use of Genetic Programming (GP) to optimise increasingly large software code has been enabled through biasing the application of GP operators to code areas relevant to the optimisation of interest. As previous approaches have used various forms of static bias applied before the application of GP, we show the emergence of bias learnt within the GP process itself which improves solution finding probability in a similar way. As this variant technique is sensitive to the evolutionary lineage, we argue that it may more accurately provide bias in programs which have undergone heavier modification and thus find solutions addressing more complex issues.", notes = "http://ssbse.info/2013/accepted-papers/", } @PhdThesis{BCK-thesis, author = "Brendan Cody-Kenny", title = "Genetic Programming Bias with Software Performance Analysis", school = "Trinity College Dublin", year = "2015", address = "Ireland", month = jan, keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, locoGP, Java, AST", URL = "http://www.tara.tcd.ie/bitstream/handle/2262/76251/BCK.thesis.april.2016%5b1%5d.pdf", URL = "http://www.tara.tcd.ie/handle/2262/76251", size = "190 pages", abstract = "The complexities of modern software systems make their engineering costly and time consuming. This thesis explores and develops techniques to improve software by automating re-design. Source code can be randomly modified and subsequently tested for correctness to search for improvements in existing software. By iteratively selecting useful programs for modification a randomised search of program variants can be guided toward improved programs. Genetic Programming (GP) is a search algorithm which crucially relies on selection to guide the evolution of programs. Applying GP to software improvement represents a scalability challenge given the number of possible modification locations in even the smallest of programs. The problem addressed in this thesis is locating performance improvements within programs. By randomly modifying a location within a program and measuring the change in performance and functionality we determine the probability of finding a performance improvement at that location under further modification. Locating performance improvements can be performed during GP as GP relies on mutation. A probabilistic overlay of bias values for modification emerges as GP progresses and the software evolves. Measuring different aspects of program change can fine-tune the GP algorithm To focus on code which is particularly relevant to the measured aspect. Measuring execution cost reduction can indicate where an improvement is likely to exist and increase the chances of finding an improvement during GP.", notes = "Appendix B Code Listings http://rosettacode.org Bubble Sort, Shell Sort, Selection Sort, Selection Sort 2, Radix Sort, Quick Sort, Merge Sort, Deceptive Bubble Sort, Insertion Sort, Heap Sort, Cocktail Sort, Huffman Codebook Generator. Supervisor Stephen Barrett", } @InProceedings{Cody-Kenny:2015:gi, author = "Brendan Cody-Kenny and Edgar Galvan Lopez and Stephen Barrett", title = "{locoGP:} Improving Performance by Genetic Programming {Java} Source Code", booktitle = "Genetic Improvement 2015 Workshop", year = "2015", editor = "William B. Langdon and Justyna Petke and David R. White", pages = "811--818", address = "Madrid", publisher_address = "New York, NY, USA", month = "11-15 " # jul, organisation = "SIGEvo", publisher = "ACM", keywords = "genetic algorithms, genetic programming, Genetic Improvement, Metrics complexity measures, performance measures, Execution Cost, Implementation, Performance Improvement, Java", isbn13 = "978-1-4503-3488-4", broken = "https://www.scss.tcd.ie/~codykenb/locoGP.html", URL = "http://gpbib.cs.ucl.ac.uk/gi2015/locoGP_improving_performance_by_genetic_programming_java_source_code.pdf", URL = "http://doi.acm.org/10.1145/2739482.2768419", DOI = "doi:10.1145/2739482.2768419", slides_url = "http://gpbib.cs.ucl.ac.uk/gi2015/gi_2015_slides/cody-kenny/locoGP-publication-slides.pdf", code_url = "https://github.com/codykenb/locoGP/", size = "8 pages", abstract = "We present locoGP, a Genetic Programming (GP) system written in Java for evolving Java source code. locoGP was designed to improve the performance of programs as measured in the number of operations executed. Variable test cases are used to maintain functional correctness during evolution. The operation of locoGP is demonstrated on a number of typically constructed off-the-shelf hand-written implementations of sort and prefix-code programs. locoGP was able to find improvement opportunities in all test problems.", notes = "Seeding, modify AST tree, back to Java source code, compile, run. 11 types of sort and huffman codebook. Also known as \cite{2768419} Distributed at GECCO-2015.", } @InProceedings{Cody-Kenny:2017:GI, author = "Brendan Cody-Kenny and Michael Fenton and Michael O'Neill", title = "From Problem Landscapes to Language Landscapes: Questions in Genetic Improvement", booktitle = "GI-2017", year = "2017", editor = "Justyna Petke and David R. White and W. B. Langdon and Westley Weimer", pages = "1509--1510", address = "Berlin", month = "15-19 " # jul, publisher = "ACM", keywords = "genetic algorithms, genetic programming, genetic improvement, Software Engineering, Search, Learning", isbn13 = "978-1-4503-4939-0", URL = "http://geneticimprovementofsoftware.com/wp-content/uploads/2017/05/codykenny2017_landscape_questions.pdf", DOI = "doi:10.1145/3067695.3082522", size = "2 pages", abstract = "Managing and curating software is a time consuming process particularly as programming languages, libraries, and execution environments change. To support the engineering of software, we propose applying a GP-based continuous learning system to all useful software. We take the position that search-based itemization and analysis of all commonly used software is feasible, in large part, because the requirements that people place on software can be used to bound the search space to software which is of high practical use. By repeatedly reusing the information generated during the search process we hope to attain a higher-level, but also more rigorous, understanding of our engineering material: source code.", notes = "'learning system which uses search techniques to explore and curate a continuously expanding library of useful programs' Always-on GP 'How many tests are required?' http://crest.cs.ucl.ac.uk/cow/50/ ", } @InProceedings{Cody-Kenny:2017:GECCOa, author = "Brendan Cody-Kenny and Michael Fenton and Adrian Ronayne and Eoghan Considine and Thomas McGuire and Michael O'Neill", title = "A Search for Improved Performance in Regular Expressions", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4920-8", address = "Berlin, Germany", pages = "1280--1287", size = "8 pages", URL = "http://doi.acm.org/10.1145/3071178.3071196", DOI = "doi:10.1145/3071178.3071196", acmid = "3071196", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, performance, regular expressions", month = "15-19 " # jul, abstract = "The primary aim of automated performance improvement is to reduce the running time of programs while maintaining (or improving on) functionality. In this paper, Genetic Programming is used to find performance improvements in regular expressions for an array of target programs, representing the first application of automated software improvement for run-time performance in the Regular Expression language. This particular problem is interesting as there may be many possible alternative regular expressions which perform the same task while exhibiting subtle differences in performance. A benchmark suite of candidate regular expressions is proposed for improvement. We show that the application of Genetic Programming techniques can result in performance improvements in all cases. As we start evolution from a known good regular expression, diversity is critical in escaping the local optima of the seed expression. In order to understand diversity during evolution we compare an initial population consisting of only seed programs with a population initialised using a combination of a single seed individual with individuals generated using PI Grow and Ramped-half-and-half initialisation mechanisms.", notes = "Also known as \cite{Cody-Kenny:2017:SIP:3071178.3071196} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @Article{Cody-Kenny:2017:sigevolution, author = "Brendan Cody-Kenny", title = "Genetic Improvement Workshop at GECCO 2017", journal = "SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation", year = "2017", volume = "10", number = "3", pages = "7--8", month = oct, keywords = "genetic algorithms, genetic programming, Genetic Improvement", URL = "http://www.sigevolution.org/issues/SIGEVOlution1003.pdf", DOI = "doi:10.1145/3231560.3231562", size = "2 pages", } @InProceedings{Cody-Kenny:2018:evoApplications, author = "Brendan Cody-Kenny and Umberto Manganiello and John Farrelly and Adrian Ronayne and Eoghan Considine and Thomas McGuire and Michael O'Neill", title = "Investigating the Evolvability of Web Page Load Time", booktitle = "21st International Conference on the Applications of Evolutionary Computation, EvoSET 2018", year = "2018", editor = "Anna I. Esparcia-Alcazar and Sara Silva", series = "LNCS", volume = "10784", publisher = "Springer", pages = "769--777", address = "Parma, Italy", month = "4-6 " # apr, organisation = "Species", keywords = "genetic algorithms, genetic programming, genetic improvement, Search-based software engineering, SBSE, Javascript, Performance, Web applications", isbn13 = "978-3-319-77537-1", URL = "https://arxiv.org/pdf/1803.01683", DOI = "doi:10.1007/978-3-319-77538-8_51", size = "9 pages", abstract = "Client-side Javascript execution environments (browsers) allow anonymous functions and event-based programming concepts such as callbacks. We investigate whether a mutate-and-test approach can be used to optimise web page load time in these environments. First, we characterise a web page load issue in a benchmark web page and derive performance metrics from page load event traces.We parse Javascript source code to an AST and make changes to method calls which appear in a web page load event trace.We present an operator based solely on code deletion and evaluate an existing community-contributed performance optimising code transform. By exploring Javascript code changes and exploiting combinations of non-destructive changes, we can optimise page load time by 41percent in our benchmark web page.", notes = "EvoApplications2018 held in conjunction with EuroGP'2018 EvoCOP2018 and EvoMusArt2018 http://www.evostar.org/2018/cfp_evoapps.php", } @InProceedings{Cody-Kenny:2018:GI, author = "Brendan Cody-Kenny and Michael O'Neill and Stephen Barrett", title = "Performance Localisation", booktitle = "GI-2018, ICSE workshops proceedings", year = "2018", editor = "Justyna Petke and Kathryn Stolee and William B. Langdon and Westley Weimer", pages = "27--34", address = "Gothenburg, Sweden", month = "2 " # jun, publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, genetic improvement", isbn13 = "978-1-4503-5753-1", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/icse2018/gi2018/papers/Cody-Kenny_2018_GI.pdf", DOI = "doi:10.1145/3194810.3194815", size = "8 pages", abstract = "Profiling techniques highlight where performance issues manifest and provide a starting point for tracing cause back through a program. While people diagnose and understand the cause of performance to guide formulation of a performance improvement, we seek automated techniques for highlighting performance improvement opportunities to guide search algorithms. We investigate mutation-based approaches for highlighting where a performance improvement is likely to exist. For all modification locations in a program, we make all possible modifications and analyse how often modifications reduce execution count. We compare the resulting code location rankings against rankings derived using a profiler and find that mutation analysis provides the higher accuracy in highlighting performance improvement locations in a set of benchmark problems, though at a much higher execution cost. We see both approaches as complimentary and consider how they may be used to further guide Genetic Programming in finding performance improvements", notes = "Slides: http://geneticimprovementofsoftware.com/wp-content/uploads/2018/06/PerformanceLocalisation.pdf https://github.com/codykenb/locoGP https://codykenb.github.io/locoGP/locoGP-ImprovementsFound.html GI-2018 http://geneticimprovementofsoftware.com/events/papers#icse2018 part of \cite{Petke:2018:ICSEworkshop}", } @Article{Coelho2010494, author = "Andre L. V. Coelho and Everlandio Fernandes and Katti Faceli", title = "Inducing multi-objective clustering ensembles with genetic programming", journal = "Neurocomputing", volume = "74", number = "1-3", pages = "494--498", year = "2010", note = "Artificial Brains", ISSN = "0925-2312", DOI = "doi:10.1016/j.neucom.2010.09.014", URL = "http://www.sciencedirect.com/science/article/B6V10-517YN4X-P/2/7322b78e25061d5ecbaa12f058216cd0", keywords = "genetic algorithms, genetic programming, Cluster analysis, Ensembles, Multi-objective optimization", abstract = "The recent years have witnessed a growing interest in two advanced strategies to cope with the data clustering problem, namely, clustering ensembles and multi-objective clustering. In this paper, we present a genetic programming based approach that can be considered as a hybrid of these strategies, thereby allowing that different hierarchical clustering ensembles be simultaneously evolved taking into account complementary validity indices. Results of computational experiments conducted with artificial and real datasets indicate that, in most of the cases, at least one of the Pareto optimal partitions returned by the proposed approach compares favourably or go in par with the consensual partitions yielded by two well-known clustering ensemble methods in terms of clustering quality, as gauged by the corrected Rand index.", } @Article{Coelho2011, author = "Andre L. V. Coelho and Everlandio Fernandes and Katti Faceli", title = "Multi-objective design of hierarchical consensus functions for clustering ensembles via genetic programming", journal = "Decision Support Systems", year = "2011", volume = "51", number = "4", pages = "794--809", ISSN = "0167-9236", DOI = "doi:10.1016/j.dss.2011.01.014", keywords = "genetic algorithms, genetic programming, Cluster analysis, Clustering ensembles, Multi-objective clustering, Hierarchical fusion, Partition selection", abstract = "This paper investigates a genetic programming (GP) approach aimed at the multi-objective design of hierarchical consensus functions for clustering ensembles. By this means, data partitions obtained via different clustering techniques can be continuously refined (via selection and merging) by a population of fusion hierarchies having complementary validation indices as objective functions. To assess the potential of the novel framework in terms of efficiency and effectiveness, a series of systematic experiments, involving eleven variants of the proposed GP-based algorithm and a comparison with basic as well as advanced clustering methods (of which some are clustering ensembles and/or multi-objective in nature), have been conducted on a number of artificial, benchmark and bioinformatics datasets. Overall, the results corroborate the perspective that having fusion hierarchies operating on well-chosen subsets of data partitions is a fine strategy that may yield significant gains in terms of clustering robustness.", notes = "Recent Advances in Data, Text, and Media Mining & Information Issues in Supply Chain and in Service System Design", } @InProceedings{Coelho:2009:ICCI, author = "Lucio Coelho and Ben Goertzel and Cassio Pennachin and Chris Heward", title = "Classifier ensemble based analysis of a genome-wide SNP dataset concerning Late-Onset Alzheimer Disease", booktitle = "8th IEEE International Conference on Cognitive Informatics, ICCI '09", year = "2009", month = jun, pages = "469--475", keywords = "genetic algorithms, genetic programming, OpenBiomind toolkit, SLC6A15, brain genes, classifier ensemble based analysis, genome-wide SNP dataset, important features analysis, late-onset Alzheimer disease, local search methods, single-nucleotide polymorphisms, brain, diseases, genetic engineering, learning (artificial intelligence), medical administrative data processing, search problems", DOI = "doi:10.1109/COGINF.2009.5250695", abstract = "The OpenBiomind toolkit is used to apply GA, GP and local search methods to analyze a large SNP dataset concerning late-onset Alzheimer's disease (LOAD). Classification models identifying LOAD with statistically significant accuracy are identified, and ensemble-based important features analysis is used to identify brain genes related to LOAD, most notably the solute carrier gene SLC6A15. Ensemble analysis is used to identify potentially significant interactions between genes in the context of LOAD.", notes = "Also known as \cite{5250695}", } @Article{coello:2004:GPEM, author = "Carlos A. {Coello Coello} and Nareli {Cruz Cortes}", title = "Solving Multiobjective Optimization Problems Using an Artificial Immune System", journal = "Genetic Programming and Evolvable Machines", year = "2005", volume = "6", number = "2", pages = "163--190", month = jun, keywords = "AIS, artificial immune system, multiobjective optimization, clonal selection", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-005-6164-x", abstract = "we propose an algorithm based on the clonal selection principle to solve multiobjective optimisation problems (either constrained or unconstrained). The proposed approach uses Pareto dominance and feasibility to identify solutions that deserve to be cloned, and uses two types of mutation: uniform mutation is applied to the clones produced and non-uniform mutation is applied to the 'not so good' antibodies (which are represented by binary strings that encode the decision variables of the problem to be solved). We also use a secondary (or external) population that stores the nondominated solutions found along the search process. Such secondary population constitutes the elitist mechanism of our approach and it allows it to move towards the true Pareto front of a problem over time. Our approach is compared with three other algorithms that are representative of the state-of-the-art in evolutionary multiobjective optimisation. For our comparative study, three metrics are adopted and graphical comparisons with respect to the true Pareto front of each problem are also included. Results indicate that the proposed approach is a viable alternative to solve multiobjective optimisation problems.", } @InProceedings{DBLP:conf/esann/CofalaEK20, author = "Tim Cofala and Lars Elend and Oliver Kramer", title = "Tournament Selection Improves Cartesian Genetic Programming for Atari Games", booktitle = "28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, {ESANN} 2020, Bruges, Belgium, October 2-4, 2020", pages = "345--350", year = "2020", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", URL = "https://www.esann.org/sites/default/files/proceedings/2020/ES2020-204.pdf", timestamp = "Fri, 12 Nov 2021 00:00:00 +0100", biburl = "https://dblp.org/rec/conf/esann/CofalaEK20.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{Cohen:2014:GECCOcomp, author = "Adam T. S. Cohen and Tony White", title = "CityBreeder: city design with evolutionary computation", booktitle = "GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion", year = "2014", editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges", isbn13 = "978-1-4503-2881-4", keywords = "genetic algorithms, genetic programming: Poster", pages = "133--134", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2598394.2598495", DOI = "doi:10.1145/2598394.2598495", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "The process of creating city designs is complex and time-consuming. This paper presents CityBreeder, a system which uses Evolutionary Computation to enable the rapid, user-guided development of city designs based on the blending of multiple existing designs.", notes = "Also known as \cite{2598495} Distributed at GECCO-2014.", } @Article{Cohen:2015:ieeeTPAMI, author = "Andrew R. Cohen and Paul M. B. Vitanyi", title = "Normalized Compression Distance of Multisets with Applications", journal = "IEEE Transactions on Pattern Analysis and Machine Intelligence", year = "2015", volume = "37", number = "8", pages = "1602--1614", month = aug, keywords = "genetic algorithms, genetic programming", ISSN = "0162-8828", DOI = "doi:10.1109/TPAMI.2014.2375175", abstract = "Pairwise normalized compression distance (NCD) is a parameter-free, feature-free, alignment-free, similarity metric based on compression. We propose an NCD of multisets that is also metric. Previously, attempts to obtain such an NCD failed. For classification purposes it is superior to the pairwise NCD in accuracy and implementation complexity. We cover the entire trajectory from theoretical underpinning to feasible practice. It is applied to biological (stem cell, organelle transport) and OCR classification questions that were earlier treated with the pairwise NCD. With the new method we achieved significantly better results. The theoretic foundation is Kolmogorov complexity.", notes = "Also known as \cite{6967789}", } @InProceedings{Cohen:2023:CDC, author = "Benjamin Cohen and Burcu Beykal and George Bollas", booktitle = "2023 62nd IEEE Conference on Decision and Control (CDC)", title = "Dynamic System Identification from Scarce and Noisy Data Using Symbolic Regression", year = "2023", pages = "3670--3675", abstract = "A framework for dynamic system model identification from scarce and noisy data is proposed. This framework uses symbolic regression via genetic programming with a gradient-based parameter estimation step to identify a differential equation model and its parameters from available system data. The effectiveness of the method is demonstrated by identifying four synthetic systems: an ideal plug flow reactor (PFR) with an irreversible chemical reaction, an ideal continuously stirred tank reactor (CSTR) with an irreversible chemical reaction, a system described by Burgers' Equation, and an ideal PFR with a reversible chemical reaction. The results show that this framework can identify PDE models of systems from broadly spaced and noisy data. When the data was not sufficiently rich, the framework discovered a surrogate model that described the observations in equal or fewer terms than the true system model. Additionally, the method can select relevant physics terms to describe a system from a list of candidate arguments, providing valuable models for use in controls applications.", keywords = "genetic algorithms, genetic programming, Parameter estimation, Chemical reactions, Mathematical models, Data models, Noise measurement, Inductors", DOI = "doi:10.1109/CDC49753.2023.10383906", ISSN = "2576-2370", month = dec, notes = "Also known as \cite{10383906}", } @Article{Cohen:2024:jcp, author = "Benjamin G. Cohen and Burcu Beykal and George M. Bollas", title = "Physics-informed genetic programming for discovery of partial differential equations from scarce and noisy data", journal = "Journal of Computational Physics", year = "2024", volume = "514", pages = "113261", keywords = "genetic algorithms, genetic programming, Model discovery, Symbolic regression, Partial differential equations", ISSN = "0021-9991", URL = "https://www.sciencedirect.com/science/article/pii/S0021999124005096", DOI = "doi:10.1016/j.jcp.2024.113261", abstract = "A novel framework is proposed that uses symbolic regression via genetic programming to identify free-form partial differential equations from scarce and noisy data. The framework successfully identified ground truth models for four synthetic systems (an isothermal plug flow reactor, a continuously stirred tank reactor, a nonisothermal reactor, and viscous flow governed by Burgers' equation) from time-variant data collected at one location. A comparative analysis against the so-called weak Sparse Identification of Nonlinear Dynamics (SINDy) demonstrated the proposed framework's superior ability to identify meaningful partial differential equation (PDE) models when data was scarce. The framework was further tested for robustness to noise and scarcity, showing successful model recovery from as few as eight time series data points collected at a single point in space with 50percent noise. These results emphasize the potential of the proposed framework for the discovery of PDE models when data collection is expensive or otherwise difficult", } @InProceedings{Cohen:2023:GI, author = "Myra B. Cohen", title = "It's all in the Semantics: When are Genetically Improved Programs Still Correct?", booktitle = "12th International Workshop on Genetic Improvement @ICSE 2023", year = "2023", editor = "Vesna Nowack and Markus Wagner and Gabin An and Aymeric Blot and Justyna Petke", pages = "ix", address = "Melbourne, Australia", month = "20 " # may, publisher = "IEEE", note = "Invited Keynote", keywords = "genetic algorithms, genetic programming, Genetic Improvement", isbn13 = "979-8-3503-1232-4", URL = "http://gpbib.cs.ucl.ac.uk/gi2023/keynote_2023_gi.pdf", DOI = "doi:10.1109/GI59320.2023.00008", video_url = "https://www.youtube.com/watch?v=Mo3HiZr4kJQ&list=PLI8fiFpB7BoJLh6cUpGBjyeB1hM9DET1V&index=1", size = "1 page", abstract = "Genetic improvement (GI) is a powerful technique to automatically optimise programs, often for nonfunctional properties. As such, we expect to retain the original program semantics, hence GI is guided by both a functional test suite and at least one other objective such as program efficiency, memory usage, energy efficiency, etc. An assumption made is that it is possible to improve a program's non-functional objective while retaining the program's correctness, however, this assumption may not hold for all types of non-functional properties. In this talk I show why GI is naturally a multi-objective optimization problem and argue that it may be necessary to relax part of the program correctness to satisfy our non-functional goals. I discuss a few recent examples where we have had to balance functional correctness and non-functional objectives and demonstrate how this may lead to programs that are of higher quality in the end. This raises an important question about when it is possible to completely satisfy multiple (potentially competing) program objectives during GI, and when it is semantically impossible. This leads to the ultimate question of what it means for a program to be correct when using GI.", notes = "GI @ ICSE 2023, part of \cite{Nowack:2023:GI}", } @MastersThesis{Coia:mastersthesis, author = "Corrado Coia", title = "Automatic Evolution of Conceptual Building Architectures", school = "Brock University", year = "2011", month = nov, keywords = "genetic algorithms, genetic programming", URL = "http://hdl.handle.net/10464/3961", URL = "https://dr.library.brocku.ca/bitstream/handle/10464/3961/Brock_Coia_Corrado_2011.pdf", size = "109 pages", abstract = "This thesis describes research in which genetic programming is used to automatically evolve shape grammars that construct three dimensional models of possible external building architectures. A completely automated fitness function is used, which evaluates the three dimensional building models according to different geometric properties such as surface normals, height, building footprint, and more. In order to evaluate the buildings on the different criteria, a multi-objective fitness function is used. The results obtained from the automated system were successful in satisfying the multiple objective criteria as well as creating interesting and unique designs that a human-aided system might not discover. In this study of evolutionary design, the architectures created are not meant to be fully functional and structurally sound blueprints for constructing a building, but are meant to be inspirational ideas for possible architectural designs. The evolved models are applicable for today's architectural industries as well as in the video game and movie industries. Many new avenues for future work have also been discovered and highlighted.", } @InProceedings{Coia:2011:AEoCBA, title = "Automatic Evolution of Conceptual Building Architectures", author = "Corrado Coia and Brian Ross", pages = "1145--1152", booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary Computation", year = "2011", editor = "Alice E. Smith", month = "5-8 " # jun, address = "New Orleans, USA", organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, 3D building model, automatic 3D building topologies generation, automatic shape grammar, conceptual building architecture, geometric specification, model geometry, target height requirement, user supplied criteria, building, shapes (structures), solid modelling, structural engineering computing", DOI = "doi:10.1109/CEC.2011.5949745", abstract = "An evolutionary approach to the automatic generation of 3D building topologies is presented. Genetic programming is used to evolve shape grammars. When interpreted, the shape grammars generate 3D models of buildings. Fitness evaluation considers user-specified criteria that evaluate different aspects of the model geometry. Such criteria might include maximising the number of unique normals, satisfying target height requirements, and conforming to supplied shape contours. Multi-objective evaluation is used to analyse and rank model fitness, based on the varied user-supplied criteria. A number of interesting models complying to given geometric specifications have been successfully evolved with the approach. A motivation for this research application is that it can be used as a generator of conceptual designs, to be used as inspirations for refinement or further exploration.", notes = "CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.", } @InProceedings{conf/arc/CoimbraL16, author = "Vitor Coimbra and Marcus Vinicius Lamar", title = "Design and Optimization of Digital Circuits by Artificial Evolution Using Hybrid Multi Chromosome Cartesian Genetic Programming", bibdate = "2016-03-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/arc/arc2016.html#CoimbraL16", booktitle = "Applied Reconfigurable Computing - 12th International Symposium, {ARC} 2016, Mangaratiba, {RJ}, Brazil, March 22-24, 2016, Proceedings", publisher = "Springer", year = "2016", volume = "9625", editor = "Vanderlei Bonato and Christos Bouganis and Marek Gorgon", isbn13 = "978-3-319-30480-9", pages = "195--206", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming", URL = "http://dx.doi.org/10.1007/978-3-319-30481-6", } @Article{Cojbasic:2016:PE, author = "Zarko Cojbasic and Dalibor Petkovic and Shahaboddin Shamshirband and Chong Wen Tong and Sudheer Ch and Predrag Jankovic and Nedeljko Ducic and Jelena Baralic", title = "Surface roughness prediction by extreme learning machine constructed with abrasive water jet", journal = "Precision Engineering", volume = "43", pages = "86--92", year = "2016", ISSN = "0141-6359", DOI = "doi:10.1016/j.precisioneng.2015.06.013", URL = "http://www.sciencedirect.com/science/article/pii/S0141635915001154", abstract = "In this study, the novel method based on extreme learning machine (ELM) is adapted to estimate roughness of surface machined with abrasive water jet. Roughness of surface is one of the main attributes of quality of products derived from water jet processing, and directly depends on the cutting parameters, such as thickness of the workpiece, abrasive flow rate, cutting speed and others. In this study, in order to provide data on influence of parameters on surface roughness, extensive experiments were carried out for different cutting regimes. Measured data were used to model the process by using ELM model. Estimation and prediction results of ELM model were compared with genetic programming (GP) and artificial neural networks (ANNs) models. The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the ELM approach in comparison with GP and ANN. Moreover, achieved results indicate that developed ELM models can be used with confidence for further work on formulating novel model predictive strategy for roughness of the surface machined with abrasive water jet. In conclusion, it is conclusively found that application of ELM is particularly promising as an alternative method to estimate the roughness of the surface machined with abrasive water jet.", keywords = "genetic algorithms, genetic programming, Abrasive water jet, Cutting, Surface roughness, Estimation, Extreme learning machine (ELM)", } @InProceedings{Coker:SEAMS:2015, author = "Zack Coker and David Garlan and Claire {Le Goues}", title = "{SASS}: Self-adaptation using stochastic search", booktitle = "10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems", year = "2015", editor = "Gerardo Canfora and Sebastian Elbaum and Antonia Bertolino", pages = "168--174", address = "Florence Italy", month = may # " 18-19", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE", isbn13 = "978-0-7695-5567-6", URL = "https://www.cs.cmu.edu/~clegoues/docs/seams15-position.pdf", DOI = "doi:10.1109/SEAMS.2015.16", size = "7 pages", abstract = "Future-generation self-adaptive systems will need to be able to optimise for multiple interrelated, difficult to measure, and evolving quality properties. To navigate this complex search space, current self-adaptive planning techniques need to be improved. In this position paper, we argue that the research community should more directly pursue the application of stochastic search techniques, such as hill climbing or genetic algorithms, that incorporate an element of randomness to self-adaptive systems research. These techniques are well-suited to handling multi-dimensional search spaces and complex problems, situations which arise often for self-adaptive systems. We believe that recent advances in both fields make this a particularly promising research trajectory. We demonstrate one way to apply some of these advances in a search-based planning prototype technique to illustrate both the feasibility and the potential of the proposed research. This strategy informs a number of potentially interesting research directions and problems. In the long term, this general technique could enable sophisticated plan generation techniques that improve domain specific knowledge, decrease human effort, and increase the application of self-adaptive systems.", notes = "Znn.com, Java JAGP, PRISM, MAPE School of Computer Science Carnegie Mellon University, Pittsburgh, PA 15213 SEAMS'15 http://2015.icse-conferences.org/", } @Article{Colak2007657, author = "Oguz Colak and Cahit Kurbanoglu and M. Cengiz Kayacan", title = "Milling surface roughness prediction using evolutionary programming methods", journal = "Materials \& Design", volume = "28", number = "2", pages = "657--666", year = "2007", ISSN = "0261-3069", DOI = "DOI:10.1016/j.matdes.2005.07.004", URL = "http://www.sciencedirect.com/science/article/B6TX5-4GYNXVH-3/2/9f33fbb56f37b01600d2773bc207696f", keywords = "genetic algorithms, genetic programming, gene expression programming, Surface roughness, CNC end milling, Genetic expression programming", abstract = "CNC milling has become one of the most competent, productive and flexible manufacturing methods, for complicated or sculptured surfaces. In order to design, optimize, built up to sophisticated, multi-axis milling centers, their expected manufacturing output is at least beneficial. Therefore data, such as the surface roughness, cutting parameters and dynamic cutting behavior are very helpful, especially when they are computationally produced, by artificial intelligent techniques. Predicting of surface roughness is very difficult using mathematical equations. In this study gene expression programming method is used for predicting surface roughness of milling surface with related to cutting parameters. Cutting speed, feed and depth of cut of end milling operations are collected for predicting surface roughness. End of the study a linear equation is predicted for surface roughness related to experimental study.", } @Article{Colbourn2011366, author = "E. A. Colbourn and S. J. Roskilly and R. C. Rowe and P. York", title = "Modelling formulations using gene expression programming - A comparative analysis with artificial neural networks", journal = "European Journal of Pharmaceutical Sciences", volume = "44", number = "3", pages = "366--374", year = "2011", ISSN = "0928-0987", DOI = "doi:10.1016/j.ejps.2011.08.021", URL = "http://www.sciencedirect.com/science/article/pii/S0928098711002958", keywords = "genetic algorithms, genetic programming, gene expression programming, Neural networks, Modelling, Formulation", abstract = "This study has investigated the utility and potential advantages of gene expression programming (GEP) - a new development in evolutionary computing for modelling data and automatically generating equations that describe the cause-and-effect relationships in a system- to four types of pharmaceutical formulation and compared the models with those generated by neural networks, a technique now widely used in the formulation development. Both methods were capable of discovering subtle and non-linear relationships within the data, with no requirement from the user to specify the functional forms that should be used. Although the neural networks rapidly developed models with higher values for the ANOVA R2 these were black box and provided little insight into the key relationships. However, GEP, although significantly slower at developing models, generated relatively simple equations describing the relationships that could be interpreted directly. The results indicate that GEP can be considered an effective and efficient modelling technique for formulation data.", } @InProceedings{DBLP:conf/itng/Coleman06, author = "Ron Coleman", title = "Boosting Blackjack Returns with Machine Learned Betting Criteria", year = "2006", publisher = "IEEE Computer Society", booktitle = "Third International Conference on Information Technology: New Generations (ITNG 2006)", pages = "669--673", address = "Las Vegas, Nevada, USA", month = "10-12 " # apr, keywords = "genetic algorithms, genetic programming", ISBN = "0-7695-2497-4", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1109/ITNG.2006.40", bibsource = "DBLP, http://dblp.uni-trier.de", } @InProceedings{coletti:1999:CPLEMGA, author = "Mark Coletti and Thomas D. Lash and Ryszard Michalski and Craig Mandsager and Rida Moustafa", title = "Comparing Performance of the Learnable Evolution Model and Genetic Algorithms", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "779", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-386.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-386.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Coletti:2020:GECCOcomp, author = "Mark A. Coletti and Eric O. Scott and Jeffrey K. Bassett", title = "Library for Evolutionary Algorithms in Python {(LEAP)}", year = "2020", editor = "Richard Allmendinger and Hugo Terashima Marin and Efren Mezura Montes and Thomas Bartz-Beielstein and Bogdan Filipic and Ke Tang and David Howard and Emma Hart and Gusz Eiben and Tome Eftimov and William {La Cava} and Boris Naujoks and Pietro Oliveto and Vanessa Volz and Thomas Weise and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Rui Wang and Ran Cheng and Guohua Wu and Miqing Li and Hisao Ishibuchi and Jonathan Fieldsend and Ozgur Akman and Khulood Alyahya and Juergen Branke and John R. Woodward and Daniel R. Tauritz and Marco Baioletti and Josu Ceberio Uribe and John McCall and Alfredo Milani and Stefan Wagner and Michael Affenzeller and Bradley Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and Nayat Sanchez-Pi and Luis Marti and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and Matthew Johns and Nick Ross and Ed Keedwell and Herman Mahmoud and David Walker and Anthony Stein and Masaya Nakata and David Paetzel and Neil Vaughan and Stephen Smith and Stefano Cagnoni and Robert M. Patton and Ivanoe {De Falco} and Antonio {Della Cioppa} and Umberto Scafuri and Ernesto Tarantino and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Erik Hemberg and Riyad Alshammari and Tokunbo Makanju and Fuijimino-shi and Ivan Zelinka and Swagatam Das and Ponnuthurai Nagaratnam and Roman Senkerik", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion", pages = "1571--1579", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", isbn13 = "9781450371278", URL = "https://www.osti.gov/biblio/1649229", URL = "https://doi.org/10.1145/3377929.3398147", DOI = "doi:10.1145/3377929.3398147", code_url = "https://github.com/leap-ec", size = "9 pages", abstract = "There are generally three types of scientific software users: users that solve problems using existing science software tools, researchers that explore new approaches by extending existing code, and educators that teach students scientific concepts. Python is a general-purpose programming language that is accessible to beginners, such as students, but also as a language that has a rich scientific programming ecosystem that facilitates writing research software. Additionally, as high-performance computing (HPC) resources become more readily available, software support for parallel processing becomes more relevant to scientific software. There currently are no Python-based evolutionary computation frameworks that adequately support all three types of scientific software users. Moreover, some support synchronous concurrent fitness evaluation that do not efficiently use HPC resources. We pose here a new Python-based EC framework that uses an established generalized unified approach to EA concepts to provide an easy to use toolkit for users wishing to use an EA to solve a problem, for researchers to implement novel approaches, and for providing a low-bar to entry to EA concepts for students. Additionally, this toolkit provides a scalable asynchronous fitness evaluation implementation friendly to HPC that has been vetted on hardware ranging from laptops to the worlds fastest supercomputer, Summit.", notes = "Also known as \cite{osti_1649229} Also known as \cite{10.1145/3377929.3398147} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @Article{colin:1997:DMGP, author = "Andre Colin", title = "Data-Mining and Genetic Programming", journal = "PC AI", year = "1997", volume = "11", number = "5", pages = "23", month = sep # "/" # oct, publisher = "Knowledge Technology, Inc.", address = "Phoenix, AZ, USA", email = "acolin@zurich.com.au", keywords = "genetic algorithms, genetic programming, data mining", ISSN = "0894-0711", URL = "http://www.pcai.com/web/issues/pcai_11_5_toc.html", size = "3 pages", abstract = "To make intelligent real-world decisions, a data-mining package must often align with other technologies. One such technology is genetic programming, which derives rules by looking through a {"}space{"} of possibilities. Andrew Colin shows how data-mining can use genetic programming in important applications.", notes = "easy going introduction to GP but little data mining information. Code available on line http://www.primenet.com/pcai/New_Home_Page/pcai_info/All_Lists.html broken Feb 2012", } @Article{Colins:2017:pone, author = "Andrea Colins and Ziomara P. Gerdtzen and Marco T. Nunez and J. Cristian Salgado", title = "Mathematical Modeling of Intestinal Iron Absorption Using Genetic Programming", journal = "PLOS one", year = "2017", volume = "12", number = "1", pages = "e0169601", month = jan # " 10", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1371/journal.pone.0169601", size = "24 pages", abstract = "Iron is a trace metal, key for the development of living organisms. Its absorption process is complex and highly regulated at the transcriptional, translational and systemic levels. Recently, the internalization of the DMT1 transporter has been proposed as an additional regulatory mechanism at the intestinal level, associated to the mucosal block phenomenon. The short-term effect of iron exposure in apical uptake and initial absorption rates was studied in Caco-2 cells at different apical iron concentrations, using both an experimental approach and a mathematical modelling framework. This is the first report of short-term studies for this system. A non-linear behaviour in the apical uptake dynamics was observed, which does not follow the classic saturation dynamics of traditional biochemical models. We propose a method for developing mathematical models for complex systems, based on a genetic programming algorithm. The algorithm is aimed at obtaining models with a high predictive capacity, and considers an additional parameter fitting stage and an additional Jack-knife stage for estimating the generalization error. We developed a model for the iron uptake system with a higher predictive capacity than classic biochemical models. This was observed both with the apical uptake dataset used for generating the model and with an independent initial rates dataset used to test the predictive capacity of the model. The model obtained is a function of time and the initial apical iron concentration, with a linear component that captures the global tendency of the system, and a non-linear component that can be associated to the movement of DMT1 transporters. The model presented in this paper allows the detailed analysis, interpretation of experimental data, and identification of key relevant components for this complex biological process. This general method holds great potential for application to the elucidation of biological mechanisms and their key components in other complex systems.", } @Misc{oai:arXiv.org:1504.05811, author = "Michele Colledanchise and Ramviyas Parasuraman and Petter Oegren", title = "Learning of Behavior Trees for Autonomous Agents", year = "2015", month = apr # "~22", abstract = "Definition of an accurate system model for Automated Planner (AP) is often impractical, especially for real-world problems. Conversely, off-the-shelf planners fail to scale up and are domain dependent. These drawbacks are inherited from conventional transition systems such as Finite State Machines (FSMs) that describes the action-plan execution generated by the AP. On the other hand, Behaviour Trees (BTs) represent a valid alternative to FSMs presenting many advantages in terms of modularity, reactiveness, scalability and domain-independence. In this paper, we propose a model-free AP framework using Genetic Programming (GP) to derive an optimal BT for an autonomous agent to achieve a given goal in unknown (but fully observable) environments. We illustrate the proposed framework using experiments conducted with an open source benchmark Mario AI for automated generation of BTs that can play the game character Mario to complete a certain level at various levels of difficulty to include enemies and obstacles.", bibsource = "OAI-PMH server at export.arxiv.org", oai = "oai:arXiv.org:1504.05811", keywords = "genetic algorithms, genetic programming, computer science - robotics, computer science - artificial intelligence, computer science - learning", URL = "http://arxiv.org/abs/1504.05811", } @Article{Colledanchise:2018:ieeeTOG, author = "Michele Colledanchise and Ramviyas Nattanmai Parasuraman and Petter Ogren", journal = "IEEE Transactions on Games", title = "Learning of Behavior Trees for Autonomous Agents", year = "2018", keywords = "genetic algorithms, genetic programming, Artificial intelligence, Games, Genetics, Heuristic algorithms, Planning, Safety, Stochastic processes", ISSN = "2475-1502", DOI = "doi:10.1109/TG.2018.2816806", abstract = "we study the problem of automatically synthesizing a successful Behaviour Tree (BT) in an a-priori unknown dynamic environment. Starting with a given set of behaviours, a reward function, and sensing in terms of a set of binary conditions, the proposed algorithm incrementally learns a switching structure in terms of a BT, that is able to handle the situations encountered. Exploiting the fact that BTs generalise And-Or-Trees and also provide very natural chromosome mappings for genetic programming, we combine the long term performance of Genetic Programming with a greedy element and use the And-Or analogy to limit the size of the resulting structure. Finally, earlier results on BTs enable us to provide certain safety guarantees for the resulting system. Using the testing environment Mario AI we compare our approach to alternative methods for learning BTs and Finite State Machines. The evaluation shows that the proposed approach generated solutions with better performance, and often fewer nodes than the other two methods.", notes = "Also known as \cite{8319483}", } @InProceedings{collet:1999:IGAVRCP, author = "Pierre Collet and Evelyne Lutton and Frederic Raynal and Marc Schoenauer", title = "Individual GP: an Alternative Viewpoint for the Resolution of Complex Problems", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "974--981", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, IFS, fractals", ISBN = "1-55860-611-4", URL = "http://minimum.inria.fr/evo-lab/Publications/GP-467.ps.gz", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-467.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-467.ps", abstract = "An unususal GP implementation is proposed, based on a more {"}economic{"} exploitation of the GP algorithm: the {"}individual{"} approach, where each individual of the population embodies a single function rather than a set of functions. The final solution is then a set of individuals. Examples are presented where results are obtained more rapidly than with the conventional approach, where all individuals of the final generation but one are discarded.", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @TechReport{collet:1999:RR-3849, author = "Pierre Collet and Evelyne Lutton and Frederic Raynal and Marc Schoenauer", title = "Polar IFS + Individual Genetic Programming = Efficient {IFS} Inverse Problem Solving", institution = "INRIA", year = "1999", number = "RR-3849", address = "Domaine de Voluceau - Rocquencourt - B.P. 105 78153 Le Chesnay Cedex France", month = dec, keywords = "genetic algorithms, genetic programming", URL = "http://minimum.inria.fr/evo-lab/Publications/RR-PolarIFS.ps.gz", abstract = "The inverse problem for Iterated Functions Systems (finding an IFS whose attractor is a target 2D shape) with non-affine IFS is a very complex task. Successful approaches have been made using Genetic Programming, but there is still room for improvement in both the IFS and the GP parts. The main difficulty with non-linear IFS is the efficient handling of contractance constraints. This paper introduces Polar IFS, a specific representation of IFS functions that shrinks the search space to mostly contractive functions. Moreover, the Polar representation gives direct access to the fixed points of the functions, whereas the fixed point of general non-linear IFS can only be numerically estimated. On the evolutionary side, the {"}individual{"} approach is similar to the Michigan approach of Classifier Systems: each individual of the population embodies a single function rather than the whole IFS. A solution to the inverse problem is then built from a set of individuals. Both improvements show a drastic cut-down on CPU-time: good results are obtained with small populations in few generations.", abstract = "Lorsque l'on s'interesse aux IFS (systemes de fonctions iterees) non affines, la resolution du probleme inverse (c'est-a-dire trouver l'IFS dont l'attracteur approxime au mieux une forme bidimensionnelle donnee) devient un probleme tres complexe. Ce probleme a deja ete resolu avec succes a l'aide de strategies de programmation genetique, fondees sur une representation des fonctions sous forme d'arbres. La principale difficulte de cette approche etant la gestion efficace des contraintes de contractance sur les fonctions, nous proposons ici l'emploi d'une representation polaire des IFS non affines, centree sur le point fixe de chaque fonction. Cette representation a deux principaux avantages : une contrainte simple sur la definition de la composante radiale de chaque fonction assure sa convergence vers un point fixe (le point central de sa representation polaire), l'acces au point fixe de chaque fonction est direct (il n'est plus necessaire de l'estimer comme dans l'approche en coordonnees cartesiennes). Nous presentons ensuite une strategie originale de programmation genetique, fondee sur une exploitation plus {"}economique{"} des strategies evolutionnaires : l'approche {"}individuelle{"}, o\`{u} chaque individu de la population represente une seule fonction (au lieu d'un IFS complet). La solution au probleme etant fournie par un ensemble d'individus de la population finale, des resultats sont obtenus de fa\c{c}on plus rapide et plus efficace que dans la version classique o\`{u} tous les individus de la population finale sauf un (le meilleur) sont ecartes.", notes = "in english", size = "30 pages", } @InProceedings{ColletPPSN2000, author = "Pierre Collet and Evelyne Lutton and Marc Schoenauer and Jean Louchet", title = "Take it {EASEA}", booktitle = "Parallel Problem Solving from Nature - PPSN VI 6th International Conference", editor = "Marc Schoenauer and Kalyanmoy Deb and G{\"u}nter Rudolph and Evelyne Lutton Xin Yao and Juan Julian Merelo and Hans-Paul Schwefel", year = "2000", publisher = "Springer-Verlag", address = "Paris, France", month = sep # " 16-20", volume = "1917", series = "LNCS", pages = "891--901", keywords = "genetic algorithms, genetic programming", URL = "http://minimum.inria.fr/evo-lab/Publications/PPSNVI.ps.gz", abstract = "Evolutionary algorithms are not straightforward to implement and the lack of any specialised language forces users to reinvent the wheel every time they want to write a new program. Over the last years, evolutionary libraries have appeared, trying to reduce the amount of work involved in writing such algorithms from scratch, by offering standard engines, strategies and tools. Unfortunately, most of these libraries are quite complex to use, and imply a deep knowledge of object programming and C++. To further reduce the amount of work needed to implement a new algorithm, without however throwing down the drain all the man-years already spent in the development of such libraries, we have designed EASEA (acronym for EAsy Specification of Evolutionary Algorithms): a new high-level language dedicated to the specification of evolutionary algorithms. EASEA compiles .ez files into C++ object files, containing function calls to a chosen existing library. The resulting C++ file is in turn compiled and linked with the library to produce an executable file implementing the evolutionary algorithm specified in the original .ez file.", notes = "online (http://minimum.inria.fr/evo-lab/Publications/PPSNVI.ps.gz) not identical format to published", } @Article{collet:2000:IFSpGP, author = "Pierre Collet and Evelyne Lutton and Frederic Raynal and Marc Schoenauer", title = "Polar {IFS}+Parisian Genetic Programming=Efficient {IFS} Inverse Problem Solving", journal = "Genetic Programming and Evolvable Machines", year = "2000", volume = "1", number = "4", pages = "339--361", month = oct, keywords = "genetic algorithms, genetic programming, fractals, Iterated Functions System, inverse problem for IFS, polar IFS", ISSN = "1389-2576", URL = "http://minimum.inria.fr/evo-lab/Publications/PolarIFS-GPEM-New.ps.gz", URL = "http://www.lri.fr/~marc/EEAAX/papers/marc/gpem2000.ps.gz", DOI = "doi:10.1023/A:1010065123132", URL = "http://citeseer.ist.psu.edu/374242.html", abstract = "This paper proposes a new method for treating the inverse problem for Iterated Functions Systems (IFS) using Genetic Programming. This method is based on two original aspects. On the fractal side, a new representation of the IFS functions, termed Polar Iterated Functions Systems, is designed, shrinking the search space to mostly contractive functions. Moreover, the Polar representation gives direct access to the fixed points of the functions. On the evolutionary side, a new variant of GP, the Parisian approach is presented. The paper explains its similarity to the Michigan approach of Classifier Systems: each individual of the population only represents a part of the global solution. The solution to the inverse problem for IFS is then built from a set of individuals. A local contribution to the global fitness of an IFS is carefully defined for each one of its member functions and plays a major role in the fitness of each individual. It is argued here that both proposals result in a large improvement in the algorithms. We observe a drastic cut-down on CPU-time, obtaining good results with small populations in few generations.", notes = "also known as \cite{Journal-PolarIFS-2000} Article ID: 273811", } @TechReport{collet:2001:RR4421, author = "Pierre Collet and Marc Schoenauer and Evelyne Lutton and Jean Louchet", title = "EASEA : un langage de specification pour les algorithmes evolutionnaires", institution = "INRIA", year = "2001", number = "RR-4218", address = "Domaine de Voluceau - Rocquencourt - B.P. 105 78153 Le Chesnay Cedex France", month = jun, keywords = "genetic algorithms, genetic programming, EASEA, Java", URL = "ftp://ftp.inria.fr/INRIA/publication/publi-pdf/RR/RR-4218.pdf", URL = "https://hal.inria.fr/inria-00000849", URL = "https://hal.inria.fr/inria-00000849/file/19_RR4218.pdf", hal_id = "inria-00000849", hal_version = "v1", size = "17 pages", abstract = "Contrairement aux apparences, il n'est pas simple d'ecrire un programme informatique realisant un algorithme evolutionnaire, d'autant que le manque de langage specialise oblige l'utilisateur a utiliser C, C++ ou JAVA. La plupart des algorithmes evolutionnaires, cependant, possedent une structure commune, et la part reellement specifique est constituee par une faible portion du code. Ainsi, il semble que rien ne s'oppose en theorie a ce qu'un utilisateur puisse construire, puis faire tourner son algorithme evolutionnaire a partir d'une interface graphique, afin de limiter son effort de programmation a la fonction a optimiser. L'ecriture d'une telle interface graphique pose tout d'abord le probleme de sauver et de recharger l'algorithme evolutionnaire sur lequel l'utilisateur travaille, puis celui de transformer ces informations en code compilable. Cela ressemble fort a un language de specification et son compilateur. Le logiciel EASEA a ete cree dans ce but, et a notre connaissance, il est actuellement le seul et unique compilateur de langage specifique aux algorithmes evolutionnaires. Ce rapport decrit comment EASEA a ete construit et quels sont les problemes qui restent a resoudre pour achever son implantation informatique.", abstract = "Evolutionary algorithms are not straightforward to implement and the lack of any specialised language forces users to write their algorithms in C, C++ or JAVA. However, most evolutionary algorithms follow a similar design, and the only really specific part is the code representing the problem to be solved. Therefore, it seems that nothing, in theory, could prevent a user from being able to design and run his evolutionary algorithm from a Graphic User Interface, without any other programming effort than the function to be optimised. Writing such a GUI rapidly poses the problem of saving and reloading the evolutionary algorithm on which the user is working, and translating the information into compilable code. This very much sounds like a specifying language and its compiler. The EASEA software was created on this purpose, and to our knowledge, it is the first and only usable compiler of a language specific to evolutionary algorithms. This reprot describes how EASEA has been designed and the problems which needed to be solved to achieve its implementation.", notes = "In english. Also known as \cite{CSLL-juin2001}", } @InProceedings{Collet:2002:IotOoEAC, author = "Pierre Collet and Jean Louchet and Evelyne Lutton", title = "Issues on the Optimisation of Evolutionary Algorithms Code", booktitle = "Proceedings of the 2002 Congress on Evolutionary Computation CEC2002", editor = "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and Mark Shackleton", pages = "1103--1108", year = "2002", publisher = "IEEE Press", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", organisation = "IEEE Neural Network Council (NNC), Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", keywords = "genetic algorithms, genetic programming, code optimisation, computation time, computational complexity, evolutionary algorithms, fitness, testbenches, computational complexity, evolutionary computation,", ISBN = "0-7803-7278-6", month = "12-17 " # may, DOI = "doi:10.1109/CEC.2002.1004397", abstract = "The aim of this paper is to show that the common belief, in the evolutionary community, that evaluation time usually takes over 90percent of the total time, is far from being always true. In fact, many real-world applications showed a much lower percentage. This raises several questions, one of them being the balance between fitness and operators computational complexity: what is the use of elaborating smart evolutionary operators to reduce the number of evaluations if as a result, the total computation time is increased?", notes = "CEC 2002 - A joint meeting of the IEEE, the Evolutionary Programming Society, and the IEE. Held in connection with the World Congress on Computational Intelligence (WCCI 2002)", } @InProceedings{collet:2003:EA, author = "Pierre Collet and Marc Schoenauer", title = "{GUIDE}: Unifying Evolutionary Engines through a Graphical User Interface", booktitle = "Evolution Artificielle, 6th International Conference", year = "2003", editor = "Pierre Liardet and Pierre Collet and Cyril Fonlupt and Evelyne Lutton and Marc Schoenauer", volume = "2936", series = "Lecture Notes in Computer Science", pages = "203--215", address = "Marseilles, France", month = "27-30 " # oct, publisher = "Springer", note = "Revised Selected Papers", keywords = "genetic algorithms, genetic programming, Artificial Evolution", ISBN = "3-540-21523-9", DOI = "doi:10.1007/b96080", DOI = "doi:10.1007/978-3-540-24621-3_17", abstract = "Many kinds of Evolutionary Algorithms (EAs) have been described in the literature since the last 30 years. However, though most of them share a common structure, no existing software package allows the user to actually shift from one model to another by simply changing a few parameters, e.g. in a single window of a Graphical User Interface. GUIDE, a graphical user interface for DREAM experiments that, among other user-friendly features, unifies all kinds of EAs into a single panel, as far as evolution parameters are concerned. Such a window can be used either to ask for one of the well known ready-to-use algorithms, or to very easily explore new combinations that have not yet been studied. Another advantage of grouping all necessary elements to describe virtually all kinds of EAs is that it creates a fantastic pedagogic tool to teach EAs to students and newcomers to the field.", bibsource = "DBLP, http://dblp.uni-trier.de", notes = "EA'03 general tool not specifically for GP", } @Proceedings{collet:2006:GP, title = "Proceedings of the 9th European Conference on Genetic Programming", year = "2006", editor = "Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art", volume = "3905", series = "Lecture Notes in Computer Science", address = "Budapest, Hungary", publisher = "Springer", month = "10 - 12 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-33143-3", DOI = "doi:10.1007/11729976", size = "361 pages", notes = "EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006", } @InCollection{collet:2007:nicem, author = "Pierre Collet", title = "Genetic Programming", booktitle = "Handbook of Research on Nature-Inspired Computing for Economics and Management", editor = "Jean-Philippe Rennard", publisher = "Idea Group Inc.", year = "2007", volume = "I", chapter = "V", pages = "59--73", address = "1200 E. Colton Ave", keywords = "genetic algorithms, genetic programming, GP-std/same, homologous crossover, interval arithmetic, problem dependence, over fitting and bloat", ISBN = "1-59140-984-5", DOI = "doi:10.4018/978-1-59140-984-7.ch005", abstract = "The aim of genetic programming is to evolve programs or functions (symbolic regression) thanks to artificial evolution. This technique is now mature and can routinely yield results on par with (or even better than) human intelligence. This chapter sums up the basics of genetic programming and outlines the main subtleties one should be aware of in order to obtain good results.", size = "15 pages", } @Article{Collet:2009:GPEM, author = "Pierre Collet", title = "Husbands, Holland, and Wheeler (eds): Review of the book {"}The Mechanical Mind in History{"} MIT Press, 2008, ISBN 978-0-262-08377-5", journal = "Genetic Programming and Evolvable Machines", year = "2009", volume = "10", number = "1", pages = "91--93", month = mar, keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dR8iK", DOI = "doi:10.1007/s10710-008-9070-1", size = "2.1 pages", notes = "Book Review", } @Article{Collet:2012:GPEM, author = "Pierre Collet and Man Leung Wong", title = "Evolutionary algorithms for data mining", journal = "Genetic Programming and Evolvable Machines", year = "2012", volume = "13", number = "1", pages = "69--70", month = mar, note = "Editorial Introduction to Special Section on Evolutionary Algorithms for Data Mining", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-011-9156-z", size = "2 pages", affiliation = "Universite de Strasbourg, Alsace, France", } @InProceedings{Collet:2012:GECCOcomp, author = "Pierre Collet and Simon Harding", title = "Evolutionary algorithms and genetic programming on graphic processing units (GPU)", booktitle = "GECCO 2012 Specialized techniques and applications tutorials", year = "2012", editor = "Gabriela Ochoa", isbn13 = "978-1-4503-1178-6", keywords = "genetic algorithms, genetic programming, GPU", pages = "1117--1138", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330784.2330933", publisher = "ACM", publisher_address = "New York, NY, USA", notes = "Also known as \cite{2330933} Distributed at GECCO-2012. ACM Order Number 910122.", } @InProceedings{collins:1998:mbiaia, author = "J. J. Collins", title = "Modeling the Behaviour of Interacting Autonomous Intelligent Agents", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "29 and 253", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "genetic algorithms, genetic programming", URL = "http://www.csis.ul.ie/staff/jjcollins/gp98.html", size = "1+1 page", notes = "GP-98LB, GP-98PhD Student Workshop", } @InProceedings{collins:1999:GPMRNS, author = "J. J. Collins and Lucia Sheehan and Conor Casey", title = "Genetic Planner for a Mobile Robot Navigation System", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "782", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-399.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-399.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{collins:1999:NFOPI, author = "J. J. Collins and Conor Ryan", title = "Non-stationary Function Optimization using Polygenic Inheritance", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "781", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-398.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-398.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{collins:2004:nue:mcol, author = "M. Collins", title = "Counting Solutions in Reduced {Boolean} Parity", editor = "R. Poli and S. Cagnoni and M. Keijzer and E. Costa and F. Pereira and G. Raidl and S. C. Upton and D. Goldberg and H. Lipson and E. {de Jong} and J. Koza and H. Suzuki and H. Sawai and I. Parmee and M. Pelikan and K. Sastry and D. Thierens and W. Stolzmann and P. L. Lanzi and S. W. Wilson and M. O'Neill and C. Ryan and T. Yu and J. F. Miller and I. Garibay and G. Holifield and A. S. Wu and T. Riopka and M. M. Meysenburg and A. W. Wright and N. Richter and J. H. Moore and M. D. Ritchie and L. Davis and R. Roy and M. Jakiela", booktitle = "GECCO 2004 Workshop Proceedings", year = "2004", month = "26-30 " # jun, address = "Seattle, Washington, USA", keywords = "genetic algorithms, genetic programming", URL = "http://gpbib.cs.ucl.ac.uk/gecco2004/WNUE001.pdf", notes = "GECCO-2004WKS Distributed on CD-ROM at GECCO-2004 See also \cite{Collins:2006:GPEM}", } @InProceedings{1068282, author = "M. Collins", title = "Finding needles in haystacks is harder with neutrality", booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation", year = "2005", editor = "Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and Edwin D. {de Jong} and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler", volume = "2", ISBN = "1-59593-010-8", pages = "1613--1618", address = "Washington DC, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2005/docs/p1613.pdf", DOI = "doi:10.1145/1068009.1068282", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "25-29 " # jun, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, reduced Boolean parity, search space", notes = "GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM Order Number 910052 See also \cite{Collins:2006:GPEM}", } @Article{Collins:2006:GPEM, author = "Mark Collins", title = "Finding needles in haystacks is harder with neutrality", journal = "Genetic Programming and Evolvable Machines", year = "2006", volume = "7", number = "2", pages = "131--144", month = aug, note = "Special Issue: Best of GECCO 2005", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, random sampling, solution density", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-006-9001-y", abstract = "an extended analysis of the reported successes of the Cartesian Genetic Programming method on a simplified form of the Boolean parity problem. We show the method of sampling used by the CGP is significantly less effective at locating solutions than the solution density of the corresponding formula space would warrant. We present results indicating that the loss of performance is caused by the sampling bias of the CGP, due to the neutrality friendly representation. We implement a simple intron free random sampling algorithm which performs considerably better on the same problem and then explain how such performance is possible.", notes = "Reduced parity=given XOR and EQ only.", } @PhdThesis{Collins:thesis, author = "Mark Collins", title = "An Algorithm for Evolving Protocol Constraints", school = "Artificial Intelligence Applications Institute, School of Informatics, University of Edinburgh", year = "2006", keywords = "genetic algorithms", URL = "http://www.cisa.informatics.ed.ac.uk/ssp/pubs/collins_phd.pdf", size = "approx 220 pages", abstract = "We present an investigation into the design of an evolutionary mechanism for multiagent protocol constraint optimisation. Starting with a review of common population based mechanisms we discuss the properties of the mechanisms used by these search methods. We derive a novel algorithm for optimisation of vectors of real numbers and empirically validate the efficacy of the design by comparing against well known results from the literature. We discuss the application of an optimiser to a novel problem and remark upon the relevance of the no free lunch theorem. We show the relative performance of the optimiser is strong and publish details of a new best result for the Keane optimisation problem. We apply the final algorithm to the multi-agent protocol optimisation problem and show the design process was successful.", } @InProceedings{collins:1999:ACSSVT, author = "Trevor D. Collins", title = "A Comparison of Search Space Visualization Techniques", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "780", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-395.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-395.pdf", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Collins:2019:NAECON, author = "Zachary Collins and Bayley King and Rashmi Jha and David Kapp and Anca Ralescu", booktitle = "2019 IEEE National Aerospace and Electronics Conference (NAECON)", title = "Evolvable Hardware for Security through Diverse Variants", year = "2019", pages = "257--261", month = jul, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/NAECON46414.2019.9058062", ISSN = "2379-2027", abstract = "Evolvable hardware is attractive as a design strategy to hardware engineers, but suffers due to its lack of scalability to larger hardware systems. This work examines how hardware designers can make use of evolvable hardware to improve the security of their systems, and to create hardware systems that are better resistant to reverse engineering.", notes = "Also known as \cite{9058062}", } @PhdThesis{Colmant:thesis, author = "Maxime Colmant", title = "Multi-Dimensional Analysis of Software Power Consumptions in Multi-Core Architectures", school = "University de Lille", year = "2016", address = "France", month = "30 " # nov, keywords = "genetic improvement, SBSE, software power consumption, software-defined power meter, machine learning", URL = "https://tel.archives-ouvertes.fr/tel-01403559/file/colmant-thesis.pdf", hal_id = "tel-01403559", hal_version = "v2", URL = "https://tel.archives-ouvertes.fr/tel-01403559v2", size = "141 pages", abstract = "Energy-efficient computing is becoming increasingly important. Among the reasons, one can mention the massive consumption of large data centres that consume as much as 180,000 homes. This trend, combined with environmental concerns, makes energy efficiency a prime technological and societal challenge. Currently, widely used power distribution units (PDUs) are often shared amongst nodes to deliver aggregated power consumption reports, in the range of hours and minutes. However, in order to improve the energy efficiency of software systems, we need to support process-level power estimation in real-time, which goes beyond the capacity of a PDUs. In particular, the CPU is considered by the research community as the major power consumer within a node and draws attention while trying to model the system power consumption. Over the last few years, a dozen of ad hoc power models have been proposed to cope with the wide diversity and the growing complexity of modern CPU architectures. In this thesis, we rather propose PowerAPI for learning power models and building software-defined power meters that provide accurate power estimation on modern architectures. With the emergence of cloud computing, we propose BitWatts and WattsKit for leveraging software power estimation in virtual machines VMs and clusters. A finer level of estimation may be required to further evaluate the effectiveness of the software optimizations and we therefore propose codEnergy for helping developers to understand how the energy is really consumed by a software. We deeply assessed all above approaches, thus demonstrating the usefulness of PowerAPI to better understand the software power consumption on modern architectures.", notes = "Only brief mention of GP. In english. Also known as \cite{colmant:tel-01403559} Supervisors: Romain Rouvoy and Lionel Seinturier.", } @InProceedings{Colmenar:2010:gecco, author = "J. Manuel Colmenar and Jose L. Risco-Martin and David Atienza and Oscar Garnica and J. Ignacio Hidalgo and Juan Lanchares", title = "Improving reliability of embedded systems through dynamic memory manager optimization using grammatical evolution", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "1227--1234", keywords = "genetic algorithms, genetic programming, grammatical evolution, genetic improvement, SBSE", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830483.1830705", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Technology scaling has offered advantages to embedded systems, such as increased performance, more available memory and reduced energy consumption. However, scaling also brings a number of problems like reliability degradation mechanisms. The intensive activity of devices and high operating temperatures are key factors for reliability degradation in latest technology nodes. Focusing on embedded systems, the memory is prone to suffer reliability problems due to the intensive use of dynamic memory on wireless and multimedia applications. In this work we present a new approach to automatically design dynamic memory managers considering reliability, and improving performance, memory footprint and energy consumption. Our approach, based on Grammatical Evolution, obtains a maximum improvement of 39percent in execution time, 38percent in memory usage and 50percent in energy consumption over state-of-the-art dynamic memory managers for several real-life applications. In addition, the resulting distributions of memory accesses improve reliability. To the best of our knowledge, this is the first proposal for automatic dynamic memory manager design that considers reliability. Categories and Subject", notes = "evolves garbage collector DMM, C++, p1230 six line BNF grammar given. Reliability fall assumed from increased temperature due to concentrated memory usage. Fitness = weighted sum of run time, bytes and energy used. GEVA applied offline to multi gigabyte profiling logs from VDrift and Physiscs3D. \cite{DBLP:journals/todaes/AtienzaMMSC06} Also known as \cite{1830705} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @InProceedings{Colmenar:2011:GECCO, author = "J. Manuel Colmenar and Jose L. Risco-Martin and David Atienza and J. Ignacio Hidalgo", title = "Multi-objective optimization of dynamic memory managers using grammatical evolution", booktitle = "GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0557-0", pages = "1819--1826", keywords = "genetic algorithms, genetic programming, grammatical evolution, genetic improvement, SBSE, Real world applications", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001576.2001820", publisher = "ACM", publisher_address = "New York, NY, USA", size = "8 pages", abstract = "The dynamic memory manager (DMM) is a key element whose customization for a target application reports great benefits in terms of execution time, memory usage and energy consumption. Previous works presented algorithms to automatically obtain custom DMMs for a given application. Nevertheless, those approaches are based on grammatical evolution where the fitness is built as an aggregate objective function, which does not completely exploit the search space, returning the designer the DMM solution with best fitness. However, this approach may not find solutions that could fit in a concrete hardware platform due to a very low value of one of the objectives while the others remain high, which may represent a high fitness. In this work we present the first multi-objective optimisation methodology applied to DMM optimisation where the Pareto dominance is considered, thus providing the designer with a set of non-dominated DMM implementations on each optimisation run. Our results show that the multi-objective optimisation provides Pareto-optimal alternatives due to a better exploitation of the search space obtaining better hypervolume values than the aggregate objective function approach.", notes = "Garbage collector. Energy consumption. NSGA-2. Pop=40. Also known as \cite{2001820} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)", } @InProceedings{Colmenar:2013:GECCOcomp, author = "J. Manuel Colmenar and Alfredo Cuesta-Infante and Jose L. Risco-Martin and J. Ignacio Hidalgo", title = "An evolutionary methodology for automatic design of finite state machines", booktitle = "GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion", year = "2013", editor = "Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A. N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks", isbn13 = "978-1-4503-1964-5", keywords = "genetic algorithms, genetic programming", pages = "139--140", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2464576.2464645", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "We propose an evolutionary flow for finite state machine inference through the cooperation of grammatical evolution and a genetic algorithm. This coevolution has two main advantages. First, a high-level description of the target problem is accepted by the flow, being easier and affordable for system designers. Second, the designer does not need to define a training set of input values because it is automatically generated by the genetic algorithm at run time. Our experiments on the sequence recogniser and the vending machine problems obtained the FSM solution in 99.96percent and 100percent of the optimisation runs, respectively.", notes = "Also known as \cite{2464645} Distributed at GECCO-2013.", } @InProceedings{conf/evoW/ColmenarHLGRCSV16, author = "J. Manuel Colmenar and Jose Ignacio Hidalgo and Juan Lanchares and Oscar Garnica and Jose L. Risco-Martin and Ivan Contreras and Almudena Sanchez and J. Manuel Velasco", title = "Compilable Phenotypes: Speeding-Up the Evaluation of Glucose Models in Grammatical Evolution", booktitle = "19th European Conference on Applications of Evolutionary Computation, EvoApplications 2016", year = "2016", editor = "Giovanni Squillero and Paolo Burelli", volume = "9598", series = "Lecture Notes in Computer Science", pages = "118--133", address = "Porto, Portugal", month = mar # " 30 -- " # apr # " 1", organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Grammatical evolution, Model identification, Diabetes mellitus, EvoPAR", bibdate = "2016-03-29", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/evoW/evoappl2016-2.html#ColmenarHLGRCSV16", isbn13 = "978-3-319-31153-1", DOI = "doi:10.1007/978-3-319-31153-1_9", abstract = "This paper presents a method for accelerating the evaluation of individuals in Grammatical Evolution. The method is applied for identification and modelling problems, where, in order to obtain the fitness value of one individual, we need to compute a mathematical expression for different time events. We propose to evaluate all necessary values of each individual using only one mathematical Java code. For this purpose we take profit of the flexibility of grammars, which allows us to generate Java compilable expressions. We test the methodology with a real problem: modeling glucose level on diabetic patients. Experiments confirms that our approach (compilable phenotypes) can get up to 300x reductions in execution time.", notes = "EvoApplications2016 (Part II) held inconjunction with EuroGP'2016, EvoCOP2016 and EvoMUSART 2016", } @InProceedings{Colmenar:2016:GECCOcomp, author = "J. Manuel Colmenar and Stephan M. Winkler and Gabriel Kronberger and Esther Maqueda and Marta Botella and J. Ignacio Hidalgo", title = "Predicting Glycemia in Diabetic Patients By Evolutionary Computation and Continuous Glucose Monitoring", booktitle = "GECCO '16 Companion: Proceedings of the Companion Publication of the 2016 Annual Conference on Genetic and Evolutionary Computation", year = "2016", editor = "Tobias Friedrich and Frank Neumann and Andrew M. Sutton and Martin Middendorf and Xiaodong Li and Emma Hart and Mengjie Zhang and Youhei Akimoto and Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and Daniele Loiacono and Julian Togelius and Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and Faustino Gomez and Carlos M. Fonseca and Heike Trautmann and Alberto Moraglio and William F. Punch and Krzysztof Krawiec and Zdenek Vasicek and Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and Boris Naujoks and Enrique Alba and Gabriela Ochoa and Simon Poulding and Dirk Sudholt and Timo Koetzing", isbn13 = "978-1-4503-4323-7", pages = "1393--1400", address = "Denver, Colorado, USA", month = "20-24 " # jul, keywords = "genetic algorithms, genetic programming, grammatical evolution", organisation = "SIGEVO", DOI = "doi:10.1145/2908961.2931734", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Diabetes mellitus is a disease that affects more than three hundreds million people worldwide. Maintaining a good control of the disease is critical to avoid not only severe long-term complications but also dangerous short-term situations. Diabetics need to decide the appropriate insulin injection, thus they need to be able to estimate the level of glucose they are going to have after a meal. In this paper we use machine learning techniques for predicting glycemia in diabetic patients. The algorithms use data collected from real patients by a continuous glucose monitoring system, the estimated number of carbohydrates, and insulin administration for each meal. We compare (1) non-linear regression with fixed model structure, (2) identification of prognosis models by symbolic regression using genetic programming, (3) prognosis by k-nearest-neighbour time series search, and (4) identification of prediction models by grammatical evolution. We consider predictions horizons of 30, 60, 90 and 120 minutes.", notes = "Distributed at GECCO-2016.", } @Article{COLMENAR:2018:Energy, author = "J. M. Colmenar and J. I. Hidalgo and S. Salcedo-Sanz", title = "Automatic generation of models for energy demand estimation using Grammatical Evolution", journal = "Energy", volume = "164", pages = "183--193", year = "2018", keywords = "genetic algorithms, genetic programming, Grammatical evolution, Energy demand estimation, Macro-economic variables, Meta-heuristics", ISSN = "0360-5442", DOI = "doi:10.1016/j.energy.2018.08.199", URL = "http://www.sciencedirect.com/science/article/pii/S0360544218317353", abstract = "The estimation of total energy demand in a country from macro-economic variables is an important problem useful to evaluate the robustness of the country's economy. Since the first years of this century, meta-heuristics approaches have been successfully applied to this problem, for different countries and problem's parameterizations. Many of these works optimize prediction models which are based on classical polynomial or simple exponential relationships, which may not be the best option for an accurate energy demand estimation prediction. In this paper the use of Grammatical Evolution is proposed to generate new models for total energy demand estimation at country level. Grammatical Evolution is a class of Genetic Programming algorithm, which is able to automatically generate new models from input variables. In this case, Grammatical Evolution considers macro-economic variables from which it is able to generate new models for total energy demand estimation of a country, with a temporal prediction horizon of one year. The models generated by the Grammatical Evolution are further optimized in order to adjust their parameters to the energy demand estimation. This process is carried out by means of a Differential Evolution approach, which is run for every model generated by the Grammatical Evolution. Thus, the algorithmic proposal consists of a hybrid method, involving Grammatical Evolution for model generation and a Differential Evolution meta-heuristic for the models' parameter tuning. The performance of the proposed approach has been evaluated in two different problems of total energy demand estimation in Spain and France, with excellent results in terms of prediction accuracy", } @InProceedings{Colmenar:2022:evoapplications, author = "J. {Manuel Colmenar} and Raul Martin-Santamaria and J. Ignacio Hidalgo", title = "{WebGE}: An Open-Source Tool for Symbolic Regression Using Grammatical Evolution", booktitle = "25th International Conference, EvoApplications 2022", year = "2022", month = "20-22 " # apr, editor = "Juan Luis Jimenez Laredo and J. Ignacio Hidalgo and Kehinde Oluwatoyin Babaagba", series = "LNCS", volume = "13224", publisher = "Springer", address = "Madrid", pages = "269--282", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Differential Evolution Symbolic regression, Open-source software", isbn13 = "978-3-031-02461-0", DOI = "doi:10.1007/978-3-031-02462-7_18", abstract = "Many frameworks and libraries are available for researchers working on optimization. However, the majority of them require programming knowledge, lack of a friendly user interface and cannot be run on different operating systems. WebGE is a new optimization tool which provides a web-based graphical user interface allowing any researcher to use Grammatical Evolution and Differential Evolution on symbolic regression problems. In addition, the fact that it can be deployed on any server as a web service also incorporating user authentication, makes it a versatile and portable tool that can be shared by multiple researchers. Finally, the modular software architecture allows to easily extend WebGE to other algorithms and types of problems.", notes = "http://www.evostar.org/2022/ EvoApplications2022 held in conjunction with EuroGP'2022, EvoCOP2022 and EvoMusArt2022", } @InProceedings{Colton:evows09, author = "Simon Colton and Cameron Browne", title = "Evolving Simple Art-based Games", booktitle = "Applications of Evolutionary Computing, EvoWorkshops2009: {EvoCOMNET}, {EvoENVIRONMENT}, {EvoFIN}, {EvoGAMES}, {EvoHOT}, {EvoIASP}, {EvoINTERACTION}, {EvoMUSART}, {EvoNUM}, {EvoPhD}, {EvoSTOC}, {EvoTRANSLOG}", year = "2009", month = "15-17 " # apr, editor = "Mario Giacobini and Ivanoe {De Falco} and Marc Ebner", series = "LNCS", volume = "5484", publisher = "Springer Verlag", address = "Tubingen, Germany", pages = "283--292", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-01128-3", DOI = "doi:10.1007/978-3-642-01129-0_32", abstract = "Evolutionary art has a long and distinguished history, and genetic programming is one of only a handful of AI techniques which is used in graphic design and the visual arts. A recent trend in so-called 'new media' art is to design online pieces which are dynamic and have an element of interaction and sometimes simple game-playing aspects. This defines the challenge addressed here: to automatically evolve dynamic, interactive art pieces with game elements. We do this by extending the Avera user-driven evolutionary art system to produce programs which generate spirograph-style images by repeatedly placing, scaling, rotating and colouring geometric objects such as squares and circles. Such images are produced in an inherently causal way which provides the dynamic element to the pieces.We further extend the system to produce programs which react to mouse clicks, and to evolve sequential patterns of clicks for the user to uncover. We wrap the programs in a simple front end which provides the user with feedback on how close they are to uncovering the pattern, adding a lightweight gameplaying element to the pieces. The evolved interactive artworks are a preliminary step in the creation of more sophisticated multimedia pieces.", notes = "EvoWorkshops2009", } @Article{Comellas:1998:GPD, author = "F. Comellas and G. Gim{\'e}nez", title = "Genetic Programming to Design Communication Algorithms for Parallel Architectures", journal = "Parallel Processing Letters", year = "1998", volume = "8", number = "4", pages = "549--560", keywords = "genetic algorithms, genetic programming, broadcasting, networks, butterfly graph", ISSN = "0129-6264", URL = "http://www-mat.upc.es/~comellas/genprog/genprog_f.pdf", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/173/http:zSzzSzwww-mat.upc.eszSz~comellaszSzgenprogzSzgenprog_f.pdf/comellas98genetic.pdf", URL = "http://citeseer.ist.psu.edu/comellas98genetic.html", DOI = "doi:10.1142/S0129626498000547", size = "12 pages", CODEN = "PPLTEE", ISSN = "0129-6264", URL = "https://mat-web.upc.edu/people/francesc.comellas/old-files/genprog/genprog.html", acknowledgement = ack-nhfb, bibdate = "Mon Nov 09 07:22:43 1998", abstract = "Broadcasting is an information dissemination problem in which a message originating at one node of a communication network (modelled as a graph) is to be sent to all other nodes as quickly as possible. This paper describes a new way of producing broadcasting schemes using genetic programming. This technique has proven successful by easily finding optimal algorithms for several well-known families of networks (grids, hypercubes and cycle connected cubes) and has indeed generated a new scheme for butterflies that improves the known upper bound for the broadcasting time of these networks.", notes = "GPQUICK. Tried on 4 problems (5x5 directed grid, toroidal, hypercube, cube connected cycles) finds known optima. '5.5 Butterfly graph For these graphs no optimal broadcasting algorithm is known... we improve the upper bound to BF_k \le 2k-2' for k=7,8...16", } @InProceedings{comellas:evows04, author = "Francesc Comellas and Cristina Dalfo", title = "Using Genetic Programming to Design Broadcasting Algorithms for Manhattan Street Networks", booktitle = "Applications of Evolutionary Computing, EvoWorkshops2004: {EvoBIO}, {EvoCOMNET}, {EvoHOT}, {EvoIASP}, {EvoMUSART}, {EvoSTOC}", year = "2004", month = "5-7 " # apr, editor = "Guenther R. Raidl and Stefano Cagnoni and Jurgen Branke and David W. Corne and Rolf Drechsler and Yaochu Jin and Colin R. Johnson and Penousal Machado and Elena Marchiori and Franz Rothlauf and George D. Smith and Giovanni Squillero", series = "LNCS", volume = "3005", address = "Coimbra, Portugal", publisher = "Springer Verlag", publisher_address = "Berlin", pages = "170--177", keywords = "genetic algorithms, genetic programming, evolutionary computation", ISBN = "3-540-21378-3", DOI = "doi:10.1007/978-3-540-24653-4_18", abstract = "Broadcasting is the process of disseminating a message from a node of a communication network to all other nodes as quickly as possible. We consider Manhattan Street Networks (MSNs) which are mesh-structured, toroidal, directed, regular networks such that locally they resemble the geographical topology of the avenues and streets of Manhattan. With the use of genetic programming we have generated broadcasting algorithms for 2-dimensional and 3-dimensional MSNs.", notes = "EvoWorkshops2004", } @InProceedings{Comellas:2005:PDCN, author = "Francesc Comellas and Cristina Dalfo", title = "Optimal Broadcasting in 2-Dimensional Manhattan Street Networks", booktitle = "Parallel and Distributed Computing and Networks - 2005", year = "2005", editor = "T. Fahringer and M. H. Hamza", volume = "246", pages = "135--140", address = "Innsbruck, Austria", month = feb # " 15-17", publisher = "Acta Press", keywords = "genetic algorithms, genetic programming, Manhattan Street Networks, broadcasting, communication networks", ISBN = "0-88986-468-3", ISSN = "1027-2666", URL = "http://www.actapress.com/Abstract.aspx?paperId=19188", URL = "http://www.actapress.com/Content_of_Proceeding.aspx?proceedingID=287", URL = "https://pdfs.semanticscholar.org/67cf/692b7fe9ccd29d9932709d3d51ebd833c2b4.pdf", size = "6 pages", abstract = "Broadcasting is the process of disseminating a message from a node of a communication network to all other nodes as quickly as possible. In this paper we consider Manhattan Street Networks (MSNs) which are mesh-structured, toroidal, directed, regular networks such that locally they resemble the geographical topology of the avenues and streets of Manhattan. Previous work on these networks has been mainly devoted to the study of the average distance and point-to-point routing schemes. Here we provide an algorithm which broadcasts optimally in a 2-dimensional M N Manhattan Street Network (M and N even).", notes = "Comparison with GP results Department of Mathematics Applications IV, Universitat Politecnica de Catalunya, EPSC, Avda. Canal Olimpic s/n, Castelldefels (Barcelona), Catalonia, Spain", } @InProceedings{Comte:2009:CIGPU, author = "Pascal Comte", title = "Design \& Implementation of Parallel Linear GP for the IBM Cell Processor", booktitle = "GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation", year = "2009", editor = "Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano {van Hemert} and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique Alba", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-60558-325-9", DOI = "doi:10.1145/1569901.1596274", abstract = "We present two different single-core parallel SIMD linear genetic programming (LGP) systems for the IBM Cell Processor on the Playstation3. Our algorithms harness their computational power from the parallel capabilities of the Cell Processor. We implement two evolutionary algorithms and look at the classical problem of symbolic regression of functions. The first LGP generates a single offspring and selection from the population occurs randomly. The second algorithm generates two offspring and selection from the population is performed using k-tournament with k = 2. Mutation occurs at macro and micro levels. Both SIMD instructions and register operands are subject to mutation. We use a static population of 648 individuals due to memory and data transfer restrictions and, experiments are constrained to 300 seconds of computational time. Our results indicate that both EAs perform equally well though the first algorithm is faster and outperforms the 2nd algorithm in some cases. We speculate that the speed at which generations are iterated through is significantly greater than that of a typical tree-based GP and sequential linear GP.", notes = "Also known as \cite{1596274}. Omitted when CD was pressed. CIGPU-2009 GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009).", } @InProceedings{Compte:2009:CIGPU2, author = "Pascal Comte", title = "Design \& Implementation of Real-time Parallel GA Operators on the IBM Cell Processor", booktitle = "GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation", year = "2009", editor = "Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano {van Hemert} and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique Alba", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms", isbn13 = "978-1-60558-325-9", DOI = "doi:10.1145/1569901.1596275", abstract = "We present a set of single-core designed parallel SIMD Genetic Algorithm (GA) operators aimed at increasing computational speed of genetic algorithms. We use a discrete-valued chromosome representation. The explored operators include: single gene mutation, uniform crossover and a fitness evaluation function. We discuss their low-level hardware implementations on the Cell Processor. We use the Knapsack problem as a proof of concept, demonstrating performances of our operators. We measure the scalability in terms of generations per second. Using the architecture of the Cell Processor and a static population size of 648 individuals, we achieved 11.6 million generations per second on one Synergetic Processing Element (SPE) core for a problem size n = 8 and 9.5 million generations per second for a problem size n = 16. Generality for a problem size n multiple of 8 is also shown. Executing six independent concurrent GA runs, one per SPE core, allows for a rough overall estimate of 70 million generations per second and 57 million generations per second for problem sizes of n = 8 and n = 16 respectively.", notes = "Also known as \cite{1596275}. Omitted when CD was pressed. CIGPU-2009 GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009).", } @Article{ga95aCona:1995:dGPs, author = "John Cona", title = "Developing a Genetic Programming System", journal = "AI Expert", year = "1995", pages = "20--29", month = feb, keywords = "genetic algorithms, genetic programming, C++, Object Orientated", ISSN = "0888-3785", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/ga95aCona_1995_dGPs.pdf", size = "10 pages", abstract = "We can use an object-oriented C++ approach to develop gentic base classes. Discusses practical speed/memory tradeoffs for an (IBM) PC environment.", notes = "BLDSC shelfmark 0772.341000, UK Floor 6-1 'Exciting prospects of language and communication', 'memory', notes on more recent features of C++. Refers to Scott A. Kennedy, AI Expert, Five ways to a smarter genetic algorithm, AI Expert 8(12):35-38, December 1993. ", } @InProceedings{Conca:2009:AHS, author = "Piero Conca and Giuseppe Nicosia and Giovanni Stracquadanio and Jon Timmis", title = "Nominal-Yield-Area Tradeoff in Automatic Synthesis of Analog Circuits: A Genetic Programming Approach using Immune-Inspired Operators", booktitle = "NASA/ESA Conference on Adaptive Hardware and Systems (AHS-2009)", year = "2009", editor = "Tughrul Arslan and Didier Keymeulen", pages = "399--406", address = "San Francisco, California, USA", month = jul # " 29-" # aug # " 1", keywords = "genetic algorithms, genetic programming, AIS, ElP, Pareto Front, analog circuit automatic synthesis, analog circuit design, circuit reliability, elitist immune programming, evolutionary algorithm, frequency response, genetic programming approach, immune-inspired operators, industrial components series, low-pass filter synthesis, nominal-yield-area tradeoff, Pareto optimisation, analogue circuits, circuit CAD, circuit reliability, frequency response, low-pass filters", DOI = "doi:10.1109/AHS.2009.32", abstract = "The synthesis of analog circuits is a complex and expensive task; whilst there are various approaches for the synthesis of digital circuits, analog design is intrinsically more difficult since analog circuits process voltages in a continuous range. In the field of analog circuit design, the genetic programming approach has received great attention, affording the possibility to design and optimize a circuit at the same time. However, these algorithms have limited industrial relevance, since they work with ideal components. Starting from the well known results of Koza and co-authors, we introduce a new evolutionary algorithm, called elitist Immune Programming (EIP), that is able to synthesize an analog circuit using industrial components series in order to produce reliable and low cost circuits. The algorithm has been used for the synthesis of low-pass filters; the results were compared with the genetic programming, and the analysis shows that EIP is able to design better circuits in terms of frequency response and number of components. In addition we conduct a complete yield analysis of the discovered circuits, and discover that EIP circuits attain a higher yield than the circuits generated via a genetic programming approach, and, in particular, the algorithm discovers a Pareto Front which respects nominal performance (sizing), number of components (area) and yield (robustness).", notes = "Co-located with Design Automation Conference (DAC-2009) http://www.see.ed.ac.uk/~ahs2009/ Also known as \cite{5325428}", } @InProceedings{Concepcion:2020:HNICEM, author = "Ronnie S. Concepcion and Elmer P. Dadios and Joy N. Carpio and Argel A. Bandala and Edwin Sybingco", booktitle = "2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)", title = "Phytopigments Profiling of Lactuca Sativa Leaf Chloroplast Photosystems via Vision-based Planar Chromatography", year = "2020", abstract = "Phytopigments are essential indicators of plant growth. However, current methodologies use expensive laboratory devices. In this study, a low-cost approach of lettuce leaf phytopigments profiling is employed using a consumer-grade camera and integrated computational intelligence via paper chromatography. Hybrid neighborhood component analysis and ReliefF selected the blue reflectance extracted from chromatography to have the most significant impact with other leaf biophysical signatures. Chl b exhibits more complex reflectance spectrum than other pigments and considered as strong indicator of energy absorbance variations. Xanthophyll and carotenoid have the strongest and weakest retardation factor and retention time, respectively. Chl a-b has weak affinity to acetone and their average blue reflectance is confirmed to absorb the highest number of photons in white light cultivation. Leaf absorbance varies by plus-minus1307.04 μmol m^-2 s^-1 PPFD per plus-minus0.1 of blue reflectance. Among other machine learning models, Gaussian processing regression bested out multigene symbolic genetic programming and recurrent neural network in predicting the average chloroplast photosystems I and II blue reflectance with R^2 of 0.9974. This developed approach extends the application of paper chromatography from segmenting to phytopigment profiling.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/HNICEM51456.2020.9400156", month = dec, notes = "Also known as \cite{9400156}", } @InProceedings{Concepcion:2020:HTC, author = "Ronnie {Concepcion II} and Sandy Lauguico and Jonnel Alejandrino and Justin {De Guia} and Elmer Dadios and Argel Bandala", title = "Aquaphotomics Determination of Total Organic Carbon and Hydrogen Biomarkers on Aquaponic Pond Water and Concentration Prediction Using Genetic Programming", booktitle = "2020 IEEE 8th R10 Humanitarian Technology Conference (R10-HTC)", year = "2020", abstract = "Crops that are cultivated in aquaponics setup highly relies on the nutrients supplied by the aqueous system through fish effluents. Continuous monitoring of essential elemental nutrients requires expensive sensors and arrays of it for full scale deployment. However, sustainable agriculture demands energy consumption reduction and cost-effectiveness. This study employed device minimization by using a combination of physical water sensors, namely temperature and electrical conductivity sensors, to predict total organic carbon (TOC) and hydrogen ion (H) concentrations in pond water. Aquaphotomics through ultraviolet (UV) and visible light (Vis) wavelength sweeping from 250 to 500 nm was explored to determine the nutrient biomarkers of pond water samples that undergoes temperature perturbation from 16 to 36 degreeC with 2 degreeC increment per testing. Principal component analysis (PCA) selected the most relevant activated water bands which are 275 nm for TOC and 415 nm for H. Direct spectrophotometric TOC concentration data was passed through a Savitzky-Golay filter to smoothen the nutrient signal. Recurrent neural network (RNN) exhibited the fastest inference time of 3.5 seconds on the average with R2 of 0.8583 and 0.9686 for predicting TOC and H concentrations. Multigene symbolic regression genetic programming (MSRGP) exhibited the best R2 performances of 0.9280 and 0.9693 in predicting TOC and H concentrations by using only the temperature and electrical conductivity sensoracquired data. This developed model is an innovative approach on measuring chemical concentrations of water using physical limnological sensors which resulted to energy consumption reduction of 50percent for complete 42-day crop life cycle of lettuce.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/R10-HTC49770.2020.9357030", ISSN = "2572-7621", month = dec, notes = "Also known as \cite{9357030}", } @Article{CONCEPCION:2021:IPA, author = "Ronnie Concepcion and Sandy Lauguico and Jonnel Alejandrino and Elmer Dadios and Edwin Sybingco and Argel Bandala", title = "Aquaphotomics determination of nutrient biomarker for spectrophotometric parameterization of crop growth primary macronutrients using genetic programming", journal = "Information Processing in Agriculture", year = "2021", ISSN = "2214-3173", DOI = "doi:10.1016/j.inpa.2021.12.007", URL = "https://www.sciencedirect.com/science/article/pii/S2214317321000998", keywords = "genetic algorithms, genetic programming, Aquaphotomics, Plant nutrients, Physicochemical composition, Spectrophotometry, Water quality monitoring", abstract = "Water quality assessment is currently based on time-consuming and costly laboratory procedures and numerous expensive physicochemical sensors deployment. In response to the trend of device minimization and reduced outlays in sustainable aquaponic water monitoring, the integration of aquaphotomics and computational intelligence is presented in this paper. This study used the combination of temperature, pH, and electrical conductivity sensors in predicting crop growth primary macronutrient concentration (nitrate, phosphate, and potassium (NPK)), thus, limiting the number of deployed sensors. A total of 220 water samples collected from an outdoor artificial aquaponic pond were temperature perturbed from 16 to 36 degreeC with 2 degreeC increments to mimic ambient range, which varies water compositional structure. Aquaphotomics was applied on ultraviolet, visible light, and near-infrared spectral regions, 100 to 1 000 nm, to determine NPK compounds. Principal component analysis emphasized nutrient dynamics through selecting the highly correlated water absorption bands resulting in 250 nm, 840 nm, and 765 nm for nitrate, phosphate, and potassium respectively. These activated water bands were used as wavelength protocols to spectrophotometrically measure macronutrient concentrations. Experiments have shown that multigene symbolic regression genetic programming (MSRGP) obtained the optimal performance in parameterizing and predicting nitrate, phosphate, and potassium concentrations based on water physical properties with an accuracy of 87.63percent, 88.73percent, and 99.91percent, respectively. The results have shown the established 4-dimensional nutrient dynamics map reveals that temperature significantly strengthens nitrate and potassium above 30 degreeC and phosphate below 25 degreeC with pH and electrical conductivity ranging between 7 and 8 and 0.1 to 0.2 mS cm-1 respectively. This novel approach of developing a physicochemical estimation model predicted macronutrient concentrations in real-time using physical limnological sensors with a 50percent reduction of energy consumption. This same approach can be extended to measure secondary macronutrients and micronutrients", } @InProceedings{Concepcion:2023:IMCOM, author = "Ronnie Concepcion and R-Jay Relano and Kate Francisco and Jonah Jahara Baun and Adrian Genevie Janairo and Joseph Aristotle {De Leon} and Llewelyn Espiritu and Andres Philip Mayol and Mike Louie Enriquez and Ryan Rhay Vicerra and Argel Bandala", booktitle = "2023 17th International Conference on Ubiquitous Information Management and Communication (IMCOM)", title = "Optimizing Low Power Near L-band Capacitive Resistive Antenna Design for in Silico Plant Root Tomography Based on Genetic Big Bang-Big Crunch", year = "2023", abstract = "Root system architecture (RSA) phenotyping is essential in formulating suitable organic fertilizers, irrigation, and protective regiments concerning its functional role in resource acquisition for plant growth. However, Ground Penetrating Radar, and Magnetic Resonance, Positron Emission, and X-Ray Micro Computed Tomography Scanning have high power requirements, and RGB imaging demands an intrusive scheme. Existing antenna-based imaging systems are not intelligently optimized yet. To address these challenges, this study developed a low power (10 W) near L-band capacitive resistive antenna system for in silico maize root tomography optimized using three novel advanced evolutionary computing, namely, Genetic Particle Collision Algorithm (gPCA), Genetic Integrated Radiation Algorithm (gIRA), and Genetic Big Bang-Big Crunch Algorithm (gBB-BC). Two capacitive resistive antenna designs were developed using CADFEKO: single parallel plate and 90-electrode dipole-dipole, where root information acquisition and processing from healthy maize seedling inside a PVC pipe intact with soil were done. Maize root permittivity and soil quality were set to resemble actual biological experiments. Transmitter frequency was determined using multigene (10 genes) genetic programming (MGGP) integrated with PCA, IRA, and BB-BC to determine the global maximum voltage at the receiver dipole. Based on in silico experiments, gBB-BC resulted in 0.984463 GHz operating frequency that lies within the global solutions of gPCA (> 1 GHz) and gIRA (< gBB-BC). The root tomography generated from electric field mapping using the gBB-BC-based antenna exhibited more pronounced RSA, while gIRA-based antenna is sensitive only to root tips. Hence, the established root imaging protocol here supports faster, low-power, and non-destructive approaches.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IMCOM56909.2023.10035574", month = jan, notes = "Also known as \cite{10035574}", } @InProceedings{Concepcion:2021:HNICEM, author = "Ronnie Concepcion and Bernardo Duarte and Argel Bandala and Joel Cuello and Ryan Rhay Vicerra and Elmer Dadios", booktitle = "2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)", title = "Characterization of Potassium Chloride Stress on Philippine Vigna radiata Varieties in Temperature-stabilized Hydroponics Using Genetic Programming", year = "2021", month = "28-30 " # nov, address = "Manila, Philippines", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-6654-0168-5", DOI = "doi:10.1109/HNICEM54116.2021.9731922", abstract = "Chloride is an important micronutrient for crop plant life. Excess chloride dehydrates the plant system and accumulates salt-like residue in leaves causing them to undergo chlorosis and necrosis. Micronutrient stress through potassium chloride that is used as fertilizers to common and industrial farms was not yet comprehensively explored concerning mung beans. This study aims to characterize the effects of potassium chloride (KCl) fertilization on stems and roots of two Philippine mung bean (Vigna radiata L.) varieties which are the yellow and green mongo. A temperature-stabilized hydroponics setup was developed based on Peltier technology. Three KCl treatments were employed: 50 μM (control), 10 μM (deficient), and 100 μM (toxic or excess). Morphological assay confirmed that KCl deficient mung beans have longer root and shoot systems and higher number of spanning leaves. Lowering KCl concentration to 10 10 μM also increases the germination rate by 111.53percent than the control. Light microscopy was performed and confirmed that there is thicker cortex, denser vascular cambium, broader xylem and phloem vessels, and larger parenchyma cells in KCl deficient seedlings. Only the green mung bean seedling variety exposed in excess KCl have formed trichomes within 14 days. Multigene genetic programming was applied to generate mathematical models of seedling architectural traits as functions of KCl concentration and cultivation period. It was found out that less than 0.05 mM, 0.9 mM 0.7 mM, 4 mM of KCl promotes root growth, shoot length, leaf expansion, and the number of spanning leaves, respectively. Overall, chloride deficiency improves mung bean growth.", notes = "Also known as \cite{9731922}", } @Article{CONCEPCION:2023:inpa, author = "Ronnie Concepcion and Bernardo Duarte and Maria {Gemel Palconit} and Jonah Jahara Baun and Argel Bandala and Ryan {Rhay Vicerra} and Elmer Dadios", title = "Screen-printed graphite electrode on polyvinyl chloride and parchment strips integrated with genetic programming for in situ nitrate sensing of aquaponic pond water", journal = "Information Processing in Agriculture", year = "2023", ISSN = "2214-3173", DOI = "doi:10.1016/j.inpa.2023.02.002", URL = "https://www.sciencedirect.com/science/article/pii/S2214317323000124", keywords = "genetic algorithms, genetic programming, aquaponic water quality, electrochemical technology, graphite electrode, nitrate sensor, precision agriculture, printed electronics, scanning electron microscopy, screen-printed electrode, voltammetry", abstract = "Nitrate is the primary water-soluble macronutrient essential for plant growth that is converted from excess fish feeds, fish effluents, and degrading biomaterials on the aquaponic pond floor, and when aquacultural malpractices occur, large amounts of it retain in the water system causing increase rate in eutrophication and toxifies fish and aquaculture plants. Recent nitrate sensor prototypes still require performing the additional steps of water sample deionization and dilution and were constructed with expensive materials. In response to the challenge of sensor enhancement and aquaponic water quality monitoring, this study developed sensitive, repeatable, and reproducible screen-printed graphite electrodes on polyvinyl chloride and parchment paper substrates with silver as electrode material and 60:40 graphite powder:nail polish formulated conductive ink for electrical traces, integrated with 9-gene genetic expression model as a function of peak anodic current and electrochemical test time for nitrate concentration prediction that is embedded into low-power Arduino ESP32 for in situ nitrate sensing in aquaponic pond water. Five SPE electrical traces were designed on the two types of substrates. Scanning electron microscopy with energy dispersive X-ray confirmed the electrode surface morphology. Electrochemical cyclic voltammetry using 10 to 100 mg/L KNO3 and water from three-depth regions of the actual pond established the electrochemical test time (10.5 s) and electrode potential (0.135 V) protocol necessary to produce peak current that corresponds to the strength of nitrate ions during redox. The findings from in situ testing revealed that the proposed sensors have strong linear predictions (R2=0.968 MSE=1.659 for nSPEv and R2=0.966 MSE=4.697 for nSPEp) in the range of 10 to 100 mg/L and best detection limit of 3.15 ?g/L, which are comparable to other sensors of more complex construction. The developed three-electrode electrochemical nitrate sensor confirms that it is reliable for both biosensing in controlled solutions and in situ aquaponic pond water systems", } @InProceedings{Concepcion:2023:ICBIR, author = "Ronnie {Concepcion II} and Adrian Genevie Janairo and Raneiel Angelo Martinez and R-Jay Relano and Marielet Guillermo and Argel Bandala and Ryan Rhay Vicerra", booktitle = "2023 8th International Conference on Business and Industrial Research (ICBIR)", title = "Genetic Termite Colony-Optimized Arbuscular Mycorrhizal Fungi Concentration for Glycophyte Plant Resilience to Saline Environment", year = "2023", pages = "812--817", abstract = "Saline environments, such as coastal and agricultural areas with excessive fertigation that remained uncultivated, impede glycophyte growth. However, arbuscular mycorrhizal fungi (AMF) can help regulate plant water balance, but its overpopulation exhibits competition with roots. To address this challenge, this study developed three hybrid evolutionary and bio-inspired optimisation models namely, genetic termite colony (GTC), genetic bacterial foraging, and genetic sperm swarm in determining the optimum concentration of Glomus spp. AMF inoculant to induce papaya var. Sinta F1 plant growth in terms of root and stem lengths, stem thickness, leaf count, and total leaf chlorophyll when exposed to a saline environment after 15 and 30 days of sowing. Four treatments were performed: control, and mycorrhizal with 5, 10, and 15 mg/L concentrations. Salinity was maintained at 6 dS/m using NaCl solution. Variance-based Factor Analysis confirmed stem length $(L_{s})$ as the most significant phenotype with highest communality. A 4-gene Genetic Programming model was formulated for $L_{s}$ fitness function. With most acceptable results, GTC recommended 11.149 mg/L which resulted in 1937.5percent, 388.43percent, 650percent, 480percent, and 238.889percent improvement in root and stem lengths, stem thickness, leaf count, and total leaf chlorophyll respectively, than the non-mycorrhizal plant. This established protocol increased glycophyte resistance to high salinity.", keywords = "genetic algorithms, genetic programming, Fungi, Reactive power, Protocols, Salinity (geophysical), Plants (biology), Sea measurements, abiotic stress, bio-inspired optimisation, digital agriculture, evolutionary computing, mycorrhizal fungi", DOI = "doi:10.1109/ICBIR57571.2023.10147624", month = may, notes = "Also known as \cite{10147624}", } @Article{CONCEPCIONII:2023:renene, author = "Ronnie {Concepcion II} and Kate Francisco and Adrian Genevie Janairo and Jonah Jahara Baun and Luigi Gennaro Izzo", title = "Genetic atom search-optimized in vivo bioelectricity harnessing from live dragon fruit plant based on intercellular two-electrode placement", journal = "Renewable Energy", volume = "219", pages = "119528", year = "2023", ISSN = "0960-1481", DOI = "doi:10.1016/j.renene.2023.119528", URL = "https://www.sciencedirect.com/science/article/pii/S096014812301443X", keywords = "genetic algorithms, genetic programming, Affordable and clean energy, Alternative energy source, Bioenergy, Low-carbon power, Renewable energy, Sustainable agriculture", abstract = "Bioelectricity is a promising alternative renewable energy source that can be produced from live plants and trees. However, previous experimental studies mostly applied non-sustainable bioelectricity extraction techniques from cut-off stem or leaves and neglected the optimum placement of electrodes for maximizing energy extraction without impeding plant growth. Electrode placement and penetration are crucial in energy extraction since they greatly influence electrical generated output enhancement. Relatively, along with the common plants used for bioelectricity extraction, the dragon fruit tree has the potential to be explored as an alternative bioelectricity source since it is widely abundant in many regions. With that, this work introduced a novel integrated genetic-population metaheuristic-based optimization model that was developed centered on in vivo stem bioelectricity extraction from dragon fruit tree to determine the exact optimum distance of silver-coated copper pin-type anodes and cathodes for maximum bioelectricity extraction through intercellular across vascular bundle (icVB) and inter-parenchymal cells (iPC) electrode penetration techniques, and incorporated the cradle-to-gate Life Cycle Assessment methodology to properly account the environmental impacts of the two intercellular penetration approaches. Multigene genetic programming was performed to formulate the fitness function followed by a comparative atom search (ASO), shuffle frog-leaping, and elephant herding-based bioelectricity harnessing optimization. Thus, ASO demonstrated the highest attainable fitness value and conformed well with both electrode placement treatments. This subsequently verified that ASO-based iPC penetration, yielding 58.923 J, surpasses icVB, which only yielded 13.909 J in terms of the total harnessed energy stored throughout the 30-day experiment. Overall, the genetic ASO-iPC with an electrode distance of 4.488 inches produced a higher yield of harnessed bioelectricity while incurring no significant damage and causing fewer environmental impacts compared to the ASO-icVB treatment. This developed technique can minimize greenhouse gas emissions while also expanding the application of evolutionary computing in agriculture and alternative energy domains", } @Article{Concepcion:2024:stress, author = "Ronnie {Concepcion II} and R-Jay Relano and Adrian Genevie Janairo and Kate Francisco and Lance Garcia and Hugo Montanvert", title = "Effects of optimized interaction of short-term hypergravity stimulation and nitrate-deficient cultivation in maize root using genetic-immunological algorithms", journal = "Plant Stress", year = "2024", volume = "14", pages = "100702", keywords = "genetic algorithms, genetic programming, Abiotic stress, Biostimulant, Complex environment, Digital agriculture, Evolutionary computing, Optimization algorithm, Seed germination, Simulated gravity, Space agriculture, Sustainable agriculture", ISSN = "2667-064X", URL = "https://www.sciencedirect.com/science/article/pii/S2667064X24003555", DOI = "doi:10.1016/j.stress.2024.100702", abstract = "Nitrate is a macronutrient substantial for plant root and shoot growth, however, the availability of nitrate within soil-based and soilless cultivation environments is not consistently optimal, presenting a significant challenge for plant growth and development. Traditional seed stimulation includes scarification, soaking, hormone application and microbial application but they are all invasive. This study pioneered an experimental approach to address the challenges posed by nutrient deficiency in hydroponic environment by integrating Multigene Genetic Programming (MGGP) with immunological computation algorithms, namely Clonal Selection Algorithm (CSA), Ant Colony Optimisation Algorithm (ACOA), and COVID Optimisation Algorithm (COVIDOA) in determining the exact optimal time exposure to 2 g hypergravity that can induced the growth of three maize genotypes (PSB 92-97, NSIC CN 302, and NSIC CN 282). Through varying dry seed exposure times to hypergravity (6, 12, and 24 h), labeled models gCSA, gACOA, and gCOVIDOA converged to 20.120 h, 22.466, and 19.700 h, respectively, based on the formulated 2-gene model of root-to-shoot ratio as a function of exposure time. Exposure time between 20 and 24 h increased the root-to-shoot ratio (R/S) by at least a factor of 2.631 and the seedling's dry weight by 13.430 g while between 10 and 15 h of exposure reduced the overall biomass. gACOA-treated seedings exhibited an R/S of 3.732 plus-minus 0.067 having the highest uniformity among the control, gCSA, and gCOVIDOA treatments. gACOA-treated seedlings have healthier root hair compared to unexposed seeds after 14 days and revealed the highest rate of increase in metaxylem, xylem, phloem, and radicle diameters with a factor of 3.651 mum/hr, 1.440 mum/hr, 0.872 mum/hr, and 71.602 mum/hr of exposure in 2 g hypergravity. This study implies that stimulating corn seeds using hypergravity can help lessen the introduction of nutrient fertilizers in the long run which could help in reducing the farm expenses", } @InProceedings{congdon:2000:GA, author = "Clare Bates Congdon and Emily F. Greenfest", title = "Gaphyl: A genetic algorithm approach to cladistics", booktitle = "Data Mining with Evolutionary Algorithms", year = "2000", editor = "Alex A. Freitas and William Hart and Natalio Krasnogor and Jim Smith", pages = "85--88", address = "Las Vegas, Nevada, USA", month = "8 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.colby.edu/~congdon/Publications/gaphyl-gecco00.ps", URL = "http://citeseer.ist.psu.edu/426598.html", abstract = "his research investigates the use of genetic algorithms (GA's) to solve problems from cladistics --- a technique used by biologists to hypothesise the evolutionary relationships between organisms. Since exhaustive search is not practical in this domain, typical cladistics software packages use heuristic search methods to navigate through the space of possible trees in an attempt to find one or more {"}best{"} solutions. We have developed a system called GAphyl, which uses the GA...", size = "4 pages", notes = "GECCO-2000WKS Part of \cite{wu:2000:GECCOWKS}", } @InProceedings{congdon:2003:ptuesipiegtwwgd, author = "Clare Bates Congdon and Kevin J. Septor", title = "Phylogenetic trees using evolutionary search: Initial progress in extending gaphyl to work with genetic data", booktitle = "Proceedings of the 2003 Congress on Evolutionary Computation CEC2003", editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon", pages = "320--326", year = "2003", publisher = "IEEE Press", address = "Canberra", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "8-12 " # dec, organisation = "IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", keywords = "genetic algorithms, genetic programming, Application software, Computer science, DNA, Drives, Educational institutions, Evolutionary computation, Genetics, Organisms, Phylogeny, Sequences, biology computing, evolutionary computation, genetics, tree searching, trees (mathematics), Gaphyl, Wagner parsimony, binary attributes, datasets, evolutionary algorithm application, evolutionary relationships, evolutionary search, exhaustive search, genetic data, heuristic search method, phylogenetic software package, phylogenetic trees, phylogenetic work, tree evaluation", ISBN = "0-7803-7804-0", DOI = "doi:10.1109/CEC.2003.1299592", abstract = "Gaphyl is an application of evolutionary algorithms to phylogenetics, an approach used by biologists to investigate evolutionary relationships among organisms. For datasets larger than 20-30 species, exhaustive search is not practical in this domain. Gaphyl uses an evolutionary search mechanism to search the space of possible phylogenetic trees, in an attempt to find the most plausible evolutionary hypotheses, while typical phylogenetic software packages use heuristic search methods. In previous work, Gaphyl has been shown to be a promising approach for searching for phylogentic trees using data with binary attributes and Wagner parsimony to evaluate the trees. In the work reported here, Gaphyl is extended to work with genetic data. Initial results with this extension further suggest that evolutionary search is a promising approach for phylogenetic work.", notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.", } @Article{Connolly:2004:drdobbs, author = "Brian Conolly", title = "{SQL}, Data Mining, \& Genetic Programming", journal = "Dr. Dobb's", year = "2004", month = apr # " 01", keywords = "genetic algorithms, genetic programming, Database", URL = "https://www.drdobbs.com/database/sql-data-mining-genetic-programming/184405616", abstract = "Evolutionary algorithms solve problems by mimicking the process of natural evolution. The practical side of evolutionary algorithms", notes = "May 2022 only start of article found... COiL Challenge 2000, BeneLearn 1999 \cite{langdon:1999:benelearn1} \cite{langdon:2000:seed}", } @Article{Connolly:2004:MSDN, author = "Brian Connolly", title = "Genetic Algorithms Survival of the Fittest: Natural Selection with Windows Forms", journal = "MSDN Magazine", year = "2004", volume = "19", number = "8", month = aug, publisher = "Microsoft", keywords = "genetic algorithms, genetic programming", URL = "http://msdn.microsoft.com/en-gb/magazine/cc163934.aspx", URL = "http://download.microsoft.com/download/3/a/7/3a7fa450-1f33-41f7-9e6d-3aa95b5a6aea/MSDNMagazineAugust2004en-us.chm", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Connolly_2004_MSDN.pdf", size = "16 pages", abstract = "Genetic Programming is an evolutionary algorithm that employs reproduction and natural selection to breed better and better executable computer programs. It can create programs that implement subtle, non-intuitive solutions to complex problems. By taking a well-known example from the Genetic Programming community and implementing it with the .NET Framework, this article demonstrates that CodeDOM and Reflection provide all the facilities that are needed to do Genetic Programming effectively This article discusses: * Genetic programming definition * Breeding new algorithm generations * Cross breeding * Mutations * Increasing fitness", notes = "Santa Fe ant trail. .net Reflection CodeDOM. .chm Microsoft Compiled HTML Help documented by https://en.wikipedia.org/wiki/Microsoft_Compiled_HTML_Help Connolly_2004_MSDN.pdf created from MSDNMagazineAugust2004en-us.chm using print to file and then ps2pdf but figures are missing from pdf.", } @InProceedings{Connor:2019:CEC, author = "Mark Connor and David Fagan and Michael O'Neill", title = "Optimising Team Sport Training Plans With Grammatical Evolution", booktitle = "2019 IEEE Congress on Evolutionary Computation, CEC 2019", year = "2019", editor = "Carlos A. Coello Coello", pages = "2474--2481", address = "Wellington, New Zealand", month = "10-13 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, grammatical evolution, sports analytics", isbn13 = "978-1-7281-2152-6", DOI = "doi:10.1109/CEC.2019.8790369", size = "8 pages", abstract = "We present a novel approach to generating seasonal training plans for elite athletes using the grammatical evolution approach to genetic programming. A grammatical encoding of a team sport training plan dictates the plan structure. The quality of the training plan is calculated using the widely adopted fitness-fatigue model, which in this study incorporates four performance metrics, namely distance covered at low to medium speed, distance covered at high speed, distance covered accelerating, and distance covered decelerating. We compare performance of the evolved training plans to a control set up which generates plans using a pseudo-random search process, and baseline against the training plan adopted by an elite team of Gaelic Football Players. Significant potential performance gains are achieved over the control setup and baseline elite team plan.", notes = "Also known as \cite{8790369} IEEE Catalog Number: CFP19ICE-ART", } @InCollection{Con88, author = "Michael Conrad", title = "The Price of Programmability", booktitle = "The Universal {Turing} Machine A Half-Century Survey", publisher = "Oxford University Press", year = "1988", editor = "Rolf Herken", pages = "285--307", keywords = "genetic algorithms, genetic programming, cellular automata, evolvable hardware, quantum computing, DNA and molecular computing", ISBN = "0-19-853741-7", isbn13 = "978-3211826379", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Con88.pdf", URL = "https://dl.acm.org/citation.cfm?id=57260", DOI = "doi:10.1093/oso/9780198537748.003.0011", size = "23 pages", abstract = "Programmability and computational efficiency are fundamental attributes of computing systems. A third attribute is evolutionary adaptability, the ability of a system to self-organise through a variation and selection process. The author has previously proposed that these three attributes of computing are linked by a trade-off principle, which may be roughly stated thus: a computing system cannot at the same time have high programmability, high computational efficiency, and high evolutionary adaptability (e.g., Conrad 1972, 1974, 1985). The purpose of the present paper is to outline the reasons for the trade-off principle in a manner which, though not entirely formal, is sufficiently detailed to allow for a well-defined formulation. We also consider the implications of the principle, first for alternative computer architectures. suited to solving problems by methods of evolutionary search and second, for limits on the capacity of programmable machines to simulate nature and duplicate intelligence.", notes = "Aug 2018 Springer DOI broken (removed Nov 2018) DOI and abstract refer to second edition published by in 1995 Springer as Volume 2 in their Computerkultur Series ISSN: 0946-9613 (Print). p261-281. isbn 978-3-211-82637-9 Rest refers to OUP original publication. Reviewed by Chris Hankin: https://academic.oup.com/logcom/article-pdf/1/6/884/2779480/1-6-884.pdf Essex Library Classmark Q 312 Evolution of programs. p287-288 'A real system is (effectively) programmable'...if 'the user's manual' is 'finite'. 'as programs become large it is inevitable that they will in some measure be incorrect (see Avizienis 1983)'. p294 stuff about evolution which seems to have wrong mutation rates and ignore the possibility of neutral mutations or gene duplications. p296 fitness landscapes referred to as 'adaptive surface'. Redundancy...'opens up extradimensional bypasses to higher adaptive peaks (Conrad 1979). p303-304 'Quantum mechanical tunnelling...and electron diffusion...(Biological macromolecules, eg DNA) excellent type of dynamics for (computing) modules in an evolutionary architecture'. p304 'Evolutionary programming'. p304 'substrate is of such immense importance'. p305 'Human intelligence (brain)...we cannot understand them in terms of a computer program and at the same time put our understanding to the test by running the program on a machine.", } @InProceedings{conrads:1998:ssdGP, author = "Markus Conrads and Peter Nordin and Wolfgang Banzhaf", title = "Speech Sound Discrimination With Genetic Programming", booktitle = "Proceedings of the First European Workshop on Genetic Programming", year = "1998", editor = "Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer and Terence C. Fogarty", volume = "1391", series = "LNCS", pages = "113--129", address = "Paris", publisher_address = "Berlin", month = "14-15 " # apr, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-64360-5", DOI = "doi:10.1007/BFb0055932", abstract = "The question that we investigate in this paper is, whether it is possible for Genetic Programming to extract certain regularities from raw time series data of human speech. We examine whether a genetic programming algorithm can find programs that are able to discriminate certain spoken vowels and consonan ts. We present evidence that this can indeed be achieved with a surprisingly simple approach that does not need preprocessing. The data we have collec ted on the system's behavior show that even speaker-independent discriminatio n is possible with GP.", notes = "EuroGP'98", affiliation = "University of Dortmund Dept. of Computer Science Dortmund Germany Dortmund Germany", } @InProceedings{Contador:2019:GECCOcomp, author = "Sergio Contador and J. Ignacio Hidalgo and Oscar Garnica and J. Manuel Velasco and Juan Lanchares", title = "Can clustering improve glucose forecasting with genetic programming models?", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", isbn13 = "978-1-4503-6748-6", pages = "1829--1836", address = "Prague, Czech Republic", DOI = "doi:10.1145/3319619.3326809", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{3326809} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InProceedings{Contador:2020:SAC, author = "Sergio Contador and J. Manuel Velasco and Oscar Garnica and J. Ignacio Hidalgo", title = "Profiled Glucose Forecasting using Genetic Programming and Clustering", booktitle = "The 35th ACM/SIGAPP Symposium On Applied Computing", year = "2020", editor = "Federico Divina and Miguel Garcia Torres", pages = "529--536", address = "Brno, Czech Republic", month = mar # " 30 -- " # apr # " 3", publisher = "ACM", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1145/3341105.3374003", abstract = "This paper proposes a method to obtain accurate forecastings of the subcutaneous glucose values from diabetic patients. Statistical techniques are applied to identify everyday situations of glucose behaviors and discover glucose profiles. This knowledge is used to create predictive models with genetic programming. The time series of glucose values, measured using continuous glucose monitoring systems, are divided into 4-hour, non-overlapping slots and clustered using a technique based on decision trees called chi-square automatic interaction detection. The glucose profiles are classified using the decision variables in order to customize the models for different profiles. Genetic programming models created with glucose values from the original dataset are compared to those of models created with classified glucose values. Significant differences and associations are observed between the glucose profiles. In general, using profiled glucose models improves the accuracy of the predictions with respect to those of models created with the original dataset.", notes = "Applications of Evolutionary Computing Track Universidad Rey Juan Carlos, Spain Universidad Complutense de Madrid, Spain https://www.sigapp.org/sac/sac2020/program.html", } @InProceedings{Contador:2020:evoapplications, author = "Sergio Contador and J. Manuel Colmenar and Oscar Garnica and J. Ignacio Hidalgo", title = "Short and Medium Term Blood Glucose Prediction Using Multi-objective Grammatical Evolution", booktitle = "23rd International Conference, EvoApplications 2020", year = "2020", month = "15-17 " # apr, editor = "Pedro A. Castillo and Juan Luis {Jimenez Laredo} and Francisco {Fernandez de Vega}", series = "LNCS", volume = "12104", publisher = "Springer Verlag", address = "Seville, Spain", pages = "494--509", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Multi-objective optimization, medicine, Human Glucose blood concentration prediction, Diabetes", isbn13 = "978-3-030-43721-3", video_url = "https://www.youtube.com/watch?v=OyNQC5Drbx4", DOI = "doi:10.1007/978-3-030-43722-0_32", abstract = "we investigate the benefits of applying a multi-objective approach for solving a symbolic regression problem by means of grammatical evolution. In particular, we continue with previous research about finding expressions to model the glucose levels in blood of diabetic patients. We use here a multi-objective Grammatical Evolution approach based on NSGA-II algorithm, considering the root mean squared error and an ad-hoc fitness function as objectives. This ad-hoc function is based on the Clarke Error Grid analysis, which is useful for showing the potential danger of mispredictions. Experimental results show that the multi-objective approach improves previous results in terms of Clarke Error Grid analysis reducing the number of dangerous mispredictions.", notes = "Universidad Complutense de Madrid, Spain http://www.evostar.org/2020/ EvoApplications2020 held in conjunction with EuroGP'2020, EvoMusArt2020 and EvoCOP2020", } @Article{CONTADOR:2021:ASC, author = "Sergio Contador and J. Manuel Velasco and Oscar Garnica and J. Ignacio Hidalgo", title = "Glucose forecasting using genetic programming and latent glucose variability features", journal = "Applied Soft Computing", volume = "110", pages = "107609", year = "2021", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2021.107609", URL = "https://www.sciencedirect.com/science/article/pii/S1568494621005305", keywords = "genetic algorithms, genetic programming, Diabetes, Continuous glucose monitoring, Glucose variability", abstract = "This paper investigates a set of genetic programming methods to obtain accurate predictions of subcutaneous glucose values from diabetic patients. We explore the usefulness of different features that identify the latent glucose variability. New features, including average glucose, glucose variability and glycemic risk, are generated as input variables of the genetic programming algorithm in order to improve the accuracy of the models in the prediction phase. The performance of traditional genetic programming, and models created with classified glucose values, are compared to those using latent glucose variability features. We experimented with a set of 18 different features and we also performed a study of the importance of the variables in the models. The Bayesian statistical analysis shows that the use of glucose variability as latent variables improved the predictions of the models, not only in terms of RMSE, but also in the reduction of dangerous predictions, i.e., those predictions that could lead to wrong decisions in the clinical practice", } @Article{Contador:2022:GPEM, author = "Sergio Contador and J. Manuel Colmenar and Oscar Garnica and J. Manuel Velasco and J. Ignacio Hidalgo", title = "Blood glucose prediction using multi-objective grammatical evolution: analysis of the ``agnostic'' and ``what-if'' scenarios", journal = "Genetic Programming and Evolvable Machines", year = "2022", volume = "23", number = "2", pages = "161--192", month = jun, keywords = "genetic algorithms, genetic programming, Grammatical evolution, Multi-objective optimization, MOGA, Glucose prediction, Diabetes", ISSN = "1389-2576", URL = "https://rdcu.be/cBKAs", DOI = "doi:10.1007/s10710-021-09424-6", size = "32 pages", abstract = "we investigate the benefits of applying a multi-objective approach for solving a symbolic regression problem by means of Grammatical Evolution. In particular, we extend previous work, obtaining mathematical expressions to model glucose levels in the blood of diabetic patients. Here we use a multi-objective Grammatical Evolution approach based on the NSGA-II algorithm, considering the root-mean-square error and an ad-hoc fitness function as objectives. This ad-hoc function is based on the Clarke Error Grid analysis, which is useful for showing the potential danger of mispredictions in diabetic patients. we use two datasets to analyse two different scenarios: What-if and Agnostic, the most common in daily clinical practice. In the What-if scenario, where future events are evaluated, results show that the multi-objective approach improves previous results in terms of Clarke Error Grid analysis by reducing the number of dangerous mispredictions. In the Agnostic situation, with no available information about future events, results suggest that we can obtain good predictions with only information from the previous hour for both Grammatical Evolution and Multi-Objective Grammatical Evolution.", notes = "Universidad Rey Juan Carlos, Mostoles, Spain", } @InProceedings{Contreras:evoapps13, author = "Ivan Contreras and J. Ignacio Hidalgo and Laura Nunez-Letamendia", title = "Combining Technical Analysis and Grammatical Evolution in a Trading System", booktitle = "Applications of Evolutionary Computing, EvoApplications 2013: EvoCOMNET, EvoCOMPLEX, EvoENERGY, EvoFIN, EvoGAMES, EvoIASP, EvoINDUSTRY, EvoNUM, EvoPAR, EvoRISK, EvoROBOT, EvoSTOC", year = "2013", month = "3-5 " # apr, editor = "Anna I. Esparcia-Alcazar and Antonio Della Cioppa and Ivanoe {De Falco} and Ernesto Tarantino and Carlos Cotta and Robert Schaefer and Konrad Diwold and Kyrre Glette and Andrea Tettamanzi and Alexandros Agapitos and Paolo Burrelli and J. J. Merelo and Stefano Cagnoni and Mengjie Zhang and Neil Urquhart and Kevin Sim and Aniko Ekart and Francisco {Fernandez de Vega} and Sara Silva and Evert Haasdijk and Gusz Eiben and Anabela Simoes and Philipp Rohlfshagen", series = "LNCS", volume = "7835", publisher = "Springer Verlag", address = "Vienna", publisher_address = "Berlin", pages = "244--253", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", isbn13 = "978-3-642-37191-2", DOI = "doi:10.1007/978-3-642-37192-9_25", size = "10 pages", abstract = "Trading Systems are beneficial for financial investments due to the complexity of nowadays markets. On one hand, finance markets are influenced by a great amount of factors of different sources such as government policies, natural disasters, international trade, political factors etc. On the other hand, traders, brokers or practitioners in general could be affected by human emotions, so their behaviour in the stock market becomes nonobjective. The high pressure induced by handling a large volume of money is the main reason of the so-called market psychology. Trading systems are able to avoid a great amount of these factors, allowing investors to abstract the complex flow of information and the emotions related to the investments. In this paper we compare two trading systems based on Evolutionary Computation. The first is a GA-based one and was already proposed and tested with data from 2006. The second one is a grammatical evolution approach which uses a new evaluation method. Experimental results show that the later outperforms the GA approach with a set of selected companies of the Spanish market with 2012 data.", notes = " EvoApplications2013 held in conjunction with EuroGP2013, EvoCOP2013, EvoBio'2013 and EvoMusArt2013", } @Article{Contreras:2017:GPEM, author = "Ivan Contreras and J. Ignacio Hidalgo and Laura Nunez-Letamendia and J. Manuel Velasco", title = "A meta-grammatical evolutionary process for portfolio selection and trading", journal = "Genetic Programming and Evolvable Machines", year = "2017", volume = "18", number = "4", pages = "411--431", month = dec, keywords = "genetic algorithms, Grammatical evolution, Automated trading systems, Meta-GE, Technical analysis, Fundamental analysis, Macroeconomic analysis", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-017-9304-1", size = "21 pages", abstract = "This study presents the implementation of an automated trading system that uses three critical analyses to determine time-decisions and portfolios for investment. The approach is based on a meta-grammatical evolution methodology that combines technical, fundamental and macroeconomic analysis on a hybrid top-down paradigm. First, the method provides a low-risk portfolio by analysing countries and industries. Next, aiming to focus on the most robust companies, the system filters the portfolio by analyzing their economic variables. Finally, the system analyses prices and volumes to optimize investment decisions during a given period. System validation involves a series of experiments in the European financial markets, which are reflected with a data set of over nine hundred companies. The final solutions have been compared with static strategies and other evolutionary implementations and the results show the effectiveness of the proposal.", } @Article{journals/jifs/ContrerasHN17, author = "Ivan Contreras and Jose Ignacio Hidalgo and Laura Nunez-Letamendia", title = "A hybrid automated trading system based on multi-objective grammatical evolution", journal = "Journal of Intelligent and Fuzzy Systems", year = "2017", volume = "32", number = "3", pages = "2461--2475", keywords = "genetic algorithms, genetic programming, grammatical evolution", ISSN = "1064-1246", bibdate = "2017-05-28", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/jifs/jifs32.html#ContrerasHN17", DOI = "doi:10.3233/JIFS-16435", abstract = "This paper describes a hybrid automated trading system (ATS) based on grammatical evolution and microeconomic analysis. The proposed system takes advantage from the flexibility of grammars for introducing and testing novel characteristics. The ATS introduces the self-generation of new technical indicators and multi-strategies for stopping unforeseen losses. Additionally, this work copes with a novel optimization method combining multi-objective optimization with a grammatical evolution methodology. We implemented the ATS testing three different fitness functions under three mono-objective approaches and also two multi-objective ATSs. Experimental results test and compare them to the Buy and Hold strategy and a previous approach, beating both in returns and in number of positive operations. In particular, the multi-objective approach demonstrated returns up to 20percent in very volatile periods, proving that the combination of fitness functions is beneficial for the ATS.", } @InProceedings{Contreras:2018:KDH, author = "Ivan Contreras and Arthur Bertachi and Lyvia Biagi and Josep Vehi and Silvia Oviedo", title = "Using Grammatical Evolution to Generate Short-term Blood Glucose Prediction Models", booktitle = "KDH@IJCAI-ECAI 2018 The 3rd International Workshop on Knowledge Discovery in Healthcare Data", year = "2018", editor = "Kerstin Bach and Razvan C. Bunescu and Oladimeji Farri and Aili Guo and Sadid A. Hasan and Zina M. Ibrahim and Cindy Marling and Jesse Raffa and Jonathan Rubin and Honghan Wu", volume = "Vol-2148", series = "CEUR Workshop Proceedings", pages = "91--96", address = "Stockholm", month = jul # " 13,", publisher = "CEUR-WS.org", keywords = "genetic algorithms, genetic programming, grammatical evolution", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ijcai/khd2018.html#ContrerasBBVO18", URL = "http://ceur-ws.org/Vol-2148/paper15.pdf", size = "6 pages", abstract = "Blood glucose levels prediction provides the possibility to issue early warnings related to ineffective or poor treatments. Advance notifications of adverse glycemic events can provide sufficient time windows to issue appropriate responses and adjust the therapy. Consequently, patients could avoid hyperglycemia and hypoglycemia conditions which would improve overall health, safety, and the quality of life of insulin dependent patients. This report concerns to the application of a search-based algorithm to generate models able to capture the dynamics of the blood glucose at a personalized patient level. The grammar-based feature generation allows to build complex empirical models using the data gathered by a sensor augmented therapy, a fitness band and a basic knowledge of T1D dynamics. Final model solutions provide blood glucose levels estimations using prediction horizons of 30, 60 and 90 minutes.", notes = "co-located with the 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence (IJCAI-ECAI 2018) http://ceur-ws.org/Vol-2148/ also known as \cite{conf/ijcai/ContrerasBBVO18}", } @InProceedings{Contreras-Bolton:2015:LA-CCI, author = "Carlos Contreras-Bolton and Victor Parada", booktitle = "2015 Latin America Congress on Computational Intelligence (LA-CCI)", title = "Automatic design of algorithms for optimization problems", year = "2015", abstract = "The design of efficient algorithms for difficult combinatorial optimisation problems remains a challenging field. Many heuristic, meta-heuristic and hyper-heuristic methods exist. In the specialized literature, it is observed that for some problems, the combined algorithms have better computational performance than individual performance. However, the automatic combination of the existing methods or the automatic design of new algorithms has received less attention in the literature. In this study, a method to automatically design algorithms is put into practice for two optimisation problems of recognised computational difficulty: the travelling salesman problem and the automatic clustering problem. The new algorithms are generated by means of genetic programming and are numerically evaluated with sets of typical instances for each problem. From an initial population of randomly generated algorithms, a systematic convergence towards the better algorithms is observed after a few hundred generations. Numerical results obtained from the evaluation of each of the designed algorithms suggest that for each set of instances with similar characteristics, specialized algorithms are required.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/LA-CCI.2015.7435977", month = oct, notes = "Also known as \cite{7435977}", } @Article{Contreras-Cruz:GPEM, author = "Marco A. Contreras-Cruz and Diana E. Martinez-Rodriguez and Uriel H. Hernandez-Belmonte and Victor Ayala-Ramirez", title = "A genetic programming framework in the automatic design of combination models for salient object detection", journal = "Genetic Programming and Evolvable Machines", year = "2019", volume = "20", number = "3", pages = "285--325", month = sep, keywords = "genetic algorithms, genetic programming, Visual attention, Contrast based methods, Evolutionary computation, Fusion strategies, Saliency enhancement, Saliency aggregation", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-019-09345-5", size = "41 pages", abstract = "In computer vision, the salient object detection problem consists of finding the most attention-grabbing objects in images. In the last years, many researchers have proposed salient object detection algorithms to address this problem. However, most of the algorithms perform well only on images with specific conditions and they do not solve the general problem. To cope with a more significant number of image types than those where each standalone saliency detection method performs well, novel methods search to generate a combination model that improves the overall performance of detecting salient objects in images. The contribution of this work is oriented towards the automatic design of combination models by using genetic programming. The proposed approach automatically selects the algorithms to be combined and the combination operators that result in an improvement in the overall performance. The evolutionary approach uses as input a set of candidate saliency detection methods ...", notes = "Electronics Engineering Department, Universidad de Guanajuato, Mexico", } @Manual{GPRS-kit, title = "GPRS-kit 0.2 User Guide", author = "Henry Cook", year = "2007", keywords = "genetic algorithms, genetic programming, C++, Matlab, gprscreate, aedoecreate, Linux, OSX", URL = "https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=53464b2d3973d4ae2afbd40d10d5ed8e841a3b64", size = "7 pages", notes = "gprscreate \cite{Alvarez:thesis} Genetically Programmed Response Surfaces see \cite{Cook:CS-2007-12} \cite{Cook:2008:DAC} Year approx 2007?", } @TechReport{Cook:CS-2007-12, author = "Henry Cook and Kevin Skadron", title = "Genetically Programmed Response Surfaces for Efficient Design Space Exploration", institution = "Department of Computer Science, University of Virginia", year = "2007", number = "CS-2007-12", address = "USA", month = aug, keywords = "genetic algorithms, genetic programming", URL = "https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=74e47f95ea3a0f963dc30ed86835927f8ac8cf4d", size = "15 pages", abstract = "In spite of many efforts to speed up cycle-accurate architecture simulation, exponential increases in architectural design complexity threaten to make traditional design optimization techniques completely intractable. Response surface methodologies address this challenge by transforming the optimization process from a lengthy series of detailed simulations into the tractable formulation and rapid evaluation of a marginally less accurate but easy to evaluate analytical expression—a predictive model. We propose genetic programming as a powerful method for creating these predictive response surface models out of sampled architectural performance data. Genetically programmed response surfaces (GPRSs) allow the architect to make rapid design optimizations (because only a small number of detailed simulations are needed) while simultaneously obtaining insight into the problem domain (because the resulting response surface, a non-linear polynomial in our case, exposes relationships and relative weights among the design variables). We validate our methodology on realistic datasets and compare it to recently proposed techniques for predictive design space exploration. GPRSs are highly accurate when making global predictions about architectural performance behavior based on only small samples of performance data: global predictions of IPC incur less than 3 percent mean percentage error based on sample sizes of less than 1 percent of one target processor design space, and no worse than than mean 6 percent error at sample sizes as small as 0.0000002 percent out of over one billion possible design points from a second target space. GPRSs can therefore reduce required simulation costs by up to six orders of magnitude.", notes = "\cite{GPRS-kit}", } @InProceedings{Cook:2008:DAC, author = "Henry Cook and Kevin Skadron", title = "Predictive design space exploration using genetically programmed response surfaces", booktitle = "45th ACM/IEEE Design Automation Conference, DAC 2008", year = "2008", month = jun, pages = "960--965", keywords = "genetic algorithms, genetic programming, genetically programmed response surfaces, GPRS, microarchitectural design space exploration, optimization process, predictive design space exploration, aircraft computers, computer architecture", URL = "http://www.cs.virginia.edu/~skadron/Papers/gprs_dac08.pdf", DOI = "doi:10.1145/1391469.1391711", ISSN = "0738-100X", abstract = "Exponential increases in architectural design complexity threaten to make traditional processor design optimization techniques intractable. Genetically programmed response surfaces (GPRS) address this challenge by transforming the optimization process from a lengthy series of detailed simulations into the tractable formulation and rapid evaluation of a predictive model. We validate GPRS methodology on realistic processor design spaces and compare it to recently proposed techniques for predictive microarchitectural design space exploration.", notes = "Also known as \cite{4555958} \cite{1391711} See also \cite{GPRS-kit}", } @InProceedings{Cook:2011:CIG, author = "Michael Cook and Simon Colton", title = "Multi-Faceted Evolution Of Simple Arcade Games", booktitle = "Proceedings of the 2011 IEEE Conference on Computational Intelligence and Games", year = "2011", address = "Seoul, South Korea", pages = "289--296", month = "31 " # aug # " - 3 " # sep, publisher = "IEEE", keywords = "genetic algorithms", URL = "http://cilab.sejong.ac.kr/cig2011/proceedings/CIG2011/papers/paper64.pdf", size = "8 pages", abstract = "We present a system for generating complete game designs by evolving rulesets, character layouts and terrain maps in an orchestrated way. In contrast to existing approaches to generate such game components in isolation, our ANGELINA system develops game components in unison with an appreciation for their interrelatedness. We describe this multi-faceted evolutionary approach, and give some results from a first round of experimentation.Y", notes = "fixed representation", } @Article{Cook:2021:CACM, author = "Perry R. Cook", title = "Behold the Ch!Ld", journal = "Communications of the ACM", year = "2021", volume = "64", number = "5", pages = "188--120", month = may, keywords = "genetic algorithms, genetic programming, genetic improvement, ficton", publisher = "Association for Computing Machinery", ISSN = "0001-0782", URL = "https://doi.org/10.1145/3453712", DOI = "doi:10.1145/3453712", size = "3 pages", abstract = "From the intersection of computational science and technological speculation, with boundaries limited only by our ability to imagine what could be. Opportunity can come calling when you least expect it.", notes = "author = P-Ray", } @InCollection{coon:1994:csgp, author = "Brett W. Coon", title = "Circuit Synthesis through Genetic Programming", booktitle = "Genetic Algorithms at Stanford 1994", year = "1994", editor = "John R. Koza", pages = "11--20", address = "Stanford, California, 94305-3079 USA", month = dec, organisation = "Stanford University", publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-187263-3", notes = "This volume contains 20 papers written and submitted by students describing their term projects for the course {"}Genetic Algorithms and Genetic Programming{"} (Computer Science 426) at Stanford University offered during the fall quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs426.html GP used to synthesis simple logic circuits. Able to simplify them. On some problems able to do as well as commercial tool {"}Synopsys{"}.", } @InProceedings{cooper:2002:gecco, author = "Jason Cooper and Chris Hinde", title = "Comparison Of Evolving Against Peers And Fixed Opponents Using Corewars", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "887", address = "New York", publisher_address = "San Francisco, CA 94104, USA", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "genetic algorithms, genetic programming, poster paper, Corewars", ISBN = "1-55860-878-8", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/GP082.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/GP082.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-15.pdf", notes = "GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)", } @InProceedings{cordella:evocop05, author = "Luigi Pietro Cordella and Claudio {De Stefano} and Francesco Fontanella and Angelo Marcelli", title = "EvoGeneS, a New Evolutionary Approach to Graph Generation", booktitle = "Evolutionary Computation in Combinatorial Optimization -- {EvoCOP}~2005", year = "2005", month = "30 " # mar # "-1 " # apr, editor = "G{\"{u}}nther R. Raidl and Jens Gottlieb", series = "LNCS", volume = "3448", publisher = "Springer Verlag", address = "Lausanne, Switzerland", publisher_address = "Berlin", pages = "46--57", keywords = "evolutionary computation", isbn13 = "978-3-540-25337-2", ISSN = "0302-9743", DOI = "doi:10.1007/978-3-540-31996-2_5", abstract = "Graphs are powerful and versatile data structures, useful to represent complex and structured information of interest in various fields of science and engineering. We present a system, called EvoGeneS, based on an evolutionary approach, for generating undirected graphs whose number of nodes is not a priori known. The method is based on a special data structure, called multilist, which encodes undirected attributed relational graphs. Two novel crossover and mutation operators are defined in order to evolve such structure. The developed system has been tested on a wireless network configuration and the results compared with those obtained by a genetic programming based approach recently proposed in the literature.", notes = "EvoCOP2005 Claims to be significantly better than \cite{hu:2004:wapcbgp}", } @InProceedings{cordella:2005:CEC, author = "L. P. Cordella and C. {De Stefano} and F. Fontanella and A. Marcelli", title = "Genetic Programming for Generating Prototypes in Classification Problems", booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary Computation", year = "2005", editor = "David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L. Gwenn Volkert and Dan Ashlock and Marc Schoenauer", volume = "2", pages = "1149--1155", address = "Edinburgh, UK", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "2-5 " # sep, organisation = "IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9363-5", DOI = "doi:10.1109/CEC.2005.1554820", abstract = "We propose a genetic programming based approach for generating prototypes in a classification problem. In this context, the set of prototypes to which the samples of a data set can be traced back is coded by a multitree, i.e. a set of trees, which represents the chromosome. Differently from other approaches, our chromosomes are of variable length. This allows coping with those classification problems in which one or more classes consist of subclasses. The devised approach has been tested on several problems and the results compared with those obtained by a different genetic programming based approach recently proposed in the literature.", notes = "First author is not L. P. Cordelia. CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS.", } @InProceedings{conf/iciap/CordellaSFM05, title = "A Novel Genetic Programming Based Approach for Classification Problems", author = "Luigi P. Cordella and Claudio {De Stefano} and Francesco Fontanella and Angelo Marcelli", year = "2005", pages = "727--734", editor = "Fabio Roli and Sergio Vitulano", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3617", booktitle = "Proceedings 13th International Conference Image Analysis and Processing - ICIAP 2005", address = "Cagliari, Italy", month = sep # " 6-8", bibdate = "2006-02-09", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/iciap/iciap2005.html#CordellaSFM05", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-28869-4", DOI = "doi:10.1007/11553595_89", size = "8 pages", abstract = "A new genetic programming based approach to classification problems is proposed. Differently from other approaches, the number of prototypes in the classifier is not a priori fixed, but automatically found by the system. In fact, in many problems a single class may contain a variable number of subclasses. Hence, a single prototype, may be inadequate to represent all the members of the class. The devised approach has been tested on several problems and the results compared with those obtained by a different genetic programming based approach recently proposed in the literature.", } @InProceedings{DBLP:conf/iwicpas/CordellaSFM06, author = "Luigi P. Cordella and Claudio {De Stefano} and Francesco Fontanella and Angelo Marcelli", title = "Looking for Prototypes by Genetic Programming", booktitle = "Advances in Machine Vision, Image Processing, and Pattern Analysis, International Workshop on Intelligent Computing in Pattern Analysis/Synthesis, IWICPAS 2006, Proceedings", year = "2006", pages = "152--159", editor = "Nanning Zheng and Xiaoyi Jiang and Xuguang Lan", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "4153", address = "Xi'an, China", month = aug # " 26-27", bibsource = "DBLP, http://dblp.uni-trier.de", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-37597-X", DOI = "doi:10.1007/11821045_16", abstract = "In this paper we propose a new genetic programming based approach for prototype generation in Pattern Recognition problems. Prototypes consist of mathematical expressions and are encoded as derivation trees. The devised system is able to cope with classification problems in which the number of prototypes is not a priori known. The approach has been tested on several problems and the results compared with those obtained by other genetic programming based approaches previously proposed.", } @Article{cordon:1999:sedpuhedat, author = "Oscar Cordon and Francisco Herrera and Luciano Sanchez", title = "Solving Electrical Distribution Problems Using Hybrid Evolutionary Data Analysis Techniques", journal = "Applied Intelligence", year = "1999", volume = "10", number = "1", pages = "5--24", month = jan, keywords = "genetic algorithms, genetic programming, electrical engineering, data analysis, evolutionary algorithms, genetic algorithm program, genetic fuzzy rule-based systems", ISSN = "0924-669X", URL = "ftp://decsai.ugr.es/pub/arai/tech_rep/ga-fl/tr-98106.ps.Z", notes = "Two modeling problems from the Spanish electrical system are solved. In each a comparison of statistical regression, GA-P, genetic fuzzy rule based and artificial neural networks is made. Uses modification of \cite{howard:1995:GA-P} tr-98106.ps.Z PScript preliminary version", } @InProceedings{DBLP:conf/eusflat/CordonAZ99, author = "Oscar Cordon and Felix {de Moya Anegon} and Carmen Zarco", title = "Learning Queries for a Fuzzy Information Retrieval System by means of GA-P Techniques", booktitle = "Proceedings of the EUSFLAT-ESTYLF Joint Conference", year = "1999", editor = "Gaspar Mayor and Jaume Su{\~n}er", pages = "335--338", address = "Palma de Mallorca, Spain", publisher_address = "Palma de Mallorca, Spain", month = sep # " 22-25", organisation = "European Society for Fuzzy Logica and Technology", publisher = "Universitat de les Illes Balears", keywords = "genetic algorithms, genetic programming", URL = "http://www.eusflat.org/publications/proceedings/EUSFLAT-ESTYLF_1999/papers/335-cordon.pdf", notes = "http://www.eusflat.org/publications/proceedings/EUSFLAT-ESTYLF_1999/", bibsource = "DBLP, http://dblp.uni-trier.de", } @Article{Cordon:2000:MSC, author = "O. Cordon and F. {de Moya} and C. Zarco", title = "A {GA-P} Algorithm to Automatically Formulate Extended {Boolean} Queries for a Fuzzy Information Retrieval System", journal = "Mathware \& Soft Computing", year = "2000", volume = "7", number = "2-3", pages = "309--322", organisation = "European Society for Fuzzy Logic and Technology (EUSFLAT)", keywords = "genetic algorithms, genetic programming", ISSN = "1134-5632", URL = "http://sci2s.ugr.es/sites/default/files/ficherosPublicaciones/0450_MATHWARE_2000_07_02-03_18.pdf", URL = "http://eudml.org/doc/39209", broken = "http://ic.ugr.es/Mathware/index.php/Mathware/article/view/145", broken = "http://ic.ugr.es/Mathware/index.php/Mathware/article/viewFile/145/124", size = "14 pages", abstract = "Although the fuzzy retrieval model constitutes a powerful extension of the boolean one, being able to deal with the imprecision and subjectivity existing in the Information Retrieval process, users are not usually able to express their query requirements in the form of an extended boolean query including weights. To solve this problem, different tools to assist the user in the query formulation have been proposed. In this paper, the genetic algorithm-programming technique is considered to build an algorithm of this kind that will be able to automatically learn weighted queries -modeling the user's needs- for a fuzzy information retrieval system by applying an off-line adaptive process starting from a set of relevant documents.", notes = "http://ic.ugr.es/Mathware/index.php/Mathware", } @InProceedings{Cordon:2002:ISKO, author = "O. Cordon and E. Herrera-Viedma and Maria Luque and Felix Moya and Carmen Zarco", title = "An Inductive Query by Example Technique for Extended {Boolean} Queries Based on Simulated-Annealing Programming", booktitle = "Challenges in Knowledge Representation and Organization for the 21st Century. Integration of Knowledge across Boundaries. Proceedings of the 7th International ISKO Conference (ISKO'2002)", year = "2002", editor = "M. J. Lopez-Huertas", volume = "8", series = "Advances in knowledge organization", pages = "429--436", address = "Granada, Spain", publisher_address = "Wuerzburg, Germany", month = jul # " 10-13", publisher = "Ergon", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-89913-247-2", broken = "http://www.ergon-verlag.de/en/start.htm?information-_library_sciences_advances_in_knowledge_organization.htm", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.163.4687", URL = "http://www.ergon-verlag.de/bibliotheks--informationswissenschaft/advances-in-knowledge-organization/band-8.php", notes = "http://www.isko.org/events.html", } @Article{cordon:2002:SC, author = "O. Cordon and F. Moya and C. Zarco", title = "A new evolutionary algorithm combining simulated annealing and genetic programming for relevance feedback in fuzzy information retrieval systems", journal = "Soft Computing - A Fusion of Foundations, Methodologies and Applications", year = "2002", volume = "6", number = "5", pages = "308--319", month = aug, keywords = "genetic algorithms, genetic programming, Fuzzy information retrieval, Relevance feedback, Evolutionary algorithms, Simulated annealing", ISSN = "1432-7643", DOI = "doi:10.1007/s00500-002-0184-8", abstract = "Relevance feedback techniques have demonstrated to be a powerful means to improve the results obtained when a user submits a query to an information retrieval system as the world wide web search engines. These kinds of techniques modify the user original query taking into account the relevance judgements provided by him on the retrieved documents, making it more similar to those he judged as relevant. This way, the new generated query permits to get new relevant documents thus improving the retrieval process by increasing recall. However, although powerful relevance feedback techniques have been developed for the vector space information retrieval model and some of them have been translated to the classical Boolean model, there is a lack of these tools in more advanced and powerful information retrieval models such as the fuzzy one. In this contribution we introduce a relevance feedback process for extended Boolean (fuzzy) information retrieval systems based on a hybrid evolutionary algorithm combining simulated annealing and genetic programming components. The performance of the proposed technique will be compared with the only previous existing approach to perform this task, Kraft et al.'s method, showing how our proposal outperforms the latter in terms of accuracy and sometimes also in time consumption. Moreover, it will be showed how the adaptation of the retrieval threshold by the relevance feedback mechanism allows the system effectiveness to be increased.", } @InProceedings{cordon:ppsn2002:pp710, author = "Oscar Cordon and Enrique Herrera-Viedma and Maria Luque", title = "Evolutionary Learning of {Boolean} Queries by Multiobjective Genetic Programming", booktitle = "Parallel Problem Solving from Nature - PPSN VII", address = "Granada, Spain", month = "7-11 " # sep, pages = "710--719", year = "2002", editor = "Juan J. Merelo-Guervos and Panagiotis Adamidis and Hans-Georg Beyer and Jose-Luis Fernandez-Villacanas and Hans-Paul Schwefel", number = "2439", series = "Lecture Notes in Computer Science, LNCS", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, MOGA, Pattern recognition and classification/datamining,Web services, Multi-objective", ISBN = "3-540-44139-5", DOI = "doi:10.1007/3-540-45712-7_68", abstract = "The performance of an information retrieval system is usually measured in terms of two different criteria, precision and recall. This way, the optimisation of any of its components is a clear example of a multiobjective problem. However, although evolutionary algorithms have been widely applied in the information retrieval area, in all of these applications both criteria have been combined in a single scalar fitness function by means of a weighting scheme. In this paper, we will tackle with a usual information retrieval problem, the automatic derivation of Boolean queries, by incorporating a well known Pareto-based multiobjective evolutionary approach, MOGA, into a previous proposal of a genetic programming technique for this task.", } @InProceedings{DBLP:conf/ifsa/CordonHLMZ03, author = "Oscar Cordon and Enrique Herrera-Viedma and Maria Luque and Felix {de Moya Anegon} and Carmen Zarco", title = "Analyzing the Performance of a Multiobjective GA-P Algorithm for Learning Fuzzy Queries in a Machine Learning Environment", booktitle = "Proceedings of the 10th International Fuzzy Systems Association World Congress, Fuzzy Sets and Systems - IFSA 2003", year = "2003", editor = "Taner Bilgi\c{c} and Bernard De Baets and Okyay Kaynak", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "2715", pages = "611--619", address = "Istanbul, Turkey", month = jun # " 30 - " # jul # " 2", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-40383-3", URL = "http://www.scimago.es/publications/ifsa03-cordon.pdf", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1007/3-540-44967-1_73", size = "9 pages", abstract = "The fuzzy information retrieval model was proposed some years ago to solve several limitations of the Boolean model without a need of a complete redesign of the information retrieval system. However, the complexity of the fuzzy query language makes it difficult to formulate user queries. Among other proposed approaches to solve this problem, we find the Inductive Query by Example (IQBE) framework, where queries are automatically derived from sets of documents provided by the user. In this work we test the applicability of a multiobjective evolutionary IQBE technique for fuzzy queries in a machine learning environment. To do so, the Cranfield documentary collection is divided into two different document sets, labeled training and test, and the algorithm is run on the former to obtain several queries that are then validated on the latter.", } @InProceedings{cordon:2003:WSC, author = "Oscar Cordon and Enrique Herrera-Viedma and Maria Luque and Felix Moya and Carmen Zarco", title = "A Realistic Information Retrieval Environment to Validate a Multiobjective GA-P Algorithm for Learning Fuzzy Queries", booktitle = "Proceedings if the 8th Online World Conference on Soft Computing in Industrial Applications (WSC8)", year = "2003", volume = "32", series = "Advances in Soft Computing", pages = "299--309", publisher = "Springer", note = "published by Springer 2005 as Soft Computing: Methodologies and Applications", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-25726-4", DOI = "doi:10.1007/3-540-32400-3_23", abstract = "IQBE has been shown as a promising technique to assist the users in the query formulation process. In this framework, queries are automatically derived from sets of documents provided by them. However, the different proposals found in the specialized literature are usually validated in non realistic information retrieval environments. In this work, we design several experimental setups to create real-like retrieval environments and validate the applicability of a previously proposed multiobjective evolutionary IQBE technique for fuzzy queries on them.", notes = "duplicate entry \cite{cordon:2005:SCMA}", } @InProceedings{Cordon:2004:FUZZ-IEEE, author = "Oscar Cordon and Felix {de Moya} and Carmen Zarco", title = "Fuzzy logic and multiobjective evolutionary algorithms as soft computing tools for persistent query learning in text retrieval environments", booktitle = "Proceedings of the 2004 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2004)", year = "2004", volume = "1", pages = "571--576", address = "Budapest, Hungary", publisher = "IEEE Press", month = "25-29 " # jul, keywords = "genetic algorithms, genetic programming, Boolean functions, evolutionary computation, fuzzy logic, knowledge engineering, query processing extended Boolean query structure, fuzzy logic, information retrieval systems, multiobjective evolutionary algorithms, persistent query learning, soft computing tools, text retrieval environment", DOI = "doi:10.1109/FUZZY.2004.1375799", size = "6 pages", abstract = "Persistent queries are a specific kind of queries used in information retrieval systems to represent a user's long-term standing information need. These queries can present many different structures, being the {"}bag of words{"} that most commonly used. They can be sometimes formulated by the user, although this task is usually difficult for him and the persistent query is then automatically derived from a set of sample documents he provides. In this work we aim at getting persistent queries with a more representative structure for text retrieval issues. To do so, we make use of soft computing tools: fuzzy logic is considered for representation and inference purposes by dealing with the extended Boolean query structure, and multiobjective evolutionary algorithms are applied to build the persistent fuzzy query. Experimental results show how both an expressive fuzzy logic-based query structure and a proper learning process to derive it are needed in order to get a good retrieval efficacy, when comparing our process to single-objective evolutionary methods to derive both classic Boolean and extended Boolean queries.", notes = "also known as \cite{1375799}", } @InCollection{Cordon:2004:fzi, author = "O. Cordon and F. Moya and C. Zarco", title = "Automatic Learning of Multiple Extended {Boolean} Queries by Multiobjective GA-P Algorithms", booktitle = "Fuzzy Logic and the Internet", publisher = "PHYSICA-VERLAG", year = "2004", editor = "V. Loia and M. Nikravesh and L. A. Zadeh", volume = "137", series = "STUDIES IN FUZZINESS AND SOFT COMPUTING", pages = "47--70", address = "Germany", keywords = "genetic algorithms, genetic programming", URL = "http://direct.bl.uk/research/18/0E/RN143659018.html", DOI = "doi:10.1007/978-3-540-39988-9_3", ISSN = "1434-9922", abstract = "In this contribution, a new Inductive Query by Example process is proposed to automatically derive extended Boolean queries for fuzzy information retrieval systems from a set of relevant documents provided by a user. The novelty of our approach is that it is able to simultaneously generate several queries with a different precision-recall tradeoff in a single run. To do so, it is based on an advanced. evolutionary algorithm, GA-P, specially designed to tackle with multiobjective problems by means of a Pareto-based multi-objective technique. The performance of the new proposal will be tested on the usual Cranfield collection and compared to the well-known Kraft et al.'s process.", notes = "English", } @InCollection{cordon:2005:SCMA, author = "Oscar Cordon and Enrique Herrera-Viedma and Maria Luque and Felix Moya and Carmen Zarco", title = "A Realistic Information Retrieval Environment to Validate a Multiobjective GA-P Algorithm for Learning Fuzzy Queries", booktitle = "Soft Computing: Methodologies and Applications", publisher = "Springer-Verlag", year = "2005", editor = "F. Hoffmann and M. Koppen and F. Klawonn and R. Roy", volume = "32", series = "Advances in Soft Computing", pages = "299--309", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-25726-4", DOI = "doi:10.1007/3-540-32400-3_23", abstract = "IQBE has been shown as a promising technique to assist the users in the query formulation process. In this framework, queries are automatically derived from sets of documents provided by them. However, the different proposals found in the specialized literature are usually validated in non realistic information retrieval environments. In this work, we design several experimental setups to create real-like retrieval environments and validate the applicability of a previously proposed multiobjective evolutionary IQBE technique for fuzzy queries on them.", notes = "duplicate entry \cite{cordon:2003:WSC}", } @Article{CHL:IPM:06, title = "Improving the learning of {Boolean} queries by means of a multiobjective IQBE evolutionary algorithm", author = "O. Cordon and E. Herrera-Viedma and M. Luque", journal = "Information Processing and Management", year = "2006", volume = "42", number = "3", pages = "615--632", month = may, keywords = "genetic algorithms, genetic programming, Boolean information retrieval systems, Inductive query by example, Multiobjective evolutionary algorithms, Query learning", DOI = "doi:10.1016/j.ipm.2005.02.006", abstract = "The Inductive Query By Example (IQBE) paradigm allows a system to automatically derive queries for a specific Information Retrieval System (IRS). Classic IRSs based on this paradigm [Smith, M., & Smith, M. (1997). The use of genetic programming to build Boolean queries for text retrieval through relevance feedback. Journal of Information Science, 23(6), 423-431 \cite{MartinPSmith:1997:JIS}] generate a single solution (Boolean query) in each run, that with the best fitness value, which is usually based on a weighted combination of the basic performance criteria, precision and recall. A desirable aspect of IRSs, especially of those based on the IQBE paradigm, is to be able to get more than one query for the same information needs, with high precision arid recall values or with different trade-offs between both. IQBE process is proposed combining a previous basic algorithm to automatically derive Boolean queries for Boolean IRSs [Smith, M., & Smith, M. (1997). The use of genetic programming to build Boolean queries for text retrieval through relevance feedback. Journal of Information Science, 23(6), 423-431] and an advanced evolutionary multiobjective approach [Coello, C. A., Van Veldhuizen, D. A., & Lamant, G. B. (2002). Evolutionary algorithms for solving multiobjective problems. Kluwer Academic Publishers], which obtains several queries with a different precision recall trade-off in a single run. The performance of the new proposal will be tested on the Cranfield and CACM collections and compared to the well-known Smith and Smith's algorithm, showing how it improves the learning of queries and thus it could better assist the user in the query formulation process.", } @Article{Cordova-Neira:2018:ieeeAccess, author = "Manuel A. {Cordova Neira} and Pedro R. {Mendes Junior} and Anderson Rocha and Ricardo {da S. Torres}", journal = "IEEE Access", title = "Data-Fusion Techniques for Open-set Recognition Problems", year = "2018", volume = "6", pages = "21242--21265", month = "6 " # apr, keywords = "genetic algorithms, genetic programming, Pattern recognition, Open-set Recognition, Data Fusion, Optimum-Path Forest, Majority Voting, OSOPF", ISSN = "2169-3536", DOI = "doi:10.1109/ACCESS.2018.2824240", abstract = "Most pattern classification techniques are focused on solving closed-set problems - in which a classifier is trained with samples of all classes that may appear during the testing phase. In many situations, however, samples of unknown classes, i.e., whose classes did not have any example during the training stage, need to be properly handled during testing. This specific set up is referred to in the literature as open-set recognition. Open-set problems are harder as they might be ill-sampled, not sampled at all, or even undefined. Differently from existing literature, here, we aim at solving open-set recognition problems combining different classifiers and features while, at the same time, taking care of unknown classes. Researchers have greatly benefited from combining different methods in order to achieve more robust and reliable classifiers in daring recognition conditions, but those solutions have often focused on closed-set set ups. In this work, we propose the integration of a newly designed open set graph-based Optimum-Path Forest (OSOPF) classifier with Genetic Programming (GP) and Majority Voting fusion techniques. While OSOPF takes care of learning decision boundaries more resilient to unknown classes and outliers, the GP, combines different problem features to discover appropriate similarity functions and allow a more robust classification through early fusion. Finally, the Majority-Voting approach combines different classification evidence from different classifier outcomes and features through late-fusion techniques. Performed experiments show the proposed data-fusion approaches yield effective results for open-set recognition problems, significantly outperforming existing counterparts in the literature and paving the way for investigations in this field.", notes = "Also known as \cite{8332940} CALTECH-256 and ALOI and COIL", } @Misc{coreyes2021evolving, author = "John D. Co-Reyes and Yingjie Miao and Daiyi Peng and Esteban Real and Sergey Levine and Quoc V. Le and Honglak Lee and Aleksandra Faust", title = "Evolving Reinforcement Learning Algorithms", howpublished = "ArXiv", year = "2021", month = "8 " # jan, keywords = "genetic algorithms, genetic programming, genetic improvement, computer video games", eprint = "2101.03958", archiveprefix = "arXiv", primaryclass = "cs.LG", URL = "https://arxiv.org/abs/2101.03958", video_url = "https://slideslive.com/38941399/evolving-reinforcement-learning-algorithms", size = "15 pages", abstract = "We propose a method for meta-learning reinforcement learning algorithms by searching over the space of computational graphs which compute the loss function for a value-based model-free RL agent to optimize. The learned algorithms are domain-agnostic and can generalize to new environments not seen during training. Our method can both learn from scratch and bootstrap off known existing algorithms, like DQN, enabling interpretable modifications which improve performance. Learning from scratch on simple classical control and gridworld tasks, our method rediscovers the temporal-difference (TD) algorithm. Bootstrapped from DQN, we highlight two learned algorithms which obtain good generalization performance over other classical control tasks, gridworld type tasks, and Atari games. The analysis of the learned algorithm behaviour shows resemblance to recently proposed RL algorithms that address overestimation in value-based methods.", notes = "Research at Google, Mountain View, CA 94043, USA 'bootstrapping off existing algorithms'. 'The search is done over 300 CPUs and run for roughly 72 hours, at which point around 20000 programs have been evaluated.'", } @InProceedings{Corgnati:2014:CVAUI, author = "Lorenzo Corgnati and Luca Mazzei and Simone Marini and Stefano Aliani and Alessandra Conversi and Annalisa Griffa and Bruno Isoppo and Ennio Ottaviani", booktitle = "ICPR Workshop on Computer Vision for Analysis of Underwater Imagery (CVAUI 2014)", title = "Automated Gelatinous Zooplankton Acquisition and Recognition", year = "2014", month = "24-24 " # aug, address = "Stockholm", keywords = "genetic algorithms, genetic programming, SVM, pattern recognition, gelatinous zooplankton, underwater imaging, feature selection, underwater camera, GUARD1, autonomous vehicle", isbn13 = "978-1-4799-6713-1", DOI = "doi:10.1109/CVAUI.2014.12", size = "8 pages", abstract = "Much is still unknown about marine plankton abundance and dynamics in the open and interior ocean. Especially challenging is the knowledge of gelatinous zooplankton distribution, since it has a very fragile structure and cannot be directly sampled using traditional net based techniques. In the last decades there has been an increasing interest in the oceanographic community toward imaging systems. In this paper the performance of three different methodologies, Tikhonov regularisation, Support Vector Machines, and Genetic Programming, are analysed for the recognition of gelatinous zooplankton. The three methods have been tested on images acquired in the Ligurian Sea by a low cost under-water standalone system (GUARD1). The results indicate that the three methods provide gelatinous zooplankton identification with high accuracy showing a good capability in robustly selecting relevant features, thus avoiding computational-consuming preprocessing stages. These aspects fit the requirements for running on an autonomous imaging system designed for long lasting deployments.", notes = "Also known as \cite{6961262} ISMAR-Marine Sciences Institute in La Spezia, CNR-National Research Council of Italy, Lerici, SP, Italy", } @Article{s16122124, author = "Lorenzo Corgnati and Simone Marini and Luca Mazzei and Ennio Ottaviani and Stefano Aliani and Alessandra Conversi and Annalisa Griffa", title = "Looking inside the Ocean: Toward an Autonomous Imaging System for Monitoring Gelatinous Zooplankton", journal = "Sensors", year = "2016", volume = "16", number = "12", month = "14 " # dec, note = "Special Issue Sensing Technologies for Autonomy and Cooperation in Underwater Networked Robot Systems", keywords = "genetic algorithms, genetic programming, content-based image recognition, feature selection, gelatinous zooplankton, autonomous underwater imaging, GUARD1", article_number = "2124", ISSN = "1424-8220", URL = "http://www.mdpi.com/1424-8220/16/12/2124", URL = "http://www.mdpi.com/1424-8220/16/12/2124/pdf", DOI = "doi:10.3390/s16122124", size = "28 pages", abstract = "Marine plankton abundance and dynamics in the open and interior ocean is still an unknown field. The knowledge of gelatinous zooplankton distribution is especially challenging, because this type of plankton has a very fragile structure and cannot be directly sampled using traditional net based techniques. To overcome this shortcoming, Computer Vision techniques can be successfully used for the automatic monitoring of this group.This paper presents the GUARD1 imaging system, a low-cost stand-alone instrument for underwater image acquisition and recognition of gelatinous zooplankton, and discusses the performance of three different methodologies, Tikhonov Regularization, Support Vector Machines and Genetic Programming, that have been compared in order to select the one to be run onboard the system for the automatic recognition of gelatinous zooplankton. The performance comparison results highlight the high accuracy of the three methods in gelatinous zooplankton identification, showing their good capability in robustly selecting relevant features. In particular, Genetic Programming technique achieves the same performances of the other two methods by using a smaller set of features, thus being the most efficient in avoiding computationally consuming preprocessing stages, that is a crucial requirement for running on an autonomous imaging system designed for long lasting deployments, like the GUARD1. The Genetic Programming algorithm has been installed onboard the system, that has been operationally tested in a two-months survey in the Ligurian Sea, providing satisfactory results in terms of monitoring and recognition performances.", notes = "open access", } @Article{DBLP:journals/apin/CoricEJ21, author = "Rebeka Coric and Mateja DHumic and Domagoj Jakobovic", title = "Genetic programming hyperheuristic parameter configuration using fitness landscape analysis", journal = "Applied Intelligence", year = "2021", volume = "51", number = "10", pages = "7402--7426", month = oct, keywords = "genetic algorithms, genetic programming, Fitness landscape analysis, Scheduling, Tree operators, Clustering, Parameter configuration", ISSN = "0924-669X", timestamp = "Thu, 16 Sep 2021 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/apin/CoricEJ21.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://www.bib.irb.hr:8443/1123444", URL = "https://doi.org/10.1007/s10489-021-02227-3", DOI = "doi:10.1007/s10489-021-02227-3", abstract = "Fitness landscape analysis is a tool that can help us gain insight into a problem, determine how hard it is to solve a problem using a given algorithm, choose an algorithm for solving a given problem, or choose good algorithm parameters for solving the problem. fitness landscape analysis of hyperheuristics is used for clustering instances of three scheduling problems. After that, good parameters for tree-based genetic programming that can solve a given scheduling problem are calculated automatically for every cluster. Additionally, we introduce tree editing operators which help in the calculation of fitness landscape features in tree based genetic programming. A heuristic is proposed based on introduced operators, and it calculates the distance between any two trees. The results show that the proposed approach can obtain parameters that offer better performance compared to manual parameter selection.", notes = "Department of Mathematics, J.J. Strossmayer University of Osijek, Gajev trg 6, 31000, Osijek, Croatia", } @Article{Corn:2015:IFAC-PapersOnLine, author = "Marko Corn and Maja Atanasijevic-Kunc", title = "Designing model and control system using evolutionary algorithms", journal = "IFAC-PapersOnLine", volume = "48", number = "1", pages = "526--531", year = "2015", note = "8th Vienna International Conference on Mathematical Modelling, MATHMOD 2015", ISSN = "2405-8963", DOI = "doi:10.1016/j.ifacol.2015.05.106", URL = "http://www.sciencedirect.com/science/article/pii/S240589631500107X", abstract = "In the paper several types of evolutionary algorithms have been tested regarding the dynamic nonlinear multivariable system model. We have defined three problems regarding the observed system: the first is the so-called grey box identification where we search for the characteristic of the system's valve, the second problem is black box identification where we search the model of the system with the usage of system's measurements and the third one is a system's controller design. We solved these problems with the usage of genetic algorithms, differential evolution, evolutionary strategies, genetic programming and a developed approach called AMEBA algorithm. All methods have been proven to be very useful for solving problems of the grey box identification and design of the controller for the mentioned system but AMEBA algorithm have also been successfully used in black box identification problem where it generated a suitable model.", keywords = "genetic algorithms, genetic programming, evolutionary algorithms, ameba, dynamic systems", } @InProceedings{Cornejo:2018:GAMM, author = "Guy Yoslan {Cornejo Maceda} and Francois Lusseyran and Bernd R. Noack and Marek Morzynski", title = "Optimizing {MIMO} control for fluidic pinball using machine learning", booktitle = "89th GAMM Annual Meeting", year = "2018", address = "Munich, Germany", month = "19-23 " # mar, organisation = "TUM", keywords = "genetic algorithms, genetic programming", bibsource = "OAI-PMH server at api.archives-ouvertes.fr", oai = "oai:HAL:hal-01856252v1", identifier = "hal-01856252", language = "en", URL = "https://hal.archives-ouvertes.fr/hal-01856252", abstract = "We are looking for wake stabilization in a multi input multi output (MIMO) configuration.The wake results from an obstacle made by three cylinders in an incoming flow. The meansof action are the cylinders rotation and the output is the velocity taken downstream. Previousstudies have shown that high and low frequency forcing stabilize the wake, revealing the nonlinearinteractions. Linear control being not applicable in our case we are looking for an optimal controllaw regarding drag reduction using genetic programming, a model free MLC approach. Geneticprogramming can explore a broad spectrum of laws, exploiting the nonlinearities, ranging fromopen loop control to closed loop control.", notes = "LIMSI-CNRS Also known as \cite{cornejomaceda:hal-01856252} broken Apr 2023 https://jahrestagung.gamm-ev.de/index.php/2018/2018-annual-meeting https://www.events.tum.de/frontend/index.php?page_id=2083&v=AuthorList&do=17&day=244&entity_id=22304 Dec 2019 no PDF via hal-01856252", } @PhdThesis{Cornejo-Maceda:thesis, author = "Guy Y. {Cornejo Maceda}", title = "Gradient-enriched machine learning control exemplified for shear flows in simulations and experiments", titletranslation = "Controle par apprentissage automatique et methodes de gradient applique aux ecoulements cisailles numeriques et experimentaux", school = "Universite Paris-Saclay", year = "2021", address = "France", month = "17 " # mar, keywords = "genetic algorithms, genetic programming, linear genetic programming, flow control, fluidic pinball, open cavity, machine learning control (mlc), genetic programming control (gpc), gradient-enriched machine learning control (gmlc), controle d'ecoulement, pinball fluidique, cavite ouverte, controle par apprentissage automatique (mlc), controle par programmation genetique (gpc), [PHYS, MECA, mefl]physics [physics]/mechanics [physics]/fluid mechanics [physics, class-ph], [INFO, info-ai]computer science [cs]/artificial intelligence [cs, AI], [STAT, ml]statistics [stat]/machine learning [stat, ML], [MATH, math-oc]mathematics [math]/optimisation and control [math, OC], [NLIN, nlin-cd]nonlinear sciences [physics]/chaotic dynamics [nlin, CD]", ISSN = "03217787", bibsource = "OAI-PMH server at api.archives-ouvertes.fr", contributor = "Laboratoire Interdisciplinaire des Sciences du Numerique and Bernd R. Noack and Francois Lusseyran", identifier = "NNT: 2021UPAST036; tel-03217787", language = "en", oai = "oai:HAL:tel-03217787v1", rights = "info:eu-repo/semantics/OpenAccess", URL = "https://tel.archives-ouvertes.fr/tel-03217787", URL = "https://tel.archives-ouvertes.fr/tel-03217787/document", URL = "https://tel.archives-ouvertes.fr/tel-03217787/file/91918_CORNEJO_MACEDA_2021_archivage.pdf", code_url = "https://github.com/gycm134/xMLC", size = "174 pages", abstract = "As main contribution we propose a fast and automated gradient-enriched machine learning control (gMLC) algorithm to learn feedback control laws. The framework alternates between explorative and exploitive gradient-based iterations, generalizing genetic programming control (GPC) and the Explorative Gradient Method (EGM). The gMLC algorithm has been demonstrated both numerically, with the stabilization of a MIMO system, the fluidic pinball and experimentally, with the control of the open cavity. In both cases, gMLC successfully built closed-loop control laws allowing the best performances so far. We prove, in particular, that the mechanisms behind the control of the cavity rely effectively on feedback. The benchmark of gMLC with GPC on both problems, shows that gMLC outperforms GPC both in terms of convergence speed and final solution efficiency. An acceleration of at least a factor 10 between the GPC and gMLC has been achieved, allowing the control of many experiments, e.g., with a large number of inputs and outputs or multiple parameters testing for robustness. The two developed codes are both freely available online: xMLC, based on GPC and gMLC, based on our new algorithm.", resume = "Nous proposons un algorithme rapide et automatise de controle par apprentissage automatique enrichi de methodes de gradients (gMLC) pour l{'}optimisation de lois de controle en boucle fermee. Notre methodologie alterne entre l{'}exploration de l{'}espace de recherche et l{'}exploitation des gradients locaux, et generalise la programmation genetique (GPC) et l{'}Explorative Gradient Method (EGM). L{'}algorithme gMLC est implemente et teste numeriquement, par la stabilisation d{'}un systeme multi-entrees multi-sorties, le pinball fluidique et experimentalement, par le controle de la cavite ouverte. Dans les deux cas, gMLC a construit des lois de controle en boucle fermee permettant les meilleures performances repertoriees. Nous demontrons aussi que les mecanismes de controle pour la cavite reposent effectivement sur la retroaction a partir de la mesure de l{'}etat. La comparaison entre gMLC et GPC est toujours a l{'}avantage de gMLC aussi bien en termes de vitesse de convergence que de qualite de la solution finale. Le gain en vitesse d{'}apprentissage est d{'}au moins un facteur 10, permettant d{'}envisager le controle d{'}experiences complexes avec, par exemple, un grand nombre d{'}entrees et de sorties ou des tests multi-parametres pour assurer la robustesse de l{'}apprentissage. Enfin, deux codes sont mis en ligne en libre acces: xMLC, base sur le controle par programmation genetique et gMLC, base sur notre nouvel algorithme.", notes = "In English Wake control with GP Supervisors: B. R. Noack and F. Lusseyran", } @Article{cornejo_maceda_li_lusseyran_morzynski_noack_2021, author = "Guy Y. {Cornejo Maceda} and Yiqing Li and Francois Lusseyran and Marek Morzynski and Bernd R. Noack", title = "Stabilization of the fluidic pinball with gradient-enriched machine learning control", journal = "Journal of Fluid Mechanics", year = "2021", volume = "917", pages = "A42", month = "25 " # jun, keywords = "genetic algorithms, genetic programming, flow control, machine learning, wakes", publisher = "Cambridge University Press", ISSN = "0022-1120", DOI = "doi:10.1017/jfm.2021.301", size = "43 pages", abstract = "We stabilise the flow past a cluster of three rotating cylinders, the fluidic pinball, with automated gradient-enriched machine learning algorithms. The control laws command the rotation speed of each cylinder in an open- and closed-loop manner. These laws are optimized with respect to the average distance from the target steady solution in three successively richer search spaces. First, stabilization is pursued with steady symmetric forcing. Second, we allow for asymmetric steady forcing. And third, we determine an optimal feedback controller employing nine velocity probes downstream. As expected, the control performance increases with every generalization of the search space. Surprisingly, both open- and closed-loop optimal controllers include an asymmetric forcing, which surpasses symmetric forcing. Intriguingly, the best performance is achieved by a combination of phasor control and asymmetric steady forcing. We hypothesize that asymmetric forcing is typical for pitchfork bifurcated dynamics of nominally symmetric configurations. Key enablers are automated machine learning algorithms augmented with gradient search: explorative gradient method for the open-loop parameter optimization and a gradient-enriched machine learning control (gMLC) for the feedback optimization. Gradient-enriched machine learning control learns the control law significantly faster than previously employed genetic programming control. The gMLC source code is freely available online.", } @Book{dbbs_mods_00071130, author = "Guy Y. {Cornejo Maceda} and Francois Lusseyran and Bernd R. Noack", title = "{xMLC} - A Toolkit for Machine Learning Control", publisher = "Technische Universitaet Braunschweig", year = "2022", volume = "2", series = "Machine Learning Tools in Fluid Mechanics", address = "Braunschweig, Germany", edition = "First edition", keywords = "genetic algorithms, genetic programming, MATLAB, Octave", file = ":https://leopard.tu-braunschweig.de/servlets/MCRZipServlet/dbbs_derivate_00049782:TYPE", language = "en", URL = "https://leopard.tu-braunschweig.de/receive/dbbs_mods_00071130", URL = "https://leopard.tu-braunschweig.de/servlets/MCRFileNodeServlet/dbbs_derivate_00049782/2022_xMLC_CornejoMaceda.pdf", URL = "https://doi.org/10.24355/dbbs.084-202007031022-0", DOI = "doi:10.24355/dbbs.084-202208220937-0", code_url = "https://github.com/gycm134/xMLC", size = "96 pages", abstract = "xMLC is the second book of this Machine Learning Tools in Fluid Mechanics Series and focuses on Machine Learning Control (MLC). The objectives of this book are two-fold: First, provide an introduction to MLC for students, researchers, and newcomers on the field; and second, share an open-source code, xMLC, to automatically learn open and closed-loop control laws directly in the plant with only a few executable commands", } @InProceedings{cornejo-maceda:2024:GECCOcomp, author = "Guy Y. {Cornejo Maceda} and Zhutao Jiang and Francois Lusseyran and Bernd R. Noack", title = "Fast {Self-Learning} of Turbulence Feedback Laws Using {Gradient-Enriched} Machine Learning Control", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion", year = "2024", editor = "Ting Hu and Aniko Ekart", pages = "495--498", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, hybrid method, feedback control, downhill simplex: Poster", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3654395", size = "4 pages", abstract = "We apply genetic programming (GP) to solve the most complex turbulence control problems. We simultaneously optimize up to dozens of parameters and dozens of control laws, listening to dozens of sensor signals with 100 to 1000 short test runs. Unlike reinforcement learning, our implementation of GP does not require any meta parameter tuning and is many orders of magnitudes faster than vanilla versions thanks to numerous enablers: 1. Parameter optimization over many dozens of experiments and simulations. 2. Smart formulation of control laws by preprocessing inputs and outputs. 3. Gradient-enriched simplex optimization of promising subspace. This work focuses on the resulting algorithm, referred to as gradient-enriched Machine Learning Control (gMLC). The applications comprise learning a multiple-input multiple-output control law to stabilize the flow past a cluster of three rotating cylinders in numerical simulations and the stabilization of the shear layer in an open cavity flow experiment. The learning acceleration achieved by gMLC opens the path to complex and time-limited experiments, including evaluation in varying operating conditions and optimization of distributed-input distributed-output control laws, i.e., functions with O(100) inputs and outputs.", notes = "GECCO-2024 GP A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{corney:1999:NSMUGP, author = "David Corney and Ian Parmee", title = "N-Dimensional Surface Mapping Using Genetic Programming", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1230", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-424.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-424.ps", URL = "http://dcorney.com/papers/gecco_surfaceGP.pdf", size = "1 page", abstract = "This work introduces an extension to Genetic Programming (GP), known as GP-UDF which uses multiple User-Defined Functions (UDFs) to solve surface mapping problems. UDFs are high level primitives, such as polynomials and Gaussian hills, which simplify mapping and aid human interpretation of GP results. Preliminary results show that although UDFs do not improve GP accuracy, they may aid in landscape classification.", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99) MSc Thesis -- text from http://www.cs.ucl.ac.uk/staff/D.Corney/MSc_thesis_abstract.html N-Dimensional Surface Mapping Using Genetic Programming This work introduces an extension to Genetic Programming (GP) known as {"}GP-UDF{"} which uses multiple User-Defined Functions (UDFs) to solve surface-mapping problems. These UDFs are high-level primitives, such as hills and polynomials, which compress the information required to map a surface. UDFs can be used to add real-world knowledge to a genetic search, and also to analyse and classify high-dimensional surfaces. GP-UDF also produces more readable solutions than standard GP. The results show that, for the problems considered, GP-UDF does not produce more accurate models than standard GP. However, the results also suggest that GP-UDF could be used as a {"}landscape classifier{"}, a tool for analysing high-dimensional surfaces to identify characteristic features. An important consideration in systems identification is the transparency (i.e. readability), of a model. GP-UDF is compared with neural networks (both MLP and RBF networks), and is shown to be far more readable, with the cost of being less accurate.", } @InProceedings{Cornforth:2012:GECCO, author = "Theodore Cornforth and Hod Lipson", title = "Symbolic regression of multiple-time-scale dynamical systems", booktitle = "GECCO '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference", year = "2012", editor = "Terry Soule and Anne Auger and Jason Moore and David Pelta and Christine Solnon and Mike Preuss and Alan Dorin and Yew-Soon Ong and Christian Blum and Dario Landa Silva and Frank Neumann and Tina Yu and Aniko Ekart and Will Browne and Tim Kovacs and Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and Giovanni Squillero and Nicolas Bredeche and Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and Martin Pelikan and Silja Meyer-Nienberg and Christian Igel and Greg Hornby and Rene Doursat and Steve Gustafson and Gustavo Olague and Shin Yoo and John Clark and Gabriela Ochoa and Gisele Pappa and Fernando Lobo and Daniel Tauritz and Jurgen Branke and Kalyanmoy Deb", isbn13 = "978-1-4503-1177-9", pages = "735--742", keywords = "genetic algorithms, genetic programming", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330163.2330266", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Genetic programming has been successfully used for symbolic regression of time series data in a wide variety of applications. However, previous approaches have not taken into account the presence of multiple-time-scale dynamics despite their prevalence in both natural and artificial dynamical systems. Here, we propose an algorithm that first decomposes data from such systems into components with dynamics at different time scales and then performs symbolic regression separately for each scale. Results show that this divide-and-conquer approach improves the accuracy and efficiency with which genetic programming can be used to reverse-engineer multiple-time-scale dynamical systems.", notes = "Also known as \cite{2330266} GECCO-2012 A joint meeting of the twenty first international conference on genetic algorithms (ICGA-2012) and the seventeenth annual genetic programming conference (GP-2012)", } @Article{Cornforth:2013:GPEM, author = "Theodore W. Cornforth and Hod Lipson", title = "Inference of hidden variables in systems of differential equations with genetic programming", journal = "Genetic Programming and Evolvable Machines", year = "2013", volume = "14", number = "2", pages = "155--190", month = jun, keywords = "genetic algorithms, genetic programming, Ordinary differential equations, Hidden variables, Modelling, Symbolic identification", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-012-9175-4", size = "36", abstract = "The data-driven modelling of dynamical systems is an important scientific activity, and many studies have applied genetic programming (GP) to the task of automatically constructing such models in the form of systems of ordinary differential equations (ODEs). These previous studies assumed that data measurements were available for all variables in the system, whereas in real-world settings, it is typically the case that one or more variables are unmeasured or hidden. Here, we investigate the prospect of automatically constructing ODE models of dynamical systems from time series data with GP in the presence of hidden variables. Several examples with both synthetic and physical systems demonstrate the unique challenges of this problem and the circumstances under which it is possible to reverse-engineer both the form and parameters of ODE models with hidden variables.", notes = "Error = normalised scaled mean absolute error (sum absolute error divided by observed standard deviation). GP individual = n trees (for n 'equations and dynamic variables'). Trees limited to depth five. Pop=96. Steady state population but (multi-objective) selection against old guys 'Age fitness Pareto Algorithm' \cite{Schmidt:2010:GPTP} \cite{Schmidt:2010:gecco}. Hill climbing to choose constants. 'Pareto hall of fame'. Evolved GP trees simplified by Matlab symbolic math toolbox.", } @PhdThesis{Cornforth:thesis, author = "Theodore Cornforth", title = "Data-Driven, Free-Form Modeling Of Biological Systems", school = "Cornell University", year = "2014", address = "USA", month = "27 " # jan, keywords = "genetic algorithms, genetic programming, Computational Biology", URL = "http://hdl.handle.net/1813/36187", URL = "http://dl.acm.org/citation.cfm?id=2604769", abstract = "The quantity of data available to scientists in all disciplines is increasing at an exponential rate, yet the insight necessary to distil data into scientific knowledge must still be supplied by human experts. This widening gap between data and insight can be bridged with data-driven modelling, in which computational methods shift much of the work in creating models from humans to computers. Traditional approaches to data-driven modeling require that the form of the model be fixed in advance, which requires substantial human effort and limits the complexity of problems that can be addressed. In contrast, a newer approach to automated modelling based on evolutionary computation (EC) removes such restrictions on the form of models. This free-form modelling has the potential both to reduce human effort for routine modelling and to make complex problems more tractable. Although major advances in EC-based modelling have been made in recent years, many challenges remain. These challenges include three features often seen in biological systems: complex nonlinear behaviour, multiple time scales, and hidden variables. This work addresses these challenges by developing new approaches to EC based modelling, with applications to neuroscience, systems biology, ecology, and other fields. The contributions of this work consist of three primary lines of research. In the first line of research, EC-based methods for the automated design of analogue electrical circuits are adapted for the modelling of electrical systems studied in neurophysiology that display complex, nonlinear behavior, such as ion channels. In the second line of research, EC-based methods for symbolic modelling are extended to facilitate the modelling of dynamical systems with multiple time scales, such as those found throughout ecology and other fields. Finally, in the third line of research, established EC-based algorithms are extended with the capability to model dynamical systems as systems of differential equations with hidden variables, which can contribute in an essential way to the observed dynamics of a physical system yet historically have presented a particularly difficult challenge to automated modelling.", notes = "Is this GP? Supervised by Hod Lipson", } @InProceedings{cad_sac98, author = "F. Corno and M. {Sonza Reorda} and G. Squillero", title = "The Selfish Gene Algorithm: a New Evolutionary Optimization Strategy", booktitle = "SAC: ACM Symposium on Applied Computing", year = "1998", pages = "349--355", keywords = "Genetic Algorithms, Approximate Methods, Equivalence Checking, Evolutionary Algorithms, Selfish Gene, Gate-Level, Simulation-Based Approaches", URL = "http://www.cad.polito.it/FullDB/exact/sac98.html", URL = "http://www.cad.polito.it/pap/db/sac98.pdf", abstract = "This paper proposes a new general approach for optimization algorithms in the Evolutionary Computation field. The approach is inspired by the Selfish Gene theory, an interpretation of the Darwinian theory given by the biologist Richard Dawkins, in which the basic element of evolution is the gene, rather than the individual. The paper defines the Selfish Gene Algorithm, that implements such a view of the evolution mechanism. We tested the approach by implementing a Selfish Gene Algorithm on a case study, and we found better results than those provided by a Genetic Algorithm on the same problem and with the same fitness function.", } @InProceedings{cad_iccd98a, author = "F. Corno and M. {Sonza Reorda} and G. Squillero", title = "{VEGA}: A Verification Tool Based on Genetic Algorithms", booktitle = "ICCD: International Conference on Circuit Design", year = "1998", pages = "321--326", address = "Austin, TX, USA", month = "05-07 " # oct, keywords = "genetic algorithms, genetic programming, EHW", broken = "http://www.cad.polito.it/FullDB/exact/iccd98a.html", broken = "http://www.cad.polito.it/pap/db/iccd98a.pdf", DOI = "doi:10.1109/ICCD.1998.727069", size = "6 pages", abstract = "While modern state-of-the-art optimization techniques can handle designs with up to hundreds of flip-flops, equivalence verification is still a challenging task in many industrial design flows. This paper presents a new verification methodology that, while sacrificing exactness, is able to handle larger circuits and give designers the opportunity to trade off CPU time with confidence on the result. The proposed methodology is able to fruitfully support an exact verification tool, dramatically increasing the confidence on the validity of an optimization process. A prototypical tool has been developed and preliminary experimental results that support this claim are shown in the paper.", notes = "compare to AQUILA", } @InProceedings{corno:2000:avpi, author = "Fulvio Corno and Matteo {Sonza Reorda} and Giovanni Squillero", title = "Automatic Validation of Protocol Interfaces Described in {VHDL}", booktitle = "Real-World Applications of Evolutionary Computing", year = "2000", editor = "Stefano Cagnoni and Riccardo Poli and George D. Smith and David Corne and Martin Oates and Emma Hart and Pier Luca Lanzi and Egbert Jan Willem and Yun Li and Ben Paechter and Terence C. Fogarty", volume = "1803", series = "LNCS", pages = "205--214", address = "Edinburgh", publisher_address = "Berlin", month = "17 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, ASIC, Approximate Methods, Evolutionary Algorithms, Gate-Level, Low Power, Selfish Gene, Simulation-Based Approaches", ISBN = "3-540-67353-9", URL = "http://www.cad.polito.it/FullDB/exact/evotel2000a.html", DOI = "doi:10.1007/3-540-45561-2_20", abstract = "In present days, most of the design activity is performed at a high level of abstraction, thus designers need to be sure that their designs are syntactically and semantically correct before starting the automatic synthesis process. The goal of this paper is to propose an automatic input pattern generation tool able to assist designers in the generation of a test bench for difficult parts of small or medium-sized digital protocol interfaces. The proposed approach exploit a Genetic Algorithm connected to a commercial simulator for cultivating a set of input sequence able to execute given statements in the interface description. The proposed approach has been evaluated on the new ITC-99 benchmark set, a collection of circuits offering a wide spectrum of complexity. Experimental results show that some portions of the circuits remained uncovered, and the subsequent manual analysis allowed identifying design redundancies.", old_abstract = "Modern VLSI design methodologies and manufacturing technologies are making circuits increasingly fast. The quest for higher circuit performance and integration density stems from fields such as the telecommunication one where high speed and capability of dealing with large data sets is mandatory. The design of high-speed circuits is a challenging task, and can be carried out only if designers can exploit suitable CAD tools. Among the several aspects of high-speed circuit design, controlling power consumption is today a major issue for ensuring that circuits can operate at full speed without damages. In particular, tools for fast and accurate estimation of power consumption of high-speed circuits are required. In this paper we focus on the problem of predicting the maximum power consumption of sequential circuits. We formulate the problem as a constrained optimization problem, and solve it resorting to an evolutionary algorithm. Moreover, we empirically assess the effectiveness of our problem formulation with respect to the classical unconstrained formulation. Finally, we report experimental results assessing the effectiveness of the prototypical tool we implemented.", notes = "EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM, EvoRob, and EvoFlight, Edinburgh, Scotland, UK, April 17, 2000 Proceedings http://evonet.lri.fr/evoweb/resources/books_journals/record.php?id=61 2000_Book_Real-WorldApplicationsOfEvolut.pdf LNCS 1803", } @InProceedings{Corno:2001:DATE, author = "F. Corno and M. {Sonza Reorda} and G. Squillero and M. Violante", title = "On the test of microprocessor {IP} cores", booktitle = "Proceedings of Design, Automation and Test in Europe Conference and Exhibition 2001", year = "2001", pages = "209--213", address = "Munich, Germany", month = "13-16 " # mar, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", URL = "http://www.date-conference.com/conference/instructions/gl_paper04c_2.pdf", DOI = "doi:10.1109/DATE.2001.915026", size = "5 pages", abstract = "Testing is a crucial issue in SOC development and production process. A popular solution for SOCs that include microprocessor cores is based on making them execute a test program. Thus, implementing a very attractive BIST solution. This paper describes a method for the generation of effective programs for the self-test of a processor. The method can be partially automated and combines ideas from traditional functional approaches and from the ATPG field. We assess the feasibility and effectiveness of the method by applying it to a 8051 core", notes = "Posted online: 2002-08-07 00:20:42.0 \cite{squillero:2005:GPEM} p249 says 'The approach relied on a library of fragments of code carefully and skillfully written by hand, called macros. The optimal sequence of macros was heuristically determined, and then a genetic algorithm optimized their parameters. The approach is quite effective, but hardly scalable. An Intel i8051, a very simple microprocessor, required 213 macros; and the macro list was carefully compiled by an experienced engineer in two working days.'", } @InProceedings{corno:2002:emctpi, author = "F. Corno and G. Cumani and M. {Sonza Reorda} and G. Squillero", title = "Efficient Machine-Code Test-Program Induction", booktitle = "Proceedings of the 2002 Congress on Evolutionary Computation CEC2002", year = "2002", editor = "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and Mark Shackleton", pages = "1486--1491", address = "Honolulu, Hawaii, USA", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "12-17 " # may, organisation = "IEEE Neural Network Council (NNC), Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, DAG, ATPG, Approximate Methods, Evolutionary Algorithms, Micro-Processors, Simulation-Based Approaches, SPARC processor, assembly-level semantics, conventions, database, device testing, directed acyclic graphs, evaluation speed, formalisms, genetic programming, graph nodes, instruction sets, integrated circuit manufacturing industries, machine-code test-program induction, macro library, microprocessors, registers, source-code programs, system on chip, target processor, test function, automatic test software, computer testing, directed graphs, instruction sets, integrated circuit manufacture, integrated circuit testing, macros, microprocessor chips, microprogramming, software libraries", ISBN = "0-7803-7278-6", URL = "http://www.cad.polito.it/pap/db/cec2002.pdf", URL = "http://citeseer.ist.psu.edu/502344.html", DOI = "doi:10.1109/CEC.2002.1004462", size = "6 pages", abstract = "Technology advances allow integrating on a single chip entire system, including memories and peripherals. The test of these devices is becoming a major issue for manufacturing industries. This paper presents a methodology for inducing test-programs similar to genetic programming. However, it includes the ability to explicitly specify registers and resorts to directed acyclic graphs instead of trees. Moreover, it exploits a database containing the assembly-level semantic associated to each graph node. This approach is extremely efficient and versatile: candidate solutions are translated into source-code programs allowing millions of evaluations per second. The proposed approach is extremely versatile: the macro library allows easily changing target processor and environment. The approach was verified on three processors with different instruction sets, different formalisms and different conventions. A complete set of experiments on a test function are also reported for the SPARC processor.", notes = "CEC 2002 - A joint meeting of the IEEE, the Evolutionary Programming Society, and the IEE. Held in connection with the World Congress on Computational Intelligence (WCCI 2002) Also known as \cite{Corno:2002:EMTI} \cite{squillero:2005:GPEM} says corno:2002:emctpi uses straightforward mu+lambda evolution.", } @InProceedings{corno:2002:ATS, author = "Fulvio Corno and Gianluca Cumani and Matteo {Sonza Reorda} and Giovanni Squillero", title = "Evolutionary test program induction for microprocessor design verification", booktitle = "Proceedings of the 11th Asian Test Symposium (ATS '02)", year = "2002", pages = "368--373", month = "18-20 " # nov, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", ISSN = "1081-7735", URL = "http://www.cad.polito.it/pap/db/ats02.pdf", URL = "http://citeseer.ist.psu.edu/574157.html", DOI = "doi:10.1109/ATS.2002.1181739", size = "6 pages", abstract = "Design verification is a crucial step in the design of any electronic device. Particularly when microprocessor cores are considered, devising appropriate test cases may be a difficult task. This paper presents a methodology able to automatically induce a test program for maximising a given verification metric. The methodology is based on an evolutionary paradigm and exploits a syntactical description of microprocessor assembly language and an RT-level functional model. Experimental results show the effectiveness of the approach.", notes = "Posted online: 2003-02-28 18:15:31.0 Cited by \cite{squillero:2005:GPEM}", } @InProceedings{oai:CiteSeerPSU:573140, title = "Automatic Test Program Generation for Pipelined Processors", author = "F. Corno and G. Cumani and M. {Sonza Reorda} and G. Squillero", publisher = "ACM", year = "2003", bibsource = "DBLP, http://dblp.uni-trier.de", booktitle = "Proceedings of the 2003 ACM Symposium on Applied Computing (SAC)", address = "Melbourne, FL, USA", month = "9-12 " # mar, keywords = "genetic algorithms, genetic programming", citeseer-isreferencedby = "oai:CiteSeerPSU:219188; oai:CiteSeerPSU:183962; oai:CiteSeerPSU:139723; oai:CiteSeerPSU:98608", citeseer-references = "oai:CiteSeerPSU:472349; oai:CiteSeerPSU:276822; oai:CiteSeerPSU:303540; oai:CiteSeerPSU:212034; oai:CiteSeerPSU:186935", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:573140", rights = "unrestricted", URL = "http://www.cad.polito.it/pap/db/sac03.pdf", URL = "http://citeseer.ist.psu.edu/573140.html", abstract = "The continuous advances in micro-electronics design are creating a significant challenge to design validation in general, but tackling pipelined microprocessors is remarkably more demanding. This paper presents a methodology to automatically induce a test program for a microprocessor maximising a given verification metric. The approach exploits a new evolutionary algorithm, close to Genetic Programming, able to cultivate effective assembly language programs. The proposed methodology was used to verify the DLX/pII, an open-source processor with a 5-stage pipeline. Code-coverage was adopted in the paper, since it can be considered the required starting point for any simulation-based functional verification processes. Experimental results clearly show the effectiveness of the approach.", } @InProceedings{corno:2003:ICES, author = "F. Corno and F. Cumani and G. Squillero", title = "Exploiting Auto-adaptive $\mu$-{GP} for Highly Effective Test Programs Generation", booktitle = "Evolvable Systems: From Biology to Hardware, Fifth International Conference, ICES 2003", year = "2003", editor = "Andy M. Tyrrell and Pauline C. Haddow and Jim Torresen", volume = "2606", series = "LNCS", pages = "262--273", address = "Trondheim, Norway", month = "17-20 " # mar, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-00730-X", DOI = "doi:10.1007/3-540-36553-2_24", abstract = "Integrated-circuit producers are shoved by competitive pressure; new devices require increasingly complex verifications to be performed at increasing pace. This paper presents a methodology to automatically induce a test program for a microprocessor that maximizes a given verification metric. The methodology is based on an auto-adaptive evolutionary algorithm and exploits a syntactical description of microprocessor assembly language and an RT-level functional model. Experimental results clearly show the effectiveness of the approach. Comparisons reveal how auto-adaptive mechanisms dramatically enhance both performances and quality of the results.", notes = "ICES-2003", } @InProceedings{corno03, author = "F. Corno and G. Squillero", title = "An Enhanced Framework for Microprocessor Test-Program Generation", booktitle = "Genetic Programming, Proceedings of EuroGP'2003", year = "2003", editor = "Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa", volume = "2610", series = "LNCS", pages = "307--316", address = "Essex", publisher_address = "Berlin", month = "14-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-00971-X", URL = "http://www.cad.polito.it/pap/db/eurogp03.pdf", DOI = "doi:10.1007/3-540-36599-0_28", abstract = "Test programs are fragment of code, but, unlike ordinary application programs, they are not intended to solve a problem, nor to calculate a function. Instead, they are supposed to give information about the machine that actually executes them. Today, the need for effective test programs is increasing, and, due to the inexorable increase in the number of transistor that can be integrated onto a single silicon die, devising effective test programs is getting more problematical. This paper presents GP, an efficient and versatile approach to test-program generation based on an evolutionary algorithm. The proposed methodology is highly versatile and improves previous approaches, allowing the test-program generator generating complex assembly programs that include subroutines calls.", notes = "EuroGP'2003 held in conjunction with EvoWorkshops 2003", size = "10 pages", } @InProceedings{corno:2004:oteocw, title = "On The Evolution of Corewar Warriors", author = "Fulvio Corno and Ernesto Sanchez and Giovanni Squillero", pages = "133--138", booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary Computation", year = "2004", publisher = "IEEE Press", month = "20-23 " # jun, address = "Portland, Oregon", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Evolutionary Computation and Games", URL = "http://www.cad.polito.it/pap/db/cec2004b.pdf", DOI = "doi:10.1109/CEC.2004.1330848", size = "6 page", abstract = "This paper analyzes corewar, a peculiar computer game where different programs fight in the memory of a virtual computer. An evolutionary assembly-program generator, is used to evolve efficient programs, and the game is exploited to evaluate new evolutionary techniques. The paper introduces a new migration model that exploits the polarization effect and a new hierarchical coarse-grained approach applicable whenever the final goal can be seen as a combination of semi-independent sub goals. Additionally, two general enhancements are proposed. Analyzed techniques are orthogonal and broadly applicable to different real-life contexts. Experimental results show that all these techniques are able to outperform a previous approach.", notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.", } @Article{Corno:2005:ieeeP, author = "F. Corno and E. Sanchez and M. S. Reorda and G. Squillero", title = "Automatic test generation for verifying microprocessors", journal = "IEEE Potentials", year = "2005", volume = "24", number = "1", pages = "34--37", month = feb # "-" # mar, keywords = "genetic algorithms, genetic programming", ISSN = "0278-6648", DOI = "doi:10.1109/MP.2005.1405800", abstract = "A pipelined processor with a high-level behavioural HDL description is presented in this paper. It generates a set of effective test programs by using a simulator, which is able to evaluate with respect to an RTL coverage metric. The proposed optimiser is based on a technique called microGP, an evolutionary system able to automatically device and optimizes the program written in an assembly language. Quantitative coverage measurement presented will guide the test-program generation. The approach is fully automatic and broadly applicable. The minimal test set with the programmable coverage is attained.", } @InProceedings{Corns:2006:CEC, author = "Steven M. Corns and Daniel A. Ashlock and Douglas S. McCorkle and Kenneth Mark Bryden", title = "Improving Design Diversity Using Graph Based Evolutionary Algorithms", booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary Computation", year = "2006", editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas", pages = "1037--1043", address = "Vancouver", month = "16-21 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9487-9", DOI = "doi:10.1109/CEC.2006.1688327", size = "7 pages", abstract = "Graph based evolutionary algorithms (GBEAs) have been shown to have superior performance to evolutionary algorithms on a variety of evolutionary computation test problems as well as on some engineering applications. One of the motivations for creating GBEAs was to produce a diversity of solutions with little additional computational cost. This paper tests that feature of GBEAs on three problems: a real-valued multi-modal function of varying dimension, the plus-one-recall-store (PORS) problem, and an applied engineering design problem. For all of the graphs studied the number of different solutions increased as the connectivity of the graph underlying the algorithm decreased. This indicates that the choice of graph can be used to control the diversity of solutions produced. The availability of multiple solutions is an asset in a product realization system, making it possible for an engineer to explore design alternatives.", notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D. IEEE Xplore gives pages = {"}333--339{"}", } @Article{Corns:2017:GPEM, author = "Steven Michael Corns", title = "{James Keller, Derong Liu, and David Fogel}: Fundamentals of computational intelligence: neural networks, fuzzy systems, and evolutionary computation", journal = "Genetic Programming and Evolvable Machines", year = "2017", volume = "18", number = "1", pages = "119--120", month = mar, note = "Book review", keywords = "genetic algorithms, EC, EHW, fuzzy, ANN", ISSN = "1389-2576", URL = "https://rdcu.be/dR8i0", DOI = "doi:10.1007/s10710-017-9285-0", size = "2 pages", notes = "John Wiley and Sons, 2016, 378 pp, ISBN 978-1-110-21434-2", } @InProceedings{correa:2022:IS, author = "Rafael F. R. Correa and Heder S. Bernardino and Joao M. {de Freitas} and Stenio S. R. F. Soares and Luciana B. Goncalves and Lorenza L. O. Moreno", title = "A Grammar-based Genetic Programming {Hyper-Heuristic} for Corridor Allocation Problem", booktitle = "Brazilian Conference on Intelligent Systems, BRACIS 2022, part 1", year = "2022", editor = "Joao Carlos Xavier-Junior and Ricardo Araujo Rios", volume = "13653", series = "Lecture Notes in Computer Science", pages = "504--519", address = "Campinas, Brazil", month = nov # " 28-" # dec # " 1", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-031-21686-2", URL = "http://link.springer.com/chapter/10.1007/978-3-031-21686-2_35", DOI = "doi:10.1007/978-3-031-21686-2_35", abstract = "Layout problems are the physical arrangement of facilities along a given area commonly used in practice. The Corridor Allocation Problem (CAP) is a class of layout problems in which no overlapping of rooms is allowed, no empty spaces are allowed between the rooms, and the two first facilities (one on each side) are placed on zero abscissa. This combinatorial problem is usually solved using heuristics, but designing and selecting the appropriate parameters is a complex task. Hyper-Heuristic can be used to alleviate this task by generating heuristics automatically. Thus, we propose a Grammar-based Genetic Programming Hyper-Heuristic (GGPHH) to generate heuristics for CAP. We investigate (i) the generation of heuristics using a subset of the instances of the problem and (ii) using a single instance. The results show that the proposed approach generates competitive heuristics, mainly when a subset of instances are used. Also, we found a single instance that can be used to generate heuristics that generalize to other cases.", } @Article{Correia:2020:IPL, author = "Alexandre Correia and Juliano Iyoda and Alexandre Mota", title = "Combining model finder and genetic programming into a general purpose automatic program synthesizer", journal = "Information Processing Letters", year = "2020", volume = "154", pages = "105866", month = feb, keywords = "genetic algorithms, genetic programming, Program synthesis, Alloy*, Programming languages, PSMF2, Fibonacci", ISSN = "0020-0190", URL = "http://www.sciencedirect.com/science/article/pii/S0020019019301498", DOI = "doi:10.1016/j.ipl.2019.105866", abstract = "Program synthesis aims to mechanize the task of programming from the user intent (expressed in various forms like pre/post conditions, examples, sketches, etc). There are many approaches to program synthesis that are usually implemented in isolation: deductive, syntax-based, inductive, etc. In this paper, we describe PSMF2, a program synthesizer that combines model finder and genetic programming. PSMF2 takes as user intent examples and a soft sketch: a new kind of user intent defined as a set of commands that must appear in the synthesized program (and that are in no particular order of execution). The output of PSMF2 is a general purpose imperative program. The combination of inductive synthesis and genetic programming has allowed PSMF2 to synthesize 7 programs (IntSQRT, Majority of 5, Majority of 8, Max of 4, Modulo, Factorial, and Fibonacci) found in the SyGuS competition, the iJava and IntroClass, and the Genetic programming communities. We carried out an empirical evaluation on the synthesis time of these 7 programs and the mean time varied from 56.4 seconds (Majority of 5) to 15.9 minutes (Fibonacci).", notes = "the HOL theorem proving system? also known as \cite{CORREIA2020105866},", } @InProceedings{Correia:2012:GECCOcomp, author = "Joao Correia and Penousal Machado and Juan Romero", title = "Improving haar cascade classifiers through the synthesis of new training examples", booktitle = "GECCO Companion '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion", year = "2012", editor = "Terry Soule and Anne Auger and Jason Moore and David Pelta and Christine Solnon and Mike Preuss and Alan Dorin and Yew-Soon Ong and Christian Blum and Dario Landa Silva and Frank Neumann and Tina Yu and Aniko Ekart and Will Browne and Tim Kovacs and Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and Giovanni Squillero and Nicolas Bredeche and Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and Martin Pelikan and Silja Meyer-Nienberg and Christian Igel and Greg Hornby and Rene Doursat and Steve Gustafson and Gustavo Olague and Shin Yoo and John Clark and Gabriela Ochoa and Gisele Pappa and Fernando Lobo and Daniel Tauritz and Jurgen Branke and Kalyanmoy Deb", isbn13 = "978-1-4503-1178-6", keywords = "genetic algorithms, Genetic programming: Poster", pages = "1479--1480", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330784.2331001", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "A Genetic Programming approach for the improvement of the performance of classifier systems through the synthesis of new training instances is explored. Genetic Programming is used to exploit shortcomings of classifiers systems and generate misclassified instances. The proposed approach performs multiple parallel evolutionary runs to generate a large number of potentially misclassified samples. A supervisor module determines which of the generated images have been misclassified and which should be added to the training set. New classifiers are trained based on the original training set augmented by the selected evolved instances. The results attained while using face detection classifiers are presented and discussed. Overall they indicate that significant improvements are attained when using multiple evolutionary runs.", notes = "Also known as \cite{2331001} Distributed at GECCO-2012. ACM Order Number 910122.", } @InProceedings{Correia:2014:SMGP, author = "Joao Correia and Penousal Machado", title = "Semantic Operators for Evolutionary Art", booktitle = "Semantic Methods in Genetic Programming", year = "2014", editor = "Colin Johnson and Krzysztof Krawiec and Alberto Moraglio and Michael O'Neill", address = "Ljubljana, Slovenia", month = "13 " # sep, note = "Workshop at Parallel Problem Solving from Nature 2014 conference", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.put.poznan.pl/kkrawiec/smgp2014/uploads/Site/Correia.pdf", size = "2 pages", notes = "SMGP 2014 http://www.cs.put.poznan.pl/kkrawiec/smgp/?n=Site.SMGP2014", } @InProceedings{Correia:2021:evomusart, author = "Joao Correia and Leonardo Vieira and Nereida Rodriguez-Fernandez and Juan Romero and Penousal Machado", title = "Evolving Image Enhancement Pipelines", booktitle = "10th International Conference on Computational Intelligence in Music, Sound, Art and Design, EvoMusArt 2021", year = "2021", month = "7-9 " # apr, editor = "Juan Romero and Tiago Martins and Nereida Rodriguez-Fernandez", series = "LNCS", publisher = "Springer Verlag", address = "virtual event", pages = "82--97", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Image enhancement, Image processing, Computer vision, Evolutionary computation", isbn13 = "978-3-030-72913-4", DOI = "doi:10.1007/978-3-030-72914-1_6", size = "16 pages", abstract = "Image enhancement is an image processing procedure in which the original information of the image is improved. It can be used to alter an image in several different ways, for instance, by highlighting a specific feature in order to ease post-processing analyses by a human or machine. In this work, we show our approach to image enhancement for digital real-estate-marketing. The aesthetic quality of the images for real-estate marketing is critical since it is the only input that clients have once browsing for options. Thus, improving and ensuring the aesthetic quality of the images is crucial for marketing success. The problem is that each set of images, even for the same real-estate item, is often taken under diverse conditions making it hard to find one solution that fits all. State of the art image enhancement pipelines applies a set of filters that tend to solve specific issues, so it is still hard to generalise that solves all type of issues encountered. With this in mind, we propose a Genetic Programming approach for the evolution of image enhancement pipelines, based on image filters from the literature. We report a set of experiments in image enhancement of real state images and analysed the results. The overall results suggest that it is possible to attain suitable pipelines that enhance the image visually and according to a set of image quality assessment metrics. The evolved pipelines show improvements across the validation metrics showing that it is possible to create image enhancement pipelines automatically. Moreover, during the experiments, some of the created pipelines end up creating non-photorealistic rendering effects in a moment of computational serendipity. Thus, we further analysed the different evolved non-photorealistic solutions, showing the potential of applying the evolved pipelines in other types of images.", notes = "http://www.evostar.org/2021/ EvoMusArt2021 held in conjunction with EuroGP'2021, EvoCOP2021 and EvoApplications2021", } @Article{correia:2022:AS, author = "Joao Correia and Nereida Rodriguez-Fernandez and Leonardo Vieira and Juan Romero and Penousal Machado", title = "Towards Automatic Image Enhancement with Genetic Programming and Machine Learning", journal = "Applied Sciences", year = "2022", volume = "12", number = "4", keywords = "genetic algorithms, genetic programming", ISSN = "2076-3417", URL = "https://www.mdpi.com/2076-3417/12/4/2212", DOI = "doi:10.3390/app12042212", abstract = "Image Enhancement (IE) is an image processing procedure in which the images original information is improved, highlighting specific features to ease post-processing analyses by a human or machine. State-of-the-art image enhancement pipelines apply solutions to fixed and static constraints to solve specific issues in isolation. In this work, an IE system for image marketing is proposed, more precisely, real estate marketing, where the objective is to enhance the commercial appeal of the images, while maintaining a level of realism and similarity with the original image. This work proposes a generic image enhancement pipeline that combines state-of-the-art image processing filters, Machine Learning methods, and Evolutionary approaches, such as Genetic Programming (GP), to create a dynamic framework for Image Enhancement. The GP-based system is trained to optimise 4 metrics: Neural Image Assessment (NIMA) technical and BRISQUE, which evaluate the technical quality of the images; and NIMA aesthetics and PhotoILike, that evaluate the commercial attractiveness. It is shown that the GP model was able to find the best image quality enhancement (0.97 NIMA Aesthetics), while maintaining a high level of similarity with the original images (Structural Similarity Index Measure (SSIM) of 0.88). The framework has better performance according to the image quality metrics than the off-the-shelf image enhancement tool and the frameworks isolated parts.", notes = "also known as \cite{app12042212}", } @Article{Correia:GPEM, author = "Joao Correia and Daniel Lopes and Leonardo Vieira and Nereida Rodriguez-Fernandez and Adrian Carballal and Juan Romero and Penousal Machado", title = "Experiments in evolutionary image enhancement with {ELAINE}", journal = "Genetic Programming and Evolvable Machines", year = "2022", volume = "23", number = "4", pages = "557--579", month = dec, note = "Special Issue: Evolutionary Computation in Art, Music and Design", keywords = "genetic algorithms, genetic programming, Image enhancement, Image processing, Computer vision, Evolutionary computation,", ISSN = "1389-2576", URL = "https://rdcu.be/cYSxw", DOI = "doi:10.1007/s10710-022-09445-9", size = "23 pages", abstract = "Image enhancement is an image processing procedure in which the image original information is refined, for example by highlighting specific features to ease post-processing analyses by a human or machine. This procedure remains challenging since each set of images is often taken under diverse conditions which makes it hard to find an image enhancement solution that fits all conditions. State-of-the-art image enhancement pipelines apply filters that solve specific issues; therefore, it is still hard to generalise these pipelines to all types of problems encountered. We have recently introduced a Genetic Programming approach named ELAINE (EvoLutionAry Image eNhancEment) for evolving image enhancement pipelines based on pre-defined image filters. In this paper, we showcase its potential to create solutions under a real-estate marketing scenario by comparing it with a manual approach and an existing tool for automatic image enhancement. The ELAINE obtained results far exceed those obtained by manual combinations of filters and by the one-click method, in all the metrics explored. We further explore the potential of creating non-photorealistic effects by applying the evolved pipelines to different types of images. The results highlight ELAINE potential to transform input images into either suitable real-estate images or non-photorealistic renderings, thus transforming contents and possibly enhancing its aesthetic appeal.", notes = "Department of Informatics Engineering, Centre for Informatics and Systems of the University of Coimbra, University of Coimbra, Coimbra, Portugal", } @InProceedings{Corso:2020:ITSC, author = "Anthony Corso and Mykel J. Kochenderfer", title = "Interpretable Safety Validation for Autonomous Vehicles", booktitle = "2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)", year = "2020", month = sep, keywords = "genetic algorithms, genetic programming, Trajectory, Grammar, Autonomous vehicles, Safety, Optimization, Time series analysis", DOI = "doi:10.1109/ITSC45102.2020.9294490", abstract = "An open problem for autonomous driving is how to validate the safety of an autonomous vehicle in simulation. Automated testing procedures can find failures of an autonomous system but these failures may be difficult to interpret due to their high dimensionality and may be so unlikely as to not be important. This work describes an approach for finding interpretable failures of an autonomous system. The failures are described by signal temporal logic expressions that can be understood by a human, and are optimized to produce failures that have high likelihood. Our methodology is demonstrated for the safety validation of an autonomous vehicle in the context of an unprotected left turn and a crosswalk with a pedestrian. Compared to a baseline importance sampling approach, our methodology finds more failures with higher likelihood while retaining interpretability.", notes = "Also known as \cite{9294490}", } @Article{Cortacero:2023:NatCommun, author = "Kevin Cortacero and Brienne McKenzie and Sabina Mueller and Roxana Khazen and Fanny Lafouresse and Gaelle Corsaut and Nathalie {Van Acker} and Francois-Xavier Frenois and Laurence Lamant and Nicolas Meyer and Beatrice Vergier and Dennis G. Wilson and Herve Luga and Oskar Staufer and Michael L. Dustin and Salvatore Valitutti and Sylvain Cussat-Blanc", title = "Evolutionary design of explainable algorithms for biomedical image segmentation", journal = "Nature Communications", year = "2023", volume = "14", pages = "article 7112", month = "06 " # nov, note = "Gold Humie award", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Kartezio, Cytotoxic T cells, Image processing, Machine learning", ISSN = "2041-1723", URL = "https://human-competitive.org/sites/default/files/entryform_5.txt", URL = "https://human-competitive.org/sites/default/files/paper_4.pdf", URL = "https://rdcu.be/dqvF0", DOI = "doi:10.1038/s41467-023-42664-x", slides_url = "https://human-competitive.org/sites/default/files/2024_gecco_humies.pptx", video_url = "https://mediaspace.msu.edu/media/Dennis+Wilson+Humies/1_8eteb4v4", code_url = "https://github.com/KevinCortacero/Kartezio", size = "18 pages", abstract = "An unresolved issue in contemporary biomedicine is the overwhelming number and diversity of complex images that require annotation, analysis and interpretation. Recent advances in Deep Learning have revolutionized the field of computer vision, creating algorithms that compete with human experts in image segmentation tasks. However, these frameworks require large human-annotated datasets for training and the resulting black box models are difficult to interpret. we introduce Kartezio, a modular Cartesian Genetic Programming-based computational strategy that generates fully transparent and easily interpretable image processing pipelines by iteratively assembling and parameterising computer vision functions. The pipelines thus generated exhibit comparable precision to state-of-the-art Deep Learning approaches on instance segmentation tasks, while requiring drastically smaller training datasets. This Few-Shot Learning method confers tremendous flexibility, speed, and functionality to this approach. We then deploy Kartezio to solve a series of semantic and instance segmentation problems, and demonstrate its utility across diverse images ranging from multiplexed tissue histopathology images to high resolution microscopy images. While the flexibility, robustness and practical utility of Kartezio make this fully explicable evolutionary designer a potential game-changer in the field of biomedical image processing, Kartezio remains complementary and potentially auxiliary to mainstream Deep Learning approaches.", notes = "filed patent; EP 22307041.8 See also arXiv https://arxiv.org/abs/2302.14762 2024_gecco_humies.pptx is huge perhaps try http://www.cs.ucl.ac.uk/staff/W.Langdon/gecco2024/humie_slides/Cortacero_2023_NatCommun_slides.pdf Leibniz Institute for New Materials, 66123, Saarbruecken, Germany", } @PhdThesis{Cortacero:thesis, author = "Kevin Cortacero", title = "Programmation Genetique Cartesienne pour la Segmentation d'Images Biomedicales", school = "Institut Universitaire du Cancer de Toulouse", year = "2023", address = "France", month = "8 " # nov, keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Kartezio, Image processing, Explainable Artificial Intelligence, XAI, Machine Learning, Instance Segmentation", URL = "https://theses.hal.science/tel-04747703", URL = "https://www.theses.fr/2023TOU30313", URL = "https://theses.hal.science/tel-04747703v1/file/2023TOU30313a.pdf", size = "266 pages", abstract = "Over the past decade, Artificial Intelligence (AI), and in particular Deep Learning (DL), has experienced exponential growth. Although the main focus has been on improving the accuracy of models, explainability, frugality and dataset size reduction are fundamental challenges that need to be addressed with increasing urgency. While precision remains paramount, this thesis research proposes a rebalancing of these metrics, in order to achieve strong model performance in the context of full transparency and Few-Shot learning. This thesis focuses upon the field of biomedical imaging and the segmentation of biological entities using computer vision. Herein, we introduce Kartezio, a modular Cartesian Genetic Programming (CGP)-based computational strategy that produces transparent and interpretable image processing algorithms through the iterative assembly and parametrisation of functions. The pipelines thus created demonstrate comparable performance to state-of-the-art DL approaches on image segmentation tasks, while requiring drastically smaller training datasets. This Few-Shot Learning and frugal method confers tremendous flexibility, as demonstrated herein through a series of biomedical Use Cases. In addition, this thesis explores several approaches to extract meaningful information from the structure of the generated algorithms, offering strategies to direct optimization of the algorithm in the future. Finally, this study proposes extensions of the published work wherein unlimited types of data can be integrated into a single multimodal evolutionary approach (MAGE). Through this detailed exploration, this work not only introduces a methodology but also offers an alternative or complement to dominant black-box DL paradigms. This suggests that, despite the complexity of state-of-the-art DL solutions, we may have previously underestimated frugal and transparent strategies such as CGP, particularly in the field of Image Processing. In biomedical imaging, where model predictions can have profound implications, the idea of using modern AI techniques should not be sidelined either due to computational or data constraints, or a lack of transparency. By showcasing CGP models through several practical applications, this thesis argues for the adoption of eXplainable Artificial Intelligence (XAI) in the biomedical domain.", notes = "HAL Id : tel-04747703 Supervisors: Salvatore Valitutti and Sylvain Cussat-Blanc", } @InProceedings{Cortes:2024:evoapplications, author = "Gabriel Cortes and Nuno Lourenco and Penousal Machado", title = "Towards Physical Plausibility in Neuroevolution Systems", booktitle = "27th International Conference, EvoApplications 2024", year = "2024", editor = "Stephen Smith and Joao Correia and Christian Cintrano", series = "LNCS", volume = "14635", publisher = "Springer", address = "Aberystwyth", month = "3-5 " # apr, pages = "76--90", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, grammatical evolution, ANN, Evolutionary Computation, Neuroevolution, Energy Efficiency", isbn13 = "978-3-031-56854-1", URL = "https://rdcu.be/dD0vz", DOI = "doi:10.1007/978-3-031-56855-8_5", notes = "http://www.evostar.org/2024/ EvoApplications2024 held in conjunction with EuroGP'2024, EvoCOP2024 and EvoMusArt2024", } @Article{Corus::TELO, author = "Dogan Corus and Pietro S. Oliveto", title = "On the Generalisation Performance of Geometric Semantic Genetic Programming for Boolean Functions: Learning Block Mutations", journal = "ACM Transactions on Evolutionary Learning and Optimization", year = "2024", volume = "4", number = "4", articleno = "25", month = dec, keywords = "genetic algorithms, genetic programming, Geometric Semantic Genetic Programming, Boolean functions, Runtime analysis", ISSN = "2688-299X", DOI = "doi:10.1145/3677124", size = "33 pages", abstract = "...we consider a hill-climber using the GSGP Fixed Block Mutation (FBM) operator for the domain of Boolean functions. We prove that the algorithm cannot evolve Boolean conjunctions of arbitrary size that are correct on unseen inputs chosen uniformly at random from the complete truth table i.e., it generalises poorly. Two algorithms based on the Varying Block Mutation (VBM) operator are proposed and analysed to address the issue. We rigorously prove that under the uniform distribution the first one can efficiently evolve any Boolean function of constant size with respect to the number of available variables, while the second one can efficiently evolve general conjunctions or disjunctions of any size without requiring prior knowledge of the target function class. An experimental analysis confirms the theoretical insights for realistic problem sizes and indicates the superiority of the proposed operators also for small parity functions not explicitly covered by the theory.", notes = "Department of Computer Engineering, Kadir Has University, Istanbul, 34083, Turkey https://dlnext.acm.org/journal/telo", } @InProceedings{conf/issre/CostaVPS05, title = "Modeling Software Reliability Growth with Genetic Programming", author = "Eduardo Oliveira Costa and Silvia Regina Vergilio and Aurora Trinidad Ramirez Pozo and Gustavo A. {de Souza}", year = "2005", pages = "171--180", booktitle = "16th International Symposium on Software Reliability Engineering (ISSRE 2005)", address = "Chicago, IL, USA", month = "8-11 " # nov, publisher = "IEEE Computer Society", bibdate = "2006-01-03", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/issre/issre2005.html#CostaVPS05", keywords = "genetic algorithms, genetic programming", ISBN = "0-7695-2482-6", DOI = "doi:10.1109/ISSRE.2005.29", abstract = "Reliability Models are very useful to estimate the probability of the software fail along the time. Several different models have been proposed to estimate the reliability growth, however, none of them has proven to perform well considering different project characteristics. In this work, we explore Genetic Programming (GP) as an alternative approach to derive these models. GP is a powerful machine learning technique based on the idea of genetic algorithms and has been acknowledged as a very suitable technique for regression problems. The main motivation to choose GP for this task is its capability of learning from historical data, discovering an equation with different variables and operators. experiment were conducted to confirm this hypotheses and the results were compared with traditional and Neural Network models.", } @InProceedings{conf/ictai/CostaPV06, title = "Using Boosting Techniques to Improve Software Reliability Models Based on Genetic Programming", author = "Eduardo Oliveira Costa and Aurora Pozo and Silvia Regina Vergilio", year = "2006", booktitle = "18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)", pages = "643--650", address = "Washington, D.C, USA", month = nov # " 13-15", publisher = "IEEE Computer Society", bibdate = "2007-01-04", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ictai/ictai2006.html#CostaPV06", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICTAI.2006.117", abstract = "Software reliability models are used to estimate the probability of a software fails along the time. They are fundamental to plan test activities and to ensure the quality of the software being developed. Two kind of models are generally used: time or test coverage based models. In our previous work, we successfully explored Genetic Programming (GP) to derive reliability models. However, nowadays Boosting techniques (BT) have been successfully applied with other Machine Learning techniques, including GP. BT merge several hypotheses of the training set to get better results. With the goal of improving the GP software reliability models, this work explores the combination GP and BT. The results show advantages in the use of the proposed approach.", } @InProceedings{Costa:2006:ICSMC, author = "Eduardo Oliveira Costa and Aurora Pozo", title = "A New Approach to Genetic Programming based on Evolution Strategies", booktitle = "IEEE International Conference on Systems, Man and Cybernetics, ICSMC '06", year = "2006", volume = "6", pages = "4832--4837", address = "Taipei, Taiwan", month = "8-11 " # oct, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", ISBN = "1-4244-0100-3", DOI = "doi:10.1109/ICSMC.2006.385070", abstract = "This paper proposes a new approach to induction of programs by Genetic Progranuning (GP) using the ideas of Evolutionary Strategies (ES). The goal of this work is to develop a variety of Genetic Programming algorithm by doing some modifications on the classical GP algorithm and adding some concepts of Evolutionary Strategies. The new approach was evaluated using two instances of the Symbolic Regression problem - the Binomial-3 problem (a tunably difficult problem), proposed in [5] and the Time Series problem (an application of symbolic regression) - and a problem of a different domain, the Santa Fe Artificial Ant problem. The results discovered were compared with the classical GP algorithm. The Symbolic Regression problems obtained excellent results and an improvement was detected, but this does not happened with the Artificial Ant problem.", notes = "Computer Science Department, Federal University of Parana (UFPR), PO Box 19081, 81531-970, Curitiba, Brazil,", } @InProceedings{Costa:2006:ICTAI, author = "Eduardo Oliveira Costa and Aurora Pozo", title = "A (mu + lambda) - {GP} Algorithm and its use for Regression Problems", booktitle = "8th IEEE International Conference on Tools with Artificial Intelligence, ICTAI '06", year = "2006", pages = "10--17", address = "Arlington, VA, USA", month = "13-15 " # nov, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", ISBN = "0-7695-2728-0", DOI = "doi:10.1109/ICTAI.2006.6", size = "8 pages", abstract = "The genetic programming (GP) is a powerful technique for symbolic regression. However, because it is a new area, many improvements can be obtained changing the basic behaviour of the method. In this way, this work develop a different genetic programming algorithm doing some modifications on the classical GP algorithm and adding some concepts of evolution strategies. The new approach was evaluated using two instances of symbolic regression problem - the binomial-3 problem (a tunably difficult problem), proposed in (J.M. Daida et al., 2001) and the problem of modelling software reliability growth (an application of symbolic regression). The discovered results were compared with the classical GP algorithm. The symbolic regression problems obtained excellent results and an improvement was detected using the proposed approach", notes = "Dept. of Comput. Sci., Fed. Univ. of Parana, Curitiba", } @Article{Costa:2007:ieeeTR, author = "Eduardo Oliveira Costa and Gustavo Alexandre {de Souza} and Aurora Trinidad Ramirez Pozo and Silvia Regina Vergilio", title = "Exploring Genetic Programming and Boosting Techniques to Model Software Reliability", journal = "IEEE Transactions on Reliability", year = "2007", volume = "56", number = "3", pages = "422--434", month = sep, keywords = "genetic algorithms, genetic programming, Fault prediction, machine learning techniques, software reliability models", DOI = "doi:10.1109/TR.2007.903269", ISSN = "0018-9529", abstract = "Software reliability models are used to estimate the probability that a software fails at a given time. They are fundamental to plan test activities, and to ensure the quality of the software being developed. Each project has a different reliability growth behaviour, and although several different models have been proposed to estimate the reliability growth, none has proven to perform well considering different project characteristics. Because of this, some authors have introduced the use of Machine Learning techniques, such as neural networks, to obtain software reliability models. Neural network-based models, however, are not easily interpreted, and other techniques could be explored. In this paper, we explore an approach based on Genetic Programming, and also propose the use of Boosting techniques to improve performance. We conduct experiments with reliability models based on time, and on test coverage. The obtained results show some advantages of the introduced approach. The models adapt better to the reliability curve, and can be used in projects with different characteristics.", } @Article{Costa:2010:ieeeTR, author = "Eduardo Oliveira Costa and Aurora Trinidad Ramirez Pozo and Silvia Regina Vergilio", title = "A Genetic Programming Approach for Software Reliability Modeling", journal = "IEEE Transactions on Reliability", year = "2010", keywords = "genetic algorithms, genetic programming, Fault prediction, machine learning techniques, software reliability models, SBSE", abstract = "Genetic Programming (GP) models adapt better to the reliability curve when compared with other traditional, and non-parametric models. In a previous work, we conducted experiments with models based on time, and on coverage. We introduced an approach, named Genetic Programming and Boosting (GPB), that uses boosting techniques to improve the performance of GP. This approach presented better results than classical GP, but required ten times the number of executions. Therefore, we introduce in this paper a new GP based approach, named $(mu+lambda)$ GP. To evaluate this new approach, we repeated the same experiments conducted before. The results obtained show that the $(mu+lambda)$ GP approach presents the same cost of classical GP, and that there is no significant difference in the performance when compared with the GPB approach. Hence, it is an excellent, less expensive technique to model software reliability.", DOI = "doi:10.1109/TR.2010.2040759", ISSN = "0018-9529", notes = "Also known as \cite{5409534}", } @InProceedings{costa:1999:GGOMINP, author = "Lino Costa and Pedro Oliveira", title = "GAs in Global Optimization of Mixed Integer Non-Linear Problems", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1773", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "real world applications, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-740.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-740.pdf", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Costa:1997:acsqrscgp, author = "Paolo Costa", title = "A Methodology for the Analysis of Complex Systems based on Qualitative Reasoning, Stochastic Complexity and Genetic Programming", booktitle = "Late Breaking Papers at the 1997 Genetic Programming Conference", year = "1997", editor = "John R. Koza", pages = "35--41", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California, 94305-3079, USA", month = "13--16 " # jul, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-206995-8", notes = "GP-97LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670", } @InProceedings{costelloe:2004:eurogp, author = "Dan Costelloe and Conor Ryan", title = "Genetic Programming for Subjective Fitness Function Identification", booktitle = "Genetic Programming 7th European Conference, EuroGP 2004, Proceedings", year = "2004", editor = "Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule", volume = "3003", series = "LNCS", pages = "259--268", address = "Coimbra, Portugal", publisher_address = "Berlin", month = "5-7 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-21346-5", DOI = "doi:10.1007/978-3-540-24650-3_24", abstract = "We address modelling fitness functions for Interactive Evolutionary Systems. Such systems are necessarily slow because they need human interaction for the fundamental task of fitness allocation. The research presented here demonstrates that Genetic Programming can be used to learn subjective fitness functions from human subjects, using historical data from an Interactive Evolutionary system for producing pleasing drum patterns. The results indicate that GP is capable of performing symbolic regression even when the number of training cases is substantially less than the number of inputs.", notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004", } @InProceedings{1277389, author = "Dan Costelloe and Conor Ryan", title = "Towards models of user preferences in interactive musical evolution", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "2254--2254", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p2254.pdf", DOI = "doi:10.1145/1276958.1277389", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, grammatical evolution, Real-World Applications: Poster, human factors, interactive evolution", abstract = "We describe the bottom-up construction of a system which aims to build models of human musical preferences with strong predictive power. We use Grammatical Evolution to construct models from toy datasets which mimic real world user-generated data. These models will ultimately substitute for the subjective fitness functions that human users employ during Interactive Evolution of melodies.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @InProceedings{Costelloe:2009:eurogp, author = "Dan Costelloe and Conor Ryan", title = "On Improving Generalisation in Genetic Programming", booktitle = "Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009", year = "2009", editor = "Leonardo Vanneschi and Steven Gustafson and Alberto Moraglio and Ivanoe {De Falco} and Marc Ebner", volume = "5481", series = "LNCS", pages = "61--72", address = "Tuebingen", month = apr # " 15-17", organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-01180-1", DOI = "doi:10.1007/978-3-642-01181-8_6", size = "12 pages", abstract = "This paper is concerned with the generalisation performance of GP. We examine the generalisation of GP on some well-studied test problems and also critically examine the performance of some well known GP improvements from a generalisation perspective. From this, the need for GP practitioners to provide more accurate reports on the generalisation performance of their systems on problems studied is highlighted. Based on the results achieved, it is shown that improvements in training performance thanks to GP-enhancements represent only half of the battle.", notes = "overfitting, Part of \cite{conf/eurogp/2009} EuroGP'2009 held in conjunction with EvoCOP2009, EvoBIO2009 and EvoWorkshops2009", } @PhdThesis{Costelloe:thesis, author = "Dan Costelloe", title = "Evolutionary Optimisation and Prediction in Subjective Problem Domains", school = "University of Limerick", year = "2009", address = "Limerick, Ireland", month = nov, keywords = "genetic algorithms, genetic programming", URL = "https://digitary.ul.ie/verifier/servlet/DocumentVerifierApp/template/VerifyDAT.vm?datid=k7aahpcxm1", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Costelloe_thesis.pdf", size = "158 pages", abstract = "Artificial Evolution is a powerful tool for generating realistic solutions to a large range of computationally difficult problems. It has been applied with great success to many optimisation problems in engineering and science, yet its application is not restricted to problems specific to these fields. The power of evolution can also be coupled with human supervision to tackle problems whose solutions must be (wholly or partly) subjectively evaluated. This thesis describes the design, implementation and use of Evolutionary-based system used for the evolution of such entities whose 'goodness' is commonly only subjectively defined. Additionally, this research investigates and tests formal models of subjective notions for a specific problem: the Interactive Evolution of music. It is demonstrated by this research how various evolutionary techniques can be used to generate and evolve pleasing musical sequences. It is also shown how similar techniques are used to build models of the subjective notions used by human users, when evaluating the goodness of musical pieces. The research presented here also makes it possible to understand what environmental conditions lead to the construction of artificial models that have good predictive power. Finally, an investigation of the generalisation performance of a specific Evolutionary technique, Genetic Programming, is presented in the context of more recently developed improvement techniques. It is demonstrated that any improvement must take generalisation performance into account in order to be considered a worthy addition to the field. It is also shown how a combination of recent improvement techniques make significant performance improvements on both artificial and real-world symbolic regression problems.", notes = "Supervisor: Dr. Conor Ryan", } @InProceedings{cotillon:2012:EuroGP, author = "Alban Cotillon and Philip Valencia and Raja Jurdak", title = "Android Genetic Programming Framework", booktitle = "Proceedings of the 15th European Conference on Genetic Programming, EuroGP 2012", year = "2012", month = "11-13 " # apr, editor = "Alberto Moraglio and Sara Silva and Krzysztof Krawiec and Penousal Machado and Carlos Cotta", series = "LNCS", volume = "7244", publisher = "Springer Verlag", address = "Malaga, Spain", pages = "13--24", organisation = "EvoStar", isbn13 = "978-3-642-29138-8", URL = "http://jurdak.com/eurogp12.pdf", DOI = "doi:10.1007/978-3-642-29139-5_2", size = "12 pages", keywords = "genetic algorithms, genetic programming, genetic improvement, AGP, Embedded, Smartphone", abstract = "Personalisation in smart phones requires adaptability to dynamic context based on application usage and sensor inputs. Current personalisation approaches do not provide sufficient adaptability to dynamic and unexpected context. This paper introduces the Android Genetic Programming Framework (AGP) as a personalisation method for smart phones. AGP considers the specific design challenges of smart phones, such as resource limitation and constrained programming environments. We demonstrate AGP's usefulness through empirical experiments on two applications: a news reader and energy efficient localisation. AGP successfully adapts application behaviour to user context.", notes = "Android open source, Java code. RSS reader. Online fitness monitoring. Online GP. Part of \cite{Moraglio:2012:GP} EuroGP'2012 held in conjunction with EvoCOP2012 EvoBIO2012, EvoMusArt2012 and EvoApplications2012", affiliation = "Autonomous Systems Laboratory, CSIRO ICT Centre, Brisbane, Australia", } @InProceedings{cotta:1996:edflc, author = "Carlos Cotta and E. Alba and J. M. Troya", title = "Evolutionary Design of Fuzzy Logic Controllers", booktitle = "Proceedings of the 1996 IEEE International Symposium on Intelligent Control", year = "1996", pages = "127--132", address = "Dearborn MI, USA", month = "15-18 Septmeber", publisher = "IEEE Control Systems Society", keywords = "genetic algorithms, genetic programming", URL = "http://www.lcc.uma.es/~ccottap/papers/isic96flc.pdf", size = "6 pages", abstract = "An evolutionary approach to fuzzy logic controller design is presented in this paper. We propose the use of a class of genetic algorithms to produce suboptimal fuzzy rule-bases (internally represented as constrained syntactic trees). This model has been applied to the cart centering problem. The obtained results show that a good parameterisation of the algorithm and an appropriate evaluation function lead to near-optimal solutions.", } @InProceedings{cotta:1999:ISDOFRTGR, author = "Carlos Cotta and Enrique Alba and Jose M. Troya", title = "Improving the Scalability of Dynastically Optimal Forma Recombination by Tuning the Granularity of the Representation", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "783", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/Ga-800.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-800.PS", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{cotta:ppsn2002:pp720, author = "Carlos Cotta and Pablo Moscato", title = "Inferring Phylogenetic Trees Using Evolutionary Algorithms", booktitle = "Parallel Problem Solving from Nature - PPSN VII", address = "Granada, Spain", month = "7-11 " # sep, pages = "720--729", year = "2002", editor = "Juan J. Merelo-Guervos and Panagiotis Adamidis and Hans-Georg Beyer and Jose-Luis Fernandez-Villacanas and Hans-Paul Schwefel", number = "2439", series = "Lecture Notes in Computer Science, LNCS", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, Biology and chemistry, Comparisons of representations", ISBN = "3-540-44139-5", ISBN = "3-540-44139-5", DOI = "doi:10.1007/3-540-45712-7_69", abstract = "We consider the problem of estimating the evolutionary history of a collection of organisms in terms of a phylogenetic tree. This is a hard combinatorial optimization problem for which different EA approaches are proposed and evaluated. Using two problem instances of different sizes, it is shown that an EA that directly encodes trees and uses ad-hoc operators performs better than several decoder-based EAs, but does not scale well with the problem size. A greedy-decoder EA provides the overall best results, achieving near 100percent success at a lower computational cost than the remaining approaches.", } @Article{Cotta:2007:GPEM, author = "Carlos Cotta and Juan-Julian Merelo", title = "Where is evolutionary computation going? A temporal analysis of the EC community", journal = "Genetic Programming and Evolvable Machines", year = "2007", volume = "8", number = "3", pages = "239--253", month = sep, keywords = "genetic algorithms, genetic programming, evolvable hardware, Complex networks, Evolutionary computation, Social network analysis", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-007-9031-0", size = "15 pages", abstract = "Studying an evolving complex system and drawing some conclusions from it is an integral part of nature-inspired computing; being a part of that complex system, some insight can also be gained from our knowledge of it. In this paper we study the evolution of the evolutionary computation co-authorship network using social network analysis tools, with the aim of extracting some conclusions on its mechanisms. In order to do this, we first examine the evolution of macroscopic properties of the EC co-authorship graph, and then we look at its community structure and its corresponding change along time. The EC network is shown to be in a strongly expansive phase, exhibiting distinctive growth patterns, both at the macroscopic and the mesoscopic level.", } @Article{journals/soco/CouchetMRR07, author = "Jorge Couchet and Daniel Manrique and Juan Rios and Alfonso Rodriguez-Paton", title = "Crossover and mutation operators for grammar-guided genetic programming", journal = "Soft Computing", year = "2007", volume = "11", number = "10", pages = "943--955", month = aug, keywords = "genetic algorithms, genetic programming, Grammar-guided genetic programming, Crossover, Mutation, Breast cancer prognosis", DOI = "doi:10.1007/s00500-006-0144-9", abstract = "This paper proposes a new grammar-guided genetic programming (GGGP) system by introducing two original genetic operators: crossover and mutation, which most influence the evolution process. The first, the so-called grammar-based crossover operator, strikes a good balance between search space exploration and exploitation capabilities and, therefore, enhances GGGP system performance. And the second is a grammar-based mutation operator, based on the crossover, which has been designed to generate individuals that match the syntactical constraints of the context-free grammar that defines the programs to be handled. The use of these operators together in the same GGGP system assures a higher convergence speed and less likelihood of getting trapped in local optima than other related approaches. These features are shown throughout the comparison of the results achieved by the proposed system with other important crossover and mutation methods in two experiments: a laboratory problem and the real-world task of breast cancer prognosis.", notes = "p945 'ambiguous' context free grammars. p950 PCT2 SSGA 75percent crossover 5percent mutation. p952 315 breast lesions X-ray images characteristics by human: size (apparent diameter mm), morphology (5 values), margins (5 values), density (4 values). Biopsy as ground truth, Comparison with two human experts. Evolved rule: if margins=spiculated and morphology=irregular then prognosis=malignant. p953 benefit of ambiguous grammar (not given).", bibdate = "2008-03-11", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/soco/soco11.html#CouchetMRR07", } @PhdThesis{Couet:thesis, author = "Frederic Couet", title_en = "Control of a perfusion bioreactor for growth of vascular tissue", title = "Controle d'un bioreacteur a perfusion pour la regeneration du tissu vasculaire", school = "Mines et metallurgi, Laval University", year = "2011", address = "Quebec, Canada", month = oct, keywords = "genetic algorithms, genetic programming, biomechanics, blood, vein", URL = "http://www.theses.ulaval.ca/2011/28452/28452.pdf", size = "187 pages", resume = "La disponibilit{\'e} limit{\'e}e de vaisseaux sanguins autologues pour les chirurgies vasculaires telles que le pontage coronarien ou p{\'e}riph{\'e}rique et les performances cliniques insuffisantes des proth{\`e}ses vasculaires pour le remplacement de vaisseaux sanguins de petit diam{\`e}tre ({\O} < 6 mm) justifie la recherche dans le domaine du g{\'e}nie tissulaire vasculaire. L{'}une des strat{\'e}gies explor{\'e}es -- le g{\'e}nie tissulaire fonctionnel -- vise {\`a} r{\'e}g{\'e}n{\'e}rer un vaisseau sanguin in vitro dans un environnement contr{\^o}l{\'e} appel{\'e} bior{\'e}acteur. L{'}objectif de cette th{\`e}se est de concevoir un bior{\'e}acteur {\`a} perfusion et de d{\'e}velopper un syst{\`e}me de contr{\^o}le pour ce bior{\'e}acteur afin d{'}interagir de mani{\`e}re dynamique avec une construction art{\'e}rielle dans le but de guider et de stimuler la maturation de constructions art{\'e}rielles. La principale question {\'e}tudi{\'e}e dans ce projet est de d{\'e}terminer comment choisir les conditions de culture {\`a} l{'}int{\'e}rieur d{'}un bior{\'e}acteur le plus efficacement possible. Deux grands enjeux ont {\'e}t{\'e} identifi{\'e}s : d{'}abord, le besoin de comprendre les diff{\'e}rents ph{\'e}nom{\`e}nes physiques et biologiques qui se d{\'e}roulent {\`a} l{'}int{\'e}rieur du bior{\'e}acteur. Ensuite, la n{\'e}cessit{\'e} de diriger la r{\'e}g{\'e}n{\'e}ration du tissu vasculaire. Une commande utilisant le concept de programmation g{\'e}n{\'e}tique fut d{\'e}velopp{\'e} afin de mod{\'e}liser en temps r{\'e}el la r{\'e}g{\'e}n{\'e}ration du tissu vasculaire. En utilisant les mod{\`e}les g{\'e}n{\'e}r{\'e}s, la commande recherche une strat{\'e}gie optimale de culture (d{\'e}formation circonf{\'e}rentielle, cisaillement longitudinal et fr{\'e}quence du d{\'e}bit puls{\'e}) en consid{\'e}rant un processus de d{\'e}cision Markovien r{\'e}solu par programmation dynamique. Par simulation num{\'e}rique, on montre que cette m{\'e}thode a le potentiel de favoriser une croissance plus rapide et plus s{\'e}curitaire des tissus en culture et permet d{'}identifier plus efficacement les param{\`e}tres importants pour la croissance et le remodelage des constructions art{\'e}rielles. La commande est capable de g{\'e}rer des mod{\`e}les de croissance non lin{\'e}aires. Exp{\'e}rimentalement, le syst{\`e}me d{\'e}velopp{\'e} permet de mieux comprendre l{'}{\'e}volution des propri{\'e}t{\'e}s m{\'e}caniques d{'}une construction art{\'e}rielle dans un bior{\'e}acteur.;", abstract = "The limited availability of autologous blood vessels for bypass surgeries (coronary or peripheral) and the poor patency rate of vascular prosthesis for the replacement of small diameter vessels ({\O} < 6 mm) motivate researches in the domain of vascular tissue engineering. One of the possible strategies named functional tissue engineering aims to regenerate a blood vessel in vitro in a controlled environment. The objective of this thesis is to design a perfusion bioreactor and develop a control system able to dynamically interact with a growing blood vessel in order to guide and stimulate the maturation of the vascular construct. The principal question addressed in this work is: How to choose culture conditions in a bioreactor in the most efficient way? Two main challenges have been identified: first, the need to develop a better comprehension of the physical and biological phenomenon occurring in bioreactors; second, the need to influence and optimise vascular tissue maturation. A controller based on the concept of genetic programming was developed for real-time modelling of vascular tissue regeneration. Using the produced models, the controller searches an optimal culture strategy (circumferential strain, longitudinal shear stress and frequency of the pulsed pressure signal) by using a Markov decision process solved by dynamic programming. Numerical simulations showed that the method has the potential to improve growth, safety of the process, and information gathering. The controller is able to work with common nonlinearities in tissue growth. Experimental results show that the controller is able to identify important culture parameters for the growth and remodelling of tissue engineered blood vessels. Furthermore, this bioreactor represents an interesting tool to study the evolution of the mechanical properties of a vascular construct during maturation.", bibsource = "OAI-PMH server at amican.webapps1.lac-bac.gc.ca", contributor = "Diego Mantovani", identifier = "TC-QQLA-28452", language = "EN", language = "FR", oai = "oai:collectionscanada.gc.ca:QQLA.2011/28452", rights = "{\copyright} Fr{\'e}d{\'e}ric Couet, 2011", } @InProceedings{Courte:2007:ANNIE, author = "Dale E. Courte", title = "Hybrid Evolutionary Code Generation Optimizing Both Functional Form and Parameter Values", booktitle = "ANNIE 2007, Intelligent Engineering Systems through Artificial Neural Networks", year = "2007", editor = "Cihan H. Dagli", volume = "17", address = "St. Louis, MO, USA", note = "Part III: Evolutionary Computation", keywords = "genetic algorithms, genetic programming, grammatical evolution", DOI = "doi:10.1115/1.802655.paper35", abstract = "Evolutionary computation (EC) is an effective tool in the optimisation of complex systems. It is desirable to model such a system with appropriate computer commands and parameter settings. Automated determination of both commands and settings, based on observed system behaviour, is a desirable goal. Of the many forms of evolutionary computation, one recently developed discipline is that of grammatical evolution (GE). This approach can evolve executable functions in any computer language that can be represented in BNF form. The ability to synthesise arbitrary functions from a formal grammar is an attractive alternative to the expression tree generation of the more common genetic programming (GP) approach. However, the GE approach may not be ideal for the optimisation of any real-valued parameters of the functions generated. This work combines the use of grammatical evolution for function synthesis with the use of evolutionary programming (EP) to optimise the parameters (constants) required by the synthesised functions. These two evolutionary processes combine to explore a rich and complex search space of functional forms and floating point values. A prototype system is implemented and applied to the problem of function approximation.", } @InProceedings{Courtrai:2001:Euromicro, author = "Luc Courtrai and Yves Maheo and Frederic Raimbault", title = "Java objects communication on a high performance network", booktitle = "Proceedings of the Ninth Euromicro Workshop on Parallel and Distributed Processing", year = "2001", pages = "203--210", month = "7-9 " # feb, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Java, parallel programming, Expresso, workstation clusters, Java object communication library, Java objects communication, cluster programming, high performance network, local high performance networks availability, parallel computing, workstations clusters, Availability, Communication standards, Communication system control, Computer architecture, Computer languages, Java, Libraries, Parallel processing, Proposals, Workstations", ISBN = "0-7695-0987-8", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.366.6319", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.366.6319", URL = "http://hal.archives-ouvertes.fr/docs/00/42/65/88/PDF/euromicro01.pdf", DOI = "doi:10.1109/EMPDP.2001.905044", size = "8 pages", abstract = "Local high performance networks availability already makes workstations clusters a serious alternative for parallel computing. However a high level and effective programming language for such architecture is still missing. Recent works show the interest in Java for cluster programming. One of the main issues is to handle efficiently the communication of objects to really take advantage of the network speed. The paper presents an alternative to the standard serialisation process through the proposal of a Java object communication library. Object allocation is controlled in such a way that the transfer of objects between two nodes comes to a direct memory to memory dump. We show how specific allocation mechanisms can cooperate with a Java Virtual Machine so that fast transfers of graphs of objects can be achieved. Experimental results are given for basic operations and for a genetic programming application, they demonstrate a dramatic change in the transfer speed", notes = "also known as \cite{905044}", } @InProceedings{Covert:2014:ALIFE, author = "Arthur W. {Covert, III} and Siena McFetridge and Evan DeLord", title = "Structured Populations with Limited Resources Exhibit Higher Rates of Complex Function Evolution", booktitle = "Proceedings of the Fourteenth International Conference of the Synthesis and Simulation of Living Systems, ALIFE 14", year = "2014", editor = "Hiroki Sayama and John Rieffel and Sebastian Risi and Rene Doursat and Hod Lipson", series = "Complex Adaptive Systems", pages = "129--134", address = "New York", month = "30 " # jul # "-2 " # aug, organisation = "International Society for Artificial Life", publisher = "MIT Press", keywords = "genetic algorithms, genetic programming, Avida", isbn13 = "9780262326216 ?", URL = "http://mitpress.mit.edu/sites/default/files/titles/content/alife14/ch021.html", DOI = "doi:10.7551/978-0-262-32621-6-ch021", size = "6 pages", abstract = "The impact of population structure on evolving populations is difficult to study. Populations broken up into groups of organisms and connected by low levels of migration will experience different types of geneflow than normal unstructured populations. Various studies, spanning decades of research, have lead to seemingly contradictory conclusions. Some point to population structure as a means to improve adaptation, others argue that population structure hinders evolution. We investigate how population structure impacts the evolution of complex functions in environments with limited resources. We find that structured populations with limited resources tend to evolve complex functions at a higher rate than unstructured populations, across a broad range of migration rates. This suggests that population structure may have an important impact on evolution, in both sexual and asexual populations, at least at certain migration rates.", notes = "Evolves program comprised of nand and other gates. ALIFE 14 http://mitpress.mit.edu/books/artificial-life-14 ALIFE14NYC@gmail.com", } @PhdThesis{Cowan:thesis, author = "George Smith Cowan", title = "An evolutionary computation approach to the software engineering of evolving programs", school = "Computer Science, Wayne State University", year = "1999", address = "Detroit, Michigan, USA", keywords = "genetic algorithms, genetic programming, Cultural Algorithm, Software Engineering Environment for Evolving Programs, SWEEP, Discipulus", URL = "http://search.proquest.com/docview/304534353", size = "196 pages", abstract = "The Software Engineering Environment for Evolving Programs (SWEEP) is a prototype for an evolving software development environment consisting of adaptive agents. SWEEP captures the salient features of automated assistance for the software systems of the future: distributed agents specializing in different life-cycle activities, focusing on software evolution, with an adaptive capability that will allow the agents to evolve new development processes. The programming agents for this system are Genetic Programming (GP) elements. A common architectural scheme for the agents, Cultural Algorithms (CA), facilitates the learning and interaction between the agents. The processes used by the GP agents create programs that are quite different from those of traditional processes. Two specific differences introduced with the use of the GP process are the phenomenon of bloat and the software quality issue of solution program generalizability to new problem instances. Unnecessary complexity compromises program generalization, but bloat obscures this relationship by introducing extra structural complexity into the program without affecting the program's outputs. Three levels of bloat are characterized: local bloat, global bloat , and representational bloat. Hopefully, any new assessment processes for new quality issues and new code phenomena can build on traditional assessment knowledge. The Metrics Apprentice (MA) is a prototype knowledge-based Software Quality Agent that constructs new software metrics, based on traditional software engineering metrics, for assessing software quality issues. To facilitate the learning of new concepts, the MA uses a CA shell that combines an evolutionary programming population component with a semantic network of schemata, or generalized knowledge, in a belief space. The performance of the system for a symbolic regression problem was compared to that of a traditional linear discriminant analysis (LDA) statistical approach. As more bloat was removed, the knowledge-based MA was able to outperform the LDA approach through the emergence of a hierarchical structure in its belief space. This structure allowed the MA knowledge-based approach to climb the conceptual hierarchy to use traditional software metrics that had a higher knowledge level, such as Intelligence Content, leading to an increased ability to predict the Generalizability of the GP produced code.", notes = "http://genealogy.math.ndsu.nodak.edu/id.php?id=100311 Supervisor: Robert Gene Reynolds McCabe's Cyclomatic Complexity measurements. Chapter 7 Relationship of Software Metrics to Bloat. Chapter 8 Defining a New Software Metric To Estimate Generalization Using the Metrics Apprentice UMI Microform 9956975", } @Book{Cowan:book, author = "George S. Cowan and Robert G. Reynolds", title = "Acquisition of Software Engineering Knowledge SWEEP: An Automatic Programming System Based on Genetic Programming and Cultural Algorithms", publisher = "World Scientific", year = "2003", volume = "14", series = "Software Engineering and Knowledge Engineering", address = "Singapore", month = aug, keywords = "genetic algorithms, genetic programming", ISBN = "981-02-2920-8", URL = "http://www.worldscibooks.com/compsci/3338.html", DOI = "DOI:10.1142/9789814327596_fmatter", abstract = "This is the first book that attempts to provide a framework in which to embed an automatic programming system based on evolutionary learning (genetic programming) into a traditional software engineering environment. As such, it looks at how traditional software engineering knowledge can be integrated with an evolutionary programming process in a symbiotic way. Contents: * SWEEP: A System for the Software Engineering of Evolving Programs * The Genetic Programming Element Agents * The Metrics Apprentice: Using Cultural Algorithms to Formulate Quality Metrics for Software Systems * An Example Problem for Automatic Programming: Solving the Noisy Sine Problem with Discipulus * Data Collection and Analysis * Analysis: The Relationship of Software Metrics to Bloat * Defining a New Software Metric to Estimate Generalisation Using the Metrics Apprentice", notes = "http://www.worldscibooks.com/compsci/3338.html E-book available in multiple DOI: Wayne State University, USA", size = "164 pages", } @InProceedings{Cowan:2024:evoapplications, author = "Tyler Cowan and Brian J. Ross", title = "Strategies for Evolving Diverse and Effective Behaviours in Pursuit Domains", booktitle = "27th International Conference, EvoApplications 2024", year = "2024", editor = "Stephen Smith and Joao Correia and Christian Cintrano", series = "LNCS", volume = "14635", publisher = "Springer", address = "Aberystwyth", month = "3-5 " # apr, pages = "345--360", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, games", isbn13 = "978-3-031-56854-1", URL = "https://rdcu.be/dD0mY", DOI = "doi:10.1007/978-3-031-56855-8_21", notes = "http://www.evostar.org/2024/ EvoApplications2024 held in conjunction with EuroGP'2024, EvoCOP2024 and EvoMusArt2024", } @Article{DBLP:journals/mp/CozadS18, author = "Alison Cozad and Nikolaos V. Sahinidis", title = "A global {MINLP} approach to symbolic regression", journal = "Mathematical Programming", year = "2018", volume = "170", number = "1", pages = "97--119", month = jul, note = "Special Issue: International Symposium on Mathematical Programming, Bordeaux, July 2018", keywords = "genetic algorithms, genetic programming, Integer nonlinear optimization, Machine learning, Global optimization, Symbolic regression", ISSN = "0025-5610", URL = "https://doi.org/10.1007/s10107-018-1289-x", DOI = "doi:10.1007/s10107-018-1289-x", timestamp = "Tue, 10 Jul 2018 09:45:38 +0200", biburl = "https://dblp.org/rec/journals/mp/CozadS18.bib", bibsource = "dblp computer science bibliography, https://dblp.org", abstract = "Symbolic regression methods generate expression trees that simultaneously define the functional form of a regression model and the regression parameter values. As a result, the regression problem can search many nonlinear functional forms using only the specification of simple mathematical operators such as addition, subtraction, multiplication, and division, among others. Currently, state-of-the-art symbolic regression methods leverage genetic algorithms and adaptive programming techniques. Genetic algorithms lack optimality certifications and are typically stochastic in nature. In contrast, we propose an optimization formulation for the rigorous deterministic optimization of the symbolic regression problem. We present a mixed-integer nonlinear programming (MINLP) formulation to solve the symbolic regression problem as well as several alternative models to eliminate redundancies and symmetries. We demonstrate this symbolic regression technique using an array of experiments based upon literature instances. We then use a set of 24 MINLPs from symbolic regression to compare the performance of five local and five global MINLP solvers. Finally, we use larger instances to demonstrate that a portfolio of models provides an effective solution mechanism for problems of the size typically addressed in the symbolic regression literature.", notes = "Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA Alison Cozad CMU PhD title: Data-and theory-driven techniques for surrogate-based optimization?", } @Article{Cpalka:2015:ITC, author = "Krzysztof Cpalka and Krystian Lapa and Andrzej Przybyl", title = "A new approach to design of control systems using genetic programming", journal = "Information Technology and Control", year = "2015", volume = "44", number = "4", pages = "433--422", keywords = "genetic algorithms, genetic programming, artificial intelligence, controller, selection of structure and parameters, hybrid evolutionary algorithm, controller design", ISSN = "1392-124X", URL = "http://www.itc.ktu.lt/index.php/ITC/article/view/10214", DOI = "doi:10.5755/j01.itc.44.4.10214", size = "10 pages", abstract = "In this paper a new approach to automatic design of control systems is proposed. It is based on a knowledge about modelling object and capabilities of the genetic programming. In particular, a new type of the problem encoding, new evolutionary operators (tuning operator and mutation operator) and new initialization method are proposed. Moreover, we present a modified block schema of genetic algorithm and modification of genetic operators: insertion, pruning, crossover were introduced. Combination of mentioned elements allows us to simplify a design of control systems. It also provides a lot of possibilities in the selection of the control system parameters and its structure. Our method was tested on the model of quarter car active suspension system.", } @Article{Crafa:2024:AJA, author = "Andrea Crafa and Marco Russo and Rossella Cannarella and Murat Gul and Michele Compagnone and Laura M Mongioi and Vittorio Cannarella and Rosita A Condorelli and Sandro {La Vignera} and Aldo E Calogero", title = "Predictability of varicocele repair success: preliminary results of a machine learning-based approach", journal = "Asian Journal of Andrology", note = "Online ahead of print", keywords = "genetic algorithms, genetic programming, artificial intelligence, medicine, infertility, male, total motile sperm count, varicocele", URL = "https://journals.lww.com/ajandrology/fulltext/9900/predictability_of_varicocele_repair_success_.218.aspx", URL = "https://pubmed.ncbi.nlm.nih.gov/39118538/", DOI = "doi:10.4103/aja202438", size = "7 pages", abstract = "Varicocele is a prevalent condition in the infertile male population. However, to date, which patients may benefit most from varicocele repair is still a matter of debate. The purpose of this study was to evaluate whether certain preintervention sperm parameters are predictive of successful varicocele repair, defined as an improvement in total motile sperm count (TMSC). We performed a retrospective study on 111 patients with varicocele who had undergone varicocele repair, collected from the Department of Endocrinology, Metabolic Diseases and Nutrition, University of Catania (Catania, Italy), and the Unit of Urology at the Selcuk University School of Medicine (Konya, Turkey). The predictive analysis was conducted through the use of the Brain Project, an innovative tool that allows a complete and totally unbiased search of mathematical expressions that relate the object of study to the various parameters available. Varicocele repair was considered successful when TMSC increased by at least 50% of the preintervention value. For patients with preintervention TMSC below 5e6, improvement was considered clinically relevant when the increase exceeded 50% and the absolute TMSC value was >5e6. From the preintervention TMSC alone, we found a model that predicts patients who appear to benefit little from varicocele repair with a sensitivity of 50.0% and a specificity of 81.8%. Varicocele grade and serum follicle-stimulating hormone (FSH) levels did not play a predictive role, but it should be noted that all patients enrolled in this study were selected with intermediate or high-grade varicocele and normal FSH levels. In conclusion, preintervention TMSC is predictive of the success of varicocele repair in terms of TMSC improvement in patients with intermediate or high-grade varicoceles and normal FSH levels.", notes = "PMID: 39118538 Department of Clinical and Experimental Medicine, University of Catania, Catania 95123, Italy", } @Article{craig:1999:gpds, author = "Iain Craig", title = "Genetic Programming and Data Structures", journal = "Robotica", year = "1999", volume = "17", number = "4", pages = "462", note = "Review", keywords = "genetic algorithms, genetic programming", publisher = "Cambridge University Press", URL = "http://journals.cambridge.org/abstract_S0263574799261528", URL = "http://journals.cambridge.org/action/displayAbstract?fromPage=online&aid=34651&fulltextType=BR&fileId=S0263574799261528", size = "0.25 pages", notes = "Review of \cite{langdon:book}", } @Misc{oai:CiteSeerX.psu:10.1.1.460.1644, title = "Genetic Program Feature Selection for Epistatic Problems using a {GA+ANN} Hybrid Approach", author = "Jesse Craig and Colin Rickert and Ian Kavanagh and Jane {Brooks Zurn}", year = "2006?", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.460.1644", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", keywords = "genetic algorithms, genetic programming, artificial intelligence, automatic programming, program synthesis, artificial neural networks, classification, feature selection, epistatic problems, problem", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.460.1644", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.460.1644.pdf", broken = "http://pdf.aminer.org/000/225/956/improving_gp_classifier_generalization_using_a_cluster_separation_metric.pdf", size = "8 pages", abstract = "We implemented a method to improve the accuracy of a genetic program (GP) for classifying an epistatic data population by limiting the number of population features passed to the GP. An epistatic population was generated and used, where the correct combination of true features was necessary in order to correctly classify each member of the population. Our method of limiting the number of features passed to the GP used a genetic algorithm (GA) with an artificial neural network (ANN) serving as the GA{'}s fitness function. Limiting the number of features sent to the GP with the GA+ANN method resulted in significantly better fitness (Student{'}s paired samples t-test, p < 0.000) than use of the entire feature set with the GP. The GA+ANN method also performed significantly better in the presence of noise, with better output fitness for p = 0.000 for 2.5percent mis-classified training instances in the population and p = 0.005 for 5.0percent mis-classified population training instances.", } @InProceedings{Craine:2024:GI, author = "Benjamin J. Craine and Penn {Faulkner Rainford} and Barry Porter", title = "Human Guidance Approaches for the Genetic Improvement of Software", booktitle = "13th International Workshop on Genetic Improvement @ICSE 2024", year = "2024", editor = "Gabin An and Aymeric Blot and Vesna Nowack and Oliver Krauss and Justyna Petke", pages = "21--22", address = "Lisbon", month = "16 " # apr, publisher = "ACM", keywords = "genetic algorithms, genetic programming, Genetic Improvement, source code, fitness plateau, neutral search spaces, experienced human engineer", isbn13 = "979-8-4007-0573-1/24/04", URL = "http://gpbib.cs.ucl.ac.uk/gi2024/Craine_2024_GI.pdf", DOI = "doi:10.1145/3643692.3648263", slides_url = "http://gpbib.cs.ucl.ac.uk/gi2024/gi_2024_slides/gi_icse2024_craine.pdf", video_url = "https://youtu.be/WvfuRLg_mJ0", video_url = "https://www.youtube.com/watch?v=WvfuRLg_mJ0&list=PLI8fiFpB7BoIRqJuY80XwmH-DFT_71y2S&index=7&pp=iAQB", size = "2 pages", abstract = "Existing research on Genetic Improvement (GI) of source code to improve performance \cite{rainford:2022:GECCO} has examined the mixed application of code synthesis and traditional GI mutation/crossover to gain higher-performing individuals that are tailored to particular deployment contexts, for examples such as hash tables or scheduling algorithms. While demonstrating successful improvements, this research presents a host of challenges \cite{Rainford:2021:GI}, from search space size to fitness landscape shape, which raise questions on whether GI alone is able to present a complete solution. In this position paper we propose to augment GI processes with Human Guidance (HG) to offer a co-pilot paradigm which may overcome these challenges.", notes = "GI @ ICSE 2024, part of \cite{an:2024:GI}", } @InProceedings{icga85:cramer, author = "Nichael Lynn Cramer", title = "A representation for the Adaptive Generation of Simple Sequential Programs", year = "1985", booktitle = "Proceedings of an International Conference on Genetic Algorithms and the Applications", address = "Carnegie-Mellon University, Pittsburgh, PA, USA", month = "24-26 " # jul, editor = "John J. Grefenstette", pages = "183--187", keywords = "genetic algorithms, genetic programming, memory", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/icga1985/icga85_cramer.pdf", URL = "http://gpbib.cs.ucl.ac.uk/icga/icga1985.pdf", URL = "http://www.sover.net/~nichael/nlc-publications/icga85/index.html", URL = "https://dl.acm.org/doi/10.5555/645511.657085", size = "5 pages", abstract = "An adaptive system for generating short sequential computer functions is described. The created functions are written in the simple {"}number-string{"} language JB, and in TB, a modified version of JB with a tree-like structure. These languages have the feature that they can be used to represent well-formed, useful computer programs while still being amenable to suitably defined genetic operators. The system is used to produce two-input, single-output multiplication functions that are concise and well-defined. Future work, dealing with extensions to more complicated functions and generalizations of the techniques, is also discussed.", notes = "The earliest description of the tree-like representation and operators for use in the application of Genetic Algorithms to computer programs - N.L.Cramer Evolves a multiplier, {"}72% more often than control sample{"} {"}PL- not fully Turing Equivalent{"}, addition of :SET and :BLOCK lead to JB language (nb a list of statements language). JB has problems with crossover -> TB which is as JB but instead of calls to other statements, these other statements are expanded in the first yielding a tree shaped syntax. Crossover operator changed to deal with sub trees! Both languages contain small numbers of global integers. TB Mutation restricted to frindges of tree, ie leaves or first level functions. Inversion: crossover within same program! Goldberg(1989, p 303) says {"}Cramer does not present any results from the use of JB in any genetic trials; however he abandoned these first efforts because of some limited computational experiments{"}. Fitness based, to some extent, upon internals of program. Limits on prog size via fitness. Forced timeout Goldberg(1987) says timed out progs fitness was calculated. =>Smith,S.F. IJCAI-83 Publisher not known, sponsored by USA Navy.", } @InProceedings{Cramer:2015:ieeeSSCI, author = "Sam Cramer and Michael Kampouridis and Alex A. Freitas and Antonis Alexandridis", booktitle = "2015 IEEE Symposium Series on Computational Intelligence", title = "Predicting Rainfall in the Context of Rainfall Derivatives Using Genetic Programming", year = "2015", pages = "711--718", abstract = "Rainfall is one of the most challenging variables to predict, as it exhibits very unique characteristics that do not exist in other time series data. Moreover, rainfall is a major component and is essential for applications that surround water resource planning. In particular, this paper is interested in the prediction of rainfall for rainfall derivatives. Currently in the rainfall derivatives literature, the process of predicting rainfall is dominated by statistical models, namely using a Markov-chain extended with rainfall prediction (MCRP). In this paper we outline a new methodology to be carried out by predicting rainfall with Genetic Programming (GP). This is the first time in the literature that GP is used within the context of rainfall derivatives. We have created a new tailored GP to this problem domain and we compare the performance of the GP and MCRP on 21 different data sets of cities across Europe and report the results. The goal is to see whether GP can outperform MCRP, which acts as a benchmark. Results indicate that in general GP significantly outperforms MCRP, which is the dominant approach in the literature.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SSCI.2015.108", month = dec, notes = "Sch. of Comput., Univ. of Kent, Canterbury, UK Also known as \cite{7376682}", } @InProceedings{Cramer:2016:GECCO, author = "Sam Cramer and Michael Kampouridis and Alex Freitas", title = "A Genetic Decomposition Algorithm for Predicting Rainfall within Financial Weather Derivatives", booktitle = "GECCO '16: Proceedings of the 2016 Annual Conference on Genetic and Evolutionary Computation", year = "2016", editor = "Tobias Friedrich and Frank Neumann and Andrew M. Sutton and Martin Middendorf and Xiaodong Li and Emma Hart and Mengjie Zhang and Youhei Akimoto and Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and Daniele Loiacono and Julian Togelius and Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and Faustino Gomez and Carlos M. Fonseca and Heike Trautmann and Alberto Moraglio and William F. Punch and Krzysztof Krawiec and Zdenek Vasicek and Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and Boris Naujoks and Enrique Alba and Gabriela Ochoa and Simon Poulding and Dirk Sudholt and Timo Koetzing", pages = "885--892", keywords = "genetic algorithms, genetic programming", month = "20-24 " # jul, organisation = "SIGEVO", address = "Denver, USA", publisher = "ACM", publisher_address = "New York, NY, USA", isbn13 = "978-1-4503-4206-3", DOI = "doi:10.1145/2908812.2908894", abstract = "Regression problems provide some of the most challenging research opportunities, where the predictions of such domains are critical to a specific application. Problem domains that exhibit large variability and are of chaotic nature are the most challenging to predict. Rainfall being a prime example, as it exhibits very unique characteristics that do not exist in other time series data. Moreover, rainfall is essential for applications that surround financial securities such as rainfall derivatives. This paper is interested in creating a new methodology for increasing the predictive accuracy of rainfall within the problem domain of rainfall derivatives. Currently, the process of predicting rainfall within rainfall derivatives is dominated by statistical models, namely Markov-chain extended with rainfall prediction (MCRP). In this paper, we propose a novel algorithm for decomposing rainfall, which is a hybrid Genetic Programming/Genetic Algorithm (GP/GA) algorithm. Hence, the overall problem becomes easier to solve. We compare the performance of our hybrid GP/GA, against MCRP, Radial Basis Function and GP without decomposition. We aim to show the effectiveness that a decomposition algorithm can have on the problem domain. Results show that in general decomposition has a very positive effect by statistically outperforming GP without decomposition and MCRP.", notes = "GECCO-2016 A Recombination of the 25th International Conference on Genetic Algorithms (ICGA-2016) and the 21st Annual Genetic Programming Conference (GP-2016)", } @InProceedings{Cramer:2016:CEC, author = "Sam Cramer and Michael Kampouridis and Alex A. Freitas", title = "Feature Engineering for Improving Financial Derivatives-based Rainfall Prediction", booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016)", year = "2016", editor = "Yew-Soon Ong", pages = "3483--3490", address = "Vancouver", month = "24-29 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-5090-0623-6", DOI = "doi:10.1109/CEC.2016.7744231", abstract = "Rainfall is one of the most challenging variables to predict, as it exhibits very unique characteristics that do not exist in other time series data. Moreover, rainfall is a major component and is essential for applications that surround water resource planning. In particular, this paper is interested in extending previous work carried out on the prediction of rainfall using Genetic Programming (GP) for rainfall derivatives. Currently in the rainfall derivatives literature, the process of predicting rainfall is dominated by statistical models, namely using a Markov-chain extended with rainfall prediction (MCRP). In this paper we further extend our new methodology by looking at the effect of feature engineering on the rainfall prediction process. Feature engineering will allow us to extract additional information from the data variables created. By incorporating feature engineering techniques we look to further tailor our GP to the problem domain and we compare the performance of the previous GP, which previously statistically outperformed MCRP, against our new GP using feature engineering on 21 different data sets of cities across Europe and report the results. The goal is to see whether GP can outperform its predecessor without extra features, which acts as a benchmark. Results indicate that in general GP using extra features significantly outperforms a GP without the use of extra features.", notes = "WCCI2016", } @InProceedings{Cramer:2017:evoApplications, author = "Sam Cramer and Michael Kampouridis and Alex A. Freitas and Antonis K. Alexandridis", title = "Pricing Rainfall Based Futures Using Genetic Programming", booktitle = "20th European Conference on the Applications of Evolutionary Computation", year = "2017", editor = "Giovanni Squillero", series = "LNCS", volume = "10199", publisher = "Springer", pages = "17--33", address = "Amsterdam", month = "19-21 " # apr, organisation = "Species", keywords = "genetic algorithms, genetic programming, Rainfall derivatives, Derivative pricing, Gibbs sampler", isbn13 = "978-3-319-55849-3", DOI = "doi:10.1007/978-3-319-55849-3_2", size = "18 pages", abstract = "rainfall derivatives are in their infancy since starting trading on the Chicago Mercentile Exchange (CME) since 2011. Being a relatively new class of financial instruments there is no generally recognised pricing framework used within the literature. In this paper, we propose a novel framework for pricing contracts using Genetic Programming (GP). Our novel framework requires generating a risk-neutral density of our rainfall predictions generated by GP supported by Markov chain Monte Carlo and Esscher transform. Moreover, instead of having a single rainfall model for all contracts, we propose having a separate rainfall model for each contract. We compare our novel framework with and without our proposed contract-specific models for pricing against the pricing performance of the two most commonly used methods, namely Markov chain extended with rainfall prediction (MCRP), and burn analysis (BA) across contracts available on the CME. Our goal is twofold, (i) to show that by improving the predictive accuracy of the rainfall process, the accuracy of pricing also increases. (ii) contract-specific models can further improve the pricing accuracy. Results show that both of the above goals are met, as GP is capable of pricing rainfall futures contracts closer to the CME than MCRP and BA. This shows that our novel framework for using GP is successful, which is a significant step forward in pricing rainfall derivatives.", notes = "EvoApplications2017 held in conjunction with EuroGP'2017, EvoCOP2017 and EvoMusArt2017 http://www.evostar.org/2017/cfp_evoapps.php.", } @PhdThesis{phd/ethos/Cramer17, author = "Sam Cramer", title = "New genetic programming methods for rainfall prediction and rainfall derivatives pricing", school = "School of Computing, University of Kent", year = "2017", address = "Canterbury, UK", month = oct, keywords = "genetic algorithms, genetic programming", bibdate = "2019-03-26", bibsource = "DBLP, http://dblp.uni-trier.de/https://kar.kent.ac.uk/69471/", URL = "https://kar.kent.ac.uk/69471/", URL = "https://kar.kent.ac.uk/69471/1/258thesis%20%281%29.pdf", URL = "http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.754825", size = "201 pages", abstract = "Rainfall derivatives is a part of an umbrella concept of weather derivatives, whereby the underlying weather variable determines the value of derivative, in our case the rainfall. These financial contracts are currently in their infancy as they have started trading on the Chicago Mercantile Exchange (CME) since 2011. Such contracts are very useful for investors or trading firms who wish to hedge against the direct or indirect adverse effects of the rainfall. The first crucial problem to focus on in this thesis is the prediction of the level of rainfall. In order to predict this, two techniques are routinely used. The first most commonly used approach is Markov chain extended with rainfall prediction. The second approach is Poisson-cluster model. Both techniques have some weakness in their predictive powers for rainfall data. More specifically, a large number of rainfall pathways obtained from these techniques are not representative of future rainfall levels. Additionally, the predictions are heavily influenced by the prior information, leading to future rainfall levels being the average of previously observed values. This motivates us to develop a new algorithm to the problem domain, based on Genetic Programming (GP), to improve the prediction of the underlying variable rainfall. GP is capable of producing white box (interpretable, as opposed to black box) models, which allows us to probe the models produced. Moreover, we can capture nonlinear and unexpected patterns in the data without making any strict assumptions regarding the data. The daily rainfall data represents some difficulties for GP. The difficulties include the data value being non-negative and discontinuous on the real time line. Moreover, the rainfall data consists of high volatilities and low seasonal time series. This makes the rainfall derivatives much more challenging to deal with than other weather contracts such as temperature or wind. However, GP does not perform well when it is applied directly on the daily rainfall data. We thus propose a data transformation method that improves GP's predictive power. The transformation works by accumulating the daily rainfall amounts into accumulated amounts with a sliding window. To evaluate the performance, we compare the prediction accuracy obtained by GP against the most currently used approach in rainfall derivatives, and six other machine learning algorithms. They are compared on 42 different data sets collected from different cities across the USA and Europe. We discover that GP is able to predict rainfall more accurately than the most currently used approaches in the literature and comparably to other machine learning methods. However, we find that the equations generated by GP are not able to take into account the volatilities and extreme periods of wet and dry rainfall. Thus, we propose decomposing the problem of rainfall into sub problems for GP to solve. We decompose the time series of rainfall by creating a partition to represent a selected range of the total rainfall amounts, where each partition is modeled by a separate equation from GP. We use a Genetic Algorithm to assist with the partitioning of data. We find that through the decomposition of the data, we are able to predict the underlying data better than all machine learning benchmark methods. Moreover, GP is able to provide a better representation of the extreme periods in the rainfall time series. The natural progression is to price rainfall futures contracts from rainfall prediction.Unlike other pricing domains in the trading market, there is no generally recognised pricing framework used within the literature. Much of this is due to weather derivatives(including rainfall derivatives) existing in an incomplete market, where the existing and well-studied pricing methods cannot be directly applied. There are two well-known techniques for pricing, the first is through indifference pricing and the second is through arbitrage free pricing. One of the requirements for pricing is knowing the level of risk or uncertainty that exists within the market. This allows for a contract price free of arbitrage. GP can be used to price derivatives, but the risk cannot be directly estimated. To estimate the risk, we must calculate a density of proposed rainfall values from a single GP equation, in order to calculate the most probable outcome.We propose three methods to achieve the required results. The first is through the procedure of sampling many different equations and extrapolating a density from the best of each generation over multiple runs. The second proposal builds on the first considering contract-specific equations, rather than a single equation explaining all contracts before extrapolating a density. The third method is the proposition of GP evolving and creating a collection of stochastic equations for pricing rainfall derivatives. We find that GP is a suitable method for pricing and both proposed methods are able to produce good pricing results. Our first and second methods are capable of pricing closer to the rainfall futures prices given by the CME. Moreover, we find that our third method reproduces the actual rainfall for the specified period of interest more accurately.", notes = "British Library, EThOS (EThOS hacked Oct 2023 still unavailable Sep 2024) Supervisors: Michael Kampouridis and Alex Freitas", } @Article{Cramer:2017:ESA, author = "Sam Cramer and Michael Kampouridis and Alex A. Freitas and Antonis K. Alexandridis", title = "An extensive evaluation of seven machine learning methods for rainfall prediction in weather derivatives", journal = "Expert Systems with Applications", year = "2017", volume = "85", pages = "169--181", month = "1 " # nov, keywords = "genetic algorithms, genetic programming, Weather derivatives, Rainfall, Machine learning", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2017.05.029", URL = "http://www.sciencedirect.com/science/article/pii/S0957417417303457", size = "13 pages", abstract = "Regression problems provide some of the most challenging research opportunities in the area of machine learning, and more broadly intelligent systems, where the predictions of some target variables are critical to a specific application. Rainfall is a prime example, as it exhibits unique characteristics of high volatility and chaotic patterns that do not exist in other time series data. This work's main impact is to show the benefit machine learning algorithms, and more broadly intelligent systems have over the current state-of-the-art techniques for rainfall prediction within rainfall derivatives. We apply and compare the predictive performance of the current state-of-the-art (Markov chain extended with rainfall prediction) and six other popular machine learning algorithms, namely: Genetic Programming, Support Vector Regression, Radial Basis Neural Networks, M5 Rules, M5 Model trees, and k-Nearest Neighbours. To assist in the extensive evaluation, we run tests using the rainfall time series across data sets for 42 cities, with very diverse climatic features. This thorough examination shows that the machine learning methods are able to outperform the current state-of-the-art. Another contribution of this work is to detect correlations between different climates and predictive accuracy. Thus, these results show the positive effect that machine learning-based intelligent systems have for predicting rainfall based on predictive accuracy and with minimal correlations existing across climates.", } @Article{CRAMER:2019:SEC, author = "Sam Cramer and Michael Kampouridis and Alex A. Freitas and Antonis Alexandridis", title = "Stochastic model genetic programming: Deriving pricing equations for rainfall weather derivatives", journal = "Swarm and Evolutionary Computation", year = "2019", keywords = "genetic algorithms, genetic programming, Weather derivatives, Rainfall, Pricing, Stochastic model genetic programming", ISSN = "2210-6502", DOI = "doi:10.1016/j.swevo.2019.01.008", URL = "http://www.sciencedirect.com/science/article/pii/S2210650218305145", abstract = "Rainfall derivatives are in their infancy since starting trading on the Chicago Mercantile Exchange (CME) in 2011. Being a relatively new class of financial instruments there is no generally recognised pricing framework used within the literature. In this paper, we propose a novel Genetic Programming (GP) algorithm for pricing contracts. Our novel algorithm, which is called Stochastic Model GP (SMGP), is able to generate and evolve stochastic equations of rainfall, which allows us to probabilistically transform rainfall predictions from the risky world to the risk-neutral world. In order to achieve this, SMGP's representation allows its individuals to comprise of two weighted parts, namely a seasonal component and an autoregressive component. To create the stochastic nature of an equation for each SMGP individual, we estimate the weights by using a probabilistic approach. We evaluate the models produced by SMGP in terms of rainfall predictive accuracy and in terms of pricing performance on 42 cities from Europe and the USA. We compare SMGP to 8 methods: its predecessor DGP, 5 well-known machine learning methods (M5 Rules, M5 Model trees, k-Nearest Neighbors, Support Vector Regression, Radial Basis Function), and two statistical methods, namely AutoRegressive Integrated Moving Average (ARIMA) and Monte Carlo Rainfall Prediction (MCRP). Results show that the proposed algorithm is able to statistically outperform all other algorithms", } @Article{CRAMER:2018:ASC, author = "Sam Cramer and Michael Kampouridis and Alex A. Freitas", title = "Decomposition genetic programming: An extensive evaluation on rainfall prediction in the context of weather derivatives", journal = "Applied Soft Computing", volume = "70", pages = "208--224", year = "2018", keywords = "genetic algorithms, genetic programming, Weather derivatives, Rainfall prediction, Problem decomposition", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2018.05.016", URL = "http://www.sciencedirect.com/science/article/pii/S1568494618302795", abstract = "Regression problems provide some of the most challenging research opportunities in the area of machine learning, where the predictions of some target variables are critical to a specific application. Rainfall is a prime example, as it exhibits unique characteristics of high volatility and chaotic patterns that do not exist in other time series data. Moreover, rainfall is essential for applications that surround financial securities, such as rainfall derivatives. This paper extensively evaluates a novel algorithm called Decomposition Genetic Programming (DGP), which is an algorithm that decomposes the problem of rainfall into subproblems. Decomposition allows the GP to focus on each subproblem, before combining back into the full problem. The GP does this by having a separate regression equation for each subproblem, based on the level of rainfall. As we turn our attention to subproblems, this reduces the difficulty when dealing with data sets with high volatility and extreme rainfall values, since these values can be focused on independently. We extensively evaluate our algorithm on 42 cities from Europe and the USA, and compare its performance to the current state-of-the-art (Markov chain extended with rainfall prediction), and six other popular machine learning algorithms (Genetic Programming without decomposition, Support Vector Regression, Radial Basis Neural Networks, M5 Rules, M5 Model trees, and k-Nearest Neighbours). Results show that the DGP is able to consistently and significantly outperform all other algorithms. Lastly, another contribution of this work is to discuss the effect that DGP has had on the coverage of the rainfall predictions and whether it shows robust performance across different climates", } @InCollection{crane:2005:GPTP, author = "Ellery Fussell Crane and Nicholas Freitag McPhee", title = "The effects of size and depth limits on tree based genetic programming", booktitle = "Genetic Programming Theory and Practice {III}", year = "2005", editor = "Tina Yu and Rick L. Riolo and Bill Worzel", volume = "9", series = "Genetic Programming", chapter = "15", pages = "223--240", address = "Ann Arbor", month = "12-14 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Size limits, Depth limits, Population distributions, Tree Shape, bloat", ISBN = "0-387-28110-X", DOI = "doi:10.1007/0-387-28111-8_15", size = "18 pages", abstract = "Bloat is a common and well studied problem in genetic programming. Size and depth limits are often used to combat bloat, but to date there has been little detailed exploration of the effects and biases of such limits. In this paper we present empirical analysis of the effects of size and depth limits on binary tree genetic programs. We find that size limits control population average size in much the same way as depth limits do. Our data suggests, however that size limits provide finer and more reliable control than depth limits, which has less of an impact upon tree shapes.", notes = "part of \cite{yu:2005:GPTP} Published Jan 2006 after the workshop", } @Article{Cranganu2010243, author = "Constantin Cranganu and Elena Bautu", title = "Using Gene Expression Programming to estimate sonic log distributions based on the natural gamma ray and deep resistivity logs: A case study from the Anadarko Basin, Oklahoma", journal = "Journal of Petroleum Science and Engineering", volume = "70", number = "3-4", pages = "243--255", year = "2010", ISSN = "0920-4105", DOI = "doi:10.1016/j.petrol.2009.11.017", URL = "http://www.sciencedirect.com/science/article/B6VDW-4XTNG6D-7/2/f3e31340cb8a863475bff4f643de28a9", keywords = "genetic algorithms, genetic programming, Gene Expression Programming, soft computing, sonic log, Anadarko Basin, overpressured zones", abstract = "In the oil and gas industry, characterisation of pore-fluid pressures and rock lithology, along with estimation of porosity, permeability, fluid saturation and other physical properties is of crucial importance for successful exploration and exploitation. Along with other well logging methods, the compressional acoustic (sonic) log (DT) is often used as a predictor because it responds to changes in porosity or compaction and, in turn, DT data are used to estimate formation porosity, to map abnormal pore-fluid pressure, or to perform petrophysical studies. However, despite its intrinsic value, the sonic log is not routinely recorded during well logging. Here we propose the use of a soft computing method -- Gene Expression Programming (GEP) -- to synthesise missing DT logs when only common logs (such as natural gamma ray -- GR, or deep resistivity -- REID) are present. The Gene Expression Programming approach can be divided into three steps: (1) supervised training of the model; (2) confirmation and validation of the model by blind-testing the results in wells containing both the predictor (GR, REID) and the target (DT) values used in the supervised training; and (3) applying the predicted model to wells containing the predictor data and obtaining the synthetic (simulated) DT log. GEP methodology offers significant advantages over traditional deterministic methods. It does not require a precise mathematical model equation describing the dependency between the predictor values and the target values. Unlike linear regression techniques, GEP does not over predict mean values and thereby preserves original data variability. GEP also deals greatly with uncertainty associated with the data, the immense size of the data and the diversity of the data type. A case study from the Anadarko Basin, Oklahoma, involving estimating the presence of over pressured zones, is presented. The results are promising and encouraging.", } @InProceedings{cranmer-2003, author = "Kyle S. Cranmer", title = "Multivariate Analysis from a Statistical Point of View", booktitle = "Phystat2003", year = "2003", editor = "Louis Lyons and Richard Mount and Rebecca Reitmeyer", pages = "211--214", address = "SLAC, Stanford, USA", month = sep # " 8-11", keywords = "genetic algorithms, genetic programming, VC dimension", URL = "http://www.slac.stanford.edu/econf/C030908/papers/WEJT002.pdf", URL = "http://www.citebase.org/abstract?id=oai:arXiv.org:physics/0310110", oai = "oai:arXiv.org:physics/0402030", size = "4 pages", abstract = "Multivariate Analysis is an increasingly common tool in experimental high energy physics; however, many of the common approaches were borrowed from other fields. We clarify what the goal of a multivariate algorithm should be for the search for a new particle and compare different approaches. We also translate the Neyman-Pearson theory into the language of statistical learning theory.", notes = "http://www.slac.stanford.edu/econf/C030908/ Talk from PhyStat2003, Stanford, Ca, USA, September 2003, LaTeX, 1 eps figures. PSN WEJT002 Section 6 page 214 talks about GP", } @Misc{oai:arXiv.org:physics/0402030, title = "Physics{GP}: {A} Genetic Programming Approach to Event Selection", author = "Kyle Cranmer and R. Sean Bowman", year = "2004", month = feb # "~05", abstract = "We present a novel multivariate classification technique based on Genetic Programming. The technique is distinct from Genetic Algorithms and offers several advantages compared to Neural Networks and Support Vector Machines. The technique optimizes a set of human-readable classifiers with respect to some user-defined performance measure. We calculate the Vapnik-Chervonenkis dimension of this class of learning machines and consider a practical example: the search for the Standard Model Higgs Boson at the LHC. The resulting classifier is very fast to evaluate, human-readable, and easily portable. The software may be downloaded at: http://cern.ch/~cranmer/PhysicsGP.html", note = "Comment: 16 pages 9 figures, 1 table. Submitted to Comput. Phys. Commun", oai = "oai:arXiv.org:physics/0402030", URL = "http://arXiv.org/abs/physics/0402030", keywords = "genetic algorithms, genetic programming, Triggering, Classification, VC Dimension, Neural Networks, Support Vector Machines", size = "pages", notes = "Published as \cite{cranmer:2005:CPC}. cites \cite{luke:2000:2ftcaGP}. Population is converted to C and compiled for fitness evaluation. (Details of GP including fitness definition (Gaussian/Poisson significance?) are vague). selection pressure based on inverse cumulative fitness distribution. Recentered fitness? Ring topology CORBA parallel programming. Island model. Higgs Boson, Large Hadron Collider CERN.", } @Article{cranmer:2005:CPC, author = "Kyle Cranmer and R. Sean Bowman", title = "{PhysicsGP}: A Genetic Programming approach to event selection", journal = "Computer Physics Communications", year = "2005", volume = "167", number = "3", pages = "165--176", month = "1 " # may, keywords = "genetic algorithms, genetic programming, Triggering, Classification, VC dimension, Neural networks, ANN, Support vector machines, SVM", ISSN = "0010-4655", URL = "http://arxiv.org/abs/physics/0402030", DOI = "doi:10.1016/j.cpc.2004.12.006", abstract = "We present a novel multivariate classification technique based on Genetic Programming. The technique is distinct from Genetic Algorithms and offers several advantages compared to Neural Networks and Support Vector Machines. The technique optimises a set of human-readable classifiers with respect to some user-defined performance measure. We calculate the Vapnik-Chervonenkis dimension of this class of learning machines and consider a practical example: the search for the Standard Model Higgs Boson at the LHC. The resulting classifier is very fast to evaluate, human-readable, and easily portable. The software may be downloaded at: http://cern.ch/~cranmer/PhysicsGP.html.", notes = "replaces oai:arXiv.org:physics/0402030 http://www.elsevier.com/wps/find/journaldescription.cws_home/505710/description#description p171 {"}It is meaningless to calculate the VCD (Vapnik-Chervonenkis dimension) for GP in general...{"} {"}by placing a bound on either size... or the degree of the polynomial, we can calculate a sensible VCD.{"} GP compared with ANN (backprop + momentum) and SVM with RBF kernel (BSVM-2.0). Training data subsampled. p174 {"}GP approach does not seem particularly sensitive to the size penalty of mutation rates{"}.", } @PhdThesis{cranmer:thesis, author = "Kyle S. Cranmer", title = "Searching for New Physics: Contributions to {LEP} and the {LHC}", school = "University of Wisconsin-Madison", year = "2005", address = "USA", month = "01 " # nov, keywords = "genetic algorithms, genetic programming, PhysicsGP", URL = "http://www.theoryandpractice.org/kyle/Files/cranmer_thesis.pdf", URL = "https://cds.cern.ch/record/823591", size = "233 pages", abstract = "This dissertation is divided into two parts and consists of a series of contributions to searches for new physics with LEP and the LHC. In the first part, an exhaustive comparison of ALEPH's LEP2 data and Standard Model predictions is made for several hundred final states. The observations are in agreement with predictions with the exception of the e- mu+ final state. Using the same general purpose particle identification procedure, searches for minimal supergravity signatures, excited electrons, doubly charged Higgs bosons, singly charged Higgs bosons, and the Standard Model Higgs boson were performed. The results of those searches are in agreement with previous ALEPH analyses. The second part focuses on preparation for searches for Higgs bosons with masses between 100 and 200 GeV. Improvements to the relevant Monte Carlo generators and the reconstruction of missing transverse momentum are presented. A detailed full simulation study of Vector Boson Fusion Higgs decaying to tau leptons confirms the qualitative conclusion that the channel is powerful near the LEP limit. Several novel statistical and multivariate analysis algorithms are considered, and their impact on Higgs searches is assessed. Finally, sensitivity estimates are provided for the combination of channels available for low mass Higgs searches. With 30 fb^-1 the expected ATLAS sensitivity is above five sigma for Higgs masses above 105 GeV.", notes = "'I would like to use one page to point out that if this dissertation was not double-spaced, then it would be fifty pages shorter. This is practice is silly and antiquated in the era of LATEX typesetting.' 'In total, three multivariate analyses were performed: a Neural Network analysis using back-propagation with momentum, a Support Vector Regression analysis using Radial Basis Functions, and a Genetic Programming analysis using the software described in Appendix E.' Copyright by Kyle S. Cranmer 2005 Some Rights Reserved This work is licensed under the Creative Commons Attribution-ShareAlike License. To view a copy of this license, visit http://creativecommons.org/licenses/by-sa/2.0/ or send a letter to Creative Commons, 559 Nathan Abbott Way, Stanford, California 94305, USA. https://cds.cern.ch/record/823591/files/CERN%20THESIS%202005%20011.pdf Supervisor: Wu Sau Lan Also known as \cite{Cranmer:823591}", } @Misc{cranmer2023interpretablemachinelearningscience, title = "Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl", author = "Miles Cranmer", howpublished = "arXiv", year = "2023", edition = "v3", month = "5 " # may, keywords = "genetic algorithms, genetic programming, GPU, Julia", eprint = "2305.01582", archiveprefix = "arXiv", primaryclass = "astro-ph.IM", URL = "https://arxiv.org/abs/2305.01582", code_url = "https://github.com/MilesCranmer/PySR", size = "24 pages", abstract = "PySR is an open-source library for practical symbolic regression, a type of machine learning which aims to discover human-interpretable symbolic models. PySR was developed to democratize and popularize symbolic regression for the sciences, and is built on a high-performance distributed back-end, a flexible search algorithm, and interfaces with several deep learning packages. PySR internal search algorithm is a multi-population evolutionary algorithm, which consists of a unique evolve-simplify-optimize loop, designed for optimization of unknown scalar constants in newly-discovered empirical expressions. PySR backend is the extremely optimized Julia library SymbolicRegression.jl, which can be used directly from Julia. It is capable of fusing user-defined operators into SIMD kernels at runtime, performing automatic differentiation, and distributing populations of expressions to thousands of cores across a cluster. In describing this software, we also introduce a new benchmark, EmpiricalBench, to quantify the applicability of symbolic regression algorithms in science. This benchmark measures recovery of historical empirical equations from original and synthetic datasets.", } @PhdThesis{Cranmer_princeton_0181D_14439, author = "Miles Donald Cranmer", title = "Interpretable Machine Learning for the Physical Sciences", school = "Department of Astrophysical Sciences, Princeton University", year = "2023", address = "USA", month = mar, keywords = "genetic algorithms, genetic programming, PySR", URL = "http://arks.princeton.edu/ark:/88435/dsp01sn00b201q", URL = "https://dataspace.princeton.edu/handle/88435/dsp01sn00b201q", size = "242 pages", abstract = "Would Kepler have discovered his laws if machine learning had been around in 1609? Or would he have been satisfied with the accuracy of some black box regression model, leaving Newton without the inspiration to find the law of gravitation? In this thesis I will present a review of machine learning and its use cases in the physical sciences. I will emphasize a major issue facing their use in science: a lack of interpretability. Over parameterised black box models are susceptible to memorizing spurious correlations in training data. Not only does this threaten reported research advances made with machine learning, but it also deprives scientists of our most powerful toolbox: symbolic manipulation and logical reasoning. With this in mind, I will demonstrate a framework for interpretable machine learning, using physically-motivated inductive biases and a new technique symbolic distillation. The combination of these methods allow a practitioner to translate a trained neural network model into an interpretable symbolic expression. I will first discuss the deep learning strategy for performing this distillation, and then review symbolic regression, an algorithm for optimizing symbolic expressions using evolutionary algorithms. In particular, I will describe my PySR/SymbolicRegression. software package, which is an easy-to-use high-performance symbolic regression package in Python and Julia. Tangential to this, I will discuss several physically-motivated inductive biases which make this technique more effective. In the second half of this thesis, I will review a variety of applications of this and other interpretable machine learning techniques, focusing on problems in astrophysics. These include: cosmology with cosmic voids, sub-grid scale modeling in computational fluid dynamics, optimal telescope time allocation, flexible modeling of population models in stellar and gravitational wave astronomy, and learning effective and probabilistically-rigorous models for planetary instability.", notes = "Supervisor: David N. Spergel and Shirley Ho", } @InProceedings{crapper:1997:mrrr, author = "P. F. Crapper and P. A. Whigham", title = "Modelling Rainfall-runoff Relationships", booktitle = "24th Hydrology and Water Resources Symposium", year = "1997", address = "Auckland, New Zealand", keywords = "genetic algorithms, genetic programming", URL = "http://trove.nla.gov.au/work/24556122", URL = "http://books.google.co.uk/books/about/24th_Hydrology_Water_resources_Symposium.html?id=bLC5PwAACAAJ&redir_esc=y", notes = " ", } @InProceedings{Crary:2022:CASES, author = "Christopher Crary and Wesley Piard and Britton Chesley and Greg Stitt", booktitle = "2022 International Conference on Compilers, Architecture, and Synthesis for Embedded Systems (CASES)", title = "Work-in-Progress: Toward a Robust, Reconfigurable Hardware Accelerator for Tree-Based Genetic Programming", year = "2022", pages = "17--18", abstract = "Genetic programming (GP) is a general, broadly effective procedure by which computable solutions are constructed from high-level objectives. As with other machine-learning endeavors, one continual trend for GP is to exploit ever-larger amounts of parallelism. In this paper, we explore the possibility of accelerating GP by way of modern field-programmable gate arrays (FPGAs), which is motivated by the fact that FPGAs can sometimes leverage larger amounts of both function and data parallelism-common characteristics of GP- when compared to CPUs and GPUs. As a first step towards more general acceleration, we present a preliminary accelerator for the evaluation phase of {"}tree-based GP{"}-the original, and still popular, flavor of GP-for which the FPGA dynamically compiles programs of varying shapes and sizes onto a reconfigurable function tree pipeline. Overall, when compared to a recent open-source GPU solution implemented on a modern 8nm process node, our accelerator implemented on an older 20nm FPGA achieves an average speedup of 9.7times. Although our accelerator is 7.9times slower than most examples of a state-of-the-art CPU solution implemented on a recent 7nm process node, we describe future extensions that can make FPGA acceleration provide attractive Pareto-optimal tradeoffs.", keywords = "genetic algorithms, genetic programming, Embedded systems, Shape, Pipelines, Graphics processing units, Machine learning, Parallel processing, reconfigurable computing, FPGA devices", DOI = "doi:10.1109/CASES55004.2022.00015", ISSN = "2643-1726", month = oct, notes = "Also known as \cite{9933164}", } @InProceedings{Crary:2023:EuroGP, author = "Christopher Crary and Wesley Piard and Greg Stitt and Caleb Bean and Benjamin Hicks", title = "Using {FPGA} Devices to Accelerate Tree-Based Genetic Programming: A Preliminary Exploration with Recent Technologies", booktitle = "EuroGP 2023: Proceedings of the 26th European Conference on Genetic Programming", year = "2023", month = "12-14 " # apr, editor = "Gisele Pappa and Mario Giacobini and Zdenek Vasicek", series = "LNCS", volume = "13986", publisher = "Springer Verlag", address = "Brno, Czech Republic", pages = "182--197", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Tree-based genetic programming, Field-programmable gate arrays, Hardware acceleration, DEAP, TensorGP, Operon", isbn13 = "978-3-031-29572-0", URL = "https://rdcu.be/c8UYC", DOI = "doi:10.1007/978-3-031-29573-7_12", code_url = "https://github.com/christophercrary/conference-eurogp-2023", size = "16 pages", abstract = "we explore the prospect of accelerating tree-based genetic programming (TGP) by way of modern field-programmable gate array (FPGA) devices, which is motivated by the fact that FPGAs can sometimes leverage larger amounts of data/function parallelism, as well as better energy efficiency, when compared to general-purpose CPU/GPU systems. we introduce a fixed-depth, tree-based architecture capable of evaluating type-consistent primitives that can be fully unrolled and pipelined. The current primitive constraints preclude arbitrary control structures, but they allow for entire programs to be evaluated every clock cycle. Using a variety of floating-point primitives and random programs, we compare to the recent TensorGP tool executing on a modern 8 nm GPU, and we show that our accelerator implemented on a 14 nm FPGA achieves an average speedup of 43 times. When compared to the popular baseline tool DEAP executing", notes = "Part of \cite{Pappa:2023:GP} EuroGP'2023 held in conjunction with EvoCOP2023, EvoMusArt2023 and EvoApplications2023", } @InProceedings{Crary:2024:GPTP, author = "Christopher Crary and Greg Stitt and Bogdan Burlacu and Wolfgang Banzhaf", title = "It's Time to Revisit the Use of {FPGA}s for Genetic Programming", booktitle = "Genetic Programming Theory and Practice XXI", year = "2024", editor = "Stephan M. Winkler and Wolfgang Banzhaf and Ting Hu and Alexander Lalejini", series = "Genetic and Evolutionary Computation", pages = "275--295", address = "University of Michigan, USA", month = jun # " 6-8", publisher = "Springer", keywords = "genetic algorithms, genetic programming, FPGA", isbn13 = "978-981-96-0076-2", DOI = "doi:10.1007/978-981-96-0077-9_14", notes = "Published in 2025 after the workshop", } @InProceedings{Crary:2024:PPSN, author = "Christopher Crary and Bogdan Burlacu and Wolfgang Banzhaf", title = "Enhancing the Computational Efficiency of Genetic Programming through Alternative Floating-Point Primitives", booktitle = "18th International Conference on Parallel Problem Solving from Nature", year = "2024", editor = "Heike Trautmann and Tea Tusar and Penousal Machado and Thomas Baeck", volume = "15148", series = "Lecture Notes in Computer Science", pages = "322--339", address = "University of Applied Sciences Upper Austria, Hagenberg, Austria", month = "13-18 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Field-programmable gate arrays, Approximate computing, Floating-point, Symbolic regression", isbn13 = "978-3-031-70054-5", URL = "https://rdcu.be/d5mBZ", DOI = "doi:10.1007/978-3-031-70055-2_20", code_url = "https://github.com/christophercrary/conference-ppsn-2024", notes = "https://ppsn2024.fh-ooe.at/program/", } @Article{Crary:2025:GPEM, author = "Christopher Crary and Wesley Piard and Greg Stitt and Benjamin Hicks and Caleb Bean and Bogdan Burlacu and Wolfgang Banzhaf", title = "Using {FPGA} devices to accelerate the evaluation phase of tree-based genetic programming: an extended analysis", journal = "Genetic Programming and Evolvable Machines", year = "2025", volume = "26", pages = "Article no 8", note = "Online first", keywords = "genetic algorithms, genetic programming, Tree-based genetic programming, Field-programmable gate array, Domain-specific architecture, Hardware acceleration, FPGA, DEAP, Operon", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-024-09505-2", size = "48 pages", abstract = "we establish the potential of accelerating the evaluation phase of tree-based genetic programming through contemporary field-programmable gate array (FPGA) technology. This exploration stems from the fact that FPGAs can sometimes leverage increased levels of both data and function parallelism, as well as superior power/energy efficiency, when compared to general-purpose CPU/GPU systems. we introduce a fixed-depth, tree-based architecture that can fully parallelize tree evaluation for type-consistent primitives that are unrolled and pipelined. We show that our accelerator on a 14nm FPGA achieves an average speedup of 43 times when compared to a recent open-source GPU solution, TensorGP, implemented on 8nm process-node technology, and an average speedup of 4902 times when compared to a popular baseline GP software tool, DEAP, running parallelised across all cores of a 2-socket, 28-core (56-thread), 14nm CPU server. Despite our single-FPGA accelerator being 2.4 times slower on average when compared to the recent state-of-the-art Operon tool executing on the same 2-processor, 28-core CPU system, we show that this single-FPGA system is 1.4 times better than Operon in terms of performance-per-watt. we also describe six future extensions that could provide at least a 64 to 192 times speedup over our current design. Therefore, our initial results provide considerable motivation for the continued exploration of FPGA-based GP systems. Overall, any success in significantly improving runtime and energy efficiency could potentially enable novel research efforts through faster and/or less costly GP runs, similar to how GPUs unlocked the power of deep learning during the past fifteen years.", } @InProceedings{Crary:2025:GPTP, author = "Chris Crary", title = "{FPGA}-Based Streaming Processors for Tree-Based Genetic Programming", booktitle = "Genetic Programming Theory and Practice XXII", year = "2025", editor = "Wolfgang Banzhaf and Bogdan Burlacu and Stephen Kelly and Alexander Lalejini and Fabricio {Olivetti de Franca}", address = "Michigan State University, USA", month = jun # " 5-7", note = "lightning talk", keywords = "genetic algorithms, genetic programming", notes = "To be published after the workshop", } @Misc{DBLP:journals/corr/abs-2403-14139, author = "Ben Cravens and Andrew Lensen and Paula Maddigan and Bing Xue", title = "Genetic Programming for Explainable Manifold Learning", howpublished = "arXiv", volume = "abs/2403.14139", year = "2024", keywords = "genetic algorithms, genetic programming, XAI", URL = "https://doi.org/10.48550/arXiv.2403.14139", DOI = "doi:10.48550/ARXIV.2403.14139", eprinttype = "arXiv", eprint = "2403.14139", timestamp = "Tue, 09 Apr 2024 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/corr/abs-2403-14139.bib", bibsource = "dblp computer science bibliography, https://dblp.org", notes = "See \cite{Cravens:ETCI}", } @Article{Cravens:TETCI, author = "Ben Cravens and Andrew Lensen and Paula Maddigan and Bing Xue", title = "Genetic Programming for Explainable Manifold Learning", journal = "IEEE Transactions on Emerging Topics in Computational Intelligence", keywords = "genetic algorithms, genetic programming, Complexity theory, Manifold learning, Measurement, Optimisation, Computational modelling, Feature extraction, High dimensional data, Evolutionary computation, Syntactics, dimensionality reduction, explainable artificial intelligence", ISSN = "2471-285X", DOI = "doi:10.1109/TETCI.2025.3561666", abstract = "Manifold learning techniques play a pivotal role in machine learning by revealing lower-dimensional embeddings within high-dimensional data, thereby enhancing the efficiency, interpretability, and scalability of data analysis. Despite their utility, current manifold learning methods often lack explicit functional mappings, which are critical for ensuring explainability in regulated and high-stakes applications. This paper introduces Genetic Programming for Explainable Manifold Learning (GP-EMaL), a novel integration of Genetic Programming (GP) and Explainable Artificial Intelligence (XAI). GP-EMaL leverages the inherently interpretable, tree-based structures of GP to generate explicit, functional mappings while directly addressing complexity challenges through innovative penalties for tree size, symmetry, and operator selection. By enabling customisable complexity metrics, GP-EMaL adapts to diverse application needs, achieving high manifold quality and significantly improved explainability. Comprehensive experiments demonstrate that GP-EMaL matches or exceeds the performance of existing approaches, producing simpler and more interpretable models. This work advances the state of explainable manifold learning, paving the way for its adoption in domains such as healthcare, environmental modelling, and financial analysis.", notes = "Also known as \cite{10979286} see \cite{DBLP:journals/corr/abs-2403-14139,}", } @InProceedings{crawford:1999:MGTNSV, author = "Kelly D. Crawford and Michael D. McCormack and Donald J. MacAllister", title = "Modified Gradient Techniques for Normalized Solution Vectors", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1498--1503", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "real world applications", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-720.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-720.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{crawford-marks:2002:gecco, author = "Raphael Crawford-Marks and Lee Spector", title = "Size Control Via Size Fair Genetic Operators In The {PushGP} Genetic Programming System", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "733--739", address = "New York", publisher_address = "San Francisco, CA 94104, USA", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-878-8", URL = "http://alum.hampshire.edu/~rpc01/gp234.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/gp234.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf", notes = "GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)", } @Article{crawford-marks:2004:ascribe, key = "Crawford-Marks", title = "Hampshire College Student Uses J.K. Rowling's Quidditch as Basis for Artificial Intelligence Experiment", journal = "AScribe Newswire", year = "2004", month = "4 " # may, note = "online", keywords = "genetic algorithms, genetic programming", broken = "http://www.ascribe.org/cgi-bin/spew4th.pl?ascribeid=20040504.114704&time=12%2033%20PDT&year=2004&public=1", URL = "http://www.snitchseeker.com/harry-potter-news/college-student-uses-quidditch-for-an-experiment-15270/", abstract = "Although enrolled in Hampshire College, not Hogwarts Academy, Raphael Crawford-Marks has spent the past year fine-tuning his Quidditch skills. Crawford-Marks - set to graduate on May 22 - has created a computerized version of the rapid-fire game played by young witches and warlocks in J.K. Rowling's series of Harry Potter novels. But Crawford-Marks is doing far more than playing a video game: he's running an artificial intelligence experiment that involves computerized generation of teams that either proceed in competition or fall by the wayside according to their ability to adapt to the Quidditch environment.", notes = "references \cite{spector:2001:vqacpapsa} Nov 2015 snitchseeker.com appears to have a copy of ascribe.org", } @Misc{crawford-marks:2004:senior, author = "Raphael Crawford-Marks", title = "Virtual Witches and Warlocks: Computational Evolution of Teamwork and Strategy in a Dynamic, Heterogeneous and Noisy {3D} Environment", school = "School of Cognitive Science, Hampshire College", year = "2004", type = "Division III (senior) Thesis", month = "18 " # may, keywords = "genetic algorithms, genetic programming, Coevolution, breve, Push", URL = "http://alum.hampshire.edu/~rpc01/div3.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.148.949", size = "64 pages, (pdf 460KB)", language = "en", oai = "oai:CiteSeerXPSU:10.1.1.148.949", abstract = "Games make excellent challenge problems for Artificial Intelligence. Two-player turn-based games (Backgammon, Checkers, Chess) are easy to program, and AI players can be benchmarked against humans of varying skill levels. Recently, more complicated real-time team games have received attention from researchers in the Distributed Artificial Intelligence (DAI) and Multi-Agent Systems (MAS) fields because of the dynamic environments and necessity for coordination. The RoboCup Soccer Simulator is the most popular and well-known of these environments. However, the soccer simulator is restricted to only two dimensions, and does not realistically model physics. This Division III thesis describes a simulator of the imaginary game Quidditch, and the automatic programming of quidditch-playing teams by Genetic Programming. These evolved teams of heterogeneous agents have offensive and defensive behaviours, and show the beginnings of real teamwork. Now, I want a nice fair game, all of you, she said, once they were all gathered around her. Harry noticed that she seemed to be speaking particularly to the Slytherin Captain, Marcus Flint, a sixth year. Harry thought Flint looked as if he had some troll blood in him. Out of the corner of his eye he saw the fluttering banner high above, flashing Potter for President over the crowd. His heart skipped. He felt braver. Mount your brooms, please.", notes = "http://www.spiderland.org/breve.", } @InProceedings{crawford-marks:2004:lbp, author = "Raphael Crawford-Marks and Lee Spector and Jon Klein", title = "Virtual Witches and Warlocks: A Quidditch Simulator and Quidditch-Playing Teams Coevolved via Genetic Programming", booktitle = "Late Breaking Papers at the 2004 Genetic and Evolutionary Computation Conference", year = "2004", editor = "Maarten Keijzer", address = "Seattle, Washington, USA", month = "26 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://gpbib.cs.ucl.ac.uk/gecco2004/LBP046.pdf", abstract = "Games make excellent challenge problems for Artificial Intelligence. Two-player turn-based games (Backgammon, Checkers, Chess) are easy to program, and AI players can be benchmarked against humans of varying skill levels. Recently, more complicated real-time team games have received attention because of their dynamic environments and the necessity for coordination. The RoboCup Soccer Simulator is the most popular and well-known of these environments. However, the soccer simulator is restricted to only two dimensions, and does not realistically model physics. In 2001, Spector et al. proposed creating a simulator of the imaginary game Quidditch from the Harry Potter Books by J.K. Rowling. This article describes such a simulator and the coevolved quidditch-playing teams created for it using Genetic Programming.", notes = "Part of \cite{keijzer:2004:GECCO:lbp}", } @Article{Creevey:2023:SciRep, author = "Floyd M. Creevey and Charles D. Hill and Lloyd C. L. Hollenberg", title = "{GASP}: a genetic algorithm for state preparation on quantum computers", journal = "Scientific Reports", year = "2023", volume = "13", pages = "11956", month = "24 " # jul, keywords = "genetic algorithms, genetic programming, CNOT, Quantum information, Qubits", ISSN = "2045-2322", URL = "https://rdcu.be/dlYH8", DOI = "doi:10.1038/s41598-023-37767-w", size = "8 pages", abstract = "The efficient preparation of quantum states is an important step in the execution of many quantum algorithms. In the noisy intermediate-scale quantum (NISQ) computing era, this is a significant challenge given quantum resources are scarce and typically only low-depth quantum circuits can be implemented on physical devices. We present a genetic algorithm for state preparation (GASP) which generates relatively low-depth quantum circuits for initialising a quantum computer in a specified quantum state. The method uses a basis set of Rx, Ry, Rz, and CNOT gates and a genetic algorithm to systematically generate circuits to synthesize the target state to the required fidelity. GASP can produce more efficient circuits of a given accuracy with lower depth and gate counts than other methods. This variability of the required accuracy facilitates overall higher accuracy on implementation, as error accumulation in high-depth circuits can be avoided. We directly compare the method to the state initialisation technique based on an exact synthesis technique by implemented in IBM Qiskit simulated with noise and implemented on physical IBM Quantum devices. Results achieved by GASP outperform Qiskit's exact general circuit synthesis method on a variety of states such as Gaussian states and W-states, and consistently show the method reduces the number of gates required for the quantum circuits to generate these quantum states to the required accuracy.", notes = "'GASP significantly outperforms the exact approach through superior circuit compression, by more than an order of magnitude' School of Physics, University of Melbourne, Melbourne 3010, Australia.", } @InCollection{creighton:2000:SSOGA, author = "Steven L. Creighton", title = "Structural Shape Optimization using a Genetic Algorithm", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "108--116", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms", notes = "part of \cite{koza:2000:gagp}", } @InProceedings{crepeau:1995:GEMS, author = "Ronald L. Crepeau", title = "Genetic Evolution of Machine Language Software", booktitle = "Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications", year = "1995", editor = "Justinian P. Rosca", pages = "121--134", address = "Tahoe City, California, USA", month = "9 " # jul, keywords = "genetic algorithms, genetic programming, memory", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/GEMS_Article.pdf", size = "14 pages", abstract = "Genetic Programming (GP) has a proven capability to routinely evolve software that provides a solution function for the specified problem. Prior work in this area has been based upon the use of relatively small sets of pre-defined operators and terminals germane to the problem domain. This paper reports on GP experiments involving a large set of general purpose operators and terminals. Specifically, a microprocessor architecture with 660 instructions and 255 bytes of memory provides the operators and terminals for a GP environment. Using this environment, GP is applied to the beginning programmer problem of generating a desired string output, e.g., {"}Hello World{"}. Results are presented on: the feasibility of using this large operator set and architectural representation; and, the computations required to breed string outputting programs vs. the size of the string and the GP parameters employed.", notes = "Z80 Machine code evolved to write {"}Hello World{"} HWP 660 instructions and 255 byte RAM (modular arithmetic used to address indexed memory) GEMS genetic evolution of machine language software Breeding system similar to crowding and Tackett's Softbrood selection (max litter size of 12). GA like crossover acts on code and contents of memory. Pool of 1500 member 0.20 mutation rate. {"}indicates that the problem difficulty, over the range of the test and in terms of required spawns, while increasing rapidly, does not appear to be combinatorial or exponential{"} (suggests O(n**3) ). Discussion of statistics of number of useful terminals in random and later populations. Memory initialised to random values. {"}Cultural memory{"} cf \cite{spector:1996:ctiGP}. Steady state GA. 2 types of Mutation (20 percent). While JP jump and subroutines are discussed the problem does not need iteration to solve it. part of \cite{rosca:1995:ml}", } @Article{Crepinsek:2006:ENTCS, author = "Matej Crepinsek and Marjan Mernik and Barrett R. Bryant and Faizan Javed and Alan Sprague", title = "Inferring Context-Free Grammars for Domain-Specific Languages", journal = "Electronic Notes in Theoretical Computer Science", year = "2005", volume = "141", number = "4", pages = "99--116", month = "12 " # dec, note = "Proceedings of the Fifth Workshop on Language Descriptions, Tools, and Applications (LDTA 2005)", keywords = "genetic algorithms, genetic programming, Grammar induction, Grammar inference, Learning from positive and negative examples, Exhaustive search", ISSN = "1571-0661", DOI = "doi:10.1016/j.entcs.2005.02.055", abstract = "In the area of programming languages, context-free grammars (CFGs) are of special importance since almost all programming languages employ CFG's in their design. Recent approaches to CFG induction are not able to infer context-free grammars for general-purpose programming languages. In this paper it is shown that syntax of a small domain-specific language can be inferred from positive and negative programs provided by domain experts. In our work we are using the genetic programming approach in grammatical inference. Grammar-specific heuristic operators and nonrandom construction of the initial population are proposed to achieve this task. Suitability of the approach is shown by examples where underlying context-free grammars are successfully inferred.", } @InCollection{Cretin:al:EA95, author = "Guillaume Cretin and Evelyne Lutton and Jacques Levy-Vehel and Philippe Glevarec and Cedric Roll", title = "Mixed {IFS}: Resolution of the Inverse Problem Using Genetic Programming", booktitle = "Artificial Evolution", publisher = "Springer Verlag", year = "1996", editor = "Jean-Marc Alliot and Evelyne Lutton and Edmund Ronald and Marc Schoenauer and Dominique Snyers", volume = "1063", series = "LNCS", pages = "247--258", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-61108-0", DOI = "doi:10.1007/3-540-61108-8_42", size = "11 pages", abstract = "We address here the resolution of the so-called inverse problem for IFS. This problem has already been widely considered, and some studies have been performed for affine IFS, using deterministic or stochastic methods (simulated annealing or Genetic Algorithm) [9, 12, 6]. When dealing with non affine IFS, the usual techniques do not perform well, except if some a priori hypotheses on the structure of IFS (number and type functions) are made. A Genetic Programming method is investigated to solve the general inverse problem, which permits to perform at the same time a numeric and a symbolic optimisation. The use of mixed IFS, as we call them, may enlarge the scope of some applications, as for example image compression, because they allow to code a wider range of shapes.", notes = "Selected papers from two conferences: Evolution Artificielle 94 and Evolution Artificielle 95 http://www.cmap.polytechnique.fr/www.eark/ea95.html see also \cite{lutton:1995:IFScs} and \cite{lutton:1995:IFScs}", affiliation = "INRIA-Rocquencourt B.P. 105 78153 Le Chesnay Cedex France B.P. 105 78153 Le Chesnay Cedex France", } @Article{CRETON:2022:FPE, author = "Benoit Creton and Benjamin Veyrat and Marie-Helene Klopffer", title = "Fuel sorption into polymers: Experimental and machine learning studies", journal = "Fluid Phase Equilibria", year = "2022", volume = "556", pages = "113403", month = may, keywords = "genetic algorithms, genetic programming, Polymer, Fuel, Machine learning, Sorption", ISSN = "0378-3812", URL = "https://www.sciencedirect.com/science/article/pii/S0378381222000280", DOI = "doi:10.1016/j.fluid.2022.113403", size = "11 pages", abstract = "In the automotive industry, the introduction of alternative fuels in the market or even the consideration of new fluids such as lubricants requires continuous efforts in research and development to predict and evaluate impacts on materials (e.g., polymers) in contact with these fluids. We address here the compatibility between polymers and fluids by means of both experimental and modelling techniques. Three polymers were considered: a nitrile butadiene rubber (NBR), a fluorinated elastomer (FKM) and a fluorosilicon rubber (FVMQ), and a series of hydrocarbons mixtures were formulated to study the swelling of the polymers. The swelling of samples has been investigated in terms of weight and not volume variations as the measure of this former is assumed to be more accurate. Multi-gene genetic programming (MGGP) was applied to experimental data obtained in order to derive models to predict: (i) the maximum value of the mass gain (Delta-M) and (ii) the sorption kinetics, i.e. the time evolution of DeltaM. Predicted values are in excellent agreement with experimental data (with R-squared greater than 0.99), and models have demonstrated their predictive capabilities when applied to external fluids (not considered during the training procedure). Combining experiments and modelling, as proposed in this work, leads to accurate models which drastically reduce the time necessary to quantify polymeric materials compatibility with a fluid candidates as compared to experiments", notes = "IFP Energies nouvelles, 1et 4 avenuede Bois-Preau, 92852 Rueil-Malmaison, France", } @InProceedings{cribbs:1999:AMGAA, author = "H. Brown {Cribbs III}", title = "Aircraft Maneuvering via Genetics-Based Adaptive Agent", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1249--1256", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "artificial life, adaptive behavior and agents", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-035.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-035.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @PhdThesis{Crochepierre:thesis, author = "Laure Crochepierre", title = "Apprentissage automatique interactif pour les operateurs du reseau electrique", school = "l'Universite de Lorraine", year = "2022", address = "France", month = "27 " # sep, keywords = "genetic algorithms, genetic programming, Grammatical Evolution", URL = "https://www.loria.fr/event/soutenance-de-these-de-laure-crochepierre/", URL = "http://docnum.univ-lorraine.fr/public/DDOC_T_2022_0112_CROCHEPIERRE.pdf", size = "186 pages", notes = "EDF french electricity grid dispatch. mostly in French UMR 7503 Supervisor: Lydia Boudjeloud-Assala", } @Article{doi:10.1142/S012918319700028X, author = "G. A. Cronje and Willi-Hans Steeb", title = "Genetic Algorithms in a Distributed Computing Environment Using {PVM}", journal = "International Journal of Modern Physics C", volume = "8", number = "2", pages = "327--344", year = "1997", keywords = "genetic algorithms, genetic programming, Object-Oriented Programming, Parallel Virtual Machines", DOI = "doi:10.1142/S012918319700028X", URL = "https://doi.org/10.1142/S012918319700028X", abstract = "The Parallel Virtual Machine (PVM) is a software system that enables a collection of heterogeneous computer systems to be used as a coherent and flexible concurrent computation resource. We show that genetic algorithms can be implemented using a Parallel Virtual Machine and C++. Problems with constraints are also discussed.", } @TechReport{oai:CiteSeerPSU:265557, title = "Defending a Computer System using Autonomous Agents", author = "Mark Crosbie and Gene Spafford", institution = "Department of Computer Science, Perdue University", year = "1994", type = "Technical Report", number = "95-022", address = "West Lafayette, IN, USA", month = "11 " # mar, keywords = "genetic algorithms, genetic programming", URL = "http://www.cerias.purdue.edu/homes/spaf/tech-reps/9522.ps", URL = "https://www.cerias.purdue.edu/assets/pdf/bibtex_archive/95-02.pdf", URL = "https://www.cerias.purdue.edu/apps/reports_and_papers/view/19", URL = "http://citeseer.ist.psu.edu/265557.html", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:265557", rights = "unrestricted", size = "11 pages", abstract = "This report presents a prototype architecture of a defense mechanism for computer systems. The intrusion detection problem is introduced and some of the key aspects of any solution are explained. Standard intrusion detection systems are built as a single monolithic module. A finer-grained approach is proposed, where small, independent agents monitor the system. These agents are taught how to recognise intrusive behaviour. The learning mechanism in the agents is built using Genetic Programming. This is explained, and some sample agents are described. The flexibility, scalability and resilience of the agent approach are discussed. Future issues are also outlined.", notes = "See also \cite{Crosbie:IDIOT} citeseer 1999 oct 21", } @InProceedings{crosbie:1995:aGPid, title = "Applying Genetic Programming to Intrusion Detection", author = "Mark Crosbie and Eugene H. Spafford", booktitle = "Working Notes for the AAAI Symposium on Genetic Programming", year = "1995", editor = "E. V. Siegel and J. R. Koza", pages = "1--8", address = "MIT, Cambridge, MA, USA", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025, USA", month = "10--12 " # nov, publisher = "AAAI", keywords = "genetic algorithms, genetic programming", URL = "http://www.aaai.org/Papers/Symposia/Fall/1995/FS-95-01/FS95-01-001.pdf", URL = "http://www.aaai.org/Library/Symposia/Fall/fs95-01.php", URL = "http://citeseer.ist.psu.edu/197480.html", size = "8 pages", abstract = "This paper presents a potential solution to the intrusion detection problem in computer security. It uses a combination of work in the fields of Artificial Life and computer security. It shows how an intrusion detection system can be implemented using autonomous agents, and how these agents can be built using Genetic Programming. It also shows how Automatically Defined Functions (ADFs) can be used to evolve genetic programs that contain multiple data types and yet retain type-safety. Future work arising from this is also discussed.", notes = "AAAI-95f GP. Part of \cite{siegel:1995:aaai-fgp} {\em Telephone:} 415-328-3123 {\em Fax:} 415-321-4457 {\em email} info@aaai.org {\em URL:} http://www.aaai.org/", } @InProceedings{crosbie:1996:eedp, author = "Mark Crosbie and Eugene H. Spafford", title = "Evolving Event-Driven Programs", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "273--278", address = "Stanford University, CA, USA", publisher = "MIT Press", URL = "http://citeseer.ist.psu.edu/rd/13718071%2C200806%2C1%2C0.25%2CDownload/http://citeseer.ist.psu.edu/cache/papers/cs/8415/http:zSzzSzwww.best.comzSz%7EmcrosbiezSzResearchzSzgp96.pdf/crosbie96evolving.pdf", URL = "http://citeseer.ist.psu.edu/200806.html", size = "6 pages", abstract = "This paper examines how Genetic Programming has shortcomings in an event-driven environment. The need for event-driven programming is motivated by some examples. We then describe the difficulty in handling these examples using the traditional genetic programming approach. A potential solution that uses colored Petri nets is outlined. We present an experimental setup to test our theory.", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap34.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96", } @TechReport{Crosbie:IDIOT, author = "Mark Crosbie and Bryn Dole and Todd Ellis and Ivan Krsul and Eugene H. Spafford", title = "{IDIOT} - Users Guide", institution = "Department of Computer Science, Perdue University", year = "1996", type = "Technical Report", number = "CSD-TR 96-050", address = "West Lafayette, IN 47907, USA", month = "4 " # sep, keywords = "genetic algorithms, genetic programming", URL = "https://docs.lib.purdue.edu/cstech/1304/", URL = "https://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=2303&context=cstech", URL = "https://www.cerias.purdue.edu/assets/pdf/bibtex_archive/bibtex_archive/96-04.pdf", size = "65 pages", abstract = "This manual gives a detailed technical description of the IDIOT intrusion detection system from the COAST Laboratory at Purdue University. It is intended to help anyone who wishes to use, extend or test the IDIOT system. Familiarity with security issues, and intrusion detection in particular, is assumed.", notes = "See \cite{oai:CiteSeerPSU:265557} etc", } @PhdThesis{Cross-AI-2018-PhD-Thesis, author = "Andreea-Ingrid Cross", title = "Reconfigurable computing for advanced trading strategies", school = "Department of Computing, Imperial College London", year = "2018", address = "UK", month = mar, keywords = "genetic algorithms, genetic programming, FPGA, EHW, PSO, Valgrind,", URL = "https://spiral.imperial.ac.uk/handle/10044/1/64787", URL = "http://hdl.handle.net/10044/1/64787", URL = "https://spiral.imperial.ac.uk/bitstream/10044/1/64787/3/Cross-AI-2018-PhD-Thesis.pdf", DOI = "doi:10.25560/64787", size = "196 pages", abstract = "Financial markets are rapidly evolving to exploit powerful computational and statistical tools to construct both risk management and alpha strategies. This research seeks to develop new tools to identify efficient trading strategies through the use of genetic programming and some mathematical optimisation methods such as adaptive elastic net regularisation while leveraging the powerful hardware acceleration capabilities of Field Programmable Gate Array technology. The first contribution of this thesis represents a Field Programmable Gate Array based algorithmic trading system which supports multiple trading strategies that can be either run in parallel or switched at run-time according to changes in market volatility, for more elaborate trading strategies. Three types of hardware designs are compared: a static reconfiguration, a full reconfiguration, and a partial reconfiguration design. We evaluate our approach using both synthetic and historical market data and we notice that our system can obtain a considerable speedup when compared to its software implementation counterpart. The second contribution of this thesis presents an evolutionary hybrid genetic program which uses aspects of swarm intelligence to seek reliable and profitable trading patterns to enhance trading strategies. We use Field Programmable Gate Array technology to accelerate the fitness evaluation step, one of the most computationally demanding operations in genetic programming. The proposed design is based on run-time reconfiguration to improve hardware use, being substantially faster than an optimised, multi-threaded software implementation while achieving comparable financial returns. The third contribution of this thesis represents a Field Programmable Gate Array based custom regularisation and regression solver, CRRS. We also introduce an Adaptive Elastic Net pipelined architecture implemented on Field Programmable Gate Arrays for maximum parallelism performance. We further show how CRRS can provide an efficient, scalable solution, allowing us to handle large-scale datasets that cannot fit the on-board DRAM of a single FPGA. Our solver proves to be efficient in different scenarios. For example, when applied to dimensionality reduction for a portfolio of foreign exchange rates, which uses the efficient kitchen-sink regression approach within the Parametric Portfolio Policies technique.", notes = "Nee Funie? UBS Supervisors: Wayne Luk and Mark Salmon", } @InProceedings{Cruz-Salinas:2017:GECCO, author = "Andres Felipe Cruz-Salinas and Jonatan Gomez Perdomo", title = "Self-adaptation of Genetic Operators Through Genetic Programming Techniques", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4920-8", address = "Berlin, Germany", pages = "913--920", size = "8 pages", URL = "http://doi.acm.org/10.1145/3071178.3071214", DOI = "doi:10.1145/3071178.3071214", acmid = "3071214", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, evolutionary algorithms, real optimization, self-adaptation, self-adapted operators", month = "15-19 " # jul, abstract = "Here we propose an evolutionary algorithm that self modifies its operators at the same time that candidate solutions are evolved. This tackles convergence and lack of diversity issues, leading to better solutions. Operators are represented as trees and are evolved using genetic programming (GP) techniques. The proposed approach is tested with real benchmark functions and an analysis of operator evolution is provided.", notes = "Also known as \cite{Cruz-Salinas:2017:SGO:3071178.3071214} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @InProceedings{Csaba:2019:SYNASC, author = "Sulyok Csaba", title = "Towards Automated Quality Assessment Methods in Algorithmic Music Composition", booktitle = "2019 21st International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)", year = "2019", pages = "155--158", month = "4-7 " # sep, address = "Timisoara, Romania", keywords = "genetic algorithms, genetic programming, fitness raters, linear genetic programming, evolutionary algorithms, algorithmic music composition, MIDI", isbn13 = "978-1-7281-5725-2", DOI = "doi:10.1109/SYNASC49474.2019.00029", size = "4 pages", abstract = "The current work in progress paper describes a proof of concept for an automatic fitness evaluator in an evolutionary music composition setting. The associated research project proposes a novel algorithmic music creation mechanism. It uses linear genetic programming to create short musical pieces statistically similar to real-world pieces from a corpus. We present two fully automatic quality assessment methods for music, both used as fitness functions in the genetic algorithm: one proposed in a previous research stage, as well as a novel one involving n-grams. Experiments are proposed and described for comparing these measurement mechanisms to each other as well as to other automated methods present in the literature.", notes = "Also known as \cite{9049871}", } @Article{Csukas:1996:CCE, author = "B. Csukas and R. Lakner and K. Varga and S. Balogh", title = "Combining generated structural models with genetic programming in evolutionary synthesis", journal = "Computers \& Chemical Engineering", year = "1996", volume = "20", pages = "S61--S66", number = "Supplement 1", note = "European Symposium on Computer Aided Process Engineering-6", keywords = "genetic algorithms, genetic programming", ISSN = "0098-1354", DOI = "doi:10.1016/0098-1354(96)00021-X", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6TFT-48JC24K-F/2/d8223cad7192932d658ef2274794f502", abstract = "A new methodology has been proposed that combines structural modelling with genetic programming, and establishes an integrated toolkit for chemical process engineering. The principle is that similarly to the engineering way of thinking the modelling is based on the a priori known structures, while the final evaluation is made in the knowledge of the best detailed simulation experiences. The basic features of the method are the following: - -The conservational processes are mapped directly onto a descriptive computer program that can be executed by the help of a general purpose simulator automatically.- -The applied structural modelling technique, separating the invariant and the problem specific actual knowledge, supports the integrated problem solving.- -The genetic model of the typical time varied process engineering networks is synthesised automatically.- -There is an evaluation feedback from the synthesised and simulated variants to the genetic elements.", } @Article{Csukas:1998:CI, author = "Bela Csukas and Sandor Balogh", title = "Combining genetic programming with generic simulation models in evolutionary synthesis", journal = "Computers in Industry", volume = "36", pages = "181--197", year = "1998", number = "3", month = "1 " # jun, keywords = "genetic algorithms, genetic programming, Generic simulation, Genetic evolution, Process design, Structural modeling, Multicriteria evaluation", URL = "http://www.sciencedirect.com/science/article/B6V2D-3VW737S-3/1/87e285c0690af97d9d081c4f2582fdcd", DOI = "doi:10.1016/S0166-3615(98)00071-2", abstract = "In the proposed combined model of the engineering synthesis, the simulation and the parametric design are organized by the genetic building elements, while the genetic possibilities are evaluated by the experiences, obtained from the detailed dynamic simulation. Using this methodology, a new, integrated toolkit can de developed for the creative problem solving in (chemical) process engineering. The combination of the structural modeling with the genetic programming suggests a possible theoretical framework and proposes a practical methodology for the solution of the various synthesis (design, planning, scheduling, ...) problems.", } @Article{csuvik:2025:TSE, author = "Viktor Csuvik and Daniel Horvath and Mark Lajko and Laszlo Vidacs", title = "{GenProgJS}: a Baseline System for Test-based Automated Repair of JavaScript Programs", journal = "IEEE Transactions on Software Engineering", year = "2025", volume = "51", number = "2", pages = "325--343", month = feb, keywords = "genetic algorithms, genetic programming, genetic improvement, APR, GENPROGJS, JavaScript, Maintenance engineering, Computer bugs, Codes, Software development management, Source coding, Genetic operators, Software algorithms, Software, Automated program repair, software debugging", ISSN = "0098-5589", DOI = "doi:10.1109/TSE.2024.3497798", code_url = "https://github.com/GenProgJS", code_url = "https://genprogjs.github.io/GenProgJS/", size = "19 pages", notes = "also known as \cite{10759840} Mocha, BugJS, Esprima Department of Software Engineering, University of Szeged, Szeged, Hungary", } @Misc{DBLP:journals/corr/abs-2005-01207, author = "Edwin Camilo Cubides and Jonatan Gomez", title = "Obtaining Basic Algebra Formulas with Genetic Programming and Functional Rewriting", howpublished = "arXiv", volume = "abs/2005.01207", year = "2020", keywords = "genetic algorithms, genetic programming", URL = "https://arxiv.org/abs/2005.01207", archiveprefix = "arXiv", eprint = "2005.01207", timestamp = "Fri, 08 May 2020 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/corr/abs-2005-01207.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{CUEVAS:2018:ESA, author = "Erik Cuevas and Luis Enriquez and Daniel Zaldivar and Marco Perez-Cisneros", title = "A selection method for evolutionary algorithms based on the Golden Section", journal = "Expert Systems with Applications", volume = "106", pages = "183--196", year = "2018", keywords = "genetic algorithms, genetic programming, Evolutionary algorithms, Golden Section, Selection methods, Evolutionary strategies (ES), Evolutionary computation", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2018.03.064", URL = "http://www.sciencedirect.com/science/article/pii/S0957417418302215", abstract = "During millions of years, nature has developed patterns and processes with interesting characteristics. They have been used as inspiration for a significant number of innovative models that can be extended to solve complex engineering and mathematical problems. One of the most famous patterns present in nature is the Golden Section (GS). It defines an especial proportion that allows the adequate formation, selection, partition, and replication in several natural phenomena. On the other hand, Evolutionary algorithms (EAs) are stochastic optimization methods based on the model of natural evolution. One important process in these schemes is the operation of selection which exerts a strong influence on the performance of their search strategy. Different selection methods have been reported in the literature. However, all of them present an unsatisfactory performance as a consequence of the deficient relations between elitism and diversity of their selection procedures. In this paper, a new selection method for evolutionary computation algorithms is introduced. In the proposed approach, the population is segmented into several groups. Each group involves a certain number of individuals and a probability to be selected, which are determined according to the GS proportion. Therefore, the individuals are divided into categories where each group contains individual with similar quality regarding their fitness values. Since the possibility to choose an element inside the group is the same, the probability of selecting an individual depends exclusively on the group from which it belongs. Under these conditions, the proposed approach defines a better balance between elitism and diversity of the selection strategy. Numerical simulations show that the proposed method achieves the best performance over other selection algorithms, in terms of its solution quality and convergence speed", } @InProceedings{cui:2022:BOMTA, author = "Henning Cui and Andreas Margraf and Joerg Haehner", title = "Refining Mutation Variants in Cartesian Genetic Programming", booktitle = "Bioinspired Optimization Methods and Their Applications", year = "2022", editor = "Marjan Mernik and Tome Eftimov and Matej Crepinsek", volume = "13627", series = "LNCS", pages = "185--200", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", isbn13 = "978-3-031-21094-5", URL = "http://link.springer.com/chapter/10.1007/978-3-031-21094-5_14", DOI = "doi:10.1007/978-3-031-21094-5_14", abstract = "we improve upon two frequently used mutation algorithms and therefore introduce three refined mutation strategies for Cartesian Genetic Programming. At first, we take the probabilistic concept of a mutation rate and split it into two mutation rates, one for active and inactive nodes respectively. Afterwards, the mutation method Single is taken and extended. Single mutates nodes until an active node is hit. Here, our extension mutates nodes until more than one but still predefined number n of active nodes are hit. At last, this concept is taken and a decay rate for n is introduced. Thus, we decrease the required number of active nodes hit per mutation step during CGP's training process. We show empirically on different classification, regression and boolean regression benchmarks that all methods lead to better fitness values. This is then further supported by probabilistic comparison methods such as the Bayesian comparison of classifiers and the Mann-Whitney-U-Test. However, these improvements come with the cost of more mutation steps needed which in turn lengthens the training time. The third variant, in which n is decreased, does not differ from the second mutation strategy listed.", } @InProceedings{Cui:2023:FOGA, author = "Henning Cui and David Paetzel and Andreas Margraf and Joerg Haehner", title = "Weighted Mutation of Connections To Mitigate Search Space Limitations in Cartesian Genetic Programming", booktitle = "Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms", year = "2023", editor = "Francisco Chicano and Franz Rothlauf", pages = "50--60", address = "Potsdam, Germany", month = "30 " # aug # "-1 " # sep, organisation = "SIGEVO", publisher = "Association for Computing Machinery", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, CGP, Evolutionary Algorithm, Mutation, DAG", isbn13 = "9798400702020", DOI = "doi:10.1145/3594805.3607130", size = "11 pages", abstract = "we present and evaluate modification to mutation operators for Cartesian Genetic Programming (CGP). We discuss and highlight a so far unresearched limitation of how CGP explores its search space which is caused by certain nodes being inactive for long periods of time. Our new mutation operator is intended to avoid this by associating each node with a dynamically changing weight. When mutating a connection between nodes, those weights are then used to bias the probability distribution in favour of inactive nodes. This way, inactive nodes have a higher probability of becoming active again. We include our mutation operator into two variants of CGP and benchmark both versions on four Boolean learning tasks. We analyse the average numbers of iterations a node is inactive and show that our modification has the intended effect on node activity. The influence of our modification on the number of iterations until a solution is reached is ambiguous if the same number of nodes is used as in the baseline without our modification. However, our results show that our new mutation operator leads to fewer nodes being required for the same performance; this saves CPU time in each iteration.", notes = "Parity, Encode, Decode, Multiply FOGA17", } @InProceedings{DBLP:conf/ijcci/CuiMH23, author = "Henning Cui and Andreas Margraf and Joerg Haehner", editor = "Niki {van Stein} and Francesco Marcelloni and H. K. Lam and Marie Cottrell and Joaquim Filipe", title = "Equidistant Reorder Operator for Cartesian Genetic Programming", booktitle = "Proceedings of the 15th International Joint Conference on Computational Intelligence, {IJCCI} 2023, Rome, Italy, November 13-15, 2023", pages = "64--74", publisher = "{SCITEPRESS}", year = "2023", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", URL = "https://doi.org/10.5220/0012174100003595", DOI = "doi:10.5220/0012174100003595", timestamp = "Fri, 08 Dec 2023 12:42:00 +0100", biburl = "https://dblp.org/rec/conf/ijcci/CuiMH23.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{DBLP:conf/ijcci/CuiMHH23, author = "Henning Cui and Andreas Margraf and Michael Heider and Joerg Haehner", editor = "Niki {van Stein} and Francesco Marcelloni and H. K. Lam and Marie Cottrell and Joaquim Filipe", title = "Towards Understanding Crossover for Cartesian Genetic Programming", booktitle = "Proceedings of the 15th International Joint Conference on Computational Intelligence, {IJCCI} 2023, Rome, Italy, November 13-15, 2023", pages = "308--314", publisher = "{SCITEPRESS}", year = "2023", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", URL = "https://doi.org/10.5220/0012231400003595", DOI = "doi:10.5220/0012231400003595", timestamp = "Fri, 08 Dec 2023 00:00:00 +0100", biburl = "https://dblp.org/rec/conf/ijcci/CuiMHH23.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{Cui:2024:PPSN, author = "Henning Cui and Michael Heider and Joerg Haehner", title = "Positional Bias Does Not Influence Cartesian Genetic Programming with Crossover", booktitle = "18th International Conference on Parallel Problem Solving from Nature", year = "2024", editor = "Heike Trautmann and Tea Tusar and Penousal Machado and Thomas Baeck", volume = "15148", series = "Lecture Notes in Computer Science", pages = "151--167", address = "University of Applied Sciences Upper Austria, Hagenberg, Austria", month = "13-18 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, CGP, Crossover, Recombination, Positional Bias", isbn13 = "978-3-031-70054-5", URL = "https://opus.bibliothek.uni-augsburg.de/opus4/frontdoor/index/index/docId/115315", URL = "https://rdcu.be/d5mCS", DOI = "doi:10.1007/978-3-031-70055-2_10", size = "17 pages", notes = "https://ppsn2024.fh-ooe.at/program/", } @InProceedings{Cui:2024:evomusart, author = "Wenqian Cui and Pedro Sarmento and Mathieu Barthet", title = "MoodLoopGP: Generating Emotion-Conditioned Loop Tablature Music with Multi-granular Features", booktitle = "13th International Conference on Artificial Intelligence in Music, Sound, Art and Design, EvoMusArt 2024", year = "2024", editor = "Colin Johnson and Sergio M. Rebelo and Iria Santos", series = "LNCS", volume = "14633", publisher = "Springer", address = "Aberystwyth", month = "3-5 " # apr, pages = "97--113", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Controllable Music Generation, Symbolic Music Generation, Deep Learning, ANN, Transformers, Guitar Tablatures, Guitar Pro", isbn13 = "978-3-031-56991-3", URL = "https://rdcu.be/dD0Ek", DOI = "doi:10.1007/978-3-031-56992-0_7", notes = "http://www.evostar.org/2024/ EvoMusArt2024 held in conjunction with EuroGP'2024, EvoCOP2024 and EvoApplications2024", } @InProceedings{football, author = "Tianxiang Cui and Jingpeng Li and John R. Woodward and Andrew J. Parkes", title = "An Ensemble Based Genetic Programming System to Predict English Football Premier League Games", booktitle = "2013 IEEE Symposium Series on Computational Intelligence", year = "2013", editor = "P. N. Suganthan", pages = "138--143", address = "Singapore", month = "16-19 " # apr, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/EAIS.2013.6604116", size = "6 pages", abstract = "Predicting the result of a football game is challenging due to the complexity and uncertainties of many possible influencing factors involved. Genetic Programming (GP) has been shown to be very successful at evolving novel and unexpected ways of solving problems. In this work, we apply GP to the problem of predicting the outcomes of English Premier League games with the result being either win, lose or draw. We select 25 features from each game as the inputs to our GP system, which will then generate a function to predict the result. The experimental test on the prediction accuracy of a single GP generated function is promising. One advantage of our GP system is, by implementing different runs or using different settings, it can generate as many high quality functions as we want. It has been showed that combining the decisions of a number of classifiers can provide better results than a single one. In this work, we combine 43 different GP-generated functions together and achieve significantly improved system performance.", notes = "http://www.ntu.edu.sg/home/epnsugan/index_files/SSCI2013/ EAIS 2013, also known as \cite{6604116}", } @InProceedings{DBLP:conf/gecco/CuiBO09, author = "Wei Cui and Anthony Brabazon and Michael O'Neill", title = "Efficient trade execution using a genetic algorithm in an order book based artificial stock market", booktitle = "GECCO-2009 Late-Breaking Papers", year = "2009", editor = "Anna I. Esparcia and Ying-ping Chen and Gabriela Ochoa and Ender Ozcan and Marc Schoenauer and Anne Auger and Hans-Georg Beyer and Nikolaus Hansen and Steffen Finck and Raymond Ros and Darrell Whitley and Garnett Wilson and Simon Harding and W. B. Langdon and Man Leung Wong and Laurence D. Merkle and Frank W. Moore and Sevan G. Ficici and William Rand and Rick Riolo and Nawwaf Kharma and William R. Buckley and Julian Miller and Kenneth Stanley and Jaume Bacardit and Will Browne and Jan Drugowitsch and Nicola Beume and Mike Preuss and Stephen L. Smith and Stefano Cagnoni and Jim DeLeo and Alexandru Floares and Aaron Baughman and Steven Gustafson and Maarten Keijzer and Arthur Kordon and Clare Bates Congdon and Laurence D. Merkle and Frank W. Moore", pages = "2023--2028", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1570256.1570270", abstract = "Although there is a plentiful literature on the use of evolutionary methodologies for the trading of financial assets, little attention has been paid to the issue of efficient trade execution. Trade execution is concerned with the actual mechanics of buying or selling the desired amount of a financial instrument of interest. This paper introduces the concept of trade execution and outlines the limited prior work applying evolutionary computing methods for this task. Furthermore, we build an Agent-based Artificial Stock Market and apply a Genetic Algorithm to evolve an efficient trade execution strategy. Finally we suggest a number of opportunities for future research.", notes = "Distributed on CD-ROM at GECCO-2009. ACM Order Number 910092.", } @InProceedings{cui:2010:evofin, author = "Wei Cui and Anthony Brabazon and Michael O'Neill", title = "Evolving Dynamic Trade Execution Strategies Using Grammatical Evolution", booktitle = "EvoFIN", year = "2010", editor = "Cecilia {Di Chio} and Anthony Brabazon and Gianni A. {Di Caro} and Marc Ebner and Muddassar Farooq and Andreas Fink and Jorn Grahl and Gary Greenfield and Penousal Machado and Michael O'Neill and Ernesto Tarantino and Neil Urquhart", volume = "6025", series = "LNCS", pages = "192--201", address = "Istanbul", month = "7-9 " # apr, organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming, grammatical evolution", isbn13 = "978-3-642-12241-5", DOI = "doi:10.1007/978-3-642-12242-2_20", abstract = "Although there is a plentiful literature on the use of evolutionary methodologies for the trading of financial assets, little attention has been paid to potential use of these methods for efficient trade execution. Trade execution is concerned with the actual mechanics of buying or selling the desired amount of a financial instrument of interest. Grammatical Evolution (GE) is an evolutionary automatic programming methodology which can be used to evolve rule sets. In this paper we use a GE algorithm to discover dynamic, efficient, trade execution strategies which adapt to changing market conditions. The strategies are tested in an artificial limit order market. GE was found to be able to evolve quality trade execution strategies which are highly competitive with two benchmark trade execution strategies.", notes = "EvoFIN'2010 held in conjunction with EuroGP'2010 EvoCOP2010 EvoBIO2010", } @InProceedings{cui_etal:cec2010, author = "Wei Cui and Anthony Brabazon and Michael O'Neill", title = "Evolving Efficient Limit Order Strategy using Grammatical Evolution", booktitle = "2010 IEEE World Congress on Computational Intelligence", pages = "2408--2413", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, grammatical evolution", isbn13 = "978-1-4244-6910-9", DOI = "doi:10.1109/CEC.2010.5586040", abstract = "Trade execution is concerned with the actual mechanics of buying or selling the desired amount of a financial instrument of interest. A practical problem in trade execution is how to trade a large order as efficiently as possible. A trade execution strategy is designed for this task to minimise total trade cost. Grammatical Evolution (GE) is an evolutionary automatic programming methodology which can be used to evolve rule sets. It has been proved successfully to be able to evolve quality trade execution strategies in our previous work. In this paper, the previous work is extended by adopting two different limit order lifetimes and three benchmark limit order strategies. GE is used to evolve efficient limit order strategies which can determine the aggressiveness levels of limit orders. We found that GE evolved limit order strategies were highly competitive against three benchmark strategies and the limit order strategies with long-term lifetime performed better than those with short-term lifetime.", notes = "WCCI 2010. Also known as \cite{5586040}", } @InCollection{CuiBO:2010:NCCFECTE, author = "Wei Cui and Anthony Brabazon and Michael O'Neill", title = "Evolutionary Computation and Trade Execution (Volume 3)", booktitle = "Natural Computing in Computational Finance", editor = "Anthony Brabazon and Michael O'Neill and Dietmar Maringer", chapter = "4", publisher = "Springer", year = "2010", volume = "293", series = "Studies in Computational Intelligence", pages = "45--62", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-13949-9", DOI = "doi:10.1007/978-3-642-13950-5_4", abstract = "Although there is a plentiful literature on the use of evolutionary methodologies for the trading of financial assets, little attention has been paid to the issue of efficient trade execution. Trade execution is concerned with the actual mechanics of buying or selling the desired amount of a financial instrument of interest. This chapter introduces the concept of trade execution and outlines the limited prior work applying evolutionary computing methods for this task. Furthermore, we build an Agent-based Artificial Stock Market and apply a Genetic Algorithm to evolve an efficient trade execution strategy. Finally, we suggest a number of opportunities for future research.", } @Article{CuiBO:2011:IJFMDDTEAGEA, author = "Wei Cui and Anthony Brabazon and Michael O'Neill", title = "Dynamic Trade Execution: A Grammatical Evolution Approach", journal = "International Journal of Financial Markets and Derivatives", year = "2011", volume = "2", number = "1-2", pages = "4--31", note = "Special Issue on Computational Methods For Financial Engineering Guest Editors: Dr. Nikolaos S. Thomaidis and Dr. Christos Floros", keywords = "genetic algorithms, genetic programming, grammatical evolution, algorithmic trading, trade execution, artificial stock markets, evolutionary computation, financial markets, market impact, opportunity cost, agent-based systems.", ISSN = "1756-7130", URL = "http://www.inderscience.com/info/inarticle.php?artid=38526", DOI = "doi:10.1504/IJFMD.2011.038526", abstract = "Trade execution is concerned with the actual mechanics of buying or selling the desired amount of a financial instrument. Investors wishing to execute large orders face a tradeoff between market impact and opportunity cost. Trade execution strategies are designed to balance out these costs, thereby minimising total trading cost. Despite the importance of optimising the trade execution process, this is difficult to do in practice due to the dynamic nature of markets and due to our imperfect understanding of them. In this paper, we adopt a novel approach, combining an evolutionary methodology whereby we evolve high-quality trade execution strategies, with an agent-based artificial stock market, wherein the evolved strategies are tested. The evolved strategies are found to outperform a series of benchmark strategies and several avenues are suggested for future work.", notes = "http://www.inderscience.com/jhome.php?jcode=ijfmd", } @PhdThesis{wei:thesis, author = "Wei Cui", title = "An Empirical Investigation of Price Impact: An Agent-based Modelling Approach", school = "Michael Smurfit School of Business University College Dublin", year = "2012", address = "Ireland", month = nov, keywords = "genetic algorithms, genetic programming", URL = "http://ncra.ucd.ie/papers/wei_thesis.pdf", size = "268 pages", abstract = "Understanding price impact is a fundamental task in finance. Many execution algorithms, used to execute a large order by dividing and spreading it over time, are based on price effects and in particular on the way how volume affects prices. Moreover, the analysis of price impact is helpful for understanding how financial markets function as price impact is one of the mechanisms determining price formation. The thesis is motivated by the recent emergence of algorithmic trading which requires a good understanding of price impact. This thesis addresses three questions concerning price impact in order to gain a better understanding on the intraday behaviour of price impact, and the factors affecting price impact. The first study examines the intraday behaviours of price impact and market liquidity. The data is drawn from the NYSE-Euronext TAQ database and the LSE ROB database. Six stocks from the US markets and six stocks from the UK markets are analysed. The intraday patterns on price volatility, bid-ask spread, trading volume and market depth are documented and generally confirm findings in prior studies on intraday phenomena. In particular, a reverse S-shaped intraday pattern on price impact is found for both US and UK stocks for the first time. The second study investigates whether agent intelligence plays an important role in determining the magnitude of price impact. This chapter constructs an artificial stock market composed of zero-intelligence agents, and calibrates it using the LSE ROB data. The result shows that the price impact in the artificial market is generally larger than that in the real market. This is consistent with the hypothesis that agent intelligence plays an important role in determining the magnitude of price impact. It supports the selective liquidity argument in Farmer et al. (2004) & Hopman (2007). The third study addresses whether order choice affects the price impact of trading a large order. A typical approach in trading a large order is to devise a strategy which divides it into numerous pieces and spreads it over time (usually one trading day). In this study, several execution strategies with various order types, and a number of simple strategies with one order type as benchmarks are constructed and evaluated by their effects on prices. Novelly, these strategies are evolved and evaluated in simulated artificial markets. The results show that the combined strategies outperform the simple strategies significantly, suggesting that order choice plays an important role in determining the price impact of trading large orders. The results in this thesis suggest that time-of-the-day, agent intelligence and order choice are important factors affecting price impact, and need to be considered in the theoretical microstructure models and in the design of trading strategies.", notes = "Research Supervisor: Prof. Anthony Brabazon", } @Article{Evolution_of_the_Discrete_Cosine_Transform_Using_Genetic_Programming, author = "Xiang Biao Cui and Martin Johnson", title = "Evolution of the Discrete Cosine Transform Using Genetic Programming", journal = "Research Letters in the Information and Mathematical Sciences", year = "2002", volume = "3", pages = "117--125", keywords = "genetic algorithms, genetic programming", URL = "http://mro.massey.ac.nz/handle/10179/4332", URL = "http://mro.massey.ac.nz/handle/10179/4363", URL = "http://hdl.handle.net/10179/4363", size = "10 pages", abstract = "Compression of 2 dimensional data is important for the efficient transmission, storage and manipulation of Images. The most common technique used for lossy image compression relies on fast application of the Discrete Cosine Transform (DCT). The cosine transform has been heavily researched and many efficient methods have been determined and successfully applied in practice; this paper presents a novel method for evolving a DCT algorithm using genetic programming. We show that it is possible to evolve a very close approximation to a 4 point transform. In theory, an 8 point transform could also be evolved using the same technique.", notes = "Available online at http://www.massey.ac.nz/~wwiims/research/letters/ ", } @InProceedings{Cuibus:2012:AQTR, author = "Octavian P. Cuibus and Tiberiu S. Letia", title = "Genetic programming synthesis of discrete event controllers applied to urban vehicle traffic control", booktitle = "IEEE International Conference on Automation Quality and Testing Robotics (AQTR 2012)", year = "2012", month = "24-27 " # may, pages = "79--84", size = "6 pages", abstract = "The paper presents a new method for generating discrete event systems as control units for a type of plants which can be modelled as delay time Petri nets. The control unit contains many transitions joined together by a set of operands and it is generated by means of the genetic programming method using a Lisp representation of the solution. Transitions are characterised by an enabling condition (reaction or feedback) and an effect (control), which represent the interaction with the plant. Crossover and mutation operators are defined for Lisp expressions. The method is applied to urban vehicle traffic control.", keywords = "genetic algorithms, genetic programming, Lisp representation, control unit, crossover operators, delay time Petri nets, discrete event controllers, feedback condition, genetic programming synthesis, mutation operators, reaction condition, urban vehicle traffic control, LISP, Petri nets, control engineering computing, delays, discrete event systems, road traffic control, road vehicles", DOI = "doi:10.1109/AQTR.2012.6237679", notes = "Also known as \cite{6237679}", } @InProceedings{DBLP:conf/ices/Cullen08, author = "Jamie Cullen", title = "Evolutionary Meta Compilation: Evolving Programs Using Real World Engineering Tools", booktitle = "Proceedings of the 8th International Conference Evolvable Systems: From Biology to Hardware, ICES 2008", year = "2008", editor = "Gregory Hornby and Luk{\'a}s Sekanina and Pauline C. Haddow", series = "Lecture Notes in Computer Science", volume = "5216", pages = "414--419", address = "Prague, Czech Republic", month = sep # " 21-24", publisher = "Springer", bibsource = "DBLP, http://dblp.uni-trier.de", keywords = "genetic algorithms, genetic programming, grammatical evolution", isbn13 = "978-3-540-85856-0", DOI = "doi:10.1007/978-3-540-85857-7_38", size = "6 pages", abstract = "A general purpose system and technique is presented for the separation of target program compilation and fitness evaluation from the primary evolutionary computation system. Preliminary results are presented for two broadly different domains: (1) Software generated in the C programming language, (2) Hardware designs in Verilog, suitable for synthesis. The presented approach frees the developer from implementing and debugging a complex interpreter, and potentially enables the rapid integration of previously unsupported languages, as well as complex methods of fitness evaluation, by leveraging the availability of external tools. It also enables engineers (especially those in industry) to use preferred/approved tools for which source code may not be readily available, or which may be cost or time prohibitive to reimplement. Efficiency gains are also expected, particularly for complex domains where the fitness evaluation is computationally intensive.", notes = "Artificial Intelligence Laboratory, University of New South Wales, Sydney, NSW. Santa Fe ant. Taxi problem (loops). gcc. tiny c (tcc), full adder circuit", } @InProceedings{DBLP:conf/seal/Cullen08, author = "Jamie Cullen", title = "Evolving Digital Circuits in an Industry Standard Hardware Description Language", booktitle = "Proceedings of the 7th International Conference on Simulated Evolution And Learning (SEAL '08)", year = "2008", editor = "Xiaodong Li and Michael Kirley and Mengjie Zhang and David G. Green and Victor Ciesielski and Hussein A. Abbass and Zbigniew Michalewicz and Tim Hendtlass and Kalyanmoy Deb and Kay Chen Tan and J{\"u}rgen Branke and Yuhui Shi", volume = "5361", series = "Lecture Notes in Computer Science", pages = "514--523", address = "Melbourne, Australia", month = dec # " 7-10", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", isbn13 = "978-3-540-89693-7", DOI = "doi:10.1007/978-3-540-89694-4_52", abstract = "Evolutionary Meta Compilation (EMC) is a recent technique that enables unmodified external applications to seamlessly perform target program compilation and fitness evaluation for an Evolutionary Computation system. Grammatical Evolution (GE) is a method for evolving computer programs in an arbitrary programming language using a grammar specified in Backus-Naur Form. This paper combines these techniques to demonstrate the evolution of both sequential and combinational digital circuits in an Industry Standard Hardware Description Language (Verilog) using an external hardware synthesis engine and simulator. Overall results show the successful evolution of core digital circuit components. An extension to GE is also presented to attempt to increase the probability of maintaining an evolved program's semantic integrity after crossover operations are performed. Early results show performance improvements in applying this technique to the majority of the presented test cases. It is suggested that this feature may also be considered for use in the evolution of software programs in C and other languages.", bibsource = "DBLP, http://dblp.uni-trier.de", } @InProceedings{Cullen:2009:GEC, author = "Jamie Cullen", title = "Evolving common LISP programs in a linear-genotype evolutionary computation system", booktitle = "GEC '09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation", year = "2009", editor = "Lihong Xu and Erik D. Goodman and Guoliang Chen and Darrell Whitley and Yongsheng Ding", bibsource = "DBLP, http://dblp.uni-trier.de", pages = "75--80", address = "Shanghai, China", organisation = "SigEvo", DOI = "doi:10.1145/1543834.1543846", publisher = "ACM", publisher_address = "New York, NY, USA", month = jun # " 12-14", isbn13 = "978-1-60558-326-6", keywords = "genetic algorithms, genetic programming", abstract = "Evolutionary Meta Programming (EMP) is an approach to Evolutionary Computation, which allows freedom of programming language choice in the evolved programs, as well as the ready use of external tools and testbenches, with which to perform fitness evaluation. The current implementation of EMP uses a linear genotype in a manner similar to Grammatical Evolution (GE). In contrast, traditional Genetic Programming (GP) typically uses a subset of the LISP programming language to represent target programs in a tree-based structure. The ability of EMP to leverage external tools and arbitrary languages enables the rapid prototyping of possibly novel approaches to Evolutionary Computation. One such experiment is presented herein: The evolution of Common LISP language constructs using a linear genotype and associated grammar, and evaluation using a real external LISP interpreter. An exploratory study is performed with three classic problems: Symbolic Regression, Ant Trail, and Towers of Hanoi. Solutions to these problems were evolved in both Common LISP and ANSI C versions, and runtime and performance results collected. Present results are relatively unintuitive, when compared to conventional programming wisdom, with some problems apparently favoring a paradigm not traditionally suited to them in a non-evolutionary programming setting.", notes = "Also known as \cite{DBLP:conf/gecco/Cullen09} part of \cite{DBLP:conf/gec/2009}", } @InProceedings{Cullen:2009:GECa, author = "Jamie Cullen", title = "Evolutionary meta programming", booktitle = "GEC '09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation", year = "2009", editor = "Lihong Xu and Erik D. Goodman and Guoliang Chen and Darrell Whitley and Yongsheng Ding", bibsource = "DBLP, http://dblp.uni-trier.de", pages = "81--88", address = "Shanghai, China", organisation = "SigEvo", DOI = "doi:10.1145/1543834.1543847", publisher = "ACM", publisher_address = "New York, NY, USA", month = jun # " 12-14", isbn13 = "978-1-60558-326-6", keywords = "genetic algorithms, genetic programming", abstract = "The Evolutionary Meta Programming (EMP) approach towards the evolution of computer programs is presented. An EMP system is divided into two interacting parts: The Host Environment, and the Target Environment. Programs are evolved in an arbitrary target language by the Host Environment and are injected into the Target Environment, where they are evaluated for fitness in their `natural surroundings'. Early results from three significantly different domains are discussed: (1) Compiling C programs using a well-known compiler (GNU C compiler) (2) Circuit synthesis of digital hardware in an industry standard Hardware Description Language (Verilog), and (3) Functional Programming in an external Common LISP interpreter. The presented approach has now been used to evolve solutions to some well-known problems in the field of Evolutionary Computation, as well as enabling the initial examination of some novel problem domains that are typically not amenable to exploration by common techniques. Possible strengths of this approach, when compared to techniques such as Genetic Programming, include more rapid and natural problem specification and testbench development for some types of problems, reduced software development time, and the potential to more readily examine problems that require complex methods of fitness evaluation.", notes = "Also known as \cite{DBLP:conf/gecco/Cullen09a} part of \cite{DBLP:conf/gec/2009}", } @InProceedings{cummins:2004:lbp, author = "Ronan Cummins and Colm O'Riordan", title = "Using Genetic Programming to Evolve Weighting Schemes for the Vector Space Model of Information Retrieval", booktitle = "Late Breaking Papers at the 2004 Genetic and Evolutionary Computation Conference", year = "2004", editor = "Maarten Keijzer", address = "Seattle, Washington, USA", month = "26 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://gpbib.cs.ucl.ac.uk/gecco2004/LBP038.pdf", abstract = "Term weighting in many Information Retrieval models is of crucial importance in the research and development of accurate retrieval systems. This paper explores a method to automatically determine suitable term weighting schemes for the vector space model. Genetic Programming is used to automatically evolve weighting schemes that return a high average precision. These weighting functions are tested on well-known test collections and compared to the tf-idf based weighting scheme using standard Information Retrieval performance metrics.", notes = "Part of \cite{keijzer:2004:GECCO:lbp}", } @InProceedings{cummins:2004:AICS, author = "Ronan Cummins and Colm O'Riordan", title = "Determining General Term Weighting Schemes for the Vector Space Model of Information Retrieval Using Genetic Programming", booktitle = "15th Artificial Intelligence and Cognitive Science Conference (AICS 2004)", year = "2004", editor = "Lorraine McGinty", address = "Galway-Mayo Institute of Technology, Castlebar Campus, Ireland", month = "8-10 " # sep, keywords = "genetic algorithms, genetic programming, NLP", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.99.5031", URL = "http://ww2.it.nuigalway.ie/cirg/localpubs/CumminsAICS2004.pdf", size = "10 pages", abstract = "Term weighting schemes play a vital role in the performance of many Information Retrieval models. The vector space model is one such model in which the weights applied to the document terms are of crucial importance to the accuracy of the retrieval system. This paper outlines a procedure using genetic programming to automatically determine term weighting schemes that achieve a high average precision. The schemes are tested on standard test collections and are shown to perform consistently better than the traditional tf-idf weighting schemes. We present an analysis of the evolved weighting schemes to explain their increase in performance. These term weighting schemes are shown to be general across various collections and are shown to adhere to Luhn's theory as both high and low frequency terms are assigned a low weight.", notes = "Broken Jan 2013 http://www.gmit.ie/aics_2004/", } @TechReport{Cummins:2004:071204, author = "Ronan Cummins and Colm O'Riordan", title = "Evolving, Analysing and Improving Global Term-Weighting Schemes in Information Retrieval", institution = "National University of Ireland, Galway", year = "2004", type = "Technical Report", number = "NUIG-IT-071204", address = "Ireland", keywords = "genetic algorithms, genetic programming, information retrieval, term-weighting", URL = "http://www.it.nuigalway.ie/Publications/TR/abstracts/NUIG-IT-071204.pdf", abstract = "The ability of a term to distinguish documents, and ultimately topics, is crucial to the performance of many Information Retrieval models. We present and analyse global weighting schemes for the vector space model developed by means of evolutionary computation. The global schemes presented are shown to increase average precision over the IDF measure on TREC data. The global schemes are also shown to be consistent with Luhns theory of resolving power as certain middle frequency terms are assigned the highest weight. The use of the collection frequency measure of a term is seen as crucial to the performance of these schemes. We also show that the analysis of these evolved schemes is an important step to understanding and improving their performance.", notes = "25 January 2005", size = "11 pages", } @TechReport{Cummins:2005:201205, author = "Ronan Cummins and Colm O'Riordan", title = "Evolving Term-Selection Schemes for Pseudo-Relevance Feedback in Information Retrieval", institution = "National University of Ireland, Galway", year = "2005", number = "NUIG-IT-201205", address = "Ireland", keywords = "genetic algorithms, genetic programming", URL = "http://www.it.nuigalway.ie/publications/TR/abstracts/NUIG-IT-201205.ps", notes = "Problem displaying page 1", size = "9 pages", } @InProceedings{cummins:2005:CIKM, author = "Ronan Cummins and Colm O'Riordan", title = "An evaluation of evolved term-weighting schemes in information retrieval", year = "2005", booktitle = "CIKM '05: Proceedings of the 14th ACM international conference on Information and knowledge management", editor = "Otthein Herzog and Hans-Jorg Schek and Norbert Fuhr and Abdur Chowdhury and Wilfried Teiken", pages = "305--306", address = "Bremen, Germany", publisher_address = "New York, NY, USA", month = "31 " # oct # " - 5 " # nov, organisation = "ACM", publisher = "ACM press", keywords = "genetic algorithms, genetic programming, information retrieval, term-weighting, Poster Session", ISBN = "1-59593-140-6", URL = "http://portal.acm.org/citation.cfm?doid=1099639", DOI = "doi:10.1145/1099554.1099639", size = "2 pages", abstract = "presents an evaluation of evolved term-weighting schemes on short, medium and long TREC queries. A previously evolved global (collection-wide) term-weighting scheme is evaluated on unseen TREC data and is shown to increase mean average precision over idf. A local (within-document) evolved term-weighting scheme is presented which is dependent on the best performing global scheme. The full evolved scheme (i.e. the combined local and global scheme) is compared to both the BM25 scheme and the Pivoted Normalisation scheme. Our results show that the local evolved solution does not perform well on some collections due to its document normalisation properties and we conclude that Okapi-tf can be tuned to interact effectively with the evolved global weighting scheme presented and increase mean average precision over the standard BM25 scheme.", order_no = "605050", notes = "Proceedings of the 14th ACM international conference on Information and knowledge management", } @InProceedings{cummins:2005:AICS, author = "Ronan Cummins and Colm O'Riordan", title = "Evolving Co-occurrence Based Query Expansion Schemes in Information Retrieval Using Genetic Programming", booktitle = "The 16th Irish conference on Artificial Intelligence and Cognitive Science (AICS05)", year = "2005", editor = "Norman Creaney", pages = "137--146", address = "School of Computing and Information Engineering, University of Ulster", publisher_address = "Cromore Road, Coleraine, BT52 1SA, UK", month = "7-9 " # sep, publisher = "University of Ulster", keywords = "genetic algorithms, genetic programming, information retrieval, query expansion", ISBN = "1-85923-197-7", URL = "http://www.infc.ulst.ac.uk/~norman/aics05/AICS05_Proceedings_V3.pdf", abstract = "Global query expansion techniques have long been proposed as a solution to overcome the problem of term mismatch between a query and its relevant documents. This paper describes a method which automatically tackles the problems of how to find the best terms for the expansion of a particular query and secondly, how to weight these terms for use with the original query. Genetic Programming is used to evolve schemes for term selection using global (collection-wide) co-occurrence measures. The schemes evolved are also used to weight the term in the expanded query as they are a measure of the term's importance in relation to the query. As a result, the genetic program has to learn a suitable scheme for identifying the best correlates for the query concept and also a scheme that correctly weights these in relation to each other. These schemes are tested on standard test collections and show a significant increase in performance on the training data but only modest improvement on the collections that are not included in training.", notes = "http://www.infc.ulst.ac.uk/~norman/aics05/", } @Article{Cummins:2005:AIR, author = "Ronan Cummins and Colm O'Riordan", title = "Evolving General Term-Weighting Schemes for Information Retrieval: Tests on Larger Collections", journal = "Artificial Intelligence Review", year = "2005", volume = "24", number = "3-4", pages = "277--299", month = nov, email = "ronan.cummins@nuigalway.ie", keywords = "genetic algorithms, genetic programming, term-weighting schemes, Information Retrieval", ISSN = "0269-2821", DOI = "doi:10.1007/s10462-005-9001-y", abstract = "Term-weighting schemes are vital to the performance of Information Retrieval models that use term frequency characteristics to determine the relevance of a document. The vector space model is one such model in which the weights assigned to the document terms are of crucial importance to the accuracy of the retrieval system. We describe a genetic programming framework used to automatically determine term-weighting schemes that achieve a high average precision. These schemes are tested on standard test collections and are shown to perform as well as, and often better than, the modern BM25 weighting scheme. We present an analysis of the schemes evolved to explain the increase in performance. Furthermore, we show that the global (collection wide) part of the evolved weighting schemes also increases average precision over idf on larger TREC data. These global weighting schemes are shown to adhere to Luhn's resolving power as middle frequency terms are assigned the highest weight. However, the complete weighting schemes evolved on small collections do not perform as well on large collections. We conclude that in order to evolve improved local (within-document) weighting schemes it is necessary to evolve these on large collections.", notes = "www.kluweronline.com/issn/0269-2821", } @Article{Cummins:2006:IR, author = "Ronan Cummins and Colm O'Riordan", title = "Evolving local and global weighting schemes in information retrieval", journal = "Information Retrieval", year = "2006", volume = "9", number = "3", pages = "311--330", month = jun, keywords = "genetic algorithms, genetic programming, Information Retrieval, Term-Weighting Schemes", ISSN = "1386-4564", DOI = "doi:10.1007/s10791-006-1682-6", abstract = "This paper describes a method, using Genetic Programming, to automatically determine term weighting schemes for the vector space model. Based on a set of queries and their human determined relevant documents, weighting schemes are evolved which achieve a high average precision. In Information Retrieval (IR) systems, useful information for term weighting schemes is available from the query, individual documents and the collection as a whole. We evolve term weighting schemes in both local (within-document) and global (collection-wide) domains which interact with each other correctly to achieve a high average precision. These weighting schemes are tested on well-known test collections and are compared to the traditional tf-idf weighting scheme and to the BM25 weighting scheme using standard IR performance metrics. Furthermore, we show that the global weighting schemes evolved on small collections also increase average precision on larger TREC data. These global weighting schemes are shown to adhere to Luhn's resolving power as both high and low frequency terms are assigned low weights. However, the local weightings evolved on small collections do not perform as well on large collections. We conclude that in order to evolve improved local (within-document) weighting schemes it is necessary to evolve these on large collections.", } @InProceedings{Cummins:2006:ECAI, author = "Ronan Cummins and Colm O'Riordan", title = "Term-Weighting in Information Retrieval using Genetic Programming: A Three Stage Process", booktitle = "The 17th European Conference on Artificial Intelligence, ECAI-2006", year = "2006", editor = "Gerhard Brewka and Silvia Coradeschi and Anna Perini and Paolo Traverso", pages = "793--794", address = "Riva del Garda, Italy", month = aug # " 28th - " # sep # " 1st", publisher = "IOS Press", bibdate = "2006-10-25", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ecai/ecai2006.html#CumminsO06", keywords = "genetic algorithms, genetic programming, poster, information retrieval, term-weighting", ISBN = "1-58603-642-4", URL = "http://ww2.it.nuigalway.ie/cirg/localpubs/CumminsECAI2006.pdf", size = "2 pages", notes = "ECAI-2001 http://ecai2006.itc.it/cda/aree/index.php?section=76&area=13", } @InProceedings{rc-tir06, author = "Ronan Cummins and Colm O'Riordan", title = "A Framework for the study of Evolved Term-Weighting Schemes in Information Retrieval", booktitle = "TIR-06 Text based Information Retrieval, Workshop. ECAI 2006", year = "2006", editor = "Benno Stein and Odej Kao", address = "Riva del Garda, Italy", month = "29 " # aug, keywords = "genetic algorithms, genetic programming, information retrieval, phenotype distance", URL = "http://ww2.it.nuigalway.ie/cirg/localpubs/CumminsECAI2006-Workshop.pdf", URL = "http://www-ai.upb.de/aisearch/tir-06/proceedings/cummins06-framework-for-the-study-evolved-term-weighting-schemes-IR.pdf", abstract = "Evolutionary algorithms and, in particular, Genetic Programming (GP) are increasingly being applied to the problem of evolving term-weighting schemes in Information Retrieval (IR). One fundamental problem with the solutions generated by these stochastic processes is that they are often difficult to analyse. A number of questions regarding these evolved term-weighting schemes remain unanswered. One interesting question is; do different runs of the GP process bring us to similar points in the solution space? This paper deals with determining a number of measures of the distance between the ranked lists (phenotype) returned by different term-weighting schemes. Using these distance measures, we develop trees that show the phenotypic distance between these termweighting schemes. This framework gives us a representation of where these evolved solutions lie in the solution space. Finally, we evolve several global term-weighting schemes and show that this framework is indeed useful for determining the relative closeness of these schemes and for determining the expected performance on general test data.", notes = "TIR-06 http://www.aisearch.de/tir-06/", } @InProceedings{Cummins:2006:AICS, author = "Ronan Cummins and Colm O'Riordan", title = "An analysis of the Solution Space for Genetically Programmed Term-Weighting Schemes in Information Retrieval", booktitle = "17th Irish Artificial Intelligence and Cognitive Science Conference (AICS 2006)", year = "2006", editor = "D. A. Bell", address = "Queen's University, Belfast", month = "11th-13th " # sep, organisation = "Artificial Intelligence Association of Ireland", keywords = "genetic algorithms, genetic programming", URL = "http://ir.dcs.gla.ac.uk/~ronanc/papers/cumminsAICS06.pdf", size = "10 pages", abstract = "Evolutionary algorithms and Genetic Programming (GP) in particular are increasingly being applied to the problem of evolving term-weighting schemes in Information Retrieval (IR). One fundamental problem with the solutions generated by this stochastic, non-deterministic process is that they are often difficult to analyse. We develop a number of different distance measures between the phenotypes (ranked lists) of the solutions (term-weighting schemes) returned by a GP process. Using these distance measures, we develop trees which show how different solutions are clustered in the solution space. Using this framework we show that our evolved solutions lie in a different part of the solution space than two of the best benchmark term-weighting schemes available.", notes = "http://www.cs.qub.ac.uk/aics06/aics.html", } @InProceedings{1277390, author = "Ronan Cummins and Colm O'Riordan", title = "Using genetic programming for information retrieval: local and global query expansion", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "2255--2255", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p2255.pdf", DOI = "doi:10.1145/1276958.1277390", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Real-World Applications: Poster, information retrieval, query-expansion", abstract = "This poster presents results for two approaches using Genetic Programming (GP) to overcome the problem of term mismatch in Information Retrieval (IR). We use automatic query expansion techniques which add terms to a user's initial query in the hope that these words better describe the information need and ultimately return more relevant documents to the user.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @InProceedings{Cummins:2007:AICS, author = "Ronan Cummins and Colm O'Riordan", title = "An Axiomatic Comparison of Learned Term-weighting Schemes in Information Retrieval", booktitle = "18th Irish Conference on Artificial Intelligence and Cognitive Science", year = "2007", editor = "Sarah Jane Delany and Michael Madden", pages = "41--50", address = "Dublin Institute of Technology", month = "29-31 " # aug, keywords = "genetic algorithms, genetic programming", notes = "http://www.comp.dit.ie/aics07/program.html Feb 2013 http://datamining.it.nuigalway.ie/images/.../aics07-proceedings-frontmatter.... See also \cite{cummins:2007a:AIR}", } @Article{cummins:2007:AIR, author = "Ronan Cummins and Colm O'Riordan", title = "Evolved term-weighting schemes in Information Retrieval: an analysis of the solution space", journal = "Artificial Intelligence Review", year = "2006", volume = "26", number = "1-2", pages = "35--47", month = oct, keywords = "genetic algorithms, genetic programming, Information Retrieval, Term-weighting schemes", DOI = "doi:10.1007/s10462-007-9034-5", abstract = "Evolutionary computation techniques are increasingly being applied to problems within Information Retrieval (IR). Genetic programming (GP) has previously been used with some success to evolve term-weighting schemes in IR. However, one fundamental problem with the solutions generated by this stochastic, non-deterministic process, is that they are often difficult to analyse. In this paper, we introduce two different distance measures between the phenotypes (ranked lists) of the solutions (term-weighting schemes) returned by a GP process. Using these distance measures, we develop trees which show how different solutions are clustered in the solution space. We show, using this framework, that our evolved solutions lie in a different part of the solution space than two of the best benchmark term-weighting schemes available.", notes = "Published online: 12 September 2007", } @Article{cummins:2007a:AIR, author = "Ronan Cummins and Colm O'Riordan", title = "An axiomatic comparison of learned term-weighting schemes in information retrieval: clarifications and extensions", journal = "Artificial Intelligence Review", year = "2007", volume = "28", number = "1", pages = "51--68", month = jun, keywords = "genetic algorithms, genetic programming, Information retrieval, Axiomatic constraints", DOI = "doi:10.1007/s10462-008-9074-5", size = "51 pages", abstract = "Machine learning approaches to information retrieval are becoming increasingly widespread. In this paper, we present term-weighting functions reported in the literature that were developed by four separate approaches using genetic programming. Recently, a number of axioms (constraints), from which all good term-weighting schemes should be deduced, have been developed and shown to be theoretically and empirically sound. We introduce a new axiom and empirically validate it by modifying the standard BM25 scheme. Furthermore, we analyse the BM25 scheme and the four learned schemes presented to determine if the schemes are consistent with the axioms. We find that one learned term-weighting approach is consistent with more axioms than any of the other schemes. An empirical evaluation of the schemes on various test collections and query lengths shows that the scheme that is consistent with more of the axioms outperforms the other schemes.", notes = "Published online: 13 September 2008", } @InProceedings{Cummins:2007:SIGIR, author = "Ronan Cummins and Colm O'Riordan", title = "An Axiomatic Study of Learned Term-Weighting Schemes", booktitle = "SIGIR 2007 workshop: Learning to Rank for Information Retrieval", year = "2007", editor = "Thorsten Joachims and Hang Li and Tie-Yan Liu and ChengXiang Zhai", month = "27 " # jul, organisation = "Microsoft", keywords = "genetic algorithms, genetic programming", URL = "http://ww2.it.nuigalway.ie/cirg/localpubs/axioms.pdf", abstract = "At present, there exists many term-weighting schemes each based on different underlying models of retrieval. Learn- ing approaches are increasingly being applied to the term- weighting problem, further increasing the number of useful term-weighting approaches available. Many of these term- weighting schemes have certain features and properties in common. As such, it is beneficial to formally model these common features and properties. In this paper, we introduce a term-weighting scheme that has been developed incrementally using an evolutionary learn- ing approach. We analyse one such term-weighting function produced from the evolutionary approach by decomposing it into inductive query and document growth functions. Con- sequently, we show that it is consistent with a number of axioms previously postulated for term-weighting schemes. Interestingly, we show that a further constraint can be de- rived from the resultant scheme. Finally, we empirically validate our analysis, and the newly developed constraint, by showing that the newly developed nonparametric term-weighting scheme can outperform BM25 and the pivoted document length normalisation scheme over many different query types and collections. We conclude that the scheme produced from the learning approach adds further evidence to the validity of the axioms.", notes = "https://research.microsoft.com/en-us/um/beijing/events/LR4IR-2007/", } @PhdThesis{Cummins:thesis, author = "Ronan Cummins", title = "The Evolution and Analysis of Term-Weighting Schemes in Information Retrieval", school = "National University of Ireland, Galway", year = "2008", address = "Ireland", month = may, keywords = "genetic algorithms, genetic programming", URL = "http://www3.it.nuigalway.ie/cirg/rcummins_thesis.pdf", size = "201 pages", abstract = "Information Retrieval is concerned with the return of relevant documents from a document collection given a user query. Term-weighting schemes assign weights to keywords (terms) based on how useful they are likely to be in identifying the topic of a document and are one of the most crucial aspects in relation to the performance of Information Retrieval systems. Much research has focused on developing both term-weighting schemes and theories to support them. Genetic Programming is a biologically-inspired search algorithm useful for searching large complex search spaces. It uses a Darwinian-inspired survival of the fittest approach to search for solutions of a suitable fitness. This thesis outlines experiments that use Genetic Programming to search for term-weighting schemes. A study of term-weighting schemes in the literature is undertaken and consequently, the function space is separated into three areas that represent three fundamental concepts in term weighting. Experiments using Genetic Programming to search these three function spaces show that term-weighting schemes that outperform state of the art term-weighting benchmarks can be found. These experiments also show that the new term-weighting schemes have general properties as they achieve high performance on unseen test data. An analysis of the solution space of the term-weighting schemes shows that the evolved solutions exist in a different part of the space than the current benchmarks. These experiments show that the Genetic Programming approach consistently evolves solutions that return similar ranked lists in each of the three function spaces. Furthermore, the best performing term-weighting schemes are formally analysed and are shown to satisfy a number of axioms in Information Retrieval. A detailed analysis of the existing axioms is presented together with some amendments and additions to the existing axioms. This analysis aids in theoretically validating the term-weighting schemes evolved in the framework. Finally, a secondary application of Genetic Programming to Information Retrieval is presented to show the potential for Genetic Programming in addressing other issues in Information Retrieval. This experiment shows that Genetic Programming can be used to combine further evidence in the retrieval process to enhance performance. This approach evolves schemes for use with two automatic query expansion techniques to increase retrieval effectiveness.", notes = "Supervisor: Colm O'Riordan", } @InProceedings{Cummins:2009:SIGIR, author = "Ronan Cummins and Colm O'Riordan", title = "Learning in a pairwise term-term proximity framework for information retrieval", booktitle = "SIGIR '09: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval", year = "2009", editor = "James Allan and Javed Aslam", pages = "251--258", address = "Boston, MA, USA", publisher_address = "New York, NY, USA", publisher = "ACM", keywords = "genetic algorithms, genetic programming, information retrieval, learning to rank, proximity", isbn13 = "978-1-60558-483-6", DOI = "doi:10.1145/1571941.1571986", abstract = "Traditional ad hoc retrieval models do not take into account the closeness or proximity of terms. Document scores in these models are primarily based on the occurrences or non-occurrences of query-terms considered independently of each other. Intuitively, documents in which query-terms occur closer together should be ranked higher than documents in which the query-terms appear far apart. This paper outlines several term-term proximity measures and develops an intuitive framework in which they can be used to fully model the proximity of all query-terms for a particular topic. As useful proximity functions may be constructed from many proximity measures, we use a learning approach to combine proximity measures to develop a useful proximity function in the framework. An evaluation of the best proximity functions show that there is a significant improvement over the baseline ad hoc retrieval model and over other more recent methods that employ the use of single proximity measures.", notes = "Also known as \cite{1571986}", } @InProceedings{DBLP:conf/ecai/CumminsLO10, author = "Ronan Cummins and Mounia Lalmas and Colm O'Riordan", title = "Learning Aggregation Functions for Expert Search", year = "2010", booktitle = "Proceedings of the 19th European Conference on Artificial Intelligence, ECAI 2010", editor = "Helder Coelho and Rudi Studer and Michael Wooldridge", volume = "215", series = "Frontiers in Artificial Intelligence and Applications", pages = "535--540", address = "Lisbon, Portugal", month = aug # " 16-20", publisher = "IOS Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-60750-605-8", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", bibsource = "DBLP, http://dblp.uni-trier.de", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.419.500", URL = "http://ebooks.iospress.nl/publication/5831", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.419.500", URL = "http://ir.dcs.gla.ac.uk/~mounia/Papers/ECAI10.pdf", URL = "http://www.booksonline.iospress.nl/Content/View.aspx?piid=17702", URL = "http://dx.doi.org/10.3233/978-1-60750-606-5-535", size = "6 pages", abstract = "Machine learning techniques are increasingly being applied to problems in the domain of information retrieval and text mining. In this paper we present an application of evolutionary computation to the area of expert search. Expert search in the context of enterprise information systems deals with the problem of finding and ranking candidate experts given an information need (query). A difficult problem in the area of expert search is finding relevant information given an information need and associating that information with a potential expert. We attempt to improve the effectiveness of a benchmark expert search approach by adopting a learning model (genetic programming) that learns how to aggregate the documents/information associated with each expert. In particular, we perform an analysis of the aggregation of document information and show that different numbers of documents should be aggregated for different queries in order to achieve optimal performance. We then attempt to learn a function that optimises the effectiveness of an expert search system by aggregating different numbers of documents for different queries. Furthermore, we also present experiments for an approach that aims to learn the best way to aggregate documents for individual experts. We find that substantial improvements in performance can be achieved, over standard analytical benchmarks, by the latter of these approaches.", notes = "ECAI", } @Article{Cunge:2003:JH, author = "Jean A Cunge", title = "Of data and models", journal = "Journal of Hydroinformatics", year = "2003", volume = "5", number = "2", pages = "75--98", month = apr, keywords = "genetic algorithms, genetic programming", ISSN = "1464-7141", URL = "http://www.iwaponline.com/jh/005/0075/0050075.pdf", DOI = "doi:10.2166/hydro.2003.0007", size = "24 pages", abstract = "Relationship between the data, such as direct observations of nature and recorded measurements, and the models is very complicated in the 'water domain'. It is not at all as clear and explicit as it is often presented by teachers to students, by consultants to clients, or by authors to readers of publications. A number of aspects of this relationship are discussed using examples to illustrate the author's views. Limitations of data-driven tools (correlations, Artificial Neuronal Networks, Genetic Algorithms, etc.) and data-mining, when applied without physical knowledge of the relevant phenomena, are discussed, as are those of deterministic models. The currently used 'good practice' paradigm in modelling (the model is to be set up, calibrated, validated and run) is rejected when deterministic models are concerned. They should not be calibrated. A new paradigm, a new 'code of good practice', is proposed instead. Strategic and tactical aspects of various available approaches to modelling of physical phenomena and data exploitation have practical engineering and financial consequences, most often immediate and sometimes very important: hence the significance of the subject that concerns the everyday occupations of modellers, their clients and end-users.", notes = "GP amongst others", } @Article{Cunha:2018:ietMAP, author = "Alexandre Ashade L. Cunha and Marco Aurelio Pacheco", journal = "IET Microwaves, Antennas Propagation", title = "Efficient model based on genetic programming and spline functions to find modes of unconventional waveguides", year = "2018", volume = "12", number = "7", pages = "1099--1106", abstract = "The contribution of this work is twofold: the authors developed an accurate model to solve the vector wave equation of radially-layered inhomogeneous wave guides based on spline function expansions and automated grid construction by genetic programming, and then employed this model to analyse the propagation of electromagnetic waves within oil wells. The developed model uses a spline expansion of the fields to convert the wave equation into a quadratic eigenvalue problem where eigenvectors represent the coefficients of the splines and eigenvalues represent the propagation constant of the eigenmode. The present study compared the proposed model using the classical winding number technique. The results obtained for the first eigenstates of a typical oil well geometry were more accurate than those obtained by the winding number method. Moreover, the authors model could find a larger amount of eigenmodes for a fixed azimuthal parameter than the standard approach.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1049/iet-map.2017.0490", ISSN = "1751-8725", notes = "Also known as \cite{8371456}", } @Article{Cunkas:2011:Energy_Sources, author = "M. Cunkas and U. Taskiran", title = "Turkey's Electricity Consumption Forecasting Using Genetic Programming", journal = "Energy Sources, Part B: Economics, Planning, and Policy", year = "2011", volume = "6", number = "4", pages = "406--416", month = jul, keywords = "genetic algorithms, genetic programming, electricity consumption, energy forecasting, Turkey", publisher = "Taylor \& Francis", ISSN = "1556-7249", DOI = "doi:10.1080/15567240903047558", size = "11 pages", abstract = "Turkey's energy demand has been increasing rapidly as a result of rapid urbanization and industrialization. The energy investment requirement will be US$130 billion by the year 2020. Electricity energy has a vital importance among the energy sector. In this study the current state of the electricity energy production and consumption of Turkey is investigated and the electricity energy consumption is forecasted by using genetic algorithm. The obtained results are compared with conventional regression analyses techniques, and the estimated values of the Ministry of Energy and Natural Resources. The electricity demand in the year 2020 is estimated to be 315.02 billion kWh compared to the 189.52 billion kWh needed in the year 2007.", notes = "Technical Education Faculty, Department of Electronics & Computer Education, Selcuk University, Konya, Turkey", } @InProceedings{cunningham:2011:AAL.D+vN, author = "Alan Cunningham and Colm O'Riordan", title = "A Genetic Programming Approach to an Appropriation Common Pool Game", booktitle = "Advances in Artificial Life. {Darwin} Meets {von Neumann}", year = "2011", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-642-21314-4_21", DOI = "doi:10.1007/978-3-642-21314-4_21", } @PhdThesis{Cunningham:thesis, title = "The Evolution of Groups for Common Pool Resource Sharing Applications of Genetic Programming to Groups for Computer Game Artificial Intelligence", author = "Alan Cunningham", school = "NUI Galway", year = "2013", address = "Ireland", month = sep, keywords = "genetic algorithms, genetic programming", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.453.4470", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.453.4470", URL = "http://hdl.handle.net/10379/3771", URL = "http://aran.library.nuigalway.ie/xmlui/bitstream/handle/10379/3771/thesis.pdf", size = "251 pages", abstract = "The scope and scale of computer games has increased such that, creating unique hand-made behaviours for each character becomes unfeasible. Throughout a typical computer game there are many AI characters with which the player will meet and interact, but only some of these characters will be central to the main story. There is a tendency to rely on template behaviours which are replicated throughout the game world. This thesis concerns the creation of groups of characters which, through the use simple actions, cooperate and coordinate to survive together. These groups are created automatically using Evolutionary Computation (EC) methods. In order to apply EC algorithms to a domain, the problem being solved will be executed and evaluated a large number of times as solutions are created, altered and refined towards a good solution. As computer games tend to be resource intensive, running thousands of simulations using a game world is not feasible. An abstract representation of a game world is needed. Selecting group based dilemmas from the social science and economic literature provides a suitable abstract representation. A Common Pool Resource (CPR) dilemma is chosen which models a group's use of a shared resource. Previous studies of human behaviours with this game environment allow for the comparison of the automatically generated solutions against expected behaviours and human performance. It is shown that by introducing irrationality into the solution creation, human-like play can be generated automatically. .........", notes = "Supervisor: Colm O'Riordan", } @InCollection{cunningham:2003:UGAEWSO, author = "Tucker Cunningham", title = "Using the Genetic Algorithm to Evolve a Winning Strategy for Othello", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2003", year = "2003", editor = "John R. Koza", pages = "31--37", address = "Stanford, California, 94305-3079 USA", month = "4 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms", URL = "http://www.genetic-programming.org/sp2003/Cunningham.pdf", notes = "part of \cite{koza:2003:gagp}", } @InProceedings{Cupertino:2011:CIGPU, author = "Leandro F. Cupertino and Cleomar P. Silva and Douglas M. Dias and Marco Aurelio C. Pacheco and Cristiana Bentes", title = "Evolving {CUDA PTX} programs by quantum inspired linear genetic programming", booktitle = "GECCO 2011 Computational intelligence on consumer games and graphics hardware (CIGPU)", year = "2011", editor = "Simon Harding and W. B. Langdon and Man Leung Wong and Garnett Wilson and Tony Lewis", isbn13 = "978-1-4503-0690-4", keywords = "genetic algorithms, genetic programming, EDA, Artificial Intelligence, automatic programming, program synthesis, Performance, GPU, CUDA, PTX, quantum-inspired algorithms", pages = "399--406", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001858.2002026", publisher = "ACM", publisher_address = "New York, NY, USA", size = "8 pages", abstract = "The tremendous computing power of Graphics Processing Units (GPUs) can be used to accelerate the evolution process in Genetic Programming (GP). The automatic generation of code using the GPU usually follows two different approaches: compiling each evolved or interpreting multiple programs. Both approaches, however, have performance drawbacks. In this work, we propose a novel approach where the GPU pseudo-assembly language, PTX (Parallel Thread Execution), is evolved. Evolving PTX programs is faster, since the compilation of a PTX program takes orders of magnitude less time than a CUDA program compilation on the CPU, and no interpreter is necessary. Another important aspect of our approach is that the evolution of PTX programs follows the Quantum Inspired Linear Genetic Programming (QILGP). Our approach, called QILGP3U (QILGP + GPGPU), enables the evolution on a single machine in a reasonable time, enhances the quality of the model with the use of PTX, and for big databases can be much faster than the CPU implementation.", notes = "No absolute speed measure given (cf. \cite{langdon:2008:eurogp}). Mexican Hat. Almost all time spent compiling PTX. Header-body(evolved)-foot. nVidia Tesla C1060 GPU. Also known as \cite{2002026} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @InProceedings{Curran:2010:gecco, author = "Dara Curran and Eugene Freuder and Thomas Jansen", title = "Incremental evolution of local search heuristics", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "981--982", keywords = "genetic algorithms, genetic programming, incremental evolution, genetic programming, local search heuristics, graph colouring, hyperheuristics, Poster", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830483.1830660", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "In evolutionary computation, incremental evolution refers to the process of employing an evolutionary environment that becomes increasingly complex over time. We present an implementation of this approach to develop randomised local search heuristics for constraint satisfaction problems, combining research on incremental evolution with local search heuristics evolution. A population of local search heuristics is evolved using a genetic programming framework on a simple problem for a short period and is then allowed to evolve on a more complex problem. Experiments compare the performance of this population with that of a randomly initialised population evolving directly on the more complex problem. The results obtained show that incremental evolution can represent a significant improvement in terms of optimisation speed, solution quality and solution structure.", notes = "Culberon's random graph generator. Also known as \cite{1830660} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @InProceedings{currey:2004:CSCSI, author = "Robert Curry and Malcolm I. Heywood", title = "Towards Efficient Training on Large Datasets for Genetic Programming", booktitle = "17th Conference of the Canadian Society for Computational Studies of Intelligence", year = "2004", editor = "Ahmed Y. Tawfik and Scott D. Goodwin", volume = "3060", series = "LNAI", pages = "161--174", address = "London, Ontario, Canada", month = "17-19 " # may, publisher = "Springer-Verlag", email = "mheywood@cs.dal.ca", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-22004-6", URL = "http://users.cs.dal.ca/~mheywood/X-files/Publications/robert-CaAI04.pdf", DOI = "doi:10.1007/b97823", abstract = "Genetic programming (GP) has the potential to provide unique solutions to a wide range of supervised learning problems. The technique, however, does suffer from a widely acknowledged computational overhead. As a consequence applications of GP are often confined to datasets consisting of hundreds of training exemplars as opposed to tens of thousands of exemplars, thus limiting the widespread applicability of the approach. In this work we propose and thoroughly investigate a data sub-sampling algorithm hierarchical dynamic subset selection that filters the initial training dataset in parallel with the learning process. The motivation being to focus the GP training on the most difficult or least recently visited exemplars. To do so, we build on the dynamic sub-set selection algorithm of Gathercole \cite{ga94aGathercole} and extend it into a hierarchy of subset selections, thus matching the concept of a memory hierarchy supported in modern computers. Such an approach provides for the training of GP solutions to data sets with hundreds of thousands of exemplars in tens of minutes whilst matching the classification accuracies of more classical approaches.", } @Article{curry:2007:SMC, author = "Robert Curry and Peter Lichodzijewski and Malcolm I. Heywood", title = "Scaling Genetic Programming to Large Datasets Using Hierarchical Dynamic Subset Selection", journal = "IEEE Transactions on Systems, Man, and Cybernetics: Part B - Cybernetics", year = "2007", volume = "37", number = "4", pages = "1065--1073", month = aug, email = "mheywood@cs.dal.ca", keywords = "genetic algorithms, genetic programming, active learning, classification, unbalanced data, hierarchical DSS, RSS, linear genetic programming, casGP", ISSN = "1083-4419", URL = "http://www.cs.dal.ca/~mheywood/X-files/GradPubs.html#curry", DOI = "doi:10.1109/TSMCB.2007.896406", size = "9 pages", abstract = "The computational overhead of Genetic Programming (GP) may be directly addressed without recourse to hardware solutions using active learning algorithms based on the Random or Dynamic Subset Selection heuristics (RSS or DSS). This work begins by presenting a family of hierarchical DSS algorithms: RSS-DSS, cascaded RSS-DSS, and the Balanced Block DSS algorithm; where the latter has not been previously introduced. Extensive benchmarking over four unbalanced real-world binary classification problems with 30,000 to 500,000 training exemplars demonstrates that both the cascade and Balanced Block algorithms are able to reduce the likelihood of degenerates, whilst providing a significant improvement in classification accuracy relative to the original RSS-DSS algorithm. Moreover, comparison with GP trained without an active learning algorithm indicates that classification performance is not compromised, while training is completed in minutes as opposed to half a day.", notes = "max prog length=8, comparsion with lilGP, binary classification, unbalanced training sets, selecting balanced training subsets, page based crossover", } @InProceedings{Curry:2007:SMCb, author = "R. Curry and M. I. Heywood", title = "One-Class Learning with Multi-Objective Genetic Programming", booktitle = "Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics", year = "2007", pages = "1938--1945", address = "Montreal", month = "7-10 " # oct, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, evolutionary multi-criteria optimisation, one-class learning", ISBN = "1-4244-0991-8", URL = "http://users.cs.dal.ca/~mheywood/X-files/GradPubs.html#curry", abstract = "One-class classification naturally only provides one class of exemplars on which to construct the classification model. In this work, multi-objective genetic programming (GP) allows the one-class learning problem to be decomposed by multiple GP classifiers, each attempting to identify only a subset of the target data to classify. In order for GP to identify appropriate subsets of the one-class data, artificial outclass data is generated in and around the provided inclass data. A local Gaussian wrapper is employed where this reinforces a novelty detection as opposed to a discrimination approach to classification. Furthermore, a hierarchical subset selection strategy is used to deal with the necessarily large number of generated outclass exemplars. The proposed approach is demonstrated on three one-class classification datasets and was found to be competitive with a one-class SVM classifier and a binary SVM classifier.", notes = "http://www.smc2007.org/program.html rcurry_SMC07.pdf is twenty pages", URL = "http://users.cs.dal.ca/~mheywood/X-files/Publications/rcurry_SMC07.pdf", } @InProceedings{Curry:2009:eurogp, author = "Robert Curry and Malcolm Heywood", title = "One-Class Genetic Programming", booktitle = "Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009", year = "2009", editor = "Leonardo Vanneschi and Steven Gustafson and Alberto Moraglio and Ivanoe {De Falco} and Marc Ebner", volume = "5481", series = "LNCS", pages = "1--12", address = "Tuebingen", month = apr # " 15-17", organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-01180-1", DOI = "doi:10.1007/978-3-642-01181-8_1", notes = "Part of \cite{conf/eurogp/2009} EuroGP'2009 held in conjunction with EvoCOP2009, EvoBIO2009 and EvoWorkshops2009", } @Article{cus:2003:ME, author = "Franci Cus and Joze Balic and Uros Zuperl", title = "Genetic algorithm based optimisation of end milling parameters", journal = "Machine Engineering", year = "2003", volume = "3", number = "1/2", pages = "116--126", keywords = "genetic algorithms", ISSN = "1642-6568", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/cus_2003_ME.pdf", size = "10 pages", abstract = "The paper proposes a new optimization technique based on genetic algorithms for the determination of the cutting parameters in machining operations. In metal cutting processes, cutting conditions have an influence on reducing the production cost and time and deciding the quality of a final product. This paper presents a new methodology for continual improvement of cutting conditions with GA (Genetic Algorithms). It performs the following: the modification of recommended cutting conditions obtained from a machining data, learning of obtained cutting conditions using neural networks and the substitution of better cutting conditions for those learned previously by a proposed GA. Operators usually select the machining parameters according to handbooks or their experience, and the selected machining parameters are usually conservative to avoid machining failure. Compared to traditional optimisation methods, a GA is robust, global and may be applied generally without recourse to domain-specific heuristics. Experimental results show that the proposed genetic algorithm- based procedure for solving the optimisation problem is both effective and efficient, and can be integrated into an intelligent manufacturing system for solving complex machining optimisation problems.", notes = "Also appears as: {"}Manufacturing flexibility design and development{"}, Jerzy Jedrzejewski (editor), NOT, Karpacz, Poland, Wroclaw: Editorial institution of the Wroclaw board of federation of scientific societies. For Machine Engineering journal see also \cite{kusiak:2001:ME}", } @Article{cus:2003:RCIM, author = "Franci Cus and Joze Balic", title = "Optimization of cutting process by GA approach", journal = "Robotics and Computer-Integrated Manufacturing", year = "2003", volume = "19", number = "1-2", pages = "113--121", month = feb # "-" # apr, keywords = "genetic algorithms, genetic programming, Cutting parameters, Manufacturing, simulation", ISSN = "0736-5845", DOI = "doi:10.1016/S0736-5845(02)00068-6", abstract = "The paper proposes a new optimization technique based on genetic algorithms (GA) for the determination of the cutting parameters in machining operations. In metal cutting processes, cutting conditions have an influence on reducing the production cost and time and deciding the quality of a final product. This paper presents a new methodology for continual improvement of cutting conditions with GA. It performs the following: the modification of recommended cutting conditions obtained from a machining data, learning of obtained cutting conditions using neural networks and the substitution of better cutting conditions for those learned previously by a proposed GA. Experimental results show that the proposed genetic algorithm-based procedure for solving the optimisation problem is both effective and efficient, and can be integrated into an intelligent manufacturing system for solving complex machining optimisation problems.", notes = "http://www.elsevier.com/wps/find/journaldescription.cws_home/704/description#description", } @Article{cus:2004:ME, author = "Franc Cus and Matjaz Milfelner and Joze Balic", title = "Optimization of cutting forces in ball-end milling by GA", journal = "Machine Engineering", year = "2004", volume = "4", number = "1/2", pages = "281--288", keywords = "genetic algorithms, genetic programming", ISSN = "1642-6568", abstract = "This paper presents the system for optimization of ball-end milling process. The system combines different methods and technologies like evolutionary methods, manufacturing technology, measuring and control technology and intelligent process technology with the adequate hardware and software support. The system for optimization of ball-end milling process combines the process monitoring system of ball-end milling process and the optimization model. The monitoring system is designed for monitoring and collecting variables of the milling process by means of sensors and transformation of those data into numerical values which are a starting point for the optimization of the ball-end milling process. The optimization model is used for the optimisation of milling parameters with genetic algorithms. The optimization is based on the analytic and genetic cutting force model and tool wear model. The developed methods can be used for the cutting force estimation and optimization of cutting parameters. The integration of the proposed system will lead to the reduction in production costs and production time, flexibility in machining parameter selection, and improvement of product quality. The system for optimization of ball-end milling process of steels can be extended to machining different materials and to other cutting techniques such as conventional turning, drilling, grinding and high speed turning.", notes = "Also appears as: 'Machine tools and factories of the knowledge', Jerzy Jedrzejewsk (editor). http://www.not.pl/wydawnictwo/abstract.html _starting_ 2006 :-( For Machine Engineering journal see also \cite{kusiak:2001:ME}", } @Article{Cus200690, author = "F. Cus and M. Milfelner and J. Balic", title = "An intelligent system for monitoring and optimization of ball-end milling process", journal = "Journal of Materials Processing Technology", volume = "175", number = "1-3", pages = "90--97", year = "2006", note = "Achievements in Mechanical and Materials Engineering", ISSN = "0924-0136", DOI = "DOI:10.1016/j.jmatprotec.2005.04.041", URL = "http://www.sciencedirect.com/science/article/B6TGJ-4GJKTR6-4/2/5b1e17c8ac5f2a7435ab419b4db98260", keywords = "Genetic algorithm, Ball-end milling, Cutting forces, Monitoring, Optimization", abstract = "The paper presents an intelligent system for on-line monitoring and optimization of the cutting process on the model of the ball-end milling. An intelligent system for monitoring and optimization in ball-end milling is developed both in hardware and software. It is based on a PC, which is connected to the CNC main processor module through a serial-port so that control and communication can be realised. The monitoring system is based on LabVIEW software, the data acquisition system and the measuring devices (sensors) for the cutting force measuring. The system collects the variables of the cutting process by means of sensors. The measured values are delivered to the computer program through the data acquisition system for data processing and analysis. The optimization technique is based on genetic algorithms for the determination of the cutting conditions in machining operations. In metal cutting processes, cutting conditions have an influence on reducing the production cost and time and deciding the quality of a final product. Experimental results show that the proposed genetic algorithm-based procedure for solving the optimization problem is effective and efficient, and can be integrated into a real-time intelligent manufacturing system for solving complex machining optimization problems.", } @InProceedings{alifexi_cussatblanc_134, author = "Sylvain Cussat-Blanc and Herve Luga and Yves Duthen", title = "From single cell to simple creature morphology and metabolism", editor = "S. Bullock and J. Noble and R. Watson and M. A. Bedau", booktitle = "Artificial Life XI: Proceedings of the Eleventh International Conference on the Simulation and Synthesis of Living Systems", publisher = "MIT Press", month = "5-8 " # aug, address = "Winchester, Hants", publisher_address = "Cambridge, MA, USA", year = "2008", pages = "134--141", isbn13 = "978-0-262-75017-2", URL = "http://www.alifexi.org/papers/ALIFExi_pp134-141.pdf", keywords = "genetic algorithms, genetic programming", } @InProceedings{Cussat-Blanc:2009:cec, author = "Sylvain Cussat-Blanc and Herve Luga and Yves Duthen", title = "Cell2Organ: Self-Repairing Artificial Creatures Thanks to a Healthy Metabolism", booktitle = "2009 IEEE Congress on Evolutionary Computation", year = "2009", editor = "Andy Tyrrell", pages = "2708--2715", address = "Trondheim, Norway", month = "18-21 " # may, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-2959-2", file = "P391.pdf", DOI = "doi:10.1109/CEC.2009.4983282", abstract = "For living organisms, the robustness property is capital. For almost all of them, robustness rhymes with self repairing. Indeed, organisms are subject to various injuries brought by the environment. To maintain their integrity, organisms are able to regenerate dead parts of themselves. This mechanism, commonly named self-repairing, is interesting to reproduce. Many works exist about self-repairing in robotics and electronics but fewer are in our domain of interest, artificial embryogenesis. In this paper, we show the self-repairing abilities of our model, Cell2Organ, designed to generate artificial creatures for artificial worlds. This model has previously been presented in \cite{alifexi_cussatblanc_134}.", keywords = "genetic algorithms, genetic programming", notes = "Gene Regulartory Network GRN, promoter, enhance, inhibitor. Java. Grid5000.fr powered parallel GA \cite{Cussat-Blanc:2008:gecco} has three chromosomes. ProActive. CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR", } @Article{Cussat-Blanc:2018:alife, author = "Sylvain Cussat-Blanc and Kyle Harrington and Wolfgang Banzhaf", title = "Artificial Genetic Regulatory Networks - A Review", journal = "Artificial Life", year = "2018", volume = "24", number = "4", pages = "296--328", month = "Fall", keywords = "genetic algorithms, genetic programming, artificial regulatory networks, Gene regulatory networks, evolutionary algorithms, morphogenesis, control dynamics, neuromodulation", ISSN = "1064-5462", DOI = "doi:10.1162/artl_a_00267", size = "33 pages", abstract = "In nature, gene regulatory networks are a key mediator between the information stored in the DNA of living organisms (their genotype) and the structural and behavioral expression this finds in their bodies, surviving in the world (their phenotype). They integrate environmental signals, steer development, buffer stochasticity, and allow evolution to proceed. In engineering, modeling and implementations of artificial gene regulatory networks have been an expanding field of research and development over the past few decades. This review discusses the concept of gene regulation, describes the current state of the art in gene regulatory networks, including modeling and simulation, and reviews their use in artificial evolutionary settings. We provide evidence for the benefits of this concept in natural and the engineering domains.", notes = "University of Toulouse, IRIT, CNRS, UMR5505", } @InProceedings{Cussat-Blanc:2025:GPTP, author = "Sylvain Cussat-Blanc", title = "On Interpretability in Multimodal Biomedical Image Analysis", booktitle = "Genetic Programming Theory and Practice XXII", year = "2025", editor = "Wolfgang Banzhaf and Bogdan Burlacu and Stephen Kelly and Alexander Lalejini and Fabricio {Olivetti de Franca}", address = "Michigan State University, USA", month = jun # " 5-7", keywords = "genetic algorithms, genetic programming", notes = "To be published after the workshop", } @InProceedings{Custode:2022:POMA, author = "Leonardo Lucio Custode and Federico Mento and Sajjad Afrakhteh and Francesco Tursi and Andrea Smargiassi and Riccardo Inchingolo and Tiziano Perrone and Libertario Demi and Giovanni Iacca", title = "Neuro-symbolic interpretable {AI} for automatic {COVID-19} patient-stratification based on standardised lung ultrasound data", booktitle = "182nd Meeting of the Acoustical Society of America", year = "2022", volume = "46", pages = "Paper 2pBAb6", address = "Denver, Colorado, USA", month = "23-27 " # may # " 2022", publisher = "Acoustical Society of America", keywords = "genetic algorithms, genetic programming, grammatical evolution, NSGA-II", URL = "https://human-competitive.org/sites/default/files/custodeentryform.txt", URL = "https://human-competitive.org/sites/default/files/custodepapera.pdf", DOI = "doi:10.1121/2.0001600", size = "12 pages", abstract = "In the current pandemic, being able to efficiently stratify patients depending on their probability to develop a severe form of COVID-19 can improve the outcome of treatments and optimize the use of the available resources. To this end, recent studies proposed to use deep-networks to perform automatic stratification of COVID-19 patients based on lung ultrasound (LUS) data. In this work, we present a novel neuro-symbolic approach able to provide video-level predictions by aggregating results from frame-level analysis made by deep-networks. Specifically, a decision tree was trained, which provides direct access to the decision process and a high-level explainability. This approach was tested on 1808 LUS videos acquired from 100 patients diagnosed as COVID-19 positive by a RT-PCR swab test. Each video was scored by LUS experts according to a 4-level scoring system specifically developed for COVID-19. This information was used for both the training and testing of the algorithms. A five-folds cross-validation process was used to assess the performance of the presented approach and compare it with results achieved by deep-learning models alone. Results show that this novel approach achieves better performance (82percent of mean prognostic agreement) than a threshold-based ensemble of deep-learning models (78percent of mean prognostic agreement).", notes = "Entered 2023 HUMIES http://acousticalsociety.org/ See also Journal of the Acoustical Society of America 2022 151 A112--A113 https://pubs.aip.org/asa/jasa/article/151/4_Supplement/A112/2838489/Neuro-symbolic-interpretable-AI-for-automatic doi:10.1121/10.0010820", } @Article{Custode:2023:ASC, author = "Leonardo Lucio Custode and Federico Mento and Francesco Tursi and Andrea Smargiassi and Riccardo Inchingolo and Tiziano Perrone and Libertario Demi and Giovanni Iacca", title = "Multi-objective automatic analysis of lung ultrasound data from COVID-19 patients by means of deep learning and decision trees", journal = "Applied Soft Computing", year = "2023", volume = "133", pages = "109926", month = jan, keywords = "genetic algorithms, genetic programming, grammatical evolution, DEAP, AI, ANN, COVID-19, Lung ultrasound, Decision trees, Evolutionary algorithms, Neuro-symbolic artificial intelligence", ISSN = "1568-4946", URL = "https://human-competitive.org/sites/default/files/custodeentryform.txt", URL = "https://human-competitive.org/sites/default/files/custodepaperb.pdf", URL = "https://www.sciencedirect.com/science/article/pii/S1568494622009759", DOI = "doi:10.1016/j.asoc.2022.109926", code_url = "https://gitlab.com/leocus/neurosymbolic-covid19-scoring", size = "34 pages", abstract = "COVID-19 raised the need for automatic medical diagnosis, to increase the physicians' efficiency in managing the pandemic. Among all the techniques for evaluating the status of the lungs of a patient with COVID-19, lung ultrasound (LUS) offers several advantages: portability, cost-effectiveness, safety. Several works approached the automatic detection of LUS imaging patterns related COVID-19 by using deep neural networks (DNNs). However, the decision processes based on DNNs are not fully explainable, which generally results in a lack of trust from physicians. This, in turn, slows down the adoption of such systems. In this work, we use two previously built DNNs as feature extractors at the frame level, and automatically synthesize, by means of an evolutionary algorithm, a decision tree (DT) that aggregates in an interpretable way the predictions made by the DNNs, returning the severity of the patients conditions according to a LUS score of prognostic value. Our results show that our approach performs comparably or better than previously reported aggregation techniques based on an empiric combination of frame-level predictions made by DNNs. Furthermore, when we analyse the evolved DTs, we discover properties about the DNNs used as feature extractors. We make our data publicly available for further development and reproducibility.", notes = "Entered 2023 HUMIES Also known as \cite{CUSTODE2023109926}", } @PhdThesis{Custode:thesis, author = "Leonardo Lucio Custode", title = "Evolutionary Optimization of Decision Trees for Interpretable Reinforcement Learning", school = "Department of Information Engineering and Computer Science, Universita degli Studi di Trento", year = "2023", address = "Italy", month = "27 " # apr, keywords = "genetic algorithms, genetic programming, Interpretable AI, XAI, Reinforcement Learning, Evolutionary Computation, UMDA", URL = "https://iris.unitn.it/handle/11572/375447", URL = "https://iris.unitn.it/retrieve/bafe6bda-8244-42c6-8fff-6a98994cc61b/phd_unitn_leonardolucio_custode.pdf", DOI = "doi:10.15168/11572_375447", size = "173 pages", abstract = "While Artificial Intelligence (AI) is making giant steps, it is also raising concerns about its trustworthiness, due to the fact that widely-used black-box models cannot be exactly understood by humans. One of the ways to improve humans trust towards AI is to use interpretable AI models, i.e., models that can be thoroughly understood by humans, and thus trusted. However, interpretable AI models are not typically used in practice, as they are thought to be less performing than blackbox models. This is more evident in Reinforcement Learning, where relatively little work addresses the problem of performing Reinforcement Learning with interpretable models. we address this gap, proposing methods for Interpretable Reinforcement Learning. For this purpose, we optimize Decision Trees by combining Reinforcement Learning with Evolutionary Computation techniques, which allows us to overcome some of the challenges tied to optimizing Decision Trees in Reinforcement Learning scenarios. The experimental results show that these approaches are competitive with the state-of-the-art score while being extremely easier to interpret. Finally, we show the practical importance of Interpretable AI by digging into the inner working of the solutions obtained.", notes = "Defended on 27-apr-2023 In English. p111 7.2 'Neuro-Symbolic Analysis of COVID-19 Patients Data' Supervisor: Giovanni Iacca", } @InProceedings{custode:2024:GECCOcomp, author = "Leonardo Lucio Custode and Chiara Camilla {Migliore Rambaldi} and Marco Roveri and Giovanni Iacca", title = "Comparing Large Language Models and Grammatical Evolution for Code Generation", booktitle = "Large Language Models for and with Evolutionary Computation Workshop", year = "2024", editor = "Erik Hemberg and Roman Senkerik and Una-May O'Reilly and Michal Pluhacek and Tome Eftimov", pages = "1830--1837", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, grammatical evolution, large language models, code generation, ANN", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3664162", size = "8 pages", abstract = "Code generation is one of the most valuable applications of AI, as it allows for automated programming and {"}self-building{"} programs. Both Large Language Models (LLMs) and evolutionary methods, such as Genetic Programming (GP) and Grammatical Evolution (GE), are known to be capable of performing code generation with reasonable performance. However, to the best of our knowledge, little work has been done so far on a systematic comparison between the two approaches. Most importantly, the only studies that conducted such comparisons used benchmarks from the GP community, which, in our opinion, may have provided possibly GP-biased results. In this work, we perform a comparison of LLMs and evolutionary methods, in particular GE, using instead a well-known benchmark originating from the LLM community. Our results show that, in this scenario, LLMs can solve significantly more tasks than GE, indicating that GE struggles to match the performance of LLMs on code generation tasks that have different properties from those commonly used in the GP community.", notes = "GECCO-2024 LLMfwEC A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{Custode:2025:evoapplications, author = "Leonardo Lucio Custode and Giovanni Iacca", title = "Social Interpretable Reinforcement Learning", booktitle = "28th International Conference, EvoApplications 2025", year = "2025", editor = " Pablo Garcia-Sanchez and Emma Hart and Sarah L. Thomson", series = "LNCS", volume = "15613", pages = "3--19", publisher = "Springer", address = "Trieste, Italy", month = "3-5 " # apr, organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Interpretability, Social Learning", isbn13 = "978-3-031-90065-5", timestamp = "Fri, 09 May 2025 20:28:51 +0200", biburl = "https://dblp.org/rec/conf/evoapps/CustodeI25.bib", bibsource = "dblp computer science bibliography, https://dblp.org", DOI = "doi:10.1007/978-3-031-90065-5_1", notes = "DBLP:conf/evoapps/CustodeI25, http://www.evostar.org/2025/ EvoApplications2025 held in conjunction with EuroGP'2025, EvoCOP2025 and EvoMusArt2025", } @InProceedings{Custodio:2023:ICBIR, author = "Jose Miguel Custodio and John Vincent Cortez and Arianna Elise Chua and Ronnie Concepcion", booktitle = "2023 8th International Conference on Business and Industrial Research (ICBIR)", title = "Development of a Quality Grading Model for Processed Milk through Sensor Data and Symbolic Genetic Programming", year = "2023", pages = "483--488", abstract = "Milk and other dairy products continue to be important components to the human diet. Large quantities of milk are produced globally for consumption and processing into other products. In the interest of food safety, quality inspection of milk becomes a necessity to ensure that only milk with favorable attributes is released for consumption. For this task, a mathematical model was developed with multigene symbolic regression genetic programming (MSRGP) that grades milk based on seven key input traits: pH level, temperature, taste, odor, fat content, turbidity, and colour. By integrating the results of these attributes, the model can give an overall grade to milk samples. An online dataset was used to train and test the model. The developed model had an R2 of 0.95441, highlighting its accuracy in predicting milk sample quality. Sensitivity analysis was also performed on the model to check how certain inputs affect the outputs. While there were outputs that went beyond the expected range, this was attributed to input combinations that were improbable in real life and thus, was not accounted for in the dataset. Overall, the model was able to create an accurate assessment of milk based on the dataset.", keywords = "genetic algorithms, genetic programming, Dairy products, Sensitivity analysis, Production, Mathematical models, Data models, Software, food inspection, food grading, milk quality, multigene genetic programming, predictive models", DOI = "doi:10.1109/ICBIR57571.2023.10147437", month = may, notes = "Also known as \cite{10147437}", } @InProceedings{DBLP:conf/dexa/CuzzocreaLM21, author = "Alfredo Cuzzocrea and Kristijan Lenac and Enzo Mumolo", editor = "Christine Strauss and Gabriele Kotsis and A Min Tjoa and Ismail Khalil", title = "Enhancing Scan Matching Algorithms via Genetic Programming for Supporting Big Moving Objects Tracking and Analysis in Emerging Environments", booktitle = "Database and Expert Systems Applications - 32nd International Conference, {DEXA} 2021, Virtual Event, September 27-30, 2021, Proceedings, Part {I}", series = "Lecture Notes in Computer Science", volume = "12923", pages = "348--360", publisher = "Springer", year = "2021", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/978-3-030-86472-9_32", DOI = "doi:10.1007/978-3-030-86472-9_32", timestamp = "Thu, 14 Oct 2021 10:01:26 +0200", biburl = "https://dblp.org/rec/conf/dexa/CuzzocreaLM21.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{cvetkovic:1999:UPGMO, author = "Dragan Cvetkovic and Ian C. Parmee", title = "Use of Preferences for GA-based Multi-objective Optimisation", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1504--1509", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "real world applications", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-764.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-764.pdf", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{czajkowski:2013:EuroGP, author = "Marcin Czajkowski and Marek Kretowski", title = "Global Top-Scoring Pair Decision Tree for Gene Expression Data Analysis", booktitle = "Proceedings of the 16th European Conference on Genetic Programming, EuroGP 2013", year = "2013", month = "3-5 " # apr, editor = "Krzysztof Krawiec and Alberto Moraglio and Ting Hu and A. Sima Uyar and Bin Hu", series = "LNCS", volume = "7831", publisher = "Springer Verlag", address = "Vienna, Austria", pages = "229--240", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, evolutionary algorithms, decision tree, top-scoring pair, classification, gene expression, micro-array", isbn13 = "978-3-642-37206-3", DOI = "doi:10.1007/978-3-642-37207-0_20", abstract = "Extracting knowledge from gene expression data is still a major challenge. Relative expression algorithms use the ordering relationships for a small collection of genes and are successfully applied for micro-array classification. However, searching for all possible subsets of genes requires a significant number of calculations, assumptions and limitations. In this paper we propose an evolutionary algorithm for global induction of top-scoring pair decision trees. We have designed several specialised genetic operators that search for the best tree structure and the splits in internal nodes which involve pairwise comparisons of the gene expression values. Preliminary validation performed on real-life micro-array datasets is promising as the proposed solution is highly competitive to other relative expression algorithms and allows exploring much larger solution space.", notes = "Part of \cite{Krawiec:2013:GP} EuroGP'2013 held in conjunction with EvoCOP2013, EvoBIO2013, EvoMusArt2013 and EvoApplications2013", } @Article{d'angelo:2022:SC, author = "Gianni D'Angelo and Maria Nunzia Scoppettuolo and Anna Lisa Cammarota and Alessandra Rosati and Francesco Palmieri", title = "A genetic programming-based approach for classifying pancreatic adenocarcinoma: the {SICED} experience", journal = "Soft Computing", year = "2022", volume = "26", number = "19", keywords = "genetic algorithms, genetic programming, Ductal adenocarcinoma, Pancreas, Machine learning", URL = "http://link.springer.com/article/10.1007/s00500-022-07383-3", URL = "https://rdcu.be/dbFaV", DOI = "doi:10.1007/s00500-022-07383-3", size = "12 pages", abstract = "Ductal adenocarcinoma of the pancreas is a cancer with a high mortality rate. Among the main reasons for this baleful prognosis is that, in most patients, this neoplasm is diagnosed at a too advanced stage. Clinical oncology research is now particularly focused on decoding the cancer molecular onset by understanding the complex biological architecture of tumor cell proliferation. In this direction, machine learning has proved to be a valid solution in many sectors of the biomedical field, thanks to its ability to mine useful knowledge by biological and genetic data. Since the major risk factor is represented by genetic predisposition, the aim of this study is to find a mathematical model describing the complex relationship existing between genetic mutations of the involved genes and the onset of the disease. To this end, an approach based on evolutionary algorithms is proposed. In particular, genetic programming is used, which allows solving a symbolic regression problem through the use of genetic algorithms. The identification of these correlations is a typical objective of the diagnostic approach and is one of the most critical and complex activities in the presence of large amounts of data that are difficult to correlate through traditional statistical techniques. The mathematical model obtained highlights the importance of the complex relationship existing between the different gene mutations present in the tumor tissue of the group of patients considered.", notes = "p10073 'comparison with the state-of-the-art has proven the superiority of the proposed approach.' Department of Computer Science, University of Salerno, ViaGiovanni Paolo II, 132, 84084 Fisciano, SA, Italy", } @InProceedings{d'angelo:2024:ICCSA, author = "Gianni D'Angelo and Francesco Palmieri and Antonio Robustelli", title = "An Android Malware Multi-class Classification Explained Through Genetic Programming", booktitle = "Computational Science and Its Applications - ICCSA 2024 Workshops", year = "2024", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "https://link.springer.com/chapter/10.1007/978-3-031-65223-3_4", DOI = "doi:10.1007/978-3-031-65223-3_4", } @InProceedings{d'emilio:2020:IBESAFF, author = "A. D'Emilio", title = "Modeling Soil Thermal Regimes During a Solarization Treatment in Closed Greenhouse by Means of Symbolic Regression via Genetic Programming", booktitle = "Innovative Biosystems Engineering for Sustainable Agriculture, Forestry and Food Production", year = "2020", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-030-39299-4_32", DOI = "doi:10.1007/978-3-030-39299-4_32", } @InProceedings{da-silva:2024:CEC, author = "Jose Eduardo H. {da Silva} and Heder S. Bernardino and Itamar L. {de Oliveira} and Jose J. Camata and Patrick {de Carvalho}", title = "Inferring Gene Regulatory Networks from Single-Cell {RNA}-Sequencing Experimental Data using Cartesian Genetic Programming", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Computational modeling, Systems biology, Evolutionary computation, Prediction algorithms, Inference algorithms, Data models, Gene Regulatory Network, Experimental Data, Boolean Models", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10611826", abstract = "Systems Biology is an interdisciplinary field that aims to understand the interactions among biological components. A central focus of this field is modelling gene regulatory networks (GRN) and understanding how gene expression varies. ScRNA -Seq technology has enabled the ability to explore gene expression at the single-cell level, unlike previous technologies where only an average view of gene expression was possible. As a result, the literature has observed a significant increase in the number of inference methods, taking into account the specificities of the data from scRNA -Seq profiling, such as batch effects, biological variations, and dropouts. However, recent studies have shown that the performance of GRN inference algorithms when considering scRNA -Seq technology is close to random predictors. Furthermore, algorithms that perform well on synthetic and curated data are different from those that perform well on experimental data, indicating a lack of robustness. Considering that experimental data is more interesting for biology, as the modelling of its GRNs enables the understanding of biological phenomena, in this paper we show that the CGPGRN framework can deal with experimental data. Computational experiments are carried out and the results indicate that CGPGRN can outperform state-of-the-art algorithms in several situations and is the only one capable of obtaining correct regulatory relationships in all situations considered.", notes = "also known as \cite{10611826} WCCI 2024", } @Article{DAAJI:2023:jss, author = "Marwa Daaji and Ali Ouni and Mohamed Mohsen Gammoudi and Salah Bouktif and Mohamed Wiem Mkaouer", title = "{BPEL} process defects prediction using multi-objective evolutionary search", journal = "Journal of Systems and Software", volume = "204", pages = "111767", year = "2023", ISSN = "0164-1212", DOI = "doi:10.1016/j.jss.2023.111767", URL = "https://www.sciencedirect.com/science/article/pii/S0164121223001620", keywords = "genetic algorithms, genetic programming, BPEL process, Anti-patterns, Multi-objective algorithms", abstract = "Web services are becoming increasingly popular technologies for modern organizations to improve their cooperation and collaboration through building new software systems by composing pre-built services. Such services are typically composed and executed through BPEL (Business Process Execution Language) processes. Like any other software artifact, such processes are frequently changed to add new or modify existing functionalities or adapt to environmental changes. However, poorly planned changes may introduce BPEL process design defects known as anti-patterns or defects. The presence of defects often leads to a regression in software quality. In this paper, we introduce an automated approach to predict the presence of defects in BPEL code using Multi-Objective Genetic Programming (MOGP). Our approach consists of learning from real-world instances of each service-based business process defect (i.e., anti-pattern) type to infer prediction rules based on the combinations of process metrics and their associated threshold values. We evaluate our approach based on a dataset of 178 real-world business processes that belong to various application domains, and a variety of BPEL process defect types such as data flow and portability defects. The statistical analysis of the achieved results shows the effectiveness of our approach in identifying defects compared with state-of-the-art techniques with a median accuracy of 91percent", } @Article{Daaji:2025:eswa, author = "Marwa Daaji and Mohamed-Amin Benatia and Ali Ouni and Mohamed Mohsen Gammoudi", title = "Predicting wind turbines faults using Multi-Objective Genetic Programming", journal = "Expert Systems with Applications", year = "2025", volume = "281", pages = "127487", keywords = "genetic algorithms, genetic programming, Wind turbine, Faults, Evolutionary search, Multi-objective optimization, ANN", ISSN = "0957-4174", URL = "https://www.sciencedirect.com/science/article/pii/S0957417425011091", DOI = "doi:10.1016/j.eswa.2025.127487", abstract = "Wind turbines are a key component of renewable energy, converting wind into electricity with minimal environmental impact. Ensuring their continuous operation is crucial for maximizing energy production and reducing costly downtimes. To extend their operational lifespan, proactive maintenance strategies that predict and address potential faults are essential. While Machine Learning (ML) and Deep Learning (DL) algorithms have demonstrated significant promise in detecting wind turbine faults, they often prioritize maximizing the detection of failures without giving sufficient attention to false alarms. In practice, false alarms are just as problematic as undetected failures, as they reduce efficiency and waste resources. In this paper, we propose a novel optimisation approach using Multi-Objective Genetic Programming (MOGP) to predict wind turbine faults. Our approach seeks to identify the best combination of features and their threshold values by optimising two conflicting objectives: maximizing fault detection while minimizing false alarms. This dual-objective strategy ensures reliable fault prediction while minimizing unnecessary maintenance actions. We assess the effectiveness of our approach using real-world Supervisory Control and Data Acquisition (SCADA) data from a wind turbine in southern Ireland. The results demonstrate the efficiency of our approach in fault identification, achieving a competitive balance between recall (91percent) and false positive rate (21percent). While machine learning (ML), specifically Random Forest (RF), shows promising performance with a recall of 91percent and a 10percent false alarm rate, it remains a black-box model. RF lacks interpretability, making it challenging to extract meaningful insights into the relationships between sensor features and fault occurrences", } @InProceedings{Dabhi:2011:ICIIP, author = "Vipul K. Dabhi and Sanjay K. Vij", title = "Empirical modeling using symbolic regression via postfix Genetic Programming", booktitle = "International Conference on Image Information Processing (ICIIP 2011)", year = "2011", month = "3-5 " # nov, address = "Himachal Pradesh", size = "6 pages", abstract = "Developing mathematical model of a process or system from experimental data is known as empirical modelling. Traditional mathematical techniques are unsuitable to solve empirical modelling problems due to their nonlinearity and multimodality. So, there is a need of an artificial expert that can create model from experimental data. In this paper, we explored the suitability of Neural Network (NN) and symbolic regression via Genetic Programming (GP) to solve empirical modelling problems and conclude that symbolic regression via GP can deal efficiently with these problems. This paper aims to introduce a novel GP approach to symbolic regression for solving empirical modelling problems. The main contribution includes: (i) a new method of chromosome representation (postfix based) and evaluation (stack based) to reduce space-time complexity of algorithm (ii) comparison of our approach with Gene Expression Programming (GEP), a GP variant (iii) algorithms for generating valid chromosomes (in postfix notation) and identifying non-coding region of chromosome to improve efficiency of evolutionary process. Experimental results showed that empirical modelling problems can be solved efficiently using symbolic regression via postfix GP approach.", keywords = "genetic algorithms, genetic programming, Gene Expression Programming, chromosome evaluation, chromosome representation, empirical modelling problem, evolutionary process, gene expression programming, neural network, postfix genetic programming, space-time complexity reduction, symbolic regression, computational complexity, modelling, neural nets", DOI = "doi:10.1109/ICIIP.2011.6108857", notes = "Also known as \cite{6108857}", } @Misc{Dabhi:2012:arXiv, author = "Vipul K. Dabhi and Sanjay Chaudhary", title = "A Survey on Techniques of Improving Generalization Ability of Genetic Programming Solutions", howpublished = "arXiv", year = "2012", month = "6 " # nov, keywords = "genetic algorithms, genetic programming", URL = "http://arxiv.org/abs/1211.1119", size = "7 pages", abstract = "In the field of empirical modelling using Genetic Programming (GP), it is important to evolve solution with good generalisation ability. Generalisation ability of GP solutions get affected by two important issues: bloat and over-fitting. We surveyed and classified existing literature related to different techniques used by GP research community to deal with these issues. We also point out limitation of these techniques, if any. Moreover, the classification of different bloat control approaches and measures for bloat and over-fitting are also discussed. We believe that this work will be useful to GP practitioners in following ways: (i) to better understand concepts of generalisation in GP (ii) comparing existing bloat and over-fitting control techniques and (iii) selecting appropriate approach to improve generalisation ability of GP evolved solutions.", notes = "Information Technology Department, Dharmsinh Desai University, Nadiad, INDIA. DA-IICT, Gandhinagar, Gujarat, INDIA", } @InProceedings{conf/bic-ta/DabhiC12, author = "Vipul K. Dabhi and Sanjay Chaudhary", title = "Semantic Sub-tree Crossover Operator for Postfix Genetic Programming", booktitle = "Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012)", year = "2012", editor = "Jagdish Chand Bansal and Pramod Kumar Singh and Kusum Deep and Millie Pant and Atulya Nagar", volume = "201", series = "Advances in Intelligent Systems and Computing", pages = "391--402", publisher_address = "India", publisher = "Springer", language = "English", keywords = "genetic algorithms, genetic programming, Postfix genetic programming, Symbolic regression, Empirical modelling, Semantic sub-tree crossover operator", isbn13 = "978-81-322-1037-5", bibdate = "2013-01-16", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/bic-ta/bic-ta2012-1.html#DabhiC12", DOI = "doi:10.1007/978-81-322-1038-2_33", abstract = "Design of crossover operator plays a crucial role in Genetic Programming (GP). The most studied issues related to crossover operator in GP are: (1) ensuring that crossover operator always produces syntactically valid individuals (2) improving search efficiency of crossover operator. These issues become crucial when the individuals are represented using linear string representation. This paper aims to introduce postfix GP approach to symbolic regression for solving empirical modelling problems. The main contribution includes (1) a linear string (postfix notation) based genome representation method and stack based evaluation to reduce space-time complexity of GP algorithm (2) ensuring that sub-tree crossover operator always produces syntactically valid genomes in linear string representation (3) using semantic information of sub-trees, to be swapped, while designing crossover operator for linear genome representation to provide additional search guidance. The proposed method is tested on two real valued symbolic regression problems. Two different constant creation techniques for Postfix GP, one that explicitly use list of constants and another without use of the list, are presented to evolve useful numeric constants for symbolic regression problems. The results on tested problems show that postfix GP comprised of semantic sub-tree crossover offers a new possibility for efficiently solving empirical modelling problems.", } @InProceedings{Dabhi:2014:ACCT, author = "Vipul K. Dabhi and Sanjay Chaudhary", booktitle = "Fourth International Conference on Advanced Computing Communication Technologies (ACCT 2014)", title = "Time Series Modeling and Prediction Using Postfix Genetic Programming", year = "2014", month = feb, pages = "307--314", keywords = "genetic algorithms, genetic programming, series Modelling, Postfix Genetic Programming, One-step ahead prediction, Multi-step ahead prediction", DOI = "doi:10.1109/ACCT.2014.33", size = "8 pages", abstract = "Traditional techniques for time series modelling can capture linear behaviour of data and lack the ability to identify nonlinear patterns in time series. Therefore, machine learning techniques like Neural Network or Genetic Programming (GP) are used by practitioners for modelling nonlinear and irregular time series. GP is preferred over other techniques because it does not presume model structure a priori. This paper introduces the use of Postfix-GP, a postfix notation based GP, for real-world nonlinear time series modelling problems. The Postfix-GP uses linear genome representation and stack based evaluation to reduce space-time complexity of GP. The Postfix-GP is applied on two real time series modelling problems: sunspots and river flow series. Performance of evolved Postfix-GP models on training data and out-of-sample data are compared with those obtained by others using EGIPSYS. The obtained results indicate that Postfix-GP offers a new possibility for solving time series modelling and prediction problems.", notes = "Also known as \cite{6783469}", } @Article{Dabhi:2014:IJMHEUR, author = "Vipul K. Dabhi and Sanjay Chaudhary", title = "Performance comparison of crossover operators for postfix genetic programming", journal = "Int. J. of Metaheuristics", year = "2014", month = oct # "~24", volume = "3", number = "3", pages = "244--264", keywords = "genetic algorithms, genetic programming, ga-like one-point crossover, sub-tree crossover, semantic awareness, postfix genetic programming, performance comparison", publisher = "Inderscience Publishers", ISSN = "1755-2184", bibsource = "OAI-PMH server at www.inderscience.com", language = "eng", URL = "http://www.inderscience.com/link.php?id=65189", DOI = "DOI:10.1504/IJMHEUR.2014.065189", abstract = "In this article, we present three crossover operators for postfix-GP, a GP system that adopts postfix notation for an individual representation. These crossover operators are: GA-like one-point, sub-tree, and semantic aware sub-tree. The algorithm and implementation details for each of these crossover operators are presented. The operators are applied on a set of real-valued symbolic regression problems. The performance comparison of the crossover operators is carried out using two measures, number of successful runs and mean best adjusted fitness. The significance of the obtained results is tested using statistical test. The results suggest that semantic aware sub-tree crossover outperforms GA-like one-point and sub-tree crossovers on all problems.", } @Article{journals/cas/DabhiC15, author = "Vipul K. Dabhi and Sanjay Chaudhary", title = "Solution Modeling Using Postfix Genetic Programming", journal = "Cybernetics and Systems", year = "2015", number = "8", volume = "46", bibdate = "2015-11-27", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/cas/cas46.html#DabhiC15", pages = "605--640", keywords = "genetic algorithms, genetic programming", URL = "http://dx.doi.org/10.1080/01969722.2015.1058662", } @Article{journals/nc/DabhiC15, author = "Vipul K. Dabhi and Sanjay Chaudhary", title = "Empirical modeling using genetic programming: a survey of issues and approaches", journal = "Natural Computing", year = "2015", number = "2", volume = "14", bibdate = "2015-05-28", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/nc/nc14.html#DabhiC15", pages = "303--330", keywords = "genetic algorithms, genetic programming", URL = "http://dx.doi.org/10.1007/s11047-014-9416-y", } @Misc{oai:arXiv.org:1507.01687, title = "Developing Postfix-{GP} Framework for Symbolic Regression Problems", note = "Comment: 8 pages, 6 figures", author = "Vipul K. Dabhi and Sanjay Chaudhary", year = "2015", month = jul # "~07", keywords = "genetic algorithms, genetic programming", abstract = "This paper describes Postfix-GP system, postfix notation based Genetic Programming (GP), for solving symbolic regression problems. It presents an object-oriented architecture of Postfix-GP framework. It assists the user in understanding of the implementation details of various components of Postfix-GP. Postfix-GP provides graphical user interface which allows user to configure the experiment, to visualize evolved solutions, to analyse GP run, and to perform out-of-sample predictions. The use of Postfix-GP is demonstrated by solving the benchmark symbolic regression problem. Finally, features of Postfix-GP framework are compared with that of other GP systems.", bibsource = "OAI-PMH server at export.arxiv.org", identifier = "doi:10.1109/ACCT.2015.114", oai = "oai:arXiv.org:1507.01687", URL = "http://arxiv.org/abs/1507.01687", notes = "2016 Sixth International Conference on Advanced Computing & Communication Technologies ACCT 2016?", } @InProceedings{DaCosta:2005:ICANNGA, author = "Luis E. {Da Costa} and Jacques-Andre Landry", title = "Generating grammatical plant models with genetic algorithms", booktitle = "Proceedings of the seventh International Conference Adaptive and Natural Computing Algorithms", year = "2005", editor = "Bernardete Ribeiro and Rudolf F. Albrecht and Andrej Dobnikar and David W. Pearson and Nigel C. Steele", address = "Coimbra, Portugal", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Lindenmayer L-System", isbn13 = "978-3-211-24934-5", URL = "https://link.springer.com/chapter/10.1007/3-211-27389-1_55", URL = "https://doi.org/10.1007/b138998", DOI = "doi:10.1007/3-211-27389-1_55", abstract = "A method for synthesizing grammatical models of natural plants is presented. It is an attempt at solving the inverse problem of generating the model that best describes a plant growth process, presented in a set of 2D pictures. A geometric study is undertaken before translating it into grammatical meaning; a genetic algorithm, coupled with a deterministic rule generation algorithm, is then applied for navigating through the space of possible solutions. Preliminary results together with a detailed description of the method are presented.", notes = "The inverse problem for biological plant growth ", } @InProceedings{1144158, author = "Luis E. {Da Costa} and Jacques-Andre Landry", title = "Relaxed genetic programming", booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation", year = "2006", editor = "Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos {Coello Coello} and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens", volume = "1", ISBN = "1-59593-186-4", pages = "937--938", address = "Seattle, Washington, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p937.pdf", DOI = "doi:10.1145/1143997.1144158", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "8-12 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming: Poster, bloat, generalisation error, measurement", notes = "GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM Order Number 910060", } @InProceedings{conf/ae/CostaLL07, title = "Treating Noisy Data Sets with Relaxed Genetic Programming", author = "Luis E. {Da Costa} and Jacques-Andre Landry and Yan Levasseur", year = "2007", volume = "4926", bibdate = "2008-05-16", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ae/ae2007.html#CostaLL07", booktitle = "Artificial Evolution", editor = "Nicolas Monmarch{\'e} and El-Ghazali Talbi and Pierre Collet and Marc Schoenauer and Evelyne Lutton", isbn13 = "978-3-540-79304-5", pages = "1--12", series = "Lecture Notes in Computer Science", address = "Tours, France", month = "31-29 " # oct, publisher = "Springer", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1007/978-3-540-79305-2_1", abstract = "In earlier papers we presented a technique (RelaxGP) for improving the performance of the solutions generated by Genetic Programming (GP) applied to regression and approximation of symbolic functions. RelaxGP changes the definition of a perfect solution: in standard symbolic regression, a perfect solution provides exact values for each point in the training set. RelaxGP allows a perfect solution to belong to a certain interval around the desired values. We applied RelaxGP to regression problems where the input data is noisy. This is indeed the case in several real-world problems, where the noise comes, for example, from the imperfection of sensors. We compare the performance of solutions generated by GP and by RelaxGP in the regression of 5 noisy sets. We show that RelaxGP with relaxation values of 10percent to 100percent of the Gaussian noise found in the data can outperform standard GP, both in terms of generalization error reached and in resources required to reach a given test error.", notes = "EA'07", } @Article{DadeRobertson:2014:CAD, author = "Martyn Dade-Robertson and Carolina {Ramirez Figueroa} and Meng Zhang", title = "Material ecologies for synthetic biology: Biomineralization and the state space of design", journal = "Computer-Aided Design", year = "2015", volume = "60", pages = "28--39", month = mar, ISSN = "0010-4485", URL = "http://www.sciencedirect.com/science/article/pii/S0010448514000451", keywords = "Synthetic biology, Material ecologies, Self assembly, Emergence, State space", DOI = "doi:10.1016/j.cad.2014.02.012", size = "12 pages", abstract = "This paper discusses the role that material ecologies might have in the emerging engineering paradigm of Synthetic Biology (hereafter SB). In this paper we suggest that, as a result of the paradigm of SB, a new way of considering the relationship between computation and material forms is needed, where computation is embedded into the material elements themselves through genetic programming. The paper discusses current trends to promote SB in traditional engineering terms and contrast this from design speculations in terms of bottom-up processes of emergence and self-organisation. The paper suggests that, to reconcile these positions, it is necessary to think about the design of new material systems derived from engineering living organisms in terms of a state space of production. The paper analyses this state space using the example of mineralisation, with illustrations from simple experiments on bacteria-induced calcium carbonate. The paper suggests a framework involving three interconnected state spaces defined as: cellular (the control of structures within the cell structures within a cell, and specifically DNA and its expression through the process of transcription and translation); chemical (considered to occur outside the cell, but in direct chemical interaction with the interior of the cell itself); physical (which constitutes the physical forces and energy within the environment). We also illustrate, in broad terms, how such spaces are interconnected. Finally the paper will conclude by suggesting how a material ecologies approach might feature in the future development of SB.", notes = "Not GP but synthetic biology", } @Article{Dafflon:2020:HBM, author = "Jessica Dafflon and Walter H. L. Pinaya and Federico Turkheimer and James H. Cole and Robert Leech and Mathew A. Harris and Simon R. Cox and Heather C. Whalley and Andrew M. McIntosh and Peter J. Hellyer", title = "An automated machine learning approach to predict brain age from cortical anatomical measures", journal = "Human Brain Mapping", year = "2020", volume = "41", number = "13", pages = "3555--3566", keywords = "genetic algorithms, genetic programming, TPOT, age prediction, automated machine learning, cortical features, neuroimaging, predictive modeling, structural imaging", ISSN = "1065-9471", URL = "https://arxiv.org/abs/1910.03349", URL = "https://onlinelibrary.wiley.com/doi/abs/10.1002/hbm.25028", DOI = "doi:10.1002/hbm.25028", size = "12 pages", abstract = "The use of machine learning (ML) algorithms has significantly increased in neuroscience. However, from the vast extent of possible ML algorithms, which one is the optimal model to predict the target variable? What are the hyperparameters for such a model? Given the plethora of possible answers to these questions, in the last years, automated ML (autoML) has been gaining attention. Here, we apply an autoML library called Tree‐based Pipeline Optimisation Tool (TPOT) which uses a tree‐based representation of ML pipelines and conducts a genetic programming based approach to find the model and its hyperparameters that more closely predicts the subject's true age. To explore autoML and evaluate its efficacy within neuroimaging data sets, we chose a problem that has been the focus of previous extensive study: brain age prediction. Without any prior knowledge, TPOT was able to scan through the model space and create pipelines that outperformed the state‐of‐the‐art accuracy for Freesurfer‐based models using only thickness and volume information for anatomical structure. In particular, we compared the performance of TPOT (mean absolute error [MAE]: 4.612 plus/minus .124 years) and a relevance vector regression (MAE 5.474 plus/minus .140 years). TPOT also suggested interesting combinations of models that do not match the current most used models for brain prediction but generalise well to unseen data. AutoML showed promising results as a data‐driven approach to find optimal models for neuroimaging applications.", notes = "14000 GP entry. PMID: 32415917 Age UK‐funded", } @Article{Daga:2009:NH, author = "Mansi Daga and M. C. Deo", title = "Alternative data-driven methods to estimate wind from waves by inverse modeling", journal = "Natural Hazards", year = "2009", volume = "49", number = "2", pages = "293--310", month = may, keywords = "genetic algorithms, genetic programming, Locally weighted learning, Model trees, Inverse modeling, Wind estimation, LWOR, MT, GP", ISSN = "0921-030X", DOI = "doi:10.1007/s11069-008-9299-2", size = "18 pages", abstract = "An attempt is made to derive wind speed from wave measurements by carrying out an inverse modeling. This requirement arises out of difficulties occasionally encountered in collecting wave and wind data simultaneously. The wind speed at every 3-h interval is worked out from corresponding simultaneous measurements of significant wave height and average wave periods with the help of alternative data-driven methods such as program-based genetic programming, model trees, and locally weighted projection regression. Five different wave buoy locations in Arabian Sea, representing nearshore and offshore as well as shallow and deep water conditions, are considered. The duration of observations ranged from 15 months to 29 months for different sites. The testing performance of calibrated models has been evaluated with the help of eight alternative error statistics, and the best model for all locations is determined by averaging out the error measures into a single evaluation index. All the three methods satisfactorily estimated the wind speed from known wave parameters through inverse modeling. The genetic programming is found to be the most suitable tool in majority of the cases.", notes = "Discussed by \cite{Gandomi:2010:NH} Discipulus. Goa, Minicoy Island, Marmagoa. Storm modelling Department of Civil Engineering, Indian Institute of Technology, Bombay, Mumbai, 400076, India", } @InCollection{DAGDIA:2020:KDBDAEO, author = "Zaineb Chelly Dagdia and Miroslav Mirchev", title = "When Evolutionary Computing Meets Astro- and Geoinformatics", year = "2020", editor = "Petr Skoda and Fathalrahman Adam", booktitle = "Knowledge Discovery in Big Data from Astronomy and Earth Observation", publisher = "Elsevier", pages = "283--306", chapter = "15", keywords = "genetic algorithms, genetic programming, Evolutionary computation, Bio-inspired computing, Metaheuristics, Astroinformatics, Geoinformatics", isbn13 = "978-0-12-819154-5", URL = "http://www.sciencedirect.com/science/article/pii/B9780128191545000266", URL = "https://hal.inria.fr/hal-02880731", URL = "https://hal.inria.fr/hal-02880731/document", URL = "https://hal.inria.fr/hal-02880731/file/chapter13.pdf", URL = "https://www.sciencedirect.com/science/article/pii/B9780128191545000266", DOI = "doi:10.1016/B978-0-12-819154-5.00026-6", size = "43 pages", abstract = "Knowledge discovery from data typically includes solving some type of an optimization problem that can be efficiently addressed using algorithms belonging to the class of evolutionary and bio-inspired computation. In this chapter, we give an overview of the various kinds of evolutionary algorithms, such as genetic algorithms, evolutionary strategy, evolutionary and genetic programming, differential evolution, and coevolutionary algorithms, as well as several other bio-inspired approaches, like swarm intelligence and artificial immune systems. After elaborating on the methodology, we provide numerous examples of applications in astronomy and geoscience and show how these algorithms can be applied within a distributed environment, by making use of parallel computing, which is essential when dealing with Big Data", } @Article{daghistani:2023:Geosciences, author = "Firas Daghistani and Abolfazl Baghbani and Hossam {Abuel Naga} and Roohollah Shirani Faradonbeh", title = "Internal Friction Angle of Cohesionless Binary Mixture {Sand-Granular} Rubber Using Experimental Study and Machine Learning", journal = "Geosciences", year = "2023", volume = "13", number = "7", pages = "Article No. 197", keywords = "genetic algorithms, genetic programming", ISSN = "2076-3263", URL = "https://www.mdpi.com/2076-3263/13/7/197", DOI = "doi:10.3390/geosciences13070197", abstract = "This study aimed to examine the shear strength characteristics of sand-granular rubber mixtures in direct shear tests. Two different sizes of rubber and one of sand were used in the experiment, with the sand being mixed with various percentages of rubber (0percent, 10percent, 20percent, 30percent, and 50percent). The mixtures were prepared at three different densities (loose, slightly dense, and dense), and shear stress was tested at four normal stresses (30, 55, 105, and 200 kPa). The results of 80 direct shear tests were used to calculate the peak and residual internal friction angles of the mixtures, and it was found that the normal stress had a significant effect on the internal friction angle, with an increase in normal stress leading to a decrease in the internal friction angle. These results indicated that the Mohr-Coulomb theory, which applies to rigid particles only, is not applicable in sand-rubber mixtures, where stiff particles (sand) and soft particles (rubber) are mixed. The shear strength of the mixtures was also influenced by multiple factors, including particle morphology (size ratio, shape, and gradation), mixture density, and normal stress. For the first time in the literature, genetic programming, classification and regression random forests, and multiple linear regression were used to predict the peak and residual internal friction angles. The genetic programming resulted in the creation of two new equations based on mixture unit weight, normal stress, and rubber content. Both artificial intelligence models were found to be capable of accurately predicting the peak and residual internal friction angles of sand-rubber mixtures.", notes = "also known as \cite{geosciences13070197}", } @Article{Dai:2011:Jsoftware, author = "Chaofan Dai and Yanghe Feng and Jianmai Shi", title = "Evolutioinary Combination of models in DSS based on Genetic Programming", journal = "Journal of Software", year = "2011", volume = "6", number = "3", pages = "444--451", month = mar, keywords = "genetic algorithms, genetic programming, model base, automatic model selection", ISSN = "1796-217X", URL = "http://ojs.academypublisher.com/index.php/jsw/article/download/0603444451/2812", DOI = "doi:10.4304/jsw.6.3.444-451", size = "8 pages", abstract = "The efficiency of model-aided decision making relies on the intelligent level of model selection. The purpose of this paper is to develop a new algorithm for model selection based on genetic programming. In the algorithm, the meta-models are classified according to the characteristics of the sample data, and the combined models are built as tree format. The genetic operations are performed under some constraints to produce combination models for users' reference. The process of the algorithm greatly decreases users' dependence on domain knowledge.", notes = "JSW", } @Article{Dai:2025:foodcont, author = "Jingbo Dai and Xiaobin Chen and Yao Zhang and Min Zhang and Yunyuan Dong and Qifu Zheng and Jianming Liao and Ying Zhao", title = "Machine learning-enhanced color recognition of test strips for rapid pesticide residue detection in fruits and vegetables", journal = "Food Control", year = "2025", volume = "174", pages = "111256", keywords = "genetic algorithms, genetic programming, Pesticide residues, Color feature index, Machine vision, Image processing", ISSN = "0956-7135", URL = "https://www.sciencedirect.com/science/article/pii/S0956713525001252", DOI = "doi:10.1016/j.foodcont.2025.111256", abstract = "Food safety, particularly the risks posed by pesticide residues, has become a critical public health concern. Existing detection methods are often slow, expensive, and require complex equipment, limiting their widespread use. This study introduces a rapid test strips system for pesticide residues, focusing on cholinesterase and organophosphate pesticides. The system combines a colourimetric reaction with machine vision to automate image analysis. Key image processing techniques, including noise reduction and threshold extraction, are used to analyse RGB values from the test strips. Multicolour feature indices are then derived to process the data. Additionally, an improved genetic programming-symbolic regression (GP-SR) model is developed to establish the relationship between these indices and pesticide residue levels. Experimental results show that the enhanced GP-SR model increases the R2 value by up to 0.195 after normalization, improves the coefficient of determination by 2.5percent, and reduces the RMSE by 16percent. This approach offers a more efficient and accurate method for detecting pesticide residues in fruits and vegetables, contributing to improved food safety monitoring", } @InProceedings{Dai:2019:WCNC, author = "Rui Dai and Yicheng Gao and Sai Huang and Fan Ning and Zhiyong Feng", booktitle = "2019 IEEE Wireless Communications and Networking Conference (WCNC)", title = "Multi-objective Genetic Programming based Automatic Modulation Classification", year = "2019", abstract = "Automatic modulation classification (AMC) plays a crucial role in the cognitive radio networks, to which feature-based (FB) methods are the dominating solutions. However, the original features in FB methods are redundant, leading to the ambiguity of classification. To tackle this problem, this paper proposes a novel multi-objective modulation classification (MOMC) method. To reduce the redundant features, the original multi-features are recombined into a single feature by multiobjective genetic programming (MOGP) algorithm. Two quantitative objectives, the classification error rate and the variance for robustness, are then presented to jointly optimize the algorithm as two fitness functions. Furthermore, the single feature generated by MOGP is classified by logistic regression (LR) with low computational complexity. Simulation results verify the enhanced robustness and classification accuracy performance yielded by our proposed MOMC method compared to the existing classification methods.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/WCNC.2019.8885738", ISSN = "1558-2612", month = apr, notes = "Also known as \cite{8885738}", } @InProceedings{Daian:2014:ICSTCC, author = "G. I. Daian and M. M. Santa and T. S. Letia", booktitle = "18th International Conference System Theory, Control and Computing (ICSTCC 2014)", title = "Evolutionary method for railway monitoring systems", year = "2014", month = oct, pages = "627--632", address = "Sinaia, Romania", size = "6 pages", abstract = "The paper presents an evolutionary method based on genetic programming (GP) for synthesising of a monitor alarm system for the railway control traffic unit. Automatic supervision of railway traffic control is a very important and complex task. A wrong control signal can lead to very serious incidents or accidents. A well-designed monitoring system can prevent these accidents by a simple alarm which signals the appearance of a wrong control signal. The railway network or the plant is modelled by Delay Time Petri Nets (DTPN) and the railway traffic control unit by Time Petri Nets (TPN). The alarm monitor contains transitions joined to the plant and control unit in order to achieve the information on the positions of trains, respectively the control signals of the control unit, and generates an alarm whenever the control signal can cause to an incident or accident. The TPN model of the monitor system is generated by means of the genetic programming method using a Lisp representation of the solution.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICSTCC.2014.6982487", notes = "Also known as \cite{6982487}", } @InProceedings{daida:1995:bsmch, author = "J. M. Daida and S. J. Ross and B. C. Hannan", title = "Biological Symbiosis as a Metaphor for Computational Hybridization", booktitle = "Genetic Algorithms: Proceedings of the Sixth International Conference (ICGA95)", year = "1995", editor = "Larry J. Eshelman", pages = "248--255", address = "Pittsburgh, PA, USA", publisher_address = "San Francisco, CA, USA", month = "15-19 " # jul, publisher = "Morgan Kaufmann", keywords = "Genetic Algorithms", ISBN = "1-55860-370-0", URL = "ftp://ftp.eecs.umich.edu/people/daida/papers/icga95.pdf", size = "8 pages", } @InProceedings{Daida:1995:SARice, author = "J. M. Daida and J. D. Hommes and S. J. Ross and J. F. Vesecky", title = "Extracting curvilinear features from SAR images of arctic ice: Algorithm discovery using the genetic programming paradigm", booktitle = "Proceedings of IEEE International Geoscience and Remote Sensing", year = "1995", editor = "T. Stein", pages = "673--675", address = "Florence, Italy", publisher_address = "Washington", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", URL = "ftp://ftp.eecs.umich.edu/people/daida/papers/igarss95_GP.pdf", URL = "http://citeseer.ist.psu.edu/406479.html", notes = "This paper focuses on how a method for automated programming (i.e., genetic programming) applies in the computeraided discovery of algorithms that enhance and extract features from remotely sensed images. Highlighted as a case study is the use of this method in the problem of extracting pressure ridge features from ERS-1 SAR imagery; a problem for which there has been no known satisfactory solution. The research on algorithm discovery uses the genetic programming paradigm to assist geoscientists in extracting textural features from satellite synthetic aperture radar imagery (i.e., ERS-1). Manual methods are extremely time consuming and limited to a few frames (in this case, a 1k by 1k low-res data product, or a 8k by 8k hi-res data product). Desirable are semi-automated, automated, or computer-assisted algorithm developmental tools for data analysis. (gp-list 13 Apr 95) Firenze, Italy", } @InProceedings{Daida:1995:ehspsSAR, author = "J. M. Daida and A. Freeman and R. Onstott", title = "Evaluation of hybrid symbiotic systems on segmenting SAR imagery", booktitle = "Proceedings of IEEE International Geoscience and Remote Sensing", year = "1995", editor = "T. Stein", pages = "1415--1417", address = "Florence, Italy", publisher_address = "Washington", publisher = "IEEE Press", keywords = "genetic algorithms", URL = "ftp://ftp.eecs.umich.edu/people/daida/papers/igarss95_symbiosis.pdf", notes = " Invited Paper Firenze, Italy", } @InProceedings{Daida:1995:mtssw, author = "J. M. Daida and D. E. Lund and C. Wolf and G. A. Meadows and K. Schroeder and J. F. Vesecky and D. R. Lyzenga and R. Bertram", title = "Measuring topography of small-scale waves", booktitle = "Proceedings of IEEE International Geoscience and Remote Sensing", year = "1995", editor = "T. Stein", pages = "1881--1883", address = "Florence, Italy", publisher_address = "Washington", publisher = "IEEE Press", keywords = "genetic algorithms", URL = "ftp://ftp.eecs.umich.edu/people/daida/papers/igarss95_GA.pdf", notes = " Firenze, Italy", } @InCollection{daida:1996:aigp2, author = "Jason M. Daida and Jonathan D. Hommes and Tommaso F. Bersano-Begey and Steven J. Ross and John F. Vesecky", title = "Algorithm Discovery Using the Genetic Programming Paradigm: Extracting Low-Contrast Curvilinear Features from {SAR} Images of Arctic Ice", booktitle = "Advances in Genetic Programming 2", publisher = "MIT Press", year = "1996", editor = "Peter J. Angeline and K. E. {Kinnear, Jr.}", pages = "417--442", chapter = "21", address = "Cambridge, MA, USA", keywords = "genetic algorithms, genetic programming, GAIA", ISBN = "0-262-01158-1", URL = "http://sitemaker.umich.edu/daida/files/GP2_cha21.pdf", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6277526", DOI = "doi:10.7551/mitpress/1109.003.0028", size = "26 pages", abstract = "We discuss the application of genetic programming (GP) to image analysis problems in geoscience and remote sensing and describes how a GP can be adapted for processing large data sets (in our case, 1024 x 1024 pixel images plus texture channels). The featured problem is one that has not been adequately solved for this type of imagery. We describe the placement of GP in the overall scheme of algorithm discovery in geoscience image analysis and describe how GP complements a scientist's hypothesis-test derivation of such algorithms. The featured solution consists of a standard non-ADF GP that incorporates a dynamic fitness function.", notes = "see also http://www.sprl.umich.edu/acers/gaia/aigpGaia.html", } @InProceedings{daida:1996:scas, author = "J. M. Daida and C. S. Grasso and S. A. Stanhope and S. J. Ross", title = "Symbionticism and Complex Adaptive Systems {I}: Implications of Having Symbiosis Occur in Nature", booktitle = "Evolutionary Programming V: Proceedings of the Fifth Annual Conference on Evolutionary Programming", year = "1996", editor = "Lawrence J. Fogel and Peter J. Angeline and Thomas Baeck", pages = "177--186", address = "San Diego", publisher_address = "Cambridge, MA, USA", month = feb # " 29-" # mar # " 3", publisher = "MIT Press", ISBN = "0-262-06190-2", URL = "ftp://ftp.eecs.umich.edu/people/daida/papers/EP96_symbiosis.pdf", notes = "EP-96, Invited Paper http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=8383", } @InProceedings{daida:1996:cadic, author = "Jason M. Daida and Tommaso F. Bersano-Begey and Steven J. Ross and John F. Vesecky", title = "Computer-Assisted Design of Image Classification Algorithms: Dynamic and Static Fitness Evaluations in a Scaffolded Genetic Programming Environment", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "279--284", address = "Stanford University, CA, USA", publisher = "MIT Press", URL = "ftp://ftp.eecs.umich.edu/people/daida/papers/GP96_image.pdf", size = "6 pages", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap35.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96", } @InProceedings{daida:1996:ircERSSARias, author = "J. M. Daida and R. G. Onstott and T. F. Bersano-Begey and S. J. Ross and J. F. Vesecky", title = "Ice Roughness Classification and ERS SAR Imagery of Arctic Sea Ice: Evaluation of Feature-Extraction Algorithms by Genetic Programming", booktitle = "Proceedings of the 1996 International Geoscience and Remote Sensing Symposium", year = "1996", pages = "1520--1522", address = "Lincoln, NE, USA", publisher_address = "Washington", month = "31-31 " # may, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", URL = "ftp://ftp.eecs.umich.edu/people/daida/papers/igarss96_GP_Valid.pdf", DOI = "doi:10.1109/IGARSS.1996.516717", size = "3 pages", abstract = "This paper describes a validation of accuracy associated with a recent algorithm that has been designed to extract ridge and rubble features from multiyear ice. Results show that the algorithm performs well with low-resolution ERS SAR data products.", } @InProceedings{daida:1996:efxa, author = "J. M. Daida and T. F. Bersano-Begey and S. J. Ross and J. F. Vesecky", title = "Evolving Feature-Extraction Algorithms: Adapting Genetic Programming for Image Analysis in Geoscience and Remote Sensing", booktitle = "Proceedings of the 1996 International Geoscience and Remote Sensing Symposium", year = "1996", pages = "2077--2079", publisher_address = "Washington", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", URL = "ftp://ftp.eecs.umich.edu/people/daida/papers/igarss96_GP.pdf", } @InProceedings{daida:1996:, author = "J. M. Daida and R. R. Bertram and D. R. Lyzenga and C. Wolf and D. T. Walker and S. A. Stanhope and G. A. Meadows and J. F. Vesecky and D. E. Lund", title = "Measuring Small-Scale Water Surface Waves: Nonlinear Interpolation and Integration Techniques for Slope Image Data", booktitle = "Proceedings of the 1996 International Geoscience and Remote Sensing Symposium", year = "1996", pages = "2219--2221", publisher_address = "Washington", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", URL = "ftp://ftp.eecs.umich.edu/people/daida/papers/igarss96_GA/igarss96_GAfig.pdf", notes = "note: these pages are reverse ordered", } @InProceedings{daida:1997:vrmGP, author = "Jason Daida and Steven Ross and Jeffrey McClain and Derrick Ampy and Michael Holczer", title = "Challenges with Verification, Repeatability, and Meaningful Comparisons in Genetic Programming", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming", pages = "64--69", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", URL = "ftp://ftp.eecs.umich.edu/people/daida/papers/GP97challenges.pdf", notes = "GP-97", } @InProceedings{Daida:1997:taging, author = "Jason M. Daida and Robert R. Bertram and Catherine S. Grasso and Stephen A. Stanhope", title = "Tagging as a Means for Self-Adaptive Hybridization", booktitle = "Late Breaking Papers at the 1997 Genetic Programming Conference", year = "1997", editor = "John R. Koza", pages = "42--50", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California, 94305-3079, USA", month = "13--16 " # jul, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-206995-8", notes = "GP-97LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670", } @InCollection{daida:1999:aigp3, author = "Jason M. Daida and Robert R. Bertram and John A. {Polito~2} and Stephen A. Stanhope", title = "Analysis of Single-Node (Building) Blocks in Genetic Programming", booktitle = "Advances in Genetic Programming 3", publisher = "MIT Press", year = "1999", editor = "Lee Spector and William B. Langdon and Una-May O'Reilly and Peter J. Angeline", chapter = "10", pages = "217--241", address = "Cambridge, MA, USA", month = jun, keywords = "genetic algorithms, genetic programming", ISBN = "0-262-19423-6", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/aigp3/ch10.pdf", language = "en", oai = "oai:CiteSeerXPSU:10.1.1.141.1123", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.141.1123", DOI = "doi:10.7551/mitpress/1110.003.0014", abstract = "What is a building block in genetic programming? by examining the smallest subtree possible--a single leaf node. The analysis of these subtrees indicates a considerably more complex portrait of what exactly is meant by a building block in GP than what has traditionally been considered.", notes = "AiGP3 See http://cognet.mit.edu", } @InProceedings{daida:1999:fogp, author = "Jason M. Daida", title = "Reconnoiter by Candle: Identifying Assumptions in Genetic Programming", booktitle = "Foundations of Genetic Programming", year = "1999", editor = "Thomas Haynes and William B. Langdon and Una-May O'Reilly and Riccardo Poli and Justinian Rosca", pages = "53--54", address = "Orlando, Florida, USA", month = "13 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/fogp/daida.ps.gz", size = "2 pages", notes = "GECCO'99 WKSHOP, part of \cite{haynes:1999:fogp} GECCO-99WKS Part of wu:1999:GECCOWKS", } @InProceedings{daida:1999:CVRMCGPGM, author = "Jason M. Daida and Derrick S. Ampy and Michael Ratanasavetavadhana and Hsiaolei Li and Omar A. Chaudhri", title = "Challenges with Verification, Repeatability, and Meaningful Comparison in Genetic Programming: {Gibson's} Magic", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1851--1858", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, methodology, pedagogy and philosophy", ISBN = "1-55860-611-4", URL = "ftp://ftp.eecs.umich.edu/people/daida/papers/GECCO99challenges.pdf", URL = "https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=7ebfbe68a7dae6ad5ddbad6b75e146a471cb47e9", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/MP-604.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/MP-604.ps", size = "8 pages", abstract = "This paper examines some of the reporting and research practices concerning empirical work in genetic programming. We describe several common loopholes and offer three case studies---two in data modeling and one in robotics---that illustrate each. We show that by exploiting these loopholes, one can achieve performance gains of up two orders of magnitude without any substantiative changes to GP. We subsequently offer several recommendations.", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{daida:1999:MSWMPGATDPGP, author = "Jason M. Daida and John A. Polito and Steven A. Stanhope and Robert R. Bertram and Jonathan C. Khoo and Shahbaz A. Chaudhary", title = "What Makes a Problem GP-Hard? Analysis of a Tunably Difficult Problem in Genetic Programming", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "982--989", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-611-4", URL = "ftp://ftp.eecs.umich.edu/people/daida/papers/GECCO99landscape.pdf", URL = "http://citeseer.ist.psu.edu/240700.html", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-444.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-444.ps", abstract = "This paper addresses the issue of what makes a problem GP-hard by considering the binomial-3 problem. In the process, we discuss the efficacy of the metaphor of an adaptive fitness landscape to explain what is GP-hard. We show that for at least this problem, the metaphor is misleading.", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{daida:1999:OMDDGPC, author = "Jason M. Daida and Seth P. Yalcin and Paul M. Litvak and Gabriel A. Eickhoff and John A. Polito", title = "Of Metaphors and Darwinism: Deconstructing Genetic Programming's Chimera", booktitle = "Proceedings of the Congress on Evolutionary Computation", year = "1999", editor = "Peter J. Angeline and Zbyszek Michalewicz and Marc Schoenauer and Xin Yao and Ali Zalzala", volume = "1", pages = "453--462", address = "Mayflower Hotel, Washington D.C., USA", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "6-9 " # jul, organisation = "Congress on Evolutionary Computation, IEEE / Neural Networks Council, Evolutionary Programming Society, Galesia, IEE", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, biomodeling, Darwinism, evolutionary biology, evolutionary computation, genetic programming theory, historical lineage, metaphors, evolution (biological), evolutionary computation", ISBN = "0-7803-5536-9 (softbound)", ISBN = "0-7803-5537-7 (Microfiche)", URL = "ftp://ftp.eecs.umich.edu/people/daida/papers/CEC99metaphors.pdf", URL = "http://citeseer.ist.psu.edu/242099.html", DOI = "doi:10.1109/CEC.1999.781959", size = "10 pages", abstract = "This paper discusses several metaphors from Darwinism that have influenced the development of genetic programming (GP) theory. It specifically examines the historical lineage of these metaphors in evolutionary computation and their corresponding concepts in evolutionary biology and Darwinism. It identifies problems that can arise from using these metaphors in the development of GP theory", notes = "CEC-99 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. Library of Congress Number = 99-61143", } @Article{daida:2001:GPEM, author = "Jason M. Daida and Robert R. Bertram and Stephen A. Stanhope and Jonathan C. Khoo and Shahbaz A. Chaudhary and Omer A. Chaudhri and John A. {Polito II}", title = "What Makes a Problem {GP}-Hard? Analysis of a Tunably Difficult Problem in Genetic Programming", journal = "Genetic Programming and Evolvable Machines", year = "2001", volume = "2", number = "2", pages = "165--191", month = jun, keywords = "genetic algorithms, genetic programming, problem difficulty, test problems, fitness landscapes, GP theory", ISSN = "1389-2576", broken = "http://ipsapp009.lwwonline.com/content/getfile/4723/5/5/fulltext.pdf", DOI = "doi:10.1023/A:1011504414730", abstract = "This paper addresses the issue of what makes a problem genetic programming (GP)-hard by considering the binomial-3 problem. In the process, we discuss the efficacy of the metaphor of an adaptive fitness landscape to explain what is GP-hard. We indicate that, at least for this problem, the metaphor is misleading.", notes = "patched lilgp. Mersenne Twister. Size and Shape of solutions to 3 binomial - tunably difficult by changing random constants used. Edvard Munch Scream. Inconsistency of ERC value within parse tree context. Destructive crossover. P180 {"}the fitness function did not need to be rugged for GP to encounter difficulty.{"} GP as error correcting. Mathematica. p186 {"}increased population meant more individuals gathered around the{"} suboptimal {"}0.8 attractor{"}. Article ID: 335714", } @InProceedings{daida:2002:lteigplm, author = "Jason M. Daida", title = "Limits to Expression in Genetic Programming: Lattice-Aggregate Modeling", booktitle = "Proceedings of the 2002 Congress on Evolutionary Computation CEC2002", editor = "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and Mark Shackleton", pages = "273--278", year = "2002", publisher = "IEEE Press", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", organisation = "IEEE Neural Network Council (NNC), Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", ISBN = "0-7803-7278-6", month = "12-17 " # may, notes = "CEC 2002 - A joint meeting of the IEEE, the Evolutionary Programming Society, and the IEE. Held in connection with the World Congress on Computational Intelligence (WCCI 2002)", keywords = "genetic algorithms, genetic programming, GP search space, ballistic accretion, expression limits, four-region partition, genetic programming, lattice-aggregate modelling, shape evolution, size evolution, theoretical model, evolutionary computation, search problems", URL = "http://sitemaker.umich.edu/daida/files/CEC7272.pdf", DOI = "doi:10.1109/CEC.2002.1006246", abstract = "This paper describes a general theoretical model of size and shape evolution in genetic programming. The proposed model incorporates a mechanism that is analogous to ballistic accretion in physics. The model indicates a four-region partition of GP search space. It further suggests that two of these regions are not searchable by GP.", } @InCollection{daida:2003:GPTP, author = "Jason M. Daida", title = "What Makes a Problem GP-Hard? A Look at How Structure Affects Content", booktitle = "Genetic Programming Theory and Practice", publisher = "Kluwer", year = "2003", editor = "Rick L. Riolo and Bill Worzel", chapter = "7", pages = "99--118", keywords = "genetic algorithms, genetic programming, GP theory, tree structures, problem difficulty, GP-hard, test problems", ISBN = "1-4020-7581-2", URL = "http://www.springer.com/computer/ai/book/978-1-4020-7581-0", DOI = "doi:10.1007/978-1-4419-8983-3_7", abstract = "Theoretical work at the University of Michigan that concerns the question 'What makes a problem difficult for genetic programming to solve?' Specifically describes linkages between content, tree structures, and problem difficulty in genetic programming. The significance of structure in influencing problem difficulty.", notes = "great pictures Part of \cite{RioloWorzel:2003}", size = "19 pages", } @InProceedings{daida0:2003:gecco, author = "Jason M. Daida and Adam M. Hilss", title = "Identifying Structural Mechanisms in Standard Genetic Programming", booktitle = "Genetic and Evolutionary Computation -- GECCO-2003", editor = "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller", year = "2003", pages = "1639--1651", address = "Chicago", publisher_address = "Berlin", month = "12-16 " # jul, volume = "2724", series = "LNCS", ISBN = "3-540-40603-4", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, theory, Binary Tree, Mathematical Entity, Symbolic Regression, Tree Shape", URL = "http://sitemaker.umich.edu/daida/files/LNCS2724lattice.pdf", DOI = "doi:10.1007/3-540-45110-2_58", size = "13 pages", abstract = "hypothesis about an undiscovered class of mechanisms that exist in standard GP. Rather than being intentionally designed, these mechanisms would be an unintended consequence of using trees as information structures. A model is described that predicts outcomes in GP that would arise solely from such mechanisms. Comparisons with empirical results from GP lend support to the existence of these mechanisms.", notes = "GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003). Also known as \cite{daida:2003:gecco}", } @InProceedings{daida:2003:gecco, author = "Jason M. Daida and Adam M. Hilss and David J. Ward and Stephen L. Long", title = "Visualizing Tree Structures in Genetic Programming", booktitle = "Genetic and Evolutionary Computation -- GECCO-2003", editor = "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller", year = "2003", pages = "1652--1664", address = "Chicago", publisher_address = "Berlin", month = "12-16 " # jul, volume = "2724", series = "LNCS", ISBN = "3-540-40603-4", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", URL = "http://sitemaker.umich.edu/daida/files/LNCS2724viz.pdf", DOI = "doi:10.1007/3-540-45110-2_59", size = "13 pages", abstract = "methods to visualise the structure of trees that occur in genetic programming. These allow for the inspection of structure of entire trees of arbitrary size. The methods also scale to allow for the inspection of structure for an entire population. Examples are given from a typical problem. The examples indicate further studies that might be enabled by visualising structure at these scales.", notes = "GECCO-2003 A joint meeting of the twelvth international conference on genetic algorithms (ICGA-99) and the eigth annual genetic programming conference (GP-2003) Also known as daida2:2003:gecco", } @InProceedings{daida3:2003:gecco, author = "Jason M. Daida and Hsiaolei Li and Ricky Tang and Adam M. Hilss", title = "What Makes a Problem {GP}-Hard? Validating a Hypothesis of Structural Causes", booktitle = "Genetic and Evolutionary Computation -- GECCO-2003", editor = "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller", year = "2003", pages = "1665--1677", address = "Chicago", publisher_address = "Berlin", month = "12-16 " # jul, volume = "2724", series = "LNCS", ISBN = "3-540-40603-4", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1007/3-540-45110-2_60", abstract = "empirical test of a hypothesis, which describes the effects of structural mechanisms in genetic programming. In doing so, the paper offers a test problem anticipated by this hypothesis. The problem is tunably difficult, but has this property because tuning is accomplished through changes in structure. Content is not involved in tuning. The results support a prediction of the hypothesis - that GP search space is significantly constrained as an outcome of structural mechanisms.", notes = "GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)", } @InCollection{daida:2004:GPTP, author = "Jason Daida", title = "Considering the Roles of Structure in Problem Solving by a Computer", booktitle = "Genetic Programming Theory and Practice {II}", year = "2004", editor = "Una-May O'Reilly and Tina Yu and Rick L. Riolo and Bill Worzel", chapter = "5", pages = "67--86", address = "Ann Arbor", month = "13-15 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, GP theory, tree structures, problem difficulty, GP-hard, test problems, Lid, Highlander, Binomial-3", ISBN = "0-387-23253-2", DOI = "doi:10.1007/0-387-23254-0_5", abstract = "This chapter presents a tiered view of the roles of structure in genetic programming. This view can be used to frame theory on how some problems are more difficult than others for genetic programming to solve. This chapter subsequently summarises my group's current theoretical work at the University of Michigan and extends the implications of that work to real-world problem solving.", notes = "part of \cite{oreilly:2004:GPTP2}", } @InProceedings{daida:2004:dctdwatdpfgp, title = "Demonstrating Constraints to Diversity with a Tunably Difficulty Problem for Genetic Programming", author = "Jason M. Daida and Michael E. Samples and Bryan T. Hart and Jeffry Halim and Aditya Kumar", pages = "1217--1224", booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary Computation", year = "2004", publisher = "IEEE Press", month = "20-23 " # jun, address = "Portland, Oregon", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Theoretical Foundations of Evolutionary Computation", URL = "http://sitemaker.umich.edu/daida/files/CEC04highlander.pdf", DOI = "doi:10.1109/CEC.2004.1331036", abstract = "This paper introduces a tunably difficult problem for genetic programming (GP) that probes for an upper bound to the amount of heterogeneity that can be represented by a single individual. Although GP's variable-length representation would suggest that there is no upper bound, our results indicate otherwise. The results provide insight into the dynamics that occur during the course of a GP run.", size = "8 pages", notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.", } @InProceedings{daida:2004:vtlodigp, title = "Visualizing the Loss of Diversity in Genetic Programming", author = "Jason M. Daida and David J. Ward and Adam M. Hilss and Stephen L. Long and Mark R. Hodges and Jason T. Kriesel", pages = "1225--1232", booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary Computation", year = "2004", publisher = "IEEE Press", month = "20-23 " # jun, address = "Portland, Oregon", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Theoretical Foundations of Evolutionary Computation", URL = "http://sitemaker.umich.edu/daida/files/CEC04viz.pdf", DOI = "doi:10.1109/CEC.2004.1331037", abstract = "This paper introduces visualization techniques that allow for a multivariate approach in understanding the dynamics that underlie genetic programming (GP). Emphasis is given toward understanding the relationship between problem difficulty and the loss of diversity. The visualizations raise questions about diversity and problem solving efficacy, as well as the role of the initial population in determining solution outcomes.", notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.", } @Article{daida:2005:GPEM, author = "Jason M. Daida and Adam M. Hilss and David J. Ward and Stephen L. Long", title = "Visualizing Tree Structures in Genetic Programming", journal = "Genetic Programming and Evolvable Machines", year = "2005", volume = "6", number = "1", pages = "79--110", month = mar, keywords = "genetic algorithms, genetic programming, theory", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-005-7621-2", size = "32 pages", abstract = "This paper presents methods to visualise the structure of trees that occur in genetic programming. These methods allow for the inspection of structure of entire trees even though several thousands of nodes may be involved. The methods also scale to allow for the inspection of structure for entire populations and for complete trials even though millions of nodes may be involved. Examples are given that demonstrate how this new way of seeing can afford a potentially rich way of understanding dynamics that underpin genetic programming. The examples indicate further studies that might be enabled by visualising structure at these scales.", notes = "Mathematica source code at http://library.wolfram.com/infocenter/MathSource/5163 See also http://www.cs.ucl.ac.uk/staff/W.Langdon/gp2lattice/gp2lattice.html", } @InCollection{daida:2005:GPTP, author = "Jason Daida", title = "Challenges in Open-Ended Problem Solving with Genetic Programming", booktitle = "Genetic Programming Theory and Practice {III}", year = "2005", editor = "Tina Yu and Rick L. Riolo and Bill Worzel", volume = "9", series = "Genetic Programming", chapter = "17", pages = "259--274", address = "Ann Arbor", month = "12-14 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, open-ended problem solving, McMaster Problem Solving", ISBN = "0-387-28110-X", DOI = "doi:10.1007/0-387-28111-8_17", size = "16 pages", abstract = "how GP might be integrated as a tool into the human context of discovery. To accomplish this, a comparison is made between GP and a well-regarded strategy in open-ended problem solving. The comparison not only indicates which tasks and skills are likely to be complemented by GP, but also the kinds of problems that may or may not be suited for it. Furthermore, the comparison indicates directions in research that may need to be taken for GP to be further leveraged as a tool that assists discovery.", notes = "part of \cite{yu:2005:GPTP} Published Jan 2006 after the workshop", } @InProceedings{1068284, author = "Jason M. Daida", title = "Towards identifying populations that increase the likelihood of success in genetic programming", booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation", year = "2005", editor = "Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and Edwin D. {de Jong} and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler", volume = "2", ISBN = "1-59593-010-8", pages = "1627--1634", address = "Washington DC, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2005/docs/p1627.pdf", DOI = "doi:10.1145/1068009.1068284", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "25-29 " # jun, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, binomial-3, building blocks, experimentation, genetic programming problem difficulty, initial populations, performance, population dynamics, selection methods, theory", notes = "GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM Order Number 910052", } @InProceedings{1068295, author = "Jason M. Daida and Michael E. Samples and Matthew J. Byom", title = "Probing for limits to building block mixing with a tunably-difficult problem for genetic programming", booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation", year = "2005", editor = "Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and Edwin D. {de Jong} and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler", volume = "2", ISBN = "1-59593-010-8", pages = "1713--1720", address = "Washington DC, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2005/docs/p1713.pdf", DOI = "doi:10.1145/1068009.1068295", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "25-29 " # jun, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, building blocks, experimentation, highlander problem, initial populations, performance, tunably-difficult problems, theory", notes = "GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM Order Number 910052", } @InCollection{Daida:2006:GPTP, author = "Jason M. Daida and Ricky Tang and Michael E. Samples and Matthew J. Byom", title = "Phase Transitions in Genetic Programming Search", booktitle = "Genetic Programming Theory and Practice {IV}", year = "2006", editor = "Rick L. Riolo and Terence Soule and Bill Worzel", volume = "5", series = "Genetic and Evolutionary Computation", pages = "237--256", address = "Ann Arbor", month = "11-13 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming", ISBN = "0-387-33375-4", DOI = "doi:10.1007/978-0-387-49650-4_15", abstract = "Phase transitions and critical phenomena occur not only in thermodynamic systems but also in nonphysical systems that occur in computation. Of particular interest is the possibility that phase transitions occur in GP search. If this were so, it would allow for a statistical mechanics approach that would allow for quantitative comparisons of GP with a broad variety of rigorously described systems. This chapter summarises our research group's work in this area and describes a case study that illustrates what is involved in establishing the existence of phase transitions in GP search.", notes = "part of \cite{Riolo:2006:GPTP} Published Jan 2007 after the workshop", } @InProceedings{1144140, author = "Jason M. Daida", title = "Characterizing the dynamics of symmetry breaking in genetic programming", booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation", year = "2006", editor = "Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos {Coello Coello} and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens", volume = "1", ISBN = "1-59593-186-4", pages = "799--806", address = "Seattle, Washington, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p799.pdf", DOI = "doi:10.1145/1143997.1144140", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "8-12 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, analysis methods, computational geometry, data structures, design patterns, graphics techniques, languages, measurement, patterns, program synthesis, symmetry breaking, synthesis, theory, tree", notes = "GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM Order Number 910060", } @InProceedings{dain:1997:GPmrwfa, author = "Robert A. Dain", title = "Genetic Programming For Mobile Robot Wall-Following Algorithms", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming", pages = "70", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1997/dain_1997_GPmrwfa.pdf", size = "1 page", notes = "Cf also Marco robot written description in Encoder, the newsletter of the Seattle Robotics Society, 103, December 1995 http://www.seattlerobotics.org/encoder/Encoder-1995-12.pdf HTR Labs Human touch Robotics Laboratories GP-97", } @Article{dain:1998:GPmrwfa, author = "Robert A. Dain", title = "Developing Mobile Robot Wall-Following Algorithms Using Genetic Programming", journal = "Applied Intelligence", year = "1998", volume = "8", number = "5", pages = "33--41", month = jan, keywords = "genetic algorithms, genetic programming, computational genetics, machine learning, adaptive systems", ISSN = "0924-669X", DOI = "doi:10.1023/A:1008216530547", size = "9 pages", abstract = "This paper demonstrates the use of genetic programming (GP) for the development of mobile robot wall-following behaviours. Algorithms are developed for a simulated mobile robot that uses an array of range finders for navigation. Navigation algorithms are tested in a variety of differently shaped environments to encourage the development of robust solutions, and reduce the possibility of solutions based on memorisation of a fixed set of movements. A brief introduction to GP is presented. A typical wall-following robot evolutionary cycle is analysed, and results are presented. GP is shown to be capable of producing robust wall-following navigation algorithms that perform well in each of the test environments used.", notes = "Special Issues on Evolutionary Learning, Xin Yao and Don Potter, Guest Editors", } @InCollection{dain:1999:dmrwaugp, author = "Robert A. Dain", title = "Development of Mobile Robot Wall-Following Algorithms Using Genetic Programming", year = "1999", pages = "269--283", booktitle = "Industrial Applications of Genetic Algorithms", editor = "Charles L. Karr and L. Michael Freeman", address = "Boca Raton, FL, USA", publisher = "CRC Press", series = "Computational Intelligence", keywords = "genetic algorithms, genetic programming", ISBN = "0-8493-9801-0", URL = "http://www.crcpress.com/product/isbn/9780849398018", } @InProceedings{Dakhama:2023:SSBSE, author = "Aidan Dakhama and Karine Even-Mendoza and W. B. Langdon and Hector {Menendez Benito} and Justyna Petke", title = "{SearchGEM5}: Towards Reliable gem5 with Search Based Software Testing and Large Language Models", booktitle = "SSBSE 2023: Challenge Track", year = "2023", editor = "Paolo Arcaini and Tao Yue and Erik Fredericks", organisers = "Erik Fredericks and Paolo Arcaini and Tao Yue and Rebecca Moussa and Thomas Vogel and Gregory Gay and Max Hort and Bobby R. Bruce and Jose Miguel Rojas and Vali Tawosi", volume = "14415", series = "LNCS", pages = "60--166", address = "San Francisco, USA", month = "8 " # dec, publisher = "Springer", note = "winner best challenge track paper", keywords = "genetic improvement, gem5, c-testsuite, AI, LLM, SBSE, SBFT, AFL++, mutations, X86, genetic improvement of tests, evolutionary computing", isbn13 = "978-3-031-48795-8", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Dakhama_2023_SSBSE.pdf", video_url = "https://youtu.be/D3nO8aMSR-0?si=wEi7mYDPe9MOyB4o", code_url = "https://github.com/karineek/SearchGEM5/", code_url = "https://zenodo.org/record/8316685", DOI = "doi:10.1007/978-3-031-48796-5_14", size = "7 pages", abstract = "We introduce a novel automated testing technique that combines LLM and search-based fuzzing. We use ChatGPT to parameterise C programs. We compile the resultant code snippets, and feed compilable ones to SearchGEM5 - our extension to AFL++ fuzzer with customised new mutation operators. We run thus created 4005 binaries through our system under test, gem5, increasing its existing test coverage by more than 1000 lines. We discover 231 instances where gem5 simulation of the binary differs from the binary's expected behaviour", notes = "not GP Also known as \cite{EvenMendoza2023Gem5} co-located with ESEC/FSE 2023. https://conf.researchr.org/track/ssbse-2023/ssbse-2023-challenge#Accepted-papers ", } @PhdThesis{Daler:thesis, author = "Ludovic Daler", title = "Adaptive Morphology for Multi-Modal Locomotion", school = "EPFL", year = "2015", address = "Switzerland", month = "9th " # jun, keywords = "flying robots, multi-modal robots, integrated design, adaptive morphology, aerial robotics", URL = "http://actu.epfl.ch/news/thesis-defense-ludovic-daler/", URL = "https://infoscience.epfl.ch/record/208777/files/EPFL_TH6608.pdf", URL = "https://infoscience.epfl.ch/record/208777", size = "127 pages", abstract = "There is a growing interest in using robots in dangerous environments, such", notes = "Thesis number 6608 (2015) Supervisers Dario Floreano and Auke Ijspeert Not on GP", } @InProceedings{Dalinghaus:2023:CS, author = "Charline Dalinghaus and Giovanni Coco and Pablo Higuera", title = "Using Genetic Programming for Ensemble Predictions of Wave Setup", booktitle = "Coastal Sediments", year = "2023", editor = "Elizabeth Royer and Julie D Rosati and Ping Wang", pages = "1933--1939", publisher = "World Sientific", keywords = "genetic algorithms, genetic programming", isbn13 = "9789811275142", DOI = "doi:10.1142/9789811275135_0177", blog_url = "https://coastalhub.science/f/genetic-programming-as-a-tool-to-predict-coastal-processes", abstract = "We applied an evolutionary-based genetic programming model to improve the accuracy of maximum wave setup predictions. To develop the algorithm, we used a previously published well-known dataset representing a variety of beach and wave conditions. As a result, we present ten new empirical predictors of wave setup and propose using an ensemble model of the top five and ten new empirical equations. All new predictors outperform the ones in the literature, with the model ensembles presenting an even better fit than the individual parametrizations to the testing data. Genetic programming models and the use of ensemble predictions are capable of providing some physical insights and increase the predictive capability.", } @Article{dalinghaus:2023:nhess, author = "Charline Dalinghaus and Giovanni Coco and Pablo Higuera", title = "A predictive equation for wave setup using genetic programming", journal = "Natural Hazards and Earth System Sciences", year = "2023", volume = "23", number = "6", pages = "2157--2169", keywords = "genetic algorithms, genetic programming", ISSN = "1684-9981", URL = "https://nhess.copernicus.org/articles/23/2157/2023/", DOI = "doi:10.5194/nhess-23-2157-2023", abstract = "We applied machine learning to improve the accuracy of present predictors of wave setup. Namely, we used an evolutionary-based genetic programming model and a previously published dataset, which includes various beach and wave conditions. Here, we present two new wave setup predictors: a simple predictor, which is a function of wave height, wavelength, and foreshore beach slope, and a fitter, but more complex predictor, which is also a function of sediment diameter. The results show that the new predictors outperform existing formulas. We conclude that machine learning models are capable of improving predictive capability (when compared to existing predictors) and also of providing a physically sound description of wave setup.", notes = "Also known as \cite{nhess-23-2157-2023} European Geosciences Union", } @TechReport{dallaway:1993:GPcm, author = "Richard Dallaway", title = "Genetic programming and cognitive models", institution = "School of Cognitive \& Computing Sciences, University of Sussex,", year = "1993", number = "CSRP 300", address = "Brighton, UK", note = "In: Brook \& Arvanitis, eds., 1993 The Sixth White House Papers: Graduate Research in the Cognitive \& Computing Sciences at Sussex", keywords = "genetic algorithms, genetic programming", URL = "http://www.dallaway.com/acad/evolution/evocog.html", size = "7 pages", abstract = "Genetic programming (GP) is a general purpose method for evolving symbolic computer programs (e.g. Lisp code). Concepts from genetic algorithms are used to evolve a population of initially random programs so that they are able to solve the problem at hand. This paper describes genetic programming and discuss the usefulness of the method for building cognitive models. Although it appears that an arbitrary fit to the training examples will be evolved, it is shown that GP can be constrained to produce small, general programs.", notes = "symbolic regression of 2.719x^2 + 3.14161x from 20 random points, parsimony pressure used Broken Sep 2018 http://cogslib.cogs.susx.ac.uk/csr_abs.php?type=csrp&num=300&id=7657", } @InProceedings{Damasevicius:ITA:2008, author = "Robertas Damasevicius", title = "Derivation of context-free stochastic {L}-Grammar rules for promoter sequence modeling using Support Vector Machine", booktitle = "XI-th Joint International Scientific Events on Informatics, Book 2, Advanced Research in Artificial Intelligence", year = "2008", editor = "K. Markov and {K. Ivanova} and I. Mitov", series = "Information Science and Computing", pages = "98--104", address = "Varna, Bulgaria", publisher_address = "Sofia, Bulgaria", month = "23 " # jun # " - 03 " # jul, publisher = "Ithea", keywords = "genetic algorithms, genetic programming, pattern recognition, J, 3 life and medical sciences", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.386.8512", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.386.8512", URL = "http://www.foibg.com/ibs_isc/ibs-02/ibs-02.htm", URL = "http://www.foibg.com/ibs_isc/ibs-02/IBS-02-p13.pdf", size = "7 pages", abstract = "Formal grammars can used for describing complex repeatable structures such as DNA sequences. In this paper, we describe the structural composition of DNA sequences using a context-free stochastic L-grammar. L-grammars are a special class of parallel grammars that can model the growth of living organisms, e.g. plant development, and model the morphology of a variety of organisms. We believe that parallel grammars also can be used for modelling genetic mechanisms and sequences such as promoters. Promoters are short regulatory DNA sequences located upstream of a gene. Detection of promoters in DNA sequences is important for successful gene prediction. Promoters can be recognised by certain patterns that are conserved within a species, but there are many exceptions which makes the promoter recognition a complex problem. We replace the problem of promoter recognition by induction of context-free stochastic L-grammar rules, which are later used for the structural analysis of promoter sequences. L-grammar rules are derived automatically from the drosophila and vertebrate promoter datasets using a genetic programming technique and their fitness is evaluated using a Support Vector Machine (SVM) classifier. The artificial promoter sequences generated using the derived L-grammar rules are analysed and compared with natural promoter sequences.", notes = "http://www.foibg.com/conf/itaf2008.htm", } @Article{Damasevicius2010633, author = "Robertas Damasevicius", title = "Structural analysis of regulatory DNA sequences using grammar inference and Support Vector Machine", journal = "Neurocomputing", volume = "73", number = "4-6", pages = "633--638", year = "2010", note = "Bayesian Networks / Design and Application of Neural Networks and Intelligent Learning Systems (KES 2008 / Bio-inspired Computing: Theories and Applications (BIC-TA 2007)", ISSN = "0925-2312", DOI = "doi:10.1016/j.neucom.2009.09.018", URL = "http://www.sciencedirect.com/science/article/B6V10-4XRYT4P-1/2/2e5b008bc8df4d5a39553b40fe6728c3", keywords = "genetic algorithms, genetic programming, DNA sequence analysis, Grammar inference, L-grammar, Support Vector Machine, SVM", abstract = "Regulatory DNA sequences such as promoters or splicing sites control gene expression and are important for successful gene prediction. Such sequences can be recognized by certain patterns or motifs that are conserved within a species. These patterns have many exceptions which makes the structural analysis of regulatory sequences a complex problem. Grammar rules can be used for describing the structure of regulatory sequences; however, the manual derivation of such rules is not trivial. In this paper, stochastic L-grammar rules are derived automatically from positive examples and counterexamples of regulatory sequences using genetic programming techniques. The fitness of grammar rules is evaluated using a Support Vector Machine (SVM) classifier. SVM is trained on known sequences to obtain a discriminating function which serves for evaluating a candidate grammar ruleset by determining the percentage of generated sequences that are classified correctly. The combination of SVM and grammar rule inference can mitigate the lack of structural insight in machine learning approaches such as SVM.", notes = "TATA box", } @InProceedings{DBLP:conf/ilp/dAmatoFE07, author = "Claudia d'Amato and Nicola Fanizzi and Floriana Esposito", title = "Induction of Optimal Semantic Semi-distances for Clausal Knowledge Bases", booktitle = "17th International Conference on Inductive Logic Programming, ILP 2007", year = "2007", editor = "Hendrik Blockeel and Jan Ramon and Jude W. Shavlik and Prasad Tadepalli", volume = "4894", series = "Lecture Notes in Computer Science", pages = "29--38", address = "Corvallis, OR, USA", month = jun # " 19-21", publisher = "Springer", note = "Revised Selected Papers", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/978-3-540-78469-2_7", DOI = "doi:10.1007/978-3-540-78469-2_7", timestamp = "Sun, 04 Jun 2017 10:05:36 +0200", biburl = "https://dblp.org/rec/bib/conf/ilp/dAmatoFE07", bibsource = "dblp computer science bibliography, https://dblp.org", abstract = "Several activities related to semantically annotated resources can be enabled by a notion of similarity, spanning from clustering to retrieval, matchmaking and other forms of inductive reasoning. We propose the definition of a family of semi-distances over the set of objects in a knowledge base which can be used in these activities. In the line of works on distance-induction on clausal spaces, the family is parametrized on a committee of concepts expressed with clauses. Hence, we also present a method based on the idea of simulated annealing to be used to optimize the choice of the best concept committee.", } @InProceedings{Dambrot:2018:UEMCON, author = "S. Mason Dambrot", booktitle = "2018 9th IEEE Annual Ubiquitous Computing, Electronics Mobile Communication Conference (UEMCON)", title = "{ReGene:} Blockchain backup of genome data and restoration of pre-engineered expressed phenotype", year = "2018", pages = "945--950", abstract = "Molecular and genetic therapeutics, extended lifespans, and the repair augmentation of the human body remain key cornerstones of current, emerging and future medical science and technology. While internal and external prosthetics have formed the foundation of this goal, the growing utility and ubiquity of genetic engineering and synthetic genomics-already being successful in early preventative therapeutic applications-promise a future in which cells, tissues and organs are likely to be designed to express novel biological structures and preprogrammed functions, the latter encompassing those capable of performing technological operations, including but not limited to direct communications with the exogenous world. Achieving this will require and accelerate the ongoing interdigitation of biology and technology, with these two domains eventually merging. This emergent transdisciplinarian environment will have the potential to render external and implanted technological devices obsolete, as their features are then performed by their synthetic biological and molecular replacements. There is, however, one operational concern for which a solution has yet to have been determined-that is, anomalies in synthetic genomics and genome engineering somatic expression. Here, I propose a blockchain-based solution that-rather than being limited to providing genomic privacy, security and anonymous data analysis, as is currently the case-would provide a method for reversing phenotypical expression errors should they occur. By so doing, ReGene addresses both actual and perceived risk, thereby ameliorating personal, medical, legislative and other areas of resistance to commercial applications of advanced genetic engineering and synthetic genomics.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/UEMCON.2018.8796768", month = nov, notes = "Also known as \cite{8796768}", } @InProceedings{Damper:2001:timr, author = "R. I. Damper and R. L. B. French", title = "Scaling Intelligent Behaviour in the ARBIB Autonomous Robot", booktitle = "TIMR 01 - Towards Intelligent Mobile Robots", year = "2001", editor = "Ulrich Nehmzow and Chris Melhuish", address = "Manchester, UK", publisher_address = "UK", month = "5 " # apr, organisation = "Computer Science, University of Manchester", keywords = "genetic algorithms, genetic programming, ANN, Khepera, Arbib, spiking neurons", URL = "http://apt.cs.manchester.ac.uk/ftp/pub/TR/UMCS-01-4-1.html", URL = "http://apt.cs.manchester.ac.uk/ftp/pub/TR/UMCS-01-4-1-damper.ps.Z", size = "10 pages", abstract = "A major concern when building an intelligent robot is: How can it develop increasingly intelligent behaviours? This problem is widely recognised, and present research with the ARBIB autonomous robot also addresses this issue. In this paper, we describe how ARBIB can scale in complexity in two directions. First, by allowing its neural simulator, called HiNoon, to take advantage of distributed computer hardware, ARBIB's nervous system can attain a high degree of complexity aimed at increasing its sensory-motor capabilities. Second, through evolution based on ideas of genetic programming, HiNoon is free to develop nervous system architectures whose complexity is no longer governed by initial human design and subsequent intervention. Hence, evolved nervous systems are supported by a simulator architecture that expands to take advantage of additional compute hardware when needed.", notes = "Southampton. p7 'we see this..as being closer to..' GP than GA', with hi-noon viewed as a virtual machine.' Is this GP? Proceedings British Conference on Autonomous Mobile Robotics & Autonomous Systems, 2001 Proceedings available as Technical Report UMCS-01-4-1 of the Computer Science department of the University of Manchester UMCS-01-4-1 Conference also known as TAROS 2001 http://www.cs.ox.ac.uk/conferences/TAROS2013/past.html", } @Article{DANAI:2021:MSSP, author = "Kourosh Danai and William G. {La Cava}", title = "Controller design by symbolic regression", journal = "Mechanical Systems and Signal Processing", volume = "151", pages = "107348", year = "2021", ISSN = "0888-3270", DOI = "doi:10.1016/j.ymssp.2020.107348", URL = "https://www.sciencedirect.com/science/article/pii/S0888327020307342", keywords = "genetic algorithms, genetic programming, Symbolic Regression, Nonlinear Control, Structural Adaptation, Controller Design", abstract = "A novel method of empirical controller design is introduced with the potential to produce exotic controller forms. The controllers in this method are derived by symbolic regression (SR) to be in equation form, hence, they are legible in how the controller output is computed as a function of loop variables. Because SR is computationally costly due to its extensive search of controller space, requiring evaluation of millions, if not billions, of candidate controllers, the candidate controllers cannot be evaluated in closed-loop due to the high cost of simulation associated with such evaluation. This paper offers a recourse to this closed-loop evaluation by allowing evaluations to be performed algebraically. To this end, a method of inverse solution is introduced that estimates the plant input for a desired plant output. This estimated plant input is then used as the target output for candidate controllers that can be readily evaluated algebraically based on the available time series of loop variables associated with the desired plant output. Unlike traditional control design which relies on closed-loop performance metrics to provide controller performance guarantees, the proposed open-loop approach sacrifices such guarantees in favor of new controller forms that it may yield. Therefore, the fidelity, as controllers, of candidate controllers need to be verified post-design. For this purpose, the candidate controllers are first evaluated as controllers in closed-loop simulation. Once verified by simulation, they need to be validated for closed-loop stability, as demonstrated for one of the studied cases", } @Article{DanandehMehr:2013:JH, author = "Ali {Danandeh Mehr} and Ercan Kahya and Ehsan Olyaie", title = "Streamflow prediction using linear genetic programming in comparison with a neuro-wavelet technique", journal = "Journal of Hydrology", volume = "505", pages = "240--249", year = "2013", keywords = "genetic algorithms, genetic programming, Feed forward neural networks, Wavelet transform, Data pre-processing, Hydrologic models, Stream-flow prediction", ISSN = "0022-1694", URL = "http://www.sciencedirect.com/science/article/pii/S0022169413007105", DOI = "doi:10.1016/j.jhydrol.2013.10.003", abstract = "Accurate prediction of stream flow is an essential ingredient for both water quantity and quality management. In recent years, artificial intelligence (AI) techniques have been pronounced as a branch of computer science to model wide range of hydrological processes. A number of research works have been still comparing these techniques in order to find more efficient approach in terms of accuracy and applicability. In this study, two AI techniques, including hybrid wavelet-artificial neural network (WANN) and linear genetic programming (LGP) technique have been proposed to forecast monthly stream-flow in a particular catchment and then performance of the proposed models were compared with each other in terms of root mean square error (RMSE) and Nash-Sutcliffe efficiency (NSE) measures. In this way, six different monthly streamflow scenarios based on records of two successive gauging stations have been modelled by a common three layer artificial neural network (ANN) method as the primary reference models. Then main time series of input(s) and output records were decomposed into sub-time series components using wavelet transform. In the next step, sub-time series of each model were imposed to ANN to develop WANN models as optimized version of the reference ANN models. The obtained results were compared with those that have been developed by LGP models. Our results showed the higher performance of LGP over WANN in all reference models. An explicit LGP model constructed by only basic arithmetic functions including one month-lagged records of both target and upstream stations revealed the best prediction model for the study catchment.", notes = "LGP is found to be more applicable than WANN for monthly streamflow prediction at Coruh River.", } @Article{DanandehMehr:2014:CG, author = "Ali {Danandeh Mehr} and Ercan Kahya and Cahit Yerdelen", title = "Linear genetic programming application for successive-station monthly streamflow prediction", journal = "Computer \& Geosciences", volume = "70", pages = "63--72", year = "2014", ISSN = "0098-3004", DOI = "doi:10.1016/j.cageo.2014.04.015", URL = "http://www.sciencedirect.com/science/article/pii/S0098300414001010", abstract = "In recent decades, artificial intelligence (AI) techniques have been pronounced as a branch of computer science to model wide range of hydrological phenomena. A number of researches have been still comparing these techniques in order to find more effective approaches in terms of accuracy and applicability. In this study, we examined the ability of linear genetic programming (LGP) technique to model successive-station monthly streamflow process, as an applied alternative for streamflow prediction. A comparative efficiency study between LGP and three different artificial neural network algorithms, namely feed forward back propagation (FFBP), generalised regression neural networks (GRNN), and radial basis function (RBF), has also been presented in this study. For this aim, firstly, we put forward six different successive-station monthly streamflow prediction scenarios subjected to training by LGP and FFBP using the field data recorded at two gauging stations on Coruh River, Turkey. Based on Nash-Sutcliffe and root mean squared error measures, we then compared the efficiency of these techniques and selected the best prediction scenario. Eventually, GRNN and RBF algorithms were used to restructure the selected scenario and to compare with corresponding FFBP and LGP. Our results indicated the promising role of LGP for successive-station monthly streamflow prediction providing more accurate results than those of all the ANN algorithms. We found an explicit LGP-based expression evolved by only the basic arithmetic functions as the best prediction model for the river, which uses the records of the both target and upstream stations.", keywords = "genetic algorithms, genetic programming, Artificial neural networks, Streamflow prediction, Successive stations", } @Article{DanandehMehr:2014:JH, author = "Ali {Danandeh Mehr} and Ercan Kahya and Mehmet Ozger", title = "A gene-wavelet model for long lead time drought forecasting", journal = "Journal of Hydrology", volume = "517", pages = "691--699", year = "2014", keywords = "genetic algorithms, genetic programming, Drought forecasting, Linear genetic programing, Wavelet transform, El Nino-Southern Oscillation, Palmer's modified drought index, Hydrologic models", ISSN = "0022-1694", DOI = "doi:10.1016/j.jhydrol.2014.06.012", URL = "http://www.sciencedirect.com/science/article/pii/S0022169414004727", abstract = "Summary Drought forecasting is an essential ingredient for drought risk and sustainable water resources management. Due to increasing water demand and looming climate change, precise drought forecasting models have recently been receiving much attention. Beginning with a brief discussion of different drought forecasting models, this study presents a new hybrid gene-wavelet model, namely wavelet-linear genetic programing (WLGP), for long lead-time drought forecasting. The idea of WLGP is to detect and optimise the number of significant spectral bands of predictors in order to forecast the original predict and (drought index) directly. Using the observed El Nno-Southern Oscillation indicator (NINO 3.4 index) and Palmer's modified drought index (PMDI) as predictors and future PMDI as predictand, we proposed the WLGP model to forecast drought conditions in the State of Texas with 3, 6, and 12-month lead times. We compared the efficiency of the model with those of a classic linear genetic programing model developed in this study, a neuro-wavelet (WANN), and a fuzzy-wavelet (WFL) drought forecasting models formerly presented in the relevant literature. Our results demonstrated that the classic linear genetic programing model is unable to learn the non-linearity of drought phenomenon in the lead times longer than 3 months; however, the WLGP can be effectively used to forecast drought conditions having 3, 6, and 12-month lead times. Genetic-based sensitivity analysis among the input spectral bands showed that NINO 3.4 index has strong potential effect in drought forecasting of the study area with 6-12-month lead times.", } @Article{DanandehMehr:2016:uujfe, author = "Ali {Danandeh Mehr} and Mehmet Cuneyd Demirel", title = "On the Calibration of Multigene Genetic Programming to Simulate Low Flows in the {Moselle} River", journal = "Uludag University Journal of The Faculty of Engineering", year = "2016", volume = "21", number = "2", pages = "365--376", month = dec, keywords = "genetic algorithms, genetic programming, Low flows, calibration, ANN, HBV, GR4J", URL = "http://mmfdergi.uludag.edu.tr/article/view/5000195603", URL = "http://mmfdergi.uludag.edu.tr/article/view/5000195603/5000179033", DOI = "doi:10.17482/uumfd.278107", size = "12 pages", abstract = "The aim of this paper is to calibrate a data-driven model to simulate Moselle River flows and compare the performance with three different hydrologic models from a previous study. For consistency a similar set up and error metric are used to evaluate the model results. Precipitation, potential evapotranspiration and streamflow from previous day have been used as inputs. Based on the calibration and validation results, the proposed multigene genetic programming model is the best performing model among four models. The timing and the magnitude of extreme low flow events could be captured even when we use root mean squared error as the objective function for model calibration. Although the model is developed and calibrated for Moselle River flows, the multigene genetic algorithm offers a great opportunity for hydrologic prediction and forecast problems in the river basins with scarce data issues.", } @Article{DanandehMehr:2017:JH, author = "Ali {Danandeh Mehr} and Ercan Kahya", title = "A Pareto-optimal moving average multigene genetic programming model for daily streamflow prediction", journal = "Journal of Hydrology", volume = "549", pages = "603--615", year = "2017", ISSN = "0022-1694", DOI = "doi:10.1016/j.jhydrol.2017.04.045", URL = "http://www.sciencedirect.com/science/article/pii/S0022169417302664", abstract = "Genetic programming (GP) is able to systematically explore alternative model structures of different accuracy and complexity from observed input and output data. The effectiveness of GP in hydrological system identification has been recognized in recent studies. However, selecting a parsimonious (accurate and simple) model from such alternatives still remains a question. This paper proposes a Pareto-optimal moving average multigene genetic programming (MA-MGGP) approach to develop a parsimonious model for single-station streamflow prediction. The three main components of the approach that take us from observed data to a validated model are: (1) data pre-processing, (2) system identification and (3) system simplification. The data pre-processing ingredient uses a simple moving average filter to diminish the lagged prediction effect of stand-alone data-driven models. The multigene ingredient of the model tends to identify the underlying nonlinear system with expressions simpler than classical monolithic GP and, eventually simplification component exploits Pareto front plot to select a parsimonious model through an interactive complexity-efficiency trade-off. The approach was tested using the daily streamflow records from a station on Senoz Stream, Turkey. Comparing to the efficiency results of stand-alone GP, MGGP, and conventional multi linear regression prediction models as benchmarks, the proposed Pareto-optimal MA-MGGP model put forward a parsimonious solution, which has a noteworthy importance of being applied in practice. In addition, the approach allows the user to enter human insight into the problem to examine evolved models and pick the best performing programs out for further analysis.", keywords = "genetic algorithms, genetic programming, Streamflow prediction, Pareto-optimal, Hydrological modelling", } @Article{DanandehMehr:2017:EMS, author = "Ali {Danandeh Mehr} and Vahid Nourani", title = "A {Pareto}-optimal moving average-multigene genetic programming model for rainfall-runoff modelling", journal = "Environmental Modelling \& Software", year = "2017", volume = "92", pages = "239--251", month = jun, keywords = "genetic algorithms, genetic programming, Multigene genetic programming, Rainfall-runoff modelling, Pareto-optimal model, Multilayer perceptron, Moving average filtering", ISSN = "1364-8152", DOI = "doi:10.1016/j.envsoft.2017.03.004", URL = "http://www.sciencedirect.com/science/article/pii/S1364815216308143", size = "13 pages", abstract = "The effectiveness of genetic programming (GP) in rainfall-runoff modelling has been recognized in recent studies. However, it may produce misleading estimations if autoregressive relationship between runoff and its antecedent values is not carefully considered. Meanwhile, GP evolves alternative models of different accuracy and complexity, where selecting a parsimonious model from such alternatives needs extra attention. To cope with these problems, this paper proposes a new hybrid model that integrates moving average filtering with multigene GP and uses Pareto-front plot to optimize the evolved models through an interactive complexity-efficiency trade-off. The model was applied to develop single- and multi-day-ahead rainfall-runoff models and compared to stand-alone GP, multigene GP, and multilayer perceptron as the benchmarks. The results indicated that the new model provides substantial improvements relative to the benchmarks, with prediction errors 25-60percent lower and timing accuracy 80-760percent higher. Moreover, it is explicit and parsimonious, motivating to be used in practice.", } @Article{DANANDEHMEHR2017397, author = "Ali {Danandeh Mehr} and Vahid Nourani and Bahrudin Hrnjica and Amir Molajou", title = "A binary genetic programing model for teleconnection identification between global sea surface temperature and local maximum monthly rainfall events", journal = "Journal of Hydrology", year = "2017", volume = "555", pages = "397--406", month = dec, keywords = "genetic algorithms, genetic programming, Maximum monthly rainfall, Sea surface temperature, Binary classification, Forecasting", ISSN = "0022-1694", URL = "http://www.sciencedirect.com/science/article/pii/S002216941730714X", DOI = "doi:10.1016/j.jhydrol.2017.10.039", abstract = "The effectiveness of genetic programming (GP) for solving regression problems in hydrology has been recognized in recent studies. However, its capability to solve classification problems has not been sufficiently explored so far. This study develops and applies a novel classification-forecasting model, namely Binary GP (BGP), for teleconnection studies between sea surface temperature (SST) variations and maximum monthly rainfall (MMR) events. The BGP integrates certain types of data pre-processing and post-processing methods with conventional GP engine to enhance its ability to solve both regression and classification problems simultaneously. The model was trained and tested using SST series of Black Sea, Mediterranean Sea, and Red Sea as potential predictors as well as classified MMR events at two locations in Iran as predicted. Skill of the model was measured in regard to different rainfall thresholds and SST lags and compared to that of the hybrid decision tree-association rule (DTAR) model available in the literature. The results indicated that the proposed model can identify potential teleconnection signals of surrounding seas beneficial to long-term forecasting of the occurrence of the classified MMR events.", } @Article{DanandehMehr:2018:ajees, author = "Ali {Danandeh Mehr}", title = "Month Ahead Rainfall Forecasting Using Gene Expression Programming", journal = "American Journal of Earth and Environmental Sciences", year = "2018", volume = "1", number = "2", pages = "63--70", month = "10 " # apr, keywords = "genetic algorithms, genetic programming, Gene Expression Programming, Monthly Rainfall, Time Series Modelling, State-Space Modelling", ISSN = "ISSN Pending", URL = "http://article.aascit.org/file/pdf/8100054.pdf", URL = "http://www.aascit.org/journal/archive2?journalId=810&paperId=6546", size = "8 pages", abstract = "In the present study, gene expression programming (GEP) technique was used to develop one-month ahead monthly rainfall forecasting models in two meteorological stations located at a semi-arid region, Iran. GEP was trained and tested using total monthly rainfall (TMR) time series measured at the stations. Time lagged series of TMR samples having weak stationary state were used as inputs for the modelling. Performance of the best evolved models were compared with those of classic genetic programming (GP) and autoregressive state-space (ASS) approaches using coefficient of efficiency (R2) and root mean squared error measures. The results showed good performance (0.532 less than 0.56) for GEP models at testing period. In both stations, the best model evolved by GEP outperforms the GP and are significantly superior to the ASS models.", notes = "Civil Engineering Department, Antalya Bilim University, Antalya, Turkey http://www.aascit.org/journal/ees", } @Article{DanandehMehr2018, author = "Ali {Danandeh Mehr} and Vahid Nourani", title = "Season Algorithm-Multigene Genetic Programming: A New Approach for Rainfall-Runoff Modelling", journal = "Water Resources Management", year = "2018", volume = "32", number = "8", pages = "2665--2679", month = jun, keywords = "genetic algorithms, genetic programming, multigene genetic programming", ISSN = "1573-1650", DOI = "doi:10.1007/s11269-018-1951-3", abstract = "Genetic programming (GP) is recognized as a robust machine learning method for rainfall-runoff modelling. However, it may produce lagged forecasts if autocorrelation feature of runoff series is not taken carefully into account. To enhance timing accuracy of GP-based rainfall-runoff models, the paper proposes a new rainfall-runoff model that integrates season algorithm (SA) with multigene-GP (MGGP). The proposed SA-MGGP model was trained and validated for single- and two- and three-day ahead streamflow forecasts at Haldizen Catchment, Trabzon, Turkey. Timing and prediction accuracy of the proposed model were assessed in terms of different efficiency criteria. In addition, the efficiency results were compared to those of monolithic GP, MGGP, and SA-GP forecasting models developed in the present study as the benchmarks. The outcomes indicated that SA augments timing accuracy of GP-based models in the range 250percent to 500percent. It is also found that MGGP may identify underlying structure of the rainfall-runoff process slightly better than monolithic GP at the study catchment.", } @Article{DANANDEHMEHR2018669, author = "Ali {Danandeh Mehr}", title = "An improved gene expression programming model for streamflow forecasting in intermittent streams", journal = "Journal of Hydrology", year = "2018", volume = "563", pages = "669--678", month = aug, keywords = "genetic algorithms, genetic programming, Gene expression programming, Streamflow forecasting, Evolutionary optimization, Intermittent streams", ISSN = "0022-1694", URL = "https://www.sciencedirect.com/science/article/pii/S0022169418304712", DOI = "doi:10.1016/j.jhydrol.2018.06.049", abstract = "Skilful forecasting of monthly streamflow in intermittent rivers is a challenging task in stochastic hydrology. In this study, genetic algorithm (GA) was combined with gene expression programming (GEP) as a new hybrid model for month ahead streamflow forecasting in an intermittent stream. The hybrid model was named GEP-GA in which sub-expression trees of the best evolved GEP model were rescaled by appropriate weighting coefficients through the use of GA optimizer. Auto-correlation and partial auto-correlation functions of the streamflow records as well as evolutionary search of GEP were used to identify the optimum predictors (i.e., number of lags) for the model. The proposed methodology was demonstrated using monthly streamflow data from the Shavir Creek in Iran. Performance of the GEP-GA was compared to that of classic genetic programming (GP), GEP, multiple linear regression and GEP-linear regression models developed in the present study as the benchmarks. The results showed that the GEP-GA outperforms all the benchmarks and motivated to be used in practice.", } @Article{DANANDEHMEHR:2018:JH, author = "Ali {Danandeh Mehr} and Vahid Nourani and Ercan Kahya and Bahrudin Hrnjica and Ahmed M. A. Sattar and Zaher Mundher Yaseen", title = "Genetic programming in water resources engineering: A state-of-the-art review", journal = "Journal of Hydrology", volume = "566", pages = "643--667", year = "2018", keywords = "genetic algorithms, genetic programming, Hydrology, Hydraulics, Hydroclimatology, Water resources engineering", ISSN = "0022-1694", DOI = "doi:10.1016/j.jhydrol.2018.09.043", URL = "http://www.sciencedirect.com/science/article/pii/S0022169418307376", abstract = "The state-of-the-art genetic programming (GP) method is an evolutionary algorithm for automatic generation of computer programs. In recent decades, GP has been frequently applied on various kind of engineering problems and undergone speedy advancements. A number of studies have demonstrated the advantage of GP to solve many practical problems associated with water resources engineering (WRE). GP has a unique feature of introducing explicit models for nonlinear processes in the WRE, which can provide new insight into the understanding of the process. Considering continuous growth of GP and its importance to both water industry and academia, this paper presents a comprehensive review on the recent progress and applications of GP in the WRE fields. Our review commences with brief explanations on the fundamentals of classic GP and its advanced variants (including multigene GP, linear GP, gene expression programming, and grammar-based GP), which have been proven to be useful and frequently used in the WRE. The representative papers having wide range of applications are clustered in three domains of hydrological, hydraulic, and hydroclimatological studies, and outlined or discussed at each domain. Finally, this paper was concluded with discussions of the optimum selection of GP parameters and likely future research directions in the WRE are suggested", } @Article{DANANDEHMEHR:2019:JH, author = "Ali {Danandeh Mehr} and Masood Jabarnejad and Vahid Nourani", title = "Pareto-optimal {MPSA-MGGP}: A new gene-annealing model for monthly rainfall forecasting", journal = "Journal of Hydrology", volume = "571", pages = "406--415", year = "2019", keywords = "genetic algorithms, genetic programming, Rainfall, Time series forecasting, Multigene genetic programming, Simulated annealing, Semiarid region", ISSN = "0022-1694", DOI = "doi:10.1016/j.jhydrol.2019.02.003", URL = "http://www.sciencedirect.com/science/article/pii/S0022169419301362", abstract = "Rainfall is considered the hardest weather variable to forecast, and its cause-effect relationships often cannot be expressed in simple or complex mathematical forms. This study introduces a novel hybrid model to month ahead forecasting monthly rainfall amounts which is motivated to be used in semi-arid basins. The new approach, called MPSA-MGGP, is based on integrating multi-period simulated annealing (MPSA) optimizer with multigene genetic programming (MGGP) symbolic regression so that the hybrid model reflects the periodic patterns in rainfall time series into a Pareto-optimal multigene forecasting equation. The model was trained and verified using observed rainfall at two meteorology stations located in north-west of Iran. The model accuracy was also cross-validated against two benchmarks: conventional genetic programming (GP) and MGGP. The results indicated that the proposed gene-annealing model provides slight to moderate decline in absolute error as well as noteworthy augment in Nash-Sutcliffe coefficient of efficiency. Promising efficiency together with parsimonious structure endorse the proposed model to be used for monthly rainfall forecasting in practice, particularly in semi-arid regions", } @InCollection{DANANDEHMEHR:2021:ASF, author = "Ali {Danandeh Mehr} and Mir Jafar Sadegh Safari", title = "Chapter 7 - Genetic programming for streamflow forecasting: a concise review of univariate models with a case study", editor = "Priyanka Sharma and Deepesh Machiwal", booktitle = "Advances in Streamflow Forecasting", publisher = "Elsevier", pages = "193--214", year = "2021", isbn13 = "978-0-12-820673-7", DOI = "doi:10.1016/B978-0-12-820673-7.00007-X", URL = "https://www.sciencedirect.com/science/article/pii/B978012820673700007X", keywords = "genetic algorithms, genetic programming, Gene expression programming, Sedre stream, Streamflow, Time series modeling", abstract = "The state-of-the-art genetic programming (GP) has received a great deal of attention over the past few decades and has been applied to many research areas of water resources engineering, including prediction of hydrometeorological variables, design of hydraulic structures, and recognition of hidden patterns in hydrological phenomena such as rainfall-runoff, interaction between surface water and groundwater, and time series modeling of streamflow. A fundamental advantage of this technique is the automatic generation of explicit solutions for a given problem, which may offer new insights into the problem at hand. Considering the importance of accurate streamflow forecasts in water resources management, this chapter presents a brief review on the recent applications of classical GP and its advanced versions in univariate streamflow modeling. The representative papers were selected from web of science database published in the current decade 2011-19. This chapter also includes a case study that compares two GP variants, namely classical GP and gene expression programming for 1-month ahead forecasts of the mean monthly streamflow in the Sedre Stream, a mountainous river in Antalya Basin, Turkey", } @Article{DANANDEHMEHR:2022:ecolind, author = "Ali {Danandeh Mehr} and Jaakko Erkinaro and Jan Hjort and Ali {Torabi Haghighi} and Amirhossein Ahrari and Maija Korpisaari and Jorma Kuusela and Brian Dempson and Hannu Marttila", title = "Factors affecting the presence of Arctic charr in streams based on a jittered binary genetic programming model", journal = "Ecological Indicators", volume = "142", pages = "109203", year = "2022", ISSN = "1470-160X", DOI = "doi:10.1016/j.ecolind.2022.109203", URL = "https://www.sciencedirect.com/science/article/pii/S1470160X22006756", keywords = "genetic algorithms, genetic programming, Ecohydrological modelling, Scarce data, Arctic Charr, Jittering", abstract = "Arctic charr is one of the fish species most sensitive to climate change but studies on their freshwater habitat preferences are limited, especially in the fluvial environment. Machine learning methods offer automatic and objective models for ecohydrological processes based on observed data. However, i) the number of ecological records is often much smaller than hydrological observations, and ii) ecological measurements over the long-term are costly. Consequently, ecohydrological datasets are scarce and imbalanced. To address these problems, we propose jittered binary genetic programming (JBGP) to detect the most dominant ecohydrological parameters affecting the occurrence of Arctic charr across tributaries within the large subarctic Teno River catchment, in northernmost Finland and Norway. We quantitatively assessed the accuracy of the proposed model and compared its performance with that of classic genetic programming (GP), decision tree (DT) and state-of-the-art jittered-DT methods. The JBGP achieves the highest total classification accuracy of 90percent and a Heidke skill score of 78percent, showing its superiority over its counterparts. Our results showed that the dominant factors contributing to the presence of Arctic charr in Teno River tributaries include i) a higher density of macroinvertebrates, ii) a lower percentage of mires in the catchment and iii) a milder stream channel slope", } @Article{danandehmehr:2023:Water, author = "Ali {Danandeh Mehr} and Masoud Reihanifar and Mohammad Mustafa Alee and Mahammad Amin {Vazifehkhah Ghaffari} and Mir Jafar Sadegh Safari and Babak Mohammadi", title = "{VMD-GP:} A New Evolutionary Explicit Model for Meteorological Drought Prediction at Ungauged Catchments", journal = "Water", year = "2023", volume = "15", number = "15", pages = "Article No. 2686", keywords = "genetic algorithms, genetic programming, gene expression programming", ISSN = "2073-4441", URL = "https://www.mdpi.com/2073-4441/15/15/2686", DOI = "doi:10.3390/w15152686", abstract = "Meteorological drought is a common hydrological hazard that affects human life. It is one of the significant factors leading to water and food scarcity. Early detection of drought events is necessary for sustainable agricultural and water resources management. For the catchments with scarce meteorological observatory stations, the lack of observed data is the main leading cause of unfeasible sustainable watershed management plans. However, various earth science and environmental databases are available that can be used for hydrological studies, even at a catchment scale. In this study, the Global Drought Monitoring (GDM) data repository that provides real-time monthly Standardized Precipitation and Evapotranspiration Index (SPEI) across the globe was used to develop a new explicit evolutionary model for SPEI prediction at ungauged catchments. The proposed model, called VMD-GP, uses an inverse distance weighting technique to transfer the GDM data to the desired area. Then, the variational mode decomposition (VMD), in conjunction with state-of-the-art genetic programming, is implemented to map the intrinsic mode functions of the GMD series to the subsequent SPEI values in the study area. The suggested model was applied for the month-ahead prediction of the SPEI series at Erbil, Iraq. The results showed a significant improvement in the prediction accuracy over the classic GP and gene expression programming models developed as the benchmarks.", notes = "also known as \cite{w15152686}", } @Article{dang:2024:Sustainability, author = "Chau Huyen Dang and Gianluigi Farru and Claudia Glaser and Marcus G. Fischer and Judy A. Libra", title = "Enhancing the Fuel Properties of Spent Coffee Grounds through Hydrothermal Carbonization: Output Prediction and {Post-Treatment} Approaches", journal = "Sustainability", year = "2024", volume = "16", number = "1", pages = "Article No. 338", keywords = "genetic algorithms, genetic programming", ISSN = "2071-1050", URL = "https://www.mdpi.com/2071-1050/16/1/338", DOI = "doi:10.3390/su16010338", abstract = "The reuse potential for the large annual production of spent coffee grounds (SCGs) is underexploited in most world regions. Hydrochars from SCGs produced via hydrothermal carbonization (HTC) have been recognised as a promising solid fuel alternative. To increase demand, optimisation of the HTC and two post-treatment processes, washing and agglomeration, were studied to improve hydrochar in terms of energetic properties, minimizing unwanted substances, and better handling. HTC experiments at three scales (1-18.75 L) and varying process conditions (temperature T (160-250 ?C), reaction time t (1-5 h), and solid content percentSo (6-20percent) showed that the higher heating value (HHV) can be improved by up to 46percent, and most potential emissions of trace elements from combustion reduced (up to 90percent). The HTC outputs (solid yield--SY, HHV, energy yield--EY) were modelled and compared to published genetic programming (GP) models. Both model types predicted the three outputs with low error (<15percent) and can be used for process optimisation. The efficiency of water washing depended on the HTC process temperature and type of aromatics produced. The furanic compounds were removed (69-100percent; 160 ?C), while only 34percent of the phenolic compounds (240 ?C) were washed out. Agglomeration of both wet SCG and its hydrochar is feasible; however, the finer particles of washed hydrochar (240 ?C) resulted in larger-sized spherical pellets (85percent > 2000-4000 um) compared to SCGs (only 4percent).", notes = "also known as \cite{su16010338}", } @Article{Dang:GPEM:CMA-ES-RKHS, author = "Viet-Hung Dang and Ngo Anh Vien and TaeChoong Chung", title = "A covariance matrix adaptation evolution strategy in reproducing kernel {Hilbert} space", journal = "Genetic Programming and Evolvable Machines", year = "2019", volume = "20", number = "4", pages = "479--501", month = dec, keywords = "Covariance matrix adaptation-evolution strategies, CMA-ES, Functional optimization, Policy search, Reinforcement learning, Robot learning, Kernel methods, Reproducing kernel Hilbert space", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-019-09357-1", size = "23 pages", abstract = "The covariance matrix adaptation evolution strategy (CMA-ES) is an efficient derivative-free optimization algorithm. It optimizes a black-box objective function over a well-defined parameter space in which feature functions are often defined manually. Therefore, the performance of those techniques strongly depends on the quality of the chosen features or the underlying parametric function space. Hence, enabling CMA-ES to optimize on a more complex and general function class has long been desired. In this paper, we consider modelling the input spaces in black-box optimization non-parametrically in reproducing kernel Hilbert spaces (RKHS). This modeling leads to a functional optimisation problem whose domain is a RKHS function space that enables optimisation in a very rich function class. We propose CMA-ES-RKHS, a generalized CMA-ES framework that is able to carry out black-box functional optimisation in RKHS. A search distribution on non-parametric function spaces, represen", notes = "Department of Computer Science, Duy Tan University, Da Nang, Vietnam", } @Article{dangelo:SC, author = "Gianni D'Angelo and Raffaele Pilla and Carlo Tascini and Salvatore Rampone", title = "A proposal for distinguishing between bacterial and viral meningitis using genetic programming and decision trees", journal = "Soft Computing", year = "2019", volume = "23", number = "22", pages = "11775--11791", month = nov, note = "On line first", keywords = "genetic algorithms, genetic programming, ANN, Meningitis, Meningitis etiology, Bacterial meningitis, Viral meningitis, Symbolic regression, Decision rules, Machine learning, Decision tree, Neural network", ISSN = "1432-7643", URL = "http://link.springer.com/article/10.1007/s00500-018-03729-y", DOI = "doi:10.1007/s00500-018-03729-y", size = "17 pages", abstract = "Meningitis is an inflammation of the protective membranes covering the brain and the spinal cord. Meningitis can have different causes, and discriminating between meningitis etiologies is still considered a hard task, especially when some specific clinical parameters, mostly derived from blood and cerebrospinal fluid analysis, are not completely available. Although less frequent than its viral version, bacterial meningitis can be fatal, especially when diagnosis is delayed. In addition, often unnecessary antibiotic and/or antiviral treatments are used as a solution, which is not cost or health effective. In this work, we address this issue through the use of machine learning-based methodologies. We consider two distinct cases. In one case, we take into account both blood and cerebrospinal parameters; in the other, we rely exclusively on the blood data. As a result, we have rules and formulas applicable in clinical settings. Both results highlight that a combination of the clinical parameters is required to properly distinguish between the two meningitis etiologies. The results on standard and clinical datasets show high performance. The formulas achieve 100percent of sensitivity in detecting a bacterial meningitis.", } @Article{DANGELO:2020:FGCS, author = "Gianni D'Angelo and Francesco Palmieri", title = "Knowledge elicitation based on genetic programming for non destructive testing of critical aerospace systems", journal = "Future Generation Computer Systems", volume = "102", pages = "633--642", year = "2020", ISSN = "0167-739X", DOI = "doi:10.1016/j.future.2019.09.007", URL = "http://www.sciencedirect.com/science/article/pii/S0167739X19306193", keywords = "genetic algorithms, genetic programming, Evolutionary algorithm, Symbolic regression (SR), Artificial intelligence, Machine learning, Non-destructive testing (NDT), Eddy-current testing (ECT), Composite materials, Carbon-fiber reinforced plastic (CFRP), Carbon-fiber reinforced aluminum (FRA)", abstract = "In non-destructive testing of aerospace structures' defects, the tests reliability is a crucial issue for guaranteeing security of both aircrafts and passengers. Most of the widely recognized approaches rely on precision and reliability of testing equipment, but also the methods and techniques used for processing measurement results, in order to detect defects, may heavily influence the overall quality of the testing process. The effectiveness of such methods strongly depends on specific field knowledge that is definitely not easy to be formalized and codified within the results processing practices. Although many studies have been conducted in this direction, such issue is yet an open-problem. This work describes the use of Genetic Programming for the diagnosis and modeling of aerospace structural defects. The resulting approach aims at extracting such knowledge by providing a mathematical model of the considered defects, which can be used for recognizing other similar ones. Eddy-Current Testing has been selected as a case study in order to assess both the performance and functionality of the whole framework, and a publicly available dataset of specific measures for aircraft structures has been considered. The experimental results put into evidence the effectiveness of the proposed approach in building reliable models of the aforementioned defects, so that it can be considered a successful option for building the knowledge needed by tools for controlling the quality of critical aerospace systems", } @Article{DANGELO:2023:future, author = "Gianni D'Angelo and David Della-Morte and Donatella Pastore and Giulia Donadel and Alessandro {De Stefano} and Francesco Palmieri", title = "Identifying patterns in multiple biomarkers to diagnose diabetic foot using an explainable genetic programming-based approach", journal = "Future Generation Computer Systems", year = "2023", volume = "140", pages = "138--150", month = mar, keywords = "genetic algorithms, genetic programming, Diabetic Foot, Explainable Artificial Intelligence (XAI), Interpretability, Explainability, Genetic programming (GP), Symbolic regression (SR), Machine Learning", ISSN = "0167-739X", URL = "https://www.sciencedirect.com/science/article/pii/S0167739X2200334X", DOI = "doi:10.1016/j.future.2022.10.019", size = "13 pages", abstract = "Diabetes mellitus is a global health problem, recognised as the seventh cause of death in the world. One of the most debilitating complications of diabetes mellitus is the diabetic foot (DF), resulting in an increased risk of hospitalization and significant morbidity and mortality. Amputation above or below the knee is a feared complication and the mortality in these patients is higher than for most forms of cancer. Identifying and interpreting relationships existing among the factors involved in DF diagnosis is still challenging. Although machine learning approaches have proven to achieve great accuracy in DF prediction, few advances have been performed in understanding how they make such predictions, resulting in mistrust of their use in real contexts. In this study, we present an approach based on Genetic Programming to build a simple global explainable classifier, named X-GPC, which, unlike existing tools such as LIME and SHAP, provides a global interpretation of the DFU diagnosis through a mathematical model. Also, an easy consultable 3d graph is provided, which could be used by the medical staff to figure out the patients' situation and take decisions for patients' healing. Experimental results obtained by using a real-world dataset have shown the ability of the proposal to diagnose DF with an accuracy of 100percent outperforming other techniques of the state-of-the-art", notes = "Department of Computer Science, University of Salerno, Fisciano (SA), Italy", } @Article{danglot:hal-01378523, author = "Benjamin Danglot and Philippe Preux and Benoit Baudry and Martin Monperrus", title = "Correctness attraction: a study of stability of software behavior under runtime perturbation", journal = "Empirical Software Engineering", year = "2018", month = "1 " # aug, volume = "23", number = "4", pages = "2086--2119", keywords = "genetic algorithms, genetic programming, diversity, selected, software correctness, perturbation analysis, empirical study", publisher = "Springer", ISSN = "1573-7616", hal_id = "hal-01378523", hal_version = "v2", URL = "https://hal.archives-ouvertes.fr/hal-01378523", URL = "https://hal.archives-ouvertes.fr/hal-01378523/file/correctness-attraction.pdf", URL = "https://hal.archives-ouvertes.fr/hal-01378523/file/correctness-attraction.pdf", URL = "https://hal.archives-ouvertes.fr/hal-01378523", URL = "https://arxiv.org/abs/1611.09187", DOI = "doi:10.1007/s10664-017-9571-8", size = "34 pages", abstract = "Can the execution of software be perturbed without breaking the correctness of the output? In this paper, we devise a protocol to answer this question from a novel perspective. In an experimental study, we observe that many perturbations do not break the correctness in ten subject programs. We call this phenomenon correctness attraction. The uniqueness of this protocol is that it considers a systematic exploration of the perturbation space as well as perfect oracles to determine the correctness of the output. To this extent, our findings on the stability of software under execution perturbations have a level of validity that has never been reported before in the scarce related work. A qualitative manual analysis enables us to set up the first taxonomy ever of the reasons behind correctness attraction.", notes = "Presented by Martin at ICSE 2018 See also blog https://medium.com/@martin.monperrus/correctness-attraction-software-behavior-is-stable-under-runtime-perturbation-4640af30e0fc also known as \cite{Danglot2018}", } @Article{Danglot:2018:sigevolution, author = "Benjamin Danglot", title = "Dagstuhl: Seminar on Genetic Improvement of Software", journal = "SIGEVOlution", year = "2018", volume = "11", number = "4", pages = "9--11", month = dec, keywords = "genetic algorithms, genetic programming, genetic improvement", URL = "http://www.sigevolution.org/issues/SIGEVOlution1104.pdf", DOI = "doi:10.1145/3302542.3302544", size = "3 pages", abstract = "Dagstuhl Seminar 18052 January 20th - February 2nd, 2018", } @PhdThesis{DBLP:phd/hal/Danglot19, author = "Benjamin Danglot", title = "Automatic Unit Test Amplification For {DevOps}", title_fr = "Amplification Automatique de Tests Unitaires pour DevOps", school = "University of Lille", year = "2019", address = "France", month = "14 " # nov, keywords = "APR", URL = "https://tel.archives-ouvertes.fr/tel-02396530/document", URL = "https://tel.archives-ouvertes.fr/tel-02396530", timestamp = "Tue, 21 Jul 2020 00:40:33 +0200", biburl = "https://dblp.org/rec/phd/hal/Danglot19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", size = "160 pages", abstract = "Over the last decade, strong unit testing has become an essential component of any serious software project, whether in industry or academia. The agile development movement has contributed to this cultural change with the global dissemination of test-driven development techniques. More recently, the DevOps movement has further strengthened the testing practice with an emphasis on continuous and automated testing. However, testing is tedious and costly for industry: it is hard to evaluate return on investment. Thus, developers under pressure, by lack of discipline or time might skip writing the tests. To overcome this problem, research investigates the automation of creating strong tests.The dream was that a command-line would give you a complete test suite, that verifies the whole program. Even if automatically generated test suites achieve high coverage, there are still obstacles on the adoption of such techniques by the industry. This can be explained by the difficulties to understand, integrate and maintain generated test suite. Also, most of the tools rely on weak or partial oracles, e.g.absence of run-time errors, which limits their ability to find bugs. In this thesis, I aim at addressing the lack of a tool that assists developers in regression testing. To do so, I use test suite amplification. I define test amplification and review research works that are using test amplification. Test amplification consists of exploiting the knowledge of test methods, in which developers embed input data and expected properties, in order to enhance these manually written tests with respect to an engineering goal. In the state of the art, I reveal main challenges of test amplification and the main lacks. I propose a new approach based on both test inputs transformation and assertions generation to amplify the test suite. This algorithm is implemented in a tool called DSpot. I evaluate DSpot on open-source projects from GitHub. First, I improve the mutation score of test suites and propose these improvements to developers through pull requests. This evaluation shows that developers value the output of DSpot and thus accepted to integrate amplified test methods into their test suite. This proves that DSpot can improve the quality of real projects test suites. Second, I use DSpot to detect the behavioral difference between two versions of the same program particularly to detect the behavioral change introduced by a commit. This shows that DSpot can be used in the continuous integration to achieve two crucial tasks: (1) generate amplified test methods that specify a behavioral change; (2) generate amplified test methods to improve the ability to detect potential regressions. I also expose three transversal contributions, related to the correctness of program. First, I study the programs correctness under runtime perturbations. Second, I study the presence of pseudo tested methods that are methods revealing weaknesses of the tests. Third, I study overfitting patches and test generation for automatic repair.", notes = " HAL Id: tel-02396530 in English Supervisor: Martin Monperrus", } @InProceedings{Danielson:2006:alife, author = "Peter Danielson", title = "From Artificial Morality to NERD: Models, Experiments, \& Robust Reflective Equilibrium", booktitle = "Artificial Life X. Workshop Proceedings", year = "2006", pages = "45--48", address = "Bloomington, IN, USA", month = "3-7 " # jun, URL = "http://www.alifex.org/program/wkshp_proceed.pdf", abstract = "Artificial ethics deploys the tools of computational science and social science to improve the improve ethics, conceived as pro-social engineering. This paper focuses on three key techniques used in the three stages of the research program of the Norms Evolving in Response to Dilemmas (NERD) research group: 1. Artificial Morality. Technique: Moral functionalism -- principles expressed as parameterised strategies and tested against a simplified game theoretic goal. 2. Evolving Artificial Moral Ecologies. Technique: Genetic programming, agent-based modelling and evolutionary game theory (replicator dynamics). 3. NERD (Norms Evolving in Response to Dilemmas): Computer mediated ethics for real people, problems, and clients. Technique: An experimental platform to test and improve ethical mechanisms.", } @Article{danilov:2023:Biophysics, author = "N. A. Danilov and K. N. Kozlov and S. Y. Surkova and M. G. Samsonova", title = "Cartesian Genetic Programming for Image Analysis of the Developing Drosophila Eye", journal = "Biophysics", year = "2023", volume = "68", number = "3", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", URL = "http://link.springer.com/article/10.1134/S0006350923030077", DOI = "doi:10.1134/S0006350923030077", } @InCollection{DANISH:2022:WRMCT, author = "Mohd Danish", title = "Chapter 1 - Artificial intelligence and machine learning in water resources engineering", editor = "Mohammad Zakwan and Abdul Wahid and Majid Niazkar and Uday Chatterjee", series = "Current Directions in Water Scarcity Research", publisher = "Elsevier", volume = "7", pages = "3--14", year = "2022", booktitle = "Water Resource Modeling and Computational Technologies", ISSN = "2542-7946", DOI = "doi:10.1016/B978-0-323-91910-4.00001-7", URL = "https://www.sciencedirect.com/science/article/pii/B9780323919104000017", keywords = "genetic algorithms, genetic programming, Water resources engineering, Artificial intelligence, Machine learning, Artificial neural network, Gene expression programming, Group method of data handling, Support vector machines", abstract = "Artificial intelligence (AI) and machine learning (ML) technology are bringing new opportunities in water resources engineering. ML, a subset of AI, is a significant research area of interest contributing smartly to the planning and execution of water resources projects. Still, ML in water resources engineering can explore new applications such as automatic scour detection, flood prediction and mitigation, etc. The challenges faced by the researchers in applying ML are mainly due to the acquisition of quality data and the cost involved in computational resources. This chapter reviews the history of the development of AI and ML algorithm applied in water resources. This chapter also presents the scientometric review of shallow ML algorithms, viz., linear regression, logistic regression, artificial neural network, decision trees, gene expression programming, genetic programming, multigene genetic programming, support vector machines, k-nearest neighbor, k-means clustering algorithm, AdaBoost, random forest, hidden Markov model, spectral clustering, and group method of data handling. This chapter analyzes the articles related to the shallow learning algorithms mentioned above from 1989 to 2022 and their applications in various aspects of water resource engineering", } @InProceedings{Dantas:2019:SSBSE, author = "Altino Dantas and Eduardo {Faria de Souza} and Jerffeson Souza and Celso G. Camilo-Junior", title = "Code naturalness to assist search space exploration in Search-based Program Repair methods", booktitle = "SSBSE 2019", year = "2019", editor = "Shiva Nejati and Gregory Gay", volume = "11664", series = "LNCS", pages = "164--170", address = "Tallinn, Estonia", month = "31 " # aug # " - 1 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, Automated Program Repair, Search space exploration, Code naturalness", isbn13 = "978-3-030-27454-2", URL = "https://altinodantas.github.io/sbpr-naturalness/", DOI = "doi:10.1007/978-3-030-27455-9_12", abstract = "Automated Program Repair (APR) is a research field that has recently gained attention due to its advances in proposing methods to fix buggy programs without human intervention. Search-Based Program Repair methods have difficulties to traverse the search space, mainly, because it is challenging and costly to evaluate each variant. Therefore, aiming to improve each program's variant evaluation through providing more information to the fitness function, we propose the combination of two techniques, Doc2vec and LSTM, to capture high-level differences among variants and to capture the dependence between source code statements in the fault localization region. The experiments performed with the IntroClass benchmark show that our approach captures differences between variants according to the level of changes they received, and the resulting information is useful to balance the search between the exploration and exploitation steps. Besides, the proposal might be promising to filter program variants that are adequate to the suspicious portion of the code.", notes = "Is this GP? Uses GenProg \cite{DBLP:journals/tse/GouesNFW12} See also MSc https://repositorio.bc.ufg.br/tede/bitstream/tede/10370/5/Disserta%C3%A7%C3%A3o%20-%20Eduardo%20Faria%20de%20Souza%20-%202020.pdf http://ssbse19.mines-albi.fr/", } @InProceedings{Dao:2012:CEC, title = "Evolving Approximations for the {Gaussian Q-function} by Genetic Programming with Semantic Based Crossover", author = "Ngoc Phong Dao and {Quang Uy Nguyen} and {Xuan Hoai Nguyen} and R I (Bob) McKay", pages = "2515--2520", booktitle = "Proceedings of the 2012 IEEE Congress on Evolutionary Computation", year = "2012", editor = "Xiaodong Li", month = "10-15 " # jun, DOI = "doi:10.1109/CEC.2012.6256588", address = "Brisbane, Australia", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Computational Intelligence in Communications and Networking (IEEE-CEC), Real-world applications", abstract = "The Gaussian Q-function is of great importance in the field of communications, where the noise is often characterised by the Gaussian distribution. However, no simple exact closed form of the Q-function is known. Consequently, a number of approximations have been proposed over the past several decades. In this paper, we use Genetic Programming with semantic based crossover to approximate the Q-function in two forms: the free and the exponential forms. Using this form, we found approximations in both forms that are more accurate than all previous approximations designed by human experts.", notes = "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the EPS and the IET.", } @InProceedings{darabos:evobio12, author = "Christian Darabos and Mario Giacobini and Ting Hu and Jason H. Moore", title = "Levy-Flight Genetic Programming: Towards a New Mutation Paradigm", booktitle = "10th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, {EvoBIO 2012}", year = "2012", month = "11-13 " # apr, editor = "Mario Giacobini and Leonardo Vanneschi and William S. Bush", series = "LNCS", volume = "7246", publisher = "Springer Verlag", address = "Malaga, Spain", pages = "38--49", organisation = "EvoStar", isbn13 = "978-3-642-29065-7", DOI = "doi:10.1007/978-3-642-29066-4_4", size = "12 pages", keywords = "genetic algorithms, genetic programming", abstract = "Levy flights are a class of random walks inspired directly by observing animal foraging habits, in which the stride length is drawn from a power-law distribution. This implies that the vast majority of the strides will be short. However, on rare occasions, the stride are gigantic. We use this technique to self-adapt the mutation rate used in Linear Genetic Programming. We apply this original approach to three different classes of problems: Boolean regression, quadratic polynomial regression, and surface reconstruction. We find that in all cases, our method outperforms the generic, commonly used constant mutation rate of 1 over the size of the genotype. We compare different common values of the power-law exponent to the regular spectrum of constant values used habitually. We conclude that our novel method is a viable alternative to constant mutation rate, especially because it tends to reduce the number of parameters of genetic programing.", notes = "Part of \cite{Giacobini:2012:EvoBio} EvoBio'2012 held in conjunction with EuroGP2012, EvoCOP2012, EvoMusArt2012 and EvoApplications2012", affiliation = "Computational Genetics Laboratory, Dartmouth Medical School, Dartmouth College, Hanover, NH 03755, USA", } @InCollection{Darabos:2012:GPTP, author = "Christian Darabos and Mario Giacobini and Ting Hu and Jason H. Moore", title = "A New Mutation Paradigm for Genetic Programming", booktitle = "Genetic Programming Theory and Practice X", year = "2012", series = "Genetic and Evolutionary Computation", editor = "Rick Riolo and Ekaterina Vladislavleva and Marylyn D. Ritchie and Jason H. Moore", publisher = "Springer", chapter = "4", pages = "45--58", address = "Ann Arbor, USA", month = "12-14 " # may, keywords = "genetic algorithms, genetic programming, Evolutionary computation, Levy-flight, Random walks", isbn13 = "978-1-4614-6845-5", URL = "http://dx.doi.org/10.1007/978-1-4614-6846-2_4", DOI = "doi:10.1007/978-1-4614-6846-2_4", abstract = "Levy flights are a class of random walks directly inspired by observing animal foraging habits, where a power-law distribution of the stride length can be often observed. This implies that, while the vast majority of the strides will be short, on rare occasions, the strides are gigantic. We propose a mutation mechanism in Linear Genetic Programming inspired by this ethological behaviour, thus obtaining a self-adaptive mutation rate. We experimentally test this original approach on three different classes of problems: Boolean regression, quadratic polynomial regression, and surface reconstruction. We find that in all cases, our method outperforms the generic, commonly used constant mutation rate of one over the size of the genotype. Moreover, we compare different common values of the power-law exponent to the another self-adaptive mutation mechanism directly inspired by Simulated Annealing. We conclude that our novel method is a viable alternative to constant and self-adaptive mutation rates, especially because it tends to reduce the number of parameters of genetic programming.", notes = "part of \cite{Riolo:2012:GPTP} published after the workshop in 2013", } @Article{Darbari:2014:IJAIS, author = "Manuj Darbari and Himanshu Pandey and V. K Singh and Gaurav Kumar Srivastava", title = "Article: Coalescence of Evolutionary Multi-Objective Decision Making approach and Genetic Programming for Selection of Software Quality Parameter", journal = "International Journal of Applied Information Systems", year = "2014", volume = "7", number = "11", pages = "18--22", month = nov, keywords = "genetic algorithms, genetic programming, Software Quality Parameters, Multi objective Decision Making approach and Genetic Programming", publisher = "Foundation of Computer Science, New York, USA", ISSN = "2249-0868", URL = "https://www.ijais.org/archives/volume7/number11/695-1255", URL = "https://research.ijais.org/volume7/number11/ijais14-451255.pdf", DOI = "doi:10.5120/ijais14-451255", size = "5 pages", abstract = "Selection of quality parameters for software according to customer expectation is a complex task which can be prospected as a constrained multi-objective optimization and a multiple criteria decision making problem. For a Software Quality: Usability, Reliability, Complexity, Capability, Durability, Maintainability are the major factors affecting its performance. We proffer a concept of a Multi-Objective Decision making approach using Genetic Programming to appraising the Software Quality Parameters. The paper highlights estimating the Quality Parameters of Software using Multi objective Decision Making approaches and Genetic Programming. The outcome of a Multi objective fed into Genetic Programming for further mutation, to find out the perfect combination of variables of these quantities. The above work is substantiating an optimum trade-off needs to be reached in the formation of good software.", notes = "Department of Information Technology, BBD University, Lucknow,India", } @Article{DARIANE:2024:ecoinf, author = "Alireza B. Dariane and Mohammad Reza {M. Behbahani}", title = "Maximum energy entropy: A novel signal preprocessing approach for data-driven monthly streamflow forecasting", journal = "Ecological Informatics", volume = "79", pages = "102452", year = "2024", ISSN = "1574-9541", DOI = "doi:10.1016/j.ecoinf.2023.102452", URL = "https://www.sciencedirect.com/science/article/pii/S1574954123004818", keywords = "genetic algorithms, genetic programming, Signal preprocessing, Maximum-energy-entropy, Monthly streamflow forecasting, Input variable selection, Wavelet-entropy", abstract = "In recent years, the application of Data-Driven Models (DDMs) in ecological studies has garnered significant attention due to their capacity to accurately simulate complex hydrological processes. These models have proven invaluable in comprehending and predicting natural phenomena. However, to achieve improved outcomes, certain additive components such as signal analysis models (SAM) and input variable selections (IVS) are necessary. SAMs unveil hidden characteristics within time series data, while IVS prevents the use of inappropriate input data. In the realm of ecological research, understanding these patterns is pivotal for grasping the ecological implications of streamflow dynamics and guiding effective management decisions. Addressing the need for more precise streamflow forecasting, this study proposes a novel SAM called {"}Maximum Energy Entropy (MEE){"} to forecast monthly streamflow in the Ajichai basin, located in northwestern Iran. A comparative analysis was conducted, pitting MEE against well-known methods such as Discreet Wavelet (DW) and Discreet Wavelet-Entropy (DWE), ultimately demonstrating the superiority of MEE. The results showcased the superior performance of our proposed method, with an NSE value of 0.72, compared to DW (NSE value of 0.68) and DWE (NSE value of 0.68). Furthermore, MEE exhibited greater reliability, boasting a lower Standard Deviation value of 0.13 compared to DW (0.26) and DWE (0.19). The use of MEE equips researchers and decision-makers with more accurate predictions, facilitating well-informed ecological management and water resource planning. To further evaluate MEE's accuracy using various DDMs, we integrated MEE with Artificial Neural Network (ANN) and Genetic Programming (GP). Additionally, GP served as an IVS method for selecting appropriate input variables. Ultimately, the combination of MEE and GP within the ANN forecasting model (MEE-GP-ANN) yielded the most favorable results", } @Article{DarmianDehghani:2024:ecoinf, author = "Mohsen {Dehghani Darmian} and Britta Schmalz", title = "River quality management: Integrating uncertainty, failure probability, and assimilation capacity", journal = "Ecological Informatics", year = "2024", volume = "83", pages = "102829", keywords = "genetic algorithms, genetic programming, River quality management, Failure probability, Advection-dispersion equation, Reliability simulation, Assimilation capacity, Risk assessment", ISSN = "1574-9541", URL = "https://www.sciencedirect.com/science/article/pii/S1574954124003716", DOI = "doi:10.1016/j.ecoinf.2024.102829", abstract = "Managing river water quality is challenging due to uncertainties in hydraulic and hydrologic parameters. This study integrates the symmetric exponential function (SEF) approach for solving the advection-dispersion equation with the Monte Carlo method in MATLAB. This combination allows us to explore the river's assimilation capacity and the failure probability (Pf) of maintaining desired water quality standards. Here, Pf represents the likelihood of pollutant concentrations exceeding acceptable limits under varying river conditions. A key contribution of this study is the introduction of a novel equation, developed using the Genetic Programming (GP) soft computing tool, to calculate assimilation capacity considering the failure probability of water quality provision. This equation provides a valuable tool for risk assessment in water resource management by quantifying pollutant assimilation dynamics. Its robustness is validated through high Coefficient of Determination (R2) and Overall Index (OI) values near 1, along with low Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The study identifies critical river characteristics, such as flow velocity and pollutant load, significantly influencing the reliability index (beta). By outlining how adjustments in these parameters can achieve a target reliability index (beta=4.526), our study offers a practical approach to safeguarding river ecosystems. For example, increasing flow velocity by 76 percent can shift the river from a safe state (Pf=3times10-5) to a hazardous state (Pf=1), while a 44 percent decrease in velocity allows for 57 percent more pollutant assimilation. These findings highlight the importance of flow control as a cost-effective strategy for mitigating high pollutant concentrations and ensuring sustainable water quality management. By integrating numerical approaches with reliability sampling methods and soft computing techniques, this study enhances understanding of river system dynamics and supports informed decision-making for protecting water resources", } @Article{DarmianDehghani:2025:ecoenv, author = "Mohsen {Dehghani Darmian} and Britta Schmalz", title = "Uncertainty analysis in river quality management considering failure probability: controllable and uncontrollable input pollutants", journal = "Ecotoxicology and Environmental Safety", year = "2025", volume = "289", pages = "117698", keywords = "genetic algorithms, genetic programming, Failure probability, Assimilation capacity, Reliability simulation, Dilution flow, Water quality protection, Detention time", ISSN = "0147-6513", URL = "https://www.sciencedirect.com/science/article/pii/S014765132500034X", DOI = "doi:10.1016/j.ecoenv.2025.117698", abstract = "River quality management involves complex challenges due to inherent uncertainties in various parameters, especially when dealing with controllable and uncontrollable pollutants. This study integrates a finite volume approach, called SEF (symmetric exponential function), with Monte Carlo simulations in MATLAB to solve the advection-dispersion equation, focusing on evaluating river quality protection tools by considering failure probability (Pf). Critical specifications for maintaining reliable river ecosystem performance are identified. We simulate assimilation capacity for managing river water quality against controllable pollutants to satisfy allowable pollution concentration at the high-reliability index. Using the Genetic Programming (GP) algorithm, a new accurate equation for assimilation capacity calculation is presented considering Pffor the first time. Results indicate that flow velocity significantly affects river assimilation capacity: increasing velocity can shift the river to a hazardous state while decreasing it allows for greater pollutant assimilation. Sustainable protection tools, including dilution flow and detention time, are considered to manage uncontrollable pollutants within a specific time (Tc) and river length constraints (Lc), safeguarding river water quality for both human and animal populations. Dilution flow is practical for specific base velocities but ineffective at high base flow rates. Conversely, detention time consistently protects water quality across all base flow velocities within the Lc constraint. Moreover, this study introduces the ratio of detention time to initial pollution contact duration as a vital water quality index to protect the rivers' environment. Combining numerical methods with reliability analysis and soft computing techniques, this research provides valuable insights into river system dynamics and protecting river water quality", } @TechReport{oai:infoscience.epfl.ch:181818, author = "Eva Darulova and Viktor Kuncak and Rupak Majumdar and Indranil Saha", title = "On the Generation of Precise Fixed-Point Expressions", year = "2013", institution = "Ecole Polytechnique Federale de Lausanne", number = "EPFL-REPORT-181818", address = "Switzerland", keywords = "genetic algorithms, genetic programming, fixed-point arithmetic , roundoff error, synthesis", URL = "http://infoscience.epfl.ch/record/181818/files/fixpoints_techreport_1.pdf", bibsource = "OAI-PMH server at infoscience.epfl.ch", language = "en", oai = "oai:infoscience.epfl.ch:181818", oai = "oai:CiteSeerX.psu:10.1.1.308.8582", URL = "http://infoscience.epfl.ch/record/181818", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.308.8582", abstract = "Several problems in the implementations of control systems, signal-processing systems, and scientific computing systems reduce to compiling a polynomial expression over the reals into an imperative program using fixed-point arithmetic. Fixed-point arithmetic only approximates real values, and its operators do not have the fundamental properties of real arithmetic, such as associativity. Consequently, a naive compilation process can yield a program that significantly deviates from the real polynomial, whereas a different order of evaluation can result in a program that is close to the real value on all inputs in its domain. We present a compilation scheme for real-valued arithmetic expressions to fixed-point arithmetic programs. Given a real-valued polynomial expression t, we find an expression t' that is equivalent to t over the reals, but whose implementation as a series of fixed-point operations minimises the error between the fixed-point value and the value of t over the space of all inputs. We show that the corresponding decision problem, checking whether there is an implementation t' of t whose error is less than a given constant, is NP-hard. We then propose a solution technique based on genetic programming. Our technique evaluates the fitness of each candidate program using a static analysis based on affine arithmetic. We show that our tool can significantly reduce the error in the fixed-point implementation on a set of linear control system benchmarks. For example, our tool found implementations whose errors are only one half of the errors in the original fixed-point expressions.", } @InProceedings{Darulova:2013:EMSOFT, author = "Eva Darulova and Viktor Kuncak and Rupak Majumdar and Indranil Saha", booktitle = "Proceedings of the International Conference on Embedded Software (EMSOFT 2013)", title = "Synthesis of fixed-point programs", year = "2013", month = sep # " 29-" # oct # " 4", keywords = "genetic algorithms, genetic programming, SBSE, Software Engineering, Design-Methodologies, synthesis, stochastic optimisation, embedded control software", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.368.1023", DOI = "doi:10.1109/EMSOFT.2013.6658600", abstract = "Several problems in the implementations of control systems, signal-processing systems, and scientific computing systems reduce to compiling a polynomial expression over the real numbers into an imperative program using fixed-point arithmetic. Fixed-point arithmetic only approximates real values, and its operators do not have the fundamental properties of real arithmetic, such as associativity. Consequently, a naive compilation process can yield a program that significantly deviates from the real polynomial, whereas a different order of evaluation can result in a program that is close to the real value on all inputs in its domain. We present a compilation scheme for real-valued arithmetic expressions to fixed-point arithmetic programs. Given a real-valued polynomial expression t, we find an expression t' that is equivalent to t over the reals, but whose implementation as a series of fixed-point operations minimises the error between the fixed-point value and the value of t over the space of all inputs. We show that the corresponding decision problem, checking whether there is an implementation t' of t whose error is less than a given constant, is NP-hard. We then propose a solution technique based on genetic programming. Our technique evaluates the fitness of each candidate program using a static analysis based on affine arithmetic. We show that our tool can significantly reduce the error in the fixed-point implementation on a set of linear control system benchmarks. For example, our tool found implementations whose errors are only one half of the errors in the original fixed-point expressions.", notes = "Also known as \cite{6658600}", } @PhdThesis{Darulova:thesis, author = "Eva Darulova", title = "Programming with Numerical Uncertainties", school = "EPFL", year = "2014", address = "Lausanne, Switzerland", keywords = "genetic algorithms, genetic programming, ECJ, floating-point arithmetic, fixed-point arithmetic, roundoff errors, numerical accuracy, static analysis, runtime verification, software synthesis", URL = "http://dx.doi.org/10.5075/epfl-thesis-6343", URL = "https://infoscience.epfl.ch/record/203570", URL = "https://infoscience.epfl.ch/record/203570/files/EPFL_TH6343.pdf", size = "172 pages", abstract = "Numerical software, common in scientific computing or embedded systems, inevitably uses an approximation of the real arithmetic in which most algorithms are designed. In many domains, roundoff errors are not the only source of inaccuracy and measurement as well as truncation errors further increase the uncertainty of the computed results. Adequate tools are needed to help users select suitable approximations (data types and algorithms) which satisfy their accuracy requirements, especially for safety-critical applications. Determining that a computation produces accurate results is challenging. Roundoff errors and error propagation depend on the ranges of variables in complex and non-obvious ways; even determining these ranges accurately for nonlinear programs poses a significant challenge. In numerical loops, roundoff errors grow, in general, unboundedly. Finally, due to numerical errors, the control flow in the finite-precision implementation may diverge from the ideal real-valued one by taking a different branch and produce a result that is far-off of the expected one. In this thesis, we present techniques and tools for automated and sound analysis, verification and synthesis of numerical programs. We focus on numerical errors due to roundoff from floating-point and fixed-point arithmetic, external input uncertainties or truncation errors. Our work uses interval or affine arithmetic together with Satisfiability Modulo Theories (SMT) technology as well as analytical properties of the underlying mathematical problems. This combination of techniques enables us to compute sound and yet accurate error bounds for nonlinear computations, determine closed-form symbolic invariants for unbounded loops and quantify the effects of discontinuities on numerical errors. We can furthermore certify the results of self-correcting iterative algorithms. Accuracy usually comes at the expense of resource efficiency: more precise data types need more time, space and energy. We propose a programming model where the scientist writes his or her numerical program in a real-valued specification language with explicit error annotations. It is then the task of our verifying compiler to select a suitable floating-point or fixed-point data type which guarantees the needed accuracy. Sometimes accuracy can be gained by simply re-arranging the non-associative finite-precision computation. We present a scalable technique that searches for a more optimal evaluation order and show that the gains can be substantial. We have implemented all our techniques and evaluated them on a number of benchmarks from scientific computing and embedded systems, with promising results.", notes = "'Synthesizing Accurate Fixed-Point Expressions' 'Difficulty of Simple Encoding into SMT solvers' Thesis number 6343 (2014) Supervisor Viktor Kuncak", } @Article{oai:thesai.org:10.14569/IJACSA.2017.080463, author = "Asim Darwaish and Hammad Majeed and M. Quamber Ali and Abdul Rafay", title = "Dynamic Programming Inspired Genetic Programming to Solve Regression Problems", journal = "International Journal of Advanced Computer Science and Applications (IJACSA)", publisher = "The Science and Information (SAI) Organization", year = "2017", volume = "8", number = "4", keywords = "genetic algorithms, genetic programming, evolutionary computing, machine learning, fitness landscape, semantic gp, symbolic regression and dynamic decomposition of gp", bibsource = "OAI-PMH server at thesai.org", description = "International Journal of Advanced Computer Science and Applications(IJACSA), 8(4), 2017", language = "eng", oai = "oai:thesai.org:10.14569/IJACSA.2017.080463", URL = "http://thesai.org/Downloads/Volume8No4/Paper_63-Dynamic_Programming_Inspired_Genetic.pdf", DOI = "doi:10.14569/IJACSA.2017.080463", abstract = "The candidate solution in traditional Genetic Programing is evolved through prescribed number of generations using fitness measure. It has been observed that, improvement of GP on different problems is insignificant at later generations. Furthermore, GP struggles to evolve on some symbolic regression problems due to high selective pressure, where input range is very small, and few generations are allowed. In such scenarios stagnation of GP occurs and GP cannot evolve a desired solution. Recent works address these issues by using single run to reduce residual error which is based on semantic concept. A new approach is proposed called Dynamic Decomposition of Genetic Programming (DDGP) inspired by dynamic programing. DDGP decomposes a problem into sub problems and initiates sub runs in order to find sub solutions. The algebraic sum of all the sub solutions merge into an overall solution, which provides the desired solution. Experiments conducted on well known benchmarks with varying complexities, validates the proposed approach, as the empirical results of DDGP are far superior to the standard GP. Moreover, statistical analysis has been conducted using T test, which depicted significant difference on eight datasets. Symbolic regression problems where other variants of GP stagnates and cannot evolve the required solution, DDGP is highly recommended for such symbolic regression problems.", notes = "National University of Computer and Emerging Sciences FAST Islamabad, Pakistan", } @InProceedings{icec96darwen, author = "Paul Darwen and Xin Yao", title = "Automatic Modularization by Speciation", booktitle = "Third IEEE International Conference on Evolutionary Computation", year = "1996", publisher = "IEEE press", keywords = "genetic algorithms", URL = "http://www.demo.cs.brandeis.edu/papers/icec96darwen.ps.gz", abstract = "Real-world problems are often too difficult to be solved by a single monolithic system. There are many examples of natural and artificial systems which show that a modular approach can reduce the total complexity of the system whilesolving a difficult problem satisfactorily. The success of modular artificial neural networks in speech and image processing is a typical example. However, designing a modular system is a difficult task. It relies heavily on human experts and prior knowledge about the problem. There is no systematic and automatic way to form a modular system for a problem. This paper proposes a novel evolutionary learning approach to designing a modular system automatically, without human intervention. Our starting point is speciation, using a technique based on fitness sharing. While speciation in genetic algorithms is not new, no effort has been made towards using a speciated population as a complete modular system. We harness the specialized expertise in the species of an entire population, rather than a single individual, by introducing a gating algorithm. We demonstrate our approach to automatic modularization by improving co-evolutionary game learning. Following earlier researchers, we learn to play iterated prisoner's dilemma. We review some problems of earlier co-evolutionary learning, and explain their poor generalization ability and sudden mass extinctions. The generalization ability of our approach is significantly better than past efforts. Using the specialised expertise of the entire speciated population though a gating algorithm, instead of the best individual, is the main contributor to this improvement.", } @Article{Darwish:2018:ietN, author = "Saad M. Darwish and Adel El-Zoghabi and Amr G. El-Shnawy", journal = "IET Networks", title = "Proactive cache replacement technique for mobile networks based on genetic programming", year = "2018", volume = "7", number = "6", pages = "376--383", abstract = "In the mobile environment, the movement of users, disconnected modes, many data updates, power battery consumption, limited cache size, and limited bandwidth impose significant challenges to information access. Caching is considered one of the most important concepts to deal with these challenges. There are two general topics related to the client cache policy: cache invalidation method keeps data in the cache up to date; and cache replacement method chooses the cached item(s) which should be deleted from the cache when the cache is full. The aim of this work is to propose a new technique for cache replacement in a mobile database that takes into consideration the impact of invalidation time for enhancing data availability in the mobile environment by using genetic programming. In this case, each client collects information for every cached item in the cache like access probability, cached document size, validation time and uses these factors in a fitness function to determine cached items that will be removed from the cache. The experiments were performed using Network Simulator 2 to evaluate the effectiveness of the proposed approach, and the results are compared with the existing cache replacement algorithms. It is concluded that the proposed approach performs significantly better than other approaches.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1049/iet-net.2017.0261", ISSN = "2047-4954", notes = "Also known as \cite{8519822}", } @Article{Darwish:2018:ijcomsys, author = "Saad M. Darwish and Amr G. El-Shnawy", title = "An intelligent database proactive cache replacement policy for mobile communication system based on genetic programming", journal = "International Journal of Communication Systems", year = "2018", volume = "31", number = "8", month = "25 " # may, keywords = "genetic algorithms, genetic programming, cache invalidation, cache replacement, genetic programming, mobile database", ISSN = "1099-1131", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ijcomsys/ijcomsys31.html#DarwishE18", DOI = "doi:10.1002/dac.3536", size = "14 pages", abstract = "In the mobile environment, the movement of the users, disconnected modes, many data updates, power battery consumption, limited cache size, and limited bandwidth impose significant challenges in information access. Caching is considered one of the most important concepts to deal with these challenges. There are 2 general topics related to the client cache policy: cache invalidation method keeps data in the cache up to date and cache replacement method chooses the cached element(s) that would be removed from the cache once the cache stays full. The aim of this work is to introduce a new technique for cache replacement in a mobile database that takes into consideration the impact of invalidation time for enhancing data availability in the mobile environment by using genetic programming. In this case, each client collects information for every cached item in the cache like access probability, cached document size, and validation time and uses these factors in a fitness function to determine cached items that will be removed from the cache. The experiments were carried by NS2 simulator to assess the efficiency of the proposed method, and the outcomes are judged against existing cache replacement algorithms. It is concluded that the proposed approach performs significantly better than other approaches.", notes = "'Experiments reveal that the suggested system increases byte hit ratio, cache hit ratio, and decreases average query latency in comparing to the' least recently used (LRU). Department of Information Technology, Institute of Graduate Studies and Research, Alexandria University, 163 Horreya Avenue, El Shatby 21526, PO Box 832, Alexandria, Egypt also known as \cite{journals/ijcomsys/DarwishE18}", } @Article{DARWISH:2019:CILS, author = "Saad M. Darwish and Tamer A. Shendi and Ahmed Younes", title = "Chemometrics approach for the prediction of chemical compounds' toxicity degree based on quantum inspired optimization with applications in drug discovery", journal = "Chemometrics and Intelligent Laboratory Systems", volume = "193", pages = "103826", year = "2019", ISSN = "0169-7439", DOI = "doi:10.1016/j.chemolab.2019.103826", URL = "http://www.sciencedirect.com/science/article/pii/S0169743918305495", keywords = "genetic algorithms, genetic programming, Quantum computing, Chemometrics, Prediction model", abstract = "Chemometrics, the application of mathematical and statistical methods to the analysis of chemical data, is finding ever widening applications in the chemical process environment. The reliable prediction of toxic effects of chemicals in living systems is highly desirable in domains such as cosmetics, drug discovery, food safety, and the manufacturing of chemical compounds. Toxicity prediction requires several new approaches for knowledge discovery from data to paradigm composite associations between the modules of the chemical compound; the computational demands of such techniques increase greatly with the number of chemical compounds involved. State-of-the-art prediction methods such as neural networks and multi-layer regression require either tuning parameters or complex transformations of predictor or outcome variables and do not achieve highly accurate results. This paper proposes a Quantum Inspired Genetic Programming {"}QIGP{"} model to improve prediction accuracy. Genetic Programming is used to give a linear equation for calculating the degree of toxicity more accurately. Quantum computing is employed to improve the selection of the best-of-run individuals and handles parsimony pressure to reduce the complexity of solutions. The results of the internal validation analysis indicated that the QIGP model has better goodness of fit statistics then, and significantly outperforms, the Neural Network model", } @Article{DBLP:journals/es/DarwishSY19, author = "Saad M. Darwish and Tamer A. Shendi and Ahmed Younes", title = "Quantum-inspired genetic programming model with application to predict toxicity degree for chemical compounds", journal = "Expert Syst. J. Knowl. Eng.", volume = "36", number = "4", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1111/exsy.12415", DOI = "doi:10.1111/exsy.12415", timestamp = "Tue, 26 May 2020 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/es/DarwishSY19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @PhdThesis{Das:thesis, author = "Abhishek Das", title = "Analyses of Crash Occurrence and Injury Severities on Multi Lane Highways using Machine Learning Algorithms", school = "Department of Civil, Environmental, and Construction Engineering (CECE) of the University of Central Florida", year = "2009", address = "Orlando, USA", month = "13 " # oct, keywords = "genetic algorithms, genetic programming", URL = "http://www.cecs.ucf.edu/graddefense/pdf/10", URL = "http://purl.fcla.edu/fcla/etd/CFE0002928", size = "212 pages", abstract = "Reduction of crash occurrence on the various roadway locations (mid-block segments; signalized intersections; un-signalized intersections) and the mitigation of injury severity in the event of a crash are the major concerns of transportation safety engineers. Multi lane arterial roadways (excluding freeways and expressways) account for forty-three percent of fatal crashes in the state of Florida. Significant contributing causes fall under the broad categories of aggressive driver behavior; unforgiving weather and environmental conditions; and roadway geometric and traffic factors. The objective of this research was the implementation of innovative, state-of-the-art analytical methods to identify the contributory factors for crashes and injury severity. Advances in computational methods render the use of modern statistical and machine learning algorithms. Even though most of the contributing factors are known a-priori, advanced methods unearth changing trends. Heuristic evolutionary processes such as linear genetic programming; sophisticated data mining methods like conditional inference tree; and mathematical treatments in the form of sensitivity analyses outline the major contributions in this research. Application of traditional statistical methods like simultaneous ordered probit models, identification and resolution of crash data problems are also key aspects of this study. In order to eliminate the use of unrealistic uniform intersection influence radius of 250 ft, heuristic rules were developed for assigning crashes to roadway segments, junctions with traffic lights intersection and access points using parameters, such as 'site location' and 'traffic control'. Use of Conditional Inference Forest instead of Classification and Regression Tree to identify variables of significance for injury severity analysis removed the bias towards the selection of continuous variable or variables with large number of categories. Concepts of evolutionary biology like crossover and mutation were implemented to develop models for classification and regression analyses based on the highest hit rate and minimum error rate, respectively. Annual daily traffic; friction coefficient of pavements; on-street parking; curbed medians; surface and shoulder widths; alcohol / drug usage are some of the significant factors that played a role in both the crash occurrence and injury severities. Relative sensitivity analyses were used to identify the effect of continuous variables on the variation of crash counts. This study improved the understanding of the significant factors that could play an important role in designing better safety countermeasures on multi lane highways, and hence enhance their safety by reducing the frequency of crashes and severity of injuries.", notes = "Supervisor Mohamed A. Abdel-Aty", } @Article{Das:2010:TRB, author = "Abhishek Das and Mohamed Abdel-Aty and Anurag Pande", title = "Genetic Programming to Investigate Design Parameters Contributing to Crash Occurrence on Urban Arterials", journal = "Transportation Research Record: Journal of the Transportation Research Board", year = "2010", volume = "2147", pages = "25--32", publisher = "Transportation Research Board of the National Academies", keywords = "genetic algorithms, genetic programming", ISSN = "0361-1981", URL = "http://trb.metapress.com/content/52881hl17685547l/fulltext.pdf", DOI = "doi:10.3141/2147-04", size = "8 pages", abstract = "Nonlinear models were developed to estimate crash frequency on urban arterials with partial access control. These multilane arterials consist of midblock segments joined by signalised and signalised intersections (or access points). Crashes included in the analysis are of three major types: rear-end, angle, and head-on. Each crash type is further sorted into mutually exclusive categories on the basis of the roadway element responsible for the crashes: midblock segment, signalised intersection, and access point. Genetic programming (GP) is adopted for predicting crash frequency. GP, which is primarily based on genetic algorithms, uses the concept of evolution to develop models through the processes of crossover and mutation. The GP modelling approach gives independence for model development without restrictions on distribution of data. The models developed were compared to the basic negative binomial models. Morning and afternoon peak periods are observed to have fewer occurrences of rear-end crashes at all roadway elements. Higher traffic volume results in an increased number of angle crashes. Instances of angle crashes have increased at signalised intersections, even at lower maximum posted speeds. A higher average truck factor increases the instances of head-on crashes on midblock segments and at signalised intersections.", notes = "Online Date Friday, October 01, 2010 p31 'GP outperforms NB [negative binomial] in lower MSE values (for validation data set only) in six of the nine models discussed in the study.'", } @Article{Das2010548, author = "Abhishek Das and Mohamed Abdel-Aty", title = "A genetic programming approach to explore the crash severity on multi-lane roads", journal = "Accident Analysis \& Prevention", volume = "42", number = "2", pages = "548--557", year = "2010", ISSN = "0001-4575", DOI = "doi:10.1016/j.aap.2009.09.021", URL = "http://www.sciencedirect.com/science/article/B6V5S-4XFXSWB-3/2/d3dd6df818f461070f758ebe4fb9f1f3", keywords = "genetic algorithms, genetic programming, Crash severity, Multi-lane roads, Genetic algorithm, Discipulus", abstract = "The study aims at understanding the relationship of geometric and environmental factors with injury related crashes as well as with severe crashes through the development of classification models. The Linear Genetic Programming (LGP) method is used to achieve these objectives. LGP is based on the traditional genetic algorithm, except that it evolves computer programs. The methodology is different from traditional non-parametric methods like classification and regression trees which develop only one model, with fixed criteria, for any given dataset. The LGP on the other hand not only evolves numerous models through the concept of biological evolution, and using the evolutionary operators of crossover and mutation, but also allows the investigator to choose the best models, developed over various runs, based on classification rates. Discipulus software was used to evolve the models. The results included vision obstruction which was found to be a leading factor for severe crashes. Percentage of trucks, even if small, is more likely to make the crashes injury prone. The [`]lawn and curb' median are found to be safe for angle/turning movement crashes. Dry surface conditions as well as good pavement conditions decrease the severity of crashes and so also wider shoulder and sidewalk widths. Interaction terms among variables like on-street parking with higher posted speed limit have been found to make injuries more probable.", } @Article{Das2011, author = "Abhishek Das and Mohamed A. Abdel-Aty", title = "A combined frequency-severity approach for the analysis of rear-end crashes on urban arterials", journal = "Safety Science", year = "2011", volume = "49", number = "8-9", pages = "1156--1163", ISSN = "0925-7535", DOI = "doi:10.1016/j.ssci.2011.03.007", URL = "http://www.sciencedirect.com/science/article/B6VF9-52T1BCG-2/2/dbc605442a050a3d5a59a825025f0f40", keywords = "genetic algorithms, genetic programming, Arterial safety, Injury severity, Crash frequency, Sensitivity analysis", abstract = "Analysis of both the crash count and the severity of injury are required to provide the complete picture of the safety situation of any given roadway. The randomness of crashes, the one-way dependency of injury on crash occurrence and the difference in response types have typically led researchers into developing independent statistical models for crash count and severity classification. The Genetic Programming (GP) methodology adopts the concepts of evolutionary biology such as crossover and mutation in effectively giving a common heuristic approach to model the development for the two different modelling objectives. The chosen GP models have the highest hit rate for rear-end crash classification problem and the least error for function fitting (regression) problems. Higher Average Daily Traffic (ADT) is more likely to result in more crashes. Absence of on-street parking may result in diminished severity of injuries resulting from crashes as they may provide soft crash barrier in contrast to fixed road side objects. Graphical presentation of the frequency of crashes with varying input variables shed new light on the results and its interpretation. Higher friction coefficient of roadways result in reduced frequency of crashes during the morning peak hours, with the trend being reversed during the afternoon peak hours. Crash counts have been observed to be at a maximum at a surface width of 30 ft. Sensitivity analysis results reflect that ADT is responsible for the largest variation in crash counts on urban arterials.", } @PhdThesis{Angan:Das:thesis, author = "Angan Das", title = "Algorithms for Topology Synthesis of Analog Circuits", school = "Electrical Engineering, University of Cincinnati", year = "2008", address = "USA", month = "7 " # nov, keywords = "genetic algorithms, genetic programming, EHW, automated analogue design, topology generation, optimization, evolutionary algorithms, filter design, graph grammar", URL = "https://etd.ohiolink.edu/!etd.send_file?accession=ucin1227204301.pdf", URL = "http://rave.ohiolink.edu/etdc/view?acc_num=ucin1227204301", size = "180 pages", abstract = "In today's world, with ever increasing design complexity and constantly shrinking device sizes, the microelectronics industry faces the need to develop an entire system on a single chip (SoC). This need gives rise to the responsibility of developing mature Computer-Aided-Design (CAD) tools to tackle such complexities. Unlike digital CAD tools, automated synthesis tools for the irreplaceable analogue sections are still immature. Circuit-level analog synthesis comprises of two steps: Topology formation and Sizing of the topology. Topology selection and topology generation are two approaches to topology formation. Research in topology selection has almost been discontinued owing to heavy designer dependency. But with the advent of evolutionary techniques like Genetic Algorithm (GA) and Genetic Programming (GP), topology generation gained popularity. Topology generation is the art of generating device level circuit schematics satisfying user specifications. This thesis makes a genuine endeavour to develop topology generation tools individually for both passive analogue circuits involving R, L, and C components and active circuits that involve additional MOS devices. For passive circuits, we present a GA-based synthesis framework, where the component values for the first set of circuits are set through a deterministic computational technique. Further, the crossover technique for breeding off-springs from parent solutions obeys certain constraints to ensure the formation of structurally correct circuits. The work has been further extended with the introduction of novel selection and crossover strategies. The above techniques have been successful in synthesizing various low-pass and high-pass filter designs. In the pursuit of developing an active circuit topology generator, we have developed a self-learning optimization algorithm involving multiple design variables. To measure the effectiveness of this technique, we applied it first to a relatively easier domain viz. MPLS computer network topology design. The tool produced optimal solutions for most of the test cases considered. Drawing inspiration from the above work, we have extended the technique to active analogue circuit synthesis. Here, we use a building block library that is adaptively formed based on the self-learning approach. It starts with basic elements like PMOS and NMOS and gradually includes bigger and functionally more meaningful blocks as the synthesis run progresses. Our next work on active synthesis incorporates the advantages of both a conventional GA as well as an augmented version of the dynamically formed building block library. Using the above techniques, we have synthesized two opamp and ring oscillator designs. Finally, to strengthen the analogue circuit topology design approach and increase its universal appeal further, we have developed a graph grammar based framework. Appropriate production rules are used to generate circuits through derivation trees. Our approach has certain advantages when compared to other tree-based techniques like GP. The framework also incorporates the concept of dynamic extraction and subsequent use of better building blocks. The work has been extended further to replace the numerical techniques used in quantifying the suitability of a block, with a fuzzy logic based inference system. The developed tool has been successful in synthesizing opamp and vco designs, producing both manual-like designs as well as novel designs.", notes = "Table 7.1 says not GP ucin1227204301 Supervisor Ranga Vemuri", } @InProceedings{Das:2009:DATE, author = "Angan Das and Ranga Vemuri", title = "A graph grammar based approach to automated multi-objective analog circuit design", booktitle = "Design, Automation Test in Europe Conference Exhibition, DATE '09", year = "2009", month = "20-24 " # apr, address = "Nice, France", pages = "700--705", keywords = "genetic algorithms, genetic programming, NSGA-II, HSPICE, PMOS, NMOS, VCO, automated multiobjective analog circuit design, automated topology synthesis, bookish circuits, derivation tree, design-suitable building blocks, encoding, graph grammar-based approach, opamp, analogue circuits, graph grammars, network topology, operational amplifiers, tree codes, voltage-controlled oscillators", ISSN = "1530-1591", isbn13 = "978-1-4244-3781-8", broken = "http://ieeexplore.ieee.org/search/srchabstract.jsp?tp=&arnumber=5090755", DOI = "doi:10.1109/DATE.2009.5090755", size = "6 pages", abstract = "This paper introduces a graph grammar based approach to automated topology synthesis of analog circuits. A grammar is developed to generate circuits through production rules, that are encoded in the form of a derivation tree. The synthesis has been sped up by using dynamically obtained design-suitable building blocks. Our technique has certain advantages when compared to other tree-based approaches like GP based structure generation. Experiments conducted on an opamp and a vco design show that unlike previous works, we are capable of generating both manual-like designs (bookish circuits) as well as novel designs (unfamiliar circuits) for multi-objective analog circuit design benchmarks.", notes = "Also known as \cite{5090755}", } @InProceedings{Das:2017:NASAmlw, author = "Kamalika Das", title = "Using Machine Learning to Study the Effects of Climate on the Amazon Rainforests", booktitle = "NASA Machine Learning Workshop 2017", year = "2017", editor = "Michael Lowry", address = "Moffett Field, California, USA", month = "29-31 " # aug, keywords = "genetic algorithms, genetic programming, educational timetabling, construction heuristics, hyper-heuristics", bibsource = "OAI-PMH server at ntrs.nasa.gov", identifier = "Document ID: 20170012209", oai = "oai:casi.ntrs.nasa.gov:20170012209", broken = "http://hdl.handle.net/2060/20170012209", broken = "https://ti.arc.nasa.gov/events/machinelearningworkshop2017/invitedspeakers/kamalika/", broken = "https://ti.arc.nasa.gov/news/Das-ITNG-talk-2018/", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Das_2017_NASAmlw.pdf", abstract = "The Amazonian forests are a critical component of the global carbon cycle, storing about 100 billion tons of carbon in woody biomass, and accounting for about 15 of global net primary production and 66 of its inter-annual variability. There is growing concern that these forests could succumb to precipitation reduction in a progressively warming climate causing extensive carbon release and feedback to the carbon cycle. Contradicting research, on the other hand, claims that these forests are resilient to extreme climatic events. In this work we describe a unifying machine learning and optimisation based approach to model the dependence of vegetation in the Amazon on climatic factors such as rainfall and temperature in order to answer questions about the future of the rainforests. We build a hierarchical regression tree in combination with genetic programming based symbolic regression for quantifying the climate-vegetation dynamics in the Amazon. The discovered equations reveal the true drivers of resilience (or lack thereof) of these rainforests, in the context of changing climate and extreme events.", notes = "Apr 2023 gone https://ti.arc.nasa.gov/events/machinelearningworkshop2017/ May 2018 This requested resource (Online NTRS full-text PDF) is no longer available from NTRS. help@sti.nasa.gov Also known as \cite{oai:casi.ntrs.nasa.gov:20170012209} See also https://myemail.constantcontact.com/NAMS-Newsletter-February-2019.html?soid=1129123412805&aid=Xq9IV_IXl1U", } @InProceedings{Das:2011:PACC, author = "Saptarshi Das and Indranil Pan and Shantanu Das and Amitava Gupta", title = "Genetic Algorithm Based Improved Sub-Optimal Model Reduction in Nyquist Plane for Optimal Tuning Rule Extraction of PID and {PI{$^\lambda$}D{$^\mu$}} Controllers via Genetic Programming", booktitle = "International Conference on Process Automation, Control and Computing (PACC 2011)", year = "2011", month = "20-22 " # jul, address = "Coimbatore", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, GA, GP, H2-norm based reduced order modelling techniques, Nyquist based sub-optimal model reduction, Nyquist plane, PID controllers, Pareto optimal front, control signal, fractional order PI D, controllers, optimal tuning rule extraction, weighted integral error index, control system synthesis, optimal control, reduced order systems, signal processing, three-term control", isbn13 = "978-1-61284-765-8", DOI = "doi:10.1109/PACC.2011.5978962", URL = "http://arxiv.org/abs/1202.5686", URL = "http://arxiv.org/pdf/1202.5686v1", size = "6 pages", abstract = "Genetic Algorithm (GA) has been used in this paper for a new Nyquist based sub-optimal model reduction and optimal time domain tuning of PID and fractional order (FO) PI lambda D mu controllers. Comparative studies show that the new model reduction technique outperforms the conventional H2-norm based reduced order modelling techniques. Optimum tuning rule has been developed next with a test-bench of higher order processes via Genetic Programming (GP) with minimum value of weighted integral error index and control signal. From the Pareto optimal front which is a trade-off between the complexity of the formulae and control performance, an efficient set of tuning rules has been generated for time domain optimal PID and PID controllers.", notes = "Also known as \cite{5978962}", oai = "oai:arXiv.org:1202.5686", } @Article{Das2012237, author = "Saptarshi Das and Indranil Pan and Shantanu Das and Amitava Gupta", title = "Improved Model Reduction and Tuning of Fractional Order {PI}{$\lambda$}{D}{$\mu$} Controllers for Analytical Rule Extraction with Genetic Programming", journal = "ISA Transactions", volume = "51", number = "2", pages = "237--261", year = "2012", month = mar, ISSN = "0019-0578", DOI = "doi:10.1016/j.isatra.2011.10.004", URL = "http://www.sciencedirect.com/science/article/pii/S0019057811001194", URL = "http://arxiv.org/abs/1202.5683", URL = "http://arxiv.org/pdf/1202.5683v1", keywords = "genetic algorithms, genetic programming, Automatic rule generation, Fractional-order proportional-integral-derivative (FOPID) controller, PID, Model reduction, Optimal time domain tuning, FOPID tuning rule", size = "25 pages", abstract = "Genetic algorithm (GA) has been used in this study for a new approach of suboptimal model reduction in the Nyquist plane and optimal time domain tuning of proportional-integral-derivative (PID) and fractional-order (FO) P I lambda D mu controllers. Simulation studies show that the new Nyquist-based model reduction technique outperforms the conventional H2-norm-based reduced parameter modelling technique. With the tuned controller parameters and reduced-order model parameter dataset, optimum tuning rules have been developed with a test-bench of higher-order processes via genetic programming (GP). The GP performs a symbolic regression on the reduced process parameters to evolve a tuning rule which provides the best analytical expression to map the data. The tuning rules are developed for a minimum time domain integral performance index described by a weighted sum of error index and controller effort. From the reported Pareto optimal front of the GP-based optimal rule extraction technique, a trade-off can be made between the complexity of the tuning formulae and the control performance. The efficacy of the single-gene and multi-gene GP-based tuning rules has been compared with the original GA-based control performance for the PID and P I lambda D mu controllers, handling four different classes of representative higher-order processes. These rules are very useful for process control engineers, as they inherit the power of the GA-based tuning methodology, but can be easily calculated without the requirement for running the computationally intensive GA every time. Three-dimensional plots of the required variation in PID/fractional-order PID (FOPID) controller parameters with reduced process parameters have been shown as a guideline for the operator. Parametric robustness of the reported GP-based tuning rules has also been shown with credible simulation examples.", oai = "oai:arXiv.org:1202.5683", } @InProceedings{das:2013:ISEUSAM, author = "S. K. Das and P. K. Muduli", title = "Probability-Based Method for Assessing Liquefaction Potential of Soil Using Genetic Programming", booktitle = "Proceedings of the International Symposium on Engineering under Uncertainty: Safety Assessment and Management (ISEUSAM - 2012)", year = "2013", pages = "1153--1163", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-81-322-0757-3_80", DOI = "doi:10.1007/978-81-322-0757-3_80", } @Book{Das:2018:handsonML, author = "Sibanjan Das and Umit Mert Cakmak", title = "Hands-On Automated Machine Learning", publisher = "Packt Publishing", year = "2018", address = "Birmingham, UK", month = "26 " # apr, keywords = "genetic algorithms, genetic programming, TPOT, AutoML", isbn13 = "9781788629898", URL = "https://www.amazon.co.uk/Hands-Automated-Machine-Learning-beginners/dp/1788629892", URL = "https://www.oreilly.com/library/view/hands-on-automated-machine/9781788629898/a8e93e6e-656d-4796-9bf0-f6e9206de316.xhtml", URL = "https://github.com/PacktPublishing/Hands-On-Automated-Machine-Learning", size = "approx 270 pages", notes = "Section on TPOT www.packtpub.com", } @InProceedings{das:GPVR, author = "Sumit Das and Terry Franguidakis and Michael Papka and Thomas A. DeFanti and Daniel J. Sandin", title = "A genetic programming application in virtual reality", booktitle = "Proceedings of the first IEEE Conference on Evolutionary Computation", year = "1994", publisher = "IEEE Press", volume = "1", note = "Part of 1994 IEEE World Congress on Computational Intelligence, Orlando, Florida", pages = "480--484", address = "Orlando, Florida, USA", month = "27-29 " # jun, organisation = "IEEE", size = "5 pages", keywords = "genetic algorithms, genetic programming", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/797/http:zSzzSzwww.evl.uic.eduzSzEVLzSzRESEARCHzSzPAPERSzSzPAPKAzSzgp94.pdf/a-genetic-programming-application.pdf", URL = "http://citeseer.ist.psu.edu/8701.html", abstract = "Genetic programming techniques have been applied to a variety of different problems. In this paper, the authors discuss the use of these techniques in a virtual environment. The use of genetic programming allows the authors a quick method of searching shape and sound spaces. The basic design of the system, problems encountered, and future plans are all discussed.", notes = "Displays 4 simple geometric 3dee items in virtual reality CAVE. User breeds from those he likes.", } @InProceedings{dasgupta:1999:AIASR, author = "Dipankar Dasgupta and Yuehua Cao and Congjun Yang", title = "An Immunogenetic Approach to Spectra Recognition", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "149--155", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Dasgupta:2006:Homeland, author = "D. Dasgupta", title = "Computational Intelligence in Cyber Security", booktitle = "Proceedings of the 2006 IEEE International Conference on Computational Intelligence for Homeland Security and Personal Safety", year = "2006", pages = "2--3?", address = "Alexandria, VA, USA", month = oct, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", ISBN = "1-4244-0744-3", DOI = "doi:10.1109/CIHSPS.2006.313289", abstract = "This keynote speech will be devoted to the application of the state-of-the-art CI (computational intelligence)-based technologies - fuzzy systems, evolutionary computation, genetic programming, neural networks and artificial immune systems, and highlight how CI-based technologies play critical roles in various computer and information security problems", notes = "Center for Inf. Assurance & Intelligent Security Syst. Res. Lab., Memphis Univ., TN", } @Article{DASGUPTA:2021:CMPB, author = "Pritika Dasgupta and James Alexander Hughes and Mark Daley and Ervin Sejdic", title = "Is Human Walking a Network Medicine Problem? An Analysis Using Symbolic Regression Models with Genetic Programming", journal = "Computer Methods and Programs in Biomedicine", volume = "206", pages = "106104", year = "2021", ISSN = "0169-2607", DOI = "doi:10.1016/j.cmpb.2021.106104", URL = "https://www.sciencedirect.com/science/article/pii/S0169260721001796", keywords = "genetic algorithms, genetic programming, walking, mathematical model, symbolic regression, wearables, acceleration gait measures", abstract = "Background and Objective Human walking is typically assessed using a sensor placed on the lower back or the hip. Such analyses often ignore that the arms, legs, and body trunk movements all have significant roles during walking; in other words, these body nodes with accelerometers form a body sensor network (BSN). BSN refers to a network of wearable sensors or devices on the human body that collects physiological signals. Our study proposes that human locomotion could be considered as a network of well-connected nodes. Methods While hypothesizing that accelerometer data can model this BSN, we collected accelerometer signals from six body areas from ten healthy participants performing a cognitive task. Machine learning based on genetic programming was used to produce a collection of non-linear symbolic models of human locomotion. Results With implications in precision medicine, our primary finding was that our BSN models fit the data from the lower back's accelerometer and describe subject-specific data the best compared to all other models. Across subjects, models were less effective due to the diversity of human sizes. Conclusions A BSN relationship between all six body nodes has been shown to describe the subject-specific data, which indicates that the network-medicine relationship between these nodes is essential in adequately describing human walking. Our gait analyses can be used for several clinical applications such as medical diagnostics as well as creating a baseline for healthy walking with and without a cognitive load.", } @InProceedings{Dash:2020:GI, author = "Santanu Kumar Dash and Fan Wu and Michail Basios and Lingbo Li and Leslie Kanthan", title = "Checkers: Multi-modal {Darwinian API} Optimisation", booktitle = "GI @ ICSE 2020", year = "2020", month = "3 " # jul, editor = "Shin Yoo and Justyna Petke and Westley Weimer and Bobby R. Bruce", publisher = "ACM", address = "internet", pages = "291--292", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, Java", isbn13 = "978-1-4503-7963-2", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gi2020/Dash_2020_GI.pdf", video_url = "https://youtu.be/GsNKCifm44A", DOI = "doi:10.1145/3387940.3392173", size = "2 pages", abstract = "Advent of microservices has increased the popularity of the API-first design principles. Developers have been focusing on concretising the API to a system before building the system. An API-first approach assumes that the API will be correctly used. Inevitably, most developers, even experienced ones, end-up writing sub-optimal software because of using APIs incorrectly. we discuss an automated approach for exploring API equivalence and a framework to synthesise semantically equivalent programs. Unlike existing approaches to API transplantation, we propose an amorphous or formless approach to software translation in which a single API could potentially be replaced by a synthesised sequence of APIs which ensures type progress. Our search is guided by the non-functional goals for the software, a type-theoretic notion of progress and an automatic multi-modal embedding of the API from its documentation and code analysis.", notes = "Type based semantically equvalent Android mobile telephone APIs. program's test suite. Dual chanel NLP documentation. Static analysis to improve original program. Auto-tune API parameters. University of Surrey. Turing Intelligence Technology, United Kingdom. turintech.ai Video: https://youtu.be/GsNKCifm44A (1:13:57 from start, end 1:29:38) Slides: http://geneticimprovementofsoftware.com/slides/gi2020icse/checkers_slides.pdf http://geneticimprovementofsoftware.com/gi2020icse.html", } @Article{DASHTI:2018:IJHE, author = "Amir Dashti and Hossein Riasat Harami and Mashallah Rezakazemi", title = "Accurate prediction of solubility of gases within {H2-selective} nanocomposite membranes using committee machine intelligent system", journal = "International Journal of Hydrogen Energy", volume = "43", number = "13", pages = "6614--6624", year = "2018", keywords = "genetic algorithms, genetic programming, Membranes, Nanocomposite, Modeling, Mass transfer, Diffusion", ISSN = "0360-3199", DOI = "doi:10.1016/j.ijhydene.2018.02.046", URL = "http://www.sciencedirect.com/science/article/pii/S0360319918304245", abstract = "In-depth knowledge about the gas sorption within hydrogen (H2) selective nanocomposite membranes at various conditions is crucial, particularly in petrochemical and separation processes. Hence, various artificial intelligence (AI) methods such as multilayer perceptron artificial neural network (MLP-ANN), adaptive neuro-fuzzy inference system (ANFIS), the adaptive neuro-fuzzy inference system optimized by genetic algorithm (GA-ANFIS), Genetic Programming (GP) and Committee Machine Intelligent System (CMIS) were applied to predict the sorption of gases in H2-selective nanocomposite membranes consist of porous nanoparticles as the dispersed phase and polymer matrix as continuous phase. The momentous purpose of this paper was to estimate the sorption of C3H8, H2, CH4 and CO2 within H2-selective nanocomposite membranes considering the effect of nanoparticles loading, critical temperature (gas type characteristics) and upstream pressure. Obtained data were categorized into two parts including training and testing data set. The CMIS method showed more precise results rather than other intelligent models. Having developed different intelligent approaches rely on algorithms, a powerful successor for labor-intensive experimental processes of solubility was revealed. The prediction results and experimental data were significantly consistent in approach with a correlation coefficient (R2) of 0.9999, 0.9987, 0.9998, 0.9995, and 0.9997 for CMIS, GP, GA-ANFIS, ANFIS and ANN models respectively", } @Article{DASHTI:2018:JML, author = "Amir Dashti and Morteza Asghari and Mostafa Dehghani and Mashallah Rezakazemi and Amir H. Mohammadi and Suresh K. Bhatia", title = "Molecular dynamics, grand canonical {Monte Carlo} and expert simulations and modeling of water-acetic acid pervaporation using polyvinyl alcohol/tetraethyl orthosilicates membrane", journal = "Journal of Molecular Liquids", volume = "265", pages = "53--68", year = "2018", keywords = "genetic algorithms, genetic programming, Pervaporation, Molecular simulation, ACO, ANFIS, PVA-TEOS membrane", ISSN = "0167-7322", DOI = "doi:10.1016/j.molliq.2018.05.078", URL = "http://www.sciencedirect.com/science/article/pii/S0167732217355344", abstract = "In this study, molecular dynamics (MD) and Monte Carlo (MC) simulations techniques were employed as well as artificial intelligence knowledge of ANFIS and GP to investigate water-acetic acid pervaporation (PV) separation through poly vinylalcohol (PVA)a silicone based membranes under a wide range of experimental conditions. For the first time, three new optimization algorithms, namely ant colony optimization for continuous domains (ACOR), differential evolution (DE) and genetic algorithm (GA) were employed for improving ANFIS modeling. The GP creates a mathematical function or model for the estimation of pervaporation separation index (PSI) as a function of the input variables. ACOR-ANFIS and GA-ANFIS and GP had high accuracy (R2a =a 0.9831, 0.9792 and 0.9722, respectively) but DE-ANFIS had a lower accuracy (R2a =a 0.9610) as compared to other models. On the other hand, molecular simulation methods were used and the results of all simulation models were compared fairly to each other and to the experimental results of the literature. Also, some characterizations were taking place to investigate the characteristics of the simulated membranes with MS such as WAXD, and FFV and glass transition temperature was used to estimate the thermal properties of the simulated membranes", } @Article{DASHTI:2019:CERD, author = "Amir Dashti and Mojtaba Raji and Amir Razmi and Nima Rezaei and Sohrab Zendehboudi and Morteza Asghari", title = "Efficient Hybrid Modeling of {CO2} Absorption in Aqueous Solution of Piperazine: Applications to Energy and Environment", journal = "Chemical Engineering Research and Design", year = "2019", keywords = "genetic algorithms, genetic programming, COAbsorption, Piperazine, Solubility, Deterministic Tools, Accuracy, Environmental Implication", ISSN = "0263-8762", DOI = "doi:10.1016/j.cherd.2019.01.019", URL = "http://www.sciencedirect.com/science/article/pii/S0263876219300218", abstract = "Carbon dioxide (CO2) considerably contributes to the greenhouse effects and consequently, global warming. Thus, reduction of its emissions/concentration in the atmosphere is an important goal for various industrial and environmental sectors. In this research work, we study CO2 capture by its absorption in mixtures of water and Piperazine (PZ). Experimental techniques to obtain the equilibrium data are usually costly and time consuming. Thermodynamic modeling by Equations of State (EOSs) and connectionist tools leads to more reliable and accurate results, compared to the empirical models and analytical modeling strategies. This research work uses Genetic Programming (GP) and Genetic Algorithm-Adaptive Neuro Fuzzy Inference System (GA-ANFIS) to estimate the solubility of CO2 in mixtures of water and Piperazine (PZ). In both methods, the input parameters are temperature, partial pressure of CO2, and concentration of PZ in the solution. A total number of 390 data points is collected from the literature and used to develop GP and GA-ANFIS models. Assessing the models by the statistical methods, both models are found to acceptably predict the CO2 solubility in water/PZ mixtures. However, the GP exhibits a superior performance, compared to GA-ANFIS; the Average Absolute Relative Error (AARD) are 5.3213percent and 9.7143percent for the GP and GA-ANFIS models, respectively. Such reliable predictive tools can assist engineers and researchers to effectively determine the key thermodynamic properties (e.g., solubility, vapor pressure, and compressibility factor) which are central in design and operation of the carbon capture processes in a variety of chemical plants such as power plants and refineries.", } @Article{dashti:2020:AJSE, author = "Amir Dashti and Mojtaba Raji and Abouzar Azarafza and Mashallah Rezakazemi and Saeed Shirazian", title = "Computational Simulation of {CO2} Sorption in Polymeric Membranes Using Genetic Programming", journal = "Arabian Journal for Science and Engineering", year = "2020", volume = "45", number = "9", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s13369-020-04783-1", DOI = "doi:10.1007/s13369-020-04783-1", } @Article{DASHTI:2020:JML, author = "Amir Dashti and Mojtaba Jokar and Farid Amirkhani and Amir H. Mohammadi", title = "Quantitative structure property relationship schemes for estimation of autoignition temperatures of organic compounds", journal = "Journal of Molecular Liquids", volume = "300", pages = "111797", year = "2020", ISSN = "0167-7322", DOI = "doi:10.1016/j.molliq.2019.111797", URL = "http://www.sciencedirect.com/science/article/pii/S0167732219330107", keywords = "genetic algorithms, genetic programming, Autoignition temperature, QSPR, ANFIS", abstract = "We have extended a quantitative structure-property relationship (QSPR) scheme to estimate the auto-ignition temperatures (AIT) of organic compounds by employing GA-ANFIS, PSO-ANFIS, DE-ANFIS and GP methods. The average absolute relative deviations (percentAARD) are 7.96, 6.29, 8.85 and 8.26, respectively. The range of these values appears to match that of experimental error in AIT measurements, suggesting strong models. For organic compounds, the AIT can be estimated using the above-mentioned methods, from molecular structure. This goal is possible using only 9 theoretical descriptors", } @Article{DASHTI:2021:RSER, author = "Amir Dashti and Abolfazl Sajadi Noushabadi and Javad Asadi and Mojtaba Raji and Abdoulmohammad Gholamzadeh Chofreh and Jiri Jaromir Klemes and Amir H. Mohammadi", title = "Review of higher heating value of municipal solid waste based on analysis and smart modelling", journal = "Renewable and Sustainable Energy Reviews", volume = "151", pages = "111591", year = "2021", ISSN = "1364-0321", DOI = "doi:10.1016/j.rser.2021.111591", URL = "https://www.sciencedirect.com/science/article/pii/S1364032121008686", keywords = "genetic algorithms, genetic programming, Higher heating value, Municipal solid waste, Ultimate analysis, Smart modelling, Energy recovery, Regression", abstract = "Energy recovery from 252 kinds of solid waste originating from various geographical areas under thermal waste-to-energy operation is investigated. A fast, economical, and comparative methodology is presented for evaluating the heating values resulted from burning municipal solid waste (MSW) based on prior knowledge, specialist experience, and data-mining methods. Development of models for estimating higher heating values (HHVs) of 252 MSW samples based on the ultimate analysis is conducted by simultaneously using five nonlinear models including Radial Basis Function (RBF) neural network in conjunction with Genetic Algorithm (GA), namely GA-RBF, genetic programming (GP), multivariate nonlinear regression (MNR), particle swarm optimisation adaptive neuro-fuzzy inference system (PSO-ANFIS) and committee machine intelligent system (CMIS) models to increase the accuracy of each model. Eight different equations based on MNR are developed to estimate energy recovery capacity from different MSW groups (e.g., textiles, plastics, papers, rubbers, mixtures, woods, sewage sludge and other waste). A detailed investigation is conducted to analyse the accuracy of the models. It is indicated that the CMIS model has the best performance comparing the results obtained from different models. The R2 values of the test dataset for GA-RBF are 0.888, for GP 0.979, for MNR 0.978, for PSO-ANFIS 0.965, and for CMIS 0.985. The developed models with an acceptable accuracy would be followed by a better estimation of HHV and providing reliable heating value for an automatic combustion control system. The results obtained from this study are beneficial to design and optimise sustainable thermal waste-to-energy (WTF) processes to accelerate city transition into a circular economy", } @Article{DASHTI:2021:Energy, author = "Amir Dashti and Omid Mazaheri and Farid Amirkhani and Amir H. Mohammadi", title = "Molecular descriptors-based models for estimating net heat of combustion of chemical compounds", journal = "Energy", volume = "217", pages = "119292", year = "2021", ISSN = "0360-5442", DOI = "doi:10.1016/j.energy.2020.119292", URL = "https://www.sciencedirect.com/science/article/pii/S0360544220323999", keywords = "genetic algorithms, genetic programming, Heat of combustion, QSPR, Molecular descriptor, Model, Prediction", abstract = "The heating values of fuels are determined by Heat of Combustion (?HCa)in which the higher amount is more lucrative. Moreover, one of the best methods to compare the stabilities of chemical materials is using ?HCa. Therefore, improving precise and general models to estimate this property in different areas such as industries and academic perspective should be considered. In this study, three models namely Least Square Support Vector Machine optimized by Coupled Simulated Annealing optimization algorithm (CSA-LSSVM), Genetic Programming (GP) and Adaptive-Neuro Fuzzy Inference System optimized by PSO, and GA methods (PSO-ANFIS and GA-ANFIS) were applied to estimate ?HCa Also, ?HCa can be expressed by the GP model with an equation. The input parameters of the models are total carbon atoms in a molecule (nC), sum of atomic van der Waals volumes (scaled on carbon atom) (Sv), Broto-Moreau autocorrelation of a topological structure (ATS2m), and total Eigenvalue from electronegativity weighted distance matrix (siege). In addition, two parameter models based on measureable variables of nC and Sv are proposed. In a comprehensive set, 1714 data points were used to fulfill and develop the models. Results demonstrate that the models are trustworthy and accurate (especially the PSO-ANFIS model) in comparison with other recently developed literature models", } @Article{DASHTI:2023:seppur, author = "Amir Dashti and Mojtaba Raji and Hossein {Riasat Harami} and John L. Zhou and Morteza Asghari", title = "Biochar performance evaluation for heavy metals removal from industrial wastewater based on machine learning: Application for environmental protection", journal = "Separation and Purification Technology", year = "2023", volume = "312", pages = "123399", keywords = "genetic algorithms, genetic programming, Biochar, Heavy metals, Machine learning, Modeling, Wastewater treatment", ISSN = "1383-5866", URL = "http://hdl.handle.net/10453/168317", URL = "https://www.sciencedirect.com/science/article/pii/S1383586623003076", DOI = "doi:10.1016/j.seppur.2023.123399", size = "7 pages", abstract = "Industrial wastewaters contaminated with heavy and toxic metals cause serious risks to human health and other forms of life. The performance of biochar for the elimination of heavy metals has been acclaimed. It is highly advantageous to develop efficient computational methods to predict its biosorption performance. In this research, the performance of four types of machine learning methods including adaptive neuro fuzzy inference system (ANFIS), coupled simulated annealing-least squares support vector machine (CSA-LSSVM), particle swarm optimization-ANFIS (PSO-ANFIS) and genetic programming (GP) was evaluated. The modeling was conducted on 44 types of biochar reported in 353 datasets from heavy metal adsorption experiments. All four models have demonstrated good predictive performance, especially by LSSVM, GP and PSO-ANFIS procedures. The correlation coefficient (R2) values of test dataset for ANFIS, CSA-LSSVM, PSO-ANFIS, and GP were 0.9428, 0.9832, 0.9712 and 0.9750. The values of mean squared error (MSE) and average absolute relative deviation (AARD) were 0.0020 and 0.36 for CSA-LSSVM model which has the superior capability than other models. The sensitivity analysis showed that the key parameters in heavy metal removal by biochar were the concentration ratio of heavy metals/biochar and total carbon content in biochar. A MATLAB code was developed to estimate the biosorption efficiency. Novel equation based genetic programming assists researchers to predict sorption yield of heavy metals by reducing the costs and time. Analyzing the results of this research can increase the understanding of researchers towards the effective remediation of hazardous chemicals in water resources", notes = "Separation Processes Research Group (SPRG), University of Science and Technology of Mazandaran, Behshahr, Iran", } @InProceedings{daSilva:2000:ewbss, author = "Adelino R. Ferreira {da Silva}", title = "Evolutionary Wavelet Bases in Signal Spaces", booktitle = "Real-World Applications of Evolutionary Computing", year = "2000", editor = "Stefano Cagnoni and Riccardo Poli and George D. Smith and David Corne and Martin Oates and Emma Hart and Pier Luca Lanzi and Egbert Jan Willem and Yun Li and Ben Paechter and Terence C. Fogarty", volume = "1803", series = "LNCS", pages = "44--53", address = "Edinburgh", publisher_address = "Berlin", month = "17 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-67353-9", DOI = "doi:10.1007/3-540-45561-2_5", abstract = "We introduce a test environment based on the optimization of signals approximated in function spaces in order to compare the performance of different evolutionary algorithms. An evolutionary algorithm to optimize signal representations by adaptively choosing a basis depending on the signal is presented. We show how evolutionary algorithms can be exploited to search larger waveform dictionaries for best basis selection than those considered in current standard approaches.", notes = "Evolution of wavlet trees. EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM, EvoRob, and EvoFlight, Edinburgh, Scotland, UK, April 17, 2000 Proceedings http://evonet.lri.fr/evoweb/resources/books_journals/record.php?id=61", } @InProceedings{daSilva:2000:GECCO, author = "Adelino R. Ferreira {da Silva}", title = "Genetic Algorithms for Component Analysis", pages = "243--250", year = "2000", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)", editor = "Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer", address = "Las Vegas, Nevada, USA", publisher_address = "San Francisco, CA 94104, USA", month = "10-12 " # jul, keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-708-0", URL = "http://gpbib.cs.ucl.ac.uk/gecco2000/GA050.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco2000/GA050.ps", notes = "A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of \cite{whitley:2000:GECCO}", } @InProceedings{Silva:2002:AoECiEPS, author = "Alexandre P. {Alves da Silva} and Pedro Jose Abrao", title = "Applications of Evolutionary Computation in Electric Power Systems", booktitle = "Proceedings of the 2002 Congress on Evolutionary Computation CEC2002", editor = "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and Mark Shackleton", pages = "1057--1062", year = "2002", publisher = "IEEE Press", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", organisation = "IEEE Neural Network Council (NNC), Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", month = "12-17 " # may, ISBN = "0-7803-7278-6", keywords = "genetic algorithms, genetic programming, evolutionary computation, optimisation, power system analysis computing, power system control, power system identification, search problems, IEEE Transactions, control, evolution strategies, evolutionary algorithms, evolutionary computation, evolutionary programming, model identification, optimization, particle swarm optimization, power systems, simulated annealing, tabu search", DOI = "doi:10.1109/CEC.2002.1004389", abstract = "This survey covers the broad area of evolutionary computation applications to optimization, model identification, and control in power systems [1]. Due to space limitation, all reviewed papers have been selected since 1996, from the IEEE Transactions only. A total of 85 articles are listed in this survey. It shows the development of the area and identifies the current trends. The following techniques are considered under the scope of evolutionary computation: evolutionary algorithms (e.g., genetic algorithms, evolution strategies, evolutionary programming, and genetic programming), simulated annealing, tabu search, and particle swarm optimization.", notes = "CEC 2002 - A joint meeting of the IEEE, the Evolutionary Programming Society, and the IEE. Held in connection with the World Congress on Computational Intelligence (WCCI 2002)", } @Article{daSilva2012510, author = "Andre Tavares {da Silva} and Jefersson Alex {dos Santos} and Alexandre Xavier Falcao and Ricardo {da S. Torres} and Leo Pini Magalhaes", title = "Incorporating multiple distance spaces in optimum-path forest classification to improve feedback-based learning", journal = "Computer Vision and Image Understanding", volume = "116", number = "4", pages = "510--523", year = "2012", ISSN = "1077-3142", DOI = "doi:10.1016/j.cviu.2011.12.001", URL = "http://www.sciencedirect.com/science/article/pii/S107731421100261X", keywords = "genetic algorithms, genetic programming, Content-based image retrieval, Optimum-path forest classifiers, Composite descriptor, Multi-scale parameter search, Image pattern analysis", abstract = "In content-based image retrieval (CBIR) using feedback-based learning, the user marks the relevance of returned images and the system learns how to return more relevant images in a next iteration. In this learning process, image comparison may be based on distinct distance spaces due to multiple visual content representations. This work improves the retrieval process by incorporating multiple distance spaces in a recent method based on optimum-path forest (OPF) classification. For a given training set with relevant and irrelevant images, an optimisation algorithm finds the best distance function to compare images as a combination of their distances according to different representations. Two optimisation techniques are evaluated: a multi-scale parameter search (MSPS), never used before for CBIR, and a genetic programming (GP) algorithm. The combined distance function is used to project an OPF classifier and to rank images classified as relevant for the next iteration. The ranking process takes into account relevant and irrelevant representatives, previously found by the OPF classifier. Experiments show the advantages in effectiveness of the proposed approach with both optimisation techniques over the same approach with single distance space and over another state-of-the-art method based on multiple distance spaces.", } @PhdThesis{Tese-Andre_Tavares_da_Silva, author = "Andre Tavares {da Silva}", title_en = "Content-based image retrieval based on relevance feedback and optimum-path forest classifier", title = "Recuperacao de imagens por conteudo baseada em realimentacao de relevancia e classificador por floresta de caminhos otimos", school = "DEPARTAMENTO DE COMPUTACAO E AUTOMACAO INDUSTRIAL, FACULDADE DE ENGENHARIA ELETRICA E DE COMPUTACAO, UNIVERSIDADE ESTADUAL DE CAMPINAS", year = "2011", address = "Campinas, SP, Brazil", month = "26 " # jul, keywords = "genetic algorithms, genetic programming, SVM, CBIR, MPEG7, Horse Guards Parade, Pattern recognition, Information retrieval, Image analysis, Engenharia de Computacao, Reconhecimento de padroes, Recuperacao da informacao, Analise de imagem, Pattern recognition, Information retrieval, Image analysis", URL = "https://hdl.handle.net/20.500.12733/1616039", URL = "https://bdtd.ibict.br/vufind/Record/UNICAMP-30_755750f6a29697ba188c0c2adb340442", URL = "https://www.dca.fee.unicamp.br/~leopini/private/teses-pdf/Tese-Andre_Tavares_da_Silva.pdf", size = "174 pages", abstract = "Considering the increasing amount of image collections that result from popularisation of the digital cameras and the Internet, efficient search methods are becoming increasingly necessary. In this context, this doctoral dissertation proposes new methods for content-based image retrieval based on relevance feedback and on the OPF (optimum-path forest) classifier, being also the first time that the OPF classifier is used in small training sets. This doctoral dissertation names as greedy and planned the two distinct learning paradigms for relevance feedback taking into account the returned images. The first paradigm attempts to return the images most relevant to the user at each iteration, while the second returns the images considered the most informative or difficult to be classified. The dissertation presents relevance feedback algorithms based on the OPF classifier using both paradigms with single descriptor. Two techniques for combining descriptors are also presented along with the relevance feedback methods based on OPF to improve the effectiveness of the learning process. The first one, MSPS (Multi-Scale Search Parameter), is used for the first time in content-based image retrieval and the second is a consolidated technique based on genetic programming. A new approach of relevance feedback using the OPF classifier at two levels of interest is also shown. In this approach it is possible to select the pixels in images at a level of interest and to choose the most relevant images at each iteration at another level. This dissertation shows that the use of the OPF classifier for content based image retrieval is very efficient and effective, requiring few learning iterations to produce the desired results to the users. Simulations show that the proposed methods outperform the reference methods based on multi-point query and support vector machine. Besides, the methods based on optimum-path forest have shown to be on the average 52 times faster than the SVM-based approaches.", notes = "In Portuguese. Faculty of Electrical and Computer Engineering, UNICAMP. Supervisor: Leo Pini Magalhaes, Co-orientador: Alexandre Xavier Falcao", } @InProceedings{daSilva:2021:CEC, author = "Cleber A. C. F. {da Silva} and Daniel {Carneiro Rosa} and Pericles B. C. Miranda and Filipe R. Cordeiro and Tapas Si and Andre C. A. Nascimento and Rafael F. L. Mello and Paulo S. G. {de Mattos Neto}", booktitle = "2021 IEEE Congress on Evolutionary Computation (CEC)", title = "A Multi-Objective Grammatical Evolution Framework to Generate Convolutional Neural Network Architectures", year = "2021", editor = "Yew-Soon Ong", pages = "2187--2194", address = "Krakow, Poland", month = "28 " # jun # "-1 " # jul, keywords = "genetic algorithms, genetic programming, Grammatical Evolution, ANN, Computer vision, Computer architecture, Evolutionary computation, Network architecture, Grammar, Convolutional neural networks, Optimization, Deep Neural Networks, Multi-objective optimization", isbn13 = "978-1-7281-8393-0", DOI = "doi:10.1109/CEC45853.2021.9504822", abstract = "Deep Convolutional Neural Networks (CNNs) have reached the attention in the last decade due to their successful application to many computer vision domains. Several handcrafted architectures have been proposed in the literature, with increasing depth and millions of parameters. However, the optimal architecture size and parameters setup are dataset-dependent and challenging to find. For addressing this problem, this work proposes a Multi-Objective Grammatical Evolution framework to automatically generate suitable CNN architectures (layers and parameters) for a given classification problem. For this, a Context-free Grammar is developed, representing the search space of possible CNN architectures. The proposed method seeks to find suitable network architectures considering two objectives: accuracy and F1-score. We evaluated our method on CIFAR-10, and the results obtained show that our method generates simpler CNN architectures and overcomes the results achieved by larger (more complex) state-of-the-art CNN approaches and other grammars.", notes = "Also known as \cite{9504822}", } @PhdThesis{daSilva:thesis, author = "Cleomar Pereira {da Silva}", title = "Programacao Genetica Macicamente Paralela em {GPUs}", title = "Massively Parallel Genetic Programming on {GPUs}", school = "Departamento de Engenharia Eletrica do Centro Tecnico Cientifico da Pontificia Universidade Catolica do Rio de Janeiro", year = "2014", address = "Brazil", month = "11 " # sep, keywords = "genetic algorithms, genetic programming, GPU, quantum inspired, graphics processing units, machine code, QILGP, CUBIN", URL = "http://doi.org/10.17771/PUCRio.acad.24129", URL = "http://www.maxwell.vrac.puc-rio.br/24129/24129_1.PDF", URL = "http://www.maxwell.vrac.puc-rio.br/24129/24129_2.PDF", URL = "http://www.maxwell.vrac.puc-rio.br/24129/24129_3.PDF", URL = "http://www.maxwell.vrac.puc-rio.br/24129/24129_4.PDF", URL = "http://www.maxwell.vrac.puc-rio.br/24129/24129_5.PDF", URL = "http://www.maxwell.vrac.puc-rio.br/24129/24129_6.PDF", URL = "http://www.maxwell.vrac.puc-rio.br/24129/24129_7.PDF", URL = "http://www.maxwell.vrac.puc-rio.br/24129/24129_8.PDF", URL = "http://www.maxwell.vrac.puc-rio.br/24129/24129_9.PDF", URL = "http://www.maxwell.vrac.puc-rio.br/24129/24129_10.PDF", URL = "http://www.maxwell.vrac.puc-rio.br/24129/24129_11.PDF", size = "134 pages", abstract = "Genetic Programming enables computers to solve problems automatically, without being programmed to it. Using the inspiration in the Darwin's Principle of natural selection, a population of programs or individuals is maintained, modified based on genetic variation, and evaluated according to a fitness function. Genetic programming has been successfully applied to many different applications such as automatic design, pattern recognition, robotic control, data mining and image analysis. However, the evaluation of the huge amount of individuals requires excessive computational demands, leading to extremely long computational times for large size problems. This work exploits the high computational power of graphics processing units, or GPUs, to accelerate genetic programming and to enable the automatic generation of programs for large problems. We propose two new methodologies to exploit the power of the GPU in genetic programming: intermediate language compilation and individuals creation in machine language. These methodologies have advantages over traditional methods used in the literature. The use of an intermediate language reduces the compilation steps, and works with instructions that are well-documented. The individuals creation in machine language has no compilation step, but requires reverse engineering of the instructions that are not documented at this level. Our methodologies are based on linear genetic programming and are inspired by quantum computing. The use of quantum computing allows rapid convergence, global search capability and inclusion of individuals' past history. The proposed methodologies were compared against existing methodologies and they showed considerable performance gains. It was observed a maximum performance of 2,74 trillion GPops (genetic programming operations per second) for the 20-bit Multiplexer benchmark, and it was possible to extend genetic programming for problems that have databases with up to 7 million samples.", abstract = "A Programacao Genetica permite que computadores resolvam problemas automaticamente, sem que eles tenham sido programados para tal. Utilizando a inspiracao no principio da selecao natural de Darwin, uma populacao de programas, ou individuos, e mantida, modificada baseada em variacao genetica, e avaliada de acordo com uma funcao de aptidao (fitness). A programacao genetica tem sido usada com sucesso por uma serie de aplicacoes como projeto automatico, reconhecimento de padroes, controle robotico, mineracao de dados e analise de imagens. Porem, a avaliacao da gigantesca quantidade de individuos gerados requer excessiva quantidade de computacao, levando a um tempo de execucao inviavel para problemas grandes. Este trabalho explora o alto poder computacional de unidades de processamento grafico, ou GPUs, para acelerar a programacao genetica e permitir a geracao automatica de programas para grandes problemas. Propomos duas novas metodologias para se explorar a GPU em programacao genetica: compilacao em linguagem intermediaria e a criacao de individuos em codigo de maquina. Estas metodologias apresentam vantagens em relacao as metodologias tradicionais usadas na literatura. A utilizacao de linguagem intermediaria reduz etapas de compilacao e trabalha com instrucoes que estao bem documentadas. A criacao de individuos em codigo de maquina nao possui nenhuma etapa de compilacao, mas requer engenharia reversa das instrucoes que nao estao documentadas neste nivel. Nossas metodologias sao baseadas em programacao genetica linear e inspiradas em computacao quantica. O uso de computacao quantica permite uma convergencia rapida, capacidade de busca global e inclusao da historia passada dos individuos. As metodologias propostas foram comparadas com as metodologias existentes e apresentaram ganhos consideraveis de desempenho. Foi observado um desempenho maximo de ate 2,74 trilhoes de GPops (operacoes de programacao genetica por segundo) para o benchmark Multiplexador de 20 bits e foi possivel estender a programacao genetica para problemas que apresentam bases de dados de ate 7 milhoes de amostras.", notes = "In Portuguese. 2740000 MGPOPs Supervisor: Marco Aurelio Cavalcanti Pacheco, Co-Orientador: Douglas Mota Dias, Co-Orientador: Cristiana Barbosa Bentes", } @Article{JMLR:v16:dasilva15a, author = "Cleomar Pereira {da Silva} and Douglas Mota Dias and Cristiana Bentes and Marco Aurelio Cavalcanti Pacheco and Leandro Fontoura Cupertino", title = "Evolving {GPU} Machine Code", journal = "Journal of Machine Learning Research", year = "2015", volume = "16", number = "22", pages = "673--712", month = apr, keywords = "genetic algorithms, genetic programming, GPU, PTX, CUDA", publisher = "Microtome Publishing", ISSN = "1533-7928", URL = "http://jmlr.org/papers/v16/dasilva15a.html", URL = "http://jmlr.org/papers/volume16/dasilva15a/dasilva15a.pdf", size = "40 pages", abstract = "Parallel Graphics Processing Unit (GPU) implementations of GP have appeared in the literature using three main methodologies: (i) compilation, which generates the individuals in GPU code and requires compilation; (ii) pseudo-assembly, which generates the individuals in an intermediary assembly code and also requires compilation; and (iii) interpretation, which interprets the codes. This paper proposes a new methodology that uses the concepts of quantum computing and directly handles the GPU machine code instructions. Our methodology uses a probabilistic representation of an individual to improve the global search capability. In addition, the evolution in machine code eliminates both the overhead of compiling the code and the cost of parsing the program during evaluation. We obtained up to 2.74 trillion GP operations per second for the 20-bit Boolean Multiplexer benchmark. We also compared our approach with the other three GPU-based acceleration methodologies implemented for quantum-inspired linear GP. Significant gains in performance were obtained.", notes = "20-Mux", } @Article{daSilva:2015:CEE, author = "Cleomar Pereira {da Silva} and Douglas {Mota Dias} and Cristiana Bentes and Marco Aurelio Cavalcanti Pacheco", title = "Use of graphics processing units for automatic synthesis of programs", journal = "Computer \& Electrical Engineering", volume = "46", pages = "112--122", year = "2015", ISSN = "0045-7906", DOI = "doi:10.1016/j.compeleceng.2015.04.006", URL = "http://www.sciencedirect.com/science/article/pii/S0045790615001342", abstract = "Genetic programming (GP) is an evolutionary method that allows computers to solve problems automatically. However, the computational power required for the evaluation of billions of programs imposes a serious limitation on the problem size. This work focuses on accelerating GP to support the synthesis of large problems. This is done by completely exploiting the highly parallel environment of graphics processing units (GPUs). Here, we propose a new quantum-inspired linear GP approach that implements all the GP steps in the GPU and provides the following: (1) significant performance improvements in the GP steps, (2) elimination of the overhead of copying the fitness results from the GPU to the CPU, and (3) incorporation of a new selection mechanism to recognize the programs with the best evaluations. The proposed approach outperforms the previous approach for large-scale synthetic and real-world problems. Further, it provides a remarkable speedup over the CPU execution.", keywords = "genetic algorithms, genetic programming, GPU acceleration, Machine code, Quantum-inspired algorithms, Massive parallelism", } @InProceedings{daSilva:2018:BRACIS, author = "Jose Eduardo {da Silva} and Heder Bernardino", booktitle = "2018 7th Brazilian Conference on Intelligent Systems (BRACIS)", title = "Cartesian Genetic Programming with Crossover for Designing Combinational Logic Circuits", year = "2018", pages = "145--150", abstract = "The development of an efficient crossover for Cartesian Genetic Programming (CGP) has been widely investigated, but there is not a large number of approaches using this type of operator when designing combinational logic circuits. In this paper, we introduce a new crossover for CGP when using a single genotype representation and the desired model has multiple outputs. The proposal modifies the standard evolutionary strategy commonly adopted in CGP by combining the subgraphs of the best outputs of the parent and its offspring in order to generate a new fittest individual. The proposed crossover is applied to combinational logic circuits with multiple outputs, a parameter analysis is performed, and the results obtained are compared to those found by a baseline CGP and other techniques from the literature.", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", DOI = "doi:10.1109/BRACIS.2018.00033", month = oct, notes = "Also known as \cite{8575604}", } @InProceedings{DBLP:conf/epia/SilvaB19, author = "Jose Eduardo Henriques {da Silva} and Heder Soares Bernardino", editor = "Paulo Moura Oliveira and Paulo Novais and Luis Paulo Reis", title = "A 3-Step Cartesian Genetic Programming for Designing Combinational Logic Circuits with Multiplexers", booktitle = "Progress in Artificial Intelligence - 19th {EPIA} Conference on Artificial Intelligence, {EPIA} 2019, Vila Real, Portugal, September 3-6, 2019, Proceedings, Part {I}", series = "Lecture Notes in Computer Science", volume = "11804", pages = "762--774", publisher = "Springer", year = "2019", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", URL = "https://doi.org/10.1007/978-3-030-30241-2_63", DOI = "doi:10.1007/978-3-030-30241-2_63", timestamp = "Fri, 27 Dec 2019 21:28:26 +0100", biburl = "https://dblp.org/rec/conf/epia/SilvaB19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{DBLP:conf/mod/SilvaSB19, author = "Jose Eduardo Henriques {da Silva} and Lucas Augusto {Muller de Souza} and Heder Soares Bernardino", editor = "Giuseppe Nicosia and Panos M. Pardalos and Renato Umeton and Giovanni Giuffrida and Vincenzo Sciacca", title = "Cartesian Genetic Programming with Guided and Single Active Mutations for Designing Combinational Logic Circuits", booktitle = "Machine Learning, Optimization, and Data Science - 5th International Conference, {LOD} 2019, Siena, Italy, September 10-13, 2019, Proceedings", series = "Lecture Notes in Computer Science", volume = "11943", pages = "396--408", publisher = "Springer", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/978-3-030-37599-7_33", DOI = "doi:10.1007/978-3-030-37599-7_33", timestamp = "Fri, 12 Jun 2020 01:00:00 +0200", biburl = "https://dblp.org/rec/conf/mod/SilvaSB19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{da-Silva:2020:CEC, author = "Jose Eduardo H. {da Silva} and Heder S. Bernardino and Helio J. C. Barbosa and Alex B. Vieira and Luciana C. D. Campos and Itamar L. {de Oliveira}", booktitle = "2020 IEEE Congress on Evolutionary Computation (CEC)", title = "Inferring Gene Regulatory Network Models from Time-Series Data Using Metaheuristics", year = "2020", editor = "Yaochu Jin", month = "19-24 " # jul, keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Biological system modeling, Data models, Numerical models, Computational modeling, Mathematical model, Integrated circuit modeling", isbn13 = "978-1-7281-6929-3", DOI = "doi:10.1109/CEC48606.2020.9185572", abstract = "The inference of Gene Regulatory Networks (GRNs) from gene expression data is a hard and widely addressed scientific challenge with potential industrial and health-care use. Discrete and continuous models of GRNs are often used (i) to understand the process, and (ii) to predict the values of the relevant variables. Here, we propose a procedure to infer models of GRNs from data where (i) the data is binarised, (ii) a Boolean model is created using a Cartesian Genetic Programming technique, (iii) the obtained Boolean model is converted to a system of ordinary differential equations, and (iv) an Evolution Strategy defines the parameters of the continuous model. As a result, we expect to reduce the effect of noise and to improve biological interpretability. The proposed method is applied to two ODE systems that describe the circadian rhythm network dynamic, with 5 and 10 state variables. The models created by the proposed procedure are able to reproduce the behavior observed in the original data.", notes = "Universidade Federal de Juiz de Fora, Juiz de Fora, Brazil. Also known as \cite{9185572}", } @Article{DASILVA:2024:biosystems, author = "Jose Eduardo H. {da Silva} and Patrick C. {de Carvalho} and Jose J. Camata and Itamar L. {de Oliveira} and Heder S. Bernardino", title = "A Data-Distribution and Successive Spline Points based discretization approach for evolving gene regulatory networks from sc{RNA-Seq} time-series data using Cartesian Genetic Programming", journal = "Biosystems", volume = "236", pages = "105126", year = "2024", ISSN = "0303-2647", DOI = "doi:10.1016/j.biosystems.2024.105126", URL = "https://www.sciencedirect.com/science/article/pii/S030326472400011X", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Gene regulatory network, Discretization, Data distribution, Gene expression data", abstract = "The inference of gene regulatory networks (GRNs) is a widely addressed problem in Systems Biology. GRNs can be modeled as Boolean networks, which is the simplest approach for this task. However, Boolean models need binarized data. Several approaches have been developed for the discretization of gene expression data (GED). Also, the advance of data extraction technologies, such as single-cell RNA-Sequencing (scRNA-Seq), provides a new vision of gene expression and brings new challenges for dealing with its specificities, such as a large occurrence of zero data. This work proposes a new discretization approach for dealing with scRNA-Seq time-series data, named Distribution and Successive Spline Points Discretization (DSSPD), which considers the data distribution and a proper preprocessing step. Here, Cartesian Genetic Programming (CGP) is used to infer GRNs using the results of DSSPD. The proposal is compared with CGP with the standard data handling and five state-of-the-art algorithms on curated models and experimental data. The results show that the proposal improves the results of CGP in all tested cases and outperforms the state-of-the-art algorithms in most cases", } @InProceedings{daSilvaPraxedes:2014:GECCO, author = "Eric {da Silva Praxedes} and Adriano Soares Koshiyama and Elita Selmara Abreu and Douglas Mota Dias and Marley Maria Bernardes Rebuzzi Vellasco and Marco Aurelio Cavalcanti Pacheco", title = "Lithology discrimination using seismic elastic attributes: a genetic fuzzy classifier approach", booktitle = "GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation", year = "2014", editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges", isbn13 = "978-1-4503-2662-9", pages = "1151--1158", keywords = "genetic algorithms, genetic programming", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2576768.2598319", DOI = "doi:10.1145/2576768.2598319", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "One of the most important issues in oil \& gas industry is the lithological identification. Lithology is the macroscopic description of the physical characteristics of a rock. This work proposes a new methodology for lithological discrimination, using GPF-CLASS model (Genetic Programming for Fuzzy Classification) a Genetic Fuzzy System based on Multi-Gene Genetic Programming. The main advantage of our approach is the possibility to identify, through seismic patterns, the rock types in new regions without requiring opening wells. Thus, we seek for a reliable model that provides two flexibilities for the experts: evaluate the membership degree of a seismic pattern to the several rock types and the chance to analyse at linguistic level the model output. Therefore, the final tool must afford knowledge discovery and support to the decision maker. Also, we evaluate other 7 classification models (from statistics and computational intelligence), using a database from a well located in Brazilian coast. The results demonstrate the potentialities of GPF-CLASS model when comparing to other classifiers.", notes = "Also known as \cite{2598319} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)", } @InCollection{DASILVAVELLASCO:2017:MSCS, author = "Pedro Colmar {Goncalves da Silva Vellasco} and Luciano {Rodrigues Ornelas de Lima} and Sebastiao Arthur {Lopes de Andrade} and Marley Maria {Bernardes Rebuzzi Vellasco} and Luis Alberto {Proenca Simoes da Silva}", title = "Chapter Four - Computational Intelligence Modelling", editor = "Pedro Colmar {Goncalves da Silva Vellasco} and Luciano {Rodrigues Ornelas de Lima} and Sebastiao Arthur {Lopes de Andrade} and Marley Maria {Bernardes Rebuzzi Vellasco} and Luis Alberto {Proenca Simoes da Silva}", booktitle = "Modelling Steel and Composite Structures", publisher = "Butterworth-Heinemann", pages = "383--432", year = "2017", keywords = "genetic algorithms, genetic programming, Steel and composite structures, Structural modelling, Computational intelligence modelling, Neural networks, Fuzzy logic, Hybrid intelligent systems, Neuro-fuzzy, Neuro-genetic and fuzzy-genetic models", isbn13 = "978-0-12-813526-6", DOI = "doi:10.1016/B978-0-12-813526-6.00004-0", URL = "http://www.sciencedirect.com/science/article/pii/B9780128135266000040", abstract = "The development of new materials and faster computing processes opened new frontier for the conception and development of new and audacious designs that will set the trend for the future 21th century structures. Various methods, techniques, and procedures have been, and still are being, used to improve and design these structures like optimisation processes, numerical modelling systems involving non-linear finite element analysis, etc. Concurrently, the last decade of the 20th century has been related to a large improvement and development of the so-called Computational Intelligent Techniques. These techniques are computational systems that try to mimic human behaviour, such as perception, reasoning, learning, evolution, and adaptation. They involve Neural Networks, Genetic Algorithm, Fuzzy Logic, and Hybrid Intelligent Systems, such as Neuro-Fuzzy, Neuro-Genetic, and Fuzzy-Genetic models. This chapter highlights some of the initial attempts to use Computational Intelligent methods to forecast, design, and optimise the structural behaviour. This work focuses on some of these methods to enable a deeper insight of a wide range of structural engineering applications that could be aided by their proper use. This chapter initially presents a brief description of the adopted Computational Intelligence method, followed by some applications. First two basic artificial neural network models (Multi-Layer Perceptron with Back Propagation algorithm and Bayesian Neural Networks) are introduced. This is followed by the Genetic Algorithms and Genetic Programming. Finally, a brief overview of Neuro-Fuzzy systems was provided, as well as some of its applications to structural engineering. The case studies corroborate the great potential of Computational Intelligence techniques to solve problems that were considered difficult, limited, or even impossible by many researchers in different fields. Some case studies had a performance, in some cases, beyond expected, suggesting that these techniques might be a good solution in many other structural engineering applications", } @Article{Daskalakis:2013:Psychoneuroendocrinology, author = "Nikolaos P. Daskalakis and Rosemary C. Bagot and Karen J. Parker and Christiaan H. Vinkers and E. R. {de Kloet}", title = "The three-hit concept of vulnerability and resilience: Toward understanding adaptation to early-life adversity outcome", journal = "Psychoneuroendocrinology", volume = "38", number = "9", pages = "1858--1873", year = "2013", ISSN = "0306-4530", DOI = "doi:10.1016/j.psyneuen.2013.06.008", URL = "http://www.sciencedirect.com/science/article/pii/S0306453013002254", notes = "not GP", } @Article{Dassau:Mat:06, author = "Eyal Dassau and Benyamin Grosman and Daniel R. Lewin", title = "Modeling and temperature control of rapid thermal processing", journal = "Computers and Chemical Engineering", year = "2006", volume = "30", number = "4", pages = "686--697", month = "15 " # feb, keywords = "genetic algorithms, genetic programming, Rapid thermal processing (RTP), Non-linear model predictive control (NMPC), GA, GP", URL = "http://tx.technion.ac.il/~dlewin/publications/rtp_paper_v9.pdf", DOI = "doi:10.1016/j.compchemeng.2005.11.007", size = "28 pages", abstract = "In the past few years, rapid thermal processing (RTP) has gained acceptance as mainstream technology for semiconductor manufacturing. This single wafer approach allows for faster wafer processing and better control of process parameters on the wafer. However, as feature sizes become smaller, and wafer uniformity demands become more stringent, there is an increased demand from rapid thermal (RT) equipment manufacturers to improve control, uniformity and repeatability of processes on wafers. In RT processes, the main control problem is that of temperature regulation, which is complicated due to the high non-linearity of the heating process, process parameters that often change significantly during and between the processing of each wafer, and difficulties in measuring temperature and edge effects. This paper summarises work carried out in cooperation with Steag CVD Systems, in which algorithms for steady state and dynamic temperature uniformity were developed. The steady-state algorithm involves the reverse engineering of the required power distribution, given a history of past distributions and the resulting temperature profile. The algorithm for dynamic temperature uniformity involves the development of a first-principles model of the RTP chamber and wafer, its calibration using experimental data, and the use of the model to develop a controller.", notes = "cf \cite{Dassau:thesis}", } @PhdThesis{Dassau:thesis, author = "Eyal Dassau", title = "Yield Enhancement in Bioprocessing through Integrated Design and Control", school = "Chemical Engineering, Technion Israel Institute of Technology", year = "2006", address = "Haifa 3200003, Israel", URL = "http://www.graduate.technion.ac.il/Theses/Abstracts.asp?Id=19627", abstract = "Controlling bioprocesses at their optimal states should be of considerable interest to the bio-tech industry since it enables the reduction of production costs and the increase of yields while at the same time maintaining quality. As estimated by the Food and Drug Administration (FDA), poor quality design is responsible for more than 40percent of product recalls. This work presents two main contributions to influence and improve process design and product quality. The first is a novel plant wide Process Systems Engineering (PSE) concept that integrates process design and control with six-sigma methodology as a tool to find bottlenecks and overcome them, with the main intention being to enhance yield of bioprocesses, specifically in the pharmaceutical industry. The second one is optimization-based root cause analysis to improve the search for the root cause of poor process performance as part of the six-sigma methodology. These contributions were realized using Matlab and Simulink based on first-principles modelling and physical knowledge on two examples: a section of the Penicillin production process, including the fermentation step and the first product purification stage, and Aspergillus nigger fermentation. Applying the PSE concept along with optimization-based root cause analysis on the Penicillin production process reduces the batch time by 64percent, increases the product purity by 45percent and improves the throughput yield by 25percent. In the Aspergillus nigger case study, the RCA mechanism generates a modified design that not only produces a higher concentration of the desired product without significant change to the critical-to-quality (CTQ) variables, but is obviously a cost-effective one since less supporting equipment is needed. These contributions can best serve business targets, capable of improving process quality, yield and ultimately speeding-up production time. This can make a difference in the pharmaceutical industry in terms of product quality, investment and time. A process that will show lower defects-per-million-opportunities (DPMO) level will receive faster approval by the FDA, which translates directly to a faster return on investment. Generalization of this methodology to other chemical processes or applications is relatively straightforward and is strongly recommended", notes = "Is this GP? Supervisor: Lewin Daniel", } @InProceedings{DAssuncao:2020:SBG, author = "Vinicius M. D'Assuncao and Flavio R. S. Coutinho", title = "Procedural texture generation based on Genetic Programming", booktitle = "Brazilian Symposium on Computer Games and Digital Entertainment, SBGames 2020", year = "2020", editor = "Breno Jose {Andrade de Carvalho} and Lucas {Silva Figueiredo} and Geber {Lisboa Ramalho}", address = "virtual event, Recife, PE, Brazil", month = "7-10 " # nov, organisation = "CIn/UFPE and UNICAP", note = "Computing Track -- Short Papers", keywords = "genetic algorithms, genetic programming, procedural texture", ISSN = "2179-2259", URL = "https://www.sbgames.org/proceedings2020/ComputacaoShort/209325.pdf", size = "4 pages", abstract = "The texture is one of the elements that give a realistic aspect to an object in a game or animation. Textures can be drawn by designers and also can be defined mathematically as a function. This method is also known as procedural texture generation. we present a procedural generator based on Genetic Programming that provides a set of operations capable of generating an image with similar characteristics given a sample image but not necessarily with the same features. This approach allowed us to create a tree formed by a set of image manipulation operations. Besides, we created a framework for procedural texture generation since we implemented several image manipulation operators", notes = "https://www.sbgames.org/sbgames2020/", } @InProceedings{dastani:2001:gecco, title = "Finding Perceived Pattern Structures using Genetic Programming", author = "Mehdi Dastani and Elena Marchiori and Robert Voorn", pages = "3--10", year = "2001", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)", editor = "Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon and Edmund Burke", address = "San Francisco, California, USA", publisher_address = "San Francisco, CA 94104, USA", month = "7-11 " # jul, keywords = "genetic algorithms, genetic programming, visual perception, gestalt, simplicity principle, structural information theory (SIT), perceptual regularity", ISBN = "1-55860-774-9", URL = "http://gpbib.cs.ucl.ac.uk/gecco2001/d01.pdf", URL = "http://people.cs.uu.nl/mehdi/publication/Gecco01.ps", URL = "http://dl.acm.org/citation.cfm?id=2955239.2955240", acmid = "2955240", size = "8 pages", abstract = "Structural information theory (SIT) deals with the perceptual organization, often called the gestalt structure, of visual patterns. Based on a set of empirically validated structural regularities, the perceived organization of a visual pattern is claimed to be the most regular (simplest) structure of the pattern. The problem of finding the perceptual organization of visual patterns has relevant applications in multi-media systems, robotics and automatic data visualization. This paper shows that genetic programming (GP) is a suitable approach for solving this problem.", notes = "GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of \cite{spector:2001:GECCO}", } @InProceedings{Dastgeer:2011:IWMSE, author = "Usman Dastgeer and Johan Enmyren and Christoph W. Kessler", title = "Auto-tuning {SkePU}: A Multi-backend Skeleton Programming Framework for {multi-GPU} Systems", booktitle = "Proceedings of the 4th International Workshop on Multicore Software Engineering, IWMSE-2011", year = "2011", editor = "Harald Gall and Nenad Medvidovic", pages = "25--32", address = "Waikiki, Honolulu, HI, USA", publisher_address = "New York, NY, USA", month = "21-28 " # may, acmid = "1984697", publisher = "ACM", keywords = "genetic algorithms, genetic programming, genetic improvement, auto-tuning, CUDA, data parallelism, GPU, openCL, skeleton programming", isbn13 = "978-1-4503-0577-8", URL = "http://doi.acm.org/10.1145/1984693.1984697", DOI = "doi:10.1145/1984693.1984697", size = "8 pages", abstract = "SkePU is a C++ template library that provides a simple and unified interface for specifying data-parallel computations with the help of skeletons on GPUs using CUDA and OpenCL. The interface is also general enough to support other architectures, and SkePU implements both a sequential CPU and a parallel OpenMP backend. It also supports multi-GPU systems. Currently available skeletons in SkePU include map, reduce, mapreduce, map-with-overlap, maparray, and scan. The performance of SkePU generated code is comparable to that of hand-written code, even for more complex applications such as ODE solving. In this paper, we discuss initial results from auto-tuning SkePU using an off-line, machine learning approach where we adapt skeletons to a given platform using training data. The prediction mechanism at execution time uses off-line pre-calculated estimates to construct an execution plan for any desired configuration with minimal overhead. The prediction mechanism accurately predicts execution time for repetitive executions and includes a mechanism to predict execution time for user functions of different complexity. The tuning framework covers selection between different backends as well as choosing optimal parameter values for the selected backend. We will discuss our approach and initial results obtained for different skeletons (map, mapreduce, reduce).", notes = "Although it claims to use GP it seems to be adapting parameters rather than code. also known as \cite{Dastgeer:2011:ASM:1984693.1984697} http://2011.icse-conferences.org/", } @InProceedings{Datta:2014:EVOLVE, author = "Bithin Datta and Om Prakash and Janardhanan Sreekanth", title = "Application of Genetic Programming Models Incorporated in Optimization Models for Contaminated Groundwater Systems Management", booktitle = "EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V", year = "2014", editor = "Alexandru-Adrian Tantar and Emilia Tantar and Jian-Qiao Sun and Wei Zhang and Qian Ding and Oliver Schuetze and Michael Emmerich and Pierrick Legrand and Pierre {Del Moral} and Carlos A. {Coello Coello}", volume = "288", series = "Advances in Intelligent Systems and Computing", pages = "183--199", address = "Peking", month = "1-4 " # jul, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Optimal Monitoring Network, Groundwater Pollution, Multi-Objective Optimisation, Pollution Source Identification, Simulated Annealing, Impact Factors, Ensemble Surrogates", isbn13 = "978-3-319-07493-1", DOI = "doi:10.1007/978-3-319-07494-8_13", abstract = "Two different applications of Genetic Programming (GP) for solving large scale groundwater management problems are presented here. Efficient groundwater contamination management needs solution of large sale simulation models as well as solution of complex optimal decision models. Often the best approach is to use linked simulation optimisation models. However, the integration of optimisation algorithm with large scale simulation of the physical processes, which require very large number of iterations, impose enormous computational burden. Often typical solutions need weeks of computer time. Suitably trained GP based surrogate models approximating the physical processes can improve the computational efficiency enormously, also ensuring reasonably accurate solutions. Also, the impact factors obtained from the GP models can help in the design of monitoring networks under uncertainties. Applications of GP for obtaining impact factors implicitly based on a surrogate GP model, showing the importance of a chosen monitoring location relative to a potential contaminant source is also presented. The first application uses GP models based impact factors for optimal design of monitoring networks for efficient identification of unknown contaminant sources. The second application uses GP based ensemble surrogate models within a linked simulation optimisation model for optimal management of saltwater intrusion in coastal aquifers.", } @InCollection{DATTA:2018:ISPSE, author = "Shounak Datta and Vikrant A. Dev and Mario R. Eden", title = "Developing Non-linear Rate Constant QSPR using Decision Trees and Multi-Gene Genetic Programming", booktitle = "13th International Symposium on Process Systems Engineering (PSE 2018)", editor = "Mario R. Eden and Marianthi G. Ierapetritou and Gavin P. Towler", series = "Computer Aided Chemical Engineering", publisher = "Elsevier", volume = "44", pages = "2473--2478", year = "2018", keywords = "genetic algorithms, genetic programming, Multi-gene genetic programming, hybrid algorithm, nonlinear regression, machine learning, stochastic optimization", ISSN = "1570-7946", DOI = "doi:10.1016/B978-0-444-64241-7.50407-9", URL = "http://www.sciencedirect.com/science/article/pii/B9780444642417504079", abstract = "Developing a QSPR model, which not only captures the influence of reactant structures but also the solvent effect on reaction rate, is of significance. Such QSPR models will serve as a prerequisite for the simultaneous computer-aided molecular design (CAMD) of reactants, products and solvents. They will also be useful in predicting the rate constant without entirely relying on experiments. To develop such a QSPR, recently, Datta et al. (2017) used the Diels-Alder reaction as a case study. Their model displayed great promise, but, there is scope for improvement in the model's predictive ability. In our work, we improve upon their model by introducing non-linearity. This is achieved using multi-gene genetic programming (MGGP). In our methodology, a combination of genetic algorithm (GA) and directed trees was used to develop a branched version of chromosomes, allowing additional possibilities in the generated models. In our work, prior to model development through MGGP, principal component analysis (PCA) was conducted. Lastly, models were evaluated based on metrics such as R2, Q2, and RMSE", } @InProceedings{conf/mum/DauSSHH14, title = "Phone based fall detection by genetic programming", author = "Anh Hoang Dau and Flora Dilys Salim and Andy Song and Lachlan Hedin and Margaret Hamilton", booktitle = "Proceedings of the 13th International Conference on Mobile and Ubiquitous Multimedia, MUM 2014", publisher = "ACM", year = "2014", editor = "Arkady B. Zaslavsky and Seng W. Loke and Lars Kulik and Evaggelia Pitoura", address = "Melbourne, Victoria, Australia", month = nov # " 25-28", pages = "256--257", keywords = "genetic algorithms, genetic programming, fall detection, mobile sensing", isbn13 = "978-1-4503-3304-7", bibdate = "2014-11-20", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/mum/mum2014.html#DauSSHH14", URL = "http://dl.acm.org/citation.cfm?id=2677972", DOI = "doi:10.1145/2677972.2678010", acmid = "2678010", abstract = "Elderly people are prone to fall due to the high rate of risk factors associated with ageing. Existing fall detection systems are mostly designed for a constrained environment, where various assumptions are applied. To overcome these drawbacks, we opt to use mobile phones with standard built-in sensors. Fall detection is performed on motion data collected by sensors in the phone alone. We use Genetic Programming (GP) to learn a classifier directly from raw sensor data. We compare the performance of GP with the popular approach of using threshold-based algorithm. The result shows that GP-evolved classifiers perform consistently well across different fall types and overall more reliable than the threshold-based.", } @InProceedings{conf/seal/DauSXSC14, author = "Anh Hoang Dau and Andy Song and Feng Xie and Flora Dilys Salim and Vic Ciesielski", title = "Genetic Programming for Channel Selection from Multi-stream Sensor Data with Application on Learning Risky Driving Behaviours", bibdate = "2014-11-12", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/seal/seal2014.html#DauSXSC14", booktitle = "Simulated Evolution and Learning - 10th International Conference, {SEAL} 2014, Dunedin, New Zealand, December 15-18, 2014. Proceedings", publisher = "Springer", year = "2014", volume = "8886", editor = "Grant Dick and Will N. Browne and Peter A. Whigham and Mengjie Zhang and Lam Thu Bui and Hisao Ishibuchi and Yaochu Jin and Xiaodong Li and Yuhui Shi and Pramod Singh and Kay Chen Tan and Ke Tang", isbn13 = "978-3-319-13562-5", pages = "542--553", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming", URL = "http://dx.doi.org/10.1007/978-3-319-13563-2", } @Article{dautenhahn:2002:GPEM, author = "Kerstin Dautenhahn", title = "Book Review: {Swarm} Intelligence", journal = "Genetic Programming and Evolvable Machines", year = "2002", volume = "3", number = "1", pages = "93--97", month = mar, keywords = "genetic algorithms, genetic programming, evolvable hardware", ISSN = "1389-2576", URL = "https://rdcu.be/dR8jK", DOI = "doi:10.1023/A:1014827205360", abstract = "Review of Kennedy+Eberhart's {"}Swarm Intelligence{"} http://www.mkp.com/books_catalog/catalog.asp?ISBN=1-55860-595-9 James Kennedy and Russell C. Eberhart, with Yuhui Shi, 2001, MKP ISBN 1-55860-595-9", notes = "Article ID: 395992", } @InProceedings{daVeigaCabral:2011:NaBIC, author = "Rafael {da Veiga Cabral} and Eduardo J. Spinosa", title = "Evaluating an outlier generation method for training tree-based Genetic Programming applied to one-class classification", booktitle = "Third World Congress on Nature and Biologically Inspired Computing (NaBIC 2011)", year = "2011", month = "19-21 " # oct, pages = "395--400", address = "Salamanca", abstract = "Genetic Programming (GP) has been successfully applied to supervised classification problems. This work evaluates a tree-based GP implementation in a one-class classification scenario, using artificial outliers generated by a promising method recently developed by Banhalmi et al. The proposed approach does not require the use of certain techniques employed by related works, thus providing a simpler yet effective strategy for one-class classification based on GP. Experiments presented herein explore parameter sensitivity of Banhalmi outlier generation method and compare the proposed approach to previously published results obtained by others one-class classifiers like v-SVM, one-class SVM and GMM.", keywords = "genetic algorithms, genetic programming, artificial outliers, outlier generation method, supervised classification problems, tree based genetic programming, learning (artificial intelligence)", DOI = "doi:10.1109/NaBIC.2011.6089468", notes = "Also known as \cite{6089468}", } @InProceedings{davenport:1999:RIURPR, author = "G. F. Davenport and M. D. Ryan and V. J. Rayward-Smith", title = "Rule Induction Using a Reverse Polish Representation", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "990--995", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-433.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-433.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Article{david:2017:RSTA, author = "Cristina David and Daniel Kroening", title = "Program synthesis: challenges and opportunities", journal = "Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences", year = "2017", volume = "375", number = "2104", month = "13 " # oct, note = "Discussion meeting issue ``Verified trustworthy software systems'' organised and edited by Philippa Gardner, Mike Gordon, Greg Morrisett, Peter O'Hearn and Fred Schneider", keywords = "genetic algorithms, genetic programming, verification, model checking, synthesis, software", ISSN = "1364-503X", publisher = "Royal Society", DOI = "doi:10.1098/rsta.2015.0403", size = "20 pages", abstract = "Program synthesis is the mechanized construction of software, dubbed self-writing code. Synthesis tools relieve the programmer from thinking about how the problem is to be solved; instead, the programmer only provides a description of what is to be achieved. Given a specification of what the program should do, the synthesizer generates an implementation that provably satisfies this specification. From a logical point of view, a program synthesizer is a solver for second-order existential logic. Owing to the expressiveness of second-order logic, program synthesis has an extremely broad range of applications. We survey some of these applications as well as recent trends in the algorithms that solve the program synthesis problem. In particular, we focus on an approach that has raised the profile of program synthesis and ushered in a generation of new synthesis tools, namely counter-example-guided inductive synthesis (CEGIS). We provide a description of the CEGIS architecture, followed by recent algorithmic improvements. We conjecture that the capacity of program synthesis engines will see further step change, in a manner that is transparent to the applications, which will open up an even broader range of use-cases. This article is part of the themed issue Verified trustworthy software systems.", notes = "PubMed:28871052", } @InProceedings{Davidge:1993:rr, author = "Robert Davidge", title = "Looping as a Means of Survival: Playing Russian Roulette in a Harsh Environment", booktitle = "ECAL-93 Self organisation and life: from simple rules to global complexity", year = "1993", pages = "259--273", address = "CP 231, Universite Libre de Bruxelles, Bld. du Triomphe, 1050 Brussels, Belgium, Fax 32-2-659.5767 Phone 32-2-650.5776 Email sgross@ulb.ac.be", month = "24--26 " # may, organisation = "Centre for Non-Linear Phenomena and Complex Systems", email = "robertd@cogs.susx.ac.uk", keywords = "genetic algorithms", size = "15 pages", abstract = "Cline 4bit processor runs across 2dee memory array. Controlled by 16 chromosome of micro-instruction sequences of fixed length.", notes = "There seems to be some doubt as to wether ECAL-93 was published. This copy from attendee.", } @InProceedings{davidson:1999:snr:htpa, author = "J. W. Davidson and D. A. Savic and G. A. Walters", title = "Symbolic and numerical regression: a hybrid technique for polynomial approximators", booktitle = "Proceedings of Recent Advances in Soft Computing'99", year = "1999", editor = "Robert John and Ralph Birkenhead", pages = "111--116", address = "De Montfort University, Leicester, UK", month = "1-2 " # jul, publisher = "Physica Verlag", keywords = "genetic algorithms, genetic programming, least squares, polynomial expressions, symbolic algebra, symbolic regression", ISBN = "3-7908-1257-9", URL = "http://www.amazon.com/exec/obidos/ASIN/3790812579/o/qid=953125875/sr=2-1/103-9581855-0507860", } @Article{davidson:1999:miepfftpf, author = "J. W. Davidson and D. A. Savic and G. A. Walters", title = "Method for the identification of explicit polynomial formulae for the friction in turbulent pipe flow", journal = "Journal of Hydroinformatics", year = "1999", volume = "1", number = "2", pages = "115--126", month = oct, keywords = "genetic algorithms, genetic programming, least squares, polynomial expressions, symbolic algebra, symbolic regression", ISSN = "1464-7141", URL = "http://www.iwaponline.com/jh/001/0115/0010115.pdf", DOI = "doi:10.2166/hydro.1999.0010", size = "12 pages", abstract = "The paper describes a new regression method for creating polynomial models. The method combines numerical and symbolic regression. Genetic programming finds the form of polynomial expressions, and least squares optimisation finds the values for the constants in the expressions. The incorporation of least squares optimization within symbolic regression is made possible by a rule-based component that algebraically transforms expressions to equivalent forms that are suitable for least squares optimisation. The paper describes new operators of crossover and mutation that improve performance, and a new method for creating starting solutions that avoids the problem of under-determined functions. An example application demonstrates the trade-off between model complexity and accuracy of a set of approximator functions created for the Colebrook-White formula.", notes = "Improving on Ephemeral random constants", } @InProceedings{davidson:1999:ac-wfohrm, author = "J. W. Davidson and D. A. Savic and G. A. Walters", title = "Approximators for the Colebrook-White Formula Obtained through a Hybrid Regression Method", booktitle = "Proceedings of XIII International Conference on Computational Methods in Water Resources", year = "2000", editor = "Laurence R. Bentley and Carlos A. Brebbia and William G. Gray and George F. Pinder and Jonathan F. Sykes", address = "Calgary, Canada", month = "25-29 " # jun, publisher = "Taylor \& Francis, Inc.", keywords = "genetic algorithms, genetic programming", isbn13 = "9789058091239", URL = "https://books.google.co.uk/books/about/Computational_Methods_in_Water_Resources.html?id=Neu9NAEACAAJ&redir_esc=y", notes = "http://atlas-conferences.com/cgi-bin/calendar/d/faav07 cmwr2000", } @InProceedings{davidson:2000:rrmunprm, author = "J. W. Davidson and D. A. Savic and G. A. Walters", title = "Rainfall Runoff Modeling Using a New Polynomial Regression Method", booktitle = "Proceedings of the 4th International Conference on Hydroinformatics", year = "2000", address = "Iowa City, Iowa, USA", month = "23-27 " # jul, organisation = "IAHR/IWA/IAHS Committee on Hydroinformatics, Iowa Institute of Hydraulic Research", publisher = "International Association for Hydro-Environment Engineering and Research", note = "CD-ROM only", keywords = "genetic algorithms, genetic programming", ISBN = "none", URL = "http://members.iahr.org/core/orders/product.aspx?catid=3&prodid=47", notes = "Kirkton, Scotland. Mallows Cp to avoid overfitting. GP limited to just polynomials (actually produced by post processing) constants fitted by least-squares. Comparison with previously published GP and ANN. Overfitting (consistency) v. model instability. Population size 40. (l,m) = (100,40) ?", } @InProceedings{davidson:2000:snrea, author = "J. W. Davidson and D. A. Savic and G. A. Walters", title = "Symbolic and numerical regression: experiments and applications", booktitle = "Developments in Soft Computing", year = "2001", editor = "Robert John and Ralph Birkenhead", volume = "9", series = "Advances in Soft Computing", pages = "175--182", address = "De Montfort University, Leicester, UK", month = "29-30 " # jun # " 2000.", publisher = "Physica Verlag", publisher_address = "Heidelberg, Germany", keywords = "genetic algorithms, genetic programming, least-squares, rule-based programming, stepwise regression, symbolic regression", URL = "http://buch.archinform.net/isbn/3-7908-1361-3.htm", URL = "http://www.amazon.com/gp/product/3790813613", DOI = "doi:10.1007/978-3-7908-1829-1_21", ISBN = "3-7908-1361-3", abstract = "This paper describes a new method for creating polynomial regression models. The new method is compared with stepwise regression and symbolic regression using three example problems. The first example is a polynomial equation. The two examples that follow are real-world problems, approximating the Colebrook-White equation and rainfall-runoff modelling", } @Article{davidson:2003:IS, author = "J. W. Davidson and D. A. Savic and G. A. Walters", title = "Symbolic and numerical regression: Experiments and applications", journal = "Information Sciences", year = "2003", volume = "150", pages = "95--117", number = "1-2", DOI = "doi:10.1016/S0020-0255(02)00371-7", URL = "http://www.sciencedirect.com/science/article/B6V0C-474DD2V-1/2/3368220198ea15f93a793594af73d8d1", keywords = "genetic algorithms, genetic programming, Least squares, Rule-based programming, Stepwise regression, Symbolic regression", abstract = "This paper describes a new method for creating polynomial regression models. The new method is compared with stepwise regression and symbolic regression using three example problems. The first example is a polynomial equation. The two examples that follow are real-world problems, approximating the Colebrook-White equation and rainfall-runoff modelling. The three example problems illustrate the advantages of the new method.", } @Article{David-Tabibi:2010:GPEM, author = "Omid David-Tabibi and Moshe Koppel and Nathan S. Netanyahu", title = "Expert-driven genetic algorithms for simulating evaluation functions", journal = "Genetic Programming and Evolvable Machines", year = "2011", volume = "12", number = "1", pages = "5--22", month = mar, keywords = "genetic algorithms, Computer chess, Fitness evaluation, Games, Parameter tuning", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-010-9103-4", abstract = "In this paper we demonstrate how genetic algorithms can be used to reverse engineer an evaluation function's parameters for computer chess. Our results show that using an appropriate expert (or mentor), we can evolve a program that is on par with top tournament-playing chess programs, outperforming a two-time World Computer Chess Champion. This performance gain is achieved by evolving a program that mimics the behavior of a superior expert. The resulting evaluation function of the evolved program consists of a much smaller number of parameters than the expert's. The extended experimental results provided in this paper include a report on our successful participation in the 2008 World Computer Chess Championship. In principle, our expert-driven approach could be used in a wide range of problems for which appropriate experts are available.", notes = "Not GP. A preliminary version of this paper appeared in Proceedings of the 2008 Genetic and Evolutionary Computation Conference \cite{David-Tabibi:2008:gecco} and received the Best Paper Award in the conference's Real-World Applications track.", affiliation = "Department of Computer Science, Bar-Ilan University, 52900 Ramat-Gan, Israel", } @Article{Davies:2000:AEMB, author = "Zoe S. Davies and Richard J. Gilbert and Roger J. Merry and Douglas B. Kell and Michael K. Theodorou and Gareth W. Griffith", title = "Efficient improvement of silage additives by using genetic algorithms", journal = "Applied and Environmental Microbiology", year = "2000", volume = "66", number = "4", pages = "1435--1443", month = apr, keywords = "genetic algorithms, genetic programming", publisher = "American Society for Microbiology", ISSN = "0099-2240", URL = "https://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC92005&blobtype=pdf", DOI = "doi:10.1128/aem.66.4.1435-1443.2000", size = "9 pages", abstract = "The enormous variety of substances which may be added to forage in order to manipulate and improve the ensilage process presents an empirical, combinatorial optimization problem of great complexity. To investigate the utility of genetic algorithms for designing effective silage additive combinations, a series of small-scale proof of principle silage experiments were performed with fresh rye grass. Having established that significant biochemical changes occur over an ensilage period as short as 2 days, we performed a series of experiments in which we used 50 silage additive combinations (prepared by using eight bacterial and other additives, each of which was added at six different levels, including zero [i.e., no additive]). The decrease in pH, the increase in lactate concentration, and the free amino acid concentration were measured after 2 days and used to calculate a fitness value that indicated the quality of the silage (compared to a control silage made without additives). This analysis also included a cost element to account for different total additive levels. In the initial experiment additive levels were selected randomly, but subsequently a genetic algorithm program was used to suggest new additive combinations based on the fitness values determined in the preceding experiments. The result was very efficient selection for silages in which large decreases in pH and high levels of lactate occurred along with low levels of free amino acids. During the series of five experiments, each of which comprised 50 treatments, there was a steady increase in the amount of lactate that accumulated; the best treatment combination was that used in the last experiment, which produced 4.6 times more lactate than the untreated silage. The additive combinations that were found to yield the highest fitness values in the final (fifth) experiment were assessed to determine a range of biochemical and microbiological quality parameters during full-term silage fermentation. We found that these combinations compared favorably both with uninoculated silage and with a commercial silage additive. The evolutionary computing methods described here are a convenient and efficient approach for designing silage additives.", notes = "Not GP but GA run on physical system: grass digestion PMID: 10742224; PMCID: PMC92005. https://aem.asm.org/ Institute of Grassland and Environmental Research, Plas Gogerddan, Aberystwyth,Ceredigion SY23 3EB, UK", } @InProceedings{Davila:2015:GECCOcomp, author = "Jaime J. Davila", title = "An Empirical Comparison of Genetically Evolved Programs and Evolved Neural Networks for Multi-agent Systems Operating under Dynamic Environments", booktitle = "GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation", year = "2015", editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr", isbn13 = "978-1-4503-3488-4", keywords = "genetic algorithms, genetic programming: Poster", pages = "1373--1374", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739482.2764717", DOI = "doi:10.1145/2739482.2764717", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "This paper expands on the research presented in [12] by comparing the performance of genetically evolved programs operating under dynamic game environments with that of neural networks with evolved weights. On the genetic programming side, the maximum allowed tree depth was varied in order to study its effect on the evolutionary process. For evolution of neural networks, encoding included direct encoding of weights and three different L-Systems. Empirical results show that genetic evolution of neural networks weights provided better performance under dynamic environments when evolved to choose which of several high-level actions to perform, such as defend or attack. On the other hand, genetic programming evolved better solutions for low-level actions, such as move left, move right, or accelerate. Solutions are analysed in order to explain these differences.", notes = "Also known as \cite{2764717} Distributed at GECCO-2015.", } @InCollection{davis:1994:spec, author = "James Davis", title = "Single Populations v. Co-Evolution", booktitle = "Artificial Life at Stanford 1994", year = "1994", editor = "John R. Koza", pages = "20--27", address = "Stanford, California, 94305-3079 USA", month = jun, organisation = "Stanford University", publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-182105-2", notes = "Steady state GP model. Tank control strategies co-evolved competitively against each other. This volume contains 22 papers written and submitted by students describing their term projects for the course in artificial life (Computer Science 425) at Stanford University offered during the spring quarter quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs425.html", } @Article{Davis:Nfs:06, author = "Richard A. Davis and Adrian J. Charlton and Sarah Oehlschlager and Julie C. Wilson", title = "Novel feature selection method for genetic programming using metabolomic {1H NMR} data", journal = "Chemometrics and Intelligent Laboratory Systems", year = "2006", volume = "81", number = "1", pages = "50--59", month = mar, keywords = "genetic algorithms, genetic programming, Metabolomics, Multivariate data analysis, Feature selection, NMR", DOI = "doi:10.1016/j.chemolab.2005.09.006", abstract = "A novel technique for multivariate data analysis using a two-stage genetic programming (GP) routine for feature selection is described. The method is compared with conventional genetic programming for the classification of genetically modified barley. Metabolic fingerprinting by 1H NMR spectroscopy was used to analyse the differences between transgenic and null-segregant plants. We show that the method has a number of major advantages over standard genetic programming techniques. By selecting a minimal set of characteristic features in the data, the method provides models that are easier to interpret. Moreover the new method achieves better classification results and convergence is reached significantly faster.", } @InProceedings{Davydov:2008:SYRCoSE, title = "Application of Genetic Algorithms for Construction of Moore Automaton and Systems of Interacting Mealy Automata in Artificial Ant Problem", author = "Andrey A. Davydov and Dmitry O. Sokolov and Fedor N. Tsarev and Anatoly A. Shalyto", booktitle = "Proceedings of the Spring/Summer Young Researchers' Colloquium on Software Engineering", year = "2008", volume = "1", series = "51--54", address = "St. Petersburg, Russia", month = may # " 29-30", organisation = "Saint-Petersburg State University and Institute for System Programming of RAS", keywords = "genetic algorithms, FSM, Artificial ant, automata based programming", DOI = "DOI:10.15514/SYRCOSE-2008-2-10", size = "4 pages", abstract = "a genetic algorithm for construction of Moore finite state machines is described in the paper. This algorithm can be also applied to construct systems of interacting Mealy finite state machines. An example of application of these algorithms for Artificial ant problem is also described.", notes = "Not GP. Santa Fe like ant trial. http://syrcose.ispras.ru/?q=node/16", } @InProceedings{Davydov:2009:INCOM, author = "Andrey Davydov and Dmitry Sokolov and Fedor Tsarev and Anatoly Shalyto", title = "Application of Genetic Programming for Generation of Controllers Represented by Automata", booktitle = "13th IFAC Symposium on Information Control Problems in Manufacturing, INCOM 2009", year = "2009", pages = "Paper We--C7.4", address = "Moscow, Russia", month = jun # " 3-5", note = "Invited Session {"}Advanced Software Engineering in Industrial Automation II{"} (We-C7)", keywords = "genetic algorithms, genetic programming", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.557.8188", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.557.8188", URL = "http://is.ifmo.ru/articles_en/_ifac-2009.pdf", URL = "https://ifac.papercept.net/conferences/scripts/abstract.pl?ConfID=8&Number=385", abstract = "This paper proposes an application of genetic programming for construction of state machines controlling systems with complex behaviour. Application of this method is illustrated on example of unmanned aerial vehicle (UAV) control. It helps to find control strategies of collaborative behaviour of UAV teams. Multi-agent approach is used, where every agent that controls a UAV is presented by a deterministic finite state machine. Two representations of finite state machines are used: abridged transition tables and decision trees. Novel algorithms for fixing connections between states and for removing unachievable branches of trees are proposed.", notes = "St Petersburg State University of IT, Mechanics and Optics", } @Article{day:2002:AEM, author = "Jennifer P. Day and Douglas B. Kell and Gareth W. Griffith", title = "Differentiation of Phytophthora infestans Sporangia from Other Airborne Biological Particles by Flow Cytometry", journal = "Applied and Environmental Microbiology", year = "2002", volume = "68", number = "1", pages = "37--45", month = jan, keywords = "genetic algorithms, genetic programming", URL = "http://intl-aem.asm.org/cgi/reprint/68/1/37.pdf", DOI = "doi:10.1128/AEM.68.1.37-45.2002", abstract = "The ability of two different flow cytometers, the Microcyte (Optoflow) and the PAS-III (Partec), to differentiate sporangia of the late-blight pathogen Phytophthora infestans from other potential airborne particles was compared. With the PAS-III, light scatter and intrinsic fluorescence parameters could be used to differentiate sporangia from conidia of Alternaria or Botrytis spp., rust urediniospores, and pollen of grasses and plantain. Differentiation between P. infestans sporangia and powdery mildew conidia was not possible by these two methods but, when combined with analytical rules evolved by genetic programming methods, could be achieved after staining with the fluorescent brightener Calcofluor white M2R. The potential application of these techniques to the prediction of late-blight epiphytotics in the field is discussed.", notes = "GMax-Bio", } @PhdThesis{Day:thesis, author = "Peter Day", title = "Advances in genetic programming with applications in speech and audio", school = "University of Liverpool", year = "2005", address = "UK", keywords = "genetic algorithms, genetic programming", URL = "https://library.liv.ac.uk/record=b2011018~S8", URL = "http://ethos.bl.uk/OrderDetails.do?did=1&uin=uk.bl.ethos.428373", notes = "LOCATION Harold Cohen Library THESIS 20560.DAY uk.bl.ethos.428373", } @Article{Day:2007:ASLP, title = "Robust Text-Independent Speaker Verification Using Genetic Programming", author = "Peter Day and Asoke K. Nandi", journal = "IEEE Transactions on Audio, Speech and Language Processing", year = "2007", volume = "15", number = "1", pages = "285--295", month = jan, keywords = "genetic algorithms, genetic programming, feature extraction, speaker recognition, telephone networks additive noise, convolutive noise, feature selection, remote security verification, robust text-independent speaker verification, telephone network", DOI = "doi:10.1109/TASL.2006.876765", ISSN = "1558-7916", abstract = "Robust automatic speaker verification has become increasingly desirable in recent years with the growing trend toward remote security verification procedures for telephone banking, bio-metric security measures and similar applications. While many approaches have been applied to this problem, genetic programming offers inherent feature selection and solutions that can be meaningfully analysed, making it well suited to this task. This paper introduces a genetic programming system to evolve programs capable of speaker verification and evaluates its performance with the publicly available TIMIT corpora. We also show the effect of a simulated telephone network on classification results which highlights the principal advantage, namely robustness to both additive and convolutive noise", notes = "see also IEEE Transactions on Speech and Audio Processing", } @InProceedings{Day:2008:MLSP, author = "Peter Day and Asoke K. Nandi", title = "Sunspot prediction using genetic programming augmented by Binary String Fitness Characterisation and Comparative Partner Selection", booktitle = "IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008", year = "2008", month = oct, pages = "175--180", keywords = "genetic algorithms, genetic programming, binary string fitness characterisation, comparative partner selection, pair-wise mating strategy, population-wide weaknesses, sunspot prediction, prediction theory, string matching, sunspots", DOI = "doi:10.1109/MLSP.2008.4685475", ISSN = "1551-2541", abstract = "The paper addresses the sunspot prediction problem using a novel strategy for evaluating individual's relative strengths and weaknesses, by representing these in the form of a binary string fitness characterisation (BSFC), in addition to an overall fitness value for each individual. Using a combination of the BSFC and a pair-wise mating strategy, comparative partner selection (CPS), appears to promote effective solutions by reducing population-wide weaknesses. This strategy offers better solution to the sunspot prediction problem.", notes = "Also known as \cite{4685475}", } @Article{Day:2008:TEC, author = "Peter Day and Asoke K. Nandi", title = "Binary String Fitness Characterization and Comparative Partner Selection in Genetic Programming", journal = "IEEE Transactions on Evolutionary Computation", year = "2008", volume = "12", number = "6", pages = "724--735", month = dec, keywords = "genetic algorithms, genetic programming, binary string fitness characterization, comparative partner selection, evolutionary methods, genetic programming benchmarking problems, adaptive crossover and mutation, mate selection, CPS", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2008.917201", URL = "http://results.ref.ac.uk/Submissions/Output/832803", size = "12 pages", abstract = "The premise behind all evolutionary methods is survival of the fittest and consequently, individuals require a quantitative fitness measure. This paper proposes a novel strategy for evaluating individual's relative strengths and weaknesses, as well as representing these in the form of a binary string fitness characterization (BSFC); in addition, as customary, an overall fitness value is assigned to each individual. Using the BSFC, we demonstrate both novel population evaluation measures and a pairwise mating strategy, comparative partner selection (CPS), with the aim of evolving a population that promotes effective solutions by reducing population-wide weaknesses. This strategy is tested with six standard genetic programming benchmarking problems.", notes = "Also known as \cite{4472181} 3 bit parity, 5-even parity, 11 mux, quartic, Rastrigin, Sunspot, parsimony pressure, bloat,", uk_research_excellence_2014 = "The survival of the fittest characterises evolutionary computational methods, requiring fitness measures for individuals. This paper invents novel strategies for evaluating individual's relative strengths and weaknesses, and representing them in a fundamentally new binary string fitness characterisation (BSFC). A new rigorous paradigm is created by using the BSFC in proposing a pair-wise mating strategy, Comparative Partner Selection, in evolving a population that promotes effective solutions by reducing population-wide weaknesses. Published in a high impact factor journal, this represents a significantly promising development that subsequently led to successes in breast cancer detection, communications (IEEE TWC 2012), and condition monitoring applications.", } @InCollection{day:2010:Chiong, author = "Peter Day and Asoke Nandi", title = "Genetic Programming for Robust Text Independent Speaker Verification", booktitle = "Nature-Inspired Informatics for Intelligent Applications and Knowledge Discovery: Implications in Business, Science, and Engineering", publisher = "IGI Global", year = "2010", editor = "Raymond Chiong", pages = "259--280", keywords = "genetic algorithms, genetic programming", isbn13 = "1605667056", URL = "http://www.igi-global.com/Bookstore/Chapter.aspx?TitleId=36319", DOI = "doi:10.4018/978-1-60566-705-8", abstract = "Robust Automatic Speaker Verification has become increasingly desirable in recent years with the growing trend toward remote security verification procedures for telephone banking, bio-metric security measures and similar applications. While many approaches have been applied to this problem, Genetic Programming offers inherent feature selection and solutions that can be meaningfully analyzed, making it well suited for this task. This chapter introduces a Genetic Programming system to evolve programs capable of speaker verification and evaluates its performance with the publicly available TIMIT corpora. Also presented are the effects of a simulated telephone network on classification results which highlight the principal advantage, namely robustness to both additive and convolutive noise.", notes = "http://www.igi-global.com/Bookstore/TitleDetails.aspx?TitleId=794&DetailsType=Description", } @Article{journals/es/DayN11, author = "Peter Day and Asoke K. Nandi", title = "Evolution of superFeatures through genetic programming", journal = "Expert Systems", year = "2011", volume = "28", number = "2", pages = "167--184", publisher = "Blackwell Publishing Ltd", keywords = "genetic algorithms, genetic programming, super features, classification, binary string fitness characterisation, comparative partner selection", ISSN = "1468-0394", DOI = "doi:10.1111/j.1468-0394.2010.00547.x", size = "18 pages", abstract = "The success of automatic classification is intricately linked with an effective feature selection. Previous studies on the use of genetic programming (GP) to solve classification problems have highlighted its benefits, principally its inherent feature selection (a process that is often performed independent of a learning method). In this paper, the problem of classification is recast as a feature generation problem, where GP is used to evolve programs that allow non-linear combination of features to create superFeatures, from which classification tasks can be achieved fairly easily. In order to generate superFeatures robustly, the binary string fitness characterisation along with the comparative partner selection strategy is introduced with the aim of promoting optimal convergence. The techniques introduced are applied to two illustrative problems first and then to the real-world problem of audio source classification, with competitive results.", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/es/es28.html#DayN11", } @Article{Dayik:2007:JTI, author = "M. Dayik and M. C. Kayacan and H. Calis and E. Cakmak", title = "Control of warp tension during weaving procedure using evaluation programming", journal = "The Journal of the Textile Institute", year = "2006", volume = "97", number = "4", pages = "313--324", keywords = "genetic algorithms, genetic programming, Gene Expression Programming, Weaving, warp tension, let-off control, warp break", ISSN = "0040-5000", DOI = "doi:10.1533/joti.2005.0132", abstract = "In this study, gene expression programming (GEP), one of the Evolution Programming methods, is used for the control of the let-off system in a weaving loom. For this control, the function of warp tension occurring in a complete rotation of the main shaft of weaving loom is determined by the method of GEP. The control of let-off system is implemented using this function. Particularly, during the shed opening and beat-up processes to make warp tension constant, warp beam is rotated clockwise and counterclockwise. The values of warp tension obtained by GEP are compared with the values of conventional controlled methods. As a conclusion, the obtained warp tension values are 11.2percent less than values of classical approach. At the same time it is also provided that break rate of warp tension is decreased by 20percent. It has shown that GEP is an effective tool for the decreasing of warp break rate.", notes = "1. Department of Textile Engineering, Suleyman Demirel University, Isparta, Turkey 2. Department of Textile Engineering, Suleyman Demirel University, Isparta, Turkey 3. Department of Electronics and Computer Education, Suleyman Demirel University, Isparta, Turkey 4. Department of Textile Engineering, Suleyman Demirel University, Isparta, Turkey", } @Article{de-Aguiar:2021:OGSTR, author = "Pinho {de Aguiar} and Kelly Lucia Nazareth and Luiz Carlos Magalhaes Palermo and Claudia Regina Elias Mansur", title = "Polymer viscosifier systems with potential application for enhanced oil recovery: a review", journal = "Oil \& Gas Science and Technology Review", year = "2021", volume = "76", number = "65", month = "1 " # oct, keywords = "genetic algorithms, genetic programming", ISSN = "1294-4475", annote = "Federal University of Rio de Janeiro; Institute of Macromolecules", bibsource = "OAI-PMH server at api.archives-ouvertes.fr", identifier = "hal-03362664", language = "en", oai = "oai:HAL:hal-03362664v1", rights = "info:eu-repo/semantics/OpenAccess", URL = "https://ogst.ifpenergiesnouvelles.fr/articles/ogst/pdf/2021/01/ogst210005.pdf", URL = "https://hal.archives-ouvertes.fr/hal-03362664", URL = "https://hal.archives-ouvertes.fr/hal-03362664/document", URL = "https://hal.archives-ouvertes.fr/hal-03362664/file/ogst210005.pdf", DOI = "doi:10.2516/ogst/2021044", size = "21 pages", abstract = "Due to the growing demand for oil and the large number of mature oil fields, Enhanced Oil Recovery (EOR) techniques are increasingly used to increase the oil recovery factor. Among the chemical methods, the use of polymers stands out to increase the viscosity of the injection fluid and harmonize the advance of this fluid in the reservoir to provide greater sweep efficiency. Synthetic polymers based on acrylamide are widely used for EOR, with Partially Hydrolyzed Polyacrylamide (PHPA) being used the most. However, this polymer has low stability under harsh reservoir conditions (High Temperature and Salinity -- HTHS). In order to improve the sweep efficiency of polymeric fluids under these conditions, Hydrophobically Modified Associative Polymers (HMAPs) and Thermo-Viscosifying Polymers (TVPs) are being developed. HMAPs contain small amounts of hydrophobic groups in their water-soluble polymeric chains, and above the Critical Association Concentration (CAC), form hydrophobic microdomains that increase the viscosity of the polymer solution. TVPs contain blocks or thermosensitive grafts that self-assemble and form microdomains, substantially increasing the solution{'}s viscosity. The performance of these systems is strongly influenced by the chemical group inserted in their structures, polymer concentration, salinity and temperature, among other factors. Furthermore, the application of nanoparticles is being investigated to improve the performance of injection polymers applied in EOR. In general, these systems have excellent thermal stability and salinity tolerance along with high viscosity, and therefore increase the oil recovery factor. Thus, these systems can be considered promising agents for enhanced oil recovery applications under harsh conditions, such as high salinity and temperature. Moreover, stands out the use of genetic programming and artificial intelligence to estimate important parameters for reservoir engineering, process improvement, and optimise polymer flooding in enhanced oil recovery.", notes = "Institut Francais du Petrole", } @InProceedings{De-Carlo:2023:EuroGP, author = "Matteo {De Carlo} and Eliseo Ferrante and Jacintha Ellers and Gerben Meynen and A. E. Eiben", title = "Interacting Robots in a Artificial Evolutionary Ecosystem", booktitle = "EuroGP 2023: Proceedings of the 26th European Conference on Genetic Programming", year = "2023", month = "12-14 " # apr, editor = "Gisele Pappa and Mario Giacobini and Zdenek Vasicek", series = "LNCS", volume = "13986", publisher = "Springer Verlag", address = "Brno, Czech Republic", pages = "339--354", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Evolutionary Computing, Evolutionary Robotics, Robot Interaction, Artificial Ecosystem, Interactive Robot Ecosystem: Poster", isbn13 = "978-3-031-29572-0", URL = "https://rdcu.be/c8U3U", DOI = "doi:10.1007/978-3-031-29573-7_22", size = "16 pages", abstract = "In Evolutionary Robotics where both body and brain are malleable, it is common practice to evaluate individuals in isolated environments. With the objective of implementing a more naturally plausible system, we designed a single interactive ecosystem for robots to be evaluated in. In this ecosystem robots are physically present and can interact each other and we implemented decentralized rules for mate selection and reproduction. To study the effects of evaluating robots in an interactive ecosystem has on evolution, we compare the evolutionary process with a more traditional, oracle-based approach. In our analysis, we observe how the different approach has a substantial impact on the final behaviour and morphology of the robots, while maintaining decent fitness performance.", notes = "Part of \cite{Pappa:2023:GP} EuroGP'2023 held in conjunction with EvoCOP2023, EvoMusArt2023 and EvoApplications2023", } @Article{de-Carvalho-Servia:2024:DD, author = "Miguel Angel {de Carvalho Servia} and Ilya Orson Sandoval and King Kuok (Mimi) Hii and Klaus Hellgardt and Dongda Zhang and Ehecatl {Antonio del Rio Chanona}", title = "The automated discovery of kinetic rate models - methodological frameworks", journal = "Digital Discovery", year = "2024", volume = "3", number = "5", pages = "954--968", keywords = "genetic algorithms, genetic programming", ISSN = "2635-098X", URL = "https://www.sciencedirect.com/science/article/pii/S2635098X24000676", DOI = "doi:10.1039/d3dd00212h", abstract = "The industrialization of catalytic processes requires reliable kinetic models for their design, optimisation and control. Mechanistic models require significant domain knowledge, while data-driven and hybrid models lack interpretability. Automated knowledge discovery methods, such as ALAMO (Automated Learning of Algebraic Models for Optimisation), SINDy (Sparse Identification of Nonlinear Dynamics), and genetic programming, have gained popularity but suffer from limitations such as needing model structure assumptions, exhibiting poor scalability, and displaying sensitivity to noise. To overcome these challenges, we propose two methodological frameworks, ADoK-S and ADoK-W (Automated Discovery of Kinetic rate models using a Strong/Weak formulation of symbolic regression), for the automated generation of catalytic kinetic models using a robust criterion for model selection. We leverage genetic programming for model generation and a sequential optimisation routine for model refinement. The frameworks are tested against three case studies of increasing complexity, demonstrating their ability to retrieve the underlying kinetic rate model with limited noisy data from the catalytic systems, showcasing their potential for chemical reaction engineering applications", notes = "Electronic supplementary information ({ESI)} available: (1) A detailed evaluation of various model selection criteria, leading to the adoption of {AIC} for both {ADoK-S} and {ADoK-W;} (2) an analytical discussion leading to the use of the two top-performing models from {ADoK-S} or {ADoK-W} in {MBDoE}, as opposed to using Gaussian process state space models and other naive parametric models; (3) a benchmarking study comparing state-of-the-art derivative approximation methods against our {GP-based} approach; (4) the performance of {ADoK-S} on an additional multi-reaction case study.", } @InProceedings{De-La-Cruz:2022:CEC, author = "Marina {De La Cruz Lopez} and Carlos Cervigon and Jorge Alvarado and Marta Botella-Serrano and J. Ignacio Hidalgo", title = "Evolving Classification Rules for Predicting Hypoglycemia Events", booktitle = "2022 IEEE Congress on Evolutionary Computation (CEC)", year = "2022", editor = "Carlos A. Coello Coello and Sanaz Mostaghim", address = "Padua, Italy", month = "18-23 " # jul, keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Structured Grammatical Evolution, Wearable Health Monitoring Systems, Predictive models, Prediction algorithms, Diabetes, Glucose, Grammar, Proposals, Diabetes, Hypoglycemia prediction, PWD, Rule System", isbn13 = "978-1-6654-6708-7", URL = "https://human-competitive.org/sites/default/files/humies_hidalgo.txt", URL = "https://human-competitive.org/sites/default/files/postable_version_2022_wccic_marina.pdf", DOI = "doi:10.1109/CEC55065.2022.9870380", size = "8 pages", abstract = "People with diabetes have to properly manage their blood glucose levels in order to avoid acute complications. This is a difficult task and an accurate and timely prediction may be of vital importance, specially of extreme values. Perhaps one of the main concerns of people with diabetes is to suffer an hypoglycemia (low value) event and moreover, that the event will be prolonged in time. It is crucial to predict events of hyperglycemia (high value) and hypoglycemia that may cause health damages in the short term and potential permanent damages in the long term. The aim of this paper is to describe our research on predicting hypoglycemia events using Dynamic structured Grammatical Evolution. Our proposal gives white box models induced by a grammar based on if-then-else conditions. We trained and tested our system with real data collected from 5 different diabetic patients, producing 30 minutes predictions with encouraging results.", notes = "1st author given as Marina De La Cruz, Universidad Complutense de Madrid, Madrid, Spain marcru06@ucm.es Finalist 2023 HUMIES Also known as \cite{9870380} Predict human blood glucose concentration using number of steps smartwatch => energy expended by person and wearable mobile Continuous Glucose Monitoring System", } @InProceedings{de-la-cruz-lopez:2023:GECCOcomp, author = "Marina {de la Cruz Lopez} and Oscar Garnica and J. Ignacio Hidalgo", title = "{Tree-Based} Grammatical Evolution with {Non-Encoding} Nodes", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Justyna Petke and Aniko Ekart", pages = "63--64", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, grammatical evolution, grammars, grammar-based genetic programming", isbn13 = "9798400701191", DOI = "doi:10.1145/3583133.3596944", size = "2 pages", abstract = "Grammar-guided genetic programming is a type of genetic programming that uses a grammar to restrict the solutions in the exploration of the search space. Different representations of grammar-guided genetic programming exist, each with specific properties that affect how the evolutionary process is developed. We propose a new representation that uses a tree structure with non-encoding nodes for the individuals in the population, a.k.a. Tree-Based Grammatical Evolution with Non-Encoding Nodes. Each tree's node has a set of children nodes and an associated number that determines which are used in decoding the solution and which are non-encoding nodes. This representation increases the size and complexity of the individuals while performing a more exhaustive exploration of the solution space. We compare the performance of our proposal with state-of-the-art genetic programming algorithms for the 11-multiplexer benchmark, showing encouraging results.", notes = "See also \cite{Garnica:2024:GPTP} GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @InProceedings{de-la-torre:2024:GECCOcomp, author = "Camilo {De La Torre} and Kevin Cortacero and Sylvain Cussat-Blanc and Dennis Wilson", title = "Multimodal Adaptive Graph Evolution", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion", year = "2024", editor = "Ting Hu and Aniko Ekart", pages = "499--502", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, evolutionary computation, symbolic regression: Poster", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3654347", size = "4 pages", abstract = "The problem of program synthesis involves automatically finding a function based on evaluation criteria, like matching input-output pairs. While Cartesian Genetic Programming (CGP) has excelled in various function synthesis tasks, it has primarily been limited to single data types, hindering its applicability to diverse data. Mixed-Type CGP, proposed in 2012, aimed to address this limitation but faced challenges due to search space limitations and complexity in building function libraries. In this study, we introduce Multimodal Adaptive Graph Evolution (MAGE), a generalized CGP extension that integrates functions of different data types by grouping them accordingly and imposing mutation constraints based on type. Through comparisons with standard CGP and Mixed-Type CGP on Program Synthesis Benchmark and image classification tasks, we demonstrate that MAGE's representation and mutation constraints facilitate the search for multimodal functions.", notes = "GECCO-2024 GP A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{de-la-torre:2024:GECCOcomp2, author = "Camilo {De La Torre} and Sylvain Cussat-Blanc and Dennis Wilson and Yuri Lavinas", title = "On Search Trajectory Networks for Graph Genetic Programming", booktitle = "Graph-based Genetic Programming", year = "2024", editor = "Dennis G. Wilson and Giorgia Nadizar and Giovanni Squillero and Alberto Tonda", pages = "1681--1685", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, evolutionary computation, symbolic regression", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3664169", size = "5 pages", abstract = "Cartesian Genetic Programming (CGP) allows for the optimization of interpretable function representations. However, comprehending the vast and combinatorially complex search space inherent to CGP remains challenging, particularly because multiple genotypes may correspond to identical functions. This paper studies the application of Search Trajectory Networks (STNs) to understand the search dynamics of CGP, specifically for symbolic regression tasks. Using STNs, we analyze the behavior of evolutionary search processes and uncover distinct phenomena, such as the presence of {"}portal{"} minima---critical junctures that facilitate sudden, beneficial shifts in the search trajectory, akin to findings in linear genetic programming. Our findings illustrate that while genetic interpretations are complex and often ambiguous, a functional analysis using STNs offers clear and actionable insights into the CGP search.", notes = "GECCO-2024 GGP A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{De-La-Torre:2024:PPSN, author = "Camilo {De La Torre} and Yuri Lavinas and Kevin Cortacero and Dennis Wilson and Sylvain Cussat-Blanc", title = "Multi-Modal Adaptive Graph Evolution for Program Synthesis", booktitle = "18th International Conference on Parallel Problem Solving from Nature", year = "2024", editor = "Heike Trautmann and Tea Tusar and Penousal Machado and Thomas Baeck", volume = "15148", series = "Lecture Notes in Computer Science", pages = "306--321", address = "University of Applied Sciences Upper Austria, Hagenberg, Austria", month = "13-18 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Multimodal Adaptive Graph Evolution, MAGE, Mixed-Type Cartesian Genetic Programming, Program synthesis, Evolutionary computation, Search Trajectory Networks", isbn13 = "978-3-031-70054-5", URL = "https://rdcu.be/d5mEo", DOI = "doi:10.1007/978-3-031-70055-2_19", code_url = "https://github.com/camilodlt/PPSN_MAGE_PSB2_PBS", size = "16 pages", abstract = "Multimodal Adaptive Graph Evolution, a multi-chromosome generalization of Cartesian Genetic Programming that groups functions by return type and constrains graph mutation based on node's type coherence...Program Synthesis Benchmark Suite...explores the solution space more effectively by creating candidate solutions with lower semantic redundancy", notes = "https://ppsn2024.fh-ooe.at/program/", } @InProceedings{de-la-torre:2025:EuroGP, author = "Camilo {De La Torre} and Giorgia Nadizar and Yuri Lavinas and Robin Schwob and Camille Franchet and Herve Luga and Dennis Wilson and Sylvain Cussat-Blanc", title = "Evolved and Transparent Pipelines for Biomedical Image Classification", booktitle = "European Conference on Genetic Programming, EuroGP 2025", year = "2025", editor = "Bing Xue and Luca Manzoni and Illya Bakurov", volume = "15609", series = "LNCS", pages = "173--189", address = "Trieste", month = "23-25 " # apr, publisher = "Springer Nature", keywords = "genetic algorithms, genetic programming, Image Classification, Interpretable Machine Learning", isbn13 = "978-3-031-89990-4", URL = "https://rdcu.be/ejgFt", DOI = "doi:10.1007/978-3-031-89991-1_11", abstract = "This article presents an interpretable approach to binary image classification using Genetic Programming (GP), applied to the PatchCamelyon (PCAM) dataset, which contains small tissue biopsy patches labeled as malignant or benign. While Deep Neural Networks (DNNs) achieve high performance in image classification, their opaque decision making processes, prone to overfitting behaviour and dependency on large amounts of annotated data limit their utility in critical fields like digital pathology, where interpretability is essential. To address this, we employ GP, specifically using the Multi-Modal Adaptive Graph Evolution (MAGE) framework, to evolve end-to-end image classification pipelines. We trained MAGE a hundred times with the best optimised key hyper-parameters for this task. Among all MAGE models trained, the best one achieved 78percent accuracy on the validation set and 76 percent accuracy on the test set. Among Convolutional Neural Networks (CNNs), our baseline, the best model obtained 84.5 percent accuracy on the validation set and 77.1 percent accuracy on the test set. Unlike CNNs, our GP approach enables program-level transparency, facilitating interpretability through example-based reasoning. By analysing evolved programs with medical experts, we highlight the transparency of decision-making in MAGE pipelines, offering an interpretable alternative for medical image classification tasks where model interpretability is paramount.", notes = "also known as \cite{paper_116_biomedical_image_classification} Part of \cite{Xue:2025:GP} EuroGP'2025 held in conjunction with EvoCOP2025, EvoMusArt2025 and EvoApplications2025", } @InProceedings{de-lima:2023:GECCOcomp, author = "Allan {De Lima} and Samuel Carvalho and Douglas Dias and Joseph Sullivan and Conor Ryan", title = "Leap Mapping: Improving Grammatical Evolution for Modularity Problems", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "555--558", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, grammatical evolution, mapping, introns: Poster", isbn13 = "9798400701191", DOI = "doi:10.1145/3583133.3590680", size = "4 pages", abstract = "We introduce Leap mapping, a new mapping process for Grammatical Evolution (GE), which spreads introns within the effective length of the genome (the part of the genome consumed while mapping), preserving information for future generations and performing less disruptive crossover and mutation operations than standard GE. Using the exact same genotypic representation as GE, Leap mapping reads the genome in separate parts named 'frames', where the size of each is the number of production rules in the grammar. Each codon inside a frame is responsible for mapping a different production rule of the grammar. The process keeps consuming codons from the frame until it needs to map again a production rule already mapped with that frame. At this point, the mapping starts consuming codons from the next frame. We assessed the performance of this new mapping in some benchmark problems, which require modular solutions: four Boolean problems and three versions of the Lawnmower problem. Moreover, we compared the results with the standard mapping procedure and a multi-genome version.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @InProceedings{de-sa:2024:GECCOcomp, author = "Alex G. C. {de Sa} and David B. Ascher", title = "Towards Evolutionary-based Automated Machine Learning for Small Molecule Pharmacokinetic Prediction", booktitle = "14th Workshop on Evolutionary Computation for the Automated Design of Algorithms", year = "2024", editor = "Daniel Tauritz Auburn and John R. Woodward", pages = "1544--1553", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, AutoML, bio(chem)informatics, grammar-based genetic programming, small molecules, pharmacokinetics", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3664166", size = "10 pages", abstract = "Machine learning (ML) is revolutionising drug discovery by expediting the prediction of small molecule properties essential for developing new drugs. These properties - including absorption, distribution, metabolism and excretion (ADME) - are crucial in the early stages of drug development since they provide an understanding of the course of the drug in the organism, i.e., the drug's pharmacokinetics. However, existing methods lack personalisation and rely on manually crafted ML algorithms or pipelines, which can introduce inefficiencies and biases into the process. To address these challenges, we propose a novel evolutionary-based automated ML method (AutoML) specifically designed for predicting small molecule properties, with a particular focus on pharmacokinetics. Leveraging the advantages of grammar-based genetic programming, our AutoML method streamlines the process by automatically selecting algorithms and designing predictive pipelines tailored to the particular characteristics of input molecular data. Results demonstrate AutoML's effectiveness in selecting diverse ML algorithms, resulting in comparable or even improved predictive performances compared to conventional approaches. By offering personalised ML-driven pipelines, our method promises to enhance small molecule research in drug discovery, providing researchers with a valuable tool for accelerating the development of novel therapeutic drugs.", notes = "GECCO-2024 ECADA A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{De-Souza-Abreu:2022:SSCI, author = "Joao Victor T. {De Souza Abreu} and Denis Mayr Lima Martins and Fernando Buarque {De Lima Neto}", booktitle = "2022 IEEE Symposium Series on Computational Intelligence (SSCI)", title = "Evolving Interpretable Classification Models via Readability-Enhanced Genetic Programming", year = "2022", pages = "1691--1697", abstract = "As the impact of Machine Learning (ML) on business and society grows, there is a need for making opaque ML models transparent and interpretable, especially in the light of fairness, bias, and discrimination. Nevertheless, interpreting complex opaque models is not trivial. Current interpretability approaches rely on local explanations or produce long explanations that tend to overload the user's cognitive abilities. In this paper, we address this problem by extracting interpretable, transparent models from opaque ones via a new readability-enhanced multi-objective Genetic Programming approach called REMO-GP. To achieve that, we adapt text readability metrics into model complexity proxies that support evaluating ML interpretability. We demonstrate that our approach can generate global interpretable models that mimic the decisions of complex opaque models over several datasets, while keeping model complexity low.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SSCI51031.2022.10022164", month = dec, notes = "Also known as \cite{10022164}", } @InProceedings{deakin:1996:GPtaw1, author = "Anthony G. Deakin and Derek F. Yates", title = "GP Tools Available on the Web: A First Encounter", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "420", address = "Stanford University, CA, USA", publisher_address = "Cambridge, MA, USA", publisher = "MIT Press", URL = "http://www.liv.ac.uk/~anthonyd/gp9632.ps", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap59.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", size = "1 page", notes = "GP-96 10 page version at http://www.csc.liv.ac.uk/~anthony/gp961.ps (broken 2006)", } @InProceedings{Deakin:1997:esGP, author = "Anthony G. Deakin and Derek F. Yates", title = "Economical Solutions with Genetic Programming: the Non-Hamstrung Squadcar Problem, FvM and EHP", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming", pages = "71--76", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1997/Deakin_1997_esGP.pdf", size = "6 pages", notes = "GP-97", } @InProceedings{deakin:1997:PTN, author = "Anthony G. Deakin and Derek F. Yates", title = "Phase Transition Networks: A Modelling technique supporting the Evolution of Autonomous Agents' Tactical and Operational Activities", booktitle = "Evolutionary Computing", year = "1997", editor = "David Corne and Jonathan L. Shapiro", volume = "1305", series = "Lecture Notes in Computer Science", pages = "263--273", address = "Manchester, UK", month = "11-13 " # apr, organisation = "AISB", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, agents, MPHaSys", ISBN = "3-540-63476-2", DOI = "doi:10.1007/BFb0027180", notes = "Proceedings of the Workshop on Artificial Intelligence and Simulation of Behaviour (AISB) International Workshop on Evolutionary Computing. Workshop in Manchester, UK, April 7-8, 1997 Phase Transfer Networks PTN, egs traffic lights, blood glucose regulation,", } @InProceedings{deakin:1998:eoaasGP, author = "Anthony G. Deakin and Derek F. Yates", title = "Evolving and Optimizing Autonomous Agents' Strategies with Genetic Programming", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "42--47", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-548-7", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1998/deakin_1998_eoaasGP.pdf", notes = "GP-98", } @MastersThesis{deAlmeida:masterthesis, author = "Alexy Damien Tome Julien Denis {de Almeida}", title = "Automatic Design of Autoencoders using NeuroEvolution", title_pt = "Desenho Automatico de AutoEncoders usando NeuroEvolucao", school = "Department of Informatics Engineering, Faculty of Sciences and Technology, Universidade de Coimbra", year = "2024", address = "Portugal", month = jul, keywords = "genetic algorithms, genetic programming, Grammatical Evolution., DSGE, F-DENSER, AutoEncoder, Automatic Design, Deep Neural Network, ANN, Machine Learning, Evolutionary Computation, NeuroEvolution, Denoising, Image Reconstruction, Anomaly Detection", URL = "https://hdl.handle.net/10316/116355", size = "114 pages", notes = "Supervisor: Nuno Antonio Marques Lourenco", } @InProceedings{conf/icnc/AlmeidaSNY05, title = "Application of Genetic Programming for Fine Tuning {PID} Controller Parameters Designed Through {Ziegler-Nichols} Technique", author = "Gustavo Maia {de Almeida} and Valceres Vieira Rocha {e Silva} and Erivelton Geraldo Nepomuceno and Ryuichi Yokoyama", year = "2005", pages = "313--322", editor = "Lipo Wang and Ke Chen and Yew-Soon Ong", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3612", booktitle = "Advances in Natural Computation, First International Conference, ICNC 2005, Proceedings, Part III", address = "Changsha, China", month = aug # " 27-29", bibdate = "2005-08-01", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icnc/icnc2005-3.html#AlmeidaSNY05", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-28320-X", DOI = "doi:10.1007/11539902_37", size = "10 pages", abstract = "PID optimal parameters selection have been extensively studied, in order to improve some strict performance requirements for complex systems. Ziegler-Nichols methods give estimated values for these parameters based on the system's transient response. Therefore, a fine tuning of these parameters is required to improve the system's behaviour. In this work, genetic programming is used to optimise the three parameters Kp , Ti and Td , after been tuned by Ziegler-Nichols method, to control a high-order process, a large time delay plant and a highly non-minimum phase process. The results were compared to some other tuning methods, and showed to be promising.", } @InProceedings{conf/sigir/AlmeidaGCC07, author = "Humberto Mossri {de Almeida} and Marcos Andre Goncalves and Marco Cristo and Pavel Calado", title = "A combined component approach for finding collection-adapted ranking functions based on genetic programming", booktitle = "Proceedings of the 30th Annual International ACM Conference on Research and Development in Information Retrieval, SIGIR 2007", year = "2007", editor = "Wessel Kraaij and Arjen P. {de Vries} and Charles L. A. Clarke and Norbert Fuhr and Noriko Kando", pages = "399--406", address = "Amsterdam, The Netherlands", month = jul # " 23-27", publisher = "ACM", keywords = "genetic algorithms, genetic programming, Information Retrieval, Ranking Functions, Term-weighting, Machine Learning", isbn13 = "978-1-59593-597-7", DOI = "doi:10.1145/1277741.1277810", size = "8 pages", abstract = "In this paper, we propose a new method to discover collection-adapted ranking functions based on Genetic Programming (GP). Our Combined Component Approach (CCA)is based on the combination of several term-weighting components (i.e.,term frequency, collection frequency, normalization) extracted from well-known ranking functions. In contrast to related work, the GP terminals in our CCA are not based on simple statistical information of a document collection, but on meaningful, effective, and proven components. Experimental results show that our approach was able to out perform standard TF-IDF, BM25 and another GP-based approach in two different collections. CCA obtained improvements in mean average precision up to 40.87percent for the TREC-8 collection, and 24.85percent for the WBR99 collection (a large Brazilian Web collection), over the baseline functions. The CCA evolution process also was able to reduce the over training, commonly found in machine learning methods, especially genetic programming, and to converge faster than the other GP-based approach used for comparison.", bibdate = "2007-08-24", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/sigir/sigir2007.html#AlmeidaGCC07", } @Article{deAlmeidaFarzat:2018:JSERD, author = "Fabio {de Almeida Farzat} and Marcio {de Oliveira Barros} and Guilherme {Horta Travassos}", title = "Challenges on applying genetic improvement in {JavaScript} using a high-performance computer", journal = "Journal of Software Engineering Research and Development", year = "2018", volume = "6", number = "12", month = dec, note = "20th Iberoamerican Conference on Software Engineering", keywords = "genetic algorithms, genetic programming, Grammatical evolution, Genetic Improvement, Source code Optimization, Search Based Software Engineering, SBSE", ISSN = "2195-1721", DOI = "doi:10.1186/s40411-018-0056-2", size = "19 pages", abstract = "Genetic Improvement is an area of Search Based Software Engineering that aims to apply evolutionary computing operators to the software source code to improve it according to one or more quality metrics. This article describes challenges related to experimental studies using Genetic Improvement in JavaScript (an interpreted and non-typed language). It describes our experience on performing a study with fifteen projects submitted to genetic improvement with the use of a supercomputer. The construction of specific software infrastructure to support such an experimentation environment reveals peculiarities (parallelization problems, management of threads, etc.) that must be carefully considered to avoid future research threats to validity such as dead-ends, which make it impossible to observe relevant phenomena (code transformation) to the understanding of software improvements and evolution.", notes = "Lists problems with ECJ ANTLR C-sharp Jurassic project tree GP NodeJs Typescript LOBOC supercomputer of COPPE/UFR Table 1 Thirteen libraries observed. EXECTIMER PUG LODASH MINIMIST MOMENT UNDERSCORE UUID XML2JS PLIVO-NODE GULP-CCCR EXPRESS-IFTTT TLEAF BROWSERIFY p15 in all cases 'the Optimizer was able to find variations of the original code that run in less time'", } @PhdThesis{de_Almeida_Farzat:thesis, author = "Fabio {de Almeida Farzat}", title = "Otimizacao para Reduzir o Tamanho de Codigo-Fonte Javascript", title2 = "Evolving JavaScript Code to Reduce Code Size", school = "Engenharia de Sistemas e Computacao, COPPE, da Universidade Federal do Rio de Janeiro", year = "2018", address = "Rio de Janeiro, Brazil", month = "10 " # dec, keywords = "genetic algorithms, genetic programming, Genetic Improvement, SBSE, Software Engineering", URL = "https://www.cos.ufrj.br/index.php/en/publicacoes-pesquisa/details/20/2889", URL = "https://www.cos.ufrj.br/uploadfile/publicacao/2889.pdf", size = "90 pages", abstract = "JavaScript is one of the most used programming languages for front-end development of Web application. The increase in complexity of front-end features brings concerns about performance, especially the load and execution time of JavaScript code. To reduce the size of JavaScript programs and, therefore, the time required to load and execute these programs in the front-end of Web applications. To characterise the variants of JavaScript programs and use this information to build a search procedure that scans such variants for smaller implementations that pass all test cases. We applied this procedure to 19 JavaScript programs varying from 92 to 15602 LOC and observed reductions from 0.2percent to 73.8percent of the original code, as well as a relationship between the quality of a program test suite and the ability to reduce its size.", resumo = "Esta Tese aborda o problema de otimizacao de tempo de carga de software, especificamente software escrito na linguagem de programacao JavaScript, uma linguagem interpretada, baseada em objetos e amplamente utilizada no desenvolvimento de aplicativos e sistemas para a internet. Estudos experimentais foram projetados para avaliar a hipotese de que tecnicas heuristicas ja aplicadas com sucesso em linguagens orientadas a objeto poderiam ter resultados positivos na reducao do tempo de carga de programas escritos em JavaScript. Para tanto, um ferramental que permitisse observar a aplicacao de heuristicas selecionadas em programas JavaScript foi construido e executado em um ambiente de computacao de alto desempenho. Os resultados dos estudos preliminares foram utilizados para criar um procedimento de busca que varre o codigo JavaScript criando variantes do programa que sejam menores e passem em todos os casos de teste do programa original. Aplicamos este procedimento a 19 programas JavaScript, variando de 92 a 15602 linhas de codigo, e observamos reducoes de 0.2percent a 73.8percent do codigo original, bem como uma relacao entre a qualidade do conjunto de casos de testes e a capacidade de reduzir o tamanho dos programas.", notes = "In Portuguese Suversiors: Guilherme Horta Travassos and Marcio de Oliveira Barros", } @Article{deAlmeidaFarzat:ieeeTSE, author = "Fabio {de A. Farzat} and Marcio {de O. Barros} and Guilherme H. Travassos", title = "Evolving {JavaScript} code to reduce load time", journal = "IEEE Transactions on Software Engineering", year = "2021", volume = "47", number = "8", pages = "1544--1558", month = aug, keywords = "genetic algorithms, genetic programming, genetic improvement, Software, JavaScript, source code improvement, local search", ISSN = "2326-3881", DOI = "doi:10.1109/TSE.2019.2928293", size = "14 pages", abstract = "JavaScript is one of the most used programming languages for front-end development of Web applications. The increase in complexity of front-end features brings concerns about performance, especially the load and execution time of JavaScript code. In this paper, we propose an evolutionary program improvement technique to reduce the size of JavaScript programs and, therefore, the time required to load and execute them in Web applications. To guide the development of such technique, we performed an experimental study to characterize the patches applied to JavaScript programs to reduce their size while keeping the functionality required to pass all test cases in their test suites. We applied this technique to 19 JavaScript programs varying from 92 to 15602 lines of code and observed reductions from 0.2percent to 73.8percent of the original code, as well as a relationship between the quality of a programs test suite and the ability to reduce the size of its source code.", notes = "Also known as \cite{8762190}", } @Article{deAlmeidaFarzat:GPEM, author = "Andre {de Almeida Farzat} and Marcio {de Oliveira Barros}", title = "Automatic generation of regular expressions for the Regex Golf challenge using a local search algorithm", journal = "Genetic Programming and Evolvable Machines", year = "2022", volume = "23", number = "1", pages = "105--131", month = mar, keywords = "genetic algorithms, genetic programming, Regular expressions, Regex Golf, Local search, Heuristic search", ISSN = "1389-2576", URL = "https://rdcu.be/cyKF8", DOI = "doi:10.1007/s10710-021-09411-x", size = "27 pages", abstract = "Regular expression is a technology widely used in software development for extracting textual data, validating the structure of textual documents, or formatting data. Regex Golf is a challenge that consists in finding the smallest possible regular expression given a set of sentences to perform matches and another set not to match. An algorithm capable of meeting the Regex Golf requirements is a relevant contribution to the area of semi-structured document data extraction. we propose a heuristic search algorithm based on local search, combined with a regular expression shrinker, to find valid results for Regex Golf problems. An experimental study was conducted to compare the proposed technique with an exact algorithm and a genetic programming algorithm designed for the Regex Golf challenge. The proposed local search was shown to outperform both competing algorithms in six out of fifteen problem instances, tying in another three instances. On the other hand, all algorithms still lack the ability to outperform human software developers in designing regular expressions for the challenge.", notes = "Federal University of the State of Rio de Janeiro, Av. Pasteur, 458 Urca, Rio de Janeiro, RJ, 22290-240, Brazil", } @Article{deAraujo:2018:EAAI, author = "Waldir Jesus {de Araujo Lobao} and Marco Aurelio Cavalcanti Pacheco and Douglas {Mota Dias} and Ana Carolina Alves Abreu", title = "Solving stochastic differential equations through genetic programming and automatic differentiation", journal = "Engineering Applications of Artificial Intelligence", year = "2018", volume = "68", pages = "110--120", month = feb, keywords = "genetic algorithms, genetic programming, Evolutionary algorithm, Automatic differentiation, Stochastic differential equations, Stochastic calculus, Geometric Brownian motion", ISSN = "0952-1976", URL = "https://www.sciencedirect.com/science/article/pii/S0952197617302749", DOI = "doi:10.1016/j.engappai.2017.10.021", abstract = "This paper investigates the potential of evolutionary algorithms, developed using a combination of genetic programming and automatic differentiation, to obtain symbolic solutions to stochastic differential equations. Using the MATLAB programming environment and based on the theory of stochastic calculus, we develop algorithms and conceive a new methodology of resolution. Relative to other methods, this method has the advantages of producing solutions in symbolic form and in continuous time and, in the case in which an equation of interest is completely unknown, of offering the option of algorithms that perform the specification and estimation of the solution to the equation via a real database. The last advantage is important because it determines an appropriate solution to the problem and simultaneously eliminates the difficult task of arbitrarily defining the functional form of the stochastic differential equation that represents the dynamics of the phenomenon under analysis. The equation for geometric Brownian motion, which is usually applied to model prices and returns from financial assets, was employed to illustrate and test the quality of the algorithms that were developed. The results are promising and indicate that the proposed methodology can be a very effective alternative for resolving stochastic differential equations.", } @InProceedings{de-Araujo-Pessoa:2019:CEC, author = "L. F. {de Araujo Pessoa} and B. Hellingrath and F. B. {de Lima Neto}", booktitle = "2019 IEEE Congress on Evolutionary Computation (CEC)", title = "Automatic Generation of Optimization Algorithms for Production Lot-Sizing Problems", year = "2019", pages = "1774--1781", month = jun, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2019.8789892", abstract = "Successful applications of heuristic-based methods are able to find high-quality solutions for complex problems in a feasible time frame. However, they are usually tailored towards the problem instances under consideration and any changes in the underlying problem structure might require a redesign of the algorithm, which is expensive and very time-consuming. This paper presents results of an automatic algorithm-generation approach used to find good-performing optimization methods for the multi-level capacitated lot-sizing problem, a relevant and hard combinatorial problem in production planning. A new template for generating algorithms is proposed for enabling the generation of different hybridisations between genetic algorithm-components and mathematical heuristics. Several experiments are carried out to evaluate the ability of the proposed method to generate competitive algorithms for benchmark instances, under consideration of different functions set and cutoff times. Results indicate that the method is able to generate heuristic algorithms that find high-quality solutions significantly faster than the compared human-designed algorithm.", notes = "Also known as \cite{8789892}", } @InProceedings{deAraujoPessoa:2020:GECCOcomp, author = "Luis Filipe {de Araujo Pessoa} and Bernd Hellingrath and Fernando B. {de Lima Neto}", title = "On the Sensitivity Analysis of Cartesian Genetic Programming Hyper-Heuristic", year = "2020", editor = "Richard Allmendinger and Hugo Terashima Marin and Efren Mezura Montes and Thomas Bartz-Beielstein and Bogdan Filipic and Ke Tang and David Howard and Emma Hart and Gusz Eiben and Tome Eftimov and William {La Cava} and Boris Naujoks and Pietro Oliveto and Vanessa Volz and Thomas Weise and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Rui Wang and Ran Cheng and Guohua Wu and Miqing Li and Hisao Ishibuchi and Jonathan Fieldsend and Ozgur Akman and Khulood Alyahya and Juergen Branke and John R. Woodward and Daniel R. Tauritz and Marco Baioletti and Josu Ceberio Uribe and John McCall and Alfredo Milani and Stefan Wagner and Michael Affenzeller and Bradley Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and Nayat Sanchez-Pi and Luis Marti and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and Matthew Johns and Nick Ross and Ed Keedwell and Herman Mahmoud and David Walker and Anthony Stein and Masaya Nakata and David Paetzel and Neil Vaughan and Stephen Smith and Stefano Cagnoni and Robert M. Patton and Ivanoe {De Falco} and Antonio {Della Cioppa} and Umberto Scafuri and Ernesto Tarantino and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Erik Hemberg and Riyad Alshammari and Tokunbo Makanju and Fuijimino-shi and Ivan Zelinka and Swagatam Das and Ponnuthurai Nagaratnam and Roman Senkerik", isbn13 = "9781450371278", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377929.3398142", DOI = "doi:10.1145/3377929.3398142", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion", pages = "1880--1888", size = "9 pages", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, grammatical evolution, hyper-heuristics (HH), design of algorithms, production planning, lot-sizing problem (LSP), sensitivity analysis", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", abstract = "The research on Genetic-Programming Hyper-heuristics (GPHH) for automated design of heuristic-based methods has been very active over the last years. Most efforts have focused on the development or improvements of GPHH methods or their applications to different problem domains. Studies that target on the analysis and understanding of the GPHH behavior are still scarce, despite their relevance for easing the application of GPHH in practice and for advancing this research field. In order to advance the body of knowledge on the understanding of GPHH behavior, this paper aims at analyzing the impact of its parameters on the evolution, diversity, and quality of generated algorithms. In particular, a Cartesian Genetic-Programming hyper-heuristic (CGPHH) applied to an NP-Complete problem of production planning (multi-level capacitated lot-sizing problem) is considered. The effects of five parameters on response variables that reflect various aspects of the CGPHH behavior, such as diversity and quality of generated algorithms, are analyzed based on a full-factorial design of experiments. Results indicate that mainly three factors affect the CGPHH behavior in different ways: mutation rate, the CGP representation, and the number of graph nodes. Nonetheless, the CGPHH still generates competitive algorithms, despite the changes applied to its parameters.", notes = "Also known as \cite{10.1145/3377929.3398142} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @InProceedings{conf/seal/PereiraJV10, title = "A Niched Genetic Programming Algorithm for Classification Rules Discovery in Geographic Databases", author = "Marconi {de Arruda Pereira} and Clodoveu Augusto {Davis Junior} and Joao Antonio {de Vasconcelos}", booktitle = "Simulated Evolution and Learning - 8th International Conference, {SEAL} 2010, Kanpur, India, December 1-4, 2010. Proceedings", publisher = "Springer", year = "2010", volume = "6457", editor = "Kalyanmoy Deb and Arnab Bhattacharya and Nirupam Chakraborti and Partha Chakroborty and Swagatam Das and Joydeep Dutta and Santosh K. Gupta and Ashu Jain and Varun Aggarwal and J{\"u}rgen Branke and Sushil J. Louis and Kay Chen Tan", isbn13 = "978-3-642-17297-7", pages = "260--269", series = "Lecture Notes in Computer Science", URL = "http://dx.doi.org/10.1007/978-3-642-17298-4", DOI = "doi:10.1007/978-3-642-17298-4_27", keywords = "genetic algorithms, genetic programming", abstract = "This paper presents a niched genetic programming tool, called DMGeo, which uses elitism and another techniques designed to efficiently perform classification rule mining in geographic databases. The main contribution of this algorithm is to present a way to work with geographical and conventional data in data mining tasks. In our approach, each individual in the genetic programming represents a classification rule using a Boolean predicate. The adequacy of the individual to the problem is assessed using a fitness function, which determines its chances for selection. In each individual, the predicate combines conventional attributes (Boolean, numeric) and geographic characteristics, evaluated using geometric and topological functions. Our prototype implementation of the tool was compared favourably to other classical classification ones. We show that the proposed niched genetic programming algorithm works efficiently with databases that contain geographic objects, opening up new possibilities for the use of genetic programming in geographic data mining problems.", bibdate = "2010-12-01", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/seal/seal2010.html#PereiraJV10", } @Article{deArrudaPereira:2014:Neurocomputing, author = "Marconi {de Arruda Pereira} and Clodoveu Augusto {Davis Junior} and Eduardo Gontijo Carrano and Joao Antonio {de Vasconcelos}", title = "A niching genetic programming-based multi-objective algorithm for hybrid data classification", journal = "Neurocomputing", volume = "133", pages = "342--357", year = "2014", ISSN = "0925-2312", DOI = "doi:10.1016/j.neucom.2013.12.048", URL = "http://www.sciencedirect.com/science/article/pii/S0925231214001404", keywords = "genetic algorithms, genetic programming, Classification rules, Spatial data mining, Multi-objective algorithm", } @Article{dearrudapereira:2019:SC, author = "Marconi {de Arruda Pereira} and Eduardo Gontijo Carrano and Clodoveu Augusto {Davis Junior} and Joao Antonio {de Vasconcelos}", title = "A comparative study of optimization models in genetic programming-based rule extraction problems", journal = "Soft Computing", year = "2019", volume = "23", number = "4", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s00500-017-2836-8", DOI = "doi:10.1007/s00500-017-2836-8", } @InProceedings{deAssis:2013:WebMedia, author = "Carlos A. S. {de Assis} and Adriano C. M. Pereira and Marconi {de A. Pereira} and Eduardo G. Carrano", title = "Using Genetic Programming to Detect Fraud in Electronic Transactions", booktitle = "Proceedings of the 19th Brazilian Symposium on Multimedia and the Web (WebMedia '13)", year = "2013", pages = "337--340", address = "Salvador, Brazil", publisher = "ACM", keywords = "genetic algorithms, genetic programming, fraud, web transactions", isbn13 = "978-1-4503-2559-2", URL = "http://doi.acm.org/10.1145/2526188.2526221", DOI = "doi:10.1145/2526188.2526221", acmid = "2526221", size = "4 pages", abstract = "The volume of online transactions has raised a lot in last years, mainly due to the popularity of E-commerce, such as Web retailers. We also observe a significant increase in the number of fraud cases, resulting in billions of dollars losses each year worldwide. Therefore it is important and necessary to developed and apply techniques that can assist in fraud detection, which motivates our research. This work proposes the use of Genetic Programming (GP), an Evolutionary Computation approach, to model and detect fraud (charge back) in electronic transactions, more specifically in credit card operations. In order to evaluate the technique, we perform a case study using an actual dataset of the most popular Brazilian electronic payment service, called UOL PagSeguro. Our results show good performance in fraud detection, presenting gains up to 17.72percent percent compared to the baseline, which is the actual scenario of the corporation.", notes = "In Portuguese Also known as \cite{Assis:2013:UGP:2526188.2526221}", } @PhdThesis{deAssis:thesis, author = "Carlos Alberto Silva {de Assis}", title = "Predicao de Tendencias em Series Financeiras utilizando Meta-Classificadores", school = "Federal University of Minas Gerais (UFMG)", year = "2019", address = "Belo Horizonte, Brazil", month = "24 " # apr, keywords = "genetic algorithms, genetic programming, Computational Intelligence. Meta-Classifier. Financial Series, Programacao Genetica, Inteligencia Computacional, AI, Meta-Classificador, Series Financeiras", URL = "https://www.sig.cefetmg.br/sigaa/public/programa/noticias_desc.jsf?lc=en_US&id=308¬icia=17099533", URL = "https://sig.cefetmg.br/sigaa/verArquivo?idArquivo=2333930&key=b59204be3343a9d10bb3904b0ab1996d", size = "106 pages", abstract = "Predicting the behavior of financial assets is a task that has been researched by various techniques over the last years. Despite there exists an extensive research in this area, the task to predict asset prices or trends remains an extremely difficult task because due to the uncertainties of the financial markets and other factors. This work proposes and implement a meta-classifier based on computational intelligence techniques to find price trends for the stock market assets, as the B3. Meta-classifier kernel is based on the WEKA tool, where seven classifiers are combined to be optimized in the next step by meta-classification. Tests were performed with some of the most liquidity assets of different sectors and the assets that accompany the Bovespa index of B3, are: BOVA11, CIEL3, ITUB4, PETR4, USIM5, CMIG4, GGBR4, KROT3 and GOLL4. The results were satisfactory, showing a good accuracy in the classification with up to 57 percent, in addition to financial results with gains of up to 100 percent of the capital value initially invested. We also had good results when compared to the buy-and-hold,random and inverse strategy.", resumo = "A previsao do comportamento de ativos financeiros e uma linha de pesquisa que vem sendo investigada por diversas tecnicas ao longo dos ultimos anos. Mesmo com inumeras pesquisas, prever precos de ativos ou tendencias continua sendo uma tarefa extremamente dificil, uma vez que tal comportamento esta ligado as incertezas do mercado financeiro e outros fatores. Desta forma, neste trabalho foi desenvolvido um meta-classificador baseado em metodos de inteligencia computacional para descobrir tendencias de preco para ativos de bolsa de valores, como a B3. O kernel do meta-classificador e baseado na ferramenta WEKA, onde 7 classificadores sao combinados para serem otimizados na etapa seguinte pela meta-classificacao. Testes foram realizados com alguns dos ativos mais liquidos de diferentes setores e o ativo que acompanha o indice Bovespa da B3, sao eles: BOVA11, CIEL3, ITUB4, PETR4, USIM5, CMIG4, GGBR4, KROT3 e GOLL4. Os resultados foram promissores, apresentando uma boa acuracia na classificacao com ate 57%, alem de resultados financeiros satisfatorios com ganhos de ate 100% do valor de capital inicialmente investido. Tambem tivemos bons resultados quando comparamos com os baselines buy-and-hold, aleatorio e estrategia inversa.", notes = "in Portuguese 3.3.9 Programacao Genetica (PG) Supervisor: Adriano C. Machado Pereira", } @InProceedings{deaton:1996:gsreDNA, author = "R. Deaton and M. Garzon and R. C. Murphy and J. A. Rose and D. R. Franceschetti and S. E. {Stevens, Jr.}", title = "Genetic Search of Reliable Encodings for {DNA}-Based Computation", booktitle = "Late Breaking Papers at the Genetic Programming 1996 Conference Stanford University July 28-31, 1996", year = "1996", editor = "John R. Koza", pages = "9--15", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California 94305-3079, USA", month = "28--31 " # jul, publisher = "Stanford Bookstore", ISBN = "0-18-201031-7", keywords = "genetic algorithms, genetic programming", URL = "http://www.csce.uark.edu/~rdeaton/dna/papers/gp-96.pdf", size = "7 pages", abstract = "In DNA-based computation, the problem instances are encoded in DNA oligonucleotides that must hybridise correctly to produce a solution. Depending on reaction conditions, oligonucleotides can bind with imperfect matching of complementary base pairs. These mismatched hybridisations are a potential source of errors. For reliable DNA-based computation, the encodings should be a minimum distance apart. This distance could be estimated from empirical curves of DNA melting, but they remain difficult to produce. In fact, the probability of a good encoding in a randomly chosen sample goes to zero fairly quickly with the number of errors for arbitrary encoding lengths. We use genetic programming methods to nd good encodings and analyse their performance in actual laboratory experiments.", notes = "GP-96LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670", } @InProceedings{deaton:1997:ithr, author = "R. Deaton and M. Garzon and R. C. Murphy and D. R. Franceschetti and J. A. Rose and S. E. {Stevens, Jr.}", title = "Information Transfer through Hybridization Reactions in DNA based Computing", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "DNA Computing", pages = "463--471", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", notes = "GP-97", } @InProceedings{deaton:1999:RTCDC, author = "Russell Deaton", title = "Reaction Temperature Constraints in DNA Computing", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1803--1804", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "dna and molecular computing", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/dn-101.pdf", URL = "http://csce.uark.edu/~rdeaton/dna/papers/dn-101.pdf", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{deb:1998:otsRGA, author = "Kalyanmoy Deb and Surendra Gulati and Sekhar Chakrabarti", title = "Optimal Truss-Structure Design using Real-Coded Genetic Algorithms", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "479--486", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms", notes = "SGA-98", } @InProceedings{deb:1999:CTPMO, author = "Kalyanmoy Deb", title = "Construction of Test Problems for Multi-Objective Optimization", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "164--171", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{deb:1999:SRGASBC, author = "Kalyanmoy Deb and Hans-Georg Beyer", title = "Self-Adaptation in Real-Parameter Genetic Algorithms with Simulated Binary Crossover", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "172--179", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/deb_gecco1.ps.gz", URL = "http://ls11-www.informatik.uni-dortmund.de/people/deb/papers/gecco1.ps.gz", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Proceedings{deb:2004:GECCO1, title = "Genetic and Evolutionary Computation -- GECCO-2004, Part I", year = "2004", editor = "Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell", series = "Lecture Notes in Computer Science", address = "Seattle, WA, USA", publisher_address = "Heidelberg", month = "26-30 " # jun, organisation = "ISGEC", publisher = "Springer-Verlag", volume = "3102", ISBN = "3-540-22344-4", ISSN = "0302-9743", DOI = "doi:10.1007/b98643", keywords = "genetic algorithms, genetic programming", organisation = "ISGEC, now ACM SIGEVO", size = "1445 pages", notes = "GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)", } @Proceedings{deb:2004:GECCO2, title = "Genetic and Evolutionary Computation -- GECCO-2004, Part II", year = "2004", editor = "Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell", series = "Lecture Notes in Computer Science", address = "Seattle, WA, USA", publisher_address = "Heidelberg", month = "26-30 " # jun, organisation = "ISGEC", publisher = "Springer-Verlag", volume = "3103", ISBN = "3-540-22343-6", ISSN = "0302-9743", DOI = "doi:10.1007/b98645", keywords = "genetic algorithms, genetic programming", organisation = "ISGEC, now ACM SIGEVO", size = "1439 pages", notes = "GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)", } @InProceedings{Deb:2020:GECCOcomp, author = "Kalyanmoy Deb", title = "Evolutionary Multi-Objective Optimization: Past, Present and Future", year = "2020", editor = "Richard Allmendinger and Hugo Terashima Marin and Efren Mezura Montes and Thomas Bartz-Beielstein and Bogdan Filipic and Ke Tang and David Howard and Emma Hart and Gusz Eiben and Tome Eftimov and William {La Cava} and Boris Naujoks and Pietro Oliveto and Vanessa Volz and Thomas Weise and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Rui Wang and Ran Cheng and Guohua Wu and Miqing Li and Hisao Ishibuchi and Jonathan Fieldsend and Ozgur Akman and Khulood Alyahya and Juergen Branke and John R. Woodward and Daniel R. Tauritz and Marco Baioletti and Josu Ceberio Uribe and John McCall and Alfredo Milani and Stefan Wagner and Michael Affenzeller and Bradley Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and Nayat Sanchez-Pi and Luis Marti and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and Matthew Johns and Nick Ross and Ed Keedwell and Herman Mahmoud and David Walker and Anthony Stein and Masaya Nakata and David Paetzel and Neil Vaughan and Stephen Smith and Stefano Cagnoni and Robert M. Patton and Ivanoe {De Falco} and Antonio {Della Cioppa} and Umberto Scafuri and Ernesto Tarantino and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Erik Hemberg and Riyad Alshammari and Tokunbo Makanju and Fuijimino-shi and Ivan Zelinka and Swagatam Das and Ponnuthurai Nagaratnam and Roman Senkerik", isbn13 = "9781450371278", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377929.3389864", DOI = "doi:10.1145/3377929.3389864", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion", pages = "343--372", size = "30 pages", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", note = "Tutorial", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{10.1145/3377929.3389864} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @Article{Debattista:2018:cgf, author = "Kurt Debattista", title = "Application-Specific Tone Mapping Via Genetic Programming", journal = "Computer Graphics Forum", year = "2018", volume = "37", number = "1", pages = "439--450", month = "1 " # nov, keywords = "genetic algorithms, genetic programming, high dynamic range imaging, tone mapping", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/cgf/cgf37.html#Debattista18", DOI = "doi:10.1111/cgf.13307", size = "12 pages", abstract = "High dynamic range (HDR) imagery permits the manipulation of real-world data distinct from the limitations of the traditional, low dynamic range (LDR), content. The process of retargeting HDR content to traditional LDR imagery via tone mapping operators (TMOs) is useful for visualizing HDR content on traditional displays, supporting backwards-compatible HDR compression and, more recently, is being frequently used for input into a wide variety of computer vision applications. This work presents the automatic generation of TMOs for specific applications via the evolutionary computing method of genetic programming (GP). A straightforward, generic GP method that generates TMOs for a given fitness function and HDR content is presented. Its efficacy is demonstrated in the context of three applications: Visualization of HDR content on LDR displays, feature mapping and compression. For these applications, results show good performance for the generated TMOs when compared to traditional methods. Furthermore, they demonstrate that the method is generalizable and could be used across various applications that require TMOs but for which dedicated successful TMOs have not yet been discovered.", notes = "journals/cgf/Debattista18", } @Article{Debien:2016:expF, author = "Antoine Debien and Kai A. F. F. {von Krbek} and Nicolas Mazellier and Thomas Duriez and Laurent Cordier and Bernd R. Noack and Markus W. Abel and Azeddine Kourta", title = "Closed-loop separation control over a sharp edge ramp using genetic programming", journal = "Experiments in Fluids", year = "2016", volume = "57", number = "3", keywords = "genetic algorithms, genetic programming, feedback flow control, turbulent boundary layer, active vortex generators, machine learning control", bibsource = "OAI-PMH server at export.arxiv.org", identifier = "doi:10.1007/s00348-016-2126-8", oai = "oai:arXiv.org:1508.05268", ISSN = "1432-1114", URL = "http://arxiv.org/abs/1508.05268", DOI = "doi:10.1007/s00348-016-2126-8", size = "19 pages", abstract = "We experimentally perform open and closed-loop control of a separating turbulent boundary layer downstream from a sharp edge ramp. The turbulent boundary layer just above the separation point has a Reynolds number {\$}{\$}Re{\_}{\{}{\backslash}theta {\}}{\backslash}approx 3500{\$}{\$} R e $\theta$ approx 3500 based on momentum thickness. The goal of the control is to mitigate separation and early re-attachment. The forcing employs a spanwise array of active vortex generators. The flow state is monitored with skin-friction sensors downstream of the actuators. The feedback control law is obtained using model-free genetic programming control (GPC) (Gautier et al. in J Fluid Mech 770:442--457, 2015). The resulting flow is assessed using the momentum coefficient, pressure distribution and skin friction over the ramp and stereo PIV. The PIV yields vector field statistics, e.g. shear layer growth, the back-flow area and vortex region. GPC is benchmarked against the best periodic forcing. While open-loop control achieves separation reduction by locking-on the shedding mode, GPC gives rise to similar benefits by accelerating the shear layer growth. Moreover, GPC uses less actuation energy.", } @InProceedings{deBoer2016, author = "G {de Boer} and H Wang and M Ghajari and A Alazmani and R Hewson and P Culmer", title = "Force and Topography Reconstruction Using {GP} and {MOR} for the {TACTIP} Soft Sensor System", booktitle = "Proceedings of the 17th Annual Conference Towards Autonomous Robotic Systems, TAROS 2016", year = "2016", editor = "Lyuba Alboul and Dana Damian and Jonathan M. Aitken", volume = "9716", series = "Lecture Notes in Computer Science", pages = "65--74", address = "Sheffield, UK", month = jun # " 26--" # jul # " 1", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Model Order Reduction", bibsource = "OAI-PMH server at eprints.whiterose.ac.uk", contributor = "L Alboul and D Damian and J. M. Aitken", oai = "oai:eprints.whiterose.ac.uk:101732", isbn13 = "978-3-319-40379-3", URL = "https://doi.org/10.1007/978-3-319-40379-3_7", DOI = "doi:10.1007/978-3-319-40379-3_7", size = "10 pages", abstract = "Sensors take measurements and provide feedback to the user via a calibrated system, in soft sensing the development of such systems is complicated by the presence of nonlinearities, e.g. contact, material properties and complex geometries. When designing soft-sensors it is desirable for them to be inexpensive and capable of providing high resolution output. Often these constraints limit the complexity of the sensing components and their low resolution data capture, this means that the usefulness of the sensor relies heavily upon the system design. This work delivers a force and topography sensing framework for a soft sensor. A system was designed to allow the data corresponding to the deformation of the sensor to be related to outputs of force and topography. This system used Genetic Programming (GP) and Model Order Reduction (MOR) methods to generate the required relationships. Using a range of 3D printed samples it was demonstrated that the system is capable of reconstructing the outputs within an error of one order of magnitude.", } @Article{deBoer:2017:sensors, author = "Gregory {de Boer} and Nicholas Raske and Hongbo Wang and Mazdak Ghajari and Peter Culmer and Robert Hewson", title = "Design Optimisation of a Magnetic Field Based Soft Tactile Sensor", journal = "sensors", year = "2017", volume = "17", number = "11", pages = "2539", month = nov, note = "Special Issue Tactile Sensors and Sensing)", keywords = "genetic algorithms, genetic programming, tactile sensing, sensitivity, optimisation, magnetic fields, force measurement", publisher = "MDPI", bibsource = "OAI-PMH server at eprints.whiterose.ac.uk", oai = "oai:eprints.whiterose.ac.uk:124902", URL = "http://eprints.whiterose.ac.uk/124902/1/sensors-17-02539-v2.pdf", DOI = "doi:10.3390/s17112539", size = "20 pages", abstract = "This paper investigates the design optimisation of a magnetic field based soft tactile sensor, comprised of a magnet and Hall effect module separated by an elastomer. The aim was to minimise sensitivity of the output force with respect to the input magnetic field; this was achieved by varying the geometry and material properties. Finite element simulations determined the magnetic field and structural behaviour under load. Genetic programming produced phenomenological expressions describing these responses. Optimisation studies constrained by a measurable force and stable loading conditions were conducted; these produced Pareto sets of designs from which the optimal sensor characteristics were selected. The optimisation demonstrated a compromise between sensitivity and the measurable force, a fabricated version of the optimised sensor validated the improvements made using this methodology. The approach presented can be applied in general for optimising soft tactile sensor designs over a range of applications and sensing modes.", notes = "Also known as \cite{oai:eprints.whiterose.ac.uk:124902}", } @InProceedings{Debroy:2010:ICST, author = "Vidroha Debroy and W. Eric Wong", title = "Using Mutation to Automatically Suggest Fixes for Faulty Programs", booktitle = "Third International Conference on Software Testing, Verification and Validation", year = "2010", pages = "65--74", address = "Paris, France", month = "6-10 " # apr, keywords = "genetic algorithms, genetic programming, genetic improvement, APR, SBSE, program debugging, mutation, fault localization, fault-fixing, software testing, Tarantula", DOI = "doi:10.1109/ICST.2010.66", size = "10 pages", abstract = "This paper proposes a strategy for automatically fixing faults in a program by combining the processes of mutation and fault localization. Statements that are ranked in order of their suspiciousness of containing faults can then be mutated in the same order to produce possible fixes for the faulty program. The proposed strategy is evaluated against the seven benchmark programs of the Siemens suite and the Ant program. Results indicate that the strategy is effective at automatically suggesting fixes for faults without any human intervention.", notes = "Ant is a Java-based build tool supplied by the open source Apache project. The University of Texas at Dallas, USA also known as \cite{5477098}", } @InProceedings{deCarvalho:2006:JCDL, author = "Moises G. {de Carvalho} and Marcos Andre Goncalves and Alberto H. F. Laender and Altigran S. {da Silva}", title = "Learning to deduplicate", booktitle = "Proceedings of the 6th ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL '06", year = "2006", pages = "41--50", address = "Chapel Hill, NC, USA", month = jun, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Deduplication, Digital Libraries", ISBN = "1-59593-354-9", URL = "http://delivery.acm.org/10.1145/1150000/1141760/p41-decarvalho.pdf?key1=1141760&key2=6906456911&coll=GUIDE&dl=GUIDE&CFID=45325455&CFTOKEN=75817203", DOI = "doi:10.1145/1141753.1141760", size = "10 pages", abstract = "Identifying record replicas in digital libraries and other types of digital repositories is fundamental to improve the quality of their content and services as well as to yield eventual sharing efforts. Several deduplication strategies are available, but most of them rely on manually chosen settings to combine evidence used to identify records as being replicas. In this paper, we present the results of experiments we have carried out with a novel machine learning approach we have proposed for the de duplication problem. This approach, based on genetic programming (GP), is able to automatically generate similarity functions to identify record replicas in a given repository. The generated similarity functions properly combine and weight the best evidence available among the record fields in order to tell when two distinct records represent the same real-world entity. The results of the experiments show that our approach outperforms the baseline method by Fellegi and Sunter by more than 12percent when identifying replicas in a data set containing researcher's personal data, and by more than 7percent, in a data set with article citation data", notes = "Comput. Sci. Dept., Fed. Univ. of Minas Gerais, Belo Horizonte", } @InProceedings{conf/sbbd/CarvalhoLGP08, title = "The Impact of Parameters Setup on a Genetic Programming Approach to Record Deduplication", author = "Moises G. {de Carvalho} and Alberto H. F. Laender and Marcos Andre Goncalves and Thiago C. Porto", bibdate = "2009-03-02", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/sbbd/sbbd2008.html#CarvalhoLGP08", booktitle = "{XXIII} Simp{\'o}sio Brasileiro de Banco de Dados", publisher = "SBC", year = "2008", editor = "Sandra de Amo", isbn13 = "978-85-7669-205-8", pages = "91--105", URL = "http://www.lbd.dcc.ufmg.br:8080/colecoes/sbbd/2008/007.pdf", address = "Campinas, {S}{\~a}o Paulo, Brasil", month = "13-15 " # oct, keywords = "genetic algorithms, genetic programming", size = "15 pages", abstract = "Several systems that rely on the integrity of the data in order to offer high quality services, such as digital libraries and e-commerce brokers, may be affected by the existence of duplicates, quasi-replicas, or near-duplicates entries in their repositories. Because of that, there has been a huge effort from private and government organizations in developing effective methods for removing replicas from large data repositories. This is due to the fact that cleaned, replica-free repositories not only allow the retrieval of higher-quality information but also lead to a more concise data representation and to potential savings in computational time and resources to process this data. In this work, we extend the results of a GP-based approach we proposed to record deduplication by performing a comprehensive set of experiments regarding its parameterization setup. Our experiments show that some parameter choices can improve the results to up 30percent Thus, the obtained results can be used as guidelines to suggest the most effective way to set up the parameters of our GP-based approach to record deduplication.", notes = "SDG SBBD 2008.", } @PhdThesis{deCarvalho:thesis, author = "Moises Gomes {de Carvalho}", title = "Evolutionary Approaches to Data Integration Related Problems", school = "Computer Science of the Federal University of Minas Gerais", year = "2009", address = "Belo Horizonte, Brazil", month = "26 " # oct, keywords = "genetic algorithms, genetic programming, Data Integration, Record Deduplication, Schema Matching", keywords_pt = "Programacao genetica, Integracao de dados, Deduplicacao de registros", URL = "http://www.dcc.ufmg.br/pos/cursos/defesas/901D.PDF", size = "138 pages", abstract = "Data integration aims to combine data from different sources (data repositories such as databases, digital libraries, etc.) by adopting a global data model and by detecting and resolving schema and data conflicts so that a homogeneous, unified view can be provided. Two specific problems related to data integration - schema matching and replica identification - present a large solution space. This space is computationally expensive and technically prohibitive to be intensively and exhaustively explored by traditional approaches. Moreover, the solutions for these problems usually require that multiple, sometimes conflicting, objectives must be simultaneously attended. This thesis aims to show that evolutionary-based techniques can be successfully applied to such problems, leading to novel approaches and methods that address all aforementioned requirements and, at the same time, provide efficient and high accuracy solutions. In this thesis, we first propose a genetic programming approach to record deduplication. This approach combines several different pieces of evidence extracted from the actual data present in the repositories to suggest a deduplication function that is able to identify whenever two entries in a repository are replicas or not. As shown by our experiments, our approach outperforms existing state-of-the-art methods found in the literature. Moreover, the suggested function is computationally less demanding since it uses fewer evidence. Finally, it is also important to notice that our approach is capable of automatically adapting to a given fixed replica identification boundary, freeing the user from the burden of having to choose and tune this parameter Based on the previous approach, we also devised a novel evolutionary approach, that is able to automatically find complex schema matches. Our aim was to develop a method to find semantic relationships between schema elements, in a restricted scenario in which only the data instances are available. To the best of our knowledge, this is the first approach that is capable of discovering complex schema matches using only the data instances, which is performed by exploiting record deduplication and information retrieval techniques to find schema matches during the evolutionary process. To demonstrate the effectiveness of our approach, we conducted an experimental evaluation using real-world and synthetic datasets. Our results show that our approach is able to find complex matches with high accuracy, despite using only the data instances.", notes = "supervisor: Alberto Henrique Frade Laender", } @Article{deCarvalho:2011:ieeeTKDE, author = "Moises G. {de Carvalho} and Alberto H. F. Laender and Marcos Andre Goncalves and Altigran S. {da Silva}", title = "A Genetic Programming Approach to Record Deduplication", journal = "IEEE Transactions on Knowledge and Data Engineering", year = "2012", month = mar, volume = "24", number = "3", pages = "399--412", abstract = "Several systems that rely on consistent data to offer high quality services, such as digital libraries and e-commerce brokers, may be affected by the existence of duplicates, quasi-replicas, or near-duplicate entries in their repositories. Because of that, there have been significant investments from private and government organisations in developing methods for removing replicas from its data repositories. This is due to the fact that clean and replica-free repositories not only allow the retrieval of higher-quality information but also lead to more concise data and to potential savings in computational time and resources to process this data. In this article, we propose a genetic programming approach to record deduplication that combines several different pieces of evidence extracted from the data content to find a deduplication function that is able to identify whether two entries in a repository are replicas or not. As shown by our experiments, our approach outperforms an existing state-of-the-art method found in the literature. Moreover, the suggested functions are computationally less demanding since they use fewer evidence. In addition, our genetic programming approach is capable of automatically adapting these functions to a given fixed replica identification boundary, freeing the user from the burden of having to choose and tune this parameter.", keywords = "genetic algorithms, genetic programming, computational time, data repositories, database administration, database integration, digital libraries, e-commerce brokers, fixed replica identification boundary, information retrieval, record deduplication, replica removal, replica-free repositories, information retrieval, replicated databases", size = "14 pages", DOI = "doi:10.1109/TKDE.2010.234", ISSN = "1041-4347", notes = "Also known as \cite{5645623}", } @Article{deCarvalho:2013:IS, author = "Moises Gomes {de Carvalho} and Alberto H. F. Laender and Marcos Andre Goncalves and Altigran S. {da Silva}", title = "An evolutionary approach to complex schema matching", journal = "Information Systems", volume = "38", number = "3", pages = "302--316", year = "2013", ISSN = "0306-4379", DOI = "doi:10.1016/j.is.2012.10.002", URL = "http://www.sciencedirect.com/science/article/pii/S0306437912001287", abstract = "The schema matching problem can be defined as the task of finding semantic relationships between schema elements existing in different data repositories. Despite the existence of elaborated graphic tools for helping to find such matches, this task is usually manually done. In this paper, we propose a novel evolutionary approach to addressing the problem of automatically finding complex matches between schemas of semantically related data repositories. To the best of our knowledge, this is the first approach that is capable of discovering complex schema matches using only the data instances. Since we only exploit the data stored in the repositories for this task, we rely on matching strategies that are based on record deduplication (aka, entity-oriented strategy) and information retrieval (aka, value-oriented strategy) techniques to find complex schema matches during the evolutionary process. To demonstrate the effectiveness of our approach, we conducted an experimental evaluation using real-world and synthetic datasets. The results show that our approach is able to find complex matches with high accuracy, similar to that obtained by more elaborated (hybrid) approaches, despite using only evidence based on the data instances.", keywords = "genetic algorithms, genetic programming, Complex schema matchings, Entity-oriented strategy, Value-oriented strategy", } @MastersThesis{decaux:2001:masters, author = "Robert {De Caux}", title = "Using Genetic Programming to Evolve Strategies for the Iterated Prisoner's Dilemma", school = "University College, London", year = "2001", month = sep, keywords = "genetic algorithms, genetic programming, java, gpsys, ipd, Coevolution, Pareto scoring, strongly typed", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/decaux.masters.zip", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/decaux.masters.pdf", size = "97 pages", abstract = "The technique of Genetic Programming (GP) uses Darwinian principles of natural selection to evolve simple programs with the aim of finding better or fitter solutions to a problem. Based on the technique of Genetic Algorithms (GA), a population of potential solutions stored in tree form are evaluated against a fitness function. The fittest ones are then modified by a genetic operation, and used to form the next generation. This process is repeated until certain criteria have been met. This could be an ultimate solution, or a certain number of generations having been evolved. Genetic Programming is a fast developing field with potential uses in medicine, finance and artificial intelligence. This project attempts to use the technique to evolve strategies for the game of Prisoner's Dilemma. Although a simple game, the range of possible strategies when the game is iterated is vast, but what makes it particularly interesting is the absence of an ultimate strategy and the possibility of mutual benefit by cooperation. A system was created to allow strategies to be evolved by either playing against fixed opponents or against each other (coevolution). The strategies are stored as trees, with GP used to form the next generation. The main advantage of GP over GA is that the trees do not need to be of a fixed size, so strategies can be developed which use the entire game history as opposed to just the last few moves. This implementation has advantages over previous investigations, as information about which go is being played can be used, thus allowing cleverer strategies. Work has also been conducted into a hunting phase, where strategies roam a two dimensional grid to find a suitable opponent. By studying the history of potential opponents and using GA, evidence emerged of an increase in cooperative behaviour as strategies sought out suitable opponents, demonstrating parallels with biological models of population dynamics. The system has been developed to allow a user to alter important parameters, select the evolution method and seed the population with pre-defined strategies by means of a graphical user interface.", notes = "Awarded a distinction. Supervised by Robin Hirsch. Zip archive contains msword document", } @PhdThesis{decaux:2017:thesis, author = "Robert {De Caux}", title = "An agent-based approach to modelling long-term systemic risk in networks of interacting banks", school = "Electronics and Computer Science, University of Southampton", year = "2017", address = "UK", month = jan, URL = "https://eprints.soton.ac.uk/417987/1/Final_Thesis_an_agent_based_approach_to_modelling_long_term_systemic_risk_.pdf", URL = "https://eprints.soton.ac.uk/417987/", size = "156 pages", abstract = "The recent banking crisis has led to a spate of literature investigating the concept of systemic risk, aiming to understand the stability of specific financial systems and how contagion can spread through them following stress events. However, the primary focus of this literature has been on static networks, rather than dynamic systems that evolve over time and are shaped by participant interactions. Such a long-term focus is necessary to fully understand how systems will react to policy changes.{\ensuremath{<}}br/{\ensuremath{>}}{\ensuremath{<}}br/{\ensuremath{>}}This thesis analyses two banking systems that are subject to systemic risk, but also feature both micro-level contagion dynamics and strategic interactions between participants. The first is the large value payment system CHAPS, in which participating banks face a strategic decision for how to make their payments in an optimal manner. The second is the relationship between the resolution of insolvent banks and system efficiency, including whether the moral hazard effect created by bank bailouts causes the system to evolve suboptimally. Both systems are analysed using agent-based models with respect to a long term {$\backslash$}social welfare{"} measure that balances bank profitability with the costs caused by contagion.{\ensuremath{<}}br/{\ensuremath{>}}{\ensuremath{<}}br/{\ensuremath{>}}The models generate results that would not be possible through a static analysis of the systems without adaptive banks. The payment system is shown to operate below its social optimum, as banks do not endogenise the systemic risk externalities caused by strategies that appear optimal at an individual level. This leads to insufficient liquidity in the system and the queuing of non-priority payments in an inefficient manner.{\ensuremath{<}}br/{\ensuremath{>}}{\ensuremath{<}}br/{\ensuremath{>}}In the insolvency model, a policy of regulatory intervention shapes bank risk-taking over the long term, with the short term gains of a bailout leading over time to excessive bank leverage, a higher number of insolvencies and reduced social welfare. A targeted strategy of only bailing out specific institutions that are Too-Big-To-Fail also reduces long term system efficiency.", notes = "not on GP? Also known as \cite{soton417987}", } @InCollection{deconde:2003:EPDMCTD, author = "Rob P. DeConde", title = "Evolving Programs for Distributed Multi-Agent Configuration in Two Dimensions", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2003", year = "2003", editor = "John R. Koza", pages = "38--44", address = "Stanford, California, 94305-3079 USA", month = "4 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", URL = "http://www.genetic-programming.org/sp2003/DeConde.pdf", notes = "part of \cite{koza:2003:gagp}", } @InProceedings{eurogp07:Decraene, author = "James Decraene and George G. Mitchell and Barry McMullin and Ciaran Kelly", title = "The Holland Broadcast Language and the Modeling of Biochemical Networks", editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar", booktitle = "Proceedings of the 10th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "4445", year = "2007", address = "Valencia, Spain", month = "11-13 " # apr, pages = "361--370", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-71602-0", ISBN = "3-540-71602-5", DOI = "doi:10.1007/978-3-540-71605-1_34", abstract = "The Broadcast Language is a programming formalism devised by Holland in 1975, which aims at improving the efficiency of Genetic Algorithms (GAs) during long-term evolution. The key mechanism of the Broadcast Language is to allow GAs to employ an adaptable problem representation. Fixed problem encoding is commonly used by GAs but may limit their performance in particular cases. This paper describes an implementation of the Broadcast Language and its application to modelling biochemical networks. Holland presented the Broadcast Language in his book 'Adaptation in Natural and Artificial Systems' where only a description of the language was provided, without any implementation. Our primary motivation for this work was the fact that there is currently no published implementation of the Broadcast Language available. Secondly, no additional examination of the Broadcast Language and its applications can be found in the literature. Holland proposed that the Broadcast Language would be suitable for the modeling of biochemical models. However, he did not support this belief with any experimental work. In this paper, we propose an implementation of the Broadcast Language which is then applied to the modelling of a signal transduction network. We conclude the paper by proposing that with some refinements it will be possible to use the Broadcast Language to evolve biochemical networks in silico.", notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007", } @Article{DEDOMENICO:2023:prostr, author = "Dario {De Domenico} and Giuseppe Quaranta and Qingcong Zeng and Giorgio Monti", title = "Machine-learning-enhanced variable-angle truss model to predict the shear capacity of {RC} elements with transverse reinforcement", journal = "Procedia Structural Integrity", volume = "44", pages = "1688--1695", year = "2023", note = "XIX ANIDIS Conference, Seismic Engineering in Italy", keywords = "genetic algorithms, genetic programming, Reinforced concrete beams, Reinforced concrete columns, Design code, Machine learning, Reinforced concrete, Shear capacity, Variable-angle truss model, Eurocode", ISSN = "2452-3216", URL = "https://www.sciencedirect.com/science/article/pii/S245232162300224X", DOI = "doi:10.1016/j.prostr.2023.01.216", abstract = "This contribution presents a numerical model for the shear capacity prediction of reinforced concrete (RC) elements with transverse reinforcement. The proposed model originates from one of the most popular mechanical models adopted in building codes, namely the variable-angle truss model. Starting from the formulation proposed in the Eurocode 2, two empirical coefficients governing the concrete contribution (i.e., the shear capacity ascribed to crushing of compressed struts) are adjusted and enriched through machine learning, in such a way to improve the predictive efficiency of the model against experimental results. More specifically, genetic programming is used to derive closed-form expressions of the two corrective coefficients, thus facilitating the use of this model for practical purposes. The proposed expressions are validated by comparison with a wide set of experimental results collected from the literature concerning RC beams and columns failing in shear under both monotonic and cyclic loading conditions, respectively. It is demonstrated that the proposed formulation, thanks to the two novel corrective coefficients, not only attains higher accuracy than the original Eurocode 2 formulation, but also outperforms many other existing design code provisions while preserving a sound mechanical basis", } @Article{Deepa:2013:IJCA, author = "K. Deepa and R. Rangarajan and M. Senthamil Selvi", title = "Automatic Threshold Selection using PSO for GA based Duplicate Record Detection", journal = "International Journal of Computer Applications", year = "2013", volume = "62", number = "4", month = jan, keywords = "genetic algorithms, genetic programming, GA, PSO, similarity metrics, threshold", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.303.6638", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.303.6638", URL = "http://research.ijcaonline.org/volume62/number4/pxc3884674.pdf", size = "6 pages", abstract = "Normally setting the threshold is an important issue in applications where the similarity functions are used and it relies more on human intervention. The proposed work addressed two issues: first to find the optimal equation using Genetic Algorithm (GA) and next it adopts an intelligence algorithm, Particle Swarm Optimisation (PSO) to get the optimal threshold to detect the duplicate records more accurately and also it reduces human intervention. Restaurant and CORA data repository are used to analyse the proposed algorithm and the performance of the proposed algorithm is compared against marlin method and the genetic programming with the help of evaluation metrics.", notes = "Sri Ramakrishna Engg College, Coimbatore", } @InProceedings{DeFalco:1997:GPekc, author = "M. Conte and G. Tautteur and I. {De Falco} and A. Della Cioppa and E. Tarantino", title = "Genetic Programming Estimates of Kolmogorov Complexity", booktitle = "Genetic Algorithms: Proceedings of the Seventh International Conference", year = "1997", editor = "Thomas Back", pages = "743--750", address = "Michigan State University, East Lansing, MI, USA", publisher_address = "San Francisco, CA, USA", month = "19-23 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-487-1", broken = "http://www.irsip.na.cnr.it/~hotg/papers/kc.ps", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/1152/http:zSzzSzamalfi.dis.unina.itzSz~deanzSzpaperszSzicga97.pdf/conte97genetic.pdf", URL = "http://citeseer.ist.psu.edu/355332.html", size = "7 pages", abstract = "In this paper the problem of the Kolmogorov complexity related to binary strings is faced. We propose a Genetic Programming approach which consists in evolving a population of Lisp programs looking for the optimal program that generates a given string. This evolutionary approach has permited to overcome the intractable space and time difficulties occurring in methods which perform an approximation of the Kolmogorov complexity function. The experimental results are quite significant and also show interesting computational strategies so proving the effectiveness of the implemented technique.", notes = "ICGA-97 Department of Physics, universita' degli studi di napoli federico ii", } @InProceedings{falco:1999:TSNM, author = "I. {De Falco} and A. Iazzetta and E. Tarantino and A. Della Cioppa and A. Iacuelli", title = "Towards a Simulation of Natural Mutation", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "156--163", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{DeFalco:2000:GECCO, author = "I. {De Falco} and A. Iazzetta and E. Tarantino and A. Della Cioppa and G. Trautteur", title = "A Kolmogorov Complexity-based Genetic Programming tool for string compression", pages = "427--434", year = "2000", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)", editor = "Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer", address = "Las Vegas, Nevada, USA", publisher_address = "San Francisco, CA 94104, USA", month = "10-12 " # jul, keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-708-0", URL = "http://gpbib.cs.ucl.ac.uk/gecco2000/GP124.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco2000/GP124.ps", notes = "A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of \cite{whitley:2000:GECCO}", } @Article{DeFalco:ASC, author = "I. {De Falco} and A. {Della Cioppa} and E. Tarantino", title = "Discovering interesting classification rules with genetic programming", journal = "Applied Soft Computing", year = "2001", volume = "1", number = "4", pages = "257--269", month = may, keywords = "genetic algorithms, genetic programming, Data mining, Classification", broken = "http://www.sciencedirect.com/science/article/B6W86-44KWJTS-1/1/8f98e1cb13b739a68dad80864389ca51", broken = "http://www.elsevier.com/gej-ng/10/10/65/45/43/28/article.pdf", DOI = "doi:10.1016/S1568-4946(01)00024-2", size = "13 pages", abstract = "Data mining deals with the problem of discovering novel and interesting knowledge from large amount of data. This problem is often performed heuristically when the extraction of patterns is difficult using standard query mechanisms or classical statistical methods. In this paper a genetic programming framework, capable of performing an automatic discovery of classification rules easily comprehensible by humans, is presented. A comparison with the results achieved by other techniques on a classical benchmark set is carried out. Furthermore, some of the obtained rules are shown and the most discriminating variables are evidenced.", notes = "comparison in \cite{yu:2004:ECDM}", } @InProceedings{falco:2002:usprfmibmoagpa, author = "Ivanoe {De Falco} and Antonio Della Cioppa and Ernesto Tarantino", title = "Unsupervised Spectral Pattern Recognition for Multispectral Images by means of a Genetic Programming approach", booktitle = "Proceedings of the 2002 Congress on Evolutionary Computation CEC2002", editor = "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and Mark Shackleton", pages = "231--236", year = "2002", month = "12-17 " # may, publisher = "IEEE Press", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", organisation = "IEEE Neural Network Council (NNC), Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", ISBN = "0-7803-7278-6", notes = "CEC 2002 - A joint meeting of the IEEE, the Evolutionary Programming Society, and the IEE. Held in connection with the World Congress on Computational Intelligence (WCCI 2002)", keywords = "genetic algorithms, genetic programming, clustered output image, genetic programming, genetic programming approach, multispectral images, unsupervised pixel classification, unsupervised spectral pattern recognition, pattern recognition, unsupervised learning", DOI = "doi:10.1109/CEC.2002.1006239", abstract = "An innovative approach to spectral pattern recognition for multispectral images based on Genetic Programming is introduced. The problem is faced in terms of unsupervised pixel classification. The system is tested on a multispectral image with 31 spectral bands and 256 by 256 pixels. A good quality clustered output image is obtained.", } @InProceedings{defalco:2004:wsc9, author = "I. {De Falco} and E. Tarantino and A. {Della Cioppa} and F. Fontanella", title = "An Innovative Approach to Genetic Programming-based Clustering", booktitle = "9th Online World Conference on Soft Computing in Industrial Applications", year = "2004", editor = "Ajith Abraham and Bernard {de Baets} and Mario Koeppen and Bertram Nickolay", volume = "34", series = "Advances in Soft Computing", pages = "55--64", address = "On the World Wide Web", month = "20 " # sep # " - 8 " # oct, organisation = "World Federation on Soft Computing (WFSC)", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, clustering", isbn13 = "978-3-540-31649-7", URL = "http://webuser.unicas.it/fontanella/papers/WSC04.pdf", DOI = "doi:10.1007/3-540-31662-0_4", size = "10 pages", abstract = "Most of the classical clustering algorithms are strongly dependent on, and sensitive to, parameters such as number of expected clusters and resolution level. To overcome this drawback, in this paper a Genetic Programming framework, capable of performing an automatic data clustering is presented. Moreover, a novel way of representing clusters which provides intelligible information on patterns is introduced together with an innovative clustering process. The effectiveness of the implemented partitioning system is estimated on a medical domain by means of evaluation indices", notes = "WSC9 Clusters represented using GP evolved functions. Grammar. Fitness is linear combination of cluster homogeneity and separation. Non standard crossover. Multiple classes. UCI dermatological benchmark.", } @InProceedings{conf/sac/FalcoTCG05a, title = "A novel grammar-based genetic programming approach to clustering", author = "Ivan {De Falco} and Ernesto Tarantino and Antonio {Della Cioppa} and F. Gagliardi", year = "2005", bibdate = "2006-02-10", pages = "928--932", editor = "Hisham Haddad and Lorie M. Liebrock and Andrea Omicini and Roger L. Wainwright", booktitle = "Proceedings of the 2005 ACM Symposium on Applied Computing (SAC)", publisher = "ACM", address = "Santa Fe, New Mexico, USA", month = mar # " 13-17", organisation = "ACM", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/sac/sac2005.html#FalcoTCG05a", keywords = "genetic algorithms, genetic programming, Information Storage and Retrieval, Information search and retrieval, clustering, retrieval methods, Artificial Intelligence, Problem Solving, Control Methods, and Search heuristic methods, Algorithms, Experimentation, data clustering, EM, Expectation-Maximisation", ISBN = "1-58113-964-0", DOI = "doi:10.1145/1066677.1066891", abstract = "Most of the classical methods for clustering analysis require the user setting of number of clusters. To surmount this problem, in this paper a grammar-based Genetic Programming approach to automatic data clustering is presented. An innovative clustering process is conceived strictly linked to a novel cluster representation which provides intelligible information on patterns. The efficacy of the implemented partitioning system is estimated on a medical domain by exploiting expressly defined evaluation indices. Furthermore, a comparison with other clustering tools is performed.", } @InProceedings{conf/sac/FalcoTCP05, title = "Inductive inference of chaotic series by Genetic Programming: a Solomonoff-based approach", author = "Ivan {De Falco} and Ernesto Tarantino and Antonio {Della Cioppa} and A. Passaro", year = "2005", pages = "957--958", editor = "Hisham Haddad and Lorie M. Liebrock and Andrea Omicini and Roger L. Wainwright", booktitle = "Proceedings of the 2005 ACM Symposium on Applied Computing (SAC)", publisher = "ACM", address = "Santa Fe, New Mexico, USA", month = mar # " 13-17", organisation = "ACM", bibdate = "2006-02-10", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/sac/sac2005.html#FalcoTCP05", keywords = "genetic algorithms, genetic programming, Automatic Programming, Algorithms, Experimentation, Inductive inference, Chaotic series", ISBN = "1-58113-964-0", DOI = "doi:10.1145/1066677.1066897", size = "2 pages", abstract = "A Genetic Programming approach to inductive inference of chaotic series, with reference to Solomonoff complexity, is presented. It consists in evolving a population of mathematical expressions looking for the 'optimal' one that generates a given chaotic data series. Validation is performed on the Logistic, the Henon and the Mackey-Glass series. The method is shown effective in obtaining the analytical expression of the first two series, and in achieving very good results on the third one.", } @InProceedings{conf/wilf/FalcoCPT05, title = "Genetic Programming for Inductive Inference of Chaotic Series", author = "Ivan {De Falco} and Antonio {Della Cioppa} and A. Passaro and Ernesto Tarantino", year = "2005", pages = "156--163", editor = "Isabelle Bloch and Alfredo Petrosino and Andrea Tettamanzi", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3849", booktitle = "Fuzzy Logic and Applications, 6th International Workshop, WILF 2005, Revised Selected Papers", address = "Crema, Italy", month = sep # " 15-17", bibdate = "2006-02-22", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/wilf/wilf2005.html#FalcoCPT05", keywords = "genetic algorithms, genetic programming, Solomonoff complexity, chaotic series", ISBN = "3-540-32529-8", DOI = "doi:10.1007/11676935_19", size = "8 pages", abstract = "In the context of inductive inference Solomonoff complexity plays a key role in correctly predicting the behavior of a given phenomenon. Unfortunately, Solomonoff complexity is not algorithmically computable. This paper deals with a Genetic Programming approach to inductive inference of chaotic series, with reference to Solomonoff complexity, that consists in evolving a population of mathematical expressions looking for the 'optimal' one that generates a given series of chaotic data. Validation is performed on the Logistic, the Henon and the Mackey-Glass series. The results show that the method is effective in obtaining the analytical expression of the first two series, and in achieving a very good approximation and forecasting of the Mackey-Glass series.", } @InProceedings{DeFalco:2005:SCMA, author = "I. {De Falco} and A. {Della Cioppa} and E. Tarantino", title = "A Genetic Programming System for Time Series Prediction and Its Application to {El Nino} Forecast", booktitle = "Soft Computing: Methodologies and Applications", year = "2005", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/3-540-32400-3_12", DOI = "doi:10.1007/3-540-32400-3_12", } @InProceedings{eurogp06:DeFalcoDellaCioppaMaistoTarantino, author = "Ivanoe {De Falco} and Antonio {Della Cioppa} and Domenico Maisto and Ernesto Tarantino", title = "A Genetic Programming Approach to {Solomonoff's} Probabilistic Induction", editor = "Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art", booktitle = "Proceedings of the 9th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3905", year = "2006", address = "Budapest, Hungary", month = "10 - 12 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-33143-3", pages = "24--35", DOI = "doi:10.1007/11729976_3", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "In the context of Solomonoff's Inductive Inference theory, Induction operator plays a key role in modelling and correctly predicting the behaviour of a given phenomenon. Unfortunately, this operator is not algorithmically computable. The present paper deals with a Genetic Programming approach to Inductive Inference, with reference to Solomonoff's algorithmic probability theory, that consists in evolving a population of mathematical expressions looking for the `optimal' one that generates a collection of data and has a maximal a priori probability. Validation is performed on Coulomb's Law, on the Henon series and on the Arosa Ozone time series. The results show that the method is effective in obtaining the analytical expression of the first two problems, and in achieving a very good approximation and forecasting of the third.", notes = "Part of \cite{collet:2006:GP} EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006", } @InProceedings{eurogp07:DeFalco, author = "Ivanoe {De Falco} and Antonio {Della Cioppa} and Domenico Maisto and Umberto Scafuri and Ernesto Tarantino", title = "Parsimony doesn't mean Simplicity: Genetic Programming for Inductive Inference on Noisy Data", editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar", booktitle = "Proceedings of the 10th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "4445", year = "2007", address = "Valencia, Spain", month = "11-13 " # apr, pages = "351--360", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-71602-0", ISBN = "3-540-71602-5", DOI = "doi:10.1007/978-3-540-71605-1_33", abstract = "A Genetic Programming algorithm based on Solomonoff probabilistic induction is designed and used to face an Inductive Inference task, i.e., symbolic regression. To this aim, some test functions are dressed with increasing levels of noise and the algorithm is employed to denoise the resulting function and recover the starting functions. Then, the algorithm is compared against a classical parsimony-based GP. The results shows the superiority of the Solomonoff-based approach.", notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007", } @InProceedings{DeFalco:2017:ieeeISCC, author = "Ivanoe {De Falco} and Umberto Scafuri and Ernesto Tarantino and Antonio {Della Cioppa}", booktitle = "2017 IEEE Symposium on Computers and Communications (ISCC)", title = "Accurate estimate of Blood Glucose through Interstitial Glucose by Genetic Programming", year = "2017", pages = "284--289", abstract = "Subjects suffering from Type 1 diabetes mellitus need to constantly receive insulin injections. To improve their life quality, a desirable solution is represented by the implementation of an artificial pancreas. In this paper we move a preliminary step towards this goal. Namely, we work at the knowledge base for such a device. One of the main problems is to estimate the Blood Glucose (BG) values, starting from the easily available Interstitial Glucose (IG) ones, and this is the aim of our paper. To face this regression task we avail ourselves of Genetic Programming over a real-world database containing both BG and IG measurements for several subjects suffering from Type 1 diabetes, aiming at finding an explicit relationship between BG and IG values under the form of a mathematical expression. This latter could be the core of the knowledge base part of an artificial pancreas. Experimental comparisons against the state-of-the-art models evidence the quality of the proposed approach.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ISCC.2017.8024543", month = jul, notes = "Also known as \cite{8024543}", } @InProceedings{DeFalco:2018:ISCC, author = "I. {De Falco} and U. Scafuri and E. Tarantino and A. {Della Cioppa} and A. Giugliano and Tomas Koutny and Michal Krcma", booktitle = "2018 IEEE Symposium on Computers and Communications (ISCC)", title = "An evolutionary methodology for estimating blood glucose levels from interstitial glucose measurements and their derivatives", year = "2018", pages = "01158--01163", abstract = "The patients suffering from diabetes are subjected to several serious medical risks that can lead also to fatal consequences. To enhance the quality of life of these patients there is the necessity to devise an artificial pancreas able to inject an insulin bolus when needed. This paper presents a genetic-programming based algorithm to extrapolate a regression model able to estimate the blood glucose (BG) level through interstitial glucose (IG) measurements and their derivatives. This algorithm represents a possible step in building the fundamental element of such an artificial pancreas, namely a new evolutionary computation-based methodology to derive a mathematical relationship between BG and IG. The proposed evolutionary automatic procedure is evaluated on a real-world database made up of both BG and IG measurements of people suffering from Type 1 diabetes. The discovered model is validated through a comparison with other techniques during the experimental phase.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ISCC.2018.8538682", ISSN = "1530-1346", month = jun, notes = "Also known as \cite{8538682}", } @Article{DEFALCO:2018:JNCA, author = "Ivanoe {De Falco} and Antonio {Della Cioppa} and Tomas Koutny and Michal Krcma and Umberto Scafuri and Ernesto Tarantino", title = "Genetic Programming-based induction of a glucose-dynamics model for telemedicine", journal = "Journal of Network and Computer Applications", volume = "119", pages = "1--13", year = "2018", keywords = "genetic algorithms, genetic programming, Blood glucose estimation, Interstitial glucose, Regression models, Evolutionary algorithms", ISSN = "1084-8045", DOI = "doi:10.1016/j.jnca.2018.06.007", URL = "http://www.sciencedirect.com/science/article/pii/S1084804518302157", abstract = "This paper describes our preliminary steps towards the deployment of a brand-new original feature for a telemedicine portal aimed at helping people suffering from diabetes. In fact, people with diabetes necessitate careful handling of their disease to stay healthy. As such a disease is correlated to a malfunction of the pancreas that produces very little or no insulin, a way to enhance the quality of life of these subjects is to implement an artificial pancreas able to inject an insulin bolus when needed. The goal of this paper is to extrapolate a regression model, capable of estimating the blood glucose (BG) through interstitial glucose (IG) measurements, that represents a possible revolutionizing step in constructing the fundamental element of such an artificial pancreas. In particular, a new evolutionary approach is illustrated to stem a mathematical relationship between BG and IG. To accomplish the task, an automatic evolutionary procedure is also devised to estimate the missing BG values within the investigated real-world database made up of both BG and IG measurements of people suffering from Type 1 diabetes. The discovered model is validated through a comparison with other models during the experimental phase on global and personalized data treatment. Moreover, investigation is performed about the accuracy of one single global relationship model for all the subjects involved in the study, as opposed to that obtained through a personalized model found for each of them. Once this research is clinically validated, the important feature of estimating BG will be added to a web portal for diabetic subjects for telemedicine purposes", } @Article{DEFALCO:2019:ASC, author = "I. {De Falco} and A. {Della Cioppa} and A. Giugliano and A. Marcelli and Tomas Koutny and Michal Krcma and Umberto Scafuri and E. Tarantino", title = "A genetic programming-based regression for extrapolating a blood glucose-dynamics model from interstitial glucose measurements and their first derivatives", journal = "Applied Soft Computing", volume = "77", pages = "316--328", year = "2019", keywords = "genetic algorithms, genetic programming, Blood glucose estimation, Interstitial glucose, Regression models", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2019.01.020", URL = "http://www.sciencedirect.com/science/article/pii/S1568494619300249", abstract = "This paper illustrates the development and the applicability of an Evolutionary Computation approach to enhance the treatment of Type-1 diabetic patients that necessitate insulin injections. In fact, being such a disease associated to a malfunctioning pancreas that generates an insufficient amount of insulin, a way to enhance the quality of life of these patients is to implement an artificial pancreas able to artificially regulate the insulin dosage. This work aims at extrapolating a regression model, capable of estimating the blood glucose (BG) through interstitial glucose (IG) measurements and their numerical first derivatives. Such an approach represents a viable preliminary stage in building the basic component of this artificial pancreas. In particular, considered the high complexity of the reciprocal interactions, an evolutionary-based strategy is outlined to extrapolate a mathematical relationship between BG and IG and its derivative. The investigation is carried out about the accuracy of personalized models and of a global relationship model for all of the subjects under examination. The discovered models are assessed through a comparison with other models during the experiments on personalized and global data", } @InProceedings{conf/globecom/FalcoSTCKK20, author = "Ivanoe {De Falco} and Umberto Scafuri and Ernesto Tarantino and Antonio Della Cioppa and Tomas Koutny and Michal Krcma", title = "A Grammatical Evolution Approach for Estimating Blood Glucose Levels", booktitle = "2020 IEEE Globecom Workshops (GC Wkshps)", year = "2020", address = "Taipei, Taiwan", month = "7-11 " # dec, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, grammatical evolution", isbn13 = "978-1-7281-7307-8", bibdate = "2021-03-11", bibsource = "DBLP, http://dblp.uni-trier.de/https://doi.org/10.1109/GCWkshps50303.2020.9367402", DOI = "doi:10.1109/GCWkshps50303.2020.9367402", abstract = "The management of diabetes is a very complex task, hence devising automatic procedures able to predict the glycemic level can represent a significant step towards the building of an artificial pancreas capable of providing the needed amounts of insulin boluses.This paper presents a Grammatical Evolution-based algorithm aiming at extrapolating a regression model able to estimate the blood glucose level in future instants of time through interstitial glucose measurements. The hypothesis is that the amounts of carbohydrates assumed, of basal insulin levels and of those administered with boluses are known. Experiments, performed on a real-world database made up of five patients suffering from Type 1 diabetes, are shown in terms of Clark Error Grid analysis. To evaluate the effectiveness of the predictions derived from the proposed approach, the results obtained are compared against those obtained by other state-of-the-art evolutionary-based methods very recently proposed.", } @InProceedings{conf/iscc/FalcoCKSTU21, author = "I. {De Falco} and Antonio Della Cioppa and Tomas Koutny and Umberto Scafuri and Ernesto Tarantino and Martin Ubl", title = "Grammatical Evolution-Based Approach for Extracting Interpretable Glucose-Dynamics Models", booktitle = "2021 IEEE Symposium on Computers and Communications (ISCC)", year = "2021", address = "Athens, Greece", month = "5-8 " # sep, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, grammatical evolution", isbn13 = "978-1-6654-2744-9", bibdate = "2021-12-17", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/iscc/iscc2021.html#FalcoCKSTU21", DOI = "doi:10.1109/ISCC53001.2021.9631483", abstract = "The quality of life of diabetic patients can be enhanced by devising a personalized control algorithm, integrated within an artificial pancreas, capable of dosing the insulin. A key action in the building of this artificial device is to conceive an efficient algorithm for forecasting future glucose levels. Within this paper, an evolutionary-based strategy, i.e., a Grammatical Evolution algorithm, is devised to deduce a personalized forecasting model to evaluate blood glucose values in the future on the basis of the past glucose measurements, and the knowledge of the basal and infused insulin levels and of the food consumption. The aim is to discover models that are not only interpretable but also with low complexity to be used within a control algorithm that is the main element of the artificial pancreas. A real-world database composed by Type 1 diabetic patients has been employed to evaluate the proposed evolutionary automatic procedure.", } @InProceedings{platel83, author = "Michael {Defoin Platel} and Manuel Clergue and Philippe Collard", title = "Maximum Homologous Crossover for Linear Genetic Programming", booktitle = "Genetic Programming, Proceedings of EuroGP'2003", year = "2003", editor = "Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa", volume = "2610", series = "LNCS", pages = "194--203", address = "Essex", publisher_address = "Berlin", month = "14-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-00971-X", URL = "http://www.i3s.unice.fr/~defoin/publications/eurogp_03.pdf", size = "11 pages", DOI = "doi:10.1007/3-540-36599-0_18", abstract = "We introduce a new recombination operator, the Maximum Homologous Crossover for Linear Genetic Programming. In contrast to standard crossover, it attempts to preserve similar structures from parents, by aligning them according to their homology, thanks to an algorithm used in Bio-Informatics. To highlight disruptive effects of crossover operators, we introduce the Royal Road landscapes and the Homology Driven Fitness problem, for Linear Genetic Programming. Two variants of the new crossover operator are described and tested on this landscapes. Results show a reduction in the bloat phenomenon and in the frequency of deleterious crossovers.", notes = "EuroGP'2003 held in conjunction with EvoWorkshops 2003", } @InProceedings{defoin-platel:2003:EA, author = "Michael {Defoin Platel} and Sebastien Verel and Manuel Clergue and Philippe Collard", title = "From Royal Road to Epistatic Road for Variable Length Evolution Algorithm", booktitle = "Evolution Artificielle, 6th International Conference", year = "2003", editor = "Pierre Liardet and Pierre Collet and Cyril Fonlupt and Evelyne Lutton and Marc Schoenauer", volume = "2936", series = "Lecture Notes in Computer Science", pages = "3--14", address = "Marseilles, France", month = "27-30 " # oct, publisher = "Springer", note = "Revised Selected Papers", keywords = "genetic algorithms, genetic programming, Artificial Evolution, String Edit Distance, Levenshtein distance", ISBN = "3-540-21523-9", URL = "http://www.i3s.unice.fr/~defoin/publications/ea_03.pdf", DOI = "doi:10.1007/b96080", DOI = "doi:10.1007/978-3-540-24621-3_1", size = "12 pages", abstract = "Although there are some real world applications where the use of variable length representation (VLR) in Evolutionary Algorithm is natural and suitable, an academic framework is lacking for such representations. In this work we propose a family of tunable fitness landscapes based on VLR of genotypes. The fitness landscapes we propose possess a tunable degree of both neutrality and epistasis; they are inspired, on the one hand by the Royal Road fitness landscapes, and the other hand by the NK fitness landscapes. So these landscapes offer a scale of continuity from Royal Road functions, with neutrality and no epistasis, to landscapes with a large amount of epistasis and no redundancy. To gain insight into these fitness landscapes, we first use standard tools such as adaptive walks and correlation length. Second, we evaluate the performances of evolutionary algorithms on these landscapes for various values of the neutral and the epistatic parameters; the results allow us to correlate the performances with the expected degrees of neutrality and epistasis.", bibsource = "DBLP, http://dblp.uni-trier.de", notes = "EA'03", } @InProceedings{defoin-platel:2003:hgscigp, author = "Michael {Defoin Platel} and Manuel Clergue and Philippe Collard", title = "Homology gives size control in genetic programming", booktitle = "Proceedings of the 2003 Congress on Evolutionary Computation CEC2003", editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon", pages = "281--288", year = "2003", publisher = "IEEE Press", address = "Canberra", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "8-12 " # dec, organisation = "IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", keywords = "genetic algorithms, genetic programming, Evolutionary computation, Genetic mutations, Protection, Size control, Space exploration, Testing, parity, pattern recognition, search problems, accurate control, bloat reduction, even-N parity problem, homologous crossover, search space exploration, size control", ISBN = "0-7803-7804-0", URL = "http://www.i3s.unice.fr/~defoin/publications/cec_03.pdf", DOI = "doi:10.1109/CEC.2003.1299586", size = "8 pages", abstract = "The Maximum Homologous Crossover attempts to preserve similar structures from parents by aligning them according to their homology. In this paper, it is successfully tested on the classical Even-N Parity Problem where it demonstrates interesting abilities in bloat reduction. Then, we show that this operator gives an accurate control of the size of programs during the evolution and thus, allows the development of new strategies for the search space exploration.", notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.", } @InProceedings{eurogp:Defoin-PlatelCCC05, author = "Michael Defoin-Platel and Malik Chami and Manuel Clergue and Philippe Collard", editor = "Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. {van Hemert} and Marco Tomassini", title = "Teams of Genetic Predictors for Inverse Problem Solving", booktitle = "Proceedings of the 8th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3447", year = "2005", address = "Lausanne, Switzerland", month = "30 " # mar # " - 1 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-25436-6", pages = "341--350", size = "10", URL = "http://www.obs-vlfr.fr/LOV/OMT/fichiers_PDF/Defoin_and_Chami_LNCS_05.pdf", DOI = "doi:10.1007/978-3-540-31989-4_31", DOI = "doi:10.1007/b107383", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "Genetic Programming (GP) has been shown to be a good method of predicting functions that solve inverse problems. In this context, a solution given by GP generally consists of a sole predictor. In contrast, Stack-based GP systems manipulate structures containing several predictors, which can be considered as teams of predictors. Work in Machine Learning reports that combining predictors gives good results in terms of both quality and robustness. In this paper, we use Stack-based GP to study different cooperations between predictors. First, preliminary tests and parameter tuning are performed on two GP benchmarks. Then, the system is applied to a real-world inverse problem. A comparative study with standard methods has shown limits and advantages of teams prediction, leading to encourage the use of combinations taking into account the response quality of each team member.", notes = "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005", } @InProceedings{DBLP:conf/ae/Defoin-PlatelCC05, author = "Michael Defoin-Platel and Manuel Clergue and Philippe Collard", title = "Size Control with Maximum Homologous Crossover", year = "2005", pages = "13--24", editor = "El-Ghazali Talbi and Pierre Liardet and Pierre Collet and Evelyne Lutton and Marc Schoenauer", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3871", ISBN = "3-540-33589-7", bibsource = "DBLP, http://dblp.uni-trier.de", booktitle = "7th International Conference on Artificial Evolution EA 2005", address = "Lille, France", month = oct # " 26-28", note = "Revised Selected Papers", keywords = "genetic algorithms, genetic programming, Maximum Homologous Crossover, MHC, stack-based GP", URL = "https://hal.archives-ouvertes.fr/hal-00159738/document", DOI = "doi:10.1007/11740698_2", size = "12 pages", abstract = "Most of the Evolutionary Algorithms handling variable-sized structures, like Genetic Programming, tend to produce too long solutions and the recombination operator used is often considered to be partly responsible of this phenomenon, called bloat. The Maximum Homologous Crossover (MHC) preserves similar structures from parents by aligning them according to their homology. This operator has already demonstrated interesting abilities in bloat reduction but also some weaknesses in the exploration of the size of programs during evolution. we show that MHC do not induce any specific biases in the distribution of sizes, allowing size control during evolution. Two different methods for size control based on MHC are presented and tested on a symbolic regression problem. Results show that an accurate control of the size is possible while improving performances of MHC.", notes = "published 2006", } @InProceedings{Defoin-Platel:2006:HIS, author = "M. D. Platel and M. Clergue", title = "Monitoring Genetic Variations in Variable Length Evolutionary Algorithms", booktitle = "Sixth International Conference on Hybrid Intelligent Systems, HIS '06", year = "2006", pages = "4--4?", address = "Rio de Janeiro, Brazil", month = dec, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, bloat", ISBN = "0-7695-2662-4", DOI = "doi:10.1109/HIS.2006.264887", abstract = "Initially, Artificial Evolution focuses on Evolutionary Algorithms handling solutions coded in fixed length structures. In this context, the role of crossover is clearly the mixing of information between solutions. The development of Evolutionary Algorithms operating on structures with variable length, of which genetic programming is one of the most representative instances, opens new questions on the effects of crossover. Beside mixing, two new effects are identified : the diffusion of information inside solutions and the variation of the solutions sizes. In this paper, we propose a experimental framework to study these three effects and apply it on three different crossovers for genetic programming : the Standard Crossover, the One-Point Crossover and the Maximum Homologous Crossover. Exceedingly different behaviours are reported leading us to consider the necessary future decoupling of the mixing, the diffusion and the size variation.", notes = "Laboratoire d'Oceanographie de Villefranche (LOV), France;", } @InProceedings{eurogp07:defoin, author = "Michael {Defoin Platel} and S\'ebastien Verel and Manuel Clergue and Malik Chami", title = "Density estimation with Genetic Programming for Inverse Problem solving", editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar", booktitle = "Proceedings of the 10th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "4445", year = "2007", address = "Valencia, Spain", month = "11-13 " # apr, pages = "45--54", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-71602-5", isbn13 = "978-3-540-71602-0", DOI = "doi:10.1007/978-3-540-71605-1_5", abstract = "This paper addresses the resolution, by Genetic Programming (GP) methods, of ambiguous inverse problems, where for a single input, many outputs can be expected. We propose two approaches to tackle this kind of many-to-one inversion problems, each of them based on the estimation, by a team of predictors, of a probability density of the expected outputs. In the first one, Stochastic Realisation GP, the predictors outputs are considered as the realisations of an unknown random variable which distribution should approach the expected one. The second one, Mixture Density GP, directly models the expected distribution by the mean of a Gaussian mixture model, for which genetic programming has to find the parameters. Encouraging results are obtained on four test problems of different difficulty, exhibiting the interests of such methods.", notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007", } @PhdThesis{defoinplatel:tel-00131993, author = "Michael {Defoin Platel}", title = "Homology in Genetic Programming Application to inverse problem solving", title_fr = "Homologie en Programmation Genetique Application a la resolution d'un probleme inverse", school = "{Universite Nice Sophia Antipolis}", year = "2004", address = "France", month = "19 " # nov, keywords = "genetic algorithms, genetic programming, Evolutionnary Algorithms, Homology, Recombination, Inverse Problem, Algorithmes Evolutionnaires, Programmation G{\'e}n{\'e}tique, Homologie, Recombinaison, Probl{\`e}me Inverse", hal_id = "tel-00131993", hal_version = "v1", URL = "https://tel.archives-ouvertes.fr/tel-00131993/file/these_dpm.pdf", URL = "https://tel.archives-ouvertes.fr/tel-00131993", size = "204 pages", abstract = "Evolutionary Algorithms (EA) are search methods working iteratively on a population of potential solutions that are randomly selected and modified. Genetic Programming (GP) is an EA that allows automatic search for programs, usually represented as syntax trees (TGP) or linear sequences (LGP). Two mechanisms perform the random variations needed to obtain new programs : the mutation operator (local variation) and the crossover operator (programs recombination). The crossover operator blindly exchanges parts of programs without taking the context into account, this is a brutal operation that may be responsible of the uncontrolled growth of programs during evolution. Mainly inspired by the homologous crossover of DNA strands, we introduce the Maximum Homologous Crossover for LGP. The MHC ensures, thanks to a measure of similarity, that recombination of programs is respectful. We show on classical GP benchmarks, e.g. the symbolic regression problem, that when using MHC the search process is less brutal and that an accurate control of programs size is also possible. These results are used to address a real world problem : the inversion of atmospheric components. We show that, with a constant computational effort, it is also possible to find teams of inversion predictors that outperform standard models.", notes = "In French. Francais. Supervisor: Philippe Collard", } @InProceedings{deFranca:2013:CEC, article_id = "1043", author = "Fabricio {de Franca} and Guilherme Coelho", title = "Identifying Overlapping Communities in Complex Networks with Multimodal Optimization", booktitle = "2013 IEEE Conference on Evolutionary Computation", volume = "1", year = "2013", month = jun # " 20-23", editor = "Luis Gerardo {de la Fraga}", pages = "269--276", address = "Cancun, Mexico", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-0453-2", DOI = "doi:10.1109/CEC.2013.6557580", notes = "CEC 2013 - A joint meeting of the IEEE, the EPS and the IET.", } @Article{deFranca:2018:IS, author = "Fabricio {Olivetti de Franca}", title = "A greedy search tree heuristic for symbolic regression", journal = "Information Sciences", year = "2018", volume = "442", pages = "18--32", month = may, keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1016/j.ins.2018.02.040", DOI = "doi:10.1016/j.ins.2018.02.040", publisher = "Elsevier", abstract = "Symbolic Regression tries to find a mathematical expression that describes the relationship of a set of explanatory variables to a measured variable. The main objective is to find a model that minimizes the error and, optionally, that also minimizes the expression size. A smaller expression can be seen as an interpretable model considered a reliable decision model. This is often performed with Genetic Programming, which represents their solution as expression trees. The shortcoming of this algorithm lies on this representation that defines a rugged search space and contains expressions of any size and difficulty. These pose as a challenge to find the optimal solution under computational constraints. This paper introduces a new data structure, called Interaction-Transformation (IT), that constrains the search space in order to exclude a region of larger and more complicated expressions. In order to test this data structure, it was also introduced an heuristic called SymTree. The obtained results show evidence that SymTree are capable of obtaining the optimal solution whenever the target function is within the search space of the IT data structure and competitive results when it is not. Overall, the algorithm found a good compromise between accuracy and simplicity for all the generated models.", } @Article{deFranca:EC, author = "F. O. {de Franca} and G. S. I. Aldeia", title = "Interaction-Transformation Evolutionary Algorithm for Symbolic Regression", journal = "Evolutionary Computation", year = "2021", volume = "29", number = "3", pages = "367--390", month = "Fall", keywords = "genetic algorithms, genetic programming, Symbolic Regression, Interaction-Transformation, ITEA, evolutionary algorithms", ISSN = "1063-6560", DOI = "doi:10.1162/evco_a_00285", code_url = "https://github.com/folivetti/ITEA/", size = "24 pages", abstract = "Interaction-Transformation (IT) is a new representation for Symbolic Regression that reduces the space of solutions to a set of expressions that follow a specific structure. The potential of this representation was illustrated in prior work with the algorithm called SymTree. This algorithm starts with a simple linear model and incrementally introduces new transformed features until a stop criterion is met. While the results obtained by this algorithm were competitive with the literature, it had the drawback of not scaling well with the problem dimension. This paper introduces a mutation only Evolutionary Algorithm, called ITEA, capable of evolving a population of IT expressions. One advantage of this algorithm is that it enables the user to specify the maximum number of terms in an expression. In order to verify the competitiveness of this approach, ITEA is compared to linear, nonlinear and Symbolic Regression models from the literature. The results indicate that ITEA is capable of finding equal or better approximations than other Symbolic Regression models while being competitive to state-of-the-art non-linear models. Additionally, since this representation follows a specific structure, it is possible to extract the importance of each original feature of a data set as an analytical function, enabling us to automate the explanation of any prediction. In conclusion, ITEA is competitive when comparing to regression models with the additional benefit of automating the extraction of additional information of the generated models.", notes = "Fabricio Olivetti de Franca and Guilherme Seidyo Imai Aldeia See also https://arxiv.org/abs/1902.03983 Center for Mathematics, Computation and Cognition, Heuristics, Analysis and Learning Laboratory, Federal University of ABC, Santo Andre, Brazil", } @Article{DEFRANCA:2021:NC, author = "Fabricio Olivetti {de Franca} and Maira Zabuscha {de Lima}", title = "Interaction-transformation symbolic regression with extreme learning machine", journal = "Neurocomputing", year = "2021", volume = "423", pages = "609--619", keywords = "genetic algorithms, genetic programming, ANN, Symbolic regression, Interaction-transformation, Extreme learning machines", ISSN = "0925-2312", URL = "https://www.sciencedirect.com/science/article/pii/S0925231220316398", DOI = "doi:10.1016/j.neucom.2020.10.062", size = "11 pages", abstract = "Symbolic Regression searches for a mathematical expression that fits the input data set by minimizing the approximation error. The search space explored by this technique is composed of any mathematical function representable as an expression tree. This provides more flexibility for fitting the data but it also makes the task more challenging. The search space induced by this representation becomes filled with redundancy and ruggedness, sometimes requiring a higher computational budget in order to achieve good results. Recently, a new representation for Symbolic Regression was proposed, called Interaction-Transformation, which can represent function forms as a composition of interactions between predictors and the application of a single transformation function. we show how this representation can be modeled as a multi-layer neural network with the weights adjusted following the Extreme Learning Machine procedure. The results show that this approach is capable of finding equally good or better results than the current state-of-the-art with a smaller computational cost.", notes = "Also known as \cite{DEFRANCA2021609} Universidade Federal do ABC (UFABC), Center of Mathematics, Computing and Cognition (CMCC), R. Santa Adelia 166, CEP 09210-170, Santo Andre, Brazil", } @InProceedings{deFranca:2022:GECCO, author = "Fabricio {de Franca}", title = "{Transformation-Interaction-Rational} Representation for Symbolic Regression", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference", year = "2022", editor = "Alma Rahat and Jonathan Fieldsend and Markus Wagner and Sara Tari and Nelishia Pillay and Irene Moser and Aldeida Aleti and Ales Zamuda and Ahmed Kheiri and Erik Hemberg and Christopher Cleghorn and Chao-li Sun and Georgios Yannakakis and Nicolas Bredeche and Gabriela Ochoa and Bilel Derbel and Gisele L. Pappa and Sebastian Risi and Laetitia Jourdan and Hiroyuki Sato and Petr Posik and Ofer Shir and Renato Tinos and John Woodward and Malcolm Heywood and Elizabeth Wanner and Leonardo Trujillo and Domagoj Jakobovic and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Inmaculada Medina-Bulo and Slim Bechikh and Andrew M. Sutton and Pietro Simone Oliveto", pages = "920--928", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, symbolic regression, regression", isbn13 = "978-1-4503-9237-2", DOI = "doi:10.1145/3512290.3528695", video_url = "https://vimeo.com/721571580", abstract = "Symbolic Regression searches for a function form that approximates a dataset often using Genetic Programming. Since there is usually no restriction to what form the function can have, Genetic Programming may return a hard to understand model due to non-linear function chaining or long expressions. A novel representation called Interaction-Transformation was recently proposed to alleviate this problem. In this representation, the function form is restricted to an affine combination of terms generated as the application of a single univariate function to the interaction of selected variables. This representation obtained competing solutions on standard benchmarks. Despite the initial success, a broader set of benchmarking functions revealed the limitations of the constrained representation. In this paper we propose an extension to this representation, called Transformation-Interaction-Rational representation that defines a new function form as the rational of two Interaction-Transformation functions. Additionally, the target variable can also be transformed with an univariate function. The main goal is to improve the approximation power while still constraining the overall complexity of the expression. We tested this representation with a standard Genetic Programming with crossover and mutation. The results show a great improvement when compared to its predecessor and a state-of-the-art performance for a large benchmark.", notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @Misc{defranca2023interpretablesymbolicregressiondata, author = "F. O. {de Franca} and M. Virgolin and M. Kommenda and M. S. Majumder and M. Cranmer and G. Espada and L. Ingelse and A. Fonseca and M. Landajuela and B. Petersen and R. Glatt and N. Mundhenk and C. S. Lee and J. D. Hochhalter and D. L. Randall and P. Kamienny and H. Zhang and G. Dick and A. Simon and B. Burlacu and Jaan Kasak and Meera Machado and Casper Wilstrup and W. G. {La Cava}", title = "Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition", howpublished = "arXiv 2304.01117", year = "2023", month = "3 " # jul, note = "v3", keywords = "genetic algorithms, genetic programming, Symbolic Regression, Competition, Interpretable Machine Learning, XAI", eprint = "2304.01117", archiveprefix = "arXiv", primaryclass = "cs.LG", URL = "https://arxiv.org/abs/2304.01117", size = "13 pages", abstract = "Symbolic regression searches for analytic expressions that accurately describe studied phenomena. The main attraction of this approach is that it returns an interpretable model that can be insightful to users. Historically, the majority of algorithms for symbolic regression have been based on evolutionary algorithms. However, there has been a recent surge of new proposals that instead use approaches such as enumeration algorithms, mixed linear integer programming, neural networks, and Bayesian optimization. In order to assess how well these new approaches behave on a set of common challenges often faced in real-world data, we hosted a competition at the 2022 Genetic and Evolutionary Computation Conference consisting of different synthetic and real-world datasets which were blind to entrants. For the real-world track, we assessed interpretability in a realistic way by using a domain expert to judge the trustworthiness of candidate this http URL present an in-depth analysis of the results obtained in this competition, discuss current challenges of symbolic regression algorithms and highlight possible improvements for future competitions.", notes = "submitted to IEEE Transactions on Evolutionary Computation \cite{deFranca:ieeeTEC} GECCO-2022", } @Article{deFranca:TELO, author = "Fabricio {Olivetti de Franca}", title = "Transformation-Interaction-Rational Representation for Symbolic Regression: A Detailed Analysis of {SRBench} Results", journal = "ACM Transactions on Evolutionary Learning and Optimization", year = "2023", volume = "3", number = "2", articleno = "2", month = jun, keywords = "genetic algorithms, genetic programming, regression, symbolic regression", publisher = "Association for Computing Machinery", address = "New York, NY, USA", ISSN = "2688-299X", DOI = "doi:10.1145/3597312", size = "19 pages", abstract = "Symbolic Regression searches for a parametric model with the optimal value of the parameters that best fits a set of samples to a measured target. The desired solution has a balance between accuracy and interpretability. Commonly there is no constraint in the way the functions are composed in the expression nor where the numerical parameters are placed, this can potentially lead to expressions that require a nonlinear optimization to find the optimal parameters. The representation called Interaction-Transformation alleviates this problem by describing expressions as a linear regression of the composition of functions applied to the interaction of the variables. One advantage is that any model that follows this representation is linear in its parameters, allowing an efficient computation. More recently, this representation was extended by applying a univariate function to the rational function of two Interaction-Transformation expressions, called Transformation-Interaction-Rational (TIR). The use of this representation was shown to be competitive with the current literature of Symbolic Regression. In this paper, we make a detailed analysis of these results using the SRBench benchmark. For this purpose, we split the datasets into different categories to understand the algorithm behavior in different settings. We also test the use of nonlinear optimisation to adjust the numerical parameters instead of Ordinary Least Squares. We find through the experiments that TIR has some difficulties handling high-dimensional and noisy data sets, especially when most of the variables are composed of random noise. These results point to new directions for improving the evolutionary search of TIR expressions.", notes = "The Best of GECCO 2022, Part I Universidade Federal do ABC, Center for Mathematics, Computing and Cognition, Heuristics, Analysis and Learning Laboratory (HAL), Brazi https://dlnext.acm.org/journal/telo", } @InProceedings{deFranca:2023:GPTP, author = "Matheus Campos Fernandes and Fabricio Olivetti {de Franca} and Emilio Francesquini", title = "Origami: (un)folding the Abstraction of Recursion Schemes for Program Synthesis", booktitle = "Genetic Programming Theory and Practice XX", year = "2023", editor = "Stephan Winkler and Leonardo Trujillo and Charles Ofria and Ting Hu", series = "Genetic and Evolutionary Computation", pages = "263--281", address = "Michigan State University, USA", month = jun # " 1-3", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-981-99-8412-1", DOI = "doi:10.1007/978-981-99-8413-8_14", abstract = "Program synthesis with Genetic Programming searches for a correct program that satisfies the input specification, which is usually provided as input-output examples. One particular challenge is how to effectively handle loops and recursion avoiding programs that never terminate. A helpful abstraction that can alleviate this problem is the employment of Recursion Schemes that generalize the combination of data production and consumption. Recursion Schemes are very powerful as they allow the construction of programs that can summarize data, create sequences, and perform advanced calculations. The main advantage of writing a program using Recursion Schemes is that the programs are composed of well-defined templates with only a few parts that need to be synthesized. In this paper, we make an initial study of the benefits of using program synthesis with fold and unfold templates and outline some preliminary experimental results. To highlight the advantages and disadvantages of this approach, we manually solved the entire GPSB benchmark using recursion schemes, highlighting the parts that should be evolved compared to alternative implementations. We noticed that, once the choice of which recursion scheme is made, the synthesis process can be simplified as each of the missing parts of the template are reduced to simpler functions, which are further constrained by their own input and output types.", notes = " Part of \cite{Hu:2023:GPTP} published after the workshop in 2024", } @Article{deFranca:2023:GPEM, author = "Fabricio Olivetti {de Franca}", title = "Alleviating overfitting in transformation-interaction-rational symbolic regression with multi-objective optimization", journal = "Genetic Programming and Evolvable Machines", year = "2023", volume = "24", number = "2", pages = "Article number: 13", month = dec, note = "Special Issue on Highlights of Genetic Programming 2022 Events", keywords = "genetic algorithms, genetic programming, Symbolic regression, Multi-objective, MOGP", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-023-09461-3", size = "23 pages", notes = "Center for Mathematics, Computation and Cognition, Federal University of ABC, Av. dos Estados, Santo Andre, SP 09210580, Brazil", } @Article{deFranca:ieeeTEC, author = "F. O. {de Franca} and M. Virgolin and M. Kommenda and M. S. Majumder and M. Cranmer and G. Espada and L. Ingelse and A. Fonseca and M. Landajuela and B. Petersen and R. Glatt and N. Mundhenk and C. S. Lee and J. D. Hochhalter and D. L. Randall and P. Kamienny and H. Zhang and G. Dick and A. Simon and B. Burlacu and Jaan Kasak and Meera Machado and Casper Wilstrup and W. G. {La Cava}", title = "{SRBench++}: Principled Benchmarking of Symbolic Regression With Domain-Expert Interpretation", journal = "IEEE Transactions on Evolutionary Computation", note = "Early Access", keywords = "genetic algorithms, genetic programming, XAI, Benchmark testing, Task analysis, Accuracy, Evolutionary computation, Prediction algorithms, Machine learning algorithms, Current measurement, Symbolic Regression, Competition, Interpretable Machine Learning", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2024.3423681", code_url = "https://github.com/cavalab/srbench", size = "12 pages", abstract = "... We evaluate 12 modern symbolic regression algorithms on thesebenchmarks and present an in-depth analysis of the results,discuss current challenges of symbolic regression algorithms and highlight possible improvements for the benchmark itself operon, PS-Tree, gpzgd, QLattice, uDSR, Bingo, eql, pysr, geneticengine, E2ET, LinearRegression, TaylorGP, nsga-dcgp", notes = "Also known as \cite{10586218} See also \cite{defranca2023interpretablesymbolicregressiondata} COVID-19 data in New York state, USA.", } @InProceedings{DeFreitas:2018:CECdiogo, author = "Diogo M. De-Freitas and Plinio S. Leitao-Junior and Celso G. Camilo-Junior and Rachel Harrison", booktitle = "2018 IEEE Congress on Evolutionary Computation (CEC)", title = "Mutation-Based Evolutionary Fault Localisation", year = "2018", abstract = "Fault localisation is an expensive and time-consuming stage of software maintenance. Research is continuing to develop new techniques to automate the process of reducing the effort needed for fault localisation without losing quality. For instance, spectrum-based techniques use execution information from testing to formulate measures for ranking a list of suspicious code locations at which the program may be defective: the suspiciousness formulae mainly combine variables related to code coverage and test results (pass or fail). Moreover previous research has evaluated mutation analysis data (mutation spectra) instead of coverage traces, to yield promising results. This paper reports on a Genetic Programming (GP) solution for the fault localisation problem together with a set of experiments to evaluate the GP solution with respect to baselines and benchmarks. The innovative aspects are the joint investigation of: (i) specialisation of suspiciousness formulae for certain contexts; (ii) the application of mutation spectra to GP-evolved formulae, i.e. signals other than program coverage; (iii) a comparison of the effectiveness of coverage spectra and mutation spectra in the context of evolutionary approaches; and (iv) an analysis of the mutation spectra quality. The results show the competitiveness of GP-evolved mutation spectra heuristics over coverage traces as well as over a number of baselines, and suggest that the quality of mutation-related variables increases the effectiveness of fault localisation heuristics.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2018.8477719", month = jul, notes = "Universidade Federal de Goias Also known as \cite{8477719}", } @InProceedings{deFreitas:2010:cec, author = "Junio {de Freitas} and Gisele L. Pappa and Altigran S. {da Silva} and Marcos A. Goncalves and Edleno Moura and Adriano Veloso and Alberto H. F. Laender and Moises G. {de Carvalho}", title = "Active Learning Genetic programming for record deduplication", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-6910-9", URL = "http://www.dcc.ufmg.br/~adrianov/papers/CEC10/cec10.pdf", abstract = "The great majority of genetic programming (GP) algorithms that deal with the classification problem follow a supervised approach, i.e., they consider that all fitness cases available to evaluate their models are labelled. However, in certain application domains, a lot of human effort is required to label training data, and methods following a semi-supervised approach might be more appropriate. This is because they significantly reduce the time required for data labelling while maintaining acceptable accuracy rates. This paper presents the Active Learning GP (AGP), a semi-supervised GP, and instantiates it for the data deduplication problem. AGP uses an active learning approach in which a committee of multi-attribute functions votes for classifying record pairs as duplicates or not. When the committee majority voting is not enough to predict the class of the data pairs, a user is called to solve the conflict. The method was applied to three datasets and compared to two other deduplication methods. Results show that AGP guarantees the quality of the deduplication while reducing the number of labeled examples needed.", DOI = "doi:10.1109/CEC.2010.5586104", notes = "WCCI 2010. Also known as \cite{5586104}", } @InProceedings{deFreitas:2014:CILAMCE, author = "Joao Marcos {de Freitas} and Marcus Vinicius Silva and Heder S. Bernardino and Joao N. C. Guerreiro and Helio J. C. Barbosa", title = "Aplica\c{c}\~{a}o de uma programa\c{c}\~{a}o gen\'{e}tica gramatical na infer\^{e}ncia da m\'{a}xima deforma\c{c}\~{a}o longitudinal de dutos com amassamento", title_en = "Application of a grammatical genetic programming in the inference of the maximum longitudinal deformation of ducts with kneading", booktitle = "Proceedings of the Ibero-Latin American Congress on Computational Methods in Engineering (CILAMCE)", year = "2014", editor = "Evandro Parente Junior", pages = "CILAMCE2014--0422", address = "Av. Monsenhor Tabosa, 740 - Praia de Iracema - Fortaleza - Ceara - Brazil, 60.165-010.", month = nov # " 23-26", organisation = "Ikone Eventos cilamce2014@eventosikone.com.br", keywords = "genetic algorithms, genetic programming", notes = "In Portuguese. See also \cite{joao2017aplicacao}. CILAMCE2014-0422 Aplicacao de uma programacao genetica gramatical na inferencia da maxima deformacao longitudinal de dutos com amassamento Graduate Program in Civil Engineering: Structures and Civil Construction of the Federal University of Ceara (PEC/UFC). Broken Apr 2023 http://www.swge.inf.br/sitecilamce2014/ https://www.cilamce.com.br/conteudo/view?ID_CONTEUDO=473", } @Misc{joao2017aplicacao, author = "Joao Macros {de Freitas}", title = "Aplicacao de uma Programacao Genetica Gramatical Coevolutiva no Apoio a Inferencia da Maxima Deformacao Longitudinal de dutos com Amassamento", school = "Ciencia da Computacao, Federal University of Juiz de Fora", year = "2016", type = "Bachelor in computer science", address = "Brazil", month = "9 " # dec, keywords = "genetic algorithms, genetic programming, Grammar based Genetic Programming, Co-evolution, Computational Intelligence, Pipe Engineering", URL = "http://monografias.ice.ufjf.br/tcc-web/tcc?id=266", size = "44 pages", abstract = "The extraction of underground fluid fuels is an important resource in order to produce energy. However, there are several factors that make this practice hard, such as damage that causes deformations on the pipe that extracts the fuels. The objective of this work is to determine relationships between characteristics observed on the pipe and fluid with the maximum longitudinal deformation of the pipe from data obtained through analyses using the Finite Element Method. An automatic process for knowledge discovery using an intelligent system that can evolve models in symbolic form is proposed here. Genetic Programming methods presented good results to this type of application and a grammatical approach is adopted here, where the models (programs) are inferred by means of a Formal Grammar. A grammar brings to the GP technique the benefit of generating only valid programs/models and the possibility of limiting the search space by introducing bias. Also, a coevolutionary approach is used to focus the search process on data which are harder to evaluate. Preliminary computational experiments were conducted to solve the problem of inferring a model of the maximum longitudinal deformation of pipes and the results indicate that the application of a Co-evolutionary Grammar based Genetic Programming can solve this problem with good accuracy and less computational cost.", key = "FREITAS,2017", notes = "In Portuguese Supervisor: Heder Soares Bernardino", } @InProceedings{deFreitas:2018:CEC, author = "Joao Marcos {de Freitas} and Felipe Rafael {de Souza} and Heder Bernardino", title = "Evolving Controllers for {Mario AI} Using Grammar-based Genetic Programming", booktitle = "2018 IEEE Congress on Evolutionary Computation (CEC)", year = "2018", editor = "Marley Vellasco", address = "Rio de Janeiro, Brazil", month = "8-13 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2018.8477698", size = "8 pages", abstract = "Video games mimic real-world situations and they can be used as a benchmark to evaluate computational methods in solving different types of problems. Also, machine learning methods are used nowadays to improve the quality of non-player characters in order (i) to create human like behaviours, and (ii) to increase the hardness of the games. Genetic Programming (GP) has presented good results when evolving programs in general. One of the main advantage of GP is the availability of the source-code of its solutions, helping researchers to understand the decision-making process. Also, a formal grammar can be used in order to facilitate the generation of programs in more complex languages (such as Java, C, and Python). Here, we propose the use of Grammar-based Genetic Programming (GGP) to evolve controllers for Mario AI, a popular platform to test video game controllers which simulates the Nintendo's Super Mario Bros. Also, as GP provides the source-code of the solutions, we present and analyse the best program obtained. Finally, GGP is compared to other techniques from the literature and the results show that GGP find good controllers, specially with respect to the scores obtained on higher difficulty levels.", notes = "WCCI2018", } @InProceedings{deFreitas:2022:GECCOcomp, author = "Joao {de Freitas} and Heder Bernardino and Luciana Goncalves and Stenio Soares", title = "Human Activity Recognition Using Grammar-based Genetic Programming", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion", year = "2022", editor = "Heike Trautmann and Carola Doerr and Alberto Moraglio and Thomas Bartz-Beielstein and Bogdan Filipic and Marcus Gallagher and Yew-Soon Ong and Abhishek Gupta and Anna V Kononova and Hao Wang and Michael Emmerich and Peter A. N. Bosman and Daniela Zaharie and Fabio Caraffini and Johann Dreo and Anne Auger and Konstantin Dietric and Paul Dufosse and Tobias Glasmachers and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Tea Tusar and Dimo Brockhoff and Tome Eftimov and Pascal Kerschke and Boris Naujoks and Mike Preuss and Vanessa Volz and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Mark Coletti and Catherine (Katie) Schuman and Eric ``Siggy'' Scott and Robert Patton and Paul Wiegand and Jeffrey K. Bassett and Chathika Gunaratne and Tinkle Chugh and Richard Allmendinger and Jussi Hakanen and Daniel Tauritz and John Woodward and Manuel Lopez-Ibanez and John McCall and Jaume Bacardit and Alexander Brownlee and Stefano Cagnoni and Giovanni Iacca and David Walker and Jamal Toutouh and UnaMay O'Reilly and Penousal Machado and Joao Correia and Sergio Nesmachnow and Josu Ceberio and Rafael Villanueva and Ignacio Hidalgo and Francisco {Fernandez de Vega} and Giuseppe Paolo and Alex Coninx and Antoine Cully and Adam Gaier and Stefan Wagner and Michael Affenzeller and Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and William B. Langdon and Justyna Petke and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Paetzel and Alexander Wagner and Michael Heider and Nadarajen Veerapen and Katherine Malan and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Mohammad Nabi Omidvar and Yuan Sun and Ernesto Tarantino and De Falco Ivanoe and Antonio {Della Cioppa} and Scafuri Umberto and John Rieffel and Jean-Baptiste Mouret and Stephane Doncieux and Stefanos Nikolaidis and Julian Togelius and Matthew C. Fontaine and Serban Georgescu and Francisco Chicano and Darrell Whitley and Oleksandr Kyriienko and Denny Dahl and Ofer Shir and Lee Spector and Alma Rahat and Richard Everson and Jonathan Fieldsend and Handing Wang and Yaochu Jin and Erik Hemberg and Marwa A. Elsayed and Michael Kommenda and William {La Cava} and Gabriel Kronberger and Steven Gustafson", pages = "699--702", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, human activity recognition, classification", isbn13 = "978-1-4503-9268-6/22/07", DOI = "doi:10.1145/3520304.3529076", abstract = "Smart devices provide a way of acquiring useful data for human activity recognition (HAR). The identification of activities is a task applicable to a wide range of situations, such as automatically providing aid to someone in need. Machine learning techniques can solve this problem, but their capacity in providing understanding regarding the classification is usually limited. Here, we propose a Grammar-based Genetic Programming (GGP) to generate interpretable models for HAR. A Context-free Grammar defines a language that the models belong to, providing a way to read and extract knowledge. The results show that the proposed GGP generates results better than another Genetic Programming method and machine learning approaches. Also, the models created provided an understanding of the features associated with the activities.", notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @InProceedings{oai:CiteSeerPSU:512552, author = "Hugo {de Garis}", title = "Artificial Embryology", booktitle = "Artificial Life {III}", year = "1992", address = "Santa Fe", month = jun, keywords = "genetic algorithms, cellular automata", citeseer-isreferencedby = "oai:CiteSeerPSU:81581", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:512552", rights = "unrestricted", broken = "http://www.iss.whu.edu.cn/degaris/papers/ALife92.pdf", URL = "https://pdfs.semanticscholar.org/3b71/90c9ab761841e66ed7ec88371b2fa1b99316.pdf", URL = "http://citeseer.ist.psu.edu/512552.html", size = "20 pages", abstract = "This paper introduces some ideas and early results concerning the Genetic Programming of artificial shapes. Genetic Programming (GP) [de GARIS 1990, 1991, 1992] is defined to be {"}the art of using Genetic Algorithms to build/evolve complex systems{"}. Complex systems are defined to be systems which are too complex in their structures or dynamics to be predictable or analyzable. Embryos and brains are obvious examples. This paper shows how GP techniques can be applied to (reproductive) cellular automata [WOLFRAM 1986] to build a colony of cells having a desired global shape. This paper shows that this type of work can be extended to building shapes sequentially, e.g. {"}limbs{"} can be {"}grown{"} out of {"}bodies{"}, so that a 2D {"}artificial embryo{"} is grown. It is hoped that such techniques will contribute towards the creation of a new branch of ALife, called {"}Artificial Embryology{"}, which is defined to be {"}the art of generating instructions to enable abstract cells to reproduce and differentiate in abstract media, such that a final agglomeration of cells has certain properties (such as a desired shape, or desired behaviors etc){"}. It may be possible that these ideas will be taken over into a form of {"}embryological electronics{"}, which uses GP techniques to {"}grow{"} electronic circuits in an electronic substrate, using special devices called {"}Darwin Machines{"}.", notes = "at the end of the paper, the author suggests his use of {"}genetic programming{"} is different from Koza's. Growing CAs into non-convex shapes: snow man, L, turtle. Embryological self-assembly of nanomachines. Diffentiable chromosome. {"}Shaping is simply splitting up an evolutionary process into intermediate phases, with intermediate targets{"} (section 4). References contain list of de Garis papers using {"}genetic programming{"} in their title.", } @InProceedings{deGaris:1992:dcGPssg, author = "Hugo {de Garis} and Hitoshi Iba and Tatsumi Furuya", title = "Differentiable Chromosomes: The Genetic Programming of switchable Shape-Genes", booktitle = "Parallel Problem Solving from Nature 2", month = "28-30 " # sep, year = "1992", editor = "R Manner and B Manderick", pages = "489--498", address = "Brussels, Belgium", publisher = "Elsevier Science", keywords = "genetic algorithms, genetic programming", size = "10 pages", URL = "http://www.iss.whu.edu.cn/degaris/papers/PPSN92.pdf", notes = "Wants to build machines with billions of components, proposes these grow themselves in an embryonic fashion. Does some experiments with two stage, hence differentiable, chromosomes which control the states of a cellular automata. Stages are switched on by psuedo chemical gradient. Can grow convex shapes but pretty poor at using GA to evolve concave shapes. PPSN2", } @InProceedings{degaris:1993:erGPsrca, author = "Hugo {de Garis}", title = "Evolving a Replicator The Genetic Programming of Self Reproduction in Cellular Automata", booktitle = "ECAL-93 Self organisation and life: from simple rules to global complexity", year = "1993", pages = "274--284", address = "CP 231, Universite Libre de Bruxelles, Bld. du Triomphe, 1050 Brussels, Belgium, Fax 32-2-659.5767 Phone 32-2-650.5776 Email sgross@ulb.ac.be", month = "24--26 " # may, organisation = "Centre for Non-Linear Phenomena and Complex Systems", email = "degaris@hip.att.co.jp", keywords = "genetic algorithms, genetic programming, nonotechnology, nanots, artificial life, Qantum-electronic computers, Darwin machines", URL = "http://www.iss.whu.edu.cn/degaris/papers/ECAL93.pdf", URL = "http://citeseer.ist.psu.edu/521663.html", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.17.9218&rank=7", URL = "http://alife.org/paper/ecal93/evolving-replicator-genetic-programming-self-reproduction-cellular-automata", URL = "http://alife.org/sites/default/files/collections/ECAL93-0274-0284-De-Garis.pdf", size = "11 pages", abstract = "This paper presents the results of an investigative study into the evolution of cellular automata replicators using Genetic Programming (GP) techniques (i.e. using Genetic Algorithms (GAs) to build/evolve complex systems). There are at least two reasons why such a study might be considered interesting. One reason is to explore how difficult the evolution of (CA) replicators might be, a topic of importance for Artificial Life. Another reason is the possibility that the evolution of CAs, if successful, may provide tools for next-generation quantum-electronic computers (e.g. using quantum dot arrays) which may use CAs as their operating principle.", notes = "Presents results from the evolution of cellular automata replicators using GP (ie using GAs to build/evolve systems. 1: How difficult is the evolution of CA replicators (intersity to Artificial Life), 2: Evolving CAs may provide tools for quantum-electronic computers (eg quantum dot arrays)", notes = "There seems to be some doubt as to wether ECAL-93 was published. This copy from attendee. May 2014: see CD-ROM version on alife.org/paper/ecal93 web pages. GA chromosome is fixed (1024 * 4 CA state values) encoding the CA state transition rules. {"}Evolving CA replicators is much harder than initially thought{"} Now working on CA networks cf Von Neuman, Codd, Burks. At one point erroneously ascribed to ECAL-2013 \cite{deGaris:2013:ECAL}.", } @InProceedings{deGaris:1994:CAM-BRAIN, author = "Hugo {de Garis}", title = "CAM-BRAIN The Genetic Programming of an Artificial Brain Which Grows/Evolves at Electronic Speeds in a Cellular Automata Machine", booktitle = "Proceedings of the 1994 IEEE World Congress on Computational Intelligence", year = "1994", volume = "1", pages = "337--339b", address = "Orlando, Florida, USA", month = "27-29 " # jun, publisher = "IEEE Press", keywords = "genetic algorithms, cellular automata, neural networks", size = "6 pages", DOI = "doi:10.1109/ICEC.1994.349929", abstract = "The paper reports on a project which aims to build (i.e. grow/evolve) an artificial brain by the year 2001. This artificial brain should initially contain thousands of interconnected artificial neural network modules, and be capable of controlling approximately 1000 behaviours in a robot kitten. The name given to this research project is CAM-Brain, because the neural networks (based on cellular automata) will be grown inside special hardware called cellular automata machines (CAMs). Using a family of CAMs, each with its own processor to measure the performance quality or fitness of the evolved neural circuits, will allow the neural modules and their interconnections to be grown/evolved at electronic speeds. State of the art in CAM design is about 10 to the power 9 or 10 cells. Since a neural module of about 15 connected neurons can fit inside a cube of 100 cells on a side (1 million cells), a CAM which is specially adapted for CAM-Brain could contain thousands of interconnected modules, i.e. an artificial brain", notes = "It appears growth of cellular automata are controlled by linear fixed length chromosome, ie does not use Koza style tree. The CA grow in channels which convey signals that are isolated from each other except at junctions (synapses). Artificial brain by 2000AD.", } @Misc{degaris:1996:alifeV, author = "Hugo {de Garis}", title = "Alife-V 1996 Conference Report", year = "1996", month = jul, keywords = "genetic algorithms, genetic programming, artificial life", URL = "http://www.hip.atr.co.jp/~degaris/AlifeV.txt broken", size = "7 pages", abstract = "Personal account of the 5th World Artificial Life Conference, 16-18 May 1996, Nara, Japan", } @InProceedings{garis:1999:AABPASWIMENNMCDDAI, author = "Hugo {de Garis} and Andrzej Buller and Michael Korkin and Felix Gers and Norberto Eija Nawa and Michael Hough", title = "{ATR}'s Artificial Brain {(``CAM-Brain'')} Project: A Sample of What Individual {``CoDi-1Bit''} Model Evolved Neural Net Modules Can Do with Digital and Analog {I/O}", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1233", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, ANN, poster papers", ISBN = "1-55860-611-4", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco1999/garis_1999_aabpaswimennmcddai.pdf", size = "1 page", notes = "This work presents a sample of what evolved neural net circuit modules using the socalled CoDi-1Bit neural net model [5] can do. This work is part of an 8 year research project at ATR which aims to build an artificial brain containing a billion neurons by the year 2001, that will be used to control the behaviours of a kitten robot Robokoneko [2][3][4]. It looks as though the figure is more likely to be 40 million, but the numbers are not of great concern. What is more important is the issue of evolvability of the cellular automata (CA) based neural net circuits which grow and evolve in special FPGA (Field Programmable Gate Array) hardware, at hardware speeds (e.g. updating 150 billion CA cells per second, and performing a complete run of a genetic algorithm, i.e. tens of thousands of circuit growths and fitness evaluations, to evolve the elite neural net circuit in about 1 second). The specialized hardware which performs this evolution is labeled the CAM-Brain Machine (CBM) [6]. It implements the CoDi-1Bit model, and was delivered to ATR in May 1999. The CBM should make practical the assemblage of 10,000s of evolved neural net modules into humanly defined artificial brain architectures. For the past few months, the latest hardware version of the CBM has been simulated in software to see just how evolvable and functional individual evolved modules can be. This work reports on some of the results of these simulations, for which the input/output is either binary or analog. May 2020 Notes from https://dl.acm.org/doi/10.5555/787262.787793 as close to abstract. GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99) 11 Nov 2005 Ten page version at citeseer.ist.psu.edu/22456.html See also CEC 1999", } @InProceedings{degaris:2002:gecco:lbp, title = "A Reversible Evolvable Network Architecture and Methodology to Overcome the Heat Generation Problem in Molecular Scale Brain Building", author = "Hugo {de Garis} and Jonathan Dinerstein and Ravichandra Sriram", booktitle = "Late Breaking Papers at the Genetic and Evolutionary Computation Conference (GECCO-2002)", editor = "Erick Cant{\'u}-Paz", year = "2002", month = jul, pages = "83--90", address = "New York, NY", publisher = "AAAI", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025", keywords = "genetic algorithms, genetic programming", URL = "http://www.iss.whu.edu.cn/degaris/papers/RENN.pdf", abstract = "Today's irreversible computing style, in which bits of information are routinely wiped out (e.g. a NAND gate has 2 input bits, and only 1 output bit), cannot continue. If Moore's Law remains valid until 2020, as many commentators think, then the heat generated in molecular scale circuits that Moore's Law will provide, would be so intense that they will explode [Hall 1992]. To avoid such heat generation problems, it has been known since the early 1970s [Bennet 1973] that the secret to ``heatless computation'' is to compute reversibly, i.e. not to destroy bits, by sending in the input bit-string through a computer built from reversible logic gates (e.g. Fredkin gates [Fredkin et al 1982], to record the output answer and then send the output bit-string backwards through the computer to obtain the original input bit-string. This reversible style of computing takes twice as long, but does not destroy bits, hence does not generate heat. (Landauer's principle states that the heat generated from irreversible computing is derived from the destruction of bits of information [Landauer 1961]). The first author intends to build artificial brains over the remaining 20 years of his active research career, by evolving (neural) network modules directly in electronics (at electronic speeds) in their 100,000s and assembling them into artificial brains. In the next 10-20 years, electronic circuitry will reach molecular scales; hence a conceptual problem needs to be faced. How to make evolvable (neural) networks that are reversible? This paper proposes a reversible evolvable Boolean network architecture and methodology which, it is hoped, will stimulate the evolvable hardware and evolvable neural network research communities to devote more effort towards solving this problem, which can only accentuate as Moore's Law continues to bite.", notes = "Late Breaking Papers, {GECCO-2002}. A joint meeting of the eleventh International Conference on Genetic Algorithms ({ICGA-2002}) and the seventh Annual Genetic Programming Conference ({GP-2002}) part of cantu-paz:2002:GECCO:lbp", } @Article{degaris:2005:EC, author = "Hugo {de Garis}", title = "Evolvable Hardware 2005", journal = "Evolutionary Computation", year = "2005", volume = "13", number = "4", month = "Winter", pages = "545--550", keywords = "genetic algorithms, genetic programming, EHW", DOI = "doi:10.1162/106365605774666840", notes = "Conference report http://ic.arc.nasa.gov/projects/eh2005/ IEEE Press ISBN 0-7695-2399-4, June 2005. Mentions papers by Dmitri Berenson, John Koza \cite{koza:2005:EH}, Simon Harding \cite{harding:2005:EH}, and Tim Gordon \cite{gordon:2005:EH}.", } @Article{degefa:2023:Sustainability, author = "Aron Berhanu Degefa and Seunghee Park and Beomjoo Yang and Solmoi Park", title = "Predicting the Degree of Reaction of Supplementary Cementitious Materials in Hydrated Portland Cement", journal = "Sustainability", year = "2023", volume = "15", number = "21", pages = "Article No. 15471", keywords = "genetic algorithms, genetic programming", ISSN = "2071-1050", URL = "https://www.mdpi.com/2071-1050/15/21/15471", DOI = "doi:10.3390/su152115471", abstract = "Determination and prediction of degree of reaction (DOR) of supplementary cementitious materials (SCMs) in hydrated Portland cement are important for designing concrete with lower levels of embodied carbon dioxide. Herein, a model for predicting the DOR of SCMs in hydrated cement was developed using a set of collected data and a machine learning algorithm based on genetic programming toolbox for the identification of physical systems. The results suggest that the model reliably predicts the DOR of slag, fly ash, metakaolin, and silica fume with a coefficient of determination (R2) value of 0.89. The predicted DOR of SCMs is found to be directly proportional to water-to-cement ratio and curing time, while it is highly reliant on the oxide composition and differs amongst SCMs. For instance, the DOR of slag substantially increased with a higher alumina content, while the DOR of metakaolin remained more stable, primarily influenced by the silica-to-alumina ratio. The proposed model is particularly useful for predicting phase assemblages of SCMs-blended Portland cement when experimental data and information on SCMs are limited and properties of SCMs are highly variable. The insights gained from this study offer a pathway towards more sustainable and efficient concrete design, aligning with contemporary environmental objectives.", notes = "also known as \cite{su152115471}", } @Article{DEGIORGI:2020:AST, author = "Maria Grazia {De Giorgi} and Marco Quarta", title = "Hybrid {MultiGene} Genetic Programming - Artificial neural networks approach for dynamic performance prediction of an aeroengine", journal = "Aerospace Science and Technology", year = "2020", volume = "103", pages = "105902", month = aug, keywords = "genetic algorithms, genetic programming, ANN, Jet engine, Turbojet, Dynamic performance, Machine learning, Artificial neural networks", ISSN = "1270-9638", URL = "http://www.sciencedirect.com/science/article/pii/S1270963820305848", DOI = "doi:10.1016/j.ast.2020.105902", size = "12 pages", abstract = "Dynamic aeroengine models have an important role in the design of real-time control systems. Modelling of aeroengines using dynamic performance simulations is a key step in the design process in order to reduce costs and the development period. A dynamic model can provide a numerical counterpart for the development of control systems and for the study of the engine behaviour in both steady and unsteady scenarios. The latter situation is particularly felt in the military field. The Viper 632-43 engine analysed in this work is a military turbojet, so it was necessary to develop a model that would replicate its behaviour as realistically as possible. The model was built using the Gas turbine Simulation Program (GSP) software and validated both in steady and transient conditions. Once the engine model was validated, different machine learning techniques were used to estimate (data mining) and predict an engine parameter; the Exhaust Gas Temperature (EGT) has been chosen as the key parameter. A MultiGene Genetic Programming (MGGP) technique has been used to derive simple mathematical relationships between different input parameters and the EGT. These, then, can be used to calculate the EGT value of a real Viper 632-43 engine knowing a priori the input parameters and in any operating condition. Finally, the EGT estimated by this algorithm has been added to the dataset used for the one-step-ahead EGT prediction by Artificial Neural Network (ANN). A time-series ANN was used for the EGT prediction, i.e. the Nonlinear AutoRegressive with eXogenous inputs (NARX) neural network. This network recognizes the input data as a real time series and is therefore able to predict the output in the next time step. It was chosen to use, as forecasting method, the one-step-ahead technique which allows to predict the EGT in the immediately next time step", } @Article{DEGIORGI:2020:DB, author = "Maria Grazia {De Giorgi} and Marco Quarta", title = "Data regarding dynamic performance predictions of an aeroengine", journal = "Data in Brief", volume = "31", pages = "105977", year = "2020", ISSN = "2352-3409", DOI = "doi:10.1016/j.dib.2020.105977", URL = "http://www.sciencedirect.com/science/article/pii/S2352340920308714", keywords = "genetic algorithms, genetic programming, Aeroengine, Turbojet modelling, Artificial neural network, Machine learning", abstract = "The design of aeroengine real-time control systems needs the implementation of machine learning based techniques. The lack of in-flight aeroengine performance data is a limit for the researchers interested in the development of these prediction algorithms. Dynamic aeroengine models can be used to overcome this lack. This data article presents data regarding the performance of a turbojet that were predicted by the dynamic engine model that was built using the Gas turbine Simulation Program (GSP) software. The data were also used to implement an Artificial Neural Network (ANN) that predicts the in-flight aeroengine performance, such as the Exhaust Gas Temperature (EGT). The Nonlinear AutoRegressive with eXogenous inputs (NARX) neural network was used. The neural network predictions have been also given as dataset of the present article. The data presented here are related to the article entitled {"}MultiGene Genetic Programming - Artificial Neural Networks approach for dynamic performance prediction of an aeroengine{"} [1]", } @Article{Degtyarev:2022:SCS, author = "Vitaliy V. Degtyarev and Stephen J. Hicks and Jerome F. Hajjar", title = "Design models for predicting shear resistance of studs in solid concrete slabs based on symbolic regression with genetic programming", journal = "Steel and Composite Structures", year = "2022", volume = "43", number = "3", pages = "293--309", month = may, keywords = "genetic algorithms, genetic programming, headed studs, shear resistance, steel-concrete composite structures, reliability, machine learning, symbolic regression", publisher = "Korea Institute of Science and Technology Information", ISSN = "1229-9367", URL = "https://wrap.warwick.ac.uk/id/eprint/163641/1/WRAP-Design-models-predicting-shear-resistance-studs-solid-concrete-2022.pdf", DOI = "doi:10.12989/scs.2022.43.3.293", size = "17 pages", abstract = "Accurate design models for predicting the shear resistance of headed studs in solid concrete slabs are essential for obtaining economical and safe steel-concrete composite structures. In this study, symbolic regression with genetic programming (GPSR) was applied to experimental data to formulate new descriptive equations for predicting the shear resistance of studs in solid slabs using both normal and lightweight concrete. The obtained GPSR-based nominal resistance equations demonstrated good agreement with the test results. The equations indicate that the stud shear resistance is insensitive to the secant modulus of elasticity of concrete, which has been included in many international standards following the pioneering work of Ollgaard et al. In contrast, it increases when the stud height-to-diameter ratio increases, which is not reflected by the design models in the current international standards. The nominal resistance equations were subsequently refined for use in design from reliability analyses to ensure that the target reliability index required by the Eurocodes was achieved. Resistance factors for the developed equations were also determined following US design practice. The stud shear resistance predicted by the proposed models was compared with the predictions from 13 existing models. The accuracy of the developed models exceeds the accuracy of the existing equations. The proposed models produce predictions that can be used with confidence in design, while providing significantly higher stud resistances for certain combinations of variables than those computed with the existing equations given by many standards.", notes = "Also known as \cite{warwick163641}", } @InProceedings{deHaas:2009:ismir, author = "W. Bas {de Haas} and Martin Rohrmeier and Remco C. Veltkamp and Frans Wiering", title = "Modeling Harmonic Similarity Using a Generative Grammar of Tonal Harmony", booktitle = "10th International Society for Music Information Retrieval Conference", year = "2009", editor = "Keiji Hirata and George Tzanetakis", pages = "549--554", address = "Kobe, Japan", month = "26-30 " # oct, URL = "http://ismir2009.ismir.net/proceedings/OS7-2.pdf", size = "6 pages", abstract = "In this paper we investigate a new approach to the similarity of tonal harmony. We create a fully functional remodeling of an earlier version of Rohrmeier's grammar of harmony. With this grammar an automatic harmonic analysis of a sequence of symbolic chord labels is obtained in the form of a parse tree. The harmonic similarity is determined by finding and examining the largest labeled common embeddable subtree (LLCES) of two parse trees. For the calculation of the LLCES a new O(min(n,m)nm) time algorithm is presented, where n and m are the sizes of the trees. For the analysis of the LLCES we propose six distance measures that exploit several structural characteristics of the Combined LLCES. We demonstrate in a retrieval experiment that at least one of these new methods significantly outperforms a baseline string matching approach and thereby show that using additional musical knowledge from music cognitive and music theoretic models actually helps improving retrieval performance.", } @InProceedings{Dehghani:2012:ASONAM, author = "Mostafa Dehghani and Masoud Asadpour and Azadeh Shakery", booktitle = "Advances in Social Networks Analysis and Mining (ASONAM), 2012 IEEE/ACM International Conference on", title = "An Evolutionary-Based Method for Reconstructing Conversation Threads in Email Corpora", year = "2012", pages = "1132--1137", address = "Istanbul", month = "26-29 " # aug, isbn13 = "978-1-4673-2497-7", DOI = "doi:10.1109/ASONAM.2012.195", size = "6 pages", abstract = "Email is a type of Web data which is produced in enormous quantities. It is beneficial to detect conversation threads contained in the email corpora for various applications, including discussion search, expert finding and even email clustering and classification. Conversation thread in email corpora can be defined as a cluster of exchanged emails among the same group of people by reply or forwarding on the same topic. According to this definition, we can define parent-child relation between emails, so email conversation threads seem to demonstrate tree structure. This paper presents a new approach based on genetic programming for reconstruction of conversation threads in emails data. This approach considers finding email conversation threads as an optimisation problem, and exploits genetic programming to search intelligently in the space of possible solutions. Rather than several studies that have been conducted on this problem, this work concentrates on detecting accurate structure of conversation threads in high recall. This paper provides a comprehensive evaluation on the BC3 data set. Preliminary results suggest that our method provides acceptable precision and higher recall than existing methods.", keywords = "genetic algorithms, genetic programming, Internet, electronic mail, pattern classification, pattern clustering, BC3 data set, Web data, conversation thread reconstruction, discussion search, email classification, email clustering, email corpora, evolutionary-based method, expert finding, optimisation problem, parent-child relation, Biological cells, Educational institutions, Electronic mail, Social network services, Sociology, Statistics, conversation, email, emails thread", notes = "Also known as \cite{6425605}", } @Article{DEHGHANIDARMIAN:2023:jenvman, author = "Mohsen {Dehghani Darmian} and Britta Schmalz", title = "Application of genetic programming in presenting novel equations for longitudinal dispersion coefficient in natural streams considering rivers geometry - Implementation in assimilation capacity simulation", journal = "Journal of Environmental Management", volume = "340", pages = "117985", year = "2023", ISSN = "0301-4797", DOI = "doi:10.1016/j.jenvman.2023.117985", URL = "https://www.sciencedirect.com/science/article/pii/S0301479723007739", keywords = "genetic algorithms, genetic programming, Longitudinal dispersion coefficient, Pollutant transport, Natural rivers, River sinuosity, Assimilation capacity", abstract = "Precise estimation of the longitudinal dispersion coefficient (LDC) is crucial for the accurate simulation of water quality management tools such as assimilation capacity. Previous research analyzed the LDC of natural streams in two general categories: ignoring or considering the river sinuosity (?). Genetic programming (GP) is used in this study to investigate both mentioned categories by applying two experimental datasets from 56 to 24 different rivers worldwide. The first proposed LDC equation of this research (without ?) improves the amounts of statistical measures R2 (Determination Coefficient), OI (Overall Index), NSE (Nash-Sutcliffe Efficiency), WI (Willmott's Index of Agreement), RMSE (Root Mean Square Error), and MAE (Mean Absolute Error) by 3.75percent, 4.71percent, 7.81percent, 0.85percent, 13.72percent, and 0.68percent, respectively, compared to the best values of these indicators in the previous investigations. Regarding the second category, relative and absolute sensitivity analyses are conducted, which reveal that ? is the most influential parameter in the accurate prediction of the LDC among all hydraulics and geometric parameters of the river. This part of the investigation presents four unique LDC equations that closely match the experimental results. Significant improvement of the most accurate presented LDC for statistical indices R2, OI, NSE, WI, RMSE, MAE, and accuracy percentage are obtained equal to 3.27percent, 2.41percent, 3.16percent, 0.81percent, 35.1percent, 24.47percent, 3.8percent, respectively, in comparison with the best previous relations. Also, a new indicator for measuring the efficiency of mathematical equations called Mean Normalized Statistical Index (MNSI) is introduced and applied in different parts of this research. Finally, the assimilation capacity of the Kashafrud River is determined based on the analytical method of pollution propagation for three types of water demands using the accurately presented LDC in 1993-2020. The average amount of river assimilation capacity using accurate LDC is simulated at 91.93 tons/day, much lower than the currently reported pollution entrance, which equals 540 tons/day", } @InProceedings{Dehghanpour:2017:ieeeINISTA, author = "Siamak Dehghanpour and Akbar Esfahanipour", booktitle = "2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA)", title = "A robust genetic programming model for a dynamic portfolio insurance strategy", year = "2017", pages = "201--206", abstract = "In this paper, we propose a robust genetic programming model for a dynamic strategy of stock portfolio insurance. With portfolio insurance strategy, we need to allocate part of the money in risky asset and the other part in risk-free asset. Our applied strategy is based on constant proportion portfolio insurance (CPPI) strategy. For determining the amount for investing in risky assets, the critical parameter is a constant risk multiplier which is used in traditional CPPI method so that it may not reflect the changes occurring in market condition. Thus, we propose a model in which, the risk multiplier is calculated with robust genetic programming. In our model, risk variables are used to generate equation trees for calculating the risk multiplier. We also implement an artificial neural network to enhance our model's robustness. We also combine the portfolio insurance strategy with a well-known portfolio optimisation model to get the best possible portfolio weights of risky assets for insurance. Experimental results using five stocks from New York Stock Exchange (NYSE) show that our proposed robust genetic programming model outperforms the other two models: the basic genetic programming for portfolio insurance without portfolio optimisation, and the basic genetic programming for portfolio insurance with portfolio optimisation.", keywords = "genetic algorithms, genetic programming, Robust Genetic Programming (RGP), Dynamic portfolio insurance strategy, Portfolio Optimization model, Constant Proportion Portfolio Insurance (CPPI)", DOI = "doi:10.1109/INISTA.2017.8001157", month = jul, notes = "Also known as \cite{8001157}", } @Article{Dehghanpour:jit, author = "Siamak Dehghanpour and Akbar Esfahanipour", title = "Dynamic portfolio insurance strategy: a robust machine learning approach", journal = "Journal of Information and Telecommunication", year = "2018", volume = "2", number = "4", pages = "392--410", keywords = "genetic algorithms, genetic programming, Robust genetic programming (RGP), portfolio insurance strategy, machine learning, portfolio optimization model, constant proportion portfolio insurance (CPPI)", publisher = "Taylor \& Francis", ISSN = "2475-1839", DOI = "doi:10.1080/24751839.2018.1431447", size = "19 pages", abstract = "we propose a robust genetic programming (RGP) model for a dynamic strategy of stock portfolio insurance. With portfolio insurance strategy, we divide the money in a risky asset and a risk-free asset. Our applied strategy is based on a constant proportion portfolio insurance strategy. For determining the amount for investing in the risky asset, a critical parameter is a constant risk multiplier that is calculated in our proposed model using RGP to reflect market dynamics. Our model includes four main steps: (1) Selecting the best stocks for constructing a portfolio using a density-based clustering strategy. (2) Enhancing the robustness of our proposed model with an application of the Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for forecasting the future prices of the selected stocks. The findings show that using ANFIS, instead of a regular multi-layer artificial neural network improves the prediction accuracy and our model's robustness. (3) Implementing the RGP model for calculating the risk multiplier. Risk variables are used to generate equation trees for calculating the risk multiplier. (4) Determining the optimal portfolio weights of the assets using the well-known Markowitz portfolio optimization model. Experimental results show that our proposed strategy outperforms our previous model.", notes = "Published online: 27 Feb 2018", } @Misc{oai:CiteSeerX.psu:10.1.1.372.7511, author = "Pooya Khosraviyan Dehkordi and Farshad Kumarci and Hamid Khosravi", title = "Text Summarization Based on Genetic Programming", year = "2013", month = oct # "~30", number = "1/", keywords = "genetic algorithms, genetic programming, automatic text summarisation, vectorial model", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", issue = "57-64", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.372.7511", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.372.7511", broken = "http://ijcir.org/volume3-number1/issue 57-64.pdf", abstract = "This work proposes an approach to address the problem of improving content selection in automatic text summarisation by using some statistical tools. This approach is a trainable summariser, which takes into account several features, for each sentence to generate summaries. First, we investigate the effect of each sentence feature on the summarization task. Then we use all features in combination to train genetic programming (GP), vector approach and fuzzy approach in order to construct a text summariser for each model. Furthermore, we use trained models to test summarisation performance. The proposed approach performance is measured at several compression rates on a data corpus composed of 17 English scientific articles.", } @InProceedings{Deias:2009:LAPC, author = "L. Deias and G. Mazzarella and N. Sirena", title = "Bandwidth optimization of EBG surfaces using genetic programming", booktitle = "Loughborough Antennas Propagation Conference, LAPC 2009", year = "2009", month = "16-17 " # nov, address = "Loughborough, UK", pages = "593--596", keywords = "genetic algorithms, genetic programming, bandwidth optimization, evolutionary strategy, full-wave MoM, planar periodic EBG surface, unit cell geometry, bandwidth allocation, method of moments, periodic structures, photonic band gap, surface electromagnetic waves", DOI = "doi:10.1109/LAPC.2009.5352381", abstract = "In this paper genetic programming is applied to the synthesis of planar periodic EBG. We constrained our design to the unit cell geometry and used a full-wave MoM to evaluate all individuals. The evolutionary strategy is then employed in order to find a geometry with a larger bandwidth.", notes = "Also known as \cite{5352381}", } @InProceedings{Deias:2010:APSURSI, author = "L. Deias and G. Mazzarella and N. Sirena", title = "EBG substrate synthesis for {2.45 GHz} applications using Genetic Programming", booktitle = "Antennas and Propagation Society International Symposium (APSURSI), 2010 IEEE", year = "2010", month = "11-17 " # jul, abstract = "In the last decade the study of frequency selective surfaces (FSS), i.e. periodic metal patches printed on a dielectric substrate, has regained interest both in the microwave and millimeter-wave region, with the introduction of electromagnetic band gap (EBG) materials. This entirely new class of structures, encompassing FSS as one of its subclasses (planar EBG), were named in analogy to the band gaps present in electric crystals and present some very interesting new electromagnetic properties. By choosing the proper geometry of the periodic surface we can shape the electromagnetic behaviour of EBGs structures in order to prevent the propagation of electromagnetic waves in a given frequency band. In particular, EBG surfaces can be made to act as artificial magnetic conductors (AMC) ground planes, showing a reflection coefficient with magnitude 1 and phase 0. The ultimate goal is then to design and incorporate such metamaterial-substrates in antenna structures in order to improve antenna performance. Currently there is a growing interest in antennas integrated with an EBG surface for communication system applications, covering the 2.45 GHz and the 5 GHz wireless networking bands. The main drawback of this strategy is the reduced bandwidth of the complete antenna, since the frequency range over which these EBG surfaces behave as an AMC is usually narrowband and fixed by their geometrical configuration. For this reason we focused our research both on the optimisation of EBGs and the synthesis of new promising geometries using genetic programming (GP).", keywords = "genetic algorithms, genetic programming, EBG structure electromagnetic behaviour, EBG substrate synthesis, FSS, antenna structures, artificial magnetic conductor ground planes, communication system, dielectric substrate, electric crystals, electromagnetic band gap materials, electromagnetic property, electromagnetic wave propagation, frequency 2.45 GHz, frequency 5 GHz, frequency selective surfaces, metamaterial substrate, microwave region, millimeter-wave region, periodic metal patches, reflection coefficient, wireless networking bands, UHF antennas, electromagnetic wave propagation, frequency selective surfaces, microwave antennas, photonic band gap, substrates", DOI = "doi:10.1109/APS.2010.5562232", ISSN = "1522-3965", notes = "ECJ Dept. of Electr. & Electron. Eng., Univ. of Cagliari, Cagliari, Italy. Also known as \cite{5562232}", } @InProceedings{Deias:2014:CEM, author = "Luisa Deias and Alessandro Fanti and Giuseppe Mazzarella", booktitle = "9th IET International Conference on Computation in Electromagnetics (CEM 2014)", title = "EBG substrate synthesis for dual frequency applications using genetic programming", year = "2014", month = mar, size = "2 pages", abstract = "In this paper evolutionary computation is applied to the synthesis of planar periodic EBG for dual frequency applications. We constrained our evolutionary design to the unit cell geometry and used a full-wave MoM to evaluate all individuals.", keywords = "genetic algorithms, genetic programming, method of moments, photonic band gap, EBG substrate synthesis, electromagnetic band gap, evolutionary computation, full-wave MoM, planar periodic EBG, unit cell geometry", DOI = "doi:10.1049/cp.2014.0225", notes = "University of Cagliari, Department of Electrical and Electronic Engineering (DIEE), Italy Also known as \cite{6826854}", } @InProceedings{Deiner:2020:SSBSE, author = "Adina Deiner and Christoph Fraedrich and Gordon Fraser and Sophia Geserer and Niklas Zantner", title = "Search-Based Testing for Scratch Programs", booktitle = "12th International Symposium on Search Based Software Engineering SSBSE 2020", year = "2020", editor = "Juan Pablo Galeotti and Bonita Sharif", series = "LNCS", volume = "12420", pages = "58--72", address = "Bari, Italy", month = "7-8 " # oct, publisher = "Springer", note = "Best paper", keywords = "genetic algorithms, genetic programming, grammatical evolution, SBSE, scratch, cat, Search-based testing, SBST, Block-based programming, video games, wisker", isbn13 = "978-3-030-59761-0", URL = "https://link.springer.com/chapter/10.1007/978-3-030-59762-7_5", URL = "https://arxiv.org/pdf/2009.04115.pdf", DOI = "doi:10.1007/978-3-030-59762-7_5", code_url = "https://github.com/se2p/whisker-main", size = "16 pages", abstract = "Block-based programming languages enable young learners to quickly implement fun programs and games. The Scratch programming environment is particularly successful at this, with more than 50 million registered users at the time of this writing. Although Scratch simplifies creating syntactically correct programs, learners and educators nevertheless frequently require feedback and support. Dynamic program analysis could enable automation of this support, but the test suites necessary for dynamic analysis do not usually exist for Scratch programs. It is, however, possible to cast test generation for Scratch as a search problem. In this paper, we introduce an approach for automatically generating test suites for Scratch programs using grammatical evolution. The use of grammatical evolution clearly separates the search encoding from framework-specific implementation details, and allows us to use advanced test acceleration techniques. We implemented our approach as an extension of the Whisker test framework. Evaluation on sample Scratch programs demonstrates the potential of the approach.", notes = "Code on GitHub.com Chrome and headless chrome. http://ssbse2020.di.uniba.it/", } @InProceedings{eddejong:1999:gssc, author = "Edwin D. {de Jong} and Luc Steels", title = "Generation and Selection of Sensory Channels", booktitle = "Evolutionary Image Analysis, Signal Processing and Telecommunications: First European Workshop, EvoIASP'99 and EuroEcTel'99", year = "1999", editor = "Riccardo Poli and Hans-Michael Voigt and Stefano Cagnoni and Dave Corne and George D. Smith and Terence C. Fogarty", volume = "1596", series = "LNCS", pages = "90--100", address = "Goteborg, Sweden", publisher_address = "Berlin", month = "28-29 " # may, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-65837-8", URL = "http://arti.vub.ac.be/~edwin/publications/channels.ps.gz", DOI = "doi:10.1007/10704703_7", size = "11 pages", abstract = "Sensory channels determine the way an agent views the world. We investigate the question of how sensory channels may be autonomously constructed using generation and selection. The context is the discrimination of geometric shapes. In a first experiment, elements of a solution were attributed fitness based on the part of the problem they solved. In two subsequent experiments, cooperation between elements was respectively required and encouraged by means of a fitness function which only rewards complete solutions. Differences between the approaches are discussed, and generation and selection is concluded to provide a successful mechanism for the autonomous construction of sensory channels.", notes = "EvoIASP99'99", } @InProceedings{jong:2001:gecco, title = "Reducing Bloat and Promoting Diversity using Multi-Objective Methods", author = "Edwin D. {de Jong} and Richard A. Watson and Jordan B. Pollack", pages = "11--18", year = "2001", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)", editor = "Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon and Edmund Burke", address = "San Francisco, California, USA", publisher_address = "San Francisco, CA 94104, USA", month = "7-11 " # jul, keywords = "genetic algorithms, genetic programming, code growth, bloat, introns, diversity maintenance, evolutionary multi-objective optimization, Pareto, optimality", ISBN = "1-55860-774-9", URL = "http://www.demo.cs.brandeis.edu/papers/rbpd_gecco01.pdf", URL = "http://www.demo.cs.brandeis.edu/papers/rbpd_gecco01.ps.gz", URL = "http://www.demo.cs.brandeis.edu/papers/long.html#rbpd_gecco01", URL = "http://gpbib.cs.ucl.ac.uk/gecco2001/d01.pdf", URL = "http://citeseer.ist.psu.edu/440305.html", abstract = "Two important problems in genetic programming (GP) are its tendency to find unnecessarily large trees (bloat), and the general evolutionary algorithms problem that diversity in the population can be lost prematurely. The prevention of these problems is frequently an implicit goal of basic GP. We explore the potential of techniques from multi-objective optimization to aid GP by adding explicit objectives to avoid bloat and promote diversity. The even 3, 4, and 5-parity problems were solved efficiently compared to basic GP results from the literature. Even though only non-dominated individuals were selected and populations thus remained extremely small, appropriate diversity was maintained. The size of individuals visited during search consistently remained small, and solutions of what we believe to be the minimum size were found for the 3, 4, and 5-parity problems.", notes = "GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of \cite{spector:2001:GECCO}", } @Article{dejong:2003:GPEM, author = "Edwin D. {de Jong} and Jordan B. Pollack", title = "Multi-Objective Methods for Tree Size Control", journal = "Genetic Programming and Evolvable Machines", year = "2003", volume = "4", number = "3", pages = "211--233", month = sep, keywords = "genetic algorithms, genetic programming, variable size representations, bloat, code growth, multi-objective optimization, Pareto optimality, interpretability", ISSN = "1389-2576", URL = "http://www.cs.uu.nl/~dejong/publications/bloat.ps", URL = "http://www.cs.uu.nl/~dejong/index.html#bloatgpem", DOI = "doi:10.1023/A:1025122906870", abstract = "Variable length methods for evolutionary computation can lead to a progressive and mainly unnecessary growth of individuals, known as bloat. First, we propose to measure performance in genetic programming as a function of the number of nodes, rather than trees, that have been evaluated. Evolutionary Multi-Objective Optimisation (EMOO) constitutes a principled way to optimise both size and fitness and may provide parameterless size control. Reportedly, its use can also lead to minimisation of size at the expense of fitness. We replicate this problem, and an empirical analysis suggests that multi-objective size control particularly requires diversity maintenance. Experiments support this explanation. The multi-objective approach is compared to genetic programming without size control on the 11-multiplexer, 6-parity, and a symbolic regression problem. On all three test problems, the method greatly reduces bloat and significantly improves fitness as a function of computational expense. Using the FOCUS algorithm, multi-objective size control is combined with active pursuit of diversity, and hypothesised minimum-size solutions to 3-, 4- and 5-parity are found. The solutions thus found are furthermore easily interpretable. When combined with diversity maintenance, EMOO can provide an adequate and parameterless approach to size control in variable length evolution.", notes = "Article ID: 5141122 Tue, 23 Mar 2004 01:22:36 +0100 See erratum in issue 5:1 Initial drop in size. 5-Parity given XOR!", } @InProceedings{conf/pakdd/JongN13, author = "Jill {de Jong} and Kourosh Neshatian", title = "Binary Classification Using Genetic Programming: Evolving Discriminant Functions with Dynamic Thresholds", booktitle = "Trends and Applications in Knowledge Discovery and Data Mining", editor = "Jiuyong Li and Longbing Cao and Can Wang and Kay Chen Tan and Bo Liu and Jian Pei and Vincent S. Tseng", year = "2013", volume = "7867", series = "Lecture Notes in Computer Science", pages = "464--474", address = "Gold Coast, Australia", month = apr # " 14-17", publisher = "Springer", note = "Revised Selected Papers", keywords = "genetic algorithms, genetic programming", bibdate = "2013-08-27", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/pakdd/pakdd2013-w.html#JongN13", isbn13 = "978-3-642-40318-7", URL = "http://dx.doi.org/10.1007/978-3-642-40319-4", DOI = "doi:10.1007/978-3-642-40319-4_40", size = "11 pages", abstract = "Binary classification is the problem of predicting which of two classes an input vector belongs to. This problem can be solved by using genetic programming to evolve discriminant functions which have a threshold output value that distinguishes between the two classes. The standard approach is to have a static threshold value of zero that is fixed throughout the evolution process. Items with a positive function output value are identified as one class and items with a negative function output value as the other class. We investigate a different approach where an optimum threshold is dynamically determined for each candidate function during the fitness evaluation. The optimum threshold is the one that achieves the lowest misclassification cost. It has an associated class translation rule for output values either side of the threshold value. The two approaches have been compared experimentally using four different datasets. Results suggest the dynamic threshold approach consistently achieves higher performance levels than the standard approach after equal numbers of fitness calls.", notes = "PAKDD 2013 International Workshops: DMApps, DANTH, QIMIE, BDM, CDA, CloudSD, 2013", } @InProceedings{icga87:deJong, author = "Kenneth {De Jong}", title = "On Using Genetic Algorithms to Search Program Spaces", booktitle = "Genetic Algorithms and their Applications: Proceedings of the second international conference on Genetic Algorithms", year = "1987", editor = "John J. Grefenstette", pages = "210--216", month = "28-31 " # jul, organisation = "AAAI", address = "MIT, Cambridge, MA, USA", publisher_address = "Hillsdale, NJ, USA", publisher = "Lawrence Erlbaum Associates", keywords = "genetic algorithms, genetic programming", size = "7 pages", ISBN = "0-8058-0158-8", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/icga1987/icga87_deJong.pdf", URL = "http://gpbib.cs.ucl.ac.uk/icga/icga1987.pdf", notes = "Argues against using LISP (but reference to LISP in ICGA-87) as too order dependant and fragile. Suggests instead production languages as in Holland and others classifiers. Warns new representations and crossover operators must obey schema theorem, so crossover is not disruptive and building blocks can be formed", } @InProceedings{DeJong:2019:GECCOcomp, author = "Kenneth {De Jong}", title = "Evolutionary computation: a unified approach", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", note = "Tutorial", isbn13 = "978-1-4503-6748-6", pages = "507--522", address = "Prague, Czech Republic", DOI = "doi:10.1145/3319619.3323379", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{3323379} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InProceedings{DeJong:2020:GECCOcomp, author = "Kenneth {De Jong}", title = "Evolutionary Computation: A Unified Approach", year = "2020", editor = "Richard Allmendinger and Hugo Terashima Marin and Efren Mezura Montes and Thomas Bartz-Beielstein and Bogdan Filipic and Ke Tang and David Howard and Emma Hart and Gusz Eiben and Tome Eftimov and William {La Cava} and Boris Naujoks and Pietro Oliveto and Vanessa Volz and Thomas Weise and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Rui Wang and Ran Cheng and Guohua Wu and Miqing Li and Hisao Ishibuchi and Jonathan Fieldsend and Ozgur Akman and Khulood Alyahya and Juergen Branke and John R. Woodward and Daniel R. Tauritz and Marco Baioletti and Josu Ceberio Uribe and John McCall and Alfredo Milani and Stefan Wagner and Michael Affenzeller and Bradley Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and Nayat Sanchez-Pi and Luis Marti and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and Matthew Johns and Nick Ross and Ed Keedwell and Herman Mahmoud and David Walker and Anthony Stein and Masaya Nakata and David Paetzel and Neil Vaughan and Stephen Smith and Stefano Cagnoni and Robert M. Patton and Ivanoe {De Falco} and Antonio {Della Cioppa} and Umberto Scafuri and Ernesto Tarantino and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Erik Hemberg and Riyad Alshammari and Tokunbo Makanju and Fuijimino-shi and Ivan Zelinka and Swagatam Das and Ponnuthurai Nagaratnam and Roman Senkerik", isbn13 = "9781450371278", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377929.3389871", DOI = "doi:10.1145/3377929.3389871", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion", pages = "327--342", size = "16 pages", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", note = "Tutorial", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{10.1145/3377929.3389871} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @Article{DeJong:EC, author = "Kenneth {De Jong} and Emma Hart", title = "Editorial: Reflecting on Thirty Years of {ECJ}", journal = "Evolutionary Computation", year = "2023", volume = "31", number = "2", pages = "73--79", month = "summer", keywords = "genetic algorithms, genetic programming", ISSN = "1063-6560", DOI = "doi:10.1162/evco_e_00324", size = "7 pages", abstract = "We reflect on 30 years of the journal Evolutionary Computation. Taking the articles published in the first volume in 1993 as a springboard, as the founding and current Editors-in-Chief, we comment on the beginnings of the field, evaluate the extent to which the field has both grown and itself evolved, and provide our own perpectives on where the future lies", } @InProceedings{DBLP:conf/iwinac/CruzPA05, author = "Marina {de la Cruz Echeandia} and Alfonso {Ortega de la Puente} and Manuel Alfonseca", title = "Attribute Grammar Evolution", booktitle = "Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach: First International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2005, Part II", year = "2005", editor = "Jos{\'e} Mira and Jos{\'e} R. {\'A}lvarez", series = "Lecture Notes in Computer Science", volume = "3562", pages = "182--191", address = "Las Palmas, Canary Islands, Spain", month = jun # " 15-18", publisher = "Springer", bibsource = "DBLP, http://dblp.uni-trier.de", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-26319-5", DOI = "doi:10.1007/11499305_19", size = "10 pages", abstract = "This paper describes Attribute Grammar Evolution (AGE), a new Automatic Evolutionary Programming algorithm that extends standard Grammar Evolution (GE) by replacing context-free grammars by attribute grammars. GE only takes into account syntactic restrictions to generate valid individuals. AGE adds semantics to ensure that both semantically and syntactically valid individuals are generated. Attribute grammars make it possible to semantically describe the solution. The paper shows empirically that AGE is as good as GE for a classical problem, and proves that including semantics in the grammar can improve GE performance. An important conclusion is that adding too much semantics can make the search difficult.", notes = "cited by \cite{Ortega:2007:ieeeTEC}", } @InProceedings{delaCruzEcheandia:2010:ICEC, author = "Marina {de la Cruz Echeandia} and Alba Martin Lazaro and Alfonso Ortega {de la Puente}", title = "The role of Keeping Semantic Blocks Invariant - Effects in Linear Genetic Programming Performance", booktitle = "Proceedings of the International Conference on Evolutionary Computation (ICEC 2010)", year = "2010", editor = "Joaquim Filipe and Janusz Kacprzyk", pages = "365--368", address = "Valencia, Spain", month = "24-26 " # oct, organisation = "INSTICC, AAAI, WfMC", publisher = "SciTePress", keywords = "genetic algorithms, genetic programming: Poster, Grammar evolution, Attribute grammars, Christiansen grammars, Genetic programming, Straight-line programs, symbolic regression", isbn13 = "978-989-8425-31-7", broken = "http://www.icec.ijcci.org/ICEC2010/Program_Tuesday.htm", URL = "http://www.robinbye.com/files/publications/ICEC_2010.pdf", URL = "https://www.scitepress.org/Papers/2010/30854/", DOI = "doi:10.5220/0003085403650368", size = "4 pages", abstract = "This paper is focused on two different approaches (previously proposed by the authors) that perform better than Genetic Programming in typical symbolic regression problems: straight-line program genetic programming (SLP-GP) and evolution with attribute grammars (AGE). Both approaches have different characteristics. One of the most important is that SLP-GP keeps semantic blocks invariant (the crossover operator always exchanges complete subexpressions). In this paper we compare both methods and study the possible effect on their performance of keeping these blocks invariant.", notes = "Broken http://www.icec.ijcci.org/ICEC2010/home.asp http://www.ijcci.org/Abstracts/2010/ICEC_2010_Abstracts.htm Also known as \cite{DBLP:conf/ijcci/EcheandiaLPAA10}", } @InCollection{delaCruz:2018:hbge, author = "Marina {de la Cruz Echeandia} and Younis R. SH. Elhaddad and Suzan Awinat and Alfonso Ortega", title = "{GE} and Semantics", booktitle = "Handbook of Grammatical Evolution", publisher = "Springer", year = "2018", editor = "Conor Ryan and Michael O'Neill and J. J. Collins", chapter = "8", pages = "189--218", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", isbn13 = "978-3-319-78716-9", DOI = "doi:10.1007/978-3-319-78717-6_8", abstract = "The main goal of this chapter is to explain in a comprehensible way the semantic context in formal language theory. This is necessary to properly understand the attempts to extend Grammatical Evolution (GE) to include semantics. Several approaches from different researchers to handle semantics, both directly and indirectly, will be briefly introduced. Finally, previous works by the authors will be described in depth.", notes = "Part of \cite{Ryan:2018:hbge}", } @Article{delaFuenteCastillo:2020:AS, author = "Victor {de la Fuente Castillo} and Alberto Diaz-Alvarez and Miguel-Angel Manso-Callejo and Francisco {Serradilla Garcia}", title = "Grammar Guided Genetic Programming for Network Architecture Search and Road Detection on Aerial Orthophotography", journal = "Applied Sciences", year = "2020", volume = "10", number = "11", keywords = "genetic algorithms, genetic programming", ISSN = "2076-3417", URL = "https://www.mdpi.com/2076-3417/10/11/3953", DOI = "doi:10.3390/app10113953", abstract = "Photogrammetry involves aerial photography of the Earths surface and subsequently processing the images to provide a more accurate depiction of the area (Orthophotography). It is used by the Spanish Instituto Geográfico Nacional to update road cartography but requires a significant amount of manual labor due to the need to perform visual inspection of all tiled images. Deep learning techniques (artificial neural networks with more than one hidden layer) can perform road detection but it is still unclear how to find the optimal network architecture. Our main goal is the automatic design of deep neural network architectures with grammar-guided genetic programming. In this kind of evolutive algorithm, all the population individuals (here candidate network architectures) are constrained to rules specified by a grammar that defines valid and useful structural patterns to guide the search process. Grammar used includes well-known complex structures (e.g., Inception-like modules) combined with a custom designed mutation operator (dynamically links the mutation probability to structural diversity). Pilot results show that the system is able to design models for road detection that obtain test accuracies similar to that reached by state-of-the-art models when evaluated over a dataset from the Spanish National Aerial Orthophotography Plan.", notes = "also known as \cite{app10113953}", } @InProceedings{delasCuevas:2020:evoapplications, author = "Paloma {de las Cuevas} and Pablo Garcia-Sanchez and Zaineb {Chelly Dagdia} and Maria-Isabel Garcia-Arenas and Juan Julian {Merelo Guervos}", title = "Automatic Rule Extraction from Access Rules Using Genetic Programming", booktitle = "23rd International Conference, EvoApplications 2020", year = "2020", month = "15-17 " # apr, editor = "Pedro A. Castillo and Juan Luis {Jimenez Laredo} and Francisco {Fernandez de Vega}", series = "LNCS", volume = "12104", publisher = "Springer Verlag", address = "Seville, Spain", pages = "54--69", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Security, Corporate Security Policy, Rule extraction", isbn13 = "978-3-030-43721-3", video_url = "https://www.youtube.com/watch?v=-VTUGCH3Em0", DOI = "doi:10.1007/978-3-030-43722-0_4", abstract = "The security policy rules in companies are generally proposed by the Chief Security Officer (CSO), who must, for instance, select by hand which access events are allowed and which ones should be forbidden. In this work we propose a way to automatically obtain rules that generalise these single-event based rules using Genetic Programming (GP), which, besides, should be able to present them in an understandable way. Our GP-based system obtains good dataset coverage and small ratios of false positives and negatives in the simulation results over real data, after testing different fitness functions and configurations in the way of coding the individuals.", notes = "http://www.evostar.org/2020/ EvoApplications2020 held in conjunction with EuroGP'2020, EvoMusArt2020 and EvoCOP2020", } @InProceedings{DelCarpio:2006:CEC, author = "Carlos A. {Del Carpio M.} and Mohamed Ismael and Eichiro Ichiishi and Michihisa Koyama and Momoji Kubo and Akira Miyamoto", title = "An Evolving Automaton for RNA Secondary Structure Prediction", booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary Computation", year = "2006", editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas", pages = "4533--4540", address = "Vancouver", month = "16-21 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9487-9", DOI = "doi:10.1109/IJCNN.2006.247018", size = "8 pages", abstract = "Conventional methods for RNA 2D structure prediction search for minimal free energy structures. RNA's, however, RNA's do not always adopt global minimum structures. Rather, their structure is the result of the folding pathway followed by the structure in nature, which adopts sub-optimal folds occurring along the pathway. Our algorithm consists of an automaton that generates RNA structures by searching for optimal folding pathways. The automaton is endowed of operations to travel throughout the hyperspace of conformers embedded in a base pairing matrix. Using genetic programming it evolves optimising its ability to find optimal pathways and finally 2D structures. Comparing the evolving automaton with conventional methods shows its potential.", notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D", } @Article{DelDuce:2009:ieeeJSTQE, author = "Andrea {Del Duce} and Polina Bayvel", title = "Quantum Logic Circuits and Optical Signal Generation for a Three-Qubit, Optically Controlled, Solid-State Quantum Computer", journal = "IEEE Journal of Selected Topics in Quantum Electronics", year = "2009", month = nov # "-" # dec, volume = "15", number = "6", pages = "1694--1703", keywords = "genetic algorithms, genetic programming, Deutsch-Jozsa algorithm, controlled-phase gates, entangling gates, optical control, optical signal generation, picosecond optical pulse sequences, quantum logic circuits, random fluctuations, solid-state quantum computer, logic circuits, optical control, optical pulse generation, optical signal detection, quantum computing, quantum entanglement", DOI = "doi:10.1109/JSTQE.2009.2024326", ISSN = "1077-260X", abstract = "We analyze the preparation of an experimental demonstration for a three-qubit, optically controlled, solid-state quantum computational system. First, using a genetic programming approach, we design quantum logic circuits, specifically tailored for our computational model, which implement a three-qubit refined Deutsch-Jozsa algorithm. Aiming at achieving the shortest possible computational time, we compare two design strategies based on exploiting two different sets of entangling gates. The first set comprises fast approximations of controlled-phase gates, while in the second case, we exploit arbitrary entangling gates with gate computational times shorter than those of the first set. Then, considering some recently proposed material implementations of this quantum computational system, we discuss the generation of the near-midinfrared, multi wavelength and picosecond optical pulse sequences necessary for controlling the presented quantum logic circuits. Finally, we analyze potential sources of errors and assess the impact of random fluctuations of the parameters controlling the entangling gates on the overall quantum computational system performance.", notes = "Also known as \cite{5290118} See also \cite{oai:arXiv.org:0910.1673} http://arxiv.org/abs/0910.1673", } @PhdThesis{DelDuce:thesis, author = "Andrea {Del Duce}", title = "Quantum Logic circuits for solid-state quantum information processing", school = "University College London", year = "2009", address = "UK", month = oct, keywords = "genetic algorithms, genetic programming", URL = "http://discovery.ucl.ac.uk/20166/1/20166.pdf", bibsource = "OAI-PMH server at eprints.ucl.ac.uk", language = "eng", oai = "oai:eprints.ucl.ac.uk.OAI2:20166", broken = "http://eprints.ucl.ac.uk/20166/", size = "175 pages", abstract = "This thesis describes research on the design of quantum logic circuits suitable for the experimental demonstration of a three-qubit quantum computation prototype. The design is based on a proposal for optically controlled, solid-state quantum logic gates. In this proposal, typically referred to as SFG model, the qubits are stored in the electron spin of donors in a solid-state substrate while the interactions between them are mediated through the optical excitation of control particles placed in their proximity. After a brief introduction to the area of quantum information processing, the basics of quantum information theory required for the understanding of the thesis work are introduced. Then, the literature on existing quantum computation proposals and experimental implementations of quantum computational systems is analysed to identify the main challenges of experimental quantum computation and typical system parameters of quantum computation prototypes. The details of the SFG model are subsequently described and the entangling characteristics of SFG two-qubit quantum gates are analysed by means of a geometrical approach, in order to understand what entangling gates would be available when designing circuits based on this proposal. Two numerical tools have been developed in the course of the research. These are a quantum logic simulator and an automated quantum circuit design algorithm based on a genetic programming approach. Both of these are used to design quantum logic circuits compatible with the SFG model for a three-qubit Deutsch-Jozsa algorithm. One of the design aims is to realise the shortest possible circuits in order to reduce the possibility of errors accumulating during computation, and different design procedures which have been tested are presented. The tolerance to perturbations of one of the designed circuits is then analysed by evaluating its performance under increasing fluctuations on some of the parameters relevant in the dynamics of SFG gates. Because interactions in SFG two-qubit quantum gates are mediated by the optical excitation of the control particles, the solutions for the generation of the optical control signal required for the proposed quantum circuits are discussed. Finally, the conclusions of this work are presented and areas for further research are identified.", } @InProceedings{Delfianto:2011:ICEEI, author = "Rizky Delfianto and Masayu Leylia Khodra and Aristama Roesli", title = "Content-targeted advertising using genetic programming", booktitle = "International Conference on Electrical Engineering and Informatics (ICEEI 2011)", year = "2011", month = "17-19 " # jul, address = "Bandung, Indonesia", size = "5 pages", abstract = "Content-targeted advertising is an ads placement technique which associates ads to a web page relative to (based on) the content of the web page (web page content). It introduces a challenge about how to settle the conflict of interests by selecting advertisements that are relevant to the users but also profitable to the advertisers and the publishers. This paper proposes an approach to associate ads with web pages using Genetic Programming (GP). GP is an extension of genetic algorithm in which the individual is not a stream of character but rather a program (function). This work is done in two stages. In the first stage, GP is used to learn a ranking function which leverages the structural and non structural information of the ads. The structural parts of the ads are the title and description. These are the parts that are shown when an ad is placed in a web page. The non-structural part is the set of keywords assigned to the ads. This part is used by the advertisers to determine what topic of the web page content should be to have the ads shown on it. The ranking function produced in the first stage is then used to rank ads given content of a web page in the second stage, the content-targeted advertising system. The experiment result showed that the ranking function effectiveness is just a little below the baseline method but its time efficiency is far better than the baseline at almost 12 times better. In spite of its effectiveness deficiency, the ranking function is still more suitable for content-targeted advertising system. The experiment result also proved that the mutation genetic operation contributes to the result of GP learning by creating a better-performed ranking function. The ranking function generated from GP learning which used mutation genetic operation is 0.11 more effective than the ranking function generated from GP which did not used mutation genetic operation.", keywords = "genetic algorithms, genetic programming, GP, Internet, Web page content, ads placement technique, content targeted advertising, structural information, Internet, advertising data processing", DOI = "doi:10.1109/ICEEI.2011.6021592", ISSN = "2155-6822", notes = "Also known as \cite{6021592}", } @Article{Delgado:2021:A, author = "Alberto Delgado and Giulia {Di Capua} and Kateryna Stoyka and Lixin Shi and Nicola Femia and Antonio Maffucci and Salvatore Ventre and Pedro Alou and Jesus A. Oliver and Jose A. Cobos", title = "Self and Mutual Inductance Behavioral Modeling of Rectangular {IPT} Coils with Air Gap and Ferrite Core Plates", journal = "IEEE Access", year = "2022", volume = "10", pages = "7476--7488", month = jan # " 20", keywords = "genetic algorithms, genetic programming, Behavioral modeling, finite element analysis, finite element modeling, inductive power transfer, magnetic components, optimization process, wireless power transfer", ISSN = "2169-3536", DOI = "doi:10.1109/ACCESS.2021.3138239", size = "13 pages", abstract = "The design and optimisation of coils for Inductive Power Transfer (IPT) systems is an iterative process conducted in Finite Element (FE) tools that takes a lot of time and computational resources. In order to overcome such limitations in the design process, new empirical equations for the evaluation of the self-inductance and mutual inductance values are proposed in this work. By means of a multi-objective genetic programming algorithm, the self-inductance, the mutual inductance and the coupling factor values obtained from FE simulations of IPT link are accounted by analytical equations based on the geometric parameters defining the IPT link. The behavioral modeling results are compared with both FE-based and experimental results, showing a good accuracy.", notes = "Also known as \cite{9663390}", } @InProceedings{delgado:1999:MHEDFS, author = "Myriam Delgado and Fernando {Von Zuben} and Fernando Gomide", title = "Modular and Hierarchial Evolutionary Design of Fuzzy Systems", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "180--187", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://www.dca.fee.unicamp.br/~myriam/papers/gecco99.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-850.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-850.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{delgado:2002:FUZZIEEE, author = "Myriam Regattieri Delgado and Fernando {Von Zuben} and Fernando Gomide", title = "Multi-Objective Decision Making: Towards Improvement of Accuracy, Interpretability and Design Autonomy in Hierarchical Genetic Fuzzy Systems", booktitle = "Proceedings of the 2002 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE-02", pages = "1222--1227", year = "2002", month = "12-17 " # may, address = "Hilton Hawaiian Village Hotel, Honolulu, Hawaii", publisher = "IEEE Press", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", organisation = "IEEE", ISBN = "0-7803-7280-8", keywords = "genetic algorithms, genetic programming, accuracy, classification problem, design autonomy, fitting, fuzzy modelling, fuzzy models, generalisation, hierarchical evolutionary process, hierarchical genetic fuzzy systems, interpretability, interpretation characteristics, multi-objective decision making, single-objective epsiv, -constrained decision making problems , decision theory, fuzzy systems, modelling,", DOI = "doi:10.1109/FUZZ.2002.1006678", abstract = "This paper presents fuzzy modeling as a multi-objective decision making problem considering accuracy, interpretability and autonomy as goals. The proposed approach assumes that these goals can be handled via corresponding single-objective e-constrained decision making problems whose solution is produced by a hierarchical evolutionary process. The fitting, generalization, and interpretation characteristics of the resulting fuzzy models are discussed using a classification problem.", notes = "IJCNN 2002 Held in connection with the World Congress on Computational Intelligence (WCCI 2002) The length of the chromosome, fixed by the constraint e2, determines the maximum number of fuzzy rules but smaller rule-bases are always aimed at first.", } @PhdThesis{delgado:2002:thesis, author = "Myriam Regattieri De Biase da Silva Delgado", title = "Projeto Automatico de Sistemas Nebulosos: Uma Abordagem Co-Evolutiva", school = "FACULDADE DE ENGENHARIA ELETRICA E DE COMPUTACAO, UNIVERSIDADE ESTADUAL DE CAMPINAS", year = "2002", month = "26 " # feb, keywords = "genetic algorithms, fuzzy systems", URL = "http://www.dca.fee.unicamp.br/~myriam/phdthesis.pdf", URL = "http://www.dca.fee.unicamp.br/~vonzuben/research/myriam_dout.html", size = "204 pages", abstract = "This thesis proposes a co-evolutionary-based approach to solve the problem of automatic fuzzy system design. The co-evolution supports hierarchical and collaborative relations among individuals representing different parameters of fuzzy models. The proposed approach takes species which encode partial solutions to fuzzy modeling problems, organized into four hierarchical levels. Each hierarchical level encodes membership functions, individual rules, rule-bases and fuzzy systems, respectively. A special fitness evaluation scheme is proposed to measure the performance of each individual of different species. Constraints and local objectives must be observed at all hierarchical levels to guarantee the occurrence of individuals characterized by the simplicity of fuzzy rules, rule compactness, rule base consistency and visibility in the universe partition. The approach allows the evolution of Mamdani or Takagi-Sugeno fuzzy models. In addition to performance improvement in terms of accuracy and interpretability, the co-evolutionary approach increases autonomy by minimizing user intervention, since it allows automatic tuning of a number of critical parameters, like type and total of fuzzy rules, relevant variables (for each rule and for the whole application), shape and location of membership functions, antecedent aggregation operator, and, for Mamdani models, aggregation operator, rule semantic, and the defuzzification method. The performance of the approach is evaluated via function approximation and pattern classification problems.", notes = "Prof. Dr. Fernando Jose Von Zuben (Orientador) Prof. Dr. Fernando Gomide (Co-orientador) Tese apresentada a Pos-graduacao da Faculdade de Engenharia Eletrica e de Computacao da Universidade Estadual de Campinas como requisito parcial a obtencao do grau de Doutor em Engenharia Eletrica na area de Engenharia de Computacao. Campinas, 26 de Fevereiro de 2002 In Portuguese", } @InProceedings{Delgado-Osuna:2022:CEC, author = "Jose A. Delgado-Osuna and Carlos Garcia-Martinez and Sebastian Ventura", title = "Smart Operators for Inducing Colorectal Cancer Classification Trees with {PonyGE2} Grammatical Evolution Python Package", booktitle = "2022 IEEE Congress on Evolutionary Computation (CEC)", year = "2022", editor = "Carlos A. Coello Coello and Sanaz Mostaghim", address = "Padua, Italy", month = "18-23 " # jul, keywords = "genetic algorithms, genetic programming,Grammatical Evolution, Machine learning algorithms, Machine learning, Evolutionary computation, Germanium, Classification algorithms, Grammar, Task analysis, Classification Trees, Heterogeneous features, Colorectal Cancer", isbn13 = "978-1-6654-6708-7", DOI = "doi:10.1109/CEC55065.2022.9870361", abstract = "Colorectal cancer is a disease that affects many people and requires a multidisciplinary approach, involving significant human and economic resources. We have been provided with a tabular dataset with 1.5 thousand cases of this disease. We are interested in producing interpretable classifiers for predicting the occurrence of complications. Grammatical Evolution has extensively been used for machine learning problems. In particular, it can be used to induce interpretable decision trees, with the advantage of allowing the practitioner to easily control the language by means of the grammar. PonyGE2 [1], [2] is a Python package that provides data scientists with Grammatical Evolution algorithms, which can be configured to their needs quite easily. In addition, and thanks to the benefits of the Python programming language, PonyGE2 is currently becoming more and more popular. However, the capabilities of PonyGE2 for inducing classification trees are still subject of improvement. In particular, it only uses simple equality conditions and requires to encode feature names and values with numbers. We have developed some smart operators for PonyGE2, which, not only enhance the framework in interpretability and performance when dealing with our colo-rectal cancer dataset, but also allows to produce results comparable to those of the widely known heuristic methods C4.5 and CART. We show how they could be applied to other datasets, and how they affect performance in our case.", notes = "Also known as \cite{9870361}", } @InProceedings{deLima:2022:GECCO, author = "Allan {de Lima} and Samuel Carvalho and Douglas Dias and Enrique Naredo and Joseph Sullivan and Conor Ryan", title = "Lexi2: Lexicase Selection with Lexicographic Parsimony Pressure", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference", year = "2022", editor = "Alma Rahat and Jonathan Fieldsend and Markus Wagner and Sara Tari and Nelishia Pillay and Irene Moser and Aldeida Aleti and Ales Zamuda and Ahmed Kheiri and Erik Hemberg and Christopher Cleghorn and Chao-li Sun and Georgios Yannakakis and Nicolas Bredeche and Gabriela Ochoa and Bilel Derbel and Gisele L. Pappa and Sebastian Risi and Laetitia Jourdan and Hiroyuki Sato and Petr Posik and Ofer Shir and Renato Tinos and John Woodward and Malcolm Heywood and Elizabeth Wanner and Leonardo Trujillo and Domagoj Jakobovic and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Inmaculada Medina-Bulo and Slim Bechikh and Andrew M. Sutton and Pietro Simone Oliveto", pages = "929--937", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, lexicase selection, grammatical evolution, lexicographic parsimony pressure", isbn13 = "978-1-4503-9237-2", DOI = "doi:10.1145/3512290.3528803", abstract = "Bloat, a well-known phenomenon in Evolutionary Computation, often slows down evolution and complicates the task of interpreting the results. We propose Lexi2, a new selection and bloat-control method, which extends the popular lexicase selection method, by including a tie-breaking step which considers attributes related to the size of the individuals. This new step applies lexicographic parsimony pressure during the selection process and is able to reduce the number of random choices performed by lexicase selection (which happen when more than a single individual correctly solve the selected training cases).Furthermore, we propose a new Grammatical Evolution-specific, low-cost diversity metric based on the grammar mapping modulus operations remainders, which we then use with Lexi2.We address four distinct problems, and the results show that Lexi2 is able to reduce significantly the length, the number of nodes and the depth for all problems, to maintain a high level of diversity in three of them, and to significantly improve the fitness score in two of them. In no case does it adversely impact the fitness.", notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @InProceedings{deLima:2024:EuroGP, author = "Allan {de Lima} and Samuel Carvalho and Douglas Mota Dias and Jorge Amaral and Joseph P. Sullivan and Conor Ryan", editor = "Mario Giacobini and Bing Xue and Luca Manzoni", title = "Fuzzy Pattern Trees for Classification Problems Using Genetic Programming", booktitle = "EuroGP 2024: Proceedings of the 27th European Conference on Genetic Programming", year = "2024", volume = "14631", series = "LNCS", publisher = "Springer", address = "Aberystwyth", month = "3-5 " # apr, organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming", pages = "3--20", abstract = "Fuzzy Pattern Trees (FPTs) are tree-based structures in which the internal nodes are fuzzy operators, and the leaves are fuzzy features. This work uses Genetic Programming (GP) to evolve FPTs and assesses their performance on 20 benchmark classification problems. The results show improved accuracy for most of the problems in comparison with previous works using different approaches. Furthermore, we experiment using Lexicase Selection with FPTs and demonstrate that selection methods based on aggregate fitness, such as Tournament Selection, produce more accurate models before analysing why this is the case. We also propose new parsimony pressure methods embedded in Lexicase Selection, and analyse their ability to reduce the size of the solutions. The results show that for most problems, at least one method could reduce the size significantly while keeping a similar accuracy. We also introduce a new fuzzification scheme for categorical features with too many categories by using target encoding followed by the same scheme for numerical features, which is straightforward to implement, and avoids a much higher increase in the number of fuzzy features.", isbn13 = "978-3-031-56957-9", DOI = "doi:10.1007/978-3-031-56957-9_1", notes = "Part of \cite{Giacobini:2024:GP} EuroGP'2024 held in conjunction with EvoCOP2024, EvoMusArt2024 and EvoApplications2024", } @InProceedings{deLima:2010:cec, author = "Elisa Boari {de Lima} and Gisele L. Pappa and Jussara Marques {de Almeida} and Marcos A. Goncalves and Wagner Meira", title = "Tuning Genetic Programming parameters with factorial designs", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-6910-9", abstract = "Parameter setting of Evolutionary Algorithms is a time consuming task with two main approaches: parameter tuning and parameter control. In this work we describe a new methodology for tuning parameters of Genetic Programming algorithms using factorial designs, one-factor designs and multiple linear regression. Our experiments show that factorial designs can be used to determine which parameters have the largest effect on the algorithm's performance. This way, parameter setting efforts can focus on them, largely reducing the parameter search space. Two classical GP problems were studied, with six parameters for the first problem and seven for the second. The results show the maximum tree depth as the parameter with the largest effect on both problems. A one-factor design was performed to fine-tune tree depth on the first problem and a multiple linear regression to fine-tune tree depth and number of generations on the second.", DOI = "doi:10.1109/CEC.2010.5586084", notes = "WCCI 2010. Also known as \cite{5586084}", } @Article{10.1371/journal.pcbi.1005001, author = "Elisa {Boari de Lima} and Wagner {Meira Jr.} and Raquel {Cardoso de Melo-Minardi}", journal = "PLoS Computational Biology", title = "Isofunctional Protein Subfamily Detection Using Data Integration and Spectral Clustering", year = "2016", volume = "12", number = "6", pages = "1005001", month = "27 " # jun, keywords = "genetic algorithms, genetic programming, Serine proteases, Sequence alignment, Protein domains, Dehydration (medicine), Protein kinases, Protein structure comparison, Adenylyl cyclase, Protein structure", publisher = "Public Library of Science", DOI = "doi:10.1371/journal.pcbi.1005001", size = "32 pages", abstract = "The knowledge of protein functions is central for understanding life at a molecular level and has huge biochemical and pharmaceutical implications. However, despite best research efforts, a substantial and ever-increasing number of proteins predicted by genome sequencing projects still lack functional annotations. Computational methods are required to determine protein functions quickly and reliably since experimental investigation is difficult and costly. Considering literature shows combining various types of information is crucial for functionally annotating proteins, such methods must be able to integrate data from different sources which may be scattered, non-standardized, incomplete, and noisy. Many protein families are composed of proteins with different folds and functions. In such cases, the division into subtypes which share specific functions uncommon to the family as a whole may lead to important information about the function and structure of a related protein of unknown function, as well as about the functional diversification acquired by the family during evolution. This work's purpose is to automatically detect isofunctional subfamilies in a protein family of unknown function, as well as identify residues responsible for differentiation. We integrate data and then provide it to a clustering algorithm, which creates clusters of similar proteins we found correspond to same-specificity subfamilies", notes = "International Society for Computational Biology", } @InProceedings{delima:2017:CEC, author = "Ricardo Henrique Remes {de Lima} and Aurora Trinidad Ramirez Pozo", booktitle = "2017 IEEE Congress on Evolutionary Computation (CEC)", title = "A study on auto-configuration of Multi-Objective Particle Swarm Optimization Algorithm", year = "2017", editor = "Jose A. Lozano", pages = "718--725", address = "Donostia, San Sebastian, Spain", publisher = "IEEE", month = "5-8 " # jun, keywords = "genetic algorithms, genetic programming, context-free grammars, evolutionary computation, particle swarm optimisation, statistical analysis, GE, IRACE, MOPSO algorithm, PSO performance, SMPSO, autoconfiguration study, context-free grammar, grammatical evolution, iterated racing, monoobjective particle swarm optimization algorithm, multiobjective evolutionary algorithms automatic design, multiobjective particle swarm optimization algorithm, speed-constrained MOPSO, statistical tests, velocity equation, Algorithm design and analysis, Grammar, Particle swarm optimization, Production, Space exploration", isbn13 = "978-1-5090-4601-0", DOI = "doi:10.1109/CEC.2017.7969381", abstract = "Researches point out to the importance of automatic design of multi-objective evolutionary algorithms. Because in general, algorithms automatically designed outperform traditional multi-objective evolutionary algorithms from the literature. Nevertheless, until fairly recently, most of the researches have been focused on a small group of algorithms, often based on evolutionary algorithms. On the other hand, mono-objective Particle Swarm Optimization algorithm (PSO) have been widely used due to its flexibility and competitive results in different applications. Besides, as PSO performance depends on different aspects of design like the velocity equation, its automatic design has been targeted by many researches with encouraging results. Motivated by these issues, this work studies the automatic design of Multi-Objective Particle Swarm Optimization (MOPSO). A framework that uses a context-free grammar to guide the design of the algorithms is implemented. The framework includes a set of parameters and components of different MOPSOs, and two design algorithms: Grammatical Evolution (GE) and Iterated Racing (IRACE). Evaluation results are presented, comparing MOPSOs generated by both design algorithms. Furthermore, the generated MOPSOs are compared to the Speed-constrained MOPSO (SMPSO), a well-known algorithm using a set of Multi-Objective problems, quality indicators and statistical tests.", notes = "IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969381}", } @Article{delisle:2004:CIM, author = "Robert Kirk DeLisle and Steven L. Dixon", title = "Induction of Decision Trees via Evolutionary Programming", journal = "Journal of Chemical Information and Modeling", year = "2004", volume = "44", number = "3", pages = "862--870", keywords = "genetic algorithms, genetic programming, EP, EPTree", DOI = "doi:10.1021/ci034188s", abstract = "Decision trees have been used extensively in cheminformatics for modelling various biochemical endpoints including receptor-ligand binding, ADME properties, environmental impact, and toxicity. The traditional approach to inducing decision trees based upon a given training set of data involves recursive partitioning which selects partitioning variables and their values in a greedy manner to optimise a given measure of purity. This methodology has numerous benefits including classifier interpretability and the capability of modeling nonlinear relationships. The greedy nature of induction, however, may fail to elucidate underlying relationships between the data and endpoints. Using evolutionary programming, decision trees are induced which are significantly more accurate than trees induced by recursive partitioning. Furthermore, when assessed on previously unseen data in a 10-fold cross-validated manner, evolutionary programming induced trees exhibit a significantly higher accuracy on previously unseen data. This methodology is compared to single-tree and multiple-tree recursive partitioning in two domains (aerobic biodegradability and hepatotoxicity) and shown to produce less complex classifiers with average increases in predictive accuracy of 5-10\% over the traditional method.", notes = "http://pubs.acs.org/journals/jcisd8/index.html American Chemical Society, ACS Publications Division S0095-2338(03)04188-X Department of Molecular Modeling, Pharmacopeia, P.O. Box 5350, Princeton, New Jersey 08543-5350, and Schrodinger, 120 West 45th Street, 32nd Floor, New York, New York 10036 http://ai-depot.com/Tutorial/DecisionTrees-EP.html", } @Article{Dell'Aquila:2021:cpc, author = "D. Dell'Aquila and M. Russo", title = "Automatic classification of nuclear physics data via a Constrained Evolutionary Clustering approach", journal = "Computer Physics Communications", year = "2021", volume = "259", pages = "107667", month = feb, keywords = "genetic algorithms, genetic programming, Nuclear physics data classification, Evolutionary computing, Clustering algorithms, Charged particle identification in nuclear collisions", ISSN = "0010-4655", URL = "https://www.sciencedirect.com/science/article/pii/S0010465520303234", DOI = "doi:10.1016/j.cpc.2020.107667", size = "41 pages", abstract = "This paper presents an automatic method for data classification in nuclear physics experiments based on evolutionary computing and vector quantisation. The major novelties of our approach are the fully automatic mechanism and the use of analytical models to provide physics constraints, yielding to a fast and physically reliable classification with nearly-zero human supervision. Our method is successfully validated using experimental data produced by stacks of semiconducting detectors. The resulting classification is highly satisfactory for all explored cases and is particularly robust to noise. The algorithm is suitable to be integrated in the online and offline analysis software of existing large complexity detection arrays for the study of nucleus-nucleus collisions at low and intermediate energies.", notes = "Also known as \cite{DELLAQUILA2021107667}", } @Article{Dell'Aquila:jpG, author = "Daniele Dell'Aquila and Brunilde Gnoffo and Ivano Lombardo and Francesco Porto and Marco Russo", title = "Modeling Heavy-Ion Fusion Cross Section Data via a Novel Artificial Intelligence Approach", journal = "Journal of Physics G: Nuclear and Particle Physics", year = "2022", volume = "50", number = "1", pages = "015101", month = nov, keywords = "genetic algorithms, genetic programming, BP, ANN, AI, heavy ion fusion, excitation function, artificial intelligence in nuclear data, Nuclear Experiment (nucl-ex), Nuclear Theory (nucl-th), FOS: Physical sciences, FOS: Physical sciences", publisher = "IOP Publishing", URL = "https://arxiv.org/abs/2203.10367", URL = "http://iopscience.iop.org/article/10.1088/1361-6471/ac9ad1", DOI = "doi:10.1088/1361-6471/ac9ad1", size = "22 pages", abstract = "We perform a comprehensive analysis of complete fusion cross section data with the aim to derive, in a completely data-driven way, a model suitable to predict the integrated cross section of the fusion between light to medium mass nuclei at above barrier energies. To this end, we adopted a novel artificial intelligence approach, based on a hybridization of genetic programming and artificial neural networks, capable to derive an analytical model for the description of experimental data. The approach enables to perform a global search for computationally simple models over several variables and a considerable body of nuclear data. The derived phenomenological formula can serve to reproduce the trend of fusion cross section for a large variety of light to intermediate mass collision systems in an energy domain ranging approximately from the Coulomb barrier to the onset of multi-fragmentation phenomena.", notes = "Brain Project", } @Article{Dell_Aquila:2023:JPCS, author = "Daniele Dell'Aquila and Brunilde Gnoffo and Ivano Lombardo and Francesco Porto and Luigi Redigolo and Marco Russo", title = "Understanding Heavy-ion Fusion Cross Section Data Using Novel Artificial Intelligence Approaches", journal = "Journal of Physics: Conference Series", year = "2023", volume = "2619", number = "1", pages = "012004", month = oct, note = "44th Symposium on Nuclear Physics Cocoyoc", keywords = "genetic algorithms, genetic programming, BP, AI", publisher = "IOP Publishing", ISSN = "2100-014X", URL = "https://dx.doi.org/10.1088/1742-6596/2619/1/012004", DOI = "doi:10.1088/1742-6596/2619/1/012004", size = "8 pages", abstract = "An unprecedentedly extensive dataset of complete fusion cross section data is modeled via a novel artificial intelligence approach. The analysis was focused on light-to-medium-mass nuclei, where fission-like phenomena are more difficult to occur. The method used to derive the models exploits a state-of-the-art hybridization of genetic programming and artificial neural networks and is capable to derive, in a data-driven way, an analytical expression that serves to predict integrated cross section values. We analyzed a comprehensive set of nuclear variables, including quantities related to the nuclear structure of projectile and target. In this paper, we describe the derivation of two computationally simple models that can satisfactorily describe, with a reduced number of variables and only a few parameters, a large variety of light-to-intermediate-mass collision systems in an energy domain ranging approximately from the Coulomb barrier to the oncet of multi-fragmentation phenomena. The underlying methods are of potential use for a broad domain of applications in the nuclear field.", notes = "See \cite{Dell'Aquila:2024:EPJCONF}", } @Article{Dell'Aquila:2024:EPJCONF, author = "Daniele Dell'Aquila and Brunilde Gnoffo and Ivano Lombardo and Luigi Redigolo and Francesco Porto and Marco Russo", title = "Understanding heavy-ion fusion cross section data using novel artificial intelligence approaches", journal = "EPJ Web of Conferences", year = "2024", volume = "292", note = "16th Varenna Conference on Nuclear Reaction Mechanisms (NRM2023)", keywords = "genetic algorithms, genetic programming, Heavy-Ion Reactions", conf_address = "Varenna, Italy", conf_month = "11-16 June", ISSN = "2100-014X", URL = "https://inspirehep.net/literature/2781521", DOI = "doi:10.1051/epjconf/202429205005", size = "7 pages", abstract = "We modelled an unprecedentedly large dataset of complete fusion cross section data using a novel artificial intelligence approach. Our analysis aims especially to unveil, in a data-driven way, nuclear structure effects on the fusion between heavy ions and to suggest a universal formula capable to describe all previously available data. The study focused on light-to-medium-mass nuclei, where incomplete fusion phenomena are more difficult to occur and less likely to contaminate the data. The method used to derive the models exploits a state-of-the-art hybridization of genetic programming and artificial neural networks and is capable to derive an analytical expression that serves to predict integrated cross section values. For the first time, we analysed a comprehensive set of nuclear variables, including quantities related to the nuclear structure of projectile and target. we describe the derivation of two computationally simple models that can satisfactorily describe, with a reduced number of variables and only a few parameters, a large variety of light-to-intermediate-mass collision systems in an energy domain ranging approximately from the Coulomb barrier to the oncet of multi-fragmentation phenomena. The underlying methods are particularly innovative and are of potential use for a broad domain of applications in the nuclear field.", notes = "See \cite{Dell_Aquila:2023:JPCS} Dipartimento di Fisica {"}Ettore Pancini{"}, University of Naples {"}Federico II{"}, Napoli, Italy", } @InProceedings{DeLorenzo:2013:GECCO, author = "Andrea {De Lorenzo} and Eric Medvet and Alberto Bartoli", title = "Automatic string replace by examples", booktitle = "GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference", year = "2013", editor = "Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi", isbn13 = "978-1-4503-1963-8", pages = "1253--1260", keywords = "genetic algorithms, genetic programming", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2463372.2463532", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Search-and-replace is a text processing task which may be largely automated with regular expressions: the user must describe with a specific formal language the regions to be modified (search pattern) and the corresponding desired changes (replacement expression). Writing and tuning the required expressions requires high familiarity with the corresponding formalism and is typically a lengthy, error-prone process. In this paper we propose a tool based on Genetic Programming (GP) for generating automatically both the search pattern and the replacement expression based only on examples. The user merely provides examples of the input text along with the desired output text and does not need any knowledge about the regular expression formalism nor about GP. We are not aware of any similar proposal. We experimentally evaluated our proposal on 4 different search-and-replace tasks operating on real-world datasets and found good results, which suggests that the approach may indeed be practically viable.", notes = "Also known as \cite{2463532} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)", } @Article{DeLorenzo:GPEM20, author = "Andrea {De Lorenzo} and Alberto Bartoli and Mauro Castelli and Eric Medvet and Bing Xue", title = "Genetic programming in the twenty-first century: a bibliometric and content-based analysis from both sides of the fence", journal = "Genetic Programming and Evolvable Machines", year = "2020", volume = "21", number = "1-2", pages = "181--204", month = jun, note = "Twentieth Anniversary Issue", keywords = "genetic algorithms, genetic programming, Bibliometrics, Topic modelling, Literature review, Publication habits", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-019-09363-3", size = "24 pages", abstract = "In this work we present an extensive bibliometric and content-based analysis of the scientific literature about genetic programming in the twenty-first century. Our work has two key peculiarities. First, we revealed the topics emerging from the literature based on an unsupervised analysis of the textual content of titles and abstracts. Second, we executed all of our analyses twice, once on the papers published in the venues that are typical of the evolutionary computation research community and once on those published in all the other venues. This view from both sides of the fence allows us to gain broader and deeper insights into the actual contributions of our community.", notes = "Google Scholar, Scopus 10233 documents, SPECIES website, Latent Dirichlet Allocation, TF-IDF", } @InProceedings{DelRe:2005:SAE, author = "Luigi {Del Re} and Peter Langthaler and Christian Furtmueller and Stephan Winkler and Michael Affenzeller", title = "{NOx} Virtual Sensor Based on Structure Identification and Global Optimization", booktitle = "SAE 2005 World Congress \& Exhibition", year = "2005", pages = "Paper Number: 2005--01--0050", address = "Detroit, Michigan, United States", month = "11 " # apr, organisation = "Society of Automotive Engineers", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.4271/2005-01-0050", abstract = "On-line measurement of engine NOx emissions is the object of a substantial effort, as it would strongly improve the control of CI engines. Many efforts have been directed towards hardware solutions, in particular to physical sensors, which have already reached a certain degree of maturity.", notes = "Date Published: 2005-04-11, SAE Technical Paper 2005-01-0050", } @InProceedings{delRe:2011:CIVTS, author = "Luigi {del Re} and Markus Hirsch and Daniel Alberer and Stephan Winkler", title = "The role of data choice in data driven identification for online emission models", booktitle = "IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (CIVTS 2011)", year = "2011", month = "11-15 " # apr, address = "Paris", pages = "46--51", size = "6 pages", abstract = "Data driven models are known to be a valid alternative to first principle approaches for modelling. However, in the case of complex and largely unknown systems such as the chemical reactions leading to engine emissions, experience shows that results from data driven models suffer from a significant dependence on the actual data set used for identification and are prone to an excessive complexity. This paper shows how the use of an incremental design of experiments based on polynomial models can be used to determine the appropriate complexity of the data set as well as a suitable measurement profile which yields an adequate excitation for the model parameter estimation. As this paper shows experimentally, this result is not specific to the particular identification approach used, but the same data set can be used e.g. by genetic programming (GP) algorithms which extract also the model structure from data. Results are shown using emission measurements on a modern turbocharged Diesel engine on an emission test bench.", keywords = "genetic algorithms, genetic programming, chemical reactions, complex systems, data choice, data driven identification, data set, design of experiments, emission measurements, engine emissions, model parameter estimation, modern turbocharged diesel engine, online emission models, polynomial models, air pollution, data models, design of experiments, diesel engines, large-scale systems, mechanical engineering computing, parameter estimation, polynomials", DOI = "doi:10.1109/CIVTS.2011.5949537", notes = "automobile, turbo charged diesel, NOx, particulates, NARX, time lagged inputs. HeuristicLab Also known as \cite{5949537}", } @InProceedings{del-Rosal:2011:IWINAC, author = "Emilio {del Rosal} and Marina {de la Cruz} and Alfonso {Ortega de la Puente}", title = "Towards the Automatic Programming of {NEPs}", booktitle = "Proceedings of the 4th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2011, Part I", year = "2011", editor = "Jose Manuel Ferrandez and Jose Ramon {Alvarez Sanchez} and Felix {de la Paz} and F. Javier Toledo", series = "Lecture Notes in Computer Science", pages = "303--312", volume = "6686", address = "La Palma, Canary Islands, Spain", month = may # " 30-" # jun # " 3", publisher = "Springer", keywords = "genetic algorithms, genetic programming, grammatical evolution, network of evolutionary processors", isbn13 = "978-3-642-21343-4", DOI = "doi:10.1007/978-3-642-21344-1_32", size = "10 pages", abstract = "This paper shows the platform with which we implement a general methodology to automatically design NEPs to solve specific problems. We use CGE/AGE (a new genetic programming algorithm) and jNEP (a Java NEP simulator), two applications we have previously developed. This work is just a proof of viability. We are interested on linking all the modules and generating the initial population. Building this platform is relevant, because our methodology includes several non trivial steps, such as designing a grammar, and implementing and using a simulator. For this first proof we have chosen a well known problem that other authors have solved by means of NEPs.", notes = "Java. jNEP http://jnep.e-delrosal.net p304 Just to generate an initial valid population Attribute grammar and Christianen grammars AGE/CGE but actually uses context free grammar. Computing cells. 4 Cells to rotate string. Both filter inputs and outputs to Cell. XML Cites \cite{DBLP:conf/iwinac/CruzPA05}", affiliation = "Departamento de Ingenieria Informatica, Escuela Politecnica Superior, Universidad Autonoma de Madrid, Spain", } @PhdThesis{delRosalGarcia:thesis, author = "Emilio {del Rosal Garcia}", title = "Real Life Applications of Bio-inspired Computing Models: {EAP and NEPs}", school = "Departamento de Ingenieria Informatica, Universidad Autonoma de Madrid", year = "2013", address = "C/ Francisco Tomas y Valiente 11, 28049, Madrid, Spain", month = "4 " # jul, keywords = "genetic algorithms, genetic programming, cellular automata, Christiansen Grammar", URL = "http://hdl.handle.net/10486/662031", URL = "https://repositorio.uam.es/bitstream/handle/10486/662031/rosal_garcia_emilio_del.pdf", size = "221 pages", notes = "In English. 8-queens, L-systems Supervisor: Alfonso Ortega de la Puente", } @InProceedings{deLyraRamos:2017:IEEE-APS, author = "Nieremberg J. P. {de Lyra Ramos} and Glauco Fontgalland and Alfredo Gomes Neto and Silvio Ernesto Barbin", title = "{NSGA-RF}: Elitist non-dominated sorting genetic algorithm region-focused", booktitle = "2017 IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications (APWC)", year = "2017", pages = "1936--1939", address = "Verona, Italy", month = "11-15 " # sep, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/APWC.2017.8062310", size = "4 pages", abstract = "This paper presents a proposal for the modification of the Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) whose operation is based on the concepts of dominance found in the principle of Pareto efficiency. The proposed modification is about the domain to be analysed by the algorithm, which is then restricted to a region near the point of interest, thus being faster and with a lower computational cost due to the smaller search space.", notes = "Pareto front. also known as \cite{8062310}", } @InProceedings{DeMaagd:2010:HICSS, author = "Kurt DeMaagd and Johannes Bauer", title = "A Genetic Programming Approach to Network Management Regulation", booktitle = "43rd Hawaii International Conference on System Sciences (HICSS 2010)", year = "2010", month = "5-8 " # jan, abstract = "Although next-generation information network infrastructure is prerequisite for continued economic growth, the United States is falling behind in this area relative to many other countries. Businesses and regulators have grown concerned that the U.S. lacks the correct regulatory and business incentives to upgrade its network. Due to the complex and dynamic nature of this problem, traditional analytic tools have proven inadequate. This paper discusses a Genetic Programming (GP) approach to the problem. Although only a first step towards addressing the problem, the GP discovered several interesting results stemming from the complex interactions. For example, telecommunications companies would actually be hurt by the option to charge discriminatory prices but application providers would benefit.", keywords = "genetic algorithms, genetic programming, United States, business incentives, discriminatory prices, economic growth, network management regulation, commerce, telecommunication industry, telecommunication network management, telecommunication services", DOI = "doi:10.1109/HICSS.2010.14", ISSN = "1530-1605", notes = "Also known as \cite{5428681}", } @Article{DEMBELE:2017:PCS, author = "Jean Marie Dembele and Sylvain Cussat-Blanc and Jean Disset and Yves Duthen", title = "Investigating Artificial Cells' Spatial Proliferation with a Gene Regulatory Network", journal = "Procedia Computer Science", year = "2017", volume = "114", pages = "208--215", note = "Complex Adaptive Systems Conference with Theme: Engineering Cyber Physical Systems, CAS October 30 - November 1, 2017, Chicago, Illinois, USA", keywords = "genetic algorithms, genetic programming, Artificial ontogeny, Dynamical Systems, Particle Systems, Evolutionary algorithm, Adaptive systems", ISSN = "1877-0509", URL = "http://www.sciencedirect.com/science/article/pii/S1877050917318665", DOI = "doi:10.1016/j.procs.2017.09.062", size = "8 page", abstract = "This paper discusses the combination of a Gene Regulatory Network (GRN) with a Genetic Algorithm (GA) in the context of spatial proliferation of artificial and dynamical cells. It gives the first steps in constructing and investigating simple ways of self-adaptation to furnish lifelike behaving cells. We are thus interested in growing an adaptive cells population in respect to environmental conditions. From a single cell, evolving on some nutriment field, we obtain relatively complex shapes, and functions, acquired with a GA. In a previous work, the artificial cells have been implemented with physical primitives for motion (in order to move correctly in space by convection and diffusion dynamics). The main goal of this current work is therefore to implement, for these physically moving cells, an embedded mechanism providing them with decisions capacities when it comes to choose the suitable biological routines (mitosis, apoptosis, migrationa) depending on nutriment conjuncture. To that end, we use a protein-based GRN, easily evolvable to achieve adequate behavior in response to environment inputs. In order to build such a GRN, we start from random GRNs, train them using a GA with a generic nutriment field and different fitness functions, and finally we run the obtained evolved GRN in different nutriment fields to test the robustness of our self-adaption structure", notes = "Universite Gaston Berger de Saint-Louis, UFR SAT, BP 234, Saint-Louis, Senega", } @InCollection{dembo:2002:EMSGA, author = "Adar Dembo", title = "Evolving Musical Scores using the Genetic Algorithm", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2002", year = "2002", editor = "John R. Koza", pages = "65--72", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms", URL = "http://www.genetic-programming.org/sp2002/Dembo.pdf", notes = "part of \cite{koza:2002:gagp}", } @InProceedings{DeMelo:2014:GECCO, author = "Vinicius Veloso {De Melo}", title = "Kaizen programming", booktitle = "GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation", year = "2014", editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges", isbn13 = "978-1-4503-2662-9", pages = "895--902", keywords = "genetic algorithms, genetic programming", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2576768.2598264", DOI = "doi:10.1145/2576768.2598264", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "This paper presents Kaizen Programming, an evolutionary tool based on the concepts of Continuous Improvement from Kaizen Japanese methodology. One may see Kaizen Programming as a new paradigm since, as opposed to classical evolutionary algorithms where individuals are complete solutions, in Kaizen Programming each expert proposes an idea to solve part of the problem, thus a solution is composed of all ideas together. Consequently, evolution becomes a collaborative approach instead of an egocentric one. An idea's quality (analog to an individual's fitness) is not how good it fits the data, but a measurement of its contribution to the solution, which improves the knowledge about the problem. Differently from evolutionary algorithms that simply perform trial-and-error search, one can determine, exactly, parts of the solution that should be removed or improved. That property results in the reduction in bloat, number of function evaluations, and computing time. Even more important, the Kaizen Programming tool, proposed to solve symbolic regression problems, builds the solutions as linear regression models - not linear in the variables, but linear in the parameters, thus all properties and characteristics of such statistical tool are valid. Experiments on benchmark functions proposed in the literature show that Kaizen Programming easily outperforms Genetic Programming and other methods, providing high quality solutions for both training and testing sets while requiring a small number of function evaluations.", notes = "Also known as \cite{2598264} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)", } @InProceedings{deMelo:2015:GPTP, author = "Vinicius Veloso {de Melo} and Wolfgang Banzhaf", title = "Kaizen Programming for Feature Generation", booktitle = "Genetic Programming Theory and Practice XIII", year = "2015", editor = "Rick Riolo and William P. Worzel and M. Kotanchek and A. Kordon", series = "Genetic and Evolutionary Computation", pages = "39--57", address = "Ann Arbor, USA", month = "14-16 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Kaizen programming Genetic programming Classification Decision-tree", isbn13 = "978-3-319-34223-8", URL = "http://www.springer.com/us/book/9783319342214", DOI = "doi:10.1007/978-3-319-34223-8_3", abstract = "A data set for classification is commonly composed of a set of features defining the data space representation and one attribute corresponding to the instances class. A classification tool has to discover how to separate classes based on features, but the discovery of useful knowledge may be hampered by inadequate or insufficient features. Pre-processing steps for the automatic construction of new high-level features proposed to discover hidden relationships among features and to improve classification quality. Here we present a new tool for high-level feature construction: Kaizen Programming. This tool can construct many complementary/dependent high-level features simultaneously. We show that our approach outperforms related methods on well-known binary-class medical data sets using a decision-tree classifier, achieving greater accuracy and smaller trees.", notes = " Part of \cite{Riolo:2015:GPTP} Published after the workshop in 2016", } @InProceedings{deMelo:2015:BRACIS, author = "Vinicius Veloso {de Melo} and Benjamin Fowler and Wolfgang Banzhaf", booktitle = "2015 Brazilian Conference on Intelligent Systems (BRACIS)", title = "Evaluating Methods for Constant Optimization of Symbolic Regression Benchmark Problems", year = "2015", pages = "25--30", address = "Natal, Brazil", size = "6 pages", abstract = "Constant optimisation in symbolic regression is an important task addressed by several researchers. It has been demonstrated that continuous optimization techniques are adequate to find good values for the constants by minimizing the prediction error. In this paper, we evaluate several continuous optimization methods that can be used to perform constant optimization in symbolic regression. We have selected 14 well-known benchmark problems and tested the performance of diverse optimization methods in finding the expected constant values, assuming that the correct formula has been found. The results show that Levenberg-Marquardt presented the highest success rate among the evaluated methods, followed by Powell's and Nelder-Mead's Simplex. However, two benchmark problems were not solved, and for two other problems the Levenberg-Marquardt was largely outperformed by Nelder-Mead Simplex in terms of success rate. We conclude that even though a symbolic regression technique may find the correct formula, constant optimization may fail, thus, this may also happen during the search for a formula and may guide the method towards the wrong solution. Also, the efficiency of LM in finding high-quality solutions by using only a few function evaluations could serve as inspiration for the development of better symbolic regression methods.", keywords = "genetic algorithms, genetic programming, Symbolic Regression, Curve-fitting, Least-squares, Nonlinear regression", DOI = "doi:10.1109/BRACIS.2015.55", month = "4-7 " # nov, notes = "Benchmarks p27 'taken from \cite{McDermott:2012:GECCO}'. Also known as \cite{7423910}", } @InProceedings{deMelo:2016:GECCOcomp, author = "Vinicius Veloso {de Melo} and Wolfgang Banzhaf", title = "Improving Logistic Regression Classification of Credit Approval with Features Constructed by Kaizen Programming", booktitle = "GECCO '16 Companion: Proceedings of the Companion Publication of the 2016 Annual Conference on Genetic and Evolutionary Computation", year = "2016", editor = "Tobias Friedrich and Frank Neumann and Andrew M. Sutton and Martin Middendorf and Xiaodong Li and Emma Hart and Mengjie Zhang and Youhei Akimoto and Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and Daniele Loiacono and Julian Togelius and Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and Faustino Gomez and Carlos M. Fonseca and Heike Trautmann and Alberto Moraglio and William F. Punch and Krzysztof Krawiec and Zdenek Vasicek and Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and Boris Naujoks and Enrique Alba and Gabriela Ochoa and Simon Poulding and Dirk Sudholt and Timo Koetzing", pages = "61--62", month = "20-24 " # jul, organisation = "SIGEVO", address = "Denver, USA", publisher = "ACM", keywords = "genetic algorithms, genetic programming: Poster", publisher_address = "New York, NY, USA", isbn13 = "978-1-4503-4323-7", DOI = "doi:10.1145/2908961.2908963", abstract = "we employ the recently proposed Kaizen Programming (KP) approach to find high-quality nonlinear combinations of the original features in a dataset. KP constructs many complementary features at the same time, which are selected by their importance, not by model quality. We investigated our approach in a well-known real-world credit scoring dataset. When compared to related approaches, KP reaches similar or better results, but evaluates fewer models.", notes = "Distributed at GECCO-2016.", } @InProceedings{deMelo:2016:CEC, author = "Vinicius Veloso {de Melo}", title = "Breast Cancer Detection with Logistic Regression improved by features constructed by Kaizen Programming in a hybrid approach", booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016)", year = "2016", editor = "Yew-Soon Ong", pages = "16--23", address = "Vancouver", month = "24-29 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-5090-0623-6", DOI = "doi:10.1109/CEC.2016.7743773", abstract = "Breast cancer is known as the second largest cause of cancer deaths among women, but thankfully it can be cured if diagnosed early. There have been many investigations on methods to improve the accuracy of the diagnostic, and Machine Learning (ML) and Evolutionary Computation (EC) tools are among the most successfully employed modern methods. On the other hand, Logistic Regression (LR), a traditional and popular statistical method for classification, is not commonly used by computer scientists as those modern methods usually outperform it. Here we show that LR can achieve results that are similar to those of ML and EC methods and can even outperform them when useful knowledge is discovered in the dataset. In this paper, we employ the recently proposed Kaizen Programming (KP) approach with LR to construct high-quality nonlinear combinations of the original features resulting in new sets of features. Experimental analysis indicates that the new sets provide significantly better predictive accuracy than the original ones. When compared to related work from the literature, it is shown that the proposed approach is competitive and a promising method for automatic feature construction.", notes = "WCCI2016", } @Article{deMelo:2017:Neurocomputing, author = "Vinicius Veloso {de Melo} and Wolfgang Banzhaf", title = "Improving the prediction of material properties of concrete using Kaizen Programming with Simulated Annealing", journal = "Neurocomputing", year = "2017", volume = "246", pages = "25--44", month = "12 " # jul, note = "Brazilian Conference on Intelligent Systems 2015", keywords = "genetic algorithms, genetic programming, Automatic feature engineering, Kaizen Programming, Linear regression, High-performance concrete", ISSN = "0925-2312", URL = "https://www.cs.mun.ca/~banzhaf/papers/Neurocomputing2017.pdf", URL = "http://www.sciencedirect.com/science/article/pii/S092523121730231X", DOI = "doi:10.1016/j.neucom.2016.12.077", abstract = "Predicting the properties of materials like concrete has been proven a difficult task given the complex interactions among its components. Over the years, researchers have used Statistics, Machine Learning, and Evolutionary Computation to build models in an attempt to accurately predict such properties. High-quality models are often non-linear, justifying the study of nonlinear regression tools. In this paper, we employ a traditional multiple linear regression method by ordinary least squares to solve the task. However, the model is built upon non-linear features automatically engineered by Kaizen Programming, a recently proposed hybrid method. Experimental results show that Kaizen Programming can find low-correlated features in an acceptable computational time. Such features build high-quality models with better predictive quality than results reported in the literature.", notes = "also known as \cite{VELOSODEMELO201725}", } @Article{DEMELO:2018:IS, author = "Vinicius Veloso {de Melo} and Wolfgang Banzhaf", title = "Automatic feature engineering for regression models with machine learning: An evolutionary computation and statistics hybrid", journal = "Information Sciences", volume = "430-431", pages = "287--313", year = "2018", keywords = "genetic algorithms, genetic programming, Feature engineering, Machine learning, Symbolic regression, Kaizen programming, Linear regression, Hybrid", ISSN = "0020-0255", DOI = "doi:10.1016/j.ins.2017.11.041", URL = "http://www.sciencedirect.com/science/article/pii/S0020025517311040", abstract = "Symbolic Regression (SR) is a well-studied task in Evolutionary Computation (EC), where adequate free-form mathematical models must be automatically discovered from observed data. Statisticians, engineers, and general data scientists still prefer traditional regression methods over EC methods because of the solid mathematical foundations, the interpretability of the models, and the lack of randomness, even though such deterministic methods tend to provide lower quality prediction than stochastic EC methods. On the other hand, while EC solutions can be big and uninterpretable, they can be created with less bias, finding high-quality solutions that would be avoided by human researchers. Another interesting possibility is using EC methods to perform automatic feature engineering for a deterministic regression method instead of evolving a single model; this may lead to smaller solutions that can be easy to understand. In this contribution, we evaluate an approach called Kaizen Programming (KP) to develop a hybrid method employing EC and Statistics. While the EC method builds the features, the statistical method efficiently builds the models, which are also used to provide the importance of the features; thus, features are improved over the iterations resulting in better models. Here we examine a large set of benchmark SR problems known from the EC literature. Our experiments show that KP outperforms traditional Genetic Programming - a popular EC method for SR - and also shows improvements over other methods, including other hybrids and well-known statistical and Machine Learning (ML) ones. More in line with ML than EC approaches, KP is able to provide high-quality solutions while requiring only a small number of function evaluations", } @Article{DeMelo:2018:NCA, author = "Vinicius Veloso {de Melo} and Wolfgang Banzhaf", title = "Drone Squadron Optimization: a novel self-adaptive algorithm for global numerical optimization", journal = "Neural Computing and Applications", year = "2018", volume = "30", pages = "3117--3144", month = nov, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1007/s00521-017-2881-3", abstract = "This paper proposes Drone Squadron Optimization (DSO), a new self-adaptive metaheuristic for global numerical optimization which is updated online by a hyper-heuristic. DSO is an artifact-inspired technique, as opposed to many nature-inspired algorithms used today. DSO is very flexible because it is not related to natural behaviors or phenomena. DSO has two core parts: the semiautonomous drones that fly over a landscape to explore, and the command center that processes the retrieved data and updates the drones firmware whenever necessary. The self-adaptive aspect of DSO in this work is the perturbation/movement scheme, which is the procedure used to generate target coordinates. This procedure is evolved by the command center during the global optimization process in order to adapt DSO to the search landscape. We evaluated DSO on a set of widely employed single-objective benchmark functions. The statistical analysis of the results shows that the proposed method is competitive with the other methods, but we plan several future improvements to make it more powerful and robust.", } @Misc{melo2019batch, author = "Vinicius V. Melo and Danilo Vasconcellos Vargas and Wolfgang Banzhaf", title = "Batch Tournament Selection for Genetic Programming: The quality of lexicase, the speed of Tournament", howpublished = "arXiv", year = "2019", month = "18 " # apr, note = "1904.08658", keywords = "genetic algorithms, genetic programming, Selection algorithm, Symbolic Regression", eprint = "1904.08658", primaryclass = "cs.NE", URL = "https://arxiv.org/abs/1904.08658", size = "9 pages", abstract = "Lexicase selection achieves very good solution quality by introducing ordered test cases. However, the computational complexity of lexicase selection can prohibit its use in many applications. we introduce Batch Tournament Selection (BTS), a hybrid of tournament and lexicase selection which is approximately one order of magnitude faster than lexicase selection while achieving a competitive quality of solutions. Tests on a number of regression datasets show that BTS compares well with lexicase selection in terms of mean absolute error while having a speed-up of up to 25 times. Surprisingly, BTS and lexicase selection have almost no difference in both diversity and performance. This reveals that batches and ordered test cases are completely different mechanisms which share the same general principle fostering the specialization of individuals. This work introduces an efficient algorithm that sheds light onto the main principles behind the success of lexicase, potentially opening up a new range of possibilities for algorithms to come.", notes = "See \cite{deMelo:2019:GECCO} From: Danilo Vasconcellos Vargas. Also known as \cite{DBLP:journals/corr/abs-1904-08658}", } @InProceedings{deMelo:2019:GECCO, author = "Vinicius V. {de Melo} and Danilo {Vasconcellos Vargas} and Wolfgang Banzhaf", title = "Batch tournament selection for genetic programming: the quality of lexicase, the speed of tournament", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference", year = "2019", editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and Anne Auger and Petr Posik and Leslie {Peprez Caceres} and Andrew M. Sutton and Nadarajen Veerapen and Christine Solnon and Andries Engelbrecht and Stephane Doncieux and Sebastian Risi and Penousal Machado and Vanessa Volz and Christian Blum and Francisco Chicano and Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and Jonathan Fieldsend and Jose Antonio Lozano and Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and Robin Purshouse and Thomas Baeck and Justyna Petke and Giuliano Antoniol and Johannes Lengler and Per Kristian Lehre", pages = "994--1002", address = "Prague, Czech Republic", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, lexicase, Selection algorithm, Symbolic Regression", isbn13 = "978-1-4503-6111-8", URL = "http://www.cs.mun.ca/~banzhaf/papers/GECCO-2019_Batch.pdf", DOI = "doi:10.1145/3321707.3321793", size = "9 pages", abstract = "Lexicase selection achieves very good solution quality by introducing ordered test cases. However, the computational complexity of lexicase selection can prohibit its use in many applications. In this paper, we introduce Batch Tournament Selection (BTS), a hybrid of tournament and lexicase selection which is approximately one order of magnitude faster than lexicase selection while achieving a competitive quality of solutions. Tests on a number of regression datasets show that BTS compares well with lexicase selection in terms of mean absolute error while having a speed-up of up to 25 times. Surprisingly, BTS and lexicase selection have almost no difference in both diversity and performance. This reveals that batches and ordered test cases are completely different mechanisms which share the same general principle fostering the specialization of individuals. This work introduces an efficient algorithm that sheds light onto the main principles behind the success of lexicase, potentially opening up a new range of possibilities for algorithms to come.", notes = "See \cite{melo2019batch} Also known as \cite{3321793} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InProceedings{DeMelo:2020:evoapplications, author = "Vinicius Veloso {de Melo} and Alvaro Luiz Fazenda and Leo Francoso Dal Piccol Sotto and Giovanni Iacca", title = "A {MIMD} Interpreter for Genetic Programming", booktitle = "23rd International Conference, EvoApplications 2020", year = "2020", month = "15-17 " # apr, editor = "Pedro A. Castillo and Juan Luis {Jimenez Laredo} and Francisco {Fernandez de Vega}", series = "LNCS", volume = "12104", publisher = "Springer Verlag", address = "Seville, Spain", pages = "645--658", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Genetic Programming Interpreter, parallel computing, Vectorization, Multiple Instruction", isbn13 = "978-3-030-43721-3", video_url = "https://www.youtube.com/watch?v=5rh56rZUO5w", DOI = "doi:10.1007/978-3-030-43722-0_41", size = "14 pages", abstract = "Most Genetic Programming implementations use an interpreter to execute an individual, in order to obtain its outcome. Usually, such interpreter is the main bottleneck of the algorithm, since a single individual may contain thousands of instructions that must be executed on a dataset made of a large number of samples. Although one can use SIMD (Single Instruction Multiple Data) intrinsics to execute a single instruction on a few samples at the same time, multiple passes on the dataset are necessary to calculate the result. To speed up the process, we propose using MIMD (Multiple Instruction Multiple Data) instruction sets. This way, in a single pass one can execute several instructions on the dataset. We employ AVX2 intrinsics to improve the performance even further, reaching a median peak of 7.5 billion genetic programming operations per second in a single CPU core.", notes = "See also \cite{Oliveira:2020:ERAD-SP} Federal University of Sao Paulo, Brazil http://www.evostar.org/2020/ EvoApplications2020 held in conjunction with EuroGP'2020, EvoMusArt2020 and EvoCOP2020", } @InProceedings{de-melo:2024:GECCOcomp, author = "Vinicius Veloso {De Melo} and Wolfgang Banzhaf and Giovanni Iacca", title = "Accelerating {GP} Genome Evaluation Through Real Compilation with a Multiple Program Single Data Approach", booktitle = "Symbolic Regression", year = "2024", editor = "William {La Cava} and Steven Gustafson", pages = "2041--2049", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, multiple program single data", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3664168", size = "9 pages", abstract = "Genetic Programming (GP) presents a unique challenge in fitness evaluation due to the need to repeatedly execute the evolved programs, often represented as tree structures, to assess their quality on multiple input data. Traditional approaches rely on interpreting these program trees, which can be computationally expensive. This paper proposes an optimization method that leverages code generation using a novel strategy and Just-In-Time (JIT) compilation to significantly improve the efficiency of fitness evaluation in GP. We propose to revisit using an actual compiler to transform a GP individual into native computer code executable quickly on the CPU. Our GP implementation is a simple tree-based approach that makes it easy for researchers to experiment with, while the evaluation function shows high performance. Preliminary results in Symbolic Regression on artificial datasets demonstrate that our approach is more than 6\texttimes{} faster than a popular GP framework that uses high-performance single-function evaluation but in a stack-based interpreter mode.", notes = "GECCO-2024 SymReg A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @Article{deMenezes:Fwg:06, author = "Lilian M. {de Menezes} and Nikolay Y. Nikolaev", title = "Forecasting with genetically programmed polynomial neural networks", journal = "International Journal of Forecasting", year = "2006", volume = "22", number = "2", pages = "249--265", month = apr # "-" # jun, keywords = "genetic algorithms, genetic programming, Nonlinear models, Tree-structured polynomial neural network models, Statistical learning algorithms", DOI = "doi:10.1016/j.ijforecast.2005.05.002", abstract = "Recent literature on nonlinear models has shown genetic programming to be a potential tool for forecasters. A special type of genetically programmed model, namely polynomial neural networks, is addressed. Their outputs are polynomials and, as such, they are open boxes that are amenable to comprehension, analysis, and interpretation. This paper presents a polynomial neural network forecasting system, PGP, which has three innovative features: polynomial block reformulation, local ridge regression for weight estimation, and regularised weight subset selection for pruning that uses a least absolute shrinkage and selection operator. The relative performance of this system to other established forecasting procedures is the focus of this research and is illustrated by three empirical studies. Overall, the results are very promising and indicate areas for further research.", } @InProceedings{deMesentierSilva:2016:CIG, author = "Fernando {de Mesentier Silva} and Aaron Isaksen and Julian Togelius and Andy Nealen", booktitle = "2016 IEEE Conference on Computational Intelligence and Games (CIG)", title = "Generating heuristics for novice players", year = "2016", abstract = "We consider the problem of generating compact sub-optimal game-playing heuristics that can be understood and easily executed by novices. In particular, we seek to find heuristics that can lead to good play while at the same time be expressed as fast and frugal trees or short decision lists. This has applications in automatically generating tutorials and instructions for playing games, but also in analysing game design and measuring game depth. We use the classic game Blackjack as a test-bed, and compare condition induction with the RIPPER algorithm, exhaustive-greedy search in statement space, genetic programming and axis-aligned search. We find that all of these methods can find compact well-playing heuristics under the given constraints, with axis-aligned search performing particularly well.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CIG.2016.7860407", month = sep, notes = "Also known as \cite{7860407}", } @InProceedings{deMesentier:2018:CIG, author = "Fernando {de Mesentier Silva} and Julian Togelius and Frank Lantz and Andy Nealen", booktitle = "2018 IEEE Conference on Computational Intelligence and Games (CIG)", title = "Generating Novice Heuristics for Post-Flop Poker", year = "2018", abstract = "Agents now exist that can play Texas Hold'em Poker at a very high level, and simplified versions of the game have been solved. However, this does not directly translate to learning heuristics humans can use to play the game. We address the problem of learning chains of human-learnable heuristics for playing heads-up limit Texas Hold'em, focusing on the post-flop stages of the game. By restricting the policy space to fast and frugal trees, which are sequences of if-then-else rules, we can learn such heuristics using several methods including genetic programming. This work builds on our previous work on learning such heuristic rule set for Blackjack and pre-flop Texas Hold'em, but introduces a richer language for heuristics.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CIG.2018.8490415", ISSN = "2325-4289", month = aug, notes = "Also known as \cite{8490415}", } @Article{demirbas:2022:AS, author = "Munise Didem Demirbas and Didem Cakir and Celal Ozturk and Sibel Arslan", title = "Stress Analysis of {2D-FG} Rectangular Plates with Multi-Gene Genetic Programming", journal = "Applied Sciences", year = "2022", volume = "12", number = "16", pages = "Article No. 8198", keywords = "genetic algorithms, genetic programming", ISSN = "2076-3417", URL = "https://www.mdpi.com/2076-3417/12/16/8198", DOI = "doi:10.3390/app12168198", abstract = "Functionally Graded Materials (FGMs) are designed for use in high-temperature applications. Since the mass production of FGM has not yet been made, the determination of its thermo-mechanical limits depends on the compositional gradient exponent value. In this study, an efficient working model is created for the thermal stress problem of the 2D-FG plate using Multi-gene Genetic Programming (MGGP). In our MGGP model in this study, data sets obtained from the numerical analysis results of the thermal stress problem are used, and formulas that give equivalent stress levels as output data, with the input data being the compositional gradient exponent, are obtained. For the current problem, efficient models that reduce CPU processing time are obtained by using the MGGP method.", notes = "also known as \cite{app12168198}", } @PhdThesis{Demirbilek:thesis, author = "Edip Demirbilek", title = "Machine learning based perceived quality estimation models in realtime communications", title_fr = "Modeles d'apprentissage automatique d'estimation de qualite percue dans les communications en temps reel", school = "Universite du Quebec, Institut national de la recherche scientifique", year = "2017", address = "Canada", keywords = "genetic algorithms, genetic programming, multimedia communication testbed, audiovisual quality dataset, perceived quality modeling, machine learning, random forests, bagging, deep learning, genetic programming, banc d'essai de la communication multimedia, donnees de mesure de la qualite audiovisuelle, modelisation de la qualite percue, apprentissage automatique, forets d'arbres decisionnels, techniques de bootstrap, apprentissage profond, programmation genetique", URL = "http://espace.inrs.ca/id/eprint/8018", URL = "http://espace.inrs.ca/id/eprint/8018/1/Demirbilek,%20Edip.pdf", size = "184 pages", abstract = "This research has started with the initial objective to build machine learning based models that predict the perceived audiovisual quality directly from a set of correlated parameters that are extracted from a target quality dataset. To reach that goal, we have first created a VideoLAN Video-on-Demand based testbed and generated a preliminary audiovisual quality dataset that let us experiment with various machine learning algorithms. These early experiments encouraged us to create a more robust testbed based on the GStreamer multimedia framework. With this new testbed, we have generated the INRS audiovisual quality dataset that reflects contemporary realtime configurations for video frame rate, video quantisation, noise reduction parameters and network packet loss rate. Then we have used this INRS dataset to build several machine learning based parametric and bit-stream perceived quality estimation models based on Random Forests, Bagging, Deep Learning and Genetic Programming methods. For the parametric models, all four methods have achieved high accuracy in terms of RMSE and Pearson correlation with subjective ratings. Random Forests and Bagging based models show a small edge over Deep Learning with respect to the accuracy they have achieved. Genetic Programming based models fell behind even though their accuracy is impressive as well. We have also obtained high accuracy on other publicly available audiovisual quality datasets and the performance metrics we have computed are comparable to the existing models trained and tested on these datasets. For the bit stream models, both the Random Forests and Bagging based bitstream models have outperformed the Deep Learning and Genetic Programming based bitstream models as well as all of the parametric models. However, both the Genetic Programming and Deep Learning based bitstream models fell behind the parametric models due to a significant increase in the number of features in the bitstream dataset. Overall we conclude that computing the bitstream information is worth the effort and helps to build more accurate models. However, it is useful only for the deployment of the right algorithms. In light of our results, we conclude that the Decision Trees based algorithms are well suited to the parametric models as well as to the bitstream models. Moreover, we know that extracting additional correlated data from the dataset helps us to generate more accurate models when suitable machine learning algorithms are deployed. The dataset, tools and machine learning codes that have been generated during this research are publicly available for research and development purposes.", resume = "L'objectif de notre travail est de developper des modeles d'apprentissage automatique qui predisent la qualite audiovisuelle percue. La prediction se fait a partir d'un ensemble de parametres correles derives d'un ensemble de donnees extraits de la cible. Afin d'atteindre cet objectif, nous avons tout d'abord developpe, avec VLC, un banc d'essai de la VsD (Video sur Demande) et avons genere un ensemble de donnees preliminaires de la qualite audiovisuelle. Le but etait d'etudier divers algorithmes d'apprentissage automatique. Ces premieres experimentations nous ont encourage a developper un banc d'essai plus robuste, base sur le framework multimedia GStreamer. Nous avons genere, avec ce nouveau banc d'essai, un ensemble de donnees de qualite audiovisuelle, propre a notre contexte. Ces donnees refletent les configurations contemporaines des communications interactives pour le taux d'image par seconde, la quantification video, les parametres de reduction du bruit et le taux de perte des paquets du reseau. Nous avons ensuite utilise cet ensemble de donnees afin de developper divers modeles, reposant soit sur l'information media (<< parametriques >>), soit sur les donnees reseau (<< bitstream >>), d'estimation de la qualite percue. Ces modeles sont bases sur les methodes des forets d'arbres decisionnels, des techniques dites de demarrage (<< bootstrap >>), de l'apprentissage profond et de la programmation genetique. Pour les modeles parametriques, les quatre methodes ont atteint une precision elevee en terme de correlation RMSE et de Pearson. Les modeles bases sur les forets d'arbres decisionnels et les techniques de bootstrap montrent un petit avantage par rapport a l'apprentissage profond quant a la precision qu'ils ont atteint. Les modeles bases sur la programmation genetique sont moins performants meme si leur precision est impressionnante. Nous avons egalement obtenu une precision elevee en utilisant les autres ensembles de donnees sur la qualite visuelle, accessibles au public. Les metriques de performance que nous avons calculees sont comparables aux modeles existants formes et testes sur ces ensembles de donnees. Pour les modeles bitstream, les methodes de forets d'arbres decisionnels ainsi que les techniques de bootstrap ont surpasse les modeles bases sur l'apprentissage profond et la programmation genetique ainsi que tous les modeles parametriques. Cependant, les modeles bitstream realises en programmation genetique et en apprentissage profond ont moins bien performe que les modeles parametriques a cause d'une augmentation significative du nombre de caracteristiques dans l'ensemble de donnees bitstream. Dans l'ensemble, nous concluons que le calcul de l'information bitstream merite l'effort fourni pour la generer. Ce calcul aide a construire des modeles plus precis mais demeure utile uniquement pour le deploiement de bons algorithmes. Sur la base de nos resultats, nous concluons que les algorithmes bases sur l'arbre de decision conviennent aux modeles parametriques ainsi qu'aux modeles bitstream. De plus, nous savons que l'extraction de donnees correlees supplementaires de l'ensemble de donnees nous aide a generer des modeles plus precis lorsque des algorithmes d'apprentissage automatique appropries sont deployes. L'ensemble des donnees, les outils et les codes d'apprentissage automatique qui ont ete developpes au cours de cette recherche sont gracieusement offerts a la communaute pour des fins de recherche et de developpement.", notes = "En Francais et English EMT Supervisor: Jean-Charles Gregoire", } @InProceedings{Demirbilek:2017:ieeeICME, author = "Edip Demirbilek and Jean-Charles Gregoire", booktitle = "2017 IEEE International Conference on Multimedia and Expo (ICME)", title = "Machine learning based reduced reference bitstream audiovisual quality prediction models for realtime communications", year = "2017", pages = "571--576", abstract = "Perceived quality prediction models for multimedia services vary greatly depending on the type of the data and on the amount of information related to the original signal used. In this research, we have developed machine learning-based reduced-reference bitstream audiovisual quality prediction models by using the parametric version of the publicly available INRS audiovisual quality dataset. As that original INRS dataset did not contain bitstream information but provided both reference and transmitted videos, we have computed its bitstream version to develop the reduced-reference bitstream models. We have compared the performance of the Decision Trees based ensemble methods, Genetic Programming and Deep Learning models on this bitstream version of the dataset and have also compared these results with the results of the no-reference parametric models on the parametric version of the dataset. Decision Trees based ensemble methods outperformed Deep Learning and Genetic Programming based models when reduced-reference bitstream data was used and outperformed all existing no-reference parametric models that were trained and tested on the parametric version of the dataset. Our studies show that Decision Trees based approaches are well suited for no-reference parametric models as well as for reduced-reference bitstream models.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICME.2017.8019462", month = jul, notes = "Also known as \cite{8019462}", } @Article{Demirbilek:2017:MLB, author = "Edip Demirbilek and Jean-Charles Gregoire", title = "Machine Learning-Based Parametric Audiovisual Quality Prediction Models for Real-Time Communications", journal = "ACM Transactions on Multimedia Computing, Communications, and Applications", volume = "13", number = "2", pages = "16:1--16:25", month = may, year = "2017", keywords = "genetic algorithms, genetic programming, ANN, DT, perceived quality estimation, audiovisual quality dataset, MOS, no-reference models, machine learning", articleno = "16", ISSN = "1551-6857", bibdate = "Fri Jun 16 14:48:38 MDT 2017", bibsource = "http://www.acm.org/pubs/contents/journals/tomccap/; http://www.math.utah.edu/pub/tex/bib/tomccap.bib", URL = "http://portal.acm.org/browse_dl.cfm?idx=J961", DOI = "doi:10.1145/3051482", size = "25 pages", abstract = "In order to mechanically predict audiovisual quality in interactive multimedia services, we have developed machine learning-based no-reference parametric models. We have compared Decision Trees-based ensemble methods, Genetic Programming and Deep Learning models that have one and more hidden layers. We have used the Institut national de la recherche scientifique (INRS) audiovisual quality dataset specifically designed to include ranges of parameters and degradations typically seen in real-time communications. Decision Trees, based ensemble methods have outperformed both Deep Learning, and Genetic Programming--based models in terms of Root-Mean-Square Error (RMSE) and Pearson correlation values. We have also trained and developed models on various publicly available datasets and have compared our results with those of these original models. Our studies show that Random Forests-based prediction models achieve high accuracy for both the INRS audiovisual quality dataset and other publicly available comparable datasets.", acknowledgement = "Nelson H. F. Beebe, University of Utah, Department of Mathematics, 110 LCB, 155 S 1400 E RM 233, Salt Lake City, UT 84112-0090, USA, Tel: +1 801 581 5254, FAX: +1 801 581 4148, e-mail: \path|beebe@math.utah.edu|, \path|beebe@acm.org|, \path|beebe@computer.org| (Internet), URL: \path|http://www.math.utah.edu/~beebe/|", } @Misc{journals/corr/abs-1801-05889, author = "Edip Demirbilek and Jean-Charles Gregoire", title = "Perceived Audiovisual Quality Modelling based on Decison Trees, Genetic Programming and Neural Networks", howpublished = "arXiv", year = "2017", month = "6 " # dec, keywords = "genetic algorithms, genetic programming, perceived quality, audiovisual dataset, bitstream model, machine learning", URL = "http://arxiv.org/abs/1801.05889", size = "14 pages", abstract = "Our objective is to build machine learning based models that predict audiovisual quality directly from a set of correlated parameters that are extracted from a target quality dataset. We have used the bitstream version of the INRS audiovisual quality dataset that reflects contemporary real-time configurations for video frame rate, video quantization, noise reduction parameters and network packet loss rate. We have used this dataset to build bitstream perceived quality estimation models based on the Random Forests, Bagging, Deep Learning and Genetic Programming methods. We have taken an empirical approach and have generated models varying from very simple to the most complex depending on the number of features used from the quality dataset. Random Forests and Bagging models have overall generated the most accurate results in terms of RMSE and Pearson correlation coefficient values. Deep Learning and Genetic Programming based bitstream models have also achieved good results but that high performance was observed only with a limited range of features. We have also obtained the epsilon-insensitive RMSE values for each model and have computed the significance of the difference between the correlation coefficients. Overall we conclude that computing the bitstream information is worth the effort it takes to generate and helps to build more accurate models for real-time communications. However, it is useful only for the deployment of the right algorithms with the carefully selected subset of the features. The dataset and tools that have been developed during this research are publicly available for research and development purposes.", } @Article{DEMIRCI:2023:cep, author = "Kardelen Demirci and Asli Zungur-Bastioglu and Ahmet Goerguc and Beyzanur Bayraktar and Selim Yilmaz and Fatih Mehmet Yilmaz", title = "Microwave irradiation, evolutionary algorithm and ultrafiltration can be exploited in process intensification for high-purity and advanced inulin powder production", journal = "Chemical Engineering and Processing - Process Intensification", volume = "194", pages = "109565", year = "2023", ISSN = "0255-2701", DOI = "doi:10.1016/j.cep.2023.109565", URL = "https://www.sciencedirect.com/science/article/pii/S0255270123003021", keywords = "genetic algorithms, genetic programming, Inulin powder, Extraction efficiency, Powder characterization, Hydrocolloid property", abstract = "This study investigated the effect of microwave-assisted aqueous extraction followed by ultrafiltration protocols on Jerusalem artichoke inulin powder's yield and functional properties. The standard inulin production method was also practiced, and the effectiveness of process parameters for two different techniques was investigated through response surface methodology as well as genetic algorithm. The proposed parameters of the genetic algorithm further increased the extraction yields, and the inulin contents reached 94 - 95percent after applying the 5 kDa cut-off ultrafiltration. Microwave-assisted extraction significantly improved lyophilized inulin powder's physical and chemical properties, such as Carr index, Hausner ratio, wettability, viscosity, dispersibility, and solubility. The water-holding capacity of inulin powder produced by conventional and microwave-assisted methods were 9percent and 11percent, respectively, while it was 7.2percent for untreated Jerusalem artichoke powder. As for oil binding capacity, the value of 12.9percent in the raw material increased to 14.1percent and 13.3percent, respectively. The loss modulus (G{"}) values were dominant over storage modulus (G') values, and microwave treatment resulted in lower modulus values. The Fourier transform infrared spectroscopy (FTIR) spectra and scanning electron microscope (SEM) images clearly depicted the chemical and structural modifications in the powders. Results showed that microwave application could significantly enhance inulin powder's yield and physicochemical properties. Genetic programming was beneficial in promoting the extraction parameters. Sequentially applying microwave-assisted extraction and ultrafiltration could be a convenient process for inulin powder as a functional ingredient for the food, pharmaceutical, and chemical industries", } @Article{Demirhan:2014:ECM, author = "Haydar Demirhan", title = "The problem of multicollinearity in horizontal solar radiation estimation models and a new model for {Turkey}", journal = "Energy Conversion and Management", year = "2014", volume = "84", pages = "334--345", month = aug, keywords = "genetic algorithms, genetic programming, Eccentricity correction factor, Entropy, Eureqa, Maximum possible sunshine duration, Model selection criteria, Solar declination angle, Statistical modelling", ISSN = "0196-8904", URL = "http://www.sciencedirect.com/science/article/pii/S0196890414003392", DOI = "doi:10.1016/j.enconman.2014.04.035", size = "12 pages", abstract = "Due to the considerable decrease in energy resources and increasing energy demand, solar energy is an appealing field of investment and research. There are various modelling strategies and particular models for the estimation of the amount of solar radiation reaching at a particular point over the Earth. In this article, global solar radiation estimation models are taken into account. To emphasise severity of multicollinearity problem in solar radiation estimation models, some of the models developed for Turkey are revisited. It is observed that these models have been identified as accurate under certain multicollinearity structures, and when the multicollinearity is eliminated, the accuracy of these models is controversial. Thus, a reliable model that does not suffer from multicollinearity and gives precise estimates of global solar radiation for the whole region of Turkey is necessary. A new nonlinear model for the estimation of average daily horizontal solar radiation is proposed making use of the genetic programming technique. There is no multicollinearity problem in the new model, and its estimation accuracy is better than the revisited models in terms of numerous statistical performance measures. According to the proposed model, temperature, precipitation, altitude, longitude, and monthly average daily extraterrestrial horizontal solar radiation have significant effect on the average daily global horizontal solar radiation. Relative humidity and soil temperature are not included in the model due to their high correlation with precipitation and temperature, respectively. While altitude has the highest relative impact on the average daily horizontal solar radiation, impact of temperature is greater than that of both longitude and precipitation.", } @Article{Demirhan:2015:ECM, author = "Haydar Demirhan and Yasemin Kayhan Atilgan", title = "New horizontal global solar radiation estimation models for Turkey based on robust coplot supported genetic programming technique", journal = "Energy Conversion and Management", volume = "106", pages = "1013--1023", year = "2015", ISSN = "0196-8904", DOI = "doi:10.1016/j.enconman.2015.10.038", URL = "http://www.sciencedirect.com/science/article/pii/S0196890415009607", abstract = "Renewable energy sources have been attracting more and more attention of researchers due to the diminishing and harmful nature of fossil energy sources. Because of the importance of solar energy as a renewable energy source, an accurate determination of significant covariates and their relationships with the amount of global solar radiation reaching the Earth is a critical research problem. There are numerous meteorological and terrestrial covariates that can be used in the analysis of horizontal global solar radiation. Some of these covariates are highly correlated with each other. It is possible to find a large variety of linear or non-linear models to explain the amount of horizontal global solar radiation. However, models that explain the amount of global solar radiation with the smallest set of covariates should be obtained. In this study, use of the robust coplot technique to reduce the number of covariates before going forward with advanced modelling techniques is considered. After reducing the dimensionality of model space, yearly and monthly mean daily horizontal global solar radiation estimation models for Turkey are built by using the genetic programming technique. It is observed that application of robust coplot analysis is helpful for building precise models that explain the amount of global solar radiation with the minimum number of covariates without suffering from outlier observations and the multicollinearity problem. Consequently, over a dataset of Turkey, precise yearly and monthly mean daily global solar radiation estimation models are introduced using the model spaces obtained by robust coplot technique and inferences on the sensitivity of the amount of global solar radiation to covariates and the magnitude and direction of effect of covariates on the global solar radiation are drawn.", keywords = "genetic algorithms, genetic programming, Coplot, Correlation coefficient, ESRA, ESRI, Estimation, Eureqa Pro, Horizontal global solar radiation, Modelling, Outlier, Robust estimation", } @InProceedings{dempsey:2004:gew:idem, author = "Ian Dempsey and Michael O'Neill and Anthony Brabazon", title = "Live Trading with Grammatical Evolution", editor = "R. Poli and S. Cagnoni and M. Keijzer and E. Costa and F. Pereira and G. Raidl and S. C. Upton and D. Goldberg and H. Lipson and E. {de Jong} and J. Koza and H. Suzuki and H. Sawai and I. Parmee and M. Pelikan and K. Sastry and D. Thierens and W. Stolzmann and P. L. Lanzi and S. W. Wilson and M. O'Neill and C. Ryan and T. Yu and J. F. Miller and I. Garibay and G. Holifield and A. S. Wu and T. Riopka and M. M. Meysenburg and A. W. Wright and N. Richter and J. H. Moore and M. D. Ritchie and L. Davis and R. Roy and M. Jakiela", booktitle = "GECCO 2004 Workshop Proceedings", year = "2004", month = "26-30 " # jun, address = "Seattle, Washington, USA", keywords = "genetic algorithms, genetic programming, grammatical evolution", URL = "http://gpbib.cs.ucl.ac.uk/gecco2004/WGEW001.pdf", size = "9 pages", abstract = "This study reports work in progress on the development of an on-line evolutionary automatic programming methodology for uncovering technical trading rules for the S&P 500 index. The system adopts a variable sized investment strategy based on the strength of the signals produced by the trading rules. Rogue rules, which generate excessive signals, led to poor market activity. Here we examine the viability of a signal decay constant to reduce the effect of rogue rules. The results show that an aggressive decay rate yielded more profitable results for the trading period January 1st 1991 to December 1st 1997.", notes = "GECCO-2004WKS Distributed on CD-ROM at GECCO-2004", } @InProceedings{1068289, author = "Ian Dempsey and Michael O'Neill and Anthony Brabazon", title = "Meta-grammar constant creation with grammatical evolution by grammatical evolution", booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation", year = "2005", editor = "Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and Edwin D. {de Jong} and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler", volume = "2", ISBN = "1-59593-010-8", pages = "1665--1671", address = "Washington DC, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2005/docs/p1665.pdf", DOI = "doi:10.1145/1068009.1068289", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "25-29 " # jun, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, constant creation, digit concatenation, ephemeral random constants, grammatical evolution, metagrammars, theory", notes = "GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM Order Number 910052", } @InProceedings{dempsey:gecco05ws, author = "Ian Dempsey", title = "Constant Generation for the Financial Domain using Grammatical Evolution", booktitle = "Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program", year = "2005", month = "25-29 " # jun, editor = "Franz Rothlauf and Misty Blowers and J{\"u}rgen Branke and Stefano Cagnoni and Ivan I. Garibay and Ozlem Garibay and J{\"o}rn Grahl and Gregory Hornby and Edwin D. {de Jong} and Tim Kovacs and Sanjeev Kumar and Claudio F. Lima and Xavier Llor{\`a} and Fernando Lobo and Laurence D. Merkle and Julian Miller and Jason H. Moore and Michael O'Neill and Martin Pelikan and Terry P. Riopka and Marylyn D. Ritchie and Kumara Sastry and Stephen L. Smith and Hal Stringer and Keiki Takadama and Marc Toussaint and Stephen C. Upton and Alden H. Wright", publisher = "ACM Press", address = "Washington, D.C., USA", keywords = "genetic algorithms, genetic programming, grammatical evolution", pages = "350--353", URL = "http://gpbib.cs.ucl.ac.uk/gecco2005wks/papers/0350.pdf", abstract = "This study reports the work to date on the analysis of different methodologies for constant creation with the aim of applying the most advantageous method to the dynamic real world problem of a live trading system. The methodologies explored here are Digit Concatenation and Grammatical Ephemeral Random Constants with clear advantages identified for a digit concatenation approach in combination with the ability to form new constants through their recombination within expressions.", notes = "Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006", } @InProceedings{dempsey:2006:CEC, author = "Ian Dempsey and Michael O'Neill and Anthony Brabazon", title = "Adaptive Trading with Grammatical Evolution", booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary Computation", year = "2006", pages = "9137--9142", address = "Vancouver", month = "16-21 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, grammatical evolution", ISBN = "0-7803-9487-9", DOI = "doi:10.1109/CEC.2006.1688631", size = "6 pages", abstract = "This study reports on the performance of an on-line evolutionary automatic programming methodology for uncovering technical trading rules for the S&P 500 and Nikkei 225 indices. The system adopts a variable sized investment strategy based on the strength of the signals produced by the trading rules. Two approaches are explored, one using a single population of rules which is adapted over the lifetime of the data and another whereby a new population is created for each step across the time series. The results show profitable performance for the trading periods explored with clear advantages for an adaptive population of rules.", notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE.", } @Article{Dempsey:2007:IJICA, author = "Ian Dempsey and Michael O'Neill and Anthony Brabazon", title = "Constant Creation in Grammatical Evolution", journal = "International Journal of Innovative Computing and Applications", year = "2007", volume = "1", number = "1", pages = "23--38", keywords = "genetic algorithms, genetic programming, grammatical evolution, constant creation, digit concatenation, ephemeral random constants, grammar based genetic programming, persistent random constants", URL = "http://www.inderscience.com/search/index.php?action=record&rec_id=13399&prevQuery=&ps=10&m=or", DOI = "doi:10.1504/IJICA.2007.013399", abstract = "We present an investigation into constant creation in Grammatical Evolution (GE), a form of grammar-based Genetic Programming (GP). The methods for constant creation evaluated include digit Concatenation (Cat) and a grammatical version of ephemeral random constants called persistent random constants. Experiments conducted on a diverse range of benchmark problems uncover clear advantages for a digit Cat approach.", } @InCollection{Dempsey:2007:geRBF, author = "Ian Dempsey and Anthony Brabazon and Michael O'Neill", title = "A Grammatical Genetic Programming Representation for Radial Basis Function Networks", booktitle = "Engineering Evolutionary Intelligent Systems", publisher = "Springer", year = "2007", editor = "Ajith Abraham and Crina Grosan and Witold Pedrycz", volume = "82", series = "Studies in Computational Intelligence", pages = "325--335", keywords = "genetic algorithms, genetic programming, grammatical evolution", isbn13 = "978-3-540-75395-7", DOI = "doi:10.1007/978-3-540-75396-4_11", abstract = "We present a hybrid algorithm where evolutionary computation, in the form of grammatical genetic programming, is used to generate Radial Basis Function Networks. An introduction to the underlying algorithms of the hybrid approach is outlined, followed by a description of a grammatical representation for Radial Basis Function networks. The hybrid algorithm is tested on five benchmark classification problem instances, and its performance is found to be encouraging.", notes = "http://www.springer.com/east/home/engineering?SGWID=5-175-22-173762620-0", } @PhdThesis{Dempsey:thesis, author = "Ian Dempsey", title = "Grammatical Evolution in Dynamic Environments", school = "University College Dublin", year = "2007", address = "Ireland", keywords = "genetic algorithms, genetic programming, grammatical evolution, dynamic environments", abstract = "Many real-world problems are anchored in dynamic environments, where some element of the problem domain changes with time. The application of Evolutionary Computation (EC) to dynamic environments creates challenges different to those encountered in static environments. Foremost among these, are issues of premature convergence, and the evolution of overfit solutions. This study aims to identify mechanisms that address these problems. A recent powerful addition to the stable of EC methodologies is Grammatical Evolution (GE). GE uses BNF grammars for the evolution of variable length programs. Thus far, there has been little study of the utility of GE in dynamic environments. A comprehensive analysis of prior work in EC and GE in the context of dynamic environments is presented. From this, it is seen that GE offers substantial potential due to the flexibility provided by the BNF grammar and the many-to-one genotype-to-phenotype mapping. Subsequently novel methods of constant creation are introduced that incorporate greater levels of latent evolvability through the use of BNF grammars. These methods are demonstrated to be more accurate and adaptable than the standard methods adopted. Through placing GE in the context of a dynamic real-world problem, the trading of financial indices, phenotypic diversity is demonstrated to be a function of the fitness landscape. That is, phenotypic entropy fluctuates with the universe of potentially fit solutions. Evidence is also presented of the evolution of robust solutions that provide superior out-of-sample performance over a statically trained population. The findings in this study highlight the importance of the genotype-to-phenotype mapping for evolution in dynamic environments and uncover some of the potential benefits of the incorporation of BNF grammars in GE.", notes = "See \cite{Dempsey:book}", } @Book{Dempsey:book, author = "Ian Dempsey and Michael O'Neill and Anthony Brabazon", title = "Foundations in Grammatical Evolution for Dynamic Environments", publisher = "Springer", year = "2009", volume = "194", series = "Studies in Computational Intelligence", month = apr, isbn13 = "978-3-642-00313-4", URL = "http://www.springer.com/engineering/book/978-3-642-00313-4", keywords = "genetic algorithms, genetic programming, grammatical evolution", abstract = "Table of contents Introduction.- Grammatical Evolution.- Survey of EC in Dynamic Environments.- GE in Dynamic Environments.- Constant Creation and Adaptation in Grammatical Evolution.- Constant Creation with meta-Grammars.- Controlled Static Trading with GE.- Adaptive Dynamic Trading with GE.- Conclusions & The Future.", size = "approx 190 pages", } @TechReport{Dempster:2000:wp35, author = "M. A. H. Dempster and C. M. Jones", title = "The Profitability of Intra-Day {FX} Trading Using Technical Indicators", institution = "Judge Institute of Management Studies, University of Cambridge", year = "2000", type = "Working Paper", number = "35/00", address = "Trumpington Street, Cambridge, CB2 1AG", keywords = "genetic algorithms, genetic programming, high frequency price data, market prices", broken = "http://www.cfr.statslab.cam.ac.uk/publications/papers.html#2000", broken = "http://hdl.handle.net/10068/583138", URL = "http://mahd-pc.jbs.cam.ac.uk/archive/PAPERS/1999/profitability.pdf", abstract = "Technical analysis indicators are widely used by traders to predict future price levels and hence enhance trading profitability. Traders often use high frequency price (ie. intra-day) data when calculating such indicators, which are then used as the basis for trade entry rules. Similar rules, along with standard exit rules aimed at reducing downside risk, are then used to exit these trades. In this paper we test a wide range of well known technical indicators on a set of US Dollar/British Pound Spot FX tick data from 1989-1997 aggregated to various intra-day frequencies. We find that few of the rules, whether based on well known and tested moving average crossover or on some of the more esoteric and untested indicators, are consistently profitable when traded under realistic slippage conditions. Furthermore, we vary the slippage regime to represent differences in the efficiency of trade execution eg. between a bank trader and a small hedge fund but still find the rules to be loss-making. When the rules are reversed, losses are still found indicating the losses not to be economically significant - a result that supports the efficient market hypothesis.", notes = "CU-JIMS-WP--35/2000", size = "70 pages", } @Article{Dempster:2000:QF, author = "M. A. H. Dempster and C. M. Jones", title = "A real-time adaptive trading system using genetic programming", journal = "Quantitative Finance", year = "2001", volume = "1", number = "4", pages = "397--413", keywords = "genetic algorithms, genetic programming", publisher = "Routledge", URL = "http://mahd-pc.jbs.cam.ac.uk/archive/PAPERS/2000/geneticprogramming.pdf", URL = "http://citeseer.ist.psu.edu/dempster01realtime.html", DOI = "doi:10.1088/1469-7688/1/4/301", size = "17 pages", abstract = "Technical analysis indicators are widely used by traders in financial and commodity markets to predict future price levels and enhance trading profitability. We have previously shown a number of popular indicator-based trading rules to be loss-making when applied individually in a systematic manner. However, technical traders typically use combinations of a broad range of technical indicators. Moreover, successful traders tend to adapt to market conditions by dropping trading rules as soon as they become loss-making or when more profitable rules are found. In this paper we try to emulate such traders by developing a trading system consisting of rules based on combinations of different indicators at different frequencies and lags. An initial portfolio of such rules is selected by a genetic algorithm applied to a number of indicators calculated on a set of US Dollar/British Pound spot foreign exchange tick data from 1994 to 1997 aggregated to various intraday frequencies. The genetic algorithm is subsequently used at regular intervals on out-of-sample data to provide new rules and a feedback system is used to rebalance the rule portfolio, thus creating two levels of adaptivity. Despite the individual indicators being generally loss-making over the data period, the best rule found by the developed system is found to be modestly, but significantly, profitable in the presence of realistic transaction costs.", notes = "Institute of Physics Publishing Broken Aug 2024 quant.iop.org", } @Article{Dempster:2001:trading, author = "M. A. H. Dempster and Tom W. Payne and Yazann Romahi and G. W. P. Thompson", title = "Computational learning techniques for intraday {FX} trading using popular technical indicators", journal = "IEEE Transactions on Neural Networks", year = "2001", volume = "12", number = "4", pages = "744--754", month = jul, keywords = "genetic algorithms, genetic programming, Markov processes, foreign exchange trading, learning (artificial intelligence), Markov decision, computational learning, foreign exchange trading, heuristic, reinforcement learning, technical trading, transaction costs", ISSN = "1045-9227", URL = "http://mahd-pc.jbs.cam.ac.uk/archive/PAPERS/2000/ieeetrading.pdf", DOI = "doi:10.1109/72.935088", abstract = "We consider strategies which use a collection of popular technical indicators as input and seek a profitable trading rule defined in terms of them. We consider two popular computational learning approaches, reinforcement learning and genetic programming, and compare them to a pair of simpler methods: the exact solution of an appropriate Markov decision problem, and a simple heuristic. We find that although all methods are able to generate significant in-sample and out-of-sample profits when transaction costs are zero, the genetic algorithm approach is superior for non-zero transaction costs, although none of the methods produce significant profits at realistic transaction costs. We also find that there is a substantial danger of overfitting if in-sample learning is not constrained", notes = "CODEN: ITNNEP. INSPEC Accession Number:6997298 Location: technical report WP30/2000 ", } @InProceedings{DBLP:conf/ideal/DempsterR02, author = "M. A. H. Dempster and Y. S. Romahi", title = "Intraday {FX} Trading: An Evolutionary Reinforcement Learning Approach", booktitle = "Proceedings of Third International Conference on Intelligent Data Engineering and Automated Learning - IDEAL 2002", year = "2002", editor = "Hujun Yin and Nigel M. Allinson and Richard T. Freeman and John A. Keane and Simon J. Hubbard", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "2412", pages = "347--358", address = "Manchester", month = "12-14 " # aug, keywords = "genetic algorithms, genetic programming, RL, GA", ISBN = "3-540-44025-9", bibsource = "DBLP, http://dblp.uni-trier.de", URL = "http://mahd-pc.jbs.cam.ac.uk/archive/PAPERS/2002/WP3-2002.pdf", URL = "https://rdcu.be/djwpR", DOI = "doi:10.1007/3-540-45675-9_52", size = "12 pages", abstract = "We have previously described trading systems based on unsupervised learning approaches such as reinforcement learning and genetic algorithms which take as input a collection of commonly used technical indicators and generate profitable trading decisions from them. This article demonstrates the advantages of applying evolutionary algorithms to the reinforcement learning problem using a hybrid credit assignment approach. In earlier work, the temporal difference reinforcement learning approach suffered from problems with overfitting the in-sample data. This motivated the present approach. Technical analysis has been shown previously to have predictive value regarding future movements of foreign exchange prices and this article presents methods for automated high-frequency FX trading based on evolutionary reinforcement learning about signals from a variety of technical indicators. These methods are applied to GBPUSD, USDCHF and USDJPY exchange rates at various frequencies. Statistically significant profits are made consistently at transaction costs of up to 4bp for the hybrid system while the standard RL is only able to trade profitably up to about 1bp slippage per trade.", notes = "Location: technical report WP03/2002 ", } @Article{Dempster:2006:ESA, author = "M. A. H. Dempster and V. Leemans", title = "An automated {FX} trading system using adaptive reinforcement learning", journal = "Expert Systems with Applications", year = "2006", volume = "30", number = "3", pages = "543--552", month = apr, note = "Special Issue on Financial Engineering", keywords = "genetic algorithms, genetic programming", URL = "http://mahd-pc.jbs.cam.ac.uk/archive/PAPERS/2004/WP18.pdf", DOI = "doi:10.1016/j.eswa.2005.10.012", size = "10 pages", abstract = "This paper introduces adaptive reinforcement learning (ARL) as the basis for a fully automated trading system application. The system is designed to trade foreign exchange (FX) markets and relies on a layered structure consisting of a machine learning algorithm, a risk management overlay and a dynamic utility optimisation layer. An existing machine-learning method called recurrent reinforcement learning (RRL) was chosen as the underlying algorithm for ARL. One of the strengths of our approach is that the dynamic optimization layer makes a fixed choice of model tuning parameters unnecessary. It also allows for a risk-return trade-off to be made by the user within the system. The trading system is able to make consistent gains out-of-sample while avoiding large draw-downs.", notes = "Centre for Financial Research, Judge Business School, University of Cambridge & Cambridge Systems Associates Limited, Cambridge, UK Also technical report WP18/2004 ", } @InProceedings{denardi08coevolutionary, author = "Renzo {De Nardi} and Owen E. Holland", title = "Coevolutionary modelling of a miniature rotorcraft", booktitle = "Proceedings of the 10th International Conference on Intelligent Autonomous Systems (IAS10)", year = "2008", editor = "Wolfram Burgard and Ruediger Dillmann and Christian Plagemann and Nikolaus Vahrenkamp", pages = "364--373", address = "Baden Baden", month = "23-25 " # jul, publisher = "IOS Press", isbn13 = "978-1-58603-887-8", keywords = "genetic algorithms, genetic programming, evolution, quadrotor, helicopter", URL = "http://www.cs.ucl.ac.uk/staff/R.DeNardi/DeNardi2008Coevolutionary.pdf", broken = "http://discovery.ucl.ac.uk/106645/", DOI = "doi:10.3233/978-1-58603-887-8-364", size = "8 pages", abstract = "The paper considers the problem of synthesising accurate dynamic models of a miniature rotorcraft based on minimal physical assumptions, and using the models to develop a controller. The approach is based on the idea of building models that predict accelerations, and is implemented using evolutionary programming in a particularly efficient co-evolutionary framework. Both the structure and the parameters of the nonlinear models are jointly identified from real data. The modelling method is demonstrated and validated on a miniature quadrotor rotorcraft, and a controller based on the model is then developed and tested.", notes = "Intelligent Autonomous Systems 2010, IAS-10 https://ebooks.iospress.nl/book/intelligent-autonomous-systems-10", } @PhdThesis{DeNardi2010PhD, author = "Renzo {De Nardi}", title = "Automatic Design of Controllers for Miniature Vehicles through Automatic Modelling", school = "School of Computer Science and Electronic Engineering, University Of Essex", year = "2010", address = "UK", month = sep, keywords = "genetic algorithms, genetic programming, evolution, quadrotor, helicopter", URL = "http://www.cs.ucl.ac.uk/staff/R.DeNardi/DeNardi2010PhD.pdf", URL = "http://discovery.ucl.ac.uk/1330831/", size = "433 pages", abstract = "This thesis investigates the problem of automatically designing controllers for vehicles that can be represented as a rigid body. The approach is based on the idea of automatically obtaining a dynamic model of the system of interest, and using it to design controllers automatically. A novel aspect of our approach is that of not requiring any form of platform specific knowledge, and being as a consequence both hands-off and very generic. The acquisition of models is based on data logged when a human pilot was controlling the vehicle, and is carried out by an evolutionary algorithm based on competitive coevolution. Models in the form of symbolic expressions are coevolved along with the portions of the training data that are used to compute their fitness. This results in an effective and computationally efficient way of constructing models. The modelling method is applied to a small toy car, a full sized aeroplane and two different types of small quadrotor helicopters. For comparison, models of the same vehicles are also derived using standard modelling techniques that exploit platform knowledge. The models produced by our technique are shown to be as accurate or better than those produced manually. Importantly after a limited amount of rearrangement of terms, the models also prove to be interpretable. A method is presented for reproducing in the models the noise and uncertainties that characterise real world platforms. The evolved deterministic models produced are augmented with a simple yet computationally efficient Gaussian noise model, and a principled method based on unscented Kalman filtering is used to estimate the noise parameters. The augmented models are demonstrated to reproduce most of the variability shown by real vehicles. The automatic design of controllers considers both monolithic and modular structures based on recurrent neural networks. Conventional steady state evolution is used to evolve monolithic controllers, and cooperative coevolution is applied to modular controllers. Manually designed controllers are also developed for purposes of comparison. Controllers are mainly evolved for path-following tasks, but other tasks like imitating game players' abilities are also considered. In general monolithic controllers are shown to be very effective in controlling the toy car, but have limitations when applied to the helicopters. Modular networks show a better ability to scale to more demanding platforms, and in simulation reach levels of performance comparable to or better than controllers designed manually. Tests show that for both the toy car and quadrotor helicopters, the evolved controllers successfully transfer to the real vehicles, although a certain amount of mismatch exists between the performances predicted in simulation and those on the real platforms.", notes = "examiners were Professor Alan Winfield (UWE) and Professor Huosheng Hu. http://www.essex.ac.uk/csee/department/news/newsletter/09_08_10.aspx", bibsource = "OAI-PMH server at discovery.ucl.ac.uk", contributor = "O. E. Holland", oai = "oai:eprints.ucl.ac.uk.OAI2:1330831", } @Article{Deneubourg1986176, author = "J. L. Deneubourg and S. Aron and S. Goss and J. M. Pasteels and G. Duerinck", title = "Random behaviour, amplification processes and number of participants: How they contribute to the foraging properties of ants", journal = "Physica D: Nonlinear Phenomena", volume = "22", number = "1-3", pages = "176--186", year = "1986", note = "Proceedings of the Fifth Annual International Conference", ISSN = "0167-2789", DOI = "doi:10.1016/0167-2789(86)90239-3", URL = "http://www.sciencedirect.com/science/article/B6TVK-4CVPV04-F/2/80230b3fab67ba01fc8a22aa94873a7e", notes = "Not on GP", } @InProceedings{deng:2023:GECCOcomp, author = "Chuanshuai Deng and Chenjing Zhao and Zhenghui Liu and Jiexin Zhang and Yunlong Wu and Yanzhen Wang and Hong Cheng and Xiaodong Yi", title = "Learning Behavior Trees by {Evolution-Inspired} Approaches", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "275--278", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, grammatical evolution, behavior tree: Poster", isbn13 = "9798400701191", DOI = "doi:10.1145/3583133.3590642", size = "4 pages", abstract = "As a reactive and modular policy control architecture, Behavior Tree (BT) has been used in computer games and robotics for autonomous agents' task switching. However, constructing BTs manually for complex tasks requires expert domain-knowledge and is error-prone. As a solution, researchers have proposed to auto-construct BTs using evolutionary algorithms such as Genetic Programming (GP) and Grammatical Evolution (GE). Nevertheless, their effectiveness in practical situations is in doubt and there are different drawbacks in the application.In this paper, we present a novel BT evolutionary system that integrates both GE and GP as modules and auto-checks the complexity of a given task to select which module to use. In addition, our system collects BTs that are either previously generated or manually designed by the user, which are used to further improve the convergence speed and the quality of generated trees for new tasks.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @Article{DENG:2012:PCS, author = "Shangkun Deng and Yizhou Sun and Akito Sakurai", title = "Robustness Test of Genetic Algorithm on Generating Rules for Currency Trading", journal = "Procedia Computer Science", volume = "13", pages = "86--98", year = "2012", note = "Proceedings of the International Neural Network Society Winter Conference (INNS-WC2012)", keywords = "genetic algorithms, genetic programming, Optimisation algorithm, Foreign exchange, Robustness test, Technical analysis, Financial prediction", ISSN = "1877-0509", DOI = "doi:10.1016/j.procs.2012.09.117", URL = "http://www.sciencedirect.com/science/article/pii/S1877050912007247", abstract = "In trading in currency markets, reducing te mean of absolute or squared errors of predicted values is not valuable unless it results in profits. A trading rule is a set of conditions that describe when to buy or sell a currency or to close a position, which can be used for automated trading. To optimise the rule to obtain a profit in the future, a probabilistic method such as a genetic algorithm (GA) or genetic programming (GP) is used, since the profit is a discrete and multimodal function with many parameters. Although the rules optimised by GA/GP reportedly obtain a profit in out-of-sample testing periods, it is hard to believe that they yield a profit in distant out-of-sample periods. In this paper, we first consider a framework where we optimise the parameters of the trading rule in an in-sample training period, and then execute trades according to the rule in its succeeding out-of-sample period. We experimentally show that the framework very often results in a profit. We then consider a framework in which we conduct optimization as above and then execute trades in distant out-of-sample periods. We empirically show that the results depend on the similarity of the trends in the training and testing periods.", } @Article{journals/access/DengYYZ17, author = "Song Deng and Changan Yuan and Jiquan Yang and Aihua Zhou", title = "Distributed Mining for Content Filtering Function Based on Simulated Annealing and Gene Expression Programming in Active Distribution Network", journal = "IEEE Access", year = "2017", volume = "5", pages = "2319--2328", keywords = "genetic algorithms, genetic programming, gene expression programming", ISSN = "2169-3536", bibdate = "2017-05-27", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/access/access5.html#DengYYZ17", URL = "http://ieeexplore.ieee.org/document/7857022/", DOI = "doi:10.1109/ACCESS.2017.2669106", size = "10 pages", abstract = "As an important part of the Internet of Energy, a complex access environment, flexible access modes and a massive number of access terminals, dynamic, and distributed mass data in an active distribution network will bring new challenges to the security of data transmission. To address the emerging challenge of this active distribution network, first we propose a content filtering function mining algorithm based on simulated annealing and gene expression programming (CFFM-SAGEP). In CFFM-SAGEP, genetic operation based on simulated annealing and dynamic population generation based on an adaptive coefficient are applied to improve the convergence speed and precision, the recall and the Fbeta measure value of the content filtering. Finally, based on CFFM-SAGEP, we present a distributed mining for content filtering function based on simulated annealing and gene expression programming (DMCF-SAGEP) to improve efficiency of content filtering. In DMCF-SAGEP, a local function merging strategy based on the minimum residual sum of squares is designed to obtain a global content filtering model. The results using three data sets demonstrate that compared with traditional algorithms, the algorithms proposed demonstrate strong content filtering performance.", } @Article{Deng:2018:PRL, author = "Song Deng and Changan Yuan and Lechan Yang and Liping Zhang", title = "Distributed electricity load forecasting model mining based on hybrid gene expression programming and cloud computing", journal = "Pattern Recognition Letters", year = "2018", volume = "109", pages = "72--80", keywords = "genetic algorithms, genetic programming, gene expression programming", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/prl/prl109.html#DengYYZ18", DOI = "doi:10.1016/j.patrec.2017.10.004", notes = "journals/prl/DengYYZ18", } @Article{journals/asc/DengXYYW20, author = "Song Deng and Xiangpeng Xie and Chang-An Yuan and Lechan Yang and Xindong Wu", title = "Numerical sensitive data recognition based on hybrid gene expression programming for active distribution networks", journal = "Appl. Soft Comput", year = "2020", volume = "91", pages = "106213", keywords = "genetic algorithms, genetic programming, gene expression programming", bibdate = "2020-06-12", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/asc/asc91.html#DengXYYW20", DOI = "doi:10.1016/j.asoc.2020.106213", } @Article{Deng:2024:PICEWM, author = "Yangyu Deng and Yakun Liu and Di Zhang and Ze Cao", title = "Machine learning-based prediction of scour depth around different-shaped bridge abutments", journal = "Proceedings of the Institution of Civil Engineers - Water Management", year = "2024", volume = "177", number = "5", pages = "308--326", keywords = "genetic algorithms, genetic programming, artificial intelligence, bridge abutment, hydraulics, machine learning, scour depth", ISSN = "1741-7589", URL = "https://www.sciencedirect.com/science/article/pii/S1741758924000052", DOI = "doi:10.1680/jwama.22.00087", abstract = "Accurate assessment of scour depth around bridge abutments is crucial to reasonable design of abutment structures. In this study, machine learning (ML) models are implemented, including M5' model tree (M5'MT), multivariate adaptive regression spline (MARS), locally weighted polynomial regression (LWPR) and multigene genetic programming (MGGP) to predict scour depth around vertical-wall, 45degree wing-wall and semicircular bridge abutments. Published experimental data are adopted, with four input parameters considered for the prediction of relative scour depth. The optimal input combination for each model is first determined using correlation and sensitivity analyses; results reveal that MGGP exhibits the best agreement with experimental data for vertical-wall and semicircular abutments, whereas LWPR outperforms the other models for the 45degree wing-wall abutment. In addition, compared with the empirical equations and ML models employed in the literature, the accuracy of scour depth prediction is significantly improved with the ML models used in this study. Considering the comprehensive performance for all types of abutments in terms of accuracy, reliability and interpretability, MGGP is recommended as the representative of the implemented ML models with its mean absolute percentage error of 2.40percent for a vertical-wall abutment, 3.95percent for a 45degree wing-wall abutment and 3.85percent for a semicircular abutment", } @PhdThesis{Denham:thesis, title = "Predicci{\'o}n de la Evoluci{\'o}n de los Incendios Forestales Guiada Din{\'a}micamente por los Datos", author = "Monica Malen Denham", year = "2009", school = "Universitat Autonoma de Barcelona. Departament d'Arquitectura de Computadors i Sistemes Operatius", address = "Spain", keywords = "genetic algorithms, forest fire prediction", bibsource = "OAI-PMH server at www.tdx.cesca.es", contributor = "Ana Cort{\'e}s Fit{\'e}", institution = "Universitat Aut{\`o}noma de Barcelona", language = "spa", oai = "oai:UAB.es:TDX-0322111-153520", rights = "Copyright information available at source archive", subject = "469 - DEPARTAMENT D'ARQUITECTURA DE COMPUTADORS I SISTEMES OPERATIUS", URL = "http://www.tdx.cat/bitstream/handle/10803/5776/mmd1de1.pdf", URL = "http://www.tesisenxarxa.net/TDX-0322111-153520/", URL = "http://www.tesisenxarxa.net", size = "163 pages", abstract = "Desde hace a{\~n}os los incendios forestales son una amenaza para la calidad de vida en nuestro planeta, dado que la cantidad y magnitud de los mismos se ha incrementado de forma alarmante. Actualmente, existe un intenso trabajo para la lucha contra estos incendios y la disminuci{\'o}n r{\'a}pida y efectiva de su avance, de sus consecuencias y de sus peligros. La predicci{\'o}n del comportamiento del fuego en incendios forestales es un tema que se est{\'a} desarrollando hace tiempo en este marco. Desde la inform{\'a}tica, se han desarrollado diversos simuladores del comportamiento del fuego en incendios forestales [3] [4] [5] [14] [17]. Estos simuladores calculan el avance del fuego, dependiendo de su estado inicial y de las caracter{\'i}sticas del lugar donde dicho incendio se desarrolla. Esto es, caracter{\'i}sticas de la topograf{\'i}a, vegetaci{\'o}n [2], humedad del combustible, humedad relativa del ambiente, estado del viento, etc. Estos simuladores son utilizados para predecir el avance del fuego en un lugar y momento espec{\'i}ficos. En este marco, una predicci {\'o}n es realmente {\'u}til si es de buena calidad (se corresponde con la real propagaci on del fuego) y si la respuesta est a dentro de un ll{\'i}mite de tiempo acotado. Por lo tanto, necesitamos simulaciones con alta calidad de respuesta, que realmente realmente reflejen el real avance del fuego, y respuestas que se obtengan velozmente, minimizando el tiempo de la misma. Estos dos factores son necesarios y determinan caracter {\'i}sticas importantes de nuestro trabajo Un problema frecuentemente encontrado en la utilizaci{\'o}n de estas herramientas inform{\'a}ticas para predecir el comportamiento del fuego es la cantidad y complejidad de los datos de entrada. Normalmente, este tipo de simuladores necesita numerosos datos de entrada, que describan de forma correcta el entorno donde se desarrolla el fuego. Topograf{\'i}a, meteorolog{\'i}a y vegetaci{\'o}n del entorno del fuego deben estar descriptos de forma adecuada en el nivel de abstracci{\'o}n y detalle que el simulador utilizado requiera. En la realidad, es muy dif{\'i}cil disponer de una correcta descripci{\'o}n de todas estas variables (y sus interacciones). Esta dificultad radica principalmente en: naturaleza din{\'a}mica de algunos factores (que var{\'i}an y siguen su propio patr{\'o}n de comportamiento), par{\'a}metros que no pueden ser medidos directamente (por lo que se utilizan estimaciones de los mismos), par{\'a}metros que no pueden ser medidos en todos los puntos (utiliz{\'a}ndose interpolaciones), mapas (topogr{\'a}ficos, vegetaci{\'o}n, etc.), los cuales pueden estar desactualizados, o utilizar discretizaciones que representan de forma incorrecta las caracter{\'i}sticas que est{\'a}n representando, etc. Es necesario disponer una correcta descripci{\'o}n del entorno del fuego, dado que predicciones con datos de entrada que no sean correctos, no ser{\'a}n {\'u}tiles, pues predecir{\'a}n un fuego en un entorno que no es el real. En este trabajo de investigaci{\'o}n se ha propuesto un framework donde se tiene como objetivo mejorar la calidad de los datos de entrada del simulador utilizado. Adem{\'a}s, se pone gran esfuerzo en minimizar los tiempos de respuesta. En este trabajo se utiliza el simulador fireLib [5], un simulador que implementa el modelo de propagaci{\'o}n de fuego desarrollado por Rothermel [20] [21]. Para mejorar la calidad de los datos de entrada, se realiza un procesamiento sobre el espacio de b{\'u}squeda que es el resultado de considerar todas las posibles combinaciones de los par{\'a}metros de entrada en sus rangos de variaci{\'o}n. Esto da como resultado un espacio de b{\'u}squeda muy grande. Con el objetivo de evitar que esta b{\'u}squeda penalice el tiempo de respuesta, se utiliza un algoritmo gen{\'e}tico [16] [19] guiado din{\'a}micamente por los datos: Dynamic Data Driven Genetic Algorithm [7] [8] [9].", abstract = "Forest fires are part of natural balance in our planet. Natural fires are provoked by natural factors combination: dry seasons, favorable fuel moistures, electrical storms, volcanoes, earth{$\neg$}quakes, etc. Natural forest fires can devastate overall forests as well as productive forest areas, farms, etc. Nowadays, human is arduously working on this problem in order to avoid and to reduce forest fires damages. As results of this effort there exist different kind of studies, strategies and tools used to prevent fires, to define risk areas and to reduce the fire effects when a disaster occurs. Forest fire control and manage are complex tasks, due to forest fires are related with weather, topography, human population, fire aspects, etc., having all of them its own behavior pattern. Furthermore, there exist several behavior interactions between them, that determine additional behavior characteristics, resulting in very complex fire behavior pattern. This problem is nowadays studied by di_erent areas. Technology is not an exception, and informatics tools are continuously developed. One of the most important computer tool for forest fires are forest fire behavior simulators. These kind of simulators determine the advance of the fire line, taking into account beginning fire state, topography conditions, weather aspects, fuel characteristics, etc [3], [4], [5] [14] [17] [20] [21]. Furthermore, this kind of problem has the additional requirement that time constraints add. A forest fire prediction is really useful when it is available before the real fire propagation occurs and when prediction really describes real fire progress. In order to be reliable, it is necessary high quality predictions, it means, predicted fire progress must be as similar to the real fire as possible. During this work, we are going to use the forest fire simulator called fireLib [5] holding it in a framework that attempts to improve input parameter accuracy in order to increase prediction quality. Usually, a prediction is made using a forest fire simulator which receives several inputs (fire environment description) and it returns the state of the fire for a later instant of time. Input parameters usually include: initial fire front state, topography, vegetation [2], wind, fuel hu{$\neg$}midities, and additionally, relative moisture, cover crown, cover clouds, etc. All these input parameters depend on the forest fire simulator used. Thus, having initial fire line and environmental characteristics, simulator uses some fire prop{$\neg$}agation model in order to simulate fire behavior. Taking into account this classical prediction method, we can see that it is a straight, simply method and it has the advantage of performing just one simulation (what means low processor time requirements). But these advantages are in a sense the main weak point of the method: final prediction quality depends on the suitability of the unique simulation (that means, using a unique input parameters set). As we had mentioned during previous paragraphs, the accuracy of the input parameters are really open to debate due to having its actual values is not easy, some times it is impossible. Consequently, we are going to present a method where a search of better parameter values is performed in order to reduce input data uncertainty [1]. This method consists of two stages: a new stage was added before the prediction step. This new stage is called Calibration stage, and it allows us to find a set of input parameter values that achieve a good simulation from instant ti to instant ti+1. Then, we can use this good set of input parameters to predict fire behavior during the next instant of time (ti+2). Once a combination of values for the input parameters is founded, we consider that the environmental characteristics that are good described by these values at instant ti+1 will re{$\neg$}main useful for the subsequent instant of time (from ti+1 to ti+2).", abstract = "Then we use these values for obtaining the predicted map for instant ti+2. This scheme is based on the premise that environmental features will be maintained during involved time steps. In addition, each of these input parameters have its own valid range where they can vary, and in fact, these ranges may be di_erent: they vary depending on the characteristic that it represents. Thus, the amount of di_erent combinations of these parameter values leaves us a very big search space. In order to avoid that this Calibration Stage becomes a bottle neck, we had developed a Dynamic Data Driven Genetic Algorithm [16] [19]. Strategies adopted through this application result in an e_cient search solution. Our Dynamic Data Driven Genetic Algorithm dynamically incorporates new data (from storage device or on line captured) promising more accuracy data analysis, more accurate pre{$\neg$}dictions, more precise controls and more reliable outcomes [8] [9] [7]. Taking into account that two stages method needs the information of the real fire spread from instant ti to ti + 1, useful information will be obtained from the analysis of this real fire progress. This information will be used for steering searching process through genetic algorithm, in order to improve the values of the parameters. Our genetic algorithm intents to minimize our error function (individual fitness): error value determines the di_erences between real fire line and simulated fire line. Due to simulator imple{$\neg$}ments a cellular automata model, all involved maps are a grid of cells, then, the error function is based on a cell by cell comparison (of real and simulated maps). When either slope nor wind are strong enough, fire grows forming a circular shape. But when wind or slope are presented (both or one of them), they influence fire growth in a de{$\neg$}terminant way. Shape, velocity, direction, intensity, all of these fire features are influenced by wind and slope factors. Wind velocity and direction, slope inclination and aspect combination are crucial in fire spread behavior. Thus, knowing wind and slope decisive influences and knowing the real fire shape (by the analysis of real fire at instant ti+1 disposed in calibration stage), we can combine this informa{$\neg$}tion in order to incorporate additional data that will be useful in order to improve fire spread simulations. This information will be used as feedback information in order to improve simula{$\neg$}tion accuracy. Actually, slope and real propagation are known. This information is used to calculate wind speed and wind direction needed to generate the observed fire propagation in presence of the current slope features. Once wind main characteristics are calculated they will be used through two methods for dynamically steering our genetic algorithm: Computational and Analytical Methods. In particular, Analytical Method was developed in order to validate Computational Method operation. Computational Method uses di_erent forest fires information (including fire environment) in order to discover wind main features. Forest fires data can come from historical real fires, prescribed burnings, or synthetic simulated fires (using a forest fire simulator). Forest fire main characteristics are stored through a data base. Data base information must be as complete as possible, in order to reflect the most amount of fire cases that can happen in the real world. In this data base several fire spreads are stored and we look for a fire progress similar to the real fire line observed for instant ti+1 in presence of similar or equal slope characteristics. Historical real fires information could be used in order to construct our application data base. Unfortunately, detailed real fires information was not available for us since we were deal{$\neg$}ing with prescribe and synthetic fires.", abstract = "In order to generate a suitable data base the forest fire simulator fireLib was used for obtaining a high number of detailed burning cases. Computational Method is based on following process: real forest fire progress is analyzed at time ti+1, thus, fire progress direction, velocity and distance are obtained. Then, all real forest fire characteristics are used in order to find the most similar fire into the data base. When most similar fire is founded, wind direction and velocity are injected during genetic algorithm operation. Specifically, these wind values will be used to define a subrange through the whole parameter valid range and, when mutation operator takes place, wind values will be assigned using a ran{$\neg$}dom value limited by the new subrange (taking into account data base cases incompleteness). Analytical Method was created in order to evaluate the proper operation of Computational Method. This method is based on an exhaustive study of Rothermel model and fireLib sim{$\neg$}ulator ([21] and [5]). This method is based on some calculus performed by the simulator in order to obtain fire direction and velocity, by the combination of wind, slope and environmental factors. Once the model and simulator was studied and understood, we use the steps performed by the simulator but in a suitable order, for obtaining wind characteristics from slope and real fire characteristics combination. When Analytical Method is applied, each simulation is performed using an individual (sce{$\neg$}nario) and ideal wind values are calculated and stored together with such individual. Then, these values are assigned as individual wind velocity and direction when elitism or mutation operations take place during Dynamic Data Driven genetic algorithm execution. In practice, we expect that this method will be more precise than Computational Method but, by contrast, it is severely coupled to the underlying simulator (being this fact an important method drawback). Taking into account application response time limits, non simulation dependences and simu{$\neg$}lations time requirements, proposed application was developed using the parallel paradigm [15] [18], dividing simulation processes and error calculus between di_erent parallel tasks. Master process performs genetic algorithm operations and distributes population individu{$\neg$}als between worker processes. Every time a worker process receives a group of individuals, it performs the simulation and calculates the error function with each individual. In order to avoid that application communication pattern became a bottle neck, individuals are distributed by groups (chunks) instead of individual transmissions. When a worker final{$\neg$}izes the evaluation of a specific chunk, this worker process returns the evaluated chunk to the master process. Then, master process sends another non evaluated chunk to it until all chunks are evaluated. During this work, two main objectives were considered: prediction quality improvement (what means prediction error reduction) and reduction of prediction process time requirements. Several times, these two aspects have mutual dependencies: suitable simulation accuracy can be achieved if enough time is available for prediction process. On the other hand, if prediction results are required in a short term of time, this feature can attempt on prediction quality. During this work, experimental results were analyzed and best application characteristics were studied. We could see that 2 stages prediction method achieve best results when they are compared with classical prediction. Performing a pre-search of input parameters values achieve an important error reduction due to the use of suitable input parameters values. Steering methods, Computational Method as well as Analytical Method, in most of the cases, reduce simulation errors, achieving more precise simulations during calibration stage, and consequently, more precise predictions [10] [11] [13].", abstract = "These Methods reduce total execution time, on account of the acceleration of searching convergence. Using real fire progress knowledge, genetic algorithm is fast guided to promising individual search space zones [10] [11] [13]. In order to obtain real fire progress characteristics, additional methods were developed. These methods had showed good performance when they where used for di_erent kind of maps: linear maps, elliptical maps, real cases, synthetic cases, di_erent sizes of cells, etc. Parallel application proposed was tested in order to evaluate its scalability. Master and worker process times had decreased when number of computing elements had increased. On demand dealing of work, communication reduction (because of chunks communication), etc. achieve a good performance application [12]. For most of the performed tests similar behavior could be seen: Computational Method convergences more quickly to good individual zones. Then, fewer iterations can be executed and steering methods finds good results. Thus, execution time of two stages prediction method can be reduced as well.", } @InProceedings{Heijer:2010:EvoMUSART, author = "E. {den Heijer} and A. E. Eiben", title = "Comparing Aesthetic Measures for Evolutionary Art", booktitle = "EvoMUSART", year = "2010", editor = "Cecilia {Di Chio} and Anthony Brabazon and Gianni A. {Di Caro} and Marc Ebner and Muddassar Farooq and Andreas Fink and Jorn Grahl and Gary Greenfield and Penousal Machado and Michael O'Neill and Ernesto Tarantino and Neil Urquhart", volume = "6025", series = "LNCS", pages = "311--320", address = "Istanbul", month = "7-9 " # apr, organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-12241-5", DOI = "doi:10.1007/978-3-642-12242-2_32", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.462.9840", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.462.9840", size = "10 pages", abstract = "In this paper we investigate and compare four aesthetic measures within the context of evolutionary art. We evolve visual art with an unsupervised evolutionary art system using genetic programming and an aesthetic measure as the fitness function. We perform multiple experiments with different aesthetic measures and examine their influence on the evolved images. To this end we store the 5 fittest individuals of each run and hand-pick the best 9 images after finishing the whole series. This way we create a portfolio of evolved art for each aesthetic measure for visual inspection. Additionally, we perform a cross-evaluation by calculating the aesthetic value of images evolved by measure i according to measure j. This way we investigate the flexibility of each aesthetic measure (i.e., whether the aesthetic measure appreciates different types of images). The results show that aesthetic measures have a rather clear style and that these styles can be very different. Furthermore we find that some aesthetic measures show very little flexibility and appreciate only a limited set of images.", notes = "EvoMUSART'2010 held in conjunction with EuroGP'2010 EvoCOP2010 EvoBIO2010", } @InProceedings{denHeijer:2010:cec, author = "Eelco {den Heijer} and A. E. Eiben", title = "Using aesthetic measures to evolve art", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-6910-9", size = "8 pages", abstract = "In this paper we investigate and compare three aesthetic measures within the context of evolutionary art. We evolve visual art with an unsupervised evolutionary art system using genetic programming and an aesthetic measure as the fitness function. We perform multiple experiments with different aesthetic measures and examine their influence on the evolved images. Additionally, we perform a cross-evaluation by calculating the aesthetic value of images evolved by measure i according to measure j. This way we investigate the flexibility of each aesthetic measure (i.e., whether the aesthetic measure appreciates different types of images). Last, we perform an image analysis using a fixed set of image statistics functions. The results show that aesthetic measures have a rather clear 'style' and that these styles can be very different. Furthermore we find that some aesthetic measures show little flexibility and appreciate only a limited set of images. The images in this paper might only be in colour in the electronic version.", DOI = "doi:10.1109/CEC.2010.5586245", notes = "Computational Aesthetics. Benford distribution, Shannon entropy+Kolmogorov complexity. Arabitat (Art Habitat) http://www.few.vu.nl/~eelco/(broken Apr 2019) indexed colour table, 'an image that can be compressed using PNG to 3percent or less of its original size is discarded'. Table II - very little agreement between different fitness measures. WCCI 2010. Also known as \cite{5586245}", } @InProceedings{denHeijer:2011:GECCO, author = "Eelco {den Heijer} and Agoston Endre Eiben", title = "Evolving art with scalable vector graphics", booktitle = "GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0557-0", pages = "427--434", keywords = "genetic algorithms, genetic programming, grammatical evolution, Digital entertainment technologies and arts, Artificial Intelligence, Computer Graphics Picture/Image Generation, Line and curve generation, Experimentation, Evolutionary computation, evolutionary art, SVG", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.395.8699", URL = "http://www.cs.vu.nl/~gusz/papers/2011-Evolving%20Art%20with%20Scalable%20Vector%20Graphics.pdf", DOI = "doi:10.1145/2001576.2001635", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "In this paper we introduce the use of Scalable Vector Graphics (SVG) as a representation for evolutionary art. We describe the technical aspects of using SVG in evolutionary art, and explain the genetic operators mutation and crossover. Furthermore, we compare the use of SVG with existing representations in evolutionary art. We performed a number of experiments in an unsupervised evolutionary art system using two aesthetic measures as fitness functions, and compared the outcome of the different experiments with each other and with previous work with symbolic expressions as the representation. All images and SVG code examples in this paper are available at http://www.few.vu.nl/~eelco", notes = "crossover XML grammar path gradient defs mutation fixup (genetic repair. Aim more Art than computer art. Two automatic fitness measures tried: Brian Ross, William Ralph, and Hai Zong. Evolutionary image synthesis using a model of aesthetics. In IEEE Congress on Evolutionary Computation (CEC) 2006, pages 1087-1094, \cite{Ross:EIS:cec2006}. Kresimir Matkovic, Laszlo Neumann, Attila Neumann, Thomas Psik, and Werner Purgathofer. Global contrast factor-a new approach to image contrast. In Laszlo Neumann, Mateu Sbert, Bruce Gooch, and Werner Purgathofer, editors, Computational Aesthetics, pages 159-168. Eurographics Association, 2005. See also \cite{DenHeijer:2016:IJART} Also known as \cite{2001635} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)", } @Article{denHeijer:2014:SEC, author = "Eelco {den Heijer} and A. E. Eiben", title = "Investigating aesthetic measures for unsupervised evolutionary art", journal = "Swarm and Evolutionary Computation", volume = "16", pages = "52--68", year = "2014", ISSN = "2210-6502", DOI = "doi:10.1016/j.swevo.2014.01.002", URL = "http://www.sciencedirect.com/science/article/pii/S2210650214000030", keywords = "genetic algorithms, genetic programming, Evolutionary computation, Evolutionary art, Computational aesthetics, Multi-objective optimisation", } @Article{DenHeijer:2016:IJART, title = "Using scalable vector graphics to evolve art", author = "Eelco {Den Heijer} and A. E. Eiben", journal = "Int. J. of Arts and Technology", year = "2016", month = mar # "~22", volume = "9", number = "1", pages = "59--85", keywords = "genetic algorithms, genetic programming, evolutionary computation, evolutionary art, scalable vector graphics, SVG, initialisation, genotype representation, abstract images, aesthetics, fitness functions", publisher = "Inderscience Publishers", ISSN = "1754-8861", bibsource = "OAI-PMH server at www.inderscience.com", language = "eng", URL = "http://www.inderscience.com/link.php?id=75408", DOI = "doi:10.1504/IJART.2016.075408", size = "27 pages", abstract = "In this paper, we describe our investigations of the use of scalable vector graphics as a genotype representation in evolutionary art. We describe the technical aspects of using SVG in evolutionary art, and explain our custom, SVG specific operators initialisation, mutation and crossover. We perform two series of experiments; in the first series of experiments, we investigate the feasibility of SVG as a genotype representation for evolutionary art, and evolve abstract images using a number of aesthetic measures as fitness functions. In the second series of experiments, we used existing images as source material. We also designed and implemented an ad-hoc aesthetic measure for pop-art and used this to evolve images that are visually similar to pop-art. All experiments described in this paper are done without a human in the loop. All images and SVG code examples in this paper are available at http://www.eelcodenheijer.nl/research.", } @PhdThesis{Eelco-den-Heijer-Autonomous-Evolutionary-Art-2013, author = "Eelco {den Heijer}", title = "Autonomous Evolutionary Art", school = "de Vrije Universiteit Amsterdam", year = "2013", address = "Holland", month = "12 " # dec, keywords = "genetic algorithms, genetic programming", URL = "https://research.vu.nl/files/42119961/title%20page.pdf", URL = "http://eelcodenheijer.nl/publications/Eelco-den-Heijer-Autonomous-Evolutionary-Art-2013.pdf", isbn13 = "978-94-6191-951-9", size = "213 pages", notes = "SIKS Dissertation Series No. 2013-38 Supervisor: A.E. Eiben", } @InProceedings{DBLP:conf/ices/DeniziakG08, author = "Stanislaw Deniziak and Adam Gorski", title = "Hardware/Software Co-synthesis of Distributed Embedded Systems Using Genetic Programming", booktitle = "Proceedings of the 8th International Conference Evolvable Systems: From Biology to Hardware, ICES 2008", year = "2008", editor = "Gregory Hornby and Luk{\'a}s Sekanina and Pauline C. Haddow", series = "Lecture Notes in Computer Science", volume = "5216", pages = "83--93", address = "Prague, Czech Republic", month = sep # " 21-24", publisher = "Springer", bibsource = "DBLP, http://dblp.uni-trier.de", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-85856-0", DOI = "doi:10.1007/978-3-540-85857-7_8", abstract = "This work presents a novel approach to hardware-software co-synthesis of distributed embedded systems, based on the developmental genetic programming. Unlike the other genetic approaches where chromosomes represent solutions, in our method chromosomes represent system construction procedures. Thus, not the system architecture but the co-synthesis process is evolved. Finally a tree describing a construction of a final solution is obtained. The optimisation process will be illustrated with examples. According to our best knowledge it is the first DGP approach that deals with the hardware-software co-synthesis.", notes = "Cracow University of Technology, Dept. of Computer Engineering, Warszawska 24, 31-155 Cracow, Poland", } @InProceedings{conf/ppam/DeniziakW11, author = "Stanislaw Deniziak and Karol Wieczorek", title = "Parallel Approach to the Functional Decomposition of Logical Functions Using Developmental Genetic Programming", booktitle = "9th International Conference on Parallel Processing and Applied Mathematics (PPAM 2011) Part I", year = "2011", editor = "Roman Wyrzykowski and Jack Dongarra and Konrad Karczewski and Jerzy Wasniewski", volume = "7203", series = "Lecture Notes in Computer Science", pages = "406--415", address = "Torun, Poland", month = sep # " 11-14", publisher = "Springer", note = "Revised Selected Papers", keywords = "genetic algorithms, genetic programming, developmental genetic programming, parallel processing, functional decomposition, FPGA devices", isbn13 = "978-3-642-31463-6", DOI = "doi:10.1007/978-3-642-31464-3_41", size = "10 pages", abstract = "Functional decomposition is the main step in the FPGA-oriented logic synthesis, where a function is decomposed into a set of functions, each of which must be simple enough to be implementable in one logic cell. This paper presents a method of searching for the best decomposition strategy for logical functions specified by cubes. The strategy is represented by a decision tree, where each node corresponds to a single decomposition step. In that way the multistage decomposition of complex logical functions may be specified. The tree evolves using the parallel developmental genetic programming. The goal of the evolution is to find a decomposition strategy for which the cost of FPGA implementation of a given function is minimal. Experimental results show that our approach gives significantly better results than other existing methods.", affiliation = "Departament of Computer Science, Kielce University of Technology, Poland", bibdate = "2012-07-04", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ppam/ppam2011-1.html#DeniziakW11", } @InProceedings{DBLP:conf/icaisc/DeniziakW12, author = "Stanislaw Deniziak and Karol Wieczorek", title = "Evolutionary Optimization of Decomposition Strategies for Logical Functions", booktitle = "Proceedings of the International Symposia on Swarm and Evolutionary Computation, SIDE 2012 and EC 2012", year = "2012", editor = "Leszek Rutkowski and Marcin Korytkowski and Rafal Scherer and Ryszard Tadeusiewicz and Lotfi A Zadeh and Jacek M Zurada", volume = "7269", series = "Lecture Notes in Computer Science", pages = "182--189", address = "Zakopane, Poland", month = apr # " 29-" # may # " 3", publisher = "Springer", keywords = "genetic algorithms, genetic programming, developmental genetic programming, functional decomposition, FPGA devices", isbn13 = "978-3-642-29352-8", DOI = "doi:10.1007/978-3-642-29353-5_21", size = "8 pages", abstract = "This paper presents a method of searching for the best decomposition strategy for logical functions. The strategy is represented by a decision tree, where each node corresponds to a single decomposition step. In that way the multistage decomposition of complex logical functions may be specified. The tree evolves using the developmental genetic programming. The goal of the evolution is to find a decomposition strategy for which the cost of FPGA implementation of a given function is minimal. Experimental results show that our approach gives significantly better outcomes than other existing methods.", notes = "Held in conjunction with ICAISC 2012, SIDE and EC 2012", } @InProceedings{Deniziak:2014:UCC, author = "Stanislaw Deniziak and Leszek Ciopinski and Grzegorz Pawinski and Karol Wieczorek and Slawomir Bak", booktitle = "IEEE/ACM 7th International Conference on Utility and Cloud Computing (UCC 2014)", title = "Cost Optimization of Real-Time Cloud Applications Using Developmental Genetic Programming", year = "2014", month = dec, pages = "774--779", abstract = "This paper presents the methodology for the cost optimisation of real-time applications, that are conformable to the Infrastructure as a Service (IaaS) model of cloud computing. We assume, that functions of applications are specified as a set of distributed echo algorithms with soft real-time constraints. Then our methodology schedules all tasks on available cloud infrastructure, minimising the total costs of the IaaS services, while guaranteeing the required level of the quality of services, as far as real-time requirements are concerned. It takes into account limited bandwidth of communication channels as well as the limited computation power of server nodes. The cost is optimised using the method based on the developmental genetic programming. The method reduces the cost of hiring the cloud infrastructure by sharing cloud resources between applications. We also present experimental results, that show the benefits of using our methodology.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/UCC.2014.126", notes = "Also known as \cite{7027593}", } @InCollection{Deniziak:2015:hbgpa, author = "Stanislaw Deniziak and Leszek Ciopinski and Grzegorz Pawinski", title = "Design of Real-Time Computer-Based Systems Using Developmental Genetic Programming", booktitle = "Handbook of Genetic Programming Applications", publisher = "Springer", year = "2015", editor = "Amir H. Gandomi and Amir H. Alavi and Conor Ryan", chapter = "9", pages = "221--244", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-20882-4", DOI = "doi:10.1007/978-3-319-20883-1_9", abstract = "This chapter presents applications of the developmental genetic programming (DGP) to design and optimize real-time computer-based systems. We show that the DGP approach may be efficiently used to solve the following problems: scheduling of real-time tasks in multiprocessor systems, hardware/software codesign of distributed embedded systems, budget-aware real-time cloud computing. The goal of optimization is to minimize the cost of the system, while all real-time constraints will be satisfied. Since the finding of the best solution is very complex, only efficient heuristics may be applied for real-life systems. Unlike the other genetic approaches where chromosomes represent solutions, in the DGP chromosomes represent system construction procedures. Thus, not the system architecture, but the synthesis process evolves. Finally, a tree describing the construction of a (sub-)optimal solution is obtained and the genotype-to-phenotype mapping is applied to create the target system. Some other ideas concerning other applications of the DGP for optimization of computer-based systems also are outlined.", } @InProceedings{Deniziak:2015:FedCSIS, author = "Stanislaw Deniziak and Leszek Ciopinski", booktitle = "2015 Federated Conference on Computer Science and Information Systems (FedCSIS)", title = "Synthesis of power aware adaptive schedulers for embedded systems using developmental genetic programming", year = "2015", pages = "449--459", abstract = "In this paper we present a method of synthesis of adaptive schedulers for real-time embedded systems. We assume that the system is implemented using multi-core embedded processor with low-power processing capabilities. First, the developmental genetic programming is used to generate the scheduler and the initial schedule. Then, during the system execution the scheduler modifies the schedule whenever execution time of the recently finished task occurred shorter or longer than expected. The goal of rescheduling is to minimise the power consumption while all time constraints will be satisfied. We present real-life example as well as some experimental results showing advantages of our method.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.15439/2015F313", month = sep, notes = "Also known as \cite{7321479}", } @InProceedings{Deniziak:2016:DSD, author = "Stanislaw Deniziak and Mariusz Wisniewski and Karol Wieczorek", booktitle = "2016 Euromicro Conference on Digital System Design (DSD)", title = "Synthesis of Multivalued Logical Networks for FPGA Implementations", year = "2016", pages = "657--660", abstract = "This paper presents the method of FPGA-oriented synthesis of multiple-valued logical networks. Multiple-valued network consists of modules connected by multiple-valued signals. During synthesis each module is decomposed into smaller ones, that may be implemented using one logic cell. For this purpose the symbolic decomposition is applied. Since the decomposition of modules strongly depends on encoding of multivalued inputs and outputs, the result of synthesis depends on the order, in which the consecutive modules are implemented. In our approach we optimise this order using developmental genetic programming. Experimental results showed that our approach significantly reduces the cost of implementation.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/DSD.2016.107", month = aug, notes = "Also known as \cite{7723614}", } @InProceedings{deniziak:2016:RACO, author = "Stanislaw Deniziak and Leszek Ciopinski", title = "Synthesis of Power Aware Adaptive Embedded Software Using Developmental Genetic Programming", booktitle = "Recent Advances in Computational Optimization", year = "2016", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-319-40132-4_7", DOI = "doi:10.1007/978-3-319-40132-4_7", } @InProceedings{deniziak:2017:FCSDOT, author = "Stanislaw Deniziak and Leszek Ciopinski and Grzegorz Pawinski", title = "Synthesis of {Low-Power} Embedded Software Using Developmental Genetic Programming", booktitle = "Proceedings of the 2015 Federated Conference on Software Development and Object Technologies", year = "2017", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-319-46535-7_19", DOI = "doi:10.1007/978-3-319-46535-7_19", } @InProceedings{Deniziak:2018:EPMCCS, author = "Stanislaw Deniziak and Leszek Ciopinski", booktitle = "2018 Conference on Electrotechnology: Processes, Models, Control and Computer Science (EPMCCS)", title = "Design for Self-Adaptivity of Real-Time Embedded Systems Using Developmental Genetic Programming", year = "2018", abstract = "This paper presents a method of synthesis of self-adaptable real-time embedded systems. The method assumes that the system specification is given as a task graph. Then, tasks are scheduled on distributed architecture consisting of low-power and high-performance processors. We apply the developmental genetic programming to generate the self-adaptive scheduler and the initial schedule. The initial schedule is optimized taking into consideration the cost, the power consumption, the real-time constraints as well as the self-adaptivity. The scheduler modifies the schedule, during the system execution, whenever execution time of the recently finished task occurred other than assumed during initial scheduling. The goal of rescheduling is to minimize the power consumption while all time constraints are satisfied. We present some experimental results for standard benchmarks, showing advantages of our method in comparison with worst case design used in existing approaches.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/EPMCCS.2018.8596421", month = nov, notes = "Also known as \cite{8596421}", } @InProceedings{Deniziak:2020:DDECS, author = "Stanislaw Deniziak and Leszek Ciopinski", booktitle = "2020 23rd International Symposium on Design and Diagnostics of Electronic Circuits Systems (DDECS)", title = "Synthesis of Self-Adaptable Software for Multicore Embedded Systems", year = "2020", month = apr, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/DDECS50862.2020.9095745", ISSN = "2473-2117", abstract = "This paper presents a method of synthesis of real-time software for self-adaptive multicore systems. The method assumes that the system specification is given as a task graph. Then, tasks are scheduled on multicore architecture consisting of low-power and high-performance cores. We apply the developmental genetic programming to generate the self-adaptive scheduler and the initial schedule. The initial schedule is optimized taking into consideration the power consumption, the real-time constraints as well as the self-adaptivity. The scheduler modifies the schedule, during the system execution, whenever execution time of the recently finished task occurred other than assumed during initial scheduling. We propose two models of self-adaptivity: self-optimization of power consumption and self-adaptivity of real-time scheduling. We present some experimental results for standard benchmarks, showing the advantages of our method in comparison with the worst case design used in existing approaches.", notes = "Also known as \cite{9095745}", } @Article{DENIZIAK:2021:MR, author = "Stanislaw Deniziak and Leszek Ciopinski", title = "Synthesis of self-adaptable energy aware software for heterogeneous multicore embedded systems", journal = "Microelectronics Reliability", volume = "123", pages = "114184", year = "2021", ISSN = "0026-2714", DOI = "doi:10.1016/j.microrel.2021.114184", URL = "https://www.sciencedirect.com/science/article/pii/S0026271421001505", keywords = "genetic algorithms, genetic programming, Self-adaptivity, Embedded system, Developmental genetic programing, Multicore system", abstract = "Contemporary embedded systems work in changing environments, some features (e.g., execution time, power consumption) of the system are often not completely predictable. Therefore, for systems with strong constraints, a worst-case design is applied. We observed that by enabling the self-adaptivity we may obtain highly optimized systems still guaranteeing the high quality of service. This paper presents a method of synthesis of real-time software for self-adaptive multicore systems. The method assumes that the system specification is given as a task graph. Then, the tasks are scheduled on a multicore architecture consisting of low-power and high-performance cores. We apply the developmental genetic programming to generate the self-adaptive scheduler and the initial schedule. The initial schedule is optimized, taking into consideration the power consumption, the real-time constraints as well as the self-adaptivity. The scheduler modifies the schedule during the system execution, whenever execution time of the recently finished task occurs other than assumed during the initial scheduling. We propose two models of self-adaptivity: self-optimization of power consumption and self-adaptivity of real-time scheduling. We present some experimental results for standard benchmarks, showing the advantages of our method in comparison with the worst case design used in existing approaches", } @InProceedings{Denno:2017:ICMR, author = "Peter Denno and Charles Dickerson and Jenny Harding", title = "Production System Identification with Genetic Programming", booktitle = "Advances in Manufacturing Technology XXXI: Proceedings of the 15th conference", year = "2017", editor = "James Gao and Mohammed {El Souri} and Simeon Keates", pages = "227--232", address = "University of Greenwich", month = sep, publisher = "IOS", keywords = "genetic algorithms, genetic programming, System identification, Petri nets, smart manufacturing", isbn13 = "978-1-61499-791-7", URL = "https://core.ac.uk/download/pdf/288361323.pdf", DOI = "doi:10.3233/978-1-61499-792-4-227", size = "6 pages", abstract = "Modern system-identification methodologies use artificial neural nets, integer linear programming, genetic algorithms, and swarm intelligence to discover system models. Pairing genetic programming, a variation of genetic algorithms,with Petri nets seems to offer an attractive,alternative means to discover system behaviour and structure. Yet to date, very little work has examined this pairing of technologies. Petri nets provide a grey-box model of the system, which is useful for verifying system behaviour and interpreting the meaning of operational data. Genetic programming promises a simple yet robust tool to search the space of candidate systems. Genetic programming is inherently highly parallel. This paper describes early experiences with genetic programming of Petri nets to discover the best interpretation of operational data. The systems studied are serial production lines with buffers.", notes = "ICMR 2017 National Institute of Standards and Technology, USA", } @Article{DENNO:2018:JMS, author = "Peter Denno and Charles Dickerson and Jennifer Anne Harding", title = "Dynamic production system identification for smart manufacturing systems", journal = "Journal of Manufacturing Systems", volume = "48", pages = "192--203", year = "2018", note = "Special Issue on Smart Manufacturing", keywords = "genetic algorithms, genetic programming, System identification, Production systems", ISSN = "0278-6125", DOI = "doi:10.1016/j.jmsy.2018.04.006", URL = "http://www.sciencedirect.com/science/article/pii/S0278612518300451", abstract = "This paper presents a methodology, called production system identification, to produce a model of a manufacturing system from logs of the system's operation. The model produced is intended to aid in making production scheduling decisions. Production system identification is similar to machine-learning methods of process mining in that they both use logs of operations. However, process mining falls short of addressing important requirements; process mining does not (1) account for infrequent exceptional events that may provide insight into system capabilities and reliability, (2) offer means to validate the model relative to an understanding of causes, and (3) updated the model as the situation on the production floor changes. The paper describes a genetic programming (GP) methodology that uses Petri nets, probabilistic neural nets, and a causal model of production system dynamics to address these shortcomings. A coloured Petri net formalism appropriate to GP is developed and used to interpret the log. Interpreted logs provide a relation between Petri net states and exceptional system states that can be learned by means of novel formulation of probabilistic neural nets (PNNs). A generalized stochastic Petri net and the PNNs are used to validate the GP-generated solutions. The methodology is evaluated with an example based on an automotive assembly system", } @Article{Deo2008340, author = "M. C. Deo", title = "Reply to: Discussion on {"}Genetic programming for retrieving missing information in wave records along the west coast of India{"} [Applied Ocean Research 2007; 29(3): 99-111]; A.H. Gandomi, A.H. Alavi, S.S. {Sadat Hosseini}", journal = "Applied Ocean Research", volume = "30", number = "4", pages = "340", year = "2008", keywords = "genetic algorithms, genetic programming", ISSN = "0141-1187", DOI = "doi:10.1016/j.apor.2009.02.002", URL = "http://www.sciencedirect.com/science/article/B6V1V-4VY6FSK-1/2/70a6592b22ba65b93887b8122e985f75", size = "1 page", notes = "Reply to \cite{Gandomi2008338}. Original article \cite{Kalra200799}", } @Article{Deo:2008:IJTS, author = "Omkar Deo and V. Jothiprakash and M. C. Deo", title = "Genetic Programming to Predict Spillway Scour", journal = "International Journal of Tomography \& Statistics", year = "2008", volume = "8", number = "W08", pages = "32--45", month = "Winter", keywords = "genetic algorithms, genetic programming, LGP, Discipulus, neural networks, ANN, scour predictions spillway scour, skijump bucket", ISSN = "0972-9976", URL = "http://www.ceser.in/ceserp/index.php/ijts/article/view/110", size = "14 pages", abstract = "Investigators in the past had noticed that application of a soft computing tool like artificial neural networks (ANN) in place of traditional statistics based data mining techniques produce more attractive results in hydrologic as well as hydraulic predictions. Mostly these works pertained to applications of ANN. Recently another tool of soft computing namely genetic programming (GP) has caught attention of researchers in civil engineering computing. This paper examines the usefulness of the GP based approach to predict the depth and geometry of the scour hole produced downstream of a common type of spillway, namely, the ski-jump bucket. Hydraulic model measurements were used to develop the GP models. The GP based estimations were found to be equally, and possibly more, accurate than the ANN based ones,especially when the underlying cause-effect relationship became more uncertain to model.", notes = "CESER Datta Meghe College of Engineering, Airoli, Navi Mumbai, 400708, India", } @InCollection{Deo:2020:IRMA, author = "Ravinesh C. Deo and Sujan Ghimire and Nathan J. Downs and Nawin Raj", title = "Optimization of Windspeed Prediction Using an Artificial Neural Network Compared With a Genetic Programming Model", booktitle = "Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms", publisher = "IGI Global", year = "2020", chapter = "7", pages = "116--147", month = dec, note = "Information Resources Management Association", keywords = "genetic algorithms, genetic programming", isbn13 = "9781799880486", DOI = "doi:10.4018/978-1-7998-8048-6", DOI = "doi:10.4018/978-1-7998-8048-6.ch007", abstract = "The precise prediction of windspeed is essential in order to improve and optimize wind power prediction. However, due to the sporadic and inherent complexity of weather parameters, the prediction of windspeed data using different patterns is difficult. Machine learning (ML) is a powerful tool to deal with uncertainty and has been widely discussed and applied in renewable energy forecasting. In this chapter, the authors present and compare an artificial neural network (ANN) and genetic programming (GP) model as a tool to predict windspeed of 15 locations in Queensland, Australia. After performing feature selection using neighborhood component analysis (NCA) from 11 different metrological parameters, seven of the most important predictor variables were chosen for 85 Queensland locations, 60 of which were used for training the model, 10 locations for model validation, and 15 locations for the model testing. For all 15 target sites, the testing performance of ANN was significantly superior to the GP model.", notes = "https://www.igi-global.com/book/research-anthology-multi-industry-uses/267374", } @InProceedings{conf/evoW/DeodharM10, title = "Grammatical Evolution Decision Trees for Detecting Gene-Gene Interactions", author = "Sushamna Deodhar and Alison A. Motsinger-Reif", booktitle = "8th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EvoBIO 2010)", publisher = "Springer", year = "2010", editor = "Clara Pizzuti and Marylyn D. Ritchie and Mario Giacobini", volume = "6023", pages = "98--109", series = "Lecture Notes in Computer Science", address = "Istanbul, Turkey", month = apr # " 7-9", keywords = "genetic algorithms, genetic programming, grammatical evolution", isbn13 = "978-3-642-12210-1", DOI = "doi:10.1007/978-3-642-12211-8", } @InProceedings{DBLP:conf/bracis/OliveiraM21, author = "Gustavo F. V. {de Oliveira} and Marcus H. S. Mendes", editor = "Andr{\'{e}} Britto and Karina Valdivia Delgado", title = "Improving Rule Based and Equivalent Decision Simplifications for Bloat Control in Genetic Programming Using a Dynamic Operator", booktitle = "Intelligent Systems - 10th Brazilian Conference, {BRACIS} 2021, Virtual Event, November 29 - December 3, 2021, Proceedings, Part {I}", series = "Lecture Notes in Computer Science", volume = "13073", pages = "234--248", publisher = "Springer", year = "2021", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/978-3-030-91702-9_16", DOI = "doi:10.1007/978-3-030-91702-9_16", timestamp = "Fri, 03 Dec 2021 17:38:13 +0100", biburl = "https://dblp.org/rec/conf/bracis/OliveiraM21.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{deOliveira:2013:CEC, article_id = "1585", author = "Renato Resende Ribeiro {de Oliveira} and Tales Heimfarth and Raphael Winckler {de Bettio} and Marcio {da Silva Arantes} and Claudio Fabiano Motta Toledo", title = "A Genetic Programming Based Approach to Automatically Generate Wireless Sensor Networks Applications", booktitle = "2013 IEEE Conference on Evolutionary Computation", volume = "1", year = "2013", month = jun # " 20-23", editor = "Luis Gerardo {de la Fraga}", pages = "1771--1778", address = "Cancun, Mexico", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2013.6557775", abstract = "The development of Wireless Sensor Networks (WSNs) applications is an arduous task, since the application needs to be customised for each sensor. Thus, the automatic generation of WSN's applications is desirable to reduce costs, since it drastically reduces the human effort. This paper presents the use of Genetic Programming to automatically generate WSNs applications. A scripting language based on events and actions is proposed to represent the WSN behaviour. Events represent the state of a given sensor node and actions modify these states. Some events are internal states and others are external states captured by the sensors. The genetic programming is used to automatically generate WSNs applications described using this scripting language. These scripts are executed by all network's sensors. This approach enables the application designer to define only the overall objective of the WSN. This objective is defined by means of a fitness function. An event-detection problem is presented in order to evaluate the proposed method. The results shown the capability of the developed approach to successfully solve WSNs problems through the automatic generation of applications.", notes = "CEC 2013 - A joint meeting of the IEEE, the EPS", notes = "CEC 2013 - A joint meeting of the IEEE, the EPS and the IET.", } @MastersThesis{deOliveira:masters, author = "Renato Resende Ribeiro {de Oliveira}", title = "Programacao genetica aplicada a geracao automatizada de aplicacoes para redes de sensores sem fio", school = "Departamento de Ciencia da Computacao, Universidade Federal de Lavras", year = "2014", type = "Mestre", address = "Brazil", month = "13 " # aug, keywords = "genetic algorithms, genetic programming, Rede de sensor sem fio, Middlewares, Wireless sensor network", URL = "http://repositorio.ufla.br/jspui/handle/1/2707", size = "72 pages", abstract = "The wireless sensor networks (WSN) programming is a complex task due to the low-level programming languages and the need of a specific application for each sensor. Furthermore, wireless sensors have many hardware limitations such as low processing power, small memory and energetic limitations. Hence, the automatic programming of WSNs is desirable since it can automatically address these difficulties, besides saving costs by eliminating the need to allocate a developer to program the WSN. The automatic code generation for WSNs using genetic programming has been poorly studied in the literature so far. The genetic programming has proved to be promising in code generation for many application areas. This study proposes the development and application of evolutionary algorithms to generate source codes that solve WSNs problems. The developed evolutionary algorithms should be able to solve different problems of WSNs correctly (achieve the main goal of the problem) and with satisfactory efficiency (mainly on energy savings). The obtained results show that the proposed framework is able to find optimal solutions for the Event Detection Problem for WSN with grid topology and to find satisfactory solutions for WSN with randomised topology. Thus, this study brings many contributions to the WSN area since the automatic programming of WSNs drastically reduces the human programming effort, besides saving costs on executing this task", notes = "in Portuguese", } @InProceedings{dePaiva:2018:ieeeEEEIC, author = "Gabriel Mendonca {de Paiva} and Sergio Pires Pimentel and Sonia Leva and Marco Mussetta", booktitle = "2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe)", title = "Assessment of Exogenous Variables on Intra-Day Solar Irradiance Forecasting Models", year = "2018", abstract = "Accurate and practical forecasting models are very important as tools for optimal integration of the solar energy source in smart grids. This work presents a comparison of four models of intra-day radiance forecasting based on genetic programming. These models are evaluated at two distinct locations, with completely different climate characteristics, with data structured in 10-minute averages to forecast irradiance up to 180 minutes ahead. The models differ in the addition of exogenous weather variables or exogenous deterministic irradiance components. With the use of genetic programming, and at these specific locations, the addition of exogenous weather variables did not result in permanent accuracy improvement, while addition of the deterministic irradiance component did.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/EEEIC.2018.8493938", month = jun, notes = "Also known as \cite{8493938}", } @Article{DePrisco:EC, author = "R. {De Prisco} and G. Zaccagnino and R. Zaccagnino", title = "{EvoComposer}: An Evolutionary Algorithm for 4-voice Music Compositions", journal = "Evolutionary Computation", year = "2020", volume = "28", number = "3", pages = "489--530", month = "Fall", keywords = "genetic algorithms, NSGA-II, Evolutionary Algorithms, Automatic Music Composition, Evolutionary Music", ISSN = "1063-6560", DOI = "doi:10.1162/evco_a_00265", size = "42 pages", abstract = "Evolutionary Algorithms mimic evolutionary behaviours in order to solve problems. They have been successfully applied in many areas and appear to have a special relationship with creative problems; such a relationship, over the last two decades, has resulted in a long list of applications, including several in the field of music. we provide an evolutionary algorithm able to compose music. More specifically we consider the following 4-voice harmonization problem: one of the 4 voices (which are bass, tenor, alto and soprano) is given as input and the composer has to write the other three voices in order to have a complete 4-voice piece of music with a 4-note chord for each input note. Solving such a problem means finding appropriate chords to use for each input note and also find a placement of the notes within each chord so that melodic concerns are addressed. Such a problem is known as the unfigured harmonization problem. The proposed algorithm for the unfigured harmonization problem, named EvoComposer, uses a novel representation...", notes = "Is this GP? Dipartimento di Informatica, University of Salerno, Fisciano (SA), 84084, Italy", } @InProceedings{DeRainville:2012:GECCOcomp, author = "Francois-Michel {De Rainville} and Felix-Antoine Fortin and Marc-Andre Gardner and Marc Parizeau and Christian Gagne", title = "{DEAP}: A Python Framework for Evolutionary Algorithms", booktitle = "GECCO 2012 Evolutionary Computation Software Systems (EvoSoft)", year = "2012", editor = "Stefan Wagner and Michael Affenzeller", pages = "85--92", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, Parallel Evolutionary Algorithms, Software Tools, Open BEAGLE, DEAP, Distributed Evolutionary Algorithms in Python", isbn13 = "978-1-4503-1178-6", DOI = "doi:10.1145/2330784.2330799", code_url = "https://github.com/deap", size = "8 pages", abstract = "DEAP (Distributed Evolutionary Algorithms in Python) is a novel evolutionary computation framework for rapid prototyping and testing of ideas. Its design departs from most other existing frameworks in that it seeks to make algorithms explicit and data structures transparent, as opposed to the more common black box type of frameworks. It also incorporates easy parallelism where users need not concern themselves with gory implementation details like synchronisation and load balancing, only functional decomposition. Several examples illustrate the multiple properties of DEAP.", notes = "Also known as \cite{2330799} Distributed at GECCO-2012. ACM Order Number 910122.", } @Article{de-rainville_2012_sigevolution, author = "Francois-Michel {De Rainville} and Felix-Antoine Fortin and Marc-Andre Gardner and Marc Parizeau and Christian Gagne", title = "{DEAP} - Enabling Nimbler Evolutions", journal = "SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation", year = "2012", volume = "6", number = "2", pages = "17--26", keywords = "genetic algorithms, genetic programming", ISSN = "1931-8499", URL = "http://www.sigevolution.org/issues/pdf/SIGEVOlution0602.pdf", code_url = "https://github.com/DEAP/notebooks", DOI = "doi:10.1145/2597453.2597455", size = "10 pages", abstract = "DEAP is a Distributed Evolutionary Algorithm (EA) framework written in Python and designed to help researchers developing custom evolutionary algorithms. Its design philosophy promotes explicit algorithms and transparent data structures, in contrast with most other evolutionary computation softwares that tend to encapsulate standardised algorithms using the black-box approach. This philosophy sets it apart as a rapid prototyping framework for testing of new ideas in EA research. An executable notebook version of this paper is available at https://github.com/DEAP/notebooks.", notes = "18 Feb 2014 . Building blocks for testing ideas . Rapid prototyping . Fully transparent . Parallel ready . Exhaustively documented . Available at http://deap.gel.ulaval.ca Distributed Island model. Genealogy tree The presented examples covered only a small part of DEAP's capabilities that include evolution strategies (including CMA-ES), multi-objective optimisation (NSGA-II and SPEA-II), co-evolution, particle swarm optimisation PSO, as well as many benchmarks (continuous, binary, regression, and moving peaks), and examples (more than 40). Departement de genie electrique et de genie informatique - Universite Laval - Quebec (Quebec), Canada", } @Article{DERAKHSHANI:2017:OE, author = "Ali Derakhshani", title = "Estimating uplift capacity of suction caissons in soft clay: A hybrid computational approach based on model tree and GP", journal = "Ocean Engineering", volume = "146", pages = "1--8", year = "2017", keywords = "genetic algorithms, genetic programming, Suction caisson, Uplift capacity, Formulation, Hybrid intelligent approach, M5-GP method", ISSN = "0029-8018", DOI = "doi:10.1016/j.oceaneng.2017.09.025", URL = "http://www.sciencedirect.com/science/article/pii/S0029801817305449", abstract = "Stability of suction caissons used as foundations or anchors of offshore structures is a critical challenge in marine structures engineering. To this end, many studies have been conducted including those concentrate on implementing computational intelligence methods to model the response of suction caissons under loading. In this regard, this paper aims at formulating uplift capacity of suction caissons using a hybrid artificial intelligence computational tool based on model tree (M5) and genetic programming (GP), called M5-GP. The formulae are developed in terms of several governing parameters using a reliable experimental database from the literature. The results show that the M5-GP based relationships are able to predict the uplift capacity of suction caissons precisely. Furthermore, to consider the safety in the design process, probabilistic equations are also given for various risk levels. The new formulas compare favorably with the existing relationships in the literature regarding prediction performance. In addition, the simplified formulation is compact, easy to use and physically sound. Therefore, it is especially appropriate to be used in design practice", } @InProceedings{Derbel:2020:GECCOcomp, author = "Bilel Derbel and Sebastien Verel", title = "Fitness Landscape Analysis to Understand and Predict Algorithm Performance for Single- and Multi-Objective Optimization", year = "2020", editor = "Richard Allmendinger and Hugo Terashima Marin and Efren Mezura Montes and Thomas Bartz-Beielstein and Bogdan Filipic and Ke Tang and David Howard and Emma Hart and Gusz Eiben and Tome Eftimov and William {La Cava} and Boris Naujoks and Pietro Oliveto and Vanessa Volz and Thomas Weise and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Rui Wang and Ran Cheng and Guohua Wu and Miqing Li and Hisao Ishibuchi and Jonathan Fieldsend and Ozgur Akman and Khulood Alyahya and Juergen Branke and John R. Woodward and Daniel R. Tauritz and Marco Baioletti and Josu Ceberio Uribe and John McCall and Alfredo Milani and Stefan Wagner and Michael Affenzeller and Bradley Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and Nayat Sanchez-Pi and Luis Marti and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and Matthew Johns and Nick Ross and Ed Keedwell and Herman Mahmoud and David Walker and Anthony Stein and Masaya Nakata and David Paetzel and Neil Vaughan and Stephen Smith and Stefano Cagnoni and Robert M. Patton and Ivanoe {De Falco} and Antonio {Della Cioppa} and Umberto Scafuri and Ernesto Tarantino and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Erik Hemberg and Riyad Alshammari and Tokunbo Makanju and Fuijimino-shi and Ivan Zelinka and Swagatam Das and Ponnuthurai Nagaratnam and Roman Senkerik", isbn13 = "9781450371278", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377929.3389893", DOI = "doi:10.1145/3377929.3389893", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion", pages = "993--1042", size = "50 pages", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", note = "Tutorial", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{10.1145/3377929.3389893} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @InProceedings{Derby:evoapps13, author = "Owen Derby and Kalyan Veeramachaneni and Una-May O'Reilly", title = "Cloud Driven Design of a Distributed Genetic Programming Platform", booktitle = "Applications of Evolutionary Computing, EvoApplications 2013: EvoCOMNET, EvoCOMPLEX, EvoENERGY, EvoFIN, EvoGAMES, EvoIASP, EvoINDUSTRY, EvoNUM, EvoPAR, EvoRISK, EvoROBOT, EvoSTOC", year = "2013", month = "3-5 " # apr, editor = "Anna I. Esparcia-Alcazar and Antonio Della Cioppa and Ivanoe {De Falco} and Ernesto Tarantino and Carlos Cotta and Robert Schaefer and Konrad Diwold and Kyrre Glette and Andrea Tettamanzi and Alexandros Agapitos and Paolo Burrelli and J. J. Merelo and Stefano Cagnoni and Mengjie Zhang and Neil Urquhart and Kevin Sim and Aniko Ekart and Francisco {Fernandez de Vega} and Sara Silva and Evert Haasdijk and Gusz Eiben and Anabela Simoes and Philipp Rohlfshagen", series = "LNCS", volume = "7835", publisher = "Springer Verlag", address = "Vienna", publisher_address = "Berlin", pages = "509--518", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, cloud computing, machine learning, distributed evolutionary computation, FlexGP", isbn13 = "978-3-642-37191-2", DOI = "doi:10.1007/978-3-642-37192-9_51", size = "10 pages", abstract = "We describe how we design FlexGP, a distributed genetic programming (GP) system to efficiently run on the cloud. The system has a decentralised, fault-tolerant, cascading startup where nodes start to compute while more nodes are launched. It has a peer-to-peer neighbour discovery protocol which constructs a robust communication network across the nodes. Concurrent with neighbour discovery, each node launches a GP run differing in parametrisation and training data from its neighbors. This factoring of parameters across learners produces many diverse models for use in ensemble learning.", notes = " EvoApplications2013 held in conjunction with EuroGP2013, EvoCOP2013, EvoBio'2013 and EvoMusArt2013", } @InProceedings{deResende:2016:BRACIS, author = "Damares C. O. {de Resende} and Adamo Lima {de Santana} and Fabio Manoel Franca Lobato", booktitle = "2016 5th Brazilian Conference on Intelligent Systems (BRACIS)", title = "Time Series Imputation Using Genetic Programming and Lagrange Interpolation", year = "2016", pages = "169--174", abstract = "Time series have been used in several applications such as process control, environment monitoring, financial analysis and scientific researches. However, in the presence of missing data, this study may become more complex due to a strong break of correlation among samples. Therefore, this work proposes an imputation method for time series using Genetic Programming (GP) and Lagrange Interpolation. The heuristic adopted builds an interpretable regression model that explores time series statistical features such as mean, variance and auto-correlation. It also makes use of interrelation among multivariate time series to estimate missing values. Results show that the proposed method is promising, being capable of imputing data without loosing the dataset's statistical properties, as well as allowing a better understanding of the missing data pattern from the obtained interpretable model.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/BRACIS.2016.040", month = oct, notes = "Also known as \cite{7839581}", } @InProceedings{Derghal:2018:SSD, author = "Abdellah Derghal and Noureddine Golea", booktitle = "2018 15th International Multi-Conference on Systems, Signals Devices (SSD)", title = "A Fuzzy Genetic Programming-Based Algorithm for Eco-Friendly and Economic Load Dispatch Problem", year = "2018", pages = "738--743", abstract = "In this paper, we attempt to apply genetic algorithms to the fuzzy mathematical programming problems which involve imprecise (fuzzy) and nonlinear information. The principle objective in this paper is how to attribute a fuzzy set in the building of the Environmental economic power dispatch problem. The Eco-friendly /Economic Load Dispatch problem is formulated as a multiple objective problem subject to physical constraints, Fuzzy mathematical programming is used to represent objective functions with fuzzy parameters and uncertainties in constraints set, and genetic algorithm (GA) is used to solve the reformulated problem. The performance of this solution method is examined by comparing its results with that of the existing methods through an illustrative example, these comparisons reveal the efficient and robustness of the planned approach developed in this paper.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SSD.2018.8570619", ISSN = "2474-0446", month = mar, notes = "Also known as \cite{8570619}", } @InProceedings{Derner:2018:ICRA, author = "Erik Derner and Jiri Kubalik and Robert Babuska", booktitle = "2018 IEEE International Conference on Robotics and Automation (ICRA)", title = "Data-driven Construction of Symbolic Process Models for Reinforcement Learning", year = "2018", abstract = "Reinforcement learning (RL) is a suitable approach for controlling systems with unknown or time-varying dynamics. RL in principle does not require a model of the system, but before it learns an acceptable policy, it needs many unsuccessful trials, which real robots usually cannot withstand. It is well known that RL can be sped up and made safer by using models learned online. In this paper, we propose to use symbolic regression to construct compact, parsimonious models described by analytic equations, which are suitable for real-time robot control. Single node genetic programming (SNGP) is employed as a tool to automatically search for equations fitting the available data. We demonstrate the approach on two benchmark examples: a simulated mobile robot and the pendulum swing-up problem; the latter both in simulations and real-time experiments. The results show that through this approach we can find accurate models even for small batches of training data. Based on the symbolic model found, RL can control the system well.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICRA.2018.8461182", ISSN = "2577-087X", month = may, notes = "Also known as \cite{8461182}", } @Article{DERNER:2020:ASC, author = "Erik Derner and Jiri Kubalik and Nicola Ancona and Robert Babuska", title = "Constructing parsimonious analytic models for dynamic systems via symbolic regression", journal = "Applied Soft Computing", volume = "94", pages = "106432", year = "2020", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2020.106432", URL = "http://www.sciencedirect.com/science/article/pii/S1568494620303720", keywords = "genetic algorithms, genetic programming, Symbolic regression, Model learning, Reinforcement learning", abstract = "Developing mathematical models of dynamic systems is central to many disciplines of engineering and science. Models facilitate simulations, analysis of the system's behavior, decision making and design of automatic control algorithms. Even inherently model-free control techniques such as reinforcement learning (RL) have been shown to benefit from the use of models, typically learned online. Any model construction method must address the tradeoff between the accuracy of the model and its complexity, which is difficult to strike. In this paper, we propose to employ symbolic regression (SR) to construct parsimonious process models described by analytic equations. We have equipped our method with two different state-of-the-art SR algorithms which automatically search for equations that fit the measured data: Single Node Genetic Programming (SNGP) and Multi-Gene Genetic Programming (MGGP). In addition to the standard problem formulation in the state-space domain, we show how the method can also be applied to input-output models of the NARX (nonlinear autoregressive with exogenous input) type. We present the approach on three simulated examples with up to 14-dimensional state space: an inverted pendulum, a mobile robot, and a bipedal walking robot. A comparison with deep neural networks and local linear regression shows that SR in most cases outperforms these commonly used alternative methods. We demonstrate on a real pendulum system that the analytic model found enables a RL controller to successfully perform the swing-up task, based on a model constructed from only 100 data samples", } @InProceedings{Derner:2021:IROS, author = "Erik Derner and Jiri Kubalik and Robert Babuska", title = "Guiding Robot Model Construction with Prior Features", booktitle = "2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)", year = "2021", pages = "7112--7118", abstract = "Virtually all robot control methods benefit from the availability of an accurate mathematical model of the robot. However, obtaining a sufficient amount of informative data for constructing dynamic models can be difficult, especially when the models are to be learned during robot deployment. Under such circumstances, standard data-driven model learning techniques often yield models that do not comply with the physics of the robot. We extend a symbolic regression algorithm based on Single Node Genetic Programming by including the prior model information into the model construction process. In this way, symbolic regression automatically builds models that compensate for theoretical or empirical model deficiencies. We experimentally demonstrate the approach on two real-world systems: the TurtleBot 2 mobile robot and the Parrot Bebop 2 drone. The results show that the proposed model-learning algorithm produces realistic models that fit well the training data even when using small training sets. Passing the prior model information to the algorithm significantly improves the model accuracy while speeding up the search.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IROS51168.2021.9635831", ISSN = "2153-0866", month = sep, notes = "Also known as \cite{9635831}", } @Article{Derouich:2015:AA, author = "M. Derouich and A. Radi and P. S. Barklem", title = "Unified numerical model of collisional depolarization and broadening rates that are due to hydrogen atom collisions", journal = "Astronomy and Astrophysics", year = "2015", volume = "584", month = dec, keywords = "genetic algorithms, genetic programming", oai = "oai:arXiv.org:1508.06482", URL = "http://arxiv.org/abs/1508.06482", URL = "http://dx.doi.org/10.1051/0004-6361/201526661", size = "8 pages", abstract = "Context. Accounting for partial or complete frequency redistribution when interpreting solar polarization spectra requires data on various collisional processes. Data for depolarization and polarization transfer are needed, but are often lacking, while data for collisional broadening are usually more readily available. Recently it was concluded that despite underlying similarities in the physics of collisional broadening and depolarization processes, the relations between them cannot be derived purely analytically. Aims. We aim to derive accurate numerical relations between the collisional broadening rates and the collisional depolarization and polarization transfer rates that are due to hydrogen atom collisions. These relations would enable accurate and efficient estimates of collisional data for solar applications. Methods. Using earlier results for broadening and depolarization processes based on general (i.e., not specific to a given atom), semi-classical calculations that employ interaction potentials from perturbation theory, we used genetic programming (GP) to fit the available data and generate analytical functions describing the relations between them. The predicted relations from the GP-based model were compared with the original data to estimate the accuracy of the method. Results. We obtain strongly nonlinear relations between the collisional broadening rates and the depolarization and polarization transfer rates. They are shown to reproduce the original data with an accuracy of about 5percent. Our results allow determining the depolarization and polarization transfer rates for hyperfine or fine-structure levels of simple and complex atoms. Conclusions. We show that by using a sophisticated numerical approach and a general collision theory, useful relations with sufficient accuracy for applications are possible.", } @Article{Derouich:2017:NA, author = "M. Derouich", title = "General model of depolarization and transfer of polarization of singly ionized atoms by collisions with hydrogen atoms", journal = "New Astronomy", volume = "51", pages = "32--36", year = "2017", ISSN = "1384-1076", DOI = "doi:10.1016/j.newast.2016.08.011", URL = "http://www.sciencedirect.com/science/article/pii/S1384107616300756", abstract = "Simulations of the generation of the atomic polarization is necessary for interpreting the second solar spectrum. For this purpose, it is important to rigorously determine the effects of the isotropic collisions with neutral hydrogen on the atomic polarization of the neutral atoms, ionized atoms and molecules. Our aim is to treat in generality the problem of depolarizing isotropic collisions between singly ionized atoms and neutral hydrogen in its ground state. Using our numerical code, we computed the collisional depolarization rates of the p-levels of ions for large number of values of the effective principal quantum number n* and the Unsoeld energy Ep. Then, genetic programming has been used to fit the available depolarization rates. As a result, strongly non-linear relationships between the collisional depolarization rates, n* and Ep are obtained, and are shown to reproduce the original data with accuracy clearly better than 10percent. These relationships allow quick calculations of the depolarizing collisional rates of any simple ion which is very useful for the solar physics community. In addition, the depolarization rates associated to the complex ions and to the hyperfine levels can be easily derived from our results. In this work we have shown that by using powerful numerical approach and our collisional method, general model giving the depolarization of the ions can be obtained to be exploited for solar applications.", keywords = "genetic algorithms, genetic programming, Scattering - Sun, photosphere - atomic processes - line, formation - line, profiles - polarization", } @Article{derouich:2022:Universe, author = "Moncef Derouich and Saleh Qutub and Fainana Mustajab and Badruddin Zaheer Ahmad", title = "Collisions of Electrons with Alkali, Alkaline and Complex Atoms Relevant to Solar and Stellar Atmospheres", journal = "Universe", year = "2022", volume = "8", number = "12", pages = "Article No. 613", keywords = "genetic algorithms, genetic programming", ISSN = "2218-1997", URL = "https://www.mdpi.com/2218-1997/8/12/613", DOI = "doi:10.3390/universe8120613", abstract = "In solar and stellar atmospheres, atomic excitation by impact with electrons plays an important role in the formation of spectral lines. We make use of available experimental and theoretical cross-sections to calculate the excitation rates in s–p transitions of alkali and alkaline atoms through collisions with electrons. Then, we infer a general formula for calculating the excitation rates by using genetic programming numerical methods. We propose an extension of our approach to deduce collisional excitation rates for complex atoms and atoms with hyperfine structure. Furthermore, the developed method is also applied to determine collisional polarization transfer rates. Our results are not specific to a given atom and can be applied to any s–p atomic transition. The accuracy of our results is discussed.", notes = "also known as \cite{universe8120613}", } @InProceedings{derrig:1998:hecagcs, author = "Daniel Derrig and James D. Johannes", title = "Hierarchical Exemplar Based Credit Allocation for Genetic Classifier Systems", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "622--628", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, classifiers", ISBN = "1-55860-548-7", notes = "GP-98", } @InProceedings{derrig:1998:deosc, author = "Daniel Derrig and James Johannes", title = "Deleting End-of-Sequence Classifiers", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "29--32", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "genetic algorithms, genetic programming", size = "4 pages", notes = "GP-98LB", } @InProceedings{deSa:2017:EuroGP, author = "Alex G. C. {de Sa} and Walter Jose G. S. Pinto and Luiz Otavio V. B. Oliveira and Gisele Pappa", title = "{RECIPE}: A Grammar-based Framework for Automatically Evolving Classification Pipelines", booktitle = "EuroGP 2017: Proceedings of the 20th European Conference on Genetic Programming", year = "2017", month = "19-21 " # apr, editor = "Mauro Castelli and James McDermott and Lukas Sekanina", series = "LNCS", volume = "10196", publisher = "Springer Verlag", address = "Amsterdam", pages = "246--261", organisation = "species", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-55695-6", code_url = "https://github.com/laic-ufmg/Recipe", DOI = "doi:10.1007/978-3-319-55696-3_16", abstract = "Automatic Machine Learning is a growing area of machine learning that has a similar objective to the area of hyper-heuristics: to automatically recommend optimized pipelines, algorithms or appropriate parameters to specific tasks without much dependency on user knowledge. The background knowledge required to solve the task at hand is actually embedded into a search mechanism that builds personalized solutions to the task. Following this idea, this paper proposes RECIPE (REsilient ClassifIcation Pipeline Evolution), a framework based on grammar-based genetic programming that builds customized classification pipelines. The framework is flexible enough to receive different grammars and can be easily extended to other machine learning tasks. RECIPE overcomes the drawbacks of previous evolutionary-based frameworks, such as generating invalid individuals, and organizes a high number of possible suitable data pre-processing and classification methods into a grammar. Results of f-measure obtained by RECIPE are compared to those two state-of-the-art methods, and shown to be as good as or better than those previously reported in the literature. RECIPE represents a first step towards a complete framework for dealing with different machine learning tasks with the minimum required human intervention.", notes = "cites \cite{Olson:2016:GECCO} Also known as desa2017recipe Part of \cite{Castelli:2017:GP} EuroGP'2017 held inconjunction with EvoCOP2017, EvoMusArt2017 and EvoApplications2017", } @InProceedings{deSa:2018:PPSN, author = "Alex {de Sa} and Alex Freitas and Gisele Pappa", title = "Automated Selection and Configuration of Multi-Label Classification Algorithms with Grammar-based Genetic Programming", booktitle = "15th International Conference on Parallel Problem Solving from Nature", year = "2018", editor = "Anne Auger and Carlos M. Fonseca and Nuno Lourenco and Penousal Machado and Luis Paquete and Darrell Whitley", volume = "11102", series = "LNCS", pages = "308--320", address = "Coimbra, Portugal", month = "8-12 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Automated machine learning (Auto-ML), Multi-label classification, Grammar-based genetic programming", isbn13 = "978-3-319-99258-7", URL = "https://www.springer.com/gp/book/9783319992587", DOI = "doi:10.1007/978-3-319-99259-4_25", abstract = "This paper proposes Auto-MEKAGGP, an Automated Machine Learning (Auto-ML) method for Multi-Label Classification (MLC) based on the MEKA tool, which offers a number of MLC algorithms. In MLC, each example can be associated with one or more class labels, making MLC problems harder than conventional (single-label) classification problems. Hence, it is essential to select an MLC algorithm and its configuration tailored (optimized) for the input dataset. Auto-MEKAGGP addresses this problem with two key ideas. First, a large number of choices of MLC algorithms and configurations from MEKA are represented into a grammar. Second, our proposed Grammar-based Genetic Programming (GGP) method uses that grammar to search for the best MLC algorithm and configuration for the input dataset. Auto-MEKAGGP was tested in 10 datasets and compared to two well-known MLC methods, namely Binary Relevance and Classifier Chain, and also compared to GA-Auto-MLC, a genetic algorithm we recently proposed for the same task. Two versions of Auto-MEKAGGP were tested: a full version with the proposed grammar, and a simplified version where the grammar includes only the algorithmic components used by GA-Auto-MLC. Overall, the full version of Auto-MEKAGGP achieved the best predictive accuracy among all five evaluated methods, being the winner in six out of the 10 datasets.", notes = "PPSN2018 http://ppsn2018.dei.uc.pt This two-volume set LNCS 11101 and 11102 constitutes the refereed proceedings of the 15th International Conference on Parallel Problem Solving from Nature, PPSN 2018", } @InProceedings{deSa:2020:GECCO, author = "Alex G. C. {de Sa} and Cristiano G. Pimenta and Gisele L. Pappa and Alex A. Freitas", title = "A Robust Experimental Evaluation of Automated Multi-Label Classification Methods", year = "2020", editor = "Carlos Artemio {Coello Coello} and Arturo Hernandez Aguirre and Josu Ceberio Uribe and Mario Garza Fabre and Gregorio {Toscano Pulido} and Katya Rodriguez-Vazquez and Elizabeth Wanner and Nadarajen Veerapen and Efren Mezura Montes and Richard Allmendinger and Hugo Terashima Marin and Markus Wagner and Thomas Bartz-Beielstein and Bogdan Filipic and Heike Trautmann and Ke Tang and John Koza and Erik Goodman and William B. Langdon and Miguel Nicolau and Christine Zarges and Vanessa Volz and Tea Tusar and Boris Naujoks and Peter A. N. Bosman and Darrell Whitley and Christine Solnon and Marde Helbig and Stephane Doncieux and Dennis G. Wilson and Francisco {Fernandez de Vega} and Luis Paquete and Francisco Chicano and Bing Xue and Jaume Bacardit and Sanaz Mostaghim and Jonathan Fieldsend and Oliver Schuetze and Dirk Arnold and Gabriela Ochoa and Carlos Segura and Carlos Cotta and Michael Emmerich and Mengjie Zhang and Robin Purshouse and Tapabrata Ray and Justyna Petke and Fuyuki Ishikawa and Johannes Lengler and Frank Neumann", isbn13 = "9781450371285", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377930.3390231", DOI = "doi:10.1145/3377930.3390231", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference", pages = "175--183", size = "9 pages", keywords = "genetic algorithms, genetic programming, search spaces, automated machine learning (AutoML), search methods, multi-label classification", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", abstract = "Automated Machine Learning (AutoML) has emerged to deal with the selection and configuration of algorithms for a given learning task. With the progression of AutoML, several effective methods were introduced, especially for traditional classification and regression problems. Apart from the AutoML success, several issues remain open. One issue, in particular, is the lack of ability of AutoML methods to deal with different types of data. Based on this scenario, this paper approaches AutoML for multi-label classification (MLC) problems. In MLC, each example can be simultaneously associated to several class labels, unlike the standard classification task, where an example is associated to just one class label. In this work, we provide a general comparison of five automated multi-label classification methods - two evolutionary methods, one Bayesian optimization method, one random search and one greedy search - on 14 datasets and three designed search spaces. Overall, we observe that the most prominent method is the one based on a canonical grammar-based genetic programming (GGP) search method, namely Auto-MEKAGGP. Auto-MEKAGGP presented the best average results in our comparison and was statistically better than all the other methods in different search spaces and evaluated measures, except when compared to the greedy search method.", notes = "Also known as \cite{10.1145/3377930.3390231} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @InProceedings{deschain:2000:ASTC, author = "Larry M. Deschaine and Fred A. Zafran and Janardan J. Patel and David Amick and Robert Pettit and Frank D. Francone and Peter Nordin and Edward Dilkes and Laurene V. Fausett", title = "Solving the Unsolved Using Machine Learning, Data Mining and Knowledge Discovery to Model a Complex Production Process", booktitle = "Advanced Technology Simulation Conference", year = "2000", editor = "M. Ades", address = "Wasington, DC, USA", organisation = "Society for Computer Simulations", month = "22-26 " # apr, keywords = "genetic algorithms, genetic programming, discipulus", broken = "http://pw2.netcom.com/%7elmdmit84/SoilStabilization2000.pdf", URL = "http://citeseer.ist.psu.edu/deschaine00solving.html", size = "6 pages", notes = "http://www.scs.org/confernc/astc00/final-program/BIS-details.htm Soil Stabilization via Evolutionary Computation - Linear Genetic Programming, Simulated Annealing, and ANN. Predict hydraulic condictivity, strangth, leach, of stabilised waste given grain size and grout composition. 'The speed savings alone made GP the technology of Choice.' Author orignally misspelt Larry M. Deschain:-(", } @InProceedings{Deschain:2001:ASTC, author = "Larry M. Deschaine and Janardan J. Patel and Ronald D. Guthrie and Joseph T. Grimski and M. J. Ades", title = "Using Linear Genetic Programming to Develop a {C/C++} Simulation Model of a Waste Incinerator", booktitle = "Advanced Technology Simulation Conference", year = "2001", editor = "M. Ades", pages = "41--48", address = "Seattle", month = "22-26 " # apr, organisation = "Society for Computer Simulations", keywords = "genetic algorithms, genetic programming, discipulus, DSS, 10 demes", broken = "http://pw2.netcom.com/~lmdmit84/ASTC2001-LGP-INCINERATOR.pdf", URL = "http://www.aimlearning.com/Environmental.Engineering.pdf", URL = "http://citeseer.ist.psu.edu/451766.html", URL = "http://citeseer.ist.psu.edu/396498.html", abstract = "Abstract We explore whether Linear Genetic Programming (LGP) can evolve a C/C++ computer simulation model that accurately models the performance of a waste incinerator. Human expert written simulation models are used worldwide in a variety of industrial and business applications. They are expensive to develop, may or may not be valid for the specific process that is being modeled, and may be erroneous. LGP is a machine learning technique that uses information about a process's inputs and outputs to simultaneously write the simulation model, calibrate and optimize the model's constants, and validate the solution. The result is a calibrated, validated, error-free C/C++ computer model specific to the desired process. To evaluate whether this is feasible for complex industrial processes, the method on data obtained from the operation of a hazardous waste incinerator. This process is difficult to model. Previously, in a well-conducted study, the popular machine learning technique, analytic neural networks, was unable to derive useful solutions to this problem. The present study uses various mutation rates (95%, 50%, and 10%), 10 random initial seeds per mutation rate, and a large number of generations (1,280 to 4,461). The LGP system provided accurate solutions to this problem with a validation data measure of fitness, R2, equal to 0.961. This work demonstrates the value of LGP for process simulation. The study confirms previously published results and found that the distribution of outputs from multiple genetic programming (GP) runs tends to include an extended tail of outstanding solutions. Such a tail was not found in previous studies of neural networks. This result emphasizes the need for employing a strategy of multiple runs using various initial seeds and mutation rates to find good solutions to complex problems using LGP. This result also demonstrates the value of a fast LGP algorithm implemented at the machine code level for both static scientific data mining and real-time process control. The work consumed 600 hours of CPU time; it is estimated that other GP algorithms would have required between 4 and 136 years of CPU time to achieve similar results.", notes = "ASTC 2001 http://www.scs.org/confernc/astc01/prelim-program/astc01prelim.html (broken 2020) Science Applications International Corporation Model of C02 concentration from 1 weeks live running hourly logs. Interactive Evaluation (Unclear what this means). Print out of PDF poor Author orignally misspelt Larry M. Deschain:-(", } @Article{deschain:2000:PCAI, author = "Larry M. Deschaine", title = "Tackling Real-World Environmental Challenges with Linear Genetic Programming", journal = "PCAI", year = "2000", volume = "15", number = "5", pages = "35--37", month = sep # "/" # oct, keywords = "genetic algorithms, genetic programming", URL = "http://www.pcai.com/web/issues/pcai_14_5_toc.html", notes = "advocates a unique approach to the challenges of engineering and scientific data mining, control, and process optimization by using fast linear genetic programming technique. Author orignally misspelt Larry M. Deschain:-(", } @Article{Deschaine:2001:PCAI, author = "L. M. Deschaine and Jennifer McCormack and D. Pyle and F. Francone", title = "Genetic Algorithms and Intelligent Agents Team Up: Techniques for Data Assembly, Preprocessing, Modeling, and Decision Optimization", journal = "PCAI magazine", year = "2001", volume = "15", number = "3", pages = "38--44", month = may # "/" # jun, keywords = "genetic algorithms, genetic programming", URL = "http://www.pcai.com/web/indexes/index_vol_15.html", abstract = "Discussing a set of techniques for optimal real-time decision making from distributed, heterogeneous information found in financial, industrial, and scientific data", notes = " ", } @InProceedings{deschaine:2002:FEA, author = "Larry M. Deschaine and Frank D. Francone", title = "Design Optimization Integrating the Outer Approximation Method with Process Simulators and Linear Genetic Programming", booktitle = "Proceedings of the 6th Joint Conference on Information Science", year = "2002", editor = "H. John Caulfield and Shu-Heng Chen and Heng-Da Cheng and Richard J. Duro and Vasant Honavar and Etienne E. Kerre and Mi Lu and Manuel Grana Romay and Timothy K. Shih and Dan Ventura and Paul P. Wang and Yuanyuan Yang", pages = "618--621", address = "Research Triangle Park, North Carolina, USA", month = mar # " 8-13", publisher = "JCIS / Association for Intelligent Machinery, Inc.", bibsource = "DBLP, http://dblp.uni-trier.de", keywords = "genetic algorithms, genetic programming", ISBN = "0-9707890-1-7", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/deschaine/FEA_2002_Design_Optimization.pdf", abstract = "Fast process optimisation is a challenge. Processes are often complex and the intricate simulators written to solve them can take hours or days per simulation to run. Optimization techniques that require many calls to a simulator can take days or months to solve. While advances in optimisation algorithms, such as the outer approximation method have reduced the solution time by a factor of ten or more when compared to other methods, long solutions times still can occur. This work explores the development of simulating a simulator to enable optimal solution development in an accelerated time frame. The technique used to develop the simulated simulator is linear genetic programming (LGP). LGP approximated a complex industrial process simulator that took hours to execute per run with a high fitness program - applied (testing) data set R2 fitness of 0.989. The LGP solution executes in less than a second. This success opens up the possibility of optimising functions faster using these LGP derived high fitness simulator approximations. Since the LGP simulated process simulator now executes in less than a second, as opposed to hours, using an intensive multiple call optimisation technique such as genetic algorithms and evolutionary strategies is now also feasible.", notes = " FEA2002 In conjunction with Sixth Joint Conference on Information Sciences My printer refuses to deal with this as PDF", } @InProceedings{ASTC_2002_UXOFinder_Invention_Paper, author = "Larry M. Deschaine and Richard A. Hoover and Joseph N. Skibinski and Janardan J. Patel and Frank Francone and Peter Nordin and M. J. Ades", title = "Using Machine Learning to Compliment and Extend the Accuracy of UXO Discrimination Beyond the Best Reported Results of the {Jefferson} Proving Ground Technology Demonstration", booktitle = "2002 Advanced Technology Simulation Conference", year = "2002", pages = "46--52", address = "San Diego, CA, USA", month = "14-18 " # apr, organisation = "The Society for Modeling and Simulation International", keywords = "genetic algorithms, genetic programming, Unexploded ordnance, anomaly detection, geophysics, UXO", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/deschaine/ASTC_2002_UXOFinder_Invention_Paper.pdf", broken = "http://www.scs.org/docInfo.cfm?get=1488", size = "7 pages", abstract = "The accurate discrimination of unexploded ordnance from geophysical signals is very difficult. Research has demonstrated that using a machine learning technique known as linear genetic programming in concert with human expertise can extend the accuracy of unexploded ordnance discrimination past currently published results. This paper describes how linear genetic programming offers the promise of creating real-time unexploded ordnance discrimination.", notes = "Broken 2017 http://www.scs.org/confernc/astc/astc02/ASTC02finalprogram.pdf ", } @InProceedings{Deschaine:2003:informs, author = "Larry Deschaine and Janos D. Pinter and Sudip Regmi", title = "Developing High Fidelity Approximations to Expensive Simulation Models for Expedited Optimization", booktitle = "INFORMS Annual Meeting Conference", year = "2003", editor = "Donna Llewellyn", address = "Atlanta, Georgia, USA", month = oct # " 19-22", note = "Presented at", keywords = "genetic algorithms, genetic programming", broken = "https://informs.emeetingsonline.com/emeetings/formbuilder/clustersessiondtl.asp?csnno=1278", abstract = "Integrated simulation and optimisation typically requires a sequence of 'expensive' function calls. While extremely valuable in concept, when the computation cost of simulations functions is high (hours / days) and or the optimization paradigm is inefficient (thousands of function calls), real-time or timely 'optimal' solutions are elusive. We discuss the use of machine learning to develop a high fidelity model of a process simulator that executes quickly (milliseconds). This function is then optimised using the LGO solver, thus enabling optimisation in real-time.", notes = "https://www.informs.org/Meetings-Conferences/INFORMS-Conference-Calendar/Past-Events/INFORMS-Annual-Meeting-Atlanta-2003 ", } @Article{Deschaine:2003:JMMS, author = "Larry M. Deschaine", title = "Simulation and Optimization of Large Scale Subsurface Environmental Impacts; Investigations, Remedial Design and Long Term Monitoring", journal = "Journal of Mathematical Machines and Systems", year = "2003", number = "3-4", pages = "201--218", address = "Kiev", keywords = "genetic algorithms, genetic programming", URL = "http://www.immsp.kiev.ua/publications/eng/2003_3_4/index.html", abstract = "The global impact to human health and the environment from large scale chemical / radionuclide releases is well documented. Examples are the wide spread release of radionuclides from the Chernobyl nuclear reactors and the mobilisation of arsenic in Bangladesh. The seriousness of these issues is represented by the activities of the World Health Organisation, the Environmental Protection Agencies in Europe, the United States, and the like. The fiscal costs of addressing and remediating these issues on a global scale are astronomical, but then so are the fiscal and human health costs of ignoring them. An integrated complete methodology for optimising the response(s) to these issues is presented. This work addresses development of global optimal response policy design for large scale, complex, environmental issues. It is important to note that optimization does not singularly refer to cost minimisation, but to the effective and efficient balance of cost, performance, risk, management, and societal priorities along with uncertainty analysis. This tool integrates all of these elements into a single decision framework. It provides a consistent approach to designing optimal solutions that are tractable, traceable, and defensible. Subsurface environmental processes are represented by linear and non-linear, elliptic and parabolic equations. The state equations for multi-phase flow (water, soil gas, NAPL), and multicomponent transport (radionuclides, heavy metals, volatile organics, explosives, etc.) are solved using numerical methods such as finite elements. Genetic programming is used to generate simulators from data when simulation models do not exist, to extend the accuracy of them, or to replace slow ones. To define and monitor the subsurface impacts, geostatistical numerical models, Kalman filtering and optimisation tools are integrated. Optimal plume finding is the estimation of the plume fringe(s) at a specified time using the least amount of sensors (i.e. monitoring wells). Long term monitoring extends this approach concept, and integrates the spatial-time correlations to optimise the decision variables of where to sample and when to sample over the project life cycle for least cost of achieving specified accuracy. The remediation optimization solves the multi-component, multiphase system of equations and incorporates constraints on life-cycle costs, maximum annual costs, maximum allowable annual discharge (for assessing the monitored natural attenuation solution) and constraints on where remedial system component(s) can be located. It includes management overrides to force certain solutions be chosen or precluded from the solution design. It uses a suite of optimization techniques, including the outer approximation method, lipschitz global optimization, genetic algorithms, and the like. A discussion of using the WAVE-WP algorithm for distributed optimisation is included. This system process provides the full capability to optimise multi-source, multiphase, and multicomponent sites. The results of applying just components of these algorithms have produced savings of as much as $90,000,000(US), when compared to alternative solutions. This was done without loss of effectiveness, and received an award from the Vice President of the United States.", notes = "UDC 681.3, Refs.: 45 titles. Extended Chalmers version 56 pages", } @InCollection{Deschaine:2005:IPEA, author = "L. M. Deschaine and F. D. Francone", title = "Extending the Boundaries of Design Optimization by Integrating Fast Optimization Techniques with Machine Code Based, Linear Genetic Programming", booktitle = "Information Processing with Evolutionary Algorithms", year = "2005", editor = "Manuel Grana and Richard J. Duro and Alicia d'Anjou and Paul P. Wang", series = "Advanced Information and Knowledge Processing", chapter = "2", pages = "11--30", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-85233-866-4", URL = "http://dx.doi.org/10.1007/1-84628-117-2_2", DOI = "doi:10.1007/1-84628-117-2_2", abstract = "Summary and Conclusions We are in the early stages of building a comprehensive, integrated optimisation and modelling system to handle complex industrial problems. We believe a combination of machine-code-based, LGP (for modelling) and ES CDSA (for optimization) together provides the best combination of available tools and algorithms for this task. By conceiving of design optimisation projects as integrated modeling and optimisation problems from the outset, we anticipate that engineers and researchers will be able to extend the range of problems that are solvable, given today's technology. The general approach of Deschaine and Francone is to reverse engineer a system with Linear Genetic Programming at the machine code level. This approach provides very fast and accurate models of the process that will be subject to optimisation. The optimisation process itself is performed using an Evolutionary Strategy with completely deterministic parameter self-adaptation. The authors have tested this approach in a variety of academic problems. They target industrial problems, characterised by low formalisation and high complexity. As a final illustration they deal with the design of an incinerator and the problem of subsurface unexploded ordnance detection.", notes = "cf FEA 2002 and JCIS 2002. Series editors Xindong Wu and Lakhmi Jain", } @Article{GDI0605scr, author = "Larry Deschaine", title = "Using Information fusion, machine learning, and global optimisation to increase the accuracy of finding and understanding items interest in the subsurface", journal = "GeoDrilling International", year = "2006", number = "122", pages = "30--32", month = may, address = "London", keywords = "genetic algorithms, genetic programming, Groundwater plumes, Source zones, Landmines and unexploded ordnance UXO", URL = "http://www.mining-journal.com/gdi_magazine/pdf/GDI0605scr.pdf", size = "3 pages", notes = " SUMMARY Exploration in the subsurface is expensive and complex. By appropriately using analysis tools that synergistically exploit the information content of data and other information, better decisions can be made. info@aspermontmedia.com", } @InProceedings{Deschaine:2006:euro, author = "Larry M. Deschaine and Frank D. Francone and Janos D. Pinter and Melissa McKay and Jeff Warren and Seth Blanchard", title = "Finding and Identifying Objects Based on Noisy Data: A Global Optimization Approach - Part 1: Theoretical Approach and Applicability with Deployment Examples; and Part 2 UXO Finding and Discrimination. Results from Field Production: Translation of R\&D work into Field Production Tools UXOMF", booktitle = "EURO XXI", year = "2006", editor = "Tuula Kinnunen", address = "Reykjavik, Iceland", month = "2-6 " # jul, organisation = "Icelandic Operations Research Society and The Association of European OR Societies", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Euro2006_Deschaine_Finding_Subsurface_Objects_of_Interest_6-29-06_Final.pdf", URL = "https://www.euro-online.org/euro21/display.php?page=treate_abstract&frompage=edit_session_cluster&sessionid=661&paperid=3361", abstract = "Automated object recognition of images or signals is important, to identify items of interest, or anomalies (such as tumours in tissues). In such analyses it is often necessary to deal with noise in the values observed. Such noise complicates automated search procedures, and can affect the solution. In our example, the location, orientation and dimensions of an elliptical object are determined based on noisy data from electromagnetic surveys. We then use a global optimisation approach to find the best function fit. Our results demonstrate the success of this general approach.", notes = " http://www.euro2006.org/ ", } @TechReport{Deschaine:Discipulus_Comparison, author = "Larry M. Deschaine and Frank D. Francone", title = "Comparison of Discipulus Linear Genetic Programming Software with Support Vector Machines, Classification Trees, Neural Networks and Human Experts", year = "2002", type = "White Paper", address = "USA", keywords = "genetic algorithms, genetic programming, linear genetic programming, SVM, ANN, DT", URL = "http://www.rmltech.com/doclink/Comparison.White.Paper.pdf", abstract = "Discipulus is multiple-run, linear, genetic-programming software. Various versions have been available commercially since 1998 (see, www.aimlearning.com). Discipulus creates models directly from data, like neural networks or support vector machines. This white paper reports on the result of a multi-year study of the performance of Discipulus by Science Applications International Corp (SAIC) and RML Technologies, Inc. This study compared Discipulus to several other powerful modelling tools on a wide variety of industrial problems including regression and classification problems, CRM problems, time series problems, complex signal discrimination problems and others. We compared the modeling capability of Discipulus to the following competitive modelling technologies: Vapnick Statistical Learning, Neural Networks, Decision Trees, and Rule-Based Systems. In brief summary, the other modelling tools performed inconsistently sometimes they produced very good results and sometimes mediocre or even very poor results. None of these tools produced high quality results across the board. In contrast, Discipulus (at its default settings) always produced results that were the same as or better than the best results from other modelling techniques. The results described in this white paper have all been previously published in peer-reviewed scientific publications.", size = "16 pages", } @Article{Deschaine:2008:GPEM, author = "Larry M. Deschaine", title = "Tina Yu, David Davis, Cem Baydar, Rajkumar Roy (eds): Evolutionary Computation in Practice: Studies in Computational Intelligence, Springer, 2008, 322 pp, ISBN 978-3-540-75770-2", journal = "Genetic Programming and Evolvable Machines", year = "2008", volume = "9", number = "4", pages = "371--372", month = dec, note = "Book Review", keywords = "genetic algorithms, genetic programming, evolvable hardware", ISSN = "1389-2576", URL = "https://rdcu.be/dR8j5", DOI = "doi:10.1007/s10710-008-9068-8", size = "2 pages", notes = "review of \cite{TinaYu:2008:book}", } @Article{Deschaine:2011:JMMS, author = "Larry M. Deschaine and Peter Nordin and Janos D. Pinter", title = "A computational geometric/information theoretic method to invert physics-based {MEC} models attributes for {MEC} discrimination", journal = "Journal of Mathematical Machines and Systems", year = "2011", number = "2", pages = "50--61", keywords = "genetic algorithms, genetic programming, UXB, bomb disposal, munitions and explosives of concern, computational geometric method, physics model inversion technique.", URL = "http://www.immsp.kiev.ua/publications/eng/2011_2/", URL = "http://dspace.nbuv.gov.ua/handle/123456789/83512", URL = "http://dspace.nbuv.gov.ua/bitstream/handle/123456789/83512/05-Deschaine.pdf", size = "12 pages", abstract = "The presence of subsurface munitions and explosives of concern (MEC) is a significant issue worldwide. Discrimination of MEC from non-MEC items enables resources be focused on mitigating risk. Geophysical data is collected and physically-based models inverted with the intent that the inverted model parameters form the basis for MEC discrimination. However, MEC discrimination via model inversion has significant difficulties in noisy environments and with uncertain sensor location. Our computational geometric approach is demonstrated to produce an information-rich set of attributes useful for MEC discrimination including the inverted model information content along with valuable additional information not obtainable using the inversion approach.", notes = "Chalmers version 54 pages MEC = munitions and explosives of concern. compiling genetic programming system. UXO unexploded ordnance. F.E. Warren Air Force Base. Tabl.: 4. Figs.: 11. Refs.: 10 titles. UDC 519.6; 662.2", } @PhdThesis{Deschaine:thesis, author = "Larry M. Deschaine", title = "Decision support for complex planning challenges - Combining expert systems, engineering-oriented modeling, machine learning, information theory, and optimization technology", school = "Chalmers University of Technology", year = "2014", series = "Doktorsavhandlingar vid Chalmers tekniska hogskola. Ny serie, No 3661", address = "SE-412 96 Goteborg, Sweden", month = "27 " # feb, keywords = "genetic algorithms, genetic programming, Discipulus, Decision analysis, model blending, model mixing, data modelling engineering-oriented modelling, energy, environmental, optimisation, analytic hierarchy processes, machine learning, UXO, and MEC, land mines, unexploded bombs, removal", isbn13 = "978-91-7385-980-6", URL = "http://publications.lib.chalmers.se/record/print-record/index.xsql?pubid=193490", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/deschaine/Deschaine-Chalmers-PhD-Feb-2014.pdf", slides_url = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/deschaine/LarryDeschaine-Chalmers-2-27-2014-FinalForDefence-EM-shared.pdf", size = "233 pages", abstract = "This thesis develops an approach for addressing complex industrial planning challenges. The approach provides advice to select and blend modelling techniques that produce implementable optimal solutions. Industrial applications demonstrate its effectiveness. Industries have a need for advanced analytic techniques that encompass and reconcile the full range of information available regarding a planning problem. The goal is to craft the best possible decision in the time allotted. The pertinent information can include subject matter expertise, physical processes simulated in models, and observational data. The approach described in this paper assesses the decision challenge in two ways: first according to the available knowledge profile which includes the type, amount, and quality of information available of the problem; and second, according to the analysis and decision-support techniques most appropriate to each profile. We use model-mixing techniques such as machine learning and Kalman Filtering to combine analysis methods from various disciplines that include expert systems, engineering-oriented numerical and symbolic modeling, and machine learning in a graded, principled manner. A suite of global and local optimisation methods handle the range of optimization tasks arising in the demonstrated engineering projects. The methods used include the global and local nonlinear optimization algorithms. The thesis consists of four appended papers. Paper I uses subject matter expertise modelling to provide decision analysis regarding the environmental issue of mercury retirement. Paper II provides the framework for developing optimal remediation designs for subsurface groundwater monitoring and contamination mitigation using numerical models based on physical understanding. Paper III provides the results of a machine learning study using the Compiling Genetic Programming System (CGPS) on multiple industrial data sets. This study resulted in a breakthrough for identifying underground unexploded ordnance (UXO) and munitions and explosives of concern (MEC) from inert buried objects. Paper IV develops and uses the model mixing and optimisation approach to expound on understanding the MEC identification technique. It uses the methods in the first three papers along with additional technology. Each thesis paper includes complimentary citations and web links to selected publications that further demonstrate the value of this approach; either via industrial application or inclusion in US government guidance documents.", notes = "Winner the top prize in the research category competition at the American Academy of Environmental Engineers and Scientists (aaees.org). Heavily based on thesis. 'Physics-Based Management Optimization Technology for Supporting Environmental and Water Resource Management' HydroGeoLogic, Inc. http://www.aaees.org/e3scompetition-winners-2017gp-research.php PBMO In English. Number 193490 Supervisor: Peter Nordin", } @InProceedings{Desell:2025:GPTP, author = "Travis Desell", title = "{CANTS-GP}: A Nature-Inspired Metaheuristic for Graph Based Genetic Programs", booktitle = "Genetic Programming Theory and Practice XXII", year = "2025", editor = "Wolfgang Banzhaf and Bogdan Burlacu and Stephen Kelly and Alexander Lalejini and Fabricio {Olivetti de Franca}", address = "Michigan State University, USA", month = jun # " 5-7", keywords = "genetic algorithms, genetic programming", notes = "To be published after the workshop", } @InCollection{deshpande:2002:CJSGASBS, author = "Nishant Deshpande", title = "Comparison of a Job-Shop Scheduler using Genetic Algorithms with a {SLACK} Based Scheduler", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2002", year = "2002", editor = "John R. Koza", pages = "73--82", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms", URL = "http://www.genetic-programming.org/sp2002/Deshpande.pdf", notes = "part of \cite{koza:2002:gagp}", } @InProceedings{Deshpande:2014:ICIIS, author = "Parijat D. Deshpande and Ujjawal Gupta and B. P. Gautham and Danish Khan", booktitle = "9th International Conference on Industrial and Information Systems, ICIIS 2014", title = "Modeling the steel case carburizing quenching process using statistical and machine learning techniques", year = "2014", month = "15-17 " # dec, address = "Gwalior, India", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, surrogate model, simulation, ANN, Simulation of Carburising Process, Artificial Neural Networks", isbn13 = "978-1-4799-6500-7", DOI = "doi:10.1109/ICIINFS.2014.7036589", size = "6 pages", abstract = "Simulation of various manufacturing processes such as heat treatments is rapidly gaining importance in the industry for process optimisation, enhancing efficiency and improving product quality. Case carburisation followed by quenching is one such significant heat treatment process commonly used in the automotive industry. The equations to be solved for simulation of these processes are non-linear differential equations and require the use of computationally intensive numerical techniques e.g. 3D Finite Element Modelling. Using these models for solving optimisation or inverse problems, compounded by the fact that a large number of evaluations need to be carried out becomes computationally expensive. This necessitates a simpler, computationally inexpensive representation of the process, albeit being applicable to a limited range of process parameters and conditions. In this paper, we explore the use of proven statistical techniques such as Linear Regression and machine learning techniques such as Artificial Neural Networks and Genetic Programming to create computationally inexpensive surrogate models of the carburisation quenching processes to predict surface hardness and their results are presented.", notes = "Tata Res., Dev. & Design Centre, Tata Consultancy Services, Pune, India Also known as \cite{7036589}", } @InProceedings{conf/icmla/SilvaNDL13, author = "Anthony Mihirana {De Silva} and Farzad Noorian and Richard I. A. Davis and Philip H. W. Leong", title = "A Hybrid Feature Selection and Generation Algorithm for Electricity Load Prediction Using Grammatical Evolution", publisher = "IEEE", year = "2013", volume = "2", pages = "211--217", address = "Miami, FL, USA", keywords = "genetic algorithms, genetic programming, grammatical evolution", bibdate = "2014-04-21", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icmla/icmla2013-2.html#SilvaNDL13", booktitle = "ICMLA (2)", isbn13 = "978-0-7695-5144-9", URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6784147", DOI = "doi:10.1109/ICMLA.2013.125", size = "7 pages", abstract = "Accurate load prediction plays a major role in devising effective power system control strategies. Successful prediction systems often use machine learning (ML) methods. The success of ML methods, among other things, depends on a suitable choice of input features which are usually selected by domain-experts. In this paper, we propose a novel systematic way of generating and selecting better features for daily peak electricity load prediction using kernel methods. Grammatical evolution is used to evolve an initial population of well performing individuals, which are subsequently mapped to feature subsets derived from wavelets and technical indicator type formulae used in finance. It is shown that the generated features can improve results, while requiring no domain-specific knowledge. The proposed method is focused on feature generation and can be applied to a wide range of ML architectures and applications.", notes = "See also 2013 MSc http://hdl.handle.net/2123/10278", } @Article{DESIMONE:2019:IJRMMS, author = "Marcelo {De Simone} and Lourdes M. S. Souza and Deane Roehl", title = "Estimating {DEM} microparameters for uniaxial compression simulation with genetic programming", journal = "International Journal of Rock Mechanics and Mining Sciences", volume = "118", pages = "33--41", year = "2019", ISSN = "1365-1609", DOI = "doi:10.1016/j.ijrmms.2019.03.024", URL = "http://www.sciencedirect.com/science/article/pii/S1365160918307123", keywords = "genetic algorithms, genetic programming, Discrete element method, Calibration, Uniaxial compression simulation, Young's modulus, Compressive strength", abstract = "Among the steps in modeling with the Discrete Element Method (DEM), one of the most important is parameter calibration. The commonly used trial-and-error approach brings drawbacks such as user dependence and high computational cost. As an alternative, artificial intelligence methods, such as neural networks and genetic algorithms, have been adopted. In this work, a new methodology based on Genetic Programming (GP) is presented as an alternative to calibrate DEM microparameters. From DEM models, GP provides functions relating microparameters and macro-properties. Given target macro-properties, the microparameters are obtained by an optimization procedure. The calibration procedure was evaluated for a uniaxial compression simulation and showed good accuracy for data sets with a reduced number of models. In addition, GP is less user dependent and less computationally intensive than other calibration methods. The methodology proved to be effective for DEM calibration and can be extended to other multiscale models", } @InProceedings{desjarlais:1999:CSOUGAST, author = "Lisa M. Desjarlais and Mohammad-R. Akbarzadeh-T. and Craig W. Wright", title = "Control System Optimization Using Genetic Algorithms within the {SoftLab} Toolkit", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1774", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "real world applications, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-781.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-781.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Desnos:2021:DASIP, author = "Karol Desnos and Nicolas Sourbier and Pierre-Yves Raumer and Olivier Gesny and Maxime Pelcat", title = "Gegelati: Lightweight Artificial Intelligence through Generic and Evolvable Tangled Program Graphs", booktitle = "Workshop on Design and Architectures for Signal and Image Processing, DASIP", year = "2021", pages = "35--43", address = "Budapest, Hungary", month = jan # " 18-20", publisher = "Association for Computing Machinery", keywords = "genetic algorithms, genetic programming", isbn13 = "9781450389013", language = "en", oai = "oai:HAL:hal-03057652v2", identifier = "hal-03057652", URL = "https://arxiv.org/abs/2012.08296", URL = "https://hal.archives-ouvertes.fr/hal-03057652", URL = "https://hal.archives-ouvertes.fr/hal-03057652v2/document", URL = "https://hal.archives-ouvertes.fr/hal-03057652v2/file/dasip.pdf", DOI = "doi:10.1145/3441110.3441575", size = "9 pages", abstract = "Tangled Program Graph (TPG) is a reinforcement learning technique based on genetic programming concepts. On state-of-the-art learning environments, TPGs have been shown to offer comparable competence with Deep Neural Networks (DNNs), for a fraction of their computational and storage cost. This lightness of TPGs, both for training and inference, makes them an interesting model to implement Artificial Intelligences (AIs) on embedded systems with limited computational and storage resources. In this paper, we introduce the Gegelati library for TPGs. Besides introducing the general concepts and features of the library, two main contributions are detailed in the paper: 1/ The parallelization of the deterministic training process of TPGs, for supporting heterogeneous Multiprocessor Systems-on-Chips (MPSoCs). 2/ The support for customizable instruction sets and data types within the genetically evolved programs of the TPG model. The scalability of the parallel training process is demonstrated through experiments on architectures ranging from a high-end 24-core processor to a low-power heterogeneous MPSoC. The impact of customizable instructions on the outcome of a training process is demonstrated on a state-of-the-art reinforcement learning environment.", } @InProceedings{Desnos:2022:SiPS, author = "Karol Desnos and Thomas Bourgoin and Mickael Dardaillon and Nicolas Sourbier and Olivier Gesny and Maxime Pelcat", booktitle = "2022 IEEE Workshop on Signal Processing Systems (SiPS)", title = "Ultra-Fast Machine Learning Inference through C Code Generation for Tangled Program Graphs", year = "2022", address = "Rennes, France", month = "2-4 " # nov, keywords = "genetic algorithms, genetic programming, ANN, Training, Deep learning, Codes, Neural networks, Focusing, Reinforcement learning, machine learning, Tangled Program Graph, embedded systems", isbn13 = "978-1-6654-8525-8", ISSN = "2374-7390", DOI = "doi:10.1109/SiPS55645.2022.9919237", size = "6 pages", abstract = "Tangled Program Graph (TPG) is a Reinforcement Learning (RL) technique based on genetic programming concepts. On state-of-the-art learning environments, TPGs have been shown to offer comparable competence with Deep Neural Networks (DNNs), for a fraction of their computational and storage cost. we focus on accelerating the inference of pre-trained TPGs, through the generation of standalone C code. While the training process of TPGs, based on genetic evolution principles, requires the use of flexible data structures supporting random mutations, this flexibility is no longer needed when focusing on the inference process. Evaluation of the proposed approach on four computing platforms, including embedded CPUs, produces an acceleration of the TPG inference by a factor 50 compared to state-of-the-art implementations. The inference performance obtained within a complex RL environment range between hundreds of nano-seconds to micro-seconds, making this approach highly competitive for edge Artificial Intelligence (AI).", notes = "Also known as \cite{9919237} Univ Rennes, INSA Rennes, CNRS, IETR - UMR 6164,35000 Rennes, France", } @Article{deSousa:2004:GPEM, author = "Janaina S. {de Sousa} and Lalinka {de C. T. Gomes} and George B. Bezerra and Leandro N. {de Castro} and Fernando J. {Von Zuben}", title = "An Immune-Evolutionary Algorithm for Multiple Rearrangements of Gene Expression Data", journal = "Genetic Programming and Evolvable Machines", year = "2004", volume = "5", number = "2", pages = "157--179", month = jun, keywords = "genetic algorithms, genetic programming, gene expression, microarray, artificial immune systems, clustering, evolutionary algorithms", ISSN = "1389-2576", DOI = "doi:10.1023/B:GENP.0000023686.59617.57", abstract = "Microarray technologies are employed to simultaneously measure expression levels of thousands of genes. Data obtained from such experiments allow inference of individual gene functions, help to identify genes from specific tissues, to analyse the behaviour of gene expression levels under various environmental conditions and under different cell cycle stages, and to identify inappropriately transcribed genes and several genetic diseases, among many other applications. As thousands of genes may be involved in a microarray experiment, computational tools for organising and providing possible visualisations of the genes and their relationships are crucial to the understanding and analysis of the data. This work proposes an algorithm based on artificial immune systems for organizing gene expression data in order to simultaneously reveal multiple features in large amounts of data. A distinctive property of the proposed algorithm is the ability to provide a diversified set of high-quality rearrangements of the genes, opening up the possibility of identifying various co-regulated genes from representative graphical configurations of the expression levels. This is a very useful approach for biologists, because several coregulated genes may exist under different conditions.", notes = "Special Issue on Biological Applications of Genetic and Evolutionary Computation Guest Editor(s): Wolfgang Banzhaf , James Foster Department of Computer Engineering and Industrial Automation, State University of Campinas (Unicamp), CP. 6101, Campinas, SP, 13083-970, Brazil", } @PhdThesis{deSousaAdelinodaFonseca:thesis, author = "Inacio {de Sousa Adelino da Fonseca}", title = "Manutencao de sistemas de geracao de energia renovavel eolica atraves de Redes {IP}", title_en = "Maintenance of Renewable Wind Power Generation Systems Through IP Networks", school = "Faculdade de Engenharia, Universidade do Porto", year = "2010", address = "Portugal", month = jan, keywords = "genetic algorithms, genetic programming, linear genetic programming, geradores eolicos, manutencao, redes IP, series temporais, sistemas embebidos, embedded systems, IP networks, maintenance, time series, wind generators", URL = "https://hdl.handle.net/10216/57490", URL = "https://repositorio-aberto.up.pt/handle/10216/57490", URL = "https://repositorio-aberto.up.pt/bitstream/10216/57490/1/000140049.pdf", size = "268 pages", abstract = "This dissertation presents work developed in the area of Conditional Planned Maintenance of Systems for the Generation of Renewable Energies using IP networks, with emphasis in WindGeneration. A software/hardware model and an architecture are proposed, that allow the implementation of conditional planning maintenance solutions, using the remote measurement of a set of control variables. The chosen approach to planned maintenance, uses Time Series to monitor the evolution of condition variables such as temperature, pressure, viscosity and modulus of the frequency spectrum. The knowledge of these variables allows to follow the operating state of equipment and anticipate the following state. In this context, a contribution is proposed through a modified exponential smoothing algorithm, in order to make it adaptive. Its performance has been monitored by the Mackey-Glass Series and an especially developed Series, which is in accordance with the expectation for the evolution of the signals to predict. To determine, at a macroscopic level, the state of the operating condition of a wind generator, a SVM classifier was used. This classifier, after the training phase, determines the malfunction state according to the values of several measured variables. In this context, a brief analysis on the vibrations in induction electric motors was conducted, with the perspective of establishing an analogy with the electric generators of wind turbines. In terms of optimization, a methodology to assist the decision for the sequence of visits to be made by maintenance technicians on the various wind generators is proposed. This methodology takes into account the on-condition maintenance plan previously defined. The proposed optimization method uses genetic algorithms and a specific solution to solve the sequence of visits problem.The organization of the maintenance management of wind farms is structured, integrating a set of developed hardware and software modules, as well as a set of updates and new modules, developed for the SMIT software. The proposed structure including the new modules, allows implementations based on corrective maintenance, planned maintenance and on condition maintenance. A client/server maintenance management system was developed, using open-source software whenever possible. It includes the Linux operating system, the PostgreSQL database engine, and the development tools Octave, R, Apache and PHP. The SMIT client was programmed using Delphi and interacts with the user through the Windows platform. In terms of hardware, the followed methodology relies on the use of low cost components and devices, to create a data acquisition system over IP networks. The basic idea consists on distributing a master clock to the different field equipments, to ensure the synchronous acquisition at the different data collection points. The SNTP and PTP protocols were used to implement a set of control techniques in order to achieve clock synchronization. The basic structure of the system uses data collecting devices connected through a CAN network. One of the devices, which has CAN and Ethernet connectivity, coveys the acquired information and relays it to the SMIT server. Simultaneously, this master node controls the data acquisition sequence, as well as the clock synchronization with the SMIT server. The integration of the developed hardware and software modules implies the flow of data from the acquisition nodes to the server, which sends time references to the master device, including the reference clock signal. The SMIT server, using algorithms based on Time Series, analyzes the acquired data using the Octave or R platforms, to predict possible failures or dysfunctional states. Based on these predictions, the server can anticipate the generation of the respective alerts, with the emission of the corresponding Working Orders.", resumo = "Esta dissertacao desenvolve-se no ambito da Manutencao Planeada Condicionada de Sistemasde Geracao de Energias Renovaveis utilizando redes IP, com enfase na Geracao Eolica. E propostoum modelo desoftware/hardwaree uma arquitectura que permitem a implementacao de solucoesde planeamento de manutencao de condicao, atraves da medicao remota de varias variaveis decontrolo. ...", notes = "In Portuguese translate.google.co.uk FEUP oai:digitool.fe.up.pt:227412 Supervisor: Fernando Maciel Barbosa", } @InCollection{deSouza:2009:EC, title = "Genetic Programming and Boosting Technique to Improve Time Series Forecasting", author = "Luzia Vidal {de Souza} and Aurora T. R. Pozo and Anselmo C. Neto and Joel M. C. {da Rosa}", booktitle = "Evolutionary Computation", publisher = "InTech", year = "2009", editor = "Wellington Pinheiro dos Santos", chapter = "6", month = oct, keywords = "genetic algorithms, genetic programming", isbn13 = "978-953-307-008-7", URL = "http://www.intechopen.com/download/pdf/pdfs_id/10932", URL = "http://www.intechopen.com/articles/show/title/genetic-programming-and-boosting-technique-to-improve-time-series-forecasting", DOI = "DOI:10.5772/9617", notes = "http://www.intechopen.com/books/show/title/evolutionary-computation", bibsource = "OAI-PMH server at www.intechopen.com", language = "eng", oai = "oai:intechopen.com:10932", size = "18 pages", } @Article{deSouza:2010:AI, title = "Applying correlation to enhance boosting technique using genetic programming as base learner", author = "Luzia Vidal {de Souza} and Aurora Pozo and Joel Mauricio Correa {da Rosa} and Anselmo Chaves Neto", journal = "Applied Intelligence", year = "2010", number = "3", volume = "33", pages = "291--301", keywords = "genetic algorithms, genetic programming", publisher = "Springer Netherlands", ISSN = "0924-669X", DOI = "doi:10.1007/s10489-009-0166-y", size = "11 pages", abstract = "This paper explores the Genetic Programming and Boosting technique to obtain an ensemble of regressors and proposes a new formula for the updating of weights, as well as for the final hypothesis. Differently from studies found in the literature, in this paper we investigate the use of the correlation metric as an additional factor for the error metric. This new approach, called Boosting using Correlation Coefficients (BCC) has been empirically obtained after trying to improve the results of the other methods. To validate this method, we conducted two groups of experiments. In the first group, we explore the BCC for time series forecasting, in academic series and in a widespread Monte Carlo simulation covering the entire ARMA spectrum. The Genetic Programming (GP) is used as a base learner and the mean squared error (MSE) has been used to compare the accuracy of the proposed method against the results obtained by GP, GP using traditional boosting and the traditional statistical methodology (ARMA). The second group of experiments aims at evaluating the proposed method on multivariate regression problems by choosing Cart (Classification and Regression Tree) as the base learner.", affiliation = "University of Parana (UFPR), CP 19:081, CEP: 81531-970 Curitiba, Brazil", } @InProceedings{deSouza:2018:evocop, author = "Marcelo {de Souza} and Marcus Ritt", title = "Automatic Grammar-Based Design of Heuristic Algorithms for Unconstrained Binary Quadratic Programming", booktitle = "The 18th European Conference on Evolutionary Computation in Combinatorial Optimisation, EvoCOP 2018", year = "2018", editor = "Arnaud Liefooghe and Manuel Lopez-Ibanez", series = "LNCS", volume = "10782", publisher = "Springer", pages = "67--84", address = "Parma, Italy", month = "4-6 " # apr, organisation = "Species", keywords = "genetic algorithms, genetic programming, Grammatical evolution, Automatic algorithm configuration, Metaheuristics", isbn13 = "978-3-319-77448-0", DOI = "doi:10.1007/978-3-319-77449-7_5", abstract = "Automatic methods have been applied to find good heuristic algorithms to combinatorial optimization problems. These methods aim at reducing human efforts in the trial-and-error search for promising heuristic strategies. We propose a grammar-based approach to the automatic design of heuristics and apply it to binary quadratic programming. The grammar represents the search space of algorithms and parameter values. A solution is represented as a sequence of categorical choices, which encode the decisions taken in the grammar to generate a complete algorithm.We use an iterated F-race to evolve solutions and tune parameter values. Experiments show that our approach can find algorithms which perform better than or comparable to state-of-the-art methods, and can even find new best solutions for some instances of standard benchmark sets.", notes = "EvoCOP2018 held in conjunction with EuroGP'2018 EvoMusArt2018 and EvoApplications2018 http://www.evostar.org/2018/cfp_evocop.php", } @InProceedings{deSouza:2018:CEC, author = "Marcelo {de Souza} and Marcus Ritt", title = "An Automatically Designed Recombination Heuristic for the Test-Assignment Problem", booktitle = "2018 IEEE Congress on Evolutionary Computation (CEC)", year = "2018", editor = "Marley Vellasco", address = "Rio de Janeiro, Brazil", month = "8-13 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Evolution Strategies (ES), test-assignment, binary quadratic programming, automatic algorithm configuration, metaheuristics", URL = "http://human-competitive.org/sites/default/files/souza-ritt-paper.pdf", DOI = "doi:10.1109/CEC.2018.8477801", size = "8 pages", abstract = "A way of minimizing the opportunity of cheating in exams is to assign different tests to students. The likelihood of cheating then depends on the proximity of the students' desks, and the similarity of the tests. The test-assignment problem is to find an assignment of tests to desks that minimizes that total likelihood of cheating. The problem is a variant of a graph colouring problem and is NP-hard. We propose a new heuristic solution for this problem. Our approach differs from the usual way of designing heuristics in two ways. First, we reduce test-assignment to the more general unconstrained binary quadratic programming. Second, we search for a good heuristic using an automatic algorithm configuration tool that evolves heuristics in a space of algorithms built from known components for binary quadratic programming. The best hybrid heuristics found repeatedly recombine elements of a population of elite solutions and improve them by a tabu search. Computational tests suggest that the resulting algorithms are competitive with existing heuristics that have been designed manually.", notes = "Entered 2019 Humies. Also known as \cite{SouzaAndRitt2018} WCCI2018", } @PhdThesis{Dessi:tesi, author = "Antonello Dessi", title = "Automatic subroutine discovery in Genetic Programming (Text In Italian)", title_italian = "Scoperta automatica di subroutine in Programmazione Genetica", school = "University of Pisa", year = "1998", type = "tesi di laurea", address = "Italy", keywords = "genetic algorithms, genetic programming, subroutines, ma, arl, aao", URL = "http://web.mclink.it/MC2657/tesi.html", URL = "http://web.mclink.it/MC2657/file/tesi.zip", size = "292 pages", abstract = "May 2018 Note this was translated from Italian by Google Translate..... The developments of Artificial Intelligence have shown that in dealing with complex problems the key to success is the ability to break them down into simpler subproblems, creating a hierarchical and modular structure that allows to reach a level of difficulty that can be faced. Whether you consider Genetic Programming as an effective possibility to reach a completely automatic programming in the future, whether we consider it only an alternative method for searching for algorithmic solutions for a wide range of problems, the importance of the management of the subroutines is therefore fundamental. The Genetic Programming in its original version fails to implement a real process of hierarchical decomposition of the problems, but through the introduction of the automatic discovery of the subroutine this goal is achieved. The primary purpose of the work carried out is therefore the analysis of how this mechanism can be efficiently implemented, of what the actual performances are observed, and which are the possibilities for improvement. The first chapter presents the Genetic Programming framing it within the Evolutionary Algorithms , illustrating the numerous variants present in the literature, the current research directions and the possible applications of the model. The second chapter illustrates the theoretical aspects of Genetic Programming, with particular attention to the theorems that try to model their behaviour (Scheme and Price Theorem), highlighting their intrinsic limits. The third chapter analyses the different possibilities concerning the introduction of subroutines in Genetic Programming, to arrive at identifying the ARL model as more promising . The various heuristics for the automatic discovery of the subroutines are then reviewed, and then proposed a new one ( salience ). Finally the ARL algorithm is reviewed, criticizing some aspects and proposing some variants, such as the use of the mutation for the diffusion of the subroutines ( diffusion by mutation)) and an alternative method for the dynamic era ( Maxfit ). The fourth chapter illustrates the extensive experimental analysis carried out, divided into three phases: the first concerns the direct comparison between the selection heuristics of the subroutines, the second evaluates the effectiveness of the automatic addition of the arguments to the new subroutines, the third analyses the problem when it is appropriate to insert new subroutines in the program population ( dynamic era). In this way the fundamental aspects of the ARL algorithm are analysed and the efficacy of the proposals advanced in the thesis: at the end of the chapter are reported the relative conclusions and the possible directions of future research. The appendix contains further experimental data and the source of the program developed specifically for the needs of the thesis.", notes = "In Italian Includes source code in C. The C code has elements of Module Acquisition (MA) and Adaptive Representation with Learning (ARL) relatori Antonina Starita and Antonella Giani", } @InProceedings{dessi:1999:AAASDGP, author = "Antonello Dessi and Antonella Giani and Antonina Starita", title = "An Analysis of Automatic Subroutine Discovery in Genetic Programming", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "996--1001", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-432.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-432.ps", size = "6 pages", abstract = "This paper analyses Rosca's ARL as a general framework for automatic subroutine discovery. We review and compare a number of heuristics for code selection, and experimentally test their effectiveness in the ARL framework. We also propose and analyse a new heuristic, the Saliency, and two extensions to ARL: diffusion of the new subroutines through mutation and the MaxFit technique to adaptively change the length of an epoch. In spite of the effectiveness of the proposed extensions, the main result is that any attempt to improve the selection criterion seems not able to produce better results than a simple near-random heuristic.", notes = "6-mux, symbolic regression, sort (loop, swap, memory array) GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{DeStefano:2001:VF, author = "Claudio {De Stefano} and A. {Della Cioppa} and A. Marcelli and F. Matarazzo", title = "Grouping Character Shapes by Means of Genetic Programming", booktitle = "Visual Form 2001", year = "2001", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/3-540-45129-3_46", DOI = "doi:10.1007/3-540-45129-3_46", } @Article{DeStefano:2002:PRL, author = "C. {De Stefano} and A. Della Cioppa and A. Marcelli", title = "Character preclassification based on genetic programming", journal = "Pattern Recognition Letters", year = "2002", volume = "23", pages = "1439--1448", number = "12", abstract = "This paper presents a learning system that uses genetic programming as a tool for automatically inferring the set of classification rules to be used during a pre-classification stage by a hierarchical handwritten character recognition system. Starting from a structural description of the character shape, the aim of the learning system is that of producing a set of classification rules able to capture the similarities among those shapes, independently of whether they represent characters belonging to the same class or to different ones. In particular, the paper illustrates the structure of the classification rules, the grammar used to generate them and the genetic operators devised to manipulate the set of rules, as well as the fitness function used to drive the inference process. The experimental results obtained by using a set of 10,000 digits extracted from the NIST database show that the proposed pre classification is efficient and accurate, because it provides at most 6 classes for more than 87% of the samples, and the error rate almost equals the intrinsic confusion found in the data set.", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6V15-45J91MV-4/2/3e5c2ac0c51428d0f7ea9fc0142f6790", keywords = "genetic algorithms, genetic programming, Character recognition, Preclassification", DOI = "doi:10.1016/S0167-8655(02)00104-6", } @InProceedings{DeStefano:2011:GECCOcomp, author = "Claudio {De Stefano} and Gianluigi Folino and Francesco Fontanella and Alessandra {Scotto di Freca}", title = "Using {Bayesian} networks for selecting classifiers in {GP} ensembles", booktitle = "GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0690-4", keywords = "genetic algorithms, genetic programming, Genetics based machine learning: Poster", pages = "173--174", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001858.2001955", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "In this paper we present a novel approach for combining GP-based ensembles by means of a Bayesian Network. The proposed system is able to effectively learn decision tree ensembles using two different strategies: decision trees ensembles are learnt by means of boosted GP algorithm; the responses of the learned ensembles are combined using a Bayesian network, which also implements a selection strategy that reduces the size of the built ensembles.", notes = "Also known as \cite{2001955} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @InProceedings{DeStefano:2011:MCS, author = "C. {De Stefano} and F. Fontanella and G. Folino and A. Scotto {di Freca}", title = "A {Bayesian} Approach for Combining Ensembles of {GP} Classifiers", booktitle = "Multiple Classifier Systems", year = "2011", editor = "Carlo Sansone and Josef Kittler and Fabio Roli", volume = "6713", series = "LNCS", pages = "26--35", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-21557-5", DOI = "doi:10.1007/978-3-642-21557-5_5", size = "10 pages", abstract = "Recently, ensemble techniques have also attracted the attention of Genetic Programming (GP) researchers. The goal is to further improve GP classification performances. Among the ensemble techniques, also bagging and boosting have been taken into account. These techniques improve classification accuracy by combining the responses of different classifiers by using a majority vote rule. However, it is really hard to ensure that classifiers in the ensemble be appropriately diverse, so as to avoid correlated errors. Our approach tries to cope with this problem, designing a framework for effectively combine GP-based ensemble by means of a Bayesian Network. The proposed system uses two different approaches. The first one applies a boosting technique to a GP-based classification algorithm in order to generate an effective decision trees ensemble. The second module uses a Bayesian network for combining the responses provided by such ensemble and select the most appropriate decision trees. The Bayesian network is learned by means of a specifically devised Evolutionary algorithm. Preliminary experimental results confirmed the effectiveness of the proposed approach.", } @InProceedings{conf/ppsn/StefanoFFF12, author = "Claudio {De Stefano} and Gianluigi Folino and Francesco Fontanella and Alessandra {Scotto di Freca}", title = "Pruning {GP}-Based Classifier Ensembles by {Bayesian} Networks", booktitle = "Parallel Problem Solving from Nature, PPSN XII (part 1)", year = "2012", editor = "Carlos A. {Coello Coello} and Vincenzo Cutello and Kalyanmoy Deb and Stephanie Forrest and Giuseppe Nicosia and Mario Pavone", volume = "7491", series = "Lecture Notes in Computer Science", pages = "236--245", address = "Taormina, Italy", month = sep # " 1-5", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-32936-4", DOI = "doi:10.1007/978-3-642-32937-1_24", size = "10 pages", abstract = "Classifier ensemble techniques are effectively used to combine the responses provided by a set of classifiers. Classifier ensembles improve the performance of single classifier systems, even if a large number of classifiers is often required. This implies large memory requirements and slow speeds of classification, making their use critical in some applications. This problem can be reduced by selecting a fraction of the classifiers from the original ensemble. In this work, it is presented an ensemble-based framework that copes with large datasets, however selecting a small number of classifiers composing the ensemble. The framework is based on two modules: an ensemble-based Genetic Programming (GP) system, which produces a high performing ensemble of decision tree classifiers, and a Bayesian Network (BN) approach to perform classifier selection. The proposed system exploits the advantages provided by both techniques and allows to strongly reduce the number of classifiers in the ensemble. Experimental results compare the system with well-known techniques both in the field of GP and BN and show the effectiveness of the devised approach. In addition, a comparison with a pareto optimal strategy of pruning has been performed.", bibsource = "DBLP, http://dblp.uni-trier.de", affiliation = "Universita di Cassino e del Lazio Meridionale, Italy", } @Article{DeStefano:2014:IS, author = "C. {De Stefano} and G. Folino and F. Fontanella and A. {Scotto di Freca}", title = "Using {Bayesian} networks for selecting classifiers in {GP} ensembles", journal = "Information Sciences", volume = "258", pages = "200--216", year = "2014", ISSN = "0020-0255", DOI = "doi:10.1016/j.ins.2013.09.049", URL = "http://www.sciencedirect.com/science/article/pii/S0020025513007184", keywords = "genetic algorithms, genetic programming, Classifier ensemble, Bayesian Networks, Evolutionary computation", } @InProceedings{devaney:1995:mpimake, author = "Judith E. Devaney", title = "Converting pvmmake to mpimake under LAM, and MPI and Parallel Genetic Programming", booktitle = "MPI Developers Conference", year = "1995", editor = "Andrew Lumsdaine", address = "University of Notre Dame", month = "22-23 " # jun, keywords = "genetic algorithms, genetic programming", URL = "http://www.cse.nd.edu/mpidc95/proceedings/papers/postscript/devaney.ps", URL = "http://citeseer.ist.psu.edu/devaney95experience.html", abstract = "This looks at the issues which arose in porting the pvmmake utility from pvm to mpi. Pvmmake is a pvm application which allows a user to send files, execute commands, and receive results from a single machine on any machine in the virtual machine. It's actions are controlled by the contents of an agenda file. It's most common use is to enable management of the development of a parallel program in a heterogeneous environment. A utility with the same features, mpimake, was coded up to run under LAM. Genetic programming is an algorithm which evolves a program to solve your input problem. The implementation under MPI requires the transfer of data structures such as lists and trees. The match between the requirements of this algorithm and the datatype feature in mpi will be discussed.", notes = "Data from http://www.cse.nd.edu/mpidc95/proceedings/abstracts/html/devaney/ 4 Nov 1997", } @InProceedings{devaney:2001:gpe, author = "Judith Devaney and John Hagedorn and Olivier Nicolas and Gagan Garg and Aurelien Samson and Martial Michel", title = "A Genetic Programming Ecosystem", booktitle = "Proceedings 15th International Parallel and Distributed Processing Symposium, Abstracts and CDROM", year = "2001", pages = "1323--1330", address = "Los Alamitos, CA, USA", howpublished = "Abstracts and CD-ROM", month = "23-27 " # apr, publisher = "IEEE Computer Society", keywords = "genetic algorithms, genetic programming", ISBN = "0-7695-0990-8", URL = "http://math.nist.gov/mcsd/savg/papers/bio.pdf", URL = "http://math.nist.gov/mcsd/savg/papers/bio.pp.gz", note = "IPDPS2001:WS", abstract = "Algorithms are needed in every aspect of parallel computing. Genetic Programming is an evolutionary technique for automating the design of algorithms through iterative steps of mutation and crossover operations on an initial population of randomly generated computer programs. This paper describes a novel parallel genetic programming (GP) system inspired by the symbiogenesis model of evolution, wherein new organisms are generated through the absorption of different life-forms in addition to the usual mutation and crossover operations. Different organisms are expressed in this GP system through multiple program representations. Two program representations considered in this paper are the procedural representation (PR) and the tree representation (TR). Populations of these representations evolve separately. Individuals in each population migrate to the other and participate in evolution via representation change algorithms. Parallelism is achieved through use of the AutoMap/AutoLink MPI library. The differences in the locality properties of the representations serve as a source of new ideas for creating the final algorithm.", } @InProceedings{devaney:2002:gecco:lbp, title = "The Role of Genetic Programming in Describing the Microscopic Structure of Hydrating Plaster", author = "Judith E. Devaney and John G. Hagedorn", booktitle = "Late Breaking Papers at the Genetic and Evolutionary Computation Conference ({GECCO-2002})", editor = "Erick Cant{\'u}-Paz", year = "2002", month = jul, pages = "91--98", address = "New York, NY", publisher = "AAAI", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025", keywords = "genetic algorithms, genetic programming", notes = "Late Breaking Papers, {GECCO-2002}. A joint meeting of the eleventh International Conference on Genetic Algorithms ({ICGA-2002}) and the seventh Annual Genetic Programming Conference ({GP-2002}) part of cantu-paz:2002:GECCO:lbp pruning anti-bloat, fitness based on correlation. Maple algebraic simplification. sensitivity, true-positives. C5. {"}clear and concise decision algorithm that accurately predicts{"} p96", } @InProceedings{conf/dis/DevaneyH02, author = "Judith Ellen Devaney and John G. Hagedorn", title = "Discovery in Hydrating Plaster Using Machine Learning Methods", booktitle = "5th International Conference on Discovery Science, DS 2002", year = "2002", editor = "Steffen Lange and Ken Satoh and Carl H. Smith", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "2534", pages = "47--58", address = "L{\"u}beck, Germany", month = nov # " 24-26", keywords = "genetic algorithms, genetic programming", isbn13 = "3-540-00188-3", URL = "http://math.nist.gov/mcsd/savg/papers/discov2002.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.138.2341", DOI = "doi:10.1007/3-540-36182-0_7", bibsource = "DBLP, http://dblp.uni-trier.de", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", contributor = "CiteSeerX", language = "en", oai = "oai:CiteSeerXPSU:10.1.1.138.2341", abstract = "We apply multiple machine learning methods to obtain concise rules that are highly predictive of scientifically meaningful classes in hydrating plaster over multiple time periods. We use three dimensional data obtained through X-ray microtomography at greater than one micron resolution per voxel at five times in the hydration process: powder, after 4 hours, 7 hours, 15.5 hours, and after 6 days of hydration. Using statistics based on locality, we create vectors containing eight attributes for subsets of size 1000 of the data and use the autoclass unsupervised classification system to label the attribute vectors into three separate classes. Following this, we use the C5 decision tree software to separate the three classes into two parts: class 0 and 1, and class 0 and 2. We use our locally developed procedural genetic programming system, GPP, to create simple rules for these. The resulting collection of simple rules are tested on a separate 1000 subset of the plaster datasets that had been labeled with their autoclass predictions. The rules were found to have both high sensitivity and high positive predictive value. The classes accurately identify important structural components in the hydrating plaster. Moreover, the rules identify the center of the local distribution as a critical factor in separating the classes.", } @Article{DEVARRIYA:2020:ESA, author = "Divyaansh Devarriya and Cairo Gulati and Vidhi Mansharamani and Aditi Sakalle and Arpit Bhardwaj", title = "Unbalanced breast cancer data classification using novel fitness functions in genetic programming", journal = "Expert Systems with Applications", year = "2020", volume = "140", pages = "112866", keywords = "genetic algorithms, genetic programming, Breast cancer, Unbalanced data, Fitness function", ISSN = "0957-4174", URL = "http://www.sciencedirect.com/science/article/pii/S0957417419305767", DOI = "doi:10.1016/j.eswa.2019.112866", size = "11 pages", abstract = "Breast Cancer is a common disease and to prevent it, the disease must be identified at earlier stages. Available breast cancer datasets are unbalanced in nature, i.e. there are more instances of benign (non-cancerous) cases then malignant (cancerous) ones. Therefore, it is a challenging task for most machine learning (ML) models to classify between benign and malignant cases properly, even though they have high accuracy. Accuracy is not a good metric to assess the results of ML models on breast cancer dataset because of biased results. To address this issue, we use Genetic Programming (GP) and propose two fitness functions. First one is F2 score which focuses on learning more about the minority class, which contains more relevant information, the second one is a novel fitness function known as Distance score (D score) which learns about both the classes by giving them equal importance and being unbiased. The GP framework in which we implemented D score is named as D-score GP (DGP) and the framework implemented with F2 score is named as F2GP. The proposed F2GP achieved a maximum accuracy of 99.63percent, 99.51percent and 100percent for 60-40, 70-30 partition schemes and 10 fold cross validation scheme respectively and DGP achieves a maximum accuracy of 99.63percent, 98.5percent and 100percent in 60-40, 70-30 partition schemes and 10 fold cross validation scheme respectively. The proposed models also achieves a recall of 100percent for all the test cases. This shows that using a new fitness function for unbalanced data classification improves the performance of a classifier", } @InProceedings{eurogp06:DevertBredecheSchoenauer, author = "Alexandre Devert and Nicolas Bredeche and Marc Schoenauer", title = "Blindbuilder : A new encoding to evolve Lego-like structures", editor = "Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art", booktitle = "Proceedings of the 9th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3905", year = "2006", address = "Budapest, Hungary", month = "10 - 12 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming, context free grammar", ISBN = "3-540-33143-3", pages = "61--72", URL = "http://hal.ccsd.cnrs.fr/docs/00/05/44/74/PDF/article.pdf", URL = "http://hal.inria.fr/inria-00000995/en/", DOI = "doi:10.1007/11729976_6", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "This paper introduces a new representation for assemblies of small Lego-like elements: structures are indirectly encoded as construction plans. This representation shows some interesting properties such as hierarchy, modularity and easy constructibility checking by definition. Together with this representation, efficient GP operators are introduced that allow efficient and fast evolution, as witnessed by the results on two construction problems that demonstrate that the proposed approach is able to achieve both compactness and reusability of evolved components.", annote = "Alexandre Devert ", bibsource = "OAI-PMH server at hal.ccsd.cnrs.fr", contributor = "Alexandre Devert ", coverage = "genetic programming", identifier = "inria-00000995 (version 1)", oai = "oai:hal.ccsd.cnrs.fr:inria-00000995_v1", subject = "Computer Science/Artificial Intelligence; Computer Science/Learning", type = "ARTCOLLOQUE", notes = "Part of \cite{collet:2006:GP} EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006", } @InProceedings{Devert:2006:ASPGP, title = "Evolution design of buildable objects with blind builder: an empirical study", author = "Alexandre Devert and Nicolas Bredeche and Marc Schoenauer", booktitle = "Proceedings of the Third Asian-Pacific workshop on Genetic Programming", year = "2006", editor = "The Long Pham and Hai Khoi Le and Xuan Hoai Nguyen", pages = "98--109", ISSN = "18590209", address = "Military Technical Academy, Hanoi, VietNam", keywords = "genetic algorithms, genetic programming", URL = "http://gpbib.cs.ucl.ac.uk/aspgp06/devert-bredeche-schoenauer-ASPGP2006.pdf", size = "12 pages", abstract = "In a previous paper, we presented BlindBuilder, a new representation formalism for Evolutionary Design based on construction plans. As for other indirect encoding approaches in the literature, BlindBuilder makes it possible to easily represent possible solutions but makes it difficult to perform structural optimisation. While satisfying results are provided, it becomes more and more difficult to build larger structures during the course of evolution. This is due to the high disruptive rate of variation operators as construction plans grow. In this paper, we provide an analysis of such a problem and propose new construction operators to avoid this. Then, we perform extensive experiments so as to identify the key parameters and discuss the advantages, limitations and possible perspectives of the indirect encoding approach.", notes = "broken march 2020 http://www.aspgp.org", } @Misc{deVisscher:2011:arXiv, title = "Automatic anomaly detection in high energy collider data", author = "Simon {de Visscher} and Michel Herquet", year = "2011", month = apr # "~13", keywords = "genetic algorithms, genetic programming, high energy physics, phenomenology, experiment, data analysis", abstract = "We address the problem of automatic anomaly detection in high energy collider data. Our approach is based on the random generation of analytic expressions for kinematical variables, which can then be evolved following a genetic programming procedure to enhance their discriminating power. We apply this approach to three concrete scenarios to demonstrate its possible usefulness, both as a detailed check of reference Monte-Carlo simulations and as a model independent tool for the detection of New Physics signatures.", bibsource = "OAI-PMH server at export.arxiv.org", oai = "oai:arXiv.org:1104.2404", URL = "http://arxiv.org/abs/1104.2404", notes = "Comment: 5 pages, 2 figures", } @Article{devra:2018:QIP, author = "Amit Devra and Prithviraj Prabhu and Harpreet Singh and Arvind and Kavita Dorai", title = "Efficient experimental design of high-fidelity three-qubit quantum gates via genetic programming", journal = "Quantum Information Processing", year = "2018", volume = "17", number = "3", pages = "Article 67", month = mar, keywords = "genetic algorithms, genetic programming", ISSN = "1570-0755", URL = "https://ui.adsabs.harvard.edu/abs/2018QuIP...17...67D/abstract", URL = "http://link.springer.com/article/10.1007/s11128-018-1835-8", DOI = "doi:10.1007/s11128-018-1835-8", abstract = "We have designed efficient quantum circuits for the three-qubit Toffoli (controlled-controlled-NOT) and the Fredkin (controlled-SWAP) gate, optimized via genetic programming methods. The gates thus obtained were experimentally implemented on a three-qubit NMR quantum information processor, with a high fidelity. Toffoli and Fredkin gates in conjunction with the single-qubit Hadamard gates form a universal gate set for quantum computing and are an essential component of several quantum algorithms. Genetic algorithms are stochastic search algorithms based on the logic of natural selection and biological genetics and have been widely used for quantum information processing applications. We devised a new selection mechanism within the genetic algorithm framework to select individuals from a population. We call this mechanism the Luck-Choose mechanism and were able to achieve faster convergence to a solution using this mechanism, as compared to existing selection mechanisms. The optimization was performed under the constraint that the experimentally implemented pulses are of short duration and can be implemented with high fidelity. We demonstrate the advantage of our pulse sequences by comparing our results with existing experimental schemes and other numerical optimization methods.", } @Article{devries:2014:plosone, author = "Natalie Jane {de Vries} and Jamie Carlson and Pablo Moscato", title = "A Data-Driven Approach to Reverse Engineering Customer Engagement Models: Towards Functional Constructs", journal = "PLOS ONE", year = "2014", volume = "9", number = "7", month = jul # " 18", keywords = "genetic algorithms, genetic programming", publisher = "Public Library of Science", ISSN = "1932-6203", DOI = "doi:10.1371/journal.pone.0102768", abstract = "Online consumer behavior in general and online customer engagement with brands in particular, has become a major focus of research activity fueled by the exponential increase of interactive functions of the internet and social media platforms and applications. Current research in this area is mostly hypothesis-driven and much debate about the concept of Customer Engagement and its related constructs remains existent in the literature. In this paper, we aim to propose a novel methodology for reverse engineering a consumer behavior model for online customer engagement, based on a computational and data-driven perspective. This methodology could be generalized and prove useful for future research in the fields of consumer behaviors using questionnaire data or studies investigating other types of human behaviors. The method we propose contains five main stages; symbolic regression analysis, graph building, community detection, evaluation of results and finally, investigation of directed cycles and common feedback loops. The communities of questionnaire items that emerge from our community detection method form possible functional constructs inferred from data rather than assumed from literature and theory. Our results show consistent partitioning of questionnaire items into such functional constructs suggesting the method proposed here could be adopted as a new data-driven way of human behaviour modeling.", } @Article{devries:2015:plosone, author = "Natalie Jane {de Vries} and Rodrigo Reis and Pablo Moscato", title = "Clustering Consumers Based on Trust, Confidence and Giving Behaviour: Data-Driven Model Building for Charitable Involvement in the Australian Not-For-Profit Sector", journal = "PLOS ONE", year = "2015", volume = "10", number = "4", month = apr # " 7", keywords = "genetic algorithms, genetic programming", publisher = "Public Library of Science", ISSN = "1932-6203", DOI = "doi:10.1371/journal.pone.0122133", size = "28 pages", abstract = "Organisations in the Not-for-Profit and charity sector face increasing competition to win time, money and efforts from a common donor base. Consequently, these organisations need to be more proactive than ever. The increased level of communications between individuals and organisations today, heightens the need for investigating the drivers of charitable giving and understanding the various consumer groups, or donor segments, within a population. It is contended that trust is the cornerstone of the not-for-profit sectors survival, making it an inevitable topic for research in this context. It has become imperative for charities and not-for-profit organisations to adopt for-profits research, marketing and targeting strategies. This study provides the not-for-profit sector with an easily-interpretable segmentation method based on a novel unsupervised clustering technique (MST-kNN) followed by a feature saliency method (the CM1 score). A sample of 1562 respondents from a survey conducted by the Australian Charities and Not-for-profits Commission is analysed to reveal donor segments. Each clusters most salient features are identified using the CM1 score. Furthermore, symbolic regression modeling is employed to find cluster-specific models to predict low or high involvement in clusters. The MST-kNN method found seven clusters. Based on their salient features they were labeled as: the non-institutionalist charities supporters, the resource allocation critics, the information-seeking financial sceptics, the non-questioning charity supporters, the non-trusting sceptics, the charity management believers and the institutionalist charity believers. Each cluster exhibits their own characteristics as well as different drivers of involvement. The method in this study provides the not-for-profit sector with a guideline for clustering, segmenting, understanding and potentially targeting their donor base better. If charities and not-for-profit organisations adopt these strategies, they will be more successful in todays competitive environment.", notes = "Centre for Bioinformatics, Biomarker Discovery & Information-Based Medicine, The University of Newcastle, Callaghan, New South Wales, Australia", } @InCollection{deVries2019:chpt2, author = "Natalie Jane {de Vries} and Pablo Moscato", title = "Consumer Behaviour and Marketing Fundamentals for Business Data Analytics", booktitle = "Business and Consumer Analytics: New Ideas", publisher = "Springer International Publishing", year = "2019", editor = "Pablo Moscato and Natalie Jane {de Vries}", chapter = "2", pages = "119--162", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-030-06222-4", DOI = "doi:10.1007/978-3-030-06222-4_2", abstract = "This chapter provides the reader with a brief introduction to the basics of marketing. The intention is to help a non-marketer to understand what is needed in business and consumer analytics from a marketing perspective and continue bridging the gap between data scientists and business thinkers. A brief introduction to the discipline of marketing is presented followed by several topics that are crucial for understanding marketing and computational applications within the field. A background of market segmentationMarket segmentationand targeting strategies is followed by the description of typical bases for segmenting a market. Further, consumer behaviour literature and theory is discussed as well as the current trends for businesses regarding consumer behaviour.", } @InCollection{deVries2019:chpt3, author = "Natalie Jane {de Vries} and {Lukasz P. Olech} and Pablo Moscato", title = "Introducing Clustering with a Focus in Marketing and Consumer Analysis", booktitle = "Business and Consumer Analytics: New Ideas", publisher = "Springer International Publishing", year = "2019", editor = "Pablo Moscato and Natalie Jane {de Vries}", chapter = "3", pages = "165--212", keywords = "genetic algorithms, genetic programming, Cluster analysis, Internal measures, External measures, k-Means, KNN", isbn13 = "978-3-030-06222-4", DOI = "doi:10.1007/978-3-030-06222-4_3", abstract = "Clustering has become an extremely popular methodology for consumer analysis with many business applications. Mainly, when a consumer market needs to be segmented, clustering methodologies are some of the most common ways of doing so nowadays. Clustering, however, is a hugely heterogeneous field in itself with advances and explanations coming from many different disciplines. Clustering has been discussed in debates almost as heated as those about politics or religions, yet still, researchers and professionals agree on the methodology's usefulness in data Analytics. This chapter provides the novice data scientist with a brief introduction and review of the field with links to previous large surveys and reviews for recommended further reading. The clustering contributions in this book focus largely on partitional clustering; hence, this is the type of clustering that will feature more prominently in this chapter. Besides sparking the interest of business and marketing researchers and professionals into this ever evolving methodological field, we aim at inspiring dedicated computer scientists and data analysts to continue to explore the wide application domains coming from business and consumer Analytics business and consumeranalytics in which clustering and grouping are making great strides.", } @InCollection{deVries2019:chpt5, author = "Natalie Jane {de Vries} and Jamie Carlson and Pablo Moscato", title = "Clustering Consumers and Cluster-Specific Behavioural Models", booktitle = "Business and Consumer Analytics: New Ideas", publisher = "Springer International Publishing", year = "2019", editor = "Pablo Moscato and Natalie Jane {de Vries}", chapter = "5", pages = "235--267", keywords = "genetic algorithms, genetic programming, Brand, Customer engagement, Engagement, Loyalty behaviour, Online customer engagement, Customer engagement prediction, Segmentation methodologies, Symbolic regression analysis", isbn13 = "978-3-030-06222-4", DOI = "doi:10.1007/978-3-030-06222-4_5", abstract = "Social media has almost become ubiquitous in everyday communications and interactions between customers and Brand. A novel clustering algorithm, that has shown high scalability in previous applications, is applied to analyse and segment an online consumer behaviour dataset. It is based on the computation of a Minimum-Spanning-Tree and a k-Nearest Neighbour graph (MST-kNN-kNN). Cluster-specific consumer behaviours relating to CustomerengagementEngagementcustomer engagement are predicted using Symbolic regression analysissymbolic regression analysis which, in a commercial setting, would provide the basis for personalized marketing strategies. Five major clusters were found in the dataset of 371 respondents who answered questions from theoretical marketing constructs related to online consumer behaviours. They are labelled as follows: Brand Rationalists, Passive Socializers, Immersers, Hedonic Sharers and Active participatorActive Participators. For each of these clusters, a linear model of Customerengagementcustomer engagement was predicted using Symbolic regression analysis symbolic regression analysis. These models inform possible personalized marketing strategies after proper segmentation of the customers based on their online consumer behaviour, rather than simple demographic characteristics.", } @InProceedings{devylder:2003:gecco:workshop, title = "Learning of Manipulation Behaviour by Demonstration using Genetic Programming", author = "Bart {De Vylder}", pages = "268--271", booktitle = "{GECCO 2003}: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference", editor = "Alwyn M. Barry", year = "2003", month = "11 " # jul, publisher = "AAAI", address = "Chigaco", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025", notes = "Bird-of-a-feather Workshops, GECCO-2003. A joint meeting of the twelth International Conference on Genetic Algorithms (ICGA-2003) and the eigth Annual Genetic Programming Conference (GP-2003) part of barry:2003:GECCO:workshop", keywords = "genetic algorithms, genetic programming", } @InProceedings{dewell:1999:gnc, author = "Larry D. Dewell and P. K. Menon", title = "Low-Thrust Orbit Transfer Optimization Using Genetic Search", booktitle = "AIAA Guidance, Navigation and Control Conference", year = "1999", address = "Portland, OR, USA", publisher = "American Institute of Aeronautics and Astronautics", keywords = "genetic algorithms, genetic programming", URL = "http://www.optisyn.com/research/papers/papers/1999/gnc_99.pdf", URL = "http://citeseer.ist.psu.edu/513854.html", size = "7 pages", abstract = "Most techniques for solving dynamic optimisation problems involve a series of gradient computations and one-dimensional searches at some point in the optimization process. A large class of problems, however, does not possess the necessary smoothness properties that such algorithms require for good convergence. Even when smoothness conditions are met, poor initial guesses at the solution often result in convergence to local minima or even a lack of convergence altogether. For such cases, genetic search techniques can be used to obtain a solution. In this paper, trajectory optimisation using genetic search methods is illustrated by solving a complex, nonlinear problem involving low-thrust orbit transfer.", } @PhdThesis{david-dewhurst-phd-dissertation, author = "David Rushing Dewhurst", title = "Essays on modeling and analysis of dynamic sociotechnical systems", school = "The University of Vermont", year = "2020", address = "USA", month = may, keywords = "genetic algorithms, genetic programming, Specializing in Complex Systems and Data Science, ABM, shocklet transform, game theory", URL = "https://cdanfort.w3.uvm.edu/research/david-dewhurst-phd-dissertation.pdf", size = "249 pages", abstract = "A sociotechnical system is a collection of humans and algorithms that interact under the partial supervision of a decentralized controller. These systems often display intricate dynamics and can be characterised by their unique emergent behavior. In this work, we describe, analyze, and model aspects of three distinct classes of sociotechnical systems: financial markets, social media platforms, and elections. Though our work is diverse in subject matter content, it is unified though the study of evolution-and adaptation-driven change in social systems and the development of methods used to infer this change. We first analyse evolutionary financial market microstructure dynamics in the context of an agent-based model (ABM). The ABM matching engine implements a frequent batch auction, a recently-developed type of price-discovery mechanism.We subject simple agents to evolutionary pressure using a variety of selection mechanisms, demonstrating that quantile-based selection mechanisms are associated with lower market-wide volatility. We then evolve deep neural networks in the ABM and demonstrate that elite individuals are profitable in back testing on real foreign exchange data, even though their fitness had never been evaluated on any real financial data during evolution. We then turn to the extraction of multi-timescale functional signals from large panels of time series generated by sociotechnical systems. We introduce the discrete shocklet transform (DST) and associated similarity search algorithm, the shocklet transform and ranking (STAR) algorithm, to accomplish this task. We empirically demonstrate the STAR algorithm invariance to quantitative functional parameterisation and provide use case examples. The STAR algorithm compares favorably with Twitter anomaly detection algorithm on a feature extraction task. We close by using STAR to automatically construct a narrative time-line of societally-significant events using a panel of Twitter word usage time series. Finally, we model strategic interactions between the foreign intelligence service (Red team) of a country that is attempting to interfere with an election occurring in another country, and the domestic intelligence service of the country in which the election is taking place (Blue team). We derive subgame-perfect Nash equilibrium strategies for both Red and Blue and demonstrate the emergence of arms race interference dynamics when either player has all-or-nothing attitudes about the result of the interference episode. We then confront our model with data from the 2016 USA presidential election contest, in which Russian military intelligence interfered. We demonstrate that our model captures the qualitative dynamics of this interference for most of the time under study.", notes = "Is this GP? Defense Date: February 14th, 2020 Dissertation Examination Committee: Peter Sheridan Dodds, Ph.D., Advisor: Christopher Danforth, Ph.D. Safwan Wshah, Ph.D. Nicholas Allgaier, Ph.D. Chair: Cynthia J. Forehand, Ph.D., Dean of Graduate College", } @InProceedings{dey:2024:CEC, author = "Prianka Dey and Abhinandan Khan and Goutam Saha and Rajat Kumar Pal", title = "{MiRNN:} A Mutual Information Augmented Recurrent Neural Network Framework for Reconstruction of Gene Regulatory Networks", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Proteins, Recurrent neural networks, Accuracy, Escherichia coli, Evolutionary computation, Genetics, Bayes methods, gene regulatory network, mutual information, recurrent neural network, reverse engineering", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10612057", abstract = "Genes act as the blueprint for regulating all activities of a living system. Genes produce proteins, which in turn, sit on the promoter regions of other genes to regulate their activity. Thus, a gene regulatory network is formed. This network is critical in disclosing the various mysteries in the operations of living systems. Often it is very difficult to find these networks in the Wet Lab. As a result, various computational approaches have been used to reconstruct these networks from gene ex-pression data. The techniques primarily used for this purpose include Bayesian networks, Boolean networks, recurrent neural networks, S-systems, and mutual information based methods. The contemporary literature indicates that these techniques often fail to reliably reconstruct real-life networks. In this paper, we have proposed a new technique based on a modified recurrent neural network strategy that is augmented by mutual information. The proposed methodology has been implemented on an 8-gene network of Escherichia coli and a lO-gene network, which have been extensively used by other researchers. The experimental results indicate that the proposed technique achieves satisfactory results when compared to other such techniques developed by contemporary researchers.", notes = "also known as \cite{10612057} WCCI 2024", } @Article{Dey:2017:JAC, author = "Swati Dey and Partha Dey and Shubhabrata Datta", title = "Design of novel age-hardenable aluminium alloy using evolutionary computation", journal = "Journal of Alloys and Compounds", volume = "704", pages = "373--381", year = "2017", ISSN = "0925-8388", DOI = "doi:10.1016/j.jallcom.2017.02.027", URL = "http://www.sciencedirect.com/science/article/pii/S0925838817304425", abstract = "This work considers the experimental data of tensile properties as a function of composition and processing of the three series of age-hardenable aluminium alloys, i.e. 2XXX, 6XXX and 7XXX, for designing new age hardenable alloy. Computational approach of designing better alloys with desired properties is employed with a target of breaking the barrier of class or series of age hardenable Al alloys. The alloy designed with the help of two evolutionary computation tools, viz. genetic programming and multi-objective genetic algorithm, having better combination of properties, i.e. strength and ductility, is experimentally developed. The designed Al-Zn-Cu-Mg alloy with complex aging characteristics and encouraging tensile properties seem to have the potential for further study.", keywords = "genetic algorithms, genetic programming, Age hardenable aluminium alloys, Mechanical properties, Multi-objective optimization, Experimental trial", } @InCollection{kinnear:DHaeseleer, title = "Effects of Locality in Individual and Population Evolution", author = "Patrik D'haeseleer and Jason Bluming", booktitle = "Advances in Genetic Programming", publisher = "MIT Press", editor = "Kenneth E. {Kinnear, Jr.}", year = "1994", chapter = "8", keywords = "genetic algorithms, genetic programming", pages = "177--198", URL = "http://www.amazon.co.uk/Advances-Genetic-Programming-Complex-Adaptive/dp/0262111888", URL = "http://cognet.mit.edu/sites/default/files/books/9780262277181/pdfs/9780262277181_chap8.pdf", DOI = "doi:10.7551/mitpress/1108.003.0013", size = "22 pages", abstract = "This chapter describes how introducing locality into the Genetic Programming Paradigm (GP) influences the evolutionary behaviour of both the population as a whole and its individual members. We have adopted an Artificial Life (ALife) viewpoint -- focusing more on the population as a whole rather than on individual performance-- for our observations of an illustrative system that uses this approach. We introduce locality into the GP in both the reproductive and evaluation phases. Our implementation of locality uses isolation by distance - on a linear population with wraparound - as opposed to the more commonly used fixed-sized demes.", notes = "Part of \cite{kinnear:book}", } @InProceedings{Dhaeseleer:1994:cpcGP, author = "Patrik D'haeseleer", title = "Context preserving crossover in genetic programming", booktitle = "Proceedings of the 1994 IEEE World Congress on Computational Intelligence", year = "1994", volume = "1", pages = "256--261", address = "Orlando, Florida, USA", month = "27-29 " # jun, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, S-expression tree, context-preserving crossover, crossover operators, matching coordinates, node coordinate scheme, subtrees,optimisation, path planning, programming, trees (mathematics)", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/ftp.io.com/papers/WCCI94_CPC.ps.Z", URL = "http://www.cs.unm.edu/~patrik/WCCI94_CPC.mac.ps", broken = "http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=00350006", DOI = "doi:10.1109/ICEC.1994.350006", size = "6 pages", abstract = "This paper introduces two new crossover operators for Genetic Programming (GP). Contrary to the regular GP crossover, the operators presented attempt to preserve the context in which subtrees appeared in the parent trees. A simple coordinate scheme for nodes in an S-expression tree is proposed, and crossovers are only allowed between nodes with exactly or partially matching coordinates.", notes = "Two new crossover operators for GP (Strong Context preserving (SCPC) and Weak context preserving(WCPC)). These attempt to preserve the context of swapped subtrees. SCPC best used 50percent with Koza crossover. 100percent WCPC not performing as well. Obstacle avoiding (simulated) robot, 11-multiplexor, food foraging. ", } @InCollection{Dharma:1997:amctsa, author = "Prisdha Dharma", title = "Automatic Model Construction for Time Series Analysis via Genetic Algorithm", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1997", publisher = "Stanford Bookstore", year = "1997", editor = "John R. Koza", pages = "28--35", address = "Stanford, California, 94305-3079 USA", month = "17 " # mar, keywords = "genetic algorithms, genetic programming", ISBN = "0-18-205981-2", notes = "part of \cite{koza:1997:GAGPs}", } @InCollection{dhingra:2002:ESIDAO, author = "Philip Dhingra", title = "Evolution of Simple Intelligence Distribution in Artificial Organisms", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2002", year = "2002", editor = "John R. Koza", pages = "83--92", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", URL = "http://www.genetic-programming.org/sp2002/Dhingra.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.141.1093", language = "en", oai = "oai:CiteSeerXPSU:10.1.1.141.1093", abstract = "This paper uses Genetic Programming to evolve groups of ants to push a box from the center of a room to a wall. Group sizes and ant capabilities are varied to observe the speed, effectiveness, and nature of the intelligence that evolves for each ant. As expected, larger groups compensate for lesser intelligent ants by having more of them to solve the task. The ant-boxpushing problem then becomes a coverage problem whereby solutions are found by adequately covering the space in which the task is to be completed.", notes = "part of \cite{koza:2002:gagp} GP to evolve group of ants to push a box from the centre of a room to a wall. Trade off between intelligence of individual ants and number of ants in the group. LISP", } @Article{Di2009612, author = "Wenhui Di and Bo Sun and Lixin Xu", title = "Dynamic Simulations of Nonlinear Multi-Domain Systems Based on Genetic Programming and Bond Graphs", journal = "Tsinghua Science \& Technology", volume = "14", number = "5", pages = "612--616", year = "2009", ISSN = "1007-0214", DOI = "doi:10.1016/S1007-0214(09)70125-7", URL = "http://www.sciencedirect.com/science/article/B7RKT-4XBR35X-B/2/f79f7984ea487a2629d93cc7ae6e2651", keywords = "genetic algorithms, genetic programming, bond graph (BG), evolutionary computation, system simulation", abstract = "A dynamic simulation method for non-linear systems based on genetic programming (GP) and bond graphs (BG) was developed to improve the design of nonlinear multi-domain energy conversion systems. The genetic operators enable the embryo bond graph to evolve towards the target graph according to the fitness function. Better simulation requires analysis of the optimization of the eigenvalue and the filter circuit evolution. The open topological design and space search ability of this method not only gives a more optimized convergence for the operation, but also reduces the generation time for the new circuit graph for the design of nonlinear multi-domain systems.", } @InProceedings{Di-Capua:2023:ISCAS, author = "G. {Di Capua} and N. Oliva and F. Milano and C. Bourelly and F. Porpora and A. Maffucci and N. Femia", booktitle = "2023 IEEE International Symposium on Circuits and Systems (ISCAS)", title = "A Behavioral Model for Lithium Batteries based on Genetic Programming", year = "2023", abstract = "This paper proposes a novel approach to derive analytical behavioural models of Lithium batteries, based on a Genetic Programming Algorithm (GPA). This approach is used to analytically relate the battery voltage to its State-of-Charge (SoC) and Charge/discharge rate (C-rate), during a battery discharge phase. The GPA generates optimal candidate analytical models, where the preferred one is selected by evaluating suitable metrics and imposing a sound trade-off between simplicity and accuracy. The GPA proposed model can be seen as a generalisation of the equivalent circuit models currently used for batteries, with the possible advantage to overcome some inherent limits, like the extensive laboratory characterisation for model parameters evaluation. The presented case-study refers to a Lithium Titanate Oxide battery, with SoC values going from 5 to 95percent, at C-rate values between 0.25C and 4.0C.", keywords = "genetic algorithms, genetic programming, Measurement, Analytical models, Voltage, Lithium batteries, Behavioural sciences, Batteries, Modelling, Multi-Objective Optimisation", DOI = "doi:10.1109/ISCAS46773.2023.10181456", ISSN = "2158-1525", month = may, notes = "Also known as \cite{10181456} see \cite{Di-Capua:2024:ACC}", } @Article{Di-Capua:2024:ACCESS, author = "Giulia {Di Capua} and Francesco Porpora and Filippo Milano and Nunzio Oliva and Antonio Maffucci", title = "Behavioral Models for Lithium Batteries Based on Genetic Programming", journal = "IEEE Access", year = "2024", volume = "12", pages = "108275--108290", keywords = "genetic algorithms, genetic programming, Batteries, Integrated circuit modelling, Voltage, Analytical models, Mathematical models, Discharges (electric), Computational modelling, Lithium-ion batteries, Pareto optimisation, Li-ion batteries, behavioural modelling, multi-objective optimisation", ISSN = "2169-3536", DOI = "doi:10.1109/ACCESS.2024.3434716", abstract = "This paper proposes a novel methodology based on the Genetic Programming (GP) to derive behavioural models describing the transient evolution of the terminal voltage of a battery. These models analytically relate the battery voltage to its state of charge, charge/discharge rate, and temperature. Compared to the popular equivalent circuit-based models, one of the main advantages is the significant reduction of the effort to produce the experimental dataset required to identify the model parameters. The GP generates a family of optimal 'candidate' analytical models, each associated with suitable metrics that quantify performance indicators like simplicity and accuracy. The methodology is applied to describe the transient discharge phase of a Lithium Iron Phosphate (LiFePO4 or LFP) battery under realistic operating conditions, considering the state-of-charge between 20percent and 80percent, discharge rates comprised between 0.25C and 1C, and temperature ranging from 5degreeC to 35degreeC. The GP provides different solutions that can be chosen by imposing the desired trade-off between accuracy and simplicity. Two models are selected and validated against experimental results. The chosen models guarantee a quite low level of the relative root mean square error (maximum 0.31percent and 0.22percent, respectively) over the range of analysis.", notes = "Also known as \cite{10613806} See also \cite{Di-Capua:2023:ISCAS}", } @InProceedings{Dias:2017:SLAAI, author = "M. U. B. Dias and D. D. N. {De Silva} and S. Fernando", title = "On Optimizing Deep Convolutional Neural Networks by Evolutionary Computing", booktitle = "International Conference on Artificial Intelligence (SLAAI 2017)", year = "2017", pages = "29--37", address = "University of Moratuwa, Sri Lanka", month = "31 " # oct, keywords = "genetic algorithms, genetic programming, deep Networks, Optimization, Evolutionary Computing, Speeding Up Rate of Convergent, Normalization", URL = "https://arxiv.org/abs/1808.01766", URL = "http://slaai.lk/proc/2017/s1705.pdf", size = "9 pages", abstract = "Optimization for deep networks is currently a very active area of research. As neural networks become deeper, the ability in manually optimizing the network becomes harder. Mini-batch normalization, identification of effective respective fields, momentum updates, introduction of residual blocks, learning rate adoption, etc. have been proposed to speed up the rate of convergent in manual training process while keeping the higher accuracy level. However, the problem of finding optimal topological structure for a given problem is becoming a challenging task need to be addressed immediately. Few researchers have attempted to optimize the network structure using evolutionary computing approaches. Among them, few have successfully evolved networks with reinforcement learning and long-short-term memory. A very few has applied evolutionary programming into deep convolution neural networks. These attempts are mainly evolved the network structure and then subsequently optimized the hyper-parameters of the network. However, a mechanism to evolve the deep network structure under the techniques currently being practised in manual process is still absent. Incorporation of such techniques into chromosomes level of evolutionary computing, certainly can take us to better topological deep structures. The paper concludes by identifying the gap between evolutionary based deep neural networks and deep neural networks. Further, it proposes some insights for optimizing deep neural networks using evolutionary computing techniques.", notes = "Department of Computational Mathematics, University of Moratuwa, Sri Lanka", } @InProceedings{1570271, author = "Josefa Diaz and J. Ignacio Hidalgo and Francisco Fernandez and Oscar Garnica and Sonia Lopez", title = "Improving {SMT} performance: an application of genetic algorithms to configure resizable caches", booktitle = "GECCO '09: Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference", year = "2009", isbn13 = "978-1-60558-505-5", pages = "2029--2034", month = "8-12 " # jul, address = "Montreal, Quebec, Canada", DOI = "doi:10.1145/1570256.1570271", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic improvement, EHW, adaptive caches, caches memories, optimization, reconfigurable caches, simultaneous multithreading", abstract = "Simultaneous Multithreading (SMT) is a technology aimed at improving the throughput of the processor core by applying Instruction Level Parallelism (ILP) and Thread Level Parallelism (TLP). Nevertheless a good control strategy is required when resources are shared among different threads, so that throughput is optimized. We study the application of evolutionary algorithms to improve the allocation of configurations on the cache hierarchy over a Simultaneous Multithreading (SMT) processor. In this way, resizeable caches have demonstrated their efficiency by adapting their configuration according to workload settings, at runtime. More-over, some methodologies and a number of techniques, such as dynamic resource allocation, have previously been developed to optimize the cache hit behaviour, trying to improve global SMT performance. In this paper we propose the use of a Genetic Algorithm (GA) to optimize dynamically reconfigurable cache designs. Given that different workloads feature different characteristics and needs, we apply a Genetic Algorithm (GA) for cache designing, in order to obtain a better dynamic configuration that increases the number of instructions per cycle (IPC). The obtained results show the feasibility of the approach and the potential of GAs for SMT optimization.", notes = "See \cite{Diaz-Alvarez:2016:JSS} also \cite{Diaz:2010:ICEC} GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009). ACM Order Number 910092.", } @InProceedings{Diaz:2010:ICEC, author = "Josefa Diaz and Francisco {Fernandez de Vega} and J. Ignacio Hidalgo and Oscar Garnica", title = "Parisian Approach Reducing Computational Effort to Improve {SMT} Performance by setting Resizable Caches", booktitle = "Proceedings of the International Conference on Evolutionary Computation (ICEC 2010)", year = "2010", editor = "Joaquim Filipe and Janusz Kacprzyk", pages = "275--280", address = "Valencia, Spain", month = "24-26 " # oct, organisation = "INSTICC, AAAI, WfMC", publisher = "SciTePress", keywords = "genetic algorithms, genetic improvement, EHW, Simultaneous multithreading, parallel multi-core architecture L1 memory cache, Optimisation, Parisian approach", isbn13 = "978-989-8425-31-7", URL = "https://www.scitepress.org/PublishedPapers/2010/31137/", URL = "http://www.robinbye.com/files/publications/ICEC_2010.pdf", DOI = "doi:10.5220/0003113702750280", size = "6 pages", abstract = "Evolutionary Algorithm are techniques widely used in the resolution of complex problems. On the other hand, Simultaneous Multithreading improves the throughput of the processor core taking advantage of Instruction Level Parallelism and Thread Level Parallelism. In this environment adaptation the cache configuration, at runtime according to workloads settings will be improved the processor performance. This improvement is achieved by using resizeable caches. In a previous work, we proposed a Genetic Algorithm to find the better cache configurations according to the needs and characteristics of the workloads. However the computational cost needed for the evaluation process is very high. In this paper we propose the use of the Parisian Evolution Approach to improve dynamically reconfigurable cache designs, and reduce the computational cost associated. We study the behaviour of a set of benchmarks, taking into account their needs over cache memory hierarchy in each phase of execution, in order to adapt the cache configuration and to increase the number of instructions per cycle. Experimental results show a large saving in computing time and some improvement on the instructions per cycle achieved in previous approaches.", notes = "See \cite{Diaz-Alvarez:2016:JSS}. p280 'the Parisian Evolution paradigm has been used to improve the performance of a SMT processor by selecting the optimal configuration of re-sizable cache memories, while reducing associated computational cost.' Follow up of \cite{1570271} GA. Parisian Approach and GA compared, PA much faster, neither produced anything much SPEC2000 suite of benchmarks: gzip vpr cc1 mcf crafty twolf swim applu galgel art equake lucas University of Extremadura, Merida, Spain cites \cite{collet:2000:IFSpGP}.", } @Article{Diaz-Alvarez:2016:SC, author = "Josefa {Diaz Alvarez} and J. Manuel Colmenar and Jose L. Risco-Martin and Juan Lanchares and Oscar Garnica", title = "Optimizing {L1} cache for embedded systems through grammatical evolution", journal = "Soft Computing", year = "2016", volume = "20", number = "6", pages = "2451--2465", keywords = "genetic algorithms, genetic programming, grammatical evolution, EHW, JECO, ARM9, Energy model, Cacti organization, Trimaran, SimpleScalar, Dinero IV cache simulator, Grammar for cache configuration description, LRU, FIFO, RANDOM, Mediabench suite", ISSN = "1432-7643", DOI = "doi:10.1007/s00500-015-1653-1", size = "15 pages", abstract = "Nowadays, embedded systems are provided with cache memories that are large enough to influence in both performance and energy consumption as never occurred before in this kind of systems. In addition, the cache memory system has been identified as a component that improves those metrics by adapting its configuration according to the memory access patterns of the applications being run. However, given that cache memories have many parameters which may be set to a high number of different values, designers are faced with a wide and time-consuming exploration space. In this paper, we propose an optimization framework based on Grammatical Evolution (GE) which is able to efficiently find the best cache configurations for a given set of benchmark applications. This metaheuristic allows an important reduction of the optimization runtime obtaining good results in a low number of generations. Besides, this reduction is also increased due to the efficient storage of evaluated caches. Moreover, we selected GE because the plasticity of the grammar eases the creation of phenotypes that form the call to the cache simulator required for the evaluation of the different configurations. Experimental results for the Mediabench suite show that our proposal is able to find cache configurations that obtain an average improvement of 62percent versus a real world baseline configuration", notes = "See \cite{Diaz-Alvarez:2016:JSS}. prior work \cite{1570271} \cite{Diaz:2010:ICEC} 'average improvement of 62 percent' 'we have improved the cache behavior by 97 percent in energy consumption and, at the same time, by 75percent in execution time considering a commercial base-line cache.'", } @Article{Diaz-Alvarez:2016:JSS, author = "Josefa {Diaz Alvarez} and Jose L. Risco-Martin and J. Manuel Colmenar", title = "Multi-objective optimization of energy consumption and execution time in a single level cache memory for embedded systems", journal = "Journal of Systems and Software", year = "2016", volume = "111", pages = "200--212", month = jan, keywords = "genetic algorithms, EHW, SBSE, Cache memory, Energy, Performance", ISSN = "0164-1212", DOI = "doi:10.1016/j.jss.2015.10.012", size = "13 pages", abstract = "Current embedded systems are specifically designed to run multimedia applications. These applications have a big impact on both performance and energy consumption. Both metrics can be optimized selecting the best cache configuration for a target set of applications. Multi-objective optimization may help to minimize both conflicting metrics in an independent manner. In this work, we propose an optimization method that based on Multi-Objective Evolutionary Algorithms, is able to find the best cache configuration for a given set of applications. To evaluate the goodness of candidate solutions, the execution of the optimization algorithm is combined with a static profiling methodology using several well-known simulation tools. Results show that our optimization framework is able to obtain an optimized cache for Mediabench applications. Compared to a baseline cache memory, our design method reaches an average improvement of 64.43 and 91.69percent in execution time and energy consumption, respectively.", notes = "Applications: epic unepic gsmdec gsmenc pegwitdec pegwitenc cjpeg djpeg mpegdec mpegenc rawcaudio rawdaudio prior work \cite{1570271} \cite{Diaz:2010:ICEC}", } @Article{DIAZALVAREZ2017, author = "Josefa {Diaz Alvarez} and Jose L. Risco-Martin and J. Manuel Colmenar", title = "Evolutionary design of the memory subsystem", journal = "Applied Soft Computing", year = "2018", volume = "62", pages = "1088--1101", month = jan, keywords = "genetic algorithms, genetic programming, Grammatical evolution, NSGA-II, SBSE, Hardware design optimization, Memory subsystem design", ISSN = "1568-4946", URL = "http://www.sciencedirect.com/science/article/pii/S1568494617305860", DOI = "doi:10.1016/j.asoc.2017.09.047", abstract = "The memory hierarchy has a high impact on the performance and power consumption in the system. Moreover, current embedded systems, included in mobile devices, are specifically designed to run multimedia applications, which are memory intensive. This increases the pressure on the memory subsystem and affects the performance and energy consumption. In this regard, the thermal problems, performance degradation and high energy consumption, can cause irreversible damage to the devices. We address the optimization of the whole memory subsystem with three approaches integrated as a single methodology. Firstly, the thermal impact of register file is analysed and optimized. Secondly, the cache memory is addressed by optimizing cache configuration according to running applications and improving both performance and power consumption. Finally, we simplify the design and evaluation process of general-purpose and customized dynamic memory manager, in the main memory. To this aim, we apply different evolutionary algorithms in combination with memory simulators and profiling tools. This way, we are able to evaluate the quality of each candidate solution and take advantage of the exploration of solutions given by the optimization algorithm. We also provide an experimental experience where our proposal is assessed using well-known benchmark applications.", } @Article{Diaz-Alvarez:2018:ComSIS, author = "Josefa {Diaz Alvarez} and Franciso {Chavez de la O} and Pedro A. Castillo and Juan Angel Garcia and Francisco J. Rodriguez and Francisco {Fernandez de Vega}", title = "A Fuzzy Rule-Based System to Predict Energy Consumption of Genetic Programming Algorithms", journal = "Computer Science and Information Systems", year = "2018", volume = "15", number = "3", pages = "235--254", month = oct, keywords = "genetic algorithms, genetic programming, Green computing, energy-aware computing, performance measurements, evolutionary algorithms", ISSN = "1820-0214", URL = "http://www.comsis.org/pdf.php?id=6908", DOI = "doi:10.2298/CSIS180110026A", size = "20 pages", abstract = "In recent years, the energy-awareness has become one of the most interesting areas in our environmentally conscious society. Algorithm designers have been part of this, particularly when dealing with networked devices and, mainly, when hand held ones are involved. Although studies in this area has increased, not many of them have focused on Evolutionary Algorithms. To the best of our knowledge, few attempts have been performed before for modelling their energy consumption considering different execution devices. In this work, we propose a fuzzy rule-based system to predict energy consumption of a kind of Evolutionary Algorithm, Genetic Programming, given the device in which it will be executed, its main parameters, and a measurement of the difficulty of the problem addressed. Experimental results performed show that the proposed model can predict energy consumption with very low error values.", notes = "Francisco {Chavez de la O} The international journal published by ComSIS Consortium http://www.comsis.org/", } @Article{Diaz-Alvarez:2022:MC, author = "Josefa Diaz-Alvarez and Pedro A Castillo and Francisco {Fernandez de Vega} and Francisco Chavez and Jorge Alvarado", title = "Population size influence on the energy consumption of genetic programming", journal = "Measurement and Control", year = "2022", volume = "55", number = "1-2", pages = "102--115", email = "fcofdez@unex.es", keywords = "genetic algorithms, genetic programming, energy consumption, evolutionary algorithms, energy-aware computing, performance measurements", ISSN = "0020-2940", URL = "https://journals.sagepub.com/doi/pdf/10.1177/00202940211064471", DOI = "doi:10.1177/00202940211064471", size = "14 pages", abstract = "Evolutionary Algorithms (EAs) are routinely applied to solve a large set of optimization problems. Traditionally, their performance in solving those problems is analyzed using the fitness quality and computing time, and the effect of evolutionary operators on both metrics is routinely used to compare different versions of EAs. Nevertheless, scientists face nowadays the challenge of considering the energy efficiency in addition to computational time, which requires studying the energy consumption of algorithms. This paper discusses the interest of introducing power consumption as a new metric to analyze the performance of standard genetic programming (GP). Two well-studied benchmark problems are addressed on three different computing platforms, and two different approaches to measure the power consumption have been tested.Analyzing the population size, the results demonstrates its influence on the energy consumed: a non-linear relationship was found between size and energy required to complete an experiment. This analysis was extended to the cache memory and results show an exponential growth in the number of cache misses as the population size increases, which affects the energy consumed. This study shows that not only computing time or solution quality must be analyzed, but also the energy required to find a solution. Summarizing, this paper shows that when GP is applied, specific considerations on how to select parameter values must be taken into account if the goal is to obtain solutions while searching for energy efficiency. Although the study has been performed using GP, we foresee that it could be similarly extended to EAs.", } @InProceedings{DiCapua:2016:IECON, author = "Giulia {Di Capua} and Nicola Femia and Mario Migliaro and Kateryna Stoyka", booktitle = "IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society", title = "Genetic programming approach for identification of ferrite inductors power loss models", year = "2016", pages = "1112--1117", abstract = "This paper discusses the identification of power loss models of ferrite core power inductors for high-power-density Switch Mode Power Supplies. A novel method, based on Genetic Programming (GP) approach, is herein proposed. It is aimed at discovering new loss models, starting from experimental measurements and taking into account all the operating conditions, such as switching frequency, inductor current ripple and volt-microsecond product, average and rms inductor current values, even for possible inductor operation in partial saturation. The behavioural models obtained by means of the GP approach are in good agreement with experimental measurements.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IECON.2016.7793000", month = oct, notes = "Also known as \cite{7793000}", } @InProceedings{DiCapua:2018:SMACD, author = "Giulia {Di Capua} and Nicola Femia and Kateryna Stoyka", booktitle = "2018 15th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD)", title = "Loss Behavioral Modeling for Ferrite Inductors", year = "2018", abstract = "This paper presents a new behavioural model of AC power loss for Ferrite Power Inductors (FPIs) used in Switch-Mode Power Supply (SMPS) applications, including the effects of saturation. The model has been identified by means of a genetic programming algorithm and a multi-objective optimization technique, given a large sets of power loss experimental measurements. The proposed AC power loss model uses the voltage and switching frequency imposed to the inductor as input variables, while the DC inductor current is used as a parameter expressing the impact of saturation. Experimental results prove the reliability of the power loss predictions for FPIs, also by correctly accounting for the impact of saturation.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SMACD.2018.8434859", month = jul, notes = "Also known as \cite{8434859}", } @Article{Di-Capua:2020:IE, author = "Giulia {Di Capua} and Nicola Femia and Kateryna Stoyka and Gennaro {Di Mambro} and Antonio Maffucci and Salvatore Ventre and Fabio Villone", journal = "IEEE Transactions on Industrial Electronics", title = "Mutual Inductance Behavioral Modeling for Wireless Power Transfer System Coils", year = "2020", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/TIE.2019.2962432", ISSN = "1557-9948", abstract = "This paper derives low-complexity behavioural analytical models of the mutual inductance between the coupling coils of Wireless Power Transfer Systems (WPTSs), as functions of their reciprocal position. These models are extremely useful in the characterization and design optimization of WPTSs. Multi-Objective Genetic Programming (MOGP) algorithm is adopted to generate models ensuring an optimal trade-off between accuracy and complexity. The training and validation data sets needed for the generation of the models are here obtained by performing numerical full-3D electromagnetic simulations. The resulting behavioral models allow accurate and fast evaluation of the WPTS coils mutual inductance, over a wide range of misalignment conditions, enabling easier system analysis and optimization.", notes = "DIEM, University of Salerno, 19028 Fisciano, Campania Italy Also known as \cite{8994192}", } @Article{DiCapua:2021:Sustainability, author = "Giulia {Di Capua} and Antonio Maffucci and Kateryna Stoyka and Gennaro {Di Mambro} and Salvatore Ventre and Vincenzo Cirimele and Fabio Freschi and Fabio Villone and Nicola Femia", title = "Analysis of Dynamic Wireless Power Transfer Systems Based on Behavioral Modeling of Mutual Inductance", journal = "Sustainability", year = "2021", volume = "13", number = "5", month = mar, keywords = "genetic algorithms, genetic programming, behavioural modeling, inductive coupling, mutual inductance, wireless power transfer", ISSN = "2071-1050", URL = "https://www.mdpi.com/2071-1050/13/5/2556", DOI = "doi:10.3390/su13052556", size = "15 pages", abstract = "This paper proposes a system-level approach suitable to analyse the performance of a dynamic Wireless Power Transfer System (WPTS) for electric vehicles, accounting for the uncertainty in the vehicle trajectory. The key-point of the approach is the use of an analytical behavioural model that relates mutual inductance between the coil pair to their relative positions along the actual vehicle trajectory. The behavioural model is derived from a limited training data set of simulations, by using a multi-objective genetic programming algorithm, and is validated against experimental data, taken from a real dynamic WPTS. This approach avoids the massive use of computationally expensive 3D finite element simulations, that would be required if this analysis were performed by means of look-up tables. This analytical model is here embedded into a system-level circuital model of the entire WPTS, thus allowing a fast and accurate analysis of the sensitivity of the performance as the actual vehicle trajectory deviates from the nominal one. The system-level analysis is eventually performed to assess the sensitivity of the power and efficiency of the WPTS to the vehicle misalignment from the nominal trajectory during the dynamic charging process.", notes = "also known as \cite{su13052556}", } @InProceedings{DiCapua:2022:WIVACE, author = "Giulia {Di Capua} and Mario Molinara and Francesco Fontanella and Claudio {De Stefano} and Nunzio Oliva and Nicola Femia", title = "Modeling of Ferrite Inductors Power Loss Based on Genetic Programming and Neural Networks", old_title = "Magnetic Devices Behavioral Modeling based on Genetic Programming and Neural Networks", booktitle = "WIVACE 2022, XVI International Workshop on Artificial Life and Evolutionary Computation", year = "2022", editor = "Claudio {De Stefano} and Francesco Fontanella and Leonardo Vanneschi", volume = "1780", series = "Computer and Information Science", pages = "245--253", address = "Gaeta (LT), Italy", month = sep # " 14-16", publisher = "Springer", keywords = "genetic algorithms, genetic programming, ANN, FCPI", isbn13 = "978-3-031-31183-3", DOI = "doi:10.1007/978-3-031-31183-3_20", abstract = "We compare two behavioral modeling approaches for predicting AC power loss in Ferrite-Core Power Inductors (FCPIs), normally used in Switch-Mode Power Supply (SMPS) applications. The first modeling approach relies on a genetic programming algorithm and a multi-objective optimization technique. The resulting AC power loss model uses the voltage and switching frequency imposed on the FCPI as input variables, whereas the DC inductor current is used as a parameter expressing the impact of saturation on the magnetic device. A second modeling approach involves a Multi-Layer Perceptron, with a single hidden layer. The resulting AC power loss model uses the voltage, switching frequency and DC inductor current as input variables. As a case study, a 10 microHenries FCPI has been selected and characterized by a large set of power loss experimental measurements, which have been adopted to obtain the training and test data. The experimental results confirmed the higher flexibility of the FCPI behavioral modeling based on genetic programming.", notes = "Published after the workshop. Use new title etc http://wivace2022.unicas.it/files/programWIVACE2022.pdf", } @InProceedings{DiCapua:2023:evoapplications, author = "G. {Di Capua} and C. Bourelly and C. {De Stefano} and F. Fontanella and F. Milano and M. Molinara and N. Oliva and F. Porpora", title = "Using Genetic Programming to Learn Behavioral Models of Lithium Batteries", booktitle = "26th International Conference, EvoApplications 2023", year = "2023", month = apr # " 12-14", editor = "Joao Correia and Stephen Smith and Raneem Qaddoura", series = "LNCS", volume = "13989", publisher = "Springer Verlag", address = "Brno, Czech Republic", pages = "461--474", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Lithium Batteries, Behavioural Modelling, Multi-Objective Optimization", isbn13 = "978-3-031-30229-9", DOI = "doi:10.1007/978-3-031-30229-9_30", size = "14 pages", notes = "http://www.evostar.org/2023/ EvoApplications2023 held in conjunction with EuroGP'2023, EvoCOP2023 and EvoMusArt2023", } @InProceedings{dichio:2005:gecco, author = "Riccardo Poli and Cecilia {Di Chio} and William B. Langdon", title = "Exploring extended particle swarms: a genetic programming approach", booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation", year = "2005", editor = "Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and Edwin D. {de Jong} and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler", volume = "1", ISBN = "1-59593-010-8", pages = "169--176", address = "Washington DC, USA", URL = "http://www.cs.essex.ac.uk/staff/poli/papers/geccopso2005.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco2005/docs/p169.pdf", DOI = "doi:10.1145/1068009.1068036", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "25-29 " # jun, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Swarm Intelligence, particle swarm optimisation, PSO, performance", size = "8 pages", abstract = "Particle Swarm Optimisation (PSO) uses a population of particles fly over the fitness landscape in search of an optimal solution. The particles are controlled by forces that encourage each particle to fly back both towards the best point sampled by it and towards the swarm's best point, while its momentum tries to keep it moving in its current direction. Previous research \cite{poli:2005:eurogp} started exploring the possibility of evolving the force generating equations which control the particles through the use of genetic programming (GP). We independently verify the findings of \cite{poli:2005:eurogp} and then extend it by considering additional meaningful ingredients for the PSO force-generating equations, such as global measures of dispersion and position of the swarm. We show that, on a range of problems, GP can automatically generate new PSO algorithms that outperform standard human-generated as well as some previously evolved ones.", notes = "GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM Order Number 910052, XPS, ACM gecco-2005 key 1068036", } @InProceedings{DiChio:2005:gsice, author = "Cecilia {Di Chio} and Riccardo Poli and William B. Langdon", title = "Evolution of Force-Generating Equations for {PSO} using {GP}", booktitle = "AI*IA Workshop on Evolutionary Computation, Evoluzionistico GSICE05", year = "2005", editor = "Sara Manzoni and Matteo Palmonari and Fabio Sartori", address = "University of Milan Bicocca, Italy", month = "20 " # sep, keywords = "genetic algorithms, genetic programming, particle swarm optimisation, XPS", ISBN = "88-900910-0-2", URL = "http://www.cs.essex.ac.uk/staff/poli/papers/gsice2005.pdf", size = "10 pages", abstract = "We extend our previous research on evolving the physical forces which control particle swarms by considering additional ingredients, such as the velocity of the neighbourhood best and time, and different neighbourhood topologies, namely the global and local ones. We test the evolved extended PSOs (XPSOs) on various classes of benchmark problems. We show that evolutionary computation, and in particular genetic programming (GP), can automatically generate new PSO algorithms that outperform standard PSOs designed by people as well as some previously evolved ones.", notes = "http://www.ce.unipr.it/people/cagnoni/gsice2005/ http://www.ce.unipr.it/people/cagnoni/gsice2005/gsice-eng.pdf Workshop proceedings on CD-ROM only. Workshop held in-conjunction with the IX Congress of the Italian Association for Artificial Intelligence. In English. Winner of Best Paper Award", } @InProceedings{DiChio:2006:evophd, author = "Cecilia {Di Chio}", title = "Extended Particle Swarm to Simulate Biology-Like Systems", booktitle = "European Graduate Student Workshop on Evolutionary Computation", year = "2006", editor = "Mario Giacobini and Jano {van Hemert}", pages = "31--43", address = "Budapest, Hungary", month = "10 " # apr, keywords = "genetic algorithms, genetic programming, PSO, XPS", URL = "http://www.vanhemert.co.uk/publications/EvoPhD2006.pdf", size = "13 pages", abstract = "Is it possible to simulate socio-biological behaviours using particle swarm systems? And if so, what should it be the best approach to use? These are the questions which I would like to answer with my research. Particle swarm systems have been originally developed to model social behaviours. My research will therefore follow the initial socio-biological metaphor underlying particle systems. The idea is to use a genetic programming approach to automatically evolve the particle swarm equations to model animal social behaviours. This research is intended to be a first example of application of genetic programming and particle swarm to simulate animal behaviours.", notes = "broken Jan 2021 http://evonet.lri.fr/eurogp2006/?page=evophd", } @InProceedings{eurogp07:DiChio, author = "Cecilia {Di Chio} and Paolo {Di Chio}", title = "Group-Foraging with Particle Swarms and Genetic Programming", editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar", booktitle = "Proceedings of the 10th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "4445", year = "2007", address = "Valencia, Spain", month = "11-13 " # apr, pages = "331--340", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-71602-5", URL = "https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=3a462b7e9ab5ff2c6a6ed44b562424d853e266ba", isbn13 = "978-3-540-71602-0", URL = "https://rdcu.be/ea55f", DOI = "doi:10.1007/978-3-540-71605-1_31", size = "10 pages", abstract = "This paper has been inspired by two quite different works in the field of Particle Swarm theory. Its main aims are to obtain particle swarm equations via genetic programming which perform better than hand-designed ones on the group-foraging problem, and to provide insight into behavioural ecology. With this work, we want to start a new research direction: the use of genetic programming together with particle swarm algorithms in the simulation of problems in behavioural ecology.", notes = "https://rdcu.be/ea55f poor Part of \cite{ebner:2007:GP} EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007", } @InProceedings{dick:2013:EuroGP, author = "Grant Dick and Peter A. Whigham", title = "Controlling Bloat through Parsimonious Elitist Replacement and Spatial Structure", booktitle = "Proceedings of the 16th European Conference on Genetic Programming, EuroGP 2013", year = "2013", month = "3-5 " # apr, editor = "Krzysztof Krawiec and Alberto Moraglio and Ting Hu and A. Sima Uyar and Bin Hu", series = "LNCS", volume = "7831", publisher = "Springer Verlag", address = "Vienna, Austria", pages = "13--24", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-37206-3", DOI = "doi:10.1007/978-3-642-37207-0_2", abstract = "The concept of bloat --- the increase of program size without a corresponding increase in fitness --- presents a significant drawback to the application of genetic programming. One approach to controlling bloat, dubbed spatial structure with elitism (SS+E), uses a combination of spatial population structure and local elitist replacement to implicitly constrain unwarranted program growth. However, the default implementation of SS+E uses a replacement scheme that prevents the introduction of smaller programs in the presence of equal fitness. This paper introduces a modified SS+E approach in which replacement is done under a lexicographic parsimony scheme. The proposed model, spatial structure with lexicographic parsimonious elitism (SS+LPE), exhibits an improvement in bloat reduction and, in some cases, more effectively searches for fitter solutions.", notes = "Part of \cite{Krawiec:2013:GP} EuroGP'2013 held in conjunction with EvoCOP2013, EvoBIO2013, EvoMusArt2013 and EvoApplications2013", } @InProceedings{Dick:2013:GECCOa, author = "Grant Dick", title = "An effective parse tree representation for tartarus", booktitle = "GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference", year = "2013", editor = "Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi", isbn13 = "978-1-4503-1963-8", pages = "909--916", keywords = "genetic algorithms, genetic programming", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2463372.2463497", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Recent work in genetic programming (GP) has highlighted the need for stronger benchmark problems. For benchmarking planning scenarios, the artificial ant problem is often used. With a limited number of test cases, this problem is often fairly simple to solve. A more complex planning problem is Tartarus, but as of yet no standard representation for Tartarus exists for GP. This paper examines an existing parse tree representation for Tartarus, and identifies weaknesses in the way in which it manipulates environmental information. Through this analysis, an alternative representation is proposed for Tartarus that shares many similarities with those already used in GP for planning problems. Empirical analysis suggests that the proposed representation has qualities that make it a suitable benchmark problem.", notes = "Also known as \cite{2463497} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)", } @InProceedings{Dick:2014:CEC, title = "Model Representation and Cooperative Coevolution for Finite-State Machine Evolution", author = "Grant Dick and Xin Yao", pages = "2700--2707", booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary Computation", year = "2014", month = "6-11 " # jul, editor = "Carlos A. {Coello Coello}", address = "Beijing, China", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, FSM, Evolutionary programming, Coevolutionary systems, Coevolution and collective behaviour", DOI = "doi:10.1109/CEC.2014.6900622", abstract = "The use and search of finite-state machine (FSM) representations has a long history in evolutionary computation. The flexibility of Mealy-style and Moore-style FSMs is traded against the large number of parameters required to encode machines with many states and/or large output alphabets. Recent work using Mealy FSMs on the Tartarus problem has shown good performance of the resulting machines, but the evolutionary search is slower than for other representations. The aim of this paper is two-fold: first, a comparison between Mealy and Moore representations is considered on two problems, and then the impact of cooperative coevolution on FSM evolutionary search is examined. The results suggest that the search space of Moore-style FSMs may be easier to explore through evolutionary search than the search space of an equivalent-sized Mealy FSM representation. The results presented also suggest that the tested cooperative coevolutionary algorithms struggle to appropriately manage the non-separability present in FSMs, indicating that new approaches to cooperative coevolution may be needed to explore FSMs and similar graphical structures.", notes = "WCCI2014", } @Proceedings{Dick:2014:SEAL, title = "Proceedings 10th International Conference on Simulated Evolution and Learning, SEAL 2014", year = "2014", editor = "Grant Dick and Will N. Browne and Peter Whigham and Mengjie Zhang and Lam Thu Bui and Hisao Ishibuchi and Yaochu Jin and Xiaodong Li and Yuhui Shi and Pramod Singh and Kay Chen Tan and Ke Tang", volume = "8886", series = "Lecture Notes in Computer Science", address = "Dunedin, New Zealand", month = dec # " 15-18", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-13562-5", DOI = "doi:10.1007/978-3-319-13563-2", size = "approx 870 pages", } @InProceedings{Dick:2015:EuroGP, author = "Grant Dick", title = "Improving Geometric Semantic Genetic Programming with Safe Tree Initialisation", booktitle = "18th European Conference on Genetic Programming", year = "2015", editor = "Penousal Machado and Malcolm I. Heywood and James McDermott and Mauro Castelli and Pablo Garcia-Sanchez and Paolo Burelli and Sebastian Risi and Kevin Sim", series = "LNCS", volume = "9025", publisher = "Springer", pages = "28--40", address = "Copenhagen", month = "8-10 " # apr, organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, Semantic methods, Interval arithmetic, Safe initialisation, Symbolic regression", isbn13 = "978-3-319-16500-4", DOI = "doi:10.1007/978-3-319-16501-1_3", abstract = "Researchers in genetic programming (GP) are increasingly looking to semantic methods to increase the efficacy of search. Semantic methods aim to increase the likelihood that a structural change made in an individual will be correlated with a change in behaviour. Recent work has promoted the use of geometric semantic methods, where offspring are generated within a bounded interval of the parents behavioural space. Extensions of this approach use random trees wrapped in logistic functions to parametrise the blending of parents. This paper identifies limitations in the logistic wrapper approach, and suggests an alternative approach based on safe initialisation using interval arithmetic to produce offspring. The proposed method demonstrates greater search performance than using a logistic wrapper approach, while maintaining an ability to produce offspring that exhibit good generalisation capabilities.", notes = "Part of \cite{Machado:2015:GP} EuroGP'2015 held in conjunction with EvoCOP2015, EvoMusArt2015 and EvoApplications2015", } @InProceedings{Dick:2015:GECCO, author = "Grant Dick and Aysha P. Rimoni and Peter A. Whigham", title = "A Re-Examination of the Use of Genetic Programming on the Oral Bioavailability Problem", booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation", year = "2015", editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr", isbn13 = "978-1-4503-3472-3", pages = "1015--1022", keywords = "genetic algorithms, genetic programming", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739480.2754771", DOI = "doi:10.1145/2739480.2754771", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Difficult benchmark problems are in increasing demand in Genetic Programming (GP). One problem seeing increased usage is the oral bioavailability problem, which is often presented as a challenging problem to both GP and other machine learning methods. However, few properties of the bioavailability data set have been demonstrated, so attributes that make it a challenging problem are largely unknown. This work uncovers important properties of the bioavailability data set, and suggests that the perceived difficulty in this problem can be partially attributed to a lack of pre-processing, including features within the data set that contain no information, and contradictory relationships between the dependent and independent features of the data set. The paper then re-examines the performance of GP on this data set, and contextualises this performance relative to other regression methods. Results suggest that a large component of the observed performance differences on the bioavailability data set can be attributed to variance in the selection of training and testing data. Differences in performance between GP and other methods disappear when multiple training/testing splits are used within experimental work, with performance typically no better than a null modelling approach of reporting the mean of the training data.", notes = "Also known as \cite{2754771} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)", } @InProceedings{Dick:2017:GECCO, author = "Grant Dick", title = "Sensitivity-like Analysis for Feature Selection in Genetic Programming", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4920-8", address = "Berlin, Germany", pages = "401--408", size = "8 pages", URL = "http://doi.acm.org/10.1145/3071178.3071338", DOI = "doi:10.1145/3071178.3071338", acmid = "3071338", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, CART, feature selection, random forests, symbolic regression, variable importance", month = "15-19 " # jul, abstract = "feature selection is an important process within machine learning problems. Through pressures imposed on models during evolution, genetic programming performs basic feature selection, and so analysis of the evolved models can provide some insights into the utility of input features. Previous work has tended towards a presence model of feature selection, where the frequency of a feature appearing within evolved models is a metric for its utility. In this paper, we identify some drawbacks with using this approach, and instead propose the integration of importance measures for feature selection that measure the influence of a feature within a model. Using sensitivity-like analysis methods inspired by importance measures used in random forest regression, we demonstrate that genetic programming introduces many features into evolved models that have little impact on a given model's behaviour, and this can mask the true importance of salient features. The paper concludes by exploring bloat control methods and adaptive terminal selection methods to influence the identification of useful features within the search performed by genetic programming, with results suggesting that a combination of adaptive terminal selection and bloat control may help to improve generalisation performance.", notes = "Also known as \cite{Dick:2017:SAF:3071178.3071338} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @InProceedings{Dick:2017:GECCOa, author = "Grant Dick", title = "Revisiting Interval Arithmetic for Regression Problems in Genetic Programming", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4939-0", address = "Berlin, Germany", pages = "129--130", size = "2 pages", URL = "http://doi.acm.org/10.1145/3067695.3076107", DOI = "doi:10.1145/3067695.3076107", acmid = "3076107", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, interval arithmetic, symbolic regression", month = "15-19 " # jul, abstract = "Traditional approaches to symbolic regression require the use of protected operators, which can lead to perverse model characteristics and poor generalisation. In this paper, we revisit interval arithmetic as one possible solution to allow genetic programming to perform regression using unprotected operators. Using standard benchmarks, we show that using interval arithmetic within model evaluation does not prevent invalid solutions from entering the population, meaning that search performance remains compromised. We extend the basic interval arithmetic concept with safe search operators that integrate interval information into their process, thereby greatly reducing the number of invalid solutions produced during search. The resulting algorithms are able to more effectively identify good models that generalise well to unseen data.", notes = "Extended version on https://arxiv.org/abs/1704.04998 Also known as \cite{Dick:2017:RIA:3067695.3076107} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @InProceedings{Dick:2020:GECCO, author = "Grant Dick and Caitlin A. Owen and Peter A. Whigham", title = "Feature Standardisation and Coefficient Optimisation for Effective Symbolic Regression", year = "2020", editor = "Carlos Artemio {Coello Coello} and Arturo Hernandez Aguirre and Josu Ceberio Uribe and Mario Garza Fabre and Gregorio {Toscano Pulido} and Katya Rodriguez-Vazquez and Elizabeth Wanner and Nadarajen Veerapen and Efren Mezura Montes and Richard Allmendinger and Hugo Terashima Marin and Markus Wagner and Thomas Bartz-Beielstein and Bogdan Filipic and Heike Trautmann and Ke Tang and John Koza and Erik Goodman and William B. Langdon and Miguel Nicolau and Christine Zarges and Vanessa Volz and Tea Tusar and Boris Naujoks and Peter A. N. Bosman and Darrell Whitley and Christine Solnon and Marde Helbig and Stephane Doncieux and Dennis G. Wilson and Francisco {Fernandez de Vega} and Luis Paquete and Francisco Chicano and Bing Xue and Jaume Bacardit and Sanaz Mostaghim and Jonathan Fieldsend and Oliver Schuetze and Dirk Arnold and Gabriela Ochoa and Carlos Segura and Carlos Cotta and Michael Emmerich and Mengjie Zhang and Robin Purshouse and Tapabrata Ray and Justyna Petke and Fuyuki Ishikawa and Johannes Lengler and Frank Neumann", isbn13 = "9781450371285", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377930.3390237", DOI = "doi:10.1145/3377930.3390237", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference", pages = "306--314", size = "9 pages", keywords = "genetic algorithms, genetic programming, gradient descent, symbolic regression, feature standardisation", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", abstract = "Symbolic regression is a common application of genetic programming where model structure and corresponding parameters are evolved in unison. In the majority of work exploring symbolic regression, features are used directly without acknowledgement of their relative scale or unit. This paper extends recent work on the importance of standardisation of features when conducting symbolic regression. Specifically, z-score standardisation of input features is applied to both inputs and response to ensure that evolution explores a model space with zero mean and unit variance. This paper demonstrates that standardisation allows a simpler function set to be used without increasing bias. Additionally, it is demonstrated that standardisation can significantly improve the performance of coefficient optimisation through gradient descent to produce accurate models. Through analysis of several benchmark data sets, we demonstrate that feature standardisation enables simple but effective approaches that are comparable in performance to the state-of-the-art in symbolic regression.", notes = "Also known as \cite{10.1145/3377930.3390237} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @InProceedings{dick:2022:SymReg, author = "Grant Dick", title = "Genetic Programming, Standardisation, and Stochastic Gradient Descent Revisited: Initial Findings on {SRBench}", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion", year = "2022", editor = "Heike Trautmann and Carola Doerr and Alberto Moraglio and Thomas Bartz-Beielstein and Bogdan Filipic and Marcus Gallagher and Yew-Soon Ong and Abhishek Gupta and Anna V Kononova and Hao Wang and Michael Emmerich and Peter A. N. Bosman and Daniela Zaharie and Fabio Caraffini and Johann Dreo and Anne Auger and Konstantin Dietric and Paul Dufosse and Tobias Glasmachers and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Tea Tusar and Dimo Brockhoff and Tome Eftimov and Pascal Kerschke and Boris Naujoks and Mike Preuss and Vanessa Volz and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Mark Coletti and Catherine (Katie) Schuman and Eric ``Siggy'' Scott and Robert Patton and Paul Wiegand and Jeffrey K. Bassett and Chathika Gunaratne and Tinkle Chugh and Richard Allmendinger and Jussi Hakanen and Daniel Tauritz and John Woodward and Manuel Lopez-Ibanez and John McCall and Jaume Bacardit and Alexander Brownlee and Stefano Cagnoni and Giovanni Iacca and David Walker and Jamal Toutouh and UnaMay O'Reilly and Penousal Machado and Joao Correia and Sergio Nesmachnow and Josu Ceberio and Rafael Villanueva and Ignacio Hidalgo and Francisco {Fernandez de Vega} and Giuseppe Paolo and Alex Coninx and Antoine Cully and Adam Gaier and Stefan Wagner and Michael Affenzeller and Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and William B. Langdon and Justyna Petke and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Paetzel and Alexander Wagner and Michael Heider and Nadarajen Veerapen and Katherine Malan and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Mohammad Nabi Omidvar and Yuan Sun and Ernesto Tarantino and De Falco Ivanoe and Antonio {Della Cioppa} and Scafuri Umberto and John Rieffel and Jean-Baptiste Mouret and Stephane Doncieux and Stefanos Nikolaidis and Julian Togelius and Matthew C. Fontaine and Serban Georgescu and Francisco Chicano and Darrell Whitley and Oleksandr Kyriienko and Denny Dahl and Ofer Shir and Lee Spector and Alma Rahat and Richard Everson and Jonathan Fieldsend and Handing Wang and Yaochu Jin and Erik Hemberg and Marwa A. Elsayed and Michael Kommenda and William {La Cava} and Gabriel Kronberger and Steven Gustafson", pages = "2265--2273", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, benchmarking, stochastic gradient descent, symbolic regression", isbn13 = "978-1-4503-9268-6/22/07", DOI = "doi:10.1145/3520304.3534040", abstract = "The use of Z-score standardisation to improve the performance of genetic programming is well known within symbolic regression. Additionally, Z-score standardisation is known to be a key element in the effective application of stochastic gradient descent. However, a thorough treatment of genetic programming with stochastic gradient descent (GPZGD) does not exist in the literature. This paper introduces a recalibrated variant of GPZGD and tests its performance within the recently-proposed SRBench framework: the resulting variant of GPZGD demonstrates excellent performance relative to existing symbolic regression methods. Additionally, this paper provides some exploration of SRBench itself and suggests areas of potential improvement to increase the utility of the SRBench framework.", notes = "Also known as \cite{DBLP:conf/gecco/Dick22} GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @Article{dick:2024:GPEM, author = "Grant Dick", title = "An ensemble learning interpretation of geometric semantic genetic programming", journal = "Genetic Programming and Evolvable Machines", year = "2024", volume = "25", pages = "Article no 9", month = "11 " # mar, note = "Online first", keywords = "genetic algorithms, genetic programming, Boosting, Base learner, Geometric interpretation", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-024-09482-6", size = "26 Pages", abstract = "Geometric semantic genetic programming (GSGP) is a variant of genetic programming (GP) that directly searches the semantic space of programs to produce candidate solutions. GSGP has shown considerable success in improving the performance of GP in terms of program correctness, however this comes at the expense of exponential program growth. Subsequent attempts to address this growth have not fully-exploited the fact that GSGP searches by producing linear combinations of existing solutions. This paper examines this property of GSGP and frames the method as an ensemble learning approach by redefining mutation and crossover as examples of boosting and stacking, respectively. The ensemble interpretation allows for simple integration of regularisation techniques that significantly reduce the size of the resultant programs. Additionally, this paper examines the quality of parse tree base learners within this ensemble learning interpretation of GSGP and suggests that future research could substantially improve the quality of GSGP by examining more effective initialisation techniques. The resulting ensemble learning interpretation leads to variants of GSGP that substantially improve upon the performance of traditional GSGP in regression contexts, and produce a method that frequently outperforms gradient boosting.", notes = "Department of Information Science, University of Otago, Dunedin, New Zealand", } @InProceedings{dick:2024:GECCOcomp, author = "Grant Dick and Caitlin Owen", title = "Characterising the Double Descent of Symbolic Regression", booktitle = "Symbolic Regression", year = "2024", editor = "William {La Cava} and Steven Gustafson", pages = "2050--2057", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, symbolic regression, bias-variance tradeoff, double descent", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3664176", size = "8 pages", abstract = "Recent work has argued that many machine learning techniques exhibit a 'double descent' in model risk, where increasing model complexity beyond an interpolation zone can overcome the bias-variance tradeoff to produce large, over-parameterised models that generalise well to unseen data. While the double descent characteristic has been identified in many learning methods, it has not been explored within symbolic regression research. This paper presents an initial exploration into the presence of double descent behaviour in symbolic regression over a range of parameter settings. Results suggest that symbolic regression via genetic programming does not exhibit a clear double descent risk curve relative to model size or function set. Unlike other methods, models evolved through symbolic regression do not appear to strongly interpolate training data, which promotes a degree of robustness towards noise in training data. However, models evolved by symbolic regression can still be large and do not present a strong overfitting characteristic. Given that a prime motivation for symbolic regression is to produce compact interpretable models, these results suggest that methods aimed at regularising evolved models should be a key feature of all symbolic regression methods.", notes = "GECCO-2024 SymReg A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InCollection{dickinson:1994:d-i, author = "Andrew Dickinson", title = "Evolution of Damage-Immune Programs using Genetic Programming", booktitle = "Genetic Algorithms at Stanford 1994", year = "1994", editor = "John R. Koza", pages = "21--30", address = "Stanford, California, 94305-3079 USA", month = dec, organisation = "Stanford University", publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-187263-3", notes = "This volume contains 20 papers written and submitted by students describing their term projects for the course {"}Genetic Algorithms and Genetic Programming{"} (Computer Science 426) at Stanford University offered during the fall quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs426.html", } @InCollection{dickson:1999:EOGASS, author = "Andrew Dickson", title = "Evolution of Optimum Genetic Algorithms for Specific Spaces", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1999", year = "1999", editor = "John R. Koza", pages = "41--48", address = "Stanford, California, 94305-3079 USA", month = "15 " # mar, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:1999:GAGPs}", } @InProceedings{Diez-Garcia:2019:evocop, author = "Marcos {Diez Garcia} and Alberto Moraglio", title = "A Unifying View on Recombination Spaces and Abstract Convex Evolutionary Search", booktitle = "The 19th European Conference on Evolutionary Computation in Combinatorial Optimisation, EvoCOP 2019", year = "2019", editor = "A. Liefooghe and L. Paquete", series = "LNCS", volume = "11452", publisher = "Springer", pages = "179--195", organisation = "Species", keywords = "genetic algorithms, genetic programming, Abstract convex landscape, Abstract convex search, Convex hull closure, Geometric crossover, Recombination P-structure", isbn13 = "978-3-030-16710-3", URL = "https://ore.exeter.ac.uk/repository/bitstream/handle/10871/35845/marcos-diez-garcia_alberto-moraglio_accepted-evocop-2019.pdf", URL = "http://hdl.handle.net/10871/35845", DOI = "doi:10.1007/978-3-030-16711-0_12", size = "19 pages", abstract = "Previous work proposed to unify an algebraic theory of fitness landscapes and a geometric framework of evolutionary algorithms (EAs). One of the main goals behind this unification is to develop an analytical method that verifies if a problem landscape belongs to certain abstract convex landscape classes, where certain recombination-based EAs (without mutation) have polynomial runtime performance. This paper advances such unification by showing that: (a) crossovers can be formally classified according to geometric or algebraic axiomatic properties; and (b) the population behaviour induced by certain crossovers in recombination-based EAs can be formalised in the geometric and algebraic theories. These results make a significant contribution to the basis of an integrated geometric-algebraic framework with which analyse recombination spaces and recombination based EAs.", notes = "Mention of GP crossovers EvoCOP2019 held in conjunction with EuroGP'2019 EvoMusArt2019 and EvoApplications2019 http://www.evostar.org/2019/cfp_evocop.php", } @PhdThesis{Diez-GarciaM, author = "Marcos {Diez Garcia}", title = "Unifying a Geometric Framework of Evolutionary Algorithms and Elementary Landscapes Theory", school = "Computer Science, University of Exeter", year = "2021", address = "UK", month = jan, keywords = "genetic algorithms, genetic programming", URL = "https://ore.exeter.ac.uk/repository/bitstream/handle/10871/126174/Diez-GarciaM.pdf", size = "270 pages", abstract = "Evolutionary algorithms (EAs) are randomised general-purpose strategies, inspired by natural evolution, often used for finding (near) optimal solutions to problems in combinatorial optimisation. Over the last 50 years, many theoretical approaches in evolutionary computation have been developed to analyse the performance of EAs,design EAs or measure problem difficulty via fitness landscape analysis. An open challenge is to formally explain why a general class of EAs perform better, or worse,than others on a class of combinatorial problems across representations. However,the lack of a general unified theory of EAs and fitness landscapes, across problems and representations, makes it harder to characterise pairs of general classes of EAs and combinatorial problems where good performance can be guaranteed provably. This thesis explores a unification between a geometric framework of EAs and elementary landscapes theory, not tied to a specific representation nor problem, with complementary strengths in the analysis of population-based EAs and combinatorial landscapes. This unification organises around three essential aspects: search space structure induced by crossovers, search behaviour of population-based EAs and structure of fitness landscapes. First, this thesis builds a crossover classification to systematically compare crossovers in the geometric framework and elementary landscapes theory, revealing a shared general subclass of crossovers: geometric recombination P-structures, which covers well-known crossovers. The crossover classification is then extended to a general framework for axiomatically analysing the population behaviour induced by crossover classes on associated EAs. This shows the shared general class of all EAs using geometric recombination P-structures, but no mutation, always do the same abstract form of convex evolutionary search. Finally, this thesis characterises a class of globally convex combinatorial landscapes shared by the geometric framework and elementary landscapes theory: abstract convex elementary landscapes. It is formally explained why geometric recombination P-structure EAs expectedly can out perform random search on abstract convex elementary landscapes related to low-order graph Laplacian eigenvalues. Altogether, this thesis paves a way towards a general unified theory of EAs and combinatorial fitness landscapes.", notes = "supervisor: Alberto Moraglio", } @InProceedings{digby:1999:EAABGC, author = "David Digby and William Seffens", title = "Evolutionary Algorithm Analysis of the Biological Genetic Codes", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1440", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "artificial life, adaptive behavior and agents, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-013.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-013.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @TechReport{dignum:2004:CSM400, author = "Stephen Dignum and Riccardo Poli", title = "Multi-agent Foreign Exchange Market Modelling via GP", institution = "Department of Computer Science, University of Essex", year = "2004", number = "CSM-400", address = "Colchester, UK", keywords = "genetic algorithms, genetic programming", URL = "http://cswww.essex.ac.uk/technical-reports/2004/csm400.pdf", abstract = "we combine Genetic Programming (GP) and intelligent agents to build a realistic foreign exchange currency market simulator. GP is used to express and evolve trading strategies. We analyse the decisions made in the design of the simulator with respect to authenticity of the representation and the efficiency of the system. A number of experimental results are also reported.", size = "12 pages", } @InProceedings{dignum:mfe:gecco2004, author = "Stephen Dignum and Riccardo Poli", title = "Multi-agent Foreign Exchange Market Modelling Via GP", booktitle = "Genetic and Evolutionary Computation -- GECCO-2004, Part I", year = "2004", editor = "Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell", series = "Lecture Notes in Computer Science", pages = "255--256", address = "Seattle, WA, USA", publisher_address = "Heidelberg", month = "26-30 " # jun, organisation = "ISGEC", publisher = "Springer-Verlag", volume = "3102", ISBN = "3-540-22344-4", ISSN = "0302-9743", DOI = "doi:10.1007/b98643", size = "2", keywords = "genetic algorithms, genetic programming, Poster", notes = "GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)", } @InProceedings{1277277, author = "Stephen Dignum and Riccardo Poli", title = "Generalisation of the limiting distribution of program sizes in tree-based genetic programming and analysis of its effects on bloat", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "1588--1595", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1588.pdf", DOI = "doi:10.1145/1276958.1277277", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, bloat, crossover Bias, initialisation, program Size distribution", size = "8 pages", abstract = "Recent research \cite{poli:2007:eurogp} has found that standard sub-tree crossover with uniform selection of crossover points, in the absence of fitness pressure, pushes a population of GP trees towards a Lagrange distribution of tree sizes. However, the result applied to the case of single arity function plus leaf node combinations, e.g., unary, binary, ternary, etc trees only. In this paper we extend those findings and show that the same distribution is also applicable to the more general case where the function set includes functions of mixed arities. We also provide empirical evidence that strongly corroborates this generalisation. Both predicted and observed results show a distinct bias towards the sampling of shorter programs irrespective of the mix of function arities used. Practical applications and implications of this knowledge are investigated with regard to search efficiency and program bloat. Work is also presented regarding the applicability of the theory to the traditional 0.90percent -function 0.10percent-terminal crossover node selection policy.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @InProceedings{Dignum:2008:eurogp, title = "Operator Equalisation and Bloat Free {GP}", author = "Stephen Dignum and Riccardo Poli", bibdate = "2008-04-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/eurogp/eurogp2008.html#DignumP08", booktitle = "Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008", address = "Naples", month = "26-28 " # mar, publisher = "Springer", year = "2008", volume = "4971", editor = "Michael O'Neill and Leonardo Vanneschi and Steven Gustafson and Anna Isabel {Esparcia Alcazar} and Ivanoe {De Falco} and Antonio {Della Cioppa} and Ernesto Tarantino", isbn13 = "978-3-540-78670-2", pages = "110--121", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming, Search, Bloat, Program Length, Operator Equalisation", DOI = "doi:10.1007/978-3-540-78671-9_10", abstract = "Research has shown that beyond a certain minimum program length the distributions of program functionality and fitness converge to a limit. Before that limit, however, there may be program-length classes with a higher or lower average fitness than that achieved beyond the limit. Ideally, therefore, GP search should be limited to program lengths that are within the limit and that can achieve optimum fitness. This has the dual benefits of providing the simplest/smallest solutions and preventing GP bloat thus shortening run times. Here we introduce a novel and simple technique, which we call Operator Equalisation, to control how GP will sample certain length classes. This allows us to finely and freely bias the search towards shorter or longer programs and also to search specific length classes during a GP run. This gives the user total control on the program length distribution, thereby completely freeing GP from bloat. Results show that we can automatically identify potentially optimal solution length classes quickly using small samples and that, for particular classes of problems, simple length biases can significantly improve the best fitness found during a GP run.", notes = "Also known as \cite{conf/eurogp/DignumP08} Part of \cite{conf/eurogp/2008} EuroGP'2008 held in conjunction with EvoCOP2008, EvoBIO2008 and EvoWorkshops2008", } @InProceedings{Dignum:2008:eurogp2, title = "Crossover, Sampling, Bloat and the Harmful Effects of Size Limits", author = "Stephen Dignum and Riccardo Poli", bibdate = "2008-04-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/eurogp/eurogp2008.html#DignumP08a", booktitle = "Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008", address = "Naples", month = "26-28 " # mar, publisher = "Springer", year = "2008", volume = "4971", editor = "Michael O'Neill and Leonardo Vanneschi and Steven Gustafson and Anna Isabel {Esparcia Alcazar} and Ivanoe {De Falco} and Antonio {Della Cioppa} and Ernesto Tarantino", isbn13 = "978-3-540-78670-2", pages = "158--169", series = "Lecture Notes in Computer Science", DOI = "doi:10.1007/978-3-540-78671-9_14", keywords = "genetic algorithms, genetic programming, Theory, Crossover, Search, Sampling, Bloat, Program Length, size", abstract = "Recent research \cite{poli:2007:eurogp} \cite{1277277} has enabled the accurate prediction of the limiting distribution of tree sizes for Genetic Programming with standard sub-tree swapping crossover when GP is applied to a flat fitness landscape. In that work, however, tree sizes are measured in terms of number of internal nodes. While the relationship between internal nodes and length is one-to-one for the case of a-ary trees, it is much more complex in the case of mixed arities. So, practically the length bias of subtree crossover remains unknown. This paper starts to fill this theoretical gap, by providing accurate estimates of the limiting distribution of lengths approached by tree-based GP with standard crossover in the absence of selection pressure. The resulting models confirm that short programs can be expected to be heavily resampled. Empirical validation shows that this is indeed the case. We also study empirically how the situation is modified by the application of program length limits. Surprisingly, the introduction of such limits further exacerbates the effect. However, this has more profound consequences than one might imagine at first. We analyse these consequences and predict that, in the presence of fitness, size limits may initially speed up bloat, almost completely defeating their original purpose (combating bloat). Indeed, experiments confirm that this is the case for the first 10 or 15 generations. This leads us to suggest a better way of using size limits. Finally, this paper proposes a novel technique to counteract bloat, sampling parsimony, the application of a penalty to resampling.", notes = "Also known as \cite{conf/eurogp/DignumP08a} Part of \cite{conf/eurogp/2008} EuroGP'2008 held in conjunction with EvoCOP2008, EvoBIO2008 and EvoWorkshops2008", } @InProceedings{Dignum:2008:EvoPHD, author = "Stephen Dignum", title = "An Analysis of Genetic Programming Operator Bias regarding the Sampling of Program Size with Potential Applications", booktitle = "EvoPhD 2008", year = "2008", editor = "Jano {van Hemert} and Mario Giacobini and Cecilia {Di Chio}", address = "Naples", month = "27 " # mar, keywords = "genetic algorithms, genetic programming", notes = "EvoPHD'2008 held in conjunction with EuroGP-2008, EvoCOP2008, EvoBIO2008 and EvoWorkshops2008", } @InProceedings{Dignum:2008:PPSN, author = "Stephen Dignum and Riccardo Poli", title = "Sub-Tree Swapping Crossover, Allele Diffusion and GP Convergence", booktitle = "Parallel Problem Solving from Nature - PPSN X", year = "2008", editor = "Gunter Rudolph and Thomas Jansen and Simon Lucas and Carlo Poloni and Nicola Beume", volume = "5199", series = "LNCS", pages = "368--377", address = "Dortmund", month = "13-17 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Search, Crossover Bias, Allele Diffusion, Convergence", ISBN = "3-540-87699-5", DOI = "doi:10.1007/978-3-540-87700-4_37", abstract = "We provide strong evidence that sub-tree swapping crossover when applied to tree-based representations will cause alleles (node labels) to diffuse within length classes. For a-ary trees we provide further confirmation that all programs are equally likely to be sampled within any length class when sub-tree swapping crossover is applied in the absence of selection and mutation. Therefore, we propose that this form of search is unbiased - within length classes - for a-ary trees. Unexpectedly, however, for mixed-arity trees this is not found and a more complicated form of search is taking place where certain tree shapes, hence programs, are more likely to be sampled than others within each class. We examine the reasons for such shape bias in mixed arity representations and provide the practitioner with a thorough examination of sub-tree swapping crossover bias. The results of this, when combined with crossover length bias research, explain Genetic Programming's lack of structural convergence during later stages of an experimental run. Several operators are discussed where a broader form of convergence may be detected in a similar way to that found in Genetic Algorithm experimentation.", notes = "PPSN X", } @PhdThesis{dignum:phdthesis, author = "Stephen Dignum", title = "An analysis of genetic programming sub-tree swapping crossover with applications", school = "Department of Computing and Electronic Systems, University of Essex", year = "2008", address = "UK", month = sep, keywords = "genetic algorithms, genetic programming, crossover, crossover bias, operator equalisation, TinyGP", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/dignum_phdthesis.pdf", URL = "https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.494360", size = "165 pages", abstract = "Genetic Programming (GP) is one of a number of biologically inspired search techniques known collectively as Evolutionary Algorithms (EAs). These algorithms use the metaphor of Darwinian Evolution to discover solutions to problems that humans, and/or other search methods have found difficult to solve. GP differs from the other main classes of EAs in that it specifically seeks to produce solutions that are executable computer programs. Considering the large amount of books, papers and articles on GP, over 5000 items in the official GP Bibliography, relatively few have addressed the problem of understanding the very basic biases of GP operators, i.e., how they sample program spaces. This thesis begins to address this lack of knowledge by considering GPs defining variation operator, sub-tree swapping crossover. It first analyses crossovers bias with regard to program sampling in terms of program length, providing a number of empirically verified theoretical models. With this knowledge in hand, the thesis investigates how length bias affects GP runs, particularly with regard to the sampling of unique programs and bloat. From this work a new bloat theory is presented, Crossover-Bias, and a method, Sampling Parsimony, to directly alter the rate of resampling and hence control bloat is created. To counteract the length bias of crossover a new technique is introduced, Operator Equalisation, which enables length classes to be sampled according to predetermined probability distributions. This provides essential information regarding GP runs and can be shown to improve GP performance. We then turn our attentions to the sampling of programs within length classes and its implications for structural convergence within GP. From this work we show that subtree swapping crossover will sample programs with a frequency determined by arity proportions, our length work being a specialisation of this process. A new theoretical model based on arity histograms is then provided.", notes = "ISNI: 0000 0001 3424 3527. Supervisor: Riccardo Poli", } @InProceedings{Dignum:2010:EuroGP, author = "Stephen Dignum and Riccardo Poli", title = "Sub-Tree Swapping Crossover and Arity Histogram Distributions", booktitle = "Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010", year = "2010", editor = "Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar", volume = "6021", series = "LNCS", pages = "38--49", address = "Istanbul", month = "7-9 " # apr, organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-12147-0", DOI = "doi:10.1007/978-3-642-12148-7_4", size = "12 pages", abstract = "Recent theoretical work has characterised the search bias of GP sub-tree swapping crossover in terms of program length distributions, providing an exact fixed point for trees with internal nodes of identical arity. However, only an approximate model (based on the notion of average arity) for the mixed-arity case has been proposed. This leaves a particularly important gap in our knowledge because multi-arity function sets are commonplace in GP and deep lessons could be learnt from the fixed point. In this paper, we present an accurate theoretical model of program length distributions when mixed-arity function sets are employed. The new model is based on the notion of an arity histogram, a count of the number of primitives of each arity in a program. Empirical support is provided and a discussion of the model is used to place earlier findings into a more general context.", notes = "p42 'This equation (4) has now become a multivariate Lagrange distribution of the second kind.' p43 'Lagrange distribution of the third kind' Part of \cite{Esparcia-Alcazar:2010:GP} EuroGP'2010 held in conjunction with EvoCOP2010 EvoBIO2010 and EvoApplications2010", } @InProceedings{dijk:1999:OTDGAGA, author = "S. {van Dijk} and D. Thierens and M. {de Berg}", title = "On The Design of Genetic Algorithms for Geographical Applications", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "188--195", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-809.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-809.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Article{journals/nca/Dikmen15, author = "Erkan Dikmen", title = "Gene expression programming strategy for estimation performance of {LiBr-H2O} absorption cooling system", journal = "Neural Computing and Applications", year = "2015", number = "2", volume = "26", pages = "409--415", keywords = "genetic algorithms, genetic programming, gene expression programming", bibdate = "2015-01-26", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/nca/nca26.html#Dikmen15", URL = "http://dx.doi.org/10.1007/s00521-014-1723-9", } @InProceedings{conf/ipcv/DileepC10, author = "K. V. S. Dileep and Venkatachalam Chandrasekaran", title = "Learning Data Dependent Composite Kernels for Robust Image Retrieval - A Genetic Programming Approach", booktitle = "Proceedings of the 2010 International Conference on Image Processing, Computer Vision, \& Pattern Recognition, {IPCV} 2010, July 12-15, 2010, Las Vegas, Nevada, {USA}, 2 Volumes", publisher = "CSREA Press", year = "2010", editor = "Hamid R. Arabnia and Leonidas Deligiannidis and Gerald Schaefer and Ashu M. G. Solo", isbn13 = "1-60132-154-6", pages = "294--299", keywords = "genetic algorithms, kernel methods, composite kernel, learning the kernel, image retrieval", broken_url = "ftp://amd64gcc.dyndns.org/WORLDCOMP10/2010%20Papers/IPC3842.pdf", abstract = "Kernel methods are a class of pattern recognition and machine learning algorithms that map data to a high dimensional space and perform various learning tasks like clustering or regression in that space. The mapping from the low dimensional space to the high dimension is done implicitly by the use of a kernel function. But the question of how to choose the kernel is an interesting and intriguing one. The choice of the kernel and its parameters is usually done using cross-validation. We propose a methodology of learning a kernel from data using genetic programming. With the aid of genetic algorithms, we constructed composite kernels and compared their performance with an ad-hoc kernel in the domain of image retrieval. The learned composite kernels showed consistent better performance compared to the individual kernel.", notes = "Despite abstract this is a GA not a GP", bibdate = "2010-12-08", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ipcv/ipcv2010.html#DileepC10", } @InProceedings{dill:1997:grmGA, author = "Karen M. Dill and Marek A. Perkowski", title = "Minimization of {GRM} Forms with a Genetic Algorithm", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "Genetic Algorithms", pages = "362", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", notes = "GP-97", } @InProceedings{Dill:1997:PACRIM, author = "Karen M. Dill and James H. Herzog and Marek A. Perkowski", title = "Genetic programming and its applications to the synthesis of digital logic", booktitle = "IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, PACRIM 1997", year = "1997", volume = "2", pages = "823--826", address = "Victoria, BC, Canada", month = "20-22 " # aug, note = "Networking the Pacific Rim, 10 Years PACRIM 1987-1997", keywords = "genetic algorithms, genetic programming, EHW, logic circuits, logic CAD, digital logic synthesis, arbitrary logic expressions, logic synthesis, problem applicability, optimization criterion, logic gates, population sizes, complete function coverage, experimental test results, randomly designed functions, input variables, logic equations, function coverage, training set size, small training sets, function recognition", ISBN = "0-7803-3905-3", DOI = "doi:10.1109/PACRIM.1997.620386", size = "4 pages", abstract = "Genetic programming is applied to the synthesis of arbitrary logic expressions. As a new method of logic synthesis, this technique is uniquely advantageous in its flexibility for both problem applicability and optimisation criterion. A number of experiments were conducted exploring this method with different types of logic gates and population sizes. While complete function coverage is not guaranteed, the best experimental test results over eight randomly designed functions, of four to seven input variables, have produced logic equations with a 98.4percent function coverage. In addition, the relation between the training set size for the genetic program and function coverage was also empirically explored. These experiments showed that only small training sets were necessary for function recognition.", } @InCollection{dillon:1995:EGASSSTP, author = "Thomas Dillon", title = "Evolution of General Algorithmic Solutions for Simple Sliding Tile Puzzles", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1995", year = "1995", editor = "John R. Koza", pages = "65--75", address = "Stanford, California, 94305-3079 USA", month = "11 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-195720-5", notes = "part of \cite{koza:1995:gagp}", } @Article{dimberg:2016:&, author = "Peter H. Dimberg and Christofer J. Olofsson", title = "A Comparison Between Regression Models and Genetic Programming for Predictions of Chlorophyll-a Concentrations in Northern Lakes", journal = "Environmental Modeling \& Assessment", year = "2016", volume = "21", number = "2", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s10666-015-9480-4", DOI = "doi:10.1007/s10666-015-9480-4", } @Article{Dimitriu:2009:MMP, author = "R. C. Dimitriu and H. K. D. H. Bhadeshia and C. Fillon and C. Poloni", title = "Strength of Ferritic Steels: Neural Networks and Genetic Programming", journal = "Materials and Manufacturing Processes", year = "2009", volume = "24", number = "1", pages = "10--15", month = jan, keywords = "genetic algorithms, genetic programming, ANN, Creep strength, Ferritic steels, Hot strength, Neural networks, Steel", ISSN = "1042-6914", URL = "http://www.msm.cam.ac.uk/phasetrans/2009/Dimitriu.html", DOI = "doi:10.1080/10426910802539796", size = "6 pages", abstract = "An analysis is presented of a complex set of data on the strength of steels as a function of chemical composition, heat treatment, and test temperature. The steels represent a special class designed to resist deformation at elevated temperatures (750-950 K) over time periods in excess of 30 years, whilst serving in hostile environments. The aim was to compare two methods, a neural network based on a Bayesian formulation, and genetic programming in which the data are formulated in an evolutionary procedure. It is found that in the present context, the neural network is able more readily to capture greater complexity in the data whereas a genetic program seems to require greater intervention to achieve an accurate representation.", notes = "Affiliations: Department of Materials Science and Metallurgy, University of Cambridge, Cambridge, England, UK Department of Electrical Engineering and Computer Science, University of Trieste, Trieste, Italy", } @InProceedings{dimopoulos:1999:ESPGPF, author = "Christos Dimopoulos and Ali M. S. Zalzala", title = "Evolving Scheduling Policies through a Genetic Programming Framework", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1231", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-448.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-448.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{dimopoulos:1999:AGPHOTTP, author = "Christos Dimopoulos and Ali M. S. Zalzala", title = "A Genetic Programming Heuristic for the One-Machine Total Tardiness Problem", booktitle = "Proceedings of the Congress on Evolutionary Computation", year = "1999", editor = "Peter J. Angeline and Zbyszek Michalewicz and Marc Schoenauer and Xin Yao and Ali Zalzala", volume = "3", pages = "2207--2214", address = "Mayflower Hotel, Washington D.C., USA", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "6-9 " # jul, organisation = "Congress on Evolutionary Computation, IEEE / Neural Networks Council, Evolutionary Programming Society, Galesia, IEE", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, manufacturing optimization, benchmark problems, dispatching rules, due date tardiness, due date tightness, genetic programming heuristic, local search techniques, manufacturing optimisation problems, modified genetic programming framework, one-machine total tardiness problem, permutations, dispatching, evolutionary computation, heuristic programming, optimisation, scheduling, search problems", ISBN = "0-7803-5536-9 (softbound)", ISBN = "0-7803-5537-7 (Microfiche)", DOI = "doi:10.1109/CEC.1999.785549", abstract = "Genetic programming has rarely been applied to manufacturing optimisation problems. In this report we investigate the potential use of genetic programming for the solution of the one-machine total tardiness problem. Combinations of dispatching rules are employed as an indirect way of representing permutations within a modified genetic programming framework. Hybridisation of genetic programming with local search techniques is also introduced, in an attempt to improve the quality of solutions. All the algorithms are tested on a large number of benchmark problems with different levels of tardiness and tightness of due dates", notes = "CEC-99 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. Library of Congress Number = 99-61143", } @InProceedings{chrnei99, author = "Christos Dimopoulos and Neil Mort", title = "Genetic programming for cellular manufacturing", booktitle = "Proceedings of the 2nd Workshop on European Scientific and Industrial Collaboration (WESIC-99)", year = "1999", editor = "G. N. Roberts and C. A. J. Tubb", month = "1-3 " # sep, organisation = "Mechatronics Research Centre, University of Wales College Newport", email = "chris_dimop@hotmail.com", keywords = "genetic algorithms, genetic programming, cellular manufacturing", ISBN = "1-899274-23-5", URL = "http://www.pickabook.co.uk/9781899274239.aspx", abstract = "Evolutionary computation methods have been applied successfully to a wide range of manufacturing optimisation problems. However, Genetic Programming applications to manufacturing optimisation have rarely been reported. In this paper we present a Genetic Programming methodology for the diagonalisation of binary machine-component matrices in cellular manufacturing. The procedure is based on the evolution of a similarity coefficient for each problem considered. The application of the method is illustrated with the help of a test problem taken from the literature", notes = "Geoff Roberts, Newport (south wales)", } @Article{chrams00, author = "Christos Dimopoulos and Ali M. S. Zalzala", title = "Recent developments in evolutionary computation for manufacturing optimisation: problems, solutions and comparisons", journal = "IEEE Transactions on Evolutionary Computation", year = "2000", volume = "4", number = "2", pages = "93--113", month = jul, email = "chris_dimop@hotmail.com", keywords = "genetic algorithms, genetic programming, evolutionary computation, manufacturing optimization, assembly line balancing, combinatorial NP-hard problems, evolutionary computation, flow shop scheduling, intelligent techniques, job shop scheduling, manufacturing optimisation, process planning, artificial intelligence, computational complexity, evolutionary computation, production control", ISSN = "1089-778X", DOI = "doi:10.1109/4235.850651", size = "21 pages", abstract = "The use of intelligent techniques in the manufacturing field has been growing the last decades due to the fact that most of manufacturing optimisation problems are combinatorial and NP hard. This report examines recent developments in the field of evolutionary computation for manufacturing optimisation. Significant papers in various areas are highlighted and comparisons of results are given wherever data is available. A wide range of problems is covered, from job shop and flow shop scheduling, to process planning and assembly line balancing", notes = "Also known as \cite{850651}", } @InProceedings{chrnei00, author = "Christos Dimopoulos and Neil Mort", title = "Solving cell-formation problems under alternative quality criteria and constraints with a genetic programming-based hierarchical clustering algorithm", booktitle = "Proceedings of the Sixth International Conference on Control, Automation, Robotics and Vision", year = "2000", pages = "3445--3446", address = "Singapore", month = "5-8 " # dec, keywords = "genetic algorithms, genetic programming, cell formation", abstract = "Cellular manufacturing is a modern approach to the implementation of efficient manufacturing systems. The solution of the cell formation problem is an essential step for the design of a cellular manufacturing system. In this paper we present a novel Genetic Programming-based methodology for the solution of the cell-formation problem. The proposed methodology is tested on a cell formation problem taken from the literature under alternative quality criteria and size constraints", notes = "broken Sep 2018 http://www3.ntu.edu.sg/eee/eee4/eventconference.asp ICARCV 2000 Sixth International Conference on Control, Automation, Robotics and Vision 5 - 8 December 2000 Marina Mandarin Hotel, Singapore General Chairman: Professor Sundararajan, N Technical Chairman: Associate Professor Wang, Jianliang Advisor: Associate Professor Soh Yeng Chai Proceedings : CD-ROM", } @InProceedings{dimmort00, author = "Christos Dimopoulos and Neil Mort", title = "Evolving similarity coefficients for the solution of cellular manufacturing problems", booktitle = "Proceedings of the Congress on Evolutionary Computation (CEC 2000)", year = "2000", pages = "617--624", volume = "1", address = "La Jolla Marriott Hotel La Jolla, California, USA", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "6-9 " # jul, organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", publisher = "IEEE Press", email = "chris_dimop@hotmail.com", keywords = "genetic algorithms, genetic programming, cell formation, similarity coefficients, engineering applications, Jaccard similarity coefficient, cell formation problem, cellular manufacturing problems, cellular manufacturing system, clustering procedure, evolved coefficients, evolving similarity coefficients, genetic programming algorithm, hierarchical clustering procedures, manufacturing optimisation problem, similarity coefficients, simple cell formation problems, flexible manufacturing systems, pattern clustering", DOI = "doi:10.1109/CEC.2000.870355", ISBN = "0-7803-6375-2", abstract = "The cell formation problem is a classic manufacturing optimisation problem associated with the implementation of a cellular manufacturing system. A variety of hierarchical clustering procedures have been proposed for the solution of this problem. Essential for the operation of a clustering procedure is the determination of a form of similarity between the objects that are going to be grouped. In this paper we employ a Genetic Programming algorithm for the evolution of new similarity coefficients for the solution of simple cell formation problems. Evolved coefficients are tested against the well-known Jaccard's similarity coefficient on a large number of problems taken from the literature", notes = "also called \cite{dimopoulos:2000:ESCSCMP} \cite{870355}. CEC-2000 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 00TH8512, Library of Congress Number = 00-018644", } @InProceedings{dimmortacd, author = "Christos Dimopoulos and Neil Mort", title = "A genetic programming-based hierarchical clustering procedure for the solution of the cell-formation problem", booktitle = "Adaptive Computing in Design and Manufacture (ACDM 2000)", year = "2000", editor = "I. C. Parmee", pages = "211--222", address = "University of Plymouth, Devon, UK", publisher = "Springer", email = "chris_dimop@hotmail.com", ISBN = "1-85233-300-6", isbn13 = "978-1-85233-300-3", URL = "http://www.springer.com/engineering/mechanical+engineering/book/978-1-85233-300-3", URL = "http://www.amazon.co.uk/Evolutionary-Design-Manufacture-Selected-Papers/dp/1852333006", DOI = "doi:10.1007/978-1-4471-0519-0_17", keywords = "genetic algorithms, genetic programming, cellular manufacturing", abstract = "Cellular manufacturing is the implementation of group technology in the manufacturing process. A key issue during the design of a cellular manufacturing system is the configuration of machine cells and part families within the plant. In this paper we present a hierarchical clustering procedure for the solution of the cell-formation problem which is based on the use of Genetic Programming for the evolution of similarity coefficients between pairs of machines in the plant. The performance of the methodology is illustrated on a number of test problems taken from the literature", notes = "Evolutionary design and Manufacture Selected Papers from ACDM '00. 4.4. Nov 2012, Currently out of stock", } @PhdThesis{DBLP:phd/ethos/Dimopoulos00, author = "Christos Dimopoulos", title = "Genetic programming for manufacturing optimisation", school = "University of Sheffield", year = "2000", address = "UK", month = aug, keywords = "genetic algorithms, genetic programming", URL = "http://etheses.whiterose.ac.uk/24962/", URL = "http://etheses.whiterose.ac.uk/24962/1/327668.pdf", URL = "http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.327668", timestamp = "Fri, 23 Sep 2016 17:28:49 +0200", biburl = "https://dblp.org/rec/phd/ethos/Dimopoulos00.bib", size = "233 pages", abstract = "A considerable number of optimisation techniques have been proposed for the solution of problems associated with the manufacturing process. Evolutionary computation methods, a group of non-deterministic search algorithms that employ the concept of Darwinian strife for survival to guide the search for optimal solutions, have been extensively used for this purpose. Genetic programming is an evolutionary algorithm that evolves variable-length solution representations in the form of computer programs. While genetic programming has produced successful applications in a variety of optimisation fields, genetic programming methodologies for the solution of manufacturing optimisation problems have rarely been reported. The applicability of genetic programming in the field of manufacturing optimisation is investigated in this thesis. Three well-known problems were used for this purpose: the one-machine total tardiness problem, the cell-formation problem and the multiobjective process planning selection problem. The main contribution of this thesis is the introduction of novel genetic programming frameworks for the solution of these problems. In the case of the one-machine total tardiness problem genetic programming employed combinations of dispatching rules for the indirect representation of job schedules. The hybridisation of genetic programming with alternative search algorithms was proposed for the solution of more difficult problem instances. In addition, genetic programming was used for the evolution of new dispatching rules that challenged the efficiency of man-made dispatching rules for the solution of the problem. An integrated genetic programming - hierarchical clustering approach was proposed for the solution of simple and advanced formulations of the cell-formation problem. The proposed framework produced competitive results to alternative methodologies that have been proposed for the solution of the same problem. The evolution of similarity coefficients that can be used in combination with clustering techniques for the solution of cell-formation problems was also investigated. Finally, genetic programming was combined with a number of evolutionary multiobjective techniques for the solution of the multiobjective process planning selection problem. Results on test problems illustrated the ability of the proposed methodology to provide a wealth of potential solutions to the decision-maker.", notes = "Supervisor: Neil Mort", } @Article{Dimopoulos:2001:AES, author = "C. Dimopoulos and A. M. S. Zalzala", title = "Investigating the use of genetic programming for a classic one-machine scheduling problem", journal = "Advances in Engineering Software", volume = "32", pages = "489--498", year = "2001", number = "6", month = jun, email = "cop97cd@sheffield.ac.uk", keywords = "genetic algorithms, genetic programming, Evolutionary computation, Manufacturing optimisation, Tardiness, Scheduling", URL = "http://www.sciencedirect.com/science/article/B6V1P-42YFC02-7/1/6be8f2e3206dccb17801b7a7833a6299", ISSN = "0965-9978", DOI = "doi:10.1016/S0965-9978(00)00109-5", abstract = "Genetic programming has rarely been applied to manufacturing optimisation problems. We investigate the potential use of genetic programming for the solution of the one-machine total tardiness problem. Genetic programming is used for the evolution of scheduling policies in the form of dispatching rules. These rules are trained to cope with different levels of tardiness and tightness of due dates.", } @Article{chrnei01, author = "Christos Dimopoulos and Neil Mort", title = "A hierarchical clustering methodology based on genetic programming for the solution of simple cell-formation problems", journal = "International Journal of Production Research", year = "2001", volume = "39", number = "1", pages = "1--19", email = "chris_dimop@hotmail.com", keywords = "genetic algorithms, genetic programming", ISSN = "00207543", DOI = "doi:10.1080/00207540150208835", abstract = "The problem of identifying machine cells and corresponding part families in cellular manufacturing has been extensively researched over the last thirty years. However, the complexity of the problem and the considerable number of issues involved in its solution create the need for increasingly efficient algorithms. In this paper we investigate the use of Genetic Programming for the solution of a simple version of the problem. The methodology is tested on a number of test problems taken from the literature and comparative results are presented", } @InProceedings{dimopoulos:2005:JCIS, author = "Christos Dimopoulos", title = "A Genetic Programming methodology for the solution of the multi-objective cell-formation problem", booktitle = "Proceedings of the 8th Joint Conference in Information Systems (JCIS 2005)", year = "2005", editor = "Heng-Da Cheng", pages = "1487--1494", address = "Salt Lake City, USA", month = "21-25 " # jul, email = "dimopoulos@cycollege.ac.cy", keywords = "genetic algorithms, genetic programming", notes = "http://delta.cs.cinvestav.mx/~ccoello/EMOO/EMOOconferences.html#860 Broken Aug 2018 homepage sting.cycollege.ac.cy/~dimopoulos/main.htm Broken Aug 2018 http://www.jcis.org/jcis_program/master_schedule.pdf", } @InProceedings{dimopoulos:2005:ICPR, author = "Christos Dimopoulos", title = "A Novel Approach for the Solution of the Multiobjective Cell-Formation Problem", booktitle = "Proceedings of the International Conference of Production Research (ICPR 05)", year = "2005", email = "dimopoulos@cycollege.ac.cy", keywords = "genetic algorithms, genetic programming, cellular manufacturing, production research, multiobjective optimisation", URL = "http://www.lania.mx/~ccoello/EMOO/dimopoulos05.pdf.gz", size = "6 pages", abstract = "We present a hybrid heuristic methodology for the solution of the multi-objective cell-formation problem. Traditional optimisation methodologies employ aggregating schemes in order to transform the problem into a single-objective case. In this way the designer is not presented with a set of non-dominated solutions but with a single compromise solution based on pre-specified weighting priorities. The proposed methodology combines a traditional hierarchical clustering analysis technique with a genetic programming algorithm that is based on the principles of evolutionary computation. The hybrid methodology evolves an approximation of the Pareto set of solutions for multi-objective cell-formation problems. The benefits brought by the proposed approach in comparison to traditional optimisation methodologies are illustrated using a typical example taken from the literature", notes = "http://icpr18.unisa.it/ Tuesday, August 2 - 16.00/18.00 - Room M Session 45 Cellular Manufacturing", } @Article{Dindarloo:2015:IJMST, author = "Saeid R. Dindarloo", title = "Prediction of blast-induced ground vibrations via genetic programming", journal = "International Journal of Mining Science and Technology", volume = "25", number = "6", pages = "1011--1015", year = "2015", ISSN = "2095-2686", DOI = "doi:10.1016/j.ijmst.2015.09.020", URL = "http://www.sciencedirect.com/science/article/pii/S2095268615001664", abstract = "Excessive ground vibrations, due to blasting, can cause severe damages to the nearby area. Hence, the blast-induced ground vibration prediction is an essential tool for both evaluating and controlling the adverse consequences of blasting. Since there are several effective variables on ground vibrations that have highly nonlinear interactions, no comprehensive model of the blast-induced vibrations are available. In this study, the genetic expression programming technique was employed for prediction of the frequency of the adjacent ground vibrations. Nine input variables were used for prediction of the vibration frequencies at different distances from the blasting face. A high coefficient of determination with low mean absolute percentage error (MAPE) was achieved that demonstrated the suitability of the algorithm in this case. The proposed model outperformed an artificial neural network model that was proposed by other authors for the same dataset.", keywords = "genetic algorithms, genetic programming, Blasting, Ground vibration, Artificial neural networks", } @Misc{Dindarloo:2016:ArXiv, author = "Saeid R. Dindarloo and Elnaz Siami-Irdemoosa", title = "Estimating the unconfined compressive strength of carbonate rocks using gene expression programming", howpublished = "ArXiv", year = "2016", keywords = "genetic algorithms, genetic programming, gene expression programming", bibdate = "2016-03-01", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/corr/corr1602.html#DindarlooS16", URL = "http://arxiv.org/abs/1602.03854", abstract = "Conventionally, many researchers have used both regression and black box techniques to estimate the unconfined compressive strength (UCS) of different rocks. The advantage of the regression approach is that it can be used to render a functional relationship between the predictive rock indices and its UCS. The advantage of the black box techniques is in rendering more accurate predictions. Gene expression programming (GEP) is proposed, in this study, as a robust mathematical alternative for predicting the UCS of carbonate rocks. The two parameters of total porosity and P-wave speed were selected as predictive indices. The proposed GEP model had the advantage of the both traditionally used approaches by proposing a mathematical model, similar to a regression, while keeping the prediction errors as low as the black box methods. The GEP outperformed both artificial neural networks and support vector machines in terms of yielding more accurate estimates of UCS. Both the porosity and the P-wave velocity were sufficient predictive indices for estimating the UCS of the carbonate rocks in this study. Nearly, 95percent of the observed variation in the UCS values was explained by these two parameters (i.e., R2 =0.95).", } @Article{Dindarloo:2016:JSR, author = "Saeid R. Dindarloo and Jonisha P. Pollard and Elnaz Siami-Irdemoosa", title = "Off-road truck-related accidents in U.S. mines", journal = "Journal of Safety Research", volume = "58", pages = "79--87", year = "2016", ISSN = "0022-4375", DOI = "doi:10.1016/j.jsr.2016.07.002", URL = "http://www.sciencedirect.com/science/article/pii/S0022437516301347", abstract = "AbstractIntroduction Off-road trucks are one of the major sources of equipment-related accidents in the U.S. mining industries. A systematic analysis of all off-road truck-related accidents, injuries, and illnesses, which are reported and published by the Mine Safety and Health Administration (MSHA), is expected to provide practical insights for identifying the accident patterns and trends in the available raw database. Therefore, appropriate safety management measures can be administered and implemented based on these accident patterns/trends. Methods A hybrid clustering-classification methodology using K-means clustering and gene expression programming (GEP) is proposed for the analysis of severe and non-severe off-road truck-related injuries at U.S. mines. Using the GEP sub-model, a small subset of the 36 recorded attributes was found to be correlated to the severity level. Results Given the set of specified attributes, the clustering sub-model was able to cluster the accident records into 5 distinct groups. For instance, the first cluster contained accidents related to minerals processing mills and coal preparation plants (91percent). More than two-thirds of the victims in this cluster had less than 5 years of job experience. This cluster was associated with the highest percentage of severe injuries (22 severe accidents, 3.4percent). Almost 50percent of all accidents in this cluster occurred at stone operations. Similarly, the other four clusters were characterized to highlight important patterns that can be used to determine areas of focus for safety initiatives. Conclusions The identified clusters of accidents may play a vital role in the prevention of severe injuries in mining. Further research into the cluster attributes and identified patterns will be necessary to determine how these factors can be mitigated to reduce the risk of severe injuries. Practical application Analyzing injury data using data mining techniques provides some insight into attributes that are associated with high accuracies for predicting injury severity.", keywords = "genetic algorithms, genetic programming, Off-road mining trucks, Fatalities and injuries, K-means clustering, Classification", } @Article{ding:2023:Energies, author = "Jianxin Ding and Rui Zhang and Xin Wen and Xuesong Li and Xianzhi Song and Baodong Ma and Dayu Li and Liang Han", title = "Interpretable Feature Construction and Incremental Update {Fine-Tuning} Strategy for Prediction of Rate of Penetration", journal = "Energies", year = "2023", volume = "16", number = "15", pages = "Article No. 5670", keywords = "genetic algorithms, genetic programming", ISSN = "1996-1073", URL = "https://www.mdpi.com/1996-1073/16/15/5670", DOI = "doi:10.3390/en16155670", abstract = "Prediction of the rate of penetration (ROP) is integral to drilling optimisation. Many scholars have established intelligent prediction models of the ROP. However, these models face challenges in adapting to different formation properties across well sections or regions, limiting their applicability. In this paper, we explore a novel prediction framework combining feature construction and incremental updating. The framework fine-tunes the model using a pre-trained ROP representation. Our method adopts genetic programming to construct interpretable features, which fuse bit properties with engineering and hydraulic parameters. The model is incrementally updated with constant data streams, enabling it to learn the static and dynamic data. We conduct ablation experiments to analyse the impact of interpretable features' construction and incremental updating. The results on field drilling datasets demonstrate that the proposed model achieves robustness against forgetting while maintaining high accuracy in ROP prediction. The model effectively extracts information from data streams and constructs interpretable representational features, which influence the current ROP, with a mean absolute percentage error of 7.5percent on the new dataset, 40percent lower than the static-trained model. This work provides a theoretical reference for the interpretability and transferability of ROP intelligent prediction models.", notes = "also known as \cite{en16155670}", } @InProceedings{ding:2022:LEOL, author = "Li Ding and Ryan Boldi and Thomas Helmuth and Lee Spector", title = "Lexicase Selection at Scale", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion", year = "2022", editor = "Heike Trautmann and Carola Doerr and Alberto Moraglio and Thomas Bartz-Beielstein and Bogdan Filipic and Marcus Gallagher and Yew-Soon Ong and Abhishek Gupta and Anna V Kononova and Hao Wang and Michael Emmerich and Peter A. N. Bosman and Daniela Zaharie and Fabio Caraffini and Johann Dreo and Anne Auger and Konstantin Dietric and Paul Dufosse and Tobias Glasmachers and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Tea Tusar and Dimo Brockhoff and Tome Eftimov and Pascal Kerschke and Boris Naujoks and Mike Preuss and Vanessa Volz and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Mark Coletti and Catherine (Katie) Schuman and Eric “Siggy” Scott and Robert Patton and Paul Wiegand and Jeffrey K. Bassett and Chathika Gunaratne and Tinkle Chugh and Richard Allmendinger and Jussi Hakanen and Daniel Tauritz and John Woodward and Manuel Lopez-Ibanez and John McCall and Jaume Bacardit and Alexander Brownlee and Stefano Cagnoni and Giovanni Iacca and David Walker and Jamal Toutouh and UnaMay O'Reilly and Penousal Machado and Joao Correia and Sergio Nesmachnow and Josu Ceberio and Rafael Villanueva and Ignacio Hidalgo and Francisco {Fernandez de Vega} and Giuseppe Paolo and Alex Coninx and Antoine Cully and Adam Gaier and Stefan Wagner and Michael Affenzeller and Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and William B. Langdon and Justyna Petke and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Paetzel and Alexander Wagner and Michael Heider and Nadarajen Veerapen and Katherine Malan and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Mohammad Nabi Omidvar and Yuan Sun and Ernesto Tarantino and De Falco Ivanoe and Antonio {Della Cioppa} and Scafuri Umberto and John Rieffel and Jean-Baptiste Mouret and Stephane Doncieux and Stefanos Nikolaidis and Julian Togelius and Matthew C. Fontaine and Serban Georgescu and Francisco Chicano and Darrell Whitley and Oleksandr Kyriienko and Denny Dahl and Ofer Shir and Lee Spector and Alma Rahat and Richard Everson and Jonathan Fieldsend and Handing Wang and Yaochu Jin and Erik Hemberg and Marwa A. Elsayed and Michael Kommenda and William {La Cava} and Gabriel Kronberger and Steven Gustafson", pages = "2054--2062", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, image classification, parent selection, deep learning, lexicase selection", isbn13 = "978-1-4503-9268-6/22/07", DOI = "doi:10.1145/3520304.3534026", video_url = "https://vimeo.com/723974066", abstract = "Lexicase selection is a semantic-aware parent selection method, which assesses individual test cases in a randomly-shuffled data stream. It has demonstrated success in multiple research areas including genetic programming, genetic algorithms, and more recently symbolic regression and deep learning. One potential drawback of lexicase selection and its variants is that the selection procedure requires evaluating training cases in a single data stream, making it difficult to handle tasks where the evaluation is computationally heavy or the dataset is large-scale, e.g., deep learning. In this work, we investigate how the weighted shuffle methods can be employed to improve the efficiency of lexicase selection. We propose a novel method, fast lexicase selection, which incorporates lexicase selection and weighted shuffle with partial evaluation. Experiments on both classic genetic programming and deep learning tasks indicate that the proposed method can significantly reduce the number of evaluation steps needed for lexicase selection to select an individual, improving its efficiency while maintaining the performance.", notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @InProceedings{ding:2023:GECCO, author = "Li Ding and Edward Pantridge and Lee Spector", title = "Probabilistic Lexicase Selection", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "1073--1081", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, symbolic regression, machine learning, evolutionary algorithms, program synthesis, parent selection", isbn13 = "9798400701191", DOI = "doi:10.1145/3583131.3590375", size = "9 pages", abstract = "Lexicase selection is a widely used parent selection algorithm in genetic programming, known for its success in various task domains such as program synthesis, symbolic regression, and machine learning. Due to its non-parametric and recursive nature, calculating the probability of each individual being selected by lexicase selection has been proven to be an NP-hard problem, which discourages deeper theoretical understanding and practical improvements to the algorithm. In this work, we introduce probabilistic lexicase selection (plexicase selection), a novel parent selection algorithm that efficiently approximates the probability distribution of lexicase selection. Our method not only demonstrates superior problem-solving capabilities as a semantic-aware selection method, but also benefits from having a probabilistic representation of the selection process for enhanced efficiency and flexibility. Experiments are conducted in two prevalent domains in genetic programming: program synthesis and symbolic regression, using standard benchmarks including PSB and SRBench. The empirical results show that plexicase selection achieves state-of-the-art problem-solving performance that is competitive to the lexicase selection, and significantly outperforms lexicase selection in computation efficiency.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @TechReport{Ding:2003:XBZRs, author = "Li-ying Ding and Yu-gang Li and Fang-yu Han", title = "Combinational Application of Genetic Programming and Simulated Annealing in Distillation Process Synthesis", institution = "Qingdao University of Science and Technology", year = "2003", type = "Journal of Qingdao Institute of Chemical Technology", volume = "24", number = "Supplement", address = "China", month = sep, keywords = "genetic algorithms, genetic programming, SA, simulated annealing, distillation synthesis,heat integration", URL = "http://en.cnki.com.cn/Article_en/CJFDTotal-QDHG2003S1010.htm", broken = "http://xbzr.qust.edu.cn/WEB2003-zeng/03zk11.htm", abstract = "Genetic Programming is combined with Simulated Annealing and applied in synthesis of multi-component distillation process. On one hand, Genetic Programming is used to determine the optimal distillation process structure. On the other hand, Simulated Annealing is used to optimize the continuous variables in the process, that is, the reflux. Therefore, by the combination of Genetic Programming and Simulated Annealing, an optimal distillation process is obtained. An example is given to illustrate that the method is effective.", notes = "http://xbzr.qust.edu.cn/new_page_1.htm Research Center for Computer and Chemical Engineering, Qingdao University of Science and Technology, Qingdao 266042, China College of Chemical Engineering, South China University of Technology, Guangzhou 510640, China", } @TechReport{Li-yingDing:2003:XBZRo, author = "Li-ying Ding and Yu-gang Li and Fang-yu Han", title = "Design of Complex Distillation Process Based on Genetic Programming", institution = "Qingdao University of Science and Technology", year = "2003", type = "Journal of Qingdao Institute of Chemical Technology", volume = "24", number = "5", month = oct, keywords = "genetic algorithms, genetic programming, complex distillation process", URL = "http://en.cnki.com.cn/Article_en/CJFDTotal-QDHG200305001.htm", broken = "http://xbzr.qust.edu.cn/WEB2003-5/ee5-2.HTM", abstract = "Genetic programming was applied to design multi-components complex distillation process by setting up interrelationship between the individual code and the process structure. With the help of equivalent simple distillation process, the objective value, that is the fitness, of complex process was determined. After a series of operations, such as reproduction, crossover and mutation between individuals, the process structure with optimum economic objective (the sum of equipment cost and operation cost) was obtained. An example was given to illustrate the effectiveness of this method.", notes = "http://xbzr.qust.edu.cn/new_page_1.htm 1001-4764-(2003)05-0382-05 Computer and Chemical Engineering Research Centre, Qingdao University of Science and Technology, Qingdao 266042, China College of Chemical Engineering, South China University of Technology, Guangzhou 510640, China", } @Article{Ding:2019:ACC, author = "Peng Ding and Qinrong Qian and Hua Wang and Jianyong Yao", journal = "IEEE Access", title = "A Symbolic Regression Based Residual Useful Life Model for Slewing Bearings", year = "2019", volume = "7", pages = "72076--72089", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ACCESS.2019.2919663", ISSN = "2169-3536", abstract = "Slewing bearings are vital functional components of large machinery. It is of far reaching significance to study their life prediction and health management. Many studies are based on data-driven approaches. However, part of them in the form of {"}black-box{"} lack actual physical meanings due to opacity model structures and have difficulty in choosing optimal parameters. Few kinds of literature focus on explicit model relationships for slewing bearings' life models. In this paper, a novel approach based on symbolic regression is proposed with the aim of exploring slewing bearings' explicit life models in depth and to predict residual useful life (RUL). The proposed method integrates the strengths of multiple signals describing a comprehensive response to slewing bearings' health and various genetic programming (GP) algorithms modeling life expressions. In addition, independent, hybrid, and piecewise strategies are introduced and explicit model relationships with respect to degradation indicators (DIs) are established via GPs. To verify the proposed method, three run-to-failure experiments under discrepant operating conditions of slewing bearings are carried out. Prediction results demonstrate that models generated by epigenetic linear genetic programming (ELGP) under hybrid and piecewise modeling strategy with similarity-based combination strategy perform best. More importantly, their life expressions are more succinct and intelligible than in other situations.", notes = "School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing, China Also known as \cite{8723621}", } @Article{DING:2019:Measurement, author = "Peng Ding and Hua Wang and Weigang Bao and Rongjing Hong", title = "{HYGP-MSAM} based model for slewing bearing residual useful life prediction", journal = "Measurement", volume = "141", pages = "162--175", year = "2019", ISSN = "0263-2241", DOI = "doi:10.1016/j.measurement.2019.04.039", URL = "http://www.sciencedirect.com/science/article/pii/S0263224119303574", keywords = "genetic algorithms, genetic programming, Slewing bearing, Life model expression, Symbolic regression, Condition monitoring and life prediction, Coupling between signals", abstract = "Slewing bearings are critical functional components of large machinery and their residual useful life (RUL) prediction can avoid downtime and reduce accidents and casualties. In the field of their condition monitoring and life prediction, multi-signal and multi-feature fusion (MSMFF) is a trend for over the current literatures. However, most of the existing researches only consider the independent effect of degradation indicators, thereby ignoring the coupling effect between different signals. To overcome this gap and further compensate for the lacks of transparency and practical meaning in data-driven approaches, especially for artificial intelligence ones, this paper proposes an adaptive symbolic regression based modeling strategy: hybrid genetic programming-model structure adaptive method (HYGP-MSAM), integrating the strengths of HYGP algorithm which is a realization based on symbolic regression directly obtaining explicit analytical expressions for the life model compared with {"}black box{"} modeling methods and MSAM aiming for reconstructing the initial models with coupling terms. To get better description of degradation trend, ensemble empirical mode decomposition combined with singular value decomposition (EEMD-SVD) denoising method is employed for raw signals and degradation indicators are obtained through a manifold learning based fusion algorithm. The proposed HYGP-MSAM modeling strategy is used to establish life model expressions afterwards. Finally, life models in the form of function expressions are derived and an accelerated run-to-failed experiment is carried out to test this strategy. It is shown that adaptive coupling reconstruction strategy for upgrading the symbolic regression based modeling methods can greatly improve the fault tolerance of algorithms under parametric error and effectively improve the prediction accuracy", } @Misc{arXiv:quant-ph/0610105, author = "Shengchao Ding and Zhi Jin and Qing Yang", title = "Evolving Quantum Oracles with Hybrid Quantum-inspired Evolutionary Algorithm", howpublished = "arXiv", year = "2008", month = "13 " # oct, note = "arXiv:quant-ph/0610105 v1", keywords = "genetic algorithms, genetic programming", URL = "http://arxiv.org/PS_cache/quant-ph/pdf/0610/0610105v1.pdf", size = "9 pages", abstract = "Quantum oracles play key roles in the studies of quantum computation and quantum information. But implementing quantum oracles efficiently with universal quantum gates is a hard work. Motivated by genetic programming, this paper proposes a novel approach to evolve quantum oracles with a hybrid quantum-inspired evolutionary algorithm. The approach codes quantum circuits with numerical values and combines the cost and correctness of quantum circuits into the fitness function. To speed up the calculation of matrix multiplication in the evaluation of individuals, a fast algorithm of matrix multiplication with Kronecker product is also presented. The experiments show the validity and the effects of some parameters of the presented approach. And some characteristics of the novel approach are discussed too.", notes = "1 Institute of Computing Technology, Chinese Academy of Sciences 2 Academy of Mathematics and Systems Science, Chinese Academy of Sciences 3 Graduate University of the Chinese Academy of Sciences Beijing 100080, China 4 School of Computer Science and Technology, South-Central University for Nationalities, Wuhan 430074, China", } @Article{DING:2019:TNAJEF, author = "Shusheng Ding and Tianxiang Cui and Yongmin Zhang", title = "Incorporating the {RMB} internationalization effect into its exchange rate volatility forecasting", journal = "The North American Journal of Economics and Finance", pages = "101103", year = "2019", ISSN = "1062-9408", DOI = "doi:10.1016/j.najef.2019.101103", URL = "http://www.sciencedirect.com/science/article/pii/S1062940819302840", keywords = "genetic algorithms, genetic programming, RMB internationalization, Exchange rate, Volatility forecasting, E47, F31, G15", abstract = "Recently, the Chinese government has launched the renminbi (RMB) internationalization policy as an impetus to foster China's global economic integration. The RMB internationalization effect on China's economy and the RMB exchange rate has attracted massive attention in recent financial research. In this paper, we adopt a genetic programming (GP) method to generate new RMB exchange rate volatility forecasting models incorporating the RMB internationalization effect. Our models are proved to have significant accuracy improvement in predicting both RMB/US dollar and RMB/euro exchange rate volatilities, compared with standard GARCH volatility models, which are incapable of capturing the RMB internationalization effect. Furthermore, our models display salient practical implications for policy makers to formulate monetary policies and currency traders to design effective trading strategies", } @Article{Ding:2020:jaihc, author = "Shusheng Ding and Tianxiang Cui and Xihan Xiong and Ruibin Bai", title = "Forecasting stock market return with nonlinearity: a genetic programming approach", journal = "Journal of Ambient Intelligence and Humanized Computing", year = "2020", volume = "11", number = "11", pages = "4927--4939", month = nov, keywords = "genetic algorithms, genetic programming, Return forecasting, Nonlinear models", URL = "http://eprints.nottingham.ac.uk/60489/", URL = "http://eprints.nottingham.ac.uk/60489/1/Forecasting%20stock%20market%20return%20with%20nonlinearity%20a%20genetic%20programming%20approach.pdf", URL = "https://rdcu.be/diANf", DOI = "doi:10.1007/s12652-020-01762-0", size = "13 pages", abstract = "The issue whether return in the stock market is predictable remains ambiguous. This paper attempts to establish new return forecasting models in order to contribute on addressing this issue. In contrast to existing literatures, we first reveal that the model forecasting accuracy can be improved through better model specification without adding any new variables. Instead of having a unified return forecasting model, we argue that stock markets in different countries shall have different forecasting models. Furthermore, we adopt an evolutionary procedure called Genetic programming (GP), to develop our new models with nonlinearity. Our newly-developed forecasting models are testified to be more accurate than traditional AR-family models. More importantly, the trading strategy we propose based on our forecasting models has been verified to be highly profitable in different types of stock markets in terms of stock index futures trading.", notes = "Special issue on Wearable computing techniques for smart health/Special issue on Challenges and trends in Ambient Intelligence 1 School of Business, Ningbo University, Ningbo, China 2 School of Computer Science, The University of Nottingham Ningbo China, Ningbo, China 3 Department of Mathematics, The London School of Economics and Political Science, London, UK", } @Article{DING:2023:irfa, author = "Shusheng Ding and Tianxiang Cui and Anthony Graham Bellotti and Mohammad Zoynul Abedin and Brian Lucey", title = "The role of feature importance in predicting corporate financial distress in pre and post {COVID} periods: Evidence from China", journal = "International Review of Financial Analysis", volume = "90", pages = "102851", year = "2023", ISSN = "1057-5219", DOI = "doi:10.1016/j.irfa.2023.102851", URL = "https://www.sciencedirect.com/science/article/pii/S1057521923003678", keywords = "genetic algorithms, genetic programming, Financial distress prediction, Time-varying feature selection, Extreme gradient boosting, COVID-19 crisis", abstract = "The prediction of firm financial distress during the COVID-19 crisis episode attracted massive academic attention since economic uncertainty was exacerbated. In this paper, we propose a firm financial distress prediction model based on the Extreme Gradient Boosting-Genetic Programming (XGB-GP) framework by investigating subsamples of pre-COVID and post-COVID periods. The key contribution of our paper is that we explore time-varying prediction features for pre-COVID and post-COVID periods. We illuminate that the earning financial indicator is the dominant feature for financial distress prediction during the pre-COVID period, whereas total financial leverage is the most important factor during the post-COVID period. On this basis, our XGB-GP financial distress prediction model exhibits higher prediction accuracy than the traditional models. As a result, managers can modify the financial leverage level to improve the financial situation of the firm by reducing the debt burden and increasing profitability during the post-COVID period", } @Article{Ding:2025:irfa, author = "Shusheng Ding and Xiangling Wu and Tianxiang Cui and John W. Goodell and Anna Min Du", title = "Modeling climate policy uncertainty into cryptocurrency volatilities", journal = "International Review of Financial Analysis", year = "2025", volume = "102", pages = "104030", keywords = "genetic algorithms, genetic programming, Cryptocurrency markets, Climate policy uncertainty, Volatility forecasting", ISSN = "1057-5219", URL = "https://www.sciencedirect.com/science/article/pii/S1057521925001176", DOI = "doi:10.1016/j.irfa.2025.104030", abstract = "Climate change is a highly controversial topic within the socioeconomic context. Climate Policy Uncertainty (CPU) arises from the process of climate policies formulation and implementation. This uncertainty impacts financial market volatilities, including cryptocurrency markets. In this paper, we demonstrate the substantial role of CPU in forecasting volatilities in cryptocurrency markets using Genetic Programming (GP). Our study shows that different cryptocurrency markets respond differently to CPU across time scales. Our paper contributes to the literature by illustrating the impact of CPU on cryptocurrency market volatilities and analyses it across different time horizons. Second, we build three volatility forecasting models for different cryptocurrency markets by incorporating CPU, which outperform traditional models. Our models can thereby illuminate portfolio construction and hedging strategies, providing valuable insights for investors and policymakers", } @Article{Ding:2025:eiar, author = "Shusheng Ding and Tianxiang Cui and Anna Min Du and John W. Goodell and Nanjiang Du", title = "Disentangling and hedging global warming risk: A machine learning approach", journal = "Environmental Impact Assessment Review", year = "2025", volume = "115", pages = "107987", keywords = "genetic algorithms, genetic programming, Climate finance, Extreme gradient boosting, Global warming risk, Greenhouse gas emission", ISSN = "0195-9255", URL = "https://www.sciencedirect.com/science/article/pii/S0195925525001842", DOI = "doi:10.1016/j.eiar.2025.107987", abstract = "As global warming provokes increasing attention from investors, this study disentangles global warming risk (GWR) for investors by leveraging energy futures volatilities. This study derives GWR from energy futures using an extreme gradient boosting (XGB)-genetic programming (GP) framework. Our XGB-GP framework develops volatility forecasting models for GWR from selected energy futures markets identified by XGB as key contributors to global warming, surpassing traditional models in forecasting accuracy. The originality of the study rests on the pioneering integration of the XGB-GP framework in predicting climate risk, linking energy futures markets with climate risk management and enabling feasible climate-featured portfolio hedging. Our study also sheds new insights for policymakers to design carbon trading systems and carbon pricing mechanisms, as they can use relevant energy futures prices as a basis for carbon trading calibration", } @Article{ding:2021:Processes, author = "Xin Ding and Xiaohong Wang and Peng Du and Zenghu Tian and Jingxuan Chen", title = "Prediction of New {Distillation-Membrane} Separation Integrated Process with Potential in Industrial Application", journal = "Processes", year = "2021", volume = "9", number = "2", keywords = "genetic algorithms, genetic programming", ISSN = "2227-9717", URL = "https://www.mdpi.com/2227-9717/9/2/318", DOI = "doi:10.3390/pr9020318", abstract = "In this paper, a new integrated distillation-membrane separation process solution strategy based on genetic programming (GP) was established for azeotrope separation. Then, a price evaluation method based on the theory of unit membrane area was proposed, so that those membranes which are still in the experimental stage and have no actual industrial cost for reference can also be used in the experimental research. For different characteristics and separation requirements of various azeotropic systems, the solution strategy can be matched with difference pervaporation membranes, and the optimal distillation-membrane separation integrated process can be solved quickly and accurately. Taking methanol-toluene as an example, the separation operation was optimised by using the algorithm. The effects of different feed flows and compositions on the modification of the chitosan membrane were discussed. These results provide a reliable basis for the prospects for development and modification direction of membrane materials which are still in the experimental research stage.", notes = "also known as \cite{pr9020318}", } @InProceedings{Ding:2019:GI, author = "Zhen Yu Ding and Yiwei Lyu and Christopher Timperley and Claire {Le Goues}", title = "Leveraging Program Invariants to Promote Population Diversity in Search-Based Automatic Program Repair", booktitle = "GI-2019, ICSE workshops proceedings", year = "2019", editor = "Justyna Petke and Shin Hwei Tan and William B. Langdon and Westley Weimer", pages = "2--9", address = "Montreal", month = "28 " # may, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE", isbn13 = "978-1-7281-2268-7", URL = "http://geneticimprovementofsoftware.com/paper_pdfs/ding2019leveraging.pdf", DOI = "doi:10.1109/GI.2019.00011", size = "8 pages", abstract = "Search-based automatic program repair has shown promise in reducing the cost of defects in real-world software.However, to date, such techniques have typically been most successful when constructing short or single-edit repairs. This is true even when techniques make use of heuristic search strategies, like genetic programming, that in principle support the construction of patches of arbitrary length. One key reason is that the fitness function traditionally depends entirely on test cases, which are poor at identifying partially correct solutions and lead to a fitness landscape with many plateaus. We propose a novel fitness function that optimizes for both functionality and semantic diversity, characterized using learned invariant solver intermediate behaviour. Our early results show that this new approach improves semantic diversity and fitness granularity, but does not statistically significantly improve repair performance.", notes = "Cost of buggy software in 2017. $170000000 Plateau fitness landscape. Daikon. IntroClassJava University of Pittsburgh Slides: http://geneticimprovementofsoftware.com/slides/ding2019leveraging_slides.pdf GI-2019 http://geneticimprovementofsoftware.com part of \cite{Petke:2019:ICSEworkshop}", } @Misc{Dingle:2024:GPTP, author = "Kamaludin Dingle", title = "Simplicity Bias, Algorithmic Probability, and Biological Evolution", booktitle = "Genetic Programming Theory and Practice XXI", year = "2024", editor = "Wolfgang Banzhaf and Ting Hu and Alexander Lalejini and Stephan Winkler", series = "Genetic and Evolutionary Computation", address = "University of Michigan, USA", month = jun # " 6-8", keywords = "genetic algorithms, genetic programming", notes = "Does not appear in published proceedings. See for example Dingle, K., Camargo, C.Q. & Louis, A.A. Input-output maps are strongly biased towards simple outputs. Nat Commun 9, 761 (2018). https://doi.org/10.1038/s41467-018-03101-6 https://rdcu.be/edG2t", } @Article{Dinh:2017:PLOScb, author = "Jean-Louis Dinh and Etienne Farcot and Charlie Hodgman", title = "The logic of the floral transition: Reverse-engineering the switch controlling the identity of lateral organs", journal = "PLOS Computational Biology", year = "2017", month = sep # " 20", keywords = "genetic algorithms, genetic programming", language = "en", oai = "oai:eprints.nottingham.ac.uk:46904", ISSN = "1553-7358", bibsource = "OAI-PMH server at eprints.nottingham.ac.uk", publisher = "Public Library of Science", URL = "http://eprints.nottingham.ac.uk/46904/", URL = "http://eprints.nottingham.ac.uk/46904/1/Farcot_logic.pdf", URL = "http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005744", DOI = "doi:10.1371/journal.pcbi.1005744", size = "25 pages", abstract = "Much laboratory work has been carried out to determine the gene regulatory network (GRN) that results in plant cells becoming flowers instead of leaves. However, this also involves the spatial distribution of different cell types, and poses the question of whether alternative networks could produce the same set of observed results. This issue has been addressed here through a survey of the published intercellular distribution of expressed regulatory genes and techniques both developed and applied to Boolean network models. This has uncovered a large number of models which are compatible with the currently available data. An exhaustive exploration had some success but proved to be unfeasible due to the massive number of alternative models, so genetic programming algorithms have also been employed. This approach allows exploration on the basis of both data-fitting criteria and parsimony of the regulatory processes, ruling out biologically unrealistic mechanisms. One of the conclusions is that, despite the multiplicity of acceptable models, an overall structure dominates, with differences mostly in alternative fine-grained regulatory interactions. The overall structure confirms the known interactions, including some that were not present in the training set, showing that current data are sufficient to determine the overall structure of the GRN. The model stresses the importance of relative spatial location, through explicit references to this aspect. This approach also provides a quantitative indication of how likely some regulatory interactions might be, and can be applied to the study of other developmental transitions.", notes = "'genetic programming algorithm has been employed to find suitable [graphical GRN] models that explain all observed data.' also known as \cite{oai:eprints.nottingham.ac.uk:46904}", } @PhdThesis{Dinh:thesis, author = "Jean-Louis T Q Dinh", title = "Mathematical modelling of the floral transition", school = "School of Biosciences, University of Nottingham", year = "2017", month = dec # "~14", address = "UK", keywords = "genetic algorithms, genetic programming, mathematical modelling, floral transition, ODE, boolean", language = "en", id = "45106", oai = "oai:eprints.nottingham.ac.uk:45106", bibsource = "OAI-PMH server at eprints.nottingham.ac.uk", URL = "http://eprints.nottingham.ac.uk/45106/", URL = "http://eprints.nottingham.ac.uk/45106/1/Dinh%20JL%20-%20Mathematical%20modelling%20of%20the%20floral%20transition.pdf", abstract = "The floral transition is a developmental process through which some plants commit to flowering and stop producing leaves. This is controlled by changes in gene expression in the shoot apical meristem (SAM). Many of the genes involved are known, but their interactions are usually only studied one by one, or in small sets. While it might be necessary to properly ascertain the existence of regulatory interactions from a biological standpoint, it cannot really provide insight in the functioning of the floral-transition process as a whole. For this reason, a modelling approach has been used to integrate knowledge from multiple studies. Several approaches were applied, starting with ordinary differential equation (ODE) models. It revealed in two cases - one on rice and one on Arabidopsis thaliana - that the currently available data were not sufficient to build data-driven ODE models. The main issues were the low temporal resolution of the time series, the low spatial resolution of the sampling methods used on meristematic tissue, and the lack of gene expression measurements in studies of factors affecting the floral transition. These issues made the available gene expression time series of little use to infer the regulatory mechanisms involved. Therefore, another approach based on qualitative data was investigated. It relies on data extracted from published in situ hybridization (ISH) studies, and Boolean modelling. The ISH data clearly showed that shoot apical meristems (SAM) are not homogeneous and contain multiple spatial domains corresponding to coexisting steady-states of the same regulatory network. Using genetic programming, Boolean models with the right steady-states were successfully generated. Finally, the third modelling approach builds upon one of the generated Boolean models and implements its logic into a 3D tissue of SAM. As Boolean models cannot represent quantitative spatio-temporal phenomena such as passive transport, the model had to be translated into ODEs. This model successfully reproduced the patterning of SAM genes in a static tissue structure. The main biological conclusions of this thesis are that the spatial organization of gene expression in the SAM is a crucial part of the floral transition and of the development of inflorescences, and it is mediated by the transport of mobile proteins and hormones. On the modelling front, this work shows that quantitative ODE models, despite their popularity, cannot be applied to all situations. When the data are insufficient, simpler approaches like Boolean models and ODE models with qualitatively selected parameters can provide suitable alternatives and facilitate large-scale explorations of the space of possible models, due to their low computational cost.", notes = "Supervisors: Graham Seymour and Etienne Farcot Simiilar name used for PhD thesis of Nick Pullen UEA 2014 John Innes Center", } @InProceedings{Dinh:2015:CEC, author = "Thi Thu Huong Dinh and Chu Thi Huong and Nguyen Quang Uy", title = "Transfer Learning in Genetic Programming", booktitle = "Proceedings of 2015 IEEE Congress on Evolutionary Computation (CEC 2015)", year = "2015", editor = "Yadahiko Murata", pages = "1145--1151", address = "Sendai, Japan", month = "25-28 " # may, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-7491-7", DOI = "doi:10.1109/CEC.2015.7257018", abstract = "Transfer learning is a process in which a system can apply knowledge and skills learned in previous tasks to novel tasks. This technique has emerged as a new framework to enhance the performance of learning methods in machine learning. Surprisingly, transfer learning has not deservedly received the attention from the Genetic Programming research community. In this paper, we propose several transfer learning methods for Genetic Programming (GP). These methods were implemented by transferring a number of good individuals or sub-individuals from the source to the target problem. They were tested on two families of symbolic regression problems. The experimental results showed that transfer learning methods help GP to achieve better training errors. Importantly, the performance of GP on unseen data when implemented with transfer learning was also considerably improved. Furthermore, the impact of transfer learning to GP code bloat was examined that showed that limiting the size of transferred individuals helps to reduce the code growth problem in GP.", notes = "1125 hrs 15457 CEC2015 Faculty of IT Thu Dau Mot University Binh Duong, Vietname", } @InCollection{Dini:2007:DBE, author = "Paolo Dini", title = "Natural Science: Biological development as metaphor for the construction of order", booktitle = "Digital Business Ecosystems", publisher = "European Commission: Information Society and Media", year = "2007", editor = "Francesco Nachira and Andrea Nicolai and Paolo Dini and Marion {Le Louarn} and Lorena Rivera Leon", chapter = "1-1", pages = "34--47", month = "11 " # sep, keywords = "genetic algorithms, genetic programming", URL = "http://www.digital-ecosystems.org/book/", URL = "http://www.digital-ecosystems.org/book/Section1.pdf", size = "42 pages", notes = "page 34 page 39 Mention of GP and Fraglets part of \cite{Nachira:2007:DBE}", } @InProceedings{eurogp06:DiosanOltean, author = "Laura Dio\c{s}an and Mihai Oltean", title = "Evolving crossover operators for function optimization", editor = "Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art", booktitle = "Proceedings of the 9th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3905", year = "2006", address = "Budapest, Hungary", month = "10 - 12 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-33143-3", pages = "97--108", DOI = "doi:10.1007/11729976_9", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "A new model for evolving crossover operators for evolutionary function optimisation is proposed in this paper. The model is a hybrid technique that combines a Genetic Programming (GP) algorithm and a Genetic Algorithm (GA). Each GP chromosome is a tree encoding a crossover operator used for function optimization. The evolved crossover is embedded into a standard Genetic Algorithm which is used for solving a particular problem. Several crossover operators for function optimisation are evolved using the considered model. The evolved crossover operators are compared to the human-designed convex crossover. Numerical experiments show that the evolved crossover operators perform similarly or sometimes even better than standard approaches for several well-known benchmarking problems.", notes = "Part of \cite{collet:2006:GP} EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006", } @InProceedings{1277332, author = "Laura Diosan and Mihai Oltean and Alexandrina Rogozan and Jean Pierre Pecuchet", title = "Genetically designed multiple-kernels for improving the SVM performance", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "1873--1873", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1873.pdf", DOI = "doi:10.1145/1276958.1277332", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Genetics-Based Machine Learning: Poster, kernel, Support Vector Machines, SVM", abstract = "Classical kernel-based classifiers only use a single kernel, but the real-world applications have emphasised the need to consider a combination of kernels - also known as a multiple kernel - in order to boost the performance. Our purpose is to automatically find the mathematical expression of a multiple kernel by evolutionary means. In order to achieve this purpose we propose a hybrid model that combines a Genetic Programming (GP) algorithm and a kernel-based Support Vector Machine (SVM) classifier. Each GP chromosome is a tree encoding the mathematical expression of a multiple kernel. Numerical experiments show that the SVM embedding the evolved multiple kernel performs better than the standard kernels for the considered classification problems.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071 Evolved SVM Kernel forced to remain valid because only legal combinations (ie plus, times, exp) of legal kernels (linear, plynomial, RBF) is allowed.", } @InProceedings{Diosan:2007:ICMLA, title = "Evolving kernel functions for SVMs by genetic programming", author = "Laura Diosan and Alexandrina Rogozan and Jean-Pierre Pecuchet", booktitle = "Sixth International Conference on Machine Learning and Applications, ICMLA 2007", year = "2007", month = "13-15 " # dec, pages = "19--24", address = "Cincinnati, Ohio, USA", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, support vector machines, SVM, GP chromosome, SVM kernel functions, evolved kernel, kernel expression, mathematical expression, tree encoding", isbn13 = "978-0-7695-3069-7", DOI = "doi:10.1109/ICMLA.2007.70", abstract = "hybrid model for evolving support vector machine (SVM) kernel functions is developed in this paper. The kernel expression is considered as a parameter of the SVM algorithm and the current approach tries to find the best expression for this SVM parameter. The model is a hybrid technique that combines a genetic programming (GP) algorithm and a support vector machine (SVM) algorithm. Each GP chromosome is a tree encoding the mathematical expression for the kernel function. The evolved kernel is compared to several human-designed kernels and to a previous genetic kernel on several datasets. Numerical experiments show that the SVM embedding our evolved kernel performs statistically better than standard kernels, but also than previous genetic kernel for all considered classification problems.", notes = "also known as \cite{4457202}. http://www.icmla-conference.org/icmla07/", } @InProceedings{conf/evoW/DiosanRP08, title = "Optimising Multiple Kernels for {SVM} by Genetic Programming", author = "Laura Diosan and Alexandrina Rogozan and Jean-Pierre Pecuchet", booktitle = "Proceedings of the 8th European Conference, Evolutionary Computation in Combinatorial Optimization, Evo{COP}", bibdate = "2008-04-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/evoW/evocop2008.html#DiosanRP08", publisher = "Springer", year = "2008", volume = "4972", editor = "Jano I. van Hemert and Carlos Cotta", isbn13 = "978-3-540-78603-0", pages = "230--241", series = "Lecture Notes in Computer Science", DOI = "doi:10.1007/978-3-540-78604-7_20", address = "Naples, Italy", month = mar # " 26-28", keywords = "genetic algorithms, genetic programming", } @Article{Diosan:2009:GPEM, author = "Laura Diosan and Mihai Oltean", title = "Evolutionary design of Evolutionary Algorithms", journal = "Genetic Programming and Evolvable Machines", year = "2009", volume = "10", number = "3", pages = "263--306", month = sep, keywords = "genetic algorithms, genetic programming, Evolving evolutionary algorithms, Meta genetic programming, Function optimization", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-009-9081-6", abstract = "Manual design of Evolutionary Algorithms (EAs) capable of performing very well on a wide range of problems is a difficult task. This is why we have to find other manners to construct algorithms that perform very well on some problems. One possibility (which is explored in this paper) is to let the evolution discover the optimal structure and parameters of the EA used for solving a specific problem. To this end a new model for automatic generation of EAs by evolutionary means is proposed here. The model is based on a simple Genetic Algorithm (GA). Every GA chromosome encodes an EA, which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization are generated by using the considered model. Numerical experiments show that the EAs perform similarly and sometimes even better than standard approaches for several well-known benchmarking problems.", } @Article{Diosan:2010:JAIT, author = "Laura Diosan and Alexandrina Rogozan and Jean Pierre Pecuchet", title = "Learning SVM with Complex Multiple Kernels Evolved by Genetic Programming", journal = "International Journal on Artificial Intelligence Tools", year = "2010", volume = "19", number = "5", pages = "647--677", keywords = "genetic algorithms, genetic programming, Multiple kernel learning, hybrid model, SVM", DOI = "doi:10.1142/S0218213010000352", abstract = "Classic kernel-based classifiers use only a single kernel, but the real-world applications have emphasised the need to consider a combination of kernels, also known as a multiple kernel (MK), in order to boost the classification accuracy by adapting better to the characteristics of the data. Our purpose is to automatically design a complex multiple kernel by evolutionary means. In order to achieve this purpose we propose a hybrid model that combines a Genetic Programming (GP) algorithm and a kernel-based Support Vector Machine (SVM) classifier. In our model, each GP chromosome is a tree that encodes the mathematical expression of a multiple kernel. The evolutionary search process of the optimal MK is guided by the fitness function (or efficiency) of each possible MK. The complex multiple kernels which are evolved in this manner (eCMKs) are compared to several classic simple kernels (SKs), to a convex linear multiple kernel (cLMK) and to an evolutionary linear multiple kernel (eLMK) on several real-world data sets from UCI repository. The numerical experiments show that the SVM involving the evolutionary complex multiple kernels perform better than the classic simple kernels. Moreover, on the considered data sets, the new multiple kernels outperform both the cLMK and eLMK linear multiple kernels. These results emphasise the fact that the SVM algorithm requires a combination of kernels more complex than a linear one in order to boost its performance.", notes = "IJAIT Laboratoire d'Informatique, de Traitement de l'Information et des Systemes, EA 4108, Institut National des Sciences Appliquees, Rouen, France", } @Article{journals/apin/DiosanA15, author = "Laura Diosan and Anca Andreica", title = "Multi-objective breast cancer classification by using multi-expression programming", journal = "Applied Intelligence", year = "2015", volume = "43", number = "3", pages = "499--511", month = oct, keywords = "genetic algorithms, genetic programming, multi-objective optimization, Breast cancer", bibdate = "2015-09-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/apin/apin43.html#DiosanA15", URL = "https://rdcu.be/c9fkd", DOI = "doi:10.1007/s10489-015-0668-8", size = "13 pages", abstract = "Despite many years of research, breast cancer detection is still a difficult, but very important problem to be solved. An automatic diagnosis system could establish whether a mammography presents tumours or belongs to a healthy patient and could offer, in this way, a second opinion to a radiologist that tries to establish a diagnosis. We therefore propose a system that could contribute to lowering both the costs and the work of an imaging diagnosis centre of breast cancer and in addition to increase the trust level in that diagnosis. We present a multi-objective evolutionary approach based on Multi-Expression Programming—a linear Genetic Programming method—that could classify a mammogram starting from a raw image of the breast. The processed images are represented through Histogram of Oriented Gradients and Kernel Descriptors since these image features have been reported as being very efficient in the image recognition scientific community and they have not been applied to mammograms before. Numerical experiments are performed on freely available datasets consisting of normal and abnormal film-based and digital mammograms and show the efficiency of the proposed decision support system", notes = "Department of Computer Science, Babes-Bolyai University, Cluj-Napoca, Romania", } @InProceedings{DiPaola:2006:, author = "Steve DiPaola", title = "Evolving Portrait Painter Programs using Genetic Programming to Explore Computer Creativity", booktitle = "Proceedings of iDMAa Conference (International Digital Media and Arts Association", year = "2006", editor = "Glenn Platt and Peg Faimon", address = "Miami University, Oxford, OH", month = apr # " 6-8", organisation = "International Digital Media and Arts Association", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", URL = "http://www.units.miamioh.edu/codeconference/schedule/presentations.htm", URL = "http://www.units.muohio.edu/codeconference/papers/papers/idmapaper1.pdf", size = "7 pages", abstract = "Creative systems as opposed to standard evolutionary systems favor exploration over optimization, finding innovative or novel solutions over a preconceived notion of a specific optimal solution. The best creative evolutionary systems only provide tools, allowing the evolutionary process to discover novelty and innovation on its own. We experiment with computer creativity by employing and modifying techniques from evolutionary computation to create a related family of abstract portraits. A new type of Genetic Programming (GP) system is used called Cartesian GP, which uses typical GP Darwinian evolutionary techniques (crossover, mutation, and survival), but has several features that allow the GP system to favor creative solutions over optimized solutions including accommodating for genetic drift where different genotypes map to the same phenotype, visual mapping modules and a knowledge of a painterly color space. This work with its specific goal of evolving portrait painter programs to create a portrait 'sparked' by the famous portrait of Darwin, speaks to the evolutionary processes as well as creativity, as seen by the early results where the evolving programs use recurring, emergent and merged creative strategies to become good abstract portraitists.", notes = "iDMAa, Journal of the International Digital Media and Arts Association, volume 3 published by lulu.com????", } @InProceedings{1274009, author = "Steve R. DiPaola and Liane Gabora", title = "Incorporating characteristics of human creativity into an evolutionary art algorithm", booktitle = "Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2007)}", year = "2007", month = "7-11 " # jul, editor = "Peter A. N. Bosman", isbn13 = "978-1-59593-698-1", pages = "2450--2456", address = "London, United Kingdom", keywords = "genetic algorithms, genetic programming, creative evolutionary systems, evolutionary art, mechanisms of creativity", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p2450.pdf", DOI = "doi:10.1145/1274000.1274009", publisher = "ACM Press", publisher_address = "New York, NY, USA", abstract = "A perceived limitation of evolutionary art and design algorithms is that they rely on human intervention; the artist selects the most aesthetically pleasing variants of one generation to produce the next. This paper discusses how computer generated art and design can become more creatively human-like with respect to both process and outcome. As an example of a step in this direction, we present an algorithm that overcomes the above limitation by employing an automatic fitness function. The goal is to evolve abstract portraits of Darwin, using our 2nd generation fitness function which rewards genomes that not just produce a likeness of Darwin but exhibit certain strategies characteristic of human artists. We note that in human creativity, change is less choosing amongst randomly generated variants and more capitalizing on the associative structure of a conceptual network to hone in on a vision. We discuss how to achieve this fluidity algorithmically.", notes = "Distributed on CD-ROM at GECCO-2007 ACM Order No. 910071", } @Article{DiPaola:2009:GPEM, author = "Steve DiPaola and Liane Gabora", title = "Incorporating characteristics of human creativity into an evolutionary art algorithm", journal = "Genetic Programming and Evolvable Machines", year = "2009", volume = "10", number = "2", pages = "97--110", month = jun, keywords = "genetic algorithms, genetic programming, Creative evolutionary systems, Mechanisms of creativity, Cognitive science, Evolutionary art", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-008-9074-x", abstract = "A perceived limitation of evolutionary art and design algorithms is that they rely on human intervention; the artist selects the most aesthetically pleasing variants of one generation to produce the next. This paper discusses how computer generated art and design can become more creatively human-like with respect to both process and outcome. As an example of a step in this direction, we present an algorithm that overcomes the above limitation by employing an automatic fitness function. The goal is to evolve abstract portraits of Darwin, using our 2nd generation fitness function which rewards genomes that not just produce a likeness of Darwin but exhibit certain strategies characteristic of human artists. We note that in human creativity, change is less choosing amongst randomly generated variants and more capitalizing on the associative structure of a conceptual network to hone in on a vision. We discuss how to achieve this fluidity algorithmically.", } @InCollection{DiPaola:2011:CGP, author = "Steve DiPaola and Nathan Sorenson", title = "CGP, Creativity and Art", booktitle = "Cartesian Genetic Programming", publisher = "Springer", editor = "Julian F. Miller", year = "2011", series = "Natural Computing Series", chapter = "10", pages = "293--307", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", isbn13 = "978-3-642-17309-7", URL = "http://www.springer.com/computer/theoretical+computer+science/book/978-3-642-17309-7", DOI = "doi:10.1007/978-3-642-17310-3_10", abstract = "This chapter looks at evolved art and creativity using Cartesian Genetic Programming (CGP). Besides an overview of evolutionary art, we discuss our work in modelling of artistic creativity based on the notion of contextual focus, which is the capacity for creative individuals to exhibit both intense concentration on a precise goal, as well as broad, associative thought processes, which produce radical departures from convention. We implement our model with Cartesian Genetic Programming, and CGP's genetic neutrality proves to be essential in reproducing contextual focus. The model is used to generate creative portraits of Darwin, which serve to illustrate the focused and exploratory aspects of the creative process.", notes = "part of \cite{Miller:CGP}", } @Article{DiPaola:2014:PCS, author = "Steve DiPaola", title = "Using a Contextual Focus Model for an Automatic Creativity Algorithm to Generate Art Work", journal = "Procedia Computer Science", volume = "41", pages = "212--219", year = "2014", note = "5th Annual International Conference on Biologically Inspired Cognitive Architectures, 2014 BICA", keywords = "genetic algorithms, genetic programming, Evolutionary Systems, Contextual Focus, Creativity, Computational Modelling", ISSN = "1877-0509", DOI = "doi:10.1016/j.procs.2014.11.105", URL = "http://www.sciencedirect.com/science/article/pii/S1877050914015506", abstract = "We sought to implement and determine whether incorporating cognitive based contextual focus into a genetic programming fitness function would play a crucial role in enabling the computer system to generate art that humans find creative (i.e. possessing qualities of novelty and aesthetic value typically ascribed to the output of a creative artistic process). We implemented contextual focus in the evolutionary art algorithm by giving the program the capacity to vary its level of fluidity and functional triggered dynamic control over different phases of the creative process. The domain of portrait painting was chosen because it requires both focused attention (analytical thought) to accomplish the primary goal of creating portrait sitter resemblance as well as defocused attention (associative thought) to creativity deviate from resemblance i.e., to meet the broad and often conflicting criteria of aesthetic art. Since judging creative art is subjective, rather than use quantitative analysis, a representative subset of the automatically produced art-work from this system was selected and submitted to many peer reviewed and commissioned art shows, thereby allowing it to be judged positively or negatively as creative by human art curators, reviewers and the art gallery going public.", } @PhdThesis{diplock:thesis, author = "Gary John Diplock", title = "The application of evolutionary computing techniques to spatial interaction modelling", school = "Leeds University, UK", year = "1996", address = "UK", month = Sep, email = "garyd@gmap.leeds.ac.uk", keywords = "genetic algorithms, genetic programming", broken = "ftp://gam.leeds.ac.uk/pub/gary/thesis/thesis.zip", URL = "http://lib.leeds.ac.uk/record=b1537165~S5", size = "xii, 257 pages", notes = " The research involved using both GAs and GP to build new forms of spatial models which predict the flows of products and services, population, etc between spatial areas. GAs were also used to calibrate existing spatial interaction models. The GP was implemented on the 512-processor T3D facility in Edinburgh (Scotland) using a MPI shell {"}Please note that{"} ftp://gam.leeds.ac.uk/pub/gary/thesis/thesis.zip (word for windows) is {"}a draft version which has a few typing errors, etc. but this should not be a problem{"} 5-0ct-1997 cited by \cite{streeter:masters}", } @InProceedings{dittrich:1998:lmrrm, author = "Peter Dittrich and Andreas Burgel and Wolfgang Banzhaf", title = "Learning to Move a Robot with Random Morphology", booktitle = "Proceedings of the First European Workshop on Evolutionary Robotics", year = "1998", editor = "Phil Husbands and Jean-Arcady Meyer", volume = "1468", series = "LNCS", pages = "165--178", address = "Paris", publisher_address = "Berlin", month = "16-17 " # apr, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-64957-3", URL = "http://www.cs.mun.ca/~banzhaf/papers/evorobot_final.pdf", size = "14 pages", notes = "EvoRobot'98 See also \cite{dittrich:1998:rmr}", } @InProceedings{dittrich:1999:DPFLGCRMR, author = "Peter Dittrich and Andre Skusa and Wolfgang Kantschik and Wolfgang Banzhaf", title = "Dynamical Properties of the Fitness Landscape of a GP Controlled Random Morphology Robot", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1002--1008", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, evolvable hardware, evolutionary robotics, on-line evolution, dynamical fitness landscape, reference fitness", ISBN = "1-55860-611-4", URL = "http://citeseer.ist.psu.edu/362288.html", URL = "http://citeseer.ist.psu.edu/358932.html", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-454.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-454.ps", abstract = "The aim of this contribution is: (1) to present an easy to maintain robot hardware platform which allows on-line evolutionary experiments and demonstrations; (2) to introduce a simple method to measure dynamical characteristics of the time-dependent fitness landscape by using reference individuals; (3) to demonstrate dynamical properties of the fitness landscape based on fitness measurements of reference individuals. The implication of the observations for the design of on-line EAs in time-dependent fitness landscapes are discussed.", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Article{oai:CiteSeerPSU:444392, author = "Peter Dittrich and Thomas Kron and Christian Kuck and Wolfgang Banzhaf", title = "Iterated Mutual Observation with Genetic Programming", journal = "Sozionik Aktuell", year = "2001", volume = "2", month = jul, keywords = "genetic algorithms, genetic programming", citeseer-isreferencedby = "oai:CiteSeerPSU:91154; oai:CiteSeerPSU:64418", citeseer-references = "oai:CiteSeerPSU:468369; oai:CiteSeerPSU:354356; oai:CiteSeerPSU:68864", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:444392", rights = "unrestricted", URL = "http://www.informatik.uni-hamburg.de/TGI/forschung/projekte/sozionik/journal/2/gp.pdf", URL = "http://citeseer.ist.psu.edu/444392.html", abstract = "This paper introduces a simple model of interacting agents that learn to predict each other. For learning to predict the other's intended action we apply genetic programming. The strategy of an agent is rational and fixed. It does not change like in classical iterated prisoners dilemma models. Furthermore the number of actions an agent can choose from is infinite. Preliminary simulation results are presented. They show that by varying the population size of genetic programming, different learning characteristics can easily be achieved, which lead to quite different communication patterns.", notes = "http://www.sozionik-aktuell.de/", } @Article{Dittrich:2001:AL, author = "Peter Dittrich and Jens Ziegler and Wolfgang Banzhaf", title = "Artificial Chemistries -- A Review", journal = "Artificial Life", year = "2001", volume = "7", number = "3", pages = "225--275", month = "Summer", keywords = "complex systems, evolution, self-organisation, emergence, molecular simulation, origin of life, chemical computing", DOI = "doi:10.1162/106454601753238636", abstract = "This article reviews the growing body of scientific work in artificial chemistry. First, common motivations and fundamental concepts are introduced. Second, current research activities are discussed along three application dimensions: modelling, information processing, and optimization. Finally, common phenomena among the different systems are summarized. It is argued here that artificial chemistries are {"}the right stuff{"} for the study of prebiotic and biochemical evolution, and the provide a productive framework for questions regarding the origin and evolution of organisations in general. Furthermore, artificial chemistries have a broad application range of practical problems, and shown in this review.", } @Article{Dittrich:2003:JASSS, author = "Peter Dittrich and Thomas Kron and Wolfgang Banzhaf", title = "On the Scalability of Social Order", journal = "Journal of Artificial Societies and Social Simulation", year = "2003", volume = "6", number = "1", month = jan, keywords = "genetic algorithms, genetic programming, Artificial Chemistry, Coordination, Double Contingency, Learning, Networks, Self-organization, System Theory", ISSN = "1460-7425", URL = "http://jasss.soc.surrey.ac.uk/6/1/3.html", abstract = "We investigate an algorithmic model based first of all on Luhmann's description of how social order may originate [N. Luhmann, Soziale Systeme, Frankfurt/Main, Suhrkamp, 1984, pp. 148-179]. In a basic 'dyadic' setting, two agents build up expectations during their interaction process. First, we include only two factors into the decision process of an agent, namely, its expectation about the future and its expectation about the other agent's expectation (called 'expectation-expectation' by Luhmann). Simulation experiments of the model reveal that 'social' order appears in the dyadic situation for a wide range of parameter settings, in accordance with Luhmann. If we move from the dyadic situation of two agents to a population of many interacting agents, we observe that the order usually disappears. In our simulation experiments, scalable order appears only for very specific cases, namely, if agents generate expectation- expectations based on the activity of other agents and if there is a mechanism of 'information proliferation', in our case created by observation of others. In a final demonstration we show that our model allows the transition from a more actor oriented perspective of social interaction to a systems-level perspective. This is achieved by deriving an 'activity system' from the microscopic interactions of the agents. Activity systems allow to describe situations (states) on a macroscopic level independent from the underlying population of agents. They also allow to draw conclusions on the scalability of social order.", notes = "Is this GP?", } @Article{diveev:2008:MMR, author = "A. I. Diveev and N. A. Severtsev and E. A. Sofronova", title = "Control design of a weather rocket by genetic programming method", journal = "Journal of Machinery Manufacture and Reliability", year = "2008", volume = "37", number = "5", pages = "501--505", month = "12 " # oct, keywords = "genetic algorithms, genetic programming, Optimal Control Problem, Network Operator, Mathematical Expression", ISSN = "1052-6188", URL = "https://rdcu.be/dkX3i", URL = "http://link.springer.com/article/10.3103/S1052618808050154", DOI = "doi:10.3103/S1052618808050154", size = "5 pages", abstract = "The paper considers the control design problem of a weather rocket, the latter reaching a maximum height at optimal rocket thrust. A control is sought as a nonlinear dependence of thrust on the height and rate of ascent. Such a control is robust with respect to variations of the air drag model. The genetic programming method is applied to obtain the control.", notes = "Original Russian Text A.I. Diveev, N.A. Severtsev, E.A. Sofronova, 2008, published in Problemy Mashinostroeniya i Nadezhnosti Mashin, 2008, No. 5, pp. 104-108", } @InProceedings{Diveev:2014:MED, author = "Askat Diveev and David Kazaryan and Elena Sofronova", booktitle = "22nd Mediterranean Conference of Control and Automation (MED 2014)", title = "Symbolic regression methods for control system synthesis", year = "2014", month = "16-19 " # jun, pages = "587--592", abstract = "In this paper we use symbolic regression methods for control system synthesis. We compare three methods: network operator method, genetic programming and analytical programming. We developed variational versions of genetic programming and analytical programming to improve the search process efficiency. All the methods perform search over the set of the small variations of the given basic solution. Search efficiency depends on the basic solution. We give an example of control system synthesis for the unmanned vehicle with the state constraints over the set of the initial states using these methods.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/MED.2014.6961436", notes = "Also known as \cite{6961436}", } @Article{Diveev:2015:IFAC-PapersOnLine, author = "A. I. Diveev and S. I. Ibadulla and N. B. Konyrbaev and E. Yu. Shmalko", title = "Variational Genetic Programming for Optimal Control System Synthesis of Mobile Robots", journal = "IFAC-PapersOnLine", volume = "48", number = "19", pages = "106--111", year = "2015", note = "11th IFAC Symposium on Robot Control SYROCO 2015 Salvador, Brazil, 26-28 August 2015", ISSN = "2405-8963", DOI = "doi:10.1016/j.ifacol.2015.12.018", URL = "http://www.sciencedirect.com/science/article/pii/S2405896315026427", abstract = "The paper focuses on the problem of autonomous control system synthesis for the mobile robot. The proposed numerical solution is based on a new method of symbolic regression called variational genetic programming. This method uses the principle of variations of the basic solution. An optimal solution is searched over the set of small variations of the given basic solution. Such approach allows to generate automatically a control function that describes the feedback controller. In the given example the control system is synthesized using variational genetic programming for the unmanned mobile robot that has to move to some terminal position from the different initial states avoiding obstacles.", keywords = "genetic algorithms, genetic programming, robust robot control, learning robot control, mobile robots and vehicles", } @InProceedings{Diveev:2017:ICNC-FSKD, author = "A. I. Diveev and G. I. Balandina and S. V. Konstantinov", booktitle = "2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)", title = "Binary variational genetic programming for the problem of synthesis of control system", year = "2017", pages = "186--191", abstract = "The paper describes a novel numerical symbolic regression method. It's called complete binary variational genetic programming. We use it for synthesis of optimal control. This method performs better than genetic programming at crossover, reduces the search area and speeds up search algorithm by using small variations. The efficiency of the new method is proven on the given example of control system synthesis for mobile robot.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/FSKD.2017.8393051", month = jul, notes = "Also known as \cite{8393051}", } @Article{Diveev:2017:PCS, author = "A. I. Diveev and N. B. Konyrbaev and E. A. Sofronova", title = "Method of Binary Analytic Programming to Look for Optimal Mathematical Expression", journal = "Procedia Computer Science", volume = "103", pages = "597--604", year = "2017", note = "\{XII\} International Symposium Intelligent Systems 2016, \{INTELS\} 2016, 5-7 October 2016, Moscow, Russia", ISSN = "1877-0509", DOI = "doi:10.1016/j.procs.2017.01.073", URL = "http://www.sciencedirect.com/science/article/pii/S1877050917300741", abstract = "In the known methods of symbolical regression by search of the solution with the help of a genetic algorithm, there is a problem of crossover. Genetic programming performs a crossover only in certain points. Grammatical evolution often corrects a code after a crossover. Other methods of symbolical regression use excess elements in a code for elimination of this shortcoming. The work presents a new method of symbolic regression on base of binary computing trees. The method has no problems with a crossover. Method use a coding in the form of a set of integer numbers like analytic programming. The work describes the new method and some examples of codding for mathematical expressions.", keywords = "genetic algorithms, genetic programming, symbolic regression, analytic programming", } @InProceedings{Diveev:2018:ICIEA, author = "Askhat Diveev and Elizaveta Shmalko and Elena Sofronova", booktitle = "2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA)", title = "Problem of optimal area monitoring by group of robots and its solution by evolutionary algorithm", year = "2018", pages = "141--146", abstract = "The problem of monitoring of some area by means of mobile robots is considered. In the stated problem we assume that the area with obstacles is given and randomly located marks are also given. The control object has the field of viewing. It is necessary to find the optimal control, which will move the robots from the initial state to the terminal state for a given time and scan as many marks as possible. To solve the task the evolutionary algorithm of grey wolf optimizer is used.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICIEA.2018.8397704", ISSN = "2158-2297", month = may, notes = "Also known as \cite{8397704}", } @InProceedings{DBLP:conf/intellisys/DiveevS19, author = "Askhat I. Diveev and Elena A. Sofronova", editor = "Yaxin Bi and Rahul Bhatia and Supriya Kapoor", title = "Automation of Synthesized Optimal Control Problem Solution for Mobile Robot by Genetic Programming", booktitle = "Intelligent Systems and Applications - Proceedings of the 2019 Intelligent Systems Conference, IntelliSys 2019, London, UK, September 5-6, 2019, Volume 2", series = "Advances in Intelligent Systems and Computing", volume = "1038", pages = "1054--1072", publisher = "Springer", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/978-3-030-29513-4_77", DOI = "doi:10.1007/978-3-030-29513-4_77", timestamp = "Mon, 09 Sep 2019 01:00:00 +0200", biburl = "https://dblp.org/rec/conf/intellisys/DiveevS19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{Diveev:2020:MED, author = "Askhat Diveev", booktitle = "2020 28th Mediterranean Conference on Control and Automation (MED)", title = "Cartesian Genetic Programming for Synthesis of Control System for Group of Robots", year = "2020", pages = "972--977", month = sep, keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Robots, Optimal control, Aerospace electronics, Collision avoidance, Mathematical model, synthesis of control, evolutionary algorithm, group of robots", DOI = "doi:10.1109/MED48518.2020.9183180", ISSN = "2473-3504", abstract = "A control problem for a group of robots is considered. The robots have to move from given initial conditions to terminal ones without collisions between themselves and stationary obstacles. To solve the problem, the optimal synthesized control method is used. According to this method firstly the control system synthesis problem for each robot is solved. As a result, the control system stabilizes the robot relative to some point in the state space. After that positions of these stable equilibrium points in the state space for each robot are found so that all robots can move from point to point till the terminal positions without collisions. For synthesis problem on the first stage the Cartesian genetic programming is used. This method of symbolic regression allows to find a mathematical expression for control function in the form of special code by a special genetic algorithm. It's shown, that using the symbolic regression methods directly doesn't allow to find a synthesized control function in a code space, because this search space does not have numerical measure for distance between two elements of the space. So the Cartesian genetic programming was modified and the principle of small variations of the basic solution was included in it. A computational example of controlling eight robots on the plane with phase constraints is presented.", notes = "Also known as \cite{9183180}", } @InProceedings{Diveev:2020:CoDIT, author = "Askhat Diveev and Galina Balandina", title = "Optimal Trajectories Synthesis of a Mobile Robots Group Using Cartesian Genetic Programming*", booktitle = "2020 7th International Conference on Control, Decision and Information Technologies (CoDIT)", year = "2020", volume = "1", pages = "130--135", abstract = "The paper is devoted to application of Cartesian Genetic Programming (CGP) for generating optimal trajectories of a mobile robots group. The problem of a control system synthesis for a mobile robots group is solved. The proposed algorithm uses numerical approach from the class of symbolic regression methods to which Cartesian Genetic Programming belonging. It allows to receive a control function in the form of a mathematical expression. We consider several stages to get optimal trajectories for mobile robots group moving along which the robots wouldn't collide with each other and obstacles. Initially, we solve the problem of synthesis for each robot in order to get the stabilized robot control system relative some point in the state space. At the second stage, spatial trajectories are found along which robots move from the current state to the obtained equilibrium points without collisions. It was proposed to improve an initial algorithm by using the principal of small variation of basic solution. There is considered a group of three robots and the control system for them with phase constraints in the paper.", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, Robots, Optimal control, Mathematical model, Collision avoidance, Trajectory, Aerospace electronics", DOI = "doi:10.1109/CoDIT49905.2020.9263782", ISSN = "2576-3555", month = jun, notes = "Also known as \cite{9263782}", } @InProceedings{Diveev:2021:ICIEA, author = "Askhat Diveev and Elena Sofronova and Droh Mecapeu Catherine Prisca", title = "Synthesised Optimal Control for a Robotic Group by Complete Binary Genetic Programming", booktitle = "2021 IEEE 16th Conference on Industrial Electronics and Applications (ICIEA)", year = "2021", pages = "100--105", abstract = "The paper continues the study of symbolic regression methods for control learning. The optimal control problem with phase constraints for a group of robots is considered. To solve the problem, the method of synthesized optimal control is used. At the first stage the stabilization problem is solved for each robot. Using a new hybrid evolutionary algorithm, built on the basis of the genetic algorithm, the particle swarm optimization and the gray wolf optimizer, stable equilibrium points are found. Next, the original optimization problem by piece-wise linear approximation of the equilibrium points is solved. In contrast to the known methods for solving the synthesis problem, the control learning by the complete binary genetic programming is used. The advantage of this approach is that the resulting control is realizable on board of mobile robots. Simulation is given for a group of two mobile robots.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICIEA51954.2021.9516380", ISSN = "2158-2297", month = aug, notes = "Also known as \cite{9516380}", } @Article{Diveev:Cybernetics, author = "Askhat I. Diveev and Elizaveta Y. Shmalko", title = "Machine-Made Synthesis of Stabilization System by Modified Cartesian Genetic Programming", journal = "IEEE Transactions on Cybernetics", year = "2022", volume = "52", number = "7", pages = "6627--6637", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", ISSN = "2168-2275", DOI = "doi:10.1109/TCYB.2020.3039693", abstract = "A numerical solution of the problem of the general synthesis of a stabilization system by a symbolic regression method is considered. The goal is to automatically find a feedback control function using a computer so that the control object can reach a given terminal position from anywhere in a given region of the initial conditions with an optimal value of the quality criterion. Usually, the control synthesis problem is solved analytically or technically taking into account the specific properties of the mathematical model. We suppose that modern numerical approaches of symbolic regression can be applied to find a solution without reference to specific model equations. It is proposed to use the numerical method of Cartesian genetic programming (CGP). It was developed for automatic writing of programs but has never been used to solve the synthesis problem. In the present work, the method was modified with the principle of small variations in order to reduce the search area and increase the rate of convergence. To apply the general principle of small variations to CGP, we developed special types of variations and coding. The modified CGP searches for the mathematical expression of the feedback control function in the form of a code and, at the same time, the optimal value of the parametric vector which is also a new feature--simultaneous tuning of the parameters inside the search process. This approach enables working with objects and functions of any type, which is not always possible with analytical methods. The need to use the received solution on the onboard processor of the control object imposes certain restrictions on the used basic set of elementary functions. This article proposes the theoretical foundations of the study of these functions, and the concept of the space of machine-made functions is introduced. The capabilities of the approach are demonstrated on the numerical solution of the control system synthesis problems for a mobile robot and a Duffing model.", notes = "Also known as \cite{9311765}", } @Article{diveev:2021:Symmetry, author = "Askhat Diveev and Elizaveta Shmalko", title = "Research of Trajectory Optimization Approaches in Synthesized Optimal Control", journal = "Symmetry", year = "2021", volume = "13", number = "2", keywords = "genetic algorithms, genetic programming", ISSN = "2073-8994", URL = "https://www.mdpi.com/2073-8994/13/2/336", DOI = "doi:10.3390/sym13020336", abstract = "This article presents a study devoted to the emerging method of synthesised optimal control. This is a new type of control based on changing the position of a stable equilibrium point. The object stabilization system forces the object to move towards the equilibrium point, and by changing its position over time, it is possible to bring the object to the desired terminal state with the optimal value of the quality criterion. The implementation of such control requires the construction of two control contours. The first contour ensures the stability of the control object relative to some point in the state space. Methods of symbolic regression are applied for numerical synthesis of a stabilization system. The second contour provides optimal control of the stable equilibrium point position. The present paper provides a study of various approaches to find the optimal location of equilibrium points. A new problem statement with the search of function for optimal location of the equilibrium points in the second stage of the synthesised optimal control approach is formulated. Symbolic regression methods of solving the stated problem are discussed. In the presented numerical example, a piece-wise linear function is applied to approximate the location of equilibrium points.", notes = "also known as \cite{sym13020336}", } @InProceedings{diveev:2021:(FTC), author = "Askhat Diveev", title = "Cartesian Genetic Programming for Synthesis of Optimal Control System", booktitle = "Proceedings of the Future Technologies Conference (FTC) 2020, Volume 2", year = "2021", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", URL = "http://link.springer.com/chapter/10.1007/978-3-030-63089-8_13", DOI = "doi:10.1007/978-3-030-63089-8_13", } @InProceedings{divina:2001:gecco, title = "Knowledge Based Evolutionary Programming for Inductive Learning in First-Order Logic", author = "Federico Divina and Elena Marchiori", pages = "173", year = "2001", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)", editor = "Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon and Edmund Burke", address = "San Francisco, California, USA", publisher_address = "San Francisco, CA 94104, USA", month = "7-11 " # jul, keywords = "genetic algorithms, genetic programming: Poster", ISBN = "1-55860-774-9", URL = "http://gpbib.cs.ucl.ac.uk/gecco2001/d02.pdf", notes = "GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of \cite{spector:2001:GECCO}", } @PhdThesis{Divina:thesis, author = "Federico Divina", title = "Hybrid Genetic Relational Search for Inductive Learning", year = "2004", school = "Department of Computer Science, Vrije Universiteit Amsterdam", address = "Amsterdam, the Netherlands", month = "26 month", language = "English", series = "SIKS Dissertation Series", number = "16", keywords = "genetic algorithms, genetic programming, ILP", URL = "https://dare.ubvu.vu.nl/bitstream/1871/10280/1/divina_thesis.pdf", URL = "https://research.vu.nl/en/publications/hybrid-genetic-relational-search-for-inductive-learning", size = "188 pages", abstract = "We are interested in learning concepts expressed in a fragment of first-order logic (FOL). This subject is known as Inductive Logic Programming (ILP), where the knowledge to be learn is expressed by Horn clauses, which are used in programming languages based on logic programming like Prolog. Learning systems that use a representation based on first-order logic have been successfully applied to relevant real life problems, e.g., learning a specific property related to carcinogenicity. Learning first-order hypotheses is a hard task, due to the huge search space one has to deal with. The approach used by the majority of ILP systems tries to overcome this problem by using specific search strategies, like the top-down and the inverse resolution mechanism (see chapter 2). However, the greedy selection strategies adopted for reducing the computational effort, render techniques based on this approach often incapable of escaping from local optima. An alternative approach is offered by genetic algorithms (GAs). GAs have proved to be successful in solving comparatively hard optimization problems, as well as problems like ICL. GAs represents a good approach when the problems to solve are characterised by a high number of variables, when there is interaction among variables, when there are mixed types of variables, e.g., numerical and nominal, and when the search space presents many local optima. Moreover it is easy to hybridise GAs with other techniques that are known to be good for solving some classes of problems. Another appealing feature of GAs is represented by their intrinsic parallelism, and their use of exploration operators, which give them the possibility of escaping from local optima. However this latter characteristic of GAs is also responsible for their rather poor performance on learning tasks which are easy to tackle by algorithms that use specific search strategies. These observations suggest that the two approaches above described, i.e., standard ILP strategies and GAs, are applicable to partly complementary classes of learning problems. More important, they indicate that a system incorporating features from both approaches could profit from the different benefits of the approaches. This motivates the aim of this thesis, which is to develop a system based on GAs for ILP that incorporates search strategies used in successful ILP systems. Our approach is inspired by memetic algorithms (Moscato, 1989), a population based search method for combinatorial optimization problems. In evolutionary computation memetic algorithms are GAs in which individuals can be refined during their lifetime. In particular the thesis introduces a hybrid evolutionary system called ECL (Evolutionary Concept Learner). ECL uses four intelligent mutation operators and an optimization phase that follows each mutation. Two mutation operators are used for generalisation of rules, and the other two for specialisation of rules. The optimisation phase consists of the repeated application of mutation operators until the fitness of the individual being optimised increases. A high level representation of rules is adopted, in order to enable the use of these mutation operators. Rules are represented as a list of predicates, variables and constants. In this way at each time of the evolutionary process ECL can distinguish between the various part of the rule. A selection mechanisms, called EWUS, is used in order to select individuals and to promote diversity in the population. This last aspect is very important in all EAs system of ICL. A method for handling numerical values is used, which evolves discretization intervals along with rules, so that each rule can have a discretization intervals that is good for itself. ECL proved to be competitive with other state of the art systems for ICL, both in the relational and in the propositional settings. You can obtain a copy by clicking on the picture below. Would you prefer a printed copy of the thesis, request it with an email.", notes = "Supervisors: AE Eiben and E. Marchiori", } @InProceedings{eurogp:Divina05, author = "Federico Divina", editor = "Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. {van Hemert} and Marco Tomassini", title = "Assessing the Effectiveness of Incorporating Knowledge in an Evolutionary Concept Learner", booktitle = "Proceedings of the 8th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3447", year = "2005", address = "Lausanne, Switzerland", month = "30 " # mar # " - 1 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-25436-6", pages = "13--24", URL = "http://www.cs.vu.nl/~divina/Publications/EuroGP-divina.pdf", DOI = "doi:10.1007/978-3-540-31989-4_2", DOI = "doi:10.1007/b107383", bibsource = "DBLP, http://dblp.uni-trier.de", size = "12 pages", abstract = "Classical methods for Inductive Concept Learning (ICL) rely mostly on using specific search strategies, as hill climbing and inverse resolution. These strategies have a great exploitation power, but run the risk of being incapable of escaping from local optima. An alternative approach to ICL is represented by Evolutionary Algorithms (EAs). EAs have a great exploration power, thus they have the capability of escaping from local optima, but their exploitation power is rather poor. These observations suggest that the two approaches are applicable to partly complementary classes of learning problems. More important, they indicate that a system incorporating features from both approaches could benefit from the complementary qualities of the approaches. In this paper we experimentally validate this statement. To this end, we incorporate different search strategies in a framework based on EAs for ICL. Results of experiments show that incorporating standard search strategies helps the EAs in achieving better results.", notes = "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005", } @Article{journals/aai/DivsalarJGSM11, author = "Mehdi Divsalar and Mohamad Rezi Javid and Amir Hossein Gandomi and Jahaniar Bamdad Soofi and Majid Vesali Mahmood", title = "Hybrid Genetic Programming-Based Search Algorithms for Enterprise bankruptcy Prediction", journal = "Applied Artificial Intelligence", year = "2011", volume = "25", number = "8", pages = "669--692", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1080/08839514.2011.595975", size = "24 pages", abstract = "Bankruptcy is an extremely significant worldwide problem that affects the economic well- being of all countries. The high social costs incurred by various stakeholders associated with bankrupt firms imply the need to search for better theoretical understanding and prediction quality. The main objective of this paper is to apply genetic programming with orthogonal least squares (GP/OLS) and with simulated annealing (GP/SA) algorithms to build models for bankruptcy prediction. Using the hybrid GP/OLS and GP/SA techniques, generalised relationships are obtained to classify samples of 136 bankrupt and nonbankrupt Iranian corporations based on financial ratios. Another important contribution of this paper is to identify the effective predictive financial ratios based on an extensive bankruptcy prediction literature review and a sequential feature selection (SFS) analysis. A comparative study on the classification accuracy of the GP/OLS- and GP/SA-based models is also conducted. The observed agreement between the predictions and the actual values indicates that the proposed models effectively estimate any enterprise with regard to the aspect of bankruptcy. According to the results, the proposed GP/SA model has better performance than the GP/OLS model in bankruptcy prediction.", bibdate = "2011-09-23", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/aai/aai25.html#DivsalarJGSM11", } @InProceedings{Djerriri:2015:ieeeIGARSS, author = "Khelifa Djerriri and Malki Mimoun", booktitle = "IEEE International Geoscience and Remote Sensing Symposium (IGARSS)", title = "Genetic programming and one-class classification for discovering useful spectral transformations", year = "2015", pages = "425--428", abstract = "This work presents a new approach for automatic discovering of useful spectral transformations in remotely sensed imagery. The method applies an approach based on One-class classification, ISODATA unsupervised classification and Genetic Programming (GP) to combine spectral bands. Experiments on burned areas extraction from Landsat8-Oli images show that the proposed method yields better results than the traditional spectral transformations.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IGARSS.2015.7325791", ISSN = "2153-6996", month = jul, notes = "Also known as \cite{7325791}", } @PhdThesis{Djupdal:thesis, author = "Asbjoern Djupdal", title = "Evolving Static Hardware Redundancy for Defect Tolerant {FPGAs}", school = "Department of Computer and Information Science, Faculty of Information Technology, Mathematics and Electrical Engineering, Norwegian University of Science and Technology", year = "2008", address = "Trondheim, Norway", month = "24 " # apr, isbn13 = "978-82-471-6874-5", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, EHW", URL = "http://www.idi.ntnu.no/research/doctor_theses/djupdal.pdf", size = "136 pages", abstract = "Integrated circuits have been in constant progression since the first prototype in 1958. The semiconductor industry has maintained a constant rate of miniaturisation of transistors and wires, resulting in ever increasing speed, size and complexity of circuits. One challenge that has always been present is reduced yield due to production defects. A certain amount of chips must be scrapped because production defects have rendered the chips unusable. Recent predictions suggest that the average number of production defects per chip will rise drastically in the future as CMOS scaling approaches the physical limits of what is possible to manufacture. If these predictions are true, circuits should exhibit some level of tolerance to defects so to keep yield at acceptable levels. The main contribution of the thesis is to the field of defect tolerance, with a focus on FPGAs. Apart from the widespread employment of FPGAs, two technical reasons make the FPGA especially suited for inclusion of defect tolerance techniques. The regular structure of the FPGA can be exploited for efficient redundancy techniques. In addition, the FPGA can be seen as a bridge between production and the application designer. Through defect tolerance techniques incorporated transparently in the FPGA, a fully functioning gate array can be provided to the application designer despite defects from production. The approach taken in this thesis is to search for new ways of introducing static hardware redundancy in a circuit through the application of artificial evolution. However, the challenge of applying evolutionary techniques provided a secondary contribution. The work provides a contribution to the field of artificial evolution and the subfield evolvable hardware (EHW) by addressing ways in which such techniques may be applied to search for non-specifiable structures. The work is also bridging the fields of EHW and traditional hardware design and reliability metrics have been investigated for the purpose of comparing evolved and traditionally designed circuits. Redundant structures are first evolved for gate level circuits where both voter based solutions and more intricate non-voter based solutions are achieved. Transistor level redundancy structures are targeted next to approach the main goal of defect tolerance for FPGAs. A defect tolerant inverter is evolved which forms the basis of a general defect tolerance technique, termed the Multiple Short-Open (MSO) technique. The FPGA look-up table (LUT) is one of the essential components of the FPGA and a defect tolerant LUT is, therefore, constructed applying the MSO technique. An evolutionary experiment is also conducted where a defect tolerant 1-input LUT is evolved directly.", notes = "http://www.idi.ntnu.no/news/index.php?news=112 24th of April Asbjoern Djupdal completed his trial lecture and thesis defence, and he will eventually be awarded the PhD degree. The PhD was completed at the Computer Architecture and Design group, with associate professor Pauline Haddow as supervisor. He defended his PhD thesis: Evolving Static Hardware Redunancy for Defect Tolerant FPGAs", } @Article{Djupdal:2011:GPEM, author = "Asbjoern Djupdal and Pauline Haddow", title = "The route to a defect tolerant LUT through artificial evolution", journal = "Genetic Programming and Evolvable Machines", year = "2011", volume = "12", number = "3", pages = "281--303", month = sep, note = "Special Issue Title: Evolvable Hardware Challenges", keywords = "genetic algorithms, genetic programming, evolvable hardware", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-011-9129-2", size = "23 pages", abstract = "Evolutionary techniques may be applied to search for specific structures or functions, as specified in the fitness function. This paper addresses the challenge of finding an appropriate fitness function when searching for generic rather than specific structures which, when combined with characteristics of defect tolerance on the circuit. Production defects for integrated circuits are expected to increase considerably. To avoid a corresponding drop in yield, improved defect tolerance solutions are needed. In the case of Field Programmable Gate Arrays (FPGAs), the pre-designed gate array provides a bridge between production and the application designers. Thus, introduction of defect tolerant techniques to the FPGA itself could provide a defect free gate array to the application designer, despite production defects. The search for defect tolerance presented herein is directed at finding defect tolerant structures for an important building block of FPGAs: Look-Up Tables (LUTs). Two key approaches are presented: (1) applying evolved generic building blocks to a traditional LUT design and (2) evolving the LUT design directly. The results highlight the fact that evolved generic defect tolerant structures can contribute to highly reliable circuit designs at the expense of area usage. Further, they show that applying such a technique, rather than direct evolution, has benefits with respect to evolvability of larger circuits, again at the expense of area usage.", affiliation = "CRAB Lab, IDI, NTNU, Trondheim, Norway", } @InProceedings{Djurasevic:2020:GECCO, author = "Marko Djurasevic and Domagoj Jakobovic and Stjepan Picek", title = "One Property to Rule Them All? On the Limits of Trade-Offs for {S}-Boxes", year = "2020", editor = "Carlos Artemio {Coello Coello} and Arturo Hernandez Aguirre and Josu Ceberio Uribe and Mario Garza Fabre and Gregorio {Toscano Pulido} and Katya Rodriguez-Vazquez and Elizabeth Wanner and Nadarajen Veerapen and Efren Mezura Montes and Richard Allmendinger and Hugo Terashima Marin and Markus Wagner and Thomas Bartz-Beielstein and Bogdan Filipic and Heike Trautmann and Ke Tang and John Koza and Erik Goodman and William B. Langdon and Miguel Nicolau and Christine Zarges and Vanessa Volz and Tea Tusar and Boris Naujoks and Peter A. N. Bosman and Darrell Whitley and Christine Solnon and Marde Helbig and Stephane Doncieux and Dennis G. Wilson and Francisco {Fernandez de Vega} and Luis Paquete and Francisco Chicano and Bing Xue and Jaume Bacardit and Sanaz Mostaghim and Jonathan Fieldsend and Oliver Schuetze and Dirk Arnold and Gabriela Ochoa and Carlos Segura and Carlos Cotta and Michael Emmerich and Mengjie Zhang and Robin Purshouse and Tapabrata Ray and Justyna Petke and Fuyuki Ishikawa and Johannes Lengler and Frank Neumann", isbn13 = "9781450371285", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377930.3390247", DOI = "doi:10.1145/3377930.3390247", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference", pages = "1064--1072", size = "9 pages", keywords = "genetic algorithms, genetic programming, S-boxes, cryptography, evolutionary algorithms, trade-off", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", abstract = "Substitution boxes (S-boxes) are nonlinear mappings that represent one of the core parts of many cryptographic algorithms (ciphers). If S-box does not possess good properties, a cipher would be susceptible to attacks. To design suitable S-boxes, we can use heuristics as it allows significant freedom in the selection of required cryptographic properties. Unfortunately, with heuristics, one is seldom sure how good a trade-off between cryptographic properties is reached or if optimizing for one property optimizes implicitly for another property. the most detailed analysis of trade-offs among S-box cryptographic properties. More precisely, we ask questions if one property is optimized, what is the worst possible value for some other property, and what happens if all properties are optimized. Our results show that while it is possible to reach a large variety of possible solutions, optimizing for a certain property would commonly result in good values for other properties. In turn, this suggests that a single-objective approach should be a method of choice unless some precise values for multiple properties are needed.", notes = "Also known as \cite{10.1145/3377930.3390247} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @InProceedings{DMello:2020:ICACTA, author = "Lynette D'Mello and Aditya Jeswani and Janice Johnson", title = "Stock Price Prediction Using Grammatical Evolution", booktitle = "Proceedings of 2nd International Conference on Advanced Computing Technologies and Applications, ICACTA 2020", year = "2020", editor = "Hari Vasudevan and Antonis Michalas and Narendra Shekokar and Meera Narvekar", chapter = "36", series = "Algorithms for Intelligent Systems", pages = "379--389", address = "Mumbai, India", month = "28-29 " # feb, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Grammatical evolution, PonyGE2", isbn13 = "978-981-15-3241-2", ISSN = "2524-7565", DOI = "doi:10.1007/978-981-15-3242-9_36", size = "11 pages", abstract = "Grammatical evolution is an evolutionary method that is used for the automated generation of programs. Over the years, different studies have proven the relevance and efficiency of this method in a wide array of fields. This method can substitute various other machine learning algorithms and older architectures to provide good efficiency and performance for optimization of algorithms. The paper aims to apply GE to predict the price of various stock market indices. An open source implementation PonyGE2 that was developed by the Natural Computing and Applications group at UCD has been employed in this paper. With the help of an objective function and a search space defined by the grammar, the evolutionary computation of the optimum solution is achieved. The effect of tweaking the grammar rules to provide different production options helped visualize the difference in the fitness of the functions generated and the consequential effect on the output produced.", notes = "Dwarkadas J. Sanghvi College of Engineering, Mumbai, India. http://djicacta.in/", } @Article{Do2008194, author = "Duong Q. Do and Raymond C. Rowe and Peter York", title = "Modelling drug dissolution from controlled release products using genetic programming", journal = "International Journal of Pharmaceutics", volume = "351", number = "1-2", pages = "194--200", year = "2008", ISSN = "0378-5173", DOI = "doi:10.1016/j.ijpharm.2007.09.044", URL = "http://www.sciencedirect.com/science/article/B6T7W-4PWF0M5-1/2/1931c3725d1a803010a1d39e29117a1", keywords = "genetic algorithms, genetic programming, Statistical methods, Modeling, Controlled release, Formulation", abstract = "This study has investigated and compared genetic programming (GP) - a method of automatically generating equations that describe the cause-and-effect relationships in a system - and statistical methods for modeling two controlled release formulations--a matrix tablet and microspheres. With the improved GP models exhibiting comparable predictive power, as well as simpler equations in some cases, the results obtained indicate that GP can be considered as an effective and efficient method for modelling controlled release formulations.", } @Article{Do:2024:finmec, author = "Tu Anh Do and Ba-Anh Le", title = "Machine learning approach for predicting early-age thermal cracking potential in concrete bridge piers", journal = "Forces in Mechanics", year = "2024", volume = "17", pages = "100297", keywords = "genetic algorithms, genetic programming, Bridge pier, Early-age thermal cracking, Time of cracking occurrence, Machine learning, ANN", ISSN = "2666-3597", URL = "https://www.sciencedirect.com/science/article/pii/S266635972400043X", DOI = "doi:10.1016/j.finmec.2024.100297", abstract = "In concrete construction, early-age thermal cracks in foundations, abutments, piers, and slabs can arise from non-uniform temperature distribution due to heat from cement hydration. These cracks negatively impact the integrity, load-bearing capacity, and service life of the concrete structures. This paper investigates the application of machine learning (ML) models to predict early-age thermal cracking in concrete bridge piers. The study aims to develop models to forecast thermal cracking potential (netamax) and estimate the timing of potential cracking (t) based on a dataset of various cross-sectional bridge piers and typical tropical temperatures. Four ML models-Support Vector Machine (SVM), Extreme Gradient Boosting (XGB), Artificial Neural Network (ANN), and Genetic Programming (GP)-were trained on 759 samples. The dataset, prepared using the EACTSA program, included parameters like cross-sectional dimensions, ambient temperature, and initial concrete temperature, with netamax and t as outputs. Results show that all the ML models achieved high prediction accuracy with Rsquared scores over 0.96. The GP symbolic equations offer transparency and practical implementation. Compared to conventional methods, ML models provide a rapid, effective tool to optimise concrete member dimensions, formwork removal timing, and control concrete temperature, mitigating early-age thermal cracking risk", } @Article{Dobai:2015:TRETS, author = "Roland Dobai and Lukas Sekanina", title = "Low-Level Flexible Architecture with Hybrid Reconfiguration for Evolvable Hardware", journal = "ACM Transactions on Reconfigurable Technology and Systems", year = "2015", volume = "8", number = "3", pages = "20:1--20:24", month = may, keywords = "genetic algorithms, genetic programming, cartesian genetic programming, EHW, Architecture, Zynq, circuit design, evolvable hardware, image filter, reconfigurable", publisher = "ACM", acmid = "2700414", ISSN = "1936-7406", URL = "http://www.fit.vutbr.cz/~sekanina/pubs.php.en?id=10394", DOI = "doi:10.1145/2700414", size = "24 pages", abstract = "Field-programmable gate arrays (FPGAs) can be considered to be the most popular and successful platform for evolvable hardware. They allow one to establish and later reconfigure candidate solutions. Recent work in the field of evolvable hardware includes the use of virtual and native reconfigurations. Virtual reconfiguration is based on the change of functionality by hardware components implemented on top of FPGA resources. Native reconfiguration changes the FPGA resources directly by means provided by the FPGA manufacturer. Both of these approaches have their disadvantages. The virtual reconfiguration is characterized by lower maximal operational frequency of the resulting solutions, and the native reconfiguration is slower. In this work, a hybrid approach is used merging the advantages while limiting the disadvantages of the virtual and native reconfigurations. The main contribution is the new low-level architecture for evolvable hardware in the new Zynq-7000 all-programmable system-on-chip. The proposed architecture offers high flexibility in comparison with other evolvable hardware systems by considering direct modification of the reconfigurable resources. The impact of the higher reconfiguration time of the native approach is limited by the dense placement of the proposed reconfigurable processing elements. These processing elements also ensure fast evaluation of candidate solutions. The proposed architecture is evaluated by evolutionary design of switching image filters and edge detectors. The experimental results demonstrate advantages over the previous approaches considering the time required for evolution, area overhead, and flexibility.", } @Article{DOBAI2017173, author = "Roland Dobai and Jan Korenek and Lukas Sekanina", title = "Evolutionary design of hash function pairs for network filters", journal = "Applied Soft Computing", year = "2017", volume = "56", number = "Supplement C", pages = "173--181", month = jul, keywords = "genetic algorithms, genetic programming, EHW, Evolutionary algorithm, Hash function, Network filter, Field-programmable gate array, Cuckoo", ISSN = "1568-4946", URL = "http://www.sciencedirect.com/science/article/pii/S1568494617301321", DOI = "doi:10.1016/j.asoc.2017.03.009", abstract = "Network filtering is a challenging area in high-speed computer networks, mostly because lots of filtering rules are required and there is only a limited time available for matching these rules. Therefore, network filters accelerated by field-programmable gate arrays (FPGAs) are becoming common where the fast lookup of filtering rules is achieved by the use of hash tables. It is desirable to be able to fill-up these tables efficiently, i.e. to achieve a high table-load factor in order to reduce the offline time of the network filter due to rehashing and/or table replacement. A parallel reconfigurable hash function tuned by an evolutionary algorithm (EA) is proposed in this paper for Internet Protocol (IP) address filtering in FPGAs. The EA fine-tunes the reconfigurable hash function for a given set of IP addresses. The experiments demonstrate that the proposed hash function provides high-speed lookup and achieves a higher table-load factor in comparison with conventional solutions.", notes = "Evolvable hardware paper, Not GP?", } @Article{Dobnik-Dubrovski:2002:TRL, author = "Polona {Dobnik Dubrovski} and Miran Brezocnik", title = "Using genetic programming to predict the macroporosity of woven cotton fabrics", journal = "Textile research journal", year = "2002", volume = "72", number = "3", pages = "187--194", month = mar, email = "mbrezocnik@uni-mb.si", publisher = "Sage", keywords = "genetic algorithms, genetic programming, woven cotton fabrics, macroporosity, modelling", ISSN = "0040-5175", URL = "http://cat.inist.fr/?aModele=afficheN&cpsidt=13560450", DOI = "doi:10.1177/004051750207200301", abstract = "This paper reports the effect of woven fabric construction on macroporosity properties. The area of a macropore's cross section, equivalent, maximum, and minimum pore diameters, pore density, and open porosity are observed in this research involving woven fabric construction parameters-yarn linear density, fabric tightness, weave type, and denting. Predictive models, determined by genetic programming, are derived to describe the influence of fabric construction. The results show very good agreement between the experimental and predicted values. This work provides guidelines for engineering staple-yarn cotton fabrics in a grey state in terms of macroporosity properties.", } @InCollection{Dobnik-Dubrovski:2012:GPnew, author = "Polona {Dobnik Dubrovski} and Miran Brezocnik", title = "The Usage of Genetic Methods for Prediction of Fabric Porosity", booktitle = "Genetic Programming - New Approaches and Successful Applications", publisher = "InTech", year = "2012", editor = "Sebastian Ventura", chapter = "8", pages = "171--198", keywords = "genetic algorithms, genetic programming", isbn13 = "978-953-51-0809-2", DOI = "doi:10.5772/48188", size = "28 pages", notes = "Yarn linear density, weave value, fabric tightness, denting. Part of \cite{Ventura:2012:GPnew}. Open access book CC BY 3.0 license", } @InProceedings{Dockhorn:2017:ieeeCIG, author = "Alexander Dockhorn and Rudolf Kruse", title = "Combining Cooperative and Adversarial Coevolution in the Context of {Pac-Man}", booktitle = "2017 IEEE Conference on Computational Intelligence and Games (CIG)", year = "2017", pages = "60--67", month = "22-25 " # aug, address = "New York", keywords = "genetic algorithms, genetic programming", ISSN = "2325-4289", isbn13 = "978-1-5386-3234-5", URL = "https://adockhorn.github.io/files/papers/Dockhorn,%20Kruse%20-%202017%20-%20Combining%20cooperative%20and%20adversarial%20coevolution%20in%20the%20context%20of%20pac-man.pdf", DOI = "doi:10.1109/CIG.2017.8080416", size = "8 pages", abstract = "we discuss our recent approach for evolving a diverse set of agents for both the Pac-Man and the Ghost Team track of the current Ms. Pac-Man vs. Ghost Team competition. We used genetic programming for generating various agents, which were distributed in multiple populations. The optimisation includes cooperative and adversarial subtasks, such that Pac-Man is constantly competing against the Ghost Team, whereas the Ghost Team is formed of four cooperatively evolving populations. For the generation of a Ghost Team and calculation of the associated fitness we took one individual from each population. This strict separation preserves the evolution pressure for each population such that respective Ghost Teams compete against each other in developing an efficient cooperation in catching Pac-Man. This approach not only is useful for developing a versatile set of playing agents, but also for adapting the team to the current behaviour of the competing populations. Ultimately, we aim for optimising both tasks in parallel.", notes = "Also known as \cite{8080416}", } @PhdThesis{Dockhorn2020, author = "Alexander Dockhorn", title = "{Prediction-based Search for Autonomous Game-Playing}", school = "Otto von Guericke University Magdeburg", year = "2020", address = "Germany", month = jun # " 12", URL = "https://adockhorn.github.io/phdthesis.html", URL = "https://adockhorn.github.io/files/phd_thesis.pdf", size = "231 pages", abstract = "Simulation-based search algorithms have been widely applied in the context of autonomous game-playing. Their flexibility allows for the rapid development of agents that are able to achieve satisfying performance in many problem domains. However, these algorithms share two requirements, namely, access to a forward model and full knowledge of the environment state. In this thesis, simulation-based search algorithms will be adapted to tasks in which either the forward model or the state of the environment is unknown. To play a game without a forward model, methods for learning the environments model from recent interactions between the agent and the environment are proposed. These forward model learning techniques allow the agent to predict the outcome of its actions, and therefore, enable a prediction-based search process. An analysis of environment models shows how they can be represented and learned in the form of an end-to-end forward model. Based on this general approach, three methods are proposed which reduce the number of possible models and, thus, the training time required. The proposed forward model learning techniques are evaluated according to their applicability to general game-learning tasks and validated based on a wide variety of games. The results show the applicability of prediction-based search agents for games where the forward model is not accessible. In case the environment's state cannot be fully observed by the agent and the number of possible states is low, state determinisation methods, which uniformly sample possible states have shown to perform well. However, if the number of states is high, the uniform state sampling approach performs worse than non-determinising search methods due to the search process spending too much time on unlikely states. In this thesis, two methods for predictive state determinisation are proposed. These sample probable states based on the agent partial observation of the current state and a database of previously played games, which allows the agent to focus its search process on likely states. Proposed algorithms are evaluated in terms of their prediction accuracy and game-playing performance in the context of the collectible card game Hearthstone. Results show that the implemented agent outperforms other state-of-the-art agents in case the replay database is representative for the state distribution.", notes = "Not GP?", } @InProceedings{Dockter:2025:evoapplications, author = "Steven Dockter and Deepak Karunakaran and Qi Chen and Yongshi Deng", title = "Analysis of Illicit Drug Mixtures at Festivals Using Portable Near-Infrared Spectroscopy with Genetic Programming", booktitle = "28th International Conference, EvoApplications 2025", year = "2025", editor = " Pablo Garcia-Sanchez and Emma Hart and Sarah L. Thomson", series = "LNCS", volume = "15612", pages = "103--118", publisher = "Springer", address = "Trieste, Italy", month = "3-5 " # apr, organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, , genetic programming for symbolic regression, GPSR, Near-infrared spectroscopy, NIRS, Mixture analysis, Drug harm reduction", isbn13 = "978-3-031-90062-4", timestamp = "Fri, 09 May 2025 20:28:52 +0200", biburl = "https://dblp.org/rec/conf/evoapps/DockterKCD25.bib", bibsource = "dblp computer science bibliography, https://dblp.org", DOI = "doi:10.1007/978-3-031-90062-4_7", notes = "DBLP:conf/evoapps/DockterKCD25 See Also Steven E. Dockter MSc 2025 https://doi.org/10.26686/wgtn.28941851 http://www.evostar.org/2025/ EvoApplications2025 held in conjunction with EuroGP'2025, EvoCOP2025 and EvoMusArt2025", } @Article{doerr_et_al:DR:2013:7:1, author = "Benjamin Doerr and Nikolaus Hansen and Jonathan L. Shapiro and L. Darrell Whitley", title = "Theory of Evolutionary Algorithms (Dagstuhl Seminar 13271)", journal = "Dagstuhl Reports", year = "2013", volume = "3", number = "7", pages = "1--28", month = "13 " # nov, note = "Edited in cooperation with Rachael Morgan", keywords = "genetic algorithms, genetic programming, evolutionary algorithms, optimization, search heuristics, algorithms, artificial intelligence", ISSN = "2192-5283", editor = "Benjamin Doerr and Nikolaus Hansen and Jonathan L. Shapiro and L. Darrell Whitley", publisher = "Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik", address = "Dagstuhl, Germany", URL = "http://drops.dagstuhl.de/opus/volltexte/2013/4260", DOI = "doi:10.4230/DagRep.3.7.1", size = "28 pages", abstract = "This report documents the talks and discussions of Dagstuhl Seminar 13271 'Theory of Evolutionary Algorithms'. This seminar, now in its 7th edition, is the main meeting point of the highly active theory of randomized search heuristics subcommunities in Australia, Asia, North America and Europe. Topics intensively discussed include a complexity theory for randomized search heuristics, evolutionary computation in noisy settings, the drift analysis technique, and parallel evolutionary computation.", notes = "Brief mentions of GP Seminar 30. June-5 July, 2013 http://www.dagstuhl.de/13271 1998 ACM Subject Classification G.1.6 Optimization, F.2 Analysis of Algorithms and Problem Complexity", } @InProceedings{Doerr:2017:GECCOc, author = "Benjamin Doerr and Timo Koetzing and J. A. Gregor Lagodzinski and Johannes Lengler", title = "Bounding Bloat in Genetic Programming", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4920-8", address = "Berlin, Germany", pages = "921--928", size = "8 pages", URL = "http://doi.acm.org/10.1145/3071178.3071271", DOI = "doi:10.1145/3071178.3071271", acmid = "3071271", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, mutation, run time analysis, theory", month = "15-19 " # jul, abstract = "While many optimization problems work with a fixed number of decision variables and thus a fixed-length representation of possible solutions, genetic programming (GP) works on variable-length representations. A naturally occurring problem is that of bloat (unnecessary growth of solutions) slowing down optimization. Theoretical analyses could so far not bound bloat and required explicit assumptions on the magnitude of bloat. In this paper we analyse bloat in mutation-based genetic programming for the two test functions ORDER and MAJORITY. We overcome previous assumptions on the magnitude of bloat and give matching or close-to-matching upper and lower bounds for the expected optimization time. In particular, we show that the (1+1) GP takes (i) ?(Tinit + n log n) iterations with bloat control on ORDER as well as MAJORITY; and (ii) O(Tinit log Tinit + n(log n)3) and Omega(Tinit + n log n) (and Omerga(Tinit log Tinit) for n = 1) iterations without bloat control on MAJORITY.", notes = "Also known as \cite{Doerr:2017:BBG:3071178.3071271} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @InProceedings{Doerr:2019:GECCOb, author = "Benjamin Doerr and Andrei Lissovoi and Pietro S. Oliveto", title = "Evolving {Boolean} functions with conjunctions and disjunctions via genetic programming", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference", year = "2019", editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and Anne Auger and Petr Posik and Leslie {Peprez Caceres} and Andrew M. Sutton and Nadarajen Veerapen and Christine Solnon and Andries Engelbrecht and Stephane Doncieux and Sebastian Risi and Penousal Machado and Vanessa Volz and Christian Blum and Francisco Chicano and Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and Jonathan Fieldsend and Jose Antonio Lozano and Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and Robin Purshouse and Thomas Baeck and Justyna Petke and Giuliano Antoniol and Johannes Lengler and Per Kristian Lehre", isbn13 = "978-1-4503-6111-8", pages = "1003--1011", address = "Prague, Czech Republic", DOI = "doi:10.1145/3321707.3321851", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, Theory, Run time analysis", size = "9 pages", abstract = "Recently it has been proved that simple GP systems can efficiently evolve the conjunction of n variables if they are equipped with the minimal required components. In this paper, we make a considerable step forward by analysing the behaviour and performance of a GP system for evolving a Boolean function with unknown components, i.e. the target function may consist of both conjunctions and disjunctions. We rigorously prove that if the target function is the conjunction of n variables, then a GP system using the complete truth table to evaluate program quality evolves the exact target function in O(l n log squared n) iterations in expectation, where l ge n is a limit on the size of any accepted tree. Additionally, we show that when a polynomial sample of possible inputs is used to evaluate solution quality, conjunctions with any polynomially small generalisation error can be evolved with probability 1 − O(log squared (n)/n). To produce our results we introduce a super-multiplicative drift theorem that gives significantly stronger runtime bounds when the expected progress is only slightly super-linear in the distance from the optimum.", notes = "Also known as \cite{3321851} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @Article{DBLP:journals/tcs/DoerrKLL20, author = "Benjamin Doerr and Timo Koetzing and J. A. Gregor Lagodzinski and Johannes Lengler", title = "The impact of lexicographic parsimony pressure for {ORDER/MAJORITY} on the run time", journal = "Theoretical Computer Science", year = "2020", volume = "816", pages = "144--168", month = "6 " # may, keywords = "genetic algorithms, genetic programming, Bloat control, Theory, Runtime analysis", URL = "https://doi.org/10.1016/j.tcs.2020.01.011", DOI = "doi:10.1016/j.tcs.2020.01.011", timestamp = "Fri, 03 Apr 2020 09:22:19 +0200", biburl = "https://dblp.org/rec/journals/tcs/DoerrKLL20.bib", bibsource = "dblp computer science bibliography, https://dblp.org", abstract = "While many optimization problems work with a fixed number of decision variables and thus a fixed-length representation of possible solutions, genetic programming (GP) works on variable-length representations. A naturally occurring problem is that of bloat, that is, the unnecessary growth of solution lengths, which may slow down the optimization process. So far, the mathematical runtime analysis could not deal well with bloat and required explicit assumptions limiting bloat. In this paper, we provide the first mathematical runtime analysis of a GP algorithm that does not require any assumptions on the bloat. Previous performance guarantees were only proven conditionally for runs in which no strong bloat occurs. Together with improved analyses for the case with bloat restrictions our results show that such assumptions on the bloat are not necessary and that the algorithm is efficient without explicit bloat control mechanism. More specifically, we analyzed the performance of the GP on the two benchmark functions Order and Majority. When using lexicographic parsimony pressure as bloat control, we show a tight runtime estimate of iterations both for Order and Majority. For the case without bloat control, the bounds and (and for ) hold for Majority", notes = "some maths in abstract not shown", } @InProceedings{Doerr:2020:GECCOcompb, author = "Benjamin Doerr", title = "A Gentle Introduction to Theory (for Non-Theoreticians)", year = "2020", editor = "Richard Allmendinger and Hugo Terashima Marin and Efren Mezura Montes and Thomas Bartz-Beielstein and Bogdan Filipic and Ke Tang and David Howard and Emma Hart and Gusz Eiben and Tome Eftimov and William {La Cava} and Boris Naujoks and Pietro Oliveto and Vanessa Volz and Thomas Weise and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Rui Wang and Ran Cheng and Guohua Wu and Miqing Li and Hisao Ishibuchi and Jonathan Fieldsend and Ozgur Akman and Khulood Alyahya and Juergen Branke and John R. Woodward and Daniel R. Tauritz and Marco Baioletti and Josu Ceberio Uribe and John McCall and Alfredo Milani and Stefan Wagner and Michael Affenzeller and Bradley Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and Nayat Sanchez-Pi and Luis Marti and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and Matthew Johns and Nick Ross and Ed Keedwell and Herman Mahmoud and David Walker and Anthony Stein and Masaya Nakata and David Paetzel and Neil Vaughan and Stephen Smith and Stefano Cagnoni and Robert M. Patton and Ivanoe {De Falco} and Antonio {Della Cioppa} and Umberto Scafuri and Ernesto Tarantino and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Erik Hemberg and Riyad Alshammari and Tokunbo Makanju and Fuijimino-shi and Ivan Zelinka and Swagatam Das and Ponnuthurai Nagaratnam and Roman Senkerik", isbn13 = "9781450371278", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion", pages = "373--403", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", note = "Tutorial", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1145/3377929.3389889", DOI = "doi:10.1145/3377929.3389889", size = "31 pages", notes = "Also known as \cite{10.1145/3377929.3389889} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @Misc{DBLP:journals/corr/abs-2303-07455, author = "Benjamin Doerr and Andrei Lissovoi and Pietro S. Oliveto", title = "{(1+1)} Genetic Programming With Functionally Complete Instruction Sets Can Evolve Boolean Conjunctions and Disjunctions with Arbitrarily Small Error", howpublished = "arXiv", volume = "abs/2303.07455", year = "2023", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.48550/arXiv.2303.07455", DOI = "doi:10.48550/arXiv.2303.07455", eprinttype = "arXiv", eprint = "2303.07455", timestamp = "Mon, 20 Mar 2023 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/corr/abs-2303-07455.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{Dogan:2017:ELECO, author = "Yavuz Selim Dogan and Fatih V. Celebi and Hilal Kaya", booktitle = "2017 10th International Conference on Electrical and Electronics Engineering (ELECO)", title = "Using evolutionary algorithms for designing {3D} novel objects", year = "2017", pages = "799--803", month = "30 " # nov # "-2 " # dec, address = "Bursa, Turkey", publisher = "IEEE", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-5386-1723-6", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=8266225", size = "5 pages", abstract = "Multicellular creatures start their life cycle by self-replication of the starting cell (the zygote) according to the instructions written in the DNA. The rules, written in the DNA, determine the final design of the organism. This process can be simulated in a digital environment, improved by evolutionary process and can be used to produce inventive designs. The purpose of this study is to test the ability of computers to make novel designs by mimicking embryological development. A program, developed for this, produces cube-shaped symbolic cells. Shapes emerging from the combination of hundreds of cells are compared to target shapes. DNAs of shapes are evolved by genetic programming to increase the similarity to the target shape. In the tests made with 4 different target shapes, it has been observed that the implemented system can make voxel based designs with the success rate between 53percent and 85percent.", notes = "Also known as \cite{8266225}", } @Article{Doglioni:2008:HP, author = "A. Doglioni and O. Giustolisi and D. A. Savic and B. W. Webb", title = "An investigation on stream temperature analysis based on evolutionary computing", journal = "Hydrological Processes", year = "2008", volume = "22", number = "3", pages = "315--326", month = "30 " # jan, publisher = "John Wiley & Sons, Ltd.", keywords = "genetic algorithms, genetic programming, data-driven, evolutionary modelling, multiobjective optimisation, thermal dynamics, on-line prediction, simulation", ISSN = "1099-1085", URL = "http://dx.doi.org/10.1002/hyp.6607", DOI = "doi:10.1002/hyp.6607", size = "12 pages", abstract = "The data-driven technique, evolutionary polynomial regression, has been tested and used for the study of water temperature behaviour in the River Barle (south-west England). The study aimed to produce multiple models for forecasting water temperature, using air temperature as input. In addition, river discharge data were used to describe the hydrological regime of the study stream, even if they are not involved in the modelling phase. The availability of data sampled at hourly intervals allowed behaviour to be studied at several time scales, including short-term lags between air temperature and water temperature. The approach to model building differs from previous studies in that the relationship between air temperature and water temperature is not evaluated on the basis of a multi-parameter regression, nor does it identify particular structures; rather the evolutionary technique identifies the model by itself. In fact, the non-linear relationship between air temperature and water temperature is investigated by an evolutionary search in the space of particular pseudo-polynomials structures.", } @Article{Doglioni:2010:HSJ, author = "Angelo Doglioni and Davide Mancarella and Vincenzo Simeone and Orazio Giustolisi", title = "Inferring groundwater system dynamics from hydrological time-series data", journal = "Hydrological Sciences Journal", year = "2010", volume = "55", number = "4", pages = "593--608", keywords = "genetic algorithms, genetic programming, groundwater, conceptual model, ordinary differential equations, evolutionary modelling, shallow aquifer", ISSN = "0262-6667", URL = "http://www.tandfonline.com/doi/abs/10.1080/02626661003747556", DOI = "doi:10.1080/02626661003747556", size = "16 pages", abstract = "The problem of identifying and reproducing the hydrological behaviour of groundwater systems can often be set in terms of ordinary differential equations relating the inputs and outputs of their physical components under simplifying assumptions. Conceptual linear and nonlinear models described as ordinary differential equations are widely used in hydrology and can be found in several studies. Groundwater systems can be described conceptually as an interlinked reservoir model structured as a series of nonlinear tanks, so that the groundwater table can be schematised as the water level in one of the interconnected tanks. In this work, we propose a methodology for inferring the dynamics of a groundwater system response to rainfall, based on recorded time series data. The use of evolutionary techniques to infer differential equations from data in order to obtain their intrinsic phenomenological dynamics has been investigated recently by a few authors and is referred to as evolutionary modelling. A strategy named Evolutionary Polynomial Regression (EPR) has been applied to a real hydrogeological system, the shallow unconfined aquifer of Brindisi, southern Italy, for which 528 recorded monthly data over a 44-year period are available. The EPR returns a set of non-dominated models, as ordinary differential equations, reproducing the system dynamics. The choice of the representative model can be made both on the basis of its performance against a test data set and based on its incorporation of terms that actually entail physical meaning with respect to the of the system.", notes = "In English.", } @InProceedings{Dohan:2018:GECCOcomp, author = "David Dohan and David So and Quoc Le", title = "Evolving modular neural sequence architectures with genetic programming", booktitle = "GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2018", editor = "Carlos Cotta and Tapabrata Ray and Hisao Ishibuchi and Shigeru Obayashi and Bogdan Filipic and Thomas Bartz-Beielstein and Grant Dick and Masaharu Munetomo and Silvino {Fernandez Alzueta} and Thomas Stuetzle and Pablo Valledor Pellicer and Manuel Lopez-Ibanez and Daniel R. Tauritz and Pietro S. Oliveto and Thomas Weise and Borys Wrobel and Ales Zamuda and Anne Auger and Julien Bect and Dimo Brockhoff and Nikolaus Hansen and Rodolphe {Le Riche} and Victor Picheny and Bilel Derbel and Ke Li and Hui Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Stephane Doncieux and Richard Duro and Joshua Auerbach and Harold {de Vladar} and Antonio J. Fernandez-Leiva and JJ Merelo and Pedro A. Castillo-Valdivieso and David Camacho-Fernandez and Francisco {Chavez de la O} and Ozgur Akman and Khulood Alyahya and Juergen Branke and Kevin Doherty and Jonathan Fieldsend and Giuseppe Carlo Marano and Nikos D. Lagaros and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Boris Naujoks and Vanessa Volz and Tea Tusar and Pascal Kerschke and Riyad Alshammari and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and John R. Woodward and Shin Yoo and John McCall and Nayat Sanchez-Pi and Luis Marti and Danilo Vasconcellos and Masaya Nakata and Anthony Stein and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Ivanoe {De Falco} and Antonio {Della Cioppa} and Ernesto Tarantino and Umberto Scafuri and P. G. M. Baltus and Giovanni Iacca and Ahmed Hallawa and Anil Yaman and Alma Rahat and Handing Wang and Yaochu Jin and David Walker and Richard Everson and Akira Oyama and Koji Shimoyama and Hemant Kumar and Kazuhisa Chiba and Pramudita Satria Palar", isbn13 = "978-1-4503-5764-7", pages = "37--38", address = "Kyoto, Japan", DOI = "doi:10.1145/3205651.3208782", publisher = "ACM", publisher_address = "New York, NY, USA", month = "15-19 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", abstract = "utomated architecture search has demonstrated significant success for image data, where reinforcement learning and evolution approaches now outperform the best human designed networks ([12], [8]). These successes have not transferred over to models dealing with sequential data, such as in language modelling and translation tasks. While there have been several attempts to evolve improved recurrent cells for sequence data [7], none have achieved significant gains over the standard LSTM. Recent work has introduced high performing recurrent neural network alternatives, such as Transformer [11] and Wavenet [4], but these models are the result of manual human tuning.", notes = "Also known as \cite{3208782} GECCO-2018 A Recombination of the 27th International Conference on Genetic Algorithms (ICGA-2018) and the 23rd Annual Genetic Programming Conference (GP-2018)", } @InProceedings{Doherty2006, author = "Darren Doherty and Colm O'Riordan", title = "Evolving Agent--Based Team Tactics for Combative Computer Games", booktitle = "Proceedings of the 17th Irish Artificial Intelligence and Cognitive Science Conference", pages = "52--61", year = "2006", location = "Belfast, Ireland", editor = "D. A. Bell and P. Milligan and P. P. Sage", address = "Queen's University, Belfast", month = "11th-13th " # sep, keywords = "genetic algorithms, genetic programming, team evolution", notes = "http://www.cs.qub.ac.uk/aics06/aics.html", organisation = "Artificial Intelligence Association of Ireland", URL = "http://netserver.it.nuigalway.ie/darrendoherty/publications/aics2006.pdf", size = "10 pages", abstract = "In this paper, we describe an architecture for evolving team tactics for a combative 2D gaming environment using genetic programming (GP) techniques.We describe the process used to evolve the decision-making capabilities of the team agents, the simulation environment and the teams of agents involved in the simulation before introducing some preliminary results and discussing possible future work.", } @InProceedings{Doherty2006I, author = "Darren Doherty and Colm O'Riordan", title = "Evolving Tactical Behaviours for Teams of Agents in Single Player Action Games", booktitle = "Proceedings of the 9th International Conference on Computer Games: AI, Animation, Mobile, Educational \& Serious Games", year = "2006", pages = "121--126", location = "Dublin, Ireland", editor = "Qasim Mehdi and Fred Mtenzi and Bryan Duggan and Hugh McAtamney", address = "Dublin Institute of Technology", month = "22nd-24th " # nov, keywords = "genetic algorithms, genetic programming, team evolution", notes = "http://www.comp.dit.ie/cgames/", organisation = "University of Wolverhampton", ISBN = "0-9549016-2-2", URL = "http://netserver.it.nuigalway.ie/darrendoherty/publications/cgames2006.pdf", size = "5 pages", abstract = "In this paper, we describe an architecture for evolving tactics for teams of agents in single-player combative 2D games using evolutionary computing (EC) techniques. We discuss the evolutionary process adopted and the team tactics evolved. The individual agents in the team evolve to have different capabilities that combine together as effective tactics. We also compare the performance of the evolved team against that of a team consisting of agents incorporating the built-in AI of the environment.", notes = " http://netserver.it.nuigalway.ie/darrendoherty/publications.html Recieved Best Paper Award", } @InProceedings{1277347, author = "Darren Doherty and Colm O'Riordan", title = "A phenotypic analysis of GP-evolved team behaviours", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "1951--1958", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1951.pdf", DOI = "doi:10.1145/1276958.1277347", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Real-World Applications, AI, artificial intelligence, cooperative agents, phenotypic analysis, tactical team behaviour", abstract = "This paper presents an approach to analyse the behaviours of teams of autonomous agents who work together to achieve a common goal. The agents in a team are evolved together using a genetic programming (GP) [8] approach where each team of agents is represented as a single GP tree or chromosome. A number of such teams are evolved and their behaviours analysed in an attempt to identify combinations of individual agent behaviours that constitute good (or bad) team behaviour. For each team we simulate a number of games and periodically capture the agents' behavioural information from the gaming environment during each simulation. This information is stored in a series of status records that can be later analysed. We compare and contrast the behaviours of agents in the evolved teams to see if there is a correlation between a team's performance (fitness score) and the combined behaviours of the team's agents. This approach could also be applied to other GP-evolved teams in different domains.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @InProceedings{Doherty2007I, author = "Darren Doherty and Colm O'Riordan", title = "Evolving Team Behaviours in Environments of Varying Difficulty", booktitle = "Proceedings of the 18th Irish Artificial Intelligence and Cognitive Science Conference", year = "2007", pages = "61--70", location = "Dublin, Ireland", editor = "Sarah Jane Delany and Michael Madden", address = "Dublin Institute of Technology", month = "29th-31st " # aug, keywords = "genetic algorithms, genetic programming, team evolution", notes = "http://www.comp.dit.ie/aics07/program.html", organisation = "Artificial Intelligence Association of Ireland", notes = "See \cite{Doherty:2007:AIR}", } @Article{Doherty:2007:AIR, author = "Darren Doherty and Colm O'Riordan", title = "Evolving team behaviours in environments of varying difficulty", journal = "Artificial Intelligence Review", year = "2007", volume = "27", number = "4", pages = "223--244", keywords = "genetic algorithms, genetic programming, Team behaviours, Team evolution, Shooter games", publisher = "Springer", ISSN = "0269-2821", DOI = "doi:10.1007/s10462-008-9078-1", size = "22 pages", abstract = "This paper investigates how varying the difficulty of the environment can affect the evolution of team behaviour in a combative game setting. The difficulty of the environment is altered by varying the perceptual capabilities of the agents in the game. The behaviours of the agents are evolved using a genetic program. These experiments show that the level of difficulty of the environment does have an impact on the evolvability of effective team behaviours; i.e. simpler environments are more conducive to the evolution of effective team behaviours than more difficult environments. In addition, the experiments show that no one best solution from any environment is optimal for all environments.", } @InProceedings{Doherty2008a, author = "Darren Doherty and Colm O'Riordan", title = "Effects of Communication on the Evolution of Squad Behaviours", booktitle = "Fourth Artificial Intelligence and Interactive Digital Entertainment Conference", year = "2008", editor = "Michael Mateas and Chris Darken", volume = "4", number = "1", pages = "30--35", address = "Stanford University in Palo Alto, California, USA", month = "22-24 " # oct, publisher = "AAAI", keywords = "genetic algorithms, genetic programming, computer games, AI, artificial intelligence, cooperative agents, tactical team behaviour", isbn13 = "978-1-57735-391-1", ISSN = "2326-909X", URL = "https://ojs.aaai.org/index.php/AIIDE/article/view/18668", URL = "https://ojs.aaai.org/index.php/AIIDE/article/view/18668/18446", size = "6 pages", abstract = "As the non-playable characters (NPCs) of squad-based shooter computer games share a common goal, they should work together in teams and display cooperative behaviours that are tactically sound. Our research examines genetic programming (GP) as a technique to automatically develop effective team behaviours for shooter games. GP has been used to evolve teams capable of defeating a single powerful enemy agent in a number of environments without the use of any explicit team communication. The aim of this paper is to explore the effects of communication on the evolution of effective squad behaviours. Thus, NPCs are given the ability to communicate their perceived information during evolution. The results show that communication between team members enables an improvement in average team effectiveness.", } @InProceedings{Doherty2008b, author = "Darren Doherty", title = "Evolving Tactical Teams for Shooter Games using Genetic Programming", booktitle = "Proceedings of the 3rd European Graduate Student Workshop on Evolutionary Computation", year = "2008", pages = "29--42", location = "Naples, Italy", editor = "Jano {Van Hemert} and Mario Giacobini and Cecilia {Di Chio}", address = "University of Naples Federico II", month = "27 " # mar, keywords = "genetic algorithms, genetic programming, team evolution", organisation = "Evostar", URL = "http://netserver.it.nuigalway.ie/darrendoherty/publications/evophd2008.pdf", size = "14 pages", abstract = "In recent years, there has been an emergence of squad-based shooter computer games. For a team to be tactically proficient, intelligent non-playable characters (NPCs) must be created that are able to assess their situation, choose effective courses of action and coordinate their behaviour so that they work together effectively. This is a very difficult task and game developers are still striving to create teams of NPCs that are able to display effective team behaviours. Our research examines genetic programming (GP) as a technique to automatically develop effective team behaviours for shooter games. Previous experiments have given rise to GP evolved teams capable of consistently defeating a single powerful enemy agent. The behaviours of these teams have been analysed using a technique we developed for analysing team phenotypes. In future work, we wish to incorporate explicit communication into the evolution and improve our phenotypic analysis method.", notes = "EvoPHD'2008 held in conjunction with EuroGP-2008, EvoCOP2008, EvoBIO2008 and EvoWorkshops2008", } @Article{Doherty:2009:ieeeTCIAIG, author = "Darren Doherty and Colm O'Riordan", title = "Effects of Shared Perception on the Evolution of Squad Behaviors", journal = "IEEE Transactions on Computational Intelligence and AI in Games", year = "2009", month = mar, volume = "1", number = "1", pages = "50--62", keywords = "genetic algorithms, genetic programming, artificial intelligence, interactive digital entertainment, nonplayable characters, shared perception, squad behavior evolution, squad-based shooter computer games, artificial intelligence, computer games", DOI = "doi:10.1109/TCIAIG.2009.2018701", ISSN = "1943-068X", abstract = "As the nonplayable characters (NPCs) of squad-based shooter computer games share a common goal, they should work together in teams and display cooperative behaviors that are tactically sound. Our research examines genetic programming (GP) as a technique to automatically develop effective squad behaviors for shooter games. GP has been used to evolve teams capable of defeating a single powerful enemy agent in a number of environments without the use of any explicit team communication. This paper is an extension of our paper presented at the 2008 Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE'08). Its aim is to explore the effects of shared perception on the evolution of effective squad behaviors. Thus, NPCs are given the ability to explicitly communicate their perceived information during evolution. The results show that the explicit communication of perceived information between team members enables an improvement in average team effectiveness.", notes = "Also known as \cite{4804730}", } @PhdThesis{Doherty:thesis, author = "Darren Doherty", title = "Evolving tactical teams for shooter games using genetic programming", school = "Department of Information Technology, National University of Ireland, Galway", year = "2009", address = "Ireland", keywords = "genetic algorithms, genetic programming", broken = "http://aleph20prod.nuigalway.ie/F/DKB7SUV9K8VB9TH8BPP21UHIK1CCMKHINDKJA669XGN9FRKM8D-00438?func=direct&local_base=GAL01&doc_number=000571432&pds_handle=GUEST", size = "249 pages", notes = "NPC=nonplayable characters (ie software agents). http://ww2.it.nuigalway.ie/cirg/people.html#DD Sys. no. 000571432 Unstable URL: try search at library.nuigalway.ie Sep 2022 (https://nuig.github.io/people/): Title also given as 'Evolving novel behaviours for NPCs in computer games' Supervisor: Colm O'Riordan", } @InCollection{doherty:2003:FAUGPCRI, author = "C. Gregory Doherty", title = "Fundamental Analysis Using Genetic Programming for Classification Rule Induction", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2003", year = "2003", editor = "John R. Koza", pages = "45--51", address = "Stanford, California, 94305-3079 USA", month = "4 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", URL = "http://www.genetic-programming.org/sp2003/Doherty.pdf", size = "7 pages", abstract = "This paper details the application of a genetic programming framework for induction of useful classification rules from a database of income statements, balance sheets, and cash flow statements for North American public companies. Potentially interesting classification rules are discovered. Anomalies in the discovery process merit further investigation of the application of genetic programming to the dataset for the problem domain.", notes = "part of \cite{koza:2003:gagp}", } @InProceedings{dolado:1998:GPNNlrspe, author = "Javier Dolado and Luis Fernandez", title = "Genetic Programming, Neural Networks and Linear Regression in Software Project Estimation", booktitle = "International Conference on Software Process Improvement, Research, Education and Training", year = "1998", editor = "C. Hawkins and M. Ross and G. Staples and J. B. Thompson", pages = "157--171", address = "London", month = "10-11 " # sep, publisher = "British Computer Society", keywords = "genetic algorithms, genetic programming, ANN, neural networks, linear regression, SBSE, LOC, Cocomo, MMRE", ISBN = "1-902505-03-4", URL = "http://www.sc.ehu.es/jiwdocoj/docs/inspir98.pdf", size = "15 pages", notes = "Datasets: Belady, Boehm, Albrect and Gaffney + Kemerer, 15 students 'did not provide any predictive value', Matson et al. effort v. size. broken lorien.ncl.av.uk/sorg broken INSPIRE 98 http://www2.unl.ac.uk/~11georgiadou/inspire98/", } @InProceedings{dolado:1999:lmsce, author = "J. Javier Dolado", title = "Limits to the Methods in Software Cost Estimation", booktitle = "Proceedings of the 1st International Workshop on Soft Computing Applied to Software Engineering", year = "1999", editor = "Conor Ryan and Jim Buckley", pages = "63--68", address = "University of Limerick, Ireland", month = "12-14 " # apr, organisation = "SCARE", publisher = "Limerick University Press", keywords = "genetic algorithms, genetic programming, SBSE", ISBN = "1-874653-52-6", URL = "http://www.sc.ehu.es/jiwdocoj/docs/dolado-scase99.ps", URL = "http://citeseer.ist.psu.edu/271064.html", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/scase_1999/dolado_1999_lmsce.pdf", size = "330 Kb", abstract = "We present some conclusions related to the use of classical regression, neural networks (NN) and genetic programming (GP) for software cost estimation. Although the estimates of classical regression can be improved by NN and GP, the results are not impressive. We conclude that either data points limit the usefulness of the methods, or that better ways have to be found for applying soft-computing techniques for software cost estimation.", notes = "broken http://scare.csis.ul.ie/scase99/ SCASE'99", } @InProceedings{dolado:1999:ICEIS, author = "J. Javier Dolado and Luis Fernandez and M. Carmen Otero and Leire Urkola", title = "Software Effort Estimation: the Elusive Goal in Project Management", booktitle = "International Conference on Enterprise Information Systems 1999", year = "1999", pages = "412--418", keywords = "genetic algorithms, genetic programming", ISBN = "972-98050-0-8", URL = "http://www.sc.ehu.es/jiwdocoj/docs/dofeotur.ps", abstract = "The estimation of the effort to be spent in a software project is a problem still open. Having a good estimation of the variables just at the beginning of a project makes the project manager confident about the future course of the actions, since many of the decisions taken during the development depend on, or are influenced by, the initial estimations. The root of the problems can be attributed to the different methods of analysis used, and to the way with which they are applied. On one hand we may not use the adequate independent variables for prediction and/or we may not build the correct predictive equations. On the other hand we could think that the method of prediction has some effect on the predictions, meaning that it is not the same to use classical regression or other methods of analysis. We have applied linear regression, neural networks and genetic programming to several datasets. We infer that the problem of accurate software estimation by means of mathematical analysis of simple relationships solely isn?t going to be inmediately solved.", notes = "http://www.iceis.org/iceis2003/abstracts_1999.htm", } @Article{Dolado:2000:vcmsse, author = "Jose Javier Dolado", title = "A validation of the component-based method for software size estimation", journal = "IEEE Transactions on Software Engineering", year = "2000", volume = "26", number = "10", pages = "1006--1021", month = oct, keywords = "genetic algorithms, genetic programming, software reusability, software component-based method, software size estimation, software management, work planning, lines of code, fourth-generation language, Mark II function points, software size prediction, neural networks, SBSE", ISSN = "0098-5589", URL = "http://ieeexplore.ieee.org/iel5/32/19037/00879821.pdf", size = "16 pages", abstract = "Estimation of software size is a crucial activity among the tasks of software management. Work planning and subsequent estimations of the effort required are made based on the estimate of the size of the software product. Software size can be measured in several ways: lines of code (LOC) is a common measure and is usually one of the independent variables in equations for estimating several methods for estimating the final LOC count of a software system in the early stages. We report the results of the validation of the component-based method (initially proposed by Verner and Tate, 1988) for software sizing. This was done through the analysis of 46 projects involving more than 100,000 LOC of a fourth-generation language. We present several conclusions concerning the predictive capabilities of the method. We observed that the component-based method behaves reasonably, although not as well as expected for global methods such as Mark II function points for software size prediction. The main factor observed that affects the performance is the type of component.", notes = "data files http://www.sc.ehu.es/jiwdocoj/cbm.htm", } @Article{Dolado:2001:SCF, author = "Jose J. Dolado", title = "On the Problem of the Software Cost Function", journal = "Information and Software Technology", year = "2001", volume = "43", number = "1", pages = "61--72", month = "1 " # jan, keywords = "genetic algorithms, genetic programming, SBSE, software cost function, Cost estimation, Empirical research", ISSN = "0950-5849", URL = "http://www.elsevier.com/locate/issn/09505849", DOI = "doi:10.1016/S0950-5849(00)00137-3", URL = "http://www.sciencedirect.com/science/article/B6V0B-41NK8BD-5/2/6d97db872ced4148a359673dc3b060c6", size = "12 pages", abstract = "The question of finding a function for software cost estimation is a long-standing issue in the software engineering field. The results of other works have shown different patterns for the unknown function, which relates software size to project cost (effort). In this work, the research about this problem has been made by using the technique of Genetic Programming (GP) for exploring the possible cost functions. Both standard regression analysis and GP have been applied and compared on several data sets. However, regardless of the method, the basic size-effort relationship does not show satisfactory results, from the predictive point of view, across all data sets. One of the results of this work is that we have not found significant deviations from the linear model in the software cost functions. This result comes from the marginal cost analysis of the equations with best predictive values.", } @Article{Dolado:2016:ASOC, author = "Jose Javier Dolado and Daniel Rodriguez and Mark Harman and William B. Langdon and Federica Sarro", title = "Evaluation of Estimation Models using the Minimum Interval of Equivalence", journal = "Applied Soft Computing", year = "2016", volume = "49", pages = "956--967", month = dec, keywords = "genetic algorithms, genetic programming, Software estimations, Soft computing, Equivalence Hypothesis Testing, Credible intervals, Bootstrap", URL = "http://www.sciencedirect.com/science/article/pii/S1568494616301557", DOI = "doi:10.1016/j.asoc.2016.03.026", ISSN = "1568-4946", size = "42 pages", abstract = "a new measure to compare soft computing methods for software estimation. This new measure is based on the concepts of Equivalence Hypothesis Testing (EHT). Using the ideas of EHT, a dimensionless measure is defined using the Minimum Interval of Equivalence and a random estimation. The dimensionless nature of the metric allows us to compare methods independently of the data samples used. The motivation of the current proposal comes from the biases that other criteria show when applied to the comparison of software estimation methods. In this work, the level of error for comparing the equivalence of methods is set using EHT. Several soft computing methods are compared, including genetic programming, neural networks, regression and model trees, linear regression (ordinary and least mean squares) and instance-based methods. The experimental work has been performed on several publicly available datasets. Given a dataset and an estimation method we compute the upper point of Minimum Interval of Equivalence, MIEu, on the confidence intervals of the errors. Afterwards, the new measure, MIEratio, is calculated as the relative distance of the MIEu to the random estimation. Finally, the data distributions of the MIEratios are analysed by means of probability intervals, showing the viability of this approach. In this experimental work, it can be observed that there is an advantage for the genetic programming and linear regression methods by comparing the values of the intervals.", } @InProceedings{Dolgano:2018:ai4health, author = "Anton Dolganov and Vladimir Kublanov", title = "Towards a Decision Support System for Disorders of the Cardiovascular System, Diagnosing and Evaluation of the Treatment Efficiency", booktitle = "Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: AI4Health,", year = "2018", pages = "727--733", address = "Funchal, Madeira, Portugal", organization = "INSTICC", publisher = "SciTePress", keywords = "genetic algorithms, genetic programming", isbn13 = "978-989-758-281-3", DOI = "doi:10.5220/0006753407270733", } @InProceedings{Dolganov:2019:USBEREIT, author = "Anton Dolganov", booktitle = "2019 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT)", title = "Indirect Measurement of Arterial Pressure by Means of Heart Rate Variability Signals", year = "2019", pages = "156--158", month = apr, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/USBEREIT.2019.8736632", abstract = "The article tests possibility of the indirect arterial pressure measurement by means of heart rate variability features. DEAP computational network was used as a tool for genetic programming for symbolic regression. Preliminary results have shown that on the one hand, the error of arterial pressure prediction is rather high. On the other hand, heart rate variability features can be used to predict change of the arterial pressure with relatively low error. Perspectives and future plans were described.", notes = "Also known as \cite{8736632}", } @InProceedings{Dolganov:2019:SIBIRCON, author = "Anton Dolganov and Vladimir Kublanov", booktitle = "2019 International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON)", title = "Presentation of the Indicative Factors of Heart Rate Variability for Hypertension Swift-diagnostics", year = "2019", pages = "0428--0431", month = oct, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SIBIRCON48586.2019.8958298", abstract = "The paper describes a visualization methodology of the indicative factors of the short-term heart rate variability for the arterial hypertension express-diagnostics. Biomedical signals were recorded in the course of the functional studies, which included rest state, tilt-test state and aftereffect state. Each state of the functional study was 5 minutes long. Factors complexes were obtained in earlier studies by means of the genetic programming application and quadratic discriminant analysis machine learning technique. In the article proposed alternative way to evaluate decision functions of discriminant analysis, which does not involve matrix multiplication. The proposed visualization is presented for different subjects: for volunteers with normal pressure and for patients, diagnosed with the arterial hypertension. It was shown, that for different subject's different factors are `activated' giving an input to the classification decision. The proposed methodology allowed to conclude that diagnostically indicative factors complexes are able to use personalized data of a patient in diagnostics.", notes = "Also known as \cite{8958298}", } @InCollection{dolin:2000:CPCESIITDLC, author = "Brad Dolin", title = "Co-Evolution of Populations of Chasers and Evaders that use Sonic Intensity and Interaural Time Difference as Localization Cues", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "117--124", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:2000:gagp}", } @InProceedings{dolin:2001:eh, author = "Brad Dolin and Forrest H {Bennett III} and Eleanor G. Rieffel", title = "Methods for evolving robust distributed robot control software: coevolutionary and single population techniques", booktitle = "The Third NASA/DoD workshop on Evolvable Hardware", year = "2001", editor = "Didier Keymeulen and Adrian Stoica and Jason Lohn and Ricardo S. Zebulum", pages = "21--29", address = "Long Beach, California", publisher_address = "1730 Massachusetts Avenue, N.W., Washington, DC, 20036-1992, USA", month = "12-14 " # jul, organisation = "Jet Propulsion Laboratory, California Institute of Technology", publisher = "IEEE Computer Society", keywords = "genetic algorithms, genetic programming, coevolutionary approaches, coevolutionary population techniques, distributed control software, modular robot, modular robots, random sampling, robust distributed robot control software, single population techniques, control engineering computing, distributed control, robots", ISBN = "0-7695-1180-5", DOI = "doi:10.1109/EH.2001.937943", size = "9 pages", abstract = "Previous work on evolving distributed control software for modular robots has resulted in solutions that do not generalise well to unseen test cases. In this work, we seek general solutions to an entire space of test cases. Each test case is a specific world configuration with a passage through which the modular robot must move. The space of test cases is extremely large, so a given training set can only be a sparse sample of this space. We look at several approaches for dealing with the problem of determining an effective training set: using a fixed set throughout a run, sampling randomly at each generation, and using coevolutionary approaches to evolve a population of test worlds. For this problem, random sampling outperformed the fixed sampling technique and did at least as well as the coevolutionary techniques we considered", notes = "EH2001 http://cism.jpl.nasa.gov/ehw/events/nasaeh01/ Note misspeling of Brad Dolin as {"}Dofin, B.{"}.", } @InProceedings{DBLP:conf/sac/DolinBR02, author = "Brad Dolin and Forrest H {Bennett III} and Eleanor G. Rieffel", title = "Co-evolving an effective fitness sample: experiments in symbolic regression and distributed robot control", booktitle = "Proceedings of the 2002 ACM Symposium on Applied Computing (SAC)", year = "2002", pages = "553--559", address = "Madrid, Spain", month = mar # " 10-14", publisher = "ACM", bibsource = "DBLP, http://dblp.uni-trier.de", keywords = "genetic algorithms, genetic programming, co-evolution, fitness cases, symbolic regression, robot control, distributed control", ISBN = "1-58113-445-2", URL = "https://www.fxpal.com/publications/co-evolving-an-effective-fitness-sample-experiments-in-symbolic-regression-and-distributed-robot-control.pdf", DOI = "doi:10.1145/508791.508899", size = "7 pages", abstract = "We investigate two techniques for co-evolving and sampling from a population of fitness cases, and compare these with a random sampling technique. We design three symbolic regression problems on which to test these techniques, and also measure their relative performance on a modular robot control problem. The methods have varying relative performance, but in all of our experiments, at least one of the co-evolutionary methods outperforms the random sampling method by guiding evolution, with substantially fewer fitness evaluations, toward solutions that generalize best on an out-of-sample test set.", } @Article{dolin:2002:GPEM, author = "Brad Dolin and J. J. Merelo", title = "Resource Review: A Web-Based Tour of Genetic Programming", journal = "Genetic Programming and Evolvable Machines", year = "2002", volume = "3", number = "3", pages = "311--313", month = sep, keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "http://www.cs.bgu.ac.il/~sipper/courses/papers/GPweb.pdf", URL = "https://rdcu.be/dR8kw", DOI = "doi:10.1023/A:1020167426088", size = "3 pages", abstract = "Summary of some introductions to GP, tutorials and demos, implementations and useful links for GP research", notes = "Article ID: 5091793", } @InProceedings{dolin:ppsn2002:pp142, author = "Brad Dolin and Maribel Garcia Arenas and Juan J. Merelo Guervos", title = "Opposites Attract: Complementary Phenotype Selection for Crossover in Genetic Programming", booktitle = "Parallel Problem Solving from Nature - PPSN VII", address = "Granada, Spain", month = "7-11 " # sep, pages = "142--152", year = "2002", editor = "Juan J. Merelo-Guervos and Panagiotis Adamidis and Hans-Georg Beyer and Jose-Luis Fernandez-Villacanas and Hans-Paul Schwefel", number = "2439", series = "Lecture Notes in Computer Science, LNCS", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, Evolutionary computing, Selection", ISBN = "3-540-44139-5", DOI = "doi:10.1007/3-540-45712-7_14", abstract = "Standard crossover in genetic programming (GP) selects two parents independently, based on fitness, and swaps randomly chosen portions of genetic material (subtrees). The mechanism by which the crossover operator achieves success in GP, and even whether crossover does in fact exhibit relative success compared to other operators such as mutation, is anything but clear [14]. An intuitive explanation for successful crossover would be that the operator produces fit offspring by combining the 'strengths' of each parent. However, standard selection schemes choose each parent independently of the other, and with regard to overall fitness rather than more specific phenotypic traits. We present an algorithm for choosing parents which have complementary performance on a set of fitness cases, with an eye toward enabling the crossover operator to produce offspring which combine the distinct strengths of each parent. We test Complementary Phenotype Selection in three genetic programming domains: Boolean 6-Multiplexer, Intertwined Spirals Classification, and Sunspot Prediction. We demonstrate significant performance gains over the control methods in all of them and present a preliminary analysis of these results.", } @InProceedings{Dolinsky:1998:ukmr, author = "J.-U. Dolinsky and G. J. Colquhoun and I. D. Jenkinson", title = "A comparison of techniques for modelling robot dynamics", booktitle = "Proceedings of the 14th national conference on manufacturing research", year = "1998", address = "University of Derby, UK", keywords = "ANN", notes = "copy in \cite{Dolinsky:thesis}", } @InProceedings{Dolinsky:2000:MATADOR, author = "J.-U. Dolinsky and I. D. Jenkinson and G. J. Colquhoun", title = "Structural identification and calibration of kinematic robot models by genetic search", booktitle = "Proceedings of the 33rd international MATADOR conference", year = "2000", editor = "David Robert Hayhurst and S. Hinduja and J. Atkinson and M. Burdekin and R. G. Hannam and L. Li and A. W. Labib", pages = "197--202", address = "University of Manchester, Institute for Science and Technology (UMIST), UK", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "9781852333232", URL = "http://books.google.co.uk/books?id=EedSAAAAMAAJ", URL = "http://link.springer.com/chapter/10.1007%2F978-1-4471-0777-4_31", DOI = "doi:10.1007/978-1-4471-0777-4_31", abstract = "Accurate robot modelling is of great importance to the application of enhanced robot programming tools such as Offline Programming systems. This paper describes a prototype of an automated kinematic modelling environment, which is primarily based on evolutionary computation. A genetic algorithm herein attempts to find an optimal model structure of the forward kinematic of an industrial robot based on measurements reflecting individual characteristics. Finally it will be reported on results obtained from simulation experiments.", notes = "copy in \cite{Dolinsky:thesis}", } @PhdThesis{Dolinsky:thesis, author = "Jens-Uwe Dolinsky", title = "The Development of a Genetic Programming Method For Kinematic Robot Calibration", school = "Liverpool John Moores University", year = "2001", address = "UK", month = mar, keywords = "genetic algorithms, genetic programming, coevolution, stochastic inference, robotrak, Symbolic, System identification, Evolutionary Computer software Robotics", URL = "http://www.mb.hs-wismar.de/cea/phd/dolinsky_thesis.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.70.7361", URL = "http://ethos.bl.uk/OrderDetails.do?did=21&uin=uk.bl.ethos.364488", size = "183 pages", abstract = "Kinematic robot calibration is the key requirement for the successful application of offline programming to industrial robotics. To compensate for inaccurate robot tool positioning, offline generated poses need to be corrected using a calibrated kinematic model, leading the robot to the desired poses. Conventional robot calibration techniques are heavily reliant upon numerical optimisation methods for model parameter estimation. However, the non-linearities of the kinematic equations, inappropriate model parameterisations with possible parameter discontinuities or redundancies, typically result in badly conditioned parameter identification. Research in kinematic robot calibration has therefore mainly focused on finding robot models and appropriate accommodated numerical methods to increase the accuracy of these models. This thesis presents an alternative approach to conventional kinematic robot calibration and develops a new inverse static kinematic calibration method based on the recent genetic programming paradigm. In this method the process of robot calibration is fully automated by applying symbolic model regression to model synthesis (structure and parameters) without involving iterative numerical methods for parameter identification, thus avoiding their drawbacks such as local convergence, numerical instability and parameter discontinuities. The approach developed in this work is focused on the evolutionary design and implementation of computer programs that model all error effects in particular non-geometric effects such as gear transmission errors, which considerably affect the overall positional accuracy of a robot. Genetic programming is employed to account for these effects and to induce joint correction models used to compensate for positional errors. The potential of this portable method is demonstrated in calibration experiments carried out on an industrial robot.", notes = "broken Nov 2020 http://www.ljmu.ac.uk/GERI/80097.htm uk.bl.ethos.364488", } @InProceedings{dolinsky:2004:EBPST, author = "Jens-Uwe Dolinsky and Gary Colquhoun and Ian Jenkinson", title = "Robot Calibration Using Genetic Programming", booktitle = "E-Manufacturing: Business Paradigms and Supporting Technologies", year = "2004", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-1-4419-8945-1_12", DOI = "doi:10.1007/978-1-4419-8945-1_12", } @Article{Dolinsky:2007:CI, author = "J. U. Dolinsky and I. D. Jenkinson and G. J. Colquhoun", title = "Application of genetic programming to the calibration of industrial robots", journal = "Computers in Industry", year = "2007", volume = "58", number = "3", pages = "255--264", month = apr, publisher = "Elsevier Science Publishers B. V.", publisher_address = "Amsterdam, The Netherlands", keywords = "genetic algorithms, genetic programming, Inverse static kinematic calibration, Distal supervised learning, Co-evolution", ISSN = "0166-3615", DOI = "doi:10.1016/j.compind.2006.06.003", abstract = "Robot calibration is a widely studied area for which a variety of solutions have been generated. Most of the methods proposed address the calibration problem by establishing a model structure followed by indirect, often ill-conditioned numeric parameter identification. This paper introduces a new inverse static kinematic calibration technique based on genetic programming, which is used to establish and identify model structure and parameters. The technique has the potential to identify the true calibration model avoiding the problems of conventional methods. The fundamentals of this approach are described and experimental results provided.", notes = "Codeplay Ltd., Edinburgh, UK School of Engineering, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK", } @InProceedings{Dolson:2017:GPTP, author = "Emily Dolson and Wolfgang Banzhaf and Charles Ofria", title = "Applying Ecological Principles to Genetic Programming", booktitle = "Genetic Programming Theory and Practice XV", editor = "Wolfgang Banzhaf and Randal S. Olson and William Tozier and Rick Riolo", year = "2017", series = "Genetic and Evolutionary Computation", pages = "73--88", address = "University of Michigan in Ann Arbor, USA", month = may # " 18--20", organisation = "the Center for the Study of Complex Systems", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-90511-2", URL = "https://link.springer.com/chapter/10.1007/978-3-319-90512-9_5", DOI = "doi:10.1007/978-3-319-90512-9_5", abstract = "In natural ecologies, niches are created, altered, or destroyed, driving populations to continually change and produce novel features. Here, we explore an approach to guiding evolution via the power of niches: ecologically-mediated hints. The original exploration of ecologically-mediated hints occurred in Eco-EA, an algorithm in which an experimenter provides a primary fitness function for a tough problem that they are trying to solve, as well as 'hints' that are associated with limited resources. We hypothesize that other evolutionary algorithms that create niches, such as lexicase selection, can be provided hints in a similar way. Here, we use a toy problem to investigate the expected benefits of using this approach to solve more challenging problems. Of course, since humans are notoriously bad at choosing fitness functions, user-provided advice may be misleading. Thus, we also explore the impact of misleading hints. As expected, we find that informative hints facilitate solving the problem. However, the mechanism of niche-creation (Eco-EA vs. lexicase selection) dramatically impacts the algorithm's robustness to misleading hints.", notes = "GPTP 2017, Part of \cite{Banzhaf:2017:GPTP} published after the workshop in 2018", } @InProceedings{dolson:2018:GPTP, author = "Emily Dolson and Alexander Lalejini and Charles Ofria", title = "Exploring Genetic Programming Systems with {MAP-Elites}", booktitle = "Genetic Programming Theory and Practice XVI", year = "2018", editor = "Wolfgang Banzhaf and Lee Spector and Leigh Sheneman", pages = "1--16", address = "Ann Arbor, USA", month = "17-20 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-030-04734-4", URL = "https://peerj.com/preprints/27154/", URL = "http://link.springer.com/chapter/10.1007/978-3-030-04735-1_1", DOI = "doi:10.1007/978-3-030-04735-1_1", abstract = "MAP-Elites is an evolutionary computation technique that has proven valuable for exploring and illuminating the genotype-phenotype space of a computational problem. In MAP-Elites, a population is structured based on phenotypic traits of prospective solutions; each cell represents a distinct combination of traits and maintains only the most fit organism found with those traits. The resulting map of trait combinations allows the user to develop a better understanding of how each trait relates to fitness and how traits interact. While MAP-Elites has not been demonstrated to be competitive for identifying the optimal Pareto front, the insights it provides do allow users to better understand the underlying problem. In particular, MAP-Elites has provided insight into the underlying structure of problem representations, such as the value of connection cost or modularity to evolving neural networks. Here, we extend the use of MAP-Elites to examine genetic programming representations, using aspects of program architecture as traits to explore. We demonstrate that MAP-Elites can generate programs with a much wider range of architectures than other evolutionary algorithms do (even those that are highly successful at maintaining diversity), which is not surprising as this is the purpose of MAP-Elites. Ultimately, we propose that MAP-Elites is a useful tool for understanding why genetic programming representations succeed or fail and we suggest that it should be used to choose selection techniques and tune parameters.", } @Misc{Dolson:2018:PeerJ, author = "Emily L. Dolson and Wolfgang Banzhaf and Charles Ofria", title = "Ecological theory provides insights about evolutionary computation", howpublished = "PeerJ Preprints", year = "2018", volume = "6", pages = "e27315v1", month = "2 " # nov, note = "not peer-reviewed", keywords = "genetic algorithms, genetic programming, Linear genetic programming, Computational Biology, Adaptive and Self-Organizing Systems, Data Mining and Machine Learning, Theory and Formal Methods, fitness sharing, lexicase selection, Eco-EA, interaction networks", DOI = "doi:10.7287/peerj.preprints.27315v1", code_url = "https://github.com/emilydolson/ecology_of_evolutionary_computation", size = "11 pages", abstract = "we find that selection methods affect phylogenetic diversity differently than phenotypic diversity", } @PhdThesis{Dolson_grad.msu_0128D_16624, author = "Emily L. Dolson", title = "On the constructive power of ecology in open-ended evolving systems", school = "Computer Science, Ecology, Evolutionary Biology, and Behavior, Michigan State University", year = "2019", address = "USA", keywords = "genetic algorithms, genetic programming, System theory, Evolutionary computation, Ecology", isbn13 = "9781392019689", URL = "https://d.lib.msu.edu/etd/47691", DOI = "doi:10.25335/x9p7-n282", size = "220 pages", abstract = "Ecology is a powerful force for unlocking the full potential of evolution. Ecological interactions create feedback loops that promote diversification, both in natural ecosystems and in more applied evolutionary computation frameworks. However, we currently lack a strong theoretical framework that predicts how a given ecological community will evolve.Such a framework would allow us to better understand and anticipate change in evolving systems and facilitate the harnessing of ecology as a tool for guiding evolution. I develop tools to begin the development of such theory in computational systems. Using these tools, I show that different mechanisms for creating ecology (e.g. spatial structure and varied competition schemes) produce radically different community structures and evolutionary outcomes. I explore the implications of these differences in the context of evolutionary computation and biology", notes = "Supervisor: Charles A. Ofria", } @InProceedings{Dolson:2021:GPTP, author = "Jose {Guadalupe Hernandez} and Alexander Lalejini and Emily Dolson", title = "What Can Phylogenetic Metrics Tell us About Useful Diversity in Evolutionary Algorithms?", booktitle = "Genetic Programming Theory and Practice XVIII", year = "2021", editor = "Wolfgang Banzhaf and Leonardo Trujillo and Stephan Winkler and Bill Worzel", series = "Genetic and Evolutionary Computation", pages = "63--82", address = "East Lansing, USA", month = "19-21 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-981-16-8112-7", URL = "https://arxiv.org/abs/2108.12586", DOI = "doi:10.1007/978-981-16-8113-4_4", code_url = "https://github.com/emilydolson/phylodiversity-metrics-in-EC-GPTP-2021", abstract = "It is generally accepted that diversity is associated with success in evolutionary algorithms. However, diversity is a broad concept that can be measured and defined in a multitude of ways. To date, most evolutionary computation research has measured diversity using the richness and/or evenness of a particular genotypic or phenotypic property. While these metrics are informative, we hypothesize that other diversity metrics are more strongly predictive of success. Phylogenetic diversity metrics are a class of metrics popularly used in biology, which take into account the evolutionary history of a population. Here, we investigate the extent to which 1) these metrics provide different information than those traditionally used in evolutionary computation, and 2) these metrics better predict the long-term success of a run of evolutionary computation. We find that, in most cases, phylogenetic metrics behave meaningfully differently from other diversity metrics. Moreover, our results suggest that phylogenetic diversity is indeed a better predictor of success.", notes = "Part of \cite{Banzhaf:2021:GPTP} published after the workshop in 2022", } @Article{Dolson:2022:GPEM, author = "Emily Dolson", title = "Book Review: the evolution of complexity", journal = "Genetic Programming and Evolvable Machines", year = "2022", volume = "23", number = "4", pages = "585--587", month = dec, keywords = "genetic algorithms, genetic programming, NK landscape, NKCS, Baldwin effect, evolution of haploid-diploid sex, bit strings, endosymbiont, horizontal gene transfer, multicellularity, epigenetics, eusociality, Random Boolean Network", ISSN = "1389-2576", URL = "https://rdcu.be/dR8mn", DOI = "doi:10.1007/s10710-022-09443-x", size = "3 pages", abstract = "Review of L. Bull, The Evolution of Complexity: Simple Simulations of Major Innovations (Springer, 2020). ISBN: 978-3-030-40729-2 https://doi.org/10.1007%2F978-3-030-40730-8", notes = "Department of Computer Science and Engineering, Michigan State University, 428 S. Shaw Lane, East Lansing 48824, MI, USA", } @InProceedings{Dolson:2023:GPTP, author = "Emily Dolson and Alexander Lalejini", title = "Reachability Analysis for Lexicase Selection via Community Assembly Graphs", booktitle = "Genetic Programming Theory and Practice XX", year = "2023", editor = "Stephan Winkler and Leonardo Trujillo and Charles Ofria and Ting Hu", series = "Genetic and Evolutionary Computation", pages = "283--301", address = "Michigan State University, USA", month = jun # " 1-3", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Lexicase selection, Eco-evolutionary theory, Multi-objective optimization", isbn13 = "978-981-99-8412-1", DOI = "doi:10.1007/978-981-99-8413-8_15", abstract = "Fitness landscapes have historically been a powerful tool for analysing the search space explored by evolutionary algorithms. In particular, they facilitate understanding how easily reachable an optimal solution is from a given starting point. However, simple fitness landscapes are inappropriate for analyzing the search space seen by selection schemes like lexicase selection in which the outcome of selection depends heavily on the current contents of the population (i.e. selection schemes with complex ecological dynamics). Here, we propose borrowing a tool from ecology to solve this problem: community assembly graphs. We demonstrate a simple proof-of-concept for this approach on an NK Landscape where we have perfect information. We then demonstrate that this approach can be successfully applied to a complex genetic programming problem. While further research is necessary to understand how to best use this tool, we believe it will be a valuable addition to our tool-kit and facilitate analyses that were previously impossible.", notes = " Part of \cite{Hu:2023:GPTP} published after the workshop in 2024", } @InProceedings{Domenech-Asensi:2017:ieeeISIE, author = "G. Domenech-Asensi and T. J. Kazmierski", booktitle = "2017 IEEE 26th International Symposium on Industrial Electronics (ISIE)", title = "Generation of new power processing structures exploiting genetic programming", year = "2017", pages = "729--732", abstract = "This paper describes the use of genetic algorithms to generate power processing circuits. In order to speed up the algorithm, the fitness of the circuits is evaluated using an explicit integration method based on the 4th order Adams-Bashforth formula. Different combinations of genetic primitives for the crossover and mutation processes have been tested. The algorithm is demonstrated by generating new structures of voltage multipliers, which specifically focus on energy harvesting systems. These systems require low input voltages, usually under the diode threshold value. The Adams-Bashforth method allows to achieve a simulation time that is about five times faster than that of SPICE-based simulations.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ISIE.2017.8001336", month = jun, notes = "Also known as \cite{8001336}", } @MastersThesis{domingos:thesis, author = "Roberto Pinheiro Domingos", title = "Non-Linear Nuclear Engineering Models as an Application of Genetic Programming", school = "Universidade Federal Rio de Janeiro", year = "1997", address = "Brazil", month = mar, keywords = "genetic algorithms, genetic programming", broken = "http://www.con.ufrj.br/MSc%20Dissertacoes/MSc%20Dissertacoes/dissertacoes_1997.htm", notes = "Modelos Nao-Lineares da Engenharia Nuclear como uma Aplicacao da Programacao Genetica Orientador: Prof. Roberto Schirru Data da Defesa: 11/04/1997 UFRJ details from http://www.genetic-programming.org/gpphdtheses.html Email Mon, 07 Jul 2003 20:25:57 -0300 confirms this as Masters thesis NOT PhD.", } @PhdThesis{domingos:phdthesis, author = "Roberto Pinheiro Domingos", title = "Evolutionary Neuro-Fuzzy Models Applied to Nuclear Engineering Process Identification and Control", school = "COPPE, Universidade Federal Rio de Janeiro", year = "2003", address = "Brasil", month = jun, email = "roberto.domingos@terra.com.br", keywords = "genetic algorithms, genetic programming, additive neurofuzzy", URL = "http://antigo.nuclear.ufrj.br/DScTeses/teses_2003.htm", size = "161 pages", abstract = "This work develops two soft computer models based on genetic programming system, these models are then applied to two engineering problems. At the first application obtaining an axial xenon oscillation controller of a nuclear reactor is investigated, several obtained controllers are discussed and the best one is compared with a neuro-fuzzy model. In the second application a hybrid model involving different soft computer techniques was developed and applied to a system identification benchmark problem, the identified model has its characteristics compared with models obtained through different techniques.", notes = "Modelos Neuronebulosos Evolucionarios Aplicados ao Controle de Processos Nucleares Data de Defesa: 30/06/2003. UFRJ Supervisors: Prof. Roberto Schirru and Prof. Aquilino Senra Martinez neurofuzzy genetic programming ", } @Article{domingos:2003:ASC, author = "Roberto P. Domingos and Gustavo H. F. Caldas and Claudio M. N. A. Pereira and Roberto Schirru", title = "{PWR's} Xenon oscillation control through a fuzzy expert system automatically designed by means of genetic programming", journal = "Applied Soft Computing", year = "2003", volume = "3", number = "4", pages = "317--323", month = dec, keywords = "genetic algorithms, genetic programming, Axial xenon oscillations control; Fuzzy logic", URL = "http://www.sciencedirect.com/science/article/B6W86-49MX1MH-1/2/50727e0c9a470ae05a1e62675e4555d7", DOI = "doi:10.1016/j.asoc.2003.05.002", abstract = "This work proposes the use of genetic programming (GP) for automatic design of a fuzzy expert system aimed to provide the control of axial xenon oscillations in pressurized water reactors (PWRs). The control methodology is based on three axial offsets of xenon (AOx), iodine (AOi) and neutron flux (AOf), effectively used in former work. Simulations were made using a two-point xenon oscillation model, which employs the non-linear xenon and iodine balance equations and the one group, one-dimensional neutron diffusion equation, with non-linear power reactivity feedback, also proposed in the literature. Results have demonstrated the ability of the GP in finding a good fuzzy strategy, which can effectively control the axial xenon oscillations.", } @Article{Domingos:2005:PNE, author = "Roberto P. Domingos and Roberto Schirru and Aquilino Senra Martinez", title = "Soft computing systems applied to PWR's xenon", journal = "Progress in Nuclear Energy", year = "2005", volume = "46", number = "3-4", pages = "297--308", keywords = "genetic algorithms, genetic programming, evolutionary computation, control, xenon oscillation", DOI = "doi:10.1016/j.pnucene.2005.03.011", abstract = "The present work intends to introduce a soft computing technique as an effective and robust tool available to deal with nuclear engineering problems. This goal is reached by the presentation of an application: a genetic programming system (GP) able to automatically design a controller for the axial xenon oscillations in a pressurised water reactors (PWRs). The axial xenon oscillations control methodology is based on three axial offsets: the xenon axial offset (AOx), the iodine axial offset (AOi) and the neutron flux axial offset (AOf), effectively used in former work. Simulations were made using a two-point xenon oscillation model which employs the non-linear xenon and iodine balance equations and the one group, one-dimensional neutron diffusion equation, with non-linear power reactivity feedback, also proposed in the literature. Obtained results showed the ability of the GP in finding a strategy which can effectively control the axial xenon oscillations.", } @Article{Dona:2015:JEM, author = "Carolina Dona and Ni-Bin Chang and Vicente Caselles and Juan M. Sanchez and Antonio Camacho and Jesus Delegido and Benjamin W. Vannah", title = "Integrated satellite data fusion and mining for monitoring lake water quality status of the {Albufera de Valencia in Spain}", journal = "Journal of Environmental Management", volume = "151", pages = "416--426", year = "2015", keywords = "genetic algorithms, genetic programming, Water quality, Lake management, Remote sensing, Data fusion, Data mining, Machine learning", ISSN = "0301-4797", DOI = "doi:10.1016/j.jenvman.2014.12.003", URL = "http://www.sciencedirect.com/science/article/pii/S0301479714005805", abstract = "Lake eutrophication is a critical issue in the interplay of water supply, environmental management, and ecosystem conservation. Integrated sensing, monitoring, and modelling for a holistic lake water quality assessment with respect to multiple constituents is in acute need. The aim of this paper is to develop an integrated algorithm for data fusion and mining of satellite remote sensing images to generate daily estimates of some water quality parameters of interest, such as chlorophyll a concentrations and water transparency, to be applied for the assessment of the hypertrophic Albufera de Valencia. The Albufera de Valencia is the largest freshwater lake in Spain, which can often present values of chlorophyll a concentration over 200 mg m-3 and values of transparency (Secchi Disk, SD) as low as 20 cm. Remote sensing data from Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat Thematic Mapper (TM) and Enhance Thematic Mapper (ETM+) images were fused to carry out an integrative near-real time water quality assessment on a daily basis. Landsat images are useful to study the spatial variability of the water quality parameters, due to its spatial resolution of 30 m, in comparison to the low spatial resolution (250/500 m) of MODIS. While Landsat offers a high spatial resolution, the low temporal resolution of 16 days is a significant drawback to achieve a near real-time monitoring system. This gap may be bridged by using MODIS images that have a high temporal resolution of 1 day, in spite of its low spatial resolution. Synthetic Landsat images were fused for dates with no Landsat overpass over the study area. Finally, with a suite of ground truth data, a few genetic programming (GP) models were derived to estimate the water quality using the fused surface reflectance data as inputs. The GP model for chlorophyll a estimation yielded a R2 of 0.94, with a Root Mean Square Error (RMSE) = 8 mg m-3, and the GP model for water transparency estimation using Secchi disk showed a R2 of 0.89, with an RMSE = 4 cm. With this effort, the spatiotemporal variations of water transparency and chlorophyll a concentrations may be assessed simultaneously on a daily basis throughout the lake for environmental management.", } @Article{dona:2016:Remote_Sensing, author = "Carolina Dona and Ni-Bin Chang and Vicente Caselles and Juan Manuel Sanchez and Lluis Perez-Planells and Maria Del Mar Bisquert and Vicente Garcia-Santos and Sanaz Imen and Antonio Camacho", title = "Monitoring Hydrological Patterns of Temporary Lakes Using Remote Sensing and Machine Learning Models: Case Study of La Mancha Humeda Biosphere Reserve in Central Spain", journal = "Remote Sensing", year = "2016", volume = "8", number = "8", keywords = "genetic algorithms, genetic programming", ISSN = "2072-4292", URL = "https://www.mdpi.com/2072-4292/8/8/618", DOI = "doi:10.3390/rs8080618", abstract = "The Biosphere Reserve of La Mancha Humeda is a wetland-rich area located in central Spain. This reserve comprises a set of temporary lakes, often saline, where water level fluctuates seasonally. Water inflows come mainly from direct precipitation and runoff of small lake watersheds. Most of these lakes lack surface outlets and behave as endorheic systems, where water withdrawal is mainly due to evaporation, causing salt accumulation in the lake beds. Remote sensing was used to estimate the temporal variation of the flooded area in these lakes and their associated hydrological patterns related to the seasonality of precipitation and evapotranspiration. Landsat 7 ETM+ satellite images for the reference period 2013-2015 were jointly used with ground-truth datasets. Several inverse modelling methods, such as two-band and multispectral indices, single-band threshold, classification methods, artificial neural network, support vector machine and genetic programming, were applied to retrieve information on the variation of the flooded areas. Results were compared to ground-truth data, and the classification errors were evaluated by means of the kappa coefficient. Comparative analyses demonstrated that the genetic programming approach yielded the best results, with a kappa value of 0.98 and a total error of omission-commission of 2percent. The dependence of the variations in the water-covered area on precipitation and evaporation was also investigated. The results show the potential of the tested techniques to monitor the hydrological patterns of temporary lakes in semiarid areas, which might be useful for management strategy-linked lake conservation and specifically to accomplish the goals of both the European Water Framework Directive and the Habitats Directive.", notes = "also known as \cite{rs8080618}", } @Article{Dona:2021:Remote_Sensing, author = "Carolina Dona and Daniel Morant and Antonio Picazo and Carlos Rochera and Juan Manuel Sanchez and Antonio Camacho", title = "Estimation of Water Coverage in Permanent and Temporary Shallow Lakes and Wetlands by Combining Remote Sensing Techniques and Genetic Programming: Application to the Mediterranean Basin of the Iberian Peninsula", journal = "Remote Sensing", year = "2021", volume = "13", number = "4", article-number = "652", keywords = "genetic algorithms, genetic programming, wetlands and shallow lakes, temporary and permanent lakes, Mediterranean, remote sensing, Landsat-7, Sentinel-2, water cover detection", ISSN = "2072-4292", publisher = "MDPI", URL = "https://www.mdpi.com/2072-4292/13/4/652", DOI = "doi:10.3390/rs13040652", size = "23 pages", abstract = "This work aims to validate the wide use of an algorithm developed using genetic programming (GP) techniques allowing to discern between water and non-water pixels using the near infrared band and different thresholds. A total of 34 wetlands and shallow lakes of 18 ecological types were used for validation. These include marshes, salt ponds, and saline and freshwater, temporary and permanent shallow lakes. Furthermore, based on the spectral matching between Landsat and Sentinel-2 sensors, this methodology was applied to Sentinel-2 imagery, improving the spatial and temporal resolution. When compared to other techniques, GP showed better accuracy (over 85percent in most cases) and acceptable kappa values in the estimation of water pixels (K.ge.0.7) in 10 of the 18 assayed ecological types evaluated with Landsat-7 and Sentinel-2 imagery. The improvements were especially achieved for temporary lakes and wetlands, where existing algorithms were scarcely reliable. This shows that GP algorithms applied to remote sensing satellite imagery can be a valuable tool to monitor water coverage in wetlands and shallow lakes where multiple factors cause a low resolution by commonly used water indices. This allows the reconstruction of hydrological series showing their hydrological behaviors during the last three decades, being useful to predict how their hydrological pattern may behave under future global change scenarios.", notes = "Cavanilles Institute of Biodiversity and Evolutionary Biology, University of Valencia, E-46980 Paterna, 46010 Valencia, Spain", } @InCollection{donald:1995:AEACFI, author = "Keith Mac Donald", title = "An Evolutionary Approach to CPU Fault Isolation", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1995", year = "1995", editor = "John R. Koza", pages = "199--208", address = "Stanford, California, 94305-3079 USA", month = "11 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-195720-5", notes = "part of \cite{koza:1995:gagp}", } @Article{Donarski2010987, author = "James A. Donarski and Stephen A. Jones and Mark Harrison and Malcolm Driffield and Adrian J. Charlton", title = "Identification of botanical biomarkers found in Corsican honey", journal = "Food Chemistry", volume = "118", number = "4", pages = "987--994", year = "2010", month = "15 " # feb, note = "Food Authenticity \& Traceability, Edited by Simon Kelly, Claude Guillou and Paul Brereton", ISSN = "0308-8146", DOI = "doi:10.1016/j.foodchem.2008.10.033", URL = "http://www.sciencedirect.com/science/article/B6T6R-4TRK0VB-1/2/c32107c8f3b0b36745ea2bd369053d04", keywords = "genetic algorithms, genetic programming, NMR spectroscopy, Honey, Kynurenic acid, Chestnut, Geographical origin, Botanical origin", abstract = "Honeys from specified botanical sources often command a premium price due to their organoleptic or pharmacoactive properties. To prevent the fraudulent marketing of honey, analytical techniques are required to confirm its origin. NMR spectroscopy has been used to identify biomarkers of botanical and geographical origin for European honey. One-dimensional 1H NMR spectra were acquired from 374 authentic European honeys collected during 2 years, with the majority of these (220) taken from the island of Corsica. Biomarkers of sweet chestnut, Corsican spring Maquis and Arbousier (strawberry-tree) honeys were identified. Kynurenic acid was found to be a biomarker of sweet chestnut honey. [alpha]-Isophorone and 2,5-dihydroxyphenylacetic acid were confirmed as markers of strawberry-tree honey. Additional compounds specific to strawberry-tree and Corsican spring Maquis honey were partially characterised.", } @InProceedings{doNascimentoFerreira:2016:CEC, author = "Thiago {do Nascimento Ferreira} and Josiel Neumann Kuk and Aurora Pozo and Silvia Regina Vergilio", title = "Product Selection Based on Upper Confidence Bound MOEA/D-DRA for Testing Software Product Lines", booktitle = "CEC 2016", year = "2016", editor = "Yew Soon Ong", pages = "4135--4142", address = "Vancouver", month = "25-29 " # jul, publisher = "IEEE", keywords = "genetic algorithms, SBSE, SPL", isbn13 = "978-1-5090-0623-6", DOI = "doi:10.1109/CEC.2016.7744315", abstract = "The selection of products for testing Software Product Lines (SPLs) is an optimization problem. The goal is to select a possible minimum set of products that satisfies testing criteria, such as, pairwise and mutation testing. Multi-Objective Evolutionary Algorithms (MOEAs) have been successfully used to solve this problem and other ones related to the software development. However, the use of MOEAs demands setting a number of control parameters and selection of genetic operators, to which the algorithm performance is often very sensitive. Adaptive Operator Selection (AOS) methods, such as Upper Confidence Bound (UCB) based ones can help in this task. UCB methods used with Multi-Objective Evolutionary Algorithm Based on Decomposition with Dynamical Resource Allocation (MOEA/D-DRA) have presented promising results, but they are under explored in the Search Based Software Engineering (SBSE) field. To contribute to this research area and to solve efficiently the product selection problem, this paper investigates the use of different AOS UCB-based methods with MOEA/D-DRA. The idea is to reduce effort spent by the tester. Some parameters and evolutionary operators can be automatically set. The approach is empirical evaluated using four instances of real world SPLs and three UCB methods. The UCB methods present similar results, and outperform the canonical version of MOEA/D-DRA.", notes = "http://www.cs.ucl.ac.uk/staff/W.Langdon/cec2016/", } @Article{Dong:1997:EPSR, author = "Dong Gyu Lee and Byong Whi Lee and Soon Heung Chang", title = "Genetic programming model for long-term forecasting of electric power demand", journal = "Electric Power Systems Research", year = "1997", volume = "40", pages = "17--22", number = "1", month = jan, keywords = "genetic algorithms, genetic programming, Forecasting, Electric demand", owner = "wlangdon", ISSN = "0378-7796", broken = "http://www.sciencedirect.com/science/article/B6V30-3WDCJBW-3/2/c71881481512566c7b47d81606334180", URL = "http://hdl.handle.net/10203/71273", DOI = "doi:10.1016/S0378-7796(96)01125-X", abstract = "Genetic programming (GP) involves finding both the functional form and the numeric coefficients for the model. So it does not require the assumption of any functional relationship between dependent and independent variables. The use of GP for solving long-term forecasting of the electric power demand problem is discussed; several cases which have different combinations of terminal sets and functional sets were investigated. The results of annual forecasting of electric power demand are presented for various cases using the GP model. The GP model is compared with the regression model.", notes = "Department of Nuclear Engineering, Korea Advanced Institute of Science and Technology, 373-1 Kusong-dong, Yusong-gu, Taejon 305-701, South Korea", } @Article{dong:2019:AS, author = "Guirong Dong and Xiaozhe Wang and Dianzi Liu", title = "Metaheuristic Approaches to Solve a Complex Aircraft Performance Optimization Problem", journal = "Applied Sciences", year = "2019", volume = "9", number = "15", keywords = "genetic algorithms, genetic programming", ISSN = "2076-3417", URL = "https://www.mdpi.com/2076-3417/9/15/2979", DOI = "doi:10.3390/app9152979", abstract = "The increasing demands for travelling comfort and reduction of carbon dioxide emissions have been considered substantially in the stage of conceptual aircraft design. However, the design of a modern aircraft is a multidisciplinary process, which requires the coordination of information from several specific disciplines, such as structures, aerodynamics, control, etc. To address this problem with adequate accuracy, the multidisciplinary analysis and optimisation (MAO) method is usually applied as a systematic and robust approach to solve such complex design issues arising from industries. Since MAO method is tedious and computationally expensive, genetic programming (GP)-based metamodelling techniques incorporating MAO are proposed as an effective approach to minimise the wing stiffness of a large aircraft subject to aerodynamic, aeroelastic and stability constraints in the conceptual design phase. Based on the linear small-disturbance theory, the state-space equation is employed for stability analysis. In the process of multidisciplinary analysis, aeroelastic response simulations are performed using Nastran. To construct metamodels representing the responses of the interests with high accuracy as well as less computational burden, optimal Latin hypercube design of experiments (DoE) is applied to determine the optimised distribution of sampling points. Following that, parametric optimisation is carried out on metamodels to obtain the optimal wing geometry shape, elastic axis positions and stiffness distribution, and then the solution is verified by finite element simulations. Finally, the superiority of the GP-based metamodel technique over genetic algorithm is demonstrated by multidisciplinary design optimisation of a representative beam-frame wing structure in terms of accuracy and efficiency. The results also show that GP metamodel-based strategy for solving MAO problems can provide valuable insights to tailoring parameters for the effective design of a large aircraft in the conceptual phase.", notes = "also known as \cite{app9152979}", } @InProceedings{Dong:2009:MASS, author = "Hong-Bin Dong and Jia Chen", title = "Improved Genetic Programming Based on Lineage Information", booktitle = "International Conference on Management and Service Science, MASS '09", year = "2009", month = sep, address = "Wuhan, China", pages = "1--5", keywords = "genetic algorithms, genetic programming, chromosome, effective search method, lineage information", DOI = "doi:10.1109/ICMSS.2009.5304998", abstract = "At present, it is a major challenge to adopt an effective search method in genetic programming in order to produce an acceptable model in the search space. How to improve the efficiency of GP in a short period of time to produce better solution is very important. Traditional GP use of all the chromosomes for breeding, its search space for complex issues is enormous. In this paper, we introduce lineage relationship of chromosome in GP and propose an improved lineage-based genetic programming algorithm, ILBGP: use of lineage information of several ancestors, at the same time only retains one chromosome with the same fitness randomly. This method maintains the diversity, which can search the space effectively and avoid premature convergence toward local optima.", notes = "Also known as \cite{5304998}", } @Article{Dong:TETCI, author = "Junlan Dong and Jinghui Zhong and Wei-Neng Chen and Jun Zhang", journal = "IEEE Transactions on Emerging Topics in Computational Intelligence", title = "An Efficient Federated Genetic Programming Framework for Symbolic Regression", year = "2023", volume = "7", number = "3", pages = "858--871", month = jun, keywords = "genetic algorithms, genetic programming, gene expression programming", ISSN = "2471-285X", DOI = "doi:10.1109/TETCI.2022.3201299", abstract = "Symbolic regression is an important method of data-driven modeling, which can provide explicit mathematical expressions for data analysis. However, the existing genetic programming algorithms for symbolic regression require centralized storage of all data, which is unrealistic in many practical applications that involve data privacy. If the data comes from different sources, such as hospitals and banks, it is prone to privacy breaches and security issues. To this end, we propose an efficient federated genetic programming framework that can train a global model without integrated data. Each client can process decentralized data locally in parallel, without sending the original data to the server. This method not only protects the privacy of the data but also reduces the time required for data collection. Moreover, a mean shift aggregation mechanism is developed for aggregating local fitness. Considering the samples relative importance, the mechanism improves the imbalance of symbolic regression data on real-life by incorporating weights into fitness function. Furthermore, based on this framework and self-learning gene expression programming (SL-GEP), a federated self-learning gene expression programming algorithm is developed. The experimental results show that, compared with standard SL-GEP which is a training model based on decentralized data only, our proposed federated genetic programming method is effective to protect data privacy and can have consistently better generalization performance.", notes = "Also known as \cite{9881543} School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China", } @Article{Dong:ieeeTEC, author = "Junlan Dong and Jinghui Zhong and Wei-Li Liu and Jun Zhang", title = "Evolving Equation Learner For Symbolic Regression", journal = "IEEE Transactions on Evolutionary Computation", note = "Early Access", keywords = "genetic algorithms, genetic programming, Mathematical models, Neural networks, Adaptation models, Training, Optimization, Evolutionary computation, Complexity theory, Evolving equation learner, Equation Learner, Progressive Evolutionary Structure Search", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2024.3404650", code_url = "https://github.com/Junlan96/eEQL", notes = "also known as \cite{10538006} School of Computer Science and Engineering, South China University of Technology, Guangzhou, China", } @MastersThesis{oai:collectionscanada.gc.ca:SSU.etd-08102011-153450, author = "Meng Dong", title = "Texture analysis of corpora lutea in ultrasonographic ovarian images using genetic programming and rotation invariant local binary patterns", school = "Computer Science, University of Saskatchewan", year = "2011", address = "Canada", month = jul, keywords = "genetic algorithms, genetic programming, Texture Analysis, Ultrasonography, Corpora lutea, Local Binary Patterns", URL = "https://ecommons.usask.ca/handle/10388/etd-08102011-153450", URL = "http://hdl.handle.net/10388/etd-08102011-153450", size = "4.819Mb", notes = "advisor Mark G. Eramian", } @Article{journals/mbec/DongELP13, author = "Meng Dong and Mark G. Eramian and Simone A. Ludwig and Roger A. Pierson", title = "Automatic detection and segmentation of bovine corpora lutea in ultrasonographic ovarian images using genetic programming and rotation invariant local binary patterns", journal = "Medical and Biological Engineering and Computing", year = "2013", number = "4", volume = "51", keywords = "genetic algorithms, genetic programming", bibdate = "2013-03-06", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/mbec/mbec51.html#DongELP13", pages = "405--416", URL = "http://dx.doi.org/10.1007/s11517-012-1009-2", DOI = "doi:10.1007/s11517-012-1009-2", abstract = "In this study, we propose a fully automatic algorithm to detect and segment corpora lutea (CL) using genetic programming and rotationally invariant local binary patterns. Detection and segmentation experiments were conducted and evaluated on 30 images containing a CL and 30 images with no CL. The detection algorithm correctly determined the presence or absence of a CL in 93.33 percent of the images. The segmentation algorithm achieved a mean (pm standard deviation) sensitivity and specificity of 0.8693 pm 0.1371 and 0.9136 pm 0.0503, respectively, over the 30 CL images. The mean root mean squared distance of the segmented boundary from the true boundary was 1.12 pm 0.463 mm and the mean maximum deviation (Hausdorff distance) was 3.39 pm 2.00 mm. The success of these algorithms demonstrates that similar algorithms designed for the analysis of in vivo human ovaries are likely viable.", } @Article{dong:2021:SERRA, author = "Yue Dong and Jun Niu and Qi Liu and Bellie Sivakumar and Taisheng Du", title = "A hybrid prediction model for wind speed using support vector machine and genetic programming in conjunction with error compensation", journal = "Stochastic Environmental Research and Risk Assessment", year = "2021", volume = "35", number = "12", keywords = "genetic algorithms, genetic programming, Wind speed, Hybrid prediction model, Error compensation, Support vector machine, SVM, Xinjiang", URL = "http://link.springer.com/article/10.1007/s00477-021-01996-0", DOI = "doi:10.1007/s00477-021-01996-0", } @Article{Dong:2020:Symmetry, author = "Yukun Dong and Mengying Wu and Shanchen Pang and Li Zhang2 and Wenjing Yin and Meng Wu and Haojie Li", title = "Automated Program-Semantic Defect Repair and False-Positive Elimination without Side Effects", journal = "Symmetry", year = "2020", volume = "12", number = "12", article-number = "2076", month = "14 " # dec, keywords = "genetic algorithms, genetic programming, genetic improvement, automated program repair, APR, false-positive elimination, program-semantic defect", ISSN = "2073-8994", URL = "https://www.mdpi.com/2073-8994/12/12/2076", DOI = "doi:10.3390/sym12122076", size = "16 pages", abstract = "The alarms of the program-semantic defect-detection report based on static analysis include defects and false positives. The repair of defects and the elimination of false positives are time-consuming and laborious, and new defects may be introduced in the process. To solve these problems, the safe constraints interval of related variables and methods are proposed for the semantic defects in the program, and proposes a functionally equivalent no-side-effect program-semantic defect repair and false-positive elimination strategy based on the test-equivalence theory. The automatic repair of the typical semantic defects of Java programs and the automatic elimination of false positives by adding safe constraint patches. After the repair, the program functions are equivalent and the status of each program point is within the safety range, so that the functions before and after the defect repair are consistent, and the functions and semantics before and after the false positives are eliminated. We have evaluated our approach by repairing 5 projects; our results show that the repair strategy does not require manual confirmation of alarms, automated repair of the program effectively, shortened the repair time greatly, and ensured the correctness of the program after the repair.", notes = "is this GP? Also known as \cite{sym12122076} https://www.mdpi.com/journal/symmetry College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China", } @InProceedings{Donne:2014:CEC, title = "Wave Height Quantification Using Land Based Seismic Data with Grammatical Evolution", author = "Sarah Donne and Miguel Nicolau and Christopher Bean and Michael O'Neill", pages = "2909--2916", booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary Computation", year = "2014", month = "6-11 " # jul, editor = "Carlos A. {Coello Coello}", address = "Beijing, China", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Real-world applications", DOI = "doi:10.1109/CEC.2014.6900563", abstract = "Accurate, real time, continuous ocean wave height measurements are required for the initialisation of ocean wave forecast models, model hindcasting, and climate studies. These measurements are usually obtained using in situ ocean buoys or by satellite altimetry, but are sometimes incomplete due to instrument failure or routine network upgrades. In such situations, a reliable gap filling technique is desirable to provide a continuous and accurate ocean wave field record. Recorded on a land based seismic network are continuous seismic signals known as microseisms. These microseisms are generated by the interactions of ocean waves and will be used in the estimation of ocean wave heights. Grammatical Evolution is applied in this study to generate symbolic models that best estimate ocean wave height from terrestrial seismic data, and the best model is validated against an Artificial Neural Network. Both models are tested over a five month period of 2013, and an analysis of the results obtained indicates that the approach is robust and that it is possible to estimate ocean wave heights from land based seismic data.", notes = "WCCI2014", } @InProceedings{doquet:2025:EuroGP, author = "Guillaume Doquet", title = "Unified piecewise symbolic regression", booktitle = "European Conference on Genetic Programming, EuroGP 2025", year = "2025", editor = "Bing Xue and Luca Manzoni and Illya Bakurov", volume = "15609", series = "LNCS", pages = "190--206", address = "Trieste", month = "23-25 " # apr, publisher = "Springer Nature", keywords = "genetic algorithms, genetic programming, Symbolic Regression, Interpretability, XAI", isbn13 = "978-3-031-89990-4", URL = "https://rdcu.be/ejgGv", DOI = "doi:10.1007/978-3-031-89991-1_12", abstract = "Symbolic Regression (SR) searches for a closed-form mathematical expression describing the relationship between input and output features in data. The main theoretical draw of SR compared to traditional black-box regression techniques is that the learnt models should be interpretable by design. However, typical SR methods struggle to discover sparse and accurate models when the shape of the output varies locally, depending on the values of some input features. Given that this is a common occurrence in physics, SR should be able to learn piecewise models. We introduce a new piecewise SR framework called Unified Piecewise Symbolic Regression (UPSR). UPSR simultaneously partitions the input space into subregions and learns local regressors for each subregion, forming a global model unifying all subregions. We demonstrate its effectiveness on a large synthetic SR benchmark containing both piecewise and non-piecewise data structures. UPSR is shown to outperform state-of-the-art piecewise SR approaches, both qualitatively and quantitatively.", notes = "also known as \cite{paper_26_unified_piecewise} Part of \cite{Xue:2025:GP} EuroGP'2025 held in conjunction with EvoCOP2025, EvoMusArt2025 and EvoApplications2025", } @InProceedings{Dorado:2002:EvoWorkshops, author = "Julian Dorado and Juan R. Rabu$\tilde{n}$al and Jer\'onimo Puertas and Antonino Santos and Daniel Rivero", title = "Prediction and Modelling of the Flow of a Typical Urban Basin through Genetic Programming", booktitle = "Applications of Evolutionary Computing, Proceedings of EvoWorkshops2002: EvoCOP, EvoIASP, EvoSTim/EvoPLAN", year = "2002", editor = "Stefano Cagnoni and Jens Gottlieb and Emma Hart and Martin Middendorf and G{"}unther Raidl", volume = "2279", series = "LNCS", pages = "190--201", address = "Kinsale, Ireland", publisher_address = "Berlin", month = "3-4 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, evolutionary computation, applications, hydrology, rain-fall run-off, sewage, flooding alarm, transference function, hydraulic enginnering, kinematic wave, unit hydographs, STGP", ISBN = "3-540-43432-1", DOI = "doi:10.1007/3-540-46004-7_20", size = "12 pages", abstract = "Genetic Programming (GP) is an evolutionary method that creates computer programs that represent approximate or exact solutions to a problem. This paper proposes an application of GP in hydrology, namely for modelling the effect of rain on the run-off flow in a typical urban basin. The ultimate goal of this research is to design a real time alarm system to warn of floods or subsidence in various types of urban basin. Results look promising and appear to offer some improvement over stochastic methods for analysing river basin systems such as unitary radiographs.", notes = "EvoWorkshops2002, part of cagnoni:2002:ews Vitoria, Spain, 5 minute pluviometer samples = 288 samples per day. Data for rainless days??? Replicated -288...575 three cycles {"}to avoid this discontinuity{"} p193. Sine and Cosine but no IF? No details of mutation, no fine constant adjustment, no anti-bloat measures? Fitting average day and rainy day are separated. Complex arithmetic, mutlti-typed system. {"}This execution does not return any value, it only stores the system's poles, zeros and constants{"} p197. Poles outside unit circle lead to immediate death of tree. Tested on 20 hours and 45 minutes of variable rainfall. Average error on GP model less than that of {"}SCS Unit Hydrograph{"}, Table 1.", } @InProceedings{dorado:2002:IJCNN, author = "Julian Dorado and Juan R. Rabunal and Daniel Rivero and Antonino Santos and Alejandro Pazos", title = "Automatic Recurrent ANN Rule Extraction with Genetic Programming", booktitle = "Proceedings of the 2002 International Joint Conference on Neural Networks IJCNN'02", pages = "1552--1557", year = "2002", month = "12-17 " # may, address = "Hilton Hawaiian Village Hotel, Honolulu, Hawaii", publisher = "IEEE Press", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", organisation = "IEEE", ISBN = "0-7803-7278-6", keywords = "genetic algorithms, genetic programming, artificial neural nets, automatic recurrent ANN rule extraction, rule-exploration technique, rule-extraction system, rule-extraction techniques, knowledge acquisition, knowledge based systems, neural nets", DOI = "doi:10.1109/IJCNN.2002.1007748", abstract = "Various rule-extraction techniques using ANNs have been used so far, most of them being applied on multi-layer ANNs, since they are more easily handled. In many cases, extraction methods focusing on different types of networks and training have been implemented, however, there are virtually no methods that view the extraction of rules from ANNs as systems which are independent from their architecture, training and internal distribution of weights, connections and activation functions. This paper proposes a rule-extraction system of ANNs regardless of their architecture (multi-layer or recurrent), using Genetic programming as a rule-exploration technique.", notes = "IJCNN 2002 Held in connection with the World Congress on Computational Intelligence (WCCI 2002)", } @InProceedings{dorado:ppsn2002:pp485, author = "Julian Dorado and Juan R. Rabunal and Antonino Santos and Alejandro Pazos and Daniel Rivero", title = "Automatic Recurrent ANN Rule Extraction with Genetic Programming", booktitle = "Parallel Problem Solving from Nature - PPSN VII", address = "Granada, Spain", month = "7-11 " # sep, pages = "485--494", year = "2002", editor = "Juan J. Merelo-Guervos and Panagiotis Adamidis and Hans-Georg Beyer and Jose-Luis Fernandez-Villacanas and Hans-Paul Schwefel", number = "2439", series = "Lecture Notes in Computer Science, LNCS", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, Neural Networks", ISBN = "3-540-44139-5", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.205.6971", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.205.6971", URL = "http://sci2s.ugr.es/keel/pdf/specific/congreso/springer1_4.pdf", DOI = "doi:10.1007/3-540-45712-7_47", size = "10 pages", abstract = "Various rule-extraction techniques using ANN have been used so far, most of them being applied on multi-layer ANN, since they are more easily handled. In many cases, extraction methods focusing on different types of networks and training have been implemented. However, there are virtually no methods that view the extraction of rules from ANN as systems which are independent from their architecture, training and internal distribution of weights, connections and activation functions. This paper proposes a rule extraction system of ANN regardless of their architecture (multi-layer or recurrent), using Genetic Programming as a rule-exploration technique.", } @Article{Dore:2025:geomorph, author = "Arnaud Dore and Giovanni Coco", title = "A hybrid, genetic programming and physically-based predictor of dune geometry", journal = "Geomorphology", year = "2025", volume = "468", pages = "109495", keywords = "genetic algorithms, genetic programming, Dunes, Machine learning, Global sensitivity analysis", ISSN = "0169-555X", URL = "https://www.sciencedirect.com/science/article/pii/S0169555X24004471", DOI = "doi:10.1016/j.geomorph.2024.109495", abstract = "Subaqueous sand dunes are found in many natural environments and pose significant operational challenges. However, classic dune predictors found in the literature fail at predicting equilibrium dune dimensions. In this study, we first investigated the potential of using genetic programming to derive predictive equations of dune wavelength and height. The predictors outperformed existing relationships, yet the equations remain complex due to the intricate physics governing dune evolution. We carried out a global sensitivity analysis to evaluate the most influential parameters of the GP predictors. Finally, we proposed a set of robust predictors, for equilibrium dune heights and wavelengths, relying on basic environmental parameters", } @InProceedings{Dorfer:2015:GECCOcomp, author = "Viktoria Dorfer and Sergey Maltsev and Stephan Dreiseitl and Karl Mechtler and Stephan M. Winkler", title = "A Symbolic Regression Based Scoring System Improving Peptide Identifications for MS Amanda", booktitle = "GECCO 2015 Medical Applications of Genetic and Evolutionary Computation (MedGEC'15) Workshop", year = "2015", editor = "Stephen L. Smith and Stefano Cagnoni and Robert M. Patton", isbn13 = "978-1-4503-3488-4", keywords = "genetic algorithms, genetic programming", pages = "1335--1341", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739482.2768509", DOI = "doi:10.1145/2739482.2768509", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Peptide search engines are algorithms that are able to identify peptides (i.e., short proteins or parts of proteins) from mass spectra of biological samples. These identification algorithms report the best matching peptide for a given spectrum and a score that represents the quality of the match; usually, the higher this score, the higher is the reliability of the respective match. In order to estimate the specificity and sensitivity of search engines, sets of target sequences are given to the identification algorithm as well as so-called decoy sequences that are randomly created or scrambled versions of real sequences; decoy sequences should be assigned low scores whereas target sequences should be assigned high scores. In this paper we present an approach based on symbolic regression (using genetic programming) that helps to distinguish between target and decoy matches. On the basis of features calculated for matched sequences and using the information on the original sequence set (target or decoy) we learn mathematical models that calculate updated scores. As an alternative to this white box modelling approach we also use a black box modelling method, namely random forests. As we show in the empirical section of this paper, this approach leads to scores that increase the number of reliably identified samples that are originally scored using the MS Amanda identification algorithm for high resolution as well as for low resolution mass spectra.", notes = "Also known as \cite{2768509} Distributed at GECCO-2015.", } @InProceedings{heuristicLab_GCE_2020, author = "Daniel Dorfmeister and Oliver Krauss", title = "Integrating {HeuristicLab} with Compilers and Interpreters for Non-Functional Code Optimization", booktitle = "GECCO 2020 Workshop on Evolutionary Computation Software Systems", year = "2020", month = jul # " 8-12", editor = "Stefan Wagner and Michael Affenzeller", publisher = "ACM", address = "Internet", pages = "1580--1588", organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, Optimization, Compiler, Interpreter, Distributed Computing, Architecture, Metaheuristics, HeuristicLab, Truffle, Graal", isbn13 = "978-1-4503-7127-8", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/gecco2020/companion_files/wksp139s2-file1.pdf", DOI = "doi:10.1145/3377929.3398103", size = "9 pages", abstract = "Modern compilers and interpreters provide code optimizations during compile and run time, simplifying the development process for the developer and resulting in optimized software. These optimizations are often based on formal proof, or alternatively stochastic optimizations have recovery paths as backup. The Genetic Compiler Optimization Environment (GCE) uses a novel approach, using genetic improvement to optimize the run-time performance of code with stochastic machine learning techniques. we propose an architecture to integrate GCE, which directly integrates with low-level interpreters and compilers, with HeuristicLab, a high-level optimization framework that features a wide range of heuristic and evolutionary algorithms, and a graphical user interface to control and monitor the machine learning process. The defined architecture supports parallel and distributed execution to compensate long run times of the machine learning process caused by abstract syntax tree (AST) transformations. The architecture does not depend on specific operating systems, programming languages, compilers or interpreters.", notes = "API of ZeroMQ. Paranoid Pirate pattern. Java JIT JVM AST. Python, Ruby, JavaScript and C. MiniC. future PSO? Software Competence Center Hagenberg Also known as \cite{Dorfmeister:2020:GECCOcomp}. Also known as \cite{10.1145/3377929.3398103} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @Article{DORGO:2021:CES, author = "Gyula Dorgo and Tibor Kulcsar and Janos Abonyi", title = "Genetic programming-based symbolic regression for goal-oriented dimension reduction", journal = "Chemical Engineering Science", volume = "244", pages = "116769", year = "2021", ISSN = "0009-2509", DOI = "doi:10.1016/j.ces.2021.116769", URL = "https://www.sciencedirect.com/science/article/pii/S0009250921003341", keywords = "genetic algorithms, genetic programming, Data visualisation, Software sensor, Online near-infrared-spectroscopy, Classification, Principal component analysis", abstract = "The majority of dimension reduction techniques are built upon the optimization of an objective function aiming to retain certain characteristics of the projected datapoints: the variance of the original dataset, the distance between the datapoints or their neighbourhood characteristics, etc. Building upon the optimization-based formalization of dimension reduction techniques, the goal-oriented formulation of projection cost functions is proposed. For the optimization of the application-oriented data visualization cost function, a Multi-gene genetic programming (GP)-based algorithm is introduced to optimize the structures of the equations used for mapping high-dimensional data into a two-dimensional space and to select variables that are needed to explore the internal structure of the data for data-driven software sensor development or classifier design. The main benefit of the approach is that the evolved equations are interpretable and can be used in surrogate models. The applicability of the approach is demonstrated in the benchmark wine dataset and in the estimation of the product quality in a diesel oil blending technology based on an online near-infrared (NIR) analyzer. The results illustrate that the algorithm is capable to generate goal-oriented and interpretable features, and the resultant simple algebraic equations can be directly implemented into applications when there is a need for computationally cost-effective projections of high-dimensional data as the resultant algebraic equations are computationally simpler than other solutions as neural networks", } @Book{dorigo.97, author = "Marco Dorigo and Marco Colombetti", title = "Robot Shaping: An Experiment in Behavior Engineering", publisher = "MIT Press", year = "1997", notes = "Bradford Books ", } @TechReport{dorin:1994:GPr, author = "Alan Dorin", title = "Koza, J. ``Genetic Programming'' (review)", institution = "School of Computer Science and Software Engineering, Monash University", address = "Clayton, Australia 3168", year = "1994", keywords = "genetic algorithms, genetic programming", URL = "http://www.csse.monash.edu.au/~aland/reviews/koza.rev.html", notes = "www only", size = "0.5 pages", } @PhdThesis{1_Dorn_Jonathan_2017_PHD, author = "Jonathan Dorn", title = "Optimizing Tradeoffs of Non-Functional Properties in Software", school = "Faculty of the School of Engineering and Applied Science, University of Virginia", year = "2017", address = "USA", month = aug, keywords = "genetic algorithms, Genetic Improvement, SBSE, non-functional properties, program improvement, evolutionary search", URL = "https://doi.org/10.18130/V3JJ62", URL = "https://libra2.lib.virginia.edu/downloads/4m90dv78f?filename=1_Dorn_Jonathan_2017_PHD.pdf", size = "136 pages", abstract = "Software systems have become integral to the daily life of millions of people. These systems provide much of our entertainment (e.g., video games, feature-length films, and YouTube) and our transportation (e.g., planes, trains and automobiles). They ensure that the electricity to power homes and businesses is delivered and are significant consumers of that electricity themselves. With so many people consuming software, the best balance between runtime, energy or battery use, and accuracy is different for some users than for others. With so many applications playing so many different roles and so many developers producing and modifying them, the tradeoff between maintainability and other properties must be managed as well. Existing methodologies for managing these non-functional properties require significant additional effort. Some techniques impose restrictions on how software may be designed or require time-consuming manual reviews. These techniques are frequently specific to a single application domain, programming language, or architecture, and are primarily applicable during initial software design and development. Further, modifying one property, such as runtime, often changes another property as well, such as maintainability. In this dissertation, we present a framework, exemplified by three case studies, for automatically manipulating interconnected program properties to find the optimal trade-offs. We exploit evolutionary search to explore the complex interactions of diverse properties and present the results to users. We demonstrate the applicability and effectiveness of this approach in three application domains, involving different combinations of dynamic properties (how the program behaves as it runs) and static properties (what the source code itself is like). In doing so, we describe the ways in which those domains impact the choices of how to represent programs, how to measure their properties effectively, and how to search for the best among many candidate program implementations. We show that effective choices enable the framework to take unmodified human-written programs and automatically produce new implementations with better properties, and better tradeoffs between properties, than before.", notes = "Not GP? Supervisor: Westley Weimer", } @Article{Dorn:2019:TSE, author = "Jonathan Dorn and Jeremy Lacomis and Westley Weimer and Stephanie Forrest", title = "Automatically Exploring Tradeoffs Between Software Output Fidelity and Energy Costs", journal = "IEEE Transactions on Software Engineering", year = "2019", volume = "45", number = "3", pages = "219--236", month = mar, keywords = "genetic algorithms, genetic programming, genetic improvement, power optimization, search-based software engineering, SBSE, profile-guided optimization, optimising noisy functions, accurate energy measurement", ISSN = "0098-5589", URL = "https://www.cs.cmu.edu/~jlacomis/materials/DornPowerGAUGE2017.pdf", DOI = "doi:10.1109/TSE.2017.2775634", size = "19 pages", abstract = "Data centers account for a significant fraction of global energy consumption and represent a growing business cost. Most current approaches to reducing energy use in data centers treat it as a hardware, compiler, or scheduling problem. focuses instead on the software level, showing how to reduce the energy used by programs when they execute. By combining insights from search-based software engineering, mutational robustness, profile-guided optimization, and approximate computing, the Producing Green Applications Using Genetic Exploration (POWERGAUGE) algorithm finds variants of individual programs that use less energy than the original. We apply hardware, software, and statistical techniques to manage the complexity of accurately assigning physical energy measurements to particular processes. In addition, our approach allows, but does not require, relaxing output quality requirements to achieve greater non-functional improvements. POWERGAUGE optimisations are validated using physical performance measurements. Experimental results on PARSEC benchmarks and two larger programs show average energy reductions of 14percent when requiring the preservation of original output quality and 41percent when allowing for human-acceptable levels of error.", notes = "GI mentioned in section 2", } @Article{journals/soco/DorostiGSAS20, author = "Shadi Dorosti and Saeid Jafarzadeh Ghoushchi and Elham Sobhrakhshankhah and Mohsen Ahmadi and Abbas Sharifi", title = "Application of gene expression programming and sensitivity analyses in analyzing effective parameters in gastric cancer tumor size and location", journal = "Soft Comput", year = "2020", number = "13", volume = "24", pages = "9943--9964", keywords = "genetic algorithms, genetic programming, gene expression programming", bibdate = "2020-06-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/soco/soco24.html#DorostiGSAS20", DOI = "doi:10.1007/s00500-019-04507-0", } @Article{dosi:1999:nepal:er, author = "Giovanni Dosi and Luigi Marengo and Andrea Bassanini and Marco Valente", title = "Norms as emergent properties of adaptive learning: The case of economic routines", journal = "Journal of Evolutionary Economics", year = "1999", volume = "9", number = "1", pages = "5--26", keywords = "genetic algorithms, genetic programming, computability, oligopoly", ISSN = "0936-9937", DOI = "doi:10.1007/s001910050073", abstract = "Interaction among autonomous decision-makers is usually modelled in economics in game-theoretic terms or within the framework of General Equilibrium. Game-theoretic and General Equilibrium models deal almost exclusively with the existence of equilibria and do not analyse the processes which might lead to them. Even when existence proofs can be given, two questions are still open. The first concerns the possibility of multiple equilibria, which game theory has shown to be the case even in very simple models and which makes the outcome of interaction unpredictable. The second relates to the computability and complexity of the decision procedures which agents should adopt and questions the possibility of reaching an equilibrium by means of an algorithmically implementable strategy. Some theorems have recently proved that in many economically relevant problems equilibria are not computable. A different approach to the problem of strategic interaction is a {"}constructivist{"} one. Such a perspective, instead of being based upon an axiomatic view of human behaviour grounded on the principle of optimisation, focuses on algorithmically implementable {"}satisfycing{"} decision procedures. Once the axiomatic approach has been abandoned, decision procedures cannot be deduced from rationality assumptions, but must be the evolving outcome of a process of learning and adaptation to the particular environment in which the decision must be made. This paper considers one of the most recently proposed adaptive learning models: Genetic Programming and applies it to one the mostly studied and still controversial economic interaction environment, that of oligopolistic markets. Genetic Programming evolves decision procedures, represented by elements in the space of functions, balancing the exploitation of knowledge previously obtained with the search of more productive procedures. The results obtained are consistent with the evidence from the observation of the behaviour of real economic agents.", } @InProceedings{dosSantos:2008:SIBGRAPI, author = "Jefersson Alex {dos Santos} and Cristiano Dalmaschio Ferreira and Ricardo {da Silva Torres}", title = "A Genetic Programming Approach for Relevance Feedback in Region-Based Image Retrieval Systems", booktitle = "XXI Brazilian Symposium on Computer Graphics and Image Processing, SIBGRAPI '08", year = "2008", month = oct, pages = "155--162", keywords = "genetic algorithms, genetic programming, genetic programming approach, local aggregation pattern, local image features, query session, region-based image retrieval systems, relevance feedback, image retrieval, relevance feedback", DOI = "doi:10.1109/SIBGRAPI.2008.15", ISSN = "1530-1834", abstract = "This paper presents a new relevance feedback method for content-based image retrieval using local image features. This method adopts a genetic programming approach to learn user preferences and combine the region similarity values in a query session. Experiments demonstrate that the proposed method yields more effective results than the local aggregation pattern (LAP)-based relevance feedback technique.", notes = "Also known as \cite{4654155}", } @Article{Santos2010, author = "J. A. {dos Santos} and C. D. Ferreira and R. {da S. Torres} and M. A. Goncalves and R. A. C. Lamparelli", title = "A Relevance Feedback Method based on Genetic Programming for Classification of Remote Sensing Images", journal = "Information Sciences", year = "2011", volume = "181", number = "12", pages = "2671--2684", month = "1 " # jul, keywords = "genetic algorithms, genetic programming, content-based image retrieval, region descriptors, relevance feedback, remote sensing image classification", ISSN = "0020-0255", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.460.574", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", DOI = "doi:10.1016/j.ins.2010.02.003", URL = "http://www.sciencedirect.com/science/article/B6V0C-4YBMF9K-2/2/7be908a0802e1675ad8e8258bfbc4e01", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.460.574", URL = "http://www.ic.unicamp.br/~jsantos/pdf/santos2011is.pdf", size = "14 pages", abstract = "This paper presents an interactive technique for remote sensing image classification. In our proposal, users are able to interact with the classification system, indicating regions of interest (and those which are not). This feedback information is employed by a genetic programming approach to learning user preferences and combining image region descriptors that encode spectral and texture properties. Experiments demonstrate that the proposed method is effective for image classification tasks and outperforms the traditional MaxVer method.", } @Article{dosSantos:2020:ACC, author = "Willian Antonio {dos Santos} and Joao {Ribeiro Bezerra} and Luis Fabricio {Wanderley Goes} and Flavia Magalhaes {Freitas Ferreira}", title = "Creative Culinary Recipe Generation Based on Statistical Language Models", journal = "IEEE Access", year = "2020", volume = "8", pages = "146263--146283", keywords = "genetic algorithms, genetic programming, Creativity, Brain modeling, Probability, Measurement, Computational modeling, Context modeling, Artificial intelligence, Language models, culinary recipe, computational creativity", DOI = "doi:10.1109/ACCESS.2020.3013436", ISSN = "2169-3536", abstract = "Many works have been done in an effort to create systems for automatic generation of creative culinary recipes. Although most of them are related to the recipe ingredient lists, few works have been done to evaluate and generate the preparation steps of culinary recipes. This work proposes the use of statistical Language Models, as well as the perplexity metric, for the generation of culinary recipes. In this work, we also developed a system for automatic generation of creative culinary recipes using two approaches: one based on a genetic programming algorithm guided by the proposed language model; and the other based on a decomposition of existing recipes and recomposition of new recipes through a genetic algorithm guided by the proposed language model. This second approach achieved the best results. For this approach, a total of 6 recipes were generated to evaluate, through an online survey, the influence of the Language Model in the generation of recipes with better use of secondary ingredients, oils and seasonings, throughout the preparation steps. In the comparison between these two groups of recipes, the respondents considered the recipes generated using the language model as having the best quality, presenting an average evaluation of 63.percent of the scale (i.e. between medium and good use of oils and seasonings compared to recipes from the other group). In addition, a recipe from this approach was cooked and tasted for taste assessment, obtaining an average evaluation of 9percent of the scale.", notes = "Pontical Catholic University of Minas Gerais (PUC Minas), Belo Horizonte, Brazil. Also known as \cite{9153554}", } @InProceedings{coelho:1998:xcsf, author = "Leandro {dos Santos Coelho} and Antonio Augusto Rodrigues Coelho", title = "An Experimental and Comparative Study of Fuzzy PID Controller Structures", booktitle = "Advances in Soft Computing - Engineering Design and Manufacturing", year = "1998", editor = "R. Roy and T. Furuhashi and P. K. Chawdhry", month = "21-30 " # jun, keywords = "Fuzzy logic control, Fuzzy PID Control, Experimental process, Control applications.", ISBN = "1-85233-062-7", broken_url = "https://www.cranfield.ac.uk/wsc3/tech-sessions/papers/ic-2/ic-2.htm", abstract = "Structures and design issues of fuzzy PID (proportional-integral-derivative) controllers (FLC-PIDs) are presented and evaluated in this paper. Configuration and basic characteristic of several structures of FLC-PID based on models proposed in the literature (PD + I), (PI + D conventional), incremental (PD + I), (PD + PI) are here reviewed and implemented. FLC-PIDs are assessed on a horizontal balance process, consisting of two propellers driven by two DC motors. Such process offers control complexities and can become unstable by using classical controllers. Experimental results, robustness and performance of FLC-PIDs are illustrated and discussed.", notes = "WSC3", } @Article{Coelho20091434, author = "Leandro {dos Santos Coelho} and Marcelo Wicthoff Pessoa", title = "Nonlinear model identification of an experimental ball-and-tube system using a genetic programming approach", journal = "Mechanical Systems and Signal Processing", volume = "23", number = "5", pages = "1434--1446", year = "2009", ISSN = "0888-3270", DOI = "doi:10.1016/j.ymssp.2009.02.005", URL = "http://www.sciencedirect.com/science/article/B6WN1-4VNH3WJ-1/2/f2de8e8814271f4e5d58e4cee49bd291", keywords = "genetic algorithms, genetic programming, System identification, Nonlinear models, Evolutionary algorithm", abstract = "Most processes in industry are characterized by nonlinear and time-varying behavior. The identification of mathematical models typically nonlinear systems is vital in many fields of engineering. The developed mathematical models can be used to study the behavior of the underlying system as well as for supervision, fault detection, prediction, estimation of unmeasurable variables, optimization and model-based control purposes. A variety of system identification techniques are applied to the modeling of process dynamics. Recently, the identification of nonlinear systems by genetic programming (GP) approaches has been successfully applied in many applications. GP is a paradigm of evolutionary computation field based on a structure description method that applies the principles of natural evolution to optimization problems and its nature is a generalized hierarchy computer program description. GP adopts a tree structure code to describe an identification problem. Unlike the traditional approximation methods where the structure of an approximate model is fixed, the structure of the GP tree itself is modified and optimized and, thus, there is a possibility that GP trees could be more appropriate or accurate approximate models. This paper focuses the GP method for structure selection in a system identification applications. The proposed GP method combines different techniques for tuning of crossover and mutation probabilities with an orthogonal least-squares (OLS) algorithm to estimate the contribution of the branches of the tree to the accuracy of the discrete polynomial Nonlinear AutoRegressive with eXogenous inputs (NARX) model. The nonlinear system identification procedure, based on a NARX representation and GP, is applied to empirical case study of an experimental ball-and-tube system. The results demonstrate that the GP with OLS is a promising technique for NARX modeling.", } @Article{Coelho:2014:AMM, author = "Leandro {dos Santos Coelho} and Teodoro Cardoso Bora and Carlos Eduardo Klein", title = "A genetic programming approach based on Levy flight applied to nonlinear identification of a poppet valve", journal = "Applied Mathematical Modelling", volume = "38", number = "5-6", pages = "1729--1736", year = "2014", ISSN = "0307-904X", DOI = "doi:10.1016/j.apm.2013.09.014", URL = "http://www.sciencedirect.com/science/article/pii/S0307904X1300591X", keywords = "genetic algorithms, genetic programming, Nonlinear identification, Levy flight, Poppet valves, NARX modelling", abstract = "Genetic programming (GP) is an evolutionary algorithm-based paradigm inspired by natural evolution to find a generalised hierarchy computer program description. GP adopts a tree-structured code to describe an identification problem. This paper proposed a GP method based on Levy flight to estimate discrete polynomial NARX (Nonlinear AutoRegressive with eXogenous inputs) models. The Levy flight random walks on increments distributed according to a heavy-tailed probability distribution formed by the alpha-stable distribution family. Besides, Levy flight is a Markov processes. The distance from the origin of the random walk tends to a stable distribution after a large number of steps. These sorts of movements describe not only the fluctuations in share prices, but also natural behaviours as the way in which albatrosses search for food or the flight of many insects. In this paper, the contribution of Levy flight is related to the tune of crossover and mutation probabilities in GP. The proposed GP method based on Levy flight is used in an experimental application, a poppet valve. Poppet-type of valve is normally used in combustion engines to open and close intake and exhaust ports in the cylinder head. The very well machined adjust between seat and poppet face gives the sealing feature that is improved every time that the pressure inside the cylinder rises up pushing the valve head against its seat. This type of device is also used in the automotive industry to control the emission levels on combustion engines by recirculating burned gases into the combustion chamber. Results are presented to demonstrate the utility of the proposed GP method based on Levy flight as promising technique in NARX (Nonlinear AutoRegressive with eXogenous inputs) model identification of a poppet valve.", } @InCollection{Dostal:2013:HBO, author = "Martin Dostal", title = "Modularity in Genetic Programming", booktitle = "Handbook of Optimization", publisher = "Springer", year = "2013", editor = "Ivan Zelinka and Vaclav Snasel and Ajith Abraham", volume = "38", series = "Intelligent Systems Reference Library", chapter = "15", pages = "365--393", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-30503-0", URL = "http://dx.doi.org/10.1007/978-3-642-30504-7_15", DOI = "doi:10.1007/978-3-642-30504-7_15", abstract = "This chapter provides a review of methods for automatic modularisation of programs evolved using genetic programming. We discuss several techniques used to establishing modularity in program evolution, including highly randomised techniques, techniques with beforehand specified structure of modules, techniques with evolvable structure and techniques with heuristic identification of modules. At first, simple techniques such as Encapsulation and Module Acquisition are discussed. The next two parts reviews Automatically Defined Functions and Automatically Defined Functions with Architecture Altering Operations that enable to evolve the structure of modules at the same time of evolving the modules itself. The following section is focused on Adaptive Representation through Learning, a technique with heuristic-based identification of modules. Next, Hierarchical Genetic Programming is described. Finally, establishing recursion and iteration, a code reuse technique closely related to modularization, is briefly surveyed.", notes = "University Olomouc, Czech Republic", } @InProceedings{Dou:2017:GECCO, author = "Tiantian Dou and Peter Rockett", title = "Semantic-based Local Search in Multiobjective Genetic Programming", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4939-0", address = "Berlin, Germany", pages = "225--226", size = "2 pages", URL = "http://doi.acm.org/10.1145/3067695.3076015", DOI = "doi:10.1145/3067695.3076015", acmid = "3076015", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, local search, model selection, multiobjective optimization, semantic-based genetic programming", month = "15-19 " # jul, abstract = "We report a series of experiments within a multiobjective genetic programming (GP) framework using semantic-based local GP search. We have made comparison with the Random Desired Operator (RDO) of Pawlak et al. and find that a standard generational GP followed by a carefully-designed single-objective GP implementing semantic-based local search yields results statistically comparable to those obtained with the RDO operator. The trees obtained with our GP-based local search technique are, however, around half the size of the trees resulting from the use of the RDO.", notes = "Also known as \cite{Dou:2017:SLS:3067695.3076015} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @TechReport{oai:eprints.whiterose.ac.uk:134140, author = "Tiantian Dou and Yuri Kaszubowski Lopes and Peter I. Rockett", title = "{GPML}: An {XML}-based Standard for the Interchange of Genetic Programming Trees", institution = "Department of Electronic and Electrical Engineering, University of Sheffield", year = "2018", number = "CR2018-2r2", address = "UK", month = "26 " # nov, keywords = "genetic algorithms, genetic programming", bibsource = "OAI-PMH server at eprints.whiterose.ac.uk", oai = "oai:eprints.whiterose.ac.uk:134140", URL = "http://eprints.whiterose.ac.uk/134140/", URL = "http://eprints.whiterose.ac.uk/134140/8/GPML-v2.pdf", size = "9 pages", abstract = "We propose a Genetic Programming Markup Language (GPML), an XML-based standard for the interchange of genetic programming trees, and outline the benefits such a format would bring. We present a formal definition of this standard and describe details of an implementation.", } @Article{Dou:GPEM, author = "Tiantian Dou and Peter Rockett", title = "Comparison of semantic-based local search methods for multiobjective genetic programming", journal = "Genetic Programming and Evolvable Machines", year = "2018", volume = "19", number = "4", pages = "535--563", month = dec, keywords = "genetic algorithms, genetic programming, Semantic-based genetic programming Local search Multiobjective optimization Model selection", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-018-9325-4", size = "29 pages", abstract = "We report a series of experiments that use semantic-based local search within a multiobjective genetic programming (GP) framework. We compare various ways of selecting target subtrees for local search as well as different methods for performing that search; we have also made comparison with the random desired operator of Pawlak et al. using statistical hypothesis testing. We find that a standard steady state or generational GP followed by a carefully-designed single-objective GP implementing semantic-based local search produces models that are mode accurate and with statistically smaller (or equal) tree size than those generated by the corresponding baseline GP algorithms. The depth fair selection strategy of Ito et al. is found to perform best compared with other subtree selection methods in the model refinement.", } @PhdThesis{Thesis_TiantianDou, author = "Tiantian Dou", title = "Nonlinear Dynamic System Identification and Model Predictive Control Using Genetic Programming", school = "Department of Electronic and Electrical Engineering, University of Sheffield", year = "2019", address = "UK", month = sep # " 29", keywords = "genetic algorithms, genetic programming", URL = "http://etheses.whiterose.ac.uk/25033/", URL = "https://etheses.whiterose.ac.uk/25033/1/Thesis_TiantianDou.pdf", size = "166 pages", abstract = "During the last century, a lot of developments have been made in research of complex nonlinear process control. As a powerful control methodology, model predictive control (MPC) has been extensively applied to chemical industrial applications. Core to MPC is a predictive model of the dynamics of the system being controlled. Most practical systems exhibit complex nonlinear dynamics, which imposes big challenges in system modeling. Being able to automatically evolve both model structure and numeric parameters, Genetic Programming (GP) shows great potential in identifying nonlinear dynamic systems. This thesis is devoted to GP based system identification and model-based control of nonlinear systems. To improve the generalization ability of GP models, a series of experiments that use semantic-based local search within a multiobjective GP framework are reported. The influence of various ways of selecting target subtrees for local search as well as different methods for performing that search were investigated; a comparison with the Random Desired Operator (RDO) of Pawlak et al. was made by statistical hypothesis testing. Compared with the corresponding baseline GP algorithms, models produced by a standard steady state or generational GP followed by a carefully-designed single-objective GP implementing semantic-based local search are statistically more accurate and with smaller (or equal) tree size, compared with the RDO-based GP algorithms. Considering the practical application, how to correctly and efficiently apply an evolved GP model to other larger systems is a critical research concern. Currently, the replication of GP models is normally done by repeating other?s work given the necessary algorithm parameters. However, due to the empirical and stochastic nature of GP, it is difficult to completely reproduce research findings. An XML-based standard file format, named Genetic Programming Markup Language (GPML), is proposed for the interchange of GP trees. A formal definition of this standard and details of implementation are described. GPML provides convenience and modularity for further applications based on GP models. The large-scale adoption of MPC in buildings is not economically viable due to the time and cost involved in designing and adjusting predictive models by expert control engineers. A GP-based control framework is proposed for automatically evolving dynamic nonlinear models for the MPC of buildings. An open-loop system identification was conducted using the data generated by a building simulator, and the obtained GP model was then employed to construct the predictive model for the MPC. The experimental result shows GP is able to produce models that allow the MPC of building to achieve the desired temperature band in a single zone space.", notes = "Also known as \cite{wreo25033} uk.bl.ethos.786611 Supervisor: Peter Rockett", } @Article{DOU:2020:ASC, author = "Tiantian Dou and Yuri {Kaszubowski Lopes} and Peter Rockett and Elizabeth A. Hathway and Esmail Saber", title = "Model predictive control of non-domestic heating using genetic programming dynamic models", journal = "Applied Soft Computing", year = "2020", volume = "97", number = "Part B", pages = "106695", month = dec, keywords = "genetic algorithms, genetic programming, Dynamic non-linear system identification, Model predictive control, Building energy management", ISSN = "1568-4946", URL = "http://www.sciencedirect.com/science/article/pii/S1568494620306335", URL = "https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=202002244243023276", DOI = "doi:10.1016/j.asoc.2020.106695", size = "13 pages", abstract = "We present a novel approach to obtaining dynamic nonlinear models using genetic programming (GP) for the model predictive control (MPC) of the indoor temperatures of buildings. Currently, the large-scale adoption of MPC in buildings is economically unviable due to the time and cost involved in the design and tuning of predictive models by expert control engineers. We show that GP is able to automate this process, and have performed open-loop system identification over the data produced by an industry grade building simulator. The simulated building was subject to an amplitude modulated pseudo-random binary sequence (APRBS), which allows the collected data to be sufficiently informative to capture the underlying system dynamics under relevant operating conditions. In this initial report, we detail how we employed GP to construct the predictive model for MPC for heating a single-zone building in simulation, and report results of using this model for controlling the internal environmental conditions of the simulated single-zone building. We conclude that GP shows great promise for producing models that allow the MPC of building to achieve the desired temperature band in a single zone space", } @Article{Dou:GPEM:gpml, author = "Tiantian Dou and Yuri {Kaszubowski Lopes} and Peter Rockett and Elizabeth A. Hathway and Esmail Saber", title = "{GPML}: an {XML}-based standard for the interchange of genetic programming trees", journal = "Genetic Programming and Evolvable Machines", year = "2020", volume = "21", number = "4", pages = "605--627", month = dec, keywords = "genetic algorithms, genetic programming, Interchange formats, Extensible markup language, XML, Model predictive control", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-019-09370-4", size = "23 pages", abstract = "We propose a genetic programming markup language (GPML), an XML-based standard for the interchange of genetic programming trees, and outline the benefits such a format would bring in allowing the deployment of trained genetic programming (GP) models in applications as well as the subsidiary benefit of allowing GP researchers to directly share trained trees. We present a formal definition of this standard and describe details of an implementation. In addition, we present a case study where GPML is used to implement a model predictive controller for the control of a building heating plant.", } @InProceedings{conf/eurogp/DoucetteH08, title = "{GP} Classification under Imbalanced Data sets: Active Sub-sampling and {AUC} Approximation", author = "John Doucette and Malcolm I. Heywood", bibdate = "2008-04-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/eurogp/eurogp2008.html#DoucetteH08", booktitle = "Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008", address = "Naples", month = "26-28 " # mar, publisher = "Springer", year = "2008", volume = "4971", editor = "Michael O'Neill and Leonardo Vanneschi and Steven Gustafson and Anna Isabel {Esparcia Alcazar} and Ivanoe {De Falco} and Antonio {Della Cioppa} and Ernesto Tarantino", pages = "266--277", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-78670-2", DOI = "doi:10.1007/978-3-540-78671-9_23", abstract = "The problem of evolving binary classification models under increasingly unbalanced data sets is approached by proposing a strategy consisting of two components: Sub-sampling and 'robust' fitness function design. In particular, recent work in the wider machine learning literature has recognized that maintaining the original distribution of exemplars during training is often not appropriate for designing classifiers that are robust to degenerate classifier behavior. To this end we propose a 'Simple Active Learning Heuristic' (SALH) in which a subset of exemplars is sampled with uniform probability under a class balance enforcing rule for fitness evaluation. In addition, an efficient estimator for the Area Under the Curve (AUC) performance metric is assumed in the form of a modified Wilcoxon-Mann-Whitney (WMW) statistic. Performance is evaluated in terms of six representative UCI data sets and benchmarked against: canonical GP, SALH based GP, SALH and the modified WMW statistic, and deterministic classifiers (Naive Bayes and C4.5). The resulting SALH-WMW model is demonstrated to be both efficient and effective at providing solutions maximizing performance assessed in terms of AUC.", notes = "John A. Doucette PhD 2016 University of Waterloo (not GP?) https://uwspace.uwaterloo.ca/items/1a44cde4-f734-460f-a65e-31871e22212c Part of \cite{conf/eurogp/2008} EuroGP'2008 held in conjunction with EvoCOP2008, EvoBIO2008 and EvoWorkshops2008", } @InProceedings{DBLP:conf/gecco/DoucetteLH09, author = "John Doucette and Peter Lichodzijewski and Malcolm I. Heywood", title = "Benchmarking coevolutionary teaming under classification problems with large attribute spaces", booktitle = "GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation", year = "2009", editor = "Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano {van Hemert} and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique Alba", pages = "1901--1902", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming, Poster", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1569901.1570226", abstract = "Benchmarking of a team based model of Genetic Programming demonstrates that the naturally embedded style of feature selection is usefully extended by the teaming metaphor to provide solutions in terms of exceptionally low attribute counts. To take this concept to its logical conclusion the teaming model must be able to build teams with a non-overlapping behavioral trait, from a single population. The Symbiotic Bid-Based (SBB) algorithm is demonstrated to fit this purpose under an evaluation using data sets with 650 to 5,000 attributes. The resulting solutions are one to two orders simpler than solutions identified under the alternative embedded paradigms of C4.5 and MaxEnt.", notes = "GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009). ACM Order Number 910092.", } @InCollection{Doucette:2009:GPTP, author = "John Doucette and Peter Lichodzijewski and Malcolm Heywood", title = "Evolving Coevolutionary Classifiers under Large Attribute Spaces", booktitle = "Genetic Programming Theory and Practice {VII}", year = "2009", editor = "Rick L. Riolo and Una-May O'Reilly and Trent McConaghy", series = "Genetic and Evolutionary Computation", address = "Ann Arbor", month = "14-16 " # may, publisher = "Springer", chapter = "3", pages = "37--54", keywords = "genetic algorithms, genetic programming, Problem Decomposition, Bid-based Cooperative Behaviors, Symbiotic Coevolution, Subspace Classifier, Large Attribute Spaces", DOI = "doi:10.1007/978-1-4419-1626-6_3", isbn13 = "978-1-4419-1653-2", abstract = "Model-building under the supervised learning domain potentially face a dual learning problem of identifying both the parameters of the model and the subset of (domain) attributes necessary to support the model, thus using an embedded as opposed to wrapper or filter based design. Genetic Programming (GP) has always addressed this dual problem, however, further implicit assumptions are made which potentially increase the complexity of the resulting solutions. In this work we are specifically interested in the case of classification under very large attribute spaces. As such it might be expected that multiple independent/ overlapping attribute subspaces support the mapping to class labels; whereas GP approaches to classification generally assume a single binary classifier per class, forcing the model to provide a solution in terms of a single attribute subspace and single mapping to class labels. Supporting the more general goal is considered as a requirement for identifying a 'team' of classifiers with non-overlapping classifier behaviours, in which each classifier responds to different subsets of exemplars. Moreover, the subsets of attributes associated with each team member might use a unique 'subspace' of attributes. This work investigates the utility of coevolutionary model building for the case of classification problems with attribute vectors consisting of 650 to 100,000 dimensions. The resulting team based coevolutionary evolutionary method-Symbiotic Bid-based (SBB) GP-is compared to alternative embedded classifier approaches of C4.5 and Maximum Entropy Classification (MaxEnt). SSB solutions demonstrate up to an order of magnitude lower attribute count relative to C4.5 and up to two orders of magnitude lower attribute count than MaxEnt while retaining comparable or better classification performance. Moreover, relative to the attribute count of individual models participating within a team, no more than six attributes are ever used; adding a further level of simplicity to the resulting solutions.", notes = "part of \cite{Riolo:2009:GPTP}", } @InProceedings{Doucette:2010:EuroGP, author = "John Doucette and Malcolm Heywood", title = "Novelty-based Fitness: An Evaluation under the Santa Fe Trail", booktitle = "Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010", year = "2010", editor = "Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar", volume = "6021", series = "LNCS", pages = "50--61", address = "Istanbul", month = "7-9 " # apr, organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-12147-0", DOI = "doi:10.1007/978-3-642-12148-7_5", abstract = "We present an empirical analysis of the effects of incorporating novelty-based fitness (phenotypic behavioral diversity) into Genetic Programming with respect to training, test and generalization performance. Three novelty-based approaches are considered: novelty comparison against a finite archive of behavioral archetypes, novelty comparison against all previously seen behaviors, and a simple linear combination of the first method with a standard fitness measure. Performance is evaluated on the Santa Fe Trail, a well known GP benchmark selected for its deceptiveness and established generalization test procedures. Results are compared to a standard quality-based fitness function (count of food eaten). Ultimately, the quality style objective provided better overall performance, however, solutions identified under novelty based fitness functions generally provided much better test performance than their corresponding training performance. This is interpreted as representing a requirement for layered learning/ symbiosis when assuming novelty based fitness functions in order to more quickly achieve the integration of diverse behaviors into a single cohesive strategy.", notes = "Part of \cite{Esparcia-Alcazar:2010:GP} EuroGP'2010 held in conjunction with EvoCOP2010 EvoBIO2010 and EvoApplications2010", } @InProceedings{Doucette:2011:RtAhtAeoEEPSuaEGST, title = "Revisiting the Acrobot `height' task: An example of Efficient Evolutionary Policy Search under an Episodic Goal Seeking Task", author = "John Doucette and Malcolm Heywood", pages = "468--475", booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary Computation", year = "2011", editor = "Alice E. Smith", month = "5-8 " # jun, address = "New Orleans, USA", organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, acrobot height task domain, episodic goal seeking task, evolutionary policy search approach, neural evolution of augmented topologies, stochastic sampling heuristic, symbiotic bid based genetic programming, temporal sequence learning problem, training scenarios, learning (artificial intelligence), sampling methods, search problems, stochastic processes, topology", DOI = "doi:10.1109/CEC.2011.5949655", abstract = "Evolutionary methods for addressing the temporal sequence learning problem generally fall into policy search as opposed to value function optimisation approaches. Various recent results have made the claim that the policy search approach is at best inefficient at solving episodic `goal seeking' tasks i.e., tasks under which the reward is limited to describing properties associated with a successful outcome have no qualification for degrees of failure. This work demonstrates that such a conclusion is due to a lack of diversity in the training scenarios. We therefore return to the Acrobot `height' task domain originally used to demonstrate complete failure in evolutionary policy search. This time a very simple stochastic sampling heuristic for defining a population of training configurations is introduced. Benchmarking two recent evolutionary policy search algorithms -- Neural Evolution of Augmented Topologies (NEAT) and Symbiotic Bid-Based (SBB) Genetic Programming -- under this condition demonstrates solutions as effective as those returned by advanced value function methods. Moreover this is achieved while remaining within the evaluation limit imposed by the original study.", notes = "CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.", } @Article{Doucette:2012:GPEM, author = "John A. Doucette and Andrew R. McIntyre and Peter Lichodzijewski and Malcolm I. Heywood", title = "Symbiotic coevolutionary genetic programming: a benchmarking study under large attribute spaces", journal = "Genetic Programming and Evolvable Machines", year = "2012", volume = "13", number = "1", pages = "71--101", month = mar, note = "Special Section on Evolutionary Algorithms for Data Mining", keywords = "genetic algorithms, genetic programming, Feature subspace selection, Problem decomposition, Symbiosis, Coevolution, Model complexity, Classification", ISSN = "1389-2576", URL = "https://web.cs.dal.ca/~mheywood/OpenAccess/open-doucette12a.pdf", URL = "https://rdcu.be/cUoeV", DOI = "doi:10.1007/s10710-011-9151-4", size = "31 pages", abstract = "Classification under large attribute spaces represents a dual learning problem in which attribute subspaces need to be identified at the same time as the classifier design is established. Embedded as opposed to filter or wrapper methodologies address both tasks simultaneously. The motivation for this work stems from the observation that team based approaches to Genetic Programming (GP) have the potential to design multiple classifiers per class. each with a potentially unique attribute subspace. without recourse to filter or wrapper style preprocessing steps. Specifically, competitive coevolution provides the basis for scaling the algorithm to data sets with large instance counts; whereas cooperative coevolution provides a framework for problem decomposition under a bid-based model for establishing program context. Symbiosis is used to separate the tasks of team/ensemble composition from the design of specific team members. Team composition is specified in terms of a combinatorial search performed by a Genetic Algorithm (GA); whereas the properties of individual team members and therefore subspace identification is established under an independent GP population. Teaming implies that the members of the resulting ensemble of classifiers should have explicitly non-overlapping behaviour. Performance evaluation is conducted over data sets taken from the UCI repository with 649-102,660 attributes and 2-10 classes. The resulting teams identify attribute spaces 1-4 orders of magnitude smaller than under the original data set. Moreover, team members generally consist of less than 10 instructions; thus, small attribute subspaces are not being traded for opaque models.", affiliation = "David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada", } @InProceedings{Doucette:2012:GECCO, author = "John A. Doucette and Darren Abramson", title = "Automated mechanism design with co-evolutionary hierarchical genetic programming techniques", booktitle = "GECCO '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference", year = "2012", editor = "Terry Soule and Anne Auger and Jason Moore and David Pelta and Christine Solnon and Mike Preuss and Alan Dorin and Yew-Soon Ong and Christian Blum and Dario Landa Silva and Frank Neumann and Tina Yu and Aniko Ekart and Will Browne and Tim Kovacs and Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and Giovanni Squillero and Nicolas Bredeche and Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and Martin Pelikan and Silja Meyer-Nienberg and Christian Igel and Greg Hornby and Rene Doursat and Steve Gustafson and Gustavo Olague and Shin Yoo and John Clark and Gabriela Ochoa and Gisele Pappa and Fernando Lobo and Daniel Tauritz and Jurgen Branke and Kalyanmoy Deb", isbn13 = "978-1-4503-1177-9", pages = "935--942", keywords = "genetic algorithms, genetic programming, integrative genetic and evolutionary computation", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330163.2330293", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "We present a novel form of automated game theoretic mechanism design in which mechanisms and players co-evolve. We also model the memetic propagation of strategies through a population of players, and argue that this process represents a more accurate depiction of human behavior than conventional economic models. The resulting model is evaluated by evolving mechanisms for the ultimatum game, and replicates the results of empirical studies of human economic behaviors, as well as demonstrating the ability to evaluate competing hypothesizes for the creation of economic incentives.", notes = "Also known as \cite{2330293} GECCO-2012 A joint meeting of the twenty first international conference on genetic algorithms (ICGA-2012) and the seventeenth annual genetic programming conference (GP-2012)", } @InProceedings{Dougherty:2019:GI7, author = "Ryan E. Dougherty and Erin Lanus and Charles J. Colbourn and Stephanie Forrest", title = "Genetic Algorithms for Affine Transformations to Existential t-Restrictions", booktitle = "7th edition of GI @ GECCO 2019", year = "2019", month = jul # " 13-17", editor = "Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and John R. Woodward", publisher = "ACM", publisher_address = "New York, NY, USA", address = "Prague, Czech Republic", pages = "1707--1708", organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, genetic improvement, combinatorial interaction testing, CIT, covering array, covering perfect hash family, t-restriction", isbn13 = "978-1-4503-6748-6", URL = "https://forrest.biodesign.asu.edu/data/publications/2019-dougherty-gecco-workshop.pdf", DOI = "doi:10.1145/3319619.3326823", video_url = "https://www.youtube.com/watch?v=2hFlvHZU2FI", size = "2 pages", abstract = "The subject of t-restrictions has garnered considerable interest recently as it encompasses many different types of combinatorial objects, all of which have unique and important applications. One of the most popular of these is an ingredient in the generation of covering arrays, which are used for discovering faulty interactions among software components. We focus on existential t-restrictions, which have a structure that can be exploited by genetic algorithms. In particular, recent work on such restrictions considers affine transformations while maximizing the corresponding score of the formed restriction. We propose to use genetic algorithms for existential t-restrictions by providing a general framework that can be applied to all such objects.", notes = " presentation https://www.youtube.com/watch?v=2hFlvHZU2FI Also known as \cite{Dougherty:2019:GECCOcomp} Also known as \cite{3326823} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InProceedings{Dougherty:2021:GI, author = "Ryan Dougherty and Xi Jiang", title = "A Permutation Representation of Covering Arrays", booktitle = "GI @ ICSE 2021", year = "2021", month = "30 " # may, editor = "Justyna Petke and Bobby R. Bruce and Yu Huang and Aymeric Blot and Westley Weimer and W. B. Langdon", publisher = "IEEE", address = "internet", pages = "41--42", keywords = "genetic algorithms, genetic programming, genetic improvement, covering array, permutation, evolutionary computation, mean time to failure, MTTF", isbn13 = "978-1-6654-4466-8/21", video_url = "https://www.youtube.com/watch?v=58u_bYvMUGs&list=PLI8fiFpB7BoKDaxvS7SQp0iA7fN7rrvDD&index=23", video_url = "https://www.youtube.com/watch?v=9TsoF5qIJ6w&list=PLI8fiFpB7BoKDaxvS7SQp0iA7fN7rrvDD&index=24", video_url = "https://www.youtube.com/watch?v=X8PisTY0frI&list=PLXTjhGKkSnI-se7uQneCX-pEDiDrQ7TIS&index=2", DOI = "doi:10.1109/GI52543.2021.00017", size = "2 pages", abstract = "Testing a large-scale system requires understanding how each of the components interact with each other, which is the subject of interaction testing. Covering arrays are a well-studied object, but traditional representations of these arrays in the context of genetic algorithms has not yielded much success. We propose a new representation of covering arrays based on a permutation of the rows considered. Preliminary results for reducing the mean-time-to-failure of these arrays are given.", notes = "Video X8PisTY0frI Ryan Dougherty black board style. 2:30 Discussion chair Justyna Petke 3:13 Q: Myra B. Cohen 4:08 Q: Justyna Petke scaling, A: log, good. 5:36 Q: Myra B. Cohen seeding, applicable to other types of arrays, fault localisation, auto generated test suite. 7:45 Q: like move from full AST to patches of AST? 9:05 Westley Weimer United States Military Academy, West Point, NY, USA part of \cite{Petke:2021:ICSEworkshop} http://geneticimprovementofsoftware.com/events/icse2021.html", } @InProceedings{oai:CiteSeerPSU:501552, author = "George Dounias and Hubertus Axer and Beth Bjerregaard and Diedrich {Graf von Keyserlingk} and Jan Jantzen and Athanasios Tsakonas", title = "Genetic Programming for the Generation of Crisp and Fuzzy Rule Bases in Classification and Diagnosis of Medical Data", booktitle = "First International NAISO Congress on Neuro Fuzzy Technologies", year = "2002", address = "Havana, Cuba", month = "16-19 " # jan, organisation = "NAISO (Natural and Artificial Intelligence Systems Organization)", keywords = "genetic algorithms, genetic programming", citeseer-isreferencedby = "oai:CiteSeerPSU:147332; oai:CiteSeerPSU:120858; oai:CiteSeerPSU:78616; oai:CiteSeerPSU:473489; oai:CiteSeerPSU:104720; oai:CiteSeerPSU:161453", citeseer-references = "oai:CiteSeerPSU:172707; oai:CiteSeerPSU:345471; oai:CiteSeerPSU:259217", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:501552", rights = "unrestricted", URL = "http://www2.ba.aegean.gr/members/tsakonas/DTJABK_Cuba2002.pdf", URL = "http://citeseer.ist.psu.edu/501552.html", abstract = "This paper demonstrates two methodologies for the construction of rule-based systems in medical decision making. The first approach consists of a method combining genetic programming and heuristic hierarchical rule-base construction. The second model is composed by a strongly-typed genetic programming system for the generation of fuzzy rule-based systems. Two different medical domains are used to evaluate the models. The first field is the diagnosis of subtypes of Aphasia. Two models for crisp rule-bases are presented. The first one discriminates between four major types and the second attempts the classification between all common types. A third model consisted of a GPgenerated fuzzy rule-based system is tested on the same domain. The second medical domain is the classification of Pap-Smear Test examinations where a crisp rulebased system is constructed. Results denote the effectiveness of the proposed systems. Comparisons on the system's comprehensibility and the transparency are included. These comparisons include for the Aphasia domain, previous work consisted of two neural network models.", notes = "http://www.icsc.ab.ca/conferences/nf2002/", } @Article{Dourado:2020:latin, author = "Antonio Miguel Batista Dourado and Emerson Carlos Pedrino", journal = "IEEE Latin America Transactions", title = "Evolutionary Approach for Automatic Generation of Multi-Objective Morphological Filters for Depth Images in Embedded Navigation Systems", year = "2020", volume = "18", number = "07", pages = "1320--1326", month = jul, keywords = "genetic algorithms, genetic programming, optimisation methods,Morphological operations, Embedded software, Assistive technology", DOI = "doi:10.1109/TLA.2020.9099775", ISSN = "1548-0992", size = "7 pages", abstract = "The efforts spent on the development of assistive technologies has led researches to explore many existing techniques as computer vision, image processing, etc. and apply them as embedded solutions to help people with several types of disabilities, including visual impairment. Embedded navigation systems for visually impaired people (VIP) often use RGB-D cameras to retrieve depth information from surroundings and present them as gray images with depth represented by gray level or black pixels if depth couldn't be estimated, which can be fixed by mathematical morphology. Morphological filters must be efficient to solve the problem and fast to avoid impact on performance. This paper presents an approach for automatic generation and optimization of low complexity and low error morphological filters to fix depth image's unknown distances based on NSGA-II and Cartesian Genetic Programming. Experiments were performed using two different error metrics and results showed that the presented approach managed to generate and optimize feasible morphological filters that fit within embedded navigation systems for VIP.", notes = "Insituto Federal de Sao Paulo, Sao Paulo, Brazil http://lattes.cnpq.br/3462738422154588 Also known as \cite{9099775}", } @Article{DOURADO:2020:ASC, author = "Antonio Miguel Batista Dourado and Emerson Carlos Pedrino", title = "Multi-objective Cartesian Genetic Programming optimization of morphological filters in navigation systems for Visually Impaired People", journal = "Applied Soft Computing", volume = "89", pages = "106130", year = "2020", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2020.106130", URL = "http://www.sciencedirect.com/science/article/pii/S1568494620300703", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Multi-objective optimization, NSGA-II, Mathematical morphology", abstract = "Navigation systems for Visually Impaired People (VIP) have improved in the last decade, incorporating many features to ensure navigation safety. Such systems often use grayscale depth images to segment obstacles and paths according to distances. However, this approach has the common problem of unknown distances. While this can be solved with good quality morphological filters, these might be too complex and power demanding. Considering navigation systems for VIP rely on limited energy sources that have to run multiple tasks, fixing unknown distance areas without major impacts on power consumption is a definite concern. Multi-objective optimization algorithms might improve filters' energy efficiency and output quality, which can be accomplished by means of different quality vs. complexity trade-offs. This study presents NSGA2CGP, a multi-objective optimization method that employs the NSGA-II algorithm on top of Cartesian Genetic Programming to optimize morphological filters for incomplete depth images used by navigation systems for VIP. Its goal is to minimize output errors and structuring element complexity, presenting several feasible alternatives combining different levels of filter quality and complexity-both of which affect power consumption. NSGA2CGP-optimized filters were deployed into an actual embedded platform, so as to experimentally measure power consumption and execution time. We also propose two new fitness functions based on existing approaches from literature. Results showed improvements in visual quality, performance, speed and power consumption, thanks to our proposed error function, proving NSGA2CGP as a solid method for developing and evolving efficient morphological filters", } @Article{doValleSimoes_2007_GPEM, author = "Eduardo {do Valle Simoes}", title = "Evolvable hardware, Springer, Genetic and Evolutionary Computation Series, edited by Tetsuya Higuchi, Yong Liu and Xin Yao, 224 pp, ISBN 0-387-24386-0", journal = "Genetic Programming and Evolvable Machines", year = "2007", volume = "8", number = "3", pages = "287--288", month = sep, note = "Book review", keywords = "genetic algorithms, genetic programming, evolvable hardware", ISSN = "1389-2576", URL = "https://rdcu.be/dR8mr", DOI = "doi:10.1007/s10710-007-9032-z", size = "2 pages", } @InProceedings{Dower:2011:GECCO, author = "Steve Dower and Clinton J. Woodward", title = "ESDL: a simple description language for population-based evolutionary computation", booktitle = "GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0557-0", pages = "1045--1052", keywords = "genetic algorithms, genetic programming", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001576.2001718", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "A large proportion of publications in the field of evolutionary computation describe algorithm specialisation and experimentation. Algorithms are variously described using text, tables, flowcharts, functions or pseudocode. However, ambiguity that can limit the efficiency of communication is common. Evolutionary System Definition Language (ESDL) is a conceptual model and language for describing evolutionary systems efficiently and with reduced ambiguity, including systems with multiple populations and adaptive parameters. ESDL may also be machine-interpreted, allowing algorithms to be tested without requiring a hand-coded implementation, as may already be done using the esec framework. The style is distinct from existing notations used within the field and is easily recognisable. This paper describes the case for ESDL, provides an overview of ESDL and examples of its use.", notes = "Also known as \cite{2001718} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)", } @InProceedings{Downey:2009:IVCNZ, title = "Multiclass object classification for computer vision using Linear Genetic Programming", author = "Carlton Downey and Mengjie Zhang", year = "2009", pages = "73--78", booktitle = "Proceeding of the 24th International Conference Image and Vision Computing New Zealand, IVCNZ '09", month = "23-25 " # nov, address = "Wellington", publisher = "IEEE", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-4697-1", ISSN = "2151-2205", DOI = "doi:10.1109/IVCNZ.2009.5378356", abstract = "Multiclass classification problems arise naturally in many tasks in computer vision; typical examples include image segmentation and letter recognition. These are among some of the most challenging and important tasks in the area and solutions to them are eagerly sought after. Genetic Programming (GP) is a powerful and flexible machine learning technique that has been successfully applied to many binary classification tasks. However, the traditional form of GP performs poorly on multi-class classification problems. Linear GP (LGP) is an alternative form of GP where programs are represented as sequences of instructions like Java and C++. This paper discusses results which demonstrate the superiority of LGP as a technique for multi class classification. It also discusses a new extension to LGP which results in a further improvement in the performance on multiclass classification problems.", notes = "Also known as \cite{5378356}", } @InProceedings{Downey:2010:gecco, author = "Carlton Downey and Mengjie Zhang and Will N. Browne", title = "New crossover operators in linear genetic programming for multiclass object classification", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "885--892", keywords = "genetic algorithms, genetic programming", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830483.1830644", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Genetic programming (GP) has been successfully applied to solving multiclass classification problems, but the performance of GP classifiers still lags behind that of alternative techniques. This paper investigates an alternative form of GP, Linear GP (LGP), which demonstrates great promise as a classifier as the division of classes is inherent in this technique. By combining biological inspiration with detailed knowledge of program structure two new crossover operators that significantly improve performance are developed. The first is a new crossover operator that mimics biological crossover between alleles, which helps reduce the disruptive effect on building blocks of information. The second is an extension of the first where a heuristic is used to predict offspring fitness guiding search to promising solutions.", notes = "Also known as \cite{1830644} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @InProceedings{downey:2011:EuroGP, author = "Carlton Downey and Mengjie Zhang", title = "Parallel Linear Genetic Programming", booktitle = "Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011", year = "2011", month = "27-29 " # apr, editor = "Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado", series = "LNCS", volume = "6621", publisher = "Springer Verlag", address = "Turin, Italy", pages = "178--189", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-20406-7", DOI = "doi:10.1007/978-3-642-20407-4_16", abstract = "Motivated by biological inspiration and the issue of code disruption, we develop a new form of LGP called Parallel LGP (PLGP). PLGP programs consist of n lists of instructions. These lists are executed in parallel, after which the resulting vectors are combined to produce program output. PGLP limits the disruptive effects of crossover and mutation, which allows PLGP to significantly outperform regular LGP.", notes = "Fixed linear combination rule to combine output of small but fixed number of team members. Cf work on multi classifier systems, linear GP and memory with memory. Part of \cite{Silva:2011:GP} EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011", } @InProceedings{Downey:2011:ETCfLGP, title = "Execution Trace Caching for Linear Genetic Programming", author = "Carlton Downey and Mengjie Zhang", pages = "1191--1198", booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary Computation", year = "2011", editor = "Alice E. Smith", month = "5-8 " # jun, address = "New Orleans, USA", organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2011.5949751", abstract = "In this paper we propose a new caching algorithm for LGP based on exploiting inter-generation program relationships. For each program we cache a partial summary of program execution, and use this summary to expedite the execution of all progeny. We study the theory behind our new caching algorithm and derive equations for optimising algorithm performance. Through both theoretical and empirical results we demonstrate that our caching algorithm can decrease LGP execution time by up to 50percent", notes = "CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.", } @InProceedings{Downey:2011:GECCOcomp, author = "Carlton Downey and Mengjie Zhang", title = "Caching for parallel linear genetic programming", booktitle = "GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0690-4", keywords = "genetic algorithms, genetic programming: Poster", pages = "201--202", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001858.2001970", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Parallel Linear Genetic Programming (PLGP) is an exciting new approach to Linear Genetic Programming (LGP) which decreases building block disruption and significantly improves performance by the introduction of a parallel architecture. We introduce a caching algorithm for PLGP which exploits this parallel architecture to avoid the majority of instruction executions. This allows PLGP programs to be executed an order of magnitude faster than LGP programs with an equal number of instructions.", notes = "Also known as \cite{2001970} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @Article{Downey:2012:GPEM, author = "Carlton Downey and Mengjie Zhang and Jing Liu", title = "Parallel linear genetic programming for multi-class classification", journal = "Genetic Programming and Evolvable Machines", year = "2012", volume = "13", number = "3", pages = "275--304", month = sep, note = "Special issue on selected papers from the 2011 European conference on genetic programming", keywords = "genetic algorithms, genetic programming, Linear genetic programming, Classification, Parallel structure, Caching", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-012-9162-9", size = "30 pages", abstract = "Motivated by biological inspiration and the issue of instruction disruption, we develop a new form of Linear Genetic Programming (LGP) called Parallel LGP (PLGP) for classification problems. PLGP programs consist of multiple lists of instructions. These lists are executed in parallel after which the resulting vectors are combined to produce the classification result. PLGP limits the disruptive effects of crossover and mutation, which allows PLGP to significantly outperform regular LGP. Furthermore, PLGP programs are naturally suited to caching due to their parallel architecture. Although caching techniques have been used in tree based GP, to our knowledge, there are no caching techniques specifically developed for LGP. Thus, a novel caching technique is also developed with the intrinsic properties of PLGP in mind, which can decrease fitness evaluation time by almost an order of magnitude for the classification problems.", notes = "Jing Liu = http://see.xidian.edu.cn/faculty/liujing/ EuroGP 2011 \cite{Silva:2011:GP}", affiliation = "School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand", } @InProceedings{downing:1998:GPGAes, author = "Keith Downing", title = "Combining Genetic Programming and Genetic Algorithms for Ecological Simulation", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "48--53", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-548-7", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1998/downing_1998_GPGAes.pdf", notes = "GP-98", } @Article{Downing:1998:EMS, author = "Keith Downing", title = "Using evolutionary computational techniques in environmental modelling", journal = "Environmental Modelling and Software", year = "1998", volume = "13", pages = "519--528", number = "5-6", keywords = "genetic algorithms, genetic programming, Evolutionary computation, Evolutionary ecology", owner = "wlangdon", ISSN = "1364-8152", URL = "http://www.sciencedirect.com/science/article/B6VHC-3VGHBS1-1G/2/20d163b7dea17eb9b21f06211acd3188", DOI = "doi:10.1016/S1364-8152(98)00050-4", abstract = "Evolutionary Computation (EC) is a field of computer science that borrows concepts such as natural selection and the genotype-phenotype distinction from biology in order to solve a wide range of complex problems, such as robot controller design, job-shop schedule optimisation, pattern recognition, electronic circuit design and many more. In addition, EC techniques in combination with individual-based modelling can be applied in their domain of origin, biology, to investigate the emergence and evolution of natural phenomena. This paper describes the use of EC as both (a) an empirical supplement to analytical approaches to mathematically tractable biological problems, and (b) a vital tool for analysing highly complex systems of interacting species in heterogeneous environments. Three EC applications, two tractable and one complex, are used to illustrate these points. In general, this work introduces environmental modellers to a cutting-edge computer-science technique that can be of considerable utility, especially in a modern world in which accelerated rates of large-scale environmental change heighten the need for evolutionary considerations in analyses of relatively short time-scale phenomena.", } @InProceedings{downing:2001:gecco, title = "Adaptive Genetic Programs via Reinforcement Learning", author = "Keith L. Downing", pages = "19--26", year = "2001", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)", editor = "Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon and Edmund Burke", address = "San Francisco, California, USA", publisher_address = "San Francisco, CA 94104, USA", month = "7-11 " # jul, keywords = "genetic algorithms, genetic programming, Reinforcement Learning, Baldwin Effect, Lamarckianism, Hybrid Adaptive Systems", ISBN = "1-55860-774-9", URL = "http://gpbib.cs.ucl.ac.uk/gecco2001/d01.pdf", size = "8 page", abstract = "Reinforced Genetic Programming (RGP) enhances standard tree-based genetic programming (GP) [7] with reinforcement learning (RL)[11]. Essentially, leaf nodes of GP trees become monitored action-selection points, while the internal nodes form a decision tree for classifying the current state of the problem solver. Reinforcements returned by the problem solver govern both fitness evaluation and intra-generation learning of the proper actions to take at the selection points. In theory, the hybrid RGP system hints of mutual benefits to RL and GP in controller-design applications, by, respectively, providing proper abstraction spaces for RL search, and accelerating evolutionary progress via Baldwinian or Lamarckian mechanisms. In practice, we demonstrate RGP's improvements over standard GP search on maze-search tasks", notes = "GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of \cite{spector:2001:GECCO}", } @Article{downing:2001:GPEM, author = "Keith L. Downing", title = "Reinforced Genetic Programming", journal = "Genetic Programming and Evolvable Machines", year = "2001", volume = "2", number = "3", pages = "259--288", month = sep, keywords = "genetic algorithms, genetic programming, reinforcement learning, the Baldwin Effect, Lamarckism", ISSN = "1389-2576", URL = "http://www.idi.ntnu.no/grupper/ai/eval/reinforcedGP/gpem.pdf", URL = "http://www.idi.ntnu.no/grupper/ai/eval/reinforcedGP/", DOI = "doi:10.1023/A:1011953410319", size = "27 pages", abstract = "This paper introduces the Reinforced Genetic Programming (RGP) system, which enhances standard tree-based genetic programming (GP) with reinforcement learning (RL). RGP adds a new element to the GP function set: monitored action-selection points that provide hooks to a reinforcement-learning system. Using strong typing, RGP can restrict these choice points to leaf nodes, thereby turning GP trees into classify-and-act procedures. Then, environmental reinforcements channeled back through the choice points provide the basis for both lifetime learning and general GP fitness assessment. This paves the way for evolutionary acceleration via both Baldwinian and Lamarckian mechanisms. In addition, the hybrid hints of potential improvements to RL by exploiting evolution to design proper abstraction spaces, via the problem-state classifications of the internal tree nodes. This paper details the basic mechanisms of RGP and demonstrates its application on a series of static and dynamic maze-search problems.", notes = "Article ID: 357595", } @Article{downing:2005:GPEM, author = "Keith L. Downing", title = "Tantrix: A Minute to Learn, 100 (Genetic Algorithm) Generations to Master", journal = "Genetic Programming and Evolvable Machines", year = "2005", volume = "6", number = "4", pages = "381--406", month = dec, keywords = "genetic algorithms, indirect-encoded genomes", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-005-4803-x", size = "26 pages", abstract = "The game of Tantrix provides a challenging, mathematical and graphic domain for evolutionary computation. The simple task of forming long loops of coloured arcs quickly becomes a search nightmare for humans and computers alike as the number of game pieces scales linearly. Tantrix-GA solves several types and sizes of Tantrix puzzles but still falls well short of (at least a few) human Tantrix experts. By introducing this problem to evolutionary computation researchers, we hope to motivate an evolutionary attack on the holy-grail Tantrix puzzles, one of which has yet to be solved by any intelligence, real or artificial.", } @Article{Downing:GPEM:review, author = "Keith Downing", title = "Alain Petrowski and Sana Ben-Hamida: Evolutionary Algorithms", journal = "Genetic Programming and Evolvable Machines", subtitle = "John Wiley and Sons, Inc., Hoboken, New Jersey, USA, 2017, ISBN-13: 978-1848218048, ISBN-10: 1848218044", year = "2018", volume = "19", number = "4", pages = "565--566", month = dec, note = "Book review", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dR8mR", DOI = "doi:10.1007/s10710-018-9321-8", size = "2 pages", notes = "p566 'My own copy of Evolutionary Algorithms became an instant go-to reference'", } @InProceedings{downing:2005:CEC, author = "Richard Mark Downing", title = "Evolving Binary Decision Diagrams using Implicit Neutrality", booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary Computation", year = "2005", editor = "David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L. Gwenn Volkert and Dan Ashlock and Marc Schoenauer", volume = "3", pages = "2107--2113", address = "Edinburgh, UK", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "2-5 " # sep, organisation = "IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9363-5", URL = "http://www.cs.bham.ac.uk/~rmd/pubs/evolvingbddsCEC2005.pdf", DOI = "doi:10.1109/CEC.2005.1554955", size = "7 pages", abstract = "A new algorithm is presented for evolving Binary Decision Diagrams (BDD) that employs the neutrality implicit in the BDD representation. It is shown that an effortless neutral walk is taken; that is, a neutral walk that requires no fitness evaluations. Experiments show the algorithm to be robust and scalable across a range of n-parity problems up to n = 17, and highly efficient on a range of other functions with compact BDD representations. Evolvability and modularity issues are also discussed, and the search space is shown to be free of local optima.", notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS. Sun, 18 Jun 2006 10:40:08 BST 20-mux and 17-parity.", } @InProceedings{Downing:2006:CEC, author = "Richard M. Downing", title = "Neutrality and gradualism: encouraging exploration and exploitation simultaneously with Binary Decision Diagrams", booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary Computation", year = "2006", pages = "615--622", address = "Vancouver", month = "16-21 " # jul, publisher = "IEEE Press", email = "rmd@cs.bham.ac.uk", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9487-9", URL = "http://www.cs.bham.ac.uk/~rmd/pubs/gradualism.pdf", DOI = "doi:10.1109/CEC.2006.1688367", abstract = "Search algorithms are subject to the trappings of local optima. Attempts to address the problem are often framed in the context of needing to balance, or trade-off, exploitation against exploration. Ideally, it is best to maximise both simultaneously, but this is usually seen as infeasible in the presence of multi-modal search spaces. This paper investigates the potential for exploration of both neutrality and mutation rate, and argues that the former is the more important. The most interesting result, however, is that the necessity for a trade-off between exploitation and exploration can be avoided within the context of our algorithm for evolving Binary Decision Diagrams.", notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE.", } @InProceedings{Downing:PPSN:2006, author = "Richard M. Downing", title = "Evolving Binary Decision Diagrams with emergent variable orderings", booktitle = "Parallel Problem Solving from Nature - PPSN IX", year = "2006", editor = "Thomas Philip Runarsson and Hans-Georg Beyer and Edmund Burke and Juan J. Merelo-Guervos and L. Darrell Whitley and Xin Yao", volume = "4193", pages = "798--807", series = "LNCS", address = "Reykjavik, Iceland", publisher_address = "Berlin", month = "9-13 " # sep, publisher = "Springer-Verlag", email = "rmd@cs.bham.ac.uk", ISBN = "3-540-38990-3", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.bham.ac.uk/~rmd/pubs/ppsn06.pdf", DOI = "doi:10.1007/11844297_81", size = "10 pages", abstract = "Binary Decision Diagrams (BDDs) have become the data structure of choice for representing discrete functions in some design and verification applications: They are compact and efficient to manipulate with strong theoretical underpinnings. However, and despite many appealing characteristics, BDDs are not a representation commonly considered for evolutionary computation (EC). The inherent difficulties associated with evolving graphs combined with the variable ordering problem poses a significant challenge which is yet to be overcome. This work addresses this challenge and presents a new approach to evolving BDDs that exhibits good variable orderings as an emergent property.", notes = "PPSN-IX", } @InProceedings{eurogp07:downing, author = "Richard M. Downing", title = "On population size and neutrality: facilitating the evolution of evolvability", editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar", booktitle = "Proceedings of the 10th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "4445", year = "2007", address = "Valencia, Spain", month = "11-13 " # apr, pages = "181--192", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-71602-5", isbn13 = "978-3-540-71602-0", DOI = "doi:10.1007/978-3-540-71605-1_17", abstract = "The role of population size is investigated within a neutrality induced local optima free search space. Neutrality decouples genotypic variation in evolvability from fitness variation. Population diversity and neutrality work in conjunction to facilitate evolvability exploration whilst restraining its loss to drift, ultimately facilitating the evolution of evolvability. The characterising dynamics and implications are discussed.", notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007", } @InProceedings{conf/eurogp/Downing08, title = "Evolvability Via Modularity-Induced Mutational Focussing", author = "Richard M. Downing", bibdate = "2008-04-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/eurogp/eurogp2008.html#Downing08", booktitle = "Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008", address = "Naples", month = "26-28 " # mar, publisher = "Springer", year = "2008", volume = "4971", editor = "Michael O'Neill and Leonardo Vanneschi and Steven Gustafson and Anna Isabel {Esparcia Alcazar} and Ivanoe {De Falco} and Antonio {Della Cioppa} and Ernesto Tarantino", isbn13 = "978-3-540-78670-2", pages = "194--205", series = "Lecture Notes in Computer Science", DOI = "doi:10.1007/978-3-540-78671-9_17", keywords = "genetic algorithms, genetic programming", notes = "Part of \cite{conf/eurogp/2008} EuroGP'2008 held in conjunction with EvoCOP2008, EvoBIO2008 and EvoWorkshops2008", } @PhdThesis{Downing08PhD, author = "Richard Mark Downing", title = "Artificial evolution with Binary Decision Diagrams: a study in evolvability in neutral spaces", school = "School of Computer Science, University of Birmingham", year = "2008", address = "UK", month = jun, keywords = "genetic algorithms, genetic programming", URL = "http://etheses.bham.ac.uk/862/", URL = "http://etheses.bham.ac.uk/862/1/Downing08PhD.pdf", URL = "http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.521944", size = "200 pages", abstract = "This thesis introduces a new approach to artificial evolution employing Binary Decision Diagrams as the genotypic representation, and uses it to study evolvability issues. The approach is referred to as Evolving Binary Decision Diagrams using Inherent Neutrality (EBDDIN). The aims are twofold. Firstly, to develop an evolutionary algorithm with a capability to address many of the issues facing the field of evolutionary computation today. Secondly, to develop a deep understanding of the concepts and mechanisms that facilitate within that context. The issue of evolvability, loosely defined as the capacity to evolve, permeates the field of evolutionary computation. For reasons that are not yet fully understood, current approaches to artificial evolution fail to exhibit a pace and extent of evolutionary change so readily exhibited in nature. In order to resolve this discrepancy, the field of evolutionary computation must characterise, understand and apply evolvability to artificial evolution. If this can be achieved, systems of artificial evolution will become much more capable than they are presently. The approach is developed with the primary practical and theoretical issues regarding evolvability in mind, exploiting inherent properties of the Binary Decision Diagram representation where possible. It is then used as a computational model for studying evolvability issues, giving particular emphasis to the role of neutrality, modularity, gradualism, robustness and population diversity, and the interplay between them. Carefully designed, controlled experiments elucidate the mechanisms and properties that facilitate evolvability and its evolution. The implications are then considered regarding the new understandings developed and the fidelity with the characteristics of biological evolution. Pleiotropic patterns which bias the phenotypic effects of random mutation are found to emerge. These configurations represent the variation component of evolvability and are subject to indirect selection. Higher-level structural configurations (i.e. OBDD variable orderings) that better facilitate such patterns emerge as a logical consequence. Neutrality plays the crucial role of facilitating fitness-conserving exploration and completely alleviating local optima for the domain of Boolean functions. Population diversity allows evolvability traits to compete and evolve, ultimately facilitating the evolution of evolvability. The search is insensitive to the starting point and the absence of initial diversity, requiring only minimal diversity generated from gradual genotypic variation. Gradual evolution in a search space that is free of local optima by way of neutrality can be a viable alternative to problematic evolution on multi-modal landscapes, exhibiting search characteristics that have greater fidelity to natural evolution. This is a fruitful direction for research that is directed at the problem of facilitating evolvability in artificial evolution, and it may lead to evolutionary systems that are open-ended.", notes = "oai:etheses.bham.ac.uk:862 Supervisors: Ata Kaban and Peter Hancox", } @Article{Drach:2016:SSI, author = "Zohar Drach and Shany Hershkovitz and Domenico Ferrero and Pierluigi Leone and Andrea Lanzini and Massimo Santarelli and Yoed Tsur", title = "Impedance spectroscopy analysis inspired by evolutionary programming as a diagnostic tool for {SOEC} and {SOFC}", journal = "Solid State Ionics", volume = "288", pages = "307--310", year = "2016", note = "Proceedings of the 20th International Conference on Solid State Ionics SSI-20", ISSN = "0167-2738", DOI = "doi:10.1016/j.ssi.2016.01.001", URL = "http://www.sciencedirect.com/science/article/pii/S0167273816000047", abstract = "Impedance spectroscopy (IS) is an effective tool for the analysis of solid oxide fuel cell (SOFC) and solid oxide electrolysis cell (SOEC) performance. The challenge using this characterization tool lies within the analysis method. Impedance spectroscopy genetic programming (ISGP) is a novel analysis technique for impedance spectroscopy data. The ISGP uses evolutionary programming techniques for finding the most suitable distribution function of relaxation times (DFRT). This approach leads toward a better analysis of impedance spectroscopy results as compared to other analysis tools such as equivalent circuits or deconvolution techniques. In this work, SOFC and SOEC were examined during operation by IS measurements and the results were analysed using ISGP. The aim of this work is to show examples of DFRT models which reflect the physical processes occurring during the operation. It is demonstrated that despite the low impedance (in the mOmega range) and the narrow available bandwidth, ISGP can provide consistent DFRT models.", keywords = "genetic algorithms, genetic programming, Impedance spectroscopy, Solid oxide electrolysis cell, Solid oxide fuel cell, Distribution of relaxation times", } @Article{drachal:2023:Energies, author = "Krzysztof Drachal", title = "Forecasting the Crude Oil Spot Price with Bayesian Symbolic Regression", journal = "Energies", year = "2023", volume = "16", number = "1", pages = "Article No. 4", keywords = "genetic algorithms, genetic programming", ISSN = "1996-1073", URL = "https://www.mdpi.com/1996-1073/16/1/4", DOI = "doi:10.3390/en16010004", abstract = "In this study, the crude oil spot price is forecast using Bayesian symbolic regression (BSR). In particular, the initial parameters specification of BSR is analysed. Contrary to the conventional approach to symbolic regression, which is based on genetic programming methods, BSR applies Bayesian algorithms to evolve the set of expressions (functions). This econometric method is able to deal with variable uncertainty (feature selection) issues in oil price forecasting. Secondly, this research seems to be the first application of BSR to oil price forecasting. Monthly data between January 1986 and April 2021 are analysed. As well as BSR, several other methods (also able to deal with variable uncertainty) are used as benchmark models, such as LASSO and ridge regressions, dynamic model averaging, and Bayesian model averaging. The more common ARIMA and naïve methods are also used, together with several time-varying parameter regressions. As a result, this research not only presents a novel and original application of the BSR method but also provides a concise and uniform comparison of the application of several popular forecasting methods for the crude oil spot price. Robustness checks are also performed to strengthen the obtained conclusions. It is found that the suitable selection of functions and operators for BSR initialization is an important, but not trivial, task. Unfortunately, BSR does not result in forecasts that are statistically significantly more accurate than the benchmark models. However, BSR is computationally faster than the genetic programming-based symbolic regression.", notes = "also known as \cite{en16010004}", } @InProceedings{Drachal:2023:BigData, author = "Krzysztof Drachal", booktitle = "2023 IEEE International Conference on Big Data (BigData)", title = "Forecasting Commodities Prices with the Bayesian Symbolic Regression Compared to Other Methods", year = "2023", pages = "3413--3421", abstract = "This study employs Bayesian Symbolic Regression (BSR) to forecasting spot prices of various commodities. This novel method exhibits promising potential as a forecasting tool, especially in the context of variable (feature) selection. Yet, there is no much research on symbolic regression as a forecasting tool for prices time-series in economics and finance. BSR offers valuable capabilities for tackling the challenges of variable selection (feature selection) in econometric modelling, as well as, it is expected to deal with some other issues smoothly. Herein, the analysis is specifically tailored to time-series data representing commodity markets. The accuracies of BSR models are compared with those of some alternative models: Symbolic Regression with Genetic Programming, Dynamic Model Averaging, LASSO regression, Time-Varying Parameters regression, ARIMA, no-change forecasting, etc. Unlike previous simulations of BSR, that relied on synthetic data, this study employs real-world data from commodities markets. The findings are expected to provide valuable insights for researchers and practitioners interested in applying BSR in econometric and financial contexts in the future.", keywords = "genetic algorithms, genetic programming, Uncertainty, Biological system modelling, Finance, Predictive models, Feature extraction, Data models, Bayesian econometrics, commodities prices, model averaging, symbolic regression, time-series forecasting, variable selection", DOI = "doi:10.1109/BigData59044.2023.10386819", month = dec, notes = "Also known as \cite{10386819}", } @Article{drachal:2024:IJFS, author = "Krzysztof Drachal and Michal Pawlowski", title = "Forecasting Selected Commodities' Prices with the Bayesian Symbolic Regression", journal = "International Journal of Financial Studies", year = "2024", volume = "12", number = "2", pages = "Article No. 34", keywords = "genetic algorithms, genetic programming", ISSN = "2227-7072", URL = "https://www.mdpi.com/2227-7072/12/2/34", DOI = "doi:10.3390/ijfs12020034", abstract = "This study firstly applied a Bayesian symbolic regression (BSR) to the forecasting of numerous commodities' prices (spot-based ones). Moreover, some features and an initial specification of the parameters of the BSR were analysed. The conventional approach to symbolic regression, based on genetic programming, was also used as a benchmark tool. Secondly, various other econometric methods dealing with variable uncertainty were estimated including Bayesian Model Averaging, Dynamic Model Averaging, LASSO, ridge, elastic net, and least-angle regressions, etc. Therefore, this study reports a concise and uniform comparison of an application of several popular econometric models to forecasting the prices of numerous commodities. Robustness checks and statistical tests were performed to strengthen the obtained conclusions. Monthly data beginning from January 1988 and ending in August 2021 were analysed.", notes = "also known as \cite{ijfs12020034}", } @InProceedings{Dracopoulos:1996:ukpar, author = "Dimitris C. Dracopoulos and Duncan Self", title = "Parallel Genetic Programming", booktitle = "UK Parallel'96", year = "1996", editor = "Chris R. Jesshope and Alex V. Shafarenko", pages = "151--162", address = "University of Surrey, UK", month = "3-5 " # jul, organisation = "BCS Parallel Processing Specialist Group", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-76068-9", DOI = "doi:10.1007/978-1-4471-1504-5_11", abstract = "A parallel implementation of Genetic Programming using PVM is described. Two different topologies for parallel implementation of GP are examined. Both of them are based on the island model for evolutionary algorithms. It is shown that considerable speedup of the GP execution can be achieved and that the parallel versions of the algorithm are very suitable for complex, time consuming problems.", } @InProceedings{dracopoulos:1996:sGPpBSP, author = "Dimitris C. Dracopoulos and Simon Kent", title = "Speeding up Genetic Programming: A Parallel BSP Implementation", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "421", address = "Stanford University, CA, USA", publisher = "MIT Press", size = "1 page", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap60.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96 5 page version available via citeseer.ist.psu.edu/233993.html ", } @InProceedings{Dracopoulos:1997:es, author = "Dimitris C. Dracopoulos", title = "Evolutionary Control of a Satellite", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming, space, Euler equation, torque feedback, spin control, strange attractor, Lyapunov functions, vector multiplication", pages = "77--81", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1997/Dracopoulos_1997_es.pdf", size = "5 pages", abstract = "The genetic programming approach is applied to a highly nonlinear control problem, the attitude control problem. A rigid body satellite is detumbled and controlled by using the control law derived by GP. It is shown that the discovered control regime is stable", notes = "GP-97", } @Book{dracopoulos:1997:elanac, author = "Dimitris C. Dracopoulos", title = "Evolutionary Learning Algorithms for Neural Adaptive Control", publisher = "Springer Verlag", year = "1997", series = "Perspectives in Neural Computing", address = "P.O. Box 31 13 40, D-10643 Berlin, Germany", month = aug, email = "orders@springer.de", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-76161-6", isbn13 = "978-1-4471-0903-7", URL = "http://www.amazon.co.uk/exec/obidos/ASIN/3540761616/qid%3D1106423488/202-4979008-1846244", URL = "http://www.springer.com/computer/ai/book/978-3-540-76161-7", DOI = "doi:10.1007/978-1-4471-0903-7", size = "222 pages", abstract = "Neural networks and evolutionary algorithms are constantly expanding their field of application to a variety of new domains. One area of particular interest is their applicability to control and adaptive control systems: the limitations of the classical control theory combined with the need for greater robustness, adaptivity and ``intelligence'' make neurocontrol and evolutionary control algorithms an attractive (and in some cases, the only) alternative. After an introduction to neural networks and genetic algorithms, this volume describes in detail how neural networks and evolutionary techniques (specifically genetic algorithms and genetic programming) can be applied to the adaptive control of complex dynamic systems (including chaotic ones). A number of examples are presented and useful tips are given for the application of the techniques described. The fundamentals of dynamic systems theory and classical adaptive control are also given.", notes = "Chapter 7 deals with genetic algorithms, including 8 pages on genetic programming. These include solving the problem described in \cite{Dracopoulos:1997:es}", size = "212 pages", } @InCollection{dracopoulos:1997:GAGPc, author = "Dimitris C. Dracopoulos", title = "Genetic Algorithms and Genetic Programming for Control", booktitle = "Evolutionary Algorithms in Engineering Applications", publisher = "Springer-Verlag", year = "1997", editor = "Dipankar Dasupta and Zbigniew Michalewicz", pages = "329--343", address = "Berlin", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-62021-4", isbn13 = "978-3-540-62021-1", URL = "http://www.springer.com/computer/swe/book/978-3-540-62021-1", notes = "brief survey of GA and GP in control. Principly concentrates upon using GP to control a tumbling satellite", } @InProceedings{Dracopoulos:2007:WCE, title = "Autolanding of Commercial Aircrafts by Genetic Programming", author = "Dimitris C. Dracopoulos", booktitle = "Proceedings of the World Congress on Engineering, WCE 2007", year = "2007", volume = "I", address = "London", month = jul # " 2-4", keywords = "genetic algorithms, genetic programming, autolanding, aircraft, intelligent control, evolutionary control", isbn13 = "978-988-98671-5-7", URL = "http://www.iaeng.org/publication/WCE2007/WCE2007_pp83-86.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.148.6342", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", contributor = "CiteSeerX", language = "en", oai = "oai:CiteSeerXPSU:10.1.1.148.6342", pages = "83--86", abstract = "The genetic programming approach is applied to the problem of aircraft autolanding, subject to wind disturbances. The derived control law is tested successfully, using a linearised model of a commercial aircraft. The evolutionary control of autolanding is done within the desired operational envelope.", } @InProceedings{Dracopoulos:2010:PPSN, author = "Dimitris Dracopoulos and Riccardo Piccoli", title = "Bioreactor Control by Genetic Programming", booktitle = "PPSN 2010 11th International Conference on Parallel Problem Solving From Nature", year = "2010", editor = "Robert Schaefer and Carlos Cotta and Joanna Kolodziej and Guenter Rudolph", publisher = "Springer", pages = "181--188", series = "Lecture Notes in Computer Science", address = "Krakow, Poland", month = "11-15 " # sep, volume = "6239", keywords = "genetic algorithms, genetic programming, bioreactor control, nonlinear control", DOI = "doi:10.1007/978-3-642-15871-1_19", abstract = "Genetic programming is applied to the problem of bioreactor control. This highly nonlinear problem has previously been suggested as one of the challenging benchmarks to explore new ideas for building automatic controllers. It is shown that the derived control law is successful in a number of test cases.", affiliation = "School of Electronics and Computer Science, University of Westminster, London, UK", } @InProceedings{dracopoulos:2012:EuroGP, author = "Dimitris C. Dracopoulos and Dimitrios Effraimidis", title = "Genetic Programming for Generalised Helicopter Hovering Control", booktitle = "Proceedings of the 15th European Conference on Genetic Programming, EuroGP 2012", year = "2012", month = "11-13 " # apr, editor = "Alberto Moraglio and Sara Silva and Krzysztof Krawiec and Penousal Machado and Carlos Cotta", series = "LNCS", volume = "7244", publisher = "Springer Verlag", address = "Malaga, Spain", pages = "25--36", organisation = "EvoStar", isbn13 = "978-3-642-29138-8", DOI = "doi:10.1007/978-3-642-29139-5_3", keywords = "genetic algorithms, genetic programming, Helicopter hovering, Nonlinear control, Neuroevolutionary control, Reinforcement learning", abstract = "We show how genetic programming can be applied to helicopter hovering control, a nonlinear high dimensional control problem which previously has been included in the literature in the set of benchmarks for the derivation of new intelligent controllers . The evolved controllers are compared with a neuroevolutionary approach which won the first position in the 2008 helicopter hovering reinforcement learning competition. GP performs similarly (and in some cases better) with the winner of the competition, even in the case where unknown wind is added to the dynamic system and control is based on structures evolved previously, i.e. the evolved controllers have good generalisation capability.", notes = "Part of \cite{Moraglio:2012:GP} EuroGP'2012 held in conjunction with EvoCOP2012 EvoBIO2012, EvoMusArt2012 and EvoApplications2012", } @InCollection{Dracopoulos:2012:RDIS, author = "Dimitris C. Dracopoulos and Barry D. Nichols", title = "Swing Up and Balance Control of the Acrobot Solved by Genetic Programming", booktitle = "Research and Development in Intelligent Systems XXIX", year = "2012", editor = "Max Bramer and Miltos Petridis", publisher = "Springer", pages = "229--242", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1007/978-1-4471-4739-8_19", isbn13 = "978-1-4471-4738-1", URL = "http://dx.doi.org/10.1007/978-1-4471-4739-8_19", language = "English", abstract = "The evolution of controllers using genetic programming is described for the continuous, limited torque minimum time swing-up and inverted balance problems of the acrobot. The best swing-up controller found is able to swing the acrobot up to a position very close to the inverted handstand position in a very short time, which is comparable to the results which have been achieved by other methods using similar parameters for the dynamic system. The balance controller is successful at keeping the acrobot in the unstable, inverted position when starting from the inverted position.", } @InProceedings{conf/sgai/DracopoulosN12, author = "Dimitris C. Dracopoulos and Barry D. Nichols", title = "Swing Up and Balance Control of the Acrobot Solved by Genetic Programming", booktitle = "Research and Development in Intelligent Systems {XXIX}, Incorporating Applications and Innovations in Intelligent Systems {XX}: Proceedings of {AI}-2012, The Thirty-second {SGAI} International Conference on Innovative Techniques and Applications of Artificial Intelligence", year = "2012", editor = "Max Bramer and Miltos Petridis", pages = "229--242", address = "Cambridge, UK", month = dec # " 11-13", publisher = "Springer", note = "Best Application Paper", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4471-4738-1", URL = "https://westminsterresearch.westminster.ac.uk/item/8z246/swing-up-and-balance-control-of-the-acrobot-solved-by-genetic-programming", URL = "https://eprints.mdx.ac.uk/id/eprint/17065", URL = "http://dx.doi.org/10.1007/978-1-4471-4739-8_19", DOI = "doi:10.1007/978-1-4471-4739-8_19", bibdate = "2013-01-22", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/sgai/sgai2012.html#DracopoulosN12", abstract = "The evolution of controllers using genetic programming is described for the continuous, limited torque minimum time swing-up and inverted balance problems of the acrobot. The best swing-up controller found is able to swing the acrobot up to a position very close to the inverted handstand position in a very short time, which is comparable to the results which have been achieved by other methods using similar parameters for the dynamic system. The balance controller is successful at keeping the acrobot in the unstable, inverted position when starting from the inverted position.", notes = "SGAI Conf", } @InCollection{Dracopoulos:2013:HCSR, title = "Genetic programming as a solver to challenging reinforcement learning problems", author = "Dimitris Dracopoulos and Dimitrios Effraimidis and Barry D. Nichols", publisher = "Nova Publications", year = "2013", editor = "Thomas S. Clary", volume = "8", series = "Horizons in Computer Science Research", pages = "145--174", address = "Hauppauge, NY, USA", keywords = "genetic algorithms, genetic programming", isbn13 = "9781624174131", URL = "http://www.novapublishers.org/catalog/product_info.php?products_id=38450", contributor = "Thomas S. Clary", oai = "oai:westminsterresearch.wmin.ac.uk:10982", type = "NonPeerReviewed", URL = "http://westminsterresearch.wmin.ac.uk/10982/", notes = " May 2021 journal {"}International Journal of Computer Research; ISSN: 1535-6698 Nova Science Publishers, Huttington, USA. Appears to discontinued 2017. ProQuest lists journal version as IJCR Vol. 20, Iss. 3, (2013): 351-379 Also known as \cite{oai:westminsterresearch.wmin.ac.uk:10982}", bibsource = "OAI-PMH server at westminsterresearch.wmin.ac.uk", } @Article{Dracopoulos:2015:EXSY, author = "Dimitris C. Dracopoulos and Barry D. Nichols", title = "Genetic programming for the minimum time swing up and balance control acrobot problem", journal = "Expert Systems", year = "2017", volume = "34", number = "5", pages = "e12115", month = oct, ISSN = "1468-0394", URL = "http://dx.doi.org/10.1111/exsy.12115", DOI = "doi:10.1111/exsy.12115", keywords = "genetic algorithms, genetic programming, artificial intelligence, control systems, computational intelligence", size = "9 pages", abstract = "This work describes how genetic programming is applied to evolving controllers for the minimum time swing up and inverted balance tasks of the continuous state and action: limited torque acrobot. The best swing-up controller is able to swing the acrobot up to a position very close to the inverted handstand position in a very short time, shorter than that of Coulom (2004), who applied the same constraints on the applied torque values, and to take only slightly longer than the approach by Lai et al. (2009) where far larger torque values were allowed. The best balance controller is able to balance the acrobot in the inverted position when starting from the balance position for the length of time used in the fitness function in all runs; furthermore, 47 out of 50 of the runs evolve controllers able to maintain the balance position for an extended period, an improvement on the balance controllers generated by Dracopoulos and Nichols (2012), which this paper is extended from. The most successful balance controller is also able to balance the acrobot when starting from a small offset from the balance position for this extended period.", } @PhdThesis{DBLP:phd/basesearch/Drahosova17, author = "Michaela Drahosova", title = "Coevolution of Fitness Predictors in Cartesian Genetic Programming", title2 = "Koevoluce prediktoru fitness v kartezskem genetickem programovani", school = "Brno University of Technology, Czech Republic", year = "2017", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, Evolutionary design, coevolutionary algorithms, fitness prediction", URL = "http://hdl.handle.net/11012/187309", URL = "https://dspace.vutbr.cz/xmlui/bitstream/handle/11012/187309/thesis-1.pdf", timestamp = "Tue, 09 Jun 2020 01:00:00 +0200", biburl = "https://dblp.org/rec/phd/basesearch/Drahosova17.bib", bibsource = "dblp computer science bibliography, https://dblp.org", size = "109 pages", abstract = "Cartesian genetic programming (CGP) is an evolutionary based machine learning method which can automatically design computer programs or digital circuits. CGP has been successfully applied in a number of challenging real-world problem domains. However, the computational power that the design based on CGP needs for obtaining innovative results is enormous for most applications. In CGP, every candidate program is executed to determine a fitness value, representing the degree to which it solves the problem. Typically, the most time consuming part of CGP is the fitness evaluation. This thesis proposes to introduce coevolution of fitness predictors to CGP in order to accelerate the evolutionary design performed by CGP. Fitness predictors are small subsets of the training data, which are used to estimate candidate program fitness instead of performing an expensive objective fitness evaluation. Coevolution of fitness predictors is an optimization method of the fitness modeling that reduces the fitness evaluation cost and frequency, while maintaining the evolutionary process. In this thesis, the coevolutionary algorithm is adapted for CGP and three approaches to fitness predictor encoding are introduced and examined. The proposed approach is evaluated using five symbolic regression benchmarks and in the image filter design problem. The method enabled us to significantly reduce the time of evolutionary design for considered class of problems.", notes = "In english. Supervisor: Lukas Sekanina", } @Article{Drahosova:EC, author = "Michaela Drahosova and Lukas Sekanina and Michal Wiglasz", title = "Adaptive Fitness Predictors in Coevolutionary Cartesian Genetic Programming", journal = "Evolutionary Computation", year = "2019", volume = "27", number = "3", pages = "497--523", month = "Fall", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, coevolutionary algorithms, fitness prediction, symbolic regression, evolutionary design, image processing", ISSN = "1063-6560", DOI = "doi:10.1162/evco_a_00229", size = "27 pages", abstract = "In genetic programming (GP), computer programs are often coevolved with training data subsets that are known as fitness predictors. In order to maximize performance of GP, it is important to find the most suitable parameters of coevolution, particularly the fitness predictor size. This is a very time consuming process as the predictor size depends on a given application and many experiments have to be performed to find its suitable size. A new method is proposed which enables us to automatically adapt the predictor and its size for a given problem and thus to reduce not only the time of evolution, but also the time needed to tune the evolutionary algorithm. The method was implemented in the context of Cartesian genetic programming and evaluated using five symbolic regression problems and three image filter design problems. In comparison with three different CGP implementations, the time required by CGP search was reduced while the quality of results remained unaffected.", notes = "Lena", } @InProceedings{Drake:2012:CIS, author = "John H. Drake and Matthew Hyde and Khaled Ibrahim and Ender Ozcan", title = "A Genetic Programming Hyper-Heuristic for the Multidimensional Knapsack Problem", year = "2012", booktitle = "11th IEEE International Conference on Cybernetic Intelligent Systems", editor = "N. H. Siddique and Michael O'Grady", address = "Limerick, Ireland", month = "23-24 " # aug, organisation = "IEEE Systems, Man and Cybernetics Society with the theme of Cybernetic Intelligent Systems", keywords = "genetic algorithms, genetic programming, hyper-heuristics, heuristic generation, multidimensional knapsack problem", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.368.5880", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.368.5880", URL = "http://www.cs.nott.ac.uk/~exo/docs/publications/cis2012_GP_MKP.pdf", size = "5 pages", abstract = "Hyper-heuristics are a class of high-level search techniques which operate on a search space of heuristics rather than directly on a search space of solutions. Early hyperheuristics focused on selecting and applying a low-level heuristic at each stage of a search. Recent trends in hyper-heuristic research have led to a number of approaches being developed to automatically generate new heuristics from a set of heuristic components. This work investigates the suitability of using genetic programming as a hyper-heuristic methodology to generate constructive heuristics to solve the multidimensional 0-1 knapsack problem. A population of heuristics to rank knapsack items are trained on a subset of test problems and then applied to unseen instances. The results over a set of standard benchmarks show that genetic programming can be used to generate constructive heuristics which yield human-competitive results.", notes = "May 2014 not in IEEE xplor. http://cis2012.wordpress.com/conference-programme/", } @InProceedings{drake:2013:EuroGP, author = "John H. Drake and Nikolaos Kililis and Ender Ozcan", title = "Generation of VNS Components with Grammatical Evolution for Vehicle Routing", booktitle = "Proceedings of the 16th European Conference on Genetic Programming, EuroGP 2013", year = "2013", month = "3-5 " # apr, editor = "Krzysztof Krawiec and Alberto Moraglio and Ting Hu and A. Sima Uyar and Bin Hu", series = "LNCS", volume = "7831", publisher = "Springer Verlag", address = "Vienna, Austria", pages = "25--36", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", isbn13 = "978-3-642-37206-3", DOI = "doi:10.1007/978-3-642-37207-0_3", abstract = "The vehicle routing problem (VRP) is a family of problems whereby a fleet of vehicles must service the commodity demands of a set of geographically scattered customers from one or more depots, subject to a number of constraints. Early hyper-heuristic research focused on selecting and applying a low-level heuristic at a given stage of an optimisation process. Recent trends have led to a number of approaches being developed to automatically generate heuristics for a number of combinatorial optimisation problems. Previous work on the VRP has shown that the application of hyper-heuristic approaches can yield successful results. In this paper we investigate the potential of grammatical evolution as a method to evolve the components of a variable neighbourhood search (VNS) framework. In particular two components are generated; constructive heuristics to create initial solutions and neighbourhood move operators to change the state of a given solution. The proposed method is tested on standard benchmark instances of two common VRP variants.", notes = "Part of \cite{Krawiec:2013:GP} EuroGP'2013 held in conjunction with EvoCOP2013, EvoBIO2013, EvoMusArt2013 and EvoApplications2013", } @PhdThesis{Drake:thesis, author = "John H. Drake", title = "Crossover Control in Selection Hyper-heuristics: Case Studies using {MKP} and {HyFlex}", school = "School of Computer Science, The University of Nottingham", year = "2014", address = "UK", month = jul, keywords = "genetic algorithms, genetic programming, hyper-heuristics, heuristic programming, knapsack problem, algorithms, search", URL = "http://eprints.nottingham.ac.uk/id/eprint/14276", URL = "http://eprints.nottingham.ac.uk/14276/1/thesis.pdf", size = "199 pages", abstract = "Hyper-heuristics are a class of high-level search methodologies which operate over a search space of heuristics rather than a search space of solutions. Hyper-heuristic research has set out to develop methods which are more general than traditional search and optimisation techniques. In recent years, focus has shifted considerably towards cross-domain heuristic search. The intention is to develop methods which are able to deliver an acceptable level of performance over a variety of different problem domains, given a set of low-level heuristics to work with. This thesis presents a body of work investigating the use of selection hyper-heuristics in a number of different problem domains. Specifically the use of crossover operators, prevalent in many evolutionary algorithms, is explored within the context of single-point search hyper-heuristics. A number of traditional selection hyper-heuristics are applied to instances of a well-known NP-hard combinatorial optimisation problem, the multidimensional knapsack problem. This domain is chosen as a benchmark for the variety of existing problem instances and solution methods available. The results suggest that selection hyper-heuristics are a viable method to solve some instances of this problem domain. Following this, a framework is defined to describe the conceptual level at which crossover low-level heuristics are managed in single-point selection hyper-heuristics. HyFlex is an existing software framework which supports the design of heuristic search methods over multiple problem domains, i.e. cross-domain optimisation. A traditional heuristic selection mechanism is modified in order to improve results in the context of cross-domain optimisation. Finally the effect of crossover use in cross-domain optimisation is explored.", notes = "Supervisors: Ender Ozcan", } @Article{Drake:2014:Kybernetes, author = "John H. Drake and Matthew Hyde and Khaled Ibrahim and Ender Ozcan", title = "A genetic programming hyper-heuristic for the multidimensional knapsack problem", journal = "Kybernetes", year = "2014", volume = "43", number = "9/10", pages = "1500--1511", keywords = "genetic algorithms, genetic programming, Artificial intelligence, Heuristic generation, Hyper-heuristics, Multidimensional knapsack problem", ISSN = "0368-492X", URL = "http://eprints.nottingham.ac.uk/id/eprint/32174", URL = "http://www.emeraldinsight.com/doi/full/10.1108/K-09-2013-0201", DOI = "doi:10.1108/K-09-2013-0201", abstract = "Hyper-heuristics are a class of high-level search techniques which operate on a search space of heuristics rather than directly on a search space of solutions. The purpose of this paper is to investigate the suitability of using genetic programming as a hyper-heuristic methodology to generate constructive heuristics to solve the multidimensional 0-1 knapsack problem Design/methodology/approach hyper-heuristics focused on selecting and applying a low-level heuristic at each stage of a search. Recent trends in hyper-heuristic research have led to a number of approaches being developed to automatically generate new heuristics from a set of heuristic components. A population of heuristics to rank knapsack items are trained on a subset of test problems and then applied to unseen instances. Findings The results over a set of standard benchmarks show that genetic programming can be used to generate constructive heuristics which yield human-competitive results. Originality/value In this work the authors show that genetic programming is suitable as a method to generate reusable constructive heuristics for the multidimensional 0-1 knapsack problem. This is classified as a hyper-heuristic approach as it operates on a search space of heuristics rather than a search space of solutions. To our knowledge, this is the first time in the literature a GP hyper-heuristic has been used to solve the multidimensional 0-1 knapsack problem. The results suggest that using GP to evolve ranking mechanisms merits further future research effort", notes = "An earlier version of this article was presented at the 11th IEEE Conference on Cybernetic Intelligent Systems (CIS 2012) in Limerick, Ireland, in August 2012.", } @InProceedings{Draschner:2019:evoLBA, author = "Carsten Draschner and Jens Lehmann and Hajira Jabeen", title = "Smart Chef: Evolving Recipes", booktitle = "Late Breaking Abstracts at Evo*2019", year = "2019", editor = "Antonio M. Mora and Anna I. Esparcia-Alcazar", pages = "8--9", address = "Leipzig, Germany", month = "24-26 " # apr, organisation = "Species", keywords = "genetic algorithms, genetic programming, cs.NE, evolutionary algorithm, artificial creativity, recipe, culinary, semantic creativity, food graph, recipe annotation, human readable recipe representation", eprint = "1907.12698", URL = "https://arxiv.org/abs/1907.12698", URL = "https://arxiv.org/ftp/arxiv/papers/1907/1907.12698.pdf", size = "1.5 pages", abstract = "Smart Chef demonstrates the creativity of evolution in culinary arts by autonomously evolving novel and human readable recipes. The evolutionary algorithm for Smart Chef fully automatized and does not require human feedback. The tree representation of recipes is inspired by genetic programming and is enriched with semantic annotations extracted from known recipes. The fitness identifies valid recipes and novelty. Recipe mutation exchanges ingredients by food category classification and recombination interchanges partial recipe instructions. Smart Chef has been tested on a population size of 128 and evolved for 100 generations resulting in valid and novel recipes.", notes = "part of \cite{mora2019evo}", } @InProceedings{Drchal:2012:GECCO, author = "Jan Drchal and Miroslav Snorek", title = "Distance measures for HyperGP with fitness sharing", booktitle = "GECCO '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference", year = "2012", editor = "Terry Soule and Anne Auger and Jason Moore and David Pelta and Christine Solnon and Mike Preuss and Alan Dorin and Yew-Soon Ong and Christian Blum and Dario Landa Silva and Frank Neumann and Tina Yu and Aniko Ekart and Will Browne and Tim Kovacs and Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and Giovanni Squillero and Nicolas Bredeche and Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and Martin Pelikan and Silja Meyer-Nienberg and Christian Igel and Greg Hornby and Rene Doursat and Steve Gustafson and Gustavo Olague and Shin Yoo and John Clark and Gabriela Ochoa and Gisele Pappa and Fernando Lobo and Daniel Tauritz and Jurgen Branke and Kalyanmoy Deb", isbn13 = "978-1-4503-1177-9", pages = "545--552", keywords = "genetic algorithms, genetic programming, generative and developmental systems", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330163.2330241", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "In this paper we propose a new algorithm called HyperGPEFS (HyperGP with Explicit Fitness Sharing). It is based on a HyperNEAT, which is a well-established evolutionary method employing indirect encoding of artificial neural networks. Indirect encoding in HyperNEAT is realized via special function called Compositional and Pattern Producing Network (CPPN), able to describe a neural network of arbitrary size. CPPNs are represented by network structures, which are evolved by means of a slightly modified version of another, well-known algorithm NEAT (NeuroEvolution of Augmenting Topologies). HyperGP is a variant of HyperNEAT, where the CPPNs are optimized by Genetic Programming (GP). Published results reported promising improvement in the speed of convergence. Our approach further extends HyperGP by using fitness sharing to promote a diversity of a population. Here, we thoroughly compare all three algorithms on six different tasks. Fitness sharing demands a definition of a tree distance measure. Among other five, we propose a generalized distance measure which, in conjunction with HyperGPEFS, significantly outperforms HyperNEAT and HyperGP on all, but one testing problems. Although this paper focuses on indirect encoding, the proposed distance measures are generally applicable.", notes = "Also known as \cite{2330241} GECCO-2012 A joint meeting of the twenty first international conference on genetic algorithms (ICGA-2012) and the seventeenth annual genetic programming conference (GP-2012)", } @InProceedings{conf/softcomp/DrchalS12, author = "Jan Drchal and Miroslav Snorek", title = "Genetic Programming of Augmenting Topologies for Hypercube-Based Indirect Encoding of Artificial Neural Networks", booktitle = "7th International Conference, Soft Computing Models in Industrial and Environmental Applications SOCO-2012", year = "2013", editor = "Vaclav Snasel and Ajith Abraham and Emilio S. Corchado", volume = "188", series = "Advances in Intelligent Systems and Computing", pages = "63--72", address = "Ostrava, Czech Republic", month = sep # " 5th-7th", publisher = "Springer", bibdate = "2013-01-16", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/softcomp/soco2012.html#DrchalS12", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-32921-0", DOI = "doi:10.1007/978-3-642-32922-7_7", abstract = "n this paper we present a novel algorithm called GPAT (Genetic Programming of Augmenting Topologies) which evolves Genetic Programming (GP) trees in a similar way as a well-established neuro-evolutionary algorithm NEAT (NeuroEvolution of Augmenting Topologies) does. The evolution starts from a minimal form and gradually adds structure as needed. A niching evolutionary algorithm is used to protect individuals of a variable complexity in a single population. Although GPAT is a general approach we employ it mainly to evolve artificial neural networks by means of Hypercube-based indirect encoding which is an approach allowing for evolution of large-scale neural networks having theoretically unlimited size. We perform also experiments for directly encoded problems. The results show that GPAT outperforms both GP and NEAT taking the best of both.", } @InProceedings{drechsler:1996:GAshtOKFDD, author = "Rold Drechsler and Bernd Becker and Nicole Gockel", title = "A Genetic Algorithm for the Construction of Small and Highly Testable OKFDD Circuits", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "Genetic Algorithms", pages = "473--478", address = "Stanford University, CA, USA", publisher = "MIT Press", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap78.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96 GA paper", } @InProceedings{drechsler:2001:EuroGP, author = "Nicole Drechsler and Frank Schmiedle and Daniel Grosse and Rolf Drechsler", title = "Heuristic Learning based on Genetic Programming", booktitle = "Genetic Programming, Proceedings of EuroGP'2001", year = "2001", editor = "Julian F. Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon", volume = "2038", series = "LNCS", pages = "1--10", address = "Lake Como, Italy", publisher_address = "Berlin", month = "18-20 " # apr, organisation = "EvoNET", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, Heuristic Learning, VLSI CAD, BDD, Binary Decision Diagrams", ISBN = "3-540-41899-7", DOI = "doi:10.1007/3-540-45355-5_1", size = "10 pages", abstract = "In this paper we present an approach to learning heuristics based on Genetic Programming (GP). Instead of directly solving the problem by application of GP, GP is used to develop a heuristic that is applied to the problem instance. By this, the typical large runtimes of evolutionary methods have to be invested only once in the learning phase. The resulting heuristic is very fast. The technique is applied to a field from the area of VLSI CAD, i.e. minimization of Binary Decision Diagrams (BDDs). We chose this topic due to its high practical relevance and since it matches the criteria where our algorithm works best, i.e. large problem instances where standard evolutionary techniques cannot be applied due to their large runtimes. Our experiments show that we obtain high quality results that outperform previous methods, while keeping the advantage of low runtimes.", notes = "EuroGP'2001, part of \cite{miller:2001:gp}", } @TechReport{drecourt:1999uANNGPrrmTR, author = "Jean-Philippe Drecourt", title = "Application Of Neural Networks And Genetic Programming To Rainfall Runoff modeling", institution = "Danish Hydraulic Institute (Hydro-Informatics Technologies HIT)", year = "1999", type = "D2K Technical Report", number = "D2K-0699-1", month = jun, keywords = "genetic algorithms, genetic programming", broken = "http://projects.dhi.dk/d2k/Publications/D2K-TR-0699-01.pdf", abstract = "The main problem in rainfall/runoff modeling is to obtain data about the catchment with sufficient accuracy. Since self-learning tools only need knowledge about rainfall and runoff, they can offer a good alternative to classical model. The present study focuses on Lindenborg, a Danish catchment situated in the northern part of Jutland, between Hobro and Alborg. It is characterized by high groundwater contribution and thus a very persistent flow regime. The tools used were artificial neural networks (ANN) and genetic programming (GP). The purpose was to compare the efficiency of these tools with a classic lumped model (NAM) and a naive prediction (i.e. the runoff does not change between one day and the next one). The study with GP was oriented in two directions: the prediction of the runoff, and the prediction of the variation in the runoff. In both cases GP was given the rainfall and runoff of the past days, and it was assumed that the rainfall was predicted without any error for the target day. Each strategy has its own advantages. Predicting the variation is considered to be closer to the relationships given by physics, whereas predicting the runoff takes in account the large auto-correlation of the runoff time series. Since it is difficult to predict the upper boundary of runoff, the ANN worked exclusively with the time variation. The variation in runoff is less likely to saturate the network than the runoff itself, especially in this catchment where the dynamics are relatively slow. Therefore, the sensitivity of the prediction is increased. Time lag recurrent network (TLRN) were used for this study as they allow to take in account smoothed version of the past time series, both in the input and the hidden layers. The comparison of the different models was based on the Pearson coefficient of correlation, which gives a good overview of the performance of the prediction.", notes = "Cited by \cite{Freire:2010:ICEC} See also \cite{drecourt:1999uANNGPrrm}", size = "38 pages", } @InProceedings{drecourt:1999uANNGPrrm, author = "J-P. Drecourt", title = "Using Artificial Neural Networks and Genetic Programming in rainfall/runoff modeling", booktitle = "3rd DHI Software Conference \& DHI Software Courses", year = "1999", address = "Helsingor, Denmark", month = "7-11 " # jun, organisation = "Danish Hydraulic Institute", keywords = "genetic algorithms, genetic programming", broken = "http://www.dhi.dk/softcon/abstract/102.doc", abstract = "The main problem in rainfall/runoff modeling is to obtain data about the catchment with sufficient accuracy. Since self-learning tools only need knowledge about rainfall and runoff, they can offer a good alternative to classical model. The present study focuses on Lindenborg, a Danish catchment situated in the northern part of Jutland, between Hobro and Alborg. It is characterized by high groundwater contribution and thus a very persistent flow regime. The tools used were artificial neural networks (ANN) and genetic programming (GP). The purpose was to compare the efficiency of these tools with a classic lumped model (NAM) and a naive prediction (i.e. the runoff does not change between one day and the next one). The study with GP was oriented in two directions : the prediction of the runoff, and the prediction of the variation in the runoff. In both cases GP was given the rainfall and runoff of the past days, and it was assumed that the rainfall was predicted without any error for the target day. Each strategy has its own advantages. Predicting the variation is considered to be closer to the relationships given by physics, whereas predicting the runoff takes in account the large auto-correlation of the runoff time series. Since it is difficult to predict the upper boundary of runoff, the ANN worked exclusively with the time variation. The variation in runoff is less likely to saturate the network than the runoff itself, especially in this catchment where the dynamics are relatively slow. Therefore, the sensitivity of the prediction is increased. Time lag recurrent network (TLRN) were used for this study as they allow to take in account smoothed version of the past time series, both in the input and the hidden layers. The comparison of the different models was based on the Pearson coefficient of correlation, which gives a good overview of the performance of the prediction. This study was realized in relationship with the Department of Hydrodynamics and Water Resources of DTU as a special course for the Master of Science in Environmental Engineering.", notes = "broken Sep 2018 http://www.dhi.dk/softcon/index.htm See also \cite{drecourt:1999uANNGPrrmTR}", } @InProceedings{Dreschler:1997:BEA, author = "Rolf Dreschler and Nicole Gockel and Elke Mackensen and Bernd Becker", title = "BEA: Specialized Hardware for Implementation of Evolutionary Algorithms", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "Evolvable Hardware", pages = "491", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", notes = "GP-97", } @Article{Drigas:2009:IJSHC, title = "Decade review (1999-2009): progress of application of artificial intelligence tools in student diagnosis", author = "Athanasios S. Drigas and Katerina Argyri and John Vrettaros", publisher = "Inderscience Publishers", year = "2009", volume = "1", journal = "International Journal of Social and Humanistic Computing", issue = "2", pages = "175--191", keywords = "genetic algorithms, genetic programming, student modelling, student diagnosis, fuzzy logic, neural networks, student assessment, student evaluation, adaptive learning, artificial intelligence, soft computing, educational research, intelligent tutoring", ISSN = "1752-6132", URL = "http://www.inderscience.com/link.php?id=31006", DOI = "doi:10.1504/IJSHC.2009.031006", language = "eng", bibsource = "OAI-PMH server at www.inderscience.com", abstract = "Over the last decade, artificial intelligence has offered a wide range of tools that have proved to be of vital importance for educational research. Indeed, logic, classifiers and machine learning methods, probabilistic techniques for uncertain reasoning as well as search and optimisation algorithms are only several among the various approaches that artificial intelligence has offered in dealing with real life problems. This paper attempts to explore the research that has been conducted on the application of the most typical and popular soft computing techniques [fuzzy logic, neural networks, Bayesian networks, genetic programming and hybrid approaches such as neuro-fuzzy systems and genetic programming neural networks (GPNNs)] in student modelling over the decade 1999-2009. This latest research trend is a part of every intelligent tutoring system and aims at generating and updating a student model in order to modify learning content to fit individual needs or to provide reliable assessment and feedback to student's answers. In this paper, we make a brief presentation of methods used so as to point out their qualities and then we describe the most representative studies sought in the decade of our interest after classifying them according to the principal aim they attempted to serve.", notes = "See also http://dx.doi.org/doi:10.1007/978-3-642-04757-2_59 Best Practices for the Knowledge Society. Knowledge, Learning, Development and Technology for All Second World Summit on the Knowledge Society, WSKS 2009, Chania, Crete, Greece, September 16-18, 2009. Proceedings", } @PhdThesis{Driscoll:thesis, author = "John Charles Driscoll", title = "Fractals as Basis for Design and Critique Driscoll, John Charles", school = "Systems Science, Portland State University", year = "2019", address = "Oregon, USA", month = "1 " # oct, keywords = "genetic algorithms, genetic programming, Architecture, Algorithmic design, City scaling, Fractals, Wright, Frank Lloyd Generative design, Ithaca", isbn13 = "9781687922274", URL = "https://archives.pdx.edu/ds/psu/29935", URL = "https://pdxscholar.library.pdx.edu/open_access_etds/5183/", URL = "https://pdxscholar.library.pdx.edu/cgi/viewcontent.cgi?article=6255&context=open_access_etds", URL = "https://www.proquest.com/docview/2306306963", URL = "https://www.proquest.com/dissertations-theses/fractals-as-basis-design-critique/docview/2306306963/se-2", slide_url = "http://johncdriscoll.com/wp-content/uploads/2019/07/7.21.19.pdf", DOI = "doi:10.15760/etd.7059", size = "242 pages", abstract = "The design profession is responding to the complex systems represented by architecture and planning by increasingly incorporating the power of computer technology into the design process. This represents a paradigm shift, and requires that designers rise to the challenge of both embracing modern technologies to perform increasingly sophisticated tasks without compromising their objective to create meaningful and environmentally sensitive architecture. This dissertation investigated computer-based fractal tools applied within a traditional architectural charette towards a design process with the potential to address the complex issues architects and planners face today. We developed and presented an algorithm that draws heavily from fractal mathematics and fractal theory. Fractals offer a quantitative and qualitative relation between nature, the built environment and computational mechanics and in this dissertation serve as a bridge between these realms. We investigated how qualitative/quantitative fractal tools may inform an architectural design process both in terms of generative formal solutions as well as a metric for assessing the complexity of designs and historic architecture. The primary research objective was to develop a compelling cybernetic design process and apply it to a real-world and multi-faceted case study project within a formal architectural critique. Jurors were provided a platform for evaluating design work and weighing in as practicing professional architects. Jurors comments were documented and discussed and presented as part of the dissertation. Our intention was to open up the discussion and document the effectiveness or ineffectiveness of the process we presented. First we discussed the history of generative and algorithmic design and fractals in architecture. We begin with examples in ancient Hindu temple architecture as well as Middle Eastern architecture and Gothic as well as Art Nouveau. We end this section with a discussion of fractals in the contemporary architecture of Frank Lloyd Wright and the Organic school. Next we developed a cybernetic design process incorporating a computer-based tool termed DBVgen within a closed loop designer/algorithm back and forth. The tool we developed incorporated a genetic algorithm that used fractal dimension as the primary fitness criterion. We applied our design process with mixed results as discussed by the jurors whose feedback was chunked into ten categories and assessed along with the author/designer's feedback. Generally we found that compelling designs tended to have a higher FD, whereas, the converse was not true that higher FD consistently led to more compelling designs. Finally, we further developed fractal theory towards an appropriate consideration of the significance of fractals in architecture. We articulated a nuanced definition of fractals in architecture as: designs having multi-scale and multi-functional representations of some unifying organizing principle as the result of an iterative process. We then wrapped this new understanding of fractals in architecture to precedent relevant to the case study project. We present and discuss fractals in the work of Frank Lloyd Wright as well as Dean Bryant Vollendorf. We expand on how a theory of fractals used in architecture may continue to be developed and applied as a critical tool in analyzing historic and contemporary architecture as well as a creative framework for designing new architectural solutions to better address the complex world we live in.", notes = "Supervisor: Wayne Wakeland", } @Article{Driscoll:2021:NNJ, author = "John Charles Driscoll", title = "Fractal Patterns as Fitness Criteria in Genetic Algorithms Applied as a Design Tool in Architecture", journal = "Nexus Network Journal", year = "2021", volume = "23", number = "1", pages = "21--37", month = mar, keywords = "genetic algorithms, genetic programming, fractal dimension (FD), Fractal mathematics, Fractal theory, Cybernetics, DBVgen, Vollendorf method", ISSN = "1590-5896", URL = "https://rdcu.be/cFquh", DOI = "doi:10.1007/s00004-020-00490-4", size = "17 pages", abstract = "This paper explores the generative use of a genetic algorithm incorporating a computer-based fractal dimension tool termed DBVgen. Fractals offer a quantitative and qualitative relation between nature, the built environment and computational mechanics and in this paper are explored as a bridge between these realms. The primary objective was to develop and employ a sophisticated analytic tool within a creative context using fractal dimension and the Vollendorf Method. This tool was then tested on a complex case study project and the results discussed. The design process developed for this research showed that the insertion of the DBVgen tool into a traditional schematic design phase was capable of creating unique and compelling compositions and aided in developing high level architectural solutions with respect to various parametric controls and designer feedback. A valuable aspect of this exploration was in positioning the DBVgen tool up front to aid in the creative process and better leverage downstream outcomes.", notes = "Kim Williams Books, Turin, 2020. 1459 Taughannock Blvd, Ithaca, NY, 14850, USA", } @InCollection{driscoll:2003:GPTP, author = "Joseph A. Driscoll and Bill Worzel and Duncan MacLean", title = "Classification of Gene Expression Data with Genetic Programming", booktitle = "Genetic Programming Theory and Practice", publisher = "Kluwer", year = "2003", editor = "Rick L. Riolo and Bill Worzel", chapter = "3", pages = "25--42", keywords = "genetic algorithms, genetic programming, classification, molecular diagnostics", ISBN = "1-4020-7581-2", URL = "http://www.springer.com/computer/ai/book/978-1-4020-7581-0", DOI = "doi:10.1007/978-1-4419-8983-3_3", abstract = "This paper summarises the use of a genetic programming (GP) system to develop classification rules for gene expression data that hold promise for the development of new molecular diagnostics. This work focuses on discovering simple, accurate rules that diagnose diseases based on changes of gene expression profiles within a diseased cell. GP is shown to be a useful technique for discovering classification rules in a supervised learning mode where the biological genotype is paired with a biological phenotype such as a disease state. In the process of developing these rules it is necessary to develop new techniques for establishing fitness and interpreting the results of evolutionary runs because of the large number of independent variables and the comparatively small number of samples. These techniques are described and issues of overfitting caused by small sample sizes and the behaviour of the GP system when variables are missing from the samples are discussed.", notes = "Part of \cite{RioloWorzel:2003}", size = "pages", } @InProceedings{Drobot:2014:ieeeICAQTR, author = "Radu Drobot and Cristian Dinu and Aurelian Draghia and Mary Jeanne Adler and Ciprian Corbus and Marius Matreata", booktitle = "IEEE International Conference on Automation, Quality and Testing, Robotics", title = "Simplified approach for flood estimation and propagation", year = "2014", month = may, abstract = "The river basins and water management systems are characterised by a high degree of complexity. In order to find the best operation rules of the reservoirs during floods all other time consuming data processing (like flood wave propagation) should be simplified at maximum. For this purpose, the Genetic Programming (GP) approach was used. The GP transfer functions derived for flood propagation provide an excellent agreement with the floods propagation based on Saint Venant equations, but without time and other resources consuming. The synthetic floods of the ungauged tributaries keeping the same probability of exceedance along the main river were derived using regionalization studies. The proposed approach was tested for Sitna and Miletin river, two main tributaries of Jijia river.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/AQTR.2014.6857921", notes = "Dept. of Hydrotechnic Eng., Tech. Univ. of Civil Eng., Bucharest, Romania Also known as \cite{6857921}", } @InProceedings{drost:2000:mbea, author = "Stefan Droste and Dirk Wiesmann", title = "Metric Based Evolutionary Algorithms", booktitle = "Genetic Programming, Proceedings of EuroGP'2000", year = "2000", editor = "Riccardo Poli and Wolfgang Banzhaf and William B. Langdon and Julian F. Miller and Peter Nordin and Terence C. Fogarty", volume = "1802", series = "LNCS", pages = "29--43", address = "Edinburgh", publisher_address = "Berlin", month = "15-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-67339-3", DOI = "doi:10.1007/978-3-540-46239-2_3", abstract = "In this article a set of guidelines for the design of genetic operators and the representation of the phenotype space is proposed. These guidelines should help to systematize the design of problem-specific evolutionary algorithms. Hence, they should be particularly beneficial for the design of genetic programming systems. The applicability of this concept is shown by the systematic design of a genetic programming system for finding Boolean functions. This system is the first GP-system, that reportedly found the 12 parity function.", notes = "EuroGP'2000, part of \cite{poli:2000:GP}", } @InProceedings{Droste:1997:eGPbf, author = "Stefan Droste", title = "Efficient Genetic Programming for Finding Good Generalizing {Boolean} Functions", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming", pages = "82--87", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", URL = "https://eldorado.uni-dortmund.de/dspace/bitstream/2003/5323/1/gp97.pdf", URL = "http://citeseer.ist.psu.edu/326196.html", size = "pages", abstract = "This paper shows how genetic programming (GP) can help in finding generalizing Boolean functions when only a small part of the function values are given. The selection pressure favours functions having as few subfunctions as possible while only using essential variables, so the resulting functions should have good generalization properties. For efficiency no S-expressions are used for representation, but a special case of directed acyclic graphs known as ordered binary decision diagrams (OBDDs), making it possible to learn the 20-multiplexer.", notes = "GP-97", } @InProceedings{droste:1998:GPgq, author = "Stefan Droste", title = "Genetic Programming with Guaranteed Quality", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "54--59", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-548-7", URL = "https://eldorado.uni-dortmund.de/dspace/bitstream/2003/5321/2/ci1597_doc.pdf", URL = "http://citeseer.ist.psu.edu/324287.html", abstract = "When using genetic programming (GP) or other techniques that try to approximate unknown functions, the principle of Occam's razor is often applied: find the simplest function that explains the given data, as it is assumed to be the best approximation for the unknown function. Using a well-known result from learning theory, it is shown in this paper, how Occam's razor can help GP in finding functions, so that the number of functions that differ from the unknown function by more than a certain degree can be bounded theoretically. Experiments show how these bounds can be used to get guaranteed quality assurances for practical applications, even though they are much too conservative.", notes = "GP-98", } @TechReport{oai:CiteSeerPSU:323494, author = "Stefan Droste and Dirk Wiesmann", title = "On Representation and Genetic Operators in Evolutionary Algorithms", institution = "Collaborative Research Center 531, University of Dortmund", year = "1998", type = "Computational Intelligence", number = "CI-41/98", address = "Germany", month = jul, keywords = "genetic algorithms, genetic programming", URL = "https://eldorado.uni-dortmund.de/bitstream/2003/5341/2/ci4198_doc.pdf", URL = "http://citeseer.ist.psu.edu/323494.html", citeseer-isreferencedby = "oai:CiteSeerPSU:307380; oai:CiteSeerPSU:329669; oai:CiteSeerPSU:566191; oai:CiteSeerPSU:446781; oai:CiteSeerPSU:503477; oai:CiteSeerPSU:307655; oai:CiteSeerPSU:535886; oai:CiteSeerPSU:533810; oai:CiteSeerPSU:429223; oai:CiteSeerPSU:568622; oai:CiteSeerPSU:410451; oai:CiteSeerPSU:560526; oai:CiteSeerPSU:390701; oai:CiteSeerPSU:245841; oai:CiteSeerPSU:467673; oai:CiteSeerPSU:437423; oai:CiteSeerPSU:499412; oai:CiteSeerPSU:544364; oai:CiteSeerPSU:442759; oai:CiteSeerPSU:425758; oai:CiteSeerPSU:491280; oai:CiteSeerPSU:458877; oai:CiteSeerPSU:376503; oai:CiteSeerPSU:320772; oai:CiteSeerPSU:311105; oai:CiteSeerPSU:564187; oai:CiteSeerPSU:503375; oai:CiteSeerPSU:279898; oai:CiteSeerPSU:531371; oai:CiteSeerPSU:443995; oai:CiteSeerPSU:326622; oai:CiteSeerPSU:447917; oai:CiteSeerPSU:501036; oai:CiteSeerPSU:551069; oai:CiteSeerPSU:534318; oai:CiteSeerPSU:412981; oai:CiteSeerPSU:525337; oai:CiteSeerPSU:431041; oai:CiteSeerPSU:39575; oai:CiteSeerPSU:545421; oai:CiteSeerPSU:409722; oai:CiteSeerPSU:551823; oai:CiteSeerPSU:422170", citeseer-isreferencedby = "oai:CiteSeerPSU:479554; oai:CiteSeerPSU:303654; oai:CiteSeerPSU:543930; oai:CiteSeerPSU:539151; oai:CiteSeerPSU:330474; oai:CiteSeerPSU:563371; oai:CiteSeerPSU:501369; oai:CiteSeerPSU:297037; oai:CiteSeerPSU:372595; oai:CiteSeerPSU:420664; oai:CiteSeerPSU:462007; oai:CiteSeerPSU:459145; oai:CiteSeerPSU:341471; oai:CiteSeerPSU:550055", citeseer-references = "oai:CiteSeerPSU:21876; oai:CiteSeerPSU:250966; oai:CiteSeerPSU:311874; oai:CiteSeerPSU:326196; oai:CiteSeerPSU:324287; oai:CiteSeerPSU:32327; oai:CiteSeerPSU:265991; oai:CiteSeerPSU:126133; oai:CiteSeerPSU:347272; oai:CiteSeerPSU:125144", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:323494", rights = "unrestricted", abstract = "The application of evolutionary algorithms (EAs) requires as a basic design decision the choice of a suitable representation of the variable space and appropriate genetic operators. In practice mainly problemspecific representations with specific genetic operators and miscellaneous extensions can be observed. In this connection it attracts attention that hardly any formal requirements on the genetic operators are stated. In this article we first formalize the representation problem and then propose a package of requirements to guide the design of genetic operators. By the definition of distance measures on the geno- and phenotype space it is possible to integrate problem-specific knowledge into the genetic operators. As an example we show how this package of requirements can be used to design a genetic programming (GP) system for finding Boolean functions.", size = "34 pages", } @InProceedings{droste:1999:PNFLBALFA, author = "Stefan Droste and Thomas Jansen and Ingo Wegener", title = "Perhaps Not a Free Lunch But At Least a Free Appetizer", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "833--839", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "evolution strategies and evolutionary programming", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/droste98perhaps.pdf", URL = "http://arc.cs.odu.edu:8080/dp9/getrecord/oai_dc/3050294235/oai:eldorado:0x00000307", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Misc{oai:CiteSeerPSU:411824, author = "Stefan Droste and Dominic Heutelbeck and Ingo Wegener", title = "Distributed Hybrid Genetic programming for learning {Boolean} Functions", institution = "Department of Computer Science/XI, University of Dortment", year = "2000", number = "CI-90/00", address = "44221 Dortmund, Germany", month = aug, keywords = "genetic algorithms, genetic programming", URL = "https://eldorado.uni-dortmund.de/dspace/bitstream/2003/5393/1/ci90.pdf", URL = "http://citeseer.ist.psu.edu/411824.html", abstract = "When genetic programming (GP) is used to find programs with Boolean inputs and outputs, ordered binary decision diagrams (OBDDs) are often used successfully. In all known OBDD-based GP-systems the variable ordering, a crucial factor for the size of OBDDs, is preset to an optimal ordering of the known test function. Certainly this cannot be done in practical applications, where the function to learn and hence its optimal variable ordering are unknown. Here, the first GP-system is presented that evolves the variable ordering of the OBDDs and the OBDDs itself by using a distributed hybrid approach. For the experiments presented the unavoidable size increase compared to the optimal variable ordering is quite small. Hence, this approach is a big step towards learning well-generalizing Boolean functions.", size = "pages", } @InProceedings{DrostePPSN2000, author = "Stefan Droste and Dominic Heutelbeck and Ingo Wegener", title = "Distributed Hybrid Genetic Programming for Learning {Boolean} Functions", booktitle = "Parallel Problem Solving from Nature - PPSN VI 6th International Conference", editor = "Marc Schoenauer and Kalyanmoy Deb and G{\"u}nter Rudolph and Xin Yao and Evelyne Lutton and Juan Julian Merelo and Hans-Paul Schwefel", year = "2000", publisher = "Springer Verlag", address = "Paris, France", month = "16-20 " # sep, volume = "1917", series = "LNCS", pages = "181--190", keywords = "genetic algorithms, genetic programming", URL = "http://ls2-www.cs.uni-dortmund.de/~wegener/papers/Paper93.ps", URL = "http://eldorado.uni-dortmund.de/0x81d98002_0x00034a39", URL = "http://citeseer.ist.psu.edu/322232.html", abstract = "When genetic programming (GP) is used to find programs with Boolean inputs and outputs, ordered binary decision diagrams (OBDDs) are often used successfully. In all known OBDD-based GP-systems the variable ordering, a crucial factor for the size of OBDDs, is preset to an optimal ordering of the known test function. Certainly this cannot be done in practical applications, where the function to learn and hence its optimal variable ordering are unknown. Here, the first GP-system is presented that evolves the variable ordering of the OBDDs and the OBDDs itself by using a distributed hybrid approach. For the experiments presented the unavoidable size increase compared to the optimal variable ordering is quite small. Hence, this approach is a big step towards learning well-generalizing Boolean functions", } @InCollection{droste:2003:ACI, author = "Stefan Droste and Thomas Jansen and G{\"u}nter Rudolph and Hans-Paul Schwefel and Karsten Tinnefeld and Ingo Wegener", title = "Theory of Evolutionary Algorithms and Genetic Programming", booktitle = "Advances in Computational Intelligence: Theory and Practice", publisher = "Springer", year = "2003", editor = "Hans-Paul Schwefel and Ingo Wegener and Klaus Weinert", series = "Natural Computing Series", chapter = "5", pages = "107--144", keywords = "genetic algorithms, genetic programming, NFL, Evolutionary Algorithms, Multiobjective Evolutionary Algorithms, Crossover, Takeover Times", ISBN = "3-540-43269-8", URL = "http://www.springer.com/computer/ai/book/978-3-540-43269-2", DOI = "doi:10.1007/978-3-662-05609-7_5", abstract = "Randomised search heuristics are an alternative to specialised and problem-specific algorithms. They are applied to NP-hard problems with the hope of being efficient in typical cases. They are an alternative if no problem-specific algorithm is available. And they are the only choice in black-box optimisation where the function to be optimised is not known. Evolutionary algorithms (EA) are a special class of randomised algorithms with many successful applications. However, the theory of evolutionary algorithms is in its infancy. Here many new contributions to constructing such a theory are presented and discussed.", notes = "Dynamization and Adaptation. Black-box Optimisation. Metric-Based EA (MBEA) and an Application in GP", } @Article{Drstvensek:2004:JMPT, author = "I. Drstvensek and I. Pahole and J. Balic", title = "A model of data flow in lower CIM levels", journal = "Journal of Materials Processing Technology", year = "2004", volume = "157-158", pages = "123--130", abstract = "After years of work in fields of computer-integrated manufacturing (CIM), flexible manufacturing systems (FMS), and evolutionary optimisation techniques, several models of production automation were developed in our laboratories. The last model pools the discoveries that proved their effectiveness in the past models. It is based on the idea of five levels CIM hierarchy where the technological database (TDB) represents a backbone of the system. Further on the idea of work operation determination by an analyse of the production system is taken out of a model for FMS control system, and finally the approach to the optimisation of production is supported by the results of evolutionary based techniques such as genetic algorithms and genetic programming.", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6TGJ-4DTM097-5/2/79f4a5e8d987732d6aaad71154b9cf18", month = "20 " # dec, keywords = "genetic algorithms, genetic programming", ISSN = "0924-0136", DOI = "doi:10.1016/j.jmatprotec.2004.09.010", } @InProceedings{Drstvensek:2005:TMT, author = "Igor Drstvensek and Tomaz Brajlih and Miha Kovacic and Joze Balic", title = "Assurance of Accuracy at Polymerisation of Photopolymers", booktitle = "9th International Research/Expert Conference Trends in the Development Machinery and Associated Technology", year = "2005", editor = "Sabahudin Ekinovic", pages = "677--680", address = "Antalya, Turkey", month = "26-30 " # sep, organisation = "UNIVERSITY OF ZENICA (Bosnia and Herzegovina) FACULTY OF MECHANICAL ENGINEERING ZENICA UNIVERSITAT POLITECNICA DE CATALUNYA (Spain) E.T.S.E.I.B. DEPARTAMENT D'ENGINYERIA MECANICA BAHCESEHIR UNIVERSITESI ISTANBUL (Turkey) MUHENDISLIK FAKULTESI", keywords = "genetic algorithms, genetic programming", ISBN = "9958-617-28-5", notes = " TMT05-107 Broken Aug 2018 http://www.mf.unze.ba/tmt2005/submitted3.html", } @Article{Drugan:2019:SwarmEC, author = "Madalina M. Drugan", title = "Reinforcement learning versus evolutionary computation: A survey on hybrid algorithms", journal = "Swarm and Evolutionary Computation", year = "2019", volume = "44", pages = "228--246", month = feb, keywords = "genetic algorithms, genetic programming, Reinforcement learning, Evolutionary computation, Natural paradigms, Hybrid algorithms, Survey", ISSN = "2210-6502", DOI = "doi:10.1016/j.swevo.2018.03.011", URL = "http://www.sciencedirect.com/science/article/pii/S2210650217302766", abstract = "A variety of Reinforcement Learning (RL) techniques blends with one or more techniques from Evolutionary Computation (EC) resulting in hybrid methods classified according to their goal, new focus, and their component methodologies. We denote this class of hybrid algorithmic techniques as the evolutionary computation versus reinforcement learning (ECRL) paradigm. This overview considers the entire spectrum of algorithmic aspects and proposes a novel methodology that analyses the technical resemblances and differences in ECRL. Our design analyses the motivation for each ECRL paradigm, the underlying natural models, the sub-component algorithmic techniques, as well as the properties of their ensemble.", notes = "Also known as \cite{DRUGAN2019228}", } @InProceedings{1277278, author = "Jan Drugowitsch and Alwyn M. Barry", title = "Mixing independent classifiers", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "1596--1603", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1596.pdf", DOI = "doi:10.1145/1276958.1277278", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, information fusion, learning classifier system (LCS), XCS", abstract = "In this study we deal with the mixing problem, which concerns combining the prediction of independently trained local models to form a global prediction. We deal with it from the perspective of Learning Classifier Systems where a set of classifiers provide the local models. Firstly, we formalise the mixing problem and provide both analytical and heuristic approaches to solving it. The analytical approaches are shown to not scale well with the number of local models, but are nevertheless compared to heuristic models in a set of function approximation tasks. These experiments show that we can design heuristics that exceed the performance of the current state-of-the-art Learning Classifier System XCS, and are competitive when compared to analytical solutions. Additionally, we provide an upper bound on the prediction errors for the heuristic mixing approaches.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @InProceedings{Drunpob:2005:WWERC, author = "A. Drunpob and N. B. Chang and M. Beaman", title = "Stream Flowrate Prediction Using Genetic Programming Model in a Semi-Arid Coastal Watershed", booktitle = "World Water and Environmental Resources Congress 2005", year = "2005", editor = "Raymond Walton", address = "Anchorage, Alaska, USA", month = may # " 15-19", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1061/40792(173)352", abstract = "Effective water resources management is a critically important priority across the globe. The availability of adequate fresh water is a fundamental requirement for the sustainability of human and terrestrial landscapes, and the importance of understanding and improving predictive capacity regarding all aspects of the global and regional water cycle is certain to continue to increase. One fundamental component of the water cycle is stream discharge. Stream flowrate prediction is not only related to regular water supply for human, animal, and plant populations, but also relevant for the management of natural hazards, such as drought and flood, that occur abruptly resulting in economic loss. Efforts to improve existing methods and develop new methods of stream flow prediction would support the optimal management of water resources at all scales in space and time. Recent advances in genetic programming technologies have shown potential to improve the prediction accuracy of stream flow rate in some river systems by better capturing the non-linearity of the features embedded in a system. This study elicits microclimatological factors in association with the basin-wide geological environment, exhibits the derivation of a representative genetic programming model, summarises the non-linear behaviour between the rainfall/run-off patterns, and conducts stream flow rate prediction in a river system given the influence of dynamic basin features such as soil moisture, soil texture, vegetative cover, air temperature, and precipitation rate. Three weather stations are deployed as a supplementary data-gathering network in addition to over 10 existing gage stations in the semi-arid Nueces River Basin, South Texas. An integrated database of physical basin features is developed and used to support a semi-structure genetic programming modelling approach to perform stream flowrate predictions. The genetic programming model is eventually proved useful in forecasting stream flowrate in the study area where water resources scarce issues are deemed critical.", notes = "c2005 ASCE", } @Article{journals/isse/Drusinsky17a, title = "Reverse engineering concurrent {UML} state machines using black box testing and genetic programming", author = "Doron Drusinsky", journal = "Innovations in Systems and Software Engineering", year = "2017", number = "2-3", volume = "13", pages = "117--128", keywords = "genetic algorithms, genetic programming, SBSE", bibdate = "2017-09-21", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/isse/isse13.html#Drusinsky17a", DOI = "doi:10.1007/s11334-017-0299-9", abstract = "This paper presents a technique for reverse engineering, a software system generated from a concurrent unified modelling language state machine implementation. In its first step, a primitive sequential finite-state machine (FSM) is deduced from a sequence of outputs emitted from black box tests applied to the systems input interface. Next, we provide an algorithmic technique for decomposing the sequential primitive FSM into a set of concurrent (orthogonal) primitive FSMs. Lastly, we show a genetic programming machine learning technique for discovering local variables, actions performed on local and non-binary output variables, and two types of intra-FSM loops, called counting-loops and while-loops.", } @Article{Drust-Nacarino_2015_Antioquia, author = "Ariadne Sofia Drust-Nacarino and Maritza Liliana Arganis-Juarez and Rodolfo Silva-Casarin and Edgar Gerardo Mendoza-Baldwin and Oscar Arturo Fuentes-Mariles", title = "Drought and genetic programming to approach annual agriculture production normalized curves", title2 = "Sequia y programacion genetica para aproximar curvas normalizadas de produccion agricola anual", journal = "Revista Facultad de Ingenieria Universidad de Antioquia", year = "2015", number = "77", pages = "63--74", month = oct # "--" # dec, keywords = "genetic algorithms, genetic programming, Drought, agricultural production, regionalisation, economic loss", ISSN = "0120-6230", URL = "http://www.scielo.org.co/scielo.php?pid=S0120-62302015000400009&script=sci_abstract", URL = "http://www.scielo.org.co/pdf/rfiua/n77/n77a09.pdf", DOI = "doi:10.17533/udea.redin.n77a09", size = "12 pages", abstract = "Drought is a severe, recurrent disaster for Mexican agriculture, causing huge economic losses, which could be reduced if appropriate planning and policies were carried out and the production loss could be predicted. This paper presents the application of a genetic programming scheme to obtain normalized curves of annual agricultural production for each state in Mexico as a function of the return period of drought events and, from them, compute the normalized value of the yearly production. This value, multiplied by the historic mean production of the state, gives the production expressed in Mexican pesos for a specified return period. Two techniques were used for this data analysis, the first one is general and considers each state separately; for the second technique the country was divided into six groups, depending on the value of the agricultural production variation coefficient. The results showed that for the first case large dispersion was found between the reported and computed data, while a better fit was found for the groups; specifically for groups 2, 3 and 6. The resulting functions can be used by decision makers at both federal and state levels, to better deal with drought events.", resumen = "La sequia es un severo desastre, recurrente para la agricultura mexicana, que causa enormes perdidas economicas que podrian reducirse si se contara con politicas y planeacion adecuadas y se pudiera predecir la reduccion en la produccion ante su ocurrencia. En este estudio se presenta la aplicacion de un esquema de programacion genetica para obtener curvas normalizadas de produccion agricola anual para cada estado de la Republica Mexicana en funcion del periodo de retorno de eventos de sequias y, a partir de ellas, estimar el valor normalizado de la produccion anual. Este valor al ser multiplicado por la media historica de la produccion en el estado, proporciona la produccion expresada en pesos mexicanos para un periodo de retorno especifi co. Dos tecnicas fueron utilizadas para este analisis de datos, la primera es general e incluye cada estado por separado; en la segunda tecnica el pais fue dividido en seis grupos, dependiendo del valor del coefi ciente de variacion de la produccion agricola. Los resultados mostraron que en el primer caso se tiene una gran dispersion entre los datos medidos y calculados, mientras que se hallo un mejor ajuste cuando se utilizaron grupos; especialmente en los grupos 2, 3 y 6. Las funciones encontradas pueden utilizarse por los tomadores de decisiones tanto a nivel estatal como a nivel federal, para abordar los eventos de sequia.", notes = "In English. Universidad Nacional Autonoma de Mexico. Ciudad Universitaria. C. P. 04510. Mexico, D. F., Mexico. DOI broken Dec 2020 revista.ingenieria@udea.edu.co https://revistas.udea.edu.co/index.php/ingenieria ", } @InProceedings{DuDXXWC:2009:GEC, author = "Xin Du and Lixin Ding and Chen Wang Xie and Xing Xu and Shenwen Wang and Li Chen2", title = "Convergence analysis of gene expression programming based on maintaining elitist", booktitle = "GEC '09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation", year = "2009", editor = "Lihong Xu and Erik D. Goodman and Guoliang Chen and Darrell Whitley and Yongsheng Ding", bibsource = "DBLP, http://dblp.uni-trier.de", pages = "823--826", address = "Shanghai, China", organisation = "SigEvo", DOI = "doi:10.1145/1543834.1543952", publisher = "ACM", publisher_address = "New York, NY, USA", month = jun # " 12-14", isbn13 = "978-1-60558-326-6", keywords = "genetic algorithms, genetic programming, Poster, Gene Expression Programming", abstract = "This paper analyzes the convergence of Gene Expression Programming based on maintaining elitist(ME-GEP).It is proved that ME-GEP algorithm will converge to the global optimal solution. The convergence speed of ME-GEP algorithm is estimated by the properties of transition matrices. The result hinges on four factors: population size, minimal transposition, mutation and selection probabilities.", notes = "Also known as \cite{DBLP:conf/gecco/DuDXXWC09} part of \cite{DBLP:conf/gec/2009}", } @Article{journals/chinaf/DuD10, title = "About the convergence rates of a class of gene expression programming", author = "Xin Du and Lixin Ding", journal = "SCIENCE CHINA Information Sciences", year = "2010", volume = "53", number = "4", pages = "715--728", month = apr, keywords = "genetic algorithms, genetic programming, gene expression programming, ME-GEP, convergence rates, Markov chain, revised spectral radius", DOI = "doi:10.1007/s11432-010-0041-9", size = "14 pages", abstract = "This paper studies the convergence rates of gene expression programming based on maintaining elitist (ME-GEP) by means of Markov chain and spectrum analysis. We obtain the following results: (1) MEGEP algorithm converges to the global optimum in probability. (2) The convergence rates of ME-GEP algorithm depend on the revised spectral radius of transition matrix of Markov chain corresponding to the algorithm. (3) The upper bounds of revised spectral radius are estimated, which are determined by the parameters of MEGEP algorithm. (4) As an application of the theoretical results acquired in the paper, the convergence rates of ME-GEP for the polynomial function modelling problem are also analysed, which verifies the relations between the convergence rates and the algorithm parameters.", bibdate = "2011-06-08", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/chinaf/chinaf53.html#DuD10", } @Article{journals/soco/DuNXYYX15, author = "Xin Du and Youcong Ni and Datong Xie and Xin Yao and Peng Ye and Ruliang Xiao", title = "The time complexity analysis of a class of gene expression programming", journal = "Soft Comput", year = "2015", number = "6", volume = "19", bibdate = "2015-05-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/soco/soco19.html#DuNXYYX15", pages = "1611--1625", keywords = "genetic algorithms, genetic programming, gene expression programming", URL = "http://dx.doi.org/10.1007/s00500-014-1551-y", } @InProceedings{conf/adma/DuanTZWZ06, title = "Distance Guided Classification with Gene Expression Programming", author = "Lei Duan and Changjie Tang and Tianqing Zhang and Dagang Wei and Huan Zhang", booktitle = "Advanced Data Mining and Applications, Proceedings of the Second International Conference, {ADMA}", publisher = "Springer", year = "2006", volume = "4093", editor = "Xue Li and Osmar R. Za{\"i}ane and Zhanhuai Li", pages = "239--246", series = "Lecture Notes in Computer Science", address = "Xi'an, China", month = aug # " 14-16", bibdate = "2006-08-21", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/adma/adma2006.html#DuanTZWZ06", keywords = "genetic algorithms, genetic programming, Gene Expression Programming", ISBN = "3-540-37025-0", DOI = "doi:10.1007/11811305_26", abstract = "Gene Expression Programming (GEP) aims at discovering essential rules hidden in observed data and expressing them mathematically. GEP has been proved to be a powerful tool for constructing efficient classifiers. Traditional GEP-classifiers ignore the distribution of samples, and hence decrease the efficiency and accuracy. The contributions of this paper include: (1) proposing two strategies of generating classification threshold dynamically, (2) designing a new approach called Distance Guided Evolution Algorithm (DGEA) to improve the efficiency of GEP, and (3) demonstrating the effectiveness of generating classification threshold dynamically and DGEA by extensive experiments. The results show that the new methods decrease the number of evolutional generations by 83percent to 90percent, and increase the accuracy by 20percent compared with the traditional approach.", } @InProceedings{conf/icnc/DuanTTZZ09, title = "An Effective Microarray Data Classifier Based on Gene Expression Programming", author = "Lei Duan and Changjie Tang and Liang Tang and Jie Zuo and Tianqing Zhang", booktitle = "Fifth International Conference on Natural Computation, 2009. ICNC '09", year = "2009", editor = "Haiying Wang and Kay Soon Low and Kexin Wei and Junqing Sun", month = "14-16 " # aug, address = "Tianjian, China", publisher = "IEEE Computer Society", isbn13 = "978-0-7695-3736-8", keywords = "genetic algorithms, genetic programming, gene expression programming", bibdate = "2010-01-22", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icnc/icnc2009-4.html#DuanTTZZ09", pages = "523--527", DOI = "doi:10.1109/ICNC.2009.267", } @InProceedings{conf/adma/DuanTTZZ09, title = "Mining Class Contrast Functions by Gene Expression Programming", author = "Lei Duan and Changjie Tang and Liang Tang and Tianqing Zhang and Jie Zuo", booktitle = "Proceedings 5th International Conference Advanced Data Mining and Applications {ADMA} 2009", year = "2009", volume = "5678", editor = "Ronghuai Huang and Qiang Yang and Jian Pei and Jo{\~a}o Gama and Xiaofeng Meng and Xue Li", pages = "116--127", series = "Lecture Notes in Computer Science", address = "Beijing, China", month = aug # " 17-19", publisher = "Springer", keywords = "genetic algorithms, genetic programming, gene expression programming", isbn13 = "978-3-642-03347-6", DOI = "doi:10.1007/978-3-642-03348-3", bibdate = "2009-08-18", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/adma/adma2009.html#DuanTTZZ09", } @Article{Duan:2024:cor, author = "Jianguo Duan and Fanfan Liu and Qinglei Zhang and Jiyun Qin", title = "Tri-objective lot-streaming scheduling optimization for hybrid flow shops with uncertainties in machine breakdowns and job arrivals using an enhanced genetic programming hyper-heuristic", journal = "Computer and Operations Research", year = "2024", volume = "172", pages = "106817", keywords = "genetic algorithms, genetic programming, Genetic programming hyper-heuristic, Dynamic disturbances, Hybrid flow shop, Lot-streaming scheduling, Harmony search", ISSN = "0305-0548", URL = "https://www.sciencedirect.com/science/article/pii/S0305054824002892", DOI = "doi:10.1016/j.cor.2024.106817", abstract = "Lot-streaming scheduling has been widely recognised as a means to improve shop productivity, but there is few research on lot-streaming scheduling problems under dynamic disturbances. To fill the gap, lot-streaming scheduling optimisation approach for hybrid flow shops with uncertainties in machine breakdowns and job arrivals is proposed. A mathematical model is formulated with objectives of minimizing maximum tardiness, total idle energy consumption of the machine, and maximum makespan. Since the Genetic Programming Hyper Heuristic algorithm has better results in solving dynamic scheduling problems, a Collaborative Harmony Search-based Genetic Programming Hyper Heuristic (CHS-GPHH) is presented to solve the dynamic lot-streaming hybrid flow shop scheduling problem (DLS-HFSSP) with the two dynamic events occurring simultaneously. In the improved algorithm, a neighbourhood structure based on harmony search is developed for lot splitting. To verify the effectiveness of the proposed approach, the various comparative studies c are conducted on the lot-streaming dynamic hybrid flow shop scheduling. The results demonstrate the effectiveness of each improvement component of the CHS-GPHH, and verify that CHS-GPHH is an effective approach to deal with DLS-HFSSP with the in all the scenarios", } @Article{Duan:2024:eswa, author = "Jianguo Duan and Fanfan Liu and Qinglei Zhang and Jiyun Qin and Ying Zhou", title = "Genetic programming hyper-heuristic-based solution for dynamic energy-efficient scheduling of hybrid flow shop scheduling with machine breakdowns and random job arrivals", journal = "Expert Systems with Applications", year = "2024", volume = "254", pages = "124375", keywords = "genetic algorithms, genetic programming, Hybrid flow shop, Dynamic energy-efficient scheduling, Genetic programming hyper-heuristic, Terminal sets, Multi-objective", ISSN = "0957-4174", URL = "https://www.sciencedirect.com/science/article/pii/S0957417424012417", DOI = "doi:10.1016/j.eswa.2024.124375", abstract = "Aiming at the lack of scientific methods for solving the dynamic energy-efficient scheduling problem of hybrid flow shop using scheduling rules, this paper proposes a method that can automatically generate scheduling rules based on the processing information of the shop. Firstly, a multi-objective mathematical model with the objective of minimizing the maximum tardiness, machine idle energy consumption and maximum makespan is established by combining two dynamic events, namely, machine breakdowns and random job arrivals. Secondly, a genetic programming hyper-heuristic algorithm, using terminal sets to generate high-level scheduling rules, is employed for the dynamic energy-efficient hybrid flow shop scheduling problem. Considering dynamic energy-efficient scheduling of the shop, terminal sets for two dynamic events and energy-efficient objects are designed, and the performance of the scheduling rules is improved by assigning weight coefficients to each terminal. Finally, comparisons of the scheduling rules generated by the proposed method and the benchmark scheduling rules are conducted in 36 scenarios. The result demonstrate that the algorithm has a high degree of flexibility and adaptability", } @Article{DUAN:2024:isci, author = "Junwei Duan and Yuxuan Wang and Long Chen and C. L. Philip Chen and Ronghua Zhang", title = "Employing broad learning and non-invasive risk factor to improve the early diagnosis of metabolic syndrome", journal = "iScience", volume = "27", number = "1", pages = "108644", year = "2024", ISSN = "2589-0042", DOI = "doi:10.1016/j.isci.2023.108644", URL = "https://www.sciencedirect.com/science/article/pii/S2589004223027219", keywords = "genetic algorithms, genetic programming, Risk factor, Human metabolism, Machine learning", abstract = "Metabolic syndrome (MetS) as a multifactorial disease is highly prevalent in countries and individuals. Monitoring the conventional risk factors (CRFs) would be a cost-effective strategy to target the increasing prevalence of MetS and the potential of noninvasive CRF for precisely detection of MetS in the early stage remains to be explored. From large-scale multicenter MetS clinical dataset, we discover 15 non-invasive CRFs which have strong relevance with MetS and first propose a broad learning-based approach named Genetic Programming Collaborative-competitive Broad Learning System (GP-CCBLS) with noninvasive CRF for early detection of MetS. The proposed GP-CCBLS model can significantly boost the detection performance and achieve the accuracy of 80.54percent. This study supports the potential clinical validity of noninvasive CRF to complement general diagnostic criteria for early detecting the MetS and also illustrates possible strength of broad learning in disease diagnosis comparing with other machine learning approaches.", } @InProceedings{duan:2001:gecco, title = "Estimating Stock Price Predictability Using Genetic Programming", author = "Minglei Duan and Richard J. Povinelli", pages = "174", year = "2001", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)", editor = "Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon and Edmund Burke", address = "San Francisco, California, USA", publisher_address = "San Francisco, CA 94104, USA", month = "7-11 " # jul, keywords = "genetic algorithms, genetic programming: Poster, time series, data mining, prediction", ISBN = "1-55860-774-9", URL = "http://gpbib.cs.ucl.ac.uk/gecco2001/d02.pdf", notes = "GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of \cite{spector:2001:GECCO}", } @InProceedings{duan_annie2001a, author = "Minglei Duan and Richard Povinelli", title = "Nonlinear Modeling: Genetic Programming vs. Fast Evolutionary Programming", booktitle = "Intelligent Engineering Systems Through Artificial Neural Networks (ANNIE 2001)", year = "2001", editor = "Cihan H. Dagli", pages = "171--176", address = "St. Louis, Missouri, USA", month = "4-7 " # nov, organisation = "University of Missouri-Rolla, Smart Engineering Systems Laboratory Department of Engineering Management In Cooperation with IEEE Neural Networks Council", keywords = "genetic algorithms, genetic programming", URL = "http://povinelli.eece.mu.edu/publications/papers/annie2001a.pdf", notes = "cf http://web.umr.edu/~annie/annie01/ ANNIE01 session TP3.3C Marquette University, Milwaukee, WI, USA GPsys \cite{qureshi:thesis} (java) Sunspot; Mackey-glass; Compaq (NYSE) and microsoft (NASDAQ) stock prices. GP >= EP", } @InProceedings{duan_annie2001b, author = "Minglei Duan and Richard Povinelli", title = "Estimating Time Series Predictability Using Genetic Programming", booktitle = "Intelligent Engineering Systems Through Artificial Neural Networks (ANNIE 2001)", year = "2001", editor = "Cihan H. Dagli", pages = "215--220", address = "St. Louis, Missouri, USA", month = "4-7 " # nov, organisation = "University of Missouri-Rolla, Smart Engineering Systems Laboratory Department of Engineering Management In Cooperation with IEEE Neural Networks Council", keywords = "genetic algorithms, genetic programming", URL = "http://povinelli.eece.mu.edu/publications/papers/annie2001b.pdf", notes = "cf http://web.umr.edu/~annie/annie01/ ANNIE01 session WP1.3C Marquette University, Milwaukee, WI, USA GPsys \cite{qureshi:thesis} (java) Compaq (NYSE) and general eletric GE 1999 stock prices", } @Article{Dubcakova:2011:GPEM, author = "Renata Dubcakova", title = "Eureqa: software review", journal = "Genetic Programming and Evolvable Machines", year = "2011", volume = "12", number = "2", pages = "173--178", month = jun, keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/c5oVx", DOI = "doi:10.1007/s10710-010-9124-z", size = "6 pages", } @InProceedings{dubey:2024:CEC, author = "Rahul Dubey and Simon Hickinbotham and Edgar Buchanan and Andrew Colligan and Imelda Briel and Mark Price and Andy M. Tyrrell", title = "Evolving Design For Engineering Structures", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Simulation, Evolutionary computation, Safety, Automobiles, Biological cells, Accidents, neural networks, ANN, EvoDevo, GRN, engineering design", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10611851", abstract = "In recent years, the evolutionary developmental (Evo- Devo) concept has gained traction in the field of engineering design. This paper presents a new biologically inspired approach rooted in Evo- Devo principles to iteratively develop car chassis designs based on a specified design brief. The proposed method draws inspiration from biological cell growth and differentiation behaviours to generate intricate engineering designs. Employing evolutionary algorithms, the paper aims to evolve gene regulatory networks that govern the growth of a minimal viable design. The primary goal is to achieve an optimal design capable of withstanding sudden crash impacts within safety limits. Comprehensive simulation results demonstrate that the proposed approach, using genetic algorithms, evolves gene regulatory networks that generate a spectrum of viable designs. Furthermore, the best-evolved solution exhibits generalizability and adaptability across different simulation parameters.", notes = "also known as \cite{10611851} WCCI 2024", } @Article{Dubey:2024:AlifeJ, author = "Rahul Dubey and Simon Hickinbotham and Andrew Colligan and Imelda Friel and Edgar Buchanan and Mark Price and Andy M. Tyrrell", title = "Evolving Novel Gene Regulatory Networks for Structural Engineering Designs", journal = "Artificial Life", year = "2024", volume = "30", number = "4", pages = "466--485", month = nov, keywords = "genetic algorithms, genetic programming, Evolutionary search, gene regulatory networks, NEAT, CGP, design optimisation", ISSN = "1064-5462", URL = "https://pure.qub.ac.uk/en/publications/evolving-novel-gene-regulatory-networks-for-structural-engineerin", DOI = "doi:10.1162/artl_a_00448", abstract = "Engineering design optimisation poses a significant challenge, usually requiring human expertise to discover superior solutions. Although various search techniques have been employed to generate diverse designs, their effectiveness is often limited by problem-specific parameter tuning, making them less generalizable and scalable. This article introduces a framework inspired by evolutionary and developmental (evo-devo) concepts, aiming to automate the evolution of structural engineering designs. In biological systems, evo-devo governs the growth of single-cell organisms into multicellular organisms through the use of gene regulatory networks (GRNs). GRNs are inherently complex and highly nonlinear, and this article explores the use of neural networks and genetic programming as artificial representations of GRNs to emulate such behaviours. To evolve a wide range of Pareto fronts for artificial GRNs, this article introduces a new technique, a real value-encoded neuroevolutionary method termed real-encoded NEAT (RNEAT). The performance of RNEAT is compared with that of two well-known evolutionary search techniques across different 2-D and 3-D problems. The experimental results demonstrate two key findings. First, the proposed framework effectively generates a population of GRNs that can produce diverse structures for both 2-D and 3-D problems. Second, the proposed RNEAT algorithm outperforms its competitors on more than 50percent of the problems examined. These results validate the proof of concept underlying the proposed evo-devo-based engineering design evolution.", notes = "Also known as \cite{10791473}", } @Article{Dubreuil:2006:SMC, author = "Marc Dubreuil and Christian Gagne and Marc Parizeau", title = "Analysis of a Master-Slave Architecture for Distributed Evolutionary Computations", journal = "IEEE Transactions on Systems, Man, and Cybernetics: Part B - Cybernetics", year = "2006", volume = "36", number = "1", pages = "229--235", month = feb, keywords = "genetic algorithms, genetic programming, Master-Slave Architecture, Evolutionary Computations, Distributed BEAGLE, C++ language, client-server systems, evolutionary computation, workstation clusters, C++ framework, distributed evolutionary computation, local area workstation networks", ISSN = "1083-4419", URL = "http://vision.gel.ulaval.ca/~parizeau/Publications/SMC06.pdf", DOI = "doi:10.1109/TSMCB.2005.856724", size = "7 pages", abstract = "a new mathematical model of the master-slave architecture for distributed evolutionary computations (EC). This model is validated using a concrete implementation based on the Distributed BEAGLE C++ framework. Results show that contrary to (current) popular belief, master-slave architectures are able to scale well over local area networks of workstations using off-the-shelf networking equipment. The main properties of the master-slave are also compared with those of the more mainstream island-model.", } @InProceedings{conf/iccci/DudaS11, author = "Jerzy Duda and Stanislaw Szydlo", title = "Collective Intelligence of Genetic Programming for Macroeconomic Forecasting", booktitle = "Proceedings of the Third International Conference on Computational Collective Intelligence. Technologies and Applications (ICCCI 2011) Part {II}", year = "2011", editor = "Piotr Jedrzejowicz and Ngoc Thanh Nguyen and Kiem Hoang", volume = "6923", series = "Lecture Notes in Computer Science", pages = "445--454", address = "Gdynia, Poland", month = sep # " 21-23", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-23937-3", DOI = "doi:10.1007/978-3-642-23938-0_45", size = "10 pages", abstract = "A collective approach to the problem of developing forecasts for macroeconomic indicators is presented in the paper. The main advantage of genetic programming over artificial neural networks is that it generates human readable mathematical expressions that can be interpreted by a decision-maker. Gene expression programming used in the paper is an example of collective adaptive system, but we propose to use a collective intelligence to develop not only one forecasting model, but a set of models, from which the most suitable one can be chosen automatically or manually by the decision-maker.", notes = "GDP estimation", affiliation = "Department of Applied Computer Science, AGH University, Poland", bibdate = "2011-09-13", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/iccci/iccci2011-2.html#DudaS11", } @InProceedings{Duela:2023:ICAECT, author = "Shiny Duela J and Umamageswari A and Prabavathi R and Prashanth Umapathy and Raja K", booktitle = "2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)", title = "Quantum assisted Genetic Algorithm for Sequencing Compatible Amino Acids in Drug Design", year = "2023", month = "5-6 " # jan, address = "Bhilai, India", keywords = "genetic algorithms, genetic programming, Drugs, Proteins, Sequential analysis, Qubit, Quantum mechanics, Genomics, Logic gates, Quantum genetic algorithm, Quantum computing, Qubit encoding, Drug design", DOI = "doi:10.1109/ICAECT57570.2023.10117673", size = "7 pages", abstract = "Using quantum computing for drug design is among the most promising applications in quantum technologies. Genetic algorithms with their evolutionary iterations make it a propitious approach in various tasks, including drug discovery, gene prediction, docking of ligands to receptors, and the design to combinatorial libraries. As its computational power and methods limits classical computing, quantum computing intends to break these limits with exponential computing capabilities. This application complements both quantum theory and genetic programming as we use true randomness with mutation and fitness function based on information encoded onto qubits storing quantum data. In this article, we present the results of encoding quantum data to our quantum genetic algorithm, which predicts the best possible drug structure to bind onto the target protein. The qubits hold the genome structure to perform bit string mutation over quantum gates. These results are later than compared to classical computing with various approaches in the evolutionary algorithm parameters.", notes = "Also known as \cite{10117673} Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram Campus, Chennai, India", } @PhdThesis{Duerrenmatt:thesis, author = "David Jerome Duerrenmatt", title = "Data mining and data-driven modelling approaches to support wastewater treatment plant operation", school = "ETH", year = "2011", address = "Zurich, Switzerland", keywords = "genetic algorithms, genetic programming, grammar-based genetic programming", URL = "http://hdl.handle.net/20.500.11850/42293", URL = "https://www.research-collection.ethz.ch/handle/20.500.11850/42293", URL = "https://www.dora.lib4ri.ch/eawag/islandora/object/eawag:13533", URL = "https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/42293/eth-4649-02.pdf", DOI = "doi:10.3929/ethz-a-006717398", size = "118 pages", abstract = "In wastewater treatment plants (WWTPs), much effort and money is invested in operating and maintaining dense plant-wide measuring networks. The network primarily serves as input for the advanced control scenarios that are implemented in the supervisory control and data acquisition (SCADA) system to satisfy the stringent effluent quality constraints. Due to new developments in information technology, long-term archiving has become practicable, and specialized process information systems are now available. The steadily growing amount of plant data available, however, is not systematically exploited for plant optimization because of the lack of specialized tools that allow operators and engineers alike to extract meaningful and valuable information efficiently from the massive amount of high-dimensional data. As a result, most information contained in the data is eventually lost. In the past few years, many data mining techniques have emerged that are capable of analyzing massive amounts of data. Available processing power allowed the development of efficient data-driven modeling techniques especially suited to situations in which the speed of data acquisition surpasses the time available for data analysis. However, although these methods are promising ways to provide valuable information to the operator and engineer,there is currently no fully developed interest in the application of these techniques to support WWTP operation. In this thesis, the applicability of data mining and datadriven modeling techniques in the context of WWTP operation is investigated. This context, however, implies specific characteristics that the adapted and developed techniques must satisfy to be practicable: On the one hand, the deployment of a given technique on a plant must be fast, simple and cost-effective. As a consequence, it must consider data that are already available or that can be gathered easily. On the other hand, the application must be safe, i.e., the extracted information must be reliable and communicated clearly. This thesis presents the results of four knowledge discovery projects that adapted data mining and data-driven modeling techniques to tackle problems relevant to either the operator or the process engineer. First, the extent to which data-driven modeling techniques are suitable for the automatic generation of software sensors exclusively based on measured data available in the SCADA system of the plant is investigated. These software sensors are meant to be substitutes for failure-prone and maintenance-intensive sensors and to diagnose hardware sensors. In two full-scale experiments, four modeling techniques for software-sensor development are compared and the role of expert knowledge is investigated. The investigations show that the non-linear modeling techniques outperform the linear technique and that a higher degree of expert knowledge is beneficial for long term accuracy, but can lead to reduced performance in the short term. Consequently, if frequent model re-calibration is possible, as is the case for sensor diagnosis applications, automatic development given limited expert knowledge is feasible. In contrast, optimum use of expert knowledge requires model transparency, which is only given for two of the investigated techniques: generalized least squares regression and self-organizing maps (SOMs). In the second project, WWTP operators are provided with additional information on characteristic sewage compositions arriving at their plant from clustered UV/Vis spectra measured at the influent. A two-staged clustering approach is considered that copes well with high-dimensional and noisy data. If it is possible to assign a characteristic cluster to a sewage producer in the catchment, detailed analysis of the temporal discharging pattern is possible without the need for additional measurements at the production site. In a full-scale experiment, one of five detected clusters could by assigned to an industrial laundry by analyzing the cluster centroids. In a validation experiment, 93 out of 95 discharging events were classified correctly. Successful detection depends on the uniqueness of the producer UV/Vis pattern,the dilution at the influent and the size and complexity of the catchment. In WWTPs, asymmetric feeding of reactors operating in parallel lanes can lead to operational issues and significant performance losses. A new method based on dynamic time warping is presented that makes the quantification of the discharge distribution at hydraulic flow dividers practicable. The method estimates the discharge distribution as a function of total discharge at the divider given influent and effluent measurements of some measured signal in the downstream reactors. The function can not only serve as the basis for structural modification, but it can also be used to calculate the flow to the individual lanes given the total influent, and thus avoid the assumption of equal distribution (this assumption must often be made by process engineers and scientists). Theoretical analysis reveals that the accuracy of the function depends on the hydraulic residence time, the dispersion and the reactions in the reactors downstream of the divider, in addition to the variability of the signal. A systematic application on a wide range of synthetic systems that may be found on WWTPs shows that the error is at least half that when an equal distribution is assumed if the function is used to obtain a better estimate for the flow to a reactor. In a full scale validation experiment, the discharge distribution could be accurately estimated. The fourth application presented shows that optimal hydraulic reactor models can be searched automatically using grammar-based genetic programming. This method is especially relevant for engineers who want to model the hydraulic processes of the plant and, because of the limited applicability of existing approaches, must rely solely on their experience and intuition for further insights into the reactor hydraulics. With a tree encoding that can decode program trees into hydraulic reactor models compatible with common software and with influent and effluent measurements, a palette of equally performing models can be generated. Of these the modeler then picks the most suitable one as starting point. The methodology is applied to reverse engineer synthetic systems, and because of theoretical and practical identifiability issues, several searches yield different models, which emphasizes the need for an expert to choose the most appropriate model. The method is applied to generate reactor models of a primary clarifier with unknown exact volume. The volume of the resulting models corresponds to the expectation and virtual tracer experiment performed on the synthetic models generally confirms with an experiment performed on-site. The knowledge discovery projects show that optimal model choice and complexity greatly depend on the specific problem and on the degree of available expert knowledge. In general, safe deployment on site requires transparent models that can be interpreted even with limited knowledge and intuitive and understandable communication of the model results. Because the effluent quality constraints will further tighten and progress in the fields of information technology and data analysis will continue, it is necessary to use the available data to fully exploit the plants. Data mining and data driven modeling are suitable tools.", zusammenfassung = "In Klaeranlagen wird viel Aufwand und Geld in den Betrieb und die Wartung eines anlagenweiten Messnetzes gesteckt. Dieses Messnetz dient in erster Linie als Input fuer die Regelungsvorgaenge, die im Prozessleitsystem definiert sind, um die strengen Grenzwerte bezueglich der Ablaufqualitaet zu erfuellen. Neue Errungenschaften im Bereich der Informationstechnologie ermoeglichen nun eine wirtschaftliche Langzeitarchivierung und es stehen mittlerweile sogar spezialisierte Prozessinformationssysteme zur Verfuegung. Allerdings wird die stetig wachsende Menge an verfuegbaren Anlagedaten nicht systematisch ausgenutzt und zur Optimierung der Anlagen herangezogen. Dies ist auf das Fehlen von spezialisierten Instrumenten zurueckzufuehren, die es sowohl Betreibern als auch Ingenieuren ermoeglichten, aussagekraeftige und wertvolle Informationen aus der gewaltigen Menge hochdimensionaler Daten zu extrahieren. Folglich geht ein Grossteil der in den Daten enthaltenen Informationen verloren", notes = "in english. DISS. ETH NO. 19878 Automatic reactor model synthesis with genetic programming. WWTP Kloten/Opfikon Supervisor: Willi Gujer", } @Article{Duerrenmatt:2012:WST, author = "David J. Duerrenmatt and Willi Gujer", title = "Automatic reactor model synthesis with genetic programming", journal = "Water Science \& Technology", year = "2012", volume = "65", number = "4", pages = "765--772", month = "1 " # feb, keywords = "genetic algorithms, genetic programming, grammar-based genetic programming, hydraulic reactor systems, water utility, modelling, operating data", ISSN = "0273-1223", URL = "http://www.iwaponline.com/wst/06504/0765/065040765.pdf", DOI = "doi:10.2166/wst.2012.913", size = "8 pages", abstract = "Successful modelling of waste water treatment plant (WWTP) processes requires an accurate description of the plant hydraulics. Common methods such as tracer experiments are difficult and costly and thus have limited applicability in practice; engineers are often forced to rely on their experience only. An implementation of grammar-based genetic programming with an encoding to represent hydraulic reactor models as program trees should fill this gap: The encoding enables the algorithm to construct arbitrary reactor models compatible with common software used for WWTP modeling by linking building blocks, such as continuous stirred-tank reactors. Discharge measurements and influent and effluent concentrations are the only required inputs. As shown in a synthetic example, the technique can be used to identify a set of reactor models that perform equally well. Instead of being guided by experience, the most suitable model can now be chosen by the engineer from the set. In a second example, temperature measurements at the influent and effluent of a primary clarifier are used to generate a reactor model. A virtual tracer experiment performed on the reactor model has good agreement with a tracer experiment performed on-site.", notes = "Sewage treatment plant", } @InProceedings{Dufek:2013:CEC, article_id = "1611", author = "Amanda Sabatini Dufek and Douglas Adriano Augusto and Pedro Leite {da Silva Dias} and Helio Jose Correa Barbosa", title = "Evaluating the Feasibility of Grammar-based GP in Combining Meteorological Forecast Models", booktitle = "2013 IEEE Conference on Evolutionary Computation", volume = "1", year = "2013", month = jun # " 20-23", editor = "Luis Gerardo {de la Fraga}", pages = "32--39", address = "Cancun, Mexico", keywords = "genetic algorithms, genetic programming, grammatical evolution", isbn13 = "978-1-4799-0453-2", DOI = "doi:10.1109/CEC.2013.6557550", size = "7 pages", abstract = "The purpose of this paper is to evaluate the feasibility of grammatical evolution (GE) in combining meteorological models into more accurate single forecast of rainfall amount. A set of GE experiments was performed comparing six proposed ensemble forecast grammars on three benchmark problems. We also proposed a manner of designing benchmark problems by creating arbitrary combinations of meteorological models, as well as modelling the effect of weather patterns over models as explicit functions. The results showed that the GE algorithm obtained a superior performance relative to three traditional statistical methods for all the benchmark problems. A comparison among the developed grammars showed that our most complex grammar, which allows non-linear combinations of models and an unrestricted use of patterns, turned out to be the overall best performing proposal.", notes = "CEC 2013 - A joint meeting of the IEEE, the EPS and the IET.", } @Article{DUFEK2017139, author = "Amanda S. Dufek and Douglas A. Augusto and Pedro L. S. Dias and Helio J. C. Barbosa", title = "Application of evolutionary computation on ensemble forecast of quantitative precipitation", journal = "Computers {\&} Geosciences", year = "2017", volume = "106", pages = "139--149", keywords = "genetic algorithms, genetic programming, Ensemble weather forecast, Quantitative precipitation, Evolutionary computation", ISSN = "0098-3004", URL = "http://www.sciencedirect.com/science/article/pii/S0098300417306507", DOI = "doi:10.1016/j.cageo.2017.06.011", abstract = "An evolutionary computation algorithm known as genetic programming (GP) has been explored as an alternative tool for improving the ensemble forecast of 24-h accumulated precipitation. Three GP versions and six ensembles' languages were applied to several real-world datasets over southern, south-eastern and central Brazil during the rainy period from October to February of 2008-2013. According to the results, the GP algorithms performed better than two traditional statistical techniques, with errors 27-57percent lower than simple ensemble mean and the MASTER super model ensemble system. In addition, the results revealed that GP algorithms outperformed the best individual forecasts, reaching an improvement of 34-42percent. On the other hand, the GP algorithms had a similar performance with respect to each other and to the Bayesian model averaging, but the former are far more versatile techniques. Although the results for the six ensembles languages are almost indistinguishable, our most complex linear language turned out to be the best overall proposal. Moreover, some meteorological attributes, including the weather patterns over Brazil, seem to play an important role in the prediction of daily rainfall amount.", } @InCollection{Dufek:2018:hbge, author = "Amanda Sabatini Dufek and Douglas Adriano Augusto and Helio Jose Correa Barbosa and Pedro Leite {da Silva Dias}", title = "Multi- and Many-Threaded Heterogeneous Parallel Grammatical Evolution", booktitle = "Handbook of Grammatical Evolution", publisher = "Springer", year = "2018", editor = "Conor Ryan and Michael O'Neill and J. J. Collins", chapter = "9", pages = "219--244", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", isbn13 = "978-3-319-78716-9", DOI = "doi:10.1007/978-3-319-78717-6_9", abstract = "There are some algorithms suited for inference of human-interpretable models for classification and regression tasks in machine learning, but it is hard to compete with Grammatical Evolution (GE) when it comes to powerfulness, model expressiveness and ease of implementation. On the other hand, algorithms that iteratively optimize a set of programs of arbitrary complexity (which is the case of GE) may take an inconceivable amount of running time when tackling complex problems. Fortunately, GE may scale to such problems by carefully harnessing the parallel processing of modern heterogeneous systems, taking advantage of traditional multi-core processors and many-core accelerators to speed up the execution by orders of magnitude. This chapter covers the subject of parallel GE, focusing on heterogeneous multi- and many-threaded decomposition in order to achieve a fully parallel implementation, where both the breeding and evaluation are parallelised. In the studied benchmarks, the overall parallel implementation runtime was 68 times faster than the sequential version, with the program evaluation kernel alone hitting an acceleration of 350 times. Details on how to efficiently accomplish that are given in the context of two well-established open standards for parallel computing: OpenMP and OpenCL. Decomposition strategies, optimization techniques and parallel benchmarks followed by analyses are presented", notes = "Part of \cite{Ryan:2018:hbge}", } @InProceedings{duffy:1999:CEF, author = "John Duffy and Jim Engle-Warnick", title = "Using Symbolic Regression to Infer Strategies from Experimental Data", booktitle = "Fifth International Conference: Computing in Economics and Finance", year = "1999", editor = "David A. Belsley and Christopher F. Baum", pages = "150", address = "Boston College, MA, USA", month = "24-26 " # jun, institution = "Society for Computational Economics", note = "Book of Abstracts", keywords = "genetic algorithms, genetic programming", URL = "http://www.pitt.edu/~jduffy/docs/Usr.pdf", URL = "http://www.pitt.edu/~jduffy/docs/Usr.ps", URL = "http://citeseer.ist.psu.edu/304022.html", URL = "https://ideas.repec.org/p/sce/scecf9/1033.html", size = "21 pages", abstract = "We propose the use of a new technique -- symbolic regression -- as a method for inferring the strategies that are being played by subjects in economic decision making experiments. We begin by describing symbolic regression and our implementation of this technique using genetic programming. We provide a brief overview of how our algorithm works and how it can be used to uncover simple data generating functions that have the flavor of strategic rules. We then apply symbolic regression using genetic programming to experimental data from the ultimatum game. We discuss and analyze the strategies that we uncover using symbolic regression and we conclude by arguing that symbolic regression techniques should at least complement standard regression analyses of experimental data.", notes = "also known as \cite{RePEc:sce:scecf9:1033} Handle: RePEc:sce:scecf9:1033 CEF'99 See also \cite{duffy:1999:srised} http://fmwww.bc.edu/cef99/sess/chen.cfp.html", } @InCollection{duffy:1999:srised, author = "John Duffy and Jim Engle-Warnick", title = "Using Symbolic Regression to Infer Strategies from Experimental Data", booktitle = "Evolutionary Computation in Economics and Finance", publisher = "Physica Verlag", year = "2002", editor = "Shu-Heng Chen", volume = "100", series = "Studies in Fuzziness and Soft Computing", chapter = "4", pages = "61--82", month = "2002", keywords = "genetic algorithms, genetic programming", ISBN = "3-7908-1476-8", URL = "http://www.pitt.edu/~jduffy/docs/Usr.ps", DOI = "doi:10.1007/978-3-7908-1784-3_4", abstract = "We propose the use of a new technique -- symbolic regression -- as a method for inferring the strategies that are being played by subjects in economic decision making experiments. We begin by describing symbolic regression and our implementation of this technique using genetic programming. We provide a brief overview of how our algorithm works and how it can be used to uncover simple data generating functions that have the flavor of strategic rules. We then apply symbolic regression using genetic programming to experimental data from the ultimatum game. We discuss and analyze the strategies that we uncover using symbolic regression and we conclude by arguing that symbolic regression techniques should at least complement standard regression analyses of experimental data.", notes = "Presented at CEF'99 (see \cite{duffy:1999:CEF}) http://fmwww.bc.edu/cef99/sess/chen.cfp.html", size = "21 pages", } @InProceedings{Duflo:2019:IPDPSW, author = "Gabriel Duflo and Emmanuel Kieffer and Matthias R. Brust and Gregoire Danoy and Pascal Bouvry", booktitle = "2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)", title = "A {GP} Hyper-Heuristic Approach for Generating {TSP} Heuristics", year = "2019", pages = "521--529", month = may, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IPDPSW.2019.00094", abstract = "A wide range of heuristics has been developed over the last decades as a way to obtain good quality solutions in reasonable time on large scale optimisation problems. However, heuristics are problem specific, i.e. lack of generalisation potential, while requiring time to design. Hyper-heuristics have been proposed to address these limitations by directly searching in the heuristics' space. This work more precisely focuses on a heuristic generation method, as opposed to heuristic selection, for the traveling salesman problem (TSP). Learning is achieved with a genetic programming (GP) approach, for which novel specific terminals are introduced. The performance of the proposed GP hyper-heuristic is evaluated on a large set of TSP instances and compared to state-of-the-art heuristics. Experiments demonstrate that the generated heuristics are outperforming existing ones while having similar or lower complexity.", notes = "Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, Esch-sur-Alzette, Luxembourg Also known as \cite{8778254}", } @InProceedings{Dufourq:2013:WICT, author = "Emmanuel Dufourq and Nelishia Pillay", booktitle = "Third World Congress on Information and Communication Technologies (WICT)", title = "Incorporating adaptive discretization into genetic programming for data classification", year = "2013", pages = "127--133", abstract = "Genetic programming (GP) for data classification using decision trees has been successful in creating models which obtain high classification accuracies. When categorical data is used GP is able to directly use decision trees to create models, however when the data contains continuous attributes discretization is required as a pre-processing step prior to learning. There has been no attempt to incorporate the discretization mechanism into the GP algorithm and this serves as the rationale for this paper. This paper proposes an adaptive discretization method for inclusion into the GP algorithm by randomly creating intervals during the execution of the algorithm through the use of a new genetic operator. This proposed approach was tested on five data sets and serves as an initial attempt at dynamically altering the intervals of GP decision trees while simultaneously searching for an optimal solution during the learning phase. The proposed method performs well when compared to other non-GP adaptive methods.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/WICT.2013.7113123", month = dec, notes = "Also known as \cite{7113123}", } @InProceedings{Dufourq:2013:WICTa, author = "Emmanuel Dufourq and Nelishia Pillay", booktitle = "2013 Third World Congress on Information and Communication Technologies (WICT)", title = "A comparison of genetic programming representations for binary data classification", year = "2013", pages = "134--140", abstract = "The choice of which representation to use when applying genetic programming (GP) to a problem is vital. Certain representations perform better than others and thus they should be selected wisely. This paper compares the three most commonly used GP representations for binary data classification problems, namely arithmetic trees, logical trees, and decision trees. Several different function sets were tested to determine which functions are more useful. The different representations were tested on eight data sets with different characteristics and the findings show that all three representations perform similarly in terms of classification accuracy. Decision trees obtained the highest training accuracy and logical trees obtained the highest test accuracy. In the context of GP and binary data classification the findings of this study show that any of the three representations can be used and a similar performance will be achieved. For certain data sets the arithmetic trees performed the best whereas the logical trees did not, and for the remaining data sets the logical tree performed best whereas the arithmetic tree did not.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/WICT.2013.7113124", month = dec, notes = "Also known as \cite{7113124}", } @InProceedings{Dufourq:2014:NaBIC, author = "Emmanuel Dufourq and Nelishia Pillay", booktitle = "Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC 2014)", title = "Hybridizing evolutionary algorithms for creating classifier ensembles", year = "2014", month = jul, pages = "84--90", abstract = "Genetic programming (GP) has been applied to solve data classification problems numerous times in previous studies and the findings in the literature confirm that GP is able to perform well. In more recent studies, researchers have shown that using a team of classifiers can outperform a single classifier. These teams are referred to as ensembles. Previously, several different attempts at creating ensembles have been investigated; some more complex than others. In this study, four approaches have been proposed, in which the ensemble methods hybridise a genetic algorithm with a GP algorithm in different ways. The first three approaches made use of a generational GP model, while the fourth used a steady state GP model. The four approaches were tested on eight public data sets and the findings confirm that the proposed ensembles outperform the standard GP method, and additionally outperform other GP methods found in literature.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/NaBIC.2014.6921858", notes = "Sch. of Math., Stat. & Comput. Sci., Univ. of KwaZulu-Natal, Durban, South Africa Also known as \cite{6921858}", } @InProceedings{dulewicz:2001:HIS, title = "Evolving Natural Language Parser with Genetic Programming", author = "Grzegorz Dulewicz and Olgierd Unold", editor = "Ajith Abraham and Mario Koppen", booktitle = "2001 International Workshop on Hybrid Intelligent Systems", series = "LNCS", pages = "361--378", publisher = "Springer-Verlag", address = "Adelaide, Australia", publisher_address = "Berlin", month = "11-12 " # dec, year = "2001", broken = "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-7908-1480-6", URL = "http://www.amazon.com/Hybrid-Information-Systems-Ajith-Abraham/dp/3790814806/ref=sr_1_8?s=books&ie=UTF8&qid=1326475568&sr=1-8", ISBN = "3-7908-1480-6", keywords = "genetic algorithms, genetic programming, natural language processing, edge encoding", abstract = "1 Introduction When we try to deal with natural language processing (NLP) we have to start with a grammar of a natural language. But the grammars described in linguistic literature have an informal form and many exceptions. Thus, they are not useful to create final formal models of grammars, which make machine processing of sentences possible. These grammars can be a starting point for the attempts to create basic models of natural language grammar at the most. However, it requires expert knowledge. Machine learning based on a set of sample sentences can be the better way to find the grammar rules. This kind of learning allows to avoid the preparation of knowledge about the language for the NLP system. The examples of correct and incorrect sentences allow the NLP systems with the self-evolutionary parser to try to find the right grammar. This self-evolutionary parser can be improved on basis of new examples. Thus, the knowledge acquired in this way is flexible and easyly modifiable.", notes = "HIS01", } @Article{Dumic:2018:FGCS, author = "Mateja Dumic and Dominik Sisejkovic and Rebeka Coric and Domagoj Jakobovic", title = "Evolving priority rules for resource constrained project scheduling problem with genetic programming", journal = "Future Generation Computer Systems", year = "2018", volume = "86", pages = "211--221", keywords = "genetic algorithms, genetic programming, Resource constrained scheduling, Hyper-heuristics", ISSN = "0167-739X", URL = "http://www.sciencedirect.com/science/article/pii/S0167739X1732441X", DOI = "doi:10.1016/j.future.2018.04.029", abstract = "The main task of scheduling is the allocation of limited resources to activities over time periods to optimize one or several criteria. The scheduling algorithms are devised mainly by the experts in the appropriate fields and evaluated over synthetic benchmarks or real-life problem instances. Since many variants of the same scheduling problem may appear in practice, and there are many scheduling algorithms to choose from, the task of designing or selecting an appropriate scheduling algorithm is far from trivial. Recently, hyper-heuristic approaches have been proven useful in many scheduling domains, where machine learning is applied to develop a customized scheduling method. This paper is concerned with the resource constrained project scheduling problem (RCPSP) and the development of scheduling heuristics based on Genetic programming (GP). The results show that this approach is a viable option when there is a need for a customized scheduling method in a dynamic environment, allowing the automated development of a suitable scheduling heuristic.", notes = "Also known as \cite{DUMIC2018211}", } @Article{DUMIC:2021:ASC, author = "Mateja Dumic and Domagoj Jakobovic", title = "Ensembles of priority rules for resource constrained project scheduling problem", journal = "Applied Soft Computing", volume = "110", pages = "107606", year = "2021", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2021.107606", URL = "https://www.sciencedirect.com/science/article/pii/S1568494621005275", keywords = "genetic algorithms, genetic programming, Resource constrained project scheduling problem, Hyper-heuristics, Priority rules, Ensemble, Machine learning", abstract = "Resource constrained project scheduling problem is an NP-hard problem that attracts many researchers because of its complexity and daily use. In literature there are a lot of various solving methods for this problem. The priority rules are one of the prominent methods used in practice. Because of their simplicity, speed, and possibility to react to changes in the system, they can be used in a dynamic environment. In this paper, ensembles of priority rules were created to improve the performance of priority rules created with genetic programming. For ensemble creation, four different methods will be considered: simple ensemble combination, BagGP, BoostGP, and cooperative coevolution. The priority rules that are part of the ensemble will be combined with the sum and vote methods in reaching the final decision. Additionally, the ensemble subset search method will be applied to the created ensembles to find the optimal subset of priority rules. The results achieved in this paper show that ensembles of priority rules can achieve significantly better results than those achieved when using only a single priority rule", } @Article{DUMIC:2022:cie, author = "Mateja Dumic and Domagoj Jakobovic", title = "Using priority rules for resource-constrained project scheduling problem in static environment", journal = "Computer \& Industrial Engineering", volume = "169", pages = "108239", year = "2022", ISSN = "0360-8352", DOI = "doi:10.1016/j.cie.2022.108239", URL = "https://www.sciencedirect.com/science/article/pii/S0360835222003096", keywords = "genetic algorithms, genetic programming, Resource constrained project scheduling problem, Priority rules, Iterative priority rules, Rollout, Static environment", abstract = "The resource-constrained project scheduling problem (RCPSP) is one of the scheduling problems that belong to the class of NP-hard problems. Therefore, heuristic approaches are usually used to solve it. One of the most commonly used heuristic approaches are priority rules (PRs). PRs are easy to use, fast and able to respond to system changes, which makes them applicable in a dynamic environment. The disadvantage of PRs is that when applied in a static environment, they do not achieve results of the same quality as heuristic approaches designed for a static environment. Moreover, a new PR must be evolved separately for each optimization criterion, which is a challenging process. Therefore, recently significant effort has been put into the automatic development of PRs. Although PRs are mainly used in a dynamic environment, they are also used in a static environment in situations where speed and simplicity are more important than the quality of the obtained solution. Since PRs evolved for a dynamic environment do not use all the information available in a static environment, this paper analyzes two adaptations for evolving PRs in a static environment for the RCPSP - iterative priority rules and rollout approach. This paper shows that these approaches achieve better results than the PRs evolved and used without these adaptations. The results of the approaches presented in the paper were also compared with the results obtained with the genetic algorithm as a representative of the heuristic approaches used mainly in the static environment", } @Article{Dumitriu:2007:ISI, author = "Luminita Dumitriu and Cristina Segal and Marian Craciun and Adina Cocu and Lucian P. Georgescu", title = "Model Discovery and Validation for the Qsar Problem using Association Rule Mining", journal = "International Science Index", volume = "1", number = "11", year = "2007", pages = "648--652", keywords = "genetic algorithms, genetic programming, association rules, classification, data mining, quantitative structure - activity relationship.", bibsource = "http://waset.org/Publications", publisher = "World Academy of Science, Engineering and Technology", ISSN = "1307-6892", oai = "oai:CiteSeerX.psu:10.1.1.308.3241", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.308.3241", URL = "http://www.waset.org/journals/waset/v11/v11-129.pdf", URL = "http://waset.org/publications/13096", URL = "http://waset.org/Publications?p=11", size = "5 pages", abstract = "There are several approaches in trying to solve the Quantitative Structure-Activity Relationship (QSAR) problem. These approaches are based either on statistical methods or on predictive data mining. Among the statistical methods, one should consider regression analysis, pattern recognition (such as cluster analysis, factor analysis and principal components analysis) or partial least squares. Predictive data mining techniques use either neural networks, or genetic programming, or neuro-fuzzy knowledge. These approaches have a low explanatory capability or non at all. This paper attempts to establish a new approach in solving QSAR problems using descriptive data mining. This way, the relationship between the chemical properties and the activity of a substance would be comprehensibly modelled.", notes = "International Science Index 11, 2007", } @InProceedings{Dumont:2000:primeca, author = "G. Dumont and Frederic Chapelle and O. Chocron and Philippe Bidaud", title = "Prototypage virtuel d'un micro-endoscope", booktitle = "Journee thematique PRIMECA", address = "Valenciennes, France", month = mar, note = "in french", size = "5 pages", keywords = "genetic algorithms", year = "2000", } @InProceedings{Dumont:2000:jpmr, author = "G. Dumont and Frederic Chapelle", title = "Simulation multi-physique pour la conception en micro-robotique", booktitle = "Journees du Pole Micro-robotique", address = "Cachan, France", month = jun, note = "in french", size = "9 pages", keywords = "genetic algorithms", year = "2000", } @InProceedings{Dumont:2001:isr, author = "Georges Dumont and Frederic Chapelle and Philippe Bidaud", title = "Toward virtual prototyping of active endoscopes", booktitle = "International Symposium on Robotics (ISR'01)", address = "Seoul, Korea", month = "19-20 " # apr, organization = "International Federation of Robotics", pages = "821--826", keywords = "genetic algorithms", year = "2001", notes = "http://isr2001.kist.re.kr/Teams/isr2001/sessionprogram.htm", } @InProceedings{Dunay:1994:rliGP, author = "Bertrand Daniel Dunay and Frederick E. Petry and Bill P Buckles", title = "Regular language induction with genetic programming", booktitle = "Proceedings of the 1994 IEEE World Congress on Computational Intelligence", year = "1994", pages = "396--400", volume = "1", address = "Orlando, Florida, USA", month = "27-29 " # jun, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming S-expressions, computational difficulties, deterministic finite automata, editing, formal language accepters, inductive inference, informant, population pressure, reachable states, regular language induction, renumbering, run-time determined solution size, sample strings, transition tables, translation, deterministic automata, finite automata, formal languages, inference mechanisms", DOI = "doi:10.1109/ICEC.1994.349918", size = "5 pages", abstract = "In this research, inductive inference is done with an informant on the class of regular languages. The approach is to evolve formal language accepters which are consistent with a set of sample strings from the language, and a set of sample strings known not to be in the language. Deterministic finite automata (DFA) were chosen as the formal language accepters to alleviate the computational difficulties of nondeterministic constructs such as rewrite grammars. Genetic programming (GP) offers two significant improvements for regular language induction over genetic algorithms. First, GP allows the size of the solution (the DFA) to be determined at run time in response to population pressure. Second, GP's potential for assuring correct dependencies in complex individuals can be exploited to assure that all states in a DFA are reachable from the start state. The contribution of this research is the effective translation of DFAs to S-expressions, the application of renumbering, and of editing to the problem of language induction. DFAs or transition tables form the basis of many problems. By using the techniques found in this paper, many of these problems can be directly translated into the domain of genetic programming", notes = "Considers two classes of regular language (NB series and Tomita) which can be recognised or accpeted by deterministic finite automata (Finite state machines). Can translate from DFA to tree structure. Trees are not executable programs but represent languages. crossover on trees defined. GP able to define a language given examples of it. Works on simplier examples but has difficulties with 8b, 9b, 10b and TL5.", } @InProceedings{dunay:1995:scpga, author = "Bertrand Daniel Dunay and Frederic E. Petry", title = "Solving Complex Problems with Genetic Algorithms", booktitle = "Genetic Algorithms: Proceedings of the Sixth International Conference (ICGA95)", year = "1995", editor = "Larry J. Eshelman", pages = "264--270", address = "Pittsburgh, PA, USA", publisher_address = "San Francisco, CA, USA", month = "15-19 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-370-0", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/dunay_1995_scpga.pdf", size = "7 pages", abstract = "Using GA to evolve Turing machines which recognise languages from the Chomsky heirarchy. Example for regular languages (awb), context free languages (a**nb**n) and context sensitive languages (a**nb**na**n).", } @PhdThesis{hazelthesis, author = "Hazel Duncan", title = "The Use of Data-Mining for the Automatic Formation of Tactics", school = "School of Informatics, University of Edinburgh", year = "2007", address = "UK", keywords = "genetic algorithms, genetic programming", URL = "http://hdl.handle.net/1842/1768", URL = "https://www.era.lib.ed.ac.uk/bitstream/handle/1842/1768/hazelthesis.pdf", size = "151 pages", abstract = "As functions which further the state of a proof in automated theorem proving, tactics are an important development in automated deduction. This thesis describes a method to tackle the problem of tactic formation. Tactics must currently be developed by hand, which can be a complicated and time-consuming process. A method is presented for the automatic production of useful tactics. The method presented works on the principle that commonly occurring patterns within proof corpora may have some significance and could therefore be exploited to provide novel tactics. These tactics are discovered using a three step process. Firstly a suitable corpus is chosen and processed. One example of a suitable corpus is that of the Isabelle theorem prover. A number of possible abstractions are presented for this corpus. Secondly, machine learning techniques are used to data-mine each corpus and find sequences of commonly occurring proof steps. The specifics of a proof step are defined by the specified abstraction. The formation of these tactics is completed using evolutionary techniques to combine these patterns into compound tactics. These new tactics are applied using a naive prover as well as undergoing manual evaluation. The tactics show favourable results across a selection of tests, justifying the claim that this project provides a novel method of automatically producing tactics which are both viable and useful.", notes = "Supervisors: Alan Bundy and Amos Storkey and John Levine", } @InProceedings{Duncan:2004:IJCAR_WS7, author = "Hazel Duncan and Alan Bundy and John Levine and Amos Storkey and Martin Pollet", title = "The Use of Data-Mining for the Automatic Formation of Tactics", booktitle = "Computer-Supported Mathematical Theory Development", year = "2004", editor = "Christoph Benzmueller and Wolfgang Windsteiger", number = "04-14", series = "RISC Report Series", month = jul # " 5", pages = "61--71", address = "Cork, Ireland", organisation = "RISC Institute, University of Linz", note = "Proceedings of the first ``Workshop on Computer-Supported Mathematical Theory Development'' held in the frame of IJCAR'04 Available at http://www.risc.uni-linz.ac.at/about/conferences/IJCAR-WS7/.", keywords = "genetic algorithms, genetic programming", isbn13 = "3-902276-04-5", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.305.1991", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.305.1991", URL = "https://www.risc.jku.at/conferences/IJCAR-WS7/html-files/IJCAR-WS7.pdf", size = "11 pages", abstract = "The aim of this project is to evaluate the applicability of data-mining techniques to the automatic formation of tactics from large corpuses of proofs. We data-mine information from large proof corpuses to find commonly occurring patterns. These patterns are then evolved into tactics using genetic programming techniques", } @Article{Dunn20061209, author = "Enrique Dunn and Gustavo Olague and Evelyne Lutton", title = "Parisian camera placement for vision metrology", journal = "Pattern Recognition Letters", volume = "27", number = "11", pages = "1209--1219", year = "2006", note = "Evolutionary Computer Vision and Image Understanding", ISSN = "0167-8655", DOI = "DOI:10.1016/j.patrec.2005.07.019", URL = "http://www.sciencedirect.com/science/article/B6V15-4HX477K-2/2/e82b5b25f9a7a82607ac4b30c9fb9c45", keywords = "genetic algorithms, genetic programming, Camera placement, Accurate 3D reconstruction, Photogrammetric network design, Evolutionary computation, Parisian approach", abstract = "This paper presents a novel camera network design methodology based on the Parisian evolutionary computation approach. This methodology proposes to partition the original problem into a set of homogeneous elements, whose individual contribution to the problem solution can be evaluated separately. A population comprised of these homogeneous elements is evolved with the goal of creating a single solution by a process of aggregation. The goal of the Parisian evolutionary process is to locally build better individuals that jointly form better global solutions. The implementation of the proposed approach requires addressing aspects such as problem decomposition and representation, local and global fitness integration, as well as diversity preservation mechanisms. The benefit of applying the Parisian approach to our camera placement problem is a substantial reduction in computational effort expended in the evolutionary optimization process. Moreover, experimental results coincide with previous state of the art photogrammetric network design methodologies, while incurring in only a fraction of the computational cost.", } @InProceedings{dunning:1996:eanlp, author = "Ted E. Dunning and Mark W. Davis", title = "Evolutionary Algorithms for Natural Language Processing", booktitle = "Late Breaking Papers at the Genetic Programming 1996 Conference Stanford University July 28-31, 1996", year = "1996", editor = "John R. Koza", pages = "16--23", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California 94305-3079, USA", month = "28--31 " # jul, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming, NLP", ISBN = "0-18-201031-7", notes = "GP-96LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670", } @InProceedings{dupas:1999:RAO, author = "R. Dupas and G. Cavory and G. Goncalves", title = "Real-World Applications. Optimising the throughput of a manufacturing production line using a genetic algortihm", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1775", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "real world applications, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-717.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-717.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{10.1109/CRV.2006.32, author = "Jean-Francois Dupuis and Marc Parizeau", title = "Evolving a Vision-Based Line-Following Robot Controller", year = "2006", publisher = "IEEE Computer Society", booktitle = "The 3rd Canadian Conference on Computer and Robot Vision (CRV'06)", pages = "75", address = "Quebec, Canada", month = "7-9 " # jun, keywords = "genetic algorithms, genetic programming", ISBN = "0-7695-2542-3", URL = "http://vision.gel.ulaval.ca/~jfdupuis/pubs/jfdupuisCRV2006.pdf", DOI = "doi:10.1109/CRV.2006.32", size = "7 pages", abstract = "framework for evolving a vision-based mobile robot controller using genetic programming. This framework is built on the Open BEAGLE framework for the evolutionary computations, and on OpenGL for simulating the visual environment of a physical mobile robot. The feasibility of this framework is demonstrated through a simple, yet non-trivial, line following problem.", } @PhdThesis{Dupuis2011ab, author = "Jean-Francois Dupuis", title = "Automated Design of Hybrid Systems Using Evolutionary Computation", school = "Department of Management Engineering, Engineering Design and Product Development (K\&P), Technical University of Denmark", year = "2011", address = "Lyngby, Denmark", month = apr, keywords = "genetic algorithms, genetic programming, bond graphs", URL = "http://www.jfdupuis.info/files/Dupuis2011ab.pdf", size = "172 pages", abstract = "The study of hybrid systems is becoming increasingly popular. They have enjoyed a particular growth in interest since the 1990s. Most of the focus on the subject has been oriented toward the design of controllers and on the development of a complete control theory. However, this work looks at hybrid systems from a synthesis point of view. More precisely, it aims at developing an automated design synthesis method to the design of hybrid mechatronic systems. In order to achieve that, hybrid bond graphs are used to model the physical systems, and evolutionary computation is used to explore the search space. The study of hybrid systems is becoming increasingly popular. They have enjoyed a particular growth in interest since the 1990s. Most of the focus on the subject has been oriented toward the design of controllers and on the development of a complete control theory. However, this work looks at hybrid systems from a synthesis point of view. More precisely, it aims at developing an automated design synthesis method to the design of hybrid mechatronic systems. In order to achieve that, hybrid bond graphs are used to model the physical systems, and evolutionary computation is used to explore the search space.", notes = "Three-tank system, DC-DC converter", } @Article{Dupuis:2011:ieeeTEC, author = "Jean-Francois Dupuis and Zhun Fan and Erik D. Goodman", title = "Evolutionary Design of Both Topologies and Parameters of a Hybrid Dynamical System", journal = "IEEE Transactions on Evolutionary Computation", year = "2012", volume = "16", number = "3", pages = "391--405", month = jun, keywords = "genetic algorithms, genetic programming, Embryo, Encoding, Junctions, Mechatronics, Switches, Automated design, bond graphs, evolutionary design, hybrid mechatronic systems", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2011.2159724", size = "15 pages", abstract = "This paper investigates the issue of evolutionary design of open-ended plants for hybrid dynamical systems, i.e., both their topologies and parameters. Hybrid bond graphs (HBGs) are used to represent dynamical systems involving both continuous and discrete system dynamics. Genetic programming, with some special mechanisms incorporated, is used as a search tool to explore the open-ended design space of hybrid bond graphs. Combination of these two tools, i.e., HBGs and genetic programming, leads to an approach called HBGGP that can automatically generate viable design candidates of hybrid dynamical systems that fulfill predefined design specifications. A comprehensive investigation of a case study of DC-DC converter design demonstrates the feasibility and effectiveness of the HBGGP approach. Important characteristics of the approach are also discussed, with some future research directions pointed out.", notes = "also known as \cite{6045329}", } @InProceedings{durand:1998:gxpsf, author = "Nicolas Durand and Jean-Marc Alliot", title = "Genetic crossover operator for partially separable functions", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "487--494", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms", notes = "SGA-98", } @Article{Durasevic:2016:ASC, author = "Marko Durasevic and Domagoj Jakobovic and Karlo Knezevic", title = "Adaptive scheduling on unrelated machines with genetic programming", journal = "Applied Soft Computing", year = "2016", volume = "48", pages = "419--430", month = nov, keywords = "genetic algorithms, genetic programming, Scheduling on unrelated machines, Priority scheduling", ISSN = "1568-4946", URL = "http://www.sciencedirect.com/science/article/pii/S1568494616303519", DOI = "doi:10.1016/j.asoc.2016.07.025", size = "12 pages", abstract = "This paper investigates the use of genetic programming in automatized synthesis of heuristics for the parallel unrelated machines environment with arbitrary performance criteria. The proposed scheduling heuristic consists of a manually defined meta-algorithm which uses a priority function evolved separately with genetic programming. In this paper, several different genetic programming methods for evolving priority functions, like dimensionally aware genetic programming, genetic programming with iterative dispatching rules and gene expression programming, have been tried out and described. The performance of the suggested approach is compared to existing scheduling heuristics and it is shown that it mostly outperforms them. The described approach could prove useful when used for optimizing scheduling criteria for which no adequate scheduling heuristic exists.", } @Article{Durasevic:GPEM, author = "Marko Durasevic and Domagoj Jakobovic", title = "Evolving dispatching rules for optimising many-objective criteria in the unrelated machines environment", journal = "Genetic Programming and Evolvable Machines", year = "2018", volume = "19", number = "1-2", pages = "9--51", month = jun, note = "Special Issue on Automated Design and Adaptation of Heuristics for Scheduling and Combinatorial Optimisation", keywords = "genetic algorithms, genetic programming, Dispatching rules, Many-objective optimisation, Scheduling, Unrelated machines environment", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-017-9310-3", size = "43 pages", abstract = "Dispatching rules are often a method of choice for solving various scheduling problems. Most often, they are designed by human experts in order to optimise a certain criterion. However, it is seldom the case that a schedule should optimise a single criterion all alone. More common is the case where several criteria need to be optimised at the same time. This paper deals with the problem of automatic design of dispatching rules (DRs) by the use of genetic programming, for many-objective scheduling problems. Four multi-objective and many-objective algorithms, including nondominated sorting genetic algorithm II, nondominated sorting genetic algorithm III, harmonic distance based multi-objective evolutionary algorithm and multi-objective evolutionary algorithm based on decomposition, have been used in order to obtain sets of Pareto optimal solutions for various many-objective scheduling problems. Through experiments it was shown that automatically generated multi-objective DRs not only achieve good performance when compared to standard DRs, but can also outperform automatically generated single objective DRs for most criteria combinations.", } @Article{Durasevic:2017:GPEM, author = "Marko Durasevic and Domagoj Jakobovic", title = "Comparison of ensemble learning methods for creating ensembles of dispatching rules for the unrelated machines environment", journal = "Genetic Programming and Evolvable Machines", year = "2018", volume = "19", number = "1-2", pages = "53--92", month = jun, note = "Special Issue on Automated Design and Adaptation of Heuristics for Scheduling and Combinatorial Optimisation", keywords = "genetic algorithms, genetic programming, Dispatching rules, Scheduling, Unrelated machines environment, Ensemble learning", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-017-9302-3", size = "40 pages", abstract = "Dispatching rules are often the method of choice for solving various scheduling problems, especially since they are applicable in dynamic scheduling environments. Unfortunately, dispatching rules are hard to design and are also unable to deliver results which are of equal quality as results achieved by different metaheuristic methods. As a consequence, genetic programming is commonly used in order to automatically design dispatching rules. Furthermore, a great amount of research with different genetic programming methods is done to increase the performance of the generated dispatching rules. In order to additionally improve the effectiveness of the evolved dispatching rules, in this paper the use of several different ensemble learning algorithms is proposed to create ensembles of dispatching rules for the dynamic scheduling problem in the unrelated machines environment. Four different ensemble learning approaches will be considered, which will be used in order to create ensembles of dispatching rules: simple ensemble combination (proposed in this paper), BagGP, BoostGP and cooperative coevolution. Additionally, the effectiveness of these algorithms is analysed based on some ensemble learning parameters. Finally, an additional search method, which finds the optimal combinations of dispatching rules to form the ensembles, is proposed and applied. The obtained results show that by using the aforementioned ensemble learning approaches it is possible to significantly increase the performance of the generated dispatching rules.", } @Article{Durasevic:2018:ESA, author = "Marko Durasevic and Domagoj Jakobovic", title = "A survey of dispatching rules for the dynamic unrelated machines environment", journal = "Expert Systems with Applications", year = "2018", volume = "113", pages = "555--569", month = "15 " # dec, keywords = "Dispatching rules, Unrelated machines environment, Dynamic conditions, Release times", ISSN = "0957-4174", URL = "http://www.sciencedirect.com/science/article/pii/S0957417418304159", DOI = "doi:10.1016/j.eswa.2018.06.053", size = "15 pages", abstract = "In the real world, scheduling is usually performed under dynamic conditions, which means that it is not known when new jobs will be released into the system. Therefore, the procedure which is used to create the schedule must be able to adapt to the changing conditions during the execution of the system. In dynamic conditions, dispatching rules are one of the most commonly used methods for creating the schedules. Throughout the years, various dispatching rules were defined for a wide range of scheduling criteria. However, in most cases when a new dispatching rule is proposed, it is usually tested on only one or two scheduling criteria, and compared with only a few other dispatching rules. Furthermore, there are also no recent studies which compare all the different dispatching rules with each other. Therefore, it is difficult to determine how certain dispatching rules perform on different scheduling criteria and problem types. The objective of this study was to collect a large number of dispatching rules from the literature for the unrelated machines environment, and test them on nine scheduling criteria and four problem types with various machine and job heterogeneities. For each of the tested dispatching rules it will be outlined in which situations it achieves the best results, as well as which dispatching rules are best suited for solving each of the tested scheduling criteria.", notes = "Is this GP? Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, Zagreb 10 0 0 0, Croatia Also known as \cite{DURASEVIC2018555}", } @Article{Durasevic2019, author = "Marko Durasevic and Domagoj Jakobovic", title = "Creating dispatching rules by simple ensemble combination", journal = "Journal of Heuristics", year = "2019", volume = "25", number = "6", pages = "959--1013", month = dec, keywords = "genetic algorithms, genetic programming, Dispatching rules, Unrelated machines environment, Ensemble learning, Scheduling", ISSN = "1572-9397", URL = "https://doi.org/10.1007/s10732-019-09416-x", DOI = "doi:10.1007/s10732-019-09416-x", size = "55 pages", abstract = "Dispatching rules are often the method of choice for solving scheduling problems since they are fast, simple, and adaptive approaches. In recent years genetic programming has increasingly been used to automatically create dispatching rules for various scheduling problems. Since genetic programming is a stochastic approach, it needs to be executed several times to ascertain that good dispatching rules were obtained. This paper analyses whether combining several dispatching rules into an ensemble leads to performance improvements over the individual dispatching rules. Two methods for creating ensembles of dispatching rules, based on the sum and vote methods applied in machine learning, are used and their effectiveness is analysed with regards to the size of the ensemble, the genetic programming method used to generate the dispatching rules, the size of the evolved dispatching rules, and the method used for creating the ensembles. The results demonstrate that the generated ensembles achieve significant improvements over individual automatically generated dispatching rules.", } @Article{DURASEVIC:2020:ASC, author = "Marko Durasevic and Domagoj Jakobovic", title = "Comparison of schedule generation schemes for designing dispatching rules with genetic programming in the unrelated machines environment", journal = "Applied Soft Computing", volume = "96", pages = "106637", year = "2020", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2020.106637", URL = "http://www.sciencedirect.com/science/article/pii/S1568494620305755", keywords = "genetic algorithms, genetic programming, Dispatching rules, Schedule generation scheme, Unrelated machines environment, Hyper-heuristics, Scheduling", abstract = "Automatically designing new dispatching rules (DRs) by genetic programming has become an increasingly researched topic. Such an approach enables that DRs can be designed efficiently for various scheduling problems. Furthermore, most automatically designed DRs outperform existing manually designed DRs. Most research focused solely on designing priority functions that were used to determine the order in which jobs should be scheduled. However, in some scheduling environments, besides only determining the order of the jobs, one has to additionally determine the allocation of jobs to machines. For that purpose, a schedule generation scheme (SGS), which constructs the schedule, has to be applied. Until now the influence of different choices in the design of the SGS has not been extensively researched, which could lead to the application of an SGS that would obtain inferior results. The main goal of this paper is to perform an analysis of different SGS variants. For that purpose, three SGS variants are tested, two of which are proposed in this paper. They are tested in several variations which differ in details like whether they insert idle times in the schedule, or if they select the job with the highest or lowest priority values. The obtained results demonstrate that the automatically designed DRs with the tested SGS variants perform better than manually designed DRs, but also that there is a significant difference in the performance between the different SGS types and variants. The best DRs are analysed and show that the main reason why they performed well was due to the more sophisticated decisions they made when selecting the appropriate machine for a job. The results suggest that it is best to apply SGS variants which use the evolved priority functions to choose both the next job and the appropriate machine for that job", } @InProceedings{Durasevic:2020:PPSN, author = "Marko Durasevic and Domagoj Jakobovic and Marcella Martins and Stjepan Picek and Markus Wagner", title = "Fitness landscape analysis of dimensionally-aware genetic programming featuring {Feynman} equations", booktitle = "16th International Conference on Parallel Problem Solving from Nature, Part II", year = "2020", editor = "Thomas Baeck and Mike Preuss and Andre Deutz and Hao Wang2 and Carola Doerr and Michael Emmerich and Heike Trautmann", volume = "12270", series = "LNCS", pages = "111--124", address = "Leiden, Holland", month = "7-9 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, dimensionally aware GP, Symbolic regression, fitness landscape, local optima network", isbn13 = "978-3-030-58114-5", URL = "https://arxiv.org/abs/2004.12762", DOI = "doi:10.1007/978-3-030-58115-2_8", size = "14 pages", abstract = "Genetic programming is an often-used technique for symbolic regression: finding symbolic expressions that match data from an unknown function. To make the symbolic regression more efficient, one can also use dimensionally aware genetic programming that constrains the physical units of the equation. Nevertheless, there is no formal analysis of how much dimensionality awareness helps in the regression process. In this paper, we conduct a fitness landscape analysis of dimensionally aware genetic programming search spaces on a subset of equations from Richard Feynmans well-known lectures. We define an initialisation procedure and an accompanying set of neighbourhood operators for conducting the local search within the physical unit constraints. Our experiments show that the added information about the variable dimensionality can efficiently guide the search algorithm. Still, further analysis of the differences between the dimensionally-aware and standard genetic programming landscapes is needed to help in the design of efficient evolutionary operators to be used in a dimensionally-aware regression.", notes = "cites \cite{keijzer:1999:DAGP} Feynman Lectures on Physics => Udrescu and Tegmark, Feynman Symbolic Regression Database https://space.mit.edu/home/tegmark/aifeynman.html Local Search. Crossover operators: subtree, one point, size fair, uniform, context preserved. Mutation operatorssubtree, hoist, node replace, permutation, shrink. Evolutionary computation framework http://ecf.zemris.fer.hr/ PPSN2020", } @Article{DURASEVIC2022101649, author = "Marko Durasevic and Domagoj Jakobovic", title = "Selection of dispatching rules evolved by genetic programming in dynamic unrelated machines scheduling based on problem characteristics", journal = "Journal of Computational Science", year = "2022", volume = "61", pages = "101649", month = may, keywords = "genetic algorithms, genetic programming, Dispatching rules, Genetic programming, Scheduling, Unrelated machines environment, Machine learning, Dispatching rule selection", ISSN = "1877-7503", URL = "https://www.sciencedirect.com/science/article/pii/S1877750322000667", DOI = "doi:10.1016/j.jocs.2022.101649", abstract = "Dispatching rules are fast and simple procedures for creating schedules for various kinds of scheduling problems. However, manually designing DRs for all possible scheduling conditions and scheduling criteria is practically infeasible. For this reason, much of the research has focused on the automatic design of DRs using various methods, especially genetic programming. However, even if genetic programming is used to design new DRs to optimise a particular criterion, it will not give good results for all possible problem instances to which it can be applied. Due to the stochastic nature of genetic programming, the evolution of DRs must be performed several times to ensure that good DRs have been obtained. However, in the end, usually only one rule is selected from the set of evolved DRs and used to solve new scheduling problems. In this paper, a DR selection procedure is proposed to select the appropriate DR from the set of evolved DRs based on the features of the problem instances to be solved. The proposed procedure is executed simultaneously with the execution of the system, approximating the properties of the problem instances and selecting the appropriate DR for the current conditions. The obtained results show that the proposed approach achieves better results than those obtained when only a single DR is selected and used for all problem instances.", } @InProceedings{10.1007/978-3-031-06527-9_12, author = "Marko Durasevic and Lucija Planinic and Francisco J. Gil-Gala and Domagoj Jakobovic", title = "Constructing Ensembles of Dispatching Rules for Multi-objective Problems", booktitle = "Proceedings of the 9th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2022, Part II", year = "2022", editor = "Jose Manuel Ferrandez Vicente and Jose Ramon Alvarez-Sanchez and Felix de la Paz Lopez and Hojjat Adeli", volume = "13259", series = "LNCS", pages = "119--129", address = "Puerto de la Cruz, Tenerife, Spain", month = may # " 31-" # jun # " 3", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Scheduling, Unrelated machines, Dispatching rules, Ensembles, Multi-objective optimisation", isbn13 = "978-3-031-06527-9", DOI = "doi:10.1007/978-3-031-06527-9_12", abstract = "Scheduling represents an important aspect of many real-world processes, which is why such problems have been well studied in the literature. Such problems are often dynamic and require that multiple criteria be optimised simultaneously. Dispatching rules (DRs) are the method of choice for solving dynamic problems. However, existing DRs are usually implemented for the optimisation of only a single criterion. Since manual design of DRs is difficult, genetic programming (GP) has been used to automatically design new DRs for single and multiple objectives. However, the performance of a single rule is limited, and it may not work well in all situations. Therefore, ensembles have been used to create rule sets that outperform single DRs. The goal of this study is to adapt ensemble learning methods to create ensembles that optimise multiple criteria simultaneously. The method creates ensembles of DRs with multiple objectives previously evolved by GP to improve their performance. The results show that ensembles are suitable for the considered multi-objective problem.", notes = "Published as Bio-inspired Systems and Applications: from Robotics to Ambient Intelligence", } @InProceedings{durasevic:2022:GECCO, author = "Marko Durasevic and Lucija Planinic and Francisco Javier {Gil Gala} and Domagoj Jakobovic", title = "Novel ensemble collaboration method for dynamic scheduling problems", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference", year = "2022", editor = "Alma Rahat and Jonathan Fieldsend and Markus Wagner and Sara Tari and Nelishia Pillay and Irene Moser and Aldeida Aleti and Ales Zamuda and Ahmed Kheiri and Erik Hemberg and Christopher Cleghorn and Chao-li Sun and Georgios Yannakakis and Nicolas Bredeche and Gabriela Ochoa and Bilel Derbel and Gisele L. Pappa and Sebastian Risi and Laetitia Jourdan and Hiroyuki Sato and Petr Posik and Ofer Shir and Renato Tinos and John Woodward and Malcolm Heywood and Elizabeth Wanner and Leonardo Trujillo and Domagoj Jakobovic and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Inmaculada Medina-Bulo and Slim Bechikh and Andrew M. Sutton and Pietro Simone Oliveto", pages = "893--901", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, dispatching rules, ensembles, unrelated machines, scheduling", isbn13 = "978-1-4503-9237-2", URL = "https://doi.org/10.1145/3512290.3528807", DOI = "doi:10.1145/3512290.3528807", abstract = "Dynamic scheduling problems are important optimisation problems with many real-world applications. Since in dynamic scheduling not all information is available at the start, such problems are usually solved by dispatching rules (DRs), which create the schedule as the system executes. Recently, DRs have been successfully developed using genetic programming. However, a single DR may not efficiently solve different problem instances. Therefore, much research has focused on using DRs collaboratively by forming ensembles. In this paper, a novel ensemble collaboration method for dynamic scheduling is proposed. In this method, DRs are applied independently at each decision point to create a simulation of the schedule for all currently released jobs. Based on these simulations, it is determined which DR makes the best decision and that decision is applied. The results show that the ensembles easily outperform individual DRs for different ensemble sizes. Moreover, the results suggest that it is relatively easy to create good ensembles from a set of independently evolved DRs.", notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @InProceedings{DBLP:conf/gecco/DurasevicJ022, author = "Marko Durasevic and Domagoj Jakobovic and Yi Mei and Su Nguyen and Mengjie Zhang", editor = "Jonathan E. Fieldsend and Markus Wagner", title = "Introduction to automated design of scheduling heuristics with genetic programming", booktitle = "{GECCO} '22: Genetic and Evolutionary Computation Conference, Companion Volume, Boston, Massachusetts, USA, July 9 - 13, 2022", pages = "1506--1526", publisher = "{ACM}", year = "2022", note = "Tutorial", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1145/3520304.3533667", DOI = "doi:10.1145/3520304.3533667", timestamp = "Sun, 02 Oct 2022 01:00:00 +0200", biburl = "https://dblp.org/rec/conf/gecco/DurasevicJ022.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{DURASEVIC:2022:asoc, author = "Marko Durasevic and Mateja Dumic", title = "Automated design of heuristics for the container relocation problem using genetic programming", journal = "Applied Soft Computing", volume = "130", pages = "109696", year = "2022", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2022.109696", URL = "https://www.sciencedirect.com/science/article/pii/S1568494622007451", keywords = "genetic algorithms, genetic programming, Container relocation problem, Hyper-heuristics, Relocation rules", abstract = "The container relocation problem is a challenging combinatorial optimisation problem tasked with finding a sequence of container relocations required to retrieve all containers by a given order. Due to the complexity of this problem, heuristic methods are often applied to obtain acceptable solutions in a small amount of time. These include relocation rules (RRs) that determine the relocation moves that need to be performed to efficiently retrieve the next container based on certain yard properties. Such rules are often designed manually by domain experts, which is a time-consuming and challenging task. This paper investigates the application of genetic programming (GP) to design effective RRs automatically. Experimental results show that RRs evolved by GP outperform several existing manually designed RRs. Additional analyses of the proposed approach demonstrate that the evolved rules generalise well across a wide range of unseen problems and that their performance can be further enhanced. Therefore, the proposed method presents a viable alternative to existing manually designed RRs and opens a new research direction in the area of container relocation problems", notes = "See also \cite{DURASEVIC:2023:asoc}", } @Article{DURASEVIC:2023:asoc, author = "Marko Durasevic and Mateja Dumic", title = "Corrigendum to ``Automated design of heuristics for the container relocation problem using genetic programming'', [Appl. Soft Comput. 130 (2022) 109696]", journal = "Applied Soft Computing", volume = "132", pages = "109836", year = "2023", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2022.109836", URL = "https://www.sciencedirect.com/science/article/pii/S1568494622008857", keywords = "genetic algorithms, genetic programming", notes = "Refers to \cite{DURASEVIC:2022:asoc}", } @InProceedings{Durasevic:2023:EuroGP, author = "Marko Durasevic and Francisco Javier Gil-Gala and Domagoj Jakobovic", title = "To bias or not to bias: Probabilistic initialisation for evolving dispatching rules", booktitle = "EuroGP 2023: Proceedings of the 26th European Conference on Genetic Programming", year = "2023", month = "12-14 " # apr, editor = "Gisele Pappa and Mario Giacobini and Zdenek Vasicek", series = "LNCS", volume = "13986", publisher = "Springer Verlag", address = "Brno, Czech Republic", pages = "308--323", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Dispatching rules, Unrelated machines environment, Scheduling, Individual initialisation: Poster", isbn13 = "978-3-031-29572-0", URL = "https://rdcu.be/c8U3i", DOI = "doi:10.1007/978-3-031-29573-7_20", size = "16 pages", abstract = "he automatic generation of dispatching rules (DRs) for various scheduling problems using genetic programming (GP) has become an increasingly researched topic in recent years. Creating DRs in this way relieves domain experts of the tedious task of manually designing new rules, but also often leads to the discovery of better rules than those already available. However, developing new DRs is a computationally intensive process that takes time to converge to good solutions. One possible way to improve the convergence of evolutionary algorithms is to use a more sophisticated method to generate the initial population of individuals. In this paper, we propose a simple method for initialising individuals that uses probabilistic information from previously evolved DRs. The method extracts the information on how many times each node occurs at each level of the tree and in each context. This information is then used to introduce bias in the selection of the node to be selected at a particular position during the construction of the expression tree. The experiments show that with the proposed method it is possible to improve the convergence of GP when generating new DRs, so that GP can obtain high-quality DRs in a much shorter time.", notes = "Part of \cite{Pappa:2023:GP} EuroGP'2023 held in conjunction with EvoCOP2023, EvoMusArt2023 and EvoApplications2023", } @InProceedings{durasevic:2023:GECCO, author = "Marko Durasevic and Francisco Javier Gil-Gala and Domagoj Jakobovic", title = "Divide and Conquer: Using Single Objective Dispatching Rules to Improve Convergence for {Multi-Objective} Optimisation", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "1082--1090", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, dispatching rules, scheduling, unrelated machines environment", isbn13 = "9798400701191", DOI = "doi:10.1145/3583131.3590370", size = "9 pages", abstract = "Dynamic multi-objective (MO) scheduling problems are encountered in various real-world situations. Due to dynamic events that occur in such problems, one has to resort to using simple constructive heuristics, called dispatching rules (DRs), when tackling them. Since DRs are difficult to design manually there is a lack of existing DRs suitable for solving MO problems. Due to that reason, genetic programming has successfully been applied to evolve DRs specifically for solving MO problems. The process of evolving new DRs is computationally expensive, requiring a significant amount of time to obtain DRs of good quality. For that reason it is worth investigating inwhich ways the convergence of algorithms could be improved. One option is to use DRs previously evolved for optimising individual criteria to initialise the starting population when optimising a MO problem. The goal of this study is to investigate how such an initialisation strategy affects the performance of NSGA-II and NSGA-III when evolving DRs for MO problems. Therefore, 8 MO unrelated machines scheduling problems, containing between 2 and 5 criteria, are considered. The obtained results demonstrate that using previously evolved DRs for single objective optimisation leads to a faster convergence, and in many cases significantly better results.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @InProceedings{durasevic:2023:GECCOcompA, author = "Marko Durasevic and Mateja Dumic and Rebeka Coric and Francisco Javier Gil-Gala", title = "Automated Design of Relocation Rules for Minimising Energy Consumption in the Container Relocation Problem", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "523--526", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, genetic algorithm, hyper-heuristics, container relocation problem: Poster", isbn13 = "9798400701191", DOI = "doi:10.1145/3583133.3590561", size = "4 pages", abstract = "The container relocation problem is a combinatorial optimisation problem aimed at finding a sequence of container relocations to retrieve all containers in a predetermined order by minimising a given objective. Relocation rules (RRs), which consist of a priority function and relocation scheme, are heuristics commonly used for solving the mentioned problem due to their flexibility and efficiency. Recently, in many real-world problems it is becoming increasingly important to consider energy consumption. However, for this variant no RRs exist and would need to be designed manually. One possibility to circumvent this issue is by applying hyperheuristics to automatically design new RRs. In this study we use genetic programming to obtain priority functions used in RRs whose goal is to minimise energy consumption. We compare the proposed approach with a genetic algorithm from the literature used to design the priority function. The results obtained demonstrate that the RRs designed by genetic programming achieve the best performance.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @InProceedings{durasevic:2023:GECCOcomp2, author = "Marko Durasevic and Francisco Javier Gil-Gala and Domagoj Jakobovi\'{c}", title = "Does Size Matter? On the Influence of Ensemble Size on Constructing Ensembles of Dispatching Rules", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "559--562", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, dispatching rules, unrelated machines environment, ensemble construction: Poster", isbn13 = "9798400701191", DOI = "doi:10.1145/3583133.3590562", size = "4 pages", abstract = "Recent years saw an increase in the application of genetic programming (GP) as a hyper-heuristic, i.e., a method used to generate heuristics for solving various combinatorial optimisation problems. One of its widest application is in scheduling to automatically design constructive heuristics called dispatching rules (DRs). DRs are crucial for solving dynamic scheduling environments, in which the conditions change over time. Although automatically designed DRs achieve good results, their performance is limited as a single DR cannot always perform well. Therefore, various methods were used to improve their performance, among which ensemble learning represents one of the most promising directions. Using ensembles introduces several new parameters, such as the ensemble construction method, ensemble collaboration method, and ensemble size. This study investigates the possibility to remove the ensemble size parameter when constructing ensembles. Therefore, the simple ensemble combination method is adapted to randomly select the size of the ensemble it generates, rather than using a fixed ensemble size. Experimental results demonstrate that not using a fixed ensemble size does not result in a worse performance, and that the best ensembles are of smaller sizes. This shows that the ensemble size can be eliminated without a significant influence on the performance.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @Article{DURASEVIC:2023:engappai, author = "Marko Durasevic and Francisco Javier Gil-Gala and Lucija Planinic and Domagoj Jakobovic", title = "Collaboration methods for ensembles of dispatching rules for the dynamic unrelated machines environment", journal = "Engineering Applications of Artificial Intelligence", volume = "122", pages = "106096", year = "2023", ISSN = "0952-1976", DOI = "doi:10.1016/j.engappai.2023.106096", URL = "https://www.sciencedirect.com/science/article/pii/S0952197623002804", keywords = "genetic algorithms, genetic programming, Unrelated machines environment, Scheduling, Dispatching rules, Ensembles", abstract = "Dynamic scheduling represents an important combinatorial optimisation problem that often appears in the real world. The difficulty in solving these problems arises from their dynamic nature, which limits the applicability of improvement based metaheuristics. Dynamic problems are usually solved using dispatching rules (DRs), which iteratively construct the schedule. Recently, such heuristics have been constructed using various hyperheuristic methods, most notably genetic programming. Although automatically designed DRs achieve good performance, it is still very difficult to design a single DR that would perform a good decision at every decision point. As a remedy, DRs were combined into ensembles to improve their performance. For that purpose it is required to define how ensembles are constructed and how DRs in the ensemble collaborate. This paper proposes a novel ensemble collaboration method based on a similar method applied for static scheduling problems and adapts it for dynamic problems. The goal is to obtain a collaboration method that produces better results than standard collaboration methods. Additionally, the paper investigates the application of novel ensemble construction methods for dynamic scheduling. The proposed methods are validated on dynamic unrelated machines scheduling problem and compared with existing ensemble construction and collaboration methods. The obtained results demonstrate that the proposed collaboration method performs better than standard ones. Further analyses provide additional insights into the proposed methods and outline several potential research directions in the area of hyper-heuristic ensemble construction", } @Article{DURASEVIC:2023:swevo, author = "Marko Durasevic and Francisco Javier Gil-Gala and Domagoj Jakobovic and Carlos A. {Coello Coello}", title = "Combining single objective dispatching rules into multi-objective ensembles for the dynamic unrelated machines environment", journal = "Swarm and Evolutionary Computation", volume = "80", pages = "101318", year = "2023", ISSN = "2210-6502", DOI = "doi:10.1016/j.swevo.2023.101318", URL = "https://www.sciencedirect.com/science/article/pii/S2210650223000913", keywords = "genetic algorithms, genetic programming, Dispatching rules, Hyper-heuristic, Multi-objective optimisation, Ensembles, Unrelated machines environment", abstract = "Dispatching rules (DRs), which are simple constructive methods that incrementally build the schedule, represent the most popular method for solving dynamic scheduling problems. These DRs were usually designed for optimising a single criterion and work poorly when solving multi-objective (MO) problems. In recent years, we have seen an increase of research dealing with automated design of DRs using genetic programming (GP), which has enabled the application of several evolutionary MO optimisation methods to create DRs for MO problems. However, for each considered MO problem new DRs need to be evolved, which can be computationally expensive. Motivated by this, we propose a novel methodology to combine existing DRs evolved for optimising individual criteria into ensembles appropriate for optimising multiple criteria simultaneously. For this purpose, we adapt the existing simple ensemble construction (SEC) method to construct ensembles of DRs for optimising MO problems. The method is evaluated on several MO scheduling problems and compared with DRs evolved by NSGA-II and NSGA-III. The obtained results show that for most problems the proposed method constructed ensembles that significantly outperform DRs developed with standard MO algorithms. Furthermore, we propose the application of evolved MO rules and ensembles on problems with a smaller number of criteria and demonstrate that with such a strategy similar or better performance is achieved compared to evolving DRs for such problems directly, which demonstrates theif reusability and generalisation potential", } @Article{DURASEVIC:2024:asoc, author = "Marko Durasevic and Mateja Dumic", title = "Designing relocation rules with genetic programming for the container relocation problem with multiple bays and container groups", journal = "Applied Soft Computing", volume = "150", pages = "111104", year = "2024", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2023.111104", URL = "https://www.sciencedirect.com/science/article/pii/S1568494623011225", keywords = "genetic algorithms, genetic programming, Container relocation problem, Hyper-heuristics, Relocation rules", abstract = "The container relocation problem (CRP) is an NP-hard combinatorial optimisation problem that arises in yard management. The problem is concerned with loading all containers from the storage yard to the ship in a certain order. The yard layout consists of bays where containers are placed in stacks on top of each other, and each container has a due date that determines their retrieval order. Due to its complexity, heuristic methods are used to solve CRP, ranging from relocation rules to metaheuristics. Relocation rules (RRs) are used when the goal is to obtain a solution of acceptable quality in short time. Manually designing RRs is difficult and time-consuming, which motivates the use of different methods to automatically design RRs. In this study, we investigate the application of genetic programming (GP) to design RRs for CRP with multiple bays and container groups. The GP algorithm was adapted for generating RRs by proposing a new set of terminals and several solution construction methods. The proposed method was evaluated on an extensive benchmark of existing problems. The results obtained with automatically developed RRs were compared with the results of manually designed RRs and it was found that the automatically designed RRs performed significantly better in all cases", } @Article{durasevic:2024:Axioms, author = "Marko Durasevic and Domagoj Jakobovic", title = "Heuristic Ensemble Construction Methods of Automatically Designed Dispatching Rules for the Unrelated Machines Environment", journal = "Axioms", year = "2024", volume = "13", number = "1", pages = "Article No. 37", keywords = "genetic algorithms, genetic programming, scheduling, unrelated machines environment, ensemble learning, dispatching rules", ISSN = "2075-1680", URL = "https://www.mdpi.com/2075-1680/13/1/37", DOI = "doi:10.3390/axioms13010037", abstract = "Dynamic scheduling represents an important class of combinatorial optimisation problems that are usually solved with simple heuristics, the so-called dispatching rules (DRs). Designing efficient DRs is a tedious task, which is why it has been automated through the application of genetic programming (GP). Various approaches have been used to improve the results of automatically generated DRs, with ensemble learning being one of the best-known. The goal of ensemble learning is to create sets of automatically designed DRs that perform better together. One of the main problems in ensemble learning is the selection of DRs to form the ensemble. To this end, various ensemble construction methods have been proposed over the years. However, these methods are quite computationally intensive and require a lot of computation time to obtain good ensembles. Therefore, in this study, we propose several simple heuristic ensemble construction methods that can be used to construct ensembles quite efficiently and without the need to evaluate their performance. The proposed methods construct the ensembles solely based on certain properties of the individual DRs used for their construction. The experimental study shows that some of the proposed heuristic construction methods perform better than more complex state-of-the-art approaches for constructing ensembles.", notes = "also known as \cite{axioms13010037}", } @Article{durasevic:2024:Algorithms, author = "Marko Durasevic and Domagoj Jakobovic and Stjepan Picek and Luca Mariot", title = "Assessing the Ability of Genetic Programming for Feature Selection in Constructing Dispatching Rules for Unrelated Machine Environments", journal = "Algorithms", year = "2024", volume = "17", number = "2", pages = "Article No. 67", note = "Special Issue Algorithms for Feature Selection (2nd Edition)", keywords = "genetic algorithms, genetic programming, feature selection, unrelated machine problem, feature selection, scheduling", ISSN = "1999-4893", URL = "https://www.mdpi.com/1999-4893/17/2/67", DOI = "doi:10.3390/a17020067", abstract = "The automated design of dispatching rules (DRs) with genetic programming (GP) has become an important research direction in recent years. One of the most important decisions in applying GP to generate DRs is determining the features of the scheduling problem to be used during the evolution process. Unfortunately, there are no clear rules or guidelines for the design or selection of such features, and often the features are simply defined without investigating their influence on the performance of the algorithm. However, the performance of GP can depend significantly on the features provided to it, and a poor or inadequate selection of features for a given problem can result in the algorithm performing poorly. In this study, we examine in detail the features that GP should use when developing DRs for unrelated machine scheduling problems. Different types of features are investigated, and the best combination of these features is determined using two selection methods. The obtained results show that the design and selection of appropriate features are crucial for GP, as they improve the results by about 7percent when only the simplest terminal nodes are used without selection. In addition, the results show that it is not possible to outperform more sophisticated manually designed DRs when only the simplest problem features are used as terminal nodes. This shows how important it is to design appropriate composite terminal nodes to produce high-quality DRs.", notes = "also known as \cite{a17020067}", } @InProceedings{durasevic:2024:CEC, author = "Marko Durasevic and Mateja Dumic and Francisco Javier Gil-Gala", title = "Constructing Ensembles of Automatically Designed Relocation Rules for the Container Relocation Problem", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Evolutionary computation, Containers, Ensemble learning, Marine vehicles, Optimization, container relocation problem, genetic program-ming", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10612112", abstract = "Automated design of heuristics with genetic programming (G P) has, in recent years, become an intensively researched research area. One of the most recent applications of this methodology is to generate relocation rules (RRs) for the container relocation problem (CRP). CRP is an important combinatorial optimisation problem that is found in ship ter-minals and warehouses. RRs are simple constructive heuristic methods that provide a good solution quickly, thus representing an alternative to computationally expensive exact or metaheuris-tic methods. Even though the RRs designed by GP provide an improvement over existing manually designed rules, they have limited performance. An efficient way to improve the performance of RRs generated by GP is to use ensemble learning. In this study, we apply ensemble learning on RRs generated by GP for CRP to improve the performance of individual rules. We investigate how different ensemble sizes and combination methods affect the quality of the results, as well as which rules are selected to form ensembles. The experimental study shows that ensembles constructed out of automatically designed RRs significantly improve performance compared to the individual rules.", notes = "also known as \cite{10612112} WCCI 2024", } @InProceedings{durasevic:2024:CEC2, author = "Marko Durasevic and Mateja Dumic and Francisco Javier Gil-Gala", title = "Designing Relocation Rules with Genetic Programming for the Online Container Relocation Problem", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Metaheuristics, Evolutionary computation, Containers, online container relocation problem, relocation rules", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10611835", abstract = "The container relocation problem (CRP) in shipping terminals is becoming increasingly important due to the growing amount of transferred goods. Until now, the most commonly investigated problem variant has been the offline CRP, in which the order in which the containers need to be retrieved is known beforehand. However, in many real-world situations this is not the case, which is modelled using the online CRP variant. In this variant, not all information is available from the beginning, but rather, it becomes available as the problem is being solved. Unfortunately, many traditional metaheuristic solution methods can not be applied to such a problem variant, which prompts the application of problem-specific heuristics called relocation rules (RRs). However, RRs are challenging to design manually, which prompted the application of genetic programming (GP) to design them automatically. Since GP was used only to design RRs for the offline problem variant, we apply GP to design relocation rules for the online variant in this study. The performance of GP is investigated under different levels of information availability to measure its performance. The results demonstrate that GP can evolve RRs that perform better than existing manually designed ones. Furthermore, the results show that in certain cases, rules generated for one level of information availability perform well for other levels, demonstrating that the evolved rules exhibit a good generalisation capability.", notes = "also known as \cite{10611835} WCCI 2024", } @InProceedings{Durasevic:2024:PPSN, author = "Marko Durasevic and Mateja Dumic and Francisco Javier Gil-Gala and Nikolina Frid and Domagoj Jakobovic", title = "Improving the performance of relocation rules for the container relocation problem with the rollout algorithm", booktitle = "18th International Conference on Parallel Problem Solving from Nature", year = "2024", editor = "Heike Trautmann and Tea Tusar and Penousal Machado and Thomas Baeck", volume = "15148", series = "Lecture Notes in Computer Science", pages = "184--200", address = "University of Applied Sciences Upper Austria, Hagenberg, Austria", month = "13-18 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Container relocation problem, Relocation rules, Rollout heuristic", isbn13 = "978-3-031-70054-5", URL = "https://rdcu.be/d5mES", DOI = "doi:10.1007/978-3-031-70055-2_12", notes = "https://ppsn2024.fh-ooe.at/program/", } @InProceedings{Durasevic:2024:EA, author = "Marko Durasevic and Nikolina Frid and Francisco Javier {Gil Gala}", title = "Investigation of Structures for Routing Rules Designed by Genetic Programming for the Electric Vehicle Routing Problem", booktitle = "Artificial Evolution, EA-2024", year = "2024", editor = "Pierrick Legrand", address = "Bordeaux, France", month = oct # " 29-31", keywords = "genetic algorithms, genetic programming", URL = "http://evr.zemris.fer.hr/EN_Home.html", notes = "Tuesday 29th 10.45-11.30 EA2024", } @InProceedings{Durasevic:2024:EA2, author = "Marko Durasevic and Francisco Javier {Gil Gala}", title = "Designing Less Myopic Routing Policies with Genetic Programming for the Electric Vehicle Routing Problem", booktitle = "Artificial Evolution, EA-2024", year = "2024", editor = "Pierrick Legrand", address = "Bordeaux, France", month = oct # " 29-31", keywords = "genetic algorithms, genetic programming: Poster", URL = "http://evr.zemris.fer.hr/EN_Home.html", notes = "EA2024", } @InProceedings{durasevic:2025:EuroGP, author = "Marko Durasevic and Mateja Dumic and Francisco Javier {Gil Gala} and Domagoj Jakobovic", title = "Designing Lookahead Relocation Rules for the Container Relocation Problem with Genetic Programming", booktitle = "European Conference on Genetic Programming, EuroGP 2025", year = "2025", editor = "Bing Xue and Luca Manzoni and Illya Bakurov", volume = "15609", series = "LNCS", pages = "156--172", address = "Trieste", month = "23-25 " # apr, publisher = "Springer Nature", keywords = "genetic algorithms, genetic programming, Container relocation problem, Relocation rules", isbn13 = "978-3-031-89990-4", URL = "https://rdcu.be/ejgEX", DOI = "doi:10.1007/978-3-031-89991-1_10", abstract = "The container relocation problem is an important combinatorial optimisation problem commonly found in warehouses and container ports. The goal of this problem is to retrieve all of the containers form the yard with the fewest container relocations between the stacks. Since the problem is NP-hard, various heuristics have been proposed to solve it, among which are relocation rules (RRs), simple constructive heuristics that incrementally construct the solution. However, it is quite difficult to design such RRs manually, so genetic programming has been applied to design new RRs automatically. A significant problem with RRs, whether manually or automatically designed, is that they usually have a limited view of the problem. This means that they will often make decisions that can negatively influence the future, meaning that the current decision would cause additional relocations. Therefore, this study investigates different relocation schemes that can be used within RRs to obtain rules with lookahead ability. These rules will enable containers to be relocated based on future information and, consequently, arranged better in the yard. For that purpose, three novel routing schemes for automatically designed RRs are defined and evaluated on an existing problem set. The results demonstrate that integrating additional elements to evolve lookahead RRs can significantly improve the results.", notes = "also known as \cite{paper_20_con_tainer_relo_cation_problem_with_genetic} Part of \cite{Xue:2025:GP} EuroGP'2025 held in conjunction with EvoCOP2025, EvoMusArt2025 and EvoApplications2025", } @Article{Durasevic:2025:swevo, author = "Marko Durasevic and Mateja Dumic and Francisco Javier {Gil Gala}", title = "Enhancing automatically designed relocation rules with the rollout algorithm", journal = "Swarm and Evolutionary Computation", year = "2025", volume = "96", pages = "101975", keywords = "genetic algorithms, genetic programming, Container relocation problem, Relocation rules, Rollout algorithm", ISSN = "2210-6502", URL = "https://www.sciencedirect.com/science/article/pii/S2210650225001336", DOI = "doi:10.1016/j.swevo.2025.101975", abstract = "The container relocation problem (CRP) is a complex optimisation problem in maritime transport. To solve this problem, heuristic approaches are often used, ranging from relocation rules (RRs) to metaheuristics. Although metaheuristics outperform RRs, the latter remain popular due to their simplicity and adaptability. The manual design of RRs is challenging, which is why genetic programming (GP) is used to automatically generate them. However, RRs generated by GP generally achieved inferior solutions compared to metaheuristics. To close this gap, this study applies the rollout method to improve the performance of RRs while maintaining reasonable execution times. The rollout algorithm strikes a balance between exhaustive and heuristic search by combining partial enumeration with RR-based decision evaluation. Although the rollout method improves the quality of the solution, it also leads to considerable computational cost. To solve this problem, three strategies for reducing the search space are proposed. Experimental results show that the rollout algorithm significantly improves solution quality compared to standard RRs, with the proposed search space reduction techniques effectively reducing execution time without compromising performance. In particular, the results show that the rollout algorithm can be executed 2 to 4 times faster using the proposed reduction techniques, while its performance is reduced only by 1percent", } @Article{Durasevic:2025:engappai, author = "Marko Durasevic and Mateja Dumic and Francisco Javier {Gil Gala}", title = "Multitask genetic programming for automated design of heuristics for the container relocation problem", journal = "Engineering Applications of Artificial Intelligence", year = "2025", volume = "144", pages = "110001", keywords = "genetic algorithms, genetic programming, Multitask genetic programming, Container relocation problem, Hyper-heuristics, Relocation rules", ISSN = "0952-1976", URL = "https://www.sciencedirect.com/science/article/pii/S0952197625000016", DOI = "doi:10.1016/j.engappai.2025.110001", abstract = "Automated design of heuristics with genetic programming (GP) received significant attention for various optimisation problems. One of these is the container relocation problem (CRP), encountered in shipping terminals and warehouses. Although GP can design high quality heuristics, it has certain limitations. One is that numerous variants of each optimisation problem exist, meaning that appropriate heuristics have to be designed for each. Most commonly these heuristics are evolved independently from each other, which can be regarded as wasteful since they should exhibit a certain kind of similarity because they solve the same problem. This encouraged the development of knowledge transfer methods, such as multitask learning, that share the knowledge between solutions evolved for different problem types. In multitask learning several problems are solved simultaneously, with solutions of different problems shared between the populations that solve each problem. Until now, in the context of the automated design of heuristics, multitask learning has been primarily used to solve scheduling problems with a limited number of different problem types. This motivates us to investigate multitask learning with two main objectives. First, to apply multitask learning to evolve heuristics for a different problem, namely CRP. Second, to investigate how problem types that are optimised simultaneously influence its ability to obtain efficient heuristics. The obtained results demonstrate that multitask learning improves the convergence of GP, although the overall performance of the obtained heuristics remains similar. Furthermore, the effectiveness of multitask learning significantly depends on the problem types optimised simultaneously, outlining that the choice of the problems is critical", } @Misc{DBLP:journals/corr/DurieuxMMSX15, author = "Thomas Durieux and Matias Martinez and Martin Monperrus and Romain Sommerard and Jifeng Xuan", title = "Automatic Repair of Real Bugs: An Experience Report on the {Defects4J} Dataset", year = "2015", howpublished = "ArXiv", month = "26 " # may, keywords = "genetic algorithms, genetic programming, genetic improvement, APR, SBSE, GenProg", URL = "http://arxiv.org/abs/1505.07002", timestamp = "Mon, 01 Jun 2015 14:13:54 +0200", biburl = "http://dblp.uni-trier.de/rec/bib/journals/corr/DurieuxMMSX15", bibsource = "dblp computer science bibliography, http://dblp.org", size = "12 pages", abstract = "Automatic software repair aims to reduce human effort for fixing bugs. Various automatic repair approaches have emerged in recent years. In this paper, we report on an experiment on automatically repairing 224 bugs of a real-world and publicly available bug dataset, Defects4J. We investigate the results of three repair methods, GenProg (repair via random search), Kali (repair via exhaustive search), and Nopol (repair via constraint based search). We conduct our investigation with five research questions: fixability, patch correctness, ill-defined bugs, performance, and fault localizability. Our implementations of GenProg, Kali, and Nopol fix together 41 out of 224 (18percent) bugs with 59 different patches. This can be viewed as a baseline for future usage of Defects4J for automatic repair research. In addition, manual analysis of sampling 42 of 59 generated patches shows that only 8 patches are undoubtedly correct. This is a novel piece of evidence that there is large room for improvement in the area of test suite based repair.", notes = "Cites \cite{LeGoues:2012:ICSE}", } @TechReport{durieux:hal-01272126, title = "{IntroClassJava}: A Benchmark of 297 Small and Buggy {Java} Programs", author = "Thomas Durieux and Martin Monperrus", URL = "https://hal.archives-ouvertes.fr/hal-01272126/document", institution = "Universite Lille 1", year = "2016", hal_id = "hal-01272126", notes = "https://github.com/Spirals-Team/IntroClassJava", } @Misc{oai:arXiv.org:1311.5250, title = "Attractor Control Using Machine Learning", author = "Thomas Duriez and Vladimir Parezanovic and Bernd R. Noack and Laurent Cordier and Marc Segond and Markus Abel", year = "2013", month = "22 " # nov, howpublished = "arXiv", keywords = "genetic algorithms, genetic programming, nonlinear sciences, chaotic dynamics, physics, fluid dynamics, ECJ", bibsource = "OAI-PMH server at export.arxiv.org", oai = "oai:arXiv.org:1311.5250", URL = "http://arxiv.org/abs/1311.5250", size = "5 pages", abstract = "We propose a general strategy for feedback control design of complex dynamical systems exploiting the nonlinear mechanisms in a systematic unsupervised manner. These dynamical systems can have a state space of arbitrary dimension with finite number of actuators (multiple inputs) and sensors (multiple outputs). The control law maps outputs into inputs and is optimised with respect to a cost function, containing physics via the dynamical or statistical properties of the attractor to be controlled. Thus, we are capable of exploiting nonlinear mechanisms, e.g. chaos or frequency cross-talk, serving the control objective. This optimisation is based on genetic programming, a branch of machine learning. This machine learning control is successfully applied to the stabilisation of nonlinearly coupled oscillators and maximization of Lyapunov exponent of a forced Lorenz system. We foresee potential applications to most nonlinear multiple inputs/multiple outputs control problems, particularly in experiments.", notes = "Comment: 5 pages, 4 figures", } @Misc{oai:arXiv.org:1505.01022, title = "Feedback Control of Turbulent Shear Flows by Genetic Programming", author = "Thomas Duriez and Vladimir Parezanovic and Kai {von Krbek} and Jean-Paul Bonnet and Laurent Cordier and Bernd R. Noack and Marc Segond and Markus Abel and Nicolas Gautier and Jean-Luc Aider and Cedric Raibaudo and Christophe Cuvier and Michel Stanislas and Antoine Debien and Nicolas Mazellier and Azeddine Kourta and Steven L. Brunton", year = "2015", month = may # "~05", note = "Comment: 49 pages, many figures, submitted to Phys Rev E", keywords = "genetic algorithms, genetic programming, physics - fluid dynamics", bibsource = "OAI-PMH server at export.arxiv.org", oai = "oai:arXiv.org:1505.01022", URL = "http://arxiv.org/abs/1505.01022", abstract = "Turbulent shear flows have triggered fundamental research in nonlinear dynamics, like transition scenarios, pattern formation and dynamical modelling. In particular, the control of nonlinear dynamics is subject of research since decades. In this publication, actuated turbulent shear flows serve as test-bed for a nonlinear feedback control strategy which can optimise an arbitrary cost function in an automatic self-learning manner. This is facilitated by genetic programming providing an analytically treatable control law. Unlike control based on PID laws or neural networks, no structure of the control law needs to be specified in advance. The strategy is first applied to low-dimensional dynamical systems featuring aspects of turbulence and for which linear control methods fail. This includes stabilising an unstable fixed point of a nonlinearly coupled oscillator model and maximising mixing, i.e. the Lyapunov exponent, for forced Lorenz equations. For the first time, we demonstrate the applicability of genetic programming control to four shear flow experiments with strong nonlinearities and intrinsically noisy measurements. These experiments comprise mixing enhancement in a turbulent shear layer, the reduction of the recirculation zone behind a backward facing step, and the optimised reattachment of separating boundary layers. Genetic programming control has outperformed tested optimised state-of-the-art control and has even found novel actuation mechanisms.", } @Misc{Durrett:2010:ccaGP2pmips, author = "Greg Durrett and Frank Neumann and Una-May O'Reilly", title = "Computational Complexity Analysis of Simple Genetic Programming On Two Problems Modeling Isolated Program Semantics", year = "2010", month = "27 " # jul, note = "arXiv:1007.4636v1", keywords = "genetic algorithms, genetic programming, Computational Complexity, Data Structures and Algorithms", URL = "http://arxiv.org/pdf/1007.4636v1", size = "26 pages", abstract = "Analysing the computational complexity of evolutionary algorithms for binary search spaces has significantly increased their theoretical understanding. With this paper, we start the computational complexity analysis of genetic programming. We set up several simplified genetic programming algorithms and analyze them on two separable model problems, ORDER and MAJORITY, each of which captures an important facet of typical genetic programming problems. Both analyses give first rigorous insights on aspects of genetic programming design, highlighting in particular the impact of accepting or rejecting neutral moves and the importance of a local mutation operator.", notes = "See \cite{Durrett:2011:foga}", } @InProceedings{Durrett:2011:foga, author = "Greg Durrett and Frank Neumann and Una-May O'Reilly", title = "Computational Complexity Analysis of Simple Genetic Programming On Two Problems Modeling Isolated Program Semantics", booktitle = "Foundations of Genetic Algorithms", year = "2011", editor = "Hans-Georg Beyer and W. B. Langdon", pages = "69--80", address = "Schwarzenberg, Austria", month = "5-9 " # jan, organisation = "SigEvo", publisher = "ACM", keywords = "genetic algorithms, genetic programming, Genetic Programming Theory, Computational Complexity, Hill Climbing", isbn13 = "978-1-4503-0633-1", DOI = "doi:10.1145/1967654.1967661", size = "12 pages", abstract = "Analysing the computational complexity of evolutionary algorithms (EAs) for binary search spaces has significantly informed our understanding of EAs in general. With this paper, we start the computational complexity analysis of genetic programming (GP). We set up several simplified GP algorithms and analyse them on two separable model problems, ORDER and MAJORITY, each of which captures a relevant facet of typical GP problems. Both analyses give first rigorous insights into aspects of GP design, highlighting in particular the impact of accepting or rejecting neutral moves and the importance of a local mutation operator.", notes = "See \cite{Durrett:2010:ccaGP2pmips} \cite{Nguyen:2013:foga} FOGA11 ACM order number 910114", } @InProceedings{duyvesteyn:2005:CEC, author = "Korneel Duyvesteyn and Uzay Kaymak", title = "Genetic Programming in Economic Modelling", booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary Computation", year = "2005", editor = "David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L. Gwenn Volkert and Dan Ashlock and Marc Schoenauer", volume = "2", pages = "1025--1031", address = "Edinburgh, UK", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "2-5 " # sep, organisation = "IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9363-5", DOI = "doi:10.1109/CEC.2005.1554803", abstract = "Typically, economists develop models by first selecting a model structure based on theoretical considerations and equilibrium conditions, followed by parameter estimation from available data. As more and more data become available about economic processes, the question arises whether it is possible to obtain models in which {"}data speak for themselves{"}, where both the model structure and the parameter values are identified directly from the data. In this paper, we discuss how genetic programming might be used for this purpose. We propose a framework to formulate a genetic programming search for suitable economic models. We also study a simple case and discuss future directions of research for developing the genetic programming methodology for economic modelling.", notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS.", } @InProceedings{Dvoracek:2016:EuroGP, author = "Petr Dvoracek and Lukas Sekanina", title = "Evolutionary Approximation of Edge Detection Circuits", booktitle = "EuroGP 2016: Proceedings of the 19th European Conference on Genetic Programming", year = "2016", month = "30 " # mar # "--1 " # apr, editor = "Malcolm I. Heywood and James McDermott and Mauro Castelli and Ernesto Costa and Kevin Sim", series = "LNCS", volume = "9594", publisher = "Springer Verlag", address = "Porto, Portugal", pages = "19--34", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming", isbn13 = "978-3-319-30668-1", DOI = "doi:10.1007/978-3-319-30668-1_2", abstract = "Approximate computing exploits the fact that many applications are inherently error resilient which means that some errors in their outputs can safely be exchanged for improving other parameters such as energy consumption or operation frequency. A new method based on evolutionary computing is proposed in this paper which enables to approximate edge detection circuits. Rather than evolving approximate edge detectors from scratch, key components of existing edge detector are replaced by their approximate versions obtained using Cartesian genetic programming (CGP). Various approximate edge detectors are then composed and their quality is evaluated using a database of images. The paper reports interesting edge detectors showing a good tradeoff between the quality of edge detection and implementation cost.", notes = "Part of \cite{Heywood:2016:GP} EuroGP'2016 held in conjunction with EvoCOP2016, EvoMusArt2016 and EvoApplications2016", } @TechReport{Dworman:95-01-04, author = "Garett Dworman and Steven Kimbrough and James Laing", title = "An Application of Genetic Programming to Bargaining in a Three-Agent Coalition Game", institution = "Department of Operations and Information Management, The Wharton School, University of Pennsylvania", year = "1995", number = "95-01-04", address = "Philadelphia PA 19104-6366, USA", keywords = "genetic algorithms, genetic programming", URL = "http://opim.wharton.upenn.edu/risk/downloads/archive/arch62.pdf", notes = "file cogs9506 30 Jan 1995 See also \cite{dworman:1995:b3acg}", size = "13 pages", } @InProceedings{dworman:1995:b3acg, author = "Garett Dworman and Steven O. Kimbrough and James D. Laing", title = "Bargaining in a Three-Agent Coalition Game: An Application of Genetic Programming", booktitle = "Working Notes for the AAAI Symposium on Genetic Programming", year = "1995", editor = "E. V. Siegel and J. R. Koza", pages = "9--16", address = "MIT, Cambridge, MA, USA", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025, USA", month = "10--12 " # nov, publisher = "AAAI", keywords = "genetic algorithms, genetic programming", URL = "http://www.aaai.org/Papers/Symposia/Fall/1995/FS-95-01/FS95-01-002.pdf", URL = "http://www.aaai.org/Library/Symposia/Fall/fs95-01.php", size = "8 page", abstract = "We are conducting a series of investigations whose primary objective is to demonstrate that boundedly rational agents, operating with fairly elementary computational mechanisms, can adapt to achieve approximately optimal strategies for bargaining with other agents in complex and dynamic environments of multilateral negotiations that humans find challenging. In this paper, we present results from an application of genetic programming (Koza, 1992) to model the co-evolution of simple artificial agents negotiating coalition agreements in a three agent cooperative game.", notes = "AAAI-95f GP. Part of \cite{siegel:1995:aaai-fgp}. See also \cite{Dworman:95-01-04}. {\em Telephone:} 415-328-3123 {\em Fax:} 415-321-4457 {\em email} info@aaai.org {\em URL:} http://www.aaai.org/", } @TechReport{dworman:1995:iGPSgt, author = "Garett Dworman and Steve O. Kimbrough and James D. Laing", title = "Implementation of a Genetic Programming System in a Game-Theoretic Context", institution = "University of Pennsylvania, Department of Operations and Information Management", year = "1995", type = "working paper", number = "95-01-02", keywords = "genetic algorithms, genetic programming", broken = "http://opim.wharton.upenn.edu/users/sok/comprats/GPWP01.ps", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/298/http:zSzzSzopim.wharton.upenn.eduzSz~dwormanzSzmy-paperszSzGPWP01.pdf/implementation-of-a-genetic.pdf", URL = "http://citeseer.ist.psu.edu/169645.html", URL = "http://opim.wharton.upenn.edu/home/wp/", size = "14 pages", } @Article{Dworman:1995:JMIS, author = "Garett Dworman and Steven O. Kimbrough and James D. Laing", title = "On Automated Discovery of Models Using Genetic Programming: Bargaining in a Three-Agent Coalitions Game", journal = "Journal of Management Information Systems", year = "1995", volume = "12", number = "3", pages = "97--125", month = "Winter", note = "Special Issue: Information Technology and IT Organizational Impact Guest Editors: Nunamaker Jr, Jay F and Sprague Jr., Ralph H", keywords = "genetic algorithms, genetic programming, automatic model discovery, game theory, machine learning", ISSN = "0742-1222", URL = "http://www.jmis-web.org/articles/307", URL = "http://www.tandfonline.com/doi/abs/10.1080/07421222.1995.11518093", URL = "https://oid.wharton.upenn.edu/files/?whdmsaction=public:main.file&fileID=5434.", DOI = "doi:10.1080/07421222.1995.11518093", abstract = "The creation of mathematical, as well as qualitative (or rule-based), models is difficult, time-consuming, and expensive. Recent developments in evolutionary computation hold out the prospect that, for many problems of practical import, machine learning techniques can be used to discover useful models automatically. The prospects are particularly bright, we believe, for such automated discoveries in the context of game theory. This paper reports on a series of successful experiments in which we used a genetic programming regime to discover high-quality negotiation policies. The game-theoretic context in which we conducted these experiments--a three-player coalitions game with side payments--is considerably more complex and subtle than any reported in the previous literature on machine learning applied to game theory.", notes = "fileID=5434. appears to be from Jstor.org", } @InProceedings{dworman:1996:admGPgtc, author = "Garett Dworman and Steve O. Kimbrough and James D. Laing", title = "On Automated Discovery of Models Using Genetic Programming in Game-Theoretic Contexts", booktitle = "Proceedings of the 28th Hawaii International Conference on System Sciences, Volume III: Information Systems: Decision Support and Knowledge-based Systems", year = "1995", editor = "Jay F. {Nunamaker Jr.} and Ralph H. {Sprague Jr.}", pages = "428--438", publisher_address = "Los Alamitos, CA", month = jan, publisher = "IEEE Computer Society Press", keywords = "genetic algorithms, genetic programming", broken = "http://opim.wharton.upenn.edu/users/sok/comprats/HICSSGP6.ps", broken = "http://opim.wharton.upenn.edu/users/sok/comprats/HICSSGP6-figures.eps", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/298/http:zSzzSzopim.wharton.upenn.eduzSz~dwormanzSzmy-paperszSzHICSSGP6.pdf/dworman95automated.pdf", URL = "http://citeseer.ist.psu.edu/dworman95automated.html", DOI = "doi:10.1109/HICSS.1995.375625", size = "13 pages", abstract = "The creation of mathematical, as well as qualitative (or rule-based), models is difficult, time-consuming, and expensive. Recent developments in evolutionary computation hold out the prospect that, for many problems of practical import, machine learning techniques can be used to discover useful models automatically. These prospects are particularly bright, we believe, for such automated discoveries in the context of game theory. This paper reports on a series of successful experiments in which we used a genetic programming regime to discover high-quality negotiation policies. The game-theoretic context in which we conducted these experiments-a three-player coalitions game with sidepayments-is considerably more complex and subtle than any reported in the literature on machine learning applied to game theory.", } @InProceedings{dworman:1996:baa2cg, author = "Garett Dworman and Steven O. Kimbrough and James D. Laing", title = "Bargaining by Artificial Agents in Two Coalition Games: A Study in Genetic Programming for Electronic Commerce", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "54--62", address = "Stanford University, CA, USA", publisher = "MIT Press", size = "9 pages", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap7.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96", } @Article{Dwyer19701199, author = "J. M. Dwyer and I. R. Mackay", title = "ANTIGEN-BINDING LYMPHOCYTES IN HUMAN FETAL THYMUS", journal = "The Lancet", volume = "295", number = "7658", pages = "1199--1202", year = "1970", ISSN = "0140-6736", DOI = "doi:10.1016/S0140-6736(70)91787-3", URL = "http://www.sciencedirect.com/science/article/B6T1B-498RPPJ-1MK/2/6cc03de5ebb144b1c653e0ffdc1720e8", notes = "Not on GP", } @InCollection{Dzalbs:2017:miller, author = "Ivars Dzalbs and Tatiana Kalganova", title = "Multi-step Ahead Forecasting Using Cartesian Genetic Programming", booktitle = "Inspired by Nature: Essays Presented to Julian F. Miller on the Occasion of his 60th Birthday", publisher = "Springer", year = "2017", editor = "Susan Stepney and Andrew Adamatzky", volume = "28", series = "Emergence, Complexity and Computation", chapter = "11", pages = "235--246", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", isbn13 = "978-3-319-67996-9", DOI = "doi:10.1007/978-3-319-67997-6_11", abstract = "This paper describes a forecasting method that is suitable for long range predictions. Forecasts are made by a calculating machine of which inputs are the actual data and the outputs are the forecasted values. The Cartesian Genetic Programming (CGP) algorithm finds the best performing machine out of a huge abundance of candidates via evolutionary strategy. The algorithm can cope with non-stationary highly multivariate data series, and can reveal hidden relationships among the input variables. Multiple experiments were devised by looking at several time series from different industries. Forecast results were analysed and compared using average Symmetric Mean Absolute Percentage Error (SMAPE) across all datasets. Overall, CGP achieved comparable to Support Vector Machine algorithm and performed better than Neural Networks.", notes = "part of \cite{miller60book} https://link.springer.com/bookseries/10624", } @Article{DZALBS:2018:BDR, author = "Ivars Dzalbs and Tatiana Kalganova", title = "Forecasting Price Movements in Betting Exchanges Using Cartesian Genetic Programming and ANN", journal = "Big Data Research", volume = "14", pages = "112--120", year = "2018", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Algorithmic trading, Financial series forecasting, Betting exchange", ISSN = "2214-5796", DOI = "doi:10.1016/j.bdr.2018.10.001", URL = "http://www.sciencedirect.com/science/article/pii/S221457961730374X", abstract = "Since the introduction of betting exchanges in 2000, there has been increased interest of ways to monetize on the new technology. Betting exchange markets are fairly similar to the financial markets in terms of their operation. Due to the lower market share and newer technology, there are very few tools available for automated trading for betting exchanges. The in-depth analysis of features available in commercial software demonstrates that there is no commercial software that natively supports machine learned strategy development. Furthermore, previously published academic software products are not publicly obtainable. Hence, this work concentrates on developing a full-stack solution from data capture, back-testing to automated Strategy Agent development for betting exchanges. Moreover, work also explores ways to forecast price movements within betting exchange using new machine learned trading strategies based on Artificial Neuron Networks (ANN) and Cartesian Genetic Programming (CGP). Automatically generated strategies can then be deployed on a server and require no human interaction. Data explored in this work were captured from 1st of January 2016 to 17th of May 2016 for all GB WIN Horse Racing markets (total of 204 GB of data processing). Best found Strategy agent shows promising 83percent Return on Investment (ROI) during simulated historical validation period of one month (15th of April 2016 to 16th of May 2016)", } @PhdThesis{Dzalbs:thesis, author = "Ivars Dzalbs", title = "OptPlatform: metaheuristic optimisation framework for solving complex real-world problems", school = "Dept of Electronic and Computer Engineering, Brunel University", year = "2021", address = "London, UK", month = jan, keywords = "genetic algorithms, genetic programming, GPU, Intel Xeon Phi, AVX, SIMD, ant colony optimisation, ACO, Optimization, Metaheuristics, Supply chain optimisation, Automated tuning", URL = "http://bura.brunel.ac.uk/handle/2438/22848", URL = "https://bura.brunel.ac.uk/bitstream/2438/22848/1/FulltextThesis.pdf", size = "187 pages", abstract = "We optimise daily, whether that is planning a round trip that visits the most attractions within a given holiday budget or just taking a train instead of driving a car in a rush hour. Many problems, just like these, are solved by individuals as part of our daily schedule, and they are effortless and straightforward. If we now scale that to many individuals with many different schedules, like a school timetable, we get to a point where it is just not feasible or practical to solve by hand. In such instances, optimisation methods are used to obtain an optimal solution. In this thesis, a practical approach to optimisation has been taken by developing an optimisation platform with all the necessary tools to be used by practitioners who are not necessarily familiar with the subject of optimisation. First, a high-performance metaheuristic optimisation framework (MOF) called OptPlatform is implemented, and the versatility and performance are evaluated across multiple benchmarks and real-world optimisation problems. Results show that, compared to competing MOFs, the OptPlatform outperforms in both the solution quality and computation time. Second, the most suitable hardware platform for OptPlatform is determined by an in-depth analysis of Ant Colony Optimisation scaling across CPU, GPU and enterprise Xeon Phi. Contrary to the common benchmark problems used in the literature, the supply chain problem solved could not scale on GPUs. Third, a variety of metaheuristics are implemented into OptPlatform. Including, a new metaheuristic based on Imperialist Competitive Algorithm (ICA), called ICA with Independence and Constrained Assimilation (ICAwICA) is proposed. The ICAwICA was compared against two different types of benchmark problems, and results show the versatile application of the algorithm, matching and in some cases outperforming the custom-tuned approaches. Finally, essential MOF features like automatic algorithm selection and tuning, lacking on existing frameworks, are implemented in OptPlatform. Two novel approaches are proposed and compared to existing methods. Results indicate the superiority of the implemented tuning algorithms within constrained tuning budget environment.", notes = "a little on GP? Supervisor: Tatiana Kalganova and Hongying Meng", } @InProceedings{dzeroski:1994:ECML, author = "Saso Dzeroski and Igor Petrovski", title = "Discovering dynamics with genetic programming", booktitle = "European Conference on Machine Learning, ECML-94", year = "1994", editor = "Francesco Bergadano and Luc {De Raedt}", volume = "784", series = "Lecture Notes in Computer Science", pages = "347--350", address = "Catania, Italy", month = apr # " 6-8", publisher = "Springer", keywords = "genetic algorithms, genetic programming, GPDD, Levenberg-Marquardt, background knowledge", URL = "https://rdcu.be/dq39r", URL = "http://link.springer.com/chapter/10.1007/3-540-57868-4_70", DOI = "doi:10.1007/3-540-57868-4_70", size = "4 pages", abstract = "This paper describes an application of the genetic programming paradigm to the problem of structure identification of dynamical systems. The approach is experimentally evaluated by reconstructing the models of several dynamical systems from simulated behaviours.", notes = "also known as \cite{DBLP:conf/ecml/DzeroskiP94} Institut Jozef Stefan, Jamova 39, 61111, Ljubljana, Slovenia", } @InProceedings{dzeroski:1995:dsiml, author = "Saso Dzeroski and Ljupco Todorovski and Igor Petrovski", title = "Dynamical System Identification with Machine Learning", booktitle = "Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications", year = "1995", editor = "Justinian P. Rosca", pages = "50--63", address = "Tahoe City, California, USA", month = "9 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://www-ai.ijs.si/SasoDzeroski/oldPage/publications.htm#Pub1995", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/dzeroski_1995_dsiml.pdf", size = "14 pages", abstract = "The classical approach to dynamical systems identification is to assume a fixed model (set of differential equations) with unknown parameters. Given measurements ... LAGRANGE algorithm described, brusselator, volterra-lotka model of population dynamics, monod equations, pole balancing, system identification", notes = "part of \cite{rosca:1995:ml} Nov 2023 2nd author name corrected", } @Article{DZIUBA:2023:ejor, author = "Daryna Dziuba and Christian Almeder", title = "New construction heuristic for capacitated lot sizing problems", journal = "European Journal of Operational Research", volume = "311", number = "3", pages = "906--920", year = "2023", ISSN = "0377-2217", DOI = "doi:10.1016/j.ejor.2023.06.002", URL = "https://www.sciencedirect.com/science/article/pii/S0377221723004290", keywords = "genetic algorithms, genetic programming, Heuristics, Lot sizing, Production planning", abstract = "We consider the classical single-level multi-item capacitated lot-sizing problem (CLSP) which is the core model for production planning. It is NP-hard and if setup operations consume capacity the feasibility problem itself is NP-complete. Several construction heuristics have been proposed in the research literature, but none of them achieves a sufficient solution quality and generality at the same time - meaning that they can be applied to different variations of the problem easily. We propose a general two-step construction heuristic (2-SCH) which sorts the customer orders in the first step and iteratively adds them to a preliminary production plan in the second step. Hence, various problem classes can be solved easily and fast. Computational experiments on the CLSP without setup times show that the 2-SCH outperforms the well-known Dixon-Silver (Dixon & Silver, 1981) and the ABC heuristics Maes and Van Wassenhove (1986) and provides better results than the genetic programming approach proposed recently by Hein et al. (2018). We also apply it to the CLSP with setup times where it outperforms the construction heuristic proposed by Trigeiro (1989). Finally, we show the flexibility of the 2-SCH by applying it to the CLSP with backorders and the multi-level CLSP as well as the single-level CLSP in an online environment", } @InProceedings{Dziurzanski:2020:CEC, author = "Piotr Dziurzanski and Simos Gerasimou and Dimitris Kolovos and Nicholas Matragkas", title = "Empirical Analysis of 1-edit Degree Patches in Syntax-Based Automatic Program Repair", booktitle = "2020 IEEE Congress on Evolutionary Computation (CEC)", year = "2020", month = jul # " 19-24", editor = "Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Justyna Petke and John R. Woodward", publisher = "IEEE", address = "Internet", note = "Special Session on Genetic Improvement", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, APR", isbn13 = "978-1-7281-6929-3", URL = "http://geneticimprovementofsoftware.com/paper_pdfs/E-24527.pdf", URL = "https://pure.york.ac.uk/portal/en/publications/empirical-analysis-of-1edit-degree-patches-in-syntaxbased-automatic-program-repair(83213662-3691-42ce-804e-0fc8c524f2f8).html", DOI = "doi:10.1109/CEC48606.2020.9185913", size = "10 pages", abstract = "In this paper, software patches modifying a single line (aka 1-edit degree patches) of buggy Java open-source projects have been generated automatically using computational search and experimentally evaluated. We carried out the presumably largest to date experiment related to 1-edit degree patches, consisting of almost 27000 computational jobs upper bounded with 107000 computational hours. Our experiments show the benefits and drawbacks of such kind of patches. In particular, the search space size has been shown to be reduced by several orders of magnitude. The volume of tests that can be filtered out without any negative impact while generating 1-edit degree patches has been increased by about 97percent. Finally, the effectiveness of finding 1-edit plausible patches is compared with multi-line plausible patches found with state-of-the-art syntax-based Automatic Program Repair tools. It is shown that despite patching fewer bugs in total, 1-edit degree patches have potential to patch some extra bugs.", notes = "Comparison with ARJA, GenProg, Kali, jGenProg, jKali, Nopol, 1-edit degree on Defects4J (chart, time, lang, math). University of York, UK. EPSRC-funded MANATEE project http://geneticimprovementofsoftware.com/events/wcci2020 WCCI2020", } @Misc{Earl:2024:GPTP, author = "Allison Earl", title = "Principles of Persuasion: What changes minds", booktitle = "Genetic Programming Theory and Practice XXI", year = "2024", editor = "Wolfgang Banzhaf and Ting Hu and Alexander Lalejini and Stephan Winkler", series = "Genetic and Evolutionary Computation", address = "University of Michigan, USA", month = jun # " 6-8", keywords = "genetic algorithms, genetic programming", notes = "Does not appear in published proceedsings", } @InProceedings{east:1999:IWOPUGA, author = "E. William East", title = "Infrastructure Work Order Planning Using Genetic Algorithms", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1510--1516", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "real world applications", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-728.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-728.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99) Seeding initial population Order-based chromosome", } @InProceedings{Easterbrook:2023:USNC-URSI, author = "Zion Easterbrook and Edmond Chong and Sunny Zhang and Magdy F. Iskander and Zhengqing Yun", booktitle = "2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (USNC-URSI)", title = "Broadband Metamaterial Design Using Carbon Fiber and Resistive Sheet Materials", year = "2023", pages = "1419--1420", month = "23-28 " # jul, address = "Portland, OR, USA", keywords = "genetic algorithms, genetic programming, Antenna measurements, Conferences, Sheet materials, Metamaterials, Broadband communication, Broadband antennas", ISSN = "1947-1491", DOI = "doi:10.1109/USNC-URSI52151.2023.10237910", size = "2 pages", abstract = "The objective of this paper is to compare experimental and simulated results of different broadband metamaterial absorber approaches. One such approach, which used a custom patterned fabricated board with resistive sheets, showed promising results in achieving broadband characteristics. In addition, quasi-isotropic carbon fiber patterns have been designed using genetic programming and simulated results have been obtained. We fabricate these carbon fiber designs and compare the measured results with the simulations, as well as compare with the resistive sheet design.", notes = "Also known as \cite{10237910} Hawaii Advanced Wireless Technologies Institude, The University of Hawaii at Manoa, Honolulu,HI96816, USA", } @InProceedings{Easton:2021:evomusart, author = "Edward Easton and Aniko Ekart and Ulysses Bernardet", title = "Axial Generation: A Concretism-Inspired Method for Synthesizing Highly Varied Artworks", booktitle = "EvoMUSART 2021, Artificial Intelligence in Music, Sound, Art and Design", year = "2021", month = "7-9 " # apr, editor = "J. Romero and T. Martins and N. Rodriguez-Fernandez", series = "LNCS", volume = "12693", publisher = "Springer Verlag", address = "Seville, Spain", pages = "115--130", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Evolutionary computation, 2D and 3D art generation, Concretism", isbn13 = "978-3-030-72913-4", URL = "https://research.aston.ac.uk/en/publications/axial-generation-a-concretism-inspired-method-for-synthesizing-hi", DOI = "doi:10.1007/978-3-030-72914-1_8", size = "16 pages", abstract = "Automated computer generation of aesthetically pleasing artwork has been the subject of research for several decades. The unsolved problem of interest is how to automatically please any audience without too much involvement of the said audience in the process of creation. Two dimensional pictures have received a lot of attention, however 3D artwork has remained relatively unexplored. This paper introduces the Axial Generation Process (AGP), a versatile generation algorithm that can be employed to create both 2D and 3D items within the Concretism art style. The evaluation of items generated using the process using a set of formal aesthetic measures, shows the process to be capable of generating visually varied items which generally exhibit a diverse range of values across the measures used, in both two and three dimensions.", notes = "Computer Science, Aston University, Birmingham, UK http://www.evostar.org/2020/ EvoMusArt2020 held in conjunction with EvoApplications2020, EuroGP'2020 and EvoCOP2020", } @InProceedings{Easton:2024:evomusart, author = "Edward Easton and Ulysses Bernardet and Aniko Ekart", title = "Modelling Individual Aesthetic Preferences of 3D Sculptures", booktitle = "13th International Conference on Artificial Intelligence in Music, Sound, Art and Design, EvoMusArt 2024", year = "2024", editor = "Colin Johnson and Sergio M. Rebelo and Iria Santos", series = "LNCS", volume = "14633", publisher = "Springer", address = "Aberystwyth", month = "3-5 " # apr, pages = "130--145", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Aesthetic judgement, 3D Art Generation, Aesthetic modelling", isbn13 = "978-3-031-56991-3", URL = "https://rdcu.be/dD0Gt", DOI = "doi:10.1007/978-3-031-56992-0_9", size = "16 pages", abstract = "Aesthetic preference is a complex puzzle with many subjective aspects. This subjectivity makes it incredibly difficult to computationally model aesthetic preference for an individual. Despite this complexity, individual aesthetic preference is an important part of life, impacting a multitude of aspects, including romantic and platonic relationships, decoration, product choices and artwork. Models of aesthetic preference form the basis of automated and semi-automated Evo-Art systems. These range from looking at individual aspects to more complex models considering multiple, different criteria. Effectively modelling aesthetic preference greatly increases the potential impact of these systems. This paper presents a flexible computational model of aesthetic preference, primarily focusing on generating 3D sculptures. Through demonstrating the model using several examples, it is shown that the model is flexible enough to identify and respond to individual aesthetic preferences,...", notes = "Aston Centre for AI Research and Application (ACAIRA), Aston University, Birmingham, UK http://www.evostar.org/2024/ EvoMusArt2024 held in conjunction with EuroGP'2024, EvoCOP2024 and EvoApplications2024", } @Article{Ebadi:2014:EC, author = "Toktam Ebadi and Ignas Kukenys and Will N. Browne and Mengjie Zhang", title = "Human Readable Feature Pattern Classification System using Learning Classifier Systems", journal = "Evolutionary Computation", year = "2014", volume = "22", number = "4", pages = "629--650", month = "Winter", keywords = "LCS, Learning classifier system, evolutionary computation, pattern recognition, Haar-like features.", ISSN = "1063-6560", DOI = "doi:10.1162/EVCO_a_00127", size = "22 pages", abstract = "Image pattern classification is a challenging task due to the large search space of pixel data. Supervised and subsymbolic approaches can learn problems classes. However, in the complex image recognition domain, there is a need for investigation of learning techniques that allow humans to interpret the learnt rules in order to gain an insight about the problem. Learning classifier systems (LCSs) are a machine learning technique that have been minimally explored for image classification. This work has developed the feature pattern classification system (FPCS) framework by adopting Haar-like features from the image recognition domain for feature extraction. The FPCS integrates Haar-like features with XCS, which is an accuracy-based LCS. A major contribution of this work is that the developed framework is capable of producing human-interpretable rules. The FPCS system achieved 91 plus/minus 1percent accuracy on the unseen test set of the MNIST dataset. In addition, the FPCS is capable of autonomously adjusting the rotation angle in unaligned images. This rotation adjustment raised the accuracy of FPCS to 95percent. Although the performance is competitive with equivalent approaches, this was not as accurate as subsymbolic approaches on this dataset. However, the benefit of the interpretability of rules produced by FPCS enabled us to identify the distribution of the learnt angles a normal distribution around 0degree which would have been very difficult in subsymbolic approaches. The analysable nature of FPCS is anticipated to be beneficial in domains such as speed sign recognition, where underlying reasoning and confidence of recognition needs to be human interpretable.", } @Article{Ebbels2009361, title = "Bioinformatic methods in {NMR}-based metabolic profiling", author = "Timothy M. D. Ebbels and Rachel Cavill", journal = "Progress in Nuclear Magnetic Resonance Spectroscopy", volume = "55", number = "4", pages = "361--374", year = "2009", month = nov, keywords = "genetic algorithms, genetic programming, Metabonomics, Metabolomics, Metabolic profiling, Bioinformatics, Statistical methods, Modelling, Machine learning, Pattern recognition", ISSN = "0079-6565", DOI = "doi:10.1016/j.pnmrs.2009.07.003", URL = "http://www.sciencedirect.com/science/article/pii/S0079656509000788", } @Article{Ebel1979131, author = "Roland H. Ebel and William Wagoner and Henry F. Hrubecky", title = "Get ready for the L-bomb: A preliminary social assessment of longevity technology", journal = "Technological Forecasting and Social Change", volume = "13", number = "2", pages = "131--148", year = "1979", ISSN = "0040-1625", DOI = "doi:10.1016/0040-1625(79)90108-2", URL = "http://www.sciencedirect.com/science/article/B6V71-45P0D4G-2X/2/89b1c10893ac90101be199e8be7a7a53", notes = "not on GP", } @InProceedings{Eberbach:1997:eGPdc, author = "Eugene Eberbach", title = "Enhancing Genetic Programming by \$-calculus", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming", pages = "88", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1997/Eberbach_1997_eGPdc.pdf", size = "1 page", notes = "GP-97", } @InProceedings{eberbach:1998:xECGPALAADNAClc, author = "Eugene Eberbach", title = "Expressing Evolutionary Computation, Genetic Programming, Artificial Life, Autonomous Agents and {DNA}-Based Computing in l-Calculus", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "33--41", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "genetic algorithms, genetic programming", size = "8 pages", notes = "GP-98LB see updated version \cite{eberbach:2000:eecgpalaadc}", } @InProceedings{eberbach:2000:eecgpalaadc, author = "Eugene Eberbach", title = "Expressing Evolutionary Computation, Genetic Programming, Artificial Life, Autonomous Agents, and {DNA}-Based Computing in \$-Calculus", booktitle = "Proceedings of the 2000 Congress on Evolutionary Computation CEC00", year = "2000", pages = "1361--1368", volume = "2", address = "La Jolla Marriott Hotel La Jolla, California, USA", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "6-9 " # jul, organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, new paradigms, L-calculus, DNA-based computing, artificial life, autonomous agents, cost-optimisation, distributed complex processes, distributed interactive systems, evolutionary computation, expert systems, genetic programming, machine learning, neural nets, polyadic process algebra, resource bounded computation, uncertain information, artificial life, biocomputing, evolutionary computation, process algebra, software agents, uncertainty handling", ISBN = "0-7803-6375-2", URL = "http://www.cis.umassd.edu/~eeberbach/papers/cec2000.ps", URL = "http://citeseer.ist.psu.edu/491674.html", DOI = "doi:10.1109/CEC.2000.870810", size = "8 pages", abstract = "Genetic programming, autonomous agents, artificial life and evolutionary computation share many common ideas. They generally investigate distributed complex processes, perhaps with the ability to interact. It seems to be natural to study their behavior using process algebras, which were designed to handle distributed interactive systems. \$-calculus is a higher-order polyadic process algebra for resource bounded computation. It has been designed to handle autonomous agents, evolutionary computing, neural nets, expert systems, machine learning, and distributed interactive AI systems, in general. \$-calculus has built-in cost-optimisation mechanism allowing to deal with nondeterminism, incomplete and uncertain information. In this paper, we express in \$-calculus several subareas of evolutionary computation, including genetic programming, artificial life, autonomous agents and DNA-based computing.", notes = "CEC-2000 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 00TH8512, Library of Congress Number = 00-018644 Revision of \cite{eberbach:1998:xECGPALAADNAClc} \$-calculus == cost-calculus", } @Article{Eberbach2007200, author = "Eugene Eberbach", title = "The \$-calculus process algebra for problem solving: A paradigmatic shift in handling hard computational problems", journal = "Theoretical Computer Science", volume = "383", number = "2-3", pages = "200--243", year = "2007", note = "Complexity of Algorithms and Computations", ISSN = "0304-3975", DOI = "DOI:10.1016/j.tcs.2007.04.012", URL = "http://www.sciencedirect.com/science/article/B6V1G-4NGKWGF-7/2/07c09787a0b898de98e171ac414f6ddc", keywords = "genetic algorithms, genetic programming, Problem solving, Process algebras, Anytime algorithms, SuperTuring models of computation, Bounded rational agents, $-calculus, Intractability, Undecidability, Completeness, Optimality, Search optimality, Total optimality", abstract = "The $-calculus is the extension of the [pi]-calculus, built around the central notion of cost and allowing infinity in its operators. We propose the $-calculus as a more complete model for problem solving to provide a support to handle intractability and undecidability. It goes beyond the Turing Machine model. We define the semantics of the $-calculus using a novel optimization method (the k[Omega]-optimization), which approximates a nonexisting universal search algorithm and allows the simulation of many other search methods. In particular, the notion of total optimality has been used to provide an automatic way to deal with intractability of problem solving by optimizing together the quality of solutions and search costs. The sufficient conditions needed for completeness, optimality and total optimality of problem solving search are defined. A very flexible classification scheme of problem solving methods into easy, hard and solvable in the limit classes has been proposed. In particular, the third class deals with non-recursive solutions of undecidable problems. The approach is illustrated by solutions of some intractable and undecidable problems. We also briefly overview two possible implementations of the $-calculus.", notes = "GP one technique in many", } @InProceedings{Eberbach:2009:cec, author = "Eugene Eberbach and Mark Burgin", title = "Evolutionary Automata as Foundation of Evolutionary Computation: Larry Fogel Was Right", booktitle = "2009 IEEE Congress on Evolutionary Computation", year = "2009", editor = "Andy Tyrrell", pages = "2149--2156", address = "Trondheim, Norway", month = "18-21 " # may, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-2959-2", file = "P058.pdf", DOI = "doi:10.1109/CEC.2009.4983207", abstract = "In this paper we study expressiveness of evolutionary computation. To do so we introduce evolutionary automata and define their several subclasses. To our surprise, we got the result that evolving finite automata by finite automata leads outside its class, and allows to express for example pushdown automata or Turing machines. This explains partially why Larry Fogel restricted representation in Evolutionary Programming to finite state machines only. The power of evolution is enormous indeed!", keywords = "genetic algorithms, genetic programming, EP", notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR", } @InProceedings{Eberhardinger:2014:SASOW, author = "Benedikt Eberhardinger and Wolfgang Reif and Franz Wotawa and Tom Holvoet", title = "Quality Assurance for Self-Adaptive, Self-Organising Systems (Message from the Workshop Organisers)", booktitle = "2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops", year = "2014", pages = "108--109", address = "Imperial College, London", month = "8-12 " # sep, keywords = "genetic algorithms, genetic programming, genetic improvement", isbn13 = "978-1-4799-6378-2", DOI = "doi:10.1109/SASOW.2014.30", size = "2 pages", abstract = "Welcome to the first edition of the Workshop on Quality Assurance for Self-adaptive, Self-organising Systems (QA4SASO 2014). Developing self-adaptive, self-organising systems that fulfil the requirements of different stakeholders is no simple matter. Quality assurance is required at each phase of the entire development process, starting from requirements elicitation, agent design, system architecture design, and finally in the implementation, testing, and deployment of the system. The quality of the artefacts from each development phase affects the rest of the system, since all parts are closely related to each other. Furthermore, the shift of adaptation decisions from design-time to run-time - necessitated by the need of the systems to adapt to changing circumstances - makes it difficult, but even more essential, to assure high quality standards in these kind of systems. Accordingly, the analysis and evaluation of these self-systems has to take into account the specific operational context to achieve high quality standards. As a consequence, we like to address the following challenges in the workshop on quality assurance for self-adaptive, self-organising systems: Evolutionary developing system, interleaving mechanisms, uncertainty according the system environment, open system architecture, and large number of system participants. The necessity to investigate this field has already been recognised and addressed in different communities, but there exists so far no platform to bring all these communities together. Therefore, the workshop provides an open stage for discussions about the different aspects of quality assurance for self-adaptive, self-organising systems.", notes = "Mention of Mark Harman GI keynote. SASOW Also known as \cite{7056363}", } @InProceedings{eberhardinger:2024:ICSI, author = "Manuel Eberhardinger and Florian Rupp and Johannes Maucher and Setareh Maghsudi", title = "Unveiling the Decision-Making Process in Reinforcement Learning with Genetic Programming", booktitle = "15th International Conference on Swarm Intelligence, ICSI 2024", year = "2024", editor = "Ying Tan and Yuhui Shi", volume = "14788", series = "Lecture Notes in Computer Science", address = "Xining, China", month = aug # " 23-26", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "https://link.springer.com/chapter/10.1007/978-981-97-7181-3_28", DOI = "doi:10.1007/978-981-97-7181-3_28", } @InBook{ebert:1998:tmpa, author = "David S. Ebert and F. Kenton Musgrave and Darwyn Peachey and Ken Perlin and Steven Worley", title = "Texturing and Modeling, a Procedural Approach", chapter = "15", publisher = "Morgan Kaufmann", year = "2002", keywords = "genetic algorithms, genetic programming, genetic textures", ISBN = "1-55860-848-6", URL = "http://www.amazon.ca/gp/reader/0122287304/ref=sib_rdr_next1_S00E/702-6803721-8680860?ie=UTF8&p=S00E&ns=1#reader-page", notes = "Duplicate/2nd edition/3rd edition?? of \cite{Musgrave:1998:GT} 2022, 3rd edition available? https://engineering.purdue.edu/~ebertd/book2e.html broken Dec 2012 http://www.texturingandmodeling.com/ART/MUSGRAVE/CH19/Chapter19Art.html", } @Article{EBID:2021:ASEJ, author = "Ahmed M. Ebid and A. Deifalla", title = "Prediction of shear strength of {FRP} reinforced beams with and without stirrups using ({GP)} technique", journal = "Ain Shams Engineering Journal", volume = "12", number = "3", pages = "2493--2510", year = "2021", ISSN = "2090-4479", DOI = "doi:10.1016/j.asej.2021.02.006", URL = "https://www.sciencedirect.com/science/article/pii/S2090447921000885", keywords = "genetic algorithms, genetic programming, Shear strength, FRP reinforced beams, With stirrups, Without stirrups", abstract = "Although, international codes such as (CSA-S806-12) and (ACI-440-15) proposed shear design provisions for concrete beams reinforced with (FRP), many researchers are still investigating this case. Most of the available research investigated either beam without stirrups or beam with stirrups. The purpose of this study is to propose a unified formula for the prediction of the shear strength of FRP reinforced beams with and without stirrups. A collected experimental database of 553 shear tests on FRP reinforced concrete beams was used to develop a new formula to predict the shear strength using genetic programming technique. The accuracy of the proposed formula was compared with that measured during testing and calculated using the models available from literature. The new proposed formula showed more accurate predictions than the models from the literature, besides that, it is much simpler than them", } @Article{DBLP:journals/acisc/EbidNOA21, author = "Ahmed M. Ebid and Light I. Nwobia and Kennedy C. Onyelowe and Frank I. Aneke", title = "Predicting Nanobinder-Improved Unsaturated Soil Consistency Limits Using Genetic Programming and Artificial Neural Networks", journal = "Appl. Comput. Intell. Soft Comput.", volume = "2021", pages = "5992628:1--5992628:13", year = "2021", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1155/2021/5992628", DOI = "doi:10.1155/2021/5992628", timestamp = "Wed, 01 Sep 2021 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/acisc/EbidNOA21.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{ebid:2022:Materials, author = "Ahmed Ebid and Ahmed Deifalla", title = "Using Artificial Intelligence Techniques to Predict Punching Shear Capacity of Lightweight Concrete Slabs", journal = "Materials", year = "2022", volume = "15", number = "8", pages = "Article No. 2732", keywords = "genetic algorithms, genetic programming", ISSN = "1996-1944", URL = "https://www.mdpi.com/1996-1944/15/8/2732", DOI = "doi:10.3390/ma15082732", abstract = "Although lightweight concrete is implemented in many mega projects to reduce the cost and improve the project’s economic aspect, research studies focus on investigating conventional normal-weight concrete. In addition, the punching shear failure of concrete slabs is dangerous and calls for precise and consistent prediction models. Thus, this current study investigates the prediction of the punching shear strength of lightweight concrete slabs. First, an extensive experimental database for lightweight concrete slabs tested under punching shear loading is gathered. Then, effective parameters are determined by applying the principles of statistical methods, namely, concrete density, columns dimensions, slab effective depth, concrete strength, flexure reinforcement ratio, and steel yield stress. Next, the manuscript presented three artificial intelligence models, which are genetic programming (GP), artificial neural network (ANN) and evolutionary polynomial regression (EPR). In addition, it provided guidance for future design code development, where the importance of each variable on the strength was identified. Moreover, it provided an expression showing the complicated inter-relation between affective variables. The novelty lies in developing three proposed models for the punching capacity of lightweight concrete slabs using three different (AI) techniques capable of accurately predicting the strength compared to the experimental database", notes = "also known as \cite{ma15082732}", } @Article{ebid:2022:Sustainability, author = "Ahmed M. Ebid and Ahmed Farouk Deifalla and Hisham A. Mahdi", title = "Evaluating Shear Strength of Light-Weight and Normal-Weight Concretes through Artificial Intelligence", journal = "Sustainability", year = "2022", volume = "14", number = "21", pages = "Article No. 14010", keywords = "genetic algorithms, genetic programming", ISSN = "2071-1050", URL = "https://www.mdpi.com/2071-1050/14/21/14010", DOI = "doi:10.3390/su142114010", abstract = "The strength of concrete elements under shear is a complex phenomenon, which is induced by several effective variables and governing mechanisms. Thus, each parameter’s importance depends on the values of the effective parameters and the governing mechanism. In addition, the new concrete types, including lightweight concrete and fibered concrete, add to the complexity, which is why machine learning (ML) techniques are ideal to simulate this behaviour due to their ability to handle fuzzy, inaccurate, and even incomplete data. Thus, this study aims to predict the shear strength of both normal-weight and light-weight concrete beams using three well-known machine learning approaches, namely evolutionary polynomial regression (EPR), artificial neural network (ANN) and genetic programming (GP). The methodology started with collecting a dataset of about 1700 shear test results and dividing it into training and testing subsets. Then, the three considered (ML) approaches were trained using the training subset to develop three predictive models. The prediction accuracy of each developed model was evaluated using the testing subset. Finally, the accuracies of the developed models were compared with the current international design codes (ACI, EC2 & JSCE) to evaluate the success of this research in terms of enhancing the prediction accuracy. The results showed that the prediction accuracies of the developed models were 68percent, 83percent & 76.5percent for GP, ANN & EPR, respectively, and 56percent, 40percent & 62percent for ACI, EC2 & JSCE, in that order. Hence, the results indicated that the accuracy of the worst (ML) model is better than those of design codes, and the ANN model is the most accurate one.", notes = "also known as \cite{su142114010}", } @Article{ebid:2022:Designs, author = "Ahmed M. Ebid and Kennedy C. Onyelowe and Mohamed Salah", title = "Load-Settlement Curve and Subgrade Reaction of Strip Footing on Bi-Layered Soil Using Constitutive {FEM-AI} Coupled Techniques", journal = "Designs", year = "2022", volume = "6", number = "6", pages = "Article No. 104", keywords = "genetic algorithms, genetic programming", ISSN = "2411-9660", URL = "https://www.mdpi.com/2411-9660/6/6/104", DOI = "doi:10.3390/designs6060104", abstract = "This study presents a hybrid Artificial Intelligence-Finite Element Method (AI-FEM) predictive model to estimate the modulus of a subgrade reaction of a strip footing rested on a bi-layered profile. A parametric study was carried out using 2D Plaxis FEM models for strip footings with width (B) and rested on a bi-layered profile with top layer thickness (h) and bottom layer thickness (H). The soil was modelled using the well-known Mohr-Coulomb’s constitutive law. The extracted load-settlement curve from each FEM model is approximated to hyperbolic function and its factors (a, b) were determined. The subgrade reaction value (Ks) is the (stress/settlement), hence (1/Ks = a·Δ + b). Both inputs and outputs of the parametric study were collected in a single database containing the geometrical factors (B, h & H), soil properties of the top and bottom layers (c, φ & γ) and the extracted hyperbolic factors (a, b). Finally, three AI techniques—Genetic Programming (GP), Evolutionary Polynomial Regression (EPR) and Artificial Neural Networks (ANN)—were implemented to develop three predictive models to estimate the values of (a, b) using the collected database. The three developed models showed different accuracy values of (50percent, 65percent and 80percent) for (GP, EPR and ANN), respectively. The innovation of the developed model is its ability to capture the degradation of a subgrade reaction by increasing the stress (or the settlement) according to the hyperbolic formula.", notes = "also known as \cite{designs6060104}", } @InProceedings{Ebner:1997a, author = "Marc Ebner", title = "Evolution of Hierarchical Translation-Invariant Feature Detectors with an Application to Character Recognition", booktitle = "Mustererkennung 1997, 19. DAGM-Symposium", year = "1997", editor = "Erwin Paulus and Friedrich M. Wahl", series = "Informatik Aktuell", pages = "456--463", address = "Braunschweig", publisher_address = "Berlin", month = "15-17 " # sep, publisher = "Springer-Verlag", publisher_address = "Berlin", keywords = "genetic algorithms, genetic programming, evolution strategies, structure evolution, feature detection", ISBN = "3-540-63426-6", bibsource = "DBLP, http://dblp.uni-trier.de", URL = "http://wwwi2.informatik.uni-wuerzburg.de/staff/ebner/research/publications/uniTu/evolve.ps.gz", size = "8 pages", notes = " ", } @InProceedings{Ebner:1997b, author = "Marc Ebner", title = "On the Evolution of Edge Detectors for Robot Vision using Genetic Programming", booktitle = "Workshop SOAVE '97 - Selbstorganisation von Adaptivem Verhalten, VDI Reihe 8 Nr. 663", year = "1997", pages = "127--134", editor = "Horst-Michael Gro{\ss}", address = "D{\"u}sseldorf", publisher = "VDI Verlag", keywords = "genetic algorithms, genetic programming, edge detection", ISBN = "3-18-366308-2", URL = "http://www.ra.cs.uni-tuebingen.de/mitarb/ebner/research/publications/uniTu/gpedge.ps.gz", size = "8 pages", abstract = "Genetic programming has been applied to the task of evolving edge detectors... Canny ...", notes = " ", } @InProceedings{ebner:1998:eioGP, author = "Marc Ebner", title = "On the Evolution of Interest Operators using Genetic Programming", booktitle = "Late Breaking Papers at EuroGP'98: the First European Workshop on Genetic Programming", year = "1998", editor = "Riccardo Poli and W. B. Langdon and Marc Schoenauer and Terry Fogarty and Wolfgang Banzhaf", pages = "6--10", address = "Paris, France", publisher_address = "School of Computer Science", month = "14-15 " # apr, publisher = "CSRP-98-10, The University of Birmingham, UK", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/csrp-98-10.pdf", URL = "http://www2.informatik.uni-wuerzburg.de/staff/ebner/research/publications/uniTu/gpmoravec.ps.gz", URL = "http://citeseer.ist.psu.edu/158450.html", size = "5 pages", abstract = "Interest operators play an important role in computer vision. Depending on the type of the environment some features may prove to be more advantageous than others. Thus detection of interesting features has to be made adaptive such that the best features according to some measure are extracted. We are trying to evolve such feature detectors using genetic programming. In this paper we describe our results where the desired operator, which is a Moravec interest operator, is directly specified. We show that the problem is a rather difficult one. Only an approximation to the Moravec operator could be evolved using several sets of elementary functions. 1 Motivation Interest operators play an important role in computer vision [8]. They highlight points which can be found easily using simple correlation methods. They can be used to calculate accurate distance information and for map building [23]. However no interest operator is suitable for all types of environments. A mobile robot which ma...", notes = "EuroGP'98LB part of \cite{Poli:1998:egplb}", } @InProceedings{Ebner:1998c, author = "Marc Ebner", title = "Evolution of a control architecture for a mobile robot", booktitle = "Proceedings of the Second International Conference on Evolvable Systems: From Biology to Hardware (ICES 98)", year = "1998", editor = "Moshe Sipper and Daniel Mange and Andres Perez-Uribe", volume = "1478", series = "LNCS", pages = "303--310", address = "Lausanne, Switzerland", publisher_address = "Berlin", month = "23-25 " # sep, publisher = "Springer Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-64954-9", URL = "http://www2.informatik.uni-wuerzburg.de/staff/ebner/research/publications/uniTu/gprealrob.ps.gz", URL = "http://citeseer.ist.psu.edu/512626.html", DOI = "doi:10.1007/BFb0057632", abstract = "Most work in evolutionary robotics used a neural net approach for control of a mobile robot. Genetic programming has mostly been used for computer simulations. We wanted to see if genetic programming is capable to evolve a hierarchical control architecture for simple reactive navigation on a large physical mobile robot. First, we evolved hierarchical control algorithms for a mobile robot using computer simulations. Then we repeated one of the experiments with a large physical mobile robot.", old_abstract = "Programming a robot to perform a desired task in an unknown environment is a difficult task. Due to unexpected interactions between the environment and the robot many iterations of program development are often required. Using genetic programming the robots themselves may search the space of possible programs. In an experiment which was conducted over a period of two months we evolved a behavior-based control architecture for a large sized mobile robot, a Real World Interface B21. This is the first time that a large mobile robot was used in evolutionary robotics using tree-based genetic programming. In addition, our architecture uses conditional statements to build up a hierarchical reactive control structure. Sonar sensors are used to sense the environment. Because the robot is able to exert a considerable force if it crashes into an object, safety measures had to be taken to automatically monitor the run.", notes = "ICES98", } @InProceedings{ebner:1999:eemrl, author = "Marc Ebner", title = "Evolving an Environment Model for Robot Localization", booktitle = "Genetic Programming, Proceedings of EuroGP'99", year = "1999", editor = "Riccardo Poli and Peter Nordin and William B. Langdon and Terence C. Fogarty", volume = "1598", series = "LNCS", pages = "184--192", address = "Goteborg, Sweden", publisher_address = "Berlin", month = "26-27 " # may, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-65899-8", URL = "http://wwwi2.informatik.uni-wuerzburg.de/mitarbeiter/ebner/research/publications/uniTu/gplocstat.ps.gz", URL = "http://citeseer.ist.psu.edu/395304.html", DOI = "doi:10.1007/3-540-48885-5_15", size = "9 pages", abstract = "The use of an evolutionary method for robot localization is explored. We use genetic programming to evolve an inverse function mapping sensor readings to robot locations. This inverse function is an internal model of the environment. The robot senses its environment using dense distance information which may be obtained from a laser range finder. Moments are calculated from the distance distribution. These moments are used as terminal symbols in the evolved function. Arithmetic, trigonometric functions and a conditional statement are used as primitive functions. Using this representation we evolved an inverse function to localize a robot in a simulated office environment. We also analyzed the accuracy of the resulting function. This research was done at the University of Tuebingen, Wilhelm-Schickard-Institute for Computer Science, Computer Architecture (Prof. Zell).", notes = "EuroGP'99, part of \cite{poli:1999:GP}", } @InProceedings{ebner:1999:etsio, author = "Marc Ebner and Andreas Zell", title = "Evolving a Task Specific Image Operator", booktitle = "Evolutionary Image Analysis, Signal Processing and Telecommunications: First European Workshop, EvoIASP'99 and EuroEcTel'99", year = "1999", editor = "Riccardo Poli and Hans-Michael Voigt and Stefano Cagnoni and Dave Corne and George D. Smith and Terence C. Fogarty", volume = "1596", series = "LNCS", pages = "74--89", address = "Goteborg, Sweden", publisher_address = "Berlin", month = "28-29 " # may, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-65837-8", URL = "http://www-info2.informatik.uni-wuerzburg.de/mitarbeiter/marc/research/publications/uniTu/gpmoflow.ps.gz", DOI = "doi:10.1007/10704703_6", URL = "http://citeseer.ist.psu.edu/392593.html", abstract = "Image processing is usually done by chaining a series of well known image processing operators. Using evolutionary methods this process may be automated. In this paper we address the problem of evolving task specific image processing operators. In general, the quality of the operator depends on the task and the current environment. Using genetic programming we evolved an interest operator which is used to calculate sparse optical flow. To evolve the interest operator we define a series of criteria which need to be optimized. The different criteria are combined into an overall fitness function. Finally, we present experimental results on the evolution of the interest operator.", notes = "EvoIASP99'99", } @InProceedings{ebner:1999:EF, author = "Marc Ebner and Andreas Zell", title = "Evolving a behavior-based control architecture- From simulations to the real world", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1009--1014", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-414.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-414.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{ebner:1999:OSSGPIRNSS, author = "Marc Ebner", title = "On the Search Space of Genetic Programming and Its Relation to Nature's Search Space", booktitle = "Proceedings of the Congress on Evolutionary Computation", year = "1999", editor = "Peter J. Angeline and Zbyszek Michalewicz and Marc Schoenauer and Xin Yao and Ali Zalzala", volume = "2", pages = "1357--1361", address = "Mayflower Hotel, Washington D.C., USA", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "6-9 " # jul, organisation = "Congress on Evolutionary Computation, IEEE / Neural Networks Council, Evolutionary Programming Society, Galesia, IEE", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, models of evolutionary computation, fixed size bit strings, identity function, introns, nature, neutral mutations, phenotypical behaviour, quantitative analysis, search space, sequence space, variable length structures, combinatorial mathematics, reachability analysis, search problems", ISBN = "0-7803-5536-9 (softbound)", ISBN = "0-7803-5537-7 (Microfiche)", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.473.2984", URL = "http://stubber.math-inf.uni-greifswald.de/~ebner/resources/uniTu/gpfitness.pdf", DOI = "doi:10.1109/CEC.1999.782609", size = "5 pages", abstract = "The size of the search space has been analysed for genetic programming and genetic algorithms. It is highly unlikely to find any single individual in this huge search space. However, genetic programming with variable length structures differs from standard genetic algorithms where fixed size bit strings are used in that usually many different individuals show the same pheno-typical behaviour due to introns. Therefore, finding any given behaviour is not as difficult as the size of the search space suggests. A quantitative analysis is presented for the number of individuals that code for the identity function. The identity function is important in the analysis of the search space because it can be used to construct individuals showing the same behavior as any given individual. Finally, an analogy is drawn to nature's sequence space which suggests possible directions for future research. The representation should be chosen such that all possible behaviours are reachable within a comparatively small number of steps from any given behaviour and the individuals coding for any given behaviour should be distributed randomly in the search space. In addition, long paths of neutral mutations should lead to individuals which code for the same behaviour", notes = "CEC-99 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. Library of Congress Number = 99-61143", } @PhdThesis{ebner:thesis, author = "Marc Ebner", title = "Steuerung eines mobilen Roboters mit evolvierten Merkmalsdetektoren", school = "Eberhard-Karls-Universit{\"{a}}t T{\"{a}}bingen", year = "1999", keywords = "genetic algorithms, genetic programming, computer vision, biologically inspired systems", URL = "http://www2.informatik.uni-wuerzburg.de/staff/ebner/research/publications/uniTu/diss.ps.gz", size = "10426344", } @InProceedings{ebner:2001:EuroGP, author = "Marc Ebner", title = "Evolving Color Constancy for an Artificial Retina", booktitle = "Genetic Programming, Proceedings of EuroGP'2001", year = "2001", editor = "Julian F. Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon", volume = "2038", series = "LNCS", pages = "11--22", address = "Lake Como, Italy", publisher_address = "Berlin", month = "18-20 " # apr, organisation = "EvoNET", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, Color Constancy, Artificial Retina, Image Processing", ISBN = "3-540-41899-7", DOI = "doi:10.1007/3-540-45355-5_2", size = "12 pages", abstract = "Objects retain their colour in spite of changes in the wavelength and energy composition of the light they reflect. This phenomenon is called color constancy and plays an important role in computer vision research. We have used genetic programming to automatically search the space of programs to solve the problem of color constancy for an artificial retina. This retina consists of a two dimensional array of elements each capable of exchanging information with its adjacent neighbours. The task of the program is to compute the intensities of the light illuminating the scene. These intensities are then used to calculate the reflectances of the object. Randomly generated colour Mondrians were used as fitness cases. The evolved program was tested on artificial Mondrians and natural images.", notes = "EuroGP'2001, part of \cite{miller:2001:gp}", } @InProceedings{Ebner:2001:ECAL, author = "Marc Ebner", title = "A Three-Dimensional Environment for Self-Reproducing Programs", booktitle = "Advances in Artificial Life, Proceedings 6th European Conference, ECAL 2001", year = "2001", editor = "Jozef Kelemen and Petr Sosik", volume = "2159", series = "Lecture Notes in Computer Science", pages = "306--315", address = "Prague, Czech Republic", month = sep # " 10-14", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, self-reproducing programs, artificial life", ISBN = "3-540-42567-5", URL = "http://wwwi2.informatik.uni-wuerzburg.de/staff/ebner/research/publications/uniWu/selfRep.ps.gz", DOI = "doi:10.1007/3-540-44811-X_33", size = "10 pages", abstract = "Experimental results with a three-dimensional environment for self-reproducing programs are presented. The environment consists of a cube of virtual CPUs each capable of running a single process. Each process has access to the memory of 7 CPUs, to its own as well as to the memory of 6 neighbouring CPUs. Each CPU has a particular orientation which may be changed using special opcodes of the machine language. An additional opcode may be used to move the CPU. We have used a standard machine language with two operands. Constants are coded in a separate section of each command and a special mutation operator is used to ensure strong causality. This type of environment sets itself apart from other types of environments in the use of redundant mappings. Individuals have read as well as write access to neighboring CPUs and reproduce by copying their genetic material. They need to move through space in order to spawn new individuals and avoid overwriting their own offspring. After a short time all CPUs are filled by self-reproducing individuals and competition between individuals sets in which results in an increased rate of speciation.", } @InProceedings{ebner:2002:EuroGP, title = "Coevolution Produces an Arms Race Among Virtual Plants", author = "Marc Ebner and Adrian Grigore and Alexander Heffner and Juergen Albert", editor = "James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi", booktitle = "Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002", volume = "2278", series = "LNCS", pages = "316--325", publisher = "Springer-Verlag", address = "Kinsale, Ireland", publisher_address = "Berlin", month = "3-5 " # apr, year = "2002", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-43378-3", URL = "http://wwwi2.informatik.uni-wuerzburg.de/mitarbeiter/ebner/research/publications/uniWu/evoPlant.ps.gz", DOI = "doi:10.1007/3-540-45984-7_31", abstract = "Creating interesting virtual worlds is a difficult task. We are using a variant of genetic programming to automatically create plants for a virtual environment. The plants are represented as context-free Lindenmayer systems. OpenGL is used to visualize and evaluate the plants. Our plants have to collect virtual sunlight through their leaves in order to reproduce successfully. Thus we have realized an interaction between the plant and its environment. Plants are either evaluated separately or all individuals of a population at the same time. The experiments show that during coevolution plants grow much higher compared to rather bushy plants when plants are evaluated in isolation.", notes = "EuroGP'2002, part of \cite{lutton:2002:GP}", } @InProceedings{ebner03, author = "Marc Ebner", title = "Evolutionary Design of Objects Using Scene Graphs", booktitle = "Genetic Programming, Proceedings of EuroGP'2003", year = "2003", editor = "Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa", volume = "2610", series = "LNCS", pages = "47--58", address = "Essex", publisher_address = "Berlin", month = "14-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-00971-X", DOI = "doi:10.1007/3-540-36599-0_5", abstract = "One of the main issues in evolutionary design is how to create three-dimensional shape. The representation needs to be general enough such that all possible shapes can be created, yet it has to be evolvable. That is, parent and offspring must be related. Small changes to the genotype should lead to small changes of the fitness of an individual. We have explored the use of scene graphs to evolve three-dimensional shapes. Two different scene graph representations are analyzed, the scene graph representation used by OpenInventor and the scene graph representation used by VRML. Both representations use internal floating point variables to specify three-dimensional vectors, rotation axes and rotation angles. The internal parameters are initially chosen at random, then remain fixed during the run. We also experimented with an evolution strategy to adapt the internal variables. Experimental results are presented for the evolution of a wind turbine. The VRML representation produced better results.", notes = "EuroGP'2003 held in conjunction with EvoWorkshops 2003 overview http://wwwi2.informatik.uni-wuerzburg.de/mitarbeiter/ebner/research/evoRotor/evoRotor.html", } @InProceedings{Ebner:2003:ECAL, author = "Marc Ebner", title = "Evolution and Growth of Virtual Plants", booktitle = "Advances in Artificial Life. 7th European Conference on Artificial Life", year = "2003", editor = "Wolfgang Banzhaf and Thomas Christaller and Peter Dittrich and Jan T. Kim and Jens Ziegler", volume = "2801", series = "Lecture Notes in Artificial Intelligence", pages = "228--237", address = "Dortmund, Germany", month = "14-17 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, virtual plants, L-systems, co-evolution", ISBN = "3-540-20057-6", DOI = "DOI:10.1007/b12035", abstract = "According to the Red Queen hypothesis, an evolving population may be improving some trait, even though its fitness remains constant. We have created such a scenario with a population of coevolving plants. Plants are modelled using Lindenmayer systems and rendered with OpenGL. The plants consist of branches and leaves. Their reproductive success depends on their ability to catch sunlight as well as their structural complexity. All plants are evaluated inside the same environment, which means that one plant is able to cover other plants leaves. Leaves which are placed in the shadow of other plants do not catch any sunlight. The shape of the plant also determines the area where offspring can be placed. Offspring can only be placed in the vicinity of a plant. A number of experiments were performed in different environments. The Red Queen effect was seen in all cases.", notes = "ECAL-2003", } @Article{ebner:2003:GPEM, author = "Marc Ebner", title = "Book Review: {Illustrating} Evolutionary Computation with Mathematica", journal = "Genetic Programming and Evolvable Machines", year = "2003", volume = "4", number = "3", pages = "291--294", month = sep, keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dR8nt", DOI = "doi:10.1023/A:1025180508687", notes = "Review of \cite{jacob:2001:iecm} Article ID: 5141126", } @InProceedings{eurogp:EbnerRA05, author = "Marc Ebner and Markus Reinhardt and Juergen Albert", editor = "Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. {van Hemert} and Marco Tomassini", title = "Evolution of Vertex and Pixel Shaders", booktitle = "Proceedings of the 8th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3447", year = "2005", address = "Lausanne, Switzerland", month = "30 " # mar # " - 1 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming, GPU, linear GP: Poster", ISBN = "3-540-25436-6", pages = "261--270", DOI = "doi:10.1007/978-3-540-31989-4_23", DOI = "doi:10.1007/b107383", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "In real-time rendering, objects are represented using polygons or triangles. Triangles are easy to render and graphics hardware is highly optimised for rendering of triangles. Initially, the shading computations were carried out by dedicated hardwired algorithms for each vertex and then interpolated by the rasterizer. Todays graphics hardware contains vertex and pixel shaders which can be reprogrammed by the user. Vertex and pixel shaders allow almost arbitrary computations per vertex respectively per pixel. We have developed a system to evolve such programs. The system runs on a variety of graphics hardware due to the use of NVIDIA's high level Cg shader language. Fitness of the shaders is determined by user interaction. Both fixed length and variable length genomes are supported. The system is highly customisable. Each individual consists of a series of meta commands. The resulting Cg program is translated into the low level commands which are required for the particular graphics hardware.", notes = "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005 examples http://wwwi2.informatik.uni-wuerzburg.de/mitarbeiter/ebner/research/evoShader/evoShader.html Cg", } @Article{Ebner:2006:GPEM, author = "Marc Ebner", title = "Coevolution and the Red Queen effect shape virtual plants", journal = "Genetic Programming and Evolvable Machines", year = "2006", volume = "7", number = "1", pages = "103--123", month = mar, keywords = "genetic algorithms, genetic programming, Red Queen effect, Coevolution, Lindenmayer systems, Artificial plants", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-006-7013-2", size = "21 pages", abstract = "According to the Red Queen hypothesis a population of individuals may be improving some trait even though fitness remains constant. We have tested this hypothesis using a population of virtual plants. The plants have to compete with each other for virtual sunlight. Plants are modelled using Lindenmayer systems and rendered with OpenGL. Reproductive success of a plant depends on the amount of virtual light received as well as on the structural complexity of the plant. We experiment with two different modes of evaluation. In one experiment, plants are evaluated in isolation, while in other experiments plants are evaluated using coevolution. When using coevolution plants have to compete with each other for sunlight inside the same environment. Coevolution produces much thinner and taller plants in comparison to bush-like plants which are obtained when plants are evaluated in isolation. The presence of other individuals leads to an evolutionary arms race. Because plants are evaluated inside the same environment, the leaves of one plant may be shadowed by other plants. In an attempt to gain more sunlight, plants grow higher and higher. The Red Queen effect was observed when individuals of a single population were coevolving.", notes = "L-Systems p103 'continued opened evolution'. Plants grow near parents on 2-D surface. Multiple genetic operations. Z-buffer rendering light gives (part of) fitness, other part from resources to grow plant. P112 Perlin noise 1998.", } @Article{Ebner:2006:PRL, author = "Marc Ebner", title = "Evolving color constancy", journal = "Pattern Recognition Letters", year = "2006", volume = "27", number = "11", pages = "1220--1229", month = aug, note = "Evolutionary Computer Vision and Image Understanding", keywords = "genetic algorithms, genetic programming, Colour constancy, Local space average colour", DOI = "doi:10.1016/j.patrec.2005.07.020", abstract = "The ability to compute colour constant descriptors of objects in view irrespective of the light illuminating the scene is called color constancy. We have used genetic programming to evolve an algorithm for colour constancy. The algorithm runs on a grid of processing elements. Each processing element is connected to neighbouring processing elements. Information exchange can therefore only occur locally. Randomly generated colour Mondrians were used as test cases. The evolved individual was tested on synthetic as well as real input images. Encouraged by these results we developed a parallel algorithm for colour constancy. This algorithm is based on the computation of local space average colour. Local space average colour is used to estimate the illuminant locally for each image pixel. Given an estimate of the illuminant, we can compute the reflectances of the corresponding object points. The algorithm can be easily mapped to a neural architecture and could be implemented directly in CCD or CMOS chips used in todays cameras.", } @Proceedings{ebner:2007:GP, title = "Proceedings of the 10th European Conference on Genetic Programming", year = "2007", editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar", series = "Lecture Notes in Computer Science", volume = "4445", address = "Valencia, Spain", publisher_address = "Berlin Heidelberg NewYork", month = "11-13 " # apr, publisher = "Springer", keywords = "genetic algorithms, genetic programming", ISSN = "0302-9743", ISBN = "3-540-71602-5", isbn13 = "978-3-540-71602-0", DOI = "doi:10.1007/978-3-540-71605-1", size = "390 pages", notes = "EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007", } @InBook{Ebner:2007:inCC, author = "Marc Ebner", title = "Color Constancy", pages = "198--204", publisher = "John Wiley \& Sons", year = "2007", series = "Imaging Science and Technology", edition = "1", month = apr, keywords = "genetic algorithms, genetic programming", ISBN = "0-470-05829-3", URL = "http://eu.wiley.com/WileyCDA/WileyTitle/productCd-0470058293.html", URL = "http://eu.wiley.com/WileyCDA/WileyTitle/productCd-047051048X.html", notes = "E-Book ISBN: 978-0-470-51048-3", size = "408 pages", } @InProceedings{conf/eurogp/Ebner08, title = "A Genetic Programming Approach to Deriving the Spectral Sensitivity of an Optical System", author = "Marc Ebner", bibdate = "2008-04-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/eurogp/eurogp2008.html#Ebner08", booktitle = "Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008", address = "Naples", month = "26-28 " # mar, publisher = "Springer", year = "2008", volume = "4971", editor = "Michael O'Neill and Leonardo Vanneschi and Steven Gustafson and Anna Isabel {Esparcia Alcazar} and Ivanoe {De Falco} and Antonio {Della Cioppa} and Ernesto Tarantino", isbn13 = "978-3-540-78670-2", pages = "61--72", series = "Lecture Notes in Computer Science", DOI = "doi:10.1007/978-3-540-78671-9_6", keywords = "genetic algorithms, genetic programming", notes = "Part of \cite{conf/eurogp/2008} EuroGP'2008 held in conjunction with EvoCOP2008, EvoBIO2008 and EvoWorkshops2008", } @InProceedings{Ebner:2008:SASOW, author = "Marc Ebner", title = "An Adaptive On-Line Evolutionary Visual System", booktitle = "Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshops, SASOW 2008", year = "2008", editor = "E. Hart and B. Paechter and J. Willies", pages = "84--89", address = "Venice", month = "20-24 " # oct, organisation = "IEEE", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, GPU, adaptive online evolutionary visual system, evolutionary computer vision, training images, adaptive systems, computer vision, evolutionary computation", DOI = "doi:10.1109/SASOW.2008.18", size = "6 pages", abstract = "In evolutionary computer vision, algorithms are usually evolved which address one particular computer vision problem. Quite often, a set of training images is used to evolve an algorithm. Another set of images is then used to evaluate the performance of those algorithms. In contrast of this standard form of algorithm evolution, it is proposed to develop a vision system which continuously evolves algorithms based on the task at hand. This adaptation of computer vision algorithms would happen on-line for every image which is presented to the system. Such a system would continuously adapt to new environmental conditions.", notes = "Workshop on Pervasive Adaptation. Also known as \cite{4800658}", } @InProceedings{Ebner:2009:eurogp, author = "Marc Ebner", title = "A Real-Time Evolutionary Object Recognition System", booktitle = "Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009", year = "2009", editor = "Leonardo Vanneschi and Steven Gustafson and Alberto Moraglio and Ivanoe {De Falco} and Marc Ebner", volume = "5481", series = "LNCS", pages = "268--279", address = "Tuebingen", month = apr # " 15-17", organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming, poster", isbn13 = "978-3-642-01180-1", DOI = "doi:10.1007/978-3-642-01181-8_23", notes = "Part of \cite{conf/eurogp/2009} EuroGP'2009 held in conjunction with EvoCOP2009, EvoBIO2009 and EvoWorkshops2009", } @InProceedings{Ebner:2009:CIG, author = "Marc Ebner and Thorsten Tiede", title = "Evolving driving controllers using Genetic Programming", booktitle = "IEEE Symposium on Computational Intelligence and Games, CIG 2009", year = "2009", pages = "279--286", address = "Milan, Italy", month = "7-10 " # sep, keywords = "genetic algorithms, genetic programming, computational gaming, computational learning approaches, computer gaming, driving controllers, manually crafted race car driver, virtual drivers, computer games, control engineering computing, driver information systems, learning (artificial intelligence), virtual reality", isbn13 = "978-1-4244-4814-2", URL = "https://stubber.math-inf.uni-greifswald.de/~ebner/resources/uniTu2/evoCarDriver.pdf", DOI = "doi:10.1109/CIG.2009.5286465", size = "8 pages", abstract = "Computational gaming requires the automatic generation of virtual opponents for different game levels. We have turned to artificial evolution to automatically generate such game players. In particular, we have used genetic programming to automatically evolve computer programs for computer gaming. With genetic programming, in theory, it is possible to generate any kind of program. The programs are not constrained as much as they are in other computational learning approaches, e.g. neural networks. We show how genetic programming improved upon a manually crafted race car driver (proportional controller). The open race car simulator TORCS was used to evaluate the virtual drivers.", notes = "Also known as \cite{5286465}", } @InProceedings{DBLP:conf/acivs/Ebner09, author = "Marc Ebner", title = "Engineering of Computer Vision Algorithms Using Evolutionary Algorithms", booktitle = "Proceedings of the 11th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2009", year = "2009", editor = "Jacques Blanc-Talon and Wilfried Philips and Dan Popescu and Paul Scheunders", series = "Lecture Notes in Computer Science", volume = "5807", pages = "367--378", address = "Bordeaux, France", month = sep # " 28-" # oct # " 2", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, GPU, OpenGLSL", isbn13 = "978-3-642-04696-4", URL = "http://www.ra.cs.uni-tuebingen.de/mitarb/ebner/research/publications/uniTu2/EvoCVengineering.pdf", DOI = "doi:10.1007/978-3-642-04697-1_34", size = "12 pages", abstract = "Computer vision algorithms are currently developed by looking up the available operators from the literature and then arranging those operators such that the desired task is performed. This is often a tedious process which also involves testing the algorithm with different lighting conditions or at different sites. We have developed a system for the automatic generation of computer vision algorithms at interactive frame rates using GPU accelerated image processing. The user simply tells the system which object should be detected in an image sequence. Simulated evolution, in particular Genetic Programming, is used to automatically generate and test alternative computer vision algorithms. Only the best algorithms survive and eventually provide a solution to the user's image processing task.", notes = "Interactive evolution of image processing software. Realtime 30 seconds. OpenGL shader language. mip mapping. nVidia GeForce 9600 GT/PCI/SEE2 ", } @InProceedings{Ebner:2010:EvoIASP, author = "Marc Ebner", title = "Towards Automated Learning of Object Detectors", booktitle = "EvoIASP", year = "2010", editor = "Cecilia {Di Chio} and Stefano Cagnoni and Carlos Cotta and Marc Ebner and Aniko Ekart and Anna I. Esparcia-Alcazar and Chi-Keong Goh and Juan J. Merelo and Ferrante Neri and Mike Preuss and Julian Togelius and Georgios N. Yannakakis", volume = "6024", series = "LNCS", pages = "231--240", address = "Istanbul", month = "7-9 " # apr, organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, GPU", isbn13 = "978-3-642-12238-5", DOI = "doi:10.1007/978-3-642-12239-2_24", abstract = "Recognizing arbitrary objects in images or video sequences is a difficult task for a computer vision system. We work towards automated learning of object detectors from video sequences (without user interaction). Our system uses object motion as an important cue to detect independently moving objects in the input sequence. The largest object is always taken as the teaching input, i.e. the object to be extracted. We use Cartesian Genetic Programming to evolve image processing routines which deliver the maximum output at the same position where the detected object is located. The graphics processor (GPU) is used to speed up the image processing. Our system is a step towards automated learning of object detectors.", notes = "EvoIASP'2010 held in conjunction with EuroGP'2010 EvoCOP2010 EvoBIO2010", } @InProceedings{Ebner:2010b, author = "Marc Ebner", title = "Evolving Object Detectors with a GPU Accelerated Vision System", booktitle = "Proceedings of the 9th International Conference Evolvable Systems: From Biology to Hardware, ICES 2010", year = "2010", editor = "Gianluca Tempesti and Andy M. Tyrrell and Julian F. Miller", series = "Lecture Notes in Computer Science", volume = "6274", pages = "109--120", address = "York", month = sep # " 6-8", publisher = "Springer", keywords = "genetic algorithms, genetic programming, GPU", isbn13 = "978-3-642-15322-8", DOI = "doi:10.1007/978-3-642-15323-5_10", abstract = "Using GPU processing, it is now possible to develop an evolutionary vision system working at interactive frame rates. Our system uses motion as an important cue to evolve detectors which are able to detect an object when this cue is not available. Object detectors consist of a series of high level operators which are applied to the input image. A matrix of low level point operators are used to recombine the output of the high level operators. With this contribution, we investigate, which image processing operators are most useful for object detection. It was found that the set of image processing operators could be considerably reduced without reducing recognition performance. Reducing the set of operators lead to an increase in speedup compared to a standard CPU implementation.", affiliation = "Wilhelm-Schickard-Institut fur Informatik, Eberhard-Karls-Universitat Tuebingen, Abt. Rechnerarchitektur, Sand 1, 72076 Tbingen, Germany", } @Article{EBRAHIMI:2022:JPSE, author = "Arash Ebrahimi and Amin Izadpanahi and Parirokh Ebrahimi and Ali Ranjbar", title = "Estimation of shear wave velocity in an Iranian oil reservoir using machine learning methods", journal = "Journal of Petroleum Science and Engineering", volume = "209", pages = "109841", year = "2022", ISSN = "0920-4105", DOI = "doi:10.1016/j.petrol.2021.109841", URL = "https://www.sciencedirect.com/science/article/pii/S0920410521014601", keywords = "genetic algorithms, genetic programming, Shear wave velocity, Machine learning, Dipole sonic imager (DSI), Multi-layer perceptron, Artificial neural network, Multi-gene genetic programming, Wireline logs", abstract = "Shear wave velocity is considered as one of the most important rock physical parameters which can be measured by dipole sonic imager (DSI) tool. This parameter is applied to evaluate porosity and permeability, rock mechanical parameters, lithology, fracture assessment, etc. On the other hand, this data is not available in all wells and hence, an accurate and reliable estimation of this parameter with the least uncertainty is of great importance in reservoir characterization. In this study, regression, multi-layer perceptron artificial neural network (MLP-ANN), adaptive neuro-fuzzy inference system (ANFIS) and multi-gene genetic programming (MGGP) methods are used to estimate the shear wave velocity using well log data. Also, the reported empirical correlations in the literature are also investigated in the studied field. The input data include depth, effective porosity, Vp, gamma ray logs (natural and spectral), neutron log, density log and caliper log from the Bangestan Group Formation in one of the fields in southwestern Iran. In this study, all the expressed methods are compared based on the best coefficient of determination (R2), root mean square error (RMSE), mean squared error (MSE), average absolute relative error (AARE), and average relative error (ARE). Among the used methods, MGGP was developed for using the useful features of this method including sensitivity analysis and correlation. Sensitivity analysis is performed on the input data using the MLP-ANN and MGGP method. Also, a correlation is suggested based on the MGGP method which is able to predict the shear wave velocity using the mentioned input parameters. The results show that the MLP-ANN method is more accurate, reliable and efficient compared to other methods studied in this paper. R2 for the train, validation, and test phase are 0.9973, 0.9901 and 0.9898, respectively. The results of sensitivity analysis imply that compressional wave velocity has the highest impact on the shear wave velocity. Finally, Young Dynamic Modulus and Poisson Dynamic Ratio are computed using both real and predicted shear wave velocities. The results indicate that these two parameters can be calculated with high accuracy using predicted shear wave velocity", } @Article{EBRAHIMIKHUSFI:2020:APR, author = "Zohre Ebrahimi-Khusfi and Ruhollah Taghizadeh-Mehrjardi and Maryam Mirakbari", title = "Evaluation of machine learning models for predicting the temporal variations of dust storm index in arid regions of Iran", journal = "Atmospheric Pollution Research", year = "2020", ISSN = "1309-1042", DOI = "doi:10.1016/j.apr.2020.08.029", URL = "http://www.sciencedirect.com/science/article/pii/S1309104220302579", keywords = "genetic algorithms, genetic programming, Machine learning, Remote sensing data, Climatic parameters, Dust emissions, Dry lands, Iran", abstract = "It is necessary to predict wind erosion events and specify the related effective factors to prioritize management and executive measures to combat desertification caused by wind erosion in arid areas. Therefore, this work aimed to evaluate the applicability of nine machine learning (ML) models (including multivariate adaptive regression splines, least absolute shrinkage and selection operator, k-nearest neighbors, genetic programming, support vector machine, Cubist, artificial neural networks, extreme gradient boosting, random forest) and their average for predicting the seasonal dust storm index (DSI) during 2000-2018 in arid regions of Iran. The results showed that the averaging method outperformed the other individual ML models in predicting DSI changes in all seasons. For instance, the averaging methods improved the prediction accuracies for winter, spring, summer, autumn, and dusty seasons by 22percent, 39percent, 28percent, 32percent, and 26percent, respectively, compared to the multivariate adaptive regression splines. Furthermore, the most important factors in predicting DSI were detected as follows: wind speed for winter, enhanced vegetation index for spring, maximum wind speed for summer, autumn and dusty seasons. In general, our results indicate that the combining of the individual ML models by averaging method help us to develop a more accurate approach for predicting the temporal changes of the dust events in arid regions. Furthermore, the obtained results in this study can be applicable for prioritizing measures in order to minimize the dangers of wind erosion based on the major driving factors", } @Article{EBRAHIMZADE:2018:JECE, author = "Hossein Ebrahimzade and Gholam Reza Khayati and Mahin Schaffie", title = "A novel predictive model for estimation of cobalt leaching from waste Li-ion batteries: Application of genetic programming for design", journal = "Journal of Environmental Chemical Engineering", volume = "6", number = "4", pages = "3999--4007", year = "2018", keywords = "genetic algorithms, genetic programming, Gene-expression programming, Waste lithium ion-batteries, Leaching reaction, Mathematical modeling, Cobalt", ISSN = "2213-3437", DOI = "doi:10.1016/j.jece.2018.05.045", URL = "http://www.sciencedirect.com/science/article/pii/S2213343718302914", abstract = "Leaching process is one of the most influential steps during waste lithium-ion batteries (LIBs) recycling. Therefore, the employment of beneficial reaction modeling strategies assists to distinguish and predict the behavior of operational parameters and optimized efficiency. In this study, a gene-expression programming (GEP), i.e., a new evolutionary computing approach, was applied for the prediction of cobalt leaching from waste LIBs using H2SO4 in the presence of H2O2. Several leaching experiments were carried out by consideration of the reagent concentration (Cr), the solid-liquid ratio (S/L), reaction temperature (Tr) and time (taur) as input parameters and leached cobalt percentage as output variable. The GEP-based models were able to predict the leaching of cobalt with a mean standard error (MSE) of less than 0.1 and mean R-square of 0.979. Results affirmed that the proposed model can be a powerful tool in prediction and generation of a mathematical expression for illustration of the relationship between the leaching reaction parameters and the leached percentage. Moreover, the sensitivity analysis showed that the sulfuric acid concentration and S/L ratio were the most influencing parameters on the cobalt leaching from the waste LIBs, respectively", } @InCollection{Ebstyne:1997:msm, author = "Michael J. Ebstyne", title = "Musings on Syncopation and Machines", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1997", publisher = "Stanford Bookstore", year = "1997", editor = "John R. Koza", pages = "36--46", address = "Stanford, California, 94305-3079 USA", month = "17 " # mar, keywords = "genetic algorithms, genetic programming", ISBN = "0-18-205981-2", abstract = "music", notes = "part of \cite{koza:1997:GAGPs}", } @Article{journals/asc/EbtehajBZAS15, author = "Isa Ebtehaj and Hossein Bonakdari and Amir Hossein Zaji and Hamed Azimi and Ali Sharifi", title = "Gene expression programming to predict the discharge coefficient in rectangular side weirs", journal = "Applied Soft Computing", year = "2015", volume = "35", bibdate = "2015-11-26", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/asc/asc35.html#EbtehajBZAS15", pages = "618--628", keywords = "genetic algorithms, genetic programming, gene expression programming, Discharge coefficient, Sensitivity analysis, Side weir", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2015.07.003", URL = "http://www.sciencedirect.com/science/article/pii/S1568494615004330", abstract = "In this study, gene expression programming (GEP) is employed as a new method for estimating the side weir discharge coefficient. The accuracy of existing equations in evaluating the side weir discharge coefficient is first examined. Afterwards, taking into consideration the dimensionless parameters that affect the estimation of this parameter and sensitivity analysis, five different models are presented. Coefficient determination (R2), root mean square error (RMSE), mean absolute relative error (MARE), scatter index (SI) and BIAS are used for measuring the models performance. Two sets of experimental data are applied to evaluate the models. According to the results obtained indicate that the model with Froude number (F1), dimensionless weir length (b/B), ratio of weir length to depth of upstream flow (b/y1), and ratio of weir height to its length (p/y1) parameters of R2=0.947, MARE=0.05, RMSE=0.037, BIAS=0.01 and SI=0.067, performed the best. Accordingly, this new equation proposed through GEP can be used for estimating the discharge coefficient in rectangular sharp-crested side weirs.", } @PhdThesis{Echevarria-Cartaya:thesis, author = "Yuviny {Echevarria Cartaya}", title = "Aportes a los Algoritmos de Aprendizaje Multiobjetivo para Modelos Semi-fisicos de Estimacion del Estado de Salud en Baterias", school = "Departamento de Informatica, Universidad de Oviedo", year = "2017", address = "Spain", month = jul, keywords = "genetic algorithms, genetic programming, Lithium Ion batteries", URL = "http://hdl.handle.net/10651/45012", URL = "http://digibuo.uniovi.es/dspace/bitstream/10651/45012/1/TD_YuvinyEchevarria.pdf", size = "195 pages", abstract = "Increasing the use of renewable energy sources reducing emissions of polluting gases to the environment is essential for achieving sustainable development. Nowadays, one of the main goals to reduce the world pollution is related to the control of carbon dioxide emissions produced by conventional engine automobiles. Electric vehicles are a good alternative to mitigate this environmental pollution problem. The efficiency of the electric vehicles, that use Li-Ion batteries,grows with the scientific innovation. Optimizing Li-Ion battery operation is not a simple task. Li-Ion batteries for automotive applications are complex and unstable dynamical systems with multiple inputs and outputs. Estimating the State of Health of the Li-Ion batteries is the major challenge for the developing of battery model. For this reason, developing of accurate estimation techniques, for battery management systems in electric vehicle, requires the concentration of the scientific community. This thesis proposes a new generation of dynamical models for the diagnosis of the State of Health in Li-Ion batteries. The models are based on partial knowledge of the electrochemical and thermodynamic phenomena defining the behavior of a Li-Ion battery. The semi-physical models comprise a set of differential equations with intelligent elements embedded that minimize the number of small black boxes. The learning process of the resulting Multiobjective Genetic Fuzzy Systems requires powerful algorithms. Due to the necessary approximation of the first derivative of the battery voltage respect to the stored charge. This is an expensive procedure and small changes in the voltage curve cause large excursion of the first derivative. The fitness evaluation in each generation is more than the ninety percent of the consumed time. On the other hand, existing evolutionary learning processes generate a high number of dominance-resistant individuals. All this motivates two major contributions made in this thesis. The first contribution is the knowledge injection through fuzzy preference order in to the learning process. Thus, prioritization of the individuals is altered in the survival selection stage. A tailored-made operator is used which complements Pareto Non-Dominance levels with a partial order at each level. The learned models are potentially better for the advantages of the proposed evolutive pressure mechanism. It has been shown, that accurate State of Health models for Li-Ion batteries can be obtained if a knowledge-based preference ordering of individuals is implemented. In this work, an empirical study is performed and the result of different multi and many-objectives genetic algorithms are assessed. The second contribution is focused in the learning process of a simple semi-physical model for the State of Health estimation. This model is based on the side reactions on the electrodes that can degrade a battery. In this case, the learning process requires the indirect estimation of a latent variable with human understandable structure. The contribution extends the Multiobjective Genetic Programming-Based Learning by using different survival selection strategies suitable for this problem. The proposed algorithm Grab-MO-GaP incorporates recent advances developed for many-objectives genetic algorithms. The proposed algorithm uses Grammatical Evolution to enforce the monotonicity of the latent variable respect to the model outputs and works as an evolutive pressure mechanism. The human-readable structures allow obtaining the location of the characteristic points of the negative electrode when the battery is being charged or discharged at a low current", notes = "In Spanish Supervisor: Luciano Sanchez Ramos", } @Article{ECHEVARRIA:2019:EAAI, author = "Yuviny Echevarria and Cecilio Blanco and Luciano Sanchez", title = "Learning human-understandable models for the health assessment of {Li}-ion batteries via Multi-Objective Genetic Programming", journal = "Engineering Applications of Artificial Intelligence", year = "2019", volume = "86", pages = "1--10", month = nov, keywords = "genetic algorithms, genetic programming, Multiobjective genetic programming, Grammatical evolution, Battery model, Lithium", ISSN = "0952-1976", DOI = "doi:10.1016/j.engappai.2019.08.013", URL = "http://www.sciencedirect.com/science/article/pii/S095219761930199X", abstract = "The health of automotive Li-ion batteries depends on different side reactions on the electrodes that may degrade the cells, thereby reducing their useable capacity and sometimes producing catastrophic failures with serious economic and safety implications. In this paper, a method of detection and prognosis of battery deterioration is proposed in which an intelligent soft sensor is able to synthesize human-understandable health indicators from sequences of voltages, currents and temperatures streamed via on-vehicle sensors. This soft sensor is based on a dynamic model optimizing three different criteria obtained by means of multi-objective grammatical evolution. Different survival selection strategies suitable for this problem are discussed and compared", } @InProceedings{Eddeen:2014:CSIT, author = "Lubna M. H {Nasir Eddeen} and Eman M. Saleh and Doa'a Saadah", title = "Genetic Hash Algorithm", booktitle = "6th International Conference on CSIT", year = "2014", editor = "Nael Hirzallah", pages = "23--26", address = "Amman, Jordan", month = "26-27 " # mar, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, GHA, 2D image, Fourier Transformation (FTT), Security, Hash functions, Hash Visualization, Chromosome, Fitness value", isbn13 = "987-1-4799-3999-2", DOI = "doi:10.1109/CSIT.2014.6805974", size = "4 pages", abstract = "Security is becoming a major concern in computing. New techniques are evolving every day; one of these techniques is Hash Visualization. Hash Visualization uses complex random generated images for security, these images can be used to hide data (watermarking). This proposed new technique improves hash visualization by using genetic algorithms. Genetic algorithms are a search optimization technique that is based on the evolution of living creatures. The proposed technique uses genetic algorithms to improve hash visualization. The used genetic algorithm was away faster than traditional previous ones, and it improved hash visualization by evolving the tree that was used to generate the images, in order to obtain a better and larger tree that will generate images with higher security. The security was satisfied by calculating the fitness value for each chromosome based on a specifically designed algorithm.", notes = "p23 'Hash visualization is a technique that is used to generate random images. The images are generated using a tree. The nodes of the tree are randomly assigned mathematical operations and the leaves are assigned random values. The tree is evaluated and the resulting value is assigned to a pixel.' p24 'An image is generated from each chromosome (tree)' University of Jordan Department of Computer Science Amman, Jordan", } @InProceedings{Eddy:2001:DETC, author = "John Eddy and Kemper Lewis", title = "Effective Generation of Pareto Sets Using Genetic Programming", booktitle = "Proceedings of DETC'01 ASME 2001 Design Engineering Technical Conferences and Computers and Information in Engineering Conference", year = "2001", address = "Pittsburgh, PA, USA", month = sep # " 9-12", organisation = "American Society of Mechanical Engineers", keywords = "genetic algorithms, Heuristic Optimization, Multi Objective Optimization, MOGA, Pareto Frontiers", URL = "http://does.eng.buffalo.edu/administrator/components/com_jresearch/files/publications/DETC2001_DAC21094.pdf", URL = "http://does.eng.buffalo.edu/index.php?option=com_jresearch&view=publication&task=show&id=59&Itemid=53", size = "9 pages", abstract = "Many designers concede that there is typically more than one measure of performance for an artefact. Often, a large system is decomposed into smaller subsystems each having its own set of objectives, constraints, and parameters. The performance of the final design is a function of the performances of the individual subsystems. It then becomes necessary to consider the trade-offs that occur in a multi-objective design problem. The complete solution to a multi-objective optimization problem is the entire set of non-dominated configurations commonly referred to as the Pareto set. Common methods of generating points along a Pareto frontier involve repeated conversion of multi-objective problems into single objective problems using weights. These methods have been shown to perform poorly when attempting to populate a Pareto frontier. This work presents an efficient means of generating a thorough spread of points along a Pareto frontier using genetic programming", notes = "Despite title appears to be on GAs not GP", } @InProceedings{edelson:1999:ECCFIGCUASIFPM, author = "William Edelson and Michael L. Gargano", title = "Efficient Calculation of Compute-Intensive Fitness In Genetic Computations Using A Survival Indicator For Population Members", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "784", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, classifier systems, poster papers", ISBN = "1-55860-611-4", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco1999/edelson_1999_eccfigcuasifpm.pdf", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{edmonds:1995:fuzzy, author = "Andrew N. Edmonds and Diana Burkhardt and Osei Adjei", title = "Genetic Programming of Fuzzy Logic Production Rules", booktitle = "1995 IEEE Conference on Evolutionary Computation", year = "1995", volume = "2", pages = "765", address = "Perth, Australia", publisher_address = "Piscataway, NJ, USA", month = "29 " # nov # " - 1 " # dec, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", URL = "ftp://ftp.scifi.co.uk/pub/docs/ICECPS.z broken", URL = "http://www.scientio.com/resources/NNCM95.pdf", URL = "http://ieeexplore.ieee.org/iel2/3507/10438/00487482.pdf", abstract = "John Koza has demonstrated that a form of machine learning can be constructed by using the techniques of Genetic Programming using LISP statements. We describe here an extension to this principle using Fuzzy Logic sets and operations instead of LISP expressions. We show that Genetic programming can be used to generate trees of fuzzy logic statements, the evaluation of which optimise some external process, in our example financial trading. We also show that these trees can be simply converted to natural language rules, and that these rules are easily comprehended by a lay audience. This clarity of internal function can be compared to Black Box non-parametric modelling techniques such as Neural Networks. We then show that even with minimal data preparation the technique produces rules with good out of sample performance on a range of different financial instruments.", notes = "ICEC-95 Editors not given by IEEE, Organisers David Fogel and Chris deSilva. conference details at http://ciips.ee.uwa.edu.au/~dorota/icnn95.html NNCM95.pdf is ten page version?", } @InCollection{edmonds:1997:mbrea, author = "Bruce Edmonds and Scott Moss", title = "Modelling of Boundedly Rational Agents using Evolutionary Programming Techniques", booktitle = "Evolutionary Computing", publisher = "Springer-Verlag", year = "1997", editor = "David Corne and Jonathan L. Shapiro", volume = "1305", series = "LNCS", pages = "31--42", address = "University of Manchester, UK", month = "7-8 " # apr, keywords = "genetic algorithms, genetic programming", ISBN = "3-540-63476-2", URL = "http://cogprints.ecs.soton.ac.uk/archive/00000509/", DOI = "doi:10.1007/BFb0027164", abstract = "A technique for the credible modelling of economic agents with bounded rationality based on the evolutionary techniques is described. The genetic programming paradigm is most suited due to its meaningful and flexible genome. The fact we are aiming to model agents with real characteristics implies a different approach from those evolutionary algorithms designed to efficiently solve specific problems. Some of these are that we use very small populations, it is based on different operators and uses a breeding selection mechanism. It is precisely some of the {"}pathological{"} features of this algorithm that capture the target behaviour. Some possibilities for integration of deductive logic-based approaches and the GP paradigm are suggested. An example application of an agent seeking to maximise its utility by modelling its own utility function is briefly described.", notes = "Papers in AISB-97 Evolutionary computation workshop proceedings may be revised before final publication. http://www.cs.man.ac.uk/ai/AISB97/text.html#evolut Former soviet union. Strictly declarative modelling language SDML. 3 sets of runs, agents have memory of different sizes, space for 10, 20 or 30 models. http://www.fmb.mmu.ac.uk", size = "12 pages", } @TechReport{edmonds:1998:mGPcov, author = "Bruce Edmonds", title = "Meta-Genetic Programming: Co-evolving the Operators of Variation", institution = "Centre for Policy Modelling, Manchester Metropolitan University, UK", year = "1998", type = "CPM Report", number = "98-32", address = "Aytoun St., Manchester, M1 3GH. UK", month = jan, keywords = "genetic algorithms, genetic programming, automatic programming, genetic operators, co-evolution", URL = "http://cogprints.org/513/00/mgp.pdf", URL = "http://cogprints.ecs.soton.ac.uk/archive/00000513/", URL = "http://www.cpm.mmu.ac.uk/cpmrep32.html", abstract = "The standard Genetic Programming approach is augmented by co-evolving the genetic operators. To do this the operators are coded as trees of indefinite length. In order for this technique to work, the language that the operators are defined in must be such that it preserves the variation in the base population. This technique can varied by adding further populations of operators and changing which populations act as operators for others, including itself, thus to provide a framework for a whole set of augmented GP techniques. The technique is tested on the parity problem. The pros and cons of the technique are discussed.", notes = "see \cite{edmonds:2001:mGPcov}", size = "18 pages", } @InProceedings{edmonds:1998:gsrefb, author = "Bruce Edmonds", title = "Gossip, Sexual Recombination and the {El Farol Bar:} modelling the emergence of heterogeneity", booktitle = "Proceedings of the 1998 Conference on Computation in Economics, Finance and Engineering", year = "1998", address = "Cambridge", month = jun, keywords = "genetic algorithms, genetic programming", URL = "http://cogprints.ecs.soton.ac.uk/archive/00000514/", URL = "http://cogprints.org/514/5/emhet.pdf", abstract = "Brian Arthur's `El Farol Bar' model is extended so that the agents also learn and communicate. The learning and communication is implemented using an evolutionary process acting upon a population of mental models inside each agent. The evolutionary process is based on a Genetic Programming algorithm. Each gene is composed of two tree-structures: one to control its action and one to determine its communication. A detailed case-study from the simulations show how the agents have differentiated so that by the end of the run they had taken on very different roles. Thus the introduction of a flexible learning process and an expressive internal representation has allowed the emergence of heterogeneity. agents also learn and communicate. Each gene is composed of two tree-structures: one to control its actions and one to determine communication.", notes = "coevolution, bounded rationality. Communicate (talk) one branch first. Then action (go to bar OR not go). STGP. page 3 {"}total population was 5 in this example{"}. SDML. problem specific terminal and function sets (different for two branches) See \cite{edmonds:1999:gsrefb}", } @Article{edmonds:1998:GP5, author = "Bruce Edmonds", title = "The Uses of Genetic Programming in Social Simulation: A Review of Five Books", journal = "Journal of Artificial Societies and Social Simulation", year = "1998", volume = "1", number = "4", month = "31-" # oct, note = "Book review", keywords = "genetic algorithms, genetic programming", ISSN = "1460-7425", URL = "http://jasss.soc.surrey.ac.uk/2/1/review1.html", abstract = "Genetic Programming: On the Programming of Computers by Natural Selection John R. Koza Cambridge, MA: The M.I.T. Press 1992 Cloth: ISBN 0-262-11170-5 Genetic Programming II: Automatic Discovery of Reusable Programs John R. Koza Cambridge, MA: The M.I.T. Press, A Bradford Book 1994 Cloth: ISBN 0-262-11189-6 Advances in Genetic Programming Edited by Kenneth E. Kinnear Jr. Cambridge, MA: The M.I.T. Press, A Bradford Book 1994 Cloth: ISBN 0-262-11188-8 Advance in Genetic Programming Volume 2 Edited by Peter J. Angeline and Kenneth E. Kinnear Jr. Cambridge, MA: The M.I.T. Press, A Bradford Book 1996 Cloth: ISBN 0-262-01158-1 Genetic Programming and Data Structures William B. Langdon Dordrecht: Kluwer Academic Publishers 1998 Cloth: ISBN 0-792-38135-1", notes = "Duplicates \cite{edmonds:1999:r5GP}", } @Article{edmonds:1999:gsrefb, author = "Bruce Edmonds", title = "Gossip, Sexual Recombination and the {El Farol} bar: modelling the emergence of heterogeneity", journal = "Journal of Artificial Societies and Social Simulation", year = "1999", volume = "2", number = "3", keywords = "genetic algorithms, genetic programming, differentiation, El Farol, evolution, co-evolution, emergence, heterogeneity, society, roles, social structure, SDML, naming, creativity", ISSN = "1460-7425", URL = "http://jasss.soc.surrey.ac.uk/2/3/2.html", URL = "http://cogprints.ecs.soton.ac.uk/archive/00001775/", URL = "http://cogprints.org/1775/5/emhet.pdf", size = "7 pages", abstract = "An investigation into the conditions conducive to the emergence of heterogeneity among agents is presented. This is done by using a model of creative artificial agents to investigate some of the possibilities. The simulation is based on Brian Arthur's 'El Farol Bar' model but extended so that the agents also learn and communicate. The learning and communication is implemented using an evolutionary process acting upon a population of strategies inside each agent. This evolutionary learning process is based on a Genetic Programming algorithm. This is chosen to make the agents as creative as possible and thus allow the outside edge of the simulation trajectory to be explored. A detailed case study from the simulations show how the agents have differentiated so that by the end of the run they had taken on qualitatively different roles. It provides some evidence that the introduction of a flexible learning process and an expressive internal representation has facilitated the emergence of this heterogeneity.", notes = "See also \cite{edmonds:1998:gsrefb}", } @Article{edmonds:1999:r5GP, author = "Bruce Edmonds", title = "The Uses of Genetic Programming in Social Simulation: A Review of Five Books", journal = "The Journal of Artificial Societies and Social Simulation", year = "1999", volume = "2", number = "1", month = jan, keywords = "genetic algorithms, genetic programming", ISSN = "1460-7425", URL = "http://jasss.soc.surrey.ac.uk/2/1/review1.html", size = "40957 bytes", abstract = "Moderately extensive introduction to GP followed by review of the following five books from the perspective of Social Simulation: Genetic Programming: On the Programming of Computers by Natural Selection John R. Koza Cambridge, MA: The M.I.T. Press 1992 \cite{koza:book} Genetic Programming II: Automatic Discovery of Reusable Programs John R. Koza Cambridge, MA: The M.I.T. Press, A Bradford Book 1994 \cite{koza:gp2} Advances in Genetic Programming Edited by Kenneth E. Kinnear Jr. Cambridge, MA: The M.I.T. Press, A Bradford Book 1994 \cite{kinnear:book} Advance in Genetic Programming Volume 2 Edited by Peter J. Angeline and Kenneth E. Kinnear Jr. Cambridge, MA: The M.I.T. Press, A Bradford Book 1996 \cite{book:1996:aigp2} Genetic Programming and Data Structures William B. Langdon Dordrecht: Kluwer Academic Publishers 1998 \cite{langdon:book}", notes = "JASSS Duplicates \cite{edmonds:1998:GP5}", } @Article{edmonds:2000:aigp, author = "Bruce Edmonds", title = "A Review of the ``Advances in Genetic Programming'' Series (Volumes 1, 2 and 3)", journal = "Genetic Programming and Evolvable Machines", year = "2000", volume = "1", number = "3", pages = "289--296", month = jul, keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dR8nS", DOI = "doi:10.1023/A:1010018414986", notes = "\cite{kinnear:book} \cite{book:1996:aigp2} \cite{book:1999:aigp3} Article ID: 264705", } @InCollection{edmonds:2001:MUC, author = "Bruce Edmonds", title = "Learning Appropriate Contexts", booktitle = "Modelling and Using Context: Third International and Interdisciplinary Conference, CONTEXT", publisher = "Springer-Verlag", year = "2001", editor = "Varol Akman and Paolo Bouquet and Richard Thomason and Roger Young", volume = "2116", series = "LNAI", pages = "143--155", address = "Dundee, UK", publisher_address = "Berlin / Heidelberg", month = "27-30 " # jul, email = "b.edmonds@mmu.ac.uk", keywords = "genetic algorithms, genetic programming, learning, conditions of application, context, evolutionary computing, error", ISBN = "3-540-42379-6", URL = "http://cogprints.ecs.soton.ac.uk/archive/00001772/", URL = "http://www.cpm.mmu.ac.uk/cpmrep78.html", URL = "http://cfpm.org/pub/papers/lac.pdf", URL = "http://citeseer.ist.psu.edu/534909.html", size = "13 pages", ISSN = "0302-9743", DOI = "doi:10.1007/3-540-44607-9_11", abstract = "Genetic Programming is extended so that the solutions being evolved do so in the context of local domains within the total problem domain. This produces a situation where different {"}species{"} of solution develop to exploit different {"}niches{"} of the problem indicating exploitable solutions. It is argued that for context to be fully learnable a further step of abstraction is necessary. Such contexts abstracted from clusters of solution/model domains make sense of the problem of how to identify when it is the content of a model is wrong and when it is the context. Some principles of learning to identify useful contexts are proposed.", notes = "Volume in the proceedings of the 3rd International and interdisciplinary conference, CONTEXT 2001, Dundee, UK, July 2001 http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-42379-6 Cited by \cite{ulgtsdl}. Centre for Policy Modelling, Manchester Metropolitan University, Aytoun Building, Autoun Street, Manchester, M1 3GH, UK. b.edmonds@mmu.ac.uk http://www.cpm.mmu.ac.uk/ bruce Feb 2007 duplicate entry \cite{Edmonds:2001:CONTEXT} combined with edmonds:2001:MUC", } @InProceedings{Edmonds:2001:IRC, author = "Bruce Edmonds and Scott Moss", title = "The Importance of Representing Cognitive Processes in Multi-agent Models", booktitle = "Artificial Neural Networks - ICANN 2001 : International Conference, Proceedings", year = "2001", editor = "G. Dorffner and H. Bischof and K. Hornik", volume = "2130", series = "Lecture Notes in Computer Science", pages = "759--766", address = "Vienna, Austria", month = aug # " 21-25", keywords = "genetic algorithms, genetic programming, modelling, methodology, agent, economics, neural net, representation, prediction, explanation, cognition, stock market, negotiation", CODEN = "LNCSD9", ISSN = "0302-9743", bibdate = "Sat Feb 2 13:05:31 MST 2002", URL = "http://cfpm.org/pub/papers/repcog.pdf", URL = "http://citeseer.ist.psu.edu/540672.html", DOI = "doi:10.1007/3-540-44668-0_106", acknowledgement = ack-nhfb, abstract = "We distinguish between two main types of model: predictive and explanatory. It is argued (in the absence of models that predict on unseen data) that in order for a model to increase our understanding of the target system the model must credibly represent the structure of that system, including the relevant aspects of agent cognition. Merely plugging in an existing algorithm for the agent cognition will not help in such understanding. In order to demonstrate that the cognitive model matters, we compare two multi-agent stock market models that differ only in the type of algorithm used by the agents to learn. We also present a positive example where a neural net is used to model an aspect of agent behaviour in a more descriptive manner.", } @Article{edmonds:2001:mGPcov, author = "Bruce Edmonds", title = "Meta-Genetic Programming: Co-evolving the Operators of Variation", journal = "Elektrik", year = "2001", volume = "9", number = "1", pages = "13--29", month = may, note = "Turkish Journal Electrical Engineering and Computer Sciences", keywords = "genetic algorithms, genetic programming, automatic programming, genetic operators, co-evolution", ISSN = "1300-0632", URL = "http://cogprints.ecs.soton.ac.uk/archive/00001776/", URL = "http://journals.tubitak.gov.tr/elektrik/issues/elk-01-9-1/elk-9-1-2-0008-5.pdf", URL = "http://cogprints.ecs.soton.ac.uk/archive/00001776/00/mgp.pdf", size = "18 pages", abstract = "The standard Genetic Programming approach is augmented by co-evolving the genetic operators. To do this the operators are coded as trees of indefinite length. In order for this technique to work, the language that the operators are defined in must be such that it preserves the variation in the base population. This technique can varied by adding further populations of operators and changing which populations act as operators for others, including itself, thus to provide a framework for a whole set of augmented GP techniques. The technique is tested on the parity problem. The pros and cons of the technique are discussed.", notes = "Elektrik (broken March 2020) http://www.tubitak.gov.tr/journals/elektrik/ see \cite{edmonds:1998:mGPcov} ", } @TechReport{ulgtsdl, author = "Bruce Edmonds", title = "Using Localised 'Gossip' to Structure Distributed Learning", institution = "Centre for Policy Modelling, Manchester Metropolitan University Business School", year = "2005", type = "CPM Report", number = "CPM-04-142", address = "UK", month = "15th " # may, keywords = "genetic algorithms, genetic programming", URL = "http://bruce.edmonds.name/ulgtsdl/ulgtsdl.pdf", URL = "http://cfpm.org/cpmrep142.html", abstract = "The idea of a {"}memetic{"} spread of solutions through a human culture in parallel to their development is applied as a distributed approach to learning. Local parts of a problem are associated with a set of overlapping localities in a space and solutions are then evolved in those localities. Good solutions are not only crossed with others to search for better solutions but also they propagate across the areas of the problem space where they are relatively successful. Thus the whole population co-evolves solutions with the domains in which they are found to work. This approach is compared to the equivalent global evolutionary computation approach with respect to predicting the occurrence of heart disease in the Cleveland data set. It greatly outperforms the global approach, but the space of attributes within which this evolutionary process occurs can effect its efficiency.", notes = "Presented at the {"}Engineering with Social Metaphors{"} day of the AISB Symposium on Socially Inspired Computing, University of Hertfordship, April 2005. \cite{edmonds:2005:esm}", size = "12 pages", notes = " 'geographic separation' in space of inputs. How this is done has dramatic effect on effectiveness of this approach. 'exact distance metric did not noticeable effect the results'. Global GP only using 10 percent of training data.", } @InProceedings{edmonds:2005:esm, author = "Bruce Edmonds", title = "Using Localised 'Gossip' to Structure Distributed Learning", booktitle = "AISB'05: Proceedings of the Joint Symposium on Socially Inspired Computing (Engineering with Social Metaphors)", year = "2005", editor = "Bruce Edmonds and Nigel Gilbert and Steven Gustafson and David Hales and Natalio Krasnogor", pages = "127--134", address = "University of Hertfordshire, Hatfield, UK", month = "12-15 " # apr, organisation = "AISB", note = "SSAISB 2005 Convention", keywords = "genetic algorithms, genetic programming", URL = "http://cfpm.org/sic/edmonds.pdf", size = "8 pages", abstract = "The idea of a memetic spread of solutions through a human culture in parallel to their development is applied as a distributed approach to learning. Local parts of a problem are associated with a set of overlapping localities in a space and solutions are then evolved in those localities. Good solutions are not only crossed with others to search for better solutions but also they propagate across the areas of the problem space where they are relatively successful. Thus the whole population co-evolves solutions with the domains in which they are found to work. This approach is compared to the equivalent global evolutionary computation approach with respect to predicting the occurrence of heart disease in the Cleveland data set. It outperforms a global approach, but the space of attributes within which this evolutionary process occurs can greatly effect the efficiency of the technique.", notes = "see also CPM rep 142 \cite{ulgtsdl}. In the joint-symposium ``Socially Inspired Computing'', in the AISB 2005 Convention ``Social Intelligence and Interaction in Animals, Robots and Agents''. Broken Jan 2013 http://aisb2005.feis.herts.ac.uk/ Nov 2015 http://cfpm.org/sic/edmonds.pdf differs from proceedings slightly", } @Article{Edmondson:2010:IJCNDS, author = "James Edmondson and Douglas Schmidt", title = "Multi-agent distributed adaptive resource allocation ({MADARA})", journal = "International Journal of Communication Networks and Distributed Systems", year = "2010", volume = "5", number = "3", pages = "229--245", keywords = "genetic algorithms, genetic programming", ISSN = "1754-3924", URL = "http://www.inderscience.com/link.php?id=34946", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.150.5912", bibsource = "OAI-PMH server at www.inderscience.com", language = "eng", rights = "Inderscience Copyright", abstract = "The component placement problem involves mapping a component to a particular location and maximising component utility in grid and cloud systems. It is also an NP hard resource allocation and deployment problem, so many common grid and cloud computing libraries, such as MPICH and Hadoop, do not address this problem, even though large performance gains can occur by optimising communications between nodes. This paper provides four contributions to research on the component placement problem for grid and cloud computing environments. First, we present the multi-agent distributed adaptive resource allocation (MADARA) toolkit, which is designed to address grid and cloud allocation and deployment needs. Second, we present a heuristic called the comparison-based iteration by degree (CID) heuristic, which we use to approximate optimal deployments in MADARA. Third, we analyse the performance of applying the CID heuristic to approximate common grid and cloud operations, such as broadcast, gather and reduce. Fourth, we evaluate the results of applying genetic programming mutation to improve our CID heuristic.", } @Misc{Edvardsen:undergraduatethesis, author = "Stian Edvardsen", title = "Classification of Images using Color, CBIR Distance Measures and Genetic Programming: An evolutionary Experiment", howpublished = "Undergraduate Theses from Norwegian University of Science and Technology. Faculty of Information Technology, Mathematics and Electrical Engineering, Department of Computer and Information Science", year = "2006", month = jun, type = "Undergraduate thesis Masteroppgave-level", keywords = "genetic algorithms, genetic programming", URL = "http://ntnu.diva-portal.org/smash/get/diva2:348194/FULLTEXT01.pdf", URL = "http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9327", size = "151 pages", abstract = "In this thesis a novel approach to image classification is presented. The thesis explores the use of colour feature vectors and CBIR, retrieval methods in combination with Genetic Programming to achieve a classification system able to build classes based on training sets, and determine if an image is a part of a specific class or not. A test bench has been built, with methods for extracting colour features, both segmented and whole, from images. CBIR distance-algorithms have been implemented, and the algorithms used are histogram Euclidian distance, histogram intersection distance and histogram quadratic distance. The genetic program consists of a function set for adjusting weights which corresponds to the extracted feature vectors. Fitness of the individual genomes is measured by using the CBIR distance algorithms, seeking to minimise the distance between the individual images in the training set. A classification routine is proposed, using the feature vectors from the image in question, and weights generated in the genetic program in order to determine if the image belongs to the trained class. A test-set of images is used to determine the accuracy of the method. The results shows that it is possible to classify images using this method, but that it requires further exploration to make it capable of good results.", notes = "Supervisor: Ramampiaro, Herindrasana", } @Article{edwards:1995:nature, author = "A. W. F. Edwards", title = "Forced Evolution", journal = "Nature", year = "1995", volume = "375", pages = "11", month = "6 " # jul, notes = "Notes {"}Professor of speculative learning{"} at the {"}Grand academy of Lagado{"} visited by Captain Lemuel Gulliver in his travels. Cites work claiming this travelogue was read by Charles Darwin in 1840.", } @PhdThesis{Effraimidis_Dimitros_thesis, author = "Dimitrios Effraimidis", title = "Computation Approaches for Continuous Reinforcement Learning Problems", school = "Department of Computer Science, University of Westminster", year = "2016", address = "UK", month = sep, keywords = "genetic algorithms, genetic programming", URL = "http://westminsterresearch.wmin.ac.uk/19074/", URL = "http://westminsterresearch.wmin.ac.uk/19074/1/Effraimidis_Dimitros%20_thesis.pdf", size = "127 pages", abstract = "Optimisation theory is at the heart of any control process, where we seek to control the behaviour of a system through a set of actions. Linear control problems have been extensively studied, and optimal control laws have been identified. But the world around us is highly non-linear and unpredictable. For these dynamic systems, which don't possess the nice mathematical properties of the linear counterpart, the classic control theory breaks and other methods have to be employed. But nature thrives by optimising non-linear and over-complicated systems. Evolutionary Computing (EC) methods exploit nature's way by imitating the evolution process and avoid to solve the control problem analytically. Reinforcement Learning (RL) from the other side regards the optimal control problem as a sequential one. In every discrete time step an action is applied. The transition of the system to a new state is accompanied by a sole numerical value, the reward that designate the quality of the control action. Even though the amount of feedback information is limited into a sole real number, the introduction of the Temporal Difference method made possible to have accurate predictions of the value-functions. This paved the way to optimise complex structures, like the Neural Networks, which are used to approximate the value functions. In this thesis we investigate the solution of continuous Reinforcement Learning control problems by EC methodologies. The accumulated reward of such problems throughout an episode suffices as information to formulate the required measure, fitness, in order to optimise a population of candidate solutions. Especially, we explore the limits of applicability of a specific branch of EC, that of Genetic Programming (GP). The evolving population in the GP case is comprised from individuals, which are immediately translated to mathematical functions, which can serve as a control law. The major contribution of this thesis is the proposed unification of these disparate Artificial Intelligence paradigms. The provided information from the systems are exploited by a step by step basis from the RL part of the proposed scheme and by an episodic basis from GP. This makes possible to augment the function set of the GP scheme with adaptable Neural Networks. In the quest to achieve stable behaviour of the RL part of the system a modification of the Actor-Critic algorithm has been implemented. Finally we successfully apply the GP method in multi-action control problems extending the spectrum of the problems that this method has been proved to solve. Also we investigated the capability of GP in relation to problems from the food industry. These type of problems exhibit also non-linearity and there is no definite model describing its behaviour.", } @Article{Efstathiou:jucs_20_8:efficient_multi_objective_optimisation, author = "Dionysios Efstathiou and Peter McBurney and Steffen Zschaler and Johann Bourcier", title = "Efficient Multi-Objective Optimisation of Service Compositions in Mobile Ad hoc Networks Using Lightweight Surrogate Models", journal = "Journal of Universal Computer Science", year = "2014", volume = "20", number = "8", pages = "1089--1108", month = aug, keywords = "genetic algorithms, SBSE, optimisation, service composition, surrogate models", ISSN = "0948-695x", URL = "http://www.jucs.org/jucs_20_8/efficient_multi_objective_optimisation", URL = "http://www.jucs.org/jucs_20_8/efficient_multi_objective_optimisation/jucs_20_08_1089_1108_efstathiou.pdf", DOI = "doi:10.3217/jucs-020-08-1089", size = "20 pages", abstract = "Infrastructure-less Mobile Ad hoc NETworks (MANETs) and ServiceOriented Architecture (SOA) enable the development of pervasive applications. Based on SOA, we can abstract devices' resources as software services which can be combined into value-added composite services providing complex functionalities while exhibiting specified QoS properties. Configuring compositions with optimal QoS is challenging due to dynamic network topologies and availability of resources. Existing approaches seek to optimise the selection of which services to participate in a centralised orchestration without considering the overhead for estimating their combined QoS. QoS metrics can be used as fitness functions to guide the search for optimal compositions. When composing services offered by diverse devices, there is no trivial relationship between the composition's QoS and its component services. Measuring the fitness values of a candidate composition could be done either by monitoring its actual invocation or simulating it. However, both approaches are too expensive to be used within an optimisation process. In this paper, we propose a surrogate-based multi-objective optimisation approach for exploring trade-off compositions. The evaluation results show that by replacing the expensive fitness functions with lightweight surrogate models, we can vastly accelerate the optimisation algorithm while producing trade-off solutions of high quality.", notes = "Not GP?", } @InProceedings{Eftekhar:2003:ICEECE, author = "S. M. Ashik Eftekhar and Sk. Mahbub Habib and M. M. A. Hashem", title = "Evolutionary Design of Digital Circuits Using Genetic Programming", booktitle = "Proceedings. of the 3rd International Conference on Electrical, Electronics and Computer Engineering (ICEECE 2003)", year = "2003", pages = "231--236", address = "Dhaka, Bangladesh", month = dec # " 22-24", organisation = "Institute of Engineers Bangladesh", keywords = "genetic algorithms, genetic programming", URL = "http://arxiv.org/abs/1304.2467", bibsource = "OAI-PMH server at export.arxiv.org", oai = "oai:arXiv.org:1304.2467", abstract = "For simple digital circuits, conventional method of designing circuits can easily be applied. But for complex digital circuits, the conventional method of designing circuits is not fruitfully applicable because it is time-consuming. On the contrary, Genetic Programming is used mostly for automatic program generation. The modern approach for designing Arithmetic circuits, commonly digital circuits, is based on Graphs. This graph-based evolutionary design of arithmetic circuits is a method of optimised designing of arithmetic circuits. In this paper, a new technique for evolutionary design of digital circuits is proposed using Genetic Programming (GP) with Subtree Mutation in place of Graph-based design. The results obtained using this technique demonstrates the potential capability of genetic programming in digital circuit design with limited computer algorithms. The proposed technique, helps to simplify and speed up the process of designing digital circuits, discovers a variation in the field of digital circuit design where optimised digital circuits can be successfully and effectively designed.", notes = "Recorded as \cite{oai:arXiv.org:1304.2467} 9 April 2013 From: M.M.A. Hashem", } @InProceedings{eggermont:1999:affGPdm, author = "J. Eggermont and A. E. Eiben and J. I. {van Hemert}", title = "Adapting the Fitness Function in {GP} for Data Mining", booktitle = "Genetic Programming, Proceedings of EuroGP'99", year = "1999", editor = "Riccardo Poli and Peter Nordin and William B. Langdon and Terence C. Fogarty", volume = "1598", series = "LNCS", pages = "193--202", address = "Goteborg, Sweden", publisher_address = "Berlin", month = "26-27 " # may, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, data mining: Poster", ISBN = "3-540-65899-8", URL = "http://www.liacs.nl/~jeggermo/publications/eurogp99.ps.gz", URL = "http://www.vanhemert.co.uk/publications/eurogp99.Adapting_the_fitness_function_in_GP_for_data_mining.ps.gz", DOI = "doi:10.1007/3-540-48885-5_16", abstract = "We describe how the Stepwise Adaptation of Weights (SAW) technique can be applied in genetic programming. The SAW-ing mechanism has been originally developed for and successfully used in constraint satisfaction problems. Here we identify the very basic underlying ideas behind SAW-ing and point out how it can be used for different types of problems. In particular, SAW-ing is well suited for data mining task s where the fitness of a candidate solution is composed by `local scores' on data records. We evaluate the power of the SAW-ing mechanism on a number of benchmark classification data sets. The results indicate that extending the GP with the SAW-ing feature increases its performance when different types of misclassifications are not weighted differently, but leads to worse results when they are.", notes = "EuroGP'99, part of \cite{poli:1999:GP}", } @InProceedings{EEH99b, author = "Jeroen Eggermont and Agoston E. Eiben and Jano I. {van Hemert}", title = "A comparison of genetic programming variants for data classification", booktitle = "Advances in Intelligent Data Analysis, Third International Symposium, IDA-99", year = "1999", editor = "David J. Hand and Joost N. Kok and Michael R. Berthold", volume = "1642", series = "LNCS", email = "jvhemert@cs.leidenuniv.nl", pages = "281--290", address = "Amsterdam, The Netherlands", publisher_address = "Berlin", month = "9--11 " # aug, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, classification, data mining", URL = "http://www.liacs.nl/~jeggermo/publications/ida99.ps.gz", URL = "http://www.vanhemert.co.uk/publications/ida99.A_comparison_of_genetic_programming_variants_for_data_classification.ps.gz", ISBN = "3-540-66332-0", abstract = "We report a comparative study on different variations of genetic programming applied on binary data classification problems. The first genetic programming variant is weighting data records for calculating the classification error and modifying the weights during the run. Hereby the algorithm is defining its own fitness function in an on-line fashion giving higher weights to `hard' records. Another novel feature we study is the atomic representation, where `Booleanization' of data is not performed at the root, but at the leafs of the trees and only Boolean functions are used in the trees' body. As a third aspect we look at generational and steady-state models in combination of both features.", notes = "IDA-99, Booleanization of inputs, ML: Australian credit, German Credit, Heart Disease, Pima. steady state. SAW-ing", } @InProceedings{EEH99bnaic, author = "J. Eggermont and A. E. Eiben and J. I. {van Hemert}", title = "A comparison of genetic programming variants for data classification", booktitle = "Proceedings of the Eleventh Belgium/Netherlands Conference on Artificial Intelligence (BNAIC'99)", year = "1999", editor = "Eric Postma and Marc Gyssens", pages = "253--254", address = "Kasteel Vaeshartelt, Maastricht, Holland", month = "3-4 " # nov, organisation = "BNVKI, Dutch and the Belgian AI Association", keywords = "genetic algorithms, genetic programming, data mining, classification", URL = "http://www.liacs.nl/~jeggermo/publications/bnaic00.ps.gz", URL = "http://www.vanhemert.co.uk/publications/bnaic99.shortpaper.Comparing_genetic_programming_variants_for_data_classification.ps.gz", size = "2 pages", abstract = "This article is a combined summary of two papers written by the authors. Binary data classification problems (with exactly two disjoint classes) form an important application area of machine learning techniques, in particular genetic programming (GP). We compare a number of different variants of GP applied to such problems whereby we investigate the effect of two significant changes in a fixed GP setup in combination with two different evolutionary models", notes = "resubmission of \cite{EEH99b} https://documentserver.uhasselt.be/handle/1942/4193 July 2024 broken http://www.cs.unimaas.nl/~bnvki/", } @InProceedings{eggermon:2000:bnaic, author = "J. Eggermont and J. I. {van Hemert}", title = "Stepwise Adaptation of Weights for Symbolic Regression with Genetic Programming", booktitle = "Proceedings of the Twelveth Belgium/Netherlands Conference on Artificial Intelligence (BNAIC'00)", year = "2000", editor = "Antal {van den Bosch} and Hans Weigand", pages = "259--266", address = "De Efteling, Kaatsheuvel, Holland", month = "1-2 " # nov, organisation = "BNVKI, Dutch and the Belgian AI Association", keywords = "genetic algorithms, genetic programming, data mining", URL = "http://www.liacs.nl/~jeggermo/publications/bnaic00.ps.gz", URL = "http://www.vanhemert.co.uk/publications/bnaic00.Stepwise_Adaptation_of_Weights_for_Symbolic_Regression_with_Genetic_Programming.ps.gz", URL = "http://www.vanhemert.co.uk/publications/bnaic00.Stepwise_Adaptation_of_Weights_for_Symbolic_Regression_with_Genetic_Programming.pdf", URL = "http://citeseer.ist.psu.edu/374087.html", abstract = "In this paper we continue study on the Stepwise Adaptation of Weights (SAW) technique. Previous studies on constraint satisfaction and data classification have indicated that SAW is a promising technique to boost the performance of evolutionary algorithms. Here we use SAW to boost performance of a genetic programming algorithm on simple symbolic regression problems. We measure the performance of a standard GP and two variants of SAW extensions on two different symbolic regression problems.", } @InProceedings{eggermont_adaptive:2001:EuroGP, author = "Jeroen Eggermont and Jano I. {van Hemert}", title = "Adaptive Genetic Programming Applied to New and Existing Simple Regression Problems", booktitle = "Genetic Programming, Proceedings of EuroGP'2001", year = "2001", editor = "Julian F. Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon", volume = "2038", series = "LNCS", pages = "23--35", address = "Lake Como, Italy", publisher_address = "Berlin", month = "18-20 " # apr, organisation = "EvoNET", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, Adaptation, Symbolic Regression, Problem Generator, Program Trees, data mining", ISBN = "3-540-41899-7", URL = "http://www.liacs.nl/~jeggermo/publications/eurogp2001-symreg.ps.gz", URL = "http://www.vanhemert.co.uk/publications/eurogp2001.Adaptive_Genetic_Programming_Applied_to_New_and_Existing_Simple_Regression_Problems.ps.gz", URL = "http://www.vanhemert.co.uk/publications/eurogp2001.Adaptive_Genetic_Programming_Applied_to_New_and_Existing_Simple_Regression_Problems.pdf", DOI = "doi:10.1007/3-540-45355-5_3", size = "13 pages", abstract = "In this paper we continue our study on adaptive genetic programming. We use Stepwise Adaptation of Weights (SAW) to boost performance of a genetic programming algorithm on simple symbolic regression problems. We measure the performance of a standard GP and two variants of SAW extensions on two different symbolic regression problems from literature. Also, we propose a model for randomly generating polynomials which we then use to further test all three GP variants.", notes = "EuroGP'2001, part of \cite{miller:2001:gp}", } @InProceedings{eggermont:2001:EuroGP_dead, author = "Jeroen Eggermont and Tom Lenaerts and Sanna Poyhonen and Alexandre Termier", title = "Raising the Dead: Extending Evolutionary Algorithms with a Case-based Memory", booktitle = "Genetic Programming, Proceedings of EuroGP'2001", year = "2001", editor = "Julian F. Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon", volume = "2038", series = "LNCS", pages = "280--290", address = "Lake Como, Italy", publisher_address = "Berlin", month = "18-20 " # apr, organisation = "EvoNET", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, Dynamic Fitness, Global Memory: Poster", ISBN = "3-540-41899-7", URL = "http://www.liacs.nl/~jeggermo/publications/eurogp2001-dynamic.ps.gz", URL = "http://www.lri.fr/~termier/publis/eurogp2001-dynamic.ps.gz", DOI = "doi:10.1007/3-540-45355-5_22", size = "11 pages", abstract = "In dynamically changing environments, the performance of a standard evolutionary algorithm deteriorates. This is due to the fact that the population, which is considered to contain the history of the evolutionary process, does not contain enough information to allow the algorithm to react adequately to changes in the fitness landscape. Therefore, we added a simple, global case-based memory to the process to keep track of interesting historical events. Through the introduction of this memory and a storing and replacement scheme we were able to improve the reaction capabilities of an evolutionary algorithm with a periodically changing fitness function.", notes = "EuroGP'2001, part of \cite{miller:2001:gp}", } @InProceedings{eggermont:2002:EuroGP, title = "Evolving Fuzzy Decision Trees with Genetic Programming and Clustering", author = "Jeroen Eggermont", editor = "James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi", booktitle = "Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002", volume = "2278", series = "LNCS", pages = "71--82", publisher = "Springer-Verlag", address = "Kinsale, Ireland", publisher_address = "Berlin", month = "3-5 " # apr, year = "2002", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-43378-3", URL = "http://www.liacs.nl/~jeggermo/publications/eurogp2002.ps.gz", DOI = "doi:10.1007/3-540-45984-7_7", abstract = "In this paper we present a new fuzzy decision tree representation for n-category data classification using genetic programming. The new fuzzy representation uses fuzzy clusters for handling continuous attributes. To make optimal use of the fuzzy classifications of this representation an extra fitness measure is used. The new fuzzy representation will be compared, using several machine learning data sets, to a similar non-fuzzy representation as well as to some other evolutionary and non-evolutionary algorithms from literature.", notes = "EuroGP'2002, part of \cite{lutton:2002:GP}", } @InProceedings{E02b, author = "J. Eggermont", title = "Evolving Fuzzy Decision Trees for Data Classification", booktitle = "Proceedings of the 14th Belgium/Netherlands Conference on Artificial Intelligence (BNAIC'02)", year = "2002", editor = "Hendrik Blockeel and Marc Denecker", pages = "417--418", address = "Leuven, Belgium", month = "21-22 " # oct, organisation = "BNVKI, Dutch and the Belgian AI Association", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.kuleuven.ac.be/conference/bnaic02/", URL = "http://www.liacs.nl/~jeggermo/publications/bnaic02-fuzzy.ps.gz", size = "2 pages", notes = "http://www.cs.kuleuven.ac.be/cgi-bin-dtai/publ_info.pl?id=40226 Katholieke Universiteit Leuven and Universite Libre de Bruxelles in collaboration with PharmaDM and under the auspices of BNVKI/AIABN (the Belgian-Dutch Association for Artificial Intelligence), SIKS (School for Information and Knowledge Systems), and SNN (the Foundation for Neural Networks).", } @InProceedings{EL02, author = "J. Eggermont and T. Lenaerts", title = "Dynamic Optimization using Evolutionary Algorithms with a Case-based Memory", booktitle = "Proceedings of the 14th Belgium/Netherlands Conference on Artificial Intelligence (BNAIC'02)", year = "2002", editor = "Hendrik Blockeel and Marc Denecker", address = "Leuven, Belgium", month = "21-22 " # oct, organisation = "BNVKI, Dutch and the Belgian AI Association", keywords = "genetic algorithms, genetic programming, evolutionary algorithms", URL = "http://www.liacs.nl/~jeggermo/publications/bnaic02-dynamic.ps.gz", size = "8 pages", abstract = "Dynamic environments form a dicult class of problems for evolutionary algorithms to solve. In this paper we propose a new evolutionary algorithm for this class in which we combine a case-based memory with a meta-learner.", notes = "http://www.cs.kuleuven.ac.be/conference/bnaic02/ Katholieke Universiteit Leuven and Universite Libre de Bruxelles in collaboration with PharmaDM and under the auspices of BNVKI/AIABN (the Belgian-Dutch Association for Artificial Intelligence), SIKS (School for Information and Knowledge Systems), and SNN (the Foundation for Neural Networks).", } @InProceedings{eggermont:2003:bnaic, author = "J. Eggermont and J. N. Kok and W. A. Kosters", title = "Genetic Programming for Data Classification: Refining the Search Space", booktitle = "Proceedings of the Fivteenth Belgium/Netherlands Conference on Artificial Intelligence (BNAIC'03)", year = "2003", editor = "T. Heskes and P. Lucas and L. Vuurpijl and W. Wiegerinck", pages = "123--130", address = "Nijmegen, The Netherlands", month = "23-24 " # oct, organisation = "BNVKI, Dutch and the Belgian AI Association", keywords = "genetic algorithms, genetic programming", URL = "http://www.liacs.nl/home/kosters/bnaic03-eggermont.ps", size = "8 pages", notes = "C4.5 ID3", } @InProceedings{EKK04, author = "J. Eggermont and J. N. Kok and W. A. Kosters", title = "Genetic Programming for Data Classification: {P}artitioning the Search Space", booktitle = "Proceedings of the 2004 Symposium on Applied Computing (ACM SAC'04)", year = "2004", pages = "1001--1005", address = "Nicosia, Cyprus", month = "14-17 " # mar, organisation = "ACM", notes = "duplicate of \cite{eggermont:2004:sac} keywords etc removed May 2016", } @InProceedings{eggermont:2004:sac, author = "Jeroen Eggermont and Joost N. Kok and Walter A. Kosters", title = "Genetic Programming for Data Classification: Partitioning the Search Space", booktitle = "Proceedings of the 2004 Symposium on applied computing (ACM SAC'04)", year = "2004", pages = "1001--1005", address = "Nicosia, Cyprus", month = "14-17 " # mar, keywords = "genetic algorithms, genetic programming, data classification", URL = "http://www.liacs.nl/~kosters/SAC2003final.pdf", DOI = "doi:10.1145/967900.968104", size = "5 pages", abstract = "When Genetic Programming is used to evolve decision trees for data classification, search spaces tend to become extremely large. We present several methods using techniques from the field of machine learning to refine and thereby reduce the search space sizes for decision tree evolvers. We will show that these refinement methods improve the classification performance of our algorithms.", } @InProceedings{Eggermont:PPSN:2004, author = "Jeroen Eggermont and Joost N. Kok and Walter A. Kosters", title = "Detecting and Pruning Introns for Faster Decision Tree Evolution", booktitle = "Parallel Problem Solving from Nature - PPSN VIII", year = "2004", editor = "Xin Yao and Edmund Burke and Jose A. Lozano and Jim Smith and Juan J. Merelo-Guerv\'os and John A. Bullinaria and Jonathan Rowe and Peter Ti\v{n}o Ata Kab\'an and Hans-Paul Schwefel", volume = "3242", pages = "1071--1080", series = "LNCS", address = "Birmingham, UK", publisher_address = "Berlin", month = "18-22 " # sep, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, bloat", ISBN = "3-540-23092-0", URL = "http://www.liacs.nl/~kosters/ppsn8/ppsn2004.pdf", URL = "https://rdcu.be/dc0jz", DOI = "doi:10.1007/b100601", DOI = "doi:10.1007/978-3-540-30217-9_108", size = "10 pages", abstract = "We show how the understandability and speed of genetic programming classification algorithms can be improved, without affecting the classification accuracy. By analysing the decision trees evolved we can remove the unessential parts, called introns, from the discovered decision trees. Since the resulting trees contain only useful information they are smaller and easier to understand. Moreover, by using these pruned decision trees in a fitness cache we can significantly reduce the number of unnecessary fitness calculations.", notes = "PPSN-VIII", } @PhdThesis{eggermont:thesis, author = "Jeroen Eggermont", title = "Data Mining using Genetic Programming: Classification and Symbolic Regression", school = "Institute for Programming research and Algorithmics, Leiden Institute of Advanced Computer Science, Faculty of Mathematics \& Natural Sciences, Leiden University", year = "2005", address = "The Netherlands", month = "14 " # sep, bibsource = "OAI-PMH server at openaccess.leidenuniv.nl", contributor = "Jeroen Eggermont", description = "Promotor: Prof. dr. J.N. Kok. Co-promotor: Dr. W.A. Kosters. Referent: Dr. W.B. Langdon.; With Summary in Dutch.", format = "29005 bytes; 685481 bytes", identifier = "Eggermont, J., 2005. Doctoral Thesis, Leiden University; 90-9019760-5", language = "en", relation = "IPA Dissertation Series;2005-12", keywords = "genetic algorithms, genetic programming, data mining", URL = "https://hdl.handle.net/1887/3393", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.155.3989", URL = "https://openaccess.leidenuniv.nl/dspace/bitstream/1887/3393/1/proefschriftppi-eggermont.pdf", ISBN = "90-90-19760-5", size = "179 pages", abstract = "Sir Francis Bacon said about four centuries ago: Knowledge is Power. If we look at today's society, information is becoming increasingly important. According to [73] about five exabytes (5000000000000000000 bytes) of new information were produced in 2002, 92percent of which on magnetic media (e.g., hard-disks). This was more than double the amount of information produced in 1999 (2 exabytes). However, as Albert Einstein observed: Information is not Knowledge. One of the challenges of the large amounts of information stored in databases is to find or extract potentially useful, understandable and novel patterns in data which can lead to new insights. To quote T.S. Eliot: Where is the knowledge we have lost in information? [35]. This is the goal of a process called Knowledge Discovery in Databases (KDD) [36]. The KDD process consists of several phases: in the Data Mining phase the actual discovery of new knowledge takes place. The outline of the rest of this introduction is as follows. We start with an introduction of Data Mining and more specifically the two subject areas of Data Mining we will be looking at: classification and regression. Next we give an introduction about evolutionary computation in general and tree-based genetic programming in particular. In Section 1.4 we give our motivation for using genetic programming for Data Mining. Finally, in the last sections we give an overview of the thesis and related publications.", notes = "IPA 1887/3393", } @Article{Eggermont:2009:GPEM, author = "Jeroen Eggermont", title = "Juan Romero and Penousal Machado (eds): The Art of Artificial Evolution: A Handbook on Evolutionary Art and Music Natural Computing Series, Springer Science+Business Media, 2008, 460 pp, 169 illustrations, 91 in colour, Hard Cover with DVD, ISBN: 978-3-540-72876-4", journal = "Genetic Programming and Evolvable Machines", year = "2009", volume = "10", number = "1", pages = "95--96", month = mar, keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dR8n3", DOI = "doi:10.1007/s10710-008-9071-0", size = "2 pages", } @InCollection{eglit:1994:tpfts, author = "Jason T. Eglit", title = "Trend Prediction in Financial Time Series", booktitle = "Genetic Algorithms at Stanford 1994", year = "1994", editor = "John R. Koza", pages = "31--40", address = "Stanford, California, 94305-3079 USA", month = dec, organisation = "Stanford University", publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-187263-3", notes = "This volume contains 20 papers written and submitted by students describing their term projects for the course {"}Genetic Algorithms and Genetic Programming{"} (Computer Science 426) at Stanford University offered during the fall quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs426.html", } @InProceedings{eguchi:2002:gecco:lbp, title = "Multiagent Systems with Symbiotic Learning and Evolution Using Genetic Network Programming", author = "Toru Eguchi and Kotaro Hirasawa and Jinglu Hu and Junichi Murata", booktitle = "Late Breaking Papers at the Genetic and Evolutionary Computation Conference ({GECCO-2002})", editor = "Erick Cant{\'u}-Paz", year = "2002", month = jul, pages = "130--137", address = "New York, NY", publisher = "AAAI", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025", keywords = "genetic algorithms, genetic programming", notes = "Late Breaking Papers, {GECCO-2002}. A joint meeting of the eleventh International Conference on Genetic Algorithms ({ICGA-2002}) and the seventh Annual Genetic Programming Conference ({GP-2002}) part of cantu-paz:2002:GECCO:lbp", } @InProceedings{Eguchi:2004:EGSCSUGNP, title = "Elevator Group Supervisory Control Systems Using Genetic Network Programming", author = "Toru Eguchi and Kotaro Hirasawa and Jinglu Hu and Sandor Markon", pages = "1661--1667", booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary Computation", year = "2004", publisher = "IEEE Press", month = "20-23 " # jun, address = "Portland, Oregon", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Real-world applications, Theory of evolutionary algorithms", DOI = "doi:10.1109/CEC.2004.1331095", abstract = "Genetic Network Programming (GNP) has been proposed and studied as a new method of evolutionary computations. Until now, the applicability and availability of GNP to the real-world applications have not been studied. In this paper, Elevator Group Supervisory Control Systems (EGSCSs) are considered as the real- world application for GNP, and it is reported that the design of a controller of EGSCSs has been studied using GNP. From simulations, it is clarified that better solutions are obtained by using GNP than other conventional methods and the availability of GNP to real-world applications is confirmed.", notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.", } @InProceedings{eguchi:2005:CEC, author = "Toru Eguchi and Kotaro Hirasawa and Jinglu Hu and Sandor Markon", title = "Elevator Group Supervisory Control System Using Genetic Network Programming with Functional Localization", booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary Computation", year = "2005", editor = "David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L. Gwenn Volkert and Dan Ashlock and Marc Schoenauer", volume = "1", pages = "328--335", address = "Edinburgh, UK", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "2-5 " # sep, organisation = "IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9363-5", DOI = "doi:10.1109/CEC.2005.1554702", abstract = "Genetic Network Programming (GNP) whose gene consists of directed graphs has been proposed as a new method of evolutionary computations, and it is recently applied to the Elevator Group Supervisory Control System (EGSCS), a real world problem, to confirm its effectiveness. In the previous study, although the flow of traffic in the elevator system is known and fixed, it is changed dynamically with time in real elevator systems. Therefore, the EGSCS with an adaptive control should be studied considering such changes for practical applications. In this paper, the GNP with functional localisation is applied to the EGSCS to construct such an adaptive system. In the proposed method, the switching GNP can switch the functionally localised GNPs (assigning GNPs) fitted to several kinds of traffic by detecting the change of the flow of traffic. From the simulations, the adaptability and effectiveness of the proposed method are clarified using the traffic data of a day in an office building.", notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS.", } @Article{DBLP:journals/tsmc/EguchiHHO06, author = "Toru Eguchi and Kotaro Hirasawa and Jinglu Hu and Nathan Ota", title = "A study of evolutionary multiagent models based on symbiosis", journal = "IEEE Transactions on Systems, Man, and Cybernetics, Part B", volume = "36", number = "1", year = "2006", pages = "179--193", month = feb, keywords = "genetic algorithms, genetic programming, decision making, evolutionary computation, graph theory, learning (artificial intelligence), multi-agent systems, directed graph, evolutionary multiagent models, genetic network programming, match type tile-world, nash equilibria, symbiosis multiagent systems, symbiotic evolution, symbiotic learning, virtual model, Evolutionary computation, multiagent systems, symbiosis, tile-world", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1109/TSMCB.2005.856720", size = "15 pages", abstract = "Multiagent Systems with Symbiotic Learning and Evolution (Masbiole) has been proposed and studied, which is a new methodology of Multiagent Systems (MAS) based on symbiosis in the ecosystem. Masbiole employs a method of symbiotic learning and evolution where agents can learn or evolve according to their symbiotic relations toward others, i.e., considering the benefits/losses of both itself and an opponent. As a result, Masbiole can escape from Nash Equilibria and obtain better performances than conventional MAS where agents consider only their own benefits. This paper focuses on the evolutionary model of Masbiole, and its characteristics are examined especially with an emphasis on the behaviours of agents obtained by symbiotic evolution. In the simulations, two ideas suitable for the effective analysis of such behaviors are introduced; {"}Match Type Tile-world (MTT){"} and {"}Genetic Network Programming (GNP){"}. MTT is a virtual model where tile-world is improved so that agents can behave considering their symbiotic relations. GNP is a newly developed evolutionary computation which has the directed graph type gene structure and enables to analyse the decision making mechanism of agents easily. Simulation results show that Masbiole can obtain various kinds of behaviours and better performances than conventional MAS in MTT by evolution.", } @InCollection{ehlis:2000:EITPDRUE, author = "Tobin Ehlis", title = "Evolution of Intelligent Task Prioritization in a Dynamic Randomly Updated Environment", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "125--134", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:2000:gagp}", } @Article{article1175, author = "Tobin Ehlis", title = "Application of Genetic Programming to the ``Snake Game''", journal = "Gamedev.Net", year = "2000", number = "175", keywords = "genetic algorithms, genetic programming, game strategy", URL = "http://www.gamedev.net/articles/programming/artificial-intelligence/application-of-genetic-programming-to-the-snake-r1175/", abstract = "This paper describes the evolution of a genetic program to optimise a problem featuring task prioritisation in a dynamic, randomly updated environment. The specific problem approached is the 'snake game' in which a snake confined to a rectangular board attempts to avoid the walls and its own body while eating pieces of food. The problem is particularly interesting because as the snake eats the food, its body grows, causing the space through which the snake can navigate to become more confined. Furthermore, with each piece of food eaten, a new piece of food is generated in a random location in the playing field, adding an element of uncertainty to the program. This paper will focus on the development and analysis of a successful function set that will allow the evolution of a genetic program that causes the snake to eat the maximum possible pieces of food.", notes = "this article was posted to GameDev.net: 8/10/2000 Cited by \cite{CS310GeneticAlgsProject} broken Oct 2018 http://www.dcs.warwick.ac.uk/~csvnw/CS310GeneticAlgsProject.pdf 'Evolving Ghosts in Pacman Using Evolutionary Algorithms' A 3rd year project report by James Hume [8] Ehlis Tobin. Application of Genetic programming to the 'Snake Game'. Available from the World Wide Web: http://www.gamedev.net/reference/articles/article1175.asp (Accessed 9th October 2003--10 Jun 2025). ", } @Article{Ehrenberg:2012:SN, author = "Rachel Ehrenberg", title = "Software Scientist", journal = "Science News", year = "2012", volume = "181", pages = "20", month = "14 " # jan, keywords = "genetic algorithms, genetic programming, Eureqa", URL = "http://www.sciencenews.org/view/feature/id/337207/title/Software_Scientist", size = "1 page", abstract = "With a little data, Eureqa generates fundamental laws of nature", notes = "...'Eureqa has many more papers with many different authors to its name. The program is openly available online and has been downloaded more than 25,000 times'. ...'Deep Thought in Douglas Adams' The Hitchhiker's Guide to the Galaxy.... [GP] ...gave for the meaning of everything: 42'. See also \cite{Dubcakova:2011:GPEM}", } @TechReport{ehrenburg:1995:fls, author = "Herman H. Ehrenburg and H. A. N. {van Maanen}", title = "A Finite Automaton Learning System Using Genetic Programming", institution = "Department of Computer Science, CWI, Centrum voor Wiskunde en Informmatica", year = "1994", type = "NeuroColt Tech Rep", number = "CS-R9458", address = "CWI, P.O. Box 94079, 1090 GB Amsterdam, The Netherlands", keywords = "genetic algorithms, genetic programming, Evolutionary Computing, finite automata", URL = "ftp://ftp.cwi.nl/pub/CWIreports/AA/CS-R9458.ps.Z", URL = "http://ftp.cwi.nl/CWIreports/AA/CS-R9458.pdf", URL = "http://www.neurocolt.org/abs/1995/../../tech_reps/1995/nc-tr-95-009.ps.gz", URL = "http://citeseer.ist.psu.edu/427245.html", abstract = "This report describes the Finite Automaton Learning System (FALS), an evolutionary system that is designed to find small digital circuits that duplicate the behavior of a given finite automaton. FALS is developed with the aim to get a better insight in learning systems. It is also targeted to become a general purpose automatic programming system. The system is based on the genetic programming approach to evolve programs for tasks instead of explicitly programming them. A representation of digital circuits suitable for genetic programming is given as well as an extended crossover operator that alleviates the need to specify an upper bound for the number of states in advance.", notes = " Also available as NC-TR-95-009", size = "40 pages", } @InProceedings{ehrenburg:1996:iDAGcGP, author = "Herman Ehrenburg", title = "Improved Directed Acyclic Graph Evaluation and the Combine Operator in Genetic Programming", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming, DAG", pages = "285--291", address = "Stanford University, CA, USA", publisher = "MIT Press", ISBN = "0-262-61127-9", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap36.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", size = "6 pages", abstract = "The use of a directed acyclic graph (DAG) to represent a population in genetic programming offers several advantages, only one of which is the efficient use of space. We improve on existing methods to evaluate a DAG and offer two new ways of evaluating a population. The first method uses a linked list and a negligible amount of space. In the second method, each node is evaluated only once on all fitness cases and the results are cached. We also introduce two genetic operators in connection to the use of a DAG. The first is a simpler alternative to crossover. The second is a context-preserving genetic operators based on the building block hypothesis, which accurate combined two similar trees.", notes = "GP-96 'combine' genetic operator, ancestor information. p289 Does not 'make use of the 32-fold speedup be evaluating 32 fitness cases in parallel'. p290 'Martin C. Martin' 'CMU 32-parallelization trick'.", } @Article{EHSANI:2023:conbuildmat, author = "Mehrdad Ehsani and Pouria Hajikarimi and Masoud Esfandiar and Mohammad Rahi and Behzad Rasouli and Yousef Yousefi and Fereidoon {Moghadas Nejad}", title = "Developing deterministic and probabilistic prediction models to evaluate high-temperature performance of modified bitumens", journal = "Construction and Building Materials", volume = "401", pages = "132808", year = "2023", ISSN = "0950-0618", DOI = "doi:10.1016/j.conbuildmat.2023.132808", URL = "https://www.sciencedirect.com/science/article/pii/S0950061823025242", keywords = "genetic algorithms, genetic programming, MSCR, Multi-gene genetic programming, Logistic regression, Modified bitumen, Machine learning, Prediction model", abstract = "This study aims to develop deterministic and probabilistic prediction models for the multiple stress creep and recovery (MSCR) test. For this purpose, crumb rubber, polyphosphoric acid, and styrene-butadiene-styrene bitumen modifiers have been used with different dosages to modify high-temperature performance of PG 58-28 and PG 64-22 base bitumens. The MSCR test has been performed at different temperatures. Deterministic models are developed by the multi-gene genetic programming technique for each modifier individually, and the non-recoverable creep compliance (Jnr) and percent recovery (R) parameters are predicted. The accuracy of deterministic models is suitable and the performance of R models has been better than Jnr models. Furthermore, a comprehensive probabilistic model has been developed by using the logistic regression technique to predict different traffic levels. The accuracy of the probabilistic model is 0.85. The sensitivity analysis has been performed on this model and the effect of changes in the modifier dosage and temperature on the traffic levels have been investigated. Results show that using the probabilistic model, it is possible to find a range of modifier's dosage in which the traffic level is desired", } @InProceedings{Eetal96, author = "A. E. Eiben and T. J. Euverman and W. Kowalczyk and E. Peelen and F. Slisser and J. A. M. Wesseling", title = "Comparing Adaptive and Traditional Techniques for Direct Marketing", booktitle = "Proceedings of the 4th European Congress on Intelligent Techniques and Soft Computing", year = "1996", editor = "H.-J. Zimmermann", pages = "434--437", publisher_address = "Aachen, Germany", publisher = "Verlag Mainz", keywords = "genetic algorithms, genetic programming", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/802/http:zSzzSzwww.wi.leidenuniv.nlzSz~guszzSzeufit96.pdf/eiben96comparing.pdf", URL = "http://citeseer.ist.psu.edu/eiben96comparing.html", size = "4 pages", abstract = "he paper contains results of a research project aimed at application and evaluation of modern data analysis techniques in the field of marketing. The investigated techniques were: neural networks, evolutionary algorithms, CHAID and logistic regression analysis. All techniques were applied to the problem of making optimal selections for direct mailing and the resulting models were compared w.r.t. accuracy, interpretability, transparency and time and expertise needed for their construction.", notes = " ", } @Unpublished{eiben:email:10-Nov-1997, author = "Gusz Eiben", title = "GP in Leiden", note = "electronic communication", month = "10 " # nov, year = "1997", keywords = "genetic algorithms, genetic programming", notes = " Organisation: Leiden University, Dept. of Mathematics & Computer Science, The Netherlands Dear GP-ers, Following Michele's example on > everybody could just give a list of keywords, > describing > - the main technical focus of the person or the team > - the applications. here is the item on the Leiden group. Mainly applications and application oriented research in the filed of marketing and financial services. Alas, this implies that many of our projects are confidential. Even if we are allowed to submit, we need to leave out so many technical details that the reviewers find it unacceptable. I hope you can use some of this info. Cheers, Gusz -------------------------------------------------------------------- Topics, resp. applications: - Direct marketing application for a big multinational computer manufacturer (see the publication below) - Credit-Score-Card application for a medium size Dutch bank - Creditability evaluation application for a major Dutch bank - Data mining feature selection application for a small Dutch software house - Customer retention modelling for a major Dutch investment fund see \cite{EEKS98} Master Theses co-supervised by our group members (not published) M. Keijzer, Representing Computer Programs in Genetic Programming, 1995 (in English). Supervised by A.E. Eiben and M. Gerrets. S. da Silva, Go and Genetic Programming: Playing Go with Filter Functions, 1996 (in English). Supervised by A.E. Eiben and H.J.M. Goeman. H.D. Sneep, A Genetic Algorithms for the Development of a Credit-Score-Card, 1994. Supervised by A.E. Eiben and H.J. Gaaikema. C. van Straten, Predictive Power of Genetic Programming, 1995. Supervised by A.E. Eiben and J.A.M. Wesseling. C.J. Veenman, Positional Genetic Programming, 1996 (in English). Supervised by A.E. Eiben and W.J. Kowalczyk (see \cite{veennan:mastersthesis} ) D. de Vries, Seeking for the Reliable Custumer with Darwin, 1994. Supervised by A.E. Eiben and B. Kersten. D.L.T. Zwietering, Genetic Selection of Relevant Features, 1995. Supervised by A.E. Eiben, E. Lebert and D. Thierens. Cheers ", } @InCollection{EEKS98, author = "A. E. Eiben and T. J. Euverman and W. Kowalczyk and F. Slisser", title = "Modelling Customer Retention with Statistical Techniques, Rough Data Models and Genetic Programming", booktitle = "Rough-Fuzzy Hybridization: A New Trend in Decision Making Fuzzy Sets, Rough Sets and Decision Making Processes", publisher = "Springer-Verlag", year = "1998", editor = "Sankar K. Pal and Andrzej Skowron", pages = "330--345", address = "Berlin", keywords = "genetic algorithms, genetic programming", ISBN = "981-4021-00-8", URL = "http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=DF680800F7770919CB85C7A704F50DC9?doi=10.1.1.55.7177&rep=rep1&type=pdf", size = "16 pages", abstract = "This paper contains results of a research project aiming at modelling the phenomenon of customer retention. Historical data from a database of a big mutual fund investment company have been analysed with three techniques: logistic regression, rough data models, and genetic programming. Models created by these techniques were used to gain insights into factors influencing customer behaviour and to make predictions on ending the relationship with the company in question. Because the techniques were applied independently of each other, it was possible to make a comparison of their basic features in the context of data mining.", notes = " http://www.amazon.com/Rough-Fuzzy-Hybridization-Decision-Making/dp/9814021008", } @InProceedings{eiben:1998:gmcr, author = "A. E. Eiben and A. E. Koudijs and F. Slisser", title = "Genetic Modelling of Customer Retention", booktitle = "Proceedings of the First European Workshop on Genetic Programming", year = "1998", editor = "Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer and Terence C. Fogarty", volume = "1391", series = "LNCS", pages = "178--186", address = "Paris", publisher_address = "Berlin", month = "14-15 " # apr, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-64360-5", DOI = "doi:10.1007/BFb0055937", abstract = "This paper contains results of a research project aiming at the application and evaluation of modern data analysis techniques in the field of marketing. The investigated techniques are: genetic programm ing, rough data analysis, CHAID and logistic regression analysis. All four techniques are applied independently to the problem of customer retention modelling, using a database of a financial company. Models created by these techniques are used to gain insights into factors influencing customer behaviour and to make predictions on ending the relationship with the company in question. Comparing the predictive power of the obtained models shows that the genetic technology offers the highest performance.", notes = "EuroGP'98", affiliation = "Leiden University Dept. of Comp. Sci. The Netherlands The Netherlands", } @InProceedings{eiben:1999:PA, author = "A. E. Eiben and D. Elia and J. I. van Hemert", title = "Population dynamics and emerging mental features in AEGIS", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1257--1264", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "artificial life, adaptive behavior and agents", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-038.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-038.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Article{eiben:1999:pcea, author = "Agoston Endre Eiben and Robert Hinterding and Zbigniew Michalewicz", title = "Parameter Control in Evolutionary Algorithms", journal = "IEEE Transations on Evolutionary Computation", year = "1999", volume = "3", number = "2", pages = "124--141", month = jul, keywords = "evolutionary strategies, genetic algorithms, evolutionary computation, self-adjusting systems, control mechanisms, evolutionary algorithms, parameter control, self-adaptation", ISSN = "1089-778X", size = "18 pages", abstract = "The issue of controlling values of various parameters of an evolutionary algorithm is one of the most important and promising areas of research in evolutionary computation: it has a potential of adjusting the algorithm to the problem while solving the problem. In the paper we: 1) revise the terminology, which is unclear and confusing, thereby providing a classification of such control mechanisms, and 2) survey various forms of control which have been studied by the evolutionary computation community in recent years. Our classification covers the major forms of parameter control in evolutionary computation and suggests some directions for further research", notes = "Some mention of GP, particularly Peter Angeline's work. Reference Cited: 144 CODEN: ITEVF5 Inspec Accession Number: 6290502", } @Article{Eiben:2002:IPL, author = "A. E. Eiben and M. Schoenauer", title = "Evolutionary computing", journal = "Information Processing Letters", year = "2002", volume = "82", pages = "1--6", number = "1", URL = "http://www.sciencedirect.com/science/article/B6V0F-44YWS0J-1/2/a93e1d8b3c96d1cb1a32da104588a569", keywords = "genetic algorithms, genetic programming, Evolutionary computing, Evolution strategies, Evolutionary programming", DOI = "doi:10.1016/S0020-0190(02)00204-1", size = "6 pages", abstract = "Evolutionary computing (EC) is an exciting development in Computer Science. It amounts to building, applying and studying algorithms based on the Darwinian principles of natural selection. we briefly introduce the main concepts behind evolutionary computing. We present the main components all evolutionary algorithms (EAs), sketch the differences between different types of EAs and survey application areas ranging from optimisation, modelling and simulation to entertainment.", owner = "wlangdon", } @Book{eiben:2003:book, author = "A. E. Eiben and J. E. Smith", title = "Introduction to Evolutionary Computing", publisher = "Springer", year = "2003", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-40184-9", URL = "http://www.cs.vu.nl/~gusz/ecbook/ecbook.html", DOI = "doi:10.1007/978-3-662-44874-8", size = "xii + 287 (2nd edition)", notes = "Second edition published 2015 http://www.springer.com/gb/book/9783662448731 Book review \cite{Popyack:2016:GPEM}. Chapter list 1. Introduction 2. What is an Evolutionary Algorithm? 3. Genetic Algorithms 4. Evolution Strategies 5. Evolutionary Programming 6. Genetic Programming 7. Learning Classifier Systems 8. Parameter Control in Evolutionary Algorithms 9. Multi-Modal Problems and Spatial Distribution 10. Hybridisation with Other Techniques: Memetic Algorithms 11. Theory 12. Constraint Handling 13. Special Forms of Evolution 14. Working with Evolutionary Algorithms 15. Summary 16. Appendices 17. Index 18. References", } @InProceedings{1277023, author = "Gusz Eiben and Joeri Bekker and Robert Griffioen and Evert Haasdijk", title = "Balancing quality and quantity in evolving agent systems", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "1", isbn13 = "978-1-59593-697-4", pages = "335--335", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p335.pdf", DOI = "doi:10.1145/1276958.1277023", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Artificial Life, Evolutionary Robotics, Adaptive Behaviour, Evolvable Hardware: Poster, multiagent system, NEW TIES, quality bias, quantity bias, varying population size", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @Article{Eiben:2015:nature, author = "Agoston E. Eiben and Jim Smith", title = "From evolutionary computation to the evolution of things", journal = "Nature", year = "2015", volume = "521", number = "7553", pages = "476--482", month = "28 " # may, keywords = "genetic algorithms, genetic programming, Insight, Mathematics and computing, Computer science", publisher = "Nature Publishing Group, a division of Macmillan Publishers Limited", ISSN = "0028-0836", DOI = "doi:10.1038/nature14544", size = "7 pages", abstract = "Evolution has provided a source of inspiration for algorithm designers since the birth of computers. The resulting field, evolutionary computation, has been successful in solving engineering tasks ranging in outlook from the molecular to the astronomical. Today, the field is entering a new phase as evolutionary algorithms that take place in hardware are developed, opening up new avenues towards autonomous machines that can adapt to their environment. We discuss how evolutionary computation compares with natural evolution and what its benefits are relative to other computing approaches, and we introduce the emerging area of artificial evolution in physical systems.", notes = "A little mention of GP", } @InProceedings{Eiben:2020:GECCOcomp, author = "A. E. Eiben and Emma Hart", title = "If It Evolves It Needs to Learn", year = "2020", editor = "Richard Allmendinger and Hugo Terashima Marin and Efren Mezura Montes and Thomas Bartz-Beielstein and Bogdan Filipic and Ke Tang and David Howard and Emma Hart and Gusz Eiben and Tome Eftimov and William {La Cava} and Boris Naujoks and Pietro Oliveto and Vanessa Volz and Thomas Weise and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Rui Wang and Ran Cheng and Guohua Wu and Miqing Li and Hisao Ishibuchi and Jonathan Fieldsend and Ozgur Akman and Khulood Alyahya and Juergen Branke and John R. Woodward and Daniel R. Tauritz and Marco Baioletti and Josu Ceberio Uribe and John McCall and Alfredo Milani and Stefan Wagner and Michael Affenzeller and Bradley Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and Nayat Sanchez-Pi and Luis Marti and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and Matthew Johns and Nick Ross and Ed Keedwell and Herman Mahmoud and David Walker and Anthony Stein and Masaya Nakata and David Paetzel and Neil Vaughan and Stephen Smith and Stefano Cagnoni and Robert M. Patton and Ivanoe {De Falco} and Antonio {Della Cioppa} and Umberto Scafuri and Ernesto Tarantino and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Erik Hemberg and Riyad Alshammari and Tokunbo Makanju and Fuijimino-shi and Ivan Zelinka and Swagatam Das and Ponnuthurai Nagaratnam and Roman Senkerik", isbn13 = "9781450371278", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377929.3398151", DOI = "doi:10.1145/3377929.3398151", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion", pages = "1383--1384", size = "2 pages", keywords = "genetic algorithms, genetic programming, online learning, Lamarckian evolution, evolutionary robotics", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", abstract = "We elaborate on (future) evolutionary robot systems where morphologies and controllers of real robots are evolved in the real-world. We argue that such systems must contain a learning component where a newborn robot refines its inherited controller to align with its body, which will inevitably be different from its parents.", notes = "Also known as \cite{10.1145/3377929.3398151} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @InProceedings{conf/ruleml/EichhoffR15, author = "Julian R. Eichhoff and Dieter Roller", title = "Genetic Programming for Design Grammar Rule Induction", bibdate = "2015-09-04", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ruleml/ruleml2015c.html#EichhoffR15", booktitle = "Proceedings of the Rule{ML} 2015 Challenge, the Special Track on Rule-based Recommender Systems for the Web of Data, the Special Industry Track and the Rule{ML} 2015 Doctoral Consortium hosted by the 9th International Web Rule Symposium (Rule{ML} 2015), Berlin, Germany, August 2-5, 2015", publisher = "CEUR-WS.org", year = "2015", volume = "1417", editor = "Nick Bassiliades and Paul Fodor and Adrian Giurca and Georg Gottlob and Tomas Kliegr and Grzegorz J. Nalepa and Monica Palmirani and Adrian Paschke and Mark Proctor and Dumitru Roman and Fariba Sadri and Nenad Stojanovic", series = "CEUR Workshop Proceedings", keywords = "genetic algorithms, genetic programming, Rule Induction, Graph Grammar, Machine Learning, Design Graph, Functional Decomposition", URL = "http://ceur-ws.org/Vol-1417", URL = "http://nbn-resolving.de/urn:nbn:de:0074-1417-1", URL = "http://ceur-ws.org/Vol-1417/paper3.pdf", size = "8 pages", abstract = "The knowledge engineering effort associated with defining grammar systems can become a barrier for the practical use of such systems. Existing grammar and rule induction algorithms offer rather limited support for discovering context-sensitive graph grammar rules as required by some applications in the domain of engineering design. For this task the present work proposes a rule induction method grounded on Genetic Programming. Specializations regarding the representation and evaluation of rule candidates are discussed. Results from preliminary experiments with a prototype implementation demonstrate the feasibility of the suggested approach.", notes = "Subdue", } @InCollection{Eid:2020:IRMA, author = "Heba F. Eid", title = "Application of Computational Intelligence in Network Intrusion Detection: A Review", booktitle = "Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms", publisher = "IGI Global", year = "2020", pages = "620--641", month = dec, note = "Information Resources Management Association", keywords = "genetic algorithms, genetic programming", isbn13 = "9781799880486", DOI = "doi:10.4018/978-1-7998-8048-6", DOI = "doi:10.4018/978-1-7998-8048-6.ch032", abstract = "Intrusion detection system plays an important role in network security. However, network intrusion detection (NID) suffers from several problems, such as false positives, operational issues in high dimensional data, and the difficulty of detecting unknown threats. Most of the problems with intrusion detection are caused by improper implementation of the network intrusion detection system (NIDS). Over the past few years, computational intelligence (CI) has become an effective area in extending research capabilities. Thus, NIDS based upon CI is currently attracting considerable interest from the research community. The scope of this review will encompass the concept of NID and presents the core methods of CI, including support vector machine, hidden naive Bayes, particle swarm optimization, genetic algorithm, and fuzzy logic. The findings of this review should provide useful insights into the application of different CI methods for NIDS over the literature, allowing to clearly define existing research challenges and progress, and to highlight promising new research directions.", notes = "GP mentioned in index Al Azhar University, Egypt https://www.igi-global.com/book/research-anthology-multi-industry-uses/267374", } @InProceedings{Einabadi:2022:ICWR, author = "Mahsa Einabadi and Seyed Mohammad Hossein Hasheminejad", title = "A Search-Based Method For optimizing Software Architecture Reliability", booktitle = "2022 8th International Conference on Web Research (ICWR)", year = "2022", pages = "47--54", month = may, keywords = "genetic algorithms, genetic programming, SBSE", DOI = "doi:10.1109/ICWR54782.2022.9786245", abstract = "Choosing the optimal software architecture in the search space by considering quality criteria is beyond human capabilities and is very challenging. It is necessary to search the design space automatically to improve the existing architectural features. To do this, we can use search-based software engineering approaches. In this study, we examine the methods of optimizing and evaluating software architecture and provide a search-based method to improve the reliability of software architecture. The proposed method is based on the use of NSGAII algorithm and genetic programming and the use of software architecture reliability tactics in it. In the proposed method, we optimize the software architecture in two steps. First, we use the genetic programming algorithm to extract how to apply the software architecture reliability tactics, and in the next step, we use the NSGA-II algorithm to search for the optimal allocation of components to the hardware servers. To evaluate the proposed method, we use a reporting system case study. The results of applying the proposed optimization steps show that the reliability of the whole system as well as most of its most frequent functionalities is improved.", notes = "Also known as \cite{9786245}", } @InCollection{Eisenstein:1997:GAil, author = "Jacob Eisenstein", title = "Genetic Algorithms and Incremental Learning", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1997", publisher = "Stanford Bookstore", year = "1997", editor = "John R. Koza", pages = "47--56", address = "Stanford, California, 94305-3079 USA", month = "17 " # mar, keywords = "genetic algorithms, genetic programming, seeding", ISBN = "0-18-205981-2", notes = "part of \cite{koza:1997:GAGPs}", } @TechReport{Eisenstein:2003-023, author = "Jacob Eisenstein", title = "Evolving Robocode Tank Fighters", institution = "Computer Science and Artificial Intelligence Laboratory, MIT", year = "2003", type = "AI Memo", number = "2003-023", address = "Cambridge, MA 02139, USA", month = "28 " # oct, keywords = "genetic algorithms, genetic programming", URL = "ftp://publications.ai.mit.edu/ai-publications/2003/AIM-2003-023.pdf", URL = "ftp://publications.ai.mit.edu/ai-publications/2003/AIM-2003-023.ps", abstract = "In this paper, I describe the application of genetic programming to evolve a controller for a robotic tank in a simulated environment. The purpose is to explore how genetic techniques can best be applied to produce controllers based on subsumption and behavior oriented languages such as REX. As part of my implementation, I developed TableRex, a modification of REX that can be expressed on a fixed-length genome. Using a fixed subsumption architecture of TableRex modules, I evolved robots that beat some of the most competitive hand-coded adversaries.", size = "24 pages", } @InProceedings{ekart:1998:gcd4bl, author = "Aniko Ekart", title = "Generating Class Descriptions of Four Bar Linkages", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "42--47", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "genetic algorithms, genetic programming", URL = "http://www.sztaki.hu/~ekart/asi.ps", URL = "http://citeseer.ist.psu.edu/465578.html", size = "6 pages", abstract = "Kinematic synthesis of four bar mechanisms is a design problem that is difficult to solve by generative methods. The present approach is a variant based method that combines the genetic programming and decision tree learning methods. The aim of the research is to give a structural description for the class of mechanisms that produce desired coupler curves. For finding and characterizing feasible regions of the design space constructive induction is used. The new features are created by genetic programming.", notes = "GP-98LB See also \cite{ekart:1999:ASI}", } @InProceedings{ekart:1999:ccgGPm, author = "Aniko Ekart", title = "Controlling Code Growth in Genetic Programming by Mutation", booktitle = "Late-Breaking Papers of EuroGP-99", year = "1999", editor = "W. B. Langdon and Riccardo Poli and Peter Nordin and Terry Fogarty", pages = "3--12", address = "Goteborg, Sweden", month = "26-27 " # may, organisation = "EvoGP", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/eebic/eurogp99/eurogp99_lbp.html", URL = "ftp://ftp.cwi.nl/pub/CWIreports/SEN/SEN-R9913.pdf", URL = "ftp://ftp.cwi.nl/pub/CWIreports/SEN/SEN-R9913.ps.Z", abstract = "In the paper a method that moderate code growth in genetic programming is presented. The addressed problem is symbolic regression. A special mutation operator is used for the simplification of programs. If every individual program in each generation is simplified, then performance of the genetic programming system is worsened. But if simplification is applied as a mutation operator, more compact solutions of the same or better accuracy can be obtained", notes = "EuroGP'99LB part of \cite{langdon:1999:egplb}", } @InProceedings{ekart:1999:ASI, author = "Aniko Ekart and Andras Markus", title = "Decision Trees and Genetic Programming in Synthesis of Four Bar Mechanisms", booktitle = "Life Cycle Approaches to Production Systems, Proceedings of the Advanced Summer Institute-ASI'99", year = "1999", pages = "210--208", address = "Leuven", month = "22-24 " # sep, keywords = "genetic algorithms, genetic programming", ISBN = "960-530-040-0", URL = "http://www.sztaki.hu/~ekart/asi.ps", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.22.2078", size = "8 pages", abstract = "Kinematic synthesis of four bar mechanisms is a design problem that is difficult to solve by generative methods. The present approach is a variant based method that combines the genetic programming and decision tree learning methods. The aim of the research is to give a structural description for the class of mechanisms that produce desired coupler curves. For finding and characterising feasible regions of the design space constructive induction is used. The new features are created by genetic programming", notes = "http://www.lar.ee.upatras.gr/icims/asi/asi99/asi99.htm See also \cite{ekart:1998:gcd4bl} Nice fusion of C4.5 and GP.", } @InProceedings{ekart:1999:EA, author = "Aniko Ekart", title = "Shorter Fitness Preserving Genetic Programs", booktitle = "Artificial Evolution. 4th European Conference, AE'99, Selected Papers", year = "2000", editor = "C. Fonlupt and J.-K. Hao and E. Lutton and E. Ronald and M. Schoenauer", volume = "1829", series = "LNCS", pages = "73--83", address = "Dunkerque, France", month = "3-5 " # nov, keywords = "genetic algorithms, genetic programming", ISBN = "3-540-67846-8", URL = "http://www.sztaki.hu/~ekart/ea.ps", URL = "http://citeseer.ist.psu.edu/496596.html", DOI = "doi:10.1007/10721187_5", abstract = "In the paper a method that moderates code growth in genetic programming is presented. The addressed problem is symbolic regression. A special mutation operator is used for the simplification of programs. If every individual program in each generation is simplified, then the performance of the genetic programming system is slightly worsened. But if simplification is applied as a mutation operator, more compact solutions of the same or better accuracy can be obtained.", notes = "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-67846-8 {"}Simplification is implemented in Prolog and consists of approximately 250 clauses.{"} Fig 4. plots of fitness (RMS error) times program size", } @InProceedings{ekart:2000:mGPfs, author = "Aniko Ekart and S. Z. Nemeth", title = "A metric for genetic programs and fitness sharing", booktitle = "Genetic Programming, Proceedings of EuroGP'2000", year = "2000", editor = "Riccardo Poli and Wolfgang Banzhaf and William B. Langdon and Julian F. Miller and Peter Nordin and Terence C. Fogarty", volume = "1802", series = "LNCS", pages = "259--270", address = "Edinburgh", publisher_address = "Berlin", month = "15-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-67339-3", URL = "http://www.sztaki.hu/~ekart/new_metric.ps", DOI = "doi:10.1007/978-3-540-46239-2_19", abstract = "In the paper a metric for genetic programs is constructed. This metric reflects the structural difference of the genetic programs. It is used then for applying fitness sharing to genetic programs, in analogy with fitness sharing applied to genetic algorithms. The experimental results for several parameter settings are discussed. We observe that by applying fitness sharing the code growth of genetic programs could be limited.", notes = "EuroGP'2000, part of \cite{poli:2000:GP}", } @Article{ekart:2001:genp, author = "Aniko Ekart and S. Z. Nemeth", title = "Selection Based on the Pareto Nondomination Criterion for Controlling Code Growth in Genetic Programming", journal = "Genetic Programming and Evolvable Machines", year = "2001", volume = "2", number = "1", pages = "61--73", month = mar, keywords = "genetic algorithms, genetic programming, code growth, selection scheme, multiobjective optimization", ISSN = "1389-2576", DOI = "doi:10.1023/A:1010070616149", abstract = "The rapid growth of program code is an important problem in genetic programming systems. In the present paper we investigate a selection scheme based on multiobjective optimization. Since we want to obtain accurate and small solutions, we reformulate this problem as multiobjective optimization. We show that selection based on the Pareto nondomination criterion reduces code growth and processing time without significant loss of solution accuracy.", notes = "Article ID: 319813", } @InProceedings{ekart:2001:ESI, author = "Aniko Ekart and S. Z. Nemeth", title = "Stability of Tree Based Decision Principles", booktitle = "EURO Summer Institute (ESI) XIX, Decision Analysis and Artificial Intellience", year = "2001", editor = "Alexis Tsoukias and Patrice Perny", pages = "67--75", address = "Toulouse, France", month = "9-22 " # sep, organisation = "LIP6", keywords = "genetic algorithms, genetic programming", notes = "broken http://www.inf.u-szeged.hu/kutatas/konferenciak/mot/esixix.xml http://www-desir.lip6.fr/~perny/downloads.php", } @PhdThesis{ekart:thesis, author = "Aniko Ekart", title = "Genetic programming: new performance improving methods and applications", school = "E{\"{o}}tv{\"{o}}s Lorand University", year = "2001", address = "Budapest, Hungary", month = "6 " # sep, email = "ekart@sztaki.hu", keywords = "genetic algorithms, genetic programming", URL = "http://www.sztaki.hu/~ekart/th.html", broken = "http://teo.elte.hu/~doktor/show_en.php?id=266", broken = "http://www.inf.elte.hu/karunkrol/szolgaltatasok/konyvtar/Lists/Doktori%20disszertcik%20adatbzisa/DispForm.aspx?ID=11", URL = "http://www.aston.ac.uk/EasySiteWeb/GatewayLink.aspx?alId=138898.pdf", size = "103 pages", abstract = "Genetic programming is the newest form of evolutionary computation that was conceived in the late 1980's as a possible means for automatic programming. Genetic programming performs an evolutionary search in the space of computer programs and selects the program that solves a given task according to certain criteria. In the first part of the dissertation we give an overview of evolutionary computation and in particular genetic programming. We raise key issues for genetic programming: code growth, diversity, real world applications. In the second part we present our contribution to the theory of genetic programming. We demonstrate two methods for limiting the code growth. The first method consists in applying an additional mutation operator that simplifies the structure of a genetic program without altering its behavior. The second method applies multiobjective optimization for the objectives of fitness and program size. We show that both methods are successful in reducing code growth without significant loss of accuracy. We then define a distance metric for genetic programs and use it for applying the fitness sharing technique. We propose a simple diversity measure based on our metric and study the effects of fitness sharing with the help of this diversity measure. In the third part we show the application of genetic programming in two complex real world problems. The first problem comes from mechanical engineering. Four bar mechanisms play a very important role in practical mechanism design. We describe our four bar mechanism design system. We demonstrate how genetic programming can be a vital component of a complex design system. We integrate genetic programming with decision trees into a powerful learning machine. The second problem belongs to the decision support domain of economics. The decision-makers have to make many subjective decisions. Consequently, the final decision is sensitive to even small changes in these subjective values. We present our genetic programming system that helps the decision-makers to arrive at stable decisions. That is, for small variations in the values of the involved variables, the final decision remains unchanged.", notes = "Supervisor: Dr. Andras Markus", } @InProceedings{ekart:2002:EuroGP, title = "Maintaining the Diversity of Genetic Programs", author = "Anik\'o Ek\'art and Sandor Zoltan N\'emeth", editor = "James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi", booktitle = "Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002", volume = "2278", series = "LNCS", pages = "162--171", publisher = "Springer-Verlag", address = "Kinsale, Ireland", publisher_address = "Berlin", month = "3-5 " # apr, URL = "http://www.sztaki.hu/~ekart/eurgp2.ps", year = "2002", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-43378-3", DOI = "doi:10.1007/3-540-45984-7_16", abstract = "An important problem of evolutionary algorithms is that throughout evolution they loose genetic diversity. Many techniques have been developed for maintaining diversity in genetic algorithms, but few investigations have been done for genetic programs. We define here a diversity measure for genetic programs based on our metric for genetic trees. We use this distance measure for studying the effects of fitness sharing. We then propose a method for adaptively maintaining the diversity of a population during evolution.", notes = "EuroGP'2002, part of \cite{lutton:2002:GP}", } @Article{ekart:2002:EJOP, author = "Anik\'o Ek\'art and S. Z. N\'emeth", title = "Stability analysis of tree structured decision functions", journal = "European Journal of Operational Research", year = "2005", volume = "160", number = "3", pages = "676--695", month = "1 " # feb, keywords = "genetic algorithms, genetic programming, Decision support systems, Evolutionary computation, Stability analysis, Decision functions", ISSN = "0377-2217", URL = "http://www.sciencedirect.com/science/article/B6VCT-4B6CR54-4/2/8de1437b694f9e2060da541ad1b175be", DOI = "doi:10.1016/j.ejor.2003.10.007", abstract = "In multicriteria decision problems many values must be assigned, such as the importance of the different criteria and the values of the alternatives with respect to subjective criteria. Since these assignments are approximate, it is very important to analyze the sensitivity of results when small modifications of the assignments are made. When solving a multicriteria decision problem, it is desirable to choose a decision function that leads to a solution as stable as possible. We propose here a method based on genetic programming that produces better decision functions than the commonly used ones. The theoretical expectations are validated by case studies.", } @Article{ekart:2003:AIEDAM, author = "Aniko Ekart and Andras Markus", title = "Using Genetic Programming and Decision Trees for Generating Structural Descriptions of Four Bar Mechanisms", journal = "Artificial Intelligence for Engineering Design, Analysis and Manufacturing", year = "2003", volume = "17", number = "3", pages = "205--220", month = aug, keywords = "genetic algorithms, genetic programming, decision trees, four bar mechanism synthesis, machine learning", ISSN = "0890-0604", DOI = "doi:10.1017/S0890060403173039", abstract = "Four bar mechanisms are basic components of many important mechanical device. The kinematic synthesis of four bar mechanisms is a difficult design problem. We present here a novel method that combines the genetic programming and decision tree learning methods. We give a structural description for the class of mechanisms that produce desired coupler curves. For finding and characterising feasible regions of the design space constructive induction is used. Decision trees constitute the learning engine and the new features are created by genetic programming.", notes = "http://journals.cambridge.org/action/displayJournal?jid=AIE", } @InProceedings{ekart:2004:eurogp, author = "Aniko Ekart and Steven Gustafson", title = "A Data Structure for Improved GP Analysis via Efficient Computation and Visualisation of Population Measures", booktitle = "Genetic Programming 7th European Conference, EuroGP 2004, Proceedings", year = "2004", editor = "Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule", volume = "3003", series = "LNCS", pages = "35--46", address = "Coimbra, Portugal", publisher_address = "Berlin", month = "5-7 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-21346-5", URL = "http://www.sztaki.hu/~ekart/eurgp4.ps", URL = "http://www.cs.nott.ac.uk/~smg/research/publications/eurogp-itree-2004.pdf", DOI = "doi:10.1007/978-3-540-24650-3_4", abstract = "Population measures for genetic programs are defined and analysed in an attempt to better understand the behaviour of genetic programming. Some measures are simple, but do not provide sufficient insight. The more meaningful ones are complex and take extra computation time. Here we present a unified view on the computation of population measures through an information hyper-tree (iTree). The iTree allows for a unified and efficient calculation of population measures via a basic tree traversal.", notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004", } @InProceedings{Ekart:2004:IWES, author = "Aniko Ekart", title = "Analysing the Emerging Properties of Genetic Programs through the iTrees of Populations", booktitle = "Proceedings of the 5th International Workshop on Emergent Synthesis IWES'04", year = "2004", pages = "61--66", address = "Budapest, Hungary", month = may # " 24-25", organisation = "Computer and Automation Research Institute. Hungarian Academy of Sciences", keywords = "genetic algorithms, genetic programming", URL = "http://eprints.sztaki.hu/id/eprint/3622", notes = "http://www.sztaki.hu/IWES04/", } @InProceedings{1274010, author = "Aniko Ekart", title = "Evolution of lace knitting stitch patterns by genetic programming", booktitle = "Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2007)}", year = "2007", month = "7-11 " # jul, editor = "Peter A. N. Bosman", isbn13 = "978-1-59593-698-1", pages = "2457--2461", address = "London, United Kingdom", keywords = "genetic algorithms, genetic programming, creativity, evaluation, representation", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p2457.pdf", DOI = "doi:10.1145/1274000.1274010", publisher = "ACM Press", publisher_address = "New York, NY, USA", abstract = "In this paper we study the generation of lace knitting stitch patterns by using genetic programming. We devise a genetic representation of knitting charts that accurately reflects their usage for hand knitting the pattern. We apply a basic evolutionary algorithm for generating the patterns, where the key of success is evaluation. We propose automatic evaluation of the patterns, without interaction with the user. We present some patterns generated by the method and then discuss further possibilities for bringing automatic evaluation closer to human evaluation.", notes = "Distributed on CD-ROM at GECCO-2007 ACM Order No. 910071", } @InProceedings{ekart:2008:AIIS, author = "Aniko Ekart", title = "Genetic Programming for the Design of Lace Knitting Stitch Patterns", booktitle = "Applications and Innovations in Intelligent Systems XV", year = "2008", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-1-84800-086-5_19", DOI = "doi:10.1007/978-1-84800-086-5_19", } @Article{Ekart:2014:GPEM, author = "Aniko Ekart", title = "Emergence in genetic programming", journal = "Genetic Programming and Evolvable Machines", year = "2014", volume = "15", number = "1", pages = "83--85", month = mar, keywords = "genetic algorithms, genetic programming, Emergence, Self-modification, Autoconstructive evolution, Multilevel genetic programming", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-013-9199-4", size = "3 pages", abstract = "Banzhaf explores the concept of emergence and how and where it happens in genetic programming [1]. Here we consider the question: what shall we do with it? We argue that given our ultimate goal to produce genetic programming systems that solve new and difficult problems, we should take advantage of emergence to get closer to this goal.", notes = "\cite{Banzhaf:2014:GPEM}", } @Article{Ekart:2017:GPEM, author = "Aniko Ekart and Peter R. Lewis", title = "Genotype-phenotype mapping implications for genetic programming representation: Commentary on ``On the mapping of genotype to phenotype in evolutionary algorithms'' by {Peter A. Whigham, Grant Dick, and James Maclaurin}", journal = "Genetic Programming and Evolvable Machines", year = "2017", volume = "18", number = "3", pages = "369--372", month = sep, note = "Special Peer Commentary on Mapping of Genotype to Phenotype in Evolutionary Algorithms", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-017-9291-2", size = "4 pages", abstract = "Here we comment on the article On the mapping of genotype to phenotype in evolutionary algorithms, by Peter A. Whigham, Grant Dick, and James Maclaurin \cite{Whigham:2017:GPEM}. The article reasons about analogies from molecular biology to evolutionary algorithms and discusses conditions for biological adaptations in the context of grammatical evolution, which provide a useful perspective to GP practitioners. However, the connection of the listed implications for GP is not sufficiently convincing for the reader . Therefore this commentary will (1) examine the proposed principles one by one, challenging the authors to provide more supporting evidence where felt that this was needed, and (2) propose a methodical way to GP practitioners to apply these principles when designing GP representations.", notes = "Introduction in \cite{Spector:2017:GPEM} An author's reply to this comment is available at http://dx.doi.org/10.1007/s10710-017-9289-9 \cite{Whigham:2017:GPEM2}. This comment refers to the article available at: http://dx.doi.org/10.1007/s10710-017-9288-x \cite{Whigham:2017:GPEM}.", } @InProceedings{Ekart:2017:GI, author = "Aniko Ekart and Alina Patelli and Victoria Lush and Elisabeth Ilie-Zudor", title = "Gaining Insights into Traffic Data through Genetic Improvement", booktitle = "GI-2017", year = "2017", editor = "Justyna Petke and David R. White and W. B. Langdon and Westley Weimer", pages = "1511--1512", address = "Berlin", month = "15-19 " # jul, publisher = "ACM", keywords = "genetic algorithms, genetic programming, genetic improvement, symbolic regression, data mining", isbn13 = "978-1-4503-4939-0", URL = "http://geneticimprovementofsoftware.com/wp-content/uploads/2017/05/ekart2017_road_data.pdf", URL = "https://publications.aston.ac.uk/id/eprint/31438/1/Road_traffic_data_through_genetic_improvement.pdf", URL = "http://eprints.sztaki.hu/9290/", DOI = "doi:10.1145/3067695.3082523", size = "2 pages", abstract = "We argue that Genetic Improvement can be successfully used for enhancing road traffic data mining. This would support the relevant decision makers with extending the existing network of devices that sense and control city traffic, with the end goal of improving vehicle flow and reducing the frequency of road accidents. Our position results from a set of preliminary observations emerging from the analysis of open access road traffic data collected in real time by the Birmingham City Council.", notes = "Last author also know as Angyalka Ilie Zudor Also known as \cite{sztaki9290}", } @InProceedings{Ekart:2020:CEC, author = "Aniko Ekart and Alina Patelli and Victoria Lush and Elisabeth Ilie-Zudor", title = "Genetic Programming with Transfer Learning for Urban Traffic Modelling and Prediction", booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC 2020", year = "2020", editor = "Yaochu Jin", pages = "paper id24137", address = "internet", month = "19-24 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, ITS, Transfer Learning, Symbolic Regression, Intelligent Transportation, Traffic Prediction", isbn13 = "978-1-7281-6929-3", URL = "https://eprints.sztaki.hu/10019/", DOI = "doi:10.1109/CEC48606.2020.9185880", size = "8 pages", abstract = "Intelligent transportation is a cornerstone of smart cities' infrastructure. Its practical realisation has been attempted by various technological means (ranging from machine learning to evolutionary approaches), all aimed at informing urban decision making (e.g., road layout design), in environmentally and financially sustainable ways. In this paper, we focus on traffic modelling and prediction, both central to intelligent transportation. We formulate this challenge as a symbolic regression problem and solve it using Genetic Programming, which we enhance with a lag operator and transfer learning. The resulting algorithm uses knowledge collected from other road segments in order to predict vehicle flow through a junction where traffic data are not available. The experimental results obtained on the Darmstadt case study show that our approach is successful at producing accurate models without increasing training time.", notes = "Motor road traffic. Induction loop embbeded under road surfaces at junctions, missing data due to sensor faults. Darmstadt Germany. https://darmstadt.ui-traffic.de/faces/TrafficData.xhtml GENTLE python gplearn. {"}memory mechanism{"} transfer learning between GP runs? 15 or 30 generations, GP run time ~2 minutes. Transfer Learning as a way to cope with missing data. https://wcci2020.org/ Aston University, United Kingdom; Hungarian Academy of Sciences, Hungary", } @Article{ekblad:2021:WR, author = "Liam Ekblad and Jonathan D. Herman", title = "Toward Data-Driven Generation and Evaluation of Model Structure for Integrated Representations of Human Behavior in Water Resources Systems", journal = "Water Resources Research", year = "2021", volume = "57", number = "2", pages = "e2020WR028148", keywords = "genetic algorithms, genetic programming, Data-driven modeling, human behavior, integrated systems, machine learning, multiobjective optimization, California", URL = "https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2020WR028148", eprint = "https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2020WR028148", DOI = "doi:10.1029/2020WR028148", size = "22 pages", abstract = "Abstract Simulations of human behavior in water resources systems are challenged by uncertainty in model structure and parameters. The increasing availability of observations describing these systems provides the opportunity to infer a set of plausible model structures using data-driven approaches. This study develops a three-phase approach to the inference of model structures and parameterizations from data: problem definition, model generation, and model evaluation, illustrated on a case study of land use decisions in the Tulare Basin, California. We encode the generalized decision problem as an arbitrary mapping from a high-dimensional data space to the action of interest and use multiobjective genetic programming to search over a family of functions that perform this mapping for both regression and classification tasks. To facilitate the discovery of models that are both realistic and interpretable, the algorithm selects model structures based on multiobjective optimization of (1) their performance on a training set and (2) complexity, measured by the number of variables, constants, and operations composing the model. After training, optimal model structures are further evaluated according to their ability to generalize to held-out test data and clustered based on their performance, complexity, and generalization properties. Finally, we diagnose the causes of good and bad generalization by performing sensitivity analysis across model inputs and within model clusters. This study serves as a template to inform and automate the problem-dependent task of constructing robust data-driven model structures to describe human behavior in water resources systems.", } @InProceedings{Eklund:2001:AMPGA, author = "E. Eklund", title = "A Massively Parallel {GP} Architecture", booktitle = "Evolutionary Methods for Design Optimization and Control with Applications to Industrial Problems", editor = "K. C. Giannakoglou and D. T. Tsahalis and J. P\'{e}riaux and K. D. Papailiou and T. Fogarty", pages = "103--108", year = "2001", publisher = "International Center for Numerical Methods in Engineering (Cmine)", publisher_address = "Gran Capitan s/n, 08034 Barcelona, Spain", ISBN = "84-89925-97-6", address = "Athens, Greece", month = "19-21 " # sep, keywords = "genetic algorithms, genetic programming", notes = "broken http://www.mech.ntua.gr/~eurogen2001 Proceedings of the EUROGEN2001 Conference www.gbv.de/dms/tib-ub-hannover/37304500X.pdf", } @InProceedings{Eklund:2001:MPA, author = "Sven E. Eklund", title = "A Massively Parallel Architecture for Linear Machine Code Genetic Programming", booktitle = "Evolvable Systems: From Biology to Hardware: Proceedings of 4th International Conference, ICES 2001", year = "2001", editor = "Yong Liu and Kiyoshi Tanaka and Masaya Iwata and Tetsuya Higuchi and Moritoshi Yasunaga", volume = "2210", series = "Lecture Notes in Computer Science", pages = "216--224", address = "Tokyo, Japan", publisher_address = "Heidelberg", month = oct # " 3-5", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", CODEN = "LNCSD9", ISSN = "0302-9743", bibdate = "Sat Feb 2 13:06:57 MST 2002", DOI = "doi:10.1007/3-540-45443-8_19", acknowledgement = ack-nhfb, abstract = "Over the last decades Genetic Algorithms (GA) and Genetic Programming (GP) have proven to be efficient tools for a wide range of applications. However, in order to solve human-competitive problems they require large amounts of computational power, particularly during fitness calculations. In this paper I propose the implementation of a massively parallel model in hardware in order to speed up GP. This fine-grained diffusion architecture has the advantage over the popular Island model of being VLSI-friendly and is therefore small and portable, without sacrificing scalability and effectiveness. The diffusion architecture consists of a large amount of independent processing nodes, connected through an X-net topology, that evolve a large number of small, overlapping sub-populations. Every node has its own embedded CPU, which executes a linear machine code representation of the individuals. Preliminary simulation results (low-level VHDL simulation) indicate a performance of 10.000 generations per second (depending on the application). One node requires 10-20.000 gates including the CPU (also application dependent), which makes it possible to fit up to 2.000 individuals in one FPGA (Virtex XC2V10000).", notes = "ICES-2001 A1 Dalarna University, Sweden sven.eklund@ieee.org", } @InProceedings{eklund:2002:ampgeiv, author = "Sven E. Eklund", title = "A Massively Parallel {GP} Engine in {VLSI}", booktitle = "Proceedings of the 2002 Congress on Evolutionary Computation CEC2002", editor = "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and Mark Shackleton", pages = "629--633", year = "2002", publisher = "IEEE Press", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", organisation = "IEEE Neural Network Council (NNC), Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", ISBN = "0-7803-7278-6", month = "12-17 " # may, notes = "CEC 2002 - A joint meeting of the IEEE, the Evolutionary Programming Society, and the IEE. Held in connection with the World Congress on Computational Intelligence (WCCI 2002)", keywords = "genetic algorithms, genetic programming, VHDL simulations, VLSI, diffusion model, linear machine code, massively parallel architecture, search space, VLSI, mathematics computing, parallel architectures", DOI = "doi:10.1109/CEC.2002.1006999", abstract = "In this paper we propose the implementation of a massively parallel GP model in hardware in order to speed up the genetic algorithm. This fine-grained diffusion architecture consists of a large amount of independent processing nodes that evolve a large number of small, overlapping subpopulations. Every node has an embedded CPU that executes a linear machine code GP representation at a rate of up to 20,000 generations per second.", } @InProceedings{eklund:2003:PDPS, author = "Sven E Eklund", title = "Time series forecasting using massively parallel genetic programming", booktitle = "Proceedings of Parallel and Distributed Processing International Symposium", year = "2003", pages = "143--147", month = "22-26 " # apr, organisation = "IEEE", keywords = "genetic algorithms, genetic programming, EHW, FPGA, Virtex XC2V10000, wolfe sunspot", DOI = "doi:10.1109/IPDPS.2003.1213272", URL = "http://dalea.du.se/research/?itemId=147", abstract = "a massively parallel GP model in hardware as an efficient,flexible and scaleable machine learning system.This fine-grained diffusion architecture consists of a large amount of independent processing nodes that evolve a large number of small, overlapping subpopulations.Every node has an embedded CPU that executes a linear machine code GP representation at a rate of up to 20,000 generations per second.Besides being efficient,implementing the system in VLSI makes it highly portable and makes it possible to target mobile,n-line applications.The SIMD-like architecture also makes the system scalable so that larger problems can be addressed with a system with more processing nodes.Finally,the use of GP representation and VHDL modeling makes the system highly flexible and easy to adapt to different applications.We demonstrate the effectiveness of the system on a time series forecasting application.", notes = "outperforms SETAR but not best ANN", } @InProceedings{Eklund:2003:ICONS, author = "Sven E. Eklund", title = "Handwritten Character Recognition using a massively parallel {GP} engine in {VLSI}", booktitle = "IFAC International Conference on Intelligent Control Systems and Signal Processing", year = "2003", editor = "Peter J. Fleming", address = "Faro, Portugal", month = apr # " 08-11", organisation = "IFAC", keywords = "genetic algorithms, genetic programming", notes = "broken Jan 2013 http://www.sciference.com/icons03/main.py/pre_programme", } @Article{Eklund:2004:PC, author = "Sven E. Eklund", title = "A massively parallel architecture for distributed genetic algorithms", journal = "Parallel Computing", year = "2004", volume = "30", pages = "647--676", number = "5-6", keywords = "genetic algorithms, genetic programming, Parallel architecture, Diffusion model, FPGA, Classification, Time series forecasting, Regression", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6V12-4CDS49V-1/2/5ba1531eae2c9d8b336f1e90cc0ba5e9", ISSN = "0167-8191", DOI = "doi:10.1016/j.parco.2003.12.009", abstract = "Genetic algorithms are a group of stochastic search algorithms with a broad field of application. Although highly successful in many fields, genetic algorithms in general suffer from long execution times. we describe parallel models for genetic algorithms in general and the massively parallel Diffusion Model in particular, in order to speedup the execution.Implemented in hardware, the Diffusion Model constitutes an efficient, flexible, scalable and mobile machine learning system. This fine-grained system consists of a large number of processing nodes that evolve a large number of small, overlapping subpopulations. Every processing node has an embedded CPU that executes a linear machine code representation at a rate of up to 20,000 generations per second.Besides being efficient, implemented in hardware this model is highly portable and applicable to mobile, on-line applications. The architecture is also scalable so that larger problems can be addressed with a system with more processing nodes. Finally, the use of linear machine code as genetic programming representation and VHDL as hardware description language, makes the system highly flexible and easy to adapt to different applications.Through a series of experiments we determine the settings of the most important parameters of the Diffusion Model. We also demonstrate the effectiveness and flexibility of the architecture on a set of regression problems, a classification application and a time series forecasting application.", } @InProceedings{ekman:2001:ehs, author = "Magnus Ekman and Peter Nordin", title = "Evolvable Hardware using State-machines", booktitle = "Graduate Student Workshop", year = "2001", editor = "Conor Ryan", pages = "409--412", address = "San Francisco, California, USA", month = "7 " # jul, keywords = "genetic algorithms, genetic programming", notes = "GECCO-2001WKS Part of heckendorn:2001:GECCOWKS", } @Article{ekmekcioglu:2023:IJEST, author = "O. Ekmekcioglu and E. E. Basakin and M. Ozger", title = "Exploring the practical application of genetic programming for stormwater drain inlet hydraulic efficiency estimation", journal = "International Journal of Environmental Science and Technology", year = "2023", volume = "20", number = "2", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s13762-022-04035-9", DOI = "doi:10.1007/s13762-022-04035-9", } @Article{El-Bachir-Menai:2015:IJMEI, author = "Mohamed {El Bachir Menai}", title = "Random forests for automatic differential diagnosis of erythemato-squamous diseases", journal = "International Journal of Medical Engineering and Informatics", publisher = "Inderscience Publishers", year = "2015", month = apr # "~04", volume = "7", number = "2", pages = "124--141", ISSN = "1755-0661", keywords = "genetic algorithms, genetic programming, erythemato-squamous diseases, ESD, automatic differential diagnosis, decision trees, random forests, boosting, skin diseases, dermatology, classifiers", bibsource = "OAI-PMH server at www.inderscience.com", URL = "http://www.inderscience.com/link.php?id=68506", DOI = "doi:10.1504/IJMEI.2015.068506", abstract = "Erythemato-squamous diseases (ESD) are frequent skin diseases that share some clinical features of erythema and scaling. Their automatic diagnosis was tackled using several approaches that achieved high performance accuracy. However, they generally remained unattractive for dermatologists because of the lack of direct readability of their output models. Decision trees are easy to understand, but their performance and structure are very sensitive to data changes. Ensembles of decision trees were introduced to reduce the effect of these problems, but on the expense of interpretability. In this paper, we present the results of our investigation of random forests and boosting as ensemble methods for the differential diagnosis of ESD. Experiments on clinical and histopathological data showed that the random forest outperformed the other ensemble classifiers in terms of accuracy, sensitivity and specificity. Its diagnosis accuracy, attaining more than 98percent, was also better than those of classifiers based on genetic programming, genetic algorithms and k-means clustering.", } @Article{El-Bakry:2006:IJMPB, author = "Salah Yaseen El-Bakry and Amr Radi", title = "Genetic Programming approach for electron-alkali-metal atom collisions", journal = "International Journal of Modern Physics B", year = "2006", volume = "20", number = "32", pages = "5463--5471", month = dec, keywords = "genetic algorithms, genetic programming, Condensed Matter Physics, Statistical Physics, Applied Physics, electron scattering, alkali atoms, total cross sections, dipole polarizability", URL = "http://www.genetic-programming.org/hc2007/09-Radi/Radi-Paper-A.pdf", DOI = "doi:10.1142/S0217979206035825", size = "5 pages", abstract = "New technique is presented for modelling the total cross sections of electron scattering by Na, K, Rb and Cs atoms in the low and intermediate energy regions. The calculations have been performed in the framework of genetic programming (GP) technique. The GP has been running based on the experimental data of the total collisional cross sections to produce the total cross sections for each target atom. The incident energy and atomic number as well as the static dipole polarisability have been used as input variables to find the functions that describe the total collisional cross sections of the scattering of electrons by alkali atoms. The experimental, calculated and predicted total collisional cross sections are compared. The discovered functions show a good match to the experimental data.", notes = "IJMPB 2007 HUMIES GECCO-2007 Physics Department, Faculty of Science, Taibah University, Madinah Munawwarah, P. O. Box 344, Kingdom of Saudi Arabia Physics Department, Faculty of Science, Ain Shams University, Abbassia, Cairo, Egypt", } @Article{El-Bakry:2007:AR, author = "Mostafa Y. El-Bakry and Amr Radi", title = "Genetic programming approach for flow of steady state fluid between two eccentric spheres", journal = "Applied Rheology", year = "2007", volume = "17", number = "6", pages = "68210", keywords = "genetic algorithms, genetic programming", ISSN = "1430-6395", publisher = "Kerschensteiner Verlag, Germany", URL = "http://www.genetic-programming.org/hc2007/09-Radi/Radi-Paper-C.pdf", DOI = "doi:10.3933/ApplRheol-17-68210", size = "13 pages", abstract = "Genetic Programming (GP) is used to estimate the functions that describe the torque and the force acting on the external sphere due to steady state motion of viscoelastic fluid between two eccentric spheres. The GP has been running based on experimental data of the torque at different eccentricities to produce torque for each target eccentricity. The angular velocity of the inner sphere and the eccentricity of the two spheres have been used as input variables to find the discovered functions. The experimental, calculated and predicted torque and forces are compared. The discovered function shows a good match to the experimental data.We find that the GP technique is a good new mechanism of determination of the force and torque of fluid in eccentric sphere model.", notes = "Mostafa Elbakry http://www.appliedrheology.org/ 2007 HUMIES GECCO-2007", } @Article{El-Bakry:2007:IJMPC, author = "Mostafa Y. El-Bakry and Amr Radi", title = "Genetic Programming for Hadronic Interactions at High Energies", journal = "International Journal of Modern Physics C, Computational Physics and Physical Computation", year = "2007", volume = "18", number = "3", pages = "329--334", keywords = "genetic algorithms, genetic programming, hadron-hadron interactions, Pseudo-rapidity distribution, proton2proton interaction at high energies", DOI = "doi:10.1142/S0129183107010371", abstract = "Genetic programming (GP) has been used to discover a function that describes pseudo-rapidity distribution of created pions from proton-proton (p-p) interactions at high and ultra-high energies. The predicted distributions from the GP-based model are compared with the experimental data. The discovered function of GP model has proven matching better for experimental data.", notes = "IJMPC On leave from Faculty of Education, Physics Department, Ain Shams University, Egypt. Faculty of Science, P. O. Box 838, Dammam 31113, Saudi Arabia Faculty of Science, Ain Shams University, Egypt. Faculty of Science, P. O. Box 838, Dammam 31113, Saudi Arabia", } @Article{El-Bakry:2007:IJMPC2, author = "Salah Yaseen El-Bakry and Amr Radi", title = "Discovered Function for Positron Collisions with Alkali-Metal Atoms using Genetic Programming", journal = "International Journal of Modern Physics C, Computational Physics and Physical Computation", year = "2007", volume = "18", number = "3", pages = "351--358", keywords = "genetic algorithms, genetic programming, positron collisions, alkali-metal atoms, total collisional cross sections", DOI = "doi:10.1142/S0129183107009480", abstract = "Genetic programming (GP) has been used to discover the function that describes the collisions of positrons with sodium, potassium, rubidium and caesium atoms at low and intermediate energies. The GP has been running based on experimental data of the total collisional cross sections to produce the total cross sections for each target atom. The incident energy and the static dipole polarisability of the alkali target atom have been used as input variables to find the discovered function. The experimental, calculated and predicted total collisional cross sections are compared. The discovered function shows a good match to the experimental data. We find that the GP technique is able to improve upon more traditional methods. To our knowledge, this is the first application of the GP technique to the data of positron collisions with alkali atoms at low and intermediate energies.", notes = "IJMPC Physics Department, Taibah University, Madinah Munawwarah, P. O. Box 344, Saudi Arabia Physics Department, Ain Shams University, Abbassia, Cairo, Egypt", } @Article{El-Bakry:2012:IJSER, author = "Mahmoud Y. El-Bakry and Moaaz A. Moussa and A. Radi and E. El-dahshan and M. Tantawy", title = "Genetic Programming Model for Hadronic Collisions", journal = "International Journal of Scientific \& Engineering Research", year = "2012", volume = "3", number = "3", month = mar, keywords = "genetic algorithms, genetic programming, hadronic collisions, high energy physics", ISSN = "2229-5518", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.302.5668", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.302.5668", URL = "http://www.ijser.org/researchpaper/Genetic-Programming-Model-for-Hadronic-Collisions.pdf", size = "4 pages", abstract = "High Energy Physics (HEP) is in need of powerful efficient techniques for various analysis tasks. Genetic Programming (GP) is a powerful technique that can be used for solving these tasks. In this paper, Genetic programming (GP) has been used to discover a function that calculates charged particles multiplicity distribution of created pions from antiproton-neutron ( p n) and proton-neutron ( p n) interactions at high energies. The predicted distributions from the GP-based model are compared with the available experimental data. The discovered function of GP model has proved matching better for experimental data", } @Misc{oai:CiteSeerX.psu:10.1.1.302.1666, title = "A Genetic programming for modeling Hadronnucleus Interactions at 200 {GeV/c}", author = "Mahmoud Y. El-bakry and El-sayed A. El-dahshan and A. Radi and M. Tantawy", year = "2013", month = jul # "~23", keywords = "genetic algorithms, genetic programming", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.302.1666", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.302.1666", URL = "http://www.ijser.org/researchpaper/Genetic-programming-for-modeling-Hadron.pdf", abstract = "Genetic programming (GP) is a soft computing search technique, which was used to develop a tree-structured program with the purpose of minimising the fitness value of it. It is also a powerful and flexible evolutionary technique with some special features that are suitable for building a tree representation which is always the best solution for the problem we encounter. In this paper, GP has been used to describe a function that calculates charged and negative pions multiplicity distribution for Hadron-nucleus interactions at 200 GeV/c and also compared with the parton two fireball model (PTFM). GP calculations are in accordance with the available experimental data in comparison with the conventional ones (PTFM). Finally, the calculation results showed that the GP model is superior to the traditional techniques that we have ever seen so far. Index Terms --- Genetic programming (GP), machine learning (ML), pion production, multiplicity distribution.", } @InProceedings{El-Baroudy:2008:hydropredict, author = "Ibrahim El-Baroudy and A. Elshorbagy", title = "Identification and Quantification of the Soil Moisture Response to the Atmospheric Forcing", booktitle = "International Interdisciplinary Conference on Predictions for Hydrology, Ecology, and Water Resources Management: Using Data and Models to Benefit Society", year = "2008", editor = "Jiri Bruthans and Karel Kovar and Zbynek Hrkal", pages = "71--74", address = "Prague, Czech Republic", month = "15-18 " # sep, organisation = "International Commission on Groundwater (ICGW), of the International Association of Hydrological Sciences (IAHS)", publisher = "Czech Association of Hydrogeologists", keywords = "genetic algorithms, genetic programming, Discipulus, data-driven techniques, evolutionary polynomial regression, EPR, oil sands, soil storage effect, soil moisture response", isbn13 = "978-80-903635-3-3", URL = "https://www.oieau.fr/eaudoc/system/files/documents/44/220589/220589_doc.pdf", URL = "https://web.natur.cuni.cz/hydropredict2008/download/Proceedings_HydroPredict2008.pdf", size = "5 pages", notes = "paper 41? Centre for Advanced Numerical Simulation (CANSIM), Department of Civil & Geological Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9 Canada", } @Article{El-Baroudy:2010:JH, author = "I. El-Baroudy and A. Elshorbagy and S. K. Carey and O. Giustolisi and D. Savic", title = "Comparison of three data-driven techniques in modelling the evapotranspiration process", journal = "Journal of Hydroinformatics", year = "2010", volume = "12", number = "4", pages = "365--379", keywords = "genetic algorithms, genetic programming, EPR, actual evapotranspiration, data driven techniques, eddy covariance, evolutionary polynomial regression, neural networks", ISSN = "1464-7141", URL = "http://www.iwaponline.com/jh/012/0365/0120365.pdf", DOI = "doi:10.2166/hydro.2010.029", size = "15 pages", abstract = "Evapotranspiration is one of the main components of the hydrological cycle as it accounts for more than two-thirds of the precipitation losses at the global scale. Reliable estimates of actual evapotranspiration are crucial for effective watershed modelling and water resource management, yet direct measurements of the evapotranspiration losses are difficult and expensive. This research explores the utility and effectiveness of data-driven techniques in modelling actual evapotranspiration measured by an eddy covariance system. The authors compare the Evolutionary Polynomial Regression (EPR) performance to Artificial Neural Networks (ANNs) and Genetic Programming (GP). Furthermore, this research investigates the effect of previous states (time lags) of the meteorological input variables on characterising actual evapotranspiration. The models developed using the EPR, based on the two case studies at the Mildred Lake mine, AB, Canada provided comparable performance to the models of GP and ANNs. Moreover, the EPR provided simpler models than those developed by the other data-driven techniques, particularly in one of the case studies. The inclusion of the previous states of the input variables slightly enhanced the performance of the developed model, which in turn indicates the dynamic nature of the evapotranspiration process.", } @InProceedings{el-beltagy:1999:MTFEOCEPPL, author = "Mohammed A. El-Beltagy and Prasanth B. Nair and Andy J. Keane", title = "Metamodeling Techniques For Evolutionary Optimization of Computationally Expensive Problems: Promises and Limitations", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "196--203", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-854.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-854.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{El-boghdadly:2016:CEC, author = "Tamer El-boghdadly and Mohamed Bader-El-Den and Dylan Jones", title = "Evolving local search heuristics for the integrated berth allocation and quay crane assignment problem", booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016)", year = "2016", editor = "Yew-Soon Ong", pages = "2880--2887", address = "Vancouver", month = "24-29 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Berth Allocation, Quay Crane Assignment, Container Terminal Operations, Composite dispatching rules, Optimization; Scheduling", isbn13 = "978-1-5090-0623-6", DOI = "doi:10.1109/CEC.2016.7744153", abstract = "Water Transportation is the cheapest transportation mode, which allows the transfer of very large volumes of cargo between continents. One of the most important types of ships used to transfer goods are the Container Ships, therefore, containerized trade volume is rapidly increasing. This has opened a number of challenging combinatorial optimization problems in container terminals. This paper focuses on the integrated problem Berth Allocation and Quay Crane Assignment Problem (BQCAP), which occur while planning incoming vessels in container terminals. We provide a Genetic Programming (GP) approach to evolve effective and robust composite dispatching rules (CDRs) to solve the problem and present a comparative study with the current state-of-art optimal approaches. The Computational results disclose the effectiveness of the presented approach.", notes = "WCCI2016", } @Article{ELBOSRATY:2020:ASEJ, author = "Ahmed H. El-Bosraty and Ahmed M. Ebid and Ayman L. Fayed", title = "Estimation of the undrained shear strength of east {Port-Said} clay using the genetic programming", journal = "Ain Shams Engineering Journal", year = "2020", ISSN = "2090-4479", DOI = "doi:10.1016/j.asej.2020.02.007", URL = "http://www.sciencedirect.com/science/article/pii/S2090447920300393", keywords = "genetic algorithms, genetic programming, CPT, Consistency limits, Genetic Programming (GP), Cone factor (N), Port-Said clay", abstract = "(CPT) is a widely acceptable and reliable geotechnical in situ test. It provides quick and truthful large amount of data about soil proprieties. Undrained cohesion of clay is a main soil parameter that could be estimated from (CPT) results as it is directly correlated to the tip resistance through the empirical cone factor (Nk). Several studies have been carried out to determine reliable values of the (Nk) factor. This study focused on using (GP) to correlate the (Nk) value of east Port Said clay with consistency limits that can be easily determined. Records of 102 data sets were gathered from site & lab investigations in considered region consists of (CPT) results and corresponding triaxial, unconfined compression, consistency limits and physical properties tests. The collected data were divided into training set to develop the (GP) models and validation set to test the developed formulas which show prediction accuracies between 93percent and 96percent", } @Article{el-dahshan:2011:CEJP, author = "EL-Sayed A. El-Dahshan", title = "Application of genetic programming for proton-proton interactions", journal = "Central European Journal of Physics", year = "2011", volume = "9", number = "3", pages = "874--883", keywords = "genetic algorithms, genetic programming, proton-proton interaction, multiplicity distribution, modeling, machine learning", URL = "http://link.springer.com/article/10.2478/s11534-010-0088-7", DOI = "doi:10.2478/s11534-010-0088-7", size = "10 pages", abstract = "The aim of the present work is to use one of the machine learning techniques named the genetic programming (GP) to model the p-p interactions through discovering functions. In our study, GP is used to simulate and predict the multiplicity distribution of charged pions (P(nch)), the average multiplicity (nch) and the total cross section (σtot) at different values of high energies. We have obtained the multiplicity distributionas a function of the center of mass energy sqrt(s) and charged particles (nch). Also, both the average multi-plicity and the total cross section are obtained as a function of s**0.5. Our discovered functions produced by GP technique show a good match to the experimental data. The performance of the GP models was also tested at non-trained data and was found to be in good agreement with the experimental data", notes = "Department of Physics, Faculty of Sciences, Ain Shams University,Abbassia, Cairo 11566, Egypt", } @PhdThesis{elgerari:tel-00918968, author = "Oussama {El Gerari}", title = "Some contributions to improve Genetic Programming", title_fr = "Contribution a l'amelioration des techniques de la programmation genetique", school = "Universite du Littoral Cote d'Opale", year = "2011", address = "Lille, France", month = dec, keywords = "genetic algorithms, genetic programming, Linear genetic programming, Differential evolution, Programmation g{\'e}n{\'e}tique, Programmation g{\'e}n{\'e}tique lin{\'e}aire, {\'E}volution diff{\'e}rentielle", number = "2011DUNK0335", hal_id = "tel-00918968", hal_version = "v1", URL = "https://tel.archives-ouvertes.fr/tel-00918968/file/ELGERARI.pdf", URL = "https://tel.archives-ouvertes.fr/tel-00918968", size = "81 pages", abstract = "This thesis mainly deals with genetic programming. we are interested in improving the overall performance of genetic programming (GP) when dealing with rich grammar when the terminal set is very large. We introduce the problem of attributes selection and in our work we introduce a scheme based on the weight (based on the frequency) to refine the attribute selection. In the second part of this work, we try to improve the evolution engine with the help of the differential evolution (DE) algorithm. This new engine is applied to linear genetic programming. We then present some experiments and make some comparisons on a set of classical benchmarks.", notes = "In french, Santa Fe ant Francais", } @Article{El-Henawy:2018:IJCA, author = "Ibrahim El-Henawy and Nagham Ahmed Abdelmegeed", title = "Meta-Heuristics Algorithms: A Survey", journal = "International Journal of Computer Applications", year = "2018", volume = "179", number = "22", pages = "45--54", month = feb, keywords = "genetic algorithms, genetic programming", publisher = "Foundation of Computer Science (FCS), NY, USA", address = "New York, USA", ISSN = "0975-8887", URL = "https://www.ijcaonline.org/archives/volume179/number22/29004-29004-2018916427", URL = "https://www.ijcaonline.org/archives/volume179/number22/elhenawy-2018-ijca-916427.pdf", URL = "http://www.ijcaonline.org/archives/volume179/number22/29004-2018916427", DOI = "doi:10.5120/ijca2018916427", size = "10 pages", abstract = "This paper is meant to present a meta-heuristic algorithms and their application to combinatorial optimization problems. This report contains an assessment of the rapid development of meta-heuristic thoughts, their convergence towards a unified fabric and the richness of potential application in optimization problems. The paper presents a brief survey of different meta-heuristic algorithms aiming to solve optimization problems. The meta-heuristic is divided into four broad categories Evolutionary, Physics-based, Swarm-based and Human-based algorithms.", notes = "Brief mention of GP amongst many techniques Also known as \cite{10.5120/ijca2018916427} www.ijcaonline.org ", } @PhdThesis{Elhussein:thesis, author = "Ahlam Ali Sharif {Elhussein}", title = "Classification of Diabetic Patients using Computational Intelligent Techniques", school = "Sudan University of Science and Technology", year = "2018", address = "Sudan", month = mar, keywords = "genetic algorithms, genetic programming, UCI Pima Indian, ANN, MLP, KNN, SVM, Multigene Symbolic Regression GP, GPTIPS, Matlab, Fuzzy, PSO", URL = "http://repository.sustech.edu/handle/123456789/20889", URL = "http://repository.sustech.edu/bitstream/handle/123456789/20889/Classi%ef%ac%81cation%20of%20Diabetic......pdf", size = "127 pages", abstract = "Diabetes Mellitus is one of the fatal diseases growing at a rapid rate in developing countries. This rate is also critical in the developed countries, Diabetes Mellitus being one of the major contributors to the mortality rate. Detection and diagnosis of Diabetes at an early stage is the need of the day. It is required that a classifier is be designed so as to work efficient, convenient and most importantly, accurate. Artificial Intelligence and Soft Computing Techniques mimic a great deal of human ideologies and are encouraged to involve in human related fields of application. These systems most fittingly find a place in the medical diagnosis. As much as there was a need for exact classification with accuracy, it should be understood that detection of a diabetic situation is highly beneficial to the community. The propose number of research methods expected for detection of the diabetic conditions so as to provide a sound warning before they had happened. The experimental result done using Pima Indian dataset which can even be retrieved from UCI Machine Learning Repository's web site. In this research Genetic Programming Toolbox For Multigene Symbolic Regression (GPTIPS), used to build a mathematical model for predict the diabetes class. After that simplified the model by selecting the weighted features that affected on the prediction model. The Neural Network, Fuzzy logic and Genetic Programming are used to check the accuracy when using the new features. The conclusion of that three features can be used to predict the class. The mathematical model become simple and convenient. As a feature work improving the performance by using the optimization methods like Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO).", notes = "Supervisor: Mohamed Elhafiz Co-Supervisor: Talat Wahabi", } @Article{oai:doaj.org/article:632ee97557c249c390830bfc410eff0d, author = "Esraa El-Khateeb and Amr Radi and Salah Yaseen El-Bakry and Mahmoud Yaseen El-Bakry", title = "Modeling hadronic collisions using genetic programming approach", journal = "Advanced Studies in Theoretical Physics", year = "2014", volume = "8", number = "1", pages = "1--9", ISSN = "1314-7609", bibsource = "OAI-PMH server at doaj.org", identifier = "1314-7609", language = "English; French", oai = "oai:doaj.org/article:632ee97557c249c390830bfc410eff0d", rights = "CC by", keywords = "genetic algorithms, genetic programming, Hadronic collisions, total cross section, pp collisions, anti-pp collisions", URL = "http://dx.doi.org/10.12988/astp.2014.311129", URL = "http://www.m-hikari.com/astp/astp2014/astp1-4-2014/elkhateebASTP1-4-2014.pdf", DOI = "doi:10.12988/astp.2014.311129", publisher = "Hikari Ltd.", size = "9 pages", abstract = "New technique, Genetic Programming, is presented for modeling total cross section of both pp and -pp collisions from low to high energy regions. Recent total cross section data are taken from Particle Data Group and LHC collaboration. The model seems to fit the experimental data well.", notes = "PDG pp, TOTEM", } @Article{ELMAHALAWY2021167793, author = "Ahmed M. El-Mahalawy and Kareem H. El-Safty", title = "Classical and quantum regression analysis for the optoelectronic performance of {NTCDA/p-Si UV} photodiode", journal = "Optik", year = "2021", volume = "246", pages = "167793", keywords = "genetic algorithms, genetic programming, Organic Semiconductors, Heterojunction Photodiode, Machine Learning, Quantum Machine Learning", ISSN = "0030-4026", URL = "https://www.sciencedirect.com/science/article/pii/S0030402621013826", DOI = "doi:10.1016/j.ijleo.2021.167793", size = "21 pages", abstract = "Due to the pivotal role of UV photodiodes in many technological applications in tandem with the high efficiency achieved by machine learning techniques in regression and classification problems, different artificial intelligence techniques are adopted to simulate and model the performance of organic/inorganic heterojunction UV photodiode. Herein, the performance of a fabricated Au/NTCDA/p-Si/Al photodiode is explained in a detailed manner and has shown an excellent responsivity and detectivity for UV light of intensities ranging from 20 to 80mW/cm2. A linear current irradiance relationship is exhibited by the fabricated photodiode under illumination up to 65mW/cm2. It also shows good response times of trise=408ms and tfall=490ms. Furthermore, we have not only fitted the characteristic I-V curve but also evaluated three classical algorithms; K-Nearest Neighbour, Artificial Neural Network, and Genetic Programming besides using a Quantum Neural Network to predict the behavior of the device. The models have achieved outstanding results and managed to capture the trend of the target values. The Quantum Neural Network has been used for the first time to model the photodiode characteristics. The trained models are of great significance since they can be used to reduce the characterization and measurement times.", notes = "Thin Film Laboratory, Physics Department, Faculty of Science, Suez Canal University, Ismailia, Egypt", } @InProceedings{conf/crv/El-SawahJGP07, author = "Ayman El-Sawah and Chris Joslin and Nicolas D. Georganas and Emil M. Petriu", title = "A Framework for {3D} Hand Tracking and Gesture Recognition using Elements of Genetic Programming", booktitle = "Fourth Canadian Conference on Computer and Robot Vision, CRV '07", year = "2007", pages = "495--502", address = "Montreal", month = "28-30 " # may, publisher = "IEEE Computer Society", keywords = "genetic algorithms, genetic programming, VR, 3D hand tracking, 3D hand vision-based posture hypothesis, dynamic Bayesian network model, fuzzy set theory, geometric transformation, gesture recognition, image plane, kinematics inverse transformation, probabilistic observation model, single camera, soft computing, Bayes methods, cameras, computer vision, optical tracking, pose estimation, probability", DOI = "doi:10.1109/CRV.2007.3", size = "8 pages", abstract = "In this paper we present a framework for 3D hand tracking and dynamic gesture recognition using a single camera. Hand tracking is performed in a two step process: we first generate 3D hand posture hypothesis using geometric and kinematics inverse transformations, and then validate the hypothesis by projecting the postures on the image plane and comparing the projected model with the ground truth using a probabilistic observation model. Dynamic gesture recognition is performed using a Dynamic Bayesian Network model. The framework uses elements of soft computing to resolve the ambiguity inherent in vision-based tracking by producing a fuzzy hand posture output by the hand tracking module and feeding back potential posture hypothesis from the gesture recognition module.", notes = "Univ. of Ottawa, Ottawa Almost no description of GP used", bibdate = "2007-06-01", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/crv/crv2007.html#El-SawahJGP07", } @PhdThesis{El-Sawah:thesis, author = "Ayman El-Sawah", title = "Towards context-aware gesture enabled user interfaces", school = "Ottawa-Carleton Institute of Computer Science, School of Information Technology and Engineering, University of Ottawa", year = "2008", address = "Canada", keywords = "genetic algorithms, genetic programming", isbn13 = "978-0-494-41631-0", URL = "http://hdl.handle.net/10393/29520", URL = "http://dx.doi.org/10.20381/ruor-12996", URL = "https://ruor.uottawa.ca/bitstream/10393/29520/1/NR41631.PDF", size = "154 pages", abstract = "Conventional graphical user interface techniques appear to be ill-suited for the kinds of interactive platforms that are required for future generations of computing devices. 3D graphics and immersive virtual reality applications require interactive 3D object manipulation and navigation. Perceptual user interfaces using speech and gestures are in high demand to provide a more natural human-computer interaction modality. The major challenge facing Perceptual user interfaces is the lack of a standard application programming interfaces capable of handling ambiguity and providing the means to include domain-specific knowledge about the context in which the user interface is used. we study dynamic hand gestures, which are defined as a sequence of hand postures. We emphasise the generality of our dynamic gesture model, which is capable of recognising essentially any dynamic hand gesture confined in a sequence of postures. Hand postures are static poses and are defined by an array of posture attributes. We use a generic definition hand postures capable of covering the space of hand postures at different levels of granularity and abstraction; and we timely monitor the posture variation as it unfolds within the dynamic gesture. We also study the role of context in gesture interpretation without making assumptions about a specific application. We view the hand-tracking and gesture-recognition subsystems as integral parts of a larger distributed and multi-user multi-service application, where gesture interpretation plays the role of resolving ambiguity of the recognized gesture. We identify the relevant aspects to hand gesture interpretation and we propose agent-based system architecture for gesture interpretation. We finally propose a framework for gesture-enabled system design, where context is placed in a middleware layer that interfaces with all sub modules in the system and plays a dialectic role and keeping the overall system stable.", notes = "DISCOVER Supervisor: Nicolas D. Georganas and Emil M. Petriu", } @InProceedings{el-zoghabi:2019:ICAISI, author = "Adel El-zoghabi and Amro G. {El shenawy}", title = "An Improved Cache Invalidation Policy in Wireless Environment Cooperate with Cache Replacement Policy Based on Genetic Programming", booktitle = "Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2018", year = "2019", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-319-99010-1_54", DOI = "doi:10.1007/978-3-319-99010-1_54", notes = "Department of Information Technology, Institute of Graduate Studies and Research, Alexandria University, Alexandria, Egypt", } @InProceedings{eldershaw:1999:RMG, author = "Craig Eldershaw and Stephen Cameron", title = "Real-world applications: Motion planning using GAs", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1776", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "real world applications, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-768.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-768.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Article{Eldrandaly:2008:IAJIT, author = "Khalid Eldrandaly and Abdel-Azim Negm", title = "Performance Evaluation of Gene Expression Programming for Hydraulic Data Mining", journal = "The International Arab Journal of Information Technology", year = "2008", volume = "5", number = "2", pages = "126--131", month = apr, email = "khalid_eldrandaly@yahoo.com", keywords = "genetic algorithms, genetic programming, gene expression programming, GEP, Data mining, multiple linear regression, MLR, hydraulic jump.", URL = "http://www.ccis2k.org/iajit/PDF/vol.5,no.2/4-103.pdf", size = "6 pages", abstract = "Predication is one of the fundamental tasks of data mining. In recent years, Artificial Intelligence techniques are widely being used in data mining applications where conventional statistical methods were used such as Regression and classification. The aim of this work is to show the applicability of Gene Expression Programming (GEP), a recently developed AI technique, for hydraulic data prediction and to evaluate its performance by comparing it with Multiple Linear Regression (MLR). Both GEP and MLR were used to model the hydraulic jump over a roughened bed using very large series of experimental data that contain all the important flow and roughness parameters such as the initial Froude number, the height of roughness ratio, the length of roughness ratio, the initial length ratio (from the gate) and the roughness density. The results show that GEP is a promising AI approach for hydraulic data prediction.", notes = "Information Systems Department, College of Computers, Zagazig University, Egypt http://www.iajit.org/", } @Article{Eldrandaly:2009:AJAS, title = "Integrating Gene Expression Programming and Geographic Information Systems for Solving a Multi Site Land Use Allocation Problem", author = "Khalid A. Eldrandaly", journal = "American Journal of Applied Sciences", year = "2009", volume = "6", number = "5", pages = "1021--1027", keywords = "genetic algorithms, genetic programming, gene expression programming, Multi site land use allocation, GIS, SDSS", publisher = "Science Publications", ISSN = "1546-9239", URL = "http://www.scipub.org/fulltext/ajas/ajas651021-1027.pdf", URL = "http://thescipub.com/html/10.3844/ajassp.2009.1021.1027", broken = "http://www.doaj.org/doaj?func=openurl\&genre=article\&issn=15469239\&date=2009\&volume=6\&issue=5\&spage=1021", bibsource = "OAI-PMH server at www.doaj.org", oai = "oai:doaj-articles:374b808b659956eb2527109ade485337", DOI = "doi:10.3844/ajassp.2009.1021.1027", size = "7 pages", abstract = "Problem statement: Land use planning may be defined as the process of allocating different activities or uses to specific units of area within a region. Multi sites Land Use Allocation Problems (MLUA) refer to the problem of allocating more than one land use type in an area. MLUA problem is one of the truly NP Complete (combinatorial optimisation) problems. Approach: To cope with this type of problems, intelligent techniques such as genetic algorithms and simulated annealing, have been used. In this study a new approach for solving MLUA problems was proposed by integrating Gene Expression Programming (GEP) and GIS. The feasibility of the proposed approach in solving MLUA problems was checked using a fictive case study. Results: The results indicated clearly that the proposed approach gives good and satisfactory results. Conclusion/Recommendation: Integrating GIS and GEP is a promising and efficient approach for solving MLUA problems. This research focused on minimising the development costs and maximising the compactness of the allocated land use. The optimization model can be extended in the future to maximize also the spatial contiguity of the allocated land use.", notes = "Faculty of Computers and Informatics, Zagazig University, Egypt", } @Article{Eldrandaly2009, author = "Khalid Eldrandaly", title = "A GEP-based spatial decision support system for multisite land use allocation", journal = "Applied Soft Computing", year = "2009", volume = "10", number = "3", pages = "694--702", month = jun, ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2009.07.014", URL = "http://www.sciencedirect.com/science/article/B6W86-4X2DCVV-2/2/c8addfbfae7f3e5035dc45213f378416", keywords = "genetic algorithms, genetic programming, Spatial decision support systems, Multisite land use allocation, GIS, Gene expression programming", abstract = "Multisite Land Use Allocation Problem (MLUA) refers to the problem of allocating more than one land use type in an area. MLUA problem is one of the truly NP Complete (combinatorial optimisation) problems. To cope with this type of problems, intelligent techniques such as genetic algorithms, and simulated annealing, have been used. Research in the area of Spatial Decision Support Systems (SDSS) for resource allocation issues, a new scientific area of information system applications developed to support semi-structured or unstructured spatial decisions, has recently generated attention for integrating Artificial Intelligence (AI) techniques with Geographic Information Systems (GIS). In this paper we demonstrate how GIS can be integrated with Gene Expression Programming (GEP), a recently developed AI approach, for solving MLUA problems. The feasibility of the proposed approach in solving MLUA problems was checked using a fictive case study. The results indicated that the proposed approach gives good and satisfactory results.", notes = "King Abdulaziz University, P.O. Box 80105, Jeddah 21589, Saudi Arabia", } @Misc{DBLP:journals/corr/abs-2204-00735, author = "Andrew Eldridge and Alejandro Rodriguez and Ming Hu and Jianjun Hu", title = "Genetic programming-based learning of carbon interatomic potential for materials discovery", howpublished = "arXiv", volume = "abs/2204.00735", year = "2022", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.48550/arXiv.2204.00735", DOI = "doi:10.48550/arXiv.2204.00735", eprinttype = "arXiv", eprint = "2204.00735", timestamp = "Wed, 06 Apr 2022 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/corr/abs-2204-00735.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @PhdThesis{Elfwing:thesis, author = "Stefan Elfwing", title = "Embodied Evolution of Learning Ability", school = "KTH School of Computer Science and Communication", year = "2007", type = "Doctoral Thesis", address = "SE-100 44 Stockholm, Sweden", month = nov, keywords = "genetic algorithms, genetic programming, Embodied Evolution, Evolutionary Robotics, Reinforcement Learning, Shaping Rewards, Meta-parameters, Hierarchical Reinforcement Learning, Learning and Evolution. Meta-learning, Baldwin Effect, Lamarckian Evolution", URL = "http://www.irp.oist.jp/nc/elfwing/Elfwing_thesis_final_electronic.pdf", size = "162 pages", isbn13 = "978-91-7178-787-3", abstract = "Embodied evolution is a methodology for evolutionary robotics that mimics the distributed, asynchronous, and autonomous properties of biological evolution. The evaluation, selection, and reproduction are carried out by cooperation and competition of the robots, without any need for human intervention. An embodied evolution framework is therefore well suited to study the adaptive learning mechanisms for artificial agents that share the same fundamental constraints as biological agents: self-preservation and self-reproduction. The main goal of the research in this thesis has been to develop a framework for performing embodied evolution with a limited number of robots, by using time-sharing of subpopulations of virtual agents inside each robot. The framework integrates reproduction as a directed autonomous behaviour, and allows for learning of basic behaviors for survival by reinforcement learning. The purpose of the evolution is to evolve the learning ability of the agents, by optimising meta-properties in reinforcement learning, such as the selection of basic behaviours, meta-parameters that modulate the efficiency of the learning, and additional and richer reward signals that guides the learning in the form of shaping rewards. The realization of the embodied evolution framework has been a cumulative research process in three steps: 1) investigation of the learning of a cooperative mating behaviour for directed autonomous reproduction; 2) development of an embodied evolution framework, in which the selection of pre-learned basic behaviours and the optimisation of battery recharging are evolved; and 3) development of an embodied evolution framework that includes meta-learning of basic reinforcement learning behaviors for survival, and in which the individuals are evaluated by an implicit and biologically inspired fitness function that promotes reproductive ability. The proposed embodied evolution methods have been validated in a simulation environment of the Cyber Rodent robot, a robotic platform developed for embodied evolution purposes. The evolutionarily obtained solutions have also been transferred to the real robotic platform. The evolutionary approach to meta-learning has also been applied for automatic design of task hierarchies in hierarchical reinforcement learning, and for co-evolving meta-parameters and potential-based shaping rewards to accelerate reinforcement learning, both in regards to finding initial solutions and in regards to convergence to robust policies.", notes = "TRITA-CSC-A 2007:16 ISSN-1653-5723 ISRN-KTH/CSC/A--07/16--SE Akademisk avhandling som med tillstand av Kungliga Tekniska hogskolan framlagges till offentlig granskning for avlaggande av teknologie doktorsexamen mandagen den 12 november 2007 kl. 10.00 i sal F3, Lindstedtsvagen 26, Kungliga Tekniska hogskolan, Stockholm. Stefan Elfwing, 2007 Tryck: Universitetsservice US AB", } @Article{Elfwing:2007:tec, author = "Stefan Elfwing and Eiji Uchibe and Kenji Doya and Henrik I. Christensen", title = "Evolutionary Development of Hierarchical Learning Structures", journal = "IEEE Transactions on Evolutionary Computation", year = "2007", volume = "11", number = "2", pages = "249--264", month = apr, keywords = "genetic algorithms, genetic programming, learning (artificial intelligence), Lamarckian evolutionary development, MAXQ hierarchical RL method, foraging task, genetic programming, hierarchical learning structures, hierarchical reinforcement learning, task decomposition", DOI = "doi:10.1109/TEVC.2006.890270", ISSN = "1089-778X", abstract = "Hierarchical reinforcement learning (RL) algorithms can learn a policy faster than standard RL algorithms. However, the applicability of hierarchical RL algorithms is limited by the fact that the task decomposition has to be performed in advance by the human designer. We propose a Lamarckian evolutionary approach for automatic development of the learning structure in hierarchical RL. The proposed method combines the MAXQ hierarchical RL method and genetic programming (GP). In the MAXQ framework, a subtask can optimise the policy independently of its parent task's policy, which makes it possible to reuse learned policies of the subtasks. In the proposed method, the MAXQ method learns the policy based on the task hierarchies obtained by GP, while the GP explores the appropriate hierarchies using the result of the MAXQ method. To show the validity of the proposed method, we have performed simulation experiments for a foraging task in three different environmental settings. The results show strong interconnection between the obtained learning structures and the given task environments. The main conclusion of the experiments is that the GP can find a minimal strategy, i.e., a hierarchy that minimises the number of primitive subtasks that can be executed for each type of situation. The experimental results for the most challenging environment also show that the policies of the subtasks can continue to improve, even after the structure of the hierarchy has been evolutionary stabilised, as an effect of Lamarckian mechanisms", } @Article{ELGAMEL:2023:engstruct, author = "Hana Elgamel and Mohamed K. Ismail and Ahmed Ashour and Wael El-Dakhakhni", title = "Backbone model for reinforced concrete block shear wall components and systems using controlled multigene genetic programming", journal = "Engineering Structures", volume = "274", pages = "115173", year = "2023", ISSN = "0141-0296", DOI = "doi:10.1016/j.engstruct.2022.115173", URL = "https://www.sciencedirect.com/science/article/pii/S0141029622012494", keywords = "genetic algorithms, genetic programming, Backbone model, Fully grouted, Reinforced concrete block shear walls, Multigene genetic programming, Seismic performance, Variables selection", abstract = "Reinforced concrete block shear walls (RCBSWs)have been used as an effective seismic force resisting system in low- and medium-rise buildings for many decades. However, attributed to their complex nonlinear behavior and the composite nature of their constituent materials, accurate prediction of their seismic performance, relying solely on mechanics, has been challenging. This study adopts multi-gene genetic programming (MGGP)- a class of bio-inspired artificial intelligence, to uncover the complexity of RCBSW behaviors and develop simplified procedures for predicting the full backbone curve of flexure-dominated RCBSWs under cyclic loading. A piecewise linear backbone curve was developed using five secant stiffness expressions associated with: cracking; yielding; 80percent ultimate; ultimate; and 20percent strength degradation (i.e., post-peak stage) derived through mechanics-controlled MGGP. Based on the experimental results of large-scale cyclically loaded fully-grouted RCBSWs, compiled from previously reported studies, a variable selection procedure was performed to identify the most influential variable subset governing wall behaviors. Subsequently, the MGGP stiffness expressions were trained and tested, and their accuracy was compared to that of existing models employing various statistical measures. In addition, the predictability of the developed backbone model was assessed at the system-level against experimental results of two two-story buildings available in the literature. This study demonstrates the power of the MGGP approach in addressing the complexity of the cyclic behavior of RCBSWs at both component- and system-level-offering an efficient prediction tool that can be adopted by relevant seismic design standards pertaining to RCBSW buildings", } @InProceedings{elhaggaz:1999:E, author = "Salah Elhaggaz and Brian Turton and John Brown", title = "Evolutionary algorithm for phased network topology design", booktitle = "Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference", year = "1999", editor = "Scott Brave and Annie S. Wu", pages = "80--87", address = "Orlando, Florida, USA", month = "13 " # jul, notes = "GECCO-99LB", } @InProceedings{conf/fira/HakimRHPR13, author = "Aulia {El Hakim} and Dadan Nur Ramadan and Indra Hidayatulloh and Ary Setijadi Prihatmanto and Estiko Rijanto", title = "Grammatical Evolution Algorithm for Position Prediction of the Ball in Robot-Soccer Goal Keeper Optimization", booktitle = "Proceedings of the 16th FIRA RoboWorld Congress", year = "2013", editor = "Khairuddin Omar and Md. Jan Nordin and Prahlad Vadakkepat and Anton Satria Prabuwono and Siti Norul Huda Sheikh Abdullah and Jacky Baltes and Shamsudin H. M. Amin and Wan Zuha Wan Hassan and Mohammad Faidzul Nasrudin", volume = "376", series = "Communications in Computer and Information Science", pages = "147--160", address = "Kuala Lumpur, Malaysia", month = aug # " 24-29", publisher = "Springer", keywords = "genetic algorithms, genetic programming, grammatical evolution, position prediction, robot soccer, goalkeeper", isbn13 = "978-3-642-40408-5", bibdate = "2013-08-23", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/fira/fira2013.html#HakimRHPR13", DOI = "doi:10.1007/978-3-642-40409-2_13", URL = "http://dx.doi.org/10.1007/978-3-642-40409-2_13", abstract = "Position prediction of the ball that approaches to the goal is necessary for a goalkeeper robot. In this paper, grammatical evolution is used for prediction. Grammatical evolution will be tested on grammar with linear characteristic. Data used in this research was taken from the Y-axis coordinate of the Ball and divide into 3 Home area. The research focuses on two conditions of the ball: straight movement and bouncing off the wall. From the results of this study, it was obtained three functions which can be used to predict position of the ball in goal area. The smallest mean of fitness value is 1.24729 for straight movement and 2.64366 for bouncing off the wall conditions.", notes = "Intelligent Robotics Systems: Inspiring the NEXT", } @Article{Elhenawy:2014:TRPCET, author = "Mohammed Elhenawy and Hao Chen2 and Hesham A. Rakha", title = "Dynamic travel time prediction using data clustering and genetic programming", journal = "Transportation Research Part C: Emerging Technologies", volume = "42", pages = "82--98", year = "2014", month = may, ISSN = "0968-090X", DOI = "doi:10.1016/j.trc.2014.02.016", URL = "http://www.sciencedirect.com/science/article/pii/S0968090X14000588", keywords = "genetic algorithms, genetic programming, Travel time prediction, Clustering, Sampling with replacement, Probe data", abstract = "The current state-of-practice for predicting travel times assumes that the speeds along the various roadway segments remain constant over the duration of the trip. This approach produces large prediction errors, especially when the segment speeds vary temporally. In this paper, we develop a data clustering and genetic programming approach for modelling and predicting the expected, lower, and upper bounds of dynamic travel times along motorways. The models obtained from the genetic programming approach are algebraic expressions that provide insights into the spatio-temporal interactions. The use of an algebraic equation also means that the approach is computationally efficient and suitable for real-time applications. Our algorithm is tested on a 37-mile freeway section encompassing several bottlenecks. The prediction error is demonstrated to be significantly lower than that produced by the instantaneous algorithm and the historical average averaged over seven weekdays (p-value <0.0001). Specifically, the proposed algorithm achieves more than a 25percent and 76percent reduction in the prediction error over the instantaneous and historical average, respectively on congested days. When bagging is used in addition to the genetic programming, the results show that the mean width of the travel time interval is less than 5 minutes for the 60-80 min trip.", } @Article{Elkaffas:2008:waset, author = "Saleh Mesbah Elkaffas and Ahmed A. Toony", title = "Applications of Genetic Programming in Data Mining", journal = "International Science Index", year = "2008", volume = "2", number = "5", pages = "710--714", note = "waset.org/Publication/23722", keywords = "genetic algorithms, genetic programming, data mining, classification rule", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.306.4138", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.306.4138", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.306.4138.pdf", broken = "http://www.waset.org/journals/waset/v17/v17-169.pdf", abstract = "This paper details the application of a genetic programming framework for induction of useful classification rules from a database of income statements, balance sheets, and cash flow statements for North American public companies. Potentially interesting classification rules are discovered. Anomalies in the discovery process merit further investigation of the application of genetic programming to the dataset for the problem domain.", } @InProceedings{conf/icores/ElkasabySE17, author = "Ayman Elkasaby and Akram Salah and Ehab Elfeky", title = "Multiobjective Optimization using Genetic Programming: Reducing Selection Pressure by Approximate Dominance", booktitle = "Proceedings of the 6th International Conference on Operations Research and Enterprise Systems, {ICORES} 2017, Porto, Portugal, February 23-25, 2017", editor = "Federico Liberatore and Greg H. Parlier and Marc Demange", year = "2017", pages = "424--429", publisher = "SciTePress", keywords = "genetic algorithms, genetic programming", bibdate = "2017-05-18", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icores/icores2017.html#ElkasabySE17", isbn13 = "978-989-758-218-9", DOI = "doi:10.5220/0006219504240429", } @InProceedings{elkasaby:2018:ORES, author = "Ayman Elkasaby and Akram Salah and Ehab Elfeky", title = "Approximate Dominance for Many-Objective Genetic Programming", booktitle = "Operations Research and Enterprise Systems", year = "2018", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-319-94767-9_9", DOI = "doi:10.1007/978-3-319-94767-9_9", } @Article{ELLIOTT:2018:EE, author = "Robert M. Elliott and Elizabeth R. Adkins and Patricia J. Culligan and Matthew I. Palmer", title = "Stormwater infiltration capacity of street tree pits: Quantifying the influence of different design and management strategies in New York City", journal = "Ecological Engineering", volume = "111", pages = "157--166", year = "2018", keywords = "genetic algorithms, genetic programming", ISSN = "0925-8574", DOI = "doi:10.1016/j.ecoleng.2017.12.003", URL = "http://www.sciencedirect.com/science/article/pii/S0925857417306365", abstract = "Street trees are abundant in the urban landscape and provide many ecosystem services including stormwater management. For trees housed within tree pits, the ability to mitigate stormwater runoff can be modulated by the permeability of the soil within the tree pit itself. Thus, developing a better understanding of how tree pit design and management impact soil permeability can be important to quantifying, and potentially improving, the stormwater benefits of street trees. To this end, water infiltration rate was measured at forty tree pits representing the variety of physical conditions commonly seen in New York City, including the presence or absence of a tree pit guard, the size of the tree pit, the size of the tree, the presence or absence of ground cover planting, the presence or absence of mulch, and the elevation of the pit's soil surface relative to the sidewalk. An initial analysis of results first tested the impact of each physical condition on infiltration rate individually. Genetic programming was then used to investigate interactive effects between the physical conditions, and to develop a statistical model that captured 66percent of the variability in the observed infiltration rate using simple physical features of a tree pit. Results showed that the most significant factor influencing the infiltration rate was the presence of a guard around a tree pit, with guarded tree pits having higher infiltration rates. Additionally, higher infiltration rates in guarded pits were associated with larger pit areas, built-up surface elevations (binary) and the combined presence of ground cover planting (binary) and mulch (binary). Tree size, as measured by circumference at breast height, was found to be a less significant indicator of the infiltration rate. The statistical model, together with the study measurements, can be used to estimate the stormwater benefits of different tree pit management strategies, inform designs for improved stormwater management, and help identify useful observations or measurements for a street tree census", } @Article{ellis:2002:AEM, author = "David I. Ellis and David Broadhurst and Douglas B. Kell and Jem J. Rowland and Royston Goodacre", title = "Rapid and Quantitative Detection of the Microbial Spoilage of Meat by Fourier Transform Infrared Spectroscopy and Machine Learning", journal = "Applied and Environmental Microbiology", year = "2002", volume = "68", number = "6", pages = "2822--2828", month = jun, keywords = "genetic algorithms, genetic programming", ISSN = "0099-2240", URL = "http://dbkgroup.org/Papers/app_%20env_microbiol_68_(2822).pdf", DOI = "doi:10.1128/AEM.68.6.2822-2828.2002", size = "7 pages", abstract = "Fourier transform infrared (FT-IR) spectroscopy is a rapid, noninvasive technique with considerable potential for application in the food and related industries. We show here that this technique can be used directly on the surface of food to produce biochemically interpretable ?fingerprints.? Spoilage in meat is the result of decomposition and the formation of metabolites caused by the growth and enzymatic activity of microorganisms. FT-IR was exploited to measure biochemical changes within the meat substrate, enhancing and accelerating the detection of microbial spoilage. Chicken breasts were purchased from a national retailer, comminuted for 10 s, and left to spoil at room temperature for 24 h. Every hour, FT-IR measurements were taken directly from the meat surface using attenuated total reflectance, and the total viable counts were obtained by classical plating methods. Quantitative interpretation of FT-IR spectra was possible using partial least-squares regression and allowed accurate estimates of bacterial loads to be calculated directly from the meat surface in 60 s. Genetic programming was used to derive rules showing that at levels of 10000000 bacteria per gram 1 the main biochemical indicator of spoilage was the onset of proteolysis. Thus, using FT-IR we were able to acquire a metabolic snapshot and quantify, noninvasively, the microbial loads of food samples accurately and rapidly in 60 s, directly from the sample surface. We believe this approach will aid in the Hazard Analysis Critical Control Point process for the assessment of the microbiological safety of food at the production, processing, manufacturing, packaging, and storage levels.", notes = "American Society for Microbiology PMID: 12039738", } @Article{Ellis:2004:ACA, author = "David I. Ellis and David Broadhurst and Royston Goodacre", title = "Rapid and quantitative detection of the microbial spoilage of beef by Fourier transform infrared spectroscopy and machine learning", journal = "Analytica Chimica Acta", year = "2004", volume = "514", number = "2", pages = "193--201", month = "1 " # jul, keywords = "genetic algorithms, genetic programming, Muscle foods, FT-IR spectroscopy, Food spoilage, Chemometrics, Evolutionary computation", ISSN = "0003-2670", owner = "wlangdon", broken = "http://www.sciencedirect.com/science/article/B6TF4-4CDJJ78-5/2/63df147cb89407ac7ac8bf9d093580f7", URL = "http://dbkgroup.org/dave_files/ACAbeef04.pdf", DOI = "doi:10.1016/j.aca.2004.03.060", abstract = "Beef is a commercially important and widely consumed muscle food and central to the protein intake of many societies. In the food industry no technology exists for the rapid and accurate detection of microbiologically spoiled or contaminated beef. Fourier transform infrared (FT-IR) spectroscopy is a rapid, reagentless and non-destructive analytical technique whose continued development is resulting in manifold applications across a wide range of biosciences. FT-IR was exploited to measure biochemical changes within the fresh beef substrate, enhancing and accelerating the detection of microbial spoilage. Separately packaged fresh beef rump steaks were purchased from a national retailer, comminuted for 15 s and left to spoil at ambient room temperature for 24 h. Every hour, FT-IR measurements were collected directly from the sample surface using attenuated total reflectance, in parallel the total viable counts of bacteria were obtained by classical microbiological plating methods. Quantitative interpretation of FT-IR spectra was undertaken using partial least squares regression and allowed for accurate estimates of bacterial loads to be calculated directly from the meat surface in 60 s. Machine learning methods in the form of genetic algorithms and genetic programming were used to elucidate the wavenumbers of interest related to the spoilage process. The results obtained demonstrated that using FT-IR and machine learning it was possible to detect bacterial spoilage rapidly in beef and that the most significant functional groups selected could be directly correlated to the spoilage process which arose from proteolysis, resulting in changes in the levels of amides and amines.", } @Article{Elmestikawy:2024:ijmultiphaseflow, author = "Hani Elmestikawy and Julia Reuter and Fabien Evrard and Sanaz Mostaghim and Berend {van Wachem}", title = "Deterministic drag modelling for spherical particles in Stokes regime using data-driven approaches", journal = "International Journal of Multiphase Flow", year = "2024", volume = "178", pages = "104880", keywords = "genetic algorithms, genetic programming, Particle-laden flow, Periodic Stokeslet, ANN", ISSN = "0301-9322", URL = "https://www.sciencedirect.com/science/article/pii/S0301932224001575", DOI = "doi:10.1016/j.ijmultiphaseflow.2024.104880", abstract = "In this paper, we develop a deterministic drag model for stationary spherical particles in a Stokes flow using a cascade of data-driven approaches. The model accounts for the variation in drag experienced by each particle within fixed random arrangements. The developed model is a symbolic expression that offers explainability, ease of implementation, and computational efficiency. Firstly, we generate particle-resolved direct numerical simulation data of the flow past periodic random arrangements of stationary spherical particles with volume fractions between 0.05 and 0.4 using the method of regularized Stokeslets. Secondly, we train graph neural networks (GNs) on the generated data to learn the pairwise influence of neighbouring particles on a reference particle. The GNs are converted to symbolic expressions using genetic programming (GP), unveiling repeated subexpressions. Finally, these subexpressions constitute the foundation of the proposed algebraic model, further refined via non-linear regression. The proposed model can qualitatively mimic the pairwise influences as predicted by the GN and can capture the drag variations with accuracy from 74percent and up to 84.7percent when compared to the particle-resolved simulations. Due to the interpretability of the proposed model, we are able to explore how neighbour positions alter the drag of a particle in an assembly. The proposed model is a promising tool for studying the dynamics of particle assemblies in Stokes flow", } @InProceedings{Elnabarawy:2017:GECCO, author = "Islam Elnabarawy and Daniel R. Tauritz and Donald C. Wunsch", title = "Evolutionary Computation for the Automated Design of Category Functions for Fuzzy {ART}: An Initial Exploration", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4939-0", address = "Berlin, Germany", pages = "1133--1140", size = "8 pages", URL = "http://doi.acm.org/10.1145/3067695.3082056", DOI = "doi:10.1145/3067695.3082056", acmid = "3082056", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, adaptive resonance theory, adjusted rand index, clustering, evolutionary computing, hyper-heuristics, unsupervised learning", month = "15-19 " # jul, abstract = "Fuzzy Adaptive Resonance Theory (ART) is a classic unsupervised learning algorithm. Its performance on a particular clustering problem is sensitive to the suitability of the category function for said problem. However, classic Fuzzy ART employs a fixed category function and thus is unable to benefit from the potential to adjust its category function. This paper presents an exploration into employing evolutionary computation for the automated design of category functions to obtain significantly enhanced Fuzzy ART performance through tailoring to specific problem classes. We employ a genetic programming powered hyper-heuristic approach where the category functions are constructed from a set of primitives constituting those of the original Fuzzy ART category function as well as additional hand-selected primitives. Results are presented for a set of experiments on benchmark classification tasks from the UCI Machine Learning Repository demonstrating that tailoring Fuzzy ART's category function can achieve statistically significant superior performance on the testing datasets in stratified 10-fold cross-validation procedures. We conclude with discussing the results and placing them in the context of being a first step towards automating the design of entirely new forms of ART.", notes = "Also known as \cite{Elnabarawy:2017:ECA:3067695.3082056} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @Article{ElNofely1989437, author = "A. El-Nofely and L. Sadek and N. Soliman", title = "Spacing in the human deciduous dentition in relation to tooth size and dental arch size", journal = "Archives of Oral Biology", volume = "34", number = "6", pages = "437--441", year = "1989", ISSN = "0003-9969", DOI = "doi:10.1016/0003-9969(89)90122-2", URL = "http://www.sciencedirect.com/science/article/B6T4J-4BWHJWH-10R/2/d3ad580204c24fb1b0297899cd63dc6d", notes = "Not on GP", } @Article{Elola:2017:ASC, author = "Andoni Elola and Javier {Del Ser} and Miren Nekane Bilbao and Cristina Perfecto and Enrique Alexandre and Sancho Salcedo-Sanz", title = "Hybridizing Cartesian Genetic Programming and Harmony Search for adaptive feature construction in supervised learning problems", journal = "Applied Soft Computing", volume = "52", pages = "760--770", year = "2017", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2016.09.049", URL = "http://www.sciencedirect.com/science/article/pii/S1568494616305087", abstract = "The advent of the so-called Big Data paradigm has motivated a flurry of research aimed at enhancing machine learning models by following very diverse approaches. In this context this work focuses on the automatic construction of features in supervised learning problems, which differs from the conventional selection of features in that new characteristics with enhanced predictive power are inferred from the original dataset. In particular this manuscript proposes a new iterative feature construction approach based on a self-learning meta-heuristic algorithm (Harmony Search) and a solution encoding strategy (correspondingly, Cartesian Genetic Programming) suited to represent combinations of features by means of constant-length solution vectors. The proposed feature construction algorithm, coined as Adaptive Cartesian Harmony Search (ACHS), incorporates modifications that allow exploiting the estimated predictive importance of intermediate solutions and, ultimately, attaining better convergence rate in its iterative learning procedure. The performance of the proposed ACHS scheme is assessed and compared to that rendered by the state of the art in a toy example and three practical use cases from the literature. The excellent performance figures obtained in these problems shed light on the widespread applicability of the proposed scheme to supervised learning with legacy datasets composed by already refined characteristics.", keywords = "genetic algorithms, genetic programming, Feature construction, Supervised learning, Harmony Search", } @Article{ELSAYED:2024:jenvman, author = "Ahmed Elsayed and Maysara Ghaith and Ahmed Yosri and Zhong Li and Wael El-Dakhakhni", title = "Genetic programming expressions for effluent quality prediction: Towards {AI-driven} monitoring and management of wastewater treatment plants", journal = "Journal of Environmental Management", volume = "356", pages = "120510", year = "2024", ISSN = "0301-4797", DOI = "doi:10.1016/j.jenvman.2024.120510", URL = "https://www.sciencedirect.com/science/article/pii/S0301479724004961", keywords = "genetic algorithms, genetic programming, Effluent quality, Interpretability analysis, Multi-gene genetic programming, Wastewater treatment, Water quality prediction, Wastewater monitoring and management", abstract = "Continuous effluent quality prediction in wastewater treatment processes is crucial to proactively reduce the risks to the environment and human health. However, wastewater treatment is an extremely complex process controlled by several uncertain, interdependent, and sometimes poorly characterized physico-chemical-biological process parameters. In addition, there are substantial spatiotemporal variations, uncertainties, and high non-linear interactions among the water quality parameters and process variables involved in the treatment process. Such complexities hinder efficient monitoring, operation, and management of wastewater treatment plants under normal and abnormal conditions. Typical mathematical and statistical tools most often fail to capture such complex interrelationships, and therefore data-driven techniques offer an attractive solution to effectively quantify the performance of wastewater treatment plants. Although several previous studies focused on applying regression-based data-driven models (e.g., artificial neural network) to predict some wastewater treatment effluent parameters, most of these studies employed a limited number of input variables to predict only one or two parameters characterizing the effluent quality (e.g., chemical oxygen demand (COD) and/or suspended solids (SS)). Harnessing the power of Artificial Intelligence (AI), the current study proposes multi-gene genetic programming (MGGP)-based models, using a dataset obtained from an operational wastewater treatment plant, deploying membrane aerated biofilm reactor, to predict the filtrated COD, ammonia (NH4), and SS concentrations along with the carbon-to-nitrogen ratio (C/N) within the effluent. Input features included a set of process variables characterizing the influent quality (e.g., filtered COD, NH4, and SS concentrations), water physics and chemistry parameters (e.g., temperature and pH), and operation conditions (e.g., applied air pressure). The developed MGGP-based models accurately reproduced the observations of the four output variables with correlation coefficient values that ranged between 0.98 and 0.99 during training and between 0.96 and 0.99 during testing, reflecting the power of the developed models in predicting the quality of the effluent from the treatment system. Interpretability analyses were subsequently deployed to confirm the intuitive understanding of input-output interrelations and to identify the governing parameters of the treatment process. The developed MGGP-based models can facilitate the AI-driven monitoring and management of wastewater treatment plants through devising optimal rapid operation and control schemes and assisting the plants' operators in maintaining proper performance of the plants under various normal and disruptive operational conditions", } @InProceedings{Elsayed:2015:CEC, author = "Saber Elsayed and Ruhul Sarker and Jill Slay", booktitle = "2015 IEEE Congress on Evolutionary Computation (CEC)", title = "Evaluating the performance of a differential evolution algorithm in anomaly detection", year = "2015", pages = "2490--2497", isbn13 = "978-1-4799-7491-7", abstract = "During the last few eras, evolutionary algorithms have been adopted to tackle cyber-terrorism. Among them, genetic algorithms and genetic programming were popular choices. Recently, it has been shown that differential evolution was more successful in solving a wide range of optimisation problems. However, a very limited number of research studies have been conducted for intrusion detection using differential evolution. In this paper, we will adapt differential evolution algorithm for anomaly detection, along with proposing a new fitness function to measure the quality of each individual in the population. The proposed method is trained and tested on the 10percentKDD99 cup data and compared against existing methodologies. The results show the effectiveness of using differential evolution in detecting anomalies by achieving an average true positive rate of 100percent, while the average false positive rate is only 0.582percent.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2015.7257194", ISSN = "1089-778X", month = may, notes = "Also known as \cite{7257194}", } @InProceedings{Elsey:1996:Chemeca, author = "Justin Elsey and Jorg Riepenhausen and Ben McKay and Geoffrey W. Barton", title = "Dynamic Modelling of a Cooking Extruder", booktitle = "Chemeca 96: Excellence in Chemical Engineering; 24th Australian and New Zealand Chemical Engineering Conference and Exhibition; Proceedings", year = "1996", editor = "Gordon Weiss", volume = "2", pages = "43--48", address = "Barton, ACT, Australia", organisation = "Institution of Engineers, Australia", keywords = "genetic algorithms, genetic programming", ISBN = "0-85825-658-4", URL = "http://search.informit.com.au/documentSummary;dn=893841670974616;res=IELENG", abstract = "A dynamic model of a twin-screw cooking extruder suitable for process optimisation and control purposes was implemented in MATLAB. The model is capable of predicting pressure, temperature and starch gelatinisation profiles, as well as the residence time distribution and the specific mechanical energy expended on the product. Two different rheological models were considered for their suitability in fitting experimental data. It was shown that the model proposed by Kulshreshtha et al. (1991) more accurately described the rheological behaviour of extruded starch than that used by Vergnes et al. (1987), although the latter model did provide a better prediction of the general trends observable in the data. The relevant model parameters were determined from experimental data using a least-square optimisation routine. The model predictions compared favourably with measured residence time distribution data.", notes = "National conference publication (Institution of Engineers, Australia) ; no. 96/13. cited 20 Dec 11 ", } @PhdThesis{Elsey:thesis, author = "Justin Rae Elsey", title = "Dynamic Modelling, Measurement and Control of Co-rotating Twin-Screw Extruders", school = "Department of Chemical Engineering, University of Sydney", year = "2002", address = "Australia", month = "25 " # aug, keywords = "genetic algorithms, genetic programming, twin-screw extrusion, extruder geometry, dynamic modelling, process control, acoustic sensors, image analysis, bubble growth", URL = "http://ses.library.usyd.edu.au/bitstream/2123/687/2/adt-NU20050131.14060102whole.pdf", URL = "http://hdl.handle.net/2123/687", size = "242 pages", abstract = "Co-rotating twin-screw extruders are unique and versatile machines that are used widely in the plastics and food processing industries. Due to the large number of operating variables and design parameters available for manipulation and the complex interactions between them, it cannot be claimed that these extruders are currently being optimally used. The most significant improvement to the field of twin-screw extrusion would be through the provision of a generally applicable dynamic process model that is both computationally inexpensive and accurate. This would enable product design, process optimisation and process controller design to be performed cheaply and more thoroughly on a computer than can currently be achieved through experimental trials. This thesis is divided into three parts: dynamic modelling, measurement and control. The first part outlines the development of a dynamic model of the extrusion process which satisfies the above mentioned criteria. The dynamic model predicts quasi-3D spatial profiles of the degree of fill, pressure, temperature, specific mechanical energy input and concentrations of inert and reacting species in the extruder. The individual material transport models which constitute the dynamic model are examined closely for their accuracy and computational efficiency by comparing candidate models amongst themselves and against full 3D finite volume flow models. Several new modelling approaches are proposed in the course of this investigation. The dynamic model achieves a high degree of simplicity and flexibility by assuming a slight compressibility in the process material, allowing the pressure to be calculated directly from the degree of over-fill in each model element using an equation of state. Comparison of the model predictions with dynamic temperature, pressure and residence time distribution data from an extrusion cooking process indicates a good predictive capability. The model can perform dynamic step-change calculations for typical screw configurations in approximately 30 seconds on a 600 MHz Pentium 3 personal computer. The second part of this thesis relates to the measurement of product quality attributes of extruded materials. A digital image processing technique for measuring the bubble size distribution in extruded foams from cross sectional images is presented. It is recognised that this is an important product quality attribute, though difficult to measure accurately with existing techniques. The present technique is demonstrated on several different products. A simulation study of the formation mechanism of polymer foams is also performed. The measurement of product quality attributes such as bulk density and hardness in a manner suitable for automatic control is also addressed. This is achieved through the development of an acoustic sensor for inferring product attributes using the sounds emanating from the product as it leaves the extruder. This method is found to have good prediction ability on unseen data. The third and final part of this thesis relates to the automatic control of product quality attributes using multivariable model predictive controllers based on both direct and indirect control strategies. In the given case study, indirect control strategies, which seek to regulate the product quality attributes through the control of secondary process indicators such as temperature and pressure, are found to cause greater deviations in product quality than taking no corrective control action at all. Conversely, direct control strategies are shown to give tight control over the product quality attributes, provided that appropriate product quality sensors or inferential estimation techniques are available.", notes = "Uses GP, eg in chapter 6. See also his publications pages iv-v", } @InProceedings{ElShawi:2022:ICDMW, author = "Radwa ElShawi and Sherif Sakr", booktitle = "2022 IEEE International Conference on Data Mining Workshops (ICDMW)", title = "{TPE-AutoClust:} A Tree-based Pipline Ensemble Framework for Automated Clustering", year = "2022", pages = "1144--1153", abstract = "Novel technologies in automated machine learning ease the complexity of building well-performed machine learning pipelines. However, these are usually restricted to supervised learning tasks such as classification and regression, while unsu-pervised learning, particularly clustering, remains a largely un-explored problem due to the ambiguity involved when evaluating the clustering solutions. Motivated by this shortcoming, in this paper, we introduce TPE-AutoClust, a genetic programming-based automated machine learning framework for clustering. TPE-AutoCl ust optimizes a series of feature preprocessors and machine learning models to optimize the performance on an unsupervised clustering task. TPE-AutoClust mainly consists of three main phases: meta-learning phase, optimization phase and clustering ensemble construction phase. The meta-learning phase suggests some instantiations of pipelines that are likely to perform well on a new dataset. These pipelines are used to warm start the optimization phase that adopts a multi-objective optimization technique to select pipelines based on the Pareto front of the trade-off between the pipeline length and performance. The ensemble construction phase develops a collaborative mechanism based on a clustering ensemble to combine optimized pipelines based on different internal cluster validity indices and construct a well-performing solution for a new dataset. The proposed framework is based on scikit-learn with 4 preprocessors and 6 clustering algorithms. Extensive experiments are conducted on 27 real and synthetic benchmark datasets to validate the superiority of TPE-AutoCl ust. The results show that TPE-AutoClust outperforms the state-of-the-art techniques for building automated clustering solutions.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICDMW58026.2022.00149", ISSN = "2375-9259", month = nov, notes = "Also known as \cite{10031132}", } @Article{Elshorbagy:2009:JH, author = "Amin Elshorbagy and Ibrahim El-Baroudy", title = "Investigating the capabilities of evolutionary data-driven techniques using the challenging estimation of soil moisture content", journal = "Journal of Hydroinformatics", year = "2009", volume = "11", number = "3-4", pages = "237--251", keywords = "genetic algorithms, genetic programming, evolutionary polynomial regression, EPR, prediction, soil moisture, tool uncertainty", ISSN = "1464-7141", URL = "http://www.iwaponline.com/jh/011/0237/0110237.pdf", DOI = "doi:10.2166/hydro.2009.032", size = "15 pages", abstract = "Soil moisture has a crucial role in both the global energy and hydrological cycles; it affects different ecosystem processes. Spatial and temporal variability of soil moisture add to its complex behaviour, which undermines the reliability of most current measurement methods. In this paper, two promising evolutionary data-driven techniques, namely (i) Evolutionary Polynomial Regression and (ii) Genetic Programming, are challenged with modelling the soil moisture response to the near surface atmospheric conditions. The utility of the proposed models is demonstrated through the prediction of the soil moisture response of three experimental soil covers, used for the restoration of watersheds that were disturbed by the mining industry. The results showed that the storage effect of the soil moisture response is the major challenging factor; it can be quantified using cumulative inputs better than time-lag inputs, which can be attributed to the effect of the soil layer moisture-holding capacity. This effect increases with the increase in the soil layer thickness. Three different modelling tools are tested to investigate the tool effect in data-driven modelling. Despite the promising results with regard to the prediction accuracy, the study demonstrates the need for adopting multiple data-driven modelling techniques and tools (modelling environments) to obtain reliable predictions.", notes = "Laucelli EPR toolbox, South Bison Hill, oil sands reclamation, 1 foot or more peat layer, AB Canada, Discipulus \cite{francone:manual} p242 'Discipulus produced better models than EPR'. p246 EPR provides insight. p258 GPLAB always evolved constants (not formulae).", } @Article{Elshorbagy:2010:HESS, author = "A. Elshorbagy and G. Corzo and S. Srinivasulu and D. P. Solomatine", title = "Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 1: Concepts and methodology", journal = "Hydrology and Earth System Sciences", year = "2010", volume = "14", number = "10", pages = "1931--1941", month = "14 " # oct, keywords = "genetic algorithms, genetic programming", ISSN = "1471-2164", URL = "http://www.hydrol-earth-syst-sci.net/14/1931/2010/hess-14-1931-2010.pdf", URL = "http://www.hydrol-earth-syst-sci.net/14/1931/2010/", DOI = "doi:10.5194/hess-14-1931-2010", publisher = "Copernicus GmbH", abstract = "A comprehensive data driven modelling experiment is presented in a two-part paper. In this first part, an extensive data-driven modeling experiment is proposed. The most important concerns regarding the way data driven modelling (DDM) techniques and data were handled, compared, and evaluated, and the basis on which findings and conclusions were drawn are discussed. A concise review of key articles that presented comparisons among various DDM techniques is presented. Six DDM techniques, namely, neural networks, genetic programming, evolutionary polynomial regression, support vector machines, M5 model trees, and K-nearest neighbours are proposed and explained. Multiple linear regression and na{\"i}ve models are also suggested as baseline for comparison with the various techniques. Five datasets from Canada and Europe representing evapotranspiration, upper and lower layer soil moisture content, and rainfall-runoff process are described and proposed, in the second paper, for the modelling experiment. Twelve different realisations (groups) from each dataset are created by a procedure involving random sampling. Each group contains three subsets; training, cross-validation, and testing. Each modelling technique is proposed to be applied to each of the 12 groups of each dataset. This way, both prediction accuracy and uncertainty of the modelling techniques can be evaluated. The description of the data sets, the implementation of the modeling techniques, results and analysis, and the findings of the modelling experiment are deferred to the second part of this paper.", notes = "See also \cite{Elshorbagy:2010a:HESS} Published in Hydrol. Earth Syst. Sci. Discuss.: 19 November 2009 \cite{oai:doaj-articles:09c2f3076a15547532440e3ac274c044} and \cite{oai:doaj-articles:90e50b27744c40b3f9d0243d0896b665} http://www.hydrol-earth-syst-sci-discuss.net/6/7055/2009/hessd-6-7055-2009.pdf cite{hessd-6-7055-2009}. 1 Centre for Advanced Numerical Simulation (CANSIM), Department of Civil and Geological Engineering, University of Saskatchewan, Saskatoon, SK, S7N 5A9, Canada 2 Department of Hydroinformatics and Knowledge Management, UNESCO-IHE Institute for Water Education, Delft, The Netherlands 3 Water Resources Section, Delft University of Technology, Delft, The Netherlands", } @Article{Elshorbagy:2010a:HESS, title = "Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 2: Application", author = "A. Elshorbagy and G. Corzo and S. Srinivasulu and D. P. Solomatine", journal = "Hydrology and Earth System Sciences", year = "2010", volume = "14", number = "10", pages = "1943--1961", month = "14 " # oct, keywords = "genetic algorithms, genetic programming", ISSN = "10275606", bibsource = "OAI-PMH server at www.doaj.org", language = "eng", oai = "oai:doaj-articles:0b5621edb6cf47d7aee8cedce805592b", source = "Hydrology and Earth System Sciences", URL = "http://www.hydrol-earth-syst-sci.net/14/1943/2010/hess-14-1943-2010.pdf", size = "19 pages", abstract = "In this second part of the two-part paper, the data driven modeling (DDM) experiment, presented and explained in the first part, is implemented. Inputs for the five case studies (half-hourly actual evapotranspiration, daily peat soil moisture, daily till soil moisture, and two daily rainfall-runoff datasets) are identified, either based on previous studies or using the mutual information content. Twelve groups (realisations) were randomly generated from each data set by randomly sampling without replacement from the original data set. Neural networks (ANNs), genetic programming (GP), evolutionary polynomial regression (EPR), Support vector machines (SVM), M5 model trees (M5), K-nearest neighbors (K-nn), and multiple linear regression (MLR) techniques are implemented and applied to each of the 12 realizations of each case study. The predictive accuracy and uncertainties of the various techniques are assessed using multiple average overall error measures, scatter plots, frequency distribution of model residuals, and the deterioration rate of prediction performance during the testing phase. Gamma test is used as a guide to assist in selecting the appropriate modeling technique. Unlike two nonlinear soil moisture case studies, the results of the experiment conducted in this research study show that ANNs were a sub-optimal choice for the actual evapotranspiration and the two rainfall-runoff case studies. GP is the most successful technique due to its ability to adapt the model complexity to the model ed data. EPR performance could be close to GP with datasets that are more linear than nonlinear. SVM is sensitive to the kernel choice and if appropriately selected, the performance of SVM can improve. M5 performs very well with linear and semi linear data, which cover wide range of hydrological situations. In highly nonlinear case studies, ANNs, K-nn, and GP could be more successful than other modelling techniques. K-nn is also successful in linear situations, and it should not be ignored as a potential modelling technique for hydrological applications.", notes = "See also \cite{Elshorbagy:2010:HESS}", } @InProceedings{El-Telbany:2004:ICEEC, author = "Mohammed E. El-Telbany", title = "The {Egyptian} Stock Market Return Prediction: A Genetic Programming Approach", booktitle = "International Conference on Electrical, Electronic and Computer Engineering, ICEEC-04", year = "2004", editor = "Abdel-Moniem Wahdan and Ahmed Amer and Hani Fikry and Ashraf Salem", pages = "161--164", address = "Ain Shams University, Cairo, Egypt", month = "5-7 " # sep, keywords = "genetic algorithms, genetic programming, Application software, Consumer electronics, Data mining, Economic forecasting, Electronic mail, Investments, Neural networks, Predictive models, Stock markets", DOI = "doi:10.1109/ICEEC.2004.1374410", size = "4 pages", abstract = "Applications of learning algorithms in knowledge discovery are promising and relevant area of research. It is offering new possibilities and benefits in real-world applications, helping us understand better mechanisms of our own methods of knowledge acquisition. Genetic programming as learning algorithm posses certain advantages that make it suitable for forecasting and mining the financial data. Especially the stock time series have a large number of specific properties that together makes the prediction task unusual. This paper presents the results of using genetic programming to forecast the Egyptian Sock Market return. Experiments results demonstrate the capability of genetic programming to predict accurate results, comparable to traditional machine learning algorithms i.e., neural networks.", notes = "details from ieee", } @InProceedings{Elver:2016:ieeeHPCA, author = "Marco Elver and Vijay Nagarajan", title = "{McVerSi}: A Test Generation Framework for Fast Memory Consistency Verification in Simulation", booktitle = "22nd IEEE Symposium on High Performance Computer Architecture, HPCA 2016", year = "2016", pages = "618--630", address = "Barcelona", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, MCM test generation", URL = "http://hpca22.site.ac.upc.edu/index.php/program/conference-program/", DOI = "doi:10.1109/HPCA.2016.7446099", month = "12-16 " # mar, size = "13 pages", abstract = "The memory consistency model (MCM), which formally specifies the behaviour of the memory system, is used by programmers to reason about parallel programs. It is imperative that hardware adheres to the promised MCM. For this reason, hardware designs must be verified against the specified MCM. One common way to do this is via executing tests, where specific threads of instruction sequences are generated and their executions are checked for adherence to the MCM. It would be extremely beneficial to execute such tests under simulation, i.e. when the functional design implementation of the hardware is being prototyped. Most prior verification methodologies, however, target post-silicon environments, which when applied under simulation would be too slow. We propose McVerSi, a test generation framework for fast MCM verification of a full-system design implementation under simulation. Our primary contribution is a Genetic Programming (GP) based approach to MCM test generation, which relies on a novel crossover function that prioritizes memory operations contributing to non-determinism, thereby increasing the probability of uncovering MCM bugs. To guide tests towards exercising as much logic as possible, the simulator's reported coverage is used as the fitness function. Furthermore, we increase test throughput by making the test workload simulation-aware. We evaluate our proposed framework using the Gem5 cycle accurate simulator in full-system mode with Ruby. We discover 2 new bugs due to the faulty interaction of the pipeline and the cache coherence protocol. Crucially, these bugs would not have been discovered through individual verification of the pipeline or the coherence protocol. We study 11 bugs in total. Our GP-based test generation approach finds all bugs consistently, therefore providing much higher guarantees compared to alternative approaches (pseudo-random test generation and litmus tests)", notes = "https://github.com/melver/mc2lib University of Edinburgh p618 'one of the bugs discovered would not ... have been discovered through individual verification of either pipeline or coherence protocol.' chomosome is DAG p621 'tests must be recombined in a way, such that the resulting test is likely to be more racy than its parents.' 'we design a selective crossover' crossover with mutation. Ruby GARNET x86-64 ISA. diy-litmus. https://github.com/melver/mc2lib Also known as \cite{7446099}", } @PhdThesis{Elver2016, author = "Marco Iskender Elver", title = "Memory Consistency Directed Cache Coherence Protocols for Scalable Multiprocessors", school = "University of Edinburgh", year = "2016", address = "UK", keywords = "genetic algorithms, genetic programming, SBSE", URL = "https://ac.marcoelver.com/res/melver-thesis.pdf", URL = "https://www.era.lib.ed.ac.uk/handle/1842/22073", URL = "https://www.era.lib.ed.ac.uk/bitstream/handle/1842/22073/Elver2016.pdf", URL = "http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.716641", size = "184 pages", abstract = "... We propose McVerSi, a test generation framework for fast memory consistency verification of a full-system design implementation under simulation. Our primary contribution is a Genetic Programming (GP) based approach to memory consistency test generation, which relies on a novel crossover function that prioritizes memory operations contributing to non-determinism, ...", notes = "uk.bl.ethos.716641 Supervisors: Vijayanand Nagarajan and Christian Fensch", } @Article{ElYafrani:GPEM:TTP, author = "Mohamed {El Yafrani} and Marcella Martins and Markus Wagner and Belaid Ahiod and Myriam Delgado and Ricardo Luders", title = "A hyperheuristic approach based on low-level heuristics for the travelling thief problem", journal = "Genetic Programming and Evolvable Machines", year = "2018", volume = "19", number = "1-2", pages = "121--150", month = jun, note = "Special Issue on Automated Design and Adaptation of Heuristics for Scheduling and Combinatorial Optimisation", keywords = "genetic algorithms, genetic programming, Heuristic selection, Travelling thief problem, Multi-component problems", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-017-9308-x", abstract = "In this paper, we investigate the use of hyper-heuristics for the travelling thief problem (TTP). TTP is a multi-component problem, which means it has a composite structure. The problem is a combination between the travelling salesman problem and the knapsack problem. Many heuristics were proposed to deal with the two components of the problem separately. In this work, we investigate the use of automatic online heuristic selection in order to find the best combination of the different known heuristics. In order to achieve this, we propose a genetic programming based hyper-heuristic called GPHS*, and compare it to state-of-the-art algorithms. The experimental results show that the approach is competitive with those algorithms on small and mid-sized TTP instances.", } @InProceedings{ElYafrani:2021:evocop, author = "Mohamed {El Yafrani} and Marcella Scoczynski and Inkyung Sung and Markus Wagner and Carola Doerr and Peter Nielsen", title = "{MATE}: A Model-based Algorithm Tuning Engine", booktitle = "The 21st European Conference on Evolutionary Computation in Combinatorial Optimisation, EvoCOP 2019", year = "2021", month = "7-9 " # apr, editor = "Christine Zarges and Sebastien Verel", series = "LNCS", volume = "12692", publisher = "Springer Verlag", address = "virtual event", pages = "51--67", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Parameter tuning, Model-based tuning", isbn13 = "978-3-030-72903-5", URL = "https://arxiv.org/abs/2004.12750", URL = "https://hal.sorbonne-universite.fr/hal-03233689", URL = "https://hal.sorbonne-universite.fr/hal-03233689/document", URL = "https://hal.sorbonne-universite.fr/hal-03233689/file/MATE", DOI = "doi:10.1007/978-3-030-72904-2_4", size = "16 pages", abstract = "we introduce a Model-based Algorithm Turning Engine, namely MATE, where the parameters of an algorithm are represented as expressions of the features of a target optimisation problem. In contrast to most static (feature-independent) algorithm tuning engines such as irace and SPOT, our approach aims to derive the best parameter configuration of a given algorithm for a specific problem, exploiting the relationships between the algorithm parameters and the features of the problem. We formulate the problem of finding the relationships between the parameters and the problem features as a symbolic regression problem and we use genetic programming to extract these expressions in a human-readable form. For the evaluation, we apply our approach to the configuration of the (1+1) EA and RLS algorithms for the OneMax, LeadingOnes, BinValue and Jump optimisation problems, where the theoretically optimal algorithm parameters to the problems are available as functions of the features of the problems. Our study shows that the found relationships typically comply with known theoretical results. this demonstrates (1) the potential of model-based parameter tuning as an alternative to existing static algorithm tuning engines, and (2) its potential to discover relationships between algorithm performance and problem features in human-readable form.", notes = "oai:HAL:hal-03233689v1 University of Adelaide http://www.evostar.org/2021/ EvoCOP2021 held in conjunction with EuroGP'2021, EvoApplications2021 and EvoMusArt2021", } @InProceedings{Elyasaf2011, author = "Achiya Elyasaf and Yael Zaritsky and Ami Hauptman and Moshe Sipper", title = "Evolving Solvers for {FreeCell} and the {Sliding-Tile} Puzzle", booktitle = "Proceedings of the Fourth Annual Symposium on Combinatorial Search, {SoCS 2011}", year = "2011", editor = "Daniel Borrajo and Maxim Likhachev and Carlos Linares Lopez", pages = "189--190", address = "Castell de Cardona, Barcelona, Spain", month = "15-16 " # jul, publisher = "AAAI Press", keywords = "genetic algorithms, genetic programming:Poster ?", URL = "http://www.aaai.org/ocs/index.php/SOCS/SOCS11/paper/view/4018", URL = "http://www.aaai.org/ocs/index.php/SOCS/SOCS11/paper/view/4018.pdf", size = "2 pages", abstract = "We use genetic algorithms to evolve highly successful solvers for two puzzles: FreeCell and Sliding-Tile Puzzle.", notes = "Abstracts http://socs11.org/", } @InProceedings{Elyasaf2011a, author = "Achiya Elyasaf and Ami Hauptman and Moshe Sipper", title = "{GA-FreeCell:} evolving solvers for the game of {FreeCell}", booktitle = "GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0557-0", pages = "1931--1938", keywords = "genetic algorithms, Self-* search", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", URL = "http://dl.acm.org/citation.cfm?id=2001836", DOI = "doi:10.1145/2001576.2001836", publisher = "ACM", publisher_address = "New York, NY, USA", size = "8 pages", abstract = "We evolve heuristics to guide staged deepening search for the hard game of FreeCell, obtaining top-notch solvers for this NP-Complete, human-challenging puzzle. We first devise several novel heuristic measures and then employ a Hillis-style coevolutionary genetic algorithm to find efficient combinations of these heuristics. Our results significantly surpass the best published solver to date by three distinct measures: 1) Number of search nodes is reduced by 87percent; 2) time to solution is reduced by 93percent; and 3) average solution length is reduced by 41percent. Our top solver is the best published FreeCell player to date, solving 98percent of the standard Microsoft 32K problem set, and also able to beat high-ranking human players.", notes = "Also known as \cite{2001836} \cite{Elyasaf:2011:GECCO}. GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)", } @Article{Elyasaf:2012:ieeeTCIAIG, author = "Achiya Elyasaf and Ami Hauptman and Moshe Sipper", title = "Evolutionary Design of {FreeCell} Solvers", journal = "IEEE Transactions on Computational Intelligence and AI in Games", year = "2012", volume = "4", month = dec, pages = "270--281", keywords = "genetic algorithms, genetic programming, GAs, GP, artificial intelligence, computer games, search problems, FreeCell solver, Microsoft 32 K problem set, building blocks, evolutionary design, evolutionary setup, genetic algorithm, heuristic measure, human-challenging puzzle, minimal domain knowledge, policy-based genetic programming, search node, solution length, solution time, staged deepening search, Games, Heuristic algorithms, Learning systems, Planning, Search problems, Standards, Evolutionary algorithms, heuristic, hyperheuristic", DOI = "doi:10.1109/TCIAIG.2012.2210423", URL = "http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6249736", ISSN = "1943-068X", size = "12 pages", abstract = "In this paper, we evolve heuristics to guide staged deepening search for the hard game of FreeCell, obtaining top-notch solvers for this human-challenging puzzle. We first devise several novel heuristic measures using minimal domain knowledge and then use them as building blocks in two evolutionary setups involving a standard genetic algorithm and policy-based, genetic programming. Our evolved solvers outperform the best FreeCell solver to date by three distinct measures: 1) number of search nodes is reduced by over 78percent; 2) time to solution is reduced by over 94percent; and 3) average solution length is reduced by over 30percent. Our top solver is the best published FreeCell player to date, solving 99.65percent of the standard Microsoft 32 K problem set. Moreover, it is able to convincingly beat high-ranking human players.", notes = "Entered 2012 Humies. Joint winner 2013 HUMIES GECCO 2013 Broken March 2021 http://tech.groups.yahoo.com/group/genetic_programming/message/6219 Also known as \cite{6249736} \cite{Elyasaf2012}", } @InProceedings{Elyasaf:2013:GECCOcompa, author = "Achiya Elyasaf and Michael Orlov and Moshe Sipper", title = "A heuristiclab evolutionary algorithm for {FINCH}", booktitle = "GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion", year = "2013", editor = "Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A. N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks", isbn13 = "978-1-4503-1964-5", keywords = "genetic algorithms, genetic programming", pages = "1727--1728", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2464576.2480786", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "We present a HeuristicLab plugin for FINCH. FINCH (Fertile Darwinian Bytecode Harvester) is a system designed to evolutionarily improve actual, extant software, which was not intentionally written for the purpose of serving as a GP representation in particular, nor for evolution in general. This is in contrast to existing work that uses restricted subsets of the Java bytecode instruction set as a representation language for individuals in genetic programming. The ability to evolve Java programs will hopefully lead to a valuable new tool in the software engineer's toolkit.", notes = "Also known as \cite{2480786} Distributed at GECCO-2013.", } @Article{Elyasaf:2014:GPEM, author = "Achiya Elyasaf and Moshe Sipper", title = "Software review: the HeuristicLab framework", journal = "Genetic Programming and Evolvable Machines", year = "2014", volume = "15", number = "2", pages = "215--218", month = jun, keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "http://link.springer.com/article/10.1007/s10710-014-9214-4", URL = "https://rdcu.be/dR8ow", DOI = "doi:10.1007/s10710-014-9214-4", size = "4 pages", abstract = "6 Conclusion HeuristicLab is an emerging system that is rapidly gaining popularity. The system is surprisingly fun to use, and offers an easy way to create new evolutionary algorithms, run them, and analyse the results.", } @PhdThesis{ElyasafDissertation, author = "Achiya Elyasaf", title = "Evolving Hyper-Heuristics using Genetic Programming", school = "Ben-Gurion University of the Negev", year = "2014", address = "Beer-Sheva, Israel", month = "10 " # oct, keywords = "genetic algorithms, genetic programming, Rush Hour, FreeCell, HH-Evolver, IDA*, mRNA", URL = "http://achiya.elyasaf.net/wp-content/uploads/2015/07/Achiya-Elyasaf-Ph.D.-Thesis.pdf", size = "128 pages", abstract = "The application of computational intelligence techniques within the vast domain of games has been increasing at a breathtaking speed. Over the past few years my research has produced a plethora of results in games of different natures, evidencing the success and efficiency of evolutionary algorithms in general|and genetic programming in particular|at producing top-notch, human-competitive game strategies. Studying games may advance our knowledge both in cognition and artificial intelligence, and, last but not least, games possess a competitive angle that coincides with our human nature, thus motivating researchers. In this dissertation I explore the application of genetic programming to the development of search heuristics for difficult games. I apply GP to the evolution of solvers for the Rush Hour puzzle and the game of FreeCell, along the way demonstrating a general method for evolving heuristics. My study produced two Gold and one Bronze HUMIE Awards, and an IEEE Outstanding Paper Award. Genetic Programming (GP) is a sub-class of evolutionary algorithms, in which a population of solutions to a given problem, embodied as LISP expressions, is improved over time by applying the principles of Darwinian evolution. At each stage, or generation, every solution's quality is measured and assigned a numerical value, called fitness. During the course of evolution, natural (or, in our case, artificial) selection takes place, wherein individuals with high fitness values are more likely to generate offspring. Following selection, genetic operators are applied to the selected individuals. The most widely used ones are crossover, reproduction, and mutation. The crossover (or recombination) operation is reminiscent of natural gene transfer from parents to offspring.....", notes = "supervisor: Moshe Sipper", } @InProceedings{Elyasaf:2015:GPTP, author = "Achiya Elyasaf and Pavel Vaks and Nimrod Milo and Moshe Sipper and Michal Ziv-Ukelson", title = "Casting the Problem of Mining {RNA} Sequence-Structure Motifs as One of Search and Learning Hyper-Heuristics for it", booktitle = "Genetic Programming Theory and Practice XIII", year = "2015", editor = "Rick Riolo and William P. Worzel and M. Kotanchek and A. Kordon", series = "Genetic and Evolutionary Computation", pages = "21--38", address = "Ann Arbor, USA", month = "14-16 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Hyper heuristic", isbn13 = "978-3-319-34223-8", URL = "http://www.springer.com/us/book/9783319342214", DOI = "doi:10.1007/978-3-319-34223-8_2", abstract = "The computational identification of conserved motifs in RNA molecules is a major (yet largely unsolved) problem. Structural conservation serves as strong evidence for important RNA functionality. Thus, comparative structure analysis is the gold standard for the discovery and interpretation of functional RNAs.In this paper we focus on one of the functional RNA motif types, sequence-structure motifs in RNA molecules, which marks the molecule as targets to be recognized by other molecules.We present a new approach for the detection of RNA structure (including pseudoknots), which is conserved among a set of unaligned RNA sequences. Our method extends previous approaches for this problem, which were based on first identifying conserved stems and then assembling them into complex structural motifs. The novelty of our approach is in simultaneously preforming both the identification and the assembly of these stems. We believe this novel unified approach offers a more informative model for deciphering the evolution of functional RNAs, where the sets of stems comprising a conserved motif co-evolve as a correlated functional unit.Since the task of mining RNA sequence-structure motifs can be addressed by solving the maximum weighted clique problem in an n-partite graph, we translate the maximum weighted clique problem into a state graph. Then, we gather and define domain knowledge and low-level heuristics for this domain. Finally, we learn hyper-heuristics for this domain, which can be used with heuristic search algorithms (e.g., A-star, IDA*) for the mining task.The hyper-heuristics are evolved using HH-Evolver, a tool for domain-specific, hyper-heuristic evolution. Our approach is designed to overcome the computational limitations of current algorithms, and to remove the necessity of previous assumptions that were used for sparsifying the graph.This is still work in progress and as yet we have no results to report. However, given the interest in the methodology and its previous success in other domains we are hopeful that these shall be forthcoming soon.", notes = " Part of \cite{Riolo:2015:GPTP} Published after the workshop in 2016", } @Article{EMAMIAN:2020:CBM, author = "Seyed Ali Emamian and Hamid Eskandari-Naddaf", title = "Genetic programming based formulation for compressive and flexural strength of cement mortar containing nano and micro silica after freeze and thaw cycles", journal = "Construction and Building Materials", volume = "241", pages = "118027", year = "2020", ISSN = "0950-0618", DOI = "doi:10.1016/j.conbuildmat.2020.118027", URL = "http://www.sciencedirect.com/science/article/pii/S0950061820300325", keywords = "genetic algorithms, genetic programming, Genetic expression programming, Cement mortar, Micro and nano silica, Freeze-thaw cycles, Porosity, Compressive and flexural strength, Sensitivity analysis", abstract = "Replacing cement with supplementary cementitious materials such as nano and micro-silica would improve the mechanical properties including compressive strength (Fc) and flexural strength (Ff). Also, the frost resistance of the cement mortar as adding micro and nano-silica reduces its porosity. The purpose of this investigation is to evaluating the capability of Genetic Expression Programming (GEP) to predict and formulate the hardened characteristics of cement mortar with the simultaneous addition of nano and micro-silica by considering the freeze-thaw (F-T) cycles based on experimental data. 32 mix designs were prepared with 0.4 and 0.5 water/binder ratios, 990-1200 gr of cement content, 2.667-3.222 of sand/cement ratio, to 0.051 of nano-silica/cement ratio, and 0-0.157 of micro-silica/cement ratio. The parameters modeled by GEP were porosity, Fc, and Ff by considering the F-T cycles. The results obtained from the experimental program of this study were used as input dataset for the proposed GEP models. The correlation between GEP and the experimental results was evaluated and a small dispersion was observed. The results showed the power and robustness of the GEP tool for modeling the hardened characteristics of the cement mortar comprising nano and micro-silica. It also produced a formulation to predict these properties as a function of the mixture components. Finally, a sensitivity analysis was performed and the contribution of the predictor variables on the variation of the Fc and Ff was evaluated", } @Article{EMEKSIZ:2022:compeleceng, author = "Cem Emeksiz", title = "Multi-gen genetic programming based improved innovative model for extrapolation of wind data at high altitudes, case study: Turkey", journal = "Computers and Electrical Engineering", volume = "100", pages = "107966", year = "2022", ISSN = "0045-7906", DOI = "doi:10.1016/j.compeleceng.2022.107966", URL = "https://www.sciencedirect.com/science/article/pii/S0045790622002427", keywords = "genetic algorithms, genetic programming, Log-law, Multi-gen genetic programming, Power-law, Wind shear coefficient, Wind speed extrapolation", abstract = "Wind speed is the most important input of wind energy conversion systems and has higher values at high altitudes. Therefore, tall wind measurement masts are used in the wind power industry to determine the wind speed at high altitudes. However, this situation brings many engineering problems (cost escalation, de-erection and re-erection of the masts due to the failure of the anemometer and sensors, lightning strikes, mechanical failures etc.). In this study, it is aimed to estimate the data at the hub height levels of the proposed wind power generators by placing shorter wind masts as a suitable alternative for longer masts. Therefore, we proposed an innovative model that uses multigene genetic programming to estimate wind speed at high altitudes. According to the power and logarithmic law, analysis results show that root mean square error (RMSE) values were decreased with proposed method in the wind speed estimation, 58.62percent and 58.77percent respectively", } @InProceedings{Emer.1997.ISCA, author = "Joel Emer and Nikolas Gloy", title = "A Language For Describing Predictors And Its Application To Automatic Synthesis", booktitle = "Conference Proceedings. The 24th Annual International Symposium on Computer Architecture", year = "1997", pages = "304--314", address = "Denver, USA", month = jun, publisher = "ACM", keywords = "genetic algorithms, genetic programming, SBSE, CPU branch prediction", ISSN = "1063-6897", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.598.7312", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.598.7312", broken = "http://courses.engr.illinois.edu/ece512/Papers/Emer.1997.ISCA.pdf", DOI = "doi:10.1145/384286.264212", size = "11 pages", abstract = "As processor architectures have increased their reliance on speculative execution to improve performance, the importance of accurate prediction of what to execute speculatively has increased. Furthermore, the types of values predicted have expanded from the ubiquitous branch and call/return targets to the prediction of indirect jump targets, cache ways and data values. In general, the prediction process is one of identifying the current state of the system, and making a prediction for some as yet uncomputed value based on that state. Prediction accuracy is improved by learning what is a good prediction for that state using a feedback process at the time the predicted value is actually computed. While there have been a number of efforts to formally characterize this process, we have taken the approach of providing a simple algebraic-style notation that allows one to express this state identification and feedback process. This notation allows one to describe a wide variety of predictors in a uniform way. It also facilitates the use of an efficient search technique called genetic programming, which is loosely modelled on the natural evolutionary process, to explore the design space. In this paper we describe our notation and the results of the application of genetic programming to the design of branch and indirect jump predictors.", notes = "DEC Alpha 21264. also known as \cite{604727}", } @InProceedings{figueiredopereiraemer:2002:gecco, author = "Maria Cl{\'a}udia Figueiredo Pereira Emer and Silvia Regina Vergilio", title = "{GPTesT}: {A} Testing Tool Based On Genetic Programming", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "1343--1350", address = "New York", publisher_address = "San Francisco, CA 94104, USA", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "genetic algorithms, genetic programming, search-based software engineering, fault-based testing, induction of programs, mutation analysis, software test criteria", ISBN = "1-55860-878-8", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/sbse017.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/sbse017.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-25.pdf", abstract = "Genetic Programming (GP) has recently been applied to solve problems in several areas. It has the goal of inducing programs from test cases by using the concepts of Darwin's evolution theory. On the other hand, software testing, that is a fundamental and expensive activity for software quality assurance, has the objective of generating test cases from the program being tested. In this sense, a symmetry between induction of programs based on GP and testing is noticed. Based on such symmetry, this work presents GPTesT, a testing tool based on GP. Fault-based testing criteria generally derive test data using a set of mutant operators to produce alternatives that differ from the program under testing by a simple modification. GPtesT uses a set of alternatives genetically derived, which allow the test of interactions between faults. GPTesT implements two test procedures respectively for guiding the selection and evaluation of test data sets. Examples with these procedures show that the approach can be used as a testing criterion.", notes = "GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)", } @InProceedings{sbes2002meta006, title = "Selection and evaluation of test data sets based on genetic programming", author = "Maria Claudia F. P. Emer and Silvia Regina Vergilio", year = "2002", identifier = "sbes2002article006", language = "por", rights = "Sociedade Brasileira de Computa{\c c}{\~a}o", source = "sbes2002", booktitle = "XVI Simposio Brasileiro de Engenharia de Software", pages = "82--97", address = "Gramado, Rio Grande do Sul, Brasil", keywords = "genetic algorithms, genetic programming, SBSE, GPBT, GPtesT, search based testing, SE, mutation testing", URL = "http://www.lbd.dcc.ufmg.br:8080/colecoes/sbes/2002/004.pdf", size = "16 pages", abstract = "A testing criterion is a predicate to be satisfied and generally addresses two important questions related to: 1) the selection of test cases capable of revealing as many faults as possible; and 2) the evaluation of a test set to consider the test ended. Studies show that fault based criteria, such as mutation testing, are very efficacious, but very expensive in terms of the number of test cases. Mutation testing uses mutation operators to generate alternatives for the program P under test. The goal is to derive test cases to producing different behaviours in P and its alternatives. This approach usually does not allow the test of interaction between faults since the alternative differs from P by a simple modification. This work explores the use of Genetic Programming (GP) to derive alternatives for testing P and describes two GP-based test procedures for selection and evaluation of test data. Experimental results show the GP approach applicability and allow comparison with mutation testing.", notes = "http://www.lbd.dcc.ufmg.br/bdbcomp/servlet/Trabalho?id=5248 triangle, chameleon and C, Grammar for factorial program, Proteum, Equivalent mutants", } @Article{emer:2003:SQJ, author = "Maria Claudia F. P. Emer and Silvia Regina Vergilio", title = "Selection and Evaluation of Test Data Based on Genetic Programming", journal = "Software Quality Journal", year = "2003", volume = "11", number = "2", pages = "167--186", month = jun, keywords = "genetic algorithms, genetic programming, evolutionary computation, testing criteria, mutation analysis, SBSE, software engineering", DOI = "doi:10.1023/A:1023772729494", size = "20 pages", abstract = "In the literature, we find several criteria that consider different aspects of the program to guide the testing, a fundamental activity for software quality assurance. They address two important questions: how to select test cases to reveal as many fault as possible and how to evaluate a test set T and end the test. Fault-based criteria, such as mutation testing, use mutation operators to generate alternatives for the program P being tested. The goal is to derive test cases capable of producing different behaviors in P and its alternatives. However, this approach usually does not allow the test of interaction between faults since the alternative differs from P by a simple modification. This work explores the use of Genetic Programming (GP), a field of Evolutionary Computation, to derive alternatives for testing P and introduces two GP-based procedures for selection and evaluation of test data. The procedures are related to the above questions, usually addressed by most testing criteria and tools. A tool, named GPTesT, is described and results from an experiment using this tool are also presented. The results show the applicability of our approach and allow comparison with mutation testing.", notes = "Article ID: 5122058 Interactive tool incorporating GP. GPTesT (C++ UML). Chameleon \cite{Spinosa:2001:gtgp} grammar based generates C programs. {"}Control over anomalous code (overflow, infinite loop among others){"} p171. {"}divide by zero{"} p177. v. Proteum (71 SE mutation operators) GPBT. cmm (common multiple), fat (factorial), max, cmd (common divisor) Computer Science Department, Federal University of Parana?UFPR CP: 19081, 81531-970, Curitiba, Brazil mpereira@inf.ufpr.br ", } @PhdThesis{Emer:thesis, author = "Maria Claudia Figueiredo Pereira Emer", title_pt = "Abordagem de Teste Baseada em Defeitos para Esquemas de Dados", title = "Fault-based testing approach for data schemas", school = "Faculdade de Engenharia Eletrica e de Computacao - FEEC, Universidade Estadual de Campinas", year = "2007", address = "Brazil", keywords = "Data schemas, Data integrity, Fault-based testing, XML, Database", URL = "http://repositorio.unicamp.br/bitstream/REPOSIP/261002/1/Emer_MariaClaudiaFigueiredoPereira_D.pdf", URL = "http://bdtd.ibict.br/vufind/Record/CAMP_26f71a2a67186532f02408e8e89f1bb5", URL = "http://repositorio.unicamp.br/jspui/handle/REPOSIP/261002", size = "153 pages", abstract = "Data are used in several software applications involving critical operations. In such applications to ensure the quality of the manipulated data is fundamental. Data schemas define the logical structure and the relationships among data. Testing schemas by means of specific testing approaches, criteria and tools has not been explored adequately as a way to ensure the quality of data defined by schemas. This work proposes a testing approach based on fault classes usually identified in data schemas. A data metamodel is defined to specify the schemas that can be tested and the constraints to the data in schemas. This testing approach provides means for revealing faults related to incorrect or absent definition of constraints for the data in the schema. The approach includes the automatic generation of a test set which contains data instances and queries to these instances; the data instances and queries are generated according to patterns defined in each fault class. Experiments in the contexts of Web and database applications were carried out to illustrate the testing approach application", abstract = "Dados sao manipulados em varias aplicacoes de software envolvendo operacoes criticas. Em tais aplicacoes assegurar a qualidade dos dados manipulados e fundamental. Esquemas de dados definem a estrutura logica e os relacionamentos entre os dados. O teste de esquemas por meio de abordagens, criterios e ferramentas de teste especificos e uma forma pouco explorada de assegurar a qualidade de dados definidos por esquemas. Este trabalho propoe uma abordagem de teste baseada em classes de defeitos comumente identificados em esquemas de dados. Um metamodelo de dados e definido para especificar os esquemas que podem ser testados e as restricoes aos dados nos esquemas. Defeitos possiveis de serem revelados sao os relacionados a definicao incorreta ou ausente de restricoes aos dados no esquema. A abordagem inclui a geracao automatica de um conjunto de teste que contem instancias de dados e consultas a essas instancias; as instancias de dados e as consultas sao geradas de acordo com padroes definidos em cada classe de defeito. Experimentos nos contextos de aplicacoes Web e de base de dados foram realizados para ilustrar a aplicacao da abordagem", notes = "not GP? In Portuguese http://genealogy.math.ndsu.nodak.edu/id.php?id=170763 Advisor 1: Mario Jino Advisor 2: Silvia Regina Vergilio", } @InProceedings{Endo:2002:GOB, author = "Ken Endo and Funinori Yamasaki and Takashi Maeno and Hiroaki Kitano", title = "Co-evolution of Morphology and Controller for Biped Humanoid Robot", booktitle = "{RoboCup} 2002: Robot Soccer World Cup {VI}", editor = "Gal A. Kaminka and Pedro U. Lima and Raul Rojas", volume = "2752", year = "2002", series = "Lecture Notes in Artificial Intelligence", pages = "327--341", publisher_address = "Berlin and Heidelberg", publisher = "Springer-Verlag", keywords = "genetic algorithms", CODEN = "LNCSD9", ISSN = "0302-9743", ISBN = "3-540-40666-2", DOI = "doi:10.1007/b11927", size = "15 pages", abstract = "we present a method for co-evolving structures and control circuits of bi-ped humanoid robots. Currently, biped walking humanoid robots are designed manually on trial-and-error basis. Although certain control theory exists, such as zero moment point (ZMP) compensation, these theories does not constrain design space of humanoid robot morphology or detailed control. Thus, engineers has to design control program for apriori designed morphology, neither of them shown to be optimal within a large design space. We propose evolutionary approaches that enables: (1) automated design of control program for a given humanoid morphology, and (2) co-evolution of morphology and control. An evolved controller has been applied to a humanoid PINO, and attained more stable walking than human designed controller. Coevolution was achieved in a precision dynamics simulator, and discovered unexpected optimal solutions. This indicate that a complex design task of bi-ped humanoid can be performed automatically using evolution-based approach, thus varieties of humanoid robots can be design in speedy manner. This is a major importance to the emerging robotics industries.", notes = "GA, not a GP approach", } @InProceedings{Endo:alife22, author = "Teruto Endo and Hirotake Abe and Mizuki Oka", title = "Toward automatic generation of diverse congestion control algorithms through co-evolution with simulation environments", booktitle = "Proceedings of the 2022 Conference on Artificial Life", year = "2022", editor = "Silvia Holler and Richard Loeffler and Stuart Bartlett", pages = "223--230", month = jul # " 18-22", organisation = "ISAL", publisher = "MIT Press", keywords = "genetic algorithms, genetic programming, grammatical evolution", URL = "https://direct.mit.edu/isal/proceedings-pdf/isal/34/33/2035325/isal_a_00515.pdf", DOI = "doi:10.1162/isal_a_00515", size = "8 pages", abstract = "Congestion control algorithms are used to help prevent congestion from occurring on the Internet. However, a definitive congestion control algorithm has yet to be developed. There are three reasons for this: First, the environment and usage of the Internet continue to evolve over time. Second, it is not clear what congestion control algorithms will be required as the environment evolves. Third, there is a limit to the number of the congestion control algorithms that can be developed by researchers. This paper proposes a method for automatically generating diverse congestion control algorithms and optimizing them in various environments by co-evolving network simulations as environments and congestion control algorithms as agents. In experiments conducted using co-evolution, although the algorithms generated were not on par with conventional practical congestion control algorithms, the intent of the procedures in the algorithms was interpretable from a human perspective. Furthermore, our results verify that it is possible to automatically discover a suitable environment for the evolution of a congestion control algorithm.", notes = "held virtually due to the ongoing COVID-19 pandemic. https://alife.org/conference/alife-2022/", } @InCollection{engel:1995:EESEAT, author = "David Engel", title = "Evolving Effective Solutions in Effective Amounts of Time", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1995", year = "1995", editor = "John R. Koza", pages = "76--85", address = "Stanford, California, 94305-3079 USA", month = "11 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-195720-5", notes = "part of \cite{koza:1995:gagp}", } @InCollection{Engelbrecht:2002:DMaHA, author = "A. P. Engelbrecht and L. Schoeman and Sonja Rouwhorst", title = "A Building Block Approach to Genetic Programming for Rule Discovery", booktitle = "Data Mining: A Heuristic Approach", publisher = "IGI-global", year = "2002", editor = "Hussein A. Abbass and Charles S. Newton and Ruhul Sarker", chapter = "9", pages = "174--190", address = "701 E Chocolate Avenue, Hershey PA 17033, USA", keywords = "genetic algorithms, genetic programming", isbn13 = "9781930708259", URL = "http://www.igi-global.com/chapter/building-block-approach-genetic-programming/7589", DOI = "doi:10.4018/978-1-930708-25-9.ch009", abstract = "Genetic programming has recently been used successfully to extract knowledge in the form of IF-THEN rules. For these genetic programming approaches to knowledge extraction from data, individuals represent decision trees. The main objective of the evolutionary process is therefore to evolve the best decision tree, or classifier, to describe the data. Rules are then extracted, after convergence, from the best individual. The current genetic programming approaches to evolve decision trees are computationally complex, since individuals are initialised to complete decision trees. This chapter discusses a new approach to genetic programming for rule extraction, namely the building block approach. This approach starts with individuals consisting of only one building block, and adds new building blocks during the evolutionary process when the simplicity of the individuals cannot account for the complexity in the underlying data. Experimental results are presented and compared with that of C4.5 and CN2. The chapter shows that the building block approach achieves very good accuracies compared to that of C4.5 and CN2. It is also shown that the building block approach extracts substantially less rules.", notes = "A. P. Engelbrecht (University of Pretoria, South Africa), L. Schoeman (University of Pretoria, South Africa) and Sonja Rouwhorst (Vrije Universiteit Amsterdam, The Netherlands)", } @InCollection{engelhardt:1998:LBNDSUGP, author = "Barbara Engelhardt", title = "Learning a {Bayesian} Network from Data Samples Using Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1998", year = "1998", editor = "John R. Koza", pages = "1--10", address = "Stanford, California, 94305-3079 USA", month = "17 " # mar, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-212568-8", notes = "part of \cite{koza:1998:GAGPs}", } @InProceedings{Engler:2009:ieeeAUTOTESTCON, author = "Joseph Engler", title = "Optimization of test engineering utilizing evolutionary computation", booktitle = "IEEE AUTOTESTCON, 2009", year = "2009", month = sep, pages = "447--452", keywords = "genetic algorithms, genetic programming, SBSE, adaptive memory, automated station software generation, evolutionary computation, genetic programming algorithm, test engineering optimization, test station software creation, testing requirements, automatic test pattern generation, automatic test software", DOI = "doi:10.1109/AUTEST.2009.5314025", ISSN = "1088-7725", abstract = "Test engineering often experiences pressures to produce test stations and software in a short time frame with constrained budgets. Since test is a negative influence towards product costs, it is crucial to optimize the processes of test station software creation as well as the configuration of the test station itself. This paper introduces novel methodologies for optimized station configuration and automated station software generation. These two optimizations use evolutionary computation to automatically generate software for the test station and to offer optimal configurations of the station based upon testing requirements. Presented is a modified genetic programming algorithm for the creation of test station software (e.g. COTS software drivers). The genetic algorithm is improved through use of adaptive memory to recall historic schemas of high fitness. From the automated software generation an optimal station configuration is produced based upon the requirements of the testing to be performed. This system has been implemented in industry and an actual industrial case study is presented to illustrate the efficiency of this novel optimization technique. Comparisons with standard genetic programming techniques are offered to further illustrate the efficiency of this methodology.", notes = "Also known as \cite{5314025}", } @InProceedings{English:2012:GECCOcomp, author = "Amanda English and Holly Petruso and Chong Wang", title = "Grammatical evolution decision trees for trio designs", booktitle = "Tenth GECCO Undergraduate Student Workshop", year = "2012", editor = "Sherri Goings", isbn13 = "978-1-4503-1178-6", keywords = "genetic algorithms, genetic programming, Grammatical evolution", pages = "559--562", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330784.2330873", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "The detection of gene-gene and gene-interactions in genetic association studies is an important challenge in human genetics. The detection of such interactive models presents a difficult computational and statistical challenge, especially as advances in genotyping technology have rapidly expanded the number of potential genetic predictors in such studies. The scale of these studies makes exhaustive search approaches infeasible, inspiring the application of evolutionary computation algorithms to perform variable selection and build classification models. Recently, an application of grammatical evolution to evolve decision trees (GEDT) has been introduced for detecting interaction models. Initial results were promising, but the previous applications of GEDT have been limited to case-control studies with unrelated individuals. While this study design is popular in human genetics, other designs with related individuals offer distinct advantages. Specifically, a trio-based design (with genetic data for an affected individual and their parents collected) can be a powerful approach to mapping that is robust to population heterogeneity and other potential confounders. In the current study, we extend the GEDT approach to be able to handle trio data (trioGEDT), and demonstrate its potential in simulated data with gene-gene interactions that underlie disease risk.", notes = "Also known as \cite{2330873} Distributed at GECCO-2012. ACM Order Number 910122.", } @Article{Engoren:2008:GPEM, author = "Milo Engoren and Jeffrey A. Kline", title = "Use of genetic programming to diagnose venous thromboembolism in the emergency department", journal = "Genetic Programming and Evolvable Machines", year = "2008", volume = "9", number = "1", pages = "39--51", month = mar, keywords = "genetic algorithms, genetic programming, Pulmonary embolism, Venous thromboembolic disease, Capnometry, Oximetry", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-007-9050-x", size = "13 pages", abstract = "Pulmonary thromboembolism as a cause of respiratory complaints is frequently undiagnosed and requires expensive imaging modalities to diagnose. The objective of this study was to determine if genetic programming could be used to classify patients as having or not having pulmonary thromboembolism using exhaled ventilatory and gas indices as genetic material. Using a custom-built exhaled oxygen and carbon dioxide analyser; exhaled flows, volumes, and gas partial pressures were recorded from patients for a series of deep exhalation and 30 seconds tidal volume breathing. A diagnosis of pulmonary embolism was made by contrast-enhanced computerised tomography angiography of the chest and indirect venography supplemented by 90-day follow-up. Genetic programming developed a series of genomes comprising genes of exhaled CO2, O2, flow, volume, vital signs, and patient demographics from these data and their predictions were compared to the radiological results. We found that 24 of 178 patients had pulmonary embolism. The best genome consisted of four genes: the minimum flow rate during the third 30 s period of tidal breathing, the average peak exhaled CO2 during the first 30 s period of tidal breathing, the average peak of the exhaled O2 during the first 30 s period of tidal breathing, and the average peak exhaled CO2 during the fourth period of tidal breathing, which immediately followed a deep exhalation. This had 100percent sensitivity and 91percent specificity on the construction population and 100percent and 82percent, respectively when tested on the separate validation population. Genetic programming using only data obtained from exhaled breaths was very accurate in classifying patients with suspected pulmonary thromboembolism.", notes = "Continuous variables converted to independent 11 deciles. Explicit representation of missing data via 11th decile. No concent of adjacency between deciles. Possibility of gaps between deciles. Fortran, windowsXP, 500 generations. At most 4 genes (to prevent over fitting). ROC", } @Article{engoren:2013:JCMC, author = "Milo Engoren and Robert H. Habib and John J. Dooner and Thomas A. Schwann", title = "Use of genetic programming, logistic regression, and artificial neural nets to predict readmission after coronary artery bypass surgery", journal = "Journal of Clinical Monitoring and Computing", year = "2013", volume = "27", number = "4", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s10877-013-9444-7", DOI = "doi:10.1007/s10877-013-9444-7", } @Article{EnriquezZarate:2017:ASC, author = "Josue Enriquez-Zarate and Leonardo Trujillo and Salvador {de Lara} and Mauro Castelli and Emigdio Z-Flores and Luis Munoz and Ales Popovic", title = "Automatic modeling of a gas turbine using genetic programming: An experimental study", journal = "Applied Soft Computing", year = "2017", volume = "50", month = jan, pages = "212--222", keywords = "genetic algorithms, genetic programming, Gas turbine, Data-driven modeling, Local search", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2016.11.019", URL = "http://www.sciencedirect.com/science/article/pii/S1568494616305889", sizze = "11 pages", abstract = "This work deals with the analysis and prediction of the behavior of a gas turbine (GT), the Mitsubishi single shaft Turbo-Generator Model MS6001, which has a 30 MW generation capacity. GTs such as this are of great importance in industry, as drivers of gas compressors for power generation. Because of their complexity and their execution environment, the failure rate of GTs can be high with severe consequences. These units are subjected to transient operations due to starts, load changes and sudden stops that degrade the system over time. To better understand the dynamic behavior of the turbine and to mitigate the aforementioned problems, these transient conditions need to be analyzed and predicted. In the absence of a thermodynamic mathematical model, other approaches should be considered to construct representative models that can be used for condition monitoring of the GT, to predict its behavior and detect possible system malfunctions. One way to derive such models is to use data-driven approaches based on machine learning and artificial intelligence. This work studies the use of state-of-the-art genetic programming (GP) methods to model the Mitsubishi single shaft Turbo-Generator. In particular, we evaluate and compare variants of GP and geometric semantic GP (GSGP) to build models that predict the fuel flow of the unit and the exhaust gas temperature. Results show that an algorithm, proposed by the authors, that integrates a local search mechanism with GP (GP-LS) outperforms all other state-of-the-art variants studied here on both problems, using real-world and representative data recorded during normal system operation. Moreover, results show that GP-LS outperforms seven other modeling techniques, including neural networks and isotonic regression, confirming the importance of GP-based algorithms in this domain.", notes = "Cites \cite{Martinez-Arellano:2014:UKSim}", } @InProceedings{Eppstein:gecco06lbp, author = "Margaret J. Eppstein and Joshua L. Payne and Bill C. White and Jason H. Moore", title = "Hill-climbing through {"}random chemistry{"} for detecting epistasis", booktitle = "Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2006)}", year = "2006", month = "8-12 " # jul, editor = "J{\"{o}}rn Grahl", address = "Seattle, WA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006etc/papers/lbp111.pdf", notes = "Distributed on CD-ROM at GECCO-2006", keywords = "genetic algorithms, genetic programming, Population based optimisation, epistasis, SNPs, data mining.", abstract = "There are estimated to be on the order of 1000000 single nucleotide polymorphisms (SNPs) existing as standing variation in the human genome. Certain combinations of these SNPs can interact in complex ways to predispose individuals for a variety of common diseases, even though individual SNPs may have no ill effects. Detecting these epistatic combinations is a computationally daunting task. Trying to use individual or growing subsets of SNPs as building blocks for detection of larger combinations of purely epistatic SNPs (e.g., via genetic algorithms or genetic programming) is no better than random search, since there is no predictive power in subsets of the correct set of epistatically interacting SNPs. Here, we explore the potential for hill-climbing from the other direction; that is, from large sets of candidate SNPs to smaller ones. This approach was inspired by Kauffman's {"}random chemistry{"} approach to detecting small autocatalytic sets of molecules from within large sets. Preliminary results from synthetic data sets show that the resulting algorithm can detect epistatic pairs from up to 1000 candidate SNPs in O(log N) fitness evaluations, although success rate degrades as heritability declines. The results presented herein are offered as proof of concept for the random chemistry approach.", } @Article{Eppstein:2007:GPEM, author = "Margaret J. Eppstein and Joshua L. Payne and Bill C. White and Jason H. Moore", title = "Genomic mining for complex disease traits with ``random chemistry''", journal = "Genetic Programming and Evolvable Machines", year = "2007", volume = "8", number = "4", pages = "395--411", month = dec, note = "special issue on medical applications of Genetic and Evolutionary Computation", keywords = "Evolutionary algorithms, Epistasis, Single nucleotide polymorphisms, Data mining, Genome-wide association studies, Complex traits, Feature selection", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-007-9039-5", abstract = "Our rapidly growing knowledge regarding genetic variation in the human genome offers great potential for understanding the genetic etiology of disease. This, in turn, could revolutionise detection, treatment, and in some cases prevention of disease. While genes for most of the rare monogenic diseases have already been discovered, most common diseases are complex traits, resulting from multiple gene-gene and gene-environment interactions. Detecting epistatic genetic interactions that predispose for disease is an important, but computationally daunting, task currently facing bioinformaticists. Here, we propose a new evolutionary approach that attempts to hill-climb from large sets of candidate epistatic genetic features to smaller sets, inspired by Kauffman's ``random chemistry'' approach to detecting small auto-catalytic sets of molecules from within large sets. Although the algorithm is conceptually straightforward, its success hinges upon the creation of a fitness function able to discriminate large sets that contain subsets of interacting genetic features from those that don't. Here, we employ an approximate and noisy fitness function based on the ReliefF data mining algorithm. We establish proof-of-concept using synthetic data sets, where individual features have no marginal effects. We show that the resulting algorithm can successfully detect epistatic pairs from up to 1,000 candidate single nucleotide polymorphisms in time that is linear in the size of the initial set, although success rate degrades as heritability declines. Research continues into seeking a more accurate fitness approximator for large sets and other algorithmic improvements that will enable us to extend the approach to larger data sets and to lower heritabilities.", notes = "SNP, ROC, AUC", } @Article{Eray:2018:HR, author = "Okan Eray and Cihan Mert and Ozgur Kisi", title = "Comparison of multi-gene genetic programming and dynamic evolving neural-fuzzy inference system in modeling pan evaporation", journal = "Hydrology Research", year = "2018", volume = "49", number = "4", pages = "1221--1233", month = "1 " # aug, keywords = "genetic algorithms, genetic programming, MGGP, AIC, dynamic evolving neural-fuzzy inference system, modeling, multi-gene genetic programming, pan evaporation, periodicity", ISSN = "0029-1277", URL = "https://iwaponline.com/hr/article/49/4/1221/38834/Comparison-of-multi-gene-genetic-programming-and", DOI = "doi:10.2166/nh.2017.076", size = "13 pages", abstract = "Accurately modeling pan evaporation is important in water resources planning and management and also in environmental engineering. This study compares the accuracy of two new data-driven methods, multi-gene genetic programming (MGGP) approach and dynamic evolving neural-fuzzy inference system (DENFIS), in modeling monthly pan evaporation. The climatic data, namely, minimum temperature, maximum temperature, solar radiation, relative humidity, wind speed, and pan evaporation, obtained from Antakya and Antalya stations, Mediterranean Region of Turkey were used. The MGGP and DENFIS methods were also compared with genetic programming (GP) and calibrated version of Hargreaves Samani (CHS) empirical method. For Antakya station, GP had slightly better accuracy than the MGGP and DENFIS models and all the data-driven models performed were superior to the CHS while the DENFIS provided better performance than the other models in modeling pan evaporation at Antalya station. The effect of periodicity input to the models accuracy was also investigated and it was found that adding periodicity significantly increased the accuracy of MGGP and DENFIS models.", } @InProceedings{erba:2001:EuroGP, author = "Massimiliano Erba and Roberto Rossi and Valentino Liberali and Andrea Tettamanzi", title = "An Evolutionary Approach to Automatic Generation of VHDL Code for Low-Power Digital Filters", booktitle = "Genetic Programming, Proceedings of EuroGP'2001", year = "2001", editor = "Julian F. Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon", volume = "2038", series = "LNCS", pages = "36--50", address = "Lake Como, Italy", publisher_address = "Berlin", month = "18-20 " # apr, organisation = "EvoNET", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, Evolvable Hardware, Evolutionary Algorithms, Electronic Design, Digital Filters, VHDL", ISBN = "3-540-41899-7", DOI = "doi:10.1007/3-540-45355-5_4", size = "15 pages", abstract = "An evolutionary algorithm is used to design a finite impulse response digital filter with reduced power consumption. The proposed design approach combines genetic optimization and simulation methodology, to evaluate a multi-objective fitness function which includes both the suitability of the filter transfer function and the transition activity of digital blocks. The proper choice of fitness function and selection criteria allows the genetic algorithm to perform a better search within the design space, thus exploring possible solutions which are not considered in the conventional structured design methodology. Although the evolutionary process is not guaranteed to generate a filter fully compliant to specifications in every run, experimental evidence shows that, when specifications are met, evolved filters are much better than classical designs both in terms of power consumption and in terms of area, while maintaining the same performance.", notes = "EuroGP'2001, part of \cite{miller:2001:gp}", } @InProceedings{Erdagi:2023:MT-ITS, author = "Ismet Goksad Erdagi and Nemanja Dobrota and Slavica Gavric and Aleksandar Stevanovic", booktitle = "2023 8th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)", title = "Cycle-by-cycle Delay Estimation at Signalized Intersections by using Machine Learning and Simulated Video Detection Data", year = "2023", abstract = "Accurate estimation of delay is crucial for efficient traffic signal operations. Estimation of delay in the real-time manner using traditional loop detectors requires advanced detectors (in addition to stop-bar detection). In cases when this detection layout is not in place, delay estimates are approximated with a lower accuracy. Video detection is one of the most frequently deployed detection systems at signalized intersections in recent years. In most cases video detection operates in the same way as traditional inductive loops. However, when coupled with computer vision algorithms, video detection systems could be used to retrieve additional information (e.g., vehicular arrivals and departures) that cannot be taken out from the conventional systems (e.g., long stop-bar loop detectors). Although present for several decades, video detection data were not frequently examined for delay estimation purposes. In this study, we proposed a novel delay estimation model which can be developed with only data from stop-bar video detectors. Relevant data were collected from a simulation model of 11 signalized intersections at downtown Chattanooga, TN and processed to create needed inputs for model development. With the use of multigene genetic programming the authors developed a delay model that outperforms accuracy of multi regression model. Furthermore, authors evaluated the developed model by comparison with the other benchmark delay models, such as HCM and approach delay model. It was found that the developed MGGP delay model outperforms benchmark models for a wide range of traffic and signal operation conditions.", keywords = "genetic algorithms, genetic programming, Uncertainty, Estimation, Detectors, Delay estimation, Machine learning, Benchmark testing, performance measures, delay, machine learning, traffic, video detection", DOI = "doi:10.1109/MT-ITS56129.2023.10241732", month = jun, notes = "Also known as \cite{10241732}", } @InProceedings{Erdem:2019:ICCSE, author = "Mehmet Bilgehan Erdem and Zekiye Erdem and Shahryar Rahnamayan", booktitle = "2019 14th International Conference on Computer Science Education (ICCSE)", title = "Diabetes Mellitus Prediction Using Multi-objective Genetic Programming and Majority Voting", year = "2019", pages = "953--958", month = aug, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICCSE.2019.8845515", ISSN = "2473-9464", abstract = "Diabetes is one of the most serious diseases which is becoming increasingly common in recent years. Diabetes can be treated and its consequences are prevented or delayed if predicted timely. This paper investigates an evolutionary computation approach for diabetes prediction. By using the multi-objective Genetic Programming Symbolic Regression, the prediction accuracy level of 79.1percent is achieved. Two objectives are namely prediction accuracy and complexity level of the created model (i.e., formula). Moreover, a majority-voting scheme is proposed and compared with other conventional classification algorithms. A widely studied dataset for diabetes prediction, the Pima Indian Diabetes dataset shared in University of California Irvine dataset repository, has been selected for conducting our experimental studies. The work presented here has profound implications for future applications of diabetes prediction and may one help to solve the problem of diabetes by their timely prediction.", notes = "Also known as \cite{8845515}", } @InProceedings{eriksson97, author = "Roger Eriksson and Bj{\"{o}}rn Olsson", title = "Cooperative Coevolution in Inventory Control Optimisation", year = "1997", booktitle = "Artificial Neural Nets and Genetic Algorithms: Proceedings of the International Conference, ICANNGA97", editor = "George D. Smith and Nigel C. Steele and Rudolf F. Albrecht", publisher = "Springer-Verlag", pages = "583--587", address = "University of East Anglia, Norwich, UK", note = "published in 1998", keywords = "genetic algorithms", ISBN = "3-211-83087-1", DOI = "doi:10.1007/978-3-7091-6492-1_129", notes = "ICANNGA97", } @Article{Eriksson:2004:BS, author = "R. Eriksson and B. Olsson", title = "Adapting genetic regulatory models by genetic programming", journal = "Biosystems", year = "2004", volume = "76", pages = "217--227", number = "1-3", abstract = "we focus on the task of adapting genetic regulatory models based on gene expression data from microarrays. Our approach aims at automatic revision of qualitative regulatory models to improve their fit to expression data. We describe a type of regulatory model designed for this purpose, a method for predicting the quality of such models, and a method for adapting the models by means of genetic programming. We also report experimental results highlighting the ability of the methods to infer models on a number of artificial data sets. In closing, we contrast our results with those of alternative methods, after which we give some suggestions for future work.", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6T2K-4D09KY2-7/2/1abfe196bb4afc60afc3311cadb75d66", keywords = "genetic algorithms, genetic programming, Gene networks, Evolutionary algorithms, Machine learning", DOI = "doi:10.1016/j.biosystems.2004.05.014", notes = "Papers presented at the Fifth International Workshop on Information Processing in Cells and Tissues PMID: 15351145 [PubMed - indexed for MEDLINE]", } @InProceedings{conf/ibpria/EscalanteMGM13, author = "Hugo Jair Escalante and Karlo Mendoza and Mario Graff and Alicia Morales-Reyes", title = "Genetic Programming of Prototypes for Pattern Classification", booktitle = "Proceedings of the 6th Iberian Conference on Pattern Recognition and Image Analysis, {IbPRIA 2013}", year = "2013", editor = "Joao M. Sanches and Luisa Mico and Jaime S. Cardoso", volume = "7887", series = "Lecture Notes in Computer Science", pages = "100--107", address = "Funchal, Madeira, Portugal", month = jun # " 5-7", publisher = "Springer", keywords = "genetic algorithms, genetic programming", bibdate = "2013-05-28", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ibpria/ibpria2013.html#EscalanteMGM13", isbn13 = "978-3-642-38627-5", URL = "http://dx.doi.org/10.1007/978-3-642-38628-2", DOI = "doi:10.1007/978-3-642-38628-2_11", size = "8 pages", abstract = "This paper introduces a genetic programming approach to the generation of classification prototypes. Prototype-based classification is a pattern recognition methodology in which the training set of a classification problem is represented by a small subset of instances. The assignment of labels to test instances is usually done by a 1NN rule. We propose a new prototype generation method, based on genetic programming, in which examples of each class are automatically combined to generate highly effective classification prototypes. The genetic program aims to maximise an estimate of the generalisation performance of a 1NN classifier using the prototypes. We report experimental results on a benchmark for the evaluation of prototype generation methods. Experimental results show the validity of our approach: the proposed method outperforms most of the state of the art techniques when using both small and large data sets. Better results are obtained for data sets with numeric attributes only, although the performance of our method on mixed data is very competitive as well.", } @InProceedings{conf/ciarp/EscalanteAMA13, author = "Hugo Jair Escalante and Niusvel Acosta-Mendoza and Alicia Morales-Reyes and Andres Gago Alonso", title = "Genetic Programming of Heterogeneous Ensembles for Classification", year = "2013", booktitle = "Proceedings of the 18th Iberoamerican Congress on Image Analysis, Computer Vision, and Applications (CIARP 2013) Part {I}", editor = "Jose Ruiz-Shulcloper and Gabriella Sanniti di Baja", volume = "8258", series = "Lecture Notes in Computer Science", pages = "9--16", address = "Havana, Cuba", month = nov # " 20-23", publisher = "Springer", keywords = "genetic algorithms, genetic programming", bibdate = "2013-11-17", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ciarp/ciarp2013-1.html#EscalanteAMA13", isbn13 = "978-3-642-41821-1", URL = "http://dx.doi.org/10.1007/978-3-642-41822-8", size = "8 pages", abstract = "The ensemble classification paradigm is an effective way to improve the performance and stability of individual predictors. Many ways to build ensembles have been proposed so far, most notably bagging and boosting based techniques. Evolutionary algorithms (EAs) also have been widely used to generate ensembles. In the context of heterogeneous ensembles EAs have been successfully used to adjust weights of base classifiers or to select ensemble members. Usually, a weighted sum is used for combining classifiers outputs in both classical and evolutionary approaches. This study proposes a novel genetic program that learns a fusion function for combining heterogeneous-classifiers outputs. It evolves a population of fusion functions in order to maximise the classification accuracy. Highly non-linear functions are obtained with the proposed method, subsuming the existing weighted-sum formulations. Experimental results show the effectiveness of the proposed approach, which can be used not only with heterogeneous classifiers but also with homogeneous-classifiers and under bagging/boosting based formulations.", } @Article{Escalante:2015:KBS, author = "Hugo Jair Escalante and Mauricio A. Garcia-Limon and Alicia Morales-Reyes and Mario Graff and Manuel Montes-y-Gomez and Eduardo F. Morales and Jose Martinez-Carranza", title = "Term-weighting learning via genetic programming for text classification", journal = "Knowledge-Based Systems", year = "2015", volume = "83", pages = "176--189", keywords = "genetic algorithms, genetic programming, term-weighting learning, text mining, representation learning, bag of words", ISSN = "0950-7051", bibdate = "2015-05-11", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/kbs/kbs83.html#EscalanteGMGMMM15", URL = "http://dx.doi.org/10.1016/j.knosys.2015.03.025", DOI = "doi:10.1016/j.knosys.2015.03.025", URL = "http://www.sciencedirect.com/science/article/pii/S0950705115001197", abstract = "This paper describes a novel approach to learning term-weighting schemes (TWSs) in the context of text classification. In text mining a TWS determines the way in which documents will be represented in a vector space model, before applying a classifier. Whereas acceptable performance has been obtained with standard TWS (e.g., Boolean and term-frequency schemes), the definition of TWSs has been traditionally an art. Further, it is still a difficult task to determine what is the best TWS for a particular problem and it is not clear yet, whether better schemes, than those currently available, can be generated by combining known TWS. We propose in this article a genetic program that aims at learning effective TWSs that can improve the performance of current schemes in text classification. The genetic program learns how to combine a set of basic units to give rise to discriminative TWSs. We report an extensive experimental study comprising data sets from thematic and non-thematic text classification as well as from image classification. Our study shows the validity of the proposed method; in fact, we show that TWSs learnt with the genetic program outperform traditional schemes and other TWSs proposed in recent works. Further, we show that TWSs learnt from a specific domain can be effectively used for other tasks.", notes = "See http://arxiv.org/abs/1410.0640 \cite{journals/corr/EscalanteGMGMM14}", } @InProceedings{Escalante:2015:IJCNN, author = "Hugo Jair Escalante and Jose Martinez-Carraza and Sergio Escalera and Victor Ponce-Lopez and Xavier Baro", booktitle = "2015 International Joint Conference on Neural Networks (IJCNN)", title = "Improving bag of visual words representations with genetic programming", year = "2015", abstract = "The bag of visual words is a well established representation in diverse computer vision problems. Taking inspiration from the fields of text mining and retrieval, this representation has proved to be very effective in a large number of domains. In most cases, a standard term-frequency weighting scheme is considered for representing images and videos in computer vision. This is somewhat surprising, as there are many alternative ways of generating bag of words representations within the text processing community. This paper explores the use of alternative weighting schemes for landmark tasks in computer vision: image categorization and gesture recognition. We study the suitability of using well-known supervised and unsupervised weighting schemes for such tasks. More importantly, we devise a genetic program that learns new ways of representing images and videos under the bag of visual words representation. The proposed method learns to combine term-weighting primitives trying to maximize the classification performance. Experimental results are reported in standard image and video data sets showing the effectiveness of the proposed evolutionary algorithm.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IJCNN.2015.7280799", ISSN = "2161-4393", month = jul, notes = "Also known as \cite{7280799}", } @Article{Escalante:2016:ASC, author = "Hugo Jair Escalante and Mario Graff and Alicia Morales-Reyes", title = "PGGP: Prototype Generation via Genetic Programming", journal = "Applied Soft Computing", volume = "40", pages = "569--580", year = "2016", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2015.12.015", URL = "http://www.sciencedirect.com/science/article/pii/S1568494615007942", abstract = "Prototype generation (PG) methods aim to find a subset of instances taken from a large training data set, in such a way that classification performance (commonly, using a 1NN classifier) when using prototypes is equal or better than that obtained when using the original training set. Several PG methods have been proposed so far, most of them consider a small subset of training instances as initial prototypes and modify them trying to maximize the classification performance on the whole training set. Although some of these methods have obtained acceptable results, training instances may be under-exploited, because most of the times they are only used to guide the search process. This paper introduces a PG method based on genetic programming in which many training samples are combined through arithmetic operators to build highly effective prototypes. The genetic program aims to generate prototypes that maximize an estimate of the generalization performance of an 1NN classifier. Experimental results are reported on benchmark data to assess PG methods. Several aspects of the genetic program are evaluated and compared to many alternative PG methods. The empirical assessment shows the effectiveness of the proposed approach outperforming most of the state of the art PG techniques when using both small and large data sets. Better results were obtained for data sets with numeric attributes only, although the performance of the proposed technique on mixed data was very competitive as well.", keywords = "genetic algorithms, genetic programming, Prototype generation, 1NN classification, Pattern classification", } @InProceedings{Escazut:1997:ccscts, author = "Cathy Escazut and Terence C. Fogarty", title = "Coevolving Classifier Systems to Control Traffic Signals", booktitle = "Late Breaking Papers at the 1997 Genetic Programming Conference", year = "1997", editor = "John R. Koza", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California, 94305-3079, USA", month = "13--16 " # jul, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-206995-8", notes = "GP-97LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670", } @InProceedings{escobar:2019:EMO, author = "Carlos A. Escobar and Diana M. Wegner and Abhinav Gaur and Ruben Morales-Menendez", title = "Process-Monitoring-for-Quality--A Model Selection Criterion for Genetic Programming", booktitle = "Evolutionary Multi-Criterion Optimization", year = "2019", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-030-12598-1_13", DOI = "doi:10.1007/978-3-030-12598-1_13", } @InProceedings{DBLP:conf/dexa/EscottMC20, author = "Kirita-Rose Escott and Hui Ma and Gang Chen2", title = "Genetic Programming Based Hyper Heuristic Approach for Dynamic Workflow Scheduling in the Cloud", booktitle = "Database and Expert Systems Applications - 31st International Conference, {DEXA} 2020, Bratislava, Slovakia, September 14-17, 2020, Proceedings, Part {II}", editor = "Sven Hartmann and Josef Kueng and Gabriele Kotsis and A Min Tjoa and Ismail Khalil", series = "Lecture Notes in Computer Science", volume = "12392", pages = "76--90", publisher = "Springer", year = "2020", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/978-3-030-59051-2_6", DOI = "doi:10.1007/978-3-030-59051-2_6", timestamp = "Sat, 19 Sep 2020 01:00:00 +0200", biburl = "https://dblp.org/rec/conf/dexa/EscottMC20.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{Escott:2020:SSCI, author = "Kirita-Rose Escott and Hui Ma and Gang Chen2", title = "A Genetic Programming Hyper-Heuristic Approach to Design High-Level Heuristics for Dynamic Workflow Scheduling in Cloud", booktitle = "2020 IEEE Symposium Series on Computational Intelligence (SSCI)", year = "2020", pages = "3141--3148", abstract = "Workflow scheduling in the cloud is the process of allocating tasks to scarce cloud resources, with an optimal goal. This is often achieved by adopting an effective scheduling heuristic. Workflow scheduling in cloud is challenging due to the dynamic nature of the cloud, often existing works focus on static workflows, ignoring this challenge. Existing heuristics, such as MINMIN, focus mainly on one specific aspect of the scheduling problem. High-level heuristics are heuristics constructed from existing man-made heuristics. In this paper, we introduce a new and more realistic workflow scheduling problem that considers different kinds of workflows, cloud resources and high-level heuristics. We propose a High-Level Heuristic Dynamic Workflow Scheduling Genetic Programming (HLH-DSGP) algorithm to automatically design high-level heuristics for workflow scheduling to minimise the response time of dynamically arriving task in a workflow. Our proposed HLH-DSGP can work consistently well regardless of the size and pattern of workflows, or number of available cloud resources. It is evaluated using a popular benchmark dataset using the popular WorkflowSim simulator. Our experiments show that with high-level scheduling heuristics, designed by HLH-DSGP, we can jointly use several well-known heuristics to achieve a desirable balance among multiple aspects of the scheduling problem collectively, hence improving the scheduling performance.", keywords = "genetic algorithms, genetic programming, Task analysis, Dynamic scheduling, Cloud computing, Virtual machining, Heuristic algorithms, Time factors, Dynamic programming, Cloud Computing, Dynamic Workflow Scheduling", DOI = "doi:10.1109/SSCI47803.2020.9308261", month = dec, notes = "Also known as \cite{9308261}", } @InProceedings{DBLP:conf/evoW/EscottMC23, author = "Kirita-Rose Escott and Hui Ma and Gang Chen2", editor = "Leslie Perez Caceres and Thomas Stuetzle", title = "Cooperative Coevolutionary Genetic Programming Hyper-Heuristic for Budget Constrained Dynamic Multi-workflow Scheduling in Cloud Computing", booktitle = "23rd European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2023", year = "2023", series = "Lecture Notes in Computer Science", volume = "13987", pages = "146--161", address = "Brno, Czech Republic", publisher = "Springer", month = apr # " 12-14", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/978-3-031-30035-6_10", DOI = "doi:10.1007/978-3-031-30035-6_10", timestamp = "Thu, 06 Apr 2023 11:08:46 +0200", biburl = "https://dblp.org/rec/conf/evoW/EscottMC23.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{escudero-rodrigo:2025:AI, author = "Diego Escudero-Rodrigo and Rene Alquezar and Joan Aranda", title = "Anchoring actions using conditional behavior trees and genetic programming", journal = "Applied Intelligence", year = "2025", volume = "55", pages = "article number 488", keywords = "genetic algorithms, genetic programming", URL = "https://link.springer.com/article/10.1007/s10489-025-06373-w", DOI = "doi:10.1007/s10489-025-06373-w", } @InProceedings{eurogp:EscuelaOK05, author = "Gabi Escuela and Gabriela Ochoa and Natalio Krasnogor", editor = "Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. {van Hemert} and Marco Tomassini", title = "Evolving L-Systems to Capture Protein Structure Native Conformations", booktitle = "Proceedings of the 8th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3447", year = "2005", address = "Lausanne, Switzerland", month = "30 " # mar # " - 1 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-25436-6", pages = "74--84", URL = "http://www.cs.nott.ac.uk/~nxk/PAPERS/LsysPSP05.pdf", DOI = "doi:10.1007/978-3-540-31989-4_7", DOI = "doi:10.1007/b107383", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "A protein is a linear chain of amino acids, that under certain physical conditions, folds into a unique functional structure, called its native state or tertiary structure. In this state, proteins show repeated substructures like alpha helices and beta sheets. This observation suggests that native structures may be captured by the formalism known as Lindenmayer systems (L-systems). In this paper an evolutionary algorithm is used as the inference procedure for folded structures under the HP model in 2D lattices. The EA searches in the space of possible L-systems which are then executed to obtain the phenotype, thus our approach is close to that of Grammatical Evolution. The problem is to find a set of rewriting rules that represents a target native structure on the selected lattice model. The proposed approach has produced promising results for short sequences under the 2D square lattice. Thus the foundations are set for a novel encoding based on L-systems for evolutionary approaches to both the Protein Structure Prediction and Inverse Folding Problems.", notes = "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005", } @InCollection{Esfahani:2015:hbgpa, author = "Hamed Koohpayehzadeh Esfahani and Bithin Datta", title = "Use of Genetic Programming Based Surrogate Models to Simulate Complex Geochemical Transport Processes in Contaminated Mine Sites", booktitle = "Handbook of Genetic Programming Applications", publisher = "Springer", year = "2015", editor = "Amir H. Gandomi and Amir H. Alavi and Conor Ryan", chapter = "14", pages = "359--379", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-20882-4", DOI = "doi:10.1007/978-3-319-20883-1_14", abstract = "Reactive transport of chemical species in contaminated groundwater systems, especially with multiple species, is a complex and highly non-linear geochemical process. Simulation of such complex geochemical processes using efficient numerical models is generally computationally intensive. In order to increase the model reliability for real field data, uncertainties in hydrogeological parameters and boundary conditions are needed to be considered as well. The development and performance evaluation of ensemble Genetic Programming (GP) models to serve as computationally efficient approximate simulators of complex groundwater contaminant transport process with reactive chemical species under aquifer parameters uncertainties are presented. The GP models are developed by training and testing of the models using sets of random input contaminated sources and the corresponding aquifer responses in terms of resulting spatio-temporal concentrations of the contaminants obtained as solution of the hydrogeological and geochemical numerical simulation model. Three dimensional transient flow and reactive contaminant transport process is considered. Performance evaluation of the ensemble GP models as surrogate models for the reactive species transport in groundwater demonstrates the feasibility of its use and the associated computational advantages. The evaluation results show that it is feasible to use ensemble GP models as approximate simulators of complex hydrogeologic and geochemical processes in a contaminated groundwater aquifer incorporating uncertainties in describing the physical system.", } @Article{Esfahanipour:2010:IJRIS, author = "Akbar Esfahanipour and Somaye Mousavi", title = "Genetic programming application to generate technical trading rules in stock markets", journal = "International Journal of Reasoning-based Intelligent Systems", year = "2010", volume = "2", number = "3/4", pages = "244--250", keywords = "genetic algorithms, genetic programming, technical trading rules, stock markets, tehran stock exchange, TSE, Iran, decision making, stock trading", ISSN = "1755-0564", bibsource = "OAI-PMH server at www.inderscience.com", language = "eng", URL = "http://www.inderscience.com/link.php?id=36870", DOI = "doi:10.1504/IJRIS.2010.036870", abstract = "Technical trading rules can be generated from historical data for decision making in stock trading. In this study, genetic programming (GP) as an evolutionary algorithm has been applied to automatically generate such technical trading rules on individual stocks. In order to obtain more realistic trading rules, we have included transaction costs, dividends and splits in our GP model. Our model has been applied for nine Iranian companies listed on different activity sectors of Tehran Stock Exchange (TSE). Our results show that this model could generate profitable trading rules in comparison with buy and hold strategy for companies having frequent trading in the market. Also, the effect of the above mentioned parameters on trading rule's profitability are evaluated using three separate models.", } @Article{Esfahanipour20118438, author = "Akbar Esfahanipour and Somayeh Mousavi", title = "A genetic programming model to generate risk-adjusted technical trading rules in stock markets", journal = "Expert Systems with Applications", volume = "38", number = "7", pages = "8438--8445", year = "2011", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2011.01.039", URL = "http://www.sciencedirect.com/science/article/B6V03-52178YW-J/2/5208571320b6e5c08daf35597b9f81f4", keywords = "genetic algorithms, genetic programming, Technical trading rules, Risk-adjusted measures, Conditional Sharpe ratio, Tehran Stock Exchange (TSE)", abstract = "Technical trading rules can be generated from historical data for decision making in stock markets. Genetic programming (GP) as an artificial intelligence technique is a valuable method to automatically generate such technical trading rules. In this paper, GP has been applied for generating risk-adjusted trading rules on individual stocks. Among many risk measures in the literature, conditional Sharpe ratio has been selected for this study because it uses conditional value at risk (CVaR) as an optimal coherent risk measure. In our proposed GP model, binary trading rules have been also extended to more realistic rules which are called trinary rules using three signals of buy, sell and no trade. Additionally we have included transaction costs, dividend and splits in our GP model for calculating more accurate returns in the generated rules. Our proposed model has been applied for 10 Iranian companies listed in Tehran Stock Exchange (TSE). The numerical results showed that our extended GP model could generate profitable trading rules in comparison with buy and hold strategy especially in the case of risk adjusted basis.", } @Article{Esha:2020:Ecohydrology, author = "Rijwana Esha and Monzur Alam Imteaz", title = "Pioneer use of gene expression programming for predicting seasonal streamflow in Australia using large scale climate drivers", journal = "Ecohydrology", year = "2020", volume = "13", number = "8", pages = "e2242", month = dec, keywords = "genetic algorithms, genetic programming, genetic expression programming, EMI, ENSO, GEP, IOD, PDO and streamflow", ISSN = "1936-0592", URL = "https://onlinelibrary.wiley.com/doi/abs/10.1002/eco.2242", DOI = "doi:10.1002/eco.2242", abstract = "we present development of an artificial intelligence (AI)-based model, genetic expression programming (GEP) to predict long-term streamflow using large-scale climate drivers as predictors. GEP is chosen over artificial neural networks (ANNs) model, as ANN is a black-box model, whereas GEP is able to explain the developed forecast models with mathematical expressions. As a case study, 12 streamflow measuring stations were selected from four different regions of New South Wales (NSW) in eastern Australia. A number of climate indices, Pacific Decadal Oscillation (PDO), Indian Ocean Dipole (IOD), El Nino Southern Oscillation (ENSO) and ENSO Modoki index (EMI), were selected as candidate predictors based on the findings of some preliminary studies. Higher predictabilities of the GEP-based models are evident from the Pearson correlation (r) values ranging between 0.57 and 0.97, which are mostly about twice the values achieved by multiple linear regression (MLR) models in the preliminary study. Performances of the developed models were assessed using standard statistical measures such as root relative squared error (RRSE), relative absolute error (RAE), root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (NSE) and Pearson correlation (r) values. The developed models are able to predict spring streamflow up to 5 months in advance with significantly high correlation values.", } @PhdThesis{Rijwana_Esha_Thesis, author = "Rijwana Ishat Esha", title = "Comparative Analysis of the Predictability of Linear \& Non-linear Methods for Seasonal Streamflow Forecasting: A Case Study of New South Wales (NSW)", school = "Department of Civil and Construction Engineering Faculty of Science, Engineering and Technology Swinburne University of Technology", year = "2020", address = "Hawthorn, VIC 3122 Australia", month = dec, keywords = "genetic algorithms, genetic programming, GEP", URL = "http://hdl.handle.net/1959.3/459586", URL = "https://researchbank.swinburne.edu.au/file/4c9cf5d5-4cd9-4479-8036-a33044d80c64/1/Rijwana_Esha_Thesis.pdf", size = "361 pages", abstract = "High inter-annual variability of stream-flow resulting from the extensive topographic variation and climatic inconsistency cause immense difficulties to the water users and planners of Australia. New South Wales, which is situated in the south-eastern part of Australia, is the most populous state and is one of the major contributors of Australia agricultural income. The inter-annual variation of streamflow hampers the agricultural production and proper allocation of water of the state largely. Therefore, prediction of streamflow over a large time period will enable the water allocators and agricultural producers to take the low-risk decision at an earlier stage of the crop year which will ultimately enhance the economic growth of the country. Since streamflow is largely dependent on rainfall, it appears to be a more complex phenomenon compared to rainfall. Thereby, long-lead forecasting of streamflow rather than rainfall will be more beneficial to the irrigators. To date, many researchers have attempted to predict future streamflow and rainfall using oceanic and atmospheric indices with the help of both statistical and dynamic approaches. While most of the past studies were concentrated on revealing the relationship between streamflow of single concurrent or lagged climate indices, this study makes an effort to explore the combined impact of large-scale climate drivers to forecast seasonal streamflow of New South Wales (NSW) region. To accomplish the aim of this study, several oceanic and atmospheric climate indices are selected considering their influence on the streamflow of NSW which includes but not limited to four major climate drivers of this region PDO (Pacific Decadal Oscillation), IPO (Inter Decadal Pacific Oscillation), IOD (Indian Ocean Dipole) and the ENSO (El Nino Southern Oscillation) indices. Many past research works demonstrated that different regions of NSW are influenced by different climate modes which lead the present study to divide NSW into four regions with a view to identifying the regional variation of the impacts of various climate drivers. At first single lagged co-rrelation analysis is performed to identify the individual interactions of indices with spring streamflow till nine lagged months which is, later on, exploited as the basis for selecting input variables for developing Multiple Linear Regression (MLR) models to examine the extent of the combined impact of the selected climate drivers on forecasting spring streamflow several months ahead. As many researchers have claimed that a non-linear approach may better capture the relationship between climate variables and seasonal streamflow, Multiple Non-Linear Regression (MNLR) Analysis is conducted to explore the underlying non-linear relationship between seasonal streamflow and climate indices. Finally, for further improvement, an Artificial Intelligence (AI) based method, Gene Expression Programming (GEP) is introduced to evaluate the potential of this method for forecasting seasonal streamflow of NSW. Performances of the developed models are assessed using standard statistical measures such as RRSE (Root Relative Squared Error), RAE ( Relative Absolute Error), RMSE ( Root Mean Square Error), MAE (Mean Absolute Error) and Pearson correlation (r) values. A comparative analysis is performed among the applied methods where GEP method has outperformed the other two methods. The highest predictabilities of the GEP based models are evident from the Pearson correlation (r) values ranging between 0.57 and 0.97, which are mostly about twice the values achieved by MLR and MNLR models. The developed GEP models are able to predict spring streamflow up to 5 months in advance with significantly high correlation values. The current study showed better performances while compared to the previous research studies in this field. This research concludes that GEP models can be used to predict seasonal streamflow of NSW incorporating large-scale multiple climate indices as predictors. In future, a similar concept will be applied to other regions for other seasons to explore the spatial and seasonal variation of influences different climate indices on seasonal streamflow", notes = "Supervisor: Monzur Alam Imteaz", } @Article{Eskil2008774, author = "Murat Eskil and Erdogan Kanca", title = "A new formulation for martensite start temperature of {Fe-Mn-Si} shape memory alloys using genetic programming", journal = "Computational Materials Science", volume = "43", number = "4", pages = "774--784", year = "2008", keywords = "genetic algorithms, genetic programming, Martensite start temperature, Fe-Mn-Si alloys, Shape memory effect, Formulation and modelling", ISSN = "0927-0256", URL = "http://www.sciencedirect.com/science/article/B6TWM-4S1BT8K-1/2/8c255199aba8337ed54aa30bf0ec4ab4", DOI = "doi:10.1016/j.commatsci.2008.01.042", abstract = "This study presents genetic programming (GP) soft computing technique as a new tool for the formulation of martensite start temperature (Ms) of Fe-Mn-Si shape memory alloys for various compositions and heat treatments. The objective of this study is to provide a different formulation to design composition at certain ranges and to verify the robustness of GP for the formulation of such characterization problems. The training and testing patterns of the proposed GP formulation is based on well established experimental results from the literature. The GP based formulation results are compared with experimental results and found to be quite reliable.", } @InProceedings{eskin:1999:Othello, author = "Eleazar Eskin and Eric V. Siegel", title = "Genetic Programming Applied to {Othello}: Introducing Students to Machine Learning Research", booktitle = "30th Technical Symposium of the ACM Special Interest Group in Computer Science Education", year = "1999", editor = "Daniel Joyce", volume = "31.1", series = "SIGCSE Bulletin", pages = "242--246", address = "New Orleans, LA, USA", month = "24-28 " # mar, publisher = "ACM Press", keywords = "genetic algorithms, genetic programming", ISSN = "0097-8418", URL = "http://www.cs.columbia.edu/~evs/papers/sigcse-paper.ps", URL = "https://doi.org/10.1145/384266.299771", DOI = "doi:10.1145/384266.299771", size = "5 pages", abstract = "In this paper we describe and analyze a three week assignment that was given in a Machine Learning course at Columbia University. The assignment presented students with an introduction to machine learning research. The assignment required students to apply Genetic Programming to evolve algorithms that play the board game Othello. The students were provided with an implemented experimental approach as a starting point. The students were required to perform their own experimental modifications corresponding to research issues in machine learning. The results of student experiments were good both in terms of research and in terms of student learning. All relevant code, documentation and information about GPOthello is available at the following url: http://www.cs.columbia.edu/~evs/ml/othello.html .", } @InProceedings{eskridge:2004:isamcofgp, title = "Imitating Success: A Memetic Crossover Operator for Genetic Programming", author = "Brent Eskridge and Dean Hougen", pages = "809--815", booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary Computation", year = "2004", publisher = "IEEE Press", month = "20-23 " # jun, address = "Portland, Oregon", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Theory of evolutionary algorithms, Poster Session", DOI = "doi:10.1109/CEC.2004.1330943", abstract = "For some problem domains, the evaluation of individuals is significantly more expensive than the other steps in the evolutionary process. Minimizing these evaluations is vital if we want to make genetic programming a viable strategy. In order to minimize the required evaluations, we need to maximize the amount learned from each evaluation. To accomplish this we introduce a new crossover operator for genetic programming, memetic crossover, that allows individuals to imitate the observed success of others. An individual that has done poorly in some parts of the problem may then imitate an individual that did well on those same parts. This results in an intelligent search of the feature-space and, therefore, fewer evaluations.", notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.", } @InProceedings{eskridge:mcf:gecco2004, author = "Brent E. Eskridge and Dean F. Hougen", title = "Memetic Crossover for Genetic Programming: Evolution Through Imitation", booktitle = "Genetic and Evolutionary Computation -- GECCO-2004, Part II", year = "2004", editor = "Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell", series = "Lecture Notes in Computer Science", pages = "459--470", address = "Seattle, WA, USA", publisher_address = "Heidelberg", month = "26-30 " # jun, organisation = "ISGEC", publisher = "Springer-Verlag", volume = "3103", ISBN = "3-540-22343-6", ISSN = "0302-9743", DOI = "doi:10.1007/b98645", size = "12", keywords = "genetic algorithms, genetic programming", notes = "GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)", } @InProceedings{Eskridge:2006:CEC, author = "Brent E. Eskridge and Dean F. Hougen", title = "An Analysis of Memetic Crossover's Impact on a Population", booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary Computation", year = "2006", editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas", pages = "6844--6850", address = "Vancouver", month = "16-21 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9487-9", DOI = "doi:10.1109/CEC.2006.1688546", size = "7 pages", abstract = "In problem domains such as robotic control, where the evaluation of an individual significantly dominates the rest of the evolutionary process with respect to time, the viability of an evolutionary approach can be called into question. In an effort to minimise the number of evaluations by maximising the learning that takes place during an evaluation, a new crossover operator for genetic programming, memetic crossover, was recently introduced. This work analyses the genealogical impact of this operator at varying levels. Although diversity, both in terms of individuals and nodes, is reduced in memetic crossover, we show that memetic crossover is capable of working with standard", notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D", } @Article{ESMAEILI:2018:JML, author = "Hadi Esmaeili and Hassan Hashemipour", title = "A simple correlation for determining ionic liquids surface tension", journal = "Journal of Molecular Liquids", volume = "272", pages = "692--696", year = "2018", keywords = "genetic algorithms, genetic programming, Ionic liquids, Surface tension, Multi-gene genetic programming, Correlation", ISSN = "0167-7322", DOI = "doi:10.1016/j.molliq.2018.10.011", URL = "http://www.sciencedirect.com/science/article/pii/S0167732218327089", abstract = "Nowadays, Ionic liquids (ILs) are considered as new solutions with novel and effective applications, therefore, determining their physical and chemical properties are very important. In this paper, it has been tried to present a novel and simple correlation to predict surface tension of ILs. To this purpose, one of the most powerful techniques of soft computing, Multi-Gene Genetic Programming (MGP), has been used to generate a network and to obtain a simple and accurate correlation. Reduced temperature (Tr), reduced pressure (Pr), critical compatibility factor (Zc) and acentric factor (omega) values have been selected as input parameters of the network. The obtained correlation has a simple mathematical form, which is a function of reduced temperature with a good accuracy (R2a =a 0.99). This correlation has three coefficients, which can be determined using GA or a simple curve fitting or can be found in this paper for some of the important ionic liquids. The other proposed method for determining the coefficients is to use six correlations that were presented in this work", } @Article{Esmaeili:2018:AAI, author = "Hadi Esmaeili and Hassan Hashemipour", title = "Determination of Kinetic and Equilibrium Parameters of Chromium Adsorption from Water with Carbon Nanotube Using Genetic Programming", journal = "Applied Artificial Intelligence", year = "2018", volume = "32", number = "3", pages = "335--343", keywords = "genetic algorithms, genetic programming", publisher = "Taylor \& Francis", ISSN = "0883-9514", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/aai/aai32.html#EsmaeiliH18", DOI = "doi:10.1080/08839514.2018.1448148", size = "9 pages", abstract = "In this paper Genetic Programming (GP) method was used to predict the removal of hexavalent chromium as one of main pollutant of wastewater using nanotube carbon as the adsorbent. One set of experimental data was chosen for this aim. The considered parameters as input of the network were adsorbent dosage, initial solution pH, initial concentration of Cr(VI), contact time and temperature and the output parameter of the network was final concentration of Cr(VI). GP applied for two groups of data, namely, kinetic and equilibrium and two correlations presented for these groups. The determined correlations using the GP had excellent precision. The correlations were used to determine appropriate model for both kinetic and equilibrium of adsorption. The results showed that the kinetic and equilibrium of adsorption fitted on the pseudo-second-order and Langmuir isotherm models, respectively. Activation energy and enthalpy of adsorption were determined using the models.", notes = "Also known as \cite{journals/aai/EsmaeiliH18}", } @Article{ESMAEILI:2021:JML, author = "Hadi Esmaeili and Hassan Hashemipour", title = "A simple correlation to predict surface tension of binary mixtures containing ionic liquids", journal = "Journal of Molecular Liquids", volume = "324", pages = "114660", year = "2021", ISSN = "0167-7322", DOI = "doi:10.1016/j.molliq.2020.114660", URL = "https://www.sciencedirect.com/science/article/pii/S0167732220369026", keywords = "genetic algorithms, genetic programming, Surface tension, Ionic liquids, Binary mixture, Multi-gene genetic programming, Correlation", abstract = "Ionic liquids are in a developing situation in nowadays research and industrial atmosphere. In some industrial applications and academic researches, one will face with binary mixtures containing ionic liquids, therefore; many studies have been done evaluating the properties of binary mixtures containing ionic liquids. In this study, it has been tried to find a general trend and precise correlation to predict surface tension of binary mixtures containing ionic liquids. To do this, Multi-Gene Genetic Programming (MGGP), which is one of the most powerful techniques of soft computing, has been used. Mole fraction, Temperature, Molecular weight of two components and boiling point have been used as input parameters of the network, where surface tension of the mixture was the output parameter. Using the mentioned parameters and MGGP, precise networks obtained. On top of that, using MGGP, a general correlation has been generated for obtaining surface tension of binary mixtures containing Ionic Liquids variable with just mole fraction and in constant temperature. Moreover, adding one term to the mentioned correlation gave a precise correlation for the surface tension variable with mole fraction and temperature. These two correlations are very promising and simplifying for determining the surface tension of binary mixtures containing ionic liquids. The precision of these correlations has been evaluated using correlation coefficient (R2) and AARD, which was respectively, average 0.994 and 0.9567percent for all used binary mixtures", } @InProceedings{Esmeraldo:2010:SPL, author = "Guilherme Esmeraldo and Edna Barros", title = "A Genetic Programming based approach for efficiently exploring architectural communication design space of {MPSoCs}", booktitle = "VI Southern Programmable Logic Conference (SPL 2010)", year = "2010", month = "24-26 " # mar, pages = "29--34", address = "Ipojuca, Brazil", abstract = "New integrated circuits technologies and the demand for more complex applications have created Multi-Processor System-on-Chip (MPSoC). MPSoC is a complex integrated circuit, which can be composed of microprocessors, buses, memories and others computational system components. As the number and variety of components of today's MPSoC is increasing, its communication architecture is becoming a limiting factor for applications performance and power consumption. Thus, techniques have been created for exploring the design space in order to find out the best communication architecture for a given application. Such techniques, however, are either inaccurate (by using static analysis based approaches) or very time consuming since each communication configuration of the design space must be simulated (by using simulation models) or estimated (using mixed approaches). This paper presents a new approach to explore the design space of bus-based communication architectures of MPSoCs using Generalised Linear Models and Genetic Programming. By using the proposed approach, some experiments show that it was possible to explore a subset of the design space and to identify the best communication configuration for a given application reducing 90percent of the exploration time with less of 3,8percent mean global error.", keywords = "genetic algorithms, genetic programming, MPSoC, architectural communication design space, generalised linear models, mixed approach, multiprocessor system-on-chip, simulation models, static analysis based approach, multiprocessing systems, system-on-chip", DOI = "doi:10.1109/SPL.2010.5483006", notes = "Also known as \cite{5483006}", } @InCollection{Esmeraldo:2012:GPnew, author = "Guilherme Esmeraldo and Robson Feitosa and Dilza Esmeraldo and Edna Barros", title = "Genetically Programmed Regression Linear Models for Non-Deterministic Estimates", booktitle = "Genetic Programming - New Approaches and Successful Applications", publisher = "InTech", year = "2012", editor = "Sebastian Ventura", chapter = "4", pages = "75--94", keywords = "genetic algorithms, genetic programming, genetic improvement", isbn13 = "978-953-51-0809-2", DOI = "doi:10.5772/48156", size = "20 pages", notes = "splash benchmark, sort, mutex, SystemC, MIPS, ARM Amba AHB Part of \cite{Ventura:2012:GPnew}. Open access book CC BY 3.0 license", } @PhdThesis{Esmeraldo:thesis, author = "Guilherme Alvaro Rodrigues Maia Esmeraldo", title = "Uma Abordagem Hibrida para Estimacao de Desempenho de Comunicacao em Plataformas Baseadas em Barramentos", school = "Centro de Informatica, Universidade Federal de Pernambuco", year = "2012", address = "Recife, Brazil", month = "9 " # mar, keywords = "genetic algorithms, genetic programming, Sistemas Embarcados, Barramentos, Analise de Comunicacao, Predicao de Desempenho, Programacao Genetica, Modelos Lineares Generalizados, Embedded Systems, Buses, Communication Analysis, Performance Prediction, Generalized Linear models", URL = "https://repositorio.ufpe.br/handle/123456789/10812", URL = "https://repositorio.ufpe.br/bitstream/123456789/10812/1/thesis-garme.pdf", size = "233 pages", abstract = "With the increasing of complexity and performance demand of embedded systems, as well as with the reduction of microprocessors cost, embedded systems designers have considered multiprocessors systems as the solutions for their applications. The improvement of the integration technologies made it possible to integrate billions of transistors onto a single chip. As an embedded microprocessor is composed by a few million transistors, ten or more microprocessors can be integrated into a single chip to form a Multi-Processor System-on-Chip (MPSoC). In the development of these systems, designers have to specify and validate the behaviour of the system application prior to final implementation, by using executable functional models and testbench structures. Approaches, such as Platform Based Design (PBD), have considered platform components reuse and abstract models at the system level as good practices to simplify and turn more dynamic the process of developing MPSoCs, thereby increasing the designers productivity. In this approach, the system in development is initially specified using a high level description, which will gradually be refined down to the final implementation in hardware. The system functions described in the initial specification are selected to be implemented in software or in hardware components. These components compose an architecture known as a platform, which can be modified and adapted to meet the application constraints. MPSoCs are composed by many processing components that implement concurrent communicating processes, so the on-chip communication architecture must meet the applications communication requirements. Thus, while there are several studies focusing on the partitioning/mapping processes, comparatively few research projects have addressed the communication analysis problem to support the design of systems, including efficient communication architectures. Some existing techniques to explore the configuration options of the communication structure are inaccurate, since they perform static estimates and do not take into account the dynamic effects of architecture, such as bus contention, or they are inefficient, since they have to simulate each configuration of the design space. This work aims to support communication analysis in the selection and refinement of communication architectures in the design of multi-processors systems, considering that the application has been partitioned and mapped to a platform, according to the PBD approach. By using the proposed approach, designer can have accurate estimates of the performance of the bus-based communication architecture for the entire design space, and, hence, can select a configuration that meets the communication constraints of the system.", notes = "in Portuguese CRB4-571 MEI2012-055 Supervisor: Edna Barros", } @InProceedings{esmeraldo:2022:AIEPLBR, author = "Guilherme Esmeraldo and Robson Feitosa and Cicero Samuel Mendes and Cicero Carlos Oliveira and Esdras {Bispo Junior} and Allan Carlos {de Sousa} and Gustavo Campos", title = "Using Genetic Programming and Linear Regression for Academic Performance Analysis", booktitle = "Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners' and Doctoral Consortium", year = "2022", editor = "Maria Mercedes Rodrigo and Noburu Matsuda and Alexandra I. Cristea and Vania Dimitrova", volume = "13356", series = "LNCS", pages = "174--179", address = "Durham, UK", month = jul # " 27-31", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Academic performance analysis, Linear regression", isbn13 = "978-3-031-11647-6", URL = "http://link.springer.com/chapter/10.1007/978-3-031-11647-6_30", DOI = "doi:10.1007/978-3-031-11647-6_30", abstract = "The academic evaluation process, even today, is the subject of much discussion. This process can use quantitative analysis to indicate the level of learning of students to support the decision about whether the student can attend the next curriculum phase. From this context, this paper analyzes the history of students grades in the 1st year of a technical course in informatics integrated to high school, for the years 2020 and 2021, through the linear regression method, supported by genetic programming, to find out the influence of the grades of the first two bimesters concerning the final grade. The main results show that the genetic programming algorithm favoured the search for linear regression models with a good fit to the datasets with students data. The resultant models proved accurate and explained more than 74percent of the datasets.", notes = "AIED 2022 https://aied2022.webspace.durham.ac.uk/", } @InProceedings{espada2022data, author = "Guilherme Espada and Leon Ingelse and Paulo Canelas and Pedro Barbosa and Alcides Fonseca", title = "Data Types as a More Ergonomic Frontend for Grammar-Guided Genetic Programming", booktitle = "21st ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences (GPCE 2022)", year = "2022", editor = "Bernhard Scholz and Yukiyoshi Kameyama", pages = "86--94", address = "Auckland, New Zealand", month = dec # " 6-7", publisher = "ACM", keywords = "genetic algorithms, genetic programming, STGP, Genetic Programming Framework, Grammar-guided Genetic Programming, Strongly-Typed Genetic Programming", isbn13 = "9781450399203", URL = "https://arxiv.org/abs/2210.04826", DOI = "doi:10.1145/3564719.3568697", code_url = "https://github.com/alcides/GeneticEngine", abstract = "Genetic Programming (GP) is an heuristic method that can be applied to many Machine Learning, Optimization and Engineering problems. In particular, it has been widely used in Software Engineering for Test-case generation, Program Synthesis and Improvement of Software (GI). Grammar-Guided Genetic Programming (GGGP) approaches allow the user to refine the domain of valid program solutions. Backus Normal Form is the most popular interface for describing Context-Free Grammars (CFG) for GGGP. BNF and its derivatives have the disadvantage of interleaving the grammar language and the target language of the program. We propose to embed the grammar as an internal Domain-Specific Language in the host language of the framework. This approach has the same expressive power as BNF and EBNF while using the host language type-system to take advantage of all the existing tooling: linters, formatters, type-checkers, autocomplete, and legacy code support. These tools have a practical utility in designing software in general, and GP systems in particular. We also present Meta-Handlers, user-defined overrides of the tree-generation system. This technique extends our object-oriented encoding with more practicability and expressive power than existing CFG approaches, achieving the same expressive power of Attribute Grammars, but without the grammar vs target language duality. Furthermore, we evidence that this approach is feasible, showing an example Python implementation as proof. We also compare our approach against textual BNF-representations w.r.t. expressive power and ergonomics. These advantages do not come at the cost of performance, as shown by our empirical evaluation on 5 benchmarks of our example implementation against PonyGE2. We conclude that our approach has better ergonomics with the same expressive power and performance of textual BNF-based grammar encodings.", notes = "University of Lisbon", } @TechReport{esparcia:1995:95012, author = "K. C. Sharman and A. I. Esparcia-Alcazar and Y. Li", title = "Evolving Digital Signal Processing Algorithms by Genetic Programming", institution = "Faculty of Engineering", year = "1995", type = "Technical Report", number = "CSC-95012", address = "Glasgow G12 8QQ, Scotland", month = "31 " # mar, keywords = "genetic algorithms, genetic programming, simulated annealing, digital signal processing, neural networks", URL = "http://www.mech.gla.ac.uk/Research/Control/Publications/Reports/csc95012.ps", URL = "http://www.mech.gla.ac.uk/Research/Control/Publications/Rabstracts/abs95012.html", abstract = "We introduce a novel genetic programming (GP) technique to evolve both the structure and parameters of adaptive digital signal processing algorithms. This is accomplished by defining a set of node functions and terminals to implement the basic operations commonly used in a large class of DSP algorithms. In addition, we show how simulated annealing may be employed to assist the GP in optimising the numerical parameters of expression trees. The concepts are illustrated by using GP to evolve high performance algorithms for detecting binary data sequences at the output of a noisy, non-linear communications channel.", notes = "Also submitted to: Proc. First IEE/IEEE Int. Conf. on GA in Eng. Syst.: Innovations and Appl., Sheffield, Sept. 1995, pp.473-480.", size = "8 pages", } @TechReport{esparcia:1996:96009, author = "Anna I. Esparcia-Alcazar and Ken C. Sharman", title = "Evolving Recurrent Neural Network Architectures by Genetic Programming", institution = "Faculty of Engineering", year = "1996", type = "Technical Report", number = "CSC-96009", address = "Glasgow G12 8QQ, Scotland", keywords = "genetic algorithms, genetic programming, Recurrent Neural Networks, Simulated annealing, Digital Signal Processing", URL = "http://www.mech.gla.ac.uk/Research/Control/Publications/Reports/csc96009.ps", URL = "http://www.mech.gla.ac.uk/Research/Control/Publications/Rabstracts/abs96009.html", abstract = "We propose a novel design paradigm for recurrent neural networks. This employs a two-stage Genetic Programming / Simulated Annealing hybrid algorithm to produce a neural network which satisfies a set of design constraints. The Genetic Programming part of the algorithm is used to evolve the general topology of the network, along with specifications for the neuronal transfer functions, while the Simulated Annealing component of the algorithm adapts the network's connection weights in response to a set of training data. Our approach offers important advantages over existing methods for automated network design. Firstly, it allows us to develop recurrent network connections; secondly, we are able to employ neurons with arbitrary transfer functions, and thirdly, the approach yields an efficient and easy to implement on-line training algorithm. The procedures involved in using the GP/SA hybrid algorithm are illustrated by using it to design a neural network for adaptive filtering in a signal processing application.", size = "pages", } @TechReport{esparcia:1996:96010, author = "Anna I. Esparcia-Alcazar and Ken C. Sharman", title = "Application of Genetic Programming to Signal Processing Problems", institution = "Faculty of Engineering", year = "1996", type = "Technical Report", number = "CSC-96010", address = "Glasgow G12 8QQ, Scotland", keywords = "genetic algorithms, genetic programming, Digital Signal Processing Simulated Annealing, Adaptive Filtering", URL = "http://www.mech.gla.ac.uk/Research/Control/Publications/Reports/csc96010.ps", URL = "http://www.mech.gla.ac.uk/Research/Control/Publications/Rabstracts/abs96010.html", abstract = "The field of Digital Signal Processing (DSP) is concerned with the restoration of signals which have undergone distortion and interference or noise corruption as a result of being transmitted. The usual way to recover such a signal is by adaptive filtering. Designing adaptive filters is not an easy task. It usually involves complicated algorithms whose performance depends on the skill of the designer as well as the power of the computer used. The aim of the present work is to provide a way of automating such process by means of a black box technique. In this approach, both the structure and the parameters of adaptive filters are evolved. The former is done by Genetic Programming (GP) and the latter is done by Simulated Annealing (SA). The power of the hybrid GP/SA is demonstrated with some results on three interesting DSP applications: channel equalisation, noise cancellation and interference removal.", notes = "Also submitted to: Late-breaking papers at the Genetic Programming 96 Conference, Stanford, USA, July 1996 \cite{esparcia:1996:GPdsp}", size = "pages", } @InProceedings{esparcia:1996:GPdsp, author = "Anna I. {Esparcia Alcazar} and Ken C. Sharman", title = "Some Applications of Genetic Programming in Digital Signal Processing", booktitle = "Late Breaking Papers at the Genetic Programming 1996 Conference Stanford University July 28-31, 1996", year = "1996", editor = "John R. Koza", pages = "24--31", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California 94305-3079, USA", month = "28--31 " # jul, publisher = "Stanford Bookstore", ISBN = "0-18-201031-7", keywords = "genetic algorithms, genetic programming, DSP", URL = "http://www.iti.upv.es/~anna/papers/someappsgp96.ps", notes = "GP-96LB, recursive, memory The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670 accepted for GP'96 but, due to a number of circumstances, never appeared in the proceedings. It was presented at the conference See \cite{esparcia:1996:96010}", } @InProceedings{esparcia:1996:GPerNNasp, author = "Anna I. Esparcia-Alcazar and Kenneth C. Sharman", title = "Genetic Programming Techniques that Evolve Recurrent Neural Networks Architectures for Signal Processing", booktitle = "IEEE Workshop on Neural Networks for Signal Processing", year = "1996", month = "4-6 " # sep, pages = "139--148", address = "Seiko, Kyoto, Japan", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, adaptive filtering, arbitrary transfer functions, design constraints, genetic programming techniques, neuronal transfer functions, online training algorithm, recurrent neural network architectures, signal processing, simulated annealing, adaptive filters, geometric programming, neural net architecture, recurrent neural nets, signal processing, simulated annealing, transfer functions", DOI = "doi:10.1109/NNSP.1996.548344", size = "10 pages", abstract = "We propose a novel design paradigm for recurrent neural networks. This employs a two-stage genetic programming/simulated annealing hybrid algorithm to produce a neural network which satisfies a set of design constraints. The genetic programming part of the algorithm is used to evolve the general topology of the network, along with specifications for the neuronal transfer functions, while the simulated annealing component of the algorithm adapts the network's connection weights in response to a set of training data. Our approach offers important advantages over existing methods for automated network design. Firstly, it allows us to develop recurrent network connections; secondly, we are able to employ neurones with arbitrary transfer functions, and thirdly, the approach yields an efficient and easy to implement on-line training algorithm. The procedures involved in using the GP/SA hybrid algorithm are illustrated by using it to design a neural network for adaptive filtering in a signal processing application", } @InProceedings{esparcia:1997:GPdsp, author = "Anna I. Esparcia-Alcazar and Ken Sharman", title = "Evolving Recurrent Neural Network Architectures by Genetic Programming", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming", pages = "89--94", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", URL = "http://www.iti.upv.es/~anna/papers/gp-rnn97.ps", notes = "GP-97", } @InProceedings{Esparcia-Alcazar:1997:lsGP, author = "Anna I. Esparcia-Alcazar and Ken Sharman", title = "Learning Schemes for Genetic Programming", booktitle = "Late Breaking Papers at the 1997 Genetic Programming Conference", year = "1997", editor = "John R. Koza", pages = "57--65", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California, 94305-3079, USA", month = "13--16 " # jul, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-206995-8", URL = "http://www.iti.upv.es/~anna/papers/learningGP97.ps", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/esparcia-alcazar/Esparcia-Alcazar_1997_lsGP.pdf", size = "9 pages", abstract = "A learning capability is introduced in the Genetic Programming (GP) paradigm. This is achieved by enhancing GP with Simulated Annealing (SA), where the latter adapts the parameter values (in the form of node gains) in the structures evolved by the former. A special feature of this approach is that, due to the particularities of the representation used, it allows engineering problems (in which numerical parameters are important) to be addressed, thus extending the applicability of the GP paradigm. We study two different learning schemes, which we refer to as Darwinian and Lamarckian according to whether the learned node gains are inherited or not. We compare the results obtained by these two techniques to those obtained in the absence of learning (both with node gain representation and standard GP representation). The results show the great interest of both learning schemes. The application presented is a classical Digital Signal Processing problem: the equalisation of a noisy communications channel.", notes = "GP-97LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670", } @InProceedings{Esparcia-Alcazar:1997:iGPtasp, author = "Anna I Esparcia-Alcazar", title = "An investigation into a Genetic Programming Technique for Adaptive Signal Processing", booktitle = "Late Breaking Papers at the 1997 Genetic Programming Conference", year = "1997", editor = "John R. Koza", pages = "290", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California, 94305-3079, USA", month = "13--16 " # jul, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-206995-8", notes = "GP-97LB PHD Students' workshop The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670", } @PhdThesis{Esparcia-Alcazar:1998:thesis, author = "Anna I. Esparcia-Alcazar", title = "Genetic Programming for Adaptive Signal Processing", school = "Electronics and Electrical Engineering, University of Glasgow", year = "1998", address = "UK", month = jul, keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/esparcia-alcazar/thesis.ps.gz", broken = "http://www.iti.upv.es/~anna/papers/Thesis.zip", URL = "http://ethos.bl.uk/OrderDetails.do?did=17&uin=uk.bl.ethos.244255", URL = "http://theses.gla.ac.uk/4780/", URL = "http://theses.gla.ac.uk/4780/1/1998alcazarphd.pdf", size = "142 pages", abstract = "This thesis is devoted to presenting the application of the Genetic Programming (GP) paradigm to a class of Digital Signal Processing (DSP) problems. Its main contributions are a new methodology for representing Discrete-Time Dynamic Systems (DDS) as expression trees. The objective is the state space specification of DDSs: the behaviour of a system for a time instant t_0 is completely accounted for given the inputs to the system and also a set of quantities which specify the state of the system. This means that the proposed method must incorporate a form of memory that will handle this information. For this purpose a number of node types and associated data structures are defined. These will allow for the implementation of local and time recursion and also other specific functions, such as the sigmoid commonly encountered in neural networks. An example is given by representing a recurrent NN as an expression tree. a new approach to the channel equalisation problem. A survey of existing methods for channel equalisation reveals that the main shortcoming of these techniques is that they rely on the assumption of a particular structure or model for the system addressed. This implies that knowledge about the system is available; otherwise the solution obtained will have a poor performance because it was not well matched to the problem. This gives a main motivation for applying GP to channel equalisation, which is done in this work for the first time. Firstly, to provide a unified technique for a wide class of problems, including those which are poorly understood; and secondly, to find alternative solutions to those problems which have been successfully addressed by existing techniques. In particular, in the equalisation of nonlinear channels, which have been mainly addressed with Neural Networks and various adaptation algorithms, the proposed GP approach presents itself as an interesting alternative.", abstract = "a new way of handling numerical parameters in GP, node gains. A node gain is a numerical parameter associated to a node that multiplies its output value. This concept was introduced by Sharman and Esparcia-Alcazar (1993) and is fully developed here. The motivation for a parameterised GP is addressed, together with an overview of how it has been addressed by other authors. The drawbacks of these methods are highlighted: there is no established way of determining the number of parameters to use and their placement; further, unused parameters can be unnecessarily adapted while, on the other hand, useful ones might be eliminated. The way in which node gains overcome these problems is explained. An extra advantage is the possibility of expressing complex systems in a compact way, which is labelled {"}compacting effect{"} of node gains. The costs of node gains are also pointed out: increase in the degrees of freedom and increased complexity. This, in theory, results in an increase of computational expense, due to the handling of more complex nodes and to the fact that an extra multiplication is needed per node. These costs, however, are expected to be of, at most, the same order of magnitude as those of the alternatives. Experimental analysis shows that random node gains may not be able to achieve all the potential benefits expected. It is conjectured that optimisation of the values is needed in order to attain the full benefits of node gains, which brings along the next contribution. a mathematical model is given for an adaptive GP. As concluded from the previous point, adaptation of the values of the node gains is needed in order to take full advantage of them. A Simulated Annealing (SA) algorithm is introduced as the adaptation algorithm. This is put in the context of an analogy: the adaptation of the gains by SA is equivalent to the learning process of an individual during its lifetime. This analogy gives way to the introduction of two learning schemes, labelled Lamarckian and Darwinian, which refer to the possibility of inheriting the learned gains. The Darwinian and Lamarckian learning schemes for GP are compared to the standard GP technique and also to GP with random node gains. Statistical analysis, done for both fixed and time-varying environments, shows the superiority of both learning methods over the non-learning ones, although it is not possible at this stage to determine which of the two provides a better performance. a number of interesting results in the channel equalisation problem. These are compared to those obtained by other techniques and it is concluded that the proposed method obtains better or similar performance when extreme values (maximum fitness or minimum error) are considered.", notes = "uk.bl.ethos.244255 Microfilm: BLDSC DXN018360 UofG Library Class Thesis 11183", } @InProceedings{esparcia-alcazar:1999:ppGPcdlis, author = "Anna Esparcia-Alcazar and Ken Sharman", title = "Phenotype Plasticity in Genetic Programming: A Comparison of {Darwinian} and {Lamarckian} Inheritance Schemes", booktitle = "Genetic Programming, Proceedings of EuroGP'99", year = "1999", editor = "Riccardo Poli and Peter Nordin and William B. Langdon and Terence C. Fogarty", volume = "1598", series = "LNCS", pages = "49--64", address = "Goteborg, Sweden", publisher_address = "Berlin", month = "26-27 " # may, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-65899-8", URL = "http://www.iti.upv.es/~anna/papers/eurogp99.ps", DOI = "doi:10.1007/3-540-48885-5_5", abstract = "We consider a form of phenotype plasticity in Genetic Programming (GP). This takes the form of a set of real-valued numerical parameters associated with each individual, an optimisation (or learning) algorithm for adapting their values, and an inheritance strategy for propagating learnt parameter values to offspring. We show that plastic GP has significant benefits including faster evolution and adaptation in changing environments compared with non-plastic GP. The paper also considers the differences between Darwinian and Lamarckian inheritance schemes and shows that the former is superior in dynamic environments", notes = "EuroGP'99, part of \cite{poli:1999:GP} Combination of GP and Simulated Annealing. Performs experiments were SA produced changes (ie new constants) are incorporated into genes (Lamarckian inheritance, also known as {"}repair{"} in GA circles) compared to not writing back. SA+GP claimed to be good (often).", } @InProceedings{esparcia-alcazar:1999:GPce, author = "Anna Esparcia-Alcazar and Ken Sharman", title = "Genetic Programming for Channel Equalisation", booktitle = "Evolutionary Image Analysis, Signal Processing and Telecommunications: First European Workshop, EvoIASP'99 and EuroEcTel'99", year = "1999", editor = "Riccardo Poli and Hans-Michael Voigt and Stefano Cagnoni and Dave Corne and George D. Smith and Terence C. Fogarty", volume = "1596", series = "LNCS", pages = "126--137", address = "Goteborg, Sweden", publisher_address = "Berlin", month = "28-29 " # may, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-65837-8", URL = "http://www.iti.upv.es/~anna/papers/evoiasp99.ps", DOI = "doi:10.1007/10704703_10", URL = "http://citeseer.ist.psu.edu/286482.html", abstract = "This paper is devoted to providing a comparison between classical and neural channel equalisation techniques and node gain Genetic Programming enhanced with Simulated Annealing (or GP+SA). Firstly, the shortcomings of existing techniques are exposed and the main requirements to demand of a new method enumerated. A description of the problem is followed by an account of particular cases of equalisation, exemplified by three channels, both linear and nonlinear. Results are obtained for these channels both with the proposed method and a classical technique, the Recursive Least Squares (RLS) algorithm, and they are further compared to those existing in the literature. The comparison shows the great potential of GP+SA, especially in the case of nonlinear channels. The main disadvantage of the proposed method, the computational effort involved, is also pointed out and it is concluded that, upon the whole, the method deserves further investigation.", notes = "EvoIASP99'99", } @Proceedings{Esparcia-Alcazar:2009:gecco, title = "GECCO '09: Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference", year = "2009", editor = "Anna I. Esparcia and Ying-ping Chen and Gabriela Ochoa and Ender Ozcan and Marc Schoenauer and Anne Auger and Hans-Georg Beyer and Nikolaus Hansen and Steffen Finck and Raymond Ros and Darrell Whitley and Garnett Wilson and Simon Harding and W. B. Langdon and Man Leung Wong and Laurence D. Merkle and Frank W. Moore and Sevan G. Ficici and William Rand and Rick Riolo and Nawwaf Kharma and William R. Buckley and Julian Miller and Kenneth Stanley and Jaume Bacardit and Will Browne and Jan Drugowitsch and Nicola Beume and Mike Preuss and Stephen L. Smith and Stefano Cagnoni and Jim DeLeo and Alexandru Floares and Aaron Baughman and Steven Gustafson and Maarten Keijzer and Arthur Kordon and Clare Bates Congdon and Laurence D. Merkle and Frank W. Moore", month = "8-12 " # jul, address = "Montreal, Qu\'{e}bec, Canada", publisher = "ACM", publisher_address = "New York, NY, USA", organisation = "SigEVO", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-60558-505-5", URL = "http://dl.acm.org/citation.cfm?id=1570256&picked=prox&CFID=401616080&CFTOKEN=58741794", notes = "GECCO 2009 workshops", } @Proceedings{Esparcia-Alcazar:2010:GP, title = "Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010", year = "2010", editor = "Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar", volume = "6021", series = "LNCS", address = "Istanbul", month = "7-9 " # apr, organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-12147-0", DOI = "doi:10.1007/978-3-642-12148-7", size = "310 pages", notes = "EuroGP'2010 held in conjunction with EvoCOP2010 EvoBIO2010 and EvoApplications2010", } @Article{journals/soco/Esparcia-AlcazarM13, author = "Anna I. Esparcia-Alcazar and Jaroslav Moravec", title = "Fitness approximation for bot evolution in genetic programming: Lessons learned from the UT2004 TM computer game", journal = "Soft Computing", year = "2013", volume = "17", number = "8", pages = "1479--1487", month = aug, keywords = "genetic algorithms, genetic programming, Game, Computationally expensive fitness functions, SoftBot evolution, Fitness approximation, Similarity estimation, Unreal Tournament 2004, phenotypic entropy", ISSN = "1432-7643", bibdate = "2013-07-11", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/soco/soco17.html#Esparcia-AlcazarM13", DOI = "doi:10.1007/s00500-012-0965-7", language = "English", size = "9 pages", abstract = "Estimating the fitness value of individuals in an evolutionary algorithm in order to reduce the computational expense of actually calculating the fitness has been a classical pursuit of practitioners. One area which could benefit from progress in this endeavour is bot evolution, i.e. the evolution of non-playing characters in computer games. Because assigning a fitness value to a bot (or rather, the decision tree that controls its behaviour) requires playing the game, the process is very costly. In this work, we introduce two major contributions to speed up this process in the computer game Unreal Tournament 2004. Firstly, a method for fitness value approximation in genetic programming which is based on the idea that individuals that behave in a similar fashion will have a similar fitness. Thus, similarity of individuals is taken at the performance level, in contrast to commonly employed approaches which are either based on similarity of genotypes or, less frequently, phenotypes. The approximation performs a weighted average of the fitness values of a number of individuals, attaching a confidence level which is based on similarity estimation. The latter is the second contribution of this work, namely a method for estimating the similarity between individuals. This involves carrying out a number of tests consisting of playing a static version of the game (with fixed inputs) with the individuals whose similarity is under evaluation and comparing the results. Because the tests involve a limited version of the game, the computational expense of the similarity estimation plus that of the fitness approximation is much lower than that of directly calculating the fitness. The success of the fitness approximation by similarity estimation method for bot evolution in UT2K4 allows us to expect similar results in environments that share the same characteristics.", } @InProceedings{Esparcia-Alcazar:2017:evoApplications, author = "Anna I. Esparcia-Alcazar and Francisco Almenar and Urko Rueda and Tanja E. J. Vos", title = "Evolving Rules for Action Selection in Automated Testing via Genetic Programming - A First Approach", booktitle = "20th European Conference on the Applications of Evolutionary Computation", year = "2017", editor = "Giovanni Squillero", series = "LNCS", volume = "10200", publisher = "Springer", pages = "82--95", address = "Amsterdam", month = "19-21 " # apr, organisation = "Species", keywords = "genetic algorithms, genetic programming, Automated testing via the GUI, Action selection for testing, Testing metrics", DOI = "doi:10.1007/978-3-319-55792-2_6", abstract = "Tools that perform automated software testing via the user interface rely on an action selection mechanism that at each step of the testing process decides what to do next. This mechanism is often based on random choice, a practice commonly referred to as monkey testing. In this work we evaluate a first approach to genetic programming (GP) for action selection that involves evolving IF-THEN-ELSE rules; we carry out experiments and compare the results with those obtained by random selection and also by -learning, a reinforcement learning technique. Three applications are used as Software Under Test (SUT) in the experiments, two of which are proprietary desktop applications and the other one an open source web-based application. Statistical analysis is used to compare the three action selection techniques on the three SUTs; for this, a number of metrics are used that are valid even under the assumption that access to the source code is not available and testing is only possible via the GUI. Even at this preliminary stage, the analysis shows the potential of GP to evolve action selection mechanisms.", notes = "EvoApplications2017 held in conjunction with EuroGP'2017, EvoCOP2017 and EvoMusArt2017 http://www.evostar.org/2017/cfp_evoapps.php.", } @Article{esparcia-alcazar:2018:MC, author = "Anna I. Esparcia-Alcazar and Francisco Almenar and Tanja E. J. Vos and Urko Rueda", title = "Using genetic programming to evolve action selection rules in traversal-based automated software testing: results obtained with the {TESTAR} tool", journal = "Memetic Computing", year = "2018", volume = "10", number = "3", pages = "257--265", month = sep, keywords = "genetic algorithms, genetic programming, linear genetic programming, SBSE, Automated software testing via the GUI, Action selection for testing, Testing metrics", ISSN = "1865-9284", URL = "http://link.springer.com/article/10.1007/s12293-018-0263-8", DOI = "doi:10.1007/s12293-018-0263-8", size = "9 pages", abstract = "Traversal-based automated software testing involves testing an application via its graphical user interface (GUI) and thereby taking the user's point of view and executing actions in a human-like manner. These actions are decided on the fly, as the software under test (SUT) is being run, as opposed to being set up in the form of a sequence prior to the testing, a sequence that is then used to exercise the SUT. In practice, random choice is commonly used to decide which action to execute at each state (a procedure commonly referred to as monkey testing), but a number of alternative mechanisms have also been proposed in the literature. Here we propose using genetic programming (GP) to evolve such an action selection strategy, defined as a list of IF-THEN rules. Genetic programming has proved to be suited for evolving all sorts of programs, and rules in particular, provided adequate primitives (functions and terminals) are defined. These primitives must aim to extract the most relevant information from the SUT and the dynamics of the testing process. We introduce a number of such primitives suited to the problem at hand and evaluate their usefulness based on various metrics. We carry out experiments and compare the results with those obtained by random selection and also by Q-learning, a reinforcement learning technique. Three applications are used as Software Under Test (SUT) in the experiments. The analysis shows the potential of GP to evolve action selection strategies.", notes = "13000th GP entry GUI reflection to give widget tree. No source code needed. Implicit test oracles. validate with 30 runs of 500 (psuedo user) actions each. STGP? 100 crossover plus some mutation, pop=11, Tournament size 11. Coverage metrics p263 'did not prove very useful' microsoft PowerPoint. Odoo. Testona 'The authors wish to thank Xara Sharman for her help with the graphics.'", } @Article{Esparcia-Alcazar:GPEM:nomgp, author = "Anna I. Esparcia-Alcazar and Leonardo Trujillo", title = "Special Issue on Integrating numerical optimization methods with genetic programming", journal = "Genetic Programming and Evolvable Machines", year = "2020", volume = "21", number = "3", pages = "469--470", month = sep, note = "Guest Editorial: Special Issue on Integrating numerical optimization methods with genetic programming", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-020-09381-6", size = "2 pages", abstract = "\cite{Kommenda:GPEM}, \cite{Povoa:GPEM}", } @Article{espejo:2006:AEPIA, author = "Pedro G. Espejo and Cesar Hervas and Sebastian Ventura and Cristobal Romero", title = "Eleccion de Operadores Logicos para la Induccion de Conocimiento Comprensible", journal = "Inteligencia Artificial, Revista Iberoamericana de Inteligencia Artificial", year = "2006", volume = "29", pages = "19--30", note = "Ejemplar dedicado a: Mineria de Datos", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Mineria de datos, Clasificacion, Comprensibilidad, Programacion genetica gramatical", ISSN = "1137-3601", URL = "http://sci2s.ugr.es/keel/pdf/keel/articulo/1cr-1r-2r.pdf", size = "12 pages", resumen = "Son varias las caracteristicas que determinan la calidad del conocimiento obtenido en el proceso de mineria de datos. De estas caracteristicas, a la que mas atencion se ha dedicado tradicionalmente ha sido la precision, relegandose a un segundo plano la comprensibilidad. En este trabajo desarrollamos un sistema de mineria de datos orientado a la tarea de clasificacion, utilizando reglas como formalismo de representacion. El objetivo principal es analizar el balance entre precision y comprensibilidad, centrandonos en un aspecto de la comprensibilidad poco tratado hasta la fecha: el que viene determinado por la eleccion de los operadores logicos que pueden aparecer en el antecedente de las reglas. El sistema de mineria desarrollado se basa en la programacion genetica gramatical, ya que otro objetivo de nuestro trabajo es estudiar la utilidad de esta tecnica evolutiva para llevar a cabo tareas de mineria.", abstract = "In data mining, the quality of induced knowledge is determined by several features. The focus has been usually placed on accuracy, paying much less attention to comprehensibility. In this paper, we present a rule-based data mining system for classification. Our main goal is the analysis of the trade-off between accuracy and comprehensibility, but we approach comprehensibility from a novel point of view: we are interested in gaining insight into how the use of logical operators affects comprehensibility. In addition, we study the suitability of grammar-based genetic programming for data mining", notes = "Broken Apr 2023 c AEPIA (http://www.aepia.dsic.upv.es/) In Spanish", } @Article{Espejo:2010:ieeetSMC, author = "Pedro G. Espejo and Sebastian Ventura and Francisco Herrera", title = "A Survey on the Application of Genetic Programming to Classification", journal = "IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews", year = "2010", month = mar, volume = "40", number = "2", pages = "121--144", keywords = "genetic algorithms, genetic programming, Classification, decision trees, ensemble classifiers, feature construction, feature selection, rule-based systems", abstract = "Classification is one of the most researched questions in machine learning and data mining. A wide range of real problems have been stated as classification problems, for example credit scoring, bankruptcy prediction, medical diagnosis, pattern recognition, text categorization, software quality assessment, and many more. The use of evolutionary algorithms for training classifiers has been studied in the past few decades. Genetic programming (GP) is a flexible and powerful evolutionary technique with some features that can be very valuable and suitable for the evolution of classifiers. This paper surveys existing literature about the application of genetic programming to classification, to show the different ways in which this evolutionary algorithm can help in the construction of accurate and reliable classifiers.", DOI = "doi:10.1109/TSMCC.2009.2033566", ISSN = "1094-6977", notes = "Also known as \cite{5340522}", } @InProceedings{conf/mcpr2/EspinalCOPMS14, author = "Andres Espinal and Juan Martin Carpio and Manuel Ornelas and Hector Puga and Patricia Melin and Marco Aurelio Sotelo-Figueroa", title = "Developing Architectures of Spiking Neural Networks by Using Grammatical Evolution Based on Evolutionary Strategy", keywords = "genetic algorithms, genetic programming, grammatical evolution", bibdate = "2014-06-24", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/mcpr2/mcpr2014.html#EspinalCOPMS14", booktitle = "Pattern Recognition - 6th Mexican Conference, {MCPR} 2014, Cancun, Mexico, June 25-28, 2014. Proceedings", publisher = "Springer", year = "2014", volume = "8495", editor = "Jose Francisco Martinez Trinidad and Jesus Ariel Carrasco-Ochoa and Jose Arturo Olvera-Lopez and Joaquin Salas Rodriguez and Ching Y. Suen", isbn13 = "978-3-319-07490-0", pages = "71--80", series = "Lecture Notes in Computer Science", URL = "http://dx.doi.org/10.1007/978-3-319-07491-7", } @Article{Espinal:2024:CyS, author = "Andres Espinal and Marco Aurelio Sotelo-Figueroa and Jorge Alberto Soria-Alcaraz", title = "Grammatical Evolution with Codons Selection Order as Intensification Process", journal = "Computacion y Sistemas", year = "2024", volume = "28", number = "2", pages = "837--846", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Symbolic Regression, Intensification", ISSN = "2007-9737", URL = "https://www.cys.cic.ipn.mx/ojs/index.php/CyS/article/view/5026", URL = "https://www.cys.cic.ipn.mx/ojs/index.php/CyS/article/view/5026/3705", DOI = "doi:10.13053/CyS-28-2-5026", size = "10 pages", abstract = "permutation heuristics to guide the codon selection order", notes = "p843 '3-opt heuristic are better than the random permutation and the classic Grammatical Evolution' in English Universidad de Guanajuato, Departamento de Estudios Organizacionales, Mexico", } @InProceedings{Esposito:2007:PriCKL, author = "Floriana Esposito and Nicola Fanizzi and Claudia d'Amato", title = "Conceptual Clustering Applied to Ontologies by means of Semantic Discernability", booktitle = "ECML/PKDD Workshop on Prior Conceptual Knowledge in Machine Learning and Knowledge Discovery, PriCKL'07", year = "2007", address = "Warsaw, Poland", month = sep # " 21", keywords = "genetic algorithms, genetic programming", URL = "http://www.ecmlpkdd2007.org/CD/workshops/PRICKLWM2/P_Fan/PriCKL07/PriCkl2007-final.pdf", abstract = "A clustering method is presented which can be applied to relational knowledge bases to discover interesting groupings of resources through their annotations expressed in the standard languages of the Semantic Web. The method exploits a simple (yet effective and language-independent) semi-distance measure for individuals, that is based on the semantics of the resources w.r.t. a number of dimensions corresponding to a set of concept descriptions (discriminating features). The algorithm adapts the classic BISECTING K-MEANS to work with medoids. A final experiment demonstrates the validity of the approach using absolute quality indices", notes = "Says based on GP and Simulated Annealing Dipartimento di Informatica, Universit`a degli Studi di Bari Campus Universitario, Via Orabona 4, 70125 Bari, Italy", } @InProceedings{essam:2001:acpfgp, author = "Daryl Essam and R. I. Bob McKay", title = "Adaptive Control of Partial Functions in Genetic Programming", booktitle = "Proceedings of the 2001 Congress on Evolutionary Computation CEC2001", year = "2001", pages = "895--901", address = "COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "27-30 " # may, organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Partial Functions, Fitness Evaluation", ISBN = "0-7803-6658-1", URL = "http://www.cs.adfa.edu.au/~rim/PAPERS/CEC01final.pdf", DOI = "doi:10.1109/CEC.2001.934285", size = "7 pages", abstract = "The paper investigates the use of partial functions in genetic programming. Previous work (R.I. McKay, 2000), has shown that the convergent behaviour of populations of partial functions is very similar to that of populations of total functions. However the convergence rates of populations of partial functions have been slower. The results presented demonstrate a significant improvement in the rate of convergence of populations of partial functions, and indicate that partial functions represent a realistic alternative to total functions for a range of problems", notes = "CEC-2001 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 01TH8546C, Library of Congress Number = . Convergence of populations of partial functions. recursion list membership, 6-multiplexor, 11-mux. undef, DCTG-GP cf. \cite{ross:1999:LGPDCTG} fitness sharing mitigated by non-undef. {"}A partial function is a function whose value is not defined for some argument values{"}, ie {"}undef{"}. Tree GP. Grammar DCTG-GP. Infinite recursion prevented by a depth limit of 20.", } @Article{essam:2002:GPEM, author = "Daryl Essam", title = "Book Review: {Blondie24}: Playing at the Edge of {AI}", journal = "Genetic Programming and Evolvable Machines", year = "2002", volume = "3", number = "4", pages = "389--390", month = dec, ISSN = "1389-2576", URL = "https://rdcu.be/dR8oQ", DOI = "doi:10.1023/A:1020941026832", notes = "Article ID: 5103876", } @InProceedings{Essam:2004:SEAL, author = "Daryl Essam and R. I. (Bob) McKay", title = "Heritage Diversity in Genetic Programming", booktitle = "The 5th International Conference on Simulated Evolution And Learning (SEAL'04)", year = "2004", address = "Busan, Korea", month = oct # " 26-29", keywords = "genetic algorithms, genetic programming, diversity", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.143.9916", URL = "http://sc.snu.ac.kr/PAPERS/seal04.pdf", size = "6 pages", abstract = "Previous work has examined diversity within genetic programming from the viewpoints of isolation, structural differences and behavioural differences. This paper investigates the implications of controlling diversity through the implicit genetic heritage of a population. In practise, each individual carries a genetic tag indicative of its genetic heritage, and will then not crossover with other individuals with similar tags.", } @InProceedings{DBLP:conf/icann/EstebanezVAG05, author = "C{\'e}sar Est{\'e}banez and Jos{\'e} Mar\'{\i}a Valls and Ricardo Aler and In{\'e}s Mar\'{\i}a Galv{\'a}n", title = "A First Attempt at Constructing Genetic Programming Expressions for EEG Classification", year = "2005", pages = "665--670", keywords = "genetic algorithms, genetic programming, EEG, BCI, brain computer interface, projection", bibsource = "DBLP, http://dblp.uni-trier.de", editor = "Wlodzislaw Duch and Janusz Kacprzyk and Erkki Oja and Slawomir Zadrozny", booktitle = "Artificial Neural Networks: Biological Inspirations - ICANN 2005, 15th International Conference, 2005, Proceedings, Part I", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3696", ISBN = "3-540-28752-3", DOI = "doi:10.1007/11550822_103", address = "Warsaw, Poland", month = "11-15 " # sep, abstract = "In BCI (Brain Computer Interface) research, the classification of EEG signals is a domain where raw data has to undergo some preprocessing, so that the right attributes for classification are obtained. Several transformational techniques have been used for this purpose: Principal Component Analysis, the Adaptive Autoregressive Model, FFT or Wavelet Transforms, etc. However, it would be useful to automatically build significant attributes appropriate for each particular problem. we use Genetic Programming to evolve projections that translate EEG data into a new vectorial space (coordinates of this space being the new attributes), where projected data can be more easily classified. Although our method is applied here in a straightforward way to check for feasibility, it has achieved reasonable classification results that are comparable to those obtained by other state of the art algorithms. In the future, we expect that by choosing carefully primitive functions, Genetic Programming will be able to give original results that cannot be matched by other machine learning classification algorithms.", } @InProceedings{conf/wec/EstebanezAV05, title = "Genetic Programming Based Data Projections for Classification Tasks", author = "Cesar Estebanez and Ricardo Aler and Jose Maria Valls", year = "2005", pages = "56--61", editor = "Cemal Ardil", publisher = "Enformatika, \c{C}anakkale, Turkey", booktitle = "International Enformatika Conference, IEC'05", volume = "7", address = "Prague, Czech Republic", month = aug # " 26-28", organisation = "World Enformatika Society", note = "CDROM", bibdate = "2005-10-13", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/wec/iec2005prague.html#EstebanezAV05", keywords = "genetic algorithms, genetic programming", ISBN = "975-98458-6-5", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.193.6069", URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.193.6069.pdf", size = "6 pages", abstract = "In this paper we present a GP-based method for automatically evolve projections, so that data can be more easily classified in the projected spaces. At the same time, our approach can reduce dimensionality by constructing more relevant attributes. Fitness of each projection measures how easy is to classify the dataset after applying the projection. This is quickly computed by a Simple Linear Perceptron. We have tested our approach in three domains. The experiments show that it obtains good results, compared to other Machine Learning approaches, while reducing dimensionality in many cases", } @InProceedings{eurogp06:EstebanezVallsAler, author = "C\'esar Est\'ebanez and Jos\'e M. Valls and Ricardo Aler", title = "Projecting Financial Data using Genetic Programming in Classification and Regression Tasks", editor = "Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art", booktitle = "Proceedings of the 9th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3905", year = "2006", address = "Budapest, Hungary", month = "10 - 12 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-33143-3", pages = "202--212", DOI = "doi:10.1007/11729976_18", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "The use of Constructive Induction (CI) methods for the generation of high-quality attributes is a very important issue in Machine Learning. In this paper, we present a CI method based in Genetic Programming (GP). This method is able to evolve projections that transform the dataset, constructing a new coordinates space in which the data can be more easily predicted. This coordinates space can be smaller than the original one, achieving two main goals at the same time: on one hand, improving classification tasks; on the other hand, reducing dimensionality of the problem. Also, our method can handle classification and regression problems. We have tested our approach in two financial prediction problems because their high dimensionality is very appropriate for our method. In the first one, GP is used to tackle prediction of bankruptcy of companies (classification problem). In the second one, an IPO Underpricing prediction domain (a classical regression problem) is confronted. Our method obtained in both cases competitive results and, in addition, it drastically reduced dimensionality of the problem.", notes = "Part of \cite{collet:2006:GP} EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006", } @InProceedings{1144300, author = "Cesar Estebanez and Julio Cesar Hernandez-Castro and Arturo Ribagorda and Pedro Isasi", title = "Evolving hash functions by means of genetic programming", booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation", year = "2006", editor = "Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos {Coello Coello} and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens", volume = "2", ISBN = "1-59593-186-4", pages = "1861--1862", address = "Seattle, Washington, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p1861.pdf", DOI = "doi:10.1145/1143997.1144300", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "8-12 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, genetic improvement, Real-World Applications: Poster, avalanche effect, hash functions", size = "2 pages", abstract = "The design of hash functions by means of evolutionary computation is a relatively new and unexplored problem. In this work, we use Genetic Programming (GP) to evolve robust and fast hash functions. We use a fitness function based on a non-linearity measure, producing evolved hashes with a good degree of Avalanche Effect. Efficiency is assured by using only very fast operators (both in hardware and software) and by limiting the number of nodes. Using this approach, we have created a new hash function, which we call gp-hash, that is able to outperform a set of five human-generated, widely-used hash functions.", notes = "lilgp GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM Order Number 910060", } @InProceedings{Estebanez:PPSN:2006, author = "Cesar Estebanez and Julio Cesar Hernandez-Castro and Arturo Ribagorda and Pedro Isasi", title = "Finding State-of-the-Art Non-cryptographic Hashes with Genetic Programming", booktitle = "Parallel Problem Solving from Nature - PPSN IX", year = "2006", editor = "Thomas Philip Runarsson and Hans-Georg Beyer and Edmund Burke and Juan J. Merelo-Guervos and L. Darrell Whitley and Xin Yao", volume = "4193", pages = "818--827", series = "LNCS", address = "Reykjavik, Iceland", publisher_address = "Berlin", month = "9-13 " # sep, publisher = "Springer-Verlag", ISBN = "3-540-38990-3", keywords = "genetic algorithms, genetic programming, genetic improvement", DOI = "doi:10.1007/11844297_83", size = "10 pages", abstract = "The design of non-cryptographic hash functions by means of evolutionary computation is a relatively new and unexplored problem. In this paper, we use the Genetic Programming paradigm to evolve collision free and fast hash functions. For achieving robustness against collision we use a fitness function based on a non-linearity concept, producing evolved hashes with a good degree of Avalanche Effect. The other main issue, efficiency, is assured by using only very fast operators (both in hardware and software) and by limiting the number of nodes. Using this approach, we have created a new hash function, which we call gp-hash, that is able to outperform a set of five human-generated, widely-used hash functions.", notes = "PPSN-IX cites \cite{Jenkins:1997:AAH}", } @Article{journals/ijcsa/EstebanezAV07, author = "Cesar Estebanez and Ricardo Aler and Jose Maria Valls", title = "A Method Based on Genetic Programming for Improving the Quality of Datasets in Classification Problems", journal = "International Journal of Computer Science and Applications", year = "2007", volume = "4", number = "1", pages = "69--80", keywords = "genetic algorithms, genetic programming, Classification, projections", ISSN = "0972-9038", URL = "http://www.tmrfindia.org/ijcsa/V4I17.pdf", size = "12 pages", abstract = "The problem of the representation of data is a key issue in the Machine Learning (ML) field. ML tries to automatically induct knowledge from a set of examples or instances of a problem, learning how to distinguish between the different classes. It is known that inappropriate representations of the data can drastically limit the performance of ML algorithms. On the other hand, a high-quality representation of the same data, can produce a strong improvement in classification rates. In this work we present a GP-based method for automatically evolve projections. These projections change the data space of a classification problem into a higher-quality one, thus improving the performance of ML algorithms. At the same time, our approach can reduce dimensionality by constructing more relevant attributes. We have tested our approach in four domains. The experiments show that it obtains good results, compared to other ML approaches that do not use our projections, while reducing dimensionality in many cases.", notes = "Including Mini Special Issue based on extended versions of selected papers presented during the First International Multiconference on Computer Science and Information Technology (FIMCSIT), which took place in Wisla, Poland, on November 6-10, 2006. Guest Editors: Maria Ganzha and Marcin Paprzycki Ripley Data Set, Pima Indians Diabetes, NIPS 2001 Brain Computer Interface Workshop", bibdate = "2007-10-30", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ijcsa/ijcsa4.html#EstebanezAV07", } @InProceedings{Estebanez:2009:eurogp, author = "Cesar Estebanez and Ricardo Aler and Jose M. Valls and Pablo Alonso", title = "An experimental study on fitness distributions of tree shapes in GP with One-Point Crossover", booktitle = "Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009", year = "2009", editor = "Leonardo Vanneschi and Steven Gustafson and Alberto Moraglio and Ivanoe {De Falco} and Marc Ebner", volume = "5481", series = "LNCS", pages = "244--255", address = "Tuebingen", month = apr # " 15-17", organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-01180-1", DOI = "doi:10.1007/978-3-642-01181-8_21", notes = "Part of \cite{conf/eurogp/2009} EuroGP'2009 held in conjunction with EvoCOP2009, EvoBIO2009 and EvoWorkshops2009", } @InCollection{Estebanez:2009:OTSCP, author = "C. Estebanez and R. Aler", title = "Generating Automatic Projections by Means of Genetic Programming", booktitle = "Optimization Techniques for Solving Complex Problems", publisher = "John Wiley \& Sons, Inc.", year = "2009", editor = "Enrique Alba and Christian Blum and Pedro Isasi and Coromoto Leon and Juan Antonio Gomez", series = "Parallel and Distributed Computing", chapter = "1", pages = "3--14", keywords = "genetic algorithms, genetic programming, genetic programming projection engine (GPPE), fitness function, initial public offerings (IPOs)", isbn13 = "9780470293324", URL = "http://www.amazon.com/gp/search?search-alias=stripbooks&field-isbn=978-0470293324", DOI = "doi:10.1002/9780470411353.ch1", } @PhdThesis{Tesis_Cesar_Estebanez_Tascon, author = "Cesar {Estebanez Tascon}", title = "Automatic design of Non-Chryptographic Hash Functions Using Artificial Intelligence Techniques", title_es = "Diseno automatico de funciones hash no criptograficas", school = "Computer Science, Universidad Carlos III de Madrid", year = "2011", address = "Spain", keywords = "genetic algorithms, genetic programming, Funciones hash no criptograficas, Diseno automatico", URL = "http://hdl.handle.net/10016/13694", URL = "http://e-archivo.uc3m.es/bitstream/handle/10016/13694/Tesis_Cesar_Estebanez_Tascon.pdf", size = "308 pages", abstract = "Las funciones hash no criptograficas son una de las herramientas mas ampliamente utilizadas en las ciencias de la computacion. Sus innumerables campos de aplicacion van desde analizadores lexicos y compiladores, hasta bases de datos, caches, redes de comunicac", notes = "In Spanish Supervisors D. Pedro Isasi Vinuela and D. Yago Saez Achaerandio", } @Article{Estebanez:2014:CI, author = "Cesar Estebanez and Yago Saez and Gustavo Recio and Pedro Isasi", title = "Automatic Design of Noncryptographic Hash Functions using Genetic Programming", journal = "Computational Intelligence", year = "2014", volume = "30", number = "4", pages = "798--831", month = nov, keywords = "genetic algorithms, genetic programming, genetic improvement, hash functions, evolutionary computation", ISSN = "1467-8640", DOI = "doi:10.1002/coin.12033", size = "34 pages", abstract = "Noncryptographic hash functions have an immense number of important practical applications owing to their powerful search properties. However, those properties critically depend on good designs: Inappropriately chosen hash functions are a very common source of performance losses. On the other hand, hash functions are difficult to design: They are extremely nonlinear and counter intuitive, and relationships between the variables are often intricate and obscure. In this work, we demonstrate the utility of genetic programming (GP) and avalanche effect to automatically generate noncryptographic hashes that can compete with state-of-the-art hash functions. We describe the design and implementation of our system, called GP-hash, and its fitness function, based on avalanche properties. Also, we experimentally identify good terminal and function sets and parameters for this task, providing interesting information for future research in this topic. Using GP-hash, we were able to generate two different families of noncryptographic hashes. These hashes are able to compete with a selection of the most important functions of the hashing literature, most of them widely used in the industry and created by world-class hashing experts with years of experience.", notes = "Avalanche fitness. 'automatically designed by GP-hash, ... at least as good as a selection of the best NCHF created by human experts'", } @Article{Estevez:2005:EL, author = "P. A. Estevez and N. Becerra-Yoma and N. Boric and J. A. Ramirez", title = "Genetic programming-based voice activity detection", journal = "Electronics Letters", year = "2005", volume = "41", number = "20", pages = "1141--1143", month = "29 " # sep, keywords = "genetic algorithms, genetic programming", ISSN = "0013-5194", DOI = "doi:10.1049/el:20052475", size = "2 pages", abstract = "A voice activity detection (VAD) algorithm is generated by using genetic programming (GP). The inputs of this VAD are the parameters extracted from the speech signals according to the ITU-T G.729B VAD standard. The GP-based VAD algorithm (GP-VAD) is evaluated using the AURORA-2 database. It is shown that the GP-VAD achieves approximately the same behaviour as the G.729B standard with a high artificial-to-intelligence ratio.", notes = "Dept. of Electr. Eng., Univ. de Chile, Santiago, Chile", } @Article{Estevez:2008:GPEM, author = "Pablo A. Estevez", title = "Russel C. Eberhart, Yuhui Shi: Computational Intelligence: Concepts to Implementation", journal = "Genetic Programming and Evolvable Machines", year = "2008", volume = "9", number = "4", pages = "367--369", month = dec, note = "Book review", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dR8o5", DOI = "doi:10.1007/s10710-008-9064-z", size = "3 pages", } @InProceedings{conf/acl/Estevez-Velarde19, title = "{AutoML} Strategy Based on Grammatical Evolution: A Case Study about Knowledge Discovery from Text", author = "Suilan Estevez-Velarde and Yoan Gutierrez and Andres Montoyo and Yudivian Almeida-Cruz", booktitle = "Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019", publisher = "Association for Computational Linguistics", year = "2019", address = "Florence, Italy", month = jul # " 28-" # aug # " 2", volume = "1 Long Papers", editor = "Anna Korhonen and David R. Traum and Lluis Marquez", pages = "4356--4365", keywords = "genetic algorithms, genetic programming, grammatical evolution", isbn13 = "978-1-950737-48-2", DOI = "doi:10.18653/v1/p19-1428", bibdate = "2020-01-28", bibsource = "DBLP, http://dblp.uni-trier.de/https://www.aclweb.org/anthology/P19-1428/>; DBLP, http://dblp.uni-trier.de/db/conf/acl/acl2019-1.html#Estevez-Velarde19", } @Article{journals/isci/Estevez-Velarde21, author = "Suilan Estevez-Velarde and Yoan Gutierrez and Yudivian Almeida-Cruz and Andres Montoyo", title = "General-purpose hierarchical optimisation of machine learning pipelines with grammatical evolution", journal = "Information Sciences", year = "2021", volume = "543", pages = "58--71", keywords = "genetic algorithms, genetic programming, grammatical evolution, AutoML, evolutionary computation, supervised learning, natural language processing", ISSN = "0020-0255", bibdate = "2020-11-14", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/isci/isci543.html#Estevez-Velarde21", URL = "https://www.sciencedirect.com/science/article/pii/S0020025520306988", DOI = "doi:10.1016/j.ins.2020.07.035", abstract = "This paper introduces Hierarchical Machine Learning Optimisation (HML-Opt), an AutoML framework that is based on probabilistic grammatical evolution. HML-Opt has been designed to provide a flexible framework where a researcher can define the space of possible pipelines to solve a specific machine learning problem, which can range from high-level decisions about representation and features to low-level hyper-parameter values. The evaluation of HML-Opt is presented via two different case studies, both of which demonstrate that it is competitive with existing AutoML tools on a variety of benchmarks. Furthermore, HML-Opt can be applied to novel problems, such as knowledge extraction from natural language text, whereas other techniques are insufficiently flexible to capture the complexity of these scenarios. The source code for HML-Opt is available online for the research community.", } @InProceedings{Estrada:2015:ieeeICPADS, author = "Trilce Estrada and Michael Wyatt and Michela Taufer", booktitle = "21st IEEE International Conference on Parallel and Distributed Systems (ICPADS)", title = "A Genetic Programming Approach to Design Resource Allocation Policies for Heterogeneous Workflows in the Cloud", year = "2015", pages = "372--379", abstract = "When dealing with very large applications in the cloud, higher costs do not always result in better turnaround times, particularly for complex work-flows with multiple task dependencies. Thus, resource allocation policies are needed that can determine when using expensive but faster resources is best and when it is not. Manually developing such heuristics is time consuming and limited by the subjective beliefs of the developer. To overcome such impediments, we present an automatic method that designs and evaluates a large set of policies using a genetic programming approach. Our method finds a robust set of policies that adapt to changes in workload while using resources efficiently. Our results show that our genetic programming designed policies perform better than greedy and other human designed policies do.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICPADS.2015.54", month = dec, notes = "Also known as \cite{7384317}", } @Article{Estrada-Gil:2007:BI, author = "Jesus K. Estrada-Gil and Juan C. Fernandez-Lopez and Enrique Hernandez-Lemus and Irma Silva-Zolezzi and Alfredo Hidalgo-Miranda and Gerardo Jimenez-Sanchez and Edgar E. Vallejo-Clemente", title = "GPDTI: A Genetic Programming Decision Tree Induction method to find epistatic effects in common complex diseases", journal = "Bioinformatics", year = "2007", volume = "13", number = "13", pages = "i167--i174", keywords = "genetic algorithms, genetic programming", ISSN = "1460-2059", DOI = "doi:10.1093/bioinformatics/btm205", abstract = "Motivation: The identification of risk-associated genetic variants in common diseases remains a challenge to the biomedical research community. It has been suggested that common statistical approaches that exclusively measure main effects are often unable to detect interactions between some of these variants. Detecting and interpreting interactions is a challenging open problem from the statistical and computational perspectives. Methods in computing science may improve our understanding on the mechanisms of genetic disease by detecting interactions even in the presence of very low heritabilities. Results: We have implemented a method using Genetic Programming that is able to induce a Decision Tree to detect interactions in genetic variants. This method has a cross-validation strategy for estimating classification and prediction errors and tests for consistencies in the results. To have better estimates, a new consistency measure that takes into account interactions and can be used in a genetic programming environment is proposed. This method detected five different interaction models with heritabilities as low as 0.008 and with prediction errors similar to the generated errors. Availability: Information on the generated data sets and executable code is available upon request.", notes = "PMID: 17646293 [PubMed - in process]", } @InProceedings{Eszes:2021:CINTI, author = "Tibor Eszes and Janos Botzheim", title = "Applying Genetic Programming for the Inverse Lindenmayer Problem", booktitle = "2021 IEEE 21st International Symposium on Computational Intelligence and Informatics (CINTI)", year = "2021", pages = "000043--000048", abstract = "The aim of this work is to find an automated solution for the Inverse Lindenmayer problem - that is to find the describing system for a given end-result of an L-system - using both Bacterial Programming and other related algorithms. To achieve this, several well-known L-systems were considered, their building symbols taken as the inputs for each algorithm, and the evolution results were compared with the formal definition of each system. The results indicate that this is indeed a viable area of research, as both Bacterial Programming and other different algorithms could be fitted to reverse engineer all of the considered systems.", keywords = "genetic algorithms, genetic programming, Microorganisms, Buildings, Informatics, Computational intelligence, Evolutionary Computation, Lindenmayer System", DOI = "doi:10.1109/CINTI53070.2021.9668544", ISSN = "2471-9269", month = nov, notes = "Also known as \cite{9668544}", } @Article{Etemadi20093199, title = "A genetic programming model for bankruptcy prediction: Empirical evidence from Iran", author = "Hossein Etemadi and Ali Asghar Anvary Rostamy and Hassan Farajzadeh Dehkordi", journal = "Expert Systems with Applications", volume = "36", number = "2, Part 2", pages = "3199--3207", year = "2009", ISSN = "0957-4174", DOI = "DOI:10.1016/j.eswa.2008.01.012", URL = "http://www.sciencedirect.com/science/article/B6V03-4RSRDDN-4/2/acecffea7c551388162fae4dfbe2a6e2", keywords = "genetic algorithms, genetic programming, Bankruptcy prediction, Financial ratios, Multiple discriminant analysis, Iranian companies", abstract = "Prediction of corporate bankruptcy is a phenomenon of increasing interest to investors/creditors, borrowing firms, and governments alike. Timely identification of firms' impending failure is indeed desirable. By this time, several methods have been used for predicting bankruptcy but some of them suffer from underlying shortcomings. In recent years, Genetic Programming (GP) has reached great attention in academic and empirical fields for efficient solving high complex problems. GP is a technique for programming computers by means of natural selection. It is a variant of the genetic algorithm, which is based on the concept of adaptive survival in natural organisms. In this study, we investigated application of GP for bankruptcy prediction modeling. GP was applied to classify 144 bankrupt and non-bankrupt Iranian firms listed in Tehran stock exchange (TSE). Then a multiple discriminant analysis (MDA) was used to benchmarking GP model. Genetic model achieved 94percent and 90percent accuracy rates in training and holdout samples, respectively; while MDA model achieved only 77percent and 73percent accuracy rates in training and holdout samples, respectively. McNemar test showed that GP approach outperforms MDA to the problem of corporate bankruptcy prediction.", } @TechReport{arXiv-2007.06986, author = "Khashayar Etemadi and Niloofar Tarighat and Siddharth Yadav and Matias Martinez and Martin Monperrus", title = "Longitudinal Analysis of the Applicability of Program Repair on Past Commits", institution = "arXiv", year = "2020", number = "2007.06986", month = "14 " # jul, keywords = "genetic algorithms, genetic programming, genetic improvement, APR", URL = "http://arxiv.org/pdf/2007.06986", size = "12 pages", abstract = "The applicability of program repair in the real world is a little researched topic. Existing program repair systems tend to only be tested on small bug datasets, such as Defects4J, that are not fully representative of real world projects. In this paper, we report on a longitudinal analysis of software repositories to investigate if past commits are amenable to program repair. Our key insight is to compute whether or not a commit lies in the search space of program repair systems. For this purpose, we present RSCommitDetector, which gets a Git repository as input and after performing a series of static analyses, it outputs a list of commits whose corresponding source code changes could likely be generated by notable repair systems. We call these commits the repair-space commits, meaning that they are considered in the search space of a repair system. Using RSCommitDetector, we conduct a study on 41612 commits from the history of 72 Github repositories. The results of this study show that 1.77percent of these commits are repair-space commits, they lie in the search space of at least one of the eight repair systems we consider. We use an original methodology to validate our approach and show that the precision and recall of RSCommitDetector are 77percent and 92percent, respectively. To our knowledge, this is the first study of the applicability of program repair with search space analysis.", notes = "Bibtex entry URL: bibtexbrowser.php?key=arXiv-2007.06986&bib=monperrus.bib", } @InProceedings{Etemadi:evoapps13, author = "Reza Etemadi and Nawwaf Kharma and Peter Grogono", title = "{CodeMonkey}: a GUI Driven Platform for Swift Synthesis of Evolutionary Algorithms in Java", booktitle = "Applications of Evolutionary Computing, EvoApplications 2013: EvoCOMNET, EvoCOMPLEX, EvoENERGY, EvoFIN, EvoGAMES, EvoIASP, EvoINDUSTRY, EvoNUM, EvoPAR, EvoRISK, EvoROBOT, EvoSTOC", year = "2013", month = "3-5 " # apr, editor = "Anna I. Esparcia-Alcazar and Antonio Della Cioppa and Ivanoe {De Falco} and Ernesto Tarantino and Carlos Cotta and Robert Schaefer and Konrad Diwold and Kyrre Glette and Andrea Tettamanzi and Alexandros Agapitos and Paolo Burrelli and J. J. Merelo and Stefano Cagnoni and Mengjie Zhang and Neil Urquhart and Kevin Sim and Aniko Ekart and Francisco {Fernandez de Vega} and Sara Silva and Evert Haasdijk and Gusz Eiben and Anabela Simoes and Philipp Rohlfshagen", series = "LNCS", volume = "7835", publisher = "Springer Verlag", address = "Vienna", publisher_address = "Berlin", pages = "439--448", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, Evolutionary Algorithm, Java Language, Eclipse Platform, GUI Application", isbn13 = "978-3-642-37191-2", DOI = "doi:10.1007/978-3-642-37192-9_44", size = "10 pages", abstract = "CodeMonkey is a GUI driven software development platform that allows non-experts and experts alike to turn an evolutionary algorithm design into a working Java program, with a minimal amount of manual code entry. This paper describes the concepts behind CodeMonkey, its internal architecture and manner of use. It concludes with a simple application that exhibits it for multi-dimensional function optimisation. CodeMonkey is provided free of charge, for non-commercial users, as a plug-in for the Eclipse platform", notes = " EvoApplications2013 held in conjunction with EuroGP2013, EvoCOP2013, EvoBio'2013 and EvoMusArt2013", } @InProceedings{Eto:2004:FLoGNPaiAtaPP, title = "Functional Localization of Genetic Network Programming and its Application to a Pursuit Problem", author = "Shinji Eto and Kotaro Hirasawa and Jinglu Hu", pages = "683--690", booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary Computation", year = "2004", publisher = "IEEE Press", month = "20-23 " # jun, address = "Portland, Oregon", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Evolutionary intelligent agents, Poster Session", DOI = "doi:10.1109/CEC.2004.1330925", abstract = "According to the knowledge of brain science, it is suggested that there exists cerebral functional localization, which means that a specific part of the cerebrum is activated depending on various kinds of information human receives. The aim of this paper is to build an artificial model to realize functional localization based on Genetic Network Programming (GNP), a new evolutionary computation method recently developed. GNP has a directed graph structure suitable for realizing functional localization brain has. We studied the basic characteristics of the proposed system by making GNP work in a functionally localized way.", notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.", } @InProceedings{Eto:gecco06lbp, author = "Shinji Eto and Shingo Mabu and Kotaro Hirasawa and Jinglu Hu", title = "Evolutionary method of Genetic Network Programing Considering Breadth and Depth", booktitle = "Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2006)}", year = "2006", month = "8-12 " # jul, editor = "J{\"{o}}rn Grahl", address = "Seattle, WA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006etc/papers/lbp119.pdf", notes = "Distributed on CD-ROM at GECCO-2006", keywords = "genetic algorithms, genetic programming", abstract = "Many methods of generating behaviour sequences of agents by evolution have been reported. A new evolutionary computation method named Genetic Network Programming (GNP) has also been developed recently along with these trends. In this paper, a new method for evolving GNP considering Breadth and Depth is proposed.The performance of the proposed method is shown from simulations using garbage collector problem.", } @InProceedings{Eto:2007:cec, author = "Shinji Eto and Shingo Mabu and Kotaro Hirasawa and Takayuki Huruzuki", title = "Genetic Network Programming with Control Nodes", booktitle = "2007 IEEE Congress on Evolutionary Computation", year = "2007", editor = "Dipti Srinivasan and Lipo Wang", pages = "1023--1028", address = "Singapore", month = "25-28 " # sep, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "1-4244-1340-0", file = "1128.pdf", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2007.4424582", abstract = "Many methods of generating behaviour sequences of agents by evolution have been reported. A new evolutionary computation method named Genetic Network Programming (GNP) has been also developed recently along with these trends. GNP has a directed graph structure and the search for obtaining optimal GNP becomes difficult when the scale of GNP is large. The aim of this paper is to find a well structured GNP considering Breadth and Depth of GNP searching. It has been shown that the proposed method is efficient compared with conventional GNPs from simulations using a garbage collector problem.", notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C", } @InProceedings{Evangelista:2009:ISDA, author = "Pedro Evangelista and Paulo Maia and Miguel Rocha", title = "Implementing Metaheuristic Optimization Algorithms with {JECoLi}", booktitle = "2009 Ninth International Conference on Intelligent Systems Design and Applications", year = "2009", pages = "505--510", address = "Pisa, Italy", month = "30 " # nov # "-2 " # dec, keywords = "genetic algorithms, genetic programming", ISSN = "2164-7143", DOI = "doi:10.1109/ISDA.2009.161", size = "6 pages", abstract = "This work proposes JECoLi, a novel Java-based library for the implementation of metaheuristic optimization algorithms with a focus on Genetic and Evolutionary Computation based methods. The library was developed based on the principles of flexibility, usability, adaptability, modularity, extensibility, transparency, scalability, robustness and computational efficiency. The project is open-source, so JECoLi is made available under the GPL license, together with extensive documentation and examples, all included in a community Wiki-based web site (http://darwin.di.uminho.pt/jecoli). JECoLi has been/is being used in several research projects that helped to shape its evolution, ranging application fields from Bioinformatics, to Data Mining and Computer Network optimization.", notes = "May 2019 http://darwin.di.uminho.pt/jecoli broken JECoLi implements a large set of metaheuristic algorithms, namely: * General purpose Evolutionary Algorithms, including Genetic Algorithms and Evolutionary Programming; * Differential Evolution (variants DE/rand and DE/best variants); * Genetic Programming and Linear GP; * Simulated Annealing (SA); * Cellular Automata GAs; * Multi-objective optimization Evolutionary Algorithms (NSGA II and SPEA2); Universidade do Minho Campus de Gualtar, Braga, Portugal also known as \cite{5364947}", } @PhdThesis{Evangelista:thesis, author = "Pedro Tiago Evangelista", title = "Novel approaches for dynamic modelling of {E.} coli and their application in Metabolic Engineering", school = "University of Minho", year = "2016", address = "Portugal", month = "5 " # oct, keywords = "genetic algorithms, genetic programming", URL = "http://hdl.handle.net/1822/43446", URL = "http://repositorium.sdum.uminho.pt/bitstream/1822/43446/1/PedroEvangelistaThesis.pdf", size = "331 pages", abstract = "One of the trends of modern societies is the replacement of chemical processes by biochemical ones, with new compounds being synthesized by engineered microorganisms, while some waste products are also being degraded by biotechnological means. Biotechnology holds the promise of creating a more profitable and environmental friendly industry, with a reduced number of waste products, when contrasted with the traditional chemical industry. However, in an era in which genomes are sequenced at a faster pace than ever before, and with the advent omic measurements, this information is not directly translated into the targeted design of new microorganisms, or biological processes. These experimental data in isolation do not explain how the different cell constituents interact. Reductionist approaches that dominated science in the last century study cellular entities in isolation as separate chunks, without taking into consideration interactions with other molecules. This leads to an incomplete view of biological processes, which compromises the development of new knowledge. To overcome these hurdles, a formal systems approach to Biology has been surging in the last thirty years. Systems biology can be defined as the conjugation of different fields (such as Mathematics, Computer Science, Biology),o describe formally and non-ambiguously the behaviour of the different cellular systems and their interactions, using to models and simulations. Metabolic Engineering takes advantage of these formal specifications, using mathematically based methods to derive strategies to optimize the microbial metabolism, in order to achieve a desired goal, such as the increase of the production of a relevant industrial compound. In this work, we develop a mechanistic dynamic model based on ordinary differential equations, comprised by elementary mass action descriptions of each reaction, from an existing model of Escherichia coli in the literature. We also explore different calibration processes for these reaction descriptions. We also contribute to the field of strain design by using evolutionary algorithms with a new representation scheme that allows to search for enzyme modulations, in continuous or discrete scales, as well as reaction knockouts, in existing dynamic metabolic models, aiming at the maximization of product yields. In the bioprocess optimization field, we extended the Dynamic Flux Balance Analysis formulation to incorporate the possibility to simulate fed-batch bioprocesses. This formulation is also enhanced with methods that possess the capacity to design feed profiles to attain a specific goal, such as maximizing the bioprocess yield or productivity. All the developed methods involved some form of sensitivity and identifiability analysis, to identify how model outputs are affected by their parameters. All the work was constructed under a modular software framework (developed during this thesis), that permits the interaction of distinct algorithms and languages, being a flexible tool to use in a cluster environment. The framework is available as an open-source software package, and has appeal to systems biologists describing biological processes with ordinary differential equations.", notes = "Is this GP? PhD thesis in Bioengineering supervisor: Isabel Cristina Santos Rocha, Bruce Tidor, Miguel Rocha", } @InProceedings{Evans:2018:CEC, author = "Benjamin Evans and Harith Al-Sahaf and Bing Xue and Mengjie Zhang", title = "Evolutionary Deep Learning: A Genetic Programming Approach to Image Classification", booktitle = "2018 IEEE Congress on Evolutionary Computation (CEC)", year = "2018", editor = "Marley Vellasco", address = "Rio de Janeiro, Brazil", month = "8-13 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2018.8477933", abstract = "Image classification is used for many tasks such as recognising handwritten digits, identifying the presence of pedestrians for self-driving cars, and even providing medical diagnosis from cell images. The current state-of-the-art solution for image classification, typically, uses convolutional neural networks (CNNs), however, there are limitations in this approach such as the need for manually crafted architectures and low interpretability. A genetic programming solution is proposed in this paper that aims to overcome these limitations, while also taking advantage of useful operators in CNNs such as convolutions and pooling. The new approach is tested on four widely used benchmark image datasets, and the experimental results show that the new method has achieved comparable performance to the state-of-the-art techniques. Furthermore, the automatically evolved programs are highly interpretable, and visualisations of those programs reveal interesting patterns.", notes = "WCCI2018", } @Misc{DBLP:journals/corr/abs-1909-13030, author = "Benjamin Patrick Evans and Harith Al-Sahaf and Bing Xue and Mengjie Zhang", title = "Genetic Programming and Gradient Descent: {A} Memetic Approach to Binary Image Classification", howpublished = "arXiv", volume = "abs/1909.13030", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "http://arxiv.org/abs/1909.13030", archiveprefix = "arXiv", eprint = "1909.13030", timestamp = "Wed, 02 Oct 2019 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/corr/abs-1909-13030.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{Evans:2019:GECCO, author = "Benjamin P. Evans and Bing Xue and Mengjie Zhang", title = "What's inside the black-box?: a genetic programming method for interpreting complex machine learning models", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference", year = "2019", editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and Anne Auger and Petr Posik and Leslie {Peprez Caceres} and Andrew M. Sutton and Nadarajen Veerapen and Christine Solnon and Andries Engelbrecht and Stephane Doncieux and Sebastian Risi and Penousal Machado and Vanessa Volz and Christian Blum and Francisco Chicano and Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and Jonathan Fieldsend and Jose Antonio Lozano and Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and Robin Purshouse and Thomas Baeck and Justyna Petke and Giuliano Antoniol and Johannes Lengler and Per Kristian Lehre", isbn13 = "978-1-4503-6111-8", pages = "1012--1020", address = "Prague, Czech Republic", DOI = "doi:10.1145/3321707.3321726", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, Explainable Artificial Intelligence, Interpretable Machine Learning, Evolutionary Multi-objective Optimisation", size = "9 pages", abstract = "Interpreting state-of-the-art machine learning algorithms can be difficult. For example, why does a complex ensemble predict a particular class? Existing approaches to interpretable machine learning tend to be either local in their explanations, apply only to a particular algorithm, or overly complex in their global explanations. In this work, we propose a global model extraction method which uses multi-objective genetic programming to construct accurate, simplistic and model-agnostic representations of complex black-box estimators. We found the resulting representations are far simpler than existing approaches while providing comparable reconstructive performance. This is demonstrated on a range of datasets, by approximating the knowledge of complex black-box models such as 200 layer neural networks and ensembles of 500 trees, with a single tree.", notes = "Also known as \cite{3321726} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InProceedings{Evans:2020:GECCO, author = "Benjamin P. Evans and Bing Xue and Mengjie Zhang", title = "Improving Generalisation of {AutoML} Systems with Dynamic Fitness Evaluations", year = "2020", editor = "Carlos Artemio {Coello Coello} and Arturo Hernandez Aguirre and Josu Ceberio Uribe and Mario Garza Fabre and Gregorio {Toscano Pulido} and Katya Rodriguez-Vazquez and Elizabeth Wanner and Nadarajen Veerapen and Efren Mezura Montes and Richard Allmendinger and Hugo Terashima Marin and Markus Wagner and Thomas Bartz-Beielstein and Bogdan Filipic and Heike Trautmann and Ke Tang and John Koza and Erik Goodman and William B. Langdon and Miguel Nicolau and Christine Zarges and Vanessa Volz and Tea Tusar and Boris Naujoks and Peter A. N. Bosman and Darrell Whitley and Christine Solnon and Marde Helbig and Stephane Doncieux and Dennis G. Wilson and Francisco {Fernandez de Vega} and Luis Paquete and Francisco Chicano and Bing Xue and Jaume Bacardit and Sanaz Mostaghim and Jonathan Fieldsend and Oliver Schuetze and Dirk Arnold and Gabriela Ochoa and Carlos Segura and Carlos Cotta and Michael Emmerich and Mengjie Zhang and Robin Purshouse and Tapabrata Ray and Justyna Petke and Fuyuki Ishikawa and Johannes Lengler and Frank Neumann", isbn13 = "9781450371285", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377930.3389805", DOI = "doi:10.1145/3377930.3389805", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference", pages = "324--332", size = "9 pages", keywords = "genetic algorithms, genetic programming, regularized evolution, AutoML, regularization, automated machine learning, generalisation, dynamic fitness evaluations", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", abstract = "A common problem machine learning developers are faced with is overfitting, that is, fitting a pipeline too closely to the training data that the performance degrades for unseen data. Automated machine learning aims to free (or at least ease) the developer from the burden of pipeline creation, but this overfitting problem can persist. In fact, this can become more of a problem as we look to iteratively optimise the performance of an internal cross-validation (most often k-fold). While this internal cross-validation hopes to reduce this overfitting, we show we can still risk overfitting to the particular folds used. In this work, we aim to remedy this problem by introducing dynamic fitness evaluations which approximate repeated k-fold cross-validation, at little extra cost over single k-fold, and far lower cost than typical repeated k-fold. The results show that when time equated, the proposed fitness function results in significant improvement over the current state-of-the-art baseline method which uses an internal single k-fold. Furthermore, the proposed extension is very simple to implement on top of existing evolutionary computation methods, and can provide essentially a free boost in generalisation/testing performance.", notes = "Also known as \cite{10.1145/3377930.3389805} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @InProceedings{Evans:2020:CEC, author = "Benjamin Evans and Bing Xue and Mengjie Zhang", booktitle = "2020 IEEE Congress on Evolutionary Computation (CEC)", title = "An Adaptive and Near Parameter-free Evolutionary Computation Approach Towards True Automation in {AutoML}", year = "2020", editor = "Yaochu Jin", month = "19-24 " # jul, keywords = "genetic algorithms, genetic programming, TPOT, Sociology, Statistics, Pipelines, Machine learning, Evolutionary computation, Optimization, Automation", isbn13 = "978-1-7281-6929-3", URL = "https://arxiv.org/pdf/2001.10178.pdf", DOI = "doi:10.1109/CEC48606.2020.9185770", abstract = "A common claim of evolutionary computation methods is that they can achieve good results without the need for human intervention. However, one criticism of this is that there are still hyperparameters which must be tuned in order to achieve good performance. In this work, we propose a near parameter-free genetic programming approach, which adapts the hyperparameter values throughout evolution without ever needing to be specified manually. We apply this to the area of automated machine learning (by extending TPOT), to produce pipelines which can effectively be claimed to be free from human input, and show that the results are competitive with existing state-of-the-art which use hand-selected hyperparameter values. Pipelines begin with a randomly chosen estimator and evolve to competitive pipelines automatically. This work moves towards a truly automated approach to AutoML.", notes = "Also known as \cite{9185770} Fibonacci sequence used for increasing population size", } @Article{Evans:2001:CEP, author = "C. Evans and P. J. Fleming and D. C. Hill and J. P. Norton and I. Pratt and D. Rees and K. Rodriguez-Vazquez", title = "Application of system identification techniques to aircraft gas turbine engines", journal = "Control Engineering Practice", volume = "9", pages = "135--148", year = "2001", number = "2", month = feb, keywords = "genetic algorithms, genetic programming, Gas turbines, System identification, Frequency domain, Multisine signals, Least-squares estimation, Time-varying systems, Structure selection", ISSN = "0967-0661", broken = "http://www.sciencedirect.com/science/article/B6V2H-4280YP2-3/1/24d44180070f91dea854032d98f9187a", DOI = "doi:10.1016/S0967-0661(00)00091-5", abstract = "A variety of system identification techniques are applied to the modelling of aircraft gas turbine dynamics. The motivation behind the study is to improve the efficiency and cost-effectiveness of system identification techniques currently used in the industry. Three system identification approaches are outlined in this paper. They are based upon: multisine testing and frequency-domain identification, time-varying models estimated using extended least squares with optimal smoothing, and multiobjective genetic programming to select model structure.", } @InProceedings{Evans:2008:IVCNZ, author = "H. Evans and Mengjie Zhang", title = "Particle swarm optimisation for object classification", booktitle = "23rd International Conference Image and Vision Computing New Zealand, IVCNZ 2008", year = "2008", month = nov, pages = "1--6", keywords = "genetic algorithms, genetic programming, PSO, feature partitioning, noise factor, object classification, optimal partition matrix, particle swarm optimisation, weight matrix, feature extraction, image classification, object detection, particle swarm optimisation", DOI = "doi:10.1109/IVCNZ.2008.4762143", abstract = "This paper describes a new approach to the use of particle swarm optimisation (PSO) for object classification problems. Instead of using PSO to evolve only a set of good parameter values for another machine learning method for object classification, the new approach developed in this paper can be used as a stand alone method for classification. Two new methods are developed in the new approach. The first new PSO method treats all different features equally important and finds an optimal partition matrix to separate a data set into distinct class groups. The second new PSO method considers the relative importance of each feature with the noise factor, and evolves a weight matrix to mitigate the effects of noisy partitions and feature dimensions. The two methods are examined and compared with a popular method using PSO combined with the nearest centroid and another evolutionary computing method, genetic programming, on three image data sets of increasing difficulty. The results suggest that the new weighted PSO method outperforms these existing methods on these object classification problems.", notes = "Refers to \cite{zhang:2004:eurogp} Also known as \cite{4762143}", } @InProceedings{evett:1987:rifs, author = "Ian W. Evett and E. J. Spiehler", title = "Rule Induction in Forensic Science", booktitle = "KBS in Government", year = "1987", pages = "107--118", publisher_address = "Pinner, UK", publisher = "Online Publications", keywords = "genetic algorithms, genetic programming, BEAGLE", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/evett_1987_rifs.pdf", notes = "British Library shelfmark 5088.238300 BEAGLE trial by UK Home Office forensic scientist to give binary and three way classification of class samples based on its refractive index and its composition (8 elements obtianed by scanning electron microscope). In blind trial (ten cases) 'The results reported from BEAGLE rules gave the lowest error rate.' [page 116] when compared to two standard techniques, neighest 3 neighbours (cartestian distance) and Statistical Package for the Social Sciences (SPSS). Is this the source of UCI glass dataset? https://archive.ics.uci.edu/ml/datasets/Glass+Identification BEAGLE see for example \cite{kybernetes:forsyth}", } @TechReport{Evett97-agps-tr, author = "M. Evett and T. Fernandez", title = "A Distributed System for Genetic Programming that Dynamically Allocates Processors", institution = "Dept. Computer Science and Engineering, Florida Atlantic University", year = "1997", address = "Boca Raton, FL, USA", annote = "AGPS", keywords = "genetic algorithms, genetic programming", notes = "See \cite{Evett:1997:aaaiMAL}. parallel GP system, AGPS, is based on MPI, not PVM", } @InProceedings{Evett:1997:aaaiMAL, author = "Matthew Evett and Thomas Fernandez", title = "A Distributed System for Genetic Programming that Dynamically Allocates Processors", booktitle = "Papers from the AAAI Workshop on Building Resource-Bounded Reasoning Systems", year = "1997", editor = "Shlomo Zilberstein and Louis Hoebel", pages = "43--48", organisation = "AAAI", note = "Published in AAAI Technical Report WS-97-06", keywords = "genetic algorithms, genetic programming", URL = "http://www.aaai.org/Papers/Workshops/1997/WS-97-06/WS97-06-008.pdf", size = "6 pages", abstract = "AGPS is a portable, distributed genetic programming system, implemented on MPI. AGPS views processors as a bounded resource and optimises the use of that resource by dynamically varying the number of processors that it uses during execution, adapting to the external demand for those processors. AGPS also attempts to optimize the use of available processors by automatically terminating a genetic programming run when it appears to have stalled in a local minimum so that another run can begin.", notes = "http://www.aaai.org/Library/Workshops/ws97-06.php", } @InProceedings{evett:1998:GPsqp, author = "Matthew Evett and Taghi Khoshgoftar and Pei-der Chien and Edward Allen", title = "GP-based software quality prediction", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "60--65", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, SBSE", ISBN = "1-55860-548-7", URL = "http://www.emunix.emich.edu/~evett/Publications/gp98-se.pdf", size = "6 pages", abstract = "Software development managers use software quality prediction methods to determine to which modules expensive reliability techniques should be applied. In this paper we describe a genetic programming (GP) based system for targeting software modules for reliability enhancement. The paper describes the GP system, and provides a case study using software quality data from two actual industrial projects. The system is shown to be robust enough for use in industrial domains.", notes = "GP-98", } @InProceedings{evett:1998:nmidncGP, author = "Matthew Evett and Thomas Fernandez", title = "Numeric Mutation Improves the Discovery of Numeric Constants in Genetic Programming", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "66--71", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-548-7", URL = "http://www.emunix.emich.edu/~evett/Publications/gp98-nm.pdf", size = "6 pages", abstract = "Genetic programming suffers difficulty in discovering useful numeric constants for the terminal nodes of its sexpression trees. In earlier work we postulated a solution to this problem called numeric mutation. Here, we provide empirical evidence to demonstrate that this method provides a statistically significant improvement in GP system performance on a variety of problems.", notes = "GP-98", } @InProceedings{FLAIRS98-020, author = "Matthew Evett and Thomas Fernandez", title = "Numeric Mutation: Improved Search in Genetic Programming", booktitle = "Proceedings of the Eleventh International FLAIRS Conference", year = "1998", pages = "106--109", publisher = "AAAI", keywords = "genetic algorithms, genetic programming", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.523.4494", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.523.4494", URL = "https://www.aaai.org/Library/FLAIRS/1998/flairs98-020.php", URL = "https://www.aaai.org/Papers/FLAIRS/1998/FLAIRS98-020.pdf", size = "4 page", abstract = "Genetic programming is relatively poor at discovering useful numeric constants for the terminal nodes of its s-expression trees. In this paper we outline an adaptation to genetic programming, called numeric mutation. We provide empirical evidence and analysis that demonstrate that numeric mutation makes a statistically significant increase in genetic programming's performance for symbolic regression problems.", } @InProceedings{Evett:1999:FLAIRS, author = "Matthew Evett and Taghi Khoshgoftaar and Pei-der Chien and Ed Allen", title = "Using genetic programming to determine software quality", booktitle = "Proceedings of the Twelfth International FLAIRS Conference", year = "1999", pages = "113--117", publisher = "AAAI", keywords = "genetic algorithms, genetic programming, SBSE", URL = "http://www.aaai.org/Papers/FLAIRS/1999/FLAIRS99-020.pdf", abstract = "Software development managers use software quality prediction methods to determine to which modules expensive reliability techniques should be applied. In this paper we describe a genetic programming (GP) based system that classifies software modules as {"}faulty{"} or {"}Not faulty{"}, allowing the targeting of modules for reliability enhancement. The paper describes the GP system, and provides a case study using software quality data from a very large industrial project. The demonstrated quality of the system is such that plans are under way to integrate it into a commercial software quality management system.", notes = "lil-gp p117 {"}Indeed, our current work involves the integration of this GP system into the existing EMERALD industrial software management system.{"} Department of Computer Science and Engineering Florida Atlantic University Boca Raton, Florida 33431, USA Evolved non-linear model could use EMERALD metrics: FILINCUQT, LGPATHT, VARSPNMX, USAGEA, BETA_PR, BETA_FIX, CUST_FIX, SRC_GRON, UNQ_DENS, UPD_CATR, VLO_UPD", } @InProceedings{evett:1999:MG, author = "Matthew Evett and Taghi Khoshgoftaar and Pei-der Chien and Edward Allen", title = "Modelling software quality with GP", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1232", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, poster papers, SBSE", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-462.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-462.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Article{evonews:1999:mole, key = "evonews", title = "MOLE at City University", journal = "EvoNEWS", year = "1999", volume = "11", pages = "2--3", month = "summer", keywords = "genetic algorithms, genetic programming", URL = "http://evonet.lri.fr/evoweb/files/evonews/evonews11.pdf", abstract = "Profile of research group. Introns Peter Smith application of GP to MRI brain tumors+Principal Component Analysis, NMR Helen Gray and Peter W. H. Smith (NMR in Biomedicine, 11)", } @Article{evonews:1999:art, key = "evonews", title = "Evol-artists - a new breed entirely", journal = "EvoNEWS", year = "1999", volume = "11", pages = "7--10", month = "summer", keywords = "genetic algorithms, genetic programming", URL = "http://evonet.lri.fr/evoweb/files/evonews/evonews11.pdf", abstract = "I CANT STOP. There is something compelling about this process. It feels as though the images are trying to break out of their hyperspace into the physical world. Sometimes I'll be two or three days into a run dozens of generations with one or two hundred individuals in the population when Wham! there's something familiar staring back at me from out of the computer screen, demanding to be made real.", notes = "Steven Rooke. Richard Dawkins Biomorphs. Jeffrey Ventrella. Mattias Fagerlund http://www.acacia.se/Mattias/WebGP/ Ken Musgrave. Karl Sims. Dr. Mutatis an evolutionary art tool. Jano I. van Hemert --- Pieter Mondriaan. Pensousal Machado -- NEvAr", } @InCollection{Evstigneev2009507, author = "Igor V. Evstigneev and Thorsten Hens and Klaus Reiner Schenk-Hoppe", title = "Evolutionary Finance", editor = "Thorsten Hens and Klaus Reiner Schenk-Hoppe", booktitle = "Handbook of Financial Markets: Dynamics and Evolution", publisher = "North-Holland", address = "San Diego", year = "2009", chapter = "9", pages = "507--566", keywords = "genetic algorithms, genetic programming", isbn13 = "978-0-12-374258-2", DOI = "doi:10.1016/B978-012374258-2.50013-0", URL = "http://www.sciencedirect.com/science/article/B8N8N-4W6Y2CK-9/2/d140c798e01e01356572d883e6694255", abstract = "Publisher Summary This chapter surveys current research and applications of evolutionary finance inspired by Darwinian ideas and random dynamical systems theory. This approach studies the market interaction of investment strategies, and the wealth dynamics it entails in financial markets. The emphasis in this survey was on the motivation and the heuristic justification of the results; technical details were avoided as much as possible. In contrast to the current standard paradigm in economic modelling, this approach is based on random dynamical systems. An equilibrium holds only in the short term, which reflects the model of investment behaviour explored in an evolutionary finance approach. Continuous-time evolutionary finance models are the latest development in this field. This approach can be seen as a generalisation of the workhorse model of continuous-time financial mathematics. One advantage of this model is the flexibility to have different trade frequencies and changes in dividend payments. Abstract Evolutionary finance studies the dynamic interaction of investment strategies in financial markets. This market interaction generates a stochastic wealth dynamic on a heterogenous population of traders through the fluctuation of asset prices and their random payoffs. Asset prices are endogenously determined through short-term market clearing. Investors' portfolio choices are characterized by investment strategies that provide a descriptive model of decision behavior. The mathematical framework of these models is given by random dynamical systems. This chapter surveys the recent progress made by the authors in the theory and applications of evolutionary finance models. An introduction to and the motivation of the modeling approach is followed by a theoretical part that presents results on the market selection (and coexistence) of investment strategies, discusses the relation to the Kelly Rule and implications for asset-pricing theory, and introduces a continuous-time mathematical finance version. Applications are concerned with simulation studies of market dynamics, empirical estimation of asset prices and their dynamics, and evolution of investment strategies using genetic programming.", } @InProceedings{FaHa2012-ICPE, author = "Michael Faber and Jens Happe", title = "Systematic adoption of genetic programming for deriving software performance curves", booktitle = "Proceedings of the third joint WOSP/SIPEW international conference on Performance Engineering", year = "2012", pages = "33--44", address = "Boston, USA", month = apr # " 22-25", publisher = "ACM", keywords = "genetic algorithms, genetic programming, SBSE, black-box approach, machine learning, model inference, software performance engineering", isbn13 = "978-1-4503-1202-8", URL = "http://sdqweb.ipd.kit.edu/publications/pdfs/FaHa2012-ICPE.pdf", DOI = "doi:10.1145/2188286.2188295", size = "12 pages", abstract = "Measurement-based approaches to software performance engineering apply analysis methods (e.g., statistical inference or machine learning) on raw measurement data with the goal to build a mathematical model describing the performance-relevant behaviour of a system under test (SUT). The main challenge for such approaches is to find a reasonable trade-off between minimising the amount of necessary measurement data used to build the model and maximising the model's accuracy. Most existing methods require prior knowledge about parameter dependencies or their models are limited to only linear correlations. In this paper, we investigate the applicability of genetic programming (GP) to derive a mathematical equation expressing the performance behaviour of the measured system (software performance curve). We systematically optimised the parameters of the GP algorithm to derive accurate software performance curves and applied techniques to prevent overfitting. We conducted an evaluation with a representative MySQL database system. The results clearly show that the GP algorithm outperforms other analysis techniques like inverse distance weighting (IDW) and multivariate adaptive regression splines (MARS) in terms of model accuracy.", acmid = "2188295", notes = "p43 'In a final evaluation, we show that the optimized GP algorithm outperforms MARS and IDW in terms of model accuracy.'", } @InProceedings{Fabera:2012:mendel, author = "Vit Fabera and Jan Zelenka and Maria Janesova and Vlastimil Janes", title = "Grammatical Evolution and FSM Construction", booktitle = "18th International Conference on Soft Computing, MENDEL 2012", editor = "Radomil Matousek", pages = "94--99", year = "2012", address = "Brno, Czech Republic", edition = "1st", month = "27-29 " # jun, publisher = "Brno University of Technology", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", isbn13 = "978-80-214-4540-6", size = "6 pages", notes = "http://www.mendel-conference.org/", } @InProceedings{fabrega:1999:GANNCSG, author = "Francesc Xavier Llora i Fabrega and Josep Maria Garrell i Guiu", title = "GENIFER: A Nearest Neighbour based Classifier System using GA", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "797", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-321.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-321.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Fagan:2010:EuroGP, author = "David Fagan and Michael O'Neill and Edgar Galvan-Lopez and Anthony Brabazon and Sean McGarraghy", title = "An Analysis of Genotype-Phenotype Maps in Grammatical Evolution", booktitle = "Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010", year = "2010", editor = "Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar", volume = "6021", series = "LNCS", pages = "62--73", address = "Istanbul", month = "7-9 " # apr, organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-12147-0", DOI = "doi:10.1007/978-3-642-12148-7_6", abstract = "We present an analysis of the genotype-phenotype map in Grammatical Evolution (GE). The standard map adopted in GE is a depth-first expansion of the non-terminal symbols during the derivation sequence. Earlier studies have indicated that allowing the path of the expansion to be under the guidance of evolution as opposed to a deterministic process produced significant performance gains on all of the benchmark problems analysed. In this study we extend this analysis to include a breadth-first and random map, investigate additional benchmark problems, and take into consideration the implications of recent results on alternative grammar representations with this new evidence. We conclude that it is possible to improve the performance of grammar-based Genetic Programming by the manner in which a genotype-phenotype map is performed.", notes = "Typed GP, GEVA, pi-GE, 5-parity, x+x^2+x^3+x^4, Santa Fe trail, Max \cite{langdon:1997:MAX}. Part of \cite{Esparcia-Alcazar:2010:GP} EuroGP'2010 held in conjunction with EvoCOP2010 EvoBIO2010 and EvoApplications2010", } @InProceedings{fagan_etal:cec2010, author = "David Fagan and Miguel Nicolau and Michael O'Neill and Edgar Galvan-Lopez and Anthony Brabazon and Sean McGarraghy", title = "Investigating Mapping Order in {piGE}", booktitle = "2010 IEEE World Congress on Computational Intelligence", pages = "3058--3064", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, grammatical evolution", isbn13 = "978-1-4244-6910-9", DOI = "doi:10.1109/CEC.2010.5586204", size = "7 pages", abstract = "We present an investigation into the genotype-phenotype map in Position Independent Grammatical Evolution (piGE). Previous studies have shown piGE to exhibit a performance increase over standard GE. The only difference between the two approaches is in how the genotype-phenotype mapping process is performed. GE uses a leftmost non terminal expansion, while piGE evolves the order of mapping as well as the content. In this study, we use the idea of focused search to examine which aspect of the piGE mapping process provides the lift in performance over standard GE by applying our approaches to four benchmark problems taken from specialised literature. We examined the traditional piGE approach and compared it to two setups which examined the extremes of mapping order search and content search, and against setups with varying ratios of content and order search. In all of these tests a purely content focused piGE was shown to exhibit a performance gain over the other setups.", notes = "WCCI 2010. Also known as \cite{5586204}", } @TechReport{FaganNHOB:TechReport042011, author = "David Fagan and Miguel Nicolau and Erik Hemberg and Michael O'Neill and Anthony Brabazon", title = "Dynamic Ant: Introducing a new benchmark for Genetic Programming in Dynamic Environments", institution = "UCD School of Computer Science and Informatics", year = "2011", number = "UCD-CSI-2011-04", address = "University College Dublin, Ireland", month = apr # " 14", keywords = "genetic algorithms, genetic programming, grammatical evolution", broken = "http://www.csi.ucd.ie/biblio", URL = "http://www.csi.ucd.ie/files/UCD-CSI-2011-04.pdf", size = "17 pages", abstract = "In this paper we present a new variant of the ant problem in the dynamic problem domain. This approach presents a functional dynamism to the problem landscape, where by the behaviour of the ant is driven by its ability to explore the search space being constrained. This restriction is designed in such a way as to ensure that no generalised solution to the problem is possible, thus providing a functional change in behaviour.", } @InProceedings{fagan:2011:EuroGP, author = "David Fagan and Miguel Nicolau and Erik Hemberg and Michael O'Neill and Anthony Brabazon and Sean McGarraghy", title = "Investigation of the Performance of Different Mapping Orders for GE on the Max Problem", booktitle = "Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011", year = "2011", month = "27-29 " # apr, editor = "Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado", series = "LNCS", volume = "6621", publisher = "Springer Verlag", address = "Turin, Italy", pages = "286--297", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, Grammatical Evolution: poster", isbn13 = "978-3-642-20406-7", DOI = "doi:10.1007/978-3-642-20407-4_25", abstract = "We present an analysis of how the genotype-phenotype map in Grammatical Evolution (GE) can effect performance on the Max Problem. Earlier studies have demonstrated a performance decrease for Position Independent Grammatical Evolution (pige) in this problem domain. In piGE the genotype-phenotype map is changed so that the evolutionary algorithm controls not only what the next expansion will be but also the choice of what position in the derivation tree is expanded next. In this study we extend previous work and investigate whether the ability to change the order of expansion is responsible for the performance decrease or if the problem is simply that a certain order of expansion in the genotype-phenotype map is responsible. We conclude that the reduction of performance in the Max problem domain by pi GE is rooted in the way the genotype-phenotype map and the genetic operators used with this mapping interact.", notes = "Part of \cite{Silva:2011:GP} EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011", } @InProceedings{Fagan:2011:GECCOposter, author = "David Fagan and Miguel Nicolau and Erik Hemberg and Michael O'Neill and Anthony Brabazon", title = "Dynamic ant: introducing a new benchmark for genetic programming in dynamic environments", booktitle = "GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0690-4", keywords = "genetic algorithms, genetic programming, grammatical evolution: Poster", pages = "183--184", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001858.2001961", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "In this paper we present a new variant of the Ant Problem in the Dynamic Problem Domain. This approach presents a functional dynamism to the problem landscape, where by the behaviour of the ant is driven by its ability to explore the search space being constrained. This restriction is designed in such a way as to ensure that no generalised solution to the problem is possible, thus providing a functional change in behaviour.", notes = "Also known as \cite{2001961} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @InProceedings{Fagan:2011:GECCOcomp, author = "David Fagan", title = "Genotype-phenotype mapping in dynamic environments with grammatical evolution", booktitle = "GECCO 2011 Graduate students workshop", year = "2011", editor = "Miguel Nicolau", isbn13 = "978-1-4503-0690-4", keywords = "genetic algorithms, genetic programming, grammatical evolution", pages = "783--786", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001858.2002091", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "The application of a genotype-phenotype mapping in Evolutionary Computation is not a new idea, however, how this mapping process is interpreted, and implemented varies wildly. In the majority of cases a very simple abstraction of the biological genotype-phenotype mapping is used, but as our understanding of this process increases, the deficiencies in current approaches become more evident. In this paper, an outline of what approaches have been taken in the investigation of the genotype-phenotype map in Grammatical Evolution are presented and an outline of proposed future work is introduced.", notes = "Also known as \cite{2002091} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @InProceedings{Fagan:2012:mendel, author = "David Fagan and Erik Hemberg and Michael O'Neill and Sean McGarraghy", title = "Fitness Reactive Mutation in Grammatical Evolution", booktitle = "18th International Conference on Soft Computing, MENDEL 2012", editor = "Radomil Matousek", pages = "144--149", year = "2012", address = "Brno, Czech Republic", edition = "1st", month = "27-29 " # jun, publisher = "Brno University of Technology", keywords = "genetic algorithms, genetic programming", isbn13 = "978-80-214-4540-6", size = "6 pages", notes = "http://www.mendel-conference.org/", } @InProceedings{Fagan:2012:GECCOcomp, author = "David Fagan and Erik Hemberg and Miguel Nicolau and Michael O'Neill and Sean McGarraghy", title = "Towards adaptive mutation in grammatical evolution", booktitle = "GECCO Companion '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion", year = "2012", editor = "Terry Soule and Anne Auger and Jason Moore and David Pelta and Christine Solnon and Mike Preuss and Alan Dorin and Yew-Soon Ong and Christian Blum and Dario Landa Silva and Frank Neumann and Tina Yu and Aniko Ekart and Will Browne and Tim Kovacs and Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and Giovanni Squillero and Nicolas Bredeche and Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and Martin Pelikan and Silja Meyer-Nienberg and Christian Igel and Greg Hornby and Rene Doursat and Steve Gustafson and Gustavo Olague and Shin Yoo and John Clark and Gabriela Ochoa and Gisele Pappa and Fernando Lobo and Daniel Tauritz and Jurgen Branke and Kalyanmoy Deb", isbn13 = "978-1-4503-1178-6", keywords = "genetic algorithms, Genetic programming, grammatical evolution: Poster", pages = "1481--1482", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330784.2331002", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Adaptive mutation operations have been proposed in Evolutionary Computation (EC) many times and in different varieties, but few have gained widespread use. In nature, mutation rates vary over time, however it has become common practice to use static, widely accepted, values for mutation, particularly in GP-like systems. In this study, an adaptive mutation operation is presented and applied to Grammatical Evolution (GE) over a variety of benchmark problems. The results are examined and it is determined that the new operators could replace the need to specify mutation rates in GE on the problem domains examined.", notes = "Also known as \cite{2331002} Distributed at GECCO-2012. ACM Order Number 910122.", } @InProceedings{fagan:2013:EuroGP, author = "David Fagan and Erik Hemberg and Michael O'Neill and Sean McGarraghy", title = "Understanding Expansion Order and Phenotypic Connectivity in piGE", booktitle = "Proceedings of the 16th European Conference on Genetic Programming, EuroGP 2013", year = "2013", month = "3-5 " # apr, editor = "Krzysztof Krawiec and Alberto Moraglio and Ting Hu and A. Sima Uyar and Bin Hu", series = "LNCS", volume = "7831", publisher = "Springer Verlag", address = "Vienna, Austria", pages = "37--48", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, grammatical evolution", isbn13 = "978-3-642-37206-3", DOI = "doi:10.1007/978-3-642-37207-0_4", abstract = "Since its inception, pige has used evolution to guide the order of how to construct derivation trees. It was hypothesised that this would allow evolution to adjust the order of expansion during the run and thus help with search. This research aims to identify if a specific order is reachable, how reachable it may be, and goes on to investigate what happens to the expansion order during a piGE run. It is concluded that within pige we do not evolve towards a specific order but a rather distribution of orders. The added complexity that an evolvable order gives pige can make it difficult to understand how it can effectively search, by examining the connectivity of the phenotypic landscape it is hoped to understand this. It is concluded that the addition of an evolvable derivation tree expansion order makes the phenotypic landscape associated with pige very densely connected, with solutions now linked via a single mutation event that were not previously connected.", notes = "Part of \cite{Krawiec:2013:GP} EuroGP'2013 held in conjunction with EvoCOP2013, EvoBIO2013, EvoMusArt2013 and EvoApplications2013", } @PhdThesis{fagan:PhDThesis:2014, author = "David Fagan", title = "Analysing the Genotype-Phenotype Map in Grammatical Evolution", school = "University College Dublin", year = "2013", address = "Ireland", month = "30 " # oct, keywords = "genetic algorithms, genetic programming, Grammatical Evolution, piGE", URL = "http://ncra.ucd.ie/papers/DavidFaganPhDThesis2014.pdf", size = "207 pages", abstract = "The Genotype-Phenotype Map (GPM) is an important aspect of the representation in Evolutionary Computing (EC). The GPM decouples the search space of the EC algorithm into a many-to-one mapping, allowing an abstraction of the search and solution spaces, which can bring a number of benefits to search. Grammatical Evolution (GE) is a grammar based form of Genetic Programming (GP) that incorporates a GPM at its core, which is loosely inspired by nature. This thesis investigates whether different approaches to the GPM can have a positive effect on GE's performance. By examining a range of GPMs that use differing expansion order principles it was found the one approach, Position Independent Grammatical Evolution (piGE) presented a viable alternative to the canonical GE GPM. piGE, while showing good performance, uses a variable expansion order controlled by evolution. This variable ordering increases the size of the search space that must be navigated by piGE during evolution. It is found that piGE gains a significant increase in connectivity by using an evolvable order, while also providing piGE with additional neutrality. Knowing what orders piGE uses during evolution may provide insight into new GPM approaches. With this in mind a set of measures are devised, that allow for the monitoring of piGE's population during an evolutionary run. What is found is that piGE doesn't converge to a single order but rather a distribution of GPM orders. The addition of the evolvable order in piGE provides an added degree of freedom in the mapping that is not exploited by standard genetic operations. A mutation operation is presented that will allow the algorithm to focus mutation on certain aspects of the piGE chromosome. It is found that with this ability the performance of piGE is increased.", notes = "Supervisor: Michael O'Neill", } @InProceedings{Fagan:2016:CEC, author = "David Fagan and Michael Fenton and Michael O'Neill", title = "Exploring Position Independent Initialisation in Grammatical Evolution", booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016)", year = "2016", editor = "Yew-Soon Ong", pages = "5060--5067", address = "Vancouver", month = "24-29 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", isbn13 = "978-1-5090-0623-6", DOI = "doi:10.1109/CEC.2016.7748331", abstract = "Initialisation in Grammatical Evolution (GE) is a topic that remains open to debate on many fronts. The literature falls between two mainstay approaches: random and sensible initialisation. These methods are not without their drawbacks with the type of trees generated. This paper tackles this problem by extending these traditional operators to incorporate position independence in the initialisation process in GE. This new approach to initialisation is shown to provide a viable alternative to the commonly used approaches, whilst avoiding the common pitfalls of traditional approaches to initialisation.", notes = "WCCI2016", } @InProceedings{Fagan:2017:IJCNN, author = "David Fagan and Michael Fenton and David Lynch and Stepan Kucera and Holger Claussen and Michael O'Neill", title = "Deep learning through evolution: A hybrid approach to scheduling in a dynamic environment", booktitle = "2017 International Joint Conference on Neural Networks (IJCNN)", year = "2017", pages = "775--782", month = may, publisher = "IEEE Press", email = "david.fagan@ucd.ie", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Bandwidth, ANN, Computer architecture, Downlink, Interference, Schedules, Signal to noise ratio", DOI = "doi:10.1109/IJCNN.2017.7965930", size = "8 pages", abstract = "Genetic Algorithms (GAs) have been shown to be a very effective optimisation tool on a wide variety of problems. However, they are not without their drawbacks. GAs require time to run, and evolve a bespoke solution to the desired problem in real time. This requirement can prove to be prohibitive in a high-frequency dynamic environment where on-line training time is limited. Neural Networks (NNs) on the other hand can be trained at length off-line, before being deployed on-line, allowing for fast generation of solutions on demand. This study presents a hybrid approach to time-frame scheduling in a high frequency domain. A GA approach is used to generate a dataset of optimised human-competitive solutions. Deep Learning is then deployed to extract the underlying model within the GA, enabling fast optimisation on unseen data. This hybrid approach allows for NNs to generate GA-quality schedules on-line, almost 100 times faster than running the GA.", notes = "Not GP? Comparison with GE? also known as \cite{7965930}", } @InCollection{Fagan:2018:hbge, author = "David Fagan and Eoin Murphy", title = "Mapping in Grammatical Evolution", booktitle = "Handbook of Grammatical Evolution", publisher = "Springer", year = "2018", editor = "Conor Ryan and Michael O'Neill and J. J. Collins", chapter = "4", pages = "79--108", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", isbn13 = "978-3-319-78716-9", DOI = "doi:10.1007/978-3-319-78717-6_4", abstract = "The act of going from genotype to phenotype in Grammatical Evolution requires the application of a mapping process. This mapping process works in conjunction with a grammar, to transform an ordinary string of integers into a possible solution to a problem. In this chapter, the reader is exposed to the rich vein of research exploring mappings in Grammatical Evolution. A comprehensive survey of the field of Mapping in GE is presented before the chapter focuses on the main theme, Position Independent Mappings. Firstly pi_GE is presented outlining some of the benefits of the approach, before the reader is presented with a position independent mapping that uses advances in mappings and grammars to present a very powerful variant of GE, TAGE. The chapter concludes by briefly exploring a highly complex developmental variant of the TAGE mapping.", notes = "Part of \cite{Ryan:2018:hbge}", } @InProceedings{Fagiani:2015:ieeeEEEIC, author = "Marco Fagiani and Stefano Squartini and Roberto Bonfigli and Francesco Piazza", booktitle = "15th IEEE International Conference on Environment and Electrical Engineering (EEEIC)", title = "Short-term load forecasting for smart water and gas grids: A comparative evaluation", year = "2015", pages = "1198--1203", abstract = "Moving from a recent publication of Fagiani et al. [1], short-term predictions of water and natural gas consumption are performed exploiting state-of-the-art techniques. Specifically, for two datasets, the performance of Support Vector Regression (SVR), Extreme Learning Machine (ELM), Genetic Programming (GP), Artificial Neural Networks (ANNs), Echo State Networks (ESNs), and Deep Belief Networks (DBNs) are compared adopting common evaluation criteria. Concerning the datasets, the Almanac of Minutely Power Dataset (AMPds) is used to compute predictions with domestic consumption, 2 year of recordings, and to perform further evaluations with the available heterogeneous data, such as energy and temperature. Whereas, predictions of building consumption are performed with the datasets recorded at the Department for International Development (DFID). In addition, the results achieved for the previous release of the AMPds, 1 year of recordings, are also reported, in order to evaluate the impact of seasonality in forecasting performance. Finally, the achieved results validate the suitability of ANN, SVR and ELM approaches for prediction applications in small-grid scenario. Specifically, for the domestic consumption the best performance are achieved by SVR and ANN, for natural gas and water, respectively. Whereas, the ANN shows the best results for both water and natural gas forecasting in building scenario.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/EEEIC.2015.7165339", month = jun, notes = "Also known as \cite{7165339}", } @Article{Fagiani:2015:Neurocomputing, author = "M. Fagiani and S. Squartini and L. Gabrielli and S. Spinsante and F. Piazza", title = "A review of datasets and load forecasting techniques for smart natural gas and water grids: Analysis and experiments", journal = "Neurocomputing", volume = "170", pages = "448--465", year = "2015", note = "Advances on Biological Rhythmic Pattern Generation: Experiments, Algorithms and Applications, Selected Papers from the 2013 International Conference on Intelligence Science and Big Data Engineering (IScIDE 2013)Computational Energy Management in Smart Grids", ISSN = "0925-2312", DOI = "doi:10.1016/j.neucom.2015.04.098", URL = "http://www.sciencedirect.com/science/article/pii/S0925231215009297", abstract = "In this paper, experiments concerning the prediction of water and natural gas consumption are presented, focusing on how to exploit data heterogeneity to get a reliable outcome. Prior to this, an up-to-date state-of-the-art review on the available datasets and forecasting techniques of water and natural gas consumption, is conducted. A collection of techniques (Artificial Neural Networks, Deep Belief Networks, Echo State Networks, Support Vector Regression, Genetic Programming and Extended Kalman Filter-Genetic Programming), partially selected from the state-of-the-art ones, are evaluated using the few publicly available datasets. The tests are performed according to two key aspects: homogeneous evaluation criteria and application of heterogeneous data. Experiments with heterogeneous data obtained combining multiple types of resources (water, gas, energy and temperature), aimed to short-term prediction, have been possible using the Almanac of Minutely Power dataset (AMPds). On the contrary, the Energy Information Administration (E.I.A.) data are used for long-term prediction combining gas and temperature information. At the end, the selected approaches have been evaluated using the sole Tehran water consumption for long-term forecasts (thanks to the full availability of the dataset). The AMPds and E.I.A. natural gas results show a correlation with temperature, that produce a performance improvement. The ANN and SVR approaches achieved good performance for both long/short-term predictions, while the EKF-GP showed good outcomes with the E.I.A. datasets. Finally, it is the authors times' purpose to create a valid starting point for future works that aim to develop innovative forecasting approaches, providing a fair comparison among different computational intelligence and machine learning techniques.", keywords = "genetic algorithms, genetic programming, Heterogeneous data forecasting, Short/long-term load forecasting, Smart water/gas grid, Forecasting techniques, Computational intelligence, Machine learning", } @InProceedings{faglia:1996:mpdCAMGA, author = "Rodolfo Faglia and David Vetturi", title = "Motion Planning and Design of CAM Mechanisms by Means of a Genetic Algorithm", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "Genetic Algorithms", pages = "479--484", address = "Stanford University, CA, USA", publisher = "MIT Press", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap79.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96 GA paper", } @Article{fairley:2003:gbc, author = "Peter Fairley", title = "Germs that build Circuits", journal = "IEEE Spectrim", year = "2003", pages = "36--41", month = nov, keywords = "nanotechnology", URL = "http://ieeexplore.ieee.org/iel5/6/27854/01242955.pdf", size = "5 pages", abstract = "Circuits With viruses serving as construction crews and DNA as the blueprint, biotechnology may hold the key to postlithography integrated circuits", notes = "gee wow level but good pointers? Belcher (MIT), protein genetic engineering circuit evolution peptide gallium arsenide, indium phosphate, 7x850nm, quantum dot, solar cells, flash memory, laser, semiconductors zinc sulfide and cadmium sulfide, magnetic materials cobalt-platinum and iron-platinum. viralware, fabric treatment and cosmetic products. Sensors. Carbon nanotubes. $30m ", } @InProceedings{Fajardo-Delgado:2018:ICAISC, author = "Daniel Fajardo-Delgado and Maria Guadalupe Sanchez and Raquel Ochoa-Ornelas and Ismael Edrein Espinosa-Curiel and Vicente Vidal", title = "Genetic Programming for the Classification of Levels of Mammographic Density", booktitle = "International Conference on Artificial Intelligence and Soft Computing, ICAISC 2018", year = "2018", editor = "Leszek Rutkowski and Rafal Scherer and Marcin Korytkowski and Witold Pedrycz and Ryszard Tadeusiewicz and Jacek M. Zurada", volume = "10841", series = "Lecture Notes in Computer Science", pages = "363--375", address = "Zakopane, Poland", month = jun # " 3-7", publisher = "Springer", keywords = "genetic algorithms, genetic programming, breast cancer, levels of mammographic density", isbn13 = "978-3-319-91252-3", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icaisc/icaisc2018-1.html#Fajardo-Delgado18", DOI = "doi:10.1007/978-3-319-91253-0_34", size = "13 pages", abstract = "Breast cancer is the second cause of death of adult women in Mexico. Some of the risk factors for breast cancer that are visible in a mammography are the masses, calcifications, and the levels of mammographic density. While the first two have been studied extensively through the use of digital mammographies, this is not the case for the last one. In this paper, we address the automatic classification problem for the levels of mammographic density based on an evolutionary approach. Our solution comprises the following stages: thresholding, feature extractions, and the implementation of a genetic program. We performed experiments to compare the accuracy of our solution with other conventional classifiers. Experimental results show that our solution is very competitive and even outperforms the other classifiers in some cases.", notes = "conf/icaisc/Fajardo-Delgado18", } @Article{fajardo-delgado:2024:AS, author = "Daniel Fajardo-Delgado and Ansel Y. Rodriguez-Gonzalez and Sergio Sandoval-Perez and Jesus Ezequiel Molinar-Solis and Maria Guadalupe Sanchez-Cervantes", title = "Genetic Programming to Remove Impulse Noise in Color Images", journal = "Applied Sciences", year = "2024", volume = "14", number = "1", pages = "Article No. 126", keywords = "genetic algorithms, genetic programming, digital image processing, color image denoising, impulse noise, adaptive filters", ISSN = "2076-3417", URL = "https://www.mdpi.com/2076-3417/14/1/126", DOI = "doi:10.3390/app14010126", size = "22 pages", abstract = "This paper presents a new filter to remove impulse noise in digital colour images. The filter is adaptive in the sense that it uses a detection stage to only correct noisy pixels. Detecting noisy pixels is performed by a binary classification model generated via genetic programming, a paradigm of evolutionary computing based on natural biological selection. The classification model training considers three impulse noise models in colour images: salt and pepper, uniform, and correlated. This is the first filter generated by genetic programming exploiting the correlation among the colour image channels. The correction stage consists of a vector median filter version that modifies colour channel values if some are noisy. An experimental study was performed to compare the proposed filter with others in the state-of-the-art related to colour image denoising. Their performance was measured objectively through the image quality metrics PSNR, MAE, SSIM, and FSIM. Experimental findings reveal substantial variability among filters based on noise model and image characteristics. The findings also indicate that, on average, the proposed filter consistently exhibited top-tier performance values for the three impulse noise models, surpassed only by a filter employing a deep learning-based approach. Unlike deep learning filters, which are black boxes with internal workings invisible to the user, the proposed filter has a high interpretability with a performance close to an equilibrium point for all images and noise models used in the experiment.", notes = "also known as \cite{app14010126}", } @Article{Fajfar:2016:EC, author = "Iztok Fajfar and Janez Puhan and Arpad Burmen", title = "Evolving a Nelder-Mead Algorithm for Optimization with Genetic Programming", journal = "Evolutionary Computation", year = "2017", volume = "25", number = "3", pages = "351--373", month = "Fall", keywords = "genetic algorithms, genetic programming", ISSN = "1063-6560", DOI = "doi:10.1162/EVCO_a_00174", size = "23 page", abstract = "We use genetic programming to evolve a direct search optimization algorithm, similar to that of the standard downhill simplex optimization method proposed by Nelder and Mead (1965). In training process, we use several 10-dimensional quadratic functions with randomly displaced parameters and different randomly generated starting simplices. The genetically obtained optimization algorithm shows overall better performance than the original Nelder-Mead method on a standard set of test functions. We observe that many parts of the genetically produced algorithm are seldom or never executed, which allows us to greatly simplify the algorithm by removing the redundant parts. The resulting algorithm turns out to be considerably simpler than the original Nelder-Mead method while still performing better than the original method.", } @Article{Fajfar:GPEM, author = "Iztok Fajfar and Arpad Burmen and Janez Puhan", title = "Grammatical evolution as a hyper-heuristic to evolve deterministic real-valued optimization algorithms", journal = "Genetic Programming and Evolvable Machines", year = "2018", volume = "19", number = "4", pages = "473--504", month = dec, keywords = "genetic algorithms, genetic programming, Grammatical evolution Real function minimization, Derivative-free optimization, Nelder-Mead method, Hyper-heuristics, Meta optimization", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-018-9324-5", size = "32 pages", abstract = "Hyper-heuristic methodologies have been extensively and successfully used to generate combinatorial optimization heuristics. On the other hand, there have been almost no attempts to build a hyper-heuristic to evolve an algorithm for solving real-valued optimization problems. In our previous research, we succeeded to evolve a Nelder--Mead-like real function minimization heuristic using genetic programming and the primitives extracted from the original Nelder--Mead algorithm. The resulting heuristic was better than the original Nelder--Mead method in the number of solved test problems but it was slower in that it needed considerably more cost function evaluations to solve the problems also solved by the original method. In this paper we exploit grammatical evolution as a hyper-heuristic to evolve heuristics that outperform the original Nelder--Mead method in all aspects. However, the main goal of the paper is not to build yet another real function optimization algorithm but to shed some light on the influence of different factors on the behavior of the evolution process as well as on the quality of the obtained heuristics. In particular, we investigate through extensive evolution runs the influence of the shape and dimensionality of the training function, and the impact of the size limit set to the evolving algorithms. At the end of this research we succeeded to evolve a number of heuristics that solved more test problems and in fewer cost function evaluations than the original Nelder--Mead method. Our solvers are also highly competitive with the improvements made to the original method based on rigorous mathematical convergence proofs found in the literature. Even more importantly, we identified some directions in which to continue the work in order to be able to construct a productive hyper-heuristic capable of evolving real function optimization heuristics that would outperform a human designer in all aspects.", } @Article{Fajfar:2018:Entropy, author = "Iztok Fajfar and Tadej Tuma", title = "Creation of Numerical Constants in Robust Gene Expression Programming", journal = "Entropy", year = "2018", number = "10", volume = "20", pages = "756", keywords = "genetic algorithms, genetic programming, gene expression programming, genotype/phenotype evolutionary algorithms, symbolic regression, constant creation, ephemeral random constants, numeric mutation, numeric crossover, digit-wise crossover", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/entropy/entropy20.html#FajfarT18", URL = "https://www.mdpi.com/1099-4300/20/10/756/pdf", DOI = "doi:10.3390/e20100756", article-number = "756", URL = "http://www.mdpi.com/1099-4300/20/10/756", ISSN = "1099-4300", abstract = "The problem of the creation of numerical constants has haunted the Genetic Programming (GP) community for a long time and is still considered one of the principal open research issues. Many problems tackled by GP include finding mathematical formulas, which often contain numerical constants. It is, however, a great challenge for GP to create highly accurate constants as their values are normally continuous, while GP is intrinsically suited for combinatorial optimisation. The prevailing attempts to resolve this issue either employ separate real-valued local optimisers or special numeric mutations. While the former yield better accuracy than the latter, they add to implementation complexity and significantly increase computational cost. In this paper, we propose a special numeric crossover operator for use with Robust Gene Expression Programming (RGEP). RGEP is a type of genotype/phenotype evolutionary algorithm closely related to GP, but employing linear chromosomes. Using normalised least squares error as a fitness measure, we show that the proposed operator is significantly better in finding highly accurate solutions than the existing numeric mutation operators on several symbolic regression problems. Another two important advantages of the proposed operator are that it is extremely simple to implement, and it comes at no additional computational cost. The latter is true because the operator is integrated into an existing crossover operator and does not call for an additional cost function evaluation.", notes = "University of Ljubljana, Faculty of Electrical Engineering, Trzaska 25, 1000 Ljubljana, Slovenia", } @InProceedings{Falbo:2002:IFORS, author = "Paolo Falbo and Nicola Doninelli", title = "{"}Reverse engineering{"} of managed fund market timing strategies", booktitle = "The Sixteenth Triennial Conference of the International Federation of Operational Research Societies", year = "2002", address = "University of Edinburgh", month = "8-12 " # jul, organisation = "UK Operational Research Society", note = "Conference theme: OR in a globalised, networked world economy, Invited session", keywords = "genetic algorithms, genetic programming", broken = "http://meetings.informs.org/IFORS2002/working_files/program.pdf", abstract = "In market timing studies the sensitivity of fund returns to the payoff of perfect market timing strategies is usually provided. Nothing is said about the nature of the trading strategies implemented by fund managers. In this work we present a novel method to identify timing activity more than timing ability based on genetic programming and the Henriksson-Merton model. While timing ability is necessarily associated to superior forecasting, timing activity is not. Therefore, we're not testing the EMH from the supply side but attempt to address a slightly different question: do mutual funds use timing strategies? This is an intriguing problem given that we focus on investment style more than on the average profits of market timing.", notes = "program.pdf has above abstract eurizoncapital.com??? University of Brescia, Italy", } @InProceedings{Faler:2011:28C3, author = "Wes Faler", title = "Automatic Algorithm Invention with {GPU}", booktitle = "28th Chaos Communication Congress", year = "2011", pages = "ID 4764", address = "Berlin", month = "27-30 " # dec, keywords = "genetic algorithms, genetic programming, GPU, Cartesian Genetic Programming", URL = "http://events.ccc.de/congress/2011/Fahrplan/events/4764.en.html", URL = "http://events.ccc.de/congress/2011/Fahrplan/attachments/2029_AutomaticAlgorithmInvention.pdf", abstract = "You write software. You test software. You know how to tell if the software is working. Automate your software testing sufficiently and you can let the computer do the writing for you! 'Genetic Programming', especially 'Cartesian Genetic Programming' (CGP), is a powerful tool for creating software and designing physical objects. See how to do CGP as we invent image filters for the Part Time Scientists' 3D cameras. Danger: Actual code will be shown!", notes = "Hell Yeah, it's rocket science. Slides only? http://events.ccc.de/congress/2011/ http://wesfaler.wordpress.com/2011/12/29/algorithm-invention-with-cartesian-genetic-programming/ broken March 2021 https://www.youtube.com/watch?v=xQDazGrKsuM 62 minutes", } @InProceedings{Faler:2011:28C3c, author = "Wes Faler", title = "Evolving custom communication protocols", booktitle = "28th Chaos Communication Congress", year = "2011", pages = "ID 4818", address = "Berlin", month = "27-30 " # dec, keywords = "genetic algorithms, genetic programming, GPU, Cartesian Genetic Programming", URL = "http://events.ccc.de/congress/2011/Fahrplan/events/4818.en.html", URL = "http://events.ccc.de/congress/2011/Fahrplan/attachments/2054_EvolvingCustomCommunicationProtocols.pdf", abstract = "Even after years of committee review, communication protocols can certainly be hacked, sometimes highly entertainingly. What about creating a protocol the opposite way? Start with all the hacks that can be done and search for a protocol that gets around them all. Is it even possible? Part Time Scientists has used a GPU to help design our moon mission protocols and we'll show you the what and how. Danger: Real code will be shown!", notes = "Hell Yeah, it's rocket science. Slides only? http://events.ccc.de/congress/2011/ https://mail.google.com/mail/h/wbzd6ywtnwil/?&v=c&th=1354023091e04a31", } @Article{FallahMehdipour:2012:WRM, author = "E. Fallah-Mehdipour and O. Bozorg Haddad and M. A. Marino", title = "Real-Time Operation of Reservoir System by Genetic Programming", journal = "Water Resources Management", year = "2012", volume = "26", number = "14", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s11269-012-0132-z", DOI = "doi:10.1007/s11269-012-0132-z", } @Article{FallahMehdipour:2013:WRM, author = "E. Fallah-Mehdipour and O. Bozorg Haddad and H. Orouji and M. A. Marino", title = "Application of Genetic Programming in Stage Hydrograph Routing of Open Channels", journal = "Water Resources Management", year = "2013", volume = "27", number = "9", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s11269-013-0345-9", DOI = "doi:10.1007/s11269-013-0345-9", } @Article{FallahMehdipour:2013:JHR, author = "E. Fallah-Mehdipour and O. {Bozorg Haddad} and M. A. Marino", title = "Prediction and simulation of monthly groundwater levels by genetic programming", journal = "Journal of Hydro-environment Research", year = "2013", volume = "7", number = "4", pages = "253--260", keywords = "genetic algorithms, genetic programming, Adaptive neural fuzzy inference system, Prediction, Simulation, Groundwater level", ISSN = "1570-6443", DOI = "doi:10.1016/j.jher.2013.03.005", URL = "http://www.sciencedirect.com/science/article/pii/S1570644313000270", abstract = "Groundwater level is an effective parameter in the determination of accuracy in groundwater modelling. Thus, application of simple tools to predict future groundwater levels and fill-in gaps in data sets are important issues in groundwater hydrology. Prediction and simulation are two approaches that use previous and previous-current data sets to complete time series. Artificial intelligence is a computing method that is capable to predict and simulate different system states without using complex relations. This paper investigates the capability of an adaptive neural fuzzy inference system (ANFIS) and genetic programming (GP) as two artificial intelligence tools to predict and simulate groundwater levels in three observation wells in the Karaj plain of Iran. Precipitation and evaporation from a surface water body and water levels in observation wells penetrating an aquifer system are used to fill-in gaps in data sets and estimate monthly groundwater level series. Results show that GP decreases the average value of root mean squared error (RMSE) as the error criterion for the observation wells in the training and testing data sets 8.35 and 11.33 percent, respectively, compared to the average of RMSE by ANFIS in prediction. Similarly, the average value of RMSE for different observation wells used in simulation improves the accuracy of prediction 9.89 and 8.40 percent in the training and testing data sets, respectively. These results indicate that the proposed prediction and simulation approach, based on GP, is an effective tool in determining groundwater levels.", } @InCollection{FallahMehdipour:2015:hbgpa, author = "E. Fallah-Mehdipour and O. {Bozorg Haddad}", title = "Application of Genetic Programming in Hydrology", booktitle = "Handbook of Genetic Programming Applications", publisher = "Springer", year = "2015", editor = "Amir H. Gandomi and Amir H. Alavi and Conor Ryan", chapter = "3", pages = "59--70", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-20882-4", DOI = "doi:10.1007/978-3-319-20883-1_3", abstract = "With increasing complexity and accuracy of different phenomenon modelling, attentions focus on using and improving some tools that extract system equations by simple rules. Commonly, these tools are user-friendly and try to minimize error criterion between real (observed) and obtained values by system rules. An appropriate water resource modeling requires assistance of computer model to provide connections in data sets, management and decision makers. The purpose of this chapter is to review genetic programming (GP) applications in the hydrology and consider future aspects for research and application. Previous applications of GP presented its capabilities to overcome some system characteristics such as the high-dimensional, nonlinearity, and convexity. GP is flexible to set with other systems in both internal and external states.", } @InCollection{Fallahnezhad:2012:hoANNms, author = "Mehdi Fallahnezhad and Hashem Yousefi", title = "Needle Insertion Force Modeling using Genetic Programming Polynomial Higher Order Neural Network", booktitle = "Artificial Higher Order Neural Networks for Modeling and Simulation", publisher = "IGI Global", year = "2012", editor = "Ming Zhang", chapter = "4", pages = "58--76", month = oct, keywords = "genetic algorithms, genetic programming", isbn13 = "9781466621756", DOI = "doi:10.4018/978-1-4666-2175-6.ch004", abstract = "Precise insertion of a medical needle as an end-effecter of a robotic or computer-aided system into biological tissue is an important issue and should be considered in different operations, such as brain biopsy, prostate brachytherapy, and percutaneous therapies. Proper understanding of the whole procedure leads to a better performance by an operator or system. In this chapter, the authors use a 0.98 mm diameter needle with a real-time recording of force, displacement, and velocity of needle through biological tissue during in-vitro insertions. Using constant velocity experiments from 5 mm/min up to 300 mm/min, the data set for the force-displacement graph of insertion was gathered. Tissue deformation with a small puncture and a constant velocity penetration are the two first phases in the needle insertion process. Direct effects of different parameters and their correlations during the process is being modelled using a polynomial neural network. The authors develop different networks in 2nd and 3rd order to model the two first phases of insertion separately. Modelling accuracies were 98percent and 86percent in phase 1 and 2, respectively.", notes = "Norwegian University of Science and Technology (NTNU), Norway, Amirkabir University of Technology (Tehran Polytechnic), Iran", } @Article{journals/nca/FallahpourOMKW16, author = "Alireza Fallahpour and Ezutah Udoncy Olugu and Siti Nurmaya Musa and Dariush Khezrimotlagh and Kuan Yew Wong", title = "An integrated model for green supplier selection under fuzzy environment: application of data envelopment analysis and genetic programming approach", journal = "Neural Computing and Applications", year = "2016", volume = "27", number = "3", pages = "707--725", month = apr, keywords = "genetic algorithms, genetic programming, Green supplier selection, Data envelopment analysis, DEA, Artificial intelligence, AI, GP, Parametric analysis", ISSN = "0941-0643", bibdate = "2016-03-24", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/nca/nca27.html#FallahpourOMKW16", DOI = "doi:10.1007/s00521-015-1890-3", size = "19 pages", abstract = "Supplier evaluation plays a critical role in a successful supply chain management. Hence, the evaluation and selection of the right suppliers have become a central decision of manufacturing business activities around the world. Consequently, numerous individual and integrated methods have been presented to evaluate and select suppliers. The current literature shows that hybrid artificial intelligence (AI)-based models have received much attention for supplier evaluation. Integrated data envelopment analysis-artificial neural network (DEA-ANN) is one of the combined methods that have recently garnered great attention from academics and practitioners. However, DEA-ANN model has some drawbacks, which make some limitation in the evaluation process. In this study, we aim at improving the previous DEA-AI models by integrating the Kourosh and Arash method as a robust model of DEA with a new AI approach namely genetic programming (GP) to overcome the shortcomings of previous DEA-AI models in supplier selection. Indeed, in this paper, GP provides a robust nonlinear mathematical equation for the suppliers efficiency using the determined criteria. To validate the model, adaptive neuro-fuzzy inference system as a powerful tool was used to compare the result with GP-based model. In addition, parametric analysis and unseen data set were used to validate the precision of the model.", notes = "Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia", } @Article{journals/nca/FallahpourOM17, author = "Alireza Fallahpour and Ezutah Udoncy Olugu and Siti Nurmaya Musa", title = "A hybrid model for supplier selection: integration of {AHP} and multi expression programming ({MEP})", journal = "Neural Computing and Applications", year = "2017", volume = "28", number = "3", pages = "499--504", keywords = "genetic algorithms, genetic programming", bibdate = "2017-05-26", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/nca/nca28.html#FallahpourOM17", DOI = "doi:10.1007/s00521-015-2078-6", } @Article{FALLAHPOUR:2021:JCP, author = "Alireza Fallahpour and Kuan Yew Wong and Srithar Rajoo and Guangdong Tian", title = "An evolutionary-based predictive soft computing model for the prediction of electricity consumption using multi expression programming", journal = "Journal of Cleaner Production", volume = "283", pages = "125287", year = "2021", ISSN = "0959-6526", DOI = "doi:10.1016/j.jclepro.2020.125287", URL = "https://www.sciencedirect.com/science/article/pii/S0959652620353324", keywords = "genetic algorithms, genetic programming, Electricity consumption, Energy demand, Prediction, Forecasting, Soft computing, Multi expression programming", abstract = "Proper estimation of electricity consumption is one of the influential factors for sustainability and cleaner production in both developed and developing countries. Many studies have been conducted to present accurate prediction models for forecasting electricity demand. However, researchers are still working to develop models with higher accuracy. This study applies a newer branch of Genetic Programming (GP) as a soft computing technique, known as Multi Expression Programming (MEP) to predict the electricity consumption of China for the first time based on the data collected from 1991 to 2019. Specifically, a robust mathematical model was developed using MEP for this purpose. Different predictive techniques known as Gene Expression Programming (GEP) and Adaptive Neuro Fuzzy Inference System (ANFIS) were used to compare the accuracy of the model. Based on the results, the proposed MEP model is more powerful and accurate than both GEP and ANFIS. In addition, a sensitivity analysis was conducted to present the impact of each factor on the electricity consumption of China. It was shown that among the four independent factors (Population, Gross Domestic Product (GDP), Import, and Export), Population has the highest impact, followed by Export, Import and GDP, respectively", } @Article{FALLAHPOUR:2023:compstruct, author = "Ali {Fallah Pour} and Roohollah {Shirani Faradonbeh} and Aliakbar Gholampour and Tuan D. Ngo", title = "Predicting ultimate condition and transition point on axial stress-strain curve of {FRP-confined} concrete using a meta-heuristic algorithm", journal = "Composite Structures", volume = "304", pages = "116387", year = "2023", ISSN = "0263-8223", DOI = "doi:10.1016/j.compstruct.2022.116387", URL = "https://www.sciencedirect.com/science/article/pii/S0263822322011199", keywords = "genetic algorithms, genetic programming, FRP-confined concrete, Genetic programming (GP), Ultimate axial strain, Hoop rupture strain, Axial stress at transition point, Axial strain at transition point", abstract = "Accurately predicting key reference points on the axial stress-strain curve of fiber-reinforced polymer (FRP)-confined concrete is of great importance for the pre-design and modeling of structures manufactured with this composite system. This paper presents a detailed study on the development of accurate and practical expressions for predicting the ultimate condition and transition point, as key reference points, on axial stress-strain curves of FRP-confined concrete using generic programming (GP). A comprehensive data tuning and cross-validation analysis was firstly performed to develop prediction models. Afterwards, the accuracy and performance of the developed empirical expressions were examined by sensitivity analysis, parametric analysis and model validation. Finally, a comparison was made between the performance of these proposed expressions and that of the existing best-performing expressions in the literature using statistical analysis. Based on the sensitivity and parametric analysis of the database, it is shown that: compressive strength (f'cc) and axial transition strain (epsilonc1) are more sensitive to FRP lateral stiffness (Kl); ultimate axial strain (epsiloncu) is more sensitive to Kl-to-unconfined compressive strength (f'co) ratio and fiber ultimate tensile strain (epsilonfu); hoop rupture strain (epsilonh,rup) is more sensitive to fiber elastic modulus (Ef); and axial transition strength (f'c1) is more sensitive to f'co. It is also shown that the proposed expressions provided more accurate predictions of the ultimate condition and transition point on the axial stress-strain curve of FRP-confined concrete than the existing expressions. This was achieved by using a larger number of datasets and accurately capturing the effects of the most influential input parameters in the proposed expressions", } @InProceedings{falloon:2024:GECCO, author = "Mathew Falloon and Hui Ma and Aaron Chen", title = "{Energy-Aware} Dynamic Resource Allocation and Container Migration in Cloud Servers: A Co-evolution {GPHH} Approach", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference", year = "2024", editor = "Ruhul Sarker and Patrick Siarry and Julia Handl and Xiaodong Li and Markus Wagner and Mario Garza-Fabre and Kate Smith-Miles and Richard Allmendinger and Ying Bi and Grant Dick and Amir H Gandomi and Marcella Scoczynski Ribeiro Martins and Hirad Assimi and Nadarajen Veerapen and Yuan Sun and Mario Andres Munyoz and Ahmed Kheiri and Nguyen Su and Dhananjay Thiruvady and Andy Song and Frank Neumann and Carla Silva", pages = "1219--1227", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, timetabling and scheduling, co-evolution, combinatorial optimization, Real World Applications", isbn13 = "979-8-4007-0494-9", DOI = "doi:10.1145/3638529.3654070", size = "9 pages", abstract = "Containers are a popular way of deploying software in cloud data centers. Containers are allocated to Virtual machines (VMs) which are allocated to Physical machines (PMs) within the data center. Since the resources required by containers often do not match those of VMs, where to allocate them must be decided. A poor solution can result in high energy costs. Many existing methods to solve this problem use heuristics which do not consider containers leaving the data center after being allocated. Some do consider migrating containers between VMs but few do for energy efficiency reasons. These overlooked aspects may lead to increased energy usage, particularly since studies have demonstrated that many containers run for only a brief duration. In this paper, we develop a model of the container-based cloud resource allocation problem that considers the energy impact of leaving and migrating containers. We then design a new Genetic Programming Hyper-Heuristic (GPHH) algorithm to jointly evolve three heuristics for container placement, VM placement and container migration control. We use newly designed terminals to ensure the effectiveness of our GPHH algorithm. Experiments have been conducted with results indicating that the heuristics evolved by our GPHH algorithm can achieve better performance compared to several state-of-the-art techniques.", notes = "GECCO-2024 RWA A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @Article{Fam:2012:AJBAS, author = "D. F. Fam and S. P. Koh and S. K. Tiong and K. H. Chong", title = "Global Optimal Analysis of Variant Genetic Operations in Solar Tracking", journal = "Australian Journal of Basic and Applied Sciences", year = "2012", volume = "6", number = "6", pages = "6--14", month = jun, keywords = "genetic algorithms, genetic programming, solar tracking, selective clonal mutation", ISSN = "1991-8178", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.1039.306", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1039.306", URL = "http://www.ajbasweb.com/old/ajbas_June_2012.html", URL = "http://www.ajbasweb.com/old/ajbas/2012/June/6-14.pdf", size = "9 pages", abstract = "Genetic Algorithms (GAs), Evolution Strategies (ES), Evolutionary Programming (EP) and Genetic Programming (GP) are some of the best known types of Evolutionary Algorithm (EA)where it is a class of global search algorithms inspired by natural evolution. Lots of research has been carried out in solar tracking system using different types of Evolutionary Algorithm. In this research, genetic algorithm is explored to maximise the performance of solar tracking system. This work evaluates the best combination of GA parameters by always fine-tuning the position of solar tracking prototype to receive maximum solar radiation. Both software and hardware have been developed to simulate related genetic algorithm results using a combination of variant genetic operators. Under conventional genetic algorithm operation, it is concluded that genetic algorithm with selective clonal mutation is able to produce the best fitness value at 0.98027 with both axles X and Y with inclination of +2 degree to the sun position.", notes = "Department of Electronic & Communication Engineering, Universiti Tenaga Nasional, Km 7, Jalan Ikram-Uniten, 43009, Kajang, Selangor, Malaysia", } @InProceedings{Fan:2023:FoSE, author = "Angela Fan and Beliz Gokkaya and Mark Harman and Mitya Lyubarskiy and Shubho Sengupta and Shin Yoo and Jie M. Zhang", title = "Large Language Models for Software Engineering: Survey and Open Problems", booktitle = "2023 IEEE/ACM International Conference on Software Engineering: Future of Software Engineering (ICSE-FoSE)", year = "2023", editor = "Hoa Khanh Dam and Xing Hu", pages = "31--53", address = "Melbourne, Australia", month = "17 " # may, keywords = "genetic algorithms, genetic programming, genetic improvement, ANN, AI, Automated Program Repair, APR, Documentation generation, Generative AI, Human-Computer Interaction, HCI, Large Language Models, LLM, Refactoring, Requirements engineering, Search Based Software Engineering, SBSE, Software Analytics, Software Engineering Education, Software Processes, Software Maintenance and Evolution, Software Testing", isbn13 = "979-8-3503-2497-6", URL = "https://arxiv.org/pdf/2310.03533", DOI = "doi:10.1109/ICSE-FoSE59343.2023.00008", size = "23 pages", abstract = "This paper provides a survey of the emerging area of Large Language Models (LLMs) for Software Engineering (SE). It also sets out open research challenges for the application of LLMs to technical problems faced by software engineers. LLMs emergent properties bring novelty and creativity with applications right across the spectrum of Software Engineering activities including coding, design, requirements, repair, refactoring, performance improvement, documentation and analytics. However, these very same emergent properties also pose significant technical challenges; we need techniques that can reliably weed out incorrect solutions, such as hallucinations. Our survey reveals the pivotal role that hybrid techniques (traditional SE plus LLMs) have to play in the development and deployment of reliable, efficient and effective LLM-based SE", notes = "FoSE held in conjunction with ICSE 2023 https://conf.researchr.org/track/icse-2023/fose?#program", } @InProceedings{Fan:2023:SMC, author = "Chong-Jiong Fan and Ya-Hui Jia and Wei-Neng Chen", booktitle = "2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)", title = "Automated Order Dispatching Strategies Design Using Genetic Programming for Dynamic Ridesharing Problem", year = "2023", pages = "348--355", abstract = "Ridesharing is a popular transportation mode and has become an important part of smart city development, which helps alleviate the pressure of urban travel. The ridesharing problem (RSP) is mainly to match drivers to suitable passengers. In practice, passengers appear dynamically, and the departure and the destination locations of these subsequent orders are unknown, resulting in the dynamic RSP (DRSP). To solve this dynamic optimisation problem, this paper develops a new genetic programming hyperheuristic (GPHH) method to evolve order dispatching rules (ODRs), which can guide drivers to match suitable passengers in real time. The proposed GPHH method contains a heuristic template for simulation-based hyper-heuristic optimisation. The experiment results show that the proposed GPHH method outperforms the state-of-the-art methods. Further analysis revealed some valuable insights, such as the generalizability of the generated rules and the impact of some features on the results.", keywords = "genetic algorithms, genetic programming, Shared transport, Smart cities, Dispatching, Real-time systems, Dynamic programming, Optimisation", DOI = "doi:10.1109/SMC53992.2023.10394334", ISSN = "2577-1655", month = oct, notes = "Also known as \cite{10394334}", } @InProceedings{Fan:2018:afmc, author = "Dewei Fan and Yu Zhou and Bernd Noack", title = "Artificial intelligence control of a turbulent jet", booktitle = "Proceedings of the 21st Australasian Fluid Mechanics Conference", address = "Adelaide, Australia", editor = "T. C. W. Lau and R. M. Kelso", publisher = "HAL CCSD", year = "2018", month = "10-13 " # dec, keywords = "genetic algorithms, genetic programming, engineering sciences, physics, mechanics, fluids mechanics", type = "info:eu-repo/semantics/conferenceObject", isbn13 = "978-0-646-59784-3", URL = "https://hal.archives-ouvertes.fr/hal-02398705", URL = "https://people.eng.unimelb.edu.au/imarusic/proceedings/21/Contribution_615_final.pdf", size = "4 pages", abstract = "An artificial intelligence (AI) control system is developed to manipulate a turbulent jet with a view to maximising its mixing. The system consists of sensors (two hot-wires), genetic programming for learning/ evolving and execution mechanism (6 unsteady radial minijets). Mixing performance is quantified by the jet centerline mean velocity. AI control discovers a hitherto unexplored combination of flapping and helical forcings. Such a combination of several actuation mechanisms-if not creating new ones-is practically inaccessible to conventional methods like a systematic parametric analysis and gradient search, and vastly outperforms the optimised periodic axisymmetric, helical or flapping forcing produced from conventional open-or closed-loop controls. Intriguingly, the learning process of AI control discovers all these forcings in the order of increased performance. The AI control has dismissed sensor feedback and multi-frequency components for optimisation. Our study is the first highly successful AI control experiment for a non-trivial spatially distributed actuation of a turbulent flow. The results show the great potential of AI in conquering the vast opportunity space of control laws for many actuators and sensors and manipulating turbulence.", annote = "Shenzhen Graduate School; Harbin Institute of Technology; The Hong Kong Polytechnic University [Hong Kong] (POLYU); Laboratoire d'Informatique pour la Mecanique et les Sciences de l'Ingenieur (LIMSI) ; Universite Paris Saclay (COmUE)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Universite - UFR d'Ingenierie (UFR 919) ; Sorbonne Universite (SU)-Sorbonne Universite (SU)-Universite Paris-Saclay-Universite Paris-Sud - Paris 11 (UP11)", bibsource = "OAI-PMH server at api.archives-ouvertes.fr", contributor = "Laboratoire d'Informatique pour la Mecanique et les Sciences de l'Ingenieur", coverage = "Adelaide, Australia", description = "International audience", identifier = "hal-02398705", language = "en", oai = "oai:HAL:hal-02398705v1", notes = "https://people.eng.unimelb.edu.au/imarusic/proceedings/21%20AFMC%20TOC.html", } @InProceedings{Fan:2019:FSSIC, title = "Artificial Intelligence Control of Turbulence", author = "Dewei Fan and Yu Zhou and Bernd Noack", publisher = "HAL CCSD", booktitle = "Proceedings of the 5th Symposium on Fluid Structure-Sound Interactions and Control (FSSIC)", year = "2019", pages = "1--4", address = "Minoa Palace - Resort, Chania, Crete island, Greece", month = aug # "~27", keywords = "genetic algorithms, genetic programming, jet, flow control, artificial intelligence, engineering sciences, physics, mechanics, fluids mechanics", URL = "https://hal.archives-ouvertes.fr/hal-02398697", abstract = "An artificial intelligence (AI) control system is developed to manipulate a turbulent jet targeting maximal mixing. The control system consists of sensors (two hot-wires), genetic programming for evolving the control law and actuators (6 unsteady radial minijets). The mixing performance is quantified by the jet centerline mean velocity. AI control discovers a hitherto unexplored combination of asymmetric flapping and helical forcing. Such a combination of several actuation mechanisms constitutes a large challenge for conventional methods of parametric optimisation. AI control vastly outperforms the optimised periodic axisymmetric, helical or flapping forcing produced from conventional open-or closed-loop control. Intriguingly, the learning process of AI control discovers all these forcings in the order of increased performance. Our study is the first AI control experiment which discovers a non-trivial spatially distributed actuation optimising a turbulent flow. The results show the great potential of AI in conquering the vast opportunity space of control laws for many actuators, many sensors and broadband turbulence.", notes = "slides: http://smartwing.org/FSSIC2019/presentations_keynotes_plenary/fssic2019-yz-web.pdf", annote = "Shenzhen Graduate School; Harbin Institute of Technology; The Hong Kong Polytechnic University [Hong Kong] (POLYU); Laboratoire d'Informatique pour la Mecanique et les Sciences de l'Ingenieur (LIMSI) ; Universite Paris Saclay (COmUE)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Universite - UFR d'Ingenierie (UFR 919) ; Sorbonne Universite (SU)-Sorbonne Universite (SU)-Universite Paris-Saclay-Universite Paris-Sud - Paris 11 (UP11)", bibsource = "OAI-PMH server at api.archives-ouvertes.fr", contributor = "Laboratoire d'Informatique pour la Mecanique et les Sciences de l'Ingenieur", description = "International audience", identifier = "hal-02398697", language = "en", oai = "oai:HAL:hal-02398697v1", type = "info:eu-repo/semantics/conferenceObject", } @Article{FAN:2021:COR, author = "Huali Fan and Hegen Xiong and Mark Goh", title = "Genetic programming-based hyper-heuristic approach for solving dynamic job shop scheduling problem with extended technical precedence constraints", journal = "Computer \& Operations Research", volume = "134", pages = "105401", year = "2021", month = oct, keywords = "genetic algorithms, genetic programming, Dynamic job shop scheduling, Dispatching rules, Hyper-heuristic, Extended technical precedence constraints", ISSN = "0305-0548", DOI = "doi:10.1016/j.cor.2021.105401", URL = "https://www.sciencedirect.com/science/article/pii/S0305054821001672", abstract = "Extended technical precedence constraints (ETPC) in dynamic job shop scheduling problem (DJSP) are the precedence constraints existing between different jobs instead of the conventional technical precedence constraints existing in the operations of the same job. This paper presents the mathematical programming model of the DJSP with ETPC to minimize the mean weighted tardiness of the jobs. The mathematical model contributes to the solution and modelling of the DJSP with ETPC and it is used to solve small-sized problems to optimality. To solve industry-sized problems, a constructive heuristic called the dispatching rule (DR) is employed. This paper investigates the use of genetic programming (GP) as a hyper-heuristic in the automated generation of the problem-specific DRs for solving the problem under consideration. The genetic programming-based hyper heuristic (GPHH) approach constructs the DRs which are learned from the training instances and then verified on the test instances by the simulation experiments. To enhance the efficiency of the approach when evolving effective DRs to solve the problem, the approach is improved with strategies which consist of a problem-specific attribute selection for GP and a threshold condition mechanism for fitness evaluation. The simulation results verify the effectiveness and efficiency of the evolved DRs to the problem under consideration by comparing against the existing classical DRs. The statistical analysis of the simulation results shows that the evolved DRs outperform the selected benchmark DRs on the problem under study. The sensitivity analysis also shows that the DRs generated by the GPHH approach are robust under different scheduling performance measures. Moreover, the effects of the model parameters, including the percentage of jobs with ETPC and the machine load, on the performance of the DRs are investigated", } @Article{FAN:2023:jhydrol, author = "Jinsheng Fan and Xiaofang Liu and Weidong Li", title = "Daily suspended sediment concentration forecast in the upper reach of Yellow River using a comprehensive integrated deep learning model", journal = "Journal of Hydrology", volume = "623", pages = "129732", year = "2023", ISSN = "0022-1694", DOI = "doi:10.1016/j.jhydrol.2023.129732", URL = "https://www.sciencedirect.com/science/article/pii/S0022169423006741", keywords = "genetic algorithms, genetic programming, Daily SSC prediction, Integrated deep learning, Wavelet transformation, CNN, LSTM, ANN", abstract = "The precise prediction of suspended sediment concentration (SSC) is of great importance for river reservoir construction planning, water resource management, and ecological environment restoration. This research aims to improve SSC prediction accuracy by constructing a comprehensive and integrated deep learning model Wavelet-MGGP-CNN-LSTM (ICNN-LSTM), combining wavelet transformation (WT), multi-gene genetic programming (MGGP), convolutional neural network (CNN), and long short-term memory (LSTM) simultaneously. In ICNN-LSTM, the WT decomposes the signal and extracts time and frequency domain information, while the MGGP filters out redundant information. Then, the CNN and LSTM are integrated in a parallel and loosely coupled manner to form an initial combined model CNN-LSTM (CNN combined with LSTM) to process filtered information by WT and MGGP. Furthermore, this study compares the performance of ICNN-LSTM with CNN, LSTM, CNN-LSTM, ICNN (CNN embedded with WT and MGGP), ILSTM (LSTM embedded with WT and MGGP), artificial neural network (ANN), and the traditional sediment rating curve (SRC). The evaluation of prediction accuracy for all models was conducted using root mean square error (RMSE), Nash-Sutcliffe coefficient (NSC), coefficient of determination (R2), and mean absolute error (MAE) as performance indicators. The daily discharge and suspended sediment concentration series data from Tangnaihai Hydrological Station in the upper reaches of the Yellow River spanning from 1977 to 1987 were selected to train and test the models. Results show that, first, deep learning networks such as CNN and LSTM outperform the shallow neural network ANN, with LSTM providing higher accuracy than CNN. Second, the CNN-LSTM hybrid outperforms both CNN and LSTM models, exhibiting a nearly 89percent improvement in NSC value compared to SRC in the test phase. Third, deep learning models such as ICNN, ILSTM, and ICNN-LSTM show significantly higher NSC values than CNN, LSTM, and CNN-LSTM models in the test phase, with improvements of 13.8percent, 5.7percent, and 12.1percent, respectively. Moreover, compared to SRC, the proposed ICNN-LSTM model improves NSC value by nearly 140percent in the test phase. The proposed ICNN-LSTM model, integrating the advantages of WT, deep learning, and ensemble learning, provides accurate and reliable predictions and serves as a reference for time series prediction modeling", } @InCollection{fan:1998:DADDCGP, author = "John L. Fan", title = "Design of an Adaptive Detector for Digital Communications using Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1998", year = "1998", editor = "John R. Koza", pages = "11--19", address = "Stanford, California, 94305-3079 USA", month = "17 " # mar, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-212568-8", notes = "part of \cite{koza:1998:GAGPs}", } @Article{Fan:2024:asoc, author = "Lei Fan and Zhaobing Su and Xiyang Liu and Yuping Wang", title = "Decomposition based cross-parallel multiobjective genetic programming for symbolic regression", journal = "Applied Soft Computing", year = "2024", volume = "167", pages = "112239", keywords = "genetic algorithms, genetic programming, Symbolic regression, Problem decomposition, Global regression, Local regression, Cross-parallel multiobjective genetic programming", ISSN = "1568-4946", URL = "https://www.sciencedirect.com/science/article/pii/S1568494624010135", DOI = "doi:10.1016/j.asoc.2024.112239", abstract = "Genetic Programming (GP) based Symbolic Regression (SR) algorithms suffer from the ineluctable effects over model bloat, blind search and diversity loss when determining explicit symbolic models to best depict the concealed laws in historical data, which often make them time-consuming and unstable. Most efforts often dealt with one of these effects, such that the algorithms still suffer from other effects. To deal with these effects, we propose a cross-parallel SR algorithm framework based on problem decomposition and multiobjective GP in this paper. The decomposition method is proposed to distill simple subproblems named global and local regression, which can be fast solved to produce various high quality models. In the proposed framework, by expressing the SR problem as the multiobjective optimisation model, a number of subproblems are automatically distilled and solved in parallel to reduce model bloat and accelerate the algorithm. Traditional regression methods are employed to produce high quality models to seed the evolving populations for each subtask to maintain population diversity and improve search efficiency. Elite models obtained by each subtasks will be collected and randomly sent to other subtasks to improve the model generalisation. Ablation and comparison experiments are conducted to evaluate the performance of the proposed algorithms. The ablation results show that the developed algorithm framework plays a positive role in reducing above ineluctable effects, and can fast determine concise symbolic models for the benchmarks. Comparisons by SRBench demonstrate the effectiveness of the developed algorithm on wide range problems", } @InProceedings{Fan:2020:CEC, author = "Qinglan Fan and Bing Xue and Mengjie Zhang", title = "A Region Adaptive Image Classification Approach Using Genetic Programming", booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC 2020", year = "2020", editor = "Yaochu Jin", pages = "paper id24346", address = "internet", month = "19-24 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming: Poster", isbn13 = "978-1-7281-6929-3", DOI = "doi:10.1109/CEC48606.2020.9185908", abstract = "Feature extraction, as one essential step of image classification, can potentially reduce image data dimensionality and capture effective information for improving performance. However, most existing image descriptors are designed to conduct specific tasks and might not be sufficient for different types of images. Genetic programming (GP) can automatically extract multiple important and discriminative features by incorporating diverse image descriptors into a GP program. Furthermore, different regions in an image have different structural characteristics. In this paper, we propose a region adaptive image classification approach based on GP, which can automatically extract informative image features by automatically applying different image descriptors in different regions of an image. A new flexible GP program structure with a new function set and a new terminal set is developed in this approach. The performance of the proposed method is evaluated on four various data sets and compared with other state-of-the-art classification methods. Experimental results illustrate that the proposed approach is capable of achieving better or competitive performance than these baseline methods. Further analysis of some good programs shows the high interpretability of the proposed method.", notes = "https://wcci2020.org/ Victoria University of Wellington, New Zealand", } @InProceedings{Fan:2021:GECCOcomp, author = "Qinglan Fan and Ying Bi and Bing Xue and Mengjie Zhang", title = "Genetic Programming with A New Representation and A New Mutation Operator for Image Classification", booktitle = "Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion", year = "2021", editor = "Francisco Chicano and Alberto Tonda and Krzysztof Krawiec and Marde Helbig and Christopher W. Cleghorn and Dennis G. Wilson and Georgios Yannakakis and Luis Paquete and Gabriela Ochoa and Jaume Bacardit and Christian Gagne and Sanaz Mostaghim and Laetitia Jourdan and Oliver Schuetze and Petr Posik and Carlos Segura and Renato Tinos and Carlos Cotta and Malcolm Heywood and Mengjie Zhang and Leonardo Trujillo and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Fuyuki Ishikawa and Inmaculada Medina-Bulo and Frank Neumann and Andrew M. Sutton", pages = "249--250", address = "internet", series = "GECCO '21", month = jul # " 10-14", organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, Image Classification, Representation: Poster", isbn13 = "978-1-4503-8351-6", DOI = "doi:10.1145/3449726.3459468", size = "2 pages", abstract = "Due to the high dimensionality and variations of the image data, it is challenging to develop an image classification method that is able to capture useful information from images and then conduct classification effectively. This paper proposes a new GP approach to image classification, which can perform feature extraction, feature construction, and classification simultaneously. The new approach can extract and construct multiple informative features to effectively handle image variations. Furthermore, a new mutation operator is developed to dynamically adjust the size of the evolved GP programs. The experimental results show that the proposed approach achieves significantly better or similar performance than/to the baseline methods on two datasets.", notes = " GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)", } @Article{FAN:2022:ASC, author = "Qinglan Fan and Ying Bi and Bing Xue and Mengjie Zhang", title = "Genetic programming for feature extraction and construction in image classification", journal = "Applied Soft Computing", year = "2022", volume = "118", pages = "108509", keywords = "genetic algorithms, genetic programming, Image classification, Representation, Feature extraction, Feature construction", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2022.108509", URL = "https://www.sciencedirect.com/science/article/pii/S1568494622000527", abstract = "Genetic Programming (GP) has been successfully applied to image classification and achieved promising results. However, most existing methods either address binary image classification tasks only or need a predefined classifier to perform multi-class image classification while using GP for feature extraction. This limits their flexibility since it is unknown which combinations of classifiers and features are the most effective for an image classification task. Furthermore, high image variations increase the difficulty of feature extraction and image classification. This paper proposes a GP approach with a new program representation, new functions, and new terminals. The new approach can conduct feature extraction, feature construction, and classification, automatically and simultaneously. It can extract and construct informative image features, select a suitable classification algorithm instead of relying on a predefined classifier, and perform classification for binary and multi-class image classification tasks. In addition, this paper develops a new mutation operator based on fitness of population for dynamically adjusting the size of the evolved GP programs. The experimental results on eight datasets with different variations and difficulties show that the proposed approach achieves higher classification accuracy than most of the benchmark methods. Further analysis shows that the GP evolved programs have appropriate tree sizes and potentially high interpretability", } @InProceedings{fan:2022:AI, author = "Qinglan Fan and Ying Bi and Bing Xue and Mengjie Zhang", title = "Evolving Effective Ensembles for Image Classification Using Multi-objective Multi-tree Genetic Programming", booktitle = "AI 2022: Advances in Artificial Intelligence", year = "2022", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-031-22695-3_21", DOI = "doi:10.1007/978-3-031-22695-3_21", } @Article{QinglanFan:ieeeTEC, author = "Qinglan Fan and Ying Bi and Bing Xue and Mengjie Zhang", title = "Genetic Programming for Image Classification: A New Program Representation with Flexible Feature Reuse", journal = "IEEE Transactions on Evolutionary Computation", year = "2023", volume = "27", number = "3", pages = "460--474", month = jun, keywords = "genetic algorithms, genetic programming, Image Classification,Feature Learning, Program Structure, Feature Reuse", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2022.3169490", size = "15 pages", abstract = "Extracting effective features from images is crucial for image classification, but it is challenging due to high variations across images. Genetic programming (GP) has become a promising machine learning approach to feature learning in image classification. The representation of existing GP-based image classification methods is usually the tree-based structure. These methods typically learn useful image features according to the output of the GP program root node. However, they are not flexible enough in feature learning since the features produced by internal nodes of the GP program have seldom been directly used. we propose a new image classification approach using GP with a new program structure, which can flexibly reuse features generated from different nodes including internal nodes of the GP program. The new method can automatically learn various informative image features based on the new function set and terminal set for effective and efficient image classification. Furthermore, instead of relying on a predefined classification algorithm, the proposed approach can automatically select a suitable classification algorithm based on the learned features and conduct classification simultaneously in a single evolved GP program for an image classification task. The experimental results on 12 benchmark datasets of varying difficulty suggest that the new approach achieves better performance than many state-of-the-art methods. Further analysis demonstrates the effectiveness and efficiency of the flexible feature reuse in the proposed approach. The analysis of evolved GP programs/solutions shows their potentially high interpretability.", notes = "also known as \cite{9761990}", } @PhdThesis{QinglanFan:thesis, author = "Qinglan Fan", title = "Improving Genetic Programming for Image Classification", school = "Computer Science, Victoria University of Wellington", year = "2023", address = "New Zealand", keywords = "genetic algorithms, genetic programming", URL = "https://openaccess.wgtn.ac.nz/ndownloader/files/49595061", URL = "https://openaccess.wgtn.ac.nz/articles/thesis/Improving_Genetic_Programming_for_Image_Classification/27165381", DOI = "doi:10.26686/wgtn.27165381", size = "328 pages", abstract = "Image classification is a fundamental task in computer vision. Due to the high dimensionality of the image data and high variations across images such as rotation, scale, illumination, and background variations, developing a good image classification method is challenging. Moreover, it is typically difficult and expensive to collect a large number of labeled images/instances in some computer vision applications, increasing the difficulty of training models to achieve high generalization performance. In addition to improving the classification performance, reducing the computational cost of an image classification method is crucial as many existing methods tend to take a long time to train a model. Genetic programming (GP), an evolutionary computation technique, has a flexible representation of variable length and powerful global search ability and can produce potentially interpretable models. GP has been used for image classification. However, the potential of GP in image classification has not been comprehensively investigated in terms of GP representations, i.e., program structures, functions, and terminals. Furthermore, most existing GP-based methods usually require a long computation time for fitness evaluations, posing a challenge to real-world applications. The overall goal of this thesis is to address the above issues and further explore the potential of GP in image classification. This goal is achieved by developing GP-based approaches with new representations, new fitness functions, and new genetic operators to improve classification performance and developing new surrogate models to reduce the computational costs of GP-based methods. ...", notes = "Also known as \cite{Fan2024} Supervisors: Mengjie Zhang and Bing Xue and Ying Bi", } @Article{FAN:2024:knosys, author = "Qinglan Fan and Ying Bi and Bing Xue and Mengjie Zhang", title = "A genetic programming-based method for image classification with small training data", year = "2024", journal = "Knowledge-Based Systems", volume = "283", pages = "111188", keywords = "genetic algorithms, genetic programming, Image classification, Fitness function, Crossover", ISSN = "0950-7051", URL = "https://www.sciencedirect.com/science/article/pii/S0950705123009383", DOI = "doi:10.1016/j.knosys.2023.111188", size = "15 pages", abstract = "Genetic programming (GP) has been considerably used for image classification because of its ability to learn simple and effective models. However, most GP methods require a large amount of training data to learn informative features for classification, where the generalization performance might be poor when only a few training instances are available. In addition to using classification accuracy to assess the goodness of GP individuals/solutions like in most GP methods, this paper proposes a new fitness function containing distance measures. The proposed method uses different distance measures to deal with binary and multi-class classification automatically. By simultaneously minimising the within-class distance and maximising the between-class distance, the generalisation performance can be improved. Furthermore, existing GP methods typically employ standard crossover to search for the best individuals from the whole search space. However, these methods might not completely exploit the potential local search space. Based on the niching technique, this paper develops a new crossover operator, which enables better exploitation of the global and local search space, improving learning effectiveness and classification accuracy. The new approach achieves significantly better generalization performance than almost all benchmark methods on eight datasets and is also computationally efficient. Further analysis demonstrates the significance of the new fitness function and crossover operator and shows the potentially good interpretability of the learned models", notes = "School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, 250014, China", } @Article{Qinglan_Fan:ieeeTEC2, author = "Qinglan Fan and Ying Bi and Bing Xue and Mengjie Zhang", title = "A Global and Local Surrogate-Assisted Genetic Programming Approach to Image Classification", journal = "IEEE Transactions on Evolutionary Computation", year = "2024", volume = "28", number = "3", pages = "718--732", month = jun, keywords = "genetic algorithms, genetic programming, Image Classification, Fitness Evaluations, Surrogate Models, Feature extraction, Computational modeling, Training, Evolutionary computation, Predictive models, Computational efficiency", ISSN = "1089-778X", URL = "https://ieeexplore.ieee.org/abstract/document/9919269/", DOI = "doi:10.1109/TEVC.2022.3214607", size = "15 pages", abstract = "Genetic programming (GP) has achieved promising performance in image classification. However, GP-based methods usually require a long computation time for fitness evaluations, posing a challenge to real-world applications. Surrogate models can be efficiently computable approximations of expensive fitness evaluations. However, most existing surrogate methods are designed for evolutionary computation techniques with a vector-based representation consisting of numerical values, thus cannot be directly used for GP with a tree-based representation consisting of functions/operators. The variable sizes of GP trees further increase the difficulty of building the surrogate model for fitness approximations. To address these limitations, we propose a new surrogate-assisted GP approach including global and local surrogate models, which can accelerate the evolutionary learning process and achieve competitive classification performance simultaneously. The global surrogate model can assist GP in exp", notes = "also known as \cite{9919269}", } @Article{Fan:2024:CIM, author = "Qinglan Fan and Ying Bi and Bing Xue and Mengjie Zhang", title = "A Multi-Tree Genetic Programming-Based Ensemble Approach to Image Classification With Limited Training Data [Research Frontier]", journal = "IEEE Computational Intelligence Magazine", year = "2024", volume = "19", number = "4", pages = "47--62", month = nov, keywords = "genetic algorithms, genetic programming, Training data, Accuracy, Gray-scale, Feature extraction, Computational efficiency, Classification algorithms, Ensemble learning, Image classification, Representation learning, Benchmark testing", ISSN = "1556-6048", DOI = "doi:10.1109/MCI.2024.3446148", abstract = "Large variations across images make image classification a challenging task; limited training data further increases its difficulty. Genetic programming (GP) has been considerably applied to image classification. However, most GP methods tend to directly evolve a single classifier or depend on a predefined classification algorithm, which typically does not lead to ideal generalisation performance when only a few training instances are available. Applying ensemble learning to classification often outperforms employing a single classifier. However, single-tree representation (each individual contains a single tree) is widely employed in GP. Training multiple diverse and accurate base learners/classifiers based on single-tree GP is challenging. Therefore, this article proposes a new ensemble construction method based on multi-tree GP (each individual contains multiple trees) for image classification. A single individual forms an ensemble, and its multiple trees constitute base learners. To find the best individual in which multiple trees are diverse and effectively cooperate, i.e., the nth tree can correct the errors of the previous n-1 trees, the new method assigns different weights to multiple trees using the idea of AdaBoost and performs classification via weighted majority voting. Furthermore, a new tree representation is developed to evolve diverse and accurate base learners that extract useful features and conduct classification simultaneously. The new approach achieves significantly better performance than almost all benchmark methods on eight datasets. Additional analyses highlight the effectiveness of the new ensembles and tree representation, demonstrating the potential for providing valuable interpretability in ensemble trees.", notes = "Also known as \cite{10709804}", } @Article{Qinglan_Fan:ieeeTEC3, author = "Qinglan Fan and Ying Bi and Bing Xue and Mengjie Zhang", title = "Multi-Tree Genetic Programming for Learning Color and Multi-Scale Features in Image Classification", journal = "IEEE Transactions on Evolutionary Computation", note = "Accepted for future publication", keywords = "genetic algorithms, genetic programming, Multi-tree Genetic Programming, Feature extraction, Training data, Image colour analysis, Training, Sociology, Representation learning, Gray-scale, Data-efficient Image Classification, Color Features, Multi-scale Feature Extraction, Parent Selection", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2024.3384021", size = "15 pages", notes = "also known as \cite{10488030} School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan 250014, China", } @InProceedings{DBLP:conf/ivcnz/FanB0SF024, author = "Qinglan Fan and Ying Bi and Bing Xue and Jane Symonds and Lauren M. Fletcher and Mengjie Zhang", title = "Automatic Quantification of King Salmon Farm Net Occlusion Via Biofouling Detection", booktitle = "2024 39th International Conference on Image and Vision Computing New Zealand (IVCNZ)", year = "2024", editor = "Richard Clare and Joe Chen and Le Yang", address = "Christchurch, New Zealand", month = dec # " 4-6", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Schedules, Costs, Image color analysis, Streaming media, Cleaning, Resource management, Sustainable development, Video recording, Biomedical imaging, Aquaculture, Biofouling, Farm Net Occlusion, Image Analysis, Automatic, GoPro video, detect fish net occlusion", isbn13 = "979-8-3315-1878-3", timestamp = "Mon, 03 Mar 2025 21:16:18 +0100", biburl = "https://dblp.org/rec/conf/ivcnz/FanB0SF024.bib", bibsource = "dblp computer science bibliography, https://dblp.org", DOI = "doi:10.1109/IVCNZ64857.2024.10794201", notes = "also known as \cite{10794201} School of Computing and Artificial Intelligence Shandong University of Finance and Economics Jinan, China School of Engineering and Computer Science Victoria University of Wellington Wellington, New Zealand IVCNZ2024 https://ivcnz2024.csse.canterbury.ac.nz/", } @InProceedings{WeigueFan:1999:agmfGPeir, author = "Weiguo Fan and Michael D. Gordon and Praveen Pathak", title = "Automatic generation of matching functions by genetic programming for effective information retrieval", booktitle = "Proceedings of the 1999 Americas Conference on Information Systems", year = "1999", editor = "W. David Haseman and Derek L. Nazareth", pages = "49--51", address = "Milwaukee, WI, USA", month = "13-15 " # aug, organisation = "Association for Information Systems", keywords = "genetic algorithms, genetic programming", URL = "http://filebox.vt.edu/users/wfan/paper/Amcis_final.pdf", size = "3 pages", abstract = "With the advent of the Internet, online resources are increasingly available. Many users choose popular search engines to perform an online search to satisfy their information need. However, these search engines tend to turn up many non-relevant documents, which make their retrieval precision very low. How to find appropriate ranking metrics to retrieve more relevant documents and fewer non-relevant documents for users remains a big challenge to the information retrieval community. In this paper, we propose a new framework that combines the merits of genetic programming and relevance feedback techniques to automatically generate and refine the matching functions used for document ranking. This approach overcomes the shortcoming of traditional ranking algorithms using a fixed ranking strategy. It also gives some new ideas and hints for information retrieval professionals.", notes = "AMCIS99 https://commerce.mindspring.com/www.icisnet.org/proc.html Prototype implemented in C. Fitness based on user feedback Duplicate entry \cite{Fan:1999:AMCIS} removed 21 Oct 2006", } @InProceedings{WeiguoFan:2000:icis, author = "Weiguo Fan and Michael D. Gordon and Praveen Pathak", title = "Personalization of Search Engine Services for Effective Retrieval and Knowledge Management", booktitle = "The Proceedings of the International Conference on Information Systems 2000", year = "2000", pages = "20--34", keywords = "genetic algorithms, genetic programming, information retrieval", URL = "http://filebox.vt.edu/users/wfan/paper/icis_final.pdf", abstract = "The Internet and corporate intranets provide far more information than anybody can absorb. People use search engines to find the information they require. However, these systems tend to use only one fixed term weighting strategy regardless of the context to which it applies, posing serious performance problems when characteristics of different users, queries, and text collections are taken into consideration. In this paper, we argue that the term weighting strategy should be context specific, that is, different term weighting strategies should be applied to different contexts, and we propose a new systematic approach that can automatically generate term weighting strategies for different contexts based on genetic programming (GP). The new proposed framework was tested on TREC data and the results are very promising.", } @Article{Fan2003a, author = "Weiguo Fan and Michael D. Gordon and Praveen Pathak", title = "Discovery of context-specific ranking functions for effective information retrieval using genetic programming", journal = "IEEE Transactions on Knowledge and Data Engineering", year = "2004", volume = "16", number = "4", pages = "523--527", month = apr, keywords = "genetic algorithms, genetic programming, data mining, information retrieval, search engines, tree data structures, Internet, TREC data, context-specific ranking function discovery, corporate intranets, fixed ranking strategy, information routing, intelligent contextual information retrieval, search engines, term weighting strategy, text mining", ISSN = "1041-4347", DOI = "doi:10.1109/TKDE.2004.1269663", size = "5 pages", abstract = "The Internet and corporate intranets have brought a lot of information. People usually resort to search engines to find required information. However, these systems tend to use only one fixed ranking strategy regardless of the contexts. This poses serious performance problems when characteristics of different users, queries, and text collections are taken into account. We argue that the ranking strategy should be context specific and we propose a , new systematic method that can automatically generate ranking strategies for different contexts based on genetic programming (GP). The new method was tested on TREC data and the results are very promising.", notes = "http://filebox.vt.edu/users/wfan/pub_area.html", } @Article{Fan2003b, author = "Weiguo Fan and Michael D. Gordon and Praveen Pathak", title = "A generic ranking function discovery framework by genetic programming for information retrieval", journal = "Information Processing and Management", year = "2003", volume = "40", number = "4", pages = "587--602", keywords = "genetic algorithms, genetic programming, Information retrieval; Ranking function, Text mining", DOI = "doi:10.1016/j.ipm.2003.08.001", URL = "http://filebox.vt.edu/users/wfan/paper/ARRANGER/ip&m2003.pdf", URL = "http://www.sciencedirect.com/science/article/B6VC8-49J8S58-2/2/158a3713b59ef9defad7d00e81707f66", size = "16 pages", abstract = "Ranking functions play a substantial role in the performance of information retrieval (IR) systems and search engines. Although there are many ranking functions available in the IR literature, various empirical evaluation studies show that ranking functions do not perform consistently well across different contexts (queries, collections, users). Moreover, it is often difficult and very expensive for human beings to design optimal ranking functions that work well in all these contexts. In this paper, we propose a novel ranking function discovery framework based on Genetic Programming and show through various experiments how this new framework helps automate the ranking function design/discovery process.", } @InProceedings{Fan2004, author = "Weiguo Fan and Michael D. Gordon and Praveen Pathak and Wensi Xi and Edward A. Fox", title = "Ranking Function Optimization For Effective Web Search By Genetic Programming: An Empirical Study", booktitle = "Proceedings of 37th Hawaii International Conference on System Sciences", year = "2004", pages = "105--112", address = "Hawaii", month = "5-8 " # jan, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/HICSS.2004.1265279", size = "8 pages", abstract = "Web search engines have become indispensable in our daily life to help us find the information we need. Although search engines are very fast in search response time, their effectiveness in finding useful and relevant documents at the top of the search hit list needs to be improved. In this paper, we report our experience applying Genetic Programming (GP) to the ranking function discovery problem leveraging the structural information of HTML documents. Our empirical experiments using the web track data from recent TREC conferences show that we can discover better ranking functions than existing well-known ranking strategies from IR, such as Okapi, Ptfidf. The performance is even comparable to those", notes = "http://filebox.vt.edu/users/wfan/pub_area.html", } @Article{Fan2004dsstwostage, author = "Weiguo Fan and Michael D. Gordon and Praveen Pathak", title = "A two stage integrated model for intelligent information routing", journal = "Decision Support Systems", year = "2006", volume = "42", number = "1", pages = "362--374", month = oct, keywords = "genetic algorithms, genetic programming, Information Routing, Information Retrieval, Personalization, Text Mining", abstract = "A recent surge of subscriptions to online news services exemplifies the fact that people and organizations constantly need up-to-date information to stay competitive and make better informed decisions. However, many of these news services often require users to either manually input their profiles or subscribe to existing news channel. This results in lack of intelligence and personalization, and thus make them less attractive to users. In this paper, an integrated model that combines query expansion with ranking function adaptation for online information routing is proposed and tested using two different large scale corpora. The experimental results show that this new model can deliver much better quality information than existing models.", URL = "http://filebox.vt.edu/users/wfan/pub_area.html", DOI = "doi:10.1016/j.dss.2005.01.007", } @Article{Fan2004jasist, author = "Weiguo Fan and Edward A. Fox and Praveen Pathak and Harris Wu", title = "The effects of fitness functions on genetic programming-based ranking discovery for web search", journal = "Journal of the American Society for Information Science and Technology", year = "2004", volume = "55", number = "7", pages = "628--636", keywords = "genetic algorithms, genetic programming, ranking function, text mining, web search, information retrieval", URL = "http://filebox.vt.edu/users/wfan/paper/ARRANGER/JASIST2004.pdf", DOI = "doi:10.1002/asi.20009", abstract = "Genetic-based evolutionary learning algorithms, such as genetic algorithms (GAs) and genetic programming (GP), have been applied to information retrieval (IR) since the 1980s. Recently, GP has been applied to a new IR task- discovery of ranking functions for Web search-and has achieved very promising results. However, in our prior research, only one fitness function has been used for GP-based learning. It is unclear how other fitness functions may affect ranking function discovery for Web search, especially since it is well known that choosing a proper fitness function is very important for the effectiveness and efficiency of evolutionary algorithms. In this article, we report our experience in contrasting different fitness function designs on GP-based learning using a very large Web corpus. Our results indicate that the design of fitness functions is instrumental in performance improvement. We also give recommendations on the design of fitness functions for genetic-based information retrieval experiments.", } @InProceedings{Fan2004sigir, author = "Weiguo Fan and Ming Luo and Li Wang and Wensi Xi and Edward A. Fox", title = "Tuning before feedback: combining ranking function discovery and blind feedback for robust retrieval", booktitle = "the Proceedings of the 27th Annual International ACM SIGIR Conference", year = "2004", pages = "138--145", address = "Sheffield, United Kingdom", publisher_address = "New York, NY, USA", month = "25-29 " # jul, organisation = "SIGIR", publisher = "ACM", keywords = "genetic algorithms, genetic programming, intelligent information retrieval, search engine, ranking function discovery, information retrieval, blind feedback", ISBN = "1-58113-881-4", URL = "http://filebox.vt.edu/users/wfan/paper/ARRANGER/p52-Fan.pdf", URL = "http://doi.acm.org/10.1145/1008992.1009018", DOI = "doi:10.1145/1008992.1009018", size = "8 pages", abstract = "Both ranking functions and user queries are very important factors affecting a search engine's performance. Prior research has looked at how to improve ad-hoc retrieval performance for existing queries while tuning the ranking function, or modify and expand user queries using a fixed ranking scheme using blind feedback. However, almost no research has looked at how to combine ranking function tuning and blind feedback together to improve ad-hoc retrieval performance. In this paper, we look at the performance improvement for ad-hoc retrieval from a more integrated point of view by combining the merits of both techniques. In particular, we argue that the ranking function should be tuned first, using user-provided queries, before applying the blind feedback technique. The intuition is that highly-tuned ranking offers more high quality documents at the top of the hit list, thus offers a stronger baseline for blind feedback. We verify this integrated model in a large scale heterogeneous collection and the experimental results show that combining ranking function tuning and blind feedback can improve search performance by almost 30 percent over the baseline Okapi system.", notes = "http://www.sigir.org/sigir2004/ Also known as \cite{Fan:2004:TBF:1008992.1009018}", } @Article{journals/dss/FanPW06, title = "Nonlinear ranking function representations in genetic programming-based ranking discovery for personalized search", author = "Weiguo Fan and Praveen Pathak and Linda Wallace", journal = "Decision Support Systems", year = "2006", number = "3", volume = "42", pages = "1338--1349", month = dec, bibdate = "2007-01-23", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/dss/dss42.html#FanPW06", keywords = "genetic algorithms, genetic programming, Information routing, Information retrieval, Ranking function", DOI = "doi:10.1016/j.dss.2005.11.002", abstract = "Ranking function is instrumental in affecting the performance of a search engine. Designing and optimising a search engine's ranking function remains a daunting task for computer and information scientists. Recently, genetic programming (GP), a machine learning technique based on evolutionary theory, has shown promise in tackling this very difficult problem. Ranking functions discovered by GP have been found to be significantly better than many of the other existing ranking functions. However, current GP implementations for ranking function discovery are all designed using the Vector Space model in which the same term weighting strategy is applied to all terms in a document. This may not be an ideal representation scheme at the individual query level considering the fact that many query terms should play different roles in the final ranking. In this paper, we propose a novel nonlinear ranking function representation scheme and compare this new design to the well-known Vector Space model. We theoretically show that the new representation scheme subsumes the traditional Vector Space model representation scheme as a special case and hence allows for additional flexibility in term weighting. We test the new representation scheme with the GP-based discovery framework in a personalised search (information routing) context using a TREC web corpus. The experimental results show that the new ranking function representation design outperforms the traditional Vector Space model for GP-based ranking function discovery.", } @Article{Fan2009398, author = "Weiguo Fan and Praveen Pathak and Mi Zhou", title = "Genetic-based approaches in ranking function discovery and optimization in information retrieval -- A framework", journal = "Decision Support Systems", volume = "47", number = "4", pages = "398--407", year = "2009", note = "Smart Business Networks: Concepts and Empirical Evidence", ISSN = "0167-9236", DOI = "doi:10.1016/j.dss.2009.04.005", URL = "http://www.sciencedirect.com/science/article/B6V8S-4W2W5G2-2/2/891e4aeaad9141e2bfe99d4477f96c1a", keywords = "genetic algorithms, genetic programming, Information retrieval, Artificial intelligence, Evolutionary computations, Data fusion", abstract = "An Information Retrieval (IR) system consists of document collection, queries issued by users, and the matching/ranking functions used to rank documents in the predicted order of relevance for a given query. A variety of ranking functions have been used in the literature. But studies show that these functions do not perform consistently well across different contexts. In this paper we propose a two-stage integrated framework for discovering and optimising ranking functions used in IR. The first stage, discovery process, is accomplished by intelligently leveraging the structural and statistical information available in HTML documents by using Genetic Programming techniques to yield novel ranking functions. In the second stage, the optimization process, document retrieval scores of various well-known ranking functions are combined using Genetic Algorithms. The overall discovery and optimization framework is tested on the well-known TREC collection of web documents for both the ad-hoc retrieval task and the routing task. Using our framework we observe a significant increase in retrieval performance compared to some of the well-known stand alone ranking functions.", } @InProceedings{Fan:2010:ICNC, author = "Xinqiao Fan and Yongli Zhu", title = "The application of Empirical Mode Decomposition and Gene Expression Programming to short-term load forecasting", booktitle = "Sixth International Conference on Natural Computation (ICNC 2010)", year = "2010", month = "10-12 " # aug, volume = "8", pages = "4331--4334", keywords = "genetic algorithms, genetic programming, gene expression programming, empirical mode decomposition, intrinsic mode functions, short-term load forecasting, wavelet transforms, load forecasting, statistical analysis, wavelet transforms", DOI = "doi:10.1109/ICNC.2010.5583605", abstract = "A forecasting method of combining Empirical Mode Decomposition(EMD) and Gene Expression Programming(GEP) that's called EMD and GEP method here is suggested, which is applied to short-term load forecasting and higher forecasting precision is obtained. The load samples are handled in order to eliminate the pseudo-data, and the intrinsic mode functions(IMFs) and the residual trend of different frequency are obtained according to EMD. Then the corresponding load series of the same time but different days in the IMFs and the residual trend are chosen as the training samples, and by means of the flexible expressive capacity of GEP, the models of different time points in each IMF and the residual trend are evolved according to time-sharing. And the final forecasting result is obtained by reconstructing the models of each IMF and the residual trend. The method of EMD overcomes the shortcomings of wavelet transform that it's difficult to select proper wavelet function, and the final result indicates that the IMFs can reflect the characteristics of the power load. After comparison with the results forecasted by means of Wavelet and GEP, it proves that the effect of the forecasting method of EMD and GEP in short-term load forecasting is better.", notes = "also known as \cite{5583605}", } @PhdThesis{Zhengjie_Fan:thesis, title = "Concise Pattern Learning for {RDF} Data Sets Interlinking", titletranslation = "Apprentissage de Motifs Concis pour le Liage de Donnees RDF", author = "Zhengjie Fan", year = "2014", school = "Universite de Grenoble", address = "France", month = "7 " # aug, keywords = "genetic algorithms, genetic programming, interlinking, ontology matching, machine learning", annote = "Computer mediated exchange of structured knowledge (EXMO) ; Inria Grenoble - Rh{\^o}ne-Alpes ; INRIA - INRIA - Laboratoire d'Informatique de Grenoble (LIG) ; CNRS - Universit{\'e} Pierre Mend{\`e}s France (Grenoble 2 UPMF) - Institut National Polytechnique de Grenoble (INPG) - Universit{\'e} Joseph Fourier (Grenoble 1 UJF) - CNRS - Universit{\'e} Pierre Mend{\`e}s France (Grenoble 2 UPMF) - Institut National Polytechnique de Grenoble (INPG) - Universit{\'e} Joseph Fourier (Grenoble 1 UJF); Universit{\'e} de Grenoble; J{\'e}r{\^o}me Euzenat(jerome.euzenat@inria.fr)", bibsource = "OAI-PMH server at api.archives-ouvertes.fr", contributor = "Computer mediated exchange of structured knowledge and J{\'e}r{\^o}me Euzenat and Datalift", identifier = "tel-00986104", language = "english", oai = "oai:HAL:tel-00986104v1", rights = "info:eu-repo/semantics/openAccess", type = "info:eu-repo/semantics/doctoralThesis; Theses", URL = "https://tel.archives-ouvertes.fr/tel-00986104", URL = "https://tel.archives-ouvertes.fr/tel-00986104/document", URL = "https://tel.archives-ouvertes.fr/tel-00986104/file/Thesis.pdf", size = "169 pages", abstract = "There are many data sets being published on the web with Semantic Web technology. The data sets contain analogous data which represent the same resources in the world. If these data sets are linked together by correctly building links, users can conveniently query data through a uniform interface, as if they are querying one data set. However, finding correct links is very challenging because there are many instances to compare. Many existing solutions have been proposed for this problem. (1) One straight-forward idea is to compare the attribute values of instances for identifying links, yet it is impossible to compare all possible pairs of attribute values. (2) Another common strategy is to compare instances according to attribute correspondences found by instance-based ontology matching, which can generate attribute correspondences based on instances. However, it is hard to identify the same instances across data sets because there are the same instances whose attribute values of some attribute correspondences are not equal. (3) Many existing solutions leverage Genetic Programming to construct interlinking patterns for comparing instances, while they suffer from long running time. In this thesis, an interlinking method is proposed to interlink the same instances across different data sets, based on both statistical learning and symbolic learning. The input is two data sets, class correspondences across the two data sets and a set of sample links that are assessed by users as either positive or negative. The method builds a classifier that distinguishes correct links and incorrect links across two RDF data sets with the set of assessed sample links. The classifier is composed of attribute correspondences across corresponding classes of two data sets, which help compare instances and build links. The classifier is called an interlinking pattern in this thesis. On the one hand, our method discovers potential attribute correspondences of each class correspondence via a statistical learning method, the K-medoids clustering algorithm, with instance value statistics. On the other hand, our solution builds the interlinking pattern by a symbolic learning method, Version Space, with all discovered potential attribute correspondences and the set of assessed sample links. Our method can fulfill the interlinking task that does not have a conjunctive interlinking pattern that covers all assessed correct links with a concise format. Experiments confirm that our interlinking method with only 1percent of sample links already reaches a high F-measure (around 0.94-0.99). The F-measure quickly converges, being improved by nearly 10percent than other approaches.", } @InProceedings{fan:2001:bgrgaafd, author = "Zhun Fan and Jianjun Hu and Kisung Seo and Erik D. Goodman and Ronald C. Rosenberg and Baihai Zhang", title = "Bond Graph Representation and {GP} for Automated Analog Filter Design", booktitle = "2001 Genetic and Evolutionary Computation Conference Late Breaking Papers", year = "2001", editor = "Erik D. Goodman", pages = "81--86", address = "San Francisco, California, USA", month = "9-11 " # jul, email = "fanzhun@egr.msu.edu, hujianju@egr.msu.edu", keywords = "genetic algorithms, genetic programming, STGP, bond graphs, evolutionary synthesis", URL = "https://www.cse.sc.edu/~jianjunh/paper/gecco2001_gpbg.pdf", URL = "http://citeseer.ist.psu.edu/448346.html", size = "6 pages", abstract = "We present a novel circuit representation scheme, namely bond graph, along with strong-typed genetic programming for the evolution of analog filter circuits. Bond graph is a concise and uniform language for the description of circuit systems and more general engineering systems. Many unique characteristics of bond graph makes it an attractive candidate for representing circuit in genetic programming design. The feasibility and efficiency of using bond graph as the representation technique of circuit systems are verified in our experiments with automated analogue filter design.", notes = "GECCO-2001LB, lilgp", } @InProceedings{fan:2002:gecco, author = "Zhun Fan and Kisung Seo and Ronald C. Rosenberg and Jianjun Hu and Erik D. Goodman", title = "Exploring Multiple Design Topologies Using Genetic Programming And Bond Graphs", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "1073--1080", address = "New York", publisher_address = "San Francisco, CA 94104, USA", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "genetic algorithms, genetic programming, real world applications, bond graphs, design automation, mechatronic system, topology", ISBN = "1-55860-878-8", URL = "http://garage.cse.msu.edu/papers/GARAGe02-07-03.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/RWA217.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-20.pdf", abstract = "To realize design automation of dynamic systems, there are two major issues to be dealt with: open-topology generation of dynamic systems and simulation or analysis of those models. For the first issue, we exploit the strong topology exploration capability of genetic programming to create and evolve structures representing dynamic systems. With the help of ERCs (ephemeral random constants) in genetic programming, we can also evolve the sizing of dynamic system components along with the structures. The second issue, simulation and analysis of those system models, is made more complex when they represent mixed-energy- domain systems. We take advantage of bond graphs as a tool for multi- or mixed-domain modeling and simulation of dynamic systems. Because there are many considerations in dynamic system design that are not completely captured by a bond graph, we would like to generate multiple solutions, allowing the designer more latitude in choosing a model to implement. The approach in this paper is capable of providing a variety of design choices to the designer for further analysis, comparison and trade-off. The approach is shown to be efficient and effective in an example of open-ended real- world dynamic system design application, a printer re-design problem.", notes = "GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)", } @InProceedings{ZhunFan:2003:AAAI, author = "Zhun Fan and Kisung Seo and Ronald C. Rosenberg and Jianjun Hu and Erik D. Goodman", title = "Computational Synthesis of Multi-Domain Systems", booktitle = "Proceedings of the 2003 AAAI Spring Symposium - Computational Synthesis: From Basic Building Blocks to High Level Functionality", year = "2003", pages = "59--66", address = "Stanford, California", month = mar, organisation = "AAAI", email = "hujianju@msu.edu, goodman@egr.msu.edu", keywords = "genetic algorithms, genetic programming, bond graphs, evolutionary synthesis", URL = "http://garage.cse.msu.edu/papers/GARAGe03-03-02.pdf", abstract = "Several challenging issues have to be addressed for automated synthesis of multi-domain systems. First, design of interdisciplinary (multi-domain) engineering systems, such as mechatronic systems, differs from design of single-domain systems, such as electronic circuits, mechanisms, and fluid power systems, in part because of the need to integrate the several distinct domain characteristics in predicting system behavior. Second, a mechanism is needed to automatically select useful elements from the building block repertoire, construct them into a system, evaluate the system and then reconfigure the system structure to achieve better performance. Dynamic system models based on diverse branches of engineering science can be expressed using the notation of bond graphs, based on energy and information flow. One may construct models of electrical, mechanical, magnetic, hydraulic, pneumatic, thermal, and other systems using only a rather small set of ideal elements as building blocks. Another useful tool, genetic programming, is a powerful method for creating and evolving novel design structures in an open-ended manner. Through definition of a set of constructor functions, a genotype tree is created for each individual in each generation. The process of evaluating the genotype tree maps the genotype into a phenotype -- i.e., to the abstract topological description of the design of a multi-domain system, using a bond graph along with parameters for each component, if needed. Finally, physical realization is carried out to relate each abstract element of the bond graph to corresponding components in various physical domains. To implement the above GPBG approach in a specific application domain, cautious steps have to be taken to make the evolved design represented by bond graphs realizable and manufacturable. To achieve this, one important step is to define appropriate building blocks of the design space and carefully design a realizable function set in genetic programming. We are going to illustrate this in an example of behavioral synthesis of an RF MEM circuit C a micro-mechanical band pass filter design. Finally, we have some discussions on how to extend the above approach to an integrated evolutionary synthesis environment for MEMS across a variety of design layers.", } @InProceedings{fan:2003:gecco, author = "Zhun Fan and Kisung Seo and Jianjun Hu and Ronald C. Rosenberg and Erik D. Goodman", title = "System-Level Synthesis of {MEMS} via Genetic Programming and Bond Graphs", booktitle = "Genetic and Evolutionary Computation -- GECCO-2003", editor = "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller", year = "2003", pages = "2058--2071", address = "Chicago", publisher_address = "Berlin", month = "12-16 " # jul, volume = "2724", series = "LNCS", ISBN = "3-540-40603-4", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, Real World Applications", DOI = "doi:10.1007/3-540-45110-2_103", abstract = "Initial results have been achieved for automatic synthesis of MEMS system-level lumped parameter models using genetic programming and bond graphs. This paper first discusses the necessity of narrowing the problem of MEMS synthesis into a certain specific application domain, e.g., RF MEM devices. Then the paper briefly introduces the flow of a structured MEMS design process and points out that system-level lumped-parameter model synthesis is the first step of the MEMS synthesis process. Bond graphs can be used to represent a system-level model of a MEM system. As an example, building blocks of RF MEM devices are selected carefully and their bond graph representations are obtained. After a proper and realizable function set to operate on that category of building blocks is defined, genetic programming can evolve both the topologies and parameters of corresponding RF MEM devices to meet predefined design specifications. Adaptive fitness definition is used to better direct the search process of genetic programming. Experimental results demonstrate the feasibility of the approach as a first step of an automated MEMS synthesis process. Some methods to extend the approach are also discussed.", notes = "GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)", } @InProceedings{fan:2004:hesom, title = "Hierarchical Evolutionary Synthesis of MEMS", author = "Zhun Fan and Erik Goodman and Jiachuan Wang and Ronald Rosenberg and Kisung Seo and Jianjun Hu", pages = "2320--2327", booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary Computation", year = "2004", publisher = "IEEE Press", month = "20-23 " # jun, address = "Portland, Oregon", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Evolutionary design \& evolvable hardware, Real-world applications", DOI = "doi:10.1109/CEC.2004.1331187", abstract = "In this paper, we discuss the hierarchy that is involved in a typical MEMS design and how evolutionary approaches can be used to automate the hierarchical design and synthesis process for MEMS. At the system level, the approach combining bond graphs and genetic programming can lead to satisfactory design candidates of system level models that meet the predefined behavioral specifications for designers to tradeoff. At the physical layout synthesis level, the selection of geometric parameters for component devices is formulated as a constrained optimization problem and addressed using a constrained GA approach. A multiple-resonator microsystem design is used to illustrate the integrated design automation idea using evolutionary approaches.", notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.", } @InCollection{Fan:2004:EMTP, author = "Zhun Fan and Jiachuan Wang and Kisung Seo and Jianjun Hu and Ronald Rosenberg and Janis Terpenny and Erik Goodman", title = "Automating the Hierarchical Synthesis of MEMS Using Evolutionary Approaches", year = "2004", booktitle = "Evolvable Machines: Theory \& Practice", pages = "129--149", volume = "161", series = "Studies in Fuzziness and Soft Computing", chapter = "6", editor = "Nadia Nedjah and Luiza {de Macedo Mourelle}", publisher = "Springer", address = "Berlin", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-22905-1", URL = "http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-175-22-33980449-0,00.html", notes = "Springer says published in 2005 but available Nov 2004", } @PhdThesis{ZhunFan:thesis, author = "Zhun Fan", title = "Design Automation of Mechatronic Systems", school = "Electrical and Computer Engineering, Michigan State University", year = "2004", address = "USA", keywords = "genetic algorithms, genetic programming", URL = "http://search.proquest.com/docview/305157550", URL = "http://www.worldcat.org/title/design-automation-of-mechatronic-systems-using-evolutionary-computation-and-bond-graph/oclc/060353062", broken = "https://www.msu.edu/~fanzhun/Zhun%27s%20Dissertation%20Research.htm", size = "132 pages", abstract = "Design automation is a difficult task and has been studied for some time by researchers. Most research is quite successful in automating the parameters of a given design topology. However, their limitation is that they only accept fixed design topologies. Others can design in topologically unconstrained space, but are limited or specially tailored to a single physical domain. The motivation of this research is two-fold. First, we want to find a way to generate a population of topologically open-ended design alternatives and provide for the designer, in an automated manner, a variety of satisfactory design candidates to choose among and trade off. Second, we want our method to be applicable not only in one physical domain, but in multiple domains or a mixture of them, as is required for design of mechatronic systems. To meet these ends, the capability of genetic programming to search automatically in an open-ended search space and the strong capability of bond graphs to represent and model mixed-domain systems are studied and ways to blend their merits in one unified approach are investigated. In our research, the BG/GP method, combining bond graphs and genetic programming, has been developed to automate the conceptual design process for general multidisciplinary mechatronic systems. Several design problems, in macro- and micro-domains, and in different physical domains, have been used as design examples to test the feasibility of the BG/GP approach. The analog electronic filter design problem shows the efficiency and effectiveness of the proposed approach. A vibration absorber design for a mechanical printer demonstrates that the approach can also be used for redesign and is very effective in exploring in an open-ended topology space and capable of providing designers with a variety of good design candidates for further analysis and tradeoff. A pneumatic air pump design shows how to bias design preference and implies the possibility and significance of extracting design heuristics in the evolutionary process. Finally, a MEM filter design problem shows that the BG/GP approach can be applied in a very general class of conceptual design problems with severe topology and/or parameter constraints. The results show that the BG/GP method is a powerful synergistic approach for automated, mixed-domain, and topologically open-ended design of mechatronic systems. A structured and hierarchical design methodology for Micro-Electro-Mechanical-Systems (MEMS) is also studied. MEMS are actually micro-mechatronic systems. The research of hierarchical evolutionary synthesis of MEMS in this thesis includes the system-level behavioural synthesis and second-level layout synthesis of MEMS. Preliminary results show that automated synthesis of MEMS is a very promising research area.", notes = "SF Project (DMI0084934) NSF Automated Synthesis of Mechatronic Systems by Bond graph and Genetic Programming http://garage.cse.msu.edu/gpbg/index.htm OCLC Number: 60353062. ProQuest 3146015. UMI Microform 3146015", } @InCollection{Fan:2005:CER, author = "Zhun Fan and Jiachuan Wang and Erik Goodman", title = "Exploring Open-Ended Design Space of Mechatronic Systems", booktitle = "Cutting Edge Robotics", publisher = "IntechOpen", year = "2005", editor = "Vedran Kordic and Aleksandar Lazinica and Munir Merdan", chapter = "41", pages = "707--726", keywords = "genetic algorithms, genetic programming", isbn13 = "3-86611-038-3", bibsource = "OAI-PMH server at mts.intechopen.com", identifier = "doi:10.5772/4677", language = "en", rights = "https://creativecommons.org/licenses/by-nc-sa/3.0/", oai = "oai:intechopen.com:41", URL = "http://www.intechopen.com/chapter/pdf-download/41", URL = "http://www.intechopen.com/chapter/pdf-download/41.pdf", size = "20 pages", abstract = "This research has explored a new automated approach for synthesizing designs for mechatronic systems. By taking advantage of genetic programming as a search method for competent designs and the bond graph as a representation for mechatronic systems, we have created a design environment in which open-ended topological search can be accomplished in a semi-automated and efficient manner and the design process thereby facilitated. By incorporating specific design considerations the method can be used to explore design space of special types of mechatronic systems such as robotic systems. The paper illustrates the process of using this approach in detail through a typewriter redesign problem. Bond graphs have proven to be an effective tool for both modelling and design in this problem. Also a special form of GP, Hierarchical Fair Competition-GP, has been shown to be capable of providing a diversity of competing designs with great efficiency. Our long-term target in this research is to design an integrated and interactive synthesis framework for mechatronic systems that covers the full spectrum of design processes, including customer needs analysis, product development, design requirements and constraints, automated synthesis, design verification, and life-cycle considerations.", } @Article{Fan2008579, author = "Zhun Fan and Jiachuan Wang and Sofiane Achiche and Erik Goodman and Ronald Rosenberg", title = "Structured synthesis of {MEMS} using evolutionary approaches", journal = "Applied Soft Computing", volume = "8", number = "1", pages = "579--589", year = "2008", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2007.04.001", URL = "http://www.sciencedirect.com/science/article/B6W86-4NWCGRR-6/2/6d147c9eb8cc9af8eec68e592dfd22f", keywords = "genetic algorithms, genetic programming, MEMS synthesis, Genetic programming, Bond graphs, Genetic algorithm", abstract = "In this paper, we discuss the hierarchy that is involved in a typical MEMS design and how evolutionary approaches can be used to automate the hierarchical synthesis process for MEMS. The paper first introduces the flow of a structured MEMS design process and emphasizes that system-level lumped-parameter model synthesis is the first step of the MEMS synthesis process. At the system level, an approach combining bond graphs and genetic programming can lead to satisfactory design candidates as system-level models that meet the predefined behavioral specifications for designers to trade off. Then at the physical layout synthesis level, the selection of geometric parameters for component devices and other design variables is formulated as a constrained optimization problem and addressed using a constrained genetic algorithm approach. A multiple-resonator microsystem design is used to illustrate the integrated design automation idea using these evolutionary approaches.", } @Book{Mechatronic_Design_Automation_Emerging_Research_and_Recent_Advances, author = "Zhun Fan", title = "Mechatronic Design Automation: Emerging Research and Recent Advances", publisher = "Nova publishers", year = "2010", month = apr, keywords = "genetic algorithms, genetic programming, bond graph", isbn13 = "978-1616689568", URL = "https://www.novapublishers.com/catalog/product_info.php?products_id=27671", URL = "https://www.novapublishers.com/catalog/downloadOA.php?order=1&access=true", URL = "http://www.amazon.com/Mechatronic-Design-Automation-Engineering-Applications/dp/1616689560", abstract = "This book proposes a novel design method that combines both genetic programming (GP) to automatically explore the open-ended design space and bond graphs (BG) to unify design representations of multi-domain Mechatronic systems. Results show that the method, formally called GPBG method, can successfully design analog filters, vibration absorbers, micro-electro-mechanical systems, and vehicle suspension systems, all in an automatic or semi-automatic way. It also investigates the very important issue of co-designing body-structures and dynamic controllers in automated design of Mechatronic systems.", notes = "Technical University of Denmark, Denmark", size = "161 pages", } @InProceedings{Fan:2015:CEC, author = "Zhun Fan and Youxiang Zuo and Dazhi Jiang and Xinye Cai", title = "Prediction of Acute Hypotensive Episodes Using Random Forest Based on Genetic Programming", booktitle = "Proceedings of 2015 IEEE Congress on Evolutionary Computation (CEC 2015)", year = "2015", editor = "Yadahiko Murata", pages = "688--694", address = "Sendai, Japan", month = "25-28 " # may, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-7491-7", DOI = "doi:10.1109/CEC.2015.7256957", abstract = "At Intensive Care Unit (ICU), acute hypotensive episode (AHE) can cause serious consequences. It can make the organs broken, or even the patient dead. Generally AHE is predicted by the doctor clinically. In order to forecast the AHE automatically, this paper proposes an algorithm based on the genetic programming (GP) and random forest (RF). The algorithm obtains features of the signal through the Intrinsic Mode Function (IMF) signal produced by applying empirical mode decomposition (EMD) to the arterial blood pressure (MAP) signal. Then the feature sets and the data sets are grouped to evolve decision functions via GP. Finally, a random forest is formed and the classification result is obtained by voting. The achieved accuracy of the proposed method is 77.55percent, the sensitivity is 80.55percent and specificity is 75.14percent after the five-fold cross-validation.", notes = "See also http://dx.doi.org/10.1155/2015/354807 1150 hrs 15654 CEC2015", } @Misc{DBLP:journals/corr/abs-1910-14627, author = "Zhun Fan and Zhaojun Wang and Xiaomin Zhu and Bingliang Hu and An-Min Zou and Dongwei Bao", title = "An Automatic Design Framework of Swarm Pattern Formation based on Multi-objective Genetic Programming", howpublished = "arXiv", volume = "abs/1910.14627", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "http://arxiv.org/abs/1910.14627", archiveprefix = "arXiv", eprint = "1910.14627", timestamp = "Mon, 04 May 2020 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/corr/abs-1910-14627.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InCollection{Fan:2020:beacon, author = "Zhun Fan and Guijie Zhuo and Wenji Li", title = "Mechatronic Design Automation: A Short Review", booktitle = "Evolution in Action: Past, Present and Future: A Festschrift in Honor of Erik D. Goodman", publisher = "Springer", year = "2020", editor = "Wolfgang Banzhaf and Betty H. C. Cheng and Kalyanmoy Deb and Kay E. Holekamp and Richard E. Lenski and Charles Ofria and Robert T. Pennock and William F. Punch and Danielle J. Whittaker", series = "Genetic and Evolutionary Computation book series", chapter = "30", pages = "453--466", keywords = "genetic algorithms, genetic programming, Mechatronic Systems, Design Automation, Evolutionary Design, Bond Graph (BG)", isbn13 = "978-3-030-39833-0", DOI = "doi:10.1007/978-3-030-39831-6_30", abstract = "a short review on mechatronic design automation (MDA) whose optimization method is mainly based on evolutionary computation techniques. The recent progress and research results of MDA are summarized systematically, and the challenges and future research directions in MDA are also discussed. The concept of MDA is introduced first, research results and potential challenges of MDA are analyzed. Then future research directions, focusing on constrained multiobjective optimization, surrogate-assisted constrained multi-objective optimization, and design automation by integrating constrained multiobjective evolutionary computation and knowledge extraction, are discussed. Finally, we suggest that MDA has great potential, and may be the next big technology wave after electronic design automation (EDA).", notes = "Shantou University, Shantou, China", } @Article{FAN:2023:swevo, author = "Zhun Fan and Zhaojun Wang and Wenji Li and Xiaomin Zhu and Bingliang Hu and An-Min Zou and Weidong Bao and Minqiang Gu and Zhifeng Hao and Yaochu Jin", title = "Automated pattern generation for swarm robots using constrained multi-objective genetic programming", journal = "Swarm and Evolutionary Computation", volume = "81", pages = "101337", year = "2023", ISSN = "2210-6502", DOI = "doi:10.1016/j.swevo.2023.101337", URL = "https://www.sciencedirect.com/science/article/pii/S2210650223001104", keywords = "genetic algorithms, genetic programming, Gene regulatory network (GRN), Entrapping pattern generation, Self-organization, Constrained multi-objective genetic programming (CMOGP)", abstract = "Swarm robotic systems (SRSs), which are widely used in many fields, such as search and rescue, usually comprise a number of robots with relatively simple mechanisms collaborating to accomplish complex tasks. A challenging task for SRSs is to design local interaction rules for self-organization of robots that can generate adaptive patterns to entrap moving targets. Biologically inspired approaches such as gene regulatory network (GRN) models provide a promising solution to this problem. However, the design of GRN models for generating entrapping patterns relies on the expertise of designers. As a result, the design of the GRN models is often a laborious and tedious trial-and-error process. In this study, we propose a modular design automation framework for GRN models that can generate entrapping patterns. The framework employs basic network motifs to construct GRN models automatically without requiring expertise. To this end, a constrained multi-objective genetic programming is used to simultaneously optimize the structures and parameters of the GRN models. A multi-criteria decision-making approach is adopted to choose the preferred GRN model for generating the entrapping pattern. Comprehensive simulation results demonstrate that the proposed framework can obtain novel GRN models with simpler structures than those designed by human experts yet better performance in complex and dynamic environments. Proof-of-concept experiments using e-puck robots confirmed the feasibility and effectiveness of the proposed GRN models", } @Article{Fang:2020:QF, author = "Jie Fang and Jianwu Lin and Shutao Xia and Zhikang Xia and Shenglei Hu and Xiang Liu and Yong Jiang", title = "Neural network-based automatic factor construction", journal = "Quantitative Finance", year = "2020", volume = "22", number = "12", pages = "2101--2114", note = "Special issue 7th International Conference on Futures and Other Derivatives (ICFOD)", keywords = "genetic algorithms, genetic programming, ANN", ISSN = "14697688", bibsource = "OAI-PMH server at oai.repec.org", identifier = "RePEc:taf:quantf:v:20:y:2020:i:12:p:2101-2114", oai = "oai:RePEc:taf:quantf:v:20:y:2020:i:12:p:2101-2114", URL = "http://hdl.handle.net/10.1080/14697688.2020.1814039", DOI = "doi:10.1080/14697688.2020.1814039", abstract = "Instead of conducting manual factor construction based on traditional and behavioural finance analysis, academic researchers and quantitative investment managers have leveraged Genetic Programming (GP) as an automatic feature construction tool in recent years, which builds reverse polish mathematical expressions from trading data into new factors. However, with the development of deep learning, more powerful feature extraction tools are available. This paper proposes Neural Network-based Automatic Factor Construction (NNAFC), a tailored neural network framework that can automatically construct diversified financial factors based on financial domain knowledge and a variety of neural network structures. The experiment results show that NNAFC can construct more informative and diversified factors than GP, to effectively enrich the current factor pool. For the current market, both fully connected and recurrent neural network structures are better at extracting information from financial time series than convolution neural network structures. Moreover, new factors constructed by NNAFC can always improve the return, Sharpe ratio, and the max draw-down of a multi-factor quantitative investment strategy due to their introducing more information and diversification to the existing factor pool.", } @Article{fang:2021:CIS, author = "Wei Fang and Mindan Gu", title = "{FMCGP:} frameshift mutation cartesian genetic programming", journal = "Complex \& Intelligent Systems", year = "2021", volume = "7", number = "3", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", URL = "http://link.springer.com/article/10.1007/s40747-020-00241-5", DOI = "doi:10.1007/s40747-020-00241-5", } @InProceedings{fang:2023:GECCOcomp, author = "Wen-Zhong Fang and Chi-Hsien Chang and Jung-Chun Liu and Tian-Li Yu", title = "{GP} with {Ranging-Binding} Technique for Symbolic Regression", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "563--566", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, evolutionary computation: Poster", isbn13 = "9798400701191", DOI = "doi:10.1145/3583133.3590605", size = "4 pages", abstract = "This paper proposes a model-based genetic programming algorithm for symbolic regression, called the ranging-binding genetic programming algorithm (RBGP). The goal is to allow offspring to retain the superiority of their promising parents during evolution. Inspired by the concept of model building, RBGP makes use of syntactic information and semantics information in a program to capture the hidden patterns. When compared with GP-GOMEA, ellynGP, and gplearn, RBGP outperformed the others on average in the Penn machine learning benchmarks, RBGP achieving statistically significant improvements over all other methods on 44 percent of the problems.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @InProceedings{conf/isica/FangL10, title = "A Review of Tournament Selection in Genetic Programming", author = "Yongsheng Fang and Jun Li", booktitle = "ISICA 2010", year = "2010", editor = "Zhihua Cai and Chengyu Hu and Zhuo Kang and Yong Liu", volume = "6382", series = "Lecture Notes in Computer Science", pages = "181--192", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-16492-7", DOI = "doi:10.1007/978-3-642-16493-4_19", size = "12 pages", bibdate = "2010-10-19", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/isica/isica2010.html#FangL10", abstract = "This paper provides a detailed review of tournament selection in genetic programming. It starts from introducing tournament selection and genetic programming, followed by a brief explanation of the popularity of the tournament selection in genetic programming. It then reviews issues and drawbacks in tournament selection, followed by analysis of and solutions to these issues and drawbacks. It finally points out some interesting directions for future work.", affiliation = "Department of Finance, Anhui Polytechnic University, Wuhu City, Anhui P.R. China", } @InProceedings{fang:2024:AI, author = "Zhengxin Fang and Hui Ma and Gang Chen2 and Sven Hartmann and Chen Wang", title = "Leveraging {LLM} in Genetic Programming Hyper-heuristics for Dynamic Microservice Deployment", booktitle = "37th Australasian Joint Conference on Artificial Intelligence, AI 2024", year = "2024", volume = "15442", series = "Lecture Notes in Computer Science", address = "Melbourne", month = nov # " 25-29", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "https://link.springer.com/chapter/10.1007/978-981-96-0348-0_7", DOI = "doi:10.1007/978-981-96-0348-0_7", } @InProceedings{Fang:2025:evoapplications, author = "Zhengxin Fang and Hui Ma and Gang Chen2 and Sven Hartmann and Shiping Chen", title = "A Communication-Aware and Energy-Efficient Genetic Programming Based Method for Dynamic Resource Allocation in Clouds", booktitle = "28th International Conference, EvoApplications 2025", year = "2025", editor = " Pablo Garcia-Sanchez and Emma Hart and Sarah L. Thomson", series = "LNCS", volume = "15613", pages = "421--436", publisher = "Springer", address = "Trieste, Italy", month = "3-5 " # apr, organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Dynamic resource allocation, Microservice applications", isbn13 = "978-3-031-90065-5", timestamp = "Wed, 07 May 2025 17:49:32 +0200", biburl = "https://dblp.org/rec/conf/evoapps/FangMCHC25.bib", bibsource = "dblp computer science bibliography, https://dblp.org", DOI = "doi:10.1007/978-3-031-90065-5_26", notes = "DBLP:conf/evoapps/FangMCHC25, http://www.evostar.org/2025/ EvoApplications2025 held in conjunction with EuroGP'2025, EvoCOP2025 and EvoMusArt2025", } @InProceedings{DBLP:conf/dlog/FanizzidE07, author = "Nicola Fanizzi and Claudia d'Amato and Floriana Esposito", title = "Induction of Optimal Semi-distances for Individuals based on Feature Sets", booktitle = "Proceedings of the 2007 International Workshop on Description Logics DL2007", year = "2007", editor = "Diego Calvanese and Enrico Franconi and Volker Haarslev and Domenico Lembo and Boris Motik and Anni-Yasmin Turhan and Sergio Tessaris", volume = "250", series = "CEUR Workshop Proceedings", address = "Brixen-Bressanone, near Bozen-Bolzano, Italy", month = "8-10 " # jun, publisher = "CEUR-WS.org", note = "method based on simulated annealing", keywords = "genetic algorithms, genetic programming", URL = "http://ceur-ws.org/Vol-250/paper_28.pdf", timestamp = "Mon, 30 May 2016 16:57:35 +0200", biburl = "https://dblp.org/rec/bib/conf/dlog/FanizzidE07", bibsource = "dblp computer science bibliography, https://dblp.org", size = "8 pages", abstract = "Many activities related to semantically annotated resources can be enabled by a notion of similarity among them. We propose a method for defining a family of semi-distances over the set of individuals in a knowledge base which can be used in these activities. In the line of works on distance-induction on clausal spaces, the family is parametrized on a committee of concepts. Hence, we also present a method based on the idea of simulated annealing to be used to optimize the choice of the best concept committee.", } @InProceedings{Fanizzi:2007:MCD, author = "Nicola Fanizzi and Claudia d'Amato and Floriana Esposito", title = "Clustering Individuals in Ontologies: a Distance-based Evolutionary Approach", booktitle = "Proceedings of the third ECML/PKDD international workshop on Mining Complex Data", year = "2007", editor = "Zbigniew W. Ras and Djamel Zighed and Shusaku Tsumoto", pages = "197--208", address = "Warsaw", month = "17 and 21 " # sep, keywords = "genetic algorithms, genetic programming", URL = "http://www.ecmlpkdd2007.org/CD/workshops/MCDM/18_Fanizzi/mcdws2007-final.pdf", size = "12 pages", abstract = "A clustering method is presented which can be applied to semantically annotated resources in the context of ontological knowledge bases. This method can be used to discover interesting groupings of structured objects through expressed in the standard languages employed for modeling concepts in the Semantic Web. The method exploits an effective and language-independent semidistance measure over the space of resources, that is based on their semantics w.r.t. a number of dimensions corresponding to a committee of features represented by a group of concept descriptions (discriminating features). A maximally discriminating group of features can be constructed through a feature construction method based on genetic programming. The evolutionary clustering algorithm employed is based on the notion of medoids applied to relational representations. It is able to induce a set of clusters by means of a proper fitness function based on a discernibility criterion. An experimentation with some ontologies proves the feasibility of our method.", notes = "LACAM Dipartimento di Informatica, Universit`a degli Studi di Bari Campus Universitario, Via Orabona 4 70125 Bari, Italy", } @InProceedings{Fanizzi:2007:ICSC, author = "Nicola Fanizzi and Claudia d'Amato and Floriana Esposito", booktitle = "International Conference on Semantic Computing (ICSC 2007)", title = "Evolutionary Conceptual Clustering of Semantically Annotated Resources", year = "2007", pages = "783--790", address = "Irvine, CA, USA", month = "17-19 " # sep, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICSC.2007.92", abstract = "A clustering method is presented which can be applied to knowledge bases storing semantically annotated resources. The method can be used to discover groupings of structured objects expressed in the standard concept languages employed in the Semantic Web. The method exploits effective language-independent semi-distance measures over the space of resources. These are based on their semantics w.r.t. a number of dimensions corresponding to a committee of features represented by a group of discriminating concept descriptions. We show how to obtain a maximally discriminating group of features through a feature construction procedure based on genetic programming. The evolutionary clustering algorithm employed is based on the notion of medoids applied to relational representations. It is able to induce an optimal set of clusters by means of a proper fitness function based on the defined distance and the discernibility criterion. An experimentation with some real ontologies proves the feasibility of our method.", notes = "also known as \cite{4338423}", } @InProceedings{DBLP:conf/cikm/FanizzidE07, author = "Nicola Fanizzi and Claudia d'Amato and Floriana Esposito", title = "Randomized metric induction and evolutionary conceptual clustering for semantic knowledge bases", booktitle = "Proceedings of the Sixteenth {ACM} Conference on Information and Knowledge Management, CIKM 2007", year = "2007", editor = "M{\'{a}}rio J. Silva and Alberto H. F. Laender and Ricardo A. Baeza{-}Yates and Deborah L. McGuinness and Bj{\o}rn Olstad and {\O}ystein Haug Olsen and Andr{\'{e}} O. Falc{\~{a}}o", pages = "51--60", address = "Lisbon, Portugal", month = nov # " 6-10", publisher = "{ACM}", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-59593-803-9", URL = "http://doi.acm.org/10.1145/1321440.1321450", timestamp = "Fri, 02 Jun 2017 20:47:30 +0200", biburl = "https://dblp.org/rec/bib/conf/cikm/FanizzidE07", bibsource = "dblp computer science bibliography, https://dblp.org", DOI = "doi:10.1145/1321440.1321450", abstract = "We present an evolutionary clustering method which can be applied to multi-relational knowledge bases storing semantic resource annotations expressed in the standard languages for the Semantic Web. The method exploits an effective and language-independent semi-distance measure defined for the space of individual resources, that is based on a finite number of dimensions corresponding to a committee of features represented by a group of concept descriptions (discriminating features). We show how to obtain a maximally discriminating group of features through a feature construction method based on genetic programming. The algorithm represents the possible clusterings as strings of central elements (medoids, w.r.t. the given metric) of variable length. Hence, the number of clusters is not needed as a parameter since the method can optimize it by means of the mutation operators and of a proper fitness function. We also show how to assign each cluster with a newly constructed intensional definition in the employed concept language. An experimentation with some ontologies proves the feasibility of our method and its effectiveness in terms of clustering validity indices.", notes = "Replaced by \cite{Fanizzi:2009:IS}", } @InProceedings{DBLP:conf/dexa/FanizzidE08, author = "Nicola Fanizzi and Claudia d'Amato and Floriana Esposito", title = "Evolutionary Clustering in Description Logics: Controlling Concept Formation and Drift in Ontologies", booktitle = "Proceedings of the 19th International Conference, Database and Expert Systems Applications, DEXA 2008", year = "2008", editor = "Sourav S. Bhowmick and Josef K{\"{u}}ng and Roland R. Wagner", series = "Lecture Notes in Computer Science", volume = "5181", pages = "808--821", address = "Turin, Italy", month = sep # " 1-5", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Cluster Algorithm, Description Logic, Dissimilarity Measure, Concept Drift, Concept Description", URL = "https://doi.org/10.1007/978-3-540-85654-2_73", DOI = "doi:10.1007/978-3-540-85654-2_73", timestamp = "Wed, 24 Jan 2018 12:46:36 +0100", biburl = "https://dblp.org/rec/bib/conf/dexa/FanizzidE08", bibsource = "dblp computer science bibliography, https://dblp.org", abstract = "We present a method based on clustering techniques to detect concept drift or novelty in a knowledge base expressed in Description Logics. The method exploits an effective and language-independent semi-distance measure defined for the space of individuals, that is based on a finite number of dimensions corresponding to a committee of discriminating features (represented by concept descriptions). In the algorithm, the possible clusterings are represented as strings of central elements (medoids, w.r.t. the given metric) of variable length. The number of clusters is not required as a parameter; the method is able to find an optimal choice by means of the evolutionary operators and of a fitness function. An experimentation with some ontologies proves the feasibility of our method and its effectiveness in terms of clustering validity indices. Then, with a supervised learning phase, each cluster can be assigned with a refined or newly constructed intensional definition expressed in the adopted language.", } @Article{DBLP:journals/ijswis/FanizzidE08, author = "Nicola Fanizzi and Claudia d'Amato and Floriana Esposito", title = "Evolutionary Conceptual Clustering Based on Induced Pseudo-Metrics", journal = "International Journal on Semantic Web and Information Systems", year = "2008", volume = "4", number = "3", pages = "44--67", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.4018/jswis.2008070103", DOI = "doi:10.4018/jswis.2008070103", timestamp = "Tue, 06 Jun 2017 22:21:42 +0200", biburl = "https://dblp.org/rec/bib/journals/ijswis/FanizzidE08", bibsource = "dblp computer science bibliography, https://dblp.org", abstract = "We present a method based on clustering techniques to detect possible/probable novel concepts or concept drift in a Description Logics knowledge base. The method exploits a semi-distance measure defined for individuals, that is based on a finite number of dimensions corresponding to a committee of discriminating features (concept descriptions). A maximally discriminating group of features is obtained with a randomized optimization method. In the algorithm, the possible clusterings are represented as medoids (w.r.t. the given metric) of variable length. The number of clusters is not required as a parameter, the method is able to find an optimal choice by means of evolutionary operators and a proper fitness function. An experimentation proves the feasibility of our method and its effectiveness in terms of clustering validity indices. With a supervised learning phase, each cluster can be assigned with a refined or newly constructed intensional definition expressed in the adopted language.", notes = "IJSWIS", } @Article{Fanizzi:2009:IS, author = "Nicola Fanizzi and Claudia d'Amato and Floriana Esposito", title = "Metric-based stochastic conceptual clustering for ontologies", journal = "Information Systems", year = "2009", volume = "34", pages = "792--806", number = "8", note = "Sixteenth ACM Conference on Information Knowledge and Management (CIKM 2007)", keywords = "genetic algorithms, genetic programming, Conceptual clustering", URL = "https://dblp.uni-trier.de/rec/bibtex/journals/is/FanizzidE09", DOI = "doi:10.1016/j.is.2009.03.008", ISSN = "0306-4379", URL = "http://www.sciencedirect.com/science/article/B6V0G-4W3HXC0-1/2/95a1535c9097d816c4ec5ad804772c4b", abstract = "A conceptual clustering framework is presented which can be applied to multi-relational knowledge bases storing resource annotations expressed in the standard languages for the Semantic Web. The framework adopts an effective and language-independent family of semi-distance measures defined for the space of individual resources. These measures are based on a finite number of dimensions corresponding to a committee of discriminating features represented by concept descriptions. The clustering algorithm expresses the possible clusterings in terms of strings of central elements (medoids, w.r.t. the given metric) of variable length. The method performs a stochastic search in the space of possible clusterings, exploiting a technique based on genetic programming. Besides, the number of clusters is not necessarily required as a parameter: a natural number of clusters is autonomously determined, since the search spans a space of strings of different length. An experimentation with real ontologies proves the feasibility of the clustering method and its effectiveness in terms of standard validity indices. The framework is completed by a successive phase, where a newly constructed intensional definition, expressed in the adopted concept language, can be assigned to each cluster. Finally, two possible extensions are proposed. One allows the induction of hierarchies of clusters. The other applies clustering to concept drift and novelty detection in the context of ontologies.", notes = "invited extended version \cite{DBLP:conf/cikm/FanizzidE07}", } @Article{Fanjiang:2016:IST, author = "Yong-Yi Fanjiang and Yang Syu and Jong-Yih Kuo", title = "Search based approach to forecasting QoS attributes of web services using genetic programming", journal = "Information and Software Technology", volume = "80", pages = "158--174", year = "2016", ISSN = "0950-5849", DOI = "doi:10.1016/j.infsof.2016.08.009", URL = "http://www.sciencedirect.com/science/article/pii/S0950584916301409", abstract = "AbstractContext Currently, many service operations performed in service-oriented software engineering (SOSE) such as service composition and discovery depend heavily on Quality of Service (QoS). Due to factors such as varying loads, the real value of some dynamic QoS attributes (e.g., response time and availability) changes over time. However, most of the existing QoS-based studies and approaches do not consider such changes; instead, they are assumed to rely on the unrealistic and static QoS information provided by service providers, which may seriously impair their outcomes. Objective To predict dynamic QoS values, the objective is to devise an approach that can generate a predictor to perform QoS forecasting based on past QoS observations. Method We use genetic programming (GP), which is a type of evolutionary computing used in search-based software engineering (SBSE), to forecast the QoS attributes of web services. In our proposed approach, GP is used to search and evolve expression-based, one-step-ahead QoS predictors. To evaluate the performance (accuracy) of our GP-based approach, we also implement most current time series forecasting methods; a comparison between our approach and these other methods is discussed in the context of real-world QoS data. Results Compared with common time series forecasting methods, our approach is found to be the most suitable and stable solution for the defined QoS forecasting problem. In addition to the numerical results of the experiments, we also analyze and provide detailed descriptions of the advantages and benefits of using GP to perform QoS forecasting. Additionally, possible validity threats using the GP approach and its validity for SBSE are discussed and evaluated. Conclusions This paper thoroughly and completely demonstrates that under a realistic situation (with real-world QoS data), the proposed GP-based QoS forecasting approach provides effective, efficient, and accurate forecasting and can be considered as an instance of SBSE.", keywords = "genetic algorithms, genetic programming, SBSE, Search-based software engineering, Web service, Qos attribute forecasting", } @Article{FanJiang:2020:SC, author = "Yong-Yi FanJiang and Yang Syu and Wei-Lun Huang", journal = "IEEE Transactions on Services Computing", title = "Time Series {QoS} Forecasting for Web Services Using Multi-Predictor-based Genetic Programming", year = "2020", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/TSC.2020.2994136", ISSN = "1939-1374", abstract = "Quality of service (QoS) time-series forecasting of web services has been studied for over a decade, and in recent years, this problem has been investigated in its multi-step-ahead version for the long-term rental and use of cloud computing. For multi-step-ahead QoS time-series forecasting problem, previous research has adopted single-predictor-based strategies and conventional time-series methods, such as autoregressive integrated moving average models and exponential smoothing, to solve this problem. However, this paper proposes the idea of applying genetic programming to evolve a set of multiple predictors, in which each predictor is dedicated to the forecasting task of a specific future time point. In our approach, two types of multiple predictors are proposed and tested, which are different from the consumed predictor inputs that drive each predictor to produce its QoS forecasting results. Furthermore, two techniques, namely, elite individual composition (EIC) and hybrid evolution, are proposed and applied to enhance the forecasting accuracy of our approach. Finally, based on a real-world QoS time series dataset, the proposed approach is validated and compared with conventional methods to demonstrate its superiority in terms of accuracy; in addition, the effectiveness and efficiency of the proposed approach and two techniques are also verified in the experiment.", notes = "Also known as \cite{9091301}", } @Article{Faradonbeh:2016:IJRMMS, author = "Roohollah Shirani Faradonbeh and Danial Jahed Armaghani and Masoud Monjezi and Edy Tonnizam Mohamad", title = "Genetic programming and gene expression programming for flyrock assessment due to mine blasting", journal = "International Journal of Rock Mechanics and Mining Sciences", volume = "88", pages = "254--264", year = "2016", ISSN = "1365-1609", DOI = "doi:10.1016/j.ijrmms.2016.07.028", URL = "http://www.sciencedirect.com/science/article/pii/S1365160916301563", abstract = "This research is aimed to develop new practical equations to predict flyrock distance based on genetic programming (GP) and genetic expression programming (GEP) techniques. For this purpose, 97 blasting operations in Delkan iron mine, Iran were investigated and the most effective parameters on flyrock were recorded. A database comprising of five inputs (i.e. burden, spacing, stemming length, hole depth, and powder factor) and one output (flyrock) was prepared to develop flyrock distance. Several GP and GEP models were proposed to predict flyrock considering the modeling procedures of them. To compare the performance prediction of the developed models, coefficient of determination (R2), mean absolute error (MAE), root mean squared error (RMSE) and variance account for (VAF) were computed and then, the best GP and GEP models were selected. According to the obtained results, it was found that the best flyrock predictive model is the GEP based-model. As an example, considering results of RMSE, values of 2.119 and 2.511 for training and testing datasets of GEP model, respectively show higher accuracy of this model in predicting flyrock, while, these values were obtained as 5.788 and 10.062 for GP model.", keywords = "genetic algorithms, genetic programming, Genetic expression programming, Blasting, Flyrock distance", } @Article{faradonbeh:2016:BEGE, author = "Roohollah Shirani Faradonbeh and Danial Jahed Armaghani and Masoud Monjezi", title = "Development of a new model for predicting flyrock distance in quarry blasting: a genetic programming technique", journal = "Bulletin of Engineering Geology and the Environment", year = "2016", volume = "75", number = "3", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s10064-016-0872-8", DOI = "doi:10.1007/s10064-016-0872-8", } @Article{faradonbeh:2017:EES, author = "Roohollah Shirani Faradonbeh and Alireza Salimi and Masoud Monjezi and Arash Ebrahimabadi and Christian Moormann", title = "Roadheader performance prediction using genetic programming {(GP)} and gene expression programming {(GEP)} techniques", journal = "Environmental Earth Sciences", year = "2017", volume = "76", number = "16", keywords = "genetic algorithms, genetic programming, gene expression programming", URL = "http://link.springer.com/article/10.1007/s12665-017-6920-2", DOI = "doi:10.1007/s12665-017-6920-2", } @Article{faradonbeh:2018:EMaA, author = "Roohollah Shirani Faradonbeh and Mahdi Hasanipanah and Hassan Bakhshandeh Amnieh and Danial Jahed Armaghani and Masoud Monjezi", title = "Development of {GP} and {GEP} models to estimate an environmental issue induced by blasting operation", journal = "Environmental Monitoring and Assessment", year = "2018", volume = "190", number = "6", keywords = "genetic algorithms, genetic programming, gene expression programming", URL = "http://link.springer.com/article/10.1007/s10661-018-6719-y", DOI = "doi:10.1007/s10661-018-6719-y", } @Article{faradonbeh:2018:NCaA, author = "Roohollah Shirani Faradonbeh and Danial Jahed Armaghani and Hassan Bakhshandeh Amnieh and Edy Tonnizam Mohamad", title = "Prediction and minimization of blast-induced flyrock using gene expression programming and firefly algorithm", journal = "Neural Computing and Applications", year = "2018", volume = "29", number = "6", keywords = "genetic algorithms, genetic programming, gene expression programming", URL = "http://link.springer.com/article/10.1007/s00521-016-2537-8", DOI = "doi:10.1007/s00521-016-2537-8", } @Article{Faraoun:2006:IJCIA, author = "K. M. Faraoun and A. Boukelif", title = "Genetic Programming Approach for Multi-Category Pattern Classification Applied to Network Intrusions Detection", journal = "International Journal of Computational Intelligence and Applications (IJCIA)", year = "2006", volume = "6", number = "1", pages = "77--100", month = mar, keywords = "genetic algorithms, genetic programming, patterns classification, intrusion detection", ISSN = "1469-0268", URL = "http://direct.bl.uk/bld/PlaceOrder.do?UIN=193825360&ETOC=RN&from=searchengine", DOI = "doi:10.1142/S1469026806001812", abstract = "The present paper describes a new approach of classification using genetic programming. The proposed technique consists of genetically co-evolve a population of nonlinear transformations on the input data to be classified, and map them to a new space with reduced dimension in order to get a maximum inter-classes discrimination. It is much easier to classify the new samples from the transformed data. Contrary to the existing GP-classification techniques, the proposed one uses a dynamic repartition of the transformed data in separated intervals, the efficiency of a given intervals repartition is handled by the fitness criterion, with a maximum classes discrimination. Experiments were performed using the Fisher's Iris dataset. After that, the KDD'99 Cup dataset was used to study the intrusion detection and classification problem. The results demonstrate that the proposed genetic approach outperforms the existing GP-classification methods, and provides improved results compared to other existing techniques.", } @Article{Faraoun:2006:MJCS, author = "Kamel Mohamed Faraoun and Aoued Boukelif", title = "Securing Network Traffic Using Genetically Evolved Transformations", journal = "Malaysian Journal of Computer Science", year = "2006", volume = "19", number = "1", pages = "9--28", keywords = "genetic algorithms, genetic programming, patterns classification, intrusion detection", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.531.8679", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.531.8679", URL = "http://mjcs.fsktm.um.edu.my/document.aspx?FileName=349.pdf", URL = "http://e-journal.um.edu.my/public/article-view.php?id=1026", size = "20 pages", abstract = "The paper describes a new approach of classification using genetic programming. The proposed technique consists of genetically coevolving a population of non-linear transformations on the input data to be classified, and map them to a new space with a reduced dimension, in order to get maximum inter-classes discrimination. The classification of new samples is then performed on the transformed data, and so becomes much easier. Contrary to the existing GP-classification techniques, the proposed one uses a dynamic repartition of the transformed data in separated intervals, the efficacy of a given interval repartition is handled by the fitness criterion, with maximum classes discrimination. Experiments were first performed using the Fisher's Iris dataset, and the KDD?99 Cup dataset was used to study the intrusion detection and classification problem. Obtained results demonstrate that the proposed genetic approach outperforms the existing GP-classification methods, and gives accepted results compared to other existing techniques.", } @InProceedings{Faria2010MIR, author = "Fabio Augusto Faria and Adriano Veloso and Humberto {Mossri de Almeida} and Eduardo Valle and Ricardo {da S. Torres} and Marcos Andre Goncalves and Wagner {Meira Jr.}", title = "Learning to rank for content-based image retrieval", booktitle = "Multimedia Information Retrieval (MIR)", year = "2010", pages = "285--294", address = "Philadelphia, Pennsylvania, USA", keywords = "genetic algorithms, genetic programming, SVM", bibsource = "DBLP, http://dblp.uni-trier.de", URL = "http://doi.acm.org/10.1145/1743384.1743434", DOI = "doi:10.1145/1743384.1743434", abstract = "In Content-based Image Retrieval (CBIR), accurately ranking the returned images is of paramount importance, since users consider mostly the topmost results. The typical ranking strategy used by many CBIR systems is to employ image content descriptors, so that returned images that are most similar to the query image are placed higher in the rank. While this strategy is well accepted and widely used, improved results may be obtained by combining multiple image descriptors. In this paper we explore this idea, and introduce algorithms that learn to combine information coming from different descriptors. The proposed learning to rank algorithms are based on three diverse learning techniques: Support Vector Machines (CBIR-SVM), Genetic Programming (CBIR-GP), and Association Rules (CBIR-AR). Eighteen image content descriptors(colour, texture, and shape information) are used as input and provided as training to the learning algorithms. We performed a systematic evaluation involving two complex and heterogeneous image databases (Corel e Caltech) and two evaluation measures (Precision and MAP). The empirical results show that all learning algorithms provide significant gains when compared to the typical ranking strategy in which descriptors are used in isolation. We concluded that, in general, CBIR-AR and CBIR-GP outperforms CBIR-SVM. A fine-grained analysis revealed the lack of correlation between the results provided by CBIR-AR and the results provided by the other two algorithms, which indicates the opportunity of an advantageous hybrid approach.", } @InProceedings{conf/clef/FariaCT11, author = "Fabio Augusto Faria and Rodrigo Tripodi Calumby and Ricardo {da Silva Torres}", title = "{RECOD} at {ImageCLEF} 2011: Medical Modality Classification using Genetic Programming", booktitle = "{CLEF} 2011 Labs and Workshop, Notebook Papers", year = "2011", editor = "Vivien Petras and Pamela Forner and Paul D. Clough", address = "Amsterdam, The Netherlands", month = "19-22 " # sep, keywords = "genetic algorithms, genetic programming, KNN, medical images, image classification, pattern recognition", isbn13 = "978-88-904810-1-7", bibdate = "2011-09-09", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/clef/clef2011w.html#FariaCT11", URL = "http://clef2011.org/resources/proceedings/Faria_Clef2011.pdf", data_url = "https://www.imageclef.org/2011/medical", size = "9 pages", abstract = "This paper describes the participation of the RECOD group on the ImageCLEF 2011 Medical Modality Classification sub-task. We present an approach based on genetic programming and kNN for image classification. In our approach the genetic programming is used for the learning of good functions for the combination of similarities obtained from a set of global descriptors for different visual evidences such as colour, texture, and shape. For each class of the dataset a combination function was learnt and used as a kNN classier. Final classification results were generated by a majority voting scheme with the voting functions from each class. Preliminary experiments have shown a good effectiveness of the approach and its potential for improvements.", notes = "broken June 2024 http://clef2011.org/index.php?page=pages/proceedings.php", } @InProceedings{Farinaccio:2010:gecco, author = "Antonella Farinaccio and Leonardo Vanneschi and Mario Giacobini and Giancarlo Mauri and Paolo Provero", title = "On the use of genetic programming for the prediction of survival in cancer", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "163--170", keywords = "genetic algorithms, genetic programming, Bioinformatics, computational, systems and synthetic biology, SVM, ANN, MLP, voted percenptron, RBF", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830483.1830514", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "The classification of cancer patients into risk classes is a very active field of research, with direct clinical applications. We have recently compared several machine learning methods on the well known 70-genes signature dataset. In that study, genetic programming showed promising results, given that it outperformed all the other techniques. Nevertheless, the study was preliminary, mainly because the validation dataset was preprocessed and all its features binarized in order to use logical operators for the genetic programming functional nodes. If this choice allowed simple interpretation of the solutions from the biological viewpoint, on the other hand the binarisation of data was limiting, since it amounts to a sizable loss of information. The goal of this paper is to overcome this limitation, using the 70-genes signature dataset with real-valued expression data. The results we present show that genetic programming using the number of incorrectly classified instances as fitness function is not able to outperform the other machine learning methods. However, when a weighted average between false positives and false negatives is used to calculate fitness values, genetic programming obtains performances that are comparable with the other methods in the minimisation of incorrectly classified instances and outperforms all the other methods in the minimization of false negatives, which is one of the main goals in breast cancer clinical applications. Also in this case, the solutions returned by genetic programming are simple, easy to understand, and they use a rather limited subset of the available features.", notes = "NKI 70-gene breast cancer. p168 Implicit feature selection. AF257175, NM_001809. Also known as \cite{1830514} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @PhdThesis{Farinaccio:thesis, author = "Antonella Farinaccio", title = "Computational Intelligence Approaches: from Time Series to Data Driven Gene Regulatory Network", school = "Facolta di Scienze Matematiche, Fisiche e Naturali, Universita degli Studi di Milano-Bicocca", year = "2011", address = "Italy", month = "8 " # feb, keywords = "genetic algorithms, genetic programming, computational Intelligence, System Biology, Microarray, Time Series, Gene Expression Data, Gene Regulatory Network, Reverse Engineering, Machine Learning", URL = "http://hdl.handle.net/10281/19257", URL = "https://boa.unimib.it/retrieve/handle/10281/19257/23819/Phd_unimib_716388%20.pdf", size = "180 pages", abstract = "For the past decade or so, Computational Intelligence has been an extremely hot topic among researchers working in the fields of biomedicine and bioinformatics. There are many successful applications of Computational Intelligence in such areas as computational genomics, prediction of gene expression, protein structure, and protein-protein interactions, modelling of evolution, or neuronal systems modeling and analysis. However, there are still many problems in biomedicine and bioinformatics that are in desperate need of advanced and efficient computational methodologies to deal with the tremendous amounts of data so prevalent in those kinds of research pursuits. In an attempt to fill this gap, in the last decade many tools of Systems Biology have been developed to elaborate the large quantity of data generated by high-throughput experimental techniques with the increasingly sophisticated range of mathematical modelling techniques. The aim of systems biology is to integrate models at multiple biological scales and investigate system-level properties of biological organisms. This aim includes understanding at four levels: (a) the structure of biological interaction networks; (b) their dynamics, how states change over time in different conditions; (c) the methods biological systems use to control the state of a cell; (d) the design of systems, including both how they have evolved and how they may potentially be artificially constructed. A key feature of systems biology is the integration of both theoretical modelling and empirical investigation, in which current biological knowledge informs the development of models and the analysis of these models produces a set of predictions that may then be tested in the laboratory. Many models have been proposed to describe the network, one of the most extensively used is Boolean Network, that notwithstanding its numerous successes, in some cases could suffer from being too coarse. Another widely studied candidate is the system of differential equations,which is a very powerful and flexible model to describe complex relations among components. But it is not necessarily easy to determine the suitable form of equations which represent the network. Thus, the form of the differential equations had been fixed during the learning phase in previous studies. As a result, their goal was to simply optimize parameters, i.e., coefficients in the fixed equations. In the analysis of time series of gene expression data presented in this thesis, a mathematical model has been identified and a system for the reconstruction of a Gene Regulatory Network Driven from Data has been implemented. Based on Genetic Programming, its target is to extract knowledge and properties from data and so to generate the network that underlies the behaviour of genes. For this reason the system is called Data Driven Gene Regulatory Network Generator. Planning to individualize the mutual interactions between genes, a Genetic Programming application for the extraction of the best activation function of the genes has also been developed. In order to test such a system, it has been applied to a serial temporal dataset of microarray gene expression data of breast cancer, while a study aimed at predicting the survival of a set of cancer patients has also been performed. This study has led to the definition of a Medical Decision Support System. The activation functions of genes performed by this system have been successively used to reconstruct the gene regulatory network that underlies the development, response and regulation of the biological system. With the intent to test it, a reverse engineering of a synthetic gene regulatory network has been made and a dynamic simulation has been performed allowing for the related time series reconstruction. The gene regulatory network used for the reverse engineering has been the recently published IRMA network, a yeast synthetic network for the assessment of reverse engineering networks and modelling approaches. Finally, in order to apply this system to a realistic gene regulatory network composed by thousands of genes, a new cluster kernel method has been identified and a framework driven by it has been developed. It is based on Gene Ontology to facilitate the detection of similar patterns of interacting genes, with the aim of reducing the dimension of the related serial temporal data.", notes = "In English INFORMATICA - 22R Phd_unimib_716388 Supervisors Prof. Giancarlo Mauri Prof. Leonardo Vanneschi", } @Article{Farinati:2023:INS, author = "Davide Farinati and Illya Bakurov and Leonardo Vanneschi", title = "A study of dynamic populations in geometric semantic genetic programming", journal = "Information Sciences", year = "2023", volume = "648", pages = "119513", month = nov, keywords = "genetic algorithms, genetic programming, Dynamic populations, Geometric semantic genetic programming, Semantic neighbourhood", ISSN = "0020-0255", URL = "https://www.sciencedirect.com/science/article/pii/S0020025523010988", DOI = "doi:10.1016/j.ins.2023.119513", abstract = "Allowing the population size to variate during the evolution can bring advantages to evolutionary algorithms (EAs), retaining computational effort during the evolution process. Dynamic populations use computational resources wisely in several types of EAs, including genetic programming. However, so far, a thorough study on the use of dynamic populations in Geometric Semantic Genetic Programming (GSGP) is missing. Still, GSGP is a resource-greedy algorithm, and the use of dynamic populations seems appropriate. we adapt algorithms to GSGP to manage dynamic populations that were successful for other types of EAs and introduces two novel algorithms. The novel algorithms exploit the concept of semantic neighbourhood. These methods are assessed and compared through a set of eight regression problems. The results indicate that the algorithms outperform standard GSGP, confirming the suitability of dynamic populations for GSGP. the novel algorithms that use semantic neighbourhood to manage variation in population size are particularly effective in generating robust models even for the most difficult of the studied test problems.", notes = "also known as \cite{FARINATI2023119513}", } @InProceedings{Farinati:2024:evoapplications, author = "Davide Farinati and Leonardo Vanneschi", title = "{GM4OS}: An Evolutionary Oversampling Approach for Imbalanced Binary Classification Tasks", booktitle = "27th International Conference, EvoApplications 2024", year = "2024", editor = "Stephen Smith and Joao Correia and Christian Cintrano", series = "LNCS", volume = "14634", publisher = "Springer", address = "Aberystwyth", month = "3-5 " # apr, pages = "68--82", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Oversampling, Imbalanced Data, Binary Classification", isbn13 = "978-3-031-56851-0", URL = "https://rdcu.be/dDZNg", DOI = "doi:10.1007/978-3-031-56852-7_5", size = "15 pages", abstract = "Imbalanced datasets pose a significant and longstanding challenge to machine learning algorithms, particularly in binary classification tasks. Over the past few years, various solutions have emerged, with a substantial focus on the automated generation of synthetic observations for the minority class, a technique known as oversampling. Among the various oversampling approaches, the Synthetic Minority Oversampling Technique (SMOTE) has recently garnered considerable attention as a highly promising method. SMOTE achieves this by generating new observations through the creation of points along the line segment connecting two existing minority class observations. Nevertheless, the performance of SMOTE frequently hinges upon the specific selection of these observation pairs for resampling. This research introduces the Genetic Methods for Over Sampling (GM4OS), a novel oversampling technique that addresses this challenge. In GM4OS, individuals are represented as pairs of objects. The first object assumes the form of a GP-like function, operating on vectors, while the second object adopts a GA-like genome structure containing pairs of minority class observations. By co-evolving these two elements, GM4OS conducts a simultaneous search for the most suitable resampling pair and the most effective oversampling function. Experimental results, obtained on ten imbalanced binary classification problems, demonstrate that GM4OS consistently outperforms or yields results that are at least comparable to those achieved through linear regression and linear regression when combined with SMOTE.", notes = "http://www.evostar.org/2024/ EvoApplications2024 held in conjunction with EuroGP'2024, EvoCOP2024 and EvoMusArt2024", } @Article{Farinati:2024:GPEM, author = "Davide Farinati and Leonardo Vanneschi", title = "A survey on dynamic populations in bio-inspired algorithms", journal = "Genetic Programming and Evolvable Machines", year = "2024", volume = "25", number = "2", pages = "Article no 19", month = dec, keywords = "genetic algorithms, genetic programming, Population-based algorithms, Bio-inspired algorithms, Population size, Dynamic population, Adaptive population", ISSN = "1389-2576", URL = "https://rdcu.be/dOTDV", DOI = "doi:10.1007/s10710-024-09492-4", size = "32 pages", abstract = "... a significant amount of computational effort in phases where the evolution is functioning well, and therefore a large population is not needed. This allows for reusing the previously saved computational effort when optimization becomes more challenging, and hence a greater computational effort is required. Numerous past contributions have demonstrated a notable advantage of using dynamically sized populations, often resulting in comparable results to those obtained by the standard PBBIAs but with a significant saving of computational effort. However, despite the numerous successes that have been presented, to date, there is still no comprehensive collection of past contributions on the use of dynamic populations that allows for their categorization and critical analysis. This article aims to bridge this gap by presenting a systematic literature review regarding the use of dynamic populations in PBBIAs, as well as identifying gaps in the research that can lead the path to future works.", notes = "NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312, Lisboa, Portugal", } @InProceedings{farinati:2025:EuroGP, author = "Davide Farinati and Gloria Pietropolli and Leonardo Vanneschi", title = "Exploring the Impact of Data Scale on Mutation Step Size in {SLIM-GSGP}", booktitle = "European Conference on Genetic Programming, EuroGP 2025", year = "2025", editor = "Bing Xue and Luca Manzoni and Illya Bakurov", volume = "15609", series = "LNCS", pages = "18--34", address = "Trieste", month = "23-25 " # apr, publisher = "Springer Nature", keywords = "genetic algorithms, genetic programming, Geometric Semantic Genetic Programming, Geometric Mutation, Mutation Step, Symbolic Regression", isbn13 = "978-3-031-89990-4", URL = "https://rdcu.be/ejgA5", DOI = "doi:10.1007/978-3-031-89991-1_3", abstract = "The Semantic Learning algorithm based on Inflate and deflate Mutation (SLIM) is a promising recent variant of Geometric Semantic Genetic Programming (GSGP) that introduces a new Deflate Geometric Semantic Mutation (DGSM). This operator maintains the key feature of the standard Geometric Semantic Mutation (GSM), inducing a unimodal error surface for any supervised learning problem, while generating smaller offspring than their parents, and thus allowing SLIM to generate compact, and potentially interpretable, final solutions. A key parameter controlling the evolution process in both GSGP and SLIM is the Mutation Step (MS), which regulates the extent of perturbation to the parent semantics. While it is intuitive that the optimal value of MS has a relationship with the scale of the dataset features, to the best of our knowledge no prior research has extensively explored this relationship. In this work, we provide the first comprehensive investigation into this topic. First, we hypothesize a general rule by analysing results from artificial datasets, and then we confirm these findings with more complex, real-world datasets. This approach offers a solid alternative to the typical hyperparameter tuning approach.", notes = "also known as \cite{paper_117_step_size_in_slim-gsgp} Part of \cite{Xue:2025:GP} EuroGP'2025 held in conjunction with EvoCOP2025, EvoMusArt2025 and EvoApplications2025", } @Article{Faris2013a, author = "Hossam Faris and Alaa Sheta and Ertan Oznergiz", title = "Modeling Hot Rolling Manufacturing Process Using Soft Computing Techniques", journal = "International Journal of Computer Integrated Manufacturing", year = "2013", volume = "26", number = "8", pages = "762--771", keywords = "genetic algorithms, genetic programming, hot rolling process, industrial process", publisher = "Taylor \& Francis", ISSN = "0951-192X", URL = "http://www.tandfonline.com/doi/pdf/10.1080/0951192X.2013.766937", DOI = "doi:10.1080/0951192X.2013.766937", size = "10 pages", abstract = "Steel making industry is becoming more competitive due to the high demand. In order to protect the market share, automation of the manufacturing industrial process is vital and represents a challenge. Empirical mathematical modelling of the process was used to design mill equipment, ensure productivity and service quality. This modelling approach shows many problems associated to complexity and time consumption. Evolutionary computing techniques show significant modelling capabilities on handling complex non-linear systems modelling. In this research, symbolic regression modelling via genetic programming is used to develop relatively simple mathematical models for the hot rolling industrial non-linear process. Three models are proposed for the rolling force, torque and slab temperature. A set of simple mathematical functions which represents the dynamical relationship between the input and output of these models shall be presented. Moreover, the performance of the symbolic regression models is compared to the known empirical models for the hot rolling system. A comparison with experimental data collected from the Ere[gtilde]li Iron and Steel Factory in Turkey is conducted for the verification of the promising model performance. Genetic programming shows better performance results compared to other soft computing approaches, such as neural networks and fuzzy logic.", notes = "http://www.tandfonline.com/action/journalInformation?show=aimsScope&journalCode=tcim20", } @Article{Faris2013b, author = "Hossam Faris and Alaa Sheta", title = "Identification of the Tennessee Eastman Chemical Process Reactor Using Genetic Programming", journal = "International Journal of Advanced Science and Technology", year = "2013", volume = "50", pages = "121--140", month = jan, keywords = "genetic algorithms, genetic programming, Tennessee Eastman chemical process, Artificial Neural Networks (ANN), fuzzy Logic (FL) and Neuro-Gas and Neuro-PSO", ISSN = "2005-4238", broken = "http://www.sersc.org/journals/IJAST/vol50.php", URL = "http://www.sersc.org/journals/IJAST/vol50/11.pdf", size = "18 pages", abstract = "The Tennessee Eastman chemical process is a well-defined simulation of a chemical process that has been commonly used in process control research. As chemical process plants are getting more complex, the pressure on chemical engineers to develop accurate models for monitoring and control purposes is increased. In this paper, we explore the idea of using Genetic Programming (GP) technique to model the Tennessee Eastman (TE) Chemical Process Reactor. The process is decomposed to four subsystems. They are reactor level, reactor pressure, reactor cooling water temperature, and reactor temperature subsystems. GP found to have many advantages over other techniques in developing an automated process for industrial system modelling. A comparison between the applications of GP in modeling the TE chemical reactors subsystems with respect to other soft computing techniques such as Artificial Neural Networks (ANN), fuzzy Logic (FL) and Neuro-Gas and Neuro-PSO is provided.", } @Article{Faris2014, author = "Hossam Faris and Alaa Sheta and Rania Hiary", title = "On Symbolic Regression for Optimizing Thermostable Lipase Production", journal = "International Journal of Advanced Science and Technology", year = "2014", volume = "63", number = "11", pages = "23--33", note = "Special Issue on: Computational Optimisation and Engineering Applications", keywords = "genetic algorithms, genetic programming, symbolic regression, lipase production, ANN, heuristiclab", ISSN = "2005-4238", publisher = "Science & Engineering Research Support soCiety, 20 Virginia Court, Sandy Bay, Tasmania, Australia. ijast@sersc.org", URL = "http://www.sersc.org/journals/IJAST/vol63/3.pdf", broken = "http://dx.doi.org/10.14257/ijast.2014.63.03", size = "12 pages", abstract = "Theromostable lipases have wide range of biotechnological applications in the industry. Therefore, there is always high interest in investigating their features and operating conditions. However, Lipase production is a challenging and complex process due to its nature which is highly dependent on the conditions of the process such as temperature, initial pH, incubation period, time, inoculum size and agitation rate. Efficient optimisation of the process is a common goal in order to improve the productivity and reduce the costs. In this paper, we apply a Symbolic Regression Genetic Programming (GP) approach in order to develop a mathematical model which can predict the lipase activities in submerged fermentation (SmF) system. The developed GP model is compared with a neural network model proposed in the literature. The reported evaluation results show superiority of GP in modelling and optimising the process.", notes = "http://www.sersc.org/journals/IJAST/", } @InProceedings{conf/iccci/FarisAG14, title = "A Genetic Programming Based Framework for Churn Prediction in Telecommunication Industry", author = "Hossam Faris and Bashar Al-Shboul and Nazeeh Ghatasheh", booktitle = "Computational Collective Intelligence. Technologies and Applications - 6th International Conference, {ICCCI} 2014, Seoul, Korea, September 24-26, 2014. Proceedings", publisher = "Springer", year = "2014", volume = "8733", editor = "Dosam Hwang and Jason J. Jung and Ngoc Thanh Nguyen", isbn13 = "978-3-319-11288-6", pages = "353--362", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming", bibdate = "2014-09-08", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/iccci/iccci2014.html#FarisAG14", URL = "http://dx.doi.org/10.1007/978-3-319-11289-3", } @InProceedings{faris:2020:EMLT, author = "Rana Faris and Bara'a Almasri and Hossam Faris and Faris M. AL-Oqla and Doraid Dalalah", title = "Evolving Genetic Programming Models for Predicting Quantities of Adhesive Wear in Low and Medium Carbon Steel", booktitle = "Evolutionary Machine Learning Techniques", year = "2020", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-981-32-9990-0_7", DOI = "doi:10.1007/978-981-32-9990-0_7", } @Article{faris2016mgp, author = "Hossam Faris and Alaa F. Sheta and Ertan Oznergiz", title = "{MGP-CC}: a hybrid multigene {GP-Cuckoo} search method for hot rolling manufacture process modelling", journal = "Systems Science \& Control Engineering", year = "2016", volume = "4", number = "1", pages = "39--49", keywords = "genetic algorithms, genetic programming, Artificial intelligence, internal model control, intelligent control, manufacturing", publisher = "Taylor and Francis", DOI = "doi:10.1080/21642583.2015.1124032", abstract = "Maintaining high level of quality in hot rolling manufacturing processes is very challenging problem to keep competitiveness in the iron and steel industrial market. Monitoring the quality of the output product helps enhancing the product outcomes, increase the company profit and improve the economic growth of the country. In this paper, we propose a new hybrid approach based on multigene genetic programming (MGP) and Cuckoo search (CS) algorithms for developing three rigorous models for roll force, torque and slab temperature in the hot rolling industrial process at the Ereg~li Iron and Steel Factory in Turkey. MGP is a robust variation of the standard genetic programming (GP) algorithm while CS is a new nature-inspired metaheuristic search algorithm. The performance of the developed models is evaluated and compared with those obtained for the standard MGP and GP approaches.", } @Article{FarithaBanu:2013:ijca, author = "A {Faritha Banu} and C. Chandrasekar", title = "An Optimized Approach of Modified BAT Algorithm to Record Deduplication", year = "2013", journal = "IJCA", month = jul # "~24", number = "1/", keywords = "genetic algorithms, genetic programming, deduplication function, modified bat algorithm, data mining algorithms", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.303.5521", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.303.552", URL = "http://research.ijcaonline.org/volume62/number1/pxc3884627.pdf", abstract = "The task of recognising, in a data warehouse, records that pass on to the identical real world entity despite misspelling words, kinds, special writing styles or even unusual schema versions or data types is called as the record deduplication. In existing research they offered a genetic programming (GP) approach to record deduplication. Their approach combines several different parts of substantiation extracted from the data content to generate a deduplication purpose that is capable to recognise whether two or more entries in a depository are duplications or not. Because record deduplication is a time intense task even for undersized repositories, their aspire is to promote a method that discovers a proper arrangement of the best pieces of confirmation, consequently compliant a deduplication function that maximises performance using a small representative portion of the corresponding data for preparation purposes also the optimisation of process is less. Our research deals these issues with a novel technique called modified bat algorithm for record duplication. The incentive behind is to generate a flexible and effective method that employs Data Mining algorithms. The structure distributes many similarities with evolutionary computation techniques such as Genetic programming approach. This scheme is initialised with an inhabitant of random solutions and explores for optima by updating bat inventions. Nevertheless, disparate GP, modified bat has no development operators such as crossover and mutation. We also compare the proposed algorithm with other existing algorithms, including GP from the experimental results.", } @Article{farooq:2020:AS, author = "Furqan Farooq and Muhammad {Nasir Amin} and Kaffayatullah Khan and Muhammad {Rehan Sadiq} and Muhammad {Faisal Javed} and Fahid Aslam and Rayed Alyousef", title = "A Comparative Study of Random Forest and Genetic Engineering Programming for the Prediction of Compressive Strength of High Strength Concrete {(HSC)}", journal = "Applied Sciences", year = "2020", volume = "10", number = "20", keywords = "genetic algorithms, genetic programming, gene expression programming", ISSN = "2076-3417", URL = "https://www.mdpi.com/2076-3417/10/20/7330", DOI = "doi:10.3390/app10207330", abstract = "Supervised machine learning and its algorithm is an emerging trend for the prediction of mechanical properties of concrete. This study uses an ensemble random forest (RF) and gene expression programming (GEP) algorithm for the compressive strength prediction of high strength concrete. The parameters include cement content, coarse aggregate to fine aggregate ratio, water, and superplasticizer. Moreover, statistical analyses like MAE, RSE, and RRMSE are used to evaluate the performance of models. The RF ensemble model outbursts in performance as it uses a weak base learner decision tree and gives an adamant determination of coefficient R2 = 0.96 with fewer errors. The GEP algorithm depicts a good response in between actual values and prediction values with an empirical relation. An external statistical check is also applied on RF and GEP models to validate the variables with data points. Artificial neural networks (ANNs) and decision tree (DT) are also used on a given data sample and comparison is made with the aforementioned models. Permutation features using python are done on the variables to give an influential parameter. The machine learning algorithm reveals a strong correlation between targets and predicts with less statistical measures showing the accuracy of the entire model.", notes = "also known as \cite{app10207330}", } @TechReport{farringdon:1996:in05, author = "J Farringdon", title = "Random Effects in Genetic Algorithms and Programming (\& Other Genetic Algorithm Issues)", institution = "University College London", year = "1996", type = "Internal Note", number = "IN/96/05", address = "Computer Science, Gower Street, London WC1E 6BT, UK", month = jul, keywords = "genetic algorithms, genetic programming", broken = "http://www.cs.ucl.ac.uk/staff/j.farringdon/GP/in-1996-05.html", abstract = "Phenomena known to mathematicians and psychologists seem to be as yet unexploited by genetic algorithms and genetic programming techniques. A number of genetic techniques are briefly considered here from a maths and psychology perspective, the most immediately applicable of which is the use of statistical distributions. The statistical distributions technique may be implemented by a programmer and produce returns for a user within an hour.", } @Misc{farrow:1958:Leo, author = "Steve Farrow", title = "GP in 1958!", howpublished = "Peter Bentely, GP mailing list, EC-digest", year = "2004", month = "8 " # mar, keywords = "genetic algorithms, genetic programming", URL = "http://groups.yahoo.com/group/genetic_programming/message/2492", size = "1 page", abstract = "First four members of a series are a, b, c, d. What is the fifth?", notes = "LEO II/4", } @InProceedings{Farry:1997:MEC, author = "Kristin Farry and Jaime Fernandez and Robert Abramczyk and Mara Novy and Diane Atkins", title = "Applying Genetic Programming To Control Of An Artificial Arm", booktitle = "Proceedings of the 1997 MyoElectric Controls/Powered Prosthetics Symposium, MEC 97", year = "1997", address = "Fredericton, New Brunswick, Canada", month = aug, organisation = "Institute of Biomedical Engineering, University of New Brunswick", keywords = "genetic algorithms, genetic programming", URL = "http://hdl.handle.net/10161/4883", size = "6 pages", abstract = "Robotics researchers at NASA's Johnson Space Center (JSC) and Rice University have made substantial progress in myoelectric teleoperation. A myoelectric teleoperation system translates signals generated by an able-bodied robot operator's muscles during hand motions into commands that drive a robot's hand through identical motions Farry's early work in myoelectric teleoperation used variations over time in the myoelectric spectrum as inputs to neural networks to discriminate grasp types and thumb motions; schemes yielded up to 93percent correct classification on thumb motions. More recently, Fernandez achieved 100percent correct non-realtime classification of thumb abduction, extension, and flexion on the same myoelectric data using genetic programming to develop functions that discriminate between thumb motions using myoelectric signal parameters. Genetic programming (GP) is an evolutionary programming method where the computer can modify the discriminating functions' form to improve its performance, not just adjust numerical coefficients or weights. While the function development may require much computational time and many training cases, the resulting discrimination functions can run in realtime on modest computers These results suggest that myoelectric signals might be a feasible teleoperation medium, allowing an operator to use his own hand and arm as a master to intuitively control an anthropomorphic robot in a remote location such as outer space. These early results imply that multifunction myoelectric control based on genetic programming is viable for prosthetics, since teleoperation of a robot by an operator with a complete limb is a limiting or 'best-case' scenario for myoelectric control We suggest that myoelectric signals of traumatic below-elbow amputees can control several movements of a myoelectric hand with the help of a function or functions developed with genetic programming techniques. We are now testing this hypothesis with the help of NASA/ISC under a NASA/JSC - Texas Medical Center Cooperative Grant. In this study, five adult below-elbow amputees are performing two forearm motions, two wrist motions and two grasp motions using their 'phantom' limb and sound limb while we collect myoelectric data from four sites on the residual limb and four sites from the sound limb. We will use a variety of myoelectric signal time and frequency features in a genetic programming analysis to evolve functions that discriminate between signals generated during different muscle contractions.", } @InProceedings{Farry:LPSC98, author = "K. A. Farry and J. S. Graham and F. Vilas and K. S. Jarvis", title = "Automating Asteroid Surface Composition Identification from Reflectance Spectra", booktitle = "The 29th Lunar and Planetary Science Conference", year = "1998", pages = "1661", address = "Houston, Texas, USA", month = "16-20 " # mar, keywords = "genetic algorithms, genetic programming", URL = "http://www.lpi.usra.edu/meetings/LPSC98/pdf/1661.pdf", size = "2 pages", abstract = "We are applying genetic programming, an evolutionary programming technique, to identifying the minerals in spectra of asteroids from telescopes. We have done a basic feasibility test of this new identifier concept using US Geological Survey (USGS) spectra of three terrestrial minerals likely to be present in low-albedo asteroid regoliths: Antigorite, Hematite, and Jarosite. Initial results are very promising. Functions produced by genetic programming correctly identify 96percent of 140 spectra corrupted by measurement noise, scale uncertainty, and background continua removal uncertainty.", notes = "http://www.lpi.usra.edu/meetings/LPSC98/ 1 National Research Council (NRC) Research Associate, NASA/JSC/SN3, Houston, TX 77058, farry@farry.com. 2 9311 Tree Branch, Houston, TX 77064. 3 NASA/JSC/SN3, Houston, TX 77058. 4 LMSMSS, Houston, TX 77058. See also (abstract only?) Application of Genetic Programming to Identifying Asteroid Surface Composition from Reflectance Spectra \cite{1997DPS....29.0721F} year 1997, series {Bulletin of the American Astronomical Society}, volume 29, month jul, pages {976-+}, http://adsabs.harvard.edu/abs/1997DPS....29.0721F", } @Article{Farry:2009:JPO, author = "Kristin A. Farry", title = "Phantom Limb Development in Congenitally Upper Limb-Deficient Individuals", journal = "Journal of Prosthetics and Orthotics", year = "2009", volume = "21", number = "3", pages = "145--151", month = jul, keywords = "genetic algorithms, genetic programming, lilGP, Matlab", ISSN = "1040-8800", broken = "http://www.oandp.org/jpo/library/2009_03_145.asp", URL = "https://journals.lww.com/jpojournal/Fulltext/2009/07000/Phantom_Limb_Development_in_Congenitally_Upper.4.aspx", DOI = "doi:10.1097/JPO.0b013e3181b15dff", size = "7 pages", abstract = "Myoelectric data were collected from 10 below-elbow limb-deficient volunteers for evaluation of a myoelectric prosthesis control system. Five had congenital limb deficiency and five had traumatic limb loss. The traumatic-loss volunteers all had phantom limbs, whereas the congenitally deficient volunteers reported no phantom limb experiences before the data collection. Unexpectedly, the congenitally deficient volunteers began to feel phantom-like sensations of their missing hands during the data collection, which used contralateral stimulation. Some described limits on their new perceptions remarkably similar to the phantom motion limits described by traumatic amputees (i.e., difficulty in fully opening and closing the phantom's fingers). Significant changes occurred in the volunteers' myoelectric data signatures after they began to feel the phantoms.( J Prosthet Orthot . 2009;21:145-151.)", notes = "KRISTIN A. FARRY, PhD, is affiliated with the Excalibur Technical Services Inc., 624 Graves Mill Road, Madison, Virginia, USA American Academy of Orthotists and prosthetists http://www.oandp.org/jpo/", } @Article{FARZINPOUR:2023:cscm, author = "Alireza Farzinpour and Esmaeil {Mohammadi Dehcheshmeh} and Vahid Broujerdian and Samira {Nasr Esfahani} and Amir H. Gandomi", title = "Efficient boosting-based algorithms for shear strength prediction of squat {RC} walls", journal = "Case Studies in Construction Materials", volume = "18", pages = "e01928", year = "2023", ISSN = "2214-5095", DOI = "doi:10.1016/j.cscm.2023.e01928", URL = "https://www.sciencedirect.com/science/article/pii/S2214509523001079", keywords = "genetic algorithms, genetic programming, Squat RC wall, Genetic algorithm (GA), Hyperparameter optimization, Boosting methods, Principal component analysis (PCA), Machine learning", abstract = "Reinforced concrete shear walls have been considered as an effective structural system due to their optimal cost and great behavior in resisting lateral loads. For the slender type of these walls, failure modes are mainly related to flexure, while for the squat type with height-to-length ratios less than two, shear is the dominant factor. Thus, accurate estimation of shear strength for squat shear walls is necessary for design applications and can also be complex due to the various effective parameters. In order to address this issue, first a comprehensive dataset with 558 samples of squat shear walls is conducted, and three hybrid models consisting of genetic algorithms and boosting-based ensemble learning methods, i.e., XGBoost, CatBoost, and LightGBM, are used for estimation of shear strength. The results showed high prediction accuracy, with a coefficient of determination of at least 98.6percent for all three models. Genetic algorithm has been proven to be an effective method for tuning boosting-based algorithms compared to manual testing. In addition, the results of the algorithms are compared to their default hyperparameters and other conventional regression Models. Also, multicollinearity and principal component analysis (PCA) were studied. Furthermore, the performance of three tuned models is compared with that of a mechanics-based semi-empirical model and other genetic programming (GP)-based models. Finally, parametric and sensitivity analyses were performed, to demonstrate the ability of the models to identify the most critical parameters with significant influence on shear strength", } @Article{Fasli2011, author = "Maria Fasli and Yevgeniya Kovalchuk", title = "Learning approaches for developing successful seller strategies in dynamic supply chain management", journal = "Information Sciences", year = "2011", volume = "181", number = "16", pages = "3411--3426", keywords = "genetic algorithms, genetic programming", ISSN = "0020-0255", DOI = "doi:10.1016/j.ins.2011.04.014", URL = "http://www.sciencedirect.com/science/article/B6V0C-52M4V3W-4/2/e88e5f17659c1d3f021a4e6052e7b965", abstract = "Variable, dynamic pricing is a key characteristic of the modern electronic trading environments, allowing for prices that change or fluctuate due to uncertainty and different conditions and context. Being able to manage dynamic pricing strategies is vital for companies wishing to succeed in the world of modern business. The ability to accurately predict selling prices at a given time can help organisations to maximise their profit. This paper addresses the problem of predicting customer order prices and choosing the selling strategy which can lead to a greater profit in the context of supply chain management (SCM). The potential of the Neural Networks (NN) and Genetic Programming (GP) learning techniques is explored for making price forecasts. In particular, different parameter settings and methods for preprocessing input data are investigated in the paper. Although, both techniques showed the potential for dealing with the problem of dynamic pricing in SCM, NN models outperform GP models in the context under consideration in terms of accuracy of prediction, complexity of implementation, and execution time.", } @InProceedings{Fast:2010:GECCO, author = "Ethan Fast and Claire {Le Goues} and Stephanie Forrest and Westley Weimer", title = "Designing better fitness functions for automated program repair", year = "2010", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "965--972", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, SBSE, Software repair, software engineering", URL = "http://www.cs.virginia.edu/~weimer/p/weimer-gecco2010-preprint.pdf", DOI = "doi:10.1145/1830483.1830654", size = "8 pages", abstract = "Evolutionary methods have been used to repair programs automatically, with promising results. However, the fitness function used to achieve these results was based on a few simple test cases and is likely too simplistic for larger programs and more complex bugs. We focus here on two aspects of fitness evaluation: efficiency and precision. Efficiency is an issue because many programs have hundreds of test cases, and it is costly to run each test on every individual in the population. Moreover, the precision of fitness functions based on test cases is limited by the fact that a program either passes a test case, or does not, which leads to a fitness function that can take on only a few distinct values. This paper investigates two approaches to enhancing fitness functions for program repair, incorporating (1) test suite selection to improve efficiency and (2) formal specifications to improve precision. We evaluate test suite selection on 10 programs, improving running time for automated repair by 81percent. We evaluate program invariants using the Fitness Distance Correlation (FDC) metric, demonstrating significant improvements and smoother evolution of repairs.", notes = "deroff, gcd, look, uniq, and zune nullhttpd, lighttpd, zune, tiff, leukocyte, and imagemagick. SUS. Oracle comparator, sand box, diffX. Daikon, (cites ClearView, Chianti.) pop=40. Also known as \cite{1830654} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @Article{Fatehnia2018, author = "Milad Fatehnia and Gholamreza Amirinia", title = "A review of Genetic Programming and Artificial Neural Network applications in pile foundations", journal = "International Journal of Geo-Engineering", year = "2018", month = dec, volume = "9", number = "2", keywords = "genetic algorithms, genetic programming, Pile foundation, Artificial Intelligence, AI, Artificial Neural Network, ANN", ISSN = "2198-2783", DOI = "doi:10.1186/s40703-017-0067-6", size = "20 pages", abstract = "Uncertainty in the behaviour of geotechnical materials (e.g. soil and rock) is the result of imprecise physical processes associated with their formation. This uncertainty provides complexity in modelling the behaviour of such materials. The same condition is applied to the behavior of the structural elements dealing with them. In this regard, pile foundations, as the structural elements used to transfer superstructure loads deep into the ground, are subjected to these material uncertainties and modeling complexity. Artificial Intelligence (AI) has demonstrated superior predictive ability compared to traditional methods in modelling the complex behaviour of materials. This ability has made AI a popular and particularly amenable option in geotechnical engineering applications. Genetic Programming (GP) and Artificial Neural Network (ANN) are two of the most common examples of AI techniques. This paper provides a review of GP and ANN applications in estimation of the pile foundations bearing capacity.", } @PhdThesis{Fatehnia:thesis, author = "Milad Fatehnia", title = "Automated Method for Determining Infiltration Rate in Soils Automated Method for Determining Infiltration Rate in Soils", school = "Department of Civil and Environmental Engineering, Florida State University", year = "2015", address = "USA", month = jan # " 23", keywords = "genetic algorithms, genetic programming, Engineering, Geotechnical engineering, Automation, Double Ring, Hydraulic conductivity, Infiltration", URL = "http://purl.flvc.org/fsu/fd/FSU_migr_etd-9327", URL = "https://diginole.lib.fsu.edu/islandora/object/fsu:252950/datastream/download.pdf", size = "129 pages", abstract = "The first goal of this study was determining in-situ soil's vertical saturated hydraulic conductivity (Ks) from the measured steady infiltration rate, initial soil parameters, and test arrangements of the Double Ring Infiltrometer (DRI) test. This was done by conducting 30 small scale DRI lab experiment, 9 full scale in-situ DRI, 9 in-situ Mini-Disk infiltrometer experiments, several lab measurements, and 864 simulated DRI tests using finite element program HYDRUS-2D. The effects of the ring diameter, head of ponding, ring depth, initial effective saturation, and soil macroscopic capillary length on measured steady infiltration rates was fully studied. M5' model trees and genetic programming methods were applied on the data to establish formulas for predicting the saturated hydraulic conductivity of the sand to sandy-clay materials. The accuracy of Ks measurements of each method was estimated using 30% of 864 data by comparing the predefined Ks measured from the initial assumptions of the finite element programs with the estimations of the suggested formulas. Another comparison was done by using the derived formulas to predict Ks values of the 9 field DRI experiments and comparing the predicted values with the Ks values measured with the lab falling head permeability tests. Compared to genetic programming method, M5' model had a better performance in prediction of Ks with correlation coefficient and the root mean square error values of 0.8618 and 0.2823, respectively. Tension Disc Infiltrometer was needed during the first part of the research. This test is a commonly used test setup for in-situ measurement of the soil infiltration properties. In the second part of this study, Mini Disk Infiltrometer was used in the lab to obtain the cumulative infiltration curve of the poorly graded sand for various suction rates and the hydraulic conductivity of the soil material was measured from the derived information. Various methods were proposed by several researchers for determination of hydraulic conductivity from the cumulative infiltration data derived from Tension Disc Infiltrometer. In this study, the hydraulic conductivity measurements were estimated by using eight different methods. These employed methods produced different unsaturated and saturated hydraulic conductivity values. The accuracy of each method was determined by comparing the estimated hydraulic conductivity values with the values obtained from the falling head permeability test. Finally, as the third part of the research, a system of automated DRI using Arduino microcontroller, Hall effect sensor, peristaltic pump, water level sensor, and constant-level float valve was designed and tested. The advantages of the current system compared to previous designed systems was discussed. The system configuration was illustrated for better understanding of the set-up. The system was mounted in a portable and weather resistant box and was applied to run DRI testing in the field to check the applicability and accuracy of the portable system in field measurements. Results of the DRI testing using the automated system were also presented.", notes = "FSU_migr_etd-9327 Supervisor: Kamal Sulaiman Tawfiq", } @Article{FATHI:2018:ASC, author = "Abdolhossein Fathi and Rasool Sadeghi", title = "A genetic programming method for feature mapping to improve prediction of {HIV-1} protease cleavage site", journal = "Applied Soft Computing", year = "2018", volume = "72", pages = "56--64", month = nov, keywords = "genetic algorithms, genetic programming, SVM, Amino acid encoding, Feature mapping, Amino acid sequence cleavage prediction", ISSN = "1568-4946", URL = "http://www.sciencedirect.com/science/article/pii/S156849461830379X", DOI = "doi:10.1016/j.asoc.2018.06.045", code_url = "https://github.com/rasool-sadeghi/Encoding-and-Prediction-of-Cleavage-of-Amino-Acid-Sequences-by-HIV-1", abstract = "The human immunodeficiency virus (HIV) is the cause of acquired immunodeficiency syndrome (AIDS), which has profound implications in terms of both economic burden and loss of life. Modeling and examination of the HIV protease cleavage of amino acid sequences can contribute to control of this disease and production of more effective drugs. The present paper introduces a new method for encoding and characterization of amino acid sequences and a new model for the prediction of amino acid sequence cleavage by HIV protease. The proposed encoding scheme uses a combination of amino acids' spatial and structural features in conjunction with 20 amino acid sequences to make sure that their physicochemical and sequencing features are all taken into account. The proposed HIV-1 amino acid cleavage prediction model is developed with the combination of genetic programming and support vector machine. The results of evaluations performed on various datasets demonstrate the superior performance of the proposed encoding and better accuracy of the proposed HIV-1 cleavage prediction model as compared to the state-of-the-art methods", } @Article{Fathinasab:2016:Fuel, author = "Mohammad Fathinasab and Shahab Ayatollahi", title = "On the determination of {CO2}-crude oil minimum miscibility pressure using genetic programming combined with constrained multivariable search methods", journal = "Fuel", volume = "173", pages = "180--188", year = "2016", ISSN = "0016-2361", DOI = "doi:10.1016/j.fuel.2016.01.009", URL = "http://www.sciencedirect.com/science/article/pii/S0016236116000181", abstract = "In addition to reducing carbon dioxide (CO2) emission, the high oil recovery efficiency achieved by CO2 injection processes makes CO2 injection a desirable enhance oil recovery (EOR) technique. Minimum miscibility pressure (MMP) is an important parameter in successful designation of any miscible gas injection process such as CO2 flooding; therefore, its accurate determination is of great importance. The current experimental techniques for determining MMP are expensive and time-consuming. In this study, multi-gene genetic programming has been combined with constrained multivariable search methods, and a simple empirical model has been developed which provides a reliable estimation of MMP in a wide range of reservoirs, injection gases and crude oil systems. The experimental data for developing the proposed correlation consists of 270 data points from twenty-six authenticated literature sources. This model uses reservoir temperature, molecular weight of C5+, volatile (N2 and C1) to intermediate (H2S, CO2, C2, C3, C4) ratio and pseudo critical temperature of the injection gas as input parameters. Both statistical and graphical error analyses have been employed to evaluate the accuracy and validity of the proposed model compared to the pre-existing correlations. The results showed that the new model provides an average absolute relative error of 11.76percent. Moreover, the relevancy factor indicated that the reservoir temperature has the greatest impact on the minimum miscibility pressure.", keywords = "genetic algorithms, genetic programming, Minimum miscibility pressure, Carbon dioxide, Constrained multivariable search methods", } @Article{Fathinasab:2015:FPE, author = "Mohammad Fathinasab and Shahab Ayatollahi and Abdolhossein Hemmati-Sarapardeh", title = "A rigorous approach to predict nitrogen-crude oil minimum miscibility pressure of pure and nitrogen mixtures", journal = "Fluid Phase Equilibria", volume = "399", pages = "30--39", year = "2015", ISSN = "0378-3812", DOI = "doi:10.1016/j.fluid.2015.04.003", URL = "http://www.sciencedirect.com/science/article/pii/S0378381215001946", abstract = "Nitrogen has been appeared as a competitive gas injection alternative for gas-based enhanced oil recovery (EOR) processes. Minimum miscibility pressure (MMP) is the most important parameter to successfully design N2 flooding, which is traditionally measured through time consuming, expensive and cumbersome experiments. In this communication, genetic programming (GP) and constrained multivariable search methods have been combined to create a simple correlation for accurate determination of the MMP of N2-crude oil, based on the explicit functionality of reservoir temperature as well as thermodynamic properties of crude oil and injection gas. The parameters of the developed correlation include reservoir temperature, average critical temperature of injection gas, volatile and intermediate fractions of reservoir oil and heptane plus-fraction molecular weight of crude oil. A set of experimental data pool from the literature was collected to evaluate and compare the results of the developed correlation with pre-existing correlations through statistical and graphical error analyses. The results of this study illustrate that the proposed correlation is more reliable and accurate than the pre-existing models in a wide range of thermodynamic and process conditions. The proposed correlation predicts the total data set (93 MMP data of pure and N2 mixture streams as well as lean gases) with an average absolute relative error of 10.02percent. Finally, by employing the relevancy factor, it was found that the intermediate components of crude oil have the most significant impact on the nitrogen MMP estimation.", keywords = "genetic algorithms, genetic programming, Minimum miscibility pressure, Nitrogen, Lean gas, Constrained multivariable search methods", } @InProceedings{Fatima:2010:cec, author = "Shaheen Fatima and Mohamed Bader-El-Den", title = "Co-evolutionary hyper-heuristic method for auction based scheduling", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-6910-9", abstract = "In this paper, we present a co-evolutionary hyper-heuristic method for solving a sequential auction based resource allocation problem. The method combines genetic programming (GP) for evolving agent's bidding functions for the individual auctions with genetic algorithms (GAs) for evolving an optimal ordering for auctions. The framework is evaluated in the context of the exam timetabling problem (ETTP). In this problem, there is a set of exams, which have to be assigned to a predefined set of slots. Here, the exam time tabling system is the seller that sells a set of slots in a series of auctions. There is one auction for each slot. The exams are viewed as the bidding agents in need of slots. The problem is then to find a schedule (i.e., a slot for each exam) such that the total cost of conducting the exams as per the schedule is minimised. In order to arrive at such a schedule, we find the bidders optimal bids for an auction using GP. We combine this with a GA that finds an optimal ordering for conducting the auctions. The effectiveness of this co-evolutionary method is demonstrated experimentally by comparing it with some existing benchmarks for exam timetabling.", DOI = "doi:10.1109/CEC.2010.5586319", notes = "WCCI 2010. Also known as \cite{5586319}", } @InProceedings{Fatima:2011:GECCO, author = "Shaheen Fatima and Ahmed Kattan", title = "Evolving optimal agendas for package deal negotiation", booktitle = "GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0557-0", pages = "505--512", keywords = "genetic algorithms, genetic programming, Evolutionary combinatorial optimization and metaheuristics", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001576.2001646", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "This paper presents a hyper GA system to evolve optimal agendas for package deal negotiation. The proposed system uses a Surrogate Model based on Radial Basis Function Networks (RBFNs) to speed up the evolution. The negotiation scenario is as follows. There are two negotiators/agents (a and b) and m issues/items available for negotiation. But from these m issues, the agents must choose g issues and negotiate on them. The g issues thus chosen form the agenda. The agenda is important because the outcome of negotiation depends on it. Furthermore, a and b will, in general, get different utilities/profits from different agendas. Thus, for competitive negotiation (i.e., negotiation where each agent wants to maximise its own utility), each agent wants to choose an agenda that maximizes its own profit. However, the problem of determining an agent's optimal agenda is complex, as it requires combinatorial search. To overcome this problem, we present a hyper GA method that uses a Surrogate Model based on Radial Basis Function Networks (RBFNs) to find an agent's optimal agenda. The performance of the proposed method is evaluated experimentally. The results of these experiments demonstrate that the surrogate assisted algorithm, on average, performs better than standard GA and random search.", notes = "Also known as \cite{2001646} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)", } @PhdThesis{Fattah:thesis, author = "K. Abdel El-Fattah", title = "Volatile Oil and Gas Condensate Fluid Behavior for Material Balance Calculations and Reservoir Simulation", school = "Cairo University", year = "2005", address = "Egypt", month = nov, keywords = "genetic algorithms, genetic programming, PVT Properties, Equation-of-state, Modified black-oil simulation", bad_url = "https://www.cu.edu.eg/thesis_pdf/4287%20Thesis_Volatile%20Oil%20and%20Gas%20Condensate%20Fluid%20Be.pdf", abstract_url = "https://cu.edu.eg/thesis_pdf/4287_Thesis_Volatile_Oil_and_Gas_Condensate_Fluid_Be.pdf", URL = "https://scholar.google.com/scholar?cluster=5512094531393996062", abstract = "This work presents a comparison of different methods for generating PVT properties for modified black-oil simulation of volatile oil and gas condensate reservoir fluids. These methods are evaluated by comparing the results of the modified black-oil simulation using these methods to the results of full equation-of-state (EOS) compositional simulation. Also the generalized material balance equation as straight line was used to calculate the initial-oil in place (IOIP). Comparisons between material balance calculations and simulation results were made. The methods are evaluated using nine actual reservoir fluid systems (six gas condensates, two volatile oils, and one wet gas) spanning a wide range of fluid properties. A new volatile oil-gas ratio RV correlation for volatile oil and gas condensate reservoir fluids is developed. According to our knowledge, no correlation to calculate Oil-Gas Ratio RV exists in the petroleum literature. In petroleum industry, calculation of Oil-Gas Ratio RV has to come from combination of laboratory experiments and elaborate calculation procedures using EOS models. Validation of the developed correlation is carried out by calculating IOIP using the developed correlation and comparing it with the value obtained using Whitson and Torp PVT.", notes = "Supervisor: Dr.M.H.Sayyouh", } @Article{Fattah:2012:OGB, author = "K. A. Fattah", title = "A new approach calculate oil-gas ratio for gas condensate and volatile oil reservoirs using genetic programming", journal = "Oil and Gas Business", year = "2012", number = "1", pages = "311--323", month = jan # "-" # feb, keywords = "genetic algorithms, genetic programming, Discipulus, Exploration. Geology and Geophysics, oil-gas ratio, PVT lab report, gas condensate, volatile oil, modified black oil simulation", ISSN = "1813-503X", URL = "https://faculty.ksu.edu.sa/en/kelshreef/publication/217271", URL = "http://www.ogbus.ru/eng/2012_1.shtml", URL = "http://www.ogbus.ru/eng/authors/Fattah/Fattah_2.pdf", size = "13 pages", abstract = "In this work, we develop a new approach to calculate oil-gas ratio (Rv) by matching PVT experimental data with an equation of state (EoS) model in a commercial simulator (Eclipse simulator) using genetic programming algorithm of commercial software (Discipulus). More than 3000 data values of Rv obtained from PVT laboratory analysis of eight gas condensate and five volatile oil fluid samples; selected under a wide range of composition, condensate yield, reservoir temperature and pressure, were used in this study. The hit-rate (R-squared) of the new approach was 0.9646 and the fitness variance for it was 0.00025 and the maximum absolute error was 7.73percent. This new approach was validated using the generalised material balance equation calculated with data generated from a compositional reservoir simulator (Eclipse simulator). The new approach depends only on readily available parameters in the field and can have wide applications when representative lab reports are not available.", notes = "Electronic scientific journal, Ufa State Petroleum Technological University, Russian Federation. http://www.ogbus.ru/eng/editorial_board.shtml#about UDC 622.276 In English", } @Article{Fattah2012141, author = "K. A. Fattah", title = "K-value program for crude oil components at high pressures based on PVT laboratory data and genetic programming", journal = "Journal of King Saud University - Engineering Sciences", volume = "24", number = "2", pages = "141--149", year = "2012", ISSN = "1018-3639", DOI = "doi:10.1016/j.jksues.2011.06.002", URL = "http://www.sciencedirect.com/science/article/pii/S1018363911000584", keywords = "genetic algorithms, genetic programming, K-value, Correlation, Genetic program, PVT lab report, Crude oil, High pressures", abstract = "Equilibrium ratios play a fundamental role in understanding the phase behaviour of hydrocarbon mixtures. They are important in predicting compositional changes under varying temperatures and pressures in the reservoirs, surface separators, and production and transportation facilities. In particular, they are critical for reliable and successful compositional reservoir simulation. Several techniques are available in the literature to estimate the K-values. This paper presents a new model for predicting K values with genetic programming (GP). The new model is applied to multicomponent mixtures. In this paper, 732 high-pressure K-values obtained from PVT analysis of 17 crude oil and gas samples from a number of petroleum reservoirs in Arabian Gulf are used. Constant Volume Depletion (CVD) and Differential Liberation (DL) were conducted for these samples. Material balance techniques were used to extract the K-values of crude oil and gas components from the constant volume depletion and differential liberation tests for the oil and gas samples, respectively. These K-values were then used to build the model using the Discipulus software, a commercial Genetic Programming system, and the results of K-values were compared with the values obtained from published correlations. Comparisons of results show that the currently published correlations give poor estimates of K-values for all components, while the proposed new model improved significantly the average absolute deviation error for all components. The average absolute error between experimental and predicted K-values for the new model was 4.355percent compared to 20.5percent for the Almehaideb correlation, 76.1percent for the Whitson and Torp correlation, 84.27percent for the Wilson correlation, and 105.8 for the McWilliams correlation.", } @Article{fattah:2014:JPEPT, author = "K. A. Fattah", title = "Gas-oil ratio correlation ({Rs}) for gas condensate using genetic programming", journal = "Journal of Petroleum Exploration and Production Technology", year = "2014", volume = "4", number = "3", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s13202-014-0098-x", DOI = "doi:10.1007/s13202-014-0098-x", } @Article{Fattah:2016:JKSUES, author = "K. A. Fattah and A. Lashin", title = "Improved oil formation volume factor (Bo) correlation for volatile oil reservoirs: An integrated non-linear regression and genetic programming approach", journal = "Journal of King Saud University - Engineering Sciences", year = "2018", volume = "30", number = "4", pages = "398--404", keywords = "genetic algorithms, genetic programming, Oil formation factor correlation, Volatile oil, PVT, Non-linear regression, Black oil simulation", ISSN = "1018-3639", DOI = "doi:10.1016/j.jksues.2016.05.002", URL = "http://www.sciencedirect.com/science/article/pii/S1018363916300198", abstract = "In this paper, two correlations for oil formation volume factor (Bo) for volatile oil reservoirs are developed using non-linear regression technique and genetic programming using commercial software. More than 1200 measured values obtained from PVT laboratory analyses of five representative volatile oil samples are selected under a wide range of reservoir conditions (temperature and pressure) and compositions. Matching of PVT experimental data with an equation of state (EOS) model using a commercial simulator (Eclipse Simulator), was achieved to generate the oil formation volume factor (Bo). The obtained results of the Bo as compared with the most common published correlations indicate that the new generated model has improved significantly the average absolute error for volatile oil fluids. The hit-rate (R2) of the new non-linear regression correlation is 98.99percent and the average absolute error (AAE) is 1.534percent with standard deviation (SD) of 0.000372. Meanwhile, correlation generated by genetic programming gave R2 of 99.96percent and an AAE of 0.3252percent with a SD of 0.00001584. The importance of the new correlation stems from the fact that it depends mainly on experimental field production data, besides having a wide range of applications especially when actual PVT laboratory data are scarce or incomplete.", } @InCollection{Faulkner:2017:miller, author = "Penelope Faulkner and Mihail Krastev and Angelika Sebald and Susan Stepney", title = "Sub-Symbolic Artificial Chemistries", booktitle = "Inspired by Nature: Essays Presented to Julian F. Miller on the Occasion of his 60th Birthday", publisher = "Springer", year = "2017", editor = "Susan Stepney and Andrew Adamatzky", volume = "28", series = "Emergence, Complexity and Computation", chapter = "14", pages = "287--322", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-67996-9", DOI = "doi:10.1007/978-3-319-67997-6_14", abstract = "We wish to use Artificial Chemistries to build and investigate open-ended systems. As such, we wish to minimise the number of explicit rules and properties needed. We describe here the concept of sub-symbolic Artificial Chemistries (ssAChems), where reaction properties are emergent properties of the internal structure and dynamics of the component particles. We define the components of a ssAChem, and illustrate it with two examples: RBN-world, where the particles are Random Boolean Networks, the emergent properties come from the dynamics on an attractor cycle, and composition is through rewiring the components to form a larger RBN; and SMAC, where the particles are Hermitian matrices, the emergent properties are eigenvalues and eigenvectors, and composition is through the non-associative Jordan product. We conclude with some ssAChem design guidelines.", notes = "part of \cite{miller60book} https://link.springer.com/bookseries/10624", } @InProceedings{Faunes:2012:ASE, author = "Martin Faunes and Houari Sahraoui and Mounir Boukadoum", title = "Generating model transformation rules from examples using an evolutionary algorithm", booktitle = "Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering, ASE 2012", year = "2012", editor = "Tim Menzies and Motoshi Saeki", pages = "250--253", address = "Essen, Germany", publisher_address = "New York, NY, USA", month = "3-7 " # sep, publisher = "ACM", keywords = "genetic algorithms, genetic programming, Model transformation by example", isbn13 = "978-1-4503-1204-2", URL = "http://doi.acm.org/10.1145/2351676.2351714", DOI = "doi:10.1145/2351676.2351714", acmid = "2351714", size = "4 pages", abstract = "We propose an evolutionary approach to automatically generate model transformation rules from a set of examples. To this end, genetic programming is adapted to the problem of model transformation in the presence of complex input/output relationships (i.e., models conforming to meta-models) by generating declarative programs (i.e., transformation rules in this case). Our approach does not rely on prior transformation traces for the model-example pairs, and directly generates executable, many-to-many rules with complex conditions. The applicability of the approach is illustrated with the well-known problem of transforming UML class diagrams into relational schema, using examples collected from the literature.", notes = "http://ase2012.paluno.uni-due.de/", } @InProceedings{conf/icmt/FaunesSB13, author = "Martin Faunes and Houari A. Sahraoui and Mounir Boukadoum", title = "Genetic-Programming Approach to Learn Model Transformation Rules from Examples", booktitle = "Proceedings of the 6th International Conference on Theory and Practice of Model Transformations, ICMT 2013", year = "2013", editor = "Keith Duddy and Gerti Kappel", volume = "7909", series = "Lecture Notes in Computer Science", pages = "17--32", address = "Budapest, Hungary", month = jun # " 18-19", publisher = "Springer", keywords = "genetic algorithms, genetic programming, JESS, ATL", isbn13 = "978-3-642-38882-8", DOI = "doi:10.1007/978-3-642-38883-5_2", bibdate = "2013-06-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icmt/icmt2013.html#FaunesSB13", size = "16 pages", abstract = "We propose a genetic programming-based approach to automatically learn model transformation rules from prior transformation pairs of source-target models used as examples. Unlike current approaches, ours does not need fine-grained transformation traces to produce many-to-many rules. This makes it applicable to a wider spectrum of transformation problems. Since the learnt rules are produced directly in an actual transformation language, they can be easily tested, improved and reused. The proposed approach was successfully evaluated on well-known transformation problems that highlight three modelling aspects: structure, time constraints, and nesting.", } @InProceedings{Faunes:2013:MODELS, author = "Martin Faunes and Juan Cadavid and Benoit Baudry and Houari Sahraoui and Benoit Combemale", title = "Automatically Searching for Metamodel Well-Formedness Rules in Examples and Counter-Examples", year = "2013", booktitle = "MODELS - ACM/IEEE 16th International Conference on Model Driven Engineering Languages and Systems", address = "Miami, Florida, USA", month = "29 " # sep # " 2013-4 " # oct # " 2013", keywords = "genetic algorithms, genetic programming, SBSE, computer science, software engineering", bibsource = "OAI-PMH server at hal.archives-ouvertes.fr", language = "ENG", oai = "oai:hal.inria.fr:hal-00923789", URL = "http://models2013.lcc.uma.es/technical.html", URL = "http://hal.inria.fr/hal-00923789", URL = "http://hal.inria.fr/docs/00/92/37/89/PDF/DerivingOCLInv_main.pdf", size = "16 pages", abstract = "Current meta-modelling formalisms support the definition of a metamodel with two views: classes and relations, that form the core of the meta-model, and well-formedness rules, that constraints the set of valid models. While a safe application of automatic operations on models requires a precise definition of the domain using the two views, most metamodels currently present in repositories have only the first one part. In this paper, we propose to start from valid and invalid model examples in order to automatically retrieve well-formedness rules in OCL using Genetic Programming. The approach is evaluated on metamodels for state machines and features diagrams. The experiments aim at demonstrating the feasibility of the approach and at illustrating some important design decisions that must be considered when using this technique.", notes = "Miami Models 2013 http://models2013.lcc.uma.es/ http://models2013.lcc.uma.es/downloads/models-2013-program-online.pdf Not in IEEE xplor May 2014 oai:hal.inria.fr:hal-00923789", } @PhdThesis{Faunes_Martin_2013_these, author = "Martin {Faunes Carvallo}", title = "Improving automation in model-driven engineering using examples", school = "Universite de Montreal", year = "2013", address = "Canada", month = jun, keywords = "genetic algorithms, genetic programming, SBSE, MODEL-DRIVEN ENGINEERING, SOFTWARE ENGINEERING BY EXAMPLES, AUTOMATED SOFTWARE ENGINEERING, METAMODELING, SEARCH-BASED SOFTWARE ENGINEERING, applied sciences - computer science", URL = "http://en.diro.umontreal.ca/our-department/news/une-nouvelle/news/improving-automation-in-model-driven-engineering-u-7567/", bibsource = "OAI-PMH server at amican.webapps1.lac-bac.gc.ca", contributor = "Houari Sahraoui", language = "en", oai = "oai:collectionscanada.gc.ca:QMU.1866/10562", URL = "http://hdl.handle.net/1866/10562", size = "104 pages", abstract = "This thesis aims to improve automation in Model Driven Engineering (MDE). MDE is a paradigm that promises to reduce software complexity by the mean of the intensive use of models and automatic model transformation (MT). Roughly speaking, in MDE vision, stakeholders use several models to represent the software, and produce source code by automatically transforming these models. Consequently, automation is a key factor and founding principle of MDE. In addition to MT, other MDE activities require automation, e.g. modelling language definition and software migration. In this context, the main contribution of this thesis is proposing a general approach for improving automation in MDE. Our approach is based on meta-heuristic search guided by examples. We apply our approach to two important MDE problems, (1) model transformation and (2) precise modelling languages. For transformations, we distinguish between transformations in the context of migration and general model transformations. In the case of migration, we propose a software clustering method based on a search algorithm guided by cluster examples. Similarly, for general transformations, we learn model transformations by a genetic programming algorithm taking inspiration from examples of past transformations. For the problem of precise meta modelling, we propose a meta-heuristic search method to derive well-formedness rules for metamodels with the objective of discriminating examples of valid and invalid models. Our empirical evaluation shows that the proposed approaches exhibit good results. These allow us to conclude that improving automation in MDE using meta-heuristic search and examples can contribute to a wider adoption of MDE in industry in the coming years.", notes = "Directeur de recherche : Sahraoui, Houari", } @PhdThesis{fayolle:tel-00476678, author = "Pierre-Alain Fayolle", title = "Reconstruction of {3D} objects using a functional representation", title_fr = "Reconstruction 3D d'objets par une representation fonctionnelle", school = "Universite d'Orleans", year = "2007", address = "France", month = "17 " # dec, keywords = "genetic algorithms, genetic programming, STGP, SARDF, 3D modeling, optimization, point-set modeling, Modelisation d'objets 3D, optimisation, nuage de points", hal_id = "tel-00476678", hal_version = "v1", URL = "https://tel.archives-ouvertes.fr/tel-00476678/file/These_francaise.pdf", URL = "https://tel.archives-ouvertes.fr/tel-00476678", size = "112 pages", abstract = "This dissertation focuses on modeling volumetric objects with distance-based scalar fields. The Euclidean distance from a given point in space to a set of points representing the boundary of a solid, corresponds to the shortest distance (defined using the Euclidean norm) between this given point and any other points of the set. Representing a solid by the distance to its boundary is a concise yet powerful method for defining and manipulating solids. Within that domain, we have restricted our attention to the constructive modeling of solids and how to implement set-theoretic operations by functions with certain properties such as: good approximation of the Euclidean distance and smoothness (differentiability) of the resulting function (a property useful for many applications). Constructions of the set-theoretic operations: union, intersection and difference have been introduced and discussed. These functions can then be applied to primitives, defined by the distance to the primitive's boundary, in order to recursively construct complex solids, whose defining function corresponds to an approximation of the distance to the resulting solid's boundary. These functions are a type of R-Function, obtained by modifying the contour lines of the min/max functions (traditionally used to model set operations with implicit surfaces). We call these functions SARDF for Signed Approximation Real Distance Functions. The SARDF framework, made by these operations and primitives defined by the Euclidean distance function, is used for heterogeneous material modeling, where the distance to the shape boundary and material features is used to parametrise the material distribution inside the solid. This framework is implemented as an extension of the HyperFun Java applet and the HyperFun interpreter. Modeling objects in a constructive way, i.e. by recursively applying set-theoretic operations to primitives is a well-known and powerful paradigm in solid modeling. Combined with the functional expression of the final solid and the Euclidean distance property, it provides a powerful tool for solid modeling and applications. The construction of objects following this constructive paradigm may however be tedious and sometimes repetitive. We have considered several approaches to automate this construction. The notion of template model was introduced for this automation purpose, and several algorithms were proposed for optimizing a template model to discrete point-sets (obtained for example with a laser scanner) on or near the surface of a solid. The idea of using template models comes from the observation that most of the solids can be clustered in classes. For example, several vases can have a common shape that can be abstracted by a template model. Parameters governing the shape of the vases can be extracted and then optimized using a combination of meta-heuristics such as Simulated Annealing or Genetic Algorithm and direct methods such as Levenberg-Marquardt or Newton type methods. Defining the template models using the SARDF framework is preferable as it gives better results with the optimization algorithms. Automation of the creation of a constructive model that can further be used as a template model is also considered by using two different approaches. The first approach consists in using genetic programming to create constructive models from a discrete set of points. The second approach creates a constructive model from a segmented point-set and a list of primitives. A genetic algorithm is used to find the best constructive expression involving the primitives fitted to the segmented point-set and operations from a set of possible operations. Both approaches have been implemented and their results discussed.", notes = "In French. Francais. These_francaise.pdf contains several papers in English. FRep 'similarities with genetic programming' Is thesis GP?", resume = "NOUS nous sommes essentiellement interesses a la modelisation d'objets volume-triques par des champs de distance scalaire. La distance Euclidienne d'un point aun ensemble de points representant la frontiere d'un solide, correspond a la plus petite distance (definie a partir de la norme Euclidienne) entre ce point et n'importe quel point de l'ensemble. La representation du solide par la distance a la surface du solideest une methode concise mais relativement puissante pour definir et manipuler des solides. Dans ce cadre, nous nous sommes interesses a la modelisation constructive desolides, et a la facon d'implementer les operations ensemblistes par des fonctions afinde garantir une bonne approximation de la distance ainsi que certaines proprietes de differentiabilite, necessaire pour plusieurs classes d'operations ou applications sur les solides. Nous avons construit differents types de fonctions implementant les principales operations ensemblistes (union, intersection, difference). Ces fonctions peuvent etre ensuite appliquees a des primitives, definies par la distance a la surface de la primitive, afin de construire recursivement des solides complexes, definies eux-memes par une approximation a la distance du solide. Ces fonctions correspondent en fait a une certaine classe de R-fonctions, obtenues en lissant les points critiques des fonctions min/max (qui sont elles memes des R-fonctions). Ces fonctions sont appelees Signed Approximate Real Distance Functions (SARDF). Le cadre SARDF, constitue des fonctions decrites ci-dessuset de primitives definiespar la fonction distance, a ete utilise pour la modelisation heterogene de solides. La distance, ou son approximation, a la surface du solide ou desmateriaux internesest utilisee comme un parametre pour modeliser la distribution des materiaux a l'interieur du solide. Le cadre SARDF a principalement ete implemente comme une extension de l'interpreteur d'HyperFun et a l'interieur del'applet Java d'HyperFun. La modelisation constructive de solides possede de nombreux avantages qui en font un outil puissant pour la modelisation de solides. Neanmoins, la definition constructive de solides peut etre fastidieuse et repetitive. Nous avons etudie differents aspects pour l'automatiser. Dans un premier temps, nous avons introduit la notion de modeles template, et propose differents algorithmes pour optimiser la forme d'untemplate a differentes instances correspondant a des nuages de points, sur ou aux alentours de la surface du solide. L'idee des templates vient de l'observation que les solides traditionnelle-ment modelises par ordinateur peuvent etre regroupes en differentes classes possedant des caracteristiques communes. Par exemple, differents vases peuvent avoir une forme commune. Cette forme generale est modelisee une seule fois, et differents parametresgouvernant les caracteristiques de la forme sont extraits. Ces parametres sont ensuite optimises a l'aide d'une combinaison de meta-heuristique comme le recuit simule oules algorithmes genetiques avec des methodes directes du type Newton ou Levenberg-Marquardt. L'utilisation du cadre SARDF pour la definition du modele template est preferable, car donne de meilleurs resultats avec les algorithmes d'optimisation. Nous pouvons maintenant nous demander comment le modele template est obtenu. Une pre-miere solution est d'utiliser les services d'un artiste. Neanmoins, nous pouvons aussi reflechir pour automatiser ce processus. Nous avons essentiellement etudie deux aspects pour repondre a cette question : la premiere est l'utilisation de la programmation genetique pour former un modele constructif a partir d'un nuage de points. La deuxieme solution consiste a partir d'un nuage de points segmentes etune liste de primitives optimises a ce nuage de points segmente, et d'utiliser un algorithme genetique pour determiner l'ordre et le type d'operations qui peuvent etre appliquees a ces primitives. Ces deux solutions ont ete implementees et leurs resultats discutes.", } @Article{Fayolle:2016:CD, author = "Pierre-Alain Fayolle and Alexander Pasko", title = "An evolutionary approach to the extraction of object construction trees from {3D} point clouds", journal = "Computer-Aided Design", volume = "74", pages = "1--17", year = "2016", ISSN = "0010-4485", DOI = "doi:10.1016/j.cad.2016.01.001", URL = "http://www.sciencedirect.com/science/article/pii/S0010448516000038", abstract = "In order to extract a construction tree from a finite set of points sampled on the surface of an object, we present an evolutionary algorithm that evolves set-theoretic expressions made of primitives fitted to the input point-set and modelling operations. To keep relatively simple trees, we use a penalty term in the objective function optimized by the evolutionary algorithm. We show with experiments successes but also limitations of this approach.", keywords = "genetic algorithms, genetic programming, Shape modelling, Fitting, Reverse engineering, Construction tree, Function Representation", } @Article{FAYOLLE:2024:cad, author = "Pierre-Alain Fayolle and Markus Friedrich", title = "A Survey of Methods for Converting Unstructured Data to {CSG} Models", journal = "Computer-Aided Design", volume = "168", pages = "103655", year = "2024", ISSN = "0010-4485", DOI = "doi:10.1016/j.cad.2023.103655", URL = "https://www.sciencedirect.com/science/article/pii/S0010448523001872", keywords = "genetic algorithms, genetic programming, ANN, Segmentation, Fitting, Constructive solid geometry, Point-cloud and polygon soup to CSG conversion, Shape program synthesis", abstract = "The goal of this document is to survey existing methods for recovering or extracting CSG (Constructive Solid Geometry) representations from unstructured data such as 3D point-clouds or polygon meshes. We review and discuss related topics such as the segmentation and fitting of the input data. We cover techniques from solid modeling for the conversion of a polyhedron to a CSG expression and for the conversion of a B-rep to a CSG expression. We look at approaches coming from program synthesis, evolutionary techniques (such as genetic programming or genetic algorithm), and deep learning. Finally, we conclude our survey with a discussion of techniques for the generation of computer programs involving higher-level constructs, representations, and operations for representing solids", } @InProceedings{fazenda:evoapps12, author = "Pedro Fazenda and James McDermott and Una-May O'Reilly", title = "A Library to Run Evolutionary Algorithms in the Cloud using {MapReduce}", booktitle = "Applications of Evolutionary Computing, EvoApplications2012: {EvoCOMNET}, {EvoCOMPLEX}, {EvoFIN}, {EvoGAMES}, {EvoHOT}, {EvoIASP}, {EvoNUM}, {EvoPAR}, {EvoRISK}, {EvoSTIM}, {EvoSTOC}", year = "2012", month = "11-13 " # apr, editor = "Cecilia {Di Chio} and Alexandros Agapitos and Stefano Cagnoni and Carlos Cotta and F. {Fernandez de Vega} and Gianni A. {Di Caro} and Rolf Drechsler and Aniko Ekart and Anna I Esparcia-Alcazar and Muddassar Farooq and William B. Langdon and Juan J. Merelo and Mike Preuss and Hendrik Richter and Sara Silva and Anabela Simoes and Giovanni Squillero and Ernesto Tarantino and Andrea G. B. Tettamanzi and Julian Togelius and Neil Urquhart and A. Sima Uyar and Georgios N. Yannakakis", series = "LNCS", volume = "7248", publisher = "Springer Verlag", address = "Malaga, Spain", pages = "416--425", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, MapReduce, Hadoop, EC, Amazon EC2, FlexEA", isbn13 = "978-3-642-29177-7", DOI = "doi:10.1007/978-3-642-29178-4_42", size = "10 pages", abstract = "We discuss ongoing development of an evolutionary algorithm library to run on the cloud. We relate how we have used the Hadoop open-source MapReduce distributed data processing framework to implement a single `island' with a potentially very large population. The design generalises beyond the current, one-off kind of MapReduce implementations. It is in preparation for the library becoming a modelling or optimization service in a service oriented architecture or a development tool for designing new evolutionary algorithms.", notes = "EDO-Lib, Reporter, Island Model, HDFS, p419 'FitnessEvaluator can be set by injection'. Matlab. Does not give execution time in terms of GP operation per second. Population up to 1 million. XEN.org p423 'takes much longer to design a MapReduce implementation than it would to develop a socket or MPI model.' p424 'It also results in a code base which requires more effort to support and maintain which impacts research agility.' Part of \cite{DiChio:2012:EvoApps} EvoApplications2012 held in conjunction with EuroGP2012, EvoCOP2012, EvoBio'2012 and EvoMusArt2012", } @Article{FAZIO:2024:xcrp, author = "Vincenzo Fazio and Nicola Maria Pugno and Orazio Giustolisi and Giuseppe Puglisi", title = "Physically based machine learning for hierarchical materials", journal = "Cell Reports Physical Science", volume = "5", number = "2", pages = "101790", year = "2024", ISSN = "2666-3864", DOI = "doi:10.1016/j.xcrp.2024.101790", URL = "https://www.sciencedirect.com/science/article/pii/S2666386424000109", keywords = "genetic algorithms, genetic programming, multiscale modeling, data modeling, materials science, spider silk, evolutionary polynomial regression approaches", abstract = "In multiscale phenomena, complex structure-function relationships emerge across different scales, making predictive modeling challenging. The recent scientific literature is exploring the possibility of leveraging machine learning, with a predominant focus on neural networks, excelling in data fitting, but often lacking insight into essential physical information. We propose the adoption of a symbolic data modeling technique, the {"}Evolutionary Polynomial Regression,{"} which integrates regression capabilities with the genetic programming paradigm, enabling the derivation of explicit analytical formulas, finally delivering a deeper comprehension of the analyzed physical phenomenon. To demonstrate the key advantages of our multiscale numerical approach, we consider the spider silk case. Based on a recent multiscale experimental dataset, we deduce the dependence of the macroscopic behavior from lower-scale parameters, also offering insights for improving a recent theoretical model by some of the authors. Our approach may represent a proof of concept for modeling in fields governed by multiscale, hierarchical differential equations.", } @InProceedings{Fazli:2013:worldcomp, author = "Mojtaba Sedigh Fazli and Jean-Fabrice Lebraty", title = "A Solution for Forecasting {PET} Chips Prices for both Short-Term and Long-Term Price Forecasting, Using Genetic Programming", year = "2013", booktitle = "Proceedings of the 2013 International Conference on Artificial Intelligence", address = "Las Vegas, Nevada, United States", month = "22-25 " # jul, keywords = "genetic algorithms, genetic programming, humanities and social sciences/business administration, efficient market hypothesis, financial forecasting, chemicals, artificial intelligence, decision support system, hybrid neuro fuzzy model", bibsource = "OAI-PMH server at hal.archives-ouvertes.fr", oai = "oai:hal-univ-lyon3.archives-ouvertes.fr:hal-00859457", language = "ENG", URL = "http://hal-univ-lyon3.archives-ouvertes.fr/hal-00859457", URL = "http://hal-univ-lyon3.archives-ouvertes.fr/docs/00/85/94/57/PDF/Hal-fazli-lebraty.pdf", URL = "http://hal-univ-lyon3.archives-ouvertes.fr/docs/00/85/94/57/PDF/Hal-fazli-lebraty.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.394.7024", URL = "http://halshs.archives-ouvertes.fr/docs/00/85/94/57/PDF/Hal-fazli-lebraty.pdf", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.394.7024", size = "7 pages", abstract = "Nowadays, forecasting what will happen in economic environments plays a crucial role. We showed that in PET market how a neuro-fuzzy hybrid model can assist the managers in decision-making. In this research, the target is to forecast the same item through another intelligent tool which obeys the evolutionary processing mechanisms. Again, the item for prediction here is PET (Poly Ethylene Terephthalate) which is the raw material for textile industries and it is highly sensitive against oil price fluctuations and also some other factors such as the demand and supply ratio. The main idea is coming through AHIS model which was presented by Mojtaba Sedigh Fazli and J.F. Lebraty in 2013. In this communication, the hybrid module is substituted with genetic programming. Finally, the simulation has been conducted and compared to three different answers which were presented before the results show that Genetic programming results (acting like hybrid model) which support both Fuzzy Systems and Neural Networks, satisfy the research question considerably.", notes = "http://www.world-academy-of-science.org/worldcomp13/ws/conferences/icai13/publication worldcomp 2013 The 2013 International Conference on Artificial Intelligence (ICAI'13) and The 2013 International Conference on Genetic and Evolutionary Methods (GEM'13) also known as \cite{oai:hal-univ-lyon3.archives-ouvertes.fr:hal-00859457}", } @TechReport{Fedaeff:2016:HBRC, author = "Nava Fedaeff and Nicolas Fauchereau", title = "Towards the Development of Tailored Seasonal Forecasts for the Hawke's Bay Region", institution = "Hawkes Bay Regional Council", year = "2016", number = "HBRC Publication No.4914, Report No, RM17-01", address = "New Zealand", month = jul, note = "Prepared for Hawke's Bay Regional Council", keywords = "genetic algorithms, genetic programming", URL = "https://www.hbrc.govt.nz/assets/Document-Library/Publications-Database/4914-RM17-01-Hawkes-Bay-Seasonal-Forecasting.pdf", size = "32 pages", abstract = "This report, prepared for the Hawkes Bay Regional Council, describes a tailored statistical seasonal forecasting scheme developed for the Hawkes Bay region. This forecasting scheme is based upon tercile probabilities i.e.what is the chance that rainfall/temperature over the next 3 months will be below normal, normal or above normal.... https://www.niwa.co.nz/climate/sco", notes = "NIWA CLIENT REPORT No: 2016035AK, Report date: July 2015, NIWA Project: ELF16101", } @InProceedings{federman:1998:clps, author = "Francine Federman and Susan Fife Dorchak", title = "A Study of Classifier Length and Population Size", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "629--634", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, classifiers", ISBN = "1-55860-548-7", notes = "GP-98", } @InProceedings{federman:1999:RMLCSUBC, author = "Francine Federman and Gayle Sparkman and Stephanie Watt", title = "Representation of Music in a Learning Classifier System Utilizing Bach Chorales", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "785", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems, poster papers", ISBN = "1-55860-611-4", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InCollection{fehr:1994:semo, author = "Garry Fehr", title = "Spontaneous Emergence of Multicellular Organisms From Unicellular Ancestors", booktitle = "Artificial Life at Stanford 1994", year = "1994", editor = "John R. Koza", pages = "28--34", address = "Stanford, California, 94305-3079 USA", month = jun, organisation = "Stanford University", publisher = "Stanford Bookstore", keywords = "genetic algorithms", ISBN = "0-18-182105-2", notes = "This volume contains 22 papers written and submitted by students describing their term projects for the course in artificial life (Computer Science 425) at Stanford University offered during the spring quarter quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs425.html", } @InProceedings{Fei:2023:CAC, author = "Baolin Fei and Binzi Xu and Dengchao Huang and Yao Zhang and Chun Wang and Long Yang", booktitle = "2023 China Automation Congress (CAC)", title = "Automatic Generation of Energy-Efficient Dispatching Rules for Dynamic Flexible Job Shop Scheduling", year = "2023", pages = "533--538", abstract = "Dynamic flexible job shop scheduling (DFJSS) is an important and complex combinatorial optimisation problem. Heuristic methods have been extensively studied and proven to be effective in solving the job shop scheduling problems, but they still suffer from difficulties in real-time scheduling when dealing with dynamic environments. In comparison to heuristic methods, genetic programming hyper-heuristic (GPHH) is more suitable for tackling dynamic events since it can make real-time decisions by dispatching rules (DRs) automatically generated based on the current job shop state. However, most existing DR-based studies focus on time-related optimisation objectives (e.g., makespan, tardiness, etc.), ignoring energy consumption, which is crucial for meeting the urgent needs of green manufacturing in current society. Therefore, this paper systematically designs the energy-efficient terminals for GPHH, following an in-depth analysis of energy flow in the job shop. Besides, the paper proposes the energy-efficient manually designed DRs based on the DR construction method. Experimental results demonstrate that the DRs containing the proposed energy-efficient terminals can effectively optimise energy-related objectives.", keywords = "genetic algorithms, genetic programming, Energy consumption, Green manufacturing, Job shop scheduling, Heuristic algorithms, Dynamic scheduling, Energy efficiency, dynamic flexible job shop scheduling, GPHH, dispatching rules, energy consumption", DOI = "doi:10.1109/CAC59555.2023.10451974", ISSN = "2688-0938", month = nov, notes = "Also known as \cite{10451974}", } @Article{Fei:2015:MPM, author = "Jiangang Fei and Jianjun Lu", title = "Analysis of students perceptions of seafaring career in China based on artificial neural network and genetic programming", journal = "Maritime Policy \& Management", year = "2015", volume = "42", number = "2", pages = "111--126", keywords = "genetic algorithms, genetic programming, career choice, transport, ANN", publisher = "Routledge", ISSN = "0308-8839", DOI = "doi:10.1080/03088839.2013.873545", size = "16 pages", abstract = "Research indicates that the worldwide shortage of qualified seafarers has been due to the rapidly increasing world merchant fleet on the one hand and the difficulty of attracting and retaining people in the industry on the other. The paper argues that in the context of the Chinese shipping industry, the high wastage is one of the major contributors to the shortage. The aims of the research are to examine the motives of students studying nautical courses; identify the key factors affecting nautical students to choose seafaring career, and to predict students career choice behavior. The research finds that (a) those who had a clear sense of their future career when enrolled in the nautical studies tend to choose a seafaring career and remain active for a longer period of time; (b) high wage is the most important factor affecting students' decisions to choose seafaring as a career; (c) prolonged separation from family, job opportunities ashore, and concern about children growth and education are among the top three factors for students not to choose a seafaring career or to quit it early; and (d) nautical students career choice behaviors can be predicted through the use of artificial neural network (ANN) and genetic programming (GP) with five rules being generated. This novel methodological approach, a combination of ANN and GP, has been proven to be effective in analysing complex variables and in generating rules.", notes = "p126 'the use of ANN and GP .. has been proven to be effective inpredicting the seafaring choice behaviors' www.tandfonline.com/journals/tmpm20", } @TechReport{Feiler:book, author = "Peter Feiler and Richard P. Gabriel and John Goodenough and Rick Linger and Tom Longstaff and Rick Kazman and Mark Klein and Linda Northrop and Douglas Schmidt and Kevin Sullivan and Kurt Wallnau", title = "Ultra-Large-Scale Systems -- The Software Challenge of the Future", institution = "software engineering institute, Carnegie Mellon University", year = "2006", address = "Pittsburgh, PA 15213-3890, USA", month = jun, keywords = "genetic algorithms, genetic programming", URL = "http://www.sei.cmu.edu/library/assets/ULS_Book20062.pdf", ISBN = "0-9786956-0-7", size = "150 pages", abstract = "The U. S. Department of Defense (DoD) has a goal of information dominance-to achieve and exploit superior collection, fusion, analysis, and use of information to meet mission objectives. This goal depends on increasingly complex systems characterised by thousands of platforms, sensors, decision nodes, weapons, and war fighters connected through heterogeneous wired and wireless networks. These systems will push far beyond the size of today's systems and systems of systems by every measure: number of lines of code; number of people employing the system for different purposes; amount of data stored, accessed, manipulated, and refined; number of connections and interdependencies among software components; and number of hardware elements. They will be ultra-largescale (ULS) systems.", notes = "GP described as digital evolution. USA Federal Government Contract Number FA8721-05-C-0003.", } @InProceedings{conf/ecms/FekiacZB11, author = "Jozef Fekiac and Ivan Zelinka and Juan C. Burguillo", title = "A Review Of Methods For Encoding Neural Network Topologies In Evolutionary Computation", booktitle = "25th European Conference on Modelling and Simulation, ECMS 2011", year = "2011", editor = "Tadeusz Burczynski and Joanna Kolodziej and Aleksander Byrski and Marco Carvalho", pages = "410--416", address = "Krakow, Poland", month = jun # " 7-10", publisher = "European Council for Modeling and Simulation", keywords = "genetic algorithms, genetic programming, artificial neural network, automata network, evolutionary computation, network encoding, graph grammar", timestamp = "Tue, 14 Jan 2014 17:49:51 +0100", biburl = "http://dblp2.uni-trier.de/rec/bib/conf/ecms/FekiacZB11", bibsource = "dblp computer science bibliography, http://dblp.org", isbn13 = "978-0-9564944-2-9", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.637.5825", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.637.5825", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.637.5825", URL = "http://www.scs-europe.net/conf/ecms2011/ecms2011%20accepted%20papers/is_ECMS_0081.pdf", URL = "http://www.scs-europe.net/dlib/2011/2011-0410.htm", DOI = "doi:10.7148/2011-0410-0416", size = "7 pages", abstract = "This paper describes various methods used to encode artificial neural networks to chromosomes to be used in evolutionary computation. The target of this review is to cover the main techniques of network encoding and make it easier to choose one when implementing a custom evolutionary algorithm for finding the network topology. Most of the encoding methods are mentioned in the context of neural networks; however all of them could be generalised to automata networks or even oriented graphs. We present direct and indirect encoding methods, and given examples of their genotypes. We also describe the possibilities of applying genetic operators of mutation and crossover to genotypes encoded by these methods. Also, the dependencies of using special evolutionary algorithms with some of the encodings were considered.", } @Article{feldkamp:2003:GPEM, author = "Udo Feldkamp and Hilmar Rauhe and Wolfgang Banzhaf", title = "Software Tools for {DNA} Sequence Design", journal = "Genetic Programming and Evolvable Machines", year = "2003", volume = "4", number = "2", pages = "153--171", month = jun, keywords = "DNA computing, DNA nanotechnology, molecular self-assembly, sequence design, specific hybridization", ISSN = "1389-2576", URL = "http://www.cs.mun.ca/~banzhaf/papers/softwaretools.pdf", DOI = "doi:10.1023/A:1023985029398", abstract = "The design of DNA sequences is a key problem for implementing molecular self-assembly with nucleic acid molecules. These molecules must meet several physical, chemical and logical requirements, mainly to avoid mishybridization. Since manual selection of proper sequences is too time-consuming for more than a handful of molecules, the aid of computer programs is advisable. In this paper two software tools for designing DNA sequences are presented, the DNASequenceGenerator and the DNASequenceCompiler. Both employ an approach of sequence dissimilarity based on the uniqueness of overlapping subsequences and a graph based algorithm for sequence generation. Other sequence properties like melting temperature or forbidden subsequences are also regarded, but not secondary structure errors or equilibrium chemistry. Fields of application are DNA computing and DNA-based nanotechnology. In the second part of this paper, sequences generated with the DNASequenceGenerator are compared to those from several publications of other groups, an example application for the DNASequenceCompiler is presented, and the advantages and disadvantages of the presented approach are discussed.", notes = "Special Issue on Biomolecular Machines and Artificial Evolution Article ID: 5122743", } @TechReport{feldt:1998:eGPmsv, author = "Robert Feldt", title = "An experiment on using genetic programming to develop multiple diverse software variants", institution = "Department of Computer Engineering, Chalmers University of Technology", year = "1998", type = "Technical Report", number = "98-13", address = "Gothenburg, Sweden", month = sep, keywords = "genetic algorithms, genetic programming", URL = "https://sites.google.com/site/drfeldt/feldt_1998_experiment_gp_variants.pdf", abstract = "This report includes the two previously published papers: Robert Feldt. Generating Multiple Diverse Software Versions with Genetic Programming - an Experimental Study, IEE Proceedings - Software, vol. 145, issue 6, pp. 228-236, December 1998. Robert Feldt. Generating Multiple Diverse Software Versions with Genetic Programming, Proceedings of the 24th EUROMICRO Conference,Workshop on Depdable Computing Systems, pp. 387-396, Vasteras, Sweden, August 1998", notes = "Included also in \cite{feldt:1998:midthesis}", size = "39 pages", } @TechReport{feldt:1998:scdGPsft, author = "Robert Feldt", title = "A survey of the concept of diversity in genetic programming and software fault tolerance", institution = "Department of Computer Engineering, Chalmers University of Technology", year = "1998", type = "Technical Report", number = "98-15", address = "Gothenburg, Sweden", month = oct, keywords = "genetic algorithms, genetic programming", notes = "Included also in \cite{feldt:1998:midthesis}", size = "pages", } @InProceedings{feldt:1998:gmdsvGP, author = "Robert Feldt", title = "Generating Multiple Diverse Software Versions with Genetic Programming", booktitle = "Proceedings of the 24th EUROMICRO Conference, Workshop on Dependable Computing Systems", year = "1998", pages = "387--396", address = "Vaesteraas, Sweden", month = "25-27th " # aug, keywords = "genetic algorithms, genetic programming", URL = "http://www.amp.york.ac.uk/external/sweden/sweden.htm", DOI = "doi:10.1109/EURMIC.1998.711831", abstract = "Software fault tolerance schemes often employ multiple software versions developed to meet the same specification. If the versions fail independently of each other, they can be combined to give high levels of reliability. While design diversity is a means to develop these versions, it has been questioned because it increases development costs and because reliability gains are limited by common-mode failures. We propose the use of genetic programming to generate multiple software versions and postulate that these versions can be forced to differ by varying parameters to the genetic programming algorithm. This might prove a cost-effective approach to obtain forced diversity and make possible controlled experiments with large numbers of diverse development methodologies. This paper qualitatively compares the proposed approach to design diversity and its sources of diversity. An experiment environment to evaluate whether significant diversity can be generated is outlined.", notes = "described in \cite{feldt:1998:midthesis}", } @Article{feldt:1998:gdsvGPes, author = "Robert Feldt", title = "Generating Diverse Software Versions with Genetic Programming: an Experimental Study", journal = "IEE Proceedings - Software Engineering", year = "1998", volume = "145", number = "6", pages = "228--236", month = dec, note = "Special issue on Dependable Computing Systems", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, aircraft control, program testing, programming environments, software reliability, aircraft arrestment system, aircraft controller, common-mode failure, design diversity, experimental study, multiple software versions, software development costs, software fault-tolerance, software reliability, software version generation, specification, navy aircraft carrier", ISSN = "1462-5970", broken = "http://www.iee.org.uk/publish/journals/profjrnl/cntnsen.html#SENDecember1998", DOI = "doi:10.1049/ip-sen:19982444", size = "9 pages", abstract = "Software fault-tolerance schemes often employ multiple software versions developed to meet the same specification. If the versions fail independently of each other, they can be combined to give high levels of reliability. Although design diversity is a means to develop these versions, it has been questioned because it increases development costs and because reliability gains are limited by common-mode failures. The use of genetic programming is proposed to generate multiple software versions by varying parameters of the genetic programming algorithm. An environment is developed to generate programs for a controller in an aircraft arrestment system. Eighty programs have been developed and tested on 10000 test cases. The experimental data show that failure diversity is achieved, but for the top performing programs its levels are limited", notes = "See Workshop: Managing and Optimising Multiplicity Computing, 22-23 March 2012 http://crest.cs.ucl.ac.uk/cow/18/ described in \cite{feldt:1998:midthesis}. Also known as \cite{765682} CODEN: IPSEFU INSPEC Accession Number:6150266", } @TechReport{feldt:1998:midthesis, author = "Robert Feldt", title = "Using Genetic Programming to Systematically Force Software Diversity", institution = "Department of Computer Engineering, Chalmers University of Technology", year = "1998", type = "Technical Report", number = "296L", address = "Goteborg, Sweden", month = nov, keywords = "genetic algorithms, genetic programming, SBSE, software reliability, forced design diversity, N-version programming, software diversity, software fault tolerance", ISBN = "91-7197-740-6", URL = "http://publications.lib.chalmers.se/publication/186704-using-genetic-programming-to-systematically-force-software-diversity", size = "133 pages", notes = "licentiate of Engineering thesis, Licentiatavhandling https://www.artes.uu.se/publications.shtml Supervisor: Jan Torin", } @InProceedings{feldt:1999:GPxtxsdp, author = "Robert Feldt", title = "Genetic Programming as an Explorative Tool in Early Software Development Phases", booktitle = "Proceedings of the 1st International Workshop on Soft Computing Applied to Software Engineering", year = "1999", editor = "Conor Ryan and Jim Buckley", pages = "11--20", address = "University of Limerick, Ireland", month = "12-14 " # apr, organisation = "SCARE", publisher = "Limerick University Press", keywords = "genetic algorithms, genetic programming, genetic improvement", ISBN = "1-874653-52-6", URL = "http://drfeldt.googlepages.com/feldt_1999_gp_as_explorative_tool.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/scase_1999/feldt_1999_GPxtxsdp.pdf", video_url = "http://crest.cs.ucl.ac.uk/cow/50/videos/feldt_cow50_480p.mp4", size = "10 pages", abstract = "Early in a software development project the developers lack knowledge about the problem to be solved by the software. Any knowledge that can be gained at an early stage can reduce the risk of making erroneous decisions and injecting defects that can be expensive to eliminate in later phases. This paper presents the idea of using genetic programming to explore the difficulty of different input data in the input space, determine the effects of different requirements and identify design trade-offs inherent in the problem. Data from a pilot experiment is analysed and the knowledge gained is used to question and prioritize the requirements on the target system. Coping with high-dimensional input spaces and establishing the relationship between GP- and human-developed programs are identified as the major outstanding problems. An extended experimental environment is proposed based on techniques for visual database exploration.", notes = "broken http://scare.csis.ul.ie/scase99/ SCASE'99 USAF aircraft arresting system (landing on aicraft carriers) used as example. Java GPsys. See discussion at COW50: The 50th CREST Open Workshop - Genetic Improvement http://crest.cs.ucl.ac.uk/cow/50/ http://crest.cs.ucl.ac.uk/cow/50/slides/cow50_Feldt_short.pdf http://crest.cs.ucl.ac.uk/cow/50/videos/feldt_cow50_480p.mp4 ", } @InProceedings{feldt:2000:feeeGP, author = "Robert Feldt and Peter Nordin", title = "Using Factorial Experiments to Evaluate the Effect of Genetic Programming Parameters", booktitle = "Genetic Programming, Proceedings of EuroGP'2000", year = "2000", editor = "Riccardo Poli and Wolfgang Banzhaf and William B. Langdon and Julian F. Miller and Peter Nordin and Terence C. Fogarty", volume = "1802", series = "LNCS", pages = "271--282", address = "Edinburgh", publisher_address = "Berlin", month = "15-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-67339-3", URL = "http://citeseer.ist.psu.edu/325152.html", URL = "http://drfeldt.googlepages.com/feldt_2000_factorial_exp_gp_params.pdf", DOI = "doi:10.1007/978-3-540-46239-2_20", abstract = "Statistical techniques for designing and analyzing experiments are used to evaluate the individual and combined effects of genetic programming parameters. Three binary classification problems are investigated in a total of seven experiments consisting of 1108 runs of a machine code genetic programming system. The parameters having the largest effect in these experiments are the population size and the number of generations. A large number of parameters have negligible effects. The experiments indicate that the investigated genetic programming system is robust to parameter variations, with the exception of a few important parameters.", notes = "EuroGP'2000, part of \cite{poli:2000:GP}", } @InProceedings{feldt:2000:gp-beagle, author = "Robert Feldt and Michael O'Neill and Conor Ryan and Peter Nordin and William B. Langdon", title = "{GP-Beagle:} A Benchmarking Problem Repository for the Genetic Programming Community", pages = "90--97", booktitle = "Late Breaking Papers at the 2000 Genetic and Evolutionary Computation Conference", year = "2000", editor = "Darrell Whitley", address = "Las Vegas, Nevada, USA", month = "8 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/feldt_et_al_gecco2000lb_gpbeagle.pdf", URL = "http://drfeldt.googlepages.com/20_paper7_gpbeagle.ps", URL = "http://citeseer.ist.psu.edu/302050.html", size = "8 pages", abstract = "Experimental studies in genetic programming often only use a few, artical problems. The results thus obtained may not be typical and may not reect performance on problems met in the real world. To change this we propose the use of common suites of benchmark problems and introduce a benchmarking problem repository called GP-Beagle. The basic entities in the repository are problems, problem instances, problem suites and usage information. We give examples of problems and suites that can be found in the repository and identify its WWW site location.", notes = "Part of \cite{whitley:2000:GECCOlb}", } @TechReport{Feldt:2002:tr, author = "Robert Feldt", title = "An Interactive Software Development Workbench based on Biomimetic Algorithms", institution = "Department of Computer Engineering, Chalmers University of Technology", year = "2002", address = "Gothenburg, SWEDEN", month = nov, keywords = "genetic algorithms, genetic programming, simulated annealing, multi-agent, SBSE, Ruby", URL = "http://drfeldt.googlepages.com/feldt_2002_wise_tech_report.pdf", abstract = "Based on a theory for software development that focus on the internal models of the developer this paper presents a design for an interactive workbench to support the iterative refinement of developers models. The goal for the workbench is to expose unknown features of the software being developed so that the developer can check if they correspond to his expectations. The workbench employs a biomimetic search system to find tests with novel features. The search system assembles test templates from small pieces of test code and data packaged into a cell.We describe a prototype of the workbench implemented in Ruby and focus on the module used for evolving tests.A case study show that the prototype supports development of tests that are both diverse, complete and have a meaning to the developer. Furthermore, the system can easily be extended by the developer when he comes up with new test strategies.", size = "42 pages", } @PhdThesis{Feldt:thesis, author = "Robert Feldt", title = "Biomimetic Software Engineering Techniques for Dependability", school = "Department of Computer Engineering, Chalmers University of Technology", year = "2002", address = "Gothenburg, Sweden", month = dec, keywords = "genetic algorithms, genetic programming, SBSE", URL = "http://www.cse.chalmers.se/~feldt/publications/feldt_2002_phdthesis.html", URL = "http://www.cse.chalmers.se/~feldt/publications/feldt_2002_phd_thesis.pdf", URL = "http://www.robertfeldt.net/publications/feldt_2002_phd_thesis.pdf", URL = "http://www.robertfeldt.net/publications/feldt_2002_phdthesis.html", ISBN = "91-7291-241-3", size = "206 pages", abstract = "The powerful information processing capabilities of computers have made them an indispensable part of our modern societies. As we become more reliant on computers and want them to handle more critical and difficult tasks it becomes important that we can depend on the software that controls them. Methods that help ensure software dependability is thus of utmost importance. While we struggle to keep our software dependable despite its increasing complexity, even the smallest biological system in nature shows features of dependability. This thesis applies ideas from and algorithms modelled after biological systems in the research for and development of dependable software. Based on a theory of software development focusing on the internal models of the developer and how to support their refinement we present a design for an interactive software development workbench where a biomimetic system searches for test sequences. A prototype of the workbench has been implemented and evaluated in a case study. It showed that the system successfully finds tests that show faults in both the software and its specification. Like biological systems in nature exploits a niche in the environment the biomimetic search system exploits the behaviour of the software being developed. In another study we applied genetic programming to evolve programs for an embedded control system. Although the procedure did not show much potential for use in real fault-tolerant software, the program variants could be used to visualise the difficulty of the problem domain, explore the effects of design decisions and trade off requirements. Taken together the works in this thesis support the claim that biomimetic algorithms can be used to explore requirements, design and test spaces in early software engineering phases and thus help in building dependable software.", } @InProceedings{Feldt:2018:RAISE, author = "Robert Feldt and Francisco G. {de Oliveira Neto} and Richard Torkar", title = "Ways of Applying Artificial Intelligence in Software Engineering", booktitle = "IEEE/ACM 6th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2018", year = "2018", editor = "Walter F. Tichy and Leandro Minku", pages = "35--41", address = "Gothenburg, Sweden", month = "27 " # may, publisher = "ACM", keywords = "genetic algorithms, genetic programming, SBSE", isbn13 = "978-1-4503-5723", URL = "https://arxiv.org/abs/1802.02033", DOI = "doi:10.1145/3194104.3194109", size = "7 pages", abstract = "As Artificial Intelligence (AI) techniques become more powerful and easier to use they are increasingly deployed as key components of modern software systems. While this enables new functionality and often allows better adaptation to user needs it also creates additional problems for software engineers and exposes companies to new risks. Some work has been done to better understand the interaction between Software Engineering and AI but we lack methods to classify ways of applying AI in software systems and to analyse and understand the risks this poses. Only by doing so can we devise tools and solutions to help mitigate them. This paper presents the AI in SE Application Levels (AI-SEAL) taxonomy that categorises applications according to their point of application, the type of AI technology used and the automation level allowed. We show the usefulness of this taxonomy by classifying 15 papers from previous editions of the RAISE workshop. Results show that the taxonomy allows classification of distinct AI applications and provides insights concerning the risks associated with them. We argue that this will be important for companies in deciding how to apply AI in their software applications and to create strategies for its use.", notes = "http://promisedata.org/raise/2018/ co-located with ICSE 2018", } @Article{feli:SIVP, author = "Mohammad Feli and Fardin Abdali-Mohammadi", title = "A novel recursive backtracking genetic programming-based algorithm for 12-lead {ECG} compression", journal = "Signal, Image and Video Processing", year = "2019", volume = "13", number = "5", pages = "1029--1036", month = jul, keywords = "genetic algorithms, genetic programming, Electrocardiograph, Signal compression, Backtracking algorithm", URL = "http://link.springer.com/article/10.1007/s11760-019-01441-4", DOI = "doi:10.1007/s11760-019-01441-4", size = "8 pages", abstract = "ECG signal is among medical signals used to diagnose heart problems. A large volume of medical signal's data in telemedicine systems causes problems in storing and sending tasks. In the present paper, a recursive algorithm with backtracking approach is used for ECG signal compression. This recursive algorithm constructs a mathematical estimator function for each segment of the signal using genetic programming algorithm. When all estimator functions of different segments of the signal are determined and put together, a piecewise-defined function is constructed. This function is used to generate a reconstructed signal in the receiver. The compression result is a set of compressed strings representing the piecewise-defined function which is coded through a text compression method. In order to improve the compression results in this method, the input signal is smoothed. MIT-BIH arrhythmia database is employed to test and evaluate the proposed algorithm. The results of this algorithm include the average of compression ratio that equals 30.97 and the percent root-mean-square difference that is equal to 2.38percent, suggesting its better efficiency in comparison with other state-of-the-art methods.", } @Article{FELI:2019:BSPC, author = "Mohammad Feli and Fardin Abdali-Mohammadi", title = "12 lead electrocardiography signals compression by a new genetic programming based mathematical modeling algorithm", journal = "Biomedical Signal Processing and Control", volume = "54", pages = "101596", year = "2019", ISSN = "1746-8094", DOI = "doi:10.1016/j.bspc.2019.101596", URL = "http://www.sciencedirect.com/science/article/pii/S1746809419301764", keywords = "genetic algorithms, genetic programming, Electrocardiograph, Compression, Mathematical modeling", abstract = "Telemedicine refers to a group of modern medical services that are provided on the platform of advanced telecommunication technologies. One of these services is the screening for heart diseases, which are the leading cause of mortality across the world. But the development of telemedicine systems for cardiac screening faces multiple challenges. One of these challenges is the large volume of ECG signals, which makes them difficult to store and transfer. Of the many algorithms proposed for the compression of ECG signals, most rely on the processing of data as discrete numerical values. The alternative approach followed in this study is to model the signal compression problem into a regression problem and then convert it into a text compression problem. Using this approach, the paper presents a new genetic programming based method for the compression of ECG signals. The proposed method starts with denoising and smoothing the ECG signal with discrete wavelet transform and then constructing its mathematical model with a genetic programming based algorithm. This model is a piecewise mathematical function where each sub-function models one part of the signal. Next, the model is converted to a character string and regular expressions are used to extract the function coefficients and encode the symbols contained in the string. Finally, the strings and coefficients are compressed using the LZW and arithmetic encoding methods, respectively. The efficiency of the algorithm is evaluated through compression ratio (CR), percent root-mean-square difference (PRD), root-mean-square-error (RMSE) and quality score (QS) on MIT-BIH Arrhythmia Database records. The evaluation results demonstrate the good performance of the proposed method in comparison with other state-of-the-art techniques", } @InProceedings{DBLP:conf/webi/FelinT21, author = "Remi Felin and Andrea G. B. Tettamanzi", editor = "Jing He and Rainer Unland and Eugene {Santos, Jr.} and Xiaohui Tao and Hemant Purohit and Willem-Jan {van den Heuvel} and John Yearwood and Jie Cao", title = "Using Grammar-Based Genetic Programming for Mining Subsumption Axioms Involving Complex Class Expressions", booktitle = "{WI-IAT} '21: {IEEE/WIC/ACM} International Conference on Web Intelligence, Melbourne {VIC} Australia, December 14 - 17, 2021", pages = "234--240", publisher = "{ACM}", year = "2021", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1145/3486622.3494025", DOI = "doi:10.1145/3486622.3494025", timestamp = "Sun, 02 Oct 2022 01:00:00 +0200", biburl = "https://dblp.org/rec/conf/webi/FelinT21.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{Felin:2024:EuroGP, author = "Remi Felin and Pierre Monnin and Catherine Faron and Andrea G. B. Tettamanzi", editor = "Mario Giacobini and Bing Xue and Luca Manzoni", title = "An Algorithm Based on Grammatical Evolution for Discovering SHACL Constraints", booktitle = "EuroGP 2024: Proceedings of the 27th European Conference on Genetic Programming", year = "2024", volume = "14631", series = "LNCS", publisher = "Springer", address = "Aberystwyth", month = "3-5 " # apr, organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", pages = "176--191", abstract = "The continuous evolution of heterogeneous RDF data has led to an increase of inconsistencies on the Web of data (i.e. missing data and errors) that we assume to be inherent to RDF data graphs. To improve their quality, the W3C recommendation SHACL allows to express various constraints that RDF data must conform to and detect nodes violating them. However, acquiring representative and meaningful SHACL constraints from complex and very large RDF data graphs is very challenging and tedious. Consequently, several recent works focus on the automatic generation of these constraints. We propose an approach based on grammatical evolution (GE) for extracting representative SHACL constraints by mining an RDF data graph. This approach uses a probabilistic SHACL validation framework to consider the inherent errors in RDF data. The results highlight the relevance of this approach in discovering SHACL shapes inspired by association rule patterns from a real-world RDF data graph.", isbn13 = "978-3-031-56957-9", DOI = "doi:10.1007/978-3-031-56957-9_11", notes = "Part of \cite{Giacobini:2024:GP} EuroGP'2024 held in conjunction with EvoCOP2024, EvoMusArt2024 and EvoApplications2024", } @Article{FelipeGallon:2018:ieeeLAT, author = "Igor {Felipe Gallon} and Denis {Pereira Lima} and Emerson {Carlos Pedrino}", journal = "IEEE Latin America Transactions", title = "{ASCGP} - Automatic System for Construction of Logical Circuits in {FPGA} using {CGP}", year = "2018", volume = "16", number = "7", pages = "1843--1848", month = jul, keywords = "genetic algorithms, genetic programming, cartesian genetic programming, logical circuits, FPGA, flexible hardware, sequential circuits, automatic system", ISSN = "1548-0992", DOI = "doi:10.1109/TLA.2018.8447347", size = "6 pages", abstract = "we introduce a framework for the development of an automatic system using a Cartesian Genetic Programming based approach to construct combinational and sequential logic circuits in FPGAs. The paper is comprised of two parts: the first one is based on a hybrid evolutionary algorithm that by means of parameters provided by the user looks for a logical circuit solution, and the second one is a flexible architecture developed in Verilog-HDL language that converts the solution given in the first part into a hardware implementation inside the FPGA. Good results using this hybrid evolutionary approach have been obtained in all the studied cases in relation to others similar studies in the literature. Our training speedup was about 7x. In addition, the generated hardware is able to manage sequential circuits, being this an innovation of the present project in relation to other projects found in the literature, in which almost all of them can only simulate the automatic generation of combinational circuits.", notes = "Also known as \cite{8447347}", } @InProceedings{Felix:2019:MEDICON, author = "Leonardo Bonato Felix and Quenaz Bezerra Soares and Antonio Mauricio Ferreira Leite {Miranda de Sa} and David Martin Simpson", title = "Combining objective response detectors using genetic programming", booktitle = "XV Mediterranean Conference on Medical and Biological Engineering and Computing, MEDICON 2019", year = "2019", editor = "Jorge Henriques and Nuno Neves and Paulo {de Carvalho}", volume = "76", pages = "83--92", series = "IFMBE Proceedings", address = "Coimbra, Portugal", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-030-31635-8", URL = "https://eprints.soton.ac.uk/437818/", broken = "https://eprints.soton.ac.uk/437818/1/Combining_objective_response_detectors_using_genetic_programming_VF.pdf", DOI = "doi:10.1007/978-3-030-31635-8_10", abstract = "Many Objective Response Detectors (ORD) have been proposed based on ratios extracted from statistical methods. This work proposes a new approach to automatically generate ORD techniques, based on the combination of the ex-isting ones by genetic programming. In this first study of this kind, the best ORD functions obtained with this approach were about 4percent more sensitive than the best original ORD. It is concluded that genetic programming applied to create ORD functions has a potential to find non-obvious functions with better performances than established alternatives", bibsource = "OAI-PMH server at eprints.soton.ac.uk", contributor = "Jorge Henriques and Nuno Neves and Paulo de Carvalho", format = "text", language = "en; English", oai = "oai:eprints.soton.ac.uk:437818", type = "Conference or Workshop Item; NonPeerReviewed", } @Article{Felton:2000:MDD, author = "Michael J. Felton", title = "Survival of the Fittest in Drug Design", journal = "Modern Drug Discovery", year = "2000", volume = "3", number = "9", pages = "49--50", month = nov # "/" # dec, publisher = "American Chemical Society", keywords = "genetic algorithms, genetic programming", ISSN = "1532-4486", URL = "http://pubs.acs.org/subscribe/journals/mdd/v03/i09/html/felton.html", size = "2 pages", abstract = "One of the cornerstones is the use of genetic algorithms in producing new molecules.", notes = "magazine", } @InProceedings{Femia:2016:CEC, author = "Nicola Femia and Mario Migliaro and Antonio {Della Cioppa}", title = "A Genetic Programming approach to modeling power losses of Insulate Gate Bipolar Transistors", booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016)", year = "2016", editor = "Yew-Soon Ong", pages = "4705--4712", address = "Vancouver", month = "24-29 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-5090-0623-6", DOI = "doi:10.1109/CEC.2016.7744391", abstract = "In high-power-density power electronics application, it's important to be able to predict the power losses of semiconductor devices in order to maximize global system efficiency and to avoid thermal damages of the components. In this paper a novel approach to model the power losses of Insulate Gate Bipolar Transistors (IGBT) in Induction Cooking (IC) application is proposed. The inherent lack of precise physical IGBT loss model and the uncertainty of load in IC application has stimulated the idea to identify system-level behavioural power loss models that allow to cover a variety of devices and load conditions. For this goal, a Genetic Programming approach has been adopted, that starts from measured electrical quantities and returns a set of models, each one with the same structure but with different parameters relevant to the device under test. The models generated by the proposed method based on a training set of case studies have been merged into a generalized model and verified through a validation set.", notes = "WCCI2016", } @InProceedings{Femia:2016:SMACD, author = "N. Femia and M. Migliaro and C. Pastore and D. Toledo", booktitle = "2016 13th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD)", title = "In-system IGBT power loss behavioral modeling", year = "2016", abstract = "In high-power-density power electronics applications, it is important to predict the power losses of semiconductor devices in order to maximize global system efficiency and avoid thermal damages of the components. When different effects influence the power losses, some of which difficult to be physically modelled, it is worthwhile to use empirical laws obtained starting from experimental data, like the Steinmetz's equation widely used for inductors' magnetic core losses prediction. This paper discusses a method to find empirical power loss models by using Genetic Programming (GP). In particular, the GP approach has been applied to identify power losses in Insulated Gate Bipolar Transistors for Induction Cooking application. A loss model has been obtained using an experimental training set, and the result has been successively validated.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SMACD.2016.7520723", month = jun, notes = "Also known as \cite{7520723}", } @InProceedings{feng:2024:CEC, author = "Chang-Tao Feng and Ya-Hui Jia and Qiang Yang and Wei-Neng Chen and Huaiguang Jiang", title = "A Bilevel Hybrid Genetic Algorithm for Capacitated Electric Vehicle Routing Problem", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, Vehicle routing, Sociology, Focusing, Evolutionary computation, Routing, Search problems, Genetics, Hybrid genetic algorithm, local search, capac-itated electric vehicle routing problem, combinatorial optimization", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10611987", abstract = "As electric vehicles become more prevalent, a novel vehicle routing problem (VRP) has emerged, known as the capacitated electric VRP (CEVRP). CEVRP requires determining not only the service order of customers but also the charging plans for vehicles, thereby increasing the complexity of solution construction. In response to this challenge, we propose a bilevel hybrid genetic algorithm (BHGA). BHGA models CEVRP as two levels of subproblem: 1) the upper level capacitated VRP, focusing on the service order and 2) the lower level fixed route vehicle charging problem, focusing on the charging plans. In dealing with the upper level subproblem, the hybrid genetic search algorithm is adopted to construct the routes to visit customers and an advanced screening strategy is proposed to optimise the local search process and effectively guide the evolution of population. For the lower level subproblem, an efficient heuristic method called focus enumeration is designed, which is specifically used to insert charging stations into routes to ensure battery constraint. The collaboration of the advanced screening strategy and the focus enumeration assists in more unified solving of the two subproblems. The experiments show that BHGA significantly surpasses state-of-the-art algorithms on benchmark instances and has successfully updated eleven best known solutions, demonstrating its outstanding performance.", notes = "also known as \cite{10611987} Is this GP? WCCI 2024", } @Article{Feng:2024:pmcj, author = "JiaYi Feng and Lang Li and LiuYan Yan and ChuTian Deng", title = "{INLEC:} An involutive and low energy lightweight block cipher for internet of things", journal = "Pervasive and Mobile Computing", year = "2024", volume = "105", pages = "101991", keywords = "genetic algorithms, genetic programming, Lightweight block cipher, Substitution Permutation Networks, Involutive S-box, Permutation, Low energy", ISSN = "1574-1192", URL = "https://www.sciencedirect.com/science/article/pii/S1574119224001160", DOI = "doi:10.1016/j.pmcj.2024.101991", abstract = "The Internet of Things (IoT) has emerged as a pivotal force in the global technological revolution and industrial transformation. Despite its advancements, IoT devices continue to face significant security challenges, particularly during data transmission, and are often constrained by limited battery life and energy resources. To address these challenges, a low energy lightweight block cipher (INLEC) is proposed to mitigate data leakage in IoT devices. In addition, the Structure and Components INvolution (SCIN) design is introduced. It is constructed using two similar round functions to achieve front-back symmetry. This design ensures coherence throughout the INLEC encryption and decryption processes and addresses the increased resource consumption during the decryption phase in Substitution Permutation Networks (SPN). Furthermore, a low area S-box is generated through a hardware gate-level circuit search method combined with Genetic Programming (GP). This optimisation leads to a 47.02percent reduction in area compared to the S0 of Midori, using UMC 0.18mum technology. Moreover, a chaotic function is used to generate the optimal nibble-based involutive permutation, further enhancing its efficiency. In terms of performs, the energy consumption for both encryption and decryption with INLEC is 6.88 muJ/bit, representing 25.21percent reduction compared to Midori. Finally, INLEC is implemented using STM32L475 PanDuoLa and Nexys A7 FPGA development boards, establishing an encryption platform for IoT devices. This platform provides functions for data acquisition, transmission, and encryption", } @Article{journals/soco/FengOCC16, author = "Liang Feng and Yew-Soon Ong and Caishun Chen and Xianshun Chen", title = "Conceptual modeling of evolvable local searches in memetic algorithms using linear genetic programming: a case study on capacitated vehicle routing problem", journal = "Soft Computing", year = "2016", number = "9", volume = "20", pages = "3745--3769", keywords = "genetic algorithms, genetic programming, Memetic computation, Individual learning, Linear genetic programming, Adaptive memetic algorithms, Vehicle routing problems", bibdate = "2017-05-20", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/soco/soco20.html#FengOCC16", DOI = "doi:10.1007/s00500-015-1971-3", abstract = "This paper presents a study on the conceptual modelling of memetic algorithm with evolvable local search in the form of linear programs, self-assembled by linear genetic programming based evolution. In particular, the linear program structure for local search and the associated local search self-assembling process in the lifetime learning process of memetic algorithm are proposed. Results showed that the memetic algorithm with evolvable local search provides a means of creating highly robust, self-configuring and scalable algorithms, thus generating improved or competitive results when benchmarking against several existing adaptive or human-designed state-of-the-art memetic algorithms and meta-heuristics, on a plethora of capacitated vehicle routing problem sets considered.", } @InProceedings{Feng:2017:PHM-Harbin, author = "Qi Feng and Haowei Lian and Jindong Zhu", booktitle = "2017 Prognostics and System Health Management Conference (PHM-Harbin)", title = "Multi-level genetic programming for fault data clustering", year = "2017", abstract = "Artificial intelligence theory is extensively employed in fault diagnosis, as the frequently used technologies, expert system and neural network, have their inherent disadvantages that have poor expansibility and unknown black box structure. Genetic Programming (GP), an improved evolution algorithm based on Genetic Algorithm (GA), could offset these insufficient for its explicit structure. Combining the main idea of hierarchical clustering, a new method based on GP is proposed. In this method, the multi-cluster problem is divided to many two-cluster problems, and GP serves as a classifier in two-cluster problem. Generally, the multi-level genetic programming classifier is expected to simplify the structure and improve the expansibility of classifier, and its effectiveness is proved in simulation experiment.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/PHM.2017.8079204", month = jul, notes = "odd PDF Also known as \cite{8079204}", } @InProceedings{Feng:2017:CCC, author = "Qi Feng and Haowei Lian and Jindong Zhu", booktitle = "2017 36th Chinese Control Conference (CCC)", title = "Model predictive control of nonlinear dynamical systems based on genetic programming", year = "2017", pages = "4540--4545", month = "26-28 " # jul, address = "Dalin, China", size = "5 pages", keywords = "genetic algorithms, genetic programming, model predictive control, unknown nonlinear systems, neural network", DOI = "doi:10.23919/ChiCC.2017.8028072", size = "6 pages", abstract = "Model predictive control (MPC) requires an explicit dynamic model to predict values of the output variable, so the accuracy of the model significantly affects the quality of control. Unfortunately, it's hard to obtain the explicit expression of unknown nonlinear systems in MPC applications. This paper describes the use of genetic programming (GP) to generate an empirical dynamic model of a process, and to improve the performance in providing accuracy and suitability support for MPC strategy. GP derives both a model structure and its parameter values in such a way that the process trajectory is predicted accurately. Consequently, the performance of the MPC strategy is expected to improve on the performance obtained models. Experimental results show that the GP based predictive controller can obtain satisfactory performance.", notes = "Also known as \cite{8028072}", } @InProceedings{Feng:2018:CCC, author = "Qi Feng and Baoxi Guo and Jindong Zhu", title = "Trajectory Fitting of Aerial Bomb Based on Combination of Genetic Programming and ANT Colony Optimization", booktitle = "2018 37th Chinese Control Conference (CCC)", year = "2018", pages = "4843--4848", abstract = "A new integrated genetic programming (GP) and ant colony optimization (ACO) approach for bomb trajectory fitting was proposed. First, bomb dynamics simulation is performed to generate training data for the GP model. On the basis of mapping Input-output training of these trajectory data and launching initial value by genetic programming (GP), preliminary fitting analytic expression was obtained. And then, optimize the formula parameters obtained by GP using the Ant Colony Optimization (ACO). A large number of checking calculation shows that the GP-ACO fitting method has clear physical relations and high precision, and can fast calculating.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.23919/ChiCC.2018.8482986", ISSN = "1934-1768", month = jul, notes = "School of Electronic Information, Northwestern Polytechnic University, Xi'an, 710072, China Also known as \cite{8482986}", } @InProceedings{Feng:2021:CCC, author = "Qi Feng and Jianyu Yu", title = "Research on UAV Adaptive Control Method Based on Genetic Programming", booktitle = "2021 40th Chinese Control Conference (CCC)", year = "2021", pages = "2150--2154", abstract = "In order to improve the non-linear PID control effect of a small unmanned aerial vehicle (UAV) flight, an adaptive PID height controller based on genetic programming is proposed. Firstly, the structure of the PID controller is introduced and the GP algorithm is applied in view of its characteristics of clear mapping relationship and strong non-linear fitting ability. The flight state parameters and the optimal control parameters are taken as the sample data of input and output respectively, and the intuitive functional relationship between the flight state parameters of UAV and the PID control parameters is obtained. Finally, the online adaptive tuning of the control parameters is realized. The simulation results show that the proposed PID neural network controller has faster response, smaller overshoot, higher precision, better robustness and stronger adaptive ability than the traditional PID controller, which can meet the requirements of autonomous flight.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.23919/CCC52363.2021.9550680", ISSN = "1934-1768", month = jul, notes = "Also known as \cite{9550680}", } @InProceedings{XiaobingFeng:2011:ICFCSE, author = "Xiaobing Feng", title = "Forecasting the RMB Exchange Regime", booktitle = "International Conference on Future Computer Science and Education (ICFCSE 2011)", year = "2011", month = aug, pages = "633--636", size = "4 pages", abstract = "To resolve the slow convergence and local minimum problem of BP network, an exchange rate forecast method based on Radial Basis Function Neural Network (RBFNN) is proposed. Data on economic variables is normalised, and then is put into the RBFNN in training. Corresponding parameters are got and then the exchange rate is predicted. Detailed simulation results and comparisons with Back-Propagation (BP) network show that, the operation speed of the method is faster and the forecast accuracy is higher than the traditional BP neural network can be achieved obviously. We then use genetic programming approach to achieve a better outcome compared with ANN.", keywords = "genetic algorithms, genetic programming, BP network, RMB exchange regime forecasting, back-propagation network, exchange rate forecast method, genetic programming approach, radial basis function neural network, backpropagation, exchange rates, radial basis function networks", DOI = "doi:10.1109/ICFCSE.2011.158", notes = "page635 'Thus GP outperforms ANN.' Also known as \cite{6041775}", } @InProceedings{feng:2011:AEM, author = "Xiaobing Feng", title = "Forecasting the {RMB} Exchange Regime Using Genetic Programming Approach", booktitle = "Advances in Education and Management", year = "2011", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-642-23062-2_74", DOI = "doi:10.1007/978-3-642-23062-2_74", } @Article{FENG2004750, author = "Xia-Ting Feng and J. A. Hudson and Shaojun Li and Hongbo Zhao and Wei Gao and Youliang Zhang", title = "Integrated intelligent methodology for Large-scale landslide prevention design", journal = "International Journal of Rock Mechanics and Mining Sciences", year = "2004", volume = "41", number = "Supplement 1", pages = "750--755", month = may, note = "Proceedings of the ISRM SINOROCK 2004 Symposium", keywords = "genetic algorithms, genetic programming, Landslide, intelligent design, GIS, back analysis, optimum design, support vector machine, SVM", ISSN = "1365-1609", URL = "http://www.sciencedirect.com/science/article/pii/S1365160904001777", DOI = "doi:10.1016/j.ijrmms.2004.03.130", abstract = "Considering the non-linear mechanical problem affected by many factors, integration of intelligent and global optimum methods with certain mechanic al calculations is attractive for analysis and control design of landslides. An integrated intelligent methodology is proposed for optimal prevention design of large-scale landslides. With this methodology, long-term monitoring data of landslide evolution, rainfall process, excavation process, change of underground water, and geological conditions, etc. are displayed in a three-dimensional geological information system. Geomechanical parameters of rock mass/soils and trace of the landslide are recognized using a combination of genetic algorithm and genetic programming, evolutionary support vector machine, and limited equilibrium analysis. Optimal design of landslide prevention is achieved by using evolutionary neural networks-limited equilibrium analysis. As an example, the integrated intelligent design prevention is applied to the Bachimen large landslide, Fujian, China induced due to the excavation of motorway. The results are satisfactory.", notes = "Paper 3A 06 sinorock", } @Article{Feng:2006:IJRMMS, author = "Xia-Ting Feng and Bing-Rui Chen and Chengxiang Yang and Hui Zhou and Xiuli Ding", title = "Identification of visco-elastic models for rocks using genetic programming coupled with the modified particle swarm optimization algorithm", journal = "International Journal of Rock Mechanics and Mining Sciences", year = "2006", volume = "43", number = "5", pages = "789--801", month = jul, keywords = "genetic algorithms, genetic programming, Visco-elastic models, Rock, Evolutionary algorithm, Particle swarm optimisation", DOI = "doi:10.1016/j.ijrmms.2005.12.010", abstract = "The response of rocks to stress can be highly non-linear, so sometimes it is difficult to establish a suitable constitutive model using traditional mechanics methods. It is appropriate, therefore, to consider modelling methods developed in other fields in order to provide adequate models for rock behaviour, and this particularly applies to the time-dependent behavior of rock. Accordingly, a new system identification method, based on a hybrid genetic programming with the improved particle swarm optimization (PSO) algorithm, for the simultaneous establishment of a visco-elastic rock material model structure and the related parameters is proposed. The method searches for the optimal model, not among several known models as in previous methods proposed in the literatures, but in the whole model space made up of elastic and viscous elementary components. Genetic programming is used for exploring the model's structure and the modified PSO is used to identify parameters (coefficients) in the provisional model. The evolution of the provisional models (individuals) is driven by the fitness based on the residual sum of squares of the behaviour predicted by the model and the actual behaviour of the rock given by a set of mechanical experiments. Using this proposed algorithm, visco-elastic models for the celadon argillaceous rock and fuchsia argillaceous rock in the Goupitan hydroelectric power station, China, are identified. The results show that the algorithm is feasible for rock mechanics use and has a useful ability in finding potential models. The algorithm enables the identification of models and parameters simultaneously and provides a new method for studying the mechanical characteristics of visco-elastic rocks.", notes = "a Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China b School of Resources and Civil Engineering, Northeastern University, Shenyang 110004, China c Yangtze River Scientific Research Institute, Wuhan 430010, China", } @InProceedings{Feng:2012:CEC, title = "Evolving Frame Splitters by Genetic Programming", author = "Xie Feng and Andy Song", pages = "1466--1472", booktitle = "Proceedings of the 2012 IEEE Congress on Evolutionary Computation", year = "2012", editor = "Xiaodong Li", month = "10-15 " # jun, DOI = "doi:10.1109/CEC.2012.6256161", address = "Brisbane, Australia", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Evolutionary Computer Vision", abstract = "This paper extends the application of Genetic Programming into a new area, automatically splitting video frames based on the content. A GP methodology is presented to show how to evolve a program which can analyse the difference between scenes and split them accordingly. A few different approaches have been investigated in this study. Compared with human written video splitting programs, GP generated splitters are more accurate. Moreover, it is shown that these video splitting programs have high tolerance to noises. They can still achieve reasonable performance even when the videos are not easily recognisable by eyes due to the server artificial noises.", notes = "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the EPS and the IET.", } @InProceedings{Feng:2013:CEC, article_id = "1691", author = "Xie Feng and Alexandra Uitdenbogerd and Andy Song", title = "Detecting PCB Component Placement Defects by Genetic Programming", booktitle = "2013 IEEE Conference on Evolutionary Computation", volume = "1", year = "2013", month = jun # " 20-23", editor = "Luis Gerardo {de la Fraga}", pages = "1138--1145", address = "Cancun, Mexico", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-0453-2", DOI = "doi:10.1109/CEC.2013.6557694", notes = "CEC 2013 - A joint meeting of the IEEE, the EPS and the IET.", } @InProceedings{Feng:2010:IEEC, author = "Yanghe Feng and Chaofan Dai and Jianmai Shi and Liang Mu", title = "An Automatic Model Selection Algorithm Based Genetic Programming", booktitle = "2nd International Symposium on Information Engineering and Electronic Commerce (IEEC 2010)", year = "2010", month = jul, abstract = "The usability of model-aided decision relies on intellectualized level of model selection. An algorithm of Model selection based sample data is proposed in the paper. The meta-models are classified by characters of the sample data, and the assembled models are built as tree format. The genetic operations are performed under several restrictions to provide the model selection scheme. Its process hardly depends on user's knowledge on domain.", keywords = "genetic algorithms, genetic programming, automatic model selection algorithm, genetic operations, metamodels, model-aided decision usability, metacomputing", DOI = "doi:10.1109/IEEC.2010.5533243", notes = "Also known as \cite{5533243}", } @InProceedings{fenton:2013:USNCCM12, author = "Michael Fenton and Ciaran McNally and Michael O'Neill", title = "The Interaction between Objectives and Constraints in Evolutionary Structural Engineering Optimisation", booktitle = "12th U.S. National Congress on Computational Mechanics (USNCCM12)", year = "2013", editor = "John Dolbow and Murthy Guddati", address = "Raleigh, North Carolina, USA", month = "22-25 " # jul, organization = "U.S. Association for Computational Mechanics", keywords = "genetic algorithms, genetic programming", abstract = "Selection of appropriate techniques for handling different constraints is a key part of evolutionary optimisation in all disciplines. This also applies to the field of Evolutionary Structural Engineering Optimisation where multiple conflicting constraints are present. These constraints include standard engineering parameters such as stress, strain, deflection, buckling, and weight; they can however also include more complex constraints such as an accurate estimate of the cost of the structure or a subjective assessment of the architectural form. The selection of appropriate functions for these constraints, and the subsequent management of these parameters is a crucial part of the evolutionary process. Structural engineering optimisation will often require the designer to satisfy multiple parallel objectives, and there may be overlaps between both constraints and objectives. Understanding the interaction between these constraints and the overall individual fitness will therefore have a significant impact on the quality of the designs produced. As such, a key challenge for designers when using evolutionary approaches is to find an accurate metric that will allow the designer to: a) judge individual constraints, and b) transform the performance of the individual (relative to those constraints) into a single coherent value for use by the fitness function. The effect of differing constraints on the overall population evolution is noteworthy. It is shown that the addition of more constraints does not necessarily reduce the search space or improve the final population, but can help to guide the search process where the search space is very large. The differences between varying degrees of hard and soft constraints are discussed, as are the implications of their use in different scenarios. The most appropriate methods of applying a costing constraint to a structure are discussed, and recommendations are made for which method to use. Finally, the merits of both single-objective and multiple-objective optimisation for evolutionary structural engineering optimisation are compared and contrasted.", notes = "USNCCM12 is co-hosted by Duke University and North Carolina State University. Other participating institutions include Khalifa University of Science Technology and Research (KUSTAR) and Army Research Office, Statistical and Applied Mathematical Sciences Institute (SAMSI). http://12.usnccm.org/ http://12.usnccm.org/technical-program", } @Article{Fenton:2014:AC, author = "Michael Fenton and Ciaran McNally and Jonathan Byrne and Erik Hemberg and James McDermott and Michael O'Neill", title = "Automatic innovative truss design using grammatical evolution", journal = "Automation in Construction", year = "2014", volume = "39", pages = "59--69", note = "Bronze Humie winner", keywords = "genetic algorithms, genetic programming, Grammatical evolution, Structural optimisation, Evolutionary computation, Truss design, Computer aided design", ISSN = "0926-5805", URL = "http://www.sciencedirect.com/science/article/pii/S0926580513002124", URL = "http://www.human-competitive.org/sites/default/files/fenton-paper-from-2014-for-background.pdf", URL = "http://www.human-competitive.org/sites/default/files/fenton-text.txt", DOI = "doi:10.1016/j.autcon.2013.11.009", size = "11 pages", abstract = "Truss optimization in the field of Structural Engineering is a growing discipline. The application of Grammatical Evolution, a grammar-based form of Genetic Programming (GP), has shown that it is capable of generating innovative engineering designs. Existing truss optimization methods in GP focus primarily on optimizing global topology. The standard method is to explore the search space while seeking minimum cross-sectional areas for all elements. In doing so, critical knowledge of section geometry and orientation is omitted, leading to inaccurate stress calculations and structures not meeting codes of practice. This can be addressed by constraining the optimisation method to only use standard construction elements. The aim of this paper is not to find fully optimized solutions, but rather to show that solutions very close to the theoretical optimum can be achieved using real-world elements. This methodology can be applied to any structural engineering design which can be generated by a grammar.", notes = "Humies http://www.human-competitive.org/awards https://pbs.twimg.com/media/DFHp6iTXkAQO613.jpg", } @PhdThesis{fenton:phdthesis, author = "Michael Fenton", title = "Truss Design and Optimisation Using Grammatical Evolution", school = "School of Civil, Structural and Environmental Engineering, University College Dublin", year = "2015", address = "Ireland", month = apr, keywords = "genetic algorithms, genetic programming, grammatical evolution, DO-GE, structural engineering, truss optimisation, SLFFEA, SEOIGE, Michell number, Delaunay Triangulation", URL = "http://ncra.ucd.ie/papers/FentonPhD.pdf", size = "218 pages", abstract = "Truss optimisation in the field of Structural Engineering is an ever growing subject. The field can be divided into two main disciplines, continuum and discrete topology optimisation. Continuum topology optimisation methods represent the current state of the art in engineering design optimisation. However, in large scale civil and structural engineering projects it is currently prohibitively expensive and difficult to manufacture solid structures fully optimised using these techniques due to the current limits of both computational power and manufacturing capabilities. At present, discrete beam structure optimisation methods remain more appropriate for larger scale designs, as they allow regular elements and construction methods to be used. This leads to savings in cost and weight over traditional construction methods. Existing discrete truss optimisation methods focus primarily on optimising global topology using a ground structure approach, with all possible node and beam locations being specified a priori and the algorithm selecting the most appropriate configuration from the given options. The standard method is to explore this search space, while seeking minimum cross-sectional areas for all elements in order to reduce the self-weight of the structure. In doing so, critical knowledge of section geometry and orientation is omitted. This leads to inaccurate stress calculations and structures failing to meet codes of practice. These issues can be addressed by constraining the optimisation method to only use standard construction elements. It is shown in this thesis that solutions close to the theoretical optimum can be achieved using commercially available elements. The classical ground structure discrete optimisation method has furthermore been shown to be inherently restrictive, as it severely limits the representation space to what is explicitly defined; a larger representation space can more effectively navigate through the search space. However, a larger representation space can potentially lead to difficulties in evolving any fit solution. Unfit individuals must be handled carefully in order to successfully evolve any fit solution in early generations. It is therefore imperative to design the fitness function in such a way as to minimise the risk of the algorithm becoming stuck in a local optimum, before a single fit solution has been evolved. The application of Grammatical Evolution (GE), a grammar-based form of Genetic Programming (GP), has shown that it is not only capable of generating innovative engineering designs, but that the recursive properties of formal grammars allows GE to define its own node locations for any number of nodes within a pre-specified design envelope, thereby vastly increasing its representation capabilities. Nodes are then connected via a Delaunay triangulation algorithm, leading to fully triangulated, kinematically stable structures. The net result is that discrete beam-truss structures can be optimised in a continuum manner, in a black-box fashion, without the need to know any information about the problem other than the design envelope. Existing discrete optimisation techniques are compared and contrasted, and notable savings in structure self-weight are demonstrated over traditional methods.", notes = "Student Number: 05589584 Supervisor: Ciaran McNally", } @InProceedings{fenton:cec2015, author = "Michael Fenton and David Lynch and Stepan Kucera and Holger Claussen and Michael O'Neill", title = "Load Balancing in Heterogeneous Networks using Grammatical Evolution", booktitle = "Proceedings of 2015 IEEE Congress on Evolutionary Computation (CEC 2015)", editor = "Yadahiko Murata", pages = "70--76", year = "2015", address = "Sendai, Japan", month = "25-28 " # may, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, grammatical evolution", DOI = "doi:10.1109/CEC.2015.7256876", abstract = "Grammatical Evolution (GE) is applied to the problem of load balancing in heterogeneous cellular network deployments (HetNets). HetNets are multi-tiered cellular networks for which load balancing is a scalable means to maximise network capacity, assuming similar traffic from all users. This paper describes a proof of concept study in which GE is used in a genetic algorithm-like way to evolve constants which represent cell power and selection bias in order to achieve load balancing in HetNets. A fitness metric is derived to achieve load balancing both locally in sectors and globally across tiers. Initial results show promise for GE as a heuristic for load balancing. This finding motivates a more sophisticated grammar to bring enhanced Inter-Cell Interference Coordination optimisation into an evolutionary framework.", notes = "1545 hrs 15434 CEC2015", } @InProceedings{conf/evoW/FentonLKCO16, author = "Michael Fenton and David Lynch and Stepan Kucera and Holger Claussen and Michael O'Neill", title = "Evolving Coverage Optimisation Functions for Heterogeneous Networks Using Grammatical Genetic Programming", booktitle = "19th European Conference on Applications of Evolutionary Computation, EvoApplications 2016", year = "2016", editor = "Giovanni Squillero and Paolo Burelli", volume = "9597", series = "Lecture Notes in Computer Science", pages = "219--234", address = "Porto, Portugal", month = mar # " 30 -- " # apr # " 1", organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", bibdate = "2016-03-23", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/evoW/evoappl2016-1.html#FentonLKCO16", isbn13 = "978-3-319-31204-0", DOI = "doi:10.1007/978-3-319-31204-0_15", abstract = "Heterogeneous Cellular Networks are multi-tiered cellular networks comprised of Macro Cells and Small Cells in which all cells occupy the same bandwidth. User Equipments greedily attach to whichever cell provides the best signal strength. While Macro Cells are invariant, the power and selection bias for each Small Cell can be increased or decreased (subject to pre-defined limits) such that more or fewer UEs attach to that cell. Setting optimal power and selection bias levels for Small Cells is key for good network performance. The application of Genetic Programming techniques has been proven to produce good results in the control of Heterogenous Networks. Expanding on previous works, this paper uses grammatical GP to evolve distributed control functions for Small Cells in order to vary their power and bias settings. The objective of these control functions is to evolve control functions that maximise a proportional fair utility of UE throughputs.", notes = "EvoApplications2016 held inconjunction with EuroGP'2016, EvoCOP2016 and EvoMUSART 2016", } @Article{Fenton:2016:ieeeTEC, author = "Michael Fenton and Ciaran McNally and Jonathan Byrne and Erik Hemberg and James McDermott and Michael O'Neill", title = "Discrete Planar Truss Optimization by Node Position Variation using Grammatical Evolution", journal = "IEEE Transactions on Evolutionary Computation", year = "2016", volume = "20", number = "4", pages = "577--589", month = aug, keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Civil Engineering, Computational Intelligence, Evolutionary Computation, Structural Engineering", ISSN = "1089-778X", URL = "http://www.human-competitive.org/sites/default/files/fenton-paper.pdf", URL = "http://www.human-competitive.org/sites/default/files/fenton-text.txt", DOI = "doi:10.1109/TEVC.2015.2502841", size = "13 pages", abstract = "The majority of existing discrete truss optimization methods focus primarily on optimizing global truss topology using a ground structure approach, in which all possible node and beam locations are specified a priori. The ground structure discrete optimization method has been shown to be restrictive as it limits derivable solutions to what is explicitly defined. Greater representational freedom can improve performance. In this paper Grammatical Evolution is applied. It can represent a variable number of nodes and their locations on a continuum. A novel method of connecting evolved nodes using a Delaunay triangulation algorithm shows that fully triangulated, kinematically stable structures can be generated. Discrete beamtruss structures can be optimized without the need for any information about the desired form of the solution other than the design envelope. Our technique is compared to existing discrete optimization techniques, and notable savings in structure self weight are demonstrated. In particular our new method can produce results superior to those reported in the literature in cases where the problem is ill-defined and the structure of the solution is not known a priori.", notes = "Entered 2017 Humies http://www.human-competitive.org/awards, Also known as \cite{7335624}", } @Misc{DBLP:journals/corr/FentonMFFOH17, author = "Michael Fenton and James McDermott and David Fagan and Stefan Forstenlechner and Michael O'Neill and Erik Hemberg", title = "{PonyGE2}: Grammatical Evolution in Python", howpublished = "arXiv", volume = "abs/1703.08535", year = "2017", month = "26 " # apr, keywords = "genetic algorithms, genetic programming, Grammatical Evolution", URL = "http://arxiv.org/abs/1703.08535", DOI = "doi:10.1145/3067695.3082469", timestamp = "Mon, 03 Apr 2017 12:41:34 +0200", biburl = "http://dblp.uni-trier.de/rec/bib/journals/corr/FentonMFFOH17", bibsource = "dblp computer science bibliography, http://dblp.org", size = "13 pages", abstract = "Grammatical Evolution (GE) is a population-based evolutionary algorithm, where a formal grammar is used in the genotype to phenotype mapping process. PonyGE2 is an open source implementation of GE in Python, developed at UCD's Natural Computing Research and Applications group. It is intended as an advertisement and a starting-point for those new to GE, a reference for students and researchers, a rapid-prototyping medium for our own experiments, and a Python workout. As well as providing the characteristic genotype to phenotype mapping of GE, a search algorithm engine is also provided. A number of sample problems and tutorials on how to use and adapt PonyGE2 have been developed.", notes = "https://github.com/jmmcd/PonyGE2 Proceedings of GECCO '17 Companion, Berlin, Germany, July 15-19, 2017, 8 pages", } @Article{Fenton:ieeeTCyB, author = "Michael Fenton and David Lynch and Stepan Kucera and Holger Claussen and Michael O'Neill", title = "Multilayer Optimization of Heterogeneous Networks Using Grammatical Genetic Programming", journal = "IEEE Transactions on Cybernetics", year = "2017", volume = "47", number = "9", pages = "2938--2950", month = sep, keywords = "genetic algorithms, genetic programming, grammatical evolution, Evolutionary computation, wireless communications networks", ISSN = "2168-2267", URL = "http://ieeexplore.ieee.org/abstract/document/7893786/", DOI = "doi:10.1109/TCYB.2017.2688280", size = "13 pages", abstract = "Heterogeneous cellular networks are composed of macro cells (MCs) and small cells (SCs) in which all cells occupy the same bandwidth. Provision has been made under the third generation partnership project-long term evolution framework for enhanced intercell interference coordination (eICIC) between cell tiers. Expanding on previous works, this paper instruments grammatical genetic programming to evolve control heuristics for heterogeneous networks. Three aspects of the eICIC framework are addressed including setting SC powers and selection biases, MC duty cycles, and scheduling of user equipments (UEs) at SCs. The evolved heuristics yield minimum downlink rates three times higher than a baseline method, and twice that of a state-of-the-art benchmark. Furthermore, a greater number of UEs receive transmissions under the proposed scheme than in either the baseline or benchmark cases.", notes = "PonyGE2 Python", } @InProceedings{Fenton:2017:GECCO, author = "Michael Fenton and David Lynch and Stepan Kucera and Holger Claussen and Michael O'Neill", title = "Multilayer Optimization of Heterogeneous Networks Using Grammatical Genetic Programming", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4939-0", address = "Berlin, Germany", pages = "3--4", size = "2 pages", URL = "http://doi.acm.org/10.1145/3067695.3084378", DOI = "doi:10.1145/3067695.3084378", acmid = "3084378", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, evolutionary computation, grammatical genetic programming, wireless communications networks", month = "15-19 " # jul, abstract = "Wireless communications networks are a global trillion dollar industry, where small improvements can scale to provide significant cost savings to networks operators. In a field full of NP-hard optimisation problems, heuristic optimisation techniques such as Evolutionary Computation offer a means to provide bespoke, scalable solutions. Grammatical Genetic Programming is applied to optimise three aspects of an LTE Heterogeneous Network: setting optimal Small Cell powers and biases, Macro Cell ABS patterns, and Small Cell scheduling. The evolved heuristics yield minimum downlink rates three times greater than a baseline technique, and twice that of a state-of-the-art industry standard benchmark. This work appears in full in Fenton et al., {"}Multilayer Optimization of Heterogeneous Networks using Grammatical Genetic Programming{"}, IEEE Transactions on Cybernetics, 2017. DOI: 10.1109/TCYB.2017.2688280.", notes = "Also known as \cite{Fenton:2017:MOH:3067695.3084378} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @InProceedings{Fenton:2017:GECCOa, author = "Michael Fenton and James McDermott and David Fagan and Stefan Forstenlechner and Erik Hemberg and Michael O'Neill", title = "{PonyGE2}: Grammatical Evolution in Python", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4939-0", address = "Berlin, Germany", pages = "1194--1201", size = "8 pages", URL = "http://doi.acm.org/10.1145/3067695.3082469", DOI = "doi:10.1145/3067695.3082469", acmid = "3082469", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, grammatical evolution", month = "15-19 " # jul, abstract = "Grammatical Evolution (GE) is a population-based evolutionary algorithm, where a formal grammar is used in the genotype to phenotype mapping process. PonyGE2 is an open source implementation of GE in Python, developed at UCD's Natural Computing Research and Applications group. It is intended as an advertisement and a starting-point for those new to GE, a reference for students and researchers, a rapid-prototyping medium for our own experiments, and a Python workout. As well as providing the characteristic genotype to phenotype mapping of GE, a search algorithm engine is also provided. A number of sample problems and tutorials on how to use and adapt PonyGE2 have been developed.", notes = "Also known as \cite{Fenton:2017:PGE:3067695.3082469} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @InCollection{Fenton:2018:hbge, author = "Michael Fenton and Jonathan Byrne and Erik Hemberg", title = "Design, Architecture, and Engineering with Grammatical Evolution", booktitle = "Handbook of Grammatical Evolution", publisher = "Springer", year = "2018", editor = "Conor Ryan and Michael O'Neill and J. J. Collins", chapter = "13", pages = "317--339", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", isbn13 = "978-3-319-78716-9", DOI = "doi:10.1007/978-3-319-78717-6_13", abstract = "Since its inception, Grammatical Evolution has had a rich history with design applications. The use of a formal grammar provides a convenient platform with which users can specify rules for design. Two main aspects of design evolution are the grammatical representation and the objective fitness evaluation. The field of design representation has many strands, each with its own strengths and weaknesses for particular applications. An overview is given of four popular grammatical representations for design: Lindenmayer Systems, Shape Grammars, Higher Order Functions, and Graph Grammars, with examples of each. The field of design is dominated by two often conflicting objectives: form and function. The disparity between the two is discussed: Interactive Evolutionary Design is examined in its capacity to provide a truly subjective fitness function for aesthetic form, while engineering applications of GE provide a basis for objective mathematically-based fitness evaluations. Finally, these two techniques can be combined to allow the designer to decide exactly how balance the optimisation and exploration of the process.", notes = "Part of \cite{Ryan:2018:hbge}", } @Article{Fenton:EC, author = "Michael Fenton and David Lynch and David Fagan and Stepan Kucera and Holger Claussen and Michael O'Neill", title = "Towards Automation \& Augmentation of the Design of Schedulers for Cellular Communications Networks", journal = "Evolutionary Computation", year = "2019", volume = "27", number = "2", month = "Summer", keywords = "genetic algorithms, genetic programming, grammatical evolution, Augmentation, scheduling, heterogeneous networks", ISSN = "1063-6560", URL = "https://doi.org/10.1162/evco_a_00221", DOI = "doi:10.1162/evco_a_00221", size = "31 pages", abstract = "Evolutionary Computation is used to automatically evolve small cell schedulers on a realistic simulation of a 4G-LTE heterogeneous cellular network. Evolved schedulers are then further augmented by human design to improve robustness. Extensive analysis of evolved solutions and their performance across a wide range of metrics reveals evolution has uncovered a new human-competitive scheduling technique which generalises well across cells of varying sizes. Furthermore, evolved methods are shown to conform to accepted scheduling frameworks without the evolutionary process being explicitly told the form of the desired solution. Evolved solutions are shown to outperform a human-engineered state-of-the-art benchmark by up to 50percent. Finally, the approach is shown to be flexible in that tailored algorithms can be evolved for specific scenarios and corner cases, allowing network operators to create unique algorithms for different deployments, and to postpone the need for costly hardware upgrades.", notes = "PMID: 29528725", } @InProceedings{Ferariu:2009:ICANNGA, author = "Lavinia Ferariu and Alina Patelli", title = "Multiobjective Genetic Programming for Nonlinear System Identification", year = "2009", booktitle = "9th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2009", editor = "Mikko Kolehmainen and Pekka Toivanen and Bartlomiej Beliczynski", series = "Lecture Notes in Computer Science", volume = "5495", pages = "233--242", address = "Kuopio, Finland", month = "23-25 " # apr, publisher = "Springer", note = "Revised selected papers", keywords = "genetic algorithms, genetic programming, multiobjective optimisation, nonlinear system identification", isbn13 = "978-3-642-04920-0", DOI = "doi:10.1007/978-3-642-04921-7_24", abstract = "The paper presents a novel identification method, which makes use of genetic programming for concomitant flexible selection of models structure and parameters. The case of nonlinear models, linear in parameters is addressed. To increase the convergence speed, the proposed algorithm considers customized genetic operators and a local optimisation procedure, based on QR decomposition, able to efficiently exploit the linearity of the model subject to its parameters. Both the model accuracy and parsimony are improved via a multiobjective optimization, considering different priority levels for the involved objectives. An enhanced Pareto loop is implemented, by means of a special fitness assignment technique and a migration mechanism, in order to evolve accurate and compact representations of dynamic nonlinear systems. The experimental results reveal the benefits of the proposed methodology within the framework of an industrial system identification.", notes = "ICANNGA 2009", } @InProceedings{Ferariu:2009:SACI, author = "L. Ferariu and A. Patelli", title = "Migration-based multiobjective genetic programming for nonlinear system identification", booktitle = "5th International Symposium on Applied Computational Intelligence and Informatics, SACI '09", year = "2009", month = may, pages = "475--480", keywords = "genetic algorithms, genetic programming, QR decomposition, adaptive threshold, convergence speed, dominance analysis, flexible model structure selection, migration-based multiobjective genetic programming, nonlinear system identification, optimization algorithm, quasi independent subpopulation, tree encoding, identification, nonlinear control systems, trees (mathematics)", DOI = "doi:10.1109/SACI.2009.5136295", abstract = "Nonlinear system identification is addressed by means of genetic programming. For a flexible selection of model structure and parameters, a multiobjective optimization of the tree encoded individuals is carried out, in terms of accuracy and parsimony. The paper suggests a new optimization algorithm based on the evolvement of two quasi-independent subpopulations, which makes use of a flexible migration scheme with adaptive thresholds and multiple rates. By efficiently exploiting the concept of dominance analysis, the algorithm is able to select compact and accurate models, with good generalization capabilities. The approach is compliant with nonlinear models, linear in parameters. That permits the hybridization with QR decomposition and the use of enhanced genetic operators, aimed to increase the algorithm convergence speed. The performances of the suggested design procedure are illustrated by the identification of two nonlinear industrial subsystems.", notes = "Also known as \cite{5136295}", } @InProceedings{Ferariu:2010:ICCC-CONTI, author = "L. Ferariu and B. Burlacu", title = "Graph genetic programming for hybrid neural networks design", booktitle = "International Joint Conference on Computational Cybernetics and Technical Informatics (ICCC-CONTI)", year = "2010", month = may, pages = "547--552", abstract = "This paper presents a novel approach devoted to the design of feed forward hybrid neural models. Graph genetic programming techniques are used to provide a flexible construction of partially interconnected neural structures with heterogeneous layers built as combinations of local and global neurons. By exploiting the inner modularity and the parallelism of the neural architectures, the approach suggests the encryption of the potential mathematical models as directed acyclic graphs and defines a minimally sufficient set of functions which guarantees that any combination of primitives encodes a valid neural model. The exploration capabilities of the algorithm are heightened by means of customised crossovers and mutations, which act both at the structural and the parametric level of the encrypted individuals, for producing offspring compliant with the neural networks' formalism. As the parameters of the models become the parameters of the primitive functions, the genetic operators are extended to manage the inner configuration of the functional nodes in the involved hierarchical individuals. The applicability of the proposed design algorithm is discussed on the identification of an industrial nonlinear plant.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICCCYB.2010.5491213", notes = "Also known as \cite{5491213}", } @InProceedings{Ferariu:2011:ieeeICCP, author = "Lavinia Ferariu and Bogdan Burlacu", title = "Multiobjective genetic programming with adaptive clustering", booktitle = "IEEE International Conference on Intelligent Computer Communication and Processing (ICCP 2011)", year = "2011", month = "25-27 " # aug, pages = "27--32", address = "Cluj-Napoca, Romania", size = "6 pages", abstract = "This paper presents a new approach meant to provide an automatic design of feed forward neural models by means of multiobjective graph genetic programming. The suggested algorithm can deal with partially interconnected neural architectures and various types of global and local neurons within each hidden neural layer. It concomitantly ensures the reduction of variables and the selection of convenient model structures and parameters, by working on a set of graph-based encrypted individuals built via genetic programming with the guarantee of phenotypic and genotypic validity. In order to provide a realistic assessment of the neural models, the optimisation is carried out subject to multiple objectives of different priorities. In relation to this idea, the authors propose a new Pareto-ranking strategy, which progressively guides the search towards the preferred zones of the exploration space. The fitness assignment procedure monitors the phenotypic diversity of the best individuals, as well as the convergence speed of the algorithm, and exploits the resulted heuristics for performing a preliminary clustering of individuals. The experimental trials targeting the identification of an industrial system show the capacity of the suggested approach to automatically build simple and precise models, whilst dealing with noisy data and scarce a priori information.", keywords = "genetic algorithms, genetic programming, Pareto-ranking strategy, adaptive clustering, automatic design, convergence speed, feedforward neural model, genotypic validity, graph based encrypted individual, hidden neural layer, industrial system, interconnected neural architecture, model structure, multiobjective graph genetic programming, noisy data, phenotypic validity, cryptography, feedforward neural nets, graph theory, pattern clustering", DOI = "doi:10.1109/ICCP.2011.6047840", notes = "Also known as \cite{6047840}", } @InProceedings{Ferariu:2011:ICAC, author = "Lavinia Ferariu and Bogdan Burlacu", title = "Multiobjective design of evolutionary hybrid neural networks", booktitle = "17th International Conference on Automation and Computing (ICAC 2011)", year = "2011", month = "10 " # sep, pages = "195--200", address = "Huddersfield, UK", keywords = "genetic algorithms, genetic programming, Pareto-ranking strategy, data-driven modelling, evolutionary hybrid neural networks, industrial system, interconnected structures, multiobjective design, multiobjective graph genetic programming, Pareto optimisation, data models, design, neural nets", isbn13 = "978-1-4673-0000-1", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6084926", size = "6 pages", abstract = "The paper presents a new approach to data-driven modelling. The models are flexibly configured in compliance with the neural network formalism, by accepting partially interconnected structures and various types of global and local neurons within each hidden neural layer. A simultaneous selection of convenient model structure and parameters is performed, making use of multiobjective graph genetic programming. For an efficient assessment of individuals, the authors suggest a new Pareto-ranking strategy, which permits a progressive combination between search and decision, tailored to handle objectives of different priorities. The experiments carried out for the identification of an industrial system show the capacity of the proposed approach to automatically build simple and precise models, whilst dealing with noisy data and poor aprioric information.", notes = "Also known as \cite{6084926}", } @InProceedings{Ferariu:2011:ICSTCC, author = "Lavinia Ferariu and Bogdan Burlacu", title = "Multiobjective Graph Genetic Programming with Encapsulation Applied to Neural System Identification", booktitle = "15th International Conference on System Theory, Control, and Computing (ICSTCC 2011)", year = "2011", month = "14-16 " # oct, address = "Sinaia", keywords = "genetic algorithms, genetic programming, Pareto ranking, encapsulation operator, evolutionary algorithm, feedforward hybrid neural network, industrial plant, multiobjective graph genetic programming, multiobjective optimisation, neural system identification, nonlinear system identification, Pareto optimisation, feedforward neural nets, graph theory", isbn13 = "978-1-4577-1173-2", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6085706", size = "6 pages", abstract = "This paper presents two new encapsulation operators compatible with graph genetic programming. The approach is used for the evolvement of partially interconnected, feed-forward hybrid neural networks, within the framework of nonlinear system identification. The suggested encapsulations are targeted to protect valuable terminals and useful sub-graphs directly connected with the root node. To preserve a better balance between exploitation and exploration, the quality of the inner substructures is assessed in relation with the phenotypic properties of the individuals to whom they belong. The multiobjective optimisation of accuracy and parsimony is adopted; for each generation, the requirements expressed by the decision block are progressively translated to the evolutionary algorithm, via a preliminary clustering of the individuals, performed before Pareto-ranking. The experimental results achieved on the identification of an industrial plant indicate that the proposed encapsulations are able to enforce the selection of accurate and simple models.", notes = "Also known as \cite{6085706}", } @InProceedings{Ofria:2019:GPTP, author = "Austin J. Ferguson and Jose Guadalupe Hernandez and Daniel Junghans and Alexander Lalejini and Emily Dolson and Charles Ofria", title = "Characterizing the effects of random subsampling and dilution on Lexicase selection", booktitle = "Genetic Programming Theory and Practice XVII", year = "2019", editor = "Wolfgang Banzhaf and Erik Goodman and Leigh Sheneman and Leonardo Trujillo and Bill Worzel", pages = "1--23", address = "East Lansing, MI, USA", month = "16-19 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-030-39957-3", DOI = "doi:10.1007/978-3-030-39958-0_1", abstract = "Lexicase selection is a proven parent-selection algorithm designed for genetic programming problems, especially for uncompromising test-based problems where many distinct test cases must all be passed. Previous work has shown that random subsampling techniques can improve lexicase selection problem-solving success; here, we investigate why. We test two types of random subsampling lexicase variants: down-sampled lexicase, which uses a random subset of all training cases each generation; and cohort lexicase, which collects candidate solutions and training cases into small groups for testing, reshuffling those groups each generation. We show that both of these subsampling lexicase variants improve problem-solving success by facilitating deeper evolutionary searches; that is, they allow populations to evolve for more generations (relative to standard lexicase) given a fixed number of test-case evaluations. We also demonstrate that the subsampled variants require less computational effort to find solutions, even though subsampling hinders lexicase ability to preserve specialists. Contrary to our expectations, we did not find any evidence of systematic loss of phenotypic diversity maintenance due to subsampling, though we did find evidence that cohort lexicase is significantly better at preserving phylogenetic diversity than down-sampled lexicase.", notes = "Part of \cite{Banzhaf:2019:GPTP}, published after the workshop", } @InProceedings{fernandes:1999:EAST, author = "Carlos Fernandes and Joao Paulo Caldeira and Fernando Melicio and Agostinho Rosa", title = "Evolutionary Algorithm for School Timetabling", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1777", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "real world applications, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-743.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-743.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{fernandes:2023:GECCO, author = "Matheus Campos Fernandes and Fabricio {Olivetti De Franca} and Emilio Francesquini", title = "{HOTGP} - {Higher-Order} Typed Genetic Programming", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "1091--1099", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, functional programming, inductive program synthesis", isbn13 = "9798400701191", DOI = "doi:10.1145/3583131.3590464", size = "9 pages", abstract = "Program synthesis is the process of generating a computer program following a set of specifications, which can be a high-level description of the problem and/or a set of input-output examples. The synthesis can be modeled as a search problem in which the search space is the set of all the programs valid under a grammar. As the search space is vast, brute force is usually not viable and search heuristics, such as genetic programming, also have difficulty navigating it without any guidance. In this paper we present HOTGP, a new genetic programming algorithm that synthesizes pure, typed, and functional programs. HOTGP leverages the knowledge provided by the rich data-types associated with the specification and the built-in grammar to constrain the search space and improve the performance of the synthesis. The grammar is based on Haskell's standard base library (the synthesized code can be directly compiled using any standard Haskell compiler) and includes support for higher-order functions, Λ-functions, and parametric polymorphism. Experimental results show that, when compared to 6 state-of-the-art algorithms using a standard set of benchmarks, HOTGP is competitive and capable of synthesizing the correct programs more frequently than any other of the evaluated algorithms.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @Misc{oai:CiteSeerX.psu:10.1.1.453.2194, title = "An Embodied Evolutionary System to Control a Population of Mobile Robots using Genetic Programming", author = "Anderson Luiz {Fernandes Perez} and Guilherme Bittencourt and Mauro Roisenberg", keywords = "genetic algorithms, genetic programming, evolutionary robotic, distributed genetic programming, embodied evolutionary system", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.453.2194", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.453.2194", URL = "http://www.das.ufsc.br/~gb/publications05-07/asai07.pdf", size = "10 pages", abstract = "In this paper, an embodied evolutionary system, able to control a population of mobile robots, is proposed. This system should be able to execute tasks such as collision-free navigation, box pushing and predator and prey. The proposed system has the following characteristics: i) it extends the traditional genetic programming algorithm to allow the evolution in a population of physical robots; ii) the evolutionary process occurs in an asynchronously way among the robots in the population; iii) it is fail-safe, because it allows the continuation of the evolutionary process even if only one robot remains in the population of robots; iv) it saves the information about the more adapted individuals in a kind of memory; v) it has an execution and management environment that is independent of the evolutionary process.", notes = "2007? The 36th International Conference of the Argentine Computer Science and Operational Research Society, Mar del Plata, Argentina 2007?", } @InProceedings{conf/eusflat/FernandezBJH09, title = "Genetic Cooperative-Competitive Fuzzy Rule Based Learning Method using Genetic Programming for Highly Imbalanced Data-Sets", author = "Alberto Fernandez and Francisco Jose Berlanga and Maria Jose {del Jesus} and Francisco Herrera", booktitle = "Proceedings of the Joint 2009 International Fuzzy Systems Association World Congress and 2009 European Society of Fuzzy Logic and Technology Conference", year = "2009", editor = "Jo{\~a}o Paulo Carvalho and Didier Dubois and Uzay Kaymak and Jo{\~a}o Miguel da Costa Sousa", pages = "42--47", address = "Lisbon, Portugal", month = jul # " 20-24", keywords = "genetic algorithms, genetic programming, Fuzzy Rule-Based Classification Systems, Genetic Fuzzy Systems, imbalanced Data-Sets, Interpretability", isbn13 = "978-989-95079-6-8", URL = "http://www.eusflat.org/publications/proceedings/IFSA-EUSFLAT_2009/pdf/tema_0042.pdf", bibdate = "2009-12-16", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/eusflat/eusflat2009.html#FernandezBJH09", abstract = "Classification in imbalanced domains is an important problem in Data Mining. We refer to imbalanced classification when data presents many examples from one class and few from the other class, and the less representative class is the one which has more interest from the point of view of the learning task. The aim of this work is to study the behaviour of the GP-COACH algorithm in the scenario of data-sets with high imbalance, analysing both the performance and the interpretability of the obtained fuzzy models. To develop the experimental study we will compare this approach with a well-known fuzzy rule learning algorithm, the Chi et al.'s method, and an algorithm of reference in the field of imbalanced data-sets, the C4.5 decision tree.", notes = "1.Department of Computer Science and Artificial Intelligence, University of Granada Granada, Spain 2.Department of Computer Science and Systems Engineering, University of Zaragoza Zaragoza, Spain 3.Department of Computer Science, University of Jaen Spain", } @InProceedings{FSTG99, author = "F. Fernandez and J. M. Sanchez and M. Tomassini and J. A. Gomez", title = "A Parallel Genetic Programming Tool based on PVM", booktitle = "Recent Advances in Parallel Virtual Machine and Message Passing Interface, Proceedings of the 6th European PVM/MPI Users' Group Meeting", series = "Lecture Notes in Computer Science", editor = "Jack Dongarra and Emilio Luque and Tomas Margalef", volume = "1697", pages = "241--248", publisher = "Springer-Verlag", ISBN = "3-540-66549-8", year = "1999", month = sep, address = "Barcelona, Spain", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1007/3-540-48158-3_30", abstract = "This paper presents a software package suited for investigating Parallel Genetic Programming (PGP) using Parallel Virtual Machine (PVM) language as means of communicating distributed populations. We show the usefulness of PVM by means of an example developed with this software tool. The example has been run on several processors in a parallel way.", affiliation = "Universidad de Extremadura Departamento de Informatica. Escuela Politacnica Caceres", } @InProceedings{FTVB00, author = "Francisco Fernandez and Marco Tomassini and Leonardo Vanneschi and Laurent Bucher", title = "A Distributed Computing Environment for Genetic Programming using MPI", editor = "Jack J. Dongarra and Peter Kacsuk and Norbert Podhorszki", booktitle = "Recent advances in parallel virtual machine and message passing interface: 7th European {PVM\slash MPI} Users' Group Meeting", volume = "1908", publisher = "Springer-Verlag", address = "Balatonfured, Hungary", pages = "322--329", year = "2000", ISBN = "3-540-41010-4 (softcover)", ISSN = "0302-9743", isbn13 = "978-3-540-41010-2", bibdate = "Mon Oct 16 18:31:56 MDT 2000", series = "Lecture Notes in Computer Science", month = "10-13 " # sep, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1007/3-540-45255-9_44", size = "8 pages", abstract = "This paper presents an environment for distributed genetic programming using MPI. Genetic programming is a stochastic evolutionary learning methodology that can greatly benefit from parallel/distributed implementations. We describe the distributed system, as well as a user-friendly graphical interface to the tool. The usefulness of the distributed setting is demonstrated by the results obtained to date on several difficult problems, one of which is described in the text.", } @InProceedings{fernandez:1999:SAEP, author = "Francisco Fernandez and Marco Tomassini and J. M. Sanchez", title = "Solving the Ant and the Even Parity-5 problems by means of parallel genetic programming", booktitle = "Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference", year = "1999", editor = "Scott Brave and Annie S. Wu", pages = "88--92", address = "Orlando, Florida, USA", month = "13 " # jul, keywords = "genetic algorithms, genetic programming", notes = "GECCO-99LB", } @InProceedings{fernandez:2000:esmpGP, author = "F. Fernandez and M. Tomassini and W. F. {Punch III} and J. M. Sanchez", title = "Experimental Study of Multipopulation Parallel Genetic Programming", booktitle = "Genetic Programming, Proceedings of EuroGP'2000", year = "2000", editor = "Riccardo Poli and Wolfgang Banzhaf and William B. Langdon and Julian F. Miller and Peter Nordin and Terence C. Fogarty", volume = "1802", series = "LNCS", pages = "283--293", address = "Edinburgh", publisher_address = "Berlin", month = "15-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-67339-3", URL = "http://garage.cse.msu.edu/papers/GARAGe00-03-01.pdf", URL = "http://citeseer.ist.psu.edu/445504.html", DOI = "doi:10.1007/978-3-540-46239-2_21", size = "11 pages", abstract = "The parallel execution of several populations in evolutionary algorithms has usually given good results. Nevertheless, researchers have to date drawn conflicting conclusions when using some of the parallel genetic programming models. One aspect of the conflict is population size, since published GP works do not agree about whether to use large or small populations. This paper presents an experimental study of a number of common GP test problems. Via our experiments, we discovered that an optimal range of values exists. This assists us in our choice of population size and in the selection of an appropriate parallel genetic programming model. Finding efficient parameters helps us to speed up our search for solutions. At the same time, it allows us to locate features that are common to parallel genetic programming and the classic genetic programming technique.", notes = "EuroGP'2000, part of \cite{poli:2000:GP}", } @InProceedings{vega:2000:mgpabd, author = "F. {Fernandez de Vega} and Laura M. Roa and Marco Tomassini and J. M. Sanchez", title = "Multipopulation Genetic Programing Applied to Burn Diagnosing", booktitle = "Proceedings of the 2000 Congress on Evolutionary Computation CEC00", year = "2000", pages = "1292--1296", volume = "2", address = "La Jolla Marriott Hotel La Jolla, California, USA", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "6-9 " # jul, organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, novel applications, burn diagnosis, decision support system, decision trees, explicit information, input parameter, learning classifier system, medical decision making, multipopulation genetic programming, optimization, software tools, decision support systems, decision trees, medical diagnostic computing, optimisation", ISBN = "0-7803-6375-2", DOI = "doi:10.1109/CEC.2000.870800", abstract = "Genetic programming (GP) has proved useful in optimisation problems. The way of representing individuals in this methodology is particularly good when we want to construct decision trees. Decision trees are well suited to representing explicit information and relationships among parameters studied. A set of decision trees could make up a decision support system. In this paper we set out a methodology for developing decision support systems as an aid to medical decision making. Above all, we apply it to diagnosing the evolution of a burn, which is a really difficult task even for specialists. A learning classifier system is developed by means of multipopulation genetic programming (MGP). It uses a set of parameters, obtained by specialist doctors, to predict the evolution of a burn according to its initial stages. The system is first trained with a set of parameters and results of evolutions which have been recorded over a set of clinic cases. Once the system is trained, it is useful for deciding how new cases will probably evolve. Thanks to the use of GP, an explicit expression of the input parameter is provided. This explicit expression takes the form of a decision tree which will be incorporated into software tools that help physicians In their everyday work", notes = "CEC-2000 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 00TH8512, Library of Congress Number = 00-018644", } @InProceedings{fernandez:2000:GA, author = "Francisco Fernandez and Marco Tomassini", title = "Genetic programming and reconfigurable hardware: A proposal for solving the problem of placement and routing", booktitle = "Graduate Student Workshop", year = "2000", editor = "Conor Ryan and Una-May O'Reilly and William B. Langdon", pages = "265--268", address = "Las Vegas, Nevada, USA", month = "8 " # jul, keywords = "genetic algorithms, genetic programming", notes = "GECCO-2000WKS Part of \cite{wu:2000:GECCOWKS}", } @InProceedings{Fernandez:2000:GECCO, author = "Francisco Fernandez and Marco Tomassini and William Punch and J. M. Sanchez", title = "Experimental Study of Isolated Multipopulation Genetic Programming", pages = "536", year = "2000", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)", editor = "Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer", address = "Las Vegas, Nevada, USA", publisher_address = "San Francisco, CA 94104, USA", month = "10-12 " # jul, keywords = "genetic algorithms, genetic programming, Poster", ISBN = "1-55860-708-0", URL = "http://gpbib.cs.ucl.ac.uk/gecco2000/GP159.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco2000/GP159.ps", notes = "A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of \cite{whitley:2000:GECCO}", } @InProceedings{fernandez:2000:esimgp, author = "Francisco Fernandez and Marco Tomassini and J. M. Sanchez", title = "Experimental Study of Isolated Multipopulation Genetic Programming", booktitle = "Proceedings of the 26th Annual Conference of the IEEE Industrial Electronics Society", volume = "1697", pages = "2672--2677 vol.4", publisher = "IEEE Press", ISBN = "0-7803-6456-2", year = "2000", month = oct, address = "Nagoya, Japan", keywords = "genetic algorithms, genetic programming, classic genetic programming, effort, fitness, individual distribution, isolated multipopulation genetic programming, populations", URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=00972420", DOI = "doi:10.1109/IECON.2000.972420", abstract = "In this paper we present results obtained when comparing classic genetic programming (GP) with the isolated multipopulation version. Our first discovery was that sometimes, given a certain number of individuals, it is useful to distribute them among several populations even when no communication is allowed. This consequently lead to research concentrating on three main questions: firstly, how to distribute individuals according to the problem in hand; secondly, how many populations must be employed in proportion to the effort and fitness involved when solving a problem; and finally, how to use isolated multipopulation GP in the classification of problems.", } @InProceedings{fernandez:2001:EuroGP, author = "Francisco Fernandez and Marco Tomassini and Leonardo Vanneschi", title = "Studying the Influence of Communication Topology and Migration on Distributed Genetic Programming", booktitle = "Genetic Programming, Proceedings of EuroGP'2001", year = "2001", editor = "Julian F. Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon", volume = "2038", series = "LNCS", pages = "51--63", address = "Lake Como, Italy", publisher_address = "Berlin", month = "18-20 " # apr, organisation = "EvoNET", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, Distributed Genetic Programming, Parallelism, Multipopulation structures, Parallel evolutionary algorithms", ISBN = "3-540-41899-7", DOI = "doi:10.1007/3-540-45355-5_5", size = "13 pages", abstract = "In this paper we present a systematic experimental study of some of the parameters influencing parallel and distributed genetic programming (PADGP) by using three benchmark problems. We first present results on the system's communication topology and then we study the parameters governing individual migration between subpopulations: the number of individuals sent and the frequency of exchange. Our results suggest that fitness evolution is more sensitive to the migration factor than the communication topology.", notes = "EuroGP'2001, part of \cite{miller:2001:gp}", } @InProceedings{fernandez:2001:soprvpmrp, author = "F. Fernandez and M. Tomassini", title = "Studying the Optimal Parameter Range of Values in PADGP by Means of Real-life Problems", booktitle = "Proceedings of the 2001 Congress on Evolutionary Computation CEC2001", year = "2001", pages = "436--441", address = "COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "27-30 " # may, organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Parallel Genetic Programming, FPGA, PADGP, Parallel and Distributed Genetic Programming, optimal parameter range, simulation, distributed algorithms, parallel algorithms", ISBN = "0-7803-6658-1", DOI = "doi:10.1109/CEC.2001.934424", abstract = "We present a study on a couple of real-life problems using Parallel and Distributed Genetic Programming (PADGP). The aim is to confirm the presence of an optimal parameter range of values, which has been observed on benchmark problems. This range of values establishes a relationship between two important parameters in PADGP: the number of individuals and the total number of subpopulations we use when solving a problem. The simulations presented confirm the existence of the optimal parameter range of values which allows us to extend conclusions about the existence of this region for different classes of problems, and thus to link different PADGP and also GP parameters", notes = "CEC-2001 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 01TH8546C, Library of Congress Number =", } @InProceedings{fernandez:2001:gecco, title = "A new methodology for the Placement and Routing problem based on PADGP", author = "F. Fernandez and J. M. Sanchez and M. Tomassini", pages = "175", year = "2001", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)", editor = "Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon and Edmund Burke", address = "San Francisco, California, USA", publisher_address = "San Francisco, CA 94104, USA", month = "7-11 " # jul, keywords = "genetic algorithms, genetic programming: Poster, Parallel Evolutionary Algorithms, Evolvable Hardware", ISBN = "1-55860-774-9", URL = "http://gpbib.cs.ucl.ac.uk/gecco2001/d02.pdf", notes = "GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of \cite{spector:2001:GECCO}", } @InProceedings{fernandez:2001:, author = "F. Fernandez and J. M. Sanchez and M. Tomassini", title = "Placing and Routing Circuits on {FPGA}s by Means of Parallel and Distributed Genetic Programming", booktitle = "Evolvable Systems: From Biology to Hardware, Proceedings of the 4th International Conference, ICES 2001", series = "Lecture Notes in Computer Science", editor = "Yong Liu and Kiyoshi Tanaka and Masaya Iwata and Tetsuya Higuchi and Moritoshi Yasunaga", volume = "2210", pages = "204--214", publisher = "Springer-Verlag", ISBN = "3-540-42671-X", ISSN = "0302-9743", year = "2001", month = "3-5 Octpber", address = "Tokyo, Japan", keywords = "genetic algorithms, genetic programming, evolvable hardware, PADGP", ISBN = "3-540-42671-X", ISSN = "0302-9743", DOI = "doi:10.1007/3-540-45443-8_18", size = "12 pages", abstract = "We present results on the application of a new methodology based on Parallel and Distributed Genetic Programming (PADGP). The aim for the methodology we present is to automatically perform the placement and routing of circuits on reconfigurable hardware. The system has been successfully applied to some benchmark problems. For each of the problems we have dealt with, the methodology is capable of finding several solutions. The results show the methodology's feasibility for addressing the problem of placement and routing on FPGAs.", notes = "island-based FPGAs eg Xilinx. Digital circuits. Connecting circuits given by syntax of evolved GP tree.", } @InProceedings{fernandez:2002:EuroGP, title = "Comparing Synchronous and Asynchronous Parallel and Distributed {GP} Models", author = "Francisco Fernandez and G. Galeano and J. A. Gomez", editor = "James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi", booktitle = "Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002", volume = "2278", series = "LNCS", pages = "326--335", publisher = "Springer-Verlag", address = "Kinsale, Ireland", publisher_address = "Berlin", month = "3-5 " # apr, year = "2002", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-43378-3", DOI = "doi:10.1007/3-540-45984-7_32", abstract = "In this paper we present a study that analyses the respective advantages and disadvantages of the synchronous and asynchronous versions of island-based genetic programming. We also look at different measuring systems for comparing parallel genetic programming with panmitic model. At the same time we show an interesting relationship between the bloat phenomenon and the number of individuals we use. Finally, we study a relationship between the number of subpopulations in parallel GP and the advantages of the asynchronous model.", notes = "EuroGP'2002, part of lutton:2002:GP. Santa Fe Ant, even-5-parity. padgp", } @Article{Fernandez:2003:GPEM, author = "Francisco Fernandez and Marco Tomassini and Leonardo Vanneschi", title = "An Empirical Study of Multipopulation Genetic Programming", journal = "Genetic Programming and Evolvable Machines", year = "2003", volume = "4", number = "1", pages = "21--51", month = mar, keywords = "genetic algorithms, genetic programming, distributed evolutionary algorithms, parallel algorithms, structured populations", ISSN = "1389-2576", URL = "https://rdcu.be/c5oUz", DOI = "doi:10.1023/A:1021873026259", abstract = "This paper presents an experimental study of distributed multipopulation genetic programming. Using three well-known benchmark problems and one real-life problem, we discuss the role of the parameters that characterise the evolutionary process of standard panmictic and parallel genetic programming. We find that distributing individuals between subpopulations offers in all cases studied here an advantage both in terms of the quality of solutions and of the computational effort spent, when compared to single populations. We also study the influence of communication patterns such as the communication topology, the number of individuals exchanged and the frequency of exchange on the evolutionary process. We empirically show that the topology does not have a marked influence on the results for the test cases studied here, while the frequency and number of individuals exchanged are related and there exists a suitable range for those parameters which is consistently similar for all the problems studied.", notes = "Article ID: 5113071", } @InProceedings{fernandez03, author = "Francisco Fernandez and Leonardo Vanneschi and Marco Tomassini", title = "The Effect of Plagues in Genetic Programming: A Study of Variable-Size Populations", booktitle = "Genetic Programming, Proceedings of EuroGP'2003", year = "2003", editor = "Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa", volume = "2610", series = "LNCS", pages = "317--326", address = "Essex", publisher_address = "Berlin", month = "14-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-00971-X", DOI = "doi:10.1007/3-540-36599-0_29", abstract = "A study on the effect of variable size populations in genetic programming is presented in this work. We apply the idea of plague (high desease of individuals). We show that although plagues are generally considered as negative events, they can help populations to save computing time and at the same time surviving individuals can reach high peaks in the fitness landscape.", notes = "EuroGP'2003 held in conjunction with EvoWorkshops 2003", } @PhdThesis{fernandez:thesis, author = "Francisco {Fernandez de Vega}", title = "Distributed Genetic Programming Models with Application to Logic Synthesis on FPGAs", school = "University of Extremadura", year = "2001", address = "Spain", email = "fcofdez@unex.es", keywords = "genetic algorithms, genetic programming, reconfigurable hardware, EHW, PADGP, IMGP", broken = "http://cum.unex.es/profes/profes/fcofdez/escritorio/investigacion/pgp/thesis/phd.html", URL = "http://www.researchgate.net/publication/256474009_Distributed_Genetic_Programming_Models_with_Application_to_Logic_Synthesis_on_FPGAs._PhD._Thesis._2001", size = "156 pages", notes = "For Spanish version see \cite{fernandez:thesis:espanol} CONCLUSIONS AND FINAL REMARKS We have presented a new implementation of GP - based on MPI - which allows us to make use of parallelism as well as experimenting with different communication topologies and GP parameters We have compared performances of this methodology ?PADGP ? with classic GP. The tool was applied to the study of two important parameters that affect convergence results on PADGP: the number and size of populations. By means of this study, we have observed the existence of a region of effort which defines the best number of individuals we must use when employing a given number of populations with PADGP. This region of effort has been detected both in benchmark problems and in ?real life? problems. We have also presented random topology as a way of improving convergence when using PADGP. We have used PADGP with random topology and compared it to classic GP. This comparison showed that the former gives better results. We have also compared random topology and grid topology and we have shown that results are similar. Nevertheless random topology requires a smaller amount of communication processes. We have presented a methodology that is based on PADGP, and which aids medical diagnosing. We used this problem to check the validity of results obtained in the benchmark problem, while we also proposed PADGP as an appropriate methodology for extracting medical knowledge. We have studied isolated subpopulations (IMGP) as a limit case of PADGP and we have experimentally seen that IMGP obtains similar convergence results than GP; sometimes results are even better if the total number of individuals is high. We have then dealt with an optimisation problem: the problem of placement and routing on FPGAs. We have developed a new methodology based on GP, and this allows us to represent circuits by means of GP trees. Furthermore, the methodology achieved the proposed goal: finding several ways of placing and routing circuits on reconfigurable hardware. The problem was later used for checking the conclusions which had been reached in the first part of this research. All statistical results obtained are in agreement with those obtained from benchmark problems. We think that the main goals we established at the beginning have been achieved: checking the usefulness of PADGP with random communications and developing a methodology for logic synthesis on FPGAs. In the researching process we discovered the concept of region of effort and we obtained interesting conclusions via the use of IMGP. Results we obtained during our research have been published in the main conferences and reviews that deal with the different topics addressed in this thesis (see References).", notes = "Gruau embryology. p113 Figure how recessive genes work in crossover between trees. Isolated multi-population genetic programming (IMGP)", } @PhdThesis{fernandez:thesis:espanol, author = "Francisco {Fernandez de Vega}", title = "Modelos de Programacion Genetica Paralela y Distribuida con aplicaciones a la Sintesis Logica en FPGAs", school = "University of Extremadura", year = "2001", email = "fcofdez@unex.es", keywords = "genetic algorithms, genetic programming, reconfigurable hardware", broken = "http://cum.unex.es/profes/profes/fcofdez/escritorio/investigacion/pgp/thesis/phd.html", size = "162 pages", notes = "version espa\~{n}ol. for english version see \cite{fernandez:thesis}", } @InProceedings{devega:2003:CEMAEB, author = "Francisco {Fernandez de Vega}", title = "Estudio de Poblaciones de tama\~{n}o variable en Programacion Genetica", booktitle = "Actas del II Congreso Espa\~{n}ol sobre Metaheuristicas, Algoritmos Evolutivos y Bioinspirados", year = "2003", pages = "424--428", month = feb, keywords = "genetic algorithms, genetic programming, bloat", URL = "http://cum.unex.es/profes/profes/fcofdez/escritorio/investigacion/pgp/papers/maeb04.pdf", abstract = "En este trabajo presentamos un estudio sobre el efecto de poblaciones de tama\~{n}o variable en Programacion Genetica. Por medio de una serie de experimentos mostramos que la supresion sistematica de un n\'{u}mero fijo de individuos a lo largo de varias generaciones puede ayudar a reducir el esfuerzo computacional requerido en la b\'{u}squeda de soluciones a problemas. Por otro lado, la calidad de las soluciones encontradas no se ve afectada de forma significativa por la eliminacion de un n\'{u}mero peque\~{n}o de individuos en cada generacion.", size = "6 pages", notes = "in spanish", } @InProceedings{fernandez:2003:sceigpbmop, author = "F. Fernandez and M. Tomassini and L. Vanneschi", title = "Saving computational effort in genetic programming by means of plagues", booktitle = "Proceedings of the 2003 Congress on Evolutionary Computation CEC2003", editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon", pages = "2042--2049", year = "2003", publisher = "IEEE Press", address = "Canberra", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "8-12 " # dec, organisation = "IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", keywords = "genetic algorithms, genetic programming, Biological cells, Computational efficiency, Computer science, Data structures, Evolutionary computation, Proposals, Size control, Tree data structures, computational complexity, computational cost, computational effort, computing resources, computing time, evolutionary algorithm", ISBN = "0-7803-7804-0", DOI = "doi:10.1109/CEC.2003.1299924", abstract = "A new technique for saving computing resources when using Genetic Programming is presented in this work. Instead of directly fighting bloat -the main factor explaining the large computational cost required for the evaluation of generations- by acting on individuals, we apply a new operator to the whole population: the plague. By removing some individuals every generation, we compensate for the increase in size of individuals, thus saving computing time when looking for solutions.", notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.", } @InProceedings{fernandez:2004:eurogp, author = "Francisco Fernandez and Aida Martin", title = "Saving Effort in Parallel GP by means of Plagues", booktitle = "Genetic Programming 7th European Conference, EuroGP 2004, Proceedings", year = "2004", editor = "Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule", volume = "3003", series = "LNCS", pages = "269--278", address = "Coimbra, Portugal", publisher_address = "Berlin", month = "5-7 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-21346-5", DOI = "doi:10.1007/978-3-540-24650-3_25", abstract = "Plague, a new technique that allows Genetic Programming to save computing resources, has been proposed. By removing some individuals every generation, plague aims at compensating for the increase in size of individuals, thus saving computing time when looking for solutions. By means of some test problems, we show that the technique is also useful when employing a parallel version of GP, such as that based on the island model.", notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004", } @Article{Fernandez:2004:MM, author = "Francisco {Fernandez de Vega} and J. I. Hidalgo and J. Lanchares and J. M. Sanchez", title = "A methodology for reconfigurable hardware design based upon evolutionary computation", journal = "Microprocessors and Microsystems", year = "2004", volume = "28", number = "7", pages = "363--371", month = sep, email = "fcofdez@unex.es", keywords = "genetic algorithms, genetic programming, reconfigurable hardware, Field programmable gate arrays, Compact genetic algorithm, Configurable logic blocks", ISSN = "0141-9331", URL = "http://www.sciencedirect.com/science/article/B6V0X-4C4BWW7-1/2/815fe7c17a6207d7a31f8046e4e2a5d1", DOI = "doi:10.1016/j.micpro.2004.03.017", size = "9 pages", abstract = "We present a methodology for Multi-FPGA systems (MFS) design. MFSs are used for a great variety of applications, including dynamically re-configurable hardware applications, digital circuit emulation, and numerical computation. There are a great variety of boards for MFS implementation. We have employed a set of techniques based on evolutionary algorithms, and we show that they are capable of solving all of the design tasks (partitioning placement and routing). Firstly a hybrid compact genetic algorithm solves the partitioning problem and then genetic programming is used to obtain a solution for the two other tasks.", } @InProceedings{Fernandez:PPSN:2004, author = "Francisco Fernandez-de-Vega and German Galeano Gil and Juan Antonio Gomez Pulido and Jose Luis Guisado", title = "Control of bloat in Genetic Programming by means of the Island Model", booktitle = "Parallel Problem Solving from Nature - PPSN VIII", year = "2004", editor = "Xin Yao and Edmund Burke and Jose A. Lozano and Jim Smith and Juan J. Merelo-Guerv\'os and John A. Bullinaria and Jonathan Rowe and Peter Ti\v{n}o Ata Kab\'an and Hans-Paul Schwefel", volume = "3242", pages = "263--271", series = "LNCS", address = "Birmingham, UK", publisher_address = "Berlin", month = "18-22 " # sep, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-23092-0", URL = "https://rdcu.be/dc0jJ", DOI = "doi:10.1007/b100601", DOI = "doi:10.1007/978-3-540-30217-9_27", abstract = "a new proposal for reducing bloat in Genetic Programming. This proposal is based in a well-known parallel evolutionary model: the island model. We firstly describe the theoretical motivation for this new approach to the bloat problem, and then we present a set of experiments that gives us evidence of the findings extracted from the theory. The experiments have been performed on a representative problem extracted from the GP field: the even parity 5 problem. We analyse the evolution of bloat employing different settings for the parameters employed. The conclusion is that the Island Model helps to prevent the bloat phenomenon.", notes = "PPSN-VIII", } @InCollection{fernandez2004, author = "F. Fernandez and J. I. Hidalgo and J. M. Sanchez and J. Lanchares", title = "An Evolutionary Approach to Multi-FPGAs System Synthesis", booktitle = "Evolvable Machines: Theory \& Practice", publisher = "Springer", year = "2004", editor = "Nadia Nedjah and Luiza {de Macedo Mourelle}", volume = "161", series = "Studies in Fuzziness and Soft Computing", chapter = "7", pages = "151--177", address = "Berlin Hidelberg Germany", keywords = "genetic algorithms, genetic programming, reconfigurable hardware", ISBN = "3-540-22905-1", URL = "http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-175-22-33980449-0,00.html", notes = "Springer says published in 2005 but available Nov 2004", } @InCollection{Fernandez:2005:pm, author = "Francisco Fernandez and Giandomenico Spezzano and Marco Tomassini and Leonardo Vanneschi", title = "Parallel Genetic Programming", booktitle = "Parallel Metaheuristics", publisher = "Wiley-Interscience", year = "2005", editor = "Enrique Alba", series = "Parallel and Distributed Computing", chapter = "6", pages = "127--153", address = "Hoboken, New Jersey, USA", keywords = "genetic algorithms, genetic programming, island model, grid cellular structure, placement FPGA, EHW, cellular genetic programming, ensemble of classifiers, CGPC, bagCGPC", ISBN = "0-471-67806-6", URL = "http://eu.wiley.com/WileyCDA/WileyTitle/productCd-0471678066.html", DOI = "doi:10.1002/0471739383.ch6", notes = "Last example uses UCI cens (299285 tuples), 16 linux myrinet pentium III nodes", size = "27 pages", } @InCollection{FernandezdeVega:2005:HBBAA, author = "Francisco {Fernandez de Vega}", title = "Parallel Genetic Programming: Methodology, History, and Application to Real-Life Problems", booktitle = "Handbook of Bioinspired Algorithms and Applications", publisher = "Chapman and Hall/CRC", year = "2005", editor = "Stephan Olariu and Albert Y. Zomaya", series = "Computer \& Information Science Series", chapter = "5", pages = "5--65--5--84", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-58488-475-0", URL = "http://www.amazon.com/Handbook-Bioinspired-Algorithms-Applications-Information/dp/1584884754", DOI = "doi:10.1201/9781420035063.ch5", notes = "Reviewed by Kushal Chakrabarti, The Book Review Column 40(4), 2009, William Gasarch, http://www.cs.umd.edu/~gasarch/bookrev/", } @Article{FernandezdeVega:2007:JPDC, author = "Francisco {Fernandez de Vega} and Erick Cantu-Paz", title = "Introduction to Special Issue on Parallel Bioinspired Algorithms", journal = "Journal of Parallel and Distributed Computing", year = "2006", volume = "66", number = "8", pages = "989--990", month = aug, email = "fcofdez@unex.es", keywords = "genetic algorithms, genetic programming, Parallel EAs", ISSN = "0743-7315", URL = "http://portal.acm.org/citation.cfm?id=1161625.1161626&coll=&dl=ACM", DOI = "doi:10.1016/j.jpdc.2006.05.001", size = "2 pages", } @Article{FernandezdeVega:2008:SC, author = "Francisco {Fernandez de Vega} and Erick Cantu-Paz", title = "Special Issue on Distributed Bioinspired Algorithms", journal = "Soft Computing", year = "2008", volume = "12", number = "12", pages = "1143--1144", month = oct, email = "fcofdez@unex.es", keywords = "genetic algorithms, genetic programming, Parallel EAs", ISSN = "1432-7643", DOI = "doi:10.1007/s00500-008-0299-7", } @Book{FernandezdeVega:pdci, editor = "Francisco {Fernandez de Vega} and Erick Cantu-Paz", title = "Parallel and Distributed Computational Intelligence", publisher = "Springer", year = "2010", volume = "269", series = "Studies in Computational Intelligence", edition = "1st", keywords = "genetic algorithms, genetic programming, Parallel Computing, Distributed Computing, Grid Computing, GPU", isbn13 = "978-3-642-10674-3", URL = "http://www.springer.com/engineering/mathematical/book/978-3-642-10674-3", DOI = "doi:10.1007/978-3-642-10675-0", abstract = "The growing success of biologically inspired algorithms in solving large and complex problems has spawned many interesting areas of research. Over the years, one of the mainstays in bio-inspired research has been the exploitation of parallel and distributed environments to speedup computations and to enrich the algorithms. From the early days of research on bio-inspired algorithms, their inherently parallel nature was recognised and different parallelisation approaches have been explored. Parallel algorithms promise reductions in execution time and open the door to solve increasingly larger problems. But parallel platforms also inspire new bio-inspired parallel algorithms that, while similar to their sequential counterparts, explore search spaces differently and offer improvements in solution quality. The objective in editing this book was to assemble a sample of the best work in parallel and distributed biologically inspired algorithms. The editors invited researchers in different domains to submit their work. They aimed to include diverse topics to appeal to a wide audience. Some of the chapters summarise work that has been ongoing for several years, while others describe more recent exploratory work. Collectively, these works offer a global snapshot of the most recent efforts of bioinspired algorithms researchers aiming at profiting from parallel and distributed computer architectures including GPUs, Clusters, Grids, volunteer computing and p2p networks as well as multi-core processors. This volume will be of value to a wide set of readers, including, but not limited to specialists in Bioinspired Algorithms, Parallel and Distributed Computing, as well as computer science students trying to figure out new paths towards the future of computational intelligence.", size = "354 pages", } @InProceedings{conf/iwann/VegaSC11, author = "Francisco {Fernandez de Vega} and J. G. {Abengozar Sanchez} and Carlos Cotta", title = "A Preliminary Analysis and Simulation of Load Balancing Techniques Applied to Parallel Genetic Programming", booktitle = "Proceedings of the 11th International Work-Conference on Artificial Neural Networks (IWANN 2011) Part {II}", year = "2011", editor = "Joan Cabestany and Ignacio Rojas and Gonzalo Joya Caparros", volume = "6692", series = "Lecture Notes in Computer Science", pages = "308--315", address = "Torremolinos-Malaga, Spain", month = jun # " 8-10", publisher = "Springer", note = "Advances in Computational Intelligence", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-21497-4", DOI = "doi:10.1007/978-3-642-21498-1_39", size = "8 pages", abstract = "This paper addresses the problem of Load-balancing when Parallel Genetic Programming is employed. Although load-balancing techniques are regularly applied in parallel and distributed systems for reducing makespan, their impact on the performance of different structured Evolutionary Algorithms, and particularly in Genetic Programming, have been scarcely studied. This paper presents a preliminary study and simulation of some recently proposed load balancing techniques when applied to Parallel Genetic Programming, with conclusions that may be extended to any Parallel or Distributed Evolutionary Algorithm.", notes = "http://iwann.ugr.es/2011/", affiliation = "Universidad de Extremadura, Merida, Spain", bibdate = "2011-06-06", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/iwann/iwann2011-2.html#VegaSC11", } @Article{Fernandez:2013:NC, author = "Francisco {Fernandez de Vega} and Gustavo Olague and Leonardo Trujillo and Daniel {Lombrana Gonzalez}", title = "Customizable execution environments for evolutionary computation using BOINC + virtualization", journal = "Natural Computing", year = "2013", volume = "12", number = "2", pages = "163--177", month = jun, keywords = "genetic algorithms, genetic programming", ISSN = "1572-9796", URL = "https://doi.org/10.1007/s11047-012-9343-8", DOI = "doi:10.1007/s11047-012-9343-8", abstract = "Evolutionary algorithms (EAs) consume large amounts of computational resources, particularly when they are used to solve real-world problems that require complex fitness evaluations. Beside the lack of resources, scientists face another problem: the absence of the required expertise to adapt applications for parallel and distributed computing models. Moreover, the computing power of PCs is frequently underused at institutions, as desktops are usually devoted to administrative tasks. Therefore, the proposal in this work consists of providing a framework that allows researchers to massively deploy EA experiments by exploiting the computing power of their instituions' PCs by setting up a Desktop Grid System based on the BOINC middleware. This paper presents a new model for running unmodified applications within BOINC with a web-based centralized management system for available resources. Thanks to this proposal, researchers can run scientific applications without modifying the application's source code, and at the same time manage thousands of computers from a single web page. Summarizing, this model allows the creation of on-demand customized execution environments within BOINC that can be used to harness unused computational resources for complex computational experiments, such as EAs. To show the performance of this model, a real-world application of Genetic Programming was used and tested through a centrally-managed desktop grid infrastructure. Results show the feasibility of the approach that has allowed researchers to generate new solutions by means of an easy to use and manage distributed system.", } @Article{Fernandez:2014:GPEM, author = "F. {Fernandez de Vega} and C. Cruz and L. Navarro and P. Hernandez and T. Gallego and L. Espada", title = "Unplugging Evolutionary Algorithms: an experiment on human-algorithmic creativity", journal = "Genetic Programming and Evolvable Machines", year = "2014", volume = "15", number = "4", pages = "379--402", month = dec, note = "Special issue on GECCO competitions", keywords = "genetic algorithms, genetic programming, Evolutionary art, Computational creativity, Interactive Evolutionary Algorithms", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-014-9225-1", size = "24 pages", abstract = "Understanding and emulating human creativity is a key factor when developing computer based algorithms devoted to art. This paper presents a new evolutionary approach to art and creativity aimed at comprehending human principles and motivations, behaviours and procedures from an evolutionary point of view. The results, and the collective artwork described, is the product of a new methodology derived from the Interactive Evolutionary Algorithm (IEA), that allowed a team of artists to collaborate following evolutionary procedures in a number of generations while providing interesting information from the creative process developed. Instead of relegating artists to merely evaluating the output of a standard IEA, we provided them with the fundamentals, operators and ideas extracted from IEAs, and asked them to apply those principles while creating a collective artwork. Artists thus focused on their inner creative process with an evolutionary perspective, providing insights that hopefully will allow us to improve future versions of EAs when devoted to art. This paper describes the methodology behind the work and the experiment performed, and analyses the collective work generated, that eventually became GECCO 2013 Art Design and Creativity Competition award-winning artwork in Amsterdam.", notes = " 1. Centro Universitario de Merida, University of Extremadura, Merida, Badajoz, Spain 2. Escuela de Arte, Merida, Badajoz, Spain 3. Facultad de Bellas Artes, Universidad de Sevilla, Seville, Spain 4. IES Tierra de Barros, Aceuchal, Spain", } @InProceedings{Fernandez:2016:PPSN, author = "F. {Fernandez de Vega} and F. Chavez and J. Diaz and J. A. Garcia and P. A. Castillo and Juan J. Merelo and C. Cotta", title = "A Cross-Platform Assessment of Energy Consumption in Evolutionary Algorithms Towards Energy-Aware Bioinspired Algorithms", booktitle = "14th International Conference on Parallel Problem Solving from Nature", year = "2016", editor = "Julia Handl and Emma Hart and Peter R. Lewis and Manuel Lopez-Ibanez and Gabriela Ochoa and Ben Paechter", volume = "9921", series = "LNCS", address = "Edinburgh", month = "17-21 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Green computing, Energy-aware computing, Performance measurements, Evolutionary algorithms", isbn13 = "978-3-319-45823-6", DOI = "doi:10.1007/978-3-319-45823-6_51", size = "10 pages", abstract = "Energy consumption is a matter of paramount importance in nowadays environmentally conscious society. It is also bound to be a crucial issue in light of the emergent computational environments arising from the pervasive use of networked hand-held devices and wearables. Evolutionary algorithms (EAs) are ideally suited for this kind of environments due to their intrinsic flexibility and adaptiveness, provided they operate on viable energy terms. In this work we analyse the energy requirements of EAs, and particularly one of their main flavours, genetic programming (GP), on several computational platforms and study the impact that parametrisation has on these requirements, paving the way for a future generation of energy-aware EAs. As experimentally demonstrated, handheld devices and tiny computer models mainly used for educational purposes may be the most energy efficient ones when looking for solutions by means of EAs.", notes = "PPSN2016", } @InProceedings{Fernandez:2019:GPTP, author = "Francisco {Fernandez de Vega} and Gustavo Olague and Francisco Chavez and Daniel Lanza and Wolfgang Banzhaf and Erik Goodman", title = "It is time for new perspectives on how to fight bloat in {GP}", booktitle = "Genetic Programming Theory and Practice XVII", year = "2019", editor = "Wolfgang Banzhaf and Erik Goodman and Leigh Sheneman and Leonardo Trujillo and Bill Worzel", pages = "25--38", address = "East Lansing, MI, USA", month = "16-19 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-030-39957-3", URL = "https://arxiv.org/abs/2005.00603", DOI = "doi:10.1007/978-3-030-39958-0_2", abstract = "The present and future of evolutionary algorithms depends on the proper use of modern parallel and distributed computing infrastructures. Although still sequential approaches dominate the landscape, available multi-core, many-core and distributed systems will make users and researchers to more frequently deploy parallel version of the algorithms. In such a scenario, new possibilities arise regarding the time saved when parallel evaluation of individuals are performed. And this time saving is particularly relevant in Genetic Programming. This paper studies how evaluation time influences not only time to solution in parallel/distributed systems, but may also affect size evolution of individuals in the population, and eventually will reduce the bloat phenomenon GP features. This paper considers time and space as two sides of a single coin when devising a more natural method for fighting bloat. This new perspective allows us to understand that new methods for bloat control can be derived, and the first of such a method is described and tested. Experimental data confirms the strength of the approach: using computing time as a measure of individuals complexity allows to control the growth in size of genetic programming individuals.", notes = "Part of \cite{Banzhaf:2019:GPTP}, published after the workshop", } @Article{Fernandez-de-Vega:2020:ACC, author = "Francisco {Fernandez de Vega} and Gustavo Olague and Daniel Lanza and Francisco {Chavez de la O} and Wolfgang Banzhaf and Erik Goodman and Jose Menendez-Clavijo and Axel Martinez", journal = "IEEE Access", title = "Time and Individual Duration in Genetic Programming", year = "2020", volume = "8", pages = "38692--38713", keywords = "genetic algorithms, genetic programming, 2.4GHz x86 E5530, ECJ", DOI = "doi:10.1109/ACCESS.2020.2975753", ISSN = "2169-3536", abstract = "This paper presents a new way of measuring complexity in variable-size-chromosome-based evolutionary algorithms. Dealing with complexity is particularly useful when considering bloat in Genetic Programming. Instead of analyzing size growth, we focus on the time required for individuals' fitness evaluations, which correlates with size. This way, we consider time and space as two sides of a single coin when devising a more natural method for fighting bloat. We thus view the problem from a perspective that departs from traditional methods applied in Genetic Programming. We have analyzed first the behavior of individuals across generations, taking into account their fitness evaluation times, thus providing clues about the general practice of the evolutionary process when modern parallel and distributed computers are used to run the algorithm. This new perspective allows us to understand that new methods for bloat control can be derived. Moreover, we develop from this framework a first proposal to show the usefulness of the idea: to group individuals in classes according to computing time required for evaluation, automatically accomplished by parallel and distributed systems without any change in the underlying algorithm, when they are only allowed to breed within their classes. Experimental data confirms the strength of the approach: using computing time as a measure of individuals' complexity allows control of the natural size growth of genetic programming individuals while preserving the quality of solutions in both the parallel and sequential versions of the algorithm.", notes = "parity, ant, lawnmower, multiplexer, even-6 parity, regression, image object recognition cifar-10. p38694 'address the bloat phenomenon more naturally' Also known as \cite{9006776}", } @InProceedings{fernandez:1996:wrGPmsrp, author = "Jaime J. Fernandez and Kristin A. Farry and John B. Cheatham", title = "Waveform Recognition Using Genetic Programming: The Myoelectric Signal Recognition Problem", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "63--71", address = "Stanford University, CA, USA", publisher = "MIT Press", broken = "ftp://hobbes.jsc.nasa.gov/pub/jjf/gp96.gz", size = "9 pages", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap8.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96", } @InProceedings{Fernandez:1998:ICRA, author = "Jaime J. Fernandez and Ian D. Walker", title = "Biologically inspired robot grasping using genetic programming", booktitle = "Proceedings. 1998 IEEE International Conference on Robotics and Automation", year = "1998", volume = "4", pages = "3032--3039", address = "Leuven, Belgium", month = may, keywords = "genetic algorithms, genetic programming", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.596.1632", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.596.1632", URL = "http://aass.oru.se/~tdt/ml/extra-readings/bio_inspired_robot_grasping.pdf", DOI = "doi:10.1109/ROBOT.1998.680891", size = "8 pages", abstract = "This paper describes the innovative use of a genetic algorithm to solve the grasp synthesis problem for multi-fingered robot hands. The goal of our algorithm is to select a best grasp of an object, given some information about the object geometry and some user-defined fitness functions which intuitively delineate good from bad grasp qualities. The fitness functions are used by the specially designed genetic algorithm, which iteratively selects the grasp. The approach is biologically inspired both in the use of the genetic algorithm to evolve populations of candidate grasps, and in the choice of fitness functions, which adapt intuition from nature to guide the evolution process", notes = "also known as \cite{680891} Cat. No.98CH36146", } @InProceedings{Fernandez:1997:tpsets, author = "Thomas Fernandez and Matthew Evett", title = "Training Period Size and Evolved Trading Systems", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming", pages = "95", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1997/Fernandez_1997_tpsets.pdf", size = "1 page", notes = "GP-97", } @InProceedings{fernandez:1998:nmisrGP, author = "Thomas Fernandez and Matthew Evett", title = "Numeric Mutation as an Improvement to Symbolic Regression in Genetic Programming", booktitle = "Evolutionary Programming VII: Proceedings of the Seventh Annual Conference on Evolutionary Programming", year = "1998", editor = "V. William Porto and N. Saravanan and D. Waagen and A. E. Eiben", volume = "1447", series = "LNCS", pages = "251--260", address = "Mission Valley Marriott, San Diego, California, USA", publisher_address = "Berlin", month = "25-27 " # mar, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-64891-7", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/fernandez_1998_nmisrGP.pdf", URL = "https://rdcu.be/daynn", DOI = "doi:10.1007/BFb0040778", size = "10 pages", abstract = "A weakness of genetic programming (GP) is the difficulty it suffers in discovering useful numeric constants for the terminal nodes of the s-expression trees. We examine a solution to this problem called numeric mutation based roughly on simulated annealing. We provide empirical evidence to demonstrate that this method provides a statistically significant improvement in GP system performance for symbolic regression problems. GP runs are more likely to find a solution and successful runs use fewer generations", notes = "EP-98. Florida Atlantic University, Boca Raton, FL", } @InProceedings{fernandez:vro:gecco2004, author = "Thomas Fernandez", title = "Virtual Ramping of Genetic Programming Populations", booktitle = "Genetic and Evolutionary Computation -- GECCO-2004, Part II", year = "2004", editor = "Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell", series = "Lecture Notes in Computer Science", pages = "471--482", address = "Seattle, WA, USA", publisher_address = "Heidelberg", month = "26-30 " # jun, organisation = "ISGEC", publisher = "Springer-Verlag", volume = "3103", ISBN = "3-540-22343-6", ISSN = "0302-9743", DOI = "doi:10.1007/b98645", size = "12", keywords = "genetic algorithms, genetic programming", notes = "GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)", } @PhdThesis{Thomas_Fernandez:thesis, author = "Thomas Fernandez", title = "Novel Techniques in Genetic Programming", school = "Florida Atlantic University", year = "2006", address = "Boca Raton, FL, USA", month = dec, keywords = "genetic algorithms, genetic programming", isbn13 = "978-0-542-93591-6", URL = "http://search.proquest.com/docview/305311992", size = "155 pages", abstract = "Three major problems make Genetic Programming unfeasible or impractical for real world problems. The first is the excessive time complexity. In nature the evolutionary process can take millions of years, a time frame that is clearly not acceptable for the solution of problems on a computer. In order to apply Genetic Programming to real world problems, it is essential that its efficiency be improved. The second is called overfitting (where results are inaccurate outside the training data). In a paper[36] for the Federal Reserve Bank, authors Neely and Weller state a perennial problem with using flexible, powerful search procedures like Genetic Programming is over fitting, the finding of spurious patterns in the data. Given the well-documented tendency for the genetic program to over fit the data it is necessary to design procedures to mitigate this. The third is the difficulty of determining optimal control parameters for the Genetic Programming process. Control parameters control the evolutionary process.They include settings such as, the size of the population and the number of generations to be run. In his book[45], Banzhaf describes this problem, The bad news is th at Genetic Programming is a young field and the effect of using various combinations of parameters is just beginning to be explored. We address these problems by implementing and testing a number of novel techniques and improvements to the Genetic Programming process. We conduct experiments using datasets of various degrees of difficulty to demonstrate success with a high degree of statistical confidence.", notes = "Supervisor: Borko Furht UMI Number: 3238947", } @InProceedings{fernandez:1999:ABIFFRG, author = "J. Jaime {Fernandez Jr.} and Ian D. Walker", title = "A Biologically Inspired Fitness Function for Robotic Grasping", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1517--1522", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, real world applications", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-744.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-744b.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Fernandez:2014:CLEI, author = "Nelson Fernandez and Jose Aguilar and Gustavo Marcano and Oswaldo Teran and Carlos Gershenson", booktitle = "XL Latin American Computing Conference (CLEI 2014)", title = "Modeling and specification of the aquatic ecological emergence using genetic programming", year = "2014", month = sep, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CLEI.2014.6965172", size = "9 pages", abstract = "A major endeavour of ecology is to understand the emergence of complexity. This task requires the integration of knowledge and theories, moving from physical to social sciences. We use genetic programming to develop mathematical relationships between ecological emergence and variables such as self-organisation, homeostasis, autopoiesis and complexity. These variables were initially formalised on the basis of information theory. The emergence models found were applied and tested with a case study involving an arctic lake and a tropical lake. In these lakes, the variables of limiting nutrients, biomass and physico-chemical components were taken into account for the automated generation of the model equations. The results show that the model follows in the dynamics of the aquatic ecological components selected accurately. In this context, ecological emergence can be calculated and studied.", notes = "Lab. de Hidroinformatica, Univ. de Pamplona, Pamplona, Colombia Also known as \cite{6965172}", } @InProceedings{DBLP:conf/cosecivi/Fernandez-AresGMCG14, author = "Antonio Fernandez-Ares and Pablo Garcia-Sanchez and Antonio Miguel Mora and Pedro A. Castillo and Juan Julian Merelo Guervos", title = "Designing competitive bots for a real time strategy game using genetic programming", booktitle = "Proceedings 1st Congreso de la Sociedad Espanola para las Ciencias del Videojuego, CoSECivi 2014", year = "2014", editor = "David Camacho and Marco Antonio Gomez-Martin and Pedro Antonio Gonzalez-Calero", series = "CEUR Workshop Proceedings", volume = "1196", pages = "159--172", address = "Barcelona, Spain", month = jun # " 24", publisher = "CEUR-WS.org", keywords = "genetic algorithms, genetic programming", bibsource = "dblp computer science bibliography, http://dblp.org", URL = "http://ceur-ws.org/Vol-1196", URL = "http://ceur-ws.org/Vol-1196/cosecivi14_submission_24.pdf", size = "14 pages", abstract = "The design of the Artificial Intelligence (AI) engine for an autonomous agent (bot) in a game is always a difficult task mainly done by an expert human player, who has to transform his/her knowledge into a behavioural engine. This paper presents an approach for conducting this task by means of Genetic Programming (GP) application. This algorithm is applied to design decision trees to be used as bot's AI in 1 vs 1 battles inside the RTS game Planet Wars. Using this method it is possible to create rule-based systems defining decisions and actions, in an automatic way, completely different from a human designer doing them from scratch. These rules will be optimised along the algorithm run, considering the bots' performance during evaluation matches. As GP can generate and evolve behavioural rules not taken into account by an expert, the obtained bots could perform better than human-defined ones. Due to the difficulties when applying Computational Intelligence techniques in the videogames scope, such as noise factor in the evaluation functions, three different fitness approaches have been implemented and tested in this work. Two of them try to minimise this factor by considering additional dynamic information about the evaluation matches, rather than just the final result (the winner), as the other function does. In order to prove them, the best obtained agents have been compared with a previous bot, created by an expert player (from scratch) and then optimised by means of Genetic Algorithms. The experiments show that the three used fitness functions generate bots that outperform the optimised human-defined one, being the area-based fitness function the one that produces better results.", } @Article{journals/soco/Fernandez-BlancoRGD13, author = "Enrique Fernandez-Blanco and Daniel Rivero and Marcos Gestal and Julian Dorado", title = "Classification of signals by means of Genetic Programming", journal = "Soft Computing", year = "2013", number = "10", volume = "17", pages = "1929--1937", keywords = "genetic algorithms, genetic programming, GP, Automatic feature extraction Automatic classification Signal processing", bibdate = "2013-09-09", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/soco/soco17.html#Fernandez-BlancoRGD13", URL = "http://dx.doi.org/10.1007/s00500-013-1036-4", size = "9 pages", abstract = "This paper describes a new technique for signal classification by means of Genetic Programming (GP). The novelty of this technique is that no prior knowledge of the signals is needed to extract the features. Instead of it, GP is able to extract the most relevant features needed for classification. This technique has been applied for the solution of a well-known problem: the classification of EEG signals in epileptic and healthy patients. In this problem, signals obtained from EEG recordings must be correctly classified into their corresponding class. The aim is to show that the technique described here, with the automatic extraction of features, can return better results than the classical techniques based on manual extraction of features. For this purpose, a final comparison between the results obtained with this technique and other results found in the literature with the same database can be found. This comparison shows how this technique can improve the ones found.", } @Article{fernandez-carrillo:2022:Forests, author = "Victor Hugo Fernandez-Carrillo and Victor Hugo Quej-Chi and Hector Manuel {De los Santos-Posadas} and Eugenio Carrillo-Avila", title = "Do {AI} Models Improve Taper Estimation? A Comparative Approach for Teak", journal = "Forests", year = "2022", volume = "13", number = "9", pages = "Article No. 1465", keywords = "genetic algorithms, genetic programming", ISSN = "1999-4907", URL = "https://www.mdpi.com/1999-4907/13/9/1465", DOI = "doi:10.3390/f13091465", abstract = "Correctly estimating stem diameter at any height is an essential task in determining the profitability of a commercial forest plantation, since the integration of the cross-sectional area along the stem of the trees allows estimating the timber volume. In this study the ability of four artificial intelligence (AI) models to estimate the stem diameter of Tectona grandis was assessed. Genetic Programming (PG), Gaussian Regression Process (PGR), Category Boosting (CatBoost) and Artificial Neural Networks (ANN) models’ ability was evaluated and compared with those of Fang 2000 and Kozak 2004 conventional models. Coefficient of determination (R2), Root Mean Square of Error (RMSE), Mean Error of Bias (MBE) and Mean Absolute Error (MAE) statistical indices were used to evaluate the models’ performance. Goodness of fit criterion of all the models suggests that Kozak’s model shows the best results, closely followed by the ANN model. However, PG, PGR and CatBoost outperformed the Fang model. Artificial intelligence methods can be an effective alternative to describe the shape of the stem in Tectona grandis trees with an excellent accuracy, particularly the ANN and CatBoost models.", notes = "also known as \cite{f13091465}", } @InProceedings{Fernandez-Leiva:2011:IWINAC, author = "Antonio Jose {Fernandez Leiva} and Jorge L. {O'Valle Barragan}", title = "Decision Tree-Based Algorithms for Implementing Bot AI in UT2004", booktitle = "Proceedings of the 4th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2011, Part I", year = "2011", editor = "Jose Manuel Ferrandez and Jose Ramon {Alvarez Sanchez} and Felix {de la Paz} and F. Javier Toledo", series = "Lecture Notes in Computer Science", pages = "383--392", volume = "6686", address = "La Palma, Canary Islands, Spain", month = may # " 30-" # jun # " 3", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-21343-4", DOI = "doi:10.1007/978-3-642-21344-1_40", abstract = "This paper describes two different decision tree-based approaches to obtain strategies that control the behaviour of bots in the context of the Unreal Tournament 2004. The first approach follows the traditional process existing in commercial video games to program the game artificial intelligence (AI), that is to say, it consists of coding the strategy manually according to the AI programmer's experience with the aim of increasing player satisfaction. The second approach is based on evolutionary programming techniques and has the objective of automatically generating the game AI. An experimental analysis is conducted in order to evaluate the quality of our proposals. This analysis is executed on the basis of two fitness functions that were defined intuitively to provide entertainment to the player. Finally a comparison between the two approaches is done following the subjective evaluation principles imposed by the 2k bot prize competition.", affiliation = "Dept. Lenguajes y Ciencias de la Computacion, ETSI Informatica, Campus de Teatinos, Universidad de Malaga, 29071 Malaga, Spain", } @Article{Fernandez-VillacanasMartin:2003:FGCS, author = "Jose-Luis {Fernandez-Villacanas Martin} and Mark Shackleton", title = "Investigation of the importance of the genotype-phenotype mapping in information retrieval", journal = "Future Generation Computer Systems", year = "2003", volume = "19", pages = "55--68", number = "1", keywords = "genetic algorithms, genetic programming, Genotype-phenotype mapping, Information retrieval", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6V06-478HYP6-1/2/4edc0c200ae393af0e1c9cb343c0cf5d", ISSN = "0167-739X", DOI = "doi:10.1016/S0167-739X(02)00108-5", abstract = "An investigation of the role of the genotype-phenotype mapping (G-Pm) is presented for an evolutionary optimisation task. A simple genetic algorithm (SGA) plus a mapping creates a new mapping genetic algorithm (MGA) that is used to optimize a Boolean decision tree for an information retrieval task, with the tree being created via a relatively complex mapping. Its performance is contrasted with that of a genetic programming algorithm, British Telecom Genetic Programming (BTGP) which operates directly on phenotypic trees. The mapping is observed to play an important role in the time evolution of the system allowing the MGA to achieve better results than the BTGP. We conclude that an appropriate G-Pm can improve the evolvability of evolutionary algorithms.", } @Article{FernandezAres:2017:EC, author = "A. Fernandez-Ares and A. M. Mora and P. Garcia-Sanchez and P. A. Castillo and J. J. Merelo", title = "Analysing the influence of the fitness function on genetically programmed bots for a real-time strategy game", journal = "Entertainment Computing", volume = "18", pages = "15--29", year = "2017", ISSN = "1875-9521", DOI = "doi:10.1016/j.entcom.2016.08.001", URL = "http://www.sciencedirect.com/science/article/pii/S1875952116300222", abstract = "Finding the global best strategy for an autonomous agent (bot) in a RTS game is a hard problem, mainly because the techniques applied to do this must deal with uncertainty and real-time planning in order to control the game agents. This work describes an approach applying a Genetic Programming (GP) algorithm to create the behavioural engine of bots able to play a simple RTS. Normally it is impossible to know in advance what kind of strategies will be the best in the most general case of this problem. So GP, which searches the general decision tree space, has been introduced and used successfully. However, it is not straightforward what fitness function would be the most convenient to guide the evolutionary process in order to reach the best solutions and also being less sensitive to the uncertainty present in the context of games. Thus, in this paper three different evaluation functions have been proposed, and a detailed analysis of their performance has been conducted. The paper also analyses several aspects of the obtained bots, in addition to their final performance on battles, such as the evolution of the decision trees (behavioural models) themselves, or the influence on the results of noise or uncertainty. The results show that a victory-based fitness, which prioritises the number of victories, contributes to generate better bots, on average, than other functions based on other numerical aspects of the battles, such as the number of resources gathered, or the number of units generated.", keywords = "genetic algorithms, genetic programming, Real-time strategy game, Autonomous agent, Bot, Fitness function, Uncertainty", } @Misc{fernando2023promptbreeder, author = "Chrisantha Fernando and Dylan Banarse and Henryk Michalewski and Simon Osindero and Tim Rocktaschel", title = "Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution", howpublished = "arXiv 2309.16797", year = "2023", month = "28 " # sep, keywords = "genetic algorithms, genetic programming, ANN, LLM", eprint = "2309.16797", archiveprefix = "arXiv", URL = "https://arxiv.org/pdf/2309.16797", size = "64 pages", abstract = "Popular prompt strategies like Chain-of-Thought Prompting can dramatically improve the reasoning abilities of Large Language Models (LLMs) in various domains. However, such hand-crafted prompt-strategies are often sub-optimal. we present Promptbreeder, a general-purpose self-referential self-improvement mechanism that evolves and adapts prompts for a given domain. Driven by an LLM, Promptbreeder mutates a population of task-prompts, and subsequently evaluates them for fitness on a training set. Crucially, the mutation of these task-prompts is governed by mutation-prompts that the LLM generates and improves throughout evolution in a self-referential way. That is, Promptbreeder is not just improving task-prompts, but it is also improving the mutation prompts that improve these task-prompts. Promptbreeder outperforms state-of-the-art prompt strategies such as Chain-of-Thought and Plan-and-Solve Prompting on commonly used arithmetic and commonsense reasoning benchmarks. Furthermore, Promptbreeder is able to evolve intricate task-prompts for the challenging problem of hate speech classification.", notes = "'We then run a standard binary tournament genetic algorithm' Google DeepMind", } @PhdThesis{Ruwan_Fernando_Thesis, author = "Ruwan A. Fernando", title = "Representations for Evolutionary Design Modelling", school = "Queensland University of Technology", year = "2014", address = "Australia", month = "7 " # mar, keywords = "genetic algorithms, genetic programming, Evolutionary Design, Computer Aided Design, Spatial Planning, Generative Design, Evolutionary Architecture", URL = "http://eprints.qut.edu.au/68252/", URL = "http://eprints.qut.edu.au/68252/1/Ruwan_Fernando_Thesis.pdf", size = "231 pages", abstract = "Evolutionary design modelling is a form of generative design, where processes inspired by biological evolution are used to produce populations of solutions to design problems. An important element within this strategy, is how genes are abstracted and used to represent solutions to the design problem. The basis of this thesis, is that developing this area (the representation of genes) is a good way to further the field of evolutionary design modelling. Representations used in the study of language grammars, computer algorithms and dynamic systems are examined with their potential for structuring the genetic code. The aim of this is to create find representations that are stable after genetic operations, expressive enough to represent design problems and have enough granularity that novel solutions emerge from these simulations.", notes = "Koza mentioned p136. Supervisors John Frazer, Robin Drogemulle, and Keith Duddy", } @InProceedings{Fernando:2019:ICAC, author = "M. J. D. Fernando and D. A. K. K. Pathirana and W. J. K. T. D. Jayasooriya and S. A. H. Rathnaweera and Lakmal Rupasinghe", booktitle = "2019 International Conference on Advancements in Computing (ICAC)", title = "Intelligent Flood Management System", year = "2019", pages = "79--84", month = dec, keywords = "genetic algorithms, genetic programming, Sri Lanka, ceylon", DOI = "doi:10.1109/ICAC49085.2019.9103407", abstract = "Flooding is one of the major disasters in Sri Lanka. In Sri Lanka, there are no effective pre preparedness procedures follow in a flooding situation. The setting of pre and post-disaster activities like mitigation, preparedness, response, and recovery have very important roles in reducing future hazard risk in disaster-prone areas. Lack of communication and coordination during a disaster situation has led inefficiencies in mitigating adverse, in that situation, messages requesting for any assistance are sent to a central cloud system where the system generates response automatically and communicate and coordinate with the relevant parties. The genetic programming methods have used to plan relief supply distribution and safety location allocation for the flood-affected people in Sri Lanka. The research provides a guide for the administration of flood management for decision making on flood disaster management, preparedness and mitigation damages and deaths, recovery, and development in post-disaster situations in Sri Lanka.", notes = "Faculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka Also known as \cite{9103407}", } @PhdThesis{Fernlund:thesis, author = "Hans Karl Gustav Fernlund", title = "Evolving models from observed human performance", school = "Electrical Engineering and Computer Science, University of Central Florida", year = "2004", address = "Orlando, Fla., USA", month = "Spring Term", keywords = "genetic algorithms, genetic programming, Context based reasoning, CxBR, Human behavioral modeling, Learning by observation, Simulation", URL = "http://purl.fcla.edu/fcla/etd/CFE0000013", URL = "http://purl.fcla.edu/fcla/etd/CFE0000013/Fernlund_Hans_K_200405_PhD.pdf", size = "234 pages", abstract = "To create a realistic environment, many simulations require simulated agents with human behavior patterns. Manually creating such agents with realistic behavior is often a tedious and time-consuming task. This dissertation describes a new approach that automatically builds human behaviour models for simulated agents by observing human performance. The research described in this dissertation synergistically combines Context-Based Reasoning, a paradigm especially developed to model tactical human performance within simulated agents, with Genetic Programming, a machine learning algorithm to construct the behavior knowledge in accordance to the paradigm. This synergistic combination of well-documented AI methodologies has resulted in a new algorithm that effectively and automatically builds simulated agents with human behaviour. This algorithm was tested extensively with five different simulated agents created by observing the performance of five humans driving an automobile simulator. The agents show not only the ability/capability to automatically learn and generalise the behavior of the human observed, but they also capture some of the personal behavior patterns observed among the five humans. Furthermore, the agents exhibited a performance that was at least as good as agents developed manually by a knowledgeable engineer.", notes = "Adviser: Avelino J. Gonzalez http://ucf.catalog.fcla.edu/cf.jsp?Ntt=CF001100798&Ntk=Number&Nty=1&N=29&I=0&V=D OpenGP", } @InProceedings{fernlund:2004:lbp, author = "Hans Fernlund and Avelino J. Gonzalez", title = "Using GP to Model Contextual Human Behavior - Competitive with Human Modeling Performance", booktitle = "Late Breaking Papers at the 2004 Genetic and Evolutionary Computation Conference", year = "2004", editor = "Maarten Keijzer", address = "Seattle, Washington, USA", month = "26 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://gpbib.cs.ucl.ac.uk/gecco2004/LBP015.pdf", abstract = "To create a realistic environment, some simulations require simulated agents with human behaviour pattern. Creating such agents with realistic behavior can be a tedious and time consuming work. This paper describes a new approach that automatically builds human behaviour models for simulated agents by observing human performance. With an automatic tool that builds human behavioral agents, the development cost and effort could be dramatically reduced. This research synergistically combines Context-Based Reasoning (CxBR), a paradigm especially developed to model tactical human performance within simulated agents, with the Genetic Programming machine learning algorithm able to construct the behaviour knowledge in accordance to the CxBR paradigm. This synergistic combination of AI methodologies has resulted in a new algorithm that automatically builds simulated agents with human behavior. This algorithm was exhaustively tested with five different simulated agents created by observing the performance of five humans driving an automobile simulator. The agents show, not only the capabilities to automatically learn and generalise the behaviour of the human observed, but they also exhibited a performance that was at least as good as that of agents developed manually by a knowledge engineer.", notes = "Part of \cite{keijzer:2004:GECCO:lbp}", } @InProceedings{fernlund:ugt:gecco2004, author = "Hans Fernlund and Avelino J. Gonzalez", title = "Using GP to Model Contextual Human Behavior", booktitle = "Genetic and Evolutionary Computation -- GECCO-2004, Part II", year = "2004", editor = "Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell", series = "Lecture Notes in Computer Science", pages = "704--705", address = "Seattle, WA, USA", publisher_address = "Heidelberg", month = "26-30 " # jun, organisation = "ISGEC", publisher = "Springer-Verlag", volume = "3103", keywords = "genetic algorithms, genetic programming, Poster", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.531.5772", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.531.5772", URL = "https://www.cs.york.ac.uk/rts/docs/GECCO_2004/Conference%20proceedings/papers/3103/31030704.pdf", ISBN = "3-540-22343-6", ISSN = "0302-9743", DOI = "doi:10.1007/b98645", size = "2", abstract = "This paper describes a new approach that automatically builds human behaviour models for simulated agents by observing human performance. This research synergistically combines Context-Based Reasoning, a paradigm especially developed to model tactical human performance within simulated agents, with Genetic Programming that is able to construct the behavior knowledge in accordance to the Context-Based Reasoning paradigm.", notes = "GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)", } @InProceedings{Fernlund:2004:SAWMAS, author = "Hans Fernlund and Sven Eklund and Avelino J. Gonzalez", title = "The {CxBR} Diffusion Engine -- A Tool for Modeling Human Behavior on the Battle Field", booktitle = "The Second Swedish-American Workshop on Modeling and Simulation, SAWMAS-2004", year = "2004", editor = "Avelino J. Gonzalez and Johan Jenvald and Soren Palmgren", address = "Holiday Inn, Cocoa Beach, Florida", month = feb # " 2-3", keywords = "genetic algorithms, genetic programming", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.454.2112", pages = "10", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.454.2112", URL = "http://www.mind.foi.se/SAWMAS/SAWMAS-2004/Papers/P10-SAWMAS-2004-H-Fernlund.pdf", abstract = "The option to automatically model the behaviour of different actors during live exercise training would increase the value of the after-action-review (AAR) process. If a simulated model of the actors is available right after the live exercise training, the evaluation of their behaviour would be more timely and alternative actions could also be evaluated at the same time. The CxBR Diffusion Engine merges technologies to establish a tool for automatic, on-line behaviour modelling. Context Based Reasoning (CxBR) is a proven methodology to build simulated agents with human behaviour. Genetic Programming (GP) provides the CxBR framework with learning capabilities to automatically create simulated agents with human behaviour. The final piece in the CxBR Diffusion Engine is to provide an efficient, flexible, scaleable and mobile platform to evolve the agents behaviour. This platform is the newly developed massively parallel architecture for distributed GP. The massively parallel architecture has the potential to execute the GP linear machine code representation at a rate of up to 50,000 generations per second. Implemented in an FPGA, this architecture is highly portable and applicable to mobile, on-line applications. This paper will present a theory on how the CxBR + GP can evolve simulated agents with human behaviour by observation in a massively parallel architecture. These pieces will introduce all the necessary elements to build the CxBR Diffusion Engine that could model human behaviour to enable individual AAR of trainees in the training field.", notes = "http://www.mind.foi.se/SAWMAS/SAWMAS-2004/index.html", } @Article{FGGD06, author = "Hans K. G. Fernlund and Avelino J. Gonzalez and Michael Georgiopoulos and Ronald F. DeMara", title = "Learning tactical human behavior through observation of human performance", journal = "IEEE Transactions on Systems, Man and Cybernetics, Part B", year = "2006", volume = "36", number = "1", month = feb, pages = "128--140", keywords = "genetic algorithms, genetic programming, inference mechanisms, knowledge representation, learning (artificial intelligence), software agents, context-based reasoning, human performance observation, knowledge acquisition, tactical agent development, tactical human behavioural learning, tactical knowledge elicitation, tactical knowledge representation, Context-based reasoning, human behavioral modeling, simulation", ISSN = "1083-4419", URL = "http://www.cal.ucf.edu/journal/j_fernlund_gonzalez_itsmc_04.pdf", DOI = "doi:10.1109/TSMCB.2005.855568", size = "13 pages", abstract = "It is widely accepted that the difficulty and expense involved in acquiring the knowledge behind tactical behaviours has been one limiting factor in the development of simulated agents representing adversaries and teammates in military and game simulations. Several researchers have addressed this problem with varying degrees of success. The problem mostly lies in the fact that tactical knowledge is difficult to elicit and represent through interactive sessions between the model developer and the subject matter expert. This paper describes a novel approach that employs genetic programming in conjunction with context-based reasoning to evolve tactical agents based upon automatic observation of a human performing a mission on a simulator. we describe the process used to carry out the learning. A prototype was built to demonstrate feasibility and it is described herein. The prototype was rigorously and extensively tested. The evolved agents exhibited good fidelity to the observed human performance, as well as the capacity to generalise from it.", notes = "INSPEC Accession Number:8736964 Dept. of Culture, Dalarna Univ., Borlange, Sweden", } @InProceedings{Ferrante:2013:GECCO, author = "Eliseo Ferrante and Edgar Duenez-Guzman and Ali Emre Turgut and Tom Wenseleers", title = "{GESwarm}: grammatical evolution for the automatic synthesis of collective behaviors in swarm robotics", booktitle = "GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference", year = "2013", editor = "Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi", isbn13 = "978-1-4503-1963-8", pages = "17--24", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", keywords = "genetic algorithms, genetic programming, grammatical evolution", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.309.3738", URL = "http://bio.kuleuven.be/ento/pdfs/ferrante_etal_gecco_2013.pdf", DOI = "doi:10.1145/2463372.2463385", publisher = "ACM", publisher_address = "New York, NY, USA", size = "8 pages", abstract = "In this paper we propose GESwarm, a novel tool that can automatically synthesise collective behaviours for swarms of autonomous robots through evolutionary robotics. Evolutionary robotics typically relies on artificial evolution for tuning the weights of an artificial neural network that is then used as individual behaviour representation. The main caveat of neural networks is that they are very difficult to reverse engineer, meaning that once a suitable solution is found, it is very difficult to analyse, to modify, and to tease apart the inherent principles that lead to the desired collective behaviour. In contrast, our representation is based on completely readable and analysable individual-level rules that lead to a desired collective behaviour. The core of our method is a grammar that can generate a rich variety of collective behaviours. We test GESwarm by evolving a foraging strategy using a realistic swarm robotics simulator. We then systematically compare the evolved collective behaviour against an hand-coded one for performance, scalability and flexibility, showing that collective behaviours evolved with GESwarm can outperform the hand-coded one.", notes = "Also known as \cite{2463385} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)", } @PhdThesis{FerrantePhd, author = "Eliseo Ferrante", title = "Information Transfer in a Flocking Robot Swarm", school = "Universit\'e Libre de Bruxelles", year = "2013", address = "Belgium", keywords = "genetic algorithms, genetic programming, PSO, information transfer, collective motion, statistical physics, swarm robotics, real robots", URL = "http://bio.kuleuven.be/ento/ferrante/papers/FerrantePhD.pdf", size = "162 pages", abstract = "In this dissertation, we propose and study methods for information transfer within a swarm of mobile robots that coordinately move, or flock, in a common direction. We define information transfer as the process whereby robots share directional information in order to coordinate their heading direction. We identify two paradigms of information transfer: explicit information transfer and implicit information transfer. In explicit information transfer, directional information is transferred via communication. Explicit information transfer requires mobile robots equipped with a a communication device. We propose novel communication strategies for explicit information transfer, and we perform flocking experiments in different situations: with one or two desired directions of motion that can be static or change over time. We perform experiments in simulation and with real robots. Furthermore, we show that the same explicit information transfer strategies can also be applied to another collective behaviour: collective transport with obstacle avoidance. In implicit information transfer, directional information is transferred without communication. We show that a simple motion control method is sufficient to guarantee cohesive and aligned motion without resorting to communication or elaborate sensing. We analyse the motion control method for its capability to achieve flocking with and without a desired direction of motion, both in simulation and using real robots. Furthermore, to better understand its underlying mechanism, we study this method using tools of statistical physics, showing that the process can be explained in terms of non-linear elasticity and energy-cascading dynamics.", notes = "Supervisor Marco Dorigo. in english", } @Article{oai:HAL:hal-01378166v1, author = "Eliseo Ferrante and Ali Turgut and Edgar Duenez-Guzman and Marco Dorigo and Tom Wenseleers", title = "Evolution of Self-Organized Task Specialization in Robot Swarms", journal = "PLoS Computational Biology", year = "2015", volume = "11", pages = "1004273--1004273", month = aug # " 6", keywords = "genetic algorithms, genetic programming, artificial intelligence, machine learning, multiagent systems nonlinear sciences, adaptation and self-organising systems", ISSN = "1553-734X; 1553-7358", bibsource = "OAI-PMH server at api.archives-ouvertes.fr", identifier = "hal-01378166", DOI = "doi:10.1371/journal.pcbi.1004273.s009", language = "en", oai = "oai:HAL:hal-01378166v1", relation = "info:eu-repo/semantics/altIdentifier/doi/10.1371/journal.pcbi.1004273.s009", URL = "https://hal.archives-ouvertes.fr/hal-01378166", URL = "https://hal.archives-ouvertes.fr/hal-01378166/document", URL = "https://hal.archives-ouvertes.fr/hal-01378166/file/2015_PlosComputationalBiology.pdf", DOI = "doi:10.1371/journal.pcbi.1004273", size = "21 pages", abstract = "Division of labour is ubiquitous in biological systems, as evidenced by various forms of complex task specialization observed in both animal societies and multicellular organisms. Although clearly adaptive, the way in which division of labor first evolved remains enigmatic, as it requires the simultaneous co-occurrence of several complex traits to achieve the required degree of coordination. Recently, evolutionary swarm robotics has emerged as an excellent test bed to study the evolution of coordinated group-level behaviour. Here we use this framework for the first time to study the evolutionary origin of behavioural task specialization among groups of identical robots. The scenario we study involves an advanced form of division of labour, common in insect societies and known as task partitioning, whereby two sets of tasks have to be carried out in sequence by different individuals. Our results show that task partitioning is favoured whenever the environment has features that, when exploited, reduce switching costs and increase the net efficiency of the group, and that an optimal mix of task specialists is achieved most readily when the behavioural repertoires aimed at carrying out the different subtasks are available as pre-adapted building blocks. Nevertheless, we also show for the first time that self-organized task specialization could be evolved entirely from scratch, starting only from basic, low-level behavioural primitives, using a nature-inspired evolutionary method known as Grammatical Evolution. Remarkably, division of labour was achieved merely by selecting on overall group performance, and without providing any prior information on how the global object retrieval task was best divided into smaller subtasks. We discuss the potential of our method for engineering adaptively behaving robot swarms and interpret our results in relation to the likely path that nature took to evolve complex sociality and task specialization.", notes = "Is this GP?", } @InProceedings{Ferrari:2022:ICDM, author = "Davide Ferrari and Veronica Guidetti and Federica Mandreoli", title = "Multi-Objective Symbolic Regression for Data-Driven Scoring System Management", booktitle = "2022 IEEE International Conference on Data Mining (ICDM)", year = "2022", pages = "945--950", address = "Orlando, FL, USA", month = "28 " # nov # "-1 " # dec, keywords = "genetic algorithms, genetic programming, Power measurement, Shape, Aggregates, Sociology, Robustness, Indexes, Data mining, scoring systems, multi-objective symbolic regression, NSGA-II", isbn13 = "978-1-6654-5100-0", ISSN = "2374-8486", DOI = "doi:10.1109/ICDM54844.2022.00112", size = "6 pages", abstract = "Scores are mathematical combinations of elementary indicators (EIs) widely used to measure complex phenomena. Upon the theoretical framework definition, score construction requires a method to aggregate EIs. Aggregation is usually chosen among known methodologies fixing its shape through a try and error approach. Only then are the predictive power, the distribution of the index, and its ability to stratify the population measured. we propose a novel data-driven approach that generates analytic aggregation methods relying on multi-objective symbolic regression. We translate the properties that the index must exhibit into optimization goals so that optimal index candidates replicate target variables, data balancing, and stratification. We run experiments on real data sets to solve three main score management problems: data-driven score simplification, generation, and combination. The results obtained show the effectiveness and robustness of the proposed approach.", notes = "Also known as \cite{10027776}", } @InProceedings{Ferreira:2021:ICPR, author = "Alvaro R. Ferreira and Gustavo H. {de Rosa} and Joao P. Papa and Gustavo Carneiro and Fabio A. Faria", title = "Creating Classifier Ensembles through Meta-heuristic Algorithms for Aerial Scene Classification", booktitle = "2020 25th International Conference on Pattern Recognition (ICPR)", year = "2021", pages = "415--422", abstract = "Convolutional Neural Networks (CNN) have been being widely employed to solve the challenging remote sensing task of aerial scene classification. Nevertheless, it is not straightforward to find single CNN models that can solve all aerial scene classification tasks, allowing the development of a better alternative, which is to fuse CNN-based classifiers into an ensemble. However, an appropriate choice of the classifiers that will belong to the ensemble is a critical factor, as it is unfeasible to employ all the possible classifiers in the literature. Therefore, this work proposes a novel framework based on meta-heuristic optimization for creating optimized ensembles in the context of aerial scene classification. The experimental results were performed across nine meta-heuristic algorithms and three aerial scene literature datasets, being compared in terms of effectiveness (accuracy), efficiency (execution time), and behavioral performance in different scenarios. Our results suggest that the Univariate Marginal Distribution Algorithm shows more effective and efficient results than other commonly used meta-heuristic algorithms, such as Genetic Programming and Particle Swarm Optimization.", keywords = "genetic algorithms, genetic programming, Image analysis, Splicing, Classification algorithms, Pattern recognition, Convolutional neural networks, Security, Task analysis", DOI = "doi:10.1109/ICPR48806.2021.9412938", ISSN = "1051-4651", month = jan, notes = "Also known as \cite{9412938}", } @Unpublished{Ferreira:2000:GEP, author = "Candida Ferreira", title = "Gene Expression Programming: a New Adaptive Algorithm for Solving Problems", note = "rejected for publication", year = "2000", keywords = "genetic algorithms, genetic programming", URL = "http://www.gene-expression-programming.com/webpapers/GEP.pdf", size = "22 pages", abstract = "Gene expression programming, a genome/phenome genetic algorithm (linear and non-linear), is presented here for the first time as a new technique for creation of computer programs. Gene expression programming uses character linear chromosomes composed of genes structurally organised in a head and a tail. The chromosomes function as a genome and are subjected to modification by means of mutation, transposition, root transposition, gene transposition, gene recombination, 1-point and 2-point recombination. The chromosomes encode expression trees which are the object of selection. The creation of these separate entities (genome and expression tree) with distinct functions allows the algorithm to perform with high efficiency: in the symbolic regression, sequence induction and block stacking problems it surpasses genetic programming in more than two orders of magnitude, whereas in the density-classification problem it surpasses genetic programming in more than four orders of magnitude. The suite of problems chosen to illustrate the power and versatility of gene expression programming includes, besides the above mentioned problems, two problems of Boolean concept learning: the 11-multiplexer and the GP rule problem.", notes = "Date: Tue, 14 Nov 2000 21:04:44 -0100 To: genetic-programming From: Candida Ferreira Subject: GP: Paper on gene expression programming Hi all, My paper on gene expression programming is now available as a pdf for download at my site: http://www.gene-expression-programming.com Be advised that different versions of this paper were submitted and rejected by Nature and Genetic Programming and Evolvable Machines. One of the reasons one anonymous reviewer from GPEM gave was that The performance of the GEP algorithm compared to GP seems too good to be true to me. As I really want to see other scientists using GEP in other applications, I decided to publish my paper on the web in order to make this powerful algorithm available to all. Remember, though, that there is a patent pending and GEP can not be used commercially. Best regards, Candida Ferreira", } @Misc{ferreira:2001:WSC6, author = "Candida Ferreira", title = "GEP tutorial", howpublished = "WSC6 tutorial", year = "2001", month = sep, email = "candidaf@gene-expression-programming.com", keywords = "genetic algorithms, genetic programming, Gene Expression Programming", URL = "http://www.gene-expression-programming.com/webpapers/GEPtutorial.pdf", notes = "WSC6, 6th World Conference on Soft Computing in Industrial Applications presentation: http://www.gene-expression-programming.com/webpapers/slideShow.pdf See discussion eg http://groups.yahoo.com/group/genetic_programming/message/68", } @InProceedings{ferreira:2001:wsc6Aa, author = "Candida Ferreira", title = "Gene Expression Programming in Problem Solving", booktitle = "Soft Computing and Industry Recent Applications", year = "2001", editor = "Rajkumar Roy and Mario K{\"o}ppen and Seppo Ovaska and Takeshi Furuhashi and Frank Hoffmann", pages = "635--654", month = "10--24 " # sep, publisher = "Springer-Verlag", note = "Published 2002", keywords = "genetic algorithms, genetic programming, Gene Expression Programming", ISBN = "1-85233-539-4", URL = "http://www.gene-expression-programming.com/webpapers/ferreira-WSC6.pdf", URL = "https://link.springer.com/book/10.1007/978-1-4471-0123-9", URL = "http://www.amazon.co.uk/Soft-Computing-Industry-Recent-Applications/dp/1852335394", notes = "WSC6 March 2020 hardcover out of print, available as softcover. ", } @Article{ferreira:2001:CS, author = "C\^andida Ferreira", title = "Gene Expression Programming: A New Adaptive Algorithm for Solving Problems", journal = "Complex Systems", year = "2001", volume = "13", number = "2", pages = "87--129", email = "candidaf@gene-expressionprogramming.com", keywords = "genetic algorithms, genetic programming, GEP", URL = "http://www.gene-expression-programming.com/webpapers/GEPfirst.pdf", URL = "http://www.complex-systems.com/abstracts/v13_i02_a01.html", URL = "http://arXiv.org/abs/cs/0102027", abstract = "Gene expression programming, a genotype/phenotype genetic algorithm (linear and ramified), is presented here for the first time as a new technique for the creation of computer programs. Gene expression programming uses character linear chromosomes composed of genes structurally organized in a head and a tail. The chromosomes function as a genome and are subjected to modification by means of mutation, transposition, root transposition, gene transposition, gene recombination, and one- and two-point recombination. The chromosomes encode expression trees which are the object of selection. The creation of these separate entities (genome and expression tree) with distinct functions allows the algorithm to perform with high efficiency that greatly surpasses existing adaptive techniques. The suite of problems chosen to illustrate the power and versatility of gene expression programming includes symbolic regression, sequence induction with and without constant creation, block stacking, cellular automata rules for the density-classification problem, and two problems of boolean concept learning: the 11-multiplexer and the GP rule problem.", notes = "Portuguese translation http://www.gene-expression-programming.com/webpapers/GEPPort.pdf", } @InProceedings{ferreira:2002:EuroGP, title = "Discovery of the {Boolean} Functions to the Best Density-Classification Rules Using Gene Expression Programming", author = "C\^andida Ferreira", editor = "James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi", booktitle = "Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002", volume = "2278", series = "LNCS", pages = "50--59", publisher = "Springer-Verlag", address = "Kinsale, Ireland", publisher_address = "Berlin", month = "3-5 " # apr, year = "2002", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-43378-3", DOI = "doi:10.1007/3-540-45984-7_5", abstract = "Cellular automata are idealized versions of massively parallel, decentralized computing systems capable of emergent behaviours. These complex behaviors result from the simultaneous execution of simple rules at multiple local sites. A widely studied behavior consists of correctly determining the density of an initial configuration, and both human and computer-written rules have been found that perform with high efficiency at this task. However, the two best rules for the density-classification task, Coevolution1 and Coevolution2, were discovered using a coevolutionary algorithm in which a genetic algorithm evolved the rules and, therefore, only the output bits of the rules are known. However, to understand why these and other rules perform so well and how the information is transmitted throughout the cellular automata, the Boolean expressions that orchestrate this behaviour must be known. The results presented in this work are a contribution in that direction.", notes = "EuroGP'2002, part of \cite{lutton:2002:GP}", } @InProceedings{ferreira:2002:FEA, author = "Candida Ferreira", title = "Mutation, Transposition, and Recombination: An Analysis of the Evolutionary Dynamics", booktitle = "4th International Workshop on Frontiers in Evolutionary Algorithms", year = "2002", editor = "Manuel Grana Romay and Richard Duro", address = "North Carolina, USA", month = "8-14 " # mar, keywords = "genetic algorithms, genetic programming, gene expression programming", ISBN = "0-9707890-1-7", URL = "http://www.gene-expression-programming.com/webpapers/ferreira-FEA02.pdf", abstract = "Gene expression programming (GEP) uses mutation, transposition, and crossover to create variation. Although there exists a large body of work in genetic algorithms concerning the roles of mutation and recombination, these results not only do not apply to GEP due to the genotype/phenotype representation but also seem to contradict the GEP experience. Therefore, and given the diversity of GEP operators, it is convenient to develop some kind of understanding of their power. The aim of this work is to help develop such an understanding and to show the evolutionary dynamics and the transforming power of each genetic operator, with their advantages and limitations.", notes = "Sat, 23 Mar 2002 17:52:10 GMT genetic_programming@yahoogroups.com FEA2002 In conjunction with Sixth Joint Conference on Information Sciences", } @InProceedings{ferreira:2002:ASIA, author = "Candida Ferreira", title = "Combinatorial Optimization by Gene Expression Programming: Inversion Revisited", booktitle = "Proceedings of the Argentine Symposium on Artificial Intelligence", year = "2002", editor = "J. M. Santos and A. Zapico", pages = "160--174", address = "Santa Fe, Argentina", keywords = "genetic algorithms, genetic programming, GEP", URL = "http://www.gene-expression-programming.com/webpapers/ferreira-ASAI02.pdf", size = "9 pages", abstract = "Combinatorial optimisation problems require combinatorial-specific search operators so that populations of candidate solutions can evolve efficiently. Indeed, several researchers created modifications to the basic genetic operators of mutation and recombination in order to create high performing combinatorial-specific operators. However, it is not known which operators perform better as no systematic comparisons have been done. In this work, a new algorithm that explores a new chromosomal organisation based on multigene families is used. This new organization together with several combinatorial-specific search operators, namely, inversion, gene and sequence deletion/insertion, and restricted and generalised permutation, allow the algorithm to perform with high efficiency. The performance of the new algorithm is empirically compared on the 13- and 19-cities tour travelling salesperson problem, showing that the long abandoned inversion operator is by far the most efficient of the combinatorial operators. The efficiency and potentialities of the new algorithm are further demonstrated by solving a simple task assignment problem.", notes = "ASAI02 http://www.dc.uba.ar/people/profesores/santos/asai2002.html", } @InProceedings{ferreira:2002:WSC, author = "C\^andida Ferreira", title = "Function Finding and the Creation of Numerical Constants in Gene Expression Programming", booktitle = "7th Online World Conference on Soft Computing in Industrial Applications", year = "2002", month = sep # " 23 - " # oct # " 4", note = "on line", keywords = "genetic algorithms, genetic programming, gene expression programming", URL = "http://www.gene-expression-programming.com/webpapers/Ferreira-WSC7.pdf", abstract = "Gene expression programming is a genotype/phenotype system that evolves computer programs of different sizes and shapes (the phenotype) encoded in linear chromosomes of fixed length (the geno-type). The chromosomes are composed of multiple genes, each gene encoding a smaller sub-program. Furthermore, the structural and functional organization of the linear chromosomes allows the uncon-strained operation of important genetic operators such as mutation, transposition, and recombination. In this work, three function finding problems, including a high dimensional time series prediction task, are analyzed in an attempt to discuss the question of constant creation in evolutionary computation by comparing two different approaches to the problem of constant creation. The first algorithm involves a facility to manipulate random numerical constants, whereas the second finds the numerical constants on its own or invents new ways of representing them. The results presented here show that evolutionary algorithms perform considerably worse if numerical constants are explicitly used.", notes = "WSC7 broken http://wsc7.ugr.es/", } @Article{ferreira:2002:ACS, author = "C. Ferreira", title = "Genetic Representation and Genetic Neutrality in Gene Expression Programming", journal = "Advances in Complex Systems", year = "2002", volume = "5", number = "4", pages = "389--408", keywords = "genetic algorithms, genetic programming, GEP, Genetic neutrality, gene expression programming, evolutionary computation", URL = "http://www.gene-expression-programming.com/webpapers/Ferreira-ACS2002.pdf", URL = "http://www.worldscinet.com/169/05/0504/S0219525902000626.html", URL = "http://www.gepsoft.com/gep/webpapers/abstracts.asp#09", abstract = "The neutral theory of molecular evolution states that the accumulation of neutral mutations in the genome is fundamental for evolution to occur. The genetic representation of gene expression programming, an artificial genotype/phenotype system, not only allows the existence of non-coding regions in the genome where neutral mutations can accumulate but also allows the controlled manipulation of both the number and the extent of these non-coding regions. Therefore, gene expression programming is an ideal artificial system where the neutral theory of evolution can be tested in order to gain some insights into the workings of artificial evolutionary systems. The results presented in this work show beyond any doubt that the existence of neutral regions in the genome is fundamental for evolution to occur efficiently.", notes = "Tue, 18 Mar 2003 20:01:57 GMT Wed, 28 Apr 2004 16:00:48 BST", } @Book{Ferreira:book, author = "Candida Ferreira", title = "Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence", year = "2002", keywords = "genetic algorithms, genetic programming, Gene Expression Programming", ISBN = "972-95890-5-4", broken = "http://www.gene-expression-programming.com/gep/Books/index.asp", notes = "Tue, 30 May 2006 11:21:33 BST to GP list replaced by \cite{Ferreira:book2} cf email Sun, 06 Jul 2003 18:40:43 BST GP list", size = "272 pages", } @InProceedings{HreFer02, author = "Candida Ferreira", title = "Analyzing the Founder Effect in Simulated Evolutionary Processes Using Gene Expression Programming", booktitle = "Soft Computing Systems: Design, Management and Applications", year = "2002", editor = "A. Abraham and J. Ruiz-del-Solar and M. K{\"o}ppen", pages = "153--162", organisation = "Gepsoft", publisher = "IOS Press", keywords = "genetic algorithms, genetic programming, Gene Expression Programming", ISBN = "1-58603-297-6", URL = "http://www.gene-expression-programming.com/webpapers/ferreira-his02.pdf", size = "10 pages", abstract = "Gene expression programming is a genotype/phenotype system that evolves computer programs encoded in linear chromosomes of fixed length. The interplay between genotype (chromosomes) and phenotype (expression trees) is made possible by the structural and functional organisation of the linear chromosomes. This organization allows the unconstrained operation of important genetic operators such as mutation, transposition, and recombination. Although simple, the genotype/phenotype system of gene expression programming can provide some insights into natural evolutionary processes. In this work the question of the initial diversity in evolving populations of computer programs is addressed by analysing populations undergoing either mutation or recombination. The results presented here show that populations undergoing mutation recover practically undisturbed from evolutionary bottlenecks whereas populations undergoing recombination alone depend considerably on the size of the founder population and are unable to evolve efficiently if subjected to really tight bottlenecks.", } @InCollection{ferreira:2004:rdbic, author = "Candida Ferreira", title = "Gene expression programming and the automatic evolution of computer programs", booktitle = "Recent Developments in Biologically Inspired Computing", publisher = "Idea Group Publishing", year = "2004", editor = "Leandro N. {de Castro} and Fernando J. {Von Zuben}", chapter = "6", pages = "82--103", keywords = "genetic algorithms, genetic programming, Gene Expression Programming", ISBN = "1-59140-312-X", URL = "http://www.gene-expression-programming.com/gep/webpapers/abstracts.asp#11", DOI = "doi:10.4018/978-1-59140-312-8.ch005", abstract = "In this chapter an artificial problem solver inspired in natural genotype/phenotype systems gene expression programming is presented. As an introduction, the fundamental differences between gene expression programming and its predecessors, genetic algorithms and genetic programming, are briefly summarised so that the evolutionary advantages of gene expression programming are better understood. The work proceeds with a detailed description of the architecture of the main players of this new algorithm (chromosomes and expression trees), focusing mainly on the interactions between them and how the simple yet revolutionary structure of the chromosomes allows the efficient, unconstrained exploration of the search space. And finally, the chapter closes with an advanced application in which gene expression programming is used to evolve computer programs for diagnosing breast cancer.", notes = "broken August 2020 http://www.idea-group.com/books/details.asp?id=4376 http://groups.yahoo.com/group/genetic_programming/message/3551 http://groups.yahoo.com/group/genetic_programming/message/3549", } @InProceedings{ferreira:2004:wsc9, author = "Candida Ferreira", title = "Designing Neural Networks Using Gene Expression Programming", booktitle = "9th Online World Conference on Soft Computing in Industrial Applications", year = "2004", editor = "Ajith Abraham and Bernard {de Baets} and Mario Koeppen and Bertram Nickolay", volume = "34", series = "Advances in Soft Computing", pages = "517--535", address = "On the World Wide Web", month = "20 " # sep # " - 8 " # oct, organisation = "World Federation on Soft Computing (WFSC)", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, Gene Expression Programming", isbn13 = "978-3-540-31649-7", URL = "http://www.gene-expression-programming.com/webpapers/Ferreira-WSC9.pdf", URL = "http://www.gene-expression-programming.com/webpapers/abstracts.asp#14", DOI = "doi:10.1007/3-540-31662-0_40", abstract = "An artificial neural network with all its elements is a rather complex structure, not easily constructed and/or trained to perform a particular task. Consequently, several researchers used Genetic Algorithms to evolve partial aspects of neural networks, such as the weights, the thresholds, and the network architecture. Indeed, over the last decade many systems have been developed that perform total network induction. In this work it is shown how the chromosomes of Gene Expression Programming can be modified so that a complete neural network, including the architecture, the weights and thresholds, could be totally encoded in a linear chromosome. It is also shown how this chromosomal organization allows the training/adaptation of the network using the evolutionary mechanisms of selection and modification, thus providing an approach to the automatic design of neural networks. The workings and performance of this new algorithm are tested on the 6-multiplexer and on the classical exclusive-or problems.", notes = "WSC9 genetic_programming@yahoogroups.com Wed, 15 Aug 2007 09:27:52 BST This volume presents the proceedings of the 9th Online World Conference on Soft Computing in Industrial Applications (WSC9), September 20th - October 08th, 2004, held on the World Wide Web.", } @InCollection{Ferreira:2006:GSP, author = "C\^{a}ndida Ferreira", title = "Automatically Defined Functions in Gene Expression Programming", year = "2006", booktitle = "Genetic Systems Programming: Theory and Experiences", pages = "21--56", volume = "13", series = "Studies in Computational Intelligence", editor = "Nadia Nedjah and Ajith Abraham and Luiza {de Macedo Mourelle}", publisher = "Springer", address = "Germany", keywords = "genetic algorithms, genetic programming, gene expression programming, ADF", ISBN = "3-540-29849-5", URL = "http://www.gene-expression-programming.com/webpapers/Ferreira-GSP2006.pdf", DOI = "doi:10.1007/3-540-32498-4_2", abstract = " In this chapter it is shown how Automatically Defined Functions are encoded in the genotype/phenotype system of Gene Expression Programming. As an introduction, the fundamental differences between Gene Expression Programming and its predecessors, Genetic Algorithms and Genetic Programming, are briefly summarized so that the evolutionary advantages of Gene Expression Programming are better understood. The introduction proceeds with a detailed description of the architecture of the main players of Gene Expression Programming (chromosomes and expression trees), focusing mainly on the interactions between them and how the simple, yet revolutionary, structure of the chromosomes allows the efficient, unconstrained exploration of the search space. The work proceeds with an introduction to Automatically Defined Functions and how they are implemented in Gene Expression Programming. Furthermore, the importance of Automatically Defined Functions in Evolutionary Computation is thoroughly analyzed by comparing the performance of sophisticated learning systems with Automatically Defined Functions with much simpler ones on the sextic polynomial problem.", notes = "http://www.springer.com/sgw/cda/frontpage/0,11855,5-146-22-92733168-0,00.html", } @Book{Ferreira:book2, author = "Candida Ferreira", title = "Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence", publisher = "Springer", year = "2006", edition = "2nd", month = may, keywords = "genetic algorithms, genetic programming, gene expression programming", ISBN = "3-540-32796-7", notes = "Tue, 30 May 2006 11:21:33 BST Genetic_Programming@Yahoogroups.Com 'This second edition' of \cite{Ferreira:book} 'has been substantially revised and extended with five new chapters, including a new chapter describing two new algorithms for inducing decision trees with nominal and numeric/mixed attributes.'", size = "478 pages", } @InProceedings{Ferreira:wsc9, author = "C. Ferreira", title = "Designing Neural Networks Using Gene Expression Programming", booktitle = "Applied Soft Computing Technologies: The Challenge of Complexity", year = "2006", editor = "Ajith Abraham and Bernard {de Baets} and Mario Koeppen and Bertram Nickolay", volume = "34", series = "Advances in Soft Computing", address = "WWW", month = "20 " # sep # " - 8 " # oct, publisher = "Springer-Verlag", notes = "duplicate of \cite{ferreira:2004:wsc9}", } @InProceedings{Ferreira:2014:ICINCO, author = "Cesar Ferreira and Pedro Silva and Joao Andre and Cristina P. Santos and Lino Costa", title = "Genetic Programming Applied to Biped Locomotion Control with Sensory Information", booktitle = "11th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2014)", year = "2014", month = sep, volume = "01", pages = "53--62", keywords = "genetic algorithms, genetic programming", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=7049752", DOI = "doi:10.5220/0005062700530062", abstract = "Generating biped locomotion in robotic platforms is hard. It has to deal with the complexity of the tasks which requires the synchronisation of several joints, while monitoring stability. Further, it is also expected to deal with the great heterogeneity of existing platforms. The generation of adaptable locomotion further increases the complexity of the task.", notes = "Also known as \cite{7049752}", } @InProceedings{conf/sbbd/FerreiraTGF08, title = "Image Retrieval with Relevance Feedback based on Genetic Programming", author = "Cristiano D. Ferreira and Ricardo {da Silva Torres} and Marcos Andre Goncalves and Weiguo Fan", year = "2008", booktitle = "{XXIII} Simp{\'o}sio Brasileiro de Banco de Dados", publisher = "SBC", editor = "Sandra {de Amo}", pages = "120--134", address = "Campinas, {S}{\~a}o Paulo, Brasil", month = "13-15 " # oct, keywords = "genetic algorithms, genetic programming, CIBR, relevance feedback", isbn13 = "978-85-7669-205-8", bibdate = "2009-03-02", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/sbbd/sbbd2008.html#FerreiraTGF08", URL = "http://www.lbd.dcc.ufmg.br:8080/colecoes/sbbd/2008/009.pdf", size = "15 pages", abstract = "This paper presents a new content-based image retrieval framework with relevance feedback. This framework employs Genetic Programming to discover a combination of descriptors that better characterizes the user perception of image similarity. Several experiments were conducted to validate the proposed framework. These experiments employed three different image databases and colour, shape, and texture descriptors to represent the content of database images. The proposed framework was compared with three other relevance feedback methods regarding their efficiency and effectiveness in image retrieval tasks. Experiment results demonstrate the superiority of the proposed method.", notes = "Image collections: FISH, MPEG7, COREL page 133 'genetic programming to learn the user preferences' SBBD 2008. CBIR, COREL", } @Article{Ferreira201127, author = "C. D. Ferreira and J. A. Santos and R. {da S. Torres} and M. A. Goncalves and R. C. Rezende and Weiguo Fan", title = "Relevance feedback based on genetic programming for image retrieval", journal = "Pattern Recognition Letters", volume = "32", number = "1", pages = "27--37", year = "2011", note = "Image Processing, Computer Vision and Pattern Recognition in Latin America", ISSN = "0167-8655", DOI = "doi:10.1016/j.patrec.2010.05.015", URL = "http://www.sciencedirect.com/science/article/B6V15-504123K-4/2/d925135e9c62c6da92ea517f2451d3bf", keywords = "genetic algorithms, genetic programming, Relevance feedback, Content-based image retrieval", abstract = "This paper presents two content-based image retrieval frameworks with relevance feedback based on genetic programming. The first framework exploits only the user indication of relevant images. The second one considers not only the relevant but also the images indicated as non-relevant. Several experiments were conducted to validate the proposed frameworks. These experiments employed three different image databases and colour, shape, and texture descriptors to represent the content of database images. The proposed frameworks were compared, and outperformed six other relevance feedback methods regarding their effectiveness and efficiency in image retrieval tasks.", } @InProceedings{Ferreira:2019:LA-CCI, author = "Jimena Ferreira and Ana Ines Torres and Martin Pedemonte", booktitle = "2019 IEEE Latin American Conference on Computational Intelligence (LA-CCI)", title = "A Comparative Study on the Numerical Performance of Kaizen Programming and Genetic Programming for Symbolic Regression Problems", year = "2019", month = nov, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/LA-CCI47412.2019.9036755", abstract = "Symbolic Regression (SR) is a problem that arises in the context of surrogate modeling and involves the fitting of a mathematical model to an input-output data set. Kaizen Programming (KP) is a novel algorithm for solving SR problems. This work presents a comparative analysis on the performance of KP and Genetic Programming (GP) for SR on 15 optimization benchmark functions and an industrial process application case. The experimental analysis shows that KP has a better performance than GP in almost all benchmark cases and in the application case. Also, the results of KP are competitive with state of the art algorithms reported in previous works. This work provides additional evidence on the benefits of KP and corroborates that KP represents a promising solver for SR problems.", notes = "Also known as \cite{9036755}", } @InCollection{FERREIRA:2019:PICFCPD, author = "Jimena Ferreira and Martin Pedemonte and Ana I. Torres", title = "A Genetic Programming Approach for Construction of Surrogate Models", booktitle = "Proceedings of the 9th International Conference on Foundations of Computer-Aided Process Design", year = "2019", editor = "Salvador Garcia Munoz and Carl D. Laird and Matthew J. Realff", series = "Computer Aided Chemical Engineering", volume = "47", pages = "451--456", address = "Copper Mountain, Colorado, USA", keywords = "genetic algorithms, genetic programming, Surrogate Models, Response Surface Models", publisher = "Elsevier", ISSN = "1570-7946", URL = "http://www.sciencedirect.com/science/article/pii/B9780128185971500722", DOI = "doi:10.1016/B978-0-12-818597-1.50072-2", size = "6 pages", abstract = "Surrogate models, response surface models or meta-models are `lack-'ox models that describe a system with high accuracy. We present a methodology that combines iterative Design of experiments (DOE) with Genetic Programming (GP) in order to obtain surrogate models. GP is an evolutionary technique to create computer programs. In the context of surrogate modelling. the programs are possible functional forms of the model, that are used to fit experimental data. Therefore, unlike most approaches, non-linear combinations of the basis functions are possible. The iterative DOE provides a methodology to choose data points to test current programs and build the next generation. Data is obtained from Aspen Plus based simulations and the process of data acquisition is automatized via Python. The methodology is applied to a RadFrac distillation column which is part of a corn to ethanol process and considers three input and three output variables. The results indicate that the proposed methodology is able to provide accurate surrogate models for the variables", notes = "Instituto de Computacion, Facultad de Ingenieria, Universidad de la Republica, Montevideo, Uruguay, 11300", } @InProceedings{Ferreira:2021:LA-CCI, author = "Jimena Ferreira and Ana Ines Torres and Martin Pedemonte", title = "Towards a Multi-Output Kaizen Programming Algorithm", booktitle = "2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI)", year = "2021", month = nov, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/LA-CCI48322.2021.9769841", abstract = "A model obtained from solving a symbolic regression problem is a surrogate model that represent a system with high accuracy. In the area of process system engineering, surrogate models substitute rigorous models in optimization and design process problems. As chemical processes have several outputs with a common physical-chemical phenomena, it is expected that the surrogate models generated for the outputs share terms or function basis. Kaizen Programming (KP) is a novel technique to solve symbolic regression problems, which do not assume any supposition of the form of the model in advance. This technique has shown a better performance than Genetic Programming on benchmarking functions. we propose an extension of Kaizen Programming, Multi-Output KP (MO-KP), to construct multi-output models in a single execution.The experimental evaluation was conducted on an extension of three classical benchmarking functions to multi-output scenarios, considering three different schemes of function basis sharing. The experimental results shown that MO-KP builds well fitted models, and it is even able to construct better models than single-output KP in some scenarios. The results also confirm that MO-KP favors the sharing of terms between the generated models. Finally, we found that the median execution time of MO-KP is in general shorter than the equivalent executions of single-output KP, but with larger variability in the distribution of the runtimes.", notes = "Also known as \cite{9769841}", } @InProceedings{Ferreira:2020:CEC, author = "Leonardo Augusto Ferreira and Frederico Gadelha Guimaraes and Rodrigo Silva", title = "Applying Genetic Programming to Improve Interpretability in Machine Learning Models", booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC 2020", year = "2020", editor = "Yaochu Jin", pages = "paper id24516", address = "internet", month = "19-24 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, XAI, Interpretability, Machine Learning, Explainability", isbn13 = "978-1-7281-6929-3", DOI = "doi:10.1109/CEC48606.2020.9185620", size = "8 pages", abstract = "Explainable Artificial Intelligence (or xAI) has become an important research topic in the fields of Machine Learning and Deep Learning. In this paper, we propose a Genetic Programming (GP) based approach, named Genetic Programming Explainer (GPX), to the problem of explaining decisions computed by AI systems. The method generates a noise set located in the neighborhood of the point of interest, whose prediction should be explained, and fits a local explanation model for the analyzed sample. The tree structure generated by GPX provides a comprehensible analytical, possibly non-linear, symbolic expression which reflects the local behavior of the complex model. We considered three machine learning techniques that can be recognized as complex black-box models: Random Forest, Deep Neural Network and Support Vector Machine in twenty data sets for regression and classifications problems. Our results indicate that the GPX is able to produce more accurate understanding of complex models than the state of the art. The results validate the proposed approach as a novel way to deploy GP to improve interpretability.", notes = "https://wcci2020.org/ Universidade Federal de Minas Gerais, Brazil; Universidade Federal de Ouro Preto, Brazil. Also known as \cite{9185620}", } @InProceedings{DBLP:conf/eusflat/FerreiraTV19, author = "Marco Antonio Cunha Ferreira and Ricardo Tanscheit and Marley M. B. R. Vellasco", editor = "Vilem Novak and Vladimir Marik and Martin Stepnicka and Mirko Navara and Petr Hurtik", title = "Automatic Generation of a Type-2 Fuzzy System for Time Series Forecast based on Genetic Programming", booktitle = "Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology, {EUSFLAT} 2019, Prague, Czech Republic, September 9-13, 2019", series = "Atlantis Studies in Uncertainty Modelling", volume = "1", publisher = "Atlantis Press", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.2991/eusflat-19.2019.54", DOI = "doi:10.2991/eusflat-19.2019.54", timestamp = "Wed, 13 May 2020 16:51:00 +0200", biburl = "https://dblp.org/rec/conf/eusflat/FerreiraTV19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{FERREIRA:2022:omega, author = "Cristiane Ferreira and Goncalo Figueira and Pedro Amorim", title = "Effective and interpretable dispatching rules for dynamic job shops via guided empirical learning", journal = "Omega", volume = "111", pages = "102643", year = "2022", ISSN = "0305-0483", DOI = "doi:10.1016/j.omega.2022.102643", URL = "https://www.sciencedirect.com/science/article/pii/S0305048322000512", keywords = "genetic algorithms, genetic programming, Scheduling, Dynamic Job Shop, Dispatching Rules", abstract = "The emergence of Industry 4.0 is making production systems more flexible and also more dynamic. In these settings, schedules often need to be adapted in real-time by dispatching rules. Although substantial progress was made until the '90s, the performance of these rules is still rather limited. The machine learning literature is developing a variety of methods to improve them. However, the resulting rules are difficult to interpret and do not generalise well for a wide range of settings. This paper is the first major attempt at combining machine learning with domain problem reasoning for scheduling. The idea consists of using the insights obtained with the latter to guide the empirical search of the former. We hypothesise that this guided empirical learning process should result in effective and interpretable dispatching rules that generalise well to different scenarios. We test our approach in the classical dynamic job shop scheduling problem minimising tardiness, one of the most well-studied scheduling problems. The simulation experiments include a wide spectrum of scenarios for the first time, from highly loose to tight due dates and from low use conditions to severely congested shops. Nonetheless, results show that our approach can find new state-of-the-art rules, which significantly outperform the existing literature in the vast majority of settings. Overall, the average improvement over the best combination of benchmark rules is 19percent. Moreover, the rules are compact, interpretable, and generalise well to extreme, unseen scenarios. Therefore, we believe that this methodology could be a new paradigm for applying machine learning to dynamic optimisation problems", } @InProceedings{Ferreira:2023:LA-CCI, author = "Jimena Ferreira and Ana Ines Torres and Martin Pedemonte", booktitle = "2023 IEEE Latin American Conference on Computational Intelligence (LA-CCI)", title = "Does Kaizen Programming need a physic-informed mechanism to improve the search?", year = "2023", abstract = "In recent years, the study of physics-informed machine learning has increased. Works that use information about the shape or some characteristic of the expected function, have been used with genetic programming and neural networks. In those studies, it was found that including information about the expected model makes the resulting models better.Motivated by these studies, the goal of this work is the evaluation of the inclusion of information about the shape of the function in Kaizen Programming using a penalty function. In order to answer if the inclusion of this information in the search results in better models. In order to answer that we worked with 13 benchmark functions. The functions have between 2 and 9 input variables, and all have different types of shapes.We found that there is no significant difference in the performance of the models obtained using plain Kazan Programming and the shape-constrained approach.", keywords = "genetic algorithms, genetic programming, Shape, Input variables, Neural networks, ANN, Machine learning, Continuous improvement, Kaizen Programming, Evolutionary Computation, Physic-informed machine learning, Physic-informed symbolic regression", DOI = "doi:10.1109/LA-CCI58595.2023.10409360", ISSN = "2769-7622", month = oct, notes = "Also known as \cite{10409360}", } @InProceedings{Ferreira:2023:EuroGP, author = "Jose Ferreira and Mauro Castelli and Luca Manzoni and Gloria Pietropolli", title = "A Self-Adaptive Approach to Exploit Topological Properties of Different {GA}s Crossover Operators", booktitle = "EuroGP 2023: Proceedings of the 26th European Conference on Genetic Programming", year = "2023", month = "12-14 " # apr, editor = "Gisele Pappa and Mario Giacobini and Zdenek Vasicek", series = "LNCS", volume = "13986", publisher = "Springer Verlag", address = "Brno, Czech Republic", pages = "3--18", organisation = "EvoStar, Species", keywords = "genetic algorithms", isbn13 = "978-3-031-29572-0", URL = "https://rdcu.be/c8UNr", DOI = "doi:10.1007/978-3-031-29573-7_1", size = "16 pages", abstract = "Evolutionary algorithms (EAs) are a family of optimization algorithms inspired by the Darwinian theory of evolution, and Genetic Algorithm (GA) is a popular technique among EAs. Similar to other EAs, common limitations of GAs have geometrical origins, like premature convergence, where the final population's convex hull might not include the global optimum. Population diversity maintenance is a central idea to tackle this problem but is often performed through methods that constantly diminish the search space's area. This work presents a self-adaptive approach, where the non-geometric crossover is strategically employed with geometric crossover to maintain diversity from a geometrical/topological perspective. To evaluate the performance of the proposed method, the experimental phase compares it against well-known diversity maintenance methods over well-known benchmarks. Experimental results clearly demonstrate the suitability of the proposed self-adaptive approach and the possibility of applying it to different types of crossover and EAs.", notes = " Part of \cite{Pappa:2023:GP} EuroGP'2023 held in conjunction with EvoCOP2023, EvoMusArt2023 and EvoApplications2023", } @Article{FERREIRA:2023:cor, author = "Cristiane Ferreira and Goncalo Figueira and Pedro Amorim and Alexandre Pigatti", title = "Scheduling wagons to unload in bulk cargo ports with uncertain processing times", journal = "Computer \& Operations Research", volume = "160", pages = "106364", year = "2023", ISSN = "0305-0548", DOI = "doi:10.1016/j.cor.2023.106364", URL = "https://www.sciencedirect.com/science/article/pii/S0305054823002289", keywords = "genetic algorithms, genetic programming, Dynamic scheduling, Bulk cargo ports, Dispatching rules", abstract = "Optimising operations in bulk cargo ports is of great relevance due to their major participation in international trade. In inbound operations, which are critical to meet due dates, the product typically arrives by train and must be transferred to the stockyard. This process requires several machines and is subject to frequent disruptions leading to uncertain processing times. This work focuses on the scheduling problem of unloading the wagons to the stockyard, approaching both the deterministic and the stochastic versions. For the deterministic problem, we compare three solution approaches: a Mixed Integer Programming model, a Constraint Programming model and a Greedy Randomised algorithm. The selection rule of the latter is evolved by Genetic Programming. The stochastic version is tackled by dispatching rules, also evolved via Genetic Programming. The proposed approaches are validated using real data from a leading company in the mining sector. Results show that the new heuristic presents similar results to the company's algorithm in a considerably shorter computational time. Moreover, we perform extensive computational experiments to validate the methods on a wide spectrum of randomly generated instances. Finally, as managing uncertainty is fundamental for the effectiveness of these operations, distinct strategies are compared, ranging from purely predictive to completely reactive scheduling. We conclude that re-scheduling with high frequency is the best approach to avoid performance deterioration under schedule disruptions, and using the evolved dispatching rules incur fewer deviations from the original schedule", } @InProceedings{ferrer:1995:bef, author = "Gabriel J. Ferrer and Worthy N. Martin", title = "Using Genetic Programming to Evolve Board Evaluation Functions for a Boardgame", booktitle = "1995 IEEE Conference on Evolutionary Computation", year = "1995", volume = "2", pages = "747", address = "Perth, Australia", publisher_address = "Piscataway, NJ, USA", month = "29 " # nov # " - 1 " # dec, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Senet", broken = "http://www.cs.virginia.edu/~gjf2a/work/papers/senet.ps", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/senet.ps.gz", size = "6 pages", abstract = "In this paper, we employ the genetic programming paradigm to enable a computer to learn to play strategies for the ancient Egyptian boardgame Senet by evolving board evaluation functions. Formulating the problem in terms of board evaluation functions made it feasible to evaluate the fitness of game playing strategies by using tournament-style fitness evaluation. The game has elements of both strategy and chance. Our approach learns strategies which enable the computer to play consistently at a reasonably skillful level.", notes = "ICEC-95 http://www.io.org/~causal/c_p/cpec95.htm Editors not given by IEEE, Organisers David Fogel and Chris deSilva. conference details at http://ciips.ee.uwa.edu.au/~dorota/icnn95.html Fitness given by knockout tournament, rank-proprtionate selection, mutation and crossover, generational, non-standard random initial population creation/mutation/crossover, no size limit on programs. 2 non-seeded runs, 2 seeded runs (504 random + 8 different hand-coded). No discussion of statistical significance of results. ", } @InProceedings{Ferretti:2021:SECDEF, author = "Claudio Ferretti and Martina Saletta", title = "Deceiving Neural Source Code Classifiers: Finding Adversarial Examples with Grammatical Evolution", booktitle = "Genetic and Evolutionary Computation in Defense, Security, and Risk", year = "2021", editor = "Riyad Alshammari and Erik Hemberg and Tokunbo Makanju", pages = "1889--1897", address = "internet", series = "GECCO '21", month = jul # " 10-14", organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, ANN, deep learning, adversarial examples, computer security, security assessment", isbn13 = "978-1-4503-8351-6", DOI = "doi:10.1145/3449726.3463222", size = "9 pages", abstract = "an evolutionary approach for assessing the robustness of a system trained in the detection of software vulnerabilities. By applying a Grammatical Evolution genetic algorithm, and using the output of the system being assessed as the fitness function, we show how we can easily change the classification decision (i.e. vulnerable or not vulnerable) for a given instance by simply injecting evolved features that in no wise affect the functionality of the program. Additionally, by means of the same technique, that is by simply modifying the program instances, we show how we can significantly decrease the accuracy measure of the whole system on the dataset used for the test phase. Finally we remark that these methods can be easily customized for applications in different domains and also how the underlying ideas can be exploited for different purposes, such as the exploration of the behaviour of a generic neural system.", notes = "Universita degli Studi di Milano-Bicocca GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)", } @InProceedings{FerrucciGOS10, author = "Filomena Ferrucci and Carmine Gravino and Rocco Oliveto and Federica Sarro", title = "Genetic Programming for Effort Estimation: An Analysis of the Impact of Different Fitness Functions", booktitle = "Proceedings of the 2nd International Symposium on Search Based Software Engineering (SSBSE '10)", year = "2010", pages = "89--98", address = "Benevento, Italy", month = "7-9 " # sep, publisher = "IEEE", editor = "Massimiliano {Di Penta} and Simon Poulding and Lionel Briand and John Clark", keywords = "genetic algorithms, genetic programming, SBSE", isbn13 = "978-0-7695-4195-2", DOI = "doi:10.1109/SSBSE.2010.20", owner = "Yuanyuan", timestamp = "2010.09.08", size = "10 pages", abstract = "Context: The use of search-based methods has been recently proposed for software development effort estimation and some case studies have been carried out to assess the effectiveness of Genetic Programming (GP). The results reported in the literature showed that GP can provide an estimation accuracy comparable or slightly better than some widely used techniques and encouraged further research to investigate whether varying the fitness function the estimation accuracy can be improved. Aim: Starting from these considerations, in this paper we report on a case study aiming to analyse the role played by some fitness functions for the accuracy of the estimates. Method: We performed a case study based on a publicly available dataset, i.e., Desharnais, by applying a 3-fold cross validation and employing summary measures and statistical tests for the analysis of the results. Moreover, we compared the accuracy of the obtained estimates with those achieved using some widely used estimation methods, namely Case-Based Reasoning (CBR) and Manual Step Wise Regression (MSWR). Results: The obtained results highlight that the fitness function choice significantly affected the estimation accuracy. The results also revealed that GP provided significantly better estimates than CBR and comparable with those of MSWR for the considered dataset.", notes = "IEEE Computer Society Order Number P4195 BMS Part Number: CFP1099G-PRT Library of Congress Number 2010933544 http://ssbse.info/2010/program.php", } @InProceedings{Ferrucci:2011:SSBSE, author = "Filomena Ferrucci and Carmine Gravino and Federica Sarro", title = "How Multi-Objective Genetic Programming Is Effective for Software Development Effort Estimation?", year = "2011", booktitle = "Search Based Software Engineering", editor = "Myra Cohen and Mel O'Cinneid", volume = "6956", series = "Lecture Notes in Computer Science", pages = "274--275", address = "Szeged, Hungary", month = "10-12 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, SBSE: Poster", isbn13 = "978-3-642-23715-7", DOI = "doi:10.1007/978-3-642-23716-4_28", size = "1.3 page", abstract = "The idea of exploiting search-based methods to estimate development effort is based on the observation that the effort estimation problem can be formulated as an optimisation problem. As a matter of fact, among possible estimation models, we have to identify the best one, i.e., the one providing the most accurate estimates. Nevertheless, in the context of effort estimation there does not exist a unique measure that allows us to compare different models and consistently derives the best one [1]. Rather, several evaluation criteria (e.g., MMRE and Pred(25)) covering different aspects of model performances (e.g., underestimating or overestimating) are used to assess and compare estimation models [1]. Thus, considering the effort estimation problem as an optimisation problem we should search for the model that optimises several measures. From this point of view, the effort estimation problem is inherently multi-objective. Nevertheless, all the studies that have been carried", } @Misc{oai:arXiv.org:1505.01474, author = "Robyn Ffrancon", title = "Retaining Experience and Growing Solutions", year = "2015", month = may # "~06", abstract = "Generally, when genetic programming (GP) is used for function synthesis any valuable experience gained by the system is lost from one problem to the next, even when the problems are closely related. With the aim of developing a system which retains beneficial experience from problem to problem, this paper introduces the novel Node-by-Node Growth Solver (NNGS) algorithm which features a component, called the controller, which can be adapted and improved for use across a set of related problems. NNGS grows a single solution tree from root to leaves. Using semantic backpropagation and acting locally on each node in turn, the algorithm employs the controller to assign subsequent child nodes until a fully formed solution is generated. The aim of this paper is to pave a path towards the use of a neural network as the controller component and also, separately, towards the use of meta-GP as a mechanism for improving the controller component. A proof-of-concept controller is discussed which demonstrates the success and potential of the NNGS algorithm. In this case, the controller constitutes a set of hand written rules which can be used to deterministically and greedily solve standard Boolean function synthesis benchmarks. Even before employing machine learning to improve the controller, the algorithm vastly outperforms other well known recent algorithms on run times, maintains comparable solution sizes, and has a 100percent success rate on all Boolean function synthesis benchmarks tested so far.", bibsource = "OAI-PMH server at export.arxiv.org", oai = "oai:arXiv.org:1505.01474", keywords = "genetic algorithms, genetic programming, computer science - neural and evolutionary computing", URL = "http://arxiv.org/abs/1505.01474", } @MastersThesis{Ffrancon, author = "Robyn Ffrancon", title = "Reversing Operators for Semantic Backpropagation", school = "Ecole Polytechnique", year = "2015", type = "Masters in Complex Systems Science", address = "France", month = jun, keywords = "genetic algorithms, genetic programming", URL = "https://www2.warwick.ac.uk/fac/cross_fac/complexity/study/emmcs/outcomes/studentprojects/ffrancon.pdf", size = "60 pages", abstract = "Boolean function synthesis problems have served as some of the most well studied bench-marks within Genetic Programming (GP). Recently, these problems have been addressed using Semantic Backpropagation (SB) which was introduced in GP so as to take into account the semantics (outputs over all fitness cases) of a GP tree at all intermediate states of the program execution, i.e. at each node of the tree. The mappings chosen for reversing the operators used within a GP tree are crucially important to SB. This thesis describes the work done in designing and testing three novel SB algorithms for solving Boolean and Finite Algebra function synthesis problems. These algorithms generally perform significantly better than other well known algorithms on run times and solution sizes. Furthermore, the third algorithms is deterministic, a property which makes it unique within the domain.", notes = "M2 Project part of Center for Complexity Science Erasmus Mundus Masters in Complex Systems https://www2.warwick.ac.uk/fac/cross_fac/complexity/study/emmcs/outcomes/studentprojects/ This work was conducted during a 6 month internship at TAO team, INRIA, Saclay, France Supervisor: Marc Schoenauer", } @InProceedings{Ffrancon:2015:GECCO, author = "Robyn Ffrancon and Marc Schoenauer", title = "Memetic Semantic Genetic Programming", booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation", year = "2015", editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr", isbn13 = "978-1-4503-3472-3", pages = "1023--1030", keywords = "genetic algorithms, genetic programming", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", note = "GP Track best paper", URL = "https://hal.inria.fr/hal-01169074/document", URL = "http://doi.acm.org/10.1145/2739480.2754697", DOI = "doi:10.1145/2739480.2754697", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Semantic Backpropagation (SB) was introduced in GP so as to take into account the semantics of a GP tree at all intermediate states of the program execution, i.e., at each node of the tree. The idea is to compute the optimal should-be values each subtree should return, whilst assuming that the rest of the tree is unchanged, so as to minimize the fitness of the tree. To this end, the Random Desired Output (RDO) mutation operator, proposed in [17], uses SB in choosing, from a given library, a tree whose semantics are preferred to the semantics of a randomly selected subtree from the parent tree. Pushing this idea one step further, this paper introduces the Brando (BRANDO) operator, which selects from the parent tree the overall best subtree for applying RDO, using a small randomly drawn static library. Used within a simple Iterated Local Search framework, BRANDO can find the exact solution of many popular Boolean benchmarks in reasonable time whilst keeping solution trees small, thus paving the road for truly memetic GP algorithms.", notes = "cited by \cite{Alvarez:2016:GECCO} Also known as \cite{2754697} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)", } @InProceedings{Ffrancon:2015:GECCOcomp, author = "Robyn Ffrancon and Marc Schoenauer", title = "Greedy Semantic Local Search for Small Solutions", booktitle = "GECCO 2015 Semantic Methods in Genetic Programming (SMGP'15) Workshop", year = "2015", editor = "Colin Johnson and Krzysztof Krawiec and Alberto Moraglio and Michael O'Neill", isbn13 = "978-1-4503-3488-4", keywords = "genetic algorithms, genetic programming, Semantic Methods", pages = "1293--1300", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "https://hal.inria.fr/UMR8623/hal-01169074v1", URL = "http://doi.acm.org/10.1145/2739482.2768504", DOI = "doi:10.1145/2739482.2768504", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Semantic Backpropagation (SB) was introduced in GP so as to take into account the semantics of a GP tree at all intermediate states of the program execution, i.e., at each node of the tree. The idea is to compute the optimal should-be values each subtree should return, whilst assuming that the rest of the tree is unchanged, and to choose a subtree that matches as well as possible these target values. A single tree is evolved by iteratively replacing one of its nodes with the best subtree from a static library according to this local fitness, with tree size as a secondary criterion. Previous results for standard Boolean GP benchmarks that have been obtained by the authors with another variant of SB are improved in term of tree size. SB is then applied for the first time to categorical GP benchmarks, and outperforms all known results to date for three variable finite algebras.", notes = "Also known as \cite{2768504} Distributed at GECCO-2015.", } @PhdThesis{ficici:thesis, author = "Sevan Gregory Ficici", title = "Solution Concepts in Coevolutionary Algorithms", school = "Computer Science Department, Brandeis University", year = "2004", address = "USA", month = May, keywords = "genetic algorithms, Coevolutionary Algorithms, Evolutionary Game Theory, Machine Learning", URL = "http://www.demo.cs.brandeis.edu/papers/long.html#ficici_thesis_04", size = "299 pages", abstract = "Inspired by the principle of natural selection, coevolutionary algorithms are search methods in which processes of mutual adaptation occur amongst agents that interact strategically. The outcomes of interaction reveal a reward structure that guides evolution towards the discovery of increasingly adaptive behaviors. Thus, coevolutionary algorithms are often used to search for optimal agent behaviors in domains of strategic interaction. Coevolutionary algorithms require little a priori knowledge about the domain. We assume the learning task necessitates the algorithm to 1) discover agent behaviors, 2) learn the domain's reward structure, and 3) approximate an optimal solution. Despite the many successes of coevolutionary optimization, the practitioner frequently observes a gap between the properties that actually confer agent adaptivity and those expected (or desired) to yield adaptivity, or optimality. This gap is manifested by a variety of well-known pathologies, such as cyclic dynamics, loss of fitness gradient, and evolutionary forgetting. This dissertation examines the divergence between expectation and actuality in coevolutionary algorithms---why selection pressures fail to conform to our beliefs about adaptiveness, or why our beliefs are evidently erroneous. When we confront the pathologies of coevolutionary algorithms as a collection, we find that they are essentially epiphenomena of a single fundamental problem, namely a lack of rigor in our solution concepts. A solution concept is a formalism with which to describe and understand the incentive structures of agents that interact strategically. All coevolutionary algorithms implement some solution concept, whether by design or by accident, and optimize according to it. Failures to obtain the desiderata of {"}complexity{"} or {"}optimality{"} often indicate a dissonance between the implemented solution concept and that required by our envisaged goal. We make the following contributions: 1) We show that solution concepts are the critical link between our expectations of coevolution and the outcomes actually delivered by algorithm operation, and are therefore crucial to explicating the divergence between the two, 2) We provide analytic results that show how solution concepts bring our expectations in line with algorithmic reality, and 3) We show how solution concepts empower us to construct algorithms that operate more in line with our goals.", notes = "Available as Computer Science Department Technical Report CS-03-243 Download this paper as: Postscript (ficici_thesis_04.ps) Gzipped Postscript (ficici_thesis_04.ps.gz) PDF (ficici_thesis_04.pdf)", } @Article{Ficko:2004:AJME, author = "Mirko Ficko and Miha Kovacic and Miran Brezocnik", title = "Genetic algorithms : a useful optimization method for manufacturing problems", journal = "Academic Journal of Manufacturing Engineering", year = "2004", volume = "2", number = "1", pages = "21--26", keywords = "genetic algorithms, genetic programming, optimisation, facility layout, flexible manufacturing systems", ISSN = "1583-7904", abstract = "a very useful method for solving g the manufacturing problems, and optimising the manufacturing process, i.e. the genetic algorithms (GAs). The well-known basic knowledge of the conventional GAs is briefly presented. The second part of the paper discusses an example of optimisation of the design of the flexible manufacturing system (FMS) in one row with GAs. First the reasons for studying the layout of devices in the FMS are discussed. The GA model, the most suitable way of coding the solutions into the organisms and the selected evolutionary and genetic operators are presented. In the model, the automated guided vehicles (AGVs) for transport between components of the FMS were used. In this connection, the most favourable sequence of devices in the row is established by means of GAs. In the end the test results of the application made and the analysis are discussed.", notes = "Broken Jan 2013 http://www.eng.utt.ro/auif/rev/issue/no-05/no-05.html University of Maribor, Faculty for Mechanical Engineering, Laboratory for Intelligent Manufacturing Systems", } @Article{ficko:2004:JMPT, author = "Mirko Ficko and Miran Brezocnik and Joze Balic", title = "Designing the layout of single- and multiple-rows flexible manufacturing system by genetic algorithms", journal = "Journal of Materials Processing Technology", year = "2004", volume = "157-158", pages = "150--158", month = "20 " # dec, keywords = "genetic algorithms, genetic programming, Flexible manufacturing systems (FMS), Optimisation, Facility layout", ISSN = "0924-0136", DOI = "doi:10.1016/j.jmatprotec.2004.09.012", abstract = "model of designing of the flexible manufacturing system (FMS) in one or multiple rows with genetic algorithms (GAs). First the reasons for studying the layout of devices in the FMS are discussed. After studying the properties of the FMS and perusing the methods of layout designing the genetic algorithms methods was selected as the most suitable method for designing the FMS. The genetic algorithm model, the most suitable way of coding the solutions into the organisms and the selected evolutionary and genetic operators are presented. In the model, the automated guided vehicles (AGVs) for transport between components of the FMS were used. In this connection, the most favourable number of rows and the sequence of devices in the individual row are established by means of genetic algorithms. In the end the test results of the application made and the analysis are discussed.", notes = "Special issue {"}Achievements in Mechanical and Materials Engineering Conference{"} Edited by L. A. Dobranski", } @Article{Ficko:2005:JMPT, author = "M. Ficko and I. Drstvensek and M. Brezocnik and J. Balic and B. Vaupotic", title = "Prediction of total manufacturing costs for stamping tool on the basis of {CAD}-model of finished product", journal = "Journal of Materials Processing Technology", year = "2005", volume = "164-165", pages = "1327--1335", month = "15 " # may, keywords = "genetic algorithms, genetic programming, Prediction of costs, Tool-making, Stamping, CAD-model, Intelligent systems", ISSN = "0924-0136", owner = "wlangdon", DOI = "doi:10.1016/j.jmatprotec.2005.02.013", abstract = "One of the orientations of the tool-making industry is towards shortening the time from enquiry to the supply of tools. The tool-making shops must prepare within the shortest possible time an offer for the manufacturer of the tool based on the enquiry in the form of the CAD-model of the final product. For preparation of a proper offer, the values of certain technological features occurring in the manufacture of the tool are needed. Most frequently the tool manufacturer is interested in total cost for manufacture of the tool. Because of lack of time for making a detailed analysis the total costs of tool manufacture are predicted by the expert on the basis of the experience gathered during several years of work in this area. In our work, we conceived an intelligent system for predicting of total cost of the tool manufacture. We limited ourselves to tools for manufacture of sheet metal products by stamping; the system is based on the concept of case-based reasoning. On the basis of target and source cases, the system prepares the prediction of costs. The target case is the CAD-model in whose costs we are interested, whereas the source cases are the CAD-model of products, for which the tools had already been made, and the relevant total costs are known. The system first abstracts from CAD-models the geometrical features, and then it calculates the similarities between the source cases and target case. Then the most similar cases are used for preparation of prediction by genetic programming method. The genetic programming method provides the model connecting the individual geometrical features with total costs searched for. In the experimental work, we made a system adapted for predicting of tool costs used for tool manufacture on the basis of a theoretic model. The results show that the quality of predictions made by the intelligent system is comparable to the quality assured by the experienced expert.", notes = "AMPT/AMME05 Part 2", } @InProceedings{fidelis:2000:DCCRGA, author = "M. V. Fidelis and H. S. Lopes and A. A. Freitas", title = "Discovering Comprehensible Classification Rules a Genetic Algorithm", booktitle = "Proceedings of the 2000 Congress on Evolutionary Computation CEC00", year = "2000", volume = "1", pages = "805--810", address = "La Jolla Marriott Hotel La Jolla, California, USA", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "6-9 " # jul, organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", publisher = "IEEE Press", keywords = "genetic algorithms, data mining, IF-THEN rules, breast cancer, classification algorithm, comprehensible classification rule discovery, data mining, dermatology, flexible chromosome encoding, gene number, genetic algorithm, genotype, medical domains, mutation operators, phenotype, public-domain real-world data sets, rule conditions, cancer, data mining, encoding, learning (artificial intelligence), mammography, medical expert systems, pattern classification, skin", ISBN = "0-7803-6375-2", URL = "http://www.cpgei.cefetpr.br/~hslopes/publicacoes/2000/cec2000a.zip", DOI = "doi:10.1109/CEC.2000.870381", size = "6 pages", abstract = "Presents a classification algorithm based on genetic algorithms (GAs) that discovers comprehensible IF-THEN rules, in the spirit of data mining. The proposed GA has a flexible chromosome encoding, where each chromosome corresponds to a classification rule. Although the number of genes (the genotype) is fixed, the number of rule conditions (the phenotype) is variable. The GA also has specific mutation operators for this chromosome encoding. The algorithm was evaluated on two public-domain real-world data sets (in the medical domains of dermatology and breast cancer)", notes = "CEC-2000 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 00TH8512, Library of Congress Number = 00-018644", } @InProceedings{Fieldsend:2015:GECCO, author = "Jonathan E. Fieldsend and Alberto Moraglio", title = "Strength Through Diversity: Disaggregation and Multi-Objectivisation Approaches for Genetic Programming", booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation", year = "2015", editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr", isbn13 = "978-1-4503-3472-3", pages = "1031--1038", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, optimisation, multi-objectivisation, diversity", URL = "http://doi.acm.org/10.1145/2739480.2754643", DOI = "doi:10.1145/2739480.2754643", URL = "https://core.ac.uk/download/pdf/83924170.pdf", URL = "https://github.com/fieldsend/gecco_2015_mogp", size = "8 pages", abstract = "An underlying problem in genetic programming (GP) is how to ensure sufficient useful diversity in the population during search. Having a wide range of diverse (sub)component structures available for recombination and/or mutation is important in preventing premature converge. We propose two new fitness disaggregation approaches that make explicit use of the information in the test cases (i.e., program semantics) to preserve diversity in the population. The first method preserves the best programs which pass each individual test case, the second preserves those which are non-dominated across test cases (multi-objectivisation). We use these in standard GP, and compare them to using standard fitness sharing, and using standard (aggregate) fitness in tournament selection. We also examine the effect of including a simple anti-bloat criterion in the selection mechanism. We find that the non-domination approach, employing anti-bloat, significantly speeds up convergence to the optimum on a range of standard Boolean test problems. Furthermore, its best performance occurs with a considerably smaller population size than typically employed in GP.", notes = "Also known as \cite{2754643} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)", } @Article{Filho:2025:pnucene, author = "Marcos A. G. S. Filho and Alan M. M. Lima and Victor H. C. Pinheiro", title = "Multi-gene genetic programming of critical boron concentration and power peak factor for nuclear reactor fuel reload calculations", journal = "Progress in Nuclear Energy", year = "2025", volume = "180", pages = "105596", keywords = "genetic algorithms, genetic programming, Artificial Intelligence, Power peak factor, Nuclear reload problem, ANN", ISSN = "0149-1970", URL = "https://www.sciencedirect.com/science/article/pii/S0149197024005468", DOI = "doi:10.1016/j.pnucene.2024.105596", abstract = "Nuclear fuel reload aims to search for a core configuration of partially burned and fresh nuclear fuel, optimising the operational cycle length while assuring safety limits. For each configuration, operational cycle length and safety limits are evaluated in terms of boron concentration and power peak factor, respectively. Other safety parameters are not currently predicted with MGGP. In practice, a licensed numerical model is provided by the reactor manufacturer to estimate these physical parameters, and each configuration is simulated in approximately 300 s. Considering all possible core combinations, this approach becomes computationally unfeasible. This work introduces the Multi-Gene Genetic Programming (MGGP) to generate an explicit closed form mathematical function to estimate the nuclear reload physical parameters more efficiently. Results for a study of a single cycle of the Angra-I reactor show that the algorithmically generated mathematical function calculates boron concentration and power peak factor with a coefficient of determination of 0.997 and 0.95, respectively. For each configuration, the time of assessment is approximately 8E-4 s, which is several orders of magnitude faster than common licensed numerical tools, potentially enabling expensive optimisation studies. Also, MGGP performance is compared with an implemented Artificial Neural Network (ANN), and model results are compared", } @InProceedings{Filho:2020:CEC, author = "Renato Miranda Filho and Anisio Lacerda and Gisele L. Pappa", title = "Explaining Symbolic Regression Predictions", booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC 2020", year = "2020", editor = "Yaochu Jin", pages = "paper id24598", address = "internet", month = "19-24 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-7281-6929-3", DOI = "doi:10.1109/CEC48606.2020.9185683", abstract = "The outgrowing application of machine learning methods has raised a discussion in the artificial intelligence community on model transparency. In the center of this discussion is the question of model explanation and interpretability. The genetic programming (GP) community has systematically pointed out as one of the major advantages of GP the fact that it produces models that can be interpreted by humans. However, as other interpretable supervised models, the more complex the model becomes, the less interpretable it is. This work focuses on post-hoc interpretability of GP for symbolic regression. This approach does not explain the process followed by a model to reach a decision. Instead, it justifies the predictions it makes. The proposed approach, named Explanation by Local Approximation (ELA), is simple and model agnostic: it finds the nearest neighbors of the point we want to explain and performs a linear regression using this subset of points. The coefficients of this linear regression are then used to generate a local explanation to the model. Results show that the errors of ELA are similar to those of the regression performed with all points. It also shows that simple visualizations can provide insights to the users about the most relevant attributes.", notes = "https://wcci2020.org/ UFMG/IFMG, Brazil; UFMG, Brazil. Also known as \cite{9185683}", } @Article{Fillenwarth:2015:Fuel, author = "Brian A. Fillenwarth and Shankar M. L. Sastry", title = "Development of a predictive optimization model for the compressive strength of sodium activated fly ash based geopolymer pastes", journal = "Fuel", volume = "147", pages = "141--146", year = "2015", ISSN = "0016-2361", DOI = "doi:10.1016/j.fuel.2015.01.029", URL = "http://www.sciencedirect.com/science/article/pii/S0016236115000435", abstract = "As concerns about global CO2 emissions grow, there exists a need for widespread commercialisation of lower emission building materials such as geopolymers. The commercialisation of geopolymers is currently impeded by the high variability of the materials used for their synthesis and limited knowledge of the interrelationships between mix design variables. To overcome these barriers, this work demonstrates a relationship between the compressive strength and the chemical design variables derived from experimental data using genetic programming. The developed model indicates the main chemical factors responsible for the compressive strength of sodium activated geopolymers are the contents of Na2O, reactive SiO2, and H2O. The contents of reactive Al2O3 and CaO were found to not have a significant impact on the compressive strength. The optimisation model is shown to predict the compressive strength of fully cured sodium activated fly ash based geopolymer pastes from their chemical composition to within 6.60 MPa.", keywords = "genetic algorithms, genetic programming, Alkali activated cement, Geopolymer paste, Compressive strength, Fly ash, Predictive optimisation model", } @InProceedings{eurogp06:FillonBartoli, author = "Cyril Fillon and Alberto Bartoli", title = "A Divide and Conquer strategy for improving efficiency and probability of success in Genetic Programming", editor = "Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art", booktitle = "Proceedings of the 9th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3905", year = "2006", address = "Budapest, Hungary", month = "10 - 12 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-33143-3", pages = "13--23", email = "cfillon@units.it", DOI = "doi:10.1007/11729976_2", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "A common method for improving a genetic programming search on difficult problems is either multiplying the number of runs or increasing the population size. We propose a new search strategy which attempts to obtain a higher probability of success with smaller amounts of computational resources. We call this model Divide & Conquer since our algorithm initially partitions the search space in smaller regions that are explored independently of each other. Then, our algorithm collects the most competitive individuals found in each partition and exploits them in order to get a solution. We benchmarked our proposal on three problem domains widely used in the literature. Our results show a significant improvement of the likelihood of success while requiring less computational resources than the standard algorithm.", notes = "Part of \cite{collet:2006:GP} EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006", } @InProceedings{eurogp07:fillon, author = "Cyril Fillon and Alberto Bartoli", title = "Multi-objective Genetic Programming for Improving the Performance of TCP", editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar", booktitle = "Proceedings of the 10th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "4445", year = "2007", address = "Valencia, Spain", month = "11-13 " # apr, pages = "170--180", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-71602-5", isbn13 = "978-3-540-71602-0", DOI = "doi:10.1007/978-3-540-71605-1_16", abstract = "TCP is one of the fundamental components of the Internet. The performance of TCP is heavily dependent on the quality of its round-trip time (RTT) estimator, i.e. the formula that predicts dynamically the delay experienced by packets along a network connection. In this paper we apply multi-objective genetic programming for constructing an RTT estimator. We used two different approaches for multi-objective optimisation and a collection of real traces collected at the mail server of our University. The solutions that we found outperform the RTT estimator currently used by all TCP implementations. This result could lead to several applications of genetic programming in the networking field.", notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007", } @InProceedings{Fillon:2007:cec, author = "Cyril Fillon and Alberto Bartoli", title = "Symbolic Regression of Discontinuous and Multivariate Functions by Hyper-Volume Error Separation (HVES)", booktitle = "2007 IEEE Congress on Evolutionary Computation", year = "2007", editor = "Dipti Srinivasan and Lipo Wang", pages = "23--30", address = "Singapore", month = "25-28 " # sep, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "1-4244-1340-0", file = "1757.pdf", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2007.4424450", abstract = "Symbolic regression is aimed at discovering mathematical expressions, in symbolic form, that fit a given sample of data points. While Genetic Programming (GP) constitutes a powerful tool for solving this class of problems, its effectiveness is still severely limited when the data sample requires different expressions in different regions of the input space - i.e., when the approximating function should be discontinuous. In this paper we present a new GP-based approach for symbolic regression of discontinuous functions in multivariate data-sets. We identify the portions of the input space that require different approximating functions by means of a new algorithm that we call Hyper-Volume Error Separation (HVES). To this end we run a preliminary GP evolution and partition the input space based on the error exhibited by the best individual across the data-set. Then we partition the data-set based on the partition of the input space and use each such partition for driving an independent, preliminary GP evolution. The populations resulting from such preliminary evolutions are finally merged and evolved again. We compared our approach to the standard GP search and to a GP search for discontinuous functions in univariate data-sets. Our results show that coupling HVES with GP is an effective approach and provides significant accuracy improvements while requiring less computational resources.", notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C", } @InProceedings{Fine:2017:GPTP, author = "Steven B. Fine and Erik Hemberg and Krzysztof Krawiec and Una-May O'Reilly", title = "Exploiting Subprograms in Genetic Programming", booktitle = "Genetic Programming Theory and Practice XV", editor = "Wolfgang Banzhaf and Randal S. Olson and William Tozier and Rick Riolo", year = "2017", series = "Genetic and Evolutionary Computation", pages = "1--16", address = "University of Michigan in Ann Arbor, USA", month = may # " 18--20", organisation = "the Center for the Study of Complex Systems", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-90511-2", URL = "https://link.springer.com/chapter/10.1007/978-3-319-90512-9_1", DOI = "doi:10.1007/978-3-319-90512-9_1", abstract = "Compelled by the importance of subprogram behaviour, we investigate how much Behavioural Genetic Programming is sensitive to model bias. We experimentally compare two different decision tree algorithms analysing whether it is possible to see significant performance differences given that the model techniques select different subprograms and differ in how accurately they can regress subprogram behavior on desired outputs. We find no remarkable difference between REPTree and CART in this regard, though for a modest fraction of our datasets we find that one algorithm results in superior error reduction than the other. We also investigate alternative ways to identify useful subprograms beyond examining those within one program. We propose a means of identifying subprograms from different programs that work well together. This method combines behavioral traces from multiple programs and uses the information derived from modelling the combined program traces.", notes = "GPTP 2017, Part of \cite{Banzhaf:2017:GPTP} published after the workshop in 2018", } @InCollection{finkel:2003:UGPEAFN, author = "Jenny Rose Finkel", title = "Using Genetic Programming to Evolve an Algorithm for Factoring Numbers", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2003", year = "2003", editor = "John R. Koza", pages = "52--60", address = "Stanford, California, 94305-3079 USA", month = "4 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", URL = "http://www.genetic-programming.org/sp2003/Finkel.pdf", notes = "part of \cite{koza:2003:gagp}", } @InProceedings{finley:1999:E, author = "Marion R. {Finley Jr.} and Haruo Akimaru and Evelyne B. Hausen-Tropper", title = "Element of a theoretical model of tele-learning using genetic algorithms", booktitle = "Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference", year = "1999", editor = "Scott Brave and Annie S. Wu", pages = "93--98", address = "Orlando, Florida, USA", month = "13 " # jul, keywords = "Genetic Algorithms", notes = "GECCO-99LB", } @Article{Fiore2012178, author = "Alessandra Fiore and Luigi Berardi and Giuseppe Carlo Marano", title = "Predicting torsional strength of RC beams by using Evolutionary Polynomial Regression", journal = "Advances in Engineering Software", volume = "47", number = "1", pages = "178--187", year = "2012", ISSN = "0965-9978", DOI = "doi:10.1016/j.advengsoft.2011.11.001", URL = "http://www.sciencedirect.com/science/article/pii/S0965997811003036", keywords = "genetic algorithms, genetic programming, Reinforced concrete beam, Evolutionary Polynomial Regression, Torsional strength, Building code, Theoretical model, Soft computing", abstract = "A new view for the analytical formulation of torsional ultimate strength for reinforced concrete (RC) beams by experimental data is explored by using a new hybrid regression method termed Evolutionary Polynomial Regression (EPR). In the case of torsion in RC elements, the poor assumptions in physical models often result into poor agreement with experimental results. Nonetheless, existing models have simple and compact mathematical expressions since they are used by practitioners as building codes provisions. EPR combines the best features of conventional numerical regression techniques with the effectiveness of genetic programming for constructing symbolic expressions of regression models. The EPR modelling paradigm allows to figure out existing patterns in recorded data in terms of compact mathematical expressions, according to the available physical knowledge on the phenomenon (if any). The procedure output is represented by different formulae to predict torsional strength of RC beam. The multi-objective search paradigm used by EPR allows developing a set of formulae showing different complexity of mathematical expressions as resulting into different agreement with experimental data. The efficiency of such approach is tested using experimental data of 64 rectangular RC beams reported in technical literature. The input parameters affecting the torsional strength were selected as cross-sectional area of beams, cross-sectional area of one-leg of closed stirrup, spacing of stirrups, area of longitudinal reinforcement, yield strength of stirrup and longitudinal reinforcement, concrete compressive strength. Those results are finally compared with previous studies and existing building codes for a complete comparison considering formulation complexity and experimental data fitting.", } @Article{Fiore:2014:ace, author = "Alessandra Fiore and Giuseppe Carlo Marano and Daniele Laucelli and Pietro Monaco", title = "Evolutionary Modeling to Evaluate the Shear Behavior of Circular Reinforced Concrete Columns", journal = "Advances in Civil Engineering", year = "2014", volume = "2014", pages = "Article ID 684256", keywords = "genetic algorithms, genetic programming", URL = "http://downloads.hindawi.com/journals/ace/2014/684256.pdf", DOI = "doi:10.1155/2014/684256.", size = "15 pages", abstract = "Despite their frequent occurrence in practice, only limited studies on the shear behaviour of reinforced concrete (RC) circular members are available in the literature. Such studies are based on poor assumptions about the physical model, often resulting in being too conservative, as well as technical codes that essentially propose empirical conversion rules. On this topic in this paper, an evolutionary approach named EPR is used to create a structured polynomial model for predicting the shear strength of circular sections. The adopted technique is an evolutionary data mining methodology that generates a transparent and structured representation of the behaviour of a system directly from experimental data. In this study experimental data of 61 RC circular columns, as reported in the technical literature, are used to develop the EPR models. As final result, physically consistent shear strength models for circular columns are obtained, to be used in different design situations. The proposed formulations are compared with models available from building codes and literature expressions, showing that EPR technique is capable of capturing and predicting the shear behavior of RC circular elements with very high accuracy. A parametric study is also carried out to evaluate the physical consistency of the proposed models.", notes = "Department of Science of Civil Engineering and Architecture, Technical University of Bari (Politecnico di Bari), Via Orabona 4, 70125 Bari, Italy", } @InProceedings{eurogp:FirpiGE05, author = "Hiram Firpi and Erik D. Goodman and Javier Echauz", editor = "Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. {van Hemert} and Marco Tomassini", title = "On Prediction of Epileptic Seizures by Computing Multiple Genetic Programming Artificial Features", booktitle = "Proceedings of the 8th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3447", year = "2005", address = "Lausanne, Switzerland", month = "30 " # mar # " - 1 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-25436-6", pages = "321--330", DOI = "doi:10.1007/978-3-540-31989-4_29", DOI = "doi:10.1007/b107383", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "In this paper, we present a general-purpose, systematic algorithm, consisting of a genetic programming module and a k-nearest neighbour classifier, to automatically create multiple artificial features (i.e., features that are computer-crafted and may not have a known physical meaning) directly from EEG signals that reveal patterns predictive of epileptic seizures. The algorithm was evaluated in three different patients, with prediction defined over a horizon that varies between 1 and 5 minutes before unequivocal electrographic onset. For one patient, a perfect classification was achieved. For the other two patients, a high classification accuracy was reached, predicting three seizures out of four for one, and eleven seizures out of fifteen for the other. For the latter, also, only one normal (non-seizure) signal was misclassified.", notes = "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005", } @InProceedings{1068082, author = "Hiram Firpi and Erik Goodman and Javier Echauz", title = "Epileptic seizure detection by means of genetically programmed artificial features", booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation", year = "2005", editor = "Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and Edwin D. {de Jong} and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler", volume = "1", ISBN = "1-59593-010-8", pages = "461--466", address = "Washington DC, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2005/docs/p461.pdf", DOI = "doi:10.1145/1068009.1068082", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "25-29 " # jun, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Biological Applications, design, epilepsy, feature extraction, seizure detection, state-space reconstruction", size = "6 pages", abstract = "we describe a general-purpose, systematic algorithm, consisting of a genetic programming module and a knearest neighbour classifier to automatically create artificial features?features that are computer-crafted and may not have a known physical meaning?directly from the reconstructed statespace trajectories of the EEG signals that reveal patterns indicative of epileptic seizure onset. The algorithm was evaluated in three patients and validation experiments were carried out using 267.6 hours of EEG recordings. The results with the artificial features compare favourably with previous benchmark work that used a handcrafted feature.", notes = "GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM Order Number 910052", } @PhdThesis{Firpi:thesis, author = "Hiram Alexer Firpi", title = "On prediction and detection of epileptic seizures by means of genetic programming artificial features", school = "Michigan State University", year = "2005", address = "USA", keywords = "genetic algorithms, genetic programming, Health and environmental sciences, Applied sciences, Artificial features, Epileptic seizures, Feature extraction, Pattern recognition, Electrical engineering, Biomedical research, Surgery, 0541:Biomedical research, 0544:Electrical engineering, 0564:Surgery", isbn13 = "9780542081422; 0542081423", language = "English", URL = "https://search.proquest.com/docview/305459616", size = "199 pages", abstract = "This work presents a novel, general-purpose algorithm called Genetic Programming Artificial Features (GPAF), which consists of a genetic programming (GP) algorithm and a k-nearest neighbour classifier, and which surpasses the performance of another recently published method called Genetically Found, Neurally Computed Artificial Features for addressing similar classes of problems. Unlike conventional features, which are designed based on human knowledge, experience, and/or intuition, the artificial features ( i.e., features that are computer-crafted and may not have a known physical meaning) are systematically and automatically designed by a computer from data provided. In this dissertation, we apply the GPAF algorithm to one of the most puzzling brain-disorder problems: the prediction and detection of epileptic seizures. Epilepsy is a neurological condition that makes people susceptible to brief electrical disturbance in the brain thus producing a change in sensation, awareness, and/or behaviour; and is characterized by recurrent seizures. It affects up to 1percent of the worldwide population, or sixty million people, and 25percent cannot be fully controlled by current pharmacological or surgical treatment. The possibility that an implantable device might eventually warn patients of an impending seizure is of utmost importance, allowing on-the-spot medication or safety measures. Epileptic electroencephalographic (EEG) signals were treated from a chaos theory perspective. First, we reconstructed the EEG state-space trajectories via a delay-embedding scheme. Then these pseudo-state-space vectors were input to a genetic programming algorithm, which designed one or more (non)linear features providing an artificial space where the baseline (nonseizure data) and preictal (preseizure data, or ictal data in case of detection) classes are sufficiently separated for a classifier to achieve better accuracy than using principal components analysis, our benchmark feature extractor. The GPAF algorithm was applied to data segments extracted from 730 hours of EEG recording obtained from seven patients. The machine automatically discovered one or more patient-specific features that predicted epileptic seizures with a time horizon from one to five minutes before the unequivocal electrographic onset of each seizure. Results showed that 43 of 55 seizures were correctly predicted, for a 78.19percent correct classification rate, while 55 epochs out of 59 representative of baseline conditions were classified correctly, for a low false positive rate per hour of 0.0508. In the case of detection, a low false-positive-per-hour-rate and a high detection rate were also achieved. A generic (cross-patient) model for prediction of epileptic seizures was also found, at the expense of decreased performance with an average of 69.09percent sensitivity. The GPAF algorithm was additionally investigated to design seizure detectors. Evaluating 730 hours of EEG recording showed that with customized, artificially designed detectors, 83 of 86 seizures were detected. Seven previously unreported seizures were also detected in this work.", notes = "ProQuest Dissertations and Theses UMI Microform 3171456 Supervisor Erik Goodman", } @Article{FGE:OPE:06, title = "On Prediction of Epileptic Seizures by Means of Genetic Programming Artificial Features", author = "Hiram Firpi and Erik Goodman and Javier Echauz", journal = "Annals of Biomedical Engineering", year = "2006", pages = "515--529", volume = "34", number = "3", month = mar, keywords = "genetic algorithms, genetic programming, Epilepsy, Seizure prediction, Artificial feature, Feature extraction, State-space reconstruction", DOI = "doi:10.1007/s10439-005-9039-7", abstract = "A general-purpose, systematic algorithm is presented, consisting of a genetic programming module and a k-nearest neighbour classifier to automatically create artificial features computer-crafted features possibly without a known physical meaning directly from the reconstructed state-space trajectory of intracranial EEG signals that reveal predictive patterns of epileptic seizures. The algorithm was evaluated with IEEG data from seven patients, with prediction defined over a horizon of 1-5 min before unequivocal electrographic onset. A total of 59 baseline epochs (nonseizures) and 55 preictal epochs (preseizures) were used for validation purposes. Among the results, it is shown that 12 seizures out of 55 were missed while four baseline epochs were misclassified, yielding 79per cent sensitivity and 93per cent specificity.", } @Article{Firpi:2007:BE, title = "Epileptic Seizure Detection Using Genetically Programmed Artificial Features", author = "Hiram Firpi and Erik D. Goodman and Javier Echauz", journal = "IEEE Transactions on Biomedical Engineering", year = "2007", volume = "54", number = "2", pages = "212--224", DOI = "doi:10.1109/TBME.2006.886936", ISSN = "0018-9294", month = feb, keywords = "genetic algorithms, genetic programming, diseases, electroencephalography, medical signal detection, medical signal processing, signal classification, signal reconstruction730.6 hr, epileptic seizure detection, genetic programming, genetically programmed artificial features, k-nearest neighbour classifier, patient-specific epilepsy seizure detectors, reconstructed state-space trajectories", abstract = "Patient-specific epilepsy seizure detectors were designed based on the genetic programming artificial features algorithm, a general-purpose, methodic algorithm comprised by a genetic programming module and a k-nearest neighbour classifier to create synthetic features. Artificial features are an extension to conventional features, characterised by being computer-coded and may not have a known physical meaning. In this paper, artificial features are constructed from the reconstructed state-space trajectories of the intracranial EEG signals intended to reveal patterns indicative of epileptic seizure onset. The algorithm was evaluated in seven patients and validation experiments were carried out using 730.6 hr of EEG recordings. The results with the artificial features compare favourably with previous benchmark work that used a handcrafted feature. Among other results, 88 out of 92 seizures were detected yielding a low false negative rate of 4.35percent", } @Article{Firpi2008558, author = "Hiram Firpi and George Vachtsevanos", title = "Genetically programmed-based artificial features extraction applied to fault detection", journal = "Engineering Applications of Artificial Intelligence", volume = "21", number = "4", pages = "558--568", year = "2008", ISSN = "0952-1976", DOI = "doi:10.1016/j.engappai.2007.06.004", URL = "http://www.sciencedirect.com/science/article/B6V2M-4PG2RVD-1/2/83e1929229a124416738c8ec59137146", keywords = "genetic algorithms, genetic programming, Fault detection, Feature extraction, Artificial feature, Conventional feature", abstract = "This paper presents a novel application of genetically programmed artificial features, which are computer crafted, data driven, and possibly without physical interpretation, to the problem of fault detection. Artificial features are extracted from vibration data of an accelerometer sensor to monitor and detect a crack fault or incipient failure seeded in an intermediate gearbox of a helicopter's main transmission. Classification accuracies for the artificial feature constructed from raw data exceeded 99percent over training and independent validation sets. As a benchmark, GP-based artificial features constructed from conventional ones under performed those derived from raw data by over 2percent over the training and over 11percent over the testing data.", } @InProceedings{First:2020:OOPSLA, author = "Emily First and Yuriy Brun and Arjun Guha", title = "{TacTok}: Semantics-Aware Proof Synthesis", booktitle = "Proceedings of the ACM on Programming Languages (PACMPL) Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA) issue", year = "2020", pages = "Article No. 231", publisher = "ACM", keywords = "genetic algorithms, genetic programming, Formal software verification, Coq, proof script synthesis, automated proofscript synthesis", URL = "https://people.cs.umass.edu/~brun/pubs/pubs/First20oopsla.pdf", URL = "https://doi.org/10.1145/3428299", DOI = "doi:10.1145/3428299", video_url = "https://dl.acm.org/action/downloadSupplement?doi=10.1145%2F3428299&file=oopsla20main-p664-p-video.mp4", video_url = "https://www.youtube.com/watch?v=bqkQ6KQmQCI", code_url = "https://zenodo.org/record/4088897#.ZAMETdbLdrk", size = "31 pages", abstract = "Formally verifying software correctness is a highly manual process. However, because verification proof scripts often share structure, it is possible to learn from existing proof scripts to fully automate some formal verification. The goal of this paper is to improve proof script synthesis and enable fully automating more verification. Interactive theorem provers, such as the Coq proof assistant, allow programmers to write partial proof scripts, observe the semantics of the proof state thus far, and then attempt more progress. Knowing the proof state semantics is a significant aid. Recent research has shown that the proof state can help predict the next step. In this paper, we present TacTok, the first technique that attempts to fully automate proof script synthesis by modeling proof scripts using both the partial proof script written thus far and the semantics of the proof state. Thus, TacTok more completely models the information the programmer has access to when writing proof scripts manually. We evaluate TacTok on a benchmark of 26 software projects in Coq, consisting of over 10 thousand theorems. We compare our approach to five tools. Two prior techniques, CoqHammer, the state-of-the-art proof synthesis technique, and ASTactic, a proof script synthesis technique that models proof state. And three new proof script synthesis technique we create ourselves, SeqOnly, which models only the partial proof script and the initial theorem being proven, and WeightedRandom and WeightedGreedy, which use metaheuristic search biased by frequencies of proof tactics in existing, successful proof scripts. We find that TacTok outperforms WeightedRandom and WeightedGreedy, and is complementary to CoqHammer and ASTactic: for 24 out of the 26 projects, TacTok can synthesize proof scripts for some theorems the prior tools cannot. Together with TacTok, 11.5percent more theorems can be proven automatically than by CoqHammer alone, and 20.0percent than by ASTactic alone. Compared to a combination of CoqHammer and ASTactic, TacTok can prove an additional 3.6percent more theorems, proving 115 theorems no tool could previously prove. Overall, our experiments provide evidence that partial proof script and proof state semantics, together, provide useful information for proof script modeling, and that metaheuristic search is a promising direction for proof script synthesis. TacTok is open-source and we make public all our data and a replication package of our experiments.", } @InProceedings{First:2022:ICSE, author = "Emily First and Yuriy Brun", title = "Diversity-Driven Automated Formal Verification", booktitle = "Proceedings of the 44th International Conference on Software Engineering (ICSE)", year = "2022", address = "Pittsburgh, PA, USA", month = may # " 21-29", publisher = "ACM", note = "ACM SIGSOFT Distinguished Paper Award", keywords = "genetic algorithms, genetic programming, Automated formal verification, language models, Coq, interactive proof assistants, proof synthesis, ANN, LSTM, AST", isbn13 = "978-1-4503-9221-1/22/05", URL = "https://people.cs.umass.edu/~brun/pubs/pubs/First22icse.pdf", DOI = "doi:10.1145/3510003.3510138", size = "13 pages", abstract = "Formally verified correctness is one of the most desirable properties of software systems. But despite great progress made via interactive theorem provers, such as Coq, writing proof scripts for verification remains one of the most effort-intensive (and often prohibitively difficult) software development activities. Recent work has created tools that automatically synthesize proofs or proof scripts. For example, CoqHammer can prove 26.6percent of theorems completely automatically by reasoning using precomputed facts, while TacTok and ASTactic, which use machine learning to model proof scripts and then perform biased search through the proof-script space, can prove 12.9percent and 12.3percent of the theorems, respectively. Further, these three tools are highly complementary; together, they can prove 30.4percent of the theorems fully automatically. Our key insight is that control over the learning process can produce a diverse set of models, and that, due to the unique nature of proof synthesis (the existence of the theorem prover, an oracle that infallibly judges a proof's correctness), this diversity can significantly improve these tools' proving power. Accordingly, we develop Diva, which uses a diverse set of models with TacTok's and ASTactic's search mechanism to prove 21.7percent of the theorems. That is, Diva proves 68percent more theorems than TacTok and 77percent more than ASTactic. Complementary to CoqHammer, Diva proves 781 theorems (27percent added value) that Coq-Hammer does not, and 364 theorems no existing tool has proved automatically. Together with CoqHammer, Diva proves 33.8percent of the theorems, the largest fraction to date. We explore nine dimensions for learning diverse models, and identify which dimensions lead to the most useful diversity. Further, we develop an optimization to speed up Diva's execution by 40X. Our study introduces a completely new idea for using diversity in machine learning to improve the power of state-of-the-art proof-script synthesis techniques, and empirically demonstrates that the improvement is significant on a dataset of 68K theorems from 122 open-source software projects.", } @PhdThesis{First:thesis, author = "Emily First", title = "Automating the Formal Verification of Software", school = "UMass Amherst", year = "2023", address = "USA", month = may, URL = "https://scholarworks.umass.edu/entities/publication/718c8df0-4728-4c1b-a8b1-a422e3fdc97e", URL = "https://hdl.handle.net/20.500.14394/19183", DOI = "doi:10.7275/35061393", abstract = "Formally verified correctness is one of the most desirable properties of software systems. Despite great progress made toward verification via interactive proof assistants, such as Coq and Isabelle/HOL, such verification remains one of the most effort-intensive (and often prohibitively difficult) software development activities. Recent work has created tools that automatically synthesize proofs either through reasoning using precomputed facts or using machine learning to model proofs and then perform biased search through the proof space. However, models in existing tools fail to capture the richness present in proofs, such as the information the programmer has access to when writing proofs and the natural language contained within variable names. Furthermore, these prior models do not make use of variations in the learning process and advances in large language models. In this dissertation, I develop tools to improve proof synthesis and to enable fully automating more verification. I first present TacTok, a proof-synthesis tool that models proofs using both the partial proof written thus far and the semantics of the proof state. I then present Diva, a proof-synthesis tool that controls the learning process to produce a diverse set of models and, due to the unique nature of proof synthesis (the existence of the theorem prover, an oracle that infallibly judges a proof's correctness), efficiently combines these models to improve the overall proving power. I then present Passport, a proof-synthesis tool that systematically explores different ways of encoding identifiers in proofs to improve synthesis. Finally, I present Baldur, a proof-synthesis tool that uses transformer-based pretrained large language models fine-tuned on proofs to generate and repair whole proofs at once, rather than one step at a time. This dissertation contributes new ideas for improving automated proof synthesis and empirically demonstrates that the improvement is significant on large benchmarks consisting of open-source software projects. License: http://creativecommons.org/licenses/by/4.0/", notes = "Oct 2024 'Request forms are not functional at the moment. Please email scholarworks@library.umass.edu if you are unable to download an item.' Advisors: Yuriy Brun, Arjun Guha, George Avrunin, Talia Ringer", } @InProceedings{conf/lwa/FischerJMM20, author = "Raphael Fischer and Matthias Jakobs and Sascha Muecke and Katharina Morik", title = "Solving Abstract Reasoning Tasks with Grammatical Evolution", booktitle = "Proceedings of the Conference Lernen, Wissen, Daten, Analysen, LWDA 2020", year = "2020", editor = "Daniel Trabold and Pascal Welke and Nico Piatkowski", volume = "2738", series = "CEUR Workshop Proceedings", pages = "6--10", address = "Online", month = sep # " 9-11", publisher = "CEUR-WS.org", note = "KDML Workshop", keywords = "genetic algorithms, genetic programming, grammatical evolution, machine learning, reasoning", ISSN = "1613-0073", bibdate = "2020-11-16", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/lwa/lwda2020.html#FischerJMM20", URL = "http://ceur-ws.org/Vol-2738", URL = "http://ceur-ws.org/Vol-2738/LWDA2020_paper_8.pdf", URL = "http://ceurspt.wikidata.dbis.rwth-aachen.de/Vol-2738/LWDA2020_paper_8.html", size = "5 pages", abstract = "The Abstraction and Reasoning Corpus (ARC) comprising image-based logical reasoning tasks is intended to serve as a benchmark for measuring intelligence. Solving these tasks is very difficult for off-the-shelf ML methods due to their diversity and low amount of training data. We here present our approach, which solves tasks via grammatical evolution on a domain-specific language for image transformations. With this approach, we successfully participated in an online challenge, scoring among the top 4percent out of 900 participants.", notes = "urn:nbn:de:0074-2738-5 TU Dortmund, AI Group, Dortmund, Germany", } @InCollection{fischer:1994:bmpm, author = "Ronald F. Fischer", title = "Applying Genetic Algorithms to Bitmap Pattern Matching", booktitle = "Genetic Algorithms at Stanford 1994", year = "1994", editor = "John R. Koza", pages = "41--48", address = "Stanford, California, 94305-3079 USA", month = dec, organisation = "Stanford University", publisher = "Stanford Bookstore", keywords = "genetic algorithms, GENESIS", ISBN = "0-18-187263-3", notes = "This volume contains 20 papers written and submitted by students describing their term projects for the course {"}Genetic Algorithms and Genetic Programming{"} (Computer Science 426) at Stanford University offered during the fall quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs426.html", } @TechReport{Fischer:RN/2018/12, author = "Thomas G. Fischer", title = "Reinforcement learning in financial markets - a survey", institution = "Institute for Economics, Friedrich-Alexander-Universitaet FAU", year = "2018", number = "Discussion Papers in Economics 12/2018", address = "Erlangen-Nuernberg", month = "1 " # oct, keywords = "genetic algorithms, genetic programming, Financial markets, reinforcement learning, survey, trading systems, machine learning", ISSN = "1867-6707", URL = "https://ideas.repec.org/p/zbw/iwqwdp/122018.html", duplicate = "https://www.iwf.rw.fau.de/files/2018/10/12-2018.pdf", URL = "https://www.econstor.eu/bitstream/10419/183139/1/1032172355.pdf", URL = "https://econpapers.repec.org/paper/zbwiwqwdp/122018.htm", size = "48 pages", abstract = "The advent of reinforcement learning (RL) in financial markets is driven by several advantages inherent to this field of artificial intelligence. In particular, RL allows to combine the prediction and the portfolio construction task in one integrated step, thereby closely aligning the machine learning problem with the objectives of the investor. At the same time, important constraints, such as transaction costs, market liquidity, and the investor degree of risk-aversion, can be conveniently taken into account. Over the past two decades, and albeit most attention still being devoted to supervised learning methods, the RL research community has made considerable advances in the finance domain. The present paper draws insights from almost 50 publications, and categorizes them into three main approaches, i.e. critic-only approach, actor-only approach, and actor-critic approach. Within each of these categories, the respective contributions are summarized and re- viewed along the representation of the state, the applied reward function, and the action space of the agent. This cross-sectional perspective allows us to identify recurring design decisions as well as potential levers to improve the agent performance. Finally, the individual strengths and weaknesses of each approach are discussed, and directions for future research are pointed out.", notes = "mention of GP also known as \cite{RePEc:zbw:iwqwdp:122018} https://www.iwf.rw.fau.de/discussion-papers/ ", } @PhdThesis{DBLP:phd/dnb/Fischer19, author = "Thomas G. Fischer", title = "Machine learning in financial markets", school = "Friedrich-Alexander-Universitaet, University of Erlangen-Nuremberg", year = "2019", address = "Germany", note = "Promotionspreis July 2019", keywords = "genetic algorithms, genetic programming, Data-warehouse-Konzept, Data mining, Kreditmarkt, Maschinelles Lernen, Hochschulschrift", timestamp = "Wed, 02 Oct 2019 14:18:50 +0200", biburl = "https://dblp.org/rec/phd/dnb/Fischer19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://www.statistik.rw.fau.de/forschung/dissertationen-habilitationen/", URL = "http://d-nb.info/1187154571", URL = "https://hbz-ulbms.primo.exlibrisgroup.com/permalink/49HBZ_ULM/1orud0a/alma991028011369706449", URL = "https://search.worldcat.org/title/1107344334", size = "238 pages", notes = "Is this on GP? In english OCLC Number / Unique Identifier: 1107344334", } @InProceedings{Fiser:2010:DDECS, author = "Petr Fiser and Jan Schmidt and Zdenek Vasicek and Lukas Sekanina", title = "On logic synthesis of conventionally hard to synthesize circuits using genetic programming", booktitle = "13th IEEE International Symposium on Design and Diagnostics of Electronic Circuits and Systems (DDECS), 2010", year = "2010", month = apr, pages = "346--351", abstract = "Recently, it has been shown that synthesis of some circuits is quite difficult for conventional methods. In this paper we present a method of minimisation of multi-level logic networks which can solve these difficult circuit instances. The synthesis problem is transformed on the search problem. A search algorithm called Cartesian genetic programming (CGP) is applied to synthesise various difficult circuits. Conventional circuit synthesis usually fails for these difficult circuits; specific synthesis processes must be employed to obtain satisfactory results. We have found that CGP is able to implicitly discover new efficient circuit structures. Thus, it is able to optimise circuits universally, regardless their structure. The circuit optimization by CGP has been found especially efficient when applied to circuits already optimized by a conventional synthesis. The total runtime is reduced, while the result quality is improved further more.", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, circuit optimisation, circuit synthesis, logic synthesis, multilevel logic networks, search algorithm, specific synthesis processes, logic design, search problems", DOI = "doi:10.1109/DDECS.2010.5491755", notes = "Also known as \cite{5491755}", } @Article{Fisonga:2024:engappai, author = "Marsheal Fisonga and Yongtao Hu and Shijie Han and Yongfeng Deng and Rennie B. Kaunda", title = "Deformation-related earth pressure within grid wall-pattern foundation under adjacent surcharge", journal = "Engineering Applications of Artificial Intelligence", year = "2024", volume = "137", pages = "109168", keywords = "genetic algorithms, genetic programming, Soft clay ground, Grid wall-pattern foundation, Adjacent surcharge loading, Deformation-related earth stress distribution, Numerical analysis, Machine learning", ISSN = "0952-1976", URL = "https://www.sciencedirect.com/science/article/pii/S0952197624013265", DOI = "doi:10.1016/j.engappai.2024.109168", abstract = "This study investigated the deformation-dependent earth pressure evolution on the periphery of the grid wall-pattern foundation fully embedded into soft ground. The numerical modelling was performed using the embedded Modified Cam Clay Constitutive model in the FLAC3D software platform and numerical results were validated using centrifuge results. The induced effective vertical and horizontal stresses acquired from the numerical modelling were used to compute the corresponding deformation-dependent earth pressure at different buffer zone distances and surcharge loading. Thereafter, the parameters that influence the determination of the lateral displacement, induced effective vertical stress and deformation-dependent earth pressure coefficient were investigated using machine learning and genetic programming was used to establish the physical equations that could be used to estimate parameters for similar problems. Moreover, for the deformation-dependent earth pressure to be within Rankine's active and passive earth pressure the buffer zone distance/depth ratio should be 10percent of the grid wall-pattern foundation embedment into the ground. Furthermore, it was also discovered that the structure fully embedded into the ground under adjacent loading cannot only be designed based on Rankine's active and passive earth pressure investigation on the periphery but the evaluation should be done within the entire location of the grid wall foundation", } @Article{Fister:2014:IREHM, author = "Iztok Fister and Amir H. Gandomi and Iztok {Fister, Jr.} and Mehdi Mousavi and Ali Farhadi", title = "Soft Computing in Earthquake Engineering: a Short Overview", journal = "International Journal of Earthquake Engineering and Hazard Mitigation", year = "2014", volume = "2", number = "2", pages = "42--48", month = jun, keywords = "genetic algorithms, genetic programming, Earthquake Engineering, Optimal Seismic Design, Earthquake Prediction, Data Analysis", ISSN = "2282-7226", URL = "http://www.iztok-jr-fister.eu/static/publications/19.pdf", URL = "http://www.praiseworthyprize.org/jsm/index.php?journal=irehm&page=article&op=view&path[]=15798", size = "7 pages", abstract = "Soft Computing refers to the name for solving the hardest problems with which human are confronted today that tolerates the imprecision, uncertainty, partial truth, and approximation of the solutions. Nature inspired algorithms, like evolutionary algorithms, swarm intelligence, and neural networks become one of the leading methods for solving these problems. The soft computing methods have also been applied for solving the earthquake engineering problems. In this paper, a short review of these methods is presented. In line with this, the problems solved by soft computing algorithms are identified, then, the characteristics of these algorithms are exposed and finally, the applications of the soft computing algorithms are identified. The paper concludes with an overview of the possible directions for further development.", notes = "IREHM http://www.praiseworthyprize.org/jsm/?journal=irehm", } @InProceedings{Fitch:2022:AERO, author = "Natalie Fitch and Daniel Clancy", title = "Genetic Programming + Multi-Agent Reinforcement Learning: Hybrid Approaches for Decision Processes", booktitle = "2022 IEEE Aerospace Conference (AERO)", year = "2022", month = "5-12 " # mar, address = "Big Sky, MT, USA", keywords = "genetic algorithms, genetic programming, Training, Q-learning, Sensitivity, Heuristic algorithms, Atmospheric modeling, Games", ISSN = "1095-323X", isbn13 = "978-1-6654-3761-5", DOI = "doi:10.1109/AERO53065.2022.9843637", abstract = "This paper details progress within the Multi-Agent Reinforcement Learning (MARL) research area with application to agent decision processing in complex battle-space scenarios, including air, surface, sub-surface, and space domains. We implement a Double Deep Q-Network (DDQN) with Minimax Q-Learning in order to model simultaneous, zero-sum, two team engagements involving multiple Blue agents & Red opponents. This is a game theoretic approach that models both ally and opponent policies while viewing a battle as a Multi-Stage Markov Stochastic Game (MSMSG). We contrast our agent with a DDQN + Traditional Q-Learning algorithm in a single stage 2v1 battle scenario with mixed optimal strategies. In order to help mitigate learning sensitivities and local optima convergence, we implement a Genetic Programming (GP) algorithm, which outperforms both the Minimax Q-Learning and Traditional Q-Learning DDQN agents trained using traditional stochastic gradient descent in a dynamic 1v1 battle. Lastly, we create a hybrid approach that combines stochastic gradient descent learning (Minimax Q-Learning) and gradient-free learning (GP) and apply our hybrid approach within the StarCraft II (SC2) 3m map, which simulates a 3v3 battle. We contrast this hybrid MARL approach with another state-of-the-art MARL method (QMIX) for the SC2 3m combat scenario.", notes = "Also known as \cite{9843637}", } @InProceedings{Fitzgerald:2011:GECCO, author = "Jeannie Fitzgerald and Conor Ryan", title = "Drawing boundaries: using individual evolved class boundaries for binary classification problems", booktitle = "GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0557-0", pages = "1347--1354", keywords = "genetic algorithms, genetic programming", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001576.2001758", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "This paper describes a technique which can be used with Genetic Programming (GP) to reduce implicit bias in binary classification tasks. Arbitrarily chosen class boundaries can introduce bias, but if individuals can choose their own boundaries, tailored to their function set, then their outputs are automatically scaled into a suitable range. These boundaries evolve over time as the individuals adapt to the data. Our system calculates the Evolved Class Boundary(ECB) for each individual in every generation, with the twin aims of reducing training times and improving test fitness. The method is tested on three benchmark binary classification data sets from the medical domain. The results obtained suggest that the strategy can improve training, validation and test fitness, and can also result in smaller individuals as well as reduced training times. Our approach is compared with a standard benchmark GP system, as well as with over twenty other systems from the literature, many of which use highly tuned, non-EC methods, and is shown to yield superior results in many cases.", notes = "Also known as \cite{2001758} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)", } @InProceedings{DBLP:conf/ichit/FitzgeraldR11, author = "Jeannie Fitzgerald and Conor Ryan", title = "Validation Sets for Evolutionary Curtailment with Improved Generalisation", booktitle = "5th International Conference on Convergence and Hybrid Information Technology, ICHIT 2011", year = "2011", editor = "Geuk Lee and Daniel Howard and Dominik Slezak", volume = "6935", series = "Lecture Notes in Computer Science", pages = "282--289", address = "Daejeon, Korea", month = sep # " 22-24", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-24081-2", DOI = "doi:10.1007/978-3-642-24082-9_35", size = "8 page", abstract = "This paper investigates the leveraging of a validation data set with Genetic Programming (GP) to counteract over-fitting. It considers fitness on both training and validation fitness, combined with with an early stopping mechanism to improve generalisation while significantly reducing run times. The method is tested on six benchmark binary classification data sets. Results of this preliminary investigation suggest that the strategy can deliver equivalent or improved results on test data.", notes = "ICHIT (1)", affiliation = "Jeannie Fitzgerald, BDS Group, CSIS Department, University of Limerick, Ireland", } @InProceedings{Fitzgerald:2011:SGAI, author = "Jeannie Fitzgerald and Conor Ryan", title = "Validation Sets, Genetic Programming and Generalisation", booktitle = "Proceedings of the 31st SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI-2011", year = "2011", editor = "Max Bramer and Miltos Petridis and Lars Nolle", pages = "79--92", address = "Cambridge, England", publisher_address = "London", month = dec, organisation = "BCS special interest group on Artificial Intelligence", publisher = "Springer", note = "Research and Development in Intelligent Systems XXVIII, Incorporating Applications and Innovations in Intelligent Systems XIX", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4471-2318-7", DOI = "doi:10.1007/978-1-4471-2318-7_6", abstract = "a new application of a validation set when using a three data set methodology with Genetic Programming (GP). Our system uses Validation Pressure combined with Validation Elitism to influence fitness evaluation and population structure with the aim of improving the system's ability to evolve individuals with an enhanced capacity for generalisation. This strategy facilitates the use of a validation set to reduce over-fitting while mitigating the loss of training data associated with traditional methods employing a validation set. The method is tested on five benchmark binary classification data sets and results obtained suggest that the strategy can deliver improved generalisation on unseen test data.", affiliation = "BDS Group, CSIS Department, University of Limerick, Limerick, Ireland", } @InProceedings{Fitzgerald:2012:GECCO, author = "Jeannie Fitzgerald and Conor Ryan", title = "Exploring boundaries: optimising individual class boundaries for binary classification problem", booktitle = "GECCO '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference", year = "2012", editor = "Terry Soule and Anne Auger and Jason Moore and David Pelta and Christine Solnon and Mike Preuss and Alan Dorin and Yew-Soon Ong and Christian Blum and Dario Landa Silva and Frank Neumann and Tina Yu and Aniko Ekart and Will Browne and Tim Kovacs and Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and Giovanni Squillero and Nicolas Bredeche and Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and Martin Pelikan and Silja Meyer-Nienberg and Christian Igel and Greg Hornby and Rene Doursat and Steve Gustafson and Gustavo Olague and Shin Yoo and John Clark and Gabriela Ochoa and Gisele Pappa and Fernando Lobo and Daniel Tauritz and Jurgen Branke and Kalyanmoy Deb", isbn13 = "978-1-4503-1177-9", pages = "743--750", keywords = "genetic algorithms, genetic programming", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330163.2330267", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "This paper explores a range of class boundary determination techniques that can be used to improve performance of Genetic Programming (GP) on binary classification tasks. These techniques involve selecting an individualised boundary threshold in order to reduce implicit bias that may be introduced through employing arbitrarily chosen values. Individuals that can chose their own boundaries and the manner in which they are applied, are freed from having to learn to force their outputs into a particular range or polarity and can instead concentrate their efforts on seeking a problem solution. Our investigation suggests that while a particular boundary selection method may deliver better performance for a given problem, no single method performs best on all problems studied. We propose a new flexible combined technique which gives near optimal performance across each of the tasks undertaken. This method together with seven other techniques is tested on six benchmark binary classification data sets. Experimental results obtained suggest that the strategy can improve test fitness, produce smaller less complex individuals and reduce run times. Our approach is shown to deliver superior results when benchmarked against a standard GP system, and is very competitive when compared with a range of other machine learning algorithms.", notes = "Also known as \cite{2330267} GECCO-2012 A joint meeting of the twenty first international conference on genetic algorithms (ICGA-2012) and the seventeenth annual genetic programming conference (GP-2012)", } @InProceedings{Fitzgerald:2013:mendel, author = "Jeannie Fitzgerald and Conor Ryan", title = "A Hybrid Approach to the Problem of Class Imbalance", booktitle = "19th International Conference on Soft Computing, MENDEL 2013", year = "2013", editor = "Radomil Matousek", pages = "129--137", address = "Brno, Czech Republic", month = jun # " 26-28, Brno", organisation = "Brno University of Technology", keywords = "genetic algorithms, genetic programming, class imbalance, Binary Classification, Class Imbalance Problem, Over Sampling, Under Sampling", isbn13 = "978-80-214-4755-4", URL = "https://www.researchgate.net/publication/264670826_A_Hybrid_Approach_to_the_Problem_of_Class_Imbalance?ev=prf_pub", size = "8 pages", abstract = "In Machine Learning classification tasks, the class imbalance problem is an important one which has received a lot of attention in the last few years. In binary classification, class imbalance occurs when there are significantly fewer examples of one class than the other. A variety of strategies have been applied to the problem with varying degrees of success. Typically previous approaches have involved attacking the problem either algorithmically or by manipulating the data in order to mitigate the imbalance. We propose a hybrid approach which combines Proportional Individualised Random Sampling(PIRS) with two different fitness functions designed to improve performance on imbalanced classification problems in Genetic Programming. We investigate the efficacy of the proposed methods together with that of five different algorithmic GP solutions, two of which are taken from the recent literature. We conclude that the PIRS approach combined with either average accuracy or Matthews Correlation Coefficient, delivers superior results in terms of AUC score when applied to either balanced or imbalanced datasets.", notes = "http://www.mendel-conference.org/ http://www.mendel-conference.org/tmp/ScheduleMendel2013papers.pdf", } @InProceedings{Fitzgerald:2013:GECCOcomp, author = "Jeannie Fitzgerald and R. Muhammad Atif Azad and Conor Ryan", title = "Bootstrapping to reduce bloat and improve generalisation in genetic programming", booktitle = "GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion", year = "2013", editor = "Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A. N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks", isbn13 = "978-1-4503-1964-5", keywords = "genetic algorithms, genetic programming", pages = "141--142", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2464576.2464647", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Typically, the quality of a solution in Genetic Programming (GP) is represented by a score on a given training sample. However, in Machine Learning, we are most interested in estimating the quality of the evolving individuals on unseen data. In this paper, we propose to simulate the effect of unseen data to direct training without actually using additional data, by employing a technique called bootstrapping that repeatedly re-samples with replacement from the training data and helps estimate sensitivity of the individual in question to small variations across these re-sampled data sets. We minimise this sensitivity, as measured by the Bootstrap Standard Error, alongside the training error, in a bid to evolve models that generalise better to the unseen data. We evaluate the proposed technique on four binary classification problems and compare with a standard GP approach. The results show that for the problems undertaken, the proposed method not only generalises significantly better than standard GP while the training performance improves, but also demonstrates a strong side effect of containing the tree sizes.", notes = "Also known as \cite{2464647} Distributed at GECCO-2013.", } @InProceedings{Fitzgerald:2013:GECCOcompa, author = "Jeannie Fitzgerald and R. Muhammad Atif Azad and Conor Ryan", title = "A bootstrapping approach to reduce over-fitting in genetic programming", booktitle = "GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion", year = "2013", editor = "Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A. N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks", isbn13 = "978-1-4503-1964-5", keywords = "genetic algorithms, genetic programming", pages = "1113--1120", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2464576.2482690", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Historically, the quality of a solution in Genetic Programming (GP) was often assessed based on its performance on a given training sample. However, in Machine Learning, we are more interested in achieving reliable estimates of the quality of the evolving individuals on unseen data. In this paper, we propose to simulate the effect of unseen data during training without actually using any additional data. We do this by employing a technique called bootstrapping that repeatedly re-samples with replacement from the training data and helps estimate sensitivity of the individual in question to small variations across these re-sampled data sets. We minimise this sensitivity, as measured by the Bootstrap Standard Error, together with the training error, in an effort to evolve models that generalise better to the unseen data. We evaluate the proposed technique on four binary classification problems and compare with a standard GP approach. The results show that for the problems undertaken, the proposed method not only generalises significantly better than standard GP while the training performance improves, but also demonstrates a strong side effect of containing the tree sizes.", notes = "Also known as \cite{2482690} Distributed at GECCO-2013.", } @InProceedings{Fitzgerald:2013:nabic, author = "Jeannie Fitzgerald and Conor Ryan", title = "Individualized self-adaptive genetic operators with adaptive selection in Genetic Programming", booktitle = "5th World Congress on Nature and Biologically Inspired Computing (NaBIC 2013)", year = "2013", editor = "Simone Ludwig and Patricia Melin and Ajith Abraham and Ana Maria Madureira and Kendall Nygard and Oscar Castillo and Azah Kamilah Muda and Kun Ma and Emilio Corchado", pages = "232--237", address = "Fargo, USA", month = "12-14 " # aug, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Self Adaptive, Geneteic Algorithm, Adaptive Selection", isbn13 = "978-1-4799-1415-9", URL = "http://www.mirlabs.net/nabic13/proceedings/html/paper55.xml", DOI = "doi:10.1109/NaBIC.2013.6617868", size = "6 pages", abstract = "In this paper we investigate a new method for improving generalization performance of Genetic Programming(GP) on Binary Classification tasks. The scheme of self adaptive, individualized genetic operators combined with adaptive tournament size is designed to provide balanced, self-adaptive exploration and exploitation. We test this scheme on several benchmark Binary Classification problems and find that the proposed techniques deliver superior performance when compared with both a tuned GP configuration and a feedback adaptive GP implementation.", notes = "USB only?, IEEE Catalog Number: CFP1395H-POD Also known as \cite{6617868}", } @InProceedings{Fitzgerald:2014:cicsyn, author = "Jeannie Fitzgerald and Conor Ryan", title = "Selection Bias and Generalisation Error in Genetic Programming", booktitle = "Sixth International Conference on Computational Intelligence, Communication Systems and Networks, CICSyN2014", year = "2014", editor = "David Al-Dabass and Vullnet Ameti and Fauzi Skenderi and Festim Halili", pages = "59--64", address = "Tetovo, Macedonia", month = "27-29 " # may, keywords = "genetic algorithms, genetic programming, generalisation, Tournament Size, Elitism, Replacement Strategy", isbn13 = "978-1-4799-5076-8", URL = "https://edas.info/showPaper.php?m=1569958507", URL = "https://www.researchgate.net/publication/264671201_Selection_Bias_and_Generalisation_Error_in_Genetic_Programming?ev=prf_pub", URL = "http://www.researchgate.net/profile/Jeannie_Fitzgerald/publication/264671201_long_url_1.pdf", URL = "https://core.ac.uk/download/pdf/59356329.pdf", size = "6 pages", abstract = "There have been many studies undertaken to determine the efficacy of parameters and algorithmic components of Genetic Programming, but historically, generalisation considerations have not been of central importance in such investigations. Recent contributions have stressed the importance of generalization to the future development of the field. In this paper we investigate aspects of selection bias as a component of generalisation error, where selection bias refers to the method used by the learning system to select one hypothesis over another. Sources of potential bias include the replacement strategy chosen and the means of applying selection pressure. We investigate the effects on generalisation of two replacement strategies, together with tournament selection with a range of tournament sizes. Our results suggest that larger tournaments are more prone to overfitting than smaller ones, and that a small tournament combined with a generational replacement strategy produces relatively small solutions and is least likely to over-fit.", notes = "Not in the IEEE digital library. http://cicsyn2014.info/", } @InProceedings{Fitzgerald:2014:GECCO, author = "Jeannie Fitzgerald and Conor Ryan", title = "On size, complexity and generalisation error in GP", booktitle = "GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation", year = "2014", editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges", isbn13 = "978-1-4503-2662-9", pages = "903--910", keywords = "genetic algorithms, genetic programming", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2576768.2598346", DOI = "doi:10.1145/2576768.2598346", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "For some time, Genetic Programming research has lagged behind the wider Machine Learning community in the study of generalisation, where the decomposition of generalisation error into bias and variance components is well understood. However, recent Genetic Programming contributions focusing on complexity, size and bloat as they relate to over-fitting have opened up some interesting avenues of research. In this paper, we carry out a simple empirical study on five binary classification problems. The study is designed to discover what effects may be observed when program size and complexity are varied in combination, with the objective of gaining a better understanding of relationships which may exist between solution size, operator complexity and variance error. The results of the study indicate that the simplest configuration, in terms of operator complexity, consistently results in the best average performance, and in many cases, the result is significantly better. We further demonstrate that the best results are achieved when this minimum complexity set-up is combined with a less than parsimonious permissible size.", notes = "Also known as \cite{2598346} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)", } @PhdThesis{jmfitz-thesis, author = "Jeannie Fitzgerald", title = "Bias and Variance Reduction Strategies for Improving Generalisation Performance of Genetic Programming on Binary Classification Tasks", school = "University of Limerick", year = "2014", address = "Ireland", month = may, email = "jeanniemfitzgerald@gmail.com", keywords = "genetic algorithms, genetic programming, generalisation, generalization, classification", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/jmfitz-thesis.pdf", size = "347 pages", abstract = "The central hypothesis of this thesis is that the reduction of variance and inappropriate bias in GP will lead to the evolution of more generalisable and robust numerical binary classifiers. A secondary, supporting, hypothesis is that dynamic, individualised approaches may have a role to play in reducing the magnitude of error due to bias and variance, as such approaches can introduce diversity and change into the learning system. We expect that, where an influencing parameter is applied identically to each member of the population, and remains unchanged throughout evolution, that any (undesirable) effects on bias and variance error are likely to be stronger than if individuals in the population apply the same parameter differently, and where the application of any such parameter can change in response to system behaviour. In other words, a monolithic system may suffer from monolithic bias, and we believe that the introduction of individualised, dynamic approaches may have a beneficial effect in diluting this, leading to improved generalisation in the GP learner. We explore the concepts of bias and variance as components of generalisation error for binary classification tasks, and investigate aspects of the GP paradigm which may influence these error components. Specifically, we identify sources of variance, language bias, search bias and selection bias inherent in standard GP for binary classification and pose several core questions relating to these sources. If the research can be shown to affirmatively answer these core questions, then our hypotheses will have been proved. In responding to the core questions we carry out several empirical studies with the objective of gaining a deeper understanding of the impacts of these sources of bias and variance on generalisation and we propose several novel approaches which may be used to reduce variance, or to replace inappropriate inductive biases with more appropriate ones, with a view to improving generalisation performance. Ultimately we combine several techniques, developed to address our fundamental questions, into a single, optimised GP (OGP) configuration. This is evaluated on nine different binary classification tasks and compared with the performance of several well known and respected machine learning algorithms on the same datasets. Results of these experiments demonstrate that a GP learner which has been optimised to reduce variance and bias error through individualised, dynamic and population based adaptations can deliver classification performance which is competitive with other machine learning algorithms. The empirical studies and proposed techniques described in this theses provide answers to the core questions which we believe validate our central and supporting hypotheses.", notes = "Supervisor: Prof. Conor Ryan External Examiner: Dr. Anna I. Esparcia-Alcazar Internal Examiner: Dr. Michael English", } @Article{journals/ijhis/FitzgeraldR14, author = "Jeannie Fitzgerald and Conor Ryan", title = "Balancing exploration and exploitation in genetic programming using inversion with individualized self-adaptation", journal = "International Journal of Hybrid Intelligent Systems", year = "2014", number = "4", volume = "11", pages = "273--285", keywords = "genetic algorithms, genetic programming", bibdate = "2014-09-30", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ijhis/ijhis11.html#FitzgeraldR14", URL = "http://content.iospress.com/download/international-journal-of-hybrid-intelligent-systems/his00199?id=international-journal-of-hybrid-intelligent-systems%2Fhis00199", URL = "http://dx.doi.org/10.3233/HIS-140199", DOI = "doi:10.3233/HIS-140199", size = "13 pages", abstract = "In this article we explore and develop a holistic scheme of self adaptive, individualized genetic operators combined with an adaptive tournament size together with a novel implementation of an inversion genetic operator which is suitable for tree based Genetic Programming. We test this scheme on several benchmark Binary Classification problems and find that the proposed techniques deliver superior performance when compared with both a tuned GP configuration and a feedback adaptive Genetic Programming implementation. Our results also demonstrate that an inversion operator may have a useful role to play in exploitation, particularly when used towards the end of evolution to facilitate gradual convergence of the learning system towards a good solution.", } @InProceedings{Fitzgerald:2015:GECCO, author = "Jeannie M. Fitzgerald and Conor Ryan and David Medernach and Krzysztof Krawiec", title = "An Integrated Approach to Stage 1 Breast Cancer Detection", booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation", year = "2015", editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr", isbn13 = "978-1-4503-3472-3", pages = "1199--1206", keywords = "genetic algorithms, genetic programming, Real World Applications", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739480.2754761", DOI = "doi:10.1145/2739480.2754761", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "We present an automated, end-to-end approach for Stage~1 breast cancer detection. The first phase of our proposed work-flow takes individual digital mammograms as input and outputs several smaller sub-images from which the background has been removed. Next, we extract a set of features which capture textural information from the segmented images. In the final phase, the most salient of these features are fed into a Multi-Objective Genetic Programming system which then evolves classifiers capable of identifying those segments which may have suspicious areas that require further investigation. A key aspect of this work is the examination of several new experimental configurations which focus on textural asymmetry between breasts. The best evolved classifier using such a configuration can deliver results of 100percent accuracy on true positives and a false positive per image rating of just 0.33, which is better than the current state of the art.", notes = "Also known as \cite{2754761} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)", } @InProceedings{Fitzgerald:2015:ECTA, author = "Jeannie Fitzgerald and R. Muhammad Atif Azad and Conor Ryan", title = "{GEML}: Evolutionary Unsupervised and Semi-Supervised Learning of Multi-class Classification with Grammatical Evolution", booktitle = "ECTA. 7th International Conference on Evolutionary Computation Theory and Practice", year = "2015", editor = "Agostinho Rosa and Juan Julian Merelo and Antonio Dourado and Jose M. Cadenas and Kurosh Madani and Antonio Ruano and Joaquim Filipe", address = "Lisbon, Portugal", month = "12-14 " # nov, pages = "83--94", organisation = "INSTICC - Institute for Systems and Technologies of Information, Control and Communication, IFAC - International Federation of Automatic Control, IEEE SMC - IEEE Systems, Man and Cybernetics Society", publisher = "SCITEPRESS - Science and Technology Publications", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Semi-supervised Learning, Multi-class Classification, Evolutionary Computation, Machine Learning", isbn13 = "978-9-8975-8165-6", URL = "http://www.researchgate.net/publication/283055687_GEML_Evolutionary_Unsupervised_and_Semi-Supervised_Learning_of_Multi-class_Classification_with_Grammatical_Evolution", URL = "http://ieeexplore.ieee.org/document/7529309/", size = "12 pages", abstract = "This paper introduces a novel evolutionary approach which can be applied to supervised, semi-supervised and unsupervised learning tasks. The method, Grammatical Evolution Machine Learning (GEML), adapts machine learning concepts from decision tree learning and clustering methods, and integrates these into a Grammatical Evolution framework. With minor adaptations to the objective function the system can be trivially modified to work with the conceptually different paradigms of supervised, semi-supervised and unsupervised learning.The framework generates human readable solutions which explain the mechanics behind the classification decisions, offering a significant advantage over existing paradigms for unsupervised and semi-supervised learning. GEML is studied on a range of multi-class classification problems and is shown to be competitive with several state of the art multi-class classification algorithms.", notes = "IJCCI Also known as \cite{7529309} See \cite{Fitzgerald:2015:ECTArevised}", } @InProceedings{Fitzgerald:2015:FSOW, author = "Jeannie M. Fitzgerald and Conor Ryan", title = "For Sale or Wanted: Directed Crossover in Adjudicated Space", booktitle = "Proceedings of the 7th International Joint Conference on Computational Intelligence, ECTA 2015", year = "2015", editor = "Agostinho Rosa and Juan Julian Merelo and Antonio Dourado and Jose M. Cadenas and Kurosh Madani and Antonio Ruano and Joaquim Filipe", volume = "1", pages = "95--105", address = "Lisbon, Portugal", month = "12-14 " # nov, organisation = "INSTICC - Institute for Systems and Technologies of Information, Control and Communication, IFAC - International Federation of Automatic Control, IEEE SMC - IEEE Systems, Man and Cybernetics Society", publisher = "SCITEPRESS - Science and Technology Publications", keywords = "genetic algorithms, genetic programming, search spaces, Directed crossover", isbn13 = "978-9-8975-8165-6", URL = "https://www.researchgate.net/profile/Jeannie_Fitzgerald/publication/283055763_For_Sale_or_Wanted_Directed_Crossover_in_Adjudicated_Space/", URL = "https://www.researchgate.net/profile/Jeannie_Fitzgerald/publication/283055763_For_Sale_or_Wanted_Directed_Crossover_in_Adjudicated_Space/FSOW.pdf", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=7529310", size = "11 pages", abstract = "Significant recent effort in genetic programming has focused on selecting and combining candidate solutions according to a notion of behaviour defined in semantic space and has also highlighted disadvantages of relying on a single scalar measure to capture the complexity of program performance in evolutionary search. In this paper, we take an alternative, yet complementary approach which directs crossover in what we call adjudicated space, where adjudicated space represents an abstraction of program behaviour that focuses on the success or failure of candidate solutions in solving problem sub-components. We investigate the effectiveness of several possible adjudicated strategies on a variety of classification and symbolic regression problems, and show that both of our novel pillage and barter tactics significantly outperform both a standard genetic programming and an enhanced genetic programming configuration on the fourteen problems studied.", notes = "See \cite{Fitzgerald:2015:FSOWrevised} Contact: Ana Margarida Guerreiro aguerreiro@insticc.org", } @InProceedings{Fitzgerald:2015:ECTArevised, author = "Jeannie M. Fitzgerald and R. Muhammad Atif Azad and Conor Ryan", title = "{GEML}: A Grammatical Evolution, Machine Learning Approach to Multi-class Classification", booktitle = "The 7th International Joint Conference on Computational Intelligence (IJCCI 2015)", year = "2015", editor = "Juan Julian Merelo and Agostinho Rosa and Jose M. Cadenas and Antonio Dourado Correia and Kurosh Madani and Antonio Ruano and Joaquim Filipe", volume = "669", series = "Studies in Computational Intelligence", pages = "113--134", address = "Lisbon, Portugal", month = nov # " 12-14", organisation = "INSTICC", publisher = "Springer", note = "Revised Selected Papers", keywords = "genetic algorithms, genetic programming, Grammatical evolution, Multi-class classification, Evolutionary computation, Machine learning", isbn13 = "978-3-319-48506-5", DOI = "doi:10.1007/978-3-319-48506-5_7", abstract = "In this paper, we propose a hybrid approach to solving multi-class problems which combines evolutionary computation with elements of traditional machine learning. The method, Grammatical Evolution Machine Learning (GEML) adapts machine learning concepts from decision tree learning and clustering methods and integrates these into a Grammatical Evolution framework. We investigate the effectiveness of GEML on several supervised, semi-supervised and unsupervised multi-class problems and demonstrate its competitive performance when compared with several well known machine learning algorithms. The GEML framework evolves human readable solutions which provide an explanation of the logic behind its classification decisions, offering a significant advantage over existing paradigms for unsupervised and semi-supervised learning. In addition we also examine the possibility of improving the performance of the algorithm through the application of several ensemble techniques.", notes = "Published by Springer 2017. See \cite{Fitzgerald:2015:ECTA} Biocomputing and Developmental Systems Group, University of Limerick, Limerick, Ireland", } @InProceedings{Fitzgerald:2015:FSOWrevised, author = "Jeannie M. Fitzgerald and Conor Ryan", title = "Adjudicated {GP}: A Behavioural Approach to Selective Breeding", booktitle = "The 7th International Joint Conference on Computational Intelligence (IJCCI 2015)", year = "2015", editor = "Juan Julian Merelo and Agostinho Rosa and Jose M. Cadenas and Antonio Dourado Correia and Kurosh Madani and Antonio Ruano and Joaquim Filipe", volume = "669", series = "Studies in Computational Intelligence", pages = "135--154", address = "Lisbon, Portugal", month = nov # " 12-14", organisation = "INSTICC", publisher = "Springer", note = "Revised Selected Papers", keywords = "genetic algorithms, genetic programming, Program semantics, Selective breeding", isbn13 = "978-3-319-48506-5", DOI = "doi:10.1007/978-3-319-48506-5_8", abstract = "For some time, there has been a realisation among Genetic Programming researchers that relying on a single scalar fitness value to drive evolutionary search is no longer a satisfactory approach. Instead, efforts are being made to gain richer insights into the complexity of program behaviour. To this end, particular attention has been focused on the notion of semantic space. In this paper we propose and unified hierarchical approach which decomposes program behaviour into semantic, result and adjudicated spaces, where adjudicated space sits at the top of the behavioural hierarchy and represents an abstraction of program behaviour that focuses on the success or failure of candidate solutions in solving problem sub-components. We show that better, smaller solutions are discovered when crossover is directed in adjudicated space. We investigate the effectiveness of several possible adjudicated strategies on a variety of classification and symbolic regression problems, and show that both of our novel pillage and barter tactics significantly outperform both a standard genetic programming and an enhanced genetic programming configuration on the fourteen problems studied. The proposed method is extremely effective when incorporated into a standard Genetic Programming structure but should also complement several other semantic approaches proposed in the literature.", notes = "Published by Springer 2017. See \cite{Fitzgerald:2015:FSOW} Biocomputing and Developmental Systems Group, University of Limerick, Limerick, Ireland", } @InProceedings{Fitzsimmons:2018:AI4I, author = "Jake Fitzsimmons and Pablo Moscato", title = "Symbolic Regression Modeling of Drug Responses", booktitle = "2018 First International Conference on Artificial Intelligence for Industries (AI4I)", year = "2018", pages = "52--59", address = "Laguna Hills, CA, USA", month = "26-28 " # sep, keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-5386-9463-3", DOI = "doi:10.1109/AI4I.2018.8665684", abstract = "Big pharmaceutical companies require to innovate by applying new machine learning and artificial intelligence methods to understand the large datasets produced by high-throughput technologies. In addition to reduce development costs for these industries, regression and classification models of drug response are needed for the final quest of delivering personalized treatment for cancer. An emphasis exists in developing models that allow for both prediction and ease of interpretation. In this contribution we present results obtained by symbolic regression. We employ a public domain dataset of drug responses on a large cancer cell line panel and compare with a previous method based on binarisation of the response data and the use of integer linear programming to find logic models. We present derived models of drug response for the drugs Afatinib, Dactolisib (BEZ235), Cytarabine, and Paclitaxel as well as for AZD6244, JQ12, KIN001-102, and PLX4720. We provide indication of the interpretability with a biological analysis of the results for Afatnib and Dactolisib, showing that our models introduce variables that point at known mechanisms of action of these drugs.", notes = "Also known as \cite{8665684}", } @MastersThesis{Flack:mastersthesis, author = "Robert W. J. Flack", title = "Evolution of Architectural Floor Plans", school = "Brock University", year = "2010", type = "Master of Science", address = "Ontario, Canada", month = Oct, keywords = "genetic algorithms, genetic programming", URL = "http://dr.library.brocku.ca/handle/10464/3409", URL = "http://dr.library.brocku.ca/bitstream/handle/10464/3409/Brock_Flack_Robert_2011.pdf", URL = "http://www.cosc.brocku.ca/~bross/FloorPlans/", code_url = "http://robgp.sourceforge.net/", size = "119 pages", abstract = "Layout planning is a process of sizing and placing rooms (e.g. in a house) while attempting to optimize various criteria. Often there are conflicting criteria such as construction cost, minimizing the distance between related activities, and meeting the area requirements for these activities. The process of layout planning has mostly been done by hand, with a handful of attempts to automate the process. This thesis explores some of these past attempts and describes several new techniques for automating the layout planning process using evolutionary computation. These techniques are inspired by the existing methods, while adding some of their own innovations. Additional experiments are done to test the possibility of allowing polygonal exteriors with rectilinear interior walls. Several multi-objective approaches are used to evaluate and compare fitness. The evolutionary representation and requirements specification used provide great flexibility in problem scope and depth and is worthy of considering in future layout and design attempts. The system outlined in this thesis is capable of evolving a variety of floor plans conforming to functional and geometric specifications. Many of the resulting plans look reasonable even when compared to a professional floor plan. Additionally polygonal and multi-floor buildings were also generated.", notes = "Also available as Brock University technical report CS-11-03, January 2011 http://www.cosc.brocku.ca/files/downloads/research/cs1103.pdf RobGP http://robgp.sourceforge.net/ supervised by Brian J. Ross", } @InCollection{flannery:2000:TETBPML, author = "Matthew Flannery", title = "The Evolution of Traffic Behavior Patterns on a Macroscopic Level", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "135--142", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:2000:gagp}", } @InProceedings{Flas09a, author = "Oliver Flasch and Thomas Bartz-Beielstein and Patrick Koch and Wolfgang Konen", title = "Genetic Programming Applied to Predictive Control in Environmental Engineering", booktitle = "Proceedings 19. Workshop Computational Intelligence", year = "2009", editor = "Frank Hoffmann and Eyke Huellermeier", pages = "101--113", publisher = "KIT Scientific Publishing", address = "Karlsruhe", keywords = "genetic algorithms, genetic programming", pubstate = "published", annote = "Fakult{\"a}t F{\"u}r Informatik Und Ingenieurwissenschaften; The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.301.5641", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.301.5641", URL = "http://lwibs01.gm.fh-koeln.de/blogs/konen/publications-wolfgang-konen/?tgid=20&yr&type&auth", URL = "http://www.gm.fh-koeln.de/~bartz/Papers.d/Flas09a.pdf", abstract = "We introduce a new hybrid Genetic Programming (GP) based method for time series prediction in predictive control applications. Our method combines existing state-of-the-art analytical models from predictive control with a modern typed graph GP system. The main idea is to pre-structure the GP search space with existing analytical models to improve prediction accuracy. We apply our method to a difficult predictive control problem from the water resource management industry, yielding an improved prediction accuracy, compared with both the best analytical model and with a modern GP method for time series prediction. Even if we focus this first study on predictive control, the automatic optimisation of existing models through GP shows a great potential for broader application.", } @InProceedings{Flas10f, author = "Oliver Flasch and Thomas Bartz-beielstein and Patrick Koch and Wolfgang Konen", title = "Clustering Based Niching for Genetic Programming in the R Environment", booktitle = "Proceedings 20. Workshop Computational Intelligence", year = "2010", editor = "Frank Hoffmann and Eyke Huellermeier", pages = "33--46", publisher = "Universitaetsverlag Karlsruhe", keywords = "genetic algorithms, genetic programming", pubstate = "published", annote = "Fakult{\"a}t F{\"u}r Informatik Und Ingenieurwissenschaften; The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.301.5035", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.301.5035", URL = "http://www.gm.fh-koeln.de/~bartz/Papers.d/Flas10f.pdf", URL = "http://lwibs01.gm.fh-koeln.de/blogs/konen/publications-wolfgang-konen/?tgid=20&yr&type&auth", abstract = "In this paper, we give a short introduction into RGP, a new genetic programming (GP) system based on the statistical package R. The system implements classical untyped tree-based genetic programming as well as more advanced variants including, for example, strongly typed genetic programming and Pareto genetic programming. The main part of this paper is concerned with the problem of premature convergence of GP populations, accompanied by a loss of genetic diversity, resulting in poor effectiveness of the search. We propose a clustering based niching approach to mitigate this problem. The results of preliminary experiments confirm that clustering based niching is effective in preserving genetic diversity in GP populations.", } @InProceedings{Flasch:2010:geccocomp, author = "Oliver Flasch and Olaf Mersmann and Thomas Bartz-Beielstein", title = "RGP: an open source genetic programming system for the R environment", booktitle = "GECCO 2010 Late breaking abstracts", year = "2010", editor = "Daniel Tauritz", isbn13 = "978-1-4503-0073-5", keywords = "genetic algorithms, genetic programming", pages = "2071--2072", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830761.1830867", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "RGP is a new genetic programming system based on the R environment. The system implements classical untyped tree-based genetic programming as well as more advanced variants including, for example, strongly typed genetic programming and Pareto genetic programming. It strives for high modularity through a consistent architecture that allows the customisation and replacement of every algorithm component, while maintaining accessibility for new users by adhering to the {"}convention over configuration{"} principle. Typical GP applications are supported by standard R interfaces. For example, symbolic regression via GP is supported by the same {"}formula interface{"} as linear regression in R. RGP is freely available as an open source R package.", notes = "Also known as \cite{1830867} Distributed on CD-ROM at GECCO-2010. ACM Order Number 910102.", } @InProceedings{Flasch:2010:cec, author = "Oliver Flasch and Thomas Bartz-Beielstein and Artur Davtyan and Patrick Koch and Wolfgang Konen and Tosin Daniel Oyetoyan and Michael Tamutan", title = "Comparing {SPO}-tuned {GP} and {NARX} prediction models for stormwater tank fill level prediction", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-6910-9", abstract = "The prediction of fill levels in stormwater tanks is an important practical problem in water resource management. In this study state-of-the-art CI methods, i.e., Neural Networks (NN) and Genetic Programming (GP), are compared with respect to their applicability to this problem. The performance of both methods crucially depends on their parametrisation. We compare different parameter tuning approaches, e.g. neuro-evolution and Sequential Parameter Optimization (SPO). In comparison to NN, GP yields superior results. By optimising GP parameters, GP runtime can be significantly reduced without degrading result quality. The SPO-based parameter tuning leads to results with significantly lower standard deviation as compared to the GA based parameter tuning. Our methodology can be transferred to other optimisation and simulation problems, where complex models have to be tuned.", DOI = "doi:10.1109/CEC.2010.5586172", notes = "WCCI 2010. Also known as \cite{5586172}", } @InCollection{Flasch:2012:GPTP, author = "Oliver Flasch and Thomas Bartz-Beielstein", title = "A Framework for the Empirical Analysis of Genetic Programming System Performance", booktitle = "Genetic Programming Theory and Practice X", year = "2012", series = "Genetic and Evolutionary Computation", editor = "Rick Riolo and Ekaterina Vladislavleva and Marylyn D. Ritchie and Jason H. Moore", publisher = "Springer", chapter = "11", pages = "155--169", address = "Ann Arbor, USA", month = "12-14 " # may, keywords = "genetic algorithms, genetic programming, Symbolic regression, Design of experiments, Sequential parameter optimisation, Reproducible research, Multi-objective optimisation", isbn13 = "978-1-4614-6845-5", URL = "http://dx.doi.org/10.1007/978-1-4614-6846-2_11", DOI = "doi:10.1007/978-1-4614-6846-2_11", abstract = "This chapter introduces a framework for statistically sound, reproducible empirical research in Genetic Programming (GP). It provides tools to understand GP algorithms and heuristics and their interaction with problems of varying difficulty. Following an approach where scientific claims are broken down to testable statistical hypotheses and GP runs are treated as experiments, the framework helps to achieve statistically verified results of high reproducibility.", notes = "part of \cite{Riolo:2012:GPTP} published after the workshop in 2013", } @InProceedings{Flasch:evoapps13, author = "Oliver Flasch and Martina Friese and Katya Vladislavleva and Thomas Bartz-Beielstein and Olaf Mersmann and Boris Naujoks and Joerg Stork and Martin Zaefferer", title = "Comparing Ensemble-Based Forecasting Methods for Smart-Metering Data", booktitle = "Applications of Evolutionary Computing, EvoApplications 2013: EvoCOMNET, EvoCOMPLEX, EvoENERGY, EvoFIN, EvoGAMES, EvoIASP, EvoINDUSTRY, EvoNUM, EvoPAR, EvoRISK, EvoROBOT, EvoSTOC", year = "2013", month = "3-5 " # apr, editor = "Anna I. Esparcia-Alcazar and Antonio Della Cioppa and Ivanoe {De Falco} and Ernesto Tarantino and Carlos Cotta and Robert Schaefer and Konrad Diwold and Kyrre Glette and Andrea Tettamanzi and Alexandros Agapitos and Paolo Burrelli and J. J. Merelo and Stefano Cagnoni and Mengjie Zhang and Neil Urquhart and Kevin Sim and Aniko Ekart and Francisco {Fernandez de Vega} and Sara Silva and Evert Haasdijk and Gusz Eiben and Anabela Simoes and Philipp Rohlfshagen", series = "LNCS", volume = "7835", publisher = "Springer Verlag", address = "Vienna", publisher_address = "Berlin", pages = "172--181", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-37191-2", DOI = "doi:10.1007/978-3-642-37192-9_18", size = "10 pages", abstract = "This work provides a preliminary study on applying state-of-the-art time-series forecasting methods to electrical energy consumption data recorded by smart metering equipment. We compare a custom-build commercial baseline method to modern ensemble-based methods from statistical time-series analysis and to a modern commercial GP system. Our preliminary results indicate that that modern ensemble-based methods, as well as GP, are an attractive alternative to custom-built approaches for electrical energy consumption forecasting", notes = " EvoApplications2013 held in conjunction with EuroGP2013, EvoCOP2013, EvoBio'2013 and EvoMusArt2013", } @PhdThesis{Flasch:thesis, author = "Oliver Flasch", title = "A modular genetic programming system", school = "Fakultaet fuer Informatik, Technische Universitaet Dortmund", year = "2015", address = "Germany", month = "6 " # may, keywords = "genetic algorithms, genetic programming, genetische programmierung, symbolic regression, symbolische regression, data mining, computational intelligence, big data", bibsource = "OAI-PMH server at eldorado.uni-dortmund.de", contributor = "Guenter Rudolph and", language = "eng", oai = "oai:eldorado.tu-dortmund.de:2003/34162", URL = "https://eldorado.tu-dortmund.de/bitstream/2003/34162/1/Dissertation.pdf", URL = "http://hdl.handle.net/2003/34162", DOI = "doi:10.17877/DE290R-7807", size = "208 pages", abstract = "Genetic Programming (GP) is an evolutionary algorithm for the automatic discovery of symbolic expressions, e.g. computer programs or mathematical formulae, that encode solutions to a user-defined task. Recent advances in GP systems and computer performance made it possible to successfully apply this algorithm to real-world applications. This work offers three main contributions to the state-of-the art in GP systems: (I) The documentation of RGP, a state-of-the art GP software implemented as an extension package to the popular R environment for statistical computation and graphics. GP and RPG are introduced both formally and with a series of tutorial examples. As R itself, RGP is available under an open source license. (II) A comprehensive empirical analysis of modern GP heuristics based on the methodology of Sequential Parameter Optimisation. The effects and interactions of the most important GP algorithm parameters are analysed and recommendations for good parameter settings are given. (III) Two extensive case studies based on real-world industrial applications. The first application involves process control models in steel production, while the second is about meta-model-based optimisation of cyclone dust separators. A comparison with traditional and modern regression methods reveals that GP offers equal or superior performance in both applications, with the additional benefit of understandable and easy to deploy models. Main motivation of this work is the advancement of GP in real-world application areas. The focus lies on a subset of application areas that are known to be practical for GP, first of all symbolic regression and classification. It has been written with practitioners from academia and industry in mind.", notes = "Supervisor Thomas Bartz-Beielstein LS 11. RGP. Meta Models for Cyclone Dust Separators (AppDust). Roll Train Control Models (AppSteel) Figure 3.1: Features versus costs of modern GP system offerings: RGP ECJ DataModeler Eureqa DataModeler tinyGP", } @InProceedings{Fleck:2019:EUROCAST, author = "Philipp Fleck and Manfred Kuegel and Michael Kommenda", title = "Understanding and Preparing Data of Industrial Processes for Machine Learning Applications", booktitle = "International Conference on Computer Aided Systems Theory, EUROCAST 2019", year = "2019", editor = "Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia", volume = "12013", series = "Lecture Notes in Computer Science", pages = "413--420", address = "Las Palmas de Gran Canaria, Spain", month = "17-22 " # feb, publisher = "Springer", keywords = "missing data, Machine learning, Data preprocessing, Missing values", isbn13 = "978-3-030-45092-2", DOI = "doi:10.1007/978-3-030-45093-9_50", abstract = "Industrial applications of machine learning face unique challenges due to the nature of raw industry data. Preprocessing and preparing raw industrial data for machine learning applications is a demanding task that often takes more time and work than the actual modeling process itself and poses additional challenges. This paper addresses one of those challenges, specifically, the challenge of missing values due to sensor unavailability at different production units of nonlinear production lines. In cases where only a small proportion of the data is missing, those missing values can often be imputed. In cases of large proportions of missing data, imputing is often not feasible, and removing observations containing missing values is often the only option. Use all of the available data without the need of removing large amounts of observations where data is only partially available. We do not only discuss the principal idea of the presented method, but also show different possible implementations that can be applied depending on the data at hand. Finally, we demonstrate the application of the presented method with data from a steel production plant.", notes = "Not GP?", } @InProceedings{Fleck:2021:GPTP, author = "Philipp Fleck and Stephan Winkler and Michael Kommenda and Michael Affenzeller", title = "Grammar-based Vectorial Genetic Programming for Symbolic Regression", booktitle = "Genetic Programming Theory and Practice XVIII", year = "2021", editor = "Wolfgang Banzhaf and Leonardo Trujillo and Stephan Winkler and Bill Worzel", series = "Genetic and Evolutionary Computation", pages = "21--43", address = "East Lansing, USA", month = "19-21 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-981-16-8112-7", DOI = "doi:10.1007/978-981-16-8113-4_2", abstract = "Vectorial Genetic Programming (GP) is a young branch of GP, where the training data for symbolic models not only include regular, scalar variables, but also allow vector variables. Also, the models abilities are extended to allow operations on vectors, where most vector operations are simply performed component-wise. Additionally, new aggregation functions are introduced that reduce vectors into scalars, allowing the model to extract information from vectors by itself, thus eliminating the need of prior feature engineering that is otherwise necessary for traditional GP to use vector data. And due to the white-box nature of symbolic models, the operations on vectors can be as easily interpreted as regular operations on scalars. In this paper, we extend the ideas of vectorial GP of previous authors, and propose a grammar-based approach for vectorial GP that can deal with various challenges noted. To evaluate grammar-based vectorial GP, we have designed new benchmark functions that contain both scalar and vector variables, and show that traditional GP falls short very quickly for certain scenarios. Grammar-based vectorial GP, however, is able to solve all presented benchmarks.", notes = "Part of \cite{Banzhaf:2021:GPTP} published after the workshop in 2022", } @Misc{DBLP:journals/corr/abs-2303-03200, author = "Philipp Fleck and Stephan M. Winkler and Michael Kommenda and Michael Affenzeller", title = "Vectorial Genetic Programming - Optimizing Segments for Feature Extraction", howpublished = "arXiv", volume = "abs/2303.03200", year = "2023", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.48550/arXiv.2303.03200", DOI = "doi:10.48550/arXiv.2303.03200", eprinttype = "arXiv", eprint = "2303.03200", timestamp = "Tue, 14 Mar 2023 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/corr/abs-2303-03200.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{fleck:2023:GECCOcomp, author = "Philipp Fleck and Stephan Winkler and Michael Kommenda and Sara Silva and Leonardo Vanneschi and Michael Affenzeller", title = "Evolutionary Algorithms for Segment Optimization in Vectorial {GP}", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "439--442", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, evolutionary algorithms, vectorial, symbolic regression: Poster", isbn13 = "9798400701191", DOI = "doi:10.1145/3583133.3590668", size = "4 pages", abstract = "Vectorial Genetic Programming (Vec-GP) extends regular GP by allowing vectorial input features (e.g. time series data), while retaining the expressiveness and interpretability of regular GP. The availability of raw vectorial data during training, not only enables Vec-GP to select appropriate aggregation functions itself, but also allows Vec-GP to extract segments from vectors prior to aggregation (like windows for time series data). This is a critical factor in many machine learning applications, as vectors can be very long and only small segments may be relevant. However, allowing aggregation over segments within GP models makes the training more complicated. We explore the use of common evolutionary algorithms to help GP identify appropriate segments, which we analyze using a simplified problem that focuses on optimizing aggregation segments on fixed data. Since the studied algorithms are to be used in GP for local optimization (e.g. as mutation operator), we evaluate not only the quality of the solutions, but also take into account the convergence speed and anytime performance. Among the evaluated algorithms, CMA-ES, PSO and ALPS show the most promising results, which would be prime candidates for evaluation within GP.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @Article{fleck:2024:AS, author = "Philipp Fleck and Bernhard Werth and Michael Affenzeller", title = "Population Dynamics in Genetic Programming for Dynamic Symbolic Regression", journal = "Applied Sciences", year = "2024", volume = "14", number = "2", pages = "Article No. 596", keywords = "genetic algorithms, genetic programming", ISSN = "2076-3417", URL = "https://www.mdpi.com/2076-3417/14/2/596", DOI = "doi:10.3390/app14020596", abstract = "This paper investigates the application of genetic programming (GP) for dynamic symbolic regression (SR), addressing the challenge of adapting machine learning models to evolving data in practical applications. Benchmark instances with changing underlying functions over time are defined to assess the performance of a genetic algorithm (GA) as a traditional evolutionary algorithm and an age-layered population structure (ALPS) as an open-ended evolutionary algorithm for dynamic symbolic regression. This study analyses population dynamics by examining variable frequencies and impact changes over time in response to dynamic shifts in the training data. The results demonstrate the effectiveness of both the GA and ALPS in handling changing data, showcasing their ability to recover and evolve improved solutions after an initial drop in population quality following data changes. Population dynamics reveal that variable impacts respond rapidly to data changes, while variable frequencies shift gradually across generations, aligning with the indirect measure of fitness represented by variable impacts. Notably, the GA shows a strong dependence on mutation to avoid variables becoming permanently extinct, contrasting with the ALPS's unexpected insensitivity to mutation rates owing to its reseeding mechanism for effective variable reintroduction.", notes = "also known as \cite{app14020596}", } @InCollection{flight:1997:psGPtmt, author = "John Flight", title = "The Use of Program State by a Genetic Program to Track a Moving Target", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1997", publisher = "Stanford Bookstore", year = "1997", editor = "John R. Koza", pages = "57-", address = "Stanford, California, 94305-3079 USA", month = "17 " # mar, keywords = "genetic algorithms, genetic programming", ISBN = "0-18-205981-2", abstract = "how a GP might use state variables and feedback from the fitness measure", notes = "part of \cite{koza:1997:GAGPs}", } @InCollection{Flister:1997:rational, author = "Erik D. Flister", title = "The Deceptive Problem of Rational Trading and Negotiation Strategies in Artificial Economic Communities", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1997", publisher = "Stanford Bookstore", year = "1997", editor = "John R. Koza", pages = "66--75", address = "Stanford, California, 94305-3079 USA", month = "17 " # mar, keywords = "genetic algorithms, genetic programming", ISBN = "0-18-205981-2", notes = "part of \cite{koza:1997:GAGPs}", } @InProceedings{conf/wilf/Floares05, title = "Genetic Programming and Neural Networks Feedback Linearization for Modeling and Controlling Complex Pharmacogenomic Systems", author = "Alexandru Floares", year = "2005", editor = "Isabelle Bloch and Alfredo Petrosino and Andrea Tettamanzi", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3849", booktitle = "Fuzzy Logic and Applications, 6th International Workshop, WILF 2005, Revised Selected Papers", pages = "178--187", address = "Crema, Italy", month = sep # " 15-17", bibdate = "2006-02-22", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/wilf/wilf2005.html#Floares05", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-32529-8", DOI = "doi:10.1007/11676935_22", abstract = "Modern pharmacology, combining pharmacokinetic, pharmacodynamic, and pharmacogenomic data, is dealing with high dimensional, nonlinear, stiff systems. Mathematical modelling of these systems is very difficult, but important for understanding them. At least as important is to adequately control them through inputs - drugs' dosage regimes. Genetic programming (GP) and neural networks (NN) are alternative techniques for these tasks. We use GP to automatically write the model structure in C++ and optimise the model's constants. This gives insights into the subjacent molecular mechanisms. We also show that NN feedback linearisation (FBL) can adequately control these systems, with or without a mathematical model. The drug dosage regimen will determine the output of the system to track very well a therapeutic objective. To our knowledge, this is the first time when a very large class of complex pharmacological problems are formulated and solved in terms of GP modeling and NN modeling and control.", notes = "Published 2006?", } @InProceedings{Floares:2006:CEC, author = "Alexandru G. Floares", title = "Computation Intelligence Tools for Modeling and Controlling Pharmacogenomic Systems: Genetic Programming and Neural Networks", booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary Computation", year = "2006", editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas", pages = "7510--7517", address = "Vancouver", month = "16-21 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, computational intelligences tools, computation intelligence tools, computer programming language, differential genes expression, neural networks, nonlinear coupled ordinary differential equations, pharmacogenomic systems, genetics, medical control systems, neurocontrollers, nonlinear differential equations, nonlinear equations", ISBN = "0-7803-9487-9", DOI = "doi:10.1109/IJCNN.2006.246876", size = "8 pages", abstract = "Pharmacogenomic systems (PG) are very high dimensional, nonlinear, and stiff systems. Mathematical modelling of these systems, as systems of nonlinear coupled ordinary differential equations (ODE), is considered important for understanding them; unfortunately, it is also a very difficult task. At least as important is to adequately control them through inputs, which are drugs' dosage regimes. In this paper, we investigate new approaches based on computational intelligences tools - genetic programming (GP), and neural networks (NN) - for these difficult tasks. We use GP to automatically write the model structure in a computer programming language (C+t) and to optimise the model's constants. In some circumstances, the proposed methods not only give an accurate mathematical model of the PG system, but they also give insights into the subjacent molecular mechanisms. We also show that NN feedback linearisation (FBL) can adequately control these systems, with or without a mathematical model. The drug dosage regimen will determine the output of the system to track very well a therapeutic objective. To our knowledge, this is the first time when a very large class of complex pharmacological problems are formulated and solved in terms of GP modelling and NN modeling and control.", notes = "May 2010 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1716624&tag=1 \cite{conf/ijcnn/Floares06} says this is in IJCNN 2006, 3820--3827, but his own IASTED-2007 ISBN:978-0-88986-694-2 says CEC 7510--7517. WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D", } @InProceedings{Floares:2008:ijcnn, author = "Alexandru George Floares", title = "Automatic Inferring Drug Gene Regulatory Networks with Missing Information Using Neural Networks and Genetic Programming", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "3078--3085", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1821-3", file = "NN0852.pdf", DOI = "doi:10.1109/IJCNN.2008.4634233", ISSN = "1098-7576", abstract = "Automatically inferring drug gene regulatory networks models from microarray time series data is a challenging task. The ordinary differential equations models are sensible, but difficult to build. We extended our reverse engineering algorithm for gene networks (RODES), based on genetic programming, by adding a neural networks feedback linearisation component. Thus, RODES automatically discovers the structure, estimate the parameter, and identify the molecular mechanisms, even when information is missing from the data. It produces systems of ordinary differential equations from experimental or simulated microarray time series data. On simulated data the accuracy and the CPU time were very good. This is due to reducing the reversing of an ordinary differential equations system to that of individual algebraic equations, and to the possibility of incorporating common a priori knowledge. To our knowledge, this is the first realistic reverse engineering algorithm, based on genetic programming and neural networks, applicable to large gene networks.", keywords = "genetic algorithms, genetic programming", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET. Also known as \cite{4634233}", } @Article{Floares2008379, author = "Alexandru George Floares", title = "A reverse engineering algorithm for neural networks, applied to the subthalamopallidal network of basal ganglia", journal = "Neural Networks", volume = "21", number = "2-3", pages = "379--386", year = "2008", note = "Advances in Neural Networks Research: IJCNN '07, 2007 International Joint Conference on Neural Networks IJCNN '07", ISSN = "0893-6080", DOI = "doi:10.1016/j.neunet.2007.12.017", URL = "http://www.sciencedirect.com/science/article/B6T08-4RDR1B6-1/2/5aae1d094dbe3fd190fbb3fe9acebe63", keywords = "genetic algorithms, genetic programming, Neural networks, Reverse engineering algorithm, Linear genetic programming, Systems of ordinary differential equations, Basal ganglia, Discovery science approach", abstract = "Modeling neural networks with ordinary differential equations systems is a sensible approach, but also very difficult. This paper describes a new algorithm based on linear genetic programming which can be used to reverse engineer neural networks. The RODES algorithm automatically discovers the structure of the network, including neural connections, their signs and strengths, estimates its parameters, and can even be used to identify the biophysical mechanisms involved. The algorithm is tested on simulated time series data, generated using a realistic model of the subthalamopallidal network of basal ganglia. The resulting ODE system is highly accurate, and results are obtained in a matter of minutes. This is because the problem of reverse engineering a system of coupled differential equations is reduced to one of reverse engineering individual algebraic equations. The algorithm allows the incorporation of common domain knowledge to restrict the solution space. To our knowledge, this is the first time a realistic reverse engineering algorithm based on linear genetic programming has been applied to neural networks.", } @InProceedings{Floares:2009:IJCNN, author = "Alexandru George Floares", title = "A neural networks algorithm for inferring drug gene regulatory networks from microarray time-series with missing transcription factors information", booktitle = "International Joint Conference on Neural Networks, IJCNN 2009", year = "2009", month = jun, pages = "848--854", keywords = "genetic algorithms, genetic programming, algebraic equations, drug gene regulatory networks, feedback linearization, mathematical modeling, microarray time-series, missing transcription factors information, neural networks algorithm, ordinary differential equations, reverse engineering algorithm, algebra, biology computing, data handling, differential equations, neural nets, reverse engineering, time series", DOI = "doi:10.1109/IJCNN.2009.5179081", ISSN = "1098-7576", abstract = "Mathematical modeling gene regulatory networks is important for understanding and controlling them, with various drugs and their dosage. The ordinary differential equations approach is sensible but also very difficult. Our reverse engineering algorithm (RODES), based on neural networks feedback linearization and genetic programming, takes as inputs high-throughput (e.g., microarray) time series data and automatically infer an accurate ordinary differential equations model. The algorithm decouples the systems of differential equations, reducing the problem to that of revere engineering individual algebraic equations, and is able to deal with missing information, reconstructing the temporal series of the transcription factors or drug related compounds which are usually missing in microarray experiments. It is also able to incorporate common a priori knowledge. To our knowledge, this is the first realistic reverse engineering algorithm, based on genetic programming and neural networks, applicable to large gene networks.", notes = "Also known as \cite{5179081}", } @InProceedings{Floares:2010:SOFA, author = "Alexandru Floares and Ovidiu Balacescu and Carmen Floares and Loredana Balacescu and Tiberiu Popa and Oana Vermesan", title = "Mining knowledge and data to discover intelligent molecular biomarkers: Prostate cancer i-Biomarkers", booktitle = "4th International Workshop on Soft Computing Applications (SOFA 2010)", year = "2010", month = "15-17 " # jul, pages = "113--118", abstract = "Currently, there are some paradigm shifts in medicine, from the search for a single ideal biomarker, to the search for panels of molecules, and from a reductionistic to a systemic view, placing these molecules on functional networks. There is also a general trend to favour non-invasive biomarkers. Identifying non-invasive biomarkers in high-throughput data, having thousands of features and only tens of samples is not trivial. Here, we proposed a methodology and the related concepts to develop intelligent molecular biomarkers, via knowledge mining and knowledge discovery in data, illustrated on prostate cancer diagnosis. An informed feature selection is done by mining knowledge about pathways involved in prostate cancer, in specialised data bases. A knowledge discovery in data approach, with soft computing methods, is used to identify the relevant features and discover their relationships with clinical outcomes. The intelligent non-invasive diagnosis systems, is based on a team of mathematical models, discovered with genetic programming, and taking as inputs eight serum angiogenic molecules and PSA. This systems share with other intelligent systems we build, using this methodology but different soft computing techniques, and in different clinical settings - chronic hepatitis, bladder cancer, and prostate cancer - the best published accuracy, even 100percent. Soft computing could be a strong foundation for the newly emerging Knowledge-Based-Medicine. The impact on medical practice could be enormous. Instead of offering just hints to the clinicians, like Evidence-Based-Medicine, Knowledge-Based-Medicine which is made possible and co-exists with Evidence-Based-Medicine, offers intelligent clinical decision supports systems.", keywords = "genetic algorithms, genetic programming, PSA, bladder cancer, chronic hepatitis, data mining, evidence based medicine, intelligent clinical decision supports systems, intelligent molecular biomarkers, intelligent noninvasive diagnosis systems, knowledge based medicine, knowledge mining, prostate cancer i-biomarkers, serum angiogenic molecules, soft computing techniques, data mining, decision support systems, knowledge based systems, medical computing, patient diagnosis, uncertainty handling", DOI = "doi:10.1109/SOFA.2010.5565613", notes = "Discipulus Also known as \cite{5565613}", } @InCollection{Floares:2012:CI4, author = "Alexandru Floares and Adriana Birlutiu", title = "Reverse Engineering Networks as Ordinary Differential Equations Systems", booktitle = "Computational Intelligence", publisher = "Nova", year = "2012", editor = "Alexandru Floares", chapter = "4", pages = "51--68", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-62081-959-3", URL = "https://www.novapublishers.com/catalog/product_info.php?products_id=34205", notes = " SAIA & OncoPredict, Cluj-Napoca, Romania", } @InCollection{Floares:2014:shbBNI, author = "Alexandru G. Floares and Irina Luludachi", title = "Inferring Transcription Networks from Data", booktitle = "Springer Handbook of Bio-/Neuroinformatics", publisher = "Springer", year = "2014", editor = "Nikola Kasabov", chapter = "20", pages = "311--326", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-30573-3", URL = "http://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-3-642-30573-3", DOI = "doi:10.1007/978-3-642-30574-0_20", abstract = "Reverse engineering of transcription networks is a challenging bioinformatics problem. Ordinary differential equation (ODEs) network models have their roots in the physicochemical base of these networks, but are difficult to build conventionally. Modelling automation is needed and knowledge discovery in data using computational intelligence methods is a solution. The authors have developed a methodology for automatically inferring ODE systems models from omics data, based on genetic programming (GP), and illustrate it on a real transcription network. The methodology allows the network to be decomposed from the complex of interacting cellular networks and to further decompose each of its nodes, without destroying their interactions. The structure of the network is not imposed but discovered from data, and further assumptions can be made about the parameters' values and the mechanisms involved. The algorithms can deal with unmeasured regulatory variables, like transcription factors (TFs) and microRNA (miRNA or miR). This is possible by introducing the regulome probabilities concept and the techniques to compute them. They are based on the statistical thermodynamics of regulatory molecular interactions. Thus, the resultant models are mechanistic and theoretically founded, not merely data fittings. To our knowledge, this is the first reverse engineering approach capable of dealing with missing variables, and the accuracy of all the models developed is greater than 99percent.", notes = "GP RODES, GPTIPS SAIA, OncoPredict Cancer Institute Cluj-Napoca, Str. Republicii, Nr. 34-36, 400015, Cluj-Napoca, Romania", } @InProceedings{Floreano:1997:gsrq, author = "Dario Floreano and Stefano Nolfi", title = "God Save the Red Queen! Competition in Co-Evolutionary Robotics", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "Artifical life and evolutionary robotics", pages = "398--406", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", URL = "ftp://kant.irmkant.rm.cnr.it/pub/econets/floreano.co-evolution.ps.Z", notes = "GP-97", } @InProceedings{viento, author = "Juan J. Flores and Mario Graff and Erasmo Cadenas", title = "Wind Prediction using Genetic Algorithms and Gene Expression Programming", booktitle = "Proceedings of the International Conference on Modelling and Simulation in the Enterprises. AMSE 2005", year = "2005", address = "Morelia, Mexico", month = apr, keywords = "genetic algorithms, genetic programming, Gene Expression Programming", URL = "http://www.amse-modeling.com/ind2.php?cont=03per&menu=/menu3.php&pag=/datosartic.php&vis=1&editart=1&id_art=1738", URL = "http://lsc.fie.umich.mx/~juan/PS/wind.pdf", size = "9 pages", notes = "information from Juan Flores", } @InProceedings{conf/iscis/FloresG05, title = "System Identification Using Genetic Programming and Gene Expression Programming", author = "Juan J. Flores and Mario Graff", year = "2005", pages = "503--511", booktitle = "Proceedings of the 20th International Symposium Computer and Information Sciences - ISCIS 2005", editor = "Pinar Yolum and Tunga Gungor and Fikret Gurgen and Can Ozturan", volume = "3733", series = "Lecture Notes in Computer Science", address = "Istanbul, Turkey", publisher_address = "Berlin / Heidelberg", month = oct # " 26-28", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Gene Expression Programming", bibdate = "2005-12-14", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/iscis/iscis2005.html#FloresG05", ISSN = "0302-9743", ISBN = "3-540-29414-7", DOI = "doi:10.1007/11569596", abstract = "This paper describes a computer program called ECSID that automates the process of system identification using Genetic Programming and Gene Expression Programming. ECSID uses a function set, and the observed data to determine an ODE whose behaviour is similar to the observed data. ECSID is capable to evolve linear and non-linear models of higher order systems. ECSID can also code a higher order system as a set of higher order equations. ECSID has been tested with linear pendulum, non-linear pendulum, mass-spring system, linear circuit, etc.", } @Article{flores:2019:Energies, author = "Juan. J. Flores and Jose R. {Cedeno Gonzalez} and Hector Rodriguez and Mario Graff and Rodrigo Lopez-Farias and Felix Calderon", title = "Soft Computing Methods with Phase Space Reconstruction for Wind Speed {Forecasting--A} Performance Comparison", journal = "Energies", year = "2019", volume = "12", number = "18", keywords = "genetic algorithms, genetic programming", ISSN = "1996-1073", URL = "https://www.mdpi.com/1996-1073/12/18/3545", DOI = "doi:10.3390/en12183545", abstract = "This article presents a comparison of wind speed forecasting techniques, starting with the Auto-regressive Integrated Moving Average, followed by Artificial Intelligence-based techniques. The objective of this article is to compare these methods and provide readers with an idea of what method(s) to apply to solve their forecasting needs. The Artificial Intelligence-based techniques included in the comparison are Nearest Neighbors (the original method, and a version tuned by Differential Evolution), Fuzzy Forecasting, Artificial Neural Networks (designed and tuned by Genetic Algorithms), and Genetic Programming. These techniques were tested against twenty wind speed time series, obtained from Russian and Mexican weather stations, predicting the wind speed for 10 days, one day at a time. The results show that Nearest Neighbors using Differential Evolution outperforms the other methods. An idea this article delivers to the reader is: what part of the history of the time series to use as input to a forecaster? This question is answered by the reconstruction of phase space. Reconstruction methods approximate the phase space from the available data, yielding m (the systems dimension) and τ (the sub-sampling constant), which can be used to determine the input for the different forecasting methods.", notes = "also known as \cite{en12183545}", } @Article{Flores-Campana:2020:ACC, author = "Jose L. {Flores Campana} and Allan Pinto and Manuel Alberto {Cordova Neira} and Luis Gustavo {Lorgus Decker} and Andreza Santos and Jhonatas S. Conceicao and Ricardo {Da Silva Torres}", journal = "IEEE Access", title = "On the Fusion of Text Detection Results: A Genetic Programming Approach", year = "2020", volume = "8", pages = "81257--81270", keywords = "genetic algorithms, genetic programming, Scene text detection, multi-oriented text, convolutional neural network, data fusion", DOI = "doi:10.1109/ACCESS.2020.2987869", ISSN = "2169-3536", size = "14 pages", abstract = "Hundreds of text detection methods have been proposed, motivated by their widespread use in several applications. Despite the huge progress in the area, which includes even the use of sophisticated learning schemes, ad-hoc post-processing procedures are often employed to improve the text detection rate, by removing both false positives and negatives. Another issue refers to the lack of the use of the complementary views provided by different text detection methods. This paper aims to fill these gaps. We propose the use of a soft computing framework, based on genetic programming (GP), to guide the definition of suitable post-processing procedures through the combination of basic operators, which may be applied to improve detection results provided by multiple methods at the same time. Performed experiments in the widely used ICDAR 2011, ICDAR 2013, and ICDAR 2015 datasets demonstrate that our GP-based approach leads to F1 effectiveness gains up to 5.1 percentage points, when compared to several baselines.", notes = "Also known as \cite{9066990}", } @InProceedings{DBLP:conf/iciai/FofanahG20, author = "Abdul Joseph Fofanah and Tiegang Gao", title = "Dual Watermarking for Protection of Medical Images based on Watermarking of Frequency Domain and Genetic Programming", booktitle = "{ICIAI} 2020: The 4th International Conference on Innovation in Artificial Intelligence, Xiamen, China, May 8-11, 2020", pages = "106--115", publisher = "{ACM}", year = "2020", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1145/3390557.3394308", DOI = "doi:10.1145/3390557.3394308", timestamp = "Tue, 16 Jun 2020 14:55:12 +0200", biburl = "https://dblp.org/rec/conf/iciai/FofanahG20.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{Fogel199582, author = "David B. Fogel", title = "Advances in genetic programming : Kenneth E. Kinnear, Jr., (ed.), MIT Press, Cambridge, MA, 1994, 518 pp., \$45.00", journal = "Biosystems", volume = "36", number = "1", pages = "82--85", year = "1995", keywords = "genetic algorithms, genetic programming", ISSN = "0303-2647", DOI = "doi:10.1016/0303-2647(95)90007-1", broken = "http://www.sciencedirect.com/science/article/B6T2K-4CHS0P6-5/2/2474f3669e7a25204939e72cbb4d7253", notes = "review of \cite{kinnear:book}", abstract = "Genetic programming, the use of genetic algorithms to evolve computer programs, has received considerable attention following the publication of Koza (1992). The edited volume Advances in Genetic Programming is the written record of presentations made at a workshop on genetic programming held in July, 1993 at the Fifth International Conference on Genetic Algorithms. The book is divided into three sections: 'Introduction' (two papers), 'Increasing the Power of Genetic Programming' (12 papers), and 'Innovative Applications of Genetic Programming' (10 papers). The book is designed to share recent research in genetic programming with an interdisciplinary audience. Space does not permit a careful review of each paper, but I will focus on particular papers and then offer some general observations.", } @InProceedings{fogel:1996:pedcs, author = "David B. Fogel and Lawrence J. Fogel", title = "Preliminary Experiments on Discriminating between Chaotic Signals and Noise Using Evolutionary Programming", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "Evolutionary Programming", pages = "512--520", address = "Stanford University, CA, USA", publisher = "MIT Press", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap85.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96 EP paper", } @Article{Fogel:2004:NAR, author = "Gary B. Fogel and Dana G. Weekes and Gabor Varga and Ernst R. Dow and Harry B. Harlow and Jude E. Onyia and Chen Su", title = "Discovery of sequence motifs related to coexpression of genes using evolutionary computation", journal = "Nucleic Acids Research", year = "2004", volume = "32", number = "13", pages = "3826--3835", DOI = "doi:10.1093/nar/gkh713", abstract = "Transcription factors are key regulatory elements that control gene expression. Recognition of transcription factor binding site (TFBS) motifs in the upstream region of coexpressed genes is therefore critical towards a true understanding of the regulations of gene expression. The task of discovering eukaryotic TFBSs remains a challenging problem. Here, we demonstrate that evolutionary computation can be used to search for TFBSs in upstream regions of genes known to be coexpressed. Evolutionary computation was used to search for TFBSs of genes regulated by octamer-binding factor and nuclear factor kappa B. The discovered binding sites included experimentally determined known binding motifs as well as lists of putative, previously unknown TFBSs. We believe that this method to search nucleotide sequence information efficiently for similar motifs will be useful for discovering TFBSs that affect gene regulation.", notes = "PMID:", } @TechReport{vuwlgp-report, author = "Christopher Fogelberg and Mengjie Zhang", title = "VUWLGP - An ANSI C++ Linear Genetic Programming Package", institution = "MSCS, Victoria University of Wellington", year = "2005", number = "CS-TR-05/8", address = "New Zealand", email = "christo.fogelberg@mcs.vuw.ac.nz", keywords = "genetic algorithms, genetic programming", URL = "http://www.syntilect.com/cgf/body00040.php", URL = "http://www.mcs.vuw.ac.nz/comp/Publications/CS-TR-05-08.abs.html", URL = "http://www.mcs.vuw.ac.nz/comp/Publications/archive/CS-TR-05/CS-TR-05-8.pdf", size = "15 pages", abstract = "Linear Genetic Programming (LGP) is a recently researched form of genetic programming, the automatic evolution of computer programs which can solve problems. Traditionally, genetic programs have been represented as function trees. However, LGP programs are linear sequences of instructions (e.g. register machine instructions) and are not best represented as a tree of functions and terminals. Few publicly available packages designed to support research into LGP exist and those that do are often incomplete. VUWLGP has been written in C++ and is available for use under the GPL. It is designed to be easily customised and tweaked so that slightly different variants of different problems can be researched easily.", } @InProceedings{conf/ausai/FogelbergZ05, title = "Linear Genetic Programming for Multi-class Object Classification", author = "Christopher Fogelberg and Mengjie Zhang", year = "2005", bibdate = "2005-11-29", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ausai/ausai2005.html#FogelbergZ05", pages = "369--379", booktitle = "AI 2005: Advances in Artificial Intelligence, 18th Australian Joint Conference on Artificial Intelligence, Proceedings", editor = "Shichao Zhang and Ray Jarvis", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3809", address = "Sydney, Australia", month = dec # " 5-9", bibsource = "DBLP, http://dblp.uni-trier.de", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-30462-2", DOI = "doi:10.1007/11589990_39", size = "11 pages", abstract = "Multi-class object classification is an important field of research in computer vision. In this paper basic linear genetic programming is modified to be more suitable for multi-class classification and its performance is then compared to tree-based genetic programming. The directed acyclic graph nature of linear genetic programming is exploited. The existing fitness function is modified to more accurately approximate the true feature space. The results show that the new linear genetic programming approach outperforms the basic tree-based genetic programming approach on all the tasks investigated here and that the new fitness function leads to better and more consistent results. The genetic programs evolved by the new linear genetic programming system are also more comprehensible than those evolved by the tree-based system.", } @InProceedings{Fogelson:2008:gecco, author = "Sergey V. Fogelson and Walter D. Potter", title = "A formulation for the relative permittivity of water and steam to high temperatures and pressures evolved using genetic programming", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "1335--1336", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1335.pdf", DOI = "doi:10.1145/1389095.1389351", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, relative permittivity: Poster", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389351}", } @Article{Fok:2007:ieeeIS, author = "Ka-Ling Fok and Tien-Tsin Wong and Man-Leung Wong", title = "Evolutionary Computing on Consumer Graphics Hardware", journal = "IEEE Intelligent Systems", year = "2007", volume = "22", number = "2", pages = "69--78", month = mar # "-" # apr, keywords = "genetic algorithms, GPU, EP, computer graphic equipment, computer graphics, evolutionary computation, parallel algorithms, consumer graphics card, consumer-grade graphics hardware, evolutionary computing, high-performance computer, parallel evolutionary algorithm, evolutionary algorithms, parallel algorithm, pervasive computing, scientific computing on graphics-processing units, ubiquitous computing, SIMD", ISSN = "1541-1672", URL = "http://ieeexplore.ieee.org/iel5/9670/4136845/04136862.pdf?tp=&isnumber=4136845&arnumber=4136862&punumber=9670", DOI = "doi:10.1109/MIS.2007.28", size = "10 pages", abstract = "We propose implementing a parallel EA on consumer graphics cards, which we can find in many PCs. This lets more people use our parallel algorithm to solve large-scale, real-world problems such as data mining. Parallel evolutionary algorithms run on consumer-grade graphics hardware achieve better execution times than ordinary evolutionary algorithms and offer greater accessibility than those run on high-performance computers", notes = "Chinese Univ. of Hong Kong, Shatin INSPEC Accession Number:9445531 nVidia GeForce 6800 Ultra. GPU wins for populations bigger than 800. Speedup ratio between 0.62 (slower) to 5.02", } @InProceedings{folino:1999:ACGPAC, author = "Gianluigi Folino and Clara Pizzuti and Giandomenico Spezzano", title = "A Cellular Genetic Programming Approach to Classification", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1015--1020", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-611-4", URL = "http://citeseer.ist.psu.edu/328823.html", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-427.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-427.ps", abstract = "A cellular genetic programming approach to data classification is proposed. The method uses cellular automata as a framework to enable a fine-grained parallel implementation of GP through the diffusion model. The main advantages to employ the method for classification problems consist in handling large populations in reasonable times, enabling fast convergence by reducing the number of iterations and execution time, favouring the cooperation in the search for good solutions, thus improving the accuracy of the method.", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{folino:2000:GPSAhmeDT, author = "Gianluigi Folino and Clara Pizzuti and Giandomenico Spezzano", title = "Genetic Programming and Simulated Annealing: A Hybrid Method to Evolve Decision Trees", booktitle = "Genetic Programming, Proceedings of EuroGP'2000", year = "2000", editor = "Riccardo Poli and Wolfgang Banzhaf and William B. Langdon and Julian F. Miller and Peter Nordin and Terence C. Fogarty", volume = "1802", series = "LNCS", pages = "294--303", address = "Edinburgh", publisher_address = "Berlin", month = "15-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-67339-3", URL = "http://www.icar.cnr.it/pizzuti/eurogp00.ps", URL = "http://citeseer.ist.psu.edu/326715.html", DOI = "doi:10.1007/978-3-540-46239-2_22", size = "11 pages", abstract = "A method for the data mining task of data classification, suitable to be implemented on massively parallel architectures, is proposed. The method combines genetic programming and simulated annealing to evolve a population of decision trees. A cellular automaton is used to realise a fine-grained parallel implementation of genetic programming through the diffusion model and the annealing schedule to decide the acceptance of a new solution. Preliminary experimental results, obtained by simulating the behaviour of the cellular automaton on a sequential machine, show significant better performances with respect to C4.5.", notes = "EuroGP'2000, part of \cite{poli:2000:GP}", } @InProceedings{folino:2000:DPS, author = "Gianluigi Folino and Clara Pizzuti and Giandomenico Spezzano", title = "Scalable Classification of Large Data Sets by Parallel Genetic Programming", booktitle = "Distributed and Parallel Systems", year = "2000", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-1-4615-4489-0_11", DOI = "doi:10.1007/978-1-4615-4489-0_11", } @InProceedings{folino:2001:EuroGP, author = "Gianluigi Folino and Clara Pizzuti and Giandomenico Spezzano", title = "CAGE: A Tool for Parallel Genetic Programming Applications", booktitle = "Genetic Programming, Proceedings of EuroGP'2001", year = "2001", editor = "Julian F. Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon", volume = "2038", series = "LNCS", pages = "64--73", address = "Lake Como, Italy", publisher_address = "Berlin", month = "18-20 " # apr, organisation = "EvoNET", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, Parallel programming, Cellular model", ISBN = "3-540-41899-7", URL = "http://www.icar.cnr.it/pizzuti/eurogp01.ps", DOI = "doi:10.1007/3-540-45355-5_6", size = "10 pages", abstract = "A new parallel implementation of genetic programming based on the cellular model is presented and compared with the island model approach. Although the widespread belief that cellular model is not suitable for parallel genetic programming implementations, experimental results show a better convergence with respect to the island approach, a good scale-up behaviour and a nearly linear speed-up.", notes = "EuroGP'2001, part of \cite{miller:2001:gp}", } @InProceedings{folino:2001:TAI, author = "Gianluigi Folino and Clara Pizzuti and Giandomenico Spezzano", title = "Parallel genetic programming for decision tree induction", booktitle = "Proceedings of the 13th International Conference on Tools with Artificial Intelligence", year = "2001", pages = "129--135", address = "Dallas, TX USA", month = "7-9 " # nov, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, decision trees, learning (artificial intelligence), parallel programming, J-measure, UCI machine learning repository, data sets, decision tree induction, fitness function, grid model, parallel genetic programming, scalability", URL = "http://www.icar.cnr.it/pizzuti/ictai01.ps", size = "7 pages", abstract = "A parallel genetic programming approach to induce decision trees in large data sets is presented. A population of trees is evolved by employing the genetic operators and every individual is evaluated by using a fitness function based on the J-measure. The method is able to deal with large data sets since it uses a parallel implementation of genetic programming through the grid model and an out of core technique for those data sets that do not fit in main memory. Preliminary experiments on data sets from the UCI machine learning repository give good classification outcomes and assess the scalability of the method", notes = "Inspec Accession Number: 7139478", } @Article{Folino:2001:ieeeTEVC, author = "G. Folino and C. Pizzuti and G. Spezzano", title = "Parallel hybrid method for {SAT} that couples genetic algorithms and local search", journal = "IEEE Transactions on Evolutionary Computation", year = "2001", volume = "5", number = "4", pages = "323--334", month = aug, keywords = "genetic algorithms, genetic programming", ISSN = "1089-778X", DOI = "doi:10.1109/4235.942527", abstract = "A parallel hybrid method for solving the satisfiability (SAT) problem that combines cellular genetic algorithms (GAs) and the random walk SAT (WSAT) strategy of greedy SAT (GSAT) is presented. The method, called cellular genetic WSAT (CGWSAT), uses a cellular GA to perform a global search from a random initial population of candidate solutions and a local selective generation of new strings. The global search is then specialized in local search by adopting the WSAT strategy. A main characteristic of the method is that it indirectly provides a parallel implementation of WSAT when the probability of crossover is set to zero. CGWSAT has been implemented on a Meiko CS-2 parallel machine using a 2D cellular automaton as a parallel computation model. The algorithm has been tested on randomly generated problems and some classes of problems from the DIMACS and SATLIB test set", notes = "also lnown as \cite{942527}", } @InProceedings{folino:2002:euromicro, author = "Gianluigi Folino and Clara Pizzuti and Giandomenico Spezzano", title = "Improving induction decision trees with parallel genetic programming", booktitle = "Proceedings 10th Euromicro Workshop on Parallel, Distributed and Network-based Processing", year = "2002", pages = "181--187", address = "Canary Islands", month = "9-11 " # jan, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, data mining, decision trees, learning by example, parallel programming, J-measure, UCI machine learning repository, fitness function, genetic operators, grid model, induction decision trees, large data sets, parallel genetic programming", DOI = "doi:10.1109/EMPDP.2002.994264", abstract = "A parallel genetic programming approach to induce decision trees in large data sets is presented. A population of trees is evolved by employing the genetic operators and every individual is evaluated by using a fitness function based on the J-measure. The method is able to deal with large data sets since it uses a parallel implementation of genetic programming through the grid model. Experiments on data sets from the UCI machine learning repository show better results with respect to C5. Furthermore, performance results show a nearly linear speedup", notes = "Inspec Accession Number: 7205091", } @Article{folino:2003:tec, author = "Gianluigi Folino and Clara Pizzuti and Giandomenico Spezzano", title = "A Scalable Cellular Implementation of Parallel Genetic Programming", journal = "IEEE Transactions on Evolutionary Computation", year = "2003", volume = "7", number = "1", pages = "37--53", month = feb, keywords = "genetic algorithms, genetic programming, Cellular genetic programming model, load balance, parallel processing, scalability", DOI = "doi:10.1109/TEVC.2002.806168", abstract = "A new parallel implementation of genetic programming (GP) based on the cellular model is presented and compared with both canonical GP and the island model approach. The method adopts a load-balancing policy that avoids the unequal use of the processors. Experimental results on benchmark problems of different complexity show the superiority of the cellular approach with respect to the canonical sequential implementation and the island model. A theoretical performance analysis reveals the high scalability of the implementation realized and allows to predict the size of the population when the number of processors and their efficiency are fixed.", notes = "CAGE", } @InProceedings{folino03, author = "Gianluigi Folino and Clara Pizzuti and Giandomenico Spezzano", title = "Ensemble techniques for Parallel Genetic Programming based Classifiers", booktitle = "Genetic Programming, Proceedings of EuroGP'2003", year = "2003", editor = "Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa", volume = "2610", series = "LNCS", pages = "59--69", address = "Essex", publisher_address = "Berlin", month = "14-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-00971-X", DOI = "doi:10.1007/3-540-36599-0_6", abstract = "An extension of Cellular Genetic Programming for data classification to induce an ensemble of predictors is presented. Each classifier is trained on a different subset of the overall data, then they are combined to classify new tuples by applying a simple majority voting algorithm, like bagging. Preliminary results on a large data set show that the ensemble of classifiers trained on a sample of the data obtains higher accuracy than a single classifier that uses the entire data set at a much lower computational cost.", notes = "EuroGP'2003 held in conjunction with EvoWorkshops 2003", } @InProceedings{folino:2003:daicamgp, author = "G. Folino and C. Pizzuti and G. Spezzano and L. Vanneschi and M. Tomassini", title = "Diversity analysis in cellular and multipopulation genetic programming", booktitle = "Proceedings of the 2003 Congress on Evolutionary Computation CEC2003", editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon", pages = "305--311", year = "2003", publisher = "IEEE Press", address = "Canberra", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "8-12 " # dec, organisation = "IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", keywords = "genetic algorithms, genetic programming, Computer science, Convergence, Costs, Evolutionary computation, Genetic mutations, Measurement standards, Performance analysis, Size measurement, Testing, convergence, parallel algorithms, statistical analysis, cellular genetic programming, convergence, diversity analysis, diversity measures, evolution, multipopulation genetic programming, parallel genetic programming model, population diversity", URL = "http://www.icar.cnr.it/pizzuti/cec03.pdf", DOI = "doi:10.1109/CEC.2003.1299589", ISBN = "0-7803-7804-0", abstract = "parallel genetic programming (GP) models in maintaining diversity in a population. The parallel models used are the cellular and the multipopulation one. Several measures of diversity are considered to gain a deeper understanding of the conditions under which the evolution of both models is successful. Three standard test problems are used to illustrate the different diversity measures and analyse their correlation with performance. Results show that diversity is not necessarily synonym of good convergence.", notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.", } @InProceedings{folino:2004:eurogp, author = "Gianluigi Folino and Clara Pizzuti and Giandomenico Spezzano", title = "Boosting technique for Combining Cellular GP Classifiers", booktitle = "Genetic Programming 7th European Conference, EuroGP 2004, Proceedings", year = "2004", editor = "Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule", volume = "3003", series = "LNCS", pages = "47--56", address = "Coimbra, Portugal", publisher_address = "Berlin", month = "5-7 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-21346-5", DOI = "doi:10.1007/978-3-540-24650-3_5", abstract = "An extension of Cellular Genetic Programming for data classification with the boosting technique is presented and a comparison with the bagging-like majority voting approach is performed. The method is able to deal with large data sets that do not fit in main memory since each classifier is trained on a subset of the overall training data. Experiments showed that, by using a sample of reasonable size, the extension with these voting algorithms enhances classification accuracy at a much lower computational cost.", notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004", } @InProceedings{folino:2005:gsice, author = "Gianluigi Folino and Clara Pizzuti and Giandomenico Spezzano", title = "GP Ensembles for improving multi-class prediction problems", booktitle = "AI*IA Workshop on Evolutionary Computation, Evoluzionistico GSICE05", year = "2005", editor = "Sara Manzoni and Matteo Palmonari and Fabio Sartori", address = "University of Milan Bicocca, Italy", month = "20 " # sep, keywords = "genetic algorithms, genetic programming, data mining, classification, boosting", ISBN = "88-900910-0-2", size = "10 pages", abstract = "Cellular Genetic Programming for data classification extended with the boosting technique to induce an ensemble of predictors is presented. The method implements in parallel AdaBoost.M2 to efficiently deal with multi-class problems and it is able to manage large data sets that do not fit in main memory since each classifier is trained on a subset of the overall training data. Experiments on several data sets show that, by using a training set of reduced size, better classification accuracy can be obtained at a much lower computational cost.", notes = "http://www.ce.unipr.it/people/cagnoni/gsice2005/gsice-eng.pdf http://www.ce.unipr.it/people/cagnoni/gsice2005/ Workshop proceedings on CD-ROM only. Workshop held in-conjunction with the IX Congress of the Italian Association for Artificial Intelligence. In English. ICAR-CNR, Via P.Bucci 41C, Univ. della Calabria 87036 Rende (CS), Italy See \cite{Folino:2005:ieeeTEC}", } @InProceedings{eurogp06:FolinoSpezzano, author = "Gianluigi Folino and Giandomenico Spezzano", title = "{P-CAGE:} An Environment for Evolutionary Computation in Peer-to-Peer Systems", editor = "Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art", booktitle = "Proceedings of the 9th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3905", year = "2006", address = "Budapest, Hungary", month = "10 - 12 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-33143-3", pages = "341--350", DOI = "doi:10.1007/11729976_31", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "Solving complex real-world problems using evolutionary computation is a CPU time-consuming task that requires a large amount of computational resources. Peer-to-Peer (P2P) computing has recently revealed as a powerful way to harness these resources and efficiently deal with such problems. In this paper, we present a P2P implementation of Genetic Programming based on the JXTA technology. To run genetic programs we use a distributed environment based on a hybrid multi-island model that combines the island model with the cellular model. Each island adopts a cellular genetic programming model and the migration occurs among neighbouring peers. The implementation is based on a virtual ring topology. Three different termination criteria (effort, time and max-gen) have been implemented. Experiments on some popular benchmarks show that the approach presents a accuracy at least comparable with classical distributed models, retaining the obvious advantages in terms of decentralisation, fault tolerance and scalability of P2P systems.", notes = "Part of \cite{collet:2006:GP} EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006", } @InProceedings{1144139, author = "Gianluigi Folino and Clara Pizzuti and Giandomenico Spezzano", title = "Improving cooperative GP ensemble with clustering and pruning for pattern classification", booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation", year = "2006", editor = "Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos {Coello Coello} and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens", volume = "1", ISBN = "1-59593-186-4", pages = "791--798", address = "Seattle, Washington, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p791.pdf", DOI = "doi:10.1145/1143997.1144139", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "8-12 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, classification, data mining, ensemble", notes = "GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM Order Number 910060", } @InCollection{Folino:2005:hbbaa, author = "Gianluigi Folino and Agostino Forestiero and Giandomenico Spezzano", title = "Swarming Agents for Decentralized Clustering in Spatial Data", booktitle = "Handbook of Bioinspired Algorithms and Applications", publisher = "CRC Press", year = "2005", editor = "Stephan Olariu and Albert Y. Zomaya", chapter = "7", pages = "341--358?", month = sep, keywords = "genetic algorithms, genetic programming", isbn13 = "9781584884750", notes = "https://www.crcpress.com/Handbook-of-Bioinspired-Algorithms-and-Applications/Olariu-Zomaya/p/book/9781584884750#googlePreviewContainer", } @Article{journals/jsw/FolinoFS06, author = "Gianluigi Folino and Agostino Forestiero and Giandomenico Spezzano", title = "A Jxta Based Asynchronous Peer-to-Peer Implementation of Genetic Programming", journal = "Journal of Software", year = "2006", volume = "1", number = "2", pages = "12--23", month = aug, keywords = "genetic algorithms, genetic programming", ISSN = "1796-217X", URL = "http://www.academypublisher.com/jsw/vol01/no02/jsw01021223.pdf", URL = "http://www.academypublisher.com/jsw/vol01/no02/jsw01021223.html", size = "12 pages", abstract = "Solving complex real-world problems using evolutionary computation is a CPU time-consuming task that requires a large amount of computational resources. Peer-to-Peer (P2P) computing has recently revealed as a powerful way to harness these resources and efficiently deal with such problems. In this paper, we present P-CAGE: a P2P environment for Genetic Programming based on the JXTA protocols. P-CAGE is based on a hybrid multi-island model that combines the island model with the cellular model. Each island adopts a cellular model and the migration occurs between neighbouring peers placed in a virtual ring topology. Three different termination criteria (effort, time and maxgen) have been implemented. Experiments were conducted on some popular benchmarks and scalability, accuracy and the effect of migration have been studied. Performance are at least comparable with classical distributed models, retaining the obvious advantages in terms of decentralisation, fault tolerance and scalability of P2P systems. We also demonstrated the important effect of migration in accelerating the convergence.", notes = "JSW", bibdate = "2008-08-13", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/jsw/jsw1.html#FolinoFS06", } @Article{Folino:2005:ieeeTEC, author = "Gianluigi Folino and Clara Pizzuti and Giandomenico Spezzano", title = "GP ensembles for large-scale data classification", journal = "IEEE Transactions on Evolutionary Computation", year = "2006", volume = "10", number = "5", pages = "604--616", month = oct, keywords = "genetic algorithms, genetic programming, Bagging, boosting, classification, data mining", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2005.863627", size = "13 pages", abstract = "An extension of cellular genetic programming for data classification (CGPC) to induce an ensemble of predictors is presented. Two algorithms implementing the bagging and boosting techniques are described and compared with CGPC. The approach is able to deal with large data sets that do not fit in main memory since each classifier is trained on a subset of the overall training data. The predictors are then combined to classify new tuples. Experiments on several data sets show that, by using a training set of reduced size, better classification accuracy can be obtained, but at a much lower computational cost", notes = "Also known as \cite{1705406}", } @InProceedings{eurogp07:folino, author = "Gianluigi Folino and Clara Pizzuti and Giandomenico Spezzano", title = "Mining Distributed Evolving Data Streams using Fractal GP Ensembles", editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar", booktitle = "Proceedings of the 10th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "4445", year = "2007", address = "Valencia, Spain", month = "11-13 " # apr, pages = "160--169", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-71602-5", isbn13 = "978-3-540-71602-0", DOI = "doi:10.1007/978-3-540-71605-1_15", abstract = "A Genetic Programming based boosting ensemble method for the classification of distributed streaming data is proposed. The approach handles flows of data coming from multiple locations by building a global model obtained by the aggregation of the local models coming from each node. A main characteristics of the algorithm presented is its adaptability in presence of concept drift. Changes in data can cause serious deterioration of the ensemble performance. Our approach is able to discover changes by adopting a strategy based on self-similarity of the ensemble behaviour, measured by its fractal dimension, and to revise itself by promptly restoring classification accuracy. Experimental results on a synthetic data set show the validity of the approach in maintaining an accurate and up-to-date GP ensemble.", notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007", } @InProceedings{1277301, author = "Gianluigi Folino and Clara Pizzuti and Giandomenico Spezzano", title = "StreamGP: tracking evolving GP ensembles in distributed data streams using fractal dimension", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "1751--1751", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1751.pdf", DOI = "doi:10.1145/1276958.1277301", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming: Poster, data mining, distributed streaming data, ensemble", abstract = "The paper presents an adaptive GP boosting ensemble method for the classification of distributed homogeneous streaming data that comes from multiple locations. The approach is able to handle concept drift via change detection by employing a change detection strategy, based on self-similarity of the ensemble behaviour, and measured by its fractal dimension. It is efficient since each node of the network works with its local streaming data, and communicate only the local model computed with the other peer-nodes. Furthermore, once the ensemble has been built, it is used to predict the class membership of new streams of data until concept drift is detected. Only in such a case the algorithm is executed to generate a new set of classifiers to update the current ensemble. Experimental results on a synthetic and real life data set showed the validity of the approach in maintaining an accurate and up-to-date GP ensemble.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @Article{Folino:2008:TEC, author = "Gianluigi Folino and Clara Pizzuti and Giandomenico Spezzano", title = "Training Distributed GP Ensemble With a Selective Algorithm Based on Clustering and Pruning for Pattern Classification", journal = "IEEE Transactions on Evolutionary Computation", year = "2008", month = aug, volume = "12", number = "4", pages = "458--468", keywords = "genetic algorithms, genetic programming, boosting algorithm, cellular genetic programming, decision trees, distributed hybrid environment, fittest trees, pattern classification, pruning strategies, training distributed GP ensemble, decision trees, pattern classification", DOI = "doi:10.1109/TEVC.2007.906658", ISSN = "1089-778X", abstract = "A boosting algorithm based on cellular genetic programming (GP) to build an ensemble of predictors is proposed. The method evolves a population of trees for a fixed number of rounds and, after each round, it chooses the predictors to include in the ensemble by applying a clustering algorithm to the population of classifiers. Clustering the population allows the selection of the most diverse and fittest trees that best contribute to improve classification accuracy. The method proposed runs on a distributed hybrid environment that combines the island and cellular models of parallel GP. The combination of the two models provides an efficient implementation of distributed GP, and, at the same time, the generation of low sized and accurate decision trees. The large amount of memory required to store the ensemble affects the performance of the method. This paper shows that, by applying suitable pruning strategies, it is possible to select a subset of the classifiers without increasing misclassification errors; indeed for some data sets, for up to 30percent of pruning, ensemble accuracy increases. Experimental results show that the combination of clustering and pruning enhances classification accuracy of the ensemble approach.", notes = "Also known as \cite{4439200}", } @InProceedings{Folino:2010:EuroGP, author = "Gianluigi Folino and Giuseppe Papuzzo", title = "Handling Different Categories of Concept Drifts in Data Streams using Distributed GP", booktitle = "Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010", year = "2010", editor = "Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar", volume = "6021", series = "LNCS", pages = "74--85", address = "Istanbul", month = "7-9 " # apr, organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-12147-0", DOI = "doi:10.1007/978-3-642-12148-7_7", abstract = "Using Genetic Programming (GP) for classifying data streams is problematic as GP is slow compared with traditional single solution techniques. However, the availability of cheaper and better-performing distributed and parallel architectures make it possible to deal with complex problems previously hardly solved owing to the large amount of time necessary. This work presents a general framework based on a distributed GP ensemble algorithm for coping with different types of concept drift for the task of classification of large data streams. The framework is able to detect changes in a very efficient way using only a detection function based on the incoming unclassified data. Thus, only if a change is detected a distributed GP algorithm is performed in order to improve classification accuracy and this limits the overhead associated with the use of a population-based method. Real world data streams may present drifts of different types. The introduced detection function, based on the self-similarity fractal dimension, permits to cope in a very short time with the main types of different drifts, as demonstrated by the first experiments performed on some artificial datasets. Furthermore, having an adequate number of resources, distributed GP can handle very frequent concept drifts.", notes = "BoostCGPC, cellular GP, island model, AdaBoost, Fractal dimension FD3, cloud computing, Minku Part of \cite{Esparcia-Alcazar:2010:GP} EuroGP'2010 held in conjunction with EvoCOP2010 EvoBIO2010 and EvoApplications2010", } @Article{Folino:2010:GPEM, author = "Gianluigi Folino and Clara Pizzuti and Giandomenico Spezzano", title = "An ensemble-based evolutionary framework for coping with distributed intrusion detection", journal = "Genetic Programming and Evolvable Machines", year = "2010", volume = "11", number = "2", pages = "131--146", month = jun, note = "Special issue on parallel and distributed evolutionary algorithms, part II", keywords = "genetic algorithms, genetic programming, Intrusion detection, Ensemble classifiers, Distributed evolutionary algorithms", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-010-9101-6", size = "16 pages", abstract = "A distributed data mining algorithm to improve the detection accuracy when classifying malicious or unauthorized network activity is presented. The algorithm is based on genetic programming (GP) extended with the ensemble paradigm. GP ensemble is particularly suitable for distributed intrusion detection because it allows to build a network profile by combining different classifiers that together provide complementary information. The main novelty of the algorithm is that data is distributed across multiple autonomous sites and the learner component acquires useful knowledge from this data in a cooperative way. The network profile is then used to predict abnormal behavior. Experiments on the KDD Cup 1999 Data show the capability of genetic programming in successfully dealing with the problem of intrusion detection on distributed data.", } @Article{Folino:2010:fgcs, author = "Gianluigi Folino and Carlo Mastroianni", title = "Bio-Inspired Algorithms for Distributed Systems", journal = "Future Generation Computer Systems", year = "2010", volume = "26", number = "6", pages = "835--837", month = jun, keywords = "genetic algorithms, genetic programming", URL = "http://grid.dimes.unical.it/papers/pdf/BioInspiredSI-FGCS.pdf", size = "3 pages", abstract = "This special section is dedicated to the use and evaluation of bio-inspired algorithms for the design and implementation of distributed computing systems. This issue collects the revised and extended versions of the five best papers presented at BADS 2009, the Workshop on Bio-Inspired Algorithms for Distributed Systems that was hosted by ICAC 2009, the 6th IEEE International Conference on Autonomic Computing held in Barcelona, Spain, in June 2009. The papers of this special section present bio-inspired solutions for important problems such as overlay construction and resource discovery in P2P networks, job mapping in a heterogeneous environment, and data dissemination and aggregation in wireless sensor networks, for which special attention is given to the important issue of energy saving. The five papers confirm that the bio-inspired paradigm naturally provides such characteristics as decentralization, self-organization, flexibility, and energy saving, which are essential to efficiently cope with the ever increasing complexity of distributed computing systems.", } @Article{Folino2011, author = "Gianluigi Folino and Carlo Mastroianni", title = "Special Issue: Bio-Inspired Optimization Techniques for High Performance Computing", journal = "New Generation Computing", year = "2011", volume = "29", number = "2", pages = "125--128", month = apr, keywords = "genetic algorithms, genetic programming", ISSN = "1882-7055", URL = "https://doi.org/10.1007/s00354-011-0101-8", DOI = "doi:10.1007/s00354-011-0101-8", } @InProceedings{Folino:evoapps13, author = "Gianluigi Folino and Francesco Sergio Pisani", title = "A Framework for Modeling Automatic Offloading of Mobile Applications Using Genetic Programming", booktitle = "Applications of Evolutionary Computing, EvoApplications 2013: EvoCOMNET, EvoCOMPLEX, EvoENERGY, EvoFIN, EvoGAMES, EvoIASP, EvoINDUSTRY, EvoNUM, EvoPAR, EvoRISK, EvoROBOT, EvoSTOC", year = "2013", month = "3-5 " # apr, editor = "Anna I. Esparcia-Alcazar and Antonio Della Cioppa and Ivanoe {De Falco} and Ernesto Tarantino and Carlos Cotta and Robert Schaefer and Konrad Diwold and Kyrre Glette and Andrea Tettamanzi and Alexandros Agapitos and Paolo Burrelli and J. J. Merelo and Stefano Cagnoni and Mengjie Zhang and Neil Urquhart and Kevin Sim and Aniko Ekart and Francisco {Fernandez de Vega} and Sara Silva and Evert Haasdijk and Gusz Eiben and Anabela Simoes and Philipp Rohlfshagen", series = "LNCS", volume = "7835", publisher = "Springer Verlag", address = "Vienna", publisher_address = "Berlin", pages = "62--71", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-37191-2", DOI = "doi:10.1007/978-3-642-37192-9_7", size = "10 pages", abstract = "The limited battery life of the modern mobile devices is one of the key problems limiting their usage. The offloading of computation on cloud computing platforms can considerably extend the battery duration. However, it is really hard not only to evaluate the cases in which the offloading guarantees real advantages on the basis of the requirements of application in terms of data transfer, computing power needed, etc., but also to evaluate if user requirements (i.e. the costs of using the clouds, a determined QoS required, etc.) are satisfied. To this aim, in this work it is presented a framework for generating models for taking automatic decisions on the offloading of mobile applications using a genetic programming (GP) approach. The GP system is designed using a taxonomy of the properties useful to the offloading process concerning the user, the network, the data and the application. Finally, the fitness function adopted permits to give different weights to the four categories considered during the process of building the model.", notes = " EvoApplications2013 held in conjunction with EuroGP2013, EvoCOP2013, EvoBio'2013 and EvoMusArt2013", } @Article{Folino2013, author = "Gianluigi Folino and Carlo Mastroianni and Sanaz Mostaghim", title = "Preface: nature inspired solutions for high performance computing", journal = "Natural Computing", year = "2013", volume = "12", number = "1", pages = "27--28", month = mar, keywords = "genetic algorithms, genetic programming", ISSN = "1572-9796", URL = "https://doi.org/10.1007/s11047-012-9326-9", DOI = "doi:10.1007/s11047-012-9326-9", } @Article{Folino:2014:ASC, author = "G. Folino and F. S. Pisani", title = "Automatic offloading of mobile applications into the cloud by means of genetic programming", journal = "Applied Soft Computing", volume = "25", pages = "253--265", year = "2014", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2014.09.016", URL = "http://www.sciencedirect.com/science/article/pii/S1568494614004578", abstract = "The limited battery life of modern mobile devices is one of the key problems limiting their use. Even if the offloading of computation onto cloud computing platforms can considerably extend battery duration, it is really hard not only to evaluate the cases where offloading guarantees real advantages on the basis of the requirements of the application in terms of data transfer, computing power needed, etc., but also to evaluate whether user requirements (i.e. the costs of using the cloud services, a determined QoS required, etc.) are satisfied. To this aim, this paper presents a framework for generating models to make automatic decisions on the offloading of mobile applications using a genetic programming (GP) approach. The GP system is designed using a taxonomy of the properties useful to the offloading process concerning the user, the network, the data and the application. The fitness function adopted permits different weights to be given to the four categories considered during the process of building the model. Experimental results, conducted on datasets representing different categories of mobile applications, permit the analysis of the behaviour of our algorithm in different applicative contexts. Finally, a comparison with the state of the art of the classification algorithm establishes the goodness of the approach in modelling the offloading process.", keywords = "genetic algorithms, genetic programming, Mobile computing, Cloud computing, Data mining", } @InProceedings{Folino:2015:evoApplications, author = "Gianluigi Folino and Francesco Sergio Pisani", title = "Combining Ensemble of Classifiers by using Genetic Programming for Cyber Security Applications", booktitle = "18th European Conference on the Applications of Evolutionary Computation", year = "2015", editor = "Antonio M. Mora and Giovanni Squillero", series = "LNCS", volume = "9028", publisher = "Springer", pages = "54--66", address = "Copenhagen", month = "8-10 " # apr, organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-16548-6", DOI = "doi:10.1007/978-3-319-16549-3_5", abstract = "Classification is a relevant task in the cyber security domain, but it must be able to cope with unbalanced and/or incomplete datasets and must also react in real-time to changes in the data. Ensemble of classifiers are a useful tool for classification in hard domains as they combine different classifiers that together provide complementary information. However, most of the ensemble-based algorithms require an extensive training phase and need to be re-trained in case of changes in the data. This work proposes a Genetic Programming-based framework to generate a function for combining an ensemble, having some interesting properties: the models composing the ensemble are trained only on a portion of the training set, and then, they can be combined and used without any extra phase of training; furthermore, in case of changes in the data, the function can be recomputed in an incrementally way, with a moderate computational effort. Experiments conducted on unbalanced datasets and on a well-known cyber-security dataset assess the goodness of the approach.", notes = "evoCOMNET EvoApplications2015 held in conjunction with EuroGP'2015, EvoCOP2015 and EvoMusArt2015 http://www.evostar.org/2015/cfp_evoapps.php", } @InProceedings{Folino:2016:EvoApps, author = "Gianluigi Folino and Francesco Sergio Pisani and Pietro Sabatino", title = "A Distributed Intrusion Detection Framework Based on Evolved Specialized Ensembles of Classifiers", booktitle = "EvoApplications 2016", year = "2016", editor = "Giovanni Squillero and Paolo Burelli", volume = "9597", series = "LNCS", pages = "315--331", address = "Porto, Portugal", month = mar # " 30-" # apr # " 1", organisation = "Species", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-31204-0", DOI = "doi:10.1007/978-3-319-31204-0_21", abstract = "Modern intrusion detection systems must handle many complicated issues in real-time, as they have to cope with a real data stream; indeed, for the task of classification, typically the classes are unbalanced and, in addition, they have to cope with distributed attacks and they have to quickly react to changes in the data. Data mining techniques and, in particular, ensemble of classifiers permit to combine different classifiers that together provide complementary information and can be built in an incremental way. This paper introduces the architecture of a distributed intrusion detection framework and in particular, the detector module based on a meta-ensemble, which is used to cope with the problem of detecting intrusions, in which typically the number of attacks is minor than the number of normal connections. To this aim, we explore the usage of ensembles specialized to detect particular types of attack or normal connections, and Genetic Programming is adopted to generate a non-tra", } @InProceedings{Folino:2016:GECCOcomp, author = "Gianluigi Folino and Francesco Sergio Pisani and Pietro Sabatino", title = "An Incremental Ensemble Evolved by using Genetic Programming to Efficiently Detect Drifts in Cyber Security Datasets", booktitle = "GECCO '16 Companion: Proceedings of the Companion Publication of the 2016 Annual Conference on Genetic and Evolutionary Computation", year = "2016", isbn13 = "978-1-4503-4323-7", pages = "1103--1110", keywords = "genetic algorithms, genetic programming", month = "20-24 " # jul, organisation = "SIGEVO", address = "Denver, Colorado, USA", DOI = "doi:10.1145/2908961.2931682", publisher = "ACM", abstract = "Unbalanced classes, the ability to detect changes in real-time, the speed of the streams and other peculiar characteristics make most of the data mining algorithms not apt to operate with datasets in the cyber security domain. To overcome these issues, we propose an ensemble-based algorithm, using a distributed Genetic Programming framework to generate the function to combine the classifiers and efficient strategies to react to changes in data. After that the base classifiers are trained, the combining function of the ensemble, based on non-trainable functions, can be generated without any extra phase of training, while the drift detection function adopted, together with a strategy for replacing classifiers, permits to respond in an efficient way to changes. Preliminary experiments conducted on an artificial dataset and on a real intrusion detection dataset show the effectiveness of the approach.", publisher_address = "New York, NY, USA", } @Article{Folino:2016:ASC, author = "G. Folino and F. S. Pisani", title = "Evolving meta-ensemble of classifiers for handling incomplete and unbalanced datasets in the cyber security domain", journal = "Applied Soft Computing", volume = "47", pages = "179--190", year = "2016", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2016.05.044", URL = "http://www.sciencedirect.com/science/article/pii/S156849461630254X", abstract = "Cyber security classification algorithms usually operate with datasets presenting many missing features and strongly unbalanced classes. In order to cope with these issues, we designed a distributed genetic programming (GP) framework, named CAGE-MetaCombiner, which adopts a meta-ensemble model to operate efficiently with missing data. Each ensemble evolves a function for combining the classifiers, which does not need of any extra phase of training on the original data. Therefore, in the case of changes in the data, the function can be recomputed in an incremental way, with a moderate computational effort; this aspect together with the advantages of running on parallel/distributed architectures makes the algorithm suitable to operate with the real time constraints typical of a cyber security problem. In addition, an important cyber security problem that concerns the classification of the users or the employers of an e-payment system is illustrated, in order to show the relevance of the case in which entire sources of data or groups of features are missing. Finally, the capacity of approach in handling groups of missing features and unbalanced datasets is validated on many artificial datasets and on two real datasets and it is compared with some similar approaches.", keywords = "genetic algorithms, genetic programming, Ensemble, Data mining, Cyber security, Missing features", } @Article{FOLINO:2019:ASC, author = "Gianluigi Folino and Massimo Guarascio and Giuseppe Papuzzo", title = "Exploiting fractal dimension and a distributed evolutionary approach to classify data streams with concept drifts", journal = "Applied Soft Computing", volume = "75", pages = "284--297", year = "2019", keywords = "genetic algorithms, genetic programming", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2018.11.009", URL = "http://www.sciencedirect.com/science/article/pii/S1568494618306410", abstract = "Evolutionary algorithms, i.e., Genetic Programming (GP), have been successfully used for the task of classification, mainly because they are less likely to get stuck in the local optimum, can operate on chunks of data and allow to compute more solutions in parallel. Ensemble techniques are usually more accurate than component learners constituting the ensemble and can be built in an incremental way, improving flexibility, adapting to changes and maintaining part of the history present in the data. This paper proposes a framework based on a distributed GP ensemble algorithm for coping with different types of concept drift for the task of classification of large data streams. The framework is able to detect changes in a very efficient way using only a detection function based on the fractal dimension, which can also works on new incoming unclassified data. Thus, a distributed GP algorithm is performed only when a change is detected in order to improve classification accuracy and this, together with the exploitation of an adaptive procedure, permits to answer in short time to these changes. Experiments are conducted on a real and on some artificial datasets in order to assess the capacity of the framework to detect the drift and quickly respond to it", } @InProceedings{DBLP:conf/numta/FolinoPP19, author = "Gianluigi Folino and Francesco Sergio Pisani and Luigi Pontieri", editor = "Yaroslav D. Sergeyev and Dmitri E. Kvasov", title = "A Cybersecurity Framework for Classifying Non Stationary Data Streams Exploiting Genetic Programming and Ensemble Learning", booktitle = "Numerical Computations: Theory and Algorithms - Third International Conference, {NUMTA} 2019, Crotone, Italy, June 15-21, 2019, Revised Selected Papers, Part {I}", series = "Lecture Notes in Computer Science", volume = "11973", pages = "269--277", publisher = "Springer", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/978-3-030-39081-5_24", DOI = "doi:10.1007/978-3-030-39081-5_24", timestamp = "Tue, 03 Mar 2020 00:00:00 +0100", biburl = "https://dblp.org/rec/conf/numta/FolinoPP19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{Folino:2019:GECCOcomp, author = "Gianluigi Folino and Francesco Sergio Pisani and Luigi Pontieri and Pietro Sabatino and Maryam Amir Haeri", title = "Using genetic programming for combining an ensemble of local and global outlier algorithms to detect new attacks", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", isbn13 = "978-1-4503-6748-6", pages = "167--168", address = "Prague, Czech Republic", DOI = "doi:10.1145/3319619.3322018", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{3322018} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InProceedings{fong:2023:GECCOcomp, author = "Kei Sen Fong and Shelvia Wongso and Mehul Motani", title = "Evolutionary Symbolic Regression: Mechanisms from the Perspectives of Morphology and Adaptability", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Alberto Moraglio", pages = "21--22", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, symbolic regression", isbn13 = "9798400701191", DOI = "doi:10.1145/3583133.3595830", size = "2 pages", abstract = "Symbolic Regression (SR) is the task of finding closed-form analytical expressions that describe the relationship between variables in a dataset. In this work, werethink SR and introduce mechanisms from two perspectives: morphology and adaptability. Morphology: Man-made heuristics are typically used in SR algorithms to influence the morphology (or structure) of candidate expressions, potentially introducing unintentional bias and data leakage. To address this issue, we create a depth-aware mathematical language model trained on terminal walks of expression trees, as a replacement to these heuristics. Adaptability: We promote alternating fitness functions across generations, eliminating equations that perform well in only one fitness function and as a result, discover expressions that are closer to the true functional form. We demonstrate this by alternating fitness functions that quantify faithfulness to values (via MSE) and empirical derivatives (via a novel theoretically justified fitness metric coined MSEDI). Proof-of-concept: We combine these ideas into a minimalistic evolutionary SR algorithm that outperforms a suite of benchmark and state of-the-art SR algorithms in problems with unknown constants added, which we claim are more reflective of SR performance for real-world applications. Our claim is then strengthened by reproducing the superior performance on real-world regression datasets from SRBench. This Hot-of-the-Press paper summarizes the work K.S. Fong, S. Wongso and M. Motani, {"}Rethinking Symbolic Regression: Morphology and Adaptability in the Context of Evolutionary Algorithms{"}, The Eleventh International Conference on Learning International Conference on Learning Representations (ICLR'23).", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @InProceedings{fong:2023:GECCOcomp2, author = "Kei Sen Fong and Mehul Motani", title = "{DistilSR:} A Distilled Version of Gene Expression Programming Symbolic Regression", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "567--570", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "gene expression programming, symbolic regression: Poster", isbn13 = "9798400701191", DOI = "doi:10.1145/3583133.3590736", size = "4 pages", abstract = "Symbolic Regression (SR) is the task of finding closed-form expressions that describe the relationship between variables in a dataset. Current SR methods tend to neglect a large portion of the search space of short expressions in favor of longer expressions which are less explainable. In contrast to current SR methods, we propose to prioritize expression length over prediction performance. We do so by systematically searching through the search space of short expressions, using K-expressions from Gene Expression Programming. However, the search space of 'short' expressions is large, scaling approximately exponentially with the number of variables in a dataset. To reduce the size of the search space, we propose a method, termed DistilSR, which replaces terminal symbols with weighted linear combinations of variables. We show that DistilSR exactly recovers the ground-truth equation of 16 synthetic datasets 100\% of the time, outperforming 14 benchmark SR methods in SRBench. DistilSR also shows outperformance on 14 real-world datasets when compared against 14 benchmark SR algorithms and 4 benchmark non-SR algorithms from SRBench. These equations were also consistently shorter. Finally, to further enforce sparsity of weights, we propose a method of actively setting uninfluential weights to 0, achieving even shorter expressions with competitive prediction performance.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @InProceedings{pmlr-v238-sen-fong24a, author = "Kei Sen Fong and Mehul Motani", title = "Multi-Level Symbolic Regression: Function Structure Learning for Multi-Level Data", booktitle = "Proceedings of The 27th International Conference on Artificial Intelligence and Statistics", year = "2024", editor = "Sanjoy Dasgupta and Stephan Mandt and Yingzhen Li", volume = "238", series = "Proceedings of Machine Learning Research", pages = "2890--2898", address = "Valencia", month = "2-4 " # may, publisher = "PMLR", keywords = "genetic algorithms, genetic programming: Poster", URL = "https://virtual.aistats.org/virtual/2024/poster/6930", URL = "https://proceedings.mlr.press/v238/sen-fong24a.html", URL = "https://proceedings.mlr.press/v238/sen-fong24a/sen-fong24a.pdf", code_url = "https://github.com/kentridgeai/MSR-Supplementary", size = "14 pages", abstract = "Symbolic Regression (SR) is an approach which learns a closed-form function relating the predictors to the outcome in a dataset. Datasets are often multi-level (MuL), meaning that certain features can be used to split data into groups for analysis (we refer to these features as levels). The advantage of viewing datasets as MuL is that we can exploit the high similarity of data within a group. SR is well-suited for MuL datasets, in which the learnt function structure serves as 'shared information' between the groups while the learnt parameter values capture the unique relationships within each group. In this context, this paper makes three contributions: (i) We design an algorithm, Multi-level Symbolic Regression (MSR), which runs multiple parallel SR processes for each group and merges them to produce a single function structure. (ii) To tackle datasets that are not explicitly MuL, we develop a metric termed MLICC to select the best feature to serve as a level. (iii) We also release MSRBench, a database of MuL datasets (synthetic and real-world) which we developed and collated, that can be used to evaluate MSR. Our results and ablation studies demonstrate that MSR achieves a higher recovery rate and lower error on MSRBench compared to SOTA methods for SR and MuL datasets.", notes = "Is this GP? AISTATS 2024 National University of Singapore", } @InProceedings{fong:2024:GECCO, author = "Kei Sen Fong and Mehul Motani", title = "{MetaSR:} A Meta-Learning Approach to Fitness Formulation for Frequency-Aware Symbolic Regression", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference", year = "2024", editor = "Ting Hu and Aniko Ekart and Julia Handl and Xiaodong Li and Markus Wagner and Mario Garza-Fabre and Kate Smith-Miles and Richard Allmendinger and Ying Bi and Grant Dick and Amir H Gandomi and Marcella Scoczynski Ribeiro Martins and Hirad Assimi and Nadarajen Veerapen and Yuan Sun and Mario Andres Munyoz and Ahmed Kheiri and Nguyen Su and Dhananjay Thiruvady and Andy Song and Frank Neumann and Carla Silva", pages = "878--886", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, symbolic regression, meta-learning", isbn13 = "979-8-4007-0494-9", DOI = "doi:10.1145/3638529.3654096", size = "9 pages", abstract = "State-of-the-art Symbolic Regression (SR) algorithms employ evolutionary techniques to fulfill the task of generating a concise mathematical expression that fulfills an objective. A common objective is to fit to a dataset of input-output pairs, in which the faithfulness of a predicted output to the actual output is used as the fitness measure (e.g., R-squared). In many datasets, among the candidate expressions evaluated, there tends to be a large number of pseudo-expressions, referring to expressions that achieve high fitness but do not resemble the ground-truth equation. These pseudo-expressions decrease the equation recovery rate of SR algorithms. To formulate novel fitness measures that function as better discriminators of the ground-truth equation, we introduce a novel meta-learning approach to SR, MetaSR, in which we use SR itself to discover new fitness measures that can be complex combinations of existing base measures. In this paper, we focus on frequency-aware symbolic regression, where the fitness can depend on the frequency domain. We show that our new fitness measures better discriminate the ground-truth equation from other equations and demonstrate the improved performance of our method against existing algorithms.", notes = "GECCO-2024 GP A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{fong:2024:GECCOcomp, author = "Kei Sen Fong and Mehul Motani", title = "Enhancing Prediction, Explainability, Inference and Robustness of Decision Trees via Symbolic {Regression-Discovered} Splits", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion", year = "2024", editor = "Marcus Gallagher", pages = "37--38", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, symbolic regression, decision tree", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3664067", size = "2 pages", abstract = "We introduce a hybrid machine learning algorithm that uses Genetic Programming-based Symbolic Regression (SR) to create decision trees (DT) with enhanced prediction, explainability, inference and robustness. Conventional DT algorithms for classification tasks are limited to axis-parallel splits. Thus, when the true boundaries do not align with feature axes, DT is likely to exhibit complex structures. In this work, we introduce SR-Enhanced DT (SREDT), which uses SR to increase the richness of the class of potential DT splits. We assess the performance of SREDT on both synthetic and real-world datasets. Despite its simplicity, our approach yields remarkably compact trees that surpass DT and its variant, oblique DT (ODT), in supervised classification tasks in terms of accuracy and F-score. SREDT possesses low depth, with a small number of leaves and terms, increasing explainability. SREDT also makes faster inference, even compared to DT. SREDT also demonstrates the highest robustness to noise. Furthermore, despite being a small white-box model, SREDT demonstrates competitive performance with large black-box tabular classification algorithms, including tree ensembles and deep models. This Hot-of-the-Press paper summarizes the work, K.S. Fong and M. Motani, {"}Symbolic Regression Enhanced Decision Trees for Classification Tasks{"}, The Annual AAAI Conference on Artificial Intelligence (AAAI'24).", notes = "GECCO-2024 A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{FonluptPPSN2000, author = "Cyril W. B. Fonlupt and Denis Robilliard", title = "Genetic Programming with Dynamic Fitness for a Remote Sensing Application", booktitle = "Parallel Problem Solving from Nature - PPSN VI 6th International Conference", editor = "Marc Schoenauer and Kalyanmoy Deb and G{\"u}nter Rudolph and Xin Yao and Evelyne Lutton and Juan Julian Merelo and Hans-Paul Schwefel", year = "2000", publisher = "Springer Verlag", address = "Paris, France", month = "16-20 " # sep, volume = "1917", series = "LNCS", pages = "191--200", keywords = "genetic algorithms, genetic programming, Coastal sea water, K1, K2", isbn13 = "978-3-540-45356-7", URL = "http://www-lil.univ-littoral.fr/~robillia/Publis/lil-00-2.ps.gz", URL = "https://rdcu.be/enmLp", DOI = "doi:10.1007/3-540-45356-3_19", size = "10 pages", abstract = "The ocean color problem consists in evaluating ocean components concentrations (phytoplankton, sediment, yellow substance) from sunlight reflectance or luminance values at selected wavelengths in the visible band. The interest of this application increases with the availability of new satellite sensors. Moreover monitoring phytoplankton concentrations is a key point for a wide set of problems ranging from greenhouse effect to industrial fishing and signaling toxic algae blooms. To our knowledge, it is the first attempt at this regression problem with Genetic Programming (GP). We show that GP outperforms traditional polynomial fits and rivals artificial neural nets in the case of open ocean waters. We improve on previous works by also solving a range of coastal waters types, providing detailed results on estimation errors. Some experiments were conducted with a dynamic fitness GP algorithm in order to speed up computing time through a process of progressive learning.", } @Article{fonlupt:2001:ASC, author = "C. Fonlupt", title = "Solving the ocean color problem using a genetic programming approach", journal = "Applied Soft Computing", year = "2001", volume = "1", number = "1", pages = "63--72", month = jun, keywords = "genetic algorithms, genetic programming, Ocean colour problem, Phytoplankton", URL = "http://www.sciencedirect.com/science/article/B6W86-43S6W98-6/2/ed66cf73aec7cb186639405e4a8801bb", DOI = "doi:10.1016/S1568-4946(01)00007-2", abstract = "The ocean color problem consists in evaluating ocean components concentrations (phytoplankton, sediment and yellow substance) from sunlight reflectance or luminance values at selected wavelengths in the visible band. The interest of this application increases with the availability of new satellite sensors. Moreover, monitoring phytoplankton concentrations is a key point for a wide set of problems ranging from greenhouse effect to industrial fishing and signaling toxic algae blooms. To our knowledge, it is the first attempt at this regression problem with genetic programming (GP). We show that GP outperforms traditional polynomial fits and rivals artificial neural nets in the case of open ocean waters. We improve previous works by also solving a range of coastal waters types, providing detailed results on estimation errors. To our knowledge, we are the firsts to publish numerical results regarding coastal waters. Experiments were conducted with a dynamic fitness GP algorithm in order to speed up computing time through a process of progressive learning.", } @Article{fonlupt:2005:GPEM, author = "Cyril Fonlupt", title = "Book Review: Genetic Programming IV: Routine Human-Competitive Machine Intelligence", journal = "Genetic Programming and Evolvable Machines", year = "2005", volume = "6", number = "2", pages = "231--233", month = jun, keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dR8ph", DOI = "doi:10.1007/s10710-005-7579-0", size = "3 pages", notes = " Review of \cite{koza:gp4} ISBN 1-4020-7446-8 Book authors John R. Koza, Martin A. Keane, Matthew J. Streeter, William Mydlowec, Jessen Yu, Guido Lanza", } @InProceedings{Fonlupt:2011:SDE, author = "Cyril Fonlupt and Denis Robilliard and Virginie Marion-Poty", title = "Linear imperative programming with Differential Evolution", booktitle = "IEEE Symposium on Differential Evolution (SDE 2011)", year = "2011", month = "11-15 " # apr, size = "8 pages", abstract = "Differential Evolution (DE) is an evolutionary approach for optimising non-linear continuous space functions. This method is known to be robust and easy to use. DE manipulates vectors of floats that are improved over generations by mating with best and random individuals. Recently, DE was successfully applied to the automatic generation of programs by mapping real-valued vectors to full programs trees - Tree Based Differential Evolution (TreeDE). In this paper, we propose to use DE as a method to directly generate linear sequences of imperative instructions, which we call Linear Differential Evolutionary Programming (LDEP). Unlike TreeDE, LDEP incorporates constant management for regression problems and lessens the constraints on the architecture of solutions since the user is no more required to determine the tree depth of solutions. Comparisons with standard Genetic Programming and with the CMA-ES algorithm showed that DE-based approach are well suited to automatic programming, being notably more robust than CMA-ES in this particular context.", keywords = "genetic algorithms, genetic programming, automatic programming, covariance matrix adaptation evolution strategy, linear differential evolutionary programming, linear imperative programming, nonlinear continuous space function, regression problem, automatic programming, evolutionary computation, linear programming, regression analysis", DOI = "doi:10.1109/SDE.2011.5952066", notes = "Also known as \cite{5952066}", } @InProceedings{fonlupt:2011:EuroGP, author = "Cyril Fonlupt and Denis Robilliard", title = "A Continuous Approach to Genetic Programming", booktitle = "Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011", year = "2011", month = "27-29 " # apr, editor = "Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado", series = "LNCS", volume = "6621", publisher = "Springer Verlag", address = "Turin, Italy", pages = "335--346", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming: poster", isbn13 = "978-3-642-20406-7", DOI = "doi:10.1007/978-3-642-20407-4_29", size = "12 pages", abstract = "Differential Evolution (DE) is an evolutionary heuristic for continuous optimisation problems. In DE, solutions are coded as vectors of floats that evolve by crossover with a combination of best and random individuals from the current generation. Experiments to apply DE to automatic programming were made recently by Veenhuis, coding full program trees as vectors of floats (Tree Based Differential Evolution or TreeDE). In this paper, we use DE to evolve linear sequences of imperative instructions, which we call Linear Differential Evolutionary Programming (LDEP). Unlike TreeDE, our heuristic provides constant management for regression problems and lessens the tree-depth constraint on the architecture of solutions. Comparisons with TreeDE and GP show that LDEP is appropriate to automatic programming.", notes = "refs \cite{ICSI-TR-95-012}, \cite{Veenhuis:2009:eurogp}, \cite{DBLP:conf/icai/ONeillB06}, \cite{langdon:book} \cite{langdon:1998:antspace} Part of \cite{Silva:2011:GP} EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011", } @InProceedings{Fonlupt:2012:GECCOcomp, author = "Cyril Fonlupt and Denis Robilliard", title = "Combining programs to counter code disruption", booktitle = "GECCO 2012 Late breaking abstracts workshop", year = "2012", editor = "Katya Rodriguez and Christian Blum", isbn13 = "978-1-4503-1178-6", keywords = "genetic algorithms, genetic programming", pages = "643--644", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330784.2330900", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "In usual Genetic Programming (GP) schemes, only the best programs survive from one generation to the next. This implies that useful code, that might be hidden inside introns in low fitness individuals, is often lost. In this paper, we propose a new representation borrowing from Linear GP (LGP), called PhenoGP, where solutions are coded as ordered lists of instruction blocks. The main goal of evolution is then to find the best ordering of the instruction blocks, with possible repetitions. When the fitness remains stalled, ignored instruction blocks, which have a low probability to be useful, are replaced. Experiments show that PhenoGP achieve competitive results against standard LGP.", notes = "Also known as \cite{2330900} Distributed at GECCO-2012. ACM Order Number 910122.", } @InCollection{Fonlupt:2012:GPnew, author = "Cyril Fonlupt and Denis Robilliard and Virginie Marion-Poty", title = "Continuous Schemes for Program Evolution", booktitle = "Genetic Programming - New Approaches and Successful Applications", publisher = "InTech", year = "2012", editor = "Sebastian Ventura", chapter = "2", pages = "27--48", keywords = "genetic algorithms, genetic programming", isbn13 = "978-953-51-0809-2", DOI = "doi:10.5772/50023", size = "22 pages", notes = "DE and CMA-ES linear GP, CMA-LEP symbolic regression, Santa Fe ant. Part of \cite{Ventura:2012:GPnew}. Open access book CC BY 3.0 license", } @InProceedings{fonlupt:2013:EuroGP, author = "Cyril Fonlupt and Denis Robilliard", title = "PhenoGP: Combining Programs to Avoid Code Disruption", booktitle = "Proceedings of the 16th European Conference on Genetic Programming, EuroGP 2013", year = "2013", month = "3-5 " # apr, editor = "Krzysztof Krawiec and Alberto Moraglio and Ting Hu and A. Sima Uyar and Bin Hu", series = "LNCS", volume = "7831", publisher = "Springer Verlag", address = "Vienna, Austria", pages = "49--60", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-37206-3", DOI = "doi:10.1007/978-3-642-37207-0_5", abstract = "In conventional Genetic Programming (GP), n programs are simultaneously evaluated and only the best programs will survive from one generation to the next. It is a pity as some programs might contain useful code that might be hidden or not evaluated due to the presence of introns. For example in regression, zero times (perfect code) will unfortunately not be assigned a good fitness and this program might be discarded due to the evolutionary process. In this paper, we develop a new form of GP called PhenoGP (PGP). PGP individuals consist of ordered lists of programs to be executed in which the ultimate goal is to find the best order from simple building-blocks programs. If the fitness remains stalled during the run, new building-blocks programs are generated. PGP seems to compare fairly well with canonical GP.", notes = "Part of \cite{Krawiec:2013:GP} EuroGP'2013 held in conjunction with EvoCOP2013, EvoBIO2013, EvoMusArt2013 and EvoApplications2013", } @InProceedings{Fonseca:2020:PPSN, author = "Alcides Fonseca and Paulo Santos and Sara Silva", title = "The Usability Argument for Refinement Typed Genetic Programming", booktitle = "16th International Conference on Parallel Problem Solving from Nature, Part II", year = "2020", editor = "Thomas Baeck and Mike Preuss and Andre Deutz and Hao Wang2 and Carola Doerr and Michael Emmerich and Heike Trautmann", volume = "12270", series = "LNCS", pages = "18--32", address = "Leiden, Holland", month = "7-9 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, SBSE, Refined types, Search-based software engineering", isbn13 = "978-3-030-58114-5", DOI = "doi:10.1007/978-3-030-58115-2_2", abstract = "The performance of Evolutionary Algorithms is frequently hindered by arbitrarily large search spaces. In order to overcome this challenge, domain-specific knowledge is often used to restrict the representation or evaluation of candidate solutions to the problem at hand. Due to the diversity of problems and the unpredictable performance impact, the encoding of domain-specific knowledge is a frequent problem in the implementation of evolutionary algorithms. We propose the use of Refinement Typed Genetic Programming, an enhanced hybrid of Strongly Typed Genetic Programming (STGP) and Grammar-Guided Genetic Programming (GGGP) that features an advanced type system with polymorphism and dependent and refined types. We argue that this approach is more usable for describing common problems in machine learning, optimisation and program synthesis, due to the familiarity of the language (when compared to GGGP) and the use of a unifying language to express the representation, the phenotype translation, the evaluation function and the context in which programs are executed.", notes = "LASIGE Computer Science and Engineering Research Centre PPSN2020", } @PhdThesis{Fonseca:thesis, author = "Alcides Miguel Cachulo Aguiar Fonseca", title = "Automatic Optimization of Granularity Control Algorithms for Parallel Programs", school = "Department of Informatics Engineering, University of Coimbra", year = "2016", address = "Portugal", month = sep, keywords = "genetic algorithms, genetic programming, automatic parallelisation, compilers, concurrency, work-stealing, optimisation, parallel programming, granularity, machine learning", URL = "https://old.cisuc.uc.pt/publication/show/5104", URL = "https://estudogeral.uc.pt/bitstream/10316/32304/3/Automatic%20Optimization%20of%20Granularity%20Control%20Algorithms%20for%20Parallel%20Programs.pdf", size = "156 pages", abstract = "In the last two decades, processors have changed from a single-core to a multi-core design, due to physical constrains in chip manufacturing. Furthermore, GPUs have become suitable targets for general purpose programming. This change in hardware design has had an impact on software development, resulting in a growing investment in parallel programming and parallelisation tools. Writing parallel programs is difficult and error prone. Two of the main problems in parallelization are the identification of which sections of the code can be safely parallelised and how to efficiently partition work. Automatic parallelization techniques can save programmers time identifying parallelism. In parallelization, each parallelizable section is denoted as a task, and a program is comprised of several tasks with dependencies among them. Work partition consists in deciding how many tasks will be created for a given parallel workload, thus defining the task granularity. Current techniques focus solely on loop and recursive parallelization, neglecting possible fine-grained task-level parallelism. However, if the granularity is too fine, penalizing scheduling overheads may be incurred. On the other hand, if the granularity is too coarse, there may not be enough parallelism in the program to occupy all processor cores. The ideal granularity of a program is influenced by its nature and the available resources. Our experiments have shown that a program that terminates within seconds with the correct granularity may execute for days with an unsuitable granularity. Finding the best granularity is not trivial, more so in the case of automatic parallelization, in which there is no knowledge of the program domain. The current approach consists in empirically evaluating several alternatives to find the optimal granularity. This thesis proposes a more efficient model for automatic parallelization, in which parallelism is identified at the Abstract Syntax Tree (AST) node level. Static analysis is used to obtain access permissions, representations of how an AST node interacts with others in terms of memory accesses and control-flow. Parallelism at the AST node level is very fine grained and may generate more tasks than those that can be executed simultaneously, resulting in scheduling overheads. In order to reduce these overheads, tasks may be merged in coarser tasks, thus reducing parallelism. A cost-model is proposed to dynamically adjust granularity according to the complexity of tasks, resulting in programs more efficient than the best existing alternative. Because the automatic parallelization model can generate programs that can execute either on the CPU or the GPU, it is important to automatically decide if a program should execute on the CPU with a coarse granularity, or on the GPU with a finer granularity. To perform this decision, a Machine Learning approach was built, based on static compiler-obtained and runtime features. This model performed program classification with over 95percent of accuracy and a low misclassification cost. In order to improve the performance of automatic and manually parallelised programs, new dynamic granularity algorithms are proposed for runtime aggregation of tasks. The proposed algorithms extend the state of the art by taking into consideration the usage of the number of stack frames and machine occupation, as well as using a cost-model-based prediction of the task execution time. The existing and proposed algorithms have been evaluated in both time and energy consumed, as well as number of programs completed within reasonable time. Considering both time and energy, the proposed algorithms outperformed existing ones, but no algorithm performed better than any other in all benchmark programs. These results demonstrate the importance of using the right algorithm for an individual program. An evolutionary algorithm was used to generate a global best granularity algorithm for a set of target programs. While improvements were not generalized to a larger set of programs, the evolutionary algorithm can be used to improve the execution time within 10 to 20 generations. To avoid an exhaustive search for the best granularity algorithm for each program, this thesis proposes both a ruleset and the usage of machine-learning classifiers over program features. The ruleset was obtained from the empirical evaluation of different alternatives on a selected benchmark suite. Both approaches can be used by compilers or programmers to select the granularity algorithm for each program. In a real-world benchmark suite, the ruleset has shown to outperform classifiers, but on an unseen larger synthetic benchmark suite, a misclassification-weighted Random Forest was able to achieve better results than the ruleset. Overall, this thesis proposes new approaches for automatic parallelization and granularity control that improve the performance of programs.", notes = "Brief mention of GP. SFRH/BD/84448/2012 Supervisor: Bruno Cabral", } @InProceedings{Fonseca:2021:GPTP, author = "Alcides Fonseca and Paulo Santos and Guilherme Espada and Sara Silva", title = "Grammatical Evolution Mapping for Semantically-Constrained Genetic Programming", booktitle = "Genetic Programming Theory and Practice XVIII", year = "2021", editor = "Wolfgang Banzhaf and Leonardo Trujillo and Stephan Winkler and Bill Worzel", series = "Genetic and Evolutionary Computation", pages = "45--62", address = "East Lansing, USA", month = "19-21 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, GeneticEngine, SBSE", isbn13 = "978-981-16-8112-7", DOI = "doi:10.1007/978-981-16-8113-4_3", code_url = "https://github.com/alcides/GeneticEngine", abstract = "Search-Based Software Engineering problems frequently have semantic constraints that can be used to deterministically restrict what type of programs can be generated, improving the performance of Genetic Programming. Strongly-Typed and Grammar-Guided Genetic Programming are two examples of using domain-knowledge to improve performance of Genetic Programming by preventing solutions that are known to be invalid from ever being added to the population. However, the restrictions in real world challenges like program synthesis, automated program repair or test generation are more complex than what context-free grammars or simple types can express. We address these limitations with examples, and discuss the process of efficiently generating individuals in the context of Christiansen Grammatical Evolution and Refined-Typed Genetic Programming. We present three new approaches for the population initialization procedure of semantically constrained GP that are more efficient and promote more diversity than traditional Grammatical Evolution.", notes = "https://www.lasige.pt/sara-silva-and-alcides-fonseca-gptp-2021 espada2022data Part of \cite{Banzhaf:2021:GPTP} published after the workshop in 2022", } @InProceedings{fonseca:2023:GECCO, author = "Alcides Fonseca and Diogo Pocas", title = "Comparing the Expressive Power of {Strongly-Typed} and {Grammar-Guided} Genetic Programming", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "1100--1108", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, grammatical evolution, grammar-guided genetic programming, strongly-typed genetic programming", isbn13 = "9798400701191", DOI = "doi:10.1145/3583131.3590507", size = "9 pages", abstract = "Since Genetic Programming (GP) has been proposed, several flavors of GP have arisen, each with their own strengths and limitations. Grammar-Guided and Strongly-Typed GP (GGGP and STGP, respectively) are two popular flavors that have the advantage of allowing the practitioner to impose syntactic and semantic restrictions on the generated programs. GGGP makes use of (traditionally context-free) grammars to restrict the generation of (and the application of genetic operators on) individuals. By guiding this generation according to a grammar, i.e. a set of rules, GGGP improves performance by searching for an good-enough solution on a subset of the search space. This approach has been extended with Attribute Grammars to encode semantic restrictions, while Context-Free Grammars would only encode syntactic restrictions. STGP is also able to restrict the shape of the generated programs using a very simple grammar together with a type system. In this work, we address the question of which approach has more expressive power. We demonstrate that STGP has higher expressive power than Context-Free GGGP and less expressive power than Attribute Grammatical Evolution.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @InProceedings{Fonseca:2025:GPTP, author = "Alcides Fonseca", title = "Bridging Genetic Programming and Programming Languages Research", booktitle = "Genetic Programming Theory and Practice XXII", year = "2025", editor = "Wolfgang Banzhaf and Bogdan Burlacu and Stephen Kelly and Alexander Lalejini and Fabricio {Olivetti de Franca}", address = "Michigan State University, USA", month = jun # " 5-7", note = "lightning talk", keywords = "genetic algorithms, genetic programming", notes = "To be published after the workshop", } @InProceedings{Font:2010:cec, author = "Jose M. Font and Daniel Manrique", title = "Grammar-guided evolutionary automatic system for autonomously building biological oscillators", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-6910-9", abstract = "This paper presents a grammar-guided evolutionary automatic system (GGEAS) that is capable of autonomously building special-purpose problem-solving programs. GGEAS uses a grammar-guided genetic programming (GGGP) core that generates solutions to a given problem from scratch, evolving them via selection, crossover and replacement to obtain the near-optimal solution to that problem. The GGGP core solves the closure problem and avoids code bloat. This core only outputs valid solutions and is able to freely determine their size and architecture. GGEAS is supplemented by three external modules that can be configured for any application domain: context-free grammar (CFG) generator, semantic checker and fitness module. The context-free grammar (CFG) generator creates the context-free grammar used by the GGEAS core to formalise the problem constraints. The semantic checker ensures the validity of the solutions created. Finally, the fitness module directs the population evolution towards an optimal solution to the problem. In order to test the effectiveness and the scope of the system, GGEAS has been applied to generate oscillatory biological programs codified in the BlenX language. The results show that GGEAS is effective at creating biological oscillators in silico from scratch without any prior knowledge about the solution and under a range of environmental conditions.", DOI = "doi:10.1109/CEC.2010.5586377", notes = "WCCI 2010. Also known as \cite{5586377}", } @Article{Font20107711, author = "Jose M. Font and Daniel Manrique and Juan Rios", title = "Evolutionary construction and adaptation of intelligent systems", journal = "Expert Systems with Applications", volume = "37", number = "12", pages = "7711--7720", year = "2010", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2010.04.070", URL = "http://www.sciencedirect.com/science/article/B6V03-501FPHF-C/2/9a2d947791e5706c203b3fed536a0e36", keywords = "genetic algorithms, genetic programming, Evolutionary computation, Intelligent systems, Rule-based systems, Fuzzy rule-based systems, Artificial neural networks, Medical prognosis", abstract = "This paper introduces evolutionary techniques for automatically constructing intelligent self-adapting systems, capable of modifying their inner structure in order to learn from experience and self-adapt to a changing environment. These evolutionary techniques comprise an evolutionary system that is engineered by grammar-guided genetic programming, enabling the development of sub-symbolic and symbolic intelligent systems: artificial neural networks and knowledge-based systems, respectively. A context-free-grammar based codification system for artificial neural networks and rules, an initialisation method and a crossover operator have been designed to properly balance the exploration and exploitation capabilities of the proposed system. This speeds up the convergence process and avoids trapping in local optima. This system has been applied to a medical domain: the detection of knee injuries from the analysis of isokinetic time series. The results of the evolved symbolic and sub-symbolic intelligent systems have been statistically compared with each other as part of a quantitative and qualitative performance analysis.", } @InProceedings{Font:2011:IWINAC, author = "Jose Font and Daniel Manrique and Eduardo Pascua", title = "Grammar-Guided Evolutionary Construction of {Bayesian} Networks", booktitle = "Proceedings of the 4th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2011, Part I", year = "2011", editor = "Jose Manuel Ferrandez and Jose Ramon {Alvarez Sanchez} and Felix {de la Paz} and F. Javier Toledo", series = "Lecture Notes in Computer Science", pages = "60--69", volume = "6686", address = "La Palma, Canary Islands, Spain", month = may # " 30-" # jun # " 3", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-21343-4", DOI = "doi:10.1007/978-3-642-21344-1_7", abstract = "This paper proposes the EvoBANE system. EvoBANE automatically generates Bayesian networks for solving special-purpose problems. EvoBANE evolves a population of individuals that codify Bayesian networks until it finds near optimal individual that solves a given classification problem. EvoBANE has the flexibility to modify the constraints that condition the solution search space, self-adapting to the specifications of the problem to be solved. The system extends the GGEAS architecture. GGEAS is a general-purpose grammar-guided evolutionary automatic system, whose modular structure favours its application to the automatic construction of intelligent systems. EvoBANE has been applied to two classification benchmark datasets belonging to different application domains, and statistically compared with a genetic algorithm performing the same tasks. Results show that the proposed system performed better, as it manages different complexity constraints in order to find the simplest solution that best solves every problem.", affiliation = "Departamento de Inteligencia Artificial, Universidad Politecnica de Madrid. Campus de Montegancedo, 28660 Boadilla del Monte, Spain", } @InProceedings{Font:evoapps12, author = "Jose M. Font", title = "Evolving Third-Person Shooter Enemies to Optimize Player Satisfaction in Real-Time", booktitle = "Applications of Evolutionary Computing, EvoApplications 2012: EvoCOMNET, EvoCOMPLEX, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoNUM, EvoPAR, EvoRISK, EvoSTIM, EvoSTOC", year = "2011", month = "11-13 " # apr, editor = "Cecilia {Di Chio} and Alexandros Agapitos and Stefano Cagnoni and Carlos Cotta and F. {Fernandez de Vega} and Gianni A. {Di Caro} and Rolf Drechsler and Aniko Ekart and Anna I Esparcia-Alcazar and Muddassar Farooq and William B. Langdon and Juan J. Merelo and Mike Preuss and Hendrik Richter and Sara Silva and Anabela Simoes and Giovanni Squillero and Ernesto Tarantino and Andrea G. B. Tettamanzi and Julian Togelius and Neil Urquhart and A. Sima Uyar and Georgios N. Yannakakis", series = "LNCS", volume = "7248", publisher = "Springer Verlag", address = "Malaga, Spain", publisher_address = "Berlin", pages = "204--213", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, Evolutionary computation, fuzzy rule based system, grammar-guided genetic programming, player satisfaction", isbn13 = "978-3-642-29177-7", DOI = "doi:10.1007/978-3-642-29178-4_21", abstract = "A grammar-guided genetic program is presented to automatically build and evolve populations of AI controlled enemies in a 2D third-person shooter called Genes of War. This evolutionary system constantly adapts enemy behaviour, encoded by a multi-layered fuzzy control system, while the game is being played. Thus the enemy behaviour fits a target challenge level for the purpose of maximising player satisfaction. Two different methods to calculate this challenge level are presented: 'hardwired' that allows the desired difficulty level to be programed at every stage of the gameplay, and 'adaptive' that automatically determines difficulty by analysing several features extracted from the player's gameplay. Results show that the genetic program successfully adapts armies of ten enemies to different kinds of players and difficulty distributions.", notes = "EvoGames Part of \cite{DiChio:2012:EvoApps} EvoApplications2012 held in conjunction with EuroGP2012, EvoCOP2012, EvoBio'2012 and EvoMusArt2012", } @InProceedings{Font:evoapps13, author = "Jose M. Font and Tobias Mahlmann and Daniel Manrique and Julian Togelius", title = "A Card Game Description Language", booktitle = "Applications of Evolutionary Computing, EvoApplications 2013: EvoCOMNET, EvoCOMPLEX, EvoENERGY, EvoFIN, EvoGAMES, EvoIASP, EvoINDUSTRY, EvoNUM, EvoPAR, EvoRISK, EvoROBOT, EvoSTOC", year = "2013", month = "3-5 " # apr, editor = "Anna I. Esparcia-Alcazar and Antonio Della Cioppa and Ivanoe {De Falco} and Ernesto Tarantino and Carlos Cotta and Robert Schaefer and Konrad Diwold and Kyrre Glette and Andrea Tettamanzi and Alexandros Agapitos and Paolo Burrelli and J. J. Merelo and Stefano Cagnoni and Mengjie Zhang and Neil Urquhart and Kevin Sim and Aniko Ekart and Francisco {Fernandez de Vega} and Sara Silva and Evert Haasdijk and Gusz Eiben and Anabela Simoes and Philipp Rohlfshagen", series = "LNCS", volume = "7835", publisher = "Springer Verlag", address = "Vienna", publisher_address = "Berlin", pages = "254--263", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, Game design, game description language, evolutionary computation, grammar guided genetic programming, automated game design", isbn13 = "978-3-642-37191-2", DOI = "doi:10.1007/978-3-642-37192-9_26", size = "10 pages", abstract = "We present initial research regarding a system capable of generating novel card games. We furthermore propose a method for computationally analysing existing games of the same genre. Ultimately, we present a formalisation of card game rules, and a context-free grammar G card game capable of expressing the rules of a large variety of card games. Example derivations are given for the poker variant Texas hold 'em, Blackjack and UNO. Stochastic simulations are used both to verify the implementation of these well-known games, and to evaluate the results of new game rules derived from the grammar. In future work, this grammar will be used to evolve completely novel card games using a grammar-guided genetic program.", notes = " EvoApplications2013 held in conjunction with EuroGP2013, EvoCOP2013, EvoBio'2013 and EvoMusArt2013", } @InProceedings{DBLP:conf/fdg/FontMMT13, author = "Jose Maria Font and Tobias Mahlmann and Daniel Manrique and Julian Togelius", title = "Towards the automatic generation of card games through grammar-guided genetic programming", year = "2013", booktitle = "International Conference on the Foundations of Digital Games", editor = "Georgios N. Yannakakis and Espen Aarseth and Kristine J{\o}rgensen and James C. Lester", pages = "360--363", address = "Chania, Crete, Greece", month = may # " 14-17", publisher = "Society for the Advancement of the Science of Digital Games", keywords = "genetic algorithms, genetic programming", bibsource = "DBLP, http://dblp.uni-trier.de", isbn13 = "978-0-9913982-0-1", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.299.3619", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.299.3619", biburl = "https://dblp.org/rec/conf/fdg/FontMMT13.bib", URL = "http://julian.togelius.com/Font2013Towards.pdf", URL = "http://www.fdg2013.org/program/papers/short01_font_etal.pdf", size = "4 pages", abstract = "We demonstrate generating complete and playable card games using evolutionary algorithms. Card games are represented in a previously devised card game description language, a context-free grammar. The syntax of this language allows us to use grammar-guided genetic programming. Candidate card games are evaluated through a cascading evaluation function, a multi-step process where games with undesired properties are progressively weeded out. Three representative examples of generated games are analysed. We observed that these games are reasonably balanced and have skill elements, they are not yet entertaining for human players. The particular shortcomings of the examples are discussed in regard to the generative process to be able to generate quality games.", } @InProceedings{Fontbonne:2022:EuroGP, author = "Nicolas Fontbonne and Nicolas Maudet and Nicolas Bredeche", title = "Cooperative Co-Evolution and Adaptive Team Composition for a Multi-Rover Resources Allocation Problem", booktitle = "EuroGP 2022: Proceedings of the 25th European Conference on Genetic Programming", year = "2022", editor = "Eric Medvet and Gisele Pappa and Bing Xue", series = "LNCS", volume = "13223", publisher = "Springer Verlag", address = "Madrid, Spain", pages = "179--193", month = "20-22 " # apr, organisation = "EvoStar, Species", note = "Best paper nomination", keywords = "genetic algorithms, genetic programming, ad hoc autonomous agent teams, multi-agent systems, marginal contribution, team composition, multi-robots, cooperative co-evolutionary algorithms (CCEA), evolutionary computation, evolutionary robotics", isbn13 = "978-3-031-02055-1", DOI = "doi:10.1007/978-3-031-02056-8_12", abstract = "we are interested in ad hoc autonomous agent team composition using cooperative co-evolutionary algorithms (CCEA). In order to accurately capture the individual contribution of team agents, we propose to limit the number of agents which are updated in-between team evaluations. However, this raises two important problems with respect to (1) the cost of accurately estimating the marginal contribution of agents with respect to the team learning speed and (2) completing tasks where improving team performance requires multiple agents to update their policies in a synchronized manner. We introduce a CCEA algorithm that is capable of learning how to update just the right amount of agents' policies for the task at hand. We use a variation of the El Farol Bar problem, formulated as a multi-robot resource selection problem, to provide an experimental validation of the algorithms proposed.", notes = "http://www.evostar.org/2022/eurogp/ Part of \cite{Medvet:2022:GP} EuroGP'2022 held inconjunction with EvoApplications2022 EvoCOP2022 EvoMusArt2022", } @InProceedings{fontoura:2017:CEC, author = "Vidal D. Fontoura and Aurora T. R. Pozo and Roberto Santana", booktitle = "2017 IEEE Congress on Evolutionary Computation (CEC)", title = "Automated design of hyper-heuristics components to solve the {PSP} problem with {HP} model", year = "2017", editor = "Jose A. Lozano", pages = "1848--1855", address = "Donostia, San Sebastian, Spain", month = "5-8 " # jun, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, grammatical evolution, biology computing, evolutionary computation, grammars, proteins, GEHyPSP, HP model, PSP problem, acceptance criteria, automated hyper-heuristics component design, grammatical evolution, hyper-heuristic framework, protein folding process, protein structure prediction problem, selection mechanisms, simplified protein models, Context, Grammar, Production, Sociology, Statistics, Two dimensional displays", isbn13 = "978-1-5090-4601-0", URL = "http://www.sc.ehu.es/ccwbayes/members/ZEeZE/papers/paper_17414.html", DOI = "doi:10.1109/CEC.2017.7969526", size = "8 pages", abstract = "The Protein Structure Prediction (PSP) problem is one of the modern most challenging problems from science. Simplified protein models are usually applied to simulate and study some characteristics of the protein folding process. Hence, many heuristic strategies have been applied in order to find simplified protein structures in which the protein configuration has the minimal energy. However, these strategies have difficulties in finding the optimal solutions to the longer sequences of amino-acids, due to the complexity of the problem and the huge amount of local optima. Hyper heuristics have proved to be useful in this type of context since they try to combine different heuristics strengths into a single framework. However, there is lack of work addressing the automated design of hyper-heuristics components. This paper proposes GEHyPSP, an approach which aims to achieve generation, through grammatical evolution, of selection mechanisms and acceptance criteria for a hyper-heuristic framework applied to PSP problem. We investigate the strengths and weaknesses of our approach on a benchmark of simplified protein models. GEHyPSP was able to reach the best known results for 7 instances from 11 that composed the benchmark set used to evaluate the approach.", notes = "IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969526}", } @InProceedings{Foreback:2025:ALIFE-CIS, author = "Max Foreback and Clifford Bohm and Emily Dolson", title = "Leveraging Heterogeneous Controller Representations for Evolutionary Swarm Robotics", booktitle = "2025 IEEE Symposium on Computational Intelligence in Artificial Life and Cooperative Intelligent Systems (ALIFE-CIS)", year = "2025", month = mar, keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Swarm robotics, Evolutionary computation, Intelligent systems, Biological neural networks, ANN", DOI = "doi:10.1109/ALIFE-CIS64968.2025.10979834", abstract = "A dominant approach to developing multi-agent swarm systems is the automatic design of individual agent controllers via evolutionary computation. Typically, these controllers are encoded as neural networks. However, controllers can be represented in many other ways, and recent work suggests that different representations excel at different tasks in single agent systems. In particular, Cartesian genetic programming and Markov brains are two promising representations that share neural networks' ability to be applied to generic problems without extensive task-specific experimenter input. Here, we extend prior results to agents within swarms, and show that there likely exist swarm tasks for which neural networks are not the best suited controller representation. Moreover, many swarm problems have subcomponents that would benefit from division of labor among specialist agents, making them amenable to a modular design. Given the relatively low probability that all subproblems happen to be optimally solved by the same controller type, swarms composed of multiple controller types may outperform swarms which have access to only one type of controller representation. We show that such heterogeneous swarms consistently specialize and achieve high performance by evolving to use controller representations on the subtasks for which they are best suited. Finally, we introduce a simple method to evolve the number of agents using each controller representation in a swarm. These evolved compositions perform well when evaluated against both homogeneous swarms and swarms with preset compositions.", notes = "Also known as \cite{10979834}", } @InProceedings{1068307, author = "Nate Foreman and Matthew Evett", title = "Preventing overfitting in {GP} with canary functions", booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation", year = "2005", editor = "Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and Edwin D. {de Jong} and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler", volume = "2", ISBN = "1-59593-010-8", pages = "1779--1780", address = "Washington DC, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2005/docs/p1779.pdf", DOI = "doi:10.1145/1068009.1068307", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "25-29 " # jun, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Poster, experimentation, overfitting, performance", notes = "GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM Order Number 910052 Prechelt, x4+x3+x2+x, cos(3x), generalisation loss", } @Article{FORMENTIN:2019:CE, author = "Sara Mizar Formentin and Barbara Zanuttigh", title = "A Genetic Programming based formula for wave overtopping by crown walls and bullnoses", journal = "Coastal Engineering", volume = "152", pages = "103529", year = "2019", ISSN = "0378-3839", DOI = "doi:10.1016/j.coastaleng.2019.103529", URL = "http://www.sciencedirect.com/science/article/pii/S0378383919300419", keywords = "genetic algorithms, genetic programming, Crown walls, Bullnoses, Wave overtopping, Experimental data", abstract = "The purpose of this contribution is to propose a new method for the parametrization of the reductive effects induced by crown walls and bullnoses on the average wave overtopping discharge (q) at coastal structures. The method consists of a formula for calculating an influence factor *GP to account for the single or combined effects of the structural elements. The formula for *GP is conceived to be included in the q formula by EurOtop (2018). The new formula was developed on the basis of the Genetic Programming (GP) technique trained on a database of nearly ?? data on wave overtopping at dikes with berms or promenades, crown walls and bullnoses. Part of the data are derived from new experiments carried out by the authors to extend the experience available from the literature and create a database of structure configurations sufficiently wide and appropriately assorted to be used for training the GP. The rough formula for predicting *GP obtained by the pure application of the GP was optimized to achieve a greater accuracy in the representation of both the breaking and non-breaking wave conditions. The estimations of q obtained with the new influence factor *GP are physically meaningful and satisfactory accurate, and overcome the underestimation bias affecting the predictions from the available formulae", } @Article{Forouzanfar2012496, author = "Mehdi Forouzanfar and A. Doustmohammadi and Samira Hasanzadeh and H. {Shakouri G}", title = "Transport energy demand forecast using multi-level genetic programming", journal = "Applied Energy", volume = "91", number = "1", pages = "496--503", year = "2012", ISSN = "0306-2619", DOI = "doi:10.1016/j.apenergy.2011.08.018", URL = "http://www.sciencedirect.com/science/article/pii/S0306261911005149", keywords = "genetic algorithms, genetic programming, Transport energy demand, Forecasting, Modelling", abstract = "In this paper, a new multi-level genetic programming (MLGP) approach is introduced for forecasting transport energy demand (TED) in Iran. It is shown that the result obtained here has smaller error compared with the result obtained using neural network or fuzzy linear regression approach. The forecast uses historical energy data from 1968 to 2002 and it is based on three parameters; gross domestic product (GDP), population (POP), and the number of vehicles (VEH). The approach taken in this paper is based on genetic programming (GP) and the multi-level part of the name comes from the fact that we use GP in two different levels. At the first level, GP is used to obtain the time series model of the three parameters, GDP, POP, and VEH, and forecast those parameters for the time interval that their actual data are not available, and at the second level GP is used one more time to forecast TED based on available data for TED along with the data that are either available or predicted for the three parameters discussed earlier. Actual data from 1968 to 2002 are used for training and the data for years 2003-2005 are used to test the GP model. We have limited ourselves to these data ranges so that we could compare our results with the existing ones in the literature. The estimation GP for the model is formulated as a nonlinear optimisation problem and it is solved numerically.", } @Article{Forrest13081993, author = "Stephanie Forrest", title = "Genetic Algorithms: Principles of Natural Selection Applied to Computation", journal = "Science", year = "1993", volume = "261", number = "5123", pages = "872--878", month = "13 " # aug, keywords = "genetic algorithms, genetic programming, automatic programming", URL = "http://www.sciencemag.org/content/261/5123/872.abstract", eprint = "http://www.sciencemag.org/content/261/5123/872.full.pdf", DOI = "doi:10.1126/science.8346439", size = "8 pages", abstract = "A genetic algorithm is a form of evolution that occurs on a computer. Genetic algorithms are a search method that can be used for both solving problems and modelling evolutionary systems. With various mapping techniques and an appropriate measure of fitness, a genetic algorithm can be tailored to evolve a solution for many types of problems, including optimisation of a function of determination of the proper order of a sequence. Mathematical analysis has begun to explain how genetic algorithms work and how best to use them. Recently, genetic algorithms have been used to model several natural evolutionary systems, including immune systems.", notes = "Koza GP Lisp s-expression page 875 PMID: 8346439", } @InProceedings{DBLP:conf/gecco/ForrestNWG09, author = "Stephanie Forrest and ThanhVu Nguyen and Westley Weimer and Claire {Le Goues}", title = "A genetic programming approach to automated software repair", booktitle = "GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation", year = "2009", editor = "Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano {van Hemert} and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique Alba", pages = "947--954", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", note = "GECCO 2019 10-Year Most Influential Paper Award, Best paper", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, Software Engineering, Testing and Debugging, Programming Languages, Syntax, Algorithms, Software repair, software engineering", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", DOI = "doi:10.1145/1569901.1570031", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.147.7651", URL = "http://www.cs.virginia.edu/~weimer/p/weimer-gecco2009.pdf", language = "en", oai = "oai:CiteSeerXPSU:10.1.1.147.7651", abstract = "Genetic programming is combined with program analysis methods to repair bugs in off-the-shelf legacy C programs. Fitness is defined using negative test cases that exercise the bug to be repaired and positive test cases that encode program requirements. Once a successful repair is discovered, structural differencing algorithms and delta debugging methods are used to minimize its size. Several modifications to the GP technique contribute to its success: (1) genetic operations are localized to the nodes along the execution path of the negative test case; (2) high-level statements are represented as single nodes in the program tree; (3) genetic operators use existing code in other parts of the program, so new code does not need to be invented. The paper describes the method, reviews earlier experiments that repaired 11 bugs in over 60,000 lines of code, reports results on new bug repairs, and describes experiments that analyze the performance and efficacy of the evolutionary components of the algorithm.", notes = "Best paper. Gold medal HUMIES. Autofix zune bug: microsoft Zune media player end of year bug 31 dec 2008. GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009). ACM Order Number 910092.", } @InProceedings{Forrest:2010:SPLASH, author = "Stephanie Forrest", title = "The Case for Evolvable Software", booktitle = "ACM International Conference on Systems, Programming, Languages, and Applications: Software for Humanity (SPLASH)", year = "2010", pages = "1", address = "Reno, USA", month = "17-21 " # oct, publisher = "ACM", note = "Keynote", keywords = "genetic algorithms, genetic programming, genetic improvement", isbn13 = "978-1-4503-0203-6", URL = "http://portal.acm.org/ft_gateway.cfm?id=1869539&type=pdf&CFID=114019259&CFTOKEN=22192943", DOI = "doi:10.1145/1869459.1869539", size = "1 page", abstract = "As programmers, we like to think of software as the product of our intelligent design, carefully crafted to meet well-specified goals. In reality, software evolves inadvertently through the actions of many individual programmers, often leading to unanticipated consequences. Large complex software systems are subject to constraints similar to those faced by evolving biological systems, and we have much to gain by viewing software through the lens of evolutionary biology. The talk will highlight recent research that applies the mechanisms of evolution quite directly to the problem of repairing software bugs.", notes = "Abstract only.", } @Misc{Forrest:2021:GI, author = "Stephanie Forrest", title = "Engineering and Evolving Software", booktitle = "2021 IEEE/ACM International Workshop on Genetic Improvement (GI)", editor = "Justyna Petke and Bobby R. Bruce and Yu Huang and Aymeric Blot and Westley Weimer and W. B. Langdon", year = "2021", pages = "10", month = "30 " # may, note = "Invited keynote", keywords = "genetic algorithms, genetic programming, Genetic Improvement, Software, Maintenance engineering, Artificial intelligence, Computer security, Artificial immune systems, US Government", video_url = "https://www.youtube.com/watch?v=iZnKHIZ5qVY&list=PLXTjhGKkSnI-se7uQneCX-pEDiDrQ7TIS&index=13", URL = "https://twitter.com/gi2021/status/1399027796597383168", DOI = "doi:10.1109/GI52543.2021.00008", size = "1 pages", abstract = "Provides a brief professional biography of the presenter Stephanie Forrest of Arizona State University. The complete presentation was not made available for publication as part of the conference proceedings.", notes = "Also known as \cite{9473907} Video presentation 1 hour 'Nature is a tinkerer, not an inventor.' F.Jacob. But Forrest says evolution closer to skilled engineer and the distinction between design and evolution not so sharp. Frances Arnold Nobel prize 2018 for chemistry directed evolution (ie Genetic Algorithm) applied to chemistry, eg DNA-SWCNT https://en.wikipedia.org/wiki/Frances_Arnold Xenobots Mike Levins group at Tufts University https://doi.org/10.1073/pnas.1910837117 Two types of Frog cells => 1mm robot Lenski E-coli MSU Large jump in evolution. Large jump in flu virus, eg bird and pig virus interacting. Engineering innovation like recombination of earlier ideas. NB not clean slate design. Chaos engineering, coevolutionary arms races in software security. A Jacob test: engineering or evolved? Evolutionary dynamics in software. There is Darwinian processes in today's software. Software has become more biological: stackoverflow. AB testing (run by genetic algorithm in the back ground, interactive evolution). Continuous integration (rapiarinator). Fuzzing. Chaos Engineering (robust to random changes) Cyber security arms races. \cite{miikkulainen:2021:nmi} neutral mutations, mutational robustness needed for evolution. = Test suite equivalent. bug repair, eg GenProg third of mutations are neutral. Why? Many ways it could arise. \cite{Renzullo:2018:GI} APR with larger step size \cite{Liou:2019:GI} Smith-Waterman CUDA 12 highly epistatic mutations Need more attention to existing EC work, drift, search (eg novelty search), more use of epistasis. More real biology. More hardware resources. video 46:00 Cost of robustness (eg spell checker => poorer typing) Robustness mechanism becomes essential. Eg IT systems. Challanges 47:37 discussion/Questions chair Bobby R. Bruce 47:37 Myra B. Cohen. 50:57 Giovani Guizzo. A: \cite{miikkulainen:2021:nmi}, drift, what if we had same resouces as deep learning? 52:45 Westley Weimer. 54:58 Alexandre Bergel A: Renzullo IPDS-2021 https://doi.org/10.1109/IPDPS49936.2021.00107 Compensatory evolution, less selection pressure, bigger populations. 57:20 Oliver Krauss http://geneticimprovementofsoftware.com/events/icse2021.html 2021 IEEE/ACM International Workshop on Genetic Improvement (GI)", } @InProceedings{Forstenlechner:2015:GECCOcomp, author = "Stefan Forstenlechner and Miguel Nicolau and David Fagan and Michael O'Neill", title = "Introducing Semantic-Clustering Selection in Grammatical Evolution", booktitle = "GECCO 2015 Semantic Methods in Genetic Programming (SMGP'15) Workshop", year = "2015", editor = "Colin Johnson and Krzysztof Krawiec and Alberto Moraglio and Michael O'Neill", isbn13 = "978-1-4503-3488-4", keywords = "genetic algorithms, genetic programming, grammatical evolution, Semantic Methods", pages = "1277--1284", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739482.2768502", DOI = "doi:10.1145/2739482.2768502", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Semantics has gained much attention in the last few years and new advanced crossover and mutation operations have been created which use semantic information to improve the quality and generalisability of individuals in genetic programming. In this paper we present a new selection operator in grammatical evolution which uses semantic information of individuals instead of just the fitness value. The semantic traits of an individual are stored in a vector. An unsupervised learning technique is used to cluster individuals based on their semantic vector. Individuals are only allowed to reproduce with individuals from the same cluster to preserve semantic locality and intensify the search in a certain semantic area. At the same time, multiple semantic areas are covered by the search as there exist multiple clusters which cover different areas and therefore preserve semantic diversity. This new selection operator is tested on several symbolic regression benchmark problems and compared to grammatical evolution with tournament selection to analyse its performance.", notes = "Also known as \cite{2768502} Distributed at GECCO-2015.", } @InProceedings{Forstenlechner:2016:EuroGP, author = "Stefan Forstenlechner and Miguel Nicolau and David Fagan and Michael O'Neill", title = "Grammar Design for Derivation Tree Based Genetic Programming Systems", booktitle = "EuroGP 2016: Proceedings of the 19th European Conference on Genetic Programming", year = "2016", month = "30 " # mar # "--1 " # apr, editor = "Malcolm I. Heywood and James McDermott and Mauro Castelli and Ernesto Costa and Kevin Sim", series = "LNCS", volume = "9594", publisher = "Springer Verlag", address = "Porto, Portugal", pages = "199--214", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-30668-1", DOI = "doi:10.1007/978-3-319-30668-1_13", abstract = "Grammar-based genetic programming systems have gained interest in recent decades and are widely used nowadays. Although researchers normally present the grammar used to solve a certain problem, they seldom write about processes used to construct the grammar. This paper sheds some light on how to design a grammar that not only covers the search space, but also supports the search process in finding good solutions. The focus lies on context free grammar guided systems using derivation tree crossover and mutation, in contrast to linearised grammar based systems. Several grammars are presented encompassing the search space of sorting networks and show concepts which apply to general grammar design. An analysis of the search operators on different grammar is undertaken and performance examined on the sorting network problem. The results show that the overall structure for derivation trees created by the grammar has little effect on the performance, but still affects the genetic material changed by search operators.", notes = "Part of \cite{Heywood:2016:GP} EuroGP'2016 held in conjunction with EvoCOP2016, EvoMusArt2016 and EvoApplications2016", } @InProceedings{Forstenlechner:2017:EuroGP, author = "Stefan Forstenlechner and David Fagan and Miguel Nicolau and Michael O'Neill", title = "A Grammar Design Pattern for Arbitrary Program Synthesis Problems in Genetic Programming", booktitle = "EuroGP 2017: Proceedings of the 20th European Conference on Genetic Programming", year = "2017", month = "19-21 " # apr, editor = "Mauro Castelli and James McDermott and Lukas Sekanina", series = "LNCS", volume = "10196", publisher = "Springer Verlag", address = "Amsterdam", pages = "262--277", organisation = "species", keywords = "genetic algorithms, genetic programming, G3P, PushGP, Python: Poster", isbn13 = "978-3-319-55695-6", DOI = "doi:10.1007/978-3-319-55696-3_17", size = "16 pages", abstract = "Grammar Guided Genetic Programming has been applied to many problem domains. It is well suited to tackle program synthesis, as it has the capability to evolve code in arbitrary languages. Nevertheless, grammars designed to evolve code have always been tailored to specific problems resulting in bespoke grammars, which makes them difficult to reuse. In this study a more general approach to grammar design in the program synthesis domain is presented. The approach undertaken is to create a grammar for each data type of a language and combine these grammars for the problem at hand, without having to tailor a grammar for every single problem. The approach can be applied to arbitrary problem instances of program synthesis and can be used with any programming language. The approach is also extensible to use libraries available in a given language. The grammars presented can be applied to any grammar-based Genetic Programming approach and make it easy for researches to rerun experiments or test new problems. The approach is tested on a suite of benchmark problems and compared to PushGP, as it is the only GP system that has presented results on a wide range of benchmark problems. The object of this study is to match or outperform PushGP on these problems without tuning grammars to solve each specific problem.", notes = "cites \cite{McKay:2010:GPEM} Part of \cite{Castelli:2017:GP} EuroGP'2017 held inconjunction with EvoCOP2017, EvoMusArt2017 and EvoApplications2017", } @InProceedings{Forstenlechner:2017:EA, author = "Stefan Forstenlechner and David Fagan and Miguel Nicolau and Michael O'Neill", title = "Semantics-Based Crossover for Program Synthesis in Genetic Programming", booktitle = "Artificial Evolution, EA-2017", year = "2017", editor = "Evelyne Lutton and Pierrick Legrand and Pierre Parrend and Nicolas Monmarche and Marc Schoenauer", volume = "10764", series = "LNCS", pages = "58--71", address = "Paris, France", month = oct # " 25-27", publisher = "Springer", note = "Revised Selected Papers", keywords = "genetic algorithms, genetic programming, Crossover", isbn13 = "978-3-319-78133-4", DOI = "doi:10.1007/978-3-319-78133-4_5", size = "14 pages", abstract = "Semantic information has been used to create operators that improve performance in genetic programming. As different problem domains have different semantics, extracting semantics and calculating semantic similarity is of tantamount importance to use semantic operators for each domain. To date researchers have struggled to effectively do this beyond the Boolean and regression problem domain. In this paper, a semantic similarity-based crossover is tested in the problem domain of program synthesis. For this purpose, a similarity measure based on the execution trace of a program is introduced. Subtree crossover as well as semantic similarity-based crossover are analysed on performance and semantic aspects. The goal is to introduce the Semantic Similarity-based Crossover in the program synthesis domain and to study the effects of using semantic locality. The results show that semantic crossover produces more semantically different children as well as more children that are better than their parents compared to subtree crossover", notes = "Published 2018", } @InProceedings{Forstenlechner:2018:CEC, author = "Stefan Forstenlechner and David Fagan and Miguel Nicolau and Michael O'Neill", title = "Towards Understanding and Refining the General Program Synthesis Benchmark Suite with Genetic Programming", booktitle = "2018 IEEE Congress on Evolutionary Computation (CEC)", year = "2018", editor = "Marley Vellasco", address = "Rio de Janeiro, Brazil", month = "8-13 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2018.8477953", abstract = "Program synthesis is a complex problem domain tackled by many communities via different methods. In the last few years, a lot of progress has been made with Genetic Programming (GP) on solving a variety of general program synthesis problems for which a benchmark suite has been introduced. While Genetic Programming is capable of finding correct solutions for many problems contained in a general program synthesis problems benchmark suite, the actual success rate per problem is low in most cases. In this paper, we analyse certain aspects of the benchmark suite and the computational effort required to solve its problems. A subset of problems on which GP performs poorly is identified. This subset is analysed to find measures to increase success rates for similar problems. The paper concludes with suggestions to refine performance on program synthesis problems.", notes = "WCCI2018", } @InProceedings{Forstenlechner:2018:GECCO, author = "Stefan Forstenlechner and David Fagan and Miguel Nicolau and Michael O'Neill", title = "Towards effective semantic operators for program synthesis in genetic programming", booktitle = "GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference", year = "2018", editor = "Hernan Aguirre and Keiki Takadama and Hisashi Handa and Arnaud Liefooghe and Tomohiro Yoshikawa and Andrew M. Sutton and Satoshi Ono and Francisco Chicano and Shinichi Shirakawa and Zdenek Vasicek and Roderich Gross and Andries Engelbrecht and Emma Hart and Sebastian Risi and Ekart Aniko and Julian Togelius and Sebastien Verel and Christian Blum and Will Browne and Yusuke Nojima and Tea Tusar and Qingfu Zhang and Nikolaus Hansen and Jose Antonio Lozano and Dirk Thierens and Tian-Li Yu and Juergen Branke and Yaochu Jin and Sara Silva and Hitoshi Iba and Anna I Esparcia-Alcazar and Thomas Bartz-Beielstein and Federica Sarro and Giuliano Antoniol and Anne Auger and Per Kristian Lehre", isbn13 = "978-1-4503-5618-3", pages = "1119--1126", address = "Kyoto, Japan", DOI = "doi:10.1145/3205455.3205592", publisher = "ACM", publisher_address = "New York, NY, USA", month = "15-19 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", abstract = "the use of semantic information in genetic programming operators has shown major improvements in recent years, especially in the regression and boolean domain. As semantic information is domain specific, using it in other areas poses certain problems. Semantic operators require being adapted for the problem domain they are applied to. An attempt to create a semantic crossover for program synthesis has been made with rather limited success, but the results have provided insights about using semantics in program synthesis. Based on this initial attempt, this paper presents an improved version of semantic operators for program synthesis, which contains a small but significant change to the overall functionality, as well as a novel measure for the comparison of the semantics of subtrees. The results show that the improved semantic crossover is superior to the previous semantic operator in the program synthesis domain.", notes = "Checksum, Compare String Lengths, Double Letters, Grade, Mirror Image, Small Or Large, Sum of Squares and Vector Average. Also known as \cite{3205592} GECCO-2018 A Recombination of the 27th International Conference on Genetic Algorithms (ICGA-2018) and the 23rd Annual Genetic Programming Conference (GP-2018)", } @InProceedings{Forstenlechner:2018:PPSN, author = "Stefan Forstenlechner and David Fagan and Miguel Nicolau and Michael O'Neill", title = "Extending Program Synthesis Grammars for Grammar-Guided Genetic Programming", booktitle = "15th International Conference on Parallel Problem Solving from Nature", year = "2018", editor = "Anne Auger and Carlos M. Fonseca and Nuno Lourenco and Penousal Machado and Luis Paquete and Darrell Whitley", volume = "11101", series = "LNCS", pages = "197--208", address = "Coimbra, Portugal", month = "8-12 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Grammar, Program synthesis", isbn13 = "978-3-319-99252-5", URL = "https://www.springer.com/gp/book/9783319992587", DOI = "doi:10.1007/978-3-319-99253-2_16", abstract = "Program synthesis is a problem domain that due to its importance is tackled by many different fields, one being Genetic Programming. Two variants, Grammar-Guided Genetic Programming (G3P) and PushGP, have been applied to a vast general program synthesis benchmark suite and solved a variety of problems although with varying success rates. While G3P achieved higher success rates on some problems, PushGP was able to find solutions to more problem instances. Reason why G3P fails at some problems might be missing functionality in the grammars or knowledge that has to discovered during the runs. In this paper the current shortcomings of G3P are analysed and the papers contributions include an example of extending grammars for program synthesis, a fairer comparison between PushGP and G3P with a more similar function set as well as new results on problems that have not been solved with G3P and one that has not been solved with PushGP.", notes = "PPSN2018 http://ppsn2018.dei.uc.pt This two-volume set LNCS 11101 and 11102 constitutes the refereed proceedings of the 15th International Conference on Parallel Problem Solving from Nature, PPSN 2018", } @PhdThesis{forstenlechner:phdthesis, author = "Stefan Forstenlechner", title = "Program Synthesis with Grammars and Semantics in Genetic Programming", school = "University College Dublin", year = "2019", address = "Ireland", month = jan, keywords = "genetic algorithms, genetic programming, grammar, program synthesis, General Program Synthesis Benchmark Suite, Grammar Design, Semantic Operators, SCPS, ESMPS", URL = "https://www.smurfitschool.ie/facultyresearch/phdresearch/phdgraduates/stefanforstenlechner/", URL = "http://ncra.ucd.ie/papers/PhDThesis_forstenlechner_submitted.pdf", size = "249 pages", abstract = "Program synthesis is an important field that has many use cases like bug fixing, automating repetitive tasks and discovering new algorithms. One way to approach program synthesis tasks is to specify a grammar that defines all possible programs that can be created and using a search algorithm like genetic programming to create a program. Although using grammars has the advantage that created programs are syntactically correct, the grammar has to be defined for each problem tackled. The focus of this thesis is to introduce a grammar design approach that provides the ability to tackle arbitrary program synthesis problems from input/output examples. The grammars will not be required to be tailored to a specific problem, and in contrast to many existing approaches, the code of the produced programs will be in a programming language used by practitioners. The grammar design approach is studied on a range of program synthesis problems throughout the thesis and shows results that are competitive to state of the art systems. As the search for programs with genetic programming is often done on the syntactic representation without considering the behaviour or semantics of a program, the introduction of semantic operators for program synthesis will be investigated. While in other problem domains, semantic operators have improved search performance, no such operators are available for the program synthesis domain. A definition of semantics in program synthesis will be provided, and multiple semantic measures and operators will be studied on the basis of this definition. The results show that novel semantic crossover and mutation operators for genetic programming can outperform traditional operators that do not consider semantic information.", notes = "UCD student number: 14204817. SFI 13/IA/1850 Supervisor: Professor Michael O'Neill", } @InProceedings{Forster:2009:ISSNIP, author = "Kilian Forster and Pascal Brem and Daniel Roggen and Gerhard Troster", title = "Evolving discriminative features robust to sensor displacement for activity recognition in body area sensor networks", booktitle = "5th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2009", year = "2009", month = dec, pages = "43--48", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ISSNIP.2009.5416810", abstract = "Activity and gesture recognition from body-worn acceleration sensors is an important application in body area sensor networks. The key to any such recognition task are discriminative and variation tolerant features. Furthermore good features may reduce the energy requirements of the sensor network as well as increase the robustness of the activity recognition. We propose a feature extraction method based on genetic programming. We benchmark this method using two datasets and compare the results to a feature selection which is typically used for obtaining a set of features. With one extracted feature we achieve an accuracy of 73.4percent on a fitness activity dataset, in contrast to 70.1percent using one selected standard feature. In a gesture based HCI dataset we achieved 95.0percent accuracy with one extracted feature. A selection of up to five standard features achieved 90.6percent accuracy in the same setting. On the HCI dataset we also evaluated the robustness of extracted features to sensor displacement which is a common problem in movement based activity and gesture recognition. With one extracted features we achieved an accuracy of 85.0percent on a displaced sensor position. With the best selection of standard features we achieved 55.2percent accuracy. The results show that our proposed genetic programming feature extraction method is superior to a feature selection based on standard features.", notes = "Also known as \cite{5416810}", } @Article{kybernetes:forsyth, author = "Richard Forsyth", title = "{BEAGLE} A {Darwinian} Approach to Pattern Recognition", journal = "Kybernetes", year = "1981", volume = "10", number = "3", pages = "159--166", keywords = "genetic algorithms, genetic programming, soccer foot ball pools", URL = "http://www.richardsandesforsyth.net/pubs/beagle81.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/kybernetes_forsyth.pdf", DOI = "doi:10.1108/eb005587", size = "8 pages", ISSN = "0368-492X", abstract = "BEAGLE (Biological Evolutionary Algorithm Generating Logical Expressions) is a computer package producing decision-rules by induction from a database. It works on the principle of naturalistic selection whereby rules that fit the data badly are killed off and replaced by mutations of better rules or by new rules created by mating two better adapted rules. The rules are Boolean expressions represented by tree structures. The software consists of two Pascal programs, HERB (Heuristic Evolutionary Rule Breeder) and LEAF (Logical Evaluator And Forecaster). HERB improves a given starting set of rules by running over several simulated generations, LEAF uses the rules to classify samples from a database where the correct membership may not be known. Preliminary test on three different databases have been carried out -- on hospital admissions (classing heart patients as deaths or survivors), on athletic physique (classing Olympic finalists as long-distance runners or sprinters) and on football results (categorising games into draws and non-draws) It appears from the tests that the method works better than the standard discriminant analysis technique based on a linear discriminant function, and hence that this long-neglected approach warrants further investigation.", notes = "Copy from British Library May 1994", } @Book{Forsyth:1986:mlESir, author = "Richard Forsyth and Roy Rada", title = "Machine Learning applications in Expert Systems and Information Retrieval", publisher = "Ellis Horwood", year = "1986", series = "Ellis Horwood series in artificial intelligence", address = "Chichester, UK", ISBN = "0-7458-0045-9", URL = "http://www.amazon.co.uk/Machine-Learning-Applications-Information-Retrieval/dp/0745800459", keywords = "genetic algorithms, genetic programming", notes = "Chapters on BEAGLE", size = "275 pages", } @InCollection{forsyth:1989:ei, author = "Richard Forsyth", title = "The evolution of intelligence", booktitle = "Machine Learning: Principles and Techniques", publisher = "Chapman and Hall", year = "1989", editor = "Richard Forsyth", chapter = "4", pages = "65--82", keywords = "genetic algorithms, genetic programming", ISBN = "0-412-30570-4", URL = "http://www.amazon.com/Machine-Learning-Principles-Techniques-Computing/dp/0412305704", URL = "http://books.google.co.uk/books?id=jIVQAAAAMAAJ&focus=searchwithinvolume&q=Forsyth", notes = "some general stuff on history of GAs, evolution strategy and evolution programming, cf Fogel 1966, description of Goldberg's natural gas pipeline control GA/classifier experiments. BEAGLE applied to classifiying countries by their flags etc and brief description of PC/Beagle being applied to forensic science {"}where a rule set developed with the aid of PC/BEAGLE was found to descriminate among glass fragments better than standard statistical procedures{"} [page 77]. ", } @PhdThesis{Forsyth:thesis, author = "Richard S. Forsyth", title = "Stylistic Structures A Computational Approach to Text Classification", school = "University of Nottingham", year = "1995", address = "UK", month = oct, keywords = "genetic algorithms, genetic programming", URL = "http://www.richardsandesforsyth.net/doctoral.html", URL = "http://www.richardsandesforsyth.net/doctoral/RFthesis.pdf", URL = "http://www.richardsandesforsyth.net/doctoral/appx_abcd.pdf", size = "297 pages", abstract = "The problem of authorship attribution has received attention both in the academic world (e.g. did Shakespeare or Marlowe write Edward III ?) and outside (e.g. is this confession really the words of the accused or was it made up by someone else?). Previous studies by statisticians and literary scholars have sought 'verbal habits' that characterize particular authors consistently. By and large, this has meant looking for distinctive rates of usage of specific marker words -- as in the classic study by Mosteller and Wallace of the Federalist Papers. The present study is based on the premiss that authorship attribution is just one type of text classification and that advances in this area can be made by applying and adapting techniques from the field of machine learning. Five different trainable text-classification systems are described, which differ from current stylometric practice in a number of ways, in particular by using a wider variety of marker patterns than customary and by seeking such markers automatically, without being told what to look for. A comparison of the strengths and weaknesses of these systems, when tested on a representative range of text-classification problems, confirms the importance of paying more attention than usual to alternative methods of representing distinctive differences between types of text. The thesis concludes with suggestions on how to make further progress towards the goal of a fully automatic, trainable text-classification system.", notes = "p222 GLADRAGS 'It may well prove to be a bridge between the sometimes Procrustean simplicity of traditional GA bitstrings and the somewhat unprincipled representational liberalism of Genetic Programming as advocated by \cite{koza:book} in particular.' Richard Sandes Forsyth", } @Article{Forsyth:2016:sigevolution, author = "Richard S. Forsyth", title = "The Genesis of Genetic Programming: A Frontiersman's tale", journal = "SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation", year = "2016", volume = "9", number = "3", pages = "3--11", month = oct, keywords = "genetic algorithms, genetic programming", ISSN = "1931-8499", URL = "http://www.sigevolution.org/issues/SIGEVOlution0903.pdf", DOI = "doi:10.1145/3066862.3066863", acmid = "3066863", size = "9 pages", abstract = "This article takes a participant-observer's look back at the genealogy of the computational method now known as Genetic Programming (GP for short). In so doing, it treats GP as a case study for elucidating the process of technical innovation. Working on the assumption that the contrast between sudden Eureka and stepwise improvement is a polarity rather than a sharp dichotomy, it introduces a simple technique for identifying the main steps in the march of GP from margin to mainstream. It is argued that this approach could be applied more widely to other areas of scientific or technological advance possibly even offering the prospect of resolution to some of the more belligerent academic priority disputes.", notes = "26 October 2016. http://www.richardsandesforsyth.net/software.html ", } @Article{fortin:2012:JMRL, author = "Felix-Antoine Fortin and Francois-Michel {De Rainville} and Marc-Andre Gardner and Marc Parizeau and Christian Gagne", title = "{DEAP}: Evolutionary Algorithms Made Easy", journal = "Journal of Machine Learning Research", year = "2012", volume = "13", pages = "2171--2175", month = jul, keywords = "genetic algorithms, genetic programming, distributed evolutionary algorithms, software tools", ISSN = "1533-7928", URL = "http://jmlr.org/papers/v13/", URL = "http://www.jmlr.org/papers/volume13/fortin12a/fortin12a.pdf", code_url = "https://github.com/deap", size = "5 pages", abstract = "DEAP is a novel evolutionary computation framework for rapid prototyping and testing of ideas. Its design departs from most other existing frameworks in that it seeks to make algorithms explicit and data structures transparent, as opposed to the more common black-box frameworks. Freely available with extensive documentation at http://deap.gel.ulaval.ca, DEAP is an open source project under an LGPL license.", notes = "http://deap.gel.ulaval.ca http://jmlr.org/", } @InProceedings{DBLP:conf/ssci/FossSH21, author = "Fredrik Foss and Truls Stenrud and Pauline C. Haddow", title = "Investigating Genetic Network Programming for Multiple Nest Foraging", booktitle = "{IEEE} Symposium Series on Computational Intelligence, {SSCI} 2021, Orlando, FL, USA, December 5-7, 2021", pages = "1--7", publisher = "{IEEE}", year = "2021", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1109/SSCI50451.2021.9659926", DOI = "doi:10.1109/SSCI50451.2021.9659926", timestamp = "Thu, 03 Feb 2022 00:00:00 +0100", biburl = "https://dblp.org/rec/conf/ssci/FossSH21.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{Foster:2010:gecco, author = "Blair Foster and Anil Somayaji", title = "Object-level recombination of commodity applications", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "957--964", keywords = "genetic algorithms, genetic programming, SBSE, software recombination, ObjRecombGA, object-level recombination, commodity programs", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", URL = "http://people.scs.carleton.ca/~soma/pubs/bfoster-gecco-2010.pdf", DOI = "doi:10.1145/1830483.1830653", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "This paper presents ObjRecombGA, a genetic algorithm framework for recombining related programs at the object file level. A genetic algorithm guides the selection of object files, while a robust link resolver allows working program binaries to be produced from the object files derived from two ancestor programs. Tests on compiled C programs, including a simple web browser and a well-known 3D video game, show that functional program variants can be created that exhibit key features of both ancestor programs. This work illustrates the feasibility of applying evolutionary techniques directly to commodity applications", notes = "Unix sed (8 version pairs), Dillo (6 version pairs), Quake (6 version pairs). Manual (Human interactive?) fitness. Population=12, =30, =50. Mashup. linear bit string GA, each bit refers to a C .o in two ancestor programs. unix bash shell script. GNU ld linker. open source in near future? p962 'we were able to repair bugs and merge functionality by recombining programs at the object file level.' Also known as \cite{1830653} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @Unpublished{foster:1997:ieGPb, author = "James A. Foster and Terence Soule", title = "Comments on the intron/exon distinction as it relates to genetic programming and biology", note = "Position paper at the Workshop on Exploring Non-coding Segments and Genetics-based Encodings at ICGA-97", month = "21 " # jul, year = "1997", address = "East Lansing, MI, USA", keywords = "genetic algorithms, genetic programming, introns", notes = "http://garage.cse.msu.edu/icga97/workshops/workshopsIndex.html#encodings Oct 2016 link to icga97.ws gone", size = "3 pages", } @Article{foster:2001:discipulus, author = "James A. Foster", title = "Review: Discipulus: A Commercial Genetic Programming System", journal = "Genetic Programming and Evolvable Machines", year = "2001", volume = "2", number = "2", pages = "201--203", month = jun, keywords = "genetic algorithms, genetic programming, Dynamic Subset Selection, DSS", ISSN = "1389-2576", URL = "https://rdcu.be/cT1so", DOI = "doi:10.1023/A:1011516717456", notes = "cites \cite{ga94aGathercole} Article ID: 335720", } @Article{foster:2005:GPEM, author = "James A. Foster and Erick Cantu-Paz", title = "Introduction", journal = "Genetic Programming and Evolvable Machines", year = "2005", volume = "6", number = "1", pages = "5--6", month = mar, keywords = "genetic algorithms, genetic programming, evolvable hardware", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-005-7616-z", notes = "Special issue best of GECCO-2003 \cite{GECCO2003-PartI}, \cite{GECCO2003-PartII} ", } @Article{foster:2006:sigevo, author = "James A. Foster and Jason H. Moore", title = "GECCO-2006 Highlights: Biological Applications", journal = "SIGEVOlution", year = "2006", volume = "1", number = "3", pages = "23", month = sep, keywords = "genetic algorithms, genetic programming", URL = "http://www.sigevolution.org/2006/03/issue.pdf", URL = "https://dl.acm.org/action/showFmPdf?doi=10.1145%2F1181964", size = "0.5 pages", } @Article{Foster:2013:GPEM, author = "James A. Foster", title = "Introduction to special section: Best of {EuroGP/EvoBio}", journal = "Genetic Programming and Evolvable Machines", year = "2013", volume = "14", number = "4", pages = "429--430", month = dec, keywords = "genetic algorithms, genetic programming, Bioinformatics", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-013-9194-9", size = "2 pages", } @Article{Foster:2017:GPEM, author = "James A. Foster", title = "Taking ``biology'' just seriously enough: Commentary on ``On the Mapping of Genotype to Phenotype in Evolutionary Algorithms'' by {Peter A. Whigham, Grant Dick, and James Maclaurin}", journal = "Genetic Programming and Evolvable Machines", year = "2017", volume = "18", number = "3", pages = "395--398", month = sep, note = "Special Peer Commentary on Mapping of Genotype to Phenotype in Evolutionary Algorithms", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-017-9296-x", size = "4 pages", abstract = "``On the Mapping of Genotype to Phenotype in Evolutionary Algorithms,'' by Peter A. Whigham, Grant Dick, and James Maclaurin \cite{Whigham:2017:GPEM}, is a welcome reminder that evolutionary computation practitioners should be wary of taking their biological analogies too seriously. But more importantly, it is a reminder to practitioners to consider carefully their representations and operators, rather than blindly implementing a biological analogy without sufficient attention to the constraints of software engineering. ``It works in biology, so it should work in EC'' is poor, even lazy, software design. The primary contribution of this paper is exactly what a commentary should be: to (re)ignite discussions about how biological inspiration should inform EC practice", notes = "Introduction in \cite{Spector:2017:GPEM} An author's reply to this comment is available at http://dx.doi.org/10.1007/s10710-017-9289-9 \cite{Whigham:2017:GPEM2}. This comment refers to the article available at: http://dx.doi.org/10.1007/s10710-017-9288-x \cite{Whigham:2017:GPEM}.", } @InProceedings{Foster:2023:GPTP, author = "James Foster", booktitle = "Genetic Programming Theory and Practice XX", year = "2023", editor = "Ting Hu and Charles Ofria and Leonardo Trujillo and Stephan Winkler", series = "Genetic and Evolutionary Computation", address = "Michigan State University, USA", month = jun # " 1-3", note = "Keynote", keywords = "genetic algorithms, genetic programming", notes = " Part of \cite{Hu:2023:GPTP} Not in published book", } @InProceedings{Foster:2021:ICTSS, author = "Michael Foster and John Derrick and Neil Walkinshaw", title = "Reverse-Engineering {EFSMs} with Data Dependencies", booktitle = "33rd IFIP International Conference on Testing Software and Systems", year = "2021", editor = "David Clark and Hector Menendez and Ana Rosa Cavalli", volume = "13045", series = "Lecture Notes in Computer Science", pages = "37--54", address = "virtual", month = "10-11 " # nov, keywords = "genetic algorithms, genetic programming, SBSE, EFSM Inference, Model Inference", isbn13 = "978-3-031-04673-5", URL = "https://eprints.whiterose.ac.uk/177494/", DOI = "doi:10.1007/978-3-031-04673-5_3", size = "16 pages", abstract = "EFSMs provide a way to model systems with internal data variables. In situations where they do not already exist, we need to infer them from system behaviour. A key challenge here is inferring the functions which relate inputs, outputs, and internal variables. Existing approaches either work with white-box traces, which expose variable values, or rely upon the user to provide heuristics to recognise and generalise particular data-usage patterns. This paper presents a preprocessing technique for the inference process which generalises the concrete values from the traces into symbolic functions which calculate output from input, even when this depends on values not present in the original traces. Our results show that our technique leads to more accurate models than are produced by the current state-of-the-art and that somewhat accurate models can still be inferred even when the output of particular transitions depends on values not present in the original traces.", notes = "Published 10 May 2022. Published in cooperation with http://www.ifip.org/ Department of Computer Science, The University of Sheffield, Regent Court, Sheffield, S1 4DP, UK http://ictss2021.cs.ucl.ac.uk/", } @Article{Fouladitajar:2013:Desalination, author = "Amir Fouladitajar and Farzin Zokaee Ashtiani and Ahmad Okhovat and Bahram Dabir", title = "Membrane fouling in microfiltration of oil-in-water emulsions; a comparison between constant pressure blocking laws and genetic programming (GP) model", journal = "Desalination", volume = "329", pages = "41--49", year = "2013", ISSN = "0011-9164", DOI = "doi:10.1016/j.desal.2013.09.003", URL = "http://www.sciencedirect.com/science/article/pii/S001191641300413X", keywords = "genetic algorithms, genetic programming, Membrane fouling, Blocking laws, Oil-in-water emulsion", size = "41 pages", abstract = "Microfiltration of oil-in-water emulsion with different concentrations and TMPs was experimentally performed to investigate the fouling mechanisms of oil droplets. In this work, the performance of both blocking laws and genetic programming model was evaluated. Four individual and five combined blocking models were applied to determine if they would provide acceptable fits of the experimental data. In individual models, the best predictions were obtained by the intermediate model and the cake model failed to provide any fit of the experimental data in all data sets. Although the combined models used two fitted parameters, they did not provide better fits of the data than individual models. The intermediate model combined with the cake filtration model and standard model provided the same fit as the intermediate model alone. In addition, genetic programming as a novel approach in membrane fouling was used to predict both permeate flux and oil rejection. It was found that for the studied system, the GP model not only was able to provide better fits of experimental data, but also predicted the oil rejection with an acceptable accuracy. The dominant fouling mechanisms were also identified in different operating conditions.", } @InProceedings{Fowler:2016:EuroGP, author = "Benjamin Fowler and Wolfgang Banzhaf", title = "Modelling Evolvability in Genetic Programming", booktitle = "EuroGP 2016: Proceedings of the 19th European Conference on Genetic Programming", year = "2016", month = "30 " # mar # "--1 " # apr, editor = "Malcolm I. Heywood and James McDermott and Mauro Castelli and Ernesto Costa and Kevin Sim", series = "LNCS", volume = "9594", publisher = "Springer Verlag", address = "Porto, Portugal", pages = "215--229", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, evolvability, meta-learning, artificial neural networks", isbn13 = "978-3-319-30668-1", DOI = "doi:10.1007/978-3-319-30668-1_14", abstract = "We develop a tree-based genetic programming system capable of modelling evolvability during evolution through machine learning algorithms, and exploiting those models to increase the efficiency and final fitness. Existing methods of determining evolvability require too much computational time to be effective in any practical sense. By being able to model evolvability instead, computational time may be reduced. This will be done first by demonstrating the effectiveness of modelling these properties \emph{a priori}, before expanding the system to show its effectiveness as evolution occurs.", notes = "Part of \cite{Heywood:2016:GP} EuroGP'2016 held in conjunction with EvoCOP2016, EvoMusArt2016 and EvoApplications2016", } @PhdThesis{Fowler_BenjaminDavidScott_doctoral, author = "Benjamin Fowler", title = "Modelling Evolvability in Genetic Programming", school = "Department of Computer Science, Memorial University of Newfoundland", year = "2018", address = "Saint Johns, Newfoundland, Canada", month = aug, keywords = "genetic algorithms, genetic programming, ANN, Evolvability, Artificial Neural Networks, Streaming Data", URL = "http://research.library.mun.ca/id/eprint/13413", URL = "https://research.library.mun.ca/13413/1/Fowler_BenjaminDavidScott_doctoral.pdf", size = "149 pages", abstract = "We develop a tree-based genetic programming system, capable of modeling evolvability during evolution through artificial neural networks (ANN) and exploiting those networks to increase the generational fitness of the system. This thesis is empirically focused; we study the effects of evolvability selection under varying conditions to demonstrate the effectiveness of evolvability selection. Evolvability is the capacity of an individual to improve its future fitness. In genetic programming (GP), we typically measure how well a program performs a given task at its current capacity only. We improve upon GP by directly selecting for evolvability. We construct a system, Sample-Evolvability Genetic Programming (SEGP), that estimates the true evolvability of a program by conducting a limited number of evolvability samples. Evolvability is sampled by conducting a number of genetic operations upon a program and comparing the fitnesses of resulting programs with the original. SEGP is able to achieve an increase in fitness at a cost of increased computational complexity. We then construct a system which improves upon SEGP, Model-Evolvability Genetic Programming (MEGP), that models the true evolvability of a program by training an ANN to predict its evolvability. MEGP reduces the computational cost of sampling evolvability while maintaining the fitness gains. MEGP is empirically shown to improve generational fitness for a streaming domain, in exchange for an upfront increase in computational time.", notes = "Supervisor: Wolfgang Banzhaf", } @InProceedings{conf/evoW/FradeVC08, title = "Modelling Video Games' Landscapes by Means of Genetic Terrain Programming - {A} New Approach for Improving Users' Experience", author = "Miguel Frade and F. {Fernandez de Vega} and Carlos Cotta", bibdate = "2008-04-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/evoW/evoW2008.html#FradeVC08", booktitle = "Proceedings of Evo{COMNET}, Evo{FIN}, Evo{HOT}, Evo{IASP}, Evo{MUSART}, Evo{NUM}, Evo{STOC}, and EvoTransLog, Applications of Evolutionary Computing, EvoWorkshops", publisher = "Springer", year = "2008", volume = "4974", editor = "Mario Giacobini and Anthony Brabazon and Stefano Cagnoni and Gianni {Di Caro} and Rolf Drechsler and Anik{\'o} Ek{\'a}rt and Anna Esparcia-Alc{\'a}zar and Muddassar Farooq and Andreas Fink and Jon McCormack and Michael O'Neill and Juan Romero and Franz Rothlauf and Giovanni Squillero and Sima Uyar and Shengxiang Yang", isbn13 = "978-3-540-78760-0", pages = "485--490", series = "Lecture Notes in Computer Science", DOI = "doi:10.1007/978-3-540-78761-7_52", address = "Naples", month = "26-28 " # mar, keywords = "genetic algorithms, genetic programming, terrain generation, video games, evolutionary art", abstract = "Terrain generation algorithms can provide a realistic scenario for video game experience and can help keep users interested in playing by providing new landscapes each time they play. Nowadays there are a wide range of techniques for terrain generation, but all of them are focused on providing realistic terrains. This paper proposes a new technique, Genetic Terrain Programming, based on evolutionary design with GP to allow game designers to evolve terrains according to their aesthetic feelings or desired features. The developed application produces Terrains Programs that will always generate different terrains, but consistently with the same features (e.g. valleys, lakes).", notes = "GPLAB Matlab, FFT", } @Article{Frade:2009:IJCGT, author = "Miguel Frade and Francisco {Fernandez de Vega} and Carlos Cotta", title = "Breeding Terrains with Genetic Terrain Programming: The Evolution of Terrain Generators", journal = "International Journal of Computer Games Technology", year = "2009", volume = "2009", note = "Special issue on Artificial Intelligence for Computer Games", keywords = "genetic algorithms, genetic programming, Genetic terrain programming, evolutionary systems, terrain generator, level of detail", ISSN = "1687-7047", URL = "http://downloads.hindawi.com/journals/ijcgt/2009/125714.pdf", broken = "http://www.hindawi.com/GetArticle.aspx?doi=10.1155/2009/125714", DOI = "doi:10.1155/2009/125714", size = "13 pages", abstract = "Although a number of terrain generation techniques have been proposed during the last few years, all of them have some key constraints. Modelling techniques depend highly upon designer's skills, time and effort to obtain acceptable results, and cannot be used to automatically generate terrains. The simpler methods allow only a narrow variety of terrain types and offer little control on the outcome terrain. The Genetic Terrain Programming technique, based on evolutionary design with Genetic Programming, allows designers to evolve terrains according to their aesthetic feelings or desired features. This technique evolves TPs (Terrain Programmes) that are capable of generating a family of terrains - different terrains that consistently present the same morphological characteristics. This paper presents a study about the persistence of morphological characteristics of terrains generated with different resolutions by a given TP. Results show it is possible to use low resolutions during the evolutionary phase without compromising the outcome and that terrain macro-features are scale invariant.", notes = "Article ID 125714", } @InProceedings{Frade:2010:EvoGAMES, author = "Miguel Frade and Francisco {Fernandez de Vega} and Carlos Cotta", title = "Evolution of Artificial Terrains for Video Games Based on Accessibility", booktitle = "EvoGAMES", year = "2010", editor = "Cecilia {Di Chio} and Stefano Cagnoni and Carlos Cotta and Marc Ebner and Aniko Ekart and Anna I. Esparcia-Alcazar and Chi-Keong Goh and Juan J. Merelo and Ferrante Neri and Mike Preuss and Julian Togelius and Georgios N. Yannakakis", volume = "6024", series = "LNCS", pages = "90--99", address = "Istanbul", month = "7-9 " # apr, organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming, genetic terrain programming, artificial terrains, video games", isbn13 = "978-3-642-12238-5", DOI = "doi:10.1007/978-3-642-12239-2_10", abstract = "Diverse methods have been developed to generate terrains under constraints to control terrain features, but most of them use strict restrictions. However, there are situations were more flexible restrictions are sufficient, such as ensuring that terrains have enough accessible area, which is an important trait for video games. The Genetic Terrain Program technique, based on genetic programming, was used to automatically evolve Terrain Programs (TPs - which are able to generate terrains procedurally) for the desired accessibility parameters. Results showed that the accessibility parameters have negligible influence on the evolutionary system and that the terminal set has a major role on the terrain look. TPs produced this way are already being used on Chapas video game.", notes = "EvoGAMES'2010 held in conjunction with EuroGP'2010 EvoCOP2010 EvoBIO2010", } @InProceedings{Frade:2010:cec, author = "Miguel Frade and F. {Fernandez de Vega} and Carlos Cotta", title = "Evolution of artificial terrains for video games based on obstacles edge length", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-6910-9", abstract = "Several methods have been developed to generate terrains under constraints to control terrain features, but most of them use strict restrictions. However, there are situations were more flexible restrictions are sufficient, such as ensuring that terrains have enough accessible area, which is an important trait for video games. Many terrains, generated with Genetic Terrain Program technique, based only on the desired accessibility parameters presented a single large non-accessible area. In an attempt to solve this problem a new fitness function, based on obstacles edge length, is presented on this paper. Results showed that the new metric suits our goal and also produces many terrains with novelty and aesthetic appeal. Terrains produced this way are already being used on Chapas video game.", DOI = "doi:10.1109/CEC.2010.5586032", notes = "WCCI 2010. Also known as \cite{5586032}", } @PhdThesis{TDUEX_2012_Frade, author = "Miguel {Monteiro de Sousa Frade}", title = "Evolving artificial terrains with automated genetic terrain programing", title2 = "Evolucionando terrenos artificiales con programacion genetica automatizada de terrenos", school = "Universidad de Extremadura. Departamento de Tecnologia de los Computadores y de las Comunicaciones", year = "2012", address = "Spain", month = jul, keywords = "genetic algorithms, genetic programming, GTP, Programacion genetica, Terrenos procedimentales, Estetica, Procedural terrains, Aesthetic, Videojuegos, Video games", URL = "http://hdl.handle.net/10662/426", URL = "http://dehesa.unex.es/bitstream/handle/10662/426/TDUEX_2012_Frade.pdf", size = "184 pages", abstract = "Nowadays video game industry is facing a big challenge: keep costs under control as games become bigger and more complex. Creation of game content, such as character models, maps, levels, textures, sound effects and so on, represent a big slice of total game production cost. Hence, video game industry is increasingly turning to procedural content generation to amplify the cost-effectiveness of video game designers efforts. However, creating and fine tunning procedural methods for automated content generation is a time consuming task. In this thesis we detail a Genetic Programming based procedural content technique to generate procedural terrains. Those terrains present aesthetic appeal and do not require any parametrisation to control its look. Thus, allowing to save time and help reducing production costs. To accomplish these features we devised the Genetic Terrain Programming (GTP) technique. The first implementation of GTP used an Interactive Evolutionary Computation (IEC) approach, were a user guides the evolutionary process. In spite of the good results achieved this way, this approach was limited by user fatigue (common in IEC systems). To address this issue a second version of GTP was developed where the search is automated, being guided by a direct fitness function. That function is composed by two morphological metrics: terrain accessibility and obstacle edge length. The combination of the two metrics allowed us remove the human factor form the evolutionary process and to find a wide range of aesthetic and fit terrains. Procedural terrains produced by GTP are already used in a real video game.", resumen = "La industria del videojuego afronta en la actualidad un gran reto: mantener el coste del desarrollo de los proyectos bajo control a medida que estos crecen y se hacen mas complejos. La creacion de los contenidos de los juegos, que incluye el modelado de personajes, mapas y niveles, texturas, efectos sonoros, etc, representa una parte fundamental del costo final de produccion. Por eso, la industria esta cada vez mas interesada en la utilizacion de metodos procedurales de generacion automatica de contenidos. Sin embargo, crear y afinar los metodos procedurales no es una tarea trivial. En esta memoria, se describe un metodo procedural basado en Programacion Genetica, que permite la generacion automatica de terrenos para videojuegos. Los terrenos presentan caracteristicas esteticas, y no requieren ningun tipo de parametrizacion para definir su aspecto. Asi, el ahorro de tiempo y la reduccion de costes en el proceso de produccion es notable. Para conseguir los objetivos, se utiliza Programacion Genetica de Terrenos. La primera implementacion de GTP utilizo Evolucion Interactiva, en que la presencia del usuario que guia el proceso evolutivo es imprescindible. A pesar de los buenos resultados, el metodo esta limitado por la fatiga del usuario (comun en los metodos interactivos). Para resolver esta cuestion se desarrolla un nuevo modelo de GTP en el que el proceso de busqueda es completamente automatico, y dirigido por una funcion de aptitudo. La funcion considera accesibilidad de los terrenos y perimetros de los obstaculos. Los resultados obtenidos se incluyeron como parte de un videojuego real.", notes = "In English. Supervisors: Francisco {Fernandez de Vega} and Carlos {Cotta Porras} https://core.ac.uk/download/pdf/61796844.pdf", } @Article{Frade:2012:SC, author = "Miguel Frade and Francisco {Fernandez de Vega} and Carlos Cotta", title = "Automatic evolution of programs for procedural generation of terrains for video games", journal = "Soft Computing", year = "2012", volume = "16", number = "11", pages = "1893--1914", month = nov, keywords = "genetic algorithms, genetic programming", ISSN = "1433-7479", URL = "https://doi.org/10.1007/s00500-012-0863-z", DOI = "doi:10.1007/s00500-012-0863-z", abstract = "Nowadays the video game industry is facing a big challenge to keep costs under control as games become bigger and more complex. Creation of game content, such as character models, maps, levels, textures, sound effects and so on, represent a big slice of total game production cost. Hence, the video game industry is increasingly turning to procedural content generation to amplify the cost-effectiveness of the efforts of video game designers. However, procedural methods for automated content generation are difficult to create and parametrize. In this work we study a genetic programming-based procedural content technique to generate procedural terrains that do not require parametrization, thus, allowing to save time and help reducing production costs. Generated procedural terrains present aesthetic appeal; however, unlike most techniques involving aesthetic, our approach does not require a human to perform the evaluation. Instead, the search is guided by the weighted sum of two morphological metrics: terrain accessibility and obstacle edge length. The combination of the two metrics allowed us to find a wide range of fit terrains that present more scattered obstacles in different locations than our previous approach with a single metric. Procedural terrains produced by this technique are already in use in a real video game.", } @InProceedings{Francisco:2008:geccocomp, author = "Tiago Francisco and Gustavo Miguel Jorge {dos Reis}", title = "Evolving combat algorithms to control space ships in a {2D} space simulation game with co-evolution using genetic programming and decision trees", year = "2008", editor = "Marc Ebner and Mike Cattolico and Jano {van Hemert} and Steven Gustafson and Laurence D. Merkle and Frank W. Moore and Clare Bates Congdon and Christopher D. Clack and Frank W. Moore and William Rand and Sevan G. Ficici and Rick Riolo and Jaume Bacardit and Ester Bernado-Mansilla and Martin V. Butz and Stephen L. Smith and Stefano Cagnoni and Mark Hauschild and Martin Pelikan and Kumara Sastry", isbn13 = "978-1-60558-131-6", booktitle = "GECCO-2008 Workshop: Defense Applications of Computational Intelligence (DAC)", pages = "1887--1892", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1887.pdf", DOI = "doi:10.1145/1388969.1388995", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming", notes = "Distributed on CD-ROM at GECCO-2008 ACM Order Number 910081. Also known as \cite{1388995}", } @InProceedings{Francisco2:2008:geccocomp, author = "Tiago Francisco and Gustavo Miguel Jorge {dos Reis}", title = "Evolving predator and prey behaviours with co-evolution using genetic programming and decision trees", year = "2008", editor = "Marc Ebner and Mike Cattolico and Jano {van Hemert} and Steven Gustafson and Laurence D. Merkle and Frank W. Moore and Clare Bates Congdon and Christopher D. Clack and Frank W. Moore and William Rand and Sevan G. Ficici and Rick Riolo and Jaume Bacardit and Ester Bernado-Mansilla and Martin V. Butz and Stephen L. Smith and Stefano Cagnoni and Mark Hauschild and Martin Pelikan and Kumara Sastry", isbn13 = "978-1-60558-131-6", booktitle = "GECCO-2008 Workshop: Defense Applications of Computational Intelligence (DAC)", pages = "1893--1900", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1893.pdf", DOI = "doi:10.1145/1388969.1388996", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming", notes = "Distributed on CD-ROM at GECCO-2008 ACM Order Number 910081. Also known as \cite{1388996}", } @InProceedings{Francon:2020:GECCO, author = "Olivier Francon and Santiago Gonzalez and Babak Hodjat and Elliot Meyerson and Risto Miikkulainen and Xin Qiu and Hormoz Shahrzad", title = "Effective Reinforcement Learning through Evolutionary Surrogate-Assisted Prescription", year = "2020", editor = "Carlos Artemio {Coello Coello} and Arturo Hernandez Aguirre and Josu Ceberio Uribe and Mario Garza Fabre and Gregorio {Toscano Pulido} and Katya Rodriguez-Vazquez and Elizabeth Wanner and Nadarajen Veerapen and Efren Mezura Montes and Richard Allmendinger and Hugo Terashima Marin and Markus Wagner and Thomas Bartz-Beielstein and Bogdan Filipic and Heike Trautmann and Ke Tang and John Koza and Erik Goodman and William B. Langdon and Miguel Nicolau and Christine Zarges and Vanessa Volz and Tea Tusar and Boris Naujoks and Peter A. N. Bosman and Darrell Whitley and Christine Solnon and Marde Helbig and Stephane Doncieux and Dennis G. Wilson and Francisco {Fernandez de Vega} and Luis Paquete and Francisco Chicano and Bing Xue and Jaume Bacardit and Sanaz Mostaghim and Jonathan Fieldsend and Oliver Schuetze and Dirk Arnold and Gabriela Ochoa and Carlos Segura and Carlos Cotta and Michael Emmerich and Mengjie Zhang and Robin Purshouse and Tapabrata Ray and Justyna Petke and Fuyuki Ishikawa and Johannes Lengler and Frank Neumann", isbn13 = "9781450371285", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377930.3389842", DOI = "doi:10.1145/3377930.3389842", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference", pages = "814--822", size = "9 pages", keywords = "genetic algorithms, neural networks, reinforcement learning, surrogate-assisted evolution, decision making", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", abstract = "There is now significant historical data available on decision making in organizations, consisting of the decision problem, what decisions were made, and how desirable the outcomes were. Using this data, it is possible to learn a surrogate model, and with that model, evolve a decision strategy that optimizes the outcomes. This paper introduces a general such approach, called Evolutionary Surrogate-Assisted Prescription, or ESP. The surrogate is, for example, a random forest or a neural network trained with gradient descent, and the strategy is a neural network that is evolved to maximize the predictions of the surrogate model. ESP is further extended in this paper to sequential decision-making tasks, which makes it possible to evaluate the framework in reinforcement learning (RL) benchmarks. Because the majority of evaluations are done on the surrogate, ESP is more sample efficient, has lower variance, and lower regret than standard RL approaches. Surprisingly, its solutions are also better because both the surrogate and the strategy network regularize the decision making behavior. ESP thus forms a promising foundation to decision optimization in real-world problems.", notes = "ESP is implemented as part of LEAF Cognizant Evolutionary AI platform Also known as \cite{10.1145/3377930.3389842} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @InProceedings{francone:1996:bench, author = "Frank D. Francone and Peter Nordin and Wolfgang Banzhaf", title = "Benchmarking the Generalization Capabilities of a Compiling Genetic programming System using Sparse Data Sets", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "72--80", address = "Stanford University, CA, USA", publisher = "MIT Press", URL = "http://www.cs.mun.ca/~banzhaf/papers/benchmarking.pdf", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap9.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", size = "9 pages", notes = "GP-96 Notes based upon version submitted to GP-96 Wed, 17 Apr 1996 09:20:19 PDT When I read your email (koza's), I went back and checked the output on two other problems that we ran as part of that paper. Gaussian 3D and Phoneme Classification. Each of these was a two output problem and the way the classification was set up, one would expect less than 50% correct classification from a randomly created individual. In those problems, we used 10 different random seeds, 3000 individuals per run. The following were the results for the best individual from generation 0's classification rate. Mean Best Worst gauss 0.59 0.64 0.55 iris 0.98 0.99 0.97 phoneme 0.73 0.75 0.71 Note that these figures represent the results of a random search of 30,000 individuals. As Peter Nordin points out in his email to which this is a reply, on the IRIS problem, even the worst figure is very good. In fact it was statistically indistinguishible from a highly optimized KNN beachmark run on twice as large a training set. This is because the IRIS problem is trivial. As pointed out in the above referenced paper, IRIS should probably not be used as a measure of the learning ability of any ML system, notwithstanding its status as a 'classic' problem. It is probably better characterized as a 'classic' way to make a ML system look good. On the other two problems, which were much more difficult, the genetic search improved on the random search considerably. The individuals with the best abilitiy to generalize on the test data set were respectively. Best Generalizer Gaussian 3D 72% Phoneme 85% I report these figures here because the generation 0 figures are not reported in the above paper directly. Regards Frank Francone ", } @InProceedings{banzhaf:1996:mutatation, author = "Wolfgang Banzhaf and Frank D. Francone and Peter Nordin", title = "The Effect of Extensive Use of the Mutation Operator on Generalization in Genetic Programming Using Sparse Data Sets", booktitle = "Parallel Problem Solving from Nature IV, Proceedings of the International Conference on Evolutionary Computation", year = "1996", editor = "Hans-Michael Voigt and Werner Ebeling and Ingo Rechenberg and Hans-Paul Schwefel", series = "LNCS", volume = "1141", pages = "300--309", address = "Berlin, Germany", publisher_address = "Heidelberg, Germany", month = "22-26 " # sep, publisher = "Springer Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-61723-X", DOI = "doi:10.1007/3-540-61723-X_994", size = "10 pages", abstract = "Ordinarily, Genetic Programming uses little or no mutation. Crossover is the predominant operator. This study tests the effect of a very aggressive use of the mutation operator on the generalisation performance of our Compiling Genetic Programming System (CPGS). We ran our tests on two benchmark classification problems on very sparse training sets. In all, we performed 240 complete runs of population 3000 for each of the problems, varying mutation rate between 5percent and 80percent. We found that increasing the mutation rate can significantly improve the generalization capabilities of GP. The mechanism by which mutation affects the generalization capability of GP is not entirely clear. What is clear is that changing the balance between mutation and crossover effects the course of GP training substantially - for example, increasing mutation greatly extends the number of generations for which the GP system can train before the population converges.", notes = "http://lautaro.fb10.tu-berlin.de/ppsniv.html PPSN4 machine code GP CGPS used on IRIS, Gaussian 3D and phoneme ELENA classification problems. Iris trivial. On others best performance from 50/50 mix of crossover and mutation. Answer extracted via designated hardware register. Stop runs when destructive crossover falls below 10percent (used as convergence indicator). Mutation giving rise to more complex introns. GP premature convergence", affiliation = "Dortmund University Department of Computer Science Joseph-vonFraunhofer-Str. 20 44227 Dortmund Germany", } @Unpublished{banzhaf:1997:emvsea, author = "Wolfgang Banzhaf and Frank D. Francone and Peter Nordin", title = "Some Emergent Properties of Variable Size EAs", editor = "Wolfgang Banzhaf and Inman Harvey and Hitoshi Iba and William Langdon and Una-May O'Reilly and Justinian Rosca and Byoung-Tak Zhang", note = "Position paper at the Workshop on Evolutionary Computation with Variable Size Representation at ICGA-97", month = "20 " # jul, year = "1997", address = "East Lansing, MI, USA", keywords = "genetic algorithms, genetic programming, bloat, variable size representation", notes = "http://web.archive.org/web/19971014081458/http://www.ai.mit.edu/people/unamay/icga-ws.html ", size = "4 pages", } @Unpublished{banzhaf:1997:wiGPge, author = "Wolfgang Banzhaf and Peter Nordin and Frank D. Francone", title = "Why introns in genetic programming grow exponentially", note = "Position paper at the Workshop on Exploring Non-coding Segments and Genetics-based Encodings at ICGA-97", month = "21 " # jul, year = "1997", address = "East Lansing, MI, USA", keywords = "genetic algorithms, genetic programming, introns", notes = "http://garage.cse.msu.edu/icga97/workshops/workshopsIndex.html#encodings Oct 2016 link to icga97.ws gone", size = "3 pages", } @InProceedings{francone:1999:HCGP, author = "Frank D. Francone and Markus Conrads and Wolfgang Banzhaf and Peter Nordin", title = "Homologous Crossover in Genetic Programming", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1021--1026", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, Sticky Crossover, AIMGP", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-463.pdf", size = "6 pages", abstract = "In recent years, the genetic programming crossover operator has been criticized on both theoretical and empirical grounds. This paper introduces a new crossover operator for linear genomes that encourages the emergence of positional homology in the population. Preliminary experimental results suggest that this approach is a promising direction for redesign of the mechanism of crossover.", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Misc{Francone:2000:lrtads, author = "Frank D. Francone and Peter Nordin and Wolfgang Banzhaf and Larry M. Deschaine", title = "Automatic Induction of Machine Code ({AIM}) Learning Real Time Adaptive Control Strategies", howpublished = "www document", year = "2000", month = "11 " # may, keywords = "genetic algorithms, genetic programming, discipulus automatic control, industrial control, model design, machine learning", URL = "http://www.pcai.com/web/articles/processcontrol.pdf", broken = "http://pw2.netcom.com/%7elmdmit84/AimProcessControl2000.pdf", size = "4 pages", abstract = "Advances in speed and computerized learning methods represented by AIM Learning Technologies make real time learning and control possible and effective.", notes = "high level. See also PC AI magazine, 'Fast Genetic Programming: Machine Code Evolution' Francone, F. 15.1 Jan/Feb 2001 page26. Introducing Genetic Programming and techniques for implementing computerized machine learning that accelerates program evolution dramatically", } @Manual{francone:manual, title = "Discipulus Owner's Manual", author = "Frank D. Francone", year = "2001", address = "11757 W. Ken Caryl Avenue F, PBM 512, Littleton, Colorado, 80127-3719, USA", edition = "Version 3.0 DRAFT", organisation = "Register Machine Learning Technologies, Inc.", keywords = "genetic algorithms, genetic programming", URL = "http://www.aimlearning.com/Discipulus%20Owners%20Manual.pdf", URL = "https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=d2cdee5dac9a6eb903a713ff3329574d2b5b3c9c", size = "210 pages", } @Article{francone:ebdo, author = "Frank D. Francone and Larry M. Deschaine", title = "Extending the boundaries of design optimization by integrating fast optimization techniques with machine-code-based, linear genetic programming", journal = "Information Sciences", volume = "161", number = "3-4", month = "20 " # apr, year = "2004", pages = "99--120", note = "FEA 2002", keywords = "genetic algorithms, genetic programming, Discipulus, Darcy's law", DOI = "doi:10.1016/j.ins.2003.05.006", abstract = "Optimised models of complex physical systems are difficult to create and time consuming to optimise. The physical and business processes are often not well understood and are therefore difficult to model. The models of often too complex to be well optimized with available computational resources. Too often approximate, less than optimal models result. This work presents an approach to this problem that blends three well-tested components. First: We apply Linear Genetic Programming (LGP) to those portions of the system that are not well understood -- for example, modelling data sets, such the control settings for industrial or chemical processes, geotechnical property prediction or UXO detection. LGP builds models inductively from known data about the physical system. The LGP approach we highlight is extremely fast and builds rapid to execute, high-precision models of a wide range of physical systems. Yet it requires few parameter adjustments and is very robust against overfitting. Second: We simulate those portions of the system -- for example, the cost model for the processes -- these are well understood with human built models. Finally: We optimise the resulting meta-model using Evolution Strategies (ES). ES is a fast, general-purpose optimiser that requires little pre-existing domain knowledge. We have developed this approach over a several years period and present results and examples that highlight where this approach can greatly improve the development and optimisation of complex physical systems.", notes = "Kodak, multiple GP runs, lead to new features in Discipulus, land mines, ES-CDSA", } @InProceedings{ASTC_2004_Getting_It_Right_from_the_Very_Start, author = "Frank D. Francone and Larry M. Deschaine", title = "Getting It Right at the Very Start -- Building Project Models where Data Is Expensive by Combining Human Expertise, Machine Learning and Information Theory", booktitle = "2004 Business and Industry Symposium", year = "2004", address = "Washington, DC", month = apr, organisation = "Society for Modeling and Simulation", keywords = "genetic algorithms, genetic programming, Environmental Science, geophysics, information theory, underground anomaly detection, machine learning, expert systems", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/deschaine/ASTC_2004_Getting_It_Right_from_the_Very_Start.pdf", URL = "http://www.scs.org/docInfo.cfm?get=1720", size = "7 pages", abstract = "Building models using machine learning techniques requires data. For some projects, gathering data is very expensive. In this type of project, there are two significant costs to using machine learning techniques in this type of project: (1) Machine learning models cannot even begin to make predictions until the project has already spent a lot of money gathering data; and (2) While the data is being gathered to train the machine learning system, unnecessary costs are incurred in making inefficient decisions. Engineers may address this type of problem efficiently when enough human expertise exists about the problem domain to be modelled. This work proposes an approach to combining human expertise, machine learning and information theory that makes efficient and effective decisions from the start of the project, while project data is being gathered.", notes = " ", } @InProceedings{francone:2004:lbp, author = "Frank D. Francone and Larry M. Deschaine and Tom Battenhouse and Jeffrey J. Warren", title = "Discrimination of Unexploded Ordnance from Clutter Using Linear Genetic Programming", booktitle = "Late Breaking Papers at the 2004 Genetic and Evolutionary Computation Conference", year = "2004", editor = "Maarten Keijzer", address = "Seattle, Washington, USA", month = "26 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://gpbib.cs.ucl.ac.uk/gecco2004/LBP022.pdf", URL = "http://www.aimlearning.com/UXO.GECCO.2004.pdf", abstract = "We used Linear Genetic Programming (LGP) to study the extent to which automated learning techniques may be used to improve Unexploded Ordinance (UXO) discrimination from Protem-47 and Geonics EM61 non-invasive electromagnetic sensors. We conclude that: (1) Even after geophysicists have analysed the EM61 signals and ranked anomalies in order of the likelihood that each comprises UXO, our LGP tool was able to substantially improve the discrimination of UXO from scrap-preexisting techniques require digging 62% more holes to locate all UXO on a range than do LGP derived models; (2) LGP can improve discrimination even though trained on a very small number of examples of UXO; and (3) LGP can improve UXO discrimination on data sets that contain a high-level of noise and little preprocessing.", notes = "Part of \cite{keijzer:2004:GECCO:lbp}. See also \cite{francone:2005:GPTP}.", } @InCollection{francone:2005:GPTP, author = "Frank D. Francone and Larry M. Deschaine and Tom Battenhouse and Jeffrey J. Warren", title = "Discrimination of Unexploded Ordnance from Clutter using Linear Genetic Programming", booktitle = "Genetic Programming Theory and Practice {III}", year = "2005", editor = "Tina Yu and Rick L. Riolo and Bill Worzel", volume = "9", series = "Genetic Programming", chapter = "4", pages = "49--64", address = "Ann Arbor", month = "12-14 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Unexploded Ordnance, UXO Discrimination.", ISBN = "0-387-28110-X", DOI = "doi:10.1007/0-387-28111-8_4", size = "16 pages", abstract = "We used Linear Genetic Programming (LGP) to study the extent to which automated learning techniques may be used to improve Unexploded Ordinance (UXO) discrimination from Protem-47 and Geonics EM61 non-invasive electromagnetic sensors. We conclude that: (1) Even after geophysicists have analysed the EM61 signals and ranked anomalies in order of the likelihood that each comprises UXO, our LGP tool was able to substantially improve the discrimination of UXO from scrap preexisting techniques require digging 62percent more holes to locate all UXO on a range than do LGP derived models; (2) LGP can improve discrimination even though trained on a very small number of examples of UXO; and (3) LGP can improve UXO discrimination on data sets that contain a high-level of noise and little preprocessing.", notes = "part of \cite{yu:2005:GPTP} Published Jan 2006 after the workshop", } @InProceedings{1277353, author = "Frank D. Francone and Larry M. Deschaine and Jeffrey J. Warren", title = "Discrimination of munitions and explosives of concern at {F.E. Warren AFB} using linear genetic programming", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "1999--2006", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1999.pdf", DOI = "doi:10.1145/1276958.1277353", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Real-World Applications, Discipulus, economics, EM61 MK2, geophysics, linear genetic programming, measurement, MEC, munitions and explosives of concern, unexploded ordnance, UXO, verification", abstract = "Removing underground, unexploded bombs, mortars, cannon shells and other ordnance (MEC or UXO) from former military ranges is difficult and expensive. The principal difficulty is discriminating intact, underground ordnance from other metallic items such as fragments of exploded ordnance (Clutter), magnetic rocks, and historic items such as horseshoes, barbed-wire, and refrigerators. This study represents the first, large-scale, blind-test of MEC discrimination technology on production-grade, survey-mode data from the cleanup of a real impact site. The results reported here significantly advance the state-of-the-art in MEC discrimination over alternative forward modelling/ inversion approaches to performing MEC discrimination. We combined Linear Genetic Programming (LGP) and statistical analysis to process data from the cleanup of 600 acres of the F.E.Warren Air Force Base. These data contained almost 30,000 targets of interest identified by geophysicists, including three-hundred thirty-two 75mm projectiles (75mm) and 37mm projectiles (37mm). A little under one-third of the ground truth was held back by the customer for blind-testing. Our task was to discriminate intact 37mm and 75mm from the clutter by ordering the targets from most-likely to be MEC to least-likely to be MEC in what is referred to as a prioritised dig list. We identified all 75mm by 28.2percent of the way through our prioritized dig-list and all 37mm by 64.2percent of the way through the prioritised dig list. Thus, depending on ordnance type, we reduced the number of targets that had to be excavated (false alarms) to clear the entire site by between 35percent and 72percent.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @PhdThesis{Frank_D._Francone_Licensiate_Thesis, author = "Frank D. Francone", title = "Dynamics and Performance of a Linear Genetic Programming System", school = "Department of Energy and Environment, Division of Physical Resource Theory, Chalmers University of Technology", year = "2009", type = "Licensiate", address = "SE-412 96 Goteborg, Sweden", keywords = "genetic algorithms, genetic programming, bloat, mutation, crossover, homologous crossover, machine-learning, UXO discrimination", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.476.2521", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.476.2521", broken = "http://www.tradingsystemlab.com/files/Frank D. Francone Licensiate Thesis.pdf", size = "77 pages", abstract = "Genetic Programming GP is a machine-learning algorithm. Typically, GP is a supervised learning algorithm, which trains on labelled training examples provided by the user. The solution output by GP maps known attributes to the known labels. GP is distinctive from other machine-learning algorithms in that its output is typically a computer program: hence Genetic Programming. The GP system documented here conducts learning with a series of very simple selection and transformation steps modelled loosely on biological evolution repeated over-and-over on a population of evolving computer programs. The selection step attempts to mimic natural selection. The transformation steps, crossover and mutation, loosely mimic biological eukaryotic reproduction. Although the individual steps are simple, the dynamics of a GP run are complex. This thesis traces key research elements in the design of a widely-used GP system. It also presents empirical comparisons of the GP system that resulted from these design elements to other leading machine-learning algorithms. Each of the issues addressed in this thesis touches on what was, at the time of publication, a key, and not well understood, issue regarding the dynamics and behaviour of GP runs. In particular, the design issues addressed here are threefold: (1) The emergence in GP runs of introns or code bloat. Introns in GP are segments of code that have no effect on the output of the program in which they appear. Introns are an emergent phenomenon in GP. This thesis reports results that support the hypothesis that introns emerge as a means of protecting evolving programs against the destructive effect of the traditional GP crossover transformation operator. (2) Mutation in biological reproduction is rare and usually destructive. However, we present results which establish that, in GP, using the mutation transformation operator with high probability, generates better and more robust evolved programs than using the mutation transformation operator at the low levels found in biological reproduction. (3) Finally, we return to the GP crossover operator and present results that suggest that a homologous crossover operator produces better and more robust results than the traditional GP crossover operator. The GP system that resulted from the above research has been publicly available since 1998. It has been extensively tested and compared to other machine-learning paradigms. This thesis presents results that suggest the resulting GP system produces consistently high-quality and robust solutions when compared to Vapnick statistical regression, decision trees, and neural networks over a wide range of problem domains and problem types.", } @TechReport{Francone:2010:Sibert_SLO, author = "Frank Francone and Dean A. Keiswetter", title = "{LGP} Discrimination and Residual Risk Analysis on Standardized Test Sites - {Camp Sibert} and {Camp San Luis Obispo}", institution = "ESTCP", year = "2010", number = "ESTCP Project MR-200811", address = "USA", month = jun, keywords = "genetic algorithms, genetic programming, Discipulus, UXO", URL = "http://serdp-estcp.org/Program-Areas/Munitions-Response/Land/Modeling-and-Signal-Processing/MR-200811", URL = "http://serdp-estcp.org/content/download/11922/145069/file/MR-200811-FR.pdf", abstract = "EXECUTIVE SUMMARY This report describes a two-year UXO discrimination project at two sites: former Camp Sibert, Alabama and Camp San Luis Obispo (SLO), California. The demonstrations described in this report were performed under project Environmental Security Technology Certification Program (ESTCP) MM-0811 Advanced MEC Discrimination Comparative Study on Standardised Test-Site Data Using Linear Genetic Programming (LGP) Discrimination. It was performed under the umbrella of the ESTCP Discrimination Study Pilot Program. The MM-0811 project demonstrates the application of the LGP Discrimination Process to the problem of UXO discrimination.", notes = "Cited by \cite{Deschaine:thesis}", size = "130 pages", } @MastersThesis{FrankKlahold:masters, author = "Steffen Frank and Stefan Klahold", title = "Ein System zur Untersuchung der Moglichkeiten und Beschrankungen fur Genetisches Programmieren in JAVA Bytecode", school = "Dortmund University", year = "1998", address = "Germany", month = May, keywords = "genetic algorithms, genetic programming", notes = "JAPHET? http://ls11-www.cs.uni-dortmund.de/bb/review98-99/node66.html Link broken Nov 2012 Joint project supervised by Robert Keller. See also \cite{klahold:1998:eprGPJb}", } @InProceedings{Frankland:2015:CEC, author = "Clive Frankland and Nelishia Pillay", booktitle = "IEEE Congress on Evolutionary Computation (CEC)", title = "Evolving game playing strategies for Othello", year = "2015", pages = "1498--1504", isbn13 = "978-1-4799-7491-7", abstract = "There has been a fair amount of research into the use of genetic programming for the induction of game playing strategies for board games such as chess, checkers, backgammon and Othello. A majority of this research has focused on developing evaluation functions for use with standard game playing algorithms such as the alpha-beta algorithm or Monte Carlo tree search. The research presented in this paper proposes a different approach based on heuristics. Genetic programming is used to evolve game playing strategies composed of heuristics. Each evolved strategy represents a player. While in previous work the game playing strategies are generally created offline, in this research learning and generation of the strategies takes place online, in real time. An initial population of players created using the ramped half-and-half method is iteratively refined using reproduction, mutation and crossover. Tournament selection is used to choose parents. The board game Othello, also known as Reversi, is used to illustrate and evaluate this novel approach. The evolved players were evaluated against human players, Othello WZebra, AI Factory Reversi and Math is fun Reversi. This study has revealed the potential of the proposed novel approach for evolving game playing strategies for board games. It has also identified areas for improvement and based on this future work will investigate mechanisms for incorporating mobility into the evolved players.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2015.7257065", ISSN = "1089-778X", month = may, notes = "Also known as \cite{7257065}", } @Article{FRANKLIN:2022:OM, author = "Gemma L. Franklin and Alec Torres-Freyermuth", title = "On the runup parameterisation for reef-lined coasts", journal = "Ocean Modelling", volume = "169", pages = "101929", year = "2022", ISSN = "1463-5003", DOI = "doi:10.1016/j.ocemod.2021.101929", URL = "https://www.sciencedirect.com/science/article/pii/S1463500321001797", keywords = "genetic algorithms, genetic programming, Runup, Reef, Parameterisation, Machine learning, SWASH model", abstract = "The degradation of coastal ecosystems in recent years, combined with more intense storms and greater sea levels associated with climate change, are likely to increase vulnerability to coastal flooding along reef-lined coasts. Therefore, there is a need to accurately predict extreme water levels to identify areas with high vulnerability and implement mitigation measures. Runup parameterisations allow a rapid assessment of coastal vulnerability at a regional to global scale, however these formulations are primarily developed for beaches. Hydrodynamic forcing and reef geometry are key parameters for the estimation of coastal flooding in reef environments. The present study aims to develop runup parameterisations for an idealised 2DV reef-lined coast profile using a widely validated nonlinear non-hydrostatic numerical model (SWASH). The numerical model is employed to simulate different combinations of wave conditions, water levels, and reef geometries. A machine learning (ML) approach, in the form of genetic programming, is used to identify the most suitable predictors for wave runup based on the numerical results. Analysis of runup results suggests that runup parameterisations can be improved for reef environments by incorporating the crest elevation, lagoon width, reef flat depth, and forereef slope. A dimensional and non-dimensional parameterisation that include reef geometry are presented. Further research efforts should be devoted to incorporate the effects of bed roughness and three-dimensional processes in this framework that were not taken into account in the present work", } @InProceedings{Frankola:2008:ITI, author = "Toni Frankola and Marin Golub and Domagoj Jakobovic", title = "Evolutionary algorithms for the resource constrained scheduling problem", booktitle = "30th International Conference on Information Technology Interfaces, ITI 2008", year = "2008", month = jun, pages = "715--722", keywords = "genetic algorithms, genetic programming, NP complete problems, evolutionary algorithms, optimal sequence finding, resource constrained project scheduling problem, constraint theory, project management, resource allocation, scheduling", DOI = "doi:10.1109/ITI.2008.4588499", ISSN = "1330-1012", abstract = "This paper investigates the use of evolutionary algorithms for solving resource constrained scheduling problem which belongs to the class of NP complete problems. The problem involves finding optimal sequence of activities with given resource constraints. Evolutionary algorithms used in this paper are genetic algorithms and genetic programming, for which adequate scheduling mechanisms are defined. Presented solutions are compared with existing heuristics or optimal results.", notes = "p715 'With genetic programming we describe a methodology to evolve scheduling heuristics in the form of priority rules that can be used to find a solution of an acceptable quality in a small amount of time.' Also known as \cite{4588499}", } @Article{franovic:2023:Energies, author = "Borna Franovic and Sandi {Baressi Segota} and Nikola Andelic and Zlatan Car", title = "Decentralized Smart Grid Stability Modeling with Machine Learning", journal = "Energies", year = "2023", volume = "16", number = "22", pages = "Article No. 7562", keywords = "genetic algorithms, genetic programming", ISSN = "1996-1073", URL = "https://www.mdpi.com/1996-1073/16/22/7562", DOI = "doi:10.3390/en16227562", abstract = "Predicting the stability of a Decentralized Smart Grid is key to the control of such systems. One of the key aspects that is necessary when observing the control of DSG systems is the need for rapid control. Due to this, the application of AI-based machine learning (ML) algorithms may be key to achieving a quick and precise stability prediction. In this paper, the authors use four algorithms--a multilayer perceptron (MLP), extreme gradient boosting (XGB), support vector machines (SVMs), and genetic programming (GP). A public dataset containing 30,000 points was used, with inputs consisting of ?--the time needed for a grid participant to adjust consumption/generation, p--generated power, and ?--the price elasticity coefficient for four grid elements; and outputs consisting of stab--the eigenvalue of stability and stabf, the categorical stability of the system. The system was modelled using the aforementioned methods as a regression model (targeting stab) and a classification model (targeting stabf). Modeling was performed with and without the ? values due to their low correlation. The best results were achieved with the XGB algorithm for classification, with and without the ? values as inputs--indicating them as being unnecessary.", notes = "also known as \cite{en16227562}", } @Article{FRANTZEN:2022:dajour, author = "Marcus Frantzen and Sunith Bandaru and Amos H. C. Ng", title = "Digital-twin-based decision support of dynamic maintenance task prioritization using simulation-based optimization and genetic programming", journal = "Decision Analytics Journal", volume = "3", pages = "100039", year = "2022", ISSN = "2772-6622", DOI = "doi:10.1016/j.dajour.2022.100039", URL = "https://www.sciencedirect.com/science/article/pii/S2772662222000108", keywords = "genetic algorithms, genetic programming, Decision support systems, Digital Twin, Short-term corrective maintenance priority, Simulation-based optimization, Bottleneck", abstract = "Modern decision support systems need to be connected online to equipment so that the large amount of data available can be used to guide the decisions of shop floor operators, making full use of the potential of industrial manufacturing systems. This paper investigates a novel optimization and data analytic method to implement such a decision support system, based on heuristic generation using genetic programming and simulation-based optimization running on a digital twin. Such a digital-twin-based decision support system allows the proactively searching of the best attribute combinations to be used in a data-driven composite dispatching rule for the short-term corrective maintenance task prioritization. Both the job (e.g., bottlenecks) and operator priorities use multiple criteria, including competence, operator walking distances on the shop floor, bottlenecks, work-in-process, and parallel resource availability. The data-driven composite dispatching rules are evaluated using a digital twin, built for a real-world machining line, which simulates the effects of decisions regarding disruptions. Experimental results show improved productivity because of using the composite dispatching rules generated by such heuristic generation method compared to the priority dispatching rules based on similar attributes and methods. The improvement is more pronounced when the number of operators is reduced. This paper thus offers new insights about how shop floor data can be transformed into useful knowledge with a digital-twin-based decision support system to enhance resource efficiency", } @Article{Fraser1989177, author = "A. F. Fraser", title = "Animal welfare theory: The keyboard of the maintenance ethosystem", journal = "Applied Animal Behaviour Science", volume = "22", number = "2", pages = "177--190", year = "1989", ISSN = "0168-1591", DOI = "doi:10.1016/0168-1591(89)90053-1", URL = "http://www.sciencedirect.com/science/article/B6T48-49NRPH9-GK/2/ff144de289e78408a13991fc32da018c", notes = "Not on GP", } @InProceedings{Fraser:1994:inkbiro, author = "A. P. Fraser and J. R. Rush", title = "Putting INK into a BIRo: A discussion of problem domain knowledge for evolutionary robotics", booktitle = "AISB Workshop on Evolutionary Computing", year = "1994", editor = "T. C. Fogarty", address = "Leeds, UK", month = "11-13 " # apr, organisation = "AISB", keywords = "genetic algorithms, genetic programming", notes = "Proceedings of the Workshop on Artificial Intelligence and Simulation of Behaviour Workshop on Evolutionary Computing. Workshop in Leeds, UK, April 11-13, 1994 This paper does NOT appear in the proceedings published by Springer_Verlag ", } @InProceedings{Fraser:2017:GECCO, author = "Olivia Lucca Fraser and Nur Zincir-Heywood and Malcolm Heywood and John T. Jacobs", title = "Return-oriented Programme Evolution with {ROPER}: A Proof of Concept", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4939-0", address = "Berlin, Germany", pages = "1447--1454", size = "8 pages", URL = "http://doi.acm.org/10.1145/3067695.3082508", DOI = "doi:10.1145/3067695.3082508", acmid = "3082508", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, ARM architecture, ROP attacks, exploit development", month = "15-19 " # jul, abstract = "Return-orientated programming (ROP) identifies code snippets ending in a return instruction (gadgets) and chains them together to construct exploits. Gadgets are already present in executable memory, thus avoiding the need to explicitly inject new code. As such ROP represents one of the most difficult exploit mechanisms to mitigate. ROP design is essentially driven by the skill of human hacker, limiting the ability of exploit mitigation to reacting to attacks. In this work we describe an evolutionary approach to ROP design, thus potentially pointing to the automatic detection of vulnerabilities before application code is released.", notes = "Also known as \cite{Fraser:2017:RPE:3067695.3082508} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @InProceedings{frayn:2005:JCIS, author = "Colin Frayn", title = "Genetic Programming in Finance", booktitle = "Proceedings of the 8th Joint Conference in Information Systems (JCIS 2005)", year = "2005", editor = "Heng-Da Cheng", address = "Salt Lake City, USA", month = "21-25 " # jul, keywords = "genetic algorithms, genetic programming", notes = "Broken Jan 2013 http://www.jcis.org/jcis_program/master_schedule.pdf", } @InProceedings{frazier:2024:GECCOcomp, author = "James Gunder Frazier and Thomas Helmuth", title = "Explaining Automatically Designed Software Defined Perimeters with a Two Phase Evolutionary Computation System", booktitle = "Evolutionary Computation and Decision Making", year = "2024", editor = "Tinkle Chugh and Richard Allmendinger", pages = "1527--1535", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, evolutionary computation, decision making, cybersecurity, networks", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3664155", size = "9 pages", abstract = "Software Defined Perimeter (SDP) is a zero-trust network-isolation defense technique which aims to limit security risks by giving dynamic account type assignments to network users. Despite SDP being proven as an effective defense strategy in various domains, it has yet to see wide-spread use due to its drawbacks. One of SDP's most pressing issues is the need for an expert to manually configure it for each unique application. Here we describe a novel system for designing SDP networks called SDPush which can automatically design and analyze possible configurations for a given network with user-specifications. Since there is not a systematic approach for account type design and assignment, we develop a two-step optimization system consisting of a bitstring genetic algorithm and a genetic programming sub-system for designing and evaluating SDP networks respectively. In order to evolve an SDP configuration exhibiting the user-specified characteristics while also minimizing security risk, we implement our system to support multi-objective search spaces by providing the system's training set with different cases aimed at evaluating different aspects of the network configuration. We present initial results of experiments on networks of varying size and characteristic requirements.", notes = "GECCO-2024 EC+DM A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{Fredericks:2013:GECCOcomp, author = "Erik M. Fredericks and Betty H. C. Cheng", title = "Exploring automated software composition with genetic programming", booktitle = "GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion", year = "2013", editor = "Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A. N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks", isbn13 = "978-1-4503-1964-5", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, SAGE", pages = "1733--1734", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2464576.2480790", publisher = "ACM", publisher_address = "New York, NY, USA", size = "2 pages", abstract = "Much research has been performed in investigating the numerous dimensions of software composition. Challenges include creating a composition-based design process, designing software for reuse, investigating various strategies for composition, and automating the composition process. Depending on the complexity of the relevant components, numerous composition strategies may exist, each of which may have several options and variations for aggregate steps in realising these strategies. This paper presents an evolutionary computation-based framework for automatically searching for and realising an optimal composition strategy for composing a given target module into an existing software system.", notes = "C OpenBEAGLE-Puppy http://code.google.com/p/beagle/wiki/Puppy SAGE: Encapsulate composed module. Provide reusable invocation interface. Directly insert code into source module. Transform data into specified format. fitness: pre and post conditions. integer bubble sort. Also known as \cite{2480790} Distributed at GECCO-2013.", } @InProceedings{Fredericks:2021:GI, author = "Erik Fredericks and Byron DeVries", title = "(Genetically) Improving Novelty in Procedural Story Generation", booktitle = "GI @ ICSE 2021", year = "2021", month = "30 " # may, editor = "Justyna Petke and Bobby R. Bruce and Yu Huang and Aymeric Blot and Westley Weimer and W. B. Langdon", publisher = "IEEE", address = "internet", pages = "39--40", keywords = "genetic algorithms, genetic programming, genetic improvement, grammatical evolution, novelty search, novelty metric, novelty archive, computer video game, procedural story generation, NLP, word2vec, Twine, Tracery, Simplex", isbn13 = "978-1-6654-4466-8/21", URL = "https://arxiv.org/pdf/2103.06935.pdf", video_url = "https://youtu.be/bsrHjd2_J2E", code_url = "https://efredericks.github.io/P5-Projects/", video_url = "https://www.youtube.com/watch?v=3dNaixTXeqA&list=PLI8fiFpB7BoKDaxvS7SQp0iA7fN7rrvDD&index=13", video_url = "https://www.youtube.com/watch?v=0HSm6BNyOzU&list=PLI8fiFpB7BoKDaxvS7SQp0iA7fN7rrvDD&index=14", video_url = "https://www.youtube.com/watch?v=RHGt7hiEiL0&list=PLXTjhGKkSnI-se7uQneCX-pEDiDrQ7TIS&index=6", DOI = "doi:10.1109/GI52543.2021.00016", size = "2 pages", abstract = "Procedural story generation (PCG) tailors a unique narrative experience for a player and can be accomplished via multiple techniques, from matching storylets to grammar-based generation. There exists a rich opportunity for evolutionary algorithms to be applied to this domain for intelligently constructing game narratives. We describe a conceptual procedure for applying genetic improvement to a grammar-driven procedural narrative within the context of a browser-based game.", notes = "https://www.nethack.org/ http://crawl.develz.org/ http://twinery.org/ https://www.inklestudios.com/ink/ P5.js Novelty search (fitness function). Video RHGt7hiEiL0 Erik Fredericks 2:22 Discussion chair: Yu Huang 2:30 Westley Weimer, Dwarf Fortress A: perhaps we allow every tile in a grassland to have its own story 4:15 Yu Huang, how to measure novelty. A: fun, scope for personalisation, variety. Diversity. 6:10 future. A: Scud, Astroneer, returno. GI game framework, eg for High school gamers. 7:14 Bobby R. Bruce, interactive evolution. Steam. Tracery, gamer. GI for game maps but can also do dialogue trees 8:50 game logic in GI part of \cite{Petke:2021:ICSEworkshop} http://geneticimprovementofsoftware.com/events/icse2021.html", } @InProceedings{Fredericks:2023:GI, author = "Erik M. Fredericks and Abigail C. Diller and Jared M. Moore", title = "Generative Art via Grammatical Evolution", booktitle = "12th International Workshop on Genetic Improvement @ICSE 2023", year = "2023", editor = "Vesna Nowack and Markus Wagner and Gabin An and Aymeric Blot and Justyna Petke", pages = "1--8", address = "Melbourne, Australia", month = "20 " # may, publisher = "IEEE", note = "Best paper", keywords = "genetic algorithms, genetic programming, Genetic Improvement, grammatical evolution, GenerativeGI, generative art, evolutionary algorithms, Lexicase Selection, GenerativeGI, Flow field grammar production, Novelty score", isbn13 = "979-8-3503-1232-4", URL = "http://gpbib.cs.ucl.ac.uk/gi2023/Fredericks_2023_GI.pdf", DOI = "doi:10.1109/GI59320.2023.00010", slides_url = "http://gpbib.cs.ucl.ac.uk/gi2023/Fredericks-GI2023.pdf", video_url = "http://gpbib.cs.ucl.ac.uk/gi2023/Fredericks-GI-2023-out.mp4", video_url = "https://www.youtube.com/watch?v=LH6i8M55ijs&list=PLI8fiFpB7BoJLh6cUpGBjyeB1hM9DET1V&index=2", code_url = "https://github.com/GI2023-GenerativeGI/GI2023/tree/ASE-GI-Extension", size = "8 pages", abstract = "Generative art produces artistic output via algorithmic design. Common examples include flow fields, particle motion, and mathematical formula visualization. Typically an art piece is generated with the artist/programmer acting as a domain expert to create the final output. A large amount of effort is often spent manipulating and/or refining parameters or algorithms and observing the resulting changes in produced images. Small changes to parameters of the various techniques can substantially alter the final product. We present GenerativeGI, a proof of concept evolutionary framework for creating generative art based on an input suite of artistic techniques and desired aesthetic preferences for outputs. GenerativeGI encodes artistic techniques in a grammar, thereby enabling multiple techniques to be combined and optimized via a many-objective evolutionary algorithm. Specific combinations of evolutionary objectives can help refine outputs reflecting the aesthetic preferences of the designer. Experimental results indicate that GenerativeGI can successfully produce more visually complex outputs than those found by random search.", notes = "Python's Pillow library python-pillow.org replaced by \cite{Fredericks:2024:ASE} GI @ ICSE 2023, part of \cite{Nowack:2023:GI}", } @Article{Fredericks:2024:ASE, author = "Erik M. Fredericks and Jared M. Moore and Abigail C. Diller", title = "{GenerativeGI}: creating generative art with genetic improvement", journal = "Automated Software Engineering", year = "2024", volume = "31", pages = "23", keywords = "genetic algorithms, genetic programming, Genetic improvement, Grammatical evolution, Generative art, Evolutionary algorithms, Lexicase selection", ISSN = "0928-8910", URL = "https://rdcu.be/emspE", DOI = "doi:10.1007/s10515-024-00414-3", size = "53 pages", abstract = "GenerativeGI, an evolutionary computation-based technique for creating generative art by automatically searching through combinations of artistic techniques and their accompanying parameters to produce outputs desirable by the designer. Generative art techniques and their respective parameters are encoded within a grammar that is then the target for genetic improvement. This grammar-based approach, combined with a many-objective evolutionary algorithm, enables the designer to efficiently search through a massive number of possible outputs that reflect their aesthetic preferences. We included a total of 15 generative art techniques and performed three separate empirical evaluations, each of which targets different aesthetic preferences and varying aspects of the search heuristic. Experimental results suggest that GenerativeGI can produce outputs that are significantly more novel than those generated by random or single objective search. Furthermore, GenerativeGI produces individuals with a larger number of relevant techniques used to generate their overall composition.", notes = "Extends \cite{Fredericks:2023:GI} School of Computing, Grand Valley State University, 1 Campus Dr., Allendale, MI 49401, USA", } @InProceedings{Fredericks:2024:GPTP, author = "Erik M. Fredericks and Denton Bobeldyk and Jared M. Moore", title = "Crafting Generative Art Through Genetic Improvement: Managing Creative Outputs in Diverse Fitness Landscapes", booktitle = "Genetic Programming Theory and Practice XXI", year = "2024", editor = "Stephan M. Winkler and Wolfgang Banzhaf and Ting Hu and Alexander Lalejini", series = "Genetic and Evolutionary Computation", pages = "321--335", address = "University of Michigan, USA", month = jun # " 6-8", publisher = "Springer", keywords = "genetic algorithms, genetic programming, genetic improvement", isbn13 = "978-981-96-0076-2", DOI = "doi:10.1007/978-981-96-0077-9_16", notes = "Published in 2025 after the workshop", } @Article{freeland:2002:GPEM, author = "Stephen J. Freeland", title = "The Darwinian Genetic Code: An Adaptation for Adapting?", journal = "Genetic Programming and Evolvable Machines", year = "2002", volume = "3", number = "2", pages = "113--127", month = jun, keywords = "error minimization, genetic code, evolution, adaptation", ISSN = "1389-2576", DOI = "doi:10.1023/A:1015527808424", abstract = "The genetic code is a ubiquitous interface between inert genetic information and living organisms, as such it plays a fundamental role in defining the process of evolution. There have been many attempts to identify features of the code that are themselves adaptations. So far, the strongest evidence for an adaptive code is that the assignments of amino acids (encoded objects) to codons (coding units) appear to be organized so as to minimize the change in amino acid hydrophobicity that results from random mutations. One possibility not previously discussed is that this feature of the code may in fact represent an adaptation to maximize the efficiency of adaptive evolution, particularly given the maximized connectedness of protein fitness landscapes afforded by the redundancy of the code.", notes = "Special issue on Gene Expression \cite{Kargupta:2002:GPEM} Article ID: 408585", } @InCollection{Freeland:2003, author = "Stephen Freeland", title = "Three Fundamentals of the Biological Genetic Algorithm", booktitle = "Genetic Programming Theory and Practice", publisher = "Kluwer", year = "2003", editor = "Rick L. Riolo and Bill Worzel", pages = "303--311", chapter = "19", keywords = "particulate genes, genetic code, phenotype, genotype, biology envy", URL = "http://www.springer.com/computer/ai/book/978-1-4020-7581-0", DOI = "doi:10.1007/978-1-4419-8983-3_19", ISBN = "1-4020-7581-2", abstract = "Evolutionary computing began by lifting ideas from biological evolutionary theory into computer science, and continues to look toward new biological research findings for inspiration. However, an over enthusiastic 'biology envy' can only be to the detriment of both disciplines by masking the broader potential for two-way intellectual traffic of shared insights and analogising from one another. Three fundamental features of biological evolution illustrate the potential range of intellectual flow between the two communities: particulate genes carry some subtle consequences for biological evolution that have not yet translated mainstream EC; the adaptive properties of the genetic code illustrate how both communities can contribute to a common understanding of appropriate evolutionary abstractions; finally, EC exploration of representational language seems pre-adapted to help biologists understand why life evolved a dichotomy of genotype and phenotype.", notes = "Part of \cite{RioloWorzel:2003}", } @InProceedings{Freeland:2019:GPTP, author = "Stephen Freeland", title = "Alphabets, topologies and optimization - I'll show you mine if you tell me about yours", booktitle = "Genetic Programming Theory and Practice XVII", year = "2019", editor = "Wolfgang Banzhaf and Erik Goodman and Leigh Sheneman and Leonardo Trujillo and Bill Worzel", address = "East Lansing, MI, USA", month = "16-19 " # may, keywords = "genetic algorithms, genetic programming", notes = "Workshop see \cite{Banzhaf:2019:GPTP}, Not included in published Springer post workshop proceedings", } @InProceedings{freeman:1998:lrGPcfg, author = "Jennifer J. Freeman", title = "A Linear Representation for GP using Context Free Grammars", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "72--77", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, CFG/GP, PORS", ISBN = "1-55860-548-7", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1998/freeman_1998_lrGPcfg.pdf", notes = "GP-98", } @InProceedings{Freire:2010:ICEC, author = "Ana Freire and Vanessa Aguiar-Pulido and Juan R. Rabunal and Marta Garrido", title = "Genetic Algorithm based on Differential Evolution with Variable Length Runoff Prediction on an Artificial Basin", booktitle = "Proceedings of the International Conference on Evolutionary Computation (ICEC 2010)", year = "2010", editor = "Joaquim Filipe and Janusz Kacprzyk", pages = "207--212", address = "Valencia, Spain", month = "24-26 " # oct, organisation = "INSTICC, AAAI, WfMC", publisher = "SciTePress", keywords = "genetic algorithms, genetic programming, Differential evolution, DE, Hydrology, Evolutionary computation", isbn13 = "978-989-8425-31-7", URL = "https://www.scitepress.org/PublishedPapers/2010/30814/", URL = "http://www.robinbye.com/files/publications/ICEC_2010.pdf", DOI = "doi:10.5220/0003081402070212", size = "6 pages", abstract = "Differential evolution is a successful approach to solve optimization problems. The way it performs the creation of the individual allows a spontaneous self-adaptability to the function. In this paper, a new method based on the differential evolution paradigm has been developed. An innovative feature has been added: the variable length of the genotype, so this approach can be applied to predict special time series. This approach has been tested over rainfall data for real-time prediction of changing water levels on an artificial basin. This way, a flood prediction system can be obtained.", notes = "Brief comparison with GP results. cites \cite{drecourt:1999uANNGPrrmTR} Department of Communications and Information Technologies, University of A Coruna Campus Elvina s/n, A Coruna, Spain", } @InProceedings{Freischlad:2005:ICCCE, author = "M. Freischlad and M. Schnellenbach-Held", title = "Multi-Objective Genetic Programming Based Design of Fuzzy Systems", booktitle = "Proceedings of the 2005 ASCE International Conference on Computing in Civil Engineering", year = "2005", editor = "Lucio Soibelman and Feniosky Pena-Mora", address = "Cancun, Mexico", month = jul # " 12-15", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1061/40794(179)62", abstract = "The Multi-Objective Domain Knowledge Augmented Genetic Fuzzy System (MODA-GFS) is a GP based fuzzy system for the data-driven generation of fuzzy rule based systems. The algorithm incorporates domain specific knowledge that is used by human knowledge engineers in the manual fuzzy system design process. The combination of characteristics of two individuals is most interesting if both individuals complement each other. In terms of fuzzy systems this means a potential crossover partner (parent B) has a lower approximation error in an area of the input space, where parent A has a higher error. Within MODA-GFS a method for the determination of feasible crossover mates is implemented. In addition MODA-GFS includes a method for the goal-oriented selection of parent rules that are handed down to the offspring. Especially in the domain of knowledge representation the quality of a fuzzy system is not only determined by its approximation capability but also by its transparency. In order to assure the automated generation of fuzzy systems that are both accurate and transparent multi-objective optimisation methods are implemented. Tests carried out on test functions as well as on real world data sets have shown that the incorporation of domain knowledge significantly speeds up the evolution process. Besides these test results the integration and application of the new methods for automated generation of fuzzy models within a learning expert system environment are described in this paper. Finally an outlook on the current and future work is given, i.e. the transfer of the presented findings to the evolutionary optimisation of large-scale structures.", notes = "c2005 ASCE", } @Article{Freitag:2016:AMT, author = "Michael Freitag and Torsten Hildebrandt", title = "Automatic design of scheduling rules for complex manufacturing systems by multi-objective simulation-based optimization", journal = "\{CIRP\} Annals - Manufacturing Technology", volume = "65", number = "1", pages = "433--436", year = "2016", ISSN = "0007-8506", DOI = "doi:10.1016/j.cirp.2016.04.066", URL = "http://www.sciencedirect.com/science/article/pii/S000785061630066X", abstract = "Complex manufacturing systems pose challenges for production planning and control. Amongst other objectives, orders have to be finished according to their due-dates. However, avoiding both earliness and tardiness requires a high level of process control. This article describes the use of simulation-based multi-objective optimization (multi-objective Genetic Programming) as a hyper-heuristic to automatically develop improved dispatching rules specifically for this control problem. Using a complex manufacturing scenario from semiconductor manufacturing as an example, it is shown that the resulting rules significantly outperform state-of-the-art dispatching rules from literature.", keywords = "genetic algorithms, genetic programming, Manufacturing systems, Scheduling, Hyper-heuristic", } @InProceedings{Freitas:1997:GPf2dm, author = "Alex A. Freitas", title = "A Genetic Programming Framework for Two Data Mining Tasks: Classification and Generalized Rule Induction", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming, SQL", pages = "96--101", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", URL = "http://citeseer.nj.nec.com/43454.html", URL = "http://kar.kent.ac.uk/21483/", URL = "http://kar.kent.ac.uk/21483/2/A_Genetic_Programming_Framework_for_Two_Data_Mining_Tasks_Classification_and_Generalized_Rule_Induction.pdf", size = "6 pages", abstract = "This paper proposes a genetic programming (GP) framework for two major data mining tasks, namely classification and generalised rule induction. The framework emphasises the integration between a GP algorithm and relational database systems. In particular, the fitness of individuals is computed by submitting SQL queries to a (parallel) database server. Some advantages of this integration from a data mining viewpoint are scalability, data-privacy control and automatic parallelization. The paper also proposes some genetic operators tailored for the two above data mining tasks.", notes = "GP-97 Lazy learning, separation of query tree encodes Tuple-Set Descriptor (SQL), from goal attribute. Goal subject to three types of mutation", } @InProceedings{freitas:1998:GAdkn, author = "Alex A. Freitas", title = "A Genetic Algorithm for Discovering Knowledge Nuggets", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "48", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "genetic algorithms, genetic programming", size = "1 page", notes = "GP-98LB", } @Article{freitas:2001:GPEM, author = "Alex A. Freitas", title = "Book Review: {Data} Mining Using Grammar-Based Genetic Programming and Applications", journal = "Genetic Programming and Evolvable Machines", year = "2001", volume = "2", number = "2", pages = "197--199", month = jun, keywords = "genetic algorithms, genetic programming, evolvable hardware", ISSN = "1389-2576", URL = "http://ipsapp009.lwwonline.com/content/getfile/4723/5/7/fulltext.pdf", URL = "https://rdcu.be/dR8p8", DOI = "doi:10.1023/A:1011564616547", notes = "review of \cite{ManLeungWong:book} Article ID: 335718", } @Book{freitas:2002:book, author = "Alex Freitas", title = "Data Mining and Knowledge Discovery with Evolutionary Algorithms", publisher = "Springer-Verlag", year = "2002", keywords = "genetic algorithms, genetic programming, data mining, classification, clustering, Artificial Intelligence, Computing Methodologies, Evolutionary Algorithms, Machine Learning", ISBN = "0-7923-8048-7", URL = "https://kar.kent.ac.uk/13669/", URL = "http://www.springer.com/computer/ai/book/978-3-540-43331-6", abstract = "This book integrates two areas of computer science, namely data mining and evolutionary algorithms. Both these areas have become increasingly popular in the last few years, and their integration is currently an area of active research. In general, data mining consists of extracting knowledge from data. In this book we particularly emphasise the importance of discovering comprehensible and interesting knowledge, which is potentially useful to the reader for intelligent decision making. In a nutshell, the motivation for applying evolutionary algorithms to data mining is that evolutionary algorithms are robust search methods which perform a global search in the space of candidate solutions (rules or another form of knowledge representation). In contrast, most rule induction methods perform a local, greedy search in the space of candidate rules. Intuitively, the global search of evolutionary algorithms can discover interesting rules and patterns that would be missed by the greedy search. This book presents a comprehensive review of basic concepts on both data mining and evolutionary algorithms and discusses significant advances in the integration of these two areas. It is self-contained, explaining both basic concepts and advanced topics.", size = "xiv + 264 pages", } @InCollection{Freitas:2002:SFSC, author = "Alex Freitas", title = "A review of evolutionary algorithms for e-commerce", booktitle = "E-Commerce and Intelligent Methods. Studies in Fuzziness and Soft Computing", publisher = "Springer-Verlag", year = "2002", editor = "J. Segovia and P. S. Szczepaniak and M. Niedzwiedzinski", volume = "105", series = "Studies in Fuzziness and Soft Computing", chapter = "10", pages = "159--179", keywords = "genetic algorithms, genetic programming, e-commerce", ISBN = "3-7908-1499-7", URL = "http://www.cs.kent.ac.uk/people/staff/aaf/pub_papers.dir/EA-e-com.ps", URL = "https://www.cs.kent.ac.uk/people/staff/aaf/pubs.html", size = "20 pages", } @InCollection{freitas:2002:HDMKD, author = "Alex Alves Freitas", title = "Evolutionary Computation", booktitle = "Handbook of Data Mining and Knowledge Discovery", publisher = "Oxford University Press", year = "2002", editor = "W. Klosgen and J. Zytkow", chapter = "32", pages = "698--706", keywords = "genetic algorithms, genetic programming, data mining, classification", URL = "https://www.cs.kent.ac.uk/people/staff/aaf/pubs.html", URL = "http://www.cs.kent.ac.uk/people/staff/aaf/pub_papers.dir/Hbk-dmkd.ps", URL = "http://citeseer.ist.psu.edu/460298.html", abstract = "This chapter addresses the integration of knowledge discovery in databases (KDD) and evolutionary algorithms (EAs), particularly genetic algorithms and genetic programming. First we provide a brief overview of EAs. Then the remaining text is divided into three parts. Section 2 discusses the use of EAs for KDD. The emphasis is on the use of EAs in attribute selection and in the optimization of parameters for other kinds of KDD algorithms (such as decision trees and nearest neighbour algorithms). Section 3 discusses three research problems in the design of an EA for KDD, namely: how to discover comprehensible rules with genetic programming, how to discover surprising (interesting) rules, and how to scale up EAs with parallel processing. Finally, section 4 discusses what the added value of KDD is for EAs. This section includes the remark that generalization performance on a separate test set (unseen during training, or EA run) is a basic principle for evaluating the quality of discovered knowledge, and then suggests that this principle should be followed in other EA applications.", size = "pages", } @InCollection{Freitas:2002:AiEC, author = "Alex Freitas", title = "A survey of evolutionary algorithms for data mining and knowledge discovery", chapter = "33", pages = "819--845", publisher = "Springer-Verlag", year = "2002", keywords = "genetic algorithms, genetic programming", booktitle = "Advances in Evolutionary Computation", editor = "A. Ghosh and S. Tsutsui", URL = "http://www.macs.hw.ac.uk/~dwcorne/Teaching/freitas01survey.pdf", URL = "https://www.cs.kent.ac.uk/people/staff/aaf/pubs.html", abstract = "This chapter discusses the use of evolutionary algorithms, particularly genetic algorithms and genetic programming, in data mining and knowledge discovery. We focus on the data mining task of classification. In addition, we discuss some preprocessing and postprocessing steps of the knowledge discovery process, focusing on attribute selection and pruning of an ensemble of classifiers. We show how the requirements of data mining and knowledge discovery influence the design of evolutionary algorithms. In particular, we discuss how individual representation, genetic operators and fitness functions have to be adapted for extracting high-level knowledge from data.", size = "27 pages", } @InCollection{Soft-Comp-KDDM-2007, author = "Alex A. Freitas", title = "A Review of evolutionary Algorithms for Data Mining", booktitle = "Soft Computing for Knowledge Discovery and Data Mining", publisher = "Springer", year = "2008", editor = "Oded Maimon and Lior Rokach", pages = "79--111", keywords = "genetic algorithms, genetic programming, genetic algorithm, genetic programming, classification, clustering, attribute selection, attribute construction, multi-objective optimization", isbn13 = "978-0-387-69935-6", URL = "https://www.cs.kent.ac.uk/people/staff/aaf/pub_papers.dir/Soft-Comp-KDDM-2007.pdf", DOI = "doi:10.1007/978-0-387-69935-6_4", size = "33 pages", abstract = "Evolutionary Algorithms (EAs) are stochastic search algorithms inspired by the process of neo-Darwinian evolution. The motivation for applying EAs to data mining is that they are robust, adaptive search techniques that perform a global search in the solution space. This chapter first presents a brief overview of EAs, focusing mainly on two kinds of EAs, viz. Genetic Algorithms (GAs) and Genetic Programming (GP). Then the chapter reviews the main concepts and principles used by EAs designed for solving several data mining tasks, namely: discovery of classification rules, clustering, attribute selection and attribute construction. Finally, it discusses Multi-Objective EAs, based on the concept of Pareto dominance, and their use in several data mining tasks.", } @InCollection{reference/dataware/FreitasP09, title = "Genetic Programming for Automatically Constructing Data Mining Algorithms", author = "Alex Alves Freitas and Gisele L. Pappa", booktitle = "Encyclopedia of Data Warehousing and Mining", publisher = "IGI Global", year = "2009", editor = "John Wang", chapter = "144", pages = "932--936", edition = "2", keywords = "genetic algorithms, genetic programming", isbn13 = "9781605660103", URL = "http://www.igi-global.com/bookstore/titledetails.aspx?titleid=346&detailstype=chapters", DOI = "doi:10.4018/978-1-60566-010-3", notes = "4 Volumes.", bibdate = "2011-01-18", bibsource = "DBLP, http://dblp.uni-trier.de/db/reference/dataware/dataware2009.html#FreitasP09", } @InProceedings{Freitas:2013:NICSO, author = "Alex A. Freitas", title = "Automating the Design of Data Mining Algorithms with Genetic Programming", booktitle = "VI International Workshop on Nature Inspired Cooperative Strategies for Optimization (NICSO 2013)", year = "2013", editor = "German Terrazas and Fernando Esteban Barril Otero and Antonio D. Masegosa", volume = "512", series = "Studies in Computational Intelligence", pages = "ix", address = "Canterbury, United Kingdom", month = sep # " 2-4", publisher = "Springer", note = "Plenary Talk", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-01691-7", URL = "http://link.springer.com/content/pdf/bfm%3A978-3-319-01692-4%2F1.pdf", DOI = "doi:10.1007/978-3-319-01692-4", size = "0.5 pages", abstract = "Rule induction and decision-tree induction algorithms are among the most popular types of classification algorithms in the field of data mining. Research on these two types of algorithms produced many new algorithms in the last 30 years. However, all the rule induction and decision-tree induction algorithms created over that period have in common the fact that they have been manually designed, typically by incrementally modifying a few basic rule induction or decision-tree induction algorithms. Having these basic algorithms and their components in mind, we describe the use of Genetic Programming (GP), a type of evolutionary algorithm that automatically creates computer programs, to automate the process of designing rule induction and decision-tree induction algorithms. The basic motivation is to automatically create complete rule induction and decision-tree induction algorithms in a data-driven way, trying to avoid the human biases and preconceptions incorporated in manually-designed algorithms. Two proposed GP methods (one for evolving rule induction algorithms, the other for evolving decision-tree induction algorithms) are evaluated on a number of datasets, and the results show that the machine-designed rule induction and decision-tree induction algorithms are competitive with well-known human-designed algorithms of the same type.", notes = "1 page abstract only \cite{Pappa:AEDMA} See also \cite{Vanneschi:2013:NICSO}. NICSO 2013 http://www.nicso2013.org/programme.html http://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-3-319-01691-7", } @InProceedings{french:2001:gecco, title = "Evolving a Nervous System of Spiking Neurons for a Behaving Robot", author = "R. L. B. French and R. I. Damper", pages = "1099--1106", year = "2001", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)", editor = "Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon and Edmund Burke", address = "San Francisco, California, USA", publisher_address = "San Francisco, CA 94104, USA", month = "7-11 " # jul, keywords = "genetic algorithms, genetic programming, evolutionary robotics, spiking, neurons, emergent behaviours", ISBN = "1-55860-774-9", URL = "http://gpbib.cs.ucl.ac.uk/gecco2001/french_2001_gecco.pdf", size = "8 pages", notes = "GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of \cite{spector:2001:GECCO}", } @InProceedings{frey:2001:gecco, title = "Evolving Strategies for Global Optimization - A Finite State Machine Approach", author = "Clemens Frey and Gunter Leugering", pages = "27--33", year = "2001", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)", editor = "Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon and Edmund Burke", address = "San Francisco, California, USA", publisher_address = "San Francisco, CA 94104, USA", month = "7-11 " # jul, keywords = "genetic algorithms, genetic programming, finite state machines, optimizing controllers, dynamic systems, adapted spatial optimization strategies", ISBN = "1-55860-774-9", URL = "http://gpbib.cs.ucl.ac.uk/gecco2001/d01.pdf", notes = "GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of \cite{spector:2001:GECCO}", } @Article{frey:2002a, author = "Clemens Frey", title = "Co-Evolution of Finite State Machines for Optimization: Promotion of Devices Which Search Globally", journal = "International Journal of Computational Intelligence and Applications", year = "2002", volume = "2", number = "1", pages = "1--16", month = mar, keywords = "genetic algorithms, genetic programming, Co-evolution, finite state machines, global search, robustness", ISSN = "1469-0268", broken = "http://www.mathematik.tu-darmstadt.de/~frey/", DOI = "doi:10.1142/S1469026802000397", size = "16 p.", abstract = "In this work a co-evolutionary approach is used in conjunction with Genetic Programming operators in order to find certain transition rules for two-step discrete dynamical systems. This issue is similar to the well-known artificial-ant problem. We seek the dynamic system to produce a trajectory leading from given initial values to a maximum of a given spatial functional. This problem is recast into the framework of input-output relations for controllers, and the optimisation is performed on program trees describing input filters and finite state machines incorporated by these controllers simultaneously. In the context of Genetic Programming there is always a set of test cases which has to be maintained for the evaluation of program trees. These test cases are subject to evolution here, too, so we employ a so-called host-parasitoid model in order to evolve optimising dynamical systems. Reinterpreting these systems as algorithms for finding the maximum of a functional under constraints, we have derived a paradigm for the automatic generation of adapted optimisation algorithms via optimal control. We provide numerical examples generated by the GP-system MathEvEco. These examples refer to key properties of the resulting strategies and they include statistical evidence showing that for this problem of system identification the co-evolutionary approach is superior to standard Genetic Programming.", } @PhdThesis{Frey:thesis, author = "Clemens Frey", title = "Virtual Ecosystems - Evolutionary and Genetic Programming from the perspective of modern means of ecosystem-modelling", school = "Darmstadt University of Technology", year = "2002", address = "Germany", keywords = "genetic algorithms, genetic programming", size = "pages", notes = "See Frey:2002", } @Book{frey:2002, author = "Clemens Frey", title = "Virtual Ecosystems - Evolutionary and Genetic Programming from the perspective of modern means of ecosystem-modelling", publisher = "Institute for Terrestrial Ecosystems, Bayreuth", year = "2002", volume = "93", series = "Bayreuth Forum Ecology", address = "Bayreuth, Germany", note = "(in German)", email = "frey@mathematik.tu-darmstadt.de", keywords = "genetic algorithms, genetic programming", ISSN = "0944-4122", URL = "http://www.bayceer.uni-bayreuth.de/bitoek/en/best/best/best.php?id_obj=9207", broken = "http://www.bitoek.uni-bayreuth.de/bitoek/en/pub/pub/pub_detail.php?id_obj=7556", size = "199 p.", abstract = "The realm of Evolutionary Computation covers many tools commonly used for solving complex discrete and continuous global optimization problems. These methods, which are known as Genetic Algorithms, Evolution Strategies, Evolutionary Programming and Genetic Programming, stem from efforts of modeling adaptive systems, from engineering and computer science. They are based on the idea of restating the Darwinian principles of natural evolution in algorithmic terms in order to get problem-solving methods for non-biological applications. Today Genetic Algorithms, Evolution Strategies and Evolutionary Programming mainly serve as mathematical techniques of numerical optimization. Genetic Programming likewise is an adaptation technique, but there is a different focus: based on evolutionary principles Genetic Programming enables us to automatically generate computer programs.The underlying hypotheses of this book is that the main point of natural, biological evolution is its data processing aspect. Evolution is seen as a certain way of processing individuals' and populations' genetic data. Referring to Evolutionary Computation there is a very interesting question now: Is it appropriate to employ Genetic Programming and similar algorithms in order to investigate natural evolution? Of course this means turning around the application profile of Evolutionary Computation, so where do we have to alter its algorithmic structure and the like? Finally, supposed there is a modified method, how do the results of both the classic algorithm and the modified variant compare to each other?In the first chapter we state the general notion of a search strategy. It may be a living being's policy of resource allocation, for example, but the notion covers optimization methods, too. A search strategy shall be defined in mathematical terms as being a dynamical system combined with a quality measure which is based on the trajectories the dynamical system produces. The author proposes a precise formulation for what a search strategy is generally claimed to accomplish, namely to generate dynamic behavior which gets us to the neighborhood of a predefined goal, possibly obeying certain constraints within the dynamics of the search process.Chapter two contains a gentle introduction into the field of Evolutionary Computation, namely Adaptive Systems, Genetic Algorithms, Evolution Strategies and Evolutionary Programming. We focus on Genetic Programming, however, and take a look at a paradigmatic experiment for automatically finding search strategies, i.e. the so-called artificial ant-experiment. In doing so the reader is also shown how the mathematical framework built in the first chapter may be used to formulate the artificial ant-problem.", abstract = "The following chapter addresses the issue of artificially creating evolution in virtual or simulated ecosystems and the question whether this can be done with the help of Evolutionary Computation. Since we want to analyse shortcomings of the conventional approaches and necessary adjustments, basic features of natural evolution are stated and discussed at first. Then we take a closer look at the area of Artificial Life and discuss specific software from this field. This discussion is concerned with so-called strong approaches like tierra and avida as well as weak approaches like the ecosystem-oriented Tragic++ system; besides, connections to social learning paradigms and Nouveau Artificial Intelligence are highlighted. Taking this broad view into account we conclude this chapter by listing a set of features which have to be comprised by a serious a model for evolution in virtual ecosystems. The gist of these desired features says that it is feasible to represent strategy programs as trees like in Genetic Programming, for this kind of representation causes a non-trivial, morphogenic mapping between the genotypic and the phenotypic space. It has to be conceded, however, that exogenously and a-priori given fitness-functions as well as the synchronous reproduction schemes which are almost always used in Genetic Programming are not appropriate for modeling evolution in virtual ecosystems. Chapters four to six describe how a system called MathEvEco was formulated and implemented according to these guidelines. Chapter four focuses on strongly typed tree representations of programs. Feasible sets of strongly typed program trees are defined precisely and their relationship with context-free grammars and the parameter-dependent evaluation of program trees are investigated in mathematical terms. These mathematical tools having been made available, genetic operators and initialization procedures of MathEvEco are stringently formulated in the fifth chapter. The system was supposed to be as flexible as possible. To this end the author has not only accessed a strongly typed version of the very classic crossover operator, but included a bunch of strongly typed mutation operators and the novel PTC2 algorithm for randomly generating program trees. In order to allow algorithmic comparisons the operators may be assembled in two fundamentally different ways; they may either be merged into a system of common Genetic Programming or they may be assembled as the desired system for modeling evolution in virtual ecosystems. Both of these possibilities are described, still in mathematical terms.The resulting systems are called MathEvEco-GP and MathEvEco-AL, respectively.", abstract = "While chapter five has been written in order to allow these systems to be communicated in a transparent and precise manner, chapter six shall illuminate their actual implementation within the scope of the mathematical software system Mathematica. To this end we show how program trees are handled in Mathematica, how model-specific and problem-specific knowledge is to be inserted by the user of MathEvEco, and in which way the various genetic operators have been implemented. Since MathEvEco can not only be run on a single machine but rather on clusters of workstations, there is a special treatment of aspects of parallel programming, too. Finally the functionality of MathEvEco is exemplified by means of a symbolic regression problem.The final chapter seven is dedicated to a case study. It consists of automatically generating search devices which is a special case of the general setting having been introduced in chapter one. There are a two different interpretations of this special problem. On the one hand side it may be understood in terms of numerical optimization; we presuppose an multi-modal objective function which may be imagined as a three-dimensional surface having many peaks. Strategies have to be evolved by MathEvEco-GP which are only provided with local information about this surface but are nevertheless required to lead the search devices to one of the highest peaks. On the other hand side the special problem may be understood in terms of an ecosystem where many organisms struggle for allocating a resource. It is quite important to realize that in this case there is a natural component of interaction since individual organisms consume resources from their immediate neighborhood and thus affect organisms there, too. For this kind of ecosystem simulation we have used the MathEvEco-AL evolution variant which provides implicit fitness assessment and asynchronous reproduction of 'living` organisms, i.e. devices.In both cases program trees represent strategies for potentially interacting devices. From a computer science point of view each of these devices is made up of a finite state machine and an input filter which maps continuous input from various channels into a finite set of symbols. The finite state machine's output iteratively controls the search device during a predefined maximum number of steps. The results of our various experiments with MathEvEco-GP show that if interaction and thus parallel search are introduced, it is much more likely that global optima, i.e. the highest peaks of the objective function will be located by the devices. In all experiments we were able to find robust strategies; this means that under certain conditions strategies evolved in conjunction with an objective function A will also perform well if acting on a different function B.", abstract = "We have also undertaken experiments involving the co-evolution of strategies and test cases; we show that co-evolution increases search capabilities of the strategies evolved with MathEvEco-GP.Compared to this system realizing classical yet strongly typed Genetic Programming, MathEvEco-AL is fundamentally different because of its modeling claims. The results of our experiments indicate, however, that in this system evolution of search strategies is realized, too. This is supported by many parameters of the evolving ecosystem, e.g. increases in average ages of individuals, increases in their average resource load and a steady increase of the overall population size over time. These observations point out that the virtual organisms evolve and gradually learn how to deal with exterior constraints defined mainly by the resource distribution objective function. Moreover, because we have used the same design for the devices evolved in MathEvEco-GP as well as in MathEvEco-AL, the resulting strategies compare very well. The extra advantage of the latter system is, of course, that it enables us to seriously investigate the interaction of ecosystems and their evolutionary formation without having to presuppose artifacts like explicit fitness functions. The mathematical tool for doing this are hierarchical dynamic systems. We conclude, after all, that it is possible to start from classical Genetic Programming and build a system for answering relevant questions about ecosystem-related evolution processes. Because of common building blocks of both the classical and the new system, the results can be compared quite easily. The Mathematica packages MathEvEco comprising these systems may be obtained from the author.For this book touches many different scientific issues there is an detailed section of annotations deepening biological and ecosystem modeling aspects as well as referring to the scientific history of Evolutionary Computation. An appendix covers software engineering with Mathematica. The extensive bibliography allows readers to take a closer look at the issues having been addressed. A subject index and a list of mathematical symbols conclude this work.", title_german = "Evolution{\"{a}}re und Genetische Programmierung im Lichte moderner {\"{O}}kosystemmodellierung", abstract = "Genetische Algorithmen und verwandte Evolutions-algorithmen spielen in der angewandten Mathematik und in der Informatik eine wichtige Rolle als Werkzeuge, mit deren Hilfe komplizierte Optimierungsprobleme naherungsweise gelost werden konnen. Die Methoden basieren auf der Idee, Prinzipien nat{\"{u}}rlicher Evolutionsablaufe algorithmisch zu formulieren und geeignet anzupassen, damit sie zur Problemlosung in nicht-biologischen Anwendungsfeldern eingesetzt werden konnen. Man verwendet zum Beispiel die sogenannte Genetische Programmierung zur automatischen, evolutionsbasierten Erzeugung von Computerprogrammen. Der Autor des vorliegenden Bandes geht von der Hypothese aus, dass nat{\"{u}}rliche Evolution einen Verar-beitungsprozess genetischer Information darstellt. Es wird untersucht, ob Evolutionsalgorithmen in Umkehrung ihres bisherigen Profils auch als Modell f{\"{u}}r biologische Evolution verwendet werden konnen. An welchen Stellen muss die Methodik zu diesem Zweck verandert werden? - Zur Beant-wortung dieser Fragen wird eine mathematische Formulierung des Darwinschen Evolutionsprozesses im Rahmen hierarchischer, diskreter dynamischer Systeme vorgeschlagen. Auf diesem Fundament werden bestehende Methoden (Genetische Programmierung, Artificial Life) analysiert und ein neues, individuenbasiertes Evolutionsmodell realisiert. Dieses Modell wurde als Mathematica-Paket unter dem Namen MathEvEco implementiert; es wird in diesem Band ausf{\"{u}}hrlich dargestellt, ebenso wie die vielen durchgef{\"{u}}hrten Versuche zur automatischen Erzeugung von Suchprogrammen, sowie ihre Parameter und Ergebnisse. Der Leser gewinnt also nicht nur einen Einblick in den aktuellen Stand von Evolutionsalgorithmen und Ansatzen zur Simulation von Evolution in virtuellen {\"{O}}kosystemen, sondern wird schlie\sslich auch in der Lage sein, eigene Evolu-tionsexperimente durchzuf{\"{u}}hren.", notes = "Bayreuther Forum Okologie 93, 1-199 (2002) BayCEER", } @InProceedings{Freyeretal1998, author = "Stephan Freyer and J{\"o}rg Graefe and Matthias Heinzel and Peter Marenbach", address = "Aachen, Germany", booktitle = "Eufit '98, 6th European Congress on Intelligent Techniques and Soft Computing, ELITE - European Laboratory for Intelligent TechniquesEngineering", editor = "Hans-J{\"u}rgen Zimmermann", pages = "1471--1475", title = "Evolutionary Generation and Refinement of Mathematical Process Models", volume = "III", year = "1998", keywords = "genetic algorithms, genetic programming, SMOG, bioprocess, modelling", URL = "http://www.rtr.tu-darmstadt.de/fileadmin/literature/rst_98_08.pdf", URL = "http://www.rt.e-technik.tu-darmstadt.de/LIT", size = "5 page", email = "pmarenbach@gmx.net", abstract = "Modelling of biotechnological processes is generally difficult and time consuming. In order to generate mathematical models of a studied reaction system in a short time period a new modelling technique for the optimisation of structures, based on the principles of evolution, was developed. This method generates transparent and comprehensible dynamic models in a data driven manner. In addition it is able to automatically refine sub-models or to verify model ideas. The transparent mathematical form of the generated models is a major advantage giving the user the opportunity to interpret the model and to influence the modelling process interactively. The article at hand presents two examples of biotechnological processes for which this new method was successfully applied.", notes = "http://www.eufit.org/proceedings/98/volume3.htm BASF AG laboratories, high noise. Monod, SubLimTeissier, SubLimJost, SubInhAnstrews, SubInhWebb MATLAB/SIMULINK. Stresses importance of user understandable models, using prior knowledge, parsimony versus accuracy (trade off in fitness function). Batch fed fermentation.", } @Article{Frias-Martinez:2007, author = "Enrique Frias-Martinez and Fernand Gobet", title = "Automatic Generation of Cognitive Theories using Genetic Programming", journal = "Minds and Machines", volume = "17", number = "3", year = "2007", pages = "287--309", month = oct, address = "Hingham, MA, USA", publisher = "Kluwer Academic Publishers", keywords = "genetic algorithms, genetic programming, Cognitive neuroscience, Computational neuroscience, Automatic generation of cognitive theories, Delayed-match-to-sample", ISSN = "0924-6495", DOI = "doi:10.1007/s11023-007-9070-6", size = "23 pages", abstract = "Cognitive neuroscience is the branch of neuroscience that studies the neural mechanisms underpinning cognition and develops theories explaining them. Within cognitive neuroscience, computational neuroscience focuses on modeling behavior, using theories expressed as computer programs. Up to now, computational theories have been formulated by neuroscientists. In this paper, we present a new approach to theory development in neuroscience: the automatic generation and testing of cognitive theories using genetic programming (GP). Our approach evolves from experimental data cognitive theories that explain the mental program that subjects use to solve a specific task. As an example, we have focused on a typical neuroscience experiment, the delayed-match-to-sample (DMTS) task. The main goal of our approach is to develop a tool that neuroscientists can use to develop better cognitive theories.", notes = "Artificial intelligence, cognitive memory. Fit both mean and standard deviation of experimental (people) results. Lisp. Data from Chao et al. 1999, pictures of animals and pictures of tools. WSTM. Table 3 putSTM ... {"}Write the input parameter in STM{"} Also known as \cite{1298700}", } @Article{Friedberg:1958:LMI, author = "R. M. Friedberg", title = "A learning machine: {I}", journal = "IBM Journal of Research and Development", volume = "2", number = "1", pages = "2--13", month = jan, year = "1958", CODEN = "IBMJAE", ISSN = "0018-8646", MRclass = "68.0X", MRnumber = "19,1085c", bibdate = "Mon Feb 12 08:25:35 2001", acknowledgement = "Nelson H. F. Beebe, University of Utah, Department of Mathematics, 110 LCB, 155 S 1400 E RM 233, Salt Lake City, UT 84112-0090, USA, Tel: +1 801 581 5254, FAX: +1 801 581 4148, e-mail: \path|beebe@math.utah.edu|, \path|beebe@acm.org|, \path|beebe@computer.org| (Internet), URL: \path|http://www.math.utah.edu/~beebe/|", reviewer = "M. L. Minsky", mrnumber-url = "http://www.ams.org/mathscinet-getitem?mr=19%2c1085c", keywords = "Machine Learning, intron, schema", URL = "http://www.research.ibm.com/journal/rd/021/ibmrd0201B.pdf", size = "12 pages", abstract = "Machines would be more useful if they could learn to perform tasks for which they were not given precise methods. Difficulties that attend giving a machine this ability are discussed. It is proposed that the program of a stored-program computer be gradually improved by a learning procedure which tries many programs and chooses, from the intructions that may occupy a given location, the one most often associated with a successful result. An experimental test of this principle is described in detail.", notes = "Not a GP, for example there is no population and no genetic operations, nonetheless included for general GP interest. Includes discussion of introns and schema (but not under those names). IBM 704. 64 bits of memory. 64 (fixed) instructions. Conditional branch (ie IF), AND, MOVE and NOT. Loops halted after 64 time steps and given zero fitness. Hitch hikers. Herman and Sherman shows importance of details of representation (not fully understood). Cultural evolution, cf \cite{spector:1996:ctiGP}. One bit identity (move a bit). Two bit sum (failed), high bit of sum, low bit of sum. One bit complement (not).", } @Article{Friedel20111583, author = "Michael J. Friedel", title = "A data-driven approach for modeling post-fire debris-flow volumes and their uncertainty", journal = "Environmental Modelling \& Software", volume = "26", number = "12", pages = "1583--1598", year = "2011", ISSN = "1364-8152", DOI = "doi:10.1016/j.envsoft.2011.07.014", URL = "http://www.sciencedirect.com/science/article/pii/S1364815211001757", keywords = "genetic algorithms, genetic programming, Wildfire, Debris-flow volume, Self-organising map, Multivariate, Prediction, Nonlinear models, Nonlinear uncertainty", abstract = "This study demonstrates the novel application of genetic programming to evolve nonlinear post-fire debris-flow volume equations from variables associated with a data-driven conceptual model of the western United States. The search space is constrained using a multi-component objective function that simultaneously minimises root-mean squared and unit errors for the evolution of fittest equations. An optimisation technique is then used to estimate the limits of nonlinear prediction uncertainty associated with the debris-flow equations. In contrast to a published multiple linear regression three-variable equation, linking basin area with slopes greater or equal to 30 percent, burn severity characterised as area burned moderate plus high, and total storm rainfall, the data-driven approach discovers many nonlinear and several dimensionally consistent equations that are unbiased and have less prediction uncertainty. Of the nonlinear equations, the best performance (lowest prediction uncertainty) is achieved when using three variables: average basin slope, total burned area, and total storm rainfall. Further reduction in uncertainty is possible for the nonlinear equations when dimensional consistency is not a priority and by subsequently applying a gradient solver to the fittest solutions. The data-driven modelling approach can be applied to nonlinear multivariate problems in all fields of study.", } @InProceedings{Friedlander:2011:MaFRUGPfCVTW, title = "Meta-Learning and Feature Ranking Using Genetic Programming for Classification: Variable Terminal Weighting", author = "Anna Friedlander and Kourosh Neshatian and Mengjie Zhang", pages = "940--947", booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary Computation", year = "2011", editor = "Alice E. Smith", month = "5-8 " # jun, address = "New Orleans, USA", organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, GP, feature ranking algorithms, feature selection, feature weighting vector, learning classification, meta learning, online feature weighting method, probability, variable terminal weighting, feature extraction, learning (artificial intelligence), probability", DOI = "doi:10.1109/CEC.2011.5949719", abstract = "We propose an online feature weighting method for classification by genetic programming (GP). GP's implicit feature selection was used to construct a feature weighting vector, based on the fitness of solutions in which the features were found and the frequency at which they were found. The vector was used to perform feature ranking and to perform meta-learning by biasing terminal selection in mutation. The proposed meta-learning mechanism significantly improved the quality of solutions in terms of classification accuracy on an unseen test set. The probability of success---the probability of finding the desired solution within a given number of generations (fitness evaluations)---was also higher than canonical GP. The ranking obtained by using the GP-provided feature weighting was very highly correlated with the ranking obtained by commonly-used feature ranking algorithms. Population information during evolution can help shape search behaviour (meta-learning) and obtain useful information about the problem domain such as the importance of input features with respect to each other.", notes = "CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.", } @InCollection{friedman:2000:EPPR, author = "Patri Friedman", title = "Evolving a Program to Play Rock-Paper-Scissors", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "143--152", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:2000:gagp}", } @InProceedings{friedrich:1996:emfgbb, author = "Christoph M. Friedrich and Claudio Moraga", title = "An Evolutionary Method to Find Good Building-Blocks for Architectures of Artificial Neural Networks", booktitle = "Proceedings of the Sixth International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU '96)", year = "1996", pages = "951--956", address = "Granada, Spain", keywords = "genetic algorithms, genetic programming", broken = "ftp://archive.cis.ohio-state.edu/pub/neuroprose/friedrich.ipmu96.ps.Z", URL = "http://citeseer.ist.psu.edu/friedrich96evolutionary.html", abstract = "This paper deals with the combination of Evolutionary Algorithms and Artificial Neural Networks (ANN). A new method is presented, to find good building-blocks for architectures of Artificial Neural Networks. The method is based on {\em Cellular Encoding}, a representation scheme by F. Gruau, and on Genetic Programming by J. Koza. First it will be shown that a modified Cellular Encoding technique is able to find good architectures even for non-boolean networks. With the help of a graph-database and a new graph-rewriting method, it is secondly possible to build architectures from modular structures. The information about building-blocks for architectures is obtained by statistically analyzing the data in the graph-database. Simulation results for two real-world problems are given.", } @InProceedings{Friedrich:2019:GECCO, author = "Markus Friedrich and Pierre-Alain Fayolle and Thomas Gabor and Claudia Linnhoff-Popien", title = "Optimizing evolutionary {CSG} tree extraction", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference", year = "2019", editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and Anne Auger and Petr Posik and Leslie {Peprez Caceres} and Andrew M. Sutton and Nadarajen Veerapen and Christine Solnon and Andries Engelbrecht and Stephane Doncieux and Sebastian Risi and Penousal Machado and Vanessa Volz and Christian Blum and Francisco Chicano and Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and Jonathan Fieldsend and Jose Antonio Lozano and Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and Robin Purshouse and Thomas Baeck and Justyna Petke and Giuliano Antoniol and Johannes Lengler and Per Kristian Lehre", isbn13 = "978-1-4503-6111-8", pages = "1183--1191", address = "Prague, Czech Republic", DOI = "doi:10.1145/3321707.3321771", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, Hierarchical representations, Shape modelling,3D Geometry Processing, CAD, CSG, 3D-Reconstruction, Evolutionary Algorithms", size = "9 pages", abstract = "The extraction of 3D models represented by Constructive Solid Geometry (CSG) trees from point clouds is a common problem in reverse engineering pipelines as used by Computer Aided Design (CAD) tools. We propose three independent enhancements on state-of-the-art Genetic Algorithms (GAs) for CSG tree extraction: (1) A deterministic point cloud filtering mechanism that significantly reduces the computational effort of objective function evaluations without loss of geometric precision, (2) a graph-based partitioning scheme that divides the problem domain in smaller parts that can be solved separately and thus in parallel and (3) a 2-level improvement procedure that combines a recursive CSG tree redundancy removal technique with a local search heuristic, which significantly improves GA running times. We show in an extensive evaluation that our optimized GA-based approach provides faster running times and scales better with problem size compared to state-of-the-art GA-based approaches.", notes = "Also known as \cite{3321771} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InProceedings{Friese:2012:dortmund, author = "Martina Friese and Oliver Flasch and Katya Vladislavleva and Thomas Bartz-Beielstein and Olaf Mersmann and Boris Naujoks and Joerg Stork and Martin Zaefferer", title = "Ensemble-Based Model Selection for Smart Metering Data", booktitle = "Proceedings of the 22nd Workshop on Computational Intelligence", year = "2012", editor = "Eyke Huellermeier", number = "22", series = "Schriftenreihe des Instituts fuer Angewandte Informatik - Automatisierungstechnik, Karlsruher Institut fur Technologie", pages = "215--227", address = "Dortmund, Germany", publisher_address = "Germany", month = "6-7 " # dec, publisher = "KIT Scientific Publishing", keywords = "genetic algorithms, genetic programming, Data Modeler", isbn13 = "978-3-86644-917-6", ISSN = "1614-5267", URL = "http://www.buchoffizin.de/produkt/9783866449176.html", URL = "https://publikationen.bibliothek.kit.edu/1000029917/2355761", URL = "https://publikationen.bibliothek.kit.edu/1000029917", DOI = "doi:10.5445/KSP/1000029917", size = "13 pages", abstract = "Introduction. In times of accelerating climate change and rising energy costs, increasingenergy efficiency becomes a high-priority goal for business and private households alike. Smart metering equipment records energy consumptiondata in regular intervals multiple times per hour, streaming this data to a central system, usually located at a local public utility company. Here, consumption data can be correlated and analysed to detect anomalies such as unusual high consumption...", notes = "Sonstiger Urheber F. Hoffmann URN: urn:nbn:de:0072-299175 KITopen ID: 1000029917 fh-koeln.de cited by \cite{journals/grid/VeeramachaneniA15}", } @Article{Frigo:1999:PLDI, author = "Matteo Frigo", title = "A Fast Fourier Transform Compiler", journal = "ACM SIGPLAN Notices", year = "1999", volume = "34", number = "5", pages = "169--180", month = may, keywords = "genfft, C, FFTW library, codelets, plan, Object Caml, OCaml", publisher = "Association for Computing Machinery", ISSN = "0362-1340", URL = "https://www.fftw.org/pldi99.pdf", URL = "https://doi.org/10.1145/301631.301661", DOI = "doi:10.1145/301631.301661", size = "12 pages", abstract = "The FFTW library for computing the discrete Fourier transform (DFT) has gained a wide acceptance in both academia and industry, because it provides excellent performance on a variety of machines (even competitive with or faster than equivalent libraries supplied by vendors). In FFTW, most of the performance-critical code was generated automatically by a special-purpose compiler, called genfft, that outputs C code. Written in Objective Caml, genfft can produce DFT programs for any input length, and it can specialize the DFT program for the common case where the input data are real instead of complex. Unexpectedly, genfft discovered algorithms that were previously unknown, and it was able to reduce the arithmetic complexity of some other existing algorithms. This paper describes the internals of this special-purpose compiler in some detail, and it argues that a specialized compiler is a valuable tool.", notes = "167 MHz UltraSPARC, tree shaped plan of codelet. genfft Discovered previously unknown FFT algorithms p170 'FFTW chooses the fastest plan automatically.' 'FFTW codelets [C code] are produced automatically' p171 'Achieving correctness has been surprisingly easy.' pldi99.pdf higher quality but without page numbers", } @InProceedings{froehlich:2019:LaIO, author = "Georg E. A. Froehlich and Guenter Kiechle and Karl F. Doerner", title = "Creating a {Multi-iterative-Priority-Rule} for the Job Shop Scheduling Problem with Focus on Tardy Jobs via Genetic Programming", booktitle = "Learning and Intelligent Optimization", year = "2019", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-030-05348-2_6", DOI = "doi:10.1007/978-3-030-05348-2_6", } @PhdThesis{Froehlich:thesis, author = "Saman Froehlich", title = "A Fully Fledged {HDL} Design Flow for In-Memory Computing with Approximation Support", school = "Universitaet Bremen", year = "2022", address = "Germany", month = "22 " # jan, keywords = "genetic algorithms, genetic programming, EHW, approximate computing, ReRAM, RRAM, In-Memory Computing, Symbolic Computer Algebra, PLiM, m-AIGs", URL = "https://doi.org/10.26092/elib/1397", URL = "https://media.suub.uni-bremen.de/handle/elib/5742", URL = "https://media.suub.uni-bremen.de/bitstream/elib/5742/1/Saman_Froehlich_-_A_Fully_Fledged_HDL_Design_Flow_for_In-Memory_Computing_with_Approximation_Support.pdf", DOI = "doi:10.26092/elib/1397", size = "127 pages", abstract = "Most computers today are based on the von Neumann architecture introduced by John von Neumann in 1945 which suffers from the von Neumann bottleneck. However, recently, new applications have emerged, such as deep learning and IoT. These applications pose their own challenges and requirements. In particular, due to the von Neumann bottleneck, the classical von Neumann architecture becomes very inefficient for such use-cases. Recently, ReRAM, a resistance based storage device, is emerging. ReRAM is especially appealing due to its inherent in-memory computation capabilities. In addition, ReRAMs low power consumption, scalability and fast switching capabilities make it an excellent candidate for a technological foundation for edge devices and IoT. In order to overcome the von Neumann bottleneck, an architecture for the PLiM~computer has been proposed. In addition to the control logic, the core of the PLiM computer architecture are the ReRAM arrays, which are used as storage and computational unit. In this thesis, we propose a design flow for in-memory computing and the PLiM computer architecture with support for Approximate Computing. First, we present approximation techniques, which are applicable for arbitrary circuits. Then, we introduce LiM-HDL - a HDL for the high-level specification of PLiM programs. As LiM-HDL is compatible with Verilog, it is easy to integrate into existing architectures and has a low hurdle to entry. Then, after transforming LiM-HDL to a graph, we propose graph-based synthesis algorithms. Finally, we propose additional optimization techniques, which are based on our previously presented approximate computing techniques and a novel graph structure which we call m-AIGs.", notes = "also known as \cite{elib_5742} Supervisor: Rolf Drechsler", } @Article{DBLP:journals/it/FrohlichD22, author = "Saman Froehlich and Rolf Drechsler", title = "Unlocking approximation for in-memory computing with Cartesian genetic programming and computer algebra for arithmetic circuits", journal = "it - Information Technology", volume = "64", number = "3", pages = "99--107", year = "2022", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, CGP, Approximate Computing, In-Memory Computing, ReRAM, RRAM, Symbolic Computer Algebra, SCA, PLiM, EA", timestamp = "Thu, 10 Nov 2022 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/it/FrohlichD22.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://doi.org/10.1515/itit-2021-0042", DOI = "doi:10.1515/itit-2021-0042", size = "9 pages", abstract = "... approximate computing techniques to in-memory computing. We extend existing compilation techniques for the Programmable Logic in Memory (PLiM) computer architecture, by adapting state-of-the-art approximate computing techniques for arithmetic circuits. We use Cartesian Genetic Programming for the generation of approximate circuits and evaluate them using a Symbolic Computer Algebra-based technique with respect to error-metrics. In our experiments, we show that we can outperform state-of-the-art handcrafted approximate adder designs.", } @InProceedings{Froemmgen:2015:ieeeICAC, author = "Alexander Froemmgen and Robert Rehner and Max Lehn and Alejandro Buchmann", booktitle = "2015 IEEE International Conference on Autonomic Computing (ICAC)", title = "Fossa: Learning ECA Rules for Adaptive Distributed Systems", year = "2015", pages = "207--210", abstract = "The development of adaptive distributed systems is complex. Due to a large amount of interdependencies and feedback loops between network nodes and software components, distributed systems respond nonlinearly to changes in the environment and system adaptations. Although Event Condition Action (ECA) rules allow a crisp definition of the adaptive behaviour and a loose coupling with the actual system implementation, defining concrete rules is nontrivial. It requires specifying the events and conditions which trigger adaptations, as well as the selection of appropriate actions leading to suitable new configurations. In this paper, we present the idea of Fossa, an ECA framework for adaptive distributed systems. Following a methodology that separates the adaptation logic from the actual application implementation, we propose learning ECA rules by automatically executing a multitude of tests. Rule sets are generated by algorithms such as genetic programming, and the results are evaluated using a utility function provided by the developer. Fossa therefore provides an automated offline learner that derives suitable ECA rules for a given utility function.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICAC.2015.37", month = jul, notes = "Also known as \cite{7266965}", } @InProceedings{Froemmgen:2015:ieeeLCN, author = "Alexander Froemmgen and Robert Rehner and Max Lehn and Alejandro Buchmann", booktitle = "40th IEEE Conference on Local Computer Networks (LCN)", title = "Fossa: Using genetic programming to learn ECA rules for adaptive networking applications", year = "2015", pages = "197--200", abstract = "Due to complex interdependencies and feedback loops between network layers and nodes, the development of adaptive applications is difficult. As networking applications respond nonlinearly to changes in the environment and adaptations, defining concrete adaptation rules is nontrivial. In this paper, we present the offline learner Fossa, which uses genetic programming to automatically learn suitable Event Condition Action (ECA) rules. Based on utility functions defined by the developer, the genetic programming learner generates a multitude of rule sets and evaluates them using simulations to obtain their utility. We show, for a concrete example scenario, how the genetic programming learner benefits from the clear model of the ECA rules, and that the methodology efficiently generates ECA rules which outperform nonadaptive and manually tuned solutions.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/LCN.2015.7366305", month = oct, notes = "Also known as \cite{7366305}", } @InProceedings{fry:2003:gecco, author = "Rodney Fry and Andy Tyrrell", title = "Enhancing the Performance of {GP} Using an Ancestry-Based Mate Selection Scheme", booktitle = "Genetic and Evolutionary Computation -- GECCO-2003", editor = "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller", year = "2003", pages = "1804--1805", address = "Chicago", publisher_address = "Berlin", month = "12-16 " # jul, volume = "2724", series = "LNCS", ISBN = "3-540-40603-4", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, poster", DOI = "doi:10.1007/3-540-45110-2_73", abstract = "The performance of genetic programming relies mostly on population-contained variation. If the population diversity is low then there will be a greater chance of the algorithm being unable to find the global optimum. We present a new method of approximating the genetic similarity between two individuals using ancestry information. We define a new diversity-preserving selection scheme, based on standard tournament selection, which encourages genetically dissimilar individuals to undergo genetic operation. The new method is illustrated by assessing its performance in a well-known problem domain: algebraic symbolic regression.", notes = "GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)", } @PhdThesis{Fry:thesis, author = "Rodney Fry", title = "Self-adaptive mate choice: Extending the selection model in genetic programming", school = "University of York", year = "2004", address = "UK", keywords = "genetic algorithms, genetic programming", URL = "http://ethos.bl.uk/OrderDetails.do?did=1&uin=uk.bl.ethos.437598", abstract = "This thesis documents new extensions to the selection model in genetic programming, intended to be analogous to the more complex behaviour of selection in natural evolution. More specifically, non-random mating models of negative inbreeding and negative assortative mating are presented. A model of psychological evolution is also presented, allowing the mating strategy to change throughout the evolutionary process. This approach results in a preservation of structural and nodal diversity, causing slower convergence. On average, the schemes provide an increase in the success rate of the system. An analysis of the computational effort required by each of the selection schemes is presented, concluding that some of the new selection schemes are viable with regard to success rate return on processor investment.", notes = "uk.bl.ethos.437598 ", } @InProceedings{fry:2005:CEC, author = "Rodney Fry and Stephen L. Smith and Andy M. Tyrrell", title = "A Self-Adaptive Mate Selection Model for Genetic Programming", booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary Computation", year = "2005", editor = "David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L. Gwenn Volkert and Dan Ashlock and Marc Schoenauer", volume = "3", pages = "2707--2714", address = "Edinburgh, UK", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "2-5 " # sep, organisation = "IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9363-5", DOI = "doi:10.1109/CEC.2005.1555034", abstract = "This paper documents new extensions to the selection model in genetic programming, designed to be analogous to the more complex behaviour of selection in natural evolution. Specifically, a negative assortative mating scheme is presented in conjunction with a model of psychological evolution, allowing the mating strategy to change throughout the evolutionary process. Results show that self-adaptive mate selection accelerates evolution for several well known test problems.", notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS. edit-distance, 4th and 7th order polynomials in one variable, exx+xpi, max problem 150 runs needed to find statistical effect. ", } @InProceedings{Fry:2012:ISSTA, author = "Zachary P. Fry and Bryan Landau and Westley Weimer", title = "A Human Study of Patch Maintainability", booktitle = "Proceedings of the 2012 International Symposium on Software Testing and Analysis, ISSTA 2012", year = "2012", editor = "Zhendong Su", pages = "177--187", address = "Minneapolis, MN, USA", publisher_address = "New York, NY, USA", month = "15-20 " # jul, publisher = "ACM", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, SBSE", isbn13 = "978-1-4503-1454-1", URL = "https://web.eecs.umich.edu/~weimerw/p/FryISSTA12_PREPRINT.pdf", URL = "http://doi.acm.org/10.1145/2338965.2336775", DOI = "doi:10.1145/2338965.2336775", acmid = "2336775", size = "11 pages", abstract = "Identifying and fixing defects is a crucial and expensive part of the software lifecycle. Measuring the quality of bug-fixing patches is a difficult task that affects both functional correctness and the future maintainability of the code base. Recent research interest in automatic patch generation makes a systematic understanding of patch maintainability and understandability even more critical. We present a human study involving over 150 participants, 32 real-world defects, and 40 distinct patches. In the study, humans perform tasks that demonstrate their understanding of the control flow, state, and maintainability aspects of code patches. As a baseline we use both human-written patches that were later reverted and also patches that have stood the test of time to ground our results. To address any potential lack of readability with machine-generated patches, we propose a system wherein such patches are augmented with synthesised, human-readable documentation that summarises their effects and context. Our results show that machine-generated patches are slightly less maintainable than human-written ones, but that trend reverses when machine patches are augmented with our synthesized documentation. Finally, we examine the relationship between code features (such as the ratio of variable uses to assignments) with participants' abilities to complete the study tasks and thus explain a portion of the broad concept of patch quality.", notes = "Does not mention GP but GP used in their machine-generated patches \cite{LeGoues:2012:ICSE} http://crisys.cs.umn.edu/issta2012/", } @PhdThesis{zak-phd, author = "Zachary P. Fry", title = "Leveraging Light-Weight Analyses to Aid Software Maintenance", school = "School of Engineering and Applied Science, University of Virginia", year = "2014", address = "USA", month = may, keywords = "genetic algorithms, genetic programming", URL = "https://web.eecs.umich.edu/~weimerw/students/zak-phd.pdf", DOI = "doi:10.18130/V34N7R", size = "125 pages", abstract = "While software systems have become a fundamental part of modern life, they require maintenance to continually function properly and to adapt to potential environment changes [1]. Software maintenance, a dominant cost in the software lifecycle [2], includes both adding new functionality and fixing existing problems, or bugs, in a system. Software bugs cost the world economy billions of dollars annually in terms of system down-time and the effort required to fix them [3]. This dissertation focuses specifically on corrective software maintenance. that is, the process of finding and fixing bugs. Traditionally, managing bugs has been a largely manual process [4]. This historically involved developers treating each defect as a unique maintenance concern, which results in a slow process and thus a high aggregate cost for finding and fixing bugs. Previous work has shown that bugs are often reported more rapidly than companies can address them, in practice [5]. Recently, automated techniques have helped to ease the human burden associated with maintenance activities. However, such techniques often suffer from a few key drawbacks. This thesis argues that automated maintenance tools often target narrowly scoped problems rather than more general ones. Such tools favour maximizing local, narrow success over wider applicability and potentially greater cost benefit. Additionally, this dissertation provides evidence that maintenance tools are traditionally evaluated in terms of functional correctness, while more practical concerns like ease-of-use and perceived relevance of results are often overlooked. When calculating cost savings, some techniques fail to account for the introduction of new workflow tasks while claiming to reduce the overall human burden. The work in this dissertation aims to avoid these weaknesses by providing fully automated, widely-applicable techniques that both reduce the cost of software maintenance and meet relevant human-centric quality and usability standards. This dissertation presents software maintenance techniques that reduce the cost of both finding and fixing bugs, with an emphasis on comprehensive, human-centric evaluation. The work in this thesis uses lightweight analyses to leverage latent information inherent in existing software artefacts. As a result, the associated techniques are both scalable and widely applicable to existing systems. The first of these techniques clusters closely-related, automatically generated defect reports to aid in the process of bug triage and repair. This clustering approach is complimented by an automatic program repair technique that generates and validates candidate defect patches by making sweeping optimizations to a state-of-the-art automatic bug fixing framework. To fully evaluate these techniques, experiments are performed that show net cost savings for both the clustering and program repair approaches while also suggesting that actual human developers both agree with the resulting defect report clusters and also are able to understand and use automatically generated patches. The techniques described in this dissertation are designed to address the three historically-lacking properties noted above: generality, usability, and human-centric efficacy. Notably, both presented approaches apply to many types of defects and systems, suggesting they are generally applicable as part of the maintenance process. With the goal of comprehensive evaluation in mind, this thesis provides evidence that humans both agree with the results of the techniques and could feasibly use them in practice. These and other results show that the techniques are usable, in terms of both minimizing additional human effort via full automation and also providing understandable maintenance solutions that promote continued system quality. By evaluating the associated techniques on programs spanning different languages and domains that contain thousands of bug reports and millions of lines of code, the results presented in this dissertation show potential concrete cost savings with respect to finding and fixing bugs. This work suggests the feasibility of further automation in software maintenance and thus increased reduction of the associated human burdens.", notes = "Mention of GenProg Supervisor: Westley R. Weimer", } @InProceedings{LeeannFu:1998:XCSQ, author = "Leeann L. Fu", title = "The {XCS} Classifier System and {Q}-learning", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "49", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "genetic algorithms, Classifier Systems", size = "1 page", notes = "GP-98LB", } @Article{Fu:TCAD, author = "Rongliang Fu and Robert Wille and Nobuyuki Yoshikawa and Tsung-Yi Ho", title = "Efficient Cartesian Genetic Programming-Based Automatic Synthesis Framework for Reversible Quantum-Flux-Parametron Logic Circuits", journal = "IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, Logic gates, Logic circuits, Logic, Quantum computing, Inverters, Reversible computing, Design automation, Energy efficiency, Energy dissipation, Superconducting electronics, reversible quantum-flux-parametron, logic synthesis", ISSN = "1937-4151", DOI = "doi:10.1109/TCAD.2025.3546884", abstract = "Reversible computing has garnered significant attention as a promising avenue for achieving energy-efficient computing systems, particularly within the realm of quantum computing. The reversible quantum-flux-parametron (RQFP) is the first practical reversible logic gate using adiabatic superconducting devices, with experimental evidence supporting both its logical and physical reversibility. Each RQFP logic gate operates on alternating current (AC) power and features three input ports and three output ports. Notably, each output port is capable of implementing a majority function while driving only a single fan-out. Additionally, the three inputs to each gate must arrive in the same clock phase. These inherent characteristics present substantial challenges in the design of RQFP logic circuits. To address these challenges, this paper proposes an automatic synthesis framework for RQFP logic circuit design based on efficient Cartesian genetic programming (CGP). The framework aims to minimise both the number of RQFP logic gates and the number of garbage outputs within the generated RQFP logic circuit. It incorporates the specific characteristics of the RQFP logic circuit by encoding them into the genotype of a CGP individual. It also introduces several point mutation operations to facilitate the generation of new individuals. Furthermore, the framework integrates circuit simulation with formal verification to assess the functional equivalence between the parent and its offspring. Experimental results on RevLib and reversible reciprocal circuit benchmarks demonstrate the effectiveness of our framework.", notes = "Also known as \cite{10907912} Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China", } @InProceedings{Fu:2010:ICNC, author = "Weizhong Fu and Yuntao Zhang and Zhengjun Cheng", title = "Improved gene expression programming and its application to QSAR", booktitle = "Sixth International Conference on Natural Computation (ICNC, 2010)", year = "2010", volume = "8", pages = "4057--4061", month = "10-12 " # aug, keywords = "genetic algorithms, genetic programming, gene expression programming, 0-(2-phthalimidoethyl)-n-substituted thiocarbamates, HIV-1 inhibitors, QSAR, RDF descriptors, acquired immune deficiency syndrome, descriptor selection, feature selection, improved gene expression programming, quantitative structure-activity relationship model, radial distribution function descriptors, replacement method, ring-opened congeners, biocomputing, evolutionary computation, radial basis function networks", isbn13 = "978-1-4244-5958-2", DOI = "doi:10.1109/ICNC.2010.5584850", abstract = "In the paper, the improved gene expression programming (IGEP) is proposed to develop a quantitative structure-activity relationship (QSAR) model of 70 compounds for O-(2-phthalimidoethyl)-N-substituted thiocarbamates and their ring-opened congeners as HIV-1 Inhibitors based on radial distribution function (RDF) descriptors for the first time. The replacement method (RM) is used as feature selection (descriptor selection). The five models (MLR, GEP, MC_GEP, IGEP, and SVM) are compared. The results show that IGEP has a good prediction ability.", } @InProceedings{Fu:2011:GPFEDAGA, title = "Genetic Programming For Edge Detection: A Global Approach", author = "Wenlong Fu and Mark Johnston and Mengjie Zhang", pages = "254--261", booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary Computation", year = "2011", editor = "Alice E. Smith", month = "5-8 " # jun, address = "New Orleans, USA", organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2011.5949626", abstract = "Edge detection is an important task in computer vision. This paper describes a global approach to edge detection using genetic programming (GP). Unlike most traditional edge detection methods which use local window filters, this approach directly uses an entire image as input and classifies pixels directly as edges or non-edges without preprocessing or postprocessing. Shifting operations and common standard operators are used to form the function set. Precision, recall and true negative rate are used to construct the fitness functions. This approach is examined and compared with the Laplacian and Sobel edge detectors on three sets of images providing edge detection problems of varying difficulty. The results suggest that the detectors evolved by GP outperform the Laplacian detector and compete with the Sobel detector in most cases.", notes = "CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.", } @InProceedings{conf/ausai/FuJZ11, author = "Wenlong Fu and Mark Johnston and Mengjie Zhang", title = "Genetic Programming for Edge Detection Based on Accuracy of Each Training Image", booktitle = "Proceedings of the 24th Australasian Joint Conference Advances in Artificial Intelligence (AI 2011)", year = "2011", editor = "Dianhui Wang and Mark Reynolds", volume = "7106", series = "Lecture Notes in Computer Science", pages = "301--310", address = "Perth, Australia", month = dec # " 5-8", publisher = "Springer", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1007/978-3-642-25832-9_31", size = "10 pages", abstract = "This paper investigates fitness functions based on the detecting accuracy of each training image. In general, machine learning algorithms for edge detection only focus on the accuracy based on all training pixels treated equally, but the accuracy based on every training image is not investigated. We employ genetic programming to evolve detectors with fitness functions based on the accuracy of every training image. Here, average (arithmetic mean) and geometric mean are used as fitness functions for normal natural images. The experimental results show fitness functions based on the accuracy of each training image obtain better performance, compared with the Sobel detector, and there is no obvious difference between the fitness functions with average and geometric mean.", affiliation = "School of Mathematics, Statistics and Operations Research, Victoria University of Wellington, PO Box 600, Wellington, New Zealand", bibdate = "2011-12-02", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ausai/ausai2011.html#FuJZ11", } @InProceedings{Fu:2012:CEC, title = "Soft Edge Maps From Edge Detectors Evolved by Genetic Programming", author = "Wenlong Fu and Mark Johnston and Mengjie Zhang", pages = "1356--1363", booktitle = "Proceedings of the 2012 IEEE Congress on Evolutionary Computation", year = "2012", editor = "Xiaodong Li", month = "10-15 " # jun, DOI = "doi:10.1109/CEC.2012.6256105", address = "Brisbane, Australia", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Conflict of Interest Papers, Evolutionary Computer Vision, Evolutionary programming", abstract = "Genetic Programming (GP) has been used for edge detection, but there is no previous work that analyses the outputs from a GP detector before thresholding them to binary edge maps. When the threshold used in a GP system slightly changes, the final edge map from a detector may change a lot. Mapping the outputs of a GP detector to a grayscale space by a linear transformation is not effective. In order to address the problem of the sensitivity to the threshold values, we replace the linear transformation with an S-shaped transformation. We design two new fitness functions so that the outputs from an evolved detector can obtain better edge maps after mapping into a grayscale space. Experimental results show that the S-shaped transformation obtains soft edge maps similar to the fixed threshold and the new fitness functions improve the edge detection accuracy.", notes = "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the EPS and the IET.", } @InProceedings{Fu:2012:CECb, title = "Genetic Programming for Edge Detection via Balancing Individual Training Images", author = "Wenlong Fu and Mark Johnston and Mengjie Zhang", pages = "2702--2709", booktitle = "Proceedings of the 2012 IEEE Congress on Evolutionary Computation", year = "2012", editor = "Xiaodong Li", month = "10-15 " # jun, DOI = "doi:10.1109/CEC.2012.6252879", address = "Brisbane, Australia", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Conflict of Interest Papers, Classification, clustering, data analysis and data mining", abstract = "Edge detectors trained by a machine learning algorithm are usually evaluated by the accuracy based on overall pixels in the training stage, rather than the information for each training image. However, when the evaluation for training edge detectors considers the accuracy of each image, the influence on the final detectors has not been investigated. In this study, we employ genetic programming to evolve detectors with new fitness functions containing the accuracy of training images. The experimental results show that fitness functions based on the accuracy of single training images can balance the accuracies across detection results, and the fitness function combining the accuracy of overall pixels with the accuracy of training images together can improve the detection performance.", notes = "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the EPS and the IET.", } @InProceedings{Fu:2012:GECCO, author = "Wenlong Fu and Mark Johnston and Mengjie Zhang", title = "Genetic programming for edge detection using blocks to extract features", booktitle = "GECCO '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference", year = "2012", editor = "Terry Soule and Anne Auger and Jason Moore and David Pelta and Christine Solnon and Mike Preuss and Alan Dorin and Yew-Soon Ong and Christian Blum and Dario Landa Silva and Frank Neumann and Tina Yu and Aniko Ekart and Will Browne and Tim Kovacs and Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and Giovanni Squillero and Nicolas Bredeche and Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and Martin Pelikan and Silja Meyer-Nienberg and Christian Igel and Greg Hornby and Rene Doursat and Steve Gustafson and Gustavo Olague and Shin Yoo and John Clark and Gabriela Ochoa and Gisele Pappa and Fernando Lobo and Daniel Tauritz and Jurgen Branke and Kalyanmoy Deb", isbn13 = "978-1-4503-1177-9", pages = "855--862", keywords = "genetic algorithms, genetic programming, genetics based machine learning", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330163.2330282", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Single pixels can be directly used to construct low-level edge detectors but these detectors are not good for suppressing noise and some texture. In general, features based on a small area are used to suppress noise and texture. However, there is very little guidance in the literature on how to select the area size. In this paper, we employ Genetic Programming (GP) to evolve edge detectors via automatically searching for features based on flexible blocks rather than dividing a fixed window into small areas based on different directions. Experimental results for natural images show that using blocks to extract features obtains better performance than using single pixels only to construct detectors, and that GP can successfully choose the block size for extracting features.", notes = "Also known as \cite{2330282} GECCO-2012 A joint meeting of the twenty first international conference on genetic algorithms (ICGA-2012) and the seventeenth annual genetic programming conference (GP-2012)", } @InProceedings{Fu:2012:GECCOcomp, author = "Wenlong Fu and Mark Johnston and Mengjie Zhang", title = "Genetic programming for edge detection based on figure of merit", booktitle = "GECCO Companion '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion", year = "2012", editor = "Terry Soule and Anne Auger and Jason Moore and David Pelta and Christine Solnon and Mike Preuss and Alan Dorin and Yew-Soon Ong and Christian Blum and Dario Landa Silva and Frank Neumann and Tina Yu and Aniko Ekart and Will Browne and Tim Kovacs and Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and Giovanni Squillero and Nicolas Bredeche and Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and Martin Pelikan and Silja Meyer-Nienberg and Christian Igel and Greg Hornby and Rene Doursat and Steve Gustafson and Gustavo Olague and Shin Yoo and John Clark and Gabriela Ochoa and Gisele Pappa and Fernando Lobo and Daniel Tauritz and Jurgen Branke and Kalyanmoy Deb", isbn13 = "978-1-4503-1178-6", keywords = "genetic algorithms, Genetic programming: Poster", pages = "1483--1484", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330784.2331003", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "The figure of merit (FOM) is popular for testing an edge detector's performance, but there are very few reports using FOM as an evaluation method in the learning stage of supervised learning methods. In this study, FOM is investigated as a fitness function in Genetic Programming (GP) for edge detection. Since FOM has some drawbacks from type II errors, new fitness functions are developed based on FOM in order to address these weaknesses. Experimental results show that FOM can be used to evolve GP edge detectors that perform better than the Sobel detector, and the new fitness functions clearly improve the ability of GP edge detectors to find edge points and give a single response on edges, compared with the fitness function using FOM.", notes = "Also known as \cite{2331003} Distributed at GECCO-2012. ACM Order Number 910122.", } @InProceedings{DBLP:conf/ausai/FuJZ12, author = "Wenlong Fu and Mark Johnston and Mengjie Zhang", title = "Automatic Construction of Invariant Features Using Genetic Programming for Edge Detection", booktitle = "25th Joint Conference Australasian Conference on Artificial Intelligence, AI 2012", year = "2012", editor = "Michael Thielscher and Dongmo Zhang", volume = "7691", series = "Lecture Notes in Computer Science", pages = "144--155", address = "Sydney, Australia", month = dec # " 4-7", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-35100-6", DOI = "doi:10.1007/978-3-642-35101-3_13", } @InProceedings{Fu:2012:SEAL, author = "Wenlong Fu and Mark Johnston and Mengjie Zhang", title = "Figure of Merit Based Fitness Functions in Genetic Programming for Edge Detection", booktitle = "The Ninth International Conference on Simulated Evolution And Learning, SEAL 2012", year = "2012", editor = "Lam Thu Bui and Yew-Soon Ong and Nguyen Xuan Hoai and Hisao Ishibuchi and Ponnuthurai Nagaratnam Suganthan", volume = "7673", series = "Lecture Notes in Computer Science", pages = "22--31", address = "Vietnam", month = dec # " 16-19", organisation = "Faculty of Information Technology, Le Quy Don Technical University", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Edge Detection, Figure of Merit", isbn13 = "978-3-642-34858-7", DOI = "doi:10.1007/978-3-642-34859-4_3", size = "10 pages", abstract = "The figure of merit (FOM) is popular for testing an edge detector's performance, but there are very few reports using FOM as an evaluation method in Genetic Programming (GP). In this study, FOM is investigated as a fitness function in GP for edge detection. Since FOM has some drawbacks from type II errors, new fitness functions are developed based on FOM in order to address these weaknesses. Experimental results show that FOM can be used to evolve GP edge detectors that perform better than the Sobel detector, and the new fitness functions clearly improve the ability of GP edge detectors to find edge points and give a single response on edges, compared with the fitness function using FOM.", } @InProceedings{Fu:evoapps13, author = "Wenlong Fu and Mark Johnston and Mengjie Zhang", title = "Genetic Programming for Automatic Construction of Variant Features in Edge Detection", booktitle = "Applications of Evolutionary Computing, EvoApplications 2013: EvoCOMNET, EvoCOMPLEX, EvoENERGY, EvoFIN, EvoGAMES, EvoIASP, EvoINDUSTRY, EvoNUM, EvoPAR, EvoRISK, EvoROBOT, EvoSTOC", year = "2013", month = "3-5 " # apr, editor = "Anna I. Esparcia-Alcazar and Antonio Della Cioppa and Ivanoe {De Falco} and Ernesto Tarantino and Carlos Cotta and Robert Schaefer and Konrad Diwold and Kyrre Glette and Andrea Tettamanzi and Alexandros Agapitos and Paolo Burrelli and J. J. Merelo and Stefano Cagnoni and Mengjie Zhang and Neil Urquhart and Kevin Sim and Aniko Ekart and Francisco {Fernandez de Vega} and Sara Silva and Evert Haasdijk and Gusz Eiben and Anabela Simoes and Philipp Rohlfshagen", series = "LNCS", volume = "7835", publisher = "Springer Verlag", address = "Vienna", publisher_address = "Berlin", pages = "354--364", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, Edge Detection, Feature Construction", isbn13 = "978-3-642-37191-2", DOI = "doi:10.1007/978-3-642-37192-9_36", size = "11 pages", abstract = "Basic features for edge detection, such as derivatives, can be further manipulated to improve detection performance. However, how to effectively combine different basic features remains an open issue and needs to be investigated. In this study, Genetic Programming (GP) is used to automatically and effectively construct rotation variant features based on basic features from derivatives, F-test, and histograms of images. To reduce computational cost in the training stage, the basic features only use the horizontal responses to construct new horizontal features. These new features are then combined with their own rotated versions in the vertical direction in the testing stage. The experimental results show that the rotation variant features constructed by GP combine advantages from the basic features, reduce drawbacks from basic features alone, and improve the detection performance", notes = " EvoApplications2013 held in conjunction with EuroGP2013, EvoCOP2013, EvoBio'2013 and EvoMusArt2013", } @InProceedings{Fu:evoapps13a, author = "Wenlong Fu and Mark Johnston and Mengjie Zhang", title = "Automatic Construction of Gaussian-Based Edge Detectors Using Genetic Programming", booktitle = "Applications of Evolutionary Computing, EvoApplications 2013: EvoCOMNET, EvoCOMPLEX, EvoENERGY, EvoFIN, EvoGAMES, EvoIASP, EvoINDUSTRY, EvoNUM, EvoPAR, EvoRISK, EvoROBOT, EvoSTOC", year = "2013", month = "3-5 " # apr, editor = "Anna I. Esparcia-Alcazar and Antonio Della Cioppa and Ivanoe {De Falco} and Ernesto Tarantino and Carlos Cotta and Robert Schaefer and Konrad Diwold and Kyrre Glette and Andrea Tettamanzi and Alexandros Agapitos and Paolo Burrelli and J. J. Merelo and Stefano Cagnoni and Mengjie Zhang and Neil Urquhart and Kevin Sim and Aniko Ekart and Francisco {Fernandez de Vega} and Sara Silva and Evert Haasdijk and Gusz Eiben and Anabela Simoes and Philipp Rohlfshagen", series = "LNCS", volume = "7835", publisher = "Springer Verlag", address = "Vienna", publisher_address = "Berlin", pages = "365--375", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, Edge Detection, Gaussian Filter", isbn13 = "978-3-642-37191-2", DOI = "doi:10.1007/978-3-642-37192-9_37", size = "11 pages", abstract = "Gaussian-based edge detectors have been developed for many years, but there are still problems with how to set scales for Gaussian filters and how to combine Gaussian filters. In order to address both problems, a Genetic Programming (GP) system is proposed to automatically choose scales for Gaussian filters and automatically combine Gaussian filters. In this study, the GP system is used to construct rotation invariant Gaussian-based edge detectors based on a benchmark image dataset. The experimental results show that the GP evolved Gaussian-based edge detectors are better than the Gaussian gradient and rotation invariant surround suppression to extract edge features.", notes = " EvoApplications2013 held in conjunction with EuroGP2013, EvoCOP2013, EvoBio'2013 and EvoMusArt2013", } @InProceedings{Fu:2013:CEC, article_id = "1084", author = "Wenlong Fu and Mark Johnston and Mengjie Zhang", title = "Triangular-Distribution-Based Feature Construction Using Genetic Programming for Edge Detection", booktitle = "2013 IEEE Conference on Evolutionary Computation", volume = "1", year = "2013", month = jun # " 20-23", editor = "Luis Gerardo {de la Fraga}", pages = "1732--1739", address = "Cancun, Mexico", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-0453-2", DOI = "doi:10.1109/CEC.2013.6557770", notes = "CEC 2013 - A joint meeting of the IEEE, the EPS and the IET.", } @InProceedings{Fu:2013:GECCO, author = "Wenlong Fu and Mark Johnston and Mengjie Zhang", title = "Genetic programming for edge detection using multivariate density", booktitle = "GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference", year = "2013", editor = "Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi", isbn13 = "978-1-4503-1963-8", pages = "917--924", keywords = "genetic algorithms, genetic programming", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2463372.2463485", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "The combination of local features in edge detection can generally improve detection performance. However, how to effectively combine different basic features remains an open issue and needs to be investigated. Multivariate density is a generalisation of the one-dimensional (univariate) distribution to higher dimensions. In order to effectively construct composite features with multivariate density, a Genetic Programming (GP) system is proposed to evolve Bayesian-based programs. An evolved Bayesian-based program estimates the relevant multivariate density to construct a composite feature. The results of the experiments show that the GP system constructs high-level combined features which substantially improve the detection performance.", notes = "Also known as \cite{2463485} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)", } @InProceedings{Fu:evoapps14, author = "Wenlong Fu and Mark Johnston and Mengjie Zhang", title = "Is a Single Image Sufficient for Evolving Edge Features by Genetic Programming?", booktitle = "17th European Conference on the Applications of Evolutionary Computation", year = "2014", editor = "Anna Isabel Esparcia-Alcazar and Antonio Miguel Mora", series = "LNCS", volume = "8602", publisher = "Springer", pages = "451--463", address = "Granada", month = "23-25 " # apr, organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, Edge Detection; Gaussian Filter", isbn13 = "978-3-662-45522-7", DOI = "doi:10.1007/978-3-662-45523-4_37", abstract = "Typically, a single natural image is not sufficient to train a program to extract edge features in edge detection when only training images and their ground truth are provided. However, a single training image might be considered as proper training data when domain knowledge, such as used in Gaussian-based edge detection, is provided. In this paper, we employ Genetic Programming (GP) to automatically evolve Gaussian-based edge detectors to extract edge features based on training data consisting of a single image only. The results show that a single image with a high proportion of true edge points can be used to train edge detectors which are not significantly different from rotation invariant surround suppression. When the programs separately evolved from eight single images are considered as weak classifiers, the combinations of these programs perform better than rotation invariant surround suppression.", notes = "EvoApplications2014 held in conjunction with EuroGP'2014, EvoCOP2014, EvoBIO2014, and EvoMusArt2014", } @InProceedings{Fu:2014:CEC, title = "Unsupervised Learning for Edge Detection Using Genetic Programming", author = "Wenlong Fu and Mark Johnston and Mengjie Zhang", pages = "117--124", booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary Computation", year = "2014", month = "6-11 " # jul, editor = "Carlos A. {Coello Coello}", address = "Beijing, China", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, Genetic programming, Evolutionary Computer Vision", DOI = "doi:10.1109/CEC.2014.6900444", abstract = "In edge detection, a machine learning algorithm generally requires training images with their ground truth or designed outputs to train an edge detector. Meanwhile the computational cost is heavy for most supervised learning algorithms in the training stage when a large set of training images is used. To learn edge detectors without ground truth and reduce the computational cost, an unsupervised Genetic Programming (GP) system is proposed for low-level edge detection. A new fitness function is developed from the energy functions in active contours. The proposed GP system uses single images to evolve GP edge detectors, and these evolved edge detectors are used to detect edges on a large set of test images. The results of the experiments show that the proposed unsupervised learning GP system can effectively evolve good edge detectors to quickly detect edges on different natural images.", notes = "WCCI2014", } @Article{Fu:2014:ieeec, author = "Wenlong Fu and Mark Johnston and Mengjie Zhang", title = "Low-Level Feature Extraction for Edge Detection Using Genetic Programming", journal = "IEEE Transactions on Cybernetics", year = "2014", volume = "44", number = "8", month = "1459--1472", keywords = "genetic algorithms, genetic programming, Accuracy, Detectors, Educational institutions, Feature extraction, Image edge detection, Noise, Training, Edge detection, feature extraction", ISSN = "2168-2267", DOI = "doi:10.1109/TCYB.2013.2286611", size = "14 pages", abstract = "Edge detection is a subjective task. Traditionally, a moving window approach is used, but the window size in edge detection is a tradeoff between localisation accuracy and noise rejection. An automatic technique for searching a discriminated pixel's neighbours to construct new edge detectors is appealing to satisfy different tasks. In this paper, we propose a genetic programming (GP) system to automatically search pixels (a discriminated pixel and its neighbours) to construct new low-level subjective edge detectors for detecting edges in natural images, and analyse the pixels selected by the GP edge detectors. Automatically searching pixels avoids the problem of blurring edges from a large window and noise influence from a small window. Linear and second-order filters are constructed from the pixels with high occurrences in these GP edge detectors. The experiment results show that the proposed GP system has good performance. A comparison between the filters with the pixels selected by GP and all pixels in a fixed window indicates that the set of pixels selected by GP is compact but sufficiently rich to construct good edge detectors.", notes = "also known as \cite{6649981}", } @InProceedings{conf/seal/FuJZ14, author = "Wenlong Fu and Mark Johnston and Mengjie Zhang", title = "Automatic Resolution Selection for Edge Detection Using Genetic Programming", bibdate = "2014-11-12", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/seal/seal2014.html#FuJZ14", booktitle = "Simulated Evolution and Learning - 10th International Conference, {SEAL} 2014, Dunedin, New Zealand, December 15-18, 2014. Proceedings", publisher = "Springer", year = "2014", volume = "8886", editor = "Grant Dick and Will N. Browne and Peter A. Whigham and Mengjie Zhang and Lam Thu Bui and Hisao Ishibuchi and Yaochu Jin and Xiaodong Li and Yuhui Shi and Pramod Singh and Kay Chen Tan and Ke Tang", isbn13 = "978-3-319-13562-5", pages = "810--821", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming", URL = "http://dx.doi.org/10.1007/978-3-319-13563-2", } @Article{Fu:2015:SC, author = "Wenlong Fu and Mark Johnston and Mengjie Zhang", title = "Distribution-based invariant feature construction using genetic programming for edge detection", journal = "Soft Computing", year = "2015", volume = "19", number = "8", pages = "2371--2389", month = aug, keywords = "genetic algorithms, genetic programming, SVM, Edge detection, Distribution estimation, Feature extraction", ISSN = "1432-7643", DOI = "doi:10.1007/s00500-014-1432-4", size = "19 pages", abstract = "In edge detection, constructing features with rich responses on different types of edges is a challenging problem. Genetic programming (GP) has been previously employed to construct features. Normally, the values of the features constructed by GP are calculated from raw observations. Some existing work has considered the distributions of the raw observations, but these features only poorly indicate class label probabilities. To construct features with rich responses on different types of edges, the distributions of the observations from GP programs are investigated in this study. The values of the constructed features are obtained from estimated distributions, rather than directly using the observations. These features themselves indicate probabilities for the target labels. Basic rotation-invariant features from gradients, image quality, and local histograms are used to construct new composite features. The results show that the invariant features constructed by GP combine advantages from the basic features, reduce drawbacks from basic features alone, and improve the detection performance. In terms of the quantitative and qualitative evaluations, features constructed by GP with distribution estimation are better than the combinations from a Bayesian model and a linear support vector machine approach.", } @Article{Fu:2016:SC, author = "Wenlong Fu and Mark Johnston and Mengjie Zhang", title = "Genetic programming for edge detection: a Gaussian-based approach", journal = "Soft Computing", year = "2016", volume = "20", number = "3", pages = "1231--1248", month = mar, keywords = "genetic algorithms, genetic programming, Edge detection, Sampling, Feature extraction", ISSN = "1432-7643", DOI = "doi:10.1007/s00500-014-1585-1", size = "18 pages", abstract = "Gaussian-based filtering techniques have been popularly applied to edge detection. However, how to effectively tune parameters of Gaussian filters and how to effectively combine different Gaussian filters are still open issues. In this study, a new genetic programming (GP) approach is proposed to automatically tune parameters of Gaussian filters and automatically combine different types of Gaussian filters to extract edge features. In general, it is time-consuming for GP to evolve edge detectors using a large training image dataset. To efficiently evolve edge detectors from a large training image dataset, we propose sampling techniques (randomly selecting training images) to evolve Gaussian-based edge detectors. A sampling technique only using part of a set of images obtains similar performance to the training data using all of these images. The evolved edge detectors from the proposed sampling technique perform better than the Gaussian gradient and rotation invariant surround suppression. Based on the analysis of GP evolving edge detectors, it is suggested that combining Gaussian filters should be based on different types of Gaussian filters, and the Gaussian gradient should be considered as a major filter in these combinations.", } @InProceedings{conf/seal/FuXZG17, author = "Wenlong Fu and Bing Xue and Mengjie Zhang and Xiaoying Gao", title = "Transductive Transfer Learning in Genetic Programming for Document Classification", booktitle = "Proceedings of the 11th International Conference on Simulated Evolution and Learning, SEAL 2017", year = "2017", editor = "Yuhui Shi and Kay Chen Tan and Mengjie Zhang and Ke Tang and Xiaodong Li and Qingfu Zhang and Ying Tan and Martin Middendorf and Yaochu Jin", volume = "10593", series = "Lecture Notes in Computer Science", pages = "556--568", address = "Shenzhen, China", month = nov # " 10-13", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Document classification, Transfer learning, Text classification", bibdate = "2017-11-03", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/seal/seal2017.html#FuXZG17", isbn13 = "978-3-319-68758-2", DOI = "doi:10.1007/978-3-319-68759-9_45", abstract = "Document classification tasks generally have sparse and high dimensional features. It is important to effectively extract features. In document classification tasks, there are some similarities existing in different categories or different datasets. It is possible that one document classification task does not have labelled training data. In order to obtain effective classifiers on this specific task, this paper proposes a Genetic Programming (GP) system using transductive transfer learning. The proposed GP system automatically extracts features from different source domains, and these GP extracted features are combined to form new classifiers being directly applied to a target domain. From experimental results, the proposed transductive transfer learning GP system can evolve features from source domains to effectively apply to target domains which are similar to the source domains.", } @Article{Fu:2018:ieeeCIM, author = "Wenlong Fu and Bing Xu and Mengjie Zhang and Mark Johnston", journal = "IEEE Computational Intelligence Magazine", title = "Fast Unsupervised Edge Detection Using Genetic Programming [Application Notes]", year = "2018", volume = "13", number = "4", pages = "46--58", abstract = "Edge detection has been a fundamental and important task in computer vision for many years, but it is still a challenging problem in real-time applications, especially for unsupervised edge detection, where ground truth is not available. Typical fast edge detection approaches, such as the single threshold method, are expensive to achieve in unsupervised edge detection. This study proposes a Genetic Programming (GP) based algorithm to quickly and automatically extract binary edges in an unsupervised manner. We investigate how GP can effectively evolve an edge detector from a single image without ground truth, and whether the evolved edge detector can be directly applied to other unseen/test images. The proposed method is examined and compared with a recent GP method and the Canny method on the Berkeley segmentation dataset. The results show that the proposed GP method has the ability to effectively evolve edge detectors by using only a single image as the whole training set, and significantly outperforms the two methods it is compared to. Furthermore, the binary edges detected by the evolved edge detectors have a good balance between recall and precision.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/MCI.2018.2866729", ISSN = "1556-603X", month = nov, notes = "Also known as \cite{8492376}", } @InProceedings{Fu:2019:CEC, author = "Wenlong Fu and Bing Xue and Xiaoying Gao and Mengjie Zhang", title = "Genetic Programming based Transfer Learning for Document Classification with Self-taught and Ensemble Learning", booktitle = "2019 IEEE Congress on Evolutionary Computation, CEC 2019", year = "2019", editor = "Carlos A. Coello Coello", pages = "2260--2267", address = "Wellington, New Zealand", month = "10-13 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Document Classification, Transfer Learning", isbn13 = "978-1-7281-2152-6", DOI = "doi:10.1109/CEC.2019.8790318", size = "8 pages", abstract = "Document classification is a common but challenging task in text mining, since the feature set used is often high-dimensional and sparse. Transfer learning has been applied to improve the classification performance of a (target) domain by transferring knowledge from a previously learnt (source) domain. When there are no labels provided for documents in target domains, it is challenging to effectively transfer knowledge from source domains to target domains. In this paper, we develop a new Genetic Programming (GP) based transfer learning method for document classification, which uses the evolved GP programs from the source domain to learn a set of weak GP classification models on the target domain with unlabelled documents, which is called self-taught learning. These weak classifiers are combined with the GP programs transferred from the source domain to predict the labels of test documents in the target domain. The experimental results show that the GP programs from source domains with their weak classifiers can effectively classify documents in the target domain.", notes = "Also known as \cite{8790318}. IEEE Catalog Number: CFP19ICE-ART", } @Article{fu:SC, author = "Wenlong Fu and Mengjie Zhang and Mark Johnston", title = "Bayesian genetic programming for edge detection", journal = "Soft Computing", year = "2019", volume = "23", number = "12", pages = "4097--4112", month = jun, keywords = "genetic algorithms, genetic programming, Edge detection, Bayesian model, Feature construction", URL = "http://link.springer.com/article/10.1007/s00500-018-3059-3", DOI = "doi:10.1007/s00500-018-3059-3", size = "16 pages", abstract = "In edge detection, designing new techniques to combine local features is expected to improve detection performance. However, how to effectively design combination techniques remains an open issue. In this study, an automatic design approach is proposed to combine local edge features using Bayesian programs (models) evolved by genetic programming (GP). Multi-variate density is used to estimate prior probabilities for edge points and non-edge points. Bayesian programs evolved by GP are used to construct composite features after estimating the relevant multivariate density. The results show that GP has the ability to effectively evolve Bayesian programs. These evolved programs have higher detection accuracy than the combination of local features by directly using the multivariate density (of these local features) in a simple Bayesian model. From evolved Bayesian programs, the proposed GP system has potential to effectively select features to construct Bayesian programs for performance improvement.", } @Article{FU:2021:ASC, author = "Wenlong Fu and Bing Xue and Xiaoying Gao and Mengjie Zhang", title = "Transductive transfer learning based Genetic Programming for balanced and unbalanced document classification using different types of features", journal = "Applied Soft Computing", volume = "103", pages = "107172", year = "2021", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2021.107172", URL = "https://www.sciencedirect.com/science/article/pii/S1568494621000958", keywords = "genetic algorithms, genetic programming, Document classification, Transfer learning", abstract = "Document classification is one of the predominant tasks in Natural Language Processing. However, some document classification tasks do not have ground truth while other similar datasets may have ground truth. Transfer learning can use similar datasets with ground truth to train effective classifiers on the dataset without ground truth. This paper introduces a transductive transfer learning method for document classification using two different text feature representations-the term frequency (TF) and the semantic feature doc2vec. It has three main contributions. First, it enables the sharing knowledge in a dataset using TF and a dataset using doc2vec in transductive transfer learning for performance improvement. Second, it demonstrates that the partially learned programs from TFs and from doc2vecs can be alternatively used to {"}label then learn{"} and they improve each other. Lastly, it addresses the unbalanced dataset problem by considering the unbalanced distributions on categories for evolving proper Genetic Programming (GP) programs on the target domains. Our experimental results on two popular document datasets show that the proposed technique effectively transfers knowledge from the GP programs evolved from the source domains to the new GP programs on the target domains using TF or doc2vec. There are obviously more than 10 percentages improvement achieved by the GP programs evolved by the proposed method over the GP programs directly evolved from the source domains. Also, the proposed technique effectively uses GP programs evolved from unbalanced datasets (on the source and target domains) to evolve new GP programs on the target domains, which balances predictions on different categories", } @Article{FU:2021:KS, author = "Wenlong Fu and Bing Xue and Xiaoying Gao and Mengjie Zhang", title = "Output-based transfer learning in genetic programming for document classification", journal = "Knowledge-Based Systems", volume = "212", pages = "106597", year = "2021", ISSN = "0950-7051", DOI = "doi:10.1016/j.knosys.2020.106597", URL = "https://www.sciencedirect.com/science/article/pii/S0950705120307267", keywords = "genetic algorithms, genetic programming, Transfer learning, Feature extraction, Document classification", abstract = "Transfer learning has been studied in document classification for transferring a model trained from a source domain (SD) to a relatively similar target domain (TD). In feature-based transfer learning techniques, there is an investigation on the features being transferred from SD to TD. This paper conducts an investigation on an output-based transfer learning system using Genetic Programming (GP) in document classification tasks, which automatically selects features to construct classifiers. The proposed GP system directly generates programs from a set of sparse features and only considers the output change of the evolved programs from SD to TD. A linear model is then used to combine existing GP programs from SD as features to TD. Also, new GP programs are mutated from the programs evolved in SD to improve the accuracy. Via directly using the evolved GP programs and their mutations, the feature extraction and estimation processes on TD are avoided. The results for the experiments demonstrates that the GP programs from SD can be effectively used for classifying documents in the relevant TD. The results also show that it is easy to train effective classifiers on TD when the GP programs are used as features. Furthermore, the proposed linear model, using multiple GP programs from SD as its inputs, outperforms single GP programs which are directly obtained from TD", } @InProceedings{fu:2023:IaVC, author = "Wenlong Fu and Bing Xue and Mengjie Zhang and Jan Schindler", title = "Evolving {U-Nets} Using Genetic Programming for Tree Crown Segmentation", booktitle = "37th International Conference, Image and Vision Computing, IVCNZ 2022", year = "2022", editor = "Wei Qi Yan and Minh Nguyen and Martin Stommel", volume = "13836", series = "LNCS", pages = "188--201", address = "Auckland, New Zealand", month = nov # " 24-25", publisher = "Springer", note = "Revised Selected Papers", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-031-25824-4", URL = "http://link.springer.com/chapter/10.1007/978-3-031-25825-1_14", DOI = "doi:10.1007/978-3-031-25825-1_14", abstract = "The U-Net deep learning algorithm and its variants have been developed for biomedical image segmentation, and due to their success gained popularity in other science domains including remote sensing. So far no U-Net structure has been specifically designed to segment complex tree canopies from aerial imagery. In this paper, a handcrafted convolutional block is introduced to replace the raw convolutional block used in the standard U-Net structure. Furthermore, we proposed a Genetic Programming (GP) approach to evolving convolutional blocks used in the U-Net structure. The experimental results on a tree crown dataset show that both the handcrafted block and the GP evolved blocks have better segmentation results than the standard U-Net. Additionally, the U-Net using the proposed handcrafted blocks has fewer numbers of the learning parameters than the standard U-Net. Also, the proposed GP approach can evolve convolutional blocks used in U-Nets that perform better than the handcrafted U-Net and the standard U-Net, and can also achieve automation.", notes = "Published in 2023 after the conference", } @Article{Fu:2024:CYB, author = "Wenlong Fu and Bing Xue and Xiaoying Gao and Mengjie Zhang", journal = "IEEE Transactions on Cybernetics", title = "Genetic Programming for Document Classification: A Transductive Transfer Learning System", year = "2024", volume = "54", number = "2", pages = "1119--1132", month = feb, keywords = "genetic algorithms, genetic programming, Transfer learning, Training, Training data, Task analysis, Feature extraction, Support vector machines, SVM, Data models, Document classification, pseudolabel, transductive transfer learning", ISSN = "2168-2275", DOI = "doi:10.1109/TCYB.2023.3338266", size = "14 pages", abstract = "Document classification is a challenging task to the data being high-dimensional and sparse. Many transfer learning methods have been investigated for improving the classification performance by effectively transferring knowledge from a source domain to a target domain, which is similar to but different from the source domain. However, most of the existing methods cannot handle the case that the training data of the target domain does not have labels. In this study, we propose a transductive transfer learning system, using solutions evolved by genetic programming (GP) on a source domain to automatically pseudolabel the training data in the target domain in order to train classifiers. Different from many other transfer learning techniques, the proposed system pseudolabels target-domain training data to retrains classifiers using all target-domain features. The proposed method is examined on nine transfer learning tasks, and the results show that the proposed transductive GP system has better prediction accuracy on the test data in the target domain than existing transfer learning approaches including subspace alignment-domain adaptation methods, feature-level-domain adaptation methods, and one latest pseudolabeling strategy-based method.", notes = "Also known as \cite{10367876}", } @InProceedings{Fu:2025:evoapplications, author = "Wenlong Fu and Qi Chen and Bing Xue and Mengjie Zhang", title = "Facial Geometric Feature Extraction for Dimensional Emotion Analysis Using Genetic Programming", booktitle = "28th International Conference, EvoApplications 2025", year = "2025", editor = " Pablo Garcia-Sanchez and Emma Hart and Sarah L. Thomson", series = "LNCS", volume = "15612", pages = "137--153", publisher = "Springer", address = "Trieste, Italy", month = "3-5 " # apr, organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Dimensional Emotion, AffectNet, BFsGP, MOGP", isbn13 = "978-3-031-90062-4", timestamp = "Wed, 07 May 2025 17:49:32 +0200", biburl = "https://dblp.org/rec/conf/evoapps/FuCXZ25.bib", bibsource = "dblp computer science bibliography, https://dblp.org", DOI = "doi:10.1007/978-3-031-90062-4_9", notes = "DBLP:conf/evoapps/FuCXZ25, http://www.evostar.org/2025/ EvoApplications2025 held in conjunction with EuroGP'2025, EvoCOP2025 and EvoMusArt2025", } @InProceedings{Fu:2018:ICBK, author = "Xiaoyi Fu and Xinqi Ren and Ole J. Mengshoel and Xindong Wu", booktitle = "2018 IEEE International Conference on Big Knowledge (ICBK)", title = "Stochastic Optimization for Market Return Prediction Using Financial Knowledge Graph", year = "2018", pages = "25--32", address = "Singapore", abstract = "Interactive prediction of financial instrument returns is important. It is needed for asset managers to generate trading strategies as well as for stock exchange regulators to discover pricing anomalies. In this paper, we introduce an integrated stochastic optimization technique, namely genetic programming (GP) with generalized crowding (GC), GP+GC, as an integrated approach for a market return prediction system, using a financial knowledge graph (KG). On the one hand, using time-series data for twenty-nine component stocks of the Dow Jones industrial average, we show that our stochastic local search method can give a better prediction performance by providing a comparison of its return performances with two traditional benchmarks, namely a Buy & Hold strategy and the Moving Average Convergence Divergence (MACD) technical indicator. On the other hand, we use features extracted from a time-evolving knowledge graph constructed from fifty component stocks of the SSE50 Index. These features are used to a GP variant and then incorporate the knowledge extracted from the expression learnt from GP into a KG. Overall, this work demonstrates how to integrate GP+GC with KGs in a powerful manner.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICBK.2018.00012", month = "17-18 " # nov, notes = "Also known as \cite{8588771}", } @PhdThesis{DBLP:books/daglib/0001382, author = "Dirk Fuchs", title = "Cooperation in Heterogeneous Theorem Prover Networks", school = "Kaiserslautern University of Technology", year = "2000", address = "Germany", month = jan, keywords = "genetic algorithms, genetic programming, Artificial Intelligence, Computer and Communication Sciences, Computer Science", isbn13 = "978-1-58603-124-4", isbn13 = "978-3-89838-231-1", volume = "231", series = "Dissertations in Artificial Intelligence", publisher = "IOS", URL = "https://www.iospress.nl/book/cooperation-in-heterogeneous-theorem-prover-networks/", URL = "http://d-nb.info/958744947", timestamp = "Thu, 30 Jan 2020 14:55:16 +0100", biburl = "https://dblp.org/rec/books/daglib/0001382.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://katalog.ub.tu-braunschweig.de/vufind/Search2Record/313528276", size = "262 pages", notes = "is this GP?", } @InProceedings{fuchs:1996:esnnGA, author = "Matthias Fuchs", title = "Evolving Strategies Based on the Nearest-Neighbor Rule and a Genetic Algorithm", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "Genetic Algorithms", pages = "485--490", address = "Stanford University, CA, USA", publisher = "MIT Press", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap80.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96 GA paper", } @InProceedings{Fuchs:1997:spclGP, author = "Matthias Fuchs and Dirk Fuchs and Marc Fuchs", title = "Solving Problems of Combinatory Logic with Genetic Programming", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming", pages = "102--110", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1997/Fuchs_1997_spclGP.pdf", size = "9 pages", abstract = "The lambda calculus... we demonstrate that GP... is a serious competitor for state-of-the-art theorem provers", notes = "GP-97", } @InProceedings{fuchs:1998:xmetsc, author = "Matthias Fuchs", title = "Crossover versus Mutation: An Empirical and Theoretical Case Study", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "78--85", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-548-7", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1998/fuchs_1998_xmetsc.pdf", notes = "GP-98", } @TechReport{fuchs:1998:ARP-09, author = "Matthias Fuchs", title = "A Data Mining Approach to Support the Creation of Loop Invariants Using Genetic Programming", institution = "Computer Science Laboaratory, Australian National University", year = "1999", type = "Technical Report", number = "TR-ARP-09-98", address = "Canberra, ACT 0200, Australia", month = "12 " # oct, keywords = "genetic algorithms, genetic programming", URL = "http://arp.anu.edu.au/ftp/techreports/1998/TR-ARP-09-98.ps.gz", abstract = "We describe a data-mining approach to creating central parts of loop invariants. The approach is based on producing a trace table by recording the values of program variables each time the condition of a loop is evaluated. From this trace table, functional dependencies between program variables can be extracted which may play a vital role in loop invariants. The extraction process is accomplished through the use of genetic programming which performs a symbolic regression on the data contained by the trace table. We illustrate our approach with examples.", size = "11 pages", } @InProceedings{fuchs:1999:GLTPSUGP, author = "Marc Fuchs and Dirk Fuchs and Matthias Fuchs", title = "Generating Lemmas for Tableau-based Proof Search Using Genetic Programming", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1027--1032", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, ATP, CTC, TPTP, SETHEO/SAT", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-400.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-400.ps", size = "6 pages", abstract = "Top-down or analytical provers based on the connection tableau calculus are rather powerful, yet have notable short comings regarding redundancy control. A well-known and successful technique for alleviating these shortcomings is the use of lemmas. We propose to use genetic programming to evolve useful lemmas through an interleaved process of topdown goal decomposition and bottom-up lemma generation. Experimental studies show that our method compares very favourably with existing methods, improving on run time and on the number of solvable problems", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @PhdThesis{DBLP:books/daglib/0001380, author = "Marc Fuchs", title = "Relevancy based Use of Lemmas in Connection Tableau Calculi", school = "Technical University Munich", year = "2000", address = "Germany", month = jan, keywords = "genetic algorithms, genetic programming, Artificial Intelligence, Computer and Communication Sciences, Computer Science", volume = "227", series = "Dissertations in Artificial Intelligence", publisher = "IOS", isbn13 = "978-3-89838-227-4", isbn13 = "978-1-58603-123-7", timestamp = "Thu, 30 Jan 2020 14:55:16 +0100", biburl = "https://dblp.org/rec/books/daglib/0001380.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://www.iospress.nl/book/relevancy-based-use-of-lemmas-in-connection-tableau-calculi/", URL = "http://d-nb.info/958746656", size = "255 pages", abstract = "Automated deduction is a fundamental research area in the field of artificial intelligence. The aim of an automated deduction system is to find a formal proof for a given goal based on given axioms. Essentially automated deduction can be viewed as a search problem which spans huge search spaces. One main thrust of research in automated deduction is the development of techniques for achieving a reduction of the search space. A particularly promising approach for search space reduction relies on the integration of top-down and bottom-up reasoning. A possible approach employs bottom-up generated lemmas in top-down systems. Lemma use offers the possibility to shorten proofs and to overcome weaknesses of top-down systems like poor redundancy control. In spite of the possible advantages of lemma use, however, naive approaches for lemma integration even tend to slow down top-down systems. The main problem is the increased nondeterminism in the search process. In this thesis important contributions for a successful application of lemmas in top-down deduction systems based on connection tableau calculi are made. New methods for lemma generation and for the estimation of the relevancy of lemmas are developed. As a practical contribution, the implementation of the new techniques leads to a powerful system for automated deduction which demonstrates the high potential of the new techniques.", notes = "is this GP?", } @InProceedings{fuchs:1999:LPANATBCIGP, author = "Matthias Fuchs", title = "Large Populations Are Not Always The Best Choice In Genetic Programming", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1033--1038", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-410.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-410.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Fuchs:1999:AJ, author = "Matthias Fuchs", title = "Evolving Gallery Layouts With Genetic Programming", booktitle = "Proceedings of The Third Australia-Japan Joint Workshop on Intelligent and Evolutionary Systems", year = "1999", editor = "Bob McKay and Yasuhiro Tsujimura and Ruhul Sarker and Akira Namatame and Xin Yao and Mitsuo Gen", address = "School of Computer Science Australian Defence Force Academy, Canberra, Australia", month = "22-25 " # nov, keywords = "genetic algorithms, genetic programming", notes = "Broken Nov 2012 http://www.cs.adfa.edu.au/archive/conference/aj99/programme.html The Australian National University", } @TechReport{fuchs:2000:AEASWDtr, author = "Matthias Fuchs", title = "An Evolutionary Approach To Support Web Page Design", institution = "Computer Science Laboaratory, Australian National University", year = "2000", type = "Technical Report", number = "TR-ARP-01-2000", address = "Canberra, ACT 0200, Australia", month = "4 " # jan, keywords = "Hill climbing", URL = "http://arp.anu.edu.au/ftp/techreports/2000/TR-ARP-01-00.ps.gz", URL = "http://citeseer.ist.psu.edu/295439.html", abstract = "Arranging pictures or photographs on a wall or a sheet of paper can be viewed as a layout problem that consists in placing a set of rectangles on a large rectangle so that there are no overlaps, and all edges are parallel to either the vertical or horizontal edge of the large rectangle. Automating this process is sensible in connection with web page design, in particular if frequent changes occur. For web pages, it is possible and it makes sense to scale pictures down so as to ensure that there is a solution to any such layout problem. The goal then is to find a layout that arranges the pictures in a way that is pleasing to the eye. We propose to use genetic programming to evolve layouts, using a representation similar to slicing tree structures, and a fitness measure that incorporates the idea of aesthetic appeal as minimising blank spaces.", notes = "Java {"}Preliminary experiments with GP using crossover did not reveal any benefits of the [our] crossover operator or a population-based search.{"} p7. No details of GP. Binary tree representation. XO randomise leaf labels. Some experiments on effect of different fitness measures. Code produces HTML and LaTeX code See also \cite{fuchs:2000:AEASWD}", size = "13 pages", } @InProceedings{fuchs:2000:AEASWD, author = "Matthias Fuchs", title = "An Evolutionary Approach to Support Web-Page Design", booktitle = "Proceedings of the 2000 Congress on Evolutionary Computation CEC00", year = "2000", pages = "1312--1319", volume = "2", address = "La Jolla Marriott Hotel La Jolla, California, USA", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "6-9 " # jul, organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", publisher = "IEEE Press", keywords = "Web page design, aesthetic appeal, blank space minimisation, evolutionary approach, fitness measure, layout problem, document handling, evolutionary computation, information resources, user interfaces", ISBN = "0-7803-6375-2", DOI = "doi:10.1109/CEC.2000.870803", abstract = "Arranging pictures or photographs on a wall or a sheet of paper can be viewed as a layout problem that consists in placing a set of rectangles on a large rectangle so that there are no overlaps, and all edges are parallel to either the vertical or horizontal edge of the large rectangle. Automating this process is sensible in connection with Web page design, in particular if frequent changes occur. For Web pages, it is possible and it makes sense to scale pictures down so as to ensure that there is a solution to any such layout problem. The goal then is to find a layout that arranges the pictures in a way that is pleasing to the eye. We propose to employ an evolutionary approach to evolve layouts, using a representation similar to slicing tree structures, and a fitness measure that incorporates the idea of aesthetic appeal as minimising blank spaces", notes = "CEC-2000 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 00TH8512, Library of Congress Number = 00-018644 See also \cite{fuchs:2000:AEASWDtr}", } @Article{fuente2013computational, author = "Luis A. Fuente and Michael A. Lones and Alexander P. Turner and Susan Stepney and Leo S. Caves and Andy M. Tyrrell", title = "Computational models of signalling networks for non-linear control", journal = "Biosystems", year = "2013", volume = "112", number = "2", pages = "122--130", note = "Selected papers from the 9th International Conference on Information Processing in Cells and Tissues", keywords = "genetic algorithms, genetic programming, Cellular signalling, Biochemical networks, Crosstalk, Evolutionary algorithms, Chaos control", publisher = "Elsevier", ISSN = "0303-2647", URL = "http://www.sciencedirect.com/science/article/pii/S0303264713000506", DOI = "doi:10.1016/j.biosystems.2013.03.006", abstract = "Artificial signalling networks (ASNs) are a computational approach inspired by the signalling processes inside cells that decode outside environmental information. Using evolutionary algorithms to induce complex behaviours, we show how chaotic dynamics in a conservative dynamical system can be controlled. Such dynamics are of particular interest as they mimic the inherent complexity of non-linear physical systems in the real world. Considering the main biological interpretations of cellular signalling, in which complex behaviours and robust cellular responses emerge from the interaction of multiple pathways, we introduce two ASN representations: a stand-alone ASN and a coupled ASN. In particular we note how sophisticated cellular communication mechanisms can lead to effective controllers, where complicated problems can be divided into smaller and independent tasks.", } @InProceedings{fuente2015harmonic, author = "Luis A. Fuente and Michael A. Lones and Nigel T. Crook and Tjeerd V. Olde Scheper", title = "Harmonic Versus Chaos Controlled Oscillators in Hexapedal Locomotion", booktitle = "10th International Conference on Information Processing in Cells and Tissues, IPCAT 2015", year = "2015", editor = "Michael Lones and Andy Tyrrell and Stephen Smith and Gary Fogel", volume = "9303", series = "LNCS", pages = "114--127", address = "San Diego, CA, USA", month = sep # " 14-16", publisher = "Springer International Publishing", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-23108-2", DOI = "doi:10.1007/978-3-319-23108-2_10", abstract = "The behavioural diversity of chaotic oscillator can be controlled into periodic dynamics and used to model locomotion using central pattern generators. This paper shows how controlled chaotic oscillators may improve the adaptation of the robot locomotion behaviour to terrain uncertainties when compared to nonlinear harmonic oscillators. This is quantitatively assesses by the stability, changes of direction and steadiness of the robotic movements. Our results show that the controlled Wu oscillator promotes the emergence of adaptive locomotion when deterministic sensory feedback is used. They also suggest that the chaotic nature of chaos controlled oscillators increases the expressiveness of pattern generators to explore new locomotion gaits.", notes = "Affiliated with Department of Computing and Communication Technologies, Oxford Brookes University", } @InProceedings{Fuentes-Almoguera:2025:evoapplications, author = "Angel Fuentes-Almoguera and Carlos Garcia-Martinez and Gabriel Luque", title = "{GPBus}: Genetic Programming Based Automated Machine Learning for Bus Delay Prediction", booktitle = "28th International Conference, EvoApplications 2025", year = "2025", editor = " Pablo Garcia-Sanchez and Emma Hart and Sarah L. Thomson", series = "LNCS", volume = "15613", pages = "288--302", publisher = "Springer", address = "Trieste, Italy", month = "3-5 " # apr, organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, AutoML, Regression, Automated machine learning learning", isbn13 = "978-3-031-90065-5", timestamp = "Fri, 09 May 2025 20:28:52 +0200", biburl = "https://dblp.org/rec/conf/evoapps/FuentesAlmogueraGL25.bib", bibsource = "dblp computer science bibliography, https://dblp.org", DOI = "doi:10.1007/978-3-031-90065-5_18", notes = "DBLP:conf/evoapps/FuentesAlmogueraGL25, http://www.evostar.org/2025/ EvoApplications2025 held in conjunction with EuroGP'2025, EvoCOP2025 and EvoMusArt2025", } @Article{Fuentes-Tomas:TEVC, author = "Jose-Antonio Fuentes-Tomas and Efren Mezura-Montes and Hector-Gabriel Acosta-Mesa and Aldo Marquez-Grajales", journal = "IEEE Transactions on Evolutionary Computation", title = "Tree-Based Codification in Neural Architecture Search for Medical Image Segmentation", year = "2024", volume = "28", number = "3", pages = "597--607", month = jun, keywords = "genetic algorithms, genetic programming, Image segmentation, Computer architecture, Biomedical imaging, Statistics, Sociology, Convolution, Syntactics, Neural Architecture Search, ANN, Convolutional Neural Networks, Medical Image Segmentation", ISSN = "1941-0026", DOI = "doi:10.1109/TEVC.2024.3353182", abstract = "Convolutional Neural Networks (CNNs) have shown a competitive performance in medical imaging applications, such as image segmentation. However, choosing an existing architecture capable of adapting to a specific dataset is challenging and requires design expertise. Neural Architecture Search (NAS) is employed to overcome these limitations. NAS uses techniques to design the Neural Networks architecture. Typically, the models' weights optimisation is carried out using a continuous loss function, unlike model topology optimisation, which is highly influenced by the specific problem. Genetic Programming (GP) is an Evolutionary Algorithm (EA) capable of adapting to the topology optimisation problem of CNNs by considering the attributes of its representation. A tree representation can express complex connectivity and apply variation operations. This paper presents a tree-based GP algorithm for evolving CNNs based on the well-known U-Net architecture producing compact and flexible models for medical image segmentation across multiple domains. This proposal is called Neural Architecture Search / Genetic Programming / U-Net (NASGP-Net). NASGP-Net uses a cell-based encoding and U-Net architecture as a backbone to construct CNNs based on a hierarchical arrangement of primitive operations. Our experiments indicate that our approach can produce remarkable segmentation results with fewer parameters regarding fixed architectures. Moreover, NASGP-Net presents competitive results against NAS methods. Finally, we observed notable performance improvements based on several evaluation metrics, including Dice similarity coefficient (DSC), Intersection over union (IoU), and Hausdorff Distance (HD).", notes = "Also known as \cite{10391062}", } @InProceedings{fuhner:2001:gecco, title = "EvolVision - an Evolvica visualization tool", author = "Tim Fuhner and Christian Jacob", pages = "176", year = "2001", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)", editor = "Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon and Edmund Burke", address = "San Francisco, California, USA", publisher_address = "San Francisco, CA 94104, USA", month = "7-11 " # jul, keywords = "genetic algorithms, genetic programming: Poster, EvolVision, Evolvica, visualization, Mathematica, Java, client/server application, plug-in architecture, pedigree diagrams", ISBN = "1-55860-774-9", URL = "http://gpbib.cs.ucl.ac.uk/gecco2001/d02.pdf", notes = "GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of \cite{spector:2001:GECCO}", } @InProceedings{ga:Dickinson87, author = "Cory Fujiki and John Dickinson", title = "Using the Genetic Algorithm to Generate Lisp Source Code to Solve the Prisoner's Dilemma", booktitle = "Genetic Algorithms and their Applications: Proceedings of the second international conference on Genetic Algorithms", year = "1987", editor = "John J. Grefenstette", pages = "236--240", address = "MIT, Cambridge, MA, USA", month = "28-31 " # jul, organisation = "AAAI, Naval Research Laboratory, Bolt Beranek and Newman, Inc", publisher_address = "Hillsdale, NJ, USA", publisher = "Lawrence Erlbaum Associates", keywords = "genetic algorithms", URL = "http://gpbib.cs.ucl.ac.uk/icga/icga1987.pdf", size = "5 pages", notes = "Complete Lisp S-Expressions generated but are constrained to be a (variable length??) list of condition-action pairs, each of which is an s-expresion. These S-expressions are initially created at random and do _not_ evolve. Instead Mutation, Invert and crossover create new individuals using these existing components.", } @InProceedings{DBLP:conf/snpd/FukuiH22, author = "Toshiyuki Fukui and Teruhisa Hochin", title = "Concurrency Control Program Generation in Genetic Programming Considering Depth of the Program Tree", booktitle = "23rd {ACIS} International Summer Virtual Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, {SNPD} 2022 - Summer, Kyoto City, Japan, July 4-7, 2022", pages = "56--61", publisher = "{IEEE}", year = "2022", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1109/SNPD-Summer57817.2022.00018", DOI = "doi:10.1109/SNPD-Summer57817.2022.00018", timestamp = "Fri, 03 Mar 2023 14:39:13 +0100", biburl = "https://dblp.org/rec/conf/snpd/FukuiH22.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{fukunaga:1995:dsef, author = "Alex S. Fukunaga and Andrew B. Kahng", title = "Improving the Performance of Evolutionary Optimization by Dynamically Scaling the Evolution Function", booktitle = "1995 IEEE Conference on Evolutionary Computation", year = "1995", volume = "1", pages = "182--187", address = "Perth, Australia", publisher_address = "Piscataway, NJ, USA", month = "29 " # nov # " - 1 " # dec, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", URL = "http://metahack.org/Fukunaga-Kahng-ICEC-1995.pdf", URL = "http://citeseer.ist.psu.edu/fukunaga95improving.html", size = "6 pages", abstract = "Traditional evolutionary optimization algorithms assume a static environment in which solutions are evolved. Incremental evolution is an approach through which a dynamic evaluation function is scaled over time in order to improve the performance of evolutionary optimization. In this paper, we present empirical results that demonstrate the effectiveness of this approach for genetic programming. Using two domains, a two-agent pursuit-evasion game and the Tracker trail-following task, we demonstrate that incremental evolution is most successful when applied near the beginning of an evolutionary run. We also show that incremental evolution can be successful when the intermediate evaluation functions are more difficult than the target evaluation function, as well as they are easier than the target function.", notes = "ICEC-95 Editors not given by IEEE, Organisers David Fogel and Chris deSilva. ", } @InProceedings{fukunaga:1998:gchpGP, author = "Alex Fukunaga and Andre Stechert and Darren Mutz", title = "A Genome Compiler for High Performance Genetic Programming", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "86--94", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, SPARC machine language", ISBN = "1-55860-548-7", URL = "http://metahack.org/gp98-compiler.pdf", URL = "http://fukunaga.bol.ucla.edu/gp98-compiler.pdf", URL = "http://citeseer.ist.psu.edu/fukunaga98genome.html", size = "9 pages", abstract = "Genetic Programming is very computationally expensive. For most applications, the vast majority of time is spent evaluating candidate solutions, so it is desirable to make individual evaluation as efficient as possible. We describe a genome compiler which compiles s-expressions to machine code, resulting in significant speedup of individual evaluations over standard GP systems. Based on performance results with symbolic regression, we show that the execution of the genome compiler system is comparable to the fastest alternative GP systems. We also demonstrate the utility of compilation on a real-world problem, lossless image compression. A somewhat surprising result is that in our test domains, the overhead of compilation is negligible.", notes = "'Our compiler directly generates the machine executable code that corresponds to this assembly-level code.' 'eliminates the the overhead of invoking an external compiler.' 'The genome compiler approach is similar to \cite{Nordin:1995:tcp}' target = x**9. 30 generations RISC 296 MHz UltraSparc 2 https://en.wikipedia.org/wiki/SPARC 'reaching a maximum speedup of around 50 when the number of test cases is 1000.' 'The genome compiler, due to its use of machine code, achieves about an order of magnitude speedup over HiGP' \cite{stoffel:1996:hpsbGP}. 'The genome compiler performs roughly 50-60 times faster than 'Tackett and Carmi' SGPC \cite{sgpc_readme} running on the same machine, which is comparable to the execution speeds for CGPS reported by Nordin and Banzhaf \cite{Nordin:1995:tcp}.' Lossless Image Compression future work 'compiler optimizations which use editing operations \cite{koza:book} or standard compiler optimization techniques to collapse instructions together, remove redundant operations, reorder operations, etc.,' GP-98 Thu, 25 Jun 1998 10:31:36 PDT We've recently developed a gp system based on lil-gp which evolves s-expressions and compiles it to machine code (specifically, Sparc machine code) to speed up evaluation. In our system, we've found that the overhead of compilation is negligible, since the vast majority of the time spent in execution in an s-expression interpreter (in our case, the lil-gp interpreter) is consumed by the recursive traversal of the tree. A full description, comparisons with previous GP-compiler systems and some experimental results with symbolic regression and image compression are described", } @InProceedings{fukunaga:1998:enlpmllicGP, author = "Alex Fukunaga and Andre Stechert", title = "Evolving Nonlinear Predictive Models for Lossless Image Compression with Genetic Programming", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "95--102", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-548-7", URL = "http://www.bol.ucla.edu/~fukunaga/gp98-compress.pdf", URL = "http://citeseer.ist.psu.edu/507773.html", size = "8 pages", abstract = "We describe a genetic programming system which learns nonlinear predictive models for lossless image compression. Sexpressions which represent nonlinear predictive models are learned, and the error image is compressed using a Human encoder. We show that the proposed system is capable of achieving compression ratios superior to that of the best known lossless compression algorithms, although it is significantly slower than standard algorithms.", notes = "GP-98", } @InProceedings{fukunaga:1999:PGA, author = "Alex S. Fukunaga", title = "Portfolios of Genetic Algorithms", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "786", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-840.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-840.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{fukunaga:2002:AAAI, author = "Alex Fukunaga", title = "Automated Discovery of Composite SAT Variable Selection Heuristics", booktitle = "Proceedings of the National Conference on Artificial Intelligence (AAAI)", year = "2002", pages = "641--648", keywords = "genetic algorithms, genetic programming, satisfiability, constraint satisfaction, local search", URL = "http://citeseer.nj.nec.com/506523.html", URL = "http://www.bol.ucla.edu/~fukunaga/AAAI02.pdf", abstract = "Variants of GSAT and Walksat are among the most successful SAT local search algorithms. We show that several well-known SAT local search algorithms are the results of novel combinations of a set of variable selection primitives. We describe CLASS, an automated heuristic discovery system which generates new, effective variable selection heuristic functions using a simple composition operator. New heuristics discovered by CLASS are shown to be competitive with the best Walksat variants, including Novelty and R-Novelty . We also analyse the local search behaviour of the learned heuristics using the depth, mobility, and coverage metrics recently proposed by Schuurmans and Southey.", } @InProceedings{Fukunaga:2004:sat, author = "Alex Fukunaga", title = "Efficient Implementations of SAT Local Search", booktitle = "The Seventh International Conference on Theory and Applications of Satisfiability Testing (SAT 2004)", year = "2004", address = "Vancouver, BC, Canada", month = "10-13 " # may, keywords = "Poster", URL = "http://www.satisfiability.org/SAT04/programme/106.pdf", URL = "http://metahack.org/sat2004.pdf", size = "6 pages", abstract = "Although most of the focus in SAT local search has been on search behavior (deciding which variable to flip next), the overall efficiency of an algorithm depends greatly on the efficiency of executing each variable flip and variable selection. This paper surveys, evaluates, and extends incremental data structures that have been used in SAT local search solvers (including the GSAT and Walksat families of solvers) to support efficient variable flips and selection.", notes = "cited by \cite{Fukunaga:2009:cec} Does not mention GP. Does not appear to be in LNCS", } @InProceedings{fukunaga:els:gecco2004, author = "Alex S. Fukunaga", title = "Evolving Local Search Heuristics for {SAT} Using Genetic Programming", booktitle = "Genetic and Evolutionary Computation -- GECCO-2004, Part II", year = "2004", editor = "Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell", series = "Lecture Notes in Computer Science", pages = "483--494", address = "Seattle, WA, USA", publisher_address = "Heidelberg", month = "26-30 " # jun, organisation = "ISGEC", publisher = "Springer-Verlag", volume = "3103", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-22343-6", ISSN = "0302-9743", URL = "http://alexf04.maclisp.org/gecco2004.pdf", DOI = "doi:10.1007/978-3-540-24855-2_59", DOI = "doi:10.1007/b98645", size = "12 pages", abstract = "Satisability testing (SAT) is a very active area of research today, with numerous real-world applications. We describe CLASS2.0, a genetic programming system for semi-automatically designing SAT local search heuristics. An empirical comparison shows that that the heuristics generated by our GP system outperform the state of the art human-designed local search algorithms, as well as previously proposed evolutionary approaches, with respect to both runtime as well as search efficiency (number of variable flips to solve a problem).", notes = "GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)", } @Article{Fukunaga:2008:EC, author = "Alex S. Fukunaga", title = "Automated Discovery of Local Search Heuristics for Satisfiability Testing", journal = "Evolutionary Computation", year = "2008", volume = "16", number = "1", pages = "31--61", month = "Spring", keywords = "genetic algorithms, genetic programming, STGP, satisfiability, constraint satisfaction, SAT, hyper-heuristic, hybrid genetic-local search", ISSN = "1063-6560", URL = "http://metahack.org/ecj08-web-preprint.pdf", DOI = "doi:10.1162/evco.2008.16.1.31", size = "31 pages", abstract = "The development of successful metaheuristic algorithms such as local search for a difficult problem such as satisfiability testing (SAT) is a challenging task. We investigate an evolutionary approach to automating the discovery of new local search heuristics for SAT. We show that several well-known SAT local search algorithms such as Walksat and Novelty are composite heuristics that are derived from novel combinations of a set of building blocks. Based on this observation, we developed CLASS, a genetic programming system that uses a simple composition operator to automatically discover SAT local search heuristics. New heuristics discovered by CLASS are shown to be competitive with the best Walksat variants, including Novelty+. Evolutionary algorithms have previously been applied to directly evolve a solution for a particular SAT instance. We show that the heuristics discovered by CLASS are also competitive with these previous, direct evolutionary approaches for SAT. We also analyse the local search behaviour of the learned heuristics using the depth, mobility, and coverage metrics proposed by Schuurmans and Southey.", } @InProceedings{Fukunaga:2009:lisp, author = "Alex S. Fukunaga", title = "A Parallel, Lisp-Based Genetic Programming System for Discovering Satisfiability Solvers", booktitle = "International Lisp Conference, ILC 2009", year = "2009", editor = "Guy L. {Steele, Jr.}", pages = "137--148", address = "Massachusetts Institute of Technology, Cambridge, Massachusetts, USA", month = mar # " 22-25", organisation = "ALU", keywords = "genetic algorithms, genetic programming", notes = "Global Edge Institute, Tokyo Institute of Technology https://lisphub.jp/common-lisp/users/index.cgi?Event%3AInternational%20Lisp%20Conference%3A2009 https://www.international-lisp-conference.org/2009/ Broken Aug 2018 http://www.international-lisp-conference.org/2009/speakers https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-20700131/", } @InProceedings{Fukunaga:2009:cec, author = "Alex S. Fukunaga", title = "Massively Parallel Evolution of {SAT} Heuristics", booktitle = "2009 IEEE Congress on Evolutionary Computation", year = "2009", editor = "Andy Tyrrell", pages = "1478--1485", address = "Trondheim, Norway", month = "18-21 " # may, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-2959-2", file = "P308.pdf", DOI = "doi:10.1109/CEC.2009.4983117", size = "8 pages", abstract = "Recent work has shown that it is possible to evolve heuristics for solving propositional satisfiability (SAT) problems which are competitive with the best hand-coded heuristics. However, previous work was limited by the computational resources required in order to evolve successful heuristics. In this paper, we describe a massively parallel genetic programming system for evolving SAT heuristics. Runs using up to 5.5 CPU core years of computation were executed, and resulted in new SAT heuristics which significantly outperform hand-coded heuristics.", keywords = "genetic algorithms, genetic programming, STGP, hyperheuristics, MPI", notes = "up to a million fitness evaluations. pop=10000,100 gens. 8 percent reproduction. Sometimes used normal (Koza like) crossover and mutation, in place of own SAT composition operator. Complicated, stepped, fitness function. CLASS, PCLASS. Compiled common lisp includes S-expressions simplification and caching subexpressions. Master-slave parallelism 720 2.83GHz E5440 core Sun blade X6250 cluster (claims idle time approx 2 percent). SAT/UNSAT phase transition mkcnf SATLIB. CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR", } @Article{Fukunaga:2012:GPEM, author = "Alex Fukunaga and Hideru Hiruma and Kazuki Komiya and Hitoshi Iba", title = "Evolving controllers for high-level applications on a service robot: a case study with exhibition visitor flow control", journal = "Genetic Programming and Evolvable Machines", year = "2012", volume = "13", number = "2", pages = "239--263", month = jun, keywords = "genetic algorithms, genetic programming, Evolutionary robotics, Service robotics Applications", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-011-9152-3", size = "25 pages", abstract = "We investigate the application of simulation-based genetic programming to evolve controllers that perform high-level tasks on a service robot. As a case study, we synthesise a controller for a guide robot that manages the visitor traffic flow in an exhibition space in order to maximise the enjoyment of the visitors. We used genetic programming in a low-fidelity simulation to evolve a controller for this task, which was then transferred to a service robot. An experimental evaluation of the evolved controller in both simulation and on the actual service robot shows that it performs well compared to hand-coded heuristics, and performs comparably to a human operator.", affiliation = "The University of Tokyo, Tokyo, Japan", } @InProceedings{Fukushima:2012:SICE, author = "Hiroki Fukushima and Takeshi Tsujimura and Kiyotaka Izumi and Yoshihiro Minato", booktitle = "SICE Annual Conference 2012", title = "Figure classification system of laser beam trace using Genetic Programming", year = "2012", pages = "284--289", address = "Akita, Japan", month = aug # " 20-23", isbn13 = "978-1-4673-2259-1", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6318448", size = "6 pages", abstract = "This paper proposes a shape classification system using Genetic Programming. In this research, the classification tree for shape identification is generated based on velocity vectors drawn by a laser pointer. We confirm the classification tree can be used for shape identification, by adjusting the function set, target program size, crossover rate, and mutation rate to optimise Genetic Programming. As a result, my proposal makes it possible to classify the shape of a drawn figure in high accuracy of 0.960.", keywords = "genetic algorithms, genetic programming, image classification, laser beams, classification tree, crossover rate, figure classification system, function set, laser beam trace, laser pointer, mutation rate, shape classification system, shape identification, target program size, velocity vector, Classification tree analysis, Computer vision, Image motion analysis, Laser beams, Optical imaging, Testing, image processing, laser pointer, optical flow, pattern recognition", notes = "\cite{Tsujimura:2012:FedCSIS} Also known as \cite{6318448}", } @InProceedings{fukuyama:1999:APSORPVCEPS, author = "Yoshikazu Fukuyama and Shinichi Takayama and Yosuke Nakanishi and Hirotaka Yoshida", title = "A Particle Swarm Optimization for Reactive Power and Voltage Control in Electric Power Systems", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1523--1528", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "real world applications", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-713.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-713.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @TechReport{cs-97-197, author = "Pablo Funes and Elizabeth Sklar and Hugues Juille and Jordan Pollack", title = "The Internet as a Virtual Ecology: Coevolutionary Arms Races Between Human and Artificial Populations", institution = "Computer Science, Brandeis University", year = "1997", type = "Technical Report", number = "CS-97-197", address = "415 South St., Waltham MA 02254 USA", keywords = "genetic algorithms, genetic programming, autonomous agents, adaptive software, evolutionary robotics, game learning, coevolution, Tron, interactive evolution, IEC", URL = "http://helen.cs-i.brandeis.edu/papers/cs-97-197.pdf", URL = "http://helen.cs-i.brandeis.edu/papers/cs-97-197.ps.gz", URL = "http://helen.cs-i.brandeis.edu/papers/cs-97-197.ps", URL = "http://www.demo.cs.brandeis.edu/papers/long.html#cs-97-197", abstract = "we propose that learning complex behaviours can be achieved in a coevolutionary environment where one population consists of the human users of an interactive adaptive software tool and the {"}opposing{"} population is artificial, generated by a coevolutionary learning engine. We take advantage of the Internet, a connected community where people and software coexist. A new kind of adaptive agent can exploit its interactions with thousands of users-inside a virtual {"}niche{"}-to learn in a coevolutionary human-robot arms race. Our model is Tron, a simple dynamic game where introspective self-play quickly leads to collusive stagnation. We describe an application where thousands of small programs are sent to play with people through the Java interpreter running in their web browsers. The feedback provided by these agents is collected in our server and used to augment an ever improving fitness landscape for local robot-robot games. Speciation and fitness sharing provide diversity to challenge humans with a variety of differ ent strategies. In this way, we obtain an evolving environment where human as well as artificial adaptation are simultaneously taking place.", notes = "See also \cite{funes_sab98} and http://demo.cs.brandeis.edu/tron/html/about.html Two populations: computer play (1000), playing against people (100). Generation based. non-standard selection and migration strategies. Deterministic play. Limited knowledge of game arena. Java. Problem with {"}live and let live{"} or conclusion between (evolved) players. p10 People played 22494 games in two months. p11 Marginal improvement in computer players (28% -> 35%). Humans better than computer. ", size = "20 pages", } @InProceedings{funes_ecal97, author = "Pablo Funes and Jordan Pollack", title = "Computer Evolution of Buildable Objects", booktitle = "Fourth European Conference on Artificial Life", year = "1997", editor = "P. Husbands and I. Harvey", pages = "358--367", publisher = "MIT Press", keywords = "genetic algorithms, genetic programming, evolutionary design, evolutionary robotics, computer simulation", URL = "http://www.demo.cs.brandeis.edu/papers/other/cs-97-191.html", URL = "http://www.demo.cs.brandeis.edu/papers/ecal97.pdf", URL = "http://www.demo.cs.brandeis.edu/papers/ecal97.ps.gz", size = "10 pages", abstract = "Creating artificial life forms through evolutionary robotics faces a {"}chicken and egg{"} problem: learning to control a complex body is dominated by inductive biases specific to its sensors and effectors, while building a body which is controllable is conditioned on the pre-existence of a brain. The idea of co-evolution of bodies and brains is becoming popular, but little work has been done in evolution of physical structure because of the lack of a general framework for doing it. Evolution of creatures in simulation has been constrained by the {"}reality gap{"} which implies that resultant objects are usually not buildable. The work we present takes a step in the problem of body evolution by applying evolutionary techniques to the design of structures assembled out of parts. Evolution takes place in a simulator we designed, which computes forces and stresses and predicts failure for 2-dimensional Lego structures. The final printout of our program is a schematic assembly, which can then be built physically. We demonstrate its functionality in several different evolved entities.", notes = "An earlier revision of this paper is available in html: Brandeis University Computer Science Technical Report CS-97-191", } @InProceedings{funes_sab98, author = "Pablo Funes and Elizabeth Sklar and Hugues Juille and Jordan Pollack", title = "Animal-Animat Coevolution: Using the Animal Population as Fitness Function", booktitle = "From Animals to Animats 5: Proceedings of the Fifth International Conference on Simulation of Adaptive Behavior", year = "1998", editor = "Rolf Pfeifer and Bruce Blumberg and Jean-Arcady Meyer and Stewart W. Wilson", pages = "525--533", address = "Zurich, Switzerland", month = aug # " 17-21", publisher = "MIT Press", keywords = "genetic algorithms, genetic programming, adaptive agents, internet evolution, computer game playing", ISBN = "0-262-66144-6", URL = "http://www.demo.cs.brandeis.edu/papers/tronsab98.pdf", URL = "http://www.demo.cs.brandeis.edu/papers/tronsab98.ps.gz", URL = "http://www.demo.cs.brandeis.edu/papers/tronsab98.ps", URL = "https://ieeexplore.ieee.org/document/6278699", DOI = "doi:10.7551/mitpress/3119.003.0079", size = "9 pages", abstract = "We show an artificial world where animals (humans) and animats (software agents) interact in a coevolutionary arms race. The two species each use adaptation schemes of their own. Learning through interaction with humans has been out of reach for evolutionary learning techniques because too many iterations are necessary. Our work demonstrates that the Internet is a new environment where this may be possible through an appropriate setup that creates mutualism, a relationship where human and animat species benefit from their interactions with each other.", notes = "broken June 2021 http://www.isab.org.uk/confs/sab98.php http://demo.cs.brandeis.edu/tron/html/about.html section 3.4 'Hall of Fame' fig 11 http://www.demo.cs.brandeis.edu/papers/tronsab98.html", } @TechReport{funes_cs98-198, author = "Pablo J. Funes and Jordan B. Pollack", title = "Componential Structural Simulator", institution = "Computer Science, Brandeis University", year = "1998", type = "Technical Report", number = "CS-98-198", address = "415 South St., Waltham MA 02254 USA", keywords = "genetic algorithms, genetic programming", URL = "http://www.demo.cs.brandeis.edu/papers/cs98-198.pdf", abstract = "Our componential structural simulator procedure provides an approximate simulation that predicts resistance of structures made of modular components. The simulation focuses on torque strains and is able to predict stability of a structure whose breakage depends on torque stress. Structures that can be described in this fashion include those made out of building toy bricks such as Lego bricks, a well-known type of snap-on toy bricks, which we have used in our initial applications. The model could be applied to many other kinds of structures made out of modular components. It is a prediction tool that can be programmed in a computer and used to test the stability of a structure before proceeding to its construction.", notes = "not about GP but about part of fitness function used in GP experiments, eg in \cite{funes_alife}", size = "17 pages", } @Article{funes_alife, author = "Pablo Funes and Jordan Pollack", title = "Evolutionary Body Building: Adaptive Physical Designs for Robots", journal = "Artificial Life", year = "1998", volume = "4", number = "4", pages = "337--357", month = "Fall", keywords = "genetic algorithms, genetic programming, evolutionary robotics, body and brain coevolution, adaptive bodies, evolutionary design, lego, children's building blocks", ISSN = "1064-5462", URL = "http://www.demo.cs.brandeis.edu/papers/funpolalife.pdf", URL = "http://www.demo.cs.brandeis.edu/papers/funpolalife.ps.gz", bytes14139576 = "http://www.demo.cs.brandeis.edu/papers/funpolalife.ps", broken = "http://mitpress.mit.edu/catalog/item/default.asp?sid=8F59C20B-F846-405E-9C5C-6F86770D37BB&ttype=6&tid=109", DOI = "doi:10.1162/106454698568639", size = "21 pages", abstract = "Creating artificial life forms through evolutionary robotics faces a 'chicken and egg' problem: learning to control a complex body is dominated by problems specific to its sensors and effectors, while building a body that is controllable assumes the pre-existence of a brain. The idea of co-evolution of bodies and brains is becoming popular, but little work has been done in evolution of physical structure because of the lack of a general framework for doing it. Evo-lution of creatures in simulation has usually resulted in virtual entities which are not buildable, while embodied evolution in actual robotics is constrained by the slow pace of real time. The work we present takes a step in the problem of body evolution by applying evolutionary techniques to the design of structures assembled out of elementary components which stick together. Evolution takes place in a simulator which computes forces and stresses and predicts stability of 3- dimensional brick structures. The final printout of our program is a schematic assembly, which is then built physically. We demonstrate the functionality of this approach to robot body building with many evolved artifacts.", notes = "ABS acrylonitrile butadiene styrene lego http://en.wikipedia.org/wiki/Acrylonitrile_butadiene_styrene Data in table 1 p341 appears to be wrong. Design respresented as lisp s-expression. \cite{koza:book} style crossover and mutation but with domain specific sanity checks. Details of tree pruning unclear. Unclear if repaired s-expression becomes geneotype or not. Only structually stable individuals allowed to become part of population (cf Tackett's \cite{Tackett:1995:grgsscp} soft brood selection). Saftey margin only 0.2 Cites \cite{funes_cs98-198}", } @InCollection{funes_edc98, author = "Pablo J. Funes and Jordan B. Pollack", title = "Computer Evolution of Buildable Objects", booktitle = "Evolutionary Design by Computers", publisher = "Morgan Kaufmann", year = "1999", editor = "Peter J. Bentley", chapter = "17", pages = "387--403", address = "San Francisco, USA", keywords = "genetic algorithms, genetic programming, evolutionary design, evolutionary robotics, computer simulation", ISBN = "1-55860-605-X", URL = "http://www.demo.cs.brandeis.edu/papers/edc98.pdf", URL = "http://www.demo.cs.brandeis.edu/papers/edc98.ps.gz", bytes12043580 = "http://www.demo.cs.brandeis.edu/papers/edc98.ps", size = "20 pages", abstract = "evolution of buildable designs using miniature plastic bricks as modular components. Lego bricks are well known for their flexibility when it comes to creating low cost, handy designs of vehicles and structures. Their simple modular concept make toy bricks a good ground for doing evolution of computer simulated structures which can be built and deployed.", notes = "http://www.amazon.com/exec/obidos/ASIN/155860605X/qid=1114257064/sr=2-1/ref=pd_bbs_b_2_1/103-2923288-2944615", } @PhdThesis{funes_phd, author = "Pablo Funes", title = "Evolution of Complexity in Real-World Domains", school = "Computer Science, Brandeis University", year = "2001", address = "USA", month = may, keywords = "genetic algorithms, genetic programming, AI", URL = "http://www.demo.cs.brandeis.edu/papers/funes_phd.pdf", URL = "http://www.demo.cs.brandeis.edu/papers/funes_phd.ps", URL = "http://www.demo.cs.brandeis.edu/papers/funes_phd.html", size = "167 pages", abstract = "Artificial Life research brings together methods from Artificial Intelligence (AI), philosophy and biology, studying the problem of evolution of complexity from what we might call a constructive point of view, trying to replicate adaptive phenomena using computers and robots. Here we wish to shed new light on the issue by showing how computer-simulated evolutionary learning methods are capable of discovering complex emergent properties in complex domains. Our stance is that in AI the most interesting results come from the interaction between learning algorithms and real domains, leading to discovery of emergent properties, rather than from the algorithms themselves. The theory of natural selection postulates that generate-test-regenerate dynamics, exemplified by life on earth, when coupled with the kinds of environments found in the natural world, have lead to the appearance of complex forms. But artificial evolution methods, based on this hypothesis, have only begun to be put in contact with real-world environments. In the present thesis we explore two aspects of real-world environments as they interact with an evolutionary algorithm. In our first experimental domain (chapter 2) we show how structures can be evolved under gravitational and geometrical constraints, employing simulated physics. Structures evolve that exploit features of the interaction between brick-based structures and the physics of gravitational forces. In a second experimental domain (chapter 3) we study how a virtual world gives rise to co-adaptation between human and agent species. In this case we look at the competitive interaction between two adaptive species. The purely reactive nature of artificial agents in this domain implies that the high level features observed cannot be explicit in the genotype but rather, they emerge from the interaction between genetic information and a changing domain. Emergent properties, not obvious from the lower level description, amount to what we humans call complexity, but the idea stands on concepts which resist formalisation -- such as difficulty or complicatedness. We show how simulated evolution, exploring reality, finds features of this kind which are preserved by selection, leading to complex forms and behaviours. But it does so without creating new levels of abstraction -- thus the question of evolution of modularity remains open.", } @Article{funes:2007:sigevo, author = "Pablo Jose Funes", title = "Buildable Evolution", journal = "SIGEVOlution", year = "2007", volume = "2", number = "3", pages = "6--19", month = "Autumn", keywords = "genetic algorithms, genetic programming, LEGO", URL = "http://www.sigevolution.org/issues/pdf/SIGEVOlution200703.pdf", DOI = "doi:10.1145/1366914.1366916", size = "14 pages", abstract = "The most interesting results in Artifical Life come about when some aspect of reality is captured. In the mid-1990s, Karl Sims energised the AL community with his ground-breaking work on evolved moving creatures [28, 29]. The life-like behaviour of Sims' creatures resulted from combining evolved morphology with a physics simulation based on Featherstone's earlier work [9]. The question that begged asking was: can a similar thing be done in the physical world? Can we make creatures that walk out of the computer screen and into the room? Two components were required: a language to evolve morphologies that have real-world counterparts, and a way to build them -- either in simulation or by automated building and testing. We set out to demonstrate that buildable evolution was possible using a readily available, cheap building system -- Lego bricks -- and an ad-hoc physics simulation that allowed us to study the interaction of the object with the physical world in silico; with respect to gravitational forces at least. The result [10, 14, 12, 13, 15, 16, 25, 23, 26, 24, 27] is a system that can evolve a variety of different shapes and is very easy to use, set up and replicate. Here I present an overview of the evolvable Lego structures project. Coinciding with the publication of this article, the source code is being released to the community (demo.cs.brandeis.edu/pr/buildable/source).", notes = "Published April 2008. Grammar, 2D and 3D, mutation and crossover, development, bloat, test and prune, network torque propagation, NTP, EvoCAD, MNFPs, linear programming solver, crane, long bridge, table. homepage: http://www.icosystem.com DEMO lab Brandeis.", } @InProceedings{Funie:2014:ICMLA, author = "Andreea-Ingrid Funie and Mark Salmon and Wayne Luk", booktitle = "13th International Conference on Machine Learning and Applications (ICMLA 2014)", title = "A Hybrid Genetic-Programming Swarm-Optimisation Approach for Examining the Nature and Stability of High Frequency Trading Strategies", year = "2014", month = dec, pages = "29--34", keywords = "genetic algorithms, genetic programming, PSO, FPGA", DOI = "doi:10.1109/ICMLA.2014.11", size = "6 pages", abstract = "Advances in high frequency trading in financial markets have exceeded the ability of regulators to monitor market stability, creating the need for tools that go beyond market microstructure theory and examine markets in real time, driven by algorithms, as employed in practice. This paper investigates the design, performance and stability of high frequency trading rules using a hybrid evolutionary algorithm based on genetic programming, with particle swarm optimisation layered on top to improve the genetic operators' performance. Our algorithm learns relevant trading signal information using Foreign Exchange market data. Execution time is significantly reduced by implementing computationally intensive tasks using Field Programmable Gate Array technology. This approach is shown to provide a reliable platform for examining the stability and nature of optimal trading strategies under different market conditions through robust statistical results on the optimal rules' performance and their economic value.", notes = "Dept. of Comput., Imperial Coll. London, London, UK Also known as \cite{7033087}", } @InProceedings{Funie:2015:ieeeASAP, author = "Andreea Ingrid Funie and Paul Grigoras and Pavel Burovskiy and Wayne Luk and Mark Salmon", booktitle = "26th IEEE International Conference on Application-specific Systems, Architectures and Processors (ASAP)", title = "Reconfigurable acceleration of fitness evaluation in trading strategies", year = "2015", pages = "210--217", month = "27-29 " # jul, address = "Toronto, Canada", keywords = "genetic algorithms, genetic programming, FPGA, Field programmable gate arrays, Random access memory, Clocks, Acceleration, Sociology, Statistics", ISSN = "1063-6862", URL = "https://www.doc.ic.ac.uk/~wl/papers/15/asap15if.pdf", DOI = "doi:10.1109/ASAP.2015.7245736", size = "8 pages", abstract = "Over the past years, examining financial markets has become a crucial part of both the trading and regulatory processes. Recently, genetic programs have been used to identify patterns in financial markets which may lead to more advanced trading strategies. We investigate the use of Field Programmable Gate Arrays to accelerate the evaluation of the fitness function which is an important kernel in genetic programming. Our pipelined design makes use of the massive amounts of parallelism available on chip to evaluate the fitness of multiple genetic programs simultaneously. An evaluation of our designs on both synthetic and historical market data shows that our implementation evaluates fitness function up to 21.56 times faster than a multi-threaded C++11 implementation running on two six-core Intel Xeon E5-2640 processors using OpenMP.", notes = "Also known as \cite{7245736}", } @Article{Funie2018, author = "Andreea-Ingrid Funie and Paul Grigoras and Pavel Burovskiy and Wayne Luk and Mark Salmon", title = "Run-time Reconfigurable Acceleration for Genetic Programming Fitness Evaluation in Trading Strategies", journal = "Journal of Signal Processing Systems", year = "2018", volume = "90", number = "1", pages = "39--52", month = "1 " # jan, keywords = "genetic algorithms, genetic programming, Fitness evaluation, High-frequency trading, Run-time reconfiguration", ISSN = "1939-8115", URL = "http://hdl.handle.net/10044/1/52831", URL = "https://spiral.imperial.ac.uk/bitstream/10044/1/52831/5/s11265-017-1244-8.pdf", URL = "https://rdcu.be/dnO7k", DOI = "doi:10.1007/s11265-017-1244-8", size = "14 pages", abstract = "Genetic programming can be used to identify complex patterns in financial markets which may lead to more advanced trading strategies. However, the computationally intensive nature of genetic programming makes it difficult to apply to real world problems, particularly in real-time constrained scenarios. In this work we propose the use of Field Programmable Gate Array technology to accelerate the fitness evaluation step, one of the most computationally demanding operations in genetic programming. We propose to develop a fully-pipelined, mixed precision design using run-time reconfiguration to accelerate fitness evaluation. We show that run-time reconfiguration can reduce resource consumption by a factor of 2 compared to previous solutions on certain configurations. The proposed design is up to 22 times faster than an optimised, multi-threaded software implementation while achieving comparable financial returns.", notes = "See also \cite{Cross-AI-2018-PhD-Thesis}", } @InProceedings{conf/ppam/FunikaK13, author = "Wlodzimierz Funika and Pawel Koperek", title = "Genetic Programming in Automatic Discovery of Relationships in Computer System Monitoring Data", publisher = "Springer", year = "2013", volume = "8384", keywords = "genetic algorithms, genetic programming", bibdate = "2014-05-06", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ppam/ppam2013-1.html#FunikaK13", booktitle = "PPAM (1)", editor = "Roman Wyrzykowski and Jack Dongarra and Konrad Karczewski and Jerzy Wasniewski", isbn13 = "978-3-642-55223-6", pages = "371--380", series = "Lecture Notes in Computer Science", URL = "http://dx.doi.org/10.1007/978-3-642-55224-3", } @InProceedings{Furkan-Gul:2023:SIU, author = "Muhammed {Furkan Gul} and Sibel Arslan and Bilgisayar Muhendisligi and Yazilim Muhendisligi", booktitle = "2023 31st Signal Processing and Communications Applications Conference (SIU)", title = "Mackey-Glass Time Series Prediction with Immune Plasma Programming", year = "2023", abstract = "Automatic Programming (AP) is one of the subfields of artificial intelligence that enables efficient modelling of systems. Immune Plasma Programming (IPP), one of the newly proposed AP methods, is developed taking inspiration from plasma treatment. mathematical models using IPP for time series prediction are proposed. It is also compared with well-known AP methods such as Genetic Programming and Artificial Bee Colony Programming. According to the simulation results, IPP has proven that it can be applied to real-world problems by showing superior performance on various performance criteria compared to other methods.", keywords = "genetic algorithms, genetic programming, Plasmas, Time series analysis, Mathematical models, Automatic programming, Simulation, Signal processing algorithms, automatic programming, immune plasma programming, time series prediction", DOI = "doi:10.1109/SIU59756.2023.10223926", ISSN = "2165-0608", month = jul, notes = "Also known as \cite{10223926}", } @InProceedings{furuholmen2008continuous, author = "Marcus Furuholmen and Mats Hovin and Jim Torresen and Kyrre Glette", title = "Continuous Adaptation in Robotic Systems by Indirect Online Evolution", booktitle = "ECSIS Symposium on Learning and Adaptive Behaviors for Robotic Systems, LAB-RS 2008", year = "2008", pages = "71--76", address = "Edinburgh", month = "6-8 " # aug, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Gene Expression Programming, Automatic testing, Erbium, Gene expression, Informatics, Robot sensing systems, Robotics and automation, Sensor phenomena and characterisation, Sensor systems, System testing, US Department of Energy, adaptive systems, end effectors, vectors, continuous system identification, end effector, indirect online evolution, parameter optimisation, robotic arm, training vectors, Indirect Online Evolution, Machine Learning, Robotics", isbn13 = "978-0-7695-3272-1", DOI = "doi:10.1109/LAB-RS.2008.13", size = "6 pages", abstract = "A conceptual framework for on line evolution in robotic systems called indirect online evolution (IDOE) is presented. A model specie automatically infers models of a hidden physical system by the use of gene expression programming (GEP). A parameter specie simultaneously optimises the parameters of the inferred models according to a specified target vector. Training vectors required for modelling are automatically provided online by the interplay between the two coevolving species and the physical system. At every generation, only the estimated fittest individual of the parameter specie is executed on the physical system. This approach thus limits both the evaluation time, the wear out and the potential hazards normally associated with direct online evolution (DOE) where every individual has to be evaluated on the physical system. Additionally, the approach enables continuous system identification and adaptation during normal operation. Features of IDOE are illustrated by inferring models of a simplified, robotic arm, and further optimising the parameters of the system according to a target position of the end effector. Simulated experiments indicate that the fitness of the IDOE approach is generally higher than the average fitness of DOE.", notes = "Also known as \cite{4599430}", } @InProceedings{furuholmen2008indirect, author = "Marcus Furuholmen and Kyrre Glette and Jim Torresen and Mats Hovin", title = "Indirect Online Evolution - A Conceptual Framework for Adaptation in Industrial Robotic Systems", booktitle = "8th International Conference on Evolvable Systems: From Biology to Hardware, ICES 2008", year = "2008", editor = "Gregory Hornby and Lukas Sekanina and Pauline C. Haddow", series = "Lecture Notes in Computer Science", volume = "5216", pages = "165--176", address = "Prague, Czech Republic", month = sep # " 21-24", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-85856-0", DOI = "doi:10.1007/978-3-540-85857-7_15", size = "12 pages", abstract = "A conceptual framework for online evolution in robotic systems called Indirect Online Evolution (IDOE) is presented. A model specie automatically infers models of a physical system and a parameter specie simultaneously optimises the parameters of the inferred models according to a specified target behaviour. Training vectors required for modelling are automatically provided online by the interplay between the two coevolving species and the physical system. At every generation, only the estimated fittest individual of the parameter specie is executed on the physical system, hence limiting both the evaluation time, the wear out and the potential hazards normally associated with direct online evolution (DOE), where every candidate solution has to be evaluated on the physical system. Features of IDOE are demonstrated by inferring models of a simple hidden system containing geometric shapes that are further optimized according to a target value. Simulated experiments indicate that the fitness of the IDOE approach is generally higher than the average fitness of DOE.", notes = "ICES", bibsource = "DBLP, http://dblp.uni-trier.de", } @InProceedings{Furuholmen:2009:cec, author = "Marcus Furuholmen and Kyrre Glette and Mats Hovin and Jim Torresen", title = "Coevolving Heuristics for The Distributor's Pallet Packing Problem", booktitle = "2009 IEEE Congress on Evolutionary Computation", year = "2009", editor = "Andy Tyrrell", pages = "2810--2817", address = "Trondheim, Norway", month = "18-21 " # may, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-2959-2", file = "P260.pdf", DOI = "doi:10.1109/CEC.2009.4983295", abstract = "Efficient heuristics are required for on-line optimization problems where search-based methods are unfeasible due to frequent dynamics in the environment. This is especially apparent when operating on combinatorial NP-complete problems involving a large number of items. However, designing new heuristics for these problems may be a difficult and time consuming task even for domain experts. Therefore, automating this design process may benefit the industry when facing new and difficult optimization problems. The Distributor's Pallet Packing Problem (DPPP) is the problem of loading a pallet of non-homogenous items coming off a production line and is an instance of a range of resource-constrained, NP-complete, scheduling problems that are highly relevant for practical tasks in the industry. Common heuristics for the DPPP typically decompose the problem into two sub-problems; one of prescheduling all items on the production line and one of packing the items on the pallet. In this paper we concentrate on a two dimensional version of the DPPP and the more realistic scenario of having knowledge about only a limited set of the items on the production line. This paper aims at demonstrating that such an unknown heuristic may be evolved by Gene Expression Programming and Cooperative Coevolution. By taking advantage of the natural problem decomposition, two species evolve heuristics for pre-scheduling and packing respectively. We also argue that the evolved heuristics form part of a developmental stage in the construction of the finished phenotype, that is, the loaded pallet.", keywords = "genetic algorithms, genetic programming, gene expression programming", notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR", } @InProceedings{DBLP:conf/gecco/FuruholmenGHT09, author = "Marcus Furuholmen and Kyrre Harald Glette and Mats Erling Hovin and Jim Torresen", title = "Scalability, generalization and coevolution -- experimental comparisons applied to automated facility layout planning", booktitle = "GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation", year = "2009", editor = "Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano {van Hemert} and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique Alba", pages = "691--698", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming, Gene Expression Programming", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1569901.1569997", abstract = "Several practical problems in industry are difficult to optimize, both in terms of scalability and representation. Heuristics designed by domain experts are frequently applied to such problems. However, designing optimized heuristics can be a non-trivial task. One such difficult problem is the Facility Layout Problem (FLP) which is concerned with the allocation of activities to space. This paper is concerned with the block layout problem, where the activities require a fixed size and shape (modules). This problem is commonly divided into two sub problems; one of creating an initial feasible layout and one of improving the layout by interchanging the location of activities. We investigate how to extract novel heuristics for the FLP by applying an approach called Cooperative Coevolutionary Gene Expression Programming (CCGEP). By taking advantage of the natural problem decomposition, one species evolves heuristics for pre-scheduling, and another for allocating the activities onto the plant. An experimental, comparative approach investigates various features of the CCGEP approach. The results show that the evolved heuristics converge to suboptimal solutions as the problem size grows. However, coevolution has a positive effect on optimization of single problem instances. Expensive fitness evaluations may be limited by evolving generalized heuristics applicable to unseen fitness cases of arbitrary sizes.", notes = "GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009). ACM Order Number 910092.", } @InProceedings{Furuholmen:2010:EuroGP, author = "Marcus Furuholmen and Kyrre Glette and Mats Hovin and Jim Torressen", title = "An Indirect Approach to the Three-dimensional Multi-pipe Routing Problem", booktitle = "Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010", year = "2010", editor = "Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar", volume = "6021", series = "LNCS", pages = "86--97", address = "Istanbul", month = "7-9 " # apr, organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-12147-0", DOI = "doi:10.1007/978-3-642-12148-7_8", abstract = "This paper explores an indirect approach to the Three-dimensional Multi-pipe Routing problem. Variable length pipelines are built by letting a virtual robot called a turtle navigate through space, leaving pipe segments along its route. The turtle senses its environment and acts in accordance with commands received from heuristics currently under evaluation. The heuristics are evolved by a Gene Expression Programming based Learning Classifier System. The suggested approach is compared to earlier studies using a direct encoding, where command lines were evolved directly by genetic algorithms. Heuristics generating higher quality pipelines are evolved by fewer generations compared to the direct approach, however the evaluation time is longer and the search space is more complex. The best evolved heuristic is short and simple, builds modular solutions, exhibits some degree of generalization and demonstrates good scalability on test cases similar to the training case.", notes = "Part of \cite{Esparcia-Alcazar:2010:GP} EuroGP'2010 held in conjunction with EvoCOP2010 EvoBIO2010 and EvoApplications2010", } @InProceedings{furuholmen2010evolutionary, author = "Marcus Furuholmen and Kyrre Glette and Mats H{\o}vin and Jim Torresen", title = "Evolutionary Approaches to the Three-dimensional Multi-pipe Routing Problem: A Comparative Study Using Direct Encodings", booktitle = "Evolutionary Computation in Combinatorial Optimization, 10th European Conference, EvoCOP 2010, Istanbul, Turkey, April 7-9, 2010. Proceedings", year = "2010", editor = "Peter I. Cowling and Peter Merz", volume = "6022", series = "Lecture Notes in Computer Science", pages = "71--82", publisher = "Springer", keywords = "genetic algorithms", DOI = "doi:10.1007/978-3-642-12139-5_7", notes = "'not using GP, but rather GA - however, the results are compared with GP in another paper which is in GP bib.'", bibsource = "DBLP, http://dblp.uni-trier.de", } @InProceedings{Furuholmen:2010:cec, author = "Marcus Furuholmen and Kyrre Glette and Mats Hovin and Jim Torresen", title = "A Coevolutionary, Hyper Heuristic approach to the optimization of Three-dimensional Process Plant Layouts -A comparative study", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-6910-9", abstract = "A Coevolutionary, Hyper Heuristic approach to the optimisation of Three-dimensional Process Plant Layouts (3DPPLs) is explored. By taking advantage of the natural problem decomposition, one population of layout heuristics, and another population of scheduling heuristics are coevolved. Generalised heuristics are evolved by training on multiple small problem instances, so that training time is reduced. The best generalized heuristic builds arbitrary sized 3DPPLs which reduce the cost by 18percent when compared to a handmade heuristic. Specialised heuristics are evolved by optimising each problem instance and outperforms the generalized heuristics after a fixed number of generations. Compared to a direct-encoded Genetic Algorithm, the benefit of specialized heuristics increases with the size of the problem, and costs are reduced by 30percent when compared to the handmade heuristic.", DOI = "doi:10.1109/CEC.2010.5586329", notes = "WCCI 2010. Also known as \cite{5586329}", } @InProceedings{furutani:1999:ASIPGAML, author = "Hiroshi Furutani", title = "Analytical Solutions for Infinite Population Genetic Algorithms on Multiplicative Landscape", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "204--211", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", abstract = "eigen values, eigenvectors, walsh functions", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Article{fyfe:1999:AFE, author = "Colin Fyfe and John Paul Marney and Heather F. E. Tarbert", title = "Technical analysis versus market efficiency - a genetic programming approach", journal = "Applied Financial Economics", year = "1999", volume = "9", number = "2", pages = "183--191", month = apr, keywords = "genetic algorithms, genetic programming", ISSN = "0960-3107", URL = "http://alidoro.catchword.com/vl=8080356/cl=18/nw=1/fm=docpdf/rpsv/catchword/routledg/09603107/v9n2/s7/p183", DOI = "doi:10.1080/096031099332447", size = "9 pages", abstract = "In the paper the authors maintain that the prevalence of technical analysis in professional investment argues that such techniques should perhaps be taken more seriously by academics. The new technique of genetic programming is used to investigate a long time series of price data for a quoted property investment company, to discern whether there are any patterns in the data which could be used for technical trading purposes. A successful buy rule is found which generates returns in excess of what would be expected from the best-fitting null time-series model. Nevertheless, this turns out to be a more sophisticated variant of the buy and hold rule, which the authors term timing specific buy and hold. Although the rule does outperform simple buy and hold, it really does not provide sufficient grounds for the rejection of the efficient market hypothesis, though it does suggest that further investigation of the specific conditions of applicability of the EMH may be appropriate.", notes = "Department of Computing, The University of Paisley, UK", } @Misc{Gaaloul:2021:arxiv, author = "Khouloud Gaaloul and Claudio Menghi and Shiva Nejati and Lionel C. Briand and Yago Isasi Parache", title = "Combining Genetic Programming and Model Checking to Generate Environment Assumptions", howpublished = "arXiv", year = "2021", month = "6 " # jan, keywords = "genetic algorithms, genetic programming, SBSE, Environment assumptions, Model checking, Machine learning, Decision trees, simulink, Search-based software testing, EPIcuRus, Matlab, GPLAB", URL = "https://arxiv.org/abs/2101.01933", code_url = "https://github.com/SNTSVV/EPIcuRus", size = "20 pages", abstract = "Software verification may yield spurious failures when environment assumptions are not accounted for. Environment assumptions are the expectations that a system or a component makes about its operational environment and are often specified in terms of conditions over the inputs of that system or component. we propose an approach to automatically infer environment assumptions for Cyber-Physical Systems (CPS). Our approach improves the state-of-the-art in three different ways: First, we learn assumptions for complex CPS models involving signal and numeric variables; second, the learned assumptions include arithmetic expressions defined over multiple variables; third, we identify the trade-off between soundness and informativeness of environment assumptions and demonstrate the flexibility of our approach in prioritising either of these criteria. We evaluate our approach using a public domain benchmark of CPS models from Lockheed Martin and a component of a satellite control system from LuxSpace, a satellite system provider. The results show that our approach outperforms state-of-the-art techniques on learning assumptions for CPS models, and further, when applied to our industrial CPS model, our approach is able to learn assumptions that are sufficiently close to the assumptions manually developed by engineers to be of practical value.", } @PhdThesis{Gaaloul:thesis, author = "Khouloud Gaaloul", title = "Verification of Design Models of Cyber-Physical Systems Specified in Simulink", school = "Faculty of Science, Technology and Medicine (FSTM), University of Luxembourg", year = "2021", address = "Luxembourg", month = "15 " # sep, keywords = "genetic algorithms, genetic programming, SBSE, SBST, Security, Reliability and Trust, Cyber-Physical Systems, Model-Based Verification, search-based testing, Model checking, machine learning", URL = "https://wwwfr.uni.lu/snt/news_events/phd_defense_verification_of_design_models_of_cyber_physical_systems_specified_in_simulink", URL = "http://hdl.handle.net/10993/48071", URL = "https://orbilu.uni.lu/bitstream/10993/48071/1/thesis_final.pdf", size = "131 pages", abstract = "Recent advances in cyber-physical systems (CPS) have allowed highly available technologies with interconnected systems between the physical assets and the computational software components. This has resulted in more complex systems with wider capabilities. CPS can be applied in various domains such as safe transport, efficient medical devices, integrated systems, critical infrastructure control and more. The development of such critical systems requires advanced new models, algorithms, methods and tools to verify and validate the software components and the entire system. The verification of cyber-physical systems has become challenging: (1) The complex and dynamical behaviour of systems requires resilient automated monitors and test oracles that can cope with time-varying variables of CPS. (2) Given the wide range of existing verification and testing techniques from formal to empirical methods, there is no clear guidance as to how different techniques fare in the context of CPS. (3) Due to serious issues when applying exhaustive verification to complex systems, a common practice is needed to verify system components separately. This requires adding implicit assumptions about the operational environment of system components to ensure correct verification. However, identifying environment assumptions for cyber-physical systems with complex, mathematical behaviors is not trivial. I focus on addressing these challenges by proposing a set of effective approaches to verify design models of CPS. The work presented in this dissertation is motivated by ESAIL maritime micro-satellite system, developed by LuxSpace, a leading provider of space systems, applications and services in Luxembourg. In addition to ESAIL, we use a benchmark of eleven public-domain Simulink models provided by Lockheed Martin, which are representative of different categories of CPS models in the aerospace and defence sector. To address the aforementioned challenges, we propose (1) an automated approach to translate CPS requirements specified in a logic-based language into test oracles specified in Simulink. The generated oracles are able to deal with CPS complex behaviours and interactions with the system environment; (2) An empirical study to evaluate the fault-finding capabilities of model testing and model checking techniques for Simulink models. We also provide a categorization of model types and a set of common logical patterns for CPS requirements; (3) An automated approach to synthesize environment assumptions for a component under analysis by combining search-based testing, machine learning and model checking procedures. We also propose a novel technique to guide the test generation based on the feedback received from the machine learning process; and (4) An extension of (3) to learn assumptions with arithmetic expressions over multiple signals and numerical variables.", notes = "Combining Genetic Programming and Model Checking to Generate Environment Assumptions SVV Supervisor: Shiva Nejati and Lionel Briand", } @Article{Gaaloul:TSE, author = "Khouloud Gaaloul and Claudio Menghi and Shiva Nejati and Lionel Briand and Yago {Isasi Parache}", title = "Combining Genetic Programming and Model Checking to Generate Environment Assumptions", journal = "IEEE Transactions on Software Engineering", year = "2022", volume = "48", number = "9", pages = "3664--3685", keywords = "genetic algorithms, genetic programming", URL = "http://hdl.handle.net/10993/47740", DOI = "doi:10.1109/TSE.2021.3101818", ISSN = "1939-3520", abstract = "Software verification may yield spurious failures when environment assumptions are not accounted for. Environment assumptions are the expectations that a system or a component makes about its operational environment and are often specified in terms of conditions over the inputs of that system or component. In this article, we propose an approach to automatically infer environment assumptions for Cyber-Physical Systems (CPS). Our approach improves the state-of-the-art in three different ways: First, we learn assumptions for complex CPS models involving signal and numeric variables; second, the learned assumptions include arithmetic expressions defined over multiple variables; third, we identify the trade-off between soundness and coverage of environment assumptions and demonstrate the flexibility of our approach in prioritizing either of these criteria. We evaluate our approach using a public domain benchmark of CPS models from Lockheed Martin and a component of a satellite control system from LuxSpace, a satellite system provider. The results show that our approach outperforms state-of-the-art techniques on learning assumptions for CPS models, and further, when applied to our industrial CPS model, our approach is able to learn assumptions that are sufficiently close to the assumptions manually developed by engineers to be of practical value.", notes = "Also known as \cite{9507379}", } @Article{Gabbar:2014:PSEP, author = "Hossam A. Gabbar and Sajid Hussain and Amir Hossein Hosseini", title = "Simulation-based fault propagation analysis-Application on hydrogen production plant", journal = "Process Safety and Environmental Protection", year = "2014", volume = "92", number = "6", pages = "723--731", month = nov, ISSN = "0957-5820", DOI = "doi:10.1016/j.psep.2013.12.006", URL = "http://www.sciencedirect.com/science/article/pii/S0957582013000955", keywords = "genetic algorithms, genetic programming, Fault sematic network (FSN), Cu-Cl thermochemical cycle, Aspen HYSYS, Neural networks, Process variables interaction", size = "9 pages", abstract = "Recently production of hydrogen from water through the Cu--Cl thermochemical cycle is developed as a new technology. The main advantages of this technology over existing ones are higher efficiency, lower costs, lower environmental impact and reduced greenhouse gas emissions. Considering these advantages, the usage of this technology in new industries such as nuclear and oil is increasingly developed. Due to hazards involved in hydrogen production, design and implementation of hydrogen plants require provisions for safety, reliability and risk assessment. However, very little research is done from safety point of view. This paper introduces fault semantic network (FSN) as a novel method for fault diagnosis and fault propagation analysis by using evolutionary techniques like genetic programming (GP) and neural networks (NN), to uncover process variables' interactions. The effectiveness, feasibility and robustness of the proposed method are demonstrated on simulated data obtained from the simulation of hydrogen production process in Aspen HYSYS. The proposed method has successfully achieved reasonable detection and prediction of non-linear interaction patterns among process variables.", } @InProceedings{Gabbouj:2010:IPTA, author = "Moncef Gabbouj", title = "Multidimensional particle swarm optimization and applications in data clustering and image retrieval", booktitle = "Image Processing Theory Tools and Applications (IPTA), 2010 2nd International Conference on", year = "2010", month = jul, pages = "5", abstract = "Particle swarm optimization (PSO) was introduced by Kennedy and Eberhart in 1995 as a population based stochastic search and optim", DOI = "doi:10.1109/IPTA.2010.5586831", ISSN = "2154-5111", notes = "Also known as \cite{5586831}", } @InProceedings{Gabel:2010:FSE, author = "Mark Gabel and Zhendong Su", title = "A Study of the Uniqueness of Source Code", booktitle = "Proceedings of the eighteenth ACM SIGSOFT international symposium on Foundations of software engineering", year = "2010", pages = "147--156", address = "Santa Fe, New Mexico, USA", month = "7-11 " # nov, publisher = "ACM", acmid = "1882315", keywords = "genetic algorithms, genetic programming, large scale study, software uniqueness, source code", isbn13 = "978-1-60558-791-2", URL = "http://www.cs.ucdavis.edu/~su/publications/fse10.pdf", DOI = "doi:10.1145/1882291.1882315", size = "10 pages", abstract = "This paper presents the results of the first study of the uniqueness of source code. We define the uniqueness of a unit of source code with respect to the entire body of written software, which we approximate with a corpus of 420 million lines of source code. Our high-level methodology consists of examining a collection of 6000 software projects and measuring the degree to which each project can be `assembled' solely from portions of this corpus, thus providing a precise measure of uniqueness that we call syntactic redundancy. We parametrised our study over a variety of variables, the most important of which being the level of granularity at which we view source code. Our suite of experiments together consumed approximately four months of CPU time, providing quantitative answers to the following questions: at what levels of granularity is software unique, and at a given level of granularity, how unique is software? While we believe these questions to be of intrinsic interest, we discuss possible applications to genetic programming and developer productivity tools.", notes = "Brief mention of GP and how their results apply to GP. C, C++, Java. n-grams. p147 'Singularity in software engineering's future'. p149 'syntactically redundant' p152 'striking similarity' between 30 current sourceforge projects. p155 Almost all small code fragments have been written many times (Small means 'approximately one to seven lines of source code'). Cites Jiang and Zu ISSTA 2009, \cite{koza:book} and \cite{Weimer:2009:ICES}. FSE '10, Gabel:2010:SUS:1882291.1882315", } @InProceedings{DBLP:journals/corr/GaboraD13, author = "Liane Gabora and Steve DiPaola", title = "How Did Humans Become So Creative? A Computational Approach", booktitle = "Proceedings of the International Conference on Computational Creativity", year = "2012", editor = "Mary Lou Maher", pages = "203--210", address = "Dublin, Ireland", month = may # " 31 - " # jun # " 1", keywords = "genetic algorithms, genetic programming, EVOC, ANN, Agent, ALife, chaining, artificial society, HSV color space, 80/20, portraits, images", URL = "http://computationalcreativity.net/iccc2012/wp-content/uploads/2012/05/203-Gabora.pdf", URL = "http://arxiv.org/ftp/arxiv/papers/1308/1308.5032.pdf", URL = "http://arxiv.org/abs/1308.5032", bibsource = "DBLP, http://dblp.uni-trier.de", size = "8 pages", abstract = "This paper summarises efforts to computationally model two transitions in the evolution of human creativity: its origins about two million years ago, and the big bang of creativity about 50,000 years ago. Using a computational model of cultural evolution in which neural network based agents evolve ideas for actions through invention and imitation, we tested the hypothesis that human creativity began with onset of the capacity for recursive recall. We compared runs in which agents were limited to single-step actions to runs in which they used recursive recall to chain simple actions into complex ones. Chaining resulted in higher diversity, open-ended novelty, no ceiling on the mean fitness of actions, and greater ability to make use of learning. Using a computational model of portrait painting, we tested the hypothesis that the explosion of creativity in the Middle/Upper Paleolithic was due to onset of contextual focus: the capacity to shift between associative and analytic thought. This resulted in faster convergence on portraits that resembled the sitter, employed painterly techniques, and were rated as preferable. We conclude that recursive recall and contextual focus provide a computationally plausible explanation of how humans evolved the means to transform this planet", notes = "TenderPixel Gallery in London, Emily Carr Galley in Vancouver, and Kings Art Centre at Cambridge University as well as the MIT Museum, and the High Museum in Atlanta. Images used in \cite{Padian:2008:nature}. http://computationalcreativity.net/iccc2012/the-conference-program/ Note: Each paper or demonstration abstract may be downloaded individually below, or you can download the entire proceedings http://www.computationalcreativity.net/proceedings/ICCC-2012-Proceedings.pdf as one file (23 MB).", } @InProceedings{conf/cifer/GabrielssonJK14, author = "Patrick Gabrielsson and Ulf Johansson and Rikard Konig", title = "Co-evolving online high-frequency trading strategies using grammatical evolution", booktitle = "IEEE Conference on Computational Intelligence for Financial Engineering Economics (CIFEr 2104)", year = "2014", pages = "473--480", address = "London", month = "27-28 " # mar, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, grammatical evolution", bibdate = "2014-11-06", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/cifer/cifer2014.html#GabrielssonJK14", URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6901616", DOI = "doi:10.1109/CIFEr.2014.6924111", abstract = "Numerous sophisticated algorithms exist for discovering reoccurring patterns in financial time series. However, the most accurate techniques available produce opaque models, from which it is impossible to discern the rationale behind trading decisions. It is therefore desirable to sacrifice some degree of accuracy for transparency. One fairly recent evolutionary computational technology that creates transparent models, using a user-specified grammar, is grammatical evolution (GE). In this paper, we explore the possibility of evolving transparent entry- and exit trading strategies for the E-mini S&P 500 index futures market in a high-frequency trading environment using grammatical evolution. We compare the performance of models incorporating risk into their calculations with models that do not. Our empirical results suggest that profitable, risk-averse, transparent trading strategies for the E-mini S&P 500 can be obtained using grammatical evolution together with technical indicators.", notes = "Also known as \cite{6924111}", } @InProceedings{Gaddam:2023:CEC, author = "Jyotheesh Gaddam and Thanh Thi Nguyen and Maia Angelova", title = "Grammatical Evolution with Adaptive Building Blocks for Traffic Light Control", booktitle = "2023 IEEE Congress on Evolutionary Computation (CEC)", year = "2023", editor = "Gui DeSouza and Gary Yen", address = "Chicago, USA", month = "1-5 " # jul, keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Traffic-Light Control, Swarm Optimisation, Ant Colony", isbn13 = "979-8-3503-1459-5", DOI = "doi:10.1109/CEC53210.2023.10254190", size = "10 pages", notes = " CEC2023 https://2023.ieee-cec.org/program-html/", } @InProceedings{gagne:2002:gecco, author = "Christian Gagn{\'e} and Marc Parizeau", title = "Open {BEAGLE}: {A} New {C++} Evolutionary Computation Framework", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "888", address = "New York", publisher_address = "San Francisco, CA 94104, USA", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "genetic algorithms, genetic programming, poster paper, artificial intelligence, evolutionary computation framework, object oriented genetic programming, software engineering, software tools", ISBN = "1-55860-878-8", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/GP272.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-15.pdf", size = "1 pages", abstract = "This poster introduces a new C++ Evolutionary Computation (EC) framework named Open BEAGLE. This framework is freely available on the projet's Web page at http://www.gel.ulaval.ca/~beagle.", notes = "GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)", } @InProceedings{gagne:2002:gecco:lbp, title = "Open {BEAGLE:} {A} New Versatile {C}++ Framework for Evolutionary Computation", author = "Christian Gagn{\'e} and Marc Parizeau", booktitle = "Late Breaking Papers at the Genetic and Evolutionary Computation Conference ({GECCO-2002})", editor = "Erick Cant{\'u}-Paz", year = "2002", month = jul, pages = "161--168", address = "New York, NY", publisher = "AAAI", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025", keywords = "genetic algorithms, genetic programming", URL = "http://vision.gel.ulaval.ca/en/publications/Id_43/PublDetails.php", URL = "http://vision.gel.ulaval.ca/~parizeau/Publications/beagle-gecco.pdf", code_url = "https://chgagne.github.io/beagle/", size = "8 pages", abstract = "This paper introduces a new Evolutionary Computation (EC) framework named Open BEAGLE, that we have been developing and improving since 1999. Coded in C++, this framework offers solid object oriented foundations based on design patterns. It contains a basic generic EC framework on which other specialised frameworks can easily be constructed. Release 1.0 of Open BEAGLE implements two specialized frameworks: a simple genetic algorithms framework, and a complete genetic programming framework. Its power and ease of use is demonstrated through an example of the latter for the classic symbolic regression problem.", notes = "Also known as \cite{Gagne43}. Late Breaking Papers, {GECCO-2002}. A joint meeting of the eleventh International Conference on Genetic Algorithms ({ICGA-2002}) and the seventh Annual Genetic Programming Conference ({GP-2002}) part of cantu-paz:2002:GECCO:lbp C++ STL GPL", } @InProceedings{gagne:2003:HPCS, author = "Christian Gagne and Marc Parizeau and Marc Dubreuil", title = "Distributed BEAGLE: An Environment for Parallel and Distributed Evolutionary Computations", booktitle = "Procceedings of the 17th Annual International Symposium on High Performance Computing Systems and Applications (HPCS) 2003", year = "2003", address = "Sherbrooke, Quebec, Canada", month = may # " 11-14", keywords = "genetic algorithms, genetic programming", URL = "http://vision.gel.ulaval.ca/~cgagne/pubs/hpcs03.pdf", URL = "http://vision.gel.ulaval.ca/fr/publications/Id_439/PublDetails.php", URL = "http://vision.gel.ulaval.ca/en/publications/Id_439/PublDetails.php", size = "8 pages", abstract = "Evolutionary computation is a promising artificial intelligence field involving the simulation of natural evolution to solve problems. Given its implicit parallelism and high computational requirements, evolutionary computation is the perfect candidate for high performance parallel computers. This paper presents Distributed BEAGLE, a new master-slave architecture for parallel and distributed evolutionary computations. It is designed as a robust, adaptive, and scalable system targeted for local networks of workstations and Beowulf clusters. Results obtained with a plausible deployment scenario demonstrate that system performance degrades gracefully when failures occurred, while still achieving near linear speedup in the ideal case.", } @InProceedings{Gagne:2003:gecco, author = "Christian Gagne and Marc Parizeau and Marc Dubreuil", title = "The Master-Slave Architecture for Evolutionary Computations Revisited", booktitle = "Genetic and Evolutionary Computation -- GECCO-2003", editor = "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller", year = "2003", pages = "1578--1579", address = "Chicago", publisher_address = "Berlin", month = "12-16 " # jul, volume = "2724", series = "LNCS", ISBN = "3-540-40603-4", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, poster", URL = "http://www.gel.ulaval.ca/~cgagne/pubs/master-gecco03.pdf", URL = "http://vision.gel.ulaval.ca/en/publications/Id_440/PublDetails.php", DOI = "doi:10.1007/3-540-45110-2_33", abstract = "The recent availability of cheap Beowulf clusters has generated much interest for Parallel and Distributed Evolutionary Computations (PDEC). Another often neglected source of CPU power for PDEC are networks of PCs, in many case very powerful workstations, that run idle each day for long periods of time. To exploit efficiently both Beowulfs and networks of heterogeneous workstations we argue that the classic master-slave distribution model is superior to the currently more popular island-model. Results obtained with a plausible deployment scenario demonstrate that system performance degrades gracefully when failures occurred, while still achieving near linear speedup in the ideal case.", notes = "GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eights Annual Genetic Programming Conference (GP-2003) BEAGLE", } @InProceedings{gagne:gecco03lbp, title = "A Robust Master-Slave Distribution Architecture for Evolutionary Computations", pages = "80--87", author = "Christian Gagne and Marc Parizeau and Marc Dubreuil", year = "2003", address = "Chicago, USA", month = "12--16 " # jul, editor = "Bart Rylander", booktitle = "Genetic and Evolutionary Computation Conference Late Breaking Papers", keywords = "genetic algorithms, genetic programming", URL = "http://www.gel.ulaval.ca/~cgagne/pubs/lbp-gecco03.pdf", URL = "http://vision.gel.ulaval.ca/en/publications/Id_456/PublDetails.php", abstract = "This paper presents a new robust master-slave distribution architecture for multiple populations Evolutionary Computations (EC). It discusses the main advantages and drawbacks of master-slave models over island models for parallel and distributed EC. It also formulates a mathematical model of the master-slave distribution policies in order to show that, contrary to what is implied by current mainstream developments in island models, a well designed master-slave approach can be both robust and scalable (up to a certain point). Finally, it introduces some of the details of a new {C++} framework named Distributed BEAGLE, which implements this architecture over the Open BEAGLE EC framework.", notes = "GECCO-2003LB", } @PhdThesis{gagne:thesis, author = "Christian Gagne", title = "Algorithmes evolutionnaires appliques a la reconnaissance des formes et a la conception optique", school = "Laval University", year = "2005", address = "Quebec (QC), Canada", month = may, keywords = "genetic algorithms, genetic programming", URL = "http://vision.gel.ulaval.ca/en/publications/Id_528/PublDetails.php", URL = "http://vision.gel.ulaval.ca/~cgagne/pubs/these-cgagne.pdf", URL = "http://www.theses.ulaval.ca/2005/22701/22701.pdf", size = "212 pages", abstract = "Evolutionary Algorithms (EA) encompass a family of robust search algorithms loosely inspired by natural evolution. These algorithms are particularly useful to solve problems for which classical algorithms of optimisation, learning, or automatic design cannot produce good results. In this thesis, we propose a common methodological approach for the development of EA-based intelligent systems. This methodological approach is based on five principles: 1) to use algorithms and representations that are problem specific; 2) to develop hybrids between EA and heuristics from the application field; 3) to take advantage of multi-objective evolutionary optimization; 4) to do co-evolution for the simultaneous resolution of several sub-problems of a common application and for promoting robustness; and 5) to use generic software tools for rapid development of unconventional EA. This methodological approach is illustrated on four applications of EA to hard problems. Moreover, the fifth principle is explained in the study on genericity of EA software tools. The application of EA to complex problems requires the use of generic software tool, for which we propose six genericity criteria. Many EA software tools are available in the community, but only a few are really generic. Indeed, an evaluation of some popular tools tells us that only three respect all these criteria, of which the framework Open BEAGLE, developed during the Ph.D. Open BEAGLE is organised into three main software layers. The basic layer is made of the object oriented foundations, over which there is the generic framework layer, consisting of the general mechanisms of the tool, and then the final layer, containing several specialised frameworks implementing different EA flavours. The tool also includes two extensions, respectively to distribute the computations over many computers and to visualise results. Three applications illustrate different approaches for using EA in the context of pattern recognition. First, nearest neighbour classifiers are optimised, with the prototype selection using a genetic algorithm simultaneously to the Genetic Programming (GP) of neighbourhood metrics. We add to this cooperative two species co-evolution a third co-evolving competitive species for selecting test data in order to improve the generalisation capability of solutions. A second application consists in designing representations with GP for handwritten character recognition. This evolutionary engineering is conducted with an automatic positioning of regions in a window of attention, combined with the selection of fuzzy sets for feature extraction. This application is used to automate character representation search, which is usually conducted by human experts with a trial and error process. For the third application in pattern recognition, we propose an extensible system for the hierarchical combination of classifiers into a fuzzy decision tree. In this system, the tree topology is evolved with GP while the numerical parameters of classification units are determined by specialized learning techniques. The system is tested with three simple types of classification units. All of these applications in pattern recognition have been implemented using a two-objective fitness measure in order to minimise classification errors and solutions complexity. The last application demonstrate the efficiency of EA for lens system design. Self-adaptative evolution strategies, hybridised with a specialised local optimisation technique, are used to solve two complex optical design problems. In both cases, the experiments demonstrate that hybridized EA are able to produce results that are comparable or better than those obtained by human experts. These results are encouraging from the standpoint of a fully automated optical design process. An additional experiment is also conducted with a two-objectives fitness measure that tries to maximise image quality while minimising lens system cost.", bibsource = "OAI-PMH server at oai.collectionscanada.ca", contributor = "Marc Parizeau", identifier = "TC-QQLA-22701", language = "FR", oai = "oai:collectionscanada.ca:QQLA.2005/22701", rights = "copyright Christian Gagne, 2005", notes = "Cf posting to GP-list Tue, 11 Oct 2005 09:50:18 +0200 Entirely written in French", abstract = "Les algorithmes {\'e}volutionnaires (AE) constituent une famille d{'}algorithmes inspir{\'e}s de l{'}{\'e}volution naturelle. Ces algorithmes sont particuli{\`e}rement utiles pour la r{\'e}solution de probl{\`e}mes o{\`u} les algorithmes classiques d{'}optimisation, d{'}apprentissage ou de conception automatique sont incapables de produire des r{\'e}sultats satisfaisants. On propose dans cette th{\`e}se une approche m{\'e}thodologique pour le d{\'e}veloppement de syst{\`e}mes intelligents bas{\'e}s sur les AE. Cette approche m{\'e}thodologique repose sur cinq principes : 1) utiliser des algorithmes et des repr{\'e}sentations adapt{\'e}s au probl{\`e}me ; 2) d{\'e}velopper des hybrides entre des AE et des heuristiques du domaine d{'}application ; 3) tirer profit de l{'}optimisation {\'e}volutionnaire {\`a} plusieurs objectifs ; 4) faire de la co-{\'e}volution pour r{\'e}soudre simultan{\'e}ment plusieurs sous-probl{\`e}mes d{'}une application ou favoriser la robustesse ; et 5) utiliser un outil logiciel g{\'e}n{\'e}rique pour le d{\'e}veloppement rapide d{'}AE non conventionnels. Cette approche m{\'e}thodologique est illustr{\'e}e par quatre applications des AE {\`a} des probl{\`e}mes difficiles. De plus, le cinqui{\`e}me principe est appuy{\'e} par l{'}{\'e}tude sur la g{\'e}n{\'e}ricit{\'e} dans les outils logiciels d{'}AE. Le d{\'e}veloppement d{'}applications complexes avec les AE exige l{'}utilisation d{'}un outil logiciel g{\'e}n{\'e}rique. Six crit{\`e}res sont propos{\'e}s ici pour {\'e}valuer la g{\'e}n{\'e}ricit{\'e} des outils d{'}AE. De nombreux outils logiciels d{'}AE sont disponibles dans la communaut{\'e}, mais peu d{'}entre eux peuvent {\^e}tre v{\'e}ritablement qualifi{\'e}s de g{\'e}n{\'e}riques. En effet, une {\'e}valuation de quelques outils relativement populaires nous indique que seulement trois satisfont pleinement {\`a} tous ces crit{\`e}res, dont la framework d{'}AE Open BEAGLE, d{\'e}velopp{\'e}e durant le doctorat.", abstract = "Open BEAGLE est organis{\'e} en trois couches logicielles principales, avec {\`a} la base les fondations orient{\'e}es objet, sur lesquelles s{'}ajoute une framework g{\'e}n {\'e}rique comprenant les m{\'e}canismes g{\'e}n{\'e}raux de l{'}outil, ainsi que plusieurs frameworks sp{\'e}cialis{\'e}es qui implantent diff{\'e}rentes saveurs d{'}AE. L{'}outil comporte {\'e}galement deux extensions servant {\`a} distribuer des calculs sur plusieurs ordinateurs et {\`a} visualiser des r{\'e}sultats. Ensuite, trois applications illustrent diff{\'e}rentes approches d{'}utilisation des AE dans un contexte de reconnaissance des formes. Premi{\`e}rement, on optimise des classifieurs bas{\'e}s sur la r{\`e}gle du plus proche voisin avec la s{\'e}lection de prototypes par un algorithme g{\'e}n{\'e}tique, simultan{\'e}ment {\`a} la construction de mesures de voisinage par programmation g{\'e}n{\'e}tique (PG). {\`A} cette co-{\'e}volution coop{\'e}rative {\`a} deux esp{\`e}ces, on ajoute la co-{\'e}volution comp{\'e}titive d{'}une troisi{\`e}me esp{\`e}ce pour la s{\'e}lection de donn{\'e}es de test, afin d{'}am{\'e}liorer la capacit{\'e} de g{\'e}n{\'e}ralisation des solutions. La deuxi{\`e}me application consiste en l{'}ing{\'e}nierie de repr{\'e}sentations par PG pour la reconnaissance de caract{\`e}res manuscrits. Cette ing{\'e}nierie {\'e}volutionnaire s{'}effectue par un positionnement automatique de r{\'e}gions dans la fen{\^e}tre d{'}attention jumel{\'e} {\`a} la s{\'e}lection d{'}ensembles flous pour l{'}extraction de caract{\'e}ristiques. Cette application permet d{'}automatiser la recherche de repr{\'e}sentations de caract{\`e}res, op{\'e}ration g{\'e}n{\'e}ralement effectu{\'e}e par des experts humains suite {\`a} un processus d{'}essais et erreurs. Pour la troisi{\`e}me application en reconnaissance des formes, on propose un syst{\`e}me extensible pour la combinaison hi{\'e}rarchique de classifieurs dans un arbre de d{\'e}cision flou. Dans ce syst{\`e}me, la topologie des arbres est {\'e}volu{\'e}e par PG alors que les param{\`e}tres num{\'e}riques des unit{\'e}s de classement sont d{\'e}termin {\'e}s par des techniques d{'}apprentissage sp{\'e}cialis{\'e}es. Le syst{\`e}me est test{\'e} avec trois types simples d{'}unit{\'e}s de classement. Pour toutes ces applications en reconnaissance des formes, on utilise une mesure d{'}ad{\'e}quation {\`a} deux objectifs afin de minimiser les erreurs de classement et la complexit{\'e} des solutions. Une derni{\`e}re application d{\'e}montre l{'}efficacit{\'e} des AE pour la conception de syst` emes de lentilles. On utilise des strat{\'e}gies d{'}{\'e}volution auto-adaptatives hybrid{\'e}es avec une technique d{'}optimisation locale sp{\'e}cialis{\'e}e pour la r{\'e}solution de deux probl{\`e}mes complexes de conception optique. Dans les deux cas, on d{\'e}montre que les AE hybrides sont capables de g{\'e}n{\'e}rer des r{\'e}sultats comparables ou sup{\'e}rieurs {\`a} ceux produits par des experts humains. Ces r{\'e}sultats sont prometteurs dans la perspective d{'}une automatisation plus pouss{\'e}e de la conception optique. On pr{\'e}sente {\'e}galement une exp{\'e}rience suppl{\'e}mentaire avec une mesure {\`a} deux objectifs servant {\`a} maximiser la qualit{\'e} de l{'}image et {\`a} minimiser le co{\^u}t du syst{\`e}me de lentilles.;", } @TechReport{oai:hal.ccsd.cnrs.fr:inria-00000996_v1, title = "Genetic Programming, Validation Sets, and Parsimony Pressure", author = "Christian Gagn{\'e} and Marc Schoenauer and Marc Parizeau and Marco Tomassini", publisher = "HAL - CCSd - CNRS", year = "2006", month = jan # "~09", institution = "l'Equipe TAO INRIA Futurs", type = "ARTCOLLOQUE", number = "inria-00000996", address = "LRI Bat. 490, Universite Paris Sud, 91405 Orsay CEDEX, France", annote = "Christian Gagn{\'e} ", bibsource = "OAI-PMH server at hal.ccsd.cnrs.fr", contributor = "Christian Gagn{\'e} ", identifier = "inria-00000996 (version 1)", oai = "oai:hal.ccsd.cnrs.fr:inria-00000996_v1", keywords = "genetic algorithms, genetic programming, Computer Science/Learning", URL = "http://hal.inria.fr/inria-00000996/en/", URL = "http://hal.ccsd.cnrs.fr/docs/00/05/44/78/PDF/gagne-paper.pdf", URL = "http://arxiv.org/abs/cs/0601044", size = "12 pages", abstract = "Fitness functions based on test cases are very common in Genetic Programming (GP). This process can be assimilated to a learning task, with the inference of models from a limited number of samples. This paper is an investigation on two methods to improve generalization in GP-based learning: 1) the selection of the best-of-run individuals using a three data sets methodology, and 2) the application of parsimony pressure in order to reduce the complexity of the solutions. Results using GP in a binary classification setup show that while the accuracy on the test sets is preserved, with less variances compared to baseline results, the mean tree size obtained with the tested methods is significantly reduced.", notes = "See also \cite{eurogp06:GagneSchoenauerParizeauTomassini}", } @InProceedings{eurogp06:GagneSchoenauerParizeauTomassini, author = "Christian Gagn\'e and Marc Schoenauer and Marc Parizeau and Marco Tomassini", title = "Genetic Programming, Validation Sets, and Parsimony Pressure", editor = "Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art", booktitle = "Proceedings of the 9th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3905", year = "2006", address = "Budapest, Hungary", month = "10 - 12 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-33143-3", pages = "109--120", URL = "http://hal.ccsd.cnrs.fr/docs/00/05/44/78/PDF/gagne-paper.pdf", URL = "http://hal.inria.fr/inria-00000996/en/", DOI = "doi:10.1007/11729976_10", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "Fitness functions based on test cases are very common in Genetic Programming (GP). This process can be assimilated to a learning task, with the inference of models from a limited number of samples. This paper is an investigation on two methods to improve generalisation in GP-based learning: 1) the selection of the best-of-run individuals using a three data sets methodology, and 2) the application of parsimony pressure in order to reduce the complexity of the solutions. Results using GP in a binary classification setup show that while the accuracy on the test sets is preserved, with less variances compared to baseline results, the mean tree size obtained with the tested methods is significantly reduced.", notes = "Part of \cite{collet:2006:GP} EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006 Also known as \cite{oai:hal.ccsd.cnrs.fr:inria-00000996_v1} overfitting, regularisation, V-C dimension, MDL, UCI, fit the noise.", } @Article{Gagne:2006:IJAIT, author = "Christian Gagn\'e and Marc Parizeau", title = "Genericity in Evolutionary Computation Software Tools: Principles and Case Study", journal = "International Journal on Artificial Intelligence Tools", year = "2006", volume = "15", number = "2", pages = "173--194", month = apr, keywords = "genetic algorithms, genetic programming, Evolutionary computation, software engineering, object oriented programming", URL = "http://vision.gel.ulaval.ca/~parizeau/Publications/IJAIT06.pdf", DOI = "doi:10.1142/S021821300600262X", size = "22 pages", abstract = "This paper deals with the need for generic software development tools in evolutionary computations (EC). These tools will be essential for the next generation of evolutionary algorithms where application designers and researchers will need to mix different combinations of traditional EC (e.g. genetic algorithms, genetic programming, evolutionary strategies, etc.), or to create new variations of these EC, in order to solve complex real world problems. Six basic principles are proposed to guide the development of such tools. These principles are then used to evaluate six freely available, widely used EC software tools. Finally, the design of Open BEAGLE, the framework developed by the authors, is presented in more detail.", notes = "cited by \cite{Lopez-Lopez:2019:SC} Laboratoire de Vision et Systemes Numeriques (LVSN), Departement de Genie electrique et de Genie Informatique, Universite Laval, Quebec (QC), Canada, G1K 7P4, Canada", } @Article{gagne:2006:sigevo, author = "Christian Gagn\'e and Marc Parizeau", title = "Open BEAGLE A C++ Framework for your Favorite Evolutionary Algorithm", journal = "SIGEVOlution", year = "2006", volume = "1", number = "1", pages = "12--15", month = apr, keywords = "genetic algorithms, genetic programming, CMA-ES, NSGA-II, NSGA2, coevolution, onemax", URL = "http://www.sigevolution.org/2006/01/issue.pdf", DOI = "doi:10.1145/1138470.1138473", } @InProceedings{Gagne:PPSN:2006, author = "Christian Gagne and Marc Schoenauer and Michele Sebag and Marco Tomassini", title = "Genetic Programming for Kernel-Based Learning with Co-evolving Subsets Selection", booktitle = "Parallel Problem Solving from Nature - PPSN IX", year = "2006", editor = "Thomas Philip Runarsson and Hans-Georg Beyer and Edmund Burke and Juan J. Merelo-Guervos and L. Darrell Whitley and Xin Yao", volume = "4193", pages = "1008--1017", series = "LNCS", address = "Reykjavik, Iceland", publisher_address = "Berlin", month = "9-13 " # sep, publisher = "Springer-Verlag", ISBN = "3-540-38990-3", keywords = "genetic algorithms, genetic programming, hyperheuristic, DSS, coevolution, open beagle", URL = "http://ppsn2006.raunvis.hi.is/proceedings/287.pdf", URL = "http://arxiv.org/abs/cs/0611135", DOI = "doi:10.1007/11844297_102", size = "10 pages", abstract = "Support Vector Machines (SVMs) are well-established Machine Learning (ML) algorithms. They rely on the fact that i) linear learning can be formalised as a well-posed optimisation problem; ii) nonlinear learning can be brought into linear learning thanks to the kernel trick and the mapping of the initial search space onto a high dimensional feature space. The kernel is designed by the ML expert and it governs the efficiency of the SVM approach. In this paper, a new approach for the automatic design of kernels by Genetic Programming, called the Evolutionary Kernel Machine (EKM), is presented. EKM combines a well-founded fitness function inspired from the margin criterion, and a co-evolution framework ensuring the computational scalability of the approach. Empirical validation on standard ML benchmark demonstrates that EKM is competitive using state-of-the-art SVMs with tuned hyper-parameters.", notes = "PPSN-IX evolved Kernels are forced to be symmetric functions. Mercer's condition not enforced, but evolved. 3 co-evolving populations. runtime < 1 hour. Size based parsimony pressure. Comparison with k-nn nearest neighbours and SVM, GK-SVM (both with somewhat optimised parameters). 6 undemanding UCI benchmarks.", } @Article{Gagne:2006:ijDAR, author = "Christian Gagne and Marc Parizeau", title = "Genetic Engineering of Hierarchical Fuzzy Regional Representations for Handwritten Character Recognition", journal = "International Journal on Document Analysis and Recognition", year = "2006", volume = "8", number = "4", pages = "223--231", month = sep, keywords = "genetic algorithms, genetic programming", URL = "http://vision.gel.ulaval.ca/fr/publications/Id_607/PublDetails.php", DOI = "doi:10.1007/s10032-005-0005-6", abstract = "This paper presents a genetic programming based approach for optimising the feature extraction step of a handwritten character recogniser. This recognizer uses a simple multilayer perceptron as a classifier and operates on a hierarchical feature space of orientation, curvature, and centre of mass primitives. The nodes of the hierarchy represent rectangular sub-regions of their parent node, the tree root corresponding to the character's bounding box. Within each sub-region, a variable number of fuzzy features are extracted. Genetic programming is used to simultaneously learn the best hierarchy and the best combination of fuzzy features. Moreover, the fuzzy features are not predetermined, they are inferred from the evolution process which runs a two-objective selection operator. The first objective maximises the recognition rate, and the second minimises the feature space size. Results on Unipen data show that, using this approach, robust representations could be obtained that out-performed comparable human-designed hierarchical fuzzy regional representations.", } @Article{Gagne:2007:ijPRAI, author = "Christian Gagn\'e and Marc Parizeau", title = "Co-evolution of Nearest Neighbor Classifiers", journal = "International Journal of Pattern Recognition and Artificial Intelligence", year = "2007", volume = "21", number = "5", pages = "921--946", month = aug, keywords = "genetic algorithms, genetic programming", ISSN = "0218-0014", URL = "http://vision.gel.ulaval.ca/en/publications/Id_692/PublDetails.php", DOI = "doi:10.1142/S0218001407005752", abstract = "This paper presents experiments of Nearest Neighbour (NN) classifier design using different evolutionary computation methods. Through multi-objective and co-evolution techniques, it combines genetic algorithms and genetic programming to both select NN prototypes and design a neighbourhood proximity measure, in order to produce a more efficient and robust classifier. The proposed approach is compared with the standard NN classifier, with and without the use of classic prototype selection methods, and classic data normalisation. Results on both synthetic and real data sets show that the proposed methodology performs as well or better than other methods on all tested data sets.", } @Article{Gai:2017:ieeeCLOUD, author = "Keke Gai and Meikang Qiu and Hui Zhao", journal = "IEEE Transactions on Cloud Computing", title = "Cost-Aware Multimedia Data Allocation for Heterogeneous Memory Using Genetic Algorithm in Cloud Computing", year = "2020", volume = "8", number = "4", pages = "1212--1222", month = oct # "-" # dec, keywords = "genetic algorithms, genetic programming, Cloud computing, heterogeneous memory, data allocation, multimedia big data", ISSN = "2168-7161", DOI = "doi:10.1109/TCC.2016.2594172", abstract = "Recent expansions of Internet-of-Things (IoT) applying cloud computing have been growing at a phenomenal rate. As one of the developments, heterogeneous cloud computing has enabled a variety of cloud-based infrastructure solutions, such as multimedia big data. Numerous prior researches have explored the optimisations of on-premise heterogeneous memories. However, the heterogeneous cloud memories are facing constraints due to the performance limitations and cost concerns caused by the hardware distributions and manipulative mechanisms. Assigning data tasks to distributed memories with various capacities is a combinatorial NP-hard problem. This paper focuses on this issue and proposes a novel approach, Cost-Aware Heterogeneous Cloud Memory Model (CAHCM), aiming to provision a high performance cloud-based heterogeneous memory service offerings. The main algorithm supporting CAHCM is Dynamic Data Allocation Advance (2DA) Algorithm that uses genetic programming to determine the data allocations on the cloud-based memories. In our proposed approach, we consider a set of crucial factors impacting the performance of the cloud memories, such as communication costs, data move operating costs, energy performance, and time constraints. Finally, we implement experimental evaluations to examine our proposed model. The experimental results have shown that our approach is adoptable and feasible for being a cost-aware cloud-based solution.", notes = "Department of Computer Science, Pace University, NewYork, NY 10038 USA. Also known as \cite{7523230}", } @InProceedings{Gaier:2020:GECCO, author = "Adam Gaier and Alexander Asteroth and Jean-Baptiste Mouret", title = "Discovering Representations for Black-Box Optimization", year = "2020", editor = "Carlos Artemio {Coello Coello} and Arturo Hernandez Aguirre and Josu Ceberio Uribe and Mario Garza Fabre and Gregorio {Toscano Pulido} and Katya Rodriguez-Vazquez and Elizabeth Wanner and Nadarajen Veerapen and Efren Mezura Montes and Richard Allmendinger and Hugo Terashima Marin and Markus Wagner and Thomas Bartz-Beielstein and Bogdan Filipic and Heike Trautmann and Ke Tang and John Koza and Erik Goodman and William B. Langdon and Miguel Nicolau and Christine Zarges and Vanessa Volz and Tea Tusar and Boris Naujoks and Peter A. N. Bosman and Darrell Whitley and Christine Solnon and Marde Helbig and Stephane Doncieux and Dennis G. Wilson and Francisco {Fernandez de Vega} and Luis Paquete and Francisco Chicano and Bing Xue and Jaume Bacardit and Sanaz Mostaghim and Jonathan Fieldsend and Oliver Schuetze and Dirk Arnold and Gabriela Ochoa and Carlos Segura and Carlos Cotta and Michael Emmerich and Mengjie Zhang and Robin Purshouse and Tapabrata Ray and Justyna Petke and Fuyuki Ishikawa and Johannes Lengler and Frank Neumann", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference", pages = "103--111", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", keywords = "Quality Diversity (QD) algorithm, machine learning, Evolutionary Strategies", isbn13 = "9781450371285", URL = "http://www.human-competitive.org/sites/default/files/gaier_et_al_2020.txt", URL = "http://www.human-competitive.org/sites/default/files/2003.04389.pdf", URL = "https://doi.org/10.1145/3377930.3390221", DOI = "doi:10.1145/3377930.3390221", size = "9 pages", abstract = "The encoding of solutions in black-box optimization is a delicate, handcrafted balance between expressiveness and domain knowledge --- between exploring a wide variety of solutions, and ensuring that those solutions are useful. Our main insight is that this process can be automated by generating a dataset of high-performing solutions with a quality diversity algorithm (here, MAP-Elites), then learning a representation with a generative model (here, a Variational Autoencoder) from that dataset. Our second insight is that this representation can be used to scale quality diversity optimization to higher dimensions --- but only if we carefully mix solutions generated with the learned representation and those generated with traditional variation operators. We demonstrate these capabilities by learning an low-dimensional encoding for the inverse kinematics of a thousand joint planar arm. The results show that learned representations make it possible to solve high-dimensional problems with orders of magnitude fewer evaluations than the standard MAP-Elites, and that, once solved, the produced encoding can be used for rapid optimization of novel, but similar, tasks. The presented techniques not only scale up quality diversity algorithms to high dimensions, but show that black-box optimization encodings can be automatically learned, rather than hand designed.", notes = "Entered 2021 HUMIES Also known as \cite{10.1145/3377930.3390221} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @InProceedings{gail:2023:ECML-PKDD, author = "Felix Clemens Gail and Roland Rieke and Florian Fenzl", title = "{RulEth:} Genetic {Programming-Driven} Derivation of Security Rules for Automotive Ethernet", booktitle = "Joint European Conference on Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track", year = "2023", address = "Turin, Italy", month = "18-22 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-031-43430-3_12", DOI = "doi:10.1007/978-3-031-43430-3_12", } @InProceedings{gaila_genetic_2012, author = "Maria Gaila and Vassilios Vassiliadis and Nikolaos Kondakis and George Dounias", title = "Genetic {Programming}-based trading system: An application on the {NASDAQ} 100 stock index", booktitle = "IMAEF-2012", year = "2012", editors = "Spyridon Symeonides and Nikos Benos and Yorgos Goletsis", address = "Ioannina, Greece", month = "21-22 " # jun, organisation = "Department of Economics of the University of Ioannina", keywords = "genetic algorithms, genetic programming, artificial intelligence, technical indicators, trading system", URL = "http://mde-lab.aegean.gr/images/stories/docs/CC79.pdf", size = "2 pages", abstract = "Nowadays, the vast amount of socio-economic and market information play an important role in the formation of any financial market's characteristics and overall behaviour. As a consequence, the uncertainty and complexity of the financial markets immensely increase. Based on the aforementioned, a crucial task for potential traders is to identify market trends and detect potential investment opportunities. What is more, individually traditional trading strategies based on technical indicators, such as certain statistical and econometric forecasting methods, have proven inadequate to adapt to the rapidly evolving market conditions. Conversely, when combining such indicators, there is a higher possibility of more promising results. The field of Artificial Intelligence provides a range of metaheuristic algorithms for dealing with complex tasks, as the above mentioned. Specifically, in this study an intelligent algorithm based on the principles of Darwinian evolution, namely Genetic Programming, is proposed. The main aim of the study is to combine a number of technical indicators and other financial heuristics, with the use of Genetic Programming, in order to detect potential market signals for trading. One of the main characteristics of Genetic Programming is its ability to manipulate complex technical rules/heuristics in a way that optimizes the investors expected outcome. The proposed trading system is applied to the NASDAQ 100 stock index. Particularly, the dataset comprises daily adjusted closing prices of the stock index, for the period January 1985 to December 2011. Regarding the experimental set-up, the entire dataset is divided into three sub-periods: training, validation and forecasting (trading) interval. The algorithmic trading system is applied to the training interval in order to provide a number of technical rules. The quality (fitness) of these rules is then tested in the validation period, based on the criterion of profit maximization. Finally, the fittest rule is applied to the forecasting time period, which consists of unknown data.", notes = "broken Jan 2024 http://www.econ.uoi.gr/imaef2012/programme.php broken Jan 2024 http://mde-lab.aegean.gr/research-material", } @InProceedings{Gaitan:2014:HIC, author = "Carlos F. Gaitan", title = "Rediscovering {Manning's} Equation Using Genetic Programming", booktitle = "11th International Conference on Hydroinformatics", year = "2014", pages = "Paper 323", address = "New York, USA", month = aug # " 17-21", organisation = "IAHR/IWA Joint Committee on Hydroinformatics", keywords = "genetic algorithms, genetic programming", isbn13 = "978-0-692-28129-1", URL = "http://academicworks.cuny.edu/cc_conf_hic/323/", URL = "http://academicworks.cuny.edu/cgi/viewcontent.cgi?article=1322&context=cc_conf_hic.pdf", broken = "http://www.hic2014.org/proceedings/bitstream/handle/123456789/1549/1580.pdf", size = "8 pages", abstract = "Open-channel hydraulics (OCH) research traditionally links empirical formulae to observational data. One of the most common equations in OCH is Manning's formula for open channel flow (Q) driven by gravity (also known as the Gauckler-Manning-Strickler formula). The formula relates the cross-sectional average velocity (V=Q/A), the hydraulic radius (R), and the slope of the water surface (S) with a friction coefficient n, characteristic of the channel's surface. Here we show a practical example where Genetic Programming (GP), a technique derived from Bioinformatics, can be used to derive an empirical relationship based on different synthetic datasets of the aforementioned parameters. Specifically, we evaluated if Manning's formula could be retrieved from datasets with 300 pentads of A, n, R, S, and Q (from Mannings equation) using GP. The cross-validated results show success retrieving the functional form from the synthetic data and encourage the application of GP on problems where traditional empirical relationships show high biases, like sediment transport. The results also show alternative flow equations that can be used in the absence of one of the predictors and approximate Manning equation.", notes = "Broken June 2021 http://www.hic2014.org/xmlui/", } @Article{journals/aires/GaitanBM16, author = "Carlos F. Gaitan and Venkatramani Balaji and Berrien {Moore III}", title = "Can we obtain viable alternatives to {Manning's} equation using genetic programming?", journal = "Artificial Intelligence Research", year = "2016", number = "2", volume = "5", pages = "92--101", keywords = "genetic algorithms, genetic programming", ISSN = "1927-6974", bibdate = "2017-05-18", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/aires/aires5.html#GaitanBM16", DOI = "doi:10.5430/air.v5n2p92", abstract = "Applied water research, like the one derived from open-channel hydraulics, traditionally links empirical formulas to observational data; for example Manning's formula for open channel flow driven by gravity relates the discharge (Q), cross-sectional average velocity (V), the hydraulic radius (R), and the slope of the water surface (S) with a friction coefficient n, characteristic of the channel's surface needed in the location of interest. Here we use Genetic Programming (GP), a machine learning technique inspired by nature's evolutionary rules, to derive empirical relationships based on synthetic datasets of the aforementioned parameters. Specifically, we evaluated if Manning's formula could be retrieved from datasets with: a) 300 pentads of A, n, R, S, and Q (from Manning's equation), b) from datasets containing an uncorrelated variable and the parameters from (a), and c) from a dataset containing the parameters from (b) but using values of Q containing noise. The cross-validated results show success retrieving the functional form from the synthetic data in the first two experiments, and a more complex solution of Q for the third experiment. The results encourage the application of GP on problems where traditional empirical relationships show high biases or are non-parsimonious. The results also show alternative flow equations that might be used in the absence of one or more predictors; however, these equations should be used with caution outside of the training intervals.", } @InProceedings{gaivoronski:1999:MCESAN, author = "Alexei A. Gaivoronski", title = "Modeling of Complex Economic Systems with Agent Nets", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1265--1272", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "artificial life, adaptive behavior and agents", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-041.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-041.pdf", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{conf/evoW/GajdaK08, title = "Evolving a Vision-Driven Robot Controller for Real-World Indoor Navigation", author = "Pawel Gajda and Krzysztof Krawiec", bibdate = "2008-04-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/evoW/evoW2008.html#GajdaK08", booktitle = "Proceedings of Evo{COMNET}, Evo{FIN}, Evo{HOT}, Evo{IASP}, Evo{MUSART}, Evo{NUM}, Evo{STOC}, and EvoTransLog, Applications of Evolutionary Computing, EvoWorkshops", publisher = "Springer", year = "2008", volume = "4974", editor = "Mario Giacobini and Anthony Brabazon and Stefano Cagnoni and Gianni {Di Caro} and Rolf Drechsler and Anik{\'o} Ek{\'a}rt and Anna Esparcia-Alc{\'a}zar and Muddassar Farooq and Andreas Fink and Jon McCormack and Michael O'Neill and Juan Romero and Franz Rothlauf and Giovanni Squillero and Sima Uyar and Shengxiang Yang", isbn13 = "978-3-540-78760-0", pages = "184--193", series = "Lecture Notes in Computer Science", DOI = "doi:10.1007/978-3-540-78761-7_19", address = "Naples", month = "26-28 " # mar, keywords = "genetic algorithms, genetic programming", abstract = "In this paper, we use genetic programming (GP) to evolve a vision-driven robot controller capable of navigating in a real-world environment. To this aim, we extract visual primitives from the video stream provided by a camera mounted on the robot and let them to be interpreted by a GP individual. The response of GP expressions is then used to control robot's servos. Thanks to the primitive-based approach, evolutionary process is less constrained in the process of synthesising image features. Experiments concerning navigation in indoor environment indicate that the evolved controller performs quite well despite very limited human intervention in the design phase.", } @InProceedings{Gajda:2009:cec, author = "Zbysek Gajda and Lukas Sekanina", title = "Gate-Level Optimization of Polymorphic Circuits Using Cartesian Genetic Programming", booktitle = "2009 IEEE Congress on Evolutionary Computation", year = "2009", editor = "Andy Tyrrell", pages = "1599--1604", address = "Trondheim, Norway", month = "18-21 " # may, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-2959-2", file = "P186.pdf", DOI = "doi:10.1109/CEC.2009.4983133", abstract = "Polymorphic digital circuits contain ordinary and polymorphic gates. In the past, Cartesian Genetic Programming (CGP) has been applied to synthesize polymorphic circuits at the gate level. However, this approach is not scalable. Experimental results presented in this paper indicate that larger and more efficient polymorphic circuits can be designed by a combination of conventional design methods (such as BDD, Espresso or ABC System) and evolutionary optimization (conducted by CGP). Proposed methods are evaluated on two benchmark circuits - Multiplier/Sorter and Parity/Majority circuits of variable input size.", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR", } @InProceedings{Gajda:2010:gecco, author = "Zbysek Gajda and Lukas Sekanina", title = "When does Cartesian genetic programming minimize the phenotype size implicitly?", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "983--984", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, Poster", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830483.1830661", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "A new method is proposed to minimize the number of gates in combinational circuits using Cartesian Genetic Programming (CGP). We show that when the selection of the parent individual is performed on basis of its functionality solely (neglecting thus the phenotype size) smaller circuits can be evolved even if the number of gates is not considered by a fitness function. This phenomenon is confirmed on the evolutionary design of combinational multipliers.", notes = "Also known as \cite{1830661} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @InProceedings{Gajda:2010:ICES, author = "Zbysek Gajda and Lukas Sekanina", title = "An Efficient Selection Strategy for Digital Circuit Evolution", booktitle = "Proceedings of the 9th International Conference Evolvable Systems: From Biology to Hardware, ICES 2010", year = "2010", editor = "Gianluca Tempesti and Andy M. Tyrrell and Julian F. Miller", series = "Lecture Notes in Computer Science", volume = "6274", pages = "13--24", address = "York", month = sep # " 6-8", publisher = "Springer", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", isbn13 = "978-3-642-15322-8", DOI = "doi:10.1007/978-3-642-15323-5_2", size = "12 pages", abstract = "In this paper, we propose a new modification of Cartesian Genetic Programming (CGP) that enables to optimise's digital circuits more significantly than standard CGP. We argue that considering fully functional but not necessarily smallest-discovered individual as the parent for new population can decrease the number of harmful mutations and so improve the search space exploration. This phenomenon was confirmed on common benchmarks such as combinational multipliers and the LGSynth91 circuits.", } @PhdThesis{DBLP:phd/basesearch/Gajda11, author = "Zbysek Gajda", title = "Evolutionary Approach to Synthesis and Optimization of Ordinary and Polymorphic Circuits", title_cz = "Evolu{\v{c}}n{\'{\i}} p{\v{r}}{\'{\i}}stup k synt{\'{e}}ze a optimalizaci b{\v{e}}{\v{z}}n{\'{y}}ch a polymorfn{\'{\i}}ch obvod{\r{u}}", school = "Brno University of Technology", year = "2011", address = "Bruno, Czech Republic", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Polymorphic gate, polymorphic circuit, digital circuit design, evolutionary design, evolutionary optimization", timestamp = "Wed, 04 May 2022 13:00:16 +0200", biburl = "https://dblp.org/rec/phd/basesearch/Gajda11.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://hdl.handle.net/11012/63257", URL = "https://dspace.vutbr.cz/handle/11012/63257", URL = "https://www.vut.cz/www_base/zav_prace_soubor_verejne.php?file_id=136902", size = "96 pages", abstract = "This thesis deals with the evolutionary design and optimization of ordinary and polymorphic circuits. New extensions of Cartesian Genetic Programming (CGP) that allow reducing of the computational time and obtaining more compact circuits are proposed and evaluated. Second part of the thesis is focused on new methods for synthesis of polymorphic circuits. Proposed methods, based on polymorphic binary decision diagrams and polymorphic multiplexing, extend the ordinary circuit representations with the aim of including polymorphic gates. In order to reduce the number of gates in circuits synthesized using proposed methods, an evolutionary optimization based on CGP is implemented and evaluated. The implementations of polymorphic circuits optimised by CGP represent the best known solutions if the number of gates is considered as the target criterion.", notes = "In English Supervisor: Lukas Sekanina", } @Article{GalaGil:2025:swevo, author = "Francisco J. Gil-Gala and Marko Durasevic and Domagoj Jakobovic and Ramiro Varela", title = "Genetic programming with surrogate evaluation for the electric vehicle routing problem", journal = "Swarm and Evolutionary Computation", year = "2025", volume = "96", pages = "101969", keywords = "genetic algorithms, genetic programming, Electric vehicle routing problem, Routing policies, Surrogate evaluation, Hyper-heuristics", ISSN = "2210-6502", URL = "https://www.sciencedirect.com/science/article/pii/S2210650225001270", DOI = "doi:10.1016/j.swevo.2025.101969", abstract = "The focus on environmental sustainability has made the Electric Vehicle Routing Problem (EVRP) an important area of research. Routing Policies (RPs) offer a simple and efficient approach to solving VRPs, providing advantages over methods like metaheuristics by quickly generating solutions. However, designing efficient RPs manually can be time-consuming. Therefore, there is a need to explore hyper-heuristic approaches, particularly Genetic Programming (GP), to automate the design of RPs. However, population-based evolutionary algorithms like GP often require a significant amount of computational resources, especially for fitness calculation. Therefore, surrogate evaluation is essential in enhancing efficiency, especially in GP, where multiple problem instances need to be solved to evaluate each chromosome. In this study, we employ surrogate models within GP to design RPs for EVRP with hard time windows. The experiments show that the RPs designed by GP with surrogate models outperform those produced by standard GP approaches while still requiring less computational time to be generated. Moreover, the RPs designed with GP using surrogate models are also smaller, and consequently, they are also more efficient and easier to interpret", } @Article{Galaviz-Aguilar:2019:SC, author = "Jose Alejandro Galaviz-Aguilar and Patrick Roblin and Jose Ricardo Cardenas-Valdez and Emigdio Z.-Flores and Leonardo Trujillo and Jose-Cruz Nunez Perez and Oliver Schuetze", title = "Comparison of a genetic programming approach with {ANFIS} for power amplifier behavioral modeling and {FPGA} implementation", journal = "Soft Computing", year = "2019", volume = "23", number = "7", pages = "2463--2481", keywords = "genetic algorithms, genetic programming, anfis digital predistortion linearisation power amplifier modelling radio frequency", ISSN = "1432-7643", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/soco/soco23.html#Galaviz-Aguilar19", DOI = "doi:10.1007/s00500-017-2941-8", abstract = "Accurate modelling of power amplifiers (PA) is of up most importance in the design process of wireless communication systems where a high linearity and efficiency is required. To deal with the nonlinear behaviour of PAs effectively a linearisation stage is applied to minimise the distortions of in-band and adjacent transmission channels, which translate to an improvement of the signal integrity and the operation cost of the transmitter system. This paper presents a method based on genetic programming with a local search heuristic (GP-LS) to emulate the electrical memory effects by using the characteristic conversion curves of the radio frequency (RF) PA NXP Semiconductor of 10 W GaN HEMT working at 2.34 GHz. This method is compared with an Adaptive Neuro-Fuzzy Inference Systems (ANFIS) through several performance metrics (NMSE, MAE and correlation coefficient), with GP-LS achieving a better modelling accuracy. Moreover, the models produced by GP-LS permit a reduction in the required hardware resources, when it is implemented on a Field-Programmable Gate Array through the DSP Builder tool. The models are derived using a data-driven approach, posed in two different ways. Firstly, experiments are performed using a testbed Arria V GX for a flexible vector signal generation that provides the raw data of the PA characterisation using an LTE-Advanced signal with 10-MHz bandwidth. Secondly, the modelling is derived from a filtered version of the data and then adding a high-frequency signal as a post processing step to approximate the true behaviour of the system. In both cases, the models are generated with ANFIS and GP-LS, performing extensive logic-based simulations and implementing the models on a Cyclone III development board. Both approaches are compared based on accuracy and required hardware resources, with GP-LS substantially outperforming ANFIS. These results suggest that the GP-LS models can be implemented in a digital pre-distortion chain and used in the linearization stage for a RF-PA.", notes = "journals/soco/Galaviz-Aguilar19", } @InProceedings{galeano:2002:stiosaapgople, author = "G. Galeano and F. Fernandez and M. Tomassini and L. Vanneschi", title = "Studying the influence of Synchronous and Asynchronous parallel {GP} on Programs' Length Evolution", booktitle = "Proceedings of the 2002 Congress on Evolutionary Computation CEC2002", editor = "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and Mark Shackleton", year = "2002", pages = "1727--1732", address = "Honolulu, USA", month = "12-17 " # may, publisher = "IEEE Press", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", organisation = "IEEE Neural Network Council (NNC), Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", keywords = "genetic algorithms, genetic programming, asynchronous parallel genetic programming, bloat phenomenon, distributed genetic programming models, programs length evolution, subpopulations, synchronous parallel genetic programming, parallel programming", ISBN = "0-7803-7278-6", URL = "http://dynamics.org/~altenber/UH_ICS/EC_REFS/GP_REFS/IEEE/WCCI2002/7126.pdf?origin=publication_detail", DOI = "doi:10.1109/CEC.2002.1004503", size = "6 pages", abstract = "We present a study of parallel and distributed genetic programming models and their relationships with the bloat phenomenon. The experiments that we have performed have also allowed us to find an interesting link between the number of processes, subpopulations and the model we should use when applying parallelism to GP. We study the synchronous and asynchronous version of the island-model in GP domain.", notes = "CEC 2002 - A joint meeting of the IEEE, the Evolutionary Programming Society, and the IEE. Held in connection with the World Congress on Computational Intelligence (WCCI 2002)", } @InProceedings{gallagher:1999:EADNNAIVLSM, author = "John C. Gallagher and Randall D. Beer", title = "Evolution and Analysis of Dynamical Neural Networks for Agents Integrating Vision, Locomotion, and Short-Term Memory", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1273--1280", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "artificial life, adaptive behavior and agents", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-005.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-005.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{gallagher:1999:REOFPDE, author = "Marcus Gallagher and Marcus Frean and Tom Downs", title = "Real-valued Evolutionary Optimization using a Flexible Probability Density Estimator", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "840--846", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "evolution strategies and evolutionary programming", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/gallagher_gecco99.ps.gz", URL = "http://www.itee.uq.edu.au/~marcusg/papers/gallagher_gecco99.ps.gz", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{galos:2003:gecco, author = "Peter Galos and Peter Nordin and Joel Ols{\'e}n and Kristofer Sund{\'e}n Ringn{\'e}r", title = "A General Approach to Automatic Programming Using {Occam's} Razor, Compression, and Self-Inspection", booktitle = "Genetic and Evolutionary Computation -- GECCO-2003", editor = "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller", year = "2003", pages = "1806--1807", address = "Chicago", publisher_address = "Berlin", month = "12-16 " # jul, volume = "2724", series = "LNCS", ISBN = "3-540-40603-4", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, poster", DOI = "doi:10.1007/3-540-45110-2_74", abstract = "general method for automatic programming which can be seen as a generalization of techniques such as genetic programming and ADATE. The approach builds on the assumption that data compression can be used as a metaphor for cognition and intelligence. The proof-of-concept system is evaluated on sequence prediction problems. As a starting point, the process of inferring a general law from a data set is viewed as an attempt to compress the observed data. From an artificial intelligence point of view, compression is a useful way of measuring how deeply the observed data is understood. If the sequence contains redundancy it exists a shorter description i.e. the sequence can be compressed.", notes = "GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)", } @InProceedings{lopez:2004:eurogp, author = "Edgar {Galvan Lopez} and Riccardo Poli and Carlos A. {Coello Coello}", title = "Reusing Code in Genetic Programming", booktitle = "Genetic Programming 7th European Conference, EuroGP 2004, Proceedings", year = "2004", editor = "Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule", volume = "3003", series = "LNCS", pages = "359--368", address = "Coimbra, Portugal", publisher_address = "Berlin", month = "5-7 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, PSO, code reuse, logic circuit design, evolvable hardware: Poster", ISBN = "3-540-21346-5", URL = "http://delta.cs.cinvestav.mx/~ccoello/conferences/eurogp04.pdf.gz", DOI = "doi:10.1007/978-3-540-24650-3_34", abstract = "We propose an approach to Genetic Programming based on reuse of code and we test our algorithm in the design of combinational logic circuits at the gate-level. The proposed algorithm is validated using examples taken from the evolvable hardware literature, and is compared against circuits produced by human designers, by Particle Swarm Optimization, by an n-cardinality GA and by Cartesian Genetic Programming.", notes = "p-node Part of \cite{keijzer:2004:GP} EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004", } @InProceedings{Lopez:gecco05lbp, author = "Edgar {Galvan Lopez} and Katya {Rodriguez Vazquez} and Riccardo Poli", title = "Beneficial Aspects of Neutrality in GP", booktitle = "Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}", year = "2005", month = "25-29 " # jun, editor = "Franz Rothlauf", address = "Washington, D.C., USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2005lbp/papers/45-lopez.pdf", keywords = "genetic algorithms, genetic programming, EHW", abstract = "We propose a new approach, called Multiple Outputs in a Single Tree (MOST), to Genetic Programming. The idea of this approach is to specify explicitly Neutrality and study how this improves the evolutionary process. For this sake, we have used several evolvable hardware problems of different complexity taken from the literature. Our results indicate that our approach has a better overall performance in terms of consistency to reach feasible solutions", notes = "Distributed on CD-ROM at GECCO-2005 ", } @InProceedings{Lopez:PPSN:2006, author = "Edgar Galvan-Lopez and Katya Rodriguez-Vazquez", title = "The Importance of Neutral Mutations in GP", booktitle = "Parallel Problem Solving from Nature - PPSN IX", year = "2006", editor = "Thomas Philip Runarsson and Hans-Georg Beyer and Edmund Burke and Juan J. Merelo-Guervos and L. Darrell Whitley and Xin Yao", volume = "4193", pages = "870--879", series = "LNCS", address = "Reykjavik, Iceland", publisher_address = "Berlin", month = "9-13 " # sep, publisher = "Springer-Verlag", ISBN = "3-540-38990-3", keywords = "genetic algorithms, genetic programming", URL = "http://ppsn2006.raunvis.hi.is/proceedings/208.pdf", URL = "https://mural.maynoothuniversity.ie/15437/", DOI = "doi:10.1007/11844297_88", size = "10 pages", abstract = "Understanding how neutrality works in EC systems has drawn increasing attention. However, some researchers have found neutrality to be beneficial for the evolutionary process while others have found it either useless or worse. We believe there are various reasons for these contradictory results: (a) many studies have based their conclusions using crossover and mutation as main operators rather than using only mutation (Kimura's studies were done analysing only mutations) and, (b) studies often consider problems and representation with larger complexity. The aim of this paper is to analyse how neutral mutations tend to behave in GP and establish how important they are. For this purpose we introduce an approach which has two advantages: (a) it allows us to specify neutrality and, (b) this makes possible to understand how neutrality affects the evolutionary search process.", notes = "PPSN-IX", } @InProceedings{eurogp07:Galvan-Lopez, author = "Edgar Galv\'an-L\'opez and Katya Rodriguez-V\'azquez", title = "Multiple Interactive Outputs in a Single Tree: An Empirical Investigation", editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar", booktitle = "Proceedings of the 10th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "4445", year = "2007", address = "Valencia, Spain", month = "11-13 " # apr, pages = "341--350", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-71602-5", isbn13 = "978-3-540-71602-0", DOI = "doi:10.1007/978-3-540-71605-1_32", abstract = "This paper describes Multiple Interactive Outputs in a Single Tree (MIOST), a new form of Genetic Programming (GP). Our approach is based on two ideas. Firstly, we have taken inspiration from graph-GP representations. With this idea we decided to explore the possibility of representing programs as graphs with oriented links. Secondly, our individuals could have more than one output. This idea was inspired on the divide and conquer principle, a program is decomposed in subprograms, and so, we are expecting to make the original problem easier by breaking down a problem into two or more sub-problems. To verify the effectiveness of our approach, we have used several evolvable hardware problems of different complexity taken from the literature. Our results indicate that our approach has a better overall performance in terms of consistency to reach feasible solutions.", notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007", } @InProceedings{Galvan-Lopez:2008:eurogp, author = "Edgar Galvan-Lopez and Stephen Dignum and Riccardo Poli", title = "The Effects of Constant Neutrality on Performance and Problem Hardness in {GP}", booktitle = "Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008", address = "Naples", month = "26-28 " # mar, publisher = "Springer", year = "2008", volume = "4971", editor = "Michael O'Neill and Leonardo Vanneschi and Steven Gustafson and Anna Isabel {Esparcia Alcazar} and Ivanoe {De Falco} and Antonio {Della Cioppa} and Ernesto Tarantino", pages = "312--324", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-78670-2", bibdate = "2008-04-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/eurogp/eurogp2008.html#LopezDP08", URL = "https://mural.maynoothuniversity.ie/15432/", DOI = "doi:10.1007/978-3-540-78671-9_27", size = "13 pages", abstract = "The neutral theory of molecular evolution and the associated notion of neutrality have interested many researchers in Evolutionary Computation. The hope is that the presence of neutrality can aid evolution. However, despite the vast number of publications on neutrality, there is still a big controversy on its effects. The aim of this paper is to clarify under what circumstances neutrality could aid Genetic Programming using the traditional representation (i.e. tree-like structures) . For this purpose, we use fitness distance correlation as a measure of hardness. In addition we have conducted extensive empirical experimentation to corroborate the fitness distance correlation predictions. This has been done using two test problems with very different landscape features that represent two extreme cases where the different effects of neutrality can be emphasised. Finally, we study the distances between individuals and global optimum to understand how neutrality affects evolution (at least with the one proposed in this paper).", notes = "Also known as \cite{conf/eurogp/LopezDP08} Part of \cite{conf/eurogp/2008} EuroGP'2008 held in conjunction with EvoCOP2008, EvoBIO2008 and EvoWorkshops2008", } @Article{galvan-lopez:2008:IJAC, author = "Edgar Galvan-Lopez", title = "Efficient graph-based genetic programming representation with multiple outputs", journal = "International Journal of Automation and Computing", year = "2008", volume = "5", number = "1", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s11633-008-0081-4", DOI = "doi:10.1007/s11633-008-0081-4", } @PhdThesis{Galvan:thesis, author = "Edgar Galvan", title = "An Analysis of the Effects of Neutrality on Problem Hardness for Evolutionary Algorithms", school = "School of Computer Science and Electronic Engineering, University of Essex", year = "2009", address = "United Kingdom", keywords = "genetic algorithms, genetic programming", URL = "http://www.essex.ac.uk/csee/news_and_seminars/newsEvent.aspx?e_id=5658", URL = "http://www.essex.ac.uk/csee/department/news/newsletter/08_12_08.aspx", URL = "http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.495801", size = "412 pages", notes = "ThOS Persistent ID: uk.bl.ethos.495801 http://www.essex.ac.uk/csee/department/news/newsletter/08_12_08.aspx", } @InProceedings{DBLP:conf/micai/LopezP09, author = "Edgar {Galvan Lopez} and Riccardo Poli", title = "An Empirical Investigation of How Degree Neutrality Affects {GP} Search", booktitle = "MICAI 2009: Advances in Artificial Intelligence, 8th Mexican International Conference on Artificial Intelligence, Proceedings", year = "2009", editor = "Arturo Hern{\'{a}}ndez Aguirre and Ra{\'{u}}l Monroy Borja and Carlos A. Reyes Garc{\'{\i}}a", volume = "5845", series = "Lecture Notes in Computer Science", pages = "728--739", address = "Guanajuato, Mexico", month = nov # " 9-13", publisher = "Springer", keywords = "genetic algorithms, genetic programming", timestamp = "Tue, 14 May 2019 10:00:49 +0200", biburl = "https://dblp.org/rec/conf/micai/LopezP09.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://www.cs.nuim.ie/~egalvan/papers/AnEmpiricalDegree_Galvan2009.pdf", DOI = "doi:10.1007/978-3-642-05258-3_64", size = "12 pages", abstract = "Over the last years, neutrality has inspired many researchers in the area of Evolutionary Computation (EC) systems in the hope that it can aid evolution. However, there are contradictory results on the effects of neutrality in evolutionary search. The aim of this paper is to understand how neutrality - named in this paper degree neutrality - affects GP search. For analysis purposes, we use a well-defined measure of hardness (i.e., fitness distance correlation) as an indicator of difficulty in the absence and in the presence of neutrality, we propose a novel approach to normalise distances between a pair of trees and finally, we use a problem with deceptive features where GP is well-known to have poor performance and see the effects of neutrality in GP search.", notes = "something odd both Springer and IEEE published MICAI 2009 proceedings the papers are different", } @InProceedings{Galvan-Lopez:2009:MICAI, author = "Edgar Galvan-Lopez and Michael O'Neill and Anthony Brabazon", title = "Towards Understanding the Effects of Locality in GP", booktitle = "Eighth Mexican International Conference on Artificial Intelligence, MICAI 2009", year = "2009", month = "9-13 " # nov, editor = "Arturo {Hernandez Aguirre} and Raul Monroy and Carlos Alberto {Reyes Garcia}", pages = "9--14", address = "Guanajuato, Mexico", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/MICAI.2009.17", isbn13 = "978-0-7695-3933-1", abstract = "Locality - how well neighbouring genotypes correspond to neighbouring phenotypes - has been defined as a key element in Evolutionary Computation systems to explore and exploit the search space. Locality has been studied empirically using the typical Genetic Algorithms (GAs) representation (i.e., bitstrings),and it has been argued that locality plays an important role in the performance of evolution. To our knowledge, there are no studies of locality using the typical Genetic Programming (GP)representation (i.e., tree-like structures). The aim of this paper is to shed some light on this matter by using GP. To do so, we use three different types of mutation taken from the specialised literature. We then perform extensive experiments by comparing the difference of distances at the genotype level between parent and offspring and their corresponding fitnesses. Our findings indicate that there is low-locality in GP when using these forms of mutation on a multimodal-deceptive landscape.", notes = "Also known as \cite{5372725}", } @InProceedings{galvanlopez:2010:evogames, author = "Edgar Galvan-Lopez and John Mark Swafford and Michael O'Neill and Anthony Brabazon", title = "Evolving a {Ms. PacMan} Controller Using Grammatical Evolution", booktitle = "EvoGAMES", year = "2010", editor = "Cecilia {Di Chio} and Stefano Cagnoni and Carlos Cotta and Marc Ebner and Aniko Ekart and Anna I. Esparcia-Alcazar and Chi-Keong Goh and Juan J. Merelo and Ferrante Neri and Mike Preuss and Julian Togelius and Georgios N. Yannakakis", volume = "6024", series = "LNCS", pages = "161--170", address = "Istanbul", month = "7-9 " # apr, organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming, grammatical evolution", isbn13 = "978-3-642-12238-5", DOI = "doi:10.1007/978-3-642-12239-2_17", abstract = "In this paper we propose an evolutionary approach capable of successfully combining rules to play the popular video game, Ms. Pac-Man. In particular we focus our attention on the benefits of using Grammatical Evolution to combine rules in the form of if then perform . We defined a set of high-level functions that we think are necessary to successfully manoeuvre Ms. Pac-Man through a maze while trying to get the highest possible score. For comparison purposes, we used four Ms. Pac-Man agents, including a hand-coded agent, and tested them against three different ghosts teams. Our approach shows that the evolved controller achieved the highest score among all the other tested controllers, regardless of the ghost team used.", notes = "EvoGAMES'2010 held in conjunction with EuroGP'2010 EvoCOP2010 EvoBIO2010", } @InProceedings{GalvanLopez:2010:gecco, author = "Edgar Galvan-Lopez and James McDermott and Michael O'Neill and Anthony Brabazon", title = "Towards an understanding of locality in genetic programming", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "901--908", keywords = "genetic algorithms, genetic programming", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830483.1830646", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Locality - how well neighbouring genotypes correspond to neighbouring phenotypes - has been defined as a key element affecting how Evolutionary Computation systems explore and exploit the search space. Locality has been studied empirically using the typical Genetic Algorithm (GA) representation (i.e., bitstrings), and it has been argued that locality plays an important role in EC performance. To our knowledge, there are few explicit studies of locality using the typical Genetic Programming (GP) representation (i.e., tree-like structures). The aim of this paper is to address this important research gap. We extend the genotype-phenotype definition of locality to GP by studying the relationship between genotypes and fitness. We consider a mutation-based GP system applied to two problems which are highly difficult to solve by GP (a multimodal deceptive landscape and a highly neutral landscape). To analyse in detail the locality in these instances, we adopt three popular mutation operators. We analyse the operators' genotypic step sizes in terms of three distance measures taken from the specialised literature and in terms of corresponding fitness values. We also analyse the frequencies of different sizes of fitness change.", notes = "Also known as \cite{1830646} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @InProceedings{galvan-lopez_etal:cec2010, author = "Edgar Galvan-Lopez and David Fagan and Eoin Murphy and John Mark Swafford and Alexandros Agapitos and Michael O'Neill and Anthony Brabazon", title = "Comparing the Performance of the Evolvable {PiGrammatical} Evolution Genotype-Phenotype Map to Grammatical Evolution in the Dynamic {Ms. Pac-Man} Environment", booktitle = "2010 IEEE World Congress on Computational Intelligence", pages = "1587--1594", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, grammatical evolution", isbn13 = "978-1-4244-6910-9", DOI = "doi:10.1109/CEC.2010.5586508", abstract = "In this work, we examine the capabilities of two forms of mappings by means of Grammatical Evolution (GE) to successfully generate controllers by combining high-level functions in a dynamic environment. In this work we adopted the Ms. Pac-Man game as a benchmark test bed. We show that the standard GE mapping and Position Independent GE (piGE) mapping achieve similar performance in terms of maximising the score. We also show that the controllers produced by both approaches have an overall better performance in terms of maximising the score compared to a hand-coded agent. There are, however, significant differences in the controllers produced by these two approaches: standard GE produces more controllers with invalid code, whereas the opposite is seen with piGE.", notes = "WCCI 2010. Also known as \cite{5586508}", } @InProceedings{galvan-lopez_etal_ii:cec2010, author = "Edgar Galvan-Lopez and James McDermott and Michael O'Neill and Anthony Brabazon", title = "Defining Locality in Genetic Programming to Predict Performance", booktitle = "2010 IEEE World Congress on Computational Intelligence", pages = "1828--1835", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-6910-9", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2010.5586095", abstract = "A key indicator of problem difficulty in evolutionary computation problems is the landscape's locality, that is whether the genotype-phenotype mapping preserves neighbourhood. In genetic programming the genotype and phenotype are not distinct, but the locality of the genotypefitness mapping is of interest. In this paper we extend the original standard quantitative definition of locality to cover the genotype-fitness case, considering three possible definitions. By relating the values given by these definitions with the results of evolutionary runs, we investigate which definition is the most useful as a predictor of performance.", notes = "WCCI 2010. Also known as \cite{5586095}", } @Article{GalvanLopezPKOB:2011:ESNeEAWDWK, author = "Edgar Galvan-Lopez and Riccardo Poli and Ahmed Kattan and Michael O'Neill and Anthony Brabazon", title = "Neutrality in Evolutionary Algorithms... What do we know?", journal = "Evolving Systems", year = "2011", volume = "2", number = "3", pages = "145--163", month = sep, keywords = "genetic algorithms, genetic programming, Neutrality, Phenotypic mutation rates, Problem hardness, Genotype-phenotype mappings, Evolutionary algorithms", ISSN = "1868-6478", DOI = "doi:10.1007/s12530-011-9030-5", size = "19 pages", abstract = "Over the last years, the effects of neutrality have attracted the attention of many researchers in the Evolutionary Algorithms (EAs) community. A mutation from one gene to another is considered as neutral if this modification does not affect the phenotype. This article provides a general overview on the work carried out on neutrality in EAs. Using as a framework the origin of neutrality and its study in different paradigms of EAs (e.g., Genetic Algorithms, Genetic Programming), we discuss the most significant works and findings on this topic. This work points towards open issues, which the community needs to address.", } @InProceedings{Galvan:2012:evolve, author = "Edgar Galvan and Leonardo Trujillo and James McDermott and Ahmed Kattan", title = "Locality in Continuous Fitness-Valued Cases and Genetic Programming Difficulty", booktitle = "EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation {II}", year = "2012", editor = "Oliver Schuetze and Carlos A. {Coello Coello} and Alexandru-Adrian Tantar and Emilia Tantar and Pascal Bouvry and Pierre {Del Moral} and Pierrick Legrand", volume = "175", series = "Advances in Intelligent Systems and Computing", pages = "41--56", address = "Mexico City, Mexico", month = aug # " 7-9", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-31519-0", DOI = "doi:10.1007/978-3-642-31519-0_3", abstract = "It is commonly accepted that a mapping is local if it preserves neighbourhood. In Evolutionary Computation, locality is generally described as the property that neighbouring genotypes correspond to neighbouring phenotypes. Locality has been classified in one of two categories: high and low locality. It is said that a representation has high locality if most genotypic neighbours correspond to phenotypic neighbours. The opposite is true for a representation that has low locality. It is argued that a representation with high locality performs better in evolutionary search compared to a representation that has low locality. In this work, we explore, for the first time, a study on Genetic Programming (GP) locality in continuous fitness valued cases. For this, we extended the original definition of locality (first defined and used in Genetic Algorithms using bitstrings) from genotype-phenotype mapping to the genotype-fitness mapping. Then, we defined three possible variants of locality in GP regarding neighbourhood. The experimental tests presented here use a set of symbolic regression problems, two different encoding and two different mutation operators. We show how locality can be studied in this type of scenarios (continuous fitness-valued cases) and that locality can successfully been used as a performance prediction tool.", notes = "EVOLVE-2012", affiliation = "Distributed Systems Group, School of Computer Science and Statistics, Trinity College, Dublin, Ireland", } @Article{Galvan-Lopez:2011:GPEM, author = "Edgar Galvan-Lopez and James McDermott and Michael O'Neill and Anthony Brabazon", title = "Defining locality as a problem difficulty measure in genetic programming", journal = "Genetic Programming and Evolvable Machines", year = "2012", volume = "12", number = "4", pages = "365--401", month = dec, keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-011-9136-3", size = "37 pages", abstract = "A mapping is local if it preserves neighbourhood. In Evolutionary Computation, locality is generally described as the property that neighbouring genotypes correspond to neighbouring phenotypes. A representation has high locality if most genotypic neighbours are mapped to phenotypic neighbours. Locality is seen as a key element in performing effective evolutionary search. It is believed that a representation that has high locality will perform better in evolutionary search and the contrary is true for a representation that has low locality. When locality was introduced, it was the genotype-phenotype mapping in bit string based Genetic Algorithms which was of interest; more recently, it has also been used to study the same mapping in Grammatical Evolution. To our knowledge, there are few explicit studies of locality in Genetic Programming (GP). The goal of this paper is to shed some light on locality in GP and use it as an indicator of problem difficulty. Strictly speaking, in GP the genotype and the phenotype are not distinct. We attempt to extend the standard quantitative definition of genotype-phenotype locality to the genotype-fitness mapping by considering three possible definitions. We consider the effects of these definitions in both continuous- and discrete-valued fitness functions. We compare three different GP representations (two of them induced by using different function sets and the other using a slightly different GP encoding) and six different mutation operators. Results indicate that one definition of locality is better in predicting performance.", notes = "Rothlauf. Mutation only (no crossover). Sum of surplus distances. Even-3-parity, even-4-parity (two function sets) Artificial ant \cite{langdon:1998:antspace} (two function sets) two symbolic regression. Uniform GP. Ant negative correlation (r=-.74). Normalised compression distance (Kolmogorov complexity). Size fair, size fair range crossovers \cite{langdon:2000:fairxo} Hoist, one point, subtree, _permutation_ mutations. p388 'mutations involving the discontinuous protected division operator' p390 'always a counter example'.", } @InProceedings{Galvan-Lopez:2013:CEC, article_id = "1650", author = "Edgar Galvan-Lopez and Brendan Cody-Kenny and Leonardo Trujillo and Ahmed Kattan", title = "Using Semantics in the Selection Mechanism in Genetic Programming: a Simple Method for Promoting Semantic Diversity", booktitle = "2013 IEEE Conference on Evolutionary Computation", volume = "1", year = "2013", month = jun # " 20-23", editor = "Luis Gerardo {de la Fraga}", pages = "2972--2979", address = "Cancun, Mexico", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-0453-2", DOI = "doi:10.1109/CEC.2013.6557931", notes = "CEC 2013 - A joint meeting of the IEEE, the EPS and the IET.", } @InProceedings{Galvan-Lopez:2016:PPSN, author = "Edgar Galvan-Lopez and Efren Mezura-Montes and Ouassim Ait Elhara and Marc Schoenauer", title = "On the Use of Semantics in Multi-Objective Genetic Programming", booktitle = "14th International Conference on Parallel Problem Solving from Nature", year = "2016", editor = "Julia Handl and Emma Hart and Peter R. Lewis and Manuel Lopez-Ibanez and Gabriela Ochoa and Ben Paechter", volume = "9921", series = "LNCS", pages = "353--363", address = "Edinburgh", month = "17-21 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-45823-6", DOI = "doi:10.1007/978-3-319-45823-6_33", abstract = "Research on semantics in Genetic Programming (GP) has increased dramatically over the last number of years. Results in this area clearly indicate that its use in GP can considerably increase GP performance. Motivated by these results, this paper investigates for the first time the use of Semantics in Muti-objective GP within the well-known NSGA-II algorithm. To this end, we propose two forms of incorporating semantics into a MOGP system. Results on challenging (highly) unbalanced binary classification tasks indicate that the adoption of semantics in MOGP is beneficial, in particular when a semantic distance is incorporated into the core of NSGA-II", notes = "PPSN2016", } @InProceedings{Lopez:2016:MICAI, author = "Edgar {Galvan Lopez} and Lucia Vazquez-Mendoza and Leonardo Trujillo", title = "Stochastic Semantic-Based Multi-objective Genetic Programming Optimisation for Classification of Imbalanced Data", booktitle = "Mexican International Conference on Artificial Intelligence", year = "2016", editor = "Obdulia Pichardo-Lagunas and Sabino Miranda-Jimenez", volume = "10062", series = "Lecture Notes in Computer Science", pages = "261--272", address = "Cancun, Mexico", month = "23-29 " # oct, publisher = "Springer", keywords = "genetic algorithms, genetic programming", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/micai/micai2016-2.html#LopezVT16", isbn13 = "978-3-319-62428-0", DOI = "doi:10.1007/978-3-319-62428-0_22", size = "12 pages", abstract = "Data sets with imbalanced class distribution pose serious challenges to well-established classifiers. In this work, we propose a stochastic multi-objective genetic programming based on semantics. We tested this approach on imbalanced binary classification data sets, where the proposed approach is able to achieve, in some cases, higher recall, precision and F-measure values on the minority class compared to C4.5, Naive Bayes and Support Vector Machine, without significantly decreasing these values on the majority class.", notes = "also \nown as \cite{conf/micai/LopezVT16}", } @InProceedings{Lopez:2017:EA, author = "Edgar Galvan-Lopez and Lucia Vazquez-Mendoza and Marc Schoenauer and Leonardo Trujillo", title = "On the Use of Dynamic {GP} Fitness Cases in Static and Dynamic Optimisation Problems", booktitle = "EA 2017, International Conference on Artificial Evolution", year = "2017", editor = "Evelyne Lutton and Pierrick Legrand and Pierre Parrend and Nicolas Monmarche and Marc Schoenauer", volume = "10764)", series = "LNCS", pages = "72--87", address = "Paris, France", month = oct # " 2017", publisher = "Springer", note = "Revised Selected Papers", keywords = "genetic algorithms, genetic programming", bibsource = "OAI-PMH server at api.archives-ouvertes.fr", contributor = "TAckling the Underspeficied and Laboratoire de Recherche en Informatique and Evelyne Lutton", identifier = "hal-01648365", language = "en", oai = "oai:HAL:hal-01648365v1", isbn13 = "978-3-319-78133-4", ISSN = "01648365", URL = "https://hal.inria.fr/hal-01648365", URL = "https://hal.inria.fr/hal-01648365/document", URL = "https://hal.inria.fr/hal-01648365/file/dynamic_v3.pdf", DOI = "doi:10.1007/978-3-319-78133-4_6", size = "16 pages", abstract = "In Genetic Programming (GP), the fitness of individuals is normally computed by using a set of fitness cases (FCs). Research on the use of FCs in GP has primarily focused on how to reduce the size of these sets. However, often, only a small set of FCs is available and there is no need to reduce it. In this work, we are interested in using the whole FCs set, but rather than adopting the commonly used GP approach of presenting the entire set of FCs to the system from the beginning of the search, referred as static FCs, we allow the GP system to build it by aggregation over time, named as dynamic FCs, with the hope to make the search more amenable. Moreover, there is no study on the use of FCs in Dynamic Optimisation Problems (DOPs). To this end, we also use the Kendall Tau Distance (KTD) approach, which quantifies pairwise dissimilarities among two lists of fitness values. KTD aims to capture the degree of a change in DOPs and we use this to promote structural diversity. Results on eight symbolic regression functions indicate that both approaches are highly beneficial in GP.", notes = "Evolution Artificielle https://ea2017.inria.fr/ Also known as \cite{oai:HAL:hal-01648365v1}", } @InProceedings{Galvan:2019:GECCO, author = "Edgar Galvan and Marc Schoenauer", title = "Promoting semantic diversity in multi-objective genetic programming", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference", year = "2019", editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and Anne Auger and Petr Posik and Leslie {Peprez Caceres} and Andrew M. Sutton and Nadarajen Veerapen and Christine Solnon and Andries Engelbrecht and Stephane Doncieux and Sebastian Risi and Penousal Machado and Vanessa Volz and Christian Blum and Francisco Chicano and Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and Jonathan Fieldsend and Jose Antonio Lozano and Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and Robin Purshouse and Thomas Baeck and Justyna Petke and Giuliano Antoniol and Johannes Lengler and Per Kristian Lehre", isbn13 = "978-1-4503-6111-8", pages = "1021--1029", address = "Prague, Czech Republic", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, Multi-objective Genetic Programming, MOGP, Semantics", URL = "https://mural.maynoothuniversity.ie/14365/1/EG_promoting.pdf", DOI = "doi:10.1145/3321707.3321854", size = "9 pages", abstract = "The study of semantics in Genetic Programming (GP) has increased dramatically over the last years due to the fact that researchers tend to report a performance increase in GP when semantic diversity is promoted. However, the adoption of semantics in Evolutionary Multi-objective Optimisation (EMO), at large, and in Multi-objective GP (MOGP), in particular, has been very limited and this paper intends to fill this challenging research area. We propose a mechanism wherein a semantic-based distance is used instead of the widely known crowding distance and is also used as an objective to be optimised. To this end, we use two well-known EMO algorithms: NSGA-II and SPEA2. Results on highly unbalanced binary classification tasks indicate that the proposed approach produces more and better results than the rest of the three other approaches used in this work, including the canonical aforementioned EMO algorithms.", notes = "Also known as \cite{3321854} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @Misc{DBLP:journals/corr/abs-2009-12401, author = "Edgar Galvan and Fergal Stapleton", title = "Semantic-based Distance Approaches in Multi-objective Genetic Programming", howpublished = "arXiv", volume = "abs/2009.12401", year = "2020", keywords = "genetic algorithms, genetic programming", URL = "https://arxiv.org/abs/2009.12401", archiveprefix = "arXiv", eprint = "2009.12401", timestamp = "Wed, 30 Sep 2020 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/corr/abs-2009-12401.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Misc{DBLP:journals/corr/abs-2012-04717, author = "Edgar Galvan and Fergal Stapleton", title = "Promoting Semantics in Multi-objective Genetic Programming based on Decomposition", howpublished = "arXiv", volume = "abs/2012.04717", year = "2020", keywords = "genetic algorithms, genetic programming", URL = "https://arxiv.org/abs/2012.04717", eprinttype = "arXiv", eprint = "2012.04717", timestamp = "Thu, 14 Oct 2021 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/corr/abs-2012-04717.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{Galvan:2020:SSCI, author = "Edgar Galvan and Fergal Stapleton", title = "Semantic-based Distance Approaches in Multi-objective Genetic Programming", booktitle = "2020 IEEE Symposium Series on Computational Intelligence (SSCI)", year = "2020", pages = "149--156", abstract = "Semantics in the context of Genetic Program (GP) can be understood as the behaviour of a program given a set of inputs and has been well documented in improving performance of GP for a range of diverse problems. There have been a wide variety of different methods which have incorporated semantics into single-objective GP. The study of semantics in Multi-objective (MO) GP, however, has been limited and this paper aims at tackling this issue. More specifically, we conduct a comparison of three different forms of semantics in MOGP. One semantic-based method, (i) Semantic Similarity-based Crossover (SSC), is borrowed from single-objective GP, where the method has consistently being reported beneficial in evolutionary search. We also study two other methods, dubbed (ii) Semantic-based Distance as an additional criterion (SDO) and (iii) Pivot Similarity SDO. We empirically and consistently show how by naturally handling semantic distance as an additional criterion to be optimised in MOGP leads to better performance when compared to canonical methods and SSC. Both semantic distance based approaches made use of a pivot, which is a reference point from the sparsest region of the search space and it was found that individuals which were both semantically similar and dissimilar to this pivot were beneficial in promoting diversity. Moreover, we also show how the semantics successfully promoted in single-objective optimisation does not necessary lead to a better performance when adopted in MOGP.", keywords = "genetic algorithms, genetic programming, Semantics, Optimisation, Pareto optimization, Mathematical model, Linear programming, Task analysis, Semantics, Multiobjective optimisation", DOI = "doi:10.1109/SSCI47803.2020.9308386", month = dec, notes = "Also known as \cite{9308386}", } @Article{GALVAN:2022:ASC, author = "Edgar Galvan and Leonardo Trujillo and Fergal Stapleton", title = "Semantics in Multi-objective Genetic Programming", journal = "Applied Soft Computing", year = "2022", volume = "115", pages = "108143", keywords = "genetic algorithms, genetic programming, Multi-objective Genetic Programming, Semantics, Diversity, NSGA-II, SPEA2", ISSN = "1568-4946", URL = "https://www.sciencedirect.com/science/article/pii/S1568494621010139", DOI = "doi:10.1016/j.asoc.2021.108143", abstract = "Semantics has become a key topic of research in Genetic Programming (GP). Semantics refers to the outputs (behaviour) of a GP individual when this is run on a dataset. The majority of works that focus on semantic diversity in single-objective GP indicates that it is highly beneficial in evolutionary search. Surprisingly, there is minuscule research conducted in semantics in Multi-objective GP (MOGP). In this work we make a leap beyond our understanding of semantics in MOGP and propose SDO: Semantic-based Distance as an additional criteriOn. This naturally encourages semantic diversity in MOGP. To do so, we find a pivot in the less dense region of the first Pareto front (most promising front). This is then used to compute a distance between the pivot and every individual in the population. The resulting distance is then used as an additional criterion to be optimised to favour semantic diversity. We also use two other semantic-based methods as baselines, called Semantic Similarity-based Crossover and Semantic-based Crowding Distance. Furthermore, we also use the Non-dominated Sorting Genetic Algorithm II and the Strength Pareto Evolutionary Algorithm 2 for comparison too. We use highly unbalanced binary classification problems and consistently show how our proposed SDO approach produces more non-dominated solutions and better diversity, leading to better statistically significant results, using the hypervolume results as evaluation measure, compared to the rest of the other four methods", } @InProceedings{Galvan-Lopez:2022:GECCOhop, author = "Edgar Galvan and Leonardo Trujillo and Fergal Stapleton", title = "Highlights of Semantics in Multi-objective Genetic Programming", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion", year = "2022", editor = "Marcus Gallagher", pages = "19--20", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, diversity, semantics, multi-objective genetic programming", isbn13 = "978-1-4503-9268-6/22/07", DOI = "doi:10.1145/3520304.3534073", video_url = "https://vimeo.com/723511007", abstract = "Semantics is a growing area of research in Genetic programming (GP) and refers to the behavioural output of a Genetic Programming individual when executed. This research expands upon the current understanding of semantics by proposing a new approach: Semantic-based Distance as an additional criteriOn (SDO), in the thus far, somewhat limited researched area of semantics in Multi-objective GP (MOGP). Our work included an expansive analysis of the GP in terms of performance and diversity metrics, using two additional semantic-based approaches, namely Semantic Similarity-based Crossover (SCC) and Semantic-based Crowding Distance (SCD). Each approach is integrated into two evolutionary multi-objective (EMO) frameworks: Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Strength Pareto Evolutionary Algorithm 2 (SPEA2), and along with the three semantic approaches, the canonical form of NSGA-II and SPEA2 are rigorously compared. Using highly-unbalanced binary classification datasets, we demonstrated that the newly proposed approach of SDO consistently generated more non-dominated solutions, with better diversity and improved hypervolume results.This Hot-off-the-Press paper summarises {"}Semantics in Multi-objective Genetic Programming{"} by Edgar Galv\'{a}n, Leonardo Trujillo and Fergal Stapleton, published in the journal of Applied Soft Computing 2022 [9], https://doi.org/10.1016/j.asoc.2021.108143.", notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @Article{Galvan:2022:games, author = "Edgar Galvan and Gavin Simpson and Fred Valdez Ameneyro", journal = "IEEE Transactions on Games", title = "Evolving the {MCTS} Upper Confidence Bounds for Trees Using a Semantic-inspired Evolutionary Algorithm in the Game of {Carcassonne}", year = "2023", volume = "15", number = "3", pages = "420--429", month = sep, keywords = "genetic algorithms, genetic programming, Carcassonne, MonteCarlo tree search (MCTS), semantics", ISSN = "2475-1510", DOI = "doi:10.1109/TG.2022.3203232", size = "10 pages", abstract = "Monte Carlo Tree Search (MCTS) is a sampling best-first method to search for optimal decisions. The success of MCTS depends heavily on how the tree is built and the selection process plays a fundamental role in this. One particular selection mechanism that has proved to be reliable is based on the Upper Confidence Bounds for Trees (UCT). The UCT attempts to balance exploration and exploitation by considering the values stored in the statistical tree of the MCTS. However, some tuning of the MCTS UCT is necessary for this to work well. In this work, we use Evolutionary Algorithms (EAs) to evolve mathematical expressions with the goal to substitute the UCT formula and use the evolved expressions in MCTS. More specifically, we evolve expressions by means of our proposed Semantic-inspired Evolutionary Algorithm in MCTS approach (SIEA-MCTS). This is inspired by semantics in Genetic Programming (GP), where the use of fitness cases is seen as a requirement to be adopted in GP. Fitness cases are normally used to determine the fitness of individuals and can be used to compute the semantic similarity (or dissimilarity) of individuals. However, fitness cases are not available in MCTS. We extend this notion by using multiple reward values from MCTS that allow us to determine both the fitness of an individual and its semantics. By doing so, we show how SIEA-MCTS is able to successfully evolve mathematical expressions that yield better or competitive results compared to UCT without the need of tuning these evolved expressions. We compare the performance of the proposed SIEA-MCTS against MCTS algorithms, MCTS Rapid Action Value Estimation algorithms, three variants of the *-minimax family of algorithms, a random controller and two more EA approaches. We consistently show how SIEA-MCTS outperforms most of these intelligent controllers in the challenging game of Carcassonne, whose state-space complexity is, approx., 1e40.", notes = "Also known as \cite{9872022}", } @Article{Galvan:2025:ins, author = "Edgar Galvan and Fred {Valdez Ameneyro}", title = "An analysis on the effects of evolving the Monte Carlo tree search upper confidence for trees selection policy on unimodal, multimodal and deceptive landscapes", journal = "Information Sciences", year = "2025", volume = "715", pages = "122226", keywords = "genetic algorithms, genetic programming, Evolutionary algorithms, Monte Carlo tree search, UCT", ISSN = "0020-0255", URL = "https://www.sciencedirect.com/science/article/pii/S0020025525003585", DOI = "doi:10.1016/j.ins.2025.122226", abstract = "Monte Carlo Tree Search (MCTS) is a best-first sampling/planning method used to find optimal decisions. The effectiveness of MCTS depends on the construction of its statistical tree, with the selection policy playing a crucial role. A particularly effective selection policy in MCTS is the Upper Confidence Bounds for Trees (UCT). While MCTS/UCT generally performs well, there may be variants that outperform it, leading to efforts to evolve selection policies for use in MCTS. However, these efforts have often been limited in their ability to demonstrate when these evolved policies might be beneficial. They frequently rely on single, poorly understood problems or on new methods that are not fully comprehended. To address these limitations, we use three evolutionary-inspired methods: Evolutionary Algorithm (EA)-MCTS, Semantically-inspired EA (SIEA)-MCTS as well as Self-adaptive (SA)-MCTS, which evolve online selection policies to be used in place of UCT. We compare these three methods against five variants of the standard MCTS on ten test functions of varying complexity and nature, including unimodal, multimodal, and deceptive features. By using well-defined metrics, we demonstrate how the evolution of MCTS/UCT can yield benefits in multimodal and deceptive scenarios, while MCTS/UCT remains robust across all functions used in this work", } @InProceedings{GalvezRamirez:2016:EuroGP, author = "Nicolas {Galvez Ramirez} and Youssef Hamadi and Eric Monfroy and Frederic Saubion", title = "Towards Automated Strategies in Satisfiability Modulo Theory", booktitle = "EuroGP 2016: Proceedings of the 19th European Conference on Genetic Programming", year = "2016", month = "30 " # mar # "--1 " # apr, editor = "Malcolm I. Heywood and James McDermott and Mauro Castelli and Ernesto Costa and Kevin Sim", series = "LNCS", volume = "9594", publisher = "Springer Verlag", address = "Porto, Portugal", pages = "230--245", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, hyper heuristic, SMT, Strategy, Z3, Learning algorithm", isbn13 = "978-3-319-30668-1", DOI = "doi:10.1007/978-3-319-30668-1_15", size = "16 pages", abstract = "SMT solvers include many heuristic components in order to ease the theorem proving process for different logics and problems. Handling these heuristics is a non-trivial task requiring specific knowledge of many theories that even a SMT solver developer may be unaware of. This is the first barrier to break in order to allow end-users to control heuristics aspects of any SMT solver and to successfully build a strategy for their own purposes. We present a first attempt for generating an automatic selection of heuristics in order to improve SMT solver efficiency and to allow end-users to take better advantage of solvers when unknown problems are faced. Evidence of improvement is shown and the basis for future works with evolutionary and/or learning-based algorithms are raised.", notes = "Part of \cite{Heywood:2016:GP} EuroGP'2016 held in conjunction with EvoCOP2016, EvoMusArt2016 and EvoApplications2016", } @Article{Gamage:2010:jcise, author = "L. B. Gamage and C. W. {de Silva}", title = "A System Framework With Online Monitoring and Evaluation for Design Evolution of Engineering Systems", journal = "Journal of Computing and Information Science in Engineering", year = "2010", volume = "10", number = "3", month = sep, note = "Technical Briefs", keywords = "genetic algorithms, genetic programming", ISSN = "1530-9827", DOI = "doi:10.1115/1.3462919", abstract = "This paper presents a methodology for the design evolution of engineering systems, with a mechatronic emphasis. The developed approach specifically integrates machine health monitoring and an expert system and carries out the design evolution of a multidomain dynamic system using bond graph modelling and genetic programming. The evolution of a bond graph model of a mechatronic system through genetic programming enables the exploration of the design space, thereby generating a global optimum design solution in an automated manner. Domain knowledge and expertise are used to control the design exploration and to restrict it only to a meaningful design space. As an illustrative example, the developed methodology is applied to redesign the electrohydraulic manipulator of an existing industrial fish processing machine", notes = "Industrial Automation Laboratory, Department of Mechanical Engineering, The University of British Columbia, Vancouver, BC, V6T 1Z4, Canada American Society of Mechanical Engineers", } @Article{Gamage201283, author = "L. B. Gamage and C. W. {de Silva} and R. Campos", title = "Design evolution of mechatronic systems through modelling, on-line monitoring, and evolutionary optimization", journal = "Mechatronics", volume = "22", number = "1", pages = "83--94", year = "2012", ISSN = "0957-4158", DOI = "doi:10.1016/j.mechatronics.2011.11.012", URL = "http://www.sciencedirect.com/science/article/pii/S0957415811001991", keywords = "genetic algorithms, genetic programming, Design evolution, Linear Graphs, Electro-mechanical modelling", abstract = "This paper presents a system framework to automate the design evolution of multi-domain engineering systems. The developed system integrates machine health monitoring with an expert system to monitor the performance of an existing mechatronic system and to take a decision as to whether and which part of the system a design improvement is necessary. A system model represented by Linear Graphs is evolved using genetic programming to obtain a design that optimally satisfies a specified level of performance. The developed approach allows exploration of the design space to find the optimum design solution in an automated manner. In order to control the arbitrary exploration of the design space, domain knowledge, expertise, and input from the machine health monitoring system are used. The design evolution algorithm is implemented using GPLAB, a MATLAB tool, and integrated with Simscape for modelling and simulation. The developed system is applied to redesign the electro-mechanical conveyor system of an industrial fish processing machine.", } @InProceedings{Gan:2009:ICNC, author = "Zhaohui Gan and Tao Shang and Gang Shi and Min Jiang", title = "Evolutionary Design of Combinational Logic Circuits Using an Improved Gene Expression-Based Clonal Selection Algorithm", booktitle = "Fifth International Conference on Natural Computation, ICNC '09", year = "2009", month = aug, volume = "4", pages = "37--41", abstract = "In this paper, an improved gene expression-based clonal selection algorithm (IGE-CSA) is proposed, which is aimed at solving synthesis problems of combinational logic circuits. The encoding of gene expression programming (GEP) is improved. Compared with GEP encoding, the proposed encoding is more compact and fits to represent multi-output combinational logic circuit. Clonal selection algorithm (CSA) is applied as search engine of the proposed approach. The proposed method is applied into combinational logic circuit design successfully. Two kinds of combinational logic circuits are synthesised to verify the effectiveness of the proposed approach. The experimental results show that the proposed approach can automatically generate combinational logic circuits efficiently and effectively. Compared with other method, the obtained circuits by the proposed method are optimal.", keywords = "genetic algorithms, genetic programming, gene expression programming, GEP encoding, gene expression-based clonal selection algorithm, multioutput combinational logic circuit, biomolecular electronics, combinational circuits, genetics, molecular biophysics", DOI = "doi:10.1109/ICNC.2009.308", notes = "Also known as \cite{5365151}", } @Article{Gan20093996, author = "Zhaohui Gan and Tommy W. S. Chow and W. N. Chau", title = "Clone selection programming and its application to symbolic regression", journal = "Expert Systems with Applications", volume = "36", number = "2, Part 2", pages = "3996--4005", year = "2009", ISSN = "0957-4174", DOI = "DOI:10.1016/j.eswa.2008.02.030", URL = "http://www.sciencedirect.com/science/article/B6V03-4S02048-9/2/d5a34ad92d4cf0f6f5e33f4407a2776f", keywords = "genetic algorithms, genetic programming, gene expression programming, Clone selection, Programming, Immune system, Gene expression", abstract = "A new idea [`]clone selection programming (CSP)' is introduced in this paper. The proposed methodology is used for deriving new algorithms in the area of evolutionary computing aimed at solving a wide range of problems. In CSP, antibodies represent candidate solutions, which are encoded according to the structure of antibody. The antibodies are able to keep syntax correct even they are changed with iterations. Also, the clone selection principle is developed as a search strategy. The proposed strategies have been thoroughly evaluated by intensive simulations. The results demonstrate the effectiveness and excellent convergent qualities of the CSP based search strategy. In our study, the convergence rate with respect to population size and other parameters is studied. A thorough comparative study between our proposed CSP based method with the gene expression programming (GEP), and immune programming (IP) are included. The comparative results show that the CSP based method can significantly improve the program performance. The experimental results indicate that the proposed method is very robust under all the investigated cases.", } @Article{Gan20101887, author = "Zhaohui Gan and Zhenkun Yang and Tao Shang and Tianyou Yu and Min Jiang", title = "Automated synthesis of passive analog filters using graph representation", journal = "Expert Systems with Applications", volume = "37", number = "3", pages = "1887--1898", year = "2010", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2009.07.013", URL = "http://www.sciencedirect.com/science/article/B6V03-4WXHBSP-5/2/32ead9142a06172b08c290d1ce58b362", keywords = "genetic algorithms, genetic programming, Analog passive filter synthesis, Automatic design, Clone selection algorithm, Graph-based encoding scheme", abstract = "In this paper, a novel method based on graph encoding scheme and clone selection algorithm is proposed for synthesising passive analog filters. Graph is the most natural and convenient data structure to represent analog electronic circuit. The proposed graph-based encoding scheme can represent any topologies of passive analog circuit and their component values. Combined with the efficient analog circuit encoding scheme, clone selection algorithm is employed as a search engine for automatic design of passive analog filters. The proposed method can synthesise both topology and sizing (component parameters) of circuit simultaneously. Three filter design tasks are experimented to evaluate the proposed method. The experimental results demonstrate that passive analog filters can be generated effectively with modest computation time. Taking more practical conditions into account, the proposed method can be applied into automatic design of passive analog filters for engineering application without the guidance of experienced engineers.", } @Article{Gandomi2008338, author = "A. H. Gandomi and A. H. Alavi and S. S. {Sadat Hosseini}", title = "A Discussion on {"}Genetic programming for retrieving missing information in wave records along the west coast of India{"} [Applied Ocean Research 2007; 29 (3): 99-111]", journal = "Applied Ocean Research", volume = "30", number = "4", pages = "338--339", year = "2008", ISSN = "0141-1187", DOI = "doi:10.1016/j.apor.2009.02.001", URL = "http://www.sciencedirect.com/science/article/B6V1V-4VXJVY5-1/2/f5aca485c623afab39556b3979e70bff", keywords = "genetic algorithms, genetic programming, Linear structure, Wave height", size = "2 pages", notes = "Discussion of \cite{Kalra200799}. See also reply \cite{Deo2008340}", abstract = "The discussers appreciate the work conducted by the authors for examining the potential of the application of genetic programming (GP) for filling up the missing significant wave height values at a given location based on the same being collected at the nearby stations. The proposed approach has been implemented using two different softwares, Discipulus and Kernel software. A comparison of the GP-based predictions with those of artificial neural networks (ANNs) was performed in the aforementioned study. The discussers would like to present the following important viewpoints, which the authors and potential researchers need to consider. The discussion will focus on main points that are not considered in the study.", } @InProceedings{Gandomi:2008:EASEC, author = "A. H. Gandomi and A. H. Alavi and M. G. Sahab and M. Gandomi and M. Safari Gorji", title = "Empirical Models for the Prediction of Flexural Resistance and Initial Stiffness of Welded Beam-Column Joints", booktitle = "Proceedings of the 11th East Asia-Pacific Conference on Structural Engineering \& Construction (EASEC-11)", year = "2008", address = "Taipei, Taiwan", month = "19-21 " # nov, organisation = "National Taiwan University", keywords = "genetic algorithms, genetic programming, Steel structures, Welded joints, Combined genetic programming and simulated annealing, Flexural resistance, Initial rotation stiffness", URL = "http://dc199.4shared.com/doc/beWiACYZ/preview.html", size = "10 pages", abstract = "Welded beam-column joints play a fundamental role in the global response of steel structures. The flexural resistance and initial stiffness properties of the joints are affected by different parameters. It is idealistic to develop models, relating these properties of the joints to the influencing parameters. This paper proposes a novel approach for the prediction of flexural resistance and initial stiffness of welded joints by using a hybrid search algorithm that couples genetic programming (GP) and simulated annealing (SA), called GP/SA. Column height, column flange width, column flange thickness, column flange yield stress, column web thickness, column web yield stress, beam height, beam web thickness, beam web yield stress, beam flange thickness, beam flange width, beam flange yield stress, and weld thickness are used as input variables to the models. A reliable database from the previously published literature was employed to develop the empirical models. The accuracy of the proposed models is satisfactory as compared to experimental results. GP/SA models are further compared with the corresponding design code (Eurocode 3) reference values. The results demonstrate that the GP/SA based models have better performance than Eurocode 3 models.", } @Article{Gandomi20091738, author = "A. H. Gandomi and A. H. Alavi and S. Kazemi and M. M. Alinia", title = "Behavior appraisal of steel semi-rigid joints using Linear Genetic Programming", journal = "Journal of Constructional Steel Research", volume = "65", number = "8-9", pages = "1738--1750", year = "2009", ISSN = "0143-974X", DOI = "doi:10.1016/j.jcsr.2009.04.010", URL = "http://www.sciencedirect.com/science/article/B6V3T-4W8KHNW-4/2/4833ff184048303a27710677ee1f047f", keywords = "genetic algorithms, genetic programming, Semi-rigid joints, Steel structures", abstract = "This paper proposes an alternative approach for predicting the flexural resistance and initial rotational stiffness of semi-rigid joints in steel structures using Linear Genetic Programming (LGP). Three types of steel beam-column joints i.e. end plates, welded, and end bolted joints with angles are investigated. Models are constructed by using test results available in the literature. The accuracy of the proposed models is verified by comparing the outcomes to the experimental results. LGP models are further compared to the corresponding design code (Eurocode 3), reference values and several existing models. The results demonstrate that the LGP based models in most cases provide superior performance than other models.", } @Article{Gandomi:2010:NH, author = "A. H. Gandomi and A. H. Alavi and A. Taghipour", title = "Discussion on ``Alternative data-driven methods to estimate wind from waves by inverse modeling'' by {Mansi Daga, M. C. Deo} [Natural Hazards (2008) NHAZ 524, Article 9299, DOI 10.1007/s11069-008-9299-2]", journal = "Natural Hazards", year = "2010", volume = "52", number = "3", pages = "671--673", keywords = "genetic algorithms, genetic programming, Linear genetic programming, Tree structure, Wind estimation", publisher = "Springer", ISSN = "0921-030X", DOI = "doi:10.1007/s11069-009-9400-5", size = "3 pages", notes = "Discussion of \cite{Daga:2009:NH}", } @Article{Gandomi:2010:JMMS, author = "Amir Hossein Gandomi and Amir Hossein Alavi and Parvin Arjmandi and Alireza Aghaeifar and Reza Seyednour", title = "Genetic programming and orthogonal least squares: a hybrid approach to modeling the compressive strength of {CFRP-confined} concrete cylinders", journal = "Journal of Mechanics of Materials and Structures", year = "2010", volume = "5", number = "5", pages = "735--753", keywords = "genetic algorithms, genetic programming, orthogonal least squares, CFRP confinement, concrete compressive strength, formulation", publisher = "Mathematical Sciences Publishers", ISSN = "1559-3959", URL = "http://msp.org/jomms/2010/5-5/p03.xhtml", DOI = "doi:10.2140/jomms.2010.5.735", size = "19 pages", abstract = "The main objective of this paper is to apply genetic programming (GP) with an orthogonal least squares (OLS) algorithm to derive a predictive model for the compressive strength of carbon fibre-reinforced plastic (CFRP) confined concrete cylinders. The GP/OLS model was developed based on experimental results obtained from the literature. Traditional GP-based and least squares regression analyses were performed using the same variables and data sets to benchmark the GP/OLS model. A subsequent parametric analysis was carried out and the trends of the results were confirmed via previous laboratory studies. The results indicate that the proposed formula can predict the ultimate compressive strength of concrete cylinders with an acceptable level of accuracy. The GP/OLS results are more accurate than those obtained using GP, regression, or several CFRP confinement models found in the literature. The GP/OLS-based formula is simple and straightforward, and provides a valuable tool for analysis.", } @Article{Gandomi:2010:jMST, author = "Amir Hossein Gandomi and Amir Hossein Alavi and Mohammad Ghasem Sahab and Parvin Arjmandi", title = "Formulation of elastic modulus of concrete using linear genetic programming", journal = "Journal of Mechanical Science and Technology", year = "2010", volume = "24", number = "6", pages = "1273--1278", month = jun, email = "ah_alavi@hotmail.com, a.h.gandomi@gmail.com", keywords = "genetic algorithms, genetic programming, Tangent elastic modulus, Linear genetic programming, Compressive strength, Normal and high strength concrete, Formulation", ISSN = "1738-494X", DOI = "doi:10.1007/s12206-010-0330-7", size = "6 pages", abstract = "This paper proposes a novel approach for the formulation of elastic modulus of both normal-strength concrete (NSC) and high-strength concrete (HSC) using a variant of genetic programming (GP), namely linear genetic programming (LGP). LGP-based models relate the modulus of elasticity of NSC and HSC to the compressive strength, as similarly presented in several codes of practice. The models are developed based on experimental results collected from the literature. A subsequent parametric analysis is further carried out to evaluate the sensitivity of the elastic modulus to the compressive strength variations. The results demonstrate that the proposed formulae can predict the elastic modulus with an acceptable degree of accuracy. The LGP results are found to be more accurate than those obtained using the buildings codes and various solutions reported in the literature. The LGP-based formulas are quite simple and straightforward and can be used reliably for routine design practice.", notes = "1Structural Health Monitoring Research Group, College of Civil Engineering, Tafresh University, Tafresh, Iran", } @Article{Gandomi:2010:MS, author = "Amir Hossein Gandomi and Amir Hossein Alavi and Mohammad Ghasem Sahab", title = "New formulation for compressive strength of CFRP confined concrete cylinders using linear genetic programming", journal = "Materials and Structures", year = "2010", volume = "43", number = "7", pages = "963--983", month = aug, email = "ah_alavi@hotmail.com, a.h.gandomi@gmail.com", keywords = "genetic algorithms, genetic programming, CFRP confinement, Linear genetic programming, Formulation, Concrete compressive strength", ISSN = "1359-5997", DOI = "doi:10.1617/s11527-009-9559-y", size = "21 pages", abstract = "This paper proposes a new approach for the formulation of compressive strength of carbon fibre reinforced plastic (CFRP) confined concrete cylinders using a promising variant of genetic programming (GP) namely, linear genetic programming (LGP). The LGP-based models are constructed using two different sets of input data. The first set of inputs comprises diameter of concrete cylinder, unconfined concrete strength, tensile strength of CFRP laminate and total thickness of CFRP layers. The second set includes unconfined concrete strength and ultimate confinement pressure which are the most widely used parameters in the CFRP confinement existing models. The models are developed based on experimental results collected from the available literature. The results demonstrate that the LGP-based formulae are able to predict the ultimate compressive strength of concrete cylinders with an acceptable level of accuracy. The LGP results are also compared with several CFRP confinement models presented in the literature and found to be more accurate in nearly all of the cases. Moreover, the formulas evolved by LGP are quite short and simple and seem to be practical for use. A subsequent parametric study is also carried out and the trends of the results have been confirmed via some previous laboratory studies.", notes = "College of Civil Engineering, Tafresh University, Tafresh, Iran", } @Article{Gandomi:2011:SEM, author = "A. H. Gandomi and A. H. Alavi and G. J. Yun", title = "Nonlinear modeling of shear strength of {SFRC} beams using linear genetic programming", journal = "Structural Engineering and Mechanics, An International Journal", year = "2011", volume = "38", number = "1", pages = "1--25", month = apr # " 10", keywords = "genetic algorithms, genetic programming, fiber-reinforced concrete beams, linear genetic programming, SFRC beam, shear strength, formulation.", publisher = "Techno Press, P.O. Box 33, Yuseong, Daejeon 305-600 Korea", ISSN = "1225-4568", URL = "http://technopress.kaist.ac.kr/?page=container&journal=sem&volume=38&num=1", DOI = "doi:10.12989/sem.2011.38.1.001", abstract = "A new nonlinear model was developed to evaluate the shear resistance of steel fibre reinforced concrete beams (SFRCB) using linear genetic programming (LGP). The proposed model relates the shear strength to the geometrical and mechanical properties of SFRCB. The best model was selected after developing and controlling several models with different combinations of the influencing parameters. The models were developed using a comprehensive database containing 213 test results of SFRC beams without stirrups obtained through an extensive literature review. The database includes experimental results for normal and high-strength concrete beams. To verify the applicability of the proposed model, it was employed to estimate the shear strength of a part of test results that were not included in the modelling process. The external validation of the model was further verified using several statistical criteria recommended by researchers. The contributions of the parameters affecting the shear strength were evaluated through a sensitivity analysis. The results indicate that the LGP model gives precise estimates of the shear strength of SFRCB. The prediction performance of the model is significantly better than several solutions found in the literature. The LGP-based design equation is remarkably straightforward and useful for pre-design applications.", } @Article{Gandomi:2011:KSCEjce, author = "Amir Hossein Gandomi and Amir Hossein Alavi and Gun Jin Yun", title = "Formulation of uplift capacity of suction caissons using multi expression programming", journal = "KSCE Journal of Civil Engineering", year = "2011", volume = "15", number = "2", pages = "363--373", month = feb, keywords = "genetic algorithms, genetic programming, multi expression programming, suction caissons, uplift capacity, formulation", publisher = "Korean Society of Civil Engineers", ISSN = "1226-7988", DOI = "doi:10.1007/s12205-011-1117-9", language = "English", size = "11 pages", abstract = "Suction caissons have increasingly been used as foundations and anchors for deep water offshore structures in the last decade. The increased use of suction caissons defines a serious need to develop more authentic methods for simulating their behaviour. Reliable assessment of uplift capacity of caissons in cohesive soils is a critical issue facing design engineers. This paper proposes a new approach for the formulation of the uplift capacity of suction caissons using a promising variant of Genetic Programming (GP), namely Multi Expression Programming (MEP). The proposed model is developed based on experimental results obtained from the literature. The derived MEP-based formula takes into account the effect of aspect ratio of caisson, shear strength of clayey soil, point of application and angle of inclination of loading, soil permeability and loading rate. A subsequent parametric analysis is carried out and the trends of the results are confirmed via previous studies. The results indicate that the MEP formulation can predict the uplift capacity of suction caissons with an acceptable level of accuracy. The proposed formula provides a prediction performance better than or comparable with the models found in the literature. The MEP-based simplified formulation is particularly valuable for providing an analysis tool accessible to practising engineers.", } @Article{Gandomi:2010:JMCE, author = "Amir Hossein Gandomi and Amir Hossein Alavi and Mohammad Reza Mirzahosseini and Fereidoon Moghadas Nejad", title = "Nonlinear Genetic-Based Models for Prediction of Flow Number of Asphalt Mixtures", journal = "ASCE Journal of Materials in Civil Engineering", year = "2011", volume = "23", number = "3", pages = "248--263", month = mar, email = "ah_alavi@hotmail.com, a.h.gandomi@gmail.com", keywords = "genetic algorithms, genetic programming, gene expression programming, Marshall mix design, Formulation", ISSN = "0899-1561", URL = "https://ascelibrary.org/toc/jmcee7/23/3", DOI = "doi:10.1061/(ASCE)MT.1943-5533.0000154", size = "16 pages", abstract = "Rutting has been considered as the most serious distresses in flexible pavements for many years. Flow number is an explanatory index for the evaluation of rutting potential of asphalt mixtures. In this study, a promising variant of genetic programming, namely gene expression programming (GEP) is used to predict the flow number of dense asphalt-aggregate mixtures. The proposed constitutive models relate the flow number of Marshall specimens to the coarse and fine aggregate contents, percentage of air voids, percentage of voids in mineral aggregate, Marshall stability and flow. Different correlations were developed using different combinations of the influencing parameters. The comprehensive experimental database used for the development of the correlations was established upon a series of uniaxial dynamic creep tests conducted in this study. Relative importance values of various predictor variables were calculated to determine their contributions to the flow number prediction. A multiple least squares regression (MLSR) analysis was performed using the same variables and data sets to benchmark the GEP models. For more verification, a subsequent parametric study was carried out and the trends of the results were confirmed with the results of previous studies. The results indicate that the proposed correlations are effectively capable of evaluating the flow number of asphalt mixtures. The GEP-based formulae are simple, straightforward and particularly valuable for providing an analysis tool accessible to practising engineers.", notes = "1Research Assistant, National Elites Foundation, Tehran, Iran & College of Civil Engineering, Tafresh University, Tafresh, Iran. 2PhD Student, School of Architecture, Civil and Environmental Engineering, Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, Switzerland. 3Assistant Professor, College of Civil Engineering, Iran University of Science & Technology, Tehran, Iran. 4Assistant Professor, College of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, Iran.", } @Article{Gandomi20111096, author = "Amir Hossein Gandomi and Seyed Morteza Tabatabaei and Mohammad Hossein Moradian and Ata Radfar and Amir Hossein Alavi", title = "A new prediction model for the load capacity of castellated steel beams", journal = "Journal of Constructional Steel Research", volume = "67", number = "7", pages = "1096--1105", year = "2011", ISSN = "0143-974X", DOI = "doi:10.1016/j.jcsr.2011.01.014", URL = "http://www.sciencedirect.com/science/article/B6V3T-52BVR2R-1/2/9f40e5717143288037afed5176f8d52e", keywords = "genetic algorithms, genetic programming, Castellated beam, Failure load, Gene expression programming", abstract = "In this study, a robust variant of genetic programming, namely gene expression programming (GEP), is used to build a prediction model for the load capacity of castellated steel beams. The proposed model relates the load capacity to the geometrical and mechanical properties of the castellated beams. The model is developed based on a reliable database obtained from the literature. To verify the applicability of the derived model, it is employed to estimate the load capacity of parts of the test results that were not included in the modelling process. The external validation of the model was further verified using several statistical criteria recommended by researchers. A multiple least squares regression analysis is performed to benchmark the GEP-based model. A sensitivity analysis is further carried out to determine the contributions of the parameters affecting the load capacity. The results indicate that the proposed model is effectively capable of evaluating the failure load of the castellated beams. The GEP-based design equation is remarkably straightforward and useful for pre-design applications.", } @Article{Gandomi20115227, author = "Amir Hossein Gandomi and Amir Hossein Alavi", title = "Multi-stage genetic programming: A new strategy to nonlinear system modeling", journal = "Information Sciences", volume = "181", number = "23", pages = "5227--5239", year = "2011", ISSN = "0020-0255", DOI = "doi:10.1016/j.ins.2011.07.026", URL = "http://www.sciencedirect.com/science/article/pii/S0020025511003586", keywords = "genetic algorithms, genetic programming, Nonlinear system modelling, Engineering problems, Formulation", abstract = "This paper presents a new multi-stage genetic programming (MSGP) strategy for modelling nonlinear systems. The proposed strategy is based on incorporating the individual effect of predictor variables and the interactions among them to provide more accurate simulations. According to the MSGP strategy, an efficient formulation for a problem comprises different terms. In the first stage of the MSGP-based analysis, the output variable is formulated in terms of an influencing variable. Thereafter, the error between the actual and the predicted value is formulated in terms of a new variable. Finally, the interaction term is derived by formulating the difference between the actual values and the values predicted by the individually developed terms. The capabilities of MSGP are illustrated by applying it to the formulation of different complex engineering problems. The problems analysed herein include the following: (i) simulation of pH neutralisation process, (ii) prediction of surface roughness in end milling, and (iii) classification of soil liquefaction conditions. The validity of the proposed strategy is confirmed by applying the derived models to the parts of the experimental results that were not included in the analyses. Further, the external validation of the models is verified using several statistical criteria recommended by other researchers. The MSGP-based solutions are capable of effectively simulating the nonlinear behaviour of the investigated systems. The results of MSGP are found to be more accurate than those of standard GP and artificial neural network-based models.", } @Article{Gandomi2011717, author = "Amir Hossein Gandomi and Amir Hossein Alavi and Mehdi Mousavi and Seyed Morteza Tabatabaei", title = "A hybrid computational approach to derive new ground-motion prediction equations", journal = "Engineering Applications of Artificial Intelligence", volume = "24", number = "4", pages = "717--732", year = "2011", ISSN = "0952-1976", DOI = "doi:10.1016/j.engappai.2011.01.005", URL = "http://www.sciencedirect.com/science/article/B6V2M-52C83TR-1/2/0e8d2ec5097e6a0e7eef643a7e26d527", keywords = "genetic algorithms, genetic programming, Time-domain ground-motion parameters, Prediction equations, Orthogonal least squares, Nonlinear modelling", abstract = "A novel hybrid method coupling genetic programming and orthogonal least squares, called GP/OLS, was employed to derive new ground-motion prediction equations (GMPEs). The principal ground-motion parameters formulated were peak ground acceleration (PGA), peak ground velocity (PGV) and peak ground displacement (PGD). The proposed GMPEs relate PGA, PGV and PGD to different seismic parameters including earthquake magnitude, earthquake source to site distance, average shear-wave velocity, and faulting mechanisms. The equations were established based on an extensive database of strong ground-motion recordings released by Pacific Earthquake Engineering Research Center (PEER). For more validity verification, the developed equations were employed to predict the ground-motion parameters of the Iranian plateau earthquakes. A sensitivity analysis was carried out to determine the contributions of the parameters affecting PGA, PGV and PGD. The sensitivity of the models to the variations of the influencing parameters was further evaluated through a parametric analysis. The obtained GMPEs are effectively capable of estimating the site ground-motion parameters. The equations provide a prediction performance better than or comparable with the attenuation relationships found in the literature. The derived GMPEs are remarkably simple and straightforward and can reliably be used for the pre-design purposes.", } @InCollection{Gandomi:2011:COAEI, author = "Amir Hossein Gandomi and Amir Hossein Alavi", title = "Applications of Computational Intelligence in Behavior Simulation of Concrete Materials", booktitle = "Computational Optimization and Applications in Engineering and Industry", editor = "Xin-She Yang and Slawomir Koziel", year = "2011", volume = "359", series = "Studies in Computational Intelligence", chapter = "9", pages = "221--243", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-20985-7", DOI = "doi:10.1007/978-3-642-20986-4_9", abstract = "The application of Computational Intelligence (CI) to structural engineering design problems is relatively new. This chapter presents the use of the CI techniques, and specifically Genetic Programming (GP) and Artificial Neural Network (ANN) techniques, in behaviour modelling of concrete materials. We first introduce two main branches of GP, namely Tree-based Genetic Programming (TGP) and Linear Genetic Programming (LGP), and two variants of ANNs, called Multi Layer Perceptron (MLP) and Radial Basis Function (RBF). The simulation capabilities of these techniques are further demonstrated by applying them to two conventional concrete material cases. The first case is simulation of concrete compressive strength using mix properties and the second problem is prediction of elastic modulus of concrete using its compressive strength.", } @Article{Gandomi:2011:Discussion, author = "A. H. Gandomi", title = "Discussion: Neural Network -- Genetic Programming for Sediment Transport", journal = "Maritime Engineering", year = "2010", volume = "163", number = "3", pages = "135--136", month = sep, keywords = "genetic algorithms, genetic programming, Discipulus", ISSN = "1741-7597", DOI = "doi:10.1680/maen.2010.163.3.135", size = "2 pages", abstract = "Tree v. Linear GP", notes = "Refers to \cite{Singh:2007:ICE} Proceedings of the Institution of Civil Engineers Tafresh University, Tafresh, Iran", } @Article{journals/nca/GandomiA12, author = "Amir Hossein Gandomi and Amir Hossein Alavi", title = "A new multi-gene genetic programming approach to nonlinear system modeling. Part {I}: materials and structural engineering problems", journal = "Neural Computing and Applications", year = "2012", volume = "21", number = "1", pages = "171--187", month = feb, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Data mining, Structural engineering, Multi-gene genetic programming, Formulation", ISSN = "0941-0643", DOI = "doi:10.1007/s00521-011-0734-z", abstract = "This paper presents a new approach for behavioural modelling of structural engineering systems using a promising variant of genetic programming (GP), namely multi-gene genetic programming (MGGP). MGGP effectively combines the model structure selection ability of the standard GP with the parameter estimation power of classical regression to capture the nonlinear interactions. The capabilities of MGGP are illustrated by applying it to the formulation of various complex structural engineering problems. The problems analysed herein include estimation of: (1) compressive strength of high-performance concrete (2) ultimate pure bending of steel circular tubes, (3) surface roughness in end-milling, and (4) failure modes of beams subjected to patch loads. The derived straightforward equations are linear combinations of nonlinear transformations of the predictor variables. The validity of MGGP is confirmed by applying the derived models to the parts of the experimental results that are not included in the analyses. The MGGP-based equations can reliably be employed for pre-design purposes. The results of MSGP are found to be more accurate than those of solutions presented in the literature. MGGP does not require simplifying assumptions in developing the models.", notes = "See \cite{journals/nca/GandomiA12a}", affiliation = "Department of Civil Engineering, University of Akron, Akron, OH 44325-3905, USA", bibdate = "2012-01-17", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/nca/nca21.html#GandomiA12", } @Article{journals/nca/GandomiA12a, author = "Amir Hossein Gandomi and Amir Hossein Alavi", title = "A new multi-gene genetic programming approach to non-linear system modeling. Part {II}: geotechnical and earthquake engineering problems", journal = "Neural Computing and Applications", year = "2012", number = "1", volume = "21", pages = "189--201", month = feb, publisher = "Springer", keywords = "genetic algorithms, genetic programming", ISSN = "0941-0643", DOI = "doi:10.1007/s00521-011-0735-y", size = "13 pages", abstract = "Complexity of analysis of geotechnical behaviour is due to multivariable dependencies of soil and rock responses. In order to cope with this complex behaviour, traditional forms of engineering design solutions are reasonably simplified. Incorporating simplifying assumptions into the development of the traditional methods may lead to very large errors. This paper presents an endeavour to exploit a robust multi-gene genetic programming (MGGP) method for the analysis of geotechnical and earthquake engineering systems. MGGP is a modified genetic programming approach for model structure selection combined with a classical technique for parameter estimation. To justify the abilities of MGGP, it is systematically employed to formulate the complex geotechnical engineering problems. Different classes of the problems analysed include the assessment of (i) undrained lateral load capacity of piles, (ii) undrained side resistance alpha factor for drilled shafts, (iii) settlement around tunnels, and (iv) soil liquefaction. The validity of the derived models is tested for a part of test results beyond the training data domain. Numerical examples show the superb accuracy, efficiency, and great potential of MGGP. Contrary to artificial neural networks and many other soft computing tools, MGGP provides constitutive prediction equations. The MGG-based solutions are particularly valuable for pre-design practices.", notes = "See \cite{journals/nca/GandomiA12}", affiliation = "Department of Civil Engineering, The University of Akron, Akron, OH 44325-3905, USA", bibdate = "2012-01-17", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/nca/nca21.html#GandomiA12a", } @Article{Gandomi:2012:JMCE, author = "Amir Hossein Gandomi and Saeed Karim Babanajad and Amir Hossein Alavi and Yaghoob Farnam", title = "Novel Approach to Strength Modeling of Concrete under Triaxial Compression", journal = "Journal of Materials in Civil Engineering", year = "2012", volume = "24", number = "9", pages = "1132--1143", month = sep, keywords = "genetic algorithms, genetic programming, Gene expression programming, Compressive strength, Triaxial compression, Ultimate strength", publisher = "American Society of Civil Engineers", ISSN = "0899-1561", DOI = "doi:10.1061/(ASCE)MT.1943-5533.0000494", size = "12 pages", abstract = "In this study, a robust variant of genetic programming, namely gene expression programming (GEP) was used to build a prediction model for the strength of concrete under triaxial compression loading. The proposed model relates the concrete triaxial strength to mix design parameters. A comprehensive database used for building the model was established on the basis of the results of 330 tests on concrete specimens under triaxial compression. To verify the predictability of the GEP model, it was employed to estimate the concrete strength of the specimens that were not included in the modelling process. Further, the model was externally validated using several statistical criteria recommended by researchers. A sensitivity analysis was carried out to determine the contributions of the parameters affecting the concrete strength. The proposed model is effectively capable of evaluating the ultimate strength of concrete under triaxial compression loading. The derived model performs superior when compared with other empirical models found in the literature. The GEP-based design equation can readily be used for predesign purposes or may be used as a fast check on solutions developed by more in-depth deterministic analyses.", } @Book{Gandomi:2013:MASI_book, editor = "Amir Hossein Gandomi and Xin-She Yang and Siamak Talatahari and Amir Hossein Alavi", title = "Metaheuristic Applications in Structures and Infrastructures", publisher = "Elsevier", year = "2013", month = feb, keywords = "genetic algorithms, genetic programming", isbn13 = "978-0-12-398364-0", URL = "http://www.sciencedirect.com/science/book/9780123983640#ancp3", URL = "https://www.elsevier.com/books/metaheuristic-applications-in-structures-and-infrastructures/gandomi/978-0-12-398364-0", size = "568 pages", } @InCollection{Gandomi:2013:MASI, author = "Amir Hossein Gandomi and Xin-She Yang and Siamak Talatahari and Amir Hossein Alavi", title = "Metaheuristic Algorithms in Modeling and Optimization", editor = "Amir Hossein Gandomi and Xin-She Yang and Siamak Talatahari and Amir Hossein Alavi", booktitle = "Metaheuristic Applications in Structures and Infrastructures", publisher = "Elsevier", address = "Oxford", year = "2013", chapter = "1", pages = "1--24", keywords = "genetic algorithms, genetic programming, Nature-inspired algorithm, metaheuristic algorithms, modeling, optimisation", isbn13 = "978-0-12-398364-0", DOI = "doi:10.1016/B978-0-12-398364-0.00001-2", URL = "http://www.sciencedirect.com/science/article/pii/B9780123983640000012", abstract = "Metaheuristic algorithms have become powerful tools for modelling and optimisation. This chapter provides an overview of nature-inspired metaheuristic algorithms, especially those developed in the last two decades, and their applications. We will briefly introduce algorithms such as genetic algorithms, differential evolution, genetic programming, fuzzy logic, and most importantly, swarm-intelligence-based algorithms such as ant and bee algorithms, particle swarm optimization, cuckoo search, firefly algorithm, bat algorithm, and krill herd algorithm. We also briefly describe the main characteristics of these algorithms and outline some recent applications of these algorithms.", } @InCollection{Gandomi:2013:MASI.18, author = "Amir Hossein Gandomi and Amir Hossein Alavi", title = "Expression Programming Techniques for Formulation of Structural Engineering Systems", editor = "Amir Hossein Gandomi and Xin-She Yang and Siamak Talatahari and Amir Hossein Alavi", booktitle = "Metaheuristic Applications in Structures and Infrastructures", publisher = "Elsevier", address = "Oxford", year = "2013", chapter = "18", pages = "439--455", keywords = "genetic algorithms, genetic programming, Gene expression programming, Data mining, structural engineering, expression programming, prediction", isbn13 = "978-0-12-398364-0", DOI = "doi:10.1016/B978-0-12-398364-0.00018-8", URL = "http://www.sciencedirect.com/science/article/pii/B9780123983640000188", abstract = "Modelling the real behaviour of structural systems is very difficult because of the multivariable dependencies of materials and structural responses. To deal with this complex behavior, simplifying assumptions are commonly incorporated into the development of the conventional methods. This may lead to very large errors. The present study investigates the simulation capabilities of expression programming (EP) techniques by applying them to complex structural engineering problems. Gene expression programming (GEP) and multiexpression programming (MEP) are the employed EP systems. Compared with traditional genetic programming, the EP techniques are more compatible with computer architectures. This results in a significant speedup in their execution. GEP and MEP are substantially useful in deriving empirical models for characterising the behavior of structural engineering systems by directly extracting the knowledge contained in the experimental data. The problems analysed herein include the following: (i) prediction of shear strength of reinforced concrete columns and (ii) prediction of hysteretic energy demand in steel moment resisting frames. The results obtained by GEP and MEP are compared with those provided by other equations presented in the literature and found to be more accurate. The new approaches of GEP and MEP overcome the shortcomings of different methods previously presented in the literature for the analysis of structural engineering systems. Contrary to artificial neural networks and many other soft computing tools, GEP and MEP provide reasonably simplified prediction equations. The derived equations can be used for routine design practice. Unlike the conventional methods, GEP and MEP do not require any simplifying assumptions in developing the models.", } @Article{Gandomi:2013:ACME, author = "A. H. Gandomi and A. H. Alavi and D. Mohammadzadeh Shadmehri and M. G. Sahab", title = "An empirical model for shear capacity of {RC} deep beams using genetic-simulated annealing", journal = "Archives of Civil and Mechanical Engineering", year = "2013", volume = "13", number = "3", pages = "354--369", keywords = "genetic algorithms, genetic programming, Shear capacity, RC deep beam, Genetic-simulated annealing, Empirical formula", ISSN = "1644-9665", DOI = "doi:10.1016/j.acme.2013.02.007", URL = "http://www.sciencedirect.com/science/article/pii/S1644966513000319", size = "16 pages", abstract = "This paper presents an empirical model to predict the shear strength of RC deep beams. A hybrid search algorithm coupling genetic programming (GP) and simulated annealing (SA), called genetic simulated annealing (GSA), was used to develop mathematical relationship between the experimental data. Using this algorithm, a constitutive relationship was obtained to make pertinent the shear strength of deep beams to nine mechanical and geometrical parameters. The model was developed using an experimental database acquired from the literature. The results indicate that the proposed empirical model is properly capable of evaluating the shear strength of deep beams. The validity of the proposed model was examined by comparing its results with those obtained from American Concrete Institute (ACI) and Canadian Standard Association (CSA) codes. The derived equation is notably simple and includes several effective parameters.", } @InProceedings{Gandomi:2013:MIT, author = "A. H. Gandomi and D. A. Roke", title = "Intelligent formulation of structural engineering systems", booktitle = "Seventh M.I.T. Conference on Computational Fluid and Solid Mechanics -- Focus: Multiphysics \& Multiscale", year = "2013", address = "Cambridge, MA 02142, USA", month = "12-14 " # jun, keywords = "genetic algorithms, genetic programming", notes = "http://www.seventhmitconference.org/ http://web.mit.edu/kjb/mitconf/7th_MIT_Conf_Schedule.pdf", } @InProceedings{conf/swarm/GandomiATY13, author = "Amir Hossein Gandomi and Amir Hossein Alavi and T. O. Ting and Xin-She Yang", title = "Intelligent Modeling and Prediction of Elastic Modulus of Concrete Strength via Gene Expression Programming", booktitle = "Proceedings of the 4th International Conference on Advances in Swarm Intelligence, ICSI 2013, Part {I}", year = "2013", editor = "Ying Tan and Yuhui Shi and Hongwei Mo", volume = "7928", series = "Lecture Notes in Computer Science", pages = "564--571", address = "Harbin, China", month = jun # " 12-15", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Gene expression programming, Tangent elastic modulus, Normal and High strength concrete, Compressive strength, Formulation", isbn13 = "978-3-642-38702-9", bibdate = "2013-05-23", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/swarm/icsi2013-1.html#GandomiATY13", DOI = "doi:10.1007/978-3-642-38703-6_66", size = "8 pages", abstract = "The accurate prediction of the elastic modulus of concrete can be very important in civil engineering applications. We use gene expression programming (GEP) to model and predict the elastic modulus of normal-strength concrete (NSC) and high-strength concrete (HSC). The proposed models can relate the modulus of elasticity of NSC and HSC to their compressive strength, based on reliable experimental databases obtained from the published literature. Our results show that GEP can be an effective method for deriving simplified and precise formulations for the elastic modulus of NSC and HSC. Furthermore, the comparison study in the present work indicates that the GEP predictions are more accurate than other methods.", } @Article{Gandomi:2013:EngStruct, author = "Amir H. Gandomi and David A. Roke and Kallol Sett", title = "Genetic programming for moment capacity modeling of ferrocement members", journal = "Engineering Structures", year = "2013", volume = "57", pages = "169--176", month = dec, keywords = "genetic algorithms, genetic programming, gene expression programming, Moment capacity, Ferrocement members", ISSN = "0141-0296", URL = "http://www.sciencedirect.com/science/article/pii/S0141029613004343", DOI = "doi:10.1016/j.engstruct.2013.09.022", size = "8 pages", abstract = "In this study, a robust variant of genetic programming called gene expression programming (GEP) is used to predict the moment capacity of ferrocement members. Constitutive relationships were obtained to correlate the ultimate moment capacity with mechanical and geometrical parameters using previously published experimental results. A subsequent parametric analysis was carried out and the trends of the results were confirmed. A comparative study was conducted between the results obtained by the proposed models and those of the plastic analysis, mechanism and nonlinear regression approaches, as well as two black-box models: back-propagation neural networks (BPNN) and an adaptive neuro-fuzzy inference system (ANFIS). Three GEP models are developed to capture the effect of randomising the test data subsets used to develop the models. The results indicate that the GEP models accurately estimate the moment capacity of ferrocement members. The prediction performance of the GEP models is significantly better than the plastic analysis, mechanism and nonlinear regression approaches and is comparable to that of the BPNN and ANFIS models.", } @Article{Gandomi:2014:MS, author = "Amir Hossein Gandomi and Gun Jin Yun and Amir Hossein Alavi", title = "An evolutionary approach for modeling of shear strength of RC deep beams", journal = "Materials and Structures", year = "2013", volume = "46", number = "12", pages = "2109--2119", month = dec, keywords = "genetic algorithms, genetic programming, Gene expression programming, Shear strength, RC deep beams", publisher = "Springer Netherlands", ISSN = "1359-5997", DOI = "doi:10.1617/s11527-013-0039-z", language = "English", size = "11 pages", abstract = "In this study, a new variant of genetic programming, namely gene expression programming (GEP) is used to predict the shear strength of reinforced concrete (RC) deep beams. A constitutive relationship was obtained correlating the ultimate load with seven mechanical and geometrical parameters. The model was developed using 214 experimental test results obtained from previously published papers. A comparative study was conducted between the results obtained by the proposed model and those of the American Concrete Institute (ACI) and Canadian Standard Association (CSA) models, as well as an Artificial Neural Network (ANN)-based model. A subsequent parametric analysis was carried out and the trends of the results were confirmed via some previous laboratory studies. The results indicate that the GEP model gives precise estimations of the shear strength of RC deep beams. The prediction performance of the model is significantly better than the ACI and CSA models and has a very good agreement with the ANN results. The derived design equation provides a valuable analysis tool accessible to practising engineers.", } @Article{Gandomi:2014:NCA, author = "Amir Hossein Gandomi and Amir Hossein Alavi and Abazar Asghari and Hadi Niroomand and Ali Matin Nazar", title = "An innovative approach for modeling of hysteretic energy demand in steel moment resisting frames", journal = "Neural Computing and Applications", year = "2014", volume = "24", number = "6", pages = "1285--1291", month = may, keywords = "genetic algorithms, genetic programming, hysteresis energy, Steel frames, Hybrid genetic simulated annealing, Prediction", publisher = "Springer-Verlag", ISSN = "0941-0643", DOI = "doi:10.1007/s00521-013-1342-x", language = "English", size = "7 pages", abstract = "This paper presents a new nonlinear model for the prediction of Hysteresis energy demand in steel moment resisting frames using an innovative genetic-based simulated annealing method called GSA. The hysteresis energy demand was formulated in terms of several effective parameters such as earthquake intensity, number of stories, soil type, period, strength index, and energy imparted to the structure. The performance and validity of the model were further tested using several criteria. The proposed model provides very high correlation coefficient (R = 0.985), and low root mean absolute error (RMSE = 1,346.1) and mean squared error (MAE = 1,037.6) values. The obtained results indicate that GSA is an effective method for the estimation of the hysteresis energy. The proposed GSA-based model is valuable for routine design practice. The prediction performance of the optimal GSA model was found to be better than that of the existing models.", } @Article{Gandomi:2014:JCEM, author = "Amir H. Gandomi and Ali Faramarzifar and Peyman Ghanad Rezaee and Abazar Asghari and Siamak Talatahari", title = "New Design Equations for Elastic Modulus of Concrete Using Multi Expression Programming", journal = "Journal of Civil Engineering and Management", year = "2015", volume = "21", number = "6", pages = "761--774", month = aug, keywords = "genetic algorithms, genetic programming, tangent elastic modulus, normal and high strength concrete, multi expression programming, compressive strength, formulation", ISSN = "1392-3730", DOI = "doi:10.3846/13923730.2014.893910", size = "14 pages", abstract = "An innovative multi expression programming (MEP) approach is used to derive new predictive equations for tangent elastic modulus of normal strength concrete (NSC) and high strength concrete (HSC). Similar to several building codes, the modulus of elasticity of NSC and HSC is formulated in terms of concrete compressive strength. Furthermore, a generic model is developed for the estimation of the elastic modulus of both NSC and HSC. Comprehensive databases are gathered from the literature to develop the models. For more verification, a parametric analysis is carried out and discussed. The proposed formulas are found to be accurate for the prediction of the elastic modulus of NSC and HSC. The predictions made by the MEP-based models are more accurate than those obtained by the existing models.", } @Article{Gandomi:2013:IREHM, author = "Amir Hossein Gandomi and Amir Hossein Alavi", title = "Hybridizing Genetic Programming with Orthogonal Least Squares for Modeling of Soil Liquefaction", journal = "International Journal of Earthquake Engineering and Hazard Mitigation", year = "2013", volume = "1", number = "1", pages = "2--8", month = sep, keywords = "genetic algorithms, genetic programming, Orthogonal Least Square, Modelling, Soil Liquefaction, Capacity Energy, Formulation", ISSN = "2282-7226", URL = "http://www.praiseworthyprize.it/public/papers/paper.asp?journal=IREHM&idpaper=13484&issue=VOL_1_N_1", size = "7 pages", abstract = "Precise estimation of the strain energy density required to trigger soil liquefaction, denoted as capacity energy, has been the focus of many studies. The main objective of this paper is to develop a robust prediction model for the soil capacity energy using a novel hybrid technique coupling genetic programming with orthogonal least squares, called GP/OLS. The proposed model was developed upon experimental results collected through a literature review. A traditional genetic programming analysis was performed to benchmark the GP/OLS model. The predictions made by the derived model were found to be more accurate than those provided by the genetic programming and other existing models. A subsequent parametric study was carried out and the trends of the results were confirmed via some previous laboratory studies.", } @InProceedings{Gandomi:2014:SC, author = "A. H. Gandomi and D. A. Roke", title = "Seismic Response Prediction of Self-Centering Concentrically Braced Frames Using Genetic Programming", booktitle = "Structures Congress 2014", year = "2014", editor = "Glenn Bell and Matt A. Card", pages = "1221--1232", address = "Boston, USA", month = "3-5 " # apr, organisation = "ASCE SCI", publisher = "American Society of Civil Engineers", keywords = "genetic algorithms, genetic programming, Seismic effects, Predictions, Frames, Bracing, Earthquake resistant structures", isbn13 = "978-0-7844-1335-7", DOI = "doi:10.1061/9780784413357.110", size = "12 pages", abstract = "Conventional concentrically braced frame (CBF) systems are commonly used in earthquake-resistant structural systems. However, they have limited drift capacity before brace buckling occurs. Self-centring, concentrically-braced frame (SC-CBF) systems have recently been developed to increase drift capacity prior to initiation of damage and to minimise residual drift. SC-CBFs have more complex behaviour than conventional CBFs. The seismic response of SC-CBFs depends on many new parameters such as rocking behavior, post-tensioning bars, and energy dissipation elements. Additionally, uncertainty of mechanical properties (e.g., coefficient of friction in the friction-bearings) can affect the system response. To design SC-CBF systems, an accurate prediction of the statistical parameters of roof drift demand is essential. In this study, genetic programming is used to predict the mean and standard deviation of SC-CBF peak roof drift response under the design basis earthquake using the most effective mechanical and geometric parameters. The results of this study can then be used in the future to design more efficient SC-CBF systems with a more accurate roof drift prediction.", notes = "http://content.asce.org/conferences/structures2014/", } @Article{Gandomi:2014:AiC, author = "Amir H. Gandomi and Amir H. Alavi and Sadegh Kazemi and Mostafa Gandomi", title = "Formulation of shear strength of slender {RC} beams using gene expression programming, part I: Without shear reinforcement", journal = "Automation in Construction", volume = "42", pages = "112--121", year = "2014", month = jun, keywords = "genetic algorithms, genetic programming, Gene expression programming, Shear strength, Reinforced concrete beam, Normal and high-strength concrete, Formulation", ISSN = "0926-5805", URL = "http://www.sciencedirect.com/science/article/pii/S0926580514000326", DOI = "doi:10.1016/j.autcon.2014.02.007", size = "10 pages", abstract = "In this study, a new design equation is derived to predict the shear strength of slender reinforced concrete (RC) beams without stirrups using gene expression programming (GEP). The predictor variables included in the analysis are web width, effective depth, concrete compressive strength, amount of longitudinal reinforcement, and shear span to depth ratio. A set of published database containing 1942 experimental test results is used to develop the model. An extra set of test results which is not involved in the modelling process is employed to verify the applicability of the proposed model. Sensitivity and parametric analyses are carried out to determine the contributions of the affecting parameters. The proposed model is effectively capable of estimating the ultimate shear capacity of members without shear steel. The results obtained by GEP are found to be more accurate than those obtained using several building codes. The GEP-based formula is fairly simple and useful for pre-design applications.", } @Article{Gandomi:2014:ASC, author = "Amir H. Gandomi and Danial {Mohammadzadeh S.} and Juan Luis Perez-Ordonez and Amir H. Alavi", title = "Linear genetic programming for shear strength prediction of reinforced concrete beams without stirrups", journal = "Applied Soft Computing", year = "2014", volume = "19", pages = "112--120", month = jun, keywords = "genetic algorithms, genetic programming, Linear genetic programming, Shear strength, Reinforced concrete beam, Design equation", ISSN = "1568-4946", URL = "http://www.sciencedirect.com/science/article/pii/S1568494614000751", DOI = "doi:10.1016/j.asoc.2014.02.007", size = "9 pages", abstract = "Highlights We have introduced LGP algorithm for shear capacity modelling of RC beams without stirrups. An extensive experimental database including 1938 test results gathered from literature. A simplified LGP based formula is obtained for different kinds of concrete. Our results are better than the nine different code models. A new design equation is proposed for the prediction of shear strength of reinforced concrete (RC) beams without stirrups using an innovative linear genetic programming methodology. The shear strength was formulated in terms of several effective parameters such as shear span to depth ratio, concrete cylinder strength at date of testing, amount of longitudinal reinforcement, lever arm, and maximum specified size of coarse aggregate. A comprehensive database containing 1938 experimental test results for the RC beams was gathered from the literature to develop the model. The performance and validity of the model were further tested using several criteria. An efficient strategy was considered to guarantee the generalisation of the proposed design equation. For more verification, sensitivity and parametric analysis were conducted. The results indicate that the derived model is an effective tool for the estimation of the shear capacity of members without stirrups (R = 0.921). The prediction performance of the proposed model was found to be better than that of several existing buildings codes.", } @Book{Gandomi:2015:hbgpa, editor = "Amir H. Gandomi and Amir H. Alavi and Conor Ryan", title = "Handbook of Genetic Programming Applications", publisher = "Springer", year = "2015", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-20882-4", DOI = "DOI:10.1007/978-3-319-20883-1", size = "approx 600 pages", } @InCollection{Gandomi:2015:hbgpaF, author = "Amir H. Gandomi and Amir H. Alavi and Conor Ryan", title = "Foreword", booktitle = "Handbook of Genetic Programming Applications", publisher = "Springer", year = "2015", editor = "Amir H. Gandomi and Amir H. Alavi and Conor Ryan", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-20882-4", DOI = "doi:10.1007/978-3-319-20883-1", size = "1 pages", } @Article{Gandomi:2015:AES, author = "Amir H. Gandomi and David A. Roke", title = "Assessment of artificial neural network and genetic programming as predictive tools", journal = "Advances in Engineering Software", year = "2015", volume = "88", pages = "63--72", month = oct, keywords = "genetic algorithms, genetic programming, gene expression programming, Artificial neural networks, Over-fitting, Explicit formulation, Punching shear, RC slabs, Parametric study", ISSN = "0965-9978", URL = "http://www.sciencedirect.com/science/article/pii/S0965997815000861", DOI = "doi:10.1016/j.advengsoft.2015.05.007", abstract = "Soft computing techniques have been widely used during the last two decades for nonlinear system modeling, specifically as predictive tools. In this study, the performances of two well-known soft computing predictive techniques, artificial neural network (ANN) and genetic programming (GP), are evaluated based on several criteria, including over-fitting potential. A case study in punching shear prediction of RC slabs is modelled here using a hybrid ANN (which includes simulated annealing and multi-layer perception) and an established GP variant called gene expression programming. The ANN and GP results are compared to values determined from several design codes. For more verification, external validation and parametric studies were also conducted. The results of this study indicate that model acceptance criteria should include engineering analysis from parametric studies.", } @Article{Gandomi:2015:IPSE, author = "Amir H. Gandomi and Gun Jin Yun", title = "Coupled {SelfSim} and genetic programming for non-linear material constitutive modelling", journal = "Inverse Problems in Science and Engineering", year = "2015", volume = "23", number = "7", pages = "1101--1119", keywords = "genetic algorithms, genetic programming, linear genetic programming, Discipulus, inverse analysis, artificial neural network, ANN, non-linear material constitutive model", ISSN = "1741-5977", URL = "http://www.tandfonline.com/doi/abs/10.1080/17415977.2014.968149", DOI = "doi:10.1080/17415977.2014.968149", size = "19 pages", abstract = "In the present study, an improved SelfSim is combined with a recent genetic programming technique called linear GP (LGP) for the inverse extraction of non-linear material behaviour. The SelfSim prepares a comprehensive database including stresses and strains of the structural elements. Then, a steady-state LGP is used to formulate the strain-stress relationship. In this research, a space truss with a reference material model is used as a hypothetical structure. The derived LGP-based formula is very simple and can be employed for design and pre-design purposes. The implementation of LGP-based model is also tested in a general purpose finite element programme. Since the proposed model is an explicit formula, its implementation becomes standard and practically useful. The results show that the procedure is reliable and can be used to derive and formulate the non-linear constitutive material models with a high degree of accuracy.", } @InProceedings{Gandomi:2016:MOD, author = "Amir H. Gandomi and Behnam Kiani and Robert Y. Liang", title = "Genetic programming for concrete modeling", booktitle = "The 2nd International Workshop on Machine learning, Optimization and big Data", year = "2016", editor = "Giovanni Giuffrida", address = "Volterra, Pisa, Italy", month = aug # " 26-29", organisation = "SIAF", keywords = "genetic algorithms, genetic programming", notes = "http://www.taosciences.it/mod/ Aug 2018 Not in Springer, LNCS 10122, https://link.springer.com/book/10.1007%2F978-3-319-51469-7", } @Article{Gandomi:2016:AiC, author = "Amir H. Gandomi and Siavash Sajedi and Behnam Kiani and Qindan Huang", title = "Genetic programming for experimental big data mining: A case study on concrete creep formulation", journal = "Automation in Construction", year = "2016", volume = "70", pages = "89--97", month = oct, keywords = "genetic algorithms, genetic programming, Multi-gene genetic programming, Big data, Multi-objective optimization, Non-dominated sorting, Concrete creep", ISSN = "0926-5805", URL = "http://www.sciencedirect.com/science/article/pii/S0926580516301315", DOI = "doi:10.1016/j.autcon.2016.06.010", size = "9 pages", abstract = "This paper proposes a new algorithm called multi-objective genetic programming (MOGP) for complex civil engineering systems. The proposed technique effectively combines the model structure selection ability of a standard genetic programming with the parameter estimation power of classical regression, and it simultaneously optimizes both the complexity and goodness-of-fit in a system through a non-dominated sorting algorithm. The performance of MOGP is illustrated by modelling a complex civil engineering problem: the time-dependent total creep of concrete. A Big Data is used for the model development so that the proposed concrete creep model (referred to as a genetic programming based creep model or G-C model in this study) is valid for both normal and high strength concrete with a wide range of structural properties. The G-C model is then compared with currently accepted creep prediction models. The G-C model obtained by MOGP is simple, straightforward to use, and provides more accurate predictions than other prediction models.", } @Article{Gandomi:2017:Measurement, author = "Amir H. Gandomi and Amir H. Alavi and Mostafa Gandomi and Sadegh Kazemi", title = "Formulation of shear strength of slender {RC} beams using gene expression programming, part II: With shear reinforcement", journal = "Measurement", volume = "95", pages = "367--376", year = "2017", ISSN = "0263-2241", DOI = "doi:10.1016/j.measurement.2016.10.024", URL = "http://www.sciencedirect.com/science/article/pii/S0263224116305723", abstract = "In this study, a new variant of genetic programming, namely gene expression programming (GEP) is used to predict the shear strength of reinforced concrete (RC) beams with stirrups. The derived model relates the shear strength to mechanical and geometrical properties. The model is developed using a database containing 466 experimental test results gathered from the literature. Sensitivity and parametric analyses are performed for further verification of the model. The comparative study proves the superior performance of the GEP model compared to the expressions developed in several codes of practice.", keywords = "genetic algorithms, genetic programming, Shear strength, Reinforced concrete beam, Normal and high-strength concrete, Stirrups, Gene expression programming, Formulation", } @InProceedings{Gandomi:2018:Biocene, author = "Amir H. Gandomi", title = "Evolutionary Data Mining in Aerospace", booktitle = "Biocene 2018", year = "2018", address = "Ohio Aerospace Institute, Cleveland, USA", organisation = "NASA", keywords = "genetic algorithms, genetic programming", broken = "https://www.grc.nasa.gov/vine/summit/program-2/", notes = "Stevens Institute of Technology broken Jan 2021 https://biocene2018.com/ Broken Jan 2024 https://www.bioohio.com/event/biocene-2018/", } @InProceedings{Gandomi:2018:BEACON, author = "A. H. Gandomi", title = "Multi-objective Genetic Programming for Classification and Regression Problems", booktitle = "BEACON Congress 2018", year = "2018", address = "Michigan State University, USA", month = "8-11 " # aug, keywords = "genetic algorithms, genetic programming", notes = "https://www3.beacon-center.org/beacon-events/ https://docs.google.com/spreadsheets/d/18v1p1IuThieHF4rq04xP4ZL2mVYoUwz6HpR1PM3-PN4/edit#gid=0", } @Article{Gandomi:GPEM:GPTIPS, author = "Amir H. Gandomi and Ehsan Atefi", title = "Software review: the {GPTIPS} platform", journal = "Genetic Programming and Evolvable Machines", year = "2020", volume = "21", number = "1-2", pages = "273--280", month = jun, note = "Software review", keywords = "genetic algorithms, genetic programming, GP, MGGP, SMGR", ISSN = "1389-2576", URL = "https://rdcu.be/dR8qH", DOI = "doi:10.1007/s10710-019-09366-0", size = "8 pages", abstract = "GPTIPS is a widely used genetic programming software that was developed in Matlab. The most recent version of this software, GPTIPS 2.0, provides a symbolic multi-gene regression for data analysis, in addition to traditional evolutionary algorithms. We briefly explain the GPTIPS methodology and describe its main features, including its weaknesses and strengths, and give examples of GPTIPS applications.", notes = "See \cite{GPTIPSGuide}", } @Article{Gandomi:II, author = "Amir H Gandomi and David Roke", journal = "IEEE Transactions on Industrial Informatics", title = "A Multi-Objective Evolutionary Framework for Formulation of Nonlinear Structural Systems", year = "2022", volume = "18", number = "9", pages = "5795--5803", keywords = "genetic algorithms, genetic programming, Evolutionary Computation, Feature Selection, Formulation, Self-centering concentrically braced frame, Multi-objective", ISSN = "1941-0050", URL = "https://www.human-competitive.org/sites/default/files/entryform.txt", URL = "https://www.human-competitive.org/sites/default/files/tii3126702.pdf", DOI = "doi:10.1109/TII.2021.3126702", size = "9 pages", abstract = "an evolutionary framework is proposed for seismic response formulation of self-centering concentrically braced frame (SC-CBF) systems. A total of 75 different SC-CBF systems were designed, and their responses were recorded under 170 earthquake records. To select the most important earthquake intensity measures, an evolutionary feature selection strategy is introduced, which tries to find the highest correlation. For the formulation of the SC-CBF response, a hybrid multi-objective genetic programming and regression analysis is implemented, considering both model accuracy and model complexity as objectives. In the hybrid approach, regression tries to connect multiple genes. Non-dominated models are presented, and the best model is selected based on the practical approach proposed here. The best model is compared with four other genetic programming models. The results show that the evolutionary procedure is highly effective for designing the SC-CBF system using a simple and accurate model for such a complex system.", notes = "Entered 2022 HUMIES Also known as \cite{9609645} University of Technology Sydney, Australia", } @Article{gandomi:2020:Remote_Sensing, author = "Mostafa Gandomi and Moharram {Dolatshahi Pirooz} and Iman Varjavand and Mohammad Reza Nikoo", title = "Permeable Breakwaters Performance Modeling: A Comparative Study of Machine Learning Techniques", journal = "Remote Sensing", year = "2020", volume = "12", number = "11", keywords = "genetic algorithms, genetic programming", ISSN = "2072-4292", URL = "https://www.mdpi.com/2072-4292/12/11/1856", DOI = "doi:10.3390/rs12111856", abstract = "The advantage of permeable breakwaters over more traditional types has attracted great interest in the behaviour of these structures in the field of engineering. The main objective of this study is to apply 19 well-known machine learning regressors to derive the best model of innovative breakwater hydrodynamic behaviour with reflection and transmission coefficients as the target parameters. A database of 360 laboratory tests on the low-scale breakwater is used to establish the model. The proposed models link the reflection and transmission coefficients to seven dimensionless parameters, including relative chamber width, relative rockfill height, relative chamber width in terms of wavelength, wave steepness, wave number multiplied by water depth, and relative wave height in terms of rockfill height. For the validation of the models, the cross-validation method was used for all models except the multilayer perceptron neural network (MLP) and genetic programming (GP) models. To validate the MLP and GP, the database is divided into three categories: training, validation, and testing. Furthermore, two explicit functional relationships are developed by using the GP for each target. The exponential Gaussian process regression (GPR) model in predicting the reflection coefficient (R2 = 0.95, OBJ function = 0.0273), and similarly, the exponential GPR model in predicting the transmission coefficient (R2 = 0.98, OBJ function = 0.0267) showed the best performance and the highest correlation with the actual records and can further be used as a reference for engineers in practical work. Also, the sensitivity analysis of the proposed models determined that the relative height parameter of the rockfill material has the greatest contribution to the introduced breakwater behaviour.", notes = "also known as \cite{rs12111856}", } @Article{GANDOMI:2021:ASC, author = "Mostafa Gandomi and Ali R. Kashani and Ali Farhadi and Mohsen Akhani and Amir H. Gandomi", title = "Spectral acceleration prediction using genetic programming based approaches", journal = "Applied Soft Computing", volume = "106", pages = "107326", year = "2021", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2021.107326", URL = "https://www.sciencedirect.com/science/article/pii/S1568494621002490", keywords = "genetic algorithms, genetic programming, Spectral acceleration, Ground-motion models, Multi-gene genetic programming, Gene expression programming, Multi-objective genetic programming", abstract = "Evolutionary computation (EC) is a widely used computational intelligence that facilitates the formulation of a range of complex engineering problems. This study tackled two hybrid EC techniques based on genetic programming (GP) for ground motion prediction equations (GMPEs). The first method coupled regression analysis with multi-objective genetic programming. In this way, the strategy was maximizing the accuracy and minimizing the models' complexity simultaneously. The second approach incorporated mesh adaptive direct search (MADS) into gene expression programming to optimize the obtained coefficients. A big data set provided by the Pacific Earthquake Engineering Research Centre (PEER) was used for the model development. Two explicit formulations were developed during this effort. In those formulae, we correlated spectral acceleration to a set of seismological parameters, including the period of vibration, magnitude, the closest distance to the fault ruptured area, shear wave velocity averaged over the top 30 meters, and style of faulting. The GP-based models are verified by a comprehensive comparison with the most well-known methods for GMPEs. The results show that the proposed models are quite simple and straightforward. The high degrees of accuracy of the predictions are competitive with the NGA complex models. Correlations of the predicted data using GEP-MADs and MOGP-R models with the real observations seem to be better than those available in the literature. Three statistical measures for GMPEs, such as E (percent), LLH, and EDR index, confirmed those observations", } @InProceedings{Gandomi:2023:CINTI, author = "Amir H Gandomi", booktitle = "2023 IEEE 23rd International Symposium on Computational Intelligence and Informatics (CINTI)", title = "Evolutionary Computation for Intelligent Data Analytics", year = "2023", pages = "000011--000012", abstract = "Artificial Intelligence has been widely used during the last two decades and has remained a highly-researched topic, especially for complex real-world problems. Evolutionary Computation (EC) techniques are a subset of artificial intelligence, but they are slightly different from the classical methods in the sense that the intelligence of EC comes from biological systems or nature in general. The efficiency of EC is due to their significant ability to imitate the best features of nature which have evolved by natural selection over millions of years. The central theme of this presentation is about EC techniques and their application to complex real-world problems. On this basis, first I will talk about an automated learning approach called genetic programming. Applied evolutionary learning will be presented, and then their new advances will be mentioned. Here, some of my studies on big data analytics and modelling using EC and genetic programming, in particular, will be presented. Second, EC will be presented including key applications in the optimisation of complex and nonlinear systems. It will also be explained how such algorithms have been adopted to engineering problems and how their advantages over the classical optimisation problems are used in action. Optimisation results of large-scale towers and many-objective problems will be presented which show the applicability of EC. Finally, heuristics will be explained which are adaptable with EC and they can significantly improve the optimisation results.", keywords = "genetic algorithms, genetic programming, Data analysis, Poles and towers, Evolutionary computation, Biological systems, Data models, Artificial intelligence", DOI = "doi:10.1109/CINTI59972.2023.10382125", ISSN = "2471-9269", month = nov, notes = "Also known as \cite{10382125}", } @InProceedings{Gandomi:2024:CINTI, author = "Amirhossein Gandomi", title = "{AI-Driven} Engineering: Redefining Possibilities and Problem-Solving", booktitle = "2024 IEEE 24th International Symposium on Computational Intelligence and Informatics (CINTI)", year = "2024", pages = "13--14", month = nov, keywords = "genetic algorithms, genetic programming, Optimisation methods, Biological systems, Problem-solving, Artificial intelligence, Particle swarm optimisation, Nonlinear systems, Informatics, Computational intelligence", ISSN = "2471-9269", DOI = "doi:10.1109/CINTI63048.2024.10830819", abstract = "Artificial Intelligence (AI) has been extensively applied over the past two decades and continues to be a prominent area of research, particularly in addressing complex, real-world challenges. Evolutionary and Swarm Intelligence (ESI) techniques represent a unique subset of AI, deriving their efficiency from mimicking the best features of natural and biological systems that have evolved over millions of years. This presentation focuses on the principles of ESI and their broader applications across various engineering domains. We will explore automated learning approaches, such as genetic programming, and highlight advancements in evolutionary learning for complex problem-solving. Additionally, the presentation will evaluate the general impact of AI in engineering, discussing key applications of ESI in optimising complex, nonlinear systems. We will also demonstrate the advantages of ESI over traditional optimisation methods, showcasing results from large-scale and multi-objective problems. Finally, adaptable heuristics that enhance ESI performance will be introduced, illustrating their potential to improve optimisation outcomes.", notes = "Also known as \cite{10830819}", } @Article{Ganesan20112913, author = "T. Ganesan and P. Vasant and I. Elamvazuthi", title = "Optimization of nonlinear geological structure mapping using hybrid neuro-genetic techniques", journal = "Mathematical and Computer Modelling", volume = "54", number = "11-12", pages = "2913--2922", year = "2011", ISSN = "0895-7177", DOI = "doi:10.1016/j.mcm.2011.07.012", URL = "http://www.sciencedirect.com/science/article/pii/S0895717711004225", keywords = "genetic algorithms, genetic programming, Nonlinear, Engineering problems, Geological structure mapping, Hybrid optimisation", abstract = "A fairly reasonable result was obtained for nonlinear engineering problems using the optimisation techniques such as neural network, genetic algorithms, and fuzzy logic independently in the past. Increasingly, hybrid techniques are being used to solve the nonlinear problems to obtain a better output. This paper discusses the use of neuro-genetic hybrid technique to optimise the geological structure mapping which is known as seismic survey. It involves minimisation of objective function subject to the requirement of geophysical and operational constraints. In this work, the optimization was initially performed using genetic programming, and followed by hybrid neuro-genetic programming approaches. Comparative studies and analysis were then carried out on the optimised results. The results indicate that the hybrid neuro-genetic hybrid technique produced better results compared to the stand-alone genetic programming method.", } @InProceedings{Ganesan:2013:Nostradamus, author = "T. Ganesan and I. Elamvazuthi and Ku Zilati Ku Shaari and P. Vasant", title = "Hypervolume-Driven Analytical Programming for Solar-Powered Irrigation System Optimization", booktitle = "Nostradamus 2013: Prediction, Modeling and Analysis of Complex Systems", year = "2013", editor = "Ivan Zelinka and Guanrong Chen and Otto E. R{\"o}ssler and Vaclav Snasel and Ajith Abraham", volume = "210", series = "Advances in Intelligent Systems and Computing", pages = "147--154", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Analytical Programming", isbn13 = "978-3-319-00542-3", DOI = "doi:10.1007/978-3-319-00542-3_15", abstract = "In the field of alternative energy and sustainability, optimization type problems are regularly encountered. In this paper, the Hypervolume-driven Analytical Programming (Hyp-AP) approaches were developed. This method was then applied to the multi-objective (MO) design optimization of a real-world photovoltaic (PV)-based solar powered irrigation system. This problem was multivariate, nonlinear and multiobjective. The Hyp-AP method was used to construct the approximate Pareto frontier as well as to identify the best solution option. Some comparative analysis was performed on the proposed method and the approach used in previous work.", } @Article{GANESH:2017:PM, author = "Sajaysurya Ganesh and Saravana Kumar Gurunathan", title = "Evolutionary Algorithms for Programming Pneumatic Sequential Circuit Controllers", journal = "Procedia Manufacturing", volume = "11", pages = "1726--1734", year = "2017", note = "27th International Conference on Flexible Automation and Intelligent Manufacturing, FAIM2017, 27-30 June 2017, Modena, Italy", keywords = "genetic algorithms, genetic programming, Flexible Automation, Genetic Programming: Programmable Logic Controller, Pneumatics", ISSN = "2351-9789", DOI = "doi:10.1016/j.promfg.2017.07.299", URL = "http://www.sciencedirect.com/science/article/pii/S2351978917305073", abstract = "Sequential actuation of pneumatic cylinders is a common form of automation in small and medium scale industries. By changing such actuation sequences to suit the different products being processed, flexible automation can be economically realized. However, changing the actuation sequence involves manually reprogramming Programmable Logic Controllers (PLC), which consumes time and hinders the implementation of flexible automation. This paper presents a novel methodology to automatically program PLCs by evolving logic equations using Genetic Algorithm and Genetic Programming for the desired actuation sequence. Case studies have been presented to demonstrate the possibility of using the proposed methodology to reliably implement flexible automation", } @InProceedings{gang:2004:eurogp, author = "Gang Wang and Terence Soule", title = "How to Choose Appropriate Function Sets for {GP}", booktitle = "Genetic Programming 7th European Conference, EuroGP 2004, Proceedings", year = "2004", editor = "Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule", volume = "3003", series = "LNCS", pages = "198--207", address = "Coimbra, Portugal", publisher_address = "Berlin", month = "5-7 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-21346-5", URL = "https://verso.uidaho.edu/esploro/outputs/conferencePaper/How-to-choose-appropriate-function-sets/996631100601851", URL = "https://rdcu.be/dQQce", DOI = "doi:10.1007/978-3-540-24650-3_18", size = "10 pages", abstract = "The choice of functions in a genetic program can have a significant effect on the GP's performance, but there have been no systematic studies of how to select functions to optimise performance. We investigate how to choose appropriate function sets for general genetic programming problems. For each problem multiple functions sets are tested. The results show that functions can be classified into function groups of equivalent functions. The most appropriate function set for a problem is one that is optimally diverse; a set that includes one function from each function group.", notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004", } @InProceedings{Gangwani:2018:ICLR, author = "Tanmay Gangwani and Jian Peng", title = "Policy Optimization by Genetic Distillation", booktitle = "Sixth International Conference on Learning Representations", year = "2018", editor = "Yoshua Bengio and Yann LeCun", address = "Vancouver", month = "30 " # apr # "--3 " # may, keywords = "Genetic algorithms, ANN, deep reinforcement learning, imitation learning", URL = "http://www.human-competitive.org/sites/default/files/gangwani-paper.pdf", URL = "https://arxiv.org/abs/1711.01012", URL = "https://openreview.net/forum?id=ByOnmlWC-", URL = "https://iclr.cc/Conferences/2018/Schedule?showEvent=160", size = "16 pages", abstract = "Genetic algorithms have been widely used in many practical optimization problems. Inspired by natural selection, operators, including mutation, crossover and selection, provide effective heuristics for search and black-box optimization. However, they have not been shown useful for deep reinforcement learning, possibly due to the catastrophic consequence of parameter crossovers of neural networks. Here, we present Genetic Policy Optimization (GPO), a new genetic algorithm for sample-efficient deep policy optimization. GPO uses imitation learning for policy crossover in the state space and applies policy gradient methods for mutation. Our experiments on MuJoCo tasks show that GPO as a genetic algorithm is able to provide superior performance over the state-of-the-art policy gradient methods and achieves comparable or higher sample efficiency.", notes = "Not GP? https://iclr.cc/Conferences/2018/Schedule?type=Poster Entered for 2018 HUMIES", } @Article{Gao:2016:ESwA, author = "Fei Gao and Teng Lee and Wen-Jing Cao and Xue-jing Lee and Yan-fang Deng and Heng-qing Tong", title = "Self-evolution of hyper fractional order chaos driven by a novel approach through genetic programming", journal = "Expert Systems with Applications", year = "2016", volume = "52", pages = "1--15", month = "15 " # jun # " 2016", keywords = "genetic algorithms, genetic programming, Fractional-order chaos, Self-evolution, United functional extrema model", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2015.12.033", size = "15 pages", abstract = "To find best inherent chaotic systems behind the complex phenomena is of vital important in Complexity science research. In this paper, a novel non-Lyapunov methodology is proposed to self-evolve the best hyper fractional order chaos automatically driven by a computational intelligent method, genetic programming. Rather than the unknown systematic parameters and fractional orders, the expressions of fractional-order differential equations (FODE) are taken as particular independent variables of a proper converted non-negative minimization of special functional extrema in the proposed united functional extrema model (UFEM), then it is free of the hypotheses that the definite forms of FODE are given but some parameters and fractional orders unknown. To demonstrate the potential of the proposed methodology, simulations are done to evolve a series of benchmark hyper and normal fractional chaotic systems in complexity science. The experiments results show that the proposed paradigm of fractional order chaos driven by genetic programming is a successful method for chaos automatic self-evolution, with the advantages of high precision and robustness.", notes = "Department of Mathematics, School of Science, Wuhan University of Technology, Luoshi Road 122, Wuhan, Hubei 430070, China. Signal Processing Group, Department of Electronics and Telecommunications, Norwegian University of Science and Technology, Trondheim N-7491, Norway", } @Article{Guanqiang_Gao:Cybernetics, author = "Guanqiang Gao and Yi Mei and Bin Xin and Ya-Hui Jia and Will N. Browne", title = "Automated Coordination Strategy Design Using Genetic Programming for Dynamic Multipoint Dynamic Aggregation", journal = "IEEE Transactions on Cybernetics", year = "2022", volume = "52", number = "12", pages = "13521--13535", keywords = "genetic algorithms, genetic programming", ISSN = "2168-2275", DOI = "doi:10.1109/TCYB.2021.3080044", abstract = "The multipoint dynamic aggregation (MPDA) problem of the multirobot system is of great significance for its real-world applications such as bush fire elimination. The problem is to design the optimal plan for a set of heterogeneous robots to complete some geographically distributed tasks collaboratively. In this article, we consider the dynamic version of the problem, where new tasks keep appearing after the robots are dispatched from the depot. The dynamic MPDA problem is a complicated optimization problem due to several characteristics, such as the collaboration of robots, the accumulative task demand, the relationships among robots and tasks, and the unpredictable task arrivals. In this article, a new model of the problem considering these characteristics is proposed. To solve the problem, we develop a new genetic programming hyperheuristic (GPHH) method to evolve reactive coordination strategies (RCSs), which can guide the robots to make decisions in real time. The proposed GPHH method contains a newly designed effective RCS heuristic template to generate the execution plan for the robots according to a GP tree. A new terminal set of features related to both robots and tasks and a cluster filter that assigns the robots to urgent tasks are designed. The experimental results show that the proposed GPHH significantly outperformed the state-of-the-art methods. Through further analysis, useful insights such as how to distribute and coordinate robots to execute different types of tasks are discovered.", notes = "Also known as \cite{9445736}", } @InProceedings{conf/icnc/GaoGCLG09, title = "Cyberspace Situation Prediction Based on Gene Expression Programming", author = "HongLei Gao and WenZhong Guo and GuoLong Chen and YanHua Liu and Mei Gao", booktitle = "Fifth International Conference on Natural Computation, 2009. ICNC '09", year = "2009", editor = "Haiying Wang and Kay Soon Low and Kexin Wei and Junqing Sun", month = "14-16 " # aug, address = "Tianjian, China", publisher = "IEEE Computer Society", isbn13 = "978-0-7695-3736-8", keywords = "genetic algorithms, genetic programming, gene expression programming", pages = "191--195", bibdate = "2010-01-22", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icnc/icnc2009-4.html#GaoGCLG09", DOI = "doi:10.1109/ICNC.2009.42", abstract = "The accurate prediction of cyberspace situation is fundamental to intrusion prevention in large scale networks. After analysing the cyberspace situation, a cyberspace situation prediction model based on gene expression programming (GEP-CSP) is proposed, to predict the time series of cyberspace situation. Besides, since its own intrinsic characteristics, GEP-CSP solves the problem that the traditional time series methods can't make an accurate prediction without the pre-knowledge. By employing GEP-CSP, the experiments on Abilene network flow data reached the expectation and made a precise prediction.", } @Article{Gao:2025:engstruct, author = "Jianfeng Gao and Biao Ma and Pan Liu and Jianzhong Li and Zehua Bao", title = "Experimental and numerical investigations of seismic performance of precast bridge columns with novel lap splice connections by {UHPC}", journal = "Engineering Structures", year = "2025", volume = "322", pages = "119165", keywords = "genetic algorithms, genetic programming, Genetic programming-orthogonal least square, Lap splice connection, Shake table test, Ultrahigh-performance concrete, Bond-slip effect", ISSN = "0141-0296", URL = "https://www.sciencedirect.com/science/article/pii/S0141029624017279", DOI = "doi:10.1016/j.engstruct.2024.119165", abstract = "In this study, the genetic programming-orthogonal least squares (GP-OLS) tool was used to find the optimum equation that fit a database of pullout tests to estimate the bond strength between UHPC and rebar. Then, a procedure based on the equation for bond strength was proposed to guide the design of a novel lap splice connection by UHPC used to connect bridge columns to footings. A 1/3.5 geometrically scaled bridge model was designed and tested on shake tables to investigate seismic performance of precast bridge columns with the novel connection type. Tests results show that plastic deformation and consequent damage is mainly concentrated above the lower column-footing connection segment for a precast column, while the connection segment remains minimally damaged. Furthermore, a calibrated finite-element model was developed to account for the bond-slip effect in a precast column with the novel connection type. Numerical results show good agreement with experimental counterparts when the bond-slip effect is appropriately considered", } @Article{Gao:2001:CCE, author = "Li Gao and Norman W. Loney", title = "Evolutionary polymorphic neural network in chemical process modeling", journal = "Computers \& Chemical Engineering", year = "2001", volume = "25", pages = "1403--1410", number = "11-12", keywords = "genetic algorithms, genetic programming, Evolutionary polymorphic neural network (EPNN), Neural network, Process modeling", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6TFT-449TFB0-2/2/b9c50f18933d4b739a9d8a2843b45548", ISSN = "0098-1354", DOI = "doi:10.1016/S0098-1354(01)00708-6", abstract = "Evolutionary polymorphic neural network (EPNN) is a novel approach to modelling dynamic process systems. This approach has its basis in artificial neural networks and evolutionary computing. As demonstrated in the studied dynamic CSTR system, EPNN produces less error than a traditional recurrent neural network with a less number of neurons. Furthermore, EPNN performs networked symbolic regressions for input-output data, while it performs multiple step ahead prediction through adaptable feedback structures formed during evolution. In addition, the extracted symbolic formulae from EPNN can be used for further theoretical analysis and process optimisation.", } @PhdThesis{LiGao:thesis, author = "Li Gao", title = "Evolutionary Polymorphic Neural Networks in Chemical Engineering Modeling", school = "Department of Chemical Engineering, New Jersey Institute of Technology", year = "2001", address = "USA", month = aug, keywords = "genetic algorithms, genetic programming, Evolutionary Polymorphic Neural Network (EPNN), Artificial intelligence, Evolutionary computing", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/LiGao_thesis.pdf", size = "146 pages", abstract = "Evolutionary Polymorphic Neural Network (EPNN) is a novel approach to modeling chemical, biochemical and physical processes. This approach has its basis in modern artificial intelligence, especially neural networks and evolutionary computing. EPNN can perform networked symbolic regressions for input-output data, while providing information about both the structure and complexity of a process during its own evolution. In this work three different processes are modeled: 1. A dynamic neutralisation process. 2. An aqueous two-phase system. 3. Reduction of a biodegradation model. In all three cases, EPNN shows better or at least equal performances over published data than traditional thermodynamics /transport or neural network models. Furthermore, in those cases where traditional modeling parameters are difficult to determine, EPNN can be used as an auxiliary tool to produce equivalent empirical formulae for the target process. Feedback links in EPNN network can be formed through training (evolution) to perform multiple steps ahead predictions for dynamic nonlinear systems. Unlike existing applications combining neural networks and genetic algorithms, symbolic formulae can be extracted from EPNN modeling results for further theoretical analysis and process optimisation. EPNN system can also be used for data prediction tuning. In which case, only a minimum number of initial system conditions need to be adjusted. Therefore, the network structure of EPNN is more flexible and adaptable than traditional neural networks. Due to the polymorphic and evolutionary nature of the EPNN system, the initially randomised values of constants in EPNN networks will converge to the same or similar forms of functions in separate runs until the training process ends. The EPNN system is not sensitive to differences in initial values of the EPNN population. However, if there exists significant larger noise in one or more data sets in the whole data composition, the EPNN system will probably fail to converge to a satisfactory level of prediction on these data sets. EPNN networks with a relatively small number of neurons can achieve similar or better performance than both traditional thermodynamic and neural network models. The developed EPNN approach provides alternative methods for efficiently modeling complex, dynamic or steady-state chemical processes. EPNN is capable of producing symbolic empirical formulae for chemical processes, regardless of whether or not traditional thermodynamic models are available or can be applied. The EPNN approach does overcome some of the limitations of traditional thermodynamic /transport models and traditional neural network models.", notes = "Advisory Committee: Loney, Norman W. Baltzis, Basil C. Barat, Robert B. Knox, Dana E. Blackmore, Denis Wasser, Daniel J.", } @InProceedings{Gao:2008:ICES, author = "Peng Gao and Trent McConaghy and Georges Gielen", title = "{ISCLEs:} Importance Sampled Circuit Learning Ensembles for Trustworthy Analog Circuit Topology Synthesis", booktitle = "Proceedings of the 8th International Conference on Evolvable Systems, ICES 2008", year = "2008", editor = "Gregory S. Hornby and Lukas Sekanina and Pauline C. Haddow", volume = "5216", series = "Lecture Notes in Computer Science", pages = "11--21", address = "Prague, Czech Republic", month = sep # " 21-24", publisher = "Springer", keywords = "genetic algorithms, genetic programming, EHW", isbn13 = "978-3-540-85856-0", URL = "http://trent.st/content/2008-ICES-iscles.pdf", DOI = "doi:10.1007/978-3-540-85857-7_2", size = "11 pages", abstract = "Importance Sampled Circuit Learning Ensembles (ISCLEs) is a novel analog circuit topology synthesis method that returns designer-trustworthy circuits yet can apply to a broad range of circuit design problems including novel functionality. ISCLEs uses the machine learning technique of boosting, which does importance sampling of weak learners to create an overall circuit ensemble. In ISCLEs, the weak learners are circuit topologies with near-minimal transistor sizes. In each boosting round, first a new weak learner topology and sizings are found via genetic programming-based MOJITO multi-topology optimisation, then it is combined with previous learners into an ensemble, and finally the weak-learning target is updated. Results are shown for the trustworthy synthesis of a sinusoidal function generator, and a 3-bit A/D converter.", notes = "Evolvable Systems: From Biology to Hardware", } @InProceedings{Gao:2008:ICCAD, author = "Peng Gao and Trent McConaghy and Georges Gielen", title = "Importance sampled circuit learning ensembles for robust analog IC design", booktitle = "IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2008", year = "2008", pages = "396--399", address = "San Jose, CA, USA", month = "10-13 " # nov, keywords = "genetic algorithms, genetic programming, EHW, analogue integrated circuits, analogue-digital conversion, importance sampling, integrated circuit design, waveform generators, ISCLEs-specific library, Moore's Law, analog IC design, block topology, boosting algorithm, digital-sized circuits, flash A-D converter, importance sampling, learning ensembles, multi-topology sizing technique, sinusoidal function generator, Analog integrated circuits, Assembly, Boosting, Circuit topology, Design methodology, Monte Carlo methods, Moore's Law, Robustness, Signal generators, Software libraries", isbn13 = "978-1-4244-2819-9", URL = "http://trent.st/content/2008-ICCAD-iscles.pdf", DOI = "doi:10.1109/ICCAD.2008.4681604", size = "4 pages", abstract = "This paper presents ISCLEs, a novel and robust analog design method that promises to scale with Moore's Law, by doing boosting-style importance sampling on digital-sized circuits to achieve the target analog behaviour. ISCLEs consists of: (1) a boosting algorithm developed specifically for circuit assembly; (2) an ISCLEs-specific library of possible digital-sized circuit blocks; and (3) a recently-developed multi-topology sizing technique to automatically determine each block's topology and device sizes. ISCLEs is demonstrated on design of a sinusoidal function generator and a flash A/D converter, showing promise to robustly scale with shrinking process geometries.", notes = "also known as \cite{4681604}", } @Article{Shuhua_Gao:Cybernetics, author = "Shuhua Gao and Changkai Sun and Cheng Xiang and Kairong Qin and Tong Heng Lee", title = "Learning Asynchronous Boolean Networks From Single-Cell Data Using Multiobjective Cooperative Genetic Programming", journal = "IEEE Transactions on Cybernetics", year = "2022", volume = "52", number = "5", pages = "2916--2930", keywords = "genetic algorithms, genetic programming", ISSN = "2168-2275", DOI = "doi:10.1109/TCYB.2020.3022430", abstract = "Recent advances in high-throughput single-cell technologies provide new opportunities for computational modeling of gene regulatory networks (GRNs) with an unprecedented amount of gene expression data. Current studies on the Boolean network (BN) modeling of GRNs mostly depend on bulk time-series data and focus on the synchronous update scheme due to its computational simplicity and tractability. However, such synchrony is a strong and rarely biologically realistic assumption. In this study, we adopt the asynchronous update scheme instead and propose a novel framework called SgpNet to infer asynchronous BNs from single-cell data by formulating it into a multiobjective optimization problem. SgpNet aims to find BNs that can match the asynchronous state transition graph (STG) extracted from single-cell data and retain the sparsity of GRNs. To search the huge solution space efficiently, we encode each Boolean function as a tree in genetic programming and evolve all functions of a network simultaneously via cooperative coevolution. Besides, we develop a regulator preselection strategy in view of GRN sparsity to further enhance learning efficiency. An error threshold estimation heuristic is also proposed to ease tedious parameter tuning. SgpNet is compared with the state-of-the-art method on both synthetic data and experimental single-cell data. Results show that SgpNet achieves comparable inference accuracy, while it has far fewer parameters and eliminates artificial restrictions on the Boolean function structures. Furthermore, SgpNet can potentially scale to large networks via straightforward parallelization on multiple cores.", notes = "Also known as \cite{9216610}", } @Article{Gao:2017:GPEM, author = "Shujun Gao and Clarence W. {de Silva}", title = "A univariate marginal distribution algorithm based on extreme elitism and its application to the robotic inverse displacement problem", journal = "Genetic Programming and Evolvable Machines", year = "2017", volume = "18", number = "3", pages = "283--312", month = sep, keywords = "genetic algorithms, Univariate marginal distribution algorithm, Inverse displacement problem, Top best solutions, Gaussian model, Differential evolution algorithm", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-017-9298-8", abstract = "In this paper, a univariate marginal distribution algorithm in continuous domain (UMDA based on extreme elitism (EEUMDA proposed for solving the inverse displacement problem (IDP) of robotic manipulators. This algorithm highlights the effect of a few top best solutions to form a primary evolution direction and obtains a fast convergence rate. Then it is implemented to determine the IDP of a 4-degree-of-freedom (DOF) Barrett WAM robotic arm. After that, the algorithm is combined with differential evolution (EEUMDA-DE) to solve the IDP of a 7-DOF Barrett WAM robotic arm. In addition, three other heuristic optimization algorithms (enhanced leader particle swarm optimization, intersect mutation differential evolution, and evolution strategies) are applied to find the IDP solution of the 7-DOF arm and their performance is compared with that of EEUMDA-DE.", notes = "Not GP?", } @Article{GAO:2019:JAR, author = "Wei Gao and Xin Chen and Dongliang Chen", title = "Genetic programming approach for predicting service life of tunnel structures subject to chloride-induced corrosion", journal = "Journal of Advanced Research", volume = "20", pages = "141--152", year = "2019", ISSN = "2090-1232", DOI = "doi:10.1016/j.jare.2019.07.001", URL = "http://www.sciencedirect.com/science/article/pii/S2090123219301341", keywords = "genetic algorithms, genetic programming, Chloride-induced corrosion, Tunnel structure, Service life, Prediction, Data-driven method", abstract = "A new method for predicting the service life of tunnel structures subject to chloride-induced corrosion using data from real engineering examples and genetic programming (GP) is proposed. As a data-driven method, the new approach can construct explicit expressions of the prediction model. The new method was verified by comparing it with the chloride-ion diffusion model considering eight corrosion influence factors. Moreover, 25 datasets collected from tunnel engineering examples were used to construct the new prediction model considering 17 corrosion influence factors belonged to just one classification of engineering corrosion factors. In addition, the performance of the new model was verified through a comparative study with an artificial neural network (ANN) model which is frequently used in chloride-induced corrosion prediction for reinforced concrete structures. The comparison revealed that both the computational result and efficiency of the GP method were significantly better than those of the ANN model. Finally, to comprehensively analyze the new prediction model, the effects of the two main controlling parameters (population size and sample size) were analyzed. The results indicated that as both the population size and the sample size increased, their effect on the computation error decreased, and their optimal values were suggested as 300 and 20, respectively", } @Article{GAO:2019:SE, author = "Wei Gao2 and Hossein Moayedi and Amin Shahsavar", title = "The feasibility of genetic programming and {ANFIS} in prediction energetic performance of a building integrated photovoltaic thermal ({BIPVT)} system", journal = "Solar Energy", volume = "183", pages = "293--305", year = "2019", ISSN = "0038-092X", DOI = "doi:10.1016/j.solener.2019.03.016", URL = "http://www.sciencedirect.com/science/article/pii/S0038092X19302336", keywords = "genetic algorithms, genetic programming, Building integrated photovoltaic/thermal (BIPVT), ANN, ANFIS, Optimization algorithm, Energetic performance", abstract = "The main motivation of this study is to evaluate and compare the efficacy of three computational intelligence approaches, namely artificial neural network (ANN), genetic programming (GP), and adaptive neuro-fuzzy inference system (ANFIS) in predicting the energetic performance of a building integrated photovoltaic thermal (BIPVT) system. This system is capable of cooling PV panels by ventilation/exhaust air in winter/summer and generating electricity. A performance evaluation criterion (PEC) is defined in this study to examine the overall performance of the considered BIPVT system. Then, the mentioned methods are used to identify a relationship between the input and output parameters of the system. The parameter PEC is considered as the essential output of the system, while the input parameters are the length, width, and depth of the duct underneath the PV panels and air mass flow rate. To evaluate the accuracy of produced outputs, two statistical indices of R2 and RMSE are used. As a result, all models presented excellent performance where the ANN model could slightly perform better performance compared to GP and ANFIS. Finally, the equations belonging to ANN and GP models are derived, and the GP presents a more suitable formula, due to its simplicity of use, simplicity of concept, and robustness", } @InProceedings{Gao:2006:icirs, author = "Xueshan Gao and Koki Kikuchi and Xiaobing Wu and Katsuya Kanai and Keisuke Somiya", title = "Study on the Symmetry of Evolutionary Robotic System", booktitle = "2006 IEEE/RSJ International Conference on Intelligent Robots and Systems", year = "2006", pages = "1638--1643", address = "Beijing", month = oct, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", ISBN = "1-4244-0259-X", DOI = "doi:10.1109/IROS.2006.282055", abstract = "This paper deals with the concept that effective robotic function emerges from intelligence and the balance between morphology and intelligence, the morphology and intelligence of the robot are represented respectively. Both them are automatically generated and evolved by genetic programming for a task of maintaining a certain distance between the robot and an object. And then evolutionary simulation and two experiments are performed. Furthermore, the symmetry properties which have two phases and emerge are discussed", notes = "Sch. of Mechatronic Eng., Beijing Inst. of Technol.", } @InProceedings{Gao:2019:GECCO, author = "Zheng Gao and Chun Guo and Xiaozhong Liu", title = "Efficient personalized community detection via genetic evolution", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference", year = "2019", editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and Anne Auger and Petr Posik and Leslie {Peprez Caceres} and Andrew M. Sutton and Nadarajen Veerapen and Christine Solnon and Andries Engelbrecht and Stephane Doncieux and Sebastian Risi and Penousal Machado and Vanessa Volz and Christian Blum and Francisco Chicano and Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and Jonathan Fieldsend and Jose Antonio Lozano and Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and Robin Purshouse and Thomas Baeck and Justyna Petke and Giuliano Antoniol and Johannes Lengler and Per Kristian Lehre", isbn13 = "978-1-4503-6111-8", pages = "383--391", address = "Prague, Czech Republic", DOI = "doi:10.1145/3321707.3321711", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, Personalized community detection, Graph mining, Network analysis", size = "9 pages", abstract = "Personalized community detection aims to generate communities associated with user need on graphs, which benefits many downstream tasks such as node recommendation and link prediction for users, etc. It is of great importance but lack of enough attention in previous studies which are on topics of user-independent, semi-supervised, or top-K user-centric community detection. Meanwhile, most of their models are time consuming due to the complex graph structure. Different from these topics, personalized community detection requires to provide higher-resolution partition on nodes that are more relevant to user need while coarser manner partition on the remaining less relevant nodes. In this paper, to solve this task in an efficient way, we propose a genetic model including an off-line and an on-line step. In the offline step, the user-independent community structure is encoded as a binary tree. And subsequently an online genetic pruning step is applied to partition the tree into communities. To accelerate the speed, we also deploy a distributed version of our model to run under parallel environment. Extensive experiments on multiple datasets show that our model outperforms the state-of-arts with significantly reduced running time.", notes = "Also known as \cite{3321711} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @Article{GARBRECHT:2023:jmps, author = "Karl Garbrecht and Donovan Birky and Brian Lester and John Emery and Jacob Hochhalter", title = "Complementing a continuum thermodynamic approach to constitutive modeling with symbolic regression", year = "2023", journal = "Journal of the Mechanics and Physics of Solids", volume = "181", pages = "105472", keywords = "genetic algorithms, genetic programming, Physics-informed machine learning, Constitutive modeling, Symbolic regression, Thermodynamics, Porous material, Multi-tree GPSR, Multi-objective optimization", ISSN = "0022-5096", URL = "https://www.sciencedirect.com/science/article/pii/S0022509623002764", DOI = "doi:10.1016/j.jmps.2023.105472", abstract = "An interpretable machine learning method, physics-informed genetic programming-based symbolic regression (P-GPSR), is integrated into a continuum thermodynamic approach to developing constitutive models. The proposed strategy for combining a thermodynamic analysis with P-GPSR is demonstrated by generating a yield function for an idealized material with voids, i.e., the Gurson yield function. First, a thermodynamic-based analysis is used to derive model requirements that are exploited in a custom P-GPSR implementation as fitness criteria or are strongly enforced in the solution. The P-GPSR implementation improved accuracy, generalizability, and training time compared to the same GPSR code without physics-informed fitness criteria. The yield function generated through the P-GPSR framework is in the form of a composite function that describes a class of materials and is characteristically more interpretable than GPSR-derived equations. The physical significance of the input functions learned by P-GPSR within the composite function is acquired from the thermodynamic analysis. Fundamental explanations of why the implemented P-GPSR capabilities improve results over a conventional GPSR algorithm are provided", } @PhdThesis{Garbrecht:thesis, author = "Karl Michael Garbrecht", title = "A physics-informed machine learning framework for constitutive model development", school = "Department of Mechanical Engineering, The University of Utah", year = "2024", address = "USA", month = may, keywords = "genetic algorithms, genetic programming", URL = "https://www.proquest.com/dissertations-theses/physics-informed-machine-learning-framework/docview/3112739837/se-2", size = "185 pages", abstract = "This work aims to advance machine learning-based homogenization methods for development of constitutive equations (e.g., equations relating stress and strain that are used in constitutive models). The macroscopic constitutive behavior (i.e., the mechanical stress-strain response) of a material volume element is governed by the cumulative contributions of the local constitutive behavior within that volume element. One approach to incorporating local considerations into higher length scale constitutive equations is micro-mechanical analysis. This work presents novel methods for micromechanical analysis that combine data-driven and analytical methods. Specifically, the data-driven methods consist of a physics-informed genetic programming based symbolic regression (P-GPSR) framework developed herein. The framework is such that P-GPSR is applied to address analytically intractable considerations to produce free-form data-driven equations that are embedded within what would other otherwise be analytical derivations. The capability to strongly enforce known terms in a P-GPSR produced solution reduced the search space and resulted in the data-driven equations (i.e., place holder functions) being constrained precisely where needed. In combination with tailored data sets and solution requirements, theoretical consistency and the a priori physical significance of the data-driven components can be ensured. Therefore, the P-GPSR framework is suitable for use in scientific and engineering applications. Overall, the P-GPSR framework advanced the state of P-ML by introducing a comprehensive method for identifying and implementing solution requirements for the development of accurate, theoretically consistent, and transparent data-driven models.", notes = "Order No. 31149397. Available from ProQuest Dissertations and Theses Global", } @Article{Garbrecht:2024:mechmat, author = "Karl Garbrecht and Andrea Rovinelli and Jacob Hochhalter and Paul Christodoulou and Ricardo A. Lebensohn and Laurent Capolungo", title = "Modeling plasticity-mediated void growth at the single crystal scale: A physics-informed machine learning approach", journal = "Mechanics of Materials", year = "2024", volume = "199", pages = "105151", keywords = "genetic algorithms, genetic programming, Gauge function, Ductile damage, Void interactions, Symbolic regression", ISSN = "0167-6636", URL = "https://www.sciencedirect.com/science/article/pii/S0167663624002436", DOI = "doi:10.1016/j.mechmat.2024.105151", abstract = "Modeling the evolution of voids during plastic flow as well as their effects on plastic dissipation is critical for both component manufacturing and lifetime estimation purposes. To this end, we propose a rate-dependent constitutive model to homogenize the effects of semi-randomly distributed voids on single crystal plasticity whilst capturing void interaction and plastic anisotropy. The present work focuses on the case of face centered cubic crystals to introduce an anisotropic gauge function applicable within the crystal plasticity formalism. The approach combines analytical methods to describe the micromechanics of the system in combination with symbolic regression to capture analytically intractable mechanisms from data. The hybrid framework uses a physics-informed genetic programming-based symbolic regression algorithm to solve a multiform optimisation problem simultaneously producing a new gauge function and a new strain rate equation. This is also a multi-objective optimisation problem with many competing objectives. A new search and selection step is introduced to the genetic algorithm that promotes convergence toward a global solution that better satisfies all the objectives. Overall, the symbolic equations produced leverage data-driven methods to achieve greater accuracy than comparable alternatives on an analytically intractable problem while maintaining model transparency", } @InProceedings{garces-perez:1996:sflp, author = "Jaime Garces-Perez and Dale A. Schoenefeld and Roger L. Wainwright", title = "Solving Facility Layout Problems Using Genetic Programming", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "182--190", address = "Stanford University, CA, USA", publisher = "MIT Press", isbn13 = "9780262315876", URL = "http://euler.utulsa.edu/~rogerw/papers/Garces-Perez-flp.pdf", broken = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap22.pdf", broken = "http://cognet.mit.edu/library/books/view?isbn=0262611279", DOI = "doi:10.7551/mitpress/3242.003.0025", size = "9 pages", abstract = "This research applies techniques and tools from Genetic Programming GP to the facility layout problem The facility layout problem FLP is an NP-complete combinatorial optimisation problem that has applications to efficient facility design for manufacturing and service industries. A facility layout is represented as a collection of rectangular blocks using a slicing tree structure (STS) We use a multiple purpose genetic programming kernel to generate slicing trees that are converted into candidate solutions for an FLP The utility of our techniques is established using eight previously published benchmark problems Our genetic programming techniques that evolve STSs are more natural and more flexible than all of the previously published genetic algorithm and simulated annealing techniques Previous genetic algorithm techniques use a twophase optimisation strategy The first phase uses clustering techniques to determine a near optimal fixed tree structure that is represented as a chromosome in a genetic algo rithm Within the constraints implied by the fixed tree structure genetic algorithm techniques are applied during the second phase to optimise the placement of facilities in relation to each other Our genetic programming technique is a single phase global optimization strategy using an un constrained tree structure This yields superior results", notes = "GP-96", } @PhdThesis{Garces-Perez:thesis, author = "Jaime Garces-Perez", title = "A Genetic programming approach to the facility location problem", school = "University of Tulsa", year = "1996", address = "USA", keywords = "genetic algorithms, genetic programming, Facility management", URL = "https://books.google.co.uk/books?id=60_H0AEACAAJ", notes = "garces1996genetic", } @InProceedings{Garcia:2008:gecco, author = "Beatriz Garcia and Ricardo Aler and Agapito Ledezma and Araceli Sanchis", title = "Protein-protein functional association prediction using genetic programming", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "347--348", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p347.pdf", DOI = "doi:10.1145/1389095.1389156", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, abstract = "Determining if a group of proteins are functionally associated among themselves is an open problem in molecular biology. Within our long term goal of applying Genetic Programming (GP) to this domain, this paper evaluates the feasibility of GP to predict if a given pair of proteins interacts. GP has been chosen because of its potential flexibility in many aspects, such as the definition of operations. In this paper, the if-unknown operation is defined, which semantically is the most appropriate in this domain for handling missing values. We have also used the Tarpeian bloat control method to decrease the computational time and the solution size. Our results show that GP is feasible for this domain and that the Tarpeian method can obtain large improvements in search efficiency and interpretability of solutions.", keywords = "genetic algorithms, genetic programming, bioinformatics, classifier systems, control bloat, data integration, evolutionary computation, machine learning, protein interaction prediction, computational biology: Poster", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389156} E.coli. Training on subsets 5000 positive and 5000 negatives. Missing values if_? if(unknown) then else. p348 GP about as accruate as other machine learning. ADTree KODE KStar MLP PART Simple Logistic SMO. Tarpeian bloat control \cite{poli03} effective. PhD http://hdl.handle.net/10016/15508 Universidad Carlos III de Madrid. Departamento de Informatica 'Anotacion funcional de proteinas basada en representacion relacional en el entorno de la biologia de sistemas'", } @InProceedings{DBLP:conf/iberamia/GarciaALS08, author = "Beatriz Garcia and Ricardo Aler and Agapito Ledezma and Araceli Sanchis", title = "Genetic Programming for Predicting Protein Networks", booktitle = "Proceedings of the 11th Ibero-American Conference on AI, IBERAMIA 2008", year = "2008", editor = "Hector Geffner and Rui Prada and Isabel Machado Alexandre and Nuno David", volume = "5290", series = "Lecture Notes in Computer Science", pages = "432--441", address = "Lisbon, Portugal", month = oct # " 14-17", publisher = "Springer", note = "Advances in Artificial Intelligence", keywords = "genetic algorithms, genetic programming, Protein interaction prediction, data integration, bioinformatics, evolutionary computation, machine learning, classification, control bloat", isbn13 = "978-3-540-88308-1", URL = "http://www.caos.inf.uc3m.es/~beatriz/papers/garcia_et.al._iberamia08-paper_InPress.pdf", DOI = "doi:10.1007/978-3-540-88309-8_44", size = "10 pages", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "One of the definitely unsolved main problems in molecular biology is the protein-protein functional association prediction problem. Genetic Programming (GP) is applied to this domain. GP evolves an expression, equivalent to a binary classifier, which predicts if a given pair of proteins interacts. We take advantages of GP flexibility, particularly, the possibility of defining new operations. In this paper, the missing values problem benefits from the definition of if-unknown, a new operation which is more appropriate to the domain data semantics. Besides, in order to improve the solution size and the computational time, we use the Tarpeian method which controls the bloat effect of GP. According to the obtained results, we have verified the feasibility of using GP in this domain, and the enhancement in the search efficiency and interpretability of solutions due to the Tarpeian method.", notes = "lilgp. BIND, DIP, Butland, IntAct, EcoCyc, KEGG, iHoP. P436 Training 'instances is reduced to 264,752' Actually 10000 used for training. Function set: +, -, * and protected division, if, if_?. FS 'closed always returning the unknown value ? if any of their input values is ?'. Comparison with WEKA.", } @Article{garcia:2023:Sensors, author = "Carlos A. Garcia and Manel Velasco and Cecilio Angulo and Pau Marti and Antonio Camacho", title = "Revisiting Classical Controller Design and Tuning with Genetic Programming", journal = "Sensors", year = "2023", volume = "23", number = "24", pages = "Article No. 9731", keywords = "genetic algorithms, genetic programming", ISSN = "1424-8220", URL = "https://www.mdpi.com/1424-8220/23/24/9731", DOI = "doi:10.3390/s23249731", abstract = "This paper introduces the application of a genetic programming (GP)-based method for the automated design and tuning of process controllers, representing a noteworthy advancement in artificial intelligence (AI) within the realm of control engineering. In contrast to already existing work, our GP-based approach operates exclusively in the time domain, incorporating differential operations such as derivatives and integrals without necessitating intermediate inverse Laplace transformations. This unique feature not only simplifies the design process but also ensures the practical implementability of the generated controllers within physical systems. Notably, the GP's functional set extends beyond basic arithmetic operators to include a rich repertoire of mathematical operations, encompassing trigonometric, exponential, and logarithmic functions. This broad set of operations enhances the flexibility and adaptability of the GP-based approach in controller design. To rigorously assess the efficacy of our GP-based approach, we conducted an extensive series of tests to determine its limits and capabilities. In summary, our research establishes the GP-based approach as a promising solution for automating the controller design process, offering a transformative tool to address a spectrum of control problems across various engineering applications.", notes = "also known as \cite{s23249731}", } @Misc{oai:cds.cern.ch:2255146, author = "Daniel Lanza Garcia", title = "Applying natural evolution for solving computational problems - Lecture 2", booktitle = "Inverted CERN School of Computing 2017", year = "2017", month = "8 " # mar, keywords = "genetic algorithms, genetic programming, inverted csc", bibsource = "OAI-PMH server at cds.cern.ch", identifier = "oai:cds.cern.ch:2255146", language = "eng", oai = "oai:cds.cern.ch:2255146", URL = "https://cds.cern.ch/record/2255146", video_url = "http://cds.cern.ch/record/2255146", size = "54 minutes", abstract = "Darwin's natural evolution theory has inspired computer scientists for solving computational problems. In a similar way to how humans and animals have evolved along millions of years, computational problems can be solved by evolving a population of solutions through generations until a good solution is found. In the first lecture, the fundaments of evolutionary computing (EC) will be described, covering the different phases that the evolutionary process implies. ECJ, a framework for researching in such field, will be also explained. In the second lecture, genetic programming (GP) will be covered. GP is a sub-field of EC where solutions are actual computational programs represented by trees. Bloat control and distributed evaluation will be introduced.", notes = "2017-03-08. - Streaming video 0:53:34, Lanza Garcia, Daniel (speaker) (CERN, Switzerland)", } @InProceedings{Garcia:2017:GECCO, author = "Dennis Garcia and Anthony Erb Lugo and Erik Hemberg and Una-May O'Reilly", title = "Investigating Coevolutionary Archive Based Genetic Algorithms on Cyber Defense Networks", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion", series = "GECCO '17", year = "2017", month = "15-19 " # jul, isbn13 = "978-1-4503-4939-0", address = "Berlin, Germany", pages = "1455--1462", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, coevolution, cybersecurity, evolutionary algorithms, network", URL = "http://doi.acm.org/10.1145/3067695.3076081", DOI = "doi:10.1145/3067695.3076081", acmid = "3076081", size = "8 pages", abstract = "We introduce a new cybersecurity project named RIVALS. RIVALS will assist in developing network defence strategies through modelling adversarial network attack and defense dynamics. RIVALS will focus on peer-to-peer networks and use coevolutionary algorithms. In this contribution, we describe RIVALS' current suite of coevolutionary algorithms that use archiving to maintain progressive exploration and that support different solution concepts as fitness metrics. We compare and contrast their effectiveness by executing a standard coevolutionary benchmark (Compare-on-one) and RIVALS simulations on 3 different network topologies. Currently, we model denial of service (DOS) attack strategies by the attacker selecting one or more network servers to disable for some duration. Defenders can choose one of three different network routing protocols: shortest path, flooding and a peer-to-peer ring overlay to try to maintain their performance. Attack completion and resource cost minimization serve as attacker objectives. Mission completion and resource cost minimization are the reciprocal defender objectives. Our experiments show that existing algorithms either sacrifice execution speed or forgo the assurance of consistent results. rIPCA, our adaptation of a known coevolutionary algorithm named IPC A, is able to more consistently produce high quality results, albeit without IPCA's guarantees for results with monotonically increasing performance, without sacrificing speed.", notes = "See also https://dspace.mit.edu/handle/1721.1/112841 Also known as \cite{Garcia:2017:ICA:3067695.3076081} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @InCollection{garcia:2000:EMFFASGA, author = "Guillermo Garcia", title = "Estimation of Multiple Fundamental Frequencies in Audio Signals using a Genetic Algorithm", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "153--159", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms", size = "7 pages", notes = "part of \cite{koza:2000:gagp}", } @InProceedings{oai:CiteSeerPSU:454347, author = "Ricardo A. Garcia", title = "Towards the Automatic Generation of Sound Synthesis Techniques: Preparatory Steps", booktitle = "AES 109th Convention", year = "2000", address = "Los Angeles", month = "22-25 " # sep, organisation = "Audio Engineering Society", keywords = "genetic algorithms, genetic programming", URL = "http://www.ragomusic.com/publications/ragoAES2000.pdf", URL = "http://citeseer.ist.psu.edu/454347.html", citeseer-isreferencedby = "oai:CiteSeerPSU:66836", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:454347", rights = "unrestricted", size = "7 pages", abstract = "An overview of an algorithm that searches through the space of the sound synthesis techniques is presented. A modular approach to construct sound synthesis techniques is introduced. The preparatory steps needed to use genetic programming as a search tool for this space are explained, focusing in the manipulation and evaluation of the modular descriptions of the topologies.", notes = "http://www.aes.org/events/109/", } @InProceedings{oai:CiteSeerPSU:569030, author = "Ricardo A. Garcia", title = "Automating The Design Of Sound Synthesis Techniques Using Evolutionary Methods", booktitle = "Proceedings of the COST G-6 Conference on Digital Audio Effects (DAFX-01)", year = "2001", editor = "Mikael Fernstrom", address = "Limerick, Ireland", month = dec # " 6-8", keywords = "genetic algorithms, genetic programming", citeseer-isreferencedby = "oai:CiteSeerPSU:97342", citeseer-references = "oai:CiteSeerPSU:32686; oai:CiteSeerPSU:286517", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:569030", rights = "unrestricted", URL = "http://www.csis.ul.ie/dafx01/proceedings/navig/../papers/garcia.pdf", URL = "http://citeseer.ist.psu.edu/569030.html", abstract = "Digital sound synthesizers, ubiquitous today in sound cards, software and dedicated hardware, use algorithms (Sound Synthesis Techniques, SSTs) capable of generating sounds similar to those of acoustic instruments and even totally novel sounds. The design of SSTs is a very hard problem. It is usually assumed that it requires human ingenuity to design an algorithm suitable for synthesizing a sound with certain characteristics. Many of the SSTs commonly used are the fruit of experimentation and a long refinement processes. A SST is determined by its functional form and internal parameters. Design of SSTs is usually done by selecting a fixed functional form from a handful of commonly used SSTs, and performing a parameter estimation technique to find a set of internal parameters that will best emulate the target sound. A new approach for automating the design of SSTs is proposed. It uses a set of examples of the desired behavior of the SST in the form of inputs + target sound. The approach is capable of suggesting novel functional forms and their internal parameters, suited to follow closely the given examples. Design of a SST is stated as a search problem in the SST space (the space spanned by all the possible valid functional forms and internal parameters, within certain limits to make it practical). This search is done using evolutionary methods; specifically, Genetic Programming (GP).", notes = "http://www.csis.ul.ie/dafx01/programme.html", } @InProceedings{oai:RePEc:sce:scecfa:489, author = "Alma Lilia Garcia-Almanza and Edward P. K. Tsang", title = "Forecasting stock prices using Genetic Programming and Chance Discovery", booktitle = "12th International Conference On Computing In Economics And Finance", year = "2006", pages = "number 489", month = jul, organisation = "Society for Computational Economics", bibsource = "OAI-PMH server at oai.repec.openlib.org", description = "Forecasting, Chance discovery, Genetic programming, machine learning", identifier = "RePEc:sce:scecfa:489", oai = "oai:RePEc:sce:scecfa:489", keywords = "genetic algorithms, genetic programming", URL = "http://repec.org/sce2006/up.13879.1141401469.pdf", URL = "http://privatewww.essex.ac.uk/~algarc/Publications/CEF2006.pdf", URL = "http://ideas.repec.org/p/sce/scecfa/489.html", abstract = "In recent years the computers have shown to be a powerful tool in financial forecasting. Many machine learning techniques have been used to predict movements in financial markets. Machine learning classifiers involve extending the past experiences into the future. However the rareness of some events makes difficult to create a model that detect them. For example bubbles burst and crashes are rare cases, however their detection is crucial since they have a significant impact on the investment. One of the main problems for any machine learning classifier is to deal with unbalanced classes. Specifically Genetic Programming has limitation to deal with unbalanced environments. In a previous work we described the Repository Method, it is a technique that analyses decision trees produced by Genetic Programming to discover classification rules. The aim of that work was to forecast future opportunities in financial stock markets on situations where positive instances are rare. The objective is to extract and collect different rules that classify the positive cases. It lets model the rare instances in different ways, increasing the possibility of identifying similar cases in the future. The objective of the present work is to find out the factors that work in favour of Repository Method, for that purpose a series of experiments was performed.", notes = "CEF 2006", } @InProceedings{Garcia-Almanza_2006_CEC, author = "Alma Lilia Garcia-Almanza and Edward P. K. Tsang", title = "Simplifying Decision Trees Learned by Genetic Programming", booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary Computation", year = "2006", pages = "7906--7912", address = "Vancouver", month = "6-21 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9487-9", URL = "http://privatewww.essex.ac.uk/~algarc/Publications/WCCI2006.pdf", DOI = "doi:10.1109/CEC.2006.1688571", size = "7 pages", abstract = "This work is motivated by financial forecasting using Genetic Programming. This paper presents a method to post-process decision trees. The processing procedure is based on the analysis and evaluation of the components of each tree, followed by pruning. The idea behind this approach is to identify and eliminate rules that cause misclassification. As a result we expect to keep and generate rules that enhance the classification. This method was tested on decision trees generated by a genetic program whose aim was to discover classification rules in financial stock markets. From experimental results we can conclude that our method is able to improve the accuracy and precision of the classification.", notes = "also known as \cite{1688571} WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE.", } @InProceedings{Garcia:2006a, author = "Alma L Garcia-Almanza and Edward P. K. Tsang", title = "The Repository Method for Chance Discovery in Financial Forecasting", ISSN = "0302-9743", year = "2006", editor = "Bogdan Gabrys and Robert J. Howlett and Lakhmi C. Jain", series = "Lecture Notes in Computer Science", volume = "4253", booktitle = "KES 2006, Proceedings of the 10th International Conference on Knowledge-Based Intelligent Information and Engineering Systems", pages = "30--37", address = "Bournemouth, UK", month = oct # " 9-11", publisher = "Springer-Verlag", note = "Part III", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-46542-1", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1007/11893011_5", abstract = "The aim of this work is to forecast future opportunities in financial stock markets, in particular, we focus our attention on situations where positive instances are rare, which falls into the domain of Chance Discovery. Machine learning classifiers extend the past experiences into the future. However the imbalance between positive and negative cases poses a serious challenge to machine learning techniques. Because it favours negative classifications, which has a high chance of being correct due to the nature of the data. Genetic Algorithms have the ability to create multiple solutions for a single problem. To exploit this feature we propose to analyse the decision trees created by Genetic Programming. The objective is to extract and collect different rules that classify the positive cases. It lets model the rare instances in different ways, positive cases. It lets model the rare instances in different ways, increasing the possibility of identifying similar cases in the future. To illustrate our approach, it was applied to predict investment opportunities with very high returns. From experiment results we showed that the Repository Method can consistently improve both the recall and the precision.", } @InProceedings{Garcia-Almanza:2007:cec, author = "Alma Lilia Garcia-Almanza and Edward P. K. Tsang", title = "Repository Method to Suit Different Investment Strategies", booktitle = "2007 IEEE Congress on Evolutionary Computation", year = "2007", editor = "Dipti Srinivasan and Lipo Wang", pages = "790--797", address = "Singapore", month = "25-28 " # sep, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "1-4244-1340-0", file = "1986.pdf", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2007.4424551", abstract = "This work is motivated by the interest in finding significant movements in financial stock prices. The detection of such movements is important because these could represent good opportunities for invest. However, when the number of profitable opportunities is very small the prediction of these cases is very difficult.", notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C", } @Article{journals/kes/Garcia-AlmanzaT07, author = "Alma Lilia Garcia-Almanza and Edward P. K. Tsang", title = "Detection of stock price movements using chance discovery and genetic programming", journal = "International Journal of Knowledge-Based and Intelligent Engineering Systems", year = "2007", volume = "11", number = "5", pages = "329--344", publisher = "IOS", keywords = "genetic algorithms, genetic programming", ISSN = "1327-2314", broken = "http://iospress.metapress.com/content/k30kgl00u6r42812/", DOI = "doi:10.3233/KES-2007-11509", size = "16 pages", abstract = "The aim of this work is to detect important movements in financial stock prices that may indicate future opportunities or risks. The occurrence of such movements is scarce, thus this problem falls into the domain of Chance Discovery, a new research area whose objective is to identify rare events that may represent potential opportunities and risks. In this work we propose to capture patterns of the rare instances in different ways in order to increase the probability of identifying similar cases in the future. To generate more variety of solutions we evolve a genetic program, which is an evolutionary technique that is able to create multiple solutions for a single problem. The idea is to mine the knowledge acquired by the evolutionary process to extract and collect different rules that model the positive cases in several and novel ways. Once an important movement in financial markets has been discovered, human interaction is needed to analyze the markets conditions and determine if that movement could be a good opportunity to invest or could be the principle of a bubble or another critical event that represents a risk. Standard decision trees methods capture patterns from training data sets. However, when the chances are scare, some of the patters captured by the best rules may not repeat themselves in unseen cases. In this work we propose Repository Method which comprises multiple rules to form a more reliable classifier in rare cases. To illustrate our approach, it was applied to discover important movements in stock prices. From experimental results we showed that our approach can consistently detect rare cases in extreme imbalanced data sets.", notes = "Nov 2015 http://iospress.metapress.com/content/k30kgl00u6r42812/ broken", bibdate = "2008-08-06", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/kes/kes11.html#Garcia-AlmanzaT07", } @Article{Garcia:2008, author = "Alma Lilia Garcia-Almanza and Edward P. K. Tsang", title = "Evolving Decision Rules to Predict Investment Opportunities", journal = "International Journal of Automation and Computing", year = "2008", volume = "5", number = "1", pages = "22--31", month = jan, keywords = "genetic algorithms, genetic programming, Machine learning, classification, imbalanced classes, evolution of rules", publisher = "Institute of Automation, Chinese Academy of Sciences, co-published with Springer-Verlag GmbH", ISSN = "1476-8186", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.153.2149", DOI = "doi:10.1007/s11633-008-0022-2", size = "10 pages", abstract = "This paper is motivated by the interest in finding significant movements in financial stock prices. However, when the number of profitable opportunities is scarce, the prediction of these cases is difficult. In a previous work, we have introduced evolving decision rules (EDR) to detect financial opportunities. The objective of EDR is to classify the minority class (positive cases) in imbalanced environments. EDR provides a range of classifications to find the best balance between not making mistakes and not missing opportunities. The goals of this paper are: 1) to show that EDR produces a range of solutions to suit the investor's preferences and 2) to analyse the factors that benefit the performance of EDR. A series of experiments was performed. EDR was tested using a data set from the London Financial Market. To analyze the EDR behaviour, another experiment was carried out using three artificial data sets, whose solutions have different levels of complexity. Finally, an illustrative example was provided to show how a bigger collection of rules is able to classify more positive cases in imbalanced data sets. Experimental results show that: 1) EDR offers a range of solutions to fit the risk guidelines of different types of investors, and 2) a bigger collection of rules is able to classify more positive cases in imbalanced environments.", affiliation = "University of Essex Department of Computer Science Wivenhoe Park Colchester CO4 3SQ UK", } @PhdThesis{Garcia-Almanza:thesis, author = "Alma Lilia {Garcia Almanza}", title = "New Classification Methods for Gathering Patterns in the Context of Genetic Programming", school = "Department of Computing and Electronic Systems, University of Essex", year = "2008", address = "Colchester, UK", month = jul, keywords = "genetic algorithms, genetic programming", URL = "http://www.bracil.net/finance/papers/Garcia-PhD2008.pdf", size = "244 pages", abstract = "Machine learning techniques extend the past experiences into the future. However, when the number of examples in the minority class (positive cases) is very small in comparison with the remaining classes, it poses a serious challenge to the machine learning [63],[119],[5],[81]. In this kind of problems, the prediction of the majority class is favoured because it has a high chance of being correct. This characteristic is present in many real-world problems, whose objective is to classify the minority class in imbalanced data sets. However, a prediction that detects more positive cases may be paid for with more false alarms. It is important to determine a balance between the detection of positive cases and false alarms. A range of classifications would give users the option to choose the best tradeoff between detecting positive cases and false alarms according to their requirements. On the other hand, we consider it is important to provide a comprehensive solution, which shows the real variables and conditions in the prediction. Thus, the users could combine their knowledge in order to make a more informed decision. In this thesis, we present three novel approaches: Repository Method (RM), Evolving Decision Rules (EDR) and Scenario Method (SM). We use Genetic Programming (GP) and supervised learning to build the methods proposed in this thesis. The main objectives of RM and EDR are: to predict the minority class in imbalanced environments, to generate a range of solutions to suit different users' preferences and to provide an comprehensible solution for the user. On the other hand, SM has been designed to improve the precision and accuracy of the prediction. However, such improvement is paid for with a decrease in the recall. But, the users have to make the decision of which of these parameters is more adequate to satisfy their needs. This work is illustrated predicting future opportunities in financial stock markets. Experiments of our methods were carried out, and these showed promising results for achieving our goals. RM and EDR were compared to a standard Genetic Programming, EDDIE-Arb and C5.0. The methods presented in this thesis can also be used in other fields of knowledge, these should not be limited to financial forecasting problems.", } @InProceedings{Garcia-Almanza:2010:CERMA, author = "Alma Lilia Garcia-Almanza and Biliana Alexandrova-Kabadjova and Serafin Martinez-Jaramillo", title = "Understanding Bank Failure: A Close Examination of Rules Created by Genetic Programming", booktitle = "Electronics, Robotics and Automotive Mechanics Conference (CERMA), 2010", year = "2010", month = "28 " # sep # "-" # oct # " 1", pages = "34--39", abstract = "This paper presents a novel method to predict bankruptcy, using a Genetic Programming (GP) based approach called Evolving Decision Rules (EDR). In order to obtain the optimum parameters of the classifying mechanism, we use a data set, obtained from the US Federal Deposit Insurance Corporation (FDIC). The set consists of limited financial institutions' data, presented as variables widely used to detect bank failure. The outcome is a set of comprehensible decision rules, which allows to identify cases of bankruptcy. Further, the reliability of those rules is measured in terms of the true and false positive rate, calculated over the whole data set and plot over the Receiving Operating Characteristic (ROC) space. In order to test the accuracy performance of the mechanism, we elaborate two experiments: the first, aimed to test the degree of the variables' usefulness, provides a quantitative and a qualitative analysis. The second experiment completed over 1000 different re-sampled cases is used to measure the performance of the approach. To our knowledge this is the first computational technique in this field able to give useful insights of the method's predictive structure. The main contributions of this work are three: first, we want to bring to the arena of bankruptcy prediction a competitive novel method which in pure performance terms is comparable to state of the art methods recently proposed in similar works, second, this method provides the additional advantage of transparency as the generated rules are fully interpretable in terms of simple financial ratios, third and final, the proposed method includes cutting edge techniques to handle highly unbalanced samples, something that is very common in bankruptcy applications.", keywords = "genetic algorithms, genetic programming, bank failure detection, bankruptcy prediction, data set, evolving decision rules, financial ratio, receiving operating characteristic space, banking, sensitivity analysis", DOI = "doi:10.1109/CERMA.2010.14", notes = "Also known as \cite{5692308}", } @Book{Garcia-Almanza:book, author = "Alma Lilia {Garcia Almanza} and Edward Tsang", title = "Evolutionary Applications for Financial Prediction: Classification Methods to Gather Patterns Using Genetic Programming", publisher = "VDM Verlag Dr. Muller", year = "2011", address = "Saarbrucken, Germany", keywords = "genetic algorithms, genetic programming", ISBN = "3-639-30767-4", URL = "http://www.bracil.net/finance/GarciaTsang-book2011/", URL = "http://www.amazon.com/Evolutionary-Applications-Financial-Prediction-Classification/dp/3639307674/ref=sr_1_1?ie=UTF8&qid=1305383401&sr=8-1", abstract = "This book presents three applications, based on Machine Learning and Genetic Programming, which are devoted to find useful patterns to predict future events. The objective is to train the algorithms by using past data to produce a classifier that identifies the positive cases and discriminates the false alarms. This work uses examples for predicting future opportunities in financial stock markets in cases where the number of profitable opportunities is scarce. However, when the number of positive examples is small in comparison with the number of total cases, the identification of useful patterns becomes a serious challenge. Nevertheless, the objective of many real world problems, is precisely to identify the minority class as the fraud detection problem, or medical diagnosis and many other examples. The techniques of this book are suitable to deal with imbalanced data sets, provide comprehensible results that allow users to understand the factors that are involved in the decision, as well as to generate a range of solutions that let the user choose the best trade off according to their risk preferences.", notes = "Reviewed by \cite{LeBaron:2012:GPEM}", size = "172 pages", } @InCollection{Garcia-Almanza:2011:Yap, author = "Alma Lilia {Garcia Almanza} and Serafin {Martinez Jaramillo} and Biliana Alexandrova-Kabadjova and Edward Tsang", title = "Using Genetic Programming Systems as Early Warning to Prevent Bank Failure", booktitle = "Information Systems for Global Financial Markets: Emerging Developments and Effects", publisher = "IGI global", year = "2011", editor = "Alexander Y. Yap", chapter = "14", pages = "369--382", month = nov, keywords = "genetic algorithms, genetic programming", ISBN = "1-61350-162-5", URL = "http://www.amazon.com/Information-Systems-Global-Financial-Markets/dp/1613501625", DOI = "doi:10.4018/978-1-61350-162-7.ch014", abstract = "Corporate bankruptcy has been always an active area of financial research. Furthermore, after the Lehman Brothers' default and its consequences on the global financial system, this topic has attracted even more attention from regulators and researchers. This event has brought an imperious urge to change the regulatory framework regardless of whether this is good or bad. Consequently, the need for timely signals for supervisory actions and the development of tools that help to determine which financial information is more relevant to predict distress is very important. During crisis periods the bankruptcy of a bank or a group of banks can make things far worse if contagion effects are transmitted first to other participants of the financial system and then to the real economy. In a previous work, developed by Garcia et al. (2010), an evolutionary technique named Evolving Decision Rules (EDR) was used to identify patterns in data from the Federal Deposit Insurance Corporation (FDIC) for generating a set of comprehensible rules, which were able to predict bank bankruptcy. The major contribution of that work was to show a series of decision rules constituted by simple financial ratios, despite that the method is not restricted to the use of such type of information. The main advantage of creating understandable rules is that users are able to interpret and identify the events that may trigger bankruptcy. By using the method that we propose in this work, it is possible to identify when certain financial indicators are getting close to specific thresholds, something that can turn into an undesirable situation. This is particularly relevant if the companies we are referring to are banks. The contribution of this chapter is to improve the prediction by means of a multi-population approach. The experimental results were evaluated using the Receiver Operating Characteristic (ROC) described in Fawcett and Provost (1997). We show that our approach could improve the Area Under the ROC Curve in 5percent with respect to the same method proposed in Garcia et al. (2010). Additionally, a series of experiments were performed in order to find out the reasons of success of the EDR", } @Article{Garcia-Arnau:2007:KBS, author = "M. Garcia-Arnau and D. Manrique and J. Rios and A. Rodriguez-Paton", title = "Initialization method for grammar-guided genetic programming", journal = "Knowledge-Based Systems", year = "2007", volume = "20", number = "2", pages = "127--133", month = mar, note = "AI 2006, The 26th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence", keywords = "genetic algorithms, genetic programming, Grammar-guided genetic programming, Initialisation method, Tree-generation algorithm, Breast cancer prognosis, GGGP", DOI = "doi:10.1016/j.knosys.2006.11.006", abstract = "This paper proposes a new tree-generation algorithm for grammar-guided genetic programming that includes a parameter to control the maximum size of the trees to be generated. An important feature of this algorithm is that the initial populations generated are adequately distributed in terms of tree size and distribution within the search space. Consequently, genetic programming systems starting from the initial populations generated by the proposed method have a higher convergence speed. Two different problems have been chosen to carry out the experiments: a laboratory test involving searching for arithmetical equalities and the real-world task of breast cancer prognosis. In both problems, comparisons have been made to another five important initialisation methods.", } @Article{Garcia-Capulin:2015:GPEM, author = "C. H. Garcia-Capulin and F. J. Cuevas and G. Trejo-Caballero and H. Rostro-Gonzalez", title = "A hierarchical genetic algorithm approach for curve fitting with B-splines", journal = "Genetic Programming and Evolvable Machines", year = "2015", volume = "16", number = "2", pages = "151--166", month = jun, keywords = "genetic algorithms, Genetic algorithm, Regression, Curve fitting, B-splines", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-014-9231-3", size = "16 pages", abstract = "Automatic curve fitting using splines has been widely used in data analysis and engineering applications. An important issue associated with data fitting by splines is the adequate selection of the number and location of the knots, as well as the calculation of the spline coefficients. Typically, these parameters are estimated separately with the aim of solving this non-linear problem. In this paper, we use a hierarchical genetic algorithm to tackle the B-spline curve fitting problem. The proposed approach is based on a novel hierarchical gene structure for the chromosomal representation, which allows us to determine the number and location of the knots, and the B-spline coefficients automatically and simultaneously. Our approach is able to find optimal solutions with the fewest parameters within the B-spline basis functions. The method is fully based on genetic algorithms and does not require subjective parameters like smooth factor or knot locations to perform the solution. In order to validate the efficacy of the proposed approach, simulation results from several tests on smooth functions and comparison with a successful method from the literature have been included.", notes = "Island model GA, HGA, BARS", } @InProceedings{garcia-garcia:2022:GECCOcomp, author = "Cosijopii Garcia-Garcia and Hugo Escalante and Alicia Morales-Reyes", title = "{CGP-NAS:} Real-based solutions encoding for multi-objective evolutionary neural architecture search", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion", year = "2022", editor = "Heike Trautmann and Carola Doerr and Alberto Moraglio and Thomas Bartz-Beielstein and Bogdan Filipic and Marcus Gallagher and Yew-Soon Ong and Abhishek Gupta and Anna V Kononova and Hao Wang and Michael Emmerich and Peter A. N. Bosman and Daniela Zaharie and Fabio Caraffini and Johann Dreo and Anne Auger and Konstantin Dietric and Paul Dufosse and Tobias Glasmachers and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Tea Tusar and Dimo Brockhoff and Tome Eftimov and Pascal Kerschke and Boris Naujoks and Mike Preuss and Vanessa Volz and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Mark Coletti and Catherine (Katie) Schuman and Eric ``Siggy'' Scott and Robert Patton and Paul Wiegand and Jeffrey K. Bassett and Chathika Gunaratne and Tinkle Chugh and Richard Allmendinger and Jussi Hakanen and Daniel Tauritz and John Woodward and Manuel Lopez-Ibanez and John McCall and Jaume Bacardit and Alexander Brownlee and Stefano Cagnoni and Giovanni Iacca and David Walker and Jamal Toutouh and UnaMay O'Reilly and Penousal Machado and Joao Correia and Sergio Nesmachnow and Josu Ceberio and Rafael Villanueva and Ignacio Hidalgo and Francisco {Fernandez de Vega} and Giuseppe Paolo and Alex Coninx and Antoine Cully and Adam Gaier and Stefan Wagner and Michael Affenzeller and Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and William B. Langdon and Justyna Petke and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Paetzel and Alexander Wagner and Michael Heider and Nadarajen Veerapen and Katherine Malan and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Mohammad Nabi Omidvar and Yuan Sun and Ernesto Tarantino and De Falco Ivanoe and Antonio {Della Cioppa} and Scafuri Umberto and John Rieffel and Jean-Baptiste Mouret and Stephane Doncieux and Stefanos Nikolaidis and Julian Togelius and Matthew C. Fontaine and Serban Georgescu and Francisco Chicano and Darrell Whitley and Oleksandr Kyriienko and Denny Dahl and Ofer Shir and Lee Spector and Alma Rahat and Richard Everson and Jonathan Fieldsend and Handing Wang and Yaochu Jin and Erik Hemberg and Marwa A. Elsayed and Michael Kommenda and William {La Cava} and Gabriel Kronberger and Steven Gustafson", pages = "643--646", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, neural architecture search, ANN, image classification, CNN, CGP, multi-objective evolutionary optimization", isbn13 = "978-1-4503-9268-6/22/07", DOI = "doi:10.1145/3520304.3528963", video_url = "https://vimeo.com/723973911", abstract = "Convolutional Neural Networks (CNNs) have had a remarkable performance in difficult computer vision tasks. In previous years, human experts have developed a number of specialized CNN architectures to deal with complex image datasets. However, the automatic design of CNN through Neural Architecture Search (NAS) has gained importance to reduce and possibly avoid human expert intervention. One of the main challenges in NAS is to design less complex and yet highly precise CNNs when both objectives conflict. This study extends Cartesian Genetic Programming (CGP) for CNNs representation in NAS through multi-objective evolutionary optimization for image classification tasks. The proposed CGP-NAS algorithm is built on CGP by combining real-based solutions representation and the well-established Non-dominated Sorting Genetic Algorithm II (NSGA-II). A detailed empirical assessment shows CGP-NAS achieved competitive performance when compared to other state-of-the-art proposals while significantly reduced the evolved CNNs architecture's complexity as well as GPU-days.", notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @InProceedings{Garcia-Garcia:2024:evoapplications, author = "Cosijopii Garcia-Garcia and Alicia Morales-Reyes and Hugo Jair Escalante", title = "Progressive Self-supervised Multi-objective NAS for Image Classification", booktitle = "27th International Conference, EvoApplications 2024", year = "2024", editor = "Stephen Smith and Joao Correia and Christian Cintrano", series = "LNCS", volume = "14635", publisher = "Springer", address = "Aberystwyth", month = "3-5 " # apr, pages = "180--195", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, ANN, MOGA, NSGA II, Evolutionary neural architecture search, AutoML, Evolutionary self-supervised learning", isbn13 = "978-3-031-56854-1", URL = "https://rdcu.be/dD0hO", DOI = "doi:10.1007/978-3-031-56855-8_11", abstract = "We introduce a novel progressive self-supervised framework for neural architecture search. Our aim is to search for competitive, yet significantly less complex, generic CNN architectures that can be used for multiple tasks (i.e., as a pretrained model). This is achieved through cartesian genetic programming (CGP) for neural architecture search (NAS). Our approach integrates self-supervised learning with a progressive architecture search process. This synergy unfolds within the continuous domain which is tackled via multi-objective evolutionary algorithms (MOEAs). To empirically validate our proposal, we adopted a rigorous evaluation using the non-dominated sorting genetic algorithm II (NSGA-II) for the CIFAR-100, CIFAR-10, SVHN and CINIC-10 datasets. The experimental results showcase the competitiveness of our approach in relation to state-of-the-art proposals concerning both classification performance and model complexity. Additionally, the effectiveness of this method in achieving strong generalization can be inferred.", notes = "http://www.evostar.org/2024/ EvoApplications2024 held in conjunction with EuroGP'2024, EvoCOP2024 and EvoMusArt2024", } @Article{GARCIAGARCIA:2023:asoc, author = "Cosijopii Garcia-Garcia and Alicia Morales-Reyes and Hugo Jair Escalante", title = "Continuous Cartesian Genetic Programming based representation for multi-objective neural architecture search", journal = "Applied Soft Computing", volume = "147", pages = "110788", year = "2023", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2023.110788", URL = "https://www.sciencedirect.com/science/article/pii/S1568494623008062", keywords = "genetic algorithms, genetic programming, Neural architecture search, ANN, Cartesian genetic programming, Convolutional neural network, Multi-objective optimization", abstract = "We propose a novel neural architecture search (NAS) approach for the challenge of designing convolutional neural networks (CNNs) that achieve a good tradeoff between complexity and accuracy. We rely on Cartesian genetic programming (CGP) and integrated real-based and block-chained CNN representation, for optimization using multi-objective evolutionary algorithms (MOEAs) in the continuous domain. We introduce two variants, CGP-NASV1 and CGP-NASV2, which differ in the granularity of their respective search spaces. To evaluate the proposed algorithms, we used the non-dominated sorting genetic algorithm II (NSGA-II) on the CIFAR-10, CIFAR-100,and SVHN datasets. Additionally, we extended the empirical analysis while maintaining the same solution representation to assess other searching techniques such as differential evolution (DE), the multi-objective evolutionary algorithm based on decomposition (MOEA/D), and the S metric selection evolutionary multi-objective algorithm (SMS-EMOA). The experimental results demonstrate that our approach exhibits competitive classification performance and model complexity compared to state-of-the-art methods", } @InProceedings{Garcia-Limon:2014:GECCO, author = "Mauricio Garcia-Limon and Hugo Jair Escalante and Eduardo Morales and Alicia Morales-Reyes", title = "Simultaneous generation of prototypes and features through genetic programming", booktitle = "GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation", year = "2014", editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges", isbn13 = "978-1-4503-2662-9", pages = "517--524", keywords = "genetic algorithms, genetic programming", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2576768.2598356", DOI = "doi:10.1145/2576768.2598356", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Nearest-neighbour (NN) methods are highly effective and widely used pattern classification techniques. There are, however, some issues that hinder their application for large scale and noisy data sets; including, its high storage requirements, its sensitivity to noisy instances, and the fact that test cases must be compared to all of the training instances. Prototype (PG) and feature generation (FG) techniques aim at alleviating these issues to some extent; where, traditionally, both techniques have been implemented separately. This paper introduces a genetic programming approach to tackle the simultaneous generation of prototypes and features to be used for classification with a NN classifier. The proposed method learns to combine instances and attributes to produce a set of prototypes and a new feature space for each class of the classification problem via genetic programming. An heterogeneous representation is proposed together with ad-hoc genetic operators. The proposed approach overcomes some limitations of NN without degradation in its classification performance. Experimental results are reported and compared with several other techniques. The empirical assessment provides evidence of the effectiveness of the proposed approach in terms of classification accuracy and instance/feature reduction.", notes = "Also known as \cite{2598356} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)", } @InProceedings{Garcia:2014:GECCOcomp, author = "Mauricio Garcia and Hugo J. Escalante and Manuel Montes and Alicia Morales and Eduardo Morales", title = "Towards the automated generation of term-weighting schemes for text categorization", booktitle = "GECCO 2014 Late breaking abstracts workshop", year = "2014", editor = "Dirk Sudholt", isbn13 = "978-1-4503-2881-4", keywords = "genetic algorithms, genetic programming", pages = "1459--1460", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2598394.2602286", DOI = "doi:10.1145/2598394.2602286", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "This paper describes ongoing research on the use of genetic programming to learn term-weighting schemes to be used for text classification. A term-weighting scheme (TWS) determines the way in which documents are represented before applying a text classification model. We propose a genetic program that aims at learning an effective TWS that can improve the performance in text classification. We report preliminary experimental results that give evidence of the validity of the proposal.", notes = "Also known as \cite{2602286} Distributed at GECCO-2014.", } @InProceedings{Garcia-Limon:2014:ROPEC, author = "Mauricio {Garcia Limon} and Hugo Jair Escalante and Eduardo F. Morales", booktitle = "IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC 2014)", title = "Towards simultaneous prototype and Feature Generation", year = "2014", month = nov, abstract = "Nearest-neighbour (NN) methods are among the most popular and highly effective techniques used in pattern recognition tasks. However, these methods have several drawbacks that impair their performance in large scale problems and noisy data sets. Some of these disadvantages includes its high storage requirements, its sensitivity to noisy instances, and the computational cost for estimating the distance among all instances. To address these problems different techniques like Prototype Generation (PG) to reduce the number of instances, and Feature Generation (FG) to obtain a new set of features have been proposed; traditionally, both techniques have been applied separately. This paper introduces a new method for simultaneous generation of prototypes and features in order to obtain a good tirade between accuracy of classification with a NN classifier, instance reduction rate and feature reduction rate. The method presented is based on the algorithm NSGA-II; the main idea of the proposed method is to combine instances and attributes to produce a set of prototypes and a new feature space for each class of the classification problem via genetic programming. The proposed approach overcomes some limitations of NN without compromising its performance in classification task. Experimental results are reported and compared with several other techniques.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ROPEC.2014.7036346", notes = "Inst. Nac. de Astrofis. Opt. y Electron., Tonantzintla, Mexico Also known as \cite{7036346}", } @InProceedings{garcia-martinez:2017:CEC, author = "Carlos Garcia-Martinez and Sebastian Ventura", booktitle = "2017 IEEE Congress on Evolutionary Computation (CEC)", title = "Multi-view semi-supervised learning using genetic programming interpretable classification rules", year = "2017", editor = "Jose A. Lozano", pages = "573--579", address = "Donostia, San Sebastian, Spain", publisher = "IEEE", isbn13 = "978-1-5090-4601-0", abstract = "Multi-view learning is a novel paradigm that aims at obtaining better results by examining the information from several perspectives instead of by analysing the same information from a single viewpoint. The multi-view methodology has widely been used for semi-supervised learning, where just some patterns were previously classified by an expert and there is a large amount of unlabelled ones. However to our knowledge, the multi-view learning paradigm has not been applied to produce interpretable rule-based classifiers before. In this work, we present a multi-view extension of a grammar-based genetic programming model for inducing rules for semi-supervised contexts. Its idea is to evolve several populations, and their corresponding views, favouring both the accuracy of the predictions for the labelled patterns and the prediction agreement with the other views for unlabelled ones. We have carried out experiments with two to five views, on six common datasets for fully-supervised learning that have been partially anonymised for our semi-supervised study. Our results show that the multi-view paradigm allows to obtain slightly better rule-based classifiers, and that two views becomes preferred.", keywords = "genetic algorithms, genetic programming, learning (artificial intelligence), pattern classification, grammar-based genetic programming model, interpretable classification rules, multiview semi-supervised learning, rule-based classifiers, semi-supervised contexts, Context, Kernel, Semisupervised learning, Sociology, Statistics, Training", isbn13 = "978-1-5090-4601-0", DOI = "doi:10.1109/CEC.2017.7969362", month = "5-8 " # jun, notes = "IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969362}", } @Article{Garcia-Martinez:2020:IJCIS, author = "Carlos Garcia-Martinez and Sebastian Ventura", title = "Multi-view Genetic Programming Learning to Obtain Interpretable Rule-Based Classifiers for Semi-supervised Contexts. Lessons Learnt", year = "2020", journal = "International Journal of Computational Intelligence Systems", volume = "13", number = "1", pages = "576--590", keywords = "genetic algorithms, genetic programming, Multi-view learning, Rule-based classification, Comprehensibility, Semi-supervised learning, Co-training, Grammar-based genetic programming", publisher = "Atlantis Press SARL", ISSN = "1875-6883", URL = "https://doi.org/10.2991/ijcis.d.200511.002", DOI = "doi:10.2991/ijcis.d.200511.002", abstract = "Multi-view learning analyzes the information from several perspectives and has largely been applied on semi-supervised contexts. It has not been extensively analyzed for inducing interpretable rule-based classifiers. We present a multi-view and grammar-based genetic programming model for inducing rules for semi-supervised contexts. It evolves several populations and views, and promotes both accuracy and agreement among the views. This work details how and why common practices may not produce the expected results when inducing rule-based classifiers under this methodology.", } @InProceedings{DBLP:conf/ccia/Garcia-SanchezV23, author = "Carlos {Garcia Sanchez} and Manel Velasco and Cecilio Angulo and Pau Marti and Antonio Camacho", editor = "Ismael Sanz and Raquel Ros and Jordi Nin", title = "Petri-Nets-Based Controllers Generation Using Genetic Programming", booktitle = "Artificial Intelligence Research and Development - Proceedings of the 25th International Conference of the Catalan Association for Artificial Intelligence, {CCIA} 2023, M{\'{o}}n Sant Benet, Spain, 25-27 October 2023", series = "Frontiers in Artificial Intelligence and Applications", volume = "375", pages = "120--123", publisher = "{IOS} Press", year = "2023", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.3233/FAIA230669", DOI = "doi:10.3233/FAIA230669", timestamp = "Fri, 23 Feb 2024 00:00:00 +0100", biburl = "https://dblp.org/rec/conf/ccia/Garcia-SanchezV23.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{Garcia-Sanchez:2008:gecco, author = "P. Garcia-Sanchez and J. J. Merelo and J. P. Sevilla and J. L. J. Laredo and A. M. Mora and P. A. Castillo", title = "Automatic generation of XSLT stylesheets using evolutionary algorithms", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "1701--1702", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1701.pdf", DOI = "doi:10.1145/1389095.1389417", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, evolutionary computation techniques, style sheets, XML, XSLT, Real-World application: Poster", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389417}", } @InProceedings{Garcia-Sanchez:2008:PPSN, author = "Pablo Garcia-Sanchez and Juan J. Merelo and Juan L. J. Laredo and Antonio Mora and Pedro A. Castillo", title = "Evolving {XSLT} Stylesheets for Document Transformation", booktitle = "Parallel Problem Solving from Nature - PPSN X", year = "2008", editor = "Gunter Rudolph and Thomas Jansen and Simon Lucas and Carlo Poloni and Nicola Beume", volume = "5199", series = "LNCS", pages = "1021--1030", address = "Dortmund", month = "13-17 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-87699-5", DOI = "doi:10.1007/978-3-540-87700-4_101", size = "pages", abstract = "This paper presents a new version of an evolutionary algorithm that creates XSLT programs from its intended input and output. XSLT is a general purpose, document-oriented functional language, generally used to transform XML documents (or, in general, solve any problem that can be coded as an XML document). Previously, a solution that solved the problem efficiently was proposed. In this paper, we improve on those results by testing different fitness functions, adding a new operator and changing the type of desired output document that can be obtained. The experiments show that the best results are obtained without considering the XSLT length and including this new operator.", notes = "PPSN X", } @InProceedings{DBLP:conf/evoW/Garcia-SanchezFMVGG14, author = "Pablo Garcia-Sanchez and Antonio Fernandez-Ares and Antonio Miguel Mora and Pedro A. Castillo and Jesus Gonzalez and Juan Julian Merelo Guervos", title = "Tree Depth Influence in Genetic Programming for Generation of Competitive Agents for {RTS} Games", booktitle = "Applications of Evolutionary Computation - 17th European Conference, EvoApplications 2014", year = "2014", editor = "Anna Isabel Esparcia-Alcazar and Antonio Miguel Mora", series = "Lecture Notes in Computer Science", volume = "8602", pages = "411--421", address = "Granada, Spain", month = apr # " 23-25", organisation = "EvoStar", publisher = "Springer", note = "Revised Selected Papers", keywords = "genetic algorithms, genetic programming", bibsource = "dblp computer science bibliography, http://dblp.org", isbn13 = "978-3-662-45522-7", URL = "http://dx.doi.org/10.1007/978-3-662-45523-4_34", DOI = "doi:10.1007/978-3-662-45523-4_34", size = "11 pages", abstract = "This work presents the results obtained from comparing different tree depths in a Genetic Programming Algorithm to create agents that play the Planet Wars game. Three different maximum levels of the tree have been used (3, 7 and Unlimited) and two bots available in the literature, based on human expertise, and optimised by a Genetic Algorithm have been used for training and comparison. Results show that in average, the bots obtained using our method equal or outperform the previous ones, being the maximum depth of the tree a relevant parameter for the algorithm", } @InProceedings{Garcia-Sanchez:2015:CIG, author = "Pablo Garcia-Sanchez and Alberto Tonda and Antonio Mora and Giovanni Squillero and J. J. Merelo", title = "Towards Automatic StarCraft Strategy Generation Using Genetic Programming", booktitle = "Proceedings of the IEEE Conference on Computational Intelligence and Games (CIG-2015)", year = "2015", editor = "Shi-Jim Yen and Tristan Cazenave and Philip Hingston", pages = "284--291", address = "Tainan, Taiwan", month = aug # " 31-" # sep # " 2", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, microGP, BWAPI", URL = "http://www.human-competitive.org/sites/default/files/garcia-sanchez-merelo-mora-squillero-tonda-text.txt", DOI = "doi:10.1109/CIG.2015.7317940", size = "8 pages", abstract = "Among Real-Time Strategy games few titles have enjoyed the continued success of StarCraft. Many research lines aimed at developing Artificial Intelligences, or bots, capable of challenging human players, use StarCraft as a platform. Several characteristics make this game particularly appealing for researchers, such as: asymmetric balanced factions, considerable complexity of the technology trees, large number of units with unique features, and potential for optimization both at the strategical and tactical level. In literature, various works exploit evolutionary computation to optimize particular aspects of the game, from squad formation to map exploration; but so far, no evolutionary approach has been applied to the development of a complete strategy from scratch. In this paper, we present the preliminary results of StarCraftGP, a framework able to evolve a complete strategy for StarCraft, from the building plan, to the composition of squads, up to the set of rules that define the bot's behaviour during the game. The proposed approach generates strategies as C++ classes, that are then compiled and executed inside the OpprimoBot open-source framework. In a first set of runs, we demonstrate that StarCraftGP ultimately generates a competitive strategy for a Zerg bot, able to defeat several human-designed bots.", notes = "Turing complete, obtain competitive bots from scratch, compiled C++ classes (constructor and per frame method). 2014 AIIDE competition OpprimoBot Benzene.scx evolves only high level strategies. p285 'being non-human is, in fact, one of the main advantages of GP'. p288 'evolve a Zerg strategy'. parallel 8 VirtualBox. DLL p290 'RTS games' p290 'automatically generate strategies that can defeat bots hand-coded by human experts'. Cites \cite{Sanchez:uGP:book}. 14:50 http://cig2015.nctu.edu.tw/program Entered 2016 HUMIES", } @Article{Garcia-Sanchez:2019:GPEM, author = "Pablo Garcia-Sanchez", title = "Georgios N. Yannakakis and Julian Togelius: Artificial Intelligence and Games", subtitle = "Springer, 2018, Print ISBN: 978-3-319-63518-7, Online ISBN: 978-3-319-63519-4, https://doi.org/10.1007/978-3-319-63519-4", journal = "Genetic Programming and Evolvable Machines", year = "2019", volume = "20", number = "1", pages = "143--145", month = mar, note = "Book review", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dR8ra", DOI = "doi:10.1007/s10710-018-9337-0", size = "3 pages", notes = "http://gameaibook.org", } @Article{Garcia-Valdez:2015:grid, author = "Mario Garcia-Valdez and Leonardo Trujillo and Juan Julian {Merelo Guervos} and Francisco {Fernandez de Vega} and Gustavo Olague", title = "The EvoSpace Model for Pool-Based Evolutionary Algorithms", journal = "Journal of Grid Computing", year = "2015", volume = "13", number = "3", pages = "329--349", month = sep, keywords = "genetic algorithms, genetic programming, Pool-based evolutionary algorithms, Distributed evolutionary algorithms, Heterogeneous computing platforms for bioinspired algorithms, Parameter setting", ISSN = "1572-9184", URL = "https://doi.org/10.1007/s10723-014-9319-2", DOI = "doi:10.1007/s10723-014-9319-2", abstract = "This work presents the EvoSpace model for the development of pool-based evolutionary algorithms (Pool-EA). Conceptually, the EvoSpace model is built around a central repository or population store, incorporating some of the principles of the tuple-space model and adding additional features to tackle some of the issues associated with Pool-EAs; such as, work redundancy, starvation of the population pool, unreliability of connected clients or workers, and a large parameter space. The model is intended as a platform to develop search algorithms that take an opportunistic approach to computing, allowing the exploitation of freely available services over the Internet or volunteer computing resources within a local network. A comprehensive analysis of the model at both the conceptual and implementation levels is provided, evaluating performance based on efficiency, optima found and speedup, while providing a comparison with a standard EA and an island-based model. The issues of lost connections and system parametrization are studied and validated experimentally with encouraging results, that suggest how EvoSpace can be used to develop and implement different Pool-EAs for search and optimization.", } @InProceedings{Garciarena:2016:GI, author = "Unai Garciarena and Roberto Santana", title = "Evolutionary optimization of compiler flag selection by learning and exploiting flags interactions", booktitle = "Genetic Improvement 2016 Workshop", year = "2016", editor = "Justyna Petke and David R. White and Westley Weimer", pages = "1159--1166", address = "Denver", publisher_address = "New York, NY, USA", month = jul # " 20-24", organisation = "SIGEvo", publisher = "ACM", keywords = "genetic algorithms, genetic programming, Genetic Improvement, SBSE, compiler flag selection, compiler optimization, probabilistic modeling, EDAs", URL = "http://geneticimprovementofsoftware.com/wp-content/uploads/2016/06/Evolutionary_optimization_of_compiler_flag_selection_by_learning_and_exploiting_flags_interactions.pdf", DOI = "doi:10.1145/2908961.2931696", size = "8 pages", abstract = "Compiler flag selection can be an effective way to increase the quality of executable code according to different code quality criteria. Evolutionary algorithms have been successfully applied to this optimization problem. However, previous approaches have only partially addressed the question of capturing and exploiting the interactions between compilation options to improve the search. In this paper we deal with this question comparing estimation of distribution algorithms (EDAs) and a traditional genetic algorithm approach. We show that EDAs that learn bivariate interactions can improve the results of GAs for some of the programs considered. We also show that the probabilistic models generated as a result of the search for optimal flag combinations can be used to unveil the (problem-dependent) interactions between the flags, allowing the user a more informed choice of compilation options.", notes = "GECCO-2016 Workshop http://geneticimprovementofsoftware.com/", } @Misc{DBLP:journals/corr/abs-1801-04407, author = "Unai Garciarena and Alexander Mendiburu and Roberto Santana", title = "Towards a more efficient representation of imputation operators in {TPOT}", howpublished = "arXiv", year = "2018", month = "13 " # jan, keywords = "genetic algorithms, genetic programming, TPOT, STGP, missing data, imputation methods, supervised classification, automatic machine learning, sklearn pipelines", eprint = "1801.04407", biburl = "https://dblp.org/rec/journals/corr/abs-1801-04407.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "http://arxiv.org/abs/1801.04407", size = "13 pages", abstract = "Automated Machine Learning encompasses a set of meta-algorithms intended to design and apply machine learning techniques (e.g., model selection, hyper-parameter tuning, model assessment, etc.). TPOT, a software for optimizing machine learning pipelines based on genetic programming (GP), is a novel example of this kind of applications. Recently we have proposed a way to introduce imputation methods as part of TPOT. While our approach was able to deal with problems with missing data, it can produce a high number of unfeasible pipelines. We propose a strongly-typed-GP based approach that enforces constraint satisfaction by GP solutions. The enhancement we introduce is based on the redefinition of the operators and implicit enforcement of constraints in the generation of the GP trees. We evaluate the method to introduce imputation methods as part of TPOT. We show that the method can notably increase the efficiency of the GP search for optimal pipelines.", } @InProceedings{Garciarena:2018:CEC, author = "Unai Garciarena and Roberto Santana and Alexander Mendiburu", booktitle = "2018 IEEE Congress on Evolutionary Computation (CEC)", title = "Analysis of the Complexity of the Automatic Pipeline Generation Problem", year = "2018", abstract = "Strategies to automatize the selection of Machine Learning algorithms and their parameters have gained popularity in recent years, to the point of coining the term Automated Machine Learning. The most general version of this problem is pipeline optimization, which seeks an optimal combination of preprocessors and classifiers, along with their respective parameters. In this paper we address the pipeline generation problem from a broader perspective, that of problem complexity understanding as a previous step before proposing a solution, a comprehension we consider critical. The main contribution of this work is the analysis of the characteristics of the fitness landscape. Furthermore, a recently introduced tool for pipeline generation is used to investigate how an automatic method behaves in the previously studied landscape. Results show the high complexity of the pipeline optimization problem, as it can contain several disperse optima, and suffers from a severe lack of generality. Results also suggest that, depending on the dimensions of the search, the model quality target, and the data being modelled, basic search methods can produce results that match the user's expectations.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2018.8477662", month = jul, notes = "Also known as \cite{8477662}", } @InProceedings{Gardner:2011:GECCOcomp, author = "Marc-Andre Gardner and Christian Gagne and Marc Parizeau", title = "Bloat control in genetic programming with a histogram-based accept-reject method", booktitle = "GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0690-4", keywords = "genetic algorithms, genetic programming: Poster", pages = "187--188", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001858.2001963", publisher = "ACM", publisher_address = "New York, NY, USA", size = "2 pages", abstract = "Recent bloat control methods such as dynamic depth limit (DynLimit) and Dynamic Operator Equalisation (DynOpEq) aim at modifying the tree size distribution in a population of genetic programs. Although they are quite efficient for that purpose, these techniques have the disadvantage of evaluating the fitness of many bloated Genetic Programming (GP) trees, and then rejecting most of them, leading to an important waste of computational resources. We are proposing a method that makes a histogram-based model of current GP tree size distribution, and uses the so-called accept-reject method for generating a population with the desired target size distribution, in order to make a stochastic control of bloat in the course of the evolution. Experimental results show that the method is able to control bloat as well as other state-of-the-art methods, with minimal additional computational efforts compared to standard tree-based GP.", notes = "symbolic regression, Santa Fe Ant, 6 parity. Like operator equalisation?? but does not need to evaluate fitness before deciding if child fits into desired distribution of program sizes. Cut off wrong word. Above target allow size histogram falls exponentially. Does not seem to limit small programs. Seem to be missing point about distribution of sizes actually generated by crossover. HARM-GP deap.googlecode.com Also known as \cite{2001963} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @PhdThesis{Gardner:thesis, author = "Marc-Andre Gardner", title = "Controle de la croissance de la taille des individus en programmation genetique", school = "Universite Laval", year = "2014", address = "Quebec, Canada", keywords = "genetic algorithms, genetic programming, bloat", URL = "http://hdl.handle.net/20.500.11794/25386", URL = "https://corpus.ulaval.ca/jspui/handle/20.500.11794/25386", URL = "http://www.theses.ulaval.ca/2014/31215/31215.pdf", size = "167 pages", abstract = "Genetic programming is a hyperheuristic optimization approach that has been applied to a wide range of problems involving symbolic representations or complex data structures. However, the method can be severely hindered by the increased computational resources required and premature convergence caused by uncontrolled code growth. We introduce HARM-GP, a novel operator equalization approach that adaptively shapes the genotype size distribution of individuals in order to effectively control code growth. Its probabilistic nature minimizes the overhead on the evolutionary process while its generic formulation allows this approach to remain independent of the problem and genetic operators used. Comparative results are provided over twelve problems with different dynamics, and over nine other algorithms taken from the literature. They show that HARM-GP is excellent at controlling code growth while maintaining good overall performances. Results also demonstrate the effectiveness of HARM-GP at limiting overtraining and overfitting in real-world supervised learning problems.", resume = "La programmation genetique (GP) est une hyperheuristique d'optimisation ayant ete appliquee avec succes a un large eventail de problemes. Cependant, son interet est souvent considerablement diminue du fait de son utilisation elevee en ressources de calcul et de sa convergence laborieuse. Ces problemes sont causes par une croissance immoderee de la taille des solutions et par l'apparition de structures inutiles dans celles-ci. Dans ce memoire, nous presentons HARM-GP, une nouvelle approche resolvant en grande partie ces problemes en permettant une adaptation dynamique de la distribution des tailles des solutions, tout en minimisant l'effort de calcul requis. Les performances de HARM-GP ont ete testees sur un ensemble de douze problemes et comparees avec celles de neuf techniques issues de la litterature. Les resultats montrent que HARM-GP excelle au controle de la croissance des arbres et du surapprentissage, tout en maintenant de bonnes performances sur les autres aspects", notes = "In French supervisor: Christian Gagne", } @Article{Gardner:2015:GPEM, author = "Marc-Andre Gardner and Christian Gagne and Marc Parizeau", title = "Controlling code growth by dynamically shaping the genotype size distribution", journal = "Genetic Programming and Evolvable Machines", year = "2015", volume = "16", number = "4", pages = "455--498", month = dec, keywords = "genetic algorithms, genetic programming, Bloat control, Monte Carlo methods", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-015-9242-8", size = "44 pages", abstract = "Genetic programming is a hyperheuristic optimisation approach that seeks to evolve various forms of symbolic computer programs, in order to solve a wide range of problems. However, the approach can be severely hindered by a significant computational burden and stagnation of the evolution caused by uncontrolled code growth. This paper introduces HARM-GP, a novel operator equalisation method that conducts an adaptive shaping of the genotype size distribution of individuals in order to effectively control code growth. Its probabilistic nature minimises the computational overheads on the evolutionary process while its generic formulation allows it to remain independent of both the problem and the genetic variation operators. Comparative results over twelve problems with different dynamics, and over nine other algorithms taken from the literature, show that HARM-GP is excellent at controlling code growth while maintaining good overall performance. Results also demonstrate the effectiveness of HARM-GP at limiting overfitting in real-world supervised learning problems.", } @InProceedings{Garg:2012:ICMIC, author = "A. Garg and K. Tai", title = "Review of genetic programming in modeling of machining processes", booktitle = "Proceedings of International Conference on Modelling, Identification Control (ICMIC 2012)", year = "2012", month = "24-26 " # jun, pages = "653--658", address = "Wuhan, China", keywords = "genetic algorithms, genetic programming, gene expression programming, ANN", isbn13 = "978-1-4673-1524-1", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6260225", size = "6 pages", abstract = "The mathematical modelling of machining processes has received immense attention and attracted a number of researchers because of its significant contribution to the overall cost and quality of product. The literature study demonstrates that conventional approaches such as statistical regression, response surface methodology, etc. requires physical understanding of the process for the erection of precise and accurate models. The statistical assumptions of such models induce ambiguity in the prediction ability of the model. Such limitations do not prevail in the nonconventional modelling approaches such as Genetic Programming (GP), Artificial Neural Network (ANN), Fuzzy Logic (FL), Genetic Algorithm (GA), etc. and therefore ensures trustworthiness in the prediction ability of the model. The present work discusses about the notion, application, abilities and limitations of Genetic Programming for modelling of machining processes. The characteristics of GP uncovered from the current review are compared with features of other modelling approaches applied to machining processes.", notes = "Also known as \cite{6260225}", } @InProceedings{Garg:2012:ICMIC2, author = "A. Garg and K. Tai", title = "Comparison of regression analysis, Artificial Neural Network and genetic programming in Handling the multicollinearity problem", booktitle = "Proceedings of International Conference on Modelling, Identification Control (ICMIC 2012)", year = "2012", month = "24-26 " # jun, pages = "353--358", size = "6 pages", address = "Wuhan, China", abstract = "Highly correlated predictors in a data set give rise to the multicollinearity problem and models derived from them may lead to erroneous system analysis. An appropriate predictor selection using variable reduction methods and Factor Analysis (FA) can eliminate this problem. These methods prove to be commendable particularly when used in conjunction with modelling methods that do not automate predictor selection such as Artificial Neural Network (ANN), Fuzzy Logic (FL), etc. The problem of severe multicollinearity is studied using data involving the estimation of fat content inside body. The purpose of the study is to select the subset of predictors from the set of highly correlated predictors. An attempt to identify the relevant predictors is comprehensively studied using Regression Analysis, Factor Analysis-Artificial Neural Networks (FA-ANN) and Genetic Programming (GP). The interpretation and comparisons of modelling methods are summarised in order to guide users about the proper techniques for tackling multicollinearity problems.", keywords = "genetic algorithms, genetic programming, Multicollinearity, Factor Analysis, Principal Component Analysis, Artificial Neural Network", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6260224", notes = "Also known as \cite{6260224}", } @InProceedings{Garg:2013:SSCI, author = "A. Garg and K. Tai", title = "Selection of a robust experimental design for the effective modeling of nonlinear systems using Genetic Programming", booktitle = "IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2013", year = "2013", editor_ssci-2013 = "P. N. Suganthan", editor = "Barbara Hammer and Zhi-Hua Zhou and Lipo Wang and Nitesh Chawla", pages = "287--292", address = "Singapore", month = "16-19 " # apr, keywords = "genetic algorithms, genetic programming, experimental designs, latin hypercube sampling, full factorial design, response surface design", DOI = "doi:10.1109/CIDM.2013.6597249", size = "6 pages", abstract = "The evolutionary approach of Genetic Programming (GP) has been applied extensively to model various non-linear systems. The distinct advantage of using GP is that prior assumptions for the selection of a model structure are not required. The GP automatically evolves the optimal model structure and its parameters that best describe the system characteristics. However, the evolution of an optimal model structure is highly dependent on the experimental designs used to sample the problem (system) domain and capture its characteristics. The literature reveals that very few researchers have studied the effect of various experimental designs on the performance of GP models and therefore the optimum choice of an experimental design is still unknown. This paper studies the effect of various experimental designs on the performance of GP models on two non-linear test functions. The objective of the paper is to identify the most robust (best) experimental design for effective modelling of non-linear test functions using GP. The analysis reveals that for the test function 1, the experimental design that gives best performance of GP models is response surface faced design and for test function 2, the best experimental design is 5-level full factorial design. Thus, the result concludes that the selection of the robust experimental design is a crucial preprocessing step for the effective modelling of non-linear systems using GP.", notes = "School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore CIDM 2013, http://www.ntu.edu.sg/home/epnsugan/index_files/SSCI2013/CIDM2013.htm also known as \cite{6597249}", } @InProceedings{Garg:2013:CIFEr, author = "A. Garg and S. Sriram and K. Tai", title = "Empirical Analysis of Model Selection Criteria for Genetic Programming in Modeling of Time Series System", booktitle = "2013 IEEE Symposium Series on Computational Intelligence", year = "2013", editor = "P. N. Suganthan", pages = "90--94", address = "Singapore", month = "16-19 " # apr, keywords = "genetic algorithms, genetic programming, AIC, FPE, PRESS, fitness function, model selection, stock market", DOI = "doi:10.1109/CIFEr.2013.6611702", size = "5 pages", abstract = "Genetic programming (GP) and its variants have been extensively applied for modelling of the stock markets. To improve the generalisation ability of the model, GP have been hybridised with its own variants (gene expression programming (GEP), multi expression programming (MEP)) or with the other methods such as neural networks and boosting. The generalisation ability of the GP model can also be improved by an appropriate choice of model selection criterion. In the past, several model selection criteria have been applied. In addition, data transformations have significant impact on the performance of the GP models. The literature reveals that few researchers have paid attention to model selection criterion and data transformation while modelling stock markets using GP. The objective of this paper is to identify the most appropriate model selection criterion and transformation that gives better generalised GP models. Therefore, the present work will conduct an empirical analysis to study the effect of three model selection criteria across two data transformations on the performance of GP while modelling the stock indexed in the New York Stock Exchange (NYSE). It was found that FPE criteria have shown a better fit for the GP model on both data transformations as compared to other model selection criteria.", notes = "CIFEr 2013, also known as \cite{6611702}", } @Article{Garg:2013:IJMIC, author = "Akhil Garg and Yogesh Bhalerao and Kang Tai", title = "Review of empirical modelling techniques for modelling of turning process", journal = "International Journal of Modelling, Identification and Control, Vol. 20, No. 2, 2013", year = "2013", month = aug # "~31", volume = "20", number = "2", pages = "121--129", keywords = "genetic algorithms, genetic programming, empirical modelling, turning, artificial neural networks, ANNs, review, regression analysis, fuzzy logic, support vector machines, SVM", ISSN = "1746-6180", publisher = "Inderscience Publishers", bibsource = "OAI-PMH server at www.inderscience.com", language = "eng", URL = "http://www.inderscience.com/link.php?id=56184", DOI = "DOI:10.1504/IJMIC.2013.056184", abstract = "The most widely and well known machining process used is turning. The turning process possesses higher complexity and uncertainty and therefore several empirical modelling techniques such as artificial neural networks, regression analysis, fuzzy logic and support vector machines have been used for predicting the performance of the process. This paper reviews the applications of empirical modelling techniques in modelling of turning process and unearths the vital issues related to it.", } @InProceedings{conf/semcco/GargT13, author = "Akhil Garg and Kang Tai", title = "Genetic Programming for Modeling Vibratory Finishing Process: Role of Experimental Designs and Fitness Functions", booktitle = "Proceedings of the 4th International Conference on Swarm, Evolutionary, and Memetic Computing (SEMCCO 2013), Part II", year = "2013", editor = "Bijaya Ketan Panigrahi and Ponnuthurai Nagaratnam Suganthan and Swagatam Das and Subhransu Sekhar Dash", volume = "8298", series = "Lecture Notes in Computer Science", pages = "23--31", address = "Chennai, India", month = dec # " 19-21", publisher = "Springer", keywords = "genetic algorithms, genetic programming, vibratory finishing, fitness function, vibratory modelling, GPTIPS, experimental designs, finishing modelling", isbn13 = "978-3-319-03755-4", bibdate = "2013-12-18", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/semcco/semcco2013-2.html#GargT13", URL = "http://dx.doi.org/10.1007/978-3-319-03756-1", DOI = "doi:10.1007/978-3-319-03756-1_3", abstract = "Manufacturers seek to improve efficiency of vibratory finishing process while meeting increasingly stringent cost and product requirements. To serve this purpose, mathematical models have been formulated using soft computing methods such as artificial neural network and genetic programming (GP). Among these methods, GP evolves model structure and its coefficients automatically. There is extensive literature on ways to improve the performance of GP but less attention has been paid to the selection of appropriate experimental designs and fitness functions. The evolution of fitter models depends on the experimental design used to sample the problem (system) domain, as well as on the appropriate fitness function used for improving the evolutionary search. This paper presents quantitative analysis of two experimental designs and four fitness functions used in GP for the modelling of vibratory finishing process. The results conclude that fitness function SRM and PRESS evolves GP models of higher generalisation ability, which may then be deployed by experts for optimisation of the finishing process.", } @Article{garg:2013:IJAMT, author = "A. Garg and L. Rachmawati and K. Tai", title = "Classification-driven model selection approach of genetic programming in modelling of turning process", journal = "The International Journal of Advanced Manufacturing Technology", year = "2013", volume = "69", number = "5 - 8", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s00170-013-5103-x", DOI = "doi:10.1007/s00170-013-5103-x", } @Article{garg:2014:IJAMT, author = "A. Garg and K. Tai and M. M. Savalani", title = "Formulation of bead width model of an {SLM} prototype using modified multi-gene genetic programming approach", journal = "The International Journal of Advanced Manufacturing Technology", year = "2014", volume = "73", number = "1 - 4", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s00170-014-5820-9", DOI = "doi:10.1007/s00170-014-5820-9", } @Article{garg:2014:Meccanica, author = "A. Garg and K. Tai and A. K. Gupta", title = "A modified multi-gene genetic programming approach for modelling true stress of dynamic strain aging regime of austenitic stainless steel 304", journal = "Meccanica", year = "2014", volume = "49", number = "5", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s11012-013-9873-x", DOI = "doi:10.1007/s11012-013-9873-x", } @Article{garg:2014:CG, author = "Akhil Garg and Ankit Garg and K. Tai", title = "A multi-gene genetic programming model for estimating stress-dependent soil water retention curves", journal = "Computational Geosciences", year = "2014", volume = "18", number = "1", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s10596-013-9381-z", DOI = "doi:10.1007/s10596-013-9381-z", } @Article{Garg:2014:EAAI, author = "Akhil Garg and Ankit Garg and K. Tai and S. Sreedeep", title = "An integrated SRM-multi-gene genetic programming approach for prediction of factor of safety of {3-D} soil nailed slopes", journal = "Engineering Applications of Artificial Intelligence", volume = "30", pages = "30--40", year = "2014", ISSN = "0952-1976", DOI = "doi:10.1016/j.engappai.2013.12.011", URL = "http://www.sciencedirect.com/science/article/pii/S0952197613002455", keywords = "genetic algorithms, genetic programming, FOS prediction, SRM-MGGP, GPTIPS, LS-SVM", } @Article{Garg:2014:ESA, author = "A. Garg and V. Vijayaraghavan and S. S. Mahapatra and K. Tai and C. H. Wong", title = "Performance evaluation of microbial fuel cell by artificial intelligence methods", journal = "Expert Systems with Applications", volume = "41", number = "4, Part 1", pages = "1389--1399", year = "2014", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2013.08.038", URL = "http://www.sciencedirect.com/science/article/pii/S0957417413006507", keywords = "genetic algorithms, genetic programming, MFC modelling, MFC prediction, GPTIPS, LS-SVM", abstract = "In the present study, performance of microbial fuel cell (MFC) has been modelled using three potential artificial intelligence (AI) methods such as multi-gene genetic programming (MGGP), artificial neural network and support vector regression. The effect of two input factors namely, temperature and ferrous sulfate concentrations on the output voltage were studied independently during two operating conditions (before and after start-up) using the three AI models. The data is randomly divided into training and testing samples containing 80percent and 20percent sets respectively and then trained and tested by three AI models. Based on the input factor, the proposed AI models predict output voltage of MFC at two operating conditions. Out of three methods, the MGGP method not only evolve model with better generalisation ability but also represents an explicit relationship between the output voltage and input factors of MFC. The models generated by MGGP approach have shown an excellent potential to predict the performance of MFC and can be used to gain better insights into the performance of MFC.", } @InProceedings{conf/ieaaie/0002T14, author = "Akhil Garg and Kang Tai", title = "An Improved Multi-Gene Genetic Programming Approach for the Evolution of Generalized Model in Modelling of Rapid Prototyping Process", bibdate = "2014-05-21", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ieaaie/ieaaie2014-1.html#0002T14", booktitle = "Modern Advances in Applied Intelligence - 27th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, {IEA}/{AIE} 2014, Kaohsiung, Taiwan, June 3-6, 2014, Proceedings, Part {I}", publisher = "Springer", year = "2014", volume = "8481", editor = "Moonis Ali and Jeng-Shyang Pan and Shyi-Ming Chen and Mong-Fong Horng", isbn13 = "978-3-319-07454-2", pages = "218--226", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming", URL = "http://dx.doi.org/10.1007/978-3-319-07455-9", } @Article{Garg:2014:EE, author = "Akhil Garg and Ankit Garg and K. Tai and S. Sreedeep", title = "Estimation of factor of safety of rooted slope using an evolutionary approach", journal = "Ecological Engineering", year = "2014", volume = "64", pages = "314--324", month = mar, keywords = "genetic algorithms, genetic programming, FOS prediction, Evolutionary, GPTIPS, LS-SVM, Multi-gene genetic programming", ISSN = "0925-8574", URL = "http://www.sciencedirect.com/science/article/pii/S0925857413005478", DOI = "doi:10.1016/j.ecoleng.2013.12.047", size = "11 pages", abstract = "Use of roots as one of slope stabilization technique via mechanical reinforcement has received considerable attention in the past few decades. Several mathematical models have been developed to estimate the additional cohesion due to roots, which is useful for the calculation of factor of safety (FOS) of the rooted slopes using finite element method (FEM) or finite difference method. It is well understood from the literature that the root properties such as root area ratio (RAR) and root depth affects the mobilized tensile stress per unit area of soil consequently affecting the FOS of the rooted slope. In addition, a fracture phenomenon also influences the FOS of the rooted slope and should also be considered. In the present work, a new evolutionary approach, namely, multi-gene genetic programming (MGGP) is presented, and, applied to formulate the mathematical relationship between FOS and input variables such as slope angles, root depth and RAR of the rooted slope. The performance of MGGP is compared to those of artificial neural network and support vector regression. Based on the evaluation of the performance of the models, the proposed MGGP model outperformed the two other models and is proved able to capture the characteristics of the FEM model by unveiling important parameters and hidden non-linear relationships.", notes = "School of Mechanical and Aerospace Engineering,Nanyang Technological University, 50 Nanyang Ave,Singapore 639798", } @Article{Garg:2014:SMPT, author = "A. Garg and V. Vijayaraghavan and C. H. Wong and K. Tai and Liang Gao", title = "An embedded simulation approach for modeling the thermal conductivity of {2D} nanoscale material", journal = "Simulation Modelling Practice and Theory", year = "2014", volume = "44", month = may, pages = "1--13", ISSN = "1569-190X", DOI = "doi:10.1016/j.simpat.2014.02.003", URL = "http://www.sciencedirect.com/science/article/pii/S1569190X14000276", keywords = "genetic algorithms, genetic programming, multi-gene genetic programming, Graphene modelling, Nanomaterial characteristics, Nanomaterial modelling, Thermal conductivity modelling", abstract = "The thermal property of single layer graphene sheet is investigated in this work by using an embedded approach of molecular dynamics (MD) and soft computing method. The effect of temperature and Stone-Thrower-Wales (STW) defects on the thermal conductivity of graphene sheet is first analysed using MD simulation. The data obtained using the MD simulation is then fed into the paradigm of soft computing approach, multi-gene genetic programming (MGGP), which was specifically designed to model the response of thermal conductivity of graphene sheet with changes in system temperature and STW defect concentration. We find that our proposed MGGP model is able to model the thermal conductivity of graphene sheet very well which can be used to complement the analytical solution developed by MD simulation. Additionally, we also conducted sensitivity and parametric analysis to find out specific influence and variation of each of the input system parameters on the thermal conductivity of graphene sheet. It was found that the STW defects has the most dominating influence on the thermal conductivity of graphene sheet.", } @Article{Garg:2014:AES, author = "A. Garg and K. Tai", title = "Stepwise approach for the evolution of generalized genetic programming model in prediction of surface finish of the turning process", journal = "Advances in Engineering Software", volume = "78", pages = "16--27", year = "2014", ISSN = "0965-9978", DOI = "doi:10.1016/j.advengsoft.2014.08.005", URL = "http://www.sciencedirect.com/science/article/pii/S0965997814001318", abstract = "Due to the complexity and uncertainty in the process, the soft computing methods such as regression analysis, neural networks (ANN), support vector regression (SVR), fuzzy logic and multi-gene genetic programming (MGGP) are preferred over physics-based models for predicting the process performance. The model participating in the evolutionary stage of the MGGP method is a linear weighted sum of several genes (model trees) regressed using the least squares method. In this combination mechanism, the occurrence of gene of lower performance in the MGGP model can degrade its performance. Therefore, this paper proposes a modified-MGGP (M-MGGP) method using a stepwise regression approach such that the genes of lower performance are eliminated and only the high performing genes are combined. In this work, the M-MGGP method is applied in modelling the surface roughness in the turning of hardened AISI H11 steel. The results show that the M-MGGP model produces better performance than those of MGGP, SVR and ANN. In addition, when compared to that of MGGP method, the models formed from the M-MGGP method are of smaller size. Further, the parametric and sensitivity analysis conducted validates the robustness of our proposed model and is proved to capture the dynamics of the turning phenomenon of AISI H11 steel by unveiling dominant input process parameters and the hidden non-linear relationships.", keywords = "genetic algorithms, genetic programming, Surface roughness prediction, Surface property, Turning, Stepwise regression, Support vector regression", } @Article{Garg:2014:SMPT2, author = "A. Garg and V. Vijayaraghavan and C. H. Wong and K. Tai and K. Sumithra and L. Gao and Pravin M. Singru", title = "Combined {CI-MD} approach in formulation of engineering moduli of single layer graphene sheet", journal = "Simulation Modelling Practice and Theory", volume = "48", pages = "93--111", year = "2014", keywords = "genetic algorithms, genetic programming, Mechanical properties, Defects, Nanomaterial modelling, Artificial intelligence, Molecular dynamics", ISSN = "1569-190X", DOI = "doi:10.1016/j.simpat.2014.07.008", URL = "http://www.sciencedirect.com/science/article/pii/S1569190X14001257", abstract = "An evolutionary approach of multi-gene genetic programming (GP) is used to study the effects of aspect ratio, temperature, number of atomic planes and vacancy defects on the engineering moduli viz. tensile and shear modulus of single layer graphene sheet. MD simulation based on REBO potential is used to obtain the engineering moduli. This data is then fed into the paradigm of a GP cluster comprising of genetic programming, which was specifically designed to formulate the explicit relationship of engineering moduli of graphene sheets loaded in armchair and zigzag directions with respect to aspect ratio, temperature, number of atomic planes and vacancy defects. We find that our MGGP model is able to model the engineering moduli of armchair and zigzag oriented graphene sheets well in agreement with that of experimental results. We also conducted sensitivity and parametric analysis to find out specific influence and variation of each of the input system parameters on the engineering moduli of armchair and zigzag graphene sheets. It was found that the number of defects has the most dominating influence on the engineering moduli of graphene sheets.", } @Article{journals/jim/GargTLS14, title = "A hybrid {M5'-genetic programming} approach for ensuring greater trustworthiness of prediction ability in modelling of {FDM} process", author = "A. Garg and K. Tai and C. H. Lee and M. M. Savalani", journal = "Journal of Intelligent Manufacturing", year = "2014", number = "6", volume = "25", pages = "1349--1365", keywords = "genetic algorithms, genetic programming, M5, Artificial neural network, ANN, Trustworthiness, Support vector regression, SVM, Fused deposition modelling, Rapid prototyping", ISSN = "0956-5515", bibdate = "2014-11-11", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/jim/jim25.html#GargTLS14", URL = "http://dx.doi.org/10.1007/s10845-013-0734-1", size = "17 pages", abstract = "Recent years have seen various rapid prototyping (RP) processes such as fused deposition modelling (FDM) and three-dimensional printing being used for fabricating prototypes, leading to shorter product development times and less human intervention. The literature reveals that the properties of RP built parts such as surface roughness, strength, dimensional accuracy, build cost, etc are related to and can be improved by the appropriate settings of the input process parameters. Researchers have formulated physics-based models and applied empirical modelling techniques such as regression analysis and artificial neural network for the modelling of RP processes. Physics-based models require in-depth understanding of the processes which is a formidable task due to their complexity. The issue of improving trustworthiness of the prediction ability of empirical models on test (unseen) samples is paid little attention. a hybrid M5'-genetic programming (M5'-GP) approach is proposed for empirical modelling of the FDM process with an attempt to resolve this issue of ensuring trustworthiness. This methodology is based on the error compensation achieved using a GP model in parallel with a M5' model. The performance of the proposed hybrid model is compared to those of support vector regression (SVR) and adaptive neuro fuzzy inference system (ANFIS) model and it is found that the M5'-GP model has the goodness of fit better than those of the SVR and ANFIS models.", } @Article{Garg:2014:TPM, author = "Ankit Garg and Akhil Garg and K. Tai and S. Barontini and Alexia Stokes", title = "A Computational Intelligence-Based Genetic Programming Approach for the Simulation of Soil Water Retention Curves", journal = "Transport in Porous Media", year = "2014", volume = "103", number = "3", pages = "497--513", keywords = "genetic algorithms, genetic programming, multi-gene genetic programming, soil water retention curves, swelling soils, enveloppe potential, environmental sciences/biodiversity and ecology", ISSN = "1573-1634", URL = "https://hal.archives-ouvertes.fr/hal-01268778", URL = "http://dx.doi.org/10.1007/s11242-014-0313-8", DOI = "doi:10.1007/s11242-014-0313-8", publisher = "HAL CCSD; Springer Verlag", annote = "Indian Institute of Technology; Nanyang Technological University (NTU); Department of Civil, Environmental, Architectural Engineering and Mathematics ; Universit{\`a} degli Studi di Brescia; BotAnique et BioinforMatique de l'Architecture des Plantes (AMAP) ; Universit{\'e} Montpellier 2 - Sciences et Techniques (UM2) - Institut national de la recherche agronomique (INRA) - Institut de recherche pour le d{\'e}veloppement [IRD] - Centre de coop{\'e}ration internationale en recherche agronomique pour le d{\'e}veloppement (CIRAD) - Centre National de la Recherche Scientifique (CNRS); Singapore Ministry of Education Academic Research Fund", bibsource = "OAI-PMH server at api.archives-ouvertes.fr", contributor = "BotAnique et BioinforMatique de l'Architecture des Plantes", identifier = "hal-01268778; DOI : 10.1007/s11242-014-0313-8; PRODINRA : 274701", language = "en", oai = "oai:HAL:hal-01268778v1", relation = "info:eu-repo/semantics/altIdentifier/doi/10.1007/s11242-014-0313-8", abstract = "Soil water retention curves are a key constitutive law used to describe the physical behaviour of an unsaturated soil. Various computational modelling techniques, that formulate retention curve models, are mostly based on existing soil databases, which rarely consider any effect of stress on the soil water retention. Such effects are crucial in the case of swelling soils. This study illustrates and explores the ability of computational intelligence-based genetic programming to formulate the mathematical relationship between the water content, in terms of degree of saturation, and two input variables, i.e., net stress and suction for three different soils (sand--kaolin mixture, Gaduk Silt and Firouzkouh clay). The predictions obtained from the proposed models are in good agreement with the experimental data. The parametric and sensitivity analysis conducted validates the robustness of our proposed model by unveiling important parameters and hidden non-linear relationships.", notes = "also known as \cite{oai:HAL:hal-01268778v1}", } @Article{garg:2014:GGE, author = "Ankit Garg and Akhil Garg and K. Tai and S. Sreedeep", title = "Estimation of Pore Water Pressure of Soil Using Genetic Programming", journal = "Geotechnical and Geological Engineering", year = "2014", volume = "32", number = "4", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s10706-014-9755-6", DOI = "doi:10.1007/s10706-014-9755-6", } @Article{GARG2017351, author = "Ankit Garg and Jinhui Li and Jinjun Hou and Christian Berretta and Akhil Garg", title = "A new computational approach for estimation of wilting point for green infrastructure", journal = "Measurement", year = "2017", volume = "111", pages = "351--358", month = dec, keywords = "genetic algorithms, genetic programming, wilting point, soil fractal dimension, s index, clay content, organic matter, evolutionary algorithms", ISSN = "0263-2241", bibsource = "OAI-PMH server at eprints.whiterose.ac.uk", publisher = "Elsevier", URL = "http://eprints.whiterose.ac.uk/119632/", URL = "http://www.sciencedirect.com/science/article/pii/S026322411730461X", DOI = "doi:10.1016/j.measurement.2017.07.026", size = "8 pages", abstract = "Wilting point is an important parameter indicating the inhibition of plant transpiration processes, which is essential for green infrastructures. Generalization of wilting point is very essential for analysing the hydrological performance of green infrastructures (e.g. green roofs, biofiltration systems) and ecological infrastructures (wetlands). Wilting point of various species is known to be affected by the factors such as soil clay content, soil organic matter, slope of soil water characteristic curve at inflection point (i.e., s index) and fractal dimension. Therefore, its practical usefulness forms the strong basis in developing the model that quantify wilting point with respects to the deterministic factors. This study proposes the wilting point model development task based on optimisation approach of Genetic programming (GP) with respect to the input variables (soil clay content, soil organic matter, s-index and fractal dimension) for various type of soils. The GP model developed is further investigated by sensitivity and parametric analysis to discover the relationships between wilting point and input variables and the dominant inputs. Based on newly developed model, it was found that wilting point increases with fractal dimension while behaves highly non-linear with respect to clay and organic content. The combined effect of the clay and organic content was found to greatly influence the wilting point. It implies that wilting point should not be generalised as usually done in literature.", notes = "also known as \cite{oai:eprints.whiterose.ac.uk:119632} Department of Civil and Environmental Engineering, Shantou University, Shantou 515063, China", } @Article{Garg:2015:SEC, author = "Akhil1 Garg and V. Vijayaraghavan and Jasmine Siu Lee Lam and Pravin M Singru and Liang Gao", title = "A molecular simulation based computational intelligence study of a nano-machining process with implications on its environmental performance", journal = "Swarm and Evolutionary Computation", volume = "21", pages = "54--63", year = "2015", ISSN = "2210-6502", DOI = "doi:10.1016/j.swevo.2015.01.001", URL = "http://www.sciencedirect.com/science/article/pii/S2210650215000115", abstract = "Determining the optimum input parameter settings (temperature, rotational velocity and feed rate) in optimising the properties (strength and time) of the nano-drilling process can result in an improvement in its environmental performance. This is because the rotational velocity is an essential component of power consumption during drilling and therefore by determining its appropriate value required in optimisation of properties, the trial-and-error approach that normally results in loss of power and waste of resources can be avoided. However, an effective optimisation of properties requires the formulation of the generalised and an explicit mathematical model. In the present work, the nano-drilling process of Boron Nitride Nanosheet (BNN) panels is studied using an explicit model formulated by a molecular dynamics (MD) based computational intelligence (CI) approach. The approach consists of nano scale modelling using MD simulation which is further fed into the paradigm of a CI cluster comprising genetic programming, which was specifically designed to formulate the explicit relationship of nano-machining properties of BNN panel with respect to process temperature, feed and rotational velocity of drill bit. Performance of the proposed model is evaluated against the actual results. We find that our proposed integrated CI model is able to model the nano-drilling process of BNN panel very well, which can be used to complement the analytical solution developed by MD simulation. Additionally, we also conducted sensitivity and parametric analysis and found that the temperature has the least influence, whereas the velocity has the highest influence on the properties of nano-drilling process of BNN panel.", keywords = "genetic algorithms, genetic programming, Computational intelligence, Nano-drilling, Boron nitride sheets, Materials nano-machining", } @Article{Garg:2015:Measurement, author = "Akhil1 Garg and Jasmine Siu Lee Lam", title = "Measurement of environmental aspect of {3-D} printing process using soft computing methods", journal = "Measurement", volume = "75", pages = "210--217", year = "2015", ISSN = "0263-2241", DOI = "doi:10.1016/j.measurement.2015.04.016", URL = "http://www.sciencedirect.com/science/article/pii/S0263224115002195", abstract = "For improving the environmental performance of the manufacturing industry across the globe, 3-D printing technology should be increasingly adopted as a manufacturing procedure. It is because this technology uses the polymer PLA (Polyactic acid) as a material, which is biodegradable, and saves fuel and reduces waste when fabricating prototypes. In addition, the technology can be located near to industries and fabricates raw material itself, resulting in reduction of transport costs and carbon emission. However, due to its high production cost, 3-D printing technology is not yet being adopted globally. One way of reducing the production cost and improving environmental performance is to formulate models that can be used to operate 3-D printing technology in an efficient way. Therefore, this paper aims to deploy the soft computing methods such as genetic programming (GP), support vector regression and artificial neural network in formulating the laser power-based-open porosity models. These methods are applied on the selective laser sintering (a 3-D printing process) process data. It is found that GP evolves the best model that is able to predict open porosity satisfactorily based on given values of laser power. The laser power-based-open porosity model formulated can assist decision makers in operating the SLS process in an effective and efficient way, thus increasing its viability for being adopted as a manufacturing procedure and paving the way for a sustainable environment across the globe.", keywords = "genetic algorithms, genetic programming, Selective laser sintering, Soft computing methods, Open porosity prediction, 3-D printing, Environmental aspect", } @Article{Garg:2015:Measurementa, author = "Akhil1 Garg and V. Vijayaraghavan and K. Tai and Pravin M. Singru and Vishal Jain and Nikilesh Krishnakumar", title = "Model development based on evolutionary framework for condition monitoring of a lathe machine", journal = "Measurement", volume = "73", pages = "95--110", year = "2015", ISSN = "0263-2241", DOI = "doi:10.1016/j.measurement.2015.04.025", URL = "http://www.sciencedirect.com/science/article/pii/S0263224115002389", abstract = "The present work deals with the vibro-acoustic condition monitoring of the metal lathe machine by the development of predictive models for the detection of probable faults. Firstly, the experiments were conducted to obtain vibration and acoustic signatures for the three operations (idle running, turning and facing) used for three experimental studies (overall acoustic, overall vibration and headstock vibration). In the perspective of formulating the predictive models, multi-gene genetic programming (MGGP) approach can be applied. However, it is effective functioning exhibit high dependence on the complexity term incorporated in its fitness function. Therefore, an evolutionary framework of MGGP based on its new complexity measure is proposed in formulation of the predictive models. In this proposed framework, polynomials known for their fixed complexity (order of polynomial) are used for defining the complexity of the generated models during the evolutionary stages of MGGP. The new complexity term is then incorporated in fitness function of MGGP to penalize the fitness of models. The results reveal that the proposed models outperformed the standardized MGGP models. Further, the parametric and sensitivity analysis is conducted to study the relationships between the key process parameters and to reveal dominant input process parameters.", keywords = "genetic algorithms, genetic programming, Vibration, Acoustics, Condition monitoring, Machine learning, Predictive maintenance, Machining modelling", } @Article{Garg:2015:JCP, author = "Akhil1 Garg and Jasmine Siu Lee Lam and L. Gao", title = "Energy conservation in manufacturing operations: modelling the milling process by a new complexity-based evolutionary approach", journal = "Journal of Cleaner Production", volume = "108, Part A", pages = "34--45", year = "2015", ISSN = "0959-6526", DOI = "doi:10.1016/j.jclepro.2015.06.043", URL = "http://www.sciencedirect.com/science/article/pii/S0959652615007726", abstract = "From the perspective of energy conservation, the notion of modelling of energy consumption as a vital element of environmental sustainability in any manufacturing industry remains a current and important focus of study for climate change experts across the globe. Among the manufacturing operations, machining is widely performed. Extensive studies by peer researchers reveal that the focus was on modelling and optimizing the manufacturing aspects (e.g. surface roughness, tool wear rate, dimensional accuracy) of the machining operations by computational intelligence methods such as analysis of variance, grey relational analysis, Taguchi method, and artificial neural network. Alternatively, an evolutionary based multi-gene genetic programming approach can be applied but its effective functioning depends on the complexity measure chosen in its fitness function. This study proposes a new complexity-based multi-gene genetic programming approach based on orthogonal basis functions and compares its performance to that of the standardized multi-gene genetic programming in modelling of energy consumption of the milling process. The hidden relationships between the energy consumption and the input process parameters are unveiled by conducting sensitivity and parametric analysis. From these relationships, an optimum set of input settings can be obtained which will conserve greater amount of energy from these operations. It was found that the cutting speed has the highest impact on the milling process followed by feed rate and depth of cut.", keywords = "genetic algorithms, genetic programming, Environmental sustainability, Energy conservation, Energy consumption, Machining, Computational intelligence, Milling process", } @Article{Garg:2015:JCPa, author = "Akhil1 Garg and Jasmine Siu Lee Lam", title = "Improving environmental sustainability by formulation of generalized power consumption models using an ensemble based multi-gene genetic programming approach", journal = "Journal of Cleaner Production", volume = "102", pages = "246--263", year = "2015", ISSN = "0959-6526", DOI = "doi:10.1016/j.jclepro.2015.04.068", URL = "http://www.sciencedirect.com/science/article/pii/S0959652615004436", abstract = "Environmental sustainability is an important aspect for accessing the performance of any machining industry. Growing demand of customers for better product quality has resulted in an increase in energy consumption and thus a lower environmental performance. Optimization of both product quality and energy consumption is needed for improving economic and environmental performance of the machining operations. However, for achieving the global multi-objective optimization, the models formulated must be able to generalize the data accurately. In this context, an evolutionary approach of multi-gene genetic programming (MGGP) can be used to formulate the models for product quality (surface roughness and tool life) and power consumption. MGGP develops the model structure and its coefficients based on the principles of genetic algorithm (GA). Despite being widely applied, MGGP generates models that may not give satisfactory performance on the test data. The main reason behind this is the inappropriate formulation procedure of the multi-gene model and the difficulty in model selection. Therefore, the present work proposes a new ensemble-based-MGGP (EN-MGGP) framework that makes use of statistical and classification strategies for improving the generalization ability. The EN-MGGP approach is applied on the reliable experimental database (outputs: surface roughness, tool life and power consumption) obtained from the literature, and its performance is compared to that of the standardized MGGP. The proposed EN-MGGP models outperformed the standardized MGGP models. The conducted sensitivity and parametric analysis validates the robustness of the models by unveiling the non-linear relationships between the outputs (surface roughness, tool life and power consumption) and input parameters. It was also found that the cutting speed has the most significant impact on the power consumption in turning of AISI 1045 steel and the turning of 7075 Al alloy- 15 wtpercent SIC composites. The generalized EN-MGGP models obtained can easily be optimized analytically for attaining the optimum input parameter settings that optimize the product quality and power consumption simultaneously.", keywords = "genetic algorithms, genetic programming, Environmental sustainability, Power consumption, Product quality, Machining, Surface roughness", } @Article{Garg:2015:engcomp, author = "Akhil Garg and Kang Tai", title = "Evolving genetic programming models of higher generalization ability in modelling of turning process", journal = "Engineering Computations", year = "2015", volume = "32", number = "8", pages = "2216--2234", month = nov, keywords = "genetic algorithms, genetic programming, surface roughness", ISSN = "0264-4401", URL = "https://www.emerald.com/insight/content/doi/10.1108/EC-12-2014-0252/full/html", DOI = "doi:10.1108/EC-12-2014-0252", abstract = "Generalisation ability of genetic programming (GP) models relies highly on the choice of parameter settings chosen and the fitness function used. The purpose of this paper is to conduct critical survey followed by quantitative analysis to determine the appropriate parameter settings and fitness function responsible for evolving the GP models with higher generalization ability. Design/methodology/approach For having a better understanding about the parameter settings, the present work examines the notion, applications, abilities and the issues of GP in the modeling of machining processes. A gamut of model selection criteria have been used in fitness functions of GP, but, the choice of an appropriate one is unclear. GP is applied to model the turning process to study the effect of fitness functions on its performance. Findings The results show that the fitness function, structural risk minimization (SRM) gives better generalization ability of the models than those of other fitness functions. Originality/value This study is of its first kind where two main contributions are listed addressing the need of evolving GP models with higher generalization ability. First is the survey study conducted to determine the parameter settings and second, the quantitative analysis for unearthing the best fitness function.", notes = "Mechanical and Aerospace engineering, Nanyang Technological University, Singapore, Singapore", } @Article{Garg:2016:JCP, author = "Akhil1 Garg and Jasmine Siu Lee Lam", title = "Power consumption and tool life models for the production process", journal = "Journal of Cleaner Production", year = "2016", volume = "131", pages = "754--764", ISSN = "0959-6526", DOI = "doi:10.1016/j.jclepro.2016.04.099", URL = "http://www.sciencedirect.com/science/article/pii/S0959652616303754", abstract = "For achieving the multi-objective optimization of product quality and power consumption of any production process, the formulation of generalized models is essential. Extensive research has been done on applying the traditional statistical methods (analysis of variance, response surface methodology, grey relational analysis, Taguchi method) in formulation of these models for the processes. In the present work, a detailed survey on the applications of these methods in modelling of power consumption for the production operations specifically machining is conducted. Critical issues arising from the survey are highlighted and hence form the motivation of this study. Further, three advanced soft computing methods, namely evolutionary-based genetic programming (GP), support vector regression, and multi-adaptive regression splines are proposed in predictive modelling of tool life and power consumption of a turning phenomenon in machining. Statistical comparison based on the five error metrics and hypothesis tests for the goodness of the fit reveals that the GP model outperforms the other two models. The hidden relationships between the process parameters are unveiled from the formulated models. It is found that the cutting speed parameter is the most influential input for power consumption and tool life in the turning phenomenon. The future scope comprising of the challenges in predictive modelling of production processes is highlighted in the end.", keywords = "genetic algorithms, genetic programming, Power consumption, Machining, Environmental, Tool life, Soft computing methods", } @Article{Garg:2016:JCPa, author = "Akhil1 Garg and Jasmine Siu Lee Lam and L. Gao", title = "Modeling multiple-response environmental and manufacturing characteristics of {EDM} process", journal = "Journal of Cleaner Production", year = "2016", volume = "137", pages = "1588--1601", month = "20 " # nov, ISSN = "0959-6526", DOI = "doi:10.1016/j.jclepro.2016.04.070", URL = "http://www.sciencedirect.com/science/article/pii/S0959652616303389", abstract = "Among the machining operations, Electrical discharge machining (EDM) process is widely used in production industries because of its ability to machine the materials of any hardness. However, the machining of advanced materials including ceramics, composites, and super-alloys requiring the precise surface finish and dimensional accuracy also increases the energy consumption and cost simultaneously. As such, both environmental and economic performances are compromised. Also, EDM process is itself considered hazardous because of the large toxic liquid and solid wastes and gases produced due to reaction products developed from highly energized dielectric media placed between tool and workpiece. Thus, an appropriate balance between manufacturing and environmental aspects is highly desirable for ensuring higher productivity and environmental sustainability of the process. In this context, the present work proposes two variants of optimization approach of genetic programming (GP) in modelling the multi-response characteristics, i.e. two environmental aspects (thermal energy consumption and dielectric consumption) and one manufacturing aspect (relative tool to wear ratio) of the EDM process. These variants are proposed by introducing two model selection criteria from statistical learning theory to be used as fitness functions in the framework of GP. The performance of the proposed GP models is evaluated against the experimental data based on five statistical error metrics and the two hypothesis tests. Further, the relationships between manufacturing, environmental aspects and the input process parameters are unveiled, which can be used by industry users to optimize the process economically and environmentally. It was found that the input peak current has the highest impact on the environmental aspects of the EDM process.", keywords = "genetic algorithms, genetic programming, Electrical discharge machining (EDM), Machining, Environmental, Energy consumption, Relative tool to wear ratio", } @Article{Garg:2016:CILS, author = "Akhil Garg and B. N. Panda and D. Y. Zhao and K. Tai", title = "Framework based on number of basis functions complexity measure in investigation of the power characteristics of direct methanol fuel cell", journal = "Chemometrics and Intelligent Laboratory Systems", volume = "155", pages = "7--18", year = "2016", ISSN = "0169-7439", DOI = "doi:10.1016/j.chemolab.2016.03.025", URL = "http://www.sciencedirect.com/science/article/pii/S0169743916300612", abstract = "A potential alternative to cell batteries is the air-breathing micro direct methanol fuel cell (muDMFC) because it is environmental friendly, charging-free, possesses high energy density properties and provides easy storage of the fuel. The effective functioning of the complex air-breathing uDMFC system exhibits higher dependence on its operating conditions and the parameters. The main challenge for the experts is to determine its optimum operating conditions. In this context, the mathematical modelling approach based on evolutionary framework of genetic programming (GP) can be applied. However, its successful implementation depends on the complexity chosen in its structural risk minimization (SRM) objective function. In this work, the two measures of complexity based on the standardized number of nodes and the number of basis functions in the splines is chosen. Comparison between the two GP approaches based on these two complexity measures is evaluated on the experimental procedure performed on the DMFC. The power characteristics considered in this study are power density and open-circuit voltage and the three inputs considered are methanol flow rate, methanol concentration and the cell temperature. The statistical analysis based on cross-validation, error metrics and hypothesis tests is performed to choose the best GP based power characteristics models. Further, 2-D plots for measuring the individual effects and the 3-D plots for the interaction effects of the inputs on the power characteristics is plotted based on the parametric approach. It was found that the methanol concentration influences the power characteristics (power density and OCV) of DMFC the most followed by cell temperature and methanol flow rate.", keywords = "genetic algorithms, genetic programming, Direct methanol fuel cell, DFMC, Fuel cell performance, Power characteristics", } @InProceedings{garg:2016:HSAM, author = "Akhil Garg and Jasmine Siu Lee Lam and M. M. Savalani", title = "A New Variant of Genetic Programming in Formulation of Laser Energy Consumption Model of {3D} Printing Process", booktitle = "Handbook of Sustainability in Additive Manufacturing", year = "2016", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-981-10-0549-7_3", DOI = "doi:10.1007/978-981-10-0549-7_3", } @Article{Garg:2016:JCPb, author = "Akhil Garg and Shrutidhara Sarma and B. N. Panda and Jian Zhang2 and L. Gao", title = "Study of effect of nanofluid concentration on response characteristics of machining process for cleaner production", journal = "Journal of Cleaner Production", year = "2016", volume = "135", pages = "476--489", month = "1 " # nov, ISSN = "0959-6526", DOI = "doi:10.1016/j.jclepro.2016.06.122", URL = "http://www.sciencedirect.com/science/article/pii/S0959652616307995", abstract = "With the ever-increasing concern for reducing environmental pollution and waste minimization, {"}green manufacturing{"} has been successful to draw sufficient amount of attention towards it. Minimum Quantity Lubrication (MQL) is one such technique that has revolutionized the manufacturing industry by not only reducing the amount of working fluid dramatically but also enhancing cutting tool life and reducing material costs. Past studies have reported the use of experiments in MQL based manufacturing but limited computational modeling for optimizing the process parameters Based on the past experimental procedure of machining process (micro-drilling), a computational framework such as Adaptive Neuro Fuzzy Inference System (ANFIS) and Genetic Programming (GP) in quantification of three response characteristics (torque, thrust forces and material removal rate (MRR) is proposed. The performance analysis based on the cross-validation, error metrics, curve fitting and hypothesis tests reveals that among the two models, the GP models have performed better. 2-D and 3-D surface analysis were performed to validate the robustness of the models. Among the three response characteristics, It was found that the nanofluid concentration influences torque the most, which is an important aspect for power consumption. At nanofluid concentration values of 1.4 and 4, the minimum values of torque and thrust forces is achieved respectively. When drill diameter is minimum and the spindle speed is maximum, the values of torque, thrust forces and MRR is the lowest. Thus, the feed rate, nanofluid concentration and drill diameter are most critical for obtaining higher MRR and lower values of torque and thrust force, thus enabling cleaner production and environment.", keywords = "genetic algorithms, genetic programming, Minimum quality lubrication, Green manufacturing, Micro-drilling process, Torque, Drill diameter", } @Article{Garg:2017:ASC, author = "A. Garg and Jasmine Siu Lee Lam and B. N. Panda", title = "A hybrid computational intelligence framework in modelling of coal-oil agglomeration phenomenon", journal = "Applied Soft Computing", volume = "55", pages = "402--412", year = "2017", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2017.01.054", URL = "http://www.sciencedirect.com/science/article/pii/S1568494617300777", abstract = "The phenomenon of Coal-Oil agglomeration for recovering the coal fines by agitating the coal-water slurries in oil is often practised by coal industry to ensure a safe and healthy environment. Experimental procedure for implementing this phenomenon is complex which involves three main mechanisms: crushing, ultimate and proximate analysis. Past studies have often focused on studying this phenomenon by the application of statistical modelling based on response surface designs. The response surface designs hold an assumption of pre-definition of the model structure, which may introduce uncertainty in the predictive ability of the model. Alternatively, the computational intelligence approach of Genetic programming (GP) that evolves the explicit models automatically can be used. However, the effective functioning of GP is often affected by its nature of producing the models of complex size. Therefore, this work develops a hybrid computational intelligence approach namely, Support vector regression-GP (SVR-GP) to study the coal-oil agglomeration phenomenon. Experimental studies based on five inputs, namely, oil dosage, agitation speed, agglomeration time, temperature, and pH are used to measure the organic matter recovery (OMR (percent)) from the coal water slurries. A hybrid computational intelligence approach of SVR-GP is proposed in formulating the relationship between OMR (percent) and the five inputs. The performance comparison and validation of the SVR-GP model is done based on the coefficient of determination, root mean square error and mean absolute percentage error. 2-D and 3-D surface analysis based on parametric and sensitivity approach is then conducted on the proposed model to find the relevant relationships between OMR (percent) and inputs. The findings suggest that the pH of coal slurry has a significant effect on the OMR (percent) and hence is important for reducing coal waste generation and promoting a cleaner environment.", keywords = "genetic algorithms, genetic programming, Coal waste, Coal-oil agglomeration, Organic matter recovery, Support vector regression", } @Article{GARG:2017:Energy, author = "A. Garg and Jasmine Siu Lee Lam", title = "Design of explicit models for estimating efficiency characteristics of microbial fuel cells", journal = "Energy", volume = "134", pages = "136--156", year = "2017", keywords = "genetic algorithms, genetic programming, Microbial fuel cell, MFC features modelling, MFC features prediction, Fuel cell modelling, Microbial microfluidic cell, Computational intelligence", ISSN = "0360-5442", DOI = "doi:10.1016/j.energy.2017.05.180", URL = "http://www.sciencedirect.com/science/article/pii/S0360544217308770", abstract = "Recent years have seen the use of microbial fuel cells for the generation of electricity from wastewater and renewable biomass. The efficiency characteristics (power density and voltage output) of fuel cells depend highly on their operating conditions such as current density, chemical oxygen demand concentration and anolyte concentration. Computational intelligence methods based on genetic programming and multi-adaptive regression splines are proposed in design of explicit models for estimating efficiency characteristics of microfluidic microbial fuel cells based on the operating conditions. Performance of the models evaluated against the actual data reveals that the models formulated from genetic programming outperform the multi-adaptive regression splines models. The robustness in the best models is validated by performing simulation of the models over 8000 runs based on the normal distribution of the operating conditions. 2-D and 3-D surface analysis conducted on the models reveals that the power density of the fuel cell increases with an increase in values of chemical oxygen demand concentration and current density till a certain value and then decreases. The voltage output decreases with an increase in values of current density while increases with an increase in values of chemical oxygen demand concentration to a certain limit", } @Article{GARG:2018:Measurement, author = "Akhil Garg and Xiongbin Peng and My Loan Phung Le and Kapil Pareek and C. M. M. Chin", title = "Design and analysis of capacity models for Lithium-ion battery", journal = "Measurement", volume = "120", pages = "114--120", year = "2018", keywords = "genetic algorithms, genetic programming, Battery modelling, Electric vehicle, Genetic programming (GP), Complexity, Battery capacity, Temperature, SRM", ISSN = "0263-2241", DOI = "doi:10.1016/j.measurement.2018.02.003", URL = "http://www.sciencedirect.com/science/article/pii/S0263224118300897", abstract = "Past studies on battery models is focussed on formulation of physics-based models, empirical models and fusion models derived from the battery pack data of electric vehicle. It is desirable to have an explicit, robust and accurate models for battery states estimation in-order to ensure its proper reliability and safety. The present work conducts a brief survey on battery models and will propose the evolutionary approach of Genetic programming (GP) for the battery capacity estimation. The experimental design for GP simulation comprises of the inputs such as the battery temperature and the rate of discharge. Further, the seven objective functions in GP approach is designed by introducing the complexity based on the order of polynomial. This step will ensure the precise functions evaluation in GP and drives the evolutionary search towards its optimum solutions. The design and analysis of the GP based battery capacity models involves the statistical validation of the seven objective functions based on error metrics with 2-D and 3-D surface plots. The results conclude that the GP models using Structural risk minimization (SRM) objective function accurately estimate the battery capacity based on the variations of the inputs. 2-D and 3-D surface analysis of the GP model reveals the increasing-decreasing nature of temperature-battery capacity curve with temperature the dominant input. The battery capacity model obtained using SRM as an objective function in GP is robust and thus can be integrated in the electric vehicle system for monitoring its performance and ensure its safety", } @Article{GARG:2019:JCP, author = "Akhil Garg and Liang Gao and Wei Li and Surinder Singh and Xiongbin Peng and Xujian Cui and Z. Fan and Harpreet Singh and C. M. M. Chin", title = "Evolutionary framework design in formulation of decision support models for production emissions and net profit of firm: Implications on environmental concerns of supply chains", journal = "Journal of Cleaner Production", volume = "231", pages = "1136--1148", year = "2019", ISSN = "0959-6526", DOI = "doi:10.1016/j.jclepro.2019.05.300", URL = "http://www.sciencedirect.com/science/article/pii/S0959652619318360", keywords = "genetic algorithms, genetic programming, Carbon emission, Production emissions, Emission elasticity, Green technology, Advanced multi-gene genetic programming", abstract = "There have been increased investments in cleaner technologies and adoption of a voluntarily limit on transportation emissions by the global firms to handle the environmental concerns of supply chains and to increase demand for finished goods. Consequences are the reduction in net profit for the firm. To address this trade-off between the net profit and environmental concerns, the formulation and optimization of a compact model are needed. Development of these models requires a thorough understanding of the nature of the impact of three inputs (investment coefficient, penalty per unit emission and customer's emission elasticity) on production emissions and net profit. Past studies revealed that a compact model comprising the interactive effect of these inputs on the production emissions and net profit is not yet formulated. Therefore, this study illustrates the development of an evolutionary framework of an advanced multi-gene genetic programming in the formulation of functional expressions for the net profit and production emissions based on the three inputs (investment coefficient, penalty per unit emission and customer's emission elasticity) of the monopolist firm. The sensitivity and parametric based 2-D analysis determine the relationships and found that the penalty per unit emission is dominant input for reducing emissions and maintaining net profit simultaneously. The contribution of this work lies in designing of an evolutionary framework in the development of empirical explicit expressions, which can easily be optimized analytically to keep production emissions and net profit balanced", } @Article{GARG:2019:JES, author = "Akhil Garg and Li Wei and Ankit Goyal and Xujian Cui and Liang Gao", title = "Evaluation of batteries residual energy for battery pack recycling: Proposition of stack stress-coupled-{AI} approach", journal = "Journal of Energy Storage", volume = "26", pages = "101001", year = "2019", ISSN = "2352-152X", DOI = "doi:10.1016/j.est.2019.101001", URL = "http://www.sciencedirect.com/science/article/pii/S2352152X1930790X", keywords = "genetic algorithms, genetic programming, Energy storage, Battery pack recycling, Residual energy", abstract = "It is predicted that by 2025, approximately 1 million metric tons of spent battery waste will be accumulated. How to reasonably and effectively evaluate the residual energy of the lithium-ion batteries embedded in hundreds in packs used in Electric Vehicles (EVs) grows attention in the field of battery pack recycling. The main challenges of evaluation of the residual energy come from the uncertainty of thermo-mechanical-electrochemical behavior of battery. This motivates the notion of facilitating research on establishing a model which can detect and predict the state of battery based on parameters enable to be measured, such as voltage and stack stress. Thus, the present work proposes a stack stress-coupled-artificial intelligence approach for analyzing the residual energy (remaining) in the batteries. Experiments are designed and performed to verify the fundamentals. A robust model is formulated based on artificial intelligence approach of genetic programming. The findings in the study can provide an optimized recycling strategy for spent batteries by accurately predicting the state of battery based on stack stress", } @Article{DBLP:journals/ijbic/GargSGXT20, author = "Akhil Garg and Surinder Singh and Liang Gao and Mei-Juan Xu and Chee Pin Tan", title = "Multi-objective optimisation framework of genetic programming for investigation of bullwhip effect and net stock amplification for three-stage supply chain systems", journal = "Int. J. Bio Inspired Comput.", volume = "16", number = "4", pages = "241--251", year = "2020", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1504/IJBIC.2020.112329", DOI = "doi:10.1504/IJBIC.2020.112329", timestamp = "Tue, 23 Feb 2021 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/ijbic/GargSGXT20.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{GARG:2020:swarm, author = "Akhil Garg and Shaosen Su and Fan Li and Liang Gao", title = "Framework of model selection criteria approximated genetic programming for optimization function for renewable energy systems", journal = "Swarm and Evolutionary Computation", volume = "59", year = "2020", pages = "100750", month = dec, keywords = "genetic algorithms, genetic programming, Model selection criteria, Objective function approximation, Renewable energy systems", ISSN = "2210-6502", URL = "http://www.sciencedirect.com/science/article/pii/S221065022030403X", DOI = "doi:10.1016/j.swevo.2020.100750", abstract = "For the realization of complex renewable energy systems (such as nano-fluids based direct absorption solar collector), an evolutionary system identification method such as genetic programming (GP) can be applied to develop mathematical models/functional relationships between the process parameters. The system complexity is attributed to interaction among the design variables influencing the outputs. There are also uncertainties in the system due to random and unknown variations in the design and response variables. GP suffers from the higher complexity structure of its solutions and non-optimal convergence, which leads to poor fitness values. Therefore, to address these uncertainties and problems, the framework based on the model selection criteria approximated genetic programming (MSC-GP) is proposed for the formulation of geometry design based thermal efficiency and entropy generation optimization function for direct absorption solar collector (DASC) system. In this proposed method, the four mathematical model selection criteria are used as an approximation for objective functions in GP framework for the evaluation of fitting degree and structure of the model. The results based on statistical measures (best fitness, mean fitness, standard deviation of fitness, number of nodes) show that models obtained from the mathematical selection criteria, Predicted Residual error sum of squares (PRESS), have performed the best. Based on Pareto front analysis of PRESS function, it is found that the best objective values and the number of nodes of models (complexity) follows more or less gradually slow increasing trend which is a good symbolic desirable sign of minimal increase of complexity of model with a decrease in objective values as the values of generation increases. The results of the sensitivity analysis show that the main factor affecting the efficiency of DASC is its geometry of the structure. 3-D interaction analysis shows that increasing the thickness, length and reducing the width of the collector can make the system maintain its higher thermal efficiency and a smaller entropy generation, which is useful for the optimized operation of DASC. Non-dominated sorting genetic algorithm-II (NSGA-II) is applied in the acquisition of the optimal geometric settings of DASC system based on the selected models. The optimal settings achieved is 5 cm in length, 5 cm in width, and 2 cm in thickness. Systems when operated using these settings results in a satisfactory performance with 77.8117percent in thermal efficiency and 6.0004E+3 in entropy generation)", } @Article{Garg:2020:IJER, author = "Akhil Garg and Su Shaosen and Liang Gao and Xiongbin Peng and Prashant Baredar", title = "Aging model development based on multidisciplinary parameters for lithium-ion batteries", journal = "International Journal of Energy Research", year = "2020", volume = "44", number = "4", pages = "2801--2818", keywords = "genetic algorithms, genetic programming, li, battery aging model, battery management system, diffusion coefficient, energy conversion", eprint = "https://onlinelibrary.wiley.com/doi/pdf/10.1002/er.5096", URL = "https://onlinelibrary.wiley.com/doi/abs/10.1002/er.5096", DOI = "doi:10.1002/er.5096", abstract = "a method composed of state of health (SOH) testing experiments and artificial intelligence simulation is proposed to carry out the study on the change of battery characteristic during its operation and generate mathematical models for the prediction of aging behaviour of battery. An experiment comprising of multidisciplinary parameters-based SOH detection is conducted to study the battery aging characteristics from several aspects (ie, electrochemistry, electric, thermal behaviour and mechanics). In total, 200 sets of data (corresponding 200 charging/discharging cycles) are collected from the experiment. The data obtained from the first 150 cycles are employed in generation of the models. The result of sensitivity analysis based on the obtained genetic programming models shows that it is better to apply voltage value at the end of charging step, charging time and cycle number to predict the operational performance of the battery. The average predicted accuracy of model (without stress) is 94.52 percent, whereas the average predicted accuracy of model (with stress effect) is 99.42 percent. The proposed models could be useful for defining the optimised charging strategy, fault diagnosis and spent batteries disposal strategies.", notes = "Nov 2024 open broken Huazhong University of Science and Technology", } @InProceedings{Garg:2025:ICSE, author = "Spandan Garg and Roshanak Zilouchian Moghaddam and Neel Sundaresan", title = "Improving Code Performance Using {LLMs} in Zero-Shot: {RAPGen}", booktitle = "IEEE/ACM International Conference on Software Engineering, ICSE 2025, Software Engineering in Practice", year = "2025", editor = "Ciera Jaspan and Rick Kazman", address = "Ottawa", month = apr # " 30-" # may # " 3", keywords = "genetic algorithms, genetic programming, genetic improvement, ANN, .NET, APR", URL = "https://arxiv.org/html/2306.17077v2", URL = "https://conf.researchr.org/profile/forge-2025/spandangarg", URL = "https://conf.researchr.org/track/icse-2025/icse-2025-research-track?track=ICSE%20Research%20Track%2BICSE%20SE%20In%20Practice%20(SEIP)#program", abstract = "Performance bugs are non-functional bugs that can even manifest in well-tested commercial products. Fixing these performance bugs is an important yet challenging problem. In this work, we address this challenge and present a new approach called Retrieval-Augmented Prompt Generation (RAPGen). Given a code snippet with a performance issue, RAPGen first retrieves a prompt instruction from a pre-constructed knowledge-base of previous performance bug fixes and then generates a prompt using the retrieved instruction. It then uses this prompt on a Large Language Model in zero-shot to generate a fix. We compare our approach with the various prompt variations and state of the art methods in the task of performance bug fixing. Our empirical evaluation shows that RAPGen can generate performance improvement suggestions equivalent or better than a developer in 60 percent of the cases, getting 42 percent of them verbatim, in an expert-verified dataset of past performance changes made by C# developers. Furthermore, we conduct an in-the-wild evaluation to verify the model effectiveness in practice by suggesting fixes to developers in a large software company. So far, we have shared performance fixes on 10 codebases that represent production services running in the cloud and 7 of the fixes have been accepted by the developers and integrated into the code.", notes = "Software Engineering in Practice SEIP Microsoft Corporation", } @InProceedings{Garg:2009:WEWRC, author = "Vaibhav Garg and V. Jothiprakash", title = "Reservoir Sedimentation Estimation Using Genetic Programming Technique", booktitle = "World Environmental and Water Resources Congress", year = "2009", editor = "Steve Starrett", pages = "1505--1513", month = "17-21 " # may, address = "Kansas City, Missouri, USA", publisher_address = "USA", organisation = "Environmental and Water Resources Institute, EWRI", publisher = "ASCE", keywords = "genetic algorithms, genetic programming, Reservoirs, Sediment, India", isbn13 = "9780784410363", DOI = "doi:10.1061/41036(342)149", size = "9 pages", abstract = "To a certain extent, all reservoirs are subjected to the problem of sediment deposition universally. Depending on the amount of material deposited the shortening of reservoir capacity and useful life result in several unpredictable consequences. To determine the total quantity of deposition, as well as the pattern and distribution of deposits in a reservoir, hydrographic survey is the only direct measurement method. These hydrographic survey methods are being considered as expensive, time consuming and cumbersome. In the present study, an attempt has been made to employ genetic programming (GP) soft computing technique to estimate the volume of sediment retained (Sv) in the Pong Reservoir, India. It was found that GP model captured the trend and magnitude of Sv very well. Moreover, GP model provided input-output relationship in the form of computer programs which may be easily used by end user. Also, GP can be effectively used to capture the non-linear relationship between the input and output with short length of data", notes = "Stock No. 41036. Great Rivers Department of Civil Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 400 076, Maharashtra, India", } @PhdThesis{Garg:thesis, author = "Vaibhav Garg", title = "Evaluation of reservoir sedimentation using data driven techniques", school = "Department of Civil Engineering Indian Institute of Technology Bombay", year = "2010", address = "India", keywords = "genetic algorithms, genetic programming", URL = "https://opac.library.iitb.ac.in/cgi-bin/koha/opac-detail.pl?biblionumber=206724", size = "xiv,202 pages", notes = "Call number=043:627.81:551.3.051 Gar Barcode=224869 https://www.civil.iitb.ac.in/~vprakash/guidance.html 1. Received the Awards for Excellence in Thesis Work-2010 during the 48th Convocation of IIT Bombay 2. SAARC-IAESTE-Academic Internship Program at Tokyo Institute of Technology, Tokyo, Jan 22 -Feb 13, 2009. Supervisor: V Jothiprakash", } @Article{Garg:2010:JHE, author = "Vaibhav Garg and V. Jothiprakash", title = "Modeling the Time Variation of Reservoir Trap Efficiency", journal = "Journal of Hydrologic Engineering", year = "2010", volume = "15", number = "12", pages = "1001--1015", month = dec, email = "vaibhavgarg@iitb.ac.in", keywords = "genetic algorithms, genetic programming, Sedimentation, Reservoirs, Hydrologic models, Computation, Artificial intelligence, Neural networks, ANN, India, Evolutionary computation", publisher = "American Society of Civil Engineers ASCE", ISSN = "1084-0699", URL = "http://dx.doi.org/10.1061/(ASCE)HE.1943-5584.0000273", URL = "http://ascelibrary.org/doi/abs/10.1061/%28ASCE%29HE.1943-5584.0000273", URL = "http://ascelibrary.org/doi/pdf/10.1061/(ASCE)HE.1943-5584.0000273", DOI = "doi:10.1061/(ASCE)HE.1943-5584.0000273", size = "15 pages", abstract = "All reservoirs are subjected to sediment inflow and deposition to a certain extent resulting in reduction of their capacity. Trap efficiency (Te), a most important parameter for reservoir sedimentation studies, is being estimated using conventional empirical methods till today. A limited research has been carried out on estimating the variation of Te with time. In the present study, an attempt has been made to incorporate the age of the reservoir to estimate the Te. This study investigates the suitability of conventional empirical approaches along with soft computing data-driven techniques to estimate the reservoir Te. The incorporation of reservoir age, in empirical model, has resulted in a better Te estimation. Further, to estimate Te at different time steps, soft computing approaches such as artificial neural networks (ANNs) and genetic programming (GP) have been attempted. Based on correlation analysis, it was found that ANN model (4-4-1) resulted better than conventional empirical methods but inferior to GP. The results show that the GP model is parsimonious and understandable and is well suited to estimate Te of a large reservoir.", notes = "http://ascelibrary.org/journal/jhyeff Also known as \cite{doi:10.1061/(ASCE)HE.1943-5584.0000273}", } @Article{Garg:2013:ASC, author = "Vaibhav Garg and V. Jothiprakash", title = "Evaluation of reservoir sedimentation using data driven techniques", journal = "Applied Soft Computing", year = "2013", volume = "13", number = "8", pages = "3567--3581", month = aug, keywords = "genetic algorithms, genetic programming, Reservoir sedimentation, Soft computing techniques, Artificial neural networks, Model trees", ISSN = "1568-4946", URL = "http://www.sciencedirect.com/science/article/pii/S1568494613001439", DOI = "doi:10.1016/j.asoc.2013.04.019", abstract = "The sedimentation is a pervasive complex hydrological process subjected to each and every reservoir in world at different extent. Hydrographic surveys are considered as most accurate method to determine the total volume occupied by sediment and its distribution pattern in a reservoir. But, these surveys are very cumbersome, time consuming and expensive. This complex sedimentation process can also be simulated through the well calibrated numerical models. However, these models generally are data extensive and require large computational time. Generally, the availability of such data is very scarce. Due to large constraints of these methods and models, in the present study, data driven approaches such as artificial neural networks (ANN), model trees (MT) and genetic programming (GP) have been investigated for the estimation of volume of sediment deposition incorporating the parameters influenced it along with conventional multiple linear regression data driven model. The aforementioned data driven models for the estimation of reservoir sediment deposition were initially developed and applied on Gobindsagar Reservoir. In order to generalise the developed methodology, the developed data driven models were also validated for unseen data of Pong Reservoir. The study depicted that the highly nonlinear models ANN and GP captured the trend of sediment deposition better than piecewise linear MT model, even for smaller length datasets.", } @Article{Garg:2014:NH, author = "Vaibhav Garg", title = "Modeling catchment sediment yield: a genetic programming approach", journal = "Natural Hazards", year = "2014", volume = "70", number = "1", pages = "39--50", month = jan, email = "vaibhav@iirs.gov.in", keywords = "genetic algorithms, genetic programming, Sediment yield, Modelling and simulation, Evolutionary technique, Soft computing", publisher = "Springer", ISSN = "0921-030X", language = "English", URL = "http://link.springer.com/article/10.1007%2Fs11069-011-0014-3#page-1", DOI = "doi:10.1007/s11069-011-0014-3", size = "12 pages", abstract = "Hydrologic processes are complex, non-linear, and distributed within a watershed both spatially and temporally. One such complex pervasive process is soil erosion. This problem is usually approached directly by considering the sediment yield. Most of the hydrologic models developed and used earlier in sediment yield modelling were lumped and had no provision for including spatial and temporal variability of the terrain and climate attributes. This study investigates the suitability of a recent evolutionary technique, genetic programming (GP), in estimating sediment yield considering various meteorological and geographic features of a basin. The Arno River basin in Italy, which is prone to frequent floods, has been chosen as case study to demonstrate the GP approach. The results of the present study show that GP can efficiently capture the trend of sediment yield, even with a small set of data. The major advantage of the GP analysis is that it generates simple parsimonious expression offering some possible interpretations to the underlying process.", } @InProceedings{gargano:1998:GAfssmsttec, author = "Michael L. Gargano and William Edelson and Olga Koval", title = "A Genetic Algorithm With Feasible Search Space For Minimal Spanning Trees With Time-Dependent Edge Costs", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "495", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms", notes = "SGA-98", } @Article{Garibay:2006:GPEM, author = "Ivan Garibay and Annie S. Wu and Ozlem Garibay", title = "Emergence of genomic self-similarity in location independent representations Favoring positive correlation between the form and quality of candidate solutions", journal = "Genetic Programming and Evolvable Machines", year = "2006", volume = "7", number = "1", pages = "55--80", month = mar, keywords = "genetic algorithms, Representation, Proportional genetic algorithm, Self-organisation, Genomic self-similarity, Emergence", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-006-7011-4", size = "26 pages", abstract = "A key property for predicting the effectiveness of stochastic search techniques, including evolutionary algorithms, is the existence of a positive correlation between the form and the quality of candidate solutions. In this paper we show that when the ordering of genomic symbols in a genetic algorithm is completely independent of the fitness function and therefore free to evolve along with the candidate solutions it encodes, the resulting genomes self-organise into self-similar structures that favour this key stochastic search property.", notes = "white noise", } @Article{Garibay:2010:GPEM, author = "Ivan Garibay", title = "Dario Floreano and Claudio Mattiussi (eds): Bio-inspired artificial intelligence: theories, methods, and technologies", journal = "Genetic Programming and Evolvable Machines", year = "2010", volume = "11", number = "3/4", pages = "441--443", month = sep, note = "Book review", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dR8rE", DOI = "doi:10.1007/s10710-010-9104-3", size = "3 pages", notes = "See Erratum \cite{Garibay:2011:GPEM}", } @Article{Garibay:2011:GPEM, author = "Ivan Garibay", title = "Erratum to: Dario Floreano and Claudio Mattiussi: Bio-inspired artificial intelligence: theories, methods, and technologies", journal = "Genetic Programming and Evolvable Machines", year = "2011", volume = "12", number = "1", pages = "89--89", month = mar, keywords = "Computer Science", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-010-9123-0", abstract = "The publisher regrets that the following book review incorrectly listed the authors Dario Floreano and Claudio Mattiussi as editors of their book, Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies. Dario Floreano and Claudio Mattiussi are the sole authors of this volume.", notes = "Correction to \cite{Garibay:2010:GPEM}", affiliation = "University of Central Florida, Orlando, FL USA", } @InProceedings{garmendia-doval:1998:etrsf, author = "A. Beatriz Garmendia-Doval and Chilukuri K. Mohan and Mohit K. Prasad", title = "Evolving Tree Representations of Stack Filters", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "103--108", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-548-7", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/24116/http:zSzzSzwww.scms.rgu.ac.ukzSzstaffzSzbgdzSzGP98.pdf/garmendia-doval98evolving.pdf", URL = "http://citeseer.ist.psu.edu/494447.html", size = "6 pages", abstract = "Evolutionary algorithms are applied to the design of a class of nonlinear discrete-time filters: the positive Boolean function defining a stack filter is derived from its properties specified in terms of `selection probabilities'. For window size 9, with search space of at least 2 126 , best results were obtained using a tree representation for each positive Boolean function.", notes = "GP-98", } @InProceedings{garmendia-doval:2003:EA, author = "A. Beatriz Garmendia-Doval and S. David Morley and Szilveszter Juhos", title = "Post Docking Filtering Using Cartesian Genetic Programming", booktitle = "Evolution Artificielle, 6th International Conference", year = "2003", editor = "Pierre Liardet and Pierre Collet and Cyril Fonlupt and Evelyne Lutton and Marc Schoenauer", volume = "2936", series = "Lecture Notes in Computer Science", pages = "189--200", address = "Marseilles, France", month = "27-30 " # oct, publisher = "Springer", note = "Revised Selected Papers", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Artificial Evolution", ISBN = "3-540-21523-9", DOI = "doi:10.1007/b96080", DOI = "doi:10.1007/978-3-540-24621-3_16", abstract = "Structure-based virtual screening is a technology increasingly used in drug discovery. Although successful at estimating binding modes for input ligands, these technologies are less successful at ranking true hits correctly by binding free energy. We present initial attempts to automate the removal of false positives from virtual hit sets, by evolving a post docking filter using Cartesian Genetic Programming.", bibsource = "DBLP, http://dblp.uni-trier.de", notes = "EA'03 HSP90 data. RiboTargets Ltd, Granta Park, Cambridge, England, CB1 6GB", } @InCollection{garmendia-doval:2004:GPTP, author = "A. Beatriz Garmendia-Doval and Julian Miller and S. David Morley", title = "Post Docking Filtering Using Cartesian Genetic Programming", booktitle = "Genetic Programming Theory and Practice {II}", year = "2004", editor = "Una-May O'Reilly and Tina Yu and Rick L. Riolo and Bill Worzel", chapter = "14", pages = "225--244", address = "Ann Arbor", month = "13-15 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, molecular docking prediction, virtual screening, machine learning, evolutionary algorithms, neutral evolution", ISBN = "0-387-23253-2", DOI = "doi:10.1007/0-387-23254-0_14", abstract = "Structure-based virtual screening is a technology increasingly used in drug discovery. Although successful at estimating binding modes for input ligands, these technologies are less successful at ranking true hits correctly by binding free energy. This chapter presents the automated removal of false positives from virtual hit sets, by evolving a post docking filter using Cartesian Genetic Programming(CGP). We also investigate characteristics of CGP for this problem and confirm the absence of bloat and the usefulness of neutral drift.", notes = "part of \cite{oreilly:2004:GPTP2}", } @Article{Garnica:GPEM:3ehwCS, author = "Oscar Garnica and Kyrre Glette and Jim Torresen", title = "Comparing three online evolvable hardware implementations of a classification system", journal = "Genetic Programming and Evolvable Machines", year = "2018", volume = "19", number = "1-2", pages = "211--234", month = jun, keywords = "genetic algorithms, evolvable hardware, EHW, Evolutionary algorithms, Classifier system, Field programmable gate arrays", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-017-9312-1", size = "24 pages", abstract = "In this paper, we present three implementations of an online evolvable hardware classifier of sonar signals on a 28 nm process technology FPGA, and compare their features using the most relevant metrics in the design of hardware: area, timing, power consumption, energy consumption, and performance. The three implementations are: one full-hardware implementation in which all the modules of the evolvable hardware system, the evaluation module and the Evolutionary Algorithm have been implemented on the ZedBoard Zynq Evaluation Kit (XC7-Z020 ELQ484-1); and two hardware/software implementations in which the Evolutionary Algorithm has been implemented in software and run on two different processors: Zynq XC7-Z020 and MicroBlaze. Additionally, each processor-based implementation has been tested at several processor speeds. The results prove that the full-hardware implementation always performs better than the hardware/software implementations by a considerable margin: up to times7.74 faster than MicroBlaze, between times1.39 and times2.11 faster that Zynq, and times0.198 lower power consumption. However, the hardware/software implementations have the advantage of being more flexible for testing different options during the design phase. These figures can be used as a guideline to determine the best use for each kind of implementation.", notes = "'The system has been implemented on a ZedBoard TM Zynq Evaluation Kit (XC7-Z020 ELQ484-1) although it does not use the built-in Zynq XC7-Z020 processor.' Not GP?", } @InProceedings{Garnica:2024:GPTP, author = "Marina {de la Cruz} and Oscar Garnica and J. Manuel Velasco and Daniel Parra and J. Ignacio Hidalgo", title = "Tree-Based Grammatical Evolution with Non-Encoding Nodes", booktitle = "Genetic Programming Theory and Practice XXI", year = "2024", editor = "Stephan M. Winkler and Wolfgang Banzhaf and Ting Hu and Alexander Lalejini", series = "Genetic and Evolutionary Computation", pages = "177--197", address = "University of Michigan, USA", month = jun # " 6-8", publisher = "Springer", keywords = "genetic algorithms, genetic programming, grammatical evolution", isbn13 = "978-981-96-0076-2", DOI = "doi:10.1007/978-981-96-0077-9_9", notes = "Published in 2025 after the workshop", } @PhdThesis{GarridoArmas_Marta_TD_2017, author = "Marta {Garrido Armas}", title = "Calculo de la transformacion lluvia-escorrentia mediante un modelo Saint Venant {2D}. Validacion mediante datos de campo y laboratorio", school = "Departamento de Ingenieria Civil, University of Coruna", year = "2017", type = "Tesis doctoral UDC", address = "Spain", keywords = "Escorrentia (Hidrologia), Aguas pluviales-Evacuacion, Ecuaciones de Saint-Venant", URL = "http://hdl.handle.net/2183/19820", URL = "https://ruc.udc.es/dspace/bitstream/handle/2183/19820/GarridoArmas_Marta_TD_2017.pdf", URL = "https://core.ac.uk/download/pdf/143423768.pdf", size = "287 pages", abstract = "The current PhD thesis is set within the context of the study of application of 2D distributed models to rainfall-runoff transformations. Although the application of two-dimensional models to surface runoff modeling is now a reality, the use of models that use the complete Saint Venant 2D equations (SWE 2D or 2D dynamic wave) to the entire runoff process, rather than simplified versions of them, is still incipient and object of study. This thesis also deals with the study of two other relevant aspects in the modeling of rainfall-runoff events, such as how to include urban obstacles in the model and the influence of the spacial distribution of precipitation in this kind of simulations. The work carried out includes the validation of a Saint Venant 2D model for laboratory artificial basins (specially designed for this thesis) on which different urban configurations and precipitation events are developed. It also validates the model of two well-developed real basins: an urban basin of 12 ha in Galicia (Spain) and a rural basin of 24 km2 in the experimental basin of Walnut Gulch in Arizona (USA). The sensitivity of the model to different parameters and approximations has been evaluated, and the uncertainty of the model for each case has been assessed.", abstract = "GALEGO Esta tese doctoral enmarcase dentro do estudo da aplicacion de modelos distribuidos 2D a calculos de transformacions choiva-escorrentia. Ainda que na actualidade a aplicacion de modelos bidimensionales a modelizacion de escorrentia superficial xa e unha realidade, a utilizacion de modelos que aplican as ecuacions completas de Saint Venant 2D (ou onda dinamica 2D) a todo o proceso de escorrentia, e non versions simplificadas das mesmas, e ainda incipiente e obxecto de estudo. Abordanse tamen nesta tese o estudo doutros dous aspectos relevantes na modelizacion de escorrentia superficial, como son a forma de incluir obstaculos urbanos no modelo e a influencia da distribucion especial da precipitacion neste tipo de calculos. Os traballos realizados incluen a validacion dun modelo Saint Venant 2D para cuencas artificiais de laboratorio (especialmente desenadas para esta tese) sobre a que se desenvolven diferentes configuracions urbanas e eventos de precipitacion. Tamen se valida o modelo sobre duascuencas reais ampliamente instrumentadas: unha cuenca urbana de 12 ha de superficie situada en Galicia (Espana) e unha cuenca rural de 24 km2 pertencente a cuenca experimental de Walnut Gulch en Arizona (EEUU). Hase evaluado a sensibilidade do modelo a diferentes parametros e aproximaciones, e valorado a incerteza do mesmo para cada caso.", resumen = "CASTELLANO Esta tesis doctoral se enmarca dentro del estudio de la aplicacion de modelos distribuidos 2D a calculos de transformaciones lluvia-escorrentia. Aunque en la actualidad la aplicacion de modelos bidimensionales a la modelizacion de escorrentia superficial ya es una realidad, la utilizacion de modelos que aplican las ecuaciones completas de Saint Venant 2D (u onda dinamica 2D) a todo el proceso de escorrentia, y no versiones simplificadas de las mismas, es todavia incipiente y objeto de estudio. Se abordan tambien en esta tesis el estudio de otros dos aspectos relevantes en la modelizacion de escorrentia superficial, como son la forma de incluir obstaculos urbanos en el modelo y la influencia de la distribucion especial de la precipitacion en este tipo de calculos. Los trabajos realizados incluyen la validacion de un modelo Saint Venant 2D para cuencas artificiales de laboratorio (especialmente disenadaspara esta tesis) sobre la que se desarrollan diferentes configuraciones urbanas y eventos de precipitacion. Tambien se validadel modelo sobre dos cuencas reales ampliamente instrumentadas: una cuenca urbana de 12 ha de superficie ubicada en Galicia (Espana) y una cuenca rural de 24 km2perteneciente a la cuenca experimental de Walnut Gulch en Arizona(EEUU). Se ha evaluado la sensibilidad del modelo a diferentes parametros y aproximaciones, y valorado la incertidumbre del mismo para cada caso", notes = "Not on GP? In Castellano, Galego, Spanish and 3 appendices in English,. Supervisors: Luis Cea Gomez and Jeronimo Puertas Agudo", } @InProceedings{garrow:2022:ECADA, author = "Fraser Garrow and Michael Lones and Robert Stewart", title = "Why Functional Program Synthesis Matters (In the Realm of Genetic Programming)", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion", year = "2022", editor = "Heike Trautmann and Carola Doerr and Alberto Moraglio and Thomas Bartz-Beielstein and Bogdan Filipic and Marcus Gallagher and Yew-Soon Ong and Abhishek Gupta and Anna V Kononova and Hao Wang and Michael Emmerich and Peter A. N. Bosman and Daniela Zaharie and Fabio Caraffini and Johann Dreo and Anne Auger and Konstantin Dietric and Paul Dufosse and Tobias Glasmachers and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Tea Tusar and Dimo Brockhoff and Tome Eftimov and Pascal Kerschke and Boris Naujoks and Mike Preuss and Vanessa Volz and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Mark Coletti and Catherine (Katie) Schuman and Eric ``Siggy'' Scott and Robert Patton and Paul Wiegand and Jeffrey K. Bassett and Chathika Gunaratne and Tinkle Chugh and Richard Allmendinger and Jussi Hakanen and Daniel Tauritz and John Woodward and Manuel Lopez-Ibanez and John McCall and Jaume Bacardit and Alexander Brownlee and Stefano Cagnoni and Giovanni Iacca and David Walker and Jamal Toutouh and UnaMay O'Reilly and Penousal Machado and Joao Correia and Sergio Nesmachnow and Josu Ceberio and Rafael Villanueva and Ignacio Hidalgo and Francisco {Fernandez de Vega} and Giuseppe Paolo and Alex Coninx and Antoine Cully and Adam Gaier and Stefan Wagner and Michael Affenzeller and Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and William B. Langdon and Justyna Petke and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Paetzel and Alexander Wagner and Michael Heider and Nadarajen Veerapen and Katherine Malan and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Mohammad Nabi Omidvar and Yuan Sun and Ernesto Tarantino and De Falco Ivanoe and Antonio {Della Cioppa} and Scafuri Umberto and John Rieffel and Jean-Baptiste Mouret and Stephane Doncieux and Stefanos Nikolaidis and Julian Togelius and Matthew C. Fontaine and Serban Georgescu and Francisco Chicano and Darrell Whitley and Oleksandr Kyriienko and Denny Dahl and Ofer Shir and Lee Spector and Alma Rahat and Richard Everson and Jonathan Fieldsend and Handing Wang and Yaochu Jin and Erik Hemberg and Marwa A. Elsayed and Michael Kommenda and William {La Cava} and Gabriel Kronberger and Steven Gustafson", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, Functional languages, Automatic programming, program synthesis, functional programming", isbn13 = "978-1-4503-9268-6/22/07", URL = "https://pure.hw.ac.uk/ws/portalfiles/portal/54217416/GECCO_ECADA_2022_GarrowLonesStewart_WhyFunctionalProgramSynthesisMatters.pdf", DOI = "doi:10.1145/3520304.3534045", size = "10 pages", abstract = "In Genetic Programming (GP) systems, particularly those that target general program synthesis problems, it is common to use imperative programming languages to represent evolving code. we consider the benefits of using a purely functional, rather than an imperative, approach. We then demonstrate some of these benefits via an experimental comparison of the pure functional language Haskell and the imperative language Python when solving program synthesis benchmarks within a grammar-guided GP system. Notably, we discover that the Haskell programs yield a higher success rate on unseen data, and that the evolved programs often have a higher degree of interpretability. We also discuss the broader issues of adapting a grammar-based GP system to functional languages, and highlight some of the challenges involved with carrying out comparisons using existing benchmark suites", notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @InProceedings{Garzon:1997:mDNAc, author = "M. Garzon and P. Neathery and R. Deaton and R. C. Murphy and D. R. Franschetti and S. E. {Stevens Jr.}", title = "A New Metric for DNA Computing", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "DNA Computing", pages = "472--478", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", notes = "GP-97", } @InProceedings{garzon:1999:OSG, author = "Max H. Garzon and Russell J. Deaton and Ken Barnes", title = "On Self-Assembling Graphs in vitro", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1805--1809", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "dna and molecular computing", ISBN = "1-55860-611-4", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{garzon1998:egDNAc, author = "Max Garzon and Rusell Deaton and Luis F. Nino and Ed Stevens and Michal Wittner", title = "Encoding Genomes for DNA Computing", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "684--690", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "DNA Computing", ISBN = "1-55860-548-7", URL = "http://www.csce.uark.edu/~rdeaton/dna/papers/gp98c-2.pdf", notes = "GP-98", } @Article{garzon:2003:GPEMe, author = "Max H. Garzon", title = "Biomolecular Machines and Artificial Evolution", journal = "Genetic Programming and Evolvable Machines", year = "2003", volume = "4", number = "2", pages = "107--109", month = jun, keywords = "DNA computing", ISSN = "1389-2576", DOI = "doi:10.1023/A:1023960327580", notes = "Special Issue on Biomolecular Machines and Artificial Evolution Article ID: 5122739", } @Article{garzon:2003:GPEM, author = "Max Garzon and Derrel Blain and Kiran Bobba and Andrew Neel and Michael West", title = "Self-Assembly of {DNA}-like Structures In Silico", journal = "Genetic Programming and Evolvable Machines", year = "2003", volume = "4", number = "2", pages = "185--200", month = jun, keywords = "Hamiltonian path problem, online genetic algorithms, DNA-based associative memories, efficiency of DNA computing, reaction kinetics in DNA-based computational protocols", ISSN = "1389-2576", DOI = "doi:10.1023/A:1023989130306", abstract = "Through evolution, biomolecules have resolved fundamental problems as a highly interactive parallel and distributed system that we are just beginning to decipher. Biomolecular Computing (BMC) protocols, however, are unreliable, inefficient and unscalable when compared to computational algorithms run in silico. An alternative approach is explored to exploiting these properties by building biomolecular analogs (eDNA) and virtual test tubes in electronics that would capture the best of both worlds. A distributed implementation is described of a virtual tube, Edna, on a cluster of PCs that does capture the massive asynchronous parallel interactions typical of BMC. Results are reported from over 1000 experiments that calibrate and benchmark Edna's performance, reproduce and extend Adleman's solution to the Hamiltonian Path problem for larger families of graphs than has been possible on a single processor or has been actually carried out in wet labs, and benchmark the feasibility and performance of DNA-based associative memories. The results required a million-fold less molecules and are at least as reliable as in vitro experiments, and so provide strong evidence that the paradigm of molecular computing can be implemented much more efficiently (in terms of time, cost, and probability of success) in silico than the corresponding wet experiments, at least in the range where Edna can be practically run. This approach also demonstrates intrinsic advantages in using electronic analogs of DNA as genomes for genetic algorithms and evolutionary computation.", notes = "Special Issue on Biomolecular Machines and Artificial Evolution Article ID: 5122745", } @Article{gashaw:2022:MESE, author = "Nigist Abera Gashaw and Eleyas Assefa and Costas Sachpazis", title = "Consolidation parameters conceptualization using regression analysis and genetic programming for Addis Ababa's red clay soils", journal = "Modeling Earth Systems and Environment", year = "2022", volume = "8", number = "1", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s40808-021-01127-2", DOI = "doi:10.1007/s40808-021-01127-2", } @Unpublished{gathercole:1994:stss, author = "Chris Gathercole and Peter Ross", title = "Some Training Subset Selection Methods for Supervised Learning in Genetic Programming", note = "Presented at ECAI'94 Workshop on Applied Genetic and other Evolutionary Algorithms", year = "1994", keywords = "genetic algorithms, genetic programming, LEF, DSS", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/733/ftp:zSzzSzftp.dai.ed.ac.ukzSzpubzSzuserzSzchrisgzSzchrisg_dss_paper_resubmitted_to_ecai94workshop.pdf/gathercole94some.pdf", URL = "http://citeseer.ist.psu.edu/gathercole94some.html", abstract = "When using the Genetic Programming (GP) Algorithm on a difficult problem with a large set of training cases, a large population size is needed and a very large number of function-tree evaluations must be carried out. This paper describes how to reduce the number of such evaluations by selecting a small subset of the training data set on which to actually carry out the GP algorithm. Three subset selection methods described in the paper are: Dynamic Subset Selection (DSS), using the current...", size = "13 pages", } @InProceedings{ga94aGathercole, author = "Chris Gathercole and Peter Ross", title = "Dynamic Training Subset Selection for Supervised Learning in Genetic Programming", booktitle = "Parallel Problem Solving from Nature III", year = "1994", editor = "Yuval Davidor and Hans-Paul Schwefel and Reinhard M{\"a}nner", series = "LNCS", volume = "866", pages = "312--321", address = "Jerusalem", publisher_address = "Berlin, Germany", month = "9-14 " # oct, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, DSS", ISBN = "3-540-58484-6", URL = "http://citeseer.ist.psu.edu/gathercole94dynamic.html", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/94-006.ps.gz", DOI = "doi:10.1007/3-540-58484-6_275", size = "10 pages", abstract = "When using the Genetic Programming (GP) Algorithm on a difficult problem with a large set of training cases, a large population size is needed and a very large number of function-tree evaluations must be carried out. This paper describes how to reduce the number of such evaluations by selecting a small subset of the training data set on which to actually carry out the GP algorithm. Three subset selection methods described in the paper are: Dynamic Subset Selection (DSS), using the current GP run to select difficult and/or disused cases, Historical Subset Selection (HSS), using previous GP runs, Random Subset Selection (RSS). Various runs have shown that GP+DSS can produce better results in less than 20percent of the time taken by GP. GP+HSS can nearly match the results of GP, and, perhaps surprisingly, GP+RSS can occasionally approach the results of GP. GP+DSS also produced better, more general results than those reported in a paper for a variety of Neural Networks when used on a substantial problem, known as the Thyroid problem.", notes = "PPSN3 Describes how to reduce the number of fitness case evaluations in difficult GP problems by selecting a small subset of the training data. Dynamic Subset Selection can produce better results than GP in less than 20percent of the time. Population size of 5,000 and 10,000. DSS used in Discipulus \cite{francone:manual}", } @TechReport{Gathercole, author = "Chris Gathercole and Peter Ross", title = "The MAX Problem for Genetic Programming - Highlighting an Adverse Interaction between the Crossover Operator and a Restriction on Tree Depth", institution = "Department of Artificial Intelligence, University of Edinburgh", year = "1995", address = "80 South Bridge, Edinburgh, EH1 1HN, UK ", keywords = "genetic algorithms, genetic programming", broken = "ftp://ftp.dai.ed.ac.uk/pub/user/chrisg/max-problem-in-GP.for_submission_to_gp-96.ps.gz", URL = "http://citeseer.ist.psu.edu/gathercole95max.html", abstract = "The Crossover operator is common to most implementations of Genetic Programming (GP). Another, usually unavoidable, factor is some form of restriction on the size of trees in the GP population. This paper concentrates on the interaction between the Crossover operator and a restriction on tree depth demonstrated by the MAX problem, which involves returning the largest possible value for given function and terminal sets. Some characteristics and inadequacies of Crossover in `normal' use are...", size = "10 pages", notes = " p.s. On a related theme, and only blowing my own trumpet a little bit, I have recently written a paper [Gathercole] (soon to be submitted to GP96) which looks at an unfortunate interaction in GP between the Crossover operator and restrictions on tree size. Its more or less finished Published as \cite{Gathercole:1996:aicrtd}", } @InProceedings{Gathercole:1996:aicrtd, author = "Chris Gathercole and Peter Ross", title = "An Adverse Interaction between Crossover and Restricted Tree Depth in Genetic Programming", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "291--296", address = "Stanford University, CA, USA", publisher = "MIT Press", broken = "ftp://ftp.dai.ed.ac.uk/pub/user/chrisg/chrisg_max-problem-in-GP_camera-ready-version.for-GP-96.ps.gz", URL = "http://citeseer.ist.psu.edu/153919.html", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1996/Gathercole_1996_aicrtd.pdf", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap37.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", size = "6 pages", notes = "GP-96, Update of \cite{Gathercole}. Slides at http://www.dai.ed.ac.uk/students/chrisg/gp96_slides.html {"}penalising large trees appears to work well, especially when it is used only to discriminate between trees that would otherwise have the same fitness.{"} p296 URLs broken 2006 Scan from GP'96 proceedings added June 2015", } @InProceedings{Gathercole:1997:sp, author = "Chris Gathercole and Peter Ross", title = "Small Populations over Many Generations can beat Large Populations over Few Generations in Genetic Programming", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming", pages = "111--118", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", URL = "http://citeseer.ist.psu.edu/189252.html", size = "8 pages", notes = "GP-97 slides at http://www.dai.ed.ac.uk/students/chrisg/gp97/small_pops/slides.html tictactoe, noughts and crosses, uci thyroid", } @InProceedings{Gathercole:1997:lef, author = "Chris Gathercole and Peter Ross", title = "Tackling the {Boolean} Even N Parity Problem with Genetic Programming and Limited-Error Fitness", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming", pages = "119--127", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", broken = "ftp://ftp.dai.ed.ac.uk/pub/user/chrisg/chrisg_for_public_gp97_lef.ps.gz", URL = "http://citeseer.ist.psu.edu/79389.html", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.26.1298", URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.26.1298.pdf", size = "9 pages", abstract = "This paper presents Limited Error Fitness (LEF), a modification to the standard supervised learning approach in Genetic Programming (GP), in which an individual's fitness score is based on how many cases remain uncovered in the ordered training set after the individual exceeds an error limit. The training set order and the error limit are both altered dynamically in response to the performance of the fittest individual in the previous generation. LEF allows standard GP to readily solve the Boolean Even N Parity problem (a very hard classification problem for GP) for N=6 and N=7 with a population size of 400, otherwise, Automatically Defined Functions, a more powerful representation, and much larger populations, are required for GP to solve for N>5. Individual fitness evaluations run more quickly, but LEF usually requires many more generations. Also a smaller population size allows GP to be run on smaller computers at a reasonable speed. LEF changes the dynamics of GP, preventing premature convergence and allows a hard problem to be presented, in effect, as a series of subproblems", notes = "GP-97 slides at http://www.dai.ed.ac.uk/students/chrisg/gp97/lef/slides.html", } @PhdThesis{gathercole:thesis, author = "Chris Gathercole", title = "An Investigation of Supervised Learning in Genetic Programming", school = "University of Edinburgh", year = "1998", address = "UK", keywords = "genetic algorithms, genetic programming, Supervised Learning", URL = "http://hdl.handle.net/1842/533", URL = "http://www.era.lib.ed.ac.uk/dspace/bitstream/1842/533/3/Gathercole.pdf", URL = "http://www.dai.ed.ac.uk/pub/daidb/papers/pt9810.ps.gz", URL = "http://ethos.bl.uk/OrderDetails.do?did=16&uin=uk.bl.ethos.561729", size = "207 pages", abstract = "This thesis is an investigation into Supervised Learning (SL) in Genetic Programming (GP). With its flexible tree-structured representation, GP is a type of Genetic Algorithm, using the Darwinian idea of natural selection and genetic recombination, evolving populations of solutions over many generations to solve problems. SL is a common approach in Machine Learning where the problem is presented as a set of examples. A good or fit solution is one which can successfully deal with all of the examples. In common with most Machine Learning approaches, GP has been used to solve many trivial problems. When applied to larger and more complex problems, however, several difficulties become apparent. When focusing on the basic features of GP, this thesis highlights the immense size of the GP search space, and describes an approach to measure this space. A stupendously flexible but frustratingly useless representation, Anarchically Automatically Defined Functions, is described. Some difficulties associated with the normal use of the GP operator Crossover (perhaps the most common method of combining GP trees to produce new trees) are demonstrated in the simple MAX problem. Crossover can lead to irreversible sub-optimal GP performance when used in combination with a restriction on tree size. There is a brief study of tournament selection which is a common method of selecting fit individuals from a GP population to act as parents in the construction of the next generation. The main contributions of this thesis however are two approaches for avoiding the fitness evaluation bottleneck resulting from the use of SL in GP. To establish the capability of a GP individual using SL, it must be tested or evaluated against each example in the set of training examples. Given that there can be a large set of training examples, a large population of individuals, and a large number of generations, before good solutions emerge, a very large number of evaluations must be carried out, often many tens of millions. This is by far the most time-consuming stage of the GP algorithm. Limited Error Fitness (LEF) and Dynamic Subset Selection (DSS) both reduce the number of evaluations needed by GP to successfully produce good solutions, adaptively using the capabilities of the current generation of individuals to guide the evaluation of the next generation. LEF curtails the fitness evaluation of an individual after it exceeds an error limit, whereas DSS picks out a subset of examples from the training set for each generation. Whilst LEF allows GP to solve the comparatively small but difficult Boolean Even N parity problem for large N without the use of a more powerful representation such as Automatically Defined Functions, DSS in particular has been successful in improving the performance of GP across two large classification problems, allowing the use of smaller population sizes, many fewer and faster evaluations, and has more reliably produced as good or better solutions than GP on its own. The thesis ends with an assertion that smaller populations evolving over many generations can perform more consistently and produce better results than the `established' approach of using large populations over few generations. ", notes = "uk.bl.ethos.561729", } @Article{Gatlin1963360, author = "L. L. Gatlin", title = "Triplet frequencies in DNA and the genetic program", journal = "Journal of Theoretical Biology", volume = "5", number = "3", pages = "360--371", year = "1963", ISSN = "0022-5193", DOI = "doi:10.1016/0022-5193(63)90083-3", URL = "http://www.sciencedirect.com/science/article/B6WMD-4F1J81C-T5/2/12c96a984135797062556122da338822", notes = "Not on GP", } @InProceedings{gaucel:2014:EuroGP, author = "Sebastien Gaucel and Maarten Keijzer and Evelyne Lutton and Alberto Tonda", title = "Learning Dynamical Systems Using Standard Symbolic Regression", booktitle = "17th European Conference on Genetic Programming", year = "2014", editor = "Miguel Nicolau and Krzysztof Krawiec and Malcolm I. Heywood and Mauro Castelli and Pablo Garcia-Sanchez and Juan J. Merelo and Victor M. {Rivas Santos} and Kevin Sim", series = "LNCS", volume = "8599", publisher = "Springer", pages = "25--36", address = "Granada, Spain", month = "23-25 " # apr, organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-662-44302-6", DOI = "doi:10.1007/978-3-662-44303-3_3", abstract = "Symbolic regression has many successful applications in learning free-form regular equations from data. Trying to apply the same approach to differential equations is the logical next step: so far, however, results have not matched the quality obtained with regular equations, mainly due to additional constraints and dependencies between variables that make the problem extremely hard to tackle. In this paper we propose a new approach to dynamic systems learning. Symbolic regression is used to obtain a set of first-order Eulerian approximations of differential equations, and mathematical properties of the approximation are then exploited to reconstruct the original differential equations. Advantages of this technique include the de-coupling of systems of differential equations, that can now be learnt independently; the possibility of exploiting established techniques for standard symbolic regression, after trivial operations on the original dataset; and the substantial reduction of computational effort, when compared to existing ad-hoc solutions for the same purpose. Experimental results show the efficacy of the proposed approach on an instance of the Lotka-Volterra model.", notes = "Part of \cite{Nicolau:2014:GP} EuroGP'2014 held in conjunction with EvoCOP2014, EvoBIO2014, EvoMusArt2014 and EvoApplications2014", } @InProceedings{Gaudesi:2013:GECCO, author = "Marco Gaudesi and Giovanni Squillero and Alberto Tonda", title = "An efficient distance metric for linear genetic programming", booktitle = "GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference", year = "2013", editor = "Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi", isbn13 = "978-1-4503-1963-8", pages = "925--932", keywords = "genetic algorithms, genetic programming", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2463372.2463495", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Defining a distance measure over the individuals in the population of an Evolutionary Algorithm can be exploited for several applications, ranging from diversity preservation to balancing exploration and exploitation. When individuals are encoded as strings of bits or sets of real values, computing the distance between any two can be a straightforward process; when individuals are represented as trees or linear graphs, however, quite often the user must resort to phenotype-level problem-specific distance metrics. This paper presents a generic genotype-level distance metric for Linear Genetic Programming: the information contained by an individual is represented as a set of symbols, using n-grams to capture significant recurring structures inside the genome. The difference in information between two individuals is evaluated resorting to a symmetric difference. Experimental evaluations show that the proposed metric has a strong correlation with phenotype-level problem-specific distance measures in two problems where individuals represent string of bits and Assembly-language programs, respectively.", notes = "Also known as \cite{2463495} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)", } @InProceedings{Gaudesi:2014:GECCOcomp, author = "Marco Gaudesi and Giovanni Squillero and Alberto Tonda", title = "Universal information distance for genetic programming", booktitle = "GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion", year = "2014", editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges", isbn13 = "978-1-4503-2881-4", keywords = "genetic algorithms, genetic programming: Poster", pages = "137--138", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2598394.2598440", DOI = "doi:10.1145/2598394.2598440", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "This paper presents a genotype-level distance metric for Genetic Programming (GP) based on the symmetric difference concept: first, the information contained in individuals is expressed as a set of symbols (the content of each node, its position inside the tree, and recurring parent-child structures); then, the difference between two individuals is computed considering the number of elements belonging to one, but not both, of their symbol sets.", notes = "Also known as \cite{2598440} Distributed at GECCO-2014.", } @PhdThesis{Marco_GAUDESI_thesis, author = "Marco Gaudesi", title = "Advanced Techniques for Solving Optimization Problems through Evolutionary Algorithms", school = "DAUIN - Control and Computer Engineering, Politecnico di Torino", year = "2015", address = "Italy", month = feb, keywords = "genetic algorithms, genetic programming, muGP, microGP", URL = "http://porto.polito.it/2592954/", URL = "http://www.phd-dauin.polito.it/pdfs/Marco%20GAUDESI_thesis.pdf", size = "119 pages", abstract = "Evolutionary algorithms (EAs) are machine-learning techniques that can be exploited in several applications in optimization problems in different fields. Even though the first works on EAs appeared in the scientific literature back in the 1960s, they cannot be considered a mature technology, yet. Brand new paradigms as well as improvements to existing ones are continuously proposed by scholars and practitioners. This thesis describes the activities performed on uGP, an existing EA toolkit developed in Politecnico di Torino since 2002. The works span from the design and experimentation of new technologies, to the application of the toolkit to specific industrial problems. More in detail, some studies addressed during these three years targeted: the realization of an optimal process to select genetic operators during the optimization process; the definition of a new distance metric able to calculate differences between individuals and maintaining diversity within the population (diversity preservation); the design and implementation of a new cooperative approach to the evolution able to group individuals in order to optimize a set of sub-optimal solutions instead of optimizing only one individual.", notes = "Supervisor Giovanni Squillero Ingegneria Informatica e dei Sistemi. COMPUTER SCIENCE AND SYSTEMS ENGINEERING", } @InProceedings{conf/epia/GaudlOB15, author = "Swen E. Gaudl and Joseph Carter Osborn and Joanna J. Bryson", title = "Learning from Play: Facilitating Character Design Through Genetic Programming and Human Mimicry", bibdate = "2015-08-26", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/epia/epia2015.html#GaudlOB15", booktitle = "Progress in Artificial Intelligence - 17th Portuguese Conference on Artificial Intelligence, {EPIA} 2015, Coimbra, Portugal, September 8-11, 2015. Proceedings", publisher = "Springer", year = "2015", volume = "9273", editor = "Francisco C. Pereira and Penousal Machado and Ernesto Costa and Amilcar Cardoso", isbn13 = "978-3-319-23484-7", pages = "292--297", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming", URL = "http://dx.doi.org/10.1007/978-3-319-23485-4", DOI = "doi:10.1007/978-3-319-23485-4_30", } @PhdThesis{Gaudl:thesis, author = "Swen E. Gaudl", title = "Building Robust Real-Time Game AI:Simplifying \& Automating Integral Process Steps in Multi-Platform Design", school = "University of Bath", year = "2016", address = "UK", keywords = "genetic algorithms, genetic programming, games,digital games, agent design, agent programming language, software development, artificial intelligence", idcode = "53314", URL = "http://opus.bath.ac.uk/53314/1/dissertationRoot.pdf", URL = "http://opus.bath.ac.uk/53314/", URL = "http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.698997", size = "265 pages", abstract = "Digital games are part of our culture and have gained significant attention over the last decade. The growing capabilities of home computers, gaming consoles and mobile phones allow current games to visualise 3D virtual worlds, photo-realistic characters and the inclusion of complex physical simulations. The growing computational power of those devices enables the usage of complex algorithms while visualising data. Therefore, opportunities arise for developers of interactive products such as digital games which introduce new, challenging and exciting elements to the next generation of highly interactive software systems. Two of those challenges, which current systems do not address adequately, are design support for creating Intelligent Virtual Agents and more believable non-player characters for immersive game-play. We start in this thesis by addressing the agent design support first and then extend the research, addressing the second challenge. The main contributions of this thesis are: The POSH-SHARP system is a framework for the development of game agents. The platform is modular, extendible, offers multi-platform support and advanced software development features such as behaviour inspection and behaviour versioning. The framework additionally integrates an advanced information exchange mechanism supporting loose behaviour coupling. The Agile behaviour design methodology integrates agile software development and agent design. To guide users, the approach presents a work-flow for agent design and guiding heuristics for their development. The action selection augmentation ERGo introduces a white-box solution to altering existing agent frameworks, making their agents less deterministic. It augments selected behaviours with a bio-mimetic memory to track and adjust their activation over time. With the new approach to agent design, the development of deepagent behaviour for digital adversaries and advanced tools supporting their design is given. Such mechanisms should enable developers to build robust non-player characters that act more human-like in an efficient and robust manner. Within this thesis, different strategies are identified to support the design of agents in a more robust manner and to guide developers. These discussed mechanisms are then evolved to develop and design Intelligent Virtual Agents. Because humans are still the best measurement for human-likeness, the evolutionary cycle involves feedback given by human players.", notes = "BOD using POSH, FAtiMA, ABL, StarCraft, posh-sharp ISNI: 0000 0004 5993 9610 uk.bl.ethos.698997 supervisor Joanna J. Bryson also known as \cite{bath53314}", } @Misc{gaudl:2018:platformersai, author = "Swen E. Gaudl", title = "A Genetic Programming Framework for {2D} Platform {AI}", howpublished = "arXiv", year = "2018", month = "5 " # mar, keywords = "genetic algorithms, genetic programming, Game AI, Agent Design, Platformer, AISB, JGAP, platformerAI, symbolic learning", URL = "https://arxiv.org/pdf/1803.01648", size = "3 pages", abstract = "There currently exists a wide range of techniques to model and evolve artificial players for games. Existing techniques range from black box neural networks to entirely hand-designed solutions. In this paper, we demonstrate the feasibility of a genetic programming framework using human controller input to derive meaningful artificial players which can, later on, be optimised by hand. The current state of the art in game character design relies heavily on human designers to manually create and edit scripts and rules for game characters. To address this manual editing bottleneck, current computational intelligence techniques approach the issue with fully autonomous character generators, replacing most of the design process using black box solutions such as neural networks or the like. Our GP approach to this problem creates character controllers which can be further authored and developed by a designer it also offers designers to included their play style without the need to use a programming language. This keeps the designer in the loop while reducing repetitive manual labour. Our system also provides insights into how players express themselves in games and into deriving appropriate models for representing those insights. We present our framework, supporting findings and open challenges", notes = "JGAP Gamalyzer http://www.platformersai.com", } @Article{Gaur20081166, author = "Surabhi Gaur and M. C. Deo", title = "Real-time wave forecasting using genetic programming", journal = "Ocean Engineering", volume = "35", number = "11-12", pages = "1166--1172", year = "2008", ISSN = "0029-8018", DOI = "doi:10.1016/j.oceaneng.2008.04.007", URL = "http://www.sciencedirect.com/science/article/B6V4F-4SD6SSR-1/2/619ec0df2657e8e39b38b7d533d37ec4", keywords = "genetic algorithms, genetic programming, Wave forecasts, Wave heights, Real-time forecasting", abstract = "The forecasting of ocean waves on real-time or online basis is necessary while carrying out any operational activity in the ocean. In order to obtain forecasts that are station-specific a time-series-based approach like stochastic modeling or artificial neural network was attempted by some investigators in the past. This paper presents an application of a relatively new soft computing tool called genetic programming for this purpose. Genetic programming is an extension of genetic algorithm and it is suited to explore dependency between input and output data sets. The wave rider buoy measurements available at two locations in the Gulf of Mexico are analyzed. The forecasts of significant wave heights are made over lead times of 3, 6, 12 and 24h. The sample size belonged to a period of 15 years and it included an extensive testing period of 5 years. The forecasts made by the approach of genetic programming indicated that it can be regarded as a promising tool for future applications to ocean predictions.", } @InProceedings{Gautam:2022:CICT, author = "Devnath Gautam and Saumya Bhadauria and Aditya Trivedi", booktitle = "2022 IEEE 6th Conference on Information and Communication Technology (CICT)", title = "Malware Analysis Using Modified Genetic Algorithm in Cyber-Physical Systems", year = "2022", abstract = "The integration of communication networks and the Internet of Things (IoT) into Cyber-Physical Systems (CPSs) is the reason for increased vulnerability in terms of cyber attacks. Cyber-Physical Systems (CPSs) is highly important to protect critical information and detect cyber threats. These new forms of threat come without any prebuilt signature to detect them. The proposed work aims to solve this problem by using machine learning techniques by performing dynamic malware analysis for windows executable files. Genetic Programming is used for selecting malicious features from benign files after extracting them with the help of the Cuckoo apparatus. These selected features are used to train machine learning classifiers. Later, these classifiers are used for malware detection. Furthermore, semantic control measures are applied to the existing crossover process to improve the generalization ability of Genetic Programming (GP).", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CICT56698.2022.9997991", month = nov, notes = "Also known as \cite{9997991}", } @PhdThesis{Gautier:thesis, title = "Flow control using optical sensors", titletranslation = "Contr{\^o}le d'ecoulement par capteurs optiques", author = "Nicolas Gautier", school = "Pierre and Marie Curie University - Paris VI", year = "2014", month = oct # "~08", address = "France", keywords = "genetic algorithms, genetic programming, flow control, graphic processor unit, contr{\^o}le d'ecoulement, GPU, boucle fermee, marche descendante, experimental, [PHYS, MECA, mefl] physics [physics]/mechanics [physics]/mechanics of the fluids [physics, class-ph]", publisher = "HAL CCSD", bibsource = "OAI-PMH server at api.archives-ouvertes.fr", contributor = "Physique et mecanique des milieux heterogenes and Jean-Luc Aider", identifier = "NNT : 2014PA066640; tel-01150428", language = "en", oai = "oai:HAL:tel-01150428v1", URL = "https://tel.archives-ouvertes.fr/tel-01150428", URL = "https://tel.archives-ouvertes.fr/tel-01150428/document", URL = "https://tel.archives-ouvertes.fr/tel-01150428/file/2014PA066640.pdf", size = "211 pages", abstract = "Flow control using optical sensors is experimentally investigated. Real-time computation of flow velocity fields is implemented. This novel approach featuring a camera for acquisition and a graphic processor unit (GPU) for processing is presented and detailed. Its validity with regards to speed and precision is investigated. A comprehensive guide to software and hardware optimisation is given. We demonstrate that online computation of velocity fields is not only achievable but offers advantages over traditional particle image velocimetry (PIV) setups. It shows great promise not only for flow control but for parametric studies and prototyping also. A hydrodynamic channel is used in all experiments, featuring a backward facing step for separated flow control. Jets are used to provide actuation. A comprehensive parametric study is effected to determine the effects of upstream jet injection. It is shown upstream injection can be very effective at reducing recirculation, corroborating results from the literature.", abstract = "Both open and closed loop control methods are investigated using this setup. Basic control is introduced to ascertain the effectiveness of this optical setup. The recirculation region created in the backward-facing step flow is computed in the vertical symmetry plane and the horizontal plane. We show that the size of this region can be successfully manipulated through set-point adaptive control and gradient based methods.A physically driven control approach is introduced. Previous works have shown successful reduction recirculation reduction can be achieved by periodic actuation at the natural Kelvin-Helmholtz frequency of the shear layer.A method based on vortex detection is introduced to determine this frequency, which is used in a closed loop to ensure the flow is always adequately actuated. Thus showing how recirculation reduction can be achieved through simple and elegant means using optical sensors. Next a feed-forward approach based on ARMAX models is implemented. It was successfully used in simulations to prevent amplification of upstream disturbances by the backward-facing step shear layer. We show how such an approach can be successful in an experimental setting. Higher Reynolds number flows exhibit non-linear behaviour which can be difficult to model in a satisfactory manner thus a new approach was attempted dubbed machine learning control and based on genetic programming. A number of random control laws are implemented and rated according to a given cost function. The laws that perform best are bred, mutated or copied to yield a second generation. The process carries on iteratively until cost is minimised. This approach can give surprising insights into effective control laws.", abstract = "Le contr{\^o}le d'ecoulement en utilisant des capteurs optiques est etudie dans un contexte experimental. Le calcul de champs de vitesses en temps reel en utilisant une camera pour l'acquisition et une carte graphique pour le calcul est detaille. La validite de l'approche en terme de rapidite et de precision est etudiee. Un guide complet pour l'optimisation logicielle et materielle est donne. Nous demontrons que le calcul dynamique de champs de vitesse est non seulement possible mais plus facile {\`a} gerer que l'utilisation d'un appareillage (PIV) classique. Un canal hydrodynamique est utilise pour toutes les experiences. Celui-ci comporte une marche descendante pour le contr{\^o}le d' ecoulements decolles. Les actionneurs sont des jets. Dans le cas de la marche descendante une etude parametrique approfondie est faite pour qualifier les effets d'une injection en amont des jets, celle-ci etant traditionnellement .......", annote = "oai:HAL:tel-01150428v1 Physique et mecanique des milieux heterogenes (PMMH) ; CNRS - Universite Paris Diderot - Paris 7 (UP7) - ESPCI ParisTech - Universite Pierre et Marie Curie - Paris 6 (UPMC); Universite Pierre et Marie Curie - Paris VI, English. ", } @Article{Gautier:2015:FLM, author = "N. Gautier and J.-L. Aider and T. Duriez and B. R. Noack and M. Segond and M. Abel", title = "Closed-loop separation control using machine learning", journal = "Journal of Fluid Mechanics", volume = "770", month = "5", year = "2015", ISSN = "1469-7645", pages = "442--457", oai = "oai:arXiv.org:1405.0908", keywords = "genetic algorithms, genetic programming, control theory, flow control, separated flows, physics - fluid dynamics", URL = "http://arxiv.org/abs/1405.0908", URL = "http://journals.cambridge.org/article_S0022112015000956", DOI = "doi:10.1017/jfm.2015.95", size = "16 pages", abstract = "We present the first closed-loop separation control experiment using a novel, model-free strategy based on genetic programming, which we call machine learning control. The goal is to reduce the recirculation zone of backward-facing step flow at Reh=1350 manipulated by a slotted jet and optically sensed by online particle image velocimetry. The feedback control law is optimised with respect to a cost functional based on the recirculation area and a penalization of the actuation. This optimisation is performed employing genetic programming. After 12 generations comprised of 500 individuals, the algorithm converges to a feedback law which reduces the recirculation zone by 80 percent. This machine learning control is benchmarked against the best periodic forcing which excites Kelvin-Helmholtz vortices. The machine learning control yields a new actuation mechanism resonating with the low-frequency flapping mode instability. This feedback control performs similarly to periodic forcing at the design condition but outperforms periodic forcing when the Reynolds number is varied by a factor two. The current study indicates that machine learning control can effectively explore and optimise new feedback actuation mechanisms in numerous experimental applications.", } @InProceedings{Gavrilis:2006:SPECOM, author = "Dimitris Gavrilis and Ioannis Tsoulos and Evangelos Dermatas", title = "Evolutionary Grammar Induction for Protein Relation Extraction", year = "2006", booktitle = "Proceedings XI International Conference Speech and Computer, SPECOM 2006", address = "St. Petersburg, Russia", month = "25-29 " # jun, keywords = "genetic algorithms, genetic programming, grammatical evolution, information extraction, protein relations", isbn13 = "574520074X", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.628.488", URL = "http://users.cs.uoi.gr/~itsoulos/publications/protein.pdf", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.628.488", size = "4 pages", abstract = "A novel method is presented for protein relation extraction from scientific abstracts. The proposed method is based on Meta-Grammars, a novel method for grammar inference that uses genetic programming and a BNF description to discover a tree representation of sentence structure that can be used for information extraction. A series if transformations are applied to the original corpus before the Meta-Grammars genetic algorithm is applied. The proposed method is evaluated against extracting protein relations from scientific abstracts and it is shown that it requires a train corpus which has minimum requirements from field experts and giving precision of 79.165percent.", notes = "http://www.specom.nw.ru/history.html https://www.tib.eu/en/search/id/TIBKAT%3A517762994/Proceedings-SPECOM-2006-XI-International-Conference/?tx_tibsearch_search%5Bsearchspace%5D=tn", } @Article{Gavrilis20081358, author = "Dimitris Gavrilis and Ioannis G. Tsoulos and Evangelos Dermatas", title = "Selecting and constructing features using grammatical evolution", journal = "Pattern Recognition Letters", year = "2008", volume = "29", number = "9", pages = "1358--1365", ISSN = "0167-8655", URL = "http://www.sciencedirect.com/science/article/B6V15-4S01WDH-4/2/aaff3c40c5eca125dfacb426d88fa177", DOI = "doi:10.1016/j.patrec.2008.02.007", month = jul, keywords = "genetic algorithms, genetic programming, Grammatical evolution, Artificial neural networks, ANN, Feature selection, Feature construction", ISSN = "0167-8655", abstract = "A novel method for feature selection and construction is introduced. The method improves the classification accuracy, using the well-established technique of grammatical evolution by creating non-linear mappings of the original features to artificial ones in order to improve the effectiveness of artificial intelligence tools such as multi-layer perceptron (MLP), Radial-basis-function (RBF) neural networks and nearest neighbor (KNN) classifier. The proposed method has been applied on a series of classification and regression problems and an experimental comparison is carried out against the accuracy obtained on the original features as well as on features created by the PCA method.", } @Article{Gawdzik:2020:DWT, author = "Alicja Gawdzik and Jaroslaw Gawdzik and Barbara Gawdzik and Andrzej Gawdzik and Marcin Rybotycki", title = "Prediction of {BOD5} content of the inflow to the treatment plant using different methods of black box - the case study", journal = "Desalination and Water Treatment", year = "2020", volume = "196", pages = "58--66", keywords = "genetic algorithms, genetic programming, Multivariate adaptive regression splines, Boosted trees, Organic compounds, BOD, COD, Wastewater treatment plant (WWTP)", ISSN = "1944-3986", URL = "https://www.sciencedirect.com/science/article/pii/S1944398624102329", DOI = "doi:10.5004/dwt.2020.25944", abstract = "ABSTRA", } @InProceedings{Gay:2020:SSBSE, author = "Gregory Gay and Rene Just", title = "{Defects4J} as a Challenge Case for the Search-Based Software Engineering Community", booktitle = "12th International Symposium on Search Based Software Engineering SSBSE 2020", year = "2020", editor = "Aldeida Aleti and Annibale Panichella", series = "LNCS", volume = "12420", pages = "255--261", address = "Bari, Italy", month = "7-8 " # oct, publisher = "Springer", email = "greg@greggay.com rjust@cs.washington.edu", keywords = "genetic algorithms, genetic programming, Genetic Improvement, SBSE, APR", isbn13 = "978-3-030-59761-0", video_url = "https://www.youtube.com/watch?v=8UAMmNlukh8", DOI = "doi:10.1007/978-3-030-59762-7_19", size = "16 pages", abstract = "Defects4J is a collection of reproducible bugs, extracted from real-world Java software systems, together with a supporting infrastructure for using these bugs. Defects4J has been widely used to evaluate software engineering research, including research on automated test generation, program repair, and fault localization. Defects4J has recently grown substantially, both in number of software systems and number of bugs. This report proposes that Defects4J can serve as a benchmark for Search-Based Software Engineering (SBSE) research as well as a catalyst for new innovations. Specifically, it outlines the current Defects4J dataset and infrastructure, and details how it can serve as a challenge case to support SBSE research and to expand Defects4J itself.", notes = "Defects4J 2.0 http://defects4j.org 835 bugs 17 projects Json Chalmers and the University of Gothenburg, Gothenburg, Sweden", } @InProceedings{Gay:2023:SSBSE, author = "Haozhou Lyu and Gregory Gay and Maiko Sakamoto", title = "Exploring Genetic Improvement of the Carbon Footprint of Web Pages", booktitle = "SSBSE 2023", year = "2023", editor = "Paolo Arcaini and Tao Yue and Erik Fredericks", organisers = "Erik Fredericks and Paolo Arcaini and Tao Yue and Rebecca Moussa and Thomas Vogel and Gregory Gay and Max Hort and Bobby R. Bruce and Jose Miguel Rojas and Vali Tawosi", volume = "14415", series = "LNCS", pages = "67--83", address = "San Francisco, USA", month = "8 " # dec, publisher = "Springer", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, Energy Consumption, Carbon footprint, Green AI, Software engineering, Case study, Thematic analysis, Web development", isbn13 = "978-3-031-48795-8", URL = "https://greg4cr.github.io/pdf/23cfgi.pdf", DOI = "doi:10.1007/978-3-031-48796-5_5", size = "15 pages", abstract = "we explore automated reduction of the carbon footprint of web pages through genetic improvement, a process that produces alternative versions of a program by applying program transformations intended to optimize qualities of interest. We introduce a prototype tool that imposes transformations to HTML, CSS, and JavaScript code, as well as image resources, that minimize the quantity of data transferred and memory usage while also minimizing impact to the user experience (measured through loading time and number of changes imposed). In an evaluation, our tool outperforms two baselines: the original page and randomized changes, in the average case on all projects for data transfer quantity, and 80% of projects for memory usage and load time, often with large effect size. Our results illustrate the applicability of genetic improvement to reduce the carbon footprint of web components, and offer lessons that can benefit the design of future tools.", notes = "See also MSc http://hdl.handle.net/20.500.12380/306723 https://odr.chalmers.se/items/c832b3a9-609d-40af-bc39-b96278686042 co-located with ESEC/FSE 2023. https://conf.researchr.org/home/ssbse-2023", } @InProceedings{Gayanov:2017:ISSPIT, author = "Ruslan Gayanov and Konstantin Mironov and Konstantin Mironov and Dmitriy Kurennov", booktitle = "2017 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)", title = "Estimating the trajectory of a thrown object from video signal with use of genetic programming", year = "2017", pages = "134--138", month = "18-20 " # dec, address = "Bilbao, Spain", keywords = "genetic algorithms, genetic programming, robotic catching, forecasting, machine vision, machine learning", DOI = "doi:10.1109/ISSPIT.2017.8388630", size = "5 pages", abstract = "Robotic catching of thrown objects is one of the common robotic tasks, which is explored in a number of papers. This task include subtask of tracking and forecasting the trajectory of the thrown object. Here we propose an algorithm for estimating future trajectory based on video signal from two cameras. Most of existing implementations use deterministic trajectory prediction and several are based on machine learning. We propose a combined forecasting algorithm where the deterministic motion model for each trajectory is generated via the genetic programming algorithm. Numerical experiments with real trajectories of the thrown tennis ball show that the algorithm is able to forecast the trajectory accurately.", notes = "Konstantin Mironov given as both second and third author Also known as \cite{8388630}", } @Article{GAYANOV:2018:IFAC-PapersOnLine, author = "Ruslan Gayanov and Konstantin Mironov and Ramil Mukhametshin and Aleksandr Vokhmintsev and Dmitriy Kurennov", title = "Transportation of small objects by robotic throwing and catching: applying genetic programming for trajectory estimation", journal = "IFAC-PapersOnLine", volume = "51", number = "30", pages = "533--537", year = "2018", note = "18th IFAC Conference on Technology, Culture and International Stability TECIS 2018", keywords = "genetic algorithms, genetic programming, GPU, robotic catching, forecasting, machine vision, machine learning, CUDA, parallel computing", ISSN = "2405-8963", DOI = "doi:10.1016/j.ifacol.2018.11.271", URL = "http://www.sciencedirect.com/science/article/pii/S2405896318329446", abstract = "Robotic catching of thrown objects is one of the common robotic tasks, which is explored in several works. This task includes subtask of tracking and forecasting the trajectory of the thrown object. Here we propose an algorithm for estimating future trajectory based on video signal from two cameras. Most of existing implementations use deterministic trajectory prediction and several are based on machine learning. We propose a combined forecasting algorithm where the deterministic motion model for each trajectory is generated via the genetic programming algorithm. Genetic programming is implemented on C++ with use of CUDA library and executed in parallel way on the graphical processing unit. Parallel execution allow genetic programming in real time. Numerical experiments with real trajectories of the thrown tennis ball show that the algorithm can forecast the trajectory accurately", } @InProceedings{gaylinn:2024:GECCOcomp, author = "Nathan Gaylinn and Joshua Bongard", title = "A {Meta-Evolutionary} Algorithm for Co-evolving Genotypes and Genotype / Phenotype Maps", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion", year = "2024", editor = "Alberto Moraglio and James McDermott", pages = "467--470", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, evolutionary computation, evolution strategies, gp map, generative encoding, cellular automata, game of life, gol, evolvability, diversity, exploration, CPPNs, development, General Evolutionary Computation, Hybrids: Poster", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3654259", size = "4 pages", abstract = "Evolutionary computation (EC) is often used to automatically discover solutions to optimization problems. It is valued because it allows the programmer to intuitively design a search space to fit a task, and because it is a relatively open-ended search process that favors diversity and unanticipated solutions that might be missed with gradient-based methods. Traditionally, the programmer decides on a fixed search strategy a priori, often by designing a specialized mapping from genotype to phenotype (GP map). Unfortunately, this can introduce bias and undermine the open-endedness of EC. Evolved GP maps can mitigate these concerns by automatically discovering efficient search spaces that improve evolvability. However, most research into evolved GP maps emphasizes convergence rate to a fit solution, or rate of recovery after a change in conditions. Here, we frame EC as a search over search strategies rather than a search for fit solutions. We demonstrate that a single meta-evolutionary algorithm with an evolved generative GP map can find better solutions to multiple fitness functions in the domain of 2D cellular automata than a traditional evolutionary algorithm. In the future, we hope these results will further the understanding of evolvability, its relationship to diversity, and the exploratory power of evolved GP maps.", notes = "is this GP? p468 'To render a phentoype, we walk the tree of operations in the GP map' GECCO-2024 GECH A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @Article{GE:2021:NEI, author = "Jiuhao Ge and Noritaka Yusa and Mengbao Fan", title = "Frequency component mixing of pulsed or multi-frequency eddy current testing for nonferromagnetic plate thickness measurement using a multi-gene genetic programming algorithm", journal = "ND \& E International", volume = "120", pages = "102423", year = "2021", ISSN = "0963-8695", DOI = "doi:10.1016/j.ndteint.2021.102423", URL = "https://www.sciencedirect.com/science/article/pii/S0963869521000220", keywords = "genetic algorithms, genetic programming, Eddy current testing, Gini coefficient, Linearity", abstract = "For the efficient use of frequency components, a frequency mixed feature for pulsed eddy current testing (PECT) or multi-frequency eddy current testing (MultiECT) was proposed for nonferromagnetic plate thickness measurement. An evolutionary algorithm multigene genetic programming was employed to mix the frequency components using the best linearity as a target. Time domain and frequency domain finite element simulations of PECT and MultiECT were conducted. The simulation results revealed that, in terms of thickness measurement, a mixed feature comprising two or three frequencies was more linear and accurate than the traditional peak time and decay coefficient of PECT. Experiments were conducted to validate the results of the simulations and to test the mixed feature in aluminum plate thickness evaluations. The experimental results also revealed that the use of more frequencies did not always increase the accuracy of thickness evaluations. Proper frequency component selection was more efficient than blindly increasing frequency numbers", } @InCollection{gearhart:2003:GPPSMDP, author = "Chris Gearhart", title = "Genetic Programming as Policy Search in Markov Decision Processes", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2003", year = "2003", editor = "John R. Koza", pages = "61--67", address = "Stanford, California, 94305-3079 USA", month = "4 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming, games, freecraft, ADF, lil-gp, Monte Carlo", URL = "http://www.genetic-programming.org/sp2003/Gearhart.pdf", size = "7 pages", abstract = "In this paper, we examine genetic programming as a policy search technique for planning problems representable as Markov Decision Processes. The planning task under consideration is derived from a real-time strategy war game. This problem presents unique challenges for standard genetic programming approaches; despite this, we show that genetic programming produces results competitive with standard techniques, albeit with certain trade-offs", notes = "part of \cite{koza:2003:gagp}", } @Article{gedik:2018:Water, author = "Nuray Gedik", title = "Least Squares Support Vector Mechanics to Predict the Stability Number of {Rubble-Mound} Breakwaters", journal = "Water", year = "2018", volume = "10", number = "10", keywords = "genetic algorithms, genetic programming", ISSN = "2073-4441", URL = "https://www.mdpi.com/2073-4441/10/10/1452", DOI = "doi:10.3390/w10101452", abstract = "In coastal engineering, empirical formulas grounded on experimental works regarding the stability of breakwaters have been developed. In recent years, soft computing tools such as artificial neural networks and fuzzy models have started to be employed to diminish the time and cost spent in these mentioned experimental works. To predict the stability number of rubble-mound breakwaters, the least squares version of support vector machines (LSSVM) method is used because it can be assessed as an alternative one to diverse soft computing techniques. The LSSVM models have been operated through the selected seven parameters, which are determined by Mallows Cp approach, that are, namely, breakwater permeability, damage level, wave number, slope angle, water depth, significant wave heights in front of the structure, and peak wave period. The performances of the LSSVM models have shown superior accuracy (correlation coefficients (CC) of 0.997) than that of artificial neural networks (ANN), fuzzy logic (FL), and genetic programming (GP), that are all implemented in the related literature. As a result, it is thought that this study will provide a practical way for readers to estimate the stability number of rubble-mound breakwaters with more accuracy.", notes = "also known as \cite{w10101452}", } @Article{GeethaRamani:2009:IJARS, author = "R. {Geetha Ramani} and R. Subramanian and P. Viswanath", title = "Genetic Programming Method of Evolving the Robotic Soccer Player Strategies with Ant Intelligence", journal = "International Journal of Advanced Robotic Systems", year = "2009", volume = "6", number = "2", pages = "79--90", month = jun, keywords = "genetic algorithms, genetic programming, Robotic Soccer, Social Insect Behaviours, Ant intelligence, Learning methods, ECJ simulator, Teambots.", ISSN = "1729-8806", DOI = "doi:10.5772/6790", size = "12 pages", abstract = "This paper presents the evolved soccer player strategies with ant-intelligence through genetic programming. To evolve the code for players we used the Evolutionary Computation tool (ECJ simulator- Evolutionary Computation in Java). We tested the evolved player strategies with already existing teams in soccerbots of teambots. This paper presents brief information regarding learning methods and ant behaviors. Experimental results depicts the performance of the evolved player strategies.", notes = "Football, Soccer. Dept. Of CSE & IT, Pondicherry Engineering College http://intechweb.org/journal.php?id=3", } @InProceedings{geiger:2023:GECCO, author = "Alina Geiger and Dominik Sobania and Franz Rothlauf", title = "{Down-Sampled} {Epsilon-Lexicase} Selection for {Real-World} Symbolic Regression Problems", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "1109--1117", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, parent selection, down-sampled epsilon-lexicase selection, symbolic regression", isbn13 = "9798400701191", DOI = "doi:10.1145/3583131.3590400", size = "9 pages", abstract = "Epsilon-lexicase selection is a parent selection method in genetic programming that has been successfully applied to symbolic regression problems. Recently, the combination of random subsampling with lexicase selection significantly improved performance in other genetic programming domains such as program synthesis. However, the influence of subsampling on the solution quality of real-world symbolic regression problems has not yet been studied. In this paper, we propose down-sampled epsilon-lexicase selection which combines epsilon-lexicase selection with random subsampling to improve the performance in the domain of symbolic regression. Therefore, we compare down-sampled epsilon-lexicase with traditional selection methods on common real-world symbolic regression problems and analyze its influence on the properties of the population over a genetic programming run. We find that the diversity is reduced by using down-sampled epsilon-lexicase selection compared to standard epsilon-lexicase selection. This comes along with high hyperselection rates we observe for down-sampled epsilon-lexicase selection. Further, we find that down-sampled epsilon-lexicase selection outperforms the traditional selection methods on all studied problems. Overall, with down-sampled epsilon-lexicase selection we observe an improvement of the solution quality of up to 85\% in comparison to standard epsilon-lexicase selection.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @InProceedings{Geiger:2024:EuroGP, author = "Alina Geiger and Dominik Sobania and Franz Rothlauf", editor = "Mario Giacobini and Bing Xue and Luca Manzoni", title = "A Comprehensive Comparison of Lexicase-Based Selection Methods for Symbolic Regression Problems", booktitle = "EuroGP 2024: Proceedings of the 27th European Conference on Genetic Programming", year = "2024", volume = "14631", series = "LNCS", publisher = "Springer", address = "Aberystwyth", month = "3-5 " # apr, organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming", pages = "192--208", abstract = "Lexicase selection is a parent selection method that has been successfully used in many application domains. In recent years, several variants of lexicase selection have been proposed and analyzed. However, it is still unclear which lexicase variant performs best in the domain of symbolic regression. Therefore, we compare relevant lexicase variants on a wide range of symbolic regression problems. We conduct experiments not only over a given evaluation budget but also over a given time as practitioners usually have limited time for solving their problems. Consequently we provide users a comprehensive guide for choosing the right selection method under different constraints in the domain of symbolic regression. Overall, we find that down-sampled epsilon lexicase selection outperforms other selection methods on the studied benchmark problems for the given evaluation budget and for the given time. The improvements with respect to solution quality are up to 68{\%} using down-sampled epsilon-lexicase selection given a time budget of 24 hours", isbn13 = "978-3-031-56957-9", DOI = "doi:10.1007/978-3-031-56957-9_12", notes = "Part of \cite{Giacobini:2024:GP} EuroGP'2024 held in conjunction with EvoCOP2024, EvoMusArt2024 and EvoApplications2024", } @InProceedings{geiger:2025:EuroGP, author = "Alina Geiger and Martin Briesch and Dominik Sobania and Franz Rothlauf", title = "Was Tournament Selection All We Ever Needed? A Critical Reflection on Lexicase Selection", booktitle = "European Conference on Genetic Programming, EuroGP 2025", year = "2025", editor = "Bing Xue and Luca Manzoni and Illya Bakurov", volume = "15609", series = "LNCS", pages = "207--223", address = "Trieste", month = "23-25 " # apr, publisher = "Springer Nature", keywords = "genetic algorithms, genetic programming, Tournament Selection, Lexicase Selection, Down-Sampling, Symbolic Regression", isbn13 = "978-3-031-89990-4", URL = "https://rdcu.be/ejgGT", DOI = "doi:10.1007/978-3-031-89991-1_13", abstract = "The success of lexicase selection has led to various extensions, including its combination with down-sampling, which further increased performance. However, recent work found that down-sampling also leads to significant improvements in the performance of tournament selection. This raises the question of whether tournament selection combined with down-sampling is the better choice, given its faster running times. To address this question, we run a set of experiments comparing epsilon-lexicase and tournament selection with different down-sampling techniques on synthetic problems of varying noise levels and problem sizes as well as real-world symbolic regression problems. Overall, we find that down-sampling improves generalisation and performance even when compared over the same number of generations. This means that down-sampling is beneficial even with way fewer fitness evaluations. Additionally, down-sampling successfully reduces code growth. We observe that population diversity increases for tournament selection when combined with down-sampling. Further, we find that tournament selection and $$\epsilon$$-lexicase selection with down-sampling perform similar, while tournament selection is significantly faster. We conclude that tournament selection should be further analysed and improved in future work instead of only focusing on the improvement of lexicase variants.", notes = "also known as \cite{paper_99_tournament_all_we_needed} Part of \cite{Xue:2025:GP} EuroGP'2025 held in conjunction with EvoCOP2025, EvoMusArt2025 and EvoApplications2025", } @InProceedings{gelenbe:1996:GAas, author = "Erol Gelenbe", title = "Genetic Algorithms with Analytical Solution", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "Genetic Algorithms", pages = "437--443", address = "Stanford University, CA, USA", publisher = "MIT Press", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap73.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96 GA paper", } @InProceedings{Gelev:2018:IT, author = "Saso Gelev and Ana Sokolovska and Dusica Curcic and Aleksandar Sokolovski", booktitle = "2018 23rd International Scientific-Professional Conference on Information Technology (IT)", title = "Usage of genetic algorithms in cryptography for mobile devices", year = "2018", abstract = "This paper attempts to investigate the methods of cryptography and strong authentication for mobile phones (and tablets), by using genetic programming. This is nowadays one of the main challenges having into account the increased number of internet enabled mobile phones and the increased usage of the everyday activities in the scope of mobile e-payments (or bitcoin). The primary objective is to investigate and verify if the usage of modern authentication of mobile users with the use of modern methods of cryptography like: Strong Authentication (HTTS), Mobile Authentication, NFC (Near Field Communication), OBC (On-Board Credentials), SMS-OTP (SMS - One Time Password) in combination with genetic programming algorithms (based on real biological reproductive generational models for generational building) will increase the security of the mobile phones and tablets. The main aim is to determine the best combination of cryptography tools and with genetic programming algorithms to achieve increased security over the authentication of the mobile phones users. This will be achieved with testing the cryptographic methods and the proposed genetic programming algorithms, using the NS-3 Network Simulator, Python SciPy Library under BSD / Linux. The results and conclusions of the analyses may serve as a guide for using the improved next generation of internet enabled mobile phones.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SPIT.2018.8350855", month = feb, notes = "Also known as \cite{8350855}", } @InProceedings{1068309, author = "Sylvain Gelly and Olivier Teytaud and Nicolas Bredeche and Marc Schoenauer", title = "A statistical learning theory approach of bloat", booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation", year = "2005", editor = "Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and Edwin D. {de Jong} and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler", volume = "2", ISBN = "1-59593-010-8", pages = "1783--1784", address = "Washington DC, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2005/docs/p1783.pdf", URL = "http://www.lri.fr/~teytaud/eabloat.pdf", broken = "http://www.lri.fr/~teytaud/eabloat/eabloat.html", DOI = "doi:10.1145/1068009.1068309", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "25-29 " # jun, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Poster, code bloat, code growth, reliability, statistical learning theory, theory", size = "2 pages", abstract = "Code bloat, the excessive increase of code size, is an important issue in Genetic Programming (GP). This paper proposes a theoretical analysis of code bloat in the framework of symbolic regression in GP, from the viewpoint of Statistical Learning Theory, a well grounded mathematical toolbox for Machine Learning. Two kinds of bloat must be distinguished in that context, depending whether the target function lies in the search space or not. Then, important mathematical results are proved using classical results from Statistical Learning. Namely, the Vapnik-Chervonenkis dimension of programs is computed, and further results from Statistical Learning allow to prove that a parsimonious fitness ensures Universal Consistency (the solution minimising the empirical error does converge to the best possible error when the number of examples goes to infinity). However, it is proved that the standard method consisting in choosing a maximal program size depending on the number of examples might still result in programs of infinitely increasing size with their accuracy; a more complicated modification of the fitness is proposed that theoretically avoids unnecessary bloat while nevertheless preserving the Universal Consistency. Full paper available at http://www.lri.fr/~teytaud/longBloat.pdf \cite{gelly:2005:longBloat}", notes = "GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM Order Number 910052 eabloat.pdf is substantially more complete than poster in GECCO proceedings", } @Misc{gelly:2005:longBloat, author = "Sylvain Gelly and Olivier Teytaud and Nicolas Bredeche and Marc Schoenauer", title = "A Statistical Learning Theory Approach of Bloat", howpublished = "www", year = "2005", keywords = "genetic algorithms, genetic programming, Vapnik-Chervonenkis, VC dimension, bloat", URL = "http://www.lri.fr/~teytaud/longBloat.pdf", URL = "http://www.lri.fr/~gelly/paper/antibloatGecco2005_long_version.pdf", size = "8 pages", abstract = "Code bloat, the excessive increase of code size, is an important issue in Genetic Programming (GP). This paper proposes a theoretical analysis of code bloat in the framework of symbolic regression in GP, from the viewpoint of Statistical Learning Theory, a well grounded mathematical toolbox for Machine Learning. Two kinds of bloat must be distinguished in that context, depending whether the target function lies in the search space or not. Then, important mathematical results are proved using classical results from Statistical Learning. Namely, the Vapnik-Chervonenkis dimension of programs is computed, and further results from Statistical Learning allow to prove that a parsimonious fitness ensures Universal Consistency (the solution minimising the empirical error does converge to the best possible error when the number of examples goes to infinity). However, it is proved that the standard method consisting in choosing a maximal program size depending on the number of examples might still result in programs of infinitely increasing size with their accuracy; a more complicated modification of the fitness is proposed that theoretically avoids unnecessary bloat while nevertheless preserving the Universal Consistency.", notes = "cited by \cite{1068309} Replaced by \cite{DBLP:conf/cfap/GellyTBS05} Equipe TAO - INRIA Futurs LRI, Bat. 490, University Paris-Sud 91405 Orsay Cedex. France", } @InProceedings{DBLP:conf/cfap/GellyTBS05, author = "Sylvain Gelly and Olivier Teytaud and Nicolas Bredeche and Marc Schoenauer", title = "Apprentissage statistique et programmation g{\'e}n{\'e}tique: la croissance du code est-elle in{\'e}vitable?", booktitle = "Actes de CAP 05, Conf{\'e}rence francophone sur l'apprentissage automatique", year = "2005", editor = "Fran\c{c}ois Denis", pages = "163--178", address = "Nice, France", month = "31 " # may # "-3 " # jun, publisher = "PUG", note = "A Statistical Learning Theory Approach of Bloat", bibsource = "DBLP, http://dblp.uni-trier.de", keywords = "genetic algorithms, genetic programming, VC, Bloat", URL = "http://www.lri.fr/~gelly/paper/bloatCap2005.pdf", size = "16 pages", abstract = "Code bloat, the excessive increase of code size, is an important issue in Genetic Programming (GP). This paper proposes a theoretical analysis of code bloat in the framework of symbolic regression in GP, from the viewpoint of Statistical Learning Theory, a well grounded mathematical toolbox for Machine Learning. Two kinds of bloat must be distinguished in that context, depending whether the target function lies in the search space or not. Then, important mathematical results are proved using classical results from Statistical Learning. Namely, the Vapnik-Cervonenkis dimension of programs is computed, and further results from Statistical Learning allow to prove that a parsimonious fitness ensures Universal Consistency (the solution minimising the empirical error does converge to the best possible error when the number of samples goes to infinity). However, it is proved that the standard method consisting in choosing a maximal program size depending on the number of samples might still result in programs of infinitely increasing size with their accuracy ; a more complicated modification of the fitness is proposed that theoretically avoids unnecessary bloat while nevertheless preserving the Universal Consistency.", notes = "CAP 2005 http://www.lif.univ-mrs.fr/~fdenis/cap05/ In english. an improved version of \cite{gelly:2005:longBloat} Part of DBLP:conf/cfap/2005 see also oai:CiteSeerX.psu:10.1.1.696.8127 http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.696.8127 https://hal.inria.fr/inria-00000546/document/", } @Article{oai:hal.archives-ouvertes.fr:inria-00112840_v1, author = "Sylvain Gelly and Olivier Teytaud and Nicolas Bredeche and Marc Schoenauer", title = "Universal Consistency and Bloat in {GP}", title_2 = "Some theoretical considerations about Genetic Programming from a Statistical Learning Theory viewpoint", journal = "Revue d'Intelligence Artificielle", year = "2006", volume = "20", number = "6", pages = "805--827", note = "Issue on New Methods in Machine Learning. Theory and Applications", publisher = "HAL - CCSd - CNRS", annote = "Sylvain Gelly ", bibsource = "OAI-PMH server at hal.archives-ouvertes.fr", contributor = "Sylvain Gelly ", identifier = "inria-00112840 (version 1)", oai = "oai:hal.archives-ouvertes.fr:inria-00112840_v1", keywords = "genetic algorithms, genetic programming, Computer Science/Learning, Mathematics/Optimization and Control, statistical learning theory, symbolic regression, universal consistency, bloat", ISSN = "0992-499X", URL = "http://hal.inria.fr/docs/00/11/28/40/PDF/riabloat.pdf", URL = "http://hal.inria.fr/inria-00112840/en/", broken = "http://ria.revuesonline.com/article.jsp?articleId=8936", broken = "doi:10.3166/ria.20.805-827", size = "23 pages", resume = "Dans cet article, nous proposons une etude de la Programmation Genetique (PG) du point de vue de la theorie de l'Apprentissage Statistique dans le cadre de la regression symbolique. En particulier, nous nous sommes interesses a la consistence universelle en PG, c'est-adire la convergence presque sure vers l'erreur bayesienne a mesure que le nombre d'exemples augmente, ainsi qu'au probleme bien connu en PG de la croissance incontrolee de la taille du code (i.e. le {"}bloat{"}). Les resultats que nous avons obtenus montrent d'une part que l'on peut identifier plusieurs types de bloat et d'autre part que la consistence universelle et l'absence de bloat peuvent etre obtenues sous certaines conditions. Nous proposons finalement une methode ad hoc evitant justement le bloat tout en garantissant la consistence universelle.", abstract = "In this paper, we provide an analysis of Genetic Programming (GP) from the Statistical Learning Theory viewpoint in the scope of symbolic regression. Firstly, we are interested in Universal Consistency, i.e. the fact that the solution minimising the empirical error does converge to the best possible error when the number of examples goes to infinity, and secondly, we focus our attention on the uncontrolled growth of program length (i.e. bloat), which is a well-known problem in GP. Results show that (1) several kinds of code bloats may be identified and that (2) Universal consistency can be obtained as well as avoiding bloat under some conditions. We conclude by describing an ad hoc method that makes it possible simultaneously to avoid bloat and to ensure universal consistency.", notes = "in english", } @PhdThesis{Gelly:thesis, author = "Sylvain Gelly", title = "A contribution to Reinforcement Learning: Application to Computer-Go", school = "Universite, Paris-Sud", year = "2007", address = "91405 Orsay, Cedex, France", month = "25 " # sep, keywords = "genetic algorithms, Monte-Carlo Random Trees, UCT, MoGo, OpenDP, SVM, CMA-ES", URL = "http://bibliographie.jeudego.org/these_sylvain-gelly.pdf", size = "283 pages", citeulike-article-id = "2990577", notes = "Informatique, Number 8754. Written in english. TAO, LRI.FR OpenBeagle implementation used. ", } @InProceedings{Gelly:2009:eurogp, author = "Nur Merve Amil and Nicolas Bredeche and Christian Gagn{\'e} and Sylvain Gelly and Marc Schoenauer and Olivier Teytaud", title = "A Statistical Learning Perspective of Genetic Programming", booktitle = "Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009", year = "2009", editor = "Leonardo Vanneschi and Steven Gustafson and Alberto Moraglio and Ivanoe {De Falco} and Marc Ebner", volume = "5481", series = "LNCS", pages = "327--338", address = "Tuebingen", month = apr # " 15-17", organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming, poster", isbn13 = "978-3-642-01180-1", URL = "https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.648.7930&rep=rep1&type=pdf", URL = "https://hal.inria.fr/inria-00369782/document", DOI = "doi:10.1007/978-3-642-01181-8_28", size = "13 pages", abstract = "This paper proposes a theoretical analysis of Genetic Programming (GP) from the perspective of statistical learning theory, a well grounded mathematical toolbox for machine learning. By computing the Vapnik-Chervonenkis dimension of the family of programs that can be inferred by a specific setting of GP, it is proved that a parsimonious fitness ensures universal consistency. This means that the empirical error minimization allows convergence to the best possible error when the number of test cases goes to infinity. However, it is also proved that the standard method consisting in putting a hard limit on the program size still results in programs of infinitely increasing size in function of their accuracy. It is also shown that cross-validation or hold-out for choosing the complexity level that optimizes the error rate in generalization also leads to bloat. So a more complicated modification of the fitness is proposed in order to avoid unnecessary bloat while nevertheless preserving universal consistency.", notes = "Also known as \cite{DBLP:conf/eurogp/AmilBGGST09} Part of \cite{conf/eurogp/2009} EuroGP'2009 held in conjunction with EvoCOP2009, EvoBIO2009 and EvoWorkshops2009", } @Article{GELMAN:2017:JPS, author = "Danny Gelman and Boris Shvartsev and Itamar Wallwater and Shahaf Kozokaro and Vicky Fidelsky and Adi Sagy and Alon Oz and Sioma Baltianski and Yoed Tsur and Yair Ein-Eli", title = "An aluminum - ionic liquid interface sustaining a durable Al-air battery", journal = "Journal of Power Sources", volume = "364", pages = "110--120", year = "2017", keywords = "genetic algorithms, genetic programming, Aluminum, Metal-air battery, Ionic liquid, Interface, Oligofluorohydrogenate", ISSN = "0378-7753", DOI = "doi:10.1016/j.jpowsour.2017.08.014", URL = "http://www.sciencedirect.com/science/article/pii/S0378775317310388", abstract = "A thorough study of a unique aluminum (Al)-air battery using a pure Al anode, an air cathode, and hydrophilic room temperature ionic liquid electrolyte 1-ethyl-3-methylimidazolium oligofluorohydrogenate [EMIm(HF)2.3F] is reported. The effects of various operation conditions, both at open circuit potential and under discharge modes, on the battery components were discussed. A variety of techniques were used to investigate and study the interfaces and processes involved, including electrochemical studies, electron microscopy, spectroscopy and diffraction. As a result of this intensive study, the upon-operation voltage drop (dip) obstacle, occurring in the initial stages of the Al-air battery discharge, has been resolved. In addition, the interaction of the Al anode with oligofluorohydrogenate electrolyte forms an Al-O-F layer on the Al surface, which allows both activation and low corrosion rates of the Al anode. The evolution of this layer has been studied via impedance spectroscopy genetic programming enabling a unique model of the Al-air battery", } @InProceedings{gemeinhardt:2023:GECCOcomp, author = "Felix Guenther Gemeinhardt and Stefan Klikovits and Manuel Wimmer", title = "Hybrid {Multi-Objective} Genetic Programming for Parameterized Quantum Operator Discovery", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "795--798", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, quantum circuit synthesis, hybrid search, search-based quantum software engineering: Poster", isbn13 = "9798400701191", DOI = "doi:10.1145/3583133.3590696", size = "4 pages", abstract = "The processing of quantum information is defined by quantum circuits. For applications on current quantum devices, these are usually parameterized, i.e., they contain operations with variable parameters. The design of such quantum circuits and aggregated higher-level quantum operators is a challenging task which requires significant knowledge in quantum information theory, provided a polynomial-sized solution can be found analytically at all. Moreover, finding an accurate solution with low computational cost represents a significant trade-off, particularly for the current generation of quantum computers. To tackle these challenges, we propose a multi-objective genetic programming approach---hybridized with a numerical parameter optimizer---to automate the synthesis of parameterized quantum operators. To demonstrate the benefits of the proposed approach, it is applied to a quantum circuit of a hybrid quantum-classical algorithm, and then compared to an analytical solution as well as a non-hybrid version. The results show that, compared to the non-hybrid version, our method produces more diverse solutions and more accurate quantum operators which even reach the quality of the analytical baseline.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @InProceedings{Gemeinhardt:2023:MODELS-C, author = "Felix Gemeinhardt and Martin Eisenberg and Stefan Klikovits and Manuel Wimmer", booktitle = "2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C)", title = "Model-Driven Optimization for Quantum Program Synthesis with {MOMoT}", year = "2023", pages = "614--621", abstract = "In the realm of classical software engineering, model-driven optimisation has been widely used for different problems such as (re)modularization of software systems. In this paper, we investigate how techniques from model-driven optimisation can be applied in the context of quantum software engineering. In quantum computing, creating executable quantum programs is a highly non-trivial task which requires significant expert knowledge in quantum information theory and linear algebra. Although different approaches for automated quantum program synthesis exist-e.g., based on reinforcement learning and genetic programming-these approaches represent tailor-made solutions requiring dedicated encodings for quantum programs. This paper applies the existing model-driven optimisation approach MOMoT to the problem of quantum program synthesis. We present the resulting platform for experimenting with quantum program synthesis and present a concrete demonstration for a well-known Quantum algorithm.", keywords = "genetic algorithms, genetic programming, Computational modelling, Software algorithms, Quantum mechanics, Software systems, Space exploration, Quantum circuit, Integrated circuit modelling, Quantum Circuit Synthesis, Model-Driven Optimisation, Quantum Software Engineering", DOI = "doi:10.1109/MODELS-C59198.2023.00100", month = oct, notes = "Also known as \cite{10350515}", } @PhdThesis{Gemeinhardt:thesis, author = "Felix Guenther Gemeinhardt", title = "A Model-Driven Quantum Software Development Framework for Combinatorial Optimization", school = "Institut fuer Wirtschaftsinformatik (Software Engineering), Faculty of Social Sciences, Economics and Business, University Linz", year = "2023", address = "Austria", keywords = "genetic algorithms, genetic programming, Quantencomputing, modellgetriebene Softwareentwicklung, suchbasierte Softwareentwicklung, Quantum computing, model-driven engineering, search-based software engineering, SBSE", URL = "https://epub.jku.at/obvulihs/content/titleinfo/9621944", URL = "https://permalink.obvsg.at/ULI/AC17094889", URL = "https://epub.jku.at/obvulihs/download/pdf/9621944", size = "245 pages", abstract = "Context: Quantum computers harness quantum mechanical effects to process information, i.e., they rely on fundamentally different physical principles compared to classical computers. This raises the need for novel software to optimally exploit the potential of quantum devices, giving birth to the emerging field of quantum software engineering. Here, the goal is to transfer concepts developed over decades from classical software engineering to the quantum realm. Problem: An identified key problem lies in the low levels of abstraction and automation in current quantum software engineering. This causes several practical problems for the user, e.g., quantum programming language-specific development and limited reusability, as well as the neglect of present trade-offs between different quantum program variants. These problems hold true for quantum computing in general, but also for the field of quantum combinatorial optimization. The latter refers to the application of quantum computing to solve combinatorial optimization problems. As quantum combinatorial optimization is considered a near-term application area of quantum computing, the illustrated problem is rendered even more urgent in this context. Solution: The solution proposed within this dissertation is a quantum software development framework that directly tackles these problems by raising abstraction and automation levels using methods from model-driven engineering and search-based software engineering. In particular, the solution framework advocates the use of higher-level composite quantum operations and comprises features to model, automatically synthesize, and automatically improve such quantum software. Research Method: This dissertation is framed as a design science project and follows established guidelines for design science research. Thus, within a first pilot project, experience regarding the problem domain, i.e., quantum software engineering for combinatorial optimization, was gathered. In combination with a systematic review of quantum combinatorial optimization, the foundation for the upcoming development of the solution framework was laid. The latter development was conducted in an iterative manner by alternating between design and evaluation phases. The solution framework has been validated by demonstration and comparison studies as well as experiments on representative use cases. Contributions: The core contribution of the dissertation is represented by the solution artifact, i.e., the instantiated quantum software development framework for raising abstraction and automation levels, and the corresponding developed model-driven and search-based approaches. During the research process, additional contributions have been elaborated. First, within the pilot project, two novel hybrid quantum-classical algorithms have been developed and evaluated, and a framework for applying the latter has been designed. Second, the systematic mapping study on quantum combinatorial optimization illustrates the current state of the art with respect to solution approaches and common optimization problems. Note: This cumulative dissertation includes 2 published journal articles, 1 conference paper, 2 workshop papers, and 2 submitted journal articles. In the following, a summary of the conducted research for this dissertation is provided by presenting the overall research project. Thus, the aim is to convey the problem, methodology, contributions, and results of the underlying design science project. In the appendix, the following articles contributing to this dissertation are attached: * Gemeinhardt, F. G., Wille, R., & Wimmer, M. (2021). Quantum k-community detection: algorithm proposals and cross-architectural evaluation. In Quantum Information Processing, 20(9), 302. * Gemeinhardt, F. G., Garmendia, A., Wimmer, M., Weder, B., & Leymann, F. (2023). Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study. In ACM Computing Surveys, 56(3), 1-36. * Gemeinhardt, F. G., Garmendia, A., Wimmer, M., & Wille, R. (2022). A Model-Driven Framework for Composition-Based Quantum Circuit Design. Resubmitted after Major Revision for publication to ACM Transactions on Quantum Computing in November 2023. * Gemeinhardt, F. G., Klikovits, S., & Wimmer, M. (2023). GeQuPI: Quantum Program Improvement with Multi-Objective Genetic Programming. Submitted for publication to the Journal of Systems and Software in September 2023. * Gemeinhardt, F. G., Klikovits, S., & Wimmer, M. (2023, July). Hybrid Multi-Objective Genetic Programming for Parameterized Quantum Operator Discovery. In Proceedings of the Conference on Genetic and Evolutionary Computation: In Companion Proceedings (pp. 795-798). * Gemeinhardt, F. G., Eisenberg, M., Klikovits, S., & Wimmer, M. (2023, October). Model-Driven Optimization for Quantum Program Synthesis with MOMoT. In Proceedings of the 26th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings. In print. * Gemeinhardt, F. G., Garmendia, A., & Wimmer, M. (2021, June). Towards model-driven quantum software engineering. In 2021 IEEE/ACM Proceedings of the 2nd International Workshop on Quantum Software Engineering (Q-SE) (pp. 13-15). IEEE.", notes = "urn:nbn:at:at-ubl:1-72420 Supervisors: Manuel Wimmer and Robert Wille", } @Article{Gemeinhardt:2025:JSS, author = "Felix Gemeinhardt and Stefan Klikovits and Manuel Wimmer", title = "GeQuPI: Quantum Program Improvement with Multi-Objective Genetic Programming", journal = "Journal of Systems and Software", year = "2025", volume = "219", pages = "112223", month = "Jany", keywords = "genetic algorithms, genetic programming, MOGP, Quantum computing, Evolutionary algorithms, Quantum software engineering, Quantum circuit optimization", ISSN = "0164-1212", URL = "https://www.sciencedirect.com/science/article/pii/S016412122400267X", DOI = "doi:10.1016/j.jss.2024.112223", size = "31 pages", abstract = "...Manually finding sufficiently accurate and efficient solutions is a challenging task, even for quantum computing experts. To tackle these challenges, we propose a quantum program improvement framework for an automated generation of accurate and efficient solutions, coined Genetic Quantum Program Improver (GeQuPI). In particular, we focus on the tasks of debugging and optimization of quantum programs. Our framework uses techniques from quantum information theory and applies multi-objective genetic programming, which can be further hybridized with quantum-aware optimizers. To demonstrate the benefits of GeQuPI, it is applied to 47 quantum programs reused from literature and openly published libraries. The results show that our approach is capable of correcting faulty programs and optimize inefficient ones for the majority of the studied cases, showing average optimizations of 35 percent with respect to computational cost.", notes = "also known as \cite{GEMEINHARDT2025112223}", } @InProceedings{Genetti:2025:evoapplications, author = "Stefano Genetti and Alberto Longobardi and Giovanni Iacca", title = "Evolutionary Reinforcement Learning for Interpretable Decision-Making in Supply Chain Management", booktitle = "28th International Conference, EvoApplications 2025", year = "2025", editor = " Pablo Garcia-Sanchez and Emma Hart and Sarah L. Thomson", series = "LNCS", volume = "15612", pages = "187--203", publisher = "Springer", address = "Trieste, Italy", month = "3-5 " # apr, organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Grammatical Evolution,, Supply Chain Management, Interpretable Reinforcement Learning, XAI, Decision Trees, DEAP, Python", isbn13 = "978-3-031-90062-4", timestamp = "Sat, 31 May 2025 23:09:36 +0200", biburl = "https://dblp.org/rec/conf/evoapps/GenettiLI25.bib", bibsource = "dblp computer science bibliography, https://dblp.org", DOI = "doi:10.1007/978-3-031-90062-4_12", notes = "DBLP:conf/evoapps/GenettiLI25, http://www.evostar.org/2025/ EvoApplications2025 held in conjunction with EuroGP'2025, EvoCOP2025 and EvoMusArt2025", } @InProceedings{Geng:2020:CEC, author = "Shengkai Geng and Ting Hu", title = "Sports Games Modeling and Prediction using Genetic Programming", booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC 2020", year = "2020", editor = "Yaochu Jin", pages = "paper id24100", address = "internet", month = "19-24 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming: Poster", isbn13 = "978-1-7281-6929-3", DOI = "doi:10.1109/CEC48606.2020.9185917", abstract = "Sports games are largely enjoyed by fans around the globe. Plenty of financial assets, such as betting, need a reference to determine which team is more likely to win. In addition, club coaches and managers can benefit from using a analytical tool that suggests more efficient and suitable strategies to win. Genetic programming is a powerful learning algorithm for prediction and knowledge discovery. In this research, we propose to use genetic programming to model and predict the final outcome of NBA playoffs. We use the regular season performance statistics of each team to predict their final ranks in the Playoffs. Historical data of NBA teams are collected in order to train the predictive models using genetic programming. The preliminary results show that the algorithm is able to achieve a good prediction accuracy, as well as to provide an importance assessment of various performance statistics in determining the probability of winning the final championship.", notes = "https://wcci2020.org/ Memorial University of Newfoundland, Canada; Queen's University, Canada. Also known as \cite{9185917}", } @Article{George:2009:MBT, author = "Ajish D. George and Scott A. Tenenbaum", title = "Informatic Resources for Identifying and Annotating Structural RNA Motifs", journal = "Molecular Biotechnology", year = "2009", volume = "41", number = "2", pages = "180--193", month = feb, URL = "http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2770092/pdf/nihms152441.pdf", DOI = "doi:10.1007/s12033-008-9114-z", size = "14 pages", abstract = "Post-transcriptional regulation of genes and transcripts is a vital aspect of cellular processes, and unlike transcriptional regulation, remains a largely unexplored domain. One of the most obvious and most important questions to explore is the discovery of functional RNA elements. Many RNA elements have been characterized to date ranging from cis-regulatory motifs within mRNAs to large families of non-coding RNAs. Like protein coding genes, the functional motifs of these RNA elements are highly conserved, but unlike protein coding genes, it is most often structure and not sequence that conserved. Proper characterization of these structural RNA motifs is both the key and the limiting step to understanding the post-transcriptional aspects of the genomic world. Here we focus on the task of structural motif discovery and provide a survey of the informatics resources geared towards this task.", notes = "Refers briefly to \cite{Yuh-JyhHu:2003:NAR} Gen*NY*Sis Center for Excellence in Cancer Genomics, University at Albany-SUNY, Department of Biomedical Sciences, School of Public Health, 1 Discovery Drive, Room 220, Rensselaer, NY 12144 Gen*NY*Sis Center for Excellence in Cancer Genomics, University at Albany-SUNY, Department of Biomedical Sciences, School of Public Health, 1 Discovery Drive, Room 220, Rensselaer, NY 12144, Phone (518) 591-7157; FAX (518) 591-7201 PMCID: PMC2770092", } @InProceedings{1144159, author = "Ashley George and Malcolm I. Heywood", title = "Improving GP classifier generalization using a cluster separation metric", booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation", year = "2006", editor = "Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos {Coello Coello} and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens", volume = "1", ISBN = "1-59593-186-4", pages = "939--940", address = "Seattle, Washington, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p939.pdf", DOI = "doi:10.1145/1143997.1144159", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "8-12 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming: Poster, classification, clustering, evaluation", size = "2 pages", abstract = "Genetic Programming offers freedom in the definition of the cost function that is unparalleled among supervised learning algorithms. However, this freedom goes largely unexploited in previous work. Here, we revisit the design of fitness functions for genetic programming by explicitly considering the contribution of the wrapper and cost function. Within the context of supervised learning, as applied to classification problems, a clustering methodology is introduced using cost functions which encourage maximization of separation between in and out of class exemplars. Through a series of empirical investigations of the nature of these functions, we demonstrate that classifier performance is much more dependable than previously the case under the genetic programming paradigm.", notes = "GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM Order Number 910060", } @InProceedings{Georgiev:2018:GECCOcompa, author = "Milen Georgiev and Ivan Tanev and Katsunori Shimohara", title = "Exploration of the effect of uncertainty in homogeneous and heterogeneous multi-agent societies with regard to their average characteristics", booktitle = "Workshop on Evolutionary Algorithms for Problems with Uncertainty, GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2018", editor = "Carlos Cotta and Tapabrata Ray and Hisao Ishibuchi and Shigeru Obayashi and Bogdan Filipic and Thomas Bartz-Beielstein and Grant Dick and Masaharu Munetomo and Silvino {Fernandez Alzueta} and Thomas Stuetzle and Pablo Valledor Pellicer and Manuel Lopez-Ibanez and Daniel R. Tauritz and Pietro S. Oliveto and Thomas Weise and Borys Wrobel and Ales Zamuda and Anne Auger and Julien Bect and Dimo Brockhoff and Nikolaus Hansen and Rodolphe {Le Riche} and Victor Picheny and Bilel Derbel and Ke Li and Hui Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Stephane Doncieux and Richard Duro and Joshua Auerbach and Harold {de Vladar} and Antonio J. Fernandez-Leiva and JJ Merelo and Pedro A. Castillo-Valdivieso and David Camacho-Fernandez and Francisco {Chavez de la O} and Ozgur Akman and Khulood Alyahya and Juergen Branke and Kevin Doherty and Jonathan Fieldsend and Giuseppe Carlo Marano and Nikos D. Lagaros and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Boris Naujoks and Vanessa Volz and Tea Tusar and Pascal Kerschke and Riyad Alshammari and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and John R. Woodward and Shin Yoo and John McCall and Nayat Sanchez-Pi and Luis Marti and Danilo Vasconcellos and Masaya Nakata and Anthony Stein and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Ivanoe {De Falco} and Antonio {Della Cioppa} and Ernesto Tarantino and Umberto Scafuri and P. G. M. Baltus and Giovanni Iacca and Ahmed Hallawa and Anil Yaman and Alma Rahat and Handing Wang and Yaochu Jin and David Walker and Richard Everson and Akira Oyama and Koji Shimoyama and Hemant Kumar and Kazuhisa Chiba and Pramudita Satria Palar", isbn13 = "978-1-4503-5764-7", pages = "1797--1804", address = "Kyoto, Japan", publisher = "ACM", publisher_address = "New York, NY, USA", month = "15-19 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1145/3205651.3208259", abstract = "In electrical engineering, the deviation from average values of a signal is viewed as noise to the useful measurement. In human societies, however, the diversity of the exhibited characteristics are a sign of individuality and personal worth. We investigate the effect of uncertainty variables in the environment on multi-agent societies (MAS) and the consequences of the deviation, from the average features of the modelled agents. We show the performance of heterogeneous MAS of agents in comparison to morphologically identical homogeneous systems, preserving the same average physical and sensory abilities for the system as a whole, in a dynamic environment. We are employing a form of the predator-prey pursuit problem in attempt to measure the different performance of homogeneous MAS with average parameters and its heterogeneous counterpart. The effects of uncertainty in our work is investigated from the viewpoint of (i) employing a limited number of initial situations to evolve the team of predator agents, (ii) generality to unforeseen initial situations, and (iii) robustness to perception noise. Key statistics are the efficiency of evolution of the successful behaviour of predator agents, effectiveness of their behaviour and its degradation because of newly introduced situation or noise. Preliminary results indicate that a heterogeneous system can be at least as good as its homogeneous average equivalent, in solution quality at the expense of the runtime of evolution.", notes = "Also known as \cite{3208259} GECCO-2018 A Recombination of the 27th International Conference on Genetic Algorithms (ICGA-2018) and the 23rd Annual Genetic Programming Conference (GP-2018)", } @InProceedings{Georgiev:2018:SICE, author = "Milen Georgiev and Ivan Tanev and Katsunori Shimohara", title = "Performance Analysis and Comparison on Heterogeneous and Homogeneous Multi-Agent Societies in Correlation to Their Average Capabilities", booktitle = "2018 57th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE)", year = "2018", pages = "674--679", month = "11-14 " # sep, address = "Nara, Japan", keywords = "genetic algorithms, genetic programming, multi-agent systems, predator-prey pursuit problem, evolutionary programming, heterogeneous and homogeneous system performance comparison and analysis", isbn13 = "978-1-5386-6644-9", DOI = "doi:10.23919/SICE.2018.8492713", size = "6 pages", abstract = "The purpose of this research is to investigate the performance of heterogeneous multi-agent systems of agents in comparison to morphologically identical homogeneous systems, pertaining the same average physical and sensory abilities for the system as a whole. We will be using a form of the well-known predator-prey pursuit problem to measure the efficiency of each of the systems in both speed of evolution of the exhibited behaviour and robustness of the programmatically generated solutions.", notes = "Also known as \cite{8492713}", } @Article{DBLP:journals/information/GeorgievTSR19, author = "Milen Georgiev and Ivan Tanev and Katsunori Shimohara and Thomas S. Ray", title = "Evolution, Robustness and Generality of a Team of Simple Agents with Asymmetric Morphology in Predator-Prey Pursuit Problem", journal = "Information", volume = "10", number = "2", year = "2019", article-number = "72", month = feb, keywords = "genetic algorithms, multi-agent systems, simple agents, asymmetric morphology, micro-robots, predator-prey problem", ISSN = "2078-2489", URL = "https://doi.org/10.3390/info10020072", DOI = "doi:10.3390/info10020072", timestamp = "Mon, 11 Mar 2019 16:59:20 +0100", biburl = "https://dblp.org/rec/bib/journals/information/GeorgievTSR19", size = "16 pages", abstract = "One of the most desired features of autonomous robotic systems is their ability to accomplish complex tasks with a minimum amount of sensory information. Often, however, the limited amount of information (simplicity of sensors) should be compensated by more precise and complex control. An optimal tradeoff between the simplicity of sensors and control would result in robots featuring better robustness, higher throughput of production and lower production costs, reduced energy consumption, and the potential to be implemented at very small scales. In our work we focus on a society of very simple robots (modelled as agents in a multi-agent system) that feature an extreme simplicity of both sensors and control. The agents have a single line-of-sight sensor, two wheels in a differential drive configuration as effectors, and a controller that does not involve any computing, but rather, a direct mapping of the currently perceived environmental state into a pair of velocities of the two wheels. Also, we applied genetic algorithms to evolve a mapping that results in effective behaviour of the team of predator agents, towards the goal of capturing the prey in the predator-prey pursuit problem (PPPP), and demonstrated that the simple agents featuring the canonical (straightforward) sensory morphology could hardly solve the PPPP. To enhance the performance of the evolved system of predator agents, we propose an asymmetric morphology featuring an angular offset of the sensor, relative to the longitudinal axis. The experimental results show that this change brings a considerable improvement of both the efficiency of evolution and the effectiveness of the evolved capturing behavior of agents. Finally, we verified that some of the best-evolved behaviours of predators with sensor offset of 20 degrees are both (i) general in that they successfully resolve most of the additionally introduced, unforeseen initial situations, and (ii) robust to perception noise in that they show a limited degradation of the number of successfully solved initial situations.", notes = "Is this GP? Brief mention of GP but chromosome is fixed length (8 integers). This paper is extended version of our paper presented in 18th International Conference AIMSA 2018, Varna, Bulgaria, 12-14 September 2018. \cite{Tanev:2018:AIMSA} Department of Information System Design, Doshisha University, Kyotanabe, Kyoto 610-0321, Japan Also known as \cite{info10020072}", } @InProceedings{Georgiou:2006:10WSEAS, author = "Loukas Georgiou and William J. Teahan", title = "{jGE} - A {Java} implementation of Grammatical Evolution", booktitle = "10th WSEAS International Conference on Systems", year = "2006", editor = "M. Isabel Garcia-Planas", pages = "406--411", address = "Athens, Greece", month = jul # " 10-15", keywords = "genetic algorithms, genetic programming, grammatical evolution, evolutionary computation,agents, jGE, libGE, GP, GE", ISBN = "960-8457-47-5", URL = "http://www.wseas.us/e-library/conferences/2006cscc/papers/534-869.pdf", size = "6 pages", abstract = "Grammatical Evolution (GE) is a novel evolutionary algorithm which uses an arbitrary variable-length binary string to govern which production rule of a Backus Naur Form grammar will be used in a genotype-to-phenotype mapping process. This paper introduces the Java GE project (jGE), which is an implementation of GE in the Java language. The main difference between jGE and libGE, a public domain implementation of GE in C++, is that jGE incorporates the functionality of libGE as a component and provides implementation of the Search Engine as well as the Evaluator. The main idea behind the jGE Library it can be downloaded at is to create a framework for evolutionary algorithms which can be extended to any specific implementation such as Genetic Algorithms, Genetic Programming and Grammatical Evolution.", notes = "http://www.wseas.us/e-library/conferences/2006cscc/ics/papers.htm", } @Article{Georgiou:2006:WSEAS, author = "Loukas Georgiou and William J. Teahan", title = "Implications of Prior Knowledge and Population Thinking in Grammatical Evolution: Toward a Knowledge Sharing Architecture", journal = "WSEAS Transactions on Systems", year = "2006", volume = "5", number = "10", pages = "2338--2345", month = oct, keywords = "genetic algorithms, genetic programming, grammatical evolution, evolutionary computation,agents, jGE, libGE, GP, GE", ISSN = "1109-2777", URL = "http://www.worldses.org/journals/systems/old.htm", abstract = "Grammatical Evolution (GE) is a novel evolutionary algorithm which uses an arbitrary variable-length binary string to govern which production rule of a Backus Naur Form grammar will be used in a genotype-to-phenotype mapping process. This paper introduces the Java GE project (jGE), which is an implementation of GE in the Java language, and presents the results of the first experiments which have been conducted towards a knowledge sharing approach using families of populations. The initial results show that the application of Prior Knowledge and Population Thinking in jGE is promising and this drives us toward a further investigation of the family-based approach whose main characteristics are the incorporation of genetic/phenotypic diversity in the population and the sharing of knowledge between individuals of the same groups (families).", notes = " Record 2108 of 7009 in Inspec 2005-2006 http://www.wseas.us/indexes/iee/2004-2007.txt ", } @InCollection{Georgiou:2008:K-DC, author = "Loukas Georgiou and William J. Teahan", title = "Experiments with Grammatical Evolution in Java", booktitle = "Knowledge-Driven Computing: Knowledge Engineering and Intelligent Computations", publisher = "Springer", year = "2008", editor = "C. Cotta and S. Reich and R. Schaefer and A. Ligeza", volume = "102", series = "Studies in Computational Intelligence", chapter = "4", pages = "45--62", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Evolutionary Computation, jGE, libGE, GP", isbn13 = "978-3-540-77474-7", DOI = "doi:10.1007/978-3-540-77475-4_4", abstract = "Grammatical Evolution (GE) is a novel evolutionary algorithm that uses a genotype-to-phenotype mapping process where variable-length binary strings govern which production rules of a Backus Naur Form grammar are used to generate programs. This paper describes the Java GE project (jGE), which is an implementation of GE in the Java language, as well as some proof-of-concept experiments. The main idea behind the jGE Library is to create a framework for evolutionary algorithms which can be extended to any specific implementation such as Genetic Algorithms, Genetic Programming and Grammatical Evolution.", notes = "Online version of book available http://www.springer.com/engineering/book/978-3-540-77474-7?detailsPage=toc", } @InProceedings{Georgiou:2010:ICEC, author = "Loukas Georgiou and William J. Teahan", title = "Grammatical Evolution and the {Santa Fe} Trail Problem", booktitle = "Proceedings of the International Conference on Evolutionary Computation (ICEC 2010)", year = "2010", editor = "Joaquim Filipe and Janusz Kacprzyk", pages = "10--19", address = "Valencia, Spain", month = "24-26 " # oct, organisation = "INSTICC, AAAI, WfMC", publisher = "SciTePress", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Artificial Ant Problem, Santa Fe Trail Problem, jGE, jGE NetLogo, Java, NetLogo", isbn13 = "978-989-8425-31-7", URL = "http://www.robinbye.com/files/publications/ICEC_2010.pdf", size = "10 pages", abstract = "In this paper we present the results of a series of experiments which explore the effectiveness of Grammatical Evolution for the Santa Fe Trail problem. The experiments which are presented support the claim of other published work that the comparison mentioned in the Grammatical Evolution literature between Grammatical Evolution (GE) and Genetic Programming (GP) regarding the Santa Fe Trail problem is not a fair one. Namely, GE literature claims that GE outperforms GP in the Santa Fe Trail problem, but we show that this happens only because the GE experiments described in the literature use a different and narrower search space. In order to perform the experiments, a series of tools and models have been developed and are presented: a) jGE, a Java implementation of the Grammatical Evolution system; b) jGE NetLogo, an extension of jGE for the NetLogo modelling environment; c) the Santa Fe Trail model, a simulation of the problem in NetLogo; and d) a NetLogo model for the execution of the experiments. Finally, we show that Grammatical Evolution is capable of finding solutions in the Santa Fe Trail problem that require fewer steps than the solutions mentioned in the GP and GE literature.", notes = " https://ecta.scitevents.org/ICEC2010/Program_Monday.htm Broken http://www.icec.ijcci.org/ICEC2010/home.asp broken http://www.ijcci.org/Abstracts/2010/ICEC_2010_Abstracts.htm Also known as \cite{DBLP:conf/ijcci/GeorgiouT10}", } @InProceedings{Georgiou:2011:IJCAI, author = "Loukas Georgiou and William J. Teahan", title = "Constituent Grammatical Evolution", booktitle = "Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence", year = "2011", editor = "Toby Walsh", pages = "1261--1268", address = "Barcelona, Spain", publisher_address = "Menlo Park, California, USA", month = "16-22 " # jul, organisation = "International Joint Conferences on Artificial Intelligence", publisher = "AAAI Press", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Santa Fe Trail", isbn13 = "978-1-57735-512-0", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.208.1709", URL = "http://ijcai.org/papers11/Papers/IJCAI11-214.pdf", size = "8 pages", abstract = "We present Constituent Grammatical Evolution (CGE), a new evolutionary automatic programming algorithm that extends the standard Grammatical Evolution algorithm by incorporating the concepts of constituent genes and conditional behaviour-switching. CGE builds from elementary and more complex building blocks a control program which dictates the behaviour of an agent and it is applicable to the class of problems where the subject of search is the behaviour of an agent in a given environment. It takes advantage of the powerful Grammatical Evolution feature of using a BNF grammar definition as a plug-in component to describe the output language to be produced by the system. The main benchmark problem in which CGE is evaluated is the Santa Fe Trail problem using a BNF grammar definition which defines a search space semantically equivalent with that of the original definition of the problem by Koza. Furthermore, CGE is evaluated on two additional problems, the Los Altos Hills and the Hampton Court Maze. The experimental results demonstrate that Constituent Grammatical Evolution outperforms the standard Grammatical Evolution algorithm in these problems, in terms of both efficiency (percent of solutions found) and effectiveness (number of required steps of solutions found).", notes = "Santa Fe Ant, Lost Altos Hills, Hampton Court Maze, jGE http://ijcai.org/papers11/contents.php", } @PhdThesis{Georgiou:thesis, author = "Loukas Georgiou", title = "Constituent Grammatical Evolution", school = "School of Computer Science, Bangor University", year = "2012", address = "LL57 1UT, Gwynedd, UK", month = aug, email = "loukas.georgiou@gmail.com", keywords = "genetic algorithms, genetic programming, grammatical evolution, artificial ant, maze search, grammatical bias, modularity", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Georgiou_thesis.pdf", URL = "https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.569460", size = "281 pages", abstract = "Evolutionary algorithms are a competent nature-inspired approach for complex computational problem solving. One recent development is Grammatical Evolution, a grammar-based evolutionary algorithm which uses genotypes of variable length binary strings and a unique genotype-to-phenotype mapping process based on a BNF grammar definition describing the output language that is able to create valid individuals of an arbitrary structure or programming language. This study surveys Grammatical Evolution, identifies its most important issues, investigates the competence of the algorithm in a series of agent-oriented benchmark problems, provides experimental results which cast doubt about its effectiveness and efficiency on problems involving the evolution of the behaviour of an agent, and presents Constituent Grammatical Evolution (CGE), a new innovative evolutionary automatic programming algorithm. CGE extends Grammatical Evolution by incorporating the concepts of constituent genes and conditional behaviour-switching. It builds from elementary and more complex building blocks a control program which dictates the behaviour of an agent and it is applicable to the class of problems where the subject of search is the behaviour of an agent in a given environment. Experimental results show that the new algorithm significantly improves Grammatical Evolution in all problems it has been benchmarked. Additionally, the investigation undertaken in this work required the development of a series of tools which are presented and described in detail. These tools provide an extendable open source and publicly available framework for experimentation in the area of evolutionary algorithms and their application in agent-oriented environments and complex systems.", notes = "jGE CGE NetLogo https://github.com/marcvanzee/moonlander/tree/master/jge Supervisor: William J. Teahan uk.bl.ethos.569460 ISNI: 0000 0004 2736 3960", } @InProceedings{conf/setn/GeorgopoulosZAVL08, title = "A Genetic Programming Environment for System Modeling", author = "Efstratios F. Georgopoulos and George P. Zarogiannis and Adam V. Adamopoulos and Anastasios P. Vassilopoulos and Spiridon D. Likothanassis", bibdate = "2008-09-26", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/setn/setn2008.html#GeorgopoulosZAVL08", booktitle = "5th Hellenic Conference on AI, SETN 2008", publisher = "Springer", year = "2008", volume = "5138", editor = "John Darzentas and George A. Vouros and Spyros Vosinakis and Argyris Arnellos", isbn13 = "978-3-540-87880-3", pages = "85--96", series = "Lecture Notes in Computer Science", DOI = "doi:10.1007/978-3-540-87881-0_9", address = "Syros, Greece", month = oct # " 2-4", keywords = "genetic algorithms, genetic programming, Evolutionary Algorithms, System Modeling, MEG modeling, fatigue modeling", size = "12 pages", abstract = "In the current paper we present an integrated genetic programming environment with a graphical user interface (GUI), called jGPModeling. The jGPModeling environment was developed using the JAVA programming language, and is an implementation of the steady-state genetic programming algorithm. That algorithm evolves tree based structures that represent models of input-output relation of a system. During the design and implementation of the application, we focused on the execution time optimization and tried to limit the bloat effect. In order to evaluate the performance of the jGPModeling environment, two different real world system modeling tasks were used.", notes = "SQUID, MEG", } @InProceedings{georgopoulos:2010:ISD, author = "Efstratios F. Georgopoulos and Adam V. Adamopoulos and Spiridon D. Likothanassis", title = "Genetic Programming Modeling and Complexity Analysis of the Magnetoencephalogram of Epileptic Patients", booktitle = "Information Systems Development", year = "2010", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/b137171_40", DOI = "doi:10.1007/b137171_40", } @Article{Georgoulas200769, author = "George Georgoulas and Dimitris Gavrilis and Ioannis G. Tsoulos and Chrysostomos Stylios and Joao Bernardes and Peter P. Groumpos", title = "Novel approach for fetal heart rate classification introducing grammatical evolution", journal = "Biomedical Signal Processing and Control", volume = "2", number = "2", pages = "69--79", year = "2007", ISSN = "1746-8094", DOI = "DOI:10.1016/j.bspc.2007.05.003", URL = "http://www.sciencedirect.com/science/article/B7XMN-4P9K9C1-1/2/26899c02af37c6edf88c6baa6282a061", keywords = "genetic algorithms, genetic programming, grammatical evolution, Fetal heart rate, Multilayer perceptron, Feature construction, Classification", abstract = "Fetal heart rate (FHR) variations reflect the level of oxygenation and blood pressure of the fetus. Electronic Fetal Monitoring (EFM), the continuous monitoring of the FHR, was introduced into clinical practice in the late 1960s and since then it has been considered as an indispensable tool for fetal surveillance. However, EFM evaluation and its merit is still an open field of controversy, mainly because it is not consistently reproducible and effective. In this work, we present a novel method based on grammatical evolution to discriminate acidemic from normal fetuses, using features extracted from the FHR signal during the minutes immediately preceding delivery. The proposed method identifies linear and nonlinear correlations among the originally extracted features and creates/constructs a set of new ones, which, in turn, feed a nonlinear classifier. The classifier, which also uses a hybrid method for training, along with the constructed features was tested using a set of real data achieving an overall performance of 90percent (specificity=sensitivity=90percent).", } @Article{Gepp:2009:GPEM, author = "Adrian Gepp and Phil Stocks", title = "A review of procedures to evolve quantum algorithms", journal = "Genetic Programming and Evolvable Machines", year = "2009", volume = "10", number = "2", pages = "181--228", month = jun, keywords = "genetic algorithms, genetic programming, Evolving quantum algorithms, Quantum computing, Evolutionary algorithms, Quantum algorithms", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-009-9080-7", abstract = "There exist quantum algorithms that are more efficient than their classical counterparts; such algorithms were invented by Shor in 1994 and then Grover in 1996. A lack of invention since Grover's algorithm has been commonly attributed to the non-intuitive nature of quantum algorithms to the classically trained person. Thus, the idea of using computers to automatically generate quantum algorithms based on an evolutionary model emerged. A limitation of this approach is that quantum computers do not yet exist and quantum simulation on a classical machine has an exponential order overhead. Nevertheless, early research into evolving quantum algorithms has shown promise. This paper provides an introduction into quantum and evolutionary algorithms for the computer scientist not familiar with these fields. The exciting field of using evolutionary algorithms to evolve quantum algorithms is then reviewed.", } @PhdThesis{Gerber:thesis, author = "Hans Ulrich Gerber", title = "Werkzeuge zum Gestalten interaktiver {PC}-Programme fuer den Unterricht", school = "ETH", year = "1997", address = "Zurich, Switzerland", keywords = "genetic algorithms, genetic programming, COMPUTER-AIDED INSTRUCTION", URL = "https://doi.org/10.3929/ethz-a-001854886", URL = "https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/143301/eth-40917-01.pdf", abstract = "Personal Computers are useful tools for teaching and learning. They serve well as demonstration aids and experimentation tools. Simulation programs imitate natural and technical processes. They visualise phenomena that otherwise would remain hidden from our senses. In experimentation programs or Virtual laboratories, die user interacts with the simulated processes. By exploring, he may discover relationships between cause and effect. Who should develop these programs? Teachers and experts of the different fields are the prime candidates; they know their subjects and wider? stand their target audience. Unfortunately, those who are experts in their fields are generally not Computer scientists or Software geeks at the same time. Many of them know some classic high-level programming language, but they do not have the spare time to keep track of the latest developments on the personal Computer market, to test programming tools and to separate the useful from the gimmicks. Some of them have developed program modules over the years that have proven useful and reliable, but which no longer run on new machines or operating Systems. Our experts and application programmers need tools to help membridge the gap between their proven programs and modern operating environments with graphical user interfaces. Just as users of Computer programs may rightfully expect understandable interfaces, application programmers have a right for clean and simple interfaces to their tools as well. Programmers are most productive if they can use tools that are tailored to their problems and their knowledge. Commercial toolkits often do not exhibit a simple programming model, since they have to serve a wide range of users with different needs. In this report, the author plays the role of Software toolsmith. He has crafted Software instruments which seamlessly fit into the world of our application programmers. The tools help protect the value of their programs by shielding them from too many rapid changes of the market. They are: a simple program library for commercial graphical user interfaces. Application programmers are not locked into an unwieldy toolkit from a single vendor, instead they can continue to use their familiar classic procedural languages, be it C, FORTRAN or Modula-2. Programs built with this tool look and behave like commercial applications. a Software library to adapt existing FORTRAN programs (legacy applications) to a graphical user interface. tools to create electronic books, combining program and documentation parts. The latest developments make it possible to offer such Compound documents across politicial and technical boundaries. An application example from the field of Genetic Programming shows the potential of the programming language Java to produce interactive simulation programs that can easily be published for a world-wide audience", notes = "Diss. Techn. Wiss. ETH Zurich, Nr. 12078, 1997. Ref.: Walter Schaufelberger ; Korref.: Christian Hafner. In german? dc.contributor.supervisor: Christian Hafner dc.contributor.supervisor: Walter Schaufelberger", } @InProceedings{gerber:2023:GECCOcomp2, author = "Mia Gerber and Nelishia Pillay", title = "An Analysis of Choice Functions for Fuzzy {ART} Using Grammatical Evolution", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "571--574", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, grammatical evolution, fuzzy art, automated design: Poster", isbn13 = "9798400701191", DOI = "doi:10.1145/3583133.3590554", size = "4 pages", abstract = "The Fuzzy Adaptive Resonance Theory (ART) algorithm is effective for unsupervised clustering. The Fuzzy ART choice function is an integral part of the Fuzzy ART algorithm. One of the challenges is that different choice functions are effective for different datasets. This work evolves the choice function using GE. The study compares the evolved choice functions to manually created choice functions. This study compares two different grammars for the GE, a basic grammar that includes only functions from the Fuzzy ART algorithm and an extended grammar that includes additional functions. This work also compares different fitness functions for GE. Analysis is done using ten UCI benchmark datasets and three real-world sentiment analysis datasets, it is found that the evolved functions using the extended grammar perform better than the manually created functions. The best fitness function to use for the GE is dataset dependent.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @InProceedings{gerber:2024:CEC, author = "Mia Gerber and Nelishia Pillay", title = "Dynamic Function Generation for Text Classification", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Machine learning algorithms, Reviews, Heuristic algorithms, Neural networks, Text categorization, Subspace constraints, Germanium, dynamic function generation, text classification, grammatical evolution", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10611777", abstract = "Genetic programming and its variants, such as grammatical evolution, have been predominantly used for generating functions for machine learning techniques, such as loss or activation functions for neural networks and choice functions for the Fuzzy ART algorithm. These functions are evolved offline prior to the execution of the neural network and remains the same during the execution of the learning algorithm. We refer to this as static function generation (SFG). This study examines generating these functions in real-time at different points during the execution of the machine learning algorithm. We refer to this as dynamic function generation (DFG). Grammatical evolution (GE) is used to generate the function. Each function is generated at every $m$ epochs of the learning algorithm. Furthermore, the grammar used by GE also changes every $g$ generations of the GE algorithm. A selection perturbative hyper-heuristic is used to determine the options to include in the grammar. In previous work the effectiveness of using GE to evolve the choice function for the Fuzzy Art algorithm was shown. We use this as a case study to investigate DFG given the success of generating choice functions for this learning algorithm in previous work. However, DFG can be used with any neural network learning algorithm. Static and dynamic function generation is evaluated for text classification using the Enron, SMS Spam, Chat GPT tweets, IMDB movie reviews and Amazon product reviews datasets. DFG improved on the performance of SFG for all datasets. Additionally, DFG was found to be competitive with the state of the art and improved on the best known results for the SMS Spam and Chat GPT Tweets datasets.", notes = "also known as \cite{10611777} WCCI 2024", } @InProceedings{gerber:2024:ICAISC, author = "Mia Gerber and Nelishia Pillay", title = "A Comparison of Genetic Programming and Grammatical Evolution for the Generation of Fuzzy {ART} Choice Functions", booktitle = "Artificial Intelligence and Soft Computing", year = "2024", volume = "15165", address = "Zakopane, Poland", month = jun # " 16–20", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", URL = "https://link.springer.com/chapter/10.1007/978-3-031-84356-3_4", DOI = "doi:10.1007/978-3-031-84356-3_4", } @PhdThesis{Gerrard_PhD_thesis_2014_Computational, author = "Claire E. Gerrard", title = "Computational aspects of cellular intelligence and their role in artificial intelligence", school = "Robert Gordon University", year = "2014", address = "Aberdeen", month = jul, URL = "http://hdl.handle.net/10059/1138", URL = "https://openair.rgu.ac.uk/bitstream/handle/10059/1138/Gerrard%20PhD%20thesis%202014%20Computational.pdf", size = "276 pages", abstract = "The work presented in this thesis is concerned with an exploration of the computational aspects of the primitive intelligence associated with single-celled organisms. The main aim is to explore this Cellular Intelligence and its role within Artificial Intelligence. The findings of an extensive literature search into the biological characteristics, properties and mechanisms associated with Cellular Intelligence, its underlying machinery - Cell Signalling Networks and the existing computational methods used to capture it are reported. The results of this search are then used to fashion the development of a versatile new connectionist representation, termed the Artificial Reaction Network (ARN). The ARN belongs to the branch of Artificial Life known as Artificial Chemistry and has properties in common with both Artificial Intelligence and Systems Biology techniques, including: Artificial Neural Networks, Artificial Biochemical Networks, Gene Regulatory Networks, Random Boolean Networks, Petri Nets, and S-Systems. The thesis outlines the following original work: The ARN is used to model the chemotaxis pathway of Escherichia coli and is shown to capture emergent characteristics associated with this organism and Cellular Intelligence more generally. The computational properties of the ARN and its applications in robotic control are explored by combining functional motifs found in biochemical network to create temporal changing waveforms which control the gaits of limbed robots. This system is then extended into a complete control system by combining pattern recognition with limb control in a single ARN. The results show that the ARN can offer increased flexibility over existing methods. Multiple distributed cell-like ARN based agents termed Cytobots are created. These are first used to simulate aggregating cells based on the slime mould Dictyostelium discoideum. The Cytobots are shown to capture emergent behaviour arising from multiple stigmergic interactions. Applications of Cytobots within swarm robotics are investigated by applying them to benchmark search problems and to the task of cleaning up a simulated oil spill. The results are compared to those of established optimization algorithms using similar cell inspired strategies, and to other robotic agent strategies. Consideration is given to the advantages and disadvantages of the technique and suggestions are made for future work in the area. The report concludes that the Artificial Reaction Network is a versatile and powerful technique which has application in both simulation of chemical systems, and in robotic control, where it can offer a higher degree of flexibility and computational efficiency than benchmark alternatives. Furthermore, it provides a tool which may possibly throw further light on the origins and limitations of the primitive intelligence associated with cells.", notes = "Not GP? advisor John McCall, George M.Coghill, Christopher Macleod", } @InProceedings{Gerules:2016:CEC, author = "George Gerules and Cezary Janikow", title = "A Survey of Modularity in Genetic Programming", booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016)", year = "2016", editor = "Yew-Soon Ong", pages = "5034--5043", address = "Vancouver", month = "24-29 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-5090-0623-6", URL = "http://www.cs.umsl.edu/~janikow/publications/2016/PID4170473.pdf", DOI = "doi:10.1109/CEC.2016.7748328", size = "10 pages", abstract = "Here, in this paper, we survey work on modularity in Genetic Programming GP. The motivation for modularity was driven by research efforts, as we shall see, to make GP programs smaller and more efficient. In the literature, modularity has commonly used Koza's term, Automatically Defined Functions ADF. But, we shall see, that the modularity concept has undergone many name and design changes. From the early ideas of Koza and Price's Defined Building Blocks DBB to Binard and Felty's work with System F and GP Briggs and O'Neill's work with Combinators in GP. Our goal in this paper is to survey the literature on this evolution. This will include Automatically Defined Functions ADFs, Automatically Defined Macros ADM, Adaptive Representation Through Learning ARL, Module Acquisition MA, Hierarchically Defined Local Modules HGP, Higher Order Functions using lambda calculus LC and Combinators. We also include critiques by researchers on the viability these various efforts.", notes = "WCCI2016", } @PhdThesis{Gerules:thesis, author = "George W. Gerules", title = "Enhancing Scalability in Genetic Programming With Adaptable Constraints, Type Constraints and Automatically Defined Functions", school = "University of Missouri -- St. Louis", year = "2019", address = "612 Clark Ave, USA", month = "11 " # jul, email = "ggerules@gmail.com", keywords = "genetic algorithms, genetic programming, ADF, scalability, types, bloat control", URL = "https://irl.umsl.edu/dissertation/867/", URL = "https://search.proquest.com/docview/2302013388?accountid=14511", size = "127 pages", abstract = "Genetic Programming is a type of biological inspired machine learning. It is composed of a population of stochastic individuals. Those individuals can exchange portions of themselves with others in the population through the crossover operation that draws its inspiration from biology. Other biologically inspired operations include mutation and reproduction. The form an individual takes can be many things. It, however, is represented most of the time as a computer program. Constructing correct efficient programs can be notoriously difficult. Various grammar, typing, function constraint, or counting mechanisms can guide creation and evolution of those individuals. These mechanisms can reduce search space and improve scalability of genetic program solutions. Finding correct combinations of individuals, however, can be extremely challenging when using methods found in GP such as Automatically Defined Functions or other Architecturally Altering Operations. This work extends and combines in a unique way previous work on Constrained Genetic Programming, Adaptive Constrained Genetic Programming and Automatically Defined Functions. This dissertation shows, compared to previous stand alone mechanisms, that a new combination of genetic programming constraint mechanisms and Automatically Defined Functions improve scalability for a number of benchmark problems. The combination of constraint mechanisms include delayed max tree size per evolved generations, typing on the evolved programs, use of automatically defined functions, and use of adaptive heuristics for function and terminals on the evolved programs. Initial results show that this combination of methodologies create smaller efficient individuals capable of handling larger problems. Moreover, this combined methodology works particularly well for constraints can be applied ahead of time.", notes = "CGP2.1 CGPF2.1 lil-gp, Lawn Mower, Bumble Bee, MA. HLDM Example, hGP Test Problems. For loop example for ADI, For loop example for ADL, Recursion example for ADR in C. implementation lilgp1.03 (modified int to long for statistics variables) Frameworks created, CGPF2.1, ACGPF2.1 Source Code https://github.com/ggerules/lilgp1.03 Supervisor: Cezary Z. Janikow Co-Advisor Uday Chakraborty", } @Article{Gervas:2023:GPEM, author = "Pablo Gervas and Gonzalo Mendez and Eugenio Concepcion", title = "Evolutionary combination of connected event schemas into meaningful plots", journal = "Genetic Programming and Evolvable Machines", year = "2023", volume = "24", pages = "Article number: 7", note = "Special Issue on Evolutionary Computation in Art, Music and Design", keywords = "genetic algorithms, genetic programming, Story generation, Subplot patterns, Evolutionary approach, Metrics on pattern compatibility, Character instantiation", ISSN = "1389-2576", URL = "https://rdcu.be/deav6", DOI = "doi:10.1007/s10710-023-09454-2", size = "38 pages", abstract = "Many of the stories we are exposed to are built from small schemas of connected events involving a set of characters: boy meets girl leads to a relationship or crime leads to revenge. We propose an evolutionary solution to the task of putting together a story by combining a set of such schemas. This approach presents three challenges: how to mix up the elements in the different schemas, how to instantiate the characters across the schemas and how to tell acceptable combinations from the rest. We apply an evolutionary solution that relies on a genetic representation for these combinations of schemas, and applies as fitness functions a set of metrics on compatibility constraints across schema combinations. Outputs of this procedure are evaluated by human judges in comparison with baseline solutions in which the values for genes are assigned at random. The proposed solution generates a population of story drafts that resemble plot descriptions for simple stories. The results of the comparative evaluation by human judges are positive. The genetic representation of pattern combinations and the metrics on compatibility across pattern pairs provide a valid evolutionary solution for constructing simple plots.", notes = "Facultad de Informatica, Universidad Complutense de Madrid, c/ Profesor Garcia Santesmases, 9, 28040 Madrid, Spain", } @InProceedings{Gervasi:2019:EuroGP, author = "Riccardo Gervasi and Irene Azzali and Donal Bisanzio and Mario Giacobini and Andrea Mosca and Luigi Bertolotti", title = "A Genetic Programming Approach to Predict Mosquitoes Abundance", booktitle = "EuroGP 2019: Proceedings of the 22nd European Conference on Genetic Programming", year = "2019", month = "24-26 " # apr, editor = "Lukas Sekanina and Ting Hu and Nuno Lourenco", series = "LNCS", volume = "11451", publisher = "Springer Verlag", address = "Leipzig, Germany", pages = "35--48", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-030-16669-4", URL = "https://www.springer.com/us/book/9783030166694", DOI = "doi:10.1007/978-3-030-16670-0_3", size = "16 pages", abstract = "In ecology, one of the main interests is to understand species population dynamics and to describe its link with various environmental factors, such as habitat characteristics and climate. It is especially important to study the behaviour of animal species that can hosts pathogens, as they can be potential disease reservoirs and/or vectors. Pathogens of vector borne diseases can only be transmitted from an infected to a susceptible individual by a vector. Thus, vector ecology is a crucial factor influencing the transmission dynamics of vector borne diseases and their complexity. The formulation of models able to predict vector abundance are essential tools to implement intervention plans aiming to reduce the spread of vector-borne diseases (e.g. West Nile Virus). The goal of this paper is to explore the possible advantages in using Genetic Programming (GP) in the field of vector ecology. In this study, we present the application of GP to predict the distribution of Culex pipiens, a mosquito species vector of West Nile virus (WNV), in Piedmont, Italy. Our modeling approach took into consideration the ecological factors which affect mosquitoes abundance. Our results showed that GP was able to outperform a statistical model that was used to address the same problem in a previous work. Furthermore, GP performed an implicit feature selection, discovered automatically relationships among variables and produced fully explorable models.", notes = "http://www.evostar.org/2019/cfp_eurogp.php#abstracts Part of \cite{Sekanina:2019:GP} EuroGP'2019 held in conjunction with EvoCOP2019, EvoMusArt2019 and EvoApplications2019", } @InProceedings{conf/asc/GestalRDP06, title = "Description of {RANNs} and their generalisation capabilities by means of rule extraction by genetic programming", author = "Marcos Gestal and Juan R. Rabu{\~n}al and Julian Dorado and Javier {Pereira Loureiro}", booktitle = "Artificial Intelligence and Soft Computing", publisher = "IASTED/ACTA Press", year = "2006", editor = "Angel P. Del Pobil", ISBN = "0-88986-612-0", pages = "323--328", address = "Palma de Mallorca, Spain", month = aug # " 28-30", bibdate = "2007-01-26", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/asc/asc2006.html#GestalRDP06", keywords = "genetic algorithms, genetic programming, Recurrent Artificial Neural Networks, Rule Extraction, Algorithm of Example Generation, Generalisation Capabilities, Series Prediction", URL = "http://www.actapress.com/PaperInfo.aspx?PaperID=28200", URL = "http://sabia.tic.udc.es/sabia/secciones/publications/?id=311", abstract = "Artificial Neural Networks have achieved satisfactory results in different fields such as example classification or image identification. Real-world processes usually have a temporal evolution, and they are the type of processes where Recurrent Networks have special success. Nevertheless they are still reluctantly used, mainly due to the fact that they do not adequately justify their response. But, if ANNs offer good results, why giving them up? Suffice it to find a method that might search an explanation to the outputs that the ANN provides. This work presents a technique, totally independent from ANN architecture and the learning algorithm used, which makes possible the justification of the ANN outputs by means of expression trees.", } @InCollection{Geyer:1990:pfss, author = "A. Geyer and Andreas Geyer-Schulz and A. Taudes", title = "A Fuzzy Times Series Analyzer", booktitle = "Progress in Fuzzy Sets and Systems", publisher = "Kluwer Academic Publishers", year = "1990", editor = "Wolfgang H. Janko and Marc Roubens and H.-J. Zimmermann", volume = "5", series = "Series D: Systems Theory, Knowledge Engineering and Problem Solving", pages = "63--74", address = "The Netherlands", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Geyer_1990_pfss.pdf", size = "12 pages", } @InProceedings{GeyerSchulz92d, crossref = "Hoehle92", author = "Andreas Geyer--Schulz", title = "Fuzzy Rule Languages and Genetic Algorithms", year = "1992", pages = "36--38", keywords = "genetic algorithms, genetic programming", notes = "In \cite{Hoehle92}", } @Proceedings{Hoehle92, editor = "Ulrich H{\"o}hle and Peter Klement", booktitle = "$14^{th}$ Linz Seminar on Fuzzy Set Theory: Non-Classical Logics and their Applications", title = "$14^{th}$ Linz Seminar on Fuzzy Set Theory: Non-Classical Logics and their Applications", year = "1992", publisher = "Johannes Kepler Universit{\"a}t Linz", address = "Linz", keywords = "genetic algorithms, genetic programming", } @InProceedings{GeyerSchulz92b, crossref = "Lowen92", author = "Andreas Geyer--Schulz", title = "Fuzzy Classifier Systems", year = "1992", pages = "345--354", notes = "In \cite{Lowen92}", } @Proceedings{Lowen92, editor = "Robert Lowen and Marc Roubens", booktitle = "Fuzzy Logic: State of the Art", title = "Fuzzy Logic: State of the Art", year = "1993", series = "Series D: System Theory, Knowledge Engineering and Problem Solving", organisation = "IFSA", publisher = "Kluwer Academic Publishers", address = "Dordrecht", } @InProceedings{GeyerSchulz92c, crossref = "Bandemer92", author = "Andreas Geyer--Schulz", title = "On the Specification of Fuzzy Data in Management", year = "1992", pages = "105--110", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/GeyerSchulz92c.pdf", keywords = "genetic algorithms, genetic programming", size = "6 pages", notes = "In \cite{Bandemer92}", } @Proceedings{Bandemer92, editor = "Hans Bandemer", booktitle = "Modelling Uncertain Data", title = "Modelling Uncertain Data", year = "1993", volume = "68", series = "Mathematical Research", organisation = "GAMM", publisher = "Akademie Verlag", address = "Berlin", ISBN = "3-05-501578-9", URL = "http://books.google.co.uk/books?id=FzjvAAAAMAAJ", keywords = "genetic algorithms, genetic programming", } @InProceedings{GeyerSchulz93b, crossref = "Frisch93", author = "Andreas Geyer--Schulz", title = "{Z}ur {B}eschleunigung des {L}ernens genetischer {A}lgorithmen mittels unscharfer {R}egelsprachen", year = "1993", pages = "73--85", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/GeyerSchulz93b.pdf", keywords = "genetic algorithms, genetic programming", notes = "In \cite{Frisch93}. In German", } @Proceedings{Frisch93, editor = "Walter Frisch and Alfred Taudes", booktitle = "Informationswirtschaft", title = "Informationswirtschaft, Aktuelle Entwicklungen und Perspektiven : Symposion", year = "1993", month = "29-30 " # sep, publisher = "Physica-Verlag", publisher_address = "Heidelberg", address = "Vienna", ISBN = "3-7908-0727-3", URL = "http://books.google.co.uk/books?id=PAXYPQAACAAJ", keywords = "genetic algorithms, genetic programming", } @InProceedings{Geyer-Schulz:1993:EUFIT, author = "Andreas Geyer-Schulz", title = "Speeding Up Genetic Machine Learning -- A case for Fuzzy Rule Languages", booktitle = "First European Congress on Fuzzy and Intelligent Technologies, EUFIT'93", year = "1993", volume = "2", pages = "1083--1089", address = "Aachen, Germany", publisher_address = "D-52076 Aachen", month = "7-10 " # sep, publisher = "Elite-Foundation", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Geyer-Schulz_1993_EUFIT.pdf", size = "7 pages", notes = "Boston Consulting Group rule language grammar", } @Book{GeyerSchulz95a, author = "Andreas Geyer--Schulz", title = "Fuzzy Rule-Based Expert Systems and Genetic Machine Learning", publisher = "Physica-Verlag", address = "Heidelberg", year = "1995", volume = "3", series = "Studies in Fuzziness", URL = "http://www.amazon.com/Rule-Based-Systems-Learning-Fuzziness-Computing/dp/3790809640", ISBN = "3-7908-0830-X", keywords = "genetic algorithms, genetic programming", notes = "reviewed by Dick Bowman, Dogon Research http://www.apl.demon.co.uk/aplandj/fuzzy.htm", } @TechReport{GeyerSchulz95c, author = "Andreas Geyer--Schulz", title = "Genetic Machine Learning", institution = "ACM SIGAPL", address = "New York, N.Y.", year = "1995", note = "Tutorial held at APL'95 at San Antonio, Texas", keywords = "genetic algorithms, genetic programming", } @InProceedings{GeyerSchulz96a, crossref = "Herrera96", author = "Andreas Geyer--Schulz", title = "The {M}{I}{T} Beer Distribution Game Revisited: Genetic Machine Learning and Managerial Behavior in a Dynamic Decision Making Experiment", year = "1996", pages = "658--682", keywords = "genetic algorithms, genetic programming, Experimental economics, organizational learning, simulation, gaming, system dynamics, fuzzy genetic programming.", abstract = "The paper reports on the experiment of applying genetic machine learning methods to breeding heuristic for playing the MIT beer distribution game. In the MIT beer distribution game a team of four subjects acts as managers of a simulated industrial production and distribution system with the aim of minimising total inventory. The system consists of a chain of ofur coupled stock management systems with uncertain demand, tiem delays, feedbacks, multiple actors, non-linearities and restricted information availability. The complexity of the system - it is a 23rd order non-linear difference equation - renders calculation of the optimal behaviour intractable. In the experiment threee genetic machine learning methods (a simple genetic algorithm, genetic programming, and fuzzy genetic programming) are applied to the beer distribution game. The results of the methods are compared with the previously known best solution and with the performance of a group of subjects which actually played the game.", notes = "In \cite{Herrera96} http://decsai.ugr.es/~herrera/abstracts.html#c30", } @Proceedings{Herrera96, editor = "F. Herrera and J. L. Verdegay", booktitle = "Genetic Algorithms and Soft Computing", title = "Genetic Algorithms and Soft Computing", year = "1996", month = sep, volume = "8", series = "Studies in Fuzziness and Soft Computing", organisation = "Physica-Verlag", publisher = "Physica-Verlag", address = "Heidelberg", ISBN = "3-7908-0956-X", broken = "http://decsai.ugr.es/~herrera/ga-sc.html", URL = "http://www.amazon.co.uk/gp/search?index=books&linkCode=qs&keywords=379080956X", keywords = "genetic algorithms, genetic programming", } @Book{GeyerSchulz96b, author = "Andreas Geyer--Schulz", title = "Fuzzy Rule-Based Expert Systems and Genetic Machine Learning", publisher = "Physica-Verlag", address = "Heidelberg", year = "1996", volume = "3", series = "Studies in Fuzziness and Soft Computing", edition = "2nd revised", keywords = "genetic algorithms, genetic programming", URL = "http://www.amazon.co.uk/gp/search?index=books&linkCode=qs&keywords=3790809640", } @Proceedings{Biethahn96, editor = "J. Biethahn and A. H{\"o}hnerloh and J. Kuhl and V. Nissen", booktitle = "Betriebliche Anwendungen von Fuzzy Technologien", title = "Betriebliche Anwendungen von Fuzzy Technologien", year = "1996", organisation = "AFN -- Arbeitsgemeinschaft Fuzzy Logik und Softcomputing Norddeutschland", publisher = "Georg-August Universit{\"a}t G{\"o}ttingen, Institut f{\"u}r Wirtschaftsinformatik", address = "G{\"o}ttingen", keywords = "genetic algorithms, genetic programming", URL = "http://www.amazon.de/Betriebliche-Anwendungen-von-Fuzzy-Technologien-Softcomputing/dp/B003E8W9ZE", } @InProceedings{GeyerSchulz96c, crossref = "Biethahn96", author = "Andreas Geyer--Schulz", title = "{D}as {L}ernen von {B}estellregeln in {D}istributionsketten: {E}ine betriebswirtschaftliche {A}nwendung von {F}uzzy {G}enetic {P}rogramming", year = "1996", pages = "92--106", keywords = "genetic algorithms, genetic programming", notes = "In \cite{Biethahn96}", } @Article{GeyerSchulz96d, author = "Andreas Geyer--Schulz", title = "Fuzzy Genetic Programming and Dynamic Decision Making", journal = "Proc. ICSE'96", year = "1996", month = jun, pages = "686--691", keywords = "genetic algorithms, genetic programming", } @InProceedings{GeyerSchulz96e, author = "Andreas Geyer--Schulz", title = "Compound Derivations in Fuzzy Genetic Programming", booktitle = "1996 Biennial Conference of the North American Fuzzy Information Processing Society, NAFIPS", year = "1996", month = jul, pages = "510--514", DOI = "doi:10.1109/NAFIPS.1996.534787", keywords = "genetic algorithms, genetic programming, a priori knowledge, compound derivations, context-free language, equivalence transformations, fuzzy genetic programming, grammar, k-bounded context-free languages, lambda abstraction, machine-learning method, nonlinear transformations, speedup theorems, context-free languages, fuzzy logic, grammars, heuristic programming, learning (artificial intelligence)", size = "5 pages", abstract = "We introduce the concept of compound derivations in fuzzy genetic programming as an alternative to lambda abstraction. We show that in fuzzy genetic programming based on simple genetic algorithms over k-bounded context-free languages compound derivations provide a powerful tool for generating automatically equivalence transformations on the grammar of a context-free language. Although such transformations do not change the language generated by the grammar, the probability of generating words can be transformed almost at will. We apply this property to: nonlinear transformations of the probability of generating words for initialising a population,; incorporating a priori knowledge; the new genetic operator compound which provides an alternative to lambda abstraction; and proving speedup theorems", } @InProceedings{GeyerSchulz96f, author = "Andreas Geyer--Schulz", title = "Learning Strategies for Managing New and Innovative Products", booktitle = "Classification and Knowledge Organization Proceedings of the 20th Annual Conference of the Gesellschaft fuer Klassifikation e.V., GfKl'96", editor = "Ruediger Klar and Otto Opitz", volume = "XX", year = "1996", month = "6-8 " # mar, series = "Studies in Classification, Data Analysis, and Knowledge Organization", pages = "262--269", address = "University of Freiburg, Germany", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-62981-8", URL = "http://www.springer.com/economics/book/978-3-540-62981-8?cm_mmc=Google-_-Book%20Search-_-Springer-_-0", size = "8 pages", notes = "published 1997 (2012 Currently out of stock)", } @Article{GeyerSchulz96g, author = "Andreas Geyer--Schulz", title = "Fuzzy Genetic Algorithms", journal = "Handbook of Fuzzy Systems", year = "1996", month = apr, note = "Work in progress", keywords = "genetic algorithms, genetic programming", } @InProceedings{Geyer-Schulz:1997:700, author = "Andreas Geyer-Schulz", title = "The Next 700 Programming Languages for Genetic Programming", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming", pages = "128--136", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1997/Geyer-Schulz_1997_700.pdf", size = "9 pages", notes = "GP-97", } @InProceedings{AGeyer-Schulz1998, author = "Andreas Geyer-Schulz", title = "The Genetic Programming Cookbook", booktitle = "APL 1998", year = "1998", editor = "Paolo {Di Chio}", address = "Rome", month = "27th-31st " # jul, organisation = "ACM SIGAPL", note = "Plenary Talk and Tutorial", keywords = "genetic algorithms, genetic programming", URL = "http://www.sigapl.org/Archives/Conferences/apl98/", URL = "http://www.sigapl.org/Archives/Conferences/apl98/apl98/howto/works/plenary-abs.html", abstract = "This talk is about the art and science of genetic programming. In the science part we introduce simple genetic algorithms over k-bounded context-free languages as a general theoretical framework for genetic programming and we present a survey of the (theoretical) results achieved in this setting: e.g. uniform initialization, generalization of various genetic programming approaches, equivalence transformations on grammars, compound derivations, abstraction and speedup. We compare genetic programming with simple genetic algorithms and show that the transition matters: Because there is no best grammar for genetic programming, a search for better grammars usually pays. However, in all practical applications there is an element of art involved: the design of a (little) language for genetic programming. The second part of this talk is devoted to the art of genetic programming.We discuss language design principles and prescribe recipes for genetic programming in various environments. The purpose of these recipes is to show informally, how to use a grammar to solve specific problems. Examples range from agent languages to layout languages, the application domains from complex dynamic systems to combinatorial optimization problems. To conclude:``The language, like a seed, is the genetic system which gives ourmillions of small acts the power to form a whole.`` (From Christopher Alexander, The Timeless Way of Building, 1979.)", notes = "Plenary Talk and Tutorial held at APL'98 at Rome, Italy. I: Genetic Programming Theory. II: Language Design and Language Cookbook. III: Combinatorial Optimization, Rewrite Systems, and Test Cases", } @Article{Ghaddar:2016:EJOR, author = "Bissan Ghaddar and Nizar Sakr and Yaw Asiedu", title = "Spare parts stocking analysis using genetic programming", journal = "European Journal of Operational Research", volume = "252", number = "1", pages = "136--144", year = "2016", ISSN = "0377-2217", DOI = "doi:10.1016/j.ejor.2015.12.041", URL = "http://www.sciencedirect.com/science/article/pii/S0377221715011807", abstract = "Optimal solutions to the Level of Repair Analysis (LORA) and the Spare Parts Stocking (SPS) problems are essential in achieving a desired system/equipment operational availability. Although these two problems are interdependent, they are seldom solved simultaneously due to the complicating nature of the relationships between spare levels and system availability (or expected backorder) thus leading to sub-optimal solutions for both problems. This paper uses genetic programming-based symbolic regression methodology to evolve simpler mathematical expressions for the expected backorder equation. In addition to making the SPS problem more tractable, the simpler mathematical expressions make it possible for a combined SPS and LORA model to be formulated and solved using standard optimization techniques. Three sets of spare parts stocking problems are presented to study the feasibility of the proposed approach. Further, a case study for the joint problem is solved which shows that the proposed methodology can tackle the integrated problem.", keywords = "genetic algorithms, genetic programming, Spare parts, Level of Repair Analysis, Symbolic regression, Optimization", } @Article{GHAFARI:2022:CBM, author = "Sepehr Ghafari and Mehrdad Ehsani and Fereidoon {Moghadas Nejad}", title = "Prediction of low-temperature fracture resistance curves of unmodified and crumb rubber modified hot mix asphalt mixtures using a machine learning approach", journal = "Construction and Building Materials", volume = "314", pages = "125332", year = "2022", ISSN = "0950-0618", DOI = "doi:10.1016/j.conbuildmat.2021.125332", URL = "https://www.sciencedirect.com/science/article/pii/S0950061821030737", keywords = "genetic algorithms, genetic programming, R-curve, Crack propagation, Hot mix asphalt, Machine learning, Artificial neural networks, Multi-gene genetic programming", abstract = "Fracture resistance curves (R-curves) provide a robust tool for a comprehensive insight into the crack propagation regime in engineering materials. In this paper, an extensive research program is conducted to determine R-curves for hot mix asphalt (HMA) mixtures with varying properties. The experimental results are then used to develop R-curve prediction models following a machine learning approach. Three-point single-edge notched beam (SE(B)) experiments were conducted on HMA mixtures incorporating 0percent, 5percent, 10percent, 15percent, and 20percent crumb rubber at low temperatures. The temperature ranged from + 5 degreeC to -20 degreeC while limestone and siliceous aggregate with two gradations were used in developing mixtures with two base bitumen having performance grades of PG58-22 and PG64-22. It was observed that as the temperature is declined to -20 degreeC, the stable crack growth region is significantly diminished in the R-curves, and the mixtures undergo a brittle fracture with abrupt failure of the specimen. A temperature of -15 degreeC could be determined where the transition from quasi-brittle to brittle fracture occurs. Mixtures fabricated incorporating 20percent crumb rubber exhibited a progressively rising R-curve at the lowest test temperature (-20 degreeC) even in the unstable crack propagation phase, which is a desirable material characteristic. Two prediction models were developed for R-curves. Artificial neural networks (ANN) were used in the first model resulting in an R-square value of 0.965. Due to the black-box nature of the ANN, the multi-gene genetic programming approach was also applied in the prediction of the R-curves to derive a mathematical equation between the input data and the outputs. The R-square equaled 0.870 in this method. R-curves could successfully be predicted by both methods considering the negligible to fair errors", } @Article{Ghanbari:2017:Fuel, author = "M. Ghanbari and G. Najafi and B. Ghobadian and T. Yusaf and A. P. Carlucci and M. Kiani Deh Kiani", title = "Performance and emission characteristics of a {CI} engine using nano particles additives in biodiesel-diesel blends and modeling with {GP} approach", journal = "Fuel", year = "2017", volume = "202", pages = "699--716", month = "15 " # aug, keywords = "genetic algorithms, genetic programming, Nano additives, Diesel-biodiesel blends, Ultrasonic", ISSN = "0016-2361", URL = "https://iris.unisalento.it/bitstream/11587/414004/5/PROOFFUEL2017.pdf", URL = "http://www.sciencedirect.com/science/article/pii/S0016236117305380", DOI = "doi:10.1016/j.fuel.2017.04.117", abstract = "The performance and the exhaust emissions of a diesel engine operating on nano-diesel-biodiesel blended fuels has been investigated. Multi wall carbon nano tubes (CNT) (40, 80 and 120 ppm) and nano silver particles (40, 80 and 120 ppm) were produced and added as additive to the biodiesel-diesel blended fuel. Six cylinders, four-stroke diesel engine was fuelled with these new blended fuels and operated at different engine speeds. Experimental test results indicated the fact that adding nano particles to diesel and biodiesel fuels, increased diesel engine performance variables including engine power and torque output up to 2percent and brake specific fuel consumption (bsfc) was decreased 7.08percent compared to the net diesel fuel. CO2 emission increased maximum 17.03percent and CO emission in a biodiesel-diesel fuel with nano-particles was lower significantly (25.17percent) compared to pure diesel fuel. UHC emission with silver nano-diesel-biodiesel blended fuel decreased (28.56percent) while with fuels that contains CNT nano particles increased maximum 14.21percent. With adding nano particles to the blended fuels, NOx increased 25.32percent compared to the net diesel fuel. This study also presents genetic programming (GP) based model to predict the performance and emission parameters of a CI engine in terms of nano-fuels and engine speed. Experimental studies were completed to obtain training and testing data. The optimum models were selected according to statistical criteria of root mean square error (RMSE) and coefficient of determination (R2). It was observed that the GP model can predict engine performance and emission parameters with correlation coefficient (R2) in the range of 0.93-1 and RMSE was found to be near zero. The simulation results demonstrated that GP model is a good tool to predict the CI engine performance and emission parameters.", notes = "Also known as \cite{GHANBARI2017699}", } @Article{GHANE:2022:bbe, author = "Mostafa Ghane and Mei Choo Ang and Mehrbakhsh Nilashi and Shahryar Sorooshian", title = "Enhanced decision tree induction using evolutionary techniques for {Parkinson's} disease classification", year = "2022", journal = "Biocybernetics and Biomedical Engineering", volume = "42", number = "3", pages = "902--920", keywords = "genetic algorithms, genetic programming, Decision tree induction, Decision tree algorithm (J48), Parkinson's disease", ISSN = "0208-5216", URL = "https://www.sciencedirect.com/science/article/pii/S0208521622000663", DOI = "doi:10.1016/j.bbe.2022.07.002", abstract = "The diagnosis of Parkinson's disease (PD) is important in neurological pathology for appropriate medical therapy. Algorithms based on decision tree induction (DTI) have been widely used for diagnosing PD through biomedical voice disorders. However, DTI for PD diagnosis is based on a greedy search algorithm which causes overfitting and inferior solutions. This paper improved the performance of DTI using evolutionary-based genetic algorithms. The goal was to combine evolutionary techniques, namely, a genetic algorithm (GA) and genetic programming (GP), with a decision tree algorithm (J48) to improve the classification performance. The developed model was applied to a real biomedical dataset for the diagnosis of PD. The results showed that the accuracy of the J48, was improved from 80.51percent to 89.23percent and to 90.76percent using the GA and GP, respectively", } @InProceedings{Ghanea-Hercock:1994:Earca, author = "R. Ghanea-Hercock and A. P. Fraser", title = "Evolution of autonomous robot control architectures", booktitle = "Evolutionary Computing, AISB workshop", year = "1994", editor = "T. C. Fogarty", address = "Leeds, UK", month = "11-13 " # apr, organisation = "AISB", keywords = "genetic algorithms, genetic programming", notes = "Does NOT appear in the published proceedings LNCS 865 DOI: 10.1007/3-540-58483-8 Proceedings of the Workshop on Artificial Intelligence and Simulation of Behaviour Workshop on Evolutionary Computing. Workshop in Leeds, UK, April 11-13, 1994", } @InProceedings{ghanea-hercock:1999:DGPMA, author = "Robert Ghanea-Hercock and Divine T. Ndumu and Jaron Collis", title = "Distributed Genetic Programming with Mobile Agents", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1441", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, artificial life, adaptive behavior and agents, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-004.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-004.ps", abstract = "java based mobil agents, MATS", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{GhaniC09, author = "Kamran Ghani and John A. Clark", title = "Widening the Goal Posts: Program Stretching to Aid Search Based Software Testing", booktitle = "Proceedings of the 1st International Symposium on Search Based Software Engineering (SSBSE'09)", year = "2009", address = "Cumberland Lodge, Windsor, UK", month = "13-15 " # may, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, SBSE", DOI = "doi:10.1109/SSBSE.2009.26", abstract = "Search based software testing has emerged in recent years as an important research area within automated software test data generation. The general approach of couching the satisfaction of test goals as numerical optimisation problems has been applied to a variety of problems such as satisfying structural coverage criteria, specification falsification, exception generation, breaking unit pre-conditions and software hazard discovery. However, some test goals may be hard to satisfy. For example, a program branch may be difficult to reach via a search based technique, because the domain of the data that causes it to be taken is exceedingly small or the non-linearity of the fitness landscape precludes the provision of effective guidance to the search for test data. In this paper we propose to stretch relevant conditions within a program to make them easier to satisfy. We find test data that satisfies the corresponding test goal of the stretched program. We then seek to transform the stretched program by stages back to the original, simultaneously migrating the obtained test data to produce test data that satisfies the goal for the original program. The stretching device is remarkably simple and shows significant promise for obtaining hard-to-find test data and also gives efficiency improvements over standard search based testing approaches.", } @InProceedings{Ghani:2009:ICSEA, author = "Kamran Ghani and John A. Clark", title = "Automatic Test Data Generation for Multiple Condition and MCDC Coverage", booktitle = "Fourth International Conference on Software Engineering Advances, ICSEA'09", year = "2009", month = sep, pages = "152--157", note = "Winner of top paper prize", keywords = "genetic algorithms, genetic programming, SBSE, MCDC coverage, automatic test data generation, search based software engineering, search based test data generation, search based testing, software engineering community, software functional property, software nonfunctional property, structural testing, automatic testing, program testing, software engineering", isbn13 = "978-0-7695-3675-0", DOI = "doi:10.1109/ICSEA.2009.31", abstract = "Recently search based software engineering (SBSE) has evolved as a major research field in the software engineering community. SBSE has been applied successfully to many software engineering activities ranging from requirement engineering to software maintenance and quality assessment. One area where SBSE has seen much application is test data generation. Search based test data generation techniques have been applied to automatically generate data for testing functional and non-functional properties of softwares. For structural testing, most of the time, the criterion used, is branch coverage. However, this is not enough. For the wider acceptance of search based test data generation techniques, much stronger criteria are needed. we propose an automatic framework that extend search based testing techniques to more stronger criteria such as multiple condition and MCDC coverage.", notes = "Also known as \cite{5298463}", } @Article{ghanizadeh:2024:SR, author = "Ali Reza Ghanizadeh and Amir Tavana Amlashi and Alireza Bahrami and Haytham F. Isleem and Samer Dessouky", title = "A formulation for asphalt concrete air void during service life by adopting a hybrid evolutionary polynomial regression and multi-gene genetic programming", journal = "Scientific Reports", year = "2024", keywords = "genetic algorithms, genetic programming", URL = "https://link.springer.com/article/10.1038/s41598-024-61313-x", DOI = "doi:10.1038/s41598-024-61313-x", } @Article{Gharagheizi201271, author = "Farhad Gharagheizi and Poorandokht Ilani-Kashkouli and Nasrin Farahani and Amir H. Mohammadi", title = "Gene expression programming strategy for estimation of flash point temperature of non-electrolyte organic compounds", journal = "Fluid Phase Equilibria", volume = "329", month = "5 " # sep, pages = "71--77", year = "2012", ISSN = "0378-3812", DOI = "doi:10.1016/j.fluid.2012.05.015", URL = "http://www.sciencedirect.com/science/article/pii/S0378381212002130", keywords = "genetic algorithms, genetic programming, Gene expression programming, Flammability characteristics, Flash point", abstract = "The accuracy and predictability of correlations and models to determine the flammability characteristics of chemical compounds are of drastic significance in various chemical industries. In the present study, the main focus is on introducing and applying the gene expression programming (GEP) mathematical strategy to develop a comprehensive empirical method for this purpose. This work deals with presenting an empirical correlation to predict the flash point temperature of 1471 (non-electrolyte) organic compounds from 77 different chemical families. The parameters of the correlation include the molecular weight, critical temperature, critical pressure, acentric factor, and normal boiling point of the compounds. The obtained statistical parameters including root mean square of error of the results from DIPPR 801 data (8.8, 8.9, 8.9 K for training, optimisation and prediction sets, respectively) demonstrate improved accuracy of the results of the presented correlation with respect to previously-proposed methods available in open literature.", } @InProceedings{Ghareeb:2013:EPECS, author = "W. T. Ghareeb and E. F. {El Saadany}", booktitle = "3rd International Conference on Electric Power and Energy Conversion Systems (EPECS 2013)", title = "Multi-Gene Genetic Programming for Short Term Load Forecasting", year = "2013", month = "2-4 " # oct, keywords = "genetic algorithms, genetic programming, Short-term load forecasting, multi-gene genetic programming, radial basis function", DOI = "doi:10.1109/EPECS.2013.6713061", abstract = "The Short Term Load Forecasting (STLF) plays a critical role in power system operation. The accuracy of the STLF is very important since it affects the generation scheduling and the electricity prices and hence an accurate STLF method should be used. This paper presents a new variant of genetic programming namely: Multi-Gene Genetic Programming (MGGP) for the problem of STLF. In order to demonstrate this technique capability, the MGGP has been compared with the RBF network and the standard single-gene Genetic Programming (GP) in terms of the forecasting accuracy. The data used in this study is a real data set of the Egyptian electrical network. The weather factors represented by the minimum and the maximum daily temperature have been included in this study. The MGGP has successfully predicted the future load with high accuracy compared to that of the Radial Basis Function (RBF) network and that of the standard single-gene Genetic Programming (GP).", notes = "Also known as \cite{6713061}", } @MastersThesis{Ghareeb:msc, author = "Wael Taha {Ghareeb Elsayed}", title = "A Fully Decentralized Approach for Solving the Economic Dispatch Problem", school = "Electrical and Computer Engineering, University of Waterloo", year = "2014", type = "Master of Applied Science", address = "Canada", month = "14 " # aug, keywords = "genetic algorithms, genetic programming, Non-convex economic dispatch problem, Fully decentralised approach, Multi-agent systems, Electrical and Computer Engineering", URL = "http://hdl.handle.net/10012/8631", abstract = "A practical formulation of the economic dispatch problem is based on treating the problem as a non-convex optimisation problem in which the practical non-convex cost functions are taken into consideration. Formulating the economic dispatch problem as a non-convex optimization problem and finding a better quality solution to this problem has consumed a large portion of the research for decades. Almost all previously presented solutions to the non-convex economic dispatch problem are centralised solutions. Recently, as a result of current research directions towards enabling the smart grid, a new research trend has emerged. This new research trend is to solve the economic dispatch problem using decentralised and distributed mechanisms. Among these mechanisms, the consensus on lambda approach is the best known mechanism. A drawback of this approach is that it can solve only the economic dispatch problem with convex cost functions; in addition, it lacks the appropriate mechanism for incorporating the transmission losses. This thesis presents a new decentralized approach for solving the economic dispatch problem. The proposed approach consists of either two or three stages. In the first stage, a flooding-based consensus algorithm is proposed in order to achieve consensus among the agents with respect to the units and system data. In the second stage, a suitable algorithm is used for solving the economic dispatch problem locally by each agent. For cases in which a non-deterministic method is used in the second stage, a third stage is applied to achieve consensus on the final solution of the problem, with a flooding-based consensus algorithm for sharing the information required during this stage. The required communication time by the proposed approach has been approximated using JADE software. Four case studies were examined for validation purposes. The results show that the proposed approach is highly effective for both solving the non-convex formulation of the economic dispatch problem and incorporating transmission losses accurately in a fully decentralised manner. Moreover, the proposed approach can also be applied with some adaptation to solve the economic dispatch problem with convex cost functions; in this case, it is very competitive to the consensus on lambda approach.", } @InProceedings{Ghareeb:2013:EPEC, author = "W. T. Ghareeb and E. F. {El Saadany}", booktitle = "IEEE Electrical Power Energy Conference (EPEC 2013)", title = "A hybrid genetic radial basis function network with fuzzy corrector for short term load forecasting", year = "2013", month = aug, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/EPEC.2013.6802948", size = "5 pages", abstract = "The short term load forecasting plays a critical role in power system operation and economics. The accuracy of short term load forecasting is very important since it affects generation scheduling and electricity prices, and hence an accurate short term load forecasting method should be used. This paper proposes a Genetic Algorithm optimised Radial Basis Function network (GA-RBF) with a fuzzy corrector for the problem of short term load forecasting. In order to demonstrate this system capability, the system has been compared with four well known techniques in the area of load forecasting. These techniques are the multi-layer feed forward neural network, the RBF network, the adaptive neuro-fuzzy inference System and the genetic programming. The data used in this study is a real data of the Egyptian electrical network. The weather factors represented in the minimum and the maximum daily temperature have been included in this study. The GA-RBF with the fuzzy corrector has successfully forecast the future load with high accuracy compared to that of the other load forecasting techniques included in this study.", notes = "Dept. of Electr. & Comput. Eng., Univ. of Waterloo, Waterloo, ON, Canada Also known as \cite{6802948}", } @Article{Gharehbaghi:2018:ESA, author = "Sadjad Gharehbaghi and A. H. Gandomi and S. Achakpour and Mohammad Nabi Omidvar", title = "A hybrid computational approach for seismic energy demand prediction", journal = "Expert Systems with Applications", year = "2018", volume = "110", pages = "335--351", month = nov, keywords = "genetic algorithms, genetic programming, Evolutionary computation, Regression analysis, Input energy, Hysteretic energy, Seismic energy spectra", ISSN = "0957-4174", URL = "https://eprints.whiterose.ac.uk/156298/1/Manuscript-R2-v4.pdf", URL = "http://www.sciencedirect.com/science/article/pii/S0957417418303543", DOI = "doi:10.1016/j.eswa.2018.06.009", abstract = "In this paper, a hybrid genetic programming (GP) with multiple genes is implemented for developing prediction models of spectral energy demands. A multi-objective strategy is used for maximizing the accuracy and minimizing the complexity of the models. Both structural properties and earthquake characteristics are considered in prediction models of four demand parameters. Here, the earthquake records are classified based on soil type assuming that different soil classes have linear relationships in terms of GP genes. Therefore, linear regression analysis is used to connect genes for different soil types, which results in a total of sixteen prediction models. The accuracy and effectiveness of these models were assessed using different performance metrics and their performance was compared with several other models. The results indicate that not only the proposed models are simple, but also they outperform other spectral energy demand models proposed in the literature.", notes = "Also known as \cite{GHAREHBAGHI2018335}", } @Article{GHAREHBAGHI:2021:CS, author = "Sadjad Gharehbaghi and Mostafa Gandomi and Vagelis Plevris and Amir H. Gandomi", title = "Prediction of seismic damage spectra using computational intelligence methods", journal = "Computer \& Structures", volume = "253", pages = "106584", year = "2021", ISSN = "0045-7949", DOI = "doi:10.1016/j.compstruc.2021.106584", URL = "https://www.sciencedirect.com/science/article/pii/S0045794921001061", keywords = "genetic algorithms, genetic programming, Computational intelligence, Artificial neural networks, Regression analysis, Seismic damage spectra, Inelastic SDOF systems, Park-Ang damage index, Resiliency", abstract = "Predicting seismic damage spectra, capturing both structural and earthquake features, is useful in performance-based seismic design and quantifying the potential seismic damage of structures. The objective of this paper is to accurately predict the seismic damage spectra using computational intelligence methods. For this purpose, an inelastic single-degree-of-freedom system subjected to a set of earthquake ground motion records is used to compute the (exact) spectral damage. The Park-Ang damage index is used to quantify the seismic damage. Both structural and earthquake features are involved in the prediction models where multi-gene genetic programming (MGGP) and artificial neural networks (ANNs) are applied. Common performance metrics were used to assess the models developed for seismic damage spectra, and indicated that their accuracy was higher than a corresponding model in the literature. Although the performance metrics revealed that the ANN model is more accurate than the MGGP model, the explicit MGGP-based mathematical model renders it more practical in quantifying the potential seismic damage of structures", } @InProceedings{gharoun:2024:GECCOcomp, author = "Hassan Gharoun and Mohammad Sadegh Khorshidi and Navid Yazdanjue and Fang Chen and Amir H. Gandomi", title = "Shadow Gene Guidance: A Novel Approach for Elevating Genetic Programming Classifications and Boosting Predictive Confidence", booktitle = "GECCO Student Workshop", year = "2024", editor = "Amir H Gandomi", pages = "2095--2098", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, crossover, uncertainty-aware classification", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3664175", size = "4 pages", abstract = "The primary purpose of the proposed method is to enhance future generations of GP, through continuously refining the genetic makeup of the population for improved classification results. Accordingly, this paper developed the novel method by modifying Boruta feature selection method in such a way that allows to evaluate the significance of individuals' genes. This method creates modified versions of the genes called {"}shadow genes{"}, evaluates their impact on model performance in competing with shadow genes, and identifies key genes. These key genes are then used to enhance future generations. The obtained results demonstrated that the proposed method not only enhances the fitness of the individuals but also steers the population toward optimal solutions. Furthermore, empirical validation on multiple datasets reveals that the proposed method significantly outperforms classic GP models in both accuracy and reduced prediction entropy, showcasing its superior ability to generate confident and reliable predictions.", notes = "GECCO-2024 Student Workshop A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @Article{Gharun:2015:AFM, author = "Mana Gharun and Tarryn L. Turnbull and Joseph Henry and Mark A. Adams", title = "Mapping spatial and temporal variation in tree water use with an elevation model and gridded temperature data", journal = "Agricultural and Forest Meteorology", volume = "200", pages = "249--257", year = "2015", ISSN = "0168-1923", DOI = "doi:10.1016/j.agrformet.2014.09.027", URL = "http://www.sciencedirect.com/science/article/pii/S0168192314002512", abstract = "Tree water use is a major component of the water balance in forested catchments of semi-arid areas, as more than 80percent of the incoming rainfall may be used by overstory trees. Managers are unable to easily predict water use and thus water yield, for the majority of eucalypt-dominated catchments in south-east Australia, owing to the variety of dominant and co-dominant species, their distributions with respect to landform, and the lack of species- and landform-specific knowledge of the regulation of water use. Moreover, the costs incurred to quantify input variables for available complex, process-based models, generally encourage finding alternative approaches. This study tested the adequacy of using just two easily measured variables for estimating rates of tree water use, using a model derived from data-learning techniques. The inputs are (1) measured daily atmospheric demand for water and (2) potential incoming radiation derived from surface topography and solar declination. Artificial neural networks (ANNs) and genetic programming (GP) models were trained and validated using in situ observations of vapour pressure deficit (VPD) and estimates of potential solar radiation (Qpot), for a period of two years, at each of 10 forest stands across the high country of the states of New South Wales and Victoria. The models were tested using a random 50percent of the collected data that was independent, i.e. not used in model development. Atmospheric demand was selected because it strongly affects tree water use irrespective of site and species. Potential solar radiation was selected as a proxy for radiation, because it is relatively easy to estimate for any location for which elevation data are available in digital format, and since radiation strongly controls photosynthesis (through stomatal behaviour) and thermal balance. Genetic programming resulted in models better able to predict rates of sap flux. A selected GP model was able to describe the relationship between tree sap flux, VPD, and potential radiation with good accuracy, and was used to map tree water use across the catchment.", keywords = "genetic algorithms, genetic programming, Potential incoming radiation, Sap flux, Eucalypt, Neural networks", } @Article{GHASEMI:2021:ASC, author = "Amir Ghasemi and Amir Ashoori and Cathal Heavey", title = "Evolutionary Learning Based Simulation Optimization for Stochastic Job Shop Scheduling Problems", journal = "Applied Soft Computing", volume = "106", pages = "107309", year = "2021", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2021.107309", URL = "https://www.sciencedirect.com/science/article/pii/S1568494621002325", keywords = "genetic algorithms, genetic programming, Stochastic Job Shop Scheduling Problem, Simulation Optimization, Ordinal Optimization, Genetic Programming (GP), Simulation based metaheuristics, Learning based simulation optimization", abstract = "Simulation Optimization (SO) techniques refer to a set of methods that have been applied to stochastic optimization problems, structured so that the optimizer(s) are integrated with simulation experiments. Although SO techniques provide promising solutions for large and complex stochastic problems, the simulation model execution is potentially expensive in terms of computation time. Thus, the overall purpose of this research is to advance the evolutionary SO methods literature by researching the use of metamodeling within these techniques. Accordingly, we present a new Evolutionary Learning Based Simulation Optimization (ELBSO) method embedded within Ordinal Optimization. In ELBSO a Machine Learning (ML) based simulation metamodel is created using Genetic Programming (GP) to replace simulation experiments aimed at reducing computation. ELBSO is evaluated on a Stochastic Job Shop Scheduling Problem (SJSSP), which is a well known complex production planning problem in most industries such as semiconductor manufacturing. To build the metamodel from SJSSP instances that replace simulation replications, we employ a novel training vector to train GP. This then is integrated into an evolutionary two-phased Ordinal Optimization approach to optimize an SJSSP which forms the ELBSO method. Using a variety of experimental SJSSP instances, ELBSO is compared with evolutionary optimization methods from the literature and typical dispatching rules. Our findings include the superiority of ELBSO over all other algorithms in terms of the quality of solutions and computation time. Furthermore, the integrated procedures and results provided within this article establish a basis for future SO applications to large and complex stochastic problems", } @InProceedings{Ghasemi:2022:WSC, author = "Amir Ghasemi and Kamil Erkan Kabak and Cathal Heavey", booktitle = "2022 Winter Simulation Conference (WSC)", title = "Demonstration of the Feasibility of Real Time Application of Machine Learning to Production Scheduling", year = "2022", pages = "3406--3417", abstract = "Industry 4.0 has placed an emphasis on real-time decision making in the execution of systems, such as semiconductor manufacturing. This article will evaluate a scheduling methodology called Evolutionary Learning Based Simulation Optimization (ELBSO) using data generated by a Manufacturing Execution System (MES) for scheduling a Stochastic Job Shop Scheduling Problem (SJSSP). ELBSO is embedded within Ordinal Optimization (OO), where in the first phase it uses a meta model, which previously was trained by a Discrete Event Simulation model of a SJSSP. The meta model used within ELBSO uses Genetic Programming (GP)-based Machine Learning (ML). Therefore, instead of using the DES model to train and test the meta model, this article uses historical data from a frontend fab to train and test. The results were statistically evaluated for the quality of the fit generated by the meta-model.", keywords = "genetic algorithms, genetic programming, Job shop scheduling, Decision making, Metamodelling, Machine learning, Semiconductor device manufacture, Real-time systems, Data models", DOI = "doi:10.1109/WSC57314.2022.10015436", ISSN = "1558-4305", month = dec, notes = "Also known as \cite{10015436}", } @Misc{DBLP:journals/corr/abs-2401-05382, author = "Zahra Ghasemi and Mehdi Neshat and Chris Aldrich and John Karageorgos and Max Zanin and Frank Neumann and Lei Chen", title = "Enhanced Genetic Programming Models with Multiple Equations for Accurate Semi-Autogenous Grinding Mill Throughput Prediction", howpublished = "arXiv", volume = "abs/2401.05382", year = "2024", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.48550/arXiv.2401.05382", DOI = "doi:10.48550/ARXIV.2401.05382", eprinttype = "arXiv", eprint = "2401.05382", timestamp = "Fri, 02 Feb 2024 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/corr/abs-2401-05382.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{GHASEMI:2024:mineng, author = "Zahra Ghasemi and Frank Neumann and Max Zanin and John Karageorgos and Lei Chen", title = "A comparative study of prediction methods for semi-autogenous grinding mill throughput", journal = "Minerals Engineering", volume = "205", pages = "108458", year = "2024", ISSN = "0892-6875", DOI = "doi:10.1016/j.mineng.2023.108458", URL = "https://www.sciencedirect.com/science/article/pii/S0892687523004727", keywords = "genetic algorithms, genetic programming, Prediction, Grinding mill, Throughput, Machine learning", abstract = "The mining industry is experiencing a growing amount of stored production data, yet the full potential of these datasets in process modelling remains unexplored. Semi-autogenous grinding (SAG) mills are extensively used in the grinding circuit of mining plants. Precise prediction of SAG mill throughput can result in significant economic benefits, as it can be used for better parameter settings to achieve higher throughputs. Furthermore, the development of an accurate throughput prediction model can assist in informed decision-making for long-term planning. The model's ability to reveal the overall effect of various inputs and estimate the potential throughput change associated with altering each input, can be helpful for determining whether to invest in altering inputs that are laborious and expensive. Numerous SAG mill models have been investigated in the literature; however, a few studies were aimed at forecasting mill throughput. In this research the most accurate prediction model for SAG mill throughput will be investigated through comparing six machine learning models, including genetic programming, recurrent neural networks, support vector regression, regression trees, random forest regression, and linear regression. To achieve this purpose, a real-world data set comprised of 20,161 records from a gold mining complex in Western Australia is investigated and the effective parameters are identified as SAG mill turning speed, power draw of SAG mill, inlet water, and input particle size. As the data set is in the form of time series, the time-dependent nature of the data is considered for prepossessing, model selection, and final comparison. Specially for the first time in this research, delays in data are investigated and used to improve prediction performance. Moreover, hyperparameter tuning is performed to determine the best parameter setting for each model prior to implementation. The comparison results demonstrate that the recurrent neural network is the most accurate prediction model, followed by genetic programming and support vector regression. The genetic programming approach is also able to provide a mathematical equation for the SAG mill throughput prediction, which is highly valued by experts in the industry. Sensitivity analysis revealed that the two factors that most significantly affect SAG mill throughput are turning speed and inlet water. It is anticipated that the SAG mill throughput will rise as the SAG mill turning speed increases and the input water decreases", } @InProceedings{ghasemi:2024:CEC, author = "Zahra Ghasemi and Mehdi Neshat and Chris Aldrich and John Karageorgos and Max Zanin and Frank Neumann and Lei Chen", title = "Enhanced Genetic Programming Models with Multiple Equations for Accurate Semi-Autogenous Grinding Mill Throughput Prediction", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Measurement, Accuracy, Training data, Euclidean distance, Predictive models, Throughput, Prediction, Clustering, Grinding mill", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10611827", abstract = "Semi-autogenous grinding (SAG) mills play a pivotal role in the grinding circuit of mineral processing plants. Accurate prediction of SAG mill throughput as a crucial performance metric is of utmost importance. The potential of applying genetic programming (GP) for this purpose has yet to be thoroughly investigated. This study introduces an enhanced GP approach entitled multi-equation GP (MEGP) for more accurate prediction of SAG mill throughput. In the new proposed method multiple equations, each accurately predicting mill throughput for specific clusters of training data are extracted. These equations are then employed to predict mill throughput for test data using various approaches. To assess the effect of distance measures, four different distance measures are employed in MEGP method. Comparative analysis reveals that the best MEGP approach achieves an average improvement of 10.74percent in prediction accuracy compared with standard GP. In this approach all extracted equations are used and both the number of data points in each data cluster and the distance to clusters are incorporated for calculating the final prediction. Further investigation of distance measures indicates that among four different metrics employed including Euclidean, Manhattan, Chebyshev, and Cosine distance, the Euclidean distance measure yields the most accurate results for the majority of data splits.", notes = "also known as \cite{10611827} WCCI 2024", } @Article{GHASEMZADEHMAHANI:2021:CBM, author = "Ahmad {Ghasemzadeh Mahani} and Payam Bazoobandi and Seyed Mohsen Hosseinian and Hassan Ziari", title = "Experimental investigation and multi-objective optimization of fracture properties of asphalt mixtures containing nano-calcium carbonate", journal = "Construction and Building Materials", volume = "285", pages = "122876", year = "2021", ISSN = "0950-0618", DOI = "doi:10.1016/j.conbuildmat.2021.122876", URL = "https://www.sciencedirect.com/science/article/pii/S095006182100636X", keywords = "genetic algorithms, genetic programming, Fracture toughness, Semi-circular bending, Nano-calcium carbonate, GMDH, GP, Two-objective optimization", abstract = "The low temperature fracture is one of the most important challenges in asphalt mixtures, which, if not paid, will lead to high maintenance costs. Therefore, researchers are looking for different materials to enhance the fracture behavior of mixtures. As the asphalt surface is affected by different types of loading throughout their operational lifetime, this study explores the impact of nano-calcium carbonate (NCC) on the fracture behavior of mixtures in different fracture modes. For this purpose, semi-circular bending (SCB) tests were applied to specify the fracture toughness. Two bitumen types of PG 64-22 and PG 58-28 were modified with 1percent, 3percent, 5percent and 7percent NCC. Finally, the fracture toughness of samples in pure mode-I, pure mode-II and mixed-mode I/II was investigated at -10 degreeC. Moreover, the prediction models of multivariate regression (MVR), group method of data handling (GMDH) and genetic programming (GP) were provided to present the best model with higher accuracy in order to obtain optimum NCC content with two objectives of KIf and KIIf (the fracture toughness in modes I and II). The results indicated that NCC had a notable influence on the fracture toughness. However, in each mode and bitumen, the additive percentage that is associated with the highest fracture toughness was different. From the fracture tests, the most optimal percentage of NCC was determined between 3percent and 7percent and 3percent to 5percent for mixtures made with bitumen types of PG 64-22 and PG 58-28, respectively. Among the various models, GMDH had the greatest R2 so that the R2 amount of GMDH for KIf and KIIf was 98.68percent and 99.02percent, respectively. The two-objective optimization results showed that 4.17percent, 3.62percent and 6.29percent NCC were the best optimal amounts to maximize KIf and KIIf amounts simultaneously for all mixtures and mixtures made with bitumen types of PG 64-22 and PG 58-28, respectively", } @Article{GHAZOUANI:2020:ESA, author = "Haythem Ghazouani and Walid Barhoumi", title = "Genetic programming-based learning of texture classification descriptors from Local Edge Signature", journal = "Expert Systems with Applications", volume = "161", pages = "113667", year = "2020", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2020.113667", URL = "http://www.sciencedirect.com/science/article/pii/S0957417420304917", keywords = "genetic algorithms, genetic programming, Texture classification, Texture descriptor, Feature extraction, Local Edge Signature", abstract = "Describing texture is a very challenging problem for many image-based expert and intelligent systems (e.g. defective product detection, people re-identification, abnormality investigation in medical imaging and remote sensing applications) since the process of texture classification relies on the quality of the extracted features. Indeed, detecting and extracting features is a hard and time-consuming task that requires the intervention of an expert, notably when dealing with challenging textures. Thus, machine learning-based descriptors have emerged as another alternative to deal with the difficulty of feature extracting. In this work, we propose a new operator, which we named Local Edge Signature (LES) descriptor, to locally represent texture. The proposed texture descriptor is based on statistical information on edge pixels' arrangement and orientation in a specific local region, and it is insensitive to rotation and scale changes. A genetic programming-based approach is then fitted to automatically learn a global texture descriptor that we called Genetic Texture Signature (GTS). In fact, a tree representation of individuals is used to generate global texture features by applying elementary operations on LES elements at a set of keypoints, and a fitness function evaluates the descriptors considering intra-class homogeneity and inter-class discrimination properties of their generated features. The obtained results, on six challenging texture datasets (Brodatz, Outex_TC_00000, Outex_TC_00013, KTH-TIPS, KTH-TIPS2b and UIUCTex), show that the proposed classification method, which is fully automated, achieves state-of-the-art performance, especially when the number of available training samples is limited", } @InProceedings{DBLP:conf/eann/GhazouaniBA20, author = "Haythem Ghazouani and Walid Barhoumi and Yosra Antit", editor = "Lazaros Iliadis and Plamen Parvanov Angelov and Chrisina Jayne and Elias Pimenidis", title = "A Genetic Programming Method for Scale-Invariant Texture Classification", booktitle = "Proceedings of the 21st {EANN} (Engineering Applications of Neural Networks) 2020 Conference - Proceedings of the {EANN} 2020, Halkidiki, Greece, June 5-7, 2020", series = "Proceedings of the International Neural Networks Society", volume = "2", pages = "593--604", publisher = "Springer", year = "2020", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/978-3-030-48791-1_47", DOI = "doi:10.1007/978-3-030-48791-1_47", timestamp = "Tue, 21 Jul 2020 12:07:35 +0200", biburl = "https://dblp.org/rec/conf/eann/GhazouaniBA20.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{GHAZOUANI:2021:CBM, author = "Haythem Ghazouani and Walid Barhoumi", title = "Towards non-data-hungry and fully-automated diagnosis of breast cancer from mammographic images", journal = "Computers in Biology and Medicine", year = "2021", volume = "139", pages = "105011", month = dec, keywords = "genetic algorithms, genetic programming, Mammograms, Feature extraction, Content-based image retrieval, Texture representation", ISSN = "0010-4825", URL = "https://www.sciencedirect.com/science/article/pii/S0010482521008052", DOI = "doi:10.1016/j.compbiomed.2021.105011", abstract = "Analysing local texture and generating features are two key issues for automatic cancer detection in mammographic images. Recent researches have shown that deep neural networks provide a promising alternative to hand-driven features which suffer from curse of dimensionality and low accuracy rates. However, large and balanced training data are foremost requirements for deep learning-based models and these data are not always available publicly. In this work, we propose a fully-automated method for breast cancer diagnosis that performs training using small sets of data. Feature extraction from mammographic images is performed using a genetic-programming-based descriptor that exploits statistics on a local binary pattern-like local distribution defined in each pixel. The effectiveness of the suggested method is demonstrated on two challenging datasets, (1) the digital database for screening mammography and (2) the mammographic image analysis society digital mammogram database, for content-based image retrieval as well as for abnormality/malignancy classification. The experimental results show that the proposed method outperforms or achieves comparable results with deep learning-based methods even those with transfer learning and/or data-augmentation", } @Article{GHAZOUANI:2021:ASC, author = "Haythem Ghazouani", title = "A genetic programming-based feature selection and fusion for facial expression recognition", journal = "Applied Soft Computing", volume = "103", pages = "107173", year = "2021", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2021.107173", URL = "https://www.sciencedirect.com/science/article/pii/S156849462100096X", keywords = "genetic algorithms, genetic programming, Facial expression recognition, Feature selection, Feature fusion, Geometric feature, Texture feature", abstract = "Emotion recognition has become one of the most active research areas in pattern recognition due to the emergence of human-machine interaction systems. Describing facial expression is a very challenging problem since it relies on the quality of the face representation. A multitude of features have been proposed in the literature to describe facial expression. None of these features is universal for accurately capturing all the emotions since facial expressions vary according to the person, gender and type of emotion (posed or spontaneous). Therefore, some research works have considered combining several features to enhance the recognition rate. But they faced significant problems because of information redundancy and high dimensionality of the resulting features. In this work, we propose a genetic programming framework for feature selection and fusion for facial expression recognition, which we called GP-FER. The main component of this framework is a tree-based genetic program with a three functional layers (feature selection, feature fusion and classification). The proposed genetic program is a binary classifier that performs discriminative feature selection and fusion differently for each pair of expression classes. The final emotion is captured by performing a unique tournament elimination between all the classes using the binary programs. Three different geometric and texture features were fused using the proposed GP-FER. The obtained results, on four posed and spontaneous facial expression datasets (DISFA, DISFA+, CK+ and MUG), show that the proposed facial expression recognition method has outperformed, or achieved a comparable performance to the state-of-the-art methods", } @PhdThesis{ghazouani:habilitation, author = "Haythem Ghazouani", title = "Image Analysis and Understanding: Application to texture classification, facial expression recognition and breast cancer diagnosis", school = "Ecole Nationale d'Ingenieurs de Carthage, Universite de Carthage", year = "2023", type = "Habilitation Universitaire", address = "Tunisia", month = "1 " # feb, keywords = "genetic algorithms, genetic programming, HL-GP, HOG-LBP, edge detection, tree GP, SVM", URL = "https://theses.hal.science/tel-03985799", biburl = "https://dblp.org/rec/books/hal/Ghazouani23.bib", URL = "https://www.researchgate.net/profile/Haythem-Ghazouani/MmoiredeSynthsedeRecherche.pdf", URL = "https://hal-lirmm.ccsd.cnrs.fr/tel-03985799v1", URL = "https://hal-lirmm.ccsd.cnrs.fr/tel-03985799v1/file/M%C3%A9moire%20de%20Synth%C3%A8se%20de%20Recherche.pdf", size = "95 pages", abstract = "As one of the most active research areas in computer vision, image analysis and understanding attempts to detect low-level and high-level features, to locate, recognize objects, to detect anomalies and to classify them into classes and categories from images and videos. This dissertation focuses on the automation of visual feature extraction, selection and fusion for image classification. The main contribution presented in this manuscript is the fully automation of the process of local feature extraction and aggregation using genetic programming with different applications ranging from texture classification to breast cancer diagnosis including facial expression recognition. More precisely, low-level texture features are defined based on edge arrangements and automatically aggregated for texture image classification under different changes. The same framework is used to extract texture cues from human faces and fuse them with geometric features representing face landmark distances in order to capture wrinkles and face distortions to detect human affects. Facial expression recognition from 3D/4D facial images is also performed based on mesh-local binary pattern difference descriptor representing a unified set of geometric and appearance features of different facial regions. Texture is explored more intensely in breast tissue from mammography images to diagnosis cancer. A more powerful texture description is proposed to detect malignant tumor in breast tissue. A fully automated framework based on genetic programming for feature extraction, selection and fusion is also presented to perform content based retrieval and breast cancer diagnosis. For all the investigated applications, the presented frameworks perform training with small number of instances and tackle the problem of the unavailability of labeled data.", notes = "In english. Also known as \cite{DBLP:books/hal/Ghazouani23} HAL Id: tel-03985799 UIUCTex", } @Article{GHAZVINEI:2017:CEA, author = "Pezhman Taherei Ghazvinei and Shahaboddin Shamshirband and Shervin Motamedi and Hossein Hassanpour Darvishi and Ely Salwana", title = "Performance investigation of the dam intake physical hydraulic model using Support Vector Machine with a discrete wavelet transform algorithm", journal = "Computers and Electronics in Agriculture", volume = "140", pages = "48--57", year = "2017", keywords = "genetic algorithms, genetic programming, Head loss, Dam Intake structure, Support Vector Machine, SVM, Wavelet algorithm, Hydraulic performance", ISSN = "0168-1699", DOI = "doi:10.1016/j.compag.2017.05.033", URL = "http://www.sciencedirect.com/science/article/pii/S0168169917306737", abstract = "In the present study hydraulic scaled model was conducted to evaluate an intake structure and checking its safety hydraulic performance. An investigation on the structural and mechanical equipment performance was performed by testing a scaled model to determine discharge capacity and head losses. In addition, the novel method established on Support Vector Machines (SVM) coupled through discrete wavelet transform was designed and adapted to estimate head loss at inlet and outlet section of the horizontal intake structure. Estimation and prediction results of SVM-WAVELET model was compared with genetic programming (GP) and artificial neural networks (ANNs) models. The model test results of SVM WAVELET approach reveal more accuracy in prediction and also attain improved generalization capabilities than GP and ANN. Furthermore, results specified that advanced SVM-WAVELET model can be applied confidently for auxiliary research to formulate predictive model for head loss at inlet and outlet section. Consequently, it was found that using of SVM-WAVELET is principally encouraging as an alternate strategy to predict the head loss as a representative of inner pressure head at intake structure", } @Article{Ghazvinei:2018:eaCFM, author = "Pezhman Taherei Ghazvinei and Hossein Hassanpour Darvishi and Amir Mosavi and Khamaruzaman {bin Wan Yusof} and Meysam Alizamir and Shahaboddin Shamshirband and Kwok-wing Chau", title = "Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network", journal = "Engineering Applications of Computational Fluid Mechanics", year = "2018", volume = "12", number = "1", pages = "738--749", keywords = "genetic algorithms, genetic programming, sustainable production, sugar cane, machine learning, growth model, estimation, extreme learning machine, prediction", publisher = "Taylor \& Francis", ISSN = "19942060", bibsource = "OAI-PMH server at www.db-thueringen.de", language = "eng", oai = "oai:www.db-thueringen.de:dbt_mods_00037448", rights = "https://creativecommons.org/licenses/by-nc/4.0/; info:eu-repo/semantics/openAccess", URL = "https://doi.org/10.1080/19942060.2018.1526119", URL = "https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20181017-38129", URL = "https://www.db-thueringen.de/receive/dbt_mods_00037448", URL = "https://www.db-thueringen.de/servlets/MCRFileNodeServlet/dbt_derivate_00043598/Mosavi_Amir_Sugarcane%20growth%20prediction.pdf", DOI = "doi:10.1080/19942060.2018.1526119", size = "13 pages", abstract = "Management strategies for sustainable sugarcane production need to deal with the increasing complexity and variability of the whole sugar system. Moreover, they need to accommodate the multiple goals of different industry sectors and the wider community. Traditional disciplinary approaches are unable to provide integrated management solutions, and an approach based on whole systems analysis is essential to bring about beneficial change to industry and the community. The application of this approach to water management, environmental management and cane supply management is outlined, where the literature indicates that the application of extreme learning machine (ELM) has never been explored in this realm. Consequently, the leading objective of the current research was set to filling this gap by applying ELM to launch swift and accurate model for crop production data-driven. The key learning has been the need for innovation both in the technical aspects of system function underpinned by modelling of sugarcane growth. Therefore, the current study is an attempt to establish an integrate model using ELM to predict the concluding growth amount of sugarcane. Prediction results were evaluated and further compared with artificial neural network (ANN) and genetic programming models. Accuracy of the ELM model is calculated using the statistics indicators of Root Means Square Error (RMSE), Pearson Coefficient (r), and Coefficient of Determination (R2) with promising results of 0.8, 0.47, and 0.89, respectively. The results also show better generalisation ability in addition to faster learning curve. Thus, proficiency of the ELM for supplementary work on advancement of prediction model for sugarcane growth was approved with promising results.", notes = "Also known as \cite{oai:www.db-thueringen.de:dbt_mods_00037448}", } @Article{GHEZELBASH:2022:est, author = "Ghazal Ghezelbash and Mojtaba Babaelahi and Mahdi Saadatfar", title = "New analytical solution and optimization of a thermocline solar energy storage using differential quadrature method and genetic programming", journal = "Journal of Energy Storage", volume = "52", pages = "104806", year = "2022", ISSN = "2352-152X", DOI = "doi:10.1016/j.est.2022.104806", URL = "https://www.sciencedirect.com/science/article/pii/S2352152X22008155", keywords = "genetic algorithms, genetic programming, Thermocline energy storage, Differential quadrature method, Solar energy, Optimization", abstract = "This paper aims to present an analytical correlation to investigate heat transfer characteristics in thermocline storage tanks based on numerical solution results. Thermocline tanks are used to store solar thermal energy to ensure the stable operation of the solar system. For the evaluation of thermocline energy storage, the mass and energy balance equations for the heat transfer fluid and the material used in the tank are extracted and simplified. The governing equations for two different configurations, including concrete blocks with vertical holes and concrete plates, are considered. Depending on the type of governing equations, an efficient numerical method called the Differential Quadrature Method (DQM) has been used to achieve an accurate solution in a short time, and the results have been validated in special cases based on previous research. Based on the numerical solution results, temperature distribution, thermodynamic efficiency, energy stored in charge and discharge mode, and thermocline tank capacity are calculated; and the effect of different variables on these parameters are evaluated. Based on the results, the effective variables are selected as the decision variable, and for different values of these variables, the evaluation parameters were calculated using DQM. Based on the results obtained from the DQM, a comprehensive database has been created and used as an input of genetic programming tools. Then the analytical correlations are presented to evaluate the evaluating parameters. Based on the prepared analytical correlations, different multi-objective optimization has been performed to maximize the stored energy (charge/discharge mode), thermodynamic efficiency, and power; and minimization of costs", } @Article{GHIMIRE:2018:RSE, author = "Sujan Ghimire and Ravinesh C. Deo and Nathan J. Downs and Nawin Raj", title = "Self-adaptive differential evolutionary extreme learning machines for long-term solar radiation prediction with remotely-sensed MODIS satellite and Reanalysis atmospheric products in solar-rich cities", journal = "Remote Sensing of Environment", volume = "212", pages = "176--198", year = "2018", keywords = "genetic algorithms, genetic programming, Satellite solar prediction model, Particle swarm optimization, Neural network, Support vector machine, Grid search, Giovanni, ECMWF, Extreme learning machine", ISSN = "0034-4257", DOI = "doi:10.1016/j.rse.2018.05.003", URL = "http://www.sciencedirect.com/science/article/pii/S0034425718302165", abstract = "Designing predictive models of global solar radiation can be an effective renewable energy feasibility studies approach to resolve future problems associated with the supply, reliability and dynamical stability of consumable energy demands generated by solar-powered electrical plants. In this paper we design and present a new approach to predict the monthly mean daily solar radiation (GSR) by constructing an extreme learning machine (ELM) model integrated with the Moderate Resolution Imaging Spectroradiometer (MODIS)-based satellite and the European Center for Medium Range Weather Forecasting (ECMWF) Reanalysis data for solar rich cities: Brisbane and Townsville, Australia. A self-adaptive differential evolutionary ELM (i.e., SaDE-ELM) is proposed, using a swarm-based ant colony optimization (ACO) feature selection to select the most important predictors for GSR, and the SaDE-ELM is then benchmarked with nine different data-driven models: a basic ELM, genetic programming (GP), online sequential ELM with fixed (OS-ELM) and varying (OSVARY-ELM) input sizes, and hybridized model including the particle swarm optimized-artificial neural network model (PSO-ANN), genetic algorithm optimized ANN (GA-ANN), PSO-support vector machine model (PSO-SVR), genetic algorithm optimized-SVR model (GA-SVR) and the SVR model optimized with grid search (GS-SVR). A comprehensive evaluation of the SaDE-ELM model is performed, considering key statistical metrics and diagnostic plots of measured and forecasted GSR. The results demonstrate excellent forecasting capability of the SaDE-ELM model in respect to the nine benchmark models. SaDE-ELM outperformed all comparative models for both tested study sites with a relative mean absolute and a root mean square error (RRMSE) of 2.6percent and 2.3percent (for Brisbane) and 0.8percent and 0.7percent (for Townsville), respectively. Majority of the forecasted errors are recorded in the lowest magnitude frequency band, to demonstrate the preciseness of the SaDE-ELM model. When tested for daily solar radiation forecasting using the ECMWF Reanalysis data for Brisbane study site, the performance resulted in an RRMSE approx 10.5percent. Alternative models evaluated with the input data classified into El Nino, La Nina and the positive and negative phases of the Indian Ocean Dipole moment (considering the impacts of synoptic-scale climate phenomenon), confirms the superiority of the SaDE-ELM model (with RRMSEa lteqa 13percent). A seasonal analysis of all developed models depicts SaDE-ELM as the preferred tool over the basic ELM and the hybridized version of ANN, SVR and GP model. In accordance with the results obtained through MODIS satellite and ECMWF Reanalysis data products, this study ascertains that the proposed SaDE-ELM model applied with ACO feature selection, integrated with satellite-derived data is adoptable as a qualified tool for monthly and daily GSR predictions and long-term solar energy feasibility study especially in data sparse and regional sites where a satellite footprint can be identified", } @Article{GHIMIRE:2019:JCP, author = "Sujan Ghimire and Ravinesh C. Deo and Nathan J. Downs and Nawin Raj", title = "Global solar radiation prediction by {ANN} integrated with European Centre for medium range weather forecast fields in solar rich cities of Queensland Australia", journal = "Journal of Cleaner Production", year = "2019", volume = "216", pages = "288--310", month = "10 " # apr, keywords = "genetic algorithms, genetic programming, ECMWF-Based solar prediction model, Temperature models, Machine learning models, Neural networks, Feature selection", ISSN = "0959-6526", URL = "http://www.sciencedirect.com/science/article/pii/S0959652619301775", DOI = "doi:10.1016/j.jclepro.2019.01.158", abstract = "To support alternative forms of energy resources, the prediction of global incident solar radiation (Irad) is critical to establish the efficacy of solar energy resources as a free and clean energy, and to identify and screen solar powered sites. Solar radiation data for construction of energy feasibility studies are not available in many locations due to the absence of meteorological stations, especially in remote or regional sites. To surmount the challenge in solar energy site identification, the universally gridded data integrated into predictive models used to generate reliable Irad forecasts can be considered as a viable medium for future energy. The objective of this paper is to review, develop and evaluate a suite of machine learning (ML) models based on the artificial neural network (ANN) versus several other kinds of data-driven models such as support vector regression (SVR), Gaussian process machine learning (GPML) and genetic programming (GP) models for the prediction of daily Irad generated through the European Centre for Medium Range Weather Forecasting (ECMWF) Reanalysis fields. The performance of the ML models are benchmarked against several statistical tools: auto regressive moving integrated average (ARIMA), Temperature Model (TM), Time series and Fourier Series (TSFS) models. To train these models, 87 different predictor variables from the ERA-Interim reanalysis dataset (01-January-1979 to 31-December-2015) were extracted for 5 solar-rich metropolitan sites (i.e., Brisbane, Gold Coast, Sunshine Coast, Ipswich and Toowoomba, Australia) targeted against surface level Irad available from the measured Scientific Information for Land Owners dataset. For daily forecast models, a total of the 20 most important predictors related to the Irad dataset were screened with nearest component analysis: {"}fsrnca{"} feature selection, and partitioned into training (70percent), validation (15percent) and testing (15percent) sets for model design. To benchmark the ANN, TSFS and TM models were developed with Fourier series and regression analysis, respectively and the statistical performance was benchmarked with root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (ENS), Willmott's Index (WI), Mean Bias error (MBE), Legates and McCabe Index (E1), and relative MAE, RMSE and diagnostic plots. The performance of ANN was significantly better than the other models (SVR, GPML, GP, TM), resulting in lower RMSE (1.715-2.27 MJm-2/day relative to 2.14-5.90 MJm-2/day), relative RMSE (9.07-12.47 vs 10.98-29.15), relative RMAE (7.97-11.74 vs 9.27-33.96) and larger WI, ENS and E1 (0.938-0.967 vs. 0.462-0.955, 0.935-0.872 vs. 0.355-0.915, 0.672-0.783 vs. 0.252-0.740). Additionally, models assessed with predictors grouped into El Nino, La Nina and the positive, negative and neutral periods of Indian Ocean Dipole, affirmed the merits of ANN model (RRMSEa lteqa 11percent). Seasonal analysis showed that ANN was an elite tool over SVR, GPML and GP for Irad prediction. The study concludes that an ANN approach integrated with ECMWF fields, incorporating physical interactions of Irad with atmospheric data, is an efficacious alternative to forecast solar energy and assist with energy modelling for solar-rich sites that have diverse climatic conditions to further support clean energy", } @PhdThesis{Ghimire:thesis, author = "Sujan Ghimire", title = "Predictive modelling of global solar radiation with artificial intelligence approaches using MODIS satellites and atmospheric reanalysis data for Australia", school = "University of Southern Queensland", year = "2019", address = "Australia", keywords = "genetic algorithms, genetic programming, ANN", URL = "https://eprints.usq.edu.au/39892/", URL = "http://eprints.usq.edu.au/39892/1/All_word_1.pdf", size = "87 pages", abstract = "Global solar radiation (GSR) prediction is a prerequisite task for agricultural management and agronomic decisions, including photovoltaic (PV) power generation, biofuel exploration and several other bio-physical applications. Since short-term variabilities in the GSR incorporate stochastic and intermittent behaviours (such as periodic fluctuations, jumps and trends) due to the dynamicity of atmospheric variables, GSR predictions, as required for solar energy generation, is a challenging endeavour to satisfactorily predict the solar generated electricity in a PV system. Additionally, the solar radiation data, as required for solar energy monitoring purposes, are not available in all geographic locations due to the absence of meteorological stations and this is especially true for remote and regional solar powered sites. To surmount these challenges, the universally (and freely available) atmospheric gridded datasets (e.g., reanalysis and satellite variables) integrated into solar radiation predictive models to generate reliable GSR predictions can be considered as a viable medium for future solar energy exploration, use and management. Hence, this doctoral thesis aims to design and evaluate novel Artificial Intelligence (AI; Machine Learning and Deep Learning) based predictive models for GSR predictions, using the European Centre for Medium Range Weather Forecasting (ECMWF) Interim-ERA reanalysis and Moderate Resolution Imaging Spectroradiometer (MODIS) Satellite variables enriched with ground-based weather station datasets for the prediction of both long-term (i.e., monthly averaged daily) as well as the short-term (i.e., daily and half-hourly) GSR. The focus of the study region is Queensland, the sunshine state, as well as a number of major solar cities in Australia where solar energy use is actively being promoted by the Australian State and Federal Government agencies. Firstly, the Artificial Neural Networks (ANN), a widely used Machine Learning model is implemented to predict daily GSR at five different cities in Australia using ECMWF Reanalysis fields obtained from the European Centre for Medium Range Weather Forecasting repository. Secondly, the Self-Adaptive Differential Evolutionary Extreme Learning Machine (i.e., SaDE-ELM) is also proposed for monthly averaged daily GSR prediction trained with ECMWF reanalysis and MODIS satellite data from the Moderate Resolution Imaging Spectroradiometer. Thirdly, a three-phase Support Vector Regression (SVR; Machine Learning) model is developed to predict monthly averaged daily GSR prediction where the MODIS data are used to train and evaluate the model and the Particle Swarm Algorithm (PSO) is used as an input selection algorithm. The PSO selected inputs are further transformed into wavelet subseries via non-decimated Discrete Wavelet Transform to unveil the embedded features leading to a hybrid PSO-W-SVR model, seen to outperform the comparative hybrid models. Fourthly, to improve the accuracy of conventional techniques adopted for GSR prediction, Deep Learning (DL) approach based on Deep Belief Network (DBN) and Deep Neural Network (DNN) algorithms are developed to predict the monthly averaged daily GSR prediction using MODIS-based dataset. Finally, the Convolutional Neural Network (CNN) integrated with a Long Short-Term Memory Network (LSTM) model is used to construct a hybrid CLSTM model which is tested to predict the half-hourly GSR values over multiple time-step horizons (i.e., 1-Day, 1-Week, 2-Week, and 1-Month periods). Here, several statistical, Machine Learning and Deep Learning models are adopted to benchmark the proposed DNN and CLSTM models against conventional models (ANN, SaDE-ELM, SVR, DBN). In this doctoral research thesis, a Global Sensitivity Analysis method that attempts to use the Gaussian Emulation Machine (GEM-SA) algorithm is employed for a sensitivity analysis of the model predictors. Sensitivity analysis of selected predictors ascertains that the variables: aerosol, cloud, and water vapour parameters used as input parameters for GSR prediction play a significant role and the most important predictors are seen to vary with the geographic location of the tested study site. A suite of alternative models are also developed to evaluate the input datasets classified into El Nino, La Nina and the positive and negative phases of the Indian Ocean Dipole moment. This considers the impact of synoptic-scale climate phenomenon on long-term GSR predictions. A seasonal analysis of models applied at the tested study sites showed that proposed predictive models are an ideal tool over several other comparative models used for GSR prediction. This study also ascertains that an Artificial Intelligence based predictive model integrated with ECMWF reanalysis and MODIS satellite data incorporating physical interactions of the GSR (and its variability) with the other important atmospheric variables can be considered to be an efficient method to predict GSR. In terms of their practical use, the models developed can be used to assist with solar energy modelling and monitoring in solar-rich sites that have diverse climatic conditions, to further support cleaner energy. The outcomes of this doctoral research program are expected to lead to new applications of Artificial Intelligence based predictive tools for GSR prediction, as these tools are able to capture the non-linear relationships between the predictor and the target variable (GSR). The Artificial Intelligence models can therefore assist climate adaptation and energy policy makers to devise new energy management devices not only for Australia but also globally, to enable optimal management of solar energy resources and promote renewable energy to combat current issues of climate change. Additionally, the proposed predictive models may also be applied to other renewable energy areas such as wind, drought, streamflow, flood and electricity demand for prediction.", notes = "Supervisors: Deo, Ravinesh C.; Raj, Nawin; Downs, Nathan; Mi, Jianchun", } @InProceedings{Gholami:2014:GECCO, author = "Mohammad M. O. Gholami and Brian J. Ross", title = "Passive solar building design using genetic programming", booktitle = "GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation", year = "2014", editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges", isbn13 = "978-1-4503-2662-9", pages = "1111--1118", keywords = "genetic algorithms, genetic programming", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2576768.2598211", DOI = "doi:10.1145/2576768.2598211", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Passive solar building design considers the effect that sunlight has on energy usage. The goal is to reduce the need for artificial cooling and heating devices, thereby saving energy costs. A number of competing design objectives can arise. Window heat gain during winter requires large windows. These same windows, however, reduce energy efficiency during nights and summers. Other model requirements add further complications, which creates a challenging optimisation problem. We use genetic programming for passive solar building design. The EnergyPlus system is used to evaluate energy consumption. It considers factors ranging from model construction (shape, windows, materials) to location particulars (latitude/longitude, weather, time of day/year). We use a strongly typed design language to build 3D models, and multi-objective fitness to evaluate the multiple design objectives. Experimental results showed that balancing window heat gain and total energy use is challenging, although our multi-objective strategy could find interesting compromises. Many factors (roof shape, material selection) were consistently optimised by evolution. We also found that geographic aspects of the location play a critical role in the final building design.", notes = "See also Brock University technical report CS-14-02 Jan 2014. Also known as \cite{2598211} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)", } @Article{gholami:NCaA, author = "Azadeh Gholami and Hossein Bonakdari and Mohammad Zeynoddin and Isa Ebtehaj and Bahram Gharabaghi and Saeed Reza Khodashenas", title = "Reliable method of determining stable threshold channel shape using experimental and gene expression programming techniques", journal = "Neural Computing and Applications", year = "2019", volume = "31", number = "10", pages = "5799--5817", month = oct, keywords = "genetic algorithms, genetic programming, Gene expression programming, Bank profile shape, Threshold channel, Sensitivity analysis", URL = "http://link.springer.com/article/10.1007/s00521-018-3411-7", DOI = "doi:10.1007/s00521-018-3411-7", abstract = "The geometric dimensions and bank profile shape of channels with boundaries containing particles on the verge of motion (threshold channels) are significant factors in channel design. In this study, extensive experimental work was done at different flow velocities to propose a reliable method capable of estimating stable channel bank profile. The proposed method is based on gene expression programming (GEP). Laboratorial datasets obtained from Mikhailova et al. (Hydro Tech Constr 14:714-722, 1980), Ikeda (J Hydraul Div ASCE 107:389-406 1981), Diplas (J Hydraul Eng ASCE 116:707-728, 1990) and Hassanzadeh et al. (J Civil Environ Eng 43(4):59-68, 2014) were used to train, test, validate and examine the GEP model in various geometric and hydraulic conditions. The obtained results demonstrate that the proposed model can estimate bank profile characteristics with great accuracy (determination coefficient of 0.973 and mean absolute relative error of 0.147). Moreover, for practi", } @InProceedings{Gholaminezhad:2014:ICRoM, author = "Iman Gholaminezhad and Ali Jamali and Hirad Assimi", booktitle = "Second RSI/ISM International Conference on Robotics and Mechatronics (ICRoM 2014)", title = "Automated synthesis of optimal controller using multi-objective genetic programming for two-mass-spring system", year = "2014", month = oct, pages = "041--046", abstract = "There are much research effort in the literature using genetic programming as an efficient tool for design of controllers for industrial systems. In this paper, multi-objective uniform-diversity genetic programming (MUGP) is used for automated synthesis of both structure and parameter tuning of optimal controllers as a many-objective optimisation problem. In the proposed evolutionary design methodology, each candidate controller illustrated by a transfer function, whose optimal structure and parameters, obtained based on performance optimisation of each candidate controller. The performance indices of each controller are treated as separate objective functions, and thus solved using the multi-objective method of this work. A two-mass-spring system is considered to show the efficiency of the proposed method using performance optimisation of open loop and closed loop control system characteristics. The results show that the proposed method is a computationally efficient framework compared to other methods in the literature for automatically designing both structure and parameter tuning of optimal controllers.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICRoM.2014.6990874", notes = "Dept. of Mech. Eng., Univ. of Guilan, Rasht, Iran Also known as \cite{6990874}", } @Article{Ghomi:2014:MAS, author = "Ahmad Attari Ghomi and Ayyub Ansarinejad and Hamid Razaghi and Davood Hafezi and Morteza Barazande", title = "A Novel Electric Power Plants Performance Assessment Technique Based on Genetic Programming Approach", publisher = "Canadian Center of Science and Education", journal = "Modern Applied Science", year = "2014", number = "3", volume = "8", keywords = "genetic algorithms, genetic programming", ISSN = "1913-1844; 1913-1852", bibsource = "OAI-PMH server at doaj.org", identifier = "1913-1844; 1913-1852; 10.5539/mas.v8n3p43", language = "English", oai = "oai:doaj.org/article:b014fb4bffa34393b358de0db9db0008", pages = "43", rights = "CC BY", URL = "http://www.ccsenet.org/journal/index.php/mas/article/view/35890", DOI = "doi:10.5539/mas.v8n3p43", abstract = "This paper presents a novel nonparametric efficiency analysis technique based on the Genetic Programming (GP) in order to measure efficiency of Iran electric power plants. GP model was used to predict the output of power plants with respect to input data. The method, we presented here, is capable of finding a best performance among power plant based on the set of input data, GP predicted results and real outputs. The advantage of using GP over traditional statistical methods is that in prediction with GP, the researcher does not need to assume the data characteristic of the dependent variable or output and the independent variable or input. In this proposed methodology to calculate the efficiency scores, a novel algorithm was introduced which worked on the basis of predicted and real output values. To validate our model, the results of proposed algorithm for calculating efficiency rank of power plants were compared to traditional method. Real data was presented for illustrative our proposed methodology. Results showed that by using the capability of input-output pattern recognition of GP, this method provides more realistic results and outperform in identification of efficient units than the conventional methods.", } @Article{GHOMISHEH:2020:JMGM, author = "Zahra Ghomisheh and Ali Ebrahimpoor Gorji and Mohammad Amin Sobati", title = "Prediction of critical properties of sulfur-containing compounds: New {QSPR} models", journal = "Journal of Molecular Graphics and Modelling", volume = "101", pages = "107700", year = "2020", ISSN = "1093-3263", DOI = "doi:10.1016/j.jmgm.2020.107700", URL = "http://www.sciencedirect.com/science/article/pii/S1093326320304897", keywords = "genetic algorithms, genetic programming, Critical properties, Sulfur-containing compounds, Quantitative structure-property relationship (QSPR), Genetic programming (GP), The domain of applicability", abstract = "In this study, new models have been proposed for the prediction of different critical properties (critical temperature (TC), critical pressure (PC), critical volume (VC), and acentric factor (omega)) of the sulfur-containing compounds based on quantitative structure-property relationship (QSPR). An extensive data set containing experimental data of over 130 different sulfur-containing compounds was employed. Enhanced Replacement Method (ERM) was applied for subset variable selection. Based on ERM selected descriptors, two different models, including linear model and genetic programming (GP) based non-linear model have been proposed for each critical property. The predicted values of each target were in good agreement with the experimental data. For GP-based models, the values of the coefficient of determination (R2) were 0.936, 0.976, 0.990, and 0.917 for TC, PC, VC, and omega, respectively. After revisiting the available QSPR models, it was found that the domain of applicability of new models has been expanded", } @Article{GHOMISHEH:2022:JSC, author = "Zahra Ghomisheh and Mohammad Amin Sobati and Ali Ebrahimpoor Gorji", title = "New empirical correlations for the prediction of critical properties and acentric factor of {S}-containing compounds", journal = "Journal of Sulfur Chemistry", year = "2022", volume = "43", number = "3", pages = "327--351", month = jun, keywords = "genetic algorithms, genetic programming, Empirical correlations, critical properties, S-containing compounds, Enhanced Replacement Method (ERM), Genetic programming (GP)", publisher = "Taylor & Francis", ISSN = "1741-5993", URL = "https://www.sciencedirect.com/science/article/pii/S1741599322000630", DOI = "doi:10.1080/17415993.2021.2017936", abstract = "In the present study, simple empirical correlations have been developed to estimate the critical properties (i.e. TC, PC, and VC) and acentric factor (omega) of S-containing compounds. The variables of correlations are a set of simple parameters, including normal boiling point temperature (Tb), molecular weight (MW), and the number of atoms and bonds. A comprehensive dataset containing more than 130 S-containing compounds, including thiophenes, sulfides, mercaptans, siloxanes, and others, has been used for the model development. The parameter selection of the models has been carried out using the Enhanced Replacement Method. Two specific linear and non-linear models have been separately developed for each critical property and omega. The genetic programming method was applied for the development of the non-linear model. Statistical evaluation of the developed models confirmed the satisfactory capability of the models to predict the critical properties and omega of new compounds. Indeed, the values of the coefficient of determination (R2) of the non-linear models for TC, PC, VC, and omega were 0.9690, 0.9076, 0.9890, and 0.9467, respectively. In addition, the values of the average absolute relative deviation (AARDpercent) of the non-linear models for TC, PC, VC, and omega were 2.1677, 7.8375, 3.8919, and 9.9344, respectively.", } @Article{Ghorbani:2012:ijsce, author = "Mohammad Ali Ghorbani and Hossein Jabbari Khamnei and Hakimeh Asadi and Peyman Yousefi", title = "Application of Chaos Theory and Genetic Programming in Runoff Time Series", journal = "International Journal of Structural and Civil Engineering", year = "2012", volume = "1", number = "2", pages = "26--34", month = feb, keywords = "genetic algorithms, genetic programming, chaos, runoff, lighvan basin", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.1011.4775", ISSN = "2277-7032", URL = "http://vixra.org/abs/1405.0106", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1011.4775", URL = "http://vixra.org/pdf/1405.0106v1.pdf", size = "9 page", abstract = "Nowadays, prediction of runoff is very important in water resources management and their permanent development. Along with scientific advances in recent years, various intelligent methods and regression and mathematical methods have been presented to estimate the runoff. In this paper, Two different methods are used, Chaos analysis and genetic programming. The performances of models are analysed and result showed that runoff have had chaotic behaviour. Application of genetic programming models in estimating the runoff is also studied in this paper. The data that has been used has chaotic behaviour and a mathematical model of genetic programming with rainfall and runoff as model inputs was result.", notes = "p33 'prediction with GP is good.' http://ijsce.com/", } @Article{GHORBANI2018455, author = "Mohammad Ali Ghorbani and Rahman Khatibi and Ali {Danandeh Mehr} and Hakimeh Asadi", title = "Chaos-based multigene genetic programming: A new hybrid strategy for river flow forecasting", journal = "Journal of Hydrology", year = "2018", volume = "562", pages = "455--467", keywords = "genetic algorithms, genetic programming, Multigene genetic programming (MGGP), Chaos theory, Forecasting, Hybrid models, Phase-Space Reconstruction (PSR), River flow", ISSN = "0022-1694", URL = "http://www.sciencedirect.com/science/article/pii/S002216941830307X", DOI = "doi:10.1016/j.jhydrol.2018.04.054", abstract = "Chaos theory is integrated with Multi-Gene Genetic Programming (MGGP) engine as a new hybrid model for river flow forecasting. This is to be referred to as Chaos-MGGP and its performance is tested using daily historic flow time series at four gauging stations in two countries with a mix of both intermittent and perennial rivers. Three models are developed: (i) Local Prediction Model (LPM); (ii) standalone MGGP; and (iii) Chaos-MGGP, where the first two models serve as the benchmark for comparison purposes. The Phase-Space Reconstruction (PSR) parameters of delay time and embedding dimension form the dominant input signals derived from original time series using chaos theory and these are transferred to Chaos-MGGP. The paper develops a procedure to identify global optimum values of the PSR parameters for the construction of a regression-type prediction model to implement the Chaos-MGGP model. The inter-comparison of the results at the selected four gauging stations shows that the Chaos-MGGP model provides more accurate forecasts than those of stand-alone MGGP or LPM models.", } @Article{ghorbani:2023:Materials, author = "Ali Ghorbani and Hadi Hasanzadehshooiili and Mohammad Ali {Somti Foumani} and Jurgis Medzvieckas and Romualdas Kliukas", title = "Liquefaction Potential of Saturated Sand Reinforced by Cement-Grouted Micropiles: An Evolutionary Approach Based on Shaking Table Tests", journal = "Materials", year = "2023", volume = "16", number = "6", pages = "Article No. 2194", keywords = "genetic algorithms, genetic programming", ISSN = "1996-1944", URL = "https://www.mdpi.com/1996-1944/16/6/2194", DOI = "doi:10.3390/ma16062194", abstract = "Cement-grouted injections are increasingly employed as a countermeasure material against liquefaction in active seismic areas; however, there is no methodology to thoroughly and directly evaluate the liquefaction potential of saturated sand materials reinforced by the cement grout-injected micropiles. To this end, first, a series of 1 g shaking table model tests are conducted. Time histories of pore water pressures, excess pore water pressure ratios (ru), and the number of required cycles (Npeak) to liquefy the soil are obtained and modified lower and upper boundaries are suggested for the potential of liquefaction of both pure and grout-reinforced sand. Next, adopting genetic programming and the least square method in the framework of the evolutionary polynomial regression technique, high-accuracy predictive equations are developed for the estimation of rumax. Based on the results of a three-dimensional, graphical, multiple-variable parametric (MVP) analysis, and introducing the concept of the critical, boundary inclination angle, the inclination of micropiles is shown to be more effective in view of liquefaction resistivity for loose sands. Due to a lower critical boundary inclination angle, the applicability range for inclining micropiles is narrower for the medium-dense sands. MVP analyses show that the effects of a decreasing spacing ratio on decreasing rumax are amplified while micropiles are inclined.", notes = "also known as \cite{ma16062194}", } @Article{Ghosh:2025:spinee, author = "Attri Ghosh and Philip J. Freda and Shane Shahrestani and Andre E. Boyke and Alena Orlenko and Hyunjun Choi and Nicholas Matsumoto and Tayo Obafemi-Ajayi and Jason H. Moore and Corey T. Walker", title = "Preoperative anemia is an unsuspecting driver of machine learning prediction of adverse outcomes after lumbar spinal fusion", journal = "The Spine Journal", year = "2025", keywords = "genetic algorithms, genetic programming, Adverse surgical outcomes, Anemia, Automated machine learning, Hemoglobin, Predictive modeling, Spinal fusion", ISSN = "1529-9430", URL = "https://www.sciencedirect.com/science/article/pii/S152994302500052X", DOI = "doi:10.1016/j.spinee.2025.01.031", abstract = "BACKGROUND CONTE", } @InProceedings{ghosh:2021:CDC, author = "Snigdhajyoti Ghosh and Damodar Goswami and Chira Ranjan Datta", title = "An Approach to Geometric Modeling Using Genetic Programming", booktitle = "Computers and Devices for Communication", year = "2021", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-981-15-8366-7_13", DOI = "doi:10.1007/978-981-15-8366-7_13", } @InCollection{GHOSH:2024:ILHA, author = "Somnath Ghosh", title = "Chapter 4 - Machine learning-enabled parametrically upscaled constitutive models for bridging length scales in Ti and Ni alloys", booktitle = "Innovative Lightweight and High-Strength Alloys", publisher = "Elsevier", year = "2024", editor = "Mohammed A. Zikry", pages = "97--139", keywords = "genetic algorithms, genetic programming, Parametrically upscaled constitutive model, Parametrically upscaled crystal plasticity model, Dual-phase titanium alloy, Ni-based superalloys, Machine learning, ANN", isbn13 = "978-0-323-99539-9", URL = "https://www.sciencedirect.com/science/article/pii/B9780323995399000047", DOI = "doi:10.1016/B978-0-323-99539-9.00004-7", abstract = "This chapter provides an overview of the development of the parametrically upscaled constitutive model (PUCM) for Ti alloys like Ti-6AL-4V, and the parametrically upscaled crystal plasticity model (PUCPM) for single crystals in Ni-based superalloys. These thermodynamically consistent constitutive models bridge multiple spatial scales through the explicit representation of representative aggregated microstructural parameters. They enable computationally efficient simulations with significant speedup over detailed lower scale models. A host of computational tools and machine learning algorithms are developed to create an automated pipeline for parametric upscaling. The novel algorithms used include genetic algorithm with support vector regression, Sobol analysis-based global sensitivity analysis, artificial neural network for emulating the crystal plasticity finite element models, nonlinear optimisation scheme using k-means acceleration, and genetic programming symbolic regression for functional representation. The computational tool chain outputs the highly efficient PUCMs/PUCPMs, which are invaluable tools for multiscale analysis of deformation and failure with implications in location-specific design", } @Article{ghosh:2020:RS, author = "Sujit Madhab Ghosh and Mukunda Dev Behera and Somnath Paramanik", title = "Canopy Height Estimation Using Sentinel Series Images through Machine Learning Models in a Mangrove Forest", journal = "Remote Sensing", year = "2020", volume = "12", number = "9", month = "1 " # may, keywords = "genetic algorithms, genetic programming, symbolic regression, random forest, Sentinel-1, Sentinel-2, ICESat, Bhitarkanika", ISSN = "2072-4292", URL = "https://www.mdpi.com/2072-4292/12/9/1519", DOI = "doi:10.3390/rs12091519", abstract = "Canopy height serves as a good indicator of forest carbon content. Remote sensing-based direct estimations of canopy height are usually based on Light Detection and Ranging (LiDAR) or Synthetic Aperture Radar (SAR) interferometric data. LiDAR data is scarcely available for the Indian tropics, while Interferometric SAR data from commercial satellites are costly. High temporal decorrelation makes freely available Sentinel-1 interferometric data mostly unsuitable for tropical forests. Alternatively, other remote sensing and biophysical parameters have shown good correlation with forest canopy height. The study objective was to establish and validate a methodology by which forest canopy height can be estimated from SAR and optical remote sensing data using machine learning models i.e., Random Forest (RF) and Symbolic Regression (SR). Here, we analysed the potential of Sentinel-1 interferometric coherence and Sentinel-2 biophysical parameters to propose a new method for estimating canopy height in the study site of the Bhitarkanika wildlife sanctuary, which has mangrove forests. The results showed that interferometric coherence, and biophysical variables (Leaf Area Index (LAI) and Fraction of Vegetation Cover (FVC)) have reasonable correlation with canopy height. The RF model showed a Root Mean Squared Error (RMSE) of 1.57 m and R2 value of 0.60 between observed and predicted canopy heights; whereas, the SR model through genetic programming demonstrated better RMSE and R2 values of 1.48 and 0.62 m, respectively. The SR also established an interpretable model, which is not possible via any other machine learning algorithms. The FVC was found to be an essential variable for predicting forest canopy height. The canopy height maps correlated with ICESat-2 estimated canopy height, albeit modestly. The study demonstrated the effectiveness of Sentinel series data and the machine learning models in predicting canopy height. Therefore, in the absence of commercial and rare data sources, the methodology demonstrated here offers a plausible alternative for forest canopy height estimation.", notes = "also known as \cite{rs12091519}", } @PhdThesis{Ghosh:thesis, author = "Sujit Madhab Ghosh", title = "Above Ground Biomass Estimation in Tropical Forests Using Multi-Sensor Data Synergy", school = "IIT Kharagpur", year = "2020", address = "India", month = jun, keywords = "genetic algorithms, genetic programming, Tropical forests biomass and carbon, Data synergy, Water cloud model, Mangrove forests, Remote sensing based biomass", URL = "http://www.idr.iitkgp.ac.in/xmlui/handle/123456789/9616", abstract = "The aboveground biomass of forests is an important indicator of its productive and carbon sequestration capability. The accuracy of earth observation data based aboveground biomass estimation methods is increasing steadily with the advances made in machine learning algorithms and the availability of data from state of the art satellite sensors. However, the applicability of these datasets and methods remains relatively unexplored for the tropical forests of India. In this thesis, different pathways were examined for the aboveground biomass estimation of two Indian tropical forest sites by using different satellite data and machine learning algorithms. The canopy height of tropical forests shows a good correlation with its biomass. Therefore, canopy height models for two separate sites were established at first using different satellite data. GLAS data-based models establish through multiple linear regression displayed low accuracy in estimating canopy height with an RMSE of 14.29 m for the Western Ghats. Sentinel data derived parameters proved to be a good indicator for the canopy height of Bhitarkanika WLS mangroves when used in a machine learning model. The random forest model showed an RMSE of 1.57 m, while the symbolic regression-based model had an RMSE of 1.48 m. Established semi-empirical models like Water Cloud Model or Interferometric Water Cloud Model did not perform well in estimating biomass of mangroves while using Sentinel-1 data. It showed a very high RMSE of 158.5 Mg/ha with an R-squared value of 0.24 between ground measured and predicted biomass. However, modern machine learning algorithms like deep learning works much better in the same context. The use of machine learning improves the RMSE up to 94.098 Mg/ha, with a maximum R2 of 0.42 between field-measured and predicted biomass. Synergistic use of data from multiple sensors shows to improve the aboveground biomass estimation accuracy for the tropical broadleaved forests of Katerniaghat WLS. The vegetation indices from Sentinel-2 data acted as an excellent predictor of biomass. However, using it together with Sentinel-1 data improved the results to a great extent. A high temporal variation of the satellite-derived parameters can be observed for the Bhitarkanika WLS while using multitemporal datasets. The primary reason behind this variation can be traced back to the rainfall pattern for the study area. It was observed from the study that the inclusion of multi-temporal features improved the accuracy from 79.007 Mg/ha to 71.279 Mg/ha. Correlation between field-measured and predicted biomass also improved significantly. The result of this study will encourage the use of machine learning algorithms and datasets from the latest sensors for improved biomass estimation of Indian tropical forests.", notes = "NB16693", } @Article{GhotbiRavandi:2013:IJMST, author = "Ebrahim {Ghotbi Ravandi} and Reza Rahmannejad and Amir Ehsan Feili Monfared and Esmaeil {Ghotbi Ravandi}", title = "Application of numerical modeling and genetic programming to estimate rock mass modulus of deformation", journal = "International Journal of Mining Science and Technology", year = "2013", volume = "23", number = "5", month = sep, pages = "733--737", ISSN = "2095-2686", DOI = "doi:10.1016/j.ijmst.2013.08.018", URL = "http://www.sciencedirect.com/science/article/pii/S209526861300147X", keywords = "genetic algorithms, genetic programming, Modulus of deformation (Em), Displacement, Numerical modelling, Back analysis", } @InProceedings{ghozeil:1996:dpspdEP, author = "Adam Ghozeil and David B. Fogel", title = "Discovering Patterns in Spatial Data using Evolutionary Programming", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "Evolutionary Programming", pages = "521--527", address = "Stanford University, CA, USA", publisher = "MIT Press", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap86.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96 EP paper", } @InProceedings{Ghugare:2016:ICC, author = "Suhas B. Ghugare and Sanjeev S. Tambe", booktitle = "2016 Indian Control Conference (ICC)", title = "Development of genetic programming based softsensor model for styrene polymerization process and its application in model based control", year = "2016", pages = "238--244", abstract = "In recent years, soft sensors have been established as a valuable alternative to the traditional hardware sensors for the acquisition of critical information regarding difficult-to-measure process variables and/or parameters in chemical process monitoring and control. Soft-sensors can also be modified as a novel process identification tool for process monitoring and model based control. Often, in polymer industries the main polymerization reaction is highly nonlinear and complex to model accurately by the conventional first principle's approach. In such cases, genetic programming (GP) - a novel artificial intelligence-based exclusively data-driven modelling technique - can be employed for process identification. In this work GP-based soft sensors have been developed for a continuous styrene polymerization reactor. The resulting GP-based models (soft sensor) showed high prediction and generalisation performances. The best performing model was successfully used in designing a model predictive control (MPC) scheme for the polymerization reactor.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/INDIANCC.2016.7441134", month = jan, notes = "Also known as \cite{7441134}", } @Article{Ghugare:2016:JEI, author = "Suhas B. Ghugare and Sanjeev S. Tambe", title = "Genetic programming based high performing correlations for prediction of higher heating value of coals of different ranks and from diverse geographies", journal = "Journal of the Energy Institute", year = "2017", volume = "90", number = "3", pages = "476--484", month = jun, keywords = "genetic algorithms, genetic programming, Coal, Higher heating value, Proximate analysis, Ultimate analysis", ISSN = "1743-9671", DOI = "doi:10.1016/j.joei.2016.03.002", URL = "http://www.sciencedirect.com/science/article/pii/S1743967115304578", abstract = "The higher heating value (HHV) is the most important indicator of a coal's potential energy yield. It is commonly used in the efficiency and optimal design calculations pertaining to the coal combustion and gasification processes. Since the experimental determination of coal's HHV is tedious and time-consuming, a number of proximate and/or ultimate analyses based correlations-which are mostly linear-have been proposed for its estimation. Owing to the fact that relationships between some of the constituents of the proximate/ultimate analyses and the HHV are nonlinear, the linear models make suboptimal predictions. Also, a majority of the currently available HHV models are restricted to the coals of specific ranks or particular geographical regions. Accordingly, in this study three proximate and ultimate analysis based nonlinear correlations have been developed for the prediction of HHV of coals by using the computational intelligence (CI) based genetic programming (GP) formalism. Each of these correlations possesses following noteworthy characteristics: (i) the highest HHV prediction accuracy and generalization capability as compared to the existing models, (ii) wider applicability for coals of different ranks and from diverse geographies, and (iii) structurally lower complex than the other CI-based existing HHV models. It may also be noted that in this study, the GP technique has been used for the first time for developing coal-specific HHV models. Owing to the stated attractive features, the GP-based models proposed here possess a significant potential to replace the existing models for predicting the HHV of coals.", } @InProceedings{giacobini:2002:gecco, author = "Mario Giacobini and Marco Tomassini and Leonardo Vanneschi", title = "How Statistics Can Help In Limiting The Number Of Fitness Cases In Genetic Programming", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "889", address = "New York", publisher_address = "San Francisco, CA 94104, USA", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "genetic algorithms, genetic programming, poster paper, entropy, fitness Cases, statistics", ISBN = "1-55860-878-8", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/GP073.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/GP073.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-15.pdf", notes = "GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)", } @InProceedings{giacobini:ppsn2002:pp371, author = "Mario Giacobini and Marco Tomassini and Leonardo Vanneschi", title = "Limiting the Number of Fitness Cases in Genetic Programming Using Statistics", booktitle = "Parallel Problem Solving from Nature - PPSN VII", address = "Granada, Spain", month = "7-11 " # sep, pages = "371--380", year = "2002", editor = "Juan J. Merelo-Guervos and Panagiotis Adamidis and Hans-Georg Beyer and Jose-Luis Fernandez-Villacanas and Hans-Paul Schwefel", number = "2439", series = "Lecture Notes in Computer Science", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, Parameter tuning, Fitness Evaluation, Theory of evolutionary computing, Central Limit Theorem, Entropy", ISBN = "3-540-44139-5", DOI = "doi:10.1007/3-540-45712-7_36", URL = "https://rdcu.be/cJz75", size = "10 pages", abstract = "Fitness evaluation is often a time consuming activity in genetic programming applications and it is thus of interest to find criteria that can help in reducing the time without compromising the quality of the results. We use well-known results in statistics and information theory to limit the number of fitness cases that are needed for reliable function reconstruction in genetic programming. By using two numerical examples, we show that the results agree with our theoretical predictions. Since our approach is problem-independent, it can be used together with techniques for choosing an efficient set of fitness cases.", } @InCollection{Giacobini:2014:evcoal, author = "Marco Giacobini and Paolo Provero and Leonardo Vanneschi and Giancarlo Mauri", title = "Towards the Use of Genetic Programming for the Prediction of Survival in Cancer", booktitle = "Evolution, Complexity and Artificial Life", publisher = "Springer", year = "2014", editor = "Stefano Cagnoni and Marco Mirolli and Marco Villani", pages = "177--192", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-37576-7", URL = "http://dx.doi.org/10.1007/978-3-642-37577-4_12", DOI = "doi:10.1007/978-3-642-37577-4_12", abstract = "Risk stratification of cancer patients, that is the prediction of the outcome of the pathology on an individual basis, is a key ingredient in making therapeutic decisions. In recent years, the use of gene expression profiling in combination with the clinical and histological criteria traditionally used in such a prediction has been successfully introduced. Sets of genes whose expression values in a tumour can be used to predict the outcome of the pathology (gene expression signatures) were introduced and tested by many research groups. A well-known such signature is the 70-genes signature, on which we recently tested several machine learning techniques in order to maximise its predictive power. Genetic Programming (GP) was shown to perform significantly better than other techniques including Support Vector Machines, Multilayer Perceptrons, and Random Forests in classifying patients. Genetic Programming has the further advantage, with respect to other methods, of performing an automatic feature selection. Importantly, by using a weighted average between false positives and false negatives in the definition of the fitness, we showed that GP can outperform all the other methods in minimising false negatives (one of the main goals in clinical applications) without compromising the overall minimization of incorrectly classified instances. The solutions returned by GP are appealing also from a clinical point of view, being simple, easy to understand, and built out of a rather limited subset of the available features.", language = "English", notes = "This is actually Mario Giacobini a selection of the best papers presented at WIVACE 2012, Parma, Italy, thoroughly revised and extended by the authors", } @Proceedings{Giacobini:2024:GP, title = "EuroGP 2024: Proceedings of the 27th European Conference on Genetic Programming", editor = "Mario Giacobini and Bing Xue and Luca Manzoni", year = "2024", volume = "14631", series = "LNCS", address = "Aberystwyth, UK", month = "3-5 " # apr, organisation = "EvoStar, Species", publisher = "Springer Nature", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-031-56957-9", URL = "https://www.evostar.org/2024/eurogp/", DOI = "doi:10.1007/978-3-031-56957-9", size = "x+ 227 pages", notes = "EuroGP'2024 held in conjunction with EvoCOP2024, EvoMusArt2024 and EvoApplications2024", } @InProceedings{Giacometto:2015:IECON, author = "Francisco Giacometto and Enric Sala and Konstantinos Kampouropoulos and Luis Romeral", booktitle = "41st Annual Conference of the IEEE Industrial Electronics Society, IECON 2015", title = "Short-term load forecasting using Cartesian Genetic Programming: An efficient evolutive strategy: Case: Australian electricity market", year = "2015", pages = "005087--005094", abstract = "Currently, the Cartesian Genetic Programming approaches applied to regression problems tackle the evolution strategy from a static point of view. They are confident on the evolving capacity of the genetic algorithm, with less attention being paid over alternative methods to enhance the generalisation error of the trained models or the convergence time of the algorithm. On this article, we propose a novel efficient strategy to train models using Cartesian Genetic Programming at a faster rate than its basic implementation. This proposal achieves greater generalisation and enhances the error convergence. Finally, the complete methodology is tested using the Australian electricity market as a case study.", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", DOI = "doi:10.1109/IECON.2015.7392898", month = nov, notes = "Also known as \cite{7392898}", } @InProceedings{Giagkos:2009:TAROS, author = "Alexandros Giagkos and Myra S. Wilson", title = "A Cross-layer Design for Bee-Inspired Routing Protocols in MANETs", booktitle = "TAROS 2009 Towards Autonomous Robotic Systems", year = "2009", editor = "Theocharis Kyriacou and Ulrich Nehmzow and Chris Melhuish and Mark Witkowski", series = "Intelligent Systems Research Centre Technical Report Series", pages = "25--32", address = "University of Ulster, Londonderry, United Kingdom", month = aug # " 31 - " # sep # " 2", keywords = "wireless, mobile, ad hoc, bee-inspired, crosslayering, routing", URL = "http://isrc.ulster.ac.uk/images/stories/publications/report-series/TAROS_2009.pdf", size = "8 pages", abstract = "The field of robotics relies heavily on various technologies such as mechanical and electronic engineering, computing systems, and wireless communication. The latter plays a significant role in the area of mobile robotics by supporting remote interactions. An effective, fast, and reliable communication among homogeneous or heterogeneous robots, as well as the ability to adapt to the rapidly changing environmental conditions predicates the robots success and completion of their tasks. In this paper we present our research position in the area of adaptive nature-inspired routing protocols for mobile ad hoc networks (MANETs). Our approach is based on the honeybee foraging behaviour and ability to find and exchange information about productive sources of food in a rapidly changing environment. We describe the research problem, present a brief review of the relative literature, and illustrate our future plan.", notes = "Inspired by GP? http://www.infm.ulst.ac.uk/~ulrich/Taros09/", } @InProceedings{giani:1998:spccs, author = "Antonella Giani", title = "A Study of Parallel Cooperative Classifier Systems", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "50", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "genetic algorithms, genetic programming", broken = "http://www.ai.mit.edu/people/unamay/phd-ws-abstracts/gianni.ps", size = "1 page", notes = "GP-98LB, GP-98PhD Student Workshop see http://www.di.unipi.it/phd/tesi/tesi_1999.html", } @Article{GIANNETTI:2023:ijheatmasstransfer, author = "N. Giannetti and J. C. S. Garcia and C. Kim and Y. Sei and K. Enoki and K. Saito", title = "Circuitry optimization using genetic programming for the advancement of next generation refrigerants", journal = "International Journal of Heat and Mass Transfer", volume = "217", pages = "124648", year = "2023", ISSN = "0017-9310", DOI = "doi:10.1016/j.ijheatmasstransfer.2023.124648", URL = "https://www.sciencedirect.com/science/article/pii/S0017931023007937", keywords = "genetic algorithms, genetic programming, Refrigerant circuitry optimization, Refrigerant evaluation, Refrigerant blends", abstract = "In this study, a new evolutionary method, which can handle the implementation of genetic operators with unrestrained number and locations of splitting and merging nodes for the optimization of heat exchanger circuitries, is developed. Accordingly, this technique expands the search space of previous optimization studies. To this end, a finned-tube heat exchanger simulator is structured around a bijective mathematical representation of a refrigerant circuitry (the tube-tube adjacency matrix), which is used in combination with traversing algorithms from graph theory to recognize infeasible circuitries and constrain the evolutionary search to coherent and feasible offspring. The performance of three refrigerants, namely R32, R410A, and R454C, commonly used in air-conditioning applications was assessed for the optimized circuitries of a 36-tube evaporator while converging to a given cooling capacity, degree of superheating, and heat source boundary conditions. At a given output capacity and air outlet temperature, larger coefficient-of-performance improvements (up to 9.99percent with reference to a common serpentine configuration) were realized for zeotropic refrigerant mixtures, such as R454C, where appropriate matching of the temperature glide with the temperature variation of the air yielded the possibility of further reducing the required compression ratio under the corresponding operating conditions. Hence, it was demonstrated that low-GWP zeotropic mixtures with temperature glide can realize a performance comparable to that of R32 and higher than that of R410A by approaching the Lorenz cycle operation", } @Article{Giannetti:2024:applthermaleng, author = "Niccolo Giannetti and Adriano Milazzo and John Carlo Garcia and Cheol-Hwan Kim and Yuichi Sei and Koji Enoki and Kiyoshi Saito", title = "Thermodynamic optimization of heat exchanger circuitry via genetic programming", journal = "Applied Thermal Engineering", year = "2024", volume = "252", pages = "123623", keywords = "genetic algorithms, genetic programming, Thermodynamic analysis, Circuitry optimization, Zeotropic refrigerant mixture", ISSN = "1359-4311", URL = "https://www.sciencedirect.com/science/article/pii/S1359431124012912", DOI = "doi:10.1016/j.applthermaleng.2024.123623", abstract = "In this study, an evolutionary thermodynamic technique was developed for the optimisation of heat exchanger circuitry. The proposed technique is capable of handling the unrestrained implementation of genetic operators while ensuring circuitry feasibility and basic manufacturability. The optimisation tool was used to clarify the optimal heat transfer features in relation to the characteristics of the refrigerant and to provide a thermodynamic interpretation of the optimisation results to extract general design guidelines. Evaporator circuitry optimisation was conducted under given cooling capacity, superheating degree, and boundary conditions representative of air-conditioning applications. The consistency between the minimum entropy generation and the maximum coefficient of performance (COP) was demonstrated under these settings. Accordingly, heat exchanger configurations that take maximum advantage of the thermodynamic benefits of each refrigerant are proposed by optimising the distribution of friction and heat transfer irreversibility. Consequently, the evaporator outlet pressure increases, thus lowering the compression ratio and maximizing the COP. The developed optimisation method maximizes the benefits of low-GWP alternative refrigerants and shows that zeotropic mixtures may exhibit performance analogous to that of R32 and higher than that of R410A by approaching a Lorenz cycle operation", } @InProceedings{gibbs:1996:eikGP, author = "Jonathan Gibbs", title = "Easy Inverse Kinematics using Genetic Programming", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "422", address = "Stanford University, CA, USA", publisher = "MIT Press", size = "1 page", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap61.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96", } @Article{gibbs:1996:GP96review, author = "W. Wayt Gibbs", title = "Programming with Primordial Ooze", journal = "Scientific American", year = "1996", volume = "275", number = "4", pages = "30--31", month = oct, keywords = "genetic algorithms, genetic programming", URL = "http://www.genetic-programming.com/published/scientificamerican1096.html", size = "1 page", abstract = "Computer programmers ascended the economic food chain by inventing clever algorithms to make manufacturing and service laborers redundant. But some programmers may one day find themselves automated out of a job. In university labs, scientists are teaching computers how to write their own programs. Borrowing from the principles of natural selection, the researchers have built artificial ecosystems that, for a few problems at least, can evolve solutions better than any yet devised by humans. Someday such systems may even be able to design new kinds of computers. The idea of evolving rather than inducing algorithms is not new. John H. Holland of the University of Michigan worked out the method 21 years ago. But Holland's strategy, based on a rigorous analogy to chromosomes, is limited to problems whose solutions can be expressed as mathematical formulas. It works well only if a human programmer figures out how many numbers the computer should plug into the formula.", notes = "Summary Report on GP96. Notes on papers by Jamie J. Fernandez, Conor Ryan, Brian Howley, Lee Spector and Adrian Thompson", } @Article{gibbs:2001:sciam, author = "W. Wayt Gibbs", title = "Cybernetic Cells", journal = "Scientific American", year = "2001", volume = "265", number = "2", pages = "42--47", keywords = "genetic algorithms, genetic programming", URL = "http://www.scientificamerican.com/article/cybernetic-cells/", size = "6 pages", notes = "favourable mention of Koza's psb 2001 work \cite{koza:2001:PSB} PMID: 11478002 [PubMed - indexed for MEDLINE]", } @InCollection{gibbs:2002:IENBCGP, author = "Kevin A. Gibbs", title = "Implementation and Evaluation of a Novel {``}Branch{''} Construct for Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2002", year = "2002", editor = "John R. Koza", pages = "93--101", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", URL = "http://www.genetic-programming.org/sp2002/Gibbs.pdf", language = "en", oai = "oai:CiteSeerXPSU:10.1.1.141.205", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.141.205", abstract = "This paper describes a technique for implementing a novel type of {"}branch {"} operator within a genetic programming system. This branch construct is a new operator type that allows arbitrary branching from one location in an individual{'}s execution tree to another. The branch can be understood as alternatively allowing arbitrary code reuse or approximating access to a potentially infinite number of automatically defined functions. This paper describes the proposed design of this branch operator. This proposed design is then implemented in a real world system, and the performance effects of the branch operator are evaluated in two well known genetic programming problems: the artificial ant problem and the lawnmower problem. [1,2] The branch is found to provide some performance benefits in both of these problems, and areas for further investigation are outlined. Introduction and Overview In the day-to-day programming done by humans, most all control structures in code originate from a high level. Whether programming in a low-level language like C or a higher-level language like LISP, we are accustomed to using high-level control constructs like functions, loops, if statements, and recursion to control the path of execution and maximize code reuse. The thought of using a branch, or goto or jump", notes = "part of \cite{koza:2002:gagp} Artificial ant. Lawn Mower. {"}allowing arbitrary code reuse{"} or {"}potentially infinite number of ADFs{"}. Goto. {"}branch{"} function with {"}random{"} destination p95. Limits on total number of instructions and number of branch instructions, defaults given if limits reached. lilgp. Branch destinations stored as relative offsets into the array of instructions. ", } @InProceedings{Gibson:2014:HIC, author = "Mike J. Gibson and Edward C. Keedwell and Dragan A. Savic", title = "Genetic Programming For Cellular Automata Urban Inundation Modelling", booktitle = "11th International Conference on Hydroinformatics", year = "2014", address = "New York, USA", month = aug # " 17-21", organisation = "IAHR/IWA Joint Committee on Hydroinformatics", keywords = "genetic algorithms, genetic programming", isbn13 = "978-0-692-28129-1", URL = "http://www.hic2014.org/proceedings/bitstream/handle/123456789/1650/1723.pdf", size = "8 pages", abstract = "Recent advances in Cellular Automata (CA) represent a new, computationally efficient method of simulating flooding in urban areas. A number of recent publications in this field have shown that CAs can be much more computationally efficient than methods that use standard shallow water equations (Saint Venant/Navier-Stokes equations). CAs operate using local state-transition rules that determine the progression of the flow from one cell in the grid to another cell, and in a number of publications the Manning's Formula is used as a simplified local state transition rule. Through the distributed interactions of the CA, computationally simplified urban flooding can be simulated, although these methods are limited by the approximation represented by the Manning's formula. An alternative approach is to learn the state transition rule using an artificial intelligence approach. One such approach is Genetic Programming (GP) that has the potential to be used to optimise state transition rules to maximise accuracy and minimise computation time. In this paper we present some preliminary findings on the use of genetic programming (GP) for deriving these rules automatically. The experimentation compares GP-derived rules with human created solutions based on the Manning's formula and findings indicate that the GP rules can improve on these approaches.", notes = "Broken June 2021 http://www.hic2014.org/xmlui/", } @Article{gibson:2015:jpdc, author = "Michael J. Gibson and Edward C. Keedwell and Dragan A. Savic", title = "An investigation of the efficient implementation of cellular automata on multi-core CPU and GPU hardware", journal = "Journal of Parallel and Distributed Computing", year = "2015", volume = "77", pages = "11--25", month = mar, keywords = "Cellular automata, CA, General purpose graphic processing unit, GPGPU, OpenCL, Single Instruction Multiple Data, SIMD, Single Instruction Multiple Thread, SIMT, OpenMP, CA", ISSN = "0743-7315", DOI = "doi:10.1016/j.jpdc.2014.10.011", size = "15 pages", abstract = "Cellular automata (CA) have proved to be excellent tools for the simulation of a wide variety of phenomena in the natural world. They are ideal candidates for acceleration with modern general purpose-graphical processing units (GPU/GPGPU) hardware that consists of large numbers of small, tightly-coupled processors. In this study the potential for speeding up CA execution using multi-core CPUs and GPUs is investigated and the scalability of doing so with respect to standard CA parameters such as lattice and neighbourhood sizes, number of states and generations is determined. Additionally the impact of Activity (the number of alive cells) within a given CA simulation is investigated in terms of both varying the random initial distribution levels of alive cells, and via the use of novel state transition rules; where a change in the dynamics of these rules (i.e. the number of states) allows for the investigation of the variable complexity within.", notes = "Is this GP?", } @PhdThesis{phd/ethos/Gibson15, title = "Genetic programming and cellular automata for fast flood modelling on multi-core {CPU} and many-core {GPU} computers", author = "Michael John Gibson", year = "2015", school = "University of Exeter", address = "UK", month = "24 " # aug, keywords = "genetic algorithms, genetic programming, GPU", URL = "https://ore.exeter.ac.uk/repository/bitstream/handle/10871/20364/GibsonM.pdf", URL = "http://hdl.handle.net/10871/20364", URL = "http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.681895", size = "257 pages", abstract = "Many complex systems in nature are governed by simple local interactions, although a number are also described by global interactions. For example, within the field of hydraulics the Navier-Stokes equations describe free-surface water flow, through means of the global preservation of water volume, momentum and energy. However, solving such partial differential equations (PDEs) is computationally expensive when applied to large 2D flow problems. An alternative which reduces the computational complexity, is to use a local derivative to approximate the PDEs, such as finite difference methods, or Cellular Automata (CA). The high speed processing of such simulations is important to modern scientific investigation especially within urban flood modelling, as urban expansion continues to increase the number of impervious areas that need to be modelled. Large numbers of model runs or large spatial or temporal resolution simulations are required in order to investigate, for example, climate change, early warning systems, and sewer design optimisation. The recent introduction of the Graphics Processor Unit (GPU) as a general purpose computing device (General Purpose Graphical Processor Unit, GPGPU) allows this hardware to be used for the accelerated processing of such locally driven simulations. A novel CA transformation for use with GPUs is proposed here to make maximum use of the GPU hardware. CA models are defined by the local state transition rules, which are used in every cell in parallel, and provide an excellent platform for a comparative study of possible alternative state transition rules. Writing local state transition rules for CA systems is a difficult task for humans due to the number and complexity of possible interactions, and is known as the inverse problem for CA. Therefore, the use of Genetic Programming (GP) algorithms for the automatic development of state transition rules from example data is also investigated in this thesis. GP is investigated as it is capable of searching the intractably large areas of possible state transition rules, and producing near optimal solutions. However, such population-based optimisation algorithms are limited by the cost of many repeated evaluations of the fitness function, which in this case requires the comparison of a CA simulation to given target data. Therefore, the use of GPGPU hardware for the accelerated learning of local rules is also developed. Speed-up factors of up to 50 times over serial Central Processing Unit (CPU) processing are achieved on simple CA, up to 5-10 times speedup over the fully parallel CPU for the learning of urban flood modelling rules. Furthermore, it is shown GP can generate rules which perform competitively when compared with human formulated rules. This is achieved with generalisation to unseen terrains using similar input conditions and different spatial/temporal resolutions in this important application domain.", notes = "British Library, EThOS", } @Article{Gielen1994120, author = "C. Gielen", title = "Genetic programming: J.R. Koza. The MIT Press, Cambridge, MA. ISBN 0-262-11170-5. 819 pp., \$ 74,25", journal = "Neurocomputing", year = "1994", volume = "6", number = "1", pages = "120--122", month = feb, note = "Backpropagation, Part III", keywords = "genetic algorithms, genetic programming", ISSN = "0925-2312", broken = "http://www.sciencedirect.com/science/article/B6V10-48TCT75-5M/2/118608d812226c1e01a24920532a2702", DOI = "doi:10.1016/0925-2312(94)90038-8", notes = "\cite{koza:book} Dept. of Medical Physics and Biophysics University of Nijmegen, The Netherlands", } @InProceedings{Gielen:2007:ECCTD, author = "Georges Gielen and Tom Eeckelaert and Ewout Martens and Trent McConaghy", title = "Automated synthesis of complex analog circuits", booktitle = "18th European Conference on Circuit Theory and Design, ECCTD 2007", year = "2007", pages = "20--23", address = "Seville", month = "27-30 " # aug, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, EHW, CMOS analogue integrated circuits, Pareto optimisation, analogue integrated circuits, integrated circuit design, CMOS technology, Pareto-optimal performance model, architectural synthesis, automated synthesis, complex analog circuits, evolution-generated yield model, hierarchical optimisation, Analog circuits, CMOS analog integrated circuits, CMOS technology, Circuit simulation, Circuit synthesis, Design optimisation, Integrated circuit technology, Radio frequency, Response surface methodology, Voltage", isbn13 = "978-1-4244-1341-6", DOI = "doi:10.1109/ECCTD.2007.4529526", size = "4 pages", abstract = "CMOS technology is evolving deeper and deeper into the nanometre era, which makes the integration of entire systems possible, many of which are mixed-signal in nature, including analog and/or RF parts. This demands for efficient automated synthesis techniques for these analog circuits that include the variability of the circuit parameters. A technique is presented for the efficient yield optimisation of analog circuits based on evolution-generated yield models. A hierarchical optimization method is described that optimises complex circuits based on combining Pareto-optimal performance models in a bottom-up way. Finally, an evolution-based method for the true architectural synthesis of analog systems is presented. This is illustrated with several examples.", notes = "Also known as \cite{4529526}", } @InProceedings{gigure:1998:psosGA1, author = "Philippe Gigure and David E. Goldberg", title = "Population Sizing for Optimum Sampling with Genetic Algorithms: A Case Study of the Onemax Problem", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "496--503", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms", notes = "SGA-98", } @InProceedings{DBLP:conf/pkdd/GijsbersVO17, author = "Pieter Gijsbers and Joaquin Vanschoren and Randal S. Olson", title = "Layered {TPOT:} Speeding up Tree-based Pipeline Optimization", booktitle = "Proceedings of the International Workshop on Automatic Selection, Configuration and Composition of Machine Learning Algorithms", year = "2017", editor = "Pavel Brazdil and Joaquin Vanschoren and Frank Hutter and Holger H. Hoos", volume = "1998", series = "CEUR Workshop Proceedings", pages = "49--68", address = "Skopje, Macedonia", month = sep # " 22", publisher = "CEUR-WS.org", note = "co-located with the European Conference on Machine Learning {\&} Principles and Practice of Knowledge Discovery in Databases, AutoML@PKDD/ECML 2017", keywords = "genetic algorithms, genetic programming, TPOT", URL = "http://ceur-ws.org/Vol-1998/paper_06.pdf", timestamp = "Wed, 12 Feb 2020 16:44:28 +0100", biburl = "https://dblp.org/rec/conf/pkdd/GijsbersVO17.bib", bibsource = "dblp computer science bibliography, https://dblp.org", size = "20 pages", abstract = "With the demand for machine learning increasing, so does the demand for tools which make it easier to use. Automated machine learning (AutoML) tools have been developed to address this need, such as the Tree-Based Pipeline Optimization Tool (TPOT) which uses genetic programming to build optimal pipelines. We introduce Layered TPOT, a modification to TPOT which aims to create pipelines equally good as the original, but in significantly less time. This approach evaluates candidate pipelines on increasingly large subsets of the data according to their fitness, using a modified evolutionary algorithm to allow for separate competition between pipelines trained on different sample sizes.Empirical evaluation shows that, on sufficiently large datasets, Layered TPOT indeed finds better models faster", notes = "LTPOP See also \cite{DBLP:journals/corr/abs-1801-06007}", } @Misc{DBLP:journals/corr/abs-1801-06007, author = "Pieter Gijsbers and Joaquin Vanschoren and Randal S. Olson", title = "Layered {TPOT:} Speeding up Tree-based Pipeline Optimization", howpublished = "arXiv", year = "2018", volume = "abs/1801.06007", month = "12 " # mar, keywords = "genetic algorithms, genetic programming, TPOT", URL = "http://arxiv.org/abs/1801.06007", timestamp = "Mon, 13 Aug 2018 16:48:02 +0200", biburl = "https://dblp.org/rec/journals/corr/abs-1801-06007.bib", bibsource = "dblp computer science bibliography, https://dblp.org", size = "24 pages", abstract = "With the demand for machine learning increasing, so does the demand for tools which make it easier to use. Automated machine learning (AutoML) tools have been developed to address this need, such as the Tree-Based Pipeline Optimization Tool (TPOT) which uses genetic programming to build optimal pipelines. We introduce Layered TPOT, a modification to TPOT which aims to create pipelines equally good as the original, but in significantly less time. This approach evaluates candidate pipelines on increasingly large subsets of the data according to their fitness, using a modified evolutionary algorithm to allow for separate competition between pipelines trained on different sample sizes. Empirical evaluation shows that, on sufficiently large datasets, Layered TPOT indeed finds better models faster.", notes = "LTPOT See \cite{DBLP:conf/pkdd/GijsbersVO17}", } @Article{Gilani:2006:IJASET, author = "Labiba Gilani and Asifullah Khan and Anwar M. Mirza", title = "Distortion Estimation in Digital Image Watermarking using Genetic Programming", journal = "International Journal of Applied Science, Engineering and Technology", year = "2006", volume = "15", number = "20", pages = "103--108", keywords = "genetic algorithms, genetic programming", URL = "http://www.waset.org/ijaset/v15/v15-20.pdf", size = "6 pages", abstract = "This paper introduces a technique of distortion estimation in image watermarking using Genetic Programming (GP). The distortion is estimated by considering the problem of obtaining a distorted watermarked signal from the original watermarked signal as a function regression problem. This function regression problem is solved using GP, where the original watermarked signal is considered as an independent variable. GP-based distortion estimation scheme is checked for Gaussian attack and Jpeg compression attack. We have used Gaussian attacks of different strengths by changing the standard deviation. JPEG compression attack is also varied by adding various distortions. Experimental results demonstrate that the proposed technique is able to detect the watermark even in the case of strong distortions and is more robust against attacks.", notes = "Broken Nov 2020 http://www.waset.org/ijaset/ Lena", } @InProceedings{Gilbert:2016:CEC, author = "Jeremy Gilbert and Daniel Ashlock", title = "Evolvable Warps for Data Normalization", booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016)", year = "2016", editor = "Yew-Soon Ong", pages = "1562--1569", address = "Vancouver", month = "24-29 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-5090-0623-6", DOI = "doi:10.1109/CEC.2016.7743975", size = "8 pages", abstract = "The traditional method of fitting an approximate cumulative probability distribution to a data set is to bin the data in narrow bins and obtain a step function approximation. This technique suffices for many applications, but the resulting object is not a differentiable function making recovery of the underlying probability distribution function impossible. In this study, a unique group theoretic representation is used to define evolvable data warps that can be used to recover continuous, infinitely differentiable versions of the inverse cumulative distribution function. The use of a group theoretic representation permits a simple calculation to transform the evolved object into a cumulative distribution function and, via differentiation, into a probability distribution function. The group used to define the evolvable data warps is the group of bijections of the unit interval. The generators used by evolution are chosen to be differentiable in order to enable the computation of probability distribution functions. Experiments are run using a simple type of evolutionary algorithm to evolve approximate CDFs on seven data sets. The first data set is used to perform a parameter study on the representation length used to evolve the approximate CDFs and comparing two variations of the representation - one of which uses a representational control called gene expression and one of which does not.", notes = "WCCI2016", } @PhdThesis{Gilbert:thesis, author = "Jeremy Alexander Gilbert", title = "Applications of Group Theory to Representation for Computational Intelligence", school = "Department of Mathematics and Statistics, The University of Guelph", year = "2022", address = "Ontario, Canada", month = jan, keywords = "genetic algorithms, genetic programming, Evolutionary Computation, Evolutionary Algorithms, Representation for Evolutionary Computation, The Representation Problem, Point Packing, Population Initialization, Generalized Julia Set, Real Optimization, Data Normalization, Finding approximate cumulative distribution function, Group Theory Applications", URL = "https://hdl.handle.net/10214/26680", URL = "https://atrium.lib.uoguelph.ca/xmlui/bitstream/handle/10214/26680/Gilbert_Jeremy_202201_PhD.pdf", size = "158 pages", abstract = "Representations Arising From Group Theory. This thesis introduces a novel approach to developing representations for evolutionary computation, using group theory as a foundation. The goal is to develop new representations which are better suited for navigating treacherous fitness landscapes, yielding improvements to algorithm performance over traditional methods. To construct such a representation, a selection of elements from a group are specified and used as generators to form a subgroup. The representation takes the form of words over the set of generators. An evolutionary algorithm is then able to search the space of words, which is a standard form of evolutionary algorithm. Multiple new representations are presented, built from additive vector groups, bijections of the unit interval, and affine transformations on Euclidean space. These representations can be used in a variety of applications, including real optimization, data normalization, image generation and modification, and point packing generation. Some can also be used to discretise a continuous search space, allowing the use of algorithms such as Monte Carlo Tree Search. The discrete nature of these representations also allows for use of a dictionary of previous optimal solutions. This permits an algorithm to find a diverse set of best fit solutions, by using the dictionary to exclude parts of the search space near solutions that have already been found, realized as prefixes of stored words. A parameter study is performed for each representation, and they are compared to conventional methods on a variety of test problems.", notes = "Section 6.5.1 Relationship to Genetic Programming Supervisors Daniel Ashlock and Rajesh Pereira", } @InProceedings{gilbert:1998:GPvshdd, author = "Richard J. Gilbert and Royston Goodacre and Beverly Shann and Douglas B. Kell and Janet Taylor and Jem J. Rowland", title = "Genetic Programming-Based Variable Selection for High-Dimensional Data", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "109--115", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-548-7", URL = "http://dbkgroup.org/Papers/Gilbert%20et%20al%201998%20Genetic%20programming-based%20variable%20selection.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1998/gilbert_1998_GPvshdd.pdf", size = "7 pages", abstract = "A major advantage of the genetic programming [GP] approach to data modeling is the automatic ability of the GP to select input variables that contribute beneficially to the model and to disregard those that do not. GPs are thus able to reduce substantially the dimensionality of the model, with consequent interpretation benefits. Experimental analytical techniques frequently generate data with very high dimensionality, typically measuring many tens or even hundreds of variables per sample. It is often not apparent which of the measured variables can best be used to derive a predictive model describing the data. The identification of these variables often provides a better understanding of the physical, chemical or biological mechanism underlying the experimental observations. The ability of a GP to perform variable selection is assessed with regard to a binary classification of the sporulation state of bacterial strains. The analytical technique used, Curie-point pyrolysis mass spectrometry, generates data for 150 variables per sample. The GP-derived predictive rules for the~e data contain a substantially smaller subset of these variables, typically just 6-9. Inspection of these rules leads to the somewhat counter-intuitive conclusion that the best predictive models use both highly characteristic and highly non-characteristic variables.", notes = "GP-98", } @Article{gilbert:1997:, author = "Richard J. Gilbert and Royston Goodacre and Andrew M. Woodward and Douglas B. Kell", title = "Genetic programming: A novel method for the quantitative analysis of pyrolysis mass spectral data", journal = "ANALYTICAL CHEMISTRY", year = "1997", volume = "69", number = "21", pages = "4381--4389", keywords = "genetic algorithms, genetic programming", URL = "http://pubs.acs.org/journals/ancham/article.cgi/ancham/1997/69/i21/pdf/ac970460j.pdf", DOI = "doi:10.1021/ac970460j", size = "9 pages", abstract = "A technique for the analysis of multivariate data by genetic programming (GP) is described, with particular reference to the quantitative analysis of orange juice adulteration data collected by pyrolysis mass spectrometry (PyMS). The dimensionality of the input space was reduced by ranking variables according to product moment correlation or mutual information with the outputs. The GP technique as described gives predictive errors equivalent to, if not better than, more widespread methods such as partial least squares and artificial neural networks but additionally can provide a means for easing the interpretation of the correlation between input and output variables. The described application demonstrates that by using the GP method for analyzing PyMS data the adulteration of orange juice with 10% sucrose solution can be quantified reliably over a 0-20% range with an RMS error in the estimate of ? 1%.", notes = " ", } @InProceedings{gilbert:1999:, author = "Richard J. Gilbert and Helen E. Johnson and Michael K. Winson and Jem J. Rowland and Royston Goodacre and Aileen R. Smith and Michael A. Hall and Douglas B. Kell", title = "Genetic Programming as an Analytical Tool for Metabolome Data", booktitle = "Late-Breaking Papers of EuroGP-99", year = "1999", editor = "W. B. Langdon and Riccardo Poli and Peter Nordin and Terry Fogarty", pages = "23--33", address = "Goteborg, Sweden", month = "26-27 " # may, organisation = "EvoGP", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/eebic/eurogp99/eurogp99_lbp.html", URL = "ftp://ftp.cwi.nl/pub/CWIreports/SEN/SEN-R9913.pdf", URL = "ftp://ftp.cwi.nl/pub/CWIreports/SEN/SEN-R9913.ps.Z", abstract = "Genetic programming, in conjunction with advanced analytical instruments, is a novel tool for the investigation of complex biological systems at the whole-tissue level. In this study, samples from tomato fruit grown hydroponically under both high- and low-salt conditions were analysed using Fourier-transform infrared spectroscopy (FTIR), with the aim of identifying spectral and biochemical features linked to salinity in the growth environment. FTIR spectra are not amenable to direct visual analysis, so supervised machine learning was used to generate models capable of classifying the samples based on their spectral characteristics. The genetic programming (GP) method was chosen, since it has previously been shown to perform with the same accuracy as conventional data modelling methods, but in a readily-interpretable form. Examination of the GP-derived models showed that there was a small number of spectral regions that were consistently being used. In particular, the spectral region containing absorbances potentially due to a cyanide/nitrile functional group was identified as discriminatory. The explanatory power of the GP models enabled a chemical interpretation of the biochemical differences to be proposed. The combination of FTIR and GP is therefore a powerful and novel analytical tool which, in this study, improves our understanding of the biochemistry of salt tolerance in tomato plants.", notes = "EuroGP'99LB part of \cite{langdon:1999:egplb}", } @Misc{gilbert:p450, author = "Richard Gilbert and Kris Birchall and William Bains", title = "Classification of Cytochrome {P450 3A4} Ligands Using Genetic Programming", year = "2002", email = "info@amedis-pharma.com", keywords = "genetic algorithms, genetic programming", broken = "http://www.amedis-pharma.com/Docs/3A4_ligand_poster.ppt", abstract = "The cytochrome P450 [CYP] family is a set of haem-containing oxidoreductase enzymes which are involved in the first-pass metabolism of xenobiotic compounds such as drug molecules. CYP 3A4 is the most abundant of these enzymes in humans, and is capable of metabolising approximately 80percent of drugs to some extent. As CYP3A4 has a limited capacity, both competing substrates and inhibitors can affect the rate at which CYP3A4 metabolises drugs, and hence the amount of the compound that reaches systemic circulation. Identifying whether a compound is metabolised by CYPs in general, and CYP3A4 in particular, is important for judging its potential as a drug. We describe an approach to the computational identification of CYP3A4 ligands (substrates and inhibitors) that is based on a type of evolutionary computing called genetic programming. The method is a supervised learning system, i.e. it is guided by past examples, in this case actual measured biological data on CYP ligand status. The GP system creates predictive models by Darwinian operations of mutation, crossover and fitness selection, operating on a population of potential solutions. Parent solutions are chosen according to their ability to explain the training data. New models are generated by mutation or crossover, and may replace less-fit individuals already in the population. After sufficient iterations, the population comprises models able to explain the observations much more effectively than the initial random population. Applying this to publicly available CYP3A4 data, we show that we can predict the ligand status of a diverse set of known drugs to approximately 90percent accuracy, and to predict whether a ligand will be a substrate or an inhibitor to approximately 85percent accuracy. The GP method also identifies structural characteristics of the molecule which it is using to build the decision algorithms, and these are consistent with more exhaustive, quantum mechanical predictions of substrate status. The evolutionary nature of GPs allows generation of multiple solutions, which allow statistical validation of the results.", notes = "Amedis Pharmaceuticals Limited, Upton House, Baldock Street, Royston, Herts SG8 5AY, UK", } @Article{EVL-2000-444, author = "Andrew Gildfind and Michael A. Gigante and Ghassan Al-Qaimari", title = "Evolving performance control systems for digital puppetry", journal = "Journal of Visualization and Computer Animation", year = "2000", volume = "11", number = "4", pages = "169--183", month = "3 " # oct, publisher = "John Wiley & Sons, Ltd.", keywords = "genetic algorithms, genetic programming, performance animation, motion capture, performance control systems, puppetry, adaptive user interfaces", ISSN = "1546-4261", broken = "http://www3.interscience.wiley.com/cgi-bin/abstract/73502730/ABSTRACT", broken = "http://visinfo.zib.de/EVlib/Show?EVL-2000-444", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/21796/http:zSzzSzgoanna.cs.rmit.edu.auzSz~gildfindzSzthesiszSzpdfzSzjvca.pdf/gildfind00evolving.pdf", URL = "http://citeseer.ist.psu.edu/438189.html", DOI = "doi:10.1002/1099-1778(200009)11:4%3C169::AID-VIS217%3E3.0.CO%3B2-L", size = "16 pages", abstract = "We describe a new approach for creating performance control systems for digital puppetry. Genetic programming with fitness values specified directly by the puppeteer is used. A generic device and model representation combined with the inherent domain independence of the genetic programming paradigm allows this approach to create control systems for arbitrary combinations of input devices and models. In addition, a number of unique interaction techniques have been developed to support the user-directed search. In this paper we introduce the approach, describe the implementation and user interface and present the results from a comprehensive evaluation with expert users. We show that a search-based approach can provide an effective alternative to manually designing performance control systems and an elegant mechanism for unifying low-level input devices with a broad range of model control modes.", } @PhdThesis{Gildfind:thesis, author = "Andrew Joseph Alexander Gildfind", title = "Evolving Performance Control Systems for Digital Puppetry", school = "Computer Science, RMIT University", year = "2001", address = "Melbourne, Australia", keywords = "genetic algorithms, genetic programming, Performance animation, motion capture, performance control systems, puppetry, interactive evolution, adaptive user interfaces, usability, human-computer interaction, HCI", URL = "https://collections.pomona.edu/labs/record/?pdb=3234", abstract = "Digital puppetry is an emerging form of computer animation in which performers control the movements of an animated model in real-time, using specialised input devices such as instrumented gloves and motion trackers. A performance control system is the software which converts puppeteer inputs to the movements of a digital puppet. This thesis introduces a new approach for creating performance control systems. Developing a performance control system involves three tasks: 1) exploring the capabilities of the puppet; 2) designing the interface through which a puppeteer will control the puppet; and, 3) implementing the low-level functionality of the performance control system itself. In addition to understanding the techniques required to implement the performance control system, these tasks can require a detailed knowledge of both the parameterisation of the model and the structure of the input devices. Current performance animation systems adopt this low-level view. Typically the underlying structure of the input devices and models is exposed, and the designer is required to specify how each input should be associated with each output. Such systems provide a range of facilities for transforming this input data. While this provides a general and powerful infrastructure for implementing control systems, such systems provide little support for the design process itself. This has two consequences. The first is that control system developers are forced to work at a very low-level of abstraction — many potential control system designs can be very difficult to conceptualise and implement in an explicit fashion such as this. The second is that the end-users of these control systems, the puppeteers, are typically dependent on others to understand their requirements and then to design and implement the control systems they perform with. This thesis describes a new approach that unifies and automates much of the process of developing performance control systems. Where, traditionally, the puppeteer communicates their requirements to designers and programmers who implement the control system, this work explores the possibility of replacing these human developers with an interactive, search-based optimiser. Performance control systems are evolved using a variation of the genetic programming algorithm. The fitness metric, that drives the search process, is provided interactively by the puppeteer. From an initial, randomly generated, population of control systems, user ratings for each potential solution determine those control system traits that are successful and should be explored further, and those that can be discarded. Subsequent generations are created by applying the standard genetic operations: reproduction, crossover and mutation. Each intermediate solution is a complete, functioning control system. The search process completely automates and hides the details of control system implementation and provides a powerful mechanism through which design possibilities can be explored and refined. It provides a natural mechanism through which puppeteers can tackle each of the tasks required to create a performance control system directly, without help from designers and programmers. The continuous dialog with the user allows a search process to arrive at solutions that are potentially better customised to their preferences, their dexterity and the requirements of specific performances than control systems created by others. A significant contribution of this research is the design and development of APECS — the Adaptive Performance Control System. APECS is a prototype performance animation system which has served as the testbed for the ideas of this work. Within APECS control systems are created using the iterative user-guided search process. Two requirements have strongly influenced the design of APECS. The first is the need for flexibility and generalisability. The architecture of APECS has been designed to exploit the inherent domain independence and generalisability of the genetic programming algorithm. It makes no assumptions about the input devices and models that might be used to create a performance control system. Models and devices are represented internally in a generic form. To the search algorithm they appear the same — with the only distinguishing factor the number of data inputs or outputs. The second requirement is the need for usability. Usability in this context means that puppeteers can understand the search process and learn to control it successfully; and that they can create effective performance control systems for real performance tasks in a reasonable length of time. The search process hides the details of device and model parameterisation and takes care of the underlying implementation details. However, a large part of the design task remains with the user. Early exploratory evaluations of the APECS system suggested that the burden on the user could become very high. As a result, considerable work has been done exploring improvements to APECS both at the architectural and user interface levels. This has resulted in a range of techniques which make the process of experimenting with and evaluating control systems more efficient. These include an expansion to the underlying genetic programming algorithm, the development of interaction techniques for selecting and exploring the functionality of unfamiliar control systems, and the development of a novel structuring for the search process that uses recorded input sequences, or gestures, to manage the complexity of the evaluation task. Usability, however, has not been achieved at the expense of the flexibility of the approach. All of the interaction techniques and algorithmic extensions are applicable to any device or model that might be integrated into the system. The effectiveness of the search-based approach, and the success of the APECS design have been assessed via a comprehensive usability study. Ten experienced puppeteers participated in this evaluation. Each was required to use the APECS system to evolve control systems for a realistic animation task. Comprehensive pre- and post-test questionnaires were used to gauge the success of the approach. The results of this evaluation showed, among other things, that: puppeteers were extremely enthusiastic about the approach and appreciated the ability to tackle the control system design problem themselves; puppeteers were able to create control systems using APECS that allowed them to successfully complete the animation tasks; and that, rated in terms of comfort, flexibility, and a range of other quality metrics, participants found the evolved control systems to be successful. The detailed evaluation process provided the opportunity to experiment with a wide range of different potential solutions and discuss the results with puppeteers. The last part of this thesis explores some of these insights. What are the features of usable performance control systems? This question is explored and a new form of real-time interaction, that has a number of potential advantages compared to existing approaches is characterised. It has been termed direct gestural manipulation. Finally, the APECS system is used to evolve performance control systems supporting this interaction technique — providing a proof-of-concept implementation and demonstrating the power and generalisability of the search-based approach.", notes = "unseen 2024, data from Leonardo.info Leonardo Abstracts Service LABS ID 00128 Supervisor: Ghassan Al'Qaimari and Michael Gigante", } @Article{GILGALA:2019:ASC, author = "Francisco J. Gil-Gala and Carlos Mencia and Maria R. Sierra and Ramiro Varela", title = "Evolving priority rules for on-line scheduling of jobs on a single machine with variable capacity over time", journal = "Applied Soft Computing", year = "2019", volume = "85", pages = "105782", keywords = "genetic algorithms, genetic programming, Scheduling, One machine scheduling, Priority rules, Hyperheuristics, Electric Vehicle Charging Scheduling", ISSN = "1568-4946", URL = "https://digibuo.uniovi.es/dspace/bitstream/handle/10651/53756/ASOC-D-18-04073R2_Reducido-1.pdf", URL = "http://www.sciencedirect.com/science/article/pii/S1568494619305630", DOI = "doi:10.1016/j.asoc.2019.105782", abstract = "On-line scheduling is often required in a number of real-life settings. This is the case of distributing charging times for a large fleet of electric vehicles arriving stochastically to a charging station working under power constraints. In this paper, we consider a scheduling problem derived from a situation of this type: one machine scheduling with variable capacity and tardiness minimization, denoted ??. The goal is to develop new priority rules to improve the results from some classical ones as Earliest Due Date (EDD) or Apparent Tardiness Cost (ATC). To this end, we developed a Genetic Programming (GP) approach. The efficiency of this algorithm relies on some smart representation of the expression trees. Besides, we restrict the search space to that of dimensionally compliant expressions, which allows GP to reach single and clear solutions. We conducted an experimental study showing that GP is able to evolve new rules that outperform ATC and EDD using the same problem attributes and operations", } @InProceedings{Gil-Gala:2021:GECCOcomp, author = "Francisco J. Gil-Gala and Maria R. Sierra and Carlos Mencia and Ramiro Varela", title = "The Optimal Filtering set Problem with Application to Surrogate Evaluation in Genetic Programming", booktitle = "Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion", year = "2021", editor = "Francisco Chicano and Alberto Tonda and Krzysztof Krawiec and Marde Helbig and Christopher W. Cleghorn and Dennis G. Wilson and Georgios Yannakakis and Luis Paquete and Gabriela Ochoa and Jaume Bacardit and Christian Gagne and Sanaz Mostaghim and Laetitia Jourdan and Oliver Schuetze and Petr Posik and Carlos Segura and Renato Tinos and Carlos Cotta and Malcolm Heywood and Mengjie Zhang and Leonardo Trujillo and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Fuyuki Ishikawa and Inmaculada Medina-Bulo and Frank Neumann and Andrew M. Sutton", pages = "129--130", address = "internet", series = "GECCO '21", month = jul # " 10-14", organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, Evolutionary computation, Surrogate models, Scheduling, Hyper-heuristics: Poster", isbn13 = "978-1-4503-8351-6", DOI = "doi:10.1145/3449726.3459484", size = "2 pages", abstract = "Surrogate evaluation is common in population-based evolutionary algorithms where exact fitness calculation may be extremely time consuming. We consider a Genetic Program (GP) that evolves scheduling rules, which have to be evaluated on a training set of instances of a scheduling problem, and propose exploiting a small set of low size instances, called filter, so that the evaluation of a rule in a filter estimates the actual evaluation of the rule on the training set. The calculation of filters is modeled as an optimal subset problem and solved by a genetic algorithm. As case study,we consider the problem of scheduling jobs in a machine with time-varying capacity and show that the combination of the surrogate model with the GP termed SM-GP, outperforms the original GP", notes = " GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)", } @Article{GILGALA:2021:SEC, author = "Francisco J. Gil-Gala and Maria R. Sierra and Carlos Mencia and Ramiro Varela", title = "Genetic programming with local search to evolve priority rules for scheduling jobs on a machine with time-varying capacity", journal = "Swarm and Evolutionary Computation", year = "2021", volume = "66", pages = "100944", month = oct, note = "Special Issue on Memetic Computing: Accelerating Optimization Heuristics with Problem-Dependent Local Search Methods", keywords = "genetic algorithms, genetic programming, One machine scheduling, Priority rules, Local search, Memetic algorithm", ISSN = "2210-6502", URL = "https://www.sciencedirect.com/science/article/pii/S2210650221001061", DOI = "doi:10.1016/j.swevo.2021.100944", size = "13 pages", abstract = "Priority rules combined with schedule generation schemes are a usual approach to online scheduling. These rules are commonly designed by experts on the problem domain. However, some automatic method may be better as it could capture some characteristics of the problem that are not evident to the human eye. Furthermore, automatic methods could devise priority rules adapted to particular sets of instances of the problem at hand. In this paper we propose a Memetic Algorithm, which combines a Genetic Program and a Local Search algorithm, to evolve priority rules for the problem of scheduling a set of jobs on a machine with time-varying capacity. We propose a number of neighbourhood structures that are specifically designed to this problem. These structures were analyzed theoretically and also experimentally on the version of the problem with tardiness minimization, which provided interesting insights on this problem. The results of the experimental study show that a proper selection and combination of neighbourhood structures allows the Memetic Algorithm to outperform previous approaches to the same problem", notes = "Department of Computer Science, University of Oviedo, Gijon 33204, Spain", } @InProceedings{10.1007/978-3-031-06527-9_13, author = "Francisco J. Gil-Gala and Marko Durasevic and Maria R. Sierra and Ramiro Varela", title = "Building Heuristics and Ensembles for the Travel Salesman Problem", booktitle = "Proceedings of the 9th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2022, Part II", year = "2022", editor = "Jose Manuel Ferrandez Vicente and Jose Ramon Alvarez-Sanchez and Felix de la Paz Lopez and Hojjat Adeli", volume = "13259", series = "LNCS", pages = "130--139", address = "Puerto de la Cruz, Tenerife, Spain", month = may # " 31-" # jun # " 3", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-031-06527-9", DOI = "doi:10.1007/978-3-031-06527-9_13", abstract = "The Travel Salesman Problem (TSP) is one of the most studied optimization problems due to its high difficulty and its practical interest. In some real-life applications of this problem the solution methods must be very efficient to deal with dynamic environments or large problem instances. For this reasons, low time consuming heuristics as priority rules are often used. Even though such a single heuristic may be good to solve many instances, it may not be robust enough to take the best decisions in all situations so, we hypothesise that an ensemble of heuristics could be much better than the best of those heuristic. We view an ensemble as a set of heuristics that collaboratively build a single solution by combining the decisions of each individual heuristic. In this paper, we study the application of single heuristics and ensembles to the TSP. The individual heuristics are evolved by Genetic Programming (GP) and then Genetic Algorithms (GA) are used to build ensembles from a pool of single heuristics. We conducted an experimental study on a set of instances taken from the TSPLIB. The results of this study provided interesting insights about the behaviour of rules and ensembles.", notes = "Published as Bio-inspired Systems and Applications: from Robotics to Ambient Intelligence", } @InProceedings{gala:2022:GECCOcomp, author = "Francisco Javier {Gil Gala} and Marko Durasevic and Domagoj Jakobovic", title = "Genetic programming for electric vehicle routing problem with soft time windows", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion", year = "2022", editor = "Heike Trautmann and Carola Doerr and Alberto Moraglio and Thomas Bartz-Beielstein and Bogdan Filipic and Marcus Gallagher and Yew-Soon Ong and Abhishek Gupta and Anna V Kononova and Hao Wang and Michael Emmerich and Peter A. N. Bosman and Daniela Zaharie and Fabio Caraffini and Johann Dreo and Anne Auger and Konstantin Dietric and Paul Dufosse and Tobias Glasmachers and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Tea Tusar and Dimo Brockhoff and Tome Eftimov and Pascal Kerschke and Boris Naujoks and Mike Preuss and Vanessa Volz and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Mark Coletti and Catherine (Katie) Schuman and Eric ``Siggy'' Scott and Robert Patton and Paul Wiegand and Jeffrey K. Bassett and Chathika Gunaratne and Tinkle Chugh and Richard Allmendinger and Jussi Hakanen and Daniel Tauritz and John Woodward and Manuel Lopez-Ibanez and John McCall and Jaume Bacardit and Alexander Brownlee and Stefano Cagnoni and Giovanni Iacca and David Walker and Jamal Toutouh and UnaMay O'Reilly and Penousal Machado and Joao Correia and Sergio Nesmachnow and Josu Ceberio and Rafael Villanueva and Ignacio Hidalgo and Francisco {Fernandez de Vega} and Giuseppe Paolo and Alex Coninx and Antoine Cully and Adam Gaier and Stefan Wagner and Michael Affenzeller and Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and William B. Langdon and Justyna Petke and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Paetzel and Alexander Wagner and Michael Heider and Nadarajen Veerapen and Katherine Malan and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Mohammad Nabi Omidvar and Yuan Sun and Ernesto Tarantino and De Falco Ivanoe and Antonio {Della Cioppa} and Scafuri Umberto and John Rieffel and Jean-Baptiste Mouret and Stephane Doncieux and Stefanos Nikolaidis and Julian Togelius and Matthew C. Fontaine and Serban Georgescu and Francisco Chicano and Darrell Whitley and Oleksandr Kyriienko and Denny Dahl and Ofer Shir and Lee Spector and Alma Rahat and Richard Everson and Jonathan Fieldsend and Handing Wang and Yaochu Jin and Erik Hemberg and Marwa A. Elsayed and Michael Kommenda and William {La Cava} and Gabriel Kronberger and Steven Gustafson", pages = "542--545", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, routing policies, hyperheuristics, electric vehicle routing problem", isbn13 = "978-1-4503-9268-6/22/07", DOI = "doi:10.1145/3520304.3528994", abstract = "Vehicle routing problems (VRPs) that model transport processes have been intensively studied. Due to environmental concerns, the electric VRP (EVRP), which uses only electric vehicles, has recently attracted more attention. In many cases, such problems need to be solved in a short time, either due to their complexity or because of their dynamic nature. Routing policies (RPs), simple heuristics that build the solution incrementally, are a suitable choice to solve these problems. However, it is difficult to design efficient RPs manually. Therefore, in this paper, we consider the application of genetic programming (GP) to automatically generate new RPs. For this purpose, three RP variants and several domain-specific terminal nodes are defined to optimise three criteria. The results show that GP is able to automatically designed RPs perform, and it finds RPs with good generalisation properties that can effectively solve unseen problems.", notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @InProceedings{gil-gala:2023:GECCO, author = "Francisco Javier Gil-Gala and Sezin Afsar and Marko Durasevic and Juan Jose Palacios and Murat Afsar", title = "Genetic Programming for the Vehicle Routing Problem with {Zone-Based} Pricing", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "1118--1126", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, vehicle routing problem, zone-based pricing, routing policies, hyper-heuristics", isbn13 = "9798400701191", DOI = "doi:10.1145/3583131.3590366", size = "9 pages", abstract = "The vehicle routing problem (VRP) is one of the most interesting NP-Hard problems due to the multitude of applications in the real world. This work tracks a VRP with zone-based prices inwhich each customer belongs to a particular zone, and the goal is to maximize the profit. The particularity of this VRP variant is that the provider needs to determine the prices for each zone and routes for all vehicles. However, depending on the selected zone prices, only a subset of customers will have to be visited. In this work, we propose a novel route generation scheme (RGS) that considers both decisions simultaneously. The RGS is guided by a priority function (PF), which determines the next customer to visit. Since designing efficient PFs manually is a difficult and time-consuming task, hyper-heuristic methods, specifically genetic programming (GP), have been used in this study to generate them automatically. Furthermore, to test the performance of the generated PFs, a genetic algorithm is also used to exploit the RGS to construct the solution. The experimental analysis shows that the evolved heuristics provide reasonable quality solutions quickly, in contrast with the current state-of-the-art. Furthermore, GP produces better results than GA for some problem instances.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @InProceedings{gil-gala:2023:GECCOcomp, author = "Francisco Javier {Gil Gala} and Marko Durasevic and Mateja Dumic and Rebeka Coric and Domagoj Jakobovic", title = "An Analysis of Training Models to Evolve Heuristics for the Travelling Salesman Problem", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "575--578", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, hyper-heuristics, travelling salesman problem: Poster", isbn13 = "9798400701191", DOI = "doi:10.1145/3583133.3590559", size = "4 pages", abstract = "Designing heuristics is an arduous task, usually approached with hyper-heuristic methods such as genetic programming (GP). In this setting, the goal of GP is to evolve new heuristics that generalise well, i.e., that work well on a large number of problems. To achieve this, GP must use a good training model to evolve new heuristics and also to evaluate their generalisation ability. For this reason, dozens of training models have been used in the literature. However, there is a lack of comparison between different models to determine their effectiveness, which makes it difficult to choose the right one. Therefore, in this paper, we compare different training models and evaluate their effectiveness. We consider the well-known Travelling Salesman Problem (TSP) as a case study to analyse the performance of different training models and gain insights about training models. Moreover, this research opens new directions for the future application of hyper-heuristics.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @Article{GILGALA:2023:ins, author = "Francisco J. Gil-Gala and Marko Durasevic and Ramiro Varela and Domagoj Jakobovic", title = "Ensembles of priority rules to solve one machine scheduling problem in real-time", journal = "Information Sciences", volume = "634", pages = "340--358", year = "2023", ISSN = "0020-0255", DOI = "doi:10.1016/j.ins.2023.03.114", URL = "https://www.sciencedirect.com/science/article/pii/S0020025523004395", keywords = "genetic algorithms, genetic programming, Scheduling, Priority rules, Ensembles, Metaheuristics, Hyperheuristics", abstract = "Priority rules are one of the most common and popular approaches to real-time scheduling. Over the last decades, several methods have been developed to generate rules automatically. In addition, it has been shown that combining rules into ensembles is better than using a single rule in many cases. In this paper, we analyze different ways to create and use ensembles previously developed through genetic programming. In our study, we classify ensembles as either collaborative or coordinated, depending on how the rules are used. In the first case, all the rules contribute to the creation of the same solution, while in the second case, each rule works independently on its own solution, and the best of them is selected as the solution of the ensemble. We found that each method has its own strengths and weaknesses, which leads us to use them in combination. Based on this hypothesis, we developed new methods to design and combine collaborative and coordinated ensembles and evaluated these methods for the One Machine Scheduling Problem with time-varying capacity and minimization of total tardiness. The results of the experimental study provided interesting insights into the use of ensembles and showed that our proposals outperform previous methods", } @Article{GILGALA:2023:eswa, author = "Francisco J. Gil-Gala and Maria R. Sierra and Carlos Mencia and Ramiro Varela", title = "Surrogate model for memetic genetic programming with application to the one machine scheduling problem with time-varying capacity", journal = "Expert Systems with Applications", volume = "233", pages = "120916", year = "2023", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2023.120916", URL = "https://www.sciencedirect.com/science/article/pii/S0957417423014185", keywords = "genetic algorithms, genetic programming, Evolutionary computation, Surrogate model, Scheduling, Hyper-heuristics", abstract = "Surrogate evaluation is a useful, if not the unique, technique in population-based evolutionary algorithms where exact fitness calculation is too expensive. This situation arises, for example, in Genetic Programming (GP) applied to evolve scheduling priority rules, since the evaluation of a candidate rule amounts to solve a large number of problem instances acting as training set. In this paper, a simplified model is proposed that relies on finding and then exploiting a small set of small problem instances, termed filter, such that the evaluation of a rule on the filter may help to estimate the performance of the same rule in solving the training set. The problem of finding the best filter is formulated as a variant of the optimal subset problem, which is solved by means of a Genetic Algorithm (GA). The surrogate evaluation of a new candidate rule consist in solving the instances of the filter. This model is exploited in combination with a Memetic Genetic Program (MGP); the resulting algorithm is termed Surrogate Model MGP (SM-MGP). An experimental study was performed on the problem of scheduling a set of jobs on a machine with varying capacity over time, denoted (1,Cap(t)||SigmaTi). The results of this study provided interesting insights into the problems of filter and rules calculation, and showcase that the priority rules evolved by SM-MGP outperform those evolved by MGP", } @Article{GilGala:2024:AI, author = "Francisco J. Gil-Gala and Marko Durasevic and Domagoj Jakobovic", title = "Evolving routing policies for electric vehicles by means of genetic programming", journal = "Applied Intelligence", year = "2024", volume = "54", pages = "12391--12419", month = dec, keywords = "genetic algorithms, genetic programming, Electric vehicle routing problem, Hyper-heuristics, Routing policies, EVRPs", URL = "https://rdcu.be/dZ4aj", DOI = "doi:10.1007/s10489-024-05803-5", size = "29 pages", } @InCollection{Gillespie:1997:GAspspsd, author = "Jaysen Gillespie", title = "A Genetic Algorithm Solution to the Project Selection Problem Using Static and Dynamic Fitness Functions", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1997", publisher = "Stanford Bookstore", year = "1997", editor = "John R. Koza", pages = "76--85", address = "Stanford, California, 94305-3079 USA", month = "17 " # mar, keywords = "genetic algorithms, genetic programming", ISBN = "0-18-205981-2", notes = "part of \cite{koza:1997:GAGPs}", } @Article{journals/ijaip/GilliesPAW12, author = "Christopher E. Gillies and Nilesh V. Patel and Jan Akervall and George D. Wilson", title = "Gene expression classification using binary rule majority voting genetic programming classifier", journal = "International Journal of Advanced Intelligence Paradigms", year = "2012", volume = "4", number = "3/4", pages = "241--255", publisher = "Inderscience", bibdate = "2013-04-23", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ijaip/ijaip4.html#GilliesPAW12", keywords = "genetic algorithms, genetic programming", ISSN = "1755-0386", DOI = "doi:10.1504/IJAIP.2012.052068", abstract = "The results of a gene expression study are difficult to interpret. To increase interpretability, researchers have developed classification techniques that produce rules to classify gene expression profiles. Genetic programming is one method to produce classification rules. These rules are difficult to interpret because they are based on complicated functions of gene expression values. We propose the binary rule majority voting genetic programming classifier (BRMVGPC) that classifies samples using binary rules based on the detection calls for genes instead of the gene expression values. BRMVGPC increases rule interpretability. We evaluate BRMVGPC on two public datasets, one brain and one prostate cancer, and achieved 88.89percent and 86.39percent accuracy respectively. These results are comparable to other classifiers in the gene expression profile domain. Specific contributions include a classification technique BRMVGPC and an iterative k-nearest neighbour technique for handling marginal detection call values.", } @InProceedings{1998APS..MAR.U2403G, author = "S. D. Gillmor and Q. Liu and L. Wang and C. E. Jordan and A. G. Frutos and A. J. Theil and T. C. Stother and A. E. Condon and R. M. Corn and L. M. Smith and M. G. Lagally", title = "Addressed-Array Approach to {DNA} Computation Readout through {UV} Photopatterning", booktitle = "1998 March Meeting of the American Physical Society", year = "1998", month = "16-20 " # mar, pages = "2403-+", address = "Los Angeles", organisation = "APS", keywords = "genetic algorithms, genetic programming", URL = "http://flux.aps.org/meetings/YR98/BAPSMAR98/abs/S4160003.html", abstract = "Surfaced-based DNA computation allows for the efficient manipulation of operations on DNA strands. The readout operation determines the DNA strand sequence that encodes the solution of a combinatorial problem of interest; to perform it, densely addressed arrays are a necessity. In our surfaced-based approach, we photopattern self-assembled monolayers (SAMs) attached to a gold surface creating specific regions of hydrophilic islands in a hydrophobic background, and we characterise the chemically modified surface through reflection FTIR and fluorometry. Subsequently, the DNA strands, short 31 base-pair oligonucleotides that encode 4-8 bits of data, attach to the hydrophilic islands and form addressed arrays with feature sizes in the submillimeter range. With simple addressed arrays, we can perform the readout operation for a combinatorial problem. Expanding this simple technique, possibly with ink jet printer technology, readout can be modified to solve complex combinatorial problems employing arrays of 16 by 16 or larger with features sizes on the micrometer scale.", adsurl = "http://adsabs.harvard.edu/cgi-bin/nph-bib_query?bibcode=1998APS..MAR.U2403G&db_key=PHY", adsnote = "Provided by the Smithsonian/NASA Astrophysics Data System", notes = "See \cite{gillmor:1998:aaaDNAcrUVp}", } @InProceedings{gillmor:1998:aaaDNAcrUVp, author = "S. D. Gillmor and Q. Liu and L. Wang and C. E. Jordan and A. G. Frutos and A. J. Theil and T. C. Stother and A. E. Condon and R. M. Corn and L. M. Smith and M. G. Lagally", title = "Addressed-Array Approach to DNA Computation Readout through UV Photopatterning", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "51 and 254--255", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "DNA computing", size = "1+2 pages", notes = "GP-98LB, GP-98PhD Student Workshop Longer paper at the American Physical Society, Annual March Meeting, March 16-20, 1998 Los Angeles, CA, abstract #U24.03 ? http://esoads.eso.org/abs/1998APS..MAR.U2403G See \cite{1998APS..MAR.U2403G} ", } @PhdThesis{Giordani:thesis, author = "Ilaria Giordani", title = "Relational clustering for knowledge discovery in life sciences", school = "Universita degli Studi di Milano-Bicocca", year = "2009", address = "Italy", month = oct, keywords = "genetic algorithms, genetic programming, Relational Clustering, Feature Selection, Knowledge integration, Mixed data types", URL = "http://boa.unimib.it/handle/10281/7830", URL = "http://hdl.handle.net/10281/7830", URL = "http://boa.unimib.it/bitstream/10281/7830/1/phd_unimib_032791.pdf", language = "eng", size = "144 pages", abstract = "Clustering is one of the most common machines learning technique, which has been widely applied in genomics, proteomics and more generally in Life Sciences. In particular, clustering is an unsupervised technique that, based on geometric concepts like distance or similarity, partitions objects into groups, such that objects with similar characteristics are clustered together and dissimilar objects are in different clusters. In many domains where clustering is applied, some background knowledge is available in different forms: labelled data (specifying the category to which an instance belongs); complementary information about 'true' similarity between pairs of objects or about the relationships structure present in the input data; user preferences (for example specifying whether two instances should be in same or different clusters). In particular, in many real-world applications like biological data processing, social network analysis and text mining, data do not exist in isolation, but a rich structure of relationships subsists between them. A simple example can be viewed in biological domain, where there are al lot of relationships between genes and proteins based on many experimental conditions. Another example, maybe common, is the Web search domain where there are relations between documents and words in a text or web pages, search queries and web users. Our research is focused on how this background knowledge can be incorporated into traditional clustering algorithms to optimise the process of pattern discovery (clustering) between instances.", abstract = "provide an overview of traditional clustering methods with some important distance measures and then we analyse three particular challenges that we try to overcome with different proposed methods: 'feature selection' to reduce high dimensional input space and remove noise from data; 'mixed data types' to handle in clustering procedure both numeric and categorical values, typically of life science applications; finally, 'knowledge integration' in order to improve the semantic value of clustering incorporating the background knowledge. Regarding the first challenge we propose a novel approach based on using of genetic programming, an evolutionary algorithm-based methodology, in order to automatically perform feature selection. Different clustering algorithms are been investigated regarding the second challenge. A modify version of a particular algorithm is proposed and applied to clinical data. Particularly attention is given to the final challenge, the most important objective of this Thesis: the development of a new relational clustering framework in order to improve the semantic value of clustering taking into account in the clustering algorithm relationships learnt from background knowledge. We investigate and classify existing clustering methods into two principal categories: - Structure driven approaches: that are bound to data structure. The data clustering problem is tackled from several dimensions: clustering concurrently columns and rows of a given dataset, like biclustering algorithm or vertical 3-D clustering. - Knowledge driven approaches: where domain information is used to drive the clustering process and interpret its results: semi-supervised clustering, that using both labelled and unlabeled data, has attracted significant attention. This kind of clustering algorithms represents the first step to implement the proposed general framework that it is classified into this category. In particular the thesis focuses on the development of a general framework for relational clustering instantiating it for three different life science applications: the first one with the aim of finding groups of gene with similar behaviour respect to their expression and regulatory profile. The second one is a pharmacogenomics application, in which the relational clustering framework is applied on a benchmark dataset (NCI60) to identify a drug treatment to a given cell line based both on drug activity pattern and gene expression profile. Finally, the proposed framework is applied on clinical data: a particular dataset containing different information about patients in anticoagulant therapy has been analyzed to find group of patients with similar behaviour and responses to the therapy.", notes = "NCI60, Saccharomyces Genome Database, Oral anticoagulation therapy Also known as \cite{10281_7830}", } @InProceedings{Giot:2010:ICPR, author = "Romain Giot and Baptiste Hemery and Christophe Rosenberger", title = "Low Cost and Usable Multimodal Biometric System Based on Keystroke Dynamics and 2D Face Recognition", booktitle = "20th International Conference on Pattern Recognition (ICPR 2010)", year = "2010", month = "23-26 " # aug, pages = "1128--1131", abstract = "We propose in this paper a low cost multimodal biometric system combining keystroke dynamics and 2D face recognition. The objective of the proposed system is to be used while keeping in mind: good performances, acceptability, and aspect of privacy. Different fusion methods have been used (min, max, mul, svm, weighted sum configured with genetic algorithms, and, genetic programming) on the scores of three keystroke dynamics algorithms and two 2D face recognition ones. This multimodal biometric system improves the recognition rate in comparison with each individual method. On a chimeric database composed of 100 individuals, the best keystroke dynamics method obtains an EER of 8.77percent, the best face recognition one has an EER of 6.38percent, while the best proposed fusion system provides an EER of 2.22percent.", keywords = "genetic algorithms, genetic programming, 2D face recognition, chimeric database, fusion methods, keystroke dynamics, multimodal biometric system, privacy, biometrics (access control), data privacy, face recognition, keyboards", DOI = "doi:10.1109/ICPR.2010.282", ISSN = "1051-4651", notes = "GREYC Lab., Univ. of CAEN, Caen, France Also known as \cite{5595872}", } @Article{Giot20121837, author = "Romain Giot and Christophe Rosenberger", title = "Genetic programming for multibiometrics", journal = "Expert Systems with Applications", volume = "39", number = "2", pages = "1837--1847", year = "2012", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2011.08.066", URL = "http://www.sciencedirect.com/science/article/pii/S095741741101178X", keywords = "genetic algorithms, genetic programming, Multibiometrics, Score fusion, Authentication", abstract = "Biometric systems suffer from some drawbacks: a biometric system can provide in general good performances except with some individuals as its performance depends highly on the quality of the capture One solution to solve some of these problems is to use multibiometrics where different biometric systems are combined together (multiple captures of the same biometric modality, multiple feature extraction algorithms, multiple biometric modalities). In this paper, we are interested in score level fusion functions application (i.e., we use a multibiometric authentication scheme which accept or deny the claimant for using an application). In the state of the art, the weighted sum of scores (which is a linear classifier) and the use of an SVM (which is a non linear classifier) provided by different biometric systems provide one of the best performances. We present a new method based on the use of genetic programming giving similar or better performances (depending on the complexity of the database). We derive a score fusion function by assembling some classical primitives functions (+, ., -, a ). We have validated the proposed method on three significant biometric benchmark datasets from the state of the art.", } @PhdThesis{giot:tel-00748915, author = "Romain Giot", title = "Contribution to keystroke dynamics: mulitbiometrics, soft biometrics and template update", school = "Universite de Caen", year = "2012", address = "France", month = "23 " # oct, keywords = "genetic algorithms, genetic programming, SVN, Biometrics, Keystroke Dynamics, Template update, Evolutionary computing, Information fusion, Biometrie, Dynamique de frappe au clavier, Mise a jour de la reference, Algorithmes evolutionnaires, Fusion d'information", URL = "https://www.greyc.fr/node/1676", URL = "https://www.greyc.fr/sites/default/files/secretariat/theses-soutenues-2012/These-Romain-Giot-2012.pdf", URL = "https://tel.archives-ouvertes.fr/tel-00748915", URL = "https://tel.archives-ouvertes.fr/tel-00748915/file/PhD_giot.pdf", hal_id = "tel-00748915", hal_version = "v1", size = "208 pages", resume = "La dynamique de frappe au clavier est une modalite biometrique comportementale qui permet d'authentifier des individus selon leur facon de taper au clavier. Un tel systeme est peu couteux, car il ne necessite pas de materiel d'acquisition autre que le clavier de l'ordinateur, et est facilement accepte pour l'utilisateur. Nous nous sommes principalement interesse aux systemes statiques ou le texte saisit par l'utilisateur est connu a l'avance par la machine. Malheureusement, les performances de cette modalite sont plutot mediocres en raison de la forte variabilite de la donnee biometrique. Cette variabilite est due a l'etat emotionnel de la personne, l'apprentissage de la facon de taper... Nous proposons dans cette these differentes contributions permettant d'ameliorer les performances de reconnaissance de systemes de dynamique de frappe au clavier (DDF). Nous effectuons egalement une analyse des bases publiques permettant d'evaluer la performance de nouveaux systemes de reconnaissance. Une contribution est la mise au point d'un systeme de DDF par mot de passe partage. Nous etudions ensuite la fusion multibiometrique avec la dynamique de frappe au clavier et la reconnaissance faciale afin d'augmenter les performances des deux systemes. Nous montrons, sur deux jeux de donnees differents, qu'il est possible de reconnaitre le genre d'un individu suivant sa facon de taper au clavier. Enfin, nous presentons une nouvelle methode de mise a jour de la reference biometrique qui permet de prendre en compte le vieillissement de la donnee biometrique, afin de ne pas avoir une diminution des performances de reconnaissance au cours du temps.", abstract = "Keystroke dynamics is a behavioural biometry which allows to authenticate individuals through there way of typing on a keyboard. Such systems are cheap, as they do not need specific devices different from the keyboard of the computer. They are also well accepted by the user. We are mainly interested in static systems where the text typed by the user is known in advance by the machine. Sadly, the performance of this modality are rather mediocre because of the high variability of the biometric data which comes from emotional state of the individual, the learning of they way to type In this thesis, we propose various contributions which allow to improve the recognition performance of keystroke dynamics systems. We also do an analysis of the public datasets allowing to evaluate the performance of new recognition systems. One contribution is the creation of a system which allows the authentication of users with a shared password. Then, we study the biometric fusion with face recognition and keystroke dynamics in order to increase the performance of the two systems. We show, on two different datasets, that it is possible to guess the gender of an individual through its way of typing to a keyboard. Finally, we present a new template update method which allows to take into account the ageing of the biometric data in order to not observe a decrease of performance overtime.", notes = "In French. Little mention of GP Membres du Jury Hubert Cardot Andrzej Drygajlo Jean-luc Dugelay Alain Rakotomamonjy Christophe Rosenberger Equipe Monetique & Biometrie - Laboratoire GREYC - UMR6072", } @InProceedings{WSEAS_644_Gir, author = "Ra{\'u}l Gir{\'a}ldez and Roberto Ruiz", title = "Applying Genetic Programming to obtain Separation Surfaces", address = "Puerto De La Cruz, Tenerife, Spain", year = "2001", month = feb # "~11-15", booktitle = "WSEAS NNA-FSFS-EC 2001", pages = "paper ID number 644", organisation = "The World Scientific and Engineering Academy and Society (WSEAS)", keywords = "genetic algorithms, genetic programming, Classification, Dynamical systems", notes = "broken www.wseas.com/2001.xls Nov 2012 Not in http://www.wseas.us/e-library/conferences/tenerife2001/ec01.htm", } @TechReport{Giraldi:2004:0403003, author = "Gilson A. Giraldi and Renato Portugal and Ricardo N. Thess", title = "Genetic Algorithms and Quantum Computation", institution = "National Laboratory for Scientific Computing, Petropolis, RJ, Brazil", year = "2004", number = "0403003", keywords = "genetic algorithms, genetic programming, Quantum Computing, Evolutionary Strategies", URL = "http://arxiv.org/PS_cache/cs/pdf/0403/0403003.pdf", URL = "http://arxiv.org/pdf/cs.NE/0403003", abstract = "Recently, researchers have applied genetic algorithms (GAs) to address some problems in quantum computation. Also, there has been some works in the designing of genetic algorithms based on quantum theoretical concepts and techniques. The so called Quantum Evolutionary Programming has two major sub-areas: Quantum Inspired Genetic Algorithms (QIGAs) and Quantum Genetic Algorithms (QGAs). The former adopts qubit chromosomes as representations and employs quantum gates for the search of the best solution. The later tries to solve a key question in this field: what GAs will look like as an implementation on quantum hardware? As we shall see, there is not a complete answer for this question. An important point for QGAs is to build a quantum algorithm that takes advantage of both the GA and quantum computing parallelism as well as true randomness provided by quantum computers. In the first part of this paper we present a survey of the main works in GAs plus quantum computing including also our works in this area. Henceforth, we review some basic concepts in quantum computation and GAs and emphasise their inherent parallelism. Next, we review the application of GAs for learning quantum operators and circuit design. Then, quantum evolutionary programming is considered. Finally, we present our current research in this field and some perspectives.", size = "27 pages", } @Article{Gircys:2019:Complexity, author = "Michael Gircys and Brian J. Ross", title = "Image Evolution Using {2D} Power Spectra", journal = "Complexity", year = "2019", volume = "2019", pages = "Article ID 7293193", month = "2 " # jan, keywords = "genetic algorithms, genetic programming, power spectra, evolutionary art", ISSN = "1076-2787", URL = "https://www.hindawi.com/journals/complexity/2019/7293193/", URL = "http://downloads.hindawi.com/journals/complexity/2019/7293193.pdf", DOI = "doi:10.1155/2019/7293193", size = "21 pages", abstract = "Procedurally generated images and textures have been widely explored in evolutionary art. One active research direction in the field is the discovery of suitable heuristics for measuring perceived characteristics of evolved images. This is important in order to help influence the nature of evolved images and thereby evolve more meaningful and pleasing art. In this regard, particular challenges exist for quantifying aspects of style and shape. In an attempt to bridge the divide between computer vision and cognitive perception, we propose the use of measures related to image spatial frequencies. Based on existing research that uses power spectral density of spatial frequencies as an effective metric for image classification and retrieval, we posit that Fourier decomposition can be effective for guiding image evolution. We refine fitness measures based on Fourier analysis and spatial frequency and apply them within a genetic programming environment for image synthesis. We implement fitness strategies using 2D Fourier power spectra and phase, with the goal of evolving images that share spectral properties of supplied target images. Adaptations and extensions of the fitness strategies are considered for their utility in art systems. Experiments were conducted using a variety of greyscale and colour target images, spatial fitness criteria, and procedural texture languages. Results were promising, in that some target images were trivially evolved, while others were more challenging to characterize. We also observed that some evolved images which we found discordant and uncomfortable show a previously identified spectral phenomenon. Future research should further investigate this result, as it could extend the use of 2D power spectra in fitness evaluations to promote new aesthetic properties.", notes = "https://www.facebook.com/128652667171184/posts/congratulations-to-michael-gircys-who-successfully-defended-his-msc-thesis-image/1586676308035472/ Brock University, Department of Computer Science, 1812 Sir Isaac Brock Way, St. Catharines, ON, Canada L2S 3A1", } @InProceedings{conf/synasc/GirdeaC07, author = "Marta Girdea and Liviu Ciortuz", title = "A Hybrid Genetic Programming and Boosting Technique for Learning Kernel Functions from Training Data", booktitle = "Proceedings of the Ninth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2007", year = "2007", editor = "Viorel Negru and Tudor Jebelean and Dana Petcu and Daniela Zaharie", pages = "395--402", address = "Timisoara, Romania", month = sep # " 26-29", publisher = "IEEE Computer Society", keywords = "genetic algorithms, genetic programming, InfoBoost procedure, RBF kernel function learning, boosting technique, nonlinear SVM classification, training data, learning (artificial intelligence), pattern classification, radial basis function networks, support vector machines", isbn13 = "978-0-7695-3078-9", DOI = "doi:10.1109/SYNASC.2007.71", size = "8 pages", abstract = "This paper proposes a technique for learning kernel functions that can be used in non-linear SVM classification. The technique uses genetic programming to evolve kernel functions as additive or multiplicative combinations of linear, polynomial and RBF kernels, while a procedure inspired from InfoBoost helps the evolved kernels concentrate on the most difficult objects to classify. The kernels obtained at each boosting round participate in the training of non-linear SVMs which are combined, along with their confidence coefficients, into a final classifier. We compared on several data sets the performance of the kernels obtained in this manner with the performance of classic RBF kernels and of kernels evolved using a pure GP method, and we concluded that the boosted GP kernels are generally better.", notes = "'Alexandru loan Cuza' Univ. of Iasi, Iasi", bibdate = "2008-11-28", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/synasc/synasc2007.html#GirdeaC07", } @InProceedings{conf/eurogp/GirginP08, title = "Feature Discovery in Reinforcement Learning Using Genetic Programming", author = "Sertan Girgin and Philippe Preux", bibdate = "2008-04-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/eurogp/eurogp2008.html#GirginP08", booktitle = "Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008", address = "Naples", month = "26-28 " # mar, publisher = "Springer", year = "2008", volume = "4971", editor = "Michael O'Neill and Leonardo Vanneschi and Steven Gustafson and Anna Isabel {Esparcia Alcazar} and Ivanoe {De Falco} and Antonio {Della Cioppa} and Ernesto Tarantino", isbn13 = "978-3-540-78670-2", pages = "218--229", series = "Lecture Notes in Computer Science", DOI = "doi:10.1007/978-3-540-78671-9_19", keywords = "genetic algorithms, genetic programming", notes = "See also http://hal.inria.fr/inria-00187997/en/ Part of \cite{conf/eurogp/2008} EuroGP'2008 held in conjunction with EvoCOP2008, EvoBIO2008 and EvoWorkshops2008", } @Article{Giri:2013:MMP, author = "Brijesh Kumar Giri and Frank Pettersson and Henrik Saxen and Nirupam Chakraborti", title = "Genetic Programming Evolved through Bi-Objective Genetic Algorithms Applied to a Blast Furnace", journal = "Materials and Manufacturing Processes", year = "2013", volume = "28", number = "7", pages = "776--782", note = "Special Issue on Genetic Algorithms", keywords = "genetic algorithms, genetic programming, BiOGP", DOI = "doi:10.1080/10426914.2013.763953", size = "7 pages", abstract = "In this study, a new Bi-objective Genetic Programming (BioGP) technique was developed that initially attempts to minimise training error through a single objective procedure and subsequently switches to bi-objective evolution to work out a Pareto-trade-off between model complexity and accuracy. For a set of highly noisy industrial data from an operational iron making blast furnace (BF) this method was pitted against an Evolutionary Neural Network (EvoNN) developed earlier by the authors. The BiOGP procedure was found to produce very competitive results for this complex modelling problem and because of its generic nature, opens a new avenue for data-driven modeling in many other domains.", } @Article{Giri:2013:ASC, author = "Brijesh Kumar Giri and Jussi Hakanen and Kaisa Miettinen and Nirupam Chakraborti", title = "Genetic programming through bi-objective genetic algorithms with a study of a simulated moving bed process involving multiple objectives", journal = "Applied Soft Computing", year = "2013", volume = "13", number = "5", pages = "2613--2623", month = may, keywords = "genetic algorithms, genetic programming, Evolutionary algorithms, Neural networks, ANN, Multi-objective optimisation, MOGP, Computational cost, Meta-models, Simulation-based optimisation", ISSN = "1568-4946", URL = "http://www.sciencedirect.com/science/article/pii/S1568494612005091", DOI = "doi:10.1016/j.asoc.2012.11.025", size = "11 pages", abstract = "A new bi-objective genetic programming (BioGP) technique has been developed for meta-modelling and applied in a chromatographic separation process using a simulated moving bed (SMB) process. The BioGP technique initially minimises training error through a single objective optimisation procedure and then a trade-off between complexity and accuracy is worked out through a genetic algorithm based bi-objective optimization strategy. A benefit of the BioGP approach is that an expert user or a decision maker (DM) can flexibly select the mathematical operations involved to construct a meta-model of desired complexity or accuracy. It is also designed to combat bloat, a perennial problem in genetic programming along with over fitting and under fitting problems. In this study the meta-models constructed for SMB reactors were compared with those obtained from an evolutionary neural network (EvoNN) developed earlier and also with a polynomial regression model. Both BioGP and EvoNN were compared for subsequent constrained bi-objective optimization studies for the SMB reactor involving four objectives. The results were also compared with the previous work in the literature. The BioGP technique produced acceptable results and is now ready for data-driven modelling and optimization studies at large.", } @InProceedings{Gite:2017:ieeeCC, author = "Balasaheb Gite and Khalid Sayed and Navin Mutha and Saurabhkumar Marpadge and Kshitij Patil", booktitle = "2017 Computing Conference", title = "Surveying various genetic programming (GP) approaches to forecast real-time trends prices in the stock market", year = "2017", pages = "131--134", abstract = "The share prices in the stock market are known for their extreme unpredictability and attempts to identify any familiar patterns in the prices poses a confounding problem for both fundamental & technical analysts. This article attempts to use symbolic regression capabilities of GP and a market trend indicator (RSI) to predict the price and trend of the particular stock as accurately as possible. The use of a market indicator to independently forecast the trend without any role of GP serves as a verification mechanism to the price predicted by GP for the next day to further validate the authenticity of the price of the stock in the context of the real-time stock market. Extensive testing has been done on the various evolution parameters and functions of GP to customize the GP approach as much as possible to suit the current application and optimise the results. Though obtained results can never be fully relied on by real technical analysts of the stock market, it could definitely be used as a decision making support.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SAI.2017.8252093", month = jul, notes = "Also known as \cite{8252093}", } @InProceedings{Giustolisi:2004:WM, author = "Orazio Giustolisi and Dragan A. Savic and Daniele Laucelli", title = "Data Mining for Management and Rehabilitation of Water Systems: The Evolutionary Polynomial Regression Approach", booktitle = "Wasserbauliche Mitteilungen (2004) Heft 27", year = "2004", pages = "285--296", address = "Germany", organisation = "Technische Universitaet Dresden, Institut fuer Wasserbau und technische Hydromechanik", keywords = "genetic algorithms, genetic programming, Evolutionary Polynomial Regression, EPR, Water Distribution Systems, Bust Risk Analysis, Data Mining, Modeling", URL = "https://hdl.handle.net/20.500.11970/103889", URL = "https://izw.baw.de/publikationen/dresdner-wasserbauliche-mitteilungen/0/2004_Wasserbauliche_Mitteilungen_Risiken_bei_der_Bemessung_und_Bewirtschaftung_von_Flie%c3%9fgew%c3%a4ssern_und_Stauanlagen.pdf", size = "11 pages", abstract = "Risk-based management and rehabilitation of water distribution systems requires that company asset data are collected and also that a methodology is available to efficiently extract information from data. The process of extracting useful information from data is called knowledge discovery and at its core is data mining. This automated analysis of large or complex datasets is performed to determine significant patterns among data. There are many data mining technologies (Decision Tree, Rule Induction, Statistical analysis, Artificial Neural Networks, etc.), but not all are useful for every type of problem. This paper deals with a novel data mining methodology for pipe burst analysis, which integrates numerical and symbolic regression. This new technique is named Evolutionary Polynomial Regression and uses polynomial structures whose exponents are selected by an evolutionary search, thus providing symbolic expressions. A case study from UK is presented to illustrate the application of the Evolutionary Polynomial Regression methodology to prediction of main bursts and to identification of the network features influencing them.", notes = "in English", } @Article{Giustolisi:2004:JH, author = "Orazio Giustolisi", title = "Using genetic programming to determine {Chezy} resistance coefficient in corrugated channels", journal = "Journal of Hydroinformatics", year = "2004", volume = "6", number = "3", pages = "157--173", month = jul, keywords = "genetic algorithms, genetic programming, evolutionary strategies, data mining, corrugated pipes", ISSN = "1464-7141", URL = "http://jh.iwaponline.com/content/6/3/157", URL = "http://www.iwaponline.com/jh/006/0157/0060157.pdf", DOI = "doi:10.2166/hydro.2004.0013", size = "17 pages", abstract = "Genetic Programming has been used to determine Chezy resistance coefficient for full circular corrugated channels. Three corrugated plastic pipes have been experimentally studied in order to generate data. The tests aim at measuring hydraulic parameters of the open-channel flow for some slopes, from 3.49-17.37percent (2-10), in order to discover the dependence of the channel resistance coefficient when wake-interference flow occurs. The monomial formula for the Chezy resistance coefficient performs well on experimental data, both from measurement errors and from a technical point of view. In this paper, we present some very parsimonious formulae that have been created by Genetic Programming with few constants and which fit the data better than the monomial formula. Moreover, two of the Genetic Programming formulae, after 'physical post-refinement', seem to better explain the role of the roughness in the Chezy resistance coefficient for corrugated channels with respect to its traditional expression for rough channels. This fact suggests that at least the structure of those formulae can be extrapolated to other types of corrugated channels. Finally, the work stresses the fact that the Genetic Programming hypothesis can be easily manipulated by means of 'human' physical insight. Therefore, Genetic Programming should be considered more than a simple data-driven technique, especially when it is used to perform scientific discovery.", notes = "Morris' wake interference. Cadim", } @Article{Giustolisi:2006:CEES, author = "Orazio Giustolisi and Daniele Laucelli and Dragan A. Savic", title = "Development of rehabilitation plans for water mains replacement considering risk and cost-benefit assessment", journal = "Civil Engineering and Environmental Systems", year = "2006", volume = "23", number = "6", pages = "175--190", note = "Special Issue: Papers selected from the Eighth International Conference on Computing and Control for the Water Industry", keywords = "genetic algorithms, genetic programming, Pipe burst modelling, Water mains rehabilitation, Investment/benefit optimisation, Renewal planning", ISSN = "1028-6608", DOI = "doi:10.1080/10286600600789375", size = "16 pages", abstract = "The economic and social costs of pipe bursts in water distribution networks (WDNs) are very significant. Water managers need reliable replacement plans for critical pipes, balancing investment with expected benefits in a risk-based management scenario. Thus, a robust and feasible decision support tool for water system rehabilitation is required. This kind of tool should incorporate (i) a model to forecast pipe failures and (ii) a strategy to solve a multi-objective optimisation problem trading investment vs. benefits. The former requires the collection of company asset data and the statistical modelling of pipe bursts. In this article, the burst modelling is performed by the evolutionary polynomial regression technique, providing a symbolic model for predicting pipe bursts. The benefits of burst reduction achieved by mains rehabilitation are evaluated by a multi-objective optimisation model over a short-term planning horizon (taken to be one year in this study). The multi-objective strategy is embedded in a genetic algorithm search methodology. The procedure identifies different subsets of pipes scheduled for rehabilitation, ranging from no-replacement (i.e., no reduction of the predicted number of bursts) to the complete replacement scheme (i.e. maximum reduction of the predicted number of bursts), trading cost of rehabilitation against achieved benefits. The result of the strategy is a Pareto (trade-off) front, which by itself does not provide any prioritisation of pipes for replacement. Thus, the article introduces a further processing step by which pipes are prioritised for rehabilitation based on the number of times each belongs to a solution on the Pareto front. By considering costs and such priority rating of each main, an improved investments/benefit diagram is constructed. The procedure is tested on a real-world UK WDN.", notes = "Papers using GP related results", } @Article{Giustolisi:2006:JH, author = "Orazio Giustolisi and Dragan A. Savic", title = "A symbolic data-driven technique based on evolutionary polynomial regression", journal = "Journal of Hydroinformatics", year = "2006", volume = "8", number = "3", pages = "207--222", month = jul, keywords = "genetic algorithms, genetic programming, EPR, Chezy resistance coefficient, Colebrook-White formula, data-driven modelling, evolutionary computing, regression", ISSN = "1464-7141", URL = "http://www.iwaponline.com/jh/008/0207/0080207.pdf", DOI = "doi:10.2166/hydro.2006.020b", size = "16 pages", abstract = "This paper describes a new hybrid regression method that combines the best features of conventional numerical regression techniques with the genetic programming symbolic regression technique. The key idea is to employ an evolutionary computing methodology to search for a model of the system/process being modelled and to employ parameter estimation to obtain constants using least squares. The new technique, termed Evolutionary Polynomial Regression (EPR) overcomes shortcomings in the GP process, such as computational performance; number of evolutionary parameters to tune and complexity of the symbolic models. Similarly, it alleviates issues arising from numerical regression, including difficulties in using physical insight and over-fitting problems. This paper demonstrates that EPR is good, both in interpolating data and in scientific knowledge discovery. As an illustration, EPR is used to identify polynomial formulae with progressively increasing levels of noise, to interpolate the Colebrook-White formula for a pipe resistance coefficient and to discover a formula for a resistance coefficient from experimental data.", } @Article{Giustolisi:2007:EMS, author = "O. Giustolisi and A. Doglioni and D. A. Savic and B. W. Webb", title = "A multi-model approach to analysis of environmental phenomena", journal = "Environmental Modelling \& Software", year = "2007", volume = "22", number = "5", pages = "674--682", note = "The Implications of Complexity for Integrated Resources The Second Biannual Meeting of the International Environmental Modelling and Software Society: Complexity and Integrated Resources Management", keywords = "genetic algorithms, genetic programming, EPR, Evolutionary Polynomial Regression, Scientific knowledge discovery from data, Environmental modelling, Evolutionary computing, Data reconstruction", ISSN = "1364-8152", URL = "http://www.sciencedirect.com/science/article/pii/S1364815206000326", DOI = "doi:10.1016/j.envsoft.2005.12.026", size = "9 pages", abstract = "A data-driven methodology named Evolutionary Polynomial Regression is introduced. EPR permits the symbolic and multi-purpose modelling of physical phenomena, through the simultaneous solution of a number of models. Multi-purpose modelling or multi-modelling enables the user to make a different choice according to what the model is aiming at: (a) the scientific knowledge based on data modelling, (b) on-line and off-line forecasting, (c) data augmentation (i.e. infilling of missing data in time series) and so on. This allows a more robust model selection phase. A case study based on the application of Evolutionary Polynomial Regression to the study of the thermal behaviour of a stream is presented.", } @Article{Giustolisi:2008:WRR, author = "O. Giustolisi and A. Doglioni and D. A. Savic and F. {di Pierro}", title = "An evolutionary multiobjective strategy for the effective management of groundwater resources", journal = "Water Resources Research", year = "2008", volume = "44", number = "1", month = jan, keywords = "genetic algorithms, genetic programming, EPR, Data-driven, modelling, evolutionary search, multiobjective, groundwater resources, efficient management, planning", ISSN = "1944-7973", publisher = "American Geophysical Union", DOI = "doi:10.1029/2006WR005359", size = "14 pages", abstract = "This paper introduces a modelling approach aimed at the management of groundwater resources based on a hybrid multiobjective paradigm, namely Evolutionary Polynomial Regression. Multiobjective modeling in hybrid evolutionary computing enables the user (a) to find a set of feasible symbolic models, (b) to make a robust choice of models and (c) to improve computational efficiency, simultaneously developing a set of models with diverse structural parsimony levels. Moreover, this methodology appears to be well suited to those cases where process input and the boundary conditions are not easily accessible. The multiobjective approach is based on the Pareto dominance criterion and it is fully integrated into the Evolutionary Polynomial Regression paradigm. This approach proves to be effective for modelling groundwater systems, which usually requires (a) accurate analyses of the underlying physical phenomena, (b) reliable forecasts under different hypothetical scenarios and (c) good generalisation features of the models identified. For these reasons it is important to construct easily interpretable models which are specialised for well defined purposes. The proposed methodology is tested on a case study aimed at determining the dynamic relationship between rainfall depth and water table depth for a shallow unconfined aquifer located in southeast Italy.", notes = "Brindisi. no page numbers, W01403, wrcr11027.pdf", } @Article{Giustolisi:2008:wama, author = "Orazio Giustolisi and Angelo Doglioni and Daniele Laucelli", title = "Determination of friction factor for corrugated drains", journal = "Proceedings of the ICE - Water Management", year = "2008", volume = "161", number = "1", pages = "31--42", month = "1 " # feb, keywords = "genetic algorithms, genetic programming, hydraulics, hydrodynamics, sewers, drains", ISSN = "1741-7589", DOI = "doi:10.1680/wama.2008.161.1.31", size = "12 pages", abstract = "This paper describes two approaches for evaluating the resistance factor of corrugated drains. The first employs a monomial formula and is based on dimensional analysis while the second uses a physically based formula that incorporates the relationships among hydraulic parameters measured for a set of corrugated pipes. The latter depends on an evolutionary modelling technique that renders a superior description of the hydraulics of the tested corrugated pipes, outperforming the classical monomial formula for rough pipes. The formulae derived herein accurately reproduce experimental data and highlight the influence of dimensionless factors on roughness values. Three differently sized corrugated plastic pipes with slopes ranging between 3.5percent and 17.5percent were considered. The tests were directed at measuring open channel flow hydraulic parameters in order to ascertain the role of friction factors when wake interference occurs. This hydro dynamic phenomenon, observed in the tested pipes, is situated in the rough fully turbulent flow region of the Moody diagram. From a technical standpoint, wake interference is interesting because the abnormal turbulence experienced along the channel's wall-roughness elements generates additional energy dissipation, entailing potentially significant implications for sewer networks installed on steep slopes.", notes = "Orazio Giustolisi Professor, Engineering Faculty of Taranto, Technical University of Bari, Italy Angelo Doglioni Post-doctoral Research Fellow, Engineering Faculty of Taranto, Technical University of Bari, Italy Daniele Laucelli Research Fellow, Engineering Faculty of Taranto, Technical University of Bari, Italy wama161-031.pdf", } @Article{Giustolisi:2009:JH, author = "O. Giustolisi and D. A. Savic", title = "Advances in data-driven analyses and modelling using {EPR-MOGA}", journal = "Journal of Hydroinformatics", year = "2009", volume = "11", number = "3", pages = "225--236", month = jul, keywords = "genetic algorithms, genetic programming, data-driven modelling, evolutionary computing, groundwater resources, multiobjective optimization, symbolic regression, Brindisi, multi objective, ANN", ISSN = "1464-7141", URL = "http://www.iwaponline.com/jh/011/0225/0110225.pdf", DOI = "doi:10.2166/hydro.2009.017", size = "12 pages", abstract = "Evolutionary Polynomial Regression (EPR) is a recently developed hybrid regression method that combines the best features of conventional numerical regression techniques with the genetic programming/symbolic regression technique. The original version of EPR works with formulae based on true or pseudo-polynomial expressions using a single-objective genetic algorithm. Therefore, to obtain a set of formulae with a variable number of pseudo-polynomial coefficients, the sequential search is performed in the formulae space. This article presents an improved EPR strategy that uses a multi-objective genetic algorithm instead. We demonstrate that multi-objective approach is a more feasible instrument for data analysis and model selection. Moreover, we show that EPR can also allow for simple uncertainty analysis (since it returns polynomial structures that are linear with respect to the estimated coefficients). The methodology is tested and the results are reported in a case study relating groundwater level predictions to total monthly rainfall.", notes = "Department of Civil and EnvironmentalEngineering,Technical University of Bari, Italy", } @Article{Giustolisi:2009:JWRPM, author = "Orazio Giustolisi and Luigi Berardi", title = "Prioritizing Pipe Replacement: From Multiobjective Genetic Algorithms to Operational Decision Support", journal = "Journal of Water Resources Planning and Management", year = "2009", volume = "135", number = "6", pages = "484--492", month = nov, keywords = "genetic algorithms, genetic programming, Decision support systems, Water distribution systems, Water pipelines, Multiple objective analysis, Replacement, Rehabilitation", ISSN = "0733-9496", DOI = "doi:10.1061/(ASCE)0733-9496(2009)135:6(484)", size = "9 pages", abstract = "Deterioration of water distribution systems and the optimal allocation of limited funds for their rehabilitation represent crucial challenges for water utility managers. Decision makers should be provided with a set of informed solutions to select the best rehabilitation plan with regard to available resources and management strategies. In a risk-based scenario, such an approach should result in an element-wise prioritisation scheme based on individual pipe rehabilitation/replacement effectiveness. This manuscript describes a framework for devising a short-term decision support tool for pipe replacement. The approach allows for the introduction of economic, technical, and management rationales as separate objectives to produce a pipe-wise prioritisation scheme which is achieved by ranking pipes selected during a multiobjective (MO) evolutionary optimisation of replacement scenarios. Such a procedure helps overcome the doubts in choosing among the solutions obtained by MO evolutionary optimization due to the diverse sets of pipes selected for replacement even when they are economically comparable. The effectiveness of the entire framework is demonstrated on a real U.K. water distribution system.", notes = "Publisher: ASCE, American Society of Civil Engineers. Papers using GP related results", } @Article{Giustolisi:2011:JH, author = "O. Giustolisi and L. Berardi and T. M. Walski", title = "Some explicit formulations of {Colebrook-White} friction factor considering accuracy vs. computational speed", journal = "Journal of Hydroinformatics", year = "2011", volume = "13", number = "3", pages = "401--418", month = jul, keywords = "genetic algorithms, genetic programming, Colebrook White formula, computational speed, evolutionary polynomial regression, friction factor, pipe flow", ISSN = "1464-7141", URL = "https://iwaponline.com/jh/article-pdf/13/3/401/386543/401.pdf", DOI = "doi:10.2166/hydro.2010.098", size = "18 pages", abstract = "The Colebrook-White formulation of the friction factor is implicit and requires some iterations to be solved given a correct initial search value and a target accuracy. Some new explicit formulations to efficiently calculate the Colebrook White friction factor are presented herein. The aim of this investigation is twofold: (i) to preserve the accuracy of estimates while (ii) reducing the computational burden (i.e. speed). On the one hand, the computational effectiveness is important when the intensive calculation of the friction factor (e.g. large-size water distribution networks (WDN) in optimisation problems, flooding software, etc.) is required together with its derivative. On the other hand, the accuracy of the developing formula should be realistically chosen considering the remaining uncertainties surrounding the model where the friction factor is used. In the following, three strategies for friction factor mapping are proposed which were achieved by using the Evolutionary Polynomial Regression (EPR). The result is the encapsulation of some pieces of the friction factor implicit formulae within pseudo-polynomial structures.", notes = "IWA Publishing", } @Article{Gladwin:2011:pimed, author = "D. Gladwin and Paul Stewart and Jill Stewart", title = "A novel genetic programming approach to the design of engine control systems for the voltage stabilisation of hybrid electric vehicle generator outputs", journal = "Proceedings of the Institute of Mechanical Engineers Part D - Automobile Engineering", year = "2011", volume = "225", number = "10", pages = "1334--1346", month = oct, keywords = "genetic algorithms, genetic programming, electronic and electrical engineering", ISSN = "0954-4070", DOI = "doi:10.1177/0954407011407414", size = "13 pages", publisher = "Institute of Mechanical Engineers", abstract = "This paper describes a Genetic Programming based automatic design methodology applied to the maintenance of a stable generated electrical output from a series-hybrid vehicle generator set. The generator set comprises a 3-phase AC generator whose output is subsequently rectified to DC.The engine/generator combination receives its control input via an electronically actuated throttle, whose control integration is made more complex due to the significant system time delay. This time delay problem is usually addressed by model predictive design methods, which add computational complexity and rely as a necessity on accurate system and delay models. In order to eliminate this reliance, and achieve stable operation with disturbance rejection, a controller is designed via a Genetic Programming framework implemented directly in Matlab, and particularly, Simulink. the principal objective is to obtain a relatively simple controller for the time-delay system which doesn{'}t rely on computationally expensive structures, yet retains inherent disturbance rejection properties. A methodology is presented to automatically design control systems directly upon the block libraries available in Simulink to automatically evolve robust control structures.", bibsource = "OAI-PMH server at eprints.lincoln.ac.uk", oai = "oai:eprints.lincoln.ac.uk:4352", type = "PeerReviewed", URL = "http://eprints.lincoln.ac.uk/4352/", notes = "http://www.uk.sagepub.com/journals/Journal202018", } @Article{Gladwin:2011:ijsysc, author = "Dan Gladwin and Paul Stewart and Jill Stewart", title = "Internal combustion engine control for series hybrid electric vehicles by parallel and distributed genetic programming/multiobjective genetic algorithms", journal = "International Journal of Systems Science", volume = "42", number = "2", year = "2011", pages = "249--261", note = "Computational Intelligence for Modelling and Control of Advanced Automotive Drivetrains", keywords = "genetic algorithms, genetic programming, automotive, model-reference control, time-delay, hybrid vehicles, parallel and distributed evolutionary computation, mechanical systems, PID control, distrubed evolutionary", ISSN = "0020-7721", DOI = "doi:10.1080/00207720903144479", bibsource = "DBLP, http://dblp.uni-trier.de", URL = "http://eprints.lincoln.ac.uk/3986/", URL = "http://results.ref.ac.uk/Submissions/Output/1636812", size = "13 pages", abstract = "This article addresses the problem of maintaining a stable rectified DC output from the three-phase AC generator in a series-hybrid vehicle powertrain. The series-hybrid prime power source generally comprises an internal combustion (IC) engine driving a three-phase permanent magnet generator whose output is rectified to DC. A recent development has been to control the engine/generator combination by an electronically actuated throttle. This system can be represented as a nonlinear system with significant time delay. Previously, voltage control of the generator output has been achieved by model predictive methods such as the Smith Predictor. These methods rely on the incorporation of an accurate system model and time delay into the control algorithm, with a consequent increase in computational complexity in the real-time controller, and as a necessity relies to some extent on the accuracy of the models. Two complementary performance objectives exist for the control system. Firstly, to maintain the IC engine at its optimal operating point, and secondly, to supply a stable DC supply to the traction drive inverters. Achievement of these goals minimises the transient energy storage requirements at the DC link, with a consequent reduction in both weight and cost. These objectives imply constant velocity operation of the IC engine under external load disturbances and changes in both operating conditions and vehicle speed set-points. In order to achieve these objectives, and reduce the complexity of implementation, in this article a controller is designed by the use of Genetic Programming methods in the Simulink modelling environment, with the aim of obtaining a relatively simple controller for the time-delay system which does not rely on the implementation of real time system models or time delay approximations in the controller. A methodology is presented to use the myriad of existing control blocks in the Simulink libraries to automatically evolve optimal control structures.", oai = "oai:eprints.lincoln.ac.uk:3986", uk_research_excellence_2014 = "D - Journal article", } @MastersThesis{Glaholt:mastersthesis, author = "William Edward Glaholt", title = "GP-Lab: The Genetic Programming Laboratory", school = "Computer Science, California State University, Sacramento", year = "2004", type = "Masters of Science", keywords = "genetic algorithms, genetic programming", URL = "http://www.theglaholts.net/gplab/GPLab-ThesisDoc%20Final.pdf", size = "136 pages", abstract = "Evolutionary Programming, also known as Genetic Programming ({"}GP{"}), is an Artificial Intelligence paradigm in which an algorithm is synthesised in the style of Charles Darwin's theory of Evolution. Algorithms are generated through 'reverse-engineering,' the concept in which a desired solution is known, as are the tools, functions, and objects used to generate the solution, but the algorithm that solves the solution is unknown. GP creates a random population of 'individuals', evaluates those individuals for fitness (a term used to judge how 'close' the solution is to a targeted solution), then iteratively creates new generations by 'cross-breeding' genes of the more fit individuals, evaluating, crossbreeding, and so on until the 'best' solution is found. Current tools in the discipline are generally targeted towards solving one explicit problem, or require actual source code modification of the software packages1 in order to effect such a generation. In addition, the solutions generated by existing software tools are not normally immediately usable, are obscure, or are in 'LISP-style' function format, which may be difficult to translate to the average programmer. GP-Lab is based upon, and is an extension of the tool created in a previous Master's thesis by Michael Kramer ({"}GAPS - Genetic Algorithm Programming System{"}, 1996) [1], as well as several other current tools, e.g. 'lil-gp' and 'GARAGE'. GP-Lab adds many user-flexible features, including graphic outputs, direct-to-C compile-ready code solution translation, and a full, extensible procedural programming language with user-created functions. As such, GP-Lab is a tool targeted toward the average programmer who has a known desired solution, a set of tools upon which the solution may be based, and wishes to know the algorithm used to solve that solution.", notes = "Approved by: Dr. Du Zhang, Advisor and Committee Chair W. Scott Gordon, Associate Professor", } @InProceedings{Glaholt:2004:ICTAI, author = "William E. Glaholt and Du Zhang", title = "GP-Lab: the Genetic Programming Laboratory", booktitle = "16th IEEE International Conference on Tools with Artificial Intelligence, 2004. ICTAI 2004", year = "2004", pages = "388--395", address = "Boca Raton, FL, USA", month = "15-17 " # nov, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", ISSN = "1082-3409", ISBN = "0-7695-2236-X", DOI = "doi:10.1109/ICTAI.2004.66", abstract = "Currently, tools in the field of genetic programming are either geared towards solving certain type of problems, or are not easy to use (e.g., requiring actual source code modification of the software packages in order to generate a genetic programming environment). In addition, the solutions generated by existing tools are usually not ready for deployment in applications. We describe a genetic programming tool called GP-Lab. GP-Lab is based upon, and an extension to an earlier tool reported in [Kramer, MD et al. (1996) \cite{Kramer:mastersthesis}, (2000); Zhang, D et al. (2003)] GP-Lab supports a full and extensible programming language, and allows solutions to be automatically generated in C+ + source code format ready to be compiled for deployment. It is a general tool and has many user-flexible features, including contextually aware genetic operations and graphic outputs.", } @InCollection{gleason:2000:TCDDGAGP, author = "Sean Gleason", title = "Tuning and Creation of Discrete Differentiators using Genetic Algorithms and Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "160--169", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:2000:gagp}", } @InProceedings{gl-gr-08, author = "Kyrre Glette and Thiemo Gruber and Paul Kaufmann and Jim Torresen and Bernhard Sick and Marco Platzner", title = "Comparing Evolvable Hardware to Conventional Classifiers for Electromyographic Prosthetic Hand Control", booktitle = "2008 NASA/ESA Conference on Adaptive Hardware and Systems", year = "2008", pages = "32--39", month = "22-25 " # jun, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, ECGP, prosthetic hand control, evolvable hardware, EHW, kNN, decision trees, DT, support vector machines, SVM", isbn13 = "978-0-7695-3166-3", DOI = "doi:10.1109/AHS.2008.12", size = "8 pages", abstract = "Evolvable hardware has shown to be a promising approach for prosthetic hand controllers as it features self-adaptation, fast training, and a compact system-on-chip implementation. Besides these intriguing features, the classification performance is paramount to success for any classifier. However, evolvable hardware classifiers have not yet been sufficiently compared to state-of-the-art conventional classifiers. In this paper, we compare two evolvable hardware approaches for signal classification to three conventional classification techniques: k-nearest-neighbour, decision trees, and support vector machines. We provide all classifiers with features extracted from electromyographic signals taken from forearm muscle contractions, and try to recognise eight different hand movements. Experimental results demonstrate that evolvable hardware approaches are indeed able to compete with state-of-the-art classifiers. Specifically, one of our evolvable hardware approaches delivers a generalisation performance similar to that of support vector machines.", notes = "also known as \cite{4584252}", } @InProceedings{gl-to-08, author = "Kyrre Glette and Jim Torresen and Paul Kaufmann and Marco Platzner", title = "A Comparison of Evolvable Hardware Architectures for Classification Tasks", booktitle = "8th International Conference on Evolvable Systems: From Biology to Hardware: ICES 2008", year = "2008", editor = "Gregory S. Hornby and Lukas Sekanina and Pauline C. Haddow", volume = "5216", series = "LNCS", pages = "22--33", address = "Prague, Czech Republic", month = sep # " 21-24", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-85857-7", DOI = "doi:10.1007/978-3-540-85857-7_3", size = "12 pages", abstract = "We analyse and compare four different evolvable hardware approaches for classification tasks: An approach based on a programmable logic array architecture, an approach based on two-phase incremental evolution, a generic logic architecture with automatic definition of building blocks, and a specialized coarse-grained architecture with pre-defined building blocks. We base the comparison on a common data set and report on classification accuracy and training effort. The results show that classification accuracy can be increased by using modular, specialized classifier architectures. Furthermore, function level evolution, either with predefined functions derived from domain-specific knowledge or with functions that are automatically defined during evolution, also gives higher accuracy. Incremental and function level evolution reduce the search space and thus shortens the training effort.", } @InProceedings{gl-ka-13a, author = "Kyrre Glette and Paul Kaufmann and Christopher Assad and Michael T. Wolf", title = "Investigating Evolvable Hardware Classification for the {BioSleeve} Electromyographic Interface", booktitle = "International Conference on Evolvable Systems (ICES 2013)", year = "2013", pages = "73--80", month = "27-31 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, EHW", DOI = "doi:10.1109/ICES.2013.6613285", size = "8 pages", abstract = "We investigate the applicability of an evolvable hardware classifier architecture for electromyography (EMG) data from the BioSleeve wearable human-machine interface, with the goal of having embedded training and classification. We investigate classification accuracy for datasets with 17 and 11 gestures and compare to results of Support Vector Machines (SVM) and Random Forest classifiers. Classification accuracies are 91.5percent for 17 gestures and 94.4percent for 11 gestures. Initial results for a field programmable array (FPGA) implementation of the classifier architecture are reported, showing that the classifier architecture fits in a Xilinx XC6SLX45 FPGA. We also investigate a bagging-inspired approach for training the individual components of the classifier with a subset of the full training data. While showing some improvement in classification accuracy, it also proves useful for reducing the number of training instances and thus reducing the training time for the classifier.", notes = "JPL BioSleeve prototype, FUR, 1 + 4 evolution strategy, NSGA-II, SVM, Weka Random Forests, RapidMiner, VHDL FPGA Xilinx Spartan-6 XC6SLX45 Also known as \cite{6613285}", } @InProceedings{gl-ka-14a, title = "Lookup Table Partial Reconfiguration for an Evolvable Hardware Classifier System", author = "Kyrre Glette and Paul Kaufmann", pages = "1706--1713", booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary Computation", year = "2014", month = "6-11 " # jul, editor = "Carlos A. {Coello Coello}", address = "Beijing, China", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, EHW, Hardware Aspects of Bio-Inspired Architectures and Systems (HABIAS)", DOI = "doi:10.1109/CEC.2014.6900503", size = "8 pages", abstract = "The evolvable hardware (EHW) paradigm relies on continuous run-time reconfiguration of hardware. When applied on modern FPGAs, the technically challenging reconfiguration process becomes an issue and can be approached at multiple levels. In related work, virtual reconfigurable circuits (VRC), partial reconfiguration, and lookup table (LUT) reconfiguration approaches have been investigated. In this paper, we show how fine-grained partial reconfiguration of 6-input LUTs of modern Xilinx FPGAs can lead to significantly more efficient resource use in an EHW application. Neither manual placement nor any proprietary bitstream manipulation is required in the simplest form of the employed method. We specify the goal architecture in VHDL and read out the locations of the automatically placed LUTs for use in an on line reconfiguration setting. This allows for an easy and flexible architecture specification, as well as possible implementation improvements over a hand-placed design. For demonstration, we rely on a hardware signal classifier application. Our results show that the proposed approach can fit a classification circuit 4 times larger than an equivalent VRC-based approach, and 6 times larger than a shift register-based approach, in a Xilinx Virtex-5 device. To verify the reconfiguration process, a MicroBlaze-based embedded system is implemented, and reconfiguration is carried out via the Xilinx Internal Configuration Access Port (ICAP) and driver software.", notes = "Also known as \cite{Glette:2014:CEC}", } @InProceedings{glickman:1998:ea:edsa, author = "Matthew Glickman and Katia Sycara", title = "Evolutionary Algorithms: Exploring the Dynamics of Self-Adaptation", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "762--769", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "evolutionary programming", ISBN = "1-55860-548-7", notes = "GP-98", } @InProceedings{glickman:1999:EGBLICE, author = "Matthew R. Glickman and Katia Sycara", title = "Evolution of Goal-Directed Behavior from Limited Information in a Complex Environment", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1281--1288", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "artificial life, adaptive behavior and agents", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-015.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-015.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{gligorovski:2023:GECCOcomp, author = "Nikola Gligorovski and Jinghui Zhong", title = "{LGP-VEC:} A Vectorial Linear Genetic Programming for Symbolic Regression", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "579--582", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, benchmark suite, vectorial linear genetic programming, symbolic regression: Poster", isbn13 = "9798400701191", DOI = "doi:10.1145/3583133.3590695", size = "4 pages", abstract = "Symbolic regression (SR) is a well-known regression problem, that aims to find a symbolic expression that best fits a given dataset. Linear Genetic Programming (LGP) is a good and powerful candidate for solving symbolic regression problems. However, current LGPs for SR only focus on finding scalar-valued functions, and limited work has been done on finding vector-valued functions with vectorial-based LGP. In addition, a comprehensive dataset for testing vectorial-based GP is still lacking in the literature. To this end, we propose a new extensive benchmark suite for vectorial symbolic regression. Furthermore, we propose a new vectorial LGP algorithm for symbolic regression, which directly deals with high dimensional data using vectorial representation and operations. Experimental results show that the proposed algorithm outperforms another recently published vectorial GP method on the benchmark suite for vector-valued functions and that it also generalizes better on unseen data.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @InProceedings{globus:1998:amduet, author = "Al Globus and John Lawton and Todd Wipke", title = "Automatic molecular design using evolutionary techniques", booktitle = "The Sixth Foresight Conference on Molecular Nanotechnology", year = "1998", editor = "Al Globus and Deepak Srivastava", address = "Westin Hotel in Santa Clara, CA, USA", month = nov # " 12-15, 1998", organisation = "Foresight Institute", keywords = "genetic algorithms, genetic programming, ring crossover, graphs, drugs", URL = "http://www.foresight.org/Conferences/MNT6/Papers/Globus/index.html", URL = "http://www.nas.nasa.gov/News/Techreports/1999/PDF/nas-99-005.pdf", URL = "http://www.nas.nasa.gov/Research/Reports/Techreports/1999/nas-99-005.html", abstract = "Molecular nanotechnology is the precise, three-dimensional control of materials and devices at the atomic scale. An important part of nanotechnology is the design of molecules for specific purposes. This paper describes early results using genetic software techniques to automatically design molecules under the control of a fitness function. The fitness function must be capable of determining which of two arbitrary molecules is better for a specific task. The software begins by generating a population of random molecules. The population is then evolved towards greater fitness by randomly combining parts of the better individuals to create new molecules. These new molecules then replace some of the worst molecules in the population. The unique aspect of our approach is that we apply genetic crossover to molecules represented by graphs, i.e., sets of atoms and the bonds that connect them. We present evidence suggesting that crossover alone, operating on graphs, can evolve any possible molecule given an appropriate fitness function and a population containing both rings and chains. Prior work evolved strings or trees that were subsequently processed to generate molecular graphs. In principle, genetic graph software should be able to evolve other graph representable systems such as circuits, transportation networks, metabolic pathways, computer networks, etc.", notes = "http://www.foresight.org/Conferences/MNT6/index.html", } @Article{globus:1999:Nano, title = "Automatic molecular design using evolutionary techniques", author = "Al Globus and John Lawton and Todd Wipke", journal = "Nanotechnology", volume = "10", number = "3", month = sep, year = "1999", pages = "290--299", URL = "http://ej.iop.org/links/20/wT4K9Gv4ZjM1zl3weq3M6Q/na9312.pdf", URL = "http://www.foresight.org/conference/MNT6/Papers/Globus/index.html", URL = "http://alglobus.net/NASAwork/papers/Nanotechnology98/paper.html", URL = "http://people.nas.nasa.gov/~globus/home.html", DOI = "doi:10.1088/0957-4484/10/3/312", keywords = "genetic algorithms, genetic programming", abstract = "Molecular nanotechnology is the precise, three-dimensional control of materials and devices at the atomic scale. An important part of nanotechnology is the design of molecules for specific purposes. This paper describes early results using genetic software techniques to automatically design molecules under the control of a fitness function. The fitness function must be capable of determining which of two arbitrary molecules is better for a specific task. The software begins by generating a population of random molecules. The individual molecules in a population are then evolved towards greater fitness by randomly combining parts of the better existing molecules to create new molecules. These new molecules then replace some of the less fit molecules in the population. We apply a unique genetic crossover operator to molecules represented by graphs, i.e., sets of atoms and the bonds that connect them. We present evidence suggesting that crossover alone, operating on graphs, can evolve any possible molecule given an appropriate fitness function and a population containing both rings and chains. Most prior work evolved strings or trees that were subsequently processed to generate molecular graphs. In principle, genetic graph software should be able to evolve other graph-representable systems such as circuits, transportation networks, metabolic pathways, and computer networks.", } @InProceedings{globus:2000:jgac, title = "{JavaGenes} and {Condor:} Cycle-Scavenging Genetic Algorithms", author = "Al Globus and Eric Langhirt and Miron Livny and Ravishankar Ramamurthy and Marvin Solomon and Steve Traugott", booktitle = "Java Grande 2000, sponsored by ACM SIGPLAN", address = "San Francisco, California", month = "3-4 " # jun, year = "2000", URL = "http://www.cs.wisc.edu/condor/doc/javagenes.pdf", URL = "http://people.nas.nasa.gov/~globus/papers/JavaGrande2000/JavaGrandePaper.html", keywords = "genetic algorithms, genetic programming", abstract = "A genetic algorithm code, JavaGenes, was written in Java and used to evolve pharmaceutical drug molecules and digital circuits. JavaGenes was run under the Condor cycle-scavenging batch system managing 100-170 desktop, desk-side, and rack-mounted SGI workstations. Genetic algorithms mimic biological evolution by evolving solutions to problems using crossover and mutation. While most genetic algorithms evolve strings or trees, JavaGenes evolves graphs representing (currently) molecules and circuits. Java was chosen as the implementation language because the genetic algorithm requires random splitting and recombining of graphs, a complex data structure manipulation with ample opportunities for memory leaks, loose pointers, out-of-bound indices, and other hard to find bugs. Java garbage-collection memory management, lack of pointer arithmetic, and array-bounds index checking reduces the frequency of these bugs, substantially reducing development time. While a run-time performance penalty must be paid, the only unacceptable performance we encountered was using standard Java serialization to checkpoint and restart the code. This was fixed by a two-day implementation of custom checkpointing. JavaGenes is minimally integrated with Condor; in other words, JavaGenes must do its own checkpointing and I/O redirection. A prototype Java-aware version of Condor was developed using standard Java serialization for checkpointing. For the prototype to be useful, standard Java serialization must be significantly optimized. JavaGenes is approximately 8700 lines of code and a few thousand JavaGenes jobs have been run. Most jobs ran for a few days. Results include proof that genetic algorithms can evolve directed and undirected graphs, development of a novel crossover operator for graphs, a paper in the journal Nanotechnology [Globus, et al. 1999], and another paper in preparation.", } @Misc{globus:2001:GECCOtr, author = "Al Globus and Sean Atsatt and John Lawton and Todd Wipke", title = "Graph Crossover", howpublished = "www", year = "2000", month = "5 " # may, keywords = "genetic algorithms, genetic programming", URL = "http://alglobus.net/NASAwork/papers/JavaGenes2/JavaGenesPaper.html", broken = "http://people.nas.nasa.gov/~globus/papers/JavaGenes2/JavaGenesPaper.html", size = "15 pages", abstract = "Most genetic algorithms use string or tree representations. To apply genetic algorithms to graphs, a good crossover operator is necessary. We have developed a general-purpose, novel crossover operator for directed and undirected graphs, and used this operator to evolve molecules and circuits. Unlike strings or trees, a single point in the representation cannot divide every possible graph into two parts, because graphs may contain cycles. Thus, the crossover operator is non-trivial. A steady-state, tournament selection genetic algorithm code (JavaGenes) was used test the graph crossover operator. JavaGenes has successfully evolved pharmaceutical drug molecules and simple digital circuits. For example, morphine, cholesterol, and diazepam were successfully evolved by 30-60percent of runs within 10,000 generations using a population of 1000 molecules. Since representation strongly affects genetic algorithm performance, adding graphs to the evolutionary programmer's bag-of-tricks should be beneficial. Also, since graph evolution operates directly on the phenotype, genotype to phenotype decoding is eliminated.", notes = "see \cite{globus:2001:GECCO}", } @InProceedings{globus:2001:GECCO, title = "Graph Crossover", author = "Al Globus and John Lawton and Todd Wipke", pages = "761", year = "2001", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)", editor = "Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon and Edmund Burke", address = "San Francisco, California, USA", publisher_address = "San Francisco, CA 94104, USA", month = "7-11 " # jul, keywords = "genetic algorithms, genetic programming, Poster, graphs, crossover, molecules, drug, design", ISBN = "1-55860-774-9", URL = "http://people.nas.nasa.gov/~globus/home.html", URL = "http://gpbib.cs.ucl.ac.uk/gecco2001/d04.pdf", notes = "A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of \cite{spector:2001:GECCO} see \cite{globus:2001:GECCOtr}", } @TechReport{globus:t1cpu, title = "Towards 100,000 CPU Cycle-Scavenging by Genetic Algorithms", author = "Al Globus", institution = "CSC at NASA Ames Research Center", number = "NAS-0-011", month = oct, year = "2001", URL = "http://people.nas.nasa.gov/~globus/papers/Cycle-ScavengingGA/paper.html", keywords = "genetic algorithms", abstract = "Cycle scavenging systems offer 100s to 100,000s of otherwise-idle CPUs for embarrassingly parallel computations such as genetic algorithms. While genetic algorithms are generally easy to parallelize, cycle scavenged resources come and go at random so some sophistication in necessary, particularly when hundreds of thousands of CPUs are available. In this paper we propose a master-slave architecture for multi-objective genetic algorithms on cycle scavengers. The architecture consists of slave computations running on computational nodes with relatively small populations, and a central master managing a large pareto front in a disc-based relational data base . Each slave runs an individual genetic algorithm and different slaves use different techniques, evolution-parameters and/or a subset of the objective functions as determined by the master. The slaves accept immigrants from the master and, after evolution, the best individuals emigrate back to the master. Slaves may then get new immigrants and/or evolution policy to be applied to the existing population. Allowing slaves to use different evolution techniques and parameters can, when many CPUs are available, avoid committing to a single evolution concept for a given problem. A sophisticated master can treat the running slaves as a population of evolutionary techniques and parameters that can be evolved. We examine a web-centric design using standard tools such as web servers, web browsers, PHP, and mySQL. We also consider the applicability of Information Power Grid tools such as the Globus (no relation to the author) Toolkit. We intend to implement this architecture with JavaGenes running on at least two cycle-scavengers: Condor and United Devices. JavaGenes, a genetic algorithm code written in Java, will be used to evolve multi-species reactive molecular force field parameters.", } @InProceedings{globus:jem2001, title = "{JavaGenes:} Evolving Molecular Force Field Parameters", author = "Al Globus and Charles Bauschlicher and Sandra Johan and Deepak Srivastava", booktitle = "Ninth Foresight Conference on Molecular Nanotechnology", month = "9-11 " # nov, year = "2001", address = "Santa Clara, California", URL = "http://www.foresight.org/Conferences/MNT9/Abstracts/Globus/index.html", URL = "http://people.nas.nasa.gov/~globus/home.html", keywords = "genetic algorithms", } @Misc{globus:2002:suppercomputer, author = "Al Globus and Madhu Menon and Deepak Srivastava", title = "Enabling Computational Nanotechnology through {JavaGenes} in a Cycle Scavenging Environment", howpublished = "www", year = "2002", month = jul, keywords = "genetic algorithms, Condor, Java, distributed", URL = "http://people.nas.nasa.gov/~globus/papers/JavaGenesSupercomputing2002/finalVersion.pdf", notes = "Available in MS Word, pdf and html; the pdf and html versions have problems caused by bugs in the MS conversion software..", } @Article{globus:jem, title = "{JavaGenes:} Evolving Molecular Force Field Parameters with Genetic Algorithm", author = "Al Globus and Madhu Menon and Deepak Srivastava", journal = "Computer Modeling in Engineering and Science", volume = "3", number = "5", pages = "557--574", year = "2002", URL = "http://people.nas.nasa.gov/~globus/home.html", keywords = "genetic algorithms", } @InProceedings{globus:seof, title = "Scheduling Earth Observing Fleets Using Evolutionary Algorithms: Problem Description and Approach", author = "Al Globus and James Crawford and Jason Lohn and Robert Morris", booktitle = "Proceedings of the 3rd International NASA Workshop on Planning and Scheduling for Space", address = "Houston, Texas", month = oct # " 27-29", year = "2002", URL = "http://people.nas.nasa.gov/~globus/home.html", keywords = "genetic algorithms", } @InProceedings{globus:emf, title = "Evolving Molecular Force Field Parameters for Si and Ge", author = "Al Globus and Ecleamus Ricks and Madhu Menon and Deepak Srivastava", booktitle = "Proceedings of the 2003 Nanotechnology Conference and Trade Show", month = feb # " 23-27", year = "2003", address = "San Francisco, California, U.S.A.", URL = "http://people.nas.nasa.gov/~globus/home.html", keywords = "genetic algorithms", } @InProceedings{globus:seo, title = "Scheduling Earth Observing Satellites with Evolutionary Algorithms", author = "Al Globus and James Crawford and Jason Lohn and Anna Pryor", booktitle = "International Conference on Space Mission Challenges for Information Technology (SMC-IT)", address = "Pasadena, CA, USA", month = jul, year = "2003", URL = "http://people.nas.nasa.gov/~globus/home.html", keywords = "genetic algorithms", } @InProceedings{Glover:2017:WSC, author = "Paul Glover and Simon Collander-Brown and Simon J. E. Taylor", booktitle = "2017 Winter Simulation Conference (WSC)", title = "Using a genetic programming approach to mission planning to deliver more agile campaign level modelling for military operational research", year = "2017", pages = "4465--4468", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/WSC.2017.8248165", month = dec, size = "2.1 pages", abstract = "Defence in both the UK and the US is committed to innovate in order to stay ahead. This implies the need for supporting analytical tools at least as adaptive in their focus as the potential change to the military system of systems that such innovation may suggest. Current approaches to modelling and simulation (M&S) produce monolithic, user scripted, models that are not well suited to rapidly assessing innovative ways of operating. In the UK a simulation tool set has been developed to provide the necessary adaptability, enabling new simulations to be rapidly produced. This toolset contains a modular mission planner to automate generation of courses of action in what are potentially very different ways of doing business.", notes = "odd PDF Also known as \cite{8248165}", } @Article{glucina:2023:Electronics, author = "Matko Glucina and Nikola Andelic and Ivan Lorencin and Sandi {Baressi Segota}", title = "Drive System Inverter Modeling Using Symbolic Regression", journal = "Electronics", year = "2023", volume = "12", number = "3", pages = "Article No. 638", keywords = "genetic algorithms, genetic programming", ISSN = "2079-9292", URL = "https://www.mdpi.com/2079-9292/12/3/638", DOI = "doi:10.3390/electronics12030638", abstract = "For accurate and efficient control performance of electrical drives, precise values of phase voltages are required. In order to achieve control of the electric drive, the development of mathematical models of the system and its parts is often approached. Data-driven modelling using artificial intelligence can often be unprofitable due to the large amount of computing resources required. To overcome this problem, the idea is to investigate if a genetic programming–symbolic regressor (GPSR) algorithm could be used to obtain simple symbolic expressions which could estimate the mean phase voltages (black-box inverter model) and duty cycles (black-box compensation scheme) with high accuracy using a publicly available dataset. To obtain the best symbolic expressions using GPSR, a random hyperparameter search method and 5-fold cross-validation were developed. The best symbolic expressions were chosen based on their estimation performance, which was measured using the coefficient of determination (R2), mean absolute error (MAE), and root mean squared error (RMSE). The best symbolic expressions for the estimation of mean phase voltages achieved R2, MAE, and RMSE values of 0.999, 2.5, and 2.8, respectively. The best symbolic expressions for the estimation of duty cycles achieved R2, MAE, and RMSE values of 0.9999, 0.0027, and 0.003, respectively. The originality of this work lies in the application of the GPSR algorithm, which, based on a mathematical equation it generates, can estimate the value of mean phase voltages and duty cycles in a three-phase inverter. Using the obtained model, it is possible to estimate the given aforementioned values. Such high-performing estimation represents an opportunity to replace expensive online equipment with a cheaper, more precise, and faster approach, such as a GPSR-based model. The presented procedure shows that the symbolic expression for the accurate estimation of mean phase voltages and duty cycles can be obtained using the GPSR algorithm.", notes = "also known as \cite{electronics12030638}", } @InProceedings{Go:2022:HNICEM, author = "Tyrone Ashley Go and Jose Antonio Cadavillo and Joyce Yuenlam Cai and Dmitri Chuacuco and Jonah Jahara Baun and Argel Bandala and Ronnie Concepcion", booktitle = "2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)", title = "Ubiquitous Limited Sensor-based Weather Binary Prediction Network Using Linear and Nonlinear Fittings and 14-gene Genetic Expression", year = "2022", abstract = "Temperature and humidity are two of the many factors that play vital roles in weather, and these two factors are used in determining the present weather conditions. Not only does it concern meteorology, but especially the food science and agricultural fields. Weather monitoring pertains to an activity in which the state of the atmosphere is analysed, which usually includes the variables such as wind speed, temperature, humidity, air moisture, pressure, and rainfall. This study uses the Arduino Uno board as a microcontroller and the DHT11 temperature (T) and humidity (H) sensor to gather information about the environment and display it in the LCD module. Simple linear, Gauss-Newton and Nernst-based non-linear, and 14-gene genetic programming regression models were developed and embedded to motes in four selected rain test areas in Metro Manila and Rizal province in predicting two weather states (no rain and raining). The expected result in this system is an approximation as to whether or not it would rain based on the data gathered throughout the project development. Weather data were automatically uploaded and stored in a ThingSpeak server using ESP32, which is viewed in the form of a graph. Based on the results, the temperature changes slightly during rainfall while humidity; on the other hand, changes much more drastically during rainfall and is a key telltale sign of rainfall. Linear regression outperformed other models in binary rain prediction based on temperature and humidity parameters only.", keywords = "genetic algorithms, genetic programming, Temperature sensors, Temperature distribution, Rain, Microcontrollers, Humidity, Predictive models, Liquid crystal displays, embedded system, environment monitoring, rain prediction, ubiquitous system, weather detection system", DOI = "doi:10.1109/HNICEM57413.2022.10109402", ISSN = "2770-0682", month = dec, notes = "Also known as \cite{10109402}", } @Article{Gobet:2005:SJP, author = "Fernand Gobet and Amanda Parker", title = "Evolving structure-function mappings in cognitive neuroscience using genetic programming", journal = "Swiss Journal of Psychology", year = "2005", volume = "64", number = "4", pages = "231--239", month = dec, keywords = "genetic algorithms, genetic programming, Complex systems, evolutionary computation, prefrontal cortex, scientific discovery, structure-function mapping, theory formation", ISSN = "1421-0185", DOI = "doi:10.1024/1421-0185.64.4.231", abstract = "A challenging goal of psychology and neuroscience is to map cognitive functions onto neuroanatomical structures. This paper shows how computational methods based upon evolutionary algorithms can facilitate the search for satisfactory mappings by efficiently combining constraints from neuroanatomy and physiology (the structures) with constraints from behavioural experiments (the functions). This methodology involves creation of a database coding for known neuroanatomical and physiological constraints, for mental programs made of primitive cognitive functions, and for typical experiments with their behavioural results. The evolutionary algorithms evolve theories mapping structures to functions in order to optimize the fit with the actual data. These theories lead to new, empirically testable predictions. The role of the prefrontal cortex in humans is discussed as an example. This methodology can be applied to the study of structures or functions alone, and can also be used to study other complex systems. (PsycINFO Database Record (c) 2008 APA, all rights reserved)", } @Article{Gocic:2015:CEA, author = "Milan Gocic and Shervin Motamedi and Shahaboddin Shamshirband and Dalibor Petkovic and Sudheer Ch and Roslan Hashim and Muhammad Arif", title = "Soft computing approaches for forecasting reference evapotranspiration", journal = "Computers and Electronics in Agriculture", volume = "113", pages = "164--173", year = "2015", ISSN = "0168-1699", DOI = "doi:10.1016/j.compag.2015.02.010", URL = "http://www.sciencedirect.com/science/article/pii/S0168169915000526", abstract = "Accurate estimation of reference evapotranspiration (ET0) is needed for planning and managing water resources and agricultural production. The FAO-56 Penman-Monteith equation is used to determinate ET0 based on the data collected during the period 1980-2010 in Serbia. In order to forecast ET0, four soft computing methods were analysed: genetic programming (GP), support vector machine-firefly algorithm (SVM-FFA), artificial neural network (ANN), and support vector machine-wavelet (SVM-Wavelet). The reliability of these computational models was analysed based on simulation results and using five statistical tests including Pearson correlation coefficient, coefficient of determination, root-mean-square error, absolute percentage error, and mean absolute error. The end-point result indicates that SVM-Wavelet is the best methodology for ET0 prediction, whereas SVM-Wavelet and SVM-FFA models have higher correlation coefficient as compared to ANN and GP computational methods.", keywords = "genetic algorithms, genetic programming, Soft computing, Forecasting, Firefly algorithm, Support vector machine, Wavelet, Serbia", } @InProceedings{Gockel:1997:GAsctg, author = "Nicole Gockel and Martin Keim and Rolf Drechsler and Bernd Becker", title = "A Genetic Algorithm for Sequential Circuit Test Generation based on Symbolic Fault Simulation", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "Genetic Algorithms", pages = "363--369", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", notes = "GP-97", } @Unpublished{Gockel:1997:lheavsr, author = "Nicole Gockel and Rolf Drechsler and Bernd Becker", title = "Learning Heuristics by Evolutionary Algorithms with Variable Size Representation", editor = "Wolfgang Banzhaf and Inman Harvey and Hitoshi Iba and William Langdon and Una-May O'Reilly and Justinian Rosca and Byoung-Tak Zhang", note = "Position paper at the Workshop on Evolutionary Computation with Variable Size Representation at ICGA-97", month = "20 " # jul, year = "1997", address = "East Lansing, MI, USA", keywords = "genetic algorithms, Evolvable Hardware, variable size representation", notes = "http://web.archive.org/web/19971014081458/http://www.ai.mit.edu/people/unamay/icga-ws.html ", size = "3 pages", } @InProceedings{Godoi2013JCDL, author = "Thiago A. Godoi and Ricardo {da Silva Torres} and Ariadne M. B. R. Carvalho and Marcos A. Goncalves and Anderson A. Ferreira and Weiguo Fan and Edward A. Fox", title = "A Relevance Feedback Approach for the Author Name Disambiguation Problem", booktitle = "Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries", year = "2013", series = "JCDL '13", pages = "209--218", address = "Indianapolis, Indiana, USA", publisher = "ACM", keywords = "genetic algorithms, genetic programming, name disambiguation, optimum-path forest classifier, relevance feedback", isbn13 = "978-1-4503-2077-1", acmid = "2467709", URL = "http://doi.acm.org/10.1145/2467696.2467709", DOI = "doi:10.1145/2467696.2467709", abstract = "This paper presents a new name disambiguation method that exploits user feedback on ambiguous references across iterations. An unsupervised step is used to define pure training samples, and a hybrid supervised step is employed to learn a classification model for assigning references to authors. Our classification scheme combines the Optimum-Path Forest (OPF) classifier with complex reference similarity functions generated by a Genetic Programming framework. Experiments demonstrate that the proposed method yields better results than state-of-the-art disambiguation methods on two traditional datasets.", } @InCollection{GOEBEL:2018:ESCAPE, author = "Rebecca Goebel and Tobias Glaser and Ilka Niederkleine and Mirko Skiborowski", title = "Towards predictive models for organic solvent nanofiltration", booktitle = "28th European Symposium on Computer Aided Process Engineering", editor = "Anton Friedl and Jiri J. Klemes and Stefan Radl and Petar S. Varbanov and Thomas Wallek", series = "Computer Aided Chemical Engineering", publisher = "Elsevier", volume = "43", pages = "115--120", year = "2018", keywords = "genetic algorithms, genetic programming, organic solvent nanofiltration, model identification, data-driven approach, prediction", ISSN = "1570-7946", DOI = "doi:10.1016/B978-0-444-64235-6.50022-X", URL = "http://www.sciencedirect.com/science/article/pii/B978044464235650022X", abstract = "Organic solvent nanofiltration (OSN) is a promising technology for an energy-efficient separation of organic mixtures. However, due to the lack of suitable models that allow for a quantitative prediction of the separation performance in different chemical systems OSN is rarely considered during conceptual process design. The feasibility of OSN is usually determined by means of an experimental screening of different membranes. Further experiments are conducted for a selected membrane in order to determine membrane specific parameters for a model-based description of the separation performance for a specific mixture. Obviously, this classical approach is experimentally demanding. The effort in identifying a suitable membrane in the first step could be significantly reduced if a theoretical evaluation of the separation performance was possible. The current article proposes an automatic method for the determination of a suitable predictive model for a given membrane, taking into account a limited set of experimental data. Specially, the rejection of different solutes in a specific solvent is modeled based on a set of physical and chemical descriptors. The proposed approach is based on a combination of genetic programming and global deterministic optimization, allowing for the identification of innovative models, including nonlinear parameter regression. The predictive capability of the generated models is validated on a separate data set. The identified models were able to predict the rejection of different components in the considered case studies with a deviation from the experimental values below 5percent", } @Article{GOEBEL:2020:SPT, author = "Rebecca Goebel and Mirko Skiborowski", title = "Machine-based learning of predictive models in organic solvent nanofiltration: Pure and mixed solvent flux", journal = "Separation and Purification Technology", volume = "237", pages = "116363", year = "2020", ISSN = "1383-5866", DOI = "doi:10.1016/j.seppur.2019.116363", URL = "http://www.sciencedirect.com/science/article/pii/S1383586619336421", keywords = "genetic algorithms, genetic programming, Organic solvent nanofiltration, Machine learning, Prediction, Solvent flux, Solvent mixtures", abstract = "During the last decades, the interest in organic solvent nanofiltration (OSN), both in academia and industry, increased substantially. OSN provides great potential for an energy-efficient separation of complex chemical mixtures with dissolved solutes in the range of 200-1000 Dalton. In contrast to conventional thermal separation processes, the pressure-driven membrane separation operates at mild temperatures without energy intensive phase transition. However, the complex interaction of different phenomena in the mass transfer through the membrane complicate the prediction of membrane performance severely, such that OSN is virtually not considered as an option in conceptual process design. Several attempts have been made to determine predictive models, which allow the determination of at least pure solvent flux through a given membrane. While these models correlate different important physical properties of the solvents and are derived from physical understanding, they provide a limited accuracy and not all of their parameters are identifiable based on available data. In contrast to previous approaches, this work presents a machine learning based approach for the identification of membrane-specific models for the prediction of solvent permeance. The data-driven approach, which is based on genetic programming, generates predictive models that show superior results in terms of accuracy and parameter precision when compared to previously proposed models. Applied to two respective sets of permeation data, the developed models were able to describe the permeance of various solvents with a mean percentage error below 9percent and to predict different solvents with a mean percentage error of 15percent. Further, the method was applied to solvent mixtures successfully", } @PhdThesis{Goebel:thesis, author = "Rebecca Goebel", title = "Towards reliable characterization and model-based evaluation of organic solvent nanofiltration", school = "TU Dortmund University", year = "2021", address = "Dortmund, Germany", month = "14 " # jul, keywords = "genetic algorithms, genetic programming, Organophile Nanofiltration, Modellentwicklung, Fluss, Rueckhalt, Model development, flow, support, matlab, GAMS", URL = "https://fvt.bci.tu-dortmund.de/details/doctoral-examination-of-rebecca-goebel-9923/", URL = "https://eldorado.tu-dortmund.de/handle/2003/40733", URL = "http://hdl.handle.net/2003/40733", URL = "https://eldorado.tu-dortmund.de/bitstream/2003/40733/1/211122_Dissertation%20Goebel_Finale%20Version.pdf", DOI = "doi:10.17877/DE290R-22591", size = "278 pages", abstract = "The interest in organic solvent nanofiltration (OSN) increased substantially in both academia and industry during the last decades, since it provides a great potential for energy savings. However, despite the advantages, there are still limitations, that lead to the fact that OSN is rarely considered as a competitive separation operation in process design. For a reliable evaluation of process design, the uncertainties in labscale measurements and the quantification of model parameter precision are major factors and the prediction of flux and rejection is additionally essential in order to reduce experimental effort for feasibility studies during process development. These challenges are addressed in this thesis. The evaluation of fluxes through multiple laboratory-scale membrane samples provides an accurate approximation of flux through an industrial-scale module. The results prove to be transferable to different membrane types. Furthermore, a collaborative study at different facilities demonstrates the comparability of experimental results obtained with a standardized procedure. Moreover, the consideration of experimental uncertainties in process design and membrane selection is proven to be as relevant as for the selection of an appropriate mass transfer model. In the second part of this work, a newly developed method for automatic development of predictive models for OSN shows promising results for prediction of solvent flux and solute rejection in pure and mixed solvents. The method derives the membrane specific model structure and discriminates automatically between potential, easily retrievable descriptors based on available data. For the prediction of solvent flux, a comparison with existing phenomenological models from literature points out that the new models are superior and cover effects that are not included in the fixed model structure of phenomenological models. Models developed for the prediction of rejection are more complex compared to those for solvent flux but are comparable accurate.", notes = "p91 'The data-driven approach, which is based on genetic programming, generates predictive models that show superior results in terms of accuracy and parameter precision when compared to previously proposed models.'", } @Article{Goel:2015:JCA, author = "Purva Goel and Sanket Bapat and Renu Vyas and Amruta Tambe and Sanjeev S. Tambe", title = "Genetic programming based quantitative structure-retention relationships for the prediction of Kovats retention indices", journal = "Journal of Chromatography A", year = "2015", volume = "1420", pages = "98--109", month = "13 " # nov, keywords = "genetic algorithms, genetic programming, Gas chromatography, Kovats retention index, Quantitative structure-retention relationships, Artificial intelligence, Molecular descriptors", ISSN = "0021-9673", URL = "http://www.sciencedirect.com/science/article/pii/S0021967315014193", DOI = "doi:10.1016/j.chroma.2015.09.086", abstract = "The development of quantitative structure-retention relationships (QSRR) aims at constructing an appropriate linear/nonlinear model for the prediction of the retention behaviour (such as Kovats retention index) of a solute on a chromatographic column. Commonly, multi-linear regression and artificial neural networks are used in the QSRR development in the gas chromatography (GC). In this study, an artificial intelligence based data-driven modelling formalism, namely genetic programming (GP), has been introduced for the development of quantitative structure based models predicting Kovats retention indices (KRI). The novelty of the GP formalism is that given an example dataset, it searches and optimizes both the form (structure) and the parameters of an appropriate linear/nonlinear data-fitting model. Thus, it is not necessary to pre-specify the form of the data-fitting model in the GP-based modelling. These models are also less complex, simple to understand, and easy to deploy. The effectiveness of GP in constructing QSRRs has been demonstrated by developing models predicting KRIs of light hydrocarbons (case study-I) and adamantane derivatives (case study-II). In each case study, two-, three- and four-descriptor models have been developed using the KRI data available in the literature. The results of these studies clearly indicate that the GP-based models possess an excellent KRI prediction accuracy and generalization capability. Specifically, the best performing four-descriptor models in both the case studies have yielded high (>0.9) values of the coefficient of determination (R2) and low values of root mean squared error (RMSE) and mean absolute percent error (MAPE) for training, test and validation set data. The characteristic feature of this study is that it introduces a practical and an effective GP-based method for developing QSRRs in gas chromatography that can be gainfully used for developing other types of data-driven models in chromatography science.", } @InProceedings{Goertzel:2006:CEC, author = "Ben Goertzel and Cassio Pennachin and Lucio {de Souza Coelho} and Mauricio Mudado", title = "Identifying Complex Biological Interactions based on Categorical Gene Expression Data", booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary Computation", year = "2006", editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas", pages = "5583--5590", address = "Vancouver", month = "16-21 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, poster", ISBN = "0-7803-9487-9", URL = "http://www.biomind.com/docs/WCCI_EC_feb06_06_fixed_v2.pdf", DOI = "doi:10.1109/CEC.2006.1688477", size = "8 pages", abstract = "A novel method, MUTIC ( Clustering), is described for identifying complex interactions between genes or gene-categories based on gene expression data. The method deals with binary categorical data, which consists of a set of gene expression profiles divided into two biologically meaningful categories. It does not require data from multiple time points. Gene expression profiles are represented by feature vectors whose component features are either gene expression values, or averaged expression values corresponding to Gene Ontology or Protein Information Resource categories. A supervised learning algorithm (genetic programming) is used to learn an ensemble of classification models distinguishing the two categories based on the feature vectors corresponding to their members. Each feature is associated with a model usage vector, which has an entry for each high-quality classification model found, indicating whether or not the feature was used in that model. These usage vectors are then clustered using a variant of hierarchical clustering called Omniclust. The result is a set of model-usage-based clusters, in which features are gathered together if they are often considered together by classification models which may be because they are co-expressed, or may be for subtler reasons involving multi-gene interactions. The MUTIC method is illustrated via applying it to a dataset regarding gene expression in human brains of various ages. Compared to traditional expression-based clustering, MUTIC yields clusters that have higher mathematical quality (in the sense of homogeneity and separation) and also yield novel insights into the underlying biological processes.", notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D", } @Article{Goertzel:2006:P, author = "Benjamin N Goertzel and Cassio Pennachin and Lucio {de Souza Coelho} and Elizabeth M Maloney and James F Jones and Brian Gurbaxani", title = "Allostatic load is associated with symptoms in chronic fatigue syndrome patients", journal = "Pharmacogenomics", year = "2006", volume = "7", number = "3", pages = "485--494", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.2217/14622416.7.3.485", URL = "http://www.futuremedicine.com/doi/abs/10.2217/14622416.7.3.485", abstract = "Objectives: To further explore the relationship between chronic fatigue syndrome (CFS) and allostatic load (AL), we conducted a computational analysis involving 43 patients with CFS and 60 nonfatigued, healthy controls (NF) enrolled in a population-based case-control study in Wichita (KS, USA). We used traditional biostatistical methods to measure the association of high AL to standardized measures of physical and mental functioning, disability, fatigue and general symptom severity. We also used nonlinear regression technology embedded in machine learning algorithms to learn equations predicting various CFS symptoms based on the individual components of the allostatic load index (ALI). Methods: An ALI was computed for all study participants using available laboratory and clinical data on metabolic, cardiovascular and hypothalamic-pituitary-adrenal (HPA) axis factors. Physical and mental functioning/impairment was measured using the Medical Outcomes Study 36-item Short Form Health Survey (SF-36); current fatigue was measured using the 20-item multidimensional fatigue inventory (MFI); frequency and intensity of symptoms was measured using the 19-item symptom inventory (SI). Genetic programming, a nonlinear regression technique, was used to learn an ensemble of different predictive equations rather just than a single one. Statistical analysis was based on the calculation of the percentage of equations in the ensemble that used each input variable, producing a measure of the 'utility' of the variable for the predictive problem at hand. Traditional biostatistics methods include the median and Wilcoxon tests for comparing the median levels of subscale scores obtained on the SF-36, the MFI and the SI summary score. Results: Among CFS patients, but not controls, a high level of AL was significantly associated with lower median values (indicating worse health) of bodily pain, physical functioning and general symptom frequency/intensity. Using genetic programming, the ALI was determined to be a better predictor of these three health measures than any subcombination of ALI components among cases, but not controls.", } @Article{Gogineni:2014:ICETETS, author = "Neelima Gogineni and C Ganga Bhavani and V S Giridhar Akula", title = "A New method to Design Accurate Images with Tree Structural Transformations", journal = "International Journal of Advanced Trends in Computer Science and Engineering", year = "2014", volume = "3", number = "1", pages = "429--431", note = "Special Issue of ICETETS 2014, Held on 24-25 February, 2014 in Malla Reddy Institute of Engineering and Technology, Secunderabad, 14, AP, India", keywords = "genetic algorithms, genetic programming, weight images, CUDA, island model, mcg model, optimisation speed, parenthetic values", ISSN = "2278 - 3091", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.461.2968", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.461.2968", URL = "http://www.warse.org/IJATCSE/archives/archivesDetiles/?heading=Special%20Issue%20Of%20ICETETS%202014", URL = "http://warse.org/pdfs/2014/icetetssp82.pdf", size = "3 pages", abstract = "Image recognition and segmentation techniques are playing key role in the field of image processing. Present researchers are working on the design concepts of accurate image processing. This paper explains the method for designing of accurate image processing with the help of the principle called automatic construction of tree structural image transformation and graphics processing unit as a hardware unit. Genetic algorithms are also used to obtain fast image processing on graphic processors.", notes = "Assistant Professor, Malla Reddy Institute of Technology, Secunderabad", } @InProceedings{Goh:2000:GECCO, author = "Gerard Kian-Meng Goh and James A. Foster", title = "Evolving Molecules for Drug Design Using Genetic Algorithms via Molecular Trees", pages = "27--33", year = "2000", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)", editor = "Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer", address = "Las Vegas, Nevada, USA", publisher_address = "San Francisco, CA 94104, USA", month = "10-12 " # jul, keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-708-0", URL = "http://gpbib.cs.ucl.ac.uk/gecco2000/GA141.pdf", notes = "A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of \cite{whitley:2000:GECCO}", } @InProceedings{goh:2001:gadsacpc, author = "C. Goh and Y. Li", title = "GA Automated Design and Synthesis of Analog Circuits with Practical Constrains", booktitle = "Proceedings of the 2001 Congress on Evolutionary Computation CEC2001", year = "2001", pages = "170--177", address = "COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "27-30 " # may, organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, CAD, Circuit Synthesis, preferred value components, PSpice, active networks, analog circuit design, analog circuit synthesis, evolutionary search techniques, genetic algorithm based growing technique, network topology, passive filter networks, practical constraints, value optimisation, analogue circuits, circuit CAD, network topology, passive filters", ISBN = "0-7803-6658-1", DOI = "doi:10.1109/CEC.2001.934386", size = "8 pages", abstract = "The paper develops a genetic algorithm (GA) based 'growing' technique to design and synthesise analogue circuits with practical constraints, such as the manufacturer's preferred component values. Most existing problems when evolutionary search techniques are applied to circuit design are addressed. The developed GA technique is then applied both to synthesise the topology of a network and perform value optimisation on the components based on a set of commonly used component values (E-12 series). Passive filter networks synthesised this way are realisable, effective and of novel topology. It is anticipated that this technique can be extended to active networks", notes = "CEC-2001 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 01TH8546C, Library of Congress Number = Fixed length chromosome but inclusion of {"}null{"} makes it effectively variable length but bounded.", } @InProceedings{Goharoodi:2023:CoDIT, author = "Saeideh Khatiry Goharoodi and Jeroen Jordens and Bart {Van Doninck} and Guillaume Crevecoeur", booktitle = "2023 9th International Conference on Control, Decision and Information Technologies (CoDIT)", title = "Hybrid Modeling of an Adhesive Bonding Process, Case Study: Polyphenylene Sulfide", year = "2023", pages = "1786--1791", abstract = "Adhesive bonding is a joining process used in several industries such as aerospace, automotive, civil construction and manufacturing. Traditionally, the optimisation of the parameters for this process is performed by adhesive experts via trial and error which is expensive and time-consuming. Therefore having a process model for optimisation purposes is of great interest. In this study, we develop such process model which includes cost, visual quality and joint strength properties for Polyphenylene sulfide bonding use-case. We adopt analytical modelling approaches for those process properties that do not require extensive system knowledge and are not effected by large number of process parameters, namely cost and visual quality. Additionally, we use data-driven genetic programming approach to model the more nonlinear process property, meaning joint strength of the bond. Consequently, we employ a hybrid approach by combining available knowledge and experimental data. The process model can then be implemented for process optimisation or to create a digital twin which predicts if the product quality is in scope.", keywords = "genetic algorithms, genetic programming, Industries, Visualization, Costs, Predictive models, Product design, Quality assessment, Manufacturing", DOI = "doi:10.1109/CoDIT58514.2023.10284411", ISSN = "2576-3555", month = jul, notes = "Also known as \cite{10284411}", } @InProceedings{Goings:2014:GECCOcomp, author = "Sherri Goings and Emily P. M. Johnston and Naozumi Hiranuma", title = "The effect of communication on the evolution of cooperative behavior in a multi-agent system", booktitle = "GECCO 2014 Eighth Annual Workshop on Evolutionary Computation and Multi-Agent Systems and Simulation (ECoMASS)", year = "2014", editor = "Forrest Stonedahl and William Rand", isbn13 = "978-1-4503-2881-4", keywords = "genetic algorithms, genetic programming", pages = "999--1006", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2598394.2605443", DOI = "doi:10.1145/2598394.2605443", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "A team of agents that cooperate to solve a problem together can handle many complex tasks that would not be possible without cooperation. While the benefit is clear, there are still many open questions in how best to achieve this cooperation. In this paper we focus on the role of communication in allowing agents to evolve effective cooperation for a prey capture task. Previous studies of this task have shown mixed results for the benefit of direct communication among predators, and we investigate potential explanations for these seemingly contradictory outcomes. We start by replicating the results of a study that found that agents with the ability to communicate actually performed worse than those without when each member of a team was evolved in a separate population [8]. The simulated world used for these experiments is very simple, and we suggest that communication would become beneficial in a similar but more complex environment. We test several methods of increasing the problem complexity, but find that at best communicating predators perform equally as well as those that cannot communicate. We thus propose that the representation may hinder the success of communication in this environment. The behaviour of each predator is encoded in a neural network, and the networks with communication have 6 inputs as opposed to just 2 for the standard network, giving communicating networks more than twice as many links for which to evolve weights. Another study using a relatively similar environment but genetic programming as a representation finds that communication is clearly beneficial for prey capture [4]. We suggest that adding communication is less costly to these genetic programs as compared to the earlier neural networks and outline experiments to test this theory.", notes = "Also known as \cite{2605443} Distributed at GECCO-2014.", } @Article{golafshani:2015:MaS, author = "E. M. Golafshani and A. Rahai and M. H. Sebt", title = "Artificial neural network and genetic programming for predicting the bond strength of {GFRP} bars in concrete", journal = "Materials and Structures", year = "2015", volume = "48", number = "5", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1617/s11527-014-0256-0", DOI = "doi:10.1617/s11527-014-0256-0", } @Article{GOLAFSHANI:2018:ASCa, author = "Emadaldin Mohammadi Golafshani and Siamak Talatahari", title = "Predicting the climbing rate of slip formwork systems using linear biogeography-based programming", journal = "Applied Soft Computing", year = "2018", volume = "70", pages = "263--278", month = sep, keywords = "genetic algorithms, genetic programming, Slip formwork, Climbing rate, Linear biogeography-based programming, Linear genetic programming", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2018.05.036", URL = "http://www.sciencedirect.com/science/article/pii/S1568494618303119", abstract = "Nowadays, it is undeniable necessity to select a fast and appropriate method for construction of high rise concrete structures. Slip formwork technology, as an automatic formwork system, has many advantages for high rise buildings and can reduce the construction time and costs. However, the climbing rate of slip formwork systems is a challenging task and depends on different factors. In this paper, the potential factors in calculating the climbing rate were identified. Then, a comprehensive database including 81 slip formwork projects in Iran was gathered. Afterwards, a symbolic regression method called linear biogeography-based programming was introduced and applied for extracting a formula that obtains a good climbing rate of slip formwork systems. For evaluating the performance of the proposed method, artificial neural network and linear genetic programming were used as well. The results show that the proposed formulation has good agreement with actual values of climbing rate of slip forming systems with low error and complexity and find it to be quite confident. Moreover, weather conditions criteria is known as the most effective parameter in climbing the rate of slip formwork systems based on the performed sensitivity analysis", notes = "Department of Civil Engineering, Architecture and Art, Science and Research Branch, Islamic Azad University, Tehran, Iran", } @Article{GOLAFSHANI:2018:ASC, author = "Emadaldin Mohammadi Golafshani and Ali Behnood", title = "Automatic regression methods for formulation of elastic modulus of recycled aggregate concrete", journal = "Applied Soft Computing", volume = "64", pages = "377--400", year = "2018", keywords = "genetic algorithms, genetic programming", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2017.12.030", URL = "http://www.sciencedirect.com/science/article/pii/S156849461730755X", abstract = "The use of recycled concrete aggregate to produce new concrete can assist the sustainability in construction industry. However, the mechanical properties of this type of aggregate should be precisely investigated before its using in different applications. The elastic modulus of concrete is one of the most important design parameters in many construction applications. Because of various mix designs, the existing formulas for the elastic modulus of concrete cannot be used for recycled aggregate concrete (RAC). In recent years, there have been a few attempts for predicting the elastic modulus of RAC, especially, with various types of artificial intelligence (AI) methods: In this paper, three automatic regression methods, namely, genetic programming (GP), artificial bee colony programming (ABCP) and biogeography-based programming (BBP) were used for estimating the elastic modulus of RAC. Performances of the different automatic regression models were compared with each other. Moreover, the sensitivity analysis was performed to assess the trend of the elastic modulus as a function of effective input parameters used for developing the different automatic regression models. Overall, the results show that GP, ABCP, and BBP can be used as reliable algorithms for prediction of the elastic modulus of RAC. In addition, the water absorption of the mixed coarse aggregate and the ratio of the fine aggregate to the total aggregate were found as two of the most effective parameters affecting the elastic modulus of RAC", } @Article{Golam-Bari:GPEM:parsons, author = "A. T. M. {Golam Bari} and Alessio Gaspar and R. Paul Wiegand and Jennifer L. Albert and Anthony Bucci and Amruth N. Kumar", title = "{EvoParsons}: design, implementation and preliminary evaluation of evolutionary {Parsons} puzzle", journal = "Genetic Programming and Evolvable Machines", year = "2019", volume = "20", number = "2", pages = "213--244", month = jun, keywords = "genetic algorithms, Evolutionary algorithms, Coevolutionary algorithms, Coevolutionary dimension extraction, Introductory programming education, Concept inventory, Computer-aided learning, Parsons puzzles", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-019-09343-7", size = "32 pages", abstract = "The automated design of a set of practice problems that co-adapts to a population of learners is a challenging problem. Fortunately, co-evolutionary computation offers a rich framework to study interactions between two co-adapting populations of teachers and learners. This framework is also relevant in scenarios in which a population of students solve practice exercises that are synthesized by an evolutionary algorithm. In this study, we propose to leverage coevolutionary optimization to evolve a population of Parsons puzzles (a relatively recent new type of practice exercise for novice computer programmers). To this end, we start by experimenting with successive simulations that progressively introduce the characteristics that we anticipate finding in our target application. Using these simulations, we refine a set of guidelines that capture insights on how to successfully co-evolve Parsons puzzles. These guidelines are then used to implement the proposed EvoParsons software, with wh...", notes = "USF University of South Florida. is this GP", } @InProceedings{Golap:2019:ICAEE, author = "Md. Asaf-uddowla Golap and M. M. A. Hashem", booktitle = "2019 5th International Conference on Advances in Electrical Engineering (ICAEE)", title = "Non-Invasive Hemoglobin Concentration Measurement Using {MGGP}-Based Model", year = "2019", month = sep, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICAEE48663.2019.8975672", ISSN = "2378-2692", abstract = "Normally blood sample is collected from human body using needle and after analyzing the sample result is revealed, this type of measurement method is called invasive. On the other hand, in non-invasive method, no blood sample is required only optical data such as photoplethysmogram or non-optical like bio-impedance is enough to measure hemoglobin concentration of blood. Unlike invasive method non-invasive methods are painless, cheap, quicker and easy to carry out. This paper proposes a non-invasive hemoglobin concentration measurement method using PPG characteristic features which is obtained from fingertip video and symbolic regression of multigene genetic programming. In this paper, 39-time domain and 6 frequency-domain features were extracted from PPG signals, additionally gender and age are added to these features. A correlation-based feature selection method was applied to select best features to train and develop a mathematical model. Promising result have been found using the model both for training and testing dataset. The coefficient of determination R2 and MAE obtained by the model are 0.763 and 0.329 respectively which implies that there is a good relation between hemoglobin level and selected features. Hence, the model can be used clinically to estimate hemoglobin concentration level of human blood.", notes = "Also known as \cite{8975672}", } @Article{GOLAP:2021:BSPC, author = "Md. Asaf-uddowla Golap and S. M. Taslim Uddin Raju and Md. Rezwanul Haque and M. M. A Hashem", title = "Hemoglobin and glucose level estimation from {PPG} characteristics features of fingertip video using {MGGP-based} model", journal = "Biomedical Signal Processing and Control", volume = "67", pages = "102478", year = "2021", ISSN = "1746-8094", DOI = "doi:10.1016/j.bspc.2021.102478", URL = "https://www.sciencedirect.com/science/article/pii/S1746809421000756", keywords = "genetic algorithms, genetic programming, Multigene genetic programming (MGGP), Hemoglobin (Hb), Glucose (Gl), Photoplethysmogram (PPG), Feature selection, Feature extraction", abstract = "Hemoglobin and the glucose level can be measured after taking a blood sample using a needle from the human body and analyzing the sample, the result can be observed. This type of invasive measurement is very painful and uncomfortable for the patient who is required to measure hemoglobin or glucose regularly. However, the non-invasive method only needed a bio-signal (image or spectra) to estimate blood components with the advantages of being painless, cheap, and user-friendliness. In this work, a non-invasive hemoglobin and glucose level estimation model have been developed based on multigene genetic programming (MGGP) using photoplethysmogram (PPG) characteristic features extracted from fingertip video captured by a smartphone. The videos are processed to generate the PPG signal. Analyzing the PPG signal, its first and second derivative, and applying Fourier analysis total of 46 features have been extracted. Additionally, age and gender are also included in the feature set. Then, a correlation-based feature selection method using a genetic algorithm is applied to select the best features. Finally, an MGGP based symbolic regression model has been developed to estimate hemoglobin and glucose level. To compare the performance of the MGGP model, several classical regression models are also developed using the same input condition as the MGGP model. A comparison between MGGP based model and classical regression models have been done by estimating different error measurement indexes. Among these regression models, the best results (plus-minus0.304 for hemoglobin and plus-minus0.324 for glucose) are found using selected features and symbolic regression based on MGGP", } @Article{Gold:GPEM, author = "Nicolas E. Gold", title = "{Virginia Dignum}: Responsible Artificial Intelligence: How to Develop and Use {AI} in a Responsible Way", title2 = "Springer Nature Switzerland AG, 2019. ISBN 978-3-030-30370-9 ISBN 978-3-030-30371-6", journal = "Genetic Programming and Evolvable Machines", year = "2021", volume = "22", number = "1", pages = "137--139", month = mar, note = "Book review", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dR8sm", DOI = "doi:10.1007/s10710-020-09394-1", size = "3 pages", abstract = "Artificial Intelligence (AI) is undoubtably a key technology of our contemporary age and one that affects all of us to a greater or lesser extent... Highly recommended for all.", } @InProceedings{gold:2022:Student, author = "Robert Gold and Andrew Haydn Grant and Erik Hemberg and Chathika Gunaratne and Una-May O'Reilly", title = "{GUI-Based,} Efficient Genetic Programming For {Unity3D}", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion", year = "2022", editor = "Heike Trautmann and Carola Doerr and Alberto Moraglio and Thomas Bartz-Beielstein and Bogdan Filipic and Marcus Gallagher and Yew-Soon Ong and Abhishek Gupta and Anna V Kononova and Hao Wang and Michael Emmerich and Peter A. N. Bosman and Daniela Zaharie and Fabio Caraffini and Johann Dreo and Anne Auger and Konstantin Dietric and Paul Dufosse and Tobias Glasmachers and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Tea Tusar and Dimo Brockhoff and Tome Eftimov and Pascal Kerschke and Boris Naujoks and Mike Preuss and Vanessa Volz and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Mark Coletti and Catherine (Katie) Schuman and Eric ``Siggy'' Scott and Robert Patton and Paul Wiegand and Jeffrey K. Bassett and Chathika Gunaratne and Tinkle Chugh and Richard Allmendinger and Jussi Hakanen and Daniel Tauritz and John Woodward and Manuel Lopez-Ibanez and John McCall and Jaume Bacardit and Alexander Brownlee and Stefano Cagnoni and Giovanni Iacca and David Walker and Jamal Toutouh and UnaMay O'Reilly and Penousal Machado and Joao Correia and Sergio Nesmachnow and Josu Ceberio and Rafael Villanueva and Ignacio Hidalgo and Francisco {Fernandez de Vega} and Giuseppe Paolo and Alex Coninx and Antoine Cully and Adam Gaier and Stefan Wagner and Michael Affenzeller and Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and William B. Langdon and Justyna Petke and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Paetzel and Alexander Wagner and Michael Heider and Nadarajen Veerapen and Katherine Malan and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Mohammad Nabi Omidvar and Yuan Sun and Ernesto Tarantino and De Falco Ivanoe and Antonio {Della Cioppa} and Scafuri Umberto and John Rieffel and Jean-Baptiste Mouret and Stephane Doncieux and Stefanos Nikolaidis and Julian Togelius and Matthew C. Fontaine and Serban Georgescu and Francisco Chicano and Darrell Whitley and Oleksandr Kyriienko and Denny Dahl and Ofer Shir and Lee Spector and Alma Rahat and Richard Everson and Jonathan Fieldsend and Handing Wang and Yaochu Jin and Erik Hemberg and Marwa A. Elsayed and Michael Kommenda and William {La Cava} and Gabriel Kronberger and Steven Gustafson", pages = "2310--2313", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, Multi-agent planning, Simulation tools, Human-centered computing, GUI, Graphical user interfaces, Unity3D, Simulator, AI Planning", isbn13 = "978-1-4503-9268-6/22/07", URL = "https://hdl.handle.net/1721.1/146338", URL = "https://dspace.mit.edu/bitstream/handle/1721.1/146338/3520304.3534022.pdf", DOI = "doi:10.1145/3520304.3534022", size = "4 pages", abstract = "Unity3D is a game development environment that could be co-opted for agent-based machine learning research. We present a GUI-driven, and efficient Genetic Programming (GP) system for this purpose. Our system, ABL-Unity3D, addresses challenges entailed in co-opting Unity3D: making the simulator serve agent learning rather than humans playing a game, lowering fitness evaluation time to make learning computationally feasible, and interfacing GP with an AI Planner to support hybrid algorithms that could improve performance. We achieve this through development of a GUI using the Unity3D editor programmable interface, and performance optimisations. These optimizations result in at least a 3 fold speedup. We describe ABL-Unity3D by explaining how to use it for an example experiment using GP and AI Planning.", notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @InProceedings{gold:2022:GPTP, author = "Robert Gold and Andrew Haydn Grant and Erik Hemberg and Chathika Gunaratne and Una-May O'Reilly", title = "{GUI-Based,} Efficient Genetic Programming and {AI} Planning for {Unity3D}", booktitle = "Genetic Programming Theory and Practice XIX", year = "2022", editor = "Leonardo Trujillo and Stephan M. Winkler and Sara Silva and Wolfgang Banzhaf", series = "Genetic and Evolutionary Computation", pages = "57--79", address = "Ann Arbor, USA", month = jun # " 2-4", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-981-19-8459-4", URL = "http://link.springer.com/chapter/10.1007/978-981-19-8460-0_3", DOI = "doi:10.1007/978-981-19-8460-0_3", abstract = "We present a GUI-driven and efficient Genetic Programming (GP) and AI Planning framework designed for agent-based learning research. Our framework, ABL-Unity3D, is built in Unity3D, a game development environment. ABL-Unity3D addresses challenges entailed in co-opting Unity3D: making the simulator serve agent learning rather than humans playing a game, lowering fitness evaluation time to make learning computationally feasible, and interfacing GP with an AI Planner to support hybrid algorithms. We achieve this by developing a Graphical User Interface (GUI) using the Unity3D editor's programmable interface and performance optimizations. These optimizations result in at least a 3x speedup. In addition, we describe ABL-Unity3D by explaining how to use it for an example experiment using GP and AI Planning. We benchmark ABL-Unity3D by measuring the performance and speed of the AI Planner alone, GP alone, and the AI Planner with GP.", notes = "Part of \cite{Banzhaf:2022:GPTP} published after the workshop in 2023", } @InProceedings{Gold:2023:evoapplications, author = "Robert Gold and Henrique Branquinho and Erik Hemberg and Una-May O'Reilly and Pablo Garcia-Sanchez", title = "Genetic Programming and Coevolution to Play the {Bomberman} Video Game", booktitle = "26th International Conference, EvoApplications 2023", year = "2023", month = apr # " 12-14", editor = "Joao Correia and Stephen Smith and Raneem Qaddoura", series = "LNCS", volume = "13989", publisher = "Springer Verlag", address = "Brno, Czech Republic", pages = "765--779", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Lexicographical Fitness, Artificial Intelligence, BombermanTM", isbn13 = "978-3-031-30229-9", DOI = "doi:10.1007/978-3-031-30229-9_49", size = "15 pages", abstract = "The field of video games is of great interest to researchers in computational intelligence due to the complex, rich and dynamic nature they provide. We propose using Genetic Programming with coevolution and lexicographic fitness to generate an agent that plays the Bomberman game. We investigate two sets of Genetic Programming building blocks: one contains conditions relative to movement, and the other does not. We aim to see whether the benefits of these movement-related conditions outweigh the negatives caused by increased search space size. We show that the benefits gained do not outweigh the increase in search space size.", notes = "Also known as \cite{DBLP:conf/evoapps/GoldBHOG13} http://www.evostar.org/2023/ EvoApplications2023 held in conjunction with EuroGP'2023, EvoCOP2023 and EvoMusArt2023", } @InProceedings{goldberg:1998:good, author = "David E. Goldberg and Una-May O'Reilly", title = "Where does the Good Stuff Go, and Why? How contextual semantics influence program structure in simple genetic programming", booktitle = "Proceedings of the First European Workshop on Genetic Programming", year = "1998", editor = "Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer and Terence C. Fogarty", volume = "1391", series = "LNCS", pages = "16--36", address = "Paris", publisher_address = "Berlin", month = "14-15 " # apr, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-64360-5", broken = "http://www.ai.mit.edu/people/unamay/papers/eurogp.final.ps", URL = "http://citeseer.ist.psu.edu/96596.html", DOI = "doi:10.1007/BFb0055925", size = "21 pages", abstract = "Using deliberately designed primitive sets, we investigate the relationship between context-based expression mechanisms and the size, height and density of genetic program trees during the evolutionary process. We show that contextual semantics influence the composition, location and flows of operative code in a program. In detail we analyze these dynamics and discuss the impact of our findings on micro-level descriptions of genetic programming.", notes = "EuroGP'98 Also presented at the Canadian AI-98 Workshop on Evolutionary Computation Schedule, 17 June 1998 Simon Fraser University Harbour Center, Canada", affiliation = "University of Illinois at Urbana-Champaign 61801 IL USA 61801 IL USA", } @InProceedings{goldberg:1999:OGSH, author = "David E. Goldberg and Siegfried Voessner", title = "Optimizing Global-Local Search Hybrids", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "220--228", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-882.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-882.pdf", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{goldberg:1999:UTEGACP, author = "David E. Goldberg", title = "Using Time Efficiently: Genetic-Evolutionary Algorithms and the Continuation Problem", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "212--219", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-881.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-881.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{goldfish:1996:nwfmGP, author = "Andrew Goldfish", title = "Noisy Wall Following and Maze Navigation through Genetic Programming", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "423", address = "Stanford University, CA, USA", publisher = "MIT Press", size = "1 page", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap62.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96", } @InProceedings{Goldman:2011:GECCOcomp, author = "Brian W. Goldman and Daniel R. Tauritz", title = "Self-configuring crossover", booktitle = "GECCO 2011 1st workshop on evolutionary computation for designing generic algorithms", year = "2011", editor = "Gisele L. Pappa and Alex A. Freitas and Jerry Swan and John Woodward", isbn13 = "978-1-4503-0690-4", keywords = "genetic algorithms, genetic programming", pages = "575--582", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001858.2002051", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Crossover is a core genetic operator in many evolutionary algorithms (EAs). The performance of such EAs on a given problem is dependent on properly configuring crossover. A small set of common crossover operators is used in the vast majority of EAs, typically fixed for the entire evolutionary run. Selecting which crossover operator to use and tuning its associated parameters to obtain acceptable performance on a specific problem often is a time consuming manual process. Even then a custom crossover operator may be required to achieve optimal performance. Finally, the best crossover configuration may be dependent on the state of the evolutionary run. This paper introduces the Self-Configuring Crossover operator encoded with linear genetic programming which addresses these shortcomings while relieving the user from the burden of crossover configuration. To demonstrate its general applicability, the novel crossover operator was applied without any problem specific tuning. Results are presented showing it to outperform the traditional crossover operators arithmetic crossover, uniform crossover, and n-point crossover on the Rosenbrock, Rastrigin, Offset Rastrigin, DTrap, and NK Landscapes benchmark problems.", notes = "Also known as \cite{2002051} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @InProceedings{goldman:2013:EuroGP, author = "Brian W. Goldman and William F. Punch", title = "Reducing Wasted Evaluations in Cartesian Genetic Programming", booktitle = "Proceedings of the 16th European Conference on Genetic Programming, EuroGP 2013", year = "2013", month = "3-5 " # apr, editor = "Krzysztof Krawiec and Alberto Moraglio and Ting Hu and A. Sima Uyar and Bin Hu", series = "LNCS", volume = "7831", publisher = "Springer Verlag", address = "Vienna, Austria", pages = "61--72", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", isbn13 = "978-3-642-37206-3", DOI = "doi:10.1007/978-3-642-37207-0_6", abstract = "Cartesian Genetic Programming~(CGP) is a form of Genetic Programming~(GP) where a large proportion of the genome is identifiably unused by the phenotype. This can lead mutation to create offspring that are genotypically different but phenotypically identical, and therefore do not need to be evaluated. We investigate theoretically and empirically the effects of avoiding these otherwise wasted evaluations, and provide evidence that doing so reduces the median number of evaluations to solve four benchmark problems, as well as reducing CGP's sensitivity to the mutation rate. The similarity of results across the problem set in combination with the theoretical conclusions supports the general need for avoiding these unnecessary evaluations.", notes = "Part of \cite{Krawiec:2013:GP} EuroGP'2013 held in conjunction with EvoCOP2013, EvoBIO2013, EvoMusArt2013 and EvoApplications2013", } @InProceedings{Goldman:2013:GECCO, author = "Brian W. Goldman and William F. Punch", title = "Length bias and search limitations in cartesian genetic programming", booktitle = "GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference", year = "2013", editor = "Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi", isbn13 = "978-1-4503-1963-8", pages = "933--940", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2463372.2463482", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "In this paper we examine how Cartesian Genetic Programming's (CGP's) method for encoding directed acyclic graphs (DAGs) and its mutation operator bias the effective length of individuals as well as the distribution of inactive nodes in the genome. We investigate these biases experimentally using two CGP variants as comparisons: Reorder, a method for shuffling node ordering without effecting individual evaluation, and DAG, a method for removing the concept of node position. Experiments were performed on four problems tailored to highlight potential search limitations, with further testing on the 3-bit multiplier problem. Unlike previous work, our experiments show that CGP has an innate parsimony pressure that makes it very difficult to evolve individuals with a high percentage of active nodes. This bias is particularly prevalent as the length of an individual increases. Furthermore, these problems are compounded by CGP's positional biases which can make some problems effectively unsolvable. Both Reorder and DAG appear to avoid these problems and outperform Normal CGP on preliminary benchmark testing. Finally, these new techniques require more reasonable genome sizes than those suggested in current CGP, with some evidence that solutions are also more terse.", notes = "Also known as \cite{2463482} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)", } @Article{Goldman:2014:ieeeTEC, author = "Brian W. Goldman and William F. Punch", title = "Analysis of Cartesian Genetic Programming's Evolutionary Mechanisms", journal = "IEEE Transactions on Evolutionary Computation", year = "2015", volume = "19", number = "3", pages = "359--373", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2014.2324539", size = "15 pages", abstract = "Understanding how search operators interact with solution representation is a critical step to improving search. In Cartesian Genetic Programming (CGP), and Genetic Programming (GP) in general, the complex genotype to phenotype map makes achieving this understanding a challenge. By examining aspects such as tuned parameter values, the search quality of CGP variants at different problem difficulties, node behaviour, and offspring replacement properties we seek to better understand the characteristics of CGP search. Our focus is twofold: creating methods to prevent wasted CGP evaluations (Skip, Accumulate, and Single) and creating methods to overcome CGP's search limitations imposed by genome ordering (Reorder and DAG). Our results on Boolean problems show CGP evolves genomes that are highly inactive, very redundant, and full of seemingly useless constants. On some tested problems we found less than 1percent of the genome was actually required to encode the evolved solution. Furthermore, traditional CGP ordering results in large portions of the genome that are never used by any ancestor of the evolved solution. Reorder and DAG allow evolution to use the entire genome. More generally, our results suggest that Skip-Reorder and Single-Reorder are most likely to solve hard problems using the least number of evaluations and the least amount of time while better avoiding degenerate behaviour.", notes = "also known as \cite{6815728}", } @PhdThesis{Goldman:thesis, author = "Brian W. Goldman", title = "Out of the box optimization using the parameter-less population pyramid", school = "Computer Science and Engineering, Michigan State University", year = "2015", address = "East Lansing, MI 48824-1226, USA", month = "1 " # jul, keywords = "genetic algorithms, Artificial intelligence", URL = "http://gradworks.umi.com/37/16/3716093.html", URL = "http://www.egr.msu.edu/academics/graduate/phd-defenses/out-box-optimization-using-parameter-less-population-pyramid", abstract = "The Parameter-less Population Pyramid (P3) is a recently introduced method for performing evolutionary optimization without requiring any user-specified parameters. P3's primary innovation is to replace the generational model with a pyramid of multiple populations that are iteratively created and expanded. In combination with local search and advanced crossover, P3 scales to problem difficulty, exploiting previously learned information before adding more diversity. Across seven problems, each tested using on average 18 problem sizes, P3 outperformed all five advanced comparison algorithms. This improvement includes requiring fewer evaluations to find the global optimum and better fitness when using the same number of evaluations. Using both algorithm analysis and comparison we show P3's effectiveness is due to its ability to properly maintain, add, and exploit diversity. Unlike the best comparison algorithms, P3 was able to achieve this quality without any problem-specific tuning. Thus, unlike previous parameter-less methods, P3 does not sacrifice quality for applicability. Therefore we conclude that P3 is an efficient, general, parameter-less approach to black-box optimization that is more effective than existing state-of-the-art techniques. Furthermore, P3 can be specialized for gray-box problems, which have known, limited, non-linear relationships between variables. Gray-Box P3 leverages the Hamming-Ball Hill Climber, an exceptionally efficient form of local search, as well as a novel method for performing crossover using the known variable interactions. In doing so Gray-Box P3 is able to find the global optimum of large problems in seconds, improving over Black-Box P3 by up to two orders of magnitude.", notes = "Supervisor William F. Punch", } @Article{Goldstein:2013:CSR, author = "Evan B. Goldstein and Giovanni Coco and A. Brad Murray", title = "Prediction of wave ripple characteristics using genetic programming", journal = "Continental Shelf Research", year = "2013", volume = "71", month = "1 " # dec, pages = "1--15", ISSN = "0278-4343", DOI = "doi:10.1016/j.csr.2013.09.020", URL = "http://www.sciencedirect.com/science/article/pii/S0278434313003166", keywords = "genetic algorithms, genetic programming, geology, Ripples, Bedforms, Machine learning, Data driven prediction, Symbolic regression", abstreact = "We integrate published data sets of field and laboratory experiments of wave ripples and use genetic programming, a machine learning paradigm, in an attempt to develop a universal equilibrium predictor for ripple wavelength, height, and steepness. We train our genetic programming algorithm with data selected using a maximum dissimilarity selection routine. Thanks to this selection algorithm; we use less data to train the genetic programming software, allowing more data to be used as testing (i.e., to compare our predictor vs. common prediction schemes). Our resulting predictor is smooth and physically meaningful, different from other machine learning derived results. Furthermore our predictor incorporates wave orbital ripples that were previously excluded from empirical prediction schemes, notably ripples in coarse sediment and long wavelength, low height ripples (hummocks). This new predictor shows ripple length to be a weakly nonlinear function of both bottom orbital excursion and grain size. Ripple height and steepness are both nonlinear functions of grain size and predicted ripple length (i.e., bottom orbital excursion and grain size). We test this new prediction scheme against common (and recent) predictors and the new predictors yield a lower normalised root mean squared error using the testing data. This study further demonstrates the applicability of machine learning techniques to successfully develop well performing predictors if data sets are large in size, extensive in scope, multidimensional, and nonlinear.", } @InProceedings{Golonek:2006:ISCAS, author = "T. Golonek and D. Grzechca and J. Rutkowski", title = "Application of genetic programming to edge detector design", booktitle = "Proceedings of the IEEE International Symposium on Circuits and Systems, ISCAS 2006", year = "2006", month = "21-24 " # may, publisher = "IEEE", note = "4 pp, CD-ROM", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9389-9", DOI = "doi:10.1109/ISCAS.2006.1693675", abstract = "The new approach to edge detection is presented in this paper. The proposed method uses genetic programming (GP) to search for digital transfer function of image edge detector. The found function can be easily implemented to any programmable logic device (PLD) that allows to build a fast system of image processing.", notes = "Inst. of Electron., Silesian Univ. of Technol., Gliwice, Poland", } @InProceedings{Golonek_2003_ECCTD, author = "Tomasz Golonek and Jerzy Rutkowski", title = "Application of Genetic Programming to Analog Fault Decoder Design", booktitle = "The 16th European Conference on Circuits Theory and Design, ECCTD'03", year = "2003", address = "Electrical Engineering, AGH University of Science and Technology, Krakow, Poland", month = "1-4 " # sep, organisation = "ECS, IEEE", keywords = "genetic algorithms, genetic programming", URL = "http://platforma.polsl.pl/rau3/mod/resource/view.php?id=1324", URL = "http://platforma.polsl.pl/rau3/mod/resource/Appl.of_GP_to_AFD-ECCTD03.pdf", size = "4 pages", abstract = "Genetic Programming (GP) is an evolutionary, heuristic technique of optimisation, which allows to solve many difficult problems. A new method using GP to analog testing is proposed. After a brief introduction to the GP technique, the use of this technique to fault decoder construction is explained. The experimental results are presented and they seem to be very promising. In the last section, some conclusions are presented.", notes = "http://ecctd03.zet.agh.edu.pl/docs/program.html", } @InProceedings{golovkin:1999:PXSAUGA, author = "Igor E. Golovkin and Roberto C. Mancini and Sushil J. Louis", title = "Plasma X-ray Spectra Analysis Using Genetic Algorithms", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1529--1534", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "real world applications", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-734b.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-734b.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Golshan:2010:ISSPA, author = "F. Golshan and K. Mohamadi", title = "An intelligent watermarking algorithm based on Genetic Programming", booktitle = "10th International Conference on Information Sciences Signal Processing and their Applications (ISSPA 2010)", year = "2010", month = may, pages = "97--100", abstract = "In this paper we propose an algorithm to develop an intelligent perceptual shaping function based on Genetic Programming (GP) in DCT domain. In digital image watermarking, robustness and imperceptibility compete with each other. In this paper we applied GP to make a trade off between these two characteristics. Here, the original image is divided into 8 #x00D7;8 non-overlapping blocks and the DCT coefficients in each block are sorted by means of zigzag. One AC coefficient in each block is changed according to a perceptual shaping function. This perceptual shaping function is obtained from the GP core and is dependent on average of all block coefficients and the related AC coefficient. The experimental results show that this proposed algorithm is robust against some digital image attacks such as low pass filtering, median filtering and JPEG compression. In addition the improvement in watermarked image quality also is achieved.", keywords = "genetic algorithms, genetic programming, DCT domain, JPEG compression, digital image attacks, digital image watermarking, intelligent perceptual shaping function, intelligent watermarking algorithm, low pass filtering, median filtering, low-pass filters, median filters, watermarking", DOI = "doi:10.1109/ISSPA.2010.5605497", notes = "Fac. of Electr. Eng., Karaj Islamic Azad Univ., Rajaeeshahr, Iran Also known as \cite{5605497}", } @InProceedings{Golshan:2011:ICSEng, author = "Farzad Golshan and Karim Mohammadi", title = "A Hybrid Intelligent SVD-Based Digital Image Watermarking", booktitle = "21st International Conference on Systems Engineering (ICSEng 2011)", year = "2011", month = "16-18 " # aug, pages = "137--141", address = "Las Vegas, NV, USA", size = "5 pages", abstract = "This paper proposes an intelligent hybrid watermarking algorithm for digital images. In digital image watermarking, robustness and imperceptibility compete with each other. In this paper we applied a hybrid intelligent algorithm based on genetic programming and particle swarm optimisation to make a trade off between robustness and imperceptibility. In this way the intelligent method has been applied in DCT_DWT_SVD domain. First of all the original image is transformed into DCT domain and then a part of DCT matrix is decomposed into four subbands using discrete wavelet transform and finally the singular values of each subband are shaped perceptually by singular values of watermark image to embed the watermark. The optimisation problem which is related to a conflict between robustness and imperceptibility is solved by means of genetic programming and particle swarm optimisation, simultaneously, to achieve the best performance in robustness without losing the quality of host image. Experimental results show improvement in imperceptibility and robustness under several attacks and different images.", keywords = "genetic algorithms, genetic programming, DCT matrix, DWT, digital image watermarking, discrete wavelet transform, hybrid intelligent SVD, hybrid intelligent algorithm, intelligent hybrid watermarking algorithm, particle swarm optimisation, discrete cosine transforms, discrete wavelet transforms, image watermarking, particle swarm optimisation, singular value decomposition", DOI = "doi:10.1109/ICSEng.2011.32", notes = "Also known as \cite{6041828}", } @InProceedings{golubski:1999:eNNsmGP, author = "Wolfgang Golubski and Thomas Feuring", title = "Evolving Neural Network Structures by Means of Genetic Programming", booktitle = "Genetic Programming, Proceedings of EuroGP'99", year = "1999", editor = "Riccardo Poli and Peter Nordin and William B. Langdon and Terence C. Fogarty", volume = "1598", series = "LNCS", pages = "211--220", address = "Goteborg, Sweden", publisher_address = "Berlin", month = "26-27 " # may, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-65899-8", DOI = "doi:10.1007/3-540-48885-5_18", abstract = "The goal of this paper is to present a more efficient way to automatically construct appropriate neural network topologies as well as their initial weight settings. Our approach combines evolutionary algorithms and genetic programming techniques and is based on a new network encoding schema where instead of a string like encoding the graph representation of neural nets is used. This way of encoding reduces the computational expense and leads to a greater variety of network topologies.", notes = "EuroGP'99, part of \cite{poli:1999:GP}", } @InProceedings{golubski:2002:EuroGP, title = "New Results on Fuzzy Regression by Using Genetic Programming", author = "Wolfgang Golubski", editor = "James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi", booktitle = "Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002", volume = "2278", series = "LNCS", pages = "308--315", publisher = "Springer-Verlag", address = "Kinsale, Ireland", publisher_address = "Berlin", month = "3-5 " # apr, year = "2002", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-43378-3", DOI = "doi:10.1007/3-540-45984-7_30", abstract = "In this paper we continue the work on symbolic fuzzy regression problems. That means that we are interesting in finding a fuzzy function f with best matches k given data pairs (x,y) of fuzzy numbers. We use a genetic programming approach for finding a suitable fuzzy function and will present test results about linear, quadratic and cubic fuzzy functions.", notes = "EuroGP'2002, part of \cite{lutton:2002:GP}", } @InProceedings{WSEAS_179_Golubski, author = "Wolfgang Golubski", title = "Distributed Genetic Programming for Regression Analysis", year = "2002", month = may # "~12-16", booktitle = "WSEAS IMCCAS-ISA-SOSM and MEM-MCP", address = "Cancun, Mexico", keywords = "genetic algorithms, genetic programming, Distributed Genetic Programming, Symbolic Regression, Master-Worker", notes = "Nov 2012 not found in http://www.wseas.us/e-library/conferences/mexico2002/index.htm", } @InProceedings{WSEAS_177_Golubski, author = "Wolfgang Golubski", title = "Regression Analysis on Uncertain Data", year = "2002", month = may # "~12-16", booktitle = "WSEAS IMCCAS-ISA-SOSM and MEM-MCP", address = "Cancun, Mexico", keywords = "genetic algorithms, genetic programming, Regression Analysis, Genetic Programming, Fuzzy Numbers, Evolutionary Algorithm, Fuzzy Application", notes = "Nov 2012 not found in http://www.wseas.us/e-library/conferences/mexico2002/index.htm", } @InProceedings{Golubski:2002:GPP, author = "Wolfgang Golubski", title = "Genetic Programming: {A} Parallel Approach", booktitle = "Soft-Ware 2002: Computing in an Imperfect World : First International Conference", volume = "2311", pages = "166--173", year = "2002", CODEN = "LNCSD9", ISSN = "0302-9743", bibdate = "Tue Sep 10 19:09:25 MDT 2002", acknowledgement = ack-nhfb, editor = "D. Bustard and W. Liu and R. Sterritt", series = "Lecture Notes in Computer Science", address = "Belfast, Northern Ireland", month = "8-10 " # apr, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-43481-8", DOI = "doi:10.1007/3-540-46019-5_13", abstract = "In this paper we introduce a parallel master-worker model for genetic programming where the master and each worker have their own equal-sized populations. The workers execute in parallel starting with the same population and are synchronized after a given interval where all worker populations are replaced by a new one. The proposed model will be applied to symbolic regression problems. Test results on two test series are presented.", } @Article{GOMES:2019:KS, author = "Fabricio M. Gomes and Felix M. Pereira and Aneirson F. Silva and Messias B. Silva", title = "Multiple response optimization: Analysis of genetic programming for symbolic regression and assessment of desirability functions", journal = "Knowledge-Based Systems", volume = "179", pages = "21--33", year = "2019", ISSN = "0950-7051", DOI = "doi:10.1016/j.knosys.2019.05.002", URL = "http://www.sciencedirect.com/science/article/pii/S0950705119302096", keywords = "genetic algorithms, genetic programming, Optimization, Desirability function, Modeling", abstract = "Multiple responses optimization (MRO) consists in the search for the best settings in an problem with conflicting responses. MRO is performed following the steps: experimental design; experimental data gathering; mathematical models building; statistical validation of models; agglutination of the models responses in only one function to be optimized; optimization of agglutinated function; experimental validation of the best conditions. This work selected two MRO cases from literature aiming to compare two methods of mathematical models building and two agglutinating functions to assess the best one among the four possible combinations. The methods used in mathematical models building were the ordinary least squares performed in Minitab (v. 17) and genetic programming performed in Eureqa Formulize (v. 1.24.0). The assessment of the best method for building mathematical models was performed using the Akaike Information Criterion. The responses agglutination were performed using the desirability and modified desirability functions. In all MRO cases, the optimization step was performed by generalized reduced gradient method on Microsoft ExcelTM software. The average percentage distance between predicted and experimental results was used to both assess the best agglutination function and verify the effect of the method used in the building of the mathematical models about its fitness to estimate the best condition close to that one obtained on experimental validation step. The obtained results suggest as the better strategy for multiple responses optimization the use, jointly, of genetic programming to mathematical models building and the modified desirability function to responses agglutination", } @InProceedings{Gomes:2018:CEC, author = "Jorge Gomes and Anders Lyhne Christensen", booktitle = "2018 IEEE Congress on Evolutionary Computation (CEC)", title = "Comparing Approaches for Evolving High-Level Robot Control Based on Behaviour Repertoires", year = "2018", abstract = "Evolutionary robotics approaches have traditionally been focused on monolithic controllers. Recent studies on the evolution of hierarchical control have, however, yielded promising results. Hierarchical approaches typically rely on a repertoire of behaviour primitives (which themselves can be the result of an evolutionary process), and an evolved top-level arbitrator that continually executes primitives from the repertoire to solve a given task. In this paper, we compare different controller architectures for the evolution of top-level arbitrators. We propose two new methods, one based on neural networks and another based on decision trees induced by genetic programming. We compare the new approaches with existing ones, namely neural network regressors and non-hierarchical control, in a challenging simulated maze navigation task that requires a broad diversity of primitives. Based on empirical results, we draw a number of conclusions regarding the strengths and limitations of each of the studied approaches.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2018.8477699", month = jul, notes = "Also known as \cite{8477699}", } @InProceedings{Gomes:2023:SIU, author = "Luis Gomes and Bruno Ribeiro and Fernando Lezama and Zita Vale", booktitle = "2023 31st Signal Processing and Communications Applications Conference (SIU)", title = "A Multi-Agent System Empowered by Federated Learning and Genetic Programming", year = "2023", abstract = "The use of multi-agent systems enables the modelling of complex and decentralized solutions, giving the ability to have agents representing different entities and assets in a social environment where they can interact and pursue their individual goals. However, multi-agent systems are usually data-driven solutions in which interactions are performed based on data sharing and environmental feedback. Therefore, the integration of multi-agent systems with federated learning, a knowledge-driven approach, allows agents to share knowledge among them in a collaborative and cooperative approach. This integration can be well seen in decentralized solutions where similar entities can benefit from collaborative and cooperative environments. This is the case in industrial environments and in smart grid environments, namely for the improvement of learning models. This paper proposes a methodology composed of a multi-agent system where the agents are empowered by federated learning. The proposed methodology was tested and validated using a genetic programming model with MNIST dataset in terms of feasibility and performance.", keywords = "genetic algorithms, genetic programming, Data privacy, Federated learning, Collaboration, Signal processing, Smart grids, Multi-agent systems, data-driven, federated learning, knowledge-driven, multi-agent system", DOI = "doi:10.1109/SIU59756.2023.10223778", ISSN = "2165-0608", month = jul, notes = "Also known as \cite{10223778}", } @InProceedings{Gomez:2011:MED, author = "Francisco Manuel Fernandez Gomez and Francisco Javier Muros Ponce", title = "Automatic design of nonlinear controllers by means of coevolutive algorithms application to an inverted pendulum", booktitle = "19th Mediterranean Conference on Control Automation (MED 2011)", year = "2011", month = "20-23 " # jun, pages = "552--557", size = "6 pages", address = "Corfu", abstract = "This paper focuses on the development of a tool to design nonlinear controllers automatically. This is done by means of an automatic stochastic search: a coevolutive algorithm, inspired by the competitive and symbiotic relationships between certain species in the Nature that evolve in parallel. The algorithm developed is used to look for a law that enable control of a classic system: the inverted pendulum. One of the two species that coevolve is made up of a set of control laws (each of them is a solution). The other one consist of initial conditions (that is a population of problems). The search space of the problem comprises all the possible solutions to it. The populations of the two species that evolve are processed by two different kinds of evolutive algorithms, a genetic algorithm and an algorithm that implements the Genetic Programming paradigm.", keywords = "genetic algorithms, genetic programming, automatic design, automatic stochastic search, coevolutive algorithm, genetic programming paradigm, inverted pendulum, nonlinear controller, control system synthesis, nonlinear control systems, pendulums, search problems, stochastic processes", DOI = "doi:10.1109/MED.2011.5982996", notes = "Also known as \cite{5982996}", } @Article{Gomez:2017:GPEM, author = "Juan Carlos Gomez and Hugo Terashima-Marin", title = "Evolutionary hyper-heuristics for tackling bi-objective {2D} bin packing problems", journal = "Genetic Programming and Evolvable Machines", year = "2018", volume = "19", number = "1-2", pages = "151--181", month = jun, note = "Special Issue on Automated Design and Adaptation of Heuristics for Scheduling and Combinatorial Optimisation", keywords = "genetic algorithms, Bin packing problem, Evolutionary computation, Hyper-heuristics, Heuristics, Multi-objective optimization", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-017-9301-4", size = "31 pages", abstract = "In this article, a multi-objective evolutionary framework to build selection hyper-heuristics for solving instances of the 2D bin packing problem is presented. The approach consists of a multi-objective evolutionary learning process, using specific tailored genetic operators, to produce sets of variable length rules representing hyper-heuristics. Each hyper-heuristic builds a solution to a given problem instance by sensing the state of the instance, and deciding which single heuristic to apply at each decision point. The hyper-heuristics consider the minimization of two conflicting objectives when building a solution: the number of bins used to accommodate the pieces and the total time required to do the job. The proposed framework integrates three well-studied multi-objective evolutionary algorithms to produce sets of Pareto-approximated hyper-heuristics: the Non-dominated Sorting Genetic Algorithm-II, the Strength Pareto Evolutionary Algorithm 2, and the Generalized Differential Evolution Algorithm 3. We conduct an extensive experimental analysis using a large set of 2D bin packing problem instances containing convex and non-convex irregular pieces, under many conditions, settings and using several performance metrics. The analysis assesses the robustness and flexibility of the proposed approach, providing encouraging results when compared against a set of well-known baseline single heuristics.", notes = "Not on GP?", } @InProceedings{Gomez-Castro:2011:ESCAPE, author = "Fernando I. Gomez-Castro and Mario A. Rodriguez-Angeles and Juan G. Segovia-Hernandez and Claudia Gutierrez-Antonio and Abel Briones-Ramirez", title = "Optimal designs of multiple dividing wall columns", booktitle = "21st European Symposium on Computer Aided Process Engineering, ESCAPE 21", year = "2011", editor = "E. N. Pistikopoulos and M. C. Georgiadis and A. C. Kokossis", pages = "176--180", publisher = "Elsevier", keywords = "genetic algorithms, genetic programming", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", description = "on genetic programming", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.696.9870", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", isbn13 = "9780444538963", URL = "http://store.elsevier.com/21st-European-Symposium-on-Computer-Aided-Process-Engineering/isbn-9780444538956/", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.696.9870", URL = "http://www.segovia-hernandez.com/publicaciones/Escape-21%20%28fernando%29.pdf", size = "5 pages", abstract = "In this work, two schemes are analysed for the reduction on energy consumptions for ternary distillation: a Petlyuk column, PC, and a Petlyuk with postfractionator system, PCP. To perform the optimal design of the analysed systems, the use of multiobjective genetic algorithms has been considered. Moreover, a strategy for diameter calculation is proposed for the dividing wall column, DWC, and double dividing wall column, DDWC, which is based on their distribution of internal flows. Results show that genetic algorithm tool allows obtaining optimal designs for the PC and PCP systems, with low energy consumptions. Furthermore, the design strategy for the DWC and DDWC shows that the physical structure required for one or two dividing walls is quite similar; thereby, it appears to be an adequate method for the sizing of the dividing wall systems.", } @Article{Gomez-Pulido:2011:GPEM, author = "Juan A. Gomez-Pulido and Miguel A. Vega-Rodriguez and Juan M. Sanchez-Perez and Silvio Priem-Mendes and Vitor Carreira", title = "Accelerating floating-point fitness functions in evolutionary algorithms: a FPGA-CPU-GPU performance comparison", journal = "Genetic Programming and Evolvable Machines", year = "2011", volume = "12", number = "4", pages = "403--427", month = dec, keywords = "genetic algorithms, evolvable hardware, EHW, Evolutionary algorithms, Fitness, Reconfigurable circuits, GPU, Floating-Point, Performance, Parallelism", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-011-9137-2", size = "25 pages", abstract = "Many large combinatorial optimisation problems tackled with evolutionary algorithms often require very high computational times, usually due to the fitness evaluation. This fact forces programmers to use clusters of computers, a computational solution very useful for running applications of intensive calculus but having a high acquisition price and operation cost, mainly due to the Central Processing Unit (CPU) power consumption and refrigeration devices. A low-cost and high-performance alternative comes from reconfigurable computing, a hardware technology based on Field Programmable Gate Array devices (FPGAs). The main objective of the work presented in this paper is to compare implementations on FPGAs and CPUs of different fitness functions in evolutionary algorithms in order to study the performance of the floating-point arithmetic in FPGAs and CPUs that is often present in the optimization problems tackled by these algorithms. We have taken advantage of the parallelism at chip-level of FPGAs pursuing the acceleration of the fitness functions (and consequently, of the evolutionary algorithms) and showing the parallel scalability to reach low cost, low power and high performance computational solutions based on FPGA. Finally, the recent popularity of GPUs as computational units has moved us to introduce these devices in our performance comparisons. We analyse performance in terms of computation times and economic cost.", notes = "Nvidia Quadro FX 580, OpenCL C++ bindings. Vertex5 FPGAs (Virtex2Pro and Spartan3) ECC, Radio Network design problem (RND). Malaga, X-ray (XRAY) pseudo-Voigt. Xilinx ISE 9.2i ModelSim 6, VHDL, Handel-C. Custom designed HPs. FPGA power less than one watt. 50 times.", } @InCollection{GomezCastro2011176, author = "Fernando I. Gomez-Castro and Mario A. Rodriguez-Angeles and Juan G. Segovia-Hernandez and Claudia Gutierrez-Antonio and Abel Briones-Ramirez", title = "Optimal design of multiple dividing wall columns based on genetic programming", editor = "M. C. Georgiadis E. N. Pistikopoulos and A. C. Kokossis", booktitle = "21st European Symposium on Computer Aided Process Engineering", publisher = "Elsevier", year = "2011", volume = "29", pages = "176--180", series = "Computer Aided Chemical Engineering", ISSN = "1570-7946", DOI = "doi:10.1016/B978-0-444-53711-9.50036-5", URL = "http://www.sciencedirect.com/science/article/pii/B9780444537119500365", keywords = "genetic algorithms, genetic programming, Multiple dividing wall columns, stochastic optimisation, columns sizing", abstract = "In this work, two schemes are analysed for the reduction on energy consumptions for ternary distillation: a Petlyuk column, PC, and a Petlyuk with postfractionator system, PCP. To perform the optimal design of the analysed systems, the use of multiobjective genetic algorithms has been considered. Moreover, a strategy for diameter calculation is proposed for the dividing wall column, DWC, and double dividing wall column, DDWC, which is based on their distribution of internal flows. Results show that genetic algorithm tool allows obtaining optimal designs for the PC and PCP systems, with low energy consumptions. Furthermore, the design strategy for the DWC and DDWC shows that the physical structure required for one or two dividing walls is quite similar; thereby, it appears to be an adequate method for the sizing of the dividing wall systems.", } @Article{gomi:2003:GPEM, author = "Takashi Gomi", title = "Book Review: {Evolutionary} Robotics: the Biology, Intelligence, and Technology of Self-Organizing Machines", journal = "Genetic Programming and Evolvable Machines", year = "2003", volume = "4", number = "1", pages = "95--98", month = mar, keywords = "genetic algorithms, genetic programming, evolvable hardware, robot", ISSN = "1389-2576", URL = "https://rdcu.be/dR8sx", DOI = "doi:10.1023/A:1021829228076", abstract = "Review of ISBN:0-262-14070-5 MIT press Authors: Stefano Nolfi and Dario Floreano", notes = "Article ID: 5113075", } @Article{Goncalves:2010:JIDM, title = "Automatic Selection of Training Examples for a Record Deduplication Method Based on Genetic Programming", author = "Gabriel Silva Goncalves and Moises G. {de Carvalho} and Alberto H. F. Laender and Marcos Andre Goncalves", journal = "Journal of Information and Data Management", year = "2010", number = "2", volume = "1", pages = "213--228", month = jun, keywords = "genetic algorithms, genetic programming, replica identification, artificial intelligence", ISSN = "2178-7107", URL = "http://seer.lcc.ufmg.br/index.php/jidm/article/view/59", bibdate = "2010-11-03", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/jidm/jidm1.html#GoncalvesCLG10", size = "16 pages", abstract = "Recently, machine learning techniques have been used to solve the record deduplication problem. However, these techniques require examples, manually generated in most cases, for training purposes. This hinders the use of such techniques because of the cost required to create the set of examples. In this article, we propose an approach based on a deterministic technique to automatically suggest training examples for a deduplication method based on genetic programming. Our experiments with synthetic datasets show that, by using only 15percent of the examples suggested by our approach, it is possible to achieve results in terms of F1 that are equivalent to those obtained when using all the examples, leading to savings in training time of up to 85percent", notes = "An official publication of the Brazilian Computer Society Special Interest Group on Databases", } @InProceedings{goncalves2011experiments, author = "Ivo Goncalves and Sara Silva", title = "Experiments on Controlling Overfitting in Genetic Programming", booktitle = "Local proceedings of the 15th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence", year = "2011", series = "EPIA 2011", pages = "152--166", month = oct, keywords = "genetic algorithms, genetic programming, overfitting, generalization", isbn13 = "978-989-95618-4-7", URL = "https://old.cisuc.uc.pt/publication/show/2653", URL = "https://www.cisuc.uc.pt/publication/showfile?fn=1512948575_Experiments_on_Controlling_Overfitting_in_Genetic_Programming.pdf", size = "15 pages", abstract = "One of the most important goals of any Machine Learning approach is to find solutions that perform well not only on the cases used for learning but also on cases never seen before. This is known as generalization ability, and failure to do so is called over-fitting. In Genetic Programming this issue has not yet been given the attention it deserves, although the number of publications on this subject has been increasing in the past few years. Here we perform several experiments on a small and yet difficult toy problem specifically designed for this work, where a perfect fitting of the training data inevitably results in poor generalization on the unseen test data. The results show that, on this problem, a Random Sampling Technique with parameter settings that maximize the variation between generations can significantly reduce over fitting when compared to a standard GP approach. We also report the results of some techniques that failed to achieve better generalization.", notes = "Not in EPIA-2011 LNCS 7026 published by Springer", } @InProceedings{goncalves:2012:EuroGP, author = "Ivo Goncalves and Sara Silva and Joana B. Melo and Joao M. B. Carreiras", title = "Random Sampling Technique for Overfitting Control in Genetic Programming", booktitle = "Proceedings of the 15th European Conference on Genetic Programming, EuroGP 2012", year = "2012", month = "11-13 " # apr, editor = "Alberto Moraglio and Sara Silva and Krzysztof Krawiec and Penousal Machado and Carlos Cotta", series = "LNCS", volume = "7244", publisher = "Springer Verlag", address = "Malaga, Spain", pages = "218--229", organisation = "EvoStar", isbn13 = "978-3-642-29138-8", URL = "https://old.cisuc.uc.pt/publication/show/2682", DOI = "doi:10.1007/978-3-642-29139-5_19", size = "12 pages", keywords = "genetic algorithms, genetic programming, Over fitting, Generalisation", size = "12 pages", abstract = "One of the areas of Genetic Programming (GP) that, in comparison to other Machine Learning methods, has seen fewer research efforts is that of generalization. Generalisation is the ability of a solution to perform well on unseen cases. It is one of the most important goals of any Machine Learning method, although in GP only recently has this issue started to receive more attention. In this work we perform a comparative analysis of a particularly interesting configuration of the Random Sampling Technique (RST) against the Standard GP approach. Experiments are conducted on three multidimensional symbolic regression real world datasets, the first two on the pharmacokinetics domain and the third one on the forestry domain. The results show that the RST decreases over fitting on all datasets. This technique also improves testing fitness on two of the three datasets. Furthermore, it does so while producing considerably smaller and less complex solutions. We discuss the possible reasons for the good performance of the RST, as well as its possible limitations.", notes = "Part of \cite{Moraglio:2012:GP} EuroGP'2012 held in conjunction with EvoCOP2012 EvoBIO2012, EvoMusArt2012 and EvoApplications2012", } @InProceedings{goncalves:2013:EuroGP, author = "Ivo Goncalves and Sara Silva", title = "Balancing Learning and Overfitting in Genetic Programming with Interleaved Sampling of Training data", booktitle = "Proceedings of the 16th European Conference on Genetic Programming, EuroGP 2013", year = "2013", month = "3-5 " # apr, editor = "Krzysztof Krawiec and Alberto Moraglio and Ting Hu and A. Sima Uyar and Bin Hu", series = "LNCS", volume = "7831", publisher = "Springer Verlag", address = "Vienna, Austria", pages = "73--84", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, Overfitting, Generalisation, Pharmacokinetics, Drug Discovery", isbn13 = "978-3-642-37206-3", DOI = "doi:10.1007/978-3-642-37207-0_7", abstract = "Generalisation is the ability of a model to perform well on cases not seen during the training phase. In Genetic Programming generalization has recently been recognised as an important open issue, and increased efforts are being made towards evolving models that do not overfit. In this work we expand on recent developments that showed that using a small and frequently changing subset of the training data is effective in reducing over fitting and improving generalisation. Particularly, we build upon the idea of randomly choosing a single training instance at each generation and balance it with periodically using all training data. The motivation for this approach is based on trying to keep overfitting low (represented by using a single training instance) and still presenting enough information so that a general pattern can be found (represented by using all training data). We propose two approaches called interleaved sampling and random interleaved sampling that respectively represent doing this balancing in a deterministic or a probabilistic way. Experiments are conducted on three high-dimensional real-life datasets on the pharmacokinetics domain. Results show that most of the variants of the proposed approaches are able to consistently improve generalisation and reduce over fitting when compared to standard Genetic Programming. The best variants are even able of such improvements on a dataset where a recent and representative state-of-the-art method could not. Furthermore, the resulting models are short and hence easier to interpret, an important achievement from the applications' point of view.", notes = "Part of \cite{Krawiec:2013:GP} EuroGP'2013 held in conjunction with EvoCOP2013, EvoBIO2013, EvoMusArt2013 and EvoApplications2013", } @InProceedings{Goncalves:2015:EuroGP, author = "Ivo Goncalves and Sara Silva and Carlos M. Fonseca", title = "On the Generalization Ability of Geometric Semantic Genetic Programming", booktitle = "18th European Conference on Genetic Programming", year = "2015", editor = "Penousal Machado and Malcolm I. Heywood and James McDermott and Mauro Castelli and Pablo Garcia-Sanchez and Paolo Burelli and Sebastian Risi and Kevin Sim", series = "LNCS", volume = "9025", publisher = "Springer", pages = "41--52", address = "Copenhagen", month = "8-10 " # apr, organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, Geometric semantic genetic programming, Generalisation, Overfitting, Pharmacokinetics, Drug discovery", isbn13 = "978-3-319-16500-4", DOI = "doi:10.1007/978-3-319-16501-1_4", abstract = "Geometric Semantic Genetic Programming (GSGP) is a recently proposed form of Genetic Programming (GP) that searches directly the space of the underlying semantics of the programs. The fitness landscape seen by the GSGP variation operators is unimodal with a linear slope by construction and, consequently, easy to search. Despite this advantage, the offspring produced by these operators grow very quickly. A new implementation of the same operators was proposed that computes the semantics of the offspring without having to explicitly build their syntax. This allowed GSGP to be used for the first time in real-life multidimensional datasets. GSGP presented a surprisingly good generalisation ability, which was justified by some properties of the geometric semantic operators. In this paper, we show that the good generalization ability of GSGP was the result of a small implementation deviation from the original formulation of the mutation operator, and that without it the generalization results would be significantly worse. We explain the reason for this difference, and then we propose two variants of the geometric semantic mutation that deterministically and optimally adapt the mutation step. They reveal to be more efficient in learning the training data, and they also achieve a competitive generalization in only a single operator application. This provides a competitive alternative when performing semantic search, particularly since they produce small individuals and compute fast.", notes = "Nominated for EuroGP 2015 Best Paper. Part of \cite{Machado:2015:GP} EuroGP'2015 held in conjunction with EvoCOP2015, EvoMusArt2015 and EvoApplications2015", } @InProceedings{conf/epia/GoncalvesSF15, author = "Ivo Goncalves and Sara Silva and Carlos M. Fonseca", title = "Semantic Learning Machine: {A} Feedforward Neural Network Construction Algorithm Inspired by Geometric Semantic Genetic Programming", booktitle = "Progress in Artificial Intelligence - 17th Portuguese Conference on Artificial Intelligence, {EPIA} 2015", year = "2015", editor = "Francisco C. Pereira and Penousal Machado and Ernesto Costa and Amilcar Cardoso", volume = "9273", series = "Lecture Notes in Computer Science", pages = "280--285", address = "Coimbra, Portugal", month = sep # " 8-11", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-23484-7", bibdate = "2015-08-26", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/epia/epia2015.html#GoncalvesSF15", URL = "http://dx.doi.org/10.1007/978-3-319-23485-4", DOI = "doi:10.1007/978-3-319-23485-4_28", size = "6 pages", abstract = "Geometric Semantic Genetic Programming (GSGP) is a recently proposed form of Genetic Programming in which the fitness landscape seen by its variation operators is unimodal with a linear slope by construction and, consequently, easy to search. This is valid across all supervised learning problems. In this paper we propose a feedforward Neural Network construction algorithm derived from GSGP. This algorithm shares the same fitness landscape as GSGP, which allows an efficient search to be performed on the space of feedforward Neural Networks, without the need to use backpropagation. Experiments are conducted on real-life multidimensional symbolic regression datasets and results show that the proposed algorithm is able to surpass GSGP, with statistical significance, in terms of learning the training data. In terms of generalization, results are similar to GSGP.", } @InProceedings{Goncalves:2016:GECCOcomp, author = "Ivo Goncalves and Sara Silva and Carlos M. Fonseca and Mauro Castelli", title = "Arbitrarily Close Alignments in the Error Space: a Geometric Semantic Genetic Programming Approach", booktitle = "GECCO '16 Companion: Proceedings of the Companion Publication of the 2016 Annual Conference on Genetic and Evolutionary Computation", year = "2016", editor = "Tobias Friedrich and Frank Neumann and Andrew M. Sutton and Martin Middendorf and Xiaodong Li and Emma Hart and Mengjie Zhang and Youhei Akimoto and Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and Daniele Loiacono and Julian Togelius and Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and Faustino Gomez and Carlos M. Fonseca and Heike Trautmann and Alberto Moraglio and William F. Punch and Krzysztof Krawiec and Zdenek Vasicek and Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and Boris Naujoks and Enrique Alba and Gabriela Ochoa and Simon Poulding and Dirk Sudholt and Timo Koetzing", pages = "99--100", keywords = "genetic algorithms, genetic programming: Poster", month = "20-24 " # jul, organisation = "SIGEVO", address = "Denver, USA", publisher = "ACM", publisher_address = "New York, NY, USA", isbn13 = "978-1-4503-4323-7", DOI = "doi:10.1145/2908961.2908988", abstract = "This paper shows how arbitrarily close alignments in the error space can be achieved by Genetic Programming. The consequences for the generalization ability of the resulting individuals are explored.", notes = "Distributed at GECCO-2016.", } @PhdThesis{goncalvesPhdThesis, author = "Ivo Carlos Pereira Goncalves", title = "An Exploration of Generalization and Overfitting in Genetic Programming: Standard and Geometric Semantic Approaches", school = "Department of Informatics Engineering, University of Coimbra", year = "2016", address = "Coimbra, Portugal", month = nov, keywords = "genetic algorithms, genetic programming, Evolutionary Computation, Geometric Semantic Genetic Programming, Supervised Learning, Generalization, Overfitting, Neural Networks, Semantic Learning Machine", URL = "https://hdl.handle.net/10316/32725", URL = "https://estudogeral.sib.uc.pt/jspui/bitstream/10316/32725/3/An_Exploration_of_Generalization_and_Overfitting_in_Genetic_Programming.pdf", size = "202 pages", abstract = "Computational learning refers to the task of inducing a general pattern from a provided set of examples. A learning method is expected to generalize to unseen examples of the same pattern. A common issue in computational learning is the possibility that the resulting models could be simply learning the provided set of examples, instead of learning the underlying pattern. A model that is incurring in such a behaviour is commonly said to be over fitting. This dissertation explores the task of computational learning and the related concepts of generalization and overfitting, in the context of Genetic Programming (GP). GP is a computational method inspired by natural evolution that considers a set of primitive functions and terminals that can be combined without any considerable constraints on the structure of the models being evolved. This flexibility can help in learning complex patterns but it also increases the risk of overfitting. The contributions of this dissertation cover the most common form of GP (Standard GP), as well as the recently proposed Geometric Semantic GP (GSGP). The initial set of approaches relies on dynamically selecting different training data subsets during the evolutionary process. These approaches can avoid overfitting and improve the resulting generalization without restricting the flexibility of GP. Besides improving the generalization, these approaches also produce considerably smaller individuals. An analysis of the generalization ability of GSGP is performed, which shows that the generalization outcome is greatly dependent on particular characteristics of the mutation operator. It is shown that, as Standard GP, the original formulation of GSGP is prone to overfitting. The necessary conditions to avoid overfitting are presented. When such conditions are in place, GSGP can achieve a particularly competitive generalization. A novel geometric semantic mutation that substantially improves the effectiveness and efficiency of GSGP is proposed. Besides considerably improving the training data learning rate, it also achieves a competitive generalization with only a few applications of the mutation operator. The final set of contributions covers the domain of Neural Networks (NNs). These contributions originated as an extension of the research conducted within GSGP. This set of contributions includes the definition of a NN construction algorithm based on an extension of the mutation operator defined in GSGP. Similarly to GSGP, the proposed algorithm searches over a space without local optima. This allows for an effective and efficient stochastic search in the space of NNs, without the need to use backpropagation to adjust the weights of the network. Finally, two search stopping criteria are proposed, which can be directly used in the proposed NN construction algorithm and in GSGP. These stopping criteria are able to detect when the risk of overfitting increases significantly. It is shown that the stopping points detected result in a competitive generalization.", notes = "Supervisors: Carlos Fonseca and Sara Silva", } @InProceedings{Goncalves:2017:GECCO, author = "Ivo Goncalves and Sara Silva and Carlos M. Fonseca and Mauro Castelli", title = "Unsure when to Stop?: Ask Your Semantic Neighbors", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4920-8", address = "Berlin, Germany", pages = "929--936", size = "8 pages", URL = "http://doi.acm.org/10.1145/3071178.3071328", DOI = "doi:10.1145/3071178.3071328", acmid = "3071328", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, generalization, geometric semantic genetic programming, overfitting, semantic learning machine, stopping criteria", month = "15-19 " # jul, abstract = "In iterative supervised learning algorithms it is common to reach a point in the search where no further induction seems to be possible with the available data. If the search is continued beyond this point, the risk of overfitting increases significantly. Following the recent developments in inductive semantic stochastic methods, this paper studies the feasibility of using information gathered from the semantic neighbourhood to decide when to stop the search. Two semantic stopping criteria are proposed and experimentally assessed in Geometric Semantic Genetic Programming (GSGP) and in the Semantic Learning Machine (SLM) algorithm (the equivalent algorithm for neural networks). The experiments are performed on real-world high-dimensional regression datasets. The results show that the proposed semantic stopping criteria are able to detect stopping points that result in a competitive generalization for both GSGP and SLM. This approach also yields computationally efficient algorithms as it allows the evolution of neural networks in less than 3 seconds on average, and of GP trees in at most 10 seconds. The usage of the proposed semantic stopping criteria in conjunction with the computation of optimal mutation/learning steps also results in small trees and neural networks.", notes = "Also known as \cite{Goncalves:2017:USA:3071178.3071328} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @InProceedings{Goncalves:2019:GPTP, author = "Ivo Goncalves and Marta Seca and Mauro Castelli", title = "Explorations of the Semantic Learning Machine Neuroevolution Algorithm: Dynamic Training Data Use, Ensemble Construction Methods, and Deep Learning Perspectives", booktitle = "Genetic Programming Theory and Practice XVII", year = "2019", editor = "Wolfgang Banzhaf and Erik Goodman and Leigh Sheneman and Leonardo Trujillo and Bill Worzel", pages = "39--62", address = "East Lansing, MI, USA", month = "16-19 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Semantic learning machine, Neuroevolution, Evolutionary machine learning, Artificial neural networks, ANN, Deep learning Deep semantic learning machine", isbn13 = "978-3-030-39957-3", DOI = "doi:10.1007/978-3-030-39958-0_3", abstract = "The recently proposed Semantic Learning Machine (SLM) neuroevolution algorithm is able to construct Neural Networks (NNs) over unimodal error landscapes in any supervised learning problem where the error is measured as a distance to the known targets. This chapter studies how different methods of dynamically using the training data affect the resulting generalization of the SLM algorithm. Across four real-world binary classification datasets, SLM is shown to outperform the Multi-layer Perceptron, with statistical significance, after parameter tuning is performed in both algorithms. Furthermore, this chapter also studies how different ensemble constructions methods influence the resulting generalization. The results show that the stochastic nature of SLM already confers enough diversity to the ensembles such that Bagging and Boosting cannot improve upon a simple averaging ensemble construction method. Finally, some initial results with SLM and Convolutional NNs are presented and future Deep Learning perspectives are discussed.", notes = "Part of \cite{Banzhaf:2019:GPTP}, published after the workshop", } @InProceedings{Goncalves-Moreira:2019:CEC, author = "Joao Pedro {Goncalves Moreira} and Marcus Ritt", booktitle = "2019 IEEE Congress on Evolutionary Computation (CEC)", title = "Evolving task priority rules for heterogeneous assembly line balancing", year = "2019", pages = "1423--1430", month = jun, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2019.8790332", abstract = "Assembly lines in sheltered work centers are a strategy for integrating persons with disabilities in the work force. In such centers, the assignment of tasks to workers must consider particularities of each worker and precedences between the tasks, and must be done in a way that maximizes the production rate of the assembly line. In this paper we present an application of genetic programming for evolving task selection rules that are competitive with rules manually created and described in literature. These rules are useful in constructive heuristics for rapidly producing solutions of good quality, and can be embedded into more sophisticated heuristic methods to improve them.", notes = "Also known as \cite{8790332}", } @InProceedings{Goncalves-Moreira:2021:EuroGP, author = "Joao Pedro {Goncalves Moreira} and Marcus Ritt", title = "Evolving Allocation Rules for Beam Search Heuristics in Assembly Line Balancing", booktitle = "EuroGP 2021: Proceedings of the 24th European Conference on Genetic Programming", year = "2021", editor = "Ting Hu and Nuno Lourenco and Eric Medvet", series = "LNCS", volume = "12691", publisher = "Springer Verlag", address = "Virtual Event", pages = "214--228", month = "7-9 " # apr, organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Combinatorial optimization, Allocation rules, Station-based allocation procedures, Assembly line balancing: Poster", isbn13 = "978-3-030-72811-3", DOI = "doi:10.1007/978-3-030-72812-0_14", abstract = "We study the evolution of rules that define how to assign tasks to workstations in heuristic procedures for assembly line balancing. In assembly line balancing, a set of partially ordered tasks has to be assigned to workstations. The variant we consider, known as the assembly line worker assignment and balancing problem (ALWABP), has a fixed number of machines and workers, and different workers need different times to execute the tasks. A solution is an assignment of tasks and workers to workstations satisfying the partial order of the tasks, and the problem is to find a solution that maximizes the production rate of the assembly line. These problems are often solved by station-based assignment procedures, which use heuristic rules to select the tasks to be assigned to stations. There are many selection rules available in the literature. We show how efficient rules can be evolved, and demonstrate that rules evolved for simple assignment procedures are also effective in stochastic heuristic procedures using beam search, leading to improved heuristics.", notes = "UFRGS disabled workers on assembly line in Spain. ALWABP-2. Minimise cycle time. Beam (125 best solutions) non-deterministic expansion of members of beam. SBAP. Seed 7 human heuristics. Subtree mutation and subtree crossover, pop=700. http://www.evostar.org/2021/eurogp/ Part of \cite{Hu:2021:GP} EuroGP'2021 held in conjunction with EvoCOP2021, EvoMusArt2021 and EvoApplications2021", } @InProceedings{Goncharenko:2020:ACIT, author = "Dmytro Goncharenko and Andrii Oliinyk and Ievgen Fedorchenko and Serhii Korniienko` and Alexander Stepanenko and Anastasia Kharchenko and Yuliia Fedorchenko", title = "Genetic Algorithm for Solution of the Problem of Optimal Location of the Distributed Electrical Networks", booktitle = "2020 10th International Conference on Advanced Computer Information Technologies (ACIT)", year = "2020", pages = "380--385", abstract = "The problem of the optimisation of location of elements in the complex distributed power supply systems is considered in this paper. The article proposes a mathematical model of solving the problem of optimal location of multiple power supply and consumer assigning in the electric supply system. A modified genetic algorithm for solving this problem has been developed based on the methods of evolutionary modeling and genetic programming. The results of experiments showed good performance of the proposed genetic algorithm and the possibility of its application for the optimal placement of power sources in a distributed electrical network.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ACIT49673.2020.9208888", month = sep, notes = "Also known as \cite{9208888}", } @Article{GONG:2021:OE, author = "Shangpeng Gong and Jie Chen and Changbo Jiang and Sudong Xu and Fei He and Zhiyuan Wu", title = "Prediction of solitary wave attenuation by emergent vegetation using genetic programming and artificial neural networks", journal = "Ocean Engineering", volume = "234", pages = "109250", year = "2021", ISSN = "0029-8018", DOI = "doi:10.1016/j.oceaneng.2021.109250", URL = "https://www.sciencedirect.com/science/article/pii/S0029801821006764", keywords = "genetic algorithms, genetic programming, Emergent vegetation, Wave attenuation, Transmission coefficient, Genetic programming (GP), Artificial neural networks (ANNs)", abstract = "Analyzing the attenuation of extreme waves by coastal emergent vegetation provides crucial information on revetment planning. In this study, three kinds of laboratory experiments of wave attenuation by rigid vegetation are performed. Transmission coefficient (Kt) was used to characterize the effect of wave attenuation. The influence of dimensionless factors including relative wave height (H/h), relative width (B/L), relative height (hv/h) and solid volume fraction (?) on the Kt under the action of solitary wave was explored by Genetic Programming (GP), Artificial Neural Networks (ANNs) and multivariate non-linear regression (MNLR). Prediction formulae (R2 is up to 0.95) of the Kt in different models were established by GP method, and the sensitivity of each dimensionless factor was analyzed by statistical analysis. ANNs were used to compare the weight of each factor. The power function relationships between Kt and factors was obtained by MNLR. The results show that GP can qualitatively acquire the sensitivity of parameters and is suitable for the sensitivity analysis of the vegetation wave disspation model, providing a more efficient and accurate prediction method. The results can provide guidelines for vegetation planting as well as the scientific basis for vegetation revetment engineering", } @Article{Gong:2015:ASC, author = "Yue-Jiao Gong and Wei-Neng Chen and Zhi-Hui Zhan and Jun Zhang and Yun Li and Qingfu Zhang and Jing-Jing Li", title = "Distributed evolutionary algorithms and their models: A survey of the state-of-the-art", journal = "Applied Soft Computing", year = "2015", volume = "34", pages = "286--300", month = sep, keywords = "genetic algorithms, genetic programming, Distributed evolutionary computation, Coevolutionary computation, Evolutionary algorithms, Global optimization, Multiobjective optimization", ISSN = "1568-4946", URL = "http://repository.essex.ac.uk/13796/1/1-s2.0-S1568494615002987-main.pdf", URL = "https://www.sciencedirect.com/science/article/pii/S1568494615002987", DOI = "doi:10.1016/j.asoc.2015.04.061", size = "15 pages", abstract = "The increasing complexity of real-world optimization problems raises new challenges to evolutionary computation. Responding to these challenges, distributed evolutionary computation has received considerable attention over the past decade. This article provides a comprehensive survey of the state-of-the-art distributed evolutionary algorithms and models, which have been classified into two groups according to their task division mechanism. Population-distributed models are presented with master-slave, island, cellular, hierarchical, and pool architectures, which parallelise an evolution task at population, individual, or operation levels. Dimension-distributed models include coevolution and multi-agent models, which focus on dimension reduction. Insights into the models, such as synchronization, homogeneity, communication, topology, speedup, advantages and disadvantages are also presented and discussed. The study of these models helps guide future development of different and/or improved algorithms. Also highlighted are recent hotspots in this area, including the cloud and MapReduce-based implementations, GPU and CUDA-based implementations, distributed evolutionary multiobjective optimization, and real-world applications. Further, a number of future research directions have been discussed, with a conclusion that the development of distributed evolutionary computation will continue to flourish.", notes = "Brief mention of GP Also known as \cite{GONG2015286} School of Advanced Computing,Sun Yat-Sen University, Guangzhou, China", } @InProceedings{Gonzales:2007:cec, author = "Eloy Gonzales and Karla Taboada and Kaoru Shimada and Shingo Mabu and Kotaro Hirasawa and Jinglu Hu", title = "Class Association Rule Mining for Large and Dense Databases with Parallel Processing of Genetic Network Programming", booktitle = "2007 IEEE Congress on Evolutionary Computation", year = "2007", editor = "Dipti Srinivasan and Lipo Wang", pages = "4615--4622", address = "Singapore", month = "25-28 " # sep, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "1-4244-1340-0", file = "1045.pdf", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2007.4425077", abstract = "Among several methods of extracting association rules that have been reported, a new evolutionary computation method named Genetic Network Programming (GNP) has also shown its effectiveness for small datasets that have a relatively small number of attributes.", notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C", } @InProceedings{Gonzales:2010:gecco, author = "Eloy Gonzales and Shingo Mabu and Karla Taboada and Kotaro Hirasawa and Kaoru Shimada", title = "Pruning association rules using statistics and genetic relation algoritm", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "419--420", keywords = "genetic algorithms, genetic programming, Evolution strategies and evolutionary programming, Poster", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830483.1830562", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Most of the classification methods proposed produces too many rules for humans to read over, that is, the number of generated rules is thousands or millions which means complex and hardly understandable for the users. In this paper, a new post-processing pruning method for class association rules is proposed by a combination of statistics and an evolutionary method named Genetic Relation Algorithm (GRA). The algorithm is carried out in two phases. In the first phase the rules are pruned depending on their matching degree and in the second phase GRA selects the most interesting rules using the distance between them and their strength.", notes = "Also known as \cite{1830562} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @InProceedings{DBLP:conf/pdp/GonzalezV07, author = "Daniel Lombrana Gonzalez and Francisco {Fernandez de Vega}", title = "On the Intrinsic Fault-Tolerance Nature of Parallel Genetic Programming", booktitle = "15th Euromicro Conference on Parallel, Distributed and Network-based Processing", year = "2007", editor = "Pasqua D'Ambra and Mario R. Guarracino", pages = "450--458", bibsource = "DBLP, http://dblp.uni-trier.de", address = "Naples", month = "7-9 " # feb, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, fault tolerance, parallel genetic programming", ISBN = "0-7695-2784-1", ISSN = "1066-6192", DOI = "doi:10.1109/PDP.2007.56", abstract = "In this paper we show how Parallel Genetic Programming can run on a distributed system with volatile resources without any lack of efficiency. By means of a series of experiments, we test whether Parallel GP -and consistently Evolutionary Algorithms- are intrinsically fault-tolerant. The interest of this result is crucial for researchers dealing with real-life problems in which parallel and distributed systems are required for obtaining results on a reasonable time. In that case, parallel GP tools will not require the inclusion of fault-tolerant computing techniques or libraries when running on Meta-systems undergoing volatility, such us Desktop Grids offering Public Resource Computing. We test the performance of the algorithm by studying the quality of solutions when running over distributed resources undergoing processors failures, when compared with a fault-free environment. This new feature, which shows its advantages, improves the dependability of the Parallel Genetic Programming Algorithm.", notes = "PDP 2007 http://www.na.icar.cnr.it/~pdp2007", } @InProceedings{1277302, author = "Daniel Lombrana Gonzalez and Francisco {Fernandez de Vega}", title = "Dynamic populations and length evolution: key factors for analyzing fault tolerance on parallel genetic programming", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "1752--1752", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1752.pdf", DOI = "doi:10.1145/1276958.1277302", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming: Poster, management, measurement, parallel and distributed evolutionary algorithm, reliability, size evolution, bloat", size = "1 page", abstract = "This paper presents an experimental research on the size of individuals when fixed and dynamic size populations are employed with Genetic Programming (GP). We propose an improvement to the Plague operator (PO), that we have called Random Plague (RPO). Then by further studies based on the RPO results we analysed the Fault Tolerance on Parallel Genetic Programming.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071 'Each removed individual [ie selected against] is a lost/error computer in a simulation of a fine grained parallel GP.' cites \cite{DBLP:conf/pdp/GonzalezV07}", } @InProceedings{Gonzalez:2007:cec, author = "Daniel Lombrana Gonzalez and Francisco {Fernandez de Vega}", title = "Analyzing Fault Tolerance on Parallel Genetic Programming by Means of Dynamic-Size Populations", booktitle = "2007 IEEE Congress on Evolutionary Computation", year = "2007", editor = "Dipti Srinivasan and Lipo Wang", pages = "4392--4398", address = "Singapore", month = "25-28 " # sep, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "1-4244-1340-0", file = "1666.pdf", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2007.4425045", abstract = "This paper presents an experimental research on the size of individuals when dynamic size populations are employed with Genetic Programming (GP). By analysing the individual's size evolution, some ideas are presented for reducing the length of the best individual while also improving the quality. This research has been performed studying both individual's size and quality of solutions, considering the fixed-size populations and also dynamic size by means of the plague operator. We propose an improvement to the Plague operator, that we have called Random Plague, that positively affects the quality of solutions and also influences the individuals' size. The results are then considered from a quite different point of view, the presence of processors failures when parallel execution over distributed computing environments are employed. We show that results strongly encourage the use of Parallel GP on non fault-tolerant computing resources: experiments shows the fault tolerant nature of Parallel GP.", notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C", } @InProceedings{Gonzalez:2008:ibergrid, author = "Daniel Lombrana Gonzalez and Francisco {Fernandez de Vega} and L. Trujillo and G. Olague and M. Cardenas and L. Araujo and P. Castillo and K. Sharman and A. Silva", title = "Interpreted Applications within BOINC Infrastructure", booktitle = "IBERGRID 2nd Iberian Grid Infrastructure Conference Proceedings", year = "2008", editor = "Fernando Silva and Gaspar Barreira and Ligia Ribeiro", pages = "261--272", address = "Porto, Portugal", publisher_address = "Oleiros (La Coruna), Spain", month = "12-14 " # may, publisher = "netbiblo.com", keywords = "genetic algorithms, genetic programming, BOINC, Interpreted Applications, Virtualization", isbn13 = "978-84-9745-288-5", URL = "http://nlp.uned.es/~lurdes/araujo/ibergrid08.pdf", size = "12 pages", abstract = "BOINC is one of the most employed middlewares in the scientific community. However, the development of BOINC applications could be difficult if the target application is an Interpreted Application such as Matlab, R or Java. The BOINC team provides an intermediate solution, the wrapper, which can run statically linked programs. Nevertheless when the application has lots of dependencies, BOINC will not be able to deploy it. In this paper, we propose to exploit the BOINC infrastructure with Interpreted Applications by complementing the wrapper program with a new application and extending the whole BOINC infrastructure by adding a new vitalisation layer, and best of all without modifying the source code of the interpreted application. Three experiments using well-known interpreted applications -Java, R and Matlab- are performed to demonstrate the viability of running unmodified interpreted applications inside a BOINC infrastructure.", notes = "Slides http://www.ibergrid.eu/2008/presentations/Dia%2014/6a_paper1.pdf IBERGRID, ECJ 42 runs 1 week. 41 PCs. R script (required extension via virtualisation of BOINC framework). IAP. Java.", } @InProceedings{Gonzalez:2009:PDP, author = "Daniel Lombrana Gonzalez and Francisco {Fernandez de Vega} and Leonardo Trujillo and Gustavo Olague and Lourdes Araujo and Pedro Castillo and Juan Julian Merelo and Ken Sharman", title = "Increasing {GP} Computing Power for Free via Desktop {GRID} Computing and Virtualization", booktitle = "17th Euromicro International Conference on Parallel, Distributed and Network-based Processing", year = "2009", month = "18-20 " # feb, pages = "419--423", address = "Weimar, Germany", isbn13 = "978-0-7695-3544-9", keywords = "genetic algorithms, genetic programming, BOINC framework, GP source code, desktop grid computing, evolutionary algorithms, genetic programming computing power, volunteer computing, grid computing, software engineering", DOI = "doi:10.1109/PDP.2009.25", ISSN = "1066-6192", abstract = "This paper presents how it is possible to increase the genetic programming (GP) computing power (CP) for free, via volunteer computing (VC), using the well known framework BOINC plus a new ``virtualization'' layer which adds all the benefits from the virtualization paradigm. Two different experiments, employing a standard GP tool and a complex GP system, are performed with distributed PCs over several cities to show the free achieved CP by means of VC, without the necessity of modifying or adapting the original GP source code. The methodology can be easily extended to evolutionary algorithms (EAs).", notes = "See also arXiv https://arxiv.org/abs/0801.1210 Also known as \cite{4912963}", } @InProceedings{Gonzalez:2009:BADS, author = "Daniel Lombrana Gonzalez and Francisco {Fernandez de Vega} and Henri Casanova", title = "Characterizing fault tolerance in genetic programming", booktitle = "BADS '09: Proceedings of the 2009 workshop on Bio-inspired algorithms for distributed systems", year = "2009", editor = "Gianluigi Folino and Natalio Krasnogor and Carlo Mastroianni and Franco Zambonelli", pages = "1--10", address = "Barcelona, Spain", publisher_address = "New York, NY, USA", month = jun # " 15-19", publisher = "ACM", keywords = "genetic algorithms, genetic programming, Fault-tolerance, parallel genetic programming, desktop grids", isbn13 = "978-1-60558-584-0", URL = "http://navet.ics.hawaii.edu/~casanova/homepage/papers/lombrana_bads2007.pdf", DOI = "doi:10.1145/1555284.1555286", size = "10 pages", abstract = "Evolutionary Algorithms (EAs), and particularly Genetic Programming (GP), are techniques frequently employed to solve difficult real-life problems, which can require up to days or months of computation. One approach to reduce the time to solution is to use parallel computing on distributed platforms. Distributed platforms are prone to failures, and when these platforms are large and/or low-cost, failures are expected events rather than catastrophic exceptions. Therefore, fault tolerance and recovery techniques often become necessary. It turns out that Parallel GP (PGP) applications have an inherent ability to tolerate failures. This ability is quantified via simulation experiments performed using failure traces from real-world distributed platforms, namely, desktop grids (DGs), for two well-known GP problems. A simple technique is then proposed by which PGP applications can better tolerate the different, and often high, failures rates seen in different platforms.", notes = "Also known as \cite{1555286} even-5-parity, 11-multiplexor", } @PhdThesis{LombranaGonzalez:thesis, author = "D. Daniel {Lombrana Gonzalez}", title = "Programacion genetica tolerante a fallos: despliegue de programacion genetica sobre computacion grid de sobremesa", school = "Universidad de Extremadura", year = "2010", address = "Spain", keywords = "genetic algorithms, genetic programming", URL = "https://www.educacion.es/teseo/imprimirFicheroTesis.do?fichero=16774#2010lombrprogr.pdf", URL = "http://dialnet.unirioja.es/servlet/tesis?codigo=21131", URL = "http://biblioteca.unex.es/tesis/9788469352939.pdf", size = "161 pages", abstract = "En esta tesis se presenta un estudio sobre la tolerancia a fallos de programacion genetica en entornos desktop grid, En la primera parte de la tesis se analizan las caracteristicas principales de los sistemas destkop grid, explicando por que son una buena plataforma para ejecutar algoritmos evolutivos, en general, y programacion genetica paralela en particular. Ademas, se proponen dos mejoras para estos sistemas (una herramienta de gestion de recursos y un sistema de entornos de ejecucion a medida) con el objetivo de acercar estos sistemas a los investigadores de algoritmos evolutivos. En la segunda parte de la tesis se analizan las caracteristicas de la programacion genetica paralela desde el punto de vista de la tolerancia a fallos y se estudia la posibilidad de ejecutar estas aplicaciones en entornos desktop grid sin la utilizacion de tecnicas de tolerancia a fallos. El estudio se realiza utilizando datos de tres sistemas desktop grid reales, llegando a la conclusion de que la programacion genetica paralela es tolerante a fallos por naturaleza.", notes = "In english Running Parallel Evolutionary Algorithms in Desktop Grid Systems. Evolutionary Algorithms. Parallel and Distributed Systems. Desktop Grid Computing. Improving BOINC Based Desktop Grid Systems. Studying the fault-tolerance nature of Parallel Genetic Programming. within Desktop Grid Systems. Fault Tolerance. Computer Failures and Genetic Programming Bloat. Plague Operator and Computer Failures. Supervisor: Francisco Fernandez de Vega Resumen en Espanol", } @Article{Gonzalez:2010:FGCS, author = "Daniel {Lombrana Gonzalez} and Francisco {Fernandez de Vega} and Henri Casanova", title = "Characterizing fault tolerance in genetic programming", journal = "Future Generation Computer Systems", year = "2010", volume = "26", number = "6", pages = "847--856", month = jun, keywords = "genetic algorithms, genetic programming, Fault tolerance, Parallel genetic programming, Desktop grids", ISSN = "0167-739X", URL = "http://www.sciencedirect.com/science/article/B6V06-4YDT3S4-2/2/0a9075d8d9c6905e388ad608f0c81e79", DOI = "doi:10.1016/j.future.2010.02.006", size = "10 pages", abstract = "Evolutionary algorithms, including genetic programming (GP), are frequently employed to solve difficult real-life problems, which can require up to days or months of computation. An approach for reducing the time-to-solution is to use parallel computing on distributed platforms. Large platforms such as these are prone to failures, which can even be commonplace events rather than rare occurrences. Thus, fault tolerance and recovery techniques are typically necessary. The aim of this article is to show the inherent ability of parallel GP to tolerate failures in distributed platforms without using any fault-tolerant technique. This ability is quantified via simulation experiments performed using failure traces from real-world distributed platforms, namely, desktop grids, for two well-known problems.", notes = "5.1.1. Even parity 5 5.1.2. 11-bit multiplexer", } @Article{gonzalez:2003:GPEM, author = "Fabio A. Gonzalez and Dipankar Dasgupta", title = "Anomaly Detection Using Real-Valued Negative Selection", journal = "Genetic Programming and Evolvable Machines", year = "2003", volume = "4", number = "4", pages = "383--403", month = dec, keywords = "artificial immune systems, anomaly detection, negative selection, matching rule, self-organizing maps", ISSN = "1389-2576", DOI = "doi:10.1023/A:1026195112518", abstract = "a real-valued representation for the negative selection algorithm and its applications to anomaly detection. In many anomaly detection applications, only positive (normal) samples are available for training purpose. However, conventional classification algorithms need samples for all classes (e.g. normal and abnormal) during the training phase. This approach uses only normal samples to generate abnormal samples, which are used as input to a classification algorithm. This hybrid approach is compared against an anomaly detection technique that uses self-organising maps to cluster the normal data sets (samples). Experiments are performed with different data sets and some results are reported.", notes = "Special issue on artificial immune systems Article ID: 5144849", } @InProceedings{DBLP:conf/gecco/GonzalezH09, author = "Gerardo Gonzalez and Dean F. Hougen", title = "Elitism, fitness, and growth", booktitle = "GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation", year = "2009", editor = "Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano {van Hemert} and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique Alba", pages = "1851--1852", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming, Poster", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1569901.1570199", abstract = "Bloat may occur when evolution allows chromosome growth. Recently it has been shown that elitism can inhibit bloat. Here we study interactions between growth, elitism, and fitness landscapes. Our results show that in some cases elitism neither constrains growth nor increases the rate of fitness accumulation, and when elitism does constrain growth it may stall the search completely.", notes = "GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009). ACM Order Number 910092.", } @InProceedings{Gonzalez:2020:CEC, author = "Santiago Gonzalez and Risto Miikkulainen", booktitle = "2020 IEEE Congress on Evolutionary Computation (CEC)", title = "Improved Training Speed, Accuracy, and Data Utilization Through Loss Function Optimization", year = "2020", editor = "Yaochu Jin", month = "19-24 " # jul, keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-7281-6929-3", DOI = "doi:10.1109/CEC48606.2020.9185777", abstract = "As the complexity of neural network models has grown, it has become increasingly important to optimize their design automatically through meta-learning. Methods for discovering hyperparameters, topologies, and learning rate schedules have lead to significant increases in performance. This paper shows that loss functions can be optimized with meta-learning as well, and result in similar improvements. The method, Genetic Loss function Optimization (GLO), discovers loss functions de novo, and optimizes them for a target task. Leveraging techniques from genetic programming, GLO builds loss functions hierarchically from a set of operators and leaf nodes. These functions are repeatedly recombined and mutated to find an optimal structure, and then a covariance-matrix adaptation evolutionary strategy (CMA-ES) is used to find optimal coefficients. Networks trained with GLO loss functions are found to outperform the standard cross-entropy loss on standard image classification tasks. Training with these new loss functions requires fewer steps, results in lower test error, and allows for smaller datasets to be used. Loss function optimization thus provides a new dimension of metalearning, and constitutes an important step towards AutoML.", notes = "Also known as \cite{9185777}", } @InProceedings{Gonzalez:2021:GECCO, author = "Santiago Gonzalez and Risto Miikkulainen", title = "Optimizing Loss Functions Through Multi-Variate {Taylor} Polynomial Parameterization", booktitle = "Proceedings of the 2021 Genetic and Evolutionary Computation Conference", year = "2021", editor = "Francisco Chicano and Alberto Tonda and Krzysztof Krawiec and Marde Helbig and Christopher W. Cleghorn and Dennis G. Wilson and Georgios Yannakakis and Luis Paquete and Gabriela Ochoa and Jaume Bacardit and Christian Gagne and Sanaz Mostaghim and Laetitia Jourdan and Oliver Schuetze and Petr Posik and Carlos Segura and Renato Tinos and Carlos Cotta and Malcolm Heywood and Mengjie Zhang and Leonardo Trujillo and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Fuyuki Ishikawa and Inmaculada Medina-Bulo and Frank Neumann and Andrew M. Sutton", pages = "305--313", address = "internet", series = "GECCO '21", month = jul # " 10-14", organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, ANN, Neural networks, Loss Functions, Metalearning, Evolutionary Strategies", isbn13 = "9781450383509", URL = "https://nn.cs.utexas.edu/downloads/papers/gonzalez.gecco21.pdf", DOI = "doi:10.1145/3449639.3459277", size = "9 pages", abstract = "Metalearning of deep neural network (DNN) architectures and hyperparameters has become an increasingly important area of research. Loss functions are a type of metaknowledge that is crucial to effective training of DNNs, however, their potential role in metalearning has not yet been fully explored. Whereas early work focused on genetic programming (GP) on tree representations, this paper proposes continuous CMA-ES optimisation of multivariate Taylor polynomial parameterizations. This approach, TaylorGLO, makes it possible to represent and search useful loss functions more effectively. In MNIST, CIFAR-10, and SVHN benchmark tasks, TaylorGLO finds new loss functions that outperform the standard cross-entropy loss as well as novel loss functions previously discovered through GP, in fewer generations. These functions serve to regularise the learning task by discouraging overfitting to the labels, which is particularly useful in tasks where limited training data is available. The results thus demonstrate that loss function optimization is a productive new avenue for metalearning", notes = "University of Texas at Austin GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)", } @Article{Gonzalez-Alvarez:2015:GPEM, author = "David L. Gonzalez-Alvarez and Miguel A. Vega-Rodriguez and Alvaro Rubio-Largo", title = "Multiobjective optimization algorithms for motif discovery in DNA sequences", journal = "Genetic Programming and Evolvable Machines", year = "2015", volume = "16", number = "2", pages = "167--209", month = jun, keywords = "genetic algorithms, Computer science, Multiobjective optimisation, Metaheuristics, Motif discovery, Bioinformatics", ISSN = "1389-2576", URL = "http://dx.doi.org/10.1007/s10710-014-9232-2", DOI = "doi:10.1007/s10710-014-9232-2", size = "43 pages", abstract = "Optimisation techniques have become powerful tools for approaching multiple NP-hard optimization problems. In this kind of problem it is practically impossible to obtain optimal solutions, thus we must apply approximation strategies such as metaheuristics. In this paper, seven metaheuristics have been used to address an important biological problem known as the motif discovery problem. As it is defined as a multiobjective optimization problem, we have adapted the proposed algorithms to this optimization context. We evaluate the proposed metaheuristics on 54 sequence datasets that belong to four organisms with different numbers of sequences and sizes. The results have been analysed in order to discover which algorithm performs best in each case. The algorithms implemented and the results achieved can assist biological researchers in the complicated task of finding DNA patterns with an important biological relevance.", } @InProceedings{Gonzalez-Campos:2014:MICAI, author = "G. Gonzalez-Campos and L. M. Torres-Trevino and E. Luevano-Hipolito and A. {Martinez-de la Cruz}", booktitle = "13th Mexican International Conference on Artificial Intelligence (MICAI)", title = "Modeling Synthesis Processes of Photocatalysts Using Symbolic Regression alpha-beta", year = "2014", pages = "174--179", abstract = "Symbolic regression is an application of genetic programming and is used for modelling different dynamic processes. Industrial processes problems have been solved using this technique. In this work a symbolic regression algorithm is used for modelling the synthesis process of the oxides Bi2MoO6 and V2O5 in order to provide a model. These oxides are used on heterogeneous photo catalysis. Genetic programming, artificial neural network and linear regression are compared with symbolic regression models using statistics metrics to evaluate them.", keywords = "genetic algorithms, genetic programming, Symbolic regression, photocatalysis, industrial modelling", DOI = "doi:10.1109/MICAI.2014.33", month = nov, notes = "Fac. de Ingeniena Mec. y Electr., Univ. Autonoma de Nuevo Leon, San Nicolás de los Garza, Mexico Also known as \cite{7222861}", } @InProceedings{GonzalezMunoz:2005:RVK, author = "David {Gonzalez Munoz} and Oscar Gustafsson and Lars Wanhammar", title = "Evolution of filter order equations for linear-phase {FIR} filters using gene expression programming", booktitle = "RVK 2005 RadioVetenskap och Kommunikation", year = "2005", pages = "679--682", address = "Linkoping, Sweden", month = "14-16 " # jun, organisation = "FOI", keywords = "genetic algorithms, genetic programming, gene expression programming", URL = "http://www.es.isy.liu.se/publications/papers_and_reports/2005/RVK05_oscarg_FIRorder.pdf", abstract = "Estimation of the minimum filter order for linear-phase FIR filters is commonly performed during the design of DSP systems. In this work gene expression programming is used to discover new equations for the linear-phase FIR filter order. The results are shown to be as least as accurate as previously proposed estimates.", notes = "MSc 2005 ? broken September 2020 http://www.rvk05.foi.se/ P18T http://www.rvk05.foi.se/Sessions_final.html Linkoping University, Linkoping, Sweden ", } @InProceedings{GonzalezPadilla:2010:CERMA, author = "Omar Alfrego {Gonzalez Padilla} and Felix Francisco {Ramos Corchado} and Jean-Paul Bartes", title = "Genetic Programming for Task Selection in Dialogue Systems", booktitle = "Electronics, Robotics and Automotive Mechanics Conference (CERMA), 2010", year = "2010", month = "28 " # sep # "-1 " # oct, pages = "180--184", abstract = "Natural language is too complex and ambiguous to be understood by a computer using currently known methods. However, in some cases natural language interfaces are possible because interaction is limited by the set of tasks the system can perform. In this context, when a user starts a dialog, the system tries to identify the intended task, which determines the course of the dialog. Modelling tasks in order to allow selecting one is labour intensive and may cause conflicts if the system performs many tasks. We propose using ripple down rules as a task selection mechanism, and genetic programming for automatic generation of such rules. Advantages of this approach are ease of generation and possibility to learn from user interaction. We tested the approach in a multi-agent system named OMAS, where agents interact with users using natural language.", keywords = "genetic algorithms, genetic programming, automatic generation, dialogue systems, multi-agent system, natural language interfaces, ripple down rules, task selection mechanism, user interaction, interactive systems, multi-agent systems, natural language interfaces", DOI = "doi:10.1109/CERMA.2010.30", notes = "Also known as \cite{5692333}", } @InProceedings{Gonzalez-Pardo:2011:AoGEAtREI, title = "Analysis of Grammatical Evolution Approaches to Regular Expression Induction", author = "Antonio Gonzalez-Pardo and David Camacho", pages = "632--639", booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary Computation", year = "2011", editor = "Alice E. Smith", month = "5-8 " # jun, address = "New Orleans, USA", organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, grammatical evolution, Data mining", DOI = "doi:10.1109/CEC.2011.5949679", abstract = "Regular expressions, or regexes, have been used traditionally as a pattern matching tool to search for structures in a set of objects, like files, text documents or folders. Pattern matching can be used to look for files whose name contains a given string, to search files that contain a specific pattern within them, or simply to extract text in a set of documents. It is very popular to apply regexes to detect and extract patterns that represent phone numbers, URLs, email addresses, etc. These kind of information can be characterised because it has a well defined structure. Nevertheless, regexes are not very frequently used because its high complexity in both, syntax and grammatical rules, makes regexes difficult to understand. For this reason, the development of programs able to automatically generate, and evaluate, regexes has become a valuable task. This work analyses the performance of different grammatical evolutionary approaches in the generation of regexes able to extract URL patterns. Four different types of grammars have been evaluated: a context-free grammar, a context-free grammar with a penalised fitness function, an extensible context-free grammar, and a Christiansen grammar. For the considered problem, the experimental results show that the best performance of the system, measured as cumulative success rate, is achieved using Christiansen grammars.", notes = "CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.", } @Article{GonzalezTaboada:2016:CBM, author = "Iris Gonzalez-Taboada and Belen Gonzalez-Fonteboa and Fernando Martinez-Abella and Juan Luis Perez-Ordonez", title = "Prediction of the mechanical properties of structural recycled concrete using multivariable regression and genetic programming", journal = "Construction and Building Materials", year = "2016", volume = "106", pages = "480--499", month = "1 " # mar, keywords = "genetic algorithms, genetic programming, Structural recycled concrete, Database, Mixing procedure, Mechanical properties, Multivariable regression", ISSN = "0950-0618", URL = "http://www.sciencedirect.com/science/article/pii/S0950061815308072", DOI = "doi:10.1016/j.conbuildmat.2015.12.136", abstract = "This study focuses on the prediction of some of the most important properties of structural recycled concrete (compressive strength, modulus of elasticity and splitting tensile strength) taking into account, not only the recycled percentage and the quality of the recycled aggregates used, but also the production method. For said purpose, a database has been developed with 1831 mixes obtained from 81 papers. Firstly, in this manner, these properties have been compared with those of conventional concrete. Then, the need to adapt the prediction code expressions (adjusted for conventional concretes) was analysed to take into account the use of recycled concrete, developing, if finally necessary, the correction coefficients which allow engineers to predict the recycled properties with the same approximation degree as in conventional concretes. These correction coefficients have been adjusted using multivariable regression, and have been analysed using different statistical indexes. Lastly, specific expressions used to predict these properties in structural recycled concretes have been optimized. Two different tools have been used to develop these expressions: multivariable regression and genetic programming. The proposed expressions have been analysed using statistical parameters which have been compared with those obtained using the expressions proposed by other authors. In this regard, and finally, the best prediction expressions for the modulus of elasticity and the splitting tensile strength of structural recycled concretes have been proposed.", } @PhdThesis{GonzalezTaboada_Iris_TD_2016, author = "Iris {Gonzalez Taboada}", title = "Self-compacting recycled concrete: basic mechanical properties, rheology, robustness and thixotropy", school = "Departamento de Tecnoloxia da Construcion, Programa de Doutoramento en Enxenaria Civil, E.T.S. Enxeneiros de Caminos, Canais e Portos, University of A Coruna", year = "2016", address = "Spain", month = nov, keywords = "genetic algorithms, genetic programming", URL = "https://ruc.udc.es/dspace/handle/2183/18183", URL = "https://ruc.udc.es/dspace/bitstream/handle/2183/18183/GonzalezTaboada_Iris_TD_2016.pdf", size = "323 pages", abstract = "This work is focused on the study of hardened and fresh behaviour of self-compacting recycled concrete (SCRC) with different replacement percentages of recycled concrete coarse aggregate (0 percent, 20 percent, 50 percent and 100 percent). Regarding hardened behaviour (compressive strength, modulus of elasticity and splitting tensile strength), it has been analysed how the incorporation of recycled coarse aggregate affects self-compacting concrete (SCC). To do so, a database was created with published results regarding vibrated recycled concrete. Different correction coefficients were adjusted to adapt code expressions to this type of concrete. Also, specific prediction expressions for vibrated recycled concretes were adjusted as an alternative to code formulations. Lastly, it has been concluded that both the correction coefficients and the specific expressions can be used with the same accuracy in SCRC as in vibrated recycled concrete. Regarding fresh behaviour, the relationships between rheological and empirical parameters were analysed concluding that they show the same trend in SCRC as in SCC. In this context, the research has also studied the specificity of SCRC rheology and its influence on the fresh behaviour over time. The achieved conclusions lead to state that this specificity lies in the extra water added to compensate the recycled aggregate absorption and in the intrinsic characteristics of this aggregate. Moreover, the SCRC robustness has been analysed through sensitivity parameters and a statistical approach, defining which factors affect it to a greater extent and which tests provide more sensitivity when this property is studied. Finally, SCRC thixotropy has been evaluated measuring also its influence on interlayer bond strength.", notes = "In English. Programa regulado polo RD 1393/2007: Programa de doutoramento de Enxenaria Civil Supervisor: Belen Gonzalez Fonteboa", } @InProceedings{goodacre:1999:ERDEC, author = "R. Goodacre and B. Shann and R. J. Gilbert and E. M. Timmins and A. C. McGovern and B. K. Alsberg and N. A. Logan and D. B. Kell", title = "The characterisation of Bacillus species from {PyMS} and {FT IR} data", booktitle = "Proceedings of the 1997 ERDEC Scientific Conference on Chemical and Biological Defense Research", year = "1997", editor = "Dorothy A. Berg", number = "ERDEC-SP-063", address = "Aberdeen Proving Ground, USA", publisher = "Edgewood Research, Development \& Engineering Center, U.S. Army Chemical and Biological Defense Command", keywords = "genetic algorithms, genetic programming", URL = "https://books.google.co.uk/books?id=kqGbGwAACAAJ", notes = "Published 1998?", } @Article{goodacre:1999:dcvbcppmsGP, author = "Royston Goodacre and Richard J. Gilbert", title = "The detection of caffeine in a variety of beverages using Curie-point pyrolysis mass spectrometry and genetic programming", journal = "The Analyst", year = "1999", volume = "124", number = "7", pages = "1069--1074", keywords = "genetic algorithms, genetic programming", URL = "http://pubs.rsc.org/en/content/articlelanding/1999/an/a901062i", DOI = "doi:10.1039/A901062I", size = "6 pages", abstract = "Freeze dried coffee, filter coffee, tea and cola were analysed by Curie-point pyrolysis mass spectrometry (PyMS). Cluster analysis showed, perhaps not surprisingly, that the discrimination between coffee, tea and cola was very easy. However, cluster analysis also indicated that there was a secondary difference between these beverages which could be attributed to whether they were caffeine- containing or decaffeinated. Artificial neural networks (ANNs) could be trained, with the pyrolysis mass spectra from some of the freeze dried coffees, to classify correctly the caffeine status of the unseen spectra of freeze dried coffee, filter coffee, tea and cola in an independent test set. However, the information in terms of which masses in the mass spectrum are important was not available, which is why ANNs are often perceived as a 'black box' approach to modelling spectra. By contrast, genetic programs (GPs) could also be used to classify correctly the caffeine status of the beverages, but which evolved function trees (or mathematical rules) enabling the deconvolution of the spectra and which highlighted that m/z 67, 109 and 165 were the most significant massed for this classification. Moreover, the chemical structure of these mass ions could be assigned to the reproducible pyrolytic degradation products from caffeine.", } @Article{goodacre:2000:ddabmbscppmsftis, author = "Royston Goodacre and Beverley Shann and Richard J. Gilbert and Eadaoin M. Timmins and Aoife C. McGovern and Bjorn K. Alsberg and Douglas B. Kell and Niall A. Logan", title = "The detection of the dipicolinic acid biomarker in Bacillus spores using Curie-point pyrolysis mass spectrometry and Fourier-transform infrared spectroscopy", journal = "Analytical Chemistry", year = "2000", volume = "72", number = "1", pages = "119--127", month = "1 " # jan, publisher = "American Chamical Society", keywords = "genetic algorithms, genetic programming", URL = "http://pubs.acs.org/cgi-bin/article.cgi/ancham/2000/72/i01/html/ac990661i.html", DOI = "doi:10.1021/ac990661i", abstract = "Thirty-six strains of aerobic endospore-forming bacteria confirmed by polyphasic taxonomic methods to belong to Bacillus amyloliquefaciens, Bacillus cereus, Bacillus licheniformis, Bacillus megaterium, Bacillus subtilis (including Bacillus niger and Bacillus globigii), Bacillus sphaericus, and Brevi laterosporus were grown axenically on nutrient agar, and vegetative and sporulated biomasses were analyzed by Curie-point pyrolysis mass spectrometry (PyMS) and diffuse reflectance-absorbance Fourier-transform infrared spectroscopy (FT-IR). Chemometric methods based on rule induction and genetic programming were used to determine the physiological state (vegetative cells or spores) correctly, and these methods produced mathematical rules which could be simply interpreted in biochemical terms. For PyMS it was found that m/z 105 was characteristic and is a pyridine ketonium ion (C6H3ON+) obtained from the pyrolysis of dipicolinic acid (pyridine-2,6-dicarboxylic acid; DPA), a substance found in spores but not in vegetative cells; this was confirmed using pyrolysis-gas chromatography/mass spectrometry. In addition, a pyridine ring vibration at 1447-1439 cm-1 from DPA was found to be highly characteristic of spores in FT-IR analysis. Thus, although the original data sets recorded hundreds of spectral variables from whole cells simultaneously, a simple biomarker can be used for the rapid and unequivocal detection of spores of these organisms.", notes = " PMID: 10655643", } @InCollection{Goodacre:2003:MP13, author = "Royston Goodacre and Douglas B. Kell", title = "Evolutionary Computation for the Interpretation of Metabolomic Data", booktitle = "Metabolic Profiling: Its Role in Biomarker Discovery and Gene Function Analysis", publisher = "Kluwer Academic Publishers", year = "2003", editor = "George G. Harrigan and Royston Goodacre", chapter = "13", pages = "239--256", address = "Boston, USA", month = jan, keywords = "genetic algorithms, genetic programming", ISBN = "1-4020-7370-4", isbn13 = "978-1-4613-5025-5", broken = "http://www.wkap.nl/prod/b/1-4020-7370-4", DOI = "doi:10.1007/978-1-4615-0333-0_13", language = "English", abstract = "Post-genomic science is producing bounteous data floods, and as the above quotation indicates the extraction of the most meaningful parts of these data is key to the generation of useful new knowledge. Atypical metabolic fingerprint or metabolomics experiment is expected to generate thousands of data points (samples times variables) of which only a handful might be needed to describe the problem adequately. Evolutionary algorithms are ideal strategies for mining such data to generate useful relationships, rules and predictions. This chapter describes these techniques and highlights their exploitation in metabolomics.", notes = "Pharmacia Corporation, Chesterfield, MO, USA University of Manchester Institute of Science and Technology (UMIST), UK", } @Article{goodacre:2003:cdupx, author = "Royston Goodacre and Emma V. York and James K. Heald and Ian M. Scott", title = "Chemometric discrimination of unfractionated plant extracts analyzed by electrospray mass spectrometry", journal = "Phytochemistry", year = "2003", volume = "62", number = "6", pages = "859--863", month = mar, keywords = "genetic algorithms, genetic programming, Pharbitis nil, Convolvulaceae, Japanese Morning Glory, Electrospray ionization mass spectrometry, Neural networks, Metabolic fingerprinting", URL = "http://www.sciencedirect.com/science/article/B6TH7-47WBXD4-7/2/91ff09f988be54824c55a1cb596f7839", DOI = "doi:10.1016/S0031-9422(02)00718-5", abstract = "Metabolic fingerprints were obtained from unfractionated Pharbitis nil leaf sap samples by direct infusion into an electrospray ionization mass spectrometer. Analyses took less than 30 s per sample and yielded complex mass spectra. Various chemometric methods, including discriminant function analysis and the machine-learning methods of artificial neural networks and genetic programming, could discriminate the metabolic fingerprints of plants subjected to different photoperiod treatments. This rapid automated analytical procedure could find use in a variety of phytochemical applications requiring high sample throughput.", notes = "GMax-Bio, Plant Metabolomics", } @Article{Goodacre:2003:VS, author = "Royston Goodacre", title = "Explanatory analysis of spectroscopic data using machine learning of simple, interpretable rules", journal = "Vibrational Spectroscopy", year = "2003", volume = "32", pages = "33--45", number = "1", month = "5 " # aug, note = "A collection of Papers Presented at Shedding New Light on Disease: Optical Diagnostics for the New Millennium (SPEC 2002) Reims, France 23-27 June 2002", keywords = "genetic algorithms, genetic programming, Artificial neural networks, ANN, FT-IR", ISSN = "0924-2031", URL = "http://www.biospec.net/learning/Metab06/Goodacre-FTIRmaps.pdf", URL = "http://www.sciencedirect.com/science/article/B6THW-48XJP5P-2/2/64840c1f311b856106e124993425ab92", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.147.8811", DOI = "doi:10.1016/S0924-2031(03)00045-6", language = "en", oai = "oai:CiteSeerXPSU:10.1.1.147.8811", abstract = "Whole organism or tissue profiling by vibrational spectroscopy produces vast amounts of seemingly unintelligible data. However, the characterisation of the biological system under scrutiny is generally possible only in combination with modern supervised machine learning techniques, such as artificial neural networks (ANNs). Nevertheless, the interpretation of the calibration models from ANNs is often very difficult, and the information in terms of which vibrational modes in the infrared or Raman spectra are important is not readily available. ANNs are often perceived as 'black box' approaches to modelling spectra, and to allow the deconvolution of complex hyperspectral data it is necessary to develop a system that itself produces 'rules' that are readily comprehensible. Evolutionary computation, and in particular genetic programming (GP), is an ideal method to achieve this. An example of how GP can be used for Fourier transform infrared (FT-IR) image analysis is presented, and is compared with images produced by principal components analysis (PCA), discriminant function analysis (DFA) and partial least squares (PLS) regression.", owner = "wlangdon", } @Article{goodacre:2004:TB, author = "Royston Goodacre and Seetharaman Vaidyanathan and Warwick B. Dunn and George G. Harrigan and Douglas B. Kell", title = "Metabolomics by numbers: acquiring and understanding global metabolite data", journal = "Trends in Biotechnology", year = "2004", volume = "22", number = "5", pages = "245--252", month = "1 " # may, keywords = "genetic algorithms, genetic programming, ILP", URL = "http://dbkgroup.org/Papers/trends%20in%20biotechnology_22_(245).pdf", DOI = "doi:10.1016/j.tibtech.2004.03.007", abstract = "In this postgenomic era, there is a specific need to assign function to orphan genes in order to validate potential targets for drug therapy and to discover new biomarkers of disease. Metabolomics is an emerging field that is complementary to the other 'omics and proving to have unique advantages. As in transcriptomics or proteomics, a typical metabolic fingerprint or metabolomic experiment is likely to generate thousands of data points, of which only a handful might be needed to describe the problem adequately. Extracting the most meaningful elements of these data is thus key to generating useful new knowledge with mechanistic or explanatory power.", notes = "many topics covered not just GP", } @Article{Goodacre:2007:m, author = "Royston Goodacre and David Broadhurst and Age K. Smilde and Bruce S. Kristal and J. David Baker and Richard Beger and Conrad Bessant and Susan Connor and Giorgio Capuani and Andrew Craig and Tim Ebbels and Douglas B. Kell and Cesare Manetti and Jack Newton and Giovanni Paternostro and Ray Somorjai and Michael Sjostrom and Johan Trygg and Florian Wulfert", title = "Proposed minimum reporting standards for data analysis in metabolomics", journal = "Metabolomics", year = "2007", volume = "3", pages = "231--241", keywords = "genetic algorithms, genetic programming, Chemometrics, Multivariate, Megavariate Unsupervised learning, Supervised learning, Informatics Bioinformatics, Statistics, Biostatistics", URL = "http://dbkgroup.org/Papers/goodacre_MSIdataanalysis07.pdf", DOI = "doi:10.1007/s11306-007-0081-3", size = "11 pages", abstract = "The goal of this group is to define the reporting requirements associated with the statistical analysis (including univariate, multivariate, informatics, machine learning etc.) of metabolite data with respect to other measured/collected experimental data (often called metadata). These definitions will embrace as many aspects of a complete metabolomics study as possible at this time. In chronological order this will include: Experimental Design, both in terms of sample collection/matching, and data acquisition scheduling of samples through whichever spectroscopic technology used; Deconvolution (if required); Pre-processing, for example, data cleaning, outlier detection, row/column scaling, or other transformations; Definition and parameterization of subsequent visualizations and Statistical/Machine learning Methods applied to the dataset; If required, a clear definition of the Model Validation Scheme used (including how data are split into training/validation/test sets); Formal indication on whether the data analysis has been Independently Tested (either by experimental reproduction, or blind hold out test set). Finally, data interpretation and the visual representations and hypotheses obtained from the data analyses.", notes = "R. Goodacre D. Broadhurst (&) D. B. Kell School of Chemistry and Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess Street, Manchester M1 7ND, UK A. K. Smilde Biosystems Data Analysis, Swammerdam Institute for Life Sciences, University of Amsterdam, Nieuwe Achtergracht 166, Amsterdam 1018 WV, Netherlands A. K. Smilde TNO Quality of Life, Utrechtseweg 48, P.O. Box 360, Zeist 3700 AJ, Netherlands B. S. Kristal Department of Neurosurgery, Brigham and Women\u2019s Hospital, 221 Longwood Ave, Boston, MA 02115, USA J. D. Baker Pfizer, Inc, Ann Arbor, MI, USA R. Beger Division of Systems Toxicology, National Center for Toxicological Research, 3900 NCTR Road, Jefferson, AR 72079, USA C. Bessant C. Manetti Cranfield University, Silsoe, Bedfordshire MK45 4DT, UK S. Connor Safety Assessment, GlaxoSmithKline, Park Road, Ware, Herts SG12 0DP, UKG. Capuani Dipartimento di Chimica, Universita` degli Studi di Roma Piazzale Aldo Moro 5, Rome 00185, Italy A. Craig BlueGnome Ltd, Breaks House, Mill Court, Great Shelford, Cambridge CB2 5LD, UK T. Ebbels Department of Biomolecular Medicine, Imperial College London, London SW7 2AZ, UK J. Newton Chenomx Inc, Suite 800, 10050 112 St, T5K 2J1 Edmonton, AB, Canada G. Paternostro Burnham Institute for Medical Research, 10901 North Torrey Pines Road, La Jolla, CA 92037, USA R. Somorjai Institute for Biodiagnostics, NRCC, 435 Ellice Ave, R3B 1Y6 Winnipeg, MB, Canada M. Sjostrom J. Trygg Research Group for Chemometrics, Organic Chemistry, Department of Chemistry, Umea University, Umea 901 87, Sweden F. Wulfert Division of Food Sciences, University of Nottingham, Sutton Bonington Campus, Loughborough LE12 5RD, UK", } @Proceedings{goodman:2001:GECCOlb, title = "Late Breaking Papers at the 2001 Genetic and Evolutionary Computation Conference", year = "2001", editor = "Erik Goodman", address = "San Francisco, California, USA", month = "7-11 " # jul, size = "pages", } @InProceedings{GARAGe02-01-01, author = "Erik D. Goodman and Kisung Seo and Ronald C. Rosenberg and Zhun Fan and Jianjun Hu and Baihai Zhang", title = "Automated Design Methodology for Mechatronic Systems Using Bond Graphs and Genetic Programming", booktitle = "Proceedings 2002 NSF Design, Service and Manufacturing Grantees and Research Conference", year = "2002", pages = "206--221", address = "San Juan, Puerto Rico", month = jan, organization = "National Science Foundation", publisher = "National Science Foundation", keywords = "genetic algorithms, genetic programming", URL = "http://garage.cse.msu.edu/papers/GARAGe02-01-01.pdf", size = "16 pages", abstract = "We suggest an automated design methodology for synthesising designs for multi-domain systems, such as mechatronic systems. The domain of mechatronic systems includes mixtures of, for example, electrical, mechanical, hydraulic, pneumatic, and thermal components, making it difficult to design a system to meet specified performance goals with a single design tool. The multi-domain design approach is not only efficient for mixed domain problems, but is also useful for addressing separate single-domain design problems with a single tool. Bond graphs are domain independent, allow free composition, and are efficient for classification and analysis of models, allowing rapid determination of various types of acceptability or feasibility of candidate designs. This can sharply reduce the time needed for analysis of designs that are infeasible or otherwise unattractive. Genetic programming is well recognised as a powerful tool for open-ended search. The combination of these two powerful methods is therefore an appropriate target for a better system for synthesis of complex multi-domain systems. The approach described here will evolve new designs (represented as bond graphs) with ever-improving performance, in an iterative loop of synthesis, analysis, and feedback to the synthesis process. The suggested design methodology has been applied here to two design examples. One is domain independent, an eigenvalues-placement design problem which is tested for some sample target sets of eigenvalues. The other is in the electrical domain -- namely, design of analog filters to achieve specified performance over a given frequency range.", notes = "https://www.nsf.gov/awardsearch/showAward?AWD_ID=0101000", } @Article{goodman:2004:GPEM, author = "Erik D. Goodman", title = "A Word from the Chair of {ISGEC}", journal = "Genetic Programming and Evolvable Machines", year = "2004", volume = "5", number = "1", pages = "9", month = mar, ISSN = "1389-2576", DOI = "doi:10.1023/B:GENP.0000017052.83908.eb", notes = "Chair of the Executive Board, International Society for Genetic and Evolutionary Computation Article ID: 5264732", } @Article{Goodman:2019:sigevolution, author = "Erik Goodman", title = "Human-Competitive Results Awards - ``{Humies}'' 2019 - Announces Winners at {GECCO}", journal = "SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation", year = "2019", volume = "12", number = "3", pages = "3--5", keywords = "genetic algorithms, genetic programming", ISSN = "1931-8499", URL = "https://evolution.sigevo.org/issues/SIGEVOlution1203.pdf", size = "3 pages", notes = "Gold \cite{Lynch:2019:Networking} Sliver: Evaluation of bi-objective treatment planning for high-dose-rate prostate brachytherapy - A retrospective observer study Bronze: \cite{Basios:2018:FSE}", } @Article{Goodman:2020:sigevolution, author = "Erik Goodman", title = "Humies 2020 Competition Yields Spectacular Winners!", journal = "SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation", year = "2020", volume = "13", number = "3", pages = "4--7", month = "Autumn", keywords = "genetic algorithms, genetic programming", ISSN = "1931-8499", URL = "https://evolution.sigevo.org/issues/HTML/sigevolution-13-3/home.html#h.yp51nkj2uiru", URL = "https://dl.acm.org/action/showFmPdf?doi=10.1145%2F3430913", size = "3 pages", abstract = "The 17th Annual Human-Competitive Results Awards were held as part of GECCO 2020, July 8-12, 2020. AKA the Humies, this competition annually awards 10000 USA dollars in cash prizes for computational results that are deemed to be competitive with results produced by human beings, but are generated automatically by computer. This year, like the rest of GECCO, the final presentations by the entrants in the competition could not be done as planned in Cancun, Mexico, but were instead made as videos shown during a virtual GECCO session. An advantage of this is that the presentations are all available on both the GECCO site and for the general public at the Humies website, www.human-competitive.org, so anyone can watch them.", notes = "GP Silver \cite{Meza-Sanchez:IETcta} and {Meza-Sanchez:2019:IS}", } @Article{Goodman:2021:sigevolution, author = "Erik Goodman", title = "2021 Humies Winners Awarded at {GECCO}!", journal = "SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation", year = "2021", volume = "14", number = "3", pages = "2--5", month = oct, keywords = "genetic algorithms, genetic programming", ISSN = "1931-8499", URL = "https://evolution.sigevo.org/issues/HTML/sigevolution-14-3/home.html", URL = "https://dl.acm.org/citation.cfm?id=3357514", DOI = "doi:10.1145/3490676.3490677", size = "3.5 pages", abstract = "Gold Award \cite{pmlr-v119-real20a} Silver Award \cite{Virgolin:2020:JMI} Bronze Award \cite{Blasco:2021:JSS}", notes = "HUMIES BEACON Centre, Michigan State University, USA", } @Article{Goodman:2022:sigevolution, author = "Erik Goodman", title = "2022 Humies Winners Awarded at GECCO in Boston!", journal = "SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation", year = "2022", volume = "15", number = "3", month = "Fall", keywords = "genetic algorithms, genetic programming", ISSN = "1931-8499", URL = "https://evolution.sigevo.org/issues/HTML/sigevolution-15-3/index.htm#2022_Humies_Winners_Awarded_at_GECCO_in_Boston", URL = "https://dl.acm.org/action/showFmPdf?doi=10.1145%2F3578482", size = "4 pages", abstract = "1st prize Gold team of Jonas Schmitt, Sebastian Kuckuk, and Harald Koestler, from Universitaet Erlangen-Nuernberg in Erlangen, Germany \cite{Schmitt:2022:GECCO} and \cite{Schmitt:GPEM}. 2nd Silver went to Risto Miikkulainen, Elliot Meyerson, Xin Qiu, Ujjayant Sinha, Raghav Kumar, Karen Hofmann, Yiyang Matt Yan, Michael Ye, Jingyuan Yang, Damon Caiazza, Stephanie Manson Brown, from Cognizant, Abbvie, and University of Texas Austin, USA. https://doi.org/10.1145/3449639.3459378 One of the 2nd Silver went to a large team which was represented by Alberto Tonda, who gave the presentation, and included Eric Claassen, Etienne Coz, Johan Garssen, Aletta D. Kraneveld, Alejandro Lopez Rincon, Lucero Mendoza Maldonado, Carmina A. Perez Romero, Jessica Vanhomwegen, who are distributed among France, Netherlands and Mexico. https://doi.org/10.1145/3449639.3459359 https://www.biorxiv.org/content/10.1101/2021.01.20.427043v3", notes = "HUMIES https://evolution.sigevo.org/", } @Article{Goodman:2023:sigevolution, author = "Erik Goodman", title = "The 2023 Humies Awards", journal = "SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation", year = "2023", volume = "16", number = "3", articleno = "1", month = sep, keywords = "genetic algorithms, genetic programming", ISSN = "1931-8499", URL = "https://evolution.sigevo.org/issues/HTML/sigevolution-16-3/index.htm", DOI = "doi:10.1145/3629719.3629720", size = "4 pages", abstract = "1st prize CryptOpt \cite{Kuepper:2023:PLDI} Joint 2nd prize: \cite{MacLachlan:2023:GECCO} and \cite{LaCava:2023:npjdm} Bronze \cite{Reuter:2023:EuroGP} The GECCO 2023 Conference was held in hybrid mode again this year. It was attended by more than 600 people on site in Lisbon, Portugal, plus more than 200 attending virtually. It was held July 15-19, with the finalists in the Humies competition presenting in a plenary session on Tuesday, July 18 that was attended by more than 150 people. Eight finalists presented their work in 10-minute talks, or, in one case, a pre-recorded video.", notes = "HUMIES https://evolution.sigevo.org/", } @Article{Goodman:2024:sigevolution, author = "Erik Goodman", title = "21st Human-Competitive Results Awards (``Humies'') 2024", journal = "SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation", year = "2024", volume = "17", number = "3", month = sep, keywords = "genetic algorithms, genetic programming", ISSN = "1931-8499", URL = "https://evolution.sigevo.org/issues/HTML/sigevolution-17-3/index.html#21st_Human-Competitive_Results_Awards_Humies_2024", abstract = "Gold Award Dennis Wilson 'Evolutionary design of explainable algorithms for biomedical image segmentation' \cite{Cortacero:2023:NatCommun} Silver Award Penousal Machado 'From Pixels to Metal: AI-Empowered Numismatic Art' \cite{ijcai2024coin} Bronze Award (Tie) Chao Qian 'Macro Placement by Wire-Mask-Guided Black-Box Optimization' https://dl.acm.org/doi/10.5555/3666122.3666421 Bronze Award (Tie) David Howard 'Freeform generative design of complex functional structures' https://www.nature.com/articles/s41598-024-62830-5", notes = "HUMIES https://evolution.sigevo.org/", } @InProceedings{Gopalakrishnan:2010:ANNIE, author = "Kasthurirangan Gopalakrishnan and Halil Ceylan and Sunghwan Kim and Siddhartha K. Khaitan", title = "Natural Selection of Asphalt Mix Stiffness Predictive Models with Genetic Programming", booktitle = "ANNIE 2010, Intelligent Engineering Systems through Artificial Neural Networks", year = "2010", editor = "Cihan H. Dagli", volume = "20", pages = "paper 48", address = "St. Louis, Mo, USA", month = nov # " 1-3", organisation = "Smart Engineering Systems Laboratory, Systems Engineering Graduate Programs, Missouri University of Science and Technology, 600 W. 14th St., Rolla, MO 65409 USA", publisher = "ASME", keywords = "genetic algorithms, genetic programming", isbn13 = "9780791859599", DOI = "doi:10.1115/1.859599.paper48", abstract = "Genetic Programming (GP) is a systematic, domain-independent evolutionary computation technique that stochastically evolves populations of computer programs to perform a user-defined task. Similar to Genetic Algorithms (GA) which evolves a population of individuals to better ones, GP iteratively transforms a population of computer programs into a new generation of programs by applying biologically inspired operations such as crossover, mutation, etc. In this paper, a population of Hot-Mix Asphalt (HMA) dynamic modulus stiffness prediction models is genetically evolved to better ones by applying the principles of genetic programming. The HMA dynamic modulus (|E*|), one of the stiffness measures, is the primary HMA material property input in the new Mechanistic Empirical Pavement Design Guide (MEPDG) developed under National Cooperative Highway Research Program (NCHRP) 1-37A (2004) for the American State Highway and Transportation Officials (AASHTO). It is shown that the evolved HMA model through GP is reasonably compact and contains both linear terms and low-order non-linear transformations of input variables for simplification.", notes = "http://annie.mst.edu/conference_schedule/ConferenceSchedule.html ASME Order Number: 859599", } @Article{Goranova:2019:sigevolution, author = "Mila Goranova and Gabriela Ochoa and Marco Tomassini", title = "{EuroGP} vs {EvoCOP}: Contrasting the Collaboration Networks", journal = "SIGEVOlution", year = "2019", volume = "12", number = "1", pages = "6--12", month = apr, keywords = "genetic algorithms, genetic programming, DBLP", publisher = "ACM", address = "New York, NY, USA", ISSN = "1931-8499", URL = "http://doi.acm.org/10.1145/3328473.3328475", DOI = "doi:10.1145/3328473.3328475", acmid = "3328475", size = "7 pages", abstract = "Using an online database with bibliographic information on major computer science publications we have constructed collaboration networks for the two main EvoStar (the he leading European event on Bio-Inspired Computation) conferences: EuroGP (European Conference on Genetic Programming) and EvoCOP (Evolutionary Computation in Combinatorial Optimisation). In these networks two authors are connected if they have coauthored one or more papers appearing in these conferences since their inception until 2018. The networks are then visualised and analysed using a number of network statistics. Our main focus is to reveal and contrast the patterns of collaboration and the most active researchers in both conferences. EuroGP's network shows a large central component of connected authors, whereas EvoCOP authors appear to work in small groups without direct interaction between groups. This could be explained by the different origins and composition of these two communities.", notes = "Goranova:2019:EVE:3328473.3328475,", } @InProceedings{gordillo:1997:ocipGPpa, author = "F. Gordillo and A. Bernal", title = "Optimal Control of an Inverted Pendulum Using Genetic Programming: Practical Aspects", booktitle = "Artificial Neural Nets and Genetic Algorithms: Proceedings of the International Conference, ICANNGA97", year = "1997", editor = "George D. Smith and Nigel C. Steele and Rudolf F. Albrecht", pages = "393--396", address = "University of East Anglia, Norwich, UK", publisher = "Springer-Verlag", note = "published in 1998", keywords = "genetic algorithms, genetic programming", ISBN = "3-211-83087-1", DOI = "doi:10.1007/978-3-7091-6492-1_86", abstract = "During the past several years, numerous papers and applications designing control systems with genetic algorithms (GAs) have been written. Many of these studies end when the simulated behaviour of the system with the controller is satisfactory. They suppose that the final stage of application of the controller to the real system will be similar to the one from the traditional design. This paper explains the conclusions of the real application of one such publication: the control of an inverted pendulum using a well-known variant of GAs: genetic programming (GP). The aim of this paper is to study the existence of possible special problems in the application stage of genetic-designed controllers. As will be seen, the application stage is more difficult for GAs than for traditional methods, and more knowledge is needed about the system.", notes = "http://www.sys.uea.ac.uk/Research/ResGroups/MAG/ICANNGA97/papers_frame.html", } @InProceedings{gordillo:1999:ATSDGCA, author = "Francisco Gordillo and Ismael Alcala and Javier Aracil", title = "A Tool for Solving Differential Games with Co-evolutionary Algorithms", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1535--1542", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "real world applications", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-775.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-775.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InCollection{gordon:1994:usni, author = "Benjamin M. Gordon", title = "Exploring the Underlying Structure of Natural Images Through Genetic Programming", booktitle = "Genetic Algorithms at Stanford 1994", year = "1994", editor = "John R. Koza", pages = "49--56", address = "Stanford, California, 94305-3079 USA", month = dec, organisation = "Stanford University", publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming, MSE, pixels", ISBN = "0-18-187263-3", notes = "This volume contains 20 papers written and submitted by students describing their term projects for the course {"}Genetic Algorithms and Genetic Programming{"} (Computer Science 426) at Stanford University offered during the fall quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs426.html", } @Article{Gordon:2006:IS, title = "Adaptive Web Search: Evolving a Program That Finds Information", author = "Michael Gordon and Weiguo (Patrick) Fan and Praveen Pathak", journal = "IEEE Intelligent Systems", year = "2006", volume = "21", number = "5", pages = "72--77", month = sep # "-" # oct, keywords = "genetic algorithms, genetic programming, Internet, information needs, relevance feedback, search engines, Web pages, adaptive Web search, document relevance feedback, genetic programming, retrieval algorithms, retrieval technique, search engines, user judgement feedback, user persistent information needs", ISSN = "1541-1672", DOI = "doi:10.1109/MIS.2006.86", size = "6 pages", abstract = "Anyone who's used a computer to find information on the Web knows that the experience can be frustrating. Search engines are incorporating new techniques (such as examining document link structures) to increase effectiveness. However, searchers all too often face one of two outcomes: reviewing many more Web pages than they'd prefer or failing to find as much useful information as they really want. We introduce a new retrieval technique that exploits users' persistent information needs. These users might include business analysts specialising in genetic technologies, stockbrokers keeping abreast of wireless communications, and legislators needing to understand computer privacy and security developments. To help such searchers, we evolve effective search programs by using feedback based on users' judgments about the relevance of the documents they've retrieved. This approach uses genetic programming to automatically evolve new retrieval algorithms based on a user's evaluation of previously viewed documents", notes = "IR, cosine nearness measure, keyword weighting. Log. Pop=200. TREC 80000 documents. Large number (500) papers returned to user. GP way better in comparison with SMART (Singhal, 1996) and ANN.", } @InProceedings{gordon:1999:TGAMPST, author = "V. Scott Gordon and Rebecca Pirie and Adam Wachter and Scottie Sharp", title = "Terrain-Based Genetic Algorithm (TBGA): Modeling Parameter Space as Terrain", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "229--235", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/tbga.pdf", URL = "http://ecs.csus.edu/~gordonvs/papers/tbga.pdf", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Article{gordon:2001:GPEM, author = "Timothy G. W. Gordon", title = "Book Review: {Hardware} evolution: automatic design of electronic circuits in reconfigurable hardware by artificial evolution", journal = "Genetic Programming and Evolvable Machines", year = "2001", volume = "2", number = "4", pages = "409--411", month = dec, keywords = "genetic algorithms, evolvable hardware", ISSN = "1389-2576", URL = "https://rdcu.be/dR8sJ", DOI = "doi:10.1023/A:1012930922211", notes = "Book review of ISBN: 3-540-76253-1 Author: Adrian Thompson Publisher: Springer-Verlag London Ltd. 1998. Article ID: 386364", } @InProceedings{gordon:2005:EH, author = "Timothy G. W. Gordon and Peter J. Bentley", title = "Development Brings Scalability to Hardware Evolution", booktitle = "Proceedings of the 2005 NASA/DoD Conference on Evolvable Hardware", year = "2005", editor = "Jason Lohn and David Gwaltney and Gregory Hornby and Ricardo Zebulum and Didier Keymeulen and Adrian Stoica", pages = "272--279", address = "Washington, DC, USA", month = "29 " # jun # "-1 " # jul, publisher = "IEEE Press", publisher_address = "IEEE Service Center 445 Hoes Lane Asia P.O. Box 1331 Piscataway, NJ 08855-1331", organisation = "NASA, DoD", keywords = "genetic algorithms, genetic programming, EHW", ISBN = "0-7695-2399-4", URL = "http://www.cs.ucl.ac.uk/staff/t.gordon/gordont_scalability.pdf", DOI = "doi:10.1109/EH.2005.18", size = "8 pages", abstract = "The scalability problem is a major impediment to the use of hardware evolution for real-world circuit design problems. A potential solution is to model the map between genotype and phenotype on biological development. Although development has been shown to improve scalability for a few toy problems, it has not been demonstrated for any circuit design problems. This paper presents such a demonstration for two problems, the n-bit adder with carry and even n-bit parity problems, and shows that development imposes, and benefits from, fewer constraints on evolutionary innovation than other approaches to scalability.", notes = "EH2005 IEEE Computer Society Order Number P2399", } @PhdThesis{tgordon, author = "Timothy Glennie Wilson Gordon", title = "Exploiting Development to Enhance the Scalability of Hardware Evolution", school = "University College, London", year = "2005", address = "UK", month = jul, keywords = "genetic algorithms, genetic programming, EHW", URL = "https://discovery.ucl.ac.uk/id/eprint/1444775/", URL = "https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.420417", URL = "http://www.bcs.org/upload/pdf/tgordon.pdf", size = "302 pages", abstract = "Evolutionary algorithms do not scale well to the large, complex circuit design problems typical of the real world. Although techniques based on traditional design decomposition have been proposed to enhance hardware evolution's scalability, they often rely on traditional domain knowledge that may not be appropriate for evolutionary search and might limit evolution's opportunity to innovate. It has been proposed that reliance on such knowledge can be avoided by introducing a model of biological development to the evolutionary algorithm, but this approach has not yet achieved its potential. Prior demonstrations of how development can enhance scalability used toy problems that are not indicative of evolving hardware. Prior attempts to apply development to hardware evolution have rarely been successful and have never explored its effect on scalability in detail. This thesis demonstrates that development can enhance scalability in hardware evolution, primarily through a statistical comparison of hardware evolution's performance with and without development using circuit design problems of various sizes. This is reinforced by proposing and demonstrating three key mechanisms that development uses to enhance scalability: the creation of modules, the reuse of modules, and the discovery of design abstractions. The thesis includes several minor contributions: hardware is evolved using a common reconfigurable architecture at a lower level of abstraction than reported elsewhere. It is shown that this can allow evolution to exploit the architecture more efficiently and perhaps search more effectively. Also the benefits of several features of developmental models are explored through the biases they impose on the evolutionary search. Features that are explored include the type of environmental context development uses and the constraints on symmetry and information transmission they impose, genetic operators that may improve the robustness of gene networks, and how development is mapped to hardware. Also performance is compared against contemporary developmental models.", notes = "Evolvable hardware rather than GP Runner up 2006 Distinguished Dissertations http://www.bcs.org/server.php?show=conWebDoc.10343 Exploiting Development to Enhance the Scalability of Hardware Evolution Tim Gordon University College London Supervised by Peter Rounce Timothy Gordon received the B.Sc. in Chemistry, the M.Sc. in Information Technology and the Ph.D. in Computer Science from University College London in 1994, 1995 and 2005 respectively. His Ph.D. research focussed on the application of evolutionary algorithms and computational development to hardware design. His recent interests include the use of evolutionary algorithms in finance. He currently works for a London hedge fund. UMI U592084", } @InProceedings{gorges-schleuter:1999:AALSES, author = "Martina Gorges-Schleuter", title = "An Analysis of Local Selection in Evolution Strategies", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "847--854", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "evolution strategies and evolutionary programming", ISBN = "1-55860-611-4", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Goribar:2015:NEO, author = "Carlos Goribar and Yazmin Maldonado and Leonardo Trujillo", title = "Automatic Random Tree Generator on {FPGA}", booktitle = "NEO 2015: Results of the Numerical and Evolutionary Optimization Workshop NEO 2015 held at September 23-25 2015 in Tijuana, Mexico", year = "2015", editor = "Oliver Schuetze and Leonardo Trujillo and Pierrick Legrand and Yazmin Maldonado", volume = "663", series = "Studies in Computational Intelligence", chapter = "4", pages = "89--104", publisher = "Springer", keywords = "genetic algorithms, genetic programming, EHW, FPGA, VHDL", isbn13 = "978-3-319-44003-3", DOI = "doi:10.1007/978-3-319-44003-3_4", abstract = "In this work we propose the implementation of an automatic random tree generator on an FPGA for genetic programming (GP). While most authors in specialized literature avoid the use of the tree data structure in their implementations of GP on Field Programmable Gate Arrays (FPGAs), due to the impossibility of using pointers (references) in the Very High Speed Integrated Circuit Hardware Description Language (VHDL), we propose two methods for a single matrix implementation and one for a vector implementation. All trees in the population are created in concurrent processes leading to significant time savings. We present pseudocode and results of hardware consumption for matrix and vector implementations. Results show that up to 100 trees can be implemented in a Spartan-6 FPGA using the representation of one tree in a single matrix in parallel processes. Moreover, this implementation requires less resources than the apparently simpler vector representation.", notes = "Published 2017. See also \cite{Goribar-Jimenez:2016:GECCOcomp}", } @InProceedings{Goribar-Jimenez:2016:GECCOcomp, author = "Carlos A. {Goribar Jimenez} and Yazmin Maldonado and Leonardo Trujillo", title = "Random Tree Generator for an FPGA-based Genetic Programming System", booktitle = "GECCO '16 Companion: Proceedings of the Companion Publication of the 2016 Annual Conference on Genetic and Evolutionary Computation", year = "2016", isbn13 = "978-1-4503-4323-7", pages = "1023--1026", keywords = "genetic algorithms, genetic programming", month = "20-24 " # jul, organisation = "SIGEVO", address = "Denver, Colorado, USA", DOI = "doi:10.1145/2908961.2931665", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "the implementation of an automatic random tree generator on an FPGA, this implementation is intended to be part of a complete genetic programming embedded system. We propose two methods for a matrix implementations and one for a vector implementation. All trees in the population are created in concurrent processes leading to significant time savings. We present pseudocode and results of hardware consumption for the three implementations.", } @InProceedings{goribar-jimenez:2017:CEC, author = "Carlos Goribar-Jimenez and Yazmin Maldonado and Leonardo Trujillo and Mauro Castelli and Ivo Goncalves and Leonardo Vanneschi", booktitle = "2017 IEEE Congress on Evolutionary Computation (CEC)", title = "Towards the development of a complete GP system on an FPGA using geometric semantic operators", year = "2017", editor = "Jose A. Lozano", pages = "1932--1939", address = "Donostia, San Sebastian, Spain", month = "5-8 " # jun, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, convergence, embedded systems, field programmable gate arrays, geometry, parallel processing, search problems, FPGA, GP algorithm, GP system development, GSGP, embedded system, empirical convergence, error space alignment, field programmable gate array, geometric semantic genetic programming, geometric semantic operators, local search strategies, maintenance requirements, optimal mutation step, parallel processes, power consumption, standard geometric semantic mutation, time savings, unimodal fitness landscape, Arrays, GSM, Proposals, Semantics, Standards", isbn13 = "978-1-5090-4601-0", DOI = "doi:10.1109/CEC.2017.7969537", size = "8 pages", abstract = "Genetic Programming (GP) has been around for over two decades and has been used in a wide range of practical applications producing human competitive results in several domains. In this paper we present a discussion and a proposal of a GP algorithm that could be conveniently implemented on an embedded system, as part of a broader research project that pursues the implementation of a complete GP system in a Field Programmable Gate Array (FPGA). Motivated by the significant time savings associated with such a platform, as well as low power consumption, low maintenance requirements, small size of the system and the possibility of performing several parallel processes. The proposal is focused on the Geometric Semantic Genetic Programming (GSGP) approach that has been recently introduced with promising results. GSGP induces a unimodal fitness landscape, simplifying the search process. The experimental work considers five variants of GSGP, that incorporate local search strategies, optimal mutations and alignment in error space. Best results were obtained by a simple variant that uses both the optimal mutation step and the standard geometric semantic mutation, using three difficult real-world problems to evaluate the methods, outperforming the original GSGP formulation in terms of fitness and empirical convergence.", notes = "predict the energy consumption of residential buildings HAVC. UCI Housing IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969537}", } @Article{GORKEMLI:2019:IS, author = "Beyza Gorkemli and Dervis Karaboga", title = "A quick semantic artificial bee colony programming (qs{ABCP)} for symbolic regression", journal = "Information Sciences", volume = "502", pages = "346--362", year = "2019", ISSN = "0020-0255", DOI = "doi:10.1016/j.ins.2019.06.052", URL = "http://www.sciencedirect.com/science/article/pii/S0020025519305900", keywords = "genetic algorithms, genetic programming, Artificial bee colony programming (ABCP), Semantic ABCP, Quick ABCP, Quick semantic ABCP, Symbolic regression", abstract = "Artificial bee colony programming (ABCP) is a novel evolutionary computation based automatic programming method, which uses the basic structure of artificial bee colony (ABC) algorithm. In this paper, some studies were conducted to improve the performance of ABCP and three new versions of ABCP are introduced. One of these improvements is related to the convergence performance of ABCP. In order to increase the local search ability and achieve higher quality solutions in early cycles, quick ABCP algorithm was developed. Experimental studies validate the enhancement of the convergence performance when the quick ABC approach is used in ABCP. The second improvement introduced in this paper is about providing high locality. Using semantic similarity based operators in the information sharing mechanism of ABCP, semantic ABCP was developed and experiment results show that semantic based information sharing improves solution quality. Finally, combining these two methods, quick semantic ABCP is introduced. Performance of these novel methods was compared with some well known automatic programming algorithms on literature test problems. Additionally, ABCP based methods were used to find approximations of the Colebrook equation for flow friction. Simulation results show that, the proposed methods can be used to solve symbolic regression problems effectively", } @Article{gorsevski:2021:NH, author = "Pece V. Gorsevski", title = "An evolutionary approach for spatial prediction of landslide susceptibility using {LiDAR} and symbolic classification with genetic programming", journal = "Natural Hazards", year = "2021", volume = "108", number = "2", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s11069-021-04780-z", DOI = "doi:10.1007/s11069-021-04780-z", } @InProceedings{Gorski:2015:PECCS, author = "Adam Gorski and Maciej Ogorzalek", booktitle = "2015 International Conference on Pervasive and Embedded Computing and Communication Systems (PECCS)", title = "GP-based methodology for HW/SW co-synthesis of multiprocessor embedded systems with increasing number of individuals obtained by mutation", year = "2015", pages = "275--280", month = "11--13 " # feb, address = "Angers, France", keywords = "genetic algorithms, genetic programming, Embedded Systems, Architecture, Hardware/Software Co-Design, Multiprocessor System", isbn13 = "978-989-758-137-3", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=7483773", size = "6 pages", abstract = "In this work, a genetic programming methodology for co-synthesis of multiprocessor systems is presented. Genotype is a tree which nodes include system construction procedures. Thus the design methodology is evolving. Next generations are obtained using genetic operators: mutation, reproduction and crossover. Unlike other algorithms in presented methodology number of individuals obtained by mutation operator is not constant. Therefore number of individuals in each population is increasing. The size of final generation is found by the algorithm.", notes = "Also known as \cite{7483773}", } @InProceedings{DBLP:conf/sensornets/GorskiO21, author = "Adam Gorski and Maciej Ogorzalek", editor = "Rangarao Venkatesha Prasad and Nirwan Ansari and C{\'{e}}sar Benavente-Peces", title = "Genetic Programming based Iterative Improvement Algorithm for {HW/SW} Cosynthesis of Distributted Embedded Systems", booktitle = "Proceedings of the 10th International Conference on Sensor Networks, {SENSORNETS} 2021, Online Streaming, February 9-10, 2021", pages = "120--125", publisher = "{SCITEPRESS}", year = "2021", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.5220/0010391501200125", DOI = "doi:10.5220/0010391501200125", timestamp = "Tue, 02 Mar 2021 22:50:18 +0100", biburl = "https://dblp.org/rec/conf/sensornets/GorskiO21.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{DBLP:conf/icsoft/GorskiO21, author = "Adam Gorski and Maciej Ogorzalek", editor = "Hans-Georg Fill and Marten {van Sinderen} and Leszek A. Maciaszek", title = "Genetic Programming based Constructive Algorithm with Penalty Function for Hardware/Software Cosynthesis of Embedded Systems", booktitle = "Proceedings of the 16th International Conference on Software Technologies, {ICSOFT} 2021, Online Streaming, July 6-8, 2021", pages = "583--588", publisher = "{SCITEPRESS}", year = "2021", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.5220/0010605005830588", DOI = "doi:10.5220/0010605005830588", timestamp = "Wed, 28 Jul 2021 16:06:40 +0200", biburl = "https://dblp.org/rec/conf/icsoft/GorskiO21.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{DBLP:conf/sensornets/GorskiO22, author = "Adam Gorski and Maciej Ogorzalek", editor = "Rangarao Venkatesha Prasad and Dirk Pesch and Nirwan Ansari and Cesar Benavente-Peces", title = "Genetic Programming based Algorithm for {HW/SW} Cosynthesis of Distributed Embedded Systems Specified using Conditional Task Graph", booktitle = "Proceedings of the 11th International Conference on Sensor Networks, {SENSORNETS} 2022, Online Streaming, February 7-8, 2022", pages = "239--243", publisher = "{SCITEPRESS}", year = "2022", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.5220/0011011700003118", DOI = "doi:10.5220/0011011700003118", timestamp = "Sat, 12 Mar 2022 14:06:34 +0100", biburl = "https://dblp.org/rec/conf/sensornets/GorskiO22.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{gorski:2023:DIS, author = "Adam Gorski and Maciej Ogorzalek", title = "Assignment of Unexpected Tasks in Embedded System Design Process Using Genetic Programming", booktitle = "Dynamics of Information Systems", year = "2023", volume = "14321", series = "LNCS", pages = "93--101", month = "3-6 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-031-50320-7_7", DOI = "doi:10.1007/978-3-031-50320-7_7", notes = "Published in 2024", } @InProceedings{Gorski:2024:ISOCC, author = "Adam M Gorski and Maciej Ogorzalek", title = "Detecting and assignment of unexpected tasks in {SoC} design process using genetic programming", booktitle = "2024 21st International SoC Design Conference (ISOCC)", year = "2024", pages = "398--399", month = aug, keywords = "genetic algorithms, genetic programming, Operations research, Cloning, Real-time systems, Optimisation, Standards, Genotypes, Genetic operators, Architecture, Concurrent real-time optimisation, Unexpected tasks", ISSN = "2472-9655", DOI = "doi:10.1109/ISOCC62682.2024.10762129", abstract = "During SoC design designers can meet many problems. One of it is Concurrent real-time optimisation. It is a very important problem in operation research. The problem appears when exist many potentials solutions of one optimisation issue. Each solution demands different parameters to optimise. Therefore the process can be split into two phases. Each phase impacts another in real-time. Such a problem can appear in detecting and assignment of unexpected tasks in SoC design process. In the paper we propose a genetic programming solution to detect the optimal way to solve unexpected situations which appear during the work of the system. Proposed methodology starts from randomly constructed genotypes. During the evolution process the algorithm generates new individuals using standard genetic operators: mutation, crossover, cloning and rank selection.", notes = "Also known as \cite{10762129}", } @InProceedings{Goschen:2022:CEC, author = "Jarrod Goschen and Anna S. Bosman and Stefan Gruner", title = "Genetic Micro-Programs for Automated Software Testing with Large Path Coverage", booktitle = "2022 IEEE Congress on Evolutionary Computation (CEC)", year = "2022", editor = "Carlos A. Coello Coello and Sanaz Mostaghim", address = "Padua, Italy", month = "18-23 " # jul, keywords = "genetic algorithms, genetic programming, SBSE, Software testing, Codes, Instruments, Software algorithms, Evolutionary computation, Software systems, Software testing, input domain partitioning, automated data generation", isbn13 = "978-1-6654-6708-7", DOI = "doi:10.1109/CEC55065.2022.9870310", abstract = "Ongoing progress in computational intelligence (CI) has led to an increased desire to apply CI techniques for the purpose of improving software engineering processes, particularly software testing. Existing state-of-the-art automated software testing techniques focus on using search algorithms to discover input values that achieve high execution path coverage. These algorithms are trained on the same code that they intend to test, requiring instrumentation and lengthy search times to test each software component. We outline a novel genetic programming framework, where the evolved solutions are not input values, but microprograms that can repeatedly generate input values to efficiently explore a software components input parameter domain. We also argue that our approach can be generalised such as to be applied to many different software systems, and is thus not specific to merely the particular software component on which it was trained.", notes = "Also known as \cite{9870310} 'pilot study' GMP = ADF = 'micro-programs'. lenience. fitness = path coverage on 5 tests. Types same as types of input SUT function to be tested. 'only uniform input type' (ie no mixed types tried). Loop <=250 iterations. If. SUT has no side-effects (ie non OO?) bloat", } @Article{Gosling:2012:GPEM, author = "Timothy Gosling", title = "Moshe Sipper: Evolved to Win", journal = "Genetic Programming and Evolvable Machines", year = "2012", volume = "13", number = "2", pages = "269--270", month = jun, note = "Book review", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dR8sV", DOI = "doi:10.1007/s10710-012-9157-6", notes = "Review of \cite{EvolvedToWin}", affiliation = "The Creative Assembly, Horsham, England", } @InProceedings{Gossuin:2020:GECCO, author = "Thomas Gossuin and Didier Garray and Vincent Kelner", title = "Multi-Objective Optimal Distribution of Materials in Hybrid Components", year = "2020", editor = "Carlos Artemio {Coello Coello} and Arturo Hernandez Aguirre and Josu Ceberio Uribe and Mario Garza Fabre and Gregorio {Toscano Pulido} and Katya Rodriguez-Vazquez and Elizabeth Wanner and Nadarajen Veerapen and Efren Mezura Montes and Richard Allmendinger and Hugo Terashima Marin and Markus Wagner and Thomas Bartz-Beielstein and Bogdan Filipic and Heike Trautmann and Ke Tang and John Koza and Erik Goodman and William B. Langdon and Miguel Nicolau and Christine Zarges and Vanessa Volz and Tea Tusar and Boris Naujoks and Peter A. N. Bosman and Darrell Whitley and Christine Solnon and Marde Helbig and Stephane Doncieux and Dennis G. Wilson and Francisco {Fernandez de Vega} and Luis Paquete and Francisco Chicano and Bing Xue and Jaume Bacardit and Sanaz Mostaghim and Jonathan Fieldsend and Oliver Schuetze and Dirk Arnold and Gabriela Ochoa and Carlos Segura and Carlos Cotta and Michael Emmerich and Mengjie Zhang and Robin Purshouse and Tapabrata Ray and Justyna Petke and Fuyuki Ishikawa and Johannes Lengler and Frank Neumann", isbn13 = "9781450371285", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377930.3390190", DOI = "doi:10.1145/3377930.3390190", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference", pages = "1082--1088", size = "7 pages", keywords = "genetic algorithms, omposite material, finite element analysis, hybrid components, multi-objective optimization, voronoi tessellation", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", abstract = "Genetic algorithms are 0th-order methods and therefore praised in many non-differentiable optimization problems, which encompass the majority of real world applications. In this work, a multiobjective optimization of hybrid, i.e. multi-material, components under technological constraints is performed to guide engineers towards the optimal design of manufactured parts in competition-driven industries, like in the automotive or the aerospace sector. Specifically, three of the main challenges Original Equipment Manufacturers (OEMs) face nowadays are met : simultaneously minimizing compliance, weight and cost. This is achieved by replacing pure metallic components with lightweight materials such as thermoplastics and composites. However, a mere substitution would not be appropriate because it would usually result in insufficient performances or expensive designs. The task of the genetic algorithm is hence to find the optimal material distribution using Voronoi tessellations on a fixed Finite Element (FE) mesh while complying with the manufacturing methods of thermoplastics and composites. The Voronoi encoding has a great advantage over traditional Bit-Array genotypes : its size is independent of the FE mesh granularity, therefore refining the mesh has no impact on the computational cost of the genetic algorithm's operators. Experimental results on the cantilever beam test case show Pareto optimal material distributions.", notes = "Also known as \cite{10.1145/3377930.3390190} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @Article{Gostimirovi:2012:JPE, author = "M. Gostimirovi and V. Pucovsky and P. Kovac and D. Rodic and B. Savkovic", title = "Modeling of Discharge Energy in Electrical Discharge Machining by the use of Genetic Programming", journal = "Journal of Production Engineering", year = "2012", volume = "15", number = "2", pages = "15--18", month = oct, keywords = "genetic algorithms, genetic programming, EDM, discharge energy, machining parameters", ISSN = "1821-4932", URL = "http://www.jpe.ftn.uns.ac.rs/papers/2012/no2/3-Gostimirovic-JPE.pdf", publisher = "FACULTY OF TECHNICAL SCIENCES, DEPARTMENT OF PRODUCTION ENGINEERING, 21000 NOVI SAD, Trg Dositeja Obradovica 6 SERBIA", address = "Novi Sad", size = "4 pages", abstract = "Being able to model machining process can save enormous funds and time, which will result in cheaper and more efficient production. In this paper discharge energy, which is in EDM directly transformed into thermal energy, is used as a primary machining process and because of that it presents a main point of interest in modelling procedure. Link between discharge energy and output results of machining process is found using genetic programming as a type of artificial intelligence.", abstract2 = "Modelovanje energije praznjenja u elektroerozivnoj obradi pomocu genetskog programiranja. Mogucnost modelovanja procesa obrade moze ustedeti velika sredstva i vreme a krajnji rezultat je jeftinija i efikasnija proizvodnja. U ovom radu je energija praznjenja, koja se u elektroerozivnom procesu direktno pretvara u toplotnu energiju, koriscena kao primarni parametar obrade i zbog toga predstavlja zizu interesa u procesima modelovanja. Veza izmedu energije praznjenja i izlaznih parametara procesa obrade je formirana koristeci genetsko programiranje kao vrste vestacke inteligencije. Kljucne reci: EDM, energija praznjenja, parametri obrade, genetsko programiranje", notes = "GPdotNET http://www.jpe.ftn.uns.ac.rs/", } @InProceedings{1277011, author = "Stanley Phillips Gotshall and Terence Soule", title = "Stochastic training of a biologically plausible spino-neuromuscular system model", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "1", isbn13 = "978-1-59593-697-4", pages = "253--260", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p253.pdf", DOI = "doi:10.1145/1276958.1277011", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Artificial Life, Evolutionary Robotics, Adaptive Behaviour, Evolvable Hardware, breeding swarm optimisers, neural networks, ANN, particle swarm optimiser, spiking networks, spinal cord", abstract = "A primary goal of evolutionary robotics is to create systems that are as robust and adaptive as the human body. Moving toward this goal often involves training control systems that process sensory information in a way similar to humans. Artificial neural networks have been an increasingly popular option for this because they consist of processing units that approximate the synaptic activity of biological signal processing units, i.e. neurons. In this paper we train a nonlinear recurrent spino-neuromuscular system (SNMS) model and compare the performance of genetic algorithms (GA)s, particle swarm optimisers (PSO)s, and GA/PSO hybrids. Several key features of the SNMS model have previously been modelled individually but have not been combined into a single model as is done here. The results show that each algorithm produces fit solutions and generates fundamental biological behaviours, such as tonic tension behaviors and triceps activation patterns, that are not explicitly trained.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @PhdThesis{stan_dissertation, author = "Stanley Phillips Gotshall", title = "Evolutionary training of a biologically plausible spino-neuromuscular system model", school = "Computer Science, University of Idaho", year = "2007", address = "USA", month = aug, keywords = "genetic algorithms, genetic programming, Nervous system Computer simulation, Spine--Computer simulation, Muscles, Computer simulation", URL = "http://www2.cs.uidaho.edu/~tsoule/website_with_hierarchy/stan_dissertation.pdf", URL = "http://digital.lib.uidaho.edu/cdm/ref/collection/etd/id/221", size = "105 pages", abstract = "There is an increasing need for researchers to develop a greater understanding of the neuromuscular system. The medical treatment of many diseases and disorders depends on physicians and practitioners having specific knowledge of how damage to certain spinal pathways can affect motor control. To that end, an important step in increasing our understanding of the spino-neuromuscular system (SNMS) is to develop a model in which researchers can conduct controlled virtual experiments within the spinal cord. This dissertation develops such a model while addressing limitations in current modelling methods of neuromuscular systems. This dissertation also shows that evolutionary algorithms train robust and stable SNMS models that yield key biological behaviours. This type of model is widely applicable in areas such as evolutionary robotics, neuroprosthetics, and modeling neuromuscular diseases since all these areas investigate the importance of specific components in biological or biologically related systems.", notes = "Brief mention of GP, part also in \cite{Gotshall:2007:GPEM} (see also \cite{Gotshall:2011:GPEM}) Supervisor Terence Soule", } @Article{Gotshall:2007:GPEM, author = "Stanley Gotshall and Kathy Browder and Jessica Sampson and Terence Soule and Richard Wells", title = "Stochastic optimization of a biologically plausible spino-neuromuscular system model", journal = "Genetic Programming and Evolvable Machines", year = "2007", volume = "8", number = "4", pages = "355--380", month = dec, note = "special issue on medical applications of Genetic and Evolutionary Computation", keywords = "genetic algorithms, Biological neural networks, Particle swarm optimisers, PSO, Breeding swarm optimisers", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-007-9044-8", abstract = "Simulations and modelling techniques are becoming increasingly important in understanding the behaviour of biological systems. Detailed models help researchers answer questions in diverse areas such as the behavior of bacteria and viruses and aiding in the diagnosis and treatment of injuries and diseases. However, to yield meaningful biological behaviour, biological simulations often include hundreds of parameters that correspond to biological components and characteristics. This paper demonstrates the effectiveness of genetic algorithms (GA) and particle swarm optimizer (PSO) based techniques in training biologically plausible behaviour in a neuromuscular simulation of a biceps/triceps pair. The results are compared to human subjects during flexion/extension movements to show that these algorithms are effective in training biologically plausible behaviours on both neural and gross anatomical levels. Specific behaviors of interest that emerge include tonic tensions in both muscles during resting periods, biceps/triceps coactivation patterns, and recruitment-like behaviours. These are all fundamental characteristics of biological motor control and emerge without direct selection for these behaviours. This is the first time that all of these characteristic behaviours emerge in a model of this detail without direct selective pressure.", notes = "See Erratum \cite{Gotshall:2011:GPEM}", } @Article{Gotshall:2011:GPEM, author = "Stanley Gotshall and Kathy Browder and Jessica Sampson and Terence Soule and Richard Wells", title = "Erratum to: Stochastic optimization of a biologically plausible spino-neuromuscular system model A comparison with human subjects", journal = "Genetic Programming and Evolvable Machines", year = "2011", volume = "12", number = "1", pages = "87--88", month = mar, ISSN = "1389-2576", DOI = "doi:10.1007/s10710-010-9108-z", size = "2 pages", abstract = "The on line version of the original article can be found under doi:10.1007/s10710-007-9044-8.", notes = "Fig 11, Equation 5 and Sec 4.4 in \cite{Gotshall:2007:GPEM}", affiliation = "Department of Computer Science, University of Idaho, Moscow, ID USA", } @InProceedings{gottlieb:1999:EAMKPRBFR, author = "Jens Gottlieb", title = "Evolutionary Algorithms for Multidimensional Knapsack Problems: the Relevance of the Boundary f the Feasible Region", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "787", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems, poster papers", ISBN = "1-55860-611-4", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Misc{gounares:2001:patent, author = "Alexander Gounares and Prakash Sikchi", title = "Adaptive problem solving method and apparatus utilizing evolutionary computation techniques", howpublished = "U.S. Patent", year = "2001", month = "28 " # aug, keywords = "genetic algorithms, genetic programming", URL = "http://patft.uspto.gov/netacgi/nph-Parser?Sect2=PTO1&Sect2=HITOFF&p=1&u=/netahtml/PTO/search-bool.html&r=1&f=G&l=50&d=PALL&RefSrch=yes&Query=PN/6282527", abstract = "A system for adaptively solving sequential problems in a target system using evolutionary computation techniques and in particular genetic algorithms and modified genetic algorithms. Stimuli to a target system such as a software system are represented as actions. A single sequence of actions is a chromosome. Chromosomes are generated by a goal-seeking algorithm that uses a hint database and recursion to intelligently and efficiently generate a robust chromosome population. The chromosomes are applied to the target system one action at a time and the change in properties of the target system is measured after each action is applied. A fitness rating is calculated for each chromosome based on the property changes produced in the target system by the chromosome. The fitness rating calculation is defined so that successive generations of chromosomes will converge upon desired characteristics. For example, desired characteristics for a software testing application are defect discovery and code coverage. Chromosomes with high fitness ratings are selected as parent chromosomes and various techniques are used to mate the parent chromosomes to produce children chromosomes. Children chromosomes with high fitness ratings are entered into the chromosome population. Defects in a target software system are minimised by evolving ever-shorter chromosomes that produce the same defect. Defect discovery rate, or any other desired characteristic, is thereby maximised.", notes = "6,282,527 Assignee: Microsoft Corporation (Redmond, WA)", } @Unpublished{gout2016, author = "Julien Gout and Markus Quade and Kamran Shafi and Robert K. Niven and Markus Abel", title = "Learning Optimal Control of Synchronization in Networks of Coupled Oscillators using Genetic Programming-based Symbolic Regression", howpublished = "arXiv:1612.05276", year = "2016", month = "15 " # dec, note = "Submitted to nonlinear dynamics", keywords = "genetic algorithms, genetic programming", bibdate = "2017-06-07", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/corr/corr1612.html#GoutQSNA16", URL = "http://arxiv.org/abs/1612.05276", size = "44 pages", abstract = "Networks of coupled dynamical systems provide a powerful way to model systems with enormously complex dynamics, such as the human brain. Control of synchronization in such networked systems has far reaching applications in many domains, including engineering and medicine. In this paper, we formulate the synchronization control in dynamical systems as an optimisation problem and present a multi-objective genetic programming-based approach to infer optimal control functions that drive the system from a synchronized to a non-synchronized state and vice-versa. The genetic programming-based controller allows learning optimal control functions in an interpretable symbolic form. The effectiveness of the proposed approach is demonstrated in controlling synchronization in coupled oscillator systems linked in networks of increasing order complexity, ranging from a simple coupled oscillator system to a hierarchical network of coupled oscillators. The results show that the proposed method can learn highly-effective and interpretable control functions for such systems.", notes = "Also known as \cite{journals/corr/GoutQSNA16}", } @InProceedings{Goyal:2023:ICCCI, author = "Khushi Goyal and Shaurya Singh and Muskan Gulati and A. Suresh", booktitle = "2023 International Conference on Computer Communication and Informatics (ICCCI)", title = "An Ensemble Of Machine And Deep Learning Models For Real Time Credit Card Scam Recognition", year = "2023", address = "Coimbatore, India", month = "23-25 " # jan, keywords = "Machine Leaning, Deep Learning, Credit Card, Neural Network, ANN, E-commerce, Online shopping", ISSN = "2473-7577", isbn13 = "979-8-3503-4822-4", DOI = "doi:10.1109/ICCCI56745.2023.10128473", broken = "https://www.kagele.com/datasets/mlg-ulb/crediteardfraud", broken = "https://www.kaggle.com/datasets/kartik2112/fraud-detection", data_url = "https://www.geeksforgeeks.org/local-outlier-factor/", size = "4 pages", abstract = "As the E-commerce sector is getting large, the use of electronic money and is getting wider and wider. Credit cards are the most useful and easy tools for payment. It is easy to use and reduces the efforts made by humans. But with advantages some disadvantages also come hand in hand. Many frauds take place while making the transactions and due to this many people lose millions of money. Hence, there need to be a detection system so that people can make the transactions without the fear of frauds. In today's time there are many technologies which can help in making such a system. Some technologies are Neural Network, Artificial Intelligence, Bayesian Network, Data mining, Artificial Immune System, K-nearest neighbour algorithm, Decision Tree, Fuzzy Logic Based System, Support Vector Machine, Machine learning, Genetic Programming etc. This paper will include many surveys which will be conducted in which people will use different techniques to make a strong system. The work will also be aiming at making a strong detection system using libraries like numpy, sklearn and other py libraries. The problem is solved by using a classifier which can differentiate between fraud and legit transactions based on the class and time. The dataset contains 31 columns among which 28 columns are named as v1, v2, v3a. Due to security purposes, 2 columns are time and amount [1]. The total amount of transactions were 283.806 with only 492 fraud cases and rest legit transactions. In today's time there are credit cards in the market for kids who are under 18 as well. Therefore it is important for a system to be developed for safety. Fraudsters can use the money for many illegal practices as well. This paper will use Random Forest Classifier and Decision tree to test the dataset [2]. The dataset is of some card holders from Europe.", notes = "Also known as \cite{10128473} Is this GP? Department of Networking and Communications, School of Computing, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, Chengalpattu - 603203, Chennai, Tamil Nadu, India.", } @InProceedings{Goyal:2016:ICCCA, author = "Uttara Goyal and Arunima Jaiswal", booktitle = "2016 International Conference on Computing, Communication and Automation (ICCCA)", title = "Analysing software reliability modelling aspects using soft computing methodology", year = "2016", pages = "358--363", abstract = "Software reliability is deliberated as a measurable metric, which is the probability of any software operation to be free of failure for a stated course of time in a given environment. Many Software Reliability Growth Models have been developed over the years that can calculate and anticipate the software product reliability. This paper gives an analysis of various Soft Computing Techniques and considers these soft computing techniques in terms of software reliability modelling competence.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CCAA.2016.7813745", month = apr, notes = "Also known as \cite{7813745}", } @InProceedings{Gp:2017:iCATccT, author = "Sunitha Gp and Rio D'Souza", booktitle = "2017 3rd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT)", title = "Multiclass genetic programming based approach for classification of intrusions", year = "2017", pages = "74--78", abstract = "Classification plays a major role in distinguishing the normal traffic from the intrusive ones in any intrusion detection system. Different approaches have been used by various researchers to improve the accuracy of the classifiers for binary and multi-class classification problems. Genetic programming (GP) algorithms have been applied in the previous studies and have confirmed that it performs well for classification problems. In our work, we have used a variation of the mutation operation which will be applied when the fitness of the individual does not change significantly for a specified number of generations. Several experiments were conducted using the standard GP method and using the modified mutation operation and the results obtained show that our approach gives good results for multiclass problem in comparison to the standard GP method.", keywords = "genetic algorithms, genetic programming, intrusion detection, false positive rate, detection rate, NSL-KDD, multiclass", DOI = "doi:10.1109/ICATCCT.2017.8389109", month = dec, notes = "St Joseph Engineering College, Mangaluru, India Also known as \cite{8389109}", } @InProceedings{graae:2000:svhrGP, author = "Cristopher T. M. Graae and Peter Nordin and Mats Nordahl", title = "Stereoscopic Vision for a Humanoid Robot Using Genetic Programming", booktitle = "Real-World Applications of Evolutionary Computing", year = "2000", editor = "Stefano Cagnoni and Riccardo Poli and George D. Smith and David Corne and Martin Oates and Emma Hart and Pier Luca Lanzi and Egbert Jan Willem and Yun Li and Ben Paechter and Terence C. Fogarty", volume = "1803", series = "LNCS", pages = "12--21", address = "Edinburgh", publisher_address = "Berlin", month = "17 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-67353-9", DOI = "doi:10.1007/3-540-45561-2_2", abstract = "we introduce a new approach to adaptive stereoscopic Vision. We use genetic programming, where the input to the individuals is raw pixel data from stereo image-pairs acquired by two CCD cameras. The output from the individuals is the disparity map, which is transformed to a 3D map of the captured scene using triangulation. The used genetic engine evolves machine-coded individuals, and can thereby reach high Performance on weak computer architectures. The evolved individuals have an average disparity-error of 1.5 pixels, which is equivalent to an uncertainty of about 10percent of the true distance. This work is motivated by applications to the control of autonomous humanoid robots The Humanoid at Project at Chalmers.", notes = "EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM, EvoRob, and EvoFlight, Edinburgh, Scotland, UK, April 17, 2000 Proceedings http://evonet.lri.fr/evoweb/resources/books_journals/record.php?id=61", } @InProceedings{Grabusts:2021:SIE, author = "Peteris Grabusts and Alex Zorins", title = "Evolutionary algorithms learning methods in student education", booktitle = "SOCIETY. INTEGRATION. EDUCATION. Proceedings of the International Scientific Conference", year = "2021", editor = "Velta Lubkina and Gunars Strods and Liga Danilane and Antra Kļavinska and Olga Vindaca", volume = "V COVID-19 IMPACT ON EDUCATION INFORMATION TECHNOLOGIES IN EDUCATIONINNOVATION IN LANGUAGE EDUCATION", pages = "330--339", address = "Rezekne, Latvia", month = may # " 28-29", publisher = "Rezeknes Tehnologiju akademija", keywords = "genetic algorithms, genetic programming, data analysis, evolutionary algorithms, modeling, teaching", ISSN = "1691-5887", URL = "http://journals.ru.lv/index.php/SIE/article/view/6153", URL = "http://journals.ru.lv/index.php/SIE/article/view/6153/5199.pdf", DOI = "doi:10.17770/sie2021vol5.6153", size = "10 pages", abstract = "Teaching experience shows that during educational process student perceive graphical information better than analytical relationships. As a possible solution, there could be the use of package Matlab in realization of different algorithms for IT studies. Students are very interested in modern data mining methods, such as artificial neural networks, fuzzy logic, clustering and evolution methods. Series of research were carried out in order to demonstrate the suitability of the Matlab for the purpose of visualization of various simulation models of some data mining disciplines, particularly genetic algorithms. Nowadays the possibilities of evolutionary algorithms are widely used in many optimization and classification tasks. There are four paradigms in the world of evolutionary algorithms: evolutionary programming, evolution strategies, genetic algorithms and genetic programming. This paper analyses present-day approaches of genetic algorithms and genetic programming and examines the possibilities of genetic programming that will be used in further research. Genetic algorithm learning methods are often undeservedly forgotten, although the implementation of their algorithms is relatively strong and can be implemented even for students. In the research part of the study the modelling capabilities in data mining studies were demonstrated based on genetic algorithms and real examples. We assume that students already have prior knowledge of genetic algorithms.", notes = "Rezekne Academy of Technologies, Latvia http://journals.ru.lv/index.php/SIE/index", } @InCollection{Graf:Banzhaf:EA95, author = "Jeanine Graf and Wolfgang Banzhaf", title = "Interactive Evolution for Simulated Natural Evolution", booktitle = "Artificial Evolution", publisher = "Springer Verlag", year = "1996", editor = "Jean-Marc Alliot and Evelyne Lutton and Edmund Ronald and Marc Schoenauer and Dominique Snyers", volume = "1063", series = "LNCS", pages = "259--272", keywords = "genetic algorithms, genetic programming, Growth, Paleontology, Evolutionary Algorithms, Simulation of Natural Evolution", isbn13 = "978-3-540-61108-0", DOI = "doi:10.1007/3-540-61108-8_43", size = "14 pages", abstract = "Evolutionary algorithms of selection and variation by recombination and/or mutation have been used to simulate biological evolution. This paper demonstrates how interactive evolution can be used to study the evolution of simulated natural evolution. Since interactive evolution allows the user to direct the development of models of natural systems, it can be used to direct the evolution of models of animals and plants. We show that interactivity of artificial evolution can serve as a useful tool in the ontogenesis and phylogenesis of simulated models. This may help paleontologists solve problems in identifying likely missing links and provides a technique to generate constrained conjectures regarding gaps in evolutionary data.", notes = "Selected papers from two conferences: Evolution Artificielle 94 and Evolution Artificielle 95 http://www.cmap.polytechnique.fr/www.eark/ea95.html cf also ICEC 1995", affiliation = "Informatik Centrum Dortmund (ICD) 44227 Dortmund Germany 44227 Dortmund Germany", } @InProceedings{Graff:2008:eurogp, title = "Practical Model of Genetic Programming's Performance on Rational Symbolic Regression Problems", author = "Mario Graff and Riccardo Poli", bibdate = "2008-04-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/eurogp/eurogp2008.html#GraffP08", booktitle = "Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008", address = "Naples", month = "26-28 " # mar, publisher = "Springer", year = "2008", volume = "4971", editor = "Michael O'Neill and Leonardo Vanneschi and Steven Gustafson and Anna Isabel {Esparcia Alcazar} and Ivanoe {De Falco} and Antonio {Della Cioppa} and Ernesto Tarantino", isbn13 = "978-3-540-78670-2", pages = "122--133", series = "Lecture Notes in Computer Science", DOI = "doi:10.1007/978-3-540-78671-9_11", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{conf/eurogp/GraffP08} Least Angle Regression (LAR). p151 {"}Angle between GP systems{"}. System used by Koza \cite{koza:book} and TinyGP \cite{poli08:fieldguide}. neato. Part of \cite{conf/eurogp/2008} EuroGP'2008 held in conjunction with EvoCOP2008, EvoBIO2008 and EvoWorkshops2008", } @InProceedings{Graff:2009:eurogp, author = "Mario Graff and Riccardo Poli", title = "Automatic Creation of Taxonomies of Genetic Programming Systems", booktitle = "Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009", year = "2009", editor = "Leonardo Vanneschi and Steven Gustafson and Alberto Moraglio and Ivanoe {De Falco} and Marc Ebner", volume = "5481", series = "LNCS", pages = "145--158", address = "Tuebingen", month = apr # " 15-17", organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-01180-1", DOI = "doi:10.1007/978-3-642-01181-8_13", size = "14 pages", abstract = "A few attempts to create taxonomies in evolutionary computation have been made. These either group algorithms or group problems on the basis of their similarities. Similarity is typically evaluated by manually analysing algorithms/problems to identify key characteristics that are then used as a basis to form the groups of a taxonomy. This task is not only very tedious but it is also rather subjective. As a consequence the resulting taxonomies lack universality and are sometimes even questionable. In this paper we present a new and powerful approach to the construction of taxonomies and we apply it to Genetic Programming (GP). Only one manually constructed taxonomy of problems has been proposed in GP before, while no GP algorithm taxonomy has ever been suggested. Our approach is entirely automated and objective. We apply it to the problem of grouping GP systems with their associated parameter settings. We do this on the basis of performance signatures which represent the behaviour of each system across a class of problems. These signatures are obtained thorough a process which involves the instantiation of models of GP's performance.We test the method on a large class of Boolean induction problems.", notes = "Part of \cite{conf/eurogp/2009} EuroGP'2009 held in conjunction with EvoCOP2009, EvoBIO2009 and EvoWorkshops2009", } @Article{Graff20101254, author = "Mario Graff and Riccardo Poli", title = "Practical performance models of algorithms in evolutionary program induction and other domains", journal = "Artificial Intelligence", volume = "174", number = "15", pages = "1254--1276", year = "2010", ISSN = "0004-3702", DOI = "doi:10.1016/j.artint.2010.07.005", URL = "http://www.sciencedirect.com/science/article/B6TYF-50KWG15-1/2/3fb87252c46b990fe9a47f5dbd261a82", keywords = "genetic algorithms, genetic programming, Evolution algorithms, Program induction, Performance prediction, Algorithm taxonomies, Algorithm selection problem", abstract = "Evolutionary computation techniques have seen a considerable popularity as problem solving and optimisation tools in recent years. Theoreticians have developed a variety of both exact and approximate models for evolutionary program induction algorithms. However, these models are often criticised for being only applicable to simplistic problems or algorithms with unrealistic parameters. In this paper, we start rectifying this situation in relation to what matters the most to practitioners and users of program induction systems: performance. That is, we introduce a simple and practical model for the performance of program-induction algorithms. To test our approach, we consider two important classes of problems -- symbolic regression and Boolean function induction -- and we model different versions of genetic programming, gene expression programming and stochastic iterated hill climbing in program space. We illustrate the generality of our technique by also accurately modelling the performance of a training algorithm for artificial neural networks and two heuristics for the off-line bin packing problem. We show that our models, besides performing accurate predictions, can help in the analysis and comparison of different algorithms and/or algorithms with different parameters setting. We illustrate this via the automatic construction of a taxonomy for the stochastic program-induction algorithms considered in this study. The taxonomy reveals important features of these algorithms from the performance point of view, which are not detected by ordinary experimentation.", } @PhdThesis{Graff-Guerrero:thesis, author = "Mario Graff-Guerrero", title = "Models of the Performance of Evolutionary Program Induction Algorithms", school = "Department of Computing Science and Electronic Engineering, University of Essex", year = "2010", address = "UK", month = oct, keywords = "genetic algorithms, genetic programming, Gene Expression Programming, Cartesian Genetic Programming", URL = "http://lsc.fie.umich.mx/~mgraffg/phd_thesis.pdf", size = "142 pages", abstract = "Evolutionary computation techniques have seen a considerable popularity as problem solving and optimisation tools in recent years. Theoreticians have developed a variety of both exact and approximate models of evolutionary algorithms. However, these models are often criticised for being only applicable to simplistic problems or algorithms with unrealistic parameters. Here, we start rectifying this situation in relation to what matters the most to practitioners and users of program induction systems: performance. That is, we introduce some simple and practical models for the performance of program-induction algorithms. To test our approach, we consider two important classes of problems, symbolic regression and Boolean function induction, and we model different versions of Genetic Programming, Gene Expression Programming, Cartesian Genetic Programming and Stochastic Iterated Hill Climbers. In all cases our models are able to accurately predict the performance of each algorithm on unseen problems. This allows, for example, the use of our models to solve the algorithm selection problem (i.e., the problem of deciding which is the best algorithm to solve a problem) for program induction. Besides performing accurate predictions, we show that our models can help in the analysis and comparison of different algorithms and/or algorithms with different parameters setting. This process, too, can be automatised. We illustrate this via the automatic construction of a taxonomy for the stochastic program induction algorithms considered in this study. Although our approach was initially aimed at modelling evolutionary program induction algorithms, it is in fact very general and, in principle, can be used to predict the performance of non-evolutionary learning algorithms and problem solvers. To illustrate this, we modelled one well-known training algorithm for artificial neural networks and two common heuristics of the off-line bin packing problem with very encouraging results.", notes = "supervisor Riccardo Poli", } @InProceedings{graff:2011:EuroGP, author = "Mario Graff and Riccardo Poli", title = "Performance Models for Evolutionary Program Induction Algorithms based on Problem Difficulty Indicators", booktitle = "Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011", year = "2011", month = "27-29 " # apr, editor = "Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado", series = "LNCS", volume = "6621", publisher = "Springer Verlag", address = "Turin, Italy", pages = "118--129", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", isbn13 = "978-3-642-20406-7", DOI = "doi:10.1007/978-3-642-20407-4_11", abstract = "Most theoretical models of evolutionary algorithms are difficult to apply to realistic situations. In this paper, two models of evolutionary program-induction algorithms (EPAs) are proposed which overcome this limitation. We test our approach with two important classes of problems --- symbolic regression and Boolean function induction --- and a variety of EPAs including: different versions of genetic programming, gene expression programing, stochastic iterated hill climbing in program space and one version of cartesian genetic programming. We compare the proposed models against a practical model of EPAs we previously developed and find that in most cases the new models are simpler and produce better predictions. A great deal can also be learnt about an EPA via a simple inspection of our new models. E.g., it is possible to infer which characteristics make a problem difficult or easy for the EPA.", notes = "Part of \cite{Silva:2011:GP} EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011", } @Article{Graff:2013:EC, author = "Mario Graff and Riccardo Poli and Juan J. Flores", title = "Models of Performance of Evolutionary Program Induction Algorithms Based on Indicators of Problem Difficulty", journal = "Evolutionary Computation", year = "2013", volume = "21", number = "4", pages = "533--560", month = "Winter", keywords = "genetic algorithms, genetic programming, Evolutionary program-induction algorithms, performance forecasting, hardness measures, wind speed forecasting", ISSN = "1063-6560", DOI = "doi:10.1162/EVCO_a_00096", size = "28 pages", abstract = "Modelling the behaviour of algorithms is the realm of Evolutionary Algorithm theory. From a practitioner's point of view, theory must provide some guidelines regarding which algorithm/parameters to use in order to solve a particular problem. Unfortunately, most theoretical models of evolutionary algorithms are difficult to apply to realistic situations. However, in recent work (Graff and Poli, 2008, 2010), where we developed a method to practically estimate the performance of evolutionary program induction algorithms (EPAs), we stated addressing this issue. The method was quite general; however, it suffered from some limitations: it required the identification of a set of reference problems, it required hand picking a distance measure in each particular domain, and the resulting models were opaque being typically linear combinations of one hundred features or more. In this paper, we propose a significant improvement of this technique that overcomes the three limitations of our previous method. We achieve this through the use of a novel set of features for assessing problem difficulty for EPAs which are very general, essentially being based on the notion of finite difference. To show the capabilities or our technique and compare it with our previous performance models, we create models for the same two important classes of problems - symbolic regression on rational functions and Boolean function induction - used in our previous work. We model a variety of EPAs. The comparison showed that for the majority of the algorithms and problem classes, the new method produced much simpler and more accurate models than before. To further illustrate the practicality of the technique and its generality (beyond EPAs), we have also used it to predict the performance of both auto-regressive models and EPAs on the problem of wind speed forecasting, obtaining simpler and accurate models that outperform in all cases our previous performance models.", notes = "Posted online on 8 Nov 2012. Cited by \cite{Graff2013} Neurocomputing, doi:10.1016/j.neucom.2013.05.035", } @InProceedings{Graff:2013:CEC, article_id = "1516", author = "Mario Graff and Rafael Pena and Aurelio Medina", title = "Wind Speed Forecasting using Genetic Programming", booktitle = "2013 IEEE Conference on Evolutionary Computation", volume = "1", year = "2013", month = jun # " 20-23", editor = "Luis Gerardo {de la Fraga}", pages = "408--415", address = "Cancun, Mexico", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-0453-2", DOI = "doi:10.1109/CEC.2013.6557598", notes = "CEC 2013 - A joint meeting of the IEEE, the EPS and the IET.", } @InProceedings{graff:2014:EuroGP, author = "Mario Graff and Ariel Graff-Guerrero and Jaime Cerda-Jacobo", title = "Semantic Crossover based on the Partial Derivative Error", booktitle = "17th European Conference on Genetic Programming", year = "2014", editor = "Miguel Nicolau and Krzysztof Krawiec and Malcolm I. Heywood and Mauro Castelli and Pablo Garcia-Sanchez and Juan J. Merelo and Victor M. {Rivas Santos} and Kevin Sim", series = "LNCS", volume = "8599", publisher = "Springer", pages = "37--47", address = "Granada, Spain", month = "23-25 " # apr, organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-662-44302-6", DOI = "doi:10.1007/978-3-662-44303-3_4", abstract = "There is great interest for the development of semantic genetic operators to improve the performance of genetic programming. Semantic genetic operators have traditionally been developed employing experimentally or theoretically-based approaches. Our current work proposes a novel semantic crossover developed amid the two traditional approaches. Our proposed semantic crossover operator is based on the use of the derivative of the error propagated through the tree. This process decides the crossing point of the second parent. The results show that our procedure improves the performance of genetic programming on rational symbolic regression problems.", notes = "Part of \cite{Nicolau:2014:GP} EuroGP'2014 held in conjunction with EvoCOP2014, EvoBIO2014, EvoMusArt2014 and EvoApplications2014", } @InProceedings{Graff:2014:ROPEC, author = "Mario Graff and Juan J. Flores and Jose {Ortiz Bejar}", booktitle = "IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC 2014)", title = "Genetic Programming: Semantic point mutation operator based on the partial derivative error", year = "2014", month = nov, abstract = "There is a great interest in the Genetic Programming (GP) community to develop semantic genetic operators. Most recent approaches includes the genetic programming framework for symbolic regression called Error Space Alignment GP, the geometric semantic operators, and our previous work the semantic crossover based on the partial derivative error. To the best of our knowledge, there has not been a semantic genetic operator similar to the point mutation. In this contribution, we start filling this gap by proposing a semantic point mutation based on the derivative of the error. This novel operator complements our previous semantic crossover and, as the results show, there is an improvement in performance when this novel operator is used, and, furthermore, the best performance in our setting is the system that uses the semantic crossover and the semantic point mutation.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ROPEC.2014.7036344", notes = "Also known as \cite{7036344}", } @InProceedings{Graff:2015:ROPEC, author = "Mario Graff and Eric S. Tellez and Hugo Jair Escalante and Jose Ortiz-Bejar", booktitle = "2015 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)", title = "Memetic Genetic Programming based on orthogonal projections in the phenotype space", year = "2015", abstract = "Genetic Programming (GP) is an evolutionary algorithm that has received a lot of attention lately due to its success in solving hard real-world problems. Lately, there has been a great interest in GP's community to develop semantic genetic operators, i.e., operators that work on the phenotype. In this contribution, we improve the performance of GP by making orthogonal projections in the phenotype space using the behaviour of the parents and the target, i.e., the problem at hand. The technique proposed can be easily applied to any tree based GP, and, as the result show this technique statistically improves the performance of GP. Furthermore, we experimentally show how a traditional GP system enhanced with our technique can outperform the state of the art geometric semantic GP systems.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ROPEC.2015.7395160", month = nov, notes = "Also known as \cite{7395160}", } @Article{journals/rcs/GraffTVM15, author = "Mario Graff and Eric Sadit Tellez and Elio Villasenor and Sabino Miranda-Jimenez", title = "Semantic Genetic Programming Operators Based on Projections in the Phenotype Space", journal = "Research in Computing Science", year = "2015", volume = "94", pages = "73--85", keywords = "genetic algorithms, genetic programming, semantic crossover, symbolic regression, geometric semantic genetic programming", bibdate = "2015-06-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/rcs/rcs94.html#GraffTVM15", ISSN = "1870-4069", URL = "http://www.rcs.cic.ipn.mx/2015_94/", URL = "http://rcs.cic.ipn.mx/2015_94/Semantic%20Genetic%20Programming%20Operators%20Based%20on%20Projections%20in%20the%20Phenotype%20Space.pdf", size = "13 pages", abstract = "In the Genetic Programming (GP) community there has been a great interest in developing semantic genetic operators. These type of operators use information of the phenotype to create offspring. The most recent approaches of semantic GP include the GP framework based on the alignment of error space, the geometric semantic genetic operators, and backpropagation genetic operators. Our contribution proposes two semantic operators based on projections in the phenotype space. The proposed operators have the characteristic, by construction, that the offspring's fitness is as at least as good as the fitness of the best parent; using as fitness the euclidean distance. The semantic operators proposed increment the learning capabilities of GP. These operators are compared against a traditional GP and Geometric Semantic GP in the Human oral bioavailability regression problem and 13 classification problems. The results show that a GP system with our novel semantic operators has the best performance in the training phase in all the problems tested.", notes = "https://rcs.cic.ipn.mx/", } @InProceedings{Graff:2015:NEO, author = "Mario Graff and Eric S. Tellez and Hugo Jair Escalante and Sabino Miranda-Jimenez", title = "Semantic Genetic Programming for Sentiment Analysis", booktitle = "NEO 2015: Results of the Numerical and Evolutionary Optimization Workshop NEO 2015 held at September 23-25 2015 in Tijuana, Mexico", year = "2015", editor = "Oliver Schuetze and Leonardo Trujillo and Pierrick Legrand and Yazmin Maldonado", volume = "663", series = "Studies in Computational Intelligence", pages = "43--65", publisher = "Springer", keywords = "genetic algorithms, genetic programming, semantic genetic programming, sentiment analysis", isbn13 = "978-3-319-44003-3", URL = "http://link.springer.com/chapter/10.1007/978-3-319-44003-3_2", DOI = "doi:10.1007/978-3-319-44003-3_2", abstract = "Sentiment analysis is one of the most important tasks in text mining. This field has a high impact for government and private companies to support major decision-making policies. Even though Genetic Programming (GP) has been widely used to solve real world problems, GP is seldom used to tackle this trendy problem. This contribution starts rectifying this research gap by proposing a novel GP system, namely, Root Genetic Programming, and extending our previous genetic operators based on projections on the phenotype space. The results show that these systems are able to tackle this problem being competitive with other state-of-the-art classifiers, and, also, give insight to approach large scale problems represented on high dimensional spaces.", notes = "Published 2017", } @InProceedings{Graff:2016:ROPEC, author = "Mario Graff and Eric S. Tellez and Sabino Miranda-Jimenez and Hugo Jair Escalante", title = "{EvoDAG}: A semantic {Genetic} {Programming} {Python} library", booktitle = "2016 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)", year = "2016", editor = "Jaime Cerda Jacobo", address = "Ixtapa, Vicente Guerrero, Mexico", month = "9-11 " # nov, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, semantic genetic programming, auto-sklearn, SVM", isbn13 = "978-1-5090-3794-0", URL = "http://ieeexplore.ieee.org/document/7830633/", DOI = "doi:10.1109/ROPEC.2016.7830633", size = "6 pages", abstract = "Genetic Programming (GP) is an evolutionary algorithm that has received a lot of attention lately due to its success in solving hard real-world problems. Lately, there has been considerable interest in GP's community to develop semantic genetic operators, i.e., operators that work on the phenotype. In this contribution, we describe EvoDAG (Evolving Directed Acyclic Graph) which is a Python library that implements a steady-state semantic Genetic Programming with tournament selection using an extension of our previous crossover operators based on orthogonal projections in the phenotype space. To show the effectiveness of EvoDAG, it is compared against state-of-the-art classifiers on different benchmark problems, experimental results indicate that EvoDAG is very competitive.", notes = "See \cite{Handley:1994:DAGpcp} banana titanic thyroid diabetis breast-cancer flare-solar heart ringnorm twonorm german image waveform splice http://2016.ropec.org/bank-payment-information/comite-revisor/ https://github.com/mgraffg/EvoDAG", } @Article{journals/nc/GraffEOT17, author = "Mario Graff and Hugo Jair Escalante and Fernando Ornelas-Tellez and Eric Sadit Tellez", title = "Time series forecasting with genetic programming", journal = "Natural Computing", year = "2017", volume = "16", number = "1", pages = "165--174", keywords = "genetic algorithms, genetic programming", bibdate = "2017-06-08", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/nc/nc16.html#GraffEOT17", DOI = "doi:10.1007/s11047-015-9536-z", } @InProceedings{DBLP:conf/semeval/GraffMTO19, author = "Mario Graff and Sabino Miranda-Jimenez and Eric Sadit Tellez and Daniela Alejandra Ochoa", editor = "Jonathan May and Ekaterina Shutova and Aurelie Herbelot and Xiaodan Zhu and Marianna Apidianaki and Saif M. Mohammad", title = "{INGEOTEC} at SemEval-2019 Task 5 and Task 6: {A} Genetic Programming Approach for Text Classification", booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation, SemEval@NAACL-HLT 2019, Minneapolis, MN, USA, June 6-7, 2019", pages = "639--644", publisher = "Association for Computational Linguistics", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.18653/v1/s19-2114", DOI = "doi:10.18653/v1/s19-2114", timestamp = "Tue, 28 Jan 2020 00:00:00 +0100", biburl = "https://dblp.org/rec/conf/semeval/GraffMTO19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{EvoMSA_A_Multilingual_Evolutionary_Approach_for_Sentiment_Analysis_Application_Notes, author = "Mario Graff and Sabino Miranda-Jimenez and Eric Sadit Tellez and Daniela Moctezuma", title = "{EvoMSA}: A Multilingual Evolutionary Approach for Sentiment Analysis", journal = "IEEE Computational Intelligence Magazine", year = "2020", volume = "15", number = "1", pages = "76--88", month = feb, keywords = "genetic algorithms, genetic programming, EvoDAG, multilingual NLP, arabic, english, spanish, Emoji Space, FastText", ISSN = "1556-603X", URL = "http://arxiv.org/abs/1812.02307", DOI = "doi:10.1109/MCI.2019.2954668", code_url = "https://github.com/INGEOTEC/EvoMSA", size = "13 pages", abstract = "Sentiment analysis (SA) is a task related to understanding people's feelings in written text; the starting point would be to identify the polarity level (positive, neutral or negative) of a given text, moving on to identify emotions or whether a text is humorous or not. This task has been the subject of several research competitions in a number of languages, e.g., English, Spanish, and Arabic, among others. In this contribution, we propose an SA system, namely EvoMSA, that unifies our participating systems in various SA competitions, making it domain-independent and multilingual by processing text using only language-independent techniques. EvoMSA is a classifier, based on Genetic Programming that works by combining the output of different text classifiers to produce the final prediction. We analyzed EvoMSA on different SA competitions to provide a global overview of its performance. The results indicated that EvoMSA is competitive obtaining top rankings in several SA competitions. Furthermore, we performed an analysis of EvoMSA's components to measure their contribution to the performance; the aim was to facilitate a practitioner or newcomer to implement a competitive SA classifier. Finally, it is worth to mention that EvoMSA is available as open-source software.", notes = "Also known as \cite{8956106} \cite{DBLP:journals/corr/abs-1812-02307} INSPEC Accession Number: 19260700 CONACYT-INFOTEC Centro de Investigacion e Innovacion en Tecnologias de la Informacion y Comunicacion, Aguascalientes, Mexico", } @InProceedings{graham:1998:opdidcGA, author = "Jonathan M. Graham", title = "Optimal Placement of Distributed Iterrelated Data Components using Genetic Algorithms", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "52--58", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "genetic algorithms, OX, EM", size = "7 pages", notes = "GP-98LB ", } @InProceedings{Graham:2009:eurogp, author = "Lee Graham and Rob Cattral and Franz Oppacher", title = "Beneficial Preadaptation in the Evolution of a 2D Agent Control System with Genetic Programming", booktitle = "Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009", year = "2009", editor = "Leonardo Vanneschi and Steven Gustafson and Alberto Moraglio and Ivanoe {De Falco} and Marc Ebner", volume = "5481", series = "LNCS", pages = "303--314", address = "Tuebingen", month = apr # " 15-17", organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming, poster", isbn13 = "978-3-642-01180-1", DOI = "doi:10.1007/978-3-642-01181-8_26", notes = "Part of \cite{conf/eurogp/2009} EuroGP'2009 held in conjunction with EvoCOP2009, EvoBIO2009 and EvoWorkshops2009", } @Article{graham-rowe:1999:elvis, author = "Duncan Graham-Rowe", title = "Elvis Lives", journal = "New Scientist", year = "1999", month = "21 " # aug, keywords = "genetic algorithms, genetic programming", URL = "http://www.newscientist.com/article/mg16322002.400-elvis-lives.html", size = "2 pages", abstract = "Description of Peter Nordin humanoid robot Elvis", } @Article{graham-rowe:2001:egp, author = "Duncan Graham-Rowe", title = "Evolve or die", journal = "New Scientist", year = "2001", month = "27 " # oct, keywords = "genetic algorithms, genetic programming, enzyme genetic programming", URL = "http://www.newscientist.com/article/mg17223142.200-evolve-or-die.html", size = "1 page", abstract = "ENZYMES, amino acids and genes are not normally in the computer geek's vernacular. But that could all change with the start of the next revolution in computer hardware and software which some scientists say could be a biological one.", notes = "Michael A Lones", } @Article{graham-rowe:2002:radio, author = "Duncan Graham-Rowe", title = "Radio emerges from the electronic soup", journal = "New Scientist", year = "2002", month = "13 " # aug, keywords = "genetic algorithms, evolvable hardware", URL = "http://www.newscientist.com/news/news.jsp?id=ns99992732", size = "1 page", abstract = "A self-organising electronic circuit has stunned engineers by turning itself into a radio receiver.", notes = "Paul Layzell and Jon Bird at the University of Sussex", } @Article{graham-rowe:2005:complearn, author = "Duncan Graham-Rowe", title = "{Google's} search for meaning", journal = "New Scientist", year = "2005", volume = "2484", pages = "21", month = "29 " # jan, keywords = "genetic algorithms, genetic programming, complearn", URL = "https://www.newscientist.com/article/dn6924-googles-search-for-meaning/", size = "1 page", abstract = "COMPUTERS can learn the meaning of words simply by plugging into Google. The finding could bring forward the day that true artificial intelligence is developed.", notes = "Paul Vitanyi and Rudi Cilibrasi at the www.CWI.nl \cite{cs.CL/0412098} see http://homepages.cwi.nl/~paulv/lectures/google-lecture.pdf", } @Proceedings{Grahl:2006:GECCO:lbp, title = "Late breaking papers at Genetic and Evolutionary Computation Conference {(GECCO'2006)}", year = "2006", month = "8-12 " # jul, editor = "J{\"{o}}rn Grahl", address = "Seattle, WA, USA", keywords = "genetic algorithms, genetic programming, MOO, PSO, NN, LCS", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006etc/LBP.html", notes = "Distributed on CD-ROM at GECCO-2006", } @InProceedings{grand:1997:creatures, author = "Stephen Grand and Dave Cliff and Anil Malhotra", title = "Creatures: Artificial Life Autonmous Software Agents for Home Entertainment", booktitle = "The First International Conference on Autonomous Agents (Agents '97)", year = "1997", editor = "W. Lewis Johnson", pages = "22--29", address = "Marina del Rey, California, USA", publisher_address = "1515 Broadway, New York, NY 10036, USA", month = feb # " 5-8", organisation = "ACM SIGART", publisher = "ACM Press", keywords = "Arificial Life", ISBN = "0-89791-877-0", notes = "http://www.isi.edu/isd/AA97/info.html", } @MastersThesis{grant:msc, author = "Michael S. Grant", title = "An Investigation into Genetic Programming", school = "Department of Computer Science and Applied Mathematics, Aston University", year = "1996", address = "Birmingham, UK", month = sep, email = "michael.grant@bbc.co.uk", email = "gp@michael-grant.me.uk", keywords = "genetic algorithms, genetic programming", URL = "http://www.michael-grant.me.uk/msc.zip", URL = "http://www.michael-grant.me.uk/msc_writeup.pdf", URL = "http://www.michael-grant.me.uk/msc_appendx.pdf", size = "150 pages", abstract = "An investigation was undertaken of the field of Genetic Programming, an offshoot of Genetic Algorithms. The GP system was implemented in Emacs Lisp. Study was undertaken of three alternative methods of GP - the original method, the Stack system and the Pygmy Algorithm. The implementation of the Stack system was shown to suffer from premature convergence; that of the Pygmy Algorithm was shown under certain conditions to be superior to the original method. A novel problem, that of generating mazes, was implemented and shown to be capable of solution by the GP system and by the Pygmy Algorithm.", } @PhdThesis{grant:phd, author = "Michael Sean Grant", title = "An Investigation into the Suitability of Genetic Programming for Computing Visibility Areas for Sensor Planning", school = "Department of Computing and Electrical Engineering, Heriot-Watt University", year = "2000", address = "Riccarton, Edinburgh EH14 4AS, United Kingdom", month = may, email = "gp@michael-grant.me.uk", keywords = "genetic algorithms, genetic programming", URL = "http://www.michael-grant.me.uk/phd.zip", URL = "http://hdl.handle.net/10399/555", URL = "http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.325090", URL = "http://books.google.co.uk/books?id=Mr4dHAAACAAJ", size = "293 pages", abstract = "This thesis considers the application of Genetic Programming to visibility space calculation, for Sensor Planning in Machine Vision. This is a problem considerably more complex than most for which GP has been used; no closed-form algorithm for it yet exists in the most general case. The main contributions and results are the application of GP to a new field, and the conclusion that GP is better suited to solve this complex problem by a generate-and-test approach than an analytic one. Three systems were implemented to evolve programs for calculating visibility spaces. The first used untyped GP and low-level operations, for maximum flexibility in evolution, but could solve the problem only for trivial cases. The second used high-level geometric operations and typed GP, but tended to get trapped in local optima. Approaches used, unsuccessfully, to obviate this included altering the fitness cases and function set both statically and dynamically, parameter tuning, seeding the population, using program templates, and using a simpler system for modelling evolution. The third system, which used a generate-and-test approach, evolved useful solutions. When seeded with hand-crafted partial solutions, it was able to improve them considerably. The work shows the potential of GP to evolve or refine a region-growing generate-and-test algorithm for calculating visibility spaces, a problem not hitherto approached by the GP community.", notes = "phd.zip is 3390593 bytes uk.bl.ethos.325090", } @Article{Gray:2018:GPEM, author = "Cameron C. Gray and Shatha F. Al-Maliki and Franck P. Vidal", title = "Data exploration in evolutionary reconstruction of {PET} images", journal = "Genetic Programming and Evolvable Machines", year = "2018", volume = "19", number = "3", pages = "391--419", month = sep, note = "Special issue on genetic programming, evolutionary computation and visualization", keywords = "genetic algorithms, genetic programming, Parisian Approach, Fly Algorithm, Tomography reconstruction, Information visualisation, Data exploration, Artificial evolution, Parisian evolution", ISSN = "1389-2576", URL = "https://doi.org/10.1007/s10710-018-9330-7", DOI = "doi:10.1007/s10710-018-9330-7", size = "29 pages", abstract = "This work is based on a cooperative co-evolution algorithm called Fly Algorithm, which is an evolutionary algorithm (EA) where individuals are called flies. It is a specific case of the Parisian Approach where the solution of an optimisation problem is a set of individuals (e.g. the whole population) instead of a single individual (the best one) as in typical EAs. The optimisation problem considered here is tomography reconstruction in positron emission tomography (PET). It estimates the concentration of a radioactive substance (called a radiotracer) within the body. Tomography, in this context, is considered as a difficult ill-posed inverse problem. The Fly Algorithm aims at optimising the position of 3-D points that mimic the radiotracer. At the end of the optimisation process, the fly population is extracted as it corresponds to an estimate of the radioactive concentration. During the optimisation loop a lot of data is generated by the algorithm, such as image metrics, duration, and internal states. This data is recorded in a log file that can be post-processed and visualised. We propose using information visualisation and user interaction techniques to explore the algorithm's internal data. Our aim is to better understand what happens during the evolutionary loop. Using an example, we demonstrate that it is possible to interactively discover when an early termination could be triggered. It is implemented in a new stopping criterion. It is tested on two other examples on which it leads to a 60percent reduction of the number of iterations without any loss of accuracy.", } @TechReport{gray:1996:ssi, author = "G. J. Gray and Yun Li and D. J. Murray-Smith and K. C. Sharman", title = "Structural System Identification Using Genetic Programming and a Block Diagram Oriented Simulation Tool", institution = "Department of Electronics and Electrical Engineering, University of Glasgow", year = "1996", type = "Technical Report", number = "CSC-96003", address = "Glasgow, G12 8QQ, U.K.", month = "13 " # jun, note = "Submitted to: Electronics Letters", keywords = "genetic algorithms, genetic programming, system identification, nonlinear mathematical modelling, SIMULINK", URL = "http://www.mech.gla.ac.uk/Research/Control/Publications/Reports/csc96003.ps", abstract = "Genetic programming can be used for structural optimisation. Combined with a hybrid simplex/simulated annealing algorithm, it is applied to the identification of nonlinear dynamic models from simulated experimental data. Nonlinear models similar to the original test model of the system are identified yielding both correct structures and accurate parameters", notes = "See \cite{gray:1996:ssi2} ", } @Article{gray:1996:ssi2, author = "Gary J. Gray and Yun Li and D. J. Murray-Smith and K. C. Sharman", title = "Structural system identification using genetic programming and a block diagram oriented simulation tool", journal = "Electronics Letters", year = "1996", volume = "32", number = "15", pages = "1422--1424", month = "18 " # jul, keywords = "genetic algorithms, genetic programming, structural system identification, block diagram, simulation tool, structural optimisation, hybrid simplex/simulated annealing algorithm, nonlinear dynamic model, identification, simulation, simulated annealing, nonlinear dynamical systems", ISSN = "0013-5194", URL = "http://ieeexplore.ieee.org/iel1/2220/11173/00511160.pdf?isNumber=11173", DOI = "doi:10.1049/el:19960888", size = "3 pages", abstract = "Genetic programming can be used for structural optimisation. Combined with a hybrid simplex/simulated annealing algorithm, it is applied to the identification of nonlinear dynamic models from simulated experimental data. Nonlinear models similar to the original test model of the system are identified, yielding both correct structures and accurate parameters.", notes = "See also \cite{gray:1996:ssi} SIMULINK, MATLAB. Numerical parameters optimised using combination of Nelder simplex minimisation and simulated annealing. A.P.Fraser's gpc++.", } @InProceedings{gray:1996:nmsti, author = "Gary J. Gray and David J. Murray-Smith and Yun Li and Ken C. Sharman", title = "Nonlinear Model Structure Identification Using Genetic Programming", booktitle = "Late Breaking Papers at the Genetic Programming 1996 Conference Stanford University July 28-31, 1996", year = "1996", editor = "John R. Koza", pages = "32--37", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California 94305-3079, USA", month = "28--31 " # jul, publisher = "Stanford Bookstore", ISBN = "0-18-201031-7", keywords = "genetic algorithms, genetic programming", URL = "http://www.mech.gla.ac.uk/Research/Control/Publications/Reports/csc96006.ps", URL = "http://citeseer.ist.psu.edu/60878.html", abstract = "Genetic programming can be used to evolve an algebraic expression as part of an equation representing measured inputoutput response data. Parts of the nonlinear differential equations describing a dynamic system are identified along with their numerical parameters using genetic programming. The results of several such optimisations are analysed to produce a nonlinear physical representation of the dynamic system. This method is applied to the identification of fluid flow through pipes in a coupled water tank system. A representative nonlinear model is identified.", notes = "GP-96LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670 See also FACULTY OF ENGINEERING, GLASGOW G12 8QQ, U.K. TECHNICAL REPORT: CSC-96xxx", } @InProceedings{Gray:1997:ISMM, author = "Gary J. Gray and David J. Murray-Smith and Yun Li and Ken C. Sharman", title = "Nonlinear Structural System Identification Using Genetic Programming", booktitle = "Proceedings of Second International Symposium on Mathematical modelling", year = "1997", editor = "Inge Troch and Felix Breitenecker", number = "11", series = "ARGESIM Report Series", pages = "301--306", address = "Technical University Vienna, Austria", month = "5-7 " # feb, organisation = "IMACS/IFAC", keywords = "genetic algorithms, genetic programming", ISBN = "3-901608-11-7", notes = "http://web.iti.upv.es/~ken/kenpubs.html http://polaris.dit.upm.es/~jpuente/ifac/newsletter497/mathmod.html", } @InProceedings{gray:1997:, author = "G. J. Gray and T. Weinbrenner and D. J. Murray-Smith and Y. Li and K. C. Sharman", title = "Issues in Nonlinear Model Structure Identification Using Genetic Programming", booktitle = "Second International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, GALESIA", year = "1997", editor = "Ali Zalzala", pages = "308--313", address = "University of Strathclyde, Glasgow, UK", publisher_address = "Savoy Place, London WC2R 0BL, UK", month = "1-4 " # sep, publisher = "Institution of Electrical Engineers", keywords = "genetic algorithms, genetic programming", ISBN = "0-85296-693-8", URL = "http://scitation.aip.org/getpdf/servlet/GetPDFServlet?filetype=pdf&id=IEECPS0019970CP446000308000001&idtype=cvips&prog=normal", DOI = "doi:10.1049/cp:19971198", size = "6 page", abstract = "Genetic programming (GP) is a powerful nonlinear optimisation tool which can be applied to the identification of the nonlinear structure of dynamic systems. Several issues must be considered. The model format must be defined and a simulation routine integrated with the GP optimisation code to evaluate each candidate model. Numerical parameters of the model must be identified and the model's {"}goodness-of-fit{"} must be quantified. The GP algorithm must be configured for model identification and optimised for computation time. Finally, general nonlinear modelling issues such as experimental design and model validation must be considered. All these issues are addressed in this paper.", notes = "GALESIA'97", } @Article{Gray:1998:CEP, author = "Gary J. Gray and David J. Murray-Smith and Yun Li and Ken C. Sharman and Thomas Weinbrenner", title = "Nonlinear model structure identification using genetic programming", journal = "Control Engineering Practice", volume = "6", pages = "1341--1352", year = "1998", number = "11", keywords = "genetic algorithms, genetic programming, nonlinear models, system identification, helicopter dynamics, Nonlinear control systems, Identification (control systems), Mathematical programming, Differential equations, Error analysis, Mathematical models, Computer simulation, Water tanks, Helicopter rotors, Speed control, Control system analysis", URL = "http://www.sciencedirect.com/science/article/B6V2H-3W1GPR8-4/1/047d9c74e28a6a1a117a3ed9a6d6c409", abstract = "Genetic Programming is an optimisation procedure which may be applied to the identification of the nonlinear structure of a dynamic model from experimental data. In such applications, the model structure may be described either by differential equations or by a block diagram and the algorithm is configured to minimise the sum of the squares of the error between the recorded experimental response from the real system and the corresponding simulation model output. The technique has been applied successfully to the modelling of a laboratory scale process involving a coupled water tank system and to the identification of a helicopter rotor speed controller and engine from flight test data. The resulting models provide useful physical insight.", } @InProceedings{gray:1996:GPcbtNMR, author = "H. F. Gray and R. J. Maxwell and I. Martinez-Perez and C. Arus and S. Cerdan", title = "Genetic Programming Classification of Magnetic Resonance Data", old_title = "Genetic Programming for Classification of Brain Tumours from Nuclear Magnetic Resonance Biopsy Spectra", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming, ANN, Lisp", pages = "424", address = "Stanford University, CA, USA", publisher = "MIT Press", isbn13 = "978-0-262-61127-5", URL = "https://dl.acm.org/doi/10.5555/1595536.1595602", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap63.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", size = "1 page", abstract = "Genetic programming (GP) is used to classify human brain tumours based on 1H Magnetic Resonance spectra. Good classification was achieved by GP (compared to a neural network). GP classification used simple combinations of variables, corresponding to a small group of metabolites, facilitating biochemical interpretation.", notes = "GP-96", } @InProceedings{Gray:1997:GPmcMRS, author = "H. F. Gray and R. J. Maxwell", title = "Genetic Programming for Multi-class Classification of Magnetic Resonance Spectroscopy Data", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming", pages = "137", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1997/Gray_1997_GPmcMRS.pdf", size = "1 page", notes = "GP-97", } @InProceedings{gray:1997:GPcmd, author = "Helen Gray", title = "Genetic Programming for Classification of Medical Data", booktitle = "Late Breaking Papers at the 1997 Genetic Programming Conference", year = "1997", editor = "John R. Koza", pages = "291", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California, 94305-3079, USA", month = "13--16 " # jul, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-206995-8", notes = "GP-97LB PHD Students' workshop The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670", } @Article{gray:1998:GPcfs:aNMRshbtb, author = "Helen F. Gray and Ross J. Maxwell and Irene Martinez-Perez and Carles Arus and Sebastian Cerdan", title = "Genetic programming for classification and feature selection: analysis of {1H} nuclear magnetic resonance spectra from human brain tumour biopsies", journal = "NMR Biomedicine", year = "1998", volume = "11", number = "4-5", pages = "217--224", month = jun # "-" # aug, keywords = "genetic algorithms, genetic programming, brain tumour, artificial intelligence, classification, feature selection", ISSN = "1099-1492", DOI = "doi:10.1002/(SICI)1099-1492(199806/08)11:4/5%3C217::AID-NBM512%3E3.0.CO%3B2-4", size = "8 pages", abstract = "Genetic programming (GP) is used to classify tumours based on 1H nuclear magnetic resonance (NMR) spectra of biopsy extracts. Analysis of such data would ideally give not only a classification result but also indicate which parts of the spectra are driving the classification (i.e. feature selection). Experiments on a database of variables derived from 1H NMR spectra from human brain tumour extracts (n = 75) are reported, showing GP's classification abilities and comparing them with that of a neural network. GP successfully classified the data into meningioma and non-meningioma classes. The advantage over the neural network method was that it made use of simple combinations of a small group of metabolites, in particular glutamine, glutamate and alanine. This may help in the choice of the most informative NMR spectroscopy methods for future non-invasive studies in patients.", notes = "PMID: 9719576, UI: 98384081 Computer Science Department, Arhus University, Denmark.", } @InProceedings{Gray:2000:GPO, author = "Helen Frances Gray and Ross James Maxwell", title = "Genetic Programming Optimisation of Nuclear Magnetic Resonance Pulse Shapes", booktitle = "Medical Data Analysis: First International Symposium, ISMDA 2000, Proceedings", year = "2000", editor = "R. W. Brause and E. Hanisch", volume = "1933", series = "Lecture Notes in Computer Science", pages = "242--249", address = "Frankfurt, Germany", publisher_address = "Heidelberg", month = sep, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-41089-8", DOI = "doi:10.1007/3-540-39949-6_30", CODEN = "LNCSD9", ISSN = "0302-9743", bibdate = "Tue Sep 10 19:08:54 MDT 2002", acknowledgement = ack-nhfb, size = "8 pages", abstract = "Genetic Programming is used to generate pulse sequence elements for a Nuclear Magnetic Resonance system and evaluate them directly on that system without human intervention. The method is used to optimise pulse shapes for a series of solvent suppression problems. The method proves to be successful, with results showing an improvement in fitness of up to two orders of magnitude. The method is capable of producing both simple and novel solutions.", } @PhdThesis{Gray:thesis, author = "Helen Frances Gray", title = "Evolutionary computing techniques to aid the acquisition and analysis of nuclear magnetic resonance data", school = "Department of Computing, City University", year = "2007", address = "London, UK", month = jan, keywords = "genetic algorithms, genetic programming, NMR, Brain Cancer, multiple trees, multiclass, Spin-Echo, PROBEN, 1H spectra, MR, AI medicine, ANN, DMSO, TSP", URL = "https://openaccess.city.ac.uk/id/eprint/8519/", URL = "https://openaccess.city.ac.uk/id/eprint/8519/1/Evolutionary_computing_techniques_to_aid_the_acquisition_and_analysis_of_nuclear_magnetic_resonance_data.pdf", URL = "https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.440692", size = "136 pages", abstract = "Evolutionary computation, including genetic algorithms and genetic programming have taken the ideas of evolution in biology and applied some of the characteristics to problem solving. The survival of the fittest paradigm allows a population of candidate solutions to be modified by sexual and asexual reproduction and mutation to come closer to solving the problem in question without the necessity of having prior knowledge of what a good solution looks like. The increasing importance of Nuclear Magnetic Resonance Spectroscopy in medicine has created a demand for automated data analysis for tissue classification and feature selection. The use of artificial intelligence techniques such as evolutionary computing can be used for such data analysis. This thesis applies the techniques of evolutionary computation to aid the collection and classification of Nuclear Magnetic Resonance spectroscopy data. The first section (chapters one and two) introduces Nuclear Magnetic Resonance spectroscopy and evolutionary computation and also contains a review of relevant literature. The second section focuses on classification. In the third chapter classification into two classes of brain tumors is undertaken. The fourth chapter expands this to classify tumours and tissues into more than two classes. Genetic Programming provided good solutions with relatively simple biochemical interpretation and was able to classify data into more than two classes at one time. The third section of the thesis concentrates on using evolutionary computation techniques to optimise data acquisition parameters directly from the Nuclear Magnetic Resonance hardware. Chapter five shows that Genetic Algorithms in particular are successful at suppressing signals from solvent while chapter six applies these techniques to find a way of enhancing the signals from metabolites important to the classification of brain tumours as found in chapter three. The final chapter draws conclusions as to the efficacy of evolutionary computation techniques applied to Nuclear Magnetic Resonance Spectroscopy.", notes = "uk.bl.ethos.440692 ISNI: 0000 0001 3512 7528 DAIMI Aarhus, Denmark. Gray Cancer Institute, Northwood, UK. Barcelona and Madrid, Spain. Supervisor: Peter Smith (City University)", } @InProceedings{Greeff:1997:eemmps, author = "D. J. Greeff and C. Aldrich", title = "Evolution of Empirical Models for Metallurgical Process Systems", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming", pages = "138", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1997/Greeff_1997_eemmps.pdf", size = "1 page", notes = "GP-97", } @Article{Greeff:1998:CCE, author = "D. J Greeff and C. Aldrich", title = "Empirical modelling of chemical process systems with evolutionary programming", journal = "Computers \& Chemical Engineering", year = "1998", volume = "22", pages = "995--1005", number = "7-8", abstract = "Through the use of evolutionary computation, empirical models for chemical processes can be evolved that are more cost-effective than models determined by means of classical statistical techniques. These strategies do not require explicit specification of a model structure, but explore candidate models assembled from sets of variables, parameters and simple mathematical operators. The application of the proposed strategies is illustrated by means of three examples, two of which are based on data pertaining to leaching experiments. Since the evolved models were derived from terminal sets containing only the most basic operators, their structures tended to be complicated, making for less easy interpretation, similar to neural networks and other non-parametric models. Nonetheless, the evolved models were either of comparable accuracy or significantly more accurate than those which were previously developed by means of standard least-squares methods.", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6TFT-3TKV02R-F/2/30657596f48ca16571ac48098a948833", keywords = "genetic algorithms, genetic programming, empirical modelling", DOI = "doi:10.1016/S0098-1354(97)00271-8", } @InProceedings{Green:2020:GECCO, author = "Maxfield E. Green and Todd F. DeLuca and Karl WD. Kaiser", title = "Modeling Wildfire Using Evolutionary Cellular Automata", year = "2020", editor = "Carlos Artemio {Coello Coello} and Arturo Hernandez Aguirre and Josu Ceberio Uribe and Mario Garza Fabre and Gregorio {Toscano Pulido} and Katya Rodriguez-Vazquez and Elizabeth Wanner and Nadarajen Veerapen and Efren Mezura Montes and Richard Allmendinger and Hugo Terashima Marin and Markus Wagner and Thomas Bartz-Beielstein and Bogdan Filipic and Heike Trautmann and Ke Tang and John Koza and Erik Goodman and William B. Langdon and Miguel Nicolau and Christine Zarges and Vanessa Volz and Tea Tusar and Boris Naujoks and Peter A. N. Bosman and Darrell Whitley and Christine Solnon and Marde Helbig and Stephane Doncieux and Dennis G. Wilson and Francisco {Fernandez de Vega} and Luis Paquete and Francisco Chicano and Bing Xue and Jaume Bacardit and Sanaz Mostaghim and Jonathan Fieldsend and Oliver Schuetze and Dirk Arnold and Gabriela Ochoa and Carlos Segura and Carlos Cotta and Michael Emmerich and Mengjie Zhang and Robin Purshouse and Tapabrata Ray and Justyna Petke and Fuyuki Ishikawa and Johannes Lengler and Frank Neumann", isbn13 = "9781450371285", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377930.3389836", DOI = "doi:10.1145/3377930.3389836", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference", pages = "1089--1097", size = "9 pages", keywords = "genetic algorithms, genetic programming, wildfire simulation, cellular automata", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", abstract = "With the increased size and frequency of wildfire events worldwide, accurate real-time prediction of evolving wildfire fronts is a crucial component of firefighting efforts and forest management practices. We propose a cellular automaton (CA) that simulates the spread of wildfire. We embed the CA inside of a genetic program (GP) that learns the state transition rules from spatially registered synthetic wildfire data. We demonstrate this model's predictive abilities by testing it on unseen synthetically generated landscapes. We compare the performance of a genetic program (GP) based on a set of primitive operators and restricted expression length to null and logistic models. We find that the GP is able to closely replicate the spreading behavior driven by a balanced logistic model. Our method is a potential alternative to current benchmark physics-based models.", notes = "Also known as \cite{10.1145/3377930.3389836} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @InProceedings{greene:1998:dasdd, author = "Buster Greene", title = "A Deterministic Analysis of Stationary Diploid/Dominance", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "770--776", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "evolutionary programming", ISBN = "1-55860-548-7", notes = "GP-98", } @InProceedings{Greene:2008:gecco, author = "Casey S. Greene and Bill C. White and Jason H. Moore", title = "Using expert knowledge in initialization for genome-wide analysis of epistasis using genetic programming", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "351--352", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p351.pdf", DOI = "doi:10.1145/1389095.1389158", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, abstract = "In human genetics it is now possible to measure large numbers of DNA sequence variations across the human genome. Given current knowledge about biological networks and disease processes it seems likely that disease risk can best be modelled by interactions between biological components, which may be examined as interacting DNA sequence variations. The machine learning challenge is to effectively explore interactions in these datasets to identify combinations of variations which are predictive of common human diseases. Genetic programming is a promising approach to this problem. The goal of this study is to examine the role that an expert knowledge aware initialiser can play in the framework of genetic programming. We show that this expert knowledge aware initializer outperforms both a random initializer and an enumerative initialiser.", keywords = "genetic algorithms, genetic programming, expert knowledge, genetic analysis, Initialisation, Bioinformatics, computational biology: Poster, TuRF, Relief, SNP, MDR, SDA", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389158} Comparison of three ways of loading problem inputs (10000+) into initial population to predict clinical end point (death). Artificial datasets.", } @Article{Greene:2008:sigevo, author = "Casey S. Greene and Jason H. Moore", title = "Solving complex problems in human genetics using {GP}: challenges and opportunities", old_title = "Human Genetics Using GP", journal = "SIGEVOlution", year = "2008", volume = "3", number = "2", pages = "2--8", month = "Summer", keywords = "genetic algorithms, genetic programming", URL = "http://www.sigevolution.org/issues/pdf/SIGEVOlution200802.pdf", DOI = "doi:10.1145/1527063.1527064", size = "7 pages", abstract = "The development of rapid data-collection technologies is changing the biomedical and biological sciences. In human genetics chip-based methods facilitate the measurement of thousands of DNA sequence variations from across the human genome. The collection of genetic data is no longer a major rate limiting step. Instead the new challenges are the analysis and interpretation of these high dimensional and frequently noisy datasets. The specific challenge we are interested in is the identification of combinations of interacting DNA sequence variations predictive of common human diseases. Specifically, we wish to detect epistasis or gene-gene interactions. Here we focus solely on the situation where there is an epistatic effect but no detectable main effect. The challenge for applying search algorithms to this problem is that the accuracy of a model is not indicative of the quality of the attributes within the model. Instead we use pre-processing of the dataset to provide building blocks which enable our evolutionary computation strategy to discover an optimal model.", notes = "Dartmouth College, Lebanon, NH 03756 USA", } @InProceedings{Greene:2009:cec, author = "Casey S. Greene and Jeff Kiralis and Jason H. Moore", title = "Nature-Inspired Algorithms for the Genetic Analysis of Epistasis in Common Human Diseases: Theoretical Assessment of Wrapper vs. Filter Approaches", booktitle = "2009 IEEE Congress on Evolutionary Computation", year = "2009", editor = "Andy Tyrrell", pages = "800--807", address = "Trondheim, Norway", month = "18-21 " # may, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-2959-2", file = "P153.pdf", DOI = "doi:10.1109/CEC.2009.4983027", abstract = "In human genetics, new technological methods allow researchers to collect a wealth of information about genetic variation among individuals quickly and relatively inexpensively. Studies examining more than one half of a million points of genetic variation are the new standard. Quickly analyzing these data to discover single gene effects is both feasible and often done. Unfortunately as our understanding of common human disease grows, we now believe it is likely that an individual's risk of these common diseases is not determined by simple single gene effects. Instead it seems likely that risk will be determined by nonlinear gene-gene interactions, also known as epistasis. Unfortunately searching for these nonlinear effects requires either effective search strategies or exhaustive search. Previously we have employed both filter and nature-inspired probabilistic search wrapper approaches such as genetic programming (GP) and ant colony optimization (ACO) to this problem. We have discovered that for this problem, expert knowledge is critical if we are to discover these interactions. Here we theoretically analyze both an expert knowledge filter and a simple expert-knowledge-aware wrapper. We show that under certain assumptions, the filter strategy leads to the highest power. Finally we discuss the implications of this work for this type of problem, and discuss how probabilistic search strategies which outperform a filtering approach may be designed.", keywords = "genetic algorithms, genetic programming", notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR", } @InProceedings{Greene:2009:cec2, author = "Casey S. Greene and Bill C. White and Jason H. Moore", title = "Sensible Initialization Using Expert Knowledge for Genome-Wide Analysis of Epistasis Using Genetic Programming", booktitle = "2009 IEEE Congress on Evolutionary Computation", year = "2009", editor = "Andy Tyrrell", pages = "1289--1296", address = "Trondheim, Norway", month = "18-21 " # may, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-2959-2", file = "P152.pdf", DOI = "doi:10.1109/CEC.2009.4983093", abstract = "For biomedical researchers it is now possible to measure large numbers of DNA sequence variations across the human genome. Measuring hundreds of thousands of variations is now routine, but single variations which consistently predict an individual's risk of common human disease have proven elusive. Instead of single variants determining the risk of common human diseases, it seems more likely that disease risk is best modeled by interactions between biological components. The evolutionary computing challenge now is to effectively explore interactions in these large datasets and identify combinations of variations which are robust predictors of common human diseases such as bladder cancer. One promising approach to this problem is genetic programming (GP). A GP approach for this problem will use Darwinian inspired evolution to evolve programs which find and model attribute interactions which predict an individual's risk of common human diseases. The goal of this study is to develop and evaluate two initializers for this domain. We develop a probabilistic initializer which uses expert knowledge to select attributes and an enumerative initializer which maximizes attribute diversity in the generated population.We compare these initializers to a random initializer which displays no preference for attributes. We show that the expert-knowledge-aware probabilistic initializer significantly outperforms both the random initializer and the enumerative initializer.We discuss implications of these results for the design of GP strategies which are able to detect and characterize predictors of common human diseases.", keywords = "genetic algorithms, genetic programming", notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR", } @PhdThesis{greene:thesis, author = "Francis Manwell Greene", title = "Genetic Synthesis of Signal Processing Networks Utilizing Diploid/Dominance", school = "Department of Electrical Engineering. University of Washington", year = "1997", address = "Seattle, USA", month = "6 " # mar, keywords = "genetic algorithms, genetic programming", URL = "https://digital.lib.washington.edu/dspace/handle/1773/4915/browse?rpp=20&etal=-1&type=title&starts_with=G&order=ASC&sort_by=1", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/fgPhdDissertation.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/fgDissertation.pdf", size = "183 pages", abstract = "Dissertation Proposal (July 29, 2001) Introduction This proposal is a result of research over the past two years, and whose purpose was to develop a design methodology for low cost ultrasonic blood flow and tissue quantification using signal processing. My original desire was to improve feature extraction techniques for use in statistical pattern recognition, but was almost immediately redirected along the lines of efficient genetic search of network solution spaces. Over ten years of experience with Doppler flow measurement suggests that dynamic processing of the clinical signals involved can be done with interconnected functional elements such as delays, filters, and thresholds. Some details of the processing issues and reasons for using genetic search will follow. The point of this dissertation is to study and develop a specific method for synthesising processing networks that aid in the use, interpretation, and diagnostic power of low-cost medical technology. Conclusions Results of synthesising a signal processing network that correctly recognises fiducial points in a simulated two-heart cycle, spectrally represented, wave form suggests the ability to handle similar applications with real clinical Doppler data. The solution described in the previous section made use of a delay element that matches the heart-cycle period and is otherwise sensible. Search difficulty was increased by including in the function set a number of function/operators not actually needed to solve the problem. This was done purposely to eliminate the necessity of defining a problem dependent function set as may be necessary for medical data. A multiple trial, multi-modal, partially deceptive test problem provide further evidence that the Max(f1,f2) diploid/dominance implementation can provide better than or equal processing efficiency, compared to haploid. This conclusion is supported by a similar, though less thorough, comparison using the R-wave network synthesis problem. The Max(f1,f2) approach has been observed to do about the same as haploid with either very simple (e.g., unimodal) or very difficult or poorly formulated problems. Diploid/dominance as implemented here can be used in conjunction with other improvements (e.g., more refined crossover, inversion, species formation, etc.) to the standard GA. The experiments with alternating fitness environments show that multiploid populations are capable of storing and rapidly recalling as many global optima as there are homologues in each individual chromosome and shows that diploid/dominance retains recessive alleles and schema. The diploid approach could immediately make use of a two-processor system, since the algorithm used involves two function evaluations per generations.", notes = "Supervisior Dr. Alistair Holden. fgDissertation.pdf is Dissertation Proposal (July 29, 2001)", } @InProceedings{Greene:2000:GECCO, author = "William A. Greene", title = "A Non-Linear Schema Theorem for Genetic Algorithms", pages = "189--194", year = "2000", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)", editor = "Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer", address = "Las Vegas, Nevada, USA", publisher_address = "San Francisco, CA 94104, USA", month = "10-12 " # jul, keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-708-0", URL = "http://www.cs.uno.edu/People/Faculty/bill/NonLinSchemaTheorem-GECCO-2000.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco2000/GA068.pdf", URL = "https://dl.acm.org/doi/10.5555/2933718.2933744", size = "6 pages", abstract = "We generalize Holland's Schema Theorem to the setting that genes are arranged, not necessarily in a linear sequence, but as the nodes in a connected graph. We have experimental results showing that the flourishing of building blocks can be expected for two distinct graphs we have investigated, one being a tree and the other being the lattice points in a cube in Euclidean 3-space.", notes = "A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of \cite{whitley:2000:GECCO}", } @InProceedings{greene:2001:NBAGA, author = "William A. Greene", title = "Non-Linear Bit Arrangements in Genetic Algorithms", booktitle = "2001 Genetic and Evolutionary Computation Conference Late Breaking Papers", year = "2001", editor = "Erik D. Goodman", pages = "138--144", address = "San Francisco, California, USA", month = "9-11 " # jul, keywords = "genetic algorithms, poster", URL = "http://www.cs.uno.edu/People/Faculty/bill/NonLinBits-GECCO-2001-lateBreakPaper.pdf", abstract = "In earlier research we laid out a theoretical basis for the supposition that genetic algorithms can succeed even if bits are arranged in ways other than as a linear sequence. In the present paper we report on certain experiments that show such success can occur in practice. Our experiments consider cases in which bits are arranged in two-dimensional grids, in three-dimensional cubes, and as the nodes of a complete binary tree. Moreover, our experiments consider several ways of cutting parental genetic material when performing mating with crossover, and also consider several notions of fitness. Our problems are not particularly difficult, but clearly show the convergence we seek, under these much liberalised ways of arranging bits.", notes = "GECCO-2001LB. Two dimensional grid chromosome, three_D cubes, complete binary tree. Follows up \cite{Greene:2000:GECCO} 576bit onemax. eight queens problem (also 20 queens). Three target binary trees (all 9 levels, full, each node labelled with 0 or 1). Twins, Palindrome trees. Extension of \cite{greene:2001:GECCO}.", } @InProceedings{greene:sdi:gecco2004, author = "William A. Greene", title = "Schema Disruption in Chromosomes That Are Structured as Binary Trees", booktitle = "Genetic and Evolutionary Computation -- GECCO-2004, Part I", year = "2004", editor = "Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell", series = "Lecture Notes in Computer Science", pages = "1197--1207", address = "Seattle, WA, USA", publisher_address = "Heidelberg", month = "26-30 " # jun, organisation = "ISGEC", publisher = "Springer-Verlag", volume = "3102", ISBN = "3-540-22344-4", ISSN = "0302-9743", URL = "http://www.cs.uno.edu/People/Faculty/bill/Schema-disruption-binary-trees-GECCO-2004.pdf", DOI = "doi:10.1007/978-3-540-24854-5_116", DOI = "doi:10.1007/b98643", size = "11", keywords = "genetic algorithms, genetic programming", abstract = "We are interested in schema disruption behaviour when chromosomes are structured as binary trees. We give the definition of the disruption probability dp(H) of a schema H, and also the relative diameter rel?(H) of H. We show that in the general case that dp(H) can far exceed rel?(H), but when the chromosome is a complete binary tree then the inequality dp(H) = rel?(H) holds almost always. Thus the more compactly the tree chromosome is structured, the better is the behavior to be expected from geneticism.", notes = "GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)", } @InProceedings{conf/prib/GreeneWM07, author = "Casey S. Greene and Bill C. White and Jason H. Moore", title = "An Expert Knowledge-Guided Mutation Operator for Genome-Wide Genetic Analysis Using Genetic Programming", booktitle = "Proceedings of the second IAPR International Workshop Pattern Recognition in Bioinformatics, PRIB 2007", year = "2007", editor = "Jagath C. Rajapakse and Bertil Schmidt and L. Gwenn Volkert", volume = "4774", series = "Lecture Notes in Computer Science", pages = "30--40", address = "Singapore", month = oct # " 1-2", publisher = "Springer", keywords = "genetic algorithms, genetic programming, TuRF", isbn13 = "978-3-540-75285-1", DOI = "doi:10.1007/978-3-540-75286-8_4", size = "11 page", abstract = "Human genetics is undergoing a data explosion. Methods are available to measure DNA sequence variation throughout the human genome. Given current knowledge it seems likely that common human diseases are best predicted by interactions between biological components, which can be examined as interacting DNA sequence variations. The challenge is thus to examine these high-dimensional datasets to identify combinations of variations likely to predict common diseases. The goal of this paper was to develop and evaluate a genetic programming (GP) mutator suited to this task by exploiting expert knowledge in the form of Tuned ReliefF (TuRF) scores during mutation. We show that using expert knowledge guided mutation performs similarly to expert knowledge guided selection. This study demonstrates that in the context of an expert knowledge aware GP, mutation may be an appropriate component of the GP used to search for interacting predictors in this domain.", bibdate = "2007-09-20", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/prib/prib2007.html#GreeneWM07", } @InCollection{Greene:2009:GPTP, author = "Casey S. Greene and Douglas P. Hill and Jason H. Moore", title = "Environmental Sensing of Expert Knowledge in a Computational Evolution System for Complex Problem Solving in Human Genetics", booktitle = "Genetic Programming Theory and Practice {VII}", year = "2009", editor = "Rick L. Riolo and Una-May O'Reilly and Trent McConaghy", series = "Genetic and Evolutionary Computation", address = "Ann Arbor", month = "14-16 " # may, publisher = "Springer", chapter = "2", pages = "19--36", keywords = "genetic algorithms, genetic programming, Genetic Epidemiology, Symbolic Discriminant Analysis, Epistasis", isbn13 = "978-1-4419-1653-2", DOI = "doi:10.1007/978-1-4419-1626-6_2", abstract = "The relationship between interindividual variation in our genomes and variation in our susceptibility to common diseases is expected to be complex with multiple interacting genetic factors. A central goal of human genetics is to identify which DNA sequence variations predict disease risk in human populations. Our success in this endeavour will depend critically on the development and implementation of computational intelligence methods that are able to embrace, rather than ignore, the complexity of the genotype to phenotype relationship. To this end, we have developed a computational evolution system (CES) to discover genetic models of disease susceptibility involving complex relationships between DNA sequence variations. The CES approach is hierarchically organised and is capable of evolving operators of any arbitrary complexity. The ability to evolve operators distinguishes this approach from artificial evolution approaches using fixed operators such as mutation and recombination. Our previous studies have shown that a CES that can use expert knowledge about the problem in evolved operators significantly outperforms a CES unable to use this knowledge. This environmental sensing of external sources of biological or statistical knowledge is important when the search space is both rugged and large as in the genetic analysis of complex diseases. We show here that the CES is also capable of evolving operators which exploit one of several sources of expert knowledge to solve the problem. This is important for both the discovery of highly fit genetic models and because the particular source of expert knowledge used by evolved operators may provide additional information about the problem itself. This study brings us a step closer to a CES that can solve complex problems in human genetics in addition to discovering genetic models of disease.", notes = "part of \cite{Riolo:2009:GPTP}", } @InCollection{greenfield:2000:ECAPHE, author = "Aaron Greenfield", title = "Evolution of Communication Among Prey in a Hostile Environment", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "170--179", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:2000:gagp}", } @InProceedings{greenwold:2000:AGG, author = "Simon M. Greenwold", title = "AGENCY GP: Genetic programming for architectural design", booktitle = "Graduate Student Workshop", year = "2000", editor = "Conor Ryan and Una-May O'Reilly and William B. Langdon", pages = "273--276", address = "Las Vegas, Nevada, USA", month = "8 " # jul, keywords = "genetic algorithms, genetic programming", notes = "GECCO-2000WKS Part of \cite{wu:2000:GECCOWKS}", } @InProceedings{Greenwood:1997:chaosES, author = "Garrison W. Greenwood", title = "Experimental Observation of Chaos in Evolution Strategies", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "evolutionary programming and evolution strategies", pages = "439--444", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", notes = "GP-97", } @Article{greenwood:2001:bicm, author = "Garrison W. Greenwood", title = "Book Review: {Bio-Inspired} Computing Machines: Towards Novel Computational Architectures", journal = "Genetic Programming and Evolvable Machines", year = "2001", volume = "2", number = "1", pages = "75--78", month = mar, keywords = "genetic algorithms, genetic programming, evolutionary programming, evolution strategies, evolvable hardware, FPGA, L-Systems", ISSN = "1389-2576", URL = "https://rdcu.be/dR8s6", DOI = "doi:10.1023/A:1010022700219", notes = "review of \cite{mange:1998:bicm} Article ID: 319814", } @InProceedings{Greer:2018:NAECON, author = "Jeremiah Greer and Samuel Toth and Rashmi Jha and Anca Ralescu and Nan Niu and Mitchell Hirschfeld and David Kapp", booktitle = "NAECON 2018 - IEEE National Aerospace and Electronics Conference", title = "Guiding Software Evolution with Binary Diversity", year = "2018", pages = "92--98", abstract = "Zero-day vulnerabilities offer unique vectors for breaking software integrity such as adding malicious code to software distributed to hardware or clients. We propose a novel method of generating immune software variants from binary files in a semi-guided environment where the solution and vulnerability are unknown. We analyse this process on programs of varying complexity.", keywords = "genetic algorithms, genetic programming, genetic Improvement", DOI = "doi:10.1109/NAECON.2018.8556645", ISSN = "2379-2027", month = jul, notes = "Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, USA Also known as \cite{8556645}", } @Unpublished{grefenstette:1997:vivposn, author = "John Grefenstette and Kenneth {De Jong} and Connie Ramsey and Annie Wu", title = "The Virtual Virus Project", editor = "Wolfgang Banzhaf and Inman Harvey and Hitoshi Iba and William Langdon and Una-May O'Reilly and Justinian Rosca and Byoung-Tak Zhang", note = "Position paper at the Workshop on Evolutionary Computation with Variable Size Representation at ICGA-97", month = "20 " # jul, year = "1997", address = "East Lansing, MI, USA", keywords = "genetic algorithms, variable size representation", notes = "http://web.archive.org/web/19971014081458/http://www.ai.mit.edu/people/unamay/icga-ws.html ", size = "1 page", } @InProceedings{Gregor:2012:ELEKTRO, author = "Michal Gregor and Juraj Spalek and Jan Capak", title = "Use of context blocks in genetic programming for evolution of robot morphology", booktitle = "ELEKTRO, 2012", year = "2012", month = "21-22 " # may, pages = "286--291", size = "6 pages", abstract = "The paper explores application of genetic programming to evolution of robot morphology, and co-evolution of morphology and low-level control. Extensions to standard genetic programming are presented that allow for straight-forward storage, retrieval, transfer, modification of data stored in the context of a syntactic tree, and shared by multiple nodes. These extensions are used to embed a genetic algorithm within the genetic programming approach to evolve values of constants. Experimental results are presented and evaluated.", keywords = "genetic algorithms, genetic programming, context blocks, data modification, data retrieval, data straight-forward storage, data transfer, low-level control, morphology coevolution, robot morphology evolution, syntactic tree, mobile robots, trees (mathematics)", DOI = "doi:10.1109/ELEKTRO.2012.6225655", notes = "Also known as \cite{6225655}", } @InProceedings{Gregor:2013:INES, author = "Michal Gregor and Juraj Spalek", booktitle = "17th IEEE International Conference on Intelligent Engineering Systems (INES 2013)", title = "Using context blocks to implement Node-attached Modules in genetic programming", year = "2013", month = "19-21 " # jun, pages = "317--322", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/INES.2013.6632833", abstract = "The paper presents extensions to the standard version of genetic programming. Concepts concerning contexts and context blocks as well as their possible applications are discussed. It is shown how these concepts can be used to implement a novel approach to modular genetic programming based on modules stored with the abstract syntax tree, but also attached to nodes that call them (Node-attached Modules with Ancestry Tracking). It is shown that such approach performs favourably.", notes = "Also known as \cite{6632833}", } @Misc{Gregor:2016:ArXiv, author = "Michal Gregor and Juraj Spalek", title = "Fitness-based Adaptive Control of Parameters in Genetic Programming: Adaptive Value Setting of Mutation Rate and Flood Mechanisms", howpublished = "ArXiv", year = "2016", keywords = "genetic algorithms, genetic programming", bibdate = "2016-06-01", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/corr/corr1605.html#GregorS16", URL = "http://arxiv.org/abs/1605.01514", } @InProceedings{Gregor:2016:ELEKTRO, author = "Michal Gregor and Juraj Spalek", title = "Using {LLVM}-based {JIT} compilation in genetic programming", booktitle = "2016 ELEKTRO", year = "2016", pages = "406--411", address = "Strbske Pleso, Slovakia", month = "16-18 " # may, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Clang, AST, symbolic regression, ALG", isbn13 = "978-1-4673-8699-9", URL = "https://arxiv.org/abs/1701.05730", DOI = "doi:10.1109/ELEKTRO.2016.7512108", size = "6 pages", abstract = "The paper describes an approach to implementing genetic programming, which uses the LLVM library to just-in-time compile/interpret the evolved abstract syntax trees. The solution is described in some detail, including a parser (based on FlexC++ and BisonC++) that can construct the trees from a simple toy language with C-like syntax. The approach is compared with a previous implementation (based on direct execution of trees using polymorphic functors) in terms of execution speed.", notes = "Also known as \cite{7512108} \cite{DBLP:journals/corr/GregorS17} 'The original AST is translated into LLVM's IR = intermediate representation' Calling C code, LLVM's SectionMemoryManager Part of master thesis?? Department of Control and Information Systems, Faculty of Electrical Engineering, University of Zilina, Zilina, Slovak Republic", } @InProceedings{gregory:1998:GAoddq, author = "Michael Gregory", title = "Genetic Algorithm Optimisation of Distributed Database Queries", booktitle = "Proceedings of the 1998 IEEE World Congress on Computational Intelligence", year = "1998", pages = "271--276", address = "Anchorage, Alaska, USA", month = "5-9 " # may, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, algorithm performance,combinatorial optimisation, cost reduction, distributed relational database query optimisation, local search phase, multistart, premature convergence, random search, real-time query optimisation, simulated annealing, stochastic optimisation techniques, table reduction, tailored crossover operator, tailored mutation operator, tree query, tree-structured data model, distributed databases, mathematical operators, query processing, real-time systems, relational databases, software performance evaluation, tree data structures", ISBN = "0-7803-4869-9", file = "c047.pdf", DOI = "doi:10.1109/ICEC.1998.699724", size = "6 pages", abstract = "Distributed relational database query optimisation is a combinatorial optimisation problem. This paper reports on an initial investigation into the potential for a genetic algorithm (GA) to optimise distributed queries. A genetic algorithm is developed and its performance compared with alternative stochastic optimisation techniques: random search, multistart, and simulated annealing. The problem of fully reducing all tables in a tree query is used to compare the techniques. For this problem, evaluating the fitness function is an expensive operation. The proposed GA uses a tree-structured data model with tailored crossover and mutation operators that avoid the need to fully re-evaluate the fitness function for new solutions. Query optimisation is a task that must be performed in real-time. A technique is required that performs well at the start of a search, but avoids the problem of premature convergence. The proposed GA uses a local search phase to deliver the required real-time performance. Experiments show that the proposed GA can perform better than the alternative techniques tested. The potential for a GA to deliver valuable distributed query processing cost reductions is demonstrated.", notes = "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE World Congress on Computational Intelligence. Also known as \cite{699724}", } @InProceedings{Gregson:2020:ICACR, author = "E. Gregson and M. L. Seto", title = "Linear Genetic Programming-Based Controller for Space Debris Retrieval", booktitle = "2020 4th International Conference on Automation, Control and Robots (ICACR)", year = "2020", pages = "112--121", abstract = "In this paper, we investigate the use of linear genetic programming to evolve a controller that can guide a debris removal chaser spacecraft to match the motion of an uncontrolled target debris object. The problem is treated in 2D, and the controller is required to apply forces and torques to the chaser such that it approaches the target and matches a {"}hand{"} point in the chaser-fixed frame to a {"}handle{"} point in the target-fixed frame. The training simulations are extensively parameterized, and as the population of controllers evolves, the population of training scenarios also changes through both coevolution and scheduled changes. This allows the controller population to be gradually taught the full task after starting with a simpler version. The resulting evolved controllers show promise but would benefit from a more sophisticated GP implementation than monolithic linear GP.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICACR51161.2020.9265513", month = oct, notes = "Also known as \cite{9265513}", } @InProceedings{Greig:2021:IGSS, author = "Rory Greig and Jordi Arranz", title = "Generating Agent Based Models From Scratch With Genetic Programming", booktitle = "Inverse Generative Social Science Workshop 2021", year = "2021", address = "online", month = jun # " 8-10", keywords = "genetic algorithms, genetic programming", URL = "https://www.igss-workshop.org/abstracts#greig", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/igss-workshop_2021.pdf", video_url = "https://www.youtube.com/watch?v=tKpZdJ3PftI", abstract = "Program synthesis (PS) and genetic programming (GP) allow non-trivial programs to be generated from example data.", notes = "See also \cite{Greig:2021:ALife} https://www.igss-workshop.org/schedule April 2023 igss-workshop_2021.pdf from https://www.igss-workshop.org/abstracts Improbable, United Kingdom", } @InProceedings{Greig:2021:ALife, author = "Rory Greig and Jordi Arranz", title = "Generating Agent Based Models From Scratch With Genetic Programming", booktitle = "2021 Conference on Artificial Life", year = "2021", address = "online", month = "19-23 " # jul, organisation = "ISAL", publisher = "Massachusetts Institute of Technology", note = "Best Paper Award", keywords = "genetic algorithms, genetic programming, program synthesis, genetic programming, agent-based modeling, opinion dynamics, Flocking, evolutionary computing, machine learning, artificial intelligence, model induction, program induction, structural calibration, micro-simulation, Julia", URL = "https://direct.mit.edu/isal/proceedings/isal2020/32/1/98387", URL = "https://direct.mit.edu/isal/proceedings-pdf/isal/33/64/1956387/isal_a_00383.pdf", DOI = "doi:10.1162/isal_a_00383", size = "10 page", abstract = "Program synthesis (PS) and genetic programming (GP) allow non-trivial programs to be generated from example data. Agent-based models (ABMs) are a promising field of application as their complexity at a macro level arises from simple agent-level rules. Previous attempts at using evolutionary algorithms to learn the structure of ABMs have focused on modifying and recombining existing models targeted to the domain in question, which requires prior domain knowledge. We demonstrate a new domain-independent approach which is able to evolve interpretable agent logic of an ABM from scratch. We employ a flexible domain specific language (DSL) which consists of basic mathematical building blocks. The flexibility of our method is demonstrated by learning symbolic models in two different domains: flocking and opinion dynamics, targeting data produced from reference models. We show that the evolved solutions are behaviourally identical to the reference models and generalise extremely well.", notes = "See also \cite{Greig:2021:IGSS} Prague, Czech Republic. Broken Jan 2024 isal_a_00383.pdf https://direct.mit.edu/isal/isal/volume/33", } @InProceedings{Grezer:2024:ASDAM, author = "Monika Grezer and Marina Topa and Emilian David and Andi Buzo and Georg Pelz", title = "Using Symbolic Regression Metamodels for Integrated Circuits Production", booktitle = "2024 15th International Conference on Advanced Semiconductor Devices and Microsystems (ASDAM)", year = "2024", pages = "18--21", month = oct, keywords = "genetic algorithms, genetic programming, Micromechanical devices, Semiconductor devices, Electronics industry, Production, Gaussian processes, Machine learning, Product development, Integrated circuit modelling, Optimisation", ISSN = "2474-9737", DOI = "doi:10.1109/ASDAM63148.2024.10844676", abstract = "The paper introduces a Machine Learning QLattice metamodel that can be used as a more efficient substitute for the circuit simulators in the integrated circuit industry with less compute effort. QLattice represents a Symbolic Regression metamodel that is based on Genetic Programming. The purpose of this metamodel is to find the best mathematical formula that achieves the optimal correlation between the inputs and the output. We made a comparative analysis between two regression metamodels, namely QLattice and Gaussian Process, on synthetic functions that emulate real circuits behaviour, as well as on the datasets used for real-life product development.", notes = "Also known as \cite{10844676} Technical University of Cluj-Napoca, Romania", } @Misc{DBLP:journals/corr/abs-2101-08742, author = "Ivan Gridin", title = "Soft Genetic Programming Binary Classifiers", howpublished = "arXiv", volume = "abs/2101.08742", year = "2021", keywords = "genetic algorithms, genetic programming", URL = "https://arxiv.org/abs/2101.08742", eprinttype = "arXiv", eprint = "2101.08742", timestamp = "Sat, 30 Jan 2021 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/corr/abs-2101-08742.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{Griffin:2023:GI, author = "David Griffin and Susan Stepney and Ian Vidamour", title = "{DebugNS}: Novelty Search for Finding Bugs in Simulators", booktitle = "12th International Workshop on Genetic Improvement @ICSE 2023", year = "2023", editor = "Vesna Nowack and Markus Wagner and Gabin An and Aymeric Blot and Justyna Petke", pages = "17--18", address = "Melbourne, Australia", month = "20 " # may, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Genetic Improvement, Novelty search, debugging, simulation", isbn13 = "979-8-3503-1232-4", URL = "http://gpbib.cs.ucl.ac.uk/gi2023/Griffin_2023_GI.pdf", DOI = "doi:10.1109/GI59320.2023.00012", slides_url = "http://gpbib.cs.ucl.ac.uk/gi2023/DebugNS.pdf", video_url = "http://gpbib.cs.ucl.ac.uk/gi2023/DebugNS.mp4", video_url = "https://www.youtube.com/watch?v=NkLaQGA847s&list=PLI8fiFpB7BoJLh6cUpGBjyeB1hM9DET1V&index=4", size = "2 pages", abstract = "Novelty search is used to find a range of novel behaviours in a system. Software bugs are behaviours that are a) unexpected and b) incorrect. As the intersection between ``novel'' and ``unexpected'' is non-empty, here we overview how novelty search can be employed to find bugs in simulation software. We give an example of this approach applied to the RingSim simulator.", notes = "GI @ ICSE 2023, part of \cite{Nowack:2023:GI}", } @Article{griffin:2014:IJAMT, author = "James Griffin", title = "The prediction of profile deviations when Creep Feed grinding complex geometrical features by use of neural networks and genetic programming with real-time simulation", journal = "The International Journal of Advanced Manufacturing Technology", year = "2014", volume = "74", number = "1 - 4", keywords = "genetic algorithms, genetic programming, ANN, Grinding, Cutting forces, Spindle power, Profile deviations, Neural networks, Creep Feed grinding simulation", URL = "http://link.springer.com/article/10.1007/s00170-014-5829-0", DOI = "doi:10.1007/s00170-014-5829-0", } @InProceedings{Griffioen:2008:cec, author = "A. R. Griffioen and S. K. Smit and A. E. Eiben", title = "Learning Benefits Evolution if Sex Gives Pleasure", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "2073--2080", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1823-7", file = "EC0492.pdf", URL = "http://www.cs.vu.nl/~gusz/papers/2008-CEC-Griffioen-Smit-Eiben.pdf", DOI = "doi:10.1109/CEC.2008.4631073", abstract = "In this paper the effects of individual learning on an evolving population of situated agents are investigated. We work with a novel type of system where agents can decide autonomously (by their controllers) if/when they reproduce and the bias in the agent controllers for the mating action is adaptable by individual learning. Our experiments show that in such a system reinforcement learning with the straightforward rewards system based on energy makes the agents lose their interest in mating. In other words, we see that learning frustrates evolution, killing the whole population on the long run. This effect can be counteracted by introducing a specially designated positive mating reward, pretty much like an orgasm in Nature.With this twist individual learning becomes a positive force. It can make the otherwise disappearing population viable by keeping agents alive that did not yet learn the task at hand. This hiding effect proves positive for it provides a smooth road for the population to adapt and learn the task with a lower risk of extinction.", keywords = "genetic algorithms, genetic programming", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @InProceedings{Griffiths:2017:EuroGP, author = "Thomas D. Griffiths and Aniko Ekart", title = "Improving the Tartarus Problem as a Benchmark in Genetic Programming", booktitle = "EuroGP 2017: Proceedings of the 20th European Conference on Genetic Programming", year = "2017", month = "19-21 " # apr, editor = "Mauro Castelli and James McDermott and Lukas Sekanina", series = "LNCS", volume = "10196", publisher = "Springer Verlag", address = "Amsterdam", pages = "278--293", organisation = "species", keywords = "genetic algorithms, genetic programming: Poster", isbn13 = "978-3-319-55695-6", DOI = "doi:10.1007/978-3-319-55696-3_18", abstract = "For empirical research on computer algorithms, it is essential to have a set of benchmark problems on which the relative performance of different methods and their applicability can be assessed. In the majority of computational research fields there are established sets of benchmark problems; however, the field of genetic programming lacks a similarly rigorously defined set of benchmarks. There is a strong interest within the genetic programming community to develop a suite of benchmarks. Following recent surveys, the desirable characteristics of a benchmark problem are now better defined. In this paper the Tartarus problem is proposed as a tunably difficult benchmark problem for use in Genetic Programming. The justification for this proposal is presented, together with guidance on its usage as a benchmark.", notes = "Part of \cite{Castelli:2017:GP} EuroGP'2017 held inconjunction with EvoCOP2017, EvoMusArt2017 and EvoApplications2017", } @InProceedings{Griffiths:2018:PPSN, author = "Thomas D. Griffiths and Aniko Ekart", title = "Self-Adaptive Crossover in Genetic Programming: The Case of the {Tartarus} Problem", booktitle = "15th International Conference on Parallel Problem Solving from Nature", year = "2018", editor = "Anne Auger and Carlos M. Fonseca and Nuno Lourenco and Penousal Machado and Luis Paquete and Darrell Whitley", volume = "11101", series = "LNCS", pages = "236--246", address = "Coimbra, Portugal", month = "8-12 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Self-adaptation, Crossover, Tartarus problem", isbn13 = "978-3-319-99252-5", URL = "https://www.springer.com/gp/book/9783319992587", DOI = "doi:10.1007/978-3-319-99253-2_19", abstract = "The runtime performance of many evolutionary algorithms depends heavily on their parameter values, many of which are problem specific. Previous work has shown that the modification of parameter values at runtime can lead to significant improvements in performance. In this paper we discuss both the when and how aspects of implementing self-adaptation in a Genetic Programming system, focusing on the crossover operator. We perform experiments on Tartarus Problem instances and find that the runtime modification of crossover parameters at the individual level, rather than population level, generate solutions with superior performance, compared to traditional crossover methods.", notes = "PPSN2018 http://ppsn2018.dei.uc.pt This two-volume set LNCS 11101 and 11102 constitutes the refereed proceedings of the 15th International Conference on Parallel Problem Solving from Nature, PPSN 2018", } @InProceedings{griffiths:2018:ALIA, author = "Thomas D. Griffiths and Aniko Ekart", title = "Improving the Effectiveness of Genetic Programming Using Continuous Self-adaptation", booktitle = "Artificial Life and Intelligent Agents", year = "2018", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-319-90418-4_8", DOI = "doi:10.1007/978-3-319-90418-4_8", } @InProceedings{Griffiths:2019:GECCOcomp, author = "Thomas D. Griffiths and Aniko Ekart", title = "Increasing genetic programming robustness using simulated {Dunning-Kruger} effect", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", isbn13 = "978-1-4503-6748-6", pages = "340--341", address = "Prague, Czech Republic", DOI = "doi:10.1145/3319619.3321885", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{3321885} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @Article{grigore:2024:Information, author = "Iuliana Malina Grigore and Gabriel Marques Tavares and Matheus Camilo da Silva and Paolo Ceravolo and Sylvio {Barbon Junior}", title = "Automated Trace Clustering Pipeline Synthesis in Process Mining", journal = "Information", year = "2024", volume = "15", number = "4", pages = "Article No. 241", keywords = "genetic algorithms, genetic programming", ISSN = "2078-2489", URL = "https://www.mdpi.com/2078-2489/15/4/241", DOI = "doi:10.3390/info15040241", abstract = "Business processes have undergone a significant transformation with the advent of the process-oriented view in organizations. The increasing complexity of business processes and the abundance of event data have driven the development and widespread adoption of process mining techniques. However, the size and noise of event logs pose challenges that require careful analysis. The inclusion of different sets of behaviours within the same business process further complicates data representation, highlighting the continued need for innovative solutions in the evolving field of process mining. Trace clustering is emerging as a solution to improve the interpretation of underlying business processes. Trace clustering offers benefits such as mitigating the impact of outliers, providing valuable insights, reducing data dimensionality, and serving as a preprocessing step in robust pipelines. However, designing an appropriate clustering pipeline can be challenging for non-experts due to the complexity of the process and the number of steps involved. For experts, it can be time-consuming and costly, requiring careful consideration of trade-offs. To address the challenge of pipeline creation, the paper proposes a genetic programming solution for trace clustering pipeline synthesis that optimises a multi-objective function matching clustering and process quality metrics. The solution is applied to real event logs, and the results demonstrate improved performance in downstream tasks through the identification of sub-logs.", notes = "also known as \cite{info15040241}", } @InProceedings{grimbleby:1995:, author = "J. B. Grimbleby", title = "Automatic Analogue Network Synthesis using Genetic Algorithms", booktitle = "First International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, GALESIA", year = "1995", editor = "A. M. S. Zalzala", volume = "414", pages = "53--58", address = "Sheffield, UK", publisher_address = "London, UK", month = "12-14 " # sep, publisher = "IEE", keywords = "genetic algorithms, genetic programming, analogue network synthesis, frequency-domain, linear networks, time-domain, analogue circuits, circuit CAD, circuit optimisation, linear network synthesis", ISBN = "0-85296-650-4", DOI = "doi:10.1049/cp:19951024", size = "6 pages", abstract = "Genetic algorithms provide a basis for automatic synthesis of analogue electronic networks. Passive linear networks have been generated to meet both frequency-domain and time-domain specifications. The networks generated are both novel and effective. It should be possible to extend the technique to deal with active networks", notes = "12--14 September 1995, Halifax Hall, University of Sheffield, UK see also http://www.iee.org.uk/LSboard/Conf/program/galprog.htm Evolves passive analogue circuits using a fixed length GA which allows non-ops to specify network connectivity and components forming links. 'Even a small amount of cross-over provides considerable efficiency benefits' [page 55] See also \cite{grimbleby:2000}.", } @Article{grimbleby:2000:, author = "J. B. Grimbleby", title = "Automatic analogue circuit synthesis using genetic algorithms", journal = "IEE Proceedings - Circuits, Devices and Systems", year = "2000", volume = "147", number = "6", pages = "319--323", month = dec, keywords = "genetic algorithms, genetic programming", ISSN = "1350-2409", publisher = "IET", URL = "https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=728229e95b2f8a622ad99511d5f46dd515d6e52d", DOI = "doi:10.1049/ip-cds:20000770", size = "5 pages", abstract = "Most analogue systems are designed manually because automatic circuit synthesis tools are available for only a limited range of design problems. A new approach to circuit synthesis based on genetic algorithms is presented. Using this method it is possible in principle to synthesise circuits to meet any linear or nonlinear, frequency-domain or time-domain, specification. When applied to existing filter design problems this circuit synthesis method produces design solutions that are more efficient than those resulting from formal design methods or created manually by an experienced analogue circuit designer", notes = "Nielsen filter. GA used to evolve circuit topology. Variable length comes from allowing parts of the fixed length chromosome to be empty. page 320 'This hybrid approach using a GA to select a suitable topology and numerical optimisation to choose component values is likely to be more efficient than using a GA or GP to perform both functions.' See also \cite{grimbleby:1995:} Electronic Engineering Group, The University of Reading, Reading, United Kingdom", } @InProceedings{grimes:1995:gtprtm, author = "C. A. Grimes", title = "Application of Genetic Techniques to the Planning of Railway Track Maintenance Work", booktitle = "First International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, GALESIA", year = "1995", editor = "A. M. S. Zalzala", volume = "414", pages = "467--472", address = "Sheffield, UK", publisher_address = "London, UK", month = "12-14 " # sep, publisher = "IEE", keywords = "genetic algorithms, genetic programming, scheduling, maintenance, PC-MARPAS", ISBN = "0-85296-650-4", broken = "http://scitation.aip.org/getpdf/servlet/GetPDFServlet?filetype=pdf&id=IEECPS0019950CP414000467000001&idtype=cvips&prog=normal", DOI = "doi:10.1049/cp:19951093", size = "6 pages", abstract = "Track maintenance work was planned using GA and GP, with profit as the optimisation criteria. The results where compared with an existing determinstic technique. It was found the GP method gave the best results, with the GA method giving good results for a short section (10 miles) and poor results for a long section (50 miles).", notes = "12--14 September 1995, Halifax Hall, University of Sheffield, UK see also http://www.iee.org.uk/LSboard/Conf/program/galprog.htm ", } @InProceedings{Grin:2021:CEC, author = "Aleksandr V Grin and Amir H Gandomi", booktitle = "2021 IEEE Congress on Evolutionary Computation (CEC)", title = "Advancing Genetic Programming via Information Theory", year = "2021", editor = "Yew-Soon Ong", pages = "1991--1998", address = "Krakow, Poland", month = "28 " # jun # "-1 " # jul, keywords = "genetic algorithms, genetic programming, Systematics, Input variables, Evolutionary computation, Tools, Search problems, Entropy, Information Theory, Data Analytics, Evolutionary Computation, VIES, Kotlin", isbn13 = "978-1-7281-8393-0", DOI = "doi:10.1109/CEC45853.2021.9504859", abstract = "Genetic Programming (GP) is a powerful tool often used to solve optimization problems where analytical methods are unusable. While the general technique is well understood, there exist deficiencies in the multitude of implementations currently widely available. The primary areas of improvement are computation time, search space reduction, and accuracy. Despite significant advances in GP systems, a key deficiency remains in the structural randomization of symbolic GP trees. Our initial assumptions regarding the formation of expression trees in symbolic GP trees is at best highly limited and normally simply non-existent. In this paper, we introduce a new GP methodology that incorporates both current cutting- edge GP system solutions as well as an information-theoretic approach to expression tree initialization. Through a more informed initial tree construction, this approach reduces the search space and model complexity. We introduce in this work the methodology as well as the accompanying theoretical component and comparison benchmarks from tests. A key advantage of the algorithm proposed is its high parallelization potential which is highlighted in further discussion. The method consists of two parts. The first is a variable-interaction system termed Entropy Shaving that is used for both variable selection and initial expression structure generation. The second is a GP system that uses the variable-interaction system as input to determine a final solution.", notes = "UCI Wine Also known as \cite{9504859}", } @InProceedings{Grinan:2019:SMC, author = "David Grinan and Alfredo Ibias and Manuel Nsnez", booktitle = "2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)", title = "Grammar-based Tree Swarm Optimization", year = "2019", pages = "76--81", month = oct, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SMC.2019.8914268", ISSN = "2577-1655", abstract = "Particle Swarm Optimisation (PSO) has been successfully applied to find good solutions through a guided search. This optimization technique usually works with vectors as individuals of the population conforming the search space. Nevertheless, there exist problems such that the search space cannot be transformed into a vector search space. In this paper we propose a novel technique based on the intuition behind PSO but overcoming its limitations concerning search spaces. Specifically, we present a PSO framework where the individuals conforming the search space are tree-like structures. In particular, our framework naturally includes classical PSO but also search spaces where elements are structures that can be represented as trees (in addition to usual trees, linear structures such as lists, queues and stacks).", notes = "Also known as \cite{8914268}", } @InProceedings{grinan:2020:CEC, author = "David Grinan and Alfredo Ibias", title = "Generating Tree Inputs For Testing Using Evolutionary Computation Techniques", booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC 2020", year = "2020", editor = "Yaochu Jin", pages = "paper id24267", address = "internet", month = "19-24 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, PSO, SBSE, Software Testing, Evolutionary Computation, Mutation Testing", isbn13 = "978-1-7281-6929-3", URL = "https://alfredoibias.com/wp-content/uploads/2020/04/2020-CEC.pdf", DOI = "doi:10.1109/CEC48606.2020.9185561", size = "8 pages", abstract = "Software Testing usually considers programs with parameters ranging over simple types. However, there are many programs using structured types. The main problem to test these programs is that it is not easy to select a relatively small test suite that can find most of the faults in these programs. In this paper we present a framework to generate test suites for unit testing of methods which have trees as parameters. We combine classical mutation testing with Evolutionary Computation techniques to evolve a population of trees. The final goal is to obtain a set of trees, representing good test cases, that will be used as the test suite to test the corresponding method.", notes = "https://wcci2020.org/", } @PhdThesis{oai:ufu.br:295, title = "Regress{\~a}o simb{\'o}lica via programa{\c c}{\~a}o gen{\'e}tica: um estudo de caso com modelagem geof{\'i}sica", author = "Alexandre Grings", year = "2006", type = "Tese ou Dissertacao Eletronica", school = "Biblioteca Digital da Universidade Federal de Uberl{\^a}ndia", address = "Brazil", month = "24 " # feb, bibsource = "OAI-PMH server at oai.ibict.br", contributor = "Ant{\^o}nio Eduardo Costa Pereira and Joao Bosco da Mota Alves and M{\'a}rcia Aparecida Fernandes", format = "PDF", language = "PT", oai = "oai:ufu.br:295", rights = "Liberar o conte{\'u}do dos arquivos para acesso p{\'u}blico", keywords = "genetic algorithms, genetic programming, Symbolic regression, Gene expression programming, Geophisical modeling, Regressao simbolica, Programacao genetica, Programacao da expressao genica, Modelagem geofisica, CIENCIA DA COMPUTACAO, Programacao genetica Computacao", URL = "http://www.bdtd.ufu.br//tde_busca/arquivo.php?codArquivo=550.pdf", size = "133 pages", abstract = "A regress{\~a}o simb{\'o}lica, que consiste na manipula{\c c}{\~a}o de express{\~o}es matem{\'a}ticas para descobertade fun{\c c}{\~o}es que descrevam um conjunto de dados, foi uma tarefa exclusivamente humanaat{\'e} pouco tempo atr{\'a}s. Recentemente, foram desenvolvidas v{\'a}rias t{\'e}cnicas computacionais paraautomatizar a regress{\~a}o simb{\'o}lica. Uma dessas t{\'e}cnicas {\'e} a programa{\c c}{\~a}o gen{\'e}tica, uma sub{\'a}reada computa{\c c}{\~a}o evolutiva que usa analogia {\`a} teoria da evolu{\c c}{\~a}o de Darwin e id{\'e}ias do campoda Gen{\'e}tica para desenvolver um grupo de programas de computador na busca por solu{\c c}{\~o}es atarefas computacionais. O presente trabalho visa a testar as capacidades de regress{\~a}o simb{\'o}licada programa{\c c}{\~a}o gen{\'e}tica com objetivo de verificar sua viabilidade como ferramenta paraa pesquisa de um problema geof{\'i}sico. Esse problema diz respeito a fen{\^o}menos que ocorremna ionosfera, a regi{\~a}o da atmosfera ionizada pela a{\c c}{\~a}o dos raios solares, que desempenham umpapel fundamental para as telecomunica{\c c}{\~o}es. No intercurso dessa tentativa, faz-se o uso deduas implementa{\c c}{\~o}es tradicionais de programa{\c c}{\~a}o gen{\'e}tica e de uma variante, chamada programa{\c c}{\~a}oda express{\~a}o g{\^e}nica. Problemas como o sistema estudado demandam muito tempode processamento e mem{\'o}ria, desse modo, o trabalho culmina com uma implementa{\c c}{\~a}o distribu{\'i}dade programa{\c c}{\~a}o gen{\'e}tica com o intuito de acelerar o processamento da modelagem.; Symbolic regression, which is in principal the handling of mathematical expressions for finding a function that describes a data set, was until recently carried out exclusively by humans. But now, several computational techniques of symbolic regression automatisation have appeared.One of these techniques is genetic programming, a subarea of evolutive computing that uses an analogy to Darwin{'}s evolutionary theory and some ideas from the Genetics field to develop group of computer programs in a search for solutions to computational tasks. This work aims to test the symbolic regression capabilities of genetic programming with the objective of verifying its viability as a tool for a specific geophysical research. This research concerns phenomena that occurs in the ionosphere, the region of earth{'}s atmosphere ionised by the action of solar rays,that play a fundamental role in telecommunications. In the course of this trial, we used two implementations of traditional genetic programming and one implementation of a variant, named gene expression programming. Problems like the one under study demand a lot of processor time and are memory consuming, therefore, the work culminates with a distributed implementation of genetic programming with the objective of accelerating the modelling process.", notes = "in Portuguese", } @Article{gritz:1995:GPafm, author = "L. Gritz and J. K. Hahn", title = "Genetic Programming for Articulated Figure Motion", journal = "Journal of Visualization and Computer Animation", year = "1995", volume = "6", number = "3", pages = "129--142", keywords = "genetic algorithms, genetic programming", URL = "http://www.icg.seas.gwu.edu/Publications/gpafm.ps", abstract = " Three dimensional computer animation has become increasingly popular over the past decade. Computer animation now has an important role in entertainment, education, and simulation. For computer animation of characters, the role of the animator has unfortunately stayed similar to that of a stop motion animator, rather than like a film director. Research in computer animation has tried to address this by giving higher levels of control to the animator, but these methods often result in lack of fine control over the animated characters. This is inadequate because fine control is essential to both aesthetics and the ability of the animator to direct a meaningful narrative. This dissertation presents methods of articulated figure motion control which attempt to bridge the gap between high level direction and low level control of subtle motion. These methods define motion in terms of goals and ratings. The agents are dynamically-controlled robots whose behavior is determined by robotic controller programs. The controller programs for the robots are evaluated at each time step to yield torque values which drive the dynamic simulation of the motion. We use the AI technique of Genetic Programming (GP) to automatically derive control programs for the agents which achieve the goals. This type of motion specification is an alternative to key framing which allows a highly automated, learning-based approach to generation of motion. This method of motion control is very general (it can be applied to any type of motion), yet it allows for specifications of the types of specific motion which are desired for a high quality animation. We show that complex, specific, physically plausible, and aesthetically appealing motion can be generated using these methods. Both skill-based and action-based motion can be specified in this manner. We also introduce the new paradigm of key marks, a generalization of key framing which is not subject to many of the limitations of key framing.", notes = " Larry Gritz is at Pixar: http://www.seas.gwu.edu/~graphics/papers/gritzdissert.html http://www.seas.gwu.edu/student/gritz/index.html James Hahn is at the George Washington University: http://www.seas.gwu.edu/facu lty/hahn/ ", } @InProceedings{Gritz:1997:GPec3da, author = "Larry Gritz and James K. Hahn", title = "Genetic Programming Evolution of Controllers for 3-D Character Animation", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming", pages = "139--146", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", URL = "http://www.icg.seas.gwu.edu/Publications/gpec-gp97.ps", size = "8 pages", abstract = "The dominant paradigm for 3-D character animation requires an animator to specify the values for all degrees of freedom of an articulated figure at key frames. Specifying motion that is physically believable and biologically plausible is a tedious practice requiring great skill. We use evolutionary techniques (specifically Genetic Programming) as a means of controller synthesis for character animation. Controllers which drive a dynamic simulation of the character are evolved using the goals of the animation as an objective function, resulting in physically plausible motion. We discuss the development of objective functions used to guide the controller evolution, making reusable skill controllers, and comparisons of the convergence rates for different parameters of the evolutionary runs.", notes = "GP-97 S-expression per degree of freedom in each joint in the character. Joints controlled by proportional derivative (PD) controllers. Aninmated desk lamp Luxo, Jr. L*xo 4 links and 3 internally controllable degrees of freedom. Robust = reusable. Randomly generated test cases", } @PhdThesis{gritz:dissertation, author = "Larry Israel Gritz", title = "Evolutionary Controller Synthesis for 3-D Character Animation", school = "The George Washington University", year = "1999", address = "Washington, DC, USA", month = "15 " # may, keywords = "genetic algorithms, genetic programming, computer animation", URL = "http://www.icg.seas.gwu.edu/Publications/gritzdissert.ps.gz", broken = "http://www.seas.gwu.edu/~graphics/papers/gritzdissert.html", size = "113 pages", abstract = "Three dimensional computer animation has become increasingly popular over the past decade. Computer animation now has an important role in entertainment, education, and simulation. For computer animation of characters, the role of the animator has unfortunately stayed similar to that of a stop motion animator, rather than like a film director. Research in computer animation has tried to address this by giving higher levels of control to the animator, but these methods often result in lack of fine control over the animated characters. This is inadequate because fine control is essential to both aesthetics and the ability of the animator to direct a meaningful narrative. This dissertation presents methods of articulated figure motion control which attempt to bridge the gap between high level direction and low level control of subtle motion. These methods define motion in terms of goals and ratings. The agents are dynamically-controlled robots whose behavior is determined by robotic controller programs. The controller programs for the robots are evaluated at each time step to yield torque values which drive the dynamic simulation of the motion. We use the AI technique of Genetic Programming (GP) to automatically derive control programs for the agents which achieve the goals. This type of motion specification is an alternative to key framing which allows a highly automated, learning-based approach to generation of motion. This method of motion control is very general (it can be applied to any type of motion), yet it allows for specifications of the types of specific motion which are desired for a high quality animation. We show that complex, specific, physically plausible, and aesthetically appealing motion can be generated using these methods. Both skill-based and action-based motion can be specified in this manner. We also introduce the new paradigm of key marks, a generalization of key framing which is not subject to many of the limitations of key framing.", } @InProceedings{Grochol:2015:evoApplications, author = "David Grochol and Lukas Sekanina and Martin Zadnik and Jan Korenek", title = "A Fast {FPGA}-Based Classification of Application Protocols Optimized Using {Cartesian GP}", booktitle = "18th European Conference on the Applications of Evolutionary Computation", year = "2015", editor = "Antonio M. Mora and Giovanni Squillero", series = "LNCS", volume = "9028", publisher = "Springer", pages = "67--78", address = "Copenhagen", month = "8-10 " # apr, organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming", isbn13 = "978-3-319-16548-6", DOI = "doi:10:10.1007/978-3-319-16549-3_6", abstract = "This paper deals with design of an application protocol classifier intended for high speed networks operating at 100 Gbps. Because a very low latency is the main design constraint, the classifier is constructed as a combinational circuit in a field programmable gate array. The classification is performed using the first packet carrying the application payload. In order to further reduce the latency, the circuit is optimised by Cartesian genetic programming. Using a real network data, we demonstrated viability of our approach in task of a very fast classification of three application protocols (HTTP, SMTP, SSH).", notes = "COMNET EvoApplications2015 held in conjunction with EuroGP'2015, EvoCOP2015 and EvoMusArt2015 http://www.evostar.org/2015/cfp_evoapps.php", } @Article{Grochol:2016:ASC, author = "D. Grochol and L. Sekanina and M. Zadnik and J. Korenek and V. Kosar", title = "Evolutionary circuit design for fast FPGA-based classification of network application protocols", journal = "Applied Soft Computing", volume = "38", pages = "933--941", year = "2016", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2015.09.046", URL = "http://www.sciencedirect.com/science/article/pii/S1568494615006262", abstract = "The evolutionary design can produce fast and efficient implementations of digital circuits. It is shown in this paper how evolved circuits, optimized for the latency and area, can increase the throughput of a manually designed classifier of application protocols. The classifier is intended for high speed networks operating at 100 Gbps. Because a very low latency is the main design constraint, the classifier is constructed as a combinational circuit in a field programmable gate array (FPGA). The classification is performed using the first packet carrying the application payload. The improvements in latency (and area) obtained by Cartesian genetic programming are validated using a professional FPGA design tool. The quality of classification is evaluated by means of real network data. All results are compared with commonly used classifiers based on regular expressions describing application protocols.", keywords = "genetic algorithms, genetic programming, Application protocol, Classifier, Field programmable gate array", } @InProceedings{Grochol2017, author = "David Grochol and Lukas Sekanina", title = "Comparison of Parallel Linear Genetic Programming Implementations", booktitle = "Proceedings of the 22nd International Conference on Soft Computing (MENDEL 2016)", year = "2016", editor = "Radek Matousek", volume = "576", series = "AISC", pages = "64--76", address = "Brno, Czech Republic", month = jun # " 8-10", publisher = "Springer", keywords = "genetic algorithms, genetic programming, parallel GP", isbn13 = "978-3-319-58087-6", ISSN = "2194-5357", DOI = "doi:10.1007/978-3-319-58088-3_7", abstract = "Linear genetic programming (LGP) represents candidate programs as sequences of instructions for a register machine. In order to accelerate the evaluation time of candidate programs and reduce the overall time of evolution, we propose various parallel implementations of LGP suitable for the current multi-core processors. The implementations are based on a parallel evaluation of candidate programs and the island model of the parallel evolutionary algorithm in which the subpopulations are evolved independently, but some genetic material can be exchanged by means of the migration. Proposed implementations are evaluated using three symbolic regression problems and a hash function design problem.", notes = "https://link.springer.com/book/10.1007/978-3-319-58088-3 ICSC-MENDEL 2016 Recent Advances in Soft Computing", } @InProceedings{Grochol:2016:GECCO, author = "David Grochol and Lukas Sekanina", title = "Evolutionary Design of Fast High-quality Hash Functions for Network Applications", booktitle = "GECCO '16: Proceedings of the 2016 Annual Conference on Genetic and Evolutionary Computation", year = "2016", editor = "Tobias Friedrich and Frank Neumann and Andrew M. Sutton and Martin Middendorf and Xiaodong Li and Emma Hart and Mengjie Zhang and Youhei Akimoto and Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and Daniele Loiacono and Julian Togelius and Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and Faustino Gomez and Carlos M. Fonseca and Heike Trautmann and Alberto Moraglio and William F. Punch and Krzysztof Krawiec and Zdenek Vasicek and Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and Boris Naujoks and Enrique Alba and Gabriela Ochoa and Simon Poulding and Dirk Sudholt and Timo Koetzing", pages = "901--908", keywords = "genetic algorithms, genetic programming", month = "20-24 " # jul, organisation = "SIGEVO", address = "Denver, USA", publisher = "ACM", publisher_address = "New York, NY, USA", isbn13 = "978-1-4503-4206-3", DOI = "doi:10.1145/2908812.2908825", abstract = "High speed networks operating at 100 Gbps pose many challenges for hardware and software involved in the packet processing. As the time to process one packet is very short the corresponding operations have to be optimized in terms of the execution time. One of them is non-cryptographic hashing implemented in order to accelerate traffic flow identification. In this paper, a method based on linear genetic programming is presented, which is capable of evolving high-quality hash functions primarily optimized for speed. Evolved hash functions are compared with conventional hash functions in terms of accuracy and execution time using real network data.", notes = "GECCO-2016 A Recombination of the 25th International Conference on Genetic Algorithms (ICGA-2016) and the 21st Annual Genetic Programming Conference (GP-2016)", } @InProceedings{grochol:2017:CEC, author = "David Grochol and Lukas Sekanina", booktitle = "2017 IEEE Congress on Evolutionary Computation (CEC)", title = "Multi-objective evolution of hash functions for high speed networks", year = "2017", editor = "Jose A. Lozano", pages = "1533--1540", address = "Donostia, San Sebastian, Spain", publisher = "IEEE", isbn13 = "978-1-5090-4601-0", abstract = "Hashing is a critical function in capturing and analysis of network flows as its quality and execution time influences the maximum throughput of network monitoring devices. In this paper, we propose a multi-objective linear genetic programming approach to evolve fast and high-quality hash functions for common processors. The search algorithm simultaneously optimizes the quality of hashing and the execution time. As it is very time consuming to obtain the real execution time for a candidate solution on a particular processor, the execution time is estimated in the fitness function. In order to demonstrate the superiority of the proposed approach, evolved hash functions are compared with hash functions available in the literature using real-world network data.", keywords = "genetic algorithms, genetic programming, cryptography, critical function, fitness function, hash functions, hashing, high speed networks, multiobjective evolution, multiobjective linear genetic programming, network flows, network monitoring devices, real-world network data, search algorithm, Hardware, Monitoring, Program processors, Registers", isbn13 = "978-1-5090-4601-0", DOI = "doi:10.1109/CEC.2017.7969485", month = "5-8 " # jun, notes = "IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969485}", } @InProceedings{Grochol:2018:EuroGP, author = "David Grochol and Lukas Sekanina", title = "Multi-Objective Evolution of Ultra-Fast General-Purpose Hash Functions", booktitle = "EuroGP 2018: Proceedings of the 21st European Conference on Genetic Programming", year = "2018", month = "4-6 " # apr, editor = "Mauro Castelli and Lukas Sekanina and Mengjie Zhang and Stefano Cagnoni and Pablo Garcia-Sanchez", series = "LNCS", volume = "10781", publisher = "Springer Verlag", address = "Parma, Italy", pages = "187--202", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming: Poster", isbn13 = "978-3-319-77552-4", DOI = "doi:10.1007/978-3-319-77553-1_12", abstract = "Hashing is an important function in many applications such as hash tables, caches and Bloom filters. In past, genetic programming was applied to evolve application-specific as well as general-purpose hash functions, where the main design target was the quality of hashing. As hash functions are frequently called in various time-critical applications, it is important to optimize their implementation with respect to the execution time. In this paper, linear genetic programming is combined with NSGA-II algorithm in order to obtain general-purpose, ultra-fast and high-quality hash functions. Evolved hash functions show highly competitive quality of hashing, but significantly reduced execution time in comparison with the state of the art hash functions available in literature.", notes = "Part of \cite{Castelli:2018:GP} EuroGP'2018 held in conjunction with EvoCOP2018, EvoMusArt2018 and EvoApplications2018", } @InProceedings{Grochol:2018:AHS, author = "David Grochol and Lukas Sekanina", booktitle = "2018 NASA/ESA Conference on Adaptive Hardware and Systems (AHS)", title = "Fast Reconfigurable Hash Functions for Network Flow Hashing in FPGAs", year = "2018", pages = "257--263", abstract = "Efficient monitoring of high speed computer networks operating with a 100 Gigabit per second (Gbps) data throughput requires a suitable hardware acceleration of its key components. We present a platform capable of automated designing of hash functions suitable for network flow hashing. The platform employs a multi-objective linear genetic programming developed for the hash function design. We evolved high-quality hash functions and implemented them in a field programmable gate array (FPGA). Several evolved hash functions were combined together in order to form a new reconfigurable hash function. The proposed reconfigurable design significantly reduces the area on a chip while the maximum operation frequency remains very close to the fastest hash functions. Properties of evolved hash functions were compared with the state-of-the-art hash functions in terms of the quality of hashing, area and operation frequency in the FPGA.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/AHS.2018.8541401", ISSN = "2471-769X", month = aug, notes = "Also known as \cite{8541401}", } @PhdThesis{Grochol:thesis, author = "David Grochol", title = "Evolutionary design and optimization of components used in high-speed computer networks", school = "Faculty of Information Technology, Brno University of Technology", year = "2019", address = "Brno, Czech Republic", month = sep # " 5", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Evolutionary algorithms, Linear Genetic Programming, Network Monitoring, Network Application, Computer Network, Hash Function", URL = "http://hdl.handle.net/11012/188162", URL = "https://invenio.nusl.cz/record/412930", URL = "https://www.vut.cz/www_base/zav_prace_soubor_verejne.php?file_id=206467", size = "101 pages", abstract = "The research presented in this thesis is directed toward the evolutionary optimization of selected components of network applications intended for high-speed network monitoring systems. The research started with a study of current network monitoring systems. As an experimental platform, the Software Defined Monitoring (SDM) system was chosen. Because traffic processing is an important part of all monitoring systems, it was analysed in greater detail. For detailed studies conducted in this thesis, two components were selected: the classifier of application protocols and the hash functions for network flow processing. The evolutionary computing techniques were surveyed with the aim to optimize not only the quality of processing but also the execution time of evolved components. The single-objective and multi-objective versions of evolutionary algorithms were considered and compared. A new approach to the application protocol classifier design was proposed. Accurate and relaxed versions of the classifier were optimized by means of Cartesian Genetic Programming (CGP). A significant reduction in Field-Programmable Gate Array (FPGA) resources and latency was reported. Specialised, highly optimized network hash functions were evolved by parallel Linear Genetic Programming (LGP). These hash functions provide better functionality (in terms of quality of hashing and execution time) than the state-of-the-art hash functions. Using multi-objective LGP, we even improved the hash functions evolved with the single-objective LGP. Parallel pipelined hash functions were implemented in an FPGA and evaluated for purposes of network flow hashing. A new reconfigurable hash function was developed as a combination of selected evolved hash functions. Very competitive general-purpose hash functions were also evolved by means of multi-objective LGP and evaluated using representative data sets. The multi-objective approach produced slightly better solutions than the single-objective approach. We confirmed that common LGP and CGP implementations can be used for automated design and optimization of selected components; however, it is important to properly handle the multi-objective nature of the problem and accelerate time-critical operations of GP.", notes = "BUT Supervisor: Lukas Sekanina", } @InProceedings{Grochol:2020:CEC, author = "David Grochol and Lukas Sekanina", booktitle = "2020 IEEE Congress on Evolutionary Computation (CEC)", title = "Evolutionary Design of Hash Functions for {IPv6} Network Flow Hashing", year = "2020", editor = "Yaochu Jin", month = "19-24 " # jul, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC48606.2020.9185723", isbn13 = "978-1-7281-6929-3", abstract = "Fast and high-quality network flow hashing is an essential operation in many high-speed network systems such as network monitoring probes. We propose a multi-objective evolutionary design method capable of evolving hash functions for IPv4 and IPv6 flow hashing. Our approach combines Cartesian genetic programming (CGP) with Non-dominated sorting genetic algorithm II (NSGA-II) and aims to optimize not only the quality of hashing, but also the execution time of the hash function. The evolved hash functions are evaluated on real data sets collected in computer network and compared against other evolved and conventionally created hash functions.", notes = "Also known as \cite{9185723}", } @InProceedings{DBLP:conf/mfcs/Gronemeier04, author = "Andre Gronemeier", title = "Approximating {Boolean} Functions by {OBDDs}", booktitle = "29th Symposium on Mathematical Foundations of Computer Science MFCS 2004", year = "2004", editor = "Jir\'{\i} Fiala and V{\'a}clav Koubek and Jan Kratochv\'{\i}l", series = "Lecture Notes in Computer Science", volume = "3153", pages = "251--262", address = "Prague, Czech Republic", month = aug # " 22-27", publisher = "Springer", bibsource = "DBLP, http://dblp.uni-trier.de", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-22823-3", URL = "http://ls2-www.cs.uni-dortmund.de/~gronemeier/publications/obdd-approx-mfcs.pdf", DOI = "doi:10.1007/978-3-540-28629-5_17", size = "12 pages", abstract = "In learning theory and genetic programming, OBDDs are used to represent approximations of Boolean functions. This motivates the investigation of the OBDD complexity of approximating Boolean functions with respect to given distributions on the inputs. We present a new type of reduction for one?round communication problems that is suitable for approximations. Using this new type of reduction, we prove the following results on OBDD approximations of Boolean functions: 1. We show that OBDDs approximating the well-known hidden weighted bit function for uniformly distributed inputs with constant 1/4 error have size 2?(n ) , improving a previously known result. 2. We prove that for every variable order ? the approximation of some output bits of integer multiplication with constant error requires ?-OBDDs of exponential size.", notes = "replaced by \cite{Gronemeier:2007:DAM}", } @Article{Gronemeier:2007:DAM, author = "Andre Gronemeier", title = "Approximating {Boolean} functions by {OBDD}", journal = "Discrete Applied Mathematics", year = "2007", volume = "155", number = "2", pages = "194--209", month = "15 " # jan, note = "29th Symposium on Mathematical Foundations of Computer Science MFCS 2004", keywords = "genetic algorithms, genetic programming, OBDD, Communication complexity, Approximation", DOI = "doi:10.1016/j.dam.2006.04.037", abstract = "In learning theory and genetic programming, OBDDs are used to represent approximations of Boolean functions. This motivates the investigation of the OBDD complexity of approximating Boolean functions with respect to given distributions on the inputs. We present a new type of reduction for one-round communication problems that is suitable for approximations. Using this new type of reduction, we improve a known lower bound on the size of OBDD approximations of the hidden weighted bit function for uniformly distributed inputs to an asymptotically tight bound and prove new results about OBDD approximations of integer multiplication and squaring for uniformly distributed inputs.", notes = "replaces \cite{DBLP:conf/mfcs/Gronemeier04}", } @InProceedings{gronroos:1999:ACSMENN, author = "Marko Gronroos", title = "A Comparison of Some Methods for Evolving Neural Networks", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1442", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "artificial life, adaptive behavior and agents, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-006.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-006.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InCollection{gros:2003:GENN, author = "Charles-Henri Gros", title = "Genetic Evolution of Neural Networks", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2003", year = "2003", editor = "John R. Koza", pages = "68--74", address = "Stanford, California, 94305-3079 USA", month = "4 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", URL = "http://www.genetic-programming.org/sp2003/Gros.pdf", notes = "part of \cite{koza:2003:gagp}", } @InProceedings{conf/iwinac/GrosanAH05, title = "{MEPIDS}: Multi-Expression Programming for Intrusion Detection System", author = "Crina Grosan and Ajith Abraham and Sang-Yong Han", year = "2005", booktitle = "Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach: First International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2005, Proceedings, Part II", editor = "Jose Mira and Jose R. Alvarez", volume = "3562", series = "Lecture Notes in Computer Science", pages = "163--172", address = "Las Palmas, Canary Islands, Spain", publisher_address = "Berlin / Heidelberg", month = jun # " 15-18", publisher = "Springer", keywords = "genetic algorithms, genetic programming", bibdate = "2005-06-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/iwinac/iwinac2005-2.html#GrosanAH05", ISBN = "3-540-26319-5", ISSN = "0302-9743", URL = "http://www.cs.ubbcluj.ro/~cgrosan/iwinac05.pdf", DOI = "doi:10.1007/11499305_17", size = "10 pages", abstract = "An Intrusion Detection System (IDS) is a program that analyses what happens or has happened during an execution and tries to find indications that the computer has been misused. An IDS does not eliminate the use of preventive mechanism but it works as the last defensive mechanism in securing the system. This paper evaluates the performances of Multi-Expression Programming (MEP) to detect intrusions in a network. Results are then compared with Linear Genetic Programming (LGP) approach. Empirical results clearly show that genetic programming could play an important role in designing light weight, real time intrusion detection systems.", } @InProceedings{grosan-stock, author = "Crina Grosan and Ajith Abraham and Vitorino Ramos and Sang Yong Han", title = "Stock Market Prediction Using Multi Expression Programming", booktitle = "ALEA-05, Workshop on Artificial Life and Evolutionary Algorithms at EPIA'05 - Proc. of the 12th Portuguese Conference on Artificial Intelligence", year = "2005", editor = "C. Bento and A. Cardoso and G. Dias", pages = "73--78", address = "Covilha, Portugal", month = "5-8 " # dec, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Stock Market Prediction, Multi Expression Programming, Nasdaq-100, CNX NIFTY stock index", URL = "http://www.cs.ubbcluj.ro/~cgrosan/alea.pdf", URL = "http://alfa.ist.utl.pt/~cvrm/staff/vramos/Vramos-EPIA05.pdf", DOI = "doi:10.1109/EPIA.2005.341268", size = "6 pages", abstract = "The use of intelligent systems for stock market predictions has been widely established. In this paper, we introduce a genetic programming technique (called Multi-Expression Programming) for the prediction of two stock indices. The performance is then compared with an Artificial Neural Network trained using Levenberg-Marquardt algorithm, Support Vector Machine, Takagi-Sugeno Neuro-Fuzzy model and Difference Boosting Neural Network. We considered Nasdaq-100 index of Nasdaq Stock MarketSM and the S and P CNX NIFTY stock index as test data.", notes = "Also known as \cite{4145927}. Does not appear in Springer publication LNAI 3808. ", } @InProceedings{grosan:2005:HIS, author = "C. Grosan and A. Abraham", title = "Ensemble of genetic programming models for designing reactive power controllers", booktitle = "Fifth International Conference on Hybrid Intelligent Systems, HIS-05", year = "2005", month = "6-9 " # nov, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICHIS.2005.36", abstract = "In this paper, we present an ensemble combination of two genetic programming models namely linear genetic programming (LGP) and multi expression programming (MEP). The proposed model is designed to assist the conventional power control systems with added intelligence. For on-line control, voltage and current are fed into the network after preprocessing and standardisation. The model was trained with a 24-hour load demand pattern and performance of the proposed method is evaluated by comparing the test results with the actual expected values. For performance comparison purposes, we also used an artificial neural network trained by a backpropagation algorithm. Test results reveal that the proposed ensemble method performed better than the individual GP approaches and artificial neural network in terms of accuracy and computational requirements.", } @InCollection{grosan:2006:GSP, author = "Crina Grosan and Ajith Abraham", title = "Stock Market Modeling Using Genetic Programming Ensembles", year = "2006", booktitle = "Genetic Systems Programming: Theory and Experiences", pages = "131--146", volume = "13", series = "Studies in Computational Intelligence", editor = "Nadia Nedjah and Ajith Abraham and Luiza {de Macedo Mourelle}", publisher = "Springer", address = "Germany", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-29849-5", URL = "http://www.cs.ubbcluj.ro/~cgrosan/stock-chapter.pdf", DOI = "doi:10.1007/3-540-32498-4_6", abstract = "The use of intelligent systems for stock market predictions has been widely established. This chapter introduces two Genetic Programming (GP) techniques: Multi-Expression Programming (MEP) and Linear Genetic Programming (LGP) for the prediction of two stock indices. The performance is then compared with an artificial neural network trained using Levenberg-Marquardt algorithm and Takagi-Sugeno neuro-fuzzy model. We considered Nasdaq-100 index of Nasdaq Stock Market and the S&P CNX NIFTY stock index as test data. Empirical results reveal that Genetic Programming techniques are promising methods for stock prediction. Finally formulate an ensemble of these two techniques using a multiobjective evolutionary algorithm. Results obtained by ensemble are better than the results obtained by each GP technique individually.", notes = "http://www.springer.com/sgw/cda/frontpage/0,11855,5-146-22-92733168-0,00.html", size = "17 pages", } @InProceedings{Grosman2001663, author = "Benjamin Grosman and Daniel R. Lewin", title = "MPC using nonlinear models generated by genetic programming", editor = "Rafiqul Gani and Sten Bay Jorgensen", booktitle = "European Symposium on Computer Aided Process Engineering - 11, 34th European Symposium of the Working Party on Computer Aided Process Engineering", publisher = "Elsevier", year = "2001", volume = "9", pages = "663--668", series = "Computer Aided Chemical Engineering", address = "Kolding, Denmark", month = may # " 27-30", keywords = "genetic algorithms, genetic programming", isbn13 = "0-444-5070904", ISSN = "1570-7946", DOI = "doi:10.1016/S1570-7946(01)80105-X", URL = "http://www.sciencedirect.com/science/article/B8G5G-4P40D5J-3R/2/96212e409c54e5c4c1781f7f1780816e", abstract = "Publisher Summary This chapter describes the use of genetic programming (GP) to generate an empirical dynamic model of a process and its use in a nonlinear model predictive control (NMPC) strategy. GP derives both a model structure and its parameter values in such a way that the process trajectory is predicted accurately. Consequently, the performance of the NMPC strategy, based on this model, is expected to be good. The genetic programming approach and the NMPC strategy are briefly described and demonstrated by simulation on a multivariable process. The application of GP-NMPC on the control of a mixing tank is also discussed. Discrete input-output models are generated to allow the prediction of level and concentration trajectories using the GP. Rapid acquisition of an empirical nonlinear model is achieved efficiently using GP. This model provides reliable prediction of future output trajectories in the NMPC scheme, which also accounts for both process interactions and constraint violations, and thus, allows the computation of improved control moves. Currently, work is in progress on the application of the approach on a more complex multiple-input, multiple-output (MIMO) process, a simulation of a Karr liquid-liquid extraction column.", notes = "ESCAPE-11", } @Article{Grosman:2002:CCE, author = "Benyamin Grosman and Daniel R. Lewin", title = "Automated nonlinear model predictive control using genetic programming", journal = "Computers \& Chemical Engineering", year = "2002", volume = "26", pages = "631--640", number = "4-5", owner = "wlangdon", keywords = "genetic algorithms, genetic programming, Empirical process modeling, Nonlinear model predictive control", ISSN = "0098-1354", URL = "http://www.sciencedirect.com/science/article/B6TFT-44YWM6B-B/2/b0dbb5bfa3d6c3d92f1904e01e559d3f", DOI = "doi:10.1016/S0098-1354(01)00780-3", abstract = "This paper describes the use of genetic programming (GP) to generate an empirical dynamic model of a process, and its use in a nonlinear, model predictive control (NMPC) strategy. GP derives both a model structure and its parameter values in such a way that the process trajectory is predicted accurately. Consequently, the performance of the NMPC strategy is expected to improve on the performance obtained using linear models. The GP approach and the nonlinear MPC strategy are described, and demonstrated by simulation on two multivariable process: a mixing tank, which involves only moderate nonlinearities, and the more complex Karr liquid-liquid extraction column.", } @Article{Grosman:2004:CCE, author = "Benyamin Grosman and Daniel R. Lewin", title = "Adaptive genetic programming for steady-state process modeling", journal = "Computers \& Chemical Engineering", year = "2004", volume = "28", pages = "2779--2790", number = "12", abstract = "Genetic programming is one of the computer algorithms in the family of evolutionary-computational methods, which have been shown to provide reliable solutions to complex optimisation problems. The genetic programming under discussion in this work relies on tree-like building blocks, and thus supports process modelling with varying structure. This paper, which describes an improved GP to facilitate the generation of steady-state nonlinear empirical models for process analysis and optimization, is an evolution of several works in the field. The key feature of the method is its ability to adjust the complexity of the required model to accurately predict the true process behaviour. The improved GP code incorporates a novel fitness calculation, the optimal creation of new generations, and parameter allocation. The advantages of these modifications are tested against the more commonly used approaches.", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6TFT-4DMW22F-1/2/3e0d065d49ca47901dac832951154da0", month = "15 " # nov, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1016/j.compchemeng.2004.09.001", } @Article{Grosman:2005:tSM, title = "Yield enhancement in photolithography through model-based process control: average mode control", author = "Benyamin Grosman and Sivan Lachman-Shalem and Raaya Swissa and D. R. Lewin", journal = "IEEE Transactions on Semiconductor Manufacturing", year = "2005", volume = "18", number = "1", pages = "86--93", month = feb, keywords = "genetic algorithms, genetic programming, integrated circuit manufacture, multivariable control systems, nonlinear control systems, photolithography, predictive control, process control, scanning electron microscopy, semiconductor process modelling KLA-Tencor-FINLE PROLITH package, average mode control, fabrication facility implementation, genetic programming, model based process control, multivariable feedback regulatory strategy, multivariable nonlinear model predictive controller, nonlinear empirical models, optimal parameters, optimal structure, scanning electron microscopy, setpoint values, simulated photolithography, stepper inputs, yield enhancement", DOI = "doi:10.1109/TSM.2004.836654", ISSN = "0894-6507", abstract = "This work describes the fabrication facility (FAB) implementation of a multivariable nonlinear model predictive controller (NMPC) for the regulation of critical dimensions (CD) in photolithography. The controller is based on nonlinear empirical models relating the stepper inputs, exposure dose and focus on the isolated and dense CDs measured by scanning electron microscopy. Since the adjustments are made on the basis of the average value of five measured points in each wafer, this is referred to as average mode control. The optimal structure and parameters of these empirical models were determined by genetic programming, to closely match FAB data. The tuning and testing of the NMPC regulator were facilitated by the use of a simulated photolithography track, using the KLA-Tencor-FINLE PROLITH package, suitably calibrated to match FAB conditions. On implementation in the FAB, the NMPC has been demonstrated to consistently maintain the CDs close to their setpoint values, despite unmeasured disturbances such as shifts in uncontrolled inputs. It was also shown that adopting the multivariable feedback regulatory strategy to regulate the CDs results in significant improvements in the die yield.", } @InProceedings{Grosman:2006:iscacsd, author = "B. Grosman and D. R. Lewin", title = "Lyapunov-based Stability Analysis Automated by Genetic Programming", booktitle = "IEEE International Symposium on Computer-Aided Control Systems Design, 2006", year = "2006", pages = "766--771", address = "Munich, Germany", month = "4-6 " # oct, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9797-5", DOI = "doi:10.1109/CACSD.2006.285474", abstract = "This contribution describes an automatic technique for detecting maximal domains of attraction for nonlinear systems using genetic programming (GP). The theoretical basis for the work is Lyapunov's direct method, which provides sufficient conditions for the existence of a region of attraction of a stable focus. In work presented here, our GP approach for defining Lyapunov functions that accurately predict the maximum region of attraction has been extended by defining a target function accounting for level sets. We demonstrate the approach on the analysis of two dynamic systems: (a) van der Pol's equation, which features both a stable and unstable limit cycle; and (b) a model of an exothermic, continuous stirred tank reactor (CSTR), whose stable trajectories tend to move away from the origin before converging", notes = "Dept. of Chem. Eng., Technion-Israel Inst. of Technol., Haifa", } @PhdThesis{Grosman:thesis, author = "Benyamin Grosman", title = "Stability Analysis of Nonlinear Control Systems Using Genetic Programming", school = "Department of Chemical Engineering, Technion", year = "2008", address = "Israel", keywords = "genetic algorithms, genetic programming", URL = "http://www.graduate.technion.ac.il/Theses/Abstracts.asp?Id=24203", abstract = "This thesis describes the use of genetic programming in stability analysis and control synthesis for nonlinear autonomous dynamic systems. The main ideas are associated with the Lyapunov direct method and optimal control synthesis driven by the solution of the Hamilton-Jacobi-Bellman (HJB) equation. A novel genetic programming code was written for the purpose of disclosing non-trivial Lyapunov functions. These functions were used initially for stability analysis, and subsequently for the synthesis of nonlinear optimal controllers. The work required the transformation of abstract mathematical concepts into a computer language format. This included satisfying the general Lyapunov conditions for stability, the identification of connected sets, the detection of their boundaries and other related topics. In addition it was necessary to address optimal control issues, through the near-solution of the Hamilton-Jacobi-Bellman (HJB) equation. The GP has the capacity to discover non-trivial Lyapunov functions that achieve good approximations to the domains of attraction for a variety of nonlinear dynamic systems. Moreover, the task of finding an approximation to the solution of the HJB equation around a working point was demonstrated on a number of autonomous control systems. In cases where the results included non-polynomial terms that are difficult to solve analytically, this obstacle was overcome by using high-order Taylor series expansions. These expansions were shown to be proper Lyapunov functions, which were analysed using a positivity test for multivariable polynomials. Numerous case-studies were examined, including a comparison of the method with the well-known work of Vennelli and Vidyasagar on detecting domains of attraction. Moreover, the control synthesis was compared with well-established control techniques such as feedback linearisation as well as other related works on optimal control. The methodology demonstrated in this work represents a viable attractive alternative analysis method for the investigation of nonlinear dynamic systems, both in open and closed loop, which can be harnessed in numerous fields of research where a guideline for disclosing unknown Lyapunov functions is lacking.", notes = "Supervisor: Prof. Lewin Daniel", } @Article{Grosman2009252, author = "Benyamin Grosman and Daniel R. Lewin", title = "Lyapunov-based stability analysis automated by genetic programming", journal = "Automatica", volume = "45", number = "1", pages = "252--256", year = "2009", ISSN = "0005-1098", DOI = "DOI:10.1016/j.automatica.2008.07.014", URL = "http://www.sciencedirect.com/science/article/B6V21-4V402MR-3/2/500948c7466e5824a72a3930c046e8aa", keywords = "genetic algorithms, genetic programming, Lyapunov stability", abstract = "This contribution describes an automatic technique to detect suitable Lyapunov functions for nonlinear systems. The theoretical basis for the work is Lyapunov's Direct Method, which provides sufficient conditions for stability of equilibrium points. In our proposed approach, genetic programming (GP) is used to search for suitable Lyapunov functions, that is, those that best predict the true domain of attraction. In the work presented here, our GP approach has been extended by defining a target function accounting for the Lyapunov function level sets.", } @InProceedings{gross:2002:gecco, author = "R. Gross and K. Albrecht and W. Kantschik and W. Banzhaf", title = "Evolving Chess Playing Programs", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "740--747", address = "New York", publisher_address = "San Francisco, CA 94104, USA", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "genetic algorithms, genetic programming, chess, distributed computing, evolution strategies", ISBN = "1-55860-878-8", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/GP121.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/GP121.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf", size = "8 pages", abstract = "This contribution introduces a hybrid GP/ES system for the evolution of chess playing computer programs. We discuss the basic system and examine its performance in comparison to pre-existing algorithms of the type alpha-beta and its improved variants. We can show that evolution is able to outperform these algorithms both in terms of efficiency and strength.", notes = "GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)", } @InProceedings{Grossi:2017:CIG, author = "Gina Grossi and Brian J. Ross", title = "Evolved communication strategies and emergent behaviour of multi-agents in pursuit domains", booktitle = "IEEE Conference on Computational Intelligence and Games, CIG 2017", year = "2017", pages = "110--117", address = "New York, NY, USA", month = "22-25 " # aug, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, multi-agent system, pursuit domain, communication, co-operative learning, emergent behaviour, video games", URL = "http://www.cig2017.com/wp-content/uploads/2017/08/paper_1.pdf", DOI = "doi:10.1109/CIG.2017.8080423", size = "8 pages", abstract = "This study investigates how genetic programs can be effectively used in a multi-agent system to allow agents to learn to communicate. Using the pursuit domain and a co-operative learning strategy, communication protocols are compared as multiple predator agents learn the meaning of commands in order to achieve their common goal of first finding and then tracking prey. The outcome of this study reveals a general synchronization behaviour emerging from simple message passing among agents. An additional outcome shows a learned behaviour in the best result which resembles the behaviour of guards and reinforcements that can be found in popular stealth video games.", notes = "also know as \cite{8080423}", } @InProceedings{Grouchy:2014:ALIFE, author = "Paul Grouchy and Gabriele M. T. D'Eleuterio", title = "Evolving Autonomous Agent Controllers as Analytical Mathematical Models", booktitle = "Proceedings of the Fourteenth International Conference of the Synthesis and Simulation of Living Systems, ALIFE 14", year = "2014", editor = "Hiroki Sayama and John Rieffel and Sebastian Risi and Rene Doursat and Hod Lipson", series = "Complex Adaptive Systems", pages = "681--688", address = "New York", month = "30 " # jul # "-2 " # aug, organisation = "International Society for Artificial Life", publisher = "MIT Press", keywords = "genetic algorithms, genetic programming, Evolvable Mathematical Models", isbn13 = "9780262326216 ?", URL = "https://www.mitpressjournals.org/doi/pdfplus/10.1162/978-0-262-32621-6-ch108", DOI = "doi:10.7551/978-0-262-32621-6-ch108", size = "8 pages", abstract = "A novel Artificial Life paradigm is proposed where autonomous agents are controlled via genetically encoded Evolvable Mathematical Models (EMMs). Agent/environment inputs are mapped to agent outputs via equation trees which are evolved using Genetic Programming. Equations use only the four basic mathematical operators: addition, subtraction, multiplication and division. Experiments on the discrete Double-T Maze with Homing problem are performed; the source code has been made available. Results demonstrate that autonomous controllers with learning capabilities can be evolved as analytical mathematical models of behaviour, and that neuroplasticity and neuromodulation can emerge within this paradigm without having these special functionalities specified a priori.", notes = "University of Toronto Institute for Aerospace Studies, Toronto, Ontario, Canada M3H 5T6 ALIFE 14 http://mitpress.mit.edu/books/artificial-life-14 ALIFE14NYC@gmail.com", } @PhdThesis{grouchy2014evolvable, author = "Paul Grouchy", title = "Evolvable mathematical models: A new artificial Intelligence paradigm", school = "Aerospace Science and Engineering, University of Toronto", year = "2014", address = "Canada", month = nov, keywords = "genetic algorithms, genetic programming, Artificial Intelligence, Artificial Life, Evolutionary Computation, Evolutionary Robotics", URL = "http://hdl.handle.net/1807/68193", URL = "https://tspace.library.utoronto.ca/handle/1807/68193", URL = "https://tspace.library.utoronto.ca/bitstream/1807/68193/1/Grouchy_Paul_201411_PhD_thesis.pdf", size = "150 pages", abstract = "We develop a novel Artificial Intelligence paradigm to generate autonomously artificial agents as mathematical models of behaviour. Agent/environment inputs are mapped to agent outputs via equation trees which are evolved in a manner similar to Symbolic Regression in Genetic Programming. Equations are comprised of only the four basic mathematical operators, addition, subtraction, multiplication and division, as well as input and output variables and constants. From these operations, equations can be constructed that approximate any analytic function. These Evolvable Mathematical Models (EMMs) are tested and compared to their Artificial Neural Network (ANN) counterparts on two benchmarking tasks: the double-pole balancing without velocity information benchmark and the challenging discrete Double-T Maze experiments with homing. The results from these experiments show that EMMs are capable of solving tasks typically solved by ANNs, and that they have the ability to produce agents that demonstrate learning behaviours. To further explore the capabilities of EMMs, as well as to investigate the evolutionary origins of communication, we develop NoiseWorld, an Artificial Life simulation in which inter-agent communication emerges and evolves from initially non-communicating EMM-based agents. Agents develop the capability to transmit their x and y position information over a one-dimensional channel via a complex, dialogue-based communication scheme. These evolved communication schemes are analysed and their evolutionary trajectories examined, yielding significant insight into the emergence and subsequent evolution of cooperative communication. Evolved agents from NoiseWorld are successfully transferred onto physical robots, demonstrating the transferability of EMM-based AIs from simulation into physical reality.", notes = "Supervisor: Gabriele, M.T. D'Eleuterio", } @Article{grouchy2016evolutionary, author = " Paul Grouchy and Gabriele M. T. D'Eleuterio and Morten H Christiansen and Hod Lipson", title = "On The Evolutionary Origin of Symbolic Communication", journal = "Scientific reports", year = "2016", volume = "6", number = "34615", keywords = "genetic algorithms, genetic programming", publisher = "Nature Publishing Group", DOI = "doi:10.1038/srep34615", abstract = "The emergence of symbolic communication is often cited as a critical step in the evolution of Homo sapiens, language, and human-level cognition. It is a widely held assumption that humans are the only species that possess natural symbolic communication schemes, although a variety of other species can be taught to use symbols. The origin of symbolic communication remains a controversial open problem, obfuscated by the lack of a fossil record. Here we demonstrate an unbroken evolutionary pathway from a population of initially noncommunicating robots to the spontaneous emergence of symbolic communication. Robots evolve in a simulated world and are supplied with only a single channel of communication. When their ability to reproduce is motivated by the need to find a mate, robots evolve indexical communication schemes from initially noncommunicating populations in 99percent of all experiments. Furthermore, 9percent of the populations evolve a symbolic communication scheme allowing pairs of robots to exchange information about two independent spatial dimensions over a one-dimensional channel, thereby increasing their chance of reproduction. These results suggest that the ability for symbolic communication could have emerged spontaneously under natural selection, without requiring cognitive preadaptations or preexisting iconic communication schemes as previously conjectured.", } @TechReport{Gruau:1992:cegNN, author = "F. Gruau", title = "Cellular encoding of Genetic Neural Networks", institution = "Laboratoire de l'Informatique du Parallilisme. Ecole Normale Supirieure de Lyon", year = "1992", type = "Technical report", number = "92-21", address = "France", keywords = "genetic algorithms, genetic programming", broken = "ftp://lip.ens-lyon.fr/pub/Rapports/RR/RR92/RR92-21.ps.Z", } @InProceedings{Gruau92, author = "Frederic Gruau", title = "Genetic Synthesis of {Boolean} Neural Networks with a Cell Rewriting Developmental Process", booktitle = "Proceedings of the Workshop on Combinations of Genetic Algorithms and Neural Networks (COGANN92)", editor = "J. D. Schaffer and D. Whitley", publisher = "The IEEE Computer Society Press", pages = "55--74", year = "1992", keywords = "genetic algorithms, connectionism, neural networks, 40 inputs symmetry function, 50 inputs parity function, Boolean functions, Boolean neural networks, cell rewriting developmental process, cell rewriting grammar, genetic synthesis, scalability property, Boolean functions, encoding, grammars, neural nets, rewriting systems", DOI = "doi:10.1109/COGANN.1992.273948", abstract = "Genetic algorithms (GAS) are used to generate neural networks that implement Boolean functions. Neural networks both involve an architecture that is a graph of connections, and a set of weights. The algorithm that is put forward yields both the architecture and the weights by using chromosomes that encode an algorithmic description based upon a cell rewriting grammar. The developmental process interprets the grammar for l cycles and develops a neural net parametrised by l. The encoding along with the developmental process have been designed in order to improve the existing approaches. They implement the following key-properties. The representation on the chromosome is abstract and compact. Any chromosome develops a valid phenotype. The developmental process gives modular and interpretable architectures with a powerful scalability property. The GA finds a neural net for the 50 inputs parity function, and for the 40 inputs symmetry function", } @Article{Gruau93, author = "Frederic Gruau", editor = "Simon Lucas", title = "Cellular encoding as a graph grammar", journal = "IEE Colloquium on Grammatical Inference: Theory, Applications and Alternatives", volume = "(Digest No.092)", pages = "17/1--10", publisher = "IEE", address = "London", month = "22-23 " # apr, year = "1993", keywords = "genetic algorithm connectionism neural networks cogann", abstract = "ABSTRACT Cellular encoding is a method for encoding a family of neural networks into a set of labeled trees. Such sets of trees can be evolved by the genetic algorithm so as to find a particular set of trees that encodes a family of Boolean neural networks for computing a family of Boolean functions. Cellular encoding is presented as a graph grammar. A method is proposed for translating a cellular encoding into a set of graph grammar rewriting rules of the kind used in the Berlin algebraic approach to graph rewriting. The genetic search of neural networks via cellular encoding appears as a grammatical inference process where the language to parse is implicitly specified, instead of explicitly by positive and negative examples. Experimental results shows that the genetic algorithm can infer grammars that derive neural networks for the parity, symmetry and decoder Boolean function of arbitrary large size.", } @InProceedings{icga93:gruau, author = "Frederic Gruau", title = "Genetic Synthesis of Modular Neural Networks", year = "1993", booktitle = "Proceedings of the 5th International Conference on Genetic Algorithms, ICGA-93", editor = "Stephanie Forrest", publisher = "Morgan Kaufmann", pages = "318--325", month = "17-21 " # jul, address = "University of Illinois at Urbana-Champaign", keywords = "genetic algorithms, genetic programming, ANN, Mux, parity distributed population", ISBN = "1-55860-299-2", acmid = "657409", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/icga93_gruau.pdf", URL = "http://dl.acm.org/citation.cfm?id=645513.657409", size = "8 pages", abstract = "Cellular encoding is a method for encoding families of Boolean neural networks having the same same structure, that can compute scalable Boolean functions. The current study describes how to incorporate modularity into Cellular Encoding. A Genetic Algorithm is used to find part of a modular code that yields both architecture and plus-minus 1 weights specifying the decoder Boolean function of 10 inputs and 1024 outputs. This results suggests that the GA can exploit modularity in order to find architectures within a more complex range", notes = "'solves the decoder of L inputs for arbitary large L' ", } @PhdThesis{Gruau:1994:thesis, author = "Frederic Gruau", title = "Neural Network Synthesis using Cellular Encoding and the Genetic Algorithm", title_fr = "Synthese de reseaux de neurones par codage cellulaire et algorithmes genetiques", school = "Laboratoire de l'Informatique du Parallilisme, Ecole Normale Supirieure de Lyon", year = "1994", address = "France", keywords = "genetic algorithms, genetic programming, ANN", URL = "ftp://ftp.ens-lyon.fr/pub/LIP/Rapports/PhD/PhD1994/PhD1994-01-E.ps.Z", URL = "ftp://ftp.ens-lyon.fr/pub/LIP/Rapports/PhD/PhD1994/PhD1994-01-F.ps.Z", URL = "https://theses.fr/1994LYO10019", URL = "https://www.sudoc.fr/126334544", size = "151 pages", abstract = "Artificial neural networks used to be considered only as a machine that learns using small modifications of internal parameters. Now this is changing. Such learning method do not allow to generate big neural networks for solving real world problems. This thesis defends the following three points: (1) The key word to go out of that dead-end is 'modularity'. (2) The tool that can generate modular neural networks is cellular encoding. (3) The optimization algorithm adapted to the search of cellular codes is the genetic algorithm. The first point is now a common idea. A modular neural network means a neural network that is made of several sub-networks, arranged in a hierarchical way. For example, the same sub-network can be repeated. This thesis encompasses two parts. The first part demonstrates the second point. Cellular encoding is presented as a machine language for neural networks, with a theoretical basis (it is a parallel graph grammar that checks a number of properties) and a compiler of high level language. The second part of the thesis shows the third point. Application of genetic algorithm to the synthesis of neural networks using cellular encoding is a new technology. This technology can solve problems that were still unsolved with neural networks. It can automatically and dynamically decompose a problem into a hierarchy of sub-problems, and generate a neural network solution to the problem. The structure of this network is a hierarchy of sub-networks that reflects the structure of the problem. The technology allows to experience new scientific domains like the interaction between learning and evolution, or the set up of learning algorithms that suit the GA.", notes = "PhD1994-01-E.ps.Z in english. PhD1994-01-F.ps.Z en francais. LIP-IMAG CNRS Supervisor: Michel Cosnard", } @InCollection{kinnear:gruau, title = "Genetic micro programming of Neural Networks", author = "Frederic Gruau", booktitle = "Advances in Genetic Programming", publisher = "MIT Press", editor = "Kenneth E. {Kinnear, Jr.}", year = "1994", pages = "495--518", chapter = "24", keywords = "genetic algorithms, genetic programming, ANN", URL = "http://www.amazon.co.uk/Advances-Genetic-Programming-Complex-Adaptive/dp/0262111888", URL = "http://cognet.mit.edu/sites/default/files/books/9780262277181/pdfs/9780262277181_chap24.pdf", DOI = "doi:10.7551/mitpress/1108.003.0030", size = "24 pages", abstract = "Cellular Encoding is a method for encoding families of similarly structured Boolean neural networks, that can compute scalable boolean functions. Genetic Programming uses the Genetic Algorithm to evolve LISP computer programs. This chapter demonstrates that Cellular Encoding is a micro-programming language of neural networks and that genetic search of neural networks using Cellular Coding is equivalent to Genetic Micro Programming. The concept of genetic language is defined. Cellular Encoding and LISP are two particular Genetic Programming languages. Other programming languages are proposed. A criterion is put forward to classify genetic languages with increasing complexity. With respect to this criterion Lisp is more complex than Cellular Encoding. Which language is better for Genetic Programming? We argue that Cellular Encoding is better than LISP for the synthesis is of neural networks, and LISP is better for symbolic manipulation. Ultimately, it is possible to evolve the genetic language itself.", notes = "Part of \cite{kinnear:book}", } @TechReport{Gruau:1993:ceNNile, author = "F. Gruau and D. Whitley", title = "The cellular development of neural networks: The interaction of learning and evolution", institution = "Laboratoire de l'Informatique du Parallilisme, Ecole Normale Supirieure de Lyon", year = "1993", type = "Technical report", number = "93-04", address = "France", keywords = "genetic algorithms, genetic programming", broken = "ftp://lip.ens-lyon.fr/pub/Rapports/RR/RR93/RR93-04.ps.Z", } @Article{Gruau:1993:alcdp, author = "Frederic Gruau and Darrell Whitley", title = "Adding learning to the cellular development process: a comparative study", journal = "Evolutionary Computation", year = "1993", volume = "1", number = "3", pages = "213--233", keywords = "genetic algorithms, genetic programming, neural networks, ANN, learning developmental system, cellular encoding", DOI = "doi:10.1162/evco.1993.1.3.213", abstract = "A grammar tree is used to encode a cellular developmental process that can generate whole families of Boolean neural networks for computing parity and symmetry. The development process resembles biological cell division. A genetic algorithm is used to find a grammar tree that yields both architecture and weights specifying a particular neural network for solving specific Boolean functions. The current study particularly focuses on the addition of learning to the development process and the evolution of grammar trees. Three ways of adding learning to the development process are explored. Two of these exploit the Baldwin effect by changing the fitness landscape without using Lamarckian evolution. The third strategy is Lamarckian in nature. Results for these three modes of combining learning with genetic search are compared against genetic search without learning. Our results suggest that merely using learning to change the fitness landscape can be as effective as Lamarckian strategies at improving search.", } @InProceedings{gruau:1995:plad, author = "Frederic Gruau and Darrell Whitley", title = "A Programming Language for Artificial Development", booktitle = "Evolutionary Programming {IV} Proceedings of the Fourth Annual Conference on Evolutionary Programming", year = "1995", editor = "John Robert McDonnell and Robert G. Reynolds and David B. Fogel", pages = "415--434", address = "San Diego, CA, USA", month = "1-3 " # mar, publisher = "MIT Press", keywords = "genetic algorithms, Neural Networks, parellel architectures", ISBN = "0-262-13317-2", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6300845", DOI = "doi:10.7551/mitpress/2887.003.0039", size = "20 pages", abstract = "We define an Artificial Development Process (ADP) which controls the growth and development of a neural network by means of cell division. The language controlling the development process has several characteristics of a procedural programming language. The resulting neural networks are powerful enough to emulate a functional programming language. The development language is also designed so that the resulting neural networks can be efficiently mapped to a distributed memory parallel machine.", notes = "EP-95, Extension of cellular encoding. Says can build neural network that can emulate any functional language (eg SISAL).", } @Article{DBLP:journals/tcs/GruauRW95, author = "Frederic Gruau and Jean-Yves Ratajszczak and Gilles Wiber", title = "A Neural Compiler", journal = "Theoretical Computer Science", year = "1995", volume = "141", number = "1-2", pages = "1--52", month = "17 " # apr, keywords = "genetic algorithms, genetic programming", ISSN = "0304-3975", timestamp = "Wed, 17 Feb 2021 22:00:02 +0100", biburl = "https://dblp.org/rec/journals/tcs/GruauRW95.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://pdf.sciencedirectassets.com/271538/1-s2.0-S0304397500X00394/1-s2.0-0304397594002003/main.pdf", URL = "https://doi.org/10.1016/0304-3975(94)00200-3", DOI = "doi:10.1016/0304-3975(94)00200-3", size = "52 pages", abstract = "The input of the compiler is a PASCAL Program. The compiler produces a neural network that computes what is specified by the PASCAL program. The compiler generates an intermediate code called cellular code.", notes = "Also known as \cite{GRUAU19951}", } @Article{gruau:1995:admnn, author = "Frederic Gruau", title = "Automatic Definition of Modular Neural Networks", journal = "Adaptive Behaviour", year = "1995", volume = "3", number = "2", pages = "151--183", keywords = "genetic algorithms, genetic programming, ANN, animats, cellular encoding, modularity, locomotion, automatic definition of neural subnetworks", ISSN = "1059-7123", URL = "http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/654/http:zSzzSzwww.cwi.nlzSz~gruauzSzgruauzSzAB.pdf/gruau95automatic.pdf", URL = "http://citeseer.ist.psu.edu/gruau95automatic.html", DOI = "doi:10.1177/105971239400300202", size = "33 pages", abstract = "This article illustrates an artificial developmental system that is a computationally efficient technique for the automatic generation of complex artificial neural networks (ANNs). The artificial developmental system can develop a graph grammar into a modular ANN made of a combination of simpler subnetworks. A genetic algorithm is used to evolve coded grammars that generate ANNs for controlling six-legged robot locomotion. A mechanism for the automatic definition of neural subnetworks is incorporated. Using this mechanism, the genetic algorithm can automatically decompose a problem into subproblems, generate a subANN for solving the subproblem, and instantiate copies of this subANN to build a higher-level ANN that solves the problem. We report some simulation results showing that the same problem cannot be solved if the mechanism for automatic definition of subnetworks is suppressed. We support our argument with pictures that describe the steps of development, how ANN structures are evolved, and how the ANNs compute.", notes = "ANN for controlling six legged robot locomotion, broken Dec 2022 http://www.isab.org/journal/adap3_2.php", } @InCollection{gruau:1996:aigp2, author = "Frederic Gruau", title = "On Using Syntactic Constraints with Genetic Programming", booktitle = "Advances in Genetic Programming 2", publisher = "MIT Press", year = "1996", editor = "Peter J. Angeline and K. E. {Kinnear, Jr.}", pages = "377--394", chapter = "19", address = "Cambridge, MA, USA", keywords = "genetic algorithms, genetic programming", ISBN = "0-262-01158-1", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6277529", DOI = "doi:10.7551/mitpress/1109.003.0025", size = "18 pages", abstract = "When using Genetic Programming (GP) for a non trivial problem, the GPer often is aware of potentially useful constraints on the structure of the programs. We know that the solution is likely to have some particular syntactic features. We will show that incorporating these features can in the GP algorithm is valuable. We express those features in terms of syntactic constraints. We customise the GP algorithm to make sure that the initial population of GP trees conforms these constraints, and that crossover and mutation enforces these constraints. This chapter shows that formal grammar can describe precisely any syntactic constraint, and the GP algorithm can be enhanced to handle directly a formal grammar. No additional programming effort is needed to use different syntactic constraints and thus many different and complex syntactic constraints can be tried to solve a problem. This chapter has two goals: 1 Stop to consider using syntactic constraints as a computer hacking trick, but instead as something part of the GP toolkit. 2 Create a general tool to implement syntactic constraints, easy to use, easy to report in a paper, and open a new area of experimentation.", } @InProceedings{gruau:1996:ceVdeGNN, author = "Frederic Gruau and Darrell Whitley and Larry Pyeatt", title = "A Comparison between Cellular Encoding and Direct Encoding for Genetic Neural Networks", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "81--89", address = "Stanford University, CA, USA", publisher = "MIT Press", ISBN = "9780-262-31587-6", URL = "http://www.cs.colostate.edu/~genitor/1996/gp96.ps.gz", URL = "https://dl.acm.org/doi/10.5555/1595536.1595547", broken = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap10.pdf", broken = "http://cognet.mit.edu/library/books/view?isbn=0262611279", DOI = "doi:10.7551/mitpress/3242.003.0013", size = "9 pages", abstract = "This paper compares the efficiency of two encoding schemes for Artificial Neural Networks optimized by evolutionary algorithms. Direct Encoding encodes the weights for an a priori fixed neural network architecture. Cellular Encoding encodes both weights and the architecture of the neural network. In previous studies, Direct Encoding and Cellular Encoding have been used to create neural networks for balancing 1 and 2 poles attached to a cart on a fixed track. The poles are balanced by a controller that pushes the cart to the left or the right. In some cases velocity information about the pole and cart is provided as an input; in other cases the network must learn to balance a single pole without velocity information. A careful study of the behavior of these systems suggests that it is possible to balance a single pole with velocity information as an input and without learning to compute the velocity. A new fitness function is introduced that forces the neural network to compute the velocity. By using this new fitness function and tuning the syntactic constraints used with cellular encoding, we achieve a tenfold speedup over our previous study and solve a more difficult problem: balancing two poles when no information about the velocity is provided as input.", notes = "See also \cite{HeidrichMeisner2009152}. GP-96", } @TechReport{gruau:1996:ceier, author = "Frederic Gruau and Kameel Quatramaran", title = "Cellular Encoding for Interactive Evolutionary Robotics", institution = "School of Cognitive and Computing Sciences, University of Sussex", year = "1996", type = "Cognitive Science Research Paper", number = "425", address = "Falmer, Brighton, Sussex, UK", month = jul, keywords = "genetic algorithms, genetic programming", URL = "ftp://ftp.cogs.susx.ac.uk/pub/reports/csrp/csrp425.ps.Z", broken = "http://www.cogs.susx.ac.uk/cgi-bin/htmlcogsreps?csrp425", abstract = "This work reports experiments in interactive evolutionary robotics. The goal is to evolve an Artificial Neural Network (ANN) to control the locomotion of an 8-legged robot. The ANNs are encoded using a cellular developmental process called cellular encoding. In a previous work similar experiments have been carried on successfully on a simulated robot. They took however around 1 million different ANN evaluations. In this work the fitness is determined on a real robot, and no more than a few hundreds evaluations can be performed. Various ideas were implemented so as to decrease the required number of evaluations from 1 million to 200. First we used cell cloning and link typing. Second we did as many things as possible interactively: interactive problem decomposition, interactive syntactic constraints, interactive fitness. More precisely: 1- A modular design was chosen where a controller for an individual leg, with a precise neuronal interface was developed. 2- Syntactic constraints were used to promote useful building blocs and impose an 8-fold symmetry. 3- We determine the fitness interactively by hand. We can reward features that would otherwise be very difficult to locate automatically. Interactive evolutionary robotics turns out to be quite successful, in the first bug-free run a global locomotion controller that is faster than a programmed controller could be evolved.", size = "23 pages", } @InCollection{Gruau:EA95, author = "Frederic Gruau", title = "Modular Genetic Neural Networks for Six-Legged Locomotion", booktitle = "Artificial Evolution", publisher = "Springer Verlag", year = "1996", editor = "Jean-Marc Alliot and Evelyne Lutton and Edmund Ronald and Marc Schoenauer and Dominique Snyers", volume = "1063", series = "LNCS", pages = "201--219", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-61108-0", DOI = "doi:10.1007/3-540-61108-8_39", size = "19 pages", abstract = "This paper illustrates an artificial developmental system that is a computationally efficient technique for the automatic generation of complex Artificial Neural Networks (ANN). Artificial developmental system can develop a graph grammar into a modular ANN made of a combination of more simple subnetworks. Genetic programming is used to evolve coded grammars that generates ANNs for controlling a six-legged robot locomotion. A mechanism for the automatic definition of sub-neural networks is incorporated.", notes = "Selected papers from two conferences: Evolution Artificielle 94 and Evolution Artificielle 95 http://www.cmap.polytechnique.fr/www.eark/ea95.html", affiliation = "Stanford University Psychology Department 94305 Palo Alto CA 94305 Palo Alto CA", } @InProceedings{blob_computing2004, author = "Frederic Gruau and Yves Lhuillier and Philippe Reitz and Olivier Temam", title = "{BLOB} Computing", booktitle = "Computing Frontiers", year = "2004", editor = "Jean-Luc Gaudiot and Vincenzo Piuri", pages = "125--139", address = "Ischia, Italy", month = apr # " 14-16", organisation = "ACM", publisher = "SIGMicro", keywords = "genetic algorithms, genetic programming, Scalable Architectures, Cellular Automata, Bio-inspiration, Distributed architectures, Programming Languages, Concurrent, distributed, and parallel languages", isbn13 = "1-58113-741-9/04/0004", URL = "http://blob.lri.fr/", URL = "http://blob.lri.fr/publication/2004-model-blob-machine.pdf", DOI = "doi:10.1145/977091.977111", size = "15 pages", abstract = "Current processor and multiprocessor architectures are almost all based on the Von Neumann paradigm. Based on this paradigm, one can build a general-purpose computer using very few transistors, e.g., 2250 transistors in the first Intel 4004 microprocessor. In other terms, the notion that on-chip space is a scarce resource is at the root of this paradigm which trades on-chip space for program execution time. Today, technology considerably relaxed this space constraint. Still, few research works question this paradigm as the most adequate basis for high-performance computers, even though the paradigm was not initially designed to scale with technology and space. we propose a different computing model, defining both an architecture and a language, that is intrinsically designed to exploit space; we then investigate the implementation issues of a computer based on this model, and we provide simulation results for small programs and a simplified architecture as a first proof of concept. Through this model, we also want to outline that revisiting some of the principles of today's computing paradigm has the potential of overcoming major limitations of current architectures.", notes = "CF-2004 http://www.computingfrontiers.org/2004 broken 2018 http://blob.lri.fr/publication/listeARCHITECTURE.htm Conf. avec Actes - Numero de document : 11378 Blob is a dynamic collection of particles. Particles run on regular lattice of computer processing elements (PE). Cellular encoding used for dynamic positioning", } @MastersThesis{IMM2005-03650, author = "Soren Grubov and Rasmus Hartvig", title = "AI in Computer games", year = "2005", school = "Informatics and Mathematical Modelling, Technical University of Denmark, {DTU}", address = "Richard Petersens Plads, Building 321, {DK-}2800 Kgs. Lyngby", note = "Supervisor: Thomas Bolander \& Hans Bruun", URL = "http://www2.imm.dtu.dk/pubdb/p.php?3650", abstract = "The aim of the project is to explore and demonstrate the potential of common AI techniques in computer games. We will be concentrating on some or all of the following: * Logic-based planning * Neural networks * Genetic programming * Machine learning We will be using game engines from IO Interactive as a framework for implementation, in order to demonstrate these techniques. The primary objective is to achieve a higher level of artificial intelligence in computer games by the usage of logic-based planning. This requires development of a multi agent system, for simulating human-like behaviour, within a computer game. The additionally mentioned techniques are regarded as secondary techniques, which are to be used in conjunction with planning, in order to facilitate more specific behavior like learning or adaptation. Combining one or more of the secondary techniques with the primary technique is a secondary objective. The extension of usage of secondary techniques will be decided at a later stage. Loosely formulated, the project objective is to bridge the gap between the AI planning field and the commercial computer game industry. Alternatively, to assess the distance between the AI field, and the emerging design patterns used in the gaming field. The project can be seen as an advanced application of multi agent theory, building on previous experiences from multi agent system projects.", keywords = "genetic algorithms, genetic programming", } @PhdThesis{Gruenert:thesis, author = "Gerd Gruenert", title = "Unconventional Programming: Programming Non-programmable Systems", school = "Friedrich-Schiller-Universitaet", year = "2016", address = "Jena, Germany", keywords = "genetic algorithms, genetic programming", URL = "https://www.db-thueringen.de/servlets/MCRFileNodeServlet/dbt_derivate_00038109/thesis.pdf", size = "196 pages", abstract = "Unconventional and natural computing research offers controlled information modification processes in uncommon media, for example on the molecular scale or in bacteria colonies. Promising aspects of such systems are often the non-linear behaviour and the high connectivity of the involved information processing components in analogy to neurons in the nervous system. Unfortunately, such properties make the system behavior hard to understand, hard to predict and thus also hard to program with common engineering principles like modularization and composition, leading to the term of non-programmable systems. In contrast to many unconventional computing works that are often focused on finding novel computing substrates and potential applications, unconventional programming approaches for such systems are the theme of this thesis: How can new programming concepts open up new perspectives for unconventional but hopefully also for traditional, digital computing systems? Mostly based on a model of artificial wet chemical neurons, different unconventional programming approaches from evolutionary algorithms, information theory, self-organization and self-assembly are explored. A particular emphasis is given on the problem of symbol encodings: Often there are multiple or even an unlimited number of possibilities to encode information in the phase space of dynamical systems, e.g. spike frequencies or population coding in neural networks. But different encodings will probably be differently useful, dependent on the system properties, the information transformation task and the desired connectivity to other systems. Hence methods are investigated that can evaluate, analyse as well as identify suitable symbol encoding schemes", notes = "Contents 1 Introduction, 2 Droplet Computers, 3 Evolution of Droplet Computers and Signals, 4 Information Theory Based Methods, 5 Tautological Loops, 6 Embodied Evolution, 7 Programmed Self-Assembly in Biology Supervisor: Peter Dittrich", } @Article{Grunwald2009195, author = "S. Grunwald", title = "Multi-criteria characterization of recent digital soil mapping and modeling approaches", journal = "Geoderma", volume = "152", number = "3-4", pages = "195--207", year = "2009", ISSN = "0016-7061", DOI = "doi:10.1016/j.geoderma.2009.06.003", URL = "http://www.sciencedirect.com/science/article/B6V67-4WSG2WJ-1/2/af92060815439203d2999e4ace2ae786", keywords = "genetic algorithms, genetic programming, Digital soil mapping, Digital soil modelling, Pedometrics, Quantitative methods, Soils", abstract = "The history of digital soil mapping and modelling (DSMM) is marked by adoption of new mapping tools and techniques, data management systems, innovative delivery of soil data, and methods to analyse, integrate, and visualise soil and environmental datasets. DSMM studies are diverse with specialised, mathematical prototype models tested on limited geographic regions and/or datasets and simpler, operational DSMM used for routine mapping over large soil regions. Research-focused DSMM contrasts with need-driven DSMM and agency-operated soil surveys. Since there is no universal equation or digital soil prediction model that fits all regions and purposes the proposed strategy is to characterise recent DSMM approaches to provide recommendations for future needs at local, national and global scales. Such needs are not solely soil-centered, but consider broader issues such as land and water quality, carbon cycling and global climate change, sustainable land management, and more. A literature review was conducted to review 90 DSMM publications from two high-impact international soil science journals -- Geoderma and Soil Science Society of America Journal. A selective approach was used to identify published studies that cover the multi-factorial DSMM space. The following criteria were used (i) soil properties, (ii) sampling setup, (iii) soil geographic region, (iv) spatial scale, (v) distribution of soil observations, (vi) incorporation of legacy/historic data, (vii) methods/model type, (viii) environmental covariates, (ix) quantitative and pedological knowledge, and (x) assessment method. Strengths and weaknesses of current DSMM, their potential to be operationalized in soil mapping/modelling programs, research gaps, and future trends are discussed. Modeling of soils in 3D space and through time will require synergistic strategies to converge environmental landscape data and denser soil data sets. There are needs for more sophisticated technologies to measure soil properties and processes at fine resolution and with accuracy. Although there are numerous quantitative models rooted in factorial models that predict soil properties with accuracy in select geographic regions they lack consistency in terms of environmental input data, soil properties, quantitative methods, and evaluation strategies. DSMM requires merging of quantitative, geographic and pedological expertise and all should be ideally in balance.", notes = "survey", } @Article{Gu:2024:CGJ, author = "Kyo-Young Gu and Nghiem Xuan Tran and Jayne M. Han and Kyeong-Sun Kim and Kyung-Won Ham and Sung-Ryul Kim", title = "Centrifuge study on the uplift behavior of spread foundation for transmission tower in sand", journal = "Canadian Geotechnical Journal", year = "2024", volume = "61", number = "7", pages = "1418--1432", keywords = "genetic algorithms, genetic programming, transmission tower, spread foundation, uplift capacity, sand, centrifuge tests", ISSN = "0008-3674", URL = "https://www.sciencedirect.com/science/article/pii/S0008367424000052", DOI = "doi:10.1139/cgj-2023-0065", abstract = "Realistic simulation of in-situ stresses is an essential consideration in evaluating the bearing capacities of spread foundations. Therefore, this study aimed to investigate the uplift behaviour of large spread foundations by performing a series of centrifuge tests. A total of 12 centrifuge test models were constructed considering the variation in the foundation width (3.5-6.5 m), embedment depth ratio (0.67-1.39), and relative density of dry silica sandy soil (40percent, 80percent). The measured load-displacement curves indicated that the uplift bearing capacity and the corresponding uplift displacement increased with the foundation width at a certain embedment depth ratio. Two sets of empirical equations were proposed by adopting genetic programming to estimate the uplift resistance factor and uplift displacement with consideration of field stress conditions. The proposed equations reasonably matched these parameters under various conditions reported in the literature. Furthermore, a simplified empirical influence zone was proposed on the basis of the measurement of the ground surface displacement. The empirical influence zone, which was a straight line with an inclination angle of ~0.9 times the soil friction angle measured from vertical, could be considered a significant reference for predicting the failure surface of the shallow foundation under uplift loading", } @Article{GU:2022:cie, author = "Wenbin Gu and Yuxin Li and Dunbing Tang and Xianliang Wang and Minghai Yuan", title = "Using real-time manufacturing data to schedule a smart factory via reinforcement learning", journal = "Computer \& Industrial Engineering", volume = "171", pages = "108406", year = "2022", ISSN = "0360-8352", DOI = "doi:10.1016/j.cie.2022.108406", URL = "https://www.sciencedirect.com/science/article/pii/S0360835222004466", keywords = "genetic algorithms, genetic programming, Smart factory, Real-time scheduling, Production state clustering, Reinforcement learning", abstract = "Under the background of intelligent manufacturing, internet of things and other information technologies have accumulated a large amount of data for manufacturing system. However, the traditional scheduling methods often ignore the production law and knowledge hidden in the manufacturing data. Therefore, this paper proposes a cyber-physical architecture and a communication protocol for smart factory, and a multiagent-system-based dynamic scheduling mechanism is given using contract net protocol. In the dynamic scheduling mechanism, the problem formulation module and scheduling point module are designed first. Then, a genetic programming (GP) method is proposed to form sixteen high-quality rules, which constitute the scheduling rule library. Meanwhile, combining with autoencoder, self-organizing mapping neural network and k-means clustering algorithm, the state clustering module is designed to realize the efficient clustering of production attribute vector. Moreover, an improved Q-learning algorithm is used to train the GP rule selector, so that the decision-making agent can choose the appropriate GP rule according to the production state at each scheduling point. Finally, the experimental results show that the proposed method has feasibility and superiority compared with other methods in real-time scheduling, and can effectively deal with disturbance events in the manufacturing process", } @Article{GU:2024:cie, author = "Wenbin Gu and Siqi Liu and Zhenyang Guo and Minghai Yuan and Fengque Pei", title = "Dynamic scheduling mechanism for intelligent workshop with deep reinforcement learning method based on multi-agent system architecture", journal = "Computer \& Industrial Engineering", pages = "110155", year = "2024", ISSN = "0360-8352", DOI = "doi:10.1016/j.cie.2024.110155", URL = "https://www.sciencedirect.com/science/article/pii/S0360835224002766", keywords = "genetic algorithms, genetic programming, Intelligent workshop, Multi-agent manufacturing system, Data-based with combination of virtual and physical agent (DB-VPA), Dynamic scheduling mechanism, IGP-PPO", abstract = "With the development and changes of industry and market demand, the personalized customization production mode with small batch and multiple batches has gradually become a new production mode. This makes production environment become more complex and dynamic. However, traditional production workshops cannot effectively adapt to this environment. Combining with new technologies, transforming traditional workshops into intelligent workshop to cope with new production mode become an urgent problem. Therefore, this paper proposes a multi-agent manufacturing system based on IoT for intelligent workshop. Meanwhile, this paper takes flexible job shop scheduling problem (FJSP) as a specific production scenario and establishes relevant mathematics model. To build the agent in intelligent workshop, this paper proposes a data-based with combination of virtual and physical agent (DB-VPA) which has information layer, software layer and physical layer. Then, based on the manufacturing system, this paper designs a dynamic scheduling mechanism with deep reinforcement learning (DRL) for intelligent workshop. This method contains three aspects: (1) Modeling production process based on Markov decision process (MDP). (2) Designing communication mechanism for DB-VPAs. (3) Designing scheduling model combining with improved genetic programming and proximal policy optimization (IGP-PPO) which is a DRL method. Finally, relevant experiments are executed in a prototype experiment platform. The experiments indicate that the proposed method has superiority and generality in solving scheduling problem with dynamic events", } @InProceedings{Gu:2011:SICE, author = "Yunqing Gu and Shingo Mabu and Yang Yang and Jianhua Li and Kotaro Hirasawa", title = "Trading rules on stock markets using Genetic Network Programming-Sarsa Learning with plural subroutines", booktitle = "Proceedings of SICE Annual Conference (SICE 2011)", year = "2011", month = "13-18 " # sep, pages = "143--148", address = "Waseda University, Tokyo, Japan", keywords = "genetic algorithms, genetic programming, GNP structure, automatically defined function, genetic network programming-Sarsa learning, plural subroutines, stock markets, subroutine node, trading rules, stock markets", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6060592", isbn13 = "978-1-4577-0714-8", size = "6 pages", abstract = "In this paper, Genetic Network Programming-Sarsa Learning (GNP-Sarsa) used for creating trading rules on stock markets is enhanced by adding plural subroutines. Subroutine node - a new kind of node which works like ADF (Automatically Defined Function) in Genetic Programming (GP) has been proved to have positive effects on the stock-trading model using GNP-Sarsa. In the proposed method, not only one kind of subroutine but plural subroutines with different structures are used to improve the performance of GNP-Sarsa with subroutines. Each subroutine node could indicate its own input and output node of the subroutine, which could be also evolved. In the simulations, totally 16 brands of stock from 2001 to 2004 are used to investigate the improvement of GNP-Sarsa with plural subroutines. The simulation results show that the proposed approach can obtain more flexible GNP structure and get higher profits in stock markets.", notes = "Also known as \cite{6060592}", } @Article{GU:2021:CPBE, author = "Zewen Gu and Yiding Liu and Darren J. Hughes and Jianqiao Ye and Xiaonan Hou", title = "A parametric study of adhesive bonded joints with composite material using black-box and grey-box machine learning methods: Deep neuron networks and genetic programming", journal = "Composites Part B: Engineering", volume = "217", pages = "108894", year = "2021", ISSN = "1359-8368", DOI = "doi:10.1016/j.compositesb.2021.108894", URL = "https://www.sciencedirect.com/science/article/pii/S1359836821002857", keywords = "genetic algorithms, genetic programming, Adhesive bonded joint, Composite material, Finite element model, Deep neuron network", abstract = "The aerospace, automotive and marine industries have witnessed a rapid increase of using adhesive bonded joints due to their advantages in joining dissimilar and/or new engineering materials. Joint strength is the key property in evaluating the capability of the adhesive joint. In this paper, developments of black-box and grey-box machine learning (ML) models are presented to allow accurate predictions of the failure load of single lap joints by considering a mix of continuous and discrete design (geometry and material) variables. Firstly, the failure loads of 300 single lap joint samples with different geometry/material parameters are calculated by FE models to generate a data set of which accuracy is validated by experimental results. Then, a deep neuron network (black-box) and a genetic programming (grey-box) model are developed for accurately predicting the failure load of the joint. Based on both ML models, a case study is conducted to explore the relationships between specific design variables and overall mechanical performances of the single lap adhesive joint, and optimal designs of structure and material can be obtained", } @PhdThesis{Gu:thesis, author = "Zewen Gu", title = "Static and Dynamic Analysis of Nonlinear Valve Springs Based on Finite Element Analysis and Machine Learning Algorithm", school = "Engineering Department, Lancaster University", year = "2022", address = "UK", keywords = "genetic algorithms, genetic programming", URL = "https://eprints.lancs.ac.uk/id/eprint/164746/1/2022zewenphd.pdf", URL = "https://www.proquest.com/openview/a617ff64b19c588f765378db1149dc2e/1", DOI = "doi:10.17635/lancaster/thesis/1531", size = "201 pages", abstract = "The valve spring is a fundamental type of helical spring which is essential for enabling the opening and closure of a valve in a car engine. Nowadays, it is increasingly common to use valve springs of nonlinear geometry in high-speed car engines for better dynamic performance. However, practical issues such as malfunction and pre-failure are also raised by spring researchers and manufacturers using and analysing these nonlinear springs. It is commonly stated that existing spring models and empirical formula do not allow for the analysis of these nonlinear springs. To tackle such difficulties, it is imperative that all the varied geometric parameters of a nonlinear spring be clarified in order to facilitate efficient and generalisable analysis. Past research efforts have mainly emphasized the analysis of standard valve springs of constant geometric parameters and the development of spring models for low-speed static conditions. However, these models do not take into account the full breadth of conditions and consequently are considered to be insufficient and compromised in accuracy. Therefore, it remains a challenge to effectively leverage such models in the analysis and design of nonlinear valve springs. This thesis aims to address the existing gaps and present a comprehensive study on the analysis of nonlinear valve springs and their dynamic response in high-speed engines. An advanced spring formula is developed based on simplified curved beam theory to formulate the relationships between the nonlinear spring geometry (varied coil diameter, varied pitch and coil clash) and the mechanical properties of a beehive valve spring. These nonlinear considerations deliver a higher predictive accuracy than the existing spring formulas by comparing FE and experimental results. The new spring formula is coupled with the distributed parameter model to simulate the dynamic spring IV responses. However, whilst it accurately simulates the dynamic responses at lower engine speeds (lower 5000-rpm), it fails to simulate the significant abnormal spring forces at high engine speeds (over 8000-rpm). On the contrary, the FE springs model is developed, of which static and dynamic simulation results fit well with the experimental data at both low and high engine speeds. More importantly, analysis of the dynamic FE results explains how the violent coil clash leads to significant abnormal spring forces. In the last part, a machine learning model, based on genetic programming techniques and the FE results, is developed to aid the design of nonlinear helical springs. The model enables researchers to analyse nonlinear helical spring properties directly using information extracted from FE results data, bypassing the necessity to unravel the complex inner relationships between the nonlinear spring parameters.", notes = "Also known as \cite{7c3452eedfb94c959d8d2f253d279768} supervisor: Xiaonan Hou, Jianqiao Ye", } @InProceedings{Guadalupe-Hernandez:2021:GPTP, author = "Jose {Guadalupe Hernandez} and Alex Lalejini and Charles Ofria", title = "An Exploration of Exploration: Measuring the Ability of Lexicase Selection to Find Obscure Pathways to Optimality", booktitle = "Genetic Programming Theory and Practice XVIII", year = "2021", editor = "Wolfgang Banzhaf and Leonardo Trujillo and Stephan Winkler and Bill Worzel", series = "Genetic and Evolutionary Computation", pages = "83--107", address = "East Lansing, USA", month = "19-21 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, lexicase selection", isbn13 = "978-981-16-8112-7", DOI = "doi:10.1007/978-981-16-8113-4_5", abstract = "Parent selection algorithms (selection schemes) steer populations through a problems search space, often trading off between exploitation and exploration. Understanding how selection schemes affect exploitation and exploration within a search space is crucial to tackling increasingly challenging problems. Here, we introduce an exploration diagnostic that diagnoses a selection schemes capacity for search space exploration. We use our exploration diagnostic to investigate the exploratory capacity of lexicase selection and several of its variants: epsilon lexicase, down-sampled lexicase, cohort lexicase, and novelty-lexicase. We verify that lexicase selection out-explores tournament selection, and we show that lexicase selection exploratory capacity can be sensitive to the ratio between population size and the number of test cases used for evaluating candidate solutions. Additionally, we find that relaxing lexicase elitism with epsilon lexicase can further improve exploration. Both down-sampling and cohort lexicase, two techniques for applying random subsampling to test cases, degrade lexicases exploratory capacity; however, we find that cohort partitioning better preserves lexicase exploratory capacity than down-sampling. Finally, we find evidence that novelty-lexicase addition of novelty test cases can degrade lexicase capacity for exploration. Overall, our findings provide hypotheses for further exploration and actionable insights and recommendations for using lexicase selection. Additionally, this work demonstrates the value of selection scheme diagnostics as a complement to more conventional benchmarking approaches to selection scheme analysis.", notes = "Part of \cite{Banzhaf:2021:GPTP} published after the workshop in 2022", } @PhdThesis{Guadalupe-Hernandez:thesis, author = "Jose Guadalupe Hernandez", title = "Beyond benchmarks suites: engineering diagnostic tools to characterize selection schemes", school = "Michigan State University", year = "2023", address = "USA", keywords = "genetic algorithms, genetic programming, Computer science, Artificial intelligence, AI, Evolutionary computation, Evolutionary programming (Computer science)", isbn13 = "9798379516642", URL = "https://d.lib.msu.edu/etd/51176", URL = "https://d.lib.msu.edu/etd/51176/OBJ/download", DOI = "doi:10.25335/tb48-wz75", size = "181 pages", abstract = "Evolutionary algorithms (EAs) draw inspiration from biological evolution and replicate evolutionary processes into a computational framework that can often solve challenging optimisation problems. These algorithms evolve a population of candidate solutions, where the population typically cycles through three phases: evaluation, selection, and reproduction. Specifically, the evaluation phase assesses the qualities of the candidate solutions, the selection phase identifies which regions will be searched further, and the reproduction phase identifies the next positions to search. Clearly, each phase plays a specific role in the evolutionary search that is implemented through one or more interacting components that fully specify the algorithm. Of course, interactions can make it difficult to isolate individual components in some complex EAs. As such, if we want to understand how each component affects the properties of the overall algorithm, we need a framework to formally define each component, and we need tools that characterise how each component contributes to overall problem-solving success. When a new EA is proposed, it is typically evaluated against a benchmark suite or handpicked test problems that clearly demonstrate its capabilities. Multiple benchmark suites exist to highlight which classes of problems an EA is most effective against. Such suites, however, are limited in their ability to diagnose why an EA performs the way it does. In particular, problems with complex fitness landscapes do not facilitate an intuitive understanding of how an algorithm traverses the search space. At a high level, components in an EA are well-classified for the role they are supposed to play in traversing a search space: evaluation components generate qualities for a candidate solution; selection components use these qualities to identify parents; reproduction components propagate parents and apply variation. However, it is often less clear which particular components would be most effective on a given problem or how different components will alter each others behaviour. Given the importance of component features and interactions, my aim is to disentangle the mechanistic effects of each choice on the search process so that we can better anticipate which combinations of components are most likely to produce an optimal solution to a given problem. In this dissertation, I achieved three synergistic goals: (1) I developed a formal definition for selection scheme components that provides a framework for their study within generational EAs; (2) I crafted a set of diagnostic tools that allow me to isolate the effects of individual selection scheme components within this framework; and (3) I used these diagnostics to characterize the search strategies employed by a set of common selection schemes. In the chapters below, I first present a formal framework for dividing any selection scheme into three fundamental components: population structures, trait processing, and selectors (Chapter 2). Next, I use lexicase selection as the basis of two case studies where I demonstrate how subtle alterations of this selection scheme affect performance on program synthesis problems, sometimes producing dramatic improvements, but leaving many open questions as to when and why these improvements will occur (Chapters 3 and 4). Once this motivation is established, I improve our toolset for understanding selection schemes by developing a set of diagnostics that more precisely and intuitively measure the strengths and weaknesses of a set of schemes (Chapters 5 and 6). Finally, I apply these diagnostics to a new area, island structures, to demonstrate their versatility and expected general usefulness (Chapter 7). This work emphasises the importance of properly configuring an EA for the problem at hand, and provides a precise and informative contribution to the set of available benchmark suites.", notes = "Thesis Advisors: Charles Ofria and Alexander Lalejini", } @InProceedings{Guadalupe-Hernandez:2024:GPTP, author = "Jose {Guadalupe Hernandez} and Anil Kumar Saini and Jason H. Moore", title = "Lexicase Selection Parameter Analysis: Varying Population Size and Test Case Redundancy with Diagnostic Metrics", booktitle = "Genetic Programming Theory and Practice XXI", year = "2024", editor = "Stephan M. Winkler and Wolfgang Banzhaf and Ting Hu and Alexander Lalejini", series = "Genetic and Evolutionary Computation", pages = "375--393", address = "University of Michigan, USA", month = jun # " 6-8", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-981-96-0076-2", DOI = "doi:10.1007/978-981-96-0077-9_19", notes = "Published in 2025 after the workshop", } @InProceedings{Guadalupe-Hernandez:2025:GPTP, author = "Jose {Guadalupe Hernandez}", title = "{GP} and {LLMs} for Program Synthesis with Varying Number of Input-Output Examples", booktitle = "Genetic Programming Theory and Practice XXII", year = "2025", editor = "Wolfgang Banzhaf and Bogdan Burlacu and Stephen Kelly and Alexander Lalejini and Fabricio {Olivetti de Franca}", address = "Michigan State University, USA", month = jun # " 5-7", note = "lightning talk", keywords = "genetic algorithms, genetic programming, ANN", notes = "To be published after the workshop", } @InProceedings{Gualda:2013:IPIN, author = "David Gualda and Jesus Urena and Juan C. Garcia and Enrique Garcia and Daniel Ruiz", booktitle = "International Conference on Indoor Positioning and Indoor Navigation (IPIN 2013)", title = "RSSI distance estimation based on Genetic Programming", year = "2013", month = oct, abstract = "The obtention of distances to different Access Points from RSSI readings in indoor environments is a difficult task due to intrinsic RF propagation effects like refraction, diffraction, reflection or absorption. This paper proposes a new model of distances estimation from RSSI data based on Genetic Programming; this new model estimates the distances from the receiver position to each WiFi AP depending on all RSSI WiFi measurements available in this point. Other methods, as fingerprinting, use the RSSI WiFi measures to determine directly the position but they need a careful choice of the set of calibration points. In our method, we obtain specific expressions that obtain distances to each AP taking into account the RSSI received from all the APs available in the coverage area with few restrictions about the location of such calibration points. Our model is compared with two classical propagation models (Hata-Okumura and COST 231 multi-wall) in a real scenario obtaining better results. The distances to the APs obtained can be used by any positioning algorithm (as Gauss-Newton one) to obtain the position of the receiver.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IPIN.2013.6817881", notes = "GPLAB Dept. of Electron., Univ. of Alcala, Alcala de Henares, Spain Also known as \cite{6817881}", } @InProceedings{Gubina:2024:NIPS, author = "Nina Gubina and Andrei Dmitrenko and Gleb Solovev and Lyubov Yamshchikova and Oleg Petrov and Ivan Lebedev and Nikita Serov and Grigorii Kirgizov and Nikolay Nikitin and Vladimir Vinogradov", title = "Hybrid Generative {AI} for De Novo Design of Co-Crystals with Enhanced Tabletability", booktitle = "NeurIPS 2024", year = "2024", editor = "A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang", volume = "37", pages = "84606--84644", address = "Vancouver", publisher = "Curran Associates, Inc.", keywords = "genetic algorithms, genetic programming", URL = "https://proceedings.neurips.cc/paper_files/paper/2024/file/99d27a4939a1dfcdde8394b9137bd885-Paper-Conference.pdf", URL = "https://neurips.cc/virtual/2024/poster/95931", URL = "https://arxiv.org/abs/2410.17005", URL = "https://www.human-competitive.org/sites/default/files/gubina_humies25.txt", URL = "https://www.human-competitive.org/sites/default/files/hybrid-generative-ai-for-de-novo-design-of-co-crystals-with-enhanced-tabletability-neurips-2024.pdf", code_url = "https://github.com/ai-chem/GEMCODE", size = "39 pages", abstract = "Co-crystallization is an accessible way to control physicochemical characteristics of organic crystals, which finds many biomedical applications. we present Generative Method for Co-crystal Design (GEMCODE) 1, a novel pipeline for automated co-crystal screening based on the hybridization of deep generative models and evolutionary optimization for broader exploration of the target chemical space. GEMCODE enables fast de novo co-crystal design with target tabletability profiles, which is crucial for the development of pharmaceuticals. With a series of experimental studies highlighting validation and discovery cases, we show that GEMCODE is effective even under realistic computational constraints. Furthermore, we explore the potential of language models in generating co-crystals. Finally, we present numerous previously unknown co-crystals predicted by GEM-CODE and discuss its potential in accelerating drug development", notes = "2025 HUMIES finalist. Also known as \cite{NEURIPS2024_99d27a49} \cite{gubina2024hybridgenerativeainovo} arXiv 2410.17005 https://neurips.cc/virtual/2024/index.html ITMO University, St. Petersburg, Russia", } @InProceedings{Gubrele:2017:CSNT, author = "Poorva Gubrele and Ritu Prasad and Praneet Saurabh and Bhupendra Verma", booktitle = "2017 7th International Conference on Communication Systems and Network Technologies (CSNT)", title = "Advance morphological filtering, correlation and convolution method for gesture recognition", year = "2017", pages = "153--157", month = nov, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CSNT.2017.8418528", abstract = "Hand gesture recognition system is employed to provide interface between computer and human using hand gesture. This paper presents a technique for human computer interface through common hand gesture that is efficient to commemorate 25 aspersion gestures from the American sign language hand alphabet. The prospect of this paper is to develop up an algorithm for hand gesture recognition with reasonable accuracy. This work uses a domain independent learning methodology to automatically stir low-level spatio-temporal descriptors for high-level cross recognition by Correlated variance programming. Feature extraction is the most important orientation for gesture recognition and is indeed important in terms of giving input to a classifier. In this work Canny edge detector algorithm is used to find edge of the segmented and morphological filtered image which yields boundary of hand gesture in the image then Correlated variance mean based programming applied for recognition of gesture. Experimental results very precisely indicate that the developed method outperforms the existing state of the art.", notes = "Also known as \cite{8418528}", } @InProceedings{Guermeur:2003:ICRODIC, author = "Philippe Guermeur and Jean Louchet", title = "An evolutionary algorithm for camera calibration", booktitle = "ICRODIC 2003", year = "2003", pages = "799--804", address = "Rethymnon, Crete", month = oct, keywords = "genetic algorithms, genetic programming, calibration, evolutionary algorithm, lens distortion, collinearity, geometric invariant, optimization", URL = "https://pdfs.semanticscholar.org/bd7a/f3af16513d6470e4a648192b309d97bfef7e.pdf", size = "6 pages", abstract = "Image calibration is the very first step in the low-level vision process, making it possible to reliably exploit geometrical information from images. In this paper, we address the problem of calculating and compensating camera lens distortion using a fast evolutionary algorithm. The advantages and limitations of this method are compared with classical calibration methods.", notes = "Aug 2018 on citeseerx.ist.psu.edu but no stable URL", } @InProceedings{guerra-salcedo:1998:gsfss, author = "Cesar Guerra-Salcedo and Darrell Whitley", title = "Genetic Search for Feature Subset Selection: A Comparison Between CHC and GENESIS", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "504--509", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms", notes = "SGA-98", } @InProceedings{guerra-salcedo:1999:GAFSEC, author = "Cesar Guerra-Salcedo and Darrell Whitley", title = "Genetic Approach to Feature Selection for Ensemble Creation", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "236--243", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems, data mining", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/Guerra-Salcedo_gecco99c.pdf", URL = "http://www.cs.colostate.edu/~genitor/1999/gecco99c.pdf", abstract = "boosting and bagging", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99) CHC cataclysmic mutation, uniform crossover. EDT, k-means (KMA) Statlog and UCI, LandSat DNA Segment. Big study, difficult to follow. Lots of references. ", } @Article{Guerrero-Enamorado:2016:IJCIS, author = "Alain Guerrero-Enamorado and Carlos Morell and Amin Y. Noaman and Sebastian Ventura", title = "An Algorithm Evaluation for Discovering Classification Rules with Gene Expression Programming", journal = "International Journal of Computational Intelligence Systems", year = "2016", volume = "9", number = "2", pages = "263--280", keywords = "genetic algorithms, genetic programming, Gene Expression Programming, classification rules, discriminant functions", DOI = "doi:10.1080/18756891.2016.1150000", abstract = "In recent years, evolutionary algorithms have been used for classification tasks. However, only a limited number of comparisons exist between classification genetic rule-based systems and gene expression programming rule-based systems. In this paper, a new algorithm for classification using gene expression programming is proposed to accomplish this task, which was compared with several classical state-of-the-art rule-based classifiers. The proposed classifier uses a Michigan approach; the evolutionary process with elitism is guided by a token competition that improves the exploration of fitness surface. Individuals that cover instances, covered previously by others individuals, are penalized. The fitness function is constructed by the multiplying three factors: sensibility, specificity and simplicity. The classifier was constructed as a decision list, sorted by the positive predictive value. The most numerous class was used as the default class. Until now, only numerical attributes are allowed and a mono objective algorithm that combines the three fitness factors is implemented. Experiments with twenty benchmark data sets have shown that our approach is significantly better in validation accuracy than some genetic rule-based state-of-the-art algorithms (i.e., SLAVE, HIDER, Tan, Falco, Bojarczuk and CORE) and not significantly worse than other better algorithms (i.e., GASSIST, LOGIT-BOOST and UCS).", } @Article{Alain18, author = "Alain Guerrero-Enamorado and Carlos Morell and Sebastian Ventura", title = "A gene expression programming algorithm for discovering classification rules in the multi-objective space", journal = "International Journal of Computational Intelligence Systems", volume = "11", number = "1", pages = "540--559", year = "2018", keywords = "genetic algorithms, genetic programming, Gene expression programming (GEP), Reference Point Based Multi-objective Evolutionary Algorithm (R-NSGA-II), Multi-objective Evolutionary Algorithm (MOEA), Multi-objective classification, Classification", publisher = "Atlantis Press", ISSN = "1875-6883", URL = "https://www.atlantis-press.com/journals/ijcis/25891989", DOI = "doi:10.2991/ijcis.11.1.40", size = "20 pages", abstract = "Multi-objective evolutionary algorithms have been criticized when they are applied to classification rule mining, and, more specifically, in the optimization of more than two objectives due to their computational complexity. It is known that a multi-objective space is much richer to be explored than a single-objective space. In consequence, there are only few multi-objective algorithms for classification and their empirical assessed is quite limited. On the other hand, gene expression programming has emerged as an alternative to carry out the evolutionary process at genotypic level in a really efficient way. This paper introduces a new multi-objective algorithm for discovering classification rules, AR-NSGEP (Adaptive Reference point based Non-dominated Sorting with Gene Expression Programming). It is a multi-objective evolution of a previous single-objective algorithm. In AR-NSGEP, the multi-objective search was based on the well known R-NSGA-II algorithm, replacing GA with GEP technology. Four objectives led the rules-discovery process, three of them (sensitivity, specificity and precision) were focused on promoting accuracy and the fourth (simpleness) on the interpretability of rules. AR-NSGEP was evaluated on several benchmark data sets and compared against six rule-based classifiers widely used. The AR-NSGEP, with four-objectives, achieved a significant improvement of the AUC metric with respect to most of the algorithms assessed, while the predictive accuracy and number of rules in the obtained models reached to acceptable results.", } @InProceedings{Guidetti:2023:DSAA, author = "Veronica Guidetti and Giovanni Dolci and Erica Franceschini and Erica Bacca and Giulia Jole Burastero and Davide Ferrari and Valentina Serra and Fabrizio {Di Benedetto} and Cristina Mussini and Federica Mandreoli", booktitle = "2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)", title = "Death After Liver Transplantation: Mining Interpretable Risk Factors for Survival Prediction", year = "2023", abstract = "This study introduces a novel approach to mine risk factors for short-term death after liver transplantation (LT). The method outputs intelligible survival models by combining Cox's regression with a genetic programming technique known as multi-objective symbolic regression (MOSR). We consider 485 Electronic Health Records (EHRs) of patients who underwent LT, containing information on hospitalization and preoperative conditions, with a focus on infections and colonizations by multi-resistant Gram-negative bacteria. We evaluate MOSR outcomes against several performance metrics and demonstrate that they are well-calibrated, predictive, safe, and parsimonious. Finally, we select the most promising post-LT early survival risk score based on information criteria, performance, and out-of-distribution safety. Validating this technique at a multicenter level could improve service pipeline logistics through a trustworthy machine-learning method.", keywords = "genetic algorithms, genetic programming, Measurement, Analytical models, Microorganisms, Pipelines, Liver, Machine learning, Data models, Multi-Objective Symbolic Regression, Cox's model, Liver Transplant, Survival analysis", DOI = "doi:10.1109/DSAA60987.2023.10302622", month = oct, notes = "Also known as \cite{10302622}", } @InProceedings{guigue:1999:SALGA, author = "Alexis Guigue and Sofiane Oussedik and Daniel Delahaye", title = "Sequencing Aircraft Landings by Genetic Algorithms", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "788", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-880.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-880.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Article{Guizzo:2019:sigevolution, author = "Giovani Guizzo", title = "Search-Based Software Engineering Events in 2019", journal = "SIGEVOlution", year = "2019", volume = "12", number = "4", pages = "9--13", month = dec, keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE", URL = "https://evolution.sigevo.org/issues/SIGEVOlution1204.pdf", DOI = "doi:10.1145/3386047.3386050", size = "5 pages", abstract = "The ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) was held in Tallinn, Estonia, from the 28th to the 30th of August 2019. Additionally, the 11th Symposium on Search-Based Software Engineering (SSBSE) was co-located with ESEC/FSE on the 31st of August and 1st of September.", notes = "Brief mention of Genetic Improvement SSBSE 2019 http://ssbse19.mines-albi.fr/", } @Article{Guizzo:ieeeTSE, author = "Giovani Guizzo and Federica Sarro and Jens Krinke and Silvia Regina Vergilio", title = "Sentinel: A Hyper-Heuristic for the Generation of Mutant Reduction Strategies", journal = "IEEE Transactions on Software Engineering", year = "2022", volume = "48", number = "3", pages = "803--818", month = mar, keywords = "genetic algorithms, genetic programming, Grammatical Evolution, SBSE, Mutation Testing, Mutant Reduction, Software Testing, Hyper-Heuristic, Search Based Software Testing, Search Based Software Engineering", DOI = "doi:10.1109/TSE.2020.3002496", size = "16 pages", abstract = "Mutation testing is an effective approach to evaluate and strengthen software test suites, but its adoption is currently limited by the mutants execution computational cost. Several strategies have been proposed to reduce this cost (a.k.a. mutation cost reduction strategies), however none of them has proven to be effective for all scenarios since they often need an ad-hoc manual selection and configuration depending on the software under test (SUT). we propose a novel multi-objective evolutionary hyper-heuristic approach, dubbed Sentinel, to automate the generation of optimal cost reduction strategies for every new SUT. We evaluate Sentinel by carrying out a thorough empirical study involving 40 releases of 10 open-source real-world software systems and both baseline and state-of-the-art strategies as a benchmark for a total of 4800 experiments, which results are evaluated with both quality indicators and statistical significance tests, following the most recent best practice in the literature. The results show that strategies generated by Sentinel outperform the baseline strategies in 95percent of the cases always with large effect sizes, and they also obtain statistically significantly better results than state-of-the-art strategies in 88percent of the cases with large effect sizes for 95percent of them. Also, our study reveals that the mutation strategies generated by Sentinel for a given software version can be used without any loss in quality for subsequently developed versions in 95percent of the cases. These results show that Sentinel is able to automatically generate mutation strategies that reduce mutation testing cost without affecting its testing effectiveness (i.e. mutation score), thus taking off from the testers shoulders the burden of manually selecting and configuring strategies for each SUT.", notes = "Presented at Facebook TAV 2020 FaceTAV2020 https://fbtavsymposium2020.bevylabs.com/events/details/facebook-tav-symposium-division-facebook-testing-and-verification-symposium-presents-facebook-testing-and-verification-symposium-2020/#/ Department of Computer Science, University College London, 4919 London, London United Kingdom of Great Britain and Northern Ireland WC1E 6BT", } @InProceedings{Guizzo:2021:ICSE, author = "Giovani Guizzo and Justyna Petke and Federica Sarro and Mark Harman", title = "Enhancing Genetic Improvement of Software with Regression Test Selection", booktitle = "Proceedings of the International Conference on Software Engineering, ICSE 2021", year = "2021", editor = "Arie {van Deursen} and Tao Xie and Natalia Juristo Oscar Dieste", pages = "1323--1333", address = "Madrid", month = "25-28 " # may, publisher = "IEEE", note = "Winner ACM SIGSOFT Distinguished Artifact Award", keywords = "genetic algorithms, genetic programming, Genetic Improvement, SBSE, AI, Regression Test Selection, RTS, Search Based Software Engineering, Java, Gin, Ekstazi, STARTS, non-functional improvement, optimise runtime", URL = "https://bit.ly/Guizzo-ICSE-2021", urk = "http://www.cs.ucl.ac.uk/staff/F.Sarro/resource/papers/ICSE21Guizzo.pdf", URL = "https://conf.researchr.org/details/icse-2021/icse-2021-papers/6/Enhancing-Genetic-Improvement-of-Software-with-Regression-Test-Selection", URL = "https://discovery.ucl.ac.uk/id/eprint/10120263/1/Petke_correctedICSE21Guizzo.pdf", URL = "https://research.fb.com/wp-content/uploads/2021/04/Enhancing-Genetic-Improvement-of-Software-with-Regression-Test-Selection-.pdf", URL = "https://research.facebook.com/publications/enhancing-genetic-improvement-of-software-with-regression-test-selection/", URL = "https://discovery.ucl.ac.uk/id/eprint/10120263/", DOI = "doi:10.1109/ICSE43902.2021.00120", video_url = "https://www.youtube.com/watch?v=4cxbTB8yF_M", code_url = "https://doi.org/10.5522/04/12890792", size = "11 pages", abstract = "Genetic improvement uses artificial intelligence to automatically improve software with respect to non-functional properties (AI for SE). we propose the use of existing software engineering best practice to enhance Genetic Improvement (SE for AI). We conjecture that existing Regression Test Selection (RTS) techniques (which have been proven to be efficient and effective) can and should be used as a core component of the GI search process for maximising its effectiveness. To assess our idea, we have carried out a thorough empirical study assessing the use of both dynamic and static Regression Test Selection techniques with GI to improve seven real-world software programs. The results of our empirical evaluation show that incorporation of Regression Test Selection within GI significantly speeds up the whole GI process, making it up to 78percent faster on our benchmark set, being still able to produce valid software improvements. Our findings are significant in that they can save hours to days of computational time, and can facilitate the uptake of GI in an industrial setting, by significantly reducing the time for the developer to receive feedback from such an automated technique. Therefore, we recommend the use of Regression Test Selection in future test-based automated software improvement work. Finally, we hope this successful application of SE for AI will encourage other researchers to investigate further applications in this area.", notes = "Also known as \cite{9401972} 'we recommend the use of RTS in future test-based automated software improvement work' Relative Safety measure. Relative Improvement Change (RIC). Perfect improvement v. fast improvement v. diverse improvement. 7 Java (from Apache Commons project) libraries: codec-1.14, compress-1.20, csv-1.71, fileupload-1.4, imaging-1.0, text-1.3, validator-1.6 almost all patches are valid. almost 100percent safety. Replication package https://figshare.com/s/440c2105bad3259bda6f \cite{Guizzo:2021:ICSEcomp} See also \cite{Guizzo:2024:TOSEM} Tue 25 May 2021 11:45 - 12:05 at Blended Sessions Room 2 - 1.2.2. Search-Based SE & Genetic Operations Video 4cxbTB8yF_M includes discussion at ICSE-2021 University College London", } @InProceedings{Guizzo:2021:ICSEcomp, author = "Giovani Guizzo and Justyna Petke and Federica Sarro and Mark Harman", title = "Artifact for Enhancing Genetic Improvement of Software with Regression Test Selection", booktitle = "IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)", year = "2021", editor = "Silvia Abrahao and Daniel Mendez", pages = "220", month = "25-28 " # may, keywords = "genetic algorithms, genetic programming, Genetic Improvement, SBSE", isbn13 = "978-1-6654-1219-3/21/", URL = "https://discovery.ucl.ac.uk/id/eprint/10131839/1/icse.pdf", code_url = "https://doi.org/10.5522/04/12890792", code_url = "https://hub.docker.com/repository/docker/giovaniguizzo/icse21-p66", DOI = "doi:10.1109/ICSE-Companion52605.2021.00099", size = "1 page", notes = "ACM Distinguished Artifact Award at the International Conference of Software Engineering (ICSE) 2021 Dr. Giovani Guizzo, Dr. Justyna Petke, Prof. Federica Sarro and Prof. Mark Harman have been awarded the ACM Distinguished Artifact Award at the International Conference of Software Engineering (ICSE) 2021 for the exceptional quality of the artifact accompanying their paper Enhancing Genetic Improvement of Software with Regression Test Selection, lead by Dr Giovani Guizzo. ICSE is one of the two most prestigious conference in Software Engineering and its artifacts track aims to review, promote, share, and catalog the research artifacts of accepted software engineering papers. High quality artifacts of published research papers increase the likelihood that results can be independently replicated and reproduced by other researchers, so that our confidence in a body of knowledge consequently increases when the results are similar each time. This award testifies the ongoing commitment of the SOLAR team, led by Prof. Sarro, to embrace and promote open science by publicly sharing their publications, data, models and tools. Further reading: https://conf.researchr.org/track/icse-2021/icse-2021-awards#Award-Recipients-at-ICSE-2021", notes = "Artifact Evaluation for \cite{Guizzo:2021:ICSE} Dr. Giovani Guizzo, via e-mail at g.guizzo@ucl.ac.uk. Department of Computer Science, University College London (UCL), London, United Kingdom", } @InProceedings{Guizzo:2021:SSBSE, author = "Giovani Guizzo and Aymeric Blot and James Callan and Justyna Petke and Federica Sarro", title = "Refining Fitness Functions for Search-Based Automated Program Repair: A Case Study with {ARJA} and {ARJA-e}", booktitle = "SSBSE 2021", year = "2021", editor = "Una-May O'Reilly and Xavier Devroey", volume = "12914", series = "LNCS", pages = "159--165", address = "Bari", month = "11-12 " # oct, publisher = "Springer", note = "Winner Challenge Track", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, APR, Search-based automated program repair, empirical study, Software engineering", isbn13 = "978-3-030-88105-4", xxx = "https://www.dropbox.com/s/rrkogd2bqmzuslb/SSBSE2021.pdf", URL = "https://discovery.ucl.ac.uk/id/eprint/10131848/", DOI = "doi:10.1007/978-3-030-88106-1_12", video_url = "https://www.youtube.com/watch?v=lcpYTv1TaE8", code_url = "https://figshare.com/s/35ea3fd819e737ed806b", size = "6 pages", abstract = "Automated Program Repair (APR) strives to automatically fix faulty software without human-intervention. Search-based APR iteratively generates possible patches for a given buggy software, guided by the execution of the patched program on a given test suite (i.e., a set of test cases). Search-based approaches have generally only used Boolean test case results (i.e., pass or fail), but recently more fined-grained fitness evaluations have been investigated with promising yet unsettled results. Using the most recent extension of the very popular Defects4J bug dataset, we conduct an empirical study using ARJA and ARJA-e, two state-of-the-art search-based APR systems using a Boolean and a non-Boolean fitness function, respectively. We aim to both extend previous results using new bugs from Defects4J v2.0 and to settle whether refining the fitness function helps fixing bugs present in large software. In our experiments using 151 non-deprecated and not previously evaluated bugs from Defects4J v2.0, ARJA was able to find patches for 6.62percent (10/151) of bugs, whereas ARJA-e found patches for 7.24percent (12/151) of bugs. We thus observe only small advantage to using the refined fitness function. This contrasts with the previous work using Defects4J v1.0.1 where ARJA was able to find adequate patches for 24.2percent (59/244) of the bugs and ARJA-e for 43.4percent (106/244). These results may indicate a potential overfitting of the tools towards the previous version of the Defects4J dataset.", notes = "https://conf.researchr.org/track/ssbse-2021/ssbse-2021-rene---replications-and-negative-results#event-overview", } @Article{Guizzo:2024:TOSEM, author = "Giovani Guizzo and David Williams and Mark Harman and Justyna Petke and Federica Sarro", title = "Speeding up Genetic Improvement via Regression Test Selection", journal = "ACM Transactions on Software Engineering and Methodology", year = "2024", volume = "33", number = "8", articleno = "196", month = nov, keywords = "genetic algorithms, genetic programming, Genetic Improvement, SBSE, RTS, Regression Test Selection, Search-Based Software Engineering", publisher = "Association for Computing Machinery", ISSN = "1049-331X", URL = "https://discovery.ucl.ac.uk/id/eprint/10189267", DOI = "doi:10.1145/3680466", abstract = "Genetic Improvement (GI) uses search-based optimisation algorithms to automatically improve software with respect to both functional and non-functional properties. Our previous work showed that Regression Test Selection (RTS) can help speed up the use of GI and enhance the overall results while not affecting the software system validity. This article expands upon our investigation by answering further questions about safety and applying a GI algorithm based on Local Search (LS) in addition to the previously explored Genetic Programming (GP) approach. Further, we extend the number of subjects to 12 by analysing five larger real-world open-source programs. We empirically compare two state-of-the-art RTS techniques combined with GP and LS for these 12 programs. The results show that both RTS techniques are safe to use and can reduce the cost of GI by up to 80 percent and by 31 percent on average across programs. We also observe that both search-based algorithms impact the effectiveness gains of GI differently, and that various RTS strategies achieve differing gains in terms of efficiency. These results serve as further evidence that RTS must be used as a core component of the GI search process to maximise its effectiveness and efficiency.", notes = "See also \cite{Guizzo:2021:ICSE}", } @InProceedings{Gulic:2013:MIPRO, author = "Matija Gulic and Domagoj Jakobovic", booktitle = "36th International Convention on Information Communication Technology Electronics Microelectronics (MIPRO 2013)", title = "Evolution of vehicle routing problem heuristics with genetic programming", year = "2013", month = "20-24 " # may, pages = "988--992", keywords = "genetic algorithms, genetic programming, vehicle routing problem with time windows, heuristic scheduling", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6596400", size = "5 pages", abstract = "Increasingly complex variants of the vehicle routing problem with time windows (VRPTW) are coming into focus, alleviated with advances in the computing power. VRPTW is a combination of the classical travelling salesman and bin packing problems, with many real world applications in various fields - from physical resource manipulation planning to virtual resource management in the ever more popular cloud computing domain. The basis for many VRPTW approaches is a heuristic which builds a candidate solution that is subsequently improved by a search or optimisation procedure. The choice of the appropriate heuristic may have a great impact on the resulting quality of the obtained schedules. In this paper we use genetic programming to evolve a suitable heuristic to build initial solutions for different objectives and classes of VRPTW instances. The results show great potential, since this method is applicable to different problem classes and user-defined performance objectives.", notes = "Also known as \cite{6596400}", } @PhdThesis{Gulic:thesis, author = "Matija Gulic", title = "Parallelization of vehicle routing algorithms by using database with domain-specific embedded functions", title2 = "Paralelizacija algoritama za rjesavanje problema usmjeravanja vozila koristenjem baza podataka s ugradenim domenski usmjerenim funkcijama", school = "Department of Applied Computing, University of Zagreb", year = "2017", address = "Croatia", keywords = "genetic algorithms, genetic programming, Process Computing", URL = "https://urn.nsk.hr/urn:nbn:hr:168:300689", size = "110 str.", abstract = "Increasingly complex variants of the vehicle routing problem with time windows (VRPTW) are coming into focus, alleviated with advances in the computing power. VRPTW is a combination of the classical travelling salesman and bin packing problems, with many real world applications in various fields. From physical resource manipulation planning to virtual resource management in the ever more popular cloud computing domain. The basis for many VRPTW approaches is a heuristic which builds a candidate solution that is subsequently improved by a search or optimization procedure. The choice of the appropriate heuristic may have a great impact on the quality of the obtained results. In this work genetic programming is used to evolve a suitable heuristic to build initial solutions for different objectives and classes of VRPTW instances. Additionally 2-phase parallel algorithm has been proposed to improve initial results obtained by genetic programming. Proposed solution is based on the divide and conquer paradigm, decomposing problem instances into smaller, mutually independent sub-problems which can be solved using traditional algorithms and integrated into a global solution of reasonably good quality. The results show great potential, since this method is applicable to different problem classes and user-defined performance objectives. It has been noticed that sometimes results for vehicle routing problem could not be used in real world applications, due to dynamic behaviour of transport systems (incidents or traffic congestion). Improving traffic control has been studied in this work. Solving traffic congestions represents a high priority issue in many big cities. Traditional traffic control systems are mainly based on pre-programmed, reactive and local techniques. This work presents an autonomic system that uses automated planning techniques instead. These techniques are easily configurable and modified, and can reason about the future implications of actions that change the default traffic lights behaviour. The proposed implemented system includes some autonomic properties, since it monitors the current traffic state, detects whether the system is degrading its performance, sets up new sets of goals to be achieved by the planner, triggers the planner that generates plans with control actions, and executes the selected courses of actions. The obtained results in several artificial and real world data-based simulation scenarios show that the proposed system can efficiently solve traffic congestion.", abstract = "Povecanjem brige o okolisu i teznjom za smanjenjem troskova transporta problem usmjeravanja vozila (VRP) postaje sve vaznija stavka u razvijenim drustvima. Navedeni problem je kombinacija nekoliko klasicnih optimizacijskih problema (problem trgovackog putnika, problem pakiranja). U radu je istrazeno nekoliko inovativnih metoda koje bi se mogle primijeniti na sirokom spektru problema iz stvarnog svijeta (prikupljanje otpada, dostava ...). Osnovne poteskoce na koje se nailazi su vrijeme potrebno za pronalazenje prihvatljivih rjesenja i iznimno velik prostor pretrazivanja rjesenja. Kako bi se ostvarilo bolje rezultate od postojecih potrebno je bolje usmjeravanje prilikom pretrazivanja kako se ne bi trosilo vrijeme na istrazivanje nekvalitetnih rjesenja. Koristeci geneticko programiranje i pohlepne funkcije moguce je brzo stvoriti pocetno rjesenje cjelobrojnog problema usmjeravanje vozila s ciljem posluzivanja odredenih lokacija odredenim skupom vozila, te brzo poboljsanje tako dobivenih pocetnih rjesenja. Naknadno poboljsanje pocetnih rjesenja moguce je opisanim paralelnim algoritmima za usmjeravanje vozila. Nakon sto su dobiveni rezultati za problem usmjeravanja vozila, uoceno je da te iste rezultate ponekad nije moguce primijeniti u stvarnom svijetu. Novonastali problem rijesen je stvaranjem jedinstvenog inteligentnog autonomnog prometnog sustava koji ima mogucnosti pratiti stanje prometa, otkriti moguce probleme, promijeniti stanje prometa koristenjem automatiziranog planiranja u cilju ostvarivanja bolje protocnosti prometa. Koristenjem predlozenog sustava pokazano je efikasnije upravljanje prometnim sustavima.", notes = "Language croatian Universal decimal classification (UDC) 62 URN:NBN urn:nbn:hr:168:300689 Mentor Damir Kalpic", } @InProceedings{Gulisano:2024:evoapplications, author = "Vincenzo Gulisano and Eric Medvet", title = "Evolutionary Computation Meets Stream Processing", booktitle = "27th International Conference, EvoApplications 2024", year = "2024", editor = "Stephen Smith and Joao Correia and Christian Cintrano", series = "LNCS", volume = "14634", publisher = "Springer", address = "Aberystwyth", month = "3-5 " # apr, pages = "377--393", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Parallellization, Design of EAs, Distributed computing, symbolic regressio", isbn13 = "978-3-031-56851-0", URL = "https://rdcu.be/dDZXz", DOI = "doi:10.1007/978-3-031-56852-7_24", abstract = "Evolutionary computation (EC) has a great potential of exploiting parallelisation, a feature often under emphasised when describing evolutionary algorithms (EAs). we show that the paradigm of stream processing (SP) can be used to express EAs in a way that allows the immediate exploitation of parallel and distributed computing, not at the expense of the agnosticity of the EAs with respect to the application domain. We introduce the first formal framework for EC based on SP and describe several building blocks tailored to EC. Then, we experimentally validate our framework and show that (a) it can be used to express common EAs, (b) it scales when deployed on real-world stream processing engines (SPEs), and (c) it facilitates the design of EA modifications which would require a larger effort with traditional implementation.", notes = "http://www.evostar.org/2024/ EvoApplications2024 held in conjunction with EuroGP'2024, EvoCOP2024 and EvoMusArt2024", } @InProceedings{Gulowaty:2022:IJCNN, author = "Bogdan Gulowaty and Michał Woźniak", booktitle = "2022 International Joint Conference on Neural Networks (IJCNN)", title = "Search-based framework for transparent non-overlapping ensemble models", year = "2022", abstract = "Due to their generalizing ability, classifier ensembles are considered very powerful predictive models. A typical ensemble consists of a static or dynamic pool of classifiers and a combination method, which translates predictions of many models into one. The combination step is often complex and renders the inner behavior of the whole ensemble incomprehensible to a typical user. In this work, in the light of recent interest in Explainable AI (XAI) research, we are proposing a novel approach to building an interpretable ensemble model. It is based on decision space splitting into non-overlapping regions. Every area has an assigned interpretable classifier and its boundaries are selected using the genetic programming approach. We experimentally evaluate the proposed method and compare it to Decision Tree and Random Forest. The results show that the proposed approach is competitive with the state-of-the-art techniques and prone to further expansion.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IJCNN55064.2022.9892360", ISSN = "2161-4407", month = jul, notes = "Also known as \cite{9892360}", } @InProceedings{ppdp10-synthesis, author = "Sumit Gulwani", title = "Dimensions in Program Synthesis", booktitle = "Proceedings of the 12th international ACM SIGPLAN symposium on Principles and practice of declarative programming", year = "2010", pages = "13--24", address = "Hagenberg, Austria", month = oct, publisher = "ACM", note = "Invited talk", keywords = "genetic algorithms, genetic programming, Deductive Synthesis, Inductive Synthesis, Programming by Examples, Programming by Demonstration, SAT Solving, SMT Solving, Machine Learning, Probabilistic Inference, Belief Propagation", acmid = "1836091", isbn13 = "978-1-4503-0132-9", URL = "http://research.microsoft.com/en-us/um/people/sumitg/pubs/ppdp10-synthesis.pdf", DOI = "doi:10.1145/1836089.1836091", size = "12 pages", abstract = "Program Synthesis, which is the task of discovering programs that realise user intent, can be useful in several scenarios: enabling people with no programming background to develop utility programs, helping regular programmers automatically discover tricky/mundane details, program understanding, discovery of new algorithms, and even teaching. This paper describes three key dimensions in program synthesis: expression of user intent, space of programs over which to search, and the search technique. These concepts are illustrated by brief description of various program synthesis projects that target synthesis of a wide variety of programs such as standard undergraduate textbook algorithms (e.g., sorting, dynamic programming), program inverses (e.g., decoders, deserializers), bitvector manipulation routines, deobfuscated programs, graph algorithms, text-manipulating routines, mutual exclusion algorithms, etc.", notes = "Formal Methods in Computer-Aided Design (FMCAD 2010) see also tutorial slides http://research.microsoft.com/en-us/um/people/sumitg/pubs/synthesis.html Programming assistance. bitvectors: Warren, Hacker's Delight, Addison Wesley, 2002. 'we can restrict sampling to the basis inputs' [Knuth]. Version space algebra, Mitchell, and \cite{Lau:2013:MLj}. Also known as \cite{5770924}", } @Article{Gulwani:2012:CACM, author = "Sumit Gulwani and William R. Harris and Rishabh Singh", title = "Spreadsheet Data Manipulation Using Examples", journal = "Communications of the ACM", year = "2012", volume = "55", number = "8", pages = "97--105", month = aug, keywords = "genetic algorithms, genetic programming, flash fill, Microsoft Excel, spreadsheet", publisher = "ACM", acmid = "2240260", address = "New York, NY, USA", ISSN = "0001-0782", URL = "http://research.microsoft.com/en-us/um/people/sumitg/pubs/cacm12-synthesis.pdf", URL = "http://doi.acm.org/10.1145/2240236.2240260", DOI = "doi:10.1145/2240236.2240260", size = "9 pages", abstract = "Millions of computer end users need to perform tasks over large spreadsheet data, yet lack the programming knowledge to do such tasks automatically. We present a programming by example methodology that allows end users to automate such repetitive tasks. Our methodology involves designing a domain-specific language and developing a synthesis algorithm that can learn programs in that language from user-provided examples. We present instantiations of this methodology for particular domains of tasks: (a) syntactic transformations of strings using restricted forms of regular expressions, conditionals, and loops, (b) semantic transformations of strings involving lookup in relational tables, and (c) layout transformations on spreadsheet tables. We have implemented this technology as an add-in for the Microsoft Excel Spreadsheet system and have evaluated it successfully over several benchmarks picked from various Excel help forums.", notes = "http://research.microsoft.com/en-us/news/features/flashfill-020613.aspx Also known as \cite{Gulwani:2012:SDM:2240236.2240260}", } @InProceedings{Gulwani:2012:synasc, author = "Sumit Gulwani", title = "Synthesis From Examples: Interaction Models and Algorithms", booktitle = "14th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing", year = "2012", editor = "Andrei Voronkov", pages = "8--14", month = sep # " 26-29", organisation = "West University of Timisoara, Department of Computer Science, Bd. V. Parvan 4, 300223 Timisoara, Romania", publisher = "IEEE", note = "Invited Talk Paper", keywords = "Program Synthesis, Inductive Synthesis, End User Programming, Intelligent Tutoring Systems, Domain Specific Languages, Programming By Example", URL = "http://research.microsoft.com/en-us/um/people/sumitg/pubs/synasc12.pdf", DOI = "doi:10.1109/SYNASC.2012.69", size = "7 pages", abstract = "Examples are often a natural way to specify various computational artifacts such as programs, queries, and sequences. Synthesising such artifacts from example based specifications has various applications in the domains of enduser programming and intelligent tutoring systems. Synthesis from examples involves addressing two key technical challenges: (i) design of a user interaction model to deal with the inherent ambiguity in the example based specification. (ii) design of an efficient search algorithm - these algorithms have been based on paradigms from various communities including use of SAT/SMT solvers (formal methods community), version space algebras (machine learning community), and A*-style goal-directed heuristics (AI community). This paper describes some effective user interaction models and algorithmic methodologies for synthesis from examples while discussing synthesisers for a variety of artifacts ranging from tricky bit vector algorithms, spreadsheet macros for automating repetitive data manipulation tasks, ruler/compass based geometry constructions, algebraic identities, and predictive intellisense for repetitive drawings and mathematical terms.", notes = "Microsoft Research, Redmond, WA, USA http://synasc12.info.uvt.ro/ http://synasc12.info.uvt.ro/invited-speakers/sumit-gulwani Also known as \cite{6481005}", } @TechReport{education13, author = "Sumit Gulwani", title = "Example Based Learning in Computer-Aided {STEM} Education", institution = "Microsoft Research", year = "2013", number = "MSR-TR-2013-50", month = "28 " # oct, URL = "http://research.microsoft.com/en-us/um/people/sumitg/pubs/education13.pdf", size = "8 pages", abstract = "Human learning is often structured around examples. Interestingly, example-based reasoning has also been heavily used in computer aided programming. In this article, we describe how techniques inspired from example-based program analysis and synthesis can be used for various tasks in Education including problem generation, solution generation, and feedback generation. We illustrate this using recent research results that have been applied to a variety of STEM subject domains including logic, automata theory, programming, arithmetic, algebra, and geometry. We classify these subject domains into procedural and conceptual content and highlight some general technical principles as per this classification. These results advance the state-of-the-art in intelligent tutoring, and can play a significant role in enabling personalised and interactive education in both standard classrooms and MOOCs.", notes = "See \cite{Gulwani:2014:CACM}", } @Article{Gulwani:2014:CACM, author = "Sumit Gulwani", title = "Example-based Learning in Computer-aided STEM Education", journal = "Communications of the ACM", issue_date = "August 2014", volume = "57", number = "8", month = aug, year = "2014", pages = "70--80", acmid = "2634273", publisher = "ACM", address = "New York, NY, USA", note = "Example-based reasoning, teaching", ISSN = "0001-0782", URL = "http://doi.acm.org/10.1145/2634273", DOI = "doi:10.1145/2634273", size = "11 pages", abstract = "..explores how such example-based reasoning techniques developed in the programming-languages community can also help automate certain repetitive and structured tasks in education, including problem generation, solution generation, and feedback generation...", notes = "Replaces \cite{education13} ", } @InProceedings{Gunaratne:2017:GECCO, author = "Chathika Gunaratne and Ivan Garibay", title = "Alternate Social Theory Discovery Using Genetic Programming: Towards Better Understanding the Artificial Anasazi", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4920-8", address = "Berlin, Germany", pages = "115--122", size = "8 pages", URL = "http://doi.acm.org/10.1145/3071178.3071332", DOI = "doi:10.1145/3071178.3071332", acmid = "3071332", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, agent-based modeling, artificial anasazi, calibration, theory discovery", month = "15-19 " # jul, abstract = "A pressing issue with agent-based model (ABM) replicability is the ambiguity behind micro-behaviour rules of the agents. In practice, modellers choose between competing theories, each describing separate candidate solutions. Pattern-oriented modelling (POM) and stylized facts matching recommend testing theories against patterns extracted from real-world data. Yet, manually, POM is tedious and prone to human error. In this study, we present a genetic programming strategy to evolve debatable assumptions on agent micro-behaviours. After proper modularization of the candidate micro-behaviors, genetic programming can discover candidate micro-behaviors which reproduce patterns found in real-world data. We illustrate this strategy by evolving the decision tree representing the farm-seeking strategy of agents in the Artificial Anasazi ABM. Through evolutionary theory discovery, we obtain multiple candidate decision trees for farm-seeking which fit the archaeological data better than the calibrated original model in the literature. We emphasize the necessity to explore a range of components that influence the agents' decision making process and demonstrate that this is achievable through an evolutionary process if the rules are modularized as required. The end result is a set of plausible candidate solutions that closely fit the real-world data, which can then be nominated by domain experts.", notes = "Also known as \cite{Gunaratne:2017:AST:3071178.3071332} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @PhdThesis{gunaratne:thesis, author = "Chathika S. Gunaratne", title = "Evolutionary Model Discovery: Automating Causal Inference for Generative Models of Human Social Behavior", school = "College of Engineering and Computer Science, University of Central Florida", year = "2019", address = "USA", month = "Fall", keywords = "genetic algorithms, genetic programming, Agent-based model", URL = "https://stars.library.ucf.edu/etd/6871/", URL = "https://stars.library.ucf.edu/cgi/viewcontent.cgi?article=7871&context=etd", size = "158 pages", abstract = "The desire to understand the causes of complex societal phenomena is fundamental to the social sciences. Society, at a macro-scale has many measurable characteristics in the form of statistical distributions and aggregate measures; data which is increasingly abundant with the proliferation of online social media, mobile devices, and the internet of things. However, the decision-making processes and limits of the individuals who interact to generate these statistical patterns are often difficult to unravel. Furthermore, multiple causal factors often interact to determine the outcome of a particular behavior. Quantifying the importance of these causal factors and their interactions, which make up a particular decision-making process, towards a societal outcome of interest helps extract explanations that provide a deeper understanding of social behavior. Holistic, generative modeling techniques, in particular agent-based modeling, are able to grow artificial societies that replicate emergent patterns seen in the real world. Driving the autonomous agents of these models are rules, generalized hypotheses of human behavior, which upon validation against real-world data, help assemble theories of human behavior. Yet often, multiple hypothetical causal factors can be suggested for the construction of these rules. With traditional agent-based modeling, it is often up to the modeler's discretion to decide which combination of factors best represent the rule at hand. Yet, due to the aforementioned lack of insight, the modeled agent rule is often one out of a vast space of possible rules. I introduce Evolutionary Model Discovery, a novel framework for automated causal inference, which treats such artificial societies as sandboxes for rule discovery and causal factor importance evaluation. Evolutionary Model Discovery consists of two major phases. Firstly, a rule of interest of a given agent-based model is genetically programmed with combinations of hypothesized factors, attempting to find rules which enable the agent-based model to more closely mimic real-world phenomena. Secondly, the data produced through genetic programming, regarding the correspondence of factor presence in the rule to fitness, is used to train a random forest regressor for importance evaluation. Besides its scientific contributions, this work has also led to the contribution of two Python open-source software libraries for high performance computing with NetLogo, Evolutionary Model Discovery and NL4Py. The results of applying Evolutionary Model Discovery for the causal inference of three very different cases of human social behavior are discussed, revisiting the rules underlying two widely studied models in the literature, the Artificial Anasazi and Schelling Segregation, and an ensemble model of diffusion of information and information overload. First, previously unconsidered factors driving the socio-agricultural behavior of an ancient Pueblo society are discovered, assisting in the construction of a more robust and accurate version of the Artificial Anasazi model. Second, factors that contribute to the coexistence of mixed patterns of segregation and integration are discovered on a recent extension of Schellings Segregation model. Finally, causal factors important to the prioritization of social media notifications under loss of attention due to information overload are discovered on an ensemble of a model of Extended Working Memory and the Multi-Action Cascade Model of conversation.", notes = "Supervisor: Ivan Garibay", } @InProceedings{gunaratne:2022:GECCOcomp, author = "Chathika Gunaratne and Robert Patton", title = "Genetic Programming for Understanding Cognitive Biases that Generate Polarization in Social Networks", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion", year = "2022", editor = "Heike Trautmann and Carola Doerr and Alberto Moraglio and Thomas Bartz-Beielstein and Bogdan Filipic and Marcus Gallagher and Yew-Soon Ong and Abhishek Gupta and Anna V Kononova and Hao Wang and Michael Emmerich and Peter A. N. Bosman and Daniela Zaharie and Fabio Caraffini and Johann Dreo and Anne Auger and Konstantin Dietric and Paul Dufosse and Tobias Glasmachers and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Tea Tusar and Dimo Brockhoff and Tome Eftimov and Pascal Kerschke and Boris Naujoks and Mike Preuss and Vanessa Volz and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Mark Coletti and Catherine (Katie) Schuman and Eric ``Siggy'' Scott and Robert Patton and Paul Wiegand and Jeffrey K. Bassett and Chathika Gunaratne and Tinkle Chugh and Richard Allmendinger and Jussi Hakanen and Daniel Tauritz and John Woodward and Manuel Lopez-Ibanez and John McCall and Jaume Bacardit and Alexander Brownlee and Stefano Cagnoni and Giovanni Iacca and David Walker and Jamal Toutouh and UnaMay O'Reilly and Penousal Machado and Joao Correia and Sergio Nesmachnow and Josu Ceberio and Rafael Villanueva and Ignacio Hidalgo and Francisco {Fernandez de Vega} and Giuseppe Paolo and Alex Coninx and Antoine Cully and Adam Gaier and Stefan Wagner and Michael Affenzeller and Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and William B. Langdon and Justyna Petke and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Paetzel and Alexander Wagner and Michael Heider and Nadarajen Veerapen and Katherine Malan and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Mohammad Nabi Omidvar and Yuan Sun and Ernesto Tarantino and De Falco Ivanoe and Antonio {Della Cioppa} and Scafuri Umberto and John Rieffel and Jean-Baptiste Mouret and Stephane Doncieux and Stefanos Nikolaidis and Julian Togelius and Matthew C. Fontaine and Serban Georgescu and Francisco Chicano and Darrell Whitley and Oleksandr Kyriienko and Denny Dahl and Ofer Shir and Lee Spector and Alma Rahat and Richard Everson and Jonathan Fieldsend and Handing Wang and Yaochu Jin and Erik Hemberg and Marwa A. Elsayed and Michael Kommenda and William {La Cava} and Gabriel Kronberger and Steven Gustafson", pages = "546--549", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, social network, polarization, agent-based, cognitive bias", isbn13 = "978-1-4503-9268-6/22/07", DOI = "doi:10.1145/3520304.3529069", abstract = "Recent studies have applied agent-based models to infer human-interpretable explanations of individual-scale behaviors that generate macro-scale patterns in complex social systems. Genetic programming has proven to be an ideal explainable AI tool for this purpose, where primitives may be expressed in an interpretable fashion and assembled into agent rules. Evolutionary model discovery (EMD) is a tool that combines genetic programming and random forest feature importance analysis, to infer individual-scale, human-interpretable explanations from agent-based models. We deploy EMD to investigate the cognitive biases behind the emergence of ideological polarization within a population. An agent-based model is developed to simulate a social network, where agents are able to create or sever links with one another, and update an internal ideological stance based on their neighbors' stances. Agent rules govern these actions and constitute of cognitive biases. A set of 7 cognitive biases are included as genetic program primitives in the search for rules that generate hyper-polarization among the population of agents. We find that heterogeneity in cognitive biases is more likely to generate polarized social networks. Highly polarized social networks are likely to emerge when individuals with confirmation bias are exposed to those with either attentional bias, egocentric bias, or cognitive dissonance.", notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @InProceedings{Gunnersen:2012:CEC, title = "Towards Objective Data Selection in Bankruptcy Prediction", author = "Sverre Gunnersen and Kate Smith-Miles and Vincent Lee", pages = "9--16", booktitle = "Proceedings of the 2012 IEEE Congress on Evolutionary Computation", year = "2012", editor = "Xiaodong Li", month = "10-15 " # jun, DOI = "doi:10.1109/CEC.2012.6256129", address = "Brisbane, Australia", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Conflict of Interest Papers, Computational Intelligence in Finance, Economics and Management Sciences (IEEE-CEC), Large-scale problems.", abstract = "This paper proposes and tests a methodology for selecting features and test cases with the goal of improving medium term bankruptcy prediction accuracy in large uncontrolled datasets of financial records. We propose a Genetic Programming and Neural Network based objective feature selection methodology to identify key inputs, and then use those inputs to combine multi-level Self-Organising Maps with Spectral Clustering to build clusters. Performing objective feature selection within each of those clusters, this research was able to increase out-of-sample classification accuracy from 71.3percent and 69.8percent on the Genetic Programming and Neural Network models respectively to 80.0percent and 77.3percent.", notes = "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the EPS and the IET.", } @Article{GUO:2024:jmsy, author = "Haoxin Guo and Jianhua Liu and Yue Wang and Cunbo Zhuang", title = "An improved genetic programming hyper-heuristic for the dynamic flexible job shop scheduling problem with reconfigurable manufacturing cells", journal = "Journal of Manufacturing Systems", volume = "74", pages = "252--263", year = "2024", ISSN = "0278-6125", DOI = "doi:10.1016/j.jmsy.2024.03.009", URL = "https://www.sciencedirect.com/science/article/pii/S027861252400058X", keywords = "genetic algorithms, genetic programming, Dynamic flexible job shop scheduling, Reconfigurable manufacturing cell, Hyper-heuristics", abstract = "The Dynamic Flexible Job Shop Scheduling Problem (DFJSP) is a classical and important research direction. However, current research usually considers the case where each manufacturing cell has a fixed and constant process capability. There are often situations in non-machining shops where each manufacturing cell is capable of capability reconfiguration and performs many different types of process operations, such as assembly shops and test shops. The variability of the manufacturing cell's capabilities increases the complexity of the problem compared to traditional FJSP. In this paper, we study the dynamic flexible job shop scheduling problem considering reconfigurable manufacturing cells (DFJSP-RMCs) with completion time, delay time and reconfiguration time as optimization objectives, and propose an improved Genetic Programming Hyper-Heuristic (GPHH) method to solve it. The method weighs the solution efficiency and the effectiveness of the results. In addition, an individual simplification policy (ISP) is proposed to reduce the evaluation time of the heuristic. Finally, random instances were generated under three production conditions and 10 independent runs were performed for each. Experiments show that the proposed method significantly reduces the time consumption while ensuring the quality of the results", } @Article{Guo:2025:eswa, author = "Haoxin Guo and Kunping Li and Jianhua Liu and Cunbo Zhuang and Fengque Pei", title = "Dynamic integrated process planning and scheduling under multi-resource constraints in workshops with reconfigurable manufacturing cells: a novel hyper-heuristic approach", journal = "Expert Systems with Applications", year = "2025", volume = "289", pages = "128337", keywords = "genetic algorithms, genetic programming, Integrated Process Planning and Scheduling, Multi-Resource Constraints, Reconfigurable Manufacturing Cells, Hyper-heuristic, Bloat Control", ISSN = "0957-4174", URL = "https://www.sciencedirect.com/science/article/pii/S0957417425019566", DOI = "doi:10.1016/j.eswa.2025.128337", abstract = "This study addresses the challenges of hybrid production lines, reconfigurable characteristics, frequent disturbances, and multi-resource constraints in complex aerospace product assembly and testing workshops. We propose a Dynamic Integrated Process Planning and Scheduling under Multi-Resource Constraints in Workshops with Reconfigurable Manufacturing Cells (MRC-DIPPS-RMC). By establishing an integrated mathematical model that combines process planning, cell reconfiguration, task scheduling, and resource allocation, we designed a Genetic Programming Hyper-Heuristic with Bloat Control Mechanism (GPHH-BC) based on multi-heuristic co-evolution. The algorithm employs population segmentation to co-evolve four types of heuristic rules, effectively solving five critical subproblems in dynamic environments while successfully suppressing efficiency degradation caused by rule bloating. Experimental results demonstrate that the proposed method demonstrates a 52.67 percent improvement in computational efficiency compared to conventional baseline approaches while ensuring solution feasibility; when compared to state-of-the-art algorithms, it achieves a further 7.40 percent improvement in computational efficiency", } @Article{journals/tsmc/GuoJN05, title = "Feature generation using genetic programming with application to fault classification", author = "Hong Guo and Lindsay B. Jack and Asoke K. Nandi", journal = "IEEE Transactions on Systems, Man, and Cybernetics, Part B", year = "2005", number = "1", volume = "35", pages = "89--99", month = feb, bibdate = "2006-01-23", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/tsmc/tsmcb35.html#GuoJN05", keywords = "genetic algorithms, genetic programming", ISSN = "1083-4419", DOI = "doi:10.1109/TSMCB.2004.841426", size = "11 pages", abstract = "One of the major challenges in pattern recognition problems is the feature extraction process which derives new features from existing features, or directly from raw data in order to reduce the cost of computation during the classification process, while improving classifier efficiency. Most current feature extraction techniques transform the original pattern vector into a new vector with increased discrimination capability but lower dimensionality. This is conducted within a predefined feature space, and thus, has limited searching power. Genetic programming (GP) can generate new features from the original dataset without prior knowledge of the probabilistic distribution. A GP-based approach is developed for feature extraction from raw vibration data recorded from a rotating machine with six different conditions. The created features are then used as the inputs to a neural classifier for the identification of six bearing conditions. Experimental results demonstrate the ability of GP to discover automatically the different bearing conditions using features expressed in the form of nonlinear functions. Furthermore, four sets of results-using GP extracted features with artificial neural networks (ANN) and support vector machines (SVM), as well as traditional features with ANN and SVM-have been obtained. This GP-based approach is used for bearing fault classification for the first time and exhibits superior searching power over other techniques. Additionally, it significantly reduces the time for computation compared with genetic algorithm (GA), therefore, makes a more practical realization of the solution.", } @Article{GN:PR:06, title = "Breast cancer diagnosis using genetic programming generated feature", author = "Hong Guo and Asoke K. Nandi", journal = "Pattern Recognition", year = "2006", volume = "39", number = "5", pages = "980--987", month = may, keywords = "genetic algorithms, genetic programming, Feature extraction, Fisher discriminant analysis, Pattern recognition", DOI = "doi:10.1016/j.patcog.2005.10.001", size = "8 pages", abstract = "This paper proposes a novel method for breast cancer diagnosis using the feature generated by genetic programming (GP). We developed a new feature extraction measure (modified Fisher linear discriminant analysis (MFLDA)) to overcome the limitation of Fisher criterion. GP as an evolutionary mechanism provides a training structure to generate features. A modified Fisher criterion is developed to help GP optimise features that allow pattern vectors belonging to different categories to distribute compactly and disjoint regions. First, the MFLDA is experimentally compared with some classical feature extraction methods (principal component analysis, Fisher linear discriminant analysis, alternative Fisher linear discriminant analysis). Second, the feature generated by GP based on the modified Fisher criterion is compared with the features generated by GP using Fisher criterion and an alternative Fisher criterion in terms of the classification performance. The classification is carried out by a simple classifier (minimum distance classifier). Finally, the same feature generated by GP is compared with a original feature set as the inputs to multi-layer perceptrons and support vector machine. Results demonstrate the capability of this method to transform information from high-dimensional feature space into one-dimensional space and automatically discover the relationship among data, to improve classification accuracy.", } @InProceedings{conf/biostec/GuoZN08, title = "Breast Cancer Detection using Genetic Programming", author = "Hong Guo and Qing Zhang and Asoke K. Nandi", bibdate = "2008-04-10", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/biostec/biosignals2008-2.html#GuoZN08", booktitle = "Proceedings of the First International Conference on Biomedical Electronics and Devices, BIOSIGNALS 2008", publisher = "INSTICC - Institute for Systems and Technologies of Information, Control and Communication", year = "2008", editor = "Pedro Encarna{\c c}{\~a}o and Ant{\'o}nio Veloso", pages = "334--341", volume = "2", address = "Funchal, Madeira, Portugal", month = jan # " 28-31", publisher = "SciTePress", organization = "INSTICC", keywords = "genetic algorithms, genetic programming, Feature Extraction, Classification, Breast Cancer Diagnosis", isbn13 = "978-989-8111-18-0", URL = "https://www.scitepress.org/Papers/2008/10592/", URL = "https://www2.lirmm.fr/lirmm/interne/BIBLI/CDROM/MIC/2008/BIOSTEC_2008/BIOSTEC%202008/Biosignals/Volume%202/Short%20Papers/C1_094_Nandi.pdf", DOI = "doi:10.5220/0001059203340341", size = "8 pages", abstract = "Breast cancer diagnosis have been investigated by different machine learning methods. This paper proposes a new method for breast cancer diagnosis using a single feature generated by Genetic Programming(GP). GP as an evolutionary mechanism that provides a training structure to generate features. The presented approach is experimentally compared with some kernel feature extraction methods: The Kernel Principal Component Analysis (KPCA) and Kernel Generalised Discriminant Analysis (KGDA). Results demonstrate the capability of this method to transform information from high dimensional feature space into one dimensional space for breast cancer diagnosis.", notes = "Also known as \cite{biosignals08} Broken Sep 2024 http://www.biosignals.biostec.org/Abstracts/2008/BIOSIGNALS_2008_Abstracts.htm Department of Electrical Engineering and ElectronicsThe University of Liverpool, Brownlow Hill, Liverpool, L69 3GJ, U.K.", } @PhdThesis{Guo:thesis, author = "Hong Guo", title = "Feature generation and dimensionality reduction using genetic programming", school = "Department of Electrical Engineering and Electronics, University of Liverpool", year = "2009", address = "UK", keywords = "genetic algorithms, genetic programming, KPCA, KGDA, ANN, ASK2, ASK4, BPSK, QPSK, FSK2, FSK4, QAM16, V29, V32, QAM64,", URL = "http://library.liv.ac.uk:2082/search~S8?/lTHESIS+20960.JI/lthesis+20960+ji/-3%2C-1%2C0%2CE/frameset&FF=lthesis+20960+guo&1%2C1%2C", URL = "http://ethos.bl.uk/OrderDetails.do?did=1&uin=uk.bl.ethos.511054", URL = "https://livrepository.liverpool.ac.uk/3174551/1/511054.pdf", size = "198 pages", abstract = "Generally speaking, a standard pattern recognition system consists of two components, feature extraction and pattern classification. During the feature extraction process, information relevant to the pattern classification is expected to be extracted from the data and prepared as features to the inputs of the classifier. Mathematically, this process is viewed as a transformation of the original parameter vector into a smaller feature vector with useful information for the classification task ahead. Usually the original parameter vector has fairly large number of dimensions, in the order of hundreds (in some cases, even thousands), for the coverage all the possible mathematical relations to the problem. However, this poses a significant computation demand to the classifier. The feature space associated with the original vector expands with the increase of dimensions. As a result of that, the complexity of the classifier might increase exponentially. This is famously called the curse of dimensionality. Feature extraction is introduced and designed to tackle this problem, by reducing the dimensionality in the data. In addition, it could possibly improve the classification efficiency. In modern pattern recognition systems, the functionality of feature extraction has become very important for its ability to extract discrimination information from the data and filter out unwanted interference. The success of classification is often linked to the quality of the extracted features. In this research work, endeavours have been made to address the feature extraction issue using the strength of evolutionary computation. A genetic programming based feature extraction framework is proposed for the problem. The idea is inspired by the biological evolution process, in which the stronger features survive and weaker ones are washed out. It is believed that for features under-going such evolution, the discrimination ability will have been optimised under a certain criterion. Therefore, the classification benefits from such optimisation of features. Compared to conventional feature extraction methods, such as Principal Components Analysis (PCA), Fisher Linear Discriminant Analysis (FLDA), Kernel Principal Components Analysis (KPCA) and Generalised Discriminant Analysis (GDA), this approach undertakes a genetic search in the feature space. The objective of the search is to get closer to the core of the problem. Rather than over-fitting, the genuine characteristic of each pattern is more likely to be identified. Practically, certain termination criterion arc set as the job-done points, such as a threshold on the fitness value, the maximum number of generations, etc. They are empirical values set to achieve satisfactory results for specific applications. In order to cover different scenarios and thoroughly examine the capability of the proposed method, three systems are designed, including a multi-feature extraction system for multiclass problems, a single-feature extraction system for dual-class problems, and a single feature extraction system for multi-class problems. Real data are used for the evaluation of the performance of the systems developed. A series of experiments are conducted to evaluate and compare the results obtained using the combination of different feature extraction methods and classification methods. The classifiers involved range from a simple Minimal Distance classifier (MDC) to a sophisticated Multi-Layer Perceptron (MLP) neural network. The results demonstrate that the proposed approach is superior to conventional methods for feature extraction for selected applications. The genetic programming system outperforms other systems in terms of classification success. The experimental results are promising. It is believed that this design can be implemented and applied in practical pattern recognition problems as a remarkably cost-effective solution.", notes = "LOCATION Harold Cohen Library THESIS 20960.GUO uk.bl.ethos.511054 Supervisor: Asoke K. Nandi", } @InProceedings{guo:2019:SEMED, author = "Junxia Guo and Yingying Duan and Ying Shang", title = "Multi-gene Genetic Programming Based {Defect-Ranking} Software Modules", booktitle = "Software Engineering and Methodology for Emerging Domains", year = "2019", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-981-15-0310-8_4", DOI = "doi:10.1007/978-981-15-0310-8_4", } @Article{GUO:2023:ijmecsci, author = "Liangteng Guo and Shaoyu Zhao and Yongqiang Guo and Jie Yang and Sritawat Kitipornchai", title = "Bandgaps in functionally graded phononic crystals containing graphene origami-enabled metamaterials", journal = "International Journal of Mechanical Sciences", volume = "240", pages = "107956", year = "2023", ISSN = "0020-7403", DOI = "doi:10.1016/j.ijmecsci.2022.107956", URL = "https://www.sciencedirect.com/science/article/pii/S0020740322008347", keywords = "genetic algorithms, genetic programming, Bandgaps, Phononic crystals, Elastic waves, Graphene origami, Functionally graded distributions, Auxetic metamaterials", abstract = "This paper investigates the dispersion characteristics of elastic waves propagating along the thickness direction in functionally graded laminated phononic crystals (FGLPCs) containing novel auxetic metamaterials enabled by graphene origami that is created with the aid of hydrogenation. Both graphene weight fraction and hydrogen coverage which are the key parameters governing the auxetic property are nonuniformly distributed in unit cells of FGLPCs whose material properties are determined by genetic programming-assisted micromechanical models. The dispersion relations of elastic waves in the structure are obtained based on the state space approach and the method of reverberation-ray matrix. A comprehensive parametric study is conducted to discuss the effects of graphene origami weight fraction and hydrogen coverage on bulk waves in elastic solids made of the metamaterial and elastic waves in FGLPCs. It is found that introducing auxetic metamaterials into FGLPCs can effectively manipulate elastic waves. The graded distribution of weight fraction in FGLPCs can lead to bandgaps for both transverse and longitudinal waves, while a through-thickness graded pattern in hydrogen coverage can trigger broad bandgaps for longitudinal waves only with transverse waves nearly unchanged", } @Article{GUO:2024:ultras, author = "Liangteng Guo and Shaoyu Zhao and Jie Yang and Sritawat Kitipornchai", title = "Graphene-based phononic crystal lenses: Machine learning-assisted analysis and design", journal = "Ultrasonics", volume = "138", pages = "107220", year = "2024", ISSN = "0041-624X", DOI = "doi:10.1016/j.ultras.2023.107220", URL = "https://www.sciencedirect.com/science/article/pii/S0041624X23002962", keywords = "genetic algorithms, genetic programming, Phononic crystal, Gradient index lens, Machine learning, Graphene-based composites, Inverse design", abstract = "The advance of artificial intelligence and graphene-based composites brings new vitality into the conventional design of acoustic lenses which suffers from high computation cost and difficulties in achieving precise desired refractive indices. This paper presents an efficient and accurate design methodology for graphene-based gradient-index phononic crystal (GGPC) lenses by combing theoretical formulations and machine learning methods. The GGPC lenses consist of two-dimensional phononic crystals possessing square unit cells with graphene-based composite inclusions. The plane wave expansion method is exploited to obtain the dispersion relations of elastic waves in the structures and then establish the data sets of the effective refractive indices in structures with different volume fractions of graphene fillers in composite materials and filling fractions of inclusions. Based on the database established by the theoretical formulation, genetic programming, a superior machine learning algorithm, is introduced to generate explicit mathematical expressions to predict the effective refractive indices under different structural information. The design of GGPC lenses is conducted with the assistance of the machine learning prediction model, and it will be illustrated by several typical design examples. The proposed design method offers high efficiency, accuracy as well as the ability to achieve inverse design of GGPC lenses, thus significantly facilitating the development of novel phononic crystal lenses and acoustic energy focusing", } @PhdThesis{LingGuo:thesis, author = "Ling Guo", title = "Soft Computing Techniques for Advanced Epileptic {EEG} Analysis and Classification", school = "Facultade de Informatica, Universidade da Coruna", year = "2011", address = "Spain", month = "26 " # may, keywords = "genetic algorithms, genetic programming, inteligencia artificial", URL = "https://dialnet.unirioja.es/servlet/tesis?codigo=43887", broken = "http://www.fic.udc.es/NewsContent.do?newsId=25045&urlCurrent=ViewPaginatedCategory.do", broken = "http://tesis.com.es/documentos/soft-computing-techniques-for-advanced-epileptic-eeg-analysis-and-classification/", size = "210 pages", abstract = "Epilepsy is an abnormal neurological status that makes people susceptible to brief electrical disturbance in the brain thus producing a change in sensation, awareness, and/or behaviour and is characterised by recurrent seizures. It affects up to 1percent of the population in the world. Two-thirds of the epileptic patients can be treated through medications. Another 8percent may benefit from surgery. But 25percent of people with epilepsy continue to have seizures and no treatment suits them. Electroencephalogram (EEG) is the recording of electrical activity of the brain and it contains much valuable information for understanding epilepsy. In clinic environments, the neurologists have to continuously observe the EEG recordings for better understanding epilepsy, which is time-consuming and tedious. Thus, efforts on developing automatic epileptic seizure detection on EEG background are of great importance for epilepsy diagnosis and treatment, and to improve the clinical assistance and, at last, for enhancing the whole health system. This research successfully combines soft computing techniques of Artificial Neural Networks (ANNs) and Genetic Programming (GP) with signal processing tools of wavelet transform and multiwavelet analysis for advanced epileptic EEG signal analysis and classification. The main objectives of this dissertation are specifically: on scalar wavelet processing technique. Scalar wavelets are efficient in non-stationary signal analysis. Amounts of classical features based on wavelet analysis have been used for EEG classification by many researches. In this study, new features as Relative Wavelet Energy (RWE) and Line Length (LL) are introduced and extracted from wavelet decomposed EEG signals. Combing these new extracted features with ANNs aims to distinguish epileptic and nonepileptic EEG recordings.", abstract = "Proposing a novel detection method based on multiwavelet processing technique. Multi-wavelets are parts of wavelet theory, however, they have some difference comparing with scalar wavelets. It is usual to apply scalar wavelets to analyse EEG signals, while, using multiwavelets to process EEG signals is an untapped research filed. In this proposed method, multiwavelet transform analyses EEG signals through decomposing the signal into several narrow frequency bands. Then, features sensitive in detecting epileptic activity are extracted from the decomposed signals. Finally, the seizure detection procedure is completed through inputting the extracted feature space into a classifier system. Additionally, comparisons between multiwavelets and scalar wavelets on EEG signal analysis are also investigated in this method. Applying GP to perform automatic feature extraction. The purpose of this study is to improve the performance of KNN classifier and reduce the input feature dimensionality simultaneously. GP is used to create GP-based features from a set of classical features for detecting epileptic seizures. GP is an automated routine in the family of Evolutionary Computation (EC) that can be used to generate optimal, artificial features. The features are optimal in the sense that the heuristics of EC maximise an objective function, which measures the performance of the artificial features in distinguishing epileptic activity from non-epileptic activity. The obtained GP-based features are thought to be artificial because GP returns computer-crafted results that might not have physical meanings. Over the years, it has become increasingly clear that the areas of neuroscience,soft computing techniques and EEG signal analysis are not mutually exclusive research areas. Rather, they represent different aspects and any new knowledge gained from one may be a stepping stone for the others. Hopefully, the results of this research can lead to a better diagnosis and treatment of epileptic seizures and an improved quality of life for the millions of persons affected by epilepsy.", abstract = "INTRODUCCION La epilepsia es un estado neurologico anormal provocado por una perturbacion electrica anomala y breve en una zona del cerebro, lo que produce un cambio en la sensacion, la conciencia y el comportamiento, y se caracteriza por convulsiones recurrentes. Afecta al 1percent de la poblacion en todo el mundo. Dos tercios de los pacientes epilepticos pueden ser tratados con medicamentos, mientras que otro 8percent se pueden beneficiar de la cirugia. Sin embargo, el 25percent de las personas con epilepsia seguiran teniendo convulsiones y no podran ser tratadas. El Electroencefalograma (EEG) es el registro de la actividad electrica del cerebro y contiene mucha informacion valiosa para la comprension de esta enfermedad. En los entornos clinicos, los neurologos han de observar continuamente el EEG para comprender mejor la epilepsia, proceso que es largo y tedioso. Por lo tanto, los esfuerzos para el desarrollo de sistemas de deteccion automatica de ataques epilepticos mediante el analisis de las senales de EEG son de gran importancia para el diagnostico de la epilepsia y su tratamiento. Esta investigacion combina tecnicas de Soft Computing como Redes Neuronales Artificiales (RR.NN.AA.) y Programacion Genetica (PG) con herramientas de procesamiento de senal, transformada wavelet y multiwavelet, para realizar un analisis avanzado y clasificacion de la senal de EEG relacionada con la enfermedad de epilepsia. Concretamente, los principales objetivos de esta Tesis son: Desarrollar un modelo para la deteccion de crisis epilepticas a traves de la extraccion de nuevas caracteristicas basadas en el analisis escalar wavelet. En este estudio, este analisis se utiliza para extraer nuevas caracteristicas con el objetivo de clasificar las senales de EEG. Estas nuevas caracteristicas se combinan con RR.NN.AA. con el objetivo de distinguir entre grabaciones de EEG epilepticos y no epilepticos. Proponer un nuevo metodo de deteccion basada en la tecnica de procesamiento multiwavelet. Esta es parte de la teoria de wavelets, sin embargo, tienen alguna diferencia en comparacion con los wavelets escalares. La aplicacion de wavelets escalares para analizar las senales de EEG es una tarea habitual, mientras que el uso de multiwavelets para procesar las senales de EEG es un campo de investigacion apenas sin explotar. Aplicar PG para realizar la extraccion automatica de caracteristicas. El proposito de este estudio es mejorar los resultados ofrecidos por algoritmo de clasificacion del vecino mas cercano (K-Nearest Neighbor, KNN) y reducir la dimensionalidad del conjunto de entrada simultaneamente.", abstract = "METODOLOGIA Para conseguir estos objetivos, en este trabajo se propone el uso de una metodologia que permita aplicar dichas tecnicas de la siguiente manera: En el primer metodo, las senales de EEG en primer lugar se descomponen mediante la transformada wavelet discreta en varias senales, y de cada una de ellas se extraen caracteristicas basadas en la energia relativa de wavelet y la longitud de linea, que se usan para clasificar las senales originales de EEG, mediante el uso de RR.NN.AA. como sistema de clasificacion. En el segundo metodo, se usa el analisis multiwavelet para analizar las senales de EEG en lugar de wavelets escalares. Cada senal de EEG se descompone en varias senales mediante la transformada multiwavelet. Despues de ello, de cada una de estas senales se extraen diversas caracteristicas basadas en la entropia y en distintos estadisticos, que seran utilizadas en dos sistemas clasificadores distintos: una RNA y el algoritmo kNN, con el objetivo de estudiar la posibilidad del uso del analisis multiwavelet en la clasificacion de senales de EEG. En el tercer metodo se utiliza PG para crear un nuevo conjunto de caracteristicas basado en un anterior conjunto de caracteristicas clasicas para la deteccion de ataques epilepticos. La PG es una tecnica de la familia de la Computacion Evolutiva (CE) que en este caso se puede utilizar para generar caracteristicas de forma optima y artificial. Estas caracteristicas halladas mediante PG son optimas en el sentido en que la PG maximiza una funcion objetivo que mide la precision de las caracteristicas artificiales en la tarea de distinguir la actividad epileptica de la no epileptica en las senales de EEG. Las caracteristicas obtenidas mediante PG se dicen artificiales porque este algoritmo devuelve resultados hallados de forma computerizada que podrian no tener significado fisico. CONCLUSIONES Y APORTACIONES En este trabajo se desarrollan, por lo tanto, tres metodos de deteccion automatizada de crisis epilepticas, y se evaluan mediante problemas de clasificacion clinica. El primer metodo se basa en combinar la transformada wavelet con RR.NN.AA. El primer experimento empleo la caracteristica de la energia relativa de wavelet, llegando a una precision en la clasificacion del 95.56percent al discriminar EEG normales y epilepticos. El segundo experimento se baso en caracteristicas de longitud de linea. En este caso, se consideraron tres problemas de clasificacion de dos clases para evaluar este metodo, obteniendose precisiones muy altas en los mismos. La segunda metodologia propuesta en este estudio utiliza multiwavelets, una nueva tecnica en la familia de la teoria wavelet, para analizar senales de EEG con el objetivo de detectar actividad de crisis epileptica. En este metodo se realizaron dos experimentos basados en la extraccion de distintas caracteristicas. El primero de ellos explora la posibilidad de usar caracteristicas basadas en la entropia de las senales que se derivan del analisis multiwavelet. Estas caracteristicas se combinaron con una RNA como sistema clasificador para discriminar las senales de EEG, obteniendose altas precisiones para los tres problemas de clasificacion. Adicionalmente, se realizo comparacion entre multiwavelets y wavelets escalares y los resultados demostraron que los multiwavelets ofrecieron mejores resultados en este caso de discriminacion de senales de EEG. El segundo experimento empleo caracteristicas basadas en estad isticos tomados de los coeficientes de los multiwavelets. Los resultados de estos dos experimentos probaron que los multiwavelets tienen un gran potencial en la clasificacion de EEG.", abstract = "La tercera metodologia desarrollada en este estudio consiste en aplicar PG para realizar una extraccion automatica de caracteristicas con el objetivo de mejorar los resultados ofrecidos por el clasificador kNN y, simultaneamente, reducir la dimensionalidad del espacio de entradas. Mediante el uso de estas caractersticas basadas en PG, las precisiones medias en la clasificacion de los tres problemas se mejoraron entre un 3.5percent y un 25percent comparado con el uso de las caracteristicas originales. Al mismo tiempo, la dimensionalidad de las entradas al sistema clasificador se redujo de forma drastica. Para problemas de clasificacion en dos clases, la dimension de las entradas se redujo de las 25 originales a 3. Para problemas de 3 clases, la dimension se redujo de las 25 a 4. Adicionalmente, mediante el analisis de las expresiones de las caracteristicas basadas en PG, se hallo que elementos que formaban parte de la base de datos original de caracteristicas fueron eventualmente descartados por el metodo de PG, dado que en el proceso evolutivo se puso de manifiesto que no eran utiles en la clasificacion. La clasificacion de las grabaciones de EEG entre estados normal y anormal es un paso importante en el diagnostico y tratamiento de la epilepsia. Esta Tesis tiene como contribucion principalmente el desarrollo de tres nuevos metodos para la deteccion de crisis epilepticas. En el primer metodo, se muestra como la energia relativa de wavelet en bandas de frecuencia especificas y la longitud de linea, basadas en la transformada wavelet discreta, son caracteristicas adecuadas para la deteccion de crisis epilepticas con un bajo coste computacional y una alta precision en la discriminacion. En el segundo metodo, los resultados de utilizar multiwavelets en el analisis de senales de EEG demuestran el gran potencial de esta herramienta de procesado de senales en el campo de la investigacion en epilepsia. En el tercer metodo, el uso de caractersticas basadas en PG en problemas de clasificacion de EEG indica que el uso de PG puede generar automaticamente caracteristicas sin caracter fisico, las cuales no solamente sirven para discriminar senales de EEG, sino que tambien disminuyen la carga de los sistemas de clasificacion. Con los anos, se ha hecho cada vez mas claro que las areas de Neurociencia, las tecnicas de Soft Computing y el analisis de la senal EEG no son areas de investigacion disjuntas. Mas bien, presentan diferentes aspectos y cualquier nuevo conocimiento adquirido a partir de uno puede ser un avance en los demas. Por lo tanto, se espera que los resultados de esta investigacion puedan conducir a un mejor diagnostico y tratamiento de las crisis epilepticas y una calidad de vida de los millones de personas afectadas por la epilepsia.", notes = "In English. de Doutoramento: Soft Computing Techniques for Advanced Epileptic EEG Analysis and Classification. Ling Guo. 13/05/11 Tese de Doutoramento supervisors: Dr Alejandro Pazos Sierra and Dr Daniel Rivero Cebrian. Data: 26 de maio de 2011. Hora: 12:00 horas. Director: pazos sierra, alejandro TRIBUNAL Presidente: del moral bueno, anselmo Secretario: rabunal dopico, juan ramon Vocal: lopez alonso, m. victoria Vocal: perfeito tome, ana maria Vocal: lang, elmar wolfgang", } @Article{Guo201110425, author = "Ling Guo and Daniel Rivero and Julian Dorado and Cristian R. Munteanu and Alejandro Pazos", title = "Automatic feature extraction using genetic programming: An application to epileptic {EEG} classification", journal = "Expert Systems with Applications", year = "2011", volume = "38", number = "8", pages = "10425--10436", month = aug, ISSN = "0957-4174", broken = "http://www.sciencedirect.com/science/article/B6V03-5265S7J-6/2/7bccfdf0fc39adebbc6851a1c6c408a3", DOI = "doi:10.1016/j.eswa.2011.02.118", keywords = "genetic algorithms, genetic programming, Feature extraction, K-nearest neighbour classifier (KNN), Discrete wavelet transform (DWT), Epilepsy, EEG classification", abstract = "This paper applies genetic programming (GP) to perform automatic feature extraction from original feature database with the aim of improving the discriminatory performance of a classifier and reducing the input feature dimensionality at the same time. The tree structure of GP naturally represents the features, and a new function generated in this work automatically decides the number of the features extracted. In experiments on two common epileptic EEG detection problems, the classification accuracy on the GP-based features is significant higher than on the original features. Simultaneously, the dimension of the input features for the classifier is much smaller than that of the original features.", notes = "Department of Information Technologies and Communications, University of La Coruna, Campus Elvina, 15071 A Coruna, Spain", } @InProceedings{DBLP:conf/gecco/GuoB09, author = "Pei Fang Guo and Prabir Bhattacharya", title = "An evolutionary approach to feature function generation in application to biomedical image patterns", booktitle = "GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation", year = "2009", editor = "Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano {van Hemert} and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique Alba", pages = "1883--1884", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming, Poster", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1569901.1570216", abstract = "A mechanism involving evolutionary genetic programming (GP) and the expectation maximization algorithm (EM) is proposed to generate feature functions, based on the primitive features, for an image pattern recognition system on the diagnosis of the disease OPMD. Experiments show that the propose algorithm achieves an average performance of 90.20percent recognition rate on diagnosis, while reducing the number of feature dimensions from 11 primitive features to the space of a single generated feature.", notes = "Oculopharyngeal Muscular Dystrophy, CellDB grayscale images, histogram region of interest by thresholds (HROIT). GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009). ACM Order Number 910092.", } @InProceedings{Guo:2009:SMC, author = "Pei-Fang Guo and Prabir Bhattacharya and Nawwaf Kharma", title = "An efficient image pattern recognition system using an evolutionary search strategy", booktitle = "IEEE International Conference on Systems, Man and Cybernetics, SMC 2009", year = "2009", month = oct, pages = "599--604", address = "San Antonio, Texas, USA", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, EM, GP, Gaussian mixture estimation, HROIT, OPMD disease diagnosis, efficiency 90.20 percent, evolutionary genetic programming, evolutionary search strategy, expectation maximization algorithm, feature function generation, histogram region, image pattern recognition system, image thresholding, oculopharyngeal muscular dystrophy, primitive texture feature extraction, support vector machine, Gaussian processes, diseases, expectation-maximisation algorithm, eye, feature extraction, image recognition, image segmentation, image texture, medical image processing, muscle, search problems", ISSN = "1062-922X", DOI = "doi:10.1109/ICSMC.2009.5346614", size = "6 pages", abstract = "A mechanism involving evolutionary genetic programming (GP) and the expectation maximization algorithm (EM) is proposed to generate feature functions automatically, based on the primitive features, for an image pattern recognition system on the diagnosis of the disease OPMD. Prior to the feature function generation, we introduce a novel technique of the primitive texture feature extraction, which deals with non-uniform images, from the histogram region of interest by thresholds (HROIT). Compared with the performance achieved by support vector machine (SVM) using the whole primitive texture features, the GP-EM methodology, as a whole, achieves a better performance of 90.20percent recognition rate on diagnosis, while projecting the hyperspace of the primitive features onto the space of a single generated feature.", notes = "Also known as \cite{5346614}", } @InProceedings{Guo:2010:CCECE, author = "Pei-Fang Guo and Prabir Bhattacharya and Nawwaf Kharma", title = "Automated synthesis of feature functions for pattern detection", booktitle = "23rd Canadian Conference on Electrical and Computer Engineering (CCECE), 2010", year = "2010", month = "2-5 " # may, abstract = "In pattern detection systems, the general techniques of feature extraction and selection perform linear transformations from primitive feature vectors to new vectors of lower dimensionality. At times, new extracted features might be linear combinations of some primitive features that are not able to provide better classification accuracy. To solve this problem, we propose the integration of genetic programming and the expectation maximisation algorithm (GP-EM) to automatically synthesise feature functions based on primitive input features for breast cancer detection. With the Gaussian mixture model, the proposed algorithm is able to perform nonlinear transformations of primitive feature vectors and data modelling simultaneously. Compared to the performance of other algorithms, such us the support vector machine, multi-layer perceptrons, inductive machine learning and logistic regression, which all used the entire primitive feature set, the proposed algorithm achieves a higher recognition rate by using one single synthesised feature function.", keywords = "genetic algorithms, genetic programming, Gaussian mixture model, automated synthesis, breast cancer detection, data modelling, expectation maximization algorithm, feature extraction, feature functions, inductive machine learning, logistic regression, multilayer perceptrons, pattern detection systems, primitive feature vector nonlinear transformations, support vector machine, cancer, data models, expectation-maximisation algorithm, feature extraction, medical computing, object detection, pattern classification, vectors", DOI = "doi:10.1109/CCECE.2010.5575224", ISSN = "0840-7789", notes = "Pei-Fang Guo PhD A Gaussian Mixture-Based Approach to Synthesizing Nonlinear Feature Functions for Automated Object Detection Concordia University 2010 http://users.encs.concordia.ca/~kharma/ResearchWeb/html/people/graduate%20students.html#pf_guo \cite{PeiFang_Guo:thesis} Also known as \cite{5575224}", } @PhdThesis{PeiFang_Guo:thesis, author = "Pei Fang Guo", title = "A Gaussian mixture-based approach to synthesizing nonlinear feature functions for automated object detection", school = "Electrical and Computer Engineering, Concordia University", year = "2010", address = "Canada", month = aug, keywords = "genetic algorithms, genetic programming", URL = "http://spectrum.library.concordia.ca/979537/", URL = "http://spectrum.library.concordia.ca/979537/1/NR67351.pdf", URL = "https://www.genealogy.math.ndsu.nodak.edu/id.php?id=147057", size = "92 pages", abstract = "Feature design is an important part to identify objects of interest into a known number of categories or classes in object detection. Based on the depth-first search for higher order feature functions, the technique of automated feature synthesis is generally considered to be a process of creating more effective features from raw feature data during the run of the algorithms. This dynamic synthesis of nonlinear feature functions is a challenging problem in object detection. This thesis presents a combinatorial approach of genetic programming and the expectation maximization algorithm (GP-EM) to synthesize nonlinear feature functions automatically in order to solve the given tasks of object detection. The EM algorithm investigates the use of Gaussian mixture which is able to model the behaviour of the training samples during an optimal GP search strategy. Based on the Gaussian probability assumption, the GP-EM method is capable of performing simultaneously dynamic feature synthesis and model-based generalization. The EM part of the approach leads to the application of the maximum likelihood (ML) operation that provides protection against inter-cluster data separation and thus exhibits improved convergence. Additionally, with the GP-EM method, an innovative technique, called the histogram region of interest by thresholds (HROIBT), is introduced for diagnosing protein conformation defects (PCD) from microscopic imagery. The experimental results show that the proposed approach improves the detection accuracy and efficiency of pattern object discovery, as compared to single GP-based feature synthesis methods and also a number of other object detection systems. The GP-EM method projects the hyperspace of the raw data onto lower-dimensional spaces efficiently, resulting in faster computational classification processes.", notes = "Supervisors: Prabir Bhattacharya and Nawwaf Kharma ID Code: 979537", } @Article{Guo:2013:EAAI, author = "Peifang Guo and Prabir Bhattacharya", title = "Detection of protein conformation defects from fluorescence microscopy images", journal = "Engineering Applications of Artificial Intelligence", volume = "26", number = "8", pages = "1936--1941", year = "2013", ISSN = "0952-1976", DOI = "doi:10.1016/j.engappai.2013.05.007", URL = "http://www.sciencedirect.com/science/article/pii/S0952197613000948", keywords = "genetic algorithms, genetic programming, EM, Pattern classification, Computer-aided diagnosis, Protein conformational diseases, Histogram, Microscopic images, Texture analysis", abstract = "A diagnostic method for protein conformational diseases (PCD) from microscopy images is proposed when such conformational conflicts involve muscular intra-nuclear inclusions (INIs) indicative of oculopharyngeal muscular dystrophy (OPMD), one variety of PCD. The method combines two techniques: (1) the Histogram Region of Interest Fixed by Thresholds (HRIFT) is designed to capture the colour information of INIs for basic feature extraction; (2) an automated feature synthesis, based on the HRIFT features, is designed to identify OPMD by means of Genetic Programming and the Expectation Maximisation algorithm (GP-EM) for classification improvement. With variations in size, shape, and background structure, a total of 600 microscopic images are analysed for the binary classes of healthy and sick conditions of OPMD. The integrated technique of the approach reveals a sensitivity of 0.9 and an area of 0.961 under the receiver operating characteristic (ROC) at a specificity of 0.95. Furthermore, significant improvements in classification accuracy and computational time are demonstrated by comparison with other methods.", } @Article{Guo:2025:ieeeTEC, author = "Tong Guo and Yi Mei and Mengjie Zhang and Ruofei Sun and Yanbo Zhu and Wenbo Du", title = "Genetic Programming With Multi-Fidelity Surrogates for Large-Scale Dynamic Air Traffic Flow Management", journal = "IEEE Transactions on Evolutionary Computation", note = "Early Access", keywords = "genetic algorithms, genetic programming, Aircraft, Air traffic control, Atmospheric modeling, Airports, Evolutionary computation, Dynamic scheduling, Computational modeling, Job shop scheduling, Random variables, Delays, evolutionary computation, meta-heuristic, air traffic flow management", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2024.3512552", notes = "also known as \cite{10782989}", } @Misc{DBLP:journals/corr/abs-2304-04022, author = "Yangyang Guo and Hao Wang4 and Lei He and Witold Pedrycz and Ponnuthurai N. Suganthan and Yanjie Song", title = "A Reinforcement Learning-assisted Genetic Programming Algorithm for Team Formation Problem Considering Person-Job Matching", howpublished = "arXiv", volume = "abs/2304.04022", year = "2023", month = "8 " # apr, keywords = "genetic algorithms, genetic programming, team formation, reinforcement learning, intuitionistic fuzzy numbers, ensemble population strategy, surrogate model", URL = "https://doi.org/10.48550/arXiv.2304.04022", DOI = "doi:10.48550/ARXIV.2304.04022", eprinttype = "arXiv", eprint = "2304.04022", timestamp = "Sat, 02 Mar 2024 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/corr/abs-2304-04022.bib", bibsource = "dblp computer science bibliography, https://dblp.org", size = "16 pages", } @InProceedings{Guo:2018:ICCCAS, author = "Zhen-xing Guo and Li-zhi Xu and Xue-jun Song and Chong-cun Li and Ruoyi Li", title = "The Research on Evolutionary Hardware Evolution Algorithm for Stall Effect", booktitle = "2018 10th International Conference on Communications, Circuits and Systems (ICCCAS)", year = "2018", pages = "461--465", month = dec, keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", DOI = "doi:10.1109/ICCCAS.2018.8768921", abstract = "The fitness values increase rapidly in the early stages of circuit evolution design, while the fitness values grew slowly or stagnated, at the later stages of the evolution. The phenomenon is called the Stalling effect phenomenon. In response to this problem, the evolutionary redundancy repair technique of circuit evolution design is proposed. The repair module is built using the redundant nodes and activated nodes at the later of circuit evolution design. The circuit with partially correct functions is evolved using Cartesian Genetic Programming at the early stages of the algorithm. At the later stages of the algorithm, the repair modules repair the error output of minimum items, ensure the correct output of the minimum items not modified meanwhile. The target circuit obtained traditional repair techniques include the additional repair circuit modules and the partial correct circuit. The evolutionary redundancy repair technique combines the repair circuit module and the evolution circuit. The repair module is built through the redundant nodes and the activated nodes. The experiment of a three-bit multiplier is researched. The results show that the rate of convergence of evolution program is greatly accelerated.", notes = "College of Physics and Information Engineering, Hebei Normal University, Shijiazhuang, China Also known as \cite{8768921}", } @InProceedings{conf/icist/GuogisM14, title = "Comparison of Genetic Programming, Grammatical Evolution and Gene Expression Programming Techniques", author = "Evaldas Guogis and Alfonsas Misevicius", bibdate = "2014-10-03", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icist/icist2014.html#GuogisM14", booktitle = "Information and Software Technologies - 20th International Conference, {ICIST} 2014, Druskininkai, Lithuania, October 9-10, 2014. Proceedings", publisher = "Springer", year = "2014", volume = "465", editor = "Giedre Dregvaite and Robertas Damasevicius", isbn13 = "978-3-319-11957-1", pages = "182--193", series = "Communications in Computer and Information Science", keywords = "genetic algorithms, genetic programming", URL = "http://dx.doi.org/10.1007/978-3-319-11958-8", } @InProceedings{gupt:2022:GECCOcomp, author = "Krishn Gupt and Meghana Kshirsagar and Lukas Rosenbauer and Joseph Sullivan and Douglas Dias and Conor Ryan", title = "{PreDive:} Preserving Diversity in Test Cases for Evolving Digital Circuits using Grammatical Evolution", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion", year = "2022", editor = "Heike Trautmann and Carola Doerr and Alberto Moraglio and Thomas Bartz-Beielstein and Bogdan Filipic and Marcus Gallagher and Yew-Soon Ong and Abhishek Gupta and Anna V Kononova and Hao Wang and Michael Emmerich and Peter A. N. Bosman and Daniela Zaharie and Fabio Caraffini and Johann Dreo and Anne Auger and Konstantin Dietric and Paul Dufosse and Tobias Glasmachers and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Tea Tusar and Dimo Brockhoff and Tome Eftimov and Pascal Kerschke and Boris Naujoks and Mike Preuss and Vanessa Volz and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Mark Coletti and Catherine (Katie) Schuman and Eric ``Siggy'' Scott and Robert Patton and Paul Wiegand and Jeffrey K. Bassett and Chathika Gunaratne and Tinkle Chugh and Richard Allmendinger and Jussi Hakanen and Daniel Tauritz and John Woodward and Manuel Lopez-Ibanez and John McCall and Jaume Bacardit and Alexander Brownlee and Stefano Cagnoni and Giovanni Iacca and David Walker and Jamal Toutouh and UnaMay O'Reilly and Penousal Machado and Joao Correia and Sergio Nesmachnow and Josu Ceberio and Rafael Villanueva and Ignacio Hidalgo and Francisco {Fernandez de Vega} and Giuseppe Paolo and Alex Coninx and Antoine Cully and Adam Gaier and Stefan Wagner and Michael Affenzeller and Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and William B. Langdon and Justyna Petke and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Paetzel and Alexander Wagner and Michael Heider and Nadarajen Veerapen and Katherine Malan and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Mohammad Nabi Omidvar and Yuan Sun and Ernesto Tarantino and De Falco Ivanoe and Antonio {Della Cioppa} and Scafuri Umberto and John Rieffel and Jean-Baptiste Mouret and Stephane Doncieux and Stefanos Nikolaidis and Julian Togelius and Matthew C. Fontaine and Serban Georgescu and Francisco Chicano and Darrell Whitley and Oleksandr Kyriienko and Denny Dahl and Ofer Shir and Lee Spector and Alma Rahat and Richard Everson and Jonathan Fieldsend and Handing Wang and Yaochu Jin and Erik Hemberg and Marwa A. Elsayed and Michael Kommenda and William {La Cava} and Gabriel Kronberger and Steven Gustafson", pages = "719--722", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, grammatical evolution, fitness function, test case selection, black-box testing, digital circuits design, diversity", isbn13 = "978-1-4503-9268-6/22/07", DOI = "doi:10.1145/3520304.3529006", abstract = "The ever-present challenge in the domain of digital devices is how to test their behavior efficiently. We tackle the issue in two ways. We switch to an automated circuit design using Grammatical Evolution (GE). Additionally, we provide two diversity-based methodologies to improve testing efficiency. The first approach extracts a minimal number of test cases from subsets formed through clustering. Moreover, the way we perform clustering can easily be used for other domains as it is problem-agnostic. The other uses complete test set and introduces a novel fitness function hitPlex that incorporates a test case diversity measure to speed up the evolutionary process.Experimental and statistical evaluations on six benchmark circuits establish that the automatically selected test cases result in good coverage and enable the system to evolve a highly accurate digital circuit. Evolutionary runs using hitPlex indicate promising improvements, with up to 16% improvement in convergence speed and up to 30% in success rate for complex circuits when compared to the system without the diversity extension.", notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @PhdThesis{Gupt:thesis, author = "Krishn Kumar Gupt", year = "2024", address = "Ireland", month = "30 " # apr, keywords = "genetic algorithms, genetic programming", notes = "Oct 2024 wait for uni to make available Supervisors: Joe Sullivan and Conor Ryan", } @Article{Gupt:2024:fcomp, author = "Krishn Kumar Gupt and Meghana Kshirsagar and Douglas {Mota Dias} and Joseph P. Sullivan and Conor Ryan", title = "A novel {ML}-driven test case selection approach for enhancing the performance of grammatical evolution", journal = "Frontiers in Computer Science", year = "2024", month = "27 " # jun, keywords = "genetic algorithms, genetic programming, grammatical evolution,test case selection, symbolic regression, digital circuit, clustering, fitness evaluation, solution size, time analysis", ISSN = "2624-9898", DOI = "doi:10.3389/fcomp.2024.1346149", size = "20 pages", abstract = "DBS yield superior or comparable solutions to the baseline (using the full datasets) with smaller sizes and faster", notes = "Department of Electrical and Electronic Engineering, Technological University of the Shannon:Midlands Midwest, Limerick, Ireland", } @InCollection{gupta:2000:CGGUGP, author = "Binod Gupta", title = "Context-Free Grammar Generation Using Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "180--187", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:2000:gagp}", } @Article{Gupta:2012:IJCSIS, author = "Rajan Gupta and Nasib Singh Gill", title = "Data Mining Techniques: A Key for detection of Financial Statement Fraud", journal = "International Journal of Computer Science and Information Security", year = "2012", volume = "10", number = "3", pages = "49--57", month = mar, keywords = "genetic algorithms, genetic programming", publisher = "LJS Publisher and IJCSIS Press", ISSN = "1947-5500", bibsource = "OAI-PMH server at www.doaj.org", oai = "oai:doaj-articles:e9ef76824df3c0f99e9c0cf09c334160", URL = "https://sites.google.com/site/ijcsis/vol-10-no-3-mar-2012", abstract = "In recent times, most of the news from business world is dominated by financial statement fraud. A financial statement becomes fraudulent if it has some false information incorporated by the management intentionally. This paper implements data mining techniques such as CART, Naive Bayesian classifier, Genetic Programming to identify companies those issue fraudulent financial statements. Each of these techniques is applied on a dataset from 114 companies. CART outperforms all other techniques in detection of fraud.", notes = "Dec 2015 'As of December 1st, Docstoc is closed for business.'", } @InProceedings{GuptaR08, author = "Nirmal Kumar Gupta and Mukesh Kumar Rohil", title = "Using Genetic Algorithm for Unit Testing of Object Oriented Software", booktitle = "Proceedings of the 1st International Conference on Emerging Trends in Engineering and Technology (ICETET '08)", year = "2008", pages = "308--313", month = jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, object-oriented methods, program testing, object oriented software unit testing, test case generation", bibsource = "http://crestweb.cs.ucl.ac.uk/resources/sbse_repository/repository.html", DOI = "doi:10.1109/ICETET.2008.137", size = "6 pages", abstract = "Genetic algorithms have been successfully applied in the area of software testing. The demand for automation of test case generation in object oriented software testing is increasing. Genetic algorithms are well applied in procedural software testing but a little has been done in testing of object oriented software. In this paper, we propose a method to generate test cases for classes in object oriented software using a genetic programming approach. This method uses tree representation of statements in test cases. Strategies for encoding the test cases and using the objective function to evolve them as suitable test case are proposed.", notes = "Also known as \cite{4579916} Java, HTMLparser", } @InProceedings{gupta:2024:ICTG, author = "Randhir Kumar Gupta and Sowmiya Chawla", title = "Retrofitting of Existing Railway Tracks Using Micropiles as a Ground Improvement Technique: Finite-Element and Genetic Programming Approach", booktitle = "Proceedings of the 5th International Conference on Transportation Geotechnics (ICTG) 2024, Volume 1", year = "2024", editor = "Cholachat Rujikiatkamjorn and Jianfeng Xue and Buddhima Indraratna", pages = "211--220", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "https://link.springer.com/chapter/10.1007/978-981-97-8213-0_23", DOI = "doi:10.1007/978-981-97-8213-0_23", abstract = "micropiles are proposed as an alternative tool to reinforce the existing railway subgrade without removing the existing track and without blocking existing traffic. suitability of micropiles as a ground improvement method for the existing railway track was assessed. Micropiles were modelled along the sides of the track in various orientations. A complete parametric study varying the geometrical parameters of the micropile under static and realistic moving train load conditions was performed using Finite Element Method (FEM) software. Three-dimensional numerical models with variations in length, diameter, inclination and spacing of micropiles was also performed. The results of FEM analyses were used as an input to the Genetic Programming (GP) model. The GP analysis output generates empirical equations for the prediction of track performance with micropile parameters as an input variable. The GP analysis resulted in empirical equations for the prediction of track performance, with the micropile parameters as input variables. The input variables were: length, diameter, spacing, and inclination of the micropile. These empirical equations can be used to predict the performance of the track for different reinforcement conditions. The empirical equations by GP have been used to develop a design methodology and a flowchart illustrating the overall design steps is generated. This design methodology can be adopted for practicing civil engineers to perform the upgradation of the existing tracks.", } @Article{Gupta:2024:GPEM, author = "Rohin Gupta and Sandeep {Singh Gill}", title = "A new representation in {3D VLSI} floorplan: {3D} O-Tree", journal = "Genetic Programming and Evolvable Machines", year = "2024", volume = "25", pages = "Article no 12", note = "Online first", keywords = "genetic algorithms, 3D floorplan, 3D O-Tree representation, Very large scale integration", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-024-09485-3", abstract = "The size of the implemented circuit plays a vital role in maximizing the performance of the chip. proposes a new, simple, efficient representation in 3D VLSI Floorplan named 3D O-Tree representation for Electronic Design Automation (EDA). Since the 3D floorplan packing problem is NP-hard (Nondeterministic Polynomial time), the novel representation is accompanied with an adaptive modified Memetic Algorithm with a kill strategy for fast performance. The tool presented in this paper employs Genetic Algorithm for global exploration, and an improved compatible local technique is used to exploit promising search regions for an improved solution. This representation has been found to be effective in obtaining an efficient packed 3D floorplan. In the case of okp benchmarks, the proposed algorithm has achieved the best stated minimum volume yet for okp1 and okp3 benchmarks with 4.62% and 1.87% ...", notes = "not GP? Department of Electronics and Communication Engineering, I.K. Gujral Punjab Technical University, Kapurthala, Punjab, 144603, India", } @Article{DBLP:journals/evs/GuptaK20, author = "Shweta Gupta and Vibhor Kant", title = "An aggregation approach to multi-criteria recommender system using genetic programming", journal = "Evolving Systems", volume = "11", number = "1", pages = "29--44", year = "2020", month = mar, keywords = "genetic algorithms, genetic programming, Collaborative filtering, Multi-criteria ratings, Recommender system", ISSN = "1868-6478", URL = "https://doi.org/10.1007/s12530-019-09296-3", DOI = "doi:10.1007/s12530-019-09296-3", timestamp = "Wed, 26 Aug 2020 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/evs/GuptaK20.bib", bibsource = "dblp computer science bibliography, https://dblp.org", abstract = "Recommender system is one of the emerging personalisation tools in e-commerce domains for suggesting suitable items to users. Traditional collaborative filtering (CF) based recommender systems (RSs) suggest items to users based on the overall ratings to find out similar users. Multicriteria ratings are used to capture user preferences efficiently in multi-criteria recommender systems (MCRSs), and incorporation of criteria ratings can lead to higher performance in MCRS. However, aggregation of these criteria ratings is a major concern in MCRS. In this paper, we propose a multi-criteria collaborative filtering-based RS by leveraging information derived from multi-criteria ratings through Genetic programming (GP). The proposed system consists of two parts: (1) weights of each user for every criterion are computed through our proposed modified sub-tree crossover in GP process (2) criteria weights are then incorporated in CF process to generate effective recommendations in our proposed system. The obtained results present significant improvements in prediction and recommendation qualities in comparison to heuristic approaches.", notes = "Affiliations: Department of Computer Science and Engineering, The LNM Institute of Information Technology, Jaipur, Rajasthan, 302031, India", } @InProceedings{Gupta:2020:ICCES, author = "Shweta Gupta and Vibhor Kant", booktitle = "2020 5th International Conference on Communication and Electronics Systems (ICCES)", title = "A Comparative Analysis of Genetic Programming and Genetic Algorithm on Multi-Criteria Recommender Systems", year = "2020", pages = "1338--1343", month = jun, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICCES48766.2020.9138051", abstract = "Recommender systems (RSs) are software tools that work as guides by suggesting products to users from a vast catalogue of products. Various approaches and techniques have been developed to provide effective recommendations to users. Classical collaborative filtering (CF) based RSs helps users by providing suggestions based on their overall assessment of items. However, providing suggestions based on their overall assessment is not an efficient way. So, multi-criteria recommender systems (MCRS) came into existence as an extended approach for suggesting products to users based on multiple features of products, and adding these multiple features can enhance the performance of the system. However, aggregation of these feature assessment i.e. feedback provided to multiple criteria is a key issue in MCRS. In this paper, we present a comparative analysis of genetic algorithm (GA) and genetic programming (GP) approaches to aggregate criteria ratings for predicting user preferences in MCRS. These two algorithms are bio-inspired and have great potential to solve optimization problems. In this research, GP and GA are used to solve the aggregation problem in MCRS by estimating weights for each criterion in a system. We compared the results of genetic programming and genetic algorithm approaches to show their effectiveness in multi-criteria rating systems.", notes = "The LNM Institute of Information Technology, Jaipur, India Also known as \cite{9138051}", } @Article{Gupta:2022:CCPE, author = "Shweta Gupta and Vibhor Kant", title = "A model-based approach to user preference discovery in multi-criteria recommender system using genetic programming", journal = "Concurrency and Computation: Practice and Experience", year = "2022", volume = "34", number = "11", pages = "e6899", month = "15 " # may, keywords = "genetic algorithms, genetic programming, collaborative filtering, multi-criteria ratings, preference ratings, recommender system", ISSN = "1532-0634", URL = "https://onlinelibrary.wiley.com/doi/abs/10.1002/cpe.6899", DOI = "doi:10.1002/cpe.6899", abstract = "Multi-criteria recommender systems (MCRSs) provide suggestions to users based on their preferences to various criteria. Incorporation of criteria ratings into recommendation framework can provide quality recommendations to users because these ratings can elicit users preferences efficiently. However, elicitation of user's overall preference based on criteria ratings is a key issue in MCRS. Even though several aggregation methods for the elicitation of users overall preference have been investigated in the literature, no method has been shown the superiority under all circumstances. Therefore, we propose a model based approach to user preference discovery in multi-criteria RS using genetic programming (GP). In this work, we suggest three-stage process to generate recommendations to users. First, we learn user preference transformation function to aggregate criteria ratings by using GP, and then we use the preference function, so derived, for computing similarities in MCRS. Finally, items are recommended to users. Experimental results on Yahoo! Movies dataset show the superiority of our proposed approach in comparison to other aggregation approaches.", } @Article{Gures2012, author = "Sinan Gures and Aleksander Mendyk and Renata Jachowicz and Przemyslaw Dorozynski and Peter Kleinebudde", title = "Application of artificial neural networks (ANNs) and genetic programming (GP) for prediction of drug release from solid lipid matrices", journal = "International Journal of Pharmaceutics", volume = "436", number = "1-2", pages = "877--879", year = "2012", ISSN = "0378-5173", DOI = "doi:10.1016/j.ijpharm.2012.05.021", URL = "http://www.sciencedirect.com/science/article/pii/S0378517312005054", keywords = "genetic algorithms, genetic programming, Solid lipid extrusion, Artificial neural networks, Release profile", abstract = "The aim of the present study was to develop a semi-empirical mathematical model, which is able to predict the release profiles of solid lipid extrudates of different dimensions. The development of the model was based on the application of ANNs and GP. ANN's abilities to deal with multidimensional data were exploited. GP programming was used to determine the constants of the model function, a modified Weibull equation. Differently dimensioned extrudates consisting of diprophylline, tristearin and polyethylene glycol were produced by the use of a twin-screw extruder and their dissolution behaviour was studied. Experimentally obtained dissolution curves were compared to the calculated release profiles, derived from the semi-empirical mathematical model.", } @PhdThesis{Gures:thesis, author = "Sinan Gures", title = "Experimentelle Untersuchungen und mathematisch-theoretische Vorhersagen des Freisetzungsverhaltens aus extrudierten Fettmatrices", title2 = "Experimental investigations and mathematical-theoretical prdictions of release behaviour from fat matrices", school = "der Mathematisch-Naturwissenschaftlichen Fakultat der Heinrich-Heine-Universitat Dusseldorf", year = "2011", address = "Germany", month = "21 " # dec, keywords = "genetic algorithms, genetic programming", URL = "http://docserv.uni-duesseldorf.de/servlets/DocumentServlet?id=21838", URL = "http://docserv.uni-duesseldorf.de/servlets/DerivateServlet/Derivate-23446/Gures_thesis.pdf", size = "118 pages", notes = "In German. Supervisor Prof. Peter Kleinebudde", abstract = "The present work focused on the dissolution behaviour of solid lipid extrudates. It was possible to analyse the influence of different groups of excipients on the release of a model API from solid lipid extrudates systematically. Three groups of excipients were determined, each including several substances. Pore formers, hydrocolloids and super-disintegrants were chosen. The extrudate matrix into which 5percent release modifier were incorporated basically consisted of diprophylline as a model API and tristearin as a matrix former (50:45percent w/w). In each case it was possible to obtain suitable extrudates. The DSC analysis showed that the physical properties of the physical mixture were also existent in the extrudate matrix, representing a successful extrusion process. Dissolution experiments resulted in different behaviour of the extrudates. Not all of the excipients led to a faster dissolution rate. Within the pore former group mannitol and sodium chloride did not influence the release rate, compared to the reference extrudate, consisting of diprophyllin and tristearin (55:45percent w/w). PEG of a mean molecular weight of 10.000 instead increased the release rate significantly. The extrusion temperature of 65degrees celcius could be identified as reason for this exceptional behaviour of PEG 10.000. Since its melting point of around 62degrees celcius is exceeded during extrusion process, PEG 10.000 was assumed to melt and become a fluid within the mass. At the same time, it gets better distributed in the matrix. Thus, a fine PEG network is constructed in the extrudate leading to a faster dissolution rate. These findings lead to the idea to check the influence of different polyethyleneglycols. Polyethylene glycols and polyethylene oxides of different molecular weights, varying from 1.500 up to 7.000.000 were tested by incorporating them into the same basic matrix. For these studies also a lower melting powdered lipid, trimyristin, was used. The studies led to the result, that primarily the extrusion temperature and thus, the solid state of the PEG/PEO was responsible for release enhancement.", abstract = "Within the group of hydrocolloids, the aim was to investigate the influence of different viscosity grades of different types on the dissolution rate of diprophylline. Higher viscosity grade of a hydrocolloid (HPMC 4000 and HEC 30000), led to a full disintegration of the matrix whereas lower viscosity grades (HPMC 50 and HEC 20) just resulted in locally eroded matrix surfaces. The super-disintegrants also showed different effects on the release behaviour of diprophylline containing extrudates. Croscarmellose sodium and sodium starch glycolate led to fast disintegration of the matrix and to full release within a few minutes. Crospovidone (PVP-CL) of two different mean particle sizes instead, did not cause disintegration of the lipid matrix. These two super disintegrants showed different behaviour. In the case of Kollidon CL-SF, that one with the smaller particle size, the matrix was still intact after dissolution and the drug was dissolved from pores, as it was in the pore former group. Kollidon CL containing extrudates exhibited a much higher release rate. Here, surface erosion was the case, but not disintegration. Since all the above mentioned experiments were performed using the excipients as received and as a consequence of this, the particle size influence on the release rate was not considered, additional trials with sieved excipients were performed. The excipients were sieved to a particle size range from 0-80 micrometer and the experiments were repeated. A significant influence of the particle size of the excipients could not be detected.", abstract = "A further approach of the present work was to develop mathematical and empirical models which are able to predict the release profiles of solid lipid extrudates. The suitability of these models was tested by predicting the release profiles of different dimensioned extrudates. For these investigations extrudates with the composition of diprophylline, tristearin and PEG 20.000 or Kolldion CL-SF were chosen. For the development of the mathematical model the physicochemical properties of the extrudates were analysed first. Based on these results, fickian diffusion could be identified as the main transport mechanism during dissolution. Fick's second law of diffusion for cylindrically shaped systems served as the basic equation of the model. As Fick's second law of diffusion is a partial differential equation, an analytical solution via Laplace transformation had to be derived. The result was an equation, which could directly be used to calculate the released drug amount. Extrudates of the abovementioned composition with PEG 20.000 of 0.6, 1.0, 1.5, 2.7 and 3.5 mm diameter were produced by using different die plates and were physicochemically characterised. After dissolution testing, the data of these extrudates were compared to the calculated release data, obtained by inserting diameter and length of the extrudates into the model equation. The calculation of the similarity factor f2 proved the sameness of the dissolution curve pairs (theory and experiment), indicating the good quality of the mathematical model. In order to validate the predictability of the model further experiments, considering only the length of an extrudate were performed. Extrudates of 1.0 mm diameter and the abovementioned composition were cut to different lengths. Dissolution experiments were performed and again the model equation was used to predict the release behaviour of these extrudates. Experiment and theory showed good accordance again. In order to demonstrate the limits of the mathematical model, a disintegrating extrudate (containing Kolldion CL-SF) was used. Since the model does not consider disintegration, it was not able to correctly predict the release behaviour in this case. As a comparison to the mechanistic model based on Fick's second law of diffusion, an empirical approach was applied to the same problem (extrudates of 0.6-3.5 mm diameter). Artificial neuronal networks (ANNs) are well known as empirical modelling tools, which are able to learn from a set of experimental data and to identify a pattern in these data. Three parameters, extrudate length, extrudate diameter and the dissolution time were determined as input units for the ANNs. The released drug fraction was chosen as the output unit, since this was the parameter of interest. The ANNs was able to identify the diameter and time as a crucial parameters determining the release rate. The number of input units could thus be reduced from three to two.", abstract = "In the second step, the Weibull-equation, a function mostly used in the industry to determine the lifetime of parts, was tried to use as a basis for the model. Via genetic programming (GP), the Weibull equation was developed to the final model equation, considering the time and the diameter of the extrudates, those two parameters, identified by the ANNs as the crucial parameters. This model also showed a good predictability, which could be proved by the calculation of the f2-value. The present work contributed to the understanding of the release behaviour of solid lipid extrudates of different compositions. Not least, the understanding of the influences of different excipient groups on the release rate could be enriched. Furthermore it was possible to create mathematical models being able to predict the release profiles of solid lipid extrudates. In the future, models like these ones could be useful in the formulation development in order to save time and material costs.", } @InCollection{gurganious:1999:ABWEUGA, author = "Darryl Gurganious", title = "Adaptive Beamformer Weight Estimation Using Genetic Algorithms", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1999", year = "1999", editor = "John R. Koza", pages = "49--57", address = "Stanford, California, 94305-3079 USA", month = "15 " # mar, publisher = "Stanford Bookstore", keywords = "genetic algorithms", notes = "part of \cite{koza:1999:GAGPs}", } @InProceedings{Guruprasad:2023:ACCESS, author = "Sunitha Guruprasad and Rio D'Souza {G. L.}", booktitle = "2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS)", title = "Parallel Model to Detect Attacks Using Evolutionary Based Technique", year = "2023", pages = "291--296", abstract = "Evolutionary-based algorithms emerged due to their flexibility and effectiveness in solving different varieties of problems. Optimisation-based techniques are used in finding solutions that involve multiple conflicting objectives. Parallel evolutionary-based algorithms are used to overcome the time-consuming job of finding solutions to these types of problems. In this paper, we present a parallel genetic programming-based model that runs parallelly and obtains solutions in a minimal amount of time. The model also allows the user to select the best set of objectives based on the requirements of the users. An island model is used which runs the operations on different islands parallelly. This not only decreases the execution time of the process but also increases the diversity of the population. The results obtained in different islands are fed to an ensemble classifier to get the required result. The model was trained and tested using the state-of-the-art ISCX-2012 and CICIDS2017 datasets. In our work, we have mainly focused on detecting the attacks in a system in a short duration of time. The model developed gave significant performance improvement compared to the results obtained using the normal CPU implementation.", keywords = "genetic algorithms, genetic programming, Computational modelling, Sociology, Genetics, Main-secondary, Statistics, Testing, Ensemble, Evolutionary, Parallel, Island model, Optimisation", DOI = "doi:10.1109/ACCESS57397.2023.10200912", month = may, notes = "Also known as \cite{10200912}", } @Article{Gusel:2005:MT, author = "Leo Gusel and Miran Brezocnik", title = "Genetic modeling of electrical conductivity of formed material", journal = "Materials and technology", year = "2005", volume = "39", number = "4", pages = "107--111", email = "mbrezocnik@uni-mb.si", keywords = "genetic algorithms, genetic programming, copper alloys, electrical conductivity, cold forming, modelling, genetsko programiranje, modeliranje, hladno preoblikovanje, elektricna prevodnost, bakrove zlitine", ISSN = "1580-2949", URL = "http://www.imt.si/materiali-tehnologije/", URL = "http://ctklj.ctk.uni-lj.si/kovine/izvodi/mit054/gusel.pdf", size = "5 pages", abstract = "In the paper a genetic programming method for efficient determination of accurate models for the change of electrical conductivity of cold formed alloy CuCrZr was presented. The main characteristic of genetic programming method, which is one of evolutionary methods for modelling, is its non- deterministic way of computing. No assumptions about the form and size of expressions were made in advance, but they were left to the self organisation and intelligence of evolutionary process. Only the best models, gained by genetic programming were presented in the paper. Accuracy of the best models was proved with the testing data set. The comparison between deviation of genetic models results and regression models results concerning the experimental results has showed that genetic models are much more precise and more varied then regression model. The variety of genetic models allows us, concerning the demands, to decide for an optimal genetic model for mathematical description and prediction of change of electrical conductivity in the frame of experimental environment.", abstract = "V prispevku smo predstavili metodo genetskega programiranja za uspesno dolocitev natancnih modelov spremembe elektricne prevodnosti hladno preoblikovane zlitine CuCrZr. Glavna znacilnost metode genetskega programiranja, ki spada med evolucijske metode modeliranja, je, da resitev ne iscemo po vnaprej dolocenih poteh ter da socasno obravnavamo mnozico enostavnih objektov. Cedalje natancnejsim resitvam smo se priblizevali postopoma, med postopkom simulirane evolucije. V prispevku smo predstavili le nekatere najuspesnejse oziroma najprimernejse genetske modele. Natancnost genetskih modelov je bila preverjena na mnozici preskusnih tock. Primerjali smo tudi natancnost genetsko dobljenih modelov in modela, dobljenega po deterministicni metodi regresije. Primerjava je pokazala, da se genetski modeli dosti manj odmikajo od eksperimentalnih rezultatov in da so bolj raznoliki. Prav raznolikost nam omogoca, da se, glede na zahteve, odlocimo za optimalen model, s katerim lahko matematicno opisemo ali napovedujemo spremembo elektricne prevodnosti zlitine v okviru eksperimentalnega okolja.", } @Article{Gusel:2006:CMS, author = "Leo Gusel and Miran Brezocnik", title = "Modeling of impact toughness of cold formed material by genetic programming", journal = "Computational Materials Science", year = "2006", volume = "37", number = "4", pages = "476--482", month = oct, email = "mbrezocnik@uni-mb.si", keywords = "genetic algorithms, genetic programming, evolutionary computing, metal forming, modelling, impact toughness, copper alloy", ISSN = "0927-0256", DOI = "doi:10.1016/j.commatsci.2005.11.007", abstract = "In the paper, an approach completely different from the conventional methods for determination of accurate models for the change of properties of cold formed material, is presented. This approach is genetic programming (GP) method which is based on imitation of natural evolution of living organisms. The main characteristic of GP is its non-deterministic way of computing. No assumptions about the form and size of expressions were made in advance, but they were left to the self organisation and intelligence of evolutionary process. First, copper alloy rods were cold drawn under different conditions and then impact toughness of cold drawn specimens was determined by Charpy tests. The values of independent variables (effective strain, coefficient of friction) influence the value of the dependent variable, impact toughness. On the basis of training data, different prediction models for impact toughness were developed by GP. Only the best models, gained by genetic programming were presented in the paper. Accuracy of the best models was proved with the testing data set. The comparison between deviation of genetic model results and regression model results concerning the experimental results has showed that genetic models are more precise and more varied then regression models.", } @Article{Gusel201115014, author = "Leo Gusel and Miran Brezocnik", title = "Application of genetic programming for modelling of material characteristics", journal = "Expert Systems with Applications", volume = "38", number = "12", pages = "15014--15019", year = "2011", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2011.05.045", URL = "http://www.sciencedirect.com/science/article/pii/S0957417411008293", keywords = "genetic algorithms, genetic programming, Evolutionary computation, Modelling, Metal forming, Material characteristics", abstract = "Genetic programming, which is one of the most general evolutionary computation methods, was used in this paper for the modelling of tensile strength and electrical conductivity in cold formed material. No assumptions about the form and size of expressions were made in advance, but they were left to the self organisation and intelligence of evolutionary process. Genetic programming does this by genetically breeding a population of computer programs using the principles of Darwinian's natural selection and biologically inspired operations. In our research, copper alloy was cold formed by drawing using different process parameters and then tensile strengths and electrical conductivity (dependent variables) of the specimens were determined. The values of independent variables (effective strain, coefficient of friction) influence the value of the dependent variables. Many different genetic models for both dependent variables were developed by genetic programming. The accuracies of the best models were proved by a testing data set. Also, comparison between the genetic and regression models is presented in the paper. The research showed that very accurate genetic models can be obtained by the proposed method.", } @InProceedings{gustafson:2000:GAK, author = "Steven M. Gustafson and William H. Hsu", title = "Genetic programming for strategy learning in soccer playing agents: A KDD-based architecture", booktitle = "Graduate Student Workshop", year = "2000", editor = "Conor Ryan and Una-May O'Reilly and William B. Langdon", pages = "277--280", address = "Las Vegas, Nevada, USA", month = "8 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.nott.ac.uk/~smg/research/publications/gecco-2000.ps", URL = "http://www.cs.nott.ac.uk/~smg/research/publications/gecco-2000.pdf", notes = "GECCO-2000WKS Part of \cite{wu:2000:GECCOWKS}", } @InProceedings{gustafson:2001:EuroGP, author = "Steven M. Gustafson and William H. Hsu", title = "Layered Learning in Genetic Programming for a Co-operative Robot Soccer Problem", booktitle = "Genetic Programming, Proceedings of EuroGP'2001", year = "2001", editor = "Julian F. Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon", volume = "2038", series = "LNCS", pages = "291--301", address = "Lake Como, Italy", publisher_address = "Berlin", month = "18-20 " # apr, organisation = "EvoNET", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, Layered Learning, Hierarchical abstractions, Robot soccer, Robots, Multiagent systems: Poster", ISBN = "3-540-41899-7", URL = "http://www.cs.nott.ac.uk/~smg/research/publications/eurogp-2001.ps", URL = "http://www.cs.nott.ac.uk/~smg/research/publications/eurogp-2001.pdf", DOI = "doi:10.1007/3-540-45355-5_23", size = "11 pages", abstract = "We present an alternative to standard genetic programming (GP) that applies layered learning techniques to decompose a problem. GP is applied to subproblems sequentially, where the population in the last generation of a subproblem is used as the initial population of the next subproblem. This method is applied to evolve agents to play keep-away soccer, a subproblem of robotic soccer that requires cooperation among multiple agents in a dynnamic environment. The layered learning paradigm allows GP to evolve better solutions faster than standard GP. Results show that the layered learning GP outperforms standard GP by evolving a lower fitness faster and an overall better fitness. Results indicate a wide area of future research with layered learning in GP.", notes = "EuroGP'2001, part of miller:2001:gp. See also \cite{gustafson:mastersthesis}", } @MastersThesis{gustafson:mastersthesis, author = "Steven M. Gustafson", title = "Layered learning in genetic programming for a co-operative robot soccer problem", school = "Kansas State University", year = "2000", address = "Manhattan, KS, USA", month = dec, email = "smg@cs.nott.ac.uk", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.nott.ac.uk/~smg/research/publications/msthesis-2000.ps", URL = "http://www.cs.nott.ac.uk/~smg/research/publications/msthesis-2000.pdf", URL = "http://citeseer.ist.psu.edu/450396.html", abstract = "We present an alternative to standard genetic programming (GP) that applies layered learning techniques to decompose a problem. GP is applied to subproblems sequentially, where the population in the last generation of a subproblem is used as the initial population of the next subproblem. This method is applied to evolve agents to play keep-away soccer, a subproblem of robotic soccer that requires cooperation among multiple agents in a dynamic environment. The layered learning paradigm allows GP to evolve better solutions faster than standard GP. Results show that the layered learning GP outperforms standard GP by evolving a lower tness faster and an overall better tness. Results indicate a wide area of future research with layered learning in GP.", notes = "Related Publications from Masters Thesis: William H. Hsu and Steven M. Gustafson. Wrappers for automatic parameter tuning in multi-agent optimization by genetic programming. In IJCAI-2001 Workshop on Wrappers for Performance Enhancement in Knowledge Discovery in Databases (KDD), Seattle, Washington, USA, 4 August 2001. \cite{hsu:2001:waptmaoGP} W. H. Hsu and S. M. Gustafson. Genetic Programming for Layered Learning of Multi-agent Tasks. In Late-Breaking Papers of the Genetic and Evolutionary Computation Conference (GECCO-2001), San Francisco, CA, June, 2001. \cite{hsu:2001:gpllmt} S. M. Gustafson and W. H. Hsu. Layered learning in genetic programming for a co-operative robot soccer problem. In J. F. Miller et al, editors, Proceedings of EuroGP'2001, v. 2038 of LNCS,p ages 291--301, Lake Como, Italy, 18-20 April 2001. Springer-Verlag. \cite{gustafson:2001:EuroGP}", } @InProceedings{gustafson:2002:EuroGP, title = "A Puzzle to Challenge Genetic Programming", author = "Edmund Burke and Steven Gustafson and Graham Kendall", editor = "James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi", booktitle = "Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002", volume = "2278", series = "LNCS", pages = "238--247", publisher = "Springer-Verlag", address = "Kinsale, Ireland", publisher_address = "Berlin", month = "3-5 " # apr, year = "2002", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-43378-3", URL = "http://www.cs.nott.ac.uk/~smg/research/publications/eurogp-2002.ps", URL = "http://www.cs.nott.ac.uk/~smg/research/publications/eurogp-2002.pdf", DOI = "doi:10.1007/3-540-45984-7_23", abstract = "This report represents an initial investigation into the use of genetic programming to solve the N-prisoners puzzle. The puzzle has generated a certain level of interest among the mathematical community. We believe that this puzzle presents a significant challenge to the field of evolutionary computation and to genetic programming in particular. The overall aim is to generate a solution that encodes complex decision making. Our initial results demonstrate that genetic programming can evolve good solutions. We compare these results to engineered solutions and discuss some of the implications. One of the consequences of this study is that it has highlighted a number of research issues and directions and challenges for the evolutionary computation community.We conclude the article by presenting some of these directions which range over several areas of evolutionary computation, including multi-objective fitness, coevolution and cooperation, and problem representations.", notes = "EuroGP'2002, part of \cite{lutton:2002:GP} Best poster", } @InProceedings{gustafson:2003:iidigpbaaoteop, author = "Edmund K. Burke and Steven Gustafson and Graham Kendall and Natalio Krasnogor", title = "Is increased diversity in genetic programming beneficial? An analysis of the effects on performance", booktitle = "Proceedings of the 2003 Congress on Evolutionary Computation CEC2003", editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon", pages = "1398--1405", year = "2003", publisher = "IEEE Press", address = "Canberra", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "8-12 " # dec, organisation = "IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", keywords = "genetic algorithms, genetic programming, Convergence, Entropy, Evolutionary computation, Shape, Stochastic processes, artificial life, regression analysis, artificial ant, binomial-3 function, even-5-parity, genetic lineage selection, symbolic regression", ISBN = "0-7803-7804-0", DOI = "doi:10.1109/CEC.2003.1299834", abstract = "A selection strategy based on genetic lineages is used to increase genetic diversity. A genetic lineage is defined as the path from an individual to individuals which were created from its genetic material. The method is applied to three problem domains: Artificial Ant, Even-5-Parity and symbolic regression of the Binomial-3 function. We examine how increased diversity affects problems differently and draw conclusions about the types of diversity which are more important for each problem. Results indicate that diversity in the Ant problem helps to overcome deception, while elitism in combination with diversity is likely to benefit the Parity and regression problems.", notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.", } @InProceedings{gustafson:2004:eurogp, author = "Steven Gustafson and Edmund K. Burke and Graham Kendall", title = "Sampling of Unique Structures and Behaviours in Genetic Programming", booktitle = "Genetic Programming 7th European Conference, EuroGP 2004, Proceedings", year = "2004", editor = "Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule", volume = "3003", series = "LNCS", pages = "279--288", address = "Coimbra, Portugal", publisher_address = "Berlin", month = "5-7 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-21346-5", URL = "http://www.cs.nott.ac.uk/~smg/research/publications/eurogp-sampling-2004.ps", URL = "http://www.cs.nott.ac.uk/~smg/research/publications/eurogp-sampling-2004.pdf", DOI = "doi:10.1007/978-3-540-24650-3_26", abstract = "We examine the sampling of unique structures and behaviours in genetic programming. A novel description of behaviour is used to better understand the solutions visited during genetic programming search. Results provide new insight about deception that can be used to improve the algorithm and demonstrate the capability of genetic programming to sample different large tree structures during the evolutionary process.", notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004", } @PhdThesis{gustafson:2004:phdthesis, author = "Steven Matt Gustafson", title = "An Analysis of Diversity in Genetic Programming", school = "School of Computer Science and Information Technology, University of Nottingham", year = "2004", month = feb, address = "Nottingham, England", keywords = "genetic algorithms, genetic programming, Evolutionary Algorithms, Diversity, Analysis, Populations, Meta-heuristics, Search", URL = "https://eprints.nottingham.ac.uk/10057/", URL = "http://www.cs.nott.ac.uk/~smg/research/publications/phdthesis-gustafson.pdf", URL = "http://www.cs.nott.ac.uk/~smg/research/publications/phdthesis-gustafson.ps", URL = "http://www.cs.nott.ac.uk/~smg/research/publications/phdthesis-gustafson.ps.gz", broken = "http://www.gustafsonresearch.com/thesis_html/", broken = "http://ethos.bl.uk/OrderDetails.do?did=6&uin=uk.bl.ethos.404029", size = "170 pages", abstract = "Genetic programming is a metaheuristic search method that uses a population of variable-length computer programs and a search strategy based on biological evolution. The idea of automatic programming has long been a goal of artificial intelligence, and genetic programming presents an intuitive method for automatically evolving programs. However, this method is not without some potential drawbacks. Search using procedural representations can be complex and inefficient. In addition, variable sized solutions can become unnecessarily large and difficult to interpret. The goal of this thesis is to understand the dynamics of genetic programming that encourages efficient and effective search. Toward this goal, the research focuses on an important property of genetic programming search: the population. The population is related to many key aspects of the genetic programming algorithm. In this programme of research, diversity is used to describe and analyse populations and their effect on search. A series of empirical investigations are carried out to better understand the genetic programming algorithm. the relationship between diversity and search. The effect of increased population diversity and a metaphor of search are then examined. This is followed by an investigation into the phenomenon of increased solution size and problem difficulty. The research concludes by examining the role of diverse individuals, particularly the ability of diverse individuals to affect the search process and ways of improving the genetic programming algorithm. (1) An analysis shows the complexity of the issues of diversity and the relationship between diversity and fitness, (2) The genetic programming search process is characterised by using the concept of genetic lineages and the sampling of structures and behaviours, (3) A causal model of the varied rates of solution size increase is presented, (4) A new, tunable problem demonstrates the contribution of different population members during search, and (5) An island model is proposed to improve the search by speciating dissimilar individuals into better-suited environments. Currently, genetic programming is applied to a wide range of problems under many varied contexts. From artificial intelligence to operations research, the results presented in this thesis will benefit population-based search methods, methods based on the concepts of evolution and search methods using variable-length representations.", notes = "uk.bl.ethos.404029 Supervisor: Edmund Burke and Graham Kendall", } @Article{gustafson:2004:GPEM, author = "Steven Gustafson and Aniko Ekart and Edmund Burke and Graham Kendall", title = "Problem Difficulty and Code Growth in Genetic Programming", journal = "Genetic Programming and Evolvable Machines", year = "2004", volume = "5", number = "3", pages = "271--290", month = sep, keywords = "genetic algorithms, genetic programming, population diversity, code growth, problem difficulty", ISSN = "1389-2576", URL = "http://www.gustafsonresearch.com/research/publications/gustafson-gpem2004.pdf", DOI = "doi:10.1023/B:GENP.0000030194.98244.e3", size = "20 pages", abstract = "the relationship between code growth and problem difficulty in genetic programming. The symbolic regression problem domain is used to investigate this relationship using two different types of increased instance difficulty. Results are supported by a simplified model of genetic programming and show that increased difficulty induces higher selection pressure and less genetic diversity, which both contribute toward an increased rate of code growth.", notes = "Article ID: 5272970", } @Article{gustafson:2004:IEEE, author = "Edmund K. Burke and Steven Gustafson and Graham Kendall", title = "Diversity in Genetic Programming: An Analysis of Measures and Correlation with Fitness", journal = "IEEE Transactions on Evolutionary Computation", publisher = "IEEE Press", year = "2004", volume = "8", number = "1", month = feb, pages = "47--62", keywords = "genetic algorithms, genetic programming, diversity, population dynamics", ISSN = "1089-778X", URL = "http://www.cs.nott.ac.uk/~smg/research/publications/gustafson-ieee2004-preprint.pdf", URL = "http://www.cs.nott.ac.uk/~smg/research/publications/gustafson-ieee2004-preprint.ps", DOI = "doi:10.1109/TEVC.2003.819263", size = "16 pages", abstract = "Examines measures of diversity in genetic programming. The goal is to understand the importance of such measures and their relationship with fitness. Diversity methods and measures from the literature are surveyed and a selected set of measures are applied to common standard problem instances in an experimental study. Results show the varying definitions and behaviours of diversity and the varying correlation between diversity and fitness during different stages of the evolutionary process. Populations in the genetic programming algorithm are shown to become structurally similar while maintaining a high amount of behavioural differences. Conclusions describe what measures are likely to be important for understanding and improving the search process and why diversity might have different meaning for different problem domains.", } @InProceedings{eurogp:GustafsonV05, author = "Steven Gustafson and Leonardo Vanneschi", editor = "Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. {van Hemert} and Marco Tomassini", title = "Operator-Based Distance for Genetic Programming: Subtree Crossover Distance", booktitle = "Proceedings of the 8th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3447", year = "2005", address = "Lausanne, Switzerland", month = "30 " # mar # " - 1 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-25436-6", pages = "178--189", URL = "http://www.cs.nott.ac.uk/~smg/research/publications/eurogp2005-gustafson-vanneschi.ps", URL = "http://www.cs.nott.ac.uk/~smg/research/publications/eurogp2005-gustafson-vanneschi.pdf", DOI = "doi:10.1007/978-3-540-31989-4_16", DOI = "doi:10.1007/b107383", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "This paper explores distance measures based on genetic operators for genetic programming using tree structures. The consistency between genetic operators and distance measures is a crucial point for analytical measures of problem difficulty, such as fitness distance correlation, and for measures of population diversity, such as entropy or variance. The contribution of this paper is the exploration of possible definitions and approximations of operator-based edit distance measures. In particular, we focus on the subtree crossover operator. An empirical study is presented to illustrate the features of an operator-based distance. This paper makes progress toward improved algorithmic analysis by using appropriate measures of distance and similarity.", notes = "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005", } @InProceedings{eurogp:GustafsonBK05, author = "Steven Gustafson and Edmund K. Burke and Natalio Krasnogor", editor = "Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. {van Hemert} and Marco Tomassini", title = "The Tree-String Problem: An Artificial Domain for Structure and Content Search", booktitle = "Proceedings of the 8th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3447", year = "2005", address = "Lausanne, Switzerland", month = "30 " # mar # " - 1 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-25436-6", pages = "215--226", URL = "http://www.cs.nott.ac.uk/~smg/research/publications/eurogp2005-gustafson-etal.ps", URL = "http://www.cs.nott.ac.uk/~smg/research/publications/eurogp2005-gustafson-etal.pdf", DOI = "doi:10.1007/978-3-540-31989-4_19", DOI = "doi:10.1007/b107383", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "This paper introduces the Tree-String problem for genetic programming and related search and optimisation methods. To improve the understanding of optimisation and search methods, we aim to capture the complex dynamic created by the interdependencies of solution structure and content. Thus, we created an artificial domain that is amenable for analysis, yet representative of a wide-range of real-world applications. The Tree-String problem provides several benefits, including: the direct control of both structure and content objectives, the production of a rich and representative search space, the ability to create tunably difficult and random instances and the flexibility for specialisation.", notes = "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005", } @InProceedings{gustafson:2005:CEC, author = "Steven Gustafson and Edmund K. Burke and Natalio Krasnogor", title = "On Improving Genetic Programming for Symbolic Regression", booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary Computation", year = "2005", editor = "David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L. Gwenn Volkert and Dan Ashlock and Marc Schoenauer", volume = "1", pages = "912--919", address = "Edinburgh, UK", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "2-5 " # sep, organisation = "IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9363-5", DOI = "doi:10.1109/CEC.2005.1554780", abstract = "This paper reports an improvement to genetic programming (GP) search for the symbolic regression domain, based on an analysis of dissimilarity and mating. GP search is generally difficult to characterise for this domain, preventing well motivated algorithmic improvements. We first examine the ability of various solutions to contribute to the search process. Further analysis highlights the numerous solutions produced during search with no change to solution quality. A simple algorithmic enhancement is made that reduces these events and produces a statistically significant improvement in solution quality. We conclude by verifying the generalisability of these results on several other regression instances", notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS.", } @Article{Gustafson:2006:JPDC, author = "Steven Gustafson and Edmund K. Burke", title = "The Speciating Island Model: An alternative parallel evolutionary algorithm", journal = "Journal of Parallel and Distributed Computing", year = "2006", volume = "66", number = "8", pages = "1025--1036", month = aug, note = "Parallel Bioinspired Algorithms", keywords = "genetic algorithms, genetic programming, Parallel evolutionary algorithms, Islands", DOI = "doi:10.1016/j.jpdc.2006.04.017", abstract = "This paper presents an investigation of a novel model for parallel evolutionary algorithms (EAs) based on the biological concept of species. In EA population search, new species represent solutions that could lead to good solutions but are disadvantaged due to their dissimilarity from the rest of the population. The Speciating Island Model (SIM) attempts to exploit new species when they arise by allocating them to new search processes executing on other islands (other processors). The long term goal of the SIM is to allow new species to diffuse throughout a large (conceptual) parallel computer network, where idle and unimproving processors initiate a new search process with them. In this paper, we focus on the successful identification and exploitation of new species and show that the SIM can achieve improved solution quality as compared to a canonical parallel EA.", } @Article{Gustafson:2008:TEC, title = "Crossover-Based Tree Distance in Genetic Programming", author = "Steven Gustafson and Leonardo Vanneschi", journal = "IEEE Transactions on Evolutionary Computation", year = "2008", month = aug, volume = "12", number = "4", pages = "506--524", keywords = "genetic algorithms, genetic programming, evolutionary computation, trees (mathematics)crossover-based tree distance, distance metrics, evolutionary algorithms, fitness sharing algorithm, fitness-distance correlation, genetic programming syntax trees", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2008.915993", size = "19 pages", abstract = "In evolutionary algorithms, distance metrics between solutions are often useful for many aspects of guiding and understanding the search process. A good distance measure should reflect the capability of the search: if two solutions are found to be close in distance, or similarity, they should also be close in the search algorithm sense, i.e., the variation operator used to traverse the search space should easily transform one of them into the other. This paper explores such a distance for genetic programming syntax trees. Distance measures are discussed, defined and empirically investigated. The value of such measures is then validated in the context of analysis (fitness-distance correlation is analyzed during population evolution) as well as guiding search (results are improved using our measure in a fitness sharing algorithm) and diversity (new insights are obtained as compared with standard measures).", notes = "also known as \cite{4459225}", } @Article{Gustafson:2012:ieeeCIM, author = "Steven Gustafson", title = "Evolved to Win", journal = "Computational Intelligence Magazine, IEEE", year = "2012", month = aug, volume = "7", number = "3", pages = "62--64", note = "Evolved to Win by Moshe Sipper, 2011, Book Review", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/MCI.2012.2200629", ISSN = "1556-603X", size = "2 pages", abstract = "This book contains 12 chapters, of which 8 are descriptions of how the author used genetic programming to solve different games. In order, the games Prof. Sipper describes are lose checkers, chess end games, search algorithms for regular chess, backgammon, simulated Robocode,simulated racing cars, the puzzle Rush Hour, and the puzzle FreeCell. Each chapter, and one additional detailed chapter for lose checkers, gives a comprehensive description on how the author solved the game using genetic programming. The reader can get a good understanding of the work and approach used to solve the game before delving deeper into the original conference and journal papers published by the author for more rigorous descriptions and empirical results.", notes = "Review of \cite{EvolvedToWin}. Also known as \cite{6238497}", } @InProceedings{Gustafson:2015:GPTP, author = "Steven Gustafson and Ram Narasimhan and Ravi Palla and Aisha Yousuf", title = "Using Genetic Programming for Data Science: Lessons Learned", booktitle = "Genetic Programming Theory and Practice XIII", year = "2015", editor = "Rick Riolo and William P. Worzel and M. Kotanchek and A. Kordon", series = "Genetic and Evolutionary Computation", pages = "117--135", address = "Ann Arbor, USA", month = "14-16 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Data Science, Gradient boosted regression, Machine learning, Industrial applications, Real-world application, Lessons learned, Diversity, Ensembles", isbn13 = "978-3-319-34223-8", URL = "http://www.springer.com/us/book/9783319342214", DOI = "doi:10.1007/978-3-319-34223-8_7", abstract = "In this chapter we present a case study to demonstrate how the current state-of-the-art Genetic Programming (GP) fairs as a tool for the emerging field of Data Science. Data Science refers to the practice of extracting knowledge from data, often Big Data, to glean insights useful for predicting business, political or societal outcomes. Data Science tools are important to the practice as they allow Data Scientists to be productive and accurate. GP has many features that make it amenable as a tool for Data Science, but GP is not widely considered as a Data Science method as of yet. Thus, we performed a real-world comparison of GP with a popular Data Science method to understand its strengths and weaknesses. GP proved to find equally strong solutions, leveraged the new Big Data infrastructure, and was able to provide several benefits like direct feature importance and solution confidence. GP lacked the ability to quickly build and test models, required much more intensive computing power, and, due to its lack of commercial maturity, created some challenges for productization as well as integration with data management and visualization capabilities. The lessons learned leads to several recommendations that provide a path for future research to focus on key areas to improve GP as a Data Science tool.", notes = " Part of \cite{Riolo:2015:GPTP} published after the workshop in 2016", } @InProceedings{Gustafson:2016:GPTP, author = "Steven Gustafson and Arun Subramaniyan and Aisha Yousuf", title = "Assisting Asset Model Development with Evolutionary Augmentation", booktitle = "Genetic Programming Theory and Practice XIV", year = "2016", editor = "Rick Riolo and Bill Worzel and Brian Goldman and Bill Tozier", pages = "197--210", address = "Ann Arbor, USA", month = "19-21 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, genetic programming, lifting models, machine learning, industrial applications, real-world application, knowledge capture, artificial intelligence, intelligent augmentation", isbn13 = "978-3-319-97087-5", URL = "http://ico2s.org/seminars/2016-07-26-sg.html", URL = "https://www.springer.com/us/book/9783319970875", DOI = "doi:10.1007/978-3-319-97088-2_13", abstract = "In this chapter, we explore how Genetic Programming can assist and augment the expert-driven process of developing data-driven models. In our use case, modellers must develop hundreds of models that represent individual properties of a part, components, assets, systems and meta-systems like a power plant. Each of these models is developed with an objective in mind, like estimating the useful remaining life or anomaly detection. As such, the modeller uses their expert judgement as well as available data to select the most appropriate method. In this initial paper, we examine the most basic example of when the expert selects a kind of regression modelling approach and develops a model from data. We then use that captured domain knowledge from their process as well as end model to determine if Genetic Programming can augment, assist and improve their final result. We show that while Genetic Programming can indeed find improved solutions according to an error metric, it is much harder for Genetic Programming to find models that do not increase complexity. Also, we find that one approach in particular shows promise as a way to incorporate domain knowledge.", notes = "http://ico2s.org/seminars/2016-07-26-sg.html Part of \cite{Tozier:2016:GPTP} published after the workshop", } @Article{Gutierrez-Reina:2018:sensors, author = "Daniel Gutierrez-Reina and Vishal Sharma and Ilsun You and Sergio L. Toral Marin", title = "Dissimilarity Metric Based on Local Neighboring Information and Genetic Programming for Data Dissemination in Vehicular Ad Hoc Networks ({VANETs})", journal = "Sensors", year = "2018", volume = "18", number = "7", pages = "2320", keywords = "genetic algorithms, genetic programming, VANETs, broadcasting communications, dissimilarity metrics", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/sensors/sensors18.html#Gutierrez-Reina18", URL = "https://www.mdpi.com/1424-8220/18/7/2320", URL = "https://www.mdpi.com/1424-8220/18/7/2320/htm", URL = "https://www.mdpi.com/1424-8220/18/7/2320/pdf", DOI = "doi:10.3390/s18072320", size = "18 pages", abstract = "This paper presents a novel dissimilarity metric based on local neighbouring information and a genetic programming approach for efficient data dissemination in Vehicular Ad Hoc Networks (VANETs). The primary aim of the dissimilarity metric is to replace the Euclidean distance in probabilistic data dissemination schemes, which use the relative Euclidean distance among vehicles to determine the retransmission probability. The novel dissimilarity metric is obtained by applying a metaheuristic genetic programming approach, which provides a formula that maximises the Pearson Correlation Coefficient between the novel dissimilarity metric and the Euclidean metric in several representative VANET scenarios. Findings show that the obtained dissimilarity metric correlates with the Euclidean distance up to 8.9percent better than classical dissimilarity metrics. Moreover, the obtained dissimilarity metric is evaluated when used in well-known data dissemination schemes, such as p-persistence, polynomial and irresponsible algorithm. The obtained dissimilarity metric achieves significant improvements in terms of reachability in comparison with the classical dissimilarity metrics and the Euclidean metric-based schemes in the studied VANET urban scenarios", notes = "Also known as \cite{journals/sensors/Gutierrez-Reina18}", } @Article{Guven:2008:JIDE, author = "Aytac Guven and Mustafa Gunal", title = "Genetic Programming Approach for Prediction of Local Scour Downstream of Hydraulic Structures", journal = "Journal of Irrigation and Drainage Engineering", year = "2008", volume = "134", number = "2", pages = "241--249", month = mar # "/" # apr, publisher = "American Society of Civil Engineers", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1061/(ASCE)0733-9437(2008)134:2(241)", abstract = "This is a pioneer study that presents genetic programming (GP) as a new tool for prediction of local scour downstream of grade-control structures. The objective of this study is to provide an alternative formulation to conventional regression based equations and verify the superiority of GP over regression analysis. The training and testing patterns of the proposed GP formulation are based on well established and widely dispersed experimental results from the literature. Linear and nonlinear regression-based equations were derived throughout regression analysis on dimensionless parameters obtained from dimensional analysis. The GP-based formulation results are compared with experimental results and other equations and found to be more accurate.", notes = "1Research Assistant, Dept. of Civil Engineering, Faculty of Engineering, Univ. of Gaziantep, 27310 Gaziantep, Turkey 2Associate Professor, Dept. of Civil Engineering, Univ. of Gaziantep, 27310 Gaziantep, Turkey.", } @Article{Guven:2008:clean, author = "Aytac Guven and Ali Aytek and M. Ishak Yuce and Hafzullah Aksoy", title = "Genetic Programming-Based Empirical Model for Daily Reference Evapotranspiration Estimation", journal = "CLEAN - Soil, Air, Water", year = "2008", volume = "36", number = "10-11", pages = "905--912", keywords = "genetic algorithms, genetic programming, Evapotranspiration Artificial intelligence, Gene expression programming", DOI = "DOI:10.1002/clen.200800009", abstract = "Genetic programming (GP) is presented as a new tool for the estimation of reference evapotranspiration by using daily atmospheric variables obtained from the California Irrigation Management Information System (CIMIS) database. The variables employed in the model are daily solar radiation, daily mean temperature, average daily relative humidity and wind speed. The results obtained are compared to seven conventional reference evapotranspiration models including: (1) the Penman-Monteith equation modified by CIMIS, (2) the Penman-Monteith equation modified by the Food and Agricultural Organization (FAO 56), (3) the Hargreaves-Samani equation, (4) the solar radiation-based ET0 equation, (5) the Jensen-Haise equation, (6) the Jones-Ritchie equation, and (7) the Turc method. Statistical measures such as average, standard deviation, minimum and maximum values, as well as criteria such as mean square error and determination coefficient are used to measure the performance of the model developed by employing GP. Statistics and scatter plots indicate that the new equation produces quite satisfactorily results and can be used as an alternative to the conventional models.", notes = "Acta hydrochimica et hydrobiologica Correspondence to Ali Aytek, Gaziantep University, Department of Civil Engineering, Hydraulics Division, Gaziantep, Turkey", } @Article{Guven:2009:JESS, author = "Aytac Guven", title = "Linear genetic programming for time-series modelling of daily flow rate", journal = "Journal of Earth System Science", year = "2009", volume = "118", number = "2", pages = "137--146", month = apr, publisher = "Springer", keywords = "genetic algorithms, genetic programming, neural networks, daily flows, flow forecasting", ISSN = "0253-4126", URL = "http://www.ias.ac.in/jess/apr2009/137.pdf", size = "10 pages", abstract = "In this study linear genetic programming (LGP),which is a variant of Genetic Programming,and two versions of Neural Networks (NNs)are used in predicting time-series of daily flow rates at a station on Schuylkill River at Berne,PA,USA.Daily flow rate at present is being predicted based on different time-series scenarios.For this purpose,various LGP and NN models are calibrated with training sets and validated by testing sets.Additionally,the robustness of the proposed LGP and NN models are evaluated by application data,which are used neither in training nor at testing stage.The results showed that both techniques predicted the flow rate data in quite good agreement with the observed ones,and the predictions of LGP and NN are challenging.The performance of LGP,which was moderately better than NN,is very promising and hence supports the use of LGP in predicting of river flow data.", notes = "Civil Engineering Department, Gaziantep University, 27310 Gaziantep, Turkey.", } @Article{Guven:2009:JHE, author = "Aytac Guven and Ali Aytek", title = "New Approach for Stage-Discharge Relationship: Gene-Expression Programming", journal = "Journal of Hydrologic Engineering", year = "2009", volume = "14", number = "8", pages = "812--820", month = aug, keywords = "genetic algorithms, genetic programming, gene expression programming", ISSN = "1084-0699", DOI = "doi:10.1061/(ASCE)HE.1943-5584.0000044", abstract = "This study presents gene-expression programming (GEP), which is an extension to genetic programming, as an alternative approach to modelling stage discharge relationship. The results obtained are compared to more conventional methods, stage rating curve and multiple linear regression techniques. Statistical measures such as average, standard deviation, minimum and maximum values, as well as criteria such as mean square error and determination coefficient, the coefficient of efficiency, and the adjusted coefficient of efficiency are used to measure the performance of the models developed by employing GEP. Also, the explicit formulations of the developed GEP models are presented. Statistics and scatter plots indicate that the proposed equations produce quite satisfactory results and perform superior to conventional models.", } @Article{Guven2009985, author = "Aytac Guven and H. Md. Azamathulla and N. A. Zakaria", title = "Linear genetic programming for prediction of circular pile scour", journal = "Ocean Engineering", volume = "36", number = "12-13", pages = "985--991", year = "2009", ISSN = "0029-8018", DOI = "doi:10.1016/j.oceaneng.2009.05.010", URL = "http://www.sciencedirect.com/science/article/B6V4F-4WCTX10-3/2/805df81deb25d8c99465f876a03fc1e5", keywords = "genetic algorithms, genetic programming, Scour, Neuro-fuzzy, Circular pile, Regression", abstract = "Genetic programming (GP) has nowadays attracted the attention of researchers in the prediction of hydraulic data. This study presents linear genetic programming (LGP), which is an extension to GP, as an alternative tool in the prediction of scour depth around a circular pile due to waves in medium dense silt and sand bed. Field measurements were used to develop LGP models. The proposed LGP models were compared with adaptive neuro-fuzzy inference system (ANFIS) model results. The predictions of LGP models were observed to be in good agreement with measured data, and quite better than ANFIS and regression-based equation of scour depth at circular piles. The results were tabulated in terms of statistical error measures and illustrated via scatter plots.", } @Article{Guven:2011:WRM, author = "Aytac Guven and Ozgur Kisi", title = "Estimation of Suspended Sediment Yield in Natural Rivers Using Machine-coded Linear Genetic Programming", journal = "Water Resources Management", year = "2011", volume = "25", number = "2", pages = "691--704", month = jan, keywords = "genetic algorithms, genetic programming, gene expression programming, Suspended sediment yield, Modelling, Linear genetic programming, ANN, Neural networks", publisher = "Springer", ISSN = "0920-4741", DOI = "doi:10.1007/s11269-010-9721-x", size = "14 pages", abstract = "Estimation of suspended sediment yield is subject to uncertainty and bias. Many methods have been developed for estimating sediment yield but they still lack accuracy and robustness. This paper investigates the use of a machine-coded linear genetic programming (LGP) in daily suspended sediment estimation. The accuracy of LGP is compared with those of the Gene-expression programming (GEP), which is another branch of GP, and artificial neural network (ANN) technique. Daily streamflow and suspended sediment data from two stations on the Tongue River in Montana, USA, are used as case studies. Root mean square error (RMSE) and determination coefficient (R2) statistics are used for evaluating the accuracy of the models. Based on the comparison of the results, it is found that the LGP performs better than the GEP and ANN techniques. The GEP was also found to be better than the ANN. For the upstream and downstream stations, it is found that the LGP models with RMSE = 175 ton/day, R2 = 0.941 and RMSE = 254 ton/day, R2 = 0.959 in test period is superior in estimating daily suspended sediments than the best accurate GEP model with RMSE = 231 ton/day, R2 = 0.941 and RMSE = 331 ton/day, R2 = 0.934, respectively.", affiliation = "Civil Engineering Department, Hydraulics Division, Gaziantep University, 27310 Gaziantep, Turkey", } @Article{Guven:2011:IS, author = "Aytac Guven and Ozgur Kisi", title = "Daily pan evaporation modeling using linear genetic programming technique", journal = "Irrigation Science", year = "2011", volume = "29", number = "2", pages = "135--145", keywords = "genetic algorithms, genetic programming, gene expression programming", ISSN = "0342-7188", publisher = "Springer", DOI = "doi:10.1007/s00271-010-0225-5", size = "11 pages", abstract = "This paper investigates the ability of linear genetic programming (LGP), which is an extension to genetic programming (GP) technique, in daily pan evaporation modelling. The daily climatic data, air temperature, solar radiation, wind speed, pressure and humidity of three automated weather stations, Fresno, Los Angeles and San Diego in California, are used as inputs to the LGP to estimate pan evaporation. The LGP estimates are compared with those of the Gene-expression programming (GEP), which is another branch of GP, multilayer perceptrons (MLP), radial basis neural networks (RBNN), generalised regression neural networks (GRNN) and Stephens-Stewart (SS) models. The performances of the models are evaluated using root mean square errors (RMSE), mean absolute error (MAE) and determination coefficient (R 2) statistics. Based on the comparisons, it was found that the LGP technique could be employed successfully in modeling evaporation process from the available climatic data.", affiliation = "Civil Engineering Department, Hydraulics Division, Gaziantep University, 27310 Gaziantep, Turkey", } @Article{Guven:2013:JH, author = "Aytac Guven and Ozgur Kisi", title = "Monthly pan evaporation modeling using linear genetic programming", journal = "Journal of Hydrology", volume = "503", pages = "178--185", year = "2013", ISSN = "0022-1694", DOI = "doi:10.1016/j.jhydrol.2013.08.043", URL = "http://www.sciencedirect.com/science/article/pii/S0022169413006306", keywords = "genetic algorithms, genetic programming, Evaporation, Modelling, Fuzzy genetic, Neural networks, Neuro-fuzzy", abstract = "This study compares the accuracy of linear genetic programming (LGP), fuzzy genetic (FG), adaptive neuro-fuzzy inference system (ANFIS), artificial neural networks (ANN) and Stephens-Stewart (SS) methods in modelling pan evaporations. Monthly climatic data including solar radiation, air temperature, relative humidity, wind speed and pan evaporation from Antalya and Mersin stations, in Turkey are used in the study. The study composed of two parts. First part of the study focuses the comparison of LGP models with those of the FG, ANFIS, ANN and SS models in estimating pan evaporations of Antalya and Mersin stations, separately. From the comparison results, the LGP models are found to be better than the other models. Comparison of LGP models with the other models in estimating pan evaporations of the Mersin Station by using both stations' inputs is focused in the second part of the study. The results indicate that the LGP models better accuracy than the FG, ANFIS, ANN and SS models. It is seen that the pan evaporations can be successfully estimated by the LGP method", } @InCollection{guyaguler:2000:RPWTDRMP, author = "Baris Guyaguler", title = "Regression on Petroleum Well Test Data with the Reservoir Model as a Parameter", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "188--197", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms", notes = "part of \cite{koza:2000:gagp}", } @InProceedings{Guzman-Trampe:2012:evolve, author = "Juan Evencio Guzman-Trampe and Nareli {Cruz Cortes} and Daniel Ortiz-Arroyo", title = "Finding Similarity Functions for Classification with Genetic Programming: Preliminary Results", booktitle = "EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation {II}", year = "2012", editor = "Oliver Schuetze and Carlos A. {Coello Coello} and Alexandru-Adrian Tantar and Emilia Tantar and Pascal Bouvry and Pierre {Del Moral} and Pierrick Legrand", address = "Mexico City, Mexico", month = aug # " 7-9", keywords = "genetic algorithms, genetic programming", URL = "http://vbn.aau.dk/ws/files/68595778/TrampeaNareliOrtiz.pdf", URL = "https://www.saber.cic.ipn.mx/SABERv3/publicacions/webView/2012", size = "7 pages", abstract = "In this paper we propose a Genetic Programming algorithm designed with a coevolutive scheme for classication problems. Our algorithm searches for similarity functions that are applied to compare pairs of objects from a supervised sample. The output of these functions can be used in similarity-based classiers.", notes = "EVOLVE-2012 Not in Springer publication of proceedings", } @Article{GUZMANURBINA:2023:ijthermalsci, author = "Alexander Guzman-Urbina and Kazuki Fukushima and Hajime Ohno and Yasuhiro Fukushima", title = "Deriving local Nusselt number correlations for heat transfer of nanofluids by genetic programming", journal = "International Journal of Thermal Sciences", volume = "192", pages = "108382", year = "2023", ISSN = "1290-0729", DOI = "doi:10.1016/j.ijthermalsci.2023.108382", URL = "https://www.sciencedirect.com/science/article/pii/S1290072923002430", keywords = "genetic algorithms, genetic programming, Nanofluids, Nusselt number, Machine learning", abstract = "The application of nanoparticles in the design of heat exchange systems is anticipated to significantly enhance energy efficiency across the industrial, commercial, and residential sectors. A nanofluid results from mixing a base fluid and nanoparticles. These nanoparticles generally are made from various materials, including metals, metal oxides, and polymers. To design nanofluid-assisted energy systems, it is necessary to estimate the heat transfer achieved by the nanofluids. Most models developed to describe the heat transfer of fluids require estimating the Nusselt number (Nu), representing the ratio of convective heat transfer to conductive heat transfer in a given system. Previous work in nanofluids has focused on modeling equations for the average Nusselt number (Nuavg) in tube systems. However, an equation that allows the local Nusselt number (Nux) to be calculated is more versatile than one which calculates the Nuavg since it can be applied to any length of circular tubes. In this study, we derive equations using Genetic Programming (GP) to estimate the local Nusselt number of nanofluids (Nux,nf) that flow through horizontal circular tubes. The method uses an evolutionary algorithm to generate correlation equations for exploring the interaction of the flow regime, flow properties, system configuration, and nanoparticle properties. It comprises creating a population of candidate equations, selecting the best ones through fitness evaluation, and combining them through genetic operators (Selection, crossover, and mutation) to create a new generation of equations. Acceptable Nux,nf models (R2>0.9) were obtained with GP tree structures larger than three levels (depth 3). Findings from the Nux,nf correlations obtained show that the determinant variables for the model were the Reynolds and Prandtl numbers. This implies that the effect of the nanofluids is driven mainly by alterations made by the nanoparticles to the inertial forces of the flow and the thermal diffusivity. The results of this study highlight the potential of this machine-learning-based approach to provide insight into the physicochemical mass and heat transfer mechanisms", } @InProceedings{Gypteau:2015:evoApplications, author = "Jeremie Gypteau and Fernando Otero and Michael Kampouridis", title = "Generating Directional Change Based Trading Strategies with Genetic Programming", booktitle = "18th European Conference on the Applications of Evolutionary Computation", year = "2015", editor = "Antonio M. Mora and Giovanni Squillero", series = "LNCS", volume = "9028", publisher = "Springer", pages = "267--278", address = "Copenhagen", month = "8-10 " # apr, organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, Directional changes, Financial forecasting, Trading", isbn13 = "978-3-319-16548-6", URL = "http://www.kampouridis.net/papers/EvoFin2015.pdf", DOI = "doi:10.1007/978-3-319-16549-3_22", size = "12 pages", abstract = "The majority of forecasting tools use a physical time scale for studying price fluctuations of financial markets, making the flow of physical time discontinuous. Therefore, using a physical time scale may expose companies to risks, due to ignorance of some significant activities. In this paper, an alternative and novel approach is explored to capture important activities in the market. The main idea is to use an intrinsic time scale based on Directional Changes. Combined with Genetic Programming, the proposed approach aims to find an optimal trading strategy to forecast the future price moves of a financial market. In order to evaluate its efficiency and robustness as forecasting tool, a series of experiments was performed, where we were able to obtain valuable information about the forecasting performance. The results from the experiments indicate that this new framework is able to generate new and profitable trading strategies.", notes = "EvoFIN EvoApplications2015 held in conjunction with EuroGP'2015, EvoCOP2015 and EvoMusArt2015 http://www.evostar.org/2015/cfp_evoapps.php", } @InProceedings{Ha:2015:GECCO, author = "Sungjoo Ha and Byung-Ro Moon", title = "Fast Knowledge Discovery in Time Series with GPGPU on Genetic Programming", booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation", year = "2015", editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr", isbn13 = "978-1-4503-3472-3", pages = "1159--1166", keywords = "genetic algorithms, genetic programming, Parallel Evolutionary Systems", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739480.2754669", DOI = "doi:10.1145/2739480.2754669", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "We tackle the problem of knowledge discovery in time series data using genetic programming and GPGPUs. Using genetic programming, various precursor patterns that have certain attractive qualities are evolved to predict the events of interest. Unfortunately, evolving a set of diverse patterns typically takes huge execution time, sometimes longer than one month for this case. In this paper, we address this problem by proposing a parallel GP framework using GPGPUs, particularly in the context of big financial data. By maximally exploiting the structure of the nVidia GPGPU platform on stock market time series data, we were able see more than 250-fold reduction in the running time.", notes = "Also known as \cite{2754669} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)", } @InProceedings{Ha:2016:GECCOcomp, author = "Sungjoo Ha and Sangyeop Lee and Byung-Ro Moon", title = "Inspecting the Latent Space of Stock Market Data with Genetic Programming", booktitle = "GECCO '16 Companion: Proceedings of the Companion Publication of the 2016 Annual Conference on Genetic and Evolutionary Computation", year = "2016", editor = "Tobias Friedrich and Frank Neumann and Andrew M. Sutton and Martin Middendorf and Xiaodong Li and Emma Hart and Mengjie Zhang and Youhei Akimoto and Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and Daniele Loiacono and Julian Togelius and Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and Faustino Gomez and Carlos M. Fonseca and Heike Trautmann and Alberto Moraglio and William F. Punch and Krzysztof Krawiec and Zdenek Vasicek and Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and Boris Naujoks and Enrique Alba and Gabriela Ochoa and Simon Poulding and Dirk Sudholt and Timo Koetzing", pages = "63--64", keywords = "genetic algorithms, genetic programming: Poster", month = "20-24 " # jul, organisation = "SIGEVO", address = "Denver, USA", publisher = "ACM", publisher_address = "New York, NY, USA", isbn13 = "978-1-4503-4323-7", DOI = "doi:10.1145/2908961.2909004", abstract = "We suggest a method of inspecting the latent space of stock market data using genetic programming. Given black box patterns and (stock, day) tuples a relation matrix is constructed. Applying a low-rank matrix factorization technique to the relation matrix induces a latent vector space. By manipulating the latent vector representations of black box patterns, the geometry of the latent space can be examined. Genetic programming constructs a tree representation corresponding to an arbitrary latent vector representation, allowing us to interpret the result of the inspection.", notes = "Distributed at GECCO-2016.", } @InProceedings{Ha:2018:GECCO, author = "Sungjoo Ha and Sangyeop Lee and Byung-Ro Moon", title = "Investigation of the latent space of stock market patterns with genetic programming", booktitle = "GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference", year = "2018", editor = "Hernan Aguirre and Keiki Takadama and Hisashi Handa and Arnaud Liefooghe and Tomohiro Yoshikawa and Andrew M. Sutton and Satoshi Ono and Francisco Chicano and Shinichi Shirakawa and Zdenek Vasicek and Roderich Gross and Andries Engelbrecht and Emma Hart and Sebastian Risi and Ekart Aniko and Julian Togelius and Sebastien Verel and Christian Blum and Will Browne and Yusuke Nojima and Tea Tusar and Qingfu Zhang and Nikolaus Hansen and Jose Antonio Lozano and Dirk Thierens and Tian-Li Yu and Juergen Branke and Yaochu Jin and Sara Silva and Hitoshi Iba and Anna I Esparcia-Alcazar and Thomas Bartz-Beielstein and Federica Sarro and Giuliano Antoniol and Anne Auger and Per Kristian Lehre", isbn13 = "978-1-4503-5618-3", pages = "1254--1261", address = "Kyoto, Japan", DOI = "doi:10.1145/3205455.3205493", publisher = "ACM", publisher_address = "New York, NY, USA", month = "15-19 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", abstract = "We suggest a use of genetic programming for transformation from a vector space to an understandable graph representation, which is part of a project to inspect the latent space in matrix factorization. Given a relation matrix, we can apply standard techniques such as non-negative matrix factorization to extract low dimensional latent space in vector representation. While the vector representation of the latent space is useful, it is not intuitive and hard to interpret. The transformation with the help of genetic programming allows us to better understand the underlying latent structure. Applying the method in the context of a stock market, we show that it is possible to recover the tree representation of technical patterns from a relation matrix. Leveraging the properties of the vector representations, we are able to find patterns that correspond to cluster centres of technical patterns. We further investigate the geometry of the latent space.", notes = "Also known as \cite{3205493} GECCO-2018 A Recombination of the 27th International Conference on Genetic Algorithms (ICGA-2018) and the 23rd Annual Genetic Programming Conference (GP-2018)", } @Article{ha:Memetic_Computing, author = "Sungjoo Ha and Byung-Ro Moon", title = "Finding attractive technical patterns in cryptocurrency markets", journal = "Memetic Computing", year = "2018", volume = "10", number = "3", pages = "301--306", month = sep, keywords = "genetic algorithms, genetic programming, Technical patterns, Cryptocurrency, Algorithmic trading", ISSN = "1865-9292", URL = "http://rdcu.be/IJDd", URL = "https://doi.org/10.1007/s12293-018-0252-y", DOI = "doi:10.1007/s12293-018-0252-y", size = "6 pages", abstract = "The cryptographic currency market is an emerging venue for traders looking to diversify their investments. We investigate the use of genetic programming (GP) for finding attractive technical patterns in a cryptocurrency market. We decompose the problem of automatic trading into two parts, mining useful signals and applying them to trading strategies, and focus our attention on the former. Extensive experiments are performed to analyse the factors that affect the quality of the solutions found by the proposed GP system. With the introduction of domain knowledge through extended function sets and the inclusion of diversity preserving mechanism, we show that the proposed GP system successfully finds attractive technical patterns. Out-of-sample performance of the patterns indicates that the GP consistently finds signals that are profitable and frequent. A trading simulation with the generated patterns suggests that the captured signals are indeed useful for portfolio optimization.", notes = "School of Computer Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 151-744, Korea", } @PhdThesis{Haan:thesis, author = "Josien Carolien Haan", title = "Analysis of chromosomal copy number aberrations in gastrointestinal cancer", school = "Vrije Universiteit Amsterdam", year = "2014", address = "Holland", month = "17 " # mar, URL = "http://hdl.handle.net/1871/50550", URL = "http://www.literatuurplein.nl/boekdetail.jsp?boekId=900014", isbn13 = "978-90-5383-046-8", size = "approx 250 pages", notes = "Not on GP? Supervisors prof.dr. G.A. Meijer and prof.dr. C.J.A. Punt", } @InProceedings{Haasdijk:2008:cec, author = "E. Haasdijk and P. Vogt and A. E. Eiben", title = "Social Learning in Population-Based Adaptive Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "1386--1392", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1823-7", file = "EC0363.pdf", DOI = "doi:10.1109/CEC.2008.4630975", abstract = "The subject of the present investigation is Population-based Adaptive Systems (PAS), as implemented in the NEW TIES platform. In many existing PASs two adaptation mechanisms are combined, (non-Lamarckian) evolution and individual learning, inevitably raising the issue of `forgetful populations': individually learnt knowledge disappears when the individual that learnt it dies. We propose social learning by explicit knowledge transfer to overcome this problem. Our mechanism is based on (1) direct communication among agents in the population, (2) messages carrying rules that the sender agent uses in its controller, and (3) the ability of the recipient agent to incorporate foreign rules into its controller. Thus, knowledge can be disseminated and multiplied within the same generation, making the population a knowledge reservoir for individually acquired knowledge. We present an initial assessment of this idea and show that this social mechanism is capable of efficiently distributing knowledge and improving the performance of the population.", keywords = "genetic algorithms, genetic programming", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @InCollection{haberman:1994:aa, author = "Mike Haberman", title = "Altrusitic Ants", booktitle = "Artificial Life at Stanford 1994", year = "1994", editor = "John R. Koza", pages = "34--43", address = "Stanford, California, 94305-3079 USA", month = jun, organisation = "Stanford University", publisher = "Stanford Bookstore", keywords = "genetic algorithms", ISBN = "0-18-182105-2", notes = "Ant World mazes This volume contains 22 papers written and submitted by students describing their term projects for the course in artificial life (Computer Science 425) at Stanford University offered during the spring quarter quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs425.html", } @InProceedings{Habib:2018:ITT, author = "Maria Habib and Hossam Faris and Mohammad A. Hassonah and Ja'far Alqatawna and Alaa F. Sheta and Ala' M. Al-Zoubi", title = "Automatic Email Spam Detection using Genetic Programming with {SMOTE}", booktitle = "Fifth HCT Information Technology Trends, ITT 2018", year = "2018", editor = "Amala Rajan", pages = "185--190", address = "Dubai, UAE", month = nov # " 28-29", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CTIT.2018.8649534", size = "6 pages", abstract = "Being one of the major communication ways on the Internet, the emailing systems need to be protected from spam which represents unsolicited messages with serious threats to both individual users and organizations. Realizing this issue, it is an imperious necessity to develop more accurate and effective spam detection models for the emailing platforms. In this paper, an efficient email spam detection model based on Genetic Programming (GP) combined with Synthetic Minority Over-sampling Technique (SMOTE) is proposed to detect spam emails. The model is applied and tested on two benchmark email corpora and tested against four other well-recognized classifiers using four measures; accuracy, recall, precision and G-mean. Experimental results show that GP combined with SMOTE can effectively classify spam emails outperforming the usual classification methods.", notes = "King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan INSPEC Accession Number: 18474238 Also known as \cite{8649534}", } @Article{oai:thesai.org:10.14569/IJACSA.2017.080527, author = "Rafi Ullah and Hani Ali Alquhayz", title = "Intelligent Watermarking Scheme for image Authentication and Recovery", journal = "International Journal of Advanced Computer Science and Applications (IJACSA)", year = "2017", volume = "8", number = "5", keywords = "genetic algorithms, genetic programming, watermarking, authentication, quantisation, recovery", publisher = "The Science and Information (SAI) Organization", bibsource = "OAI-PMH server at thesai.org", description = "International Journal of Advanced Computer Science and Applications(IJACSA), 8(5), 2017", language = "eng", oai = "oai:thesai.org:10.14569/IJACSA.2017.080527", URL = "http://thesai.org/Downloads/Volume8No5/Paper_27-Intelligent_Watermarking_Scheme_for_Image_Authentication.pdf", DOI = "doi:10.14569/IJACSA.2017.080527", abstract = "Recently, researchers have proposed semi-fragile watermarking techniques with the additional capability of image recovery. However, these approaches have certain limitations with respect to capacity, imperceptibility, and robustness. In this paper, we are proposing two independent watermarks, one for image recovery and the other for authentication. The first watermark (image digest), a highly compressed version of the original image itself, is used to recover the distorted image. Unlike the traditional quantisation matrix, genetic programming based matrices are used for compression purposes. These matrices are based on the local characteristics of the original image. Furthermore, a second watermark, which is a pseudo-random binary matrix, is generated to authenticate the host image precisely. Experimental results show that the semi-fragility of the watermarks makes the proposed scheme tolerant of JPEG lossy compression and it locates the tampered regions accurately.", } @Article{Habib2019, author = "Rafi Ullah Habib and Hani Ali Alquhayz", title = "Medical Image(s) Watermarking and its Optimization using Genetic Programming", journal = "International Journal of Advanced Computer Science and Applications", year = "2019", volume = "10", number = "4", pages = "163--169", keywords = "genetic algorithms, genetic programming, Capacity, imperceptibility, image sequence, watermarking, GPLAB, ultrasound", publisher = "The Science and Information Organization", ISSN = "2158-107X", URL = "https://thesai.org/Downloads/Volume10No4/Paper_19-Medical_Images_Watermarking.pdf", URL = "http://dx.doi.org/10.14569/IJACSA.2019.0100419", DOI = "doi:10.14569/IJACSA.2019.0100419", size = "7 pages", abstract = "an medical image watermarking technique has been proposed, where intelligence has been incorporated into the encoding and decoding structure. The motion vectors of the medical image sequence are used for embedding the watermark. Instead of a manual selection of the candidate motion vectors, a generalized approach is used to select the most suitable motion vectors for embedding the watermark. Genetic programming (GP) module has been employed to develop a function in accordance with imperceptibility and watermarking capacity. Employment of intelligence in the system improves its imperceptibility, capacity, and resistance toward different attacks that can occur during communication and storing. The motion vectors are generated by applying a block-based motion estimation algorithm. In this work, Full-Search method has been used for its better performance as compared to the other methods. Experimental results show marked improvement in capacity and visual similarity as compared to the conventional approaches.", notes = "IJACSA www.ijacsa.thesai.org Department of Computer Science and Information, College of Science, Majmaah University, Majmaah 11952, Saudi Arabia", } @Article{Habib:2019:IJACSA, title = "Optimal Compression of Medical Images", author = "Rafi Ullah Habib", journal = "International Journal of Advanced Computer Science and Applications(IJACSA)", publisher = "The Science and Information (SAI) Organization", year = "2019", number = "4", volume = "10", keywords = "genetic algorithms, genetic programming, medical images, wavelet transform, JPEG2000, compression, quantization", URL = "http://thesai.org/Downloads/Volume10No4/Paper_15-Optimal_Compression_of_Medical_Images.pdf", DOI = "doi:10.14569/IJACSA.2019.0100415", abstract = "In todays healthcare system, medical images are playing a vital role in the diagnosis. The challenges arise to the hospital management systems (HMS) are to store and communicate the large volume of medical images generated by various imaging modalities. Efficient compression of medical images is required to reduce the bit rate to increase the storage capacity and speed-up the transmission without affecting its quality. Over the past few decades, several compression standards have been proposed. In this paper, an intelligent JPEG2000 compression scheme is presented to compress the medical images efficiently. Unlike the traditional compression techniques, genetic programming (GP)-based quantisation matrices are used to quantise the wavelet coefficients of the input image. Experimental results validate the usefulness of the proposed intelligent compression scheme.", bibsource = "OAI-PMH server at thesai.org", language = "eng", oai = "oai:thesai.org:10.14569/IJACSA.2019.0100415", } @InProceedings{hackworth:1999:IPARAGA, author = "Tim Hackworth", title = "India and Pakistan, a classic ``Richardson'' Arms Race: A Genetic Algorithmic approach", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1543--1550", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "real world applications", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-700.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-700.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{hackworth:1999:GS, author = "Tim Hackworth", title = "Genetic algorithms; Some effects of redundancy in chromosomes", booktitle = "Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference", year = "1999", editor = "Scott Brave and Annie S. Wu", pages = "99--106", address = "Orlando, Florida, USA", month = "13 " # jul, keywords = "Genetic Algorithms", notes = "GECCO-99LB", } @Article{HADAVIMOGHADDAM:2022:ijhydene, author = "Fahimeh Hadavimoghaddam and Mohammad-Reza Mohammadi and Saeid Atashrouz and Dragutin Nedeljkovic and Abdolhossein Hemmati-Sarapardeh and Ahmad Mohaddespour", title = "Data-driven modeling of {H2} solubility in hydrocarbons using white-box approaches", journal = "International Journal of Hydrogen Energy", volume = "47", number = "78", pages = "33224--33238", year = "2022", ISSN = "0360-3199", DOI = "doi:10.1016/j.ijhydene.2022.07.238", URL = "https://www.sciencedirect.com/science/article/pii/S0360319922033481", keywords = "genetic algorithms, genetic programming, Advanced correlation techniques, Hydrogen solubility, Hydrocarbon, GP, GMDH, Leverage technique", abstract = "As a result of technological advancements, reliable calculation of hydrogen (H2) solubility in diverse hydrocarbons is now required for the design and efficient operation of processes in chemical and petroleum processing facilities. The accuracy of equations of state (EOSs) in estimating H2 solubility is restricted, particularly in high-pressure or/and high-temperature conditions, which could result in energy loss and/or potential safety and environmental problem. Two strong machine learning techniques for building advanced correlation were used to evaluate H2 solubility in hydrocarbons in this study which were Group method of data handling (GMDH) and genetic programming (GP). For that purpose, 1332 datasets from experimental results of H2 solubility in 32 distinct hydrocarbons were collected from 68 various systems throughout a wide range of operating temperatures from 98 K to 701 K and pressures from 0.101325 MPa to 78.45 MPa. Hydrocarbons from two distinct classes include alkane, alkene, cycloalkane, aromatic, polycyclic aromatic, and terpene. Hydrocarbons have a molecular mass range of 28.054-647.2 g/mol, which corresponds to a carbon number of 2-46. Solvent molecular weight, critical pressure, and critical temperature, as well as pressure and temperature operational parameters, were used to create the features. With a regression coefficient (R2) which was equal to 0.986 and root mean square error (RMSE) which was 0.0132, the GP modeling approach estimated experimental solubility data more accurately than the GMDH approach. Operating pressure, followed by molecular weight of hydrocarbon solvents and temperature, had the greatest influence on estimation H2 solubility, according to sensitivity analysis. The GP model shown in this paper is a reliable development that may be used in the chemical and petroleum sectors as a reliable and effective estimator for H2 solubility in diverse hydrocarbons", } @Article{Hadavimoghaddam:2023:ijhydene, author = "Fahimeh Hadavimoghaddam and Mohammad-Reza Mohammadi and Saeid Atashrouz and Ali Bostani and Abdolhossein Hemmati-Sarapardeh and Ahmad Mohaddespour", title = "Modeling hydrogen solubility in alcohols using group method of data handling and genetic programming", journal = "International Journal of Hydrogen Energy", volume = "48", number = "7", pages = "2689--2704", year = "2023", ISSN = "0360-3199", DOI = "doi:10.1016/j.ijhydene.2022.10.017", URL = "https://www.sciencedirect.com/science/article/pii/S0360319922046407", keywords = "genetic algorithms, genetic programming, Hydrogen solubility, White-box approach, Correlation: GMDH, GP, Leverage technique", abstract = "Having accurate information about the solubility of hydrogen (H2) in alcoholic solvents is crucial for the design and implementation of numerous chemical processes. In this communication, two robust correlative techniques, Genetic programming (GP) and Group method of data handling (GMDH) were used to estimate H2 solubility in alcohols. For the mentioned purpose, 673 laboratory data of H2 solubility for 26 distinct alcoholic solvents were collected over a broad interval of operating pressure from 0.101 MPa to 110.3 MPa and temperature from 213.15 K to 524.9 K. These solvents include fatty alcohols, aliphatic alcohols, diols, glycols, and hydroxypolyether with molecular weights ranging from 32.042 to 242.446 g/mol. The algorithms' input parameters were selected to be molecular weight of alcohol, the temperature and pressure of the solubility system, critical temperature and pressure of alcohols. According to the graphical and statistical assessments, the GMDH model was shown to be the best choice for estimating H2 solubility in alcoholic solvents, with a root mean square error of 0.00482 and a coefficient of determination of 0.9841. Furthermore, according to sensitivity analysis, the greatest influence on H2 solubility in alcoholic solvents is dedicated to pressure, temperature, and molecular weight of alcohols. Furthermore, the Leverage technique was used to identify the application domain of the GMDH model and outlier data, with the findings indicating that GMDH has a high credit for estimating H2 dissolution in alcoholic media", } @Article{Hadavimoghaddam:2023:ijhydene2, author = "Fahimeh Hadavimoghaddam and Sajjad Ansari and Saeid Atashrouz and Ali Abedi and Abdolhossein Hemmati-Sarapardeh and Ahmad Mohaddespour", title = "Application of advanced correlative approaches to modeling hydrogen solubility in hydrocarbon fuels", journal = "International Journal of Hydrogen Energy", volume = "48", number = "51", pages = "19564--19579", year = "2023", ISSN = "0360-3199", DOI = "doi:10.1016/j.ijhydene.2023.01.155", URL = "https://www.sciencedirect.com/science/article/pii/S036031992300294X", keywords = "genetic algorithms, genetic programming, Hydrogen solubility, Hydrocarbon fuels, Robust correlation, GMDH, GP", abstract = "In petroleum and petrochemical refineries, having precise knowledge regarding H2 solubility in hydrocarbon fuels and feedstocks is critical. In this study, the hydrogen solubility in hydrocarbon fuels was estimated using genetic programming (GP) and group method of data handling (GMDH), two exemplary robust advanced models for generating correlation. To do this, 445 observations derived from labratory findings on hydrogen solubility in 17 different hydrocarbon fuels such as bitumen, atmospheric residue, heavy coking gas oil, heavy virgin gas oil, light virgin gas oil, straight run gas oil, shale fuel oil, dephenolated shale fuel oil, diesel, hydrogenated coal liquid, coal liquid, and coal oil, over a large interval of P- operating pressures and T-temperatures were collected. Temperature, pressure, as well as density at 20 degreeC, molecular weight, and weight percentage of carbon (C) and hydrogen (H) in hydrocarbon fuels, were used as input parameters in developing robust correlations. The outcomes showed the GMDH approach is more precise compared to the GP, with a root mean square error (RMSE) of 0.053302 and a determination coefficient (R2) of 0.9641. Additionally, sensitivity analysis showed that pressure, followed by temperature and H (wtpercent) of hydrocarbon fuels, has the greatest impact on hydrogen solubility in hydrocarbon fuels. Ultimately, the Leverage method's results suggested that the GMDH model could be relied on to predict hydrogen solubility in hydrocarbon fuels", } @Article{HADAVIMOGHADDAM:2023:geoen, author = "Fahimeh Hadavimoghaddam and Aboozar Garavand and Alexei Rozhenko and Masoud Mostajeran Gortani and Abdolhossein Hemmati-Sarapardeh", title = "Toward smart correlations for predicting in-situ stress: Application to evaluating subsurface energy structures", journal = "Geoenergy Science and Engineering", volume = "231", pages = "212292", year = "2023", ISSN = "2949-8910", DOI = "doi:10.1016/j.geoen.2023.212292", URL = "https://www.sciencedirect.com/science/article/pii/S2949891023008795", keywords = "genetic algorithms, genetic programming, Gene expression programming, In-situ stress, Borehole breakouts, Robust correlation, Group method of data handling (GMDH)", abstract = "The precise calculation of the in-situ stress tensor is a crucial factor in addressing the challenges associated with the development of subsurface energy structures. To establish a consistent relationship between breakout shapes and the in-situ stress using nonlinear or coupled process assumptions through numerical methods, effective techniques are required. In this regard, three white box algorithms, namely gene expression programming (GEP), genetic programming (GP), and the group method of data handling (GMDH), were developed to predict the maximum horizontal stress. To develop a robust correlation using the white box algorithms, 662 data points were obtained from numerical analysis using an elastoplastic model across a wide range of wellbore pressures. Input parameters were breakout width and depth, wellbore pressure, and minimum horizontal stress. The study results indicated that the GP algorithm demonstrates higher accuracy compared to the GEP and GMDH, with a root mean square error (RMSE) of 0.9977 and a determination coefficient (R2) of 0.97564. Additionally, both SHAP values (SHapley Additive exPlanations) and sensitivity analysis were employed. The sensitivity analysis revealed that breakout width has a greater influence on predicting the maximum in-situ stress compared to other parameters. Furthermore, the Leverage technique indicated that the GP model can be considered a reliable tool for accurately estimating the in-situ stress, making it suitable for use in the subsurface energy structures", } @InProceedings{Haddadi:evoapps13, author = "Fariba Haddadi and H. Gunes Kayacik and A. Nur Zincir-Heywood and Malcolm I. Heywood", title = "Malicious Automatically Generated Domain Name Detection Using {Stateful-SBB}", booktitle = "Applications of Evolutionary Computing, EvoApplications 2013: EvoCOMNET, EvoCOMPLEX, EvoENERGY, EvoFIN, EvoGAMES, EvoIASP, EvoINDUSTRY, EvoNUM, EvoPAR, EvoRISK, EvoROBOT, EvoSTOC", year = "2013", month = "3-5 " # apr, editor = "Anna I. Esparcia-Alcazar and Antonio Della Cioppa and Ivanoe {De Falco} and Ernesto Tarantino and Carlos Cotta and Robert Schaefer and Konrad Diwold and Kyrre Glette and Andrea Tettamanzi and Alexandros Agapitos and Paolo Burrelli and J. J. Merelo and Stefano Cagnoni and Mengjie Zhang and Neil Urquhart and Kevin Sim and Aniko Ekart and Francisco {Fernandez de Vega} and Sara Silva and Evert Haasdijk and Gusz Eiben and Anabela Simoes and Philipp Rohlfshagen", series = "LNCS", volume = "7835", publisher = "Springer Verlag", address = "Vienna", publisher_address = "Berlin", pages = "529--539", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, Security, Botnet detection, Evolutionary computation, Data mining", isbn13 = "978-3-642-37191-2", DOI = "doi:10.1007/978-3-642-37192-9_53", size = "11 pages", abstract = "This work investigates the detection of Botnet Command and Control (C&C) activity by monitoring Domain Name System (DNS) traffic. Detection signatures are automatically generated using evolutionary computation technique based on Stateful-SBB. The evaluation performed shows that the proposed system can work on raw variable length domain name strings with very high accuracy.", notes = " EvoApplications2013 held in conjunction with EuroGP2013, EvoCOP2013, EvoBio'2013 and EvoMusArt2013", } @InProceedings{Haddadi:2013:CEC, article_id = "1158", author = "Fariba Haddadi and A. Nur Zincir-Heywood", title = "Analyzing String Format-Based Classifiers For Botnet Detection: GP and SVM", booktitle = "2013 IEEE Conference on Evolutionary Computation", volume = "1", year = "2013", month = jun # " 20-23", editor = "Luis Gerardo {de la Fraga}", pages = "2626--2633", address = "Cancun, Mexico", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-0453-2", DOI = "doi:10.1109/CEC.2013.6557886", notes = "CEC 2013 - A joint meeting of the IEEE, the EPS and the IET.", } @InProceedings{Haddadi:2014:GECCOcomp, author = "Fariba Haddadi and Dylan Runkel and A. Nur Zincir-Heywood and Malcolm I. Heywood", title = "On botnet behaviour analysis using {GP} and {C4.5}", booktitle = "GECCO 2014 Workshop on genetic and evolutionary computation in defense, security and risk management (SecDef)", year = "2014", editor = "Anna I Esparcia-Alcazar and Frank W. Moore", isbn13 = "978-1-4503-2881-4", keywords = "genetic algorithms, genetic programming", pages = "1253--1260", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "https://web.cs.dal.ca/~mheywood/OpenAccess/open-haddadi14.pdf", URL = "http://doi.acm.org/10.1145/2598394.2605435", DOI = "doi:10.1145/2598394.2605435", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Botnets represent a destructive cyber security threat that aim to hide their malicious activities within legitimate Internet traffic. Part of what makes botnets so affective is that they often upgrade themselves over time, hence reacting to improved detection mechanisms. In addition, Internet common communication protocols (i.e. HTTP) are used for the purposes of constructing subversive communication channels. This work employs machine learning algorithms (genetic programming and decision trees) to detect distinct behaviours in various botnets. That is to say, botnets mimic legitimate HTTP traffic while actually serving botnet purposes. To this end, two different feature sets are employed and analysed to see how differences between three botnets - Zeus, Conficker and Torpig - can be distinguished. Specific recommendations are then made regarding the utility of different feature sets and machine learning algorithms for detecting each type of botnet.", notes = "Also known as \cite{2605435} Distributed at GECCO-2014.", } @InProceedings{Haddadi:2015:GECCOcomp, author = "Fariba Haddadi and A. Nur Zincir-Heywood", title = "Botnet Detection System Analysis on the Effect of Botnet Evolution and Feature Representation", booktitle = "SecDef'2015 - Workshop on genetic and evolutionary computation in defense, security and risk management", year = "2015", editor = "Frank W. Moore and Nur Zincir-Heywood", isbn13 = "978-1-4503-3488-4", keywords = "genetic algorithms, genetic programming", pages = "893--900", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739482.2768435", DOI = "doi:10.1145/2739482.2768435", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Botnets are known as one of the main destructive threats that have been active since 2003 in various forms. The ability to upgrade the structure and algorithms on the fly is part of what causes botnets to survive for more than a decade. Hence, one of the main concerns in designing a botnet detection system is how long such a system can be effective and useful considering the evolution of a given botnet. Furthermore, the data representation and the feature extraction components have always been an important issue in order to design a robust detection system. In this work, we employ machine learning algorithms (genetic programming and decision trees) to explore two questions: (i) How can the representation of non-numeric features effect the detection system's performance? and (ii) How long can a machine learning based detection system can perform effectively? To this end, we gathered seven Zeus botnet data sets over a period of four years and analysed three different data representation techniques to be able to explore aforementioned questions.", notes = "Also known as \cite{2768435} Distributed at GECCO-2015.", } @PhdThesis{Haddadi:thesis, author = "Fariba Haddadi", title = "Investigating a behaviour analysis-based early warning system to identify botnets using machine learning algorithms", school = "Dalhousie University", year = "2016", address = "Halifax, Nova Scotia, Canada", month = sep, keywords = "genetic algorithms, genetic programming", URL = "https://dalspaceb.library.dal.ca/server/api/core/bitstreams/a276400e-7b38-4a30-a53e-58b15689bf23/content", size = "191 pages", abstract = "Botnets represent one of the more aggressive threats against cyber security and botnet traffic analysis is one of the main approaches to study and investigate such threats. Botnets employ different techniques (e.g. fluxing and encryption), topologies (e.g. centralized and de-centralized) and communication protocols (e.g. HTTP and DNS) in different stages of their lifecycle. Therefore, identifying the botnets has become very challenging given that they can upgrade their methodology automatically at any time for one reason or another. To this end, different approaches are proposed for botnet traffic analysis and detection based on various botnet behaviours and structures. Hence, the main focus of this thesis is to investigate various botnet detection approaches based on the technique used and the available data. Specifically, two main categories of solutions are explored: application data analysis-based solutions and network analysisbased solutions. In the application data analysis category, two different approaches are explored: one with a priori knowledge and the other one without any a priori knowledge. On the other hand, flow-based botnet detection approaches are explored in the network analysis-based category focused on using minimum a priori knowledge. In this case, various feature extraction methods, machine learning algorithms, protocol filtering, non-numeric feature representation, normal behaviour representation and time generalization issues are investigated. Finally, a flow-based early warning system is proposed. The effectiveness of the solutions is shown on several botnet data sets from IRC botnets to peer-to-peer botnets. Results indicate that the proposed solutions can detect botnet behaviour with good performances. Moreover, two botnet detection systems from the literature and two publicly available malicious behaviour detection systems are employed for further evaluation of the proposed early warning system. The results indicate that the proposed system outperformed these four systems. Last but not least, the proposed system is evaluated as well on botnets in cellular networks on an exploratory basis. It is shown that the proposed system demonstrates promising performance under such circumstances as well.", notes = "supervisor: Nur Zincir-Heywood", } @Article{Haddow:2011:GPEM, author = "Pauline C. Haddow", title = "Introduction: special issue on evolvable hardware challenges", journal = "Genetic Programming and Evolvable Machines", year = "2011", volume = "12", number = "3", pages = "181--182", month = sep, note = "EDITORIAL", keywords = "genetic algorithms, evolvable hardware", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-011-9138-1", size = "2 pages", } @Article{Haddow:2011:GPEM2, author = "Pauline C. Haddow and Andy M. Tyrrell", title = "Challenges of evolvable hardware: past, present and the path to a promising future", journal = "Genetic Programming and Evolvable Machines", year = "2011", volume = "12", number = "3", pages = "183--215", month = sep, keywords = "genetic algorithms, genetic programming, evolvable hardware, EHW, Future technology, Scalability, Computation medium, Review", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-011-9141-6", size = "33 pages", abstract = "Nature is phenomenal. The achievements in, for example, evolution are everywhere to be seen: complexity, resilience, inventive solutions and beauty. Evolvable Hardware (EH) is a field of evolutionary computation (EC) that focuses on the embodiment of evolution in a physical media. If EH could achieve even a small step in natural evolution's achievements, it would be a significant step for hardware designers. Before the field of EH began, EC had already shown artificial evolution to be a highly competitive problem solver. EH thus started off as a new and exciting field with much promise. It seemed only a matter of time before researchers would find ways to convert such techniques into hardware problem solvers and further refine the techniques to achieve systems that were competitive with or better than human designs. However, 15 years on it appears that problems solved by EH are only of the size and complexity of that achievable in EC 15 years ago and seldom compete with traditional designs. A critical review of the field is presented. Whilst highlighting some of the successes, it also considers why the field is far from reaching these goals. The paper further redefines the field and speculates where the field should go in the next 10 years.", notes = "Brief mention of GP, mostly Koza, Lohn and Miller. Claims to define EHW. randomspice, ngspice. JPL, Heidelberg FTPA, Xilinx, Altera FPGA, systolic arrays, MOGA, adaptive clock skew. Overhype, secure funding, lack of theoretical work. Complexity Kolmogorov, Lempel-Ziv. Reliability, multiple faults. we need to take a fresh look at the way we think about evolvable hardware", } @InCollection{Haddow:2017:miller, author = "Pauline C. Haddow and Andy M. Tyrrell", title = "Evolvable Hardware Challenges: Past, Present and the Path to a Promising Future", booktitle = "Inspired by Nature: Essays Presented to Julian F. Miller on the Occasion of his 60th Birthday", publisher = "Springer", year = "2017", editor = "Susan Stepney and Andrew Adamatzky", volume = "28", series = "Emergence, Complexity and Computation", chapter = "1", pages = "3--37", keywords = "genetic algorithms, genetic programming, EHW", isbn13 = "978-3-319-67996-9", DOI = "doi:10.1007/978-3-319-67997-6_1", abstract = "The ability of the processes in Nature to achieve remarkable examples of complexity, resilience, inventive solutions and beauty is phenomenal. This ability has promoted engineers and scientists to look to Nature for inspiration. Evolvable Hardware (EH) is one such form of inspiration. It is a field of evolutionary computation (EC) that focuses on the embodiment of evolution in a physical media. If EH could achieve even a small step in natural evolution's achievements, it would be a significant step for hardware designers. Before the field of EH began, EC had already shown artificial evolution to be a highly competitive problem solver. EH thus started off as a new and exciting field with much promise. It seemed only a matter of time before researchers would find ways to convert such techniques into hardware problem solvers and further refine the techniques to achieve systems that were competitive (better) than human designs. However, almost 20 years on, it appears that problems solved by EH are only of the size and complexity of that achievable in EC 20 years ago and seldom compete with traditional designs. A critical review of the field is presented. Whilst highlighting some of the successes, it also considers why the field is far from reaching these goals. The chapter further redefines the field and speculates where the field should go in the next 10 years.", notes = "part of \cite{miller60book} https://link.springer.com/bookseries/10624", } @Article{HADI:2018:JH, author = "Sinan Jasim Hadi and Mustafa Tombul", title = "Monthly streamflow forecasting using continuous wavelet and multi-gene genetic programming combination", journal = "Journal of Hydrology", volume = "561", pages = "674--687", year = "2018", keywords = "genetic algorithms, genetic programming, Wavelet coherence transformation, Continuous wavelet transformation, Artificial neural network, ANN, Data-driven models", ISSN = "0022-1694", DOI = "doi:10.1016/j.jhydrol.2018.04.036", URL = "http://www.sciencedirect.com/science/article/pii/S0022169418302890", abstract = "Streamflow is an essential component of the hydrologic cycle in the regional and global scale and the main source of fresh water supply. It is highly associated with natural disasters, such as droughts and floods. Therefore, accurate streamflow forecasting is essential. Forecasting streamflow in general and monthly streamflow in particular is a complex process that cannot be handled by data-driven models (DDMs) only and requires pre-processing. Wavelet transformation is a pre-processing technique; however, application of continuous wavelet transformation (CWT) produces many scales that cause deterioration in the performance of any DDM because of the high number of redundant variables. This study proposes multigene genetic programming (MGGP) as a selection tool. After the CWT analysis, it selects important scales to be imposed into the artificial neural network (ANN). A basin located in the southeast of Turkey is selected as case study to prove the forecasting ability of the proposed model. One month ahead downstream flow is used as output, and downstream flow, upstream, rainfall, temperature, and potential evapotranspiration with associated lags are used as inputs. Before modeling, wavelet coherence transformation (WCT) analysis was conducted to analyze the relationship between variables in the time-frequency domain. Several combinations were developed to investigate the effect of the variables on streamflow forecasting. The results indicated a high localized correlation between the streamflow and other variables, especially the upstream. In the models of the standalone layout where the data were entered to ANN and MGGP without CWT, the performance is found poor. In the best-scale layout, where the best scale of the CWT identified as the highest correlated scale is chosen and enters to ANN and MGGP, the performance increased slightly. Using the proposed model, the performance improved dramatically particularly in forecasting the peak values because of the inclusion of several scales in which seasonality and irregularity can be captured. Using hydrological and meteorological variables also improved the ability to forecast the streamflow", } @InProceedings{Hadipour:2013:AIMS, author = "Sahar Hadipour and Shamsuddin Shahid and Sobri {bin Harun} and Xiao-Jun Wang", booktitle = "1st International Conference on Artificial Intelligence, Modelling and Simulation (AIMS 2013)", title = "Genetic Programming for Downscaling Extreme Rainfall Events", year = "2013", month = dec, pages = "331--334", abstract = "Downscaling extreme rainfall events is a major challenge in climate change study. A Genetic Programming (GP) based method is used in this article for the downscaling of extreme rainfall events in the East coast of peninsular Malaysia during northeast monsoon season. The principal components of Global Circulation Model (GCM) parameters at four points surrounding the study area are used as predictors. Four GP models are developed for the prediction of rainy days and extreme rainfall events such as rainfall more than 99 percentile, rainfall more than 95 percentile and rainfall more than 90 percentile in a year. All possible numerical, logical and trigonometric operators are used to find multi-level GP models for the downscaling. Daily rainfall data during monsoon season for the time periods 1961-1990 and 1991-2000 are used for model calibration and validation, respectively. The results show that the models can predict extreme rainfall events in the East coast of Malaysia with reasonable accuracy.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/AIMS.2013.61", notes = "Fac. of Civil Eng., Univ. Teknol. Malaysia, Skudai, Malaysia. Research Center for Climate Change, Ministry of Water Resources, Nanjing, 210029, China. Also known as \cite{6959939}", } @InProceedings{1274013, author = "Fatima Zohra Hadjam and Claudio Moraga and Mohamed Benmohamed", title = "Cluster-based evolutionary design of digital circuits using all improved multi-expression programming", booktitle = "Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2007)}", year = "2007", month = "7-11 " # jul, editor = "Peter A. N. Bosman", isbn13 = "978-1-59593-698-1", pages = "2475--2482", address = "London, United Kingdom", keywords = "genetic algorithms, genetic programming, improved multi-expression programming, islands model", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p2475.pdf", URL = "http://ls1-www.cs.uni-dortmund.de/pdf/Veroeffentlichungen/GECCO-2007.pdf", DOI = "doi:10.1145/1274000.1274013", publisher = "ACM Press", publisher_address = "New York, NY, USA", abstract = "Evolutionary Electronics (EE) is a research area which involves application of Evolutionary Computation in the domain of electronics. EE algorithms are generally able to find good solutions to rather small problems in a reasonable amount of time, but the need for solving more and more complex problems increases the time required to find adequate solutions. This is due to the large number of individuals to be evaluated and to the large number of generations required until the convergence process leads to the solution. As a consequence, there have been multiple efforts to make EE faster, and one of the most promising choices is to use distributed implementations. In this paper, we propose a cluster-based evolutionary design of digital circuits using a distributed improved multi expression programming method (DIMEP). DIMEP keeps, in parallel, several sub-populations that are processed by Improved Multi-Expression Programming algorithms, with each one being independent from the others. A migration mechanism produces a chromosome exchange between the subpopulations using MPI (Message Passing Interface) on a dedicated cluster of workstations (Lido Cluster, Dortmund University). This paper presents the main ideas and shows preliminary experimental results.", notes = "Distributed on CD-ROM at GECCO-2007 ACM Order No. 910071", } @InProceedings{Hadjam:2010:cec, author = "Fatima Z. Hadjam and Claudio Moraga", title = "Evolutionary design of reversible digital circuits using IMEP the case of the even parity problem", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-6910-9", abstract = "Reversible logic is an emerging research area and has attracted significant attention in recent years. Developing systematic logic synthesis algorithms for reversible logic is still an area of research. Unlike other areas of application, there are relatively few publications on applications of genetic programming -(evolutionary algorithms in general) -to reversible logic synthesis. In this paper, we are introducing a new method; a variant of IMEP. The case of digital circuits for the even-parity problem is investigated. The type of gate used to evolve such a problem is the Fredkin gate.", DOI = "doi:10.1109/CEC.2010.5586252", notes = "WCCI 2010. Also known as \cite{5586252}", } @Misc{oai:arXiv.org:1405.2226, author = "Fatima Hadjam and Claudio Moraga", title = "Introduction to {RIMEP2}: {A} Multi-Expression Programming System for the Design of Reversible Digital Circuits", note = "Comment: 17 text pages, 8 Figures, Research Report, Contact author: Fatima.Hadjam@googlemail.com", year = "2014", month = nov # "~24", abstract = "Quantum computers are considered as a future alternative to circumvent the heat dissipation problem of VLSI circuits. The synthesis of reversible circuits is a very promising area of study considering the expected further technological advances towards quantum computing. In this report, we propose a linear genetic programming system to design reversible circuits -RIMEP2-. The system has evolved reversible circuits starting from scratch without resorting to a pre-existing library. The results show that among the 26 considered benchmarks, RIMEP2 outperformed the best published solutions for 20 of them and matched the remaining 6. RIMEP2 is presented in this report as a promising method with a considerable potential for reversible circuit design. It will be considered as work reference for future studies based on this method.", bibsource = "OAI-PMH server at export.arxiv.org", oai = "oai:arXiv.org:1405.2226", keywords = "genetic algorithms, genetic programming", URL = "http://arxiv.org/abs/1405.2226", } @Article{Hadjam:2014:RED, author = "Fatima Zohra Hadjam and Claudio Moraga", title = "{RIMEP2}: Evolutionary Design of Reversible Digital Circuits", journal = "ACM Journal on Emerging Technologies in Computing Systems (JETC)", volume = "11", number = "3", pages = "27:1--27:??", month = dec, year = "2014", CODEN = "????", DOI = "doi:10.1145/2629534", keywords = "genetic algorithms, genetic programming", ISSN = "1550-4832", bibdate = "Wed Jan 7 15:40:14 MST 2015", bibsource = "http://www.acm.org/pubs/contents/journals/jetc/; http://www.math.utah.edu/pub/tex/bib/jetc.bib", abstract = "RIMEP (Reversible Improved Multi Expression Programming), is a system that has been developed for designing reversible digital circuits. This article discloses a new version of RIMEP called RIMEP2. The goal was to evolve reversible circuits in a fanout free search space. The major changes that RIMEP has undergone, are made in the structure of the chromosome and in the fitness calculation. Although the changes seem to be minor, the impact is effective. The execution time has been considerably decreased and optimal competitive solutions were found for a set of 30 selected benchmarks, where a quantum cost reduction up to 96.13percent was reached with an average of 42.17percent.", acknowledgement = "Nelson H. F. Beebe, University of Utah, Department of Mathematics, 110 LCB, 155 S 1400 E RM 233, Salt Lake City, UT 84112-0090, USA, Tel: +1 801 581 5254, FAX: +1 801 581 4148, e-mail: \path|beebe@math.utah.edu|, \path|beebe@acm.org|, \path|beebe@computer.org| (Internet), URL: \path|http://www.math.utah.edu/~beebe/|", articleno = "27", fjournal = "ACM Journal on Emerging Technologies in Computing Systems (JETC)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J967", doi-url = "http://dx.doi.org/10.1145/2629534", } @InProceedings{HM2016, author = "Fatima Hadjam and Claudio Moraga", title = "Distributed {RIMEP2}: a Comparative Study between a Hierarchical Model and the Islands Model in the context of reversible circuits design", booktitle = "Proceedings of the 12th International Workshop on Boolean Problems", year = "2016", editor = "B. Steinbach", pages = "13--20", address = "Freiberg, Germany", month = sep # ", 22-23", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.tu-dortmund.de/nps/de/Forschung/Publikationen/Graue_Reihe1/Ver__ffentlichungen_2016/853.pdf", URL = "https://ls1-www.cs.tu-dortmund.de/en/307-claudio-moraga/1702-distributed-rimep2-a-comparative-study-between-a-hierarchical-model-and-the-islands", size = "16 pages", abstract = "A distributed hierarchical evolutionary system, named DRIMEP2, for the design of reversible circuits was earlier successfully introduced. In the present work we extend the concept of distributed evolutionary design algorithm, enlarging DRIMEP2 to a family of distributed systems including the hierarchical model, the Island Model, and two hybrid architectures: one comprising a hierarchical model with islands at the lower level, and another one consisting of islands of hierarchical models. A set of 17 randomly chosen 4-bit reversible benchmarks has been evolved under similar parameter environments for the four studied systems. For each benchmark, 100 independent runs were realised and statistics such as number of successful runs, average quantum cost, average gate count and total execution time were considered in the comparison. The results show that in most cases the straight hierarchical model and the hierarchical model with islands of workers are the best in terms of quantum cost and successful runs over 100 runs, although all four distributed DRIMEP2 systems obtained a close performance.", notes = "http://www.informatik.tu-freiberg.de/prof2/ws_bp12/ Some details from technical report of the same name: Number 853, May 2016 Technische Universitaet Dortmund - Fakultaet fuer Informatik Otto-Hahn-Str. 14, 44227 Dortmund, Germany", } @Unpublished{hafner:1996:GGP, author = "Christian Hafner and Juerg Froehlich and Hansueli Gerber", title = "Generalized Genetic Program", note = "Submitted to the 'Evolutionary Computation' Journal", year = "1996", keywords = "genetic algorithms, genetic programming", broken = "http://alphard.ethz.ch/Hafner/ggp/gp.htm", broken = "http://alphard.ethz.ch/gp.htm", abstract = "A novel hybrid approach for the Symbolic Regression problem is presented. First, the classical series expansion approach and the traditional Genetic Programming approach are outlined. In order to overcome the specific problems of them, a combination is analyzed and two specific implementations are presented. Both the Extended Genetic Programming and the Generalized Genetic Programming approach are based on series expansions with genetic optimizations of the basis functions combined with linear and nonlinear parameter optimizations, but they exhibit important differences in their 'philosophy' and in the details of the implementation. The advantages of our approaches are demonstrated with simple examples that are hard to solve with traditional Genetic Programming. It is demonstrated that the performance can drastically be improved.", notes = "postscript generated by MS word appears to be faulty. GGP See GPP manual broken Oct 2021 http://alphard.ethz.ch/Hafner/ggp/ggpmanu.htm", size = "25 pages", } @InProceedings{hafner:1999:GFAUHEA, author = "Christian Hafner and Jurg Frohlich", title = "Generalized Function Analysis Using Hybrid Evolutionary Algorithms", booktitle = "Proceedings of the Congress on Evolutionary Computation", year = "1999", editor = "Peter J. Angeline and Zbyszek Michalewicz and Marc Schoenauer and Xin Yao and Ali Zalzala", volume = "1", pages = "287--294", address = "Mayflower Hotel, Washington D.C., USA", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "6-9 " # jul, organisation = "Congress on Evolutionary Computation, IEEE / Neural Networks Council, Evolutionary Programming Society, Galesia, IEE", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, time series, evolutionary computation, generalized function analysis, hybrid evolutionary algorithms, time series prediction, prominent codes, future data, symbolic regression, series expansions, parameter optimization techniques, highly complex codes, physics, economy", ISBN = "0-7803-5536-9 (softbound)", ISBN = "0-7803-5537-7 (Microfiche)", URL = "http://ieeexplore.ieee.org/iel5/6342/16952/00781938.pdf", DOI = "doi:10.1109/CEC.1999.781938", size = "8 pages", abstract = "Two novel codes for the prediction of time series are presented. Unlike most of the prominent codes based on finding a process that predicts the future data, these codes are based on function analysis and symbolic regression. Both codes are based on a generalization and combination of series expansions, parameter optimization techniques, and genetic programming. These highly complex codes are outlined and applied to different examples of physics and economy.", notes = "CEC-99 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. Library of Congress Number = 99-61143 Extrapolation. GCP v. EGP. Sunspot, Dow Jones, stock price prediction. Full Binary trees of depth 3. On http://alphard.ethz.ch/Hafner/ggp/gp.htm there is more information on GGP with links for downloading the software.", } @InProceedings{hagedorn:2001:agpsppr, author = "John G. Hagedorn and Judith E. Devaney", title = "A Genetic Programming System with a Procedural Program Representation", booktitle = "2001 Genetic and Evolutionary Computation Conference Late Breaking Papers", year = "2001", editor = "Erik D. Goodman", pages = "152--159", address = "San Francisco, California, USA", month = "9-11 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://math.nist.gov/mcsd/savg/papers/g2001.ps.gz", notes = "GECCO-2001LB, NIST", } @InProceedings{Hagenhoff:2022:CNSM, author = "Klement Hagenhoff and Eike Viehmann and Gabi Dreo Rodosek", booktitle = "2022 18th International Conference on Network and Service Management (CNSM)", title = "Time-sensitive Multi-Flow Routing in Highly Utilized {MANETs}", year = "2022", pages = "82--90", abstract = "MANETs comprise several mobile nodes, wirelessly connected with each other. These networks are self-organized, each participant is responsible for routing and data forwarding. Routing protocols only provide local or outdated topology knowledge because participants are moving continuously. Also, transmission capacities are limited which often results in over-used network segments. Capacity conform path distribution is challenging since nodes route based on their incomplete topology knowledge. Recent work showed that an up-to-date and complete network topology representation can quickly be delivered to a controller, which is instantiated on an arbitrary node. Now, routing and path deployment can be outsourced to the controller. With this knowledge, we introduce several path finding approaches to answer the question if and to which extent non over-using routes for several flows can be found where common MANET routing techniques would fail. Also, paths have to be computed quickly since topologies change due to the mobility of nodes. Our path finding techniques also focus on routes aiming for long connection lifetime. We compare our approaches regarding capacity usage, computation times, and connection lifetimes, taking into consideration typical MANET behaviour.", keywords = "genetic algorithms, genetic programming, Measurement, Runtime, Network topology, Computational modelling, Routing, Ad hoc networks, Routing protocols, MANET, Path Finding, MIP, GP", DOI = "doi:10.23919/CNSM55787.2022.9964689", ISSN = "2165-963X", month = oct, notes = "Also known as \cite{9964689}", } @InProceedings{Hagg:2019:GECCOcomp, author = "Alexander Hagg and Martin Zaefferer and Joerg Stork and Adam Gaier", title = "Prediction of neural network performance by phenotypic modeling", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", isbn13 = "978-1-4503-6748-6", pages = "1576--1582", address = "Prague, Czech Republic", DOI = "doi:10.1145/3319619.3326815", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{3326815} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InProceedings{hagiya:1998:tamc, author = "Masami Hagiya", title = "Towards Autonomous Molecular Computers", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "691--699", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "DNA Computing", ISBN = "1-55860-548-7", notes = "GP-98", } @InProceedings{DBLP:conf/eurocast/HaiderK22, author = "Christian Haider and Gabriel Kronberger", title = "Shape-Constrained Symbolic Regression with {NSGA-III}", booktitle = "18th International Conference on Computer Aided Systems Theory, EUROCAST 2022", year = "2022", editor = "Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia", volume = "13789", series = "Lecture Notes in Computer Science", pages = "164--172", address = "Las Palmas de Gran Canaria, Spain", month = feb # " 20-25", publisher = "Springer", note = "Revised Selected Papers", keywords = "genetic algorithms, genetic programming, Symbolic regression, Shape-constraints, Many-objective optimization", timestamp = "Mon, 26 Jun 2023 20:47:36 +0200", biburl = "https://dblp.org/rec/conf/eurocast/HaiderK22.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://doi.org/10.1007/978-3-031-25312-6_19", DOI = "doi:10.1007/978-3-031-25312-6_19", size = "9 pages", notes = "Is this GP? Feynman Symbolic Regression Database", } @InProceedings{haider:2022:GECCO, author = "Christian Haider and Fabricio {De Franca} and Gabriel Kronberger and Bogdan Burlacu", title = "Comparing Optimistic and Pessimistic Constraint Evaluation in Shape-constrained Symbolic Regression", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference", year = "2022", editor = "Alma Rahat and Jonathan Fieldsend and Markus Wagner and Sara Tari and Nelishia Pillay and Irene Moser and Aldeida Aleti and Ales Zamuda and Ahmed Kheiri and Erik Hemberg and Christopher Cleghorn and Chao-li Sun and Georgios Yannakakis and Nicolas Bredeche and Gabriela Ochoa and Bilel Derbel and Gisele L. Pappa and Sebastian Risi and Laetitia Jourdan and Hiroyuki Sato and Petr Posik and Ofer Shir and Renato Tinos and John Woodward and Malcolm Heywood and Elizabeth Wanner and Leonardo Trujillo and Domagoj Jakobovic and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Inmaculada Medina-Bulo and Slim Bechikh and Andrew M. Sutton and Pietro Simone Oliveto", pages = "938--945", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, prior knowledge, symbolic regression, shape constraints", isbn13 = "978-1-4503-9237-2", DOI = "doi:10.1145/3512290.3528714", abstract = "Shape-constrained Symbolic Regression integrates prior knowledge about the function shape into the symbolic regression model. This can be used to enforce that the model has desired properties such as monotonicity, or convexity, among others. Shape-constrained Symbolic Regression can also help to create models with better extrapolation behavior and reduced sensitivity to noise. The constraint evaluation can be challenging because exact evaluation of constraints may require a search for the extrema of non-convex functions. Approximations via interval arithmetic allow to efficiently find bounds for the extrema of functions. However, interval arithmetic can lead to overly wide bounds and therefore produces a pessimistic estimation. Another possibility is to use sampling which underestimates the true range. Sampling therefore produces an optimistic estimation. In this paper we evaluate both methods and compare them on different problem instances. In particular we evaluate the sensitivity to noise and the extrapolation capabilities in combination with noise data. The results indicate that the optimistic approach works better for predicting out-of-domain points (extrapolation) and the pessimistic approach works better for high noise levels.", notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @Article{HAIDER:2023:asoc, author = "C. Haider and F. O. {de Franca} and B. Burlacu and G. Kronberger", title = "Shape-constrained multi-objective genetic programming for symbolic regression", journal = "Applied Soft Computing", volume = "132", pages = "109855", year = "2023", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2022.109855", URL = "https://www.sciencedirect.com/science/article/pii/S1568494622009048", keywords = "genetic algorithms, genetic programming, Multi-objective optimization, Shape-constrained regression, Symbolic regression", abstract = "We describe and analyze algorithms for shape-constrained symbolic regression, which allow the inclusion of prior knowledge about the shape of the regression function. This is relevant in many areas of engineering - in particular, when data-driven models, which are based on data of measurements must exhibit certain properties (e.g. positivity, monotonicity, or convexity/concavity). To satisfy these properties, we have extended multi-objective algorithms with shape constraints. A soft-penalty approach is used to minimize both the constraint violations and the prediction error. We use the non-dominated sorting genetic algorithm (NSGA-II) as well as the multi-objective evolutionary algorithm based on decomposition (MOEA/D). The algorithms are tested on a set of models from physics textbooks and compared against previous results achieved with single objective algorithms. Further, we generated out-of-domain samples to test the extrapolation behavior using shape constraints and added a different level of noise on the training data to verify if shape constraints can still help maintain the prediction errors to a minimum and generate valid models. The results showed that the multi-objective algorithms were capable of finding mostly valid models, also when using a soft-penalty approach. Further, we investigated that NSGA-II achieved the best overall ranks on high noise instances", } @InProceedings{Haider:2023:GPTP, author = "Christian Haider and Fabricio Olivetti {de Franca} and Bogdan Burlacu and Florian Bachinger and Gabriel Kronberger and Michael Affenzeller", title = "Shape-constrained Symbolic Regression: Real-World Applications in Magnetization, Extrusion and Data Validation", booktitle = "Genetic Programming Theory and Practice XX", year = "2023", editor = "Stephan Winkler and Leonardo Trujillo and Charles Ofria and Ting Hu", series = "Genetic and Evolutionary Computation", pages = "225--240", address = "Michigan State University, USA", month = jun # " 1-3", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-981-99-8412-1", DOI = "doi:10.1007/978-981-99-8413-8_12", abstract = "We present different approaches for including knowledge in data-based modeling. For this, we use the model representation of symbolic regression (SR), which represents the models as short interpretable mathematical formulas. The integration of knowledge into symbolic regression via shape constraints is discussed alongside three real-world applications: modeling magnetisation curves, modeling twin-screw extruders and model-based data validation.", notes = " Part of \cite{Hu:2023:GPTP} published after the workshop in 2024", } @InProceedings{haith:1999:CPS, author = "Gary L. Haith and Silvano P. Colombano and Jason D. Lohn and Dimitris Stassinopoulos", title = "Coevolution for Problem Simplification", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "244--251", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-896.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-896.ps", abstract = "predator-prey", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Article{HAJEK:2018:COR, author = "Petr Hajek and Roberto Henriques and Mauro Castelli and Leonardo Vanneschi", title = "Forecasting performance of regional innovation systems using semantic-based genetic programming with local search optimizer", journal = "Computer \& Operations Research", year = "2018", keywords = "genetic algorithms, genetic programming", ISSN = "0305-0548", DOI = "doi:10.1016/j.cor.2018.02.001", URL = "http://www.sciencedirect.com/science/article/pii/S0305054818300327", abstract = "Innovation performance of regional innovation systems can serve as an important tool for policymaking to identify best practices and provide aid to regions in need. Accurate forecasting of regional innovation performance plays a critical role in the implementation of policies intended to support innovation because it can be used to simulate the effects of actions and strategies. However, innovation is a complex and dynamic socio-economic phenomenon. Moreover, patterns in regional innovation structures are becoming increasingly diverse and non-linear. Therefore, to develop an accurate forecasting tool for this problem represents a challenge for optimization methods. The main aim of the paper is to develop a model based on a variant of genetic programming to address the regional innovation performance forecasting problem. Using the historical data related to regional knowledge base and competitiveness, the model should accurately and effectively predict a variety of innovation outputs, including patent counts, technological and non-technological innovation activity and economic effects of innovations. We show that the proposed model outperforms state-of-the-art machine learning methods", } @Article{Hajirezaie:2017:JML, author = "Sassan Hajirezaie and Amin Pajouhandeh and Abdolhossein Hemmati-Sarapardeh and Maysam Pournik and Bahram Dabir", title = "Development of a robust model for prediction of under-saturated reservoir oil viscosity", journal = "Journal of Molecular Liquids", volume = "229", pages = "89--97", year = "2017", ISSN = "0167-7322", DOI = "doi:10.1016/j.molliq.2016.11.088", URL = "http://www.sciencedirect.com/science/article/pii/S0167732216320608", abstract = "Fluid viscosity is considered as one of the most important parameters for reservoir simulation, performance evaluation, designing production facilities, etc. In this communication, a robust model based on Genetic Programming (GP) approach was developed for prediction of under-saturated reservoir oil viscosity. A third order polynomial correlation for prediction of under-saturated oil viscosity as a function of bubble point viscosity, pressure differential (pressure minus bubble point pressure) and pressure ratio (pressure divided by bubble point pressure) was proposed. To this end, a large number of experimental viscosity databank including 601 data sets from various regions covering a wide range of reservoir conditions was collected from literature. Statistical and graphical error analyses were employed to evaluate the performance and accuracy of the model. The results indicate that the developed model is able to estimate oil viscosity with an average absolute percentage relative error of 4.47percent. These results in addition to the graphical results confirmed the robustness and superiority of the developed model compared to the most well-known existing correlations of under-saturated oil viscosity. Additionally, the investigation of relative impact of input parameters on under-saturated reservoir oil viscosity demonstrates that bubble point viscosity has the greatest impact on oil viscosity.", keywords = "genetic algorithms, genetic programming, Under-saturated reservoir oil viscosity, Statistical and graphical error analyses, Relevancy factor", } @InProceedings{Halaby:2010:ICEAC, author = "A. Halaby and M. Awad and R. Khanna", title = "Guided Search Space Genetic Programming for identifying energy aware microarchitectural designs", booktitle = "2010 International Conference on Energy Aware Computing (ICEAC)", year = "2010", month = "16-18 " # dec, keywords = "genetic algorithms, genetic programming, GSS-GP, energy aware microarchitectural design, fitness function, guided search space genetic programming, machine learning technique, resource use, convergence, learning (artificial intelligence), power aware computing, search problems", DOI = "doi:10.1109/ICEAC.2010.5702307", abstract = "Genetic Programming (GP) is being proposed as a machine learning technique in design space exploration. An evolutionary but heuristic approach by default, GP basically searches the whole input space for suboptimal values, which often translates into long convergence times, more processing and thus inefficient resource usage. We propose in this paper a Guided Search Space GP (GSS-GP) approach that improves convergence time and accuracy because of the limited search space it uses and the fitness function designed to account for the class disproportionality. Experimental results to identify energy aware microarchitectural designs show the merit of GSS-GP and motivate follow on research.", notes = "fixed representation classifier. Electrical & Computer Engineering, American University of Beirut, Beirut, Lebanon. Also known as \cite{5702307}", } @InProceedings{Halaby:2011:ICEAC, author = "Abdallah El-Halaby and Mariette Awad and Rahul Khanna", title = "Exploring energy aware microarchitectural design space via computationally efficient genetic programming", booktitle = "International Conference on Energy Aware Computing (ICEAC 2011)", year = "2011", month = "30 " # nov # " - 2 " # dec, pages = "1--5", address = "Istanbul", size = "5 pages", abstract = "Efficiently exploring the microarchitectural design space is crucial in order to find promising design subspaces satisfying better power constraints. Based on our previous work on Guided Search Space Genetic Programming (GSS-GP), we introduce a new fitness function based on Fisher Linear Discriminant, in addition to the weighted fitness function designed to improve unbalanced classification accuracy. Experimental results show that GSS-GP outperforms classical GP in both accuracy and convergence times, with a minor class accuracy improvement of 9.05 percentage points. In addition, GSS-GP resulted in a significant reduction of more than 99percent in processing time compared to other robust classifiers like Support Vector Machines.", keywords = "genetic algorithms, genetic programming, Fisher linear discriminant, GSS-GP, computationally efficient genetic programming, energy aware microarchitectural design space, guided search space genetic programming, power constraints, support vector machines, weighted fitness function, computer architecture, power aware computing, support vector machines", DOI = "doi:10.1109/ICEAC.2011.6136688", notes = "Also known as \cite{6136688}", } @Misc{Hale:2018:TPOT, author = "Jeff Hale", title = "{TPOT} Automated Machine Learning in Python", howpublished = "Blog", year = "2018", month = aug # " 22", keywords = "genetic algorithms, genetic programming, TPOT, Bioinformatics", URL = "https://towardsdatascience.com/tpot-automated-machine-learning-in-python-4c063b3e5de9", size = "27 pages", abstract = "In this post Im sharing some of my explorations with TPOT, an automated machine learning (autoML) tool in Python. The goal is to see what TPOT can do and if it merits becoming part of your machine learning workflow.", notes = "http://epistasislab.github.io/tpot/", } @Article{HALE:2020:IFAC-PapersOnLine, author = "William T. Hale and George M. Bollas", title = "Symbolic regression of uncertainty-resilient inferential sensors for fault diagnostics", journal = "IFAC-PapersOnLine", volume = "53", number = "2", pages = "11446--11451", year = "2020", note = "21st IFAC World Congress", ISSN = "2405-8963", DOI = "doi:10.1016/j.ifacol.2020.12.582", URL = "https://www.sciencedirect.com/science/article/pii/S2405896320308831", keywords = "genetic algorithms, genetic programming, fault detection, diagnosis, experiment design, AI methods for FDI", abstract = "An algorithm is presented for the design of inferential sensors for fault diagnostics in thermal management systems. The algorithm uses input and output sensed system information to improve the detection and isolation of a fault by generating inferential sensors that augment the measured information to: (i) reduce the evidence of uncertainty in the inferred variables, and thus decrease false alarm and nondetection rates; and (ii) provide distinguishable responses to faults, and thus reduce reduce the rate of misdiagnoses. The novelty of the algorithm is its use of genetic programming to evolve explainable inferential sensors that maximize information criteria specific to fault diagnostics. The chosen criteria: (i) least squares regression; and (ii) Ds -optimality (calculated from the Fisher Information Matrix), leverage symbolic mathematics and automatic differentiation to obtain parametric sensitivities of the measured outputs and inferential sensors. The algorithm is included in a standard work for fault diagnostics, where its effectiveness is assessed through k-NN classification and illustrated in an application to an aircraft cross-flow plate-fin heat exchanger", } @Article{HALE:2022:CCE, author = "William T. Hale and Efi Safikou and George M. Bollas", title = "Inference of faults through symbolic regression of system data", journal = "Computer \& Chemical Engineering", volume = "157", pages = "107619", year = "2022", ISSN = "0098-1354", DOI = "doi:10.1016/j.compchemeng.2021.107619", URL = "https://www.sciencedirect.com/science/article/pii/S0098135421003975", keywords = "genetic algorithms, genetic programming, Machine learning, Symbolic regression, Fault detection, Soft sensors, Inferential sensors", abstract = "We present the development of inferential sensors that use system input and output measurements to improve the accuracy and robustness of fault detection and isolation. These inferential sensors transform and augment the sensed information of a system to: (i) minimize the evidence of uncertainty in the inferred variables, decreasing the rates of false alarms and nondetections; and (ii) provide distinguishable estimates of the existence and/or severity of faults, decreasing the rate of misdiagnoses. The proposed method symbolically regresses the noisy and uncertain system measurements, using genetic programming, to evolve uniquely explainable mathematical functions that minimize a least-squares objective of the fault inference. A standard workflow using the proposed algorithm for fault diagnostics is presented and illustrated in the classification of the severity of fouling in a cross-flow plate-fin heat exchanger. The effectiveness and robustness of the method are explored at different test designs, assessed using k-nearest neighbors classification, and compared to other traditional fault classification methods. The extension of the inferential sensors to information theoretic metrics, where the system model is augmented to improve the evidence of fault(s) is also discussed", } @Book{hall:1995:AIsd, author = "Curt Hall and Paul Harmon", title = "AI in Software Development: Genetic Programming, Fuzzy Logic, and Neural Nets", publisher = "cutter", year = "1995", keywords = "genetic algorithms, genetic programming", broken = "http://www.cutter.com/itgroup/reports/aisoft.htm", abstract = "Neural network products are already being used for character recognition, real estate evaluation, {"}what-if{"} simulations for manufacturing, allocating airline seats, trading stocks and bonds, and detecting credit-card fraud. Two more cutting-edge technologies -- genetic programming and fuzzy-logic techniques -- are just entering the marketplace, promising many more innovative applications. AI in Software Development presents a clear overview of these exciting developments ... without hype and exaggerated projections. Drawn from issues of the monthly newsletter Intelligent Software Strategies, this practical report demonstrates the in-depth expertise and clear explanations that Curt Hall and Paul Harmon are known for.", notes = "lovering@cutter.com", size = "45 pages", } @InCollection{hall:2004:GPTP, author = "John M. Hall and Terence Soule", title = "Does Genetic Programming Inherently Adopt Structured Design Techniques?", booktitle = "Genetic Programming Theory and Practice {II}", year = "2004", editor = "Una-May O'Reilly and Tina Yu and Rick L. Riolo and Bill Worzel", chapter = "10", pages = "159--174", address = "Ann Arbor", month = "13-15 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, design, function choice, root node", ISBN = "0-387-23253-2", URL = "http://www.cs.uidaho.edu/~tsoule/research/doesDesign.ps", DOI = "doi:10.1007/0-387-23254-0_10", abstract = "Basic genetic programming (GP) techniques allow individuals to take advantage of some basic top-down design principles. In order to evaluate the effectiveness of these techniques, we define a design as an evolutionary frozen root node. We show that GP design converges quickly based primarily on the best individual in the initial random population. This leads to speculation of several mechanisms that could be used to allow basic GP techniques to better incorporate top-down design principles.", notes = "part of \cite{oreilly:2004:GPTP2} A version of Santa Fe trail artificial ant, 6-even parity (given XOR!), intertwined spirals, sin(x), Battleship GP robust to forced choice of root node. Differences in means small compared to variation between runs. In population of 100 root node almost always converges.", } @PhdThesis{Hall:thesis, author = "Mathew James Hall", title = "Improving Software Remodularisation", school = "Department of Computer Science, University of Sheffield", year = "2013", address = "UK", month = mar, keywords = "genetic algorithms, genetic programming, SBSE, remodularisation, clustering, constraint solving, metaheuristic algorithms, reverse engineering, software engineering", URL = "https://etheses.whiterose.ac.uk/4183/", URL = "https://etheses.whiterose.ac.uk/4183/1/thesis.pdf", URL = "https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.577421", URL = "https://www.sheffield.ac.uk/dcs/research/phd-theses#hall", size = "180 pages", abstract = "Maintenance is estimated to be the most expensive stage of the software development lifecycle. While documentation is widely considered essential to reduce the cost of maintaining software, it is commonly neglected. Automated reverse engineering tools present a potential solution to this problem by allowing documentation, in the form of models, to be produced cheaply. State machines, module dependency graphs (MDGs), and other software models may be extracted automatically from software using reverse engineering tools. However the models are typically large and complex due to a lack of abstraction. Solutions to this problem use transformations (state machines) or remodularisation (MDGs) to enrich the diagram with a hierarchy to uncover the system structure. This task is complicated by the subjectivity of the problem. Automated techniques aim to optimise the structure, either through design quality metrics or by grouping elements by the limited number of available features. Both of these approaches can lead to a mismatch between the algorithm output and the developer intentions. This thesis addresses the problem from two perspectives: firstly, the improvement of automated hierarchy generation to the extent possible, and then augmentation using additional expert knowledge in a refinement process. Investigation begins on the application of remodularisation to the state machine hierarchy generation problem, which is shown to be feasible, due to the common underlying graph structure present in both MDGs and statemachines. Following this success, genetic programming is investigated as a means to improve upon this result, which is found to produce hierarchies that better optimise a quality metric at higher levels. The disparity between metric-maximising performance and human-acceptable performance is then examined, resulting in the SUMO algorithm, which incorporates domain knowledge to interactively refine a modularisation. The thesis concludes with an empirical user study conducted with 35 participants, showing, while its performance is highly dependent on the individual user, SUMO allows a modularization of a 122 file component to be refined in a short period of time (within an hour for most participants).", notes = "uk.bl.ethos.577421 Supervisor: Dr Phil McMinn", } @InProceedings{Hall:2016:ICSME, author = "Mathew Hall and Neil Walkinshaw", booktitle = "2016 IEEE International Conference on Software Maintenance and Evolution (ICSME)", title = "Data and Analysis Code for GP EFSM Inference", year = "2016", pages = "611--611", abstract = "This artefact captures the workflow that we adopted for our experimental evaluation in our ICSME paper on inferring state transition functions during EFSM inference. To summarise, the paper uses Genetic Programming to infer data transformations, to enable the inference of fully 'computational' extended finite state machine models. This submission shows how we generated, transformed, analysed, and visualised our raw data. It includes everything needed to generate raw results and provides the relevant R code in the form of a re-usable Jupyter Notebook (accompanied by a descriptive narrative).", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICSME.2016.22", month = oct, notes = "Also known as \cite{7816520}", } @InProceedings{Hallawa:2020:evoapplications, author = "Ahmed Hallawa and Simon Schug and Giovanni Iacca and Gerd Ascheid", title = "Evolving Instinctive Behaviour in Resource-Constrained Autonomous Agents Using Grammatical Evolution", booktitle = "23rd International Conference, EvoApplications 2020", year = "2020", month = "15-17 " # apr, editor = "Pedro A. Castillo and Juan Luis {Jimenez Laredo} and Francisco {Fernandez de Vega}", series = "LNCS", volume = "12104", publisher = "Springer Verlag", address = "Seville, Spain", pages = "369--383", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Behavior Tree, Autonomous agents", isbn13 = "978-3-030-43721-3", video_url = "https://www.youtube.com/watch?v=ABy01VKXWME", DOI = "doi:10.1007/978-3-030-43722-0_24", abstract = "Recent developments in the miniaturization of hardware have facilitated the use of robots or mobile sensory agents in many applications such as exploration of GPS-denied, hardly accessible unknown environments. This includes underground resource exploration and water pollution monitoring. One problem in scaling-down robots is that it puts significant emphasis on power consumption due to the limited energy available online. Furthermore, the design of adequate controllers for such agents is challenging as representing the system mathematically is difficult due to complexity. In that regard, Evolutionary Algorithms (EA) is a suitable choice for developing the controllers. However, the solution space for evolving those controllers is relatively large because of the wide range of the possible tunable parameters available on the hardware, in addition to the numerous number of objectives which appear on different design levels. A recently-proposed method, dubbed as Instinct Evolution Scheme (IES), offered a way to limit the solution space in these cases. This scheme uses Behavior Trees (BTs) to represent the robot behaviour in a modular, re-usable and intelligible fashion. In this paper, we improve upon the original IES by using Grammatical evolution (GE) to implement a full BT evolution model integratable with IES. A special emphasis is put on minimizing the complexity of the BT generated by GE. To test the scheme, we consider an environment exploration task on a virtual environment. Results show 85percent correct reactions to environment stimuli and a decrease in relative complexity to 4.7percent. Finally, the evolved BT is represented in an if-else on-chip compatible format.", notes = "Chair for Integrated Signal Processing Systems, RWTH Aachen University, Germany http://www.evostar.org/2020/ EvoApplications2020 held in conjunction with EuroGP'2020, EvoMusArt2020 and EvoCOP2020", } @Article{hamda:2002:IJAI, author = "Hatem Hamda and Francois Jouve and Evelyne Lutton and Marc Schoenauer and Michele Sebag", title = "Compact Unstructured Representations for Evolutionary Design", journal = "International Journal of Applied Intelligence", year = "2002", volume = "16", number = "2", pages = "139--155", note = "Special Issue on Creative Evolutionary Systems", publisher = "Springer Netherlands", keywords = "genetic algorithms, evolution strategies, Computer Science", ISSN = "0924-669X", URL = "http://minimum.inria.fr/evo-lab/Publications/creative_soumis.ps.gz", broken = "http://www.wkap.nl/prod/j/0924-669X", DOI = "doi:10.1023/A:1013666503249", size = "29 pages", abstract = "This paper proposes a few steps to escape structured extensive representations for evolutionary solving of Topological Optimum Design (TOD) problems: early results have shown the ability of Evolutionary methods to find numerical solutions to yet unsolved TOD problems, but those approaches were limited because the complexity of the representation was that of a fixed underlying mesh. Different compact unstructured representations are introduced, the complexity of which is self-adaptive, i.e. is evolved by the algorithm itself. The Voronoi-based representations are variable length lists of alleles that are directly decoded into shapes, while the IFS representation, based on fractal theory, involves a much more complex morphogenetic process. First results demonstrates that Voronoi-based representations allow one to push further the limits of Evolutionary Topological Optimum Design by actually removing the correlation between the complexity of the representations and that of the discretization. Further comparative results among all these representations on simple test problems indicate that the complex causality in the IFS representation disfavor it compared to the Voronoi-based representations.", notes = "Bentely and Corne Special issue", } @InProceedings{hamel:2002:gecco, author = "Lutz Hamel", title = "Breeding Algebraic Structures---An Evolutionary Approach To Inductive Equational Logic Programming", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "748--755", address = "New York", publisher_address = "San Francisco, CA 94104, USA", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "genetic algorithms, genetic programming, algebraic specification, concept learning, equational logic, inductive logic programming", URL = "http://homepage.cs.uri.edu/faculty/hamel/pubs/gecco2002.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/GP034.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf", ISBN = "1-55860-878-8", size = "8 pages", abstract = "Concept learning is the induction of a description from a set of examples. Inductive logic programming can be considered a special case of the general notion of conceptlearning specifically referring to the induction of first-order theories. Both concept learning and inductive logic programming can be seen as a search over all possible sentences in some representation language for sentences that correctly explain the examples and also generalise to other sentences that are part of that concept. we explore inductive logic programming with equational logic as the representation language and genetic programming as the underlying search paradigm. Equational logic is the logic of substituting equals for equals with algebras as models and term rewriting as operational semantics.", notes = "GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)", } @InProceedings{H.:2003:Esiielp, author = "Lutz H. Hamel", title = "Evolutionary search in inductive equational logic programming", booktitle = "Proceedings of the 2003 Congress on Evolutionary Computation CEC2003", editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon", pages = "2426--2433", year = "2003", publisher = "IEEE Press", address = "Canberra", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "8-12 " # dec, organisation = "IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-7804-0", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.383.7908", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.383.7908", URL = "http://homepage.cs.uri.edu/faculty/hamel/pubs/cec2003-hamel.pdf", abstract = "Concept learning is the induction of a description from a set of examples. Inductive logic programming can be considered a special case of the general notion of concept learning specifically referring to the induction of first-order theories. Both concept learning and inductive logic programming can be seen as a search over all possible sentences in some representation language for sentences that correctly explain the examples and also generalise to other sentences that are part of that concept. In this paper we explore inductive logic programming with equational logic as the representation language. We present a high-level overview of the implementation of inductive equational logic using genetic programming and discuss encouraging results based on experiments that are intended to emulate real world scenarios.", notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.", } @InProceedings{Hamel:2007:AAIP, author = "Lutz Hamel and Chi Shen", title = "An Inductive Programming Approach to Algebraic Specification", booktitle = "Proceedings of the ECML 2007 Workshop on Approaches and Applications of Inductive Programming (AAIP'07)", year = "2007", pages = "3--15", address = "Warsaw", month = "17-21 " # sep, keywords = "genetic algorithms, genetic programming", URL = "http://homepage.cs.uri.edu/faculty/hamel/pubs/aaip07-hamel.pdf", URL = "http://www.ecmlpkdd2007.org/CD/workshops/AAIP/hamel_shen/hamel_shen.pdf", size = "12 pages", abstract = "Inductive machine learning suggests an alternative approach to the algebraic specification of software systems: rather than using test cases to validate an existing specification we use the test cases to induce a specification. In the algebraic setting test cases are ground equations that represent specific aspects of the desired system behavior or, in the case of negative test cases, represent specific behavior that is to be excluded from the system. We call this inductive equational logic programming. We have developed an algebraic semantics for inductive equational logic programming where hypotheses are cones over specification diagrams. The induction of a hypothesis or specification can then be viewed as a search problem in the category of cones over a specific specification diagram for a cone that satisfies some pragmatic criteria such as being as general as possible. We have implemented such an induction system in the functional part of the Maude specification language using evolutionary computation as a search strategy.", notes = "Department of Computer Science and Statistics University of Rhode Island Kingston, RI 02881, USA", } @Article{journals/ci/HamidaAA16, title = "Applying Dynamic Training-Subset Selection Methods Using Genetic Programming for Forecasting Implied Volatility", author = "Sana Ben Hamida and Wafa Abdelmalek and Fathi Abid", journal = "Computational Intelligence", year = "2016", volume = "32", number = "3", pages = "369--390", month = aug, keywords = "genetic algorithms, genetic programming, implied volatility forecast, static training-subset selection, dynamic training-subset selection, mean squared errors, percentage of non-fitted observations", bibdate = "2017-05-27", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ci/ci32.html#HamidaAA16", URL = "http://dx.doi.org/10.1111/coin.12057", DOI = "doi:10.1111/coin.12057", abstract = "Volatility is a key variable in option pricing, trading, and hedging strategies. The purpose of this article is to improve the accuracy of forecasting implied volatility using an extension of genetic programming (GP) by means of dynamic training-subset selection methods. These methods manipulate the training data in order to improve the out-of-sample patterns fitting. When applied with the static subset selection method using a single training data sample, GP could generate forecasting models, which are not adapted to some out-of-sample fitness cases. In order to improve the predictive accuracy of generated GP patterns, dynamic subset selection methods are introduced to the GP algorithm allowing a regular change of the training sample during evolution. Four dynamic training-subset selection methods are proposed based on random, sequential, or adaptive subset selection. The latest approach uses an adaptive subset weight measuring the sample difficulty according to the fitness cases' errors. Using real data from S&P500 index options, these techniques are compared with the static subset selection method. Based on mean squared error total and percentage of non-fitted observations, results show that the dynamic approach improves the forecasting performance of the generated GP models, especially those obtained from the adaptive-random training-subset selection method applied to the whole set of training samples.", } @Article{HAMIDA:2020:CSR, author = "Sana {Ben Hamida} and Hmida Hmida and Amel Borgi and Marta Rukoz", title = "Adaptive Sampling for Active Learning with Genetic Programming", journal = "Cognitive Systems Research", year = "2020", ISSN = "1389-0417", DOI = "doi:10.1016/j.cogsys.2020.08.008", URL = "http://www.sciencedirect.com/science/article/pii/S1389041720300541", keywords = "genetic algorithms, genetic programming, Machine Learning, Active Learning, Training data sampling, Adaptive sampling, Sampling frequency control", abstract = "Active learning is a machine learning paradigm allowing to decide which inputs to use for training. It is introduced to Genetic Programming (GP) essentially thanks to the dynamic data sampling, used to address some known issues such as the computational cost, the over-fitting problem and the imbalanced databases. The traditional dynamic sampling for GP gives to the algorithm a new sample periodically, often each generation, without considering the state of the evolution. In so doing, individuals do not have enough time to extract the hidden knowledge. An alternative approach is to use some information about the learning state to adapt the periodicity of the training data change. In this work, we propose an adaptive sampling strategy for classification tasks based on the state of solved fitness cases throughout learning. It is a flexible approach that could be applied with any dynamic sampling. We implemented some sampling algorithms extended with dynamic and adaptive controlling re-sampling frequency. We experimented them to solve the KDD intrusion detection and the Adult incomes prediction problems with GP. The experimental study demonstrates how the sampling frequency control preserves the power of dynamic sampling with possible improvements in learning time and quality. We also demonstrate that adaptive sampling can be an alternative to multi-level sampling. This work opens many new relevant extension paths", } @InCollection{hamida:2023:M4ML, author = "S. {Ben Hamida} and H. Hmida", title = "Algorithm vs Processing Manipulation to Scale Genetic Programming to Big Data Mining", booktitle = "Metaheuristics for Machine Learning", publisher = "Springer", year = "2023", editor = "Mansour Eddaly and Bassem Jarboui and Patrick Siarry", pages = "179--199", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-981-19-3888-7_7", DOI = "doi:10.1007/978-981-19-3888-7_7", } @InProceedings{Hamilton:2023:evoapplications, author = "John Rego Hamilton and Aniko Ekart and Alina Patelli", title = "Predicting Normal and Anomalous Urban Traffic with Vectorial Genetic Programming and Transfer Learning", booktitle = "26th International Conference, EvoApplications 2023", year = "2023", month = apr # " 12-14", editor = "Joao Correia and Stephen Smith and Raneem Qaddoura", series = "LNCS", volume = "13989", publisher = "Springer Verlag", address = "Brno, Czech Republic", pages = "519--535", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Nature-inspired computing for sustainability, Resilient urban development, AI-driven decision support systems, Intelligent and safe transportation, Urban traffic prediction", isbn13 = "978-3-031-30229-9", URL = "https://research.aston.ac.uk/en/publications/predicting-normal-and-anomalous-urban-traffic-with-vectorial-gene", DOI = "doi:10.1007/978-3-031-30229-9_34", size = "17 pages", abstract = "The robust and reliable prediction of urban traffic provides a pathway to reducing pollution, increasing road safety and minimising infrastructure costs. The data driven modeling of vehicle flow through major cities is an inherently complex task, given the intricate topology of real life road networks, the dynamic nature of urban traffic, often disrupted by construction work and large-scale social events, and the various failures of sensing equipment, leading to discontinuous and noisy readings. It thus becomes necessary to look beyond traditional optimisation approaches and consider evolutionary methods, such as Genetic Programming (GP). We investigate the quality of GP traffic models, under both normal and anomalous conditions (such as major sporting events), at two levels: spatial, where we enhance standard GP with Transfer Learning (TL) and diversity control in order to learn traffic patterns from areas neighbouring the one where a prediction is needed, and temporal. In the latter case, we propose two implementations of GP with TL: one that employs a lag operator to skip over a configurable number of anomalous traffic readings during training and one that leverages Vectorial GP, particularly its linear algebra operators, to smooth out the effect of anomalous data samples on model prediction quality. A thorough experimental investigation conducted on central Birmingham traffic readings collected before and during the 2022 Commonwealth Games demonstrates our models' usefulness in a variety of real-life scenarios.", notes = "http://www.evostar.org/2023/ EvoApplications2023 held in conjunction with EuroGP'2023, EvoCOP2023 and EvoMusArt2023", } @InProceedings{Hammami:2018:CEC, author = "Marwa Hammami and Slim Bechikh and Chih-Cheng Hung and Lamjed {Ben Said}", booktitle = "2018 IEEE Congress on Evolutionary Computation (CEC)", title = "A Multi-Objective Hybrid Filter-Wrapper Evolutionary Approach for Feature Construction on High-Dimensional Data", year = "2018", abstract = "Feature selection and construction are important pre-processing techniques in data mining. They may allow not only dimensionality reduction but also classifier accuracy and efficiency improvement. These two techniques are of great importance especially for the case of high-dimensional data. Feature construction for high-dimensional data is still a very challenging topic. This can be explained by the large search space of feature combinations, whose size is a function of the number of features. Recently, researchers have used Genetic Programming (GP) for feature construction and the obtained results were promising. Unfortunately, the wrapper evaluation of each feature subset, where a feature can be constructed by a combination of features, is computationally intensive since such evaluation requires running the classifier on the data sets. Motivated by this observation, we propose, in this paper, a hybrid multiobjective evolutionary approach for efficient feature construction and selection. Our approach uses two filter objectives and one wrapper objective corresponding to the accuracy. In fact, the whole population is evaluated using two filter objectives. However, only non-dominated (best) feature subsets are improved using an indicator-based local search that optimizes the three objectives simultaneously. Our approach has been assessed on six high-dimensional datasets and compared with two existing prominent GP approaches, using three different classifiers for accuracy evaluation. Based on the obtained results, our approach is shown to provide competitive and better results compared with two competitor GP algorithms tested in this study.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2018.8477771", month = jul, notes = "University of Tunis, SMART lab, ISG-Campus, Tunisia Also known as \cite{8477771}", } @InProceedings{Hammami:2020:CEC, author = "Marwa Hammami and Slim Bechikh and Mohamed Makhlouf and Chih-Cheng Hung and Lamjed {Ben Said}", title = "Class Dependent Feature Construction as a Bi-level optimization Problem", booktitle = "2020 IEEE Congress on Evolutionary Computation (CEC)", year = "2020", editor = "Yaochu Jin", month = "19-24 " # jul, keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-7281-6929-3", DOI = "doi:10.1109/CEC48606.2020.9185756", abstract = "Feature selection and construction are important pre-processing techniques in data mining. They allow not only dimensionality reduction but also classification accuracy and efficiency improvement. While feature selection consists in selecting a subset of relevant features from the original feature set, feature construction corresponds to the generation of new high-level features, called constructed features, where each one of them is a combination of a subset of original features. However, different features can have different abilities to distinguish different classes. Therefore, it may be more difficult to construct a better discriminating feature when combining features that are relevant to different classes. Based on these definitions, feature construction could be seen as a BLOP (Bi-Level optimization Problem) where the feature subset should be defined in the upper level and the feature construction is applied in the lower level by performing multiple followers, each of which generates a set class dependent constructed features. In this paper, we propose a new bi-level evolutionary approach for feature construction called BCDFC that constructs multiple features which focuses on distinguishing one class from other classes using Genetic Programming (GP). A detailed experimental study has been conducted on six high-dimensional datasets. The statistical analysis of the obtained results shows the competitiveness and the outperformance of our bi-level feature construction approach with respect to many state-of-art algorithms.", notes = "SMART lab, University of Tunis, ISG, Tunis, Tunisia. Also known as \cite{9185756}", } @Article{Hammell2010, author = "Molly Hammell", title = "Computational methods to identify miRNA targets", journal = "Seminars in Cell \& Developmental Biology", year = "2010", volume = "21", number = "7", pages = "738--744", month = sep, ISSN = "1084-9521", DOI = "doi:10.1016/j.semcdb.2010.01.004", URL = "http://www.sciencedirect.com/science/article/B6WX0-4Y5GY3K-2/2/ee338722f9ce7b4b87a41bdd717fc22e", keywords = "genetic algorithms, genetic programming, miRNA, miRNA target prediction, Computational methods", abstract = "MicroRNAs (miRNAs) are short RNA molecules that regulate the post-transcriptional expression of their target genes. This regulation may take the form of stable translational or degradation of the target transcript, although the mechanisms governing the outcome of miRNA-mediated regulation remain largely unknown. While it is becoming clear that miRNAs are core components of gene regulatory networks, elucidating precise roles for each miRNA within these networks will require an accurate means of identifying target genes and assessing the impact of miRNAs on individual targets. Numerous computational methods for predicting targets are currently available. These methods vary widely in their emphasis, accuracy, and ease of use for researchers. This review will focus on a comparison of the available computational methods in animals, with an emphasis on approaches that are informed by experimental analysis of microRNA:target complexes.", notes = "survey", } @Article{HAMMER:2021:JNFM, author = "Alexander Hammer and Wolfgang Roland and Christian Marschik and Georg Steinbichler", title = "Predicting the co-extrusion flow of non-Newtonian fluids through rectangular ducts - A hybrid modeling approach", journal = "Journal of Non-Newtonian Fluid Mechanics", volume = "295", pages = "104618", year = "2021", ISSN = "0377-0257", DOI = "doi:10.1016/j.jnnfm.2021.104618", URL = "https://www.sciencedirect.com/science/article/pii/S037702572100118X", keywords = "genetic algorithms, genetic programming, Modeling and simulation, Co-extrusion, Die flow, Power-law fluid, Shooting method", abstract = "Co-extrusion has become the state-of-the-art process technology in nearly all application areas of polymer processing. By combining different types of polymeric materials within multilayer structures, products with a broad range of property profiles can be obtained for advanced applications. Design of co-extrusion dies and feedblock systems requires extensive knowledge of process and material behavior. To accurately describe the shear-thinning behavior of polymer melts in co-extrusion processes and to predict characteristic process quantities, numerical methods are essential. We present a hybrid approach to modeling stratified co-extrusion flows of two power-law fluids through rectangular ducts. By applying the theory of similarity and transforming the problem into dimensionless representation, we identified four independent influencing parameters that fully describe the flow situation: (i) the power-law index of the first fluid, (ii) the power-law index of the second fluid, (iii) the dimensionless position of the interface, and (iv) the ratio of dimensionless pressure gradients. We varied these input parameters within ranges that cover almost all combinations of industrial relevance, creating in the process a set of more than 44,000 design points. By means of the shooting method, numerical solutions were obtained for (i) pressure-throughput behavior, (ii) interfacial shear stress, (iii) interfacial velocity, and (iv) individual volume flow rates. Finally, we used symbolic regression based on genetic programming to model these target quantities as functions of their influencing parameters and obtain algebraic relationships between them. Our mathematical models thus enable accurate prediction of several characteristic process quantities in two-layer co-extrusion flows of shear-thinning fluids through rectangular ducts. The models are not restricted to the field of polymer processing, but can be used in all industrial applications that involve such co-extrusion flows", } @Article{DBLP:journals/ecs/HamoudaHR10, author = "Eslam Hamouda and Taher Hamza and Elsayed Radwan", title = "Genetic programming with Automatically Defined Function in Isolated Arabic Optical Character Recognition", journal = "Egyptian Computer Science Journal", year = "2010", volume = "34", number = "5", month = sep, keywords = "genetic algorithms, genetic programming, Automatically Defined Function, Optical Character Recognition, Arabic Typewritten, Classification", ISSN = "1110-2586", timestamp = "Sat, 06 Apr 2013 15:32:50 +0200", biburl = "https://dblp.org/rec/bib/journals/ecs/HamoudaHR10", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "http://ecsjournal.org/JournalArticle.aspx?articleID=267", abstract = "Optical Character Recognition refers to the branch of computer science that involves reading text from paper and translating the images into a form that the computer can manipulate. This paper demonstrates the usefulness of Genetic Programming with Automatically Defined Functions for evolving Optical Character Recognition algorithms. The problem-specific information required for this technique is a set of training isolated Arabic characters in different fonts. The result is a set of algorithms that can determine which character is represented by an image.", notes = "http://ecsjournal.org/Default.aspx Faculty of Computers and Information sciences, Computer science Department, Mansoura University, Egypt", } @InProceedings{hampo:1992:new, author = "Richard Hampo", title = "Genetic Programming: A New Paradigm for Control and Analysis", booktitle = "Third ASME Symposium on Transportation Systems", year = "1992", pages = "155--163", address = "Anaheim, California, USA", month = "9--13 " # nov, note = "Invited Paper at ASME Winter Annual Meeting", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/hampo_1992_new.pdf", size = "5 pages", } @InProceedings{hampo:1992:cvs, author = "R. J. Hampo and K. A. Marko", title = "Application of Genetic Programming to Control of Vehicle Systems", booktitle = "Proceedings of the Intelligent Vehicles '92 Symposium", year = "1992", pages = "191--195", address = "Detroit, Mi, USA", month = jun # " 29 - " # jul # " 1", publisher = "IEEE", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-0747-X", DOI = "doi:10.1109/IVS.1992.252255", abstract = "The development of sophisticated and complex `intelligent' systems often requires effective means to process information and control complicated systems. An `intelligent' system gathers information and interacts with its environment under the control of microprocessors programmed to process information and to execute control actions or responses to sensory inputs. The authors review briefly the basic principles of genetic algorithms and examine some potential applications of genetic programming for intelligent vehicle systems. They demonstrate the potential of this method by examining a particular problem in detail; the discovery of a control algorithm for an active suspension system", } @Unpublished{hampo:1992:newford, author = "R. J. Hampo", title = "The Genetic Programming Paradigm: A New Tool for Analysis and Control", note = "Ford Proprietary", month = "6 " # mar, year = "1992", keywords = "genetic algorithms, genetic programming", notes = "Ford Technical Report SR-92-114", } @InProceedings{Hampo:1994:ICemdagGP, author = "Richard J. Hampo and Bruce D. Bryant and Kenneth A. Marko", title = "IC Engine Misfire Detection Algorithm Generation Using Genetic Programming", booktitle = "EUFIT'94", year = "1994", pages = "1674--1678", address = "Promenade 9, D-52076, Aachen, Germany", month = "20--23 " # sep, publisher = "ELITE-Foundation", keywords = "genetic algorithms, genetic programming", size = "5 pages", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/ftp.io.com/papers/misfire-detection.PS.Z", notes = " Presents 2 GPs and a Neural Net detecting engine missfires from test data. Both GPs better than NN. Not clear what distinction is between two GPs. Uses existing, processed input signals from engine. Says GP easier to implement in existing vehical computer. Author's address: Ford Motor Company Scientific Research Laboratory, PO BOX 2053, 20000 Rotunda Drive, MD 2036, Dearborn, Michigan 48121-2053, USA", } @InProceedings{hamza:ooc:gecco2004, author = "Karim Hamza and Kazuhiro Saitou", title = "Optimization of Constructive Solid Geometry Via a Tree-Based Multi-objective Genetic Algorithm", booktitle = "Genetic and Evolutionary Computation -- GECCO-2004, Part II", year = "2004", editor = "Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell", series = "Lecture Notes in Computer Science", pages = "981--992", address = "Seattle, WA, USA", publisher_address = "Heidelberg", month = "26-30 " # jun, organisation = "ISGEC", publisher = "Springer-Verlag", volume = "3103", ISBN = "3-540-22343-6", ISSN = "0302-9743", DOI = "doi:10.1007/b98645", size = "12", keywords = "genetic algorithms, genetic programming", notes = "GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)", } @InProceedings{Han:2006:WCICA, author = "Pu Han and Shiliang Zhou and Dongfeng Wang", title = "A Multi-objective Genetic Programming/ NARMAX Approach to Chaotic Systems Identification", booktitle = "The Sixth World Congress on Intelligent Control and Automation, WCICA 2006", year = "2006", volume = "1", pages = "1735--1739", address = "Dalian", publisher = "IEEE", keywords = "genetic algorithms, genetic programming", ISBN = "1-4244-0332-4", DOI = "doi:10.1109/WCICA.2006.1712650", abstract = "A chaotic system identification approach based on genetic programming (GP) and multi-objective optimisation is introduced. NARMAX (Nonlinear Auto Regressive Moving Average with exogenous inputs) model representation is used for the basis of the hierarchical tree encoding in GP. Criteria related to the complexity, performance and chaotic invariants obtained by chaotic time series analysis of the models are considered in the fitness evaluation, which is achieved using the concept of the non-dominated solutions. So the solution set provides a trade-off between the complexity and the performance of the models, and derived model were able to capture the dynamic characteristics of the system and reproduce the chaotic motion. The simulation results show that the proposed technique provides an efficient method to get the optimum NARMAX difference equation model of chaotic systems", notes = "Dept. of Autom., North China Electr. Power Univ., Baoding", } @InProceedings{Han:2021:SBST, author = "Seunghee Han and Jaeuk Kim and Geon Kim and Jaemin Cho and Jiin Kim and Shin Yoo", title = "Preliminary Evaluation of Path-aware Crossover Operators for Search-Based Test Data Generation for Autonomous Driving", booktitle = "2021 IEEE/ACM 14th International Workshop on Search-Based Software Testing (SBST)", year = "2021", editor = "Jie M Zhang and Erik Fredericks", address = "internet", month = "31 " # may, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, SBSE, PCG, Simulink, Autonomous Driving, Test Data Generation, index error, OBE, AsFault, Search Based Software Engineering, Procedural Content Generation", isbn13 = "978-1-6654-4571-9/21", URL = "https://coinse.kaist.ac.kr/publications/pdfs/Han2021vp.pdf", video_url = "https://youtu.be/m0jTmJPa7ts", DOI = "doi:10.1109/SBST52555.2021.00020", size = "4 pages", abstract = "As autonomous driving gains attraction, testing of autonomous vehicles has become an important issue. However,testing in the real world is not only dangerous but also expensive.Consequently, a virtual test method has emerged as an alternative. Recently, a novel testing technique based on Procedural Content Generation (PCG) and Genetic Algorithm (GA), As Fault, has been proposed to test the lane keeping functionality of autonomous vehicles. This paper proposes new crossover operators for As fault that can better preserve the coupling between genotype (representations of road segments) and phenotype (occurrences of interesting self driving behaviour). We explain our design intentions and present a preliminary evaluation of the proposed operators using the Simulink autonomous driving simulator. We report promising early results: the proposed operators can lead not only to Out of Bound Episodes but also causes more vision errors in the simulation when compared to the original", notes = "is this GP? KAIST, Daejeon, Republic of Korea https://sbst21.github.io/program/", } @Article{Han2021_Article_ModelingTheProgressionOfCOVID-, author = "Tao Han and Francisco Nauber Bernardo Gois and Ramses Oliveira and Luan {Rocha Prates} and Magda {Moura de Almeida Porto}", title = "Modeling the progression of COVID-19 deaths using {Kalman} Filter and {AutoML}", journal = "Soft Computing", year = "2023", volume = "27", pages = "3229--3244", keywords = "genetic algorithms, genetic programming, TPOT, AutoML, COVID-19, Forecast, Kalman Filter", ISSN = "1432-7643", URL = "https://rdcu.be/cC09J", DOI = "doi:10.1007/s00500-020-05503-5", size = "16 pages", abstract = "The COVID-19 pandemic continues to have a destructive effect on the health and well-being of the global population. A vital step in the battle against it is the successful screening of infected patients, together with one of the effective screening methods being radiology examination using chest radiography. Recognition of epidemic growth patterns across temporal and social factors can improve our capability to create epidemic transmission designs, including the critical job of predicting the estimated intensity of the outbreak morbidity or mortality impact at the end. The study primary motivation is to be able to estimate with a certain level of accuracy the number of deaths due to COVID-19, managing to model the progression of the pandemic. Predicting the number of possible deaths from COVID-19 can provide governments and decision-makers with indicators for purchasing respirators and pandemic prevention policies. Thus, this work presents itself as an essential contribution to combating the pandemic. Kalman Filter is a widely used method for tracking and navigation and filtering and time series. Designing and tuning machine learning methods are a labour- and time-intensive task that requires extensive experience. The field of automated machine learning Auto Machine Learning relies on automating this task. Auto Machine Learning tools enable novice users to create useful machine learning units, while experts can use them to free up valuable time for other tasks. This paper presents an objective method of forecasting the COVID-19 outbreak using Kalman Filter and Auto Machine Learning. We use a COVID-19 dataset of Ceara, one of the 27 federative units in Brazil. Ceara has more than 235222 confirmed cases of COVID-19 and 8850 deaths due to the disease. The TPOT automobile model showed the best result with a 0.99 of R2 score.", notes = "DGUT-CNAM Institute, Dongguan University of Technology, Dongguan 523106, China. Health Department of Ceara, Av. Almirante Barroso, 600, Praia de Iracema, Fortaleza, Ceara, Brazil PMID: 33424432; PMCID: PMC7783486", } @InCollection{han:2000:GHSPGA, author = "Todd Han", title = "Generating Hard Satisfiability Problems with Genetic Algorithms", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "198--205", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms", notes = "part of \cite{koza:2000:gagp}", } @Article{Han:2025:jmsy, author = "Seulki Han and Utsav Awasthi and George M. Bollas", title = "Physics-informed symbolic regression for tool wear and remaining useful life predictions in manufacturing", journal = "Journal of Manufacturing Systems", year = "2025", volume = "80", pages = "734--748", keywords = "genetic algorithms, genetic programming, Explainable AI, Hybrid model, Surrogate model, Tool wear", ISSN = "0278-6125", URL = "https://www.sciencedirect.com/science/article/pii/S0278612525000846", DOI = "doi:10.1016/j.jmsy.2025.03.023", abstract = "Prognostics and Health Management (PHM) plays a crucial role in enhancing the reliability and safety of engineering systems. Recently, physics-informed machine learning (PIML) methods have gained significant attention for their ability to incorporate domain-specific knowledge into data-driven models. This paper proposes a novel approach that integrates symbolic regression with recursive modelling to develop a robust framework for PHM of dynamic processes. Our framework was applied to a manufacturing process to build a generic model for tool wear prognostics across various machining scenarios. The proposed method integrates domain knowledge of milling processes under different conditions with recursive models using symbolic regression to achieve accurate and robust tool wear predictions. A recursive feature model and a recursive tool wear model were developedto accurately predict future tool wear, taking into consideration the strong correlation between features extracted from sensor signals and tool wear. The Genetic Programming-based Toolbox for Identification of Physical Systems (GPTIPS) was employed for symbolic regression. The results illustrate that the proposed framework can capture the dynamics of tool wear by recursively updating predictions with new data and can derive simple, interpretable mathematical expressions that represent the physical characteristics of the tool wear process. Benchmarking analysis demonstrated the effectiveness of the proposed approach, achieving lower root-mean-square error (RMSE) compared to other tool wear prognostic models in the literature", } @Article{Han:2024:mtcomm, author = "Tianlong Han and Yijie Tong and Yalin Yan and Kai Kang and Adham E. Ragab", title = "Application of graphene origami metamaterials to improve nonlinear vibrations of the cars' hood door under transient loading: Introducing machine learning method for solving the nonlinear problem", journal = "Materials Today Communications", year = "2024", volume = "39", pages = "109278", keywords = "genetic algorithms, genetic programming, GOEAM, Car's hood door, Mechanical excitation, Nonlinear vibrations, Hybrid machine learning method", ISSN = "2352-4928", URL = "https://www.sciencedirect.com/science/article/pii/S2352492824012595", DOI = "doi:10.1016/j.mtcomm.2024.109278", abstract = "Due to the importance of the car's hood door for automobiles and other related industries, in this work for the first time, some recommendations are made to enhance the nonlinear vibrations of this kind of engineering structure under external excitation. One of the recommendations is related to the used material for the fabrication of this kind of applicable structure. Functionally graded (FG) graphene origami (GOri)-enabled auxetic metamaterial (GOEAM) systems exhibit significant promise for many engineering applications owing to their remarkable physical and mechanical characteristics, including a high ratio of strength to weight, adjustable stiffness and strength, and a negative Poisson's ratio (NPR). So, due to the importance of this kind of applicable structure made of GOEAM, in this work, both hybrid machine learning algorithms and mathematical modelling are used to correctly simulate the presented nonlinear system. In the category of mathematical simulation, genetic programming is used to correctly simulate the material properties of the structure. Nonlinear strain-displacement components, Hamilton's principle, numerical solution procedure, and homotopy perturbation method are used to model and extract the responses of the current system in the mathematics domain. After obtaining the datasets using mathematics, these data are used for testing, training, and validating the results of the presented hybrid machine learning method. Finally, some recommendations for improving the efficiency and stability of the car's hood door in various situations are presented in detail", } @Article{Han:2025:CIM, author = "Xiaoxu Han and Jinghui Zhong and Zhitong Ma and Xin Mu and Nikola Gligorovski", title = "Transformer-Assisted Genetic Programming for Symbolic Regression [Research Frontier]", journal = "IEEE Computational Intelligence Magazine", year = "2025", volume = "20", number = "2", pages = "58--79", month = may, keywords = "genetic algorithms, genetic programming, Training, Accuracy, Computational modelling, Transformers, Mathematical models, Complexity theory, Problem-solving, Optimisation", ISSN = "1556-6048", DOI = "doi:10.1109/MCI.2025.3540742", abstract = "Symbolic Regression (SR) is a powerful technique for uncovering hidden mathematical expressions from observed data and has broad applications in scientific discovery and automatic programming. Genetic Programming (GP) has traditionally been the dominant technique for solving the SR, benefiting from a robust global search capability that enables the discovery of solutions with high fitting accuracy. Whereas, GP suffers from low search efficiency and may not fully exploit the accumulated knowledge to accelerate convergence. Conversely, deep learning-based methods, particularly those employing Transformer backbones, are trained offline on large-scale datasets. These methods exhibit strong generalisation capabilities for unseen tasks without additional training. However, the lack of refinement mechanisms for specific tasks renders them inferior to GP methods in accuracy. This study aims to combine the specific problem-solving capabilities of GP with the generalisation strengths of pretrained Transformer models. Specifically, we propose a pretrained model guided GP (PGGP) method, which is a GP-based method that incorporates a pretrained Transformer model to enhance SR problem-solving. New initialization and mutation operators are proposed based on the well-structured equation obtained using the pre-trained model. Extensive experiments are conducted, and the results show that our method not only surpasses comparative methods in terms of accuracy but also reduces the complexity of the generated solutions, potentially enhancing interpretability.", notes = "Also known as \cite{10976464} South China University of Technology, Guangzhou, China Pengcheng Laboratory, Shenzhen, China", } @Article{Han:2021:WRM, author = "Zheng Han and Wenxi Lu and Yue Fan and Jianan Xu and Jin Lin", title = "Surrogate-Based Stochastic Multiobjective Optimization for Coastal Aquifer Management under Parameter Uncertainty", journal = "Water Resources Management", year = "2021", volume = "35", pages = "1479--1497", keywords = "genetic algorithms, genetic programming, multigene genetic programming, seawater intrusion, uncertainty, simulation-optimisation, groundwater management, multiobjective evolutionary algorithm", publisher = "springer", bibsource = "OAI-PMH server at oai.repec.org", identifier = "RePEc:spr:waterr:v:35:y:2021:i:5:d:10.1007_s11269-021-02796-5", oai = "oai:RePEc:spr:waterr:v:35:y:2021:i:5:d:10.1007_s11269-021-02796-5", URL = "http://link.springer.com/10.1007/s11269-021-02796-5", DOI = "doi:10.1007/s11269-021-02796-5", abstract = "Linked simulation-optimisation (S/O) approaches have been extensively used as tools in coastal aquifer management. However, parameter uncertainties in seawater intrusion (SI) simulation models often undermine the reliability of the derived solutions. In this study, a stochastic S/O framework is presented and applied to a real-world case of the Longkou coastal aquifer in China. The three conflicting objectives of maximising the total pumping rate, minimising the total injection rate, and minimising the solute mass increase are considered in the optimisation model. The uncertain parameters are contained in both the constraints and the objective functions. A multiple realization approach is used to address the uncertainty in the model parameters, and a new multiobjective evolutionary algorithm (EN-NSGA2) is proposed to solve the optimisation model. EN-NSGA2 overcomes some inherent limitations in the traditional nondominated sorting genetic algorithm-II (NSGA-II) by introducing information entropy theory. The comparison results indicate that EN-NSGA2 can effectively ameliorate the diversity in Pareto-optimal solutions. For the computational challenge in the stochastic S/O process, a surrogate model based on the multigene genetic programming (MGGP) method is developed to substitute for the numerical simulation model. The results show that the MGGP surrogate model can tremendously reduce the computational burden while ensuring an acceptable level of accuracy.", } @InProceedings{Han:2024:SSE, author = "Zheng Han2 and Chen Wang and Zhengxin Fang and Hui Ma and Gang Chen2", title = "Multi-Objective Optimization of Application Deployment Strategies in Integrated Cloud-Fog Computing Environments", booktitle = "2024 IEEE International Conference on Software Services Engineering (SSE)", year = "2024", pages = "160--166", month = jul, keywords = "genetic algorithms, genetic programming, Measurement, Cloud computing, Scalability, Quality of service, Software, Fog Computing, Multi-Objective Optimisation, Genetic Programming Hyper-Heuristics", DOI = "doi:10.1109/SSE62657.2024.00033", abstract = "In the domain of Cloud computing, Fog computing is integrated with the Cloud to offer a balanced approach that combines Cloud's scalability with Fog's low latency, enabling efficient software application deployment. However, many current studies overlook the unpredictability of future user requests, such as assuming all requests are known beforehand. User requests often arrive dynamically and may have different quality of service (QoS) preferences. Therefore we need effective methods to handle dynamic application deployment with multiple objectives. This paper tackles this gap by modelling a multi-objective application deployment problem that considers dynamically arriving users' requests on application deployment in a Cloud-Fog environment. We further introduce a multi-objective Genetic Programming Hyper-Heuristic based approach to automatically generate a set of deployment rules that can be chosen according to users' QoS preferences. These rules are generated with different trade-offs of two optimisation objectives, i.e., minimizing cost and latency, which can be used for deploying applications dynamically. Our experimental evaluation using real-world data demonstrates that our GPHH approach can generate effective heuristics for deploying applications in an integrated Cloud-Fog environment.", notes = "Also known as \cite{10664389}", } @InProceedings{Hanada:2012:CEC, title = "Effectiveness of Multi-step Crossover Fusions in Genetic Programming", author = "Yoshiko Hanada and Nagahiro Hosokawa and Keiko Ono and Mitsuji Muneyasu", pages = "2389--2396", booktitle = "Proceedings of the 2012 IEEE Congress on Evolutionary Computation", year = "2012", editor = "Xiaodong Li", month = "10-15 " # jun, DOI = "doi:10.1109/CEC.2012.6256564", address = "Brisbane, Australia", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Representation and operators, Discrete and combinatorial optimization.", abstract = "Multi-step Crossover Fusion (MSXF) and deterministic MSXF (dMSXF) are promising crossover operators that perform multi-step neighbourhood search between parents, and applicable to various problems by introducing a problem-specific neighbourhood structure and a distance measure. Under their appropriate definitions, MSXF and dMSXF can successively generate offspring that acquire parents' good characteristics along the path connecting the parents. In this paper, we introduce MSXF and dMSXF to genetic programming (GP), and apply them to symbolic regression problem. To optimise trees, we define a neighbourhood structure and its corresponding distance measure based on the largest common subtree between parents with considering ordered/unordered tree structures. Experiments using symbolic regression problem instances showed the effectiveness of a GP with the proposed MSXF and dMSXF.", notes = "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the EPS and the IET.", } @InProceedings{Hand:1997:gn, author = "Charles Hand", title = "Genetic Nets", booktitle = "Late Breaking Papers at the 1997 Genetic Programming Conference", year = "1997", editor = "John R. Koza", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California, 94305-3079, USA", month = "13--16 " # jul, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-206995-8", notes = "GP-97LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670", } @Misc{hand:1994:GPreview, author = "David J. Hand", title = "Evolutionary computation", journal = "Statistics and Computing", year = "1994", volume = "4", number = "2", pages = "158", month = jun, note = "Book review of Koza's ``Genetic Programming''", DOI = "DOI:10.1007/BF00175359", keywords = "genetic algorithms, genetic programming", size = "0.7 pages", ISSN = "0960-3174", notes = "Special issue on Evolutionary Programming. Favourable review of \cite{koza:book}", } @Article{hand:2003:GPEM, author = "David J. Hand", title = "Book Review: {Data} Mining and Knowledge Discovery with Evolutionary Programs", journal = "Genetic Programming and Evolvable Machines", year = "2003", volume = "4", number = "3", pages = "287--289", month = sep, keywords = "genetic algorithms", ISSN = "1389-2576", URL = "https://rdcu.be/dR8t3", DOI = "doi:10.1023/A:1025128524617", notes = "Review of Alex A. Freitas' \cite{freitas:2002:book} Article ID: 5141125", } @InProceedings{handa:1999:CGASDCSP, author = "Hisashi Handa and Osamu Katai and Tadataka Konishi and Mitsuru Baba", title = "Coevolutionary Genetic Algorithms for Solving Dynamic Constraint Satisfaction Problems", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "252--257", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-394.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-394.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{icga93:handley, author = "Simon Handley", title = "Automatic Learning of a Detector for alpha-helices in Protein Sequences Via Genetic Programming", year = "1993", booktitle = "Proceedings of the 5th International Conference on Genetic Algorithms, ICGA-93", editor = "Stephanie Forrest", publisher = "Morgan Kaufmann", address = "University of Illinois at Urbana-Champaign", month = "17-21 " # jul, keywords = "genetic algorithms, genetic programming", pages = "271--278", size = "8 pages", abstract = "This paper reports preliminary results from an attempt to predict the secondary structure of globular proteins. The genetic programming system was used to evolve programs that classified each residue in ten proteins as being either in an a-helix or in a {"}coil{"} (everything else). The proteins were chosen to be non-homologous and to contain mostly a-helices. The ten proteins were divided in half into a training set, that was used to drive the evolution, and a testing set, that was used to test the resultant programs. The fitness of the programs was based on the correlation coefficient between the observed and the predicted a-helicity of the residues. The fittest program produced by the genetic programming system had a correlation of 0.316 between the observed classifications and the classifications predicted by the program (on the proteins in the testing set).", broken = "http://www-leland.stanford.edu/~shandley/postscript/alpha-helices.ps.gz", notes = "GP based upon balkiness and hydrophilicity of the 7 amino acid residues closest to a point along the chain (repeat for whole chain). Train on five known P test on five more. NOT GOOD, GP learns structure of the training set well but this is not a very good predictor for the others", } @InProceedings{Handley:1993:GPagplGP, author = "Simon Handley", title = "The genetic planner: The automatic generation of plans for a mobile robot via genetic programming", booktitle = "Proceedings of the Eighth IEEE International Symposium on Intelligent Control", year = "1993", pages = "190--195", address = "Chicago, USA", month = aug, organisation = "The IEEE Control System Society", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Automatic control, Calculus, Computational modeling, Computer science, Computer simulation, Particle measurements, Proportional control, Robotics and automation, mobile robots, path planning, artificial selection, fitness proportionate reproduction, genetic planner, mobile robot, recombination, sexual mixing", DOI = "doi:10.1109/ISIC.1993.397715", size = "6 pages", abstract = "Planning is the creation of programs to control an agent, such as a robot. Traditionally, planners have maintained a logical model of the agent's world and planned by reasoning about what plans do to that world. The Genetic Planner uses artificial selection, sexual mixing (recombination) and fitness proportionate reproduction to breed computer programs (i.e., to plan). The Genetic Planner uses a simulation of the world to execute candidate computer programs (i.e., candidate plans). This paper describes The Genetic Planner and shows it at work on a simple problem: a robot on a 2-D grid.", notes = "Chicago, IL, USA ", } @InProceedings{Handley:1991:agplGPADF, author = "S. Handley", title = "The automatic generation of plans for a mobile robot via genetic programming with automatically defined functions", booktitle = "Proceedings of the Fifth Workshop on Neural Networks: An International Conference on Computational Intelligence: Neural Networks, Fuzzy Systems, Evolutionary Programming, and Virtual Reality", year = "1991", organisation = "The Society for Computer Simulation", keywords = "genetic algorithms, genetic programming", } @InCollection{kinnear:handley, title = "The Automatic Generations of Plans for a Mobile Robot via Genetic Programming with Automatically Defined Functions", author = "Simon G. Handley", booktitle = "Advances in Genetic Programming", publisher = "MIT Press", editor = "Kenneth E. {Kinnear, Jr.}", year = "1994", chapter = "18", pages = "391--407", keywords = "genetic algorithms, genetic programming", broken = "http://www-leland.stanford.edu/~shandley/postscript/kinnear.ps.gz", URL = "http://www.amazon.co.uk/Advances-Genetic-Programming-Complex-Adaptive/dp/0262111888", URL = "http://cognet.mit.edu/sites/default/files/books/9780262277181/pdfs/9780262277181_chap18.pdf", DOI = "doi:10.7551/mitpress/1108.003.0024", size = "17 pages", abstract = "Planning is the creation of programs to control an agent, such as a robot. Traditionally, planners have maintained a logical model of the agent's world and planned by reasoning about what plans do to that world. In this chapter I describe a new planner, the Genetic Planner, that uses artificial selection, sexual mixing (recombination) and fitness proportionate reproduction to breed computer programs (i.e., to plan). This planner uses a simulation of the world to execute candidate computer programs (i.e., candidate plans). I first describe this planner and then I show it at work on a simple problem---a robot on a 2-D grid. Also, Koza's Automatically Defined Functions (ADFs) are used and the results compared with the non-ADF genetic programming system.", notes = " Move about 49 by 49 world, move boxes, fails to switch on light. Describes Genetic Planner (=GP plus fitness function based upon hiow close to succeeding multiple predicates are) Part of \cite{kinnear:book}", } @InProceedings{Handley:1994:DAGpcp, author = "S. Handley", title = "On the use of a directed acyclic graph to represent a population of computer programs", booktitle = "Proceedings of the 1994 IEEE World Congress on Computational Intelligence", year = "1994", pages = "154--159", volume = "1", address = "Orlando, Florida, USA", month = "27-29 " # jun, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, DAG", DOI = "doi:10.1109/ICEC.1994.350024", size = "6 pages", abstract = "This paper demonstrates a technique that reduces the time and space requirements of genetic programming. The population of parse trees is stored as a directed acyclic graph (DAG), rather than as a forest of trees. This saves space by not duplicating structurally identical subtrees. Also, the value computed by each subtree for each fitness case is cached, which saves computation both by not recomputing subtrees that appear more than once in a generation and by not recomputing subtrees that are copied from one generation to the next. I have implemented this technique for a number of problems and have seen a 15- to 28-fold reduction in the number of nodes extant per generation and an 11- to 30-fold reduction in the number of nodes evaluated per run (for populations of size 500).", notes = "Converts whole GP population to a directed Acyclic Graph, which is functionally equivelent. With primatives that have NO SIDE EFFECTS is able to cache earlier sub tree evaluations so they donot have to be re-evaluated, even if occur in a different individual. Claims speed ups of 11-30 fold. See \cite{Azad:2014:EC}", } @InProceedings{Handley:1994:alAHGP, author = "S. Handley", title = "Automated learning of a detector for the cores of a-helices in protein sequences via genetic programming", booktitle = "Proceedings of the 1994 IEEE World Congress on Computational Intelligence", year = "1994", volume = "1", pages = "474--479", address = "Orlando, Florida, USA", month = "27-29 " # jun, publisher = "IEEE Press", broken = "http://www-leland.stanford.edu/~shandley/postscript/helix_segments_paper.ps.gz", DOI = "doi:10.1109/ICEC.1994.349904", keywords = "genetic algorithms, genetic programming", size = "6 pages", abstract = "I used Koza's genetic programming to evolve programs that classified contiguous regions of proteins as being a-helix cores or not. I snipped positive and negative examples of a-helix core regions out of a set of 90 proteins. These proteins were chosen from the Brookhaven Protein Data Bank to be non-homologous. The fitness of the programs was defined as the correlation coefficient between the observed and the predicted a-helicity of the above regions. The fittest program produced by the genetic programming system that predicted the training set at least as well as the testing set had a correlation of 0.4818 between the observed classifications and the classifications predicted by the program (on the proteins in the testing set).", } @InProceedings{handley:1994:solvent, author = "Simon G. Handley", title = "The prediction of the degree of exposure to solvent of amino acid residues via genetic programming", booktitle = "Second International Conference on Intelligent Systems for Molecular Biology", year = "1994", editor = "Russ Altman and Douglas Brutlag and Peter Karp and Richard Lathrop and David Searls", pages = "156--160", address = "Stanford University, Stanford, CA, USA", publisher = "AAAI Press", keywords = "genetic algorithms, genetic programming, bioinformatics", broken = "http://www-leland.stanford.edu/~shandley/postscript/pburied.ps.gz", URL = "http://www.aaai.org/Library/ISMB/ismb94contents.php", URL = "http://www.aaai.org/Library/ISMB/1994/ismb94-019.php", size = "5 pages", abstract = "In this paper I evolve programs that predict the degree of exposure to solvent (the buriedness) of amino acid residues given only the primary structure. I use genetic programming to evolve programs that take as input the primary structure and that output the buriedness of each residue. I trained these programs on a set of 82 proteins from the Brookhaven Protein Data Bank (PDB) and cross-validated them on a separate testing set of 40 proteins, also from the PDB. The best program evolved had a correlation of 0.434 between the predicted and observed buriednesses on the testing set.", notes = "ISBM-94", } @InCollection{handley:1994:al, author = "Simon G. Handley and Tod Klingler", title = "Automated learning of a detector for a-helices in protein sequences via genetic programming", booktitle = "Artificial Life at Stanford 1993", year = "1993", editor = "John R. Koza", address = "Stanford, California, 94305-3079 USA", month = dec, organisation = "Stanford University", publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-171957-6", notes = "Part of koza:1993:alife Student works for course {"}Artificial Life{"} (Computer Science 425) at Stanford University offered during 1994 http://www-cs-faculty.stanford.edu/~koza/cs425.html", } @InProceedings{handley:1995:DNAsplice, author = "Simon Handley", title = "Predicting Whether or Not a 60-base {DNA} Sequence Contains a Centrally-Located Splice Site Using Genetic Programming", booktitle = "Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications", year = "1995", editor = "Justinian P. Rosca", pages = "98--103", address = "Tahoe City, California, USA", month = "9 " # jul, publisher = "AAAI", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/handley_1995_DNAsplice.pdf", broken = "http://www-leland.stanford.edu/~shandley/postscript/splicej.ps.gz", broken = "http://www-leland.stanford.edu/~shandley/postscript/ML95GPwkshp.ps.gz", URL = "http://www.cs.rochester.edu/u/rosca/ml95.htm", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.532.6577", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.532.6577", URL = "http://www.aaai.org/Papers/Symposia/Fall/1995/FS-95-01/FS95-01-003.pdf", size = "6 pages", abstract = "An evolutionary computation technique, genetic programming, was used to create programs that classify DNA sequences into one of three classes: (1) contains a centrally-located donor splice site, (2) contains a centrally-located acceptor splice site, and (3) contains neither a donor nor an acceptor. The performance of the programs created are competitive with previous work.", notes = "Pop size 64,000 part of \cite{rosca:1995:ml}", } @InProceedings{handley:1995:IorE, author = "Simon Handley", title = "Classifying Nucleic Acid Sub-Sequences as Introns or Exons Using Genetic Programming", booktitle = "Proceedings of the Third International Conference on Intelligent Systems for Molecular Biology (ISMB-95)", year = "1995", editor = "Christopher Rawlins and Dominic Clark and Russ Altman and Lawrence Hunter and Thomas Lengauer and Shoshana Wodak", pages = "162--169", address = "Cambridge, UK", publisher_address = "Menlo Park, CA, USA", publisher = "AAAI Press", keywords = "genetic algorithms, genetic programming", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.529.6402", URL = "http://www.aaai.org/Papers/ISMB/1995/ISMB95-020.pdf", broken = "http://www-leland.stanford.edu/~shandley/postscript/iep-ISMB.ps.gz", URL = "http://www.aaai.org/Library/ISMB/ismb95contents.php", abstract = "An evolutionary computation technique, genetic programming, was used to create programs that classify messenger RNA sequences into one of two classes: (1) the sequence is expressed as (part of) a protein (called an exon), or (2) not expressed as protein (called an intron).", notes = "PMID: 7584433 ", } @InProceedings{handley:1995:coliP, author = "Simon Handley", title = "Predicting Whether or not a Nucleic Acid Sequence is an E. coli Promoter Region using Genetic Programming", booktitle = "Proceedings of the First International Symposium on Intelligence in Neural and Biological Systems INBS-95", year = "1995", pages = "122--127", address = "Herndon, Virginia, USA", publisher_address = "Los Alamitos, California, USA", month = "29-31 " # may, organisation = "IEEE Comitteee on Pattern Analysis and Machine Intelligence (PAMI)", publisher = "IEEE Computer Society Press", keywords = "genetic algorithms, genetic programming", broken = "http://www-leland.stanford.edu/~shandley/postscript/postscript/INBS-camera-ready.ps.gz", DOI = "doi:10.1109/INBS.1995.404270", abstract = "This paper shows that an evolutionary computing technique, genetic programming, can create programs that classify DNA sequences as E. coli promoter vs non-E. coli promoter. The performance of the programs are competitive with pervious work.", notes = "Pop size 32,000 ", } @InProceedings{handley:1995:DNAspliceF, author = "Simon Handley", title = "Predicting Whether Or Not a 60-Base {DNA} Sequence Contains a Centrally-Located Splice Site Using Genetic Programming", booktitle = "Working Notes for the AAAI Symposium on Genetic Programming", year = "1995", editor = "E. V. Siegel and J. R. Koza", pages = "17--22", address = "MIT, Cambridge, MA, USA", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025, USA", month = "10--12 " # nov, publisher = "AAAI", keywords = "genetic algorithms, genetic programming", URL = "http://www.aaai.org/Papers/Symposia/Fall/1995/FS-95-01/FS95-01-003.pdf", URL = "http://www.aaai.org/Library/Symposia/Fall/fs95-01.php", size = "6 pages", abstract = "An evolutionary computation technique, genetic programming, was used to create programs that classify DNA sequences into one of three classes: (1) contains a centrally-located donor splice site, (2) contains a centrally-located acceptor splice site, and (3) contains neither donor nor an acceptor. The performance of the programs created are competitive with previous work.", notes = "AAAI-95f GP. Part of \cite{siegel:1995:aaai-fgp} {\em Telephone:} 415-328-3123 {\em Fax:} 415-321-4457 {\em email} info@aaai.org {\em URL:} http://www.aaai.org/", } @InProceedings{handley:1996:pdesaarGP, author = "Simon Handley", title = "The Prediction of the Degree of Exposure to Solvent of Amino Acid Residues via Genetic Programming", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "297--300", address = "Stanford University, CA, USA", publisher = "MIT Press", size = "4 pages", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap38.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96", } @InProceedings{handley:1996:nfsssp, author = "Simon Handley", title = "A New Class of Function Sets for Solving Sequence Problems", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "301--308", address = "Stanford University, CA, USA", publisher = "MIT Press", size = "8 pages", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap39.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96", } @PhdThesis{handley:thesis, author = "Simon Graham Handley", title = "Automatically Discovering Solutions that Flexibly Combine Iterative and non-Iterative Computations", school = "Department of Computer Science, Stanford University", year = "1997", address = "USA", month = dec, keywords = "genetic algorithms, genetic programming, ADF, computational biology, coiled coils, SCZ, E.Coli promoters, intron v exon, pinochie poker", URL = "http://searchworks.stanford.edu/view/3911278", URL = "http://search.proquest.com/docview/304454352", URL = "http://search.proquest.com/docview/304454352.pdf", URL = "https://search.worldcat.org/title/Automatically-discovering-solutions-that-flexibly-combine-iterative-and-non-iterative-computations/oclc/77834341", size = "513 pages", abstract = "This thesis investigates computational techniques that automatically discover solutions to problems. In particular, this thesis focuses on the discovery of solutions that flexibly combine iterative and non-iterative computations. Consider, for example, the problem of assigning poker hands to classes (such as full-house or two-pairs). This classification is a mapping $f\sb{\rm PKR}$: Hand $\mapsto$ Class where Hand = $\lbrack C\sb1,C\sb2,\...,C\sb{n}\rbrack,$ the $C\sb{i}$ are cards, n is typically five and $\rm Class\in\{royal-flush,\...,high-card\}.$ A solution to this problem will likely do computations such as 'count up the number of Aces', 'how many cards occur 3 or more times?' and 'is the frequency of occurrence of rank x equal to 2?'. We investigate four techniques: three adaptations of existing techniques and one new technique that partially addresses concerns with the other three techniques. These techniques automatically discover solutions that combine iterative and non-iterative computations with varying degrees of flexibility. All four techniques are based on genetic programming, an evolutionary algorithm. The appropriate criteria for analysing these techniques are discussed. One important design criterion is the degree to which representational flexibility is traded-off for run-time predictability. This trade-off is observed in many solution discovery techniques: solutions that are drawn from a search space with a high degree of representational freedom often have execution times that are difficult to predict. The techniques are demonstrated on the following problems: computing parity, classifying poker hands, generating hypotheses about coiled-coil regions, recognizing splice sites, parsing genes, recognizing E. coli promoters, secondary structure prediction, and predicting the degree of exposure to solvent of amino acid residues. By examining the problems that worked, and those that did not, we gained an understanding of (a) why these techniques work, (b) the types of problems on which they work, and (c) the types of problems on which they don't work.", notes = "John R. Koza, primary advisor. http://genealogy.math.ndsu.nodak.edu/id.php?id=71265 Special Collections - University Archives Request at service desk 3781 1998 H must be paged/requested for in-library use only Items in Stacks 3781 1998 H UMI Microform 9837102 OCLC Number: 77834341 Copyright 1997 by Simon Graham Handley", } @Article{oai:biomedcentral.com:1471-2105-8-23, title = "Motif kernel generated by genetic programming improves remote homology and fold detection", author = "Tony Handstad and Arne J H Hestnes and Pal Saetrom", journal = "BMC Bioinformatics", year = "2007", volume = "8", number = "23", month = jan # "~25", publisher = "BioMed Central Ltd.", bibsource = "OAI-PMH server at www.biomedcentral.com", language = "en", oai = "oai:biomedcentral.com:1471-2105-8-23", rights = "Copyright 2007 H{\aa}ndstad et al; licensee BioMed Central Ltd.", keywords = "genetic algorithms, genetic programming, GPkernel, SVM, MISD, boosting", ISSN = "1471-2105", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.276.5386", URL = "http://www.biomedcentral.com/content/pdf/1471-2105-8-23.pdf", URL = "http://www.biomedcentral.com/1471-2105/8/23", DOI = "doi:10.1186/1471-2105-8-23", broken_undergraduate_thesis = "http://www.diva-portal.org/diva/getDocument?urn_nbn_no_ntnu_diva-1030-1__fulltext.pdf", size = "16 pages", abstract = "Background Protein remote homology detection is a central problem in computational biology. Most recent methods train support vector machines to discriminate between related and unrelated sequences and these studies have introduced several types of kernels. One successful approach is to base a kernel on shared occurrences of discrete sequence motifs. Still, many protein sequences fail to be classified correctly for a lack of a suitable set of motifs for these sequences. Results We introduce the GPkernel, which is a motif kernel based on discrete sequence motifs where the motifs are evolved using genetic programming. All proteins can be grouped according to evolutionary relations and structure, and the method uses this inherent structure to create groups of motifs that discriminate between different families of evolutionary origin. When tested on two SCOP benchmarks, the superfamily and fold recognition problems, the GPkernel gives significantly better results compared to related methods of remote homology detection. Conclusion The GPkernel gives particularly good results on the more difficult fold recognition problem compared to the other methods. This is mainly because the method creates motif sets that describe similarities among subgroups of both the related and unrelated proteins. This rich set of motifs give a better description of the similarities and differences between different folds than do previous motif-based methods.", notes = "PMID: 17254344 Undergraduate thesis: Protein Remote Homology Detection using Motifs made with Genetic Programming Handstad, Tony. broken http://urn.ub.uu.se/resolve?urn=urn:nbn:no:ntnu:diva-1030 (2007-03-30) 118 pages. Binary feature vectors. 2 seconds run time (PC+search chip). GPboost, SCOP, eMOTIF kernel, ROC, classifier combination. 'GPkernel performs significantly better than the other motif-based methods' p5. GPextended. evolves regular expressions. 'In addition to [the 20] amino acid characters, the motifs are also made from the disjunction operator (|) wildcard (.) and Hamming distance {:p>=x} that specifies the minimum number of characters that must match in the pattern.' p13.", } @InCollection{hahn:1994:p-p, author = "Mark S. Hanh", title = "Simulating Evolution In a Kolmogorov Predator-Prey Model With Genetic Extensions", booktitle = "Artificial Life at Stanford 1994", year = "1994", editor = "John R. Koza", pages = "44--53", address = "Stanford, California, 94305-3079 USA", month = jun, organisation = "Stanford University", publisher = "Stanford Bookstore", keywords = "genetic algorithms", ISBN = "0-18-182105-2", notes = "This volume contains 22 papers written and submitted by students describing their term projects for the course in artificial life (Computer Science 425) at Stanford University offered during the spring quarter quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs425.html", } @Misc{hanna2023reinforcement, author = "Carol Hanna and Aymeric Blot and Justyna Petke", title = "Reinforcement Learning for Mutation Operator Selection in Automated Program Repair", howpublished = "arXiv", year = "2023", month = "9 " # jun, keywords = "genetic algorithms, genetic programming, genetic improvement, APR, JaRFly, Defects4J", eprint = "2306.05792", archiveprefix = "arXiv", primaryclass = "cs.SE", URL = "https://arxiv.org/abs/2306.05792", size = "12 pages", abstract = "Automated program repair techniques aim to aid software developers with the challenging task of fixing bugs. In heuristic-based program repair, a search space of program variants is created by applying mutation operations on the source code to find potential patches for bugs. Most commonly, every selection of a mutation operator during search is performed uniformly at random. The inefficiency of this critical step in the search creates many variants that do not compile or break intended functionality, wasting considerable resources as a result. we address this issue and propose a reinforcement learning-based approach to optimise the selection of mutation operators in heuristic-based program repair. Our solution is programming language, granularity-level, and search strategy agnostic and allows for easy augmentation into existing heuristic-based repair tools. We conduct extensive experimentation on four operator selection techniques, two reward types, two credit assignment strategies, two integration methods, and three sets of mutation operators using 22300 independent repair attempts. We evaluate our approach on 353 real-world bugs from the Defects4J benchmark. Results show that the epsilon-greedy multi-armed bandit algorithm with average credit assignment is best for mutation operator selection. Our approach exhibits a 17.3percent improvement upon the baseline, by generating patches for 9 additional bugs for a total of 61 patched bugs in the Defects4J benchmark.", notes = "V2 6 May 2024. Replaced by \cite{Hanna:2025:ASEJ} V2 also known as \cite{hanna2024reinforcementlearningmutationoperator}", } @Article{Hanna:2025:ASEJ, author = "Carol Hanna and Aymeric Blot and Justyna Petke", title = "Reinforcement learning for mutation operator selection in automated program repair", journal = "Automated Software Engineering", year = "2025", volume = "32", number = "2", pages = "Article number 31", month = nov, keywords = "genetic algorithms, genetic programming, Genetic improvement, Automated program repair, APR, Machine learning, Mutation operators, Reinforcement learning, Arja, ASTOR, GenProg, KGenProg, jGenProg, RSRepair, jMutRepair, HistoricalFix, LIANA", ISSN = "1573-7535", URL = "https://discovery.ucl.ac.uk/id/eprint/10205221/", URL = "https://rdcu.be/eeuzB", DOI = "doi:10.1007/s10515-025-00501-z", data_url = "https://github.com/carolhanna01/jarFly-learner/blob/operator-selection/reports/Tools.pdf", code_url = "https://zenodo.org/records/7579947", size = "33 pages", abstract = "Automated program repair techniques aim to aid software developers with the challenging task of fixing bugs. In heuristic-based program repair, a search space of mutated program variants is explored to find potential patches for bugs. Most commonly, every selection of a mutation operator during search is performed uniformly at random, which can generate many buggy, even uncompilable programs. Our goal is to reduce the generation of variants that do not compile or break intended functionality which waste considerable resources. We investigate the feasibility of a reinforcement learning-based approach for the selection of mutation operators in heuristic-based program repair. Our proposed approach is programming language, granularity-level, and search strategy agnostic and allows for easy augmentation into existing heuristic-based repair tools. We conducted an extensive empirical evaluation of four operator selection techniques, two reward types, two credit assignment strategies, two integration methods, and three sets of mutation operators using 30080 independent repair attempts. We evaluated our approach on 353 real-world bugs from the Defects4J benchmark. The reinforcement learning-based mutation operator selection results in a higher number of test-passing variants, but does not exhibit a noticeable improvement in the number of bugs patched in comparison with the baseline, uniform random selection. While reinforcement learning has been previously shown to be successful in improving the search of evolutionary algorithms, often used in heuristic-based program repair, it has yet to demonstrate such improvements when applied to this area of research.", notes = "Also known as \cite{Hanna2025} Replaces \cite{hanna2023reinforcement} University College London, London, UK", } @InProceedings{Hanselmann:1996:Chemeca, author = "K. Hanselmann and G. W. Barton and B. McKay and M. J. Willis", title = "Modelling a Transformer Oil Regeneration Process Using Genetic Programming", booktitle = "Chemeca 96: Excellence in Chemical Engineering; Proceedings of the 24th Australian and New Zealand Chemical Engineering Conference and Exhibition", year = "1996", editor = "Gordon Weiss", number = "96/13", series = "National conference publication", pages = "9--84 [in volume 2]", address = "Barton, ACT, Australia", publisher_address = "Australia", publisher = "Institution of Engineers", keywords = "genetic algorithms, genetic programming, Data processing, Neural networks (Computer science), Mathematical models, Linear programming, Mathematical models, Offshore oil industry, Electric insulators and insulation, Oils", ISBN = "0-85825-658-4", URL = "http://search.informit.com.au/documentSummary;dn=894065266629714;res=IELENG", abstract = "Genetic programming and neural network techniques were both used to predict the product distribution and yield of product oil from a reactor in a transformer oil regeneration process. All reactor models were developed by fitting laboratory-scale data. For the (relatively) small experimental data set available, it was found that the accuracy of the reactor model was significantly better when using genetic programming than neural network modelling techniques. A flowsheet of a pilot-scale version of the process was developed (using commercial simulation packages) based on the reactor model obtained using genetic programming, and the optimal operating conditions determined so as to give the maximum yield of regenerated transformer oil.", notes = "Broken Sep 2018 http://lorien.ncl.ac.uk/ming/infer/inferrefs.htm (1) CSIRO Division of Coal and Energy Technology, Lucas Heights, Sydney, Australia (2) Department of Chemical Engineering, University of Sydney, Australia (3) Department of Chemical Engineering, University of Sydney, Australia (4) Department of Chemical and Process Engineering, University of Newcastle, UK", } @Article{HANSEN:2024:ijfatigue, author = "Cooper K. Hansen and Gary F. Whelan and Jacob D. Hochhalter", title = "Interpretable machine learning for microstructure-dependent models of fatigue indicator parameters", journal = "International Journal of Fatigue", volume = "178", pages = "108019", year = "2024", ISSN = "0142-1123", DOI = "doi:10.1016/j.ijfatigue.2023.108019", URL = "https://www.sciencedirect.com/science/article/pii/S0142112323005200", keywords = "genetic algorithms, genetic programming, Machine learning, Microstructure, FIP, Crack initiation, Fatigue, XAI", abstract = "Fatigue indicator parameters (FIPs) are typically computed using crystal plasticity finite element modeling (CPFEM) and used to predict microscale crack initiation. While informative, computing FIPs in this manner can limit their application in engineering use cases due to the computational demand of CPFEM. To address this limitation, an interpretable machine learning approach is developed and used to model FIPs in additive manufactured IN625 single-phase microstructures. Genetic programming based symbolic regression is used to evolve inherently interpretable expressions of FIPs from microstructure features. Once developed, these FIP models act as an efficient surrogate for CPFEM and, due to their symbolic representation, can be readily combined with engineering workflows", } @Article{Hansen:2003:GPEM, author = "James V. Hansen", title = "Genetic Programming Experiments with Standard and Homologous Crossover Methods", journal = "Genetic Programming and Evolvable Machines", year = "2003", volume = "4", number = "1", pages = "53--66", month = mar, keywords = "genetic algorithms, genetic programming, homologous crossover, regression, classifications", ISSN = "1389-2576", DOI = "doi:10.1023/A:1021825110329", size = "14 pages", abstract = "While successful applications have been reported using standard GP crossover, limitations of this approach have been identified by several investigators. Among the most compelling alternatives to standard GP crossover are those that use some form of homologous crossover, where code segments that are exchanged are structurally or syntactically aligned in order to preserve context and worth. This paper reports the results of an empirical comparison of GP using standard crossover methods with GP using homologous crossover methods. Ten problems are tested, five each of pattern recognition and regression. Results suggest that in terms of generalisation accuracy, homologous crossover does generate consistently better performance. In addition, there is a consistently lower fraction of introns that are generated in the solution code.", notes = "Article ID: 5113072", } @Article{hansen:2004:COR, author = "James V. Hansen", title = "Genetic search methods in air traffic control", journal = "Computers and Operations Research", year = "2004", volume = "31", number = "3", pages = "445--459", month = mar, keywords = "genetic algorithms, genetic programming, Aircraft traffic control, Genetic search, Heuristics, Scheduling", broken = "http://www.sciencedirect.com/science/article/B6VC5-480622F-4/2/468055c77aed02e9629b07b8dc6b0dbe", DOI = "doi:10.1016/S0305-0548(02)00228-9", abstract = "Of primary importance to the efficient operation and profitability of an airline is adherence to its flight schedule. This paper examines that segment of air traffic control, termed traffic management adviser (TMA), which is charged with the complex task of scheduling arriving aircraft to available runways in a manner that minimises delays and satisfies safety constraints. In particular, we investigate the effectiveness and efficiency of using genetic search methods to support the scheduling decisions made by TMA. Four different genetic search methods are tested on TMA problems suggested by recent work at the NASA Ames Research Center. For problems of realistic size, optimal or near-optimal assignments of aircraft to runways are achieved in real time. Scope and purpose. We report the application of genetic search algorithms to solve certain complexities associated with air traffic control. Air traffic control is an important practical problem that is difficult to solve by other methods because of non-convex, non-linear, or non-analytic characteristics. Four genetic search algorithms are applied, with consistent advantage being demonstrated by an algorithm based on genetic programming functions. Good results are achieved, with evidence that solutions can be achieved in real time.", owner = "wlangdon", } @Article{Hansen:2006:DSS, author = "James V. Hansen and Paul Benjamin Lowry and Rayman D. Meservy and Daniel M. McDonald", title = "Genetic programming for prevention of cyberterrorism through dynamic and evolving intrusion detection", journal = "Decision Support Systems", year = "2007", volume = "43", number = "4", pages = "1362--1374", month = aug, note = "Special Issue Clusters", keywords = "genetic algorithms, genetic programming, Cyberterrorism, Homologous crossover, Intrusion detection, Pattern recognition, Information security", DOI = "doi:10.1016/j.dss.2006.04.004", abstract = "Because malicious intrusions into critical information infrastructures are essential to the success of cyberterrorists, effective intrusion detection is also essential for defending such infrastructures. Cyberterrorism thrives on the development of new technologies; and, in response, intrusion detection methods must be robust and adaptive, as well as efficient. We hypothesise that genetic programming algorithms can aid in this endeavour. To investigate this proposition, we conducted an experiment using a very large dataset from the 1999 Knowledge Discovery in Database (KDD) Cup data, supplied by the Defense Advanced Research Projects Agency (DARPA) and MIT's Lincoln Laboratories. Using machine-coded linear genomes and a homologous crossover operator in genetic programming, promising results were achieved in detecting malicious intrusions. The resulting programs execute in real time, and high levels of accuracy were realised in identifying both positive and negative instances.", } @Article{hansen:2016:bams, author = "Johannes Hansen and Marc Ebner", title = "Is depth information and optical flow helpful for visual control?", journal = "Bio-Algorithms and Med-Systems", year = "2016", volume = "12", number = "1", pages = "9--18", month = mar, keywords = "genetic algorithms, genetic programming, depth map, optical flow, visual control, ECJ, game play", ISSN = "1895-9091", publisher = "De Gruyter, Berlin", DOI = "doi:10.1515/bams-2015-0044", size = "10 pages", abstract = "The human visual system was shaped through natural evolution. We have used artificial evolution to investigate whether depth information and optical flow are helpful for visual control. Our experiments were carried out in simulation. The task was controlling a simulated racing car. We have used The Open Racing Car Simulator for our experiments. Genetic programming was used to evolve visual algorithms that transform input images (colour, optical flow, or depth information) to control commands for a simulated racing car. We found that significantly better solutions were found when color, depth, and optical flow were available as input together compared with colour, depth, or optical flow alone.", notes = "openCV, torcs p17 'in our experiments, we found that significantly better results in driving a racing car along its track are obtained when colour, depth, and optical flow are provided together.'", } @InProceedings{Hanskunatai:2020:ICCAR, author = "Anantaporn Hanskunatai", booktitle = "2020 6th International Conference on Control, Automation and Robotics (ICCAR)", title = "Automatic Parameter Tuning in Aluminum Extrusion Based on Genetic Programming", year = "2020", pages = "39--43", abstract = "This work applies artificial intelligence in the aluminum extrusion process for automatic setting the ram speed of a machine according to the requirements of the industry. The automatic parameter tuning system computes the ram speed with the equation created by genetic programming (GP). In model evaluation, MAE and MAPE are used to measure a predictive performance of the models. In addition to GP, linear and polynomial regression are used to generate the automatic parameter tuning model for comparing a performance with GP. The experimental results on the test set show that GP performs the best in predictive performance with 0.130 of MAE and 4.2percent of MAPE. Finally, the GP model has been developed as a software to calculate the ram speed and display it on a screen. This system will help users who are not proficient in aluminum extrusion or new users to have better control of production.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICCAR49639.2020.9107980", ISSN = "2251-2446", month = apr, notes = "Also known as \cite{9107980}", } @InCollection{Haque2019, author = "Mohammad Nazmul Haque and Natalie Jane {de Vries} and Pablo Moscato", title = "A Multi-objective Meta-Analytic Method for Customer Churn Prediction", booktitle = "Business and Consumer Analytics: New Ideas", publisher = "Springer International Publishing", year = "2019", editor = "Pablo Moscato and Natalie Jane {de Vries}", chapter = "20", pages = "781--813", keywords = "genetic algorithms, genetic programming, Churn, Customer churn prediction, Ensemble of classifiers, Ensemble learning, Multi-objective ensemble, NSGA-II algorithm, Symbolic regression", isbn13 = "978-3-030-06222-4", DOI = "doi:10.1007/978-3-030-06222-4_20", abstract = "The term metaheuristic was introduced in 1986 as a way to label a higher-level procedure designed to guide a lower-level heuristic or algorithm to find solutions for tasks posed as mathematical optimization problems. Analogously, the term meta-analytics can be used to refer to a higher-level procedure that guides ad hoc data analysis techniques. Heuristics that guide ensemble learning of heterogeneous classifier systems would be one of those procedures that can be referred to as meta-analytics. In general, researchers use single-objective approaches for ensemble learning. In this contribution we investigate the use of a multi-objective evolutionary algorithm and we apply it to the problem of Customer churn prediction Prediction customer churn customer churn prediction. We compare the results with those of a symbolic regression-based approach. Each has its own merits. While the multi-objective approach excels at prediction, it lacks in interpretability for business insights. Oppositely, the symbolic regression-based approach has lower Accuracy accuracy but can give business analysts some actionable tools. Depending on the nature of the business scenario, we recommend that both be employed together to maximise our understanding of consumer behaviour. High-quality individualised prediction based on multi-objective optimization can help a company to direct a message to a particular individual, while the results of a global symbolic regression-based approach may help large marketing campaigns or big changes in policies, cost structures and/or product offerings.", } @InProceedings{hara:1999:EAADG, author = "Akira Hara and Tomoharu Nagao", title = "Emergence of the cooperative behavior using {ADG}; Automatically Defined Groups", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1039--1046", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-415.ps", URL = "http://www.ints.info.hiroshima-cu.ac.jp/~ahara/GECCO99.ps", size = "8 pages", abstract = "In producing a multi-agent team which solves problem cooperatively by means of Genetic Programming (GP), it seems that a heterogeneous team performs better than a homogeneous team. In a heterogeneous team, however, as the number of agents increases, the size of the search space becomes vast and the efficiency of search decreases. One of the solutions of this problem is to divide a team into the proper number of groups, and to provide the same program for the all agents belonging to the same group. However it is difficult to know the adequate team structure beforehand. In order to solve these we have proposed a method called Automatically Defined Groups. we applied this method to a simple transportation problem and a modified Tile World problem, and confirmed that the optimal team structure was acquired in each problem.", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Hara:2011:SMC, author = "Akira Hara and Manabu Watanabe and Tetsuyuki Takahama", title = "Cartesian Ant Programming", booktitle = "IEEE International Conference on Systems, Man, and Cybernetics (SMC 2011)", year = "2011", month = "9-12 " # oct, pages = "3161--3166", address = "Anchorage, Alaska, USA", size = "6 pages", abstract = "Genetic Programming (GP) is well-known as an evolutionary method for automatic programming. GP can optimise tree-structural programs. Cartesian GP (CGP) is one of the extensions of GP, which generates the graph structural programs. By using the graph structure, the solutions can be represented by more compact programs. Therefore, CGP is widely applied to the various problems. As a different approach from the evolution, there is the Ant Colony Optimisation (ACO), which is an optimisation method for combinatorial optimisation problems based on the cooperative behaviour of ants. By using pheromone communication, the promising solution space can be searched intensively. In this paper, we propose a new automatic programming method, which combines CGP and ACO. In this method, ants generate programs by moving in the node-network used in CGP. We call this method, Cartesian Ant Programming (CAP). We examined the effectiveness of CAP by comparing with CGP on the search performance in a symbolic regression and a classification problem.", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, ant colony optimisation, automatic programming, cartesian ant programming, classification problem, combinatorial optimisation, compact program, evolutionary method, graph structural program, pheromone communication, symbolic regression, tree-structural program optimisation, ant colony optimisation, trees (mathematics)", DOI = "doi:10.1109/ICSMC.2011.6084146", ISSN = "1062-922X", notes = "Also known as \cite{6084146}", } @InProceedings{Hara:2012:SMC, author = "Akira Hara and Yoshimasa Ueno and Tetsuyuki Takahama", booktitle = "IEEE International Conference on Systems, Man, and Cybernetics (SMC 2012)", title = "New crossover operator based on semantic distance between subtrees in Genetic Programming", year = "2012", pages = "721--726", month = oct # " 14-17", address = "Seoul, Korea", DOI = "doi:10.1109/ICSMC.2012.6377812", size = "8 pages", abstract = "Genetic Programming (GP) is an evolutionary method for generating tree structural programs. Normal subtree crossover in GP randomly selects a crossover point in each parental tree, and offspring are created by exchanging the selected subtrees. In the normal crossover, it is difficult to control the global and local search because the similarity between the subtrees is not considered. In this paper, we propose a new crossover operation based on the semantic distance between the subtrees. We call this operation Semantic Control Crossover. By using the Semantic Control Crossover, the global search can be performed in the early stage of search, and the search property can be shifted to the local search as the search proceeds. As the results of experiments, the Semantic Control Crossover showed better performance than the conventional crossover.", keywords = "genetic algorithms, genetic programming, mathematical operators, search problems, trees (mathematics), crossover operator, evolutionary method, global search control, local search control, normal subtree crossover, offspring, parental tree, semantic control crossover, semantic distance, tree structural program generation, Equations, Mathematical model, Semantics, Sociology, Statistics, Vectors, Crossover, Subtree Semantics", notes = "Also known as \cite{6377812}", } @Article{journals/ijkwi/HaraTIT12, author = "Akira Hara and Haruko Tanaka and Takumi Ichimura and Tetsuyuki Takahama", title = "Knowledge acquisition from many-attribute data by genetic programming with clustered terminal symbols", journal = "International Journal of Knowledge and Web Intelligence", year = "2012", volume = "3", number = "2", pages = "180--201", keywords = "genetic algorithms, genetic programming, knowledge acquisition, rule extraction, molecule classification, data attributes, clustering, terminal symbols, soft computing, similarities, molecules, page rank learning, information retrieval", ISSN = "1755-8255", DOI = "doi:10.1504/IJKWI.2012.050286", bibdate = "2012-11-30", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ijkwi/ijkwi3.html#HaraTIT12", abstract = "Rule extraction from database by soft computing methods is important for knowledge acquisition. For example, knowledge from the web pages can be useful for information retrieval. When genetic programming (GP) is applied to rule extraction from a database, the attributes of data are often used for the terminal symbols. However, the real databases have a large number of attributes. Therefore, the size of the terminal set increases and the search space becomes vast. For improving the search performance, we propose new methods for dealing with the large-scale terminal set. In the methods, the terminal symbols are clustered based on the similarities of the attributes. In the beginning of search, by using the clusters for terminals instead of original attributes, the number of terminal symbols can be reduced. Therefore, the search space can be reduced. In the latter stage of search, by using the original attributes for terminal symbols, the local search is performed. We applied our proposed methods to two many-attribute datasets, the classification of molecules as a benchmark problem and the page rank learning for information retrieval. By comparison with the conventional GP, the proposed methods showed the faster evolutionary speed and extracted more accurate rules", } @InProceedings{Hara:2013:IWCIA, author = "Akira Hara and Jun-ichi Kushida and Souichi Tanabe and Tetsuyuki Takahama", booktitle = "2013 IEEE Sixth International Workshop on Computational Intelligence Applications (IWCIA)", title = "Parallel Ant Programming using genetic operators", year = "2013", month = "13 " # jul, pages = "75--80", keywords = "genetic algorithms, genetic programming, Ant Colony Optimisation, Swarm Intelligence", DOI = "doi:10.1109/IWCIA.2013.6624788", ISSN = "1883-3977", abstract = "Ant Programming (AP) is an automatic programming method, which combines tree-structural representations of Genetic Programming (GP) and search mechanism by pheromone communications of ants in Ant Colony Optimisation (ACO). In AP, a single prototype tree, in which respective nodes have different pheromone tables, is prepared, and an ant searches solutions under the prototype tree. The structure of the prototype tree does not change during search. Therefore, premature convergence often occurs. To solve the problem, we propose parallel AP using genetic operators of GP. In this method, multiple prototype trees are generated and the structures change by GP operators such as selection, crossover and mutation. We applied our proposed method to symbolic regressions and logical function synthesis. As the results of experiments, our proposed method showed better performance than the conventional AP.", notes = "Also known as \cite{6624788}", } @InProceedings{Hara:2014:smc, author = "Akira Hara and Jun-ichi Kushida and Takeyuki Nobuta and Tetsuyuki Takahama", booktitle = "IEEE International Conference on Systems, Man and Cybernetics (SMC 2014)", title = "Rank-based Semantic Control Crossover in Genetic Programming", year = "2014", month = oct, pages = "501--506", abstract = "Subtree exchange crossover which is usually used in Genetic Programming (GP) can not control the search properties such as global or local search, because crossover points in parental individuals are selected at random. To overcome the problem, crossover based on semantic distance of subtrees has been studied recent years. If similar subtrees in semantic space are exchanged, the local search can be performed. In contrast, dissimilar subtrees are exchanged, the global search can be performed. In Semantic Control Crossover (SCC), the global search can be performed in early generations, and the local search can be performed in later generations. In this paper, we propose a new SCC based on the ranking information of parents, Rank-based SCC. The method controls search properties according to not generations but ranking information of parents. In case of the crossover to a pair of parents with higher ranks, similar subtrees should be exchanged for local search around the parents. In contrast, in case of the crossover to a pair of parents with lower ranks, dissimilar subtrees should be exchanged for global search. We compared the search performance of three methods, standard crossover, conventional SCC and Rank-based SCC, and showed the effectiveness of our method.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SMC.2014.6973957", notes = "Grad. Sch. of Inf. Sci., Hiroshima City Univ., Hiroshima, Japan Also known as \cite{6973957}", } @InProceedings{Hara:2014:IWCIA, author = "Akira Hara and Jun-ichi Kushida and Keita Fukuhara and Tetsuyuki Takahama", booktitle = "7th IEEE International Workshop on Computational Intelligence and Applications (IWCIA 2014)", title = "Cartesian Ant Programming with adaptive node replacements", year = "2014", month = nov, pages = "119--124", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, ACO, swarm intelligence", DOI = "doi:10.1109/IWCIA.2014.6988089", ISSN = "1883-3977", size = "6 pages", abstract = "Ant Colony Optimisation (ACO) is a swarm-based search method. Multiple ant agents search various solutions and their searches focus on around good solutions by positive feedback mechanism based on pheromone communication. ACO is effective for combinatorial optimisation problems. The attempt of applying ACO to automatic programming has been studied in recent years. As one of the attempts, we have previously proposed Cartesian Ant Programming (CAP) as an ant-based automatic programming method. Cartesian Genetic Programming (CGP) is well-known as an evolutionary optimisation method for graph-structural programs. CAP combines graph representations in CGP with pheromone communication in ACO. The connections of program primitives, terminal and functional symbols, can be optimised by ants. CAP showed better performance than CGP. However, quantities of respective symbols are limited due to the fixed assignments of functional symbols to nodes. Therefore, if the number of given nodes is not enough for representing program, the search performance becomes poor. In this paper, to solve the problem, we propose CAP with adaptive node replacements. This method finds unnecessary nodes which are not used for representing programs. Then, new functional symbols, which seems to be useful for constructing good programs, are assigned to the nodes. By this method, given nodes can be used efficiently. In order to examine the effectiveness of our method, we apply it to a symbolic regression problem. CAP with adaptive node replacements showed better results than conventional methods, CGP and CAP.", notes = "Also known as \cite{6988089}", } @InProceedings{Hara:2015:IIAI-AAI, author = "Akira Hara and Jun-Ichi Kushida and Kei Kisaka and Tetsuyuki Takahama", booktitle = "4th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI)", title = "Geometric Semantic Genetic Programming Using External Division of Parents", year = "2015", pages = "189--194", abstract = "In this paper, we focus on symbolic regression problems, in which we find functions approximating the relationships between given input and output data. If we do not have the knowledge on the structure (e.g. Degree) of the true functions, Genetic Programming (GP) is often used for evolving tree structural numerical expressions. In GP, crossover operator has a great influence on the quality of the acquired solutions. Therefore, various crossover operators have been proposed. Recently, new crossover operators based on semantics of tree structures have attracted many attentions for efficient search. In the semantics-based crossover, offspring is created from its parental individuals so that the offspring can be similar to the parents not structurally but semantically. Geometric Semantic Genetic Programming (GSGP) is a method in which offspring is produced by a convex combination of two parental individuals. This operation corresponds to the internal division of two parents. This method can optimise solutions efficiently because the crossover operator always produces better solution than a worse parent. But, in GSGP, if the true function exists outside of two parents in semantic space, it is difficult to produce better solution than both of the parents. In this paper, we propose an improved GSGP which can also consider external divisions as well as internal ones. By comparing the search performance among several crossover operators in symbolic regression problems, we showed that our methods are superior to the standard GP and conventional GSGP.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IIAI-AAI.2015.245", month = jul, notes = "Also known as \cite{7373899}", } @InProceedings{Hara:2015:ieeeSMC, author = "Akira Hara and Takuya Mototsuka and Jun-ichi Kushida and Tetsuyuki Takahama", booktitle = "2015 IEEE International Conference on Systems, Man, and Cybernetics (SMC)", title = "Genetic Programming Using the Best Individuals of Genealogies for Maintaining Population Diversity", year = "2015", pages = "2690--2696", abstract = "Genetic Programming (GP) is an evolutionary optimisation method for generating tree structural programs. It is important to maintain the population diversity for preventing GP search from falling into local optima. For this purpose, we propose a new method which introduces a concept of genealogy into the population. We call the method Genetic Programming using the Best Individuals of Genealogies (GPBIG). Information on genealogy is assigned to each individual, and the best-so-far individuals in respective genealogies are preserved as the genealogical elite individuals. The population is reconstituted every generation by selecting the individuals from the pool of the genealogical elite individuals. In addition, the search property shifts from global to local search gradually by extinguishing unnecessary genealogies. We examined the effectiveness of our method by comparing with the standard GP in search performance in three kinds of benchmark problems.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SMC.2015.470", month = oct, notes = "Also known as \cite{7379602}", } @InProceedings{Hara:2015:ieeeIWCIA, author = "Akira Hara and Jun-ichi Kushida and Tomoya Okita and Tetsuyuki Takahama", booktitle = "8th IEEE International Workshop on Computational Intelligence and Applications (IWCIA)", title = "Behavior control of multiple agents by Cartesian Genetic Programming equipped with sharing sub-programs among agents", year = "2015", pages = "71--76", address = "Hiroshima, Japan", keywords = "genetic algorithms, genetic programming, Artificial neural networks, Cloning, Mathematical model, Multi-agent systems, Optimisation, Cartesian Genetic Programming, Evolutionary Computation, Multi-Agent Systems", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7449465", DOI = "doi:10.1109/IWCIA.2015.7449465", ISSN = "1883-3977", month = "6-7 " # nov, abstract = "In this paper, we focus on evolutionary optimisation of multi-agent behaviour. There are two representative models for multi-agent control, homogeneous and heterogeneous models. In the homogeneous model, all agents are controlled by the same controller. Therefore, it is difficult to realize complex cooperative behaviour such as division of labours. In contrast, in the heterogeneous model, respective agents can play different roles for cooperative tasks. However, the search space becomes too large to optimise respective controllers. To solve the problems, we propose a new multi-agent control model based on Cartesian Genetic Programming (CGP). In CGP, each individual represents a graph-structural program and it can have multiple outputs. The feature is used for controlling multiple agents in our model. In addition, we propose a new genetic operator dedicated to multi-agent control. Our method enables multiple agents to not only take different actions according to their own roles but also share sub-programs if the same behaviour is needed for solving problems. We applied our method to a food foraging problem. The experimental results showed that the performance of our method is superior to those of the conventional models.", notes = "Also known as \cite{7449465}", } @InProceedings{Hara:2016:IWCIA, author = "Akira Hara and Hiroki Konishi and Jun-ichi Kushida and Tetsuyuki Takahama", title = "Efficiency improvement of imitation operator in multi-agent control model based on Cartesian Genetic Programming", booktitle = "2016 IEEE 9th International Workshop on Computational Intelligence and Applications", year = "2016", editor = "Shimpei Matsumoto and Tomoko Tateyam", pages = "69--74", address = "Hiroshima", month = "5 " # nov, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming", DOI = "doi:10.1109/IWCIA.2016.7805751", abstract = "In this paper, we focus on evolutionary optimization of multi-agent behaviour. In our previous work, we have proposed a multi-agent control model based on Cartesian Genetic Programming (CGP). In CGP, each individual is represented by a graph-structural program. The CGP has a characteristics that each individual has multiple output nodes. Therefore, by assigning the outputs to respective agents, we can control multiple agents by an individual. The method enables multiple agents to not only take different actions according to their own roles but also share sub-programs if the same behaviour is needed for solving problems. In addition, a new genetic operator for multi-agent control, imitation operator, has been proposed to facilitate the grouping of agents. An agent selects another agent at random for imitating the behavior. However, if the number of agents increases, the appropriate agent cannot always be selected for imitation. Therefore, in this paper, we propose a modified imitation operator for selecting useful agent. We applied our method to a food foraging problem. The experimental results showed that the performance of our method is superior to those of the conventional models.", notes = "http://www.smc-hiroshima.info.hiroshima-cu.ac.jp/events/iwcia/2016/contr_program.html Graduate School of Information Sciences, Hiroshima City University, 3-4-1, Ozuka-higashi, Asaminami-ku, Hiroshima, Japan 731-3194 Also known as \cite{7805751}", } @InProceedings{Hara:2016:IIAI-AAI, author = "A. Hara and J. I. Kushida and R. Tanemura and T. Takahama", booktitle = "2016 5th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI)", title = "Deterministic Crossover Based on Target Semantics in Geometric Semantic Genetic Programming", year = "2016", pages = "197--202", abstract = "In this paper, we focus on solving symbolic regression problems, in which we find functions approximating the relationships between given input and output data. Genetic Programming (GP) is often used for evolving tree structural numerical expressions. Recently, new crossover operators based on semantics of tree structures have attracted many attentions for efficient search. In the semantics-based crossover, offspring is created from its parental individuals so that the offspring can be similar to the parents not structurally but semantically. Geometric Semantic Genetic Programming (GSGP) is a method in which offspring is produced by a convex combination of two parental individuals. In order to improve the search performance of GSGP, we propose an improved Geometric Semantic Crossover using the information of the target semantics. In conventional GSGP, ratios of convex combinations are determined at random. On the other hand, our proposed method can use optimal ratios for affine combinations of parental individuals. We confirmed that our method showed better performance than conventional GSGP in several symbolic regression problems.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IIAI-AAI.2016.220", month = jul, notes = "Also known as \cite{7557602}", } @InProceedings{Hara:2016:SMC, author = "A. Hara and J. i. Kushida and T. Takahama", booktitle = "2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)", title = "Deterministic Geometric Semantic Genetic Programming with Optimal Mate Selection", year = "2016", pages = "003387--003392", abstract = "To solve symbolic regression problems, Genetic Programming (GP) is often used for evolving tree structural numerical expressions. Recently, new crossover operators based on semantics of tree structures have attracted many attentions. In the semantics-based crossover, offspring is created from its parental individuals so that the offspring can inherit the characteristics of the parents not structurally but semantically. Geometric Semantic GP (GSGP) is a method in which offspring is produced by a convex combination of two parental individuals. In order to improve the search performance of GSGP, deterministic Geometric Semantic Crossover using the information of the target semantics has been proposed. In conventional GSGP, ratios of convex combinations are determined at random. On the other hand, the deterministic crossover can use optimal ratios for affine combinations of parental individuals so that created offspring can be closest to the target solution. In these methods, parents which crossover operators will be applied to are selected based only on their fitness. In this paper, we propose a new selection method of parents for generating offspring which can approach to a target solution more efficiently. In this method, we select a pair of parents so that a distance between a straight line connecting the parents and a target point can be smallest in semantic space. We confirmed that our method showed better performance than conventional GSGP in several symbolic regression problems.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SMC.2016.7844757", month = oct, notes = "Also known as \cite{7844757}", } @InProceedings{Hara:2017:ieeeSMC, author = "Akira Hara and Jun-ichi Kushida and Takamichi Yamagata and Tetsuyuki Takahama", booktitle = "2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)", title = "The influence of generation alternation model on search performance in deterministic geometric semantic genetic programming", year = "2017", pages = "588--593", abstract = "In recent years, semantics-based crossover operators have attracted attention for efficient search in Genetic Programming (GP). Geometric Semantic Genetic Programming (GSGP) is one of the methods, in which a convex combination of two parents is used for creating an offspring. We have previously proposed an improved GSGP, Deterministic GSGP. In Deterministic GSGP, the convex combination is relaxed to an affine combination, and the optimum ratio for the affine combination is determined so that an offspring can always have better fitness than its parents. However, Deterministic GSGP has a problem that search might fall into local optima due to premature convergence. In this paper, we propose a new generation alternation model for maintaining population diversity. In the proposed model, all the individuals have opportunities to generate offspring as parents. We compared our proposed model with the conventional Deterministic GSGP in search performance, and showed its effectiveness.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SMC.2017.8122670", month = oct, notes = "Also known as \cite{8122670}", } @InProceedings{Hara:2018:ieeeSMC, author = "Akira Hara and Jun-Ichi Kushida and Ryota Takemoto and Tetsuyuki Takahama", booktitle = "2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)", title = "Artificial Bee Colony Programming Using Semantic Control Crossover", year = "2018", pages = "189--194", abstract = "Artificial Bee Colony Programming (ABCP), which has been inspired by intelligent foraging behaviour of honey bees, is a swarm-based automatic programming method. Tree structural programs can be optimized by three kinds of bees such as employed bees, onlooker bees and scout bees. New solutions are generated by information sharing mechanism, which is similar to subtree exchange crossover used in Genetic Programming (GP). However, it is difficult to control global or local search by the operation. To solve the problem, we introduce Semantic Control Crossover (SCC), which we have previously proposed as one of semantics-based crossovers in GP, into ABCP. In this paper, we proposed two kinds of improved ABCPs using SCC. In the Proposed Method 1, employed bees and onlooker bees have different search strategies. Employed bees perform global search and onlookers perform local search. On the other hand, in the Proposed Method 2, the search strategies are switched according to the degree of stagnation. Local search is performed while solutions have been improved successively. In contrast, global search is performed when the solutions have not been improved for a long term. We applied our proposed methods to symbolic regression problems and confirmed that our proposed methods have higher performance than the conventional ABCP.", keywords = "genetic algorithms, genetic programming, Semantics, Search problems, Information management, Switches, Artificial bee colony algorithm, Automatic programming, Swarm Intelligence, Artificial Bee Colony", DOI = "doi:10.1109/SMC.2018.00043", ISSN = "2577-1655", month = oct, notes = "Also known as \cite{8616038}", } @InProceedings{Hara:2019:SMC, author = "Akira Hara and Jun-ichi Kushida and Tetsuyuki Takahama", title = "Time Series Prediction Using Deterministic Geometric Semantic Genetic Programming", booktitle = "2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)", year = "2019", pages = "1945--1949", month = oct, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SMC.2019.8914562", ISSN = "2577-1655", abstract = "Predicting time series data is one of the most important challenges in many different application domains. Constructing the prediction models can be regarded as symbolic regressions, and the model can be optimized by Genetic Programming (GP), which is an evolutionary automatic programming method for tree structural programs. In the last decade, semantics-based genetic operators have attracted much attentions for improving search performance in the field of GP. As one of the semantics-based GP, we have previously proposed Deterministic Geometric Semantic GP (D-GSGP). Crossover operations in D-GSGP generate offspring by affine combinations of parents with the optimal combination ratios. We have shown the effectiveness in several benchmark functions in symbolic regression problems. In this research, we apply the method to a time-series forecasting problem, sunspot number series, as more practical application. The experimental results indicate that D-GSGP works effectively and the acquired programs are useful for knowledge acquisition of the application domain.", notes = "Also known as \cite{8914562}", } @InProceedings{Hara:2020:SMC, author = "Akira Hara and Jun-ichi Kushida and Tetsuyuki Takahama", title = "Maintaining Population Diversity in Deterministic Geometric Semantic Genetic Programming by e-Lexicase Selection", booktitle = "2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)", year = "2020", pages = "205--210", address = "Toronto, Canada", month = "11-14 " # oct, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, geometric semantic genetic programming, diversity, lexicase selection", isbn13 = "978-1-7281-8527-9", bibdate = "2021-01-08", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/smc/smc2020.html#HaraKT20", DOI = "doi:10.1109/SMC42975.2020.9283096", ISSN = "2577-1655", size = "6 pages", abstract = "Genetic Programming (GP) is an evolutionary method for automatic programming. In recent years, crossover operators based on the semantics of programs have attracted much attention for improving the search efficiency. We have previously proposed a semantics-based crossover that deterministically generates an optimal offspring by using the target semantics explicitly in symbolic regression problems. The GP method using this crossover is called Deterministic Geometric Semantic GP (D-GSGP). However, this operation may cause rapid convergence of the population. One of the ways to maintain diversity is to use an improved selection method. epsilon-Lexicase Selection is a method to select individuals based on their responses to a part of fitness cases. D-GSGP has a high affinity with epsilon-Lexicase Selection because the responses to a part of fitness cases are components of the semantics of the program. Therefore, in this research, we combine D-GSGP and epsilon-Lexicase Selection to maintain the diversity of the population. To verify the effectiveness of our proposed method, we applied the method to a practical symbolic regression problem, the Boston Housing Dataset.", notes = "Also known as \cite{9283096}, \cite{conf/smc/HaraKT20} Graduate School of Information Sciences, Hiroshima City University, Hiroshima, Japan", } @InProceedings{Harada:2011:NaBIC, author = "Tomohiro Harada and Keiki Takadama", title = "Adaptive mutation depending on program size in asynchronous program evolution", booktitle = "Third World Congress on Nature and Biologically Inspired Computing (NaBIC 2011)", year = "2011", month = "19-21 " # oct, pages = "433--438", address = "Salamanca", size = "6 pages", abstract = "This paper proposes an adaptive mutation method which changes a mutation rate depending on the program size in the asynchronous program evolution unlike the synchronous program evolution such as genetic programming. An intensive experiment with an evolution of calculation programs has revealed that the proposed adaptive mutation method can generate the correct and short programs in comparison with other methods.", keywords = "genetic algorithms, genetic programming, Tierra, adaptive mutation method, asynchronous program evolution, evolutionary algorithms, mutation rate", DOI = "doi:10.1109/NaBIC.2011.6089626", notes = "Also known as \cite{6089626}", } @InProceedings{Harada:2012:ISIS, author = "Tomohiro Harada and Yoshihiro Ichikawa and Keiki Takadama", booktitle = "Soft Computing and Intelligent Systems (SCIS) and 13th International Symposium on Advanced Intelligent Systems (ISIS), 2012 Joint 6th International Conference on", title = "Evolving conditional branch programs in Tierra-based Asynchronous Genetic Programming", year = "2012", pages = "1023--1028", month = "20-24 " # nov, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SCIS-ISIS.2012.6505032", size = "6 pages", abstract = "This paper explores the methods which can evolve conditional branch programs in Tierra-based Asynchronous Genetic Programming (TAGP) to improve an evolutionary ability for complex programs. For this purpose, we propose three methods, namely, the label address, the elite preserving strategy with the program size restriction, and the gradient fitness calculation. An intensive experiment on a calculation program evolution reveals the following implications: (1) the label addressing can simply construct the conditional branch; (2) the elite preserving strategy contributes to maintaining the correct programs and the program size restriction prevents the ineffective instructions; and (3) the gradient fitness calculation can correctly evaluate the multiple outputs programs; and (4) the above three methods, however, are difficult to generate the shortest size programs such as sharing instructions with different calculations.", notes = "Also known as \cite{6505032}", } @InProceedings{harada:2013:EuroGP, author = "Tomohiro Harada and Keiki Takadama", title = "Asynchronous Evaluation based Genetic Programming: Comparison of Asynchronous and Synchronous Evaluation and its Application", booktitle = "Proceedings of the 16th European Conference on Genetic Programming, EuroGP 2013", year = "2013", month = "3-5 " # apr, editor = "Krzysztof Krawiec and Alberto Moraglio and Ting Hu and A. Sima Uyar and Bin Hu", series = "LNCS", volume = "7831", publisher = "Springer Verlag", address = "Vienna, Austria", pages = "241--252", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-37206-3", DOI = "doi:10.1007/978-3-642-37207-0_21", abstract = "This paper compares an asynchronous evaluation based GP with a synchronous evaluation based GP to investigate the evolution ability of an asynchronous evaluation on the GP domain. As an asynchronous evaluation based GP, this paper focuses on Tierra-based Asynchronous GP we have proposed, which is based on a biological evolution simulator, Tierra. The intensive experiment compares TAGP with simple GP by applying them to a symbolic regression problem, and it is revealed that an asynchronous evaluation based GP has better evolution ability than a synchronous one.", notes = "Part of \cite{Krawiec:2013:GP} EuroGP'2013 held in conjunction with EvoCOP2013, EvoBIO2013, EvoMusArt2013 and EvoApplications2013", } @InProceedings{Harada:2013:ECAL, author = "Tomohiro Harada and Keiki Takadama", title = "Analyzing Program Evolution in Genetic Programming using Asynchronous Evaluation", booktitle = "Advances in Artificial Life, ECAL 2013", year = "2013", editor = "Pietro Lio and Orazio Miglino and Giuseppe Nicosia and Stefano Nolfi and Mario Pavone", series = "Complex Adaptive Systems", pages = "713--720", address = "Taormina, Italy", month = sep # " 2-6", publisher = "MIT Press", keywords = "genetic algorithms, genetic programming, TAGP", isbn13 = "978-0-262-31709-2", DOI = "doi:10.7551/978-0-262-31709-2-ch102", size = "8 pages", abstract = "This paper investigates the evolution ability of Tierra-based Asynchronous Genetic Programming (TAGP) as GP using an asynchronous evaluation. We compare TAGP with two simple GP methods, steady-state GP and GP using (mu + lambda)-selection as GP using a synchronous evaluation. Three GP methods are compared in experiment to minimise the size of an actual assembly language program in several computational problems, two arithmetic and two Boolean problems. The intensive comparisons have revealed the following implications: (1) TAGP has higher evolution ability than GP using synchronous evaluation, i.e., TAGP can evolve smaller size programs which cannot be evolved by GPs using synchronous evaluation; and (2) the diversity of the programs evolved by TAGP can derive a high evolution ability in comparison with GP using synchronous evaluation.", notes = "Not tag-GP. ECAL-2013", } @InProceedings{harada:2014:EuroGP, author = "Tomohiro Harada and Keiki Takadama", title = "Asynchronous Evolution by Reference-based Evaluation: Tertiary Parent Selection and its Archive", booktitle = "17th European Conference on Genetic Programming", year = "2014", editor = "Miguel Nicolau and Krzysztof Krawiec and Malcolm I. Heywood and Mauro Castelli and Pablo Garcia-Sanchez and Juan J. Merelo and Victor M. {Rivas Santos} and Kevin Sim", series = "LNCS", volume = "8599", publisher = "Springer", pages = "198--209", address = "Granada, Spain", month = "23-25 " # apr, organisation = "EvoStar", keywords = "genetic algorithms, genetic programming :poster", isbn13 = "978-3-662-44302-6", DOI = "doi:10.1007/978-3-662-44303-3_17", abstract = "This paper proposes a novel asynchronous reference-based evaluation (named as ARE) for an asynchronous EA that evolves individuals independently unlike general EAs that evolve all individuals at the same time. ARE is designed for an asynchronous evolution by tertiary parent selection and its archive. In particular, ARE asynchronously evolves individuals through a comparison with only three of individuals (i.e., two parents and one reference individual as the tertiary parent). In addition, ARE builds an archive of good reference individuals. This differ from synchronous evolution in EAs in which selection involves comparison with all population members. In this paper, we investigate the effectiveness of ARE, by applying it to some standard problems used in Linear GP that aim being to minimise the execution step of machine-code programs. We compare GP using ARE (ARE-GP) with steady state (synchronous) GP (SSGP) and our previous asynchronous GP (Tierra-based Asynchronous GP: TAGP). The experimental results have revealed that ARE-GP not only asynchronously evolves the machine-code programs, but also outperforms SSGP and TAGP in all test problems.", notes = "Part of \cite{Nicolau:2014:GP} EuroGP'2014 held in conjunction with EvoCOP2014, EvoBIO2014, EvoMusArt2014 and EvoApplications2014", } @InProceedings{Harada:2014:GECCO, author = "Tomohiro Harada and Keiki Takadama", title = "Asynchronously evolving solutions with excessively different evaluation time by reference-based evaluation", booktitle = "GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation", year = "2014", editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges", isbn13 = "978-1-4503-2662-9", pages = "911--918", keywords = "genetic algorithms, genetic programming", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2576768.2598330", DOI = "doi:10.1145/2576768.2598330", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "The asynchronous evolution has an advantage when evolving solutions with excessively different evaluation time since the asynchronous evolution evolves each solution independently without waiting for other evaluations, unlike the synchronous evolution requires evaluations of all solutions at the same time. As a novel asynchronous evolution approach, this paper proposes Asynchronous Reference-based Evaluation (ARE) that asynchronously selects good parents by the tournament selection using reference solution in order to evolve solutions through a crossover of the good parents. To investigate the effectiveness of ARE in the case of evolving solutions with excessively different evaluation time, this paper applies ARE to Genetic Programming (GP), and compares GP using ARE (ARE-GP) with GP using (mu+lambda) selection ((mu+lambda)-GP) as the synchronous approach in particular situation where the evaluation time of individuals differs from each other. The intensive experiments have revealed the following implications: (1) ARE-GP greatly outperforms (mu+lambda)-GP from the viewpoint of the elapsed unit time in the parallel computation environment, (2) ARE-GP can evolve individuals without decreasing the searching ability in the situation where the computing speed of each individual differs from each other and some individuals fail in their execution.", notes = "Also known as \cite{2598330} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)", } @Article{journals/jrm/HaradaT17, author = "Tomohiro Harada and Keiki Takadama", title = "Machine-Code Program Evolution by Genetic Programming Using Asynchronous Reference-Based Evaluation Through Single-Event Upset in On-Board Computer", journal = "Journal of Robotics and Mechatronics", year = "2017", number = "5", volume = "29", pages = "808--818", keywords = "genetic algorithms, genetic programming, single-event upset, machine-code program evolution, on-board computer", ISSN = "0915-3942", bibdate = "2017-11-03", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/jrm/jrm29.html#HaradaT17", DOI = "doi:10.20965/jrm.2017.p0808", abstract = "This study proposes a novel genetic programming method using asynchronous reference-based evaluation (called AREGP) to evolve computer programs through single-event upsets (SEUs) in the on-board computer in space missions. AREGP is an extension of Tierra-based asynchronous genetic programming (TAGP), which was proposed in our previous study. It is based on the idea of the biological simulator, Tierra, where digital creatures are evolved through bit inversions in a program. AREGP not only inherits the advantages of TAGP but also overcomes its limitation, i.e., TAGP cannot select good programs for evolution without an appropriate threshold. Specifically, AREGP introduces an archive mechanism to maintain good programs and a reference-based evaluation by using the archive for appropriate threshold selection and removal. To investigate the effectiveness of the proposed AREGP, simulation experiments are performed to evolve the assembly language program in the SEU environment. In these experiments, the PIC instruction set, which is carried on many types of spacecraft, is used as the evolved assembly program. The experimental results revealed that AREGP cannot only maintain the correct program through SEU with high occurrence rate, but is also better at reducing the size of programs in comparison with TAGP. Additionally, AREGP can achieve a shorter execution step and smaller size of programs, which cannot be achieved by TAGP.", notes = "College of Information Science and Engineering, Ritsumeikan University", } @InProceedings{Harada:2020:CEC, author = "Tomohiro Harada and Kei Murano and Ruck Thawonmas", title = "Proposal of Multimodal Program Optimization Benchmark and Its Application to Multimodal Genetic Programming", booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC 2020", year = "2020", editor = "Yaochu Jin", pages = "paper id24279", address = "internet", month = "19-24 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-7281-6929-3", DOI = "doi:10.1109/CEC48606.2020.9185705", abstract = "Multimodal program optimisations (MMPOs) have been studied in recent years. MMPOs aims at obtaining multiple optimal programs with different structures simultaneously. This paper proposes novel MMPO benchmark problems to evaluate the performance of the multimodal program search algorithms. In particular, we propose five MMPOs, which have different characteristics, the similarity between optimal programs, the complexity of optimal programs, and the number of local optimal programs. We apply multimodal genetic programming (MMGP) proposed in our previous work to the proposed MMPOs to verify their difficulty and effectiveness, and evaluate the performance of MMGP. The experimental results reveal that the proposed MMPOs are difficult and complex to obtain the global and local optimal programs simultaneously as compared to the conventional benchmark. In addition, the experimental results clarify mechanisms to improve the performance of MMGP.", notes = "https://wcci2020.org/ Tokyo Metropolitan University, Japan; Ritsumeikan University, Japan. Also known as \cite{9185705}", } @Article{harada:2023:ALR, author = "Tomohiro Harada and Sohei Kino and Ruck Thawonmas", title = "Investigating the influence of survival selection and fitness estimation method in genotype-based surrogate-assisted genetic programming", journal = "Artificial Life and Robotics", year = "2023", volume = "28", number = "1", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s10015-022-00821-3", DOI = "doi:10.1007/s10015-022-00821-3", } @Article{DBLP:journals/informaticaSI/HarahshehSS21, author = "Heba Harahsheh and Mohammad Shraideh and Saleh Sharaeh", title = "Performance of Malware Detection Classifier Using Genetic Programming in Feature Selection", journal = "Informatica (Slovenia)", volume = "45", number = "4", year = "2021", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.31449/inf.v45i4.3819", DOI = "doi:10.31449/inf.v45i4.3819", timestamp = "Wed, 23 Feb 2022 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/informaticaSI/HarahshehSS21.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{Haraldsson:2014:GECCOcomp, author = "Saemundur O. Haraldsson and John R. Woodward", title = "Automated design of algorithms and genetic improvement: contrast and commonalities", booktitle = "GECCO 2014 4th workshop on evolutionary computation for the automated design of algorithms", year = "2014", editor = "John Woodward and Jerry Swan and Earl Barr", pages = "1373--1380", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", publisher_address = "New York, NY, USA", publisher = "ACM", keywords = "genetic algorithms, genetic programming, Genetic Improvement, Automated Design of Algorithms (ADA), GI, Abstract Syntax Tree (AST), Search Based Software Engineering, SBSE", isbn13 = "978-1-4503-2881-4", URL = "http://doi.acm.org/10.1145/2598394.2609874", DOI = "doi:10.1145/2598394.2609874", size = "8 pages", abstract = "Automated Design of Algorithms (ADA) and Genetic Improvement (GI) are two relatively young fields of research that have been receiving more attention in recent years. Both methodologies can improve programs using evolutionary search methods and successfully produce human competitive programs. ADA and GI are used for improving functional properties such as quality of solution and non-functional properties, e.g. speed, memory and, energy consumption. Only GI of the two has been used to fix bugs, probably because it is applied globally on the whole source code while ADA typically replaces a function or a method locally. While GI is applied directly to the source code ADA works ex-situ, i.e. as a separate process from the program it is improving. Although the methodologies overlap in many ways they differ on some fundamentals and for further progress to be made researchers from both disciplines should be aware of each other's work.", notes = "Also known as \cite{2609874} Distributed at GECCO-2014. Unstable URL at citeseerx.ist.psu.edu", } @InProceedings{Haraldsson:2015:gi, author = "Saemundur O. Haraldsson and John R. Woodward", title = "Genetic Improvement of Energy Usage is only as Reliable as the Measurements are Accurate", booktitle = "Genetic Improvement 2015 Workshop", year = "2015", editor = "William B. Langdon and Justyna Petke and David R. White", pages = "831--832", address = "Madrid", publisher_address = "New York, NY, USA", month = "11-15 " # jul, organisation = "SIGEvo", publisher = "ACM", keywords = "genetic algorithms, genetic programming, Genetic Improvement", isbn13 = "978-1-4503-3488-4", URL = "http://gpbib.cs.ucl.ac.uk/gi2015/energy_optimisation_via_genetic_improvement.pdf", DOI = "doi:10.1145/2739482.2768421", size = "2 pages", abstract = "Energy has recently become an objective for Genetic Improvement. Measuring software energy use is complicated which might tempt us to use simpler measurements. However if we base the GI on inaccurate measurements we can not assure any improvements. This paper seeks to highlight important issues when evaluating energy use of programs.", notes = "position paper slides http://gpbib.cs.ucl.ac.uk/gi2015/gi_2015_slides/haraldsson ", } @InProceedings{Haraldsson:2017:EuroGP, author = "Saemundur O. Haraldsson and John R. Woodward and Alexander E. I. Brownlee and David Cairns", title = "Exploring Fitness and Edit Distance of Mutated Python Programs", booktitle = "EuroGP 2017: Proceedings of the 20th European Conference on Genetic Programming", year = "2017", month = "19-21 " # apr, editor = "Mauro Castelli and James McDermott and Lukas Sekanina", series = "LNCS", volume = "10196", publisher = "Springer Verlag", address = "Amsterdam", pages = "19--34", organisation = "species", keywords = "genetic algorithms, genetic programming, Genetic Improvement, SBSE, Search Based Software Engineering, Automatic programming, Software repair", isbn13 = "978-3-319-55695-6", URL = "https://dspace.stir.ac.uk/bitstream/1893/25251/1/haraldsson.pdf", URL = "http://hdl.handle.net/1893/25251", URL = "https://rdcu.be/dslLb", DOI = "doi:10.1007/978-3-319-55696-3_2", size = "16 pages", abstract = "Genetic Improvement (GI) is the process of using computational search techniques to improve existing software e.g. in terms of execution time, power consumption or correctness. As in most heuristic search algorithms, the search is guided by fitness with GI searching the space of program variants of the original software. The relationship between the program space and fitness is seldom simple and often quite difficult to analyse. This paper makes a preliminary analysis of GI's fitness distance measure on program repair with three small Python programs. Each program undergoes incremental mutations while the change in fitness as measured by proportion of tests passed is monitored. We conclude that the fitnesses of these programs often does not change with single mutations and we also confirm the inherent discreteness of bug fixing fitness functions. Although our findings cannot be assumed to be general for other software they provide us with interesting directions for further investigation.", notes = "Part of \cite{Castelli:2017:GP} EuroGP'2017 held in conjunction with EvoCOP2017, EvoMusArt2017 and EvoApplications2017", } @InProceedings{Haraldsson:2017:SBST, author = "Saemundur Oskar Haraldsson and John R. Woodward and Alexander E. I. Brownlee", title = "The Use of Automatic Test Data Generation for Genetic Improvement in a Live System", booktitle = "Search-Based Software Testing", year = "2017", editor = "Juan P. Galeotti and Justyna Petke", pages = "28--31", address = "Buenos Aires, Argentina", month = "22-23 " # may, publisher = "IEEE/ACM", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, Test data generation, Bug fixing, APR, Python", isbn13 = "978-1-5386-2789-1", URL = "https://www.researchgate.net/publication/315381757_The_Use_of_Automatic_Test_Data_Generation_for_Genetic_Improvement_in_a_Live_System", URL = "https://www.researchgate.net/profile/Saemundur_Haraldsson/publication/315381757_The_Use_of_Automatic_Test_Data_Generation_for_Genetic_Improvement_in_a_Live_System/links/58d3abeda6fdccd24d4595d1/The-Use-of-Automatic-Test-Data-Generation-for-Genetic-Improvement-in-a-Live-System.pdf", URL = "http://www.human-competitive.org/sites/default/files/haraldsson-text.txt", URL = "http://www.human-competitive.org/sites/default/files/haraldsson-paper-1.pdf", URL = "http://dl.acm.org/citation.cfm?id=3105433&CFID=952099816&CFTOKEN=74048713", video_url = "http://crest.cs.ucl.ac.uk/cow/50/videos/haraldsson_cow50_480p.mp4", DOI = "doi:10.1109/SBST.2017.10", size = "4 pages", abstract = "In this paper we present a bespoke live system in commercial use that has been implemented with self-improving properties. During business hours it provides overview and control for many specialists to simultaneously schedule and observe the rehabilitation process for multiple clients. However in the evening, after the last user logs out, it starts a self-analysis based on the day's recorded interactions and the self-improving process. It uses Search Based Software Testing (SBST) techniques to generate test data for Genetic Improvement (GI) to fix any bugs if exceptions have been recorded. The system has already been under testing for 4 months and demonstrates the effectiveness of simple test data generation and the power of GI for improving live code.", notes = "Janus Manager, see also video http://crest.cs.ucl.ac.uk/cow/50/videos/haraldsson_cow50_480p.mp4 co-located with ICSE 2017 http://sbst2017.lafhis.dc.uba.ar/ Entered 2017 Humies http://www.human-competitive.org/awards,", } @InProceedings{Haraldsson:2017:GI, author = "Saemundur O. Haraldsson and John R. Woodward and Alexander E. I. Brownlee and Kristin Siggeirsdottir", title = "Fixing Bugs in Your Sleep: How Genetic Improvement Became an Overnight Success", booktitle = "GI-2017", year = "2017", editor = "Justyna Petke and David R. White and W. B. Langdon and Westley Weimer", pages = "1513--1520", address = "Berlin", month = "15-19 " # jul, publisher = "ACM", note = "Best paper", keywords = "genetic algorithms, genetic programming, genetic improvement, Adaptive System, Bug fixing, APR, Python, Test data generation", isbn13 = "978-1-4503-4939-0", URL = "http://geneticimprovementofsoftware.com/wp-content/uploads/2017/05/haraldsson2017_gi_overnight.pdf", URL = "http://www.human-competitive.org/sites/default/files/haraldsson-text.txt", URL = "http://www.human-competitive.org/sites/default/files/haraldsson-paper-2.pdf", DOI = "doi:10.1145/3067695.3082517", acmid = "3082517", size = "8 pages", abstract = "We present a bespoke live system in commercial use with self-improving capability. During daytime business hours it provides an overview and control for many specialists to simultaneously schedule and observe the rehabilitation process for multiple clients. However in the evening, after the last user logs out, it starts a self-analysis based on the day's recorded interactions. It generates test data from the recorded interactions for Genetic Improvement to fix any recorded bugs that have raised exceptions. The system has already been under test for over 6 months and has in that time identified, located, and fixed 22 bugs. No other bugs have been identified by other methods during that time. It demonstrates the effectiveness of simple test data generation and the ability of GI for improving live code.", notes = "Kristin Siggeirsdottir is 10000th GP author. Janus Rehabilitation Centre. Entered 2017 Humies http://www.human-competitive.org/awards", } @InProceedings{Haraldsson:2017a:GI, author = "Saemundur O. Haraldsson and John R. Woodward and Alexander E. I. Brownlee and Albert V. Smith and Vilmundur Gudnason", title = "Genetic Improvement of Runtime in a Bioinformatics Application", booktitle = "GI-2017", year = "2017", editor = "Justyna Petke and David R. White and W. B. Langdon and Westley Weimer", address = "Berlin", month = "15-19 " # jul, keywords = "genetic algorithms, genetic programming, genetic improvement, software performance, Search-based software engineering, SBSE, Execution Time, Landscape, Bioinformatics", URL = "http://geneticimprovementofsoftware.com/wp-content/uploads/2017/05/haraldsson2017_bioinformatics.pdf", DOI = "doi:10.1145/3067695.3082526", size = "8 pages", abstract = "We present a Genetic Improvement (GI) experiment on ProbAbel, a piece of bioinformatics software for Genome Wide Association (GWA) studies. The GI framework used here has previously been successfully used on Python programs and can, with minimal adaptation, be used on source code written in other languages. We achieve improvements in execution time without the loss of accuracy in output while also exploring the vast fitness landscape that the GI framework has to search. The runtime improvements achieved on smaller data set scale up for larger data sets. Our findings are that for ProbAbel, the GI's execution time landscape is noisy but flat. We also confirm that human written code is robust with respect to small edits to the source code.", notes = "missing values, SNPs. Learn from smallest dataset but mutated C code applicable to larger dataset. Macro mutation: moving lines of code (eg delete line 321) and micromutation, in statement token changes, eg add one to integer constant, Replace col_new++ with ++col_new. GI run 8 hours. No semantic change. 'Software is Not Fragile' Significant but tiny speedup. The Icelandic Heart Association", } @Article{Haraldsson:2017:cwsbatfix, author = "Saemundur Haraldsson and Alexander Brownlee and John R. Woodward", title = "Computers will soon be able to fix themselves - are {IT} departments for the chop?", journal = "The Conversation", year = "2017", pages = "3.29pm BST", month = oct # " 12", keywords = "genetic algorithms, genetic programming, Genetic Improvement, APR, Python", URL = "http://theconversation.com/computers-will-soon-be-able-to-fix-themselves-are-it-departments-for-the-chop-85632", notes = "Science + Technology", } @PhdThesis{Haraldsson:thesis, author = "Saemundur Oskar Haraldsson", title = "Genetic Improvement of Software: From Program Landscapes to the Automatic Improvement of a Live System", school = "Institute of Computing Science and Mathematics, University of Stirling", year = "2017", address = "UK", month = may, keywords = "genetic algorithms, genetic programming, genetic improvement, software Engineering, SBSE, Automatic Programming, Bug fixing, APR, Python", URL = "http://hdl.handle.net/1893/26007", URL = "https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.725136", URL = "https://dspace.stir.ac.uk/bitstream/1893/26007/1/thesis.pdf", size = "171 pages", abstract = "In today's technology driven society, software is becoming increasingly important in more areas of our lives. The domain of software extends beyond the obvious domain of computers, tablets, and mobile phones. Smart devices and the internet-of-things have inspired the integration of digital and computational technology into objects that some of us would never have guessed could be possible or even necessary. Fridges and freezers connected to social media sites, a toaster activated with a mobile phone, physical buttons for shopping, and verbally asking smart speakers to order a meal to be delivered. This is the world we live in and it is an exciting time for software engineers and computer scientists. The sheer volume of code that is currently in use has long since outgrown beyond the point of any hope for proper manual maintenance. The rate of which mobile application stores such as Google's and Apple's have expanded is astounding. The research presented here aims to shed a light on an emerging field of research, called Genetic Improvement ( GI ) of software. It is a methodology to change program code to improve existing software. This thesis details a framework for GI that is then applied to explore fitness landscape of bug fixing Python software, reduce execution time in a C++ program, and integrated into a live system. We show that software is generally not fragile and although fitness landscapes for GI are flat they are not impossible to search in. This conclusion applies equally to bug fixing in small programs as well as execution time improvements. The framework's application is shown to be transportable between programming languages with minimal effort. Additionally, it can be easily integrated into a system that runs a live web service.", notes = "ISNI: 0000 0004 6422 5524 Supervisor John R. Woodward gismo", } @InProceedings{Haraldsson:2020:GECCOcomp, author = "Samundur O. Haraldsson and John R. Woodward and Markus Wagner", title = "Genetic Improvement: Taking Real-World Source Code and Improving It Using Genetic Programming", year = "2020", editor = "Richard Allmendinger and Hugo Terashima Marin and Efren Mezura Montes and Thomas Bartz-Beielstein and Bogdan Filipic and Ke Tang and David Howard and Emma Hart and Gusz Eiben and Tome Eftimov and William {La Cava} and Boris Naujoks and Pietro Oliveto and Vanessa Volz and Thomas Weise and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Rui Wang and Ran Cheng and Guohua Wu and Miqing Li and Hisao Ishibuchi and Jonathan Fieldsend and Ozgur Akman and Khulood Alyahya and Juergen Branke and John R. Woodward and Daniel R. Tauritz and Marco Baioletti and Josu Ceberio Uribe and John McCall and Alfredo Milani and Stefan Wagner and Michael Affenzeller and Bradley Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and Nayat Sanchez-Pi and Luis Marti and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and Matthew Johns and Nick Ross and Ed Keedwell and Herman Mahmoud and David Walker and Anthony Stein and Masaya Nakata and David Paetzel and Neil Vaughan and Stephen Smith and Stefano Cagnoni and Robert M. Patton and Ivanoe {De Falco} and Antonio {Della Cioppa} and Umberto Scafuri and Ernesto Tarantino and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Erik Hemberg and Riyad Alshammari and Tokunbo Makanju and Fuijimino-shi and Ivan Zelinka and Swagatam Das and Ponnuthurai Nagaratnam and Roman Senkerik", isbn13 = "9781450371278", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://dl.acm.org/doi/abs/10.1145/3377929.3389885", URL = "https://doi.org/10.1145/3377929.3389885", DOI = "doi:10.1145/3377929.3389885", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion", pages = "801--831", size = "31 pages", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", note = "GI tutorial", keywords = "genetic algorithms, genetic programming, genetic improvement", notes = "Also known as \cite{10.1145/3377929.3389885} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @InProceedings{Haraldsson:2021:GECCOcomp, author = "Saemundur Haraldsson and Alexander Brownlee and John R. Woodward and Markus Wagner and Bradley Alexander", title = "Genetic improvement: taking real-world source code and improving it using genetic programming", booktitle = "Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion", year = "2021", editor = "Gisele L. Pappa", pages = "786--817", address = "internet", series = "GECCO '21", month = jul # " 10-14", organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, genetic improvement", isbn13 = "978-1-4503-8351-6", URL = "https://gecco-2021.sigevo.org/Tutorials#id_Genetic%20improvement:%20Taking%20real-world%20source%20code%20and%20improving%20it%20using%20computational%20search%20methods.", DOI = "doi:10.1145/3449726.3461416", size = "32 pages", notes = "University of Stirling GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)", } @Book{Harari:Sapiens, author = "Yuval Noah Harari", title = "Sapiens", publisher = "Vintage", year = "2015", keywords = "genetic algorithms, genetic programming", ISBN = "0-09-959008-5", URL = "https://www.amazon.co.uk/Sapiens-Humankind-Yuval-Noah-Harari/dp/0099590085", size = "512 pages", notes = "One positive sentence on GP", } @Article{Hardesty:2012:MITnews, author = "Larry Hardesty", title = "The mathematics of taste", journal = "MIT news", year = "2012", month = jan # " 24", keywords = "genetic algorithms, genetic programming", URL = "http://web.mit.edu/newsoffice/2012/what-smells-good-0124.html", size = "~1 page", abstract = "By using 'genetic programming' to crossbreed algorithms, researchers help flavour companies figure out what their customers like.", notes = "See \cite{Veeramachaneni:2012:GPEM} MIT News Office 77 Massachusetts Avenue, Room 11-400, Cambridge, MA 02139-4307, 617.253.2700", } @InProceedings{Hardey:2012:GECCO, author = "Kathryn Hardey and Eren Corapcioglu and Molly Mattis and Mark Goadrich and Matthew Jadud", title = "Exploring and evolving process-oriented control for real and virtual fire fighting robots", booktitle = "GECCO '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference", year = "2012", editor = "Terry Soule and Anne Auger and Jason Moore and David Pelta and Christine Solnon and Mike Preuss and Alan Dorin and Yew-Soon Ong and Christian Blum and Dario Landa Silva and Frank Neumann and Tina Yu and Aniko Ekart and Will Browne and Tim Kovacs and Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and Giovanni Squillero and Nicolas Bredeche and Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and Martin Pelikan and Silja Meyer-Nienberg and Christian Igel and Greg Hornby and Rene Doursat and Steve Gustafson and Gustavo Olague and Shin Yoo and John Clark and Gabriela Ochoa and Gisele Pappa and Fernando Lobo and Daniel Tauritz and Jurgen Branke and Kalyanmoy Deb", isbn13 = "978-1-4503-1177-9", pages = "105--112", keywords = "genetic algorithms, genetic programming, artificial life/robotics/evolvable hardware", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330163.2330179", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Current research in evolutionary robotics is largely focused on creating controllers by either evolving neural networks or refining genetic programs based on grammar trees. We propose the use of the parallel, dataflow languages for the construction of effective robotic controllers and the evolution of new controllers using genetic programming techniques. These languages have the advantages of being built on concurrent execution frameworks that lend themselves to formal verification along with being visualized as a dataflow graph. In this paper, we compare and contrast the development and subsequent evolution of one such process-oriented control algorithm. Our control software was built from composable, communicating processes executing in parallel, and we tested our solution in an annual fire-fighting robotics competition. Subsequently, we evolved new controllers in a virtual simulation of this parallel dataflow domain, and in doing so discovered and quantified more efficient solutions. This research demonstrates the effectiveness of using process networks as the basis for evolutionary robotics.", notes = "Also known as \cite{2330179} GECCO-2012 A joint meeting of the twenty first international conference on genetic algorithms (ICGA-2012) and the seventeenth annual genetic programming conference (GP-2012)", } @InProceedings{conf/iconas/HardierRS13, author = "Georges Hardier and Clement Roos and Cedric Seren", title = "Creating Sparse Rational Approximations for Linear Fractional Representations Using Genetic Programming", booktitle = "3rd IFAC International Conference on Intelligent Control and Automation Science, ICONS 2013", year = "2013", editor = "Pedro M. Ferreira", pages = "393--398", address = "Sichuan, Chengdu, China", month = sep # " 2-4", publisher = "International Federation of Automatic Control", keywords = "genetic algorithms, genetic programming, rational approximation, Linear Fractional Representation, mu-analysis", bibdate = "2014-11-28", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/iconas/iconas2013.html#HardierRS13", isbn13 = "978-3-902823-45-8", broken = "http://www.ifac-papersonline.net/Detailed/63487.html", DOI = "doi:10.3182/20130902-3-CN-3020.00065", URL = "https://w3.onera.fr/smac/tracker", size = "6 pages", abstract = "The objective of this paper is to stress that the size of a Linear Fractional Representation (LFR) significantly depends on the way tabulated or irrational data are approximated during the modelling process. It is notably shown that rational approximants can result in much smaller LFR than polynomial ones. In this context, a new method is introduced to generate sparse rational models, which avoid data overfitting and lead to simple yet accurate LFR, thanks to a symbolic regression technique. Genetic Programming is implemented to select sparse monomials and coupled with a nonlinear iterative procedure to estimate the coefficients of the surrogate model. Furthermore, a mu-analysis based proof is given to check the nonsingularity of the resulting rational functions. The proposed method is evaluated on an aeronautical example and successfully compared to more classical approaches.", notes = "Systems Control and Flight Dynamics Department, ONERA The French Aerospace Lab,Toulouse, France. https://tc.ifac-control.org/3/2/activities/newsletter-05-ifac-tc-1-3-discrete-event-and-hybrid-system-january-2007 http: www.ifac-papersonline.net Intelligent_Control_and_Automation_Science 3rd_IFAC_International_Conference_on_Intelligent_Control_and_Automation_Science__2013_ index.htm See also https://hal.archives-ouvertes.fr/hal-01088602/document", } @Article{Harding:2016:DS, author = "John E. Harding and Paul Shepherd", title = "Meta-Parametric Design", journal = "Design Studies", year = "2016", volume = "52", pages = "73--95", month = sep, keywords = "genetic algorithms, genetic programming, parametric design, conceptual design, design cognition, human-computer interaction", ISSN = "0142-694X", URL = "http://www.sciencedirect.com/science/article/pii/S0142694X16300655", DOI = "doi:10.1016/j.destud.2016.09.005", size = "23 pages", abstract = "Parametric modelling software often maintains an explicit history of design development in the form of a graph. However, as the graph increases in complexity it quickly becomes inflexible and unsuitable for exploring a wide design space. By contrast, implicit low-level rule systems can offer wide design exploration due to their lack of structure, but often act as black boxes to human observers with only initial conditions and final designs cognisable. In response to these two extremes, the authors propose a new approach called Meta-Parametric Design, combining graph-based parametric modelling with genetic programming. The advantages of this approach are demonstrated using two real case-study projects that widen design exploration whilst maintaining the benefits of a graph representation.", } @InProceedings{Harding:2003:eh, author = "Simon Harding and Julian Francis Miller", editor = "Jason Lohn and Ricardo Zebulum and James Steincamp and Didier Keymeulen and Adrian Stoica and Michael I. Ferguson", month = "9-11 " # jul, year = "2003", title = "A Scalable Platform for Intrinsic Hardware and in materio Evolution", booktitle = "2003 {NASA/DoD} Conference on Evolvable Hardware", pages = "221--224", publisher = "IEEE Computer Society", address = "Chicago, Illinois", organisation = "NASA Ames Research Center", publisher_address = "10662 Los Vaqueros Circle, P.O. Box 3014, Los Alamitos, CA, 90720-1314, USA", email = "s.l.harding@cs.bham.ac.uk", ISBN = "0-7695-1977-6", URL = "EHW http://ehw.jpl.nasa.gov", notes = "EH2003 http://ic.arc.nasa.gov/projects/eh2003/", } @InProceedings{eurogp:HardingM05, author = "Simon Harding and Julian F. Miller", editor = "Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. {van Hemert} and Marco Tomassini", title = "Evolution of Robot Controller Using Cartesian Genetic Programming", booktitle = "Proceedings of the 8th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3447", year = "2005", address = "Lausanne, Switzerland", month = "30 " # mar # " - 1 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", ISBN = "3-540-25436-6", pages = "62--73", DOI = "doi:10.1007/978-3-540-31989-4_6", DOI = "doi:10.1007/b107383", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "Cartesian Genetic Programming is a graph based representation that has many benefits over traditional tree based methods, including bloat free evolution and faster evolution through neutral search. Here, an integer based version of the representation is applied to a traditional problem in the field : evolving an obstacle avoiding robot controller. The technique is used to rapidly evolve controllers that work in a complex environment and with a challenging robot design. The generalisation of the robot controllers in different environments is also demonstrated. A novel fitness function based on chemical gradients is presented as a means of improving evolvability in such tasks.", notes = "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005", } @InProceedings{harding:2005:EH, author = "Simon Harding and Julian F. Miller", title = "Evolution In Materio : A Real-Time Robot Controller in Liquid Crystal", booktitle = "Proceedings of the 2005 NASA/DoD Conference on Evolvable Hardware", year = "2005", editor = "Jason Lohn and David Gwaltney and Gregory Hornby and Ricardo Zebulum and Didier Keymeulen and Adrian Stoica", pages = "229--238", address = "Washington, DC, USA", month = "29 " # jun # "-1 " # jul, publisher = "IEEE Press", publisher_address = "IEEE Service Center 445 Hoes Lane Asia P.O. Box 1331 Piscataway, NJ 08855-1331", organisation = "NASA, DoD", keywords = "genetic algorithms, genetic programming, EHW", ISBN = "0-7695-2399-4", DOI = "doi:10.1109/EH.2005.22", abstract = "Although intrinsic evolution has been shown to be capable of exploiting the physical properties of materials to solve problems, most researchers have chosen to limit themselves to using standard electronic components. However, it has been previously argued that because such components are human designed and intentionally have predictable responses, they may not be the most suitable medium to use when trying to get a naturally inspired search technique to solve a problem. Indeed allowing computer controlled evolution (CCE) to manipulate novel physical media can allow much greater scope for the discovery of unconventional solutions. Last year the authors demonstrated, for the first time, that CCE could manipulate liquid crystal to perform signal processing tasks (i.e frequency discrimination). In this paper we show that CCE can use liquid crystal to solve the much harder problem of controlling a robot in real time to navigate in an environment to reach an obstructed destination point.", notes = "EH2005 IEEE Computer Society Order Number P2399", } @InProceedings{eurogp07:harding, author = "Simon Harding and Wolfgang Banzhaf", title = "Fast genetic programming on {GPUs}", editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar", booktitle = "Proceedings of the 10th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "4445", year = "2007", address = "Valencia, Spain", month = "11-13 " # apr, pages = "90--101", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, GPU, Graphics Card Acceleration, Parallel Evaluation", ISBN = "3-540-71602-5", isbn13 = "978-3-540-71602-0", URL = "http://citeseer.ist.psu.edu/viewdoc/citations;jsessionid=7CB4F09F82CEB4C8933E1E15E8EF3632?doi=10.1.1.93.1862", URL = "http://www.cs.mun.ca/~banzhaf/papers/eurogp07.pdf", URL = "https://rdcu.be/dfEPJ", DOI = "doi:10.1007/978-3-540-71605-1_9", size = "12 pages", abstract = "As is typical in evolutionary algorithms, fitness evaluation in GP takes the majority of the computational effort. In this paper we demonstrate the use of the Graphics Processing Unit (GPU) to accelerate the evaluation of individuals. We show that for both binary and floating point based data types, it is possible to get speed increases of several hundred times over a typical CPU implementation. This allows for evaluation of many thousands of fitness cases, and hence should enable more ambitious solutions to be evolved using GP.", notes = "NVidia GForce 7300 Go. p95 'GP interpreter', microsoft .NET C# visual studio, windowsXP 'The Accelerator tool kit compiles each individuals GP expression into a shader program.' floating point x^6-2x^4+x^2, C# boolean type. Two spirals. Nuclear proteins \cite{langdon:2005:CS} Part of \cite{ebner:2007:GP} EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007", } @InProceedings{1277161, author = "Simon L. Harding and Julian F. Miller and Wolfgang Banzhaf", title = "Self-modifying cartesian genetic programming", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "1", isbn13 = "978-1-59593-697-4", pages = "1021--1028", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1021.pdf", DOI = "doi:10.1145/1276958.1277161", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Generative and Developmental Systems, evolution, self modification", abstract = "In nature, systems with enormous numbers of components (i.e. cells) are evolved from a relatively small genotype. It has not yet been demonstrated that artificial evolution is sufficient to make such a system evolvable. Consequently researchers have been investigating forms of computational development that may allow more evolvable systems. The approaches taken have largely used re-writing, multi-cellularity, or genetic regulation. In many cases it has been difficult to produce general purpose computation from such systems. In this paper we introduce computational development using a form of Cartesian Genetic Programming that includes self-modification operations. One advantage of this approach is that ab initio the system can be used to solve computational problems. We present results on a number of problems and demonstrate the characteristics and advantages that self-modification brings.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @InProceedings{10.1109/HPCS.2007.17, author = "S. L. Harding and W. Banzhaf", title = "Fast Genetic Programming and Artificial Developmental Systems on GPUs", booktitle = "21st International Symposium on High Performance Computing Systems and Applications (HPCS'07)", year = "2007", pages = "2", address = "Canada", publisher = "IEEE Computer Society", keywords = "genetic algorithms, genetic programming, GPU", ISBN = "0-7695-2813-9", DOI = "doi:10.1109/HPCS.2007.17", abstract = "In this paper we demonstrate the use of the Graphics Processing Unit (GPU) to accelerate Evolutionary Computation applications, in particular Genetic Programming approaches. We show that it is possible to get speed increases of several hundred times over a typical CPU implementation, catapulting GPU processing for these applications into the realm of HPC. This increase in performance also extends to artificial developmental systems, where evolved programs are used to construct cellular systems. Feasibility of this approach to efficiently evaluate artificial developmental systems based on cellular automata is demonstrated.", } @InProceedings{Harding:2008:cec, author = "Simon Harding", title = "Evolution of Image Filters on Graphics Processor Units Using Cartesian Genetic Programming", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "1921--1928", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1823-7", file = "EC0465.pdf", DOI = "doi:10.1109/CEC.2008.4631051", abstract = "Graphics processor units are fast, inexpensive parallel computing devices. Recently there has been great interest in harnessing this power for various types of scientific computation, including genetic programming. In previous work, we have shown that using the graphics processor provides dramatic speed improvements over a standard CPU in the context of fitness evaluation. In this work, we use Cartesian Genetic Programming to generate shader programs that implement image filter operations. Using the GPU, we can rapidly apply these programs to each pixel in an image and evaluate the performance of a given filter. We show that we can successfully evolve noise removal filters that produce better image quality than a standard median filter.", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, GPU", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @Article{harding_genetic_2008, author = "S. Harding and W. Banzhaf", title = "Genetic programming on {GPUs} for image processing", journal = "International Journal of High Performance Systems Architecture", year = "2008", volume = "1", number = "4", pages = "231--240", keywords = "genetic algorithms, genetic programming, GPU, graphics processing units, image filters, image processing, parallel processing, reverse engineering", ISSN = "1751-6528", URL = "http://www.gpgpgpu.com/bibtex.html#harding_genetic_2008", DOI = "doi:10.1504/IJHPSA.2008.024207", abstract = "The evolution of image filters using genetic programming is a relatively unexplored task. This is most likely due to the high computational cost of evaluating the evolved programs. The parallel processors available on modern graphics cards can be used to greatly increase the speed of evaluation. Previous papers in this area dealt with tasks such as noise reduction and edge detection. Here we demonstrate that other more complicated processes can also be successfully evolved and that we can 'reverse engineer' the output from filters used in common graphics manipulation programs.", notes = "IJHPSA See also Technical report by Simon Harding and Wolfgang Banzhaf April 2008. http://gpgpgpu.com/papers/CGPGPUEvolvingImageFilters.pdf", } @InProceedings{Harding:2009:eurogp, author = "Simon Harding and Julian Miller and Wolfgang Banzhaf", title = "Self Modifying Cartesian Genetic Programming: Fibonacci, Squares, Regression and Summing", booktitle = "Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009", year = "2009", editor = "Leonardo Vanneschi and Steven Gustafson and Alberto Moraglio and Ivanoe {De Falco} and Marc Ebner", volume = "5481", series = "LNCS", pages = "133--144", address = "Tuebingen", month = apr # " 15-17", organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, developmental systems, Fibonacci", isbn13 = "978-3-642-01180-1", URL = "http://www.evolutioninmaterio.com/preprints/eurogp_smcgp_1.ps.pdf", DOI = "doi:10.1007/978-3-642-01181-8_12", size = "12 pages", abstract = "Self Modifying CGP (SMCGP) is a developmental form of Cartesian Genetic Programming(CGP). It is able to modify its own phenotype during execution of the evolved program. This is done by the inclusion of modification operators in the function set. Here we present the use of the technique on several different sequence generation and regression problems.", notes = "Part of \cite{conf/eurogp/2009} EuroGP'2009 held in conjunction with EvoCOP2009, EvoBIO2009 and EvoWorkshops2009", } @InProceedings{Harding:2009:cec, author = "S. Harding and J. F. Miller and W. Banzhaf", title = "Self Modifying Cartesian Genetic Programming: Parity", booktitle = "2009 IEEE Congress on Evolutionary Computation", year = "2009", editor = "Andy Tyrrell", pages = "285--292", address = "Trondheim, Norway", month = "18-21 " # may, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", isbn13 = "978-1-4244-2959-2", file = "P128.pdf", URL = "http://www.cs.mun.ca/~banzhaf/papers/smcgp_cec1.pdf", DOI = "doi:10.1109/CEC.2009.4982960", size = "8 pages", abstract = "Self Modifying CGP (SMCGP) is a developmental form of Cartesian Genetic Programming(CGP). It differs from CGP by including primitive functions which modify the program. Beginning with the evolved genotype the self-modifying functions produce a new program (phenotype) at each iteration. In this paper we have applied it to a well known digital circuit building problem: even-parity. We show that it is easier to solve difficult parity problems with SMCGP than either with CGP or Modular CGP, and that the increase in efficiency grows with problem size. More importantly, we prove that SMCGP can evolve general solutions to arbitrary-sized even parity problems.", notes = "Also known as \cite{4982960} CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR", } @InProceedings{DBLP:conf/gecco/HardingMB09, author = "Simon Harding and Julian Francis Miller and Wolfgang Banzhaf", title = "Evolution, development and learning using self-modifying cartesian genetic programming", booktitle = "GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation", year = "2009", editor = "Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano {van Hemert} and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique Alba", pages = "699--706", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1569901.1569998", abstract = "Self-Modifying Cartesian Genetic Programming (SMCGP) is a form of genetic programming that integrates developmental (self-modifying) features as a genotype-phenotype mapping. This paper asks: Is it possible to evolve a learning algorithm using SMCGP?", notes = "GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009). ACM Order Number 910092.", } @InProceedings{hardinggpem2009, author = "Simon L. Harding and Wolfgang Banzhaf", title = "Distributed Genetic Programming on {GPUs} using {CUDA}", booktitle = "Workshop on Parallel Architectures and Bioinspired Algorithms", year = "2009", editor = "Ignacio Hidalgo and Francisco Fernandez and Juan Lanchares", pages = "1--10", address = "Raleigh, NC, USA", month = "13 " # sep, publisher = "Universidad Complutense de Madrid", keywords = "genetic algorithms, genetic programming, GPU", URL = "http://www.evolutioninmaterio.com/preprints/CudaParallelCompilePP.pdf", abstract = "Using of a cluster of Graphics Processing Unit (GPU) equipped computers, it is possible to accelerate the evaluation of individuals in Genetic Programming. Program compilation, fitness case data and fitness execution are spread over the cluster of computers, allowing for the efficient processing of very large datasets. Here, the implementation is demonstrated on datasets containing over 10 million rows and several hundred megabytes in size. Populations of candidate individuals are compiled into NVidia CUDA programs and executed on a set of client computers - each with a different subset of the dataset. The paper discusses the implementation of the system and acts as a tutorial for other researchers experimenting with genetic programming and GPUs.", notes = "mono dot net. WPABA'09 http://bioinspired.dacya.ucm.es/doku.php?id=workshops http://bioinspired.dacya.ucm.es/lib/exe/fetch.php?media=2_cfp_wpaba.pdf", } @Article{Harding:2010:GPEM, author = "Simon Harding and Julian F. Miller and Wolfgang Banzhaf", title = "Developments in Cartesian Genetic Programming: self-modifying CGP", journal = "Genetic Programming and Evolvable Machines", year = "2010", volume = "11", number = "3/4", pages = "397--439", month = sep, note = "Tenth Anniversary Issue: Progress in Genetic Programming and Evolvable Machines", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Developmental systems", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-010-9114-1", URL = "http://results.ref.ac.uk/Submissions/Output/3354577", size = "43 pages", abstract = "Self-modifying Cartesian Genetic Programming (SMCGP) is a general purpose, graph-based, developmental form of Genetic Programming founded on Cartesian Genetic Programming. In addition to the usual computational functions, it includes functions that can modify the program encoded in the genotype. This means that programs can be iterated to produce an infinite sequence of programs (phenotypes) from a single evolved genotype. It also allows programs to acquire more inputs and produce more outputs during this iteration. We discuss how SMCGP can be used and the results obtained in several different problem domains, including digital circuits, generation of patterns and sequences, and mathematical problems. We find that SMCGP can efficiently solve all the problems studied. In addition, we prove mathematically that evolved programs can provide general solutions to a number of problems: n-input even-parity, n-input adder, and sequence approximation to pi", uk_research_excellence_2014 = "The paper advances evolutionary computation. Published in this special issue of the journal to mark its tenth anniversary and calling for far-reaching and foundational work. The result of collaborations with Memorial University of Newfoundland, Canada the paper introduces the concept of self-modification in Genetic Programming (GP). For the first time this allows GP to be applied to multiple instances of problems, and shows that general, mathematically provable solutions to classes of problems can be evolved.", } @InProceedings{Harding:2010:gecco, author = "Simon Harding and Julian F. Miller and Wolfgang Banzhaf", title = "Self modifying cartesian genetic programming: finding algorithms that calculate pi and e to arbitrary precision", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "579--586", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, Generative and developmental systems", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", URL = "http://www.cs.mun.ca/~banzhaf/papers/GECCO2010p579.pdf", DOI = "doi:10.1145/1830483.1830591", publisher = "ACM", publisher_address = "New York, NY, USA", size = "8 pages", abstract = "Self Modifying Cartesian Genetic Programming (SMCGP) aims to be a general purpose form of developmental genetic programming. The evolved programs are iterated thus allowing an infinite sequence of phenotypes (programs) to be obtained from a single evolved genotype. In previous work this approach has already shown that it is possible to obtain mathematically provable general solutions to certain problems. We extend this class in this paper by showing how SMCGP can be used to find algorithms that converge to mathematical constants (pi and e). Mathematical proofs are given that show that some evolved formulae converge to pi and e in the limit as the number of iterations increase.", notes = "Also known as \cite{1830591} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @InCollection{Harding:2010:GPTP, author = "Simon Harding and Wolfgang Banzhaf and Julian F. Miller", title = "A Survey of Self Modifying Cartesian Genetic Programming", booktitle = "Genetic Programming Theory and Practice VIII", year = "2010", editor = "Rick Riolo and Trent McConaghy and Ekaterina Vladislavleva", series = "Genetic and Evolutionary Computation", volume = "8", address = "Ann Arbor, USA", month = "20-22 " # may, publisher = "Springer", chapter = "6", pages = "91--107", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, developmental systems", isbn13 = "978-1-4419-7746-5", URL = "http://www.springer.com/computer/ai/book/978-1-4419-7746-5", DOI = "doi:10.1007/978-1-4419-7747-2_6", abstract = "Self-Modifying Cartesian Genetic Programming (SMCGP) is a general purpose, graph-based, developmental form of Cartesian Genetic Programming. In addition to the usual computational functions found in CGP, SMCGP includes functions that can modify the evolved program at run time. This means that programs can be iterated to produce an infinite sequence of phenotypes from a single evolved genotype. Here, we discuss the results of using SMCGP on a variety of different problems, and see that SMCGP is able to solve tasks that require scalability and plasticity. We demonstrate how SMCGP is able to produce results that would be impossible for conventional, static Genetic Programming techniques.", notes = "part of \cite{Riolo:2010:GPTP}", } @InProceedings{Harding:2011:GECCO, author = "Simon Harding and Julian F. Miller and Wolfgang Banzhaf", title = "{SMCGP2}: self modifying cartesian genetic programming in two dimensions", booktitle = "GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0557-0", pages = "1491--1498", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, developmental systems", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", URL = "http://www.cs.mun.ca/%7Ebanzhaf/papers/SMCGP2-2011.pdf", DOI = "doi:10.1145/2001576.2001777", publisher = "ACM", publisher_address = "New York, NY, USA", size = "8 pages", abstract = "Self Modifying Cartesian Genetic Programming is a general purpose, graph-based, developmental form of Cartesian Genetic Programming. Using a combination of computational functions and special functions that can modify the phenotype at runtime, it has been employed to find general solutions to certain Boolean circuits and mathematical problems. In the present work, a new version, of SMCGP is proposed and demonstrated. Compared to the original SMCGP both the representation and the function set have been simplified. However, the new representation is also two-dimensional and it allows evolution and development to have more ways to solve a given problem. Under most situations we show that the new method makes the evolution of solutions to even parity and binary addition faster than with previous version of SMCGP.", notes = "hill climbing. General solution to parity. Also known as \cite{2001777} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)", } @InProceedings{Harding:2011:GECCOcompQ, author = "Simon Harding and Julian F. Miller and Wolfgang Banzhaf", title = "SMCGP2: finding algorithms that approximate numerical constants using quaternions and complex numbers", booktitle = "GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0690-4", keywords = "genetic algorithms, genetic programming, cartesian genetic programming: Poster", pages = "197--198", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001858.2001968", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Self Modifying Cartesian Genetic Programming 2 (SMCGP2) is a general purpose, graph-based, developmental form of Cartesian Genetic Programming. Using a combination of computational functions and special functions that can modify the phenotype at runtime, it has been employed to find general solutions to a number of computational problems. Here, we apply the new SMCGP technique to find mathematical relationships between well known mathematical constants (i.e. pi, e, phi, omega etc) using a variety of functions sets. Some of formulae obtained are distinctly unusual and may be unknown in mathematics.", notes = "Also known as \cite{2001968} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @InProceedings{Harding:2011:GECCOcomp, author = "Simon Harding and Wolfgang Banzhaf", title = "Implementing cartesian genetic programming classifiers on graphics processing units using GPU.NET", booktitle = "GECCO 2011 Computational intelligence on consumer games and graphics hardware (CIGPU)", year = "2011", editor = "Simon Harding and W. B. Langdon and Man Leung Wong and Garnett Wilson and Tony Lewis", isbn13 = "978-1-4503-0690-4", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, GPU", pages = "463--470", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001858.2002034", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "This paper investigates the use of a new Graphics Processing Unit (GPU) programming tool called 'GPU.NET' for implementing a Genetic Programming fitness evaluator. We find that the tool is able to help write software that accelerates fitness evaluation. For the first time, Cartesian Genetic Programming (CGP) was used with a GPU-based interpreter. With its code reuse and compact representation, implementing CGP efficiently on the GPU required several innovations. Further, we tested the system on a very large data set, and showed that CGP is also suitable for use as a classifier.", notes = "Also known as \cite{2002034} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @InCollection{Harding:2011:CGP.ch4, author = "Simon L. Harding and Julian F. Miller and Wolfgang Banzhaf", title = "Self-Modifying Cartesian Genetic Programming", booktitle = "Cartesian Genetic Programming", publisher = "Springer", editor = "Julian F. Miller", year = "2011", series = "Natural Computing Series", chapter = "4", pages = "101--124", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", isbn13 = "978-3-642-17309-7", URL = "http://www.springer.com/computer/theoretical+computer+science/book/978-3-642-17309-7", DOI = "doi:10.1007/978-3-642-17310-3_4", abstract = "Self-modifying Cartesian genetic programming (SMCGP) is a general purpose, graph-based, form of genetic programming founded on Cartesian genetic programming. In addition to the usual computational functions, it includes functions that can modify the program encoded in the genotype. SMCGP has high scalability in that evolved programs encoded in the genotype can be iterated to produce an infinite sequence of programs (phenotypes). It also allows programs to acquire more inputs and produce more outputs during iterations. Another attractive feature of SMCGP is that it facilitates the evolution of provably general solutions to various computational problems.", notes = "part of \cite{Miller:CGP}", } @InCollection{Harding:2011:CGP.ch8, author = "Simon L. Harding and Wolfgang Banzhaf", title = "Hardware Acceleration for CGP: Graphics Processing Units", booktitle = "Cartesian Genetic Programming", publisher = "Springer", editor = "Julian F. Miller", year = "2011", series = "Natural Computing Series", chapter = "8", pages = "231--253", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, GPU", isbn13 = "978-3-642-17309-7", URL = "http://www.springer.com/computer/theoretical+computer+science/book/978-3-642-17309-7", URL = "http://www.cs.mun.ca/~banzhaf/papers/GPUCGP_book2011.pdf", DOI = "doi:10.1007/978-3-642-17310-3_8", abstract = "As with other forms of genetic programming, evaluation of the fitness function in CGP is a major bottleneck. Recently there has been a lot of interest in exploiting the parallel processing capabilities of the Graphics Processing Units that are found on modern graphics cards. Using these processors it is possible to greatly accelerate evaluation of CGP individuals.", notes = "part of \cite{Miller:CGP}", } @InCollection{Harding:2012:GPTP, author = "Simon Harding and Juergen Leitner and Juergen Schmidhuber", title = "Cartesian Genetic Programming for Image Processing", booktitle = "Genetic Programming Theory and Practice X", year = "2012", series = "Genetic and Evolutionary Computation", editor = "Rick Riolo and Ekaterina Vladislavleva and Marylyn D. Ritchie and Jason H. Moore", publisher = "Springer", chapter = "3", pages = "31--44", address = "Ann Arbor, USA", month = "12-14 " # may, keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, Image processing, Object detection", isbn13 = "978-1-4614-6845-5", URL = "http://dx.doi.org/10.1007/978-1-4614-6846-2_3", DOI = "doi:10.1007/978-1-4614-6846-2_3", abstract = "Combining domain knowledge about both imaging processing and machine learning techniques can expand the abilities of Genetic Programming when used for image processing. We successfully demonstrate our new approach on several different problem domains. We show that the approach is fast, scalable and robust. In addition, by virtue of using off-the-shelf image processing libraries we can generate human readable programs that incorporate sophisticated domain knowledge.", notes = "part of \cite{Riolo:2012:GPTP} published after the workshop in 2013", } @InProceedings{Harding:2012:GECCO, author = "Simon Harding and Vincent Graziano and Juergen Leitner and Juergen Schmidhuber", title = "MT-CGP: mixed type cartesian genetic programming", booktitle = "GECCO '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference", year = "2012", editor = "Terry Soule and Anne Auger and Jason Moore and David Pelta and Christine Solnon and Mike Preuss and Alan Dorin and Yew-Soon Ong and Christian Blum and Dario Landa Silva and Frank Neumann and Tina Yu and Aniko Ekart and Will Browne and Tim Kovacs and Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and Giovanni Squillero and Nicolas Bredeche and Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and Martin Pelikan and Silja Meyer-Nienberg and Christian Igel and Greg Hornby and Rene Doursat and Steve Gustafson and Gustavo Olague and Shin Yoo and John Clark and Gabriela Ochoa and Gisele Pappa and Fernando Lobo and Daniel Tauritz and Jurgen Branke and Kalyanmoy Deb", isbn13 = "978-1-4503-1177-9", pages = "751--758", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330163.2330268", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "The majority of genetic programming implementations build expressions that only use a single data type. This is in contrast to human engineered programs that typically make use of multiple data types, as this provides the ability to express solutions in a more natural fashion. In this paper, we present a version of Cartesian Genetic Programming that handles multiple data types. We demonstrate that this allows evolution to quickly find competitive, compact, and human readable solutions on multiple classification tasks.", notes = "Also known as \cite{2330268} GECCO-2012 A joint meeting of the twenty first international conference on genetic algorithms (ICGA-2012) and the seventeenth annual genetic programming conference (GP-2012)", } @InCollection{Harding:2012:PABA, author = "Simon Harding and W. Banzhaf", title = "Optimizing Shape Design with Distributed Parallel Genetic Programming on GPUs", booktitle = "Parallel Architectures and Bioinspired Algorithms", publisher = "Springer", year = "2012", editor = "Francisco {Fernandez de Vega} and Jose Ignacio {Hidalgo Perez} and Juan Lanchares", volume = "415", series = "Studies in Computational Intelligence", chapter = "2", pages = "51--75", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, GPU", isbn13 = "978-3-642-28788-6", URL = "http://www.amazon.com/Architectures-Bioinspired-Algorithms-Computational-Intelligence/dp/3642287883", DOI = "doi:10.1007/978-3-642-28789-3_3", abstract = "Optimised shape design is used for such applications as wing design in aircraft, hull design in ships, and more generally rotor optimisation in turbomachinery such as that of aircraft, ships, and wind turbines. We present work on optimized shape design using a technique from the area of Genetic Programming, self-modifying Cartesian Genetic Programming (SMCGP), to evolve shapes with specific criteria, such as minimised drag or maximised lift. This technique is well suited for a distributed parallel system to increase efficiency. Fitness evaluation of the genetic programming technique is accomplished through a custom implementation of a fluid dynamics solver running on graphics processing units (GPUs). Solving fluid dynamics systems is a computationally expensive task and requires optimisation in order for the evolution to complete in a practical period of time. In this chapter, we shall describe both the SMCGP technique and the GPU fluid dynamics solver that together provide a robust and efficient shape design system.", affiliation = "IDSIA (Istituto Dalle Molle di Studi sull'Intelligenza Artificiale), Lugano, Switzerland", } @InCollection{Harding:2013:ecgpu, author = "Simon Harding and Julian F. Miller", title = "Cartesian Genetic Programming on the {GPU}", booktitle = "Massively Parallel Evolutionary Computation on {GPGPUs}", publisher = "Springer", year = "2013", editor = "Shigeyoshi Tsutsui and Pierre Collet", series = "Natural Computing Series", chapter = "12", pages = "249--266", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, GPU", isbn13 = "978-3-642-37958-1", URL = "https://144c912f-b777-4dc5-89cc-51238af78a13.filesusr.com/ugd/5ef763_135e2e412fdd45d7b8aed573b6a277e1.pdf", URL = "http://www.springer.com/computer/ai/book/978-3-642-37958-1", DOI = "doi:10.1007/978-3-642-37959-8_12", abstract = "Cartesian Genetic Programming is a form of Genetic Programming based on evolving graph structures. It has a fixed genotype length and a genotype phenotype mapping that introduces neutrality into the representation. It has been used for many applications and was one of the first Genetic Programming techniques to be implemented on the GPU. In this chapter, we describe the representation in detail and discuss various GPU implementations of it. Later in the chapter, we discuss a recent implementation based on the GPU.net framework.", } @InCollection{Harding:2017:miller, author = "Simon Harding and Jan Koutnik and Juergen Schmidhuber and Andrew Adamatzky", title = "Discovering {Boolean} Gates in Slime Mould", booktitle = "Inspired by Nature: Essays Presented to Julian F. Miller on the Occasion of his 60th Birthday", publisher = "Springer", year = "2017", editor = "Susan Stepney and Andrew Adamatzky", volume = "28", series = "Emergence, Complexity and Computation", chapter = "15", pages = "323--337", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-67996-9", DOI = "doi:10.1007/978-3-319-67997-6_15", abstract = "Slime mould of Physarum polycephalum is a large cell exhibiting rich spatial non-linear electrical characteristics. We exploit the electrical properties of the slime mould to implement logic gates using a flexible hardware platform designed for investigating the electrical properties of a substrate (Mecobo). We apply arbitrary electrical signals to `configure' the slime mould, i.e. change shape of its body and, measure the slime mould's electrical response. We show that it is possible to find configurations that allow the Physarum to act as any 2-input Boolean gate. The occurrence frequency of the gates discovered in the slime was analysed and compared to complexity hierarchies of logical gates obtained in other unconventional materials. The search for gates was performed by both sweeping across configurations in the real material as well as training a neural network-based model and searching the gates therein using gradient descent.", notes = "part of \cite{miller60book} https://link.springer.com/bookseries/10624", } @InProceedings{Hardison:2008:gecco, author = "Nicholas E. Hardison and Theresa J. Fanelli and Scott M. Dudek and David M. Reif and Marylyn D. Ritchie and Alison A. Motsinger-Reif", title = "A balanced accuracy fitness function leads to robust analysis using grammatical evolution neural networks in the case of class imbalance", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "353--354", address = "Atlanta, GA, USA", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, grammatical evolution, gene-gene interactions, ANN, neural networks, SNP, single nucleotide polymorphism, Bioinformatics, computational biology: Poster", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p353.pdf", DOI = "doi:10.1145/1389095.1389159", size = "2 pages", abstract = "Grammatical Evolution Neural Networks (GENN) is a computational method designed to detect gene-gene interactions in genetic epidemiology, but has so far only been evaluated in situations with balanced numbers of cases and controls. Real data, however, rarely has such perfectly balanced classes. In the current study, we test the power of GENN to detect interactions in data with a range of class imbalance using two fitness functions (classification error and balanced error), as well as data re-sampling. We show that when using classification error, class imbalance greatly decreases the power of GENN. Re-sampling methods demonstrated improved power, but using balanced accuracy resulted in the highest power. Based on the results of this study, balanced error has replaced classification error in the GENN algorithm.", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389159}", } @InProceedings{Hardison:2011:GECCO, author = "Nicholas E. Hardison and Alison A. Motsinger-Reif", title = "The power of quantitative grammatical evolution neural networks to detect gene-gene interactions", booktitle = "GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0557-0", pages = "299--306", keywords = "genetic algorithms, genetic programming, grammatical evolution, Bioinformatics, computational, systems, and synthetic biology", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001576.2001618", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Applying grammatical evolution to evolve neural networks (GENN) has been increasing used in genetic epidemiology to detect gene-gene or gene-environment interactions, also known as epistasis, in high dimensional data. GENN approaches have previously been shown to be highly successful in a range of simulated and real case-control studies, and has recently been applied to quantitative traits. In the current study, we evaluate the potential of an application of GENN to quantitative traits (QTGENN) to a range of simulated genetic models. We demonstrate the power of the approach, and compare this power to more traditional linear regression analysis approaches. We find that the QTGENN approach has relatively high power to detect both single-locus models as well as several completely epistatic two-locus models, and favourably compares to the regression methods.", notes = "Also known as \cite{2001618} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)", } @Article{Hardy:2002:IJMPc, author = "Yorick Hardy and W.-H. Steeb", title = "Gene Expression Programming and One-dimensional chaotic maps", journal = "International Journal of Modern Physics C", year = "2002", volume = "13", number = "1", pages = "25--30", month = jan, keywords = "genetic algorithms, genetic programming, Gene expression programming, chromosomes, replication, chaotic maps", DOI = "doi:10.1142/S0129183102002912", abstract = "Gene expression programming is applied to find one-dimensional maps. A survey on gene expression programming is also given.", notes = "Computational Physics and Physical Computation Quantum? International School for Scientific Computing, at the Rand Afrikaans University, Auckland Park 2006, South Africa World Scientific Publishing Company", } @Article{hardy05a, author = "Yorick Hardy and Willi-Hans Steeb and Ruedi Stoop", title = "Genetic Algorithms, Floating Point Numbers and Applications", journal = "International Journal of Modern Physics C", volume = "16", pages = "1811--1816", year = "2005", keywords = "genetic algorithms, genetic programming, crossing, mutation, floating point numbers", DOI = "doi:10.1142/S0129183105008321", URL = "http://dx.doi.org/10.1142/S0129183105008321", abstract = "The core in most genetic algorithms is the bitwise manipulations of bit strings. We show that one can directly manipulate the bits in floating point numbers. This means the main bitwise operations in genetic algorithm mutations and crossings are directly done inside the floating point number. Thus the interval under consideration does not need to be known in advance. For applications, we consider the roots of polynomials and finding solutions of linear equations.", } @Article{hardy10a, author = "Yorick Hardy and Willi-Hans Steeb", title = "Genetic Algorithms and Optimization Problems in Quantum Computing", journal = "International Journal of Modern Physics C", year = "2010", volume = "21", number = "11", pages = "1359--1375", month = nov, keywords = "genetic algorithms, genetic programming, genetic algorithms and entanglement, tangle, three-tangle, hyper-determinant, Bell-CHSH inequality", ISSN = "0129-1831", URL = "http://dx.doi.org/10.1142/S0129183110015890", DOI = "doi:10.1142/S0129183110015890", size = "17 pages", abstract = "We solve a number of problems in quantum computing by applying genetic algorithms. We use the bitset class of C++ to represent any data type for genetic algorithms. Thus we have a flexible way to solve any optimisation problem. The Bell-CHSH inequality and entanglement measures are studied using genetic algorithms. Entangled states form the backbone for teleportation. The C++ code is also provided.", } @InProceedings{Haridass:2010:SSST, author = "Sai sri Krishna Haridass and David H. K. Hoe", title = "Fault tolerant Block Based Neural Networks", booktitle = "42nd Southeastern Symposium on System Theory (SSST 2010)", year = "2010", month = "7-9 " # mar, pages = "357--361", address = "University of Texas at Tyler, USA", abstract = "Block Based Neural Networks (BBNNs) have shown to be a practical means for implementing evolvable hardware on reconfigurable fabrics for solving a variety of problems that take advantage of the massive parallelism offered by a neural network approach. This paper proposes a method for obtaining a fault tolerant implementation of BBNNs by using a biologically inspired layered design. At the lowest level, each block has its own online detection and correcting logic combined with sufficient spare components to ensure recovery from permanent and transient errors. Another layer of hierarchy combines the blocks into clusters, where a redundant column of blocks can be used to replace blocks that cannot be repaired at the lowest level. The hierarchical approach is well-suited to a divide-and-conquer approach to genetic programming whereby complex problems are subdivided into smaller parts. The overall approach can be implemented on a reconfigurable fabric.", keywords = "genetic algorithms, genetic programming, EHW, correcting logic, divide-and-conquer approach, evolvable hardware, fault tolerant block based neural networks, massive parallelism, online detection, reconfigurable fabrics, transient errors, fault tolerant computing, neural nets, reconfigurable architectures", DOI = "doi:10.1109/SSST.2010.5442804", ISSN = "0094-2898", notes = "Is this a GP? Also known as \cite{5442804}", } @InProceedings{harik:1999:A, author = "Georges R. Harik and Fernando G. Lobo", title = "A parameter-less genetic algorithm", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "258--265", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/parameter-less-ga.ps", URL = "ftp://ftp-illigal.ge.uiuc.edu/pub/papers/Publications/lobo/parameter-less-ga.ps.Z", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Harman:2007:ICPC, author = "Mark Harman", title = "Search Based Software Engineering for Program Comprehension", booktitle = "15th International Conference on Program Comprehension (ICPC 2007)", year = "2007", editor = "Kenny Wong", address = "Banff, Canada", month = "26-29 " # jun, publisher = "IEEE", note = "Invited paper", keywords = "genetic algorithms, genetic programming", URL = "http://www.dcs.kcl.ac.uk/staff/mark/icpc07.ps", notes = "http://www-user.cs.ualberta.ca/conferences/icpc2007/", } @InProceedings{harman:2010:Manifesto, author = "Mark Harman and Yue Jia and William B. Langdon", title = "A Manifesto for Higher Order Mutation Testing", booktitle = "Mutation 2010", year = "2010", editor = "Lydie {du Bousquet} and Jeremy Bradbury and Gordon Fraser", pages = "80--89", address = "Paris", month = "6 " # apr, publisher = "IEEE Computer Society", note = "Keynote", keywords = "genetic algorithms, genetic programming, SBSE", isbn13 = "978-0-7695-4050-4", URL = "http://www.dcs.kcl.ac.uk/pg/jiayue/publications/papers/HarmanJL10.pdf", DOI = "doi:10.1109/ICSTW.2010.13", size = "10 pages", abstract = "We argue that higher order mutants are potentially better able to simulate real faults and to reveal insights into bugs than the restricted class of first order mutants. the Mutation Testing community has previously shied away from Higher Order Mutation Testing believing it to be too expensive and therefore impractical. However, this paper argues that Search Based Software Engineering can provide a solution to this apparent problem, citing results from recent work on search based optimization techniques for constructing higher order mutants. We also present a research agenda for the development of Higher Order Mutation Testing.", notes = "http://www.st.cs.uni-saarland.de/mutation2010/ held in conjunction with the 3rd International Conference on Software Testing, Verfication, and Validation (ICST'10) (6-9 April 2010). Also known as \cite{HarmanJL10}", } @Article{Harman:2010:ACM, author = "Mark Harman", title = "Automated Patching Techniques: The Fix Is In", journal = "Communications of the ACM", volume = "53", number = "5", year = "2010", ISSN = "0001-0782", pages = "108", month = jun, publisher = "ACM", address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, SBSE", ISSN = "0001-0782", DOI = "doi:10.1145/1735223.1735248", size = "1 page", abstract = "Finding bugs is technically demanding and yet economically vital. How much more difficult yet valuable would it be to automatically fix bugs?", notes = "Technical Perspective. technical perspective. Intro to \cite{Weimer:2010:ACM} Also known as \cite{1735248}", } @Article{Harman:2011:ieeeC, author = "Mark Harman", journal = "Computer", title = "Software Engineering Meets Evolutionary Computation", year = "2011", month = oct, volume = "44", number = "10", pages = "31--39", note = "Cover feature", keywords = "genetic algorithms, genetic programming, SBSE, evolutionary computation, realistic algorithm, software design, software engineering", ISSN = "0018-9162", DOI = "doi:10.1109/MC.2011.263", size = "9 pages", abstract = "The concept of evolutionary computation has affected virtually every area of software design, not merely as a metaphor, but as a realistic algorithm for exploration, insight, and improvement.", notes = "also known as \cite{6036090}", } @InProceedings{Harman:2012:ASE, author = "Mark Harman and William B. Langdon and Yue Jia and David R. White and Andrea Arcuri and John A. Clark", title = "The {GISMOE} challenge: Constructing the {Pareto} Program Surface Using Genetic Programming to Find Better Programs", booktitle = "The 27th IEEE/ACM International Conference on Automated Software Engineering (ASE 12)", year = "2012", pages = "1--14", address = "Essen, Germany", publisher_address = "New York, NY, USA", month = sep # " 3-7", publisher = "ACM", note = "keynote paper", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, Software Engineering, Algorithms, Design, Experimentation, Human Factors, Languages, Measurement, Performance, Verification, SBSE, Search Based Optimisation, Compilation, Non-functional Properties, Pareto Surface", isbn13 = "978-1-4503-1204-2", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/gismo/Harman_2012_ASE.pdf", DOI = "doi:10.1145/2351676.2351678", acmid = "2351678", size = "14 pages", abstract = "Optimising programs for non-functional properties such as speed, size, throughput, power consumption and bandwidth can be demanding; pity the poor programmer who is asked to cater for them all at once! We set out an alternate vision for a new kind of software development environment inspired by recent results from Search Based Software Engineering (SBSE). Given an input program that satisfies the functional requirements, the proposed programming environment will automatically generate a set of candidate program implementations, all of which share functionality, but each of which differ in their non-functional trade offs. The software designer navigates this diverse Pareto surface of candidate implementations, gaining insight into the trade offs and selecting solutions for different platforms and environments, thereby stretching beyond the reach of current compiler technologies. Rather than having to focus on the details required to manage complex, inter-related and conflicting, non-functional tradeoffs, the designer is thus freed to explore, to understand, to control and to decide rather than to construct.", notes = "This position paper accompanies the keynote given by Mark Harman at the 27th IEEE/ACM International Conference on Automated Software Engineering (ASE 12) in Essen, Germany. It is joint work with Bill Langdon, Yue Jia, David White, Andrea Arcuri and John Clark, funded by the EPSRC grants SEBASE (EP/D050863, EP/D050618 and EP/D052785), GISMO (EP/I033688) and DAASE (EP/J017515/) and by EU project FITTEST (257574).", } @Misc{Harman:2013:STTT, notes = "see \cite{Harman:2014:seams}", } @InProceedings{Harman:2013:WCRE, author = "Mark Harman and William B. Langdon and Westley Weimer", title = "Genetic Programming for Reverse Engineering", booktitle = "20th Working Conference on Reverse Engineering (WCRE 2013)", year = "2013", editor = "Rocco Oliveto and Romain Robbes", pages = "1--10", address = "Koblenz, Germany", month = "14-17 " # oct, organisation = "University of Koblenz-Landau", publisher = "IEEE", note = "Invited Keynote", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, GP4RE, gismo", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/gismo/Harman_2013_WCRE.pdf", video_url = "http://www.youtube.com/watch?v=MbsuejqE_sk", DOI = "doi:10.1109/WCRE.2013.6671274", size = "10 pages", abstract = "This paper overviews the application of Search Based Software Engineering (SBSE) to reverse engineering with a particular emphasis on the growing importance of recent developments in genetic programming and genetic improvement for reverse engineering. This includes work on SBSE for re-modularisation, refactoring, regression testing, syntax-preserving slicing and dependence analysis, concept assignment and feature location, bug fixing, and code migration. We also explore the possibilities for new directions in research using GP and GI for partial evaluation, amorphous slicing, and product lines, with a particular focus on code transplantation.", notes = "Slides as youtube video. http://wcre.wikidot.com/2013:keynotes This paper accompanies the keynote given by Mark Harman at the 20th Working Conference on Reverse Engineering (WCRE 2013). Also known as \cite{6671274}", } @InProceedings{Harman:2014:seams, author = "Mark Harman and Yue Jia and William B. Langdon and Justyna Petke and Iman Hemati Moghadam and Shin Yoo and Fan Wu", title = "Genetic Improvement for Adaptive Software Engineering", booktitle = "9th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS'14)", year = "2014", editor = "Gregor Engels", pages = "1--4", address = "Hyderabad, India", month = "2-3 " # jun, publisher = "ACM", note = "Keynote", keywords = "genetic algorithms, genetic programming, SBSE, Artificial Intelligence, Machine Learning, Genetic Improvement, Search Based Software Engineering", isbn13 = "978-1-4503-2864-7", URL = "http://www.cs.ucl.ac.uk/staff/mharman/seams14.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/gismo/seams14main-id100-p-22852-aede8e5-20619-submitted.pdf", DOI = "doi:10.1145/2593929.2600116", acmid = "2600116", size = "4 pages", abstract = "This paper presents a brief outline of an approach to online genetic improvement. We argue that existing progress in genetic improvement can be exploited to support adaptivity. We illustrate our proposed approach with a dreaming smart device example that combines online and offline machine learning and optimisation.", notes = " http://seams2014.uni-paderborn.de/ Co-located with ICSE 2014 978-1-4503-2864-7/14/06", } @InProceedings{Harman:2014:Babel, author = "Mark Harman and Yue Jia and William B. Langdon", title = "Babel {Pidgin}: {SBSE} Can Grow and Graft Entirely New Functionality into a Real World System", booktitle = "Proceedings of the 6th International Symposium, on Search-Based Software Engineering, SSBSE 2014", year = "2014", editor = "Claire {Le Goues} and Shin Yoo", volume = "8636", series = "LNCS", pages = "247--252", address = "Fortaleza, Brazil", month = "26-29 " # aug, publisher = "Springer", note = "Winner SSBSE 2014 Challange Track", keywords = "genetic algorithms, genetic programming, Genetic Improvement, SBSE, GGGP, GIP, gismo", isbn13 = "978-3-319-09939-2", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/gismo/Harman_2014_Babel.pdf", URL = "http://www.springer.com/computer/swe/book/978-3-319-09939-2", DOI = "doi:10.1007/978-3-319-09940-8_20", size = "6 pages", abstract = "Adding new functionality to an existing, large, and perhaps poorly-understood system is a challenge, even for the most competent human programmer. We introduce a grow and graft approach to Genetic Improvement (GI) that transplants new functionality into an existing system. We report on the trade offs between varying degrees of human guidance to the GI transplantation process. Using our approach, we successfully grew and transplanted a new Babel Fish linguistic translation feature into the Pidgin instant messaging system, creating a genetically improved system we call Babel Pidgin. This is the first time that SBSE has been used to evolve and transplant entirely novel functionality into an existing system. Our results indicate that our grow and graft approach requires surprisingly little human guidance.", notes = " SSBSE Challenge Track, Marcio Barros (organiser). http://www.ssbse.info/2014", } @InProceedings{Harman:2014:SPLC, author = "Mark Harman and Yue Jia and Jens Krinke and W. B. Langdon and Justyna Petke and Yuanyuan Zhang", title = "Search based software engineering for software product line engineering: a survey and directions for future work", booktitle = "18th International Software Product Line, SPLC 2014", year = "2014", pages = "5--18", address = "Florence, Italy", month = sep # " 15-19", note = "Invited keynote", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, Software Engineering, SPL, Program Synthesis", isbn13 = "978-1-4503-2740-4", URL = "http://www0.cs.ucl.ac.uk/staff/m.harman/splc14.pdf", DOI = "doi:10.1145/2648511.2648513", size = "14 pages", abstract = "This paper presents a survey of work on Search Based Software Engineering (SBSE) for Software Product Lines (SPLs). We have attempted to be comprehensive, in the sense that we have sought to include all papers that apply computational search techniques to problems in software product line engineering. Having surveyed the recent explosion in SBSE for SPL research activity, we highlight some directions for future work. We focus on suggestions for the development of recent advances in genetic improvement, showing how these might be exploited by SPL researchers and practitioners: Genetic improvement may grow new products with new functional and non-functional features and graft these into SPLs. It may also merge and parametrise multiple branches to cope with SPL branchmania.", notes = "The paper was written to accompany the keynote at the 15th Software Product Line Conference (SPLC 2014), given by Mark Harman, but it reflects the joint work of all the authors. Also known as \cite{Harman:2014:SBS:2648511.2648513}", } @InProceedings{Harman:2015:ICST, author = "Mark Harman and Yue Jia and Yuanyuan Zhang", title = "Achievements, Open Problems and Challenges for Search Based Software Testing", booktitle = "8th IEEE International Conference on Software Testing, Verification and Validation, ICST 2015", year = "2015", editor = "Gordon Fraser and Darko Marinov", pages = "1--12", address = "Graz, Austria", month = apr # " 14-16", publisher = "IEEE", note = "Keynote", keywords = "genetic algorithms, genetic programming, SBSE, genetic Improvement, FiFiVerify", URL = "http://www0.cs.ucl.ac.uk/staff/m.harman/icst15.pdf", DOI = "doi:10.1109/ICST.2015.7102580", size = "12 pages", abstract = "Search Based Software Testing (SBST) formulates testing as an optimisation problem, which can be attacked using computational search techniques from the field of Search Based Software Engineering (SBSE). We present an analysis of the SBST research agenda, focusing on the open problems and challenges of testing non-functional properties, in particular a topic we call 'Search Based Energy Testing' (SBET), Multi-objective SBST and SBST for Test Strategy Identification. We conclude with a vision of FIFIVERIFY tools, which would automatically find faults, fix them and verify the fixes. We explain why we think such FIFIVERIFY tools constitute an exciting challenge for the SBSE community that already could be within its reach.", notes = "Slides http://icst2015.ist.tu-graz.ac.at/icst2015_harman_s.pdf 'Genetic Programming for SBSTI:' 'Fault localisation ... and ... genetic improvement' Also known as \cite{7102580}", } @InProceedings{Harman:2015:gi, author = "Mark Harman and Justyna Petke", title = "{GI4GI}: Improving Genetic Improvement Fitness Functions", booktitle = "Genetic Improvement 2015 Workshop", year = "2015", editor = "William B. Langdon and Justyna Petke and David R. White", pages = "793--794", address = "Madrid", publisher_address = "New York, NY, USA", month = "11-15 " # jul, organisation = "SIGEvo", publisher = "ACM", keywords = "genetic algorithms, genetic programming, Genetic Improvement", isbn13 = "978-1-4503-3488-4", URL = "http://gpbib.cs.ucl.ac.uk/gi2015/giforgi.pdf", URL = "http://doi.acm.org/10.1145/2739482.2768415", DOI = "doi:10.1145/2739482.2768415", size = "2 pages", abstract = "Genetic improvement (GI) has been successfully used to optimise non-functional properties of software, such as execution time, by automatically manipulating program's source code. Measurement of non-functional properties, however, is a non-trivial task; energy consumption, for instance, is highly dependant on the hardware used. Therefore, we propose the GI4GI framework (and two illustrative applications). GI4GI first applies GI to improve the fitness function for the particular environment within which software is subsequently optimised using traditional GI.", notes = "Slides: http://gpbib.cs.ucl.ac.uk/gi2015/gi_2015_slides/petke/gi4gi.pdf position paper", } @InProceedings{Harman:2015:DeMobile, author = "Afnan Al-Subaihin and Anthony Finkelstein and Mark Harman and Yue Jia and William Martin and Federica Sarro and Yuanyuan Zhang", title = "App Store Mining and Analysis", booktitle = "Third International Workshop on Software Development Lifecycle for Mobile, DeMobile 2015", year = "2015", editor = "Aharon Abadi and Shah Rukh Humayoun and Henry Muccini", address = "Bergamo, Italy", month = "31 " # aug, note = "Keynote", keywords = "genetic algorithms, genetic programming, genetic improvement", URL = "http://www.cs.ucl.ac.uk/staff/mharman/final-demobile15-keynote.pdf", size = "2 pages", abstract = "App stores are not merely disrupting traditional software deployment practice, but also offer considerable potential benefit to scientific research. Software engineering researchers have never had available, a more rich, wide and varied source of information about software products. There is some source code availability, supporting scientific investigation as it does with more traditional open source systems. However, what is important and different about app stores, is the other data available. Researchers can access user perceptions, expressed in rating and review data. Information is also available on app popularity (typically expressed as the number or rank of downloads). For more traditional applications, this data would simply be too commercially sensitive for public release. Pricing information is also partially available, though at the time of writing, this is sadly submerging beneath a more opaque layer of in-app purchasing. This talk will review research trends in the nascent field of App Store Analysis, presenting results from the UCL app Analysis Group (UCLappA) and others, and will give some directions for future work.", notes = "http://sysrun.haifa.il.ibm.com/hrl/demobile2015/ https://www.conference-publishing.com/list.php?Event=FSEWS15DEMOBILE&Full=abs", } @InProceedings{Harman:2022:APR, author = "Mark Harman", title = "Scaling Genetic Improvement and Automated Program Repair", booktitle = "International Workshop on Automated Program Repair (APR'22)", year = "2022", editor = "Maria Kechagia and Shin Hwei Tan and Sergey Mechtaev and Lin Tan", address = "Internet", month = "19 " # may, publisher = "ACM", note = "Invited keynote", keywords = "genetic algorithms, genetic programming, genetic improvement, GI, Automated Program Repair, APR, Search Based Software Engineering, SBSE", isbn13 = "978-1-4503-9285-3/22/05", URL = "https://research.facebook.com/publications/scaling-genetic-improvement-and-automated-program-repair/", URL = "https://research.facebook.com/file/987372605236188/Scaling-Genetic-Improvement-and-Automated-Program-Repair.pdf", DOI = "doi:10.1145/3524459.3527353", video_url = "https://youtu.be/uAU_ySdEvks", size = "7 pages", abstract = "techniques and research directions for scaling genetic improvement and automated program repair, highlighting possible directions for future work and open challenges", notes = "Video Intro/Chair: Lin Tan, Purdue University, USA Scalability. Build effort repair, 20 minutes too long. New feature build cost problem. Meta = Facebook, 2900000000 users. 100000 commits per week. Cloud infrastructure for both development and emulator testing. Search based testing (SBST) at scale. Sapienz (testing) SSBSE 2018 https://engineering.fb.com/2018/05/02/developer-tools/sapienz-intelligent-automated-software-testing-at-scale/ Repair. Yue Jia, Ke Mao, Nadia Alshahwan. Alexandru Marginean SapFix \cite{Marginean:2019:ICSE} 12 week PhD \cite{Marginean_10137954_thesis_redacted} internship at Facebook. Immediate impact (12 weeks v 12-15 years (uni). Impact on 3 billion users. Sapienz and Infer Peter O'Hearn. Fixing null pointer exceptions. Developers modifying some automatically generated Sap-Fix bug fixes. Developer cannot deal with 100000000 lines of code, automated systems can. New feature build cost. Interaction between new features: feature interaction problem. Automated mock => genetic improvement solution. Automated acceptance testing. Digital twin, dove-dove cyber-cyber digital twin, hierarchy, precision v speed tradeoff. WES \cite{Ahlgren:2020:GI} detection of problem versus making it harder to cause problem. Software technology is different from physical systems. Bot emulate real users in Facebook system. Restrictions on using social graph like road traffic speed humps. Avoids rebuilding system between tests of new parameters. Dynamic co-adaptation coevolution between system bad software robots and good bots, both emulating users. GI 43 minutes: then Q+A Q: Westley Weimer 48 Q: Sergey Mechtaev 52: Q Lin Tan deployment in industry? A: No fundamental block to use in industry. Need better system level test. Unit tests often give good coverage. 56: Q Lin Tan progress. A: Fault localisation (sometimes easy), developers want to take ownership of (automatically generated) code change. 999900a001.pdf http://program-repair.org/workshop-2022/ APR@ICSE Part of \cite{Kechagia:2022:APR}", } @InProceedings{Harman:2025:FSE, author = "Mark Harman and Peter O'Hearn and Shubho Sengupta", title = "Harden and Catch for Just-in-Time Assured {LLM}-Based Software Testing: Open Research Challenges", booktitle = "Foundations of Software Engineering, FSE 2025", year = "2025", editor = "Jingyue Li", address = "Trondheim", month = "23-27 " # jun, publisher = "ACM", note = "Invited Keynote", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, SBST, SBFT, Software Engineering, Programming Languages, AI, Large Language Models, LLMs, ANN, JIT, Meta, Facebook, LLMSE, ACH, TestGenLLM", URL = "https://arxiv.org/pdf/2504.16472", URL = "https://conf.researchr.org/info/fse-2025/keynotes", size = "17 pages", abstract = "Despite decades of research and practice in automated software testing, several fundamental concepts remain ill-defined and under-explored, yet offer enormous potential real-world impact. We show that these concepts raise exciting new challenges in the context of Large Language Models for software test generation. More specifically, we formally define and investigate the properties of hardening and catching tests. A hardening test is one that seeks to protect against future regressions, while a catching test is one that catches such a regression or a fault in new functionality introduced by a code change. Hardening tests can be generated at any time and may become catching tests when a future regression is caught. We also define and motivate the Catching Just-in-Time (JiTTest) Challenge, in which tests are generated just-in-time to catch new faults before they land into production. We show that any solution to Catching JiTTest generation can also be repurposed to catch latent faults in legacy code. We enumerate possible outcomes for hardening and catching tests and JiTTests, and discuss open research problems, deployment options, and initial results from our work on automated LLM-based hardening at Meta.", notes = "real time jittest <= 8 hours. Fig 1: Hardening and catching Venn Diagram. Automatically generated test cases/asserts may fail in current build because they are designed for the new functionality, and so only pass in the revised build. sec 6.1 'Plenty of Computation Time' sec 6.3 [test]'oracle inference problem' sec 6.4 'humans drizzle oracle information into software' sec 6.4 [when creating test cases]'we have to treat executable text (the executable source code) as potentially suspicious, and focus on extraction of intended semantics from non-executable text' (eg comments) Talk UCL 15 May 2025 https://conf.researchr.org/home/fse-2025 Product Compliance and Privacy team, Meta Platforms London, UK", } @InCollection{harmeling:2000:SSPGA, author = "Stefan Harmeling", title = "Solving Satisfiability Problems with Genetic Algorithms", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "206--213", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms", notes = "part of \cite{koza:2000:gagp}", } @InProceedings{Harmon:ACo:gecco2004, author = "Scott Harmon and Edwin Rodriguez and Christopher Zhong and William Hsu", title = "A Comparison of Hybrid Incremental Reuse Strategies for Reinforcement Learning in Genetic Programming", booktitle = "Genetic and Evolutionary Computation -- GECCO-2004, Part II", year = "2004", editor = "Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell", series = "Lecture Notes in Computer Science", pages = "706--707", address = "Seattle, WA, USA", publisher_address = "Heidelberg", month = "26-30 " # jun, organisation = "ISGEC", publisher = "Springer-Verlag", volume = "3103", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-22343-6", ISSN = "0302-9743", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.1038.994", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1038.994", URL = "http://dynamics.org/Altenberg/UH_ICS/EC_REFS/GP_REFS/GECCO/2004/31030706.pdf", DOI = "doi:10.1007/b98645", DOI = "doi:10.1007/978-3-540-24855-2_79", size = "2", abstract = "Easy missions is an approach to machine learning that seeks to synthesize solutions for complex tasks from those for simpler ones. ISLES (Incrementally Staged Learning from Easier Subtasks) [1] is a genetic programming (GP) technique that achieves this by using identified goals and fitness functions for subproblems of the overall problem. Solutions evolved for these subproblems are then reused to speed up learning, either as automatically defined functions (ADF) or by seeding a new GP population. Previous positive results using both approaches for learning in multi-agent systems (MAS) showed that incremental reuse using easy missions achieves comparable or better overall fitness than single-layered GP. A key unresolved issue dealt with hybrid reuse using ADF with easy missions. Results in the keep-away soccer (KAS) [2] domain (a test bed for MAS learning) were also inconclusive on whether compactness-inducing reuse helped or hurt overall agent performance. In this paper, we compare reuse using single-layered (with and without ADF) GP and easy missions GPs to two new types of GP learning systems with incremental reuse.", notes = "GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)", } @InProceedings{harn:2023:GECCOcomp, author = "Po-Wei Harn and Bo Hui and Sai Deepthi Yeddula and Libo Sun and Min-Te Sun and Wei-Shinn Ku", title = "A Novel {Quadtree-Based} Genetic Programming Search for Searchable Encryption Optimization", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "583--586", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, searchable encryption, region quadtree: Poster", isbn13 = "9798400701191", DOI = "doi:10.1145/3583133.3590566", size = "4 pages", abstract = "The encoding method of a searchable encryption can significantly impact the performance of a location-based alert system. While there were attempts to design searchable encryption manually, Gray Encoding is considered the most preferable method. However, if the alert zones are scattered unevenly, Gray Encoding fails to achieve token aggregation. In this research, a novel Quadtree-based Genetic Programming (Quadtree-GP) is proposed to iteratively identify superior searchable encryption candidates for the location-based alert system. Quadtree-GP can be effectively applied on customized requirements and different grid maps. Extensive experimental results show that Quadtree-GP is able to find searchable encryption candidates that outperform GP search, random search, and the baseline Gray Encoding in terms of user response time, token remaining percentage, and execution time.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @InProceedings{harper:2024:CEC, author = "Matthew Harper and Ivy Liu and Bing Xue and Ross Vennell and Mengjie Zhang", title = "Evaluating Machine Learning Techniques for Predicting Salinity", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Training, Accuracy, Statistical analysis, Salinity (geophysical), System performance, Machine learning, Estuaries", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10612099", abstract = "Oyster farms provide a sustainable and profitable export for New Zealand. Oyster farms are sensitive to changes in salinity that can cause significant crop loss if they persist too long. Recent extreme weather events have been leading to increased periods of low salinity, putting the farms at risk. Machine learning based methods provide a way to predict these low salinity events and provide an early warning system, but this has not been investigated in aquaculture. In this paper, we investigate three different methods to assess the viability of salinity prediction systems. A simple statistical model, a genetic programming (GP) based symbolic regression model and a convolutional neural network (CNN) were compared as ways of solving this problem. The results show that GP based symbolic regression and CNNs are fairly good approaches to predicting salinity. However, as weather events get more extreme, the CNN approach tends to hold up better and can be generalised better, while the G P based symbolic regression models show better potential explainability with the tree based model structure. These results show promise and provide a good stepping off point at creating a generalised approach to predicting salinity in estuaries.", notes = "also known as \cite{10612099} WCCI 2024", } @InProceedings{harper:2005:CEC, author = "Robin Harper and Alan Blair", title = "A Structure Preserving Crossover In Grammatical Evolution", booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary Computation", year = "2005", editor = "David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L. Gwenn Volkert and Dan Ashlock and Marc Schoenauer", volume = "3", pages = "2537--2544", address = "Edinburgh, UK", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "2-5 " # sep, organisation = "IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", ISBN = "0-7803-9363-5", DOI = "doi:10.1109/CEC.2005.1555012", abstract = "Grammatical Evolution is an algorithm for evolving complete programs in an arbitrary language. By using a Backus Naur Form grammar the advantages of typing are achieved. A separation of genotype and phenotype allows the implementation of operators that manipulate (for instance by crossover and mutation) the genotype (in Grammatical Evolution - a sequence of bits) irrespective of the genotype to phenotype mapping (in Grammatical Evolution - an arbitrary grammar). This paper introduces a new type of crossover operator for Grammatical Evolution. The crossover operator uses information automatically extracted from the grammar to minimise any destructive impact from the crossover. The information, which is extracted at the same time as the genome is initially decoded, allows the swapping between entities of complete expansions of non-terminals in the grammar without disrupting useful blocks of code on either side of the two point crossover. In the domains tested, results confirm that the crossover is (i) more productive than hill-climbing; (ii) enables populations to continue to evolve over considerable numbers of generations without intron bloat; and (iii) allows populations (in the domains tested) to reach higher fitness levels, quicker.", notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS. LHS Replacement operator. Minesweeper, Taxi problems. Santa Fe ant too easy for GE. 2500 generations.", } @InProceedings{Harper:2006:CECx, author = "Robin Harper and Alan Blair", title = "A Self-Selecting Crossover Operator", booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary Computation", year = "2006", editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas", pages = "5569--5576", address = "Vancouver", month = "16-21 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", ISBN = "0-7803-9487-9", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.412.2946", URL = "http://www.cse.unsw.edu.au/~blair/pubs/2006HarperBlairSSCO.pdf", DOI = "doi:10.1109/CEC.2006.1688475", size = "8 pages", abstract = "This paper compares the efficacy of different crossover operators for Grammatical Evolution across a typical numeric regression problem and a typical data classification problem. Grammatical Evolution is an extension of Genetic Programming, in that it is an algorithm for evolving complete programs in an arbitrary language. Each of the two main crossover operators struggles (for different reasons) to achieve 100percent correct solutions. A mechanism is proposed, allowing the evolutionary algorithm to self-select the type of crossover used and this is shown to improve the rate of generating 100percent successful solutions.", notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D", } @InProceedings{Harper_2006_CEC, author = "Robin Harper and Alan Blair", title = "Dynamically Defined Functions In Grammatical Evolution", booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary Computation", year = "2006", pages = "9188--9195", address = "Vancouver", month = "6-21 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, grammatical evolution, grammars, search problems, Backus Naur form grammar, arbitrary language, genotype, phenotype", ISBN = "0-7803-9487-9", DOI = "doi:10.1109/CEC.2006.1688638", size = "8 pages", abstract = "Grammatical Evolution is an extension of Genetic Programming, in that it is an algorithm for evolving complete programs in an arbitrary language. a Backus Naur Form grammar the advantages of typing are achieved as well as a separation of genotype and phenotype. introduces a meta-grammar into Grammatical Evolution allowing the grammar to dynamically define functions, self adaptively at the individual level without the need for special purpose operators or constraints. The user need not determine the architecture of the dynamically defined functions. As the search proceeds through genotype/phenotype space the number and use of the functions can vary. The ability of the grammar to dynamically define such functions allows regularities in the problem space to be exploited even where such regularities were not apparent when the problem was set up.", notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. minesweeper Also known as \cite{1688638}. IEEE Xplore gives pages 2638--2645.", } @PhdThesis{Harper:thesis, author = "Robin Thomas Ross Harper", title = "Enhancing Grammatical Evolution", school = "School of Computer Science and Engineering, The University of New South Wales", year = "2009", address = "Sydney 2052, Australia", keywords = "genetic algorithms, genetic programming, grammatical evolution, Dynamically Defined Functions, DDF, SCALP", URL = "http://handle.unsw.edu.au/1959.4/44843", URL = "http://unsworks.unsw.edu.au/fapi/datastream/unsworks:8140/SOURCE1.pdf", DOI = "doi:10.26190/unsworks/23007", size = "218 pages", abstract = "Grammatical Evolution (GE) is a method of using a general purpose evolutionary algorithm to evolve programs written in an arbitrary BNF grammar. This thesis extends GE as follows: GE as an extension of Genetic Programming (GP) A novel method of automatically extracting information from the grammar is introduced. This additional information allows the use of GP style crossover which in turn allows GE to perform identically to a strongly typed GP system as well as a non-typed (or canonical) GP system. Two test problems are presented one which is more easily solved by the GP style crossover and one which favours the tradition GE Ripple Crossover. With this new crossover operator GE can now emulate GP (as well as retaining its own unique features) and can therefore now be seen as an extension of GP. Dynamically Defined Functions An extension to the BNF grammar is presented which allows the use of dynamically defined functions (DDFs). DDFs provide an alternative to the traditional approach of Automatically Defined Functions (ADFs) but have the advantage that the number of functions and their parameters do not need to be specified by the user in advance. In addition DDFs allow the architecture of individuals to change dynamically throughout the course of the run without requiring the introduction of any new form of operator. Experimental results are presented confirming the effectiveness of DDFs. Self-Selecting (or Variable) Crossover. A self-selecting operator is introduced which allows the system to determine, during the course of the run, which crossover operator to apply; this is tested over several problem domains and (especially where small populations are used) is shown to be effective in aiding the system to overcome local optima. Spatial Co-Evolution in Age Layered Planes (SCALP) A method of combining Hornby's ALPS metaheuristic and the spatial co-evolution system introduced by Mitchell is presented; the new SCALP system is tested over three problem domains of increasing difficulty and performs extremely well in each of them.", notes = "Supervisor: Alan Blair", } @InProceedings{Harper:2010:cec, author = "Robin Harper", title = "Genetic Programming -To much P and not enough G?", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-6910-9", abstract = "This paper re-visits the minesweeper problem, one of the problems used by Koza in his 1994 book, Genetic Programming II, Advances in Genetic Programming. The minesweeper problem was one of the many problems used to demonstrate how the Automatically Defined Function methodology could solve problems not able to be solved (in this case) with a no function GP. By taking advantage of advances in computing power it has become easier to allow the problem to run for many more generations. If this is done it is seen that the no function version easily outperforms the ADF alternative. A variation to the problem, which might require a more general-purpose minesweeper to be evolved (rather than one which can learn two maps) is examined and it appears that the ADF methodology solves this alternative problem more readily than the no function version.", DOI = "doi:10.1109/CEC.2010.5586050", notes = "WCCI 2010. Also known as \cite{5586050}", } @InProceedings{Harper:2010:cec2, author = "Robin Harper", title = "Spatial co-evolution in Age Layered Planes (SCALP)", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-6910-9", abstract = "This paper introduces a method of combining Greg Hornby's Age Layered Protocol System with a form of spatial co-evolution. The combined system (SCALP) is compared to these two systems and a canonical GP tournament selection scheme over three well understood domains, the sextic regression problem, a two variable regression problem and a variation on the classic minesweeper problem. In each case SCALP avoided premature convergence; solving every run of these particular problems.", DOI = "doi:10.1109/CEC.2010.5586342", notes = "WCCI 2010. Also known as \cite{5586342}", } @InProceedings{Harper:2010:cec3, author = "Robin Harper", title = "GE, explosive grammars and the lasting legacy of bad initialisation", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", isbn13 = "978-1-4244-6910-9", abstract = "This paper explores some of the initialisation schemes that can be used to create the starting population of a Grammatical Evolution (GE) run. It investigates why two typical initialisation schemes (random bit and ramped half and half) produce very different, but in each case skewed, tree types. A third methodology, Sean Luke's Probabilistic Tree-Creation version 2 (PTC2), is also examined and is shown to produce a wider variety of trees. Two experiments on different problem sets are carried out and it is shown that for each of these test cases, where the ``wrong'' initialisation method is used, the chance of achieving a successful run is decreased even if the runs are continued long enough for the populations to stagnate. This would seem to suggest that the system does not typically recover from a ``bad'' start.", DOI = "doi:10.1109/CEC.2010.5586336", notes = "WCCI 2010. Also known as \cite{5586336}", } @InProceedings{Harper:2011:GECCO, author = "Robin Harper", title = "Co-evolving robocode tanks", booktitle = "GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0557-0", pages = "1443--1450", keywords = "genetic algorithms, genetic programming, grammatical evolution", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001576.2001770", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Robocode is a Java based programming platform where robot tanks, controlled by programs written in Java, compete. In this paper Grammatical Evolution is used to evolve Java programs to control a Robocode robot. This paper demonstrates how Grammatical Evolution together with spatial co-evolution in age layered planes (SCALP) can harness co-evolution to evolve relatively complex behaviour, including robots capable of beating Robocode's sample robots as well as some more complex human coded robots. The results of the co-evolution are similar to the results obtained by direct evolution against a range of human coded robots. This indicates that co-evolution alone is able to evolve robots of a similar standard to those evolved against graded human coded robots.", notes = "Also known as \cite{2001770} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)", } @InProceedings{Harper:2011:GECCOcomp, author = "Robin Harper", title = "Dynamic L-systems in GE", booktitle = "GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0690-4", keywords = "genetic algorithms, genetic programming, grammatical evolution, Generative and developmental systems: Poster", pages = "209--210", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001858.2001975", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "In this paper, I describe how to use Grammatical Evolution to implement a parametrised Lindenmayer System (L-System), where the number of production rules of the L-System is determined by the genome of the individual, rather than being determined by the user before hand. This leaves the number of production rules as a free parameter and allows the underlying topology of the system to be optimised by the evolutionary algorithm.", notes = "Also known as \cite{2001975} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @InProceedings{Harper:2012:GECCO, author = "Robin Harper", title = "Spatial co-evolution: quicker, fitter and less bloated", booktitle = "GECCO '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference", year = "2012", editor = "Terry Soule and Anne Auger and Jason Moore and David Pelta and Christine Solnon and Mike Preuss and Alan Dorin and Yew-Soon Ong and Christian Blum and Dario Landa Silva and Frank Neumann and Tina Yu and Aniko Ekart and Will Browne and Tim Kovacs and Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and Giovanni Squillero and Nicolas Bredeche and Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and Martin Pelikan and Silja Meyer-Nienberg and Christian Igel and Greg Hornby and Rene Doursat and Steve Gustafson and Gustavo Olague and Shin Yoo and John Clark and Gabriela Ochoa and Gisele Pappa and Fernando Lobo and Daniel Tauritz and Jurgen Branke and Kalyanmoy Deb", isbn13 = "978-1-4503-1177-9", pages = "759--766", keywords = "genetic algorithms, genetic programming", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330163.2330269", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Operator equalisation is a methodology inspired by the cross-over bias theory that attempts to limit bloat in genetic programming (GP). This paper examines a bivariate regression problem and demonstrates that operator equalisation suffers from bloat like behaviour when attempting to solve this problem. This is in contrast to a spatial co-evolutionary mechanism (SCALP) that appears to avoid bloat, without any need for express bloat control mechanisms. A previously analysed real world problem (human oral bioavailability prediction) is examined. The behaviour of SCALP on this problem is quite different from that of standard GP and operator equalisation leading to short, general candidate solutions.", notes = "Also known as \cite{2330269} GECCO-2012 A joint meeting of the twenty first international conference on genetic algorithms (ICGA-2012) and the seventeenth annual genetic programming conference (GP-2012)", } @Article{Harper:2014:GPEM, author = "Robin Harper", title = "Evolving Robocode tanks for Evo Robocode", journal = "Genetic Programming and Evolvable Machines", year = "2014", volume = "15", number = "4", pages = "403--431", month = dec, note = "Special issue on GECCO competitions", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Robocode, Co-evolution, SCALP", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-014-9224-2", size = "29 pages", abstract = "Evo Robocode is a competition where the challenge is to use evolutionary techniques to create a Java based controller for a simulated robot tank. The tank competes in a closed arena against other such tanks. The Robocode game is a programming platform that allows such tanks to compete. This article discusses the use of Grammatical Evolution (a form of genetic programming) together with spatial co-evolution. This system harnessed co-evolution to evolve relatively complex behaviours, within the program size constraints of the competition. The entry for the 2013 Evo Robocode competition was not evolved against any human coded robots and yet was able to compete effectively against many previously unseen opponents. The co-evolutionary system was then compared to a system that used a handcrafted fitness gradient consisting of pre-selected human coded robots. The top robots from the co-evolved system performed as well as those evolved using a hand crafted fitness function, scoring well against such robots in head to head battles.", } @Article{Harper:2021:GPEM, author = "Robin Harper", title = "Introducing Design Automation for Quantum Computing, Alwin Zulehner and Robert Wille", journal = "Genetic Programming and Evolvable Machines", year = "2021", volume = "22", number = "3", pages = "387--389", month = sep, note = "Book review", keywords = "genetic algorithms, genetic programming, Quantum Computing", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-021-09407-7", size = "3 pages", notes = "ISBN 978-3-030-41753-6, 2020, Springer International Publishing. 222 Pages, 51 b/w illustrations, 14 illustrations in colour Sydney Quantum Academy, School of Physics, University of Sydney, Sydney, NSW, Australia", } @InProceedings{harrak:2006:IIPWM, author = "Hanane Harrak and Thai Duy Hien and Yasunori Nagata and Zensho Nakao", title = "{DCT} Watermarking Optimization by Genetic Programming", booktitle = "Intelligent Information Processing and Web Mining", year = "2006", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/3-540-33521-8_35", DOI = "doi:10.1007/3-540-33521-8_35", } @Article{Harrand:GPEM, author = "Nicolas Harrand and Simon Allier and Marcelino Rodriguez-Cancio and Martin Monperrus and Benoit Baudry", title = "A Journey Among {Java} Neutral Program Variants", journal = "Genetic Programming and Evolvable Machines", year = "2019", volume = "20", number = "4", pages = "531--580", month = dec, keywords = "genetic algorithms, genetic programming, genetic improvement", ISSN = "1389-2576", URL = "https://arxiv.org/abs/1901.02533", DOI = "doi:10.1007/s10710-019-09355-3", size = "50 pages", abstract = "Neutral program variants are functionally similar to an original program, yet implement slightly different behaviors. Techniques such as approximate computing or genetic improvement share the intuition that potential for enhancements lies in these acceptable behavioral differences (e.g., enhanced performance or reliability). Yet, the automatic synthesis of neutral program variants, through speculative transformations remains a key challenge. This work aims at characterizing plastic code regions in Java programs, i.e., the areas that are prone to the synthesis of neutral program variants. Our empirical study relies on automatic variations of 6 real-world Java programs. First, we transform these programs with three state-of-the-art speculative transformations: add, replace and delete statements. We get a pool of 23445 neutral variants, from which we gather the following novel insights: developers naturally write code that supports fine-grain behavioral changes; statement deletion is a surprisingly effective speculative transformation; high-level design decisions, such as the choice of a data structure, are natural points that can evolve while keeping functionality. Second, we design 3 novel speculative transformations, targeted at specific plastic regions. New experiments reveal that respectively 60percent, 58percent and 73percent of the synthesized variants (175688 in total) are neutral and exhibit execution traces that are different from the original.", notes = "3 Java source code mutations: add method invocation, swap sub type and loop flip (reverse order of loop). Presented at the GI Dagstuhl seminar http://www.dagstuhl.de/en/program/calendar/semhp/?semnr=18052 Also available via http://arxiv.org/abs/1901.02533 (v2) https://github.com/castor-software/journey-paper-replication Also known as \cite{DBLP:journals/corr/abs-1901-02533} ", } @InProceedings{harrell:1999:EAPFIWMDP, author = "Laura J. Harrell and S. Ranji Ranjithan", title = "Evaluation of Alternative Penalty Function Implementations in a Watershed Management Design Problem", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1551--1558", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "real world applications", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-736.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-736.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Harries:1997:eaossGP, author = "Kim Harries and Peter Smith", title = "Exploring Alternative Operators and Search Strategies in Genetic Programming", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming", pages = "147--155", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/harries.gp97_paper.ps.gz", notes = "GP-97 even-4-parity, even-5-parity, artificial ant (Santa Fe trail), regression of x^4-3x^3+9x^2-27x Depth-based crossover (depth fair, SameDepths and DiffDepths) NoBias and combinations of crossovers. SameDepths does badly on even-5-parity otherwise crossovers similar to each other. 58,100 runs Several different types of mutation (and combination of mutation) used as stochastic {"}hill climbers{"}. Steady state, tournament size=2, limit of 1000 nodes, kinnear's Hoist and mutation both at 1 percent. GP mutation generally good hill climber, small and self-crossover generally awful. See sec 4 discussion. )", } @Misc{harries:1998:cgediass, author = "K. Harries and P. W. H. Smith", title = "Code Growth, Explicitly Defined Introns and Alternative Selection Schemes", howpublished = "www", year = "1998", note = "Earlier version of Evolutionary Computation 6 (4), 336-360, 1998", keywords = "genetic algorithms, genetic programming, Introns, Bloat, Parsimony", URL = "http://www.soi.city.ac.uk/homes/peters/pub/Introns6.ps", URL = "http://citeseer.ist.psu.edu/harries98code.html", size = "26 pages", abstract = "Previous work on introns and code growth in genetic programming is expanded on and tested experimentally. Explicitly Defined Introns are introduced to tree-based representations as an aid to measuring and evaluating intron behaviour, and it is shown that though introns do create code growth they are not the only cause of it and removing them merely decreases the growth rate, not eliminates it. By systematically negating various forms of intron behaviour a deeper understanding of the causes of code growth is obtained, leading to the development of a system that keeps unnecessary bloat to a minimum. Alternative selection schemes and recombination operators are examined and improvements demonstrated over the standard methods in terms of both performance and parsimony.", notes = "Final version is \cite{PWHSmith:1998:cgediass}", } @Article{harrigan:2004:TXL, author = "George G. Harrigan and Roxanne H. LaPlante and Greg N. Cosma and Gary Cockerell and Royston Goodacre and Jane F. Maddox and James P. Luyendyk and Patricia E. Ganey and Robert A. Roth", title = "Application of high-throughput Fourier-transform infrared spectroscopy in toxicology studies: contribution to a study on the development of an animal model for idiosyncratic toxicity", journal = "Toxicology Letters", year = "2004", volume = "146", number = "3", pages = "197--205", month = "2 " # feb, keywords = "genetic algorithms, genetic programming, Bacterial lipopolysaccharide, High-throughput infrared spectroscopy, Idiosyncratic toxicity, Metabonomics", DOI = "doi:10.1016/j.toxlet.2003.09.011", abstract = "An evaluation of high-throughput Fourier-transform infrared spectroscopy (FT-IR) as a technology that could support a {"}metabonomics{"} component in toxicological studies of drug candidates is presented. The hypothesis tested in this study was that FT-IR had sufficient resolving power to discriminate between urine collected from control rat populations and rats subjected to treatment with a potent inflammatory agent, bacterial lipopolysaccharide (LPS). It was also hypothesized that co-administration of LPS with ranitidine, a drug associated with reports of idiosyncratic susceptibility, would induce hepatotoxicity in rats and that this could be detected non-invasively by an FT-IR-based metabonomics approach. The co-administration of LPS with {"}idiosyncratic{"} drugs represents an attempt to develop a predictive model of idiosyncratic toxicity and FT-IR is used herein to support characterization of this model. FT-IR spectra are high dimensional and the use of genetic programming to identify spectral sub-regions that most contribute to discrimination is demonstrated. FT-IR is rapid, reagentless, highly reproducible and inexpensive. Results from this pilot study indicate it could be extended to routine applications in toxicology and to supporting characterization of a new animal model for idiosyncratic susceptibility.", notes = "Pharmacia Corporation, GMax-Bio", } @InProceedings{1274094, author = "Kyle Ira Harrington", title = "Predicting reactions from amino acid sequences in S. cerevisiae: an evolutionary computation approach", booktitle = "Genetic and Evolutionary Computation Conference {(GECCO2007)} workshop program", year = "2007", month = "7-11 " # jul, editor = "Tina Yu", isbn13 = "978-1-59593-698-1", pages = "2725--2728", address = "London, United Kingdom", keywords = "genetic algorithms, genetic programming, GP^2, push, PushGP", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p2725.pdf", DOI = "doi:10.1145/1274000.1274094", publisher = "ACM Press", publisher_address = "New York, NY, USA", abstract = "Evolutionary computation has been used many times for protein function prediction. In this paper a new approach is taken by constraining the problem to predicting the products of enzyme catalysis. Genetic programming with the Push programming language is used to evolve predictors within multiple search spaces. Predictors are evolved within multiple search spaces to reduce the complexity of solutions and represent sequence analysis, protein domain recognition, protein folding, and informatic approaches.", notes = "Distributed on CD-ROM at GECCO-2007 ACM Order No. 910071", } @InProceedings{Harrington:2012:GECCOcomp, author = "Kyle I. Harrington and Lee Spector and Jordan B. Pollack and Una-May O'Reilly", title = "Autoconstructive evolution for structural problems", booktitle = "GECCO 2012 2nd Workshop on Evolutionary Computation for the Automated Design of Algorithms", year = "2012", editor = "Gisele L. Pappa and John Woodward and Matthew R. Hyde and Jerry Swan", isbn13 = "978-1-4503-1178-6", keywords = "genetic algorithms, genetic programming", pages = "75--82", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330784.2330797", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "While most hyper-heuristics search for a heuristic that is later used to solve classes of problems, autoconstructive evolution represents an alternative which simultaneously searches both heuristic and solution space. In this study we contrast autoconstructive evolution, in which intergenerational variation is accomplished by the evolving programs themselves, with a genetic programming system, PushGP, to understand the dynamics of this hybrid approach. A problem size scaling analysis of these genetic programming techniques is performed on structural problems. These problems involve fewer domain-specific features than most model problems while maintaining core features representative of program search. We use two such problems, Order and Majority, to study autoconstructive evolution in the Push programming language.", notes = "Also known as \cite{2330797} Distributed at GECCO-2012. ACM Order Number 910122.", } @InProceedings{Harrington:2014:ALIFE, author = "Kyle I. Harrington and Jesse Freeman and Jordan Pollack", title = "Coevolution in Hide and Seek: Camouflage and Vision", booktitle = "Proceedings of the Fourteenth International Conference of the Synthesis and Simulation of Living Systems, ALIFE 14", year = "2014", editor = "Hiroki Sayama and John Rieffel and Sebastian Risi and Rene Doursat and Hod Lipson", series = "Complex Adaptive Systems", pages = "25--32", address = "New York", month = "30 " # jul # "-2 " # aug, organisation = "International Society for Artificial Life", publisher = "MIT Press", keywords = "genetic algorithms, genetic programming", isbn13 = "9780262326216 ?", URL = "http://mitpress.mit.edu/sites/default/files/titles/content/alife14/ch005.html", DOI = "doi:10.7551/978-0-262-32621-6-ch005", size = "8 pages", abstract = "Predator-prey interactions are one of the most common coevolutionary dynamics in Nature. We consider a model of the coevolution of prey appearance and predator vision, where a successful result is visually apparent. While using a neurophysiologically-based model of vision and a rich developmental process for prey patterning, we show that predator prey coevolution can maintain engagement. Backgrounds with large regional differences generally lead to prey that appear as mixtures of the regions. Finally, we find that engagement between predators and prey is supported by greater background complexity.", notes = "ALIFE 14 http://mitpress.mit.edu/books/artificial-life-14 ALIFE14NYC@gmail.com", } @InProceedings{Harrington:2013:CEC, article_id = "1561", author = "Adrian Harrington and Brian J. Ross", title = "Generative Representations for Artificial Architecture and Passive Solar Performance", booktitle = "2013 IEEE Conference on Evolutionary Computation", volume = "1", year = "2013", month = jun # " 20-23", editor = "Luis Gerardo {de la Fraga}", pages = "537--545", address = "Cancun, Mexico", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-0453-2", DOI = "doi:10.1109/CEC.2013.6557615", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.419.3502", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.419.3502", URL = "http://www.cosc.brocku.ca/sites/all/files/downloads/research/cs1302.pdf", size = "9 pages", abstract = "This paper explores how the use of generative representations influences the quality of solutions in evolutionary design problems. A genetic programming system is developed with individuals encoded as generative representations. Two research goals motivate this work. One goal is to examine Hornby's features and measures of modularity, reuse and hierarchy in new and more complex evolutionary design problems. In particular, we consider a more difficult problem domain where the generated 3D models are no longer constrained by voxels. Experiments are carried out to generate 3D models which grow towards a set of target points. The results show that the generative representations with the three features of modularity, regularity and hierarchy performed best overall. Although the measures of these features were largely consistent with those of Hornby, a few differences were found. Our second research goal is to use the best performing encoding on some 3D modeling problems that involve passive solar performance criteria. Here, the system is challenged with generating forms that optimize exposure to the Sun. This is complicated by the fact that a model's structure can interfere with solar exposure to itself; for example, protrusions can block Sun exposure to other model elements. Furthermore, external environmental factors (geographic location, time of the day, time of the year, other buildings in the proximity) may also be relevant. Experimental results were successful, and the system was shown to scale well to the architectural problems studied.", notes = "also known as \cite{6557615}. See also technical report CS-13-02 March 2013 cs1302.pdf CEC 2013 - A joint meeting of the IEEE, the EPS and the IET.", } @Article{Harrington:2016:GPEM, author = "Kyle I. S. Harrington", title = "Wolfgang Banzhaf and Lidia Yamamoto: Artificial Chemistries, MIT Press, 2015", journal = "Genetic Programming and Evolvable Machines", year = "2016", volume = "17", number = "3", pages = "317--319", month = sep, note = "Book review", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dR8un", DOI = "doi:10.1007/s10710-016-9271-y", size = "3 pages", } @Article{Harris:2009:HNO, author = "Andrew T. Harris and Anxhela Lungari and Christopher J. Needham and Stephen L. Smith and Michael A. Lones and Sheila E. Fisher and Xuebin Yang and Nicola Cooper and Jennifer Kirkham and D. Alastair Smith and Dominic P. Martin-Hirsch and Alec S. High", title = "Potential for Raman Spectroscopy to Provide Cancer Screening Using a Peripheral Blood Sample", journal = "Head \& Neck Oncology", year = "2009", volume = "1", pages = "34", month = sep, keywords = "genetic algorithms, genetic programming", URL = "http://www.headandneckoncology.org/content/1/1/34", DOI = "doi:10.1186/1758-3284-1-34", pubmedid = "19761601", ISSN = "1758-3284", abstract = "Cancer poses a massive health burden with incidence rates expected to double globally over the next decade. In the United Kingdom screening programmes exists for cervical, breast, and colorectal cancer. The ability to screen individuals for solid malignant tumours using only a peripheral blood sample would revolutionise cancer services and permit early diagnosis and intervention. Raman spectroscopy interrogates native biochemistry through the interaction of light with matter, producing a high definition biochemical 'fingerprint' of the target material. This paper explores the possibility of using Raman spectroscopy to discriminate between cancer and non-cancer patients through a peripheral blood sample. Forty blood samples were obtained from patients with Head and Neck cancer and patients with respiratory illnesses to act as a positive control. Raman spectroscopy was carried out on all samples with the resulting spectra being used to build a classifier in order to distinguish between the cancer and respiratory patients' spectra; firstly using principal component analysis (PCA)/linear discriminant analysis (LDA), and secondly with a genetic evolutionary algorithm. The PCA/LDA classifier gave a 65percent sensitivity and specificity for discrimination between the cancer and respiratory groups. A sensitivity score of 75percent with a specificity of 75percent was achieved with a 'trained' evolutionary algorithm. In conclusion this preliminary study has demonstrated the feasibility of using Raman spectroscopy in cancer screening and diagnostics of solid tumours through a peripheral blood sample. Further work needs to be carried out for this technique to be implemented in the clinical setting.", notes = "Also known as \cite{19761601}", } @TechReport{Harris:1996:edgegpRN, author = "Christopher Harris and Bernard Buxton", title = "Evolving Edge Detectors", year = "1996", institution = "UCL", type = "Research Note", number = "RN/96/3", address = "Gower Street, London, WC1E 6BT, UK", month = jan, URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/edgegp.ps.gz", keywords = "genetic algorithms, genetic programming, Edge Detection", abstract = "Edge detection is the process of detecting discontinuities in signals and images. We apply Genetic Programming techniques to the production of high-performance edge detectors for 1-D signals and image profiles. The method, which it is intended to extend to the development of practical edge detectors for use in image processing and machine vision, uses theoretical performance measures as criteria for the experimental design.", } @InProceedings{Harris:1996:edgegp, author = "Christopher Harris and Bernard Buxton", title = "Evolving Edge Detectors with Genetic Programming", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "309--315", address = "Stanford University, CA, USA", publisher = "MIT Press", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp96edge.ps.gz", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap40.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", size = "6 pages", abstract = "Edge detection is the process of detecting discontinuities in signals and images. We apply genetic programming techniques to the production of highperformance edge detectors for 1-D signals and image profiles. The method, which it is intended to extend to the development of practical edge detectors for use in image processing and machine vision, uses theoretical performance measures as criteria for the experimental design.", notes = "GP-96", } @TechReport{Harris:1996:gpcomRN, author = "Christopher Harris and Bernard Buxton", title = "{GP-COM}: A Distributed, Component-Based Genetic Programming System in {C++}", year = "1996", institution = "UCL", type = "Research Note", number = "RN/96/2", address = "Gower Street, London, WC1E 6BT, UK", month = jan, URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gpcom.ps", keywords = "genetic algorithms, genetic programming, Software System", abstract = "Widespread adoption of Genetic Programming techniques as a domain-independent problem solving tool depends on a good underlying software structure. A system is presented that mirrors the conceptual make-up of a GP system. Consisting of a loose collection of software components, each with strict interface definitions and roles, the system maximises flexibility and minimises effort when applied to a new problem domain.", } @InProceedings{Harris:1996:gpcom, author = "Christopher Harris and Bernard Buxton", title = "{GP-COM}: A Distributed, Component-Based Genetic Programming System in {C++}", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, pages = "425", address = "Stanford University, CA, USA", publisher = "MIT Press", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp96com.ps.gz", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap64.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", size = "1 page", abstract = "A genetic programming (GP) system is presented that mirrors the conceptual structure of the genetic programming cycle, maximising flexibility and re-use of code. This reduces the effort required to apply GP to a new problem domain.", notes = "See also \cite{Harris:1996:gpcomRN} GP-96", } @TechReport{Harris:1997:ledGPpsa, author = "Christopher Harris and Bernard Buxton", title = "Low-level Edge Detection Using Genetic Programming: performance, specificity and application to real-world signals", year = "1997", institution = "UCL", type = "Research Note", number = "RN/97/7", address = "Gower Street, London, WC1E 6BT, UK", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/rn_97_7.pdf", broken = "http://citeseer.ist.psu.edu/404512.html", keywords = "genetic algorithms, genetic programming, Edge Detection", size = "23 pages", notes = " 404512.html PDF link broken 22 Oct 2004 Oct 2018 rn_97_7.pdf is scan of photocopy...", } @InProceedings{harris:1997:STGPphtexc, author = "Christopher Harris", title = "Strongly Types GP to promote hierarchy through explicit syntax constraints", booktitle = "Late Breaking Papers at the GP-97 Conference", year = "1997", editor = "John Koza", pages = "72--80", address = "Stanford, CA, USA", publisher_address = "Stanford, California, 94305-3079 USA", month = "13-16 " # jul, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming, STGP", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/c.harris/harris_1997_STGPphtexc.pdf", broken = "http://www.cs.ucl.ac.uk/staff/C.Harris/stgp_structure.ps.gz", size = "9 pages", notes = "GP-97LB harris_1997_STGPphtexc.pdf is low res scan from proceedings", } @InProceedings{harris:1997:ehSTGP, author = "Christopher Harris", title = "Enforcing Hierarchy on Solutions with Strongly Typed Genetic Programming", booktitle = "Late Breaking Papers at the 1997 Genetic Programming Conference", year = "1997", editor = "John R. Koza", pages = "292", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California, 94305-3079, USA", month = "13--16 " # jul, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-206995-8", notes = "GP-97LB PHD Students' workshop The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670", } @PhdThesis{harris:thesis, author = "Christopher Harris", title = "An investigation into the Application of Genetic Programming techniques to Signal Analysis and Feature Detection", school = "University College, London", year = "1997", address = "UK", month = "26 " # sep, keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/c.harris/thesisps.zip", URL = "http://ethos.bl.uk/OrderDetails.do?did=33&uin=uk.bl.ethos.286626", size = "186 pages", notes = "uk.bl.ethos.286626 UCL internal use:000902299", } @Article{Harris:2003:IJRBM, author = "E. L. Harris and V. Babovic and R. A. Falconer", title = "Velocity predictions in compound channels with vegetated floodplains using genetic programming", journal = "International Journal of River Basin Management", year = "2003", volume = "1", number = "2", pages = "117--123", keywords = "genetic algorithms, genetic programming, Evolutionary computation, hydrodynamic processes, floodplain vegetation", ISSN = "1571-5124", DOI = "doi:10.1080/15715124.2003.9635198", size = "7 pages", abstract = "Data collection and storage methods have improved vastly over recent years, however the processes of information and knowledge extraction from data have not mirrored this. The application of computer supported scientific knowledge discovery processes to carefully collected observations aims to improve the understanding of the processes that generated or produced these data. In this paper, these new techniques have been applied to the complex and poorly understood phenomena of flow through idealised vegetation. The ability to predict, with improved accuracy, velocities within wetlands and other vegetated areas would be advantageous as these regions are increasingly being recognised for their natural flood alleviation properties. In this study, laboratory data collected in a flume with steady flows over a deep channel with relatively shallow vegetated floodplains were used to induce the formulation of expressions using a data driven discovery technique, namely genetic programming (GP). The objective of the study was not only to gain an understanding of the effect of vegetation on velocity distributions across a channel but moreover to demonstrate an alternative discovery process. The performance of the genetic program is reported for three variations of the GP. The reported results of the experiments were found to be encouraging and further work is detailed.", notes = "PhD 2003 Environmental Hydroinformatics Tools for Water Quality Management", } @InProceedings{harris:1999:PIWRUGA, author = "S. D. Harris and R. Mustata and L. Elliott and D. B. Ingham and D. Lesnic", title = "Parameter Identification Within Rocks Using Genetic Algorithms", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1779", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "real world applications, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-758_2.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-758_2.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{harris:1999:TRCRRUGA, author = "S. D. Harris and L. Elliott and D. B. Ingham and M. Pourkashanian and C. W. Wilson", title = "The Retrieval of Chemical Reaction Rates Using Genetic Algorithms", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1780", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "real world applications, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-759_2.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-759_2.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InCollection{harris:2000:GPFF, author = "Sarah Harris", title = "Genetically-Learned 7-Input Parity Function by an 8 x 8 FPGA", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "214--220", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms", notes = "part of \cite{koza:2000:gagp}", } @InProceedings{Harris:2015:GECCOcomp, author = "Sean Harris and Travis Bueter and Daniel R. Tauritz", title = "A Comparison of Genetic Programming Variants for Hyper-Heuristics", booktitle = "GECCO 2015 5th Workshop on Evolutionary Computation for the Automated Design of Algorithms (ECADA'15)", year = "2015", editor = "John Woodward and Daniel Tauritz and Manuel Lopez-Ibanez", isbn13 = "978-1-4503-3488-4", keywords = "genetic algorithms, genetic programming", pages = "1043--1050", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739482.2768456", DOI = "doi:10.1145/2739482.2768456", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "General-purpose optimization algorithms are often not well suited for real-world scenarios where many instances of the same problem class need to be repeatedly and efficiently solved. Hyper-heuristics automate the design of algorithms for a particular scenario, making them a good match for real-world problem solving. For instance, hardware model checking induced Boolean Satisfiability Problem (SAT) instances have a very specific distribution which general SAT solvers are not necessarily well targeted to. Hyper-heuristics can automate the design of a SAT solver customized to a specific distribution of SAT instances. The first step in employing a hyper-heuristic is creating a set of algorithmic primitives appropriate for tackling a specific problem class. The second step is searching the associated algorithmic primitive space. Hyper-heuristics have typically employed Genetic Programming (GP) to execute the second step, but even in GP there are many alternatives. This paper reports on an investigation of the relationship between the choice of GP type and the performance obtained by a hyper-heuristic employing it. Results are presented on SAT, demonstrating the existence of problems for which there is a statistically significant performance differential between the use of different GP types.", notes = "Also known as \cite{2768456} Distributed at GECCO-2015.", } @InProceedings{Harris:2011:PLDI, author = "William R. Harris and Sumit Gulwani", title = "Spreadsheet table transformations from examples", booktitle = "Proceedings of the 32nd ACM SIGPLAN conference on Programming language design and implementation, PLDI'11", year = "2011", pages = "317--328", address = "San Jose, California, USA", publisher_address = "New York, NY, USA", acmid = "1993536", publisher = "ACM", keywords = "genetic algorithms, genetic programming, end-user programming, program synthesis, programming by example, spreadsheet programming, table manipulation, user intent", isbn13 = "978-1-4503-0663-8", DOI = "doi:10.1145/1993498.1993536", size = "12 pages", notes = "Also known as \cite{Harris:2011:STT:1993498.1993536}", } @Article{Harris:2011:SIGPlan, author = "William R. Harris and Sumit Gulwani", title = "Spreadsheet table transformations from examples", journal = "ACM SIGPLAN Notices", volume = "46", issue = "6", month = jun, year = "2011", pages = "317--328", keywords = "genetic algorithms, genetic programming, end-user programming, program synthesis, programming by example, spreadsheet programming, table manipulation, user intent", ISSN = "0362-1340", DOI = "doi:10.1145/1993316.1993536", size = "12 pages", acmid = "1993536", publisher = "ACM", abstract = "Every day, millions of computer end-users need to perform tasks over large, tabular data, yet lack the programming knowledge to do such tasks automatically. In this work, we present an automatic technique that takes from a user an example of how the user needs to transform a table of data, and provides to the user a program that implements the transformation described by the example. In particular, we present a language of programs TableProg that can describe transformations that real users require.We then present an algorithm ProgFromEx that takes an example input and output table, and infers a program in TableProg that implements the transformation described by the example. When the program is applied to the example input, it reproduces the example output. When the program is applied to another, potentially larger, table with a 'similar' layout as the example input table, then the program produces a corresponding table with a layout that is similar to the example output table. A user can apply ProgFromEx interactively, providing multiple small examples to obtain a program that implements the transformation that the user desires. Moreover, ProgFromEx can help identify 'noisy' examples that contain errors. To evaluate the practicality of TableProg and ProgFromEx, we implemented ProgFromEx as a module for the Microsoft Excel spreadsheet program. We applied the module to automatically implement over 50 table transformations specified by end users through examples on on line Excel help forums. In seconds, ProgFromEx found programs that satisfied the examples and could be applied to larger input tables. This experience demonstrates that TableProg and ProgFromEx can significantly automate the tasks over tabular data that users need to perform.", notes = "As \cite{Harris:2011:PLDI} ? Also known as \cite{Harris:2011:STT:1993316.1993536}", } @InProceedings{1274068, author = "Gregory Anthony Harrison and Eric W. Worden", title = "Genetically programmed learning classifier system description and results", booktitle = "Genetic and Evolutionary Computation Conference {(GECCO2007)} workshop program", year = "2007", month = "7-11 " # jul, editor = "Tina Yu", isbn13 = "978-1-59593-698-1", pages = "2729--2736", address = "London, United Kingdom", keywords = "genetic algorithms, genetic programming, agent learning, autonomous agent, bucket brigade, evolutionary computation, genetics-based machine learning (GBML), intelligent agent, learning classifier system (LCS), reinforcement learning", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p2729.pdf", DOI = "doi:10.1145/1274000.1274068", publisher = "ACM Press", publisher_address = "New York, NY, USA", abstract = "An agent population can be evolved in a complex environment to perform various tasks and optimise its job performance using Learning Classifier System (LCS) technology. Due to the complexity and knowledge content of some real-world systems, having the ability to use genetic programming, GP, to represent the LCS rules provides a great benefit. Methods have been created to extend LCS theory into operation across the power-set of GP-enabled rule content. This system uses a full bucketbrigade system for GP-LCS learning. Using GP in the LCS system allows the functions and terminals of the actual problem environment to be used internally directly in the rule set, enabling more direct interpretation of the operation of the LCS system. The system was designed and built, and underwent independent testing at an advanced technology research laboratory. This paper describes the top-level operation of the system, and includes some of the results of the testing effort, and performance figures.", notes = "Distributed on CD-ROM at GECCO-2007 ACM Order No. 910071", } @InProceedings{DBLP:conf/ppsn/HarrisonAB22, author = "Joe Harrison and Tanja Alderliesten and Peter A. N. Bosman", title = "Gene-pool Optimal Mixing in Cartesian Genetic Programming", booktitle = "Parallel Problem Solving from Nature - PPSN XVII - 17th International Conference, PPSN 2022, Proceedings, Part II", year = "2022", editor = "Guenter Rudolph and Anna V. Kononova and Hernan E. Aguirre and Pascal Kerschke and Gabriela Ochoa and Tea Tusar", volume = "13399", series = "Lecture Notes in Computer Science", pages = "19--32", address = "Dortmund, Germany", month = sep # " 10-14", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Gene-pool Optimal Mixing, Subexpression re-use, XAI, Evolutionary computation, Symbolic regression", timestamp = "Tue, 16 Aug 2022 16:15:42 +0200", biburl = "https://dblp.org/rec/conf/ppsn/HarrisonAB22.bib", bibsource = "dblp computer science bibliography, https://dblp.org", isbn13 = "978-3-031-14720-3", DOI = "doi:10.1007/978-3-031-14721-0_2", abstract = "Genetic Programming (GP) can make an important contribution to explainable artificial intelligence because it can create symbolic expressions as machine learning models. Nevertheless, to be explainable, the expressions must not become too large. This may, however, limit their potential to be accurate. The re-use of subexpressions has the unique potential to mitigate this issue. The Genetic Programming Gene-pool Optimal Mixing Evolutionary Algorithm (GP-GOMEA) is a recent model-based GP approach that has been found particularly capable of evolving small expressions. However, its tree representation offers no explicit mechanisms to re-use subexpressions. By contrast, the graph representation in Cartesian GP (CGP) is natively capable of re-use. For this reason, we introduce CGP-GOMEA, a variant of GP-GOMEA that uses graphs instead of trees. We experimentally compare various configurations of CGP-GOMEA with GP-GOMEA and find that CGP-GOMEA performs on par with GP-GOMEA on three common datasets. Moreover, CGP-GOMEA is found to produce models that re-use subexpressions more often than GP-GOMEA uses duplicate subexpressions. This indicates that CGP-GOMEA has unique added potential, allowing to find even smaller expressions than GP-GOMEA with similar accuracy.", notes = "PPSN2022", } @InProceedings{harrison:2023:GECCO, author = "Joe Harrison and Marco Virgolin and Tanja Alderliesten and Peter Bosman", title = "{Mini-Batching,} {Gradient-Clipping,} First- versus {Second-Order:} What Works in {Gradient-Based} Coefficient Optimisation for Symbolic Regression?", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "1127--1136", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, symbolic regression, gradient descent, explainable AI, coefficient optimisation", isbn13 = "9798400701191", DOI = "doi:10.1145/3583131.3590368", size = "10 pages", abstract = "The aim of Symbolic Regression (SR) is to discover interpretable expressions that accurately describe data. The accuracy of an expression depends on both its structure and coefficients. To keep the structure simple enough to be interpretable, effective coefficient optimisation becomes key. Gradient-based optimisation is clearly effective at training neural networks in Deep Learning (DL), which can essentially be viewed as large, over-parameterised expressions: in this paper, we study how gradient-based optimisation techniques as often used in DL transfer to SR. In particular, we first assess what techniques work well across random SR expressions, independent of any specific SR algorithm. We find that mini-batching and gradient-clipping can be helpful (similar to DL), while second-order optimisers outperform first-order ones (different from DL). Next, we consider whether including gradient-based optimisation in Genetic Programming (GP), a classic SR algorithm, is beneficial. On five real-world datasets, in a generation-based comparison, we find that second-order optimisation outperforms coefficient mutation (or no optimisation). However, in time-based comparisons, performance gaps shrink substantially because the computational expensiveness of second-order optimisation causes GP to perform fewer generations. The interplay of computational costs between the optimisation of structure and coefficients is thus a critical aspect to consider.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @InProceedings{Harrison:2015:evoApplications, author = "Kyle Harrison and Mario Ventresca and Beatrice Ombuki-Berman", title = "Investigating Fitness Measures for the Automatic Construction of Graph Models", booktitle = "18th European Conference on the Applications of Evolutionary Computation", year = "2015", editor = "Antonio M. Mora and Giovanni Squillero", series = "LNCS", volume = "9028", publisher = "Springer", pages = "189--200", address = "Copenhagen", month = "8-10 " # apr, organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, Complex networks, Graph models, Centrality measures, Meta-analysis:poster", isbn13 = "978-3-319-16548-6", DOI = "doi:10.1007/978-3-319-16549-3_16", abstract = "Graph models are often constructed as a tool to better understand the growth dynamics of complex networks. Traditionally, graph models have been constructed through a very time consuming and difficult manual process. Recently, there have been various methods proposed to alleviate the manual efforts required when constructing these models, using statistical and evolutionary strategies. A major difficulty associated with automated approaches lies in the evaluation of candidate models. To address this difficulty, this paper examines a number of well-known network properties using a proposed meta-analysis procedure. The meta-analysis demonstrated how these network measures interacted when used together as classifiers to determine network, and thus model, (dis)similarity. The analytical results formed the basis of a fitness evaluation scheme used in a genetic programming (GP) system to automatically construct graph models for complex networks. The GP-based automatic inference system was used to reproduce two well-known graph models, the results of which indicated that the evolved models exemplified striking similarity when compared to their respective targets on a number of structural network properties.", notes = "EvoCOMPLEX EvoApplications2015 held in conjunction with EuroGP'2015, EvoCOP2015 and EvoMusArt2015 http://www.evostar.org/2015/cfp_evoapps.php", } @Article{Harrison:2015:JCS, author = "Kyle Robert Harrison and Mario Ventresca and Beatrice M. Ombuki-Berman", title = "A meta-analysis of centrality measures for comparing and generating complex network models", journal = "Journal of Computational Science", year = "2015", ISSN = "1877-7503", DOI = "doi:10.1016/j.jocs.2015.09.011", URL = "http://www.sciencedirect.com/science/article/pii/S1877750315300259", abstract = "Complex networks are often characterized by their statistical and topological network properties such as degree distribution, average path length, and clustering coefficient. However, many more characteristics can also be considered such as graph similarity, centrality, or flow properties. These properties have been used as feedback for algorithms whose goal is to ascertain plausible network models (also called generators) for a given network. However, a good set of network measures to employ that can be said to sufficiently capture network structure is not yet known. In this paper we provide an investigation into this question through a meta-analysis that quantifies the ability of a subset of measures to appropriately compare model (dis)similarity. The results are used as fitness measures for improving a recently proposed genetic programming (GP) framework that is capable of ascertaining a plausible network model from a single network observation. It is shown that the candidate model evaluation criteria of the GP system to automatically infer existing (man-made) network models, in addition to real-world networks, is improved.", keywords = "genetic algorithms, genetic programming, Complex networks, Graph models, Cortical networks, Meta-analysis", } @InProceedings{harrison:2004:eurogp, author = "Michael L. Harrison and James A. Foster", title = "Co-evolving Faults to Improve the Fault Tolerance of Sorting Networks", booktitle = "Genetic Programming 7th European Conference, EuroGP 2004, Proceedings", year = "2004", editor = "Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule", volume = "3003", series = "LNCS", pages = "57--66", address = "Coimbra, Portugal", publisher_address = "Berlin", month = "5-7 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-21346-5", DOI = "doi:10.1007/978-3-540-24650-3_6", abstract = "Co-evolving Faults to Improve the Fault Tolerance of Sorting Networks Fault tolerance is an important objective for circuit design, so it is natural to apply genetic programming techniques that are already being used for circuit design to enhance fault tolerance. We present preliminary evidence that co-evolving faults with circuits enhances the masking of faults in evolved circuits. Our test systems are sorting networks, since these are simple enough to analyse. We show that the overall impact of faults in an evolved sorting network can be reduced proportionally to the strength of co-evolutionary pressure.", notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004", } @InProceedings{hart:1999:AISASCE, author = "Emma Hart and Peter Ross", title = "An Immune System Approach to Scheduling in Changing Environments", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1559--1566", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "real world applications", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-723.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-723.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Article{hart:2003:GPEM, author = "Emma Hart and Peter Ross", title = "Exploiting the Analogy between the Immune System and Sparse Distributed Memories", journal = "Genetic Programming and Evolvable Machines", year = "2003", volume = "4", number = "4", pages = "333--358", month = dec, keywords = "artificial immune systems, sparse distributed memory, data-clustering", ISSN = "1389-2576", DOI = "doi:10.1023/A:1026191011609", abstract = "The relationship between immunological memory and a class of associative memories known as sparse distributed memories (SDM) is well known. This paper proposes a new model for clustering non-stationary data based on a combination of salient features from the two metaphors. The resulting system embodies the important principles of both types of memory; it is self-organising, robust, scalable, dynamic and can perform anomaly detection, and is shown to be a more faithful model of the biological system than a standard SDM. The model is first applied to clustering static benchmark data-sets, and is shown to outperform another system based on immunological principles. It is then applied to clustering non-stationary data-sets with promising results. The system is also shown to be scalable therefore is of potential for clustering real-world data-sets.", notes = "Special issue on artificial immune systems Article ID: 5144847", } @Article{hart:2005:GPEM, author = "Emma Hart and Peter Ross and David Corne", title = "Evolutionary Scheduling: A Review", journal = "Genetic Programming and Evolvable Machines", year = "2005", volume = "6", number = "2", pages = "191--220", month = jun, note = "Early and seminal work which applied evolutionary computing methods to scheduling problems from 1985 onwards laid a strong and exciting foundation for the work which has been reported over the past decade or so. A survey of the current state-of-the-art was produced in 1999 for the European Network of Excellence on Evolutionary Computing EVONET", } @Article{Hart:2016:EC, author = "Emma Hart and Kevin Sim", journal = "Evolutionary Computation", title = "A Hyper-Heuristic Ensemble Method for Static Job-Shop Scheduling", year = "2016", volume = "24", number = "4", pages = "609--635", abstract = "We describe a new hyper-heuristic method NELLI-GP for solving job-shop scheduling problems (JSSP) that evolves an ensemble of heuristics. The ensemble adopts a divide-and-conquer approach in which each heuristic solves a unique subset of the instance set considered. NELLI-GP extends an existing ensemble method called NELLI by introducing a novel heuristic generator that evolves heuristics composed of linear sequences of dispatching rules: each rule is represented using a tree structure and is itself evolved. Following a training period, the ensemble is shown to outperform both existing dispatching rules and a standard genetic programming algorithm on a large set of new test instances. In addition, it obtains superior results on a set of 210 benchmark problems from the literature when compared to two state-of-the-art hyper-heuristic approaches. Further analysis of the relationship between heuristics in the evolved ensemble and the instances each solves provides new insights into features that might describe similar instances.", keywords = "genetic algorithms, genetic programming, Job-shop-scheduling, dispatching rule, genetic programming., heuristic ensemble, hyper-heuristic", DOI = "doi:10.1162/EVCO_a_00183", ISSN = "1063-6560", month = dec, notes = "Also known as \cite{7765317}", } @InProceedings{Hart:2017:GECCO, author = "Emma Hart and Kevin Sim and Barry Gardiner and Kana Kamimura", title = "A Hybrid Method for Feature Construction and Selection to Improve Wind-damage Prediction in the Forestry Sector", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", series = "GECCO '17", year = "2017", pages = "1121--1128", address = "Berlin, Germany", publisher_address = "New York, NY, USA", month = "15-19 " # jul, organisation = "SIGEvo", publisher = "ACM", keywords = "genetic algorithms, genetic programming, feature-construction, forestry, machine-learning", isbn13 = "978-1-4503-4920-8", URL = "http://www.human-competitive.org/sites/default/files/hart-paper.pdf", URL = "http://doi.acm.org/10.1145/3071178.3071217", DOI = "doi:10.1145/3071178.3071217", acmid = "3071217", size = "8 pages", abstract = "Catastrophic damage to forests resulting from major storms has resulted in serious timber and financial losses within the sector across Europe in the recent past. Developing risk assessment methods is thus one of the keys to finding forest management strategies to reduce future damage. Previous approaches to predicting damage to individual trees have used mechanistic models of wind-flow or logistical regression with mixed results. We propose a novel filter-based Genetic Programming method for constructing a large set of new features which are ranked using the Hellinger distance metric which is insensitive to skew in the data. A wrapper-based feature-selection method that uses a random forest classifier is then applied predict damage to individual trees. Using data collected from two forests within South-West France, we demonstrate significantly improved classification results using the new features, and in comparison to previously published results. The feature-selection method retains a small set of relevant variables consisting only of newly constructed features whose components provide insights that can inform forest management policies.", notes = "Bronze Winner 2018 HUMIES Also known as \cite{Hart:2017:HMF:3071178.3071217} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @Article{hart:2018:stormAI, author = "Emma Hart and Barry Gardiner", title = "Storm damage to forests costs billions: here's how artificial intelligence can help", journal = "The Conversation", year = "2018", pages = "1.33pm BST", month = apr # " 23", keywords = "genetic algorithms, genetic programming", URL = "https://theconversation.com/storm-damage-to-forests-costs-billions-heres-how-artificial-intelligence-can-help-95299", URL = "https://bit.ly/2smvN2x", size = "3 pages", abstract = "Researchers use various modelling techniques to help forest managers predict which trees are at risk of damage, but none are sufficiently accurate. Artificial intelligence has the potential to make a big difference, however. We have built a system that we believe points the way to protecting the forestry industry more effectively in future.", notes = "Science + Technology", } @InProceedings{hart:1999:CEPEPSAADDA, author = "William E. Hart", title = "Comparing Evolutionary Programs and Evolutionary Pattern Search Algorithms: A Drug Docking Application", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "855--862", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "evolution strategies and evolutionary programming", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/Har99-gecco.ps.gz", URL = "ftp://ftp.cs.sandia.gov/pub/papers/wehart/1999/Har99-gecco.ps.gz", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InCollection{hart:1995:TAGPCMPE, author = "Jonathan Joseph Hart", title = "The Application of Genetic Programming to Cooperative Movement Planning and Execution", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1995", year = "1995", editor = "John R. Koza", pages = "86--95", address = "Stanford, California, 94305-3079 USA", month = "11 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-195720-5", notes = "part of \cite{koza:1995:gagp}", } @TechReport{hart:2002:TR02-06, author = "John Hart and Martin Shepperd", title = "Evolving Software with Multiple Outputs and Multiple Populations", institution = "School of Design, Engineering and Computing, Bournemouth University", year = "2002", number = "TR02-06", address = "Royal London House, Christchurch Rd, Bournemouth, BH1 3LT, UK", month = jul, keywords = "genetic algorithms, genetic programming, evolutionary algorithms, search, embedded system", URL = "http://dec.bournemouth.ac.uk/ESERG/Technical_Reports/TR02-06/TR02-06.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.8.4223", size = "6 pages", abstract = "In this research we are concerned with the automatic evolution of programs for control applications, the particular example we use being software for a simple fridge device with two inputs and three outputs. By careful choice of the target programming language - in a similar vein to a RISC processor - we are able to represent programs as variable length strings and use evolutionary computing techniques to search for fitter individuals. We used a fitness function that summed the fitness of each output channel, by various methods, in an attempt to encourage a total solution using a single population of candidate solutions. In general we were able to successfully evolve suitable solutions, however, the search sometimes suffered from premature convergence once the functionality for two out of the three output channels had evolved. More complex fitness assessment schemes, using mechanisms such as dynamically modifying the fitness associated with an output channel without additional benefit. These difficulties in attempting to do too much with a single population pointed to a `divide and conquer' approach whereby one (or more) populations are dedicated to solving for one output channel alone - whilst being exposed to all inputs. This is seen to be an acceptable approach due to the growth in multi-tasking operating systems and multiprocessor platforms.", notes = "as \cite{hart:2002:gecco:lbp}", } @InProceedings{hart:2002:gecco:lbp, title = "Evolving Software with Multiple Outputs and Multiple Populations", author = "John Hart and Martin Shepperd", booktitle = "Late Breaking Papers at the Genetic and Evolutionary Computation Conference ({GECCO-2002})", editor = "Erick Cant{\'u}-Paz", year = "2002", month = jul, pages = "223--227", address = "New York, NY", publisher = "AAAI", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025", keywords = "genetic algorithms, genetic programming", notes = "Late Breaking Papers, {GECCO-2002}. A joint meeting of the eleventh International Conference on Genetic Algorithms ({ICGA-2002}) and the seventh Annual Genetic Programming Conference ({GP-2002}) part of cantu-paz:2002:GECCO:lbp Tries to evolve controller for fridge. Variable length string. See also \cite{hart:2002:TR02-06}", } @PhdThesis{hart:thesis, author = "John K. Hart", title = "Automatic control program creation using concurrent Evolutionary Computing", school = "Bournemouth University", year = "2004", address = "UK", month = jan, keywords = "genetic algorithms, genetic programming", URL = "http://eprints.bournemouth.ac.uk/394/", URL = "http://eprints.bournemouth.ac.uk/394/1/John_Hart.pdf", size = "112 pages", abstract = "Over the past decade, Genetic Programming (GP) has been the subject of a significant amount of research, but this has resulted in the solution of few complex real-world problems. In this work, I propose that, for some relatively simple, non safety-critical embedded control applications, GP can be used as a practical alternative to software developed by humans. Embedded control software has become a branch of software engineering with distinct temporal, interface and resource constraints and requirements. This results in a characteristic software structure, and by examining this, the effective decomposition of an overall problem into a number of smaller, simpler problems is performed. It is this type of problem amelioration that is suggested as a method whereby certain realworld problems may be rendered into a soluble form suitable for GP. In the course of this research, the body of published GP literature was examined and the most important changes to the original GP technique of Koza are noted; particular focus is made upon GP techniques involving an element of concurrency, which is central to this work. This search highlighted few applications of GP for the creation of software for complex, realworld problems, this was especially true in the case of multi thread, multi output solutions. To demonstrate this Idea, a concurrent Linear GP (LGP) system was built that creates a multiple input multiple output solution using a custom low-level evolutionary language set, combining both continuous and Boolean data types. The system uses a multi-tasking model to evolve and execute the required LGP code for each system output using separate populations: Two example problems: a simple fridge controller and a more complex washing machine controller are described, and the problems encountered and overcome during the successful solution of these problems, are detailed. The operation of the complete, evolved washing machine controller is simulated using a graphical LabVIEW application. The aim of this research is to propose a general purpose system for the automatic creation of control software for use in a range of problems from the target problem class, without requiring any system tuning: In order to assess the system search performance sensitivity, experiments were performed using various population and LGP string sizes; the experimental data collected was also used to examine the utility of abandoning stalled searches and restarting. This work is significant because it identifies a realistic application of GP that can ease the burden of finite human software design resources, whilst capitalising on accelerating computing potential.", notes = "related publications \cite{hart:2002:gecco:lbp} \cite{hart:2004:eurogp} Supervisor: Martin Shepperd and Martin Lefley", } @TechReport{hart:2004:eurogpTR, author = "John Hart and Martin Shepperd", title = "The Evolution of Concurrent Control Software Using Genetic Programming", institution = "Empirical Software Engineering Research Group School of Design, Engineering \& Computing, Bournemouth University", year = "2003", number = "TR03-08", address = "Royal London House, Christchurch Rd, Bournemouth, BH1 3LT, UK", keywords = "genetic algorithms, genetic programming, linear genetic programming, embedded software", URL = "http://dec.bournemouth.ac.uk/ESERG/Technical_Reports/TR03-08/TR03-08.pdf", abstract = "Despite considerable progress in GP over the past 10 years, there are many outstanding challenges that need to be addressed before it will be widely deployed for developing useful software. In this paper we suggest a method for the automatic creation of concurrent control software using Linear Genetic Programming (LGP) and a `divide and conquer' approach. The method involves decomposing the whole problem into a multi-task solution with multiple inputs and multiple outputs - similar to the process used to implement embedded control solutions. We describe the necessary architecture of typical embedded control systems and their relevance to this work, the software evolution scheme used and lastly demonstrate the technique for an embedded software problem, namely a washing machine controller.", notes = "See also \cite{hart:2004:eurogp}", size = "10 pages", } @InProceedings{hart:2004:eurogp, author = "John Hart and Martin Shepperd", title = "The Evolution of Concurrent Control Software Using Genetic Programming", booktitle = "Genetic Programming 7th European Conference, EuroGP 2004, Proceedings", year = "2004", editor = "Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule", volume = "3003", series = "LNCS", pages = "289--298", address = "Coimbra, Portugal", publisher_address = "Berlin", month = "5-7 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-21346-5", DOI = "doi:10.1007/978-3-540-24650-3_27", abstract = "Despite considerable progress in GP over the past 10 years, there are many outstanding challenges that need to be addressed before it will be widely deployed for developing useful software. We suggest a method for the automatic creation of concurrent control software using Linear Genetic Programming (LGP) and a divide and conquer approach. The method involves decomposing the whole problem into a multi-task solution with multiple inputs and multiple outputs -- similar to the process used to implement embedded control solutions. We describe the necessary architecture of typical embedded control systems and their relevance to this work, the software evolution scheme used and lastly demonstrate the technique for an embedded software problem, namely a washing machine controller.", notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004 See also \cite{hart:2004:eurogpTR}", } @InProceedings{Harter:2017:GECCO, author = "Adam Harter and Daniel R. Tauritz and William M. Siever", title = "Asynchronous Parallel Cartesian Genetic Programming", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4939-0", address = "Berlin, Germany", pages = "1820--1824", size = "5 pages", URL = "http://doi.acm.org/10.1145/3067695.3084210", DOI = "doi:10.1145/3067695.3084210", acmid = "3084210", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, asynchronous parallel evolution, evolutionary computing", month = "15-19 " # jul, abstract = "The run-time of evolutionary algorithms (EAs) is typically dominated by fitness evaluation. This is particularly the case when the genotypes are complex, such as in genetic programming (GP). Evaluating multiple offspring in parallel is appropriate in most types of EAs and can reduce the time incurred by fitness evaluation proportional to the number of parallel processing units. The most naive approach maintains the synchrony of evolution as employed by the vast majority of EAs, requiring an entire generation to be evaluated before progressing to the next generation. Heterogeneity in the evaluation times will degrade the performance, as parallel processing units will idle until the longest evaluation has completed. Asynchronous parallel evolution mitigates this bottleneck and techniques which experience high heterogeneity in evaluation times, such as Cartesian GP (CGP), are prime candidates for asynchrony. However, due to CGP's small population size, asynchrony has a significant impact on selection pressure and biases evolution towards genotypes with shorter execution times, resulting in poorer results compared to their synchronous counterparts. This paper: 1) provides a quick introduction to CGP and asynchronous parallel evolution, 2) introduces asynchronous parallel CGP, and 3) shows empirical results demonstrating the potential for asynchronous parallel CGP to outperform synchronous parallel CGP.", notes = "Also known as \cite{Harter:2017:APC:3067695.3084210} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @InProceedings{Harter:2019:GECCOcomp, author = "Adam Harter and Aaron Scott Pope and Daniel R. Tauritz and Chris Rawlings", title = "Empirical evidence of the effectiveness of primitive granularity control for hyper-heuristics", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", isbn13 = "978-1-4503-6748-6", pages = "1478--1486", address = "Prague, Czech Republic", DOI = "doi:10.1145/3319619.3326860", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{3326860} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InProceedings{hartley:1999:A, author = "Adrian R. Hartley", title = "Accuracy-based fitness allows similar performance to humans in static and dynamic classification environments", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "266--273", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/Hartley1999a.ps.gz", URL = "http://www.cs.bris.ac.uk/~kovacs/lcs.archive/Hartley1999a.ps.gz", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Hartmann:2002:gecco, author = "Morten Hartmann and Frode Eskelund and Pauline C. Haddow and Julian F. Miller", title = "Evolving Fault Tolerance On An Unreliable Technology Platform", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "171--177", address = "New York", publisher_address = "San Francisco, CA 94104, USA", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "evolvable hardware, digital circuits, fault tolerance, noise robustness", ISBN = "1-55860-878-8", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/EH275.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/EH275.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-04.pdf", notes = "GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)", } @InCollection{HARUN:2023:HH, author = "Mohd Afiq Harun and Aminuddin {Ab. Ghani} and Saeid Eslamian and Chun Kiat Chang", title = "Chapter 26 - Sediment transport with soft computing application for tropical rivers", editor = "Saeid Eslamian and Faezeh Eslamian", booktitle = "Handbook of Hydroinformatics", publisher = "Elsevier", pages = "379--394", year = "2023", isbn13 = "978-0-12-821962-1", DOI = "doi:10.1016/B978-0-12-821962-1.00017-9", URL = "https://www.sciencedirect.com/science/article/pii/B9780128219621000179", keywords = "genetic algorithms, genetic programming, Sediment Transport, Fluvial environment, Soft computing, Tropical rivers, Multiple linear regression, Machine learning", abstract = "This research revised the existing sediment transport equation for rivers in Malaysia. The current equations of Ariffin (2004) and Sinnakaudan et al. (2006) were modified by using MLR and machine learning programs, namely Evolutionary Polynomial Regression (EPR), Multi-Gene Genetic Programming (MGGP), and M5 tree model (M5P). Among the three machine learning models, in terms of coefficient of determination (R2), Nash-Sutcliffe coefficient of Efficiency (NSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE), EPR were able to give the best prediction model in the evidence of Revised Ariffin (2004) model (R2 = 0.922, NSE = 0.913, RMSE = 3.305, MAE = 1.552), followed by MGGP (R2 = 0.787, NSE = 0.784, RMSE = 5.217, MAE = 3.054) and M5P (R2 = 0.786, NSE = 0.762, RMSE = 5.467, MAE = 1.561). The trend was also the same for Revised Sinnakaudan et al. (2006) whereby EPR had an excellent prediction accuracy model (R2 = 0.884, NSE = 0.848, RMSE = 4.377 ,MAE = 2.137), followed by MGGP (R2 = 0.787, NSE = 0.784, RMSE = 5.207, MAE = 3.054) and M5P (R2 = 0.622, NSE = 0.615, RMSE = 6.961, MAE = 1.994). In terms of Discrepancy Ratio (DR), only M5P of both Revised Ariffin (2004) (73.46percent) and Revised Sinnakaudan (2006) (73.36percent) produced better results than MLR (66.36percent). However, the data did not distribute well and is rather flattening at the lower total bed material load rate. Machine learning is excellent at improving the prediction distribution at the high-value data but lacks accuracy compared to the observed value at the lower data value. This is mainly due to the type of regression algorithm used and sample size used in this study", } @InProceedings{harvey:1998:bcGP, author = "Brad Harvey and James A. Foster and Deborah Frincke", title = "Byte Code Genetic Programming", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "59--63", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "genetic algorithms, genetic programming", URL = "http://www.csds.uidaho.edu/deb/jvm.pdf", URL = "http://citeseer.ist.psu.edu/547985.html", size = "5 pages", abstract = "This paper explores the idea of using Genetic Programming (GP) to evolve Java Virtual Machine (JVM) byte code to solve a sample symbolic regression problem. The evolutionary process is done completely in memory using a standard Java environment.", notes = "GP-98LB", } @InProceedings{harvey:1999:TBCGP, author = "Brad Harvey and James Foster and Deborah Frincke", title = "Towards Byte Code Genetic Programming", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1234", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, poster papers", ISBN = "1-55860-611-4", URL = "http://citeseer.ist.psu.edu/468509.html", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-418.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-418.ps", abstract = "his paper uses the GP paradigm to evolve linear genotypes (individuals) that consist of Java byte code. Our prototype GP system is implemented in Java using a standard Java development kit (JDK). The evolutionary process is done completely in memory and the fitness of individuals is determined by directly executing them in the Java Virtual Machine (JVM). We validate our approach by solving a functional regression problem with a fourth degree polynomial, and a classification problem diagnosing thyroid disease. Our implementation provides a fast, effective means for evolving native machine code for the JVM.", notes = "cites \cite{lukschandl:1998:1java} \cite{klahold:1998:eprGPJb} \cite{harvey:1998:bcGP} GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Harvey:2013:HMSBS, author = "Dustin Y. Harvey and Michael D. Todd", title = "Automated extraction of damage features through genetic programming", booktitle = "Health Monitoring of Structural and Biological Systems 2013", year = "2013", editor = "Tribikram Kundu", volume = "8695", series = "Proceedings of SPIE", pages = "86950J-1--86950J-10", address = "San Diego, California, USA", month = "11 - 14 " # mar, publisher = "Society of Photo-Optical Instrumentation Engineers (SPIE)", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1117/12.2009739", size = "10 pages", abstract = "Robust damage detection algorithms are a fundamental requirement for development of practical structural health monitoring systems. Typically, structural health-related decisions are made based on measurements of structural response. Data analysis involves a two-stage process of feature extraction and classification. While classification methods are well understood, feature design is difficult, time-consuming, and requires application experts and domain-specific knowledge. Genetic programming, a method of evolutionary computing closely related to genetic algorithms, has previously shown promise when adapted to problems involving structured data such as signals and images. Genetic programming evolves a population of candidate solutions represented as computer programs to perform a well-defined task. Importantly, genetic programming conducts an efficient search without specification of the size of the desired solution. In this study, a novel formulation of genetic programming is introduced as an automated feature extractor for supervised learning problems related to structural health monitoring applications. Performance of the system is evaluated on signal processing problems with known optimal solutions.", } @PhdThesis{Harvey:thesis, title = "Automated Feature Design for Time Series Classification by Genetic Programming", author = "Dustin Yewell Harvey", year = "2014", month = jan # "~01", number = "2", school = "University of California, San Diego", address = "USA", keywords = "genetic algorithms, genetic programming", URL = "http://escholarship.org/uc/item/1864t693.pdf", bibsource = "OAI-PMH server at escholarship.org", coverage = "1 PDF (1 online resource xvii, 122 pages)", identifier = "qt1864t693", rights = "public", URL = "http://www.escholarship.org/uc/item/1864t693", broken = "http://n2t.net/ark:/20775/bb7271474z", size = "139 pages", abstract = "Time series classification (TSC) methods discover and exploit patterns in time series and other one-dimensional signals. Although many accurate, robust classifiers exist for multivariate feature sets, general approaches are needed to extend machine learning techniques to make use of signal inputs. Numerous applications of TSC can be found in structural engineering, especially in the areas of structural health monitoring and non-destructive evaluation. Additionally, the fields of process control, medicine, data analytics, econometrics, image and facial recognition, and robotics include TSC problems. This dissertation details, demonstrates, and evaluates Autofead, a novel approach to automated feature design for TSC. In Autofead, a genetic programming variant evolves a population of candidate solutions to optimise performance for the TSC or time series regression task based on training data. Solutions consist of features built from a library of mathematical and digital signal processing functions. Numerical optimisation methods, included through a hybrid search approach, ensure that the fitness of candidate feature algorithms is measured using optimal parameter values. Experimental validation and evaluation of the method is carried out on a wide range of synthetic, laboratory, and real-world data sets with direct comparison to conventional solutions and state-of-the-art TSC methods. Autofead is shown to be competitively accurate as well as producing highly interpretable solutions that are desirable for data mining and knowledge discovery tasks. Computational cost of the search is relatively high in the learning stage to design solutions; however, the computational expense for classifying new time series is very low making Autofead solutions suitable for embedded and real-time systems. Autofead represents a powerful, general tool for TSC and time series data mining researchers as well as industry practitioners. Potential applications are numerous including the monitoring of electrocardiogram signals for indications of heart failure, network traffic analysis for intrusion detection systems, vibration measurement for bearing condition determination in rotating machinery, and credit card activity for fraud detection. In addition to the development of the overall method, this dissertation provides contributions in the areas of evolutionary computation, numerical optimisation, digital signal processing, and uncertainty analysis for evaluating solution robustness", } @Article{Harvey:2014:ieeeTEC, author = "Dustin Y. Harvey and Michael D. Todd", title = "Automated Feature Design for Numeric Sequence Classification by Genetic Programming", journal = "IEEE Transactions on Evolutionary Computation", year = "2015", volume = "19", number = "4", pages = "474--489", month = aug, keywords = "genetic algorithms, genetic programming, Feature design, machine learning, pattern recognition, sequence classification, time series classification, time series data mining.", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2014.2341451", size = "16 pages", abstract = "Pattern recognition methods rely on maximum-information, minimum-dimension feature sets to reliably perform classification and regression tasks. Many methods exist to reduce feature set dimensionality and construct improved features from an initial set; however, there are few general approaches for the design of features from numeric sequences. Any information lost in preprocessing or feature measurement cannot be recreated during pattern recognition. General approaches are needed to extend pattern recognition to include feature design and selection for numeric sequences, such as time series, within the learning process itself. This paper proposes a novel genetic programming (GP) approach to automated feature design called Autofead. In this method, a GP variant evolves a population of candidate features built from a library of sequence-handling functions. Numerical optimization methods, included through a hybrid approach, ensure that the fitness of candidate algorithms is measured using optimal parameter values. Autofead represents the first automated feature design system for numeric sequences to leverage the power and efficiency of both numerical optimisation and standard pattern recognition algorithms. Potential applications include the monitoring of electrocardiogram signals for indications of heart failure, network traffic analysis for intrusion detection systems, vibration measurement for bearing condition determination in rotating machinery, and credit card activity for fraud detection.", notes = "Department of Structural Engineering, University of California, San Diego also known as \cite{6861439}", } @InProceedings{harvey:2014:TMAV, author = "Dustin Harvey and Michael Todd", title = "Automated Selection of Damage Detection Features by Genetic Programming", booktitle = "Topics in Modal Analysis, Volume 7", year = "2014", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-1-4614-6585-0_2", DOI = "doi:10.1007/978-1-4614-6585-0_2", } @Unpublished{harvey:1997:ob, author = "Inman Harvey", title = "Open the Box", editor = "Wolfgang Banzhaf and Inman Harvey and Hitoshi Iba and William Langdon and Una-May O'Reilly and Justinian Rosca and Byoung-Tak Zhang", note = "Position paper at the Workshop on Evolutionary Computation with Variable Size Representation at ICGA-97", month = "20 " # jul, year = "1997", address = "East Lansing, MI, USA", keywords = "genetic algorithms, variable size representation, SAGA", URL = "http://users.sussex.ac.uk/~inmanh/openbox.pdf", abstract = "Introduction SAGA or Species Adaptation Genetic Algorithms have been developed over the last 8 years as the modification of standard GAs necessary when one is using them not as function optimisers, but rather as incremental adaptation algorithms. This is inevitably associated with variable-length genotypes. I here give a brief background survey.", notes = "http://web.archive.org/web/19971014081458/http://www.ai.mit.edu/people/unamay/icga-ws.html ", size = "4 pages", } @InProceedings{harvey:1999:TOMSMFSRPGA, author = "K. Burton Harvey and Chrisila C. Pettey", title = "The Outlaw Method for Solving Multimodal Functions with Split Ring Parallel Genetic Algorithms", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "274--280", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-382.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-382.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{conf/evoW/HarveyPBTPYVSB00, title = "Finding Golf Courses: The Ultra High Tech Approach", author = "Neal R. Harvey and Simon Perkins and Steven P. Brumby and James Theiler and Reid B. Porter and A. Cody Young and Anil K. Varghese and John J. Szymanski and Jeffrey J. Bloch", booktitle = "Real-World Applications of Evolutionary Computing", year = "2000", editor = "Stefano Cagnoni and Riccardo Poli and Yun Li and George Smith and David Corne and Martin J. Oates and Emma Hart and Pier Luca Lanzi and Egbert J. W. Boers and Ben Paechter and Terence C. Fogarty", volume = "1803", series = "LNCS", pages = "54--64", address = "Edinburgh", publisher_address = "Berlin", month = "17 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-67353-9", bibdate = "2002-01-03", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/evoW/evoW2000.html#HarveyPBTPYVSB00", broken = "http://www.genie.lanl.gov/green/publications/harveyEvoIASP2000.pdf", DOI = "doi:10.1007/3-540-45561-2_6", size = "12 pages", abstract = "The search for a suitable golf course is a very important issue in the travel plans of any modern manager. Modern management is also infamous for its penchant for high-tech gadgetry. Here we combine these two facets of modern management life. We aim to provide the cutting edge manager with a method of finding golf courses from space! In this paper, we present Genie: a hybrid evolutionary algorithm-based system that tackles the general problem of finding features of interest in multi-spectral remotely-sensed images, including, but not limited to, golf courses. Using this system we are able to successfully locate golf courses in 10-channel satellite images of several desirable US locations.", notes = "EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM, EvoRob, and EvoFlight, Edinburgh, Scotland, UK, April 17, 2000 Proceedings", } @InProceedings{Harvey:2000:SPIE, author = "N. R. Harvey and S. P. Brumby and S. J. Perkins and R. B. Porter and J. Theiler and A. C. Young and J. J. Szymanski and J. J. Bloch", title = "Parallel evolution of image processing tools for multispectral imagery", booktitle = "Imaging Spectrometry VI, Procceedings of SPIE", year = "2000", editor = "Michael R. Descour and Sylvia S. Shen", volume = "4132", pages = "72--82", organisation = "SPIE", keywords = "genetic algorithms, genetic programming, GENIE, ALADDIN", URL = "http://public.lanl.gov/jt/Papers/harveySPIE4132.ps.gz", DOI = "doi:10.1117/12.406611", size = "11 pages", abstract = "We describe the implementation and performance of a parallel, hybrid evolutionary-algorithm based system, which optimises image processing tools for feature-finding tasks in multi-spectral imagery (MSI) data sets. Our system uses an integrated spatio-spectral approach and is capable of combining suitably-registered data from different sensors. We investigate the speed-up obtained by parallelisation of the evolutionary process via multiple processors (a workstation cluster) and develop a model for prediction of run-times for different numbers of processors. We demonstrate our system on Landsat Thematic Mapper MSI, covering the recent Cerro Grande fire at Los Alamos, NM, USA.", } @Article{oai:CiteSeerPSU:561309, author = "Neal R. Harvey and James Theiler and Steven P. Brumby and Simon Perkins and John J. Szymanski and Jeffrey J. Bloch and Reid B. Porter and Mark Galassi and A. Cody Young", title = "Comparison of {GENIE} and conventional supervised classifiers for multispectral image feature extraction", journal = "IEEE Transactions on Geoscience and Remote Sensing", year = "2002", volume = "40", number = "2", pages = "393--404", month = feb, keywords = "genetic algorithms, genetic programming, Supervised Classification, Image Processing, Evolutionary Algorithms, Multispectral Imagery, Remote Sensing, feature extraction, geophysical signal processing, geophysical techniques, geophysics computing, image classification, multidimensional signal processing, terrain mapping, GENIE, GENetic Imagery Exploitation, IR, feature extraction, geophysical measurement technique, hybrid evolutionary algorithm, image classification, image processing, infrared, land surface, multispectral remote sensing, supervised classifier, terrain mapping, visible", ISSN = "0196-2892", URL = "http://nis-www.lanl.gov/~simes/webdocs/harveyIEEE_TGARS2001.pdf", URL = "http://citeseer.ist.psu.edu/561309.html", DOI = "doi:10.1109/36.992801", size = "12 pages", abstract = "We have developed an automated feature detection/ classification system, called Genie (GENetic Imagery Exploitation), which has been designed to generate image processing pipelines for a variety of feature detection/ classification tasks. Genie is a hybrid evolutionary algorithm that addresses the general problem of finding features of interest in multi-spectral remotely-sensed images. We describe our system in detail together with experiments involving comparisons of Genie with several conventional supervised classification techniques, for a number of classification tasks using multi-spectral remotely-sensed imagery.", notes = "On line version not identical to IEEE version Inspec Accession Number: 7265352, CODEN: IGRSD2", } @InProceedings{Harwerth:2011:EuroGP, author = "Michael Harwerth", title = "Experiments on Islands", booktitle = "Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011", year = "2011", month = "27-29 " # apr, editor = "Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado", series = "LNCS", volume = "6621", publisher = "Springer Verlag", address = "Turin, Italy", pages = "239--249", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming: poster", isbn13 = "978-3-642-20406-7", DOI = "doi:10.1007/978-3-642-20407-4_21", abstract = "The use of segmented populations (Islands) has proved to be advantageous for Genetic Programming (GP). This paper discusses the application of segmentation and migration strategies to a system for Linear Genetic Programming (LGP). Besides revisiting migration topologies, a modification for migration strategies is proposed --- migration delay. It is found that highly connected topologies yield better results than those with segments coupled more loosely, and that migration delays can further improve the effect of migration.", notes = "Part of \cite{Silva:2011:GP} EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011", } @InProceedings{Hasan:2016:ICIEV, author = "Md. Kamrul Hasan and Md. Milon Islam and M. M. A. Hashem", booktitle = "2016 5th International Conference on Informatics, Electronics and Vision (ICIEV)", title = "Mathematical model development to detect breast cancer using multigene genetic programming", year = "2016", pages = "574--579", month = may, keywords = "genetic algorithms, genetic programming, Breast cancer, multigene genetic programming, cross validation, confusion matrix, symbolic regression, mathematical model", DOI = "doi:10.1109/ICIEV.2016.7760068", size = "6 pages", abstract = "Breast cancer is one of the world's leading causes of cancer death of women. Generally, human breast tissue cells emerge this cancer. This causes loss of breast as well as precious lives. Usually in people over 50 years have the risk of this types of cancer. So, early detection for this disease is very crucial to save the valuable lives. This paper develops a 10 fold cross validated mathematical model to detect breast cancer using symbolic regression of multi-gene genetic programming (MGGP). Data for MGGP is retrieved from UCI machine learning repository data set and is used for training and testing the 10 fold cross validated mathematical model. The developed model produces fast and accurate results for both training and testing data set. The error rate is very negligible for both benign and malignant type of breast cancer. The cross validated model shows the higher accuracy with respect to existing techniques.", notes = "Also known as \cite{7760068}", } @Article{Hasan:2006:PLoS, author = "Samiul Hasan and Sabine Daugelat and P. S. Srinivasa Rao and Mark Schreiber", title = "Prioritizing Genomic Drug Targets in Pathogens: Application to Mycobacterium tuberculosis", journal = "PLoS Computational Biology", year = "2006", volume = "2", number = "6", pages = "e61", month = jun, keywords = "genetic algorithms", URL = "http://compbiol.plosjournals.org/archive/1553-7358/2/6/pdf/10.1371_journal.pcbi.0020061-L.pdf", DOI = "doi:10.1371/journal.pcbi.0020061", size = "12 pages", abstract = "We have developed a software program that weights and integrates specific properties on the genes in a pathogen so that they may be ranked as drug targets. We applied this software to produce three prioritised drug target lists for Mycobacterium tuberculosis, the causative agent of tuberculosis, a disease for which a new drug is desperately needed. Each list is based on an individual criterion. The first list prioritises metabolic drug targets by the uniqueness of their roles in the M. tuberculosis metabolome (metabolic choke points) and their similarity to known druggable protein classes (i.e., classes whose activity has previously been shown to be modulated by binding a small molecule). The second list prioritizes targets that would specifically impair M. tuberculosis, by weighting heavily those that are closely conserved within the Actinobacteria class but lack close homology to the host and gut flora. M. tuberculosis can survive asymptomatically in its host for many years by adapting to a dormant state referred to as persistence. The final list aims to prioritise potential targets involved in maintaining persistence in M. tuberculosis. The rankings of current, candidate, and proposed drug targets are highlighted with respect to these lists. Some features were found to be more accurate than others in prioritising studied targets. It can also be shown that targets can be prioritised by using evolutionary programming to optimise the weights of each desired property. We demonstrate this approach in prioritizing persistence targets.", } @InProceedings{Hasan:2024:evoapplications, author = "Yumnah Hasan and Allan {de Lima} and Fatemeh Amerehi and Darian Reyes {Fernandez de Bulnes} and Patrick Healy and Conor Ryan", title = "Interpretable Solutions for Breast Cancer Diagnosis with Grammatical Evolution and Data Augmentation", booktitle = "27th International Conference, EvoApplications 2024", year = "2024", editor = "Stephen Smith and Joao Correia and Christian Cintrano", series = "LNCS", volume = "14634", publisher = "Springer", address = "Aberystwyth", month = "3-5 " # apr, pages = "224--239", organisation = "EvoStar, Species", note = "Best poster", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Augmentation, Breast Cancer, Ensemble, STEM", isbn13 = "978-3-031-56851-0", URL = "https://rdcu.be/dDZ2T", DOI = "doi:10.1007/978-3-031-56852-7_15", size = "16 pages", abstract = "models, the use of inherently understandable models makes such endeavours more fruitful. This paper addresses these issues by demonstrating how a relatively new synthetic data generation technique, STEM, can be used to produce data to train models produced by Grammatical Evolution (GE) that are inherently understandable. STEM is a recently introduced combination of the Synthetic Minority Oversampling Technique (SMOTE), Edited Nearest Neighbour (ENN), and Mixup; it has previously been successfully used to tackle both between-class and within-class imbalance issues. We test our technique on the Digital Database for Screening Mammography (DDSM) and the Wisconsin Breast Cancer (WBC) datasets and compare Area Under the Curve (AUC) results with an ensemble of the top three performing classifiers from a set of eight standard ML classifiers with varying degrees of interpretability. We demonstrate that the GE-derived models present the best AUC while still maintaining interpretable solutions.", notes = "http://www.evostar.org/2024/ EvoApplications2024 held in conjunction with EuroGP'2024, EvoCOP2024 and EvoMusArt2024", } @InProceedings{Hasan:2025:evoapplications, author = "Yumnah Hasan and Allan {de Lima} and Darian Reyes {Fernandez de Bulnes} and Douglas {Mota Dias} and Conor Ryan", title = "Grammatical Feature Construction for Enhanced Interpretability in Breast Cancer Classification", booktitle = "28th International Conference, EvoApplications 2025", year = "2025", editor = " Pablo Garcia-Sanchez and Emma Hart and Sarah L. Thomson", series = "LNCS", volume = "15612", pages = "204--220", publisher = "Springer", address = "Trieste, Italy", month = "3-5 " # apr, organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Breast Cancer, Feature Construction Evolution, Interpretability, XAI, Machine Learning", isbn13 = "978-3-031-90062-4", timestamp = "Fri, 09 May 2025 20:28:52 +0200", biburl = "https://dblp.org/rec/conf/evoapps/HasanLBDR25.bib", bibsource = "dblp computer science bibliography, https://dblp.org", DOI = "doi:10.1007/978-3-031-90062-4_13", notes = "DBLP:conf/evoapps/HasanLBDR25, http://www.evostar.org/2025/ EvoApplications2025 held in conjunction with EuroGP'2025, EvoCOP2025 and EvoMusArt2025", } @Article{oai:doaj.org/article:2b97571506b643c1881e8a9bdb4636a6, author = "Seyed Reza Hasani and Zulaiha Ali Othman and Seyed Mostafa Mousavi Kahaki", title = "hybrid feature selection algorithm for intrusion detection system", journal = "Journal of Computer Science", publisher = "Science Publications", year = "2014", volume = "10", number = "6", pages = "1015--1025", keywords = "genetic algorithms, genetic programming", ISSN = "1549-3636", bibsource = "OAI-PMH server at doaj.org", language = "English", oai = "oai:doaj.org/article:2b97571506b643c1881e8a9bdb4636a6", URL = "http://www.thescipub.com/pdf/10.3844/jcssp.2014.1015.1025", DOI = "DOI:10.3844/jcssp.2014.1015.1025", size = "11 pages", abstract = "Network security is a serious global concern. Usefulness Intrusion Detection Systems (IDS) are increasing incredibly in Information Security research using Soft computing techniques. In the previous researches having irrelevant and redundant features are recognised causes of increasing the processing speed of evaluating the known intrusive patterns. In addition, an efficient feature selection method eliminates dimension of data and reduce redundancy and ambiguity caused by none important attributes. Therefore, feature selection methods are well-known methods to overcome this problem. There are various approaches being used in intrusion detections, they are able to perform their method and relatively they are achieved with some improvements. This work is based on the enhancement of the highest Detection Rate (DR) algorithm which is Linear Genetic Programming (LGP) reducing the False Alarm Rate (FAR) incorporates with Bees Algorithm. Finally, Support Vector Machine (SVM) is one of the best candidate solutions to settle IDSs problems. In this study four sample dataset containing 4000 random records are excluded randomly from this dataset for training and testing purposes. Experimental results show that the LGP_BA method improves the accuracy and efficiency compared with the previous related research and the feature subcategory offered by LGP_BA gives a superior representation of data.", } @Article{journals/soco/HasanzadehBA18, author = "Hamid Hasanzadeh and Mahdi Bashiri and Amirhossein Amiri", title = "A new approach to optimize a hub covering location problem with a queue estimation component using genetic programming", journal = "Soft Computing", year = "2018", number = "3", volume = "22", pages = "949--961", keywords = "genetic algorithms, genetic programming, hub location problem, queuing theory, particle swarm optimisation, PSO", bibdate = "2018-02-01", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/soco/soco22.html#HasanzadehBA18", DOI = "doi:10.1007/s00500-016-2398-1", abstract = "Hub locations are NP-hard problems used in transportation systems. In this paper, we focus on a single-allocation hub covering location problem considering a queue model in which the number of servers is a decision variable. We propose a model enhanced with a queue estimation component to determine the number and location of hubs and the number of servers in each hub, and to allocate non-hub to hub nodes according to network costs, including fixed costs for establishing each hub and server, transportation costs, and waiting costs. Moreover, we consider the capacity for a queuing system in any hub node. In addition, we present a meta heuristic algorithm based on particle swarm optimisation as a solution method. To evaluate the quality of the results obtained by the proposed algorithm, we establish a tight lower bound for the proposed model. Genetic programming is used for lower bound calculation in the proposed method. The results showed better performance of the proposed lower bound compared to a lower bound obtained by a relaxed model. Finally, the computational results confirm that the proposed solution algorithm performs well in optimising the model with a minimum gap from the calculated lower bound.", } @Article{HASANZADEH:2024:engeos, author = "Maryam Hasanzadeh and Mohammad Madani", title = "Deterministic tools to predict gas assisted gravity drainage recovery factor", journal = "Energy Geoscience", volume = "5", number = "3", pages = "100267", year = "2024", ISSN = "2666-7592", DOI = "doi:10.1016/j.engeos.2023.100267", URL = "https://www.sciencedirect.com/science/article/pii/S2666759223001130", keywords = "genetic algorithms, genetic programming, Gas assisted gravity drainage, Recovery factor, Deterministic tools, Statistical evaluation", abstract = "Naturally fractured rocks contain most of the world's petroleum reserves. This significant amount of oil can be recovered efficiently by gas assisted gravity drainage (GAGD). Although, GAGD is known as one of the most effective recovery methods in reservoir engineering, the lack of available simulation and mathematical models is considerable in these kinds of reservoirs. The main goal of this study is to provide efficient and accurate methods for predicting the GAGD recovery factor using data driven techniques. The proposed models are developed to relate GAGD recovery factor to the various parameters including model height, matrix porosity and permeability, fracture porosity and permeability, dip angle, viscosity and density of wet and non-wet phases, injection rate, and production time. In this investigation, by considering the effective parameters on GAGD recovery factor, three different efficient, smart, and fast models including artificial neural network (ANN), least square support vector machine (LSSVM), and multi-gene genetic programming (MGGP) are developed and compared in both fractured and homogenous porous media. Buckingham ? theorem is also used to generate dimensionless numbers to reduce the number of input and output parameters. The efficiency of the proposed models is examined through statistical analysis of R-squared, RMSE, MSE, ARE, and AARE. Moreover, the performance of the generated MGGP correlation is compared to the traditional models. Results demonstrate that the ANN model predicts the GAGD recovery factor more accurately than the LSSVM and MGGP models. The maximum R2 of 0.9677 and minimum RMSE of 0.0520 values are obtained by the ANN model. Although the MGGP model has the lowest performance among the other used models (the R2 of 0.896 and the RMSE of 0.0846), the proposed MGGP correlation can predict the GAGD recovery factor in fractured and homogenous reservoirs with high accuracy and reliability compared to the traditional models. Results reveal that the employed models can easily predict GAGD recovery factor without requiring complicate governing equations or running complex and time-consuming simulation models. The approach of this research work improves our understanding about the most significant parameters on GAGD recovery and helps to optimize the stages of the process, and make appropriate economic decisions", } @InProceedings{Hasegawa:2017:GECCO, author = "Taku Hasegawa and Naoki Mori and Keinosuke Matsumoto", title = "Genetic Programming with Multi-layered Population Structure", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4939-0", address = "Berlin, Germany", pages = "229--230", size = "2 pages", URL = "http://doi.acm.org/10.1145/3067695.3076048", DOI = "doi:10.1145/3067695.3076048", acmid = "3076048", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, local search, population structure", month = "15-19 " # jul, abstract = "This paper focus on the control of building blocks in the population of Genetic Programming (GP). We propose a GP algorithm that employs multi-layered population and searches solutions by using local search and crossover. The computational experiments were carried out by taking several classical Boolean problems as examples.", notes = "Also known as \cite{Hasegawa:2017:GPM:3067695.3076048} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @InProceedings{Hasegawa:2018:SoMeT, author = "Taku Hasegawa and Naoki Mori and Keinosuke Matsumoto", title = "Genetic Programming with Multi-Layered Population Structure for Software Evolution", booktitle = "Proceedings of the 17th International Conference on Intelligent Software Methodologies, Tools and Techniques, SoMeT 2018", year = "2018", editor = "Hamido Fujita and Enrique Herrera-Viedma", volume = "303", series = "Frontiers in Artificial Intelligence and Applications", pages = "57--70", address = "Granada, Spain", month = "26-28 " # sep, publisher = "IOS Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-61499-899-0", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/somet/somet2018.html#HasegawaMM18", DOI = "doi:10.3233/978-1-61499-900-3-57", abstract = "Genetic Programmings (GPs) is one of the most powerful evolutionary computation (EC) for software evolution. In ECs, it is difficult to maintain efficient building blocks. In particular, the control of building blocks in the population of genetic programming (GP) is relatively difficult because of tree-shaped individuals and also because of bloat, which is the uncontrolled growth of ineffective code segments in GP. For a variety of reasons, reliable techniques to remove bloat are highly desirable. This paper introduces a novel approach of removing bloat, by proposing a novel GP called Genetic Programming with Multi-Layered Population Structure (MLPS-GP) that employs multi-layered population and searches solutions using local search and crossover. The MLPS-GP has no mutation-like operator because such kinds of operators are the source of bloats. We showed that diversity can be maintained well only controlling the tree structures by a well-structured multi-layered population. To confirm the effectiveness of the proposed method, the computational experiments were carried out taking several classical Boolean problems as examples.", notes = "http://secaba.ugr.es/SOMET2018/ Also known as \cite{conf/somet/HasegawaMM18}", } @InProceedings{Hasegawa:1997:mg2br, author = "Yasuhisa Hasegawa and Toshio Fukuda", title = "Motion Generation of Two-link Brachiation Robot", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "Artifical life and evolutionary robotics", pages = "407--412", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", notes = "GP-97", } @InProceedings{hasegawa:2004:AIS, author = "Yoshihiko Hasegawa and Hitoshi Iba", title = "Multimodal Search with Immune Based Genetic Programming", booktitle = "Artificial Immune Systems", year = "2004", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-540-30220-9_27", DOI = "doi:10.1007/978-3-540-30220-9_27", } @InProceedings{Hasegawa:2006:CEC, author = "Yoshihiko Hasegawa and Hitoshi Iba", title = "Optimizing Programs with Estimation of {Bayesian} Network", booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary Computation", year = "2006", editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas", pages = "5527--5534", address = "Vancouver", month = "16-21 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9487-9", DOI = "doi:10.1109/CEC.2006.1688469", size = "8 pages", abstract = "Genetic Programming (GP) is a powerful optimisation algorithm and has been applied to many problems. GP is an extension of Genetic Algorithm (GA) which can handle programs, functions, etc. GP evolves with genetic operators such as crossover and mutation. The crossover operator in GP however selects sub-trees randomly and this selection is done regardless of the problem. This gives rise to the destruction of good building blocks. Recently, probabilistic model building techniques have been applied to GP to estimate the building blocks properly. This type of algorithm is called Probabilistic Model Building GP (PMBGP). Because GP uses many types of nodes, prior PMBGPs have been faced with the problem of huge CPT (Conditional Probability Table) size. The large CPT not only consumes a lot of memory but also requires many samples to construct networks. We propose a new PMBGP that uses Bayesian network for generating new individuals. In our approach, a special chromosome called expanded", notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D", } @InProceedings{Hasegawa:2006:ASPGP, title = "Estimation of {Bayesian} network for program generation", author = "Yoshihiko Hasegawa and Hitoshi Iba", booktitle = "Proceedings of the Third Asian-Pacific workshop on Genetic Programming", year = "2006", editor = "The Long Pham and Hai Khoi Le and Xuan Hoai Nguyen", pages = "35--46", ISSN = "18590209", address = "Military Technical Academy, Hanoi, VietNam", keywords = "genetic algorithms, genetic programming", URL = "http://www.iba.k.u-tokyo.ac.jp/~hasegawa/hasegawa_aspgp2006.pdf", URL = "http://gpbib.cs.ucl.ac.uk/aspgp06/hasegawa.pdf", size = "12 pages", abstract = "Genetic Programming (GP) is a powerful optimisation algorithm, which employs crossover for a main genetic operator. Because a crossover operator in GP selects sub-trees randomly, the building blocks may be destroyed by crossover. Recently, algorithms called PMBGPs (Probabilistic Model Building GP) based on probabilistic techniques have been proposed in order to improve the problem above. We propose a new PMBGP employing Bayesian network for generating new individuals with a special chromosome called expanded parse tree, which much reduces the number of possible symbols at each node. Although the large number of symbols gives rise to the large conditional probability table and requires a lot of samples to estimate the interactions among nodes, a use of the expanded parse tree overcomes these problems. A computational experiment on a deceptive MAX problem (DMAX problem) demonstrates that our new PMBGP is superior to other program evolution methods.", notes = "broken march 2020 http://www.aspgp.org", } @InProceedings{Hasegawa:2007:cec, author = "Yoshihiko Hasegawa and Hitoshi Iba", title = "Estimation of Distribution Algorithm Based on Probabilistic Grammar with Latent Annotations", booktitle = "2007 IEEE Congress on Evolutionary Computation", year = "2007", editor = "Dipti Srinivasan and Lipo Wang", pages = "1043--1050", address = "Singapore", month = "25-28 " # sep, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "1-4244-1340-0", file = "1692.pdf", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2007.4424585", abstract = "Genetic Programming (GP) which mimics the natural evolution to optimise functions and programs, has been applied to many problems. In recent years, evolutionary algorithms are seen from the viewpoint of the estimation of distribution. Many algorithms called EDAs (Estimation of Distribution Algorithms) based on probabilistic techniques have been proposed. Although probabilistic context free grammar (PCFG) is often used for the function and program evolution, it assumes the independence among the production rules. With this simple PCFG, it is not able to induce the building-blocks from promising solutions. We have proposed a new function evolution algorithm based on PCFG using latent annotations which weaken the independence assumption. Computational experiments on two subjects (the royal tree problem and the DMAX problem) demonstrate that our new approach is highly effective compared to prior approaches.", notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C", } @Article{Hasegawa:2008:TEC, title = "A {Bayesian} Network Approach to Program Generation", author = "Yoshihiko Hasegawa and Hitoshi Iba", journal = "IEEE Transactions on Evolutionary Computation", year = "2008", month = dec, volume = "12", number = "6", pages = "750--764", keywords = "genetic algorithms, genetic programming, belief networks, probability, trees (mathematics)Bayesian network, conditional probability table, evolutionary algorithms, expanded parse tree, powerful optimization algorithm, probabilistic techniques, program generation", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2008.915999", size = "15 pages", abstract = "Genetic programming (GP) is a powerful optimization algorithm that has been applied to a variety of problems. This algorithm can, however, suffer from problems arising from the fact that a crossover, which is a main genetic operator in GP, randomly selects crossover points, and so building blocks may be destroyed by the action of this operator. In recent years, evolutionary algorithms based on probabilistic techniques have been proposed in order to overcome this problem. In the present study, we propose a new program evolution algorithm employing a Bayesian network for generating new individuals. It employs a special chromosome called the expanded parse tree , which significantly reduces the size of the conditional probability table (CPT). Prior prototype tree-based approaches have been faced with the problem of huge CPTs, which not only require significant memory resources, but also many samples in order to construct the Bayesian network. By applying the present approach to three distinct computational experiments, the effectiveness of this new approach for dealing with deceptive problems is demonstrated.", notes = "POLE, EPT, Kullback-Leibler. Max problem \cite{langdon:1997:MAX}. DMAX deceptive max problem. Royal tree problem. Also known as \cite{4470578}", } @Article{Hasegawa:2009:ieeeTEC, title = "Latent Variable Model for Estimation of Distribution Algorithm Based on a Probabilistic Context-Free Grammar", author = "Yoshihiko Hasegawa and Hitoshi Iba", journal = "IEEE Transactions on Evolutionary Computation", year = "2009", month = aug, volume = "13", number = "4", pages = "858--878", keywords = "genetic algorithms, genetic programming, EM algorithm, estimation of distribution algorithm, variational Bayes.context-sensitive grammars, probability context freedom assumption, distribution algorithm estimation, evolutionary algorithm, function evolution, genetic operator, genetic programming techniques, latent variable model, probabilistic context-free grammar, probabilistic program evolution, probabilistic techniques", DOI = "doi:10.1109/TEVC.2009.2015574", ISSN = "1089-778X", size = "21 pages", abstract = "Estimation of distribution algorithms are evolutionary algorithms using probabilistic techniques instead of traditional genetic operators. Recently, the application of probabilistic techniques to program and function evolution has received increasing attention, and this approach promises to provide a strong alternative to the traditional genetic programming techniques. Although a probabilistic context-free grammar (PCFG) is a widely used model for probabilistic program evolution, a conventional PCFG is not suitable for estimating interactions among nodes because of the context freedom assumption. In this paper, we have proposed a new evolutionary algorithm named programming with annotated grammar estimation based on a PCFG with latent annotations, which allows this context freedom assumption to be weakened. By applying the proposed algorithm to several computational problems, it is demonstrated that our approach is markedly more effective at estimating building blocks than prior approaches.", notes = "PAGE. Royal tree, DMAX complex arithmetic Also known as \cite{5175364}", } @InCollection{Hasegawa:2012:GPnew, author = "Yoshihiko Hasegawa", title = "Programming with Annotated Grammar Estimation", booktitle = "Genetic Programming - New Approaches and Successful Applications", publisher = "InTech", year = "2012", editor = "Sebastian Ventura", chapter = "3", pages = "49--74", keywords = "genetic algorithms, genetic programming", isbn13 = "978-953-51-0809-2", DOI = "doi:10.5772/51662", size = "26 pages", notes = "Part of \cite{Ventura:2012:GPnew}. Open access book CC BY 3.0 license", } @Article{Hashim:2016:AR, author = "Roslan Hashim and Chandrabhushan Roy and Shervin Motamedi and Shahaboddin Shamshirband and Dalibor Petkovic and Milan Gocic and Siew Cheng Lee", title = "Selection of meteorological parameters affecting rainfall estimation using neuro-fuzzy computing methodology", journal = "Atmospheric Research", volume = "171", pages = "21--30", year = "2016", ISSN = "0169-8095", DOI = "doi:10.1016/j.atmosres.2015.12.002", URL = "http://www.sciencedirect.com/science/article/pii/S0169809515003920", abstract = "Rainfall is a complex atmospheric process that varies over time and space. Researchers have used various empirical and numerical methods to enhance estimation of rainfall intensity. We developed a novel prediction model in this study, with the emphasis on accuracy to identify the most significant meteorological parameters having effect on rainfall. For this, we used five input parameters: wet day frequency (dwet), vapour pressure ( e - a ), and maximum and minimum air temperatures (Tmax and Tmin) as well as cloud cover (cc). The data were obtained from the Indian Meteorological Department for the Patna city, Bihar, India. Further, a type of soft-computing method, known as the adaptive-neuro-fuzzy inference system (ANFIS), was applied to the available data. In this respect, the observation data from 1901 to 2000 were employed for testing, validating, and estimating monthly rainfall via the simulated model. In addition, the ANFIS process for variable selection was implemented to detect the predominant variables affecting the rainfall prediction. Finally, the performance of the model was compared to other soft-computing approaches, including the artificial neural network (ANN), support vector machine (SVM), extreme learning machine (ELM), and genetic programming (GP). The results revealed that ANN, ELM, ANFIS, SVM, and GP had R2 of 0.9531, 0.9572, 0.9764, 0.9525, and 0.9526, respectively. Therefore, we conclude that the ANFIS is the best method among all to predict monthly rainfall. Moreover, dwet was found to be the most influential parameter for rainfall prediction, and the best predictor of accuracy. This study also identified sets of two and three meteorological parameters that show the best predictions.", keywords = "genetic algorithms, genetic programming, Rainfall, Forecasting, Meteorological data, Anfis, Variable selection", } @InProceedings{Hashimoto:2014:SCIS-ISIS, author = "Takahiro Hashimoto and Takeshi Tsujimura and Kiyotaka Izumi", booktitle = "SCIS and ISIS 2014", title = "Myogenic potential pattern discernment method using genetic programming for hand gesture", year = "2014", month = dec, pages = "643--648", abstract = "The authors study on the hand gesture discernment based on the surface electromyogram of forearm. In order to discern finger shapes of the rock-paper-scissors, genetic programming technique is applied to establish the optimum classification algorithm of hand gestures by composing of arithmetic functions. We measure myoelectric potential signals of forearm related to rock-paper-scissors, and applies them to genetic evolution of hand gesture classification. We also evaluated the effects of the target number of nodes, crossover rate, mutation rate of GP parameters. Realtime hand gesture identification experiments are carried out and the typical hand gestures are actually distinguished in accuracy of 99percent.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SCIS-ISIS.2014.7044713", notes = "Dept. of Mech. Eng., Saga Univ., Saga, Japan Also known as \cite{7044713}", } @Article{Hashmi20111639, author = "Muhammad Z. Hashmi and Asaad Y. Shamseldin and Bruce W. Melville", title = "Statistical downscaling of watershed precipitation using Gene Expression Programming (GEP)", journal = "Environmental Modelling \& Software", volume = "26", number = "12", pages = "1639--1646", year = "2011", ISSN = "1364-8152", DOI = "doi:10.1016/j.envsoft.2011.07.007", URL = "http://www.sciencedirect.com/science/article/pii/S136481521100168X", keywords = "genetic algorithms, genetic programming, Statistical downscaling, Gene expression programming, Data-driven, Watershed, Precipitation", abstract = "Investigation of hydrological impacts of climate change at the regional scale requires the use of a downscaling technique. Significant progress has already been made in the development of new statistical downscaling techniques. Statistical downscaling techniques involve the development of relationships between the large scale climatic parameters and local variables. When the local parameter is precipitation, these relationships are often very complex and may not be handled efficiently using linear regression. For this reason, a number of non-linear regression techniques and the use of Artificial Neural Networks (ANNs) was introduced. But due to the complexity and issues related to finding a global solution using ANN-based techniques, the Genetic Programming (GP) based techniques have surfaced as a potential better alternative. Compared to ANNs, GP based techniques can provide simpler and more efficient solutions but they have been rarely used for precipitation downscaling. This paper presents the results of statistical downscaling of precipitation data from the Clutha Watershed in New Zealand using a non-linear regression model developed by the authors using Gene Expression Programming (GEP), a variant of GP. The results show that GEP-based downscaling models can offer very simple and efficient solutions in the case of precipitation downscaling.", } @PhdThesis{Hashmi:thesis, author = "Muhammad Zia ur Rahman Hashmi", title = "Watershed Scale Climate Change Projections for Use in Hydrologic Studies: Exploring New Dimensions", school = "The University of Auckland", year = "2012", address = "New Zealand", month = jan, keywords = "genetic algorithms, genetic programming, Gene Expression Programming", URL = "http://hdl.handle.net/2292/10876", URL = "https://researchspace.auckland.ac.nz/bitstream/handle/2292/10876/02whole.pdf", size = "288 pages", abstract = "Global Circulation Models (GCMs) are considered the most reliable source to provide the necessary data for climate change studies. At present, there is a wide variety of GCMs, which can be used for future projections of climate change using different emission scenarios. However, for assessing the hydrological impacts of climate change at the watershed and the regional scale, the GCM outputs cannot be used directly due to the mismatch in the spatial resolution between the GCMs and hydrological models. In order to use the output of a GCM for conducting hydrological impact studies, downscaling is used to convert the coarse spatial resolution of the GCM output into a fine resolution. In broad terms, downscaling techniques can be classified as dynamical downscaling and statistical downscaling. Statistical downscaling approaches are further classified into three broad categories, namely: (1) weather typing; (2) weather generators; and (3) multiple regression-based. For the assessment of hydrologic impacts of climate change at the watershed scale, statistical downscaling is usually preferred over dynamical downscaling as station scale information required for such studies may not be directly obtained through dynamical downscaling. Among the variables commonly downscaled, precipitation downscaling is still quite challenging, which has been recognised by many recent studies. Moreover, statistical downscaling methods are usually considered to be not very effective for simulation of precipitation, especially extreme precipitation events. On the other hand, the frequency and intensity of extreme precipitation events are very likely to be impacted by envisaged climate change in most parts of the world, thus posing the risk of increased floods and droughts. In this situation, hydrologists should only rely on those statistical downscaling tools that are equally efficient for simulating mean precipitation as well as extreme precipitation events. There is a wide variety of statistical downscaling methods available under the three categories mentioned above, and each method has its strengths and weaknesses. Therefore, no single method has been developed which is considered universal for all kinds of conditions and all variables. In this situation there is a need for multi-model downscaling studies to produce probabilistic climate change projections rather than a point estimate of a projected change.", abstract = "In order to address some of the key issues in the field of statistical downscaling research, this thesis study includes the evaluation of two well established and popular downscaling models, i.e. the Statistical DownScaling Model (SDSM) and Long Ashton Research Station Weather Generator (LARS-WG), in terms of their ability to downscale precipitation, with its mean and extreme characteristics, for the Clutha River watershed in New Zealand. It also presents the development of a novel statistical downscaling tool using Gene Expression Programming (GEP) and compares its performance with the SDSM-a widely used tool of similar nature. The GEP downscaling model proves to be a simpler and more efficient solution for precipitation downscaling than the SDSM model. Also, a major part of this study comprises of an evaluation of all the three downscaling models i.e. the SDSM, the LARS-WG and the GEP, in terms of their ability to simulate and downscale the frequency of extreme precipitation events, by fitting a Generalised Extreme Value (GEV) distribution to the annual maximum data obtained from the three models. Out of the three models, the GEP model appears to be the least efficient in simulating the frequency of extreme precipitation events while the other two models show reasonable capability in this regard. Furthermore, the research conducted for this thesis explores the development of a novel probabilistic multi-model ensemble of the three downscaling models, involved in the thesis study, using a Bayesian statistical framework and presents probabilistic projections of precipitation change for the Clutha watershed. In this way, the thesis endeavoured to contribute in the ongoing research related to statistical downscaling by addressing some of the key modern day issues highlighted by other leading researchers.", notes = "Supervisors Asaad Y. Shamseldin and Bruce W. Melville", } @InProceedings{Haslam:2016:CEC, author = "Edward Haslam and Bing Xue and Mengjie Zhang", title = "Further Investigation on Genetic Programming with Transfer Learning for Symbolic Regression", booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016)", year = "2016", editor = "Yew-Soon Ong", pages = "3598--3605", address = "Vancouver", month = "24-29 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-5090-0623-6", DOI = "doi:10.1109/CEC.2016.7744245", abstract = "Transfer learning is an important approach in machine learning, which aims to solve a problem by using the knowledge learnt from another problem domain. There has been extensive research and great achievement on transfer learning for image analysis and other tasks, but research on transfer learning in genetic programming (GP) for symbolic regression is still in the very early stage. However, GP has a natural way of expressing knowledge by trees or subtrees, which can be automatically discovered during the evolutionary process. An initial work on GP with transfer learning was proposed to transfer knowledge through best trees or subtrees from to source domain to facilitate the learning in the target domain. However, there are still a number of important issues remaining not investigated. This paper further investigates the ability of GP with transfer learning on different types of transfer scenarios, investigates the influence of a key parameter and the effect of transfer learning on the evolutionary training process, and also analyses how the knowledge learnt from the source domain was used during the learning process on the target domain. The results show that GP with transfer learning can generally perform well on different types of transfer scenarios. The transferred knowledge can provide a good initial population for the GP learning on the target domain, speed up the convergence, and help obtain better final solutions. However, the benefits of transfer learning varies in different scenarios.", notes = "WCCI2016", } @Article{journals/mima/Osman12, author = "Osman {Hassab Elgawi}", title = "Hitoshi Iba, Yoshihiko Hasegawa, and Topon Kumar Paul: Applied Genetic Programming and Machine Learning - {CRC} Press, Boca Raton, {FL}, 2010, 349 pp, \$79.95, {ISBN} 978-1-4398-0369-1", journal = "Minds and Machines", year = "2012", volume = "22", number = "4", pages = "381--383", note = "Book review", language = "English", keywords = "genetic algorithms, genetic programming", ISSN = "0924-6495", DOI = "doi:10.1007/s11023-012-9274-2", bibdate = "2012-10-23", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/mima/mima22.html#Osman12", notes = "Review of \cite{Iba:2009:AGPML}", } @InProceedings{Hassan:2008:gecco, author = "Ghada Hassan and Christopher D. Clack", title = "Multiobjective robustness for portfolio optimization in volatile environments", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "1507--1514", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1507.pdf", DOI = "doi:10.1145/1389095.1389387", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, dynamic environment, finance, multiobjective optimisation, portfolio optimisation, robustness, Real-World application", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389387}", } @InProceedings{Hassan:2008:geccocomp, author = "Ghada Hassan", title = "Non-linear factor model for asset selection using multi objective genetic programming", year = "2008", editor = "Marc Ebner and Mike Cattolico and Jano {van Hemert} and Steven Gustafson and Laurence D. Merkle and Frank W. Moore and Clare Bates Congdon and Christopher D. Clack and Frank W. Moore and William Rand and Sevan G. Ficici and Rick Riolo and Jaume Bacardit and Ester Bernado-Mansilla and Martin V. Butz and Stephen L. Smith and Stefano Cagnoni and Mark Hauschild and Martin Pelikan and Kumara Sastry", isbn13 = "978-1-60558-131-6", booktitle = "GECCO-2008 Workshop: Advanced Research Challenges in Financial Evolutionary Computing (ARC-FEC)", pages = "1859--1862", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1859.pdf", DOI = "doi:10.1145/1388969.1388990", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, Factor models, finance, multiobjective optimisation, portfolio optimisation", notes = "Distributed on CD-ROM at GECCO-2008 ACM Order Number 910081. Also known as \cite{1388990}", } @InProceedings{DBLP:conf/gecco/HassanC09, author = "Ghada Hassan and Christopher D. Clack", title = "Robustness of multiple objective GP stock-picking in unstable financial markets: real-world applications track", booktitle = "GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation", year = "2009", editor = "Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano {van Hemert} and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique Alba", pages = "1513--1520", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1569901.1570104", abstract = "Multiple Objective Genetic Programming (MOGP) is a promising stock-picking technique for fund managers, because the Pareto front approximates the risk/reward Efficient Frontier and simplifies the choice of investment model for a given client's attitude to risk. Unfortunately GP solutions don't work well if used in an environment that is different from the training environment, and the financial markets are notoriously unstable, often lurching from one market context to another (e.g. {"}bull{"} to {"}bear{"}). This turns out to be a hard problem -- simple dynamic adaptation methods are insufficient and robust behaviour of solutions becomes extremely important. In this paper we provide the first known empirical results on the robustness of MOGP solutions in an unseen environment consisting of real-world financial data. We focus on two well-known mechanisms to determine which leads to the more robust solutions: Mating Restriction, and Diversity Preservation. We introduce novel metrics for Pareto front robustness, and a novel variation on Mating Restriction, both based on phenotypic cluster analysis.", notes = "GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009). ACM Order Number 910092.", } @PhdThesis{Hassan:thesis, title = "Multiobjective genetic programming for financial portfolio management in dynamic environments", author = "Ghada Nasr Aly Hassan", school = "Department of Computer Science, University College London", year = "2010", type = "Doctoral", address = "UK", keywords = "genetic algorithms, genetic programming, MOGP", bibsource = "OAI-PMH server at eprints.ucl.ac.uk", language = "eng", oai = "oai:eprints.ucl.ac.uk.OAI2:20456", URL = "http://discovery.ucl.ac.uk/20456/1/20456.pdf", URL = "https://discovery.ucl.ac.uk/id/eprint/20456/", URL = "https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.565018", size = "160 pages", abstract = "Multiobjective (MO) optimisation is a useful technique for evolving portfolio optimisation solutions that span a range from high-return/high-risk to low-return/low-risk. The resulting Pareto front would approximate the risk/reward Efficient Frontier [Mar52], and simplifies the choice of investment model for a given clients attitude to risk. However, the financial market is continuously changing and it is essential to ensure that MO solutions are capturing true relationships between financial factors and not merely over fitting the training data. Research on evolutionary algorithms in dynamic environments has been directed towards adapting the algorithm to improve its suitability for retraining whenever a change is detected. Little research focused on how to assess and quantify the success of multiobjective solutions in unseen environments. The multiobjective nature of the problem adds a unique feature to be satisfied to judge robustness of solutions. That is, in addition to examining whether solutions remain optimal in the new environment, we need to ensure that the solutions relative positions previously identified on the Pareto front are not altered. This thesis investigates the performance of Multiobjective Genetic Programming (MOGP) in the dynamic real world problem of portfolio optimisation. The thesis provides new definitions and statistical metrics based on phenotypic cluster analysis to quantify robustness of both the solutions and the Pareto front. Focusing on the critical period between an environment change and when retraining occurs, four techniques to improve the robustness of solutions are examined. Namely, the use of a validation data set; diversity preservation; a novel variation on mating restriction; and a combination of both diversity enhancement and mating restriction. In addition, preliminary investigation of using the robustness metrics to quantify the severity of change for optimum tracking in a dynamic portfolio optimisation problem is carried out. Results show that the techniques used offer statistically significant improvement on the solutions' robustness, although not on all the robustness criteria simultaneously. Combining the mating restriction with diversity enhancement provided the best robustness results while also greatly enhancing the quality of solutions.", notes = "Two supervisors, Christopher Clack and Philip Treleaven. uk.bl.ethos.565018 ISNI: 0000 0004 2728 0858 UCL internal:001466727", } @InProceedings{Hassan:2023:evoapplications, author = "Mujtaba Hassan and Arish Sateesan and Jo Vliegen and Stjepan Picek and Nele Mentens", title = "Evolving Non-cryptographic Hash Functions Using Genetic Programming for High-speed Lookups in Network Security Applications", booktitle = "26th International Conference, EvoApplications 2023", year = "2023", month = apr # " 12-14", editor = "Joao Correia and Stephen Smith and Raneem Qaddoura", series = "LNCS", volume = "13989", publisher = "Springer Verlag", address = "Brno, Czech Republic", pages = "302--318", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, evolutionary Computation, Non-cryptographic Hash Functions, FPGA, Avalanche Metrics", isbn13 = "978-3-031-30229-9", URL = "https://rdcu.be/daNHv", DOI = "doi:10.1007/978-3-031-30229-9_20", size = "17 pages", notes = "http://www.evostar.org/2023/ EvoApplications2023 held in conjunction with EuroGP'2023, EvoCOP2023 and EvoMusArt2023", } @InProceedings{hassan:2024:GECCO, author = "Mujtaba Hassan and Jo Vliegen and Stjepan Picek and Nele Mentens", title = "A Systematic Exploration of Evolutionary Computation for the Design of Hardware-oriented Non-cryptographic Hash Functions", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference", year = "2024", editor = "Ruhul Sarker and Patrick Siarry and Julia Handl and Xiaodong Li and Markus Wagner and Mario Garza-Fabre and Kate Smith-Miles and Richard Allmendinger and Ying Bi and Grant Dick and Amir H Gandomi and Marcella Scoczynski Ribeiro Martins and Hirad Assimi and Nadarajen Veerapen and Yuan Sun and Mario Andres Munyoz and Ahmed Kheiri and Nguyen Su and Dhananjay Thiruvady and Andy Song and Frank Neumann and Carla Silva", pages = "1255--1263", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, non-cryptographic hash functions, bloom filters, FPGA, Real World Applications", isbn13 = "979-8-4007-0494-9", DOI = "doi:10.1145/3638529.3654009", size = "9 pages", abstract = "Non-cryptographic (NC) hash functions are crucial in high-speed search applications and probabilistic data structures (PDS) such as Bloom filters and Count-Min sketches for efficient lookups and counting. These operations necessitate execution at line rates to accommodate the high-speed demands of Terabit Ethernet networks, characterized by bandwidths exceeding 100 Gbps. Consequently, a growing inclination towards hardware platforms, particularly Field Programmable Gate Arrays (FPGAs), is evident in network security applications. Given the centrality of hash functions in these structures, any enhancements to their design carry substantial implications for overall system performance. However, hash functions must exhibit independence, uniform distribution, and hardware-friendly characteristics. In this work, we employ Genetic Programming (GP) with avalanche metrics as a fitness function to devise a hardware-friendly family of NC hash functions called the Evolutionary hash (E-hash). We provide a detailed experimental analysis to offer insights on primitive set combinations involving logical operations and diverse hyperparameter settings, encompassing variables such as the number of nodes, tree height, population size, crossover and mutation rate, tournament size, number of constants, and generations. Compared to existing state-of-the-art hardware-friendly hash functions, the proposed E-hash family exhibits an 8.4\% improvement in terms of operating frequency and throughput and 7.74\% in latency on FPGA.", notes = "GECCO-2024 RWA A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @Article{Hassan:2024:asoc, author = "Mujtaba Hassan and Arish Sateesan and Jo Vliegen and Stjepan Picek and Nele Mentens", title = "A Genetic Programming approach for hardware-oriented hash functions for network security applications", journal = "Applied Soft Computing", year = "2024", volume = "165", pages = "112078", keywords = "genetic algorithms, genetic programming, Hash functions, Field Programmable Gate Arrays, Bloom filters, Evolutionary computation", ISSN = "1568-4946", URL = "https://www.sciencedirect.com/science/article/pii/S1568494624008524", DOI = "doi:10.1016/j.asoc.2024.112078", abstract = "Non-cryptographic (NC) hash functions are generally designed for speed and efficiency, which enables their use in many network security applications that require efficient lookup and counting, such as Bloom Filters and Count-Min (CM) Sketch structures. The performance of these structures heavily relies on underlying hash functions. Therefore, any advancement in the hash function design significantly impacts the overall performance of these structures. This paper presents a novel family of 32-bit NC hash functions (NCGPH-32) evolved using Genetic Programming (GP) and their corresponding implementation on Field Programmable Gate Arrays (FPGAs). This family of NC hash functions generates smaller hash values concatenated to produce larger hash outputs. Inspired by related work on 96-bit NC hash functions with GP, this work optimises the performance of 32-bit NC hash functions on FPGA while achieving high scores on specific avalanche metrics (avalanche dependence, avalanche weight, and entropy) when considering concatenated 96-bit outputs. This optimisation is of utmost importance to address the escalating demand for Terabit Ethernet networks, specifically in processing real-time network flow IDs (identification and monitoring) at line rate. The throughput, latency, operating frequency, and resource use are evaluated on an FPGA and compared against 17 state-of-the-art NC hash functions. The results show that the proposed 96-bit concatenated hash function surpasses prior GP-based and other state-of-the-art NC hash functions by at least 36percent in operating frequency, 30percent in throughput and reduces latency by 27percent. The demonstrated improvements in the hash design not only cater to the present demands of Terabit networks but also meet the expected near-future demands. Additionally, we integrate these hash functions into the Standard Bloom Filter (SBF) architecture and demonstrate comparable false positive rates (FPR) to state-of-the-art NC hash functions, affirming their effectiveness and applicability. We have also conducted several statistical tests on hash outputs of NCGPH-32 to demonstrate the high random nature and uniform distribution", } @InProceedings{Hassan:2025:evoapplications, author = "Mujtaba Hassan and Jo Vliegen and Stjepan Picek and Nele Mentens", title = "Designing Hardware-Friendly Hash Functions for Network Security Using Cartesian Genetic Programming", booktitle = "28th International Conference, EvoApplications 2025", year = "2025", editor = "Pablo Garcia-Sanchez and Emma Hart and Sarah L. Thomson", series = "LNCS", volume = "15612", pages = "221--237", publisher = "Springer", address = "Trieste, Italy", month = "3-5 " # apr, organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Non-cryptographic hash function, Bloom filters, Network flow detection", isbn13 = "978-3-031-90062-4", URL = "https://www.human-competitive.org/sites/default/files/cgp_paper_final_camera_ready_2.pdf", URL = "https://www.human-competitive.org/sites/default/files/mujtaba_hassan_v2_0.txt", URL = "https://rdcu.be/eqmlM", DOI = "doi:10.1007/978-3-031-90062-4_14", size = "17 pages", notes = "2025 HUMIES finalist. http://www.evostar.org/2025/ EvoApplications2025 held in conjunction with EuroGP'2025, EvoCOP2025 and EvoMusArt2025", } @InProceedings{Hassan:2003:rsgp, author = "Yasser Hassan and Eiichiro Tazaki", title = "Rough Set and Genetic Programming", booktitle = "Rough Set Theory and Granular Computing", year = "2003", editor = "Masahiro Inuiguchi and Shoji Hirano and Shusaku Tsumoto", volume = "125", series = "Studies in Fuzziness and Soft Computing", pages = "197--207", publisher = "Springer", keywords = "genetic algorithms, genetic programming", language = "English", isbn13 = "978-3-642-05614-7", DOI = "doi:10.1007/978-3-540-36473-3_19", abstract = "A methodology for using Rough Set for preference modelling in decision problem is presented in this paper; where we will introduce a new approach for deriving knowledge rules from database based on Rough Sets theory combined with Genetic Programming algorithm. Genetic Programming belongs to the most newly techniques in applications of Artificial Intelligence. Rough Set Theory, which emerged about twenty years ago, is nowadays rapidly developing branch of Artificial Intelligence and Soft Computing. At the first glance the two methodologies we talk about have not in common. Rough Sets construct representation of knowledge in terms of attributes, semantic decision rules, etc. On the contradictory, Genetic Programming attempts to automatically create computer programs from a high-level statement of the problem requirements. But, in spite of these differences, it is interesting to try to incorporate both approaches into one combined system. The challenge is to get as much as possible from this association.", } @Article{Hassan:2004:Kybernetes, author = "Yasser Hassan and Eiichiro Tazaki", title = "Combination method of rough set and genetic programming", journal = "Kybernetes", year = "2004", volume = "33", number = "1", pages = "98--117", keywords = "genetic algorithms, genetic programming", ISSN = "0368-492X", DOI = "doi:10.1108/03684920410514544", abstract = "A methodology for using rough set for preference modelling in decision problem is presented in this paper; where we will introduce a new approach for deriving knowledge rules from database based on rough set combined with genetic programming. Genetic programming belongs to the most new techniques in applications of artificial intelligence. Rough set theory, which emerged about 20 years back, is nowadays a rapidly developing branch of artificial intelligence and soft computing. At the first glance, the two methodologies that we discuss are not in common. Rough set construct is the representation of knowledge in terms of attributes, semantic decision rules, etc. On the contrary, genetic programming attempts to automatically create computer programs from a high-level statement of the problem requirements. But, in spite of these differences, it is interesting to try to incorporate both the approaches into a combined system. The challenge is to obtain as much as possible from this association", } @Article{journals/isca/Hassan10, author = "Yasser Fouad Hassan", title = "Rough Set Genetic Programming", journal = "International Journal of Computers and Their Applications", volume = "17", number = "3", year = "2010", pages = "161--171", bibsource = "DBLP, http://dblp.uni-trier.de", month = sep, keywords = "genetic algorithms, genetic programming", ISSN = "1076-5204", URL = "https://www.researchgate.net/publication/220085298_Rough_Set_Genetic_Programming", broken = "http://www.isca-hq.org/j-list.htm", } @InProceedings{hatanaka:2001:hmimbgp, author = "Toshiharu Hatanaka and Katsuji Uosaki", title = "Hammerstein Model Identification Method Based on Genetic Programming", booktitle = "Proceedings of the 2001 Congress on Evolutionary Computation CEC2001", year = "2001", pages = "1430--1435", address = "COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "27-30 " # may, organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, System identification, Hammerstein models, Nonlinear systems, Evolutionary computation, Akaike information criterion, Hammerstein model identification method, genetic programming, least square method, nonlinear dynamic system, nonlinear static block, system identification, training data, identification, nonlinear dynamical systems", ISBN = "0-7803-6658-1", DOI = "doi:10.1109/CEC.2001.934359", abstract = "We address a novel approach to identify a nonlinear dynamic system for a Hammerstein model. The Hammerstein model is composed of a nonlinear static block in series with a linear, dynamic system block. The aim of system identification is to provide the optimal mathematical model of both nonlinear static and linear dynamic system blocks in some appropriate sense. We use genetic programming to determine the functional structure for the nonlinear static block. Each individual in genetic programming represents a nonlinear function structure. The unknown parameters of the linear dynamic block and the nonlinear static block given by each individual are estimated with a least square method. The fitness is evaluated by AIC (Akaike information criterion) as representing the balance of model complexity and accuracy. It is calculated with the number of nodes in the genetic programming tree, the order of the linear dynamic model and the accuracy of model for the training data. The results of numerical studies indicate the usefulness of proposed approach to Hammerstein model identification", notes = "CEC-2001 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 01TH8546C, Library of Congress Number = . AIC Akaike information criterion", } @InProceedings{hatta:1998:appiGA, author = "Koichi Hatta and Shin'ichi Wakabayashi and Tetsushi Koide", title = "Adapting Parameters Based on Pedigree of Individuals in a Genetic Algorithm", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "510--517", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms", notes = "SGA-98", } @InCollection{haugh:2002:ELCDGP, author = "Justin C. Haugh", title = "Evolution of Life Cycle Differentiation using Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2002", year = "2002", editor = "John R. Koza", pages = "102--110", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", URL = "http://www.genetic-programming.org/sp2002/Haugh.pdf", oai = "oai:CiteSeerXPSU:10.1.1.140.6874", abstract = "This paper describes the emergence of age and sexual differentiation among computer programs in a digital ecosystem. Programs to control the behavior of simulated mice are randomly generated, and are evolved over time using a steadystate genetic programming system with tournament selection. Problems and early failures are described, and solutions are discussed. Evolved programs demonstrating life stage differentiation are examined with a comparison of relative fitness", notes = "part of \cite{koza:2002:gagp} 10 by 10 world. Snakes and mice. lilgp problem -> gpc++ 0.40", } @InProceedings{eurogp:HauptmanS05, author = "Ami Hauptman and Moshe Sipper", editor = "Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. {van Hemert} and Marco Tomassini", title = "{GP-EndChess}: Using Genetic Programming to Evolve Chess Endgame Players", booktitle = "Proceedings of the 8th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3447", year = "2005", address = "Lausanne, Switzerland", month = "30 " # mar # " - 1 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-25436-6", pages = "120--131", URL = "http://www.cs.bgu.ac.il/~sipper/papabs/eurogpchess-final.pdf", DOI = "doi:10.1007/978-3-540-31989-4_11", DOI = "doi:10.1007/b107383", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "We apply genetic programming to the evolution of strategies for playing chess endgames. Our evolved programs are able to draw or win against an expert human-based strategy, and draw against CRAFTY---a world-class chess program, which finished second in the 2004 Computer Chess Championship.", notes = "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005", } @InProceedings{Hauptman:gecco05lbp, author = "Ami Hauptman and Moshe Sipper", title = "Analyzing the Intelligence of a Genetically Programmed Chess Player", booktitle = "Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}", year = "2005", month = "25-29 " # jun, editor = "Franz Rothlauf", address = "Washington, D.C., USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2005lbp/papers/21-hauptmann.pdf", keywords = "genetic algorithms, genetic programming", abstract = "We investigate a strong chess endgame player, previously evolved by us through genetic programming [1]. Its performance is analysed across four games, demonstrating the chess-playing capabilities developed through evolution. We end with a discussion of our GP-evolved player\'s pros and cons", notes = "Distributed on CD-ROM at GECCO-2005", } @InProceedings{eurogp07:hauptman, author = "Ami Hauptman and Moshe Sipper", title = "Evolution of an Efficient Search Algorithm for the Mate-In-N Problem in Chess", editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar", booktitle = "Proceedings of the 10th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "4445", year = "2007", address = "Valencia, Spain", month = "11-13 " # apr, pages = "78--89", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-71602-5", isbn13 = "978-3-540-71602-0", DOI = "doi:10.1007/978-3-540-71605-1_8", abstract = "We propose an approach for developing efficient search algorithms through genetic programming. Focusing on the game of chess we evolve entire game-tree search algorithms to solve the Mate-In-N problem: find a key move such that even with the best possible counterplays, the opponent cannot avoid being mated in (or before) move N. We show that our evolved search algorithms successfully solve several instances of the Mate-In-N problem, for the hardest ones developing 47percent less game-tree nodes than CRAFTY---a state-of-the-art chess engine with a ranking of 2614 points. Improvement is thus not over the basic alpha-beta algorithm, but over a world-class program using all standard enhancements.", notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007", } @InProceedings{DBLP:conf/gecco/HauptmanESK09, author = "Ami Hauptman and Achiya Elyasaf and Moshe Sipper and Assaf Karmon", title = "{GP-rush:} using genetic programming to evolve solvers for the {Rush Hour} puzzle", booktitle = "GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation", year = "2009", editor = "Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano {van Hemert} and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique Alba", pages = "955--962", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", URL = "http://dl.acm.org/citation.cfm?id=1570032", DOI = "doi:10.1145/1569901.1570032", abstract = "We evolve heuristics to guide IDA* search for the 6x6 and 8x8 versions of the Rush Hour puzzle, a PSPACE-Complete problem, for which no efficient solver has yet been reported. No effective heuristic functions are known for this domain, and--before applying any evolutionary thinking--we first devise several novel heuristic measures, which improve (non-evolutionary) search for some instances, but hinder search substantially for many other instances. We then turn to genetic programming (GP) and find that evolution proves immensely efficacious, managing to combine heuristics of such highly variable utility into composites that are nearly always beneficial, and far better than each separate component. GP is thus able to beat both the human player of the game and also the human designers of heuristics.", notes = "Also known as \cite{Hauptman2009}. GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009). ACM Order Number 910092.", } @InProceedings{Hauptman2010, author = "Ami Hauptman and Achiya Elyasaf and Moshe Sipper", title = "Evolving hyper heuristic-based solvers for {Rush Hour} and {FreeCell}", booktitle = "Proceedings of the 3rd Annual Symposium on Combinatorial Search, {SoCS 2010}", year = "2010", editor = "Ariel Felner and Nathan R. Sturtevant", pages = "149--150", address = "Stone Mountain, Atlanta, Georgia, USA", month = jul # " 8-10", publisher = "AAAI Press", keywords = "genetic algorithms, genetic programming, computer game, heuristics, rush hour, freecell:Poster ?", isbn13 = "978-1-57735-481-9", URL = "https://aaai.org/Library/SOCS/socs10contents.php", broken = "http://www.aaai.org/ocs/index.php/SOCS/SOCS10/paper/view/2079", URL = "http://www.aaai.org/ocs/index.php/SOCS/SOCS10/paper/viewFile/2079/2522.pdf", size = "2 pages", abstract = "We use genetic programming to evolve highly successful solvers for two puzzles: Rush Hour and FreeCell.", notes = "Section: Abstracts from Other Conference Papers. Cites \cite{DBLP:conf/gecco/HauptmanESK09} Abstracts http://movingai.com/socs/ May 2021, Dec 2022 http://aaai.org/ocs/index.php/SOCS/SOCS10/paper/view/2079 etc seem to be broken", } @PhdThesis{Hauptman:thesis, author = "Ami Hauptman", title = "Evolving Search Heuristics for Combinatorial Games with Genetic Programming", school = "Department of Computer Science, Faculty of Natural Sciences, Ben-Gurian University of the Negev", year = "2009", address = "Beer-Sheva, Israel", month = dec, keywords = "genetic algorithms, genetic programming, Heuristics, Artificial Intelligence, Search, IDA*, Chess, Endgames, FreeCell, Rush Hour, Complex Systems", URL = "https://www.nli.org.il/en/dissertations/NNL_ALEPH002801620/NLI", URL = "http://aranne5.bgu.ac.il/others/HauptmanAmi.pdf", size = "175 pages", abstract = "A combinatorial game is defined as a two-player, perfect-information game, with no chance elements [54]. In this work we focus on several generalized combinatorial games, a class defined by Hearn [76], whose characteristics are: 1) having only a finite (albeit large) number of positions; 2) the number of players may vary from zero to more than two (we consider only single-player and two-player games); and, 3) it should be possible (and easy) to determine all legal moves from a given position. Game-playing programs typically consist of two main elements: 1) search-tree node generation using search techniques, to traverse relevant game positions, and 2) an evaluation scheme for assessing the value of individual positions, known as the heuristic function (or evaluation function). Genetic Programming (GP) is a sub-class of evolutionary algorithms, in which a population of solutions to a given problem, embodied as LISP expressions, is improved over time by applying the principles of Darwinian evolution. At each stage, or generation, every solution quality is measured and assigned a numerical value, called fitness. During the course of evolution, natural (or, in our case, artificial) selection takes place, wherein individuals with high fitness values are more likely to generate offspring. In this dissertation, we explore the application of Genetic Programming to the development of search heuristics, for several hard, generalized combinatorial games, including Chess, Rush Hour, and FreeCell. We start by applying GP to the evolution of strategies for playing a group of chess endgames. Our first set of experiments gives rise to GP individuals capable of drawing (or even winning) against C RAFTY, a world-class chess program. We then turn to analysing the strategic capabilities of our evolved players and show that some of them are emergent. In the second set of experiments we devise new measures for determining the effectiveness of each GP terminal, which include testing it both singly,and in conjunction with other terminals. Results show that the whole (embodied as a full-fledged GP individual) is greater than the sum of its parts (the terminals). Since one of the main conclusions following our analysis is that search must be incorporated into our players, our next set of experiments deals with a novel way to combine search and knowledge using GP, by means of search-inducing terminals and functions. We report our experiments with the Mate-in-N problem in chess, in which we demonstrate how the amount of search effort, measured by the number of nodes visited by C RAFTY (when solving non-trivial problems with N = 4 and N = 5), can be reduced by up to 46percent, which is no mean feat when comparing to such a strong chess program. In the next part of this dissertation we attack a somewhat different type of problem, namely, single-player games. We show that, although the search algorithms used with these problems are different (specifically, Iterative-deepening A* and Heineman Staged Deepening), evolution of heuristics with GP can be applied successfully across the board. We evolve the first reported solver for the Rush Hour puzzle, a PSPACE-Complete problem, using a new approach of evolving value-returning policies. Our evolved solvers successfully compete both with non-evolved search and human players for the most difficult known instances of Rush Hour. Additionally, we advance the state-of-the-art of the most difficult known instances by co-evolving solvable 8x8 configurations, requiring over 15,000,000 nodes to solve with blind search. We demonstrate the efficacy of our method for these instances as well, showing that the search effort required to solve them may also be greatly reduced. We then apply our methods to the game of FreeCell, an NP-Complete problem used as a standard benchmark domain in several International Planning Competitions (IPCs). In practice this problem is much more difficult than Rush Hour, due to its typically large instances, a fact that is evidenced by the utter failure of methods such as A* and IDA* with this problem. We challenge the best solver to date, Heinemans Staged-Deepening algorithm, tailored specifically for this problem. We demonstrate that GP-evolved policies, when equipped with several hand-crafted heuristics, again greatly reduce the search effort of the best algorithm to date, as measured in multiple ways, including time and space required for search, and the percentage of problems solved. In the final chapter we draw conclusions from both types of problems, and discuss the variants of interactions between search and knowledge when evolving solvers for them. We also propose some future research directions. The work described in this dissertation was published in [69-74, 160], and won three Humie awards: two Bronze awards: one in 2005 and one in 2009, and a Silver award in 2007.", notes = "Supervisor: Moshe Sipper. cited by \cite{EvolvedToWin}", } @Article{hauptman:2023:Bioengineering, author = "Ami Hauptman and Ganesh M. Balasubramaniam and Shlomi Arnon", title = "Machine Learning Diffuse Optical Tomography Using Extreme Gradient Boosting and Genetic Programming", journal = "Bioengineering", year = "2023", volume = "10", number = "3", pages = "Article No. 382", keywords = "genetic algorithms, genetic programming", ISSN = "2306-5354", URL = "https://www.mdpi.com/2306-5354/10/3/382", DOI = "doi:10.3390/bioengineering10030382", abstract = "Diffuse optical tomography (DOT) is a non-invasive method for detecting breast cancer; however, it struggles to produce high-quality images due to the complexity of scattered light and the limitations of traditional image reconstruction algorithms. These algorithms can be affected by boundary conditions and have a low imaging accuracy, a shallow imaging depth, a long computation time, and a high signal-to-noise ratio. However, machine learning can potentially improve the performance of DOT by being better equipped to solve inverse problems, perform regression, classify medical images, and reconstruct biomedical images. In this study, we used a machine learning model called “XGBoost” to detect tumours in inhomogeneous breasts and applied a post-processing technique based on genetic programming to improve accuracy. The proposed algorithm was tested using simulated DOT measurements from complex inhomogeneous breasts and evaluated using the cosine similarity metrics and root mean square error loss. The results showed that the use of XGBoost and genetic programming in DOT could lead to more accurate and non-invasive detection of tumours in inhomogeneous breasts compared to traditional methods, with the reconstructed breasts having an average cosine similarity of more than 0.97 ± 0.07 and average root mean square error of around 0.1270 ± 0.0031 compared to the ground truth.", notes = "also known as \cite{bioengineering10030382}", } @Article{hauser:2015:CE, author = "Florian Hauser and Jurgen Huber and Bob Kaempff", title = "Costly Information in Markets with Heterogeneous Agents: A Model with Genetic Programming", journal = "Computational Economics", year = "2015", volume = "46", number = "2", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s10614-014-9439-6", DOI = "doi:10.1007/s10614-014-9439-6", } @InProceedings{Hausknecht:2012:GECCO, author = "Matthew Hausknecht and Piyush Khandelwal and Risto Miikkulainen and Peter Stone", title = "{HyperNEAT-GGP: a hyperNEAT-based Atari} General Game Player", booktitle = "GECCO '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference", year = "2012", editor = "Terry Soule and Anne Auger and Jason Moore and David Pelta and Christine Solnon and Mike Preuss and Alan Dorin and Yew-Soon Ong and Christian Blum and Dario Landa Silva and Frank Neumann and Tina Yu and Aniko Ekart and Will Browne and Tim Kovacs and Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and Giovanni Squillero and Nicolas Bredeche and Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and Martin Pelikan and Silja Meyer-Nienberg and Christian Igel and Greg Hornby and Rene Doursat and Steve Gustafson and Gustavo Olague and Shin Yoo and John Clark and Gabriela Ochoa and Gisele Pappa and Fernando Lobo and Daniel Tauritz and Jurgen Branke and Kalyanmoy Deb", pages = "217--224", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, digital entertainment technologies and arts", isbn13 = "978-1-4503-1177-9", URL = "http://nn.cs.utexas.edu/downloads/papers/hausknecht.gecco12.pdf", DOI = "doi:10.1145/2330163.2330195", size = "8 pages", abstract = "This paper considers the challenge of enabling agents to learn with as little domain-specific knowledge as possible. The main contribution is HyperNEAT-GGP, a HyperNEAT-based General Game Playing approach to Atari games. By leveraging the geometric regularities present in the Atari game screen, HyperNEAT effectively evolves policies for playing two different Atari games, Asterix and Freeway. Results show that HyperNEAT-GGP outperforms existing benchmarks on these games. HyperNEAT-GGP represents a step towards the ambitious goal of creating an agent capable of learning and seamlessly transitioning between many different tasks.", notes = "Also known as \cite{2330195} GECCO-2012 A joint meeting of the twenty first international conference on genetic algorithms (ICGA-2012) and the seventeenth annual genetic programming conference (GP-2012)", } @InProceedings{haut:2022:GECCOcomp, author = "Nathaniel Haut and Wolfgang Banzhaf and Bill Punch", title = "Active Learning Improves Performance on Regression Tasks {inStackGP}", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion", year = "2022", editor = "Heike Trautmann and Carola Doerr and Alberto Moraglio and Thomas Bartz-Beielstein and Bogdan Filipic and Marcus Gallagher and Yew-Soon Ong and Abhishek Gupta and Anna V Kononova and Hao Wang and Michael Emmerich and Peter A. N. Bosman and Daniela Zaharie and Fabio Caraffini and Johann Dreo and Anne Auger and Konstantin Dietric and Paul Dufosse and Tobias Glasmachers and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Tea Tusar and Dimo Brockhoff and Tome Eftimov and Pascal Kerschke and Boris Naujoks and Mike Preuss and Vanessa Volz and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Mark Coletti and Catherine (Katie) Schuman and Eric ``Siggy'' Scott and Robert Patton and Paul Wiegand and Jeffrey K. Bassett and Chathika Gunaratne and Tinkle Chugh and Richard Allmendinger and Jussi Hakanen and Daniel Tauritz and John Woodward and Manuel Lopez-Ibanez and John McCall and Jaume Bacardit and Alexander Brownlee and Stefano Cagnoni and Giovanni Iacca and David Walker and Jamal Toutouh and UnaMay O'Reilly and Penousal Machado and Joao Correia and Sergio Nesmachnow and Josu Ceberio and Rafael Villanueva and Ignacio Hidalgo and Francisco {Fernandez de Vega} and Giuseppe Paolo and Alex Coninx and Antoine Cully and Adam Gaier and Stefan Wagner and Michael Affenzeller and Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and William B. Langdon and Justyna Petke and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Paetzel and Alexander Wagner and Michael Heider and Nadarajen Veerapen and Katherine Malan and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Mohammad Nabi Omidvar and Yuan Sun and Ernesto Tarantino and De Falco Ivanoe and Antonio {Della Cioppa} and Scafuri Umberto and John Rieffel and Jean-Baptiste Mouret and Stephane Doncieux and Stefanos Nikolaidis and Julian Togelius and Matthew C. Fontaine and Serban Georgescu and Francisco Chicano and Darrell Whitley and Oleksandr Kyriienko and Denny Dahl and Ofer Shir and Lee Spector and Alma Rahat and Richard Everson and Jonathan Fieldsend and Handing Wang and Yaochu Jin and Erik Hemberg and Marwa A. Elsayed and Michael Kommenda and William {La Cava} and Gabriel Kronberger and Steven Gustafson", pages = "550--553", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, symbolic regression, active learning", isbn13 = "978-1-4503-9268-6/22/07", DOI = "doi:10.1145/3520304.3528941", abstract = "This paper introduces an active learning method for symbolic regression using StackGP. The approach begins with a small number of data points for StackGP to model. To improve the model the system incrementally adds the data point characterized by maximizing prediction uncertainty as measured by the model ensemble. Symbolic regression is re-run with the larger data set. This cycle continues until the system satisfies a termination criterion. The Feynman AI benchmark set of equations is used to examine the ability of the method to find appropriate models using as few data points as possible. The approach successfully rediscovered 72 of the 100 Feynman equations without the use of domain expertise or data translation.", notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @InProceedings{Haut:2023:GPTP, author = "Nathan Haut and Wolfgang Banzhaf and Bill Punch and Dirk Colbry", title = "Accelerating Image Analysis Research with Active Learning Techniques in Genetic Programming", booktitle = "Genetic Programming Theory and Practice XX", year = "2023", editor = "Stephan Winkler and Leonardo Trujillo and Charles Ofria and Ting Hu", series = "Genetic and Evolutionary Computation", pages = "45--64", address = "Michigan State University, USA", month = jun # " 1-3", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-981-99-8412-1", DOI = "doi:10.1007/978-981-99-8413-8_3", abstract = "The efficacy of active learning in genetic programming (AL-GP) for image processing tasks was explored using two new population-based machine learning systems, decision tree genetic programming and SEE-Segment. Active learning was shown to improve the rate and consistency at which good models are found while reducing the required number of training samples to achieve good solutions in both ML systems. The importance of diversity in ensembles for AL-GP was revealed by varying the definition for diversity when performing active learning with SEE-Segment. It was also demonstrated how AL-GP was deployed in a research setting to help automate and accelerate progress by guiding labeling of training samples (human cells) to inform the development of classification models which were then used to automatically classify cells in video frames.", notes = " Part of \cite{Hu:2023:GPTP} published after the workshop in 2024", } @Misc{DBLP:journals/corr/abs-2308-00672, author = "Nathan Haut and Wolfgang Banzhaf and Bill Punch", title = "Active Learning in Genetic Programming: Guiding Efficient Data Collection for Symbolic Regression", howpublished = "arXiv", volume = "abs/2308.00672", year = "2023", month = "31 " # jul, keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.48550/arXiv.2308.00672", DOI = "doi:10.48550/ARXIV.2308.00672", eprinttype = "arXiv", eprint = "2308.00672", timestamp = "Mon, 21 Aug 2023 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/corr/abs-2308-00672.bib", bibsource = "dblp computer science bibliography, https://dblp.org", size = "26 pages", abstract = "we examines various methods of computing uncertainty and diversity for active learning in genetic programming. We found that the model population in genetic programming can be exploited to select informative training data points by using a model ensemble combined with an uncertainty metric. We explored several uncertainty metrics and found that differential entropy performed the best. We also compared two data diversity metrics and found that correlation asa diversity metric performs better than minimum Euclidean distance, although there are some drawbacks that prevent correlation from being used on all problems. Finally, we combined uncertainty and diversity using a Pareto optimisation approach to allow both to be considered in a balanced way to guide the selection of informative and unique data points for training.", notes = "See also \cite{Haut:ieeeTEC}", } @InProceedings{haut:2023:GECCOcomp, author = "Nathan Haut and Bill Punch and Wolfgang Banzhaf", title = "Active Learning Informs Symbolic Regression Model Development in Genetic Programming", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "587--590", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, active learning, symbolic regression: Poster", isbn13 = "9798400701191", DOI = "doi:10.1145/3583133.3590577", size = "4 pages", abstract = "Active learning for genetic programming using model ensemble uncertainty was explored across a range of uncertainty metrics to determine if active learning can be used with GP to minimize training set sizes by selecting maximally informative samples to guide evolution. The choice of uncertainty metric was found to have a significant impact on the success of active learning to inform model development in genetic programming. Differential evolution was found to be an effective optimizer, likely due to the non-convex nature of the uncertainty space, while differential entropy was found to be an effective uncertainty metric. Uncertainty-based active learning was compared to two random sampling methods and the results show that active learning successfully identified informative samples and can be used with GP to reduce required training set sizes to arrive at a solution.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @Article{Haut:ieeeTEC, author = "Nathan Haut and Wolfgang Banzhaf and Bill Punch", title = "Active Learning in Genetic Programming: Guiding Efficient Data Collection for Symbolic Regression", journal = "IEEE Transactions on Evolutionary Computation", note = "Early Access", keywords = "genetic algorithms, genetic programming, Training, Data models, Uncertainty, Mathematical models, Measurement, Machine learning, Labeling, Genetic programming, Training data, Computational modeling, Active learning, symbolic regression", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2024.3471341", size = "13 pages", abstract = "This paper examines various methods of computing uncertainty and diversity for active learning in genetic programming. We found that the model population in genetic programming can be exploited to select informative training data points by using a model ensemble combined with an uncertainty metric. We explored several uncertainty metrics and found that differential entropy performed the best. We also compared two data diversity metrics and found that correlation as a diversity metric performs better than minimum Euclidean distance, although there are some drawbacks that prevent correlation from being used on all problems. Finally, we combined uncertainty and diversity using a Pareto optimization approach to allow both to be considered in a balanced way to guide the selection of informative and unique data points for training.", notes = "also known as \cite{10700803} See also \cite{DBLP:journals/corr/abs-2308-00672}", } @Article{Havlicek:2013:IBH, author = "Vojtech Havlicek and Martin Hanel and Petr Maca and Michal Kuraz and Pavel Pech", title = "Incorporating basic hydrological concepts into genetic programming for rainfall-runoff forecasting", journal = "Computing", year = "2013", volume = "95", number = "1supplement", pages = "363--380", month = may, note = "Special Issue on ESCO2012.", keywords = "genetic algorithms, genetic programming, SORD!", bibdate = "Wed Jan 29 10:23:33 MST 2014", bibsource = "http://springerlink.metapress.com/openurl.asp?genre=issue&issn=0010-485X&volume=95&issue=1; http://www.math.utah.edu/pub/tex/bib/computing.bib", acknowledgement = "Nelson H. F. Beebe, University of Utah, Department of Mathematics, 110 LCB, 155 S 1400 E RM 233, Salt Lake City, UT 84112-0090, USA, Tel: +1 801 581 5254, FAX: +1 801 581 4148, e-mail: \path|beebe@math.utah.edu|, \path|beebe@acm.org|, \path|beebe@computer.org| (Internet), URL: \path|http://www.math.utah.edu/~beebe/|", ISSN = "0010-485X", URL = "http://link.springer.com/article/10.1007/s00607-013-0298-0", URL = "http://dx.doi.org/10.1007/s00607-013-0298-0", DOI = "doi:10.1007/s00607-013-0298-0", size = "18 pages", abstract = "This paper focuses on improving rainfall-runoff forecasts by a combination of genetic programming (GP) and basic hydrological modelling concepts. GP is a general optimisation technique for making an automated search of a computer program that solves some particular problem. The SORD! program was developed for the purposes of this study (in the R programming language). It is an implementation of canonical GP. Special functions are used for a combined approach of hydrological concepts and GP. The special functions are a reservoir model, a simple moving average model, and a cumulative sum and delay operator. The efficiency of the approach presented here is tested on runoff predictions for five catchments of various sizes. The input data consists of daily rainfall and runoff series. The forecast step is one day. The performance of the proposed approach is compared with the results of the artificial neural network model (ANN) and with the GP model without special functions. GP combined with these concepts provides satisfactory performance, and the simulations seem to be more accurate than the results of ANN and GP without these functions. An additional advantage of the proposed approach is that it is not necessary to determine the input lag, and there is better convergence. The SORD! program provides an easy-to-use alternative for data-oriented modelling combined with simple concepts used in hydrological modelling.", notes = "R programming language. correct acknowledgement is: This work was supported by the Technology Agency of the Czech Republic, grant TA02020139. The authors wish to acknowledge the MOPEX project staff, which are associated with data providing and management.", } @MastersThesis{haynes:1994:masters, author = "Thomas D. Haynes", title = "A Simulation of Adaptive Agents in a Hostile Environment", school = "University of Tulsa", year = "1994", address = "Tulsa, OK, USA", month = apr, keywords = "genetic algorithms, genetic programming", broken = "http://euler.mcs.utulsa.edu/~haynes/masters.ps", URL = "http://citeseer.ist.psu.edu/2240.html", size = "254 pages", abstract = "The Genetic Programming Algorithm is used to construct an Autonomous Agent capable of learning how to survive a hostile environment. Randomly generated programs, which control the interaction of the Agent with its environment, are recombined to form better programs. Each generation of the population of Agents is placed into the Simulator with the ultimate goal of producing an Agent capable of surviving any environment. The Simulator determines the raw fitness of each Agent by interpreting the associated program. General programs are evolved to solve this problem. Different environmental setups are presented to show the generality of the solution. Certain constructs always appear to facilitate the solution of subproblems of the task. This is evidenced in similar responses of the Average Fitness per Generation curves for the different runs.", notes = " ", } @TechReport{Hayes:1994:ecs, author = "Thomas Haynes and Roger Wainwright and Sandip Sen", title = "Evolving Cooperation Strategies", institution = "The University of Tulsa", year = "1994", type = "Technical Report", number = "UTULSA-MCS-94-10", address = "Tulsa, OK, USA", month = "16 " # dec, keywords = "genetic algorithms, genetic programming, ccoperation strategies", URL = "http://www.mcs.utulsa.edu/~rogerw/papers/Haynes-icmas95.pdf", abstract = "The identification, design, and implementation of strategies for cooperation is a central research issue in the field of Distributed Artificial Intelligence (DAI). We propose a novel approach to the construction of cooperation strategies for a group of problem solvers based on the Genetic Programming (GP) paradigm. GP's are a class of adaptive algorithms used to evolve solution structures that optimize a given evaluation criterion. Our approach is based on designing a representation for cooperation strategies that can be manipulated by GPs. We present results from experiments in the predator-prey domain, which has been extensively studied as an easy-to-describe but difficult-to-solve cooperation problem domain. They key aspect of our approach is the minimal reliance on domain knowledge and human intervention in the construction of good cooperation strategies. Promising comparison results with prior systems lend credence to the viability of this approach.", size = "9 pages", notes = " ", } @InProceedings{Hayes:1995:agents, author = "Thomas D. Haynes and Roger L. Wainwright", title = "A Simulation of Adaptive Agents in Hostile Environment", booktitle = "Proceedings of the 1995 ACM Symposium on Applied Computing", year = "1995", editor = "K. M. George and Janice H. Carroll and Ed Deaton and Dave Oppenheim and Jim Hightower", pages = "318--323", address = "Nashville, USA", publisher = "ACM Press", keywords = "genetic algorithms, genetic programming", URL = "http://www.mcs.utulsa.edu/~rogerw/papers/Haynes-sac95.ps", URL = "http://citeseer.ist.psu.edu/2240.html", DOI = "doi:10.1145/315891.316007", size = "8 pages", abstract = "In this paper we use the genetic programming technique to evolve programs to control an autonomous agent capable of learning how to survive in a hostile environment. In order to facilitate this goal, agents are run through random environment configurations. Randomly generated programs, which control the interaction of the agent with its environment, are recombined to form better programs. Each generation of the population of agents is placed into the Simulator with the ultimate goal of producing an agent capable of surviving any environment. The environment that an agent is presented consists of other agents, mines, and energy. The goal of this research is to construct a program which when executed will allow an agent (or agents) to correctly sense, and mark, the presence of items (energy and mines) in any environment. The Simulator determines the raw fitness of each agent by interpreting the associated program. General programs are evolved to solve this problem. Different environmental setups are presented to show the generality of the solution. These environments include one agent in a fixed environment, one agent in a fluctuating environment, and multiple agents in a fluctuating environment cooperating together. The genetic programming technique was extremely successful. The average fitness per generation in all three environments tested showed steady improvement. Programs were successfully generated that enabled an agent to handle any possible environment.", notes = "Agent has access to memory holding information on locations it has already visited. Agents are run through random environment configurations. Environment contains other agents, lethal mines and energy. Agents aims to sense and mark these. One example: multiple agents cooperating in a fluctating environment. GP generated an {"}agent to handle any possible enironment{"}.", } @InProceedings{Hayes:1995:ecsICMAS, author = "Thomas D. Haynes and Roger L. Wainwright and Sandip Sen", title = "Evolving Cooperating Strategies", booktitle = "Proceedings of the first International Conference on Multiple Agent Systems", year = "1995", editor = "Victor Lesser", pages = "450", address = "San Francisco, USA", month = "12--14 " # jun, publisher = "AAAI Press/MIT Press", note = "Poster", keywords = "genetic algorithms, genetic programming, evolutionary computation, cooperation strategies, poster", ISBN = "0-262-62102-9", URL = "http://www.mcs.utulsa.edu/~rogerw/papers/Haynes-icmas95.pdf", size = "1 page", abstract = "The identification, design, and implementation of strategies for cooperation is a central research issue in the field of Distributed Artificial Intelligence (DAI). We propose a novel approach to the construction of cooperation strategies for a group of problem solvers based on the Genetic Programming (GP) paradigm. GP's are a class of adaptive algorithms used to evolve solution structures that optimize a given evaluation criterion. Our approach is based on designing a representation for cooperation strategies that can be manipulated by GPs. We present results from experiments in the predator-prey domain, which has been extensively studied as an easy-to-describe but difficult-to-solve cooperation problem domain. They key aspect of our approach is the minimal reliance on domain knowledge and human intervention in the construction of good cooperation strategies. Promising comparison results with prior systems lend credence to the viability of this approach.", notes = "13 page version available via url ", } @InProceedings{Hayes:1995, author = "Thomas Haynes and Roger Wainwright and Sandip Sen and Dale Schoenefeld", title = "Strongly typed genetic programming in evolving cooperation strategies", booktitle = "Genetic Algorithms: Proceedings of the Sixth International Conference (ICGA95)", year = "1995", editor = "Larry J. Eshelman", pages = "271--278", address = "Pittsburgh, PA, USA", publisher_address = "San Francisco, CA, USA", month = "15-19 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-370-0", URL = "http://www.mcs.utulsa.edu/~rogerw/papers/Haynes-icga95.pdf", abstract = "A key concern in genetic programming (GP) is the size of the state-space which must be searched for large and complex problem domains. One method to reduce the state-space size is by using Strongly Typed Genetic Programming (STGP). We applied both GP and STGP to construct cooperation strategies to be used by multiple predator agents to pursue and capture a prey agent on a grid-world. This domain has been extensively studied in Distributed Artificial Intelligence (DAI) as an easy-to-describe but difficult-to-solve cooperation problem. The evolved programs from our systems are competitive with manually derived greedy algorithms. In particular the STGP paradigm evolved strategies in which the predators were able to achieve their goal without explicitly sensing the location of other predators or communicating with other predators. This represents an improvement over previous research in this area. The results of our experiments indicate that STGP is able to evolve programs that perform significantly better than GP evolved programs. In addition, the programs generated by STGP were easier to understand.", notes = "Our printers barf at graph on page 8. ", } @InProceedings{Hayes:1995:ebspp, author = "Thomas Haynes and Sandip Sen", title = "Evolving Behavioral Strategies in Predators and Prey", booktitle = "IJCAI-95 Workshop on Adaptation and Learning in Multiagent Systems", year = "1995", editor = "Sandip Sen", pages = "32--37", address = "Montreal, Quebec, Canada", publisher_address = "San Francisco, CA, USA", month = "20-25 " # aug, organisation = "IJCAII,AAAI,CSCSI", publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, cooperation strategies", broken = "http://euler.mcs.utulsa.edu/~haynes/icjai95.ps", URL = "http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/734/http:zSzzSzwww.cs.twsu.eduzSz~hayneszSzicjai95.pdf/haynes96evolving.pdf", URL = "http://citeseer.ist.psu.edu/haynes96evolving.html", abstract = "The predator/prey domain is used to conduct research in Distributed Artificial Intelligence. Genetic Programing is used to evolve behavioral strategies for the predator agents. To further the utility of the predator strategies, the prey population is allowed to evolve at the same time. The expected competitive learning cycle did not surface. This failing is investigated, and a simple prey algorithm surfaces, which is consistently able to evade capture from the predator algorithms.", notes = "see also \cite{Haynes:1996:EBS} ", } @InProceedings{Haynes95:Team, author = "Thomas Haynes and Sandip Sen and Dale Schoenefeld and Roger Wainwright", title = "Evolving a Team", booktitle = "Working Notes for the AAAI Symposium on Genetic Programming", year = "1995", editor = "E. V. Siegel and J. R. Koza", pages = "23--30", address = "MIT, Cambridge, MA, USA", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025, USA", month = "10--12 " # nov, publisher = "AAAI", keywords = "genetic algorithms, genetic programming", URL = "http://www.aaai.org/Papers/Symposia/Fall/1995/FS-95-01/FS95-01-004.pdf", URL = "http://www.mcs.utulsa.edu/~rogerw/papers/Haynes-team.pdf", URL = "http://www.aaai.org/Library/Symposia/Fall/fs95-01.php#23", size = "8 pages", abstract = "We introduce a cooperative co--evolutionary system to facilitate the development of teams of agents. Specifically, we deal with the credit assignment problem of how to fairly split the fitness of a team to all of its participants. We believe that ${k}$ different strategies for controlling the actions of a group of ${k}$ agents can combine to form a cooperation strategy which efficiently results in attaining a global goal. A concern is the amount of time needed to either evolve a good team or reach convergence. We present several crossover mechanisms to reduce this time. Even with this mechanisms, the time is large; which precluded the gathering of sufficient data for a statistical base.", notes = "AAAI-95f GP. Part of \cite{siegel:1995:aaai-fgp} {\em Telephone:} 415-328-3123 {\em Fax:} 415-321-4457 {\em email} info@aaai.org {\em URL:} http://www.aaai.org/", } @InCollection{Haynes95:Prey, author = "Thomas Haynes and Sandip Sen", title = "Evolving Behavioral Strategies in Predators and Prey", booktitle = "Adaptation and Learning in Multiagent Systems", publisher = "Springer Verlag", year = "1995", editor = "Gerhard Wei{\ss} and Sandip Sen", volume = "1042", series = "Lecture Notes in Artificial Intelligence", pages = "113--126", address = "Berlin, Germany", keywords = "genetic algorithms, genetic programming, STGP", isbn13 = "978-3-540-60923-0", DOI = "doi:10.1007/3-540-60923-7_22", abstract = "The predator/prey domain is used to conduct research in Distributed Artificial Intelligence. Genetic Programming is used to evolve behavioural strategies for the predator agents. To further the utility of the predator strategies, the prey population is allowed to evolve at the same time. The expected competitive learning cycle did not surface. This failing is investigated, and a simple prey algorithm surfaces, which is consistently able to evade capture from the predator algorithms.", size = "14 pages", notes = "Published in 1996? ", affiliation = "The University of Tulsa Department of Mathematical & Computer Sciences USA USA", } @TechReport{Haynes:1995:EMC, author = "Thomas Haynes and Sandip Sen and Dale Schoenefeld and Roger Wainwright", title = "Evolving Multiagent Coordination Strategies with Genetic Programming", number = "UTULSA-MCS-95-04", institution = "The University of Tulsa", year = "1995", month = may # " 31,", keywords = "genetic algorithms, genetic programming", URL = "http://www.mcs.utulsa.edu/~rogerw/papers/Haynes-jp.pdf", URL = "http://citeseer.ist.psu.edu/26626.html", abstract = "The design and development of behavioral strategies to coordinate the actions of multiple agents is a central issue in multiagent systems research. We propose a novel approach of evolving, rather than handcrafting, behavioral strategies. The evolution scheme used is a variant of the Genetic Programming (GP) paradigm. As a proof of principle, we evolve behavioral strategies in the predator-prey domain that has been studied widely in the Distributed Artificial Intelligence community. We use the GP to evolve behavioral strategies for individual agents, as prior literature claims that communication between predators is not necessary for successfully capturing the prey. The evolved strategy, when used by each predator, performs better than all but one of the handcrafted strategies mentioned in literature. We analyze the shortcomings of each of these strategies. The next set of experiments involve co-evolving predators and prey. To our surprise, a simple prey strategy evolves that consistently evades all of the predator strategies. We analyze the implications of the relative successes of evolution in the two sets of experiments and comment on the nature of domains for which GP based evolution is a viable mechanism for generating coordination strategies. We conclude with our design for concurrent evolution of multiple agent strategies in domains where agents need to communicate with each other to successfully solve a common problem.", notes = " ", } @InCollection{haynes:1996:aigp2, author = "Thomas D. Haynes and Dale A. Schoenefeld and Roger L. Wainwright", title = "Type Inheritance in Strongly Typed Genetic Programming", booktitle = "Advances in Genetic Programming 2", publisher = "MIT Press", year = "1996", editor = "Peter J. Angeline and K. E. {Kinnear, Jr.}", pages = "359--376", chapter = "18", address = "Cambridge, MA, USA", keywords = "genetic algorithms, genetic programming", ISBN = "0-262-01158-1", URL = "http://www.mcs.utulsa.edu/~rogerw/papers/Haynes-hier.pdf", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6277530", DOI = "doi:10.7551/mitpress/1109.003.0024", size = "17 pages", abstract = "Genetic Programming (GP) is an automatic method for generating computer programs, which are stored as data structures and manipulated to evolve better programs. An extension restricting the search space is Strongly Typed Genetic Programming (STGP), which has, as a basic premise, the removal of closure by typing both the arguments and return values of functions, and by also typing the terminal set. A restriction of STGP is that there are only two levels of typing. We extend STGP by allowing a type hierarchy, which allows more than two levels of typing.", } @InProceedings{haynes:1996:esr, author = "Thomas Haynes and Rose Gamble and Leslie Knight and Roger Wainwright", title = "Entailment for Specification Refinement", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "90--97", address = "Stanford University, CA, USA", publisher_address = "Cambridge, MA, USA", publisher = "MIT Press", URL = "http://www.mcs.utulsa.edu/~rogerw/papers/Haynes-theorem.pdf", size = "9 pages", abstract = "Specification refinement is part of formal program derivation, a method by which software is directly constructed from a provably correct specification. Because program derivation is an intensive manual exercise used for critical software systems, an automated approach would allow it to be viable for many other types of software systems. The goal of this research is to determine if genetic programming (GP) can be used to automate the specification refinement process. The initial steps toward this goal are to show that a well--known proof logic for program derivation can be encoded such that a GP--based system can infer sentences in the logic for proof of a particular sentence. The results are promising and indicate that GP can be useful in aiding program derivation.", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap11.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96", } @TechReport{Haynes:1995:CDG, author = "Thomas Haynes", title = "Clique Detection via Genetic Programming", number = "UTULSA-MCS-95-02", institution = "The University of Tulsa", year = "1995", month = apr # " 24,", keywords = "genetic algorithms, genetic programming", broken = "http://euler.mcs.utulsa.edu/~haynes/tr_clique.ps", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/2785/http:zSzzSzeuler.mcs.utulsa.eduzSz~hayneszSztr_clique.pdf/haynes95clique.pdf", URL = "http://citeseer.ist.psu.edu/135522.html", abstract = "Genetic Programming is used as a technique for detecting cliques in a network. Candidate cliques are represented in lists, and the lists are manipulated such that larger cliques are formed from the candidates. The clique detection problem has some interesting implications to the Strongly Typed Genetic Programming paradigm, namely in forming a class hierarchy. The problem is also useful in that it is easy to add noise.", } @TechReport{Haynes:1996:CDGb, author = "Thomas Haynes and Dale Schoenefeld", title = "Clique Detection via Genetic Programming", number = "UTULSA-MCS-96-05", institution = "The University of Tulsa", month = mar # " 15,", notes = "Full version of GP'96 poster", year = "1996", keywords = "genetic algorithms, genetic programming", broken = "http://euler.mcs.utulsa.edu/~haynes/clique.ps", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/4146/http:zSzzSzeuler.mcs.utulsa.eduzSz~hayneszSzclique.pdf/haynes95clique.pdf", URL = "http://citeseer.ist.psu.edu/haynes95clique.html", abstract = "Genetic programming is applied to the task of finding all of the cliques in a graph. Nodes in the graph are represented as tree structures, which are then manipulated to form candidate cliques. The intrinsic properties of clique detection complicates the design of a good fitness evaluation. We analyze those properties, and show the clique detector is found to be better at finding the maximum clique in the graph, not the set of all cliques.", } @InProceedings{haynes:1996:cdGP, author = "Thomas Haynes and Dale Schoenefeld", title = "Clique Detection via Genetic Programming", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "426", address = "Stanford University, CA, USA", publisher_address = "Cambridge, MA, USA", publisher = "MIT Press", size = "1 page", abstract = "Genetic programming is applied to the task of finding all of the cliques in a graph. Nodes in the graph are represented as tree structures, which are then manipulated to form candidate cliques. The intrinsic properties of clique detection complicates the design of a good fitness evaluation. We analyze those properties, and show the clique detector is found to be better at finding the maximum clique in the graph, not the set of all cliques.", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap65.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96 see also technical report Haynes:1995:CDGb", } @TechReport{Haynes:1996:DCSa, author = "Thomas Haynes", title = "Duplication of Coding Segments in Genetic Programming", number = "UTULSA-MCS-96-03", institution = "The University of Tulsa", month = mar # " 11,", year = "1996", keywords = "genetic algorithms, genetic programming", notes = "Longer version of AAAI '96 paper \cite{Haynes:1996:DCSb}", broken = "http://euler.mcs.utulsa.edu/~haynes/tr_duplicate.ps", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/989/http:zSzzSzwww.umsl.eduzSz~hayneszSztr_duplicate.pdf/haynes96duplication.pdf", URL = "http://citeseer.ist.psu.edu/haynes96duplication.html", abstract = "Research into the utility of non--coding segments, or introns, in genetic--based encodings has shown that they expedite the evolution of solutions in domains by protecting building blocks against destructive crossover. We consider a genetic programming system where non--coding segments can be removed, and the resultant chromosomes returned into the population. This parsimonious repair leads to premature convergence, since as we remove the naturally occurring non--coding segments, we strip away their protective backup feature. We then duplicate the coding segments in the repaired chromosomes, and place the modified chromosomes into the population. The duplication method significantly improves the learning rate in the domain we have considered. We also show that this method can be applied to other domains.", } @InProceedings{Haynes:1996:DCSb, author = "Thomas Haynes", title = "Duplication of Coding Segments in Genetic Programming", booktitle = "Proceedings of the Thirteenth National Conference on Artificial Intelligence", month = "4-6 " # aug, year = "1996", address = "Portland, USA", volume = "1", publisher = "AAAI Press / MIT Press", ISBN = "0-262-51091-X", pages = "344--349", keywords = "genetic algorithms, genetic programming", notes = "see also tech report \cite{Haynes:1996:DCSa}", abstract = "Research into the utility of non--coding segments, or introns, in genetic--based encodings has shown that they expedite the evolution of solutions in domains by protecting building blocks against destructive crossover. We consider a genetic programming system where non--coding segments can be removed, and the resultant chromosomes returned into the population. This parsimonious repair leads to premature convergence, since as we remove the naturally occurring non--coding segments, we strip away their protective backup feature. We then duplicate the coding segments in the repaired chromosomes, and place the modified chromosomes into the population. The duplication method significantly improves the learning rate in the domain we have considered. We also show that this method can be applied to other domains.", URL = "http://citeseer.ist.psu.edu/haynes96duplication.html", } @InCollection{Haynes:1996:EBS, author = "Thomas Haynes and Sandip Sen", title = "Evolving Behavioral Strategies in Predators and Prey", pages = "113--126", editor = "Gerhard Wei{\ss} and Sandip Sen", booktitle = "Adaptation and Learning in Multi--Agent Systems", year = "1996", publisher = "Springer Verlag", series = "Lecture Notes in Artificial Intelligence", address = "Berlin, Germany", keywords = "genetic algorithms, genetic programming", notes = "see also \cite{Hayes:1995:ebspp}", broken = "http://euler.mcs.utulsa.edu/~haynes/icjai95.ps", URL = "http://citeseer.ist.psu.edu/rd/13718071%2C21714%2C1%2C0.25%2CDownload/http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/734/http:zSzzSzwww.cs.twsu.eduzSz%7EhayneszSzicjai95.pdf/haynes96evolving.pdf", URL = "http://citeseer.ist.psu.edu/haynes96evolving.html", abstract = "The predator/prey domain is used to conduct research in Distributed Artificial Intelligence. Genetic Programing is used to evolve behavioral strategies for the predator agents. To further the utility of the predator strategies, the prey population is allowed to evolve at the same time. The expected competitive learning cycle did not surface. This failing is investigated, and a simple prey algorithm surfaces, which is consistently able to evade capture from the predator algorithms.", } @TechReport{Haynes:1996:CF, author = "Thomas Haynes and Sandip Sen", title = "Cooperation of the Fittest", number = "UTULSA-MCS-96-09", institution = "The University of Tulsa", year = "1996", month = apr # " 12,", size = "9+ pages", keywords = "genetic algorithms, genetic programming", broken = "http://euler.mcs.utulsa.edu/~haynes/coopevol.ps", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/2230/http:zSzzSzeuler.mcs.utulsa.eduzSz~hayneszSzcoopevol.pdf/haynes96cooperation.pdf", URL = "http://citeseer.ist.psu.edu/haynes96cooperation.html", abstract = "We introduce a cooperative co-evolutionary system to facilitate the development of teams of heterogeneous agents. We believe that $k$ different behavioral strategies for controlling the actions of a group of $k$ agents can combine to form a cooperation strategy which efficiently achieves global goals. We examine the on-line adaption of behavioral strategies using genetic programming. Specifically, we deal with the credit assignment problem of how to fairly split the fitness of a team to all of its participants. We present several crossover mechanisms in a genetic programming system to facilitate the evolution of more than one member in the team during each crossover operation. Our goal is to reduce the time needed to either evolve a good team or reach convergence.", notes = "evolution of cooperation (multi-agent,multi-tree) NOT coevolution of fitness function evolution. Our printer barfs on page 9.", } @InProceedings{haynes:1996:cms, author = "Thomas Haynes", title = "Collective Memory Search", booktitle = "Late Breaking Papers at the Genetic Programming 1996 Conference Stanford University July 28-31, 1996", year = "1996", editor = "John R. Koza", pages = "38--46", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California 94305-3079, USA", month = "28--31 " # jul, publisher = "Stanford Bookstore", ISBN = "0-18-201031-7", keywords = "genetic algorithms, genetic programming", notes = "GP-96LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670 see \cite{haynes:1997:cms}", } @InProceedings{haynes1996:cf, author = "Thomas Haynes and Sandip Sen", title = "Cooperation of the Fittest", booktitle = "Late Breaking Papers at the Genetic Programming 1996 Conference Stanford University July 28-31, 1996", year = "1996", editor = "John R. Koza", pages = "47--55", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California 94305-3079, USA", month = "28--31 " # jul, publisher = "Stanford Bookstore", ISBN = "0-18-201031-7", keywords = "genetic algorithms, genetic programming", notes = "GP-96LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670", } @InProceedings{haynes:1997:cms, author = "Thomas Haynes", title = "Collective Memory Search", booktitle = "Proceedings of the 1997 ACM Symposium on Applied Computing", year = "1997", editor = "Barrett Bryant and Janice Carroll and Dave Oppenheim and Jim Hightower and K. M. George", pages = "217--222", address = "Hyatt Sainte Claire Hotel, San Jose, California, USA", publisher_address = "New York", month = "28 " # feb # "-2 " # mar, publisher = "Association for Computing Machinery", keywords = "genetic algorithms, genetic programming", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/2284/http:zSzzSzadept.cs.twsu.eduzSz~thomaszSzcollect.pdf/haynes97collective.pdf", URL = "http://citeseer.ist.psu.edu/haynes97collective.html", abstract = "Collective action has been examined to expedite search in optimisation problems [ Dorigo et al., 1996 ] . Collective memory has been applied to learning in multiagent systems [ Garland and Alterman, 1996 ] . We integrate the simplicity of collective action with the pattern detection of collective memory to significantly improve both the gathering and processing of knowledge. We investigate the augmentation of distributed search in genetic programming based systems with collective memory. Four...", notes = "ACM SAC-97 0-89791-850-9 citeseer says twsu.edu/~thomas/collect.ps see \cite{haynes:1996:cms}", } @InProceedings{Haynes:1997:adskr, author = "Thomas Haynes", title = "On-line Adaptation of Search via Knowledge Reuse", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming, distributed search", pages = "156--161", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.54.3381", size = "8 pages", abstract = "We have integrated the distributed search of genetic programming based systems with collective memory to form a collective adaptation search method. Such a system significantly improves search as problem complexity is increased. In collective adaptation, search agents gather knowledge of their environment and deposit it in a central information repository. Process agents are then able to manipulate that focused knowledge, exploiting the exploration of the search agents. Communication is oneway, from the search agents to the process agents. As the process agents are able to refine the knowledge gathered by the search agents, we investigate two-way communication. Such communication directs the genetic programming based engine of the search agents.", notes = "GP-97", } @InProceedings{Haynes:1997:caet, author = "Thomas Haynes and Sandip Sen", title = "Crossover Operators for Evolving A Team", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming", pages = "162--167", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", URL = "http://www.mcs.utulsa.edu/~sandip/gp97.ps", size = "6 pages", notes = "GP-97", } @InProceedings{Haynes:1997:ccas, author = "Thomas Haynes", title = "Competitive Computational Agent Society", booktitle = "Late Breaking Papers at the 1997 Genetic Programming Conference", year = "1997", editor = "John R. Koza", pages = "293", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California, 94305-3079, USA", month = "13--16 " # jul, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-206995-8", notes = "GP-97LB PHD Students' workshop The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670", } @InProceedings{haynes:1997:pbbGP, author = "Thomas Haynes", title = "Phenotypical Building Blocks for Genetic Programming", booktitle = "Genetic Algorithms: Proceedings of the Seventh International Conference", year = "1997", editor = "Thomas Back", pages = "26--33", address = "Michigan State University, East Lansing, MI, USA", publisher_address = "San Francisco, CA, USA", month = "19-23 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-487-1", URL = "http://gpbib.cs.ucl.ac.uk/gp-html/haynes_1997_pbbGP.html", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/2284/http:zSzzSzadept.cs.twsu.eduzSz~thomaszSzpbb_gp.pdf/haynes97phenotypical.pdf", URL = "http://citeseer.ist.psu.edu/haynes97phenotypical.html", size = "8 pages", abstract = "The theoretical foundations of genetic algorithms (GA) rest on the shoulders of the Schema Theorem, which states that the building blocks, highly fit compact subsets of the chromosome, are more likely to survive from one generation to the next. The theory of genetic programming (GP) is tenuous, borrowing heavily from that of GA. As the GP can be considered to be a GA operating on a tree structure, this borrowing is adequate for most. Part of the problem of tying GP theory to the schema theorem is in the identification of building blocks. We discuss how a building block can be represented in a GP chromosome and the characteristics of building blocks in GP chromosomes. We also present the clique detection domain for which the detection of building blocks is easier than in previous domains used in GP research. We illustrate how the clique detection domain facilitates the construction of fitness landscapes similar to those of the Royal Road functions in GA research.", notes = "ICGA-97 citeseer says twsu.edu/~thomas/pbb_gp.ps", } @InProceedings{Haynes:1997:aaaiMAL, author = "Thomas Haynes", title = "Augmenting Collective Adaptation with Simple Process Agents", booktitle = "Papers from the AAAI Workshop on Multiagent Learning", year = "1997", editor = "Sandip Sen", pages = "41--46", organisation = "AAAI", note = "Published in AAAI Technical Report WS-97-03", keywords = "genetic algorithms, genetic programming", URL = "http://www.aaai.org/Papers/Workshops/1997/WS-97-03/WS97-03-008.pdf", size = "6 pages", abstract = "We have integrated the distributed search of genetic programming based systems with collective memory to form a collective adaptation search method. Such a system significantly improves search as problem complexity is increased. However, there is still considerable scope for improvement. In collective adaptation, search agents gather knowledge of their environment and deposit it in a central information repository. Process agents are then able to manipulate that focused knowledge, exploiting the exploration of the search agents. We examine the utility of increasing the capabilities of the centralised process agents.", notes = "http://www.aaai.org/Library/Workshops/ws97-03.php", } @InProceedings{Haynes:1998:CRS, author = "Thomas Haynes", title = "A Comparision of Random Search versus Genetic Programming as Engines for Collective Adaptation", editor = "V. William Porto and N. Saravanan and D. Waagen and A. E. Eiben", booktitle = "Evolutionary Programming VII: Proceedings of the Seventh Annual Conference on Evolutionary Programming", year = "1998", volume = "1447", series = "LNCS", pages = "683--692", address = "Mission Valley Marriott, San Diego, California, USA", publisher_address = "Berlin", month = "25-27 " # mar, organisation = "Natural Selection, Inc.", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-64891-7", broken = "http://www.cs.twsu.edu/~haynes/random.ps", DOI = "doi:10.1007/BFb0040819", size = "10 pages", abstract = "We have integrated the distributed search of genetic programming (GP) based systems with collective memory to form a collective adaptation search method. Such a system significantly improves search as problem complexity is increased. Since the pure GP approach does not scale well with problem complexity, a natural question is which of the two components is actually contributing to the search process. We investigate a collective memory search which uses a random search engine and find that it significantly outperforms the GP based search engine. We examine the solution space and show that as problem complexity and search space grow, a collective adaptive system will perform better than a collective memory search employing random search as an engine.", notes = "EP-98. {"}With collective adaptation{"}.... {"}A random search engine is more effective than a GP based one, but only at low problem complexity. As the complexity increases, the competetiveness of the GP search engine is more effective than the wide ranging exploration of random search.{"} pages 10-11.", } @PhdThesis{haynes:thesis, author = "Thomas Dunlop Haynes", title = "Collective Adaptation: The Sharing of Building Blocks", school = "Department of Mathematical and Computer Sciences, University of Tulsa", year = "1998", address = "Tulsa, OK, USA", month = apr, keywords = "genetic algorithms, genetic programming", broken = "http://www.cs.twsu.edu/~haynes/thesis.ps", URL = "http://tulsalabs.com/Documents/thesisDS.pdf", URL = "http://blogs.tulsalabs.com/?p=274", size = "147 pages (normal spacing). 208", abstract = "Weak search heuristics use minimal domain knowledge during the search process. Genetic algorithms (GA) and genetic programming (GP) are population based weak search heuristics which represent candidate solutions as chromosomes. The Schemata Theorem forms the basis of the theory of how GAs process building blocks during the domain independent search for a solution to a given problem. Building blocks are templates describing subsets of the chromosome which have a small defining length and are highly fit. The main differences between typical GP and GA implementations are a variable length tree versus a fixed length linear string representation and a n-ary versus a binary alphabet. A consequence of the differences is that what constitutes a building block has been difficult to answer for GP and has led to theories that the Schemata Theorem does not hold for GP. This thesis defines building blocks to be coding segments, which are those subsets of the chromosome that contribute fitness to the evaluation of the chromosome. Building blocks can be extracted from chromosomes and stored in a collective memory, which becomes a repository of partial solutions for both recently discovered building blocks and those discovered earlier. The contributions of this thesis are the mechanisms by which building blocks can be effectively shared both inside and outside chromosomes. The duplication of building blocks inside a chromosome is shown to increase the exploratory power of the weak search heuristics. The perturbation of a candidate solution will affect one copy of the building blocks and if the fitness of the perturbed copy is not better than the original, the duplicate copies may still maintain the overall fitness of the chromosome. The duplication of coding segments is significant in finding better partial solutions with the following weak search heuristics: GP, GA, random search (RS), hill climbing (HC), and simulated annealing (SA). Each algorithm is systematically validated in the clique detection domain against a particular family of graphs, which have the properties that the set of partial solutions is known, the set of partial solutions is larger than viable chromosome lengths, and pruning algorithms are not effective. Collective adaptation is the addition of the collective memory to the weak search heuristic. The solution no longer has to be found inside the chromosomes; the chromosomes can collectively contribute partial solutions such that the overall solution is formed inside the collective memory. Strong search heuristics can extend the partial solutions inside the collective memory and these partial solutions can be transfered back into the chromosomes. The thesis empirically demonstrates that collective adaptation finds significantly better partial solutions with weak search heuristics (GP, GA, RS, HC, and SA).", notes = "a ROUGH DRAFT available via http://citeseer.ist.psu.edu/haynes96explicit.html Directed by Sandip Sen", } @InProceedings{haynes:1998:acaspa, author = "Thomas Haynes", title = "Augmenting Collective Adaptation with Simple Process Agents", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "116--121", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-548-7", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/4146/http:zSzzSzeuler.mcs.utulsa.eduzSz~hayneszSzactive.pdf/haynes97augmenting.pdf", URL = "http://citeseer.ist.psu.edu/haynes97augmenting.html", abstract = "We have integrated the distributed search of genetic programming based systems with collective memory to form a collective adaptation search method. Such a system significantly improves search as problem complexity is increased. However, there is still considerable scope for improvement. In collective adaptation, search agents gather knowledge of their environment and deposit it in a central information repository. Process agents are then able to manipulate that focused knowledge, exploiting...", notes = "GP-98 citeseer says utulsa.edu/~haynes/active.ps", } @InProceedings{haynes:1998:prdec, author = "Thomas Haynes", title = "Perturbing the Representation, Decoding, and Evaluation of Chromosomes", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "122--127", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-548-7", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/2284/http:zSzzSzadept.cs.twsu.eduzSz~thomaszSzcook.pdf/haynes98perturbing.pdf", URL = "http://citeseer.ist.psu.edu/haynes98perturbing.html", size = "7 pages", abstract = "We investigate different genetic algorithm and genetic programming variants of representation, decoding, and evaluation of chromosomes for clique detection in graph. Small changes can drastically impact finding the evolutionary process, making fair comparisons difficult. 1 Introduction While research into the interactions of function and terminal set size is sparse to non--existent (for examples, see [ Montana, 1995 ] and [ Haynes et al., 1995 ] ), a rule of thumb for GP researchers is to...", notes = "GP-98 citeseer says twsu.edu/~thomas/cook.ps", } @Article{haynes:1998:caxcs, author = "Thomas Haynes", title = "Collective Adaptation: The Exchange of Coding Segments", journal = "Evolutionary Computation", year = "1998", volume = "6", number = "4", pages = "311--338", month = "Winter", keywords = "genetic algorithms, genetic programming, collective adaptation, coding segments, duplication of coding segments, collective memory", URL = "http://www.mitpressjournals.org/doi/pdfplus/10.1162/evco.1998.6.4.311", DOI = "doi:10.1162/evco.1998.6.4.311", size = "29 pages", abstract = "Coding segments are those subsegments of the chromosome that contribute positively to the fitness evaluation of the chromosome. Clique detection is a NP-complete problem in which we can detect such coding segments. We extract coding segments from chromosomes, and we investigate the duplication of coding segments inside the chromosome and the collection of coding segments outside of the chromosome. We find that duplication of coding segments inside the chromosomes provides a back-up mechanism for the search heuristics. We further find local search in a collective memory of coding segments outside of the chromosome, collective adaptation, enables the search heuristic to represent partial solutions that are larger than realistic chromosomes lengths and to express the solution outside of the chromosome.", notes = "Evolutionary Computation (Journal) Special Issue: Variable-Length Representation and Noncoding Segments for Evolutionary Algorithms Edited by Annie S. Wu and Wolfgang Banzhaf", } @InProceedings{Haynes:1999:DCAa, author = "Thomas Haynes", title = "Distributed Collective Adaptation Applied to a Hard Combinatorial Optimization Problem", booktitle = "Proceedings of the 1999 ACM Symposium on Applied Computing", year = "1999", editor = "Janice Carroll and Hisham Haddad and Dave Oppenheim and Barrett Bryant and Gary B. Lamont", pages = "339--343", publisher = "ACM Press", keywords = "genetic algorithms, genetic programming", broken = "http://adept.cs.twsu.edu/~thomas/mpi.ps", URL = "http://delivery.acm.org/10.1145/300000/298377/p339-haynes.pdf", DOI = "doi:10.1145/298151.298377", size = "5 pages", abstract = "We use collective memory to integrate weak and strong search heuristics to find cliques in FC, a family of graphs. We construct FC such that pruning partial solutions will be ineffective. Each weak heuristic maintains a local cache of the collective memory. We examine the impact on the distributed search of the distribution of the collective memory, the search algorithms, and our family of graphs. We find the distributed search performs better than the individual searches, even though the space of partial solutions is combinatorial.", notes = "(GA track)", } @InProceedings{Haynes:1999:DCAb, author = "Thomas Haynes", title = "Distributing Collective Adaptation via Message Passing", booktitle = "Proceedings of the 1999 ACM Symposium on Applied Computing", year = "1999", editor = "Janice Carroll and Hisham Haddad and Dave Oppenheim and Barrett Bryant and Gary B. Lamont", pages = "501--505", publisher = "ACM Press", keywords = "genetic algorithms, genetic programming", broken = "http://adept.cs.twsu.edu/~thomas/cluster.ps", DOI = "doi:10.1145/298151.298429", abstract = "We describe an architecture for implementing a distributed access to a collective memory on a cluster of PC workstations running Linux. The basic memory hierarchy of register, cache, RAM, and main storage is modeled. The message passing interface (MPI) provides the functionality of a virtual bus between the various layers of memory.", notes = "(PC Cluster track)", } @Proceedings{haynes:1999:fogp, title = "Foundations of Genetic Programming", year = "1999", editor = "Thomas Haynes and William B. Langdon and Una-May O'Reilly and Riccardo Poli and Justinian Rosca", pages = "52", address = "Orlando, Florida, USA", month = "13 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/fogp/workshop.html", size = "20 pages", notes = "GECCO'99 WKSHOP, GECCO-99WKS Part of wu:1999:GECCOWKS", } @InProceedings{hayslep:2023:GECCO, author = "Matthew Hayslep and Edward Keedwell and Raziyeh Farmani", title = "{Multi-Objective} {Multi-Gene} Genetic Programming for the Prediction of Leakage in Water Distribution Networks", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "1357--1364", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, feature construction, leakage, minimum night flow, linear regression", isbn13 = "9798400701191", DOI = "doi:10.1145/3583131.3590499", size = "8 pages", abstract = "Understanding leakage is an important challenge within the water sector to minimise waste, energy use and carbon emissions. Every Water Distribution Network (WDN) has leakage, usually approximated as Minimum Night Flow (MNF) for each District Metered Area (DMA). However, not all DMAs have instruments to monitor leakage directly, or the main dynamic factors that contribute to it. Therefore, this article will estimate the leakage of a DMA by using the recorded features of its pipes, making use of readily available asset data collected routinely by water companies. This article interprets this problem as a feature construction task and uses a multi-objective multi-gene strongly typed genetic programming approach to create a set of features. These features are used by a linear regression model to estimate the average long-term leakage in DMAs and Shapley values are used to understand the impact and importance of each tree. The methodology is applied to a dataset for a real-world WDN with over 700 DMAs and the results are compared to a previous work which used human-constructed features. The results show comparable performance with significantly fewer, and less complex features. In addition, novel features are found that were not part of the human-constructed features.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @InProceedings{hazan:evows06, author = "Amaury Hazan and Rafael Ramirez and Esteban Maestre and Alfonso Perez and Antonio Pertusa", title = "Modelling Expressive Performance: a Regression Tree Approach Based on Strongly Typed Genetic Programming", booktitle = "Applications of Evolutionary Computing, EvoWorkshops2006: {EvoBIO}, {EvoCOMNET}, {EvoHOT}, {EvoIASP}, {EvoInteraction}, {EvoMUSART}, {EvoSTOC}", year = "2006", month = "10-12 " # apr, editor = "Franz Rothlauf and Jurgen Branke and Stefano Cagnoni and Ernesto Costa and Carlos Cotta and Rolf Drechsler and Evelyne Lutton and Penousal Machado and Jason H. Moore and Juan Romero and George D. Smith and Giovanni Squillero and Hideyuki Takagi", series = "LNCS", volume = "3907", publisher = "Springer Verlag", address = "Budapest", publisher_address = "Berlin", keywords = "genetic algorithms, genetic programming, STGP", ISBN = "3-540-33237-5", pages = "676--687", DOI = "doi:10.1007/11732242_64", abstract = "Strongly-Typed Genetic Programming approach for building Regression Trees in order to model expressive music performance. The approach consists of inducing a Regression Tree model from training data (monophonic recordings of Jazz standards) for transforming an inexpressive melody into an expressive one. The work presented in this paper is an extension of [1], where we induced general expressive performance rules explaining part of the training examples. Here, the emphasis is on inducing a generative model (i.e. a model capable of generating expressive performances) which covers all the training examples. We present our evolutionary approach for a one-dimensional regression task: the performed note duration ratio prediction. We then show the encouraging results of experiments with Jazz musical material, and sketch the milestones which will enable the system to generate expressive music performance in a broader sense.", notes = "part of \cite{evows06}", } @InProceedings{Hazell:2008:geccocomp, author = "Alex Hazell and Stephen L. Smith", title = "Towards an objective assessment of Alzheimer's disease: the application of a novel evolutionary algorithm in the analysis of figure copying tasks", year = "2008", editor = "Marc Ebner and Mike Cattolico and Jano {van Hemert} and Steven Gustafson and Laurence D. Merkle and Frank W. Moore and Clare Bates Congdon and Christopher D. Clack and Frank W. Moore and William Rand and Sevan G. Ficici and Rick Riolo and Jaume Bacardit and Ester Bernado-Mansilla and Martin V. Butz and Stephen L. Smith and Stefano Cagnoni and Mark Hauschild and Martin Pelikan and Kumara Sastry", isbn13 = "978-1-60558-131-6", booktitle = "GECCO-2008 Workshop: MedGEC Medical Applications of Genetic and Evolutionary Computation", pages = "2073--2080", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p2073.pdf", DOI = "doi:10.1145/1388969.1389024", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, Alzheimer's disease, cartesian genetic programming, evolutionary algorithm(s), image analysis, medical applications", notes = "Distributed on CD-ROM at GECCO-2008 ACM Order Number 910081. Also known as \cite{1389024}", } @Article{DBLP:journals/vc/HazguiGB22, author = "Mohamed Hazgui and Haythem Ghazouani and Walid Barhoumi", title = "Genetic programming-based fusion of {HOG} and {LBP} features for fully automated texture classification", journal = "Vis. Comput.", volume = "38", number = "2", pages = "457--476", year = "2022", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/s00371-020-02028-8", DOI = "doi:10.1007/s00371-020-02028-8", timestamp = "Fri, 04 Mar 2022 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/vc/HazguiGB22.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{HE:2020:RSC_Advances, author = "Ge He and Tao Luo and Yagu Dang and Li Zhou and Yiyang Dai and Xu Ji", title = "Combined mechanistic and genetic programming approach to modeling pilot {NBR} production: influence of feed compositions on rubber Mooney viscosity: Electronic supplementary information ({ESI)} available. See DOI:10.1039/d0ra07257e", journal = "RSC Advances", volume = "11", number = "2", pages = "817--829", year = "2020", ISSN = "2046-2069", DOI = "doi:10.1039/d0ra07257e", URL = "https://www.sciencedirect.com/science/article/pii/S2046206920000121", abstract = "Mooney viscosity is an essential parameter in quality control during the production of nitrile-butadiene rubber (NBR) by emulsion polymerization. A process model that could help understand the influence of feed compositions on the Mooney viscosity of NBR products is of vital importance for its intelligent manufacture. In this work, a process model comprised of a mechanistic model based on emulsion polymerization kinetics and a data-driven model derived from genetic programming (GP) for Mooney viscosity is developed to correlate the feed compositions (including impurities) and process conditions to Mooney viscosity of NBR products. The feed compositions are inputs of the mechanistic model to generate the number-, weight-averaged molecular weights (Mn, Mw) and branching degree (BRD) of NBR polymers. With these generated data, the GP model is used to output the optimal correlation for the Mooney viscosity of NBR. In a pilot NBR production, Mooney viscosity data of NBR predicted by the process model agree quite well with experimental values. Furthermore, the process model enables the analyses of the univariate and multivariate influence of feed compositions on NBR Mooney viscosity, and the variables include the contents of vinyl acetylene and dimer in 1,3-butadiene, as well as the mass flow rate of the chain transfer agent (CTA) in the process. Based on the results, it is recommended to control the content of vinyl acetylene in the 1,3-butadiene feed below 14 ppm and the content of dimer below 1100 ppm. This developed process model would help stabilize NBR viscosity for a better control of the product quality.", keywords = "genetic algorithms, genetic programming", } @InProceedings{he:2024:GECCOcomp, author = "Jie He and Yaohong Zhang and Ji Wang and Xiaoqing Li and Weidong Bao", title = "{Hypernetwork-Based} {Multi-Objective} Optimization with {NSGA-II}", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion", year = "2024", editor = "Aneta Neumann and Elizabeth Wanner", pages = "431--434", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, multi-objective optimization, NSGA-II, crowing-distance assignment: Poster", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3654189", size = "4 pages", abstract = "Multi-objective optimization algorithms have a wide range of applications in various fields. NSGA-II (Non-dominated Sorting Genetic Algorithm II) is a well-known multi-objective optimization algorithm. However, when solving high-dimensional problems using NSGA-II, the algorithm complexity increases linearly with the problem dimension. To address this issue, this paper proposes the HNSGA-II (Hypernetwork Non-dominated Sorting Genetic Algorithm II) algorithm based on hypernetwork modeling. The HNSGA-II algorithm introduces a binary tree data structure and uses hypernetworks to model candidate solutions. A new crowding calculation is designed to improve the algorithm's convergence. Horizontal and vertical comparative experiments were conducted on standard test sets ZDT and BT using the newly proposed algorithm, NSGA-II, MOEAD, and other algorithms. The results demonstrate that the HNSGA-II algorithm achieves a maximum improvement of about 10\% in time efficiency when solving high-dimensional problems and exhibits better algorithmic convergence than the original NSGA-II algorithm.", notes = "GECCO-2024 GA A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{he:2005:EH, author = "Jingsong He and Xufa Wang and Min Zhang and Jiying Wang and Qiansheng Fang", title = "New Research on Scalability of Lossless Image Compression by GP Engine", booktitle = "Proceedings of the 2005 NASA/DoD Conference on Evolvable Hardware", year = "2005", editor = "Jason Lohn and David Gwaltney and Gregory Hornby and Ricardo Zebulum and Didier Keymeulen and Adrian Stoica", pages = "160--164", address = "Washington, DC, USA", month = "29 " # jun # "-1 " # jul, publisher = "IEEE Press", publisher_address = "IEEE Service Center 445 Hoes Lane Asia P.O. Box 1331 Piscataway, NJ 08855-1331", organisation = "NASA, DoD", keywords = "genetic algorithms, genetic programming, EHW", ISBN = "0-7695-2399-4", DOI = "doi:10.1109/EH.2005.35", abstract = "By introducing the optimal linear predictive code technic into the dynamic issue of loss less image compression, this paper presented a less complexity fitness function for Genetic Programming engine, which can reduce the cost of computational time in each evaluation for individual greatly, and can also provide further benefit with the scalability issue. To make the speed of large image compression faster in condition of not increasing the cost of computational resource and time, evaluating mechanism in the field of machine learning was used to help Genetic Programming, and the scalability issue was mapped to the task of making the approach accuracy best from lower speed sampling to higher speed sampling in the field of signal processing. In experiments for compressing large images, the cost of computational time was reduced evidently and efficiently.", notes = "EH2005 IEEE Computer Society Order Number P2399", } @Article{Jingsong_He:GPEM:pfpa, author = "Jingsong He and Jin Yin", title = "Evolutionary design model of passive filter circuit for practical application", journal = "Genetic Programming and Evolvable Machines", year = "2020", volume = "21", number = "4", pages = "571--604", month = dec, keywords = "genetic algorithms, genetic programming, evolvable hardware, Evolutionary circuit design, Analogue circuit synthesis, Differential evolution, Neighbourhood model", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-019-09369-x", size = "34 pages", abstract = "Evolutionary circuit design is a promising way to study new circuit design methodologies, and the passive filter is the most basic circuit module widely existing in modern electronic systems. Focused on the basic and fatal criterion related to the filter circuit design, this paper presents a novel evolutionary design model of passive filter circuit. The proposed model includes a circuit representation method for passive filter circuit design based on circuit cells and the corresponding real encoding scheme, a fast fitness calculation method avoiding expensive SPICE simulations, and a simple and effective cell-based differential evolution algorithm. Experimental results show that the proposed model can quickly obtain filter circuits for challenging specifications. Under harsh design criteria, the design performance of the pro- posed model is not inferior to that of some advanced professional design techniques based on traditional design ideas.", notes = "http://orcid.org/0000-0002-5404-0003", } @Article{He:2016:SC, author = "Pei He and Zelin Deng and Chongzhi Gao and Xiuni Wang and Jin Li2", title = "Model approach to grammatical evolution: deep-structured analyzing of model and representation", journal = "Soft Computing", year = "2017", volume = "21", number = "18", pages = "5413--5423", month = sep, keywords = "genetic algorithms, genetic programming, grammatical evolution, finite state automaton, model", ISSN = "1433-7479", URL = "https://rdcu.be/drca7", DOI = "doi:10.1007/s00500-016-2130-1", size = "11 pages", abstract = "Grammatical evolution (GE) is a combination of genetic algorithm and context-free grammar, evolving programs for given problems by breeding candidate programs in the context of a grammar using genetic operations. As far as the representation is concerned, classical GE as well as most of its existing variants lacks awareness of both syntax and semantics, therefore having no potential for parallelism of various evaluation methods. To this end, we have proposed a novel approach called model-based grammatical evolution (MGE) in terms of grammar model (a finite state transition system) previously. It is proved, in the present paper, through theoretical analysis and experiments that semantic embedded syntax taking the form of regex (regular expression) over an alphabet of simple cycles and paths provides with potential for parallel evaluation of fitness, thereby making it possible for MGE to have a better performance in coping with more complex problems than most existing GEs.", notes = "School of Computer Science and Educational Software, Guangzhou University, Guangzhou, 510006, China", } @InProceedings{He:2010:CIKM, author = "Qiang He and Jun Ma and Shuaiqiang Wang", title = "Directly optimizing evaluation measures in learning to rank based on the clonal selection algorithm", booktitle = "Proceedings of the 19th ACM international conference on Information and knowledge management, CIKM '10", year = "2010", pages = "1449--1452", address = "Toronto, ON, Canada", publisher = "ACM", keywords = "genetic algorithms, genetic programming, clonal selection algorithm, information retrieval, learning to rank, machine learning, ranking function: Poster", isbn13 = "978-1-4503-0099-5", DOI = "doi:10.1145/1871437.1871644", size = "4 pages", acmid = "1871644", abstract = "One fundamental issue of learning to rank is the choice of loss function to be optimised. Although the evaluation measures used in Information Retrieval (IR) are ideal ones, in many cases they can't be used directly because they do not satisfy the smooth property needed in conventional machine learning algorithms. In this paper a new method named RankCSA is proposed, which tries to use IR evaluation measure directly. It employs the clonal selection algorithm to learn an effective ranking function by combining various evidences in IR. Experimental results on the LETOR benchmark datasets demonstrate that RankCSA outperforms the baseline methods in terms of P@n, MAP and NDCG@n.", } @InProceedings{He:2006:ciea, author = "Mingyi He and Yifan Zhang and Yuzhen Xie and Na Liang and Changyun Wen", title = "Classification of Multi-spectral/Hyperspectral Data using Genetic Programming and Error-correcting Output Codes", booktitle = "1ST IEEE Conference on Industrial Electronics and Applications", year = "2006", pages = "1--6", address = "Singapore", month = "24-26 " # may, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9514-X", DOI = "doi:10.1109/ICIEA.2006.257153", abstract = "Genetic programming (GP) and error-correcting output codes (ECOC) are combined to develop a new classification method (GP-ECOC) for the multi-class problem solving in this paper. Some additional improvements on the algorithm, modified codeword matrix and group division before classification, are also proposed to settle several existing problems in multi-spectral and hyperspectral data classification. Experimental tests using both multi-spectral and hyper-spectral data are carried out for verification and illustration. It is observed from the obtained results that the classification precision with the newly proposed method is greatly enhanced compared with some existing methods using GP, and the proposed improvements are also effective. The algorithm of GP-ECOC and its improved versions can also be run on multi-terminals, which saves computational cost effectively", notes = "INSPEC Accession Number: 9096919 Sch. of Electron. & Inf., Northwestern Polytech Univ., Xi'an;", } @InProceedings{He3:2008:cec, author = "Pei He and Lishan Kang and Ming Fu", title = "Formality Based Genetic Programming", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "4080--4087", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1823-7", file = "EC0867.pdf", DOI = "doi:10.1109/CEC.2008.4631354", abstract = "Genetic programming (GP) is an illogical method for automatic programming. It shows creativity in discovering a desired program to solve problem, but in essence bases its searching principle on software testing. This paper is dedicated to establishing a novel GP which combines classical GP and formal approaches like Hoare's logic, model checking, and automaton, etc. The result indicates these methods can collaborate in the framework pretty well. As has been demonstrated by the experiment, they work in a way that preserves their advantages while each compensates for the deficiencies of the other. So, once an approximate program is obtained, we can say with certainty it is correct with respect to its corresponding pre- and post-conditions.", keywords = "genetic algorithms, genetic programming, program verification, approximate program, automatic programming, formality based genetic programming, software testing", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @Article{journals/chinaf/HeJW11, author = "Pei He and Colin G. Johnson and HouFeng Wang", title = "Modeling grammatical evolution by automaton", journal = "SCIENCE CHINA Information Sciences", year = "2011", number = "12", volume = "54", pages = "2544--2553", publisher = "Science China Press, co-published with Springer", keywords = "genetic algorithms, genetic programming, grammatical evolution, FSM", ISSN = "1674-733X", DOI = "doi:10.1007/s11432-011-4411-8", size = "10 pages", abstract = "Twelve years have passed since the advent of grammatical evolution (GE) in 1998, but such issues as vast search space, genotypic readability, and the inherent relationship among grammatical concepts, production rules and derivations have remained untouched in almost all existing GE researches. Model-based approach is an attractive method to achieve different objectives of software engineering. In this paper, we make the first attempt to model syntactically usable information of GE using an automaton, coming up with a novel solution called model-based grammatical evolution (MGE) to these problems. In MGE, the search space is reduced dramatically through the use of concepts from building blocks, but the functionality and expressiveness are still the same as that of classical GE. Besides, complex evolutionary process can visually be analysed in the context of transition diagrams.", affiliation = "State Key Laboratory of Software Engineering, Wuhan University, Wuhan, 430072 China", bibdate = "2011-12-05", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/chinaf/chinaf54.html#HeJW11", } @Article{journals/chinaf/HeKJY11, author = "Pei He and Lishan Kang and Colin G. Johnson and Shi Ying", title = "Hoare logic-based genetic programming", journal = "SCIENCE CHINA Information Sciences", year = "2011", number = "3", volume = "54", pages = "623--637", month = mar, publisher = "Science China Press, co-published with Springer", keywords = "genetic algorithms, genetic programming", ISSN = "1674-733X", DOI = "doi:10.1007/s11432-011-4200-4", size = "15 pages", abstract = "Almost all existing genetic programming systems deal with fitness evaluation solely by testing. In this paper, by contrast, we present an original approach that combines genetic programming with Hoare logic with the aid of model checking and finite state automata, hence by proposing a brand new verification-focused formal genetic programming system that makes it possible to evolve reliable programs with mathematically verified properties.", affiliation = "State Key Laboratory of Software Engineering, Wuhan University, Wuhan, 430072 China", bibdate = "2011-06-08", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/chinaf/chinaf54.html#HeKJY11", } @Article{He:2015:SC, author = "Pei He and Zelin Deng and Houfeng Wang and Zhusong Liu", title = "Model approach to grammatical evolution: theory and case study", journal = "Soft Computing", year = "2016", volume = "20", number = "9", pages = "3537--3548", month = sep, keywords = "genetic algorithms, genetic programming, Grammatical evolution, Finite state automaton, Model", ISSN = "1432-7643", publisher = "Springer", DOI = "doi:10.1007/s00500-015-1710-9", abstract = "Many deficiencies with grammatical evolution (GE) such as inconvenience in solution derivations, modularity analysis, and semantic computing can partly be explained from the angle of genotypic representations. In this paper, we deepen some of our previous work in visualising concept relationships, individual structures and total evolutionary process, contributing new ideas, perspectives, and methods in these aspects; reveal the principle hidden in early work so that to develop a practical methodology; provide formal proofs for issues of concern which will be helpful for understanding of mathematical essence of issues, establishing of an unified formal framework as well as practical implementation; exploit genotypic modularity like modular discovery systematically which for the lack of supporting mechanism, if not impossible, is done poorly in many existing systems, and finally demonstrate the possible gains through semantic analysis and modular reuse. As shown in this work, the search space and the number of nodes in the parser tree are reduced using concepts from building blocks, and concepts such as the codon-to-grammar mapping and the integer modulo arithmetic used in most existing GE can be abnegated", } @InProceedings{He:2013:IJCAI, author = "Di He and Wei Chen and Liwei Wang and Tie-Yan Liu", title = "Learning Optimal Auction Mechanism in Sponsored Search", booktitle = "Twenty-third International Conference on Artificial Intelligence, IJCAI 2013", year = "2013", editor = "Francesca Rossi", address = "Beijing, China", month = aug # " 3-9", keywords = "genetic algorithms, genetic programming, computer science - computer science and game theory, computer science - learning", URL = "http://arxiv.org/abs/1406.0728", size = "8 pages", abstract = "Sponsored search is an important monetization channel for search engines, in which an auction mechanism is used to select the ads shown to users and determine the prices charged from advertisers. There have been several pieces of work in the literature that investigate how to design an auction mechanism in order to optimise the revenue of the search engine. However, due to some unrealistic assumptions used, the practical values of these studies are not very clear. In this paper, we propose a novel game-theoretic machine learning approach, which naturally combines machine learning and game theory, and learns the auction mechanism using a bilevel optimisation framework. In particular, we first learn a Markov model from historical data to describe how advertisers change their bids in response to an auction mechanism, and then for any given auction mechanism, we use the learnt model to predict its corresponding future bid sequences. Next we learn the auction mechanism through empirical revenue maximisation on the predicted bid sequences. We show that the empirical revenue will converge when the prediction period approaches infinity, and a Genetic Programming algorithm can effectively optimise this empirical revenue. Our experiments indicate that the proposed approach is able to produce a much more effective auction mechanism than several baselines. abstract from oai:arXiv.org:1406.0728", notes = "see also A Game-theoretic Machine Learning Approach for Revenue Maximization in Sponsored Search \cite{oai:arXiv.org:1406.0728} http://ijcai13.org/program/accepted_papers", } @Article{HE:2021:JMRT, author = "Shixin He and Xuxiang Wang and Haibo Bai and Zhiwei Xu and Dan Ma", title = "Effect of fiber dispersion, content and aspect ratio on tensile strength of {PP} fiber reinforced soil", journal = "Journal of Materials Research and Technology", volume = "15", pages = "1613--1621", year = "2021", ISSN = "2238-7854", DOI = "doi:10.1016/j.jmrt.2021.08.128", URL = "https://www.sciencedirect.com/science/article/pii/S2238785421009534", keywords = "genetic algorithms, genetic programming, Tensile strength, Fiber-reinforced soil, GP model, Fiber dispersion, Fiber content, Aspect ratio", abstract = "The present study was conducted to investigate the tensile strength characteristics of polypropylene (PP) fiber reinforced soil with different fiber dispersion, content and aspect ratio. In order to investigate this, the experimental programme was comprised by the test with a wide range of fiber content (0.35percent, 0.60percent, 0.85percent), fiber aspect ratio (150, 225, 350), and mix patterns (discrete or random distribution). The results indicated that increases in fiber content caused an increment in the tensile strength whether discrete or random distribution. The increasing extent of tensile strength was different with increase of fiber aspect ratio under different fiber mix patterns. From experimental data, a genetic programming (GP) model was proposed for analyzing tensile strength contrast of the two mix patterns. In addition, the sensitive analysis of three inputs showed that aspect ratio has the greatest influence on the forecasting model. The effectiveness of the GP model was validated by the test results, and the robust model developed would provide a theoretical support for roadbase designing and construction which were reinforced with PP fibers", } @Article{he:2024:Sustainability, author = "Sizhong He and Zhenzhen Ma and Huashan Wang and Yuqin Gao", title = "Evolution in Patterns of Urban Water Consumption Accompanying {Socio-Economic} Development", journal = "Sustainability", year = "2024", volume = "16", number = "9", pages = "Article No. 3737", keywords = "genetic algorithms, genetic programming", ISSN = "2071-1050", URL = "https://www.mdpi.com/2071-1050/16/9/3737", DOI = "doi:10.3390/su16093737", abstract = "To explore the evolution in patterns of urban water consumption (UWC) accompanying socio-economic development, historical data on socio-economic conditions and water consumption in developed cities worldwide were collected. stages of evolution and patterns in UWC were identified through Pettitt tests. Through correlation analysis, the main socio-economic indicators influencing UWC were identified, and their threshold values for different trends in the evolution of UWC were statistically analysed. The use of these patterns of evolution for UWC prediction was explored taking Changsha, China as a case study. Results indicate the following: (1) UWC typically increases in the early stage of development and then stabilizes or decreases later; (2) when UWC stabilizes or decreases, socio-economic indicators, namely per capita GDP, tertiary industry's contribution to GDP, and urbanization rate, range from [USD 10,000, 60,000], [60percent, 80percent], and [85percent, 95percent], respectively, entering or approaching the developed economy stage. (3) Using patterns in this evolution, Changsha's UWC is predicted to increase until the turning point, which will occur between 2027 and 2035 (earlier than that predicted using genetic programming) and then decrease earlier than that of Hunan Province or the entirety of China. Predicted values are lower than those obtained using the quota method. This study provides a new perspective on relationships between UWC and socio-economic conditions, supporting water resources planning.", notes = "also known as \cite{su16093737}", } @InProceedings{he:2022:GECCO, author = "Baihe He and Qiang Lu and Qingyun Yang and Jake Luo and Zhiguang Wang", title = "Taylor Genetic Programming for Symbolic Regression", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference", year = "2022", editor = "Alma Rahat and Jonathan Fieldsend and Markus Wagner and Sara Tari and Nelishia Pillay and Irene Moser and Aldeida Aleti and Ales Zamuda and Ahmed Kheiri and Erik Hemberg and Christopher Cleghorn and Chao-li Sun and Georgios Yannakakis and Nicolas Bredeche and Gabriela Ochoa and Bilel Derbel and Gisele L. Pappa and Sebastian Risi and Laetitia Jourdan and Hiroyuki Sato and Petr Posik and Ofer Shir and Renato Tinos and John Woodward and Malcolm Heywood and Elizabeth Wanner and Leonardo Trujillo and Domagoj Jakobovic and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Inmaculada Medina-Bulo and Slim Bechikh and Andrew M. Sutton and Pietro Simone Oliveto", pages = "946--954", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, Taylor polynomials, symbolic regression, PMLB, FSRB", isbn13 = "978-1-4503-9237-2", DOI = "doi:10.1145/3512290.3528757", video_url = "https://vimeo.com/724011976", code_url = "https://github.com/KGAE-CUP/TaylorGP", size = "9 pages", abstract = "Genetic programming (GP) is a commonly used approach to solve symbolic regression (SR) problems. Compared with the machine learning or deep learning methods that depend on the pre-defined model and the training dataset for solving SR problems, GP is more focused on finding the solution in a search space. Although GP has good performance on large-scale benchmarks, it randomly transforms individuals to search results without taking advantage of the characteristics of the dataset. So, the search process of GP is usually slow, and the final results could be unstable. To guide GP by these characteristics, we propose a new method for SR, called Taylor genetic programming (TaylorGP)1. TaylorGP leverages a Taylor polynomial to approximate the symbolic equation that fits the dataset. It also uses the Taylor polynomial to extract the features of the symbolic equation: low order polynomial discrimination, variable separability, boundary, monotonic, and parity. GP is enhanced by these Taylor polynomial techniques. Experiments are conducted on three kinds of benchmarks: classical SR, machine learning, and physics. The experimental results show that TaylorGP not only has higher accuracy than the nine baseline methods, but also is faster in finding stable results.", notes = "Beijing Key Laboratory of Petroleum Data Mining, China University of Petroleum Beijing, China GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @InProceedings{he:2023:LAHS, author = "Yifan He and Ferrante Neri", title = "Fitness Landscape Analysis of Genetic Programming Search Spaces with Local Optima Networks", booktitle = "Workshop on Landscape-Aware Heuristic Search (LAHS 2022)", year = "2023", editor = "Sarah L. Thomson and Nadarajen Veerapen and Katherine Malan and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa", pages = "2056--2063", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, local optima networks, fitness landscape analysis", isbn13 = "9798400701191", DOI = "doi:10.1145/3583133.3596305", size = "8 pages", abstract = "Fitness landscape analysis (FLA) refers to a set of techniques to characterise optimisation problems. This paper presents an FLA of three types of genetic programming (GP) benchmarks: parity, symbolic regression, and artificial ant. We applied a modern graph-based FLA tool called Local Optima Networks and several classical FLA metrics (fitness distance correlation, neutrality, and ruggedness measures) to study the tree-based GP search spaces. Our analysis shows that the search spaces for all problems contain many local optima and are highly deceptive. The parity problems are highly rugged and neutral. Conversely, the problems of symbolic regression are less rugged and neutral. Finally, the artificial ant problem is highly rugged but less neutral. Our results indicate that a mutation in deep nodes makes finding the global optimum difficult.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @InProceedings{he:2022:GECCOcomp, author = "Yifan He and Claus Aranha and Tetsuya Sakurai", title = "Incorporating Sub-programs as Knowledge in Program Synthesis by {PushGP} and Adaptive Replacement Mutation", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion", year = "2022", editor = "Heike Trautmann and Carola Doerr and Alberto Moraglio and Thomas Bartz-Beielstein and Bogdan Filipic and Marcus Gallagher and Yew-Soon Ong and Abhishek Gupta and Anna V Kononova and Hao Wang and Michael Emmerich and Peter A. N. Bosman and Daniela Zaharie and Fabio Caraffini and Johann Dreo and Anne Auger and Konstantin Dietric and Paul Dufosse and Tobias Glasmachers and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Tea Tusar and Dimo Brockhoff and Tome Eftimov and Pascal Kerschke and Boris Naujoks and Mike Preuss and Vanessa Volz and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Mark Coletti and Catherine (Katie) Schuman and Eric ``Siggy'' Scott and Robert Patton and Paul Wiegand and Jeffrey K. Bassett and Chathika Gunaratne and Tinkle Chugh and Richard Allmendinger and Jussi Hakanen and Daniel Tauritz and John Woodward and Manuel Lopez-Ibanez and John McCall and Jaume Bacardit and Alexander Brownlee and Stefano Cagnoni and Giovanni Iacca and David Walker and Jamal Toutouh and UnaMay O'Reilly and Penousal Machado and Joao Correia and Sergio Nesmachnow and Josu Ceberio and Rafael Villanueva and Ignacio Hidalgo and Francisco {Fernandez de Vega} and Giuseppe Paolo and Alex Coninx and Antoine Cully and Adam Gaier and Stefan Wagner and Michael Affenzeller and Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and William B. Langdon and Justyna Petke and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Paetzel and Alexander Wagner and Michael Heider and Nadarajen Veerapen and Katherine Malan and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Mohammad Nabi Omidvar and Yuan Sun and Ernesto Tarantino and De Falco Ivanoe and Antonio {Della Cioppa} and Scafuri Umberto and John Rieffel and Jean-Baptiste Mouret and Stephane Doncieux and Stefanos Nikolaidis and Julian Togelius and Matthew C. Fontaine and Serban Georgescu and Francisco Chicano and Darrell Whitley and Oleksandr Kyriienko and Denny Dahl and Ofer Shir and Lee Spector and Alma Rahat and Richard Everson and Jonathan Fieldsend and Handing Wang and Yaochu Jin and Erik Hemberg and Marwa A. Elsayed and Michael Kommenda and William {La Cava} and Gabriel Kronberger and Steven Gustafson", pages = "554--557", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, program synthesis, knowledge, adaptation", isbn13 = "978-1-4503-9268-6/22/07", DOI = "doi:10.1145/3520304.3528891", abstract = "Program synthesis aims to build an intelligent agent that composes computer programs to solve problems. Genetic programming (GP) provides an evolutionary solution for the program synthesis task. A typical GP includes a random initialization, an unguided variation, and a fitness-guided selection to search for a solution program. However, several recent studies have shown the importance of using prior knowledge in different components of the GP. This study investigates the effectiveness of incorporating sub-programs as {"}prior knowledge{"} into the variation process of GP by Replacement Mutation. We further design an adaptive strategy that allows the automatic selection of the helpful sub-programs to the search process from an archive (including helpful and unhelpful ones). With handcrafted sub-program archives, we verify the effectiveness of the Adaptive Replacement Mutation method in success rate. We demonstrate the effectiveness of our approach with transferred archives on two composite problems.", notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @Misc{DBLP:journals/corr/abs-2403-14146, author = "Yifan He and Claus Aranha", title = "Evolving Benchmark Functions to Compare Evolutionary Algorithms via Genetic Programming", howpublished = "arXiv", volume = "abs/2403.14146", year = "2024", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.48550/arXiv.2403.14146", DOI = "doi:10.48550/ARXIV.2403.14146", eprinttype = "arXiv", eprint = "2403.14146", timestamp = "Tue, 09 Apr 2024 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/corr/abs-2403-14146.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{he:2024:CEC, author = "Yifan He and Claus Aranha", title = "Evolving Benchmark Functions to Compare Evolutionary Algorithms via Genetic Programming", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Measurement, Correlation, Evolutionary computation, Switches, Benchmark testing, Generators, Benchmark functions, Algorithmic behavior, MAP-Elites", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10611801", abstract = "In this study, we use Genetic Programming (GP) to compose new optimisation benchmark functions. Optimisation benchmarks have the important role of showing the differences between evolutionary algorithms, making it possible for further analysis and comparisons. We show that the benchmarks generated by GP are able to differentiate algorithms better than human-made benchmark functions. The fitness measure of the GP is the Wasserstein distance of the solutions found by a pair of optimisers. Additionally, we use MAP-Elites to both enhance the search power of the GP and also illustrate how the difference between optimisers changes by various landscape features. Our approach provides a novel way to automate the design of benchmark functions and to compare evolutionary algorithms.", notes = "also known as \cite{10611801} WCCI 2024", } @InProceedings{headleand:2014:RDIS, author = "Christopher J. Headleand and Llyr Ap Cenydd and William J. Teahan", title = "Benchmarking {Grammar-Based} Genetic Programming Algorithms", booktitle = "Research and Development in Intelligent Systems XXXI", year = "2014", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-319-12069-0_9", DOI = "doi:10.1007/978-3-319-12069-0_9", } @Article{JMLR:v16:heaton15a, author = "Jeff Heaton", title = "{Encog}: Library of Interchangeable Machine Learning Models for {Java} and {C\#}", journal = "Journal of Machine Learning Research", year = "2015", volume = "16", pages = "1243--1247", month = jun, keywords = "genetic algorithms, genetic programming", ISSN = "1533-7928", URL = "http://www.jmlr.org/papers/v16/", URL = "http://jmlr.org/papers/v16/heaton15a.html", URL = "http://www.jmlr.org/papers/volume16/heaton15a/heaton15a.pdf", abstract = "This paper introduces the Encog library for Java and C#, a scalable, adaptable, multi-platform machine learning framework that was first released in 2008. Encog allows a variety of machine learning models to be applied to data sets using regression, classification, and clustering. Various supported machine learning models can be used interchangeably with minimal recoding. Encog uses efficient multithreaded code to reduce training time by exploiting modern multicore processors. The current version of Encog can be downloaded from www.encog.org.", } @PhdThesis{Heaton:thesis, author = "Jeff Heaton", title = "Automated Feature Engineering for Deep Neural Networks with Genetic Programming", school = "Computer Science, Nova Southeastern University", year = "2017", address = "Florida, USA", keywords = "genetic algorithms, genetic programming, Applied sciences, Deep neural network, Feature engineering, Artificial intelligence, Computer science", isbn13 = "9781369660012", language = "English", URL = "https://search.proquest.com/docview/1889190846?accountid=14511", URL = "https://search.proquest.com/docview/1889190846?pq-origsite=gscholar", URL = "http://nsuworks.nova.edu/gscis_etd/994/", URL = "https://www.researchgate.net/publication/316285310_Automated_Feature_Engineering_for_Deep_Neural_Networks_with_Genetic_Programming", size = "202 pages", abstract = "Feature engineering is a process that augments the feature vector of a machine learning model with calculated values that are designed to enhance the accuracy of a model's predictions. Research has shown that the accuracy of models such as deep neural networks, support vector machines, and tree/forest-based algorithms sometimes benefit from feature engineering. Expressions that combine one or more of the original features usually create these engineered features. The choice of the exact structure of an engineered feature is dependent on the type of machine learning model in use. Previous research demonstrated that various model families benefit from different types of engineered feature. Random forests, gradient-boosting machines, or other tree-based models might not see the same accuracy gain that an engineered feature allowed neural networks, generalized linear models, or other dot-product based models to achieve on the same data set. This dissertation presents a genetic programming-based algorithm that automatically engineers features that increase the accuracy of deep neural networks for some data sets. For a genetic programming algorithm to be effective, it must prioritize the search space and efficiently evaluate what it finds. This dissertation algorithm faced a potential search space composed of all possible mathematical combinations of the original feature vector. Five experiments were designed to guide the search process to efficiently evolve good engineered features. The result of this dissertation is an automated feature engineering (AFE) algorithm that is computationally efficient, even though a neural network is used to evaluate each candidate feature. This approach gave the algorithm a greater opportunity to specifically target deep neural networks in its search for engineered features that improve accuracy. Finally, a sixth experiment empirically demonstrated the degree to which this algorithm improved the accuracy of neural networks on data sets augmented by the algorithm's engineered features.", notes = " ProQuest Dissertations Publishing, 2017. 10259604. Supervisor James D. Cannady School code 1191", } @Article{Heaton:GPEM:deep_learning, author = "Jeff Heaton", title = "{Ian Goodfellow}, {Yoshua Bengio}, and {Aaron Courville}: Deep learning", journal = "Genetic Programming and Evolvable Machines", year = "2018", volume = "19", number = "1-2", pages = "305--307", month = jun, note = "Book review", keywords = "genetic algorithms, genetic programming, ANN", ISSN = "1389-2576", URL = "https://rdcu.be/dR8uL", DOI = "doi:10.1007/s10710-017-9314-z", size = "3 pages", notes = "The MIT Press, 2016, 800 pp, ISBN: 0262035618 http://www.deeplearningbook.org/", } @Article{Heaton:2019:GPEM, author = "Jeff Heaton", title = "Evolving continuous cellular automata for aesthetic objectives", journal = "Genetic Programming and Evolvable Machines", year = "2019", volume = "20", number = "1", pages = "93--125", month = mar, keywords = "genetic algorithms, genetic programming, Cellular automata, Generative art, Multi-objective optimization", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-018-9336-1", size = "33 pages", abstract = "We present MergeLife, a genetic algorithm (GA) capable of evolving continuous cellular automata (CA) that generate full colour dynamic animations according to aesthetic user specifications. A simple 16-byte update rule is introduced that is evolved through an objective function that requires only initial human aesthetic guidelines. This update rule provides a fixed-length genome that can be successfully optimized by a GA. Also introduced are several novel fitness measures that when given human selected aesthetic guidelines encourage the evolution of complex animations that often include spaceships, oscillators, still life, and other complex emergent behaviour. The results of this research are several complex and long running update rules and the objective function parameters that produced them. Several update rules produced from this paper exhibit complex emergent behaviour through patterns, such as spaceships, guns, oscillators, and Universal Turing Machines. Because the true animated behavior of these CA cannot be observed from static images, we also present an on-line JavaScript viewer that is capable of animating any MergeLife 16-byte update rule.", } @Article{HEBBALAGUPPAEKRISHNASHETTY:2021:CSR, author = "Pradeep {Hebbalaguppae Krishnashetty} and Jasma Balasangameshwara and Sheshshayee Sreeman and Sujeet Desai and Archana {Bengaluru Kantharaju}", title = "Cognitive computing models for estimation of reference evapotranspiration: A review", journal = "Cognitive Systems Research", volume = "70", pages = "109--116", year = "2021", ISSN = "1389-0417", DOI = "doi:10.1016/j.cogsys.2021.07.012", URL = "https://www.sciencedirect.com/science/article/pii/S1389041721000620", keywords = "genetic algorithms, genetic programming, Crop water requirements, Irrigation system, Artificial neural networks, Support vector machine", abstract = "Irrigation practices can be advanced by the aid of cognitive computing models. Repeated droughts, population expansion and the impact of global warming collectively impose rigorous restrictions over irrigation practices. Reference evapotranspiration (ET0) is a vital factor to predict the crop water requirements based on climate data. There are many techniques available for the prediction of ET0. An efficient ET0 prediction model plays an important role in irrigation system to increase water productivity. In the present study, a review has been carried out over cognitive computing models used for the estimation of ET0. Review exhibits that artificial neural network (ANN) approach outperforms support vector machine (SVM) and genetic programming (GP). Second order neural network (SONN) is the most promising approach among ANN models", } @InProceedings{heckendorn:1999:PTSSGM, author = "Robert B. Heckendorn and Soraya Rana and Darrell Whitley", title = "Polynomial Time Summary Statistics for a Generalization of MAXSAT", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "281--288", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/maxsat99.pdf", URL = "http://www.cs.colostate.edu/~genitor/1999/maxsat99.pdf", abstract = "NK landscape, Walsh analysis", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Article{Hecker200986, author = "Michael Hecker and Sandro Lambeck and Susanne Toepfer and Eugene {van Someren} and Reinhard Guthke", title = "Gene regulatory network inference: Data integration in dynamic models--A review", journal = "Biosystems", volume = "96", number = "1", pages = "86--103", year = "2009", ISSN = "0303-2647", DOI = "doi:10.1016/j.biosystems.2008.12.004", URL = "http://www.sciencedirect.com/science/article/B6T2K-4V7MSTS-1/2/db669ac3459da19bab3535dc038303d5", keywords = "genetic algorithms, genetic programming, Systems biology, Reverse engineering, Biological modelling, Knowledge integration", abstract = "Systems biology aims to develop mathematical models of biological systems by integrating experimental and theoretical techniques. During the last decade, many systems biological approaches that base on genome-wide data have been developed to unravel the complexity of gene regulation. This review deals with the reconstruction of gene regulatory networks (GRNs) from experimental data through computational methods. Standard GRN inference methods primarily use gene expression data derived from microarrays. However, the incorporation of additional information from heterogeneous data sources, e.g. genome sequence and protein-DNA interaction data, clearly supports the network inference process. This review focuses on promising modelling approaches that use such diverse types of molecular biological information. In particular, approaches are discussed that enable the modelling of the dynamics of gene regulatory systems. The review provides an overview of common modelling schemes and learning algorithms and outlines current challenges in GRN modelling.", notes = "survey", } @InProceedings{Hedar:2010:ICCTD, author = "Abdel-Rahman Hedar and Mostafa Kamel Osman", title = "Scatter Programming", booktitle = "2nd International Conference on Computer Technology and Development (ICCTD), 2010", year = "2010", month = "2-4 " # nov, pages = "451--455", address = "Cairo", abstract = "The core of artificial intelligence and machine learning is to get computers to solve problems automatically. One of the great tools that attempt to achieve that goal is Genetic Programming (GP). As alternatives to GP, Scatter Programming (SP) is proposed in this paper. One of the main features of SP is to exploit local search in order to overcome some recently addressed drawbacks of GP, especially its highly disruption of its main operations; crossover and mutation. This work shows that SP has promising performance and results in solving machine learning problems.", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, grammatical evolution, artificial intelligence, machine learning, scatter programming, learning (artificial intelligence)", DOI = "doi:10.1109/ICCTD.2010.5645839", notes = "symbolic regression, 6-mux. Also known as \cite{5645839}", } @Article{journals/ijitdm/HedarMF11, author = "Abdel-Rahman Hedar and Emad Mabrouk and Masao Fukushima", title = "Tabu Programming: a New Problem Solver through Adaptive Memory Programming over Tree Data Structures", journal = "International Journal of Information Technology and Decision Making", volume = "10", number = "2", year = "2011", pages = "373--406", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1142/S0219622011004373", oai = "oai:RePEc:wsi:ijitdm:v:10:y:2011:i:02:p:373-406", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "Since the first appearance of the Genetic Programming (GP) algorithm, extensive theoretical and application studies on it have been conducted. Nowadays, the GP algorithm is considered one of the most important tools in Artificial Intelligence (AI). Nevertheless, several questions have been raised about the complexity of the GP algorithm and the disruption effect of the crossover and mutation operators. In this paper, the Tabu Programming (TP) algorithm is proposed to employ the search strategy of the classical Tabu Search algorithm with the tree data structure. Moreover, the TP algorithm exploits a set of local search procedures over a tree space in order to mitigate the drawbacks of the crossover and mutation operators. Extensive numerical experiments are performed to study the performance of the proposed algorithm for a set of benchmark problems. The results of those experiments show that the TP algorithm compares favourably to recent versions of the GP algorithm in terms of computational efforts and the rate of success. Finally, we present a comprehensive framework called Meta-Heuristics Programming (MHP) as general machine learning tools.", } @InProceedings{Hedar:2015:ieee/acisSNPD, author = "Abdel-Rahman Hedar and Mohamed A. Omer and Ahmed F. Al-Sadek and Adel A. Sewisy", booktitle = "16th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)", title = "Hybrid evolutionary algorithms for data classification in intrusion detection systems", year = "2015", abstract = "Intrusion detection systems (IDS) are important to protect our systems and networks from attacks and malicious behaviours. In this paper, we propose a new hybrid intrusion detection system by using accelerated genetic algorithm and rough set theory (AGAAR) for data feature reduction, and genetic programming with local search (GPLS) for data classification. The AGAAR method is used to select the most relevant attributes that can represent an intrusion detection dataset. In order to improve the performance of GPLS classifier, a new local search strategy is used with genetic programming operators. The main target of using local search strategy is to discover the better solution from the current. The results shown later indicate that classification accuracy improved from 75.98percent to 81.44percent after using AGAAR attribute reduction for the NSL-KDD dataset. The classification accuracies have been compared with others algorithms and shown that the proposed method can be one of the competitive classifiers for IDS.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SNPD.2015.7176208", month = jun, notes = "Also known as \cite{7176208}", } @Article{Hedberg:2005:IS, author = "Sara Resse Hedberg", title = "Evolutionary computing: the rise of electronic breeding", journal = "Intelligent Systems", year = "2005", volume = "20", number = "6", pages = "12--15", month = nov # "-" # dec, keywords = "genetic algorithms, genetic programming, biological evolution, electronic breeding, evolutionary computing", DOI = "doi:10.1109/MIS.2005.104", ISSN = "1541-1672", size = "4 pages", abstract = "GAs and their relations, which fall under the umbrella term evolutionary computing, are being harnessed to optimise designs of all sorts. GAs mimics the mechanisms of biological evolution. Populations of individuals evolve by means of reproduction, inheritance, mutation, natural selection, and recombination or crossover (two organisms swap a portion of their genetic code). The result is computational methods that build a population of individuals or designs based on a set of criteria and constraints.", notes = "high level", } @InProceedings{heddad:evows04, author = "Amine Heddad and Markus Brameier and Robert M. MacCallum", title = "Evolving Regular Expression-based Sequence Classifiers for Protein Nuclear Localisation", booktitle = "Applications of Evolutionary Computing, EvoWorkshops2004: {EvoBIO}, {EvoCOMNET}, {EvoHOT}, {EvoIASP}, {EvoMUSART}, {EvoSTOC}", year = "2004", month = "5-7 " # apr, editor = "Guenther R. Raidl and Stefano Cagnoni and Jurgen Branke and David W. Corne and Rolf Drechsler and Yaochu Jin and Colin R. Johnson and Penousal Machado and Elena Marchiori and Franz Rothlauf and George D. Smith and Giovanni Squillero", series = "LNCS", volume = "3005", address = "Coimbra, Portugal", publisher = "Springer Verlag", publisher_address = "Berlin", pages = "31--40", keywords = "genetic algorithms, genetic programming, evolutionary computation, perl, grammar, BNF, linear GP, LGP, RE, regular expressions", ISBN = "3-540-21378-3", URL = "http://www.sbc.su.se/~maccallr/publications/heddad-evobio2004.pdf", DOI = "doi:10.1007/978-3-540-24653-4_4", abstract = "A number of bioinformatics tools use regular expression (RE) matching to locate protein or DNA sequence motifs that have been discovered by researchers in the laboratory. For example, patterns representing nuclear localisation signals (NLSs) are used to predict nuclear localisation. NLSs are not yet well understood, and so the set of currently known NLSs may be incomplete. Here we use genetic programming (GP) to generate RE-based classifiers for nuclear localisation. While the approach is a supervised one (with respect to protein location), it is unsupervised with respect to already known NLSs. It therefore has the potential to discover new NLS motifs. We apply both tree based and linear GP to the problem. The inclusion of predicted secondary structure in the input does not improve performance. Benchmarking shows that our majority classifiers are competitive with existing tools. The evolved REs are usually NLS like and work is underway to analyse these for novelty.", notes = "EvoWorkshops2004, perlGP, grammar (not needed, cf p39?). Broken Dec 2021 http://www.sbc.su.se/~maccallr/nucpred/ perl eval(), grammar, stgp, matches(),, pdiv, plog, multiple classifier combination majority vote. 'No crossover is allowed between REs' p38. Removing ineffective code. 'LGP very close to PerlGP' p38. RE matching done in C. cf. \cite{brameier:nucpred}", } @InCollection{HEDDAM:2022:CEES, author = "Salim Heddam and Sungwon Kim and Ali {Danandeh Mehr} and Mohammad Zounemat-Kermani and Anurag Malik and Ahmed Elbeltagi and Ozgur Kisi", title = "Chapter 1 - Predicting dissolved oxygen concentration in river using new advanced machines learning: Long-short term memory ({LSTM)} deep learning", editor = "Hamid Reza Pourghasemi", booktitle = "Computers in Earth and Environmental Sciences", publisher = "Elsevier", pages = "1--20", year = "2022", isbn13 = "978-0-323-89861-4", DOI = "doi:10.1016/B978-0-323-89861-4.00031-2", URL = "https://www.sciencedirect.com/science/article/pii/B9780323898614000312", keywords = "genetic algorithms, genetic programming, Modeling, Dissolved oxygen, LSTM, GP, GMDH, SVR, GRP, MLR", abstract = "Accurate estimation of the dissolved oxygen concentration is critical and of significant importance for several environmental applications. Over the years, many types of models have been proposed to provide a more accurate estimation of dissolved oxygen at different time scales. Recently, the deep learning paradigm has been increasingly used in several environmental and engineering applications. This study presents the application of long short-term memory (LSTM) deep learning for dissolved oxygen (DO) prediction in rivers. The model was trained and calibrated using three predictors: (i) river water temperature (Tw), (ii) air temperature, and (iii) river discharge (Q). The variables were measured on an hourly time scale and collected from two USGS stations. The LSTM model was compared against genetic programming (GP), the group method of data handling neural network (GMDH), support vector regression (SVR), and Gaussian process regression (GPR) models. The proposed models were evaluated using well-known performance metrics, namely the coefficient of correlation (R), Nash-Sutcliffe efficiency (NSE), mean absolute error (MAE), and root mean square error (RMSE). This study demonstrates the utility and robustness of the proposed models for predicting dissolved oxygen, and the GPR was found to be slightly better than the SVR model, and significantly better than the GMDH, LSTM, GP, and MLR models. It was also demonstrated that the LSTM ranked third. Numerical results showed that using Tw, Ta, and Q as predictors combined with the periodicity (i.e., hour, day, and month number) leads to high accuracies with R, NSE, RMSE, and MAE of 0.991, 0.981, 0.085, and 0.062, respectively", } @InProceedings{Hedman:2002:gecco, author = "Karl Hedman and David Persson and Per Skoglund and Dan Wiklund and Krister Wolff and Peter Nordin", title = "Sensing And Direction In Locomotion Learning With {A} Random Morphology Robot", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "1297", address = "New York", publisher_address = "San Francisco, CA 94104, USA", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "genetic algorithms, genetic programming, evolutionary robotics, poster paper, evolutionary algorithm, random morphology", ISBN = "1-55860-878-8", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/ROB211.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/ROB211.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-23.pdf", abstract = "We describe the first instance in sensing and direction with a learning Random Morphology robot. Using GP, it learns to locomote itself in different directions and by letting different solutions master the robot in different situations it can thus follow an arbitrary path.", notes = "GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)", } @InProceedings{DBLP:conf/kdd/HeffetzVKR20, author = "Yuval Heffetz and Roman Vainshtein and Gilad Katz and Lior Rokach", title = "{DeepLine: AutoML} Tool for Pipelines Generation using Deep Reinforcement Learning and Hierarchical Actions Filtering", booktitle = "KDD 2020: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining", year = "2020", editor = "Rajesh Gupta and Yan Liu and Jiliang Tang and B. Aditya Prakash", pages = "2103--2113", address = "Virtual Event, CA, USA", month = aug # " 23-27", publisher = "ACM", keywords = "genetic algorithms, genetic programming, TPOT, AutoML, classification, deep reinforcement learning", timestamp = "Tue, 09 Mar 2021 09:46:47 +0100", biburl = "https://dblp.org/rec/conf/kdd/HeffetzVKR20.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://doi.org/10.1145/3394486.3403261", DOI = "doi:10.1145/3394486.3403261", size = "11 pages", abstract = "Automatic Machine Learning (AutoML) is an area of research aimed at automating Machine Learning (ML) activities that currently require the involvement of human experts. One of the most challenging tasks in this field is the automatic generation of end-to-end ML pipelines: combining multiple types of ML algorithms into a single architecture used for analysis of previously unseen data. This task has two challenging aspects: the first is the need to explore a large search space of algorithms and pipeline architectures. The second challenge is the computational cost of training and evaluating multiple pipelines. we present DeepLine, a reinforcement learning based approach for automatic pipeline generation. Our proposed approach uses an efficient representation of the search space together with a novel method for operating in environments with large and dynamic action spaces. By leveraging past knowledge gained from previously analysed datasets,our approach only needs to generate and evaluate few dozens of pipe lines to reach comparable or better performance than current state-of-the-art AutoML systems that evaluate hundreds and even thousands of pipelines in their optimisation process. Evaluation on 56 classification datasets demonstrates the merits of our approach", notes = "Comparison with TPOT and Auto-Sklearn etc", } @Article{HEGAB:2021:ASC, author = "H. Hegab and A. Salem and S. Rahnamayan and H. A. Kishawy", title = "Analysis, modeling, and multi-objective optimization of machining Inconel 718 with nano-additives based minimum quantity coolant", journal = "Applied Soft Computing", volume = "108", pages = "107416", year = "2021", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2021.107416", URL = "https://www.sciencedirect.com/science/article/pii/S1568494621003392", keywords = "genetic algorithms, genetic programming, Inconel 718, Minimum quantity lubrication, Nano-additives, Tool wear, Surface roughness, Energy consumption, Modeling and multi-objective optimization", abstract = "In the current study, analysis, modeling, and optimization of machining with nano-additives based minimum quantity lubrication (MQL) during turning Inconel 718 are presented and discussed. Multi-walled carbon nanotubes (MWCNTs) and aluminum oxide (Al2O3) gamma nanoparticles were used as used nano-additives. The studied design variables include cutting speed, feed rate, and nano-additives percentage (wt. percent). Three machining outputs were considered namely: flank wear, surface roughness, and energy consumption. The novelty here focuses on improving the MQL heat capacity by employing two different nano-fluids. The analysis of variance (ANOVA) technique was employed to investigate the influence of the design variables on the studied machining outputs. The results demonstrated that the usage of MQL-nanofluids improved the cutting process performance compared to the classical approach of MQL. It was found that 4 wt. percent of added MWCNTs decreased the flank wear by 45.6percent compared to the pure MQL. Similarly, it was found that 4 wt. percent of added Al2O3 nanoparticles improved the tool wear by 37.2percent. Besides, the nanotubes additives showed more improvements than Al2O3 nanoparticles in terms of tool wear, surface quality, and energy consumption. Regarding the modeling stage, artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and genetic programming (GP) are employed to model the measured outputs in terms of the studied parameters. These soft computing approaches provide various advantages through their self-learning capabilities, fuzzy principles, and evolutionary computational concept. In addition, a comparison among the developed models has been conducted to select the most accurate approach to present the machining characteristics. Finally, the non-dominated sorting genetic algorithm (NSGA-II) was used to optimize the studied cutting processes. Moreover, a comparison between the optimized results from different approaches is presented. The proposed methodology presented in this work can be further implemented in other machining cases to model, analyze as well as optimize the machining performance, especially for the hard-to-cut materials which are commonly used in different industries", } @InCollection{Heiberg:1997:lbn, author = "Vilhelm Heiberg", title = "Learning {Bayesian} Networks Using a Genetic Algorithm", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1997", publisher = "Stanford Bookstore", year = "1997", editor = "John R. Koza", pages = "86--97", address = "Stanford, California, 94305-3079 USA", month = "17 " # mar, keywords = "genetic algorithms, genetic programming", ISBN = "0-18-205981-2", abstract = "for learning baysian networks .... intelligent Greedy Search outperforms GA and SA", notes = "part of \cite{koza:1997:GAGPs}", } @Article{Heidari:2024:jobe, author = "Farahbod Heidari and Mohammadjavad Mahdavinejad and Katia Zolotovsky and Mohammadreza Bemanian", title = "Data-driven bio-integrated design method encoded by biocomputational real-time feedback loop and deep semi-supervised learning ({DSSL)}", journal = "Journal of Building Engineering", year = "2024", volume = "98", pages = "110923", keywords = "genetic algorithms, genetic programming, Data-driven bio-integrated design, Deep semi-supervised learning, transfer learning, feature extraction, Programmable living building materials", ISSN = "2352-7102", URL = "https://www.sciencedirect.com/science/article/pii/S2352710224024914", DOI = "doi:10.1016/j.jobe.2024.110923", abstract = "Today, under the imperatives of synthetic biology (Synbio), material-based design strategies are conceived as biofactories that can recapitulate the functionalities of living systems for developing building materials with controllable structural features. These strategies include biofabrication techniques such as vitro-culturing, Fabrication Information Modeling (FIM), growth parametrization, machinery systems, and genetic programming articulated bioproducts as the construction material for low-carbon buildings. However, these biophysical systems need interactive development by data-oriented models and decision-maker tools that can mine, measure, and re-configure the complexity in biological systems through monitoring and controlling in a higher integration toward systemic decision-making solutions. This research presents a real-time feedback loop as the bio-integrated design method for design and fabrication of Gluconacebacter Xylinus (GX) Bacterial Cellulose (BC) with Transfer Learning (TL) and Deep Semi-Supervised Learning (DSSL) through TensorFlow and Keras Python libraries. The loop started by mining datasets from BC growth via the VGG16 pre-trained model. In this stage, the loop can learn the material behaviour over the growth process and attribute features to a measured BC thickness. Based on this attribution, the loop can drive real-time classifying of input datasets to thickness-based classes and guide the Region of Interests (ROIs) to a user-designed thickness till the captured features are fitted to the user-input class features. The training results over 8640 images show a 0.001 learning rate, an average loss of 0.18 (540 total steps), an accuracy of 0.706, and a precision of 0.752 for the feature extractor model. For the classifier, a 0.15 average loss (3375 total steps), an accuracy of 0.829, and a precision of 0.712 demonstrate the models' efficiency in pattern recognition and classification. Also, the loop operation persistently exhibits a more than 50 percent precision rate, displaying the loop's high performance in achieving user-designed 3D shapes. This research aims to a transformative shift in bio-integrated design based on feedback loops as the foundational step towards intelligent bio-digital design platforms. Also, this ground can break designing with biological systems based on desired tasks for the new generation of designers called biodesigners", } @Article{HEIDARI:2023:molliq, author = "Zeinab Heidari and Mohammad Amin Sobati", title = "Evaluation of the flammability characteristics of alkyl esters: New {QSPR} models", journal = "Journal of Molecular Liquids", year = "2023", volume = "387", pages = "122697", keywords = "genetic algorithms, genetic programming, Flammability limits, Alkyl esters, QSPR, Random forest (RF), Support vector regression (SVR), Genetic programming (GP)", ISSN = "0167-7322", URL = "https://www.sciencedirect.com/science/article/pii/S0167732223015027", DOI = "doi:10.1016/j.molliq.2023.122697", abstract = "In the present study, new quantitative structure-property relation (QSPR) models have been developed to predict different flammability characteristics (i.e., Lower flammability temperature (LFLT), Upper flammability temperature (UFLT), Lower flammability percent (LFL(Vpercent), and Upper flammability percent (UFL(Vpercent)) of pure Alkyl esters. In this regard, new data sets containing 179 different alkyl esters from 10 different chemical categories were used. In the model development procedure, the appropriate molecular descriptors were selected for each property using the enhanced replacement method (ERM). Afterward, a multivariable linear model and three nonlinear models based on genetic programming (GP), random forest regression (RFR), and support vector regression (SVR) were developed using the molecular descriptors as input variables. The implementation of different internal and external validation methods confirmed the acceptable prediction capability of the developed models. In this regard, the average absolute error for the best models were 7.61 K for LFLT, 7.96 K for UFLT, 0.10 percent for LFL (Vpercent), and 1.10 for UFL(Vpercent) over the whole dataset. A comparison between the prediction capabilities of the developed QSPR models with previous models also confirmed the superiority of the developed models in this study. Therefore, the developed QSPR models can be employed in the evaluation of the flammability characteristics of new alkyl esters such as new fatty acid alkyl esters in the biodiesel", } @Article{HEIDARI:2023:chemolab, author = "Zeinab Heidari and Mohammad Amin Sobati", title = "New structure-based models for the prediction of flash point and autoignition temperatures of alkyl esters", journal = "Chemometrics and Intelligent Laboratory Systems", volume = "240", pages = "104877", year = "2023", ISSN = "0169-7439", DOI = "doi:10.1016/j.chemolab.2023.104877", URL = "https://www.sciencedirect.com/science/article/pii/S0169743923001272", keywords = "genetic algorithms, genetic programming, Alkyl esters, Enhanced replacement method (ERM), QSPR, Genetic programming (GP), Support vector regression (SVR), Random forest regression (RFR)", abstract = "In this study, new models based on quantitative structure-property relationship (QSPR) have been proposed for the prediction of autoignition temperature (AIT) and flash point (FP) of different alkyl esters. In this regard, data sets containing 126 and 179 alkyl esters from 10 categories were used for AIT and FP, respectively. The enhanced replacement method (ERM) was applied for choosing the appropriate molecular descriptors, and linear models were developed based on the selected descriptors. Nonlinear models were also developed for AIT and FP using genetic programming (GP), support vector regression (SVR), and random forest regression (RFR). Then, the predictive performance of each model was evaluated through internal and external validation techniques considering several statistical parameters such as coefficient of determination (R2), root mean square error (RMSE), and percent of average absolute relative deviation (AARD). The outcome of the validation techniques confirmed the satisfactory agreement between the predicted and experimental data. The AARDpercent, R2, and RMSE for the best corresponding models (GP-based nonlinear models) was reported as 4.54percent, 0.74, and 39.01 for AIT and 2.51percent, 0.98, and 12.32 for FP, respectively. The superiority of the new models over the previous models was also proved by comparing the prediction capability of different models", } @Article{oai:biomedcentral.com:1471-2156-7-23, title = "The challenge for genetic epidemiologists: how to analyze large numbers of {SNP}s in relation to complex diseases", author = "A Geert Heidema and Jolanda M A Boer and Nico Nagelkerke and Edwin C M Mariman and Daphne L {van der A} and Edith J M Feskens", year = "2006", month = apr # "~21", journal = "BMC Genetics", volume = "7", number = "23", publisher = "BioMed Central Ltd.", bibsource = "OAI-PMH server at www.biomedcentral.com", language = "en", oai = "oai:biomedcentral.com:1471-2156-7-23", rights = "Copyright 2006 Heidema et al; licensee BioMed Central Ltd.", type = "Commentary", keywords = "genetic algorithms, genetic programming", ISSN = "1471-2156", URL = "http://www.biomedcentral.com/content/pdf/1471-2156-7-23.pdf", URL = "http://www.biomedcentral.com/1471-2156/7/23", DOI = "doi:10.1186/1471-2156-7-23", size = "15 pages", abstract = "Genetic epidemiologists have taken the challenge to identify genetic polymorphisms involved in the development of diseases. Many have collected data on large numbers of genetic markers but are not familiar with available methods to assess their association with complex diseases. Statistical methods have been developed for analysing the relation between large numbers of genetic and environmental predictors to disease or disease-related variables in genetic association studies. In this commentary we discuss logistic regression analysis, neural networks, including the parameter decreasing method (PDM) and genetic programming optimised neural networks (GPNN) and several non-parametric methods, which include the set association approach, combinatorial partitioning method (CPM), restricted partitioning method (RPM), multifactor dimensionality reduction (MDR) method and the random forests approach. The relative strengths and weaknesses of these methods are highlighted. Logistic regression and neural networks can handle only a limited number of predictor variables, depending on the number of observations in the dataset. Therefore, they are less useful than the non-parametric methods to approach association studies with large numbers of predictor variables. GPNN on the other hand may be a useful approach to select and model important predictors, but its performance to select the important effects in the presence of large numbers of predictors needs to be examined. Both the set association approach and random forests approach are able to handle a large number of predictors and are useful in reducing these predictors to a subset of predictors with an important contribution to disease. The combinatorial methods give more insight in combination patterns for sets of genetic and/or environmental predictor variables that may be related to the outcome variable. As the non-parametric methods have different strengths and weaknesses we conclude that to approach genetic association studies using the case-control design, the application of a combination of several methods, including the set association approach, MDR and the random forests approach, will likely be a useful strategy to find the important genes and interaction patterns involved in complex diseases.", notes = "Open Access", } @Article{HeidrichMeisner2009152, author = "Verena Heidrich-Meisner and Christian Igel", title = "Neuroevolution strategies for episodic reinforcement learning", journal = "Journal of Algorithms", year = "2009", volume = "64", number = "4", pages = "152--168", month = oct, note = "Special Issue: Reinforcement Learning", keywords = "genetic algorithms, genetic programming, Reinforcement learning, Evolution strategy, Covariance matrix adaptation, Partially observable Markov decision process, Direct policy search", ISSN = "0196-6774", broken = "http://www.sciencedirect.com/science/article/B6WH3-4W7RY8J-3/2/22f7075bc25dab10a8ff3714e2fee303", DOI = "doi:10.1016/j.jalgor.2009.04.002", abstract = "Because of their convincing performance, there is a growing interest in using evolutionary algorithms for reinforcement learning. We propose learning of neural network policies by the covariance matrix adaptation evolution strategy (CMA-ES), a randomised variable-metric search algorithm for continuous optimisation. We argue that this approach, which we refer to as CMA Neuroevolution Strategy (CMA-NeuroES), is ideally suited for reinforcement learning, in particular because it is based on ranking policies (and therefore robust against noise), efficiently detects correlations between parameters, and infers a search direction from scalar reinforcement signals. We evaluate the CMA-NeuroES on five different (Markovian and non-Markovian) variants of the common pole balancing problem. The results are compared to those described in a recent study covering several RL algorithms, and the CMA-NeuroES shows the overall best performance.", notes = "compared against CE \cite{gruau:1996:ceVdeGNN}", } @MastersThesis{heigl_05, author = "Andreas Heigl", title = "Option Pricing by means of Genetic Programming", school = "Vienna University of Technology, Institute of Computer Graphics and Algorithms", year = "2005", type = "Diplomarbeit", address = "Vienna, Austria", month = feb, keywords = "genetic algorithms, genetic programming", URL = "https://www.ads.tuwien.ac.at/publications/bib/pdf/heigl_05.pdf", size = "68 pages", abstract = "This master thesis describes how to price options by means of Genetic Programming. The underlying model is the Generalised Autoregressive Conditional Heteroskedastic (GARCH) asset return process. The goal of this master thesis is to find a closed-form solution for the price of European call options where the underlying securities follow a GARCH process. The data are simulated over a wide range to cover a lot of existing options in one single equation. Genetic Programming is used to generate the pricing function from the data. Genetic Programming is a method of producing programs just by defining a problem dependent fitness function. The resulting equation is found via a heuristic algorithm inspired by natural evolution. Three different methods of bloat control are used. Additionally Automatic Defined Functions (ADFs) and a hybrid approach are tested, too. To ensure that a good configuration setting is used, preliminary testing of many different settings has been done, suggesting that simpler configurations are more successful in this environment. The resulting equation can be used to calculate the price of an option in the given range with minimal errors. This equation is well behaved and can be used in standard spread sheet programs. It offers a wider range of uses or a higher accuracy, respectively than other existing approaches.", zusammenfassung = "Diese Diplomarbeit beschreibt, wie Optionen mit Hilfe Genetischer Programmierung bewertet werden koennen. Das zugrunde liegende Modell nennt sich GARCH (Generalized Autoregressive Conditional Heteroskedastic) Renditeprozess. Das Ziel dieser Diplomarbeit ist eine geschlossene Formel, die als Ergebnis den Preis einer europaeischen Kaufoption liefert, dessen dahinter liegende Wertpapier einem GARCH Prozess folgt. Die Daten werden innerhalb eines breiten Wertebereiches simuliert, um die meisten existierenden Optionen mit einer Formel bewerten zu koennen. Die Formel wird mittels Genetischer Programmierung aus den Daten generiert. Genetische Programmierung ist eine Methode, bei der nur durch Definition einer zum Problem passenden Bewertungsfunktion vollstaendige Programme produziert werden koennen. Die Ergebnisgleichung wird schliesslich mittels eines der Evolution aehnlichen Algorithmus gefunden. Drei verschiedene Methoden zum Bloat Control wurden verwendet. Zusaetzlich wurden auch Automatisch De nierte Funktionen sowie ein hybrider Ansatz untersucht. Um sicherzustellen, dass eine gute Konfiguration gewaehlt wird, gibt es Vortests vieler verschiedener Konfigurationen. Es zeigt sich, dass in diesem Umfeld einfachere Konfigurationen erfolgreicher sind. Die Ergebnisgleichung kann schliesslich zur Errechnung der Optionspreise mit minimalem Fehler verwendet werden. Diese Gleichung verhaelt sich gut und kann auch in Standardtabellenkalkulationen verwendet werden. Im Vergleich mit anderen existierenden Ansaetzen, bietet diese Gleichung eine weitere Verwendbarkeit beziehungsweise eine hoehere Genauigkeit.", notes = "In English. Supervised by Guether Raidl and Michael Hanke", } @Book{Heigl:book, author = "Andreas Heigl", title = "Option Pricing by Means of Genetic Programming", subtitle = "How to Find a Closed-form Solution for the Price of European Call Options?", publisher = "VDM Verlag Dr. Mueller", year = "2008", month = "3 " # apr, keywords = "genetic algorithms, genetic programming", isbn13 = "9783836485203", URL = "https://www.amazon.com/Option-Pricing-Means-Genetic-Programming/dp/3836485206/ref=sr_1_2", broken = "http://www.word-power.co.uk/books/option-pricing-by-means-of-genetic-programming-I9783836485203/", abstract = "This master thesis describes how to price options by means of Genetic Programming. The underlying model is the Generalized Autoregressive Conditional Heteroskedastic (GARCH) asset return process. The goal is to find a closed-form solution for the price of European call options where the underlying securities follow a GARCH process. Genetic Programming is used to generate the pricing function from the data. Genetic Programming is a method of producing programs just by defining a problem dependent fitness function. The resulting equation is...", notes = "See also \cite{heigl_05}", size = "68 pages", } @InProceedings{Heimfarth:2013:ISORC, author = "Tales Heimfarth and Renato {Resende Ribeiro de Oliveira} and Raphael {Winckler de Bettio} and Ariel Felipe {Ferreira Marques} and Claudio Fabiano {Motta Toledo}", booktitle = "16th IEEE International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing (ISORC 2013)", title = "Automatic generation and configuration of Wireless Sensor Networks applications with Genetic Programming", year = "2013", month = jun, abstract = "The development of Wireless Sensor Networks (WSNs) applications is an arduous task, since the developer has to design the behaviour of the nodes and their interactions. The automatic generation of WSN's applications is desirable to reduce costs, since it drastically reduces the human effort. This paper presents the use of Genetic Programming to automatically generate WSNs applications. A scripting language based on events and actions is proposed to represent the WSN behaviour. Events represent the state of a given sensor node and actions modify these states. Some events are internal states and others are external states captured by the sensors. A parallel genetic algorithm is used to automatically generate WSNs applications in this scripting language. These scripts are executed by a middleware installed on all sensors nodes. This approach enables the application designer to define only the overall objective of the WSN. This objective is defined by means of a fitness function. An event-detection problem is presented in order to evaluate the proposed method. The results showed the capability of the developed approach to successfully solve WSNs problems through the automatic generation of applications.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ISORC.2013.6913217", notes = "Also known as \cite{6913217}", } @InProceedings{Heimfarth:2014:AINA, author = "Tales Heimfarth and Joao Paulo {De Araujo} and Renato {Resende Ribeiro de Oliveira} and Raphael {Winckler de Bettio}", booktitle = "28th IEEE International Conference on Advanced Information Networking and Applications (AINA 2014)", title = "Evaluation of a Genetic Programming Approach to Generate Wireless Sensor Network Applications", year = "2014", month = may, pages = "775--782", abstract = "This article presents a systematic evaluation of a framework based on Genetic Programming (GP) which aims the automatic generation of Wireless Sensor Network (WSN) applications. Developing WSN applications poses a challenge due to massive distribution of the network nodes. The automatic generation of applications reduces drastically costs, since the manual development is a laborious process. In our approach, the user describes the desired global behaviour as a fitness function which guides the evolution of the application by the GP. A scripting language based on events and actions is used to represent the WSN behaviour and the GP generates programs in this language. In order to evaluate the framework, a problem of multiple events detection is introduced. Several problem instances were used to appraise the performance of our method under different parameters. Results evidence the feasibility of our approach for the proposed problem, highlighting the challenges posed by the large search space and the dead end routing problem.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/AINA.2014.94", ISSN = "1550-445X", notes = "Dept. of Comput. Sci., Univ. Fed. de Lavras, Lavras, Brazil Also known as \cite{6838743}", } @Article{Heimisdottir:2021:JDR, author = "L. H. Heimisdottir and B. M. Lin and H. Cho and A. Orlenko and A. A. Ribeiro and A. Simon-Soro and J. Roach and D. Shungin and J. Ginnis and M. A. Simancas-Pallares and H. D. Spangler and A. G. {Ferreira Zandona} and J. T. Wright and P. Ramamoorthy and J. H. Moore and H. Koo and D. Wu and K. Divaris", title = "Metabolomics Insights in Early Childhood Caries", journal = "Journal of Dental Research", year = "2021", month = "9 " # jan, note = "Epub ahead of print", keywords = "genetic algorithms, genetic programming, TPOT, children, biofilm, dental caries, microbiome, machine learning, risk assessment", ISSN = "0022-0345", DOI = "doi:10.1177/0022034520982963", abstract = "Dental caries is characterized by a dysbiotic shift at the biofilm-tooth surface interface, yet comprehensive biochemical characterisations of the biofilm are scant. We used metabolomics to identify biochemical features of the supragingival biofilm associated with early childhood caries (ECC) prevalence and severity. The study analytical sample comprised 289 children ages 3 to 5 (51percent with ECC) who attended public preschools in North Carolina and were enrolled in a community-based cross-sectional study of early childhood oral health. Clinical examinations were conducted by calibrated examiners in community locations using International Caries Detection and Classification System (ICDAS) criteria. Supragingival plaque collected from the facial/buccal surfaces of all primary teeth in the upper-left quadrant was analysed using ultra-performance liquid chromatography-tandem mass spectrometry. Associations between individual metabolites and 18 clinical traits (based on different ECC definitions and sets of tooth surfaces) were quantified using Brownian distance correlations (dCor) and linear regression modeling of log2-transformed values, applying a false discovery rate multiple testing correction. A tree-based pipeline optimization tool (TPOT), machine learning process was used to identify the best-fitting ECC classification metabolite model. There were 503 named metabolites identified, including microbial, host, and exogenous biochemicals. Most significant ECC-metabolite associations were positive (i.e., upregulations/enrichments). The localized ECC case definition (ICDAS ge 1 caries experience within the surfaces from which plaque was collected) had the strongest correlation with the metabolome (dCor P = 8 10-3). Sixteen metabolites were significantly associated with ECC after multiple testing correction, including fucose (P = 3.0 10-6) and N-acetylneuraminate (p = 6.8 10-6) with higher ECC prevalence, as well as catechin (P = 4.7 10-6) and epicatechin (P = 2.9 10-6) with lower. Catechin, epicatechin, imidazole propionate, fucose, 9,10-DiHOME, and N-acetylneuraminate were among the top 15 metabolites in terms of ECC classification importance in the automated TPOT model. These supragingival biofilm metabolite findings provide novel insights in ECC biology and can serve as the basis for the development of measures of disease activity or risk assessment.", notes = "Division of Pediatric and Public Health, Adams School of Dentistry, University of North Carolina, Chapel Hill, NC, USA PMID: 33423574", } @Article{Hein:1994:TI, author = "Carl Hein and Alex Meystel", title = "A genetic technique for robotic trajectory planning", journal = "Telematics and Informatics", year = "1994", volume = "11", pages = "351--364", number = "4", abstract = "There are many multi-stage optimisation problems that are not easily solved through any known direct method when the stages are coupled. For instance, the problem of planning a vehicle's control sequence to negotiate obstacles and reach a goal in minimum time is investigated. The vehicle has a known mass, and the controlling forces have finite limits. A genetic programming technique is developed that finds admissible control trajectories that tend to minimise the vehicle's transit time through the obstacle field. The immediate application is that of a space robot that must rapidly traverse around two or three dimensional structures via application of a rotating thruster or non-rotating on-off thrusters. (An air-bearing floor test-bed for such vehicles is located at the Marshal Space Flight Center in Huntsville, Alabama.) It appears that the developed method is applicable to a general set of optimization problems in which the cost function and the multi-dimensional multi-state system can be any non-linear functions that are continuous in the operating regions. Other applications include: the planning of optimal navigation pathways through a traversability graph, the planning of control input for underwater manoeuvring vehicles which have complex control state-space relationships, the planning of control sequences for milling and manufacturing robots, the planning of control and trajectories for automated delivery vehicles, and the optimisation of control for racing vehicles and athletic training in slalom sports.", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6V1H-48V1Y16-6/2/1a0f7979e649fe0ff30f590d6fc5e0b5", keywords = "genetic algorithms, genetic programming", } @Misc{journals/corr/abs-1712-04170, author = "Daniel Hein and Steffen Udluft and Thomas A. Runkler", title = "Interpretable Policies for Reinforcement Learning by Genetic Programming", howpublished = "ArXiv", year = "2018", month = "4 " # apr, edition = "V2", keywords = "genetic algorithms, genetic programming, interpretable, reinforcement learning, model-based, symbolic regression, industrial benchmark", URL = "https://arxiv.org/abs/1712.04170", size = "15 pages", abstract = "The search for interpretable reinforcement learning policies is of high academic and industrial interest. Especially for industrial systems, domain experts are more likely to deploy autonomously learnt controllers if they are understandable and convenient to evaluate. Basic algebraic equations are supposed to meet these requirements, as long as they are restricted to an adequate complexity. Here we introduce the genetic programming for reinforcement learning (GPRL) approach based on model-based batch reinforcement learning and genetic programming, which autonomously learns policy equations from pre-existing default state-action trajectory samples. GPRL is compared to a straight-forward method which uses genetic programming for symbolic regression, yielding policies imitating an existing well-performing, but non-interpretable policy. Experiments on three reinforcement learning benchmarks, i.e., mountain car, cart-pole balancing, and industrial benchmark, demonstrate the superiority of our GPRL approach compared to the symbolic regression method. GPRL is capable of producing well-performing interpretable reinforcement learning policies from pre-existing default trajectory data.", } @Article{HEIN2018158, author = "Daniel Hein and Steffen Udluft and Thomas A. Runkler", title = "Interpretable policies for reinforcement learning by genetic programming", journal = "Engineering Applications of Artificial Intelligence", year = "2018", volume = "76", pages = "158--169", month = nov, keywords = "genetic algorithms, genetic programming, Interpretable, Reinforcement learning, Model-based, Symbolic regression, Industrial benchmark", ISSN = "0952-1976", URL = "http://www.sciencedirect.com/science/article/pii/S0952197618301933", DOI = "doi:10.1016/j.engappai.2018.09.007", abstract = "The search for interpretable reinforcement learning policies is of high academic and industrial interest. Especially for industrial systems, domain experts are more likely to deploy autonomously learned controllers if they are understandable and convenient to evaluate. Basic algebraic equations are supposed to meet these requirements, as long as they are restricted to an adequate complexity. Here we introduce the genetic programming for reinforcement learning (GPRL) approach based on model-based batch reinforcement learning and genetic programming, which autonomously learns policy equations from pre-existing default state-action trajectory samples. GPRL is compared to a straightforward method which uses genetic programming for symbolic regression, yielding policies imitating an existing well-performing, but non-interpretable policy. Experiments on three reinforcement learning benchmarks, i.e., mountain car, cart-pole balancing, and industrial benchmark, demonstrate the superiority of our GPRL approach compared to the symbolic regression method. GPRL is capable of producing well-performing interpretable reinforcement learning policies from pre-existing default trajectory data.", } @InProceedings{Hein:2018:GECCOcomp, author = "Daniel Hein and Steffen Udluft and Thomas A. Runkler", title = "Generating interpretable fuzzy controllers using particle swarm optimization and genetic programming", booktitle = "GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2018", editor = "Carlos Cotta and Tapabrata Ray and Hisao Ishibuchi and Shigeru Obayashi and Bogdan Filipic and Thomas Bartz-Beielstein and Grant Dick and Masaharu Munetomo and Silvino {Fernandez Alzueta} and Thomas Stuetzle and Pablo Valledor Pellicer and Manuel Lopez-Ibanez and Daniel R. Tauritz and Pietro S. Oliveto and Thomas Weise and Borys Wrobel and Ales Zamuda and Anne Auger and Julien Bect and Dimo Brockhoff and Nikolaus Hansen and Rodolphe {Le Riche} and Victor Picheny and Bilel Derbel and Ke Li and Hui Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Stephane Doncieux and Richard Duro and Joshua Auerbach and Harold {de Vladar} and Antonio J. Fernandez-Leiva and JJ Merelo and Pedro A. Castillo-Valdivieso and David Camacho-Fernandez and Francisco {Chavez de la O} and Ozgur Akman and Khulood Alyahya and Juergen Branke and Kevin Doherty and Jonathan Fieldsend and Giuseppe Carlo Marano and Nikos D. Lagaros and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Boris Naujoks and Vanessa Volz and Tea Tusar and Pascal Kerschke and Riyad Alshammari and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and John R. Woodward and Shin Yoo and John McCall and Nayat Sanchez-Pi and Luis Marti and Danilo Vasconcellos and Masaya Nakata and Anthony Stein and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Ivanoe {De Falco} and Antonio {Della Cioppa} and Ernesto Tarantino and Umberto Scafuri and P. G. M. Baltus and Giovanni Iacca and Ahmed Hallawa and Anil Yaman and Alma Rahat and Handing Wang and Yaochu Jin and David Walker and Richard Everson and Akira Oyama and Koji Shimoyama and Hemant Kumar and Kazuhisa Chiba and Pramudita Satria Palar", isbn13 = "978-1-4503-5764-7", pages = "1268--1275", address = "Kyoto, Japan", publisher = "ACM", publisher_address = "New York, NY, USA", month = "15-19 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, interpretable reinforcement learning, RL, fuzzy control, swarm optimization, PSO, FGPRL, FGPRL, AMIFS, industrial benchmark", URL = "https://arxiv.org/abs/1804.10960", DOI = "doi:10.1145/3205651.3208277", size = "8 pages", abstract = "Autonomously training interpretable control strategies, called policies, using pre-existing plant trajectory data is of great interest in industrial applications. Fuzzy controllers have been used in industry for decades as interpretable and efficient system controllers. In this study, we introduce a fuzzy genetic programming (GP) approach called fuzzy GP reinforcement learning (FGPRL) that can select the relevant state features, determine the size of the required fuzzy rule set, and automatically adjust all the controller parameters simultaneously. Each GP individual's fitness is computed using model-based batch reinforcement learning (RL), which first trains a model using available system samples and subsequently performs Monte Carlo roll outs to predict each policy candidate's performance. We compare FGPRL to an extended version of a related method called fuzzy particle swarm reinforcement learning (FPSRL), which uses swarm intelligence to tune the fuzzy policy parameters. Experiments using an industrial benchmark show that FGPRL is able to autonomously learn interpretable fuzzy policies with high control performance.", notes = "Fig. 2. A fuzzy GP individual Table 1: Types and complexity. Local Search. 'GP-based fuzzy policy learning approach called FGPRL ... FGPRL is better than FPSRL at creating interpretable fuzzy policies autonomously from existing transition samples' http://github.com/siemens/industrialbenchmark Siemens AG, Corporate Technology, Germany Also known as \cite{3208277} GECCO-2018 A Recombination of the 27th International Conference on Genetic Algorithms (ICGA-2018) and the 23rd Annual Genetic Programming Conference (GP-2018)", } @InProceedings{Hein:2019:GECCOcomp, author = "Daniel Hein and Steffen Udluft and Thomas A. Runkler", title = "Generating interpretable reinforcement learning policies using genetic programming", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", isbn13 = "978-1-4503-6748-6", pages = "23--24", address = "Prague, Czech Republic", DOI = "doi:10.1145/3319619.3326755", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{3326755} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @PhdThesis{DBLP:phd/dnb/Hein19, author = "Daniel Hein", title = "Interpretable Reinforcement Learning Policies by Evolutionary Computation", school = "Technische Universitaet Muenchen", year = "2019", address = "Munich, Germany", month = oct, keywords = "genetic algorithms, genetic programming, interpretable, XAI, reinforcement learning, policies, model-based, particle swarm optimization, PSO, evolutionary computation, rule-based, equation-based, PID, MPC, NFQ, industrial benchmark", URL = "http://mediatum.ub.tum.de/doc/1467616/1467616.pdf", size = "172 pages", abstract = "three novel algorithms for generating interpretable reinforcement learning policies from batches of previously generated system transitions, are proposed and evaluated. On challenging benchmarks, it is empirically shown that the algorithms generate human-interpretable control strategies of competitive performance and superior generalisation capabilities by using evolutionary computation methods. In the context of machine learning, the need for interpretability stems from an incompleteness in the problem formalisation. Since complex real-world tasks in industry are almost never completely testable, enumerating all possible outputs given all possible inputs is infeasible. Hence, we usually are unable to flag all undesirable outputs. Especially for industrial systems, domain experts are more likely to deploy automatically learned controllers if they are understandable and convenient to assess. Moreover, novel legal frameworks such as the European Union General Data Protection Regulation enforce interpretability of personal data processing systems. Two of the three novel reinforcement learning methods of this thesis learn policies represented as fuzzy rule-based controllers since fuzzy controllers have proven to serve as interpretable and efficient system controllers in industry for decades. The first method, called fuzzy particle swarm reinforcement learning (FPSRL), uses swarm intelligence to optimize parameters of a fixed fuzzy rule set, whereas the second method, called fuzzy genetic programming reinforcement learning (FGPRL), applies genetic programming to generate a new fuzzy set, including the optimization of all parameters, from available building blocks. Empirical studies on benchmark problems show that FPSRL has advantages regarding computational costs on rather simple problems, where prior expert knowledge about informative state features and rule numbers is available. However,experiments using an industrial benchmark show that FGPRL can automatically select the most informative state features as well as the most compact fuzzy rule representation for a certain level of performance. The third interpretable approach, called genetic programming reinforcement learning (GPRL), finally drops the constraint on learning rule-based policies by representing the policies as basic algebraic equations of low complexity. Experimental results show that the GPRL policies yield human-understandable and well-performing control results. Moreover, both FGPRL and GPRL return not just one solution to the problem but a whole Pareto front containing the best-performing solutions for many different levels of complexity. Comparing the results from experiments of all three interpretable reinforcement learning approaches with the performance of standard neural fitted Q iteration, a novel model predictive control approach, and a non-interpretable neural network policy method gives a comprehensive overview on the performance of the methods as well as the interpretability of the produced policies. However, choosing the most interpretable form of presentation is highly subjective and depends on many prerequisites, like the application domain, the ability to visualize solutions, or successive processing steps, for example. Therefore, it is all the more important to have methods at hand which can search domain-specific policy representation spaces automatically. The empirical studies show that, combining model-based reinforcement learning with genetic programming, is a very promising approach to achieve this goal.", abstrakt = "In dieser Dissertation werden drei neuartige Algorithmen zur Erzeugung von interpretierbaren Aktionsauswahlregeln in modelbasiertem bestaerkendem Lernen durch die Verwendung von Schwarmoptimierung und genetischer Programmierung vorgeschlagen und evaluiert: FPSRL und FGPRL erzeugen regelbasierte, GPRL erzeugt gleichungsbasierte Aktionsauswahlregeln. Es zeigt sich, dass die interpretierbaren Aktionsauswahlregeln auf einer Reihe von Testaufgaben, inklusive einer neuartigen industriellen Testumgebung, von vergleichbarer oder sogar hoeher Regelungsguete sind als Kontrollstrategien erzeugt durch PID, MPC, NFQ, oder kuenstlichen neuronalen Netzwerken.", notes = "Interpretierbare Aktionsauswahlregeln durch bestaerkendes Lernen unter Verwendung von evolutionaeren Algorithmen. In English. TUM supervisor Thomas Runkler", } @InProceedings{Hein:2021:GECCOcomp, author = "Daniel Hein and Daniel Labisch", title = "Trustworthy {AI} for Process Automation on a {Chylla-Haase} Polymerization Reactor", booktitle = "Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion", year = "2021", editor = "Francisco Chicano and Alberto Tonda and Krzysztof Krawiec and Marde Helbig and Christopher W. Cleghorn and Dennis G. Wilson and Georgios Yannakakis and Luis Paquete and Gabriela Ochoa and Jaume Bacardit and Christian Gagne and Sanaz Mostaghim and Laetitia Jourdan and Oliver Schuetze and Petr Posik and Carlos Segura and Renato Tinos and Carlos Cotta and Malcolm Heywood and Mengjie Zhang and Leonardo Trujillo and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Fuyuki Ishikawa and Inmaculada Medina-Bulo and Frank Neumann and Andrew M. Sutton", pages = "1570--1578", address = "internet", series = "GECCO '21", month = jul # " 10-14", organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, GPRL, Theory of computation, Reinforcement learning, Computing methodologies, Applied computing, Industry and manufacturing, Interpretable reinforcement learning, process automation, real-world application", isbn13 = "978-1-4503-8351-6", timestamp = "Fri, 03 Sep 2021 10:51:17 +0200", biburl = "https://dblp.org/rec/journals/corr/abs-2108-13381.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://arxiv.org/abs/2108.13381", DOI = "doi:10.1145/3449726.3463131", size = "9 pages", abstract = "genetic programming reinforcement learning (GPRL) is used to generate human-interpretable control policies for a Chylla-Haase polymerization reactor. Such continuously stirred tank reactors (CSTRs) with jacket cooling are widely used in the chemical industry, in the production of fine chemicals, pigments, polymers, and medical products. Despite appearing rather simple, controlling CSTRs in real-world applications is quite a challenging problem to tackle. GPRL uses already existing data from the reactor and generates fully automatically a set of optimized simplistic control strategies, so-called policies, the domain expert can choose from. Note that these policies are white-box models of low complexity, which makes them easy to validate and implement in the target control system, e.g., SIMATIC PCS 7. However, despite its low complexity the automatically-generated policy yields a high performance in terms of reactor temperature control deviation, which we empirically evaluate on the original reactor template.", notes = "Siemens AG, Technology, Munich, Germany. Also known as \cite{DBLP:journals/corr/abs-2108-13381}. GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)", } @Article{HEIN:2018:COR, author = "Fanny Hein and Christian Almeder and Goncalo Figueira and Bernardo Almada-Lobo", title = "Designing new heuristics for the capacitated lot sizing problem by genetic programming", journal = "Computer \& Operations Research", volume = "96", pages = "1--14", year = "2018", keywords = "genetic algorithms, genetic programming", ISSN = "0305-0548", DOI = "doi:10.1016/j.cor.2018.03.006", URL = "http://www.sciencedirect.com/science/article/pii/S0305054818300753", abstract = "This work addresses the well-known capacitated lot sizing problem (CLSP) which is proven to be an NP-hard optimization problem. Simple period-by-period heuristics are popular solution approaches due to the extremely low computational effort and their suitability for rolling planning horizons. The aim of this work is to apply genetic programming (GP) to automatically generate specialized heuristics specific to the instance class. Experiments show that we are able to obtain better solutions when using GP evolved lot sizing rules compared to state-of-the-art constructive heuristics", } @InProceedings{heinrich-litan:1998:EuroPar, author = "L. Heinrich-Litan and U. Fissgus and St. Sutter and P. Molitor and Th. Rauber", title = "Modeling the communication behavior of distributed memory machines by genetic programming", booktitle = "Euro-Par'98 Parallel Processing", year = "1998", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/BFb0057862", DOI = "doi:10.1007/BFb0057862", } @InProceedings{heiss-czedik:1997:highlevel, author = "D. Heiss-Czedik", title = "Is Genetic Programming Dependent on High-level Primitives?", booktitle = "Artificial Neural Nets and Genetic Algorithms: Proceedings of the International Conference, ICANNGA97", year = "1997", editor = "George D. Smith and Nigel C. Steele and Rudolf F. Albrecht", pages = "405--408", address = "University of East Anglia, Norwich, UK", publisher = "Springer-Verlag", note = "published in 1998", keywords = "genetic algorithms, genetic programming", ISBN = "3-211-83087-1", DOI = "doi:10.1007/978-3-7091-6492-1_89", abstract = "The aim of this paper is to refute the claim that the success of genetic programming depends on problem-specific high-level primitives. We therefore apply genetic programming to the lambda-calculus, a Turing complete formalism with only two (very low-level) primitives. Genetic programming is suited to find the predecessor function in the space of Lambda-definable functions without a priori knowledge. The predecessor function is historically important and documented to be a challenge and difficult to find.", notes = "Dorothea Heiss, nee Czedik-Eysenberg Broken Sep 2024 http://www.sys.uea.ac.uk/Research/ResGroups/MAG/ICANNGA97/papers_frame.html", } @InProceedings{Heller:2018:INISTA, author = "Lauren Heller and Michail Tsikerdekis", booktitle = "2018 Innovations in Intelligent Systems and Applications (INISTA)", title = "Selective Equation Constructor: A Scalable Genetic Algorithm", year = "2018", abstract = "Efforts to improve machine learning performance begin with defining a valuable feature set. However, datasets with copious amounts of attributes can have relevant information that is obscured by its high dimensionality, which can be caused by repetitive characteristics or irrelevant qualities. Genetic algorithms provide improvements to feature sets through dimensionality reduction and feature construction. Most genetic algorithms follow the theoretical framework of evolutionary theory where a population of features randomly evolves through generations through a series of random operations such as crossover and mutation. While successful, the randomness of feature modification operations and derived constructed features may yield children that under-perform compared to their ancestors, yet their properties are used in future generations. We developed a new genetic algorithm called Selective Equation Constructor (SEC) that evolves constructed features selectively in order to limit the shortcomings of other genetic algorithms. The algorithm leads to faster computation and better results compared to similar algorithms. Analysis of the results indicates increases in classification accuracy, decreased run time, and reduction in attribute count.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/INISTA.2018.8466278", month = jul, notes = "Also known as \cite{8466278}", } @InProceedings{Helm:2002:WSC, author = "Terry M. Helm and Steve W. Painter and W. Robert Oakes", title = "A Comparison of Three Optimization Methods for Scheduling Maintenance of High Cost, Long-Lived Capital Assets", booktitle = "Proceedings of the 2002 Winter Simulation Conference", year = "2002", editor = "E. Yucesan and C.-H. Chen and J. L. Snowdon and J. M. Charnes", volume = "2", pages = "880--1884", keywords = "genetic algorithms, genetic programming, constraint handling, financial data processing, investment, minimisation, scheduling, constraint programming, costs, investments, long-lived capital assets, maintenance scheduling, minimization, optimization", URL = "http://www.informs-sim.org/wsc02papers/259.pdf", DOI = "doi:10.1109/WSC.2002.1166483", abstract = "A range of minimization methods exist enabling planners to tackle tough scheduling problems. We compare three scheduling techniques representative of old or standard technologies, evolving technologies, and advanced technologies. The problem we address includes the complications of scheduling long-term upgrades and refurbishments essential to maintaining expensive capital assets. We concentrate on the costs of being able to do maintenance work. Using a standard technology as the baseline technique, Constraint Programming (CP) produces a 50-yr maintenance approach that is 31percent less costly. Genetic Programming produces an approach that is 60percent less costly", } @InProceedings{helmer:1999:FSUGAID, author = "Guy Helmer and Johnny Wong and Vasant Honavar and Les Miller", title = "Feature Selection Using a Genetic Algorithm for Intrusion Detection", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1781", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "real world applications, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-737.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-737.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Misc{Helmer:1999:snake, author = "Martin Helmer and Martin Hemberg", title = "Moving a Snake Robot using Genetic Programming", howpublished = "www", year = "1999", month = "15 " # dec, keywords = "genetic algorithms, genetic programming", broken = "http://www.dd.chalmers.se/~f96mahe/evcomp.html", size = "20k", abstract = "We have constructed a snake robot with five servos. Our goal was to make the snake move using an evolutionary algorithm. For fitness we attached a mouse by the tail of the snake. We used the {"}30-monkeys-in-a-bus{"} algorithm for selection. It was found possible to develop a forward movement of the snake, however not without problems. One of the biggest problems was to prevent the snake from cheating, which it often did by wagging its tail a lot or by ending in a curled-up position.", notes = "See also http://www.luxfamily.com/jimlux/robot/snakerobot.htm", } @InProceedings{Helmuth:2011:GECCOcomp, author = "Thomas Helmuth and Lee Spector and Brian Martin", title = "Size-based tournaments for node selection", booktitle = "GECCO 2011 Graduate students workshop", year = "2011", editor = "Miguel Nicolau", isbn13 = "978-1-4503-0690-4", keywords = "genetic algorithms, genetic programming", pages = "799--802", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001858.2002095", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "In genetic programming, the reproductive operators of crossover and mutation both require the selection of nodes from the reproducing individuals. Both unbiased random selection and Koza 90/10 mechanisms remain popular, despite their arbitrary natures and a lack of evidence for their effectiveness. It is generally considered problematic to select from all nodes with a uniform distribution, since this causes terminal nodes to be selected most of the time. This can limit the complexity of program fragments that can be exchanged in crossover, and it may also lead to code bloat when leaf nodes are replaced with larger new subtrees during mutation. We present a new node selection method that selects nodes based on a tournament, from which the largest participating subtree is selected. We show this method of size-based tournaments improves performance on three standard test problems with no increases in code bloat as compared to unbiased and Koza 90/10 selection methods.", notes = "Also known as \cite{2002095} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @InCollection{Helmuth:2012:GPTP, author = "Thomas Helmuth and Lee Spector", title = "Evolving SQL Queries from Examples with Developmental Genetic Programming", booktitle = "Genetic Programming Theory and Practice X", year = "2012", series = "Genetic and Evolutionary Computation", editor = "Rick Riolo and Ekaterina Vladislavleva and Marylyn D. Ritchie and Jason H. Moore", publisher = "Springer", chapter = "1", pages = "1--14", address = "Ann Arbor, USA", month = "12-14 " # may, keywords = "genetic algorithms, genetic programming, Data mining, Classification, SQL, Push, PushGP", isbn13 = "978-1-4614-6845-5", URL = "http://dx.doi.org/10.1007/978-1-4614-6846-2_1", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.466.9078", URL = "http://faculty.hampshire.edu/lspector/pubs/gptp-2012-preprint.pdf", DOI = "doi:10.1007/978-1-4614-6846-2_1", abstract = "Large databases are becoming ever more ubiquitous, as are the opportunities for discovering useful knowledge within them. Evolutionary computation methods such as genetic programming have previously been applied to several aspects of the problem of discovering knowledge in databases. The more specific task of producing human-comprehensible SQL queries has several potential applications but has thus far been explored only to a limited extent. In this chapter we show how developmental genetic programming can automatically generate SQL queries from sets of positive and negative examples. We show that a developmental genetic programming system can produce queries that are reasonably accurate while excelling in human comprehensibility relative to the well-known C5.0 decision tree generation system.", notes = "part of \cite{Riolo:2012:GPTP} published after the workshop in 2013", } @InProceedings{Helmuth:2012:GECCOcomp, author = "Thomas Helmuth and Lee Spector", title = "Empirical investigation of size-based tournaments for node selection in genetic programming", booktitle = "GECCO Companion '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion", year = "2012", editor = "Terry Soule and Anne Auger and Jason Moore and David Pelta and Christine Solnon and Mike Preuss and Alan Dorin and Yew-Soon Ong and Christian Blum and Dario Landa Silva and Frank Neumann and Tina Yu and Aniko Ekart and Will Browne and Tim Kovacs and Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and Giovanni Squillero and Nicolas Bredeche and Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and Martin Pelikan and Silja Meyer-Nienberg and Christian Igel and Greg Hornby and Rene Doursat and Steve Gustafson and Gustavo Olague and Shin Yoo and John Clark and Gabriela Ochoa and Gisele Pappa and Fernando Lobo and Daniel Tauritz and Jurgen Branke and Kalyanmoy Deb", isbn13 = "978-1-4503-1178-6", keywords = "genetic algorithms, Genetic programming: Poster", pages = "1485--1486", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330784.2331004", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "In genetic programming systems, genetic operators must select nodes upon which to act; the method by which they select nodes influences problem solving performance and possibly also code growth. A recently proposed node selection method using size-based tournaments has been shown to have potential, but variations of the method have not been studied systematically. Here we extend the ideas of size-based tournaments and test how they can improve problem-solving performance. We consider allowing tournament size to depend on whether we are selecting nodes within donors for crossover, recipients for crossover, or targets of mutation. We also consider tournaments that bias selection toward smaller trees rather than larger trees. We find that differentiating between donors and recipients is probably not worthwhile and that size 2 tournaments perform near-optimally.", notes = "Also known as \cite{2331004} Distributed at GECCO-2012. ACM Order Number 910122.", } @InProceedings{Helmuth:2013:GECCOcomp, author = "Thomas Helmuth and Lee Spector", title = "Evolving a digital multiplier with the pushgp genetic programming system", booktitle = "GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion", year = "2013", editor = "Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A. N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks", isbn13 = "978-1-4503-1964-5", keywords = "genetic algorithms, genetic programming", pages = "1627--1634", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2464576.2466814", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "A recent article on benchmark problems for genetic programming suggested that researchers focus attention on the digital multiplier problem, also known as the multiple output multiplier problem, in part because it is scalable and in part because the requirement of multiple outputs presents challenges for some forms of genetic programming [20]. Here we demonstrate the application of stack-based genetic programming to the digital multiplier problem using the PushGP genetic programming system, which evolves programs expressed in the stack-based Push programming language. We demonstrate the use of output instructions and argue that they provide a natural mechanism for producing multiple outputs in a stack-based genetic programming context. We also show how two recent developments in PushGP dramatically improve the performance of the system on the digital multiplier problem. These developments are the ULTRA genetic operator, which produces offspring via Uniform Linear Transformation with Repair and Alternation [12], and lexicase selection, which selects parents according to performance on cases considered sequentially in random order [11]. Our results using these techniques show not only their utility, but also the utility of the digital multiplier problem as a benchmark problem for genetic programming research. The results also demonstrate the exibility of stack-based genetic programming for solving problems with multiple outputs and for serving as a platform for experimentation with new genetic programming techniques.", notes = "Also known as \cite{2466814} Distributed at GECCO-2013.", } @InProceedings{Helmuth:2014:GECCO, author = "Thomas Helmuth and Lee Spector", title = "Word count as a traditional programming benchmark problem for genetic programming", booktitle = "GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation", year = "2014", editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges", isbn13 = "978-1-4503-2662-9", pages = "919--926", keywords = "genetic algorithms, genetic programming", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2576768.2598230", DOI = "doi:10.1145/2576768.2598230", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "The Unix utility program wc, which stands for word count, takes any number of files and prints the number of newlines, words, and characters in each of the files. We show that genetic programming can find programs that replicate the core functionality of the wc utility, and propose this problem as a traditional programming benchmark for genetic programming systems. This wc problem features key elements of programming tasks that often confront human programmers, including requirements for multiple data types, a large instruction set, control flow, and multiple outputs. Furthermore, it mimics the behavior of a real-world utility program, showing that genetic programming can automatically synthesize programs with general utility. We suggest statistical procedures that should be used to compare performances of different systems on traditional programming problems such as the wc problem, and present the results of a short experiment using the problem. Finally, we give a short analysis of evolved solution programs, showing how they make use of traditional programming concepts.", notes = "Also known as \cite{2598230} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)", } @InProceedings{Helmuth:2015:GPTP, author = "Thomas Helmuth and Nicholas Freitag McPhee and Lee Spector", title = "Lexicase Selection For Program Synthesis: A Diversity Analysis", booktitle = "Genetic Programming Theory and Practice XIII", year = "2015", editor = "Rick Riolo and William P. Worzel and M. Kotanchek and A. Kordon", series = "Genetic and Evolutionary Computation", pages = "151--167", address = "Ann Arbor, USA", month = "14-16 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Lexicase selection, diversity, tournament selection, implicit fitness sharing", isbn13 = "978-3-319-34223-8", URL = "http://cs.wlu.edu/~helmuth/Pubs/2015-GPTP-lexicase-diversity-analysis.pdf", URL = "http://www.springer.com/us/book/9783319342214", DOI = "doi:10.1007/978-3-319-34223-8_9", size = "16 pages", abstract = "Lexicase selection is a selection method for evolutionary computation in which individuals are selected by filtering the population according to performance on test cases, considered in random order. When used as the parent selection method in genetic programming, lexicase selection has been shown to provide significant improvements in problem-solving power. In this chapter we investigate the reasons for the success of lexicase selection, focusing on measures of population diversity. We present data from eight program synthesis problems and compare lexicase selection to tournament selection and selection based on implicit fitness sharing. We conclude that lexicase selection does indeed produce more diverse populations, which helps to explain the utility of lexicase selection for program synthesis.", notes = "Replace Space With Newline, Syllables, String Lengths Backwards, Negative To Zero, Double Letters, Scrabble Score, Checksum, Count Odds. Clojush PushGP. IFS \cite{McKay:2000:GECCO} agglomerative hierarchical clustering agnes.R not helpful?? Part of \cite{Riolo:2015:GPTP} Published after the workshop in 2016", } @InProceedings{Helmuth:2015:GECCO, author = "Thomas Helmuth and Lee Spector", title = "General Program Synthesis Benchmark Suite", booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation", year = "2015", editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr", isbn13 = "978-1-4503-3472-3", pages = "1039--1046", keywords = "genetic algorithms, genetic programming", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739480.2754769", DOI = "doi:10.1145/2739480.2754769", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Recent interest in the development and use of non-trivial benchmark problems for genetic programming research has highlighted the scarcity of general program synthesis (also called traditional programming) benchmark problems. We present a suite of 29 general program synthesis benchmark problems systematically selected from sources of introductory computer science programming problems. This suite is suitable for experiments with any program synthesis system driven by input/output examples. We present results from illustrative experiments using our reference implementation of the problems in the PushGP genetic programming system. The results show that the problems in the suite vary in difficulty and can be useful for assessing the capabilities of a program synthesis system.", notes = "See also \cite{Helmuth:2022:GPEM} Also known as \cite{2754769} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)", } @PhdThesis{Helmuth:thesis, author = "Thomas M. Helmuth", title = "General Program Synthesis from Examples Using Genetic Programming with Parent Selection Based on Random Lexicographic Orderings of Test Cases", school = "College of Information and Computer Sciences, University of Massachusetts Amherst", year = "2015", address = "USA", month = sep, keywords = "genetic algorithms, genetic programming, lexicase", URL = "https://web.cs.umass.edu/publication/details.php?id=2398", URL = "https://web.cs.umass.edu/publication/docs/2015/UM-CS-PhD-2015-005.pdf", size = "159 pages", abstract = "Software developers routinely create tests before writing code, to ensure that their programs fulfill their requirements. Instead of having human programmers write the code to meet these tests, automatic program synthesis systems can create programs to meet specifications without human intervention, only requiring examples of desired behavior. In the long-term, we envision using genetic programming to synthesize large pieces of software. This dissertation takes steps toward this goal by investigating the ability of genetic programming to solve introductory computer science programming problems. We present a suite of 29 benchmark problems intended to test general program synthesis systems, which we systematically selected from sources of introductory computer science programming problems. This suite is suitable for experiments with any program synthesis system driven by input/output examples. Unlike existing benchmarks that concentrate on constrained problem domains such as list manipulation, symbolic regression, or Boolean functions, this suite contains general programming problems that require a range of programming constructs, such as multiple data types and data structures, control flow statements, and I/O. The problems encompass a range of difficulties and requirements as necessary to thoroughly assess the capabilities of a program synthesis system. Besides describing the specifications for each problem, we make recommendations for experimental protocols and statistical methods to use with the problems. This dissertation's second contribution is an investigation of behaviour-based parent selection in genetic programming, concentrating on a new method called lexicase selection. Most parent selection techniques aggregate errors from test cases to compute a single scalar fitness value; lexicase selection instead treats test cases separately, never comparing error values of different test cases. This property allows it to select parents that specialise on some test cases even if they perform poorly on others. We compare lexicase selection to other parent selection techniques on our benchmark suite, showing better performance for lexicase selection. After observing that lexicase selection increases exploration of the search space while also increasing exploitation of promising programs, we conduct a range of experiments to identify which characteristics of lexicase selection influence its utility.", notes = "UM-CS-Phd-2015-005 Supervised by Lee Spector Broken Oct 2021 GP discussion list 27 Sep 2015: https://groups.yahoo.com/neo/groups/genetic_programming/conversations/messages/6785", } @Article{Helmuth:2015:ieeeTEC, author = "Thomas Helmuth and Lee Spector and James Matheson", title = "Solving Uncompromising Problems with Lexicase Selection", journal = "IEEE Transactions on Evolutionary Computation", year = "2015", volume = "19", number = "5", pages = "630--643", month = oct, keywords = "genetic algorithms, genetic programming, parent selection, lexicase selection, tournament selection, PushGP", ISSN = "1089-778X", URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6920034", DOI = "doi:10.1109/TEVC.2014.2362729", size = "14 pages", abstract = "We describe a broad class of problems, called uncompromising problems, characterised by the requirement that solutions must perform optimally on each of many test cases. Many of the problems that have long motivated genetic programming research, including the automation of many traditional programming tasks, are uncompromising. We describe and analyse the recently proposed lexicase parent selection algorition and show that it can facilitate the solution of uncompromising problems by genetic programming. Unlike most traditional parent selection techniques, lexicase selection does not base selection on a fitness value that is aggregated over all test cases; rather, it considers test cases one at a time in random order. We present results comparing lexicase selection to more traditional parent selection methods, including standard tournament selection and implicit fitness sharing, on four uncompromising problems: finding terms in finite algebras, designing digital multipliers, counting words in files, and performing symbolic regression of the factorial function. We provide evidence that lexicase selection maintains higher levels of population diversity than other selection methods, which may partially explain its utility as a parent selection algorithm in the context of uncompromising problems.", notes = "tree-based GP Also known as \cite{6920034}", } @InProceedings{Helmuth:2016:GPTP, author = "Thomas Helmuth and Lee Spector and Nicholas Freitag McPhee and Saul Shanabrook", title = "Linear Genomes for Structured Programs", booktitle = "Genetic Programming Theory and Practice XIV", year = "2016", editor = "Rick Riolo and Bill Worzel and Brian Goldman and Bill Tozier", pages = "85--100", address = "Ann Arbor, USA", month = "19-21 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Uniform variation, linear genomes, Push, Plush", isbn13 = "978-3-319-97087-5", URL = "http://cs.hamilton.edu/~thelmuth/Pubs/2016-GPTP-plush.pdf", URL = "https://www.springer.com/us/book/9783319970875", DOI = "doi:10.1007/978-3-319-97088-2_6", size = "15 pages", abstract = "In most genetic programming systems, candidate solution programs themselves serve as the genetic material upon which variation operators act. However, because of the hierarchical structure of computer programs, and the syntactic constraints that they must obey, it is difficult to implement variation operators that affect different parts of programs with uniform probability. This can have detrimental effects on evolutionary search. In prior work, structured programs were linearised prior to variation in order to facilitate uniformity, but this necessitated syntactic repair after variation, which reintroduced non-uniformities. In this chapter we describe a new approach that uses linear genomes, from which structured programs are expressed only for the purpose of fitness testing. We present the new approach in detail and show how it facilitates both uniform variation and the evolution of programs with meaningful structure.", notes = " Part of \cite{Tozier:2016:GPTP} published after the workshop", } @InProceedings{Helmuth:2016:GECCO, author = "Thomas Helmuth and Nicholas Freitag McPhee and Lee Spector", title = "The Impact of Hyperselection on Lexicase Selection", booktitle = "GECCO '16: Proceedings of the 2016 Annual Conference on Genetic and Evolutionary Computation", year = "2016", editor = "Tobias Friedrich", pages = "717--724", note = "Nominated for best paper", month = "20-24 " # jul, organisation = "SIGEVO", address = "Denver, USA", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, Heuristic function construction, lexicase selection, tournament selection, hyperselection, program synthesis", isbn13 = "978-1-4503-4206-3", URL = "http://cs.hamilton.edu/~thelmuth/Pubs/2016-GECCO-hyperselection.pdf", DOI = "doi:10.1145/2908812.2908851", size = "8 pages", abstract = "Lexicase selection is a parent selection method that has been shown to improve the problem solving power of genetic programming over a range of problems. Previous work has shown that it can also produce hyperselection events, in which a single individual is selected many more times than other individuals. Here we investigate the role that hyperselection plays in the problem-solving performance of lexicase selection. We run genetic programming on a set of program synthesis benchmark problems using lexicase and tournament selection, confirming that hyperselection occurs significantly more often and more drastically with lexicase selection, which also performs significantly better. We then show results from an experiment indicating that hyperselection is not integral to the problem-solving performance or diversity maintenance observed when using lexicase selection. We conclude that the power of lexicase selection stems from the collection of individuals that it selects, not from the unusual frequencies with which it sometimes selects them.", notes = "Washington and Lee University, University of Minnesota Morris, Hampshire College GECCO-2016 A Recombination of the 25th International Conference on Genetic Algorithms (ICGA-2016) and the 21st Annual Genetic Programming Conference (GP-2016)", } @InProceedings{Helmuth:2016:GECCOcomp, author = "Thomas Helmuth and Nicholas Freitag McPhee and Lee Spector", title = "Effects of Lexicase and Tournament Selection on Diversity Recovery and Maintenance", booktitle = "GECCO '16 Companion: Proceedings of the Companion Publication of the 2016 Annual Conference on Genetic and Evolutionary Computation", year = "2016", editor = "Tobias Friedrich and Frank Neumann and Andrew M. Sutton and Martin Middendorf and Xiaodong Li and Emma Hart and Mengjie Zhang and Youhei Akimoto and Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and Daniele Loiacono and Julian Togelius and Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and Faustino Gomez and Carlos M. Fonseca and Heike Trautmann and Alberto Moraglio and William F. Punch and Krzysztof Krawiec and Zdenek Vasicek and Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and Boris Naujoks and Enrique Alba and Gabriela Ochoa and Simon Poulding and Dirk Sudholt and Timo Koetzing", isbn13 = "978-1-4503-4323-7", pages = "983--990", address = "Denver, Colorado, USA", month = "20-24 " # jul, keywords = "genetic algorithms, genetic programming", organisation = "SIGEVO", DOI = "doi:10.1145/2908961.2931657", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "In genetic programming systems, parent selection algorithms select the programs from which offspring will be produced by random variation and recombination. While most parent selection algorithms select programs on the basis of aggregate performance on multiple test cases, the lexicase selection algorithm considers each test case individually, in random order, for each parent selection event. Prior work has shown that lexicase selection can produce both more diverse populations and more solutions when applied to several hard problems. Here we examine the effects of lexicase selection, compared to those of the more traditional tournament selection algorithm, on population error diversity using two program synthesis problems. We conduct experiments in which the same initial population is used to start multiple runs, each using a different random number seed. The initial populations are extracted from genetic programming runs, and fall into three categories: high diversity populations, low diversity populations, and populations that occur after diversity crashes. The reported data shows that lexicase selection can maintain high error diversity and also that it can re-diversify less-diverse populations, while tournament selection consistently produces lower diversity.", notes = "Distributed at GECCO-2016.", } @InProceedings{Helmuth:2017:GECCO, author = "Thomas Helmuth and Nicholas Freitag McPhee and Edward Pantridge and Lee Spector", title = "Improving Generalization of Evolved Programs Through Automatic Simplification", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4920-8", address = "Berlin, Germany", pages = "937--944", size = "8 pages", URL = "http://cs.hamilton.edu/~thelmuth/Pubs/2017-GECCO-simplification-for-generalization.pdf", URL = "http://doi.acm.org/10.1145/3071178.3071330", DOI = "doi:10.1145/3071178.3071330", acmid = "3071330", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, automatic simplification, generalization, overfitting, push", month = "15-19 " # jul, abstract = "Programs evolved by genetic programming unfortunately often do not generalize to unseen data. Reliable synthesis of programs that generalize to unseen data is therefore an important open problem. We present evidence that smaller programs evolved using the PushGP system tend to generalize better over a range of program synthesis problems. Like in many genetic programming systems, programs evolved by PushGP usually have pieces that can be removed without changing the behaviour of the program. We describe methods for automatically simplifying evolved programs to make them smaller and potentially improve their generalization. We present five simplification methods and analyse their strengths and weaknesses on a suite of general program synthesis benchmark problems. All of our methods use a straightforward hill-climbing procedure to remove pieces of a program while ensuring that the resulting program gives the same errors on the training data as did the original program. We show that automatic simplification, previously used both for post-run analysis and as a genetic operator, can significantly improve the generalization rates of evolved programs.", notes = "Also known as \cite{Helmuth:2017:IGE:3071178.3071330} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @InProceedings{Helmuth:2019:GECCO, author = "Thomas Helmuth and Edward Pantridge and Lee Spector", title = "Lexicase Selection of Specialists", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference", year = "2019", editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and Anne Auger and Petr Posik and Leslie {Peprez Caceres} and Andrew M. Sutton and Nadarajen Veerapen and Christine Solnon and Andries Engelbrecht and Stephane Doncieux and Sebastian Risi and Penousal Machado and Vanessa Volz and Christian Blum and Francisco Chicano and Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and Jonathan Fieldsend and Jose Antonio Lozano and Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and Robin Purshouse and Thomas Baeck and Justyna Petke and Giuliano Antoniol and Johannes Lengler and Per Kristian Lehre", isbn13 = "978-1-4503-6111-8", pages = "1030--1038", address = "Prague, Czech Republic", DOI = "doi:10.1145/3321707.3321875", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, lexicase selection, specialization", size = "9 pages", abstract = "Lexicase parent selection filters the population by considering one random training case at a time, eliminating any individuals with errors for the current case that are worse than the best error in the selection pool, until a single individual remains. This process often stops before considering all training cases, meaning that it will ignore the error values on any cases that were not yet considered. Lexicase selection can therefore select specialist individuals that have poor errors on some training cases, if they have great errors on others and those errors come near the start of the random list of cases used for the parent selection event in question. We hypothesize here that selecting these specialists, which may have poor total error, plays an important role in lexicase selection observed performance advantages over error-aggregating parent selection methods such as tournament selection, which select specialists much less frequently. We conduct experiments examining this hypothesis, and find that lexicase selection performance and diversity maintenance degrade when we deprive it of the ability of selecting specialists. These findings help explain the improved performance of lexicase selection compared to tournament selection, and suggest that specialists help drive evolution under lexicase selection toward global solutions.", notes = "Also known as \cite{3321875} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @Article{Helmuth:GPEM:lexi, author = "Thomas Helmuth and Edward Pantridge and Lee Spector", title = "On the importance of specialists for lexicase selection", journal = "Genetic Programming and Evolvable Machines", year = "2020", volume = "21", number = "3", pages = "349--373", month = sep, note = "Special Issue: Highlights of Genetic Programming 2019 Events", keywords = "genetic algorithms, genetic programming, Lexicase selection, Specialists, Parent selection, Program synthesis", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-020-09377-2", size = "25 pages", abstract = "Lexicase parent selection filters the population by considering one random training case at a time, eliminating any individual with an error for the current case that is worse than the best error of any individual in the selection pool, until a single individual remains. This process often stops before considering all training cases, meaning that it will ignore the error values on any cases that were not yet considered. Lexicase selection can therefore select specialist individuals that have high errors on some training cases, if they have low errors on others and those errors come near the start of the random list of cases used for the parent selection event in question. We hypothesize here that selecting such specialists, which may have high total error, plays an important role in lexicase selection observed performance advantages over error-aggregating parent selection methods such as tournament selection, which select specialists less frequently. We conduct experiments examining", } @InProceedings{Helmuth:2020:GECCOcomp, author = "Thomas Helmuth and Amr Abdelhady", title = "Benchmarking Parent Selection for Program Synthesis by Genetic Programming", year = "2020", editor = "Richard Allmendinger and Hugo Terashima Marin and Efren Mezura Montes and Thomas Bartz-Beielstein and Bogdan Filipic and Ke Tang and David Howard and Emma Hart and Gusz Eiben and Tome Eftimov and William {La Cava} and Boris Naujoks and Pietro Oliveto and Vanessa Volz and Thomas Weise and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Rui Wang and Ran Cheng and Guohua Wu and Miqing Li and Hisao Ishibuchi and Jonathan Fieldsend and Ozgur Akman and Khulood Alyahya and Juergen Branke and John R. Woodward and Daniel R. Tauritz and Marco Baioletti and Josu Ceberio Uribe and John McCall and Alfredo Milani and Stefan Wagner and Michael Affenzeller and Bradley Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and Nayat Sanchez-Pi and Luis Marti and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and Matthew Johns and Nick Ross and Ed Keedwell and Herman Mahmoud and David Walker and Anthony Stein and Masaya Nakata and David Paetzel and Neil Vaughan and Stephen Smith and Stefano Cagnoni and Robert M. Patton and Ivanoe {De Falco} and Antonio {Della Cioppa} and Umberto Scafuri and Ernesto Tarantino and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Erik Hemberg and Riyad Alshammari and Tokunbo Makanju and Fuijimino-shi and Ivan Zelinka and Swagatam Das and Ponnuthurai Nagaratnam and Roman Senkerik", isbn13 = "9781450371278", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377929.3389987", DOI = "doi:10.1145/3377929.3389987", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion", pages = "237--238", size = "2 pages", keywords = "genetic algorithms, genetic programming, parent selection, benchmark, program synthesis", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", abstract = "In genetic programming, the parent selection method determines which individuals in the population are selected to be parents for the next generation, and how many children they create. This process directly impacts the search performance by determining on which areas of the search space genetic programming focuses its attention and how it balances exploration and exploitation. Many parent selection methods have been proposed in the literature, with aims of improving problem-solving performance or other characteristics of the GP system. This paper aims to benchmark many recent and common parent selection methods by comparing them within a single system and set of benchmark problems. We specifically focus on the domain of general program synthesis, where solution programs must make use of multiple data types and control flow structures, and use an existing benchmark suite within the domain. We find that a few methods, all variants of lexicase selection, rise to the top and demand further study, both within the field of program synthesis and in other domains.", notes = "Also known as \cite{10.1145/3377929.3389987} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @InProceedings{Helmuth:2020:GECCOcompa, author = "Thomas Helmuth and Lee Spector and Edward Pantridge", title = "Counterexample-Driven Genetic Programming without Formal Specifications", year = "2020", editor = "Richard Allmendinger and Hugo Terashima Marin and Efren Mezura Montes and Thomas Bartz-Beielstein and Bogdan Filipic and Ke Tang and David Howard and Emma Hart and Gusz Eiben and Tome Eftimov and William {La Cava} and Boris Naujoks and Pietro Oliveto and Vanessa Volz and Thomas Weise and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Rui Wang and Ran Cheng and Guohua Wu and Miqing Li and Hisao Ishibuchi and Jonathan Fieldsend and Ozgur Akman and Khulood Alyahya and Juergen Branke and John R. Woodward and Daniel R. Tauritz and Marco Baioletti and Josu Ceberio Uribe and John McCall and Alfredo Milani and Stefan Wagner and Michael Affenzeller and Bradley Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and Nayat Sanchez-Pi and Luis Marti and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and Matthew Johns and Nick Ross and Ed Keedwell and Herman Mahmoud and David Walker and Anthony Stein and Masaya Nakata and David Paetzel and Neil Vaughan and Stephen Smith and Stefano Cagnoni and Robert M. Patton and Ivanoe {De Falco} and Antonio {Della Cioppa} and Umberto Scafuri and Ernesto Tarantino and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Erik Hemberg and Riyad Alshammari and Tokunbo Makanju and Fuijimino-shi and Ivan Zelinka and Swagatam Das and Ponnuthurai Nagaratnam and Roman Senkerik", isbn13 = "9781450371278", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377929.3389983", DOI = "doi:10.1145/3377929.3389983", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion", pages = "239--240", size = "2 pages", keywords = "genetic algorithms, genetic programming, counterexamples, program synthesis", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", abstract = "Counterexample-driven genetic programming (CDGP) uses specifications provided as formal constraints in order to generate the training cases used to evaluate the evolving programs. It has also been extended to combine formal constraints and user-provided training data to solve symbolic regression problems. Here we show how the ideas underlying CDGP can also be applied using only user-provided training data, without formal specifications. We demonstrate the application of this method, called informal CDGP, to software synthesis problems.", notes = "Also known as \cite{10.1145/3377929.3389983} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @InProceedings{Helmuth:2020:GECCOcompb, author = "Thomas Helmuth and Edward Pantridge and Grace Woolson and Lee Spector", title = "Transfer Learning of Genetic Programming Instruction Sets", year = "2020", editor = "Richard Allmendinger and Hugo Terashima Marin and Efren Mezura Montes and Thomas Bartz-Beielstein and Bogdan Filipic and Ke Tang and David Howard and Emma Hart and Gusz Eiben and Tome Eftimov and William {La Cava} and Boris Naujoks and Pietro Oliveto and Vanessa Volz and Thomas Weise and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Rui Wang and Ran Cheng and Guohua Wu and Miqing Li and Hisao Ishibuchi and Jonathan Fieldsend and Ozgur Akman and Khulood Alyahya and Juergen Branke and John R. Woodward and Daniel R. Tauritz and Marco Baioletti and Josu Ceberio Uribe and John McCall and Alfredo Milani and Stefan Wagner and Michael Affenzeller and Bradley Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and Nayat Sanchez-Pi and Luis Marti and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and Matthew Johns and Nick Ross and Ed Keedwell and Herman Mahmoud and David Walker and Anthony Stein and Masaya Nakata and David Paetzel and Neil Vaughan and Stephen Smith and Stefano Cagnoni and Robert M. Patton and Ivanoe {De Falco} and Antonio {Della Cioppa} and Umberto Scafuri and Ernesto Tarantino and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Erik Hemberg and Riyad Alshammari and Tokunbo Makanju and Fuijimino-shi and Ivan Zelinka and Swagatam Das and Ponnuthurai Nagaratnam and Roman Senkerik", isbn13 = "9781450371278", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://dl.acm.org/doi/abs/10.1145/3377929.3389988", URL = "https://doi.org/10.1145/3377929.3389988", DOI = "doi:10.1145/3377929.3389988", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion", pages = "241--242", size = "2 pages", keywords = "genetic algorithms, genetic programming, transfer learning, instruction set, PushGP", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", abstract = "The performance of a genetic programming system depends partially on the composition of the collection of elements out of which programs can be constructed, and by the relative probability of different instructions and constants being chosen for inclusion in randomly generated programs or for introduction by mutation. In this paper we develop a method for the transfer learning of instruction sets across different software synthesis problems. These instruction sets outperform unlearned instruction sets on a range of problems.", notes = "'Using a set of 25 program synthesis benchmark problems, we learn instruction sets by using solution programs from the 24 problems not being tested to make up the instruction set when testing the 25th problem.' Also known as \cite{10.1145/3377929.3389988} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @InProceedings{Helmuth:2020:ALife, author = "Thomas Helmuth and Edward Pantridge and Grace Woolson and Lee Spector", title = "Genetic Source Sensitivity and Transfer Learning in Genetic Programming", booktitle = "2020 Conference on Artificial Life", year = "2020", editor = "Josh Bongard and Juniper Lovato and Laurent Hebert-Dufresne and Radhakrishna Dasari and Lisa Soros", pages = "303--311", address = "Online", month = "13-18 " # jul, organisation = "ISAL", publisher = "Massachusetts Institute of Technology", keywords = "genetic algorithms, genetic programming, Push", URL = "https://direct.mit.edu/isal/proceedings/isal2020/32/1/98387", URL = "https://direct.mit.edu/isal/proceedings-pdf/isal2020/32/303/1908486/isal_a_00326.pdf", DOI = "doi:10.1162/isal_a_00326", abstract = "Genetic programming uses biologically-inspired processes of variation and selection to synthesize computer programs that solve problems. Here we investigate the sensitivity of genetic programming to changes in the probability that particular instructions and constants will be chosen for inclusion in randomly generated programs or for introduction by mutation. We find, contrary to conventional wisdom within the field, that genetic programming can be highly sensitive to changes in this source of new genetic material. Additionally, we find that genetic sources can be tuned to significantly improve adaptation across sets of related problems. We study the evolution of solutions to software synthesis problems using untuned genetic sources and sources that have been tuned on the basis of problem statements, human intuition, or prevalence in prior solution programs. We find significant differences in performance across these approaches, and use these lessons to develop a method for tuning genetic sources on the basis of evolved solutions to related problems. This transfer learning approach tunes genetic sources nearly as well as humans do, but by means of a fully automated process that can be applied to previously unsolved problems.", notes = "Program Synthesis Benchmark Problems Montreal, Canada, isal_a_00357.pdf", } @InProceedings{Helmuth:2020:ALife_lx, author = "Thomas Helmuth and Lee Spector", title = "Explaining and Exploiting the Advantages of Down-sampled Lexicase Selection", booktitle = "2020 Conference on Artificial Life", year = "2020", editor = "Josh Bongard and Juniper Lovato and Laurent Hebert-Dufresne and Radhakrishna Dasari and Lisa Soros", pages = "341--349", address = "online", month = "13-18 " # jul, organisation = "ISAL", publisher = "Massachusetts Institute of Technology", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1162/isal_a_00334", size = "9 pages", abstract = "In genetic programming, parent selection is ordinarily based on aggregate measures of performance across an entire training set. Lexicase selection, by contrast, selects on the basis of performance on random sequences of test cases; this has been shown to enhance problem-solving power in many circumstances. Lexicase selection can also be seen as better reflecting biological evolution, by modeling sequences of challenges that organisms face over their lifetimes. Recent work has demonstrated that the advantages of lexicase selection can be amplified by down-sampling, meaning that only a random subsample of the training cases is used each generation, which can also be seen as modeling environmental change over time. Here we provide the most extensive bench-marking of down-sampled lexicase selection to date, showing that its benefits hold up to increased scrutiny. The reasons that down-sampling helps, however, are not yet fully understood. Hypotheses include that down-sampling allows for more generations to be processed with the same budget of program evaluations; that the variation of training data across generations acts as a changing environment, encouraging adaptation; or that it reduces overfitting, leading to more general solutions. We systematically evaluate these hypotheses, finding evidence against all three, and instead draw the conclusion that down-sampled lexicase selections main benefit stems from the fact that it allows GP to examine more individuals within the same computational budget, even though each individual is examined less completely.", notes = "Montreal, Canada, isal_a_00357.pdf", } @InProceedings{Helmuth:2021:GECCO, author = "Thomas Helmuth and Peter Kelly", title = "{PSB2}: The Second Program Synthesis Benchmark Suite", booktitle = "Proceedings of the 2021 Genetic and Evolutionary Computation Conference", year = "2021", editor = "Francisco Chicano and Alberto Tonda and Krzysztof Krawiec and Marde Helbig and Christopher W. Cleghorn and Dennis G. Wilson and Georgios Yannakakis and Luis Paquete and Gabriela Ochoa and Jaume Bacardit and Christian Gagne and Sanaz Mostaghim and Laetitia Jourdan and Oliver Schuetze and Petr Posik and Carlos Segura and Renato Tinos and Carlos Cotta and Malcolm Heywood and Mengjie Zhang and Leonardo Trujillo and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Fuyuki Ishikawa and Inmaculada Medina-Bulo and Frank Neumann and Andrew M. Sutton", pages = "785--794", address = "internet", series = "GECCO '21", month = jul # " 10-14", organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", note = "Nominated for best paper", keywords = "genetic algorithms, genetic programming, automatic program synthesis, benchmarking", isbn13 = "9781450383509", URL = "https://arxiv.org/abs/2106.06086", DOI = "doi:10.1145/3449639.3459285", code_url = "https://cs.hamilton.edu/~thelmuth/PSB2/PSB2.html", size = "10 pages", abstract = "For the past six years, researchers in genetic programming and other program synthesis disciplines have used the General Program Synthesis Benchmark Suite to benchmark many aspects of automatic program synthesis systems. These problems have been used to make notable progress toward the goal of general program synthesis: automatically creating the types of software that human programmers code. Many of the systems that have attempted the problems in the original benchmark suite have used it to demonstrate performance improvements granted through new techniques. Over time, the suite has gradually become outdated, hindering the accurate measurement of further improvements. The field needs a new set of more difficult benchmark problems to move beyond what was previously possible. We describe the 25 new general program synthesis benchmark problems that make up PSB2, a new benchmark suite. These problems are curated from a variety of sources,including programming katas and college courses. We selected these problems to be more difficult than those in the original suite, and give results using PushGP showing this increase in difficulty. These new problems give plenty of room for improvement, pointing the way for the next six or more years of general program synthesis research", notes = "https://cs.hamilton.edu/~thelmuth/PSB2/PSB2.html See also \cite{Helmuth:2022:GPEM} p785 'PSB2 consists of 25 problems curated from programming challenges, programming katas, and college courses.' 'markedly harder to solve than problems in PSB1' Hamilton College, USA Also known as \cite{Helmuth:2021:GECCO:PSB2} GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)", } @Article{Helmuth:2022:ALife, author = "Thomas Helmuth and Lee Spector", title = "Problem-Solving Benefits of Down-Sampled {Lexicase} Selection", journal = "Artificial Life", year = "2021", volume = "27", number = "3-4", pages = "183--203", month = "Summer-Fall", note = "Special issue highlights from the 2020 Conference on Artificial Life", keywords = "genetic algorithms, genetic programming, parent selection, lexicase selection, down-sampled lexicase selection, program synthesis", ISSN = "1064-5462", URL = "https://direct.mit.edu/artl/article-pdf/doi/10.1162/artl_a_00341/1960075/artl_a_00341.pdf", DOI = "doi:10.1162/artl_a_00341", size = "21 pages", abstract = "In genetic programming, an evolutionary method for producing computer programs that solve specified computational problems, parent selection is ordinarily based on aggregate measures of performance across an entire training set. Lexicase selection, by contrast, selects on the basis of performance on random sequences of training cases; this has been shown to enhance problem-solving power in many circumstances. Lexicase selection can also be seen as better reflecting biological evolution, by modeling sequences of challenges that organisms face over their lifetimes. Recent work has demonstrated that the advantages of lexicase selection can be amplified by down-sampling, meaning that only a random subsample of the training cases is used each generation. This can be seen as modeling the fact that individual organisms encounter only subsets of the possible environments and that environments change over time. Here we provide the most extensive benchmarking of down-sampled lexicase selection to date, showing that its benefits hold up to increased scrutiny. The reasons that down-sampling helps, however, are not yet fully understood. Hypotheses include that down-sampling allows for more generations to be processed with the same budget of program evaluations; that the variation of training data across generations acts as a changing environment, encouraging adaptation; or that it reduces overfitting, leading to more general solutions. We systematically evaluate these hypotheses, finding evidence against all three, and instead draw the conclusion that down-sampled lexicase selection's main benefit stems from the fact that it allows the evolutionary process to examine more individuals within the same computational budget, even though each individual is examined less completely.", notes = "Also known as \cite{10.1162/artl_a_00341} Published March 2022", } @Article{Helmuth:2022:GPEM, author = "Thomas Helmuth and Peter Kelly", title = "Applying genetic programming to {PSB2}: the next generation program synthesis benchmark suite", journal = "Genetic Programming and Evolvable Machines", year = "2022", volume = "23", number = "3", pages = "375--404", month = sep, note = "Special Issue: Highlights of Genetic Programming 2021 Events", keywords = "genetic algorithms, genetic programming, Automatic program synthesis, Benchmarking, PushGP", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-022-09434-y", code_url = "https://cs.hamilton.edu/%7ethelmuth/PSB2/PSB2.html", code_url = "https://github.com/thelmuth/Clojush/releases/tag/psb2-v1.0", code_url = "https://zenodo.org/record/5084812#.YxCloPfTVmM", size = "30 pages", abstract = "For the past seven years, researchers in genetic programming and other program synthesis disciplines have used the General Program Synthesis Benchmark Suite (PSB1) to benchmark many aspects of systems that conduct programming by example, where the specifications of the desired program are given as input/output pairs. PSB1 has been used to make notable progress toward the goal of general program synthesis: automatically creating the types of software that human programmers code. Many of the systems that have attempted the problems in PSB1 have used it to demonstrate performance improvements granted through new techniques. Over time, the suite has gradually become outdated, hindering the accurate measurement of further improvements. The field needs a new set of more difficult benchmark problems to move beyond what was previously possible and ensure that systems do not overfit to one benchmark suite. In this paper, we describe the 25 new general program synthesis benchmark problems that make up PSB2, a new benchmark suite. These problems are curated from a variety of sources, including programming katas and college courses. We selected these problems to be more difficult than those in the original suite, and give results using PushGP showing this increase in difficulty. We additionally give an example of benchmarking using a state-of-the-art parent selection method, showing improved performance on PSB2 while still leaving plenty of room for improvement. These new problems will help guide program synthesis research for years to come.", notes = "Extends \cite{Helmuth:2021:GECCO} PSB1 = \cite{Helmuth:2015:GECCO} 'General program synthesis requires the manipulation of multiple data types' '4 multi-output problems in PSB2.' 'large training sets (200 examples) that have a variety of specific edge cases purposefully included.' 'we did not include any problems on which standard PushGP produced a success rate over 60 percent in initial experiments'. 'We aimed to include problems that require a large variety of data types and control flow structures to solve, with a balance between data types across problems. Most of the problems require some type of iteration and/or conditional statements. Required data types include integers, floats, Booleans, characters, strings, vectors of integers, and vectors of floats. In order to produce large datasets, we aimed to select problems that have at least 1 million possible unique inputs.'", } @InProceedings{helmuth:2023:iGECCO, author = "Thomas Helmuth and James Gunder Frazier and Yuhan Shi and Ahmed Farghali Abdelrehim", title = "{Human-Driven} Genetic Programming for Program Synthesis: A Prototype", booktitle = "Interactive Methods at GECCO", year = "2023", editor = "Matthew Johns and Ed Keedwell and Nick Ross and David Walker", pages = "1981--1989", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, interactive evolution, automatic programming", isbn13 = "9798400701191", DOI = "doi:10.1145/3583133.3596373", size = "9 pages", abstract = "End users can benefit from automatic program synthesis in a variety of applications, many of which require the user to specify the program they would like to generate. Recent advances in genetic programming allow it to generate general purpose programs similar to those humans write, but require specifications in the form of extensive, labeled training data, a barrier to using it for user-driven synthesis. Here we describe the prototype of a human-driven genetic programming system that can be used to synthesize programs from scratch. In order to address the issue of extensive training data, we draw inspiration from counterexample-driven genetic programming, allowing the user to initially provide only a few training cases and asking the user to verify the correctness of potential solutions on automatically generated potential counterexample cases. We present anecdotal experiments showing that our prototype can solve a variety of easy program synthesis problems entirely based on user input.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @InProceedings{Helmuth:2024:EuroGP, author = "Thomas Helmuth and Edward Pantridge and James Gunder Frazier and Lee Spector", editor = "Mario Giacobini and Bing Xue and Luca Manzoni", title = "Generational Computation Reduction in Informal Counterexample-Driven Genetic Programming", booktitle = "EuroGP 2024: Proceedings of the 27th European Conference on Genetic Programming", year = "2024", volume = "14631", series = "LNCS", publisher = "Springer", address = "Aberystwyth", month = "3-5 " # apr, organisation = "EvoStar, Species", pages = "21--37", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-031-56957-9", DOI = "doi:10.1007/978-3-031-56957-9_2", abstract = "Counter example-driven genetic programming (CDGP) uses specifications provided as formal constraints to generate the training cases used to evaluate evolving programs. It has also been extended to combine formal constraints and user-provided training data to solve symbolic regression problems. Here we show how the ideas underlying CDGP can also be applied using only user-provided training data, without formal specifications. We demonstrate the application of this method, called informal CDGP, to software synthesis problems. Our results show that informal CDGP finds solutions faster (i.e. with fewer program executions) than standard GP. Additionally, we propose two new variants to informal CDGP, and find that one produces significantly more successful runs on about half of the tested problems. Finally, we study whether the addition of counterexample training cases to the training set is useful by comparing informal CDGP to using a static subsample of the training set, and find that the addition of counterexamples significantly improves performance.", notes = "Part of \cite{Giacobini:2024:GP} EuroGP'2024 held in conjunction with EvoCOP2024, EvoMusArt2024 and EvoApplications2024", } @InProceedings{helmuth:2024:GECCO, author = "Thomas Helmuth and Jayden Fedoroff and Edward Pantridge and Lee Spector", title = "Facilitating Function Application in Code Building Genetic Programming", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference", year = "2024", editor = "Ting Hu and Aniko Ekart and Julia Handl and Xiaodong Li and Markus Wagner and Mario Garza-Fabre and Kate Smith-Miles and Richard Allmendinger and Ying Bi and Grant Dick and Amir H Gandomi and Marcella Scoczynski Ribeiro Martins and Hirad Assimi and Nadarajen Veerapen and Yuan Sun and Mario Andres Munyoz and Ahmed Kheiri and Nguyen Su and Dhananjay Thiruvady and Andy Song and Frank Neumann and Carla Silva", pages = "887--895", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, automatic programming, inductive program synthesis, function application", isbn13 = "979-8-4007-0494-9", DOI = "doi:10.1145/3638529.3654068", size = "9 pages", abstract = "Code Building Genetic Programming (CBGP) is a method for general inductive program synthesis that uses a genetic algorithm and a formal type system to evolve linear genomes that are compiled into type-safe programs in a host language. Prior work showed that CBGP can evolve programs that use arbitrary abstractions from existing codebases along with higher-order functions and polymorphism. In tests on benchmark problems, however, the problem solving capabilities of CBGP have been mixed. One hypothesized explanation for weak performance on some problems is that many functions encountered during the compilation process are typically not applied. Here we propose two modifications to the compilation algorithm, both of which make it more likely that functions will be applied when composing programs. The first modification changes how frequently CBGP attempts to perform function application, while the second allows the construction of function applications to backtrack. While both modifications increase solution rates on benchmark problems, the backtracking modification shows more promise with a modest increase in computational cost and no additional configuration requirements. We argue that this modification should be considered the new standard compilation algorithm for CBGP systems.", notes = "GECCO-2024 GP A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{eurogp07:hemberg, author = "Erik Hemberg and Conor Gilligan and Michael O'Neill and Anthony Brabazon", title = "A Grammatical Genetic Programming Approach to Modularity in Genetic Algorithms", editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar", booktitle = "Proceedings of the 10th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "4445", year = "2007", address = "Valencia, Spain", month = "11-13 " # apr, pages = "1--11", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-71602-5", isbn13 = "978-3-540-71602-0", DOI = "doi:10.1007/978-3-540-71605-1_1", abstract = "The ability of Genetic Programming to scale to problems of increasing difficulty operates on the premise that it is possible to capture regularities that exist in a problem environment by decomposition of the problem into a hierarchy of modules. As computer scientists and more generally as humans we tend to adopt a similar divide-and-conquer strategy in our problem solving. In this paper we consider the adoption of such a strategy for Genetic Algorithms. By adopting a modular representation in a Genetic Algorithm we can make efficiency gains that enable superior scaling characteristics to problems of increasing size. We present a comparison of two modular Genetic Algorithms, one of which is a Grammatical Genetic Programming algorithm, the meta-Grammar Genetic Algorithm (mGGA), which generates binary string sentences instead of traditional GP trees. A number of problems instances are tackled which extend the Checkerboard problem by introducing different kinds of regularity and noise. The results demonstrate some limitations of the modular GA (MGA) representation and how the mGGA can overcome these. The mGGA shows improved scaling when compared the MGA.", notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007", } @InProceedings{conf/eurogp/HembergOB08, title = "Altering Search Rates of the Meta and Solution Grammars in the m{GGA}", author = "Erik Hemberg and Michael O'Neill and Anthony Brabazon", bibdate = "2008-04-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/eurogp/eurogp2008.html#HembergOB08", booktitle = "Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008", address = "Naples", month = "26-28 " # mar, publisher = "Springer", year = "2008", volume = "4971", editor = "Michael O'Neill and Leonardo Vanneschi and Steven Gustafson and Anna Isabel {Esparcia Alcazar} and Ivanoe {De Falco} and Antonio {Della Cioppa} and Ernesto Tarantino", isbn13 = "978-3-540-78670-2", pages = "362--373", series = "Lecture Notes in Computer Science", DOI = "doi:10.1007/978-3-540-78671-9_31", keywords = "genetic algorithms, genetic programming", notes = "Part of \cite{conf/eurogp/2008} EuroGP'2008 held in conjunction with EvoCOP2008, EvoBIO2008 and EvoWorkshops2008", } @InProceedings{Hemberg:2008:cec, author = "Erik Hemberg and Michael O'Neill and Anthony Brabazon", title = "Grammatical Bias and Building Blocks in Meta-Grammar Grammatical Evolution", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "3775--3782", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1823-7", file = "EC0802.pdf", DOI = "doi:10.1109/CEC.2008.4631309", abstract = "This paper describes and tests the utility of a meta Grammar approach to Grammatical Evolution (GE). Rather than employing a fixed grammar as is the case with canonical GE, under a meta Grammar approach the grammar that is used to specify the construction of a syntactically correct solution is itself allowed to evolve. The ability to evolve a grammar in the context of GE means that useful bias towards specific structures and solutions can be evolved and directly incorporated into the grammar during a run. This approach facilitates the evolution of modularity and reuse both on structural and symbol levels and consequently could enhance both the scalability of GE and its adaptive potential in dynamic environments. In this paper an analysis of the extent that building block structures created in the grammars are used in the solution is undertaken. It is demonstrated that building block structures are incorporated into the evolving grammars and solutions at a rate higher than would be expected by random search. Furthermore, the results indicate that grammar design can be an important factor in performance.", keywords = "genetic algorithms, genetic programming, grammatical evolution", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @InProceedings{DBLP:conf/gecco/Hemberg09, author = "Erik Hemberg", title = "An exploration of learning and grammars in grammatical evolution", booktitle = "GECCO-2009 Graduate student workshop", year = "2009", editor = "Anna I. Esparcia and Ying-ping Chen and Gabriela Ochoa and Ender Ozcan and Marc Schoenauer and Anne Auger and Hans-Georg Beyer and Nikolaus Hansen and Steffen Finck and Raymond Ros and Darrell Whitley and Garnett Wilson and Simon Harding and W. B. Langdon and Man Leung Wong and Laurence D. Merkle and Frank W. Moore and Sevan G. Ficici and William Rand and Rick Riolo and Nawwaf Kharma and William R. Buckley and Julian Miller and Kenneth Stanley and Jaume Bacardit and Will Browne and Jan Drugowitsch and Nicola Beume and Mike Preuss and Stephen L. Smith and Stefano Cagnoni and Jim DeLeo and Alexandru Floares and Aaron Baughman and Steven Gustafson and Maarten Keijzer and Arthur Kordon and Clare Bates Congdon and Laurence D. Merkle and Frank W. Moore", pages = "2705--2708", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming, grammatical evolution", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1570256.1570389", abstract = "This paper is concerned with the challenge of learning solutions to problems. The method employed here is a grammar based heuristic, where domain knowledge is encoded in a generative grammar, while evolution drives the update of the population of solutions. Furthermore the method can adapt to the environment by altering the grammar. The implementation consists of the grammar-based Genetic Programming approach of Grammatical Evolution (GE). A number of different constructions of grammars and operators for manipulating the grammars and the evolutionary algorithm are investigated, as well as a meta-grammar GE which allows a more flexible grammar. The results show some benefit of using meta-grammars in GE and re-emphasize the grammar's impact on GE's performance.", notes = "Distributed on CD-ROM at GECCO-2009. ACM Order Number 910092.", } @InProceedings{Hemberg:2008:ECSummerSchool, author = "Erik Hemberg and Nic McPhee and Michael O'Neill and Anthony Brabazon", title = "Pre-, In- and Postfix grammars for Symbolic Regression in Grammatical Evolution", booktitle = "IEEE Workshop and Summer School on Evolutionary Computing", year = "2008", editor = "T. M. McGinnity", pages = "18--22", address = "University of Ulster, Derry, Northern Ireland", month = "18-22 " # aug, keywords = "genetic algorithms, genetic programming, Grammatical Evolution", URL = "http://ncra.ucd.ie/papers/HembergMcPhee_etal.pdf", size = "4 pages", abstract = "Recent research has indicated that grammar design is an important consideration when using grammar-based Genetic Programming, particularly with respect to unintended biases that may arise through rule ordering or duplication. In this study we examine how the ordering of the elements during mapping can impact performance. Here we use to the standard GE depth-first mapper and compare the performance of postfix, prefix and infix grammars on a selection of symbolic regression problem instances. We show that postfix can confer a performance advantage on the harder problems examined", notes = "broken 2016 http://isel.infm.ulst.ac.uk/conference/wssec2008/wssec08.htm", } @InProceedings{Hemberg:2009:Mendel, author = "Erik Hemberg and Michael O'Neill and Anthony Brabazon", title = "An investigation into automatically defined function representations in Grammatical Evolution", booktitle = "15th International Conference on Soft Computing, Mendel'09", year = "2009", editor = "R. Matousek and L. Nolle", address = "Brno, Czech Republic", month = "24-26 " # jun, keywords = "genetic algorithms, genetic programming, grammatical evolution", isbn13 = "978-80-214-3884-2", URL = "http://ncra.ucd.ie/papers/mendel2009ADF.pdf", size = "6 pages", abstract = "Automatically defined functions are a fundamental tool adopted in Genetic Programming to allow problem decomposition and leverage modules in order to improve scalability to larger problems. We examine a number of function representations using a grammar-based form of Genetic Programming, Grammatical Evolution. The problem instances include variants of the ant trail, static and dynamic Symbolic Regression instances. On the problems examined we find that irrespective of the function representation, the presence of automatically defined functions alone is sufficient to significantly improve performance on problems that are complex enough to justify their use.", notes = "ID09045 http://www.mendel-conference.org/tmp/ScheduleMendel2009b.pdf Also in electronic form ISSN 1803-3814", } @PhdThesis{Hemberg:thesis, author = "Erik Anders Pieter Hemberg", title = "An Exploration of Grammars in Grammatical Evolution", year = "2010", school = "University College Dublin", address = "Ireland", month = "17 " # sep, keywords = "genetic algorithms, genetic programming, grammatical evolution", URL = "http://ncra.ucd.ie/papers/exploration_of_grammars_in_grammatical_evolution.pdf", size = "265 pages", abstract = "The grammar in the grammar-based Genetic Programming (GP) approach of Grammatical Evolution (GE) is explored. The GE algorithm solves problems by using a grammar representation and an automated and parallel trial-and-error approach, Evolutionary Computation (EC). The search for solutions in EC is driven by evaluating each solution, selecting the fittest and replacing these into a population of solutions which are modified to further guide the search. Representations have a strong impact on the efficiency of search and by using a generative grammar domain knowledge is encoded into the population of solutions. The grammar in GE biases the search for solutions, and in combination with a linear representation this is what distinguishes GE from other GP-systems. After a review of grammars in EC and a description of GE, several different constructions of grammars and operators for manipulating the grammars and the evolutionary algorithm are studied. The thesis goes on to study a meta-grammar GE, which allows a larger grammar with different bias. By adopting a divide-and-conquer strategy the goal is to investigate how a modular GE approach solves problems of increasing size and in dynamically changing environments. The results show some benefit from using meta-grammars in GE, for the meta-grammar Genetic Algorithm (mGGA) and they re-emphasise the grammar's impact on GE's performance. In addition, GE and meta-grammars are more formally described. The bias, both declarative and search, arising from the use of a Context-Free Grammar representation and the constraints of GE and the mGGA are analysed and their implications are examined. This is done by studying the effects of the mapping and operations on the input, single and multiple changes in input, as well as the preservation of output after a change. Furthermore, a matrix view of a grammar and different suggestions for measurements of grammars are investigated, in order to allow the practitioner to get an alternative view of the mapping process and of how operations work.", } @InProceedings{Hemberg:2011:GECCOcomp, author = "Erik Hemberg and Lester Ho and Michael O'Neill and Holger Claussen", title = "A symbolic regression approach to manage femtocell coverage using grammatical genetic programming", booktitle = "3rd symbolic regression and modeling workshop for GECCO 2011", year = "2011", editor = "Steven Gustafson and Ekaterina Vladislavleva", isbn13 = "978-1-4503-0690-4", keywords = "genetic algorithms, genetic programming, grammatical evolution", pages = "639--646", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001858.2002061", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "We present a novel application of Grammatical Evolution to the real-world application of femtocell coverage. A symbolic regression approach is adopted in which we wish to uncover an expression to automatically manage the power settings of individual femtocells in a larger femtocell group to optimise the coverage of the network under time varying load. The generation of symbolic expressions is important as it facilitates the analysis of the evolved solutions. Given the multi-objective nature of the problem we hybridise Grammatical Evolution with NSGA-II connected to tabu search. The best evolved solutions have superior power consumption characteristics than a fixed coverage femtocell deployment.", notes = "Also known as \cite{2002061} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @InCollection{Hemberg:2012:GPTP, author = "Erik Hemberg and Lester Ho and Michael O'Neill and Holger Claussen", title = "Representing Communication and Learning in Femtocell Pilot Power Control Algorithms", booktitle = "Genetic Programming Theory and Practice X", year = "2012", series = "Genetic and Evolutionary Computation", editor = "Rick Riolo and Ekaterina Vladislavleva and Marylyn D. Ritchie and Jason H. Moore", publisher = "Springer", chapter = "15", pages = "223--238", address = "Ann Arbor, USA", month = "12-14 " # may, keywords = "genetic algorithms, genetic programming, Grammatical evolution, Femtocell, Symbolic regression", isbn13 = "978-1-4614-6845-5", URL = "http://dx.doi.org/10.1007/978-1-4614-6846-2_15", DOI = "doi:10.1007/978-1-4614-6846-2_15", abstract = "The overall goal of evolving algorithms for femtocells is to create a continuous on-line evolution of the femtocell pilot power control algorithm to optimise their coverage. Two aspects of intelligence are used for increasing the complexity of the input and the behaviour, communication and learning. In this initial study we investigate how to evolve more complex behaviour in decentralised control algorithms by changing the representation of communication and learning. The communication is addressed by allowing the femtocell to identify its neighbours and take the values of its neighbours into account when making decisions regarding the increase or decrease of pilot power. Learning is considered in two variants: the use of input parameters and the implementation of a built-in reinforcement procedure. The reinforcement allows learning during the simulation in addition to the execution of fixed commands. The experiments compare the new representation in the form of different terminal symbols in a grammar. The results show that there are differences between the communication and learning combinations and that the best solution uses both communication and learning.", notes = "Also known as \cite{Hemberg:2013:GPTP} part of \cite{Riolo:2012:GPTP} published after the workshop in 2013", } @InProceedings{Hemberg:2012:GECCO, author = "Erik Hemberg and Kalyan Veeramachaneni and James McDermott and Constantin Berzan and Una-May O'Reilly", title = "An investigation of local patterns for estimation of distribution genetic programming", booktitle = "GECCO '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference", year = "2012", editor = "Terry Soule and Anne Auger and Jason Moore and David Pelta and Christine Solnon and Mike Preuss and Alan Dorin and Yew-Soon Ong and Christian Blum and Dario Landa Silva and Frank Neumann and Tina Yu and Aniko Ekart and Will Browne and Tim Kovacs and Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and Giovanni Squillero and Nicolas Bredeche and Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and Martin Pelikan and Silja Meyer-Nienberg and Christian Igel and Greg Hornby and Rene Doursat and Steve Gustafson and Gustavo Olague and Shin Yoo and John Clark and Gabriela Ochoa and Gisele Pappa and Fernando Lobo and Daniel Tauritz and Jurgen Branke and Kalyanmoy Deb", isbn13 = "978-1-4503-1177-9", pages = "767--774", keywords = "genetic algorithms, genetic programming", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330163.2330270", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "We present an improved estimation of distribution (EDA) genetic programming (GP) algorithm which does not rely upon a prototype tree. Instead of using a prototype tree, Operator-Free Genetic Programming learns the distribution of ancestor node chains, {"}n-grams{"}, in a fit fraction of each generation's population. It then uses this information, via sampling, to create trees for the next generation. Ancestral n-grams are used because an analysis of a GP run conducted by learning depth first graphical models for each generation indicated their emergence as substructures of conditional dependence. We are able to show that our algorithm, without an operator and a prototype tree, achieves, on average, performance close to conventional tree based crossover GP on the problem we study. Our approach sets a direction for pattern-based EDA GP which off ers better tractability and improvements over GP with operators or EDAs using prototype trees.", notes = "Also known as \cite{2330270} GECCO-2012 A joint meeting of the twenty first international conference on genetic algorithms (ICGA-2012) and the seventeenth annual genetic programming conference (GP-2012)", } @InProceedings{Hemberg:2012:GECCOcompA, author = "Erik Hemberg and Kalyan Veeramachaneni and Una-May O'Reilly", title = "Graphical models and what they reveal about GP when it solves a symbolic regression problem", booktitle = "GECCO 2012 Symbolic regression and modeling workshop", year = "2012", editor = "Steven Gustafson and Ekaterina Vladislavleva", isbn13 = "978-1-4503-1178-6", keywords = "genetic algorithms, genetic programming", pages = "493--494", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330784.2330860", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "We introduce the notion of using graphical models as a new and complementary means of understanding genetic programming dynamics (along with statistics such as mean tree size, etc). Graphical models reveal the dependency structure of the multivariate distribution associated with functions and terminals in solution structures. This information is more semantically rather than syntax oriented. As a first step, using the Pagie-2D problem as our exemplar, we present the generation and inter-generation dynamics of genetic programming in terms of graphical models that are largely unrestricted in structure. Open for discussion are questions such as: should a estimation of distribution genetic programming algorithm mimic standard genetic programming's search bias in terms of tree size and shape? And, does graphical model analysis indicate a better way to control the search bias for symbolic regression - by operator design, size control, bloat control or other means?", notes = "Also known as \cite{2330860} Distributed at GECCO-2012. ACM Order Number 910122.", } @InProceedings{Hemberg:2012:GECCOcomp, author = "Erik Hemberg and Lester Ho and Michael O'Neill and Holger Clausssen", title = "Comparing the robustness of grammatical genetic programming solutions for femtocell algorithms", booktitle = "GECCO Companion '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion", year = "2012", editor = "Terry Soule and Anne Auger and Jason Moore and David Pelta and Christine Solnon and Mike Preuss and Alan Dorin and Yew-Soon Ong and Christian Blum and Dario Landa Silva and Frank Neumann and Tina Yu and Aniko Ekart and Will Browne and Tim Kovacs and Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and Giovanni Squillero and Nicolas Bredeche and Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and Martin Pelikan and Silja Meyer-Nienberg and Christian Igel and Greg Hornby and Rene Doursat and Steve Gustafson and Gustavo Olague and Shin Yoo and John Clark and Gabriela Ochoa and Gisele Pappa and Fernando Lobo and Daniel Tauritz and Jurgen Branke and Kalyanmoy Deb", isbn13 = "978-1-4503-1178-6", keywords = "genetic algorithms, genetic programming, Real world applications: Poster", pages = "1525--1526", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330784.2331028", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Methods for evolving robust solutions are necessary when the evolved solutions are algorithms which are deployed in actual consumer products, e.g. Femtocells, low power, low-cost, user-deployed cellular base stations. We compare how multiple and dynamic applications of training scenarios in the evolutionary search produce different solutions and performance on training and test scenarios. For Femtocells, robustness is especially important since each fitness evaluation is a simulation that is computationally expensive. Previous studies in robustness and dynamic environments have not shown differences in the robustness of the solution when a dynamic or multiple setup is used, or if they are negligible. In the dynamic setup the solution gets exposed to a multitude of scenarios during the evolution. Therefore a solution could be evolved which is capable of surviving, and is also more general. The experiments use grammar based Genetic Programming on the Femtocell problem with one grammar for generating real-values and another grammar for generating discrete values for changing the pilot power. The results show that the solutions evolved using multiple scenarios have the best test performance. Moreover, the use of a grammar which produces discrete changes to the pilot power generate better solutions on the training and the test scenarios.", notes = "Also known as \cite{2331028} Distributed at GECCO-2012. ACM Order Number 910122.", } @InProceedings{conf/ppsn/HembergHOC12, author = "Erik Hemberg and Lester Ho and Michael O'Neill and Holger Claussen", title = "Evolving Femtocell Algorithms with Dynamic \& Stationary Training Scenarios", booktitle = "Parallel Problem Solving from Nature, PPSN XII (part 2)", year = "2012", editor = "Carlos A. {Coello Coello} and Vincenzo Cutello and Kalyanmoy Deb and Stephanie Forrest and Giuseppe Nicosia and Mario Pavone", volume = "7492", series = "Lecture Notes in Computer Science", pages = "518--527", address = "Taormina, Italy", month = sep # " 1-5", publisher = "Springer", keywords = "genetic algorithms, genetic programming, grammatical evolution, femtocell", isbn13 = "978-3-642-32963-0", DOI = "doi:10.1007/978-3-642-32964-7_52", size = "10 pages", abstract = "We analyse the impact of dynamic training scenarios when evolving algorithms for femtocells, which are low power, low-cost, user-deployed cellular base stations. Performance is benchmarked against an alternative stationary training strategy where all scenarios are presented to each individual in the evolving population during each fitness evaluation. In the dynamic setup, different training scenarios are gradually exposed to the population over successive generations. The results show that the solutions evolved using the stationary training scenarios have the best out-of-sample performance. Moreover, the use of a grammar which produces discrete changes to the pilot power generate better solutions on the training and out-of-sample scenarios.", bibsource = "DBLP, http://dblp.uni-trier.de", affiliation = "Natural Computing Research and Applications Group, University College Dublin, Ireland", } @InProceedings{Hemberg:2013:foga, author = "Erik Hemberg and Kalyan Veeramachaneni and Constantin Berzan and Una-May O'Reilly", title = "Introducing Graphical Models to Analyze Genetic Programming Dynamics", booktitle = "Foundations of Genetic Algorithms", year = "2013", editor = "Frank Neumann and Kenneth {De Jong}", pages = "75--86", address = "Adelaide, Australia", month = "16-20 " # jan, organisation = "SigEvo", publisher = "ACM", keywords = "genetic algorithms, genetic programming, Bayesian networks, graphical models", isbn13 = "978-1-4503-1990-4", URL = "http://doi.acm.org/10.1145/2460239.2460247", DOI = "doi:10.1145/2460239.2460247", acmid = "2460247", size = "12 pages", abstract = "We propose graphical models as a new means of understanding genetic programming dynamics. Herein, we describe how to build an unbiased graphical model from a population of genetic programming trees. Graphical models both express information about the conditional dependency relations among a set of random variables and they support probabilistic inference regarding the likelihood of a random variable's outcome. We focus on the former information: by their structure, graphical models reveal structural dependencies between the nodes of genetic programming trees. We identify graphical model properties of potential interest in this regard - edge quantity and dependency among nodes expressed in terms of family relations. Using a simple symbolic regression problem we generate a graphical model of the population each generation. Then we interpret the graphical models with respect to conventional knowledge about the influence of subtree crossover and mutation upon tree structure.", notes = "Also known as \cite{Hemberg:2013:IGM:2460239.2460247} http://www.sigevo.org/foga-2013/index.html", } @Article{Hemberg:2013:GPEM, author = "Erik Hemberg and Lester Ho and Michael O'Neill and Holger Claussen", title = "A comparison of grammatical genetic programming grammars for controlling femtocell network coverage", journal = "Genetic Programming and Evolvable Machines", year = "2013", volume = "14", number = "1", pages = "65--93", month = mar, keywords = "genetic algorithms, genetic programming, Grammatical evolution, Grammars, Femtocell, Symbolic regression", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-012-9171-8", size = "20 pages", abstract = "We study grammars used in grammatical genetic programming (GP) which create algorithms that control the base station pilot power in a femtocell network. The overall goal of evolving algorithms for femtocells is to create a continuous online evolution of the femtocell pilot power control algorithm in order to optimise their coverage. We compare the performance of different grammars and analyse the femtocell simulation model using the grammatical genetic programming method called grammatical evolution. The grammars consist of conditional statements or mathematical functions as are used in symbolic regression applications of GP, as well as a hybrid containing both kinds of statements. To benchmark and gain further information about our femtocell network simulation model we also perform random sampling and limited enumeration of femtocell pilot power settings. The symbolic regression based grammars require the most configuration of the evolutionary algorithm and more fitness evaluations, whereas the conditional statement grammar requires more domain knowledge to set the parameters. The content of the resulting femtocell algorithms shows that the evolutionary computation (EC) methods are exploiting the assumptions in the model. The ability of EC to exploit bias in both the fitness function and the underlying model is vital for identifying the current system and improves the model and the EC method. Finally, the results show that the best fitness and engineering performances for the grammars are similar over both test and training scenarios. In addition, the evolved solutions' performance is superior to those designed by humans.", notes = "Recommended by Una-May O'Reilly and Steven Gustafson.", affiliation = "Complex and Adaptive Systems Laboratory, School of Computer Science and Informatics, University College Dublin, Dublin, Ireland", } @InProceedings{Hemberg:2015:ICAIL, author = "Erik Hemberg and Jacob Rosen and Geoff Warner and Sanith Wijesinghe and Una-May O'Reilly", title = "Tax Non-compliance Detection Using Co-evolution of Tax Evasion Risk and Audit Likelihood", booktitle = "Proceedings of the 15th International Conference on Artificial Intelligence and Law, ICAIL-2015", year = "2015", editor = "Katie Atkinson and Ted Sichelman", pages = "79--88", address = "San Diego, USA", publisher_address = "New York, NY, USA", publisher = "ACM", keywords = "genetic algorithms, genetic programming, grammatical evolution, coevolution, auditing policy, innovative applications, tax evasion", isbn13 = "978-1-4503-3522-5", URL = "http://doi.acm.org/10.1145/2746090.2746099", DOI = "doi:10.1145/2746090.2746099", acmid = "2746099", size = "10 pages", abstract = "We detect tax law abuse by simulating the co-evolution of tax evasion schemes and their discovery through audits. Tax evasion accounts for billions of dollars of lost income each year. When the IRS pursues a tax evasion scheme and changes the tax law or audit procedures, the tax evasion schemes evolve and change into undetectable forms. The arms race between tax evasion schemes and tax authorities presents a serious compliance challenge. Tax evasion schemes are sequences of transactions where each transaction is individually compliant. However, when all transactions are combined they have no other purpose than to evade tax and are thus non-compliant. Our method consists of an ownership network and a sequence of transactions, which outputs the likelihood of conducting an audit, and requires no prior tax return or audit data. We adjust audit procedures for a new generation of evolved tax evasion schemes by simulating the gradual change of tax evasion schemes and audit points, i.e. methods used for detecting non-compliance. Additionally, we identify, for a given audit scoring procedure, which tax evasion schemes will likely escape auditing. The approach is demonstrated in the context of partnership tax law and the Installment Bogus Optional Basis tax evasion scheme. The experiments show the oscillatory behaviour of a co-adapting system and that it can model the co-evolution of tax evasion schemes and their detection.", notes = "Presented at GI COW45 http://crest.cs.ucl.ac.uk/cow/45/ Also known as \cite{Hemberg:2015:TND:2746090.2746099}", } @InProceedings{Hemberg:2016:GPTP, author = "Erik Hemberg and Jacob Rosen and Una-May O'Reilly", title = "Investigating Multi population Competetive Coevolution for Anticipating of Tax Evasion", booktitle = "Genetic Programming Theory and Practice XIV", year = "2016", editor = "Rick Riolo and Bill Worzel and Brian Goldman and Bill Tozier", pages = "35--51", address = "Ann Arbor, USA", month = "19-21 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-97087-5", URL = "https://www.springer.com/us/book/9783319970875", DOI = "doi:10.1007/978-3-319-97088-2_3", abstract = "We investigate the application of a version of Genetic Programming with grammars, called Grammatical Evolution, and a multi population competitive coevolutionary algorithm for anticipating tax evasion in the domain of U.S. Partnership tax regulations. A problem in tax auditing is that as soon as an evasion scheme is detected a new, slightly mutated, variant of the scheme appears. Multi population competitive coevolutionary algorithms are disposed to explore adversarial problems, such as the arms-race between tax evader and auditor. Furthermore, we use Genetic Programming and grammars to represent and search the transactions of tax evaders and tax audit policies. Grammars are helpful for representing and biasing the search space. The feasibility of the method is explored with an example of adversarial coevolution in tax evasion. We study the dynamics and the solutions of the competing populations in this scenario, and note that we are able to replicate some of the expected behaviour.", notes = " Part of \cite{Tozier:2016:GPTP} published after the workshop", } @Article{Hemberg:2016:AIL, author = "Erik Hemberg and Jacob B. Rosen and Geoff Warner and Sanith Wijesinghe and Una-May O'Reilly", title = "Detecting tax evasion: a co-evolutionary approach", journal = "Artificial Intelligence and Law", year = "2016", volume = "24", number = "2", pages = "149--182", month = jun, keywords = "genetic algorithms, genetic programming, Grammatical evolution, Tax evasion, Co-evolution, Auditing policy, Partnership tax", timestamp = "Thu, 18 May 2017 09:54:58 +0200", biburl = "https://dblp.org/rec/journals/ail/HembergRWWO16.bib", ISSN = "0924-8463", URL = "https://core.ac.uk/download/pdf/78071385.pdf", DOI = "doi:10.1007/s10506-016-9181-6", size = "34 pages", abstract = "We present an algorithm that can anticipate tax evasion by modelling the co-evolution of tax schemes with auditing policies. Malicious tax non-compliance, or evasion, accounts for billions of lost revenue each year. Unfortunately when tax administrators change the tax laws or auditing procedures to eliminate known fraudulent schemes another potentially more profitable scheme takes it place. Modeling both the tax schemes and auditing policies within a single framework can therefore provide major advantages. In particular we can explore the likely forms of tax schemes in response to changes in audit policies. This can serve as an early warning system to help focus enforcement efforts. In addition, the audit policies can be fine tuned to help improve tax scheme detection. We demonstrate our approach using the iBOB tax scheme and show it can capture the co-evolution between tax evasion and audit policy. Our experiments shows the expected oscillatory behaviour of a biological co-evolving system.", notes = "also known as \cite{DBLP:journals/ail/HembergRWWO16}", } @InCollection{Hemberg:2018:hbge, author = "Erik Hemberg", title = "Theory of Disruption in {GE}", booktitle = "Handbook of Grammatical Evolution", publisher = "Springer", year = "2018", editor = "Conor Ryan and Michael O'Neill and J. J. Collins", chapter = "5", pages = "109--135", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", isbn13 = "978-3-319-78716-9", DOI = "doi:10.1007/978-3-319-78717-6_5", abstract = "We formalize and describe the mapping process of integer input (genotype) to an output sentence (phenotype) in Grammatical Evolution (GE). The aim is to study the grammatical and search bias which is produced by the mapping. We investigate changes in input and the effect on output and analyse the neighbouring solutions as well as the effect of changes and bias in representation. Different types of changes are defined to allow classification of the effects that input changes (operators) have. The changes are a part of identifying what the neighbourhood for GE search looks like. We call this disruption in GE. Furthermore, a schema theorem is introduced for investigating preservation of material during application of variation operators, an attempt to identify the population effects.", notes = "Part of \cite{Ryan:2018:hbge}", } @InCollection{Hemberg:2018:hbge2, author = "Erik Hemberg and Anthony Erb Lugo and Dennis Garcia and Una-May O'Reilly", title = "Grammatical Evolution with Coevolutionary Algorithms in Cyber Security", booktitle = "Handbook of Grammatical Evolution", publisher = "Springer", year = "2018", editor = "Conor Ryan and Michael O'Neill and J. J. Collins", chapter = "17", pages = "407--431", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", isbn13 = "978-3-319-78716-9", DOI = "doi:10.1007/978-3-319-78717-6_17", abstract = "We apply Grammatical Evolution (GE), and multi population competitive coevolutionary algorithms to the domain of cybersecurity. Our interest (and concern) is the evolution of network denial of service attacks. In these cases, when attackers are deterred by a specific defence, they evolve their strategies until variations find success. Defenders are then forced to counter the new variations and an arms race ensues. We use GE and grammars to conveniently express and explore the behaviour of network defences and denial of service attacks under different mission and network scenarios. We use coevolution to model competition between attacks and defenses and the larger scale arms race. This allows us to study the dynamics and the solutions of the competing adversaries.", notes = "Part of \cite{Ryan:2018:hbge}", } @InProceedings{Hemberg:2019:GECCO, author = "Erik Hemberg and Jonathan Kelly and Una-May O'Reilly", title = "On domain knowledge and novelty to improve program synthesis performance with grammatical evolution", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference", year = "2019", editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and Anne Auger and Petr Posik and Leslie {Peprez Caceres} and Andrew M. Sutton and Nadarajen Veerapen and Christine Solnon and Andries Engelbrecht and Stephane Doncieux and Sebastian Risi and Penousal Machado and Vanessa Volz and Christian Blum and Francisco Chicano and Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and Jonathan Fieldsend and Jose Antonio Lozano and Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and Robin Purshouse and Thomas Baeck and Justyna Petke and Giuliano Antoniol and Johannes Lengler and Per Kristian Lehre", pages = "1039--1046", address = "Prague, Czech Republic", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, grammatical evolution, Multi-agent systems, grammar, program synthesis, novelty", isbn13 = "978-1-4503-6111-8", URL = "https://alfagroup.csail.mit.edu/sites/default/files/documents/2019Domain_Knowledge_and_Novelty_to_Improve_Program_Synthesis_Performance_with_Grammatical_Evolution.pdf", DOI = "doi:10.1145/3321707.3321865", size = "8 pages", abstract = "Programmers solve coding problems with the support of both programming and problem specific knowledge. They integrate this domain knowledge to reason by computational abstraction. Correct and readable code arises from sound abstractions and problem solving. We attempt to transfer insights from such human expertise to genetic programming (GP) for solving automatic program synthesis. We draw upon manual and non-GP Artificial Intelligence methods to extract knowledge from synthesis problem definitions to guide the construction of the grammar that Grammatical Evolution uses and to supplement its fitness function. We examine the impact of using such knowledge on 21 problems from the GP program synthesis benchmark suite. Additionally, we investigate the compounding impact of this knowledge and novelty search. The resulting approaches exhibit improvements in accuracy on a majority of problems in the fields benchmark suite of program synthesis problems.", notes = "Also known as \cite{3321865} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @Misc{hemberg2024evolvingcodelargelanguage, author = "Erik Hemberg and Stephen Moskal and Una-May O'Reilly", title = "Evolving Code with A Large Language Model", year = "2024", howpublished = "arXiv", month = "13 " # jan, keywords = "genetic algorithms, genetic programming, ANN, Large Language Models, Evolutionary Algorithm, Operators, Code Llama Instruct, HumanEval, MBPP, APPS, MultiPL-E, GSM8Km, Tutorial-LLMGP", eprint = "2401.07102", archiveprefix = "arXiv", primaryclass = "cs.NE", URL = "https://arxiv.org/abs/2401.07102", size = "34 pages", abstract = "We present LLM GP, a formalised LLM-based evolutionary algorithm designed to evolve code. Like GP, it uses evolutionary operators, but its designs and implementations of those operators radically differ from GP because they enlist an LLM, using prompting and the LLMs pre-trained pattern matching and sequence completion capability. We also present a demonstration-level variant of LLM GP and share its code. By addressing algorithms that range from the formal to hands-on, we cover design and LLM-usage considerations as well as the scientific challenges that arise when using an LLM for genetic programming.", } @InProceedings{Hemberg:2024:GPTP, author = "Erik Hemberg and Steven Jorgensen and Una-May O'Reilly", title = "Survey of Genetic Programming and Large Language Models", booktitle = "Genetic Programming Theory and Practice XXI", year = "2024", editor = "Stephan M. Winkler and Wolfgang Banzhaf and Ting Hu and Alexander Lalejini", series = "Genetic and Evolutionary Computation", pages = "67--86", address = "University of Michigan, USA", month = jun # " 6-8", publisher = "Springer", keywords = "genetic algorithms, genetic programming, LLM, AI, ANN", isbn13 = "978-981-96-0076-2", DOI = "doi:10.1007/978-981-96-0077-9_4", notes = "Published in 2025 after the workshop", } @Article{Hemberg:2024:GPEM, author = "Erik Hemberg and Stephen Moskal and Una-May O'Reilly", title = "Evolving code with a large language model", journal = "Genetic Programming and Evolvable Machines", year = "2024", volume = "25", number = "2", pages = "Article no: 21", month = dec, keywords = "genetic algorithms, genetic programming, Artificial Intelligence, AI, LLM, ANN, LLM-GP, Evolutionary algorithm, Operators", ISSN = "1389-2576", URL = "https://rdcu.be/dUBQG", DOI = "doi:10.1007/s10710-024-09494-2", size = "36 pages", abstract = "Algorithms that use Large Language Models (LLMs) to evolve code arrived on the Genetic Programming (GP) scene very recently. We present LLM_GP, a general LLM-based evolutionary algorithm designed to evolve code. Like GP, it uses evolutionary operators, but its designs and implementations of those operators significantly differ from GP because they enlist an LLM, using prompting and the LLM pre-trained pattern matching and sequence completion capability. We also present a demonstration-level variant of LLM_GP and share its code. By presentations that range from formal to hands-on, we cover design and LLM-usage considerations as well as the scientific challenges that arise when using an LLM for genetic programming.", notes = "EECS, MIT CSAIL, 32 Vassar St, Cambridge 02139, MA, USA", } @MastersThesis{hemberg:2001:masters, author = "Martin Hemberg", title = "{GENR8} - A Design Tool for Surface Generation", school = "Department of Physical Resource Theory", year = "2001", address = "Chalmers University, Sweden", month = jun # " 29", keywords = "genetic algorithms, genetic programming, lindenmayer system, development, grammatical evolution", URL = "http://www.ai.mit.edu/projects/emergentDesign/genr8/main.pdf", size = "90 pages", abstract = "GENR8 is an architect's design tool that generates surfaces. It is powerful and innovative because it fuses expressively powerful universes of growth languages with evolutionary search. Unlike traditional CAD-tools, GENR8 can create new designs and help the user to come up with new ideas. Developed via the API of AliasjWavefront's Maya, it combines 3D map L-systems, that are extended to an abstract physical environment with evolutionary computation. GENR8 uses Grammatical Evolution and a BNF of the grammar to specify the grammar that governs the growth. GENR8 addresses key issues arising from exploiting evolutionary adaption within a creative interactive tool framework. EAs typically adapt `off-line' but GENR8 is designed to sensitively accommodate the nature of the back and forth control exchange between user and tool during on-line evolutionary adaptation. GENR8 addresses how users may interrupt, intervene and then resume an EA tool. It also forgoes interactive subjective design evaluation for computationalized multi-criteria evaluation that permits wider search in shorter time spans.", notes = "Master of Science Engineering Physics", } @InProceedings{hemberg:2001:adtsg, author = "Martin Hemberg and Una-May O'Reilly and Peter Nordin", title = "{GENR8} - A Design Tool for Surface Generation", booktitle = "2001 Genetic and Evolutionary Computation Conference Late Breaking Papers", year = "2001", editor = "Erik D. Goodman", pages = "160--167", address = "San Francisco, California, USA", month = "9-11 " # jul, keywords = "genetic algorithms, genetic programming, grammatical evolution, architecture, Lindenmayer systems, BNF grammar, HEMLS, Alias|Wavefront Maya", URL = "http://www.ai.mit.edu/projects/emergentDesign/genr8/lateGecco.pdf", abstract = "GENR8 is an architect's design tool that generates surfaces. It is powerful and innovative because it fuses expressively powerful universes of growth languages with evolutionary search. Developed via the API of Alias|Wavefront's Maya, it combines 3D map L-systems, that are extended to an abstract physical environment, with Grammatical Evolution. GENR8 addresses key issues arising from exploiting evolutionary adaption within a creative interactive tool framework. EAs typically adapt off-line but GENR8 is designed to sensitively accommodate the nature of the back and forth control exchange between user and tool during on-line evolutionary adaptation. It addresses how users may interrupt, intervene and then resume an EA tool. It also forgoes interactive subjective design evaluation for computational multi-criteria evaluation that permits wider search in shorter time spans.", notes = "GECCO-2001LB", } @InProceedings{hemberg:2001:adtsg2, author = "Martin Hemberg and Una-May O'Reilly", title = "{GENR8} - A Design Tool for Surface Generation", booktitle = "Graduate Student Workshop", year = "2001", editor = "Conor Ryan", pages = "413--416", address = "San Francisco, California, USA", month = "7 " # jul, keywords = "genetic algorithms, genetic programming", notes = "GECCO-2001WKS Part of heckendorn:2001:GECCOWKS, see \cite{hemberg:2001:adtsg}, GENR8", } @InProceedings{hemberg:2002:gecco:workshop, title = "{GENR8} - Using Grammatical Evolution In {A} Surface Design Tool", author = "Martin Hemberg and Una-May O'Reilly", pages = "120--123", booktitle = "{GECCO 2002}: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference", editor = "Alwyn M. Barry", year = "2002", month = "8 " # jul, publisher = "AAAI", address = "New York", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025", keywords = "genetic algorithms, genetic programming, grammatical evolution", URL = "http://www.ai.mit.edu/projects/emergentDesign/genr8/gecco2002.pdf", size = "4 pages", notes = "Bird-of-a-feather Workshops, GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002) part of barry:2002:GECCO:workshop", } @InProceedings{hemberg:2004:eurogp, author = "Martin Hemberg and Una-May O'Reilly", title = "Extending Grammatical Evolution to Evolve Digital Surfaces with Genr8", booktitle = "Genetic Programming 7th European Conference, EuroGP 2004, Proceedings", year = "2004", editor = "Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule", volume = "3003", series = "LNCS", pages = "299--308", address = "Coimbra, Portugal", publisher_address = "Berlin", month = "5-7 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, grammatical evolution: Poster", ISBN = "3-540-21346-5", URL = "http://www.ai.mit.edu/projects/emergentDesign/genr8/euroGPpaper.pdf", DOI = "doi:10.1007/978-3-540-24650-3_28", abstract = "Genr8 is a surface design tool for architects. It uses a grammar-based generative growth model that produces surfaces with an organic quality. Grammatical Evolution is used to help the designer search the universe of possible surfaces. We describe how we have extended Grammatical Evolution, in a general manner, in order to handle the grammar used by Genr8.", notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004 GENR8 http://www.ai.mit.edu/projects/emergentDesign/genr8/euroGPposter.pdf", } @InProceedings{hemberg:2004:ALwks, author = "Martin Hemberg and Una-May O'Reilly", title = "Using Generative Growth Systems to Design Architectural Form", booktitle = "Workshop and Tutorial Proceedings Ninth International Conference on the Simulation and Synthesis of Living Systems(Alife {XI})", year = "2004", editor = "Mark Bedau and Phil Husbands and Tim Hutton and Sanjeev Kumar and Hideaki Sizuki", pages = "33--36", address = "Boston, Massachusetts", month = "12 " # sep, note = "Self-organisation and development in artificial and natural systems workshop.", keywords = "genetic algorithms, genetic programming, gramatical evolution, Genr8, HEMLS, Lindenmayer (L-systems), BNF", URL = "http://www.cs.ucl.ac.uk/staff/S.Kumar/hemberg-oreilly.zip", abstract = "Inspired by biological growth, we are using generative systems influenced by simulated environmental factors to create scalable and complex form designs. We describe how a generative system language in combination with simulated physics can crudely mimic biology with respect to parallel, non-linear spatial growth reacting to the environment. We also present a categorization of selected creative design tools in terms of how they address environment, genomic representation, search and development.", notes = "ALIFE9 http://www.cs.ucl.ac.uk/staff/S.Kumar/sodans.htm 3D surfaces. Nice (concise) survey of creative design tools (CDT) generative architectural systems. \cite{broughton:1999:e3DwlsGPwww} Rosenman and John S Gero, GADES, GENRE, The Groningen Twister, Jackson, J. Frazer, MoSS, AgencyGP \cite{o'reilly:2001:aagpd}, Genr8 \cite{hemberg:2001:masters}", } @InCollection{hemberg:2008:aae, author = "Martin Hemberg and Una-May O'Reilly and Achim Menges and Katrin Jonas and Michel {da Costa Goncalves} and Steven R. Fuchs", title = "Genr8: Architects' Experience with an Emergent Design Tool", booktitle = "The Art of Artificial Evolution: A Handbook on Evolutionary Art and Music", publisher = "Springer", year = "2008", editor = "Juan Romero and Penousal Machado", chapter = "8", pages = "167--188", keywords = "genetic algorithms, genetic programming, grammatical evolution", isbn13 = "978-3-540-72877-1", DOI = "doi:10.1007/978-3-540-72877-1_8", size = "22 pages", abstract = "We present the computational design tool Genr8 and six different architectural projects making extensive use of Genr8. Genr8 is based on ideas from Evolutionary Computation (EC) and Artificial Life and it produces surfaces using an organic growth algorithm inspired by how plants grow. These algorithms have been implemented as an architect's design tool and the chapter provides an illustration of the possibilities that the tool provides.", } @MastersThesis{Hemert:mastersthesis:1998, author = "J. I. {van Hemert}", title = "Applying Adaptive Evolutionary Algorithms to Hard Problems", school = "Leiden University", year = "1998", month = "31 " # aug, URL = "http://www.vanhemert.co.uk/publications/IR-98-19.ps.gz", keywords = "constraint satisfaction; data mining", abstract = "Supervised by A.E. Eiben and E. Marchiori", type = "Master's thesis", } @TechReport{tr-01-01, title = "An Engineering Approach to Evolutionary Art", author = "J. I. {van Hemert} and M. L. M. Jansen", year = "2001", month = "31 " # jan, institution = "Leiden University", number = "TR-01-01", URL = "http://www.vanhemert.co.uk/publications/tr01-01.An_Engineering_Approach_to_Evolutionary_Art.pdf", URL = "http://www.vanhemert.co.uk/publications/tr01-01.An_Engineering_Approach_to_Evolutionary_Art.ps.gz", keywords = "genetic algorithms, genetic programming, evolutionary art", abstract = "We present a general system that evolves art on the Internet. The system runs on a server which enables it to collect information about its usage world wide; its core uses operators and representations from genetic programming. The output consists of images that are decoded from tree structures. We show how this general system can be used to evolve two types of art: A Mondriaan like art and a type known as mandala. Both types are implemented with the mind of an engineer.", size = "pages", } @TechReport{tr-01-02, title = "A ``Futurist'' approach to dynamic environments", author = "Jano I. {van Hemert} and Clarissa {Van Hoyweghen} and Eduard Lukschandl and Katja Verbeeck", year = "2001", month = "31 " # jan, institution = "Leiden University", number = "{TR-01-02}", URL = "http://www.vanhemert.co.uk/publications/tr01-02.A_Futurist_Approach_to_Dynamic_Environments.pdf", URL = "http://www.vanhemert.co.uk/publications/tr01-02.A_Futurist_Approach_to_Dynamic_Environments.ps.gz", keywords = "genetic algorithms, genetic programming, dynamic problems, interactive evolution", abstract = "We present a general system that evolves art on the Internet. The system runs on a server which enables it to collect information about its usage world wide; its core uses operators and representations from genetic programming. The output consists of images that are decoded from tree structures. We show how this general system can be used to evolve two types of art: A Mondriaan like art and a type known as mandala. Both types are implemented with the mind of an engineer.", } @InProceedings{hemert:2001:gecco, title = "An Engineering Approach to Evolutionary Art", author = "J. I. {van Hemert} and M. L. M. Jansen", pages = "177", year = "2001", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)", editor = "Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon and Edmund Burke", address = "San Francisco, California, USA", publisher_address = "San Francisco, CA 94104, USA", month = "7-11 " # jul, keywords = "genetic algorithms, genetic programming: Poster, art, abstract, Internet, human induced fitness function, subjective, gene bank, evolutionary art", ISBN = "1-55860-774-9", URL = "http://www.vanhemert.co.uk/publications/gecco2001.An_Engineering_Approach_to_Evolutionary_Art.pdf", URL = "http://www.vanhemert.co.uk/publications/gecco2001.An_Engineering_Approach_to_Evolutionary_Art.ps.gz", URL = "http://gpbib.cs.ucl.ac.uk/gecco2001/d02.pdf", abstract = "We present a general system that evolves art on the Internet. The system runs on a server which enables it to collect information about its usage world wide; its core uses operators and representations from genetic programming. We show two types of art that can be evolved using this general system.", notes = "GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of \cite{spector:2001:GECCO} See also \cite{tr-01-01}", } @InCollection{HEMMATISARAPARDEH:2020:AAITPI, author = "Abdolhossein Hemmati-Sarapardeh and Aydin Larestani and Menad {Nait Amar} and Sassan Hajirezaie", title = "Chapter 2 - Intelligent models", editor = "Abdolhossein Hemmati-Sarapardeh and Aydin Larestani and Menad {Nait Amar} and Sassan Hajirezaie", booktitle = "Applications of Artificial Intelligence Techniques in the Petroleum Industry", publisher = "Gulf Professional Publishing", pages = "23--50", year = "2020", isbn13 = "978-0-12-818680-0", DOI = "doi:10.1016/B978-0-12-818680-0.00002-3", URL = "http://www.sciencedirect.com/science/article/pii/B9780128186800000023", keywords = "genetic algorithms, genetic programming, Intelligent models, neurons, layers, networks, algorithm, artificial neural network, support vector machine, fuzzy logic, adaptive neuro-fuzzy inference system, decision tree, group method of data handling, gene expression programming, case-based reasoning, committee machine intelligent system", abstract = "In recent decades, a vast number of intelligent approaches are applied for different engineering problems. These intelligent approaches are based on different (Artificial Intelligence) AI modeling techniques. In this chapter, various intelligent modeling techniques are discussed in detail. These models include Artificial neural networks (ANN), Fuzzy logic systems (FLS), Adaptive neuro-fuzzy inference system (ANFIS), Support vector machine (SVM), Decision tree (DT), Group method of data handling (GMDH), Genetic programming (GP), Gene expression programming (GEP), Case-based reasoning (CBR), and Committee machine intelligent system (CMIS)", } @InCollection{Henderson1999223, author = "Robert W. Henderson and Robert Powell", title = "West Indian Herpetoecology", editor = "Brian I. Crother", booktitle = "Caribbean Amphibians and Reptiles", publisher = "Academic Press", address = "San Diego", year = "1999", pages = "223--268", isbn13 = "978-0-12-197955-3", DOI = "doi:10.1016/B978-012197955-3/50019-7", URL = "http://www.sciencedirect.com/science/article/B87C3-4PN0BJP-K/2/14f280906c919939952ffbddf6b96c6c", notes = "Not on GP", } @InProceedings{oai:CiteSeerPSU:536164, author = "S. Hengpraprohm and P. Chongstitvatana", title = "Selective Crossover in Genetic Programming", booktitle = "ISCIT International Symposium on Communications and Information Technologies", year = "2001", address = "ChiangMai Orchid, ChiangMai Thailand", month = "14-16 " # nov, keywords = "genetic algorithms, genetic programming", citeseer-isreferencedby = "oai:CiteSeerPSU:87839; oai:CiteSeerPSU:272763; oai:CiteSeerPSU:322608", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:536164", rights = "unrestricted", URL = "http://www.cp.eng.chula.ac.th/~piak/paper/ISCIT534.pdf", URL = "http://citeseer.ist.psu.edu/536164.html", abstract = "Performance of Genetic Programming depends its genetic operators, especially the crossover operator. The simple crossover randomly swaps subtrees of the parents. The {"}good{"} subtree can be destroyed by an inappropriate choice of the crossover point. This work proposes a crossover operator that identifies a good subtree by measuring its impact on the fitness value and recombines good subtrees from parents. The proposed operator, called selective crossover, has been tested on two problems with satisfactory results.", notes = "http://www.ecti.or.th/conferences/ISCIT/ Chulalongkorn University, Thailand", } @InProceedings{Hengpraprohm:2007:FBIT, author = "S. Hengpraprohm and P. Chongstitvatana", title = "Selecting Informative Genes from Microarray Data for Cancer Classification with Genetic Programming Classifier Using K-Means Clustering and SNR Ranking", booktitle = "Proceedings of the 2007 International Conference Frontiers in the Convergence of Bioscience and Information Technologies (FBIT 2007)", year = "2007", pages = "211--218", address = "Jeju Island, Korea", month = oct # " 11-13", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-0-7695-2999-8", URL = "http://www.computer.org/portal/web/csdl/doi/10.1109/FBIT.2007.84", DOI = "doi:10.1109/FBIT.2007.84", abstract = "This paper presents a method for selecting informative features using K-Means clustering and SNR ranking. The performance of the proposed method was tested on cancer classification problems. Genetic Programming is employed as a classifier. The experimental results indicate that the proposed method yields higher accuracy than using the SNR ranking alone and higher than using all of the genes in classification. The clustering step assures that the selected genes have low redundancy, hence the classifier can exploit these features to obtain better performance.", notes = "Dept. of Comput. Eng., Chulalongkorn Univ., Chulalongkorn ", } @InProceedings{Hengpraprohm:2008:ICICIC, author = "Supoj Hengpraprohm and Prabhas Chongstitvatana", title = "A Genetic Programming Ensemble Approach to Cancer Microarray Data Classification", booktitle = "3rd International Conference on Innovative Computing Information and Control, ICICIC '08", year = "2008", month = jun # " 18-" # jun # " 20", pages = "340--340", address = "Dalian, Liaoning China", isbn13 = "978-0-7695-3161-8", keywords = "genetic algorithms, genetic programming, K-means clustering, cancer microarray data classification, ensemble approach, evolutionary algorithm, feature selection, machine learning, cancer, feature extraction, learning (artificial intelligence), medical computing, pattern classification, pattern clustering", URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4603529", DOI = "doi:10.1109/ICICIC.2008.35", abstract = "This paper presents a method for building an ensemble of classifiers for cancer microarray data. The proposed method exploits the advantage of a clustering technique, namely K-means clustering, combined with a feature selection technique, namely SNR feature selection. An evolutionary algorithm, namely Genetic Programming, is used to construct a number of classifiers which are assembled into an ensemble. The performance of the proposed method was tested on six cancer microarray data sets. The experimental results indicate that the proposed method yields a good prediction accuracy with a small standard deviation.", notes = "Also known as \cite{4603529}", } @PhdThesis{Hengpraprohm:thesis, author = "Supoj Hengpraprohm", title = "Ensemble genetic programming classifier for microarray data", school = "Computer Engineering, Chulalongkorn University", year = "2008", address = "Thailand", keywords = "genetic algorithms, genetic programming, Classification, Ensemble method, Microarray data analysis, feature selection", URL = "http://cuir.car.chula.ac.th/handle/123456789/16946", URL = "http://home.npru.ac.th/supoj/research/FullReport/4771832221-MyThesis.pdf", size = "90 pages", abstract = "This thesis presents an algorithm for generating an ensemble of Genetic Programming classifiers for microarray data. The number of data is small and it has high dimensions. In order to construct an ensemble, each classifier must have high efficiency and at the same time it must be different from other classifiers. The proposed method uses K-Means clustering for grouping the features of data which are similar into the same group. The SNR (Signal-to-Noise Ratio) feature selection is used to select informative features. The feature with the ith best SNR score in each group is selected to form a set of features. This feature set is used to train the ith Genetic Programming classifier. The proposed method creates a good Genetic Programming classifier where each classifier is different from the others. They contain different set of features. As a result, the performance of the ensemble is improved", notes = "In Thai? Australian Credit, Pima Indians Diabetes supervisor: Prabhas Chongstitvatana Supot Hengpraprohm Supoj_He.pdf Thai Library Integrated System (ThaiLIS) :: Thai Digital Collection (TDC) | Union Catalog (UC) | Thai Academic Reference Database (TAR)", } @Article{journals/kbs/HennessyMCR05, title = "An improved genetic programming technique for the classification of Raman spectra", author = "Kenneth Hennessy and Michael G. Madden and Jennifer Conroy and Alan G. Ryder", journal = "Knowledge Based Systems", year = "2005", number = "4-5", volume = "18", pages = "217--224", month = aug, note = "AI-2004, Cambridge, England, 13th-15th December 2004", bibdate = "2005-11-24", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/kbs/kbs18.html#HennessyMCR05", keywords = "genetic algorithms, genetic programming, Machine learning, Neural networks, Spectroscopy, Raman", DOI = "doi:10.1016/j.knosys.2004.10.001", abstract = "The aim of this study is to evaluate the effectiveness of genetic programming relative to that of more commonly-used methods for the identification of components within mixtures of materials using Raman spectroscopy. A key contribution of the genetic programming technique proposed in this research is that it explicitly aims to optimise the certainty levels associated with discovered rules, so as to minimize the chance of misclassification of future samples.", } @InCollection{henry:1994:ca, author = "Kelvin C. Henry", title = "Exploring Cellular Automata Using a Two-Dimensional Genetic Algorithm", booktitle = "Genetic Algorithms at Stanford 1994", year = "1994", editor = "John R. Koza", pages = "57--66", address = "Stanford, California, 94305-3079 USA", month = dec, organisation = "Stanford University", publisher = "Stanford Bookstore", keywords = "genetic algorithms, life, GENESIS", ISBN = "0-18-187263-3", notes = "GA generates rules for Cellular Automata aimming to select those that support proporgating structures. Life used as comparison. This volume contains 20 papers written and submitted by students describing their term projects for the course {"}Genetic Algorithms and Genetic Programming{"} (Computer Science 426) at Stanford University offered during the fall quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs426.html", } @Article{Hens20126774, author = "Akhil Bandhu Hens and Manoj Kumar Tiwari", title = "Computational time reduction for credit scoring: An integrated approach based on support vector machine and stratified sampling method", journal = "Expert Systems with Applications", volume = "39", number = "8", pages = "6774--6781", year = "2012", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2011.12.057", URL = "http://www.sciencedirect.com/science/article/pii/S0957417411017283", keywords = "genetic algorithms, genetic programming, Support vector machine, Credit scoring, F score, Stratified sampling", abstract = "With the rapid growth of credit industry, credit scoring model has a great significance to issue a credit card to the applicant with a minimum risk. So credit scoring is very important in financial firm like bans etc. With the previous data, a model is established. From that model is decision is taken whether he will be granted for issuing loans, credit cards or he will be rejected. There are several methodologies to construct credit scoring model i.e. neural network model, statistical classification techniques, genetic programming, support vector model etc. Computational time for running a model has a great importance in the 21st century. The algorithms or models with less computational time are more efficient and thus gives more profit to the banks or firms. In this study, we proposed a new strategy to reduce the computational time for credit scoring. In this approach we have used SVM incorporated with the concept of reduction of features using F score and taking a sample instead of taking the whole dataset to create the credit scoring model. We run our method two real dataset to see the performance of the new method. We have compared the result of the new method with the result obtained from other well known method. It is shown that new method for credit scoring model is very much competitive to other method in the view of its accuracy as well as new method has a less computational time than the other methods.", } @Article{Hens2018, author = "Thorsten Hens and Terje Lensberg and Klaus Reiner Schenk-Hoppe", title = "Front-running and market quality: An evolutionary perspective on high frequency trading", journal = "International Review of Finance", year = "2018", volume = "18", number = "4", pages = "727--741", month = dec, keywords = "genetic algorithms, genetic programming", URL = "https://onlinelibrary.wiley.com/doi/abs/10.1111/irfi.12159", DOI = "doi:10.1111/irfi.12159", size = "15 pages", abstract = "We study front-running by high-frequency traders (HFTs) in a limit order model with continuous trading. The model describes an evolutionary equilibrium of low-frequency traders who compete in portfolio management services by offering investment styles. The introduction of front-runners inflicts heavy losses on speculators, while leaving passive investors relatively unscathed. This encourages investment in the market portfolio and markedly reduces overall turnover. Speculative trading persists despite its lower profitability. By most measures, market quality is not affected to any significant extent by front-running HFTs.", notes = "JEL Codes: D53; D47; C63; C73", } @InCollection{Henson:2017:miller, author = "Benjamin Henson and James Alfred Walker and Martin A. Trefzer and Andy M. Tyrrell", title = "Designing Digital Systems Using Cartesian Genetic Programming and {VHDL}", booktitle = "Inspired by Nature: Essays Presented to Julian F. Miller on the Occasion of his 60th Birthday", publisher = "Springer", year = "2017", editor = "Susan Stepney and Andrew Adamatzky", volume = "28", series = "Emergence, Complexity and Computation", chapter = "3", pages = "57--86", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, EHW", isbn13 = "978-3-319-67996-9", DOI = "doi:10.1007/978-3-319-67997-6_3", abstract = "This chapter describes the use of biologically inspired Evolutionary Algorithms (EAs) to create designs for implementation on a reconfigurable logic device. Previous work on Evolvable Hardware (EHW) is discussed with a focus on timing problems for digital circuits. An EA is developed that describes the circuit using a Hardware Description Language (HDL) in a Cartesian Genetic Programming (CGP) framework. The use of an HDL enabled a commercial hardware simulator to be used to evaluate the evolved circuits. Timing models are included in the simulation allowing sequential circuits to be created and assessed. The aim of the work is to develop an EA that is able to create time dependent circuity using the versatility of a HDL and a hardware timing simulator. The variation in the circuit timing from the placement of the logic components, led to an environment with a selection pressure that promoted a more robust design. The results show the creation of both combinatorial and sequential circuits.", notes = "part of \cite{miller60book} https://link.springer.com/bookseries/10624", } @Article{Heo:2025:csite, author = "Hyo Beom Heo and Jun Ho Lee and Jeong Won Yoon and Sangseok Yu and Byoung Jae Kim and Seokyeon Im and Seung Hwan Park", title = "Explainable surrogate modeling for predicting temperature separation performance of the vortex tube", journal = "Case Studies in Thermal Engineering", year = "2025", volume = "66", pages = "105728", keywords = "genetic algorithms, genetic programming, Vortex tube, Temperature separation, Surrogate model, White-box modeling, Symbolic regression", ISSN = "2214-157X", URL = "https://www.sciencedirect.com/science/article/pii/S2214157X24017593", DOI = "doi:10.1016/j.csite.2024.105728", abstract = "A vortex tube is a device that separates compressed air at ambient temperature into cold and hot air. Compared to other air conditioning devices, it has a more straightforward structure and does not require a separate power source, making it widely used in various industrial fields. Numerous studies have proposed a data-driven surrogate model to predict the temperature at an outlet. These data-driven models are a narrow model that is suitable for the specific device. Furthermore, due to the complex internal flow field within the vortex tube, no theoretical formula has been established to explain the temperature separation phenomenon. Therefore, this study aims to develop a general surrogate model for predicting the performance of the vortex tube using symbolic regression, a representative white-box machine learning model. A white-box machine learning model is one that allows users to understand how it was able to produce its output. Non-dimensionalization is applied to ensure unit consistency across the symbolic regression and to enhance the generalizability of the surrogate model. This study also introduces genetic programming permutation importance (GPPI), a variable selection method designed to prevent model overfitting. An intuitive surrogate model are created using the cold outlet orifice hold diameter, cold mass fraction, pressure ratio, nozzle area ratio, and tube aspect ratio from counter-flow vortex tube and it was verified with new experimental data. The existing black box model was suitable only for specific experiments, However, the proposed white-box model demonstrated suitability for new experimental data, achieving a maximum performance of R2 = 0.8625", } @InCollection{Heralic:2007:hrnd, author = "Almir Heralic and Krister Wolff and Mattias Wahde", title = "Central Pattern Generators for Gait Generation in Bipedal Robots", booktitle = "Humanoid Robots: New Developments", publisher = "I-Tech Education and Publishing", year = "2007", editor = "Armando Carlos {de Pina Filho}", chapter = "17", pages = "285--304", month = jun, note = "Invited book chapter", address = "Vienna, Austria", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-902613-00-4", URL = "http://www.intechopen.com/download/pdf/pdfs_id/237", URL = "http://www.intechopen.com/articles/show/title/central_pattern_generators_for_gait_generation_in_bipedal_robots", DOI = "doi:10.5772/4873", abstract = "An obvious problem confronting humanoid robotics is the generation of stable and efficient gaits. Whereas wheeled robots normally are statically balanced and remain upright regardless of the torques applied to the wheels, a bipedal robot must be actively balanced, particularly if it is to execute a human-like, dynamic gait. The success of gait generation methods based on classical control theory, such as the zero-moment point (ZMP) method (Takanishi et al., 1985), relies on the calculation of reference trajectories for the robot to follow. In the ZMP method, control torques are generated in order to keep the zero-moment point within the convex hull of the support area defined by the feet. When the robot is moving in a well-known environment, the ZMP method certainly works well. However, when the robot finds itself in a dynamically changing real-world environment, it will encounter unexpected situations that cannot be accounted for in advance. Hence, reference trajectories can rarely be specified under such circumstances. In order to address this problem, alternative, biologically inspired control methods have been proposed, which do not require the specification of reference trajectories. The aim of this chapter is to describe one such method, based on central pattern generators (CPGs), for control of bipedal robots.", size = "20 pages", } @InProceedings{Heravi:2016:CEC, author = "Ashkan Entezari Heravi and Sheridan Houghten", title = "A Methodology for Disease Gene Association using Centrality Measures", booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016)", year = "2016", editor = "Yew-Soon Ong", pages = "24--31", address = "Vancouver", month = "24-29 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-5090-0623-6", DOI = "doi:10.1109/CEC.2016.7743774", abstract = "Disease-gene association attempts to determine which genes are involved with genetic diseases. Various methodologies have been applied to this problem for different diseases. In earlier work, two evolutionary approaches were used to analyse the complex network of gene interaction. This paper presents an improvement upon the genetic programming approach using a variety of centrality measures to analyze the networks. This approach is applied to both Parkinson's disease and breast cancer.", notes = "WCCI2016", } @Article{Hermanovsky:2017:JH, author = "M. Hermanovsky and V. Havlicek and M. Hanel and P. Pech", title = "Regionalization of runoff models derived by genetic programming", journal = "Journal of Hydrology", year = "2017", volume = "547", pages = "544--556", keywords = "genetic algorithms, genetic programming, Physical similarity, PUB, Regionalization, Runoff modelling", ISSN = "0022-1694", DOI = "doi:10.1016/j.jhydrol.2017.02.018", URL = "http://www.sciencedirect.com/science/article/pii/S0022169417300951", abstract = "The aim of this study is to assess the potential of hydrological models derived by genetic programming (GP) to estimate runoff at ungauged catchments by regionalization. A set of 176 catchments from the MOPEX (Model Parameter Estimation Experiment) project was used for our analysis. Runoff models for each catchment were derived by genetic programming (hereafter GP models). A comparison of efficiency was made between GP models and three conceptual models (SAC-SMA, BTOPMC, GR4J). The efficiency of the GP models was in general comparable with that of the SAC-SMA and BTOPMC models but slightly lower (up to 10percent for calibration and 15percent in validation) than for the GR4J model. The relationship between the efficiency of the GP models and catchment descriptors (CDs) was investigated. From 13 available CDs the aridity index and mean catchment elevation explained most of the variation in the efficiency of the GP models. The runoff for each catchment was then estimated considering GP models from single or multiple physically similar catchments (donors). Better results were obtained with multiple donor catchments. Increasing the number of CDs used for quantification of physical similarity improves the efficiency of the GP models in runoff simulation. The best regionalization results were obtained with 6 CDs together with 6 donors. Our results show that transfer of the GP models is possible and leads to satisfactory results when applied at physically similar catchments. The GP models can be therefore used as an alternative for runoff modelling at ungauged catchments if similar gauged catchments can be identified and successfully simulated.", } @Misc{DBLP:journals/corr/abs-1904-01095, author = "Alberto Hernandez and Adarsh Balasubramanian and Fenglin Yuan and Simon Mason and Tim Mueller", title = "Fast, accurate, and transferable many-body interatomic potentials by genetic programming", howpublished = "arXiv", volume = "abs/1904.01095", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "http://arxiv.org/abs/1904.01095", archiveprefix = "arXiv", eprint = "1904.01095", timestamp = "Mon, 22 Jul 2019 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/corr/abs-1904-01095.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{hernandez:1999:SDMEACS, author = "German Hernandez and Jerome A. Goldstein and Fernando Niao", title = "Stochastic Differential Model for Evolutionary Algorithms over Continuous Spaces", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "863--870", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "evolution strategies and evolutionary programming", ISBN = "1-55860-611-4", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Hernandez:2019:GECCOcomp, author = "Jose Guadalupe Hernandez and Alexander Lalejini and Emily Dolson and Charles Ofria", title = "Random subsampling improves performance in lexicase selection", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", isbn13 = "978-1-4503-6748-6", pages = "2028--2031", address = "Prague, Czech Republic", DOI = "doi:10.1145/3319619.3326900", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{3326900} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InProceedings{hernandez:2024:GECCOcomp, author = "Jose Guadalupe Hernandez and Anil Kumar Saini and Jason H Moore", title = "Lexidate: Model Evaluation and Selection with Lexicase", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion", year = "2024", editor = "Jean-Baptiste Mouret and Kai Qin", pages = "279--282", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, lexicase selection, AutoML, cross-validation, model evaluation, model selection, model complexity, Evolutionary Machine Learning: Poster", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3654265", size = "4 pages", abstract = "Automated machine learning streamlines the task of finding effective machine learning pipelines by automating model training, evaluation, and selection. Traditional evaluation strategies, like cross-validation (CV), generate one value that averages the accuracy of a pipeline's predictions. This single value, however, may not fully describe the generalizability of the pipeline. Here, we present Lexicase-based Validation (lexidate), a method that uses multiple, independent prediction values for selection. Lexidate splits training data into a learning set and a selection set. Pipelines are trained on the learning set and make predictions on the selection set. The predictions are graded for correctness and used by lexicase selection to identify parent pipelines. Compared to 10-fold CV, lexicase reduces the training time. We test the effectiveness of three lexi-date configurations within the Tree-based Pipeline Optimization Tool 2 (TPOT2) package on six OpenML classification tasks. In one configuration, we detected no difference in the accuracy of the final model returned from TPOT2 on most tasks compared to 10-fold CV. All configurations studied here returned similar or less complex final pipelines compared to 10-fold CV.", notes = "GECCO-2024 EML A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{hernandez:2004:otdospngbmogp, title = "On the design of state-of-the-art pseudorandom number generators by means of genetic programming", author = "Julio Cesar Hernandez and Andre Seznec and Pedro Isasi", pages = "1510--1516", booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary Computation", year = "2004", publisher = "IEEE Press", month = "20-23 " # jun, address = "Portland, Oregon", ISBN = "0-7803-8515-2", DOI = "doi:10.1109/CEC.2004.1331075", keywords = "genetic algorithms, genetic programming, Evolutionary Computation in Cryptology and Computer Security, cellular automata, fitness function, pseudorandom number generators, cellular automata, random number generation", abstract = "The design of pseudorandom number generators by means of evolutionary computation is a classical problem. To day, it has been mostly and better accomplished by means of cellular automata and not many proposals, inside or outside this paradigm, could claim to be both robust (passing many statistical tests, including the most demanding ones) and fast, as is the case of the proposal we present. Furthermore, we use a radically new approach, where our fitness function is not at all based in any measure of randomness, as is frequently the case in the literature, but of non-linearity. Efficiency is assured by using only very efficient operators, and by limiting the number of terminals in the Genetic Programming implementation.", notes = "PRNG. Also known as \cite{1331075}. CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.", } @InProceedings{hernandez-aguirre:2000:gsbfbmgp, author = "Arturo Hernandez-Aguirre and Bill P. Buckles and Carlos A. Coello-Coello", title = "Gate-level Synthesis of {Boolean} Functions using Binary Multiplexers and Genetic Programming", booktitle = "Proceedings of the 2000 Congress on Evolutionary Computation CEC00", year = "2000", pages = "675--682", address = "La Jolla Marriott Hotel La Jolla, California, USA", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "6-9 " # jul, organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, hybrid systems, 1-control line multiplexer, Boolean functions, application domain, binary multiplexers, fitness function, gate-level synthesis, logic functions, truth table, Boolean functions, binary decision diagrams, logic design, multiplexing equipment", ISBN = "0-7803-6375-2", URL = "http://www.lania.mx/~ccoello/papers/hernandez00.ps.gz", URL = "http://citeseer.ist.psu.edu/309980.html", DOI = "doi:10.1109/CEC.2000.870363", size = "8 pages", abstract = "This paper presents a genetic programming approach for the synthesis of logic functions by means of multiplexers. The approach uses the 1-control line multiplexer as the only design unit. Any logic function (defined by a truth table) can be produced through the replication of this single unit. Our fitness function works in two stages: first, it finds feasible solutions, and then it concentrates on the minimisation of the circuit. The proposed approach does not require any knowledge from the application domain.", notes = "CEC-2000 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 00TH8512, Library of Congress Number = 00-018644", } @InProceedings{Hernandez-Albarracin:2017:IGARSS, author = "Juan F. {Hernandez Albarracin} and Edemir {Ferreira, Jr.} and Jefersson A. {dos Santos} and Ricardo {da S. Torres}", booktitle = "2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)", title = "Fusion of genetic-programming-based indices in hyperspectral image classification tasks", year = "2017", pages = "554--557", abstract = "This paper introduces a two-step hyper- and multi-spectral image classification approach. The first step relies on the use of a genetic programming (GP) framework to both select and combine appropriate bands. The second step is concerned with the image classification itself. We present two strategies for multi-class classification problems based on the combination of GP-based indices defined in binary classification scenarios. Performed experiments involving well-known and widely-used datasets demonstrate that the proposed approach yields comparable or better effectiveness performance when compared to several traditional baselines.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IGARSS.2017.8127013", month = jul, notes = "Also known as \cite{8127013}", } @Article{Hernandez-Beltran:2019:swarmEC, author = "Jose Enrique Hernandez-Beltran and Victor H. Diaz-Ramirez and Leonardo Trujillo and Pierrick Legrand", title = "Design of estimators for restoration of images degraded by haze using genetic programming", journal = "Swarm and Evolutionary Computation", year = "2019", volume = "44", pages = "49--63", month = feb, keywords = "genetic algorithms, genetic programming, Image restoration, Haze removal, Image processing", ISSN = "2210-6502", identifier = "hal-01909121", language = "en", oai = "oai:HAL:hal-01909121v1", ISSN = "2210-6502", URL = "https://www.human-competitive.org/sites/default/files/hernandezramirez.txt", URL = "http://www.sciencedirect.com/science/article/pii/S2210650218301366", URL = "https://hal.inria.fr/hal-01909121", URL = "http://www.sciencedirect.com/science/article/pii/S2210650218301366", video_url = "http://www.human-competitive.org/sites/default/files/ramirezvideo_copy.mp4", DOI = "doi:10.1016/j.swevo.2018.11.008", size = "15 pages", abstract = "Restoring hazy images is challenging since it must account for several physical factors that are related to the image formation process. Existing analytical methods can only provide partial solutions because they rely on assumptions that may not be valid in practice. This research presents an effective method for restoring hazy images based on genetic programming. Using basic mathematical operators several computer programs that estimate the medium transmission function of hazy scenes are automatically evolved. Afterwards, image restoration is performed using the estimated transmission function in a physics-based restoration model. The proposed estimators are optimized with respect to the mean-absolute-error. Thus, the effects of haze are effectively removed while minimizing over processing artefacts. The performance of the evolved GP estimators given in terms of objective metrics and a subjective visual criterion, is evaluated on synthetic and real-life hazy images. Comparisons are carried out with state-of-the-art methods, showing that the evolved estimators can outperform these methods without incurring a loss in efficiency, and in most scenarios achieving improved performance that is statistically significant.", notes = "2020 HUMIES finalist. also known as \cite{HERNANDEZBELTRAN201949}", } @InProceedings{Hernandez-Beltran:2019:OPIP, author = "Jose Enrique Hernandez-Beltran and Victor H. Diaz-Ramirez and Rigoberto Juarez-Salazar", title = "Real-time image dehazing using genetic programming", booktitle = "Optics and Photonics for Information Processing XIII", year = "2019", editor = "Khan M. Iftekharuddin and Abdul A. S. Awwal and Victor H. Diaz-Ramirez and Andres Marquez", volume = "11136", pages = "222--230", address = "San Diego, California, United States", month = "6 " # sep, organization = "International Society for Optics and Photonics", publisher = "SPIE", keywords = "genetic algorithms, genetic programming, Image dehazing, Genetic programming, Real-time image processing", URL = "https://www.human-competitive.org/sites/default/files/hernandezramirez.txt", URL = "https://doi.org/10.1117/12.2528510", video_url = "http://www.human-competitive.org/sites/default/files/ramirezvideo_copy.mp4", DOI = "doi:10.1117/12.2528510", abstract = "A real-time system for restoration of images degraded by haze is presented. First, a transmission function estimator is automatically constructed using genetic programming. Next, the resultant estimator is employed to compute the transmission function of the scene by processing an input hazy image. Finally, the estimated transmission function and the hazy image are used in a restoration model based on atmospheric optics to obtain a haze-free image. The proposed method is implemented in a laboratory prototype for high-rate image processing. The performance of the proposed approach is evaluated in terms of objective metrics using synthetic and real-world images.", notes = "2020 HUMIES finalist.", } @Article{Hernandez-Castaneda:2023:ACC, author = "Angel Hernandez-Castaneda and Rene Arnulfo Garcia-Hernandez and Yulia Ledeneva", journal = "IEEE Access", title = "Toward the Automatic Generation of an Objective Function for Extractive Text Summarization", year = "2023", volume = "11", pages = "51455--51464", abstract = "A fitness function is a type of objective function that quantifies the optimality of a solution; the correct formulation of this function is relevant, in evolutionary-based ATS systems, because it must indicate the quality of the summaries. Several unsupervised evolutionary methods for the automatic text summarization (ATS) task proposed in current standards require authors to manually construct an objective function that guides the algorithms to create good-quality summaries. In this sense, it is necessary to test each fitness function created to measure its performance; however, this process is time consuming and only a few functions are analysed. This study proposes the automatic generation of heuristic functions, through genetic programming (GP), to be applied in the ATS task. Therefore, our proposed method for ATS provides an automatically generated fitness function for cluster-based unsupervised approaches. The results of this study, using two standard collections, demonstrate to automatically obtain an orientation function that leads to good quality abstracts.", keywords = "genetic algorithms, genetic programming, Heuristic algorithms, Natural language processing, NLP, Mathematical models, Training, Data mining, Text recognition, Automatic text summarization, clustering, heuristic functions", DOI = "doi:10.1109/ACCESS.2023.3279101", ISSN = "2169-3536", notes = "Also known as \cite{10131908}", } @InProceedings{Hernandez-Castro:2003:KBIIES, author = "Julio Cesar {Hernandez Castro} and Pedro Isasi Vinuela and Cristobal {Luque del Arco-Calderon}", title = "Finding Efficient Nonlinear Functions by Means of Genetic Programming", booktitle = "Knowledge-Based Intelligent Information and Engineering Systems", year = "2003", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-540-45224-9_161", DOI = "doi:10.1007/978-3-540-45224-9_161", } @InProceedings{Hernandez-Castro:2006:CEC, author = "Julio C. Hernandez-Castro and Juan M. Estevez-Tapiador and Arturo Ribagorda-Garnacho and Benjamin Ramos-Alvarez", title = "Wheedham: An Automatically Designed Block Cipher by means of Genetic Programming", booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary Computation", year = "2006", editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas", pages = "499--506", address = "Vancouver", month = "16-21 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9487-9", DOI = "doi:10.1109/CEC.2006.1688308", size = "8 pages", abstract = "we present a general scheme for the design of block ciphers by means of Genetic Programming. In this vein, we try to evolve highly nonlinear and efficient functions to be used for the key expansion and the F-function of a Feistel network. Following this scheme, we propose a new block cipher design called Wheedham, that operates on 512 bit blocks and keys of 256 bits, of which we offer its C code (directly translated from the GP Trees) and some preliminary security results.", notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D", } @InProceedings{Herranz:2022:ICARSC, author = "Guillermo Legarda Herranz and Sabine Hauert and Simon Jones", booktitle = "2022 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)", title = "Decentralised Negotiation for Multi-Object Collective Transport with Robot Swarms", year = "2022", pages = "186--191", abstract = "Recent developments of robot swarms with richer capabilities for sensing and manipulation of the environment have opened the door to more complex applications of swarm robotics. The introduction of such swarms in intralogistics, where workers are still at risk of injury, is of particular interest. We present a method to control a swarm of robots to simultaneously transport multiple items that are too heavy or too large for a single robot to carry. We introduce a decentralised negotiation strategy based on inter-robot communication, which allows the robots to coordinate with subgroups of the swarm. We then use genetic programming to evolve behaviour tree controllers that generate the desired action of each robot, which is then fed to the negotiation strategy to produce the final output.", keywords = "genetic algorithms, genetic programming, Robot kinematics, Conferences, Swarm robotics, Robot sensing systems, Sensors, Robots, swarm robotics, collective transport, negotiation", DOI = "doi:10.1109/ICARSC55462.2022.9784801", month = apr, notes = "Also known as \cite{9784801}", } @Article{HERRERA:2022:ASC, author = "Leonardo Herrera and M. C. Rodriguez-Linan and Eddie Clemente and Marlen Meza-Sanchez and Luis Monay-Arredondo", title = "Evolved Extended {Kalman} Filter for first-order dynamical systems with unknown measurements noise covariance", journal = "Applied Soft Computing", year = "2022", volume = "115", pages = "108174", keywords = "genetic algorithms, genetic programming, Extended Kalman Filter, Analytic behaviors, Nonlinear first-order dynamical systems, Logistic map system", ISSN = "1568-4946", URL = "https://www.human-competitive.org/sites/default/files/humiesentry-eekf.txt", URL = "https://www.human-competitive.org/sites/default/files/papereekf.pdf", URL = "https://www.sciencedirect.com/science/article/pii/S1568494621010280", DOI = "doi:10.1016/j.asoc.2021.108174", size = "13 pages", abstract = "We focus on an open problem in the design of Extended Kalman filters: the lack of knowledge of the measurement noise covariance. A novel extension of the analytic behaviors framework, which integrates a theoretical formulation and evolutionary computing, has been introduced as a design methodology for the construction of this unknown parameter. The proposed methodology is developed and applied for the design of Evolved Extended Kalman Filters for nonlinear first-order dynamical systems. The proposed methodology applies an offline evolutionary synthesis of analytic nonlinear functions, to be used as measurement noise covariance, aiming to minimize the Kalman criterion. The virtues of the methodology are exemplified through a complex, highly nonlinear, first-order dynamical system, for which 2649 optimised replacements of the measurement noise covariance are found. Under different scenarios, the performance of the Evolved Extended Kalman Filter with unknown measurement noise covariance is compared with that of the conventional Extended Kalman Filter where the measurement noise covariance is known. The robustness of the Evolved Extended Kalman Filter is demonstrated through numerical evaluation", notes = "2022 HUMIES finalist. Mechanical and Aerospace Engineering Department, NPS, Monterey, USA", } @InProceedings{Herrera-Sanchez:2022:ISCMI, author = "David Herrera-Sanchez and Efren Mezura-Montes and Hector-Gabriel Acosta-Mesa", booktitle = "2022 9th International Conference on Soft Computing \& Machine Intelligence (ISCMI)", title = "Feature Construction, Feature Reduction and Search Space Reduction Using Genetic Programming", year = "2022", pages = "152--156", abstract = "Feature construction and feature selection are essential pre-processing techniques in data mining, especially for high-dimensional data. The principal goals of such techniques are to increase accuracy in classification tasks and reduce runtime in the learning process. Genetic programming is used to construct a new high-level feature space. Additionally, the feature selection process, immersed in the task, is seized. Therefore, a set of features with relevant information is obtained. This paper presents an approach to reducing the features of high-dimensional data throughout genetic programming. Moreover, reducing the search space eliminates features that do not have considerable information over the generations of the search process. Although the approach is simple, competitive results are achieved. In the implementation, the wrapper approach is used for the classifier to lead the searching process.", keywords = "genetic algorithms, genetic programming, Runtime, Feature extraction, Data mining, Task analysis, Machine intelligence, feature reduction, feature construction, high-dimensional data", DOI = "doi:10.1109/ISCMI56532.2022.10068452", ISSN = "2640-0146", month = nov, notes = "Also known as \cite{10068452}", } @InProceedings{herrera-sanchez:2023:MICAI, author = "David Herrera-Sanchez and Hector-Gabriel Acosta-Mesa and Efren Mezura-Montes", title = "Auto Machine Learning Based on Genetic Programming for Medical Image Classification", booktitle = "Advances in Computational Intelligence. MICAI 2023 International Workshops", year = "2023", address = "Yucatan, Mexico", month = nov # " 13-18", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-031-51940-6_26", DOI = "doi:10.1007/978-3-031-51940-6_26", notes = "Published 2024", } @InProceedings{herrera-sanchez:2024:GECCOcomp, author = "David Herrera-Sanchez and Hector-Gabriel Acosta-Mesa and Efren Mezura-Montes and Aldo Marquez-Grajales", title = "Shifting Color Space for Image Classification using Genetic Programming", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion", year = "2024", editor = "Jean-Baptiste Mouret and Kai Qin", pages = "283--286", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, color space, image classification, Evolutionary Machine Learning: Poster", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3654430", size = "4 pages", abstract = "Image classification is an important application in different areas of computer vision. Genetic Programming (GP) has demonstrated an excellent capability to deal with this task. However, many approaches transform images to grayscale due to the computational cost of working with color spaces. Therefore, possible significant image features for classification could be discarded early. Consequently, our proposal incorporates features extracted from different color spaces for image classification through GP in three datasets. Furthermore, the GP's flexibility allows the images' color space conversion during the evolutionary process. According to the dataset, the final solution determines whether a specific color space was used or if it was necessary to change to another to extract meaningful features from the images. The results demonstrate that changing the color space in some data sets was necessary to achieve competitive classification accuracy.", notes = "GECCO-2024 EML A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @Article{herrera-sanchez:2024:NCA, author = "David Herrera-Sanchez and Efren Mezura-Montes and Hector-Gabriel Acosta-Mesa and Aldo Marquez-Grajales", title = "Feature selection, construction and search space reduction based on genetic programming for high-dimensional datasets", journal = "Neural Computing and Applications", year = "2024", keywords = "genetic algorithms, genetic programming", URL = "https://link.springer.com/article/10.1007/s00521-024-10567-7", DOI = "doi:10.1007/s00521-024-10567-7", } @InProceedings{herrera-sanchez:2024:MICAI, author = "David Herrera-Sanchez and Jose-Antonio Fuentes-Tomas and Hector-Gabriel Acosta-Mesa and Efren Mezura-Montes", title = "Experimental Study for Automatic Feature Construction to Segment Images of Lungs Affected by {COVID-19} Using Genetic Programming", booktitle = "Advances in Computational Intelligence. MICAI 2024 International Workshops", year = "2024", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "https://link.springer.com/chapter/10.1007/978-3-031-83882-8_15", DOI = "doi:10.1007/978-3-031-83882-8_15", } @InProceedings{Hershkovitz:2008:ESDA, author = "Shany Hershkovitz and Sioma Baltianski and Yoed Tsur", title = "{Nb}-Doped Barium Titanate: Concentration-Properties Relations", booktitle = "9th Biennial Conference on Engineering Systems Design and Analysis (ESDA2008)", year = "2008", volume = "1", pages = "499--504", address = "Haifa, Israel", month = jul # " 7-9", publisher = "ASME", keywords = "genetic algorithms, genetic programming", isbn13 = "978-0-7918-4836-4", DOI = "doi:10.1115/ESDA2008-59049", abstract = "Nb doped barium titanate (BT) experiences unique phenomena over a range of dopant concentrations. One important phenomenon is the resistivity behaviour as a function of donor concentration. The role of the grains and the grain boundaries in this system is not fully established yet. There are diverse opinions on this subject, since this system is usually only in partial equilibrium and hence very complex. We examine the system using Impedance Spectroscopy (IS). Two new analysis methods for IS based on evolutionary programming techniques, which are inspired by biological evolution, have been developed in our lab. Those evolutionary programming techniques are called Genetic Programming (GP) and Genetic Algorithm (GA). This is an approach to solve (or in the case of GA suggest solution for) such ill-posed inverse problems. By implementation and improvement of the use of those techniques for analysing IS results, we believe that the role of the grains and the grain boundaries can be separated and the physical processes occur can be analysed.", notes = "Technion-Israel Institute of Technology, Haifa, Israel", } @Article{Hershkovitz2011104, author = "Shany Hershkovitz and Sioma Baltianski and Yoed Tsur", title = "Harnessing evolutionary programming for impedance spectroscopy analysis: A case study of mixed ionic-electronic conductors", journal = "Solid State Ionics", volume = "188", number = "1", pages = "104--109", year = "2011", note = "9th International Symposium on Systems with Fast Ionic Transport", ISSN = "0167-2738", DOI = "doi:10.1016/j.ssi.2010.10.004", URL = "http://www.sciencedirect.com/science/article/B6TY4-51D5RFW-2/2/78396a47420bfca2e3d664e88b21c461", keywords = "genetic algorithms, genetic programming, Impedance spectroscopy, Warburg elements, Parametric analysis", abstract = "A modified Genetic Programming (GP) method has been developed for the analysis of impedance spectroscopy data. It gives a functional form of the distribution function of relaxation times (DFRT) in the sample. The evolution force is composed of lowering the discrepancy between the model's prediction and the measured data, while keeping the model simple in terms of the number of free parameters. The DFRT that the program seeks for has the form of a peak or a sum of several peaks. All the peaks are known mathematical functions (e.g., Gaussians). The user can let the program search for many types of peaks or to limit the search. Finding a functional form of the underlying DFRT has two main assets. (a) DFRT is unique and (b) a functional form makes it possible to develop a physical model and compare it to the function. In addition, if more than one peak is present and each peak can be related to a different phenomenon, the peaks can be directly separated for further analysis. The analysis method is demonstrated using synthetic data as well as experimental data of Gd0.1Ce0.9O1.95 (GDC).", } @PhdThesis{Hershkovitz:thesis, author = "Shany Hershkovitz", title = "Harnessing Evolutionary Programming for Impedance Spectroscopy Analysis", school = "Department of Chemical Engineering, Technion", year = "2011", address = "Israel", keywords = "genetic algorithms, genetic programming", URL = "http://www.graduate.technion.ac.il/Theses/Abstracts.asp?Id=24411", abstract = "In this research, a novel analysis technique for impedance spectroscopy (IS) measurements is introduced and applied to the investigation of symmetric cells. IS is a powerful and non destructive method of characterizing electrical properties of materials. The analysis program is based on genetic programming (GP) which is an evolutionary-based optimization algorithm. The GP computing approach allows the evolutions of both the model and the numerical parameters of a certain model based on its fitness to a general mathematical problem. In contrast to the conventional analysis methods used for impedance spectroscopy measurements, e.g. equivalent circuits, our program seeks the distribution of relaxation times, DFRT, that has the form of a peak or a sum of several peaks, assuming the Debye kernel. Using this method one finds a functional (parametric) form of the distribution of relaxation times. By finding a functional form of the DFRT, one may develop a physical model and examine its behaviour. This analysis technique is used to investigate the oxygen reduction reaction at the cathode side of solid oxide fuel cells (SOFC). Two symmetric cell configurations (SCC) where chosen : (i) The first configuration is composed of Pt│GDC│Pt, where the Pt layer serves as cathode material as well as current collector; GDC is the electrolyte material. (ii) The Second configuration is composed of Pt│LSCF│GDC│LSCF│Pt, where LSCF serves as the electrode material. IS measurements combined with I-V measurements were employed on several samples at several temperatures and several oxygen partial pressures in order to investigate their influence on the oxygen reduction reaction. The resulting IS data was analyzed using the ISGP program and the resulting peaks constructing the DFRTs were assigned for different processes that occur at the cathode side. The activation energies as well as the dependence of the processes on the oxygen partial pressure were also evaluated. The polarization curves obtained were analyzed using the Butler-Volmer (B-V) relations and a proposed model was suggested for the behavior of the examined cell.", notes = "March 2017: Access is available only to users in the Technion Campus premises. Supervisors Tsur Yoed and Sioma Baltianski", } @InProceedings{hervas:2001:MTRSI, author = "Paul L. Rosin and Javier Hervas", title = "Image Thresholding For Landslide Detection By Genetic Programming", booktitle = "Proceedings of the First International Workshop on Multitemporal Remote Sensing Images", year = "2001", editor = "Lorenzo Bruzzone and Paul Smits", volume = "2", series = "Remote Sensing", pages = "67--74", address = "University of Trento, Italy", month = "13-14 " # sep, publisher = "World Scientific Publishing", keywords = "genetic algorithms, genetic programming, Tessina landslide", ISBN = "981-02-4955-1", URL = "https://users.cs.cf.ac.uk/Paul.Rosin/resources/papers/gp2.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.11.7996", URL = "https://www.amazon.com/Analysis-Multi-Temporal-Remote-Sensing-Images/dp/9810249551", broken = "http://www.abebooks.co.uk/servlet/BookDetailsPL?bi=8579480676&searchurl=isbn%3D9810249551%26x%3D37%26y%3D8", DOI = "doi:10.1142/9789812777249_0005", size = "8 pages", abstract = "This paper describes an approach to image thresholding that combines various multiscale and morphological features, including texture, shape and edge filtering, by using genetic programming, to detect the presence of landslides and their active sectors in change detected multitemporal aerial images", notes = "lil-gp see also \cite{oai:CiteSeerPSU:555070} MultiTemp-2001 broken June 2021 http://www.ing.unitn.it/~multi/Multitemp2001/", } @Misc{oai:CiteSeerPSU:555070, title = "Image Thresholding For Landslide Detection By Genetic Programming", author = "Javier Hervas and Paul L. Rosin", year = "2003", month = jan # "~02", abstract = "This paper describes an approach to image thresholding that combines various multiscale and morphological features, including texture, shape and edge filtering, by using genetic programming, to detect the presence of landslides and their active sectors in change detected multitemporal aerial images", citeseer-isreferencedby = "oai:CiteSeerPSU:93111", citeseer-references = "oai:CiteSeerPSU:557560", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:555070", rights = "unrestricted", URL = "http://www.cs.cf.ac.uk/User/Paul.Rosin/resources/papers/gp2.pdf", URL = "http://citeseer.ist.psu.edu/555070.html", keywords = "genetic algorithms, genetic programming", size = "8 pages", abstract = "This paper describes an approach to image thresholding that combines various multiscale and morphological features, including texture, shape and edge filtering, by using genetic programming, to detect the presence of landslides and their active sectors in change detected multitemporal aerial images", notes = "see also \cite{hervas:2001:MTRSI} http://www.worldscibooks.com/compsci/4997.html", } @Article{HERZOG:2020:NN, author = "Sebastian Herzog and Christian Tetzlaff and Florentin Woergoetter", title = "Evolving artificial neural networks with feedback", journal = "Neural Networks", volume = "123", pages = "153--162", year = "2020", ISSN = "0893-6080", DOI = "doi:10.1016/j.neunet.2019.12.004", URL = "http://www.sciencedirect.com/science/article/pii/S089360801930396X", keywords = "genetic algorithms, genetic programming, Deep learning, Feedback, Transfer entropy, Convolutional neural network", abstract = "Neural networks in the brain are dominated by sometimes more than 60percent feedback connections, which most often have small synaptic weights. Different from this, little is known how to introduce feedback into artificial neural networks. Here we use transfer entropy in the feed-forward paths of deep networks to identify feedback candidates between the convolutional layers and determine their final synaptic weights using genetic programming. This adds about 70percent more connections to these layers all with very small weights. Nonetheless performance improves substantially on different standard benchmark tasks and in different networks. To verify that this effect is generic we use 36000 configurations of small (2-10 hidden layer) conventional neural networks in a non-linear classification task and select the best performing feed-forward nets. Then we show that feedback reduces total entropy in these networks always leading to performance increase. This method may, thus, supplement standard techniques (e.g. error backprop) adding a new quality to network learning", } @InProceedings{Heshmati:2009:icce, author = "A. A. R. Heshmati and M. G. Sahab and A. H. Alavi and A. H. Gandomi", title = "Soil Classification Using a Combined Algorithm of Simulated Annealing and Genetic Programming", booktitle = "The 8th International Congress on Civil Engineering", year = "2009", editor = "Nasser Talebbeydokbti", pages = "Number G0273", address = "Shiraz University, Shiraz, Iran", month = may # " 11-13", keywords = "genetic algorithms, genetic programming, Soil classification, Combined genetic programming and simulated annealing, Neural network, IS classification system", URL = "https://en.symposia.ir/ICCE08", URL = "https://www.civilica.com/Paper-ICCE08-ICCE08_042.html", URL = "https://www.civilica.com/PdfExport-ICCE08_042=Soil-Classification-Using-a-Combined-Algorithm-of-Simulated-Annealing-and-Genetic-Programming.pdf", URL = "http://www.civilica.com/EnPaper--ICCE08_042.html", size = "8 pages??", abstract = "This paper presents a novel approach for the determination of soil classification using a hybrid search algorithm that couples genetic programming (GP) and simulated annealing (SA), as a combined algorithm, called GP/SA. Properties of soil namely, plastic limit, liquid limit, colour of soil, percentage of gravel, sand, and fine grained particles were used as input variables to the models to determine the classification of soils. The models were developed using a reliable database obtained from the previously published literature. The results of GP/SA based formulations were found to be more accurate as compared to the experimental, numerical and analytical results obtained by other researchers.", notes = "EnPaper--ICCE08_042.html in Persian? PDF may be available? Also known as \cite{ICCE08_042} Year also given as 1388", } @InProceedings{hetland:2002:RASC, author = "Magnus Lie Hetland and Pal Saetrom", title = "Temporal Rule Discovery using Genetic Programming and Specialized Hardware", booktitle = "Proceedings of the 4th International Conference on Recent Advances in Soft Computing", year = "2002", editor = "Ahmad Lotfi and Jon Garibaldi and Robert John", pages = "182--188", address = "Nottingham, United Kingdom", publisher_address = "Nottingham, United Kingdom", month = "12-13 " # dec, publisher = "The Nottingham Trent University", keywords = "genetic algorithms, genetic programming, Time series, sequence mining, rule discovery, pattern matching hardware", ISBN = "1-84233-076-4", URL = "http://hetland.org/research/2002/sc2103.pdf", URL = "http://citeseer.ist.psu.edu/549830.html", abstract = "Discovering association rules is a well-established problem in the field of data mining, with many existing solutions. In later years, several methods have been proposed for mining rules from sequential and temporal data. This paper presents a novel technique based on genetic programming and specialized pattern matching hardware. The advantages of this method are its exibility and adaptability, and its ability to produce intelligible rules of considerable complexity.", notes = "RASC broken Dec 2024 http://www.rasc2002.info/ https://irep.ntu.ac.uk/id/eprint/7246 See also \cite{hetland:2005:ML}", } @Article{hetland:2005:ML, author = "Magnus Lie Hetland and Pal Saetrom", title = "Evolutionary Rule Mining in Time Series Databases", journal = "Machine Learning", year = "2005", volume = "58", number = "2-3", pages = "107--125", month = feb, keywords = "genetic algorithms, genetic programming, sequence mining, knowledge discovery, time series, specialised hardware", ISSN = "0885-6125", DOI = "doi:10.1007/s10994-005-5823-8", abstract = "Data mining in the form of rule discovery is a growing field of investigation. A recent addition to this field is the use of evolutionary algorithms in the mining process. While this has been used extensively in the traditional mining of relational databases, it has hardly, if at all, been used in mining sequences and time series. In this paper we describe our method for evolutionary sequence mining, using a specialized piece of hardware for rule evaluation, and show how the method can be applied to several different mining tasks, such as supervised sequence prediction, unsupervised mining of interesting rules, discovering connections between separate time series, and investigating tradeoffs between contradictory objectives by using multiobjective evolution.", } @PhdThesis{Heule:thesis, author = "Stefan Heule", title = "Guided Randomized Search over Programs for Synthesis and Program Optimization", school = "Stanford University", year = "2018", address = "USA", month = jun, keywords = "Opaque code, Formal semantics, Guided randomized search, Computer science, 0984:Computer science", isbn13 = "9798662556355", language = "English", URL = "http://theory.stanford.edu/~aiken/publications/theses/heule.pdf", URL = "https://www.proquest.com/dissertations-theses/guided-randomized-search-over-programs-synthesis/docview/2435778805/se-2?accountid=14511", size = "97 pages", abstract = "The ability to automatically reason about programs and extract useful information from them is very important and has received a lot of attention from both the academic community as well as practitioners in industry. Scaling such program analyses to real system is a significant challenge, as real systems tend to be very large, very complex, and often at least part of the system is not available for analysis. A common solution to this problem is to manually write models for the parts of the system that are not analysable. However, writing these models is both challenging and time consuming. Instead, we propose the use of guided randomized search to find models automatically, and we show how this idea can be applied in three diverse contexts. First, we show how we can use guided randomized search to automatically find models for opaque code, a common problem in program analysis. Opaque code is code that is executable but whose source code is unavailable or difficult to process. We present a technique to first observe the opaque code by collecting partial program traces and then automatically synthesize a model. We demonstrate our method by learning models for a collection of array-manipulating routines. Second, we tackle automatically learning a formal specification for the x86-64 instruction set. Many software analysis and verification tools depend, either explicitly or implicitly, on correct modelling of the semantics of x86-64 instructions. However, formal semantics for the x86-64 ISA are difficult to obtain and often written manually with great effort. Instead, we show how to automatically synthesize formal semantics for 1795 instruction variants of x86-64. Crucial to our success is a new technique, stratified synthesis, that allows us to scale to longer programs. We evaluate the specification we learned and find that it contains no errors, unlike all manually written specifications we compare against. Third, we consider the problem of program optimization on recent CPU architectures. These modern architectures are incredibly complex and make it difficult to statically determine the performance of a program. Using guided randomized search with a new cost function we are able to outperform the previous state-of-the-art on several metrics, sometimes by a wide margin.", notes = "Not GP Supervisor: Alex Aiken", } @Misc{Hewgill:Final, author = "Adam Hewgill", title = "COSC 4P77 Final Project Improvements to lilgp Genetic Programming System", howpublished = "www", note = "Brock Strongly Typed lilgp", keywords = "genetic algorithms, genetic programming", URL = "http://www.cosc.brocku.ca/Offerings/5P71/bstlilgp/bstlilgp_unix/lilgp%20Improvments.pdf", size = "11 pages", notes = "bstlilgp-0.5.0 http://www.cosc.brocku.ca/Offerings/5P71/bstlilgp/bstlilgp_unix/bstlilgp-0.5.0.zip", } @InProceedings{hewgill:2002:gecco:lbp, title = "Real-Time Competitive Evolutionary Computation", author = "Adam Hewgill", booktitle = "Late Breaking Papers at the Genetic and Evolutionary Computation Conference ({GECCO-2002})", editor = "Erick Cant{\'u}-Paz", year = "2002", month = jul, pages = "228--232", address = "New York, NY", publisher = "AAAI", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025", keywords = "genetic algorithms, genetic programming, alife", URL = "http://www.cosc.brocku.ca/files/downloads/research/cs0217.pdf", notes = "Late Breaking Papers, {GECCO-2002}. A joint meeting of the eleventh International Conference on Genetic Algorithms ({ICGA-2002}) and the seventh Annual Genetic Programming Conference ({GP-2002}) part of cantu-paz:2002:GECCO:lbp lil-gp used to evolve brains for robot fish in simulated aquarium", } @TechReport{hewgill:2003:06, author = "Adam Hewgill and Brian J. Ross", title = "Procedural {3D} Texture Synthesis Using Genetic Programming", institution = "Brock University, Department of Computer Science", year = "2003", type = "Technical Report", number = "CS-03-06", address = "St. Catharines, Ontario, Canada L2S 3A1", month = apr # " 2003", keywords = "genetic algorithms, genetic programming, procedural textures, evolution", URL = "http://www.cosc.brocku.ca/files/downloads/research/cs0306.pdf", URL = "http://citeseer.ist.psu.edu/559621.html", abstract = "The automatic synthesis of procedural textures for 3D surfaces using genetic programming is investigated. Genetic algorithms employ a search strategy inspired by Darwinian natural evolution. Genetic programming uses genetic algorithms on tree structures, which are interpretable as computer programs or mathematical formulae. We use a texture generation language as a target language for genetic programming, and then use it to evolve textures having particular characteristics of interest. The texture generation language used here includes operators useful for texture creation, for example, mathematical operators, and colour and noise functions. In order to be practical for 3D model rendering, the language includes primitives that access surface information for the point being rendered, such as coordinates values, normal vectors, and surface gradients. A variety of experiments successfully generated procedural textures that displayed visual characteristics similar to the target textures used during training.", notes = "see also \cite{hewgill:2004:CG}", size = "26 pages", } @InProceedings{hewgill:gecco03lbp, title = "The Evolution of {3D} Procedural Textures", pages = "146--147", author = "Adam Hewgill and Brian J. Ross", year = "2003", address = "Chicago, USA", month = "12 " # jul, editor = "Bart Rylander", keywords = "genetic algorithms, genetic programming, STGP", URL = "http://adamhewgill.com/research/gen3d_LBP.pdf", booktitle = "Genetic and Evolutionary Computation Conference Late Breaking Papers", notes = "GECCO-2003LB, lilgp", } @Article{hewgill:2004:CG, author = "Adam Hewgill and Brian J. Ross", title = "Procedural {3D} Texture Synthesis Using Genetic Programming", journal = "Computers and Graphics", year = "2004", volume = "28", number = "4", pages = "569--584", month = aug, keywords = "genetic algorithms, genetic programming, Procedural textures, Evolution, grammar BNF", URL = "http://www.cosc.brocku.ca/~bross/research/HewgillRoss04.pdf", URL = "http://www.sciencedirect.com/science/article/B6TYG-4CS4FCT-1/2/b8a5d381a1371ba6545d194a470dfa89", ISSN = "0097-8493", DOI = "doi:10.1016/j.cag.2004.04.012", abstract = "The automatic synthesis of procedural textures for 3D surfaces using genetic programming is investigated. Genetic algorithms employ a search strategy inspired by Darwinian natural evolution. Genetic programming uses genetic algorithms on tree structures, which are interpretable as computer programs or mathematical formulae. We define a texture generation language in the genetic programming system, which is then used to evolve textures having particular characteristics of interest. The texture generation language used here includes operators useful for texture creation, for example, mathematical operators, colour functions and noise functions. In order to be practical for 3D model rendering, the language includes primitives that access surface information for the point being rendered, such as coordinates values, normal vectors, and surface gradients. A variety of experiments successfully generated procedural textures that displayed visual characteristics similar to the target textures used during training.", notes = "\cite{hewgill:2003:06} is prelimary version. lilgp. Ten runs in parallel on 16-CPU Silicon Graphics Origin 2000 server. Fig 9 Woman's clothing training points", } @InCollection{hewlett:1998:RNUGPVFDO, author = "William R. Hewlett", title = "Reynolds Numbers: Using Genetic Programming and Vite to find Formulas to Describe Organizations", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1998", year = "1998", editor = "John R. Koza", pages = "20--28", address = "Stanford, California, 94305-3079 USA", month = "17 " # mar, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-212568-8", notes = "part of \cite{koza:1998:GAGPs}", } @InProceedings{heywood:2000:rbGPFPGA, author = "M. I. Heywood and A. N. Zincir-Heywood", title = "Register Based Genetic Programming on FPGA Computing Platforms", booktitle = "Genetic Programming, Proceedings of EuroGP'2000", year = "2000", editor = "Riccardo Poli and Wolfgang Banzhaf and William B. Langdon and Julian F. Miller and Peter Nordin and Terence C. Fogarty", volume = "1802", series = "LNCS", pages = "44--59", address = "Edinburgh", publisher_address = "Berlin", month = "15-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-67339-3", URL = "http://users.cs.dal.ca/~mheywood/X-files/Publications/EuroGP-2k0.pdf", DOI = "doi:10.1007/978-3-540-46239-2_4", abstract = "The use of FPGA based custom computing platforms is proposed for implementing linearly structured Genetic Programs. Such a context enables consideration of micro architectural and instruction design issues not normally possible when using classical Von Neumann machines. More importantly, the desirability of minimising memory management overheads results in the imposition of additional constraints to the crossover operator. Specifically, individuals are described in terms of the number of pages and page length, where the page length is common across individuals of the population. Pairwise crossover therefore results in the swapping of equal length pages, hence minimising memory overheads. Simulation of the approach demonstrates that the method warrants further study.", notes = "EuroGP'2000, part of \cite{poli:2000:GP} http://users.cs.dal.ca/~mheywood/X-files/Publications/EuroGP-2k0.pdf has additional revisions.", } @InProceedings{Heywood:2000:PBGP, author = "M. I. Heywood and A. N. Zincir-Heywood", title = "Page-based linear genetic programming", booktitle = "Systems, Man, and Cybernetics, 2000 IEEE International Conference", year = "2000", volume = "5", pages = "3823--3828", address = "Nashville, TN, USA", month = "8-11 " # oct, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, page-based linear genetic programming, evolutionary computation, computational overheads, fitness of individuals, crossover operator, equal length code fragments, register-machine, a priori internal register external output definitions", ISBN = "0-7803-6583-6", URL = "http://ieeexplore.ieee.org/iel5/7099/19140/00886606.pdf?isNumber=19140", DOI = "doi:10.1109/ICSMC.2000.886606", size = "6 pages", abstract = "Genetic programming arguably represents the most general form of evolutionary computation. However, such generality is not without significant computational overheads. Particularly, the cost of evaluating the fitness of individuals in any form of evolutionary computation represents the single most significant computational bottleneck. A less widely acknowledged computational overhead in GP involves the implementation of the crossover operator. To this end a page-based definition of individuals is used to restrict crossover to equal length code fragments. Moreover, by using a register-machine context, the significance of a priori internal register external output definitions is emphasized.", } @Article{heywood:2002:SMCB, author = "M. I. Heywood and A. N. Zincir-Heywood", title = "Dynamic Page Based Crossover in Linear Genetic Programming", journal = "IEEE Transactions on Systems, Man, and Cybernetics: Part B - Cybernetics", year = "2002", volume = "32", number = "3", pages = "380--388", month = jun, email = "mheywood@cs.dal.ca", keywords = "genetic algorithms, genetic programming, linear genetic programming, crossover operator, homologous crossover, natural selection", DOI = "doi:10.1109/TSMCB.2002.999814", ISSN = "1083-4419", abstract = "Page-based Linear Genetic Programming (GP) is proposed in which individuals are described in terms of a number of pages. Pages are expressed in terms of a fixed number of instructions, constant for all individuals in the population. Pairwise crossover results in the swapping of single pages, thus individuals are of a fixed number of instructions. Head-to-head comparison with Tree structured GP and block-based Linear GP indicates that the page-based approach evolves succinct solutions without penalising generalisation ability.", } @Article{Heywood:2015:GPEM, author = "Malcolm I. Heywood", title = "Evolutionary model building under streaming data for classification tasks: opportunities and challenges", journal = "Genetic Programming and Evolvable Machines", year = "2015", volume = "16", number = "3", pages = "283--326", month = sep, keywords = "genetic algorithms, genetic programming, Streaming data, Non-stationary processes, Dynamic environment, Imbalanced data, Task decomposition, Ensemble learning, Active learning, Evolvability, Diversity, Memory", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-014-9236-y", size = "44 pages", abstract = "Streaming data analysis potentially represents a significant shift in emphasis from schemes historically pursued for offline (batch) approaches to the classification task. In particular, a streaming data application implies that: (1) the data itself has no formal start or end; (2) the properties of the process generating the data are non-stationary, thus models that function correctly for some part(s) of a stream may be ineffective elsewhere; (3) constraints on the time to produce a response, potentially implying an anytime operational requirement; and (4) given the prohibitive cost of employing an oracle to label a stream, a finite labelling budget is necessary. The scope of this article is to provide a survey of developments for model building under streaming environments from the perspective of both evolutionary and non-evolutionary frameworks. In doing so, we bring attention to the challenges and opportunities that developing solutions to streaming data classification tasks are likely to face using evolutionary approaches.", } @Proceedings{Heywood:2016:GP, title = "Proceedings of the 19th European Conference on Genetic Programming, {EuroGP 2016}", year = "2016", editor = "Malcolm I. Heywood and James McDermott and Mauro Castelli and Ernesto Costa", volume = "9594", series = "LNCS", address = "Porto, Portugal", month = "30 " # mar # "--1 " # apr, organisation = "EvoStar", publisher = "Springer Verlag", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-30668-1", DOI = "doi:10.1007/978-3-319-30668-1", size = "311 pages", notes = "EuroGP'2016 held in conjunction with EvoCOP2016, EvoMusArt2016 and EvoApplications2016", } @Article{heywood:2023:GPEM, author = "Malcolm I. Heywood", title = "{W. B. Langdon ``Jaws 30''}", journal = "Genetic Programming and Evolvable Machines", year = "2023", volume = "24", number = "2", pages = "Article number: 25", month = dec, note = "Special Issue: Thirtieth Anniversary of Genetic Programming: On the Programming of Computers by Means of Natural Selection", keywords = "genetic algorithms, genetic programming, GPU, Hardware acceleration, Competitive coevolution, Cooperative coevolution", ISSN = "1389-2576", URL = "https://rdcu.be/drZd3", DOI = "doi:10.1007/s10710-023-09473-z", size = "5 pages", abstract = "At the 30th anniversary of Jaws, the Genetic programming field has much to celebrate. However, in order continue to build on these successes, it might be necessary to look more deeply into the less successful and/or less explored topics. We consider the role of FPGA and GPU platforms from the former and coevolution from the latter.", notes = "Response to \cite{langdon:jaws30} Peer commentary editors: Leonardo Vanneschi and Leonardo Trujillo \cite{Vanneschi:2023:GPEM} See also \cite{jaws30_reply}", } @InProceedings{Heywood:2025:GPTP, author = "Malcolm Heywood", title = "Offline reinforcement learning: A new challenge for symbolic regression?", booktitle = "Genetic Programming Theory and Practice XXII", year = "2025", editor = "Wolfgang Banzhaf and Bogdan Burlacu and Stephen Kelly and Alexander Lalejini and Fabricio {Olivetti de Franca}", address = "Michigan State University, USA", month = jun # " 5-7", keywords = "genetic algorithms, genetic programming", notes = "To be published after the workshop", } @InProceedings{Hidalgo:2014:GECCOcomp, author = "J. Ignacio Hidalgo and J. Manuel Colmenar and Jose L. Risco-Martin and Esther Maqueda and Marta Botella and Jose Antonio Rubio and Alfredo Cuesta-Infante and Oscar Garnica and Juan Lanchares", title = "Clarke and parkes error grid analysis of diabetic glucose models obtained with evolutionary computation", booktitle = "GECCO 2014 Workshop on Medical Applications of Genetic and Evolutionary Computation (MedGEC)", year = "2014", editor = "Stephen L. Smith and Stefano Cagnoni and Robert M. Patton", isbn13 = "978-1-4503-2881-4", keywords = "genetic algorithms, genetic programming, grammatical evolution", pages = "1305--1312", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2598394.2609856", DOI = "doi:10.1145/2598394.2609856", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Diabetes mellitus is a disease that affects to hundreds of millions of people worldwide. Maintaining a good control of the disease is critical to avoid severe long-term complications. In recent years, a lot of research has been made to improve the quality of life of the diabetic patient, especially in the automation of glucose level control. One of the main problems that arises in the (semi) automatic control of diabetes, is to obtain a model that explains the behaviour of blood glucose levels with insulin, food intakes and other external factors, fitting the characteristics of each individual or patient. Recently, Grammatical Evolution (GE), has been proposed to solve this lack of models. A proposal based on GE was able to obtain customised models of five in-silico patient data with a mean percentage average error of 13.69percent, modelling well also both hyper and hypoglycemic situations. In this paper we have extended the study of Error Grid Analysis (EGA) to prediction models in up to 8 in-silico patients. EGA is commonly used in Endocrinology to test the clinical significance of differences between measurements and real value of blood glucose, but has not been used before as a metric in obtention of glycemia models.", notes = "Also known as \cite{2609856} Distributed at GECCO-2014.", } @Article{Hidalgo:2014:ASC, author = "J. Ignacio Hidalgo and J. Manuel Colmenar and Jose L. Risco-Martin and Alfredo Cuesta-Infante and Esther Maqueda and Marta Botella and Jose Antonio Rubio", title = "Modeling glycemia in humans by means of Grammatical Evolution", journal = "Applied Soft Computing", year = "2014", volume = "20", pages = "40--53", month = jul, keywords = "genetic algorithms, genetic programming, Grammatical Evolution", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2013.11.006", URL = "http://www.sciencedirect.com/science/article/pii/S156849461300402X", size = "14 pages", abstract = "Diabetes mellitus is a disease that affects to hundreds of millions of people worldwide. Maintaining a good control of the disease is critical to avoid severe long-term complications. In recent years, several artificial pancreas systems have been proposed and developed, which are increasingly advanced. However there is still a lot of research to do. One of the main problems that arises in the (semi) automatic control of diabetes, is to get a model explaining how glycemia (glucose levels in blood) varies with insulin, food intakes and other factors, fitting the characteristics of each individual or patient. This paper proposes the application of evolutionary computation techniques to obtain customised models of patients, unlike most of previous approaches which obtain averaged models. The proposal is based on a kind of genetic programming based on grammars known as Grammatical Evolution (GE). The proposal has been tested with in silico patient data and results are clearly positive. We present also a study of four different grammars and five objective functions. In the test phase the models characterised the glucose with a mean percentage average error of 13.69percent, modelling well also both hyper and hypoglycemic situations.", } @InProceedings{Hidalgo:2017:evoApplications, author = "Jose Ignacio Hidalgo and Carlos Cervigon and Jose Manuel Velasco and J. Manuel Colmenar and Carlos Garcia Sanchez and Guillermo Botella", title = "Embedded Grammars for Grammatical Evolution on {GPGPU}", booktitle = "20th European Conference on the Applications of Evolutionary Computation", year = "2017", editor = "Giovanni Squillero", series = "LNCS", volume = "10199", publisher = "Springer", pages = "789--805", address = "Amsterdam", month = "19-21 " # apr, organisation = "Species", keywords = "genetic algorithms, genetic programming, grammatical evolution, GPU", isbn13 = "978-3-319-55848-6; 978-3-319-55849-3", bibdate = "2017-05-21", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/evoW/evoappl2017-1.html#HidalgoCVCSB17", DOI = "doi:10.1007/978-3-319-55849-3_51", notes = "also known as \cite{conf/evoW/HidalgoCVCSB17}, \cite{Hidalgo2017} EvoApplications2017 held in conjunction with EuroGP'2017, EvoCOP2017 and EvoMusArt2017 http://www.evostar.org/2017/cfp_evoapps.php.", } @InProceedings{DBLP:conf/eurocast/HidalgoCKW17, author = "Jose Ignacio Hidalgo and J. Manuel Colmenar and Gabriel Kronberger and Stephan M. Winkler", title = "Glucose Prognosis by Grammatical Evolution", booktitle = "16th International Conference on Computer Aided Systems Theory, EUROCAST 2017, Part I", year = "2017", editor = "Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia", volume = "10671", series = "Lecture Notes in Computer Science", pages = "455--463", address = "Las Palmas de Gran Canaria, Spain", month = feb # " 19-24", publisher = "Springer", note = "Revised Selected Papers", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", isbn13 = "978-3-319-74717-0", URL = "https://doi.org/10.1007/978-3-319-74718-7_55", DOI = "doi:10.1007/978-3-319-74718-7_55", timestamp = "Fri, 26 Jan 2018 12:44:51 +0100", biburl = "https://dblp.org/rec/bib/conf/eurocast/BurlacuAKKW17", bibsource = "dblp computer science bibliography, https://dblp.org", size = "9 pages", abstract = "Patients suffering from Diabetes Mellitus illness need to control their levels of sugar by a restricted diet, a healthy life and in the cases of those patients that do not produce insulin (or with a severe defect on the action of the insulin they produce), by injecting synthetic insulin before and after the meals. The amount of insulin, namely bolus, to be injected is usually estimated based on the experience of the doctor and of the own patient. During the last years, several computational tools have been designed to suggest the boluses for each patient. Some of the successful approaches to solve this problem are based on obtaining a model of the glucose levels which is then applied to estimate the most appropriate dose of insulin. In this paper we describe some advances in the application of evolutionary computation to obtain those models. In particular, we extend some previous works with Grammatical Evolution, a branch of Genetic Programming. We present results for ten real patients on the prediction on several time horizons. We obtain reliable and individualized predictive models of the glucose regulatory system, eliminating restrictions such as linearity or limitation on the input parameters.", } @Article{Hidalgo2017b, author = "J. Ignacio Hidalgo and J. Manuel Colmenar and Gabriel Kronberger and Stephan M. Winkler and Oscar Garnica and Juan Lanchares", title = "Data Based Prediction of Blood Glucose Concentrations Using Evolutionary Methods", journal = "Journal of Medical Systems", year = "2017", volume = "41", number = "9", pages = "142", month = sep, note = "Special issue on Patient Facing Systems", keywords = "genetic algorithms, genetic programming, grammatical evolution, Diabetes, Glucose prediction, Continuous glucose monitoring, Evolutionary computation", ISSN = "1573-689X", DOI = "doi:10.1007/s10916-017-0788-2", size = "20 pages", abstract = "Predicting glucose values on the basis of insulin and food intakes is a difficult task that people with diabetes need to do daily. This is necessary as it is important to maintain glucose levels at appropriate values to avoid not only short-term, but also long-term complications of the illness. Artificial intelligence in general and machine learning techniques in particular have already lead to promising results in modelling and predicting glucose concentrations. In this work, several machine learning techniques are used for the modeling and prediction of glucose concentrations using as inputs the values measured by a continuous monitoring glucose system as well as also previous and estimated future carbohydrate intakes and insulin injections. In particular, we use the following four techniques: genetic programming, random forests, k-nearest neighbours, and grammatical evolution. We propose two new enhanced modeling algorithms for glucose prediction, namely (i) a variant of grammatical evolution which uses an optimized grammar, and (ii) a variant of tree-based genetic programming which uses a three-compartment model for carbohydrate and insulin dynamics. The predictors were trained and tested using data of ten patients from a public hospital in Spain. We analyse our experimental results using the Clarke error grid metric and see that 90percent of the forecasts are correct (i.e., Clarke error categories A and B), but still even the best methods produce 5 to 10percent of serious errors (category D) and approximately 0.5percent of very serious errors (category E). We also propose an enhanced genetic programming algorithm that incorporates a three-compartment model into symbolic regression models to create smoothed time series of the original carbohydrate and insulin time series.", } @InCollection{Hidalgo:2018:hbge, author = "J. Ignacio Hidalgo and J. Manuel Colmenar and J. Manuel Velasco and Gabriel Kronberger and Stephan M. Winkler and Oscar Garnica and Juan Lanchares", title = "Identification of Models for Glucose Blood Values in Diabetics by Grammatical Evolution", booktitle = "Handbook of Grammatical Evolution", publisher = "Springer", year = "2018", editor = "Conor Ryan and Michael O'Neill and J. J. Collins", chapter = "15", pages = "367--393", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", isbn13 = "978-3-319-78716-9", DOI = "doi:10.1007/978-3-319-78717-6_15", abstract = "One the most relevant application areas of artificial intelligence and machine learning in general is medical research. We here focus on research dedicated to diabetes, a disease that affects a high percentage of the population worldwide and that is an increasing threat due to the advance of the sedentary life in the big cities. Most recent studies estimate that it affects about more than 410 million people in the world. In this chapter we discuss a set of techniques based on GE to obtain mathematical models of the evolution of blood glucose along the time. These models help diabetic patients to improve the control of blood sugar levels and thus, improve their quality of life. We summarize some recent works on data preprocessing and design of grammars that have proven to be valuable in the identification of prediction models for type 1 diabetics. Furthermore, we explain the data augmentation method which is used to sample new data sets.", notes = "Part of \cite{Ryan:2018:hbge}", } @Article{journals/asc/HidalgoBVGCMAML20, author = "Jose Ignacio Hidalgo and Marta Botella and J. Manuel Velasco and Oscar Garnica and Carlos Cervigon and Remedios Martinez and Aranzazu Aramendi and Esther Maqueda and Juan Lanchares", title = "Glucose forecasting combining Markov chain based enrichment of data, random grammatical evolution and Bagging", journal = "Appl. Soft Comput", year = "2020", volume = "88", keywords = "genetic algorithms, genetic programming, grammatical evolution", bibdate = "2020-03-19", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/asc/asc88.html#HidalgoBVGCMAML20", pages = "105923", DOI = "doi:10.1016/j.asoc.2019.105923", } @InProceedings{Hidalgo:2023:GPTP, author = "J. Ignacio Hidalgo and Jose Manuel Velasco and Daniel Parra and Oscar Garnica", title = "Genetic Programming Techniques for Glucose Prediction in People with Diabetes", booktitle = "Genetic Programming Theory and Practice XX", year = "2023", editor = "Stephan Winkler and Leonardo Trujillo and Charles Ofria and Ting Hu", series = "Genetic and Evolutionary Computation", pages = "105--124", address = "Michigan State University, USA", month = jun # " 1-3", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", isbn13 = "978-981-99-8412-1", DOI = "doi:10.1007/978-981-99-8413-8_6", abstract = "Accurately predicting blood glucose levels in individuals with diabetes is essential for effectively managing and preventing complications. This paper explores the application of Grammatical Evolution, a genetic programming technique, for glucose prediction. It discusses how Grammatical Evolution has been employed in addressing various challenges related to glucose prediction, such as limited actual recorded data, prediction safety, interpretability of models, consideration of latent variables, and prognosis of hypoglycemia episodes. Building upon this research, the paper presents a comprehensive framework for glucose control that uses evolutionary techniques, primarily emphasising structured grammatical evolution. The framework encompasses several stages, including data gathering, data augmentation, extraction of latent variability features, scenario clustering, structured grammatical evolution training, development of interpretable personal models, derivation of classification rules, glucose prediction, hypoglycemia alert, and glucose control. By harnessing the power of evolutionary algorithms, the framework optimises model performance and adapts to individual patient characteristics. The proposed framework presents a promising approach to improve glucose monitoring and control, thereby contributing to better diabetes management and improved quality of life for patients.", notes = " Jose Ignacio Hidalgo Perez Part of \cite{Hu:2023:GPTP} published after the workshop in 2024", } @InProceedings{hidalgo:2024:EUROCAST, author = "J. Ignacio Hidalgo and Elisabeth Mayrhuber and Stephan M. Winkler and Daniel Parra and J. Manuel Velasco and Jose J. Zamorano-Leon and Oscar Garnica", title = "Modelling the Risk of Overweight and Obesity Based on the {GenObiA} Dataset Using Genetic Programming", booktitle = "Computer Aided Systems Theory - EUROCAST 2024", year = "2024", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "https://link.springer.com/chapter/10.1007/978-3-031-82957-4_31", DOI = "doi:10.1007/978-3-031-82957-4_31", } @TechReport{hiden:1996:npcaGP, author = "H. G. Hiden and M. J. Willis and P. Turner and M. T. Tham and G. A. Montague", title = "Non-linear Principal Components Analysis Using Genetic Programming", institution = "Chemical Engineering, Newcastle University", year = "1996", address = "UK", note = "Extended Abstract, ICANNGA '97, Norwich, UK", keywords = "genetic algorithms, genetic programming", broken = "http://lorien.ncl.ac.uk/sorg/paper9a.ps", abstract = "The recent explosion of low-cost computing power and information storage has brought with it a corresponding mushrooming in the amount of data on almost any subject conceivable that is available. The philosophy that you cant have enough information seems to have been applied to every situation with great enthusiasm. By adopting such an approach, much useful data can be gathered, however it is all too frequently swamped by irrelevant information. The distinction must be made between useful information and information for the sake of having it. The chemical industry also has not been immune to the data collection bug. The equipment required to collect, process and store data is more affordable than ever, a fact which the designers of chemical processes are beginning to exploit. Unfortunately, this data is not particularly useful on its own. It is very easy to collect data, but difficult to analyse it productively. It is this situation that has spawned a wide variety of data analysis tools, the objective of which is to determine underlying relationships and structures within large data sets.", notes = "MSword postscript not compatible with unix. ", } @InProceedings{hiden:1997:ndddmGP, author = "Hugo Hiden and Mark Willis and Ben McKay and Gary Montague", title = "Non-Linear And Direction Dependent Dynamic Modelling Using Genetic Programming", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming", pages = "168--173", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1997/hiden_1997_ndddmGP.pdf", size = "6 pages", notes = "GP-97", } @InProceedings{hinden:1997:npcaGAL, author = "Hugo Hiden and Mark Willis and Ming Tham and Paul Turner and Gary Montague", title = "Non-Linear Principal Components Analysis using Genetic Programming", booktitle = "Second International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, GALESIA", year = "1997", editor = "Ali Zalzala", pages = "302--307", address = "University of Strathclyde, Glasgow, UK", publisher_address = "Savoy Place, London WC2R 0BL, UK", month = "1-4 " # sep, publisher = "Institution of Electrical Engineers", keywords = "genetic algorithms, genetic programming, data analysis, multivariate statistics", ISBN = "0-85296-693-8", broken = "http://lorien.ncl.ac.uk/sorg/paper13.ps", URL = "http://scitation.aip.org/getpdf/servlet/GetPDFServlet?filetype=pdf&id=IEECPS0019970CP446000302000001&idtype=cvips&prog=normal", DOI = "doi:10.1049/cp:19971197", size = "6 pages", abstract = "Principal Components Analysis (PCA) is a standard statistical technique, which is frequently employed in the analysis of large highly correlated data-sets. As it stands, PCA is a linear technique which can limit its relevance to the highly non-linear systems frequently encountered in the chemical process industries. Several attempts to extend linear PCA to cover non-linear data sets have been made, and will be briefly reviewed in this paper. We propose a symbolically oriented technique for non-linear PCA, which is based on the Genetic Programming (GP) paradigm. Its applicability will be demonstrated using two simple non-linear systems and industrial data collected from a distillation column. It is suggested that the use of the GP based non-linear PCA algorithm achieves the objectives of non-linear PCA, while giving high a degree of structural parsimony.", notes = "GALESIA'97 see also \cite{hiden:1999:CCE}", } @InProceedings{hiden:1997:GPndmcps, author = "H. G. Hiden and M. J. Willis and G. A. Montague", title = "Using Genetic Programming to Develop Non-Linear Dynamic Models of Chemical Process Systems", booktitle = "IChemE Jubilee Research Event", year = "1997", volume = "2", pages = "789--792", address = "Nottingham, UK", month = "8-9 " # apr, organisation = "Institute of Chemical Engineers", keywords = "genetic algorithms, genetic programming", notes = "Comparison of GP with feedforward ANN and finite Impulse response model", } @InProceedings{hiden:1998:plsGP, author = "Hugo Hiden and Ben McKay and Mark Willis and Gary Montague", title = "Non-Linear Partial Least Squares using Genetic Programming", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "128--133", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-548-7", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1998/hiden_1998_plsGP.pdf", size = "6 pages", notes = "GP-98", } @Article{hiden:1999:CCE, author = "H. G. Hiden and M. J. Willis and M. T. Tham and G. A. Montague", title = "Non-linear principal components analysis using genetic programming", journal = "Computers and Chemical Engineering", year = "1999", volume = "23", number = "3", pages = "413--425", month = "28 " # feb, keywords = "genetic algorithms, genetic programming, data analysis, multivariate statistics, statistical methods, data reduction, mathematical programming, distillation columns, nonlinear systems, chemical operations, chemical plants, principal component analysis, multivariate statistics", DOI = "doi:10.1016/S0098-1354(98)00284-1", size = "13 pages", abstract = "Principal components analysis (PCA) is a standard statistical technique, which is frequently employed in the analysis of large highly correlated data sets. As it stands, PCA is a linear technique which can limit its relevance to the non-linear systems frequently encountered in the chemical process industries. Several attempts to extend linear PCA to cover non-linear data sets have been made, and will be briefly reviewed in this paper. We propose a symbolically oriented technique for non-linear PCA, which is based on the genetic programming (GP) paradigm. Its applicability will be demonstrated using two simple non-linear systems and data collected from an industrial distillation column.", notes = "Matlab, Maple, pop=60", } @PhdThesis{Hiden:thesis, author = "Hugo George Hiden", title = "Data-based modelling using genetic programming", school = "University of Newcastle upon Tyne", year = "1998", address = "UK", keywords = "genetic algorithms, genetic programming", URL = "http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.246137", notes = "Ming Tham - I don't think the thesis is online. uk.bl.ethos.246137", } @Article{HIEN:2020:OE, author = "Nguyen Thi Hien and Cao Truong Tran and Xuan Hoai Nguyen and Sooyoul Kim and Vu Dinh Phai and Nguyen Ba Thuy and Ngo {Van Manh}", title = "Genetic Programming for storm surge forecasting", journal = "Ocean Engineering", volume = "215", pages = "107812", year = "2020", ISSN = "0029-8018", DOI = "doi:10.1016/j.oceaneng.2020.107812", URL = "http://www.sciencedirect.com/science/article/pii/S0029801820307885", keywords = "genetic algorithms, genetic programming, Storm surge, Typhoon, Surge deviation, White-box forecasting", abstract = "Storm surge is a genuine common fiasco coming from the ocean. Therefore, an exact forecast of surges is a vital assignment to dodge property misfortunes and to decrease a chance caused by tropical storm surge. Genetic Programming (GP) is an evolution-based model learning technique that can simultaneously find the functional form and the numeric coefficients for the model. Therefore, GP has been widely applied to build models for predictive problems. However, GP has seldom been applied to the problem of storm surge forecasting. In this paper, we propose a new method to use GP for evolving models for storm surge forecasting. Experimental results on datasets collected from the Tottori coast of Japan show that GP can evolve accurate storm surge forecasting models. Moreover, GP can automatically select relevant features when evolving storm surge forecasting models, and the models evolved by GP are interpretable", } @InProceedings{Highnam:2016:DSN-W, author = "Kate Highnam and Kevin Angstadt and Kevin Leach and Westley Weimer and Aaron Paulos and Patrick Hurley", title = "An Uncrewed Aerial Vehicle Attack Scenario and Trustworthy Repair Architecture", booktitle = "2016 46th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshop (DSN-W)", year = "2016", editor = "Domenico Cotroneo and Cristina Nita-Rotaru)", pages = "222--225", address = "Toulouse, France", month = "28 " # jun # "-1 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, genetic improvement, APR", isbn13 = "978-1-5090-3688-2", URL = "https://web.eecs.umich.edu/~angstadt/papers/dsn16-industrial.pdf", DOI = "doi:10.1109/DSN-W.2016.63", size = "4 pages", abstract = "With the growing ubiquity of uncrewed aerial vehicles (UAVs), mitigating emergent threats in such systems has become increasingly important. In this short paper, we discuss an indicative class of UAVs and a potential attack scenario in which a benign UAV completing a mission can be compromised by a malicious attacker with an antenna and a commodity computer with open-source ground station software. We attest to the relevance of such a scenario for both enterprise and defense applications. We describe a system architecture for resiliency and trustworthiness in the face of these attacks. Our system is based on the quantitative assessment of trust from domain-specific telemetry data and the application of program repair techniques to UAV flight plans. We conclude with a discussion of restoring trust in post-repair UAV mission integrity.", notes = "GenProg, Slides: https://web.eecs.umich.edu/~weimerw/p/DSN16-Industrial%20Attack%20and%20Arch.pdf https://dsn-2016.sciencesconf.org/ INSPEC Accession Number: 16355400 also known as \cite{7575381}", } @InProceedings{higuchi:1994:evaa, author = "Tetsuya Higuchi and Hitoshi Iba and Bernard Manderick", title = "Applying Evolvable Hardware to Autonomous Agents", booktitle = "Parallel Problem Solving from Nature III", year = "1994", editor = "Yuval Davidor and Hans-Paul Schwefel and Reinhard M{\"a}nner", series = "LNCS", volume = "866", pages = "524--533", address = "Jerusalem", publisher_address = "Berlin, Germany", month = "9-14 " # oct, publisher = "Springer-Verlag", keywords = "genetic algorithms, reinforcement learning, Evovable Hardware", ISBN = "3-540-58484-6", URL = "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-58484-6", DOI = "doi:10.1007/3-540-58484-6_295", size = "10 pages", abstract = "In this paper, we describe a parallel processing architecture for Evolvable Hardware (EHW) which changes its own hardware structure in order to adapt to the environment in which it is embedded. This adaptation process is a combination of genetic learning with reinforcement learning. As an example of EHW applications, the arbitration in behaviour-based robot is discussed. Our goal by implementing adaptation in hardware is to produce a flexible and fault-tolerant architecture which responds in real-time to a changing environment.", notes = "Describes software reconfigurable logic device which changes its own hardware to adapt to its environment. PPSN3", } @InProceedings{hikage:1998:cemse, author = "Tomofumi Hikage and Hitoshi Hemmi and Katsunori Shimohara", title = "Comparison of Evolutionary Methods for Smoother Evolution", booktitle = "Proceedings of the Second International Conference on Evolvable Systems: From Biology to Hardware (ICES 98)", year = "1998", editor = "Moshe Sipper and Daniel Mange and Andres Perez-Uribe", volume = "1478", series = "LNCS", pages = "115--124", address = "Lausanne, Switzerland", publisher_address = "Berlin", month = "23-25 " # sep, publisher = "Springer Verlag", keywords = "genetic algorithms, genetic programming, HDL", ISBN = "3-540-64954-9", DOI = "doi:10.1007/BFb0057613", size = "8 pages", abstract = "Hardware evolution methodologies come into their own in the construction of real-time adaptive systems. The technological requirements for such systems are not only high-speed evolution, but also steady and smooth evolution. This paper shows that the Progressive Evolution Model (PEM) and Diploid chromosomes contribute toward satisfying these requirements in the hardware evolutionary system AdAM (Adaptive Architecture Methodology). Simulations of an artificial ant problem using four combinations of two wets of variables - PEM vs. non-PEM, and Diploid AdAM vs. Haploid AdAM - show that the Diploid-PEM combination overwhelms the others.", notes = "ICES98 Chromosome is parse-tree for SFL (HDL). Simulation. Dominace recessive tags in trees. Ant is _assumed_ to be able to solve problem entirely from its current sensor readings, ie no memory", } @InProceedings{Hildebrandt:2010:gecco, author = "Torsten Hildebrandt and Jens Heger and Bernd Scholz-Reiter", title = "Towards improved dispatching rules for complex shop floor scenarios: a genetic programming approach", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "257--264", keywords = "genetic algorithms, genetic programming, Combinatorial optimization and metaheuristics", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830483.1830530", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Developing dispatching rules for manufacturing systems is a process, which is time- and cost-consuming. Since there is no good general rule for different scenarios and objectives automatic rule search mechanism are investigated. In this paper an approach using Genetic Programming (GP) is presented. The priority rules generated by GP are evaluated on dynamic job shop scenarios from literature and compared with manually developed rules yielding very promising results also interesting for Simulation Optimisation in general.", notes = "hyperheuristic Entered 2010 HUMIES GECCO 2010 Also known as \cite{1830530} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @Article{Hildebrandt:2014:EC, author = "Torsten Hildebrandt and Juergen Branke", title = "On Using Surrogates with Genetic Programming", journal = "Evolutionary Computation", year = "2015", volume = "23", number = "3", pages = "343--367", month = "Fall", keywords = "genetic algorithms, genetic programming, surrogates, phenotypic characterization, ECJ", ISSN = "1063-6560", DOI = "doi:10.1162/EVCO_a_00133", size = "25 pages", abstract = "One way to accelerate evolutionary algorithms with expensive fitness evaluations is to combine them with surrogate models. Surrogate models are efficiently computable approximations of the fitness function, derived by means of statistical or machine learning techniques from samples of fully evaluated solutions. But these models usually require a numerical representation, and therefore can not be used with the tree representation of Genetic Programming (GP). In this paper, we present a new way to use surrogate models with GP. Rather than using the genotype directly as input to the surrogate model, we propose using a phenotypic characterisation. This phenotypic characterization can be computed efficiently and allows us to define approximate measures of equivalence and similarity. Using a stochastic, dynamic job shop scenario as an example of simulation-based GP with an expensive fitness evaluation, we show how these ideas can be used to construct surrogate models and improve the convergence speed and solution quality of GP.", notes = "Posted Online June 26, 2014", } @InProceedings{Hildebrandt:2014:WSC, author = "Torsten Hildebrandt and Debkalpa Goswami and Michael Freitag", booktitle = "Winter Simulation Conference (WSC 2014)", title = "Large-scale simulation-based optimization of semiconductor dispatching rules", year = "2014", month = dec, pages = "2580--2590", keywords = "genetic algorithms, genetic programming, MIMAC FAB6", DOI = "doi:10.1109/WSC.2014.7020102", size = "11 pages", abstract = "Developing dispatching rules for complex production systems such as semiconductor manufacturing is an involved task usually performed manually. In a tedious trial-and-error process, a human expert attempts to improve existing rules, which are evaluated using discrete-event simulation. A significant improvement in this task can be achieved by coupling a discrete-event simulator with heuristic optimisation algorithms. In this paper we show that this approach is feasible for large manufacturing scenarios as well, and it is also useful to quantify the value of information for the scheduling process. Using the objective of minimising the mean cycle time of lots, we show that rules created automatically using Genetic Programming (GP) can clearly outperform standard rules. We compare their performance to manually developed rules from the literature.", notes = "BIBA-Bremer Inst. fur Produktion und Logistik GmbH at the ArcelorMittal Bremen, Univ. Bremen, Bremen, Germany Also known as \cite{7020102}", } @InProceedings{DBLP:conf/giscience/HildemannMV23, author = "Moritz J. Hildemann and Alan T. Murray and Judith Anne Verstegen", editor = "Roger Beecham and Jed A. Long and Dianna Smith and Qunshan Zhao and Sarah Wise", title = "Genetic Programming for Computationally Efficient Land Use Allocation Optimization", booktitle = "12th International Conference on Geographic Information Science, GIScience 2023, September 12-15, 2023, Leeds, {UK}", series = "LIPIcs", volume = "277", pages = "4:1--4:15", publisher = "Schloss Dagstuhl - Leibniz-Zentrum f{\"{u}}r Informatik", year = "2023", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.4230/LIPIcs.GIScience.2023.4", DOI = "doi:10.4230/LIPICS.GISCIENCE.2023.4", timestamp = "Sat, 30 Sep 2023 01:00:00 +0200", biburl = "https://dblp.org/rec/conf/giscience/HildemannMV23.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{1274014, author = "James A. Hilder and Andy M. Tyrrell", title = "An evolutionary platform for developing next-generation electronic circuits", booktitle = "Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2007)}", year = "2007", month = "7-11 " # jul, editor = "Peter A. N. Bosman", isbn13 = "978-1-59593-698-1", pages = "2483--2488", address = "London, United Kingdom", keywords = "genetic algorithms, genetic programming, EHW, analogue circuit design, SPICE", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p2483.pdf", DOI = "doi:10.1145/1274000.1274014", publisher = "ACM Press", publisher_address = "New York, NY, USA", abstract = "In this paper, a new method for evolving simple electronic circuits is discussed, with the aim of improving the reliability and performance of basic circuit blocks. Next-generation CMOS device models will be used in the simulation of circuits. Circuits are mapped to a grid layout which reflects the appearance of conventional schematic blocks. The performance of the system at designing passive lowpass filters is discussed, with an outline given of the intended future steps, towards the goal of integrating sub 100 nm MOSFET models into the circuits.", notes = "Distributed on CD-ROM at GECCO-2007 ACM Order No. 910071", } @InProceedings{Hilder:2009:PRIME, author = "James A. Hilder and James Alfred Walker and Andy M. Tyrrell", title = "Designing variability tolerant logic using evolutionary algorithms", booktitle = "Ph.D. Research in Microelectronics and Electronics, PRIME 2009", year = "2009", month = "12-17 " # jul, pages = "184--187", abstract = "This paper describes an approach to create novel, robust logic-circuit topologies, using several evolution-inspired techniques over a number of design stages. A library of 2-input logic gates are evolved and optimised for tolerance to the effects of intrinsic variability. Block-level designs are evolved using evolutionary methods (CGP). A method of selecting the optimal gates from the library to fit into the block-level designs to create variability-tolerant circuits is also proposed.", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, EHW, block-level designs, evolutionary algorithms, intrinsic variability, logic gates, robust logic circuit topology, variability tolerant logic, circuit optimisation, evolutionary computation, integrated circuit design, logic design", DOI = "doi:10.1109/RME.2009.5201345", notes = "Also known as \cite{5201345}", } @InProceedings{Hilder:2010:AHS, author = "James Hilder and James A. Walker and Andy Tyrrell", title = "Use of a multi-objective fitness function to improve cartesian genetic programming circuits", booktitle = "2010 NASA/ESA Conference on Adaptive Hardware and Systems (AHS)", year = "2010", month = "15-18 " # jun, pages = "179--185", abstract = "This paper describes an approach of using a multi-objective fitness function to improve the performance of digital circuits evolved using CGP. Circuits are initially evolved for correct functionality using conventional CGP before the NSGA-II algorithm is used to extract circuits which are more efficient in terms of design complexity and delay. This approach is used to evolve typical digital-system building block circuits with results compared to standard-CGP, other evolutionary methods and conventional designs.", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", DOI = "doi:10.1109/AHS.2010.5546262", notes = "Hex to 7-Segment Display Driver. Also known as \cite{5546262}", } @Article{Hill:2007:JH, author = "David J. Hill and Barbara S. Minsker and Albert J. Valocchi and Vladan Babovic and Maarten Keijzer", title = "Upscaling models of solute transport in porous media through genetic programming", journal = "Journal of Hydroinformatics", year = "2007", volume = "9", number = "4", pages = "251--266", publisher = "IWA Publishing", keywords = "genetic algorithms, genetic programming, data-driven modeling, knowledge discovery, solute transport", ISSN = "1464-7141", URL = "http://www.iwaponline.com/jh/009/0251/0090251.pdf", DOI = "doi:10.2166/hydro.2007.028", size = "16 pages", abstract = "Due to the considerable computational demands of modeling solute transport in heterogeneous porous media, there is a need for upscaled models that do not require explicit resolution of the small-scale heterogeneity. This study investigates the development of upscaled solute transport models using genetic programming (GP), a domain-independent modelling tool that searches the space of mathematical equations for one or more equations that describe a set of training data. An upscaling methodology is developed that facilitates both the GP search and the implementation of the resulting models. A case study is performed that demonstrates this methodology by developing vertically averaged equations of solute transport in perfectly stratified aquifers. The solute flux models developed for the case study were analysed for parsimony and physical meaning, resulting in an up scaled model of the enhanced spreading of the solute plume, due to aquifer heterogeneity, as a process that changes from predominantly advective to Fickian. This case study not only demonstrates the use and efficacy of GP as a tool for developing upscaled solute transport models, but it also provides insight into how to approach more realistic multi-dimensional problems with this methodology.", notes = "Synthetic aquifers, ALP, p264 'GP.. produce mathematical models that researchers can understand.'", } @PhdThesis{HillDissertation, author = "David J. Hill", title = "Data Mining Approaches to Complex Environmental Problems", school = "Environmental Engineering in Civil Engineering, University of Illinois at Urbana-Champaign", year = "2007", address = "Urbana, Illinois, USA", month = "23 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://gaia.rutgers.edu/docs/HillDissertation.pdf", size = "195 pages", abstract = "Understanding and predicting the behaviour of large-scale environmental systems is necessary for addressing many challenging problems of environmental interest. Unfortunately, the challenge of scaling predictive models, as well as the difficulty of parametrise these models, makes it difficult to apply them to large-scale systems. This research addresses these issues through the use of data mining. Specifically, this dissertation addresses two problems: upscaling models of solute transport in porous media and detecting anomalies in streaming environmental data. Up scaling refers to the creation of models that do not need to explicitly resolve all scales of system heterogeneity. Upscaled models require significantly fewer computational resources than do models that resolve small-scale heterogeneity. This research develops an upscaling method based on genetic programming (GP), which facilitates both the GP search and the implementation of the resulting models, and demonstrates its use and efficacy through a case study. Anomaly detection is the task of identifying data that deviate from historical patterns. It has many practical applications, such as data quality assurance and control (QA/QC), focused data collection, and event detection. The second portion of this dissertation develops a suite of data-driven anomaly detection methods, based on autoregressive datadriven models (e.g. artificial neural networks) and dynamic Bayesian network (DBN) models of the sensor data stream. All of the developed methods perform fast, incremental evaluation of data as it becomes available; scale to large quantities of data; and require no a priori information, regarding process variables or types of anomalies that may be encountered. Furthermore, the methods can be easily deployed on large heterogeneous sensor networks. The anomaly detection methods are then applied to a sensor network located in Corpus Christi Bay, Texas, and their abilities to identify both real and synthetic anomalies in meteorological data are compared. Results of these case studies indicate that DBN-based detectors, using either robust Kalman filtering or Rao-Blackwellized particle filtering, are most suitable for the Corpus Christi meteorological data.", } @InProceedings{Hill:2010:ICEC, author = "Seamus Hill and Colm O'Riordan", title = "A Genetic Algorithm with a multi-layered Genotype-Phenotype Mapping", booktitle = "Proceedings of the International Conference on Evolutionary Computation (ICEC 2010)", year = "2010", editor = "Joaquim Filipe and Janusz Kacprzyk", pages = "369--372", address = "Valencia, Spain", month = "24-26 " # oct, organisation = "INSTICC, AAAI, WfMC", publisher = "SciTePress", keywords = "genetic algorithms, genotype, Phenotype, Deception", isbn13 = "978-989-8425-31-7", URL = "https://www.scitepress.org/PublishedPapers/2010/30862/", URL = "http://www.robinbye.com/files/publications/ICEC_2010.pdf", DOI = "doi:10.5220/0003086203690372", size = "4 pages", abstract = "In this paper we investigate the introduction of a multiple-layer genotype-phenotype mapping to a Genetic Algorithm (GA) which attempts to mimic more closely, the effects of nature. The motivation for introducing multiple-layers into the genotype-phenotype mapping is to create a many-to-one genotype-phenotype mapping. The paper compares a traditional GA with a GA containing a multi-layered genotype-phenotype mapping using a number of well understood problems in an attempt to illustrate the potential benefits of including the multilayered mapping. Initial findings suggest that the multi-layered mapping between the genotype-phenotype used in conjunction with a binary representation outperforms existing traditional GA approaches on well known problems, while still allowing the use well understood genetic operators.", notes = "not GP Broken http://www.icec.ijcci.org/ICEC2010/home.asp http://www.ijcci.org/Abstracts/2010/ICEC_2010_Abstracts.htm Also known as \cite{DBLP:conf/ijcci/HillO10}", } @InProceedings{Hill:2011:EtuoaNFGMiGAtIPVoDUL, title = "Examining the use of a Non-Trivial Fixed Genotype-Phenotype Mapping in Genetic Algorithms to Induce Phenotypic Variability over Deceptive Uncertain Landscapes", author = "Seamus Hill and Colm O'Riordan", pages = "1404--1411", booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary Computation", year = "2011", editor = "Alice E. Smith", month = "5-8 " # jun, address = "New Orleans, USA", organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Representation and operators", DOI = "doi:10.1109/CEC.2011.5949780", abstract = "In nature, living organisms can be viewed as the product of their genotype-phenotype mapping (GP-map). This paper presents a GP-map loosely based on the biological phenomena of transcription and translation, to create a multi-layered GP-map which increases the level of phenotypic variability. The aim of the paper is to examine through the use of a fixed non-trivial GP-map, the impact of increased phenotypic variability, on search over a set of deceptive landscapes. The GP-map allows for a non-injective genotype-phenotype relationship, and the phenotypic variability of a number of phenotypes, introduced by the GP-map, are advanced from the genotypes used to encode them through a basic interpretation of transcription and translation. We attempt to analyse the level of variability by measuring diversity, both at a genotypic and phenotypic level. The multi-layered GP-map is incorporated into a Genetic Algorithm, the multi-layered mapping GA (MMGA), and runs over a number of GA-Hard landscapes. Initial empirical results appear to indicate that over deceptive landscapes, as the level of problem difficulty increases, so too does the benefit of using the proposed GP-map to probe the search space.", notes = "CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.", } @Misc{Hillar:2009:eureqa, author = "Christopher J. Hillar and Friedrich T. Sommer", title = "On the article ``Distilling free-form natural laws from experimental data''", howpublished = "www", year = "2009", keywords = "genetic algorithms, genetic programming", URL = "http://www.msri.org/people/members/chillar/files/hs09b.pdf", size = "4 pages", abstract = "A recent paper \cite{Science09:Schmidt} introduced the fascinating idea that natural symbolic laws (such as Lagrangians and Hamiltonians) could be learned from experimental measurements in a physical system (such as a pendulum)...", notes = "See also \cite{Schmidt:2009:rebuttal}", } @InCollection{alife92:hillis, author = "W. Daniel Hillis", title = "Co-evolving Parasites Improve Simulated Evolution as an Optimization Procedure", booktitle = "Artificial Life II", publisher = "Addison-Wesley", year = "1992", pages = "313--324", month = feb # " 1990", address = "Santa Fe Institute, New Mexico, USA", editor = "Christopher G. Langton and Charles E. Taylor and J. Doyne Farmer and Steen Rasmussen", volume = "X", keywords = "genetic algorithms", series = "Santa Fe Institute Studies in the Sciences of Complexity", abstract = "Evolves sorting networks. Tests evolved at same time lead to better solutions. Also aim to reduced testing effort.", notes = "Not in index, see page 313-324", size = "12 pages", } @TechReport{hinchcliffe:1996:c2GPcpsm, author = "Mark Hinchliffe and Mark Willis and Hugo Hiden and Ming Tham", title = "A comparison of two Genetic Programming Algorithms Applied to Chemical Process Systems Modelling", institution = "Chemical Engineering, Newcastle University", year = "1996", address = "UK", note = "Extended Abstract, submitted to: ICANNGA '97, Norwick, UK", keywords = "genetic algorithms, genetic programming", broken = "http://lorien.ncl.ac.uk/sorg/paper10a.ps", abstract = "Previous work by McKay et al (1996a,b,c) has shown that the Genetic programming (GP) methodology can be successfully applied to the development of non-linear steady state models of industrial chemical processes. Although a GP algorithm can identify the relevant input variables and evolve parsimonious system representations, the resulting model structures tend to contain little or no information relating to the mechanisms of the process itself. In this respect, the performance of the GP methodology is comparable to that of other black-box modelling techniques such as neural networks. Chemical process systems are often extremely complex and non-linear in nature. Phenomenological models are time consuming to develop and can be subject to inaccuracies caused by any simplifying assumptions made. Consequently, mechanistic models are costly to construct; an aspect which would make an automated procedure highly desirable. Phenomenological models are usually derived by applying the laws of conservation of mass, energy and momentum to the system. An examination of a number of steady-state mechanistic models shows that they tend to be made up of distinct sub-groups which, when added together, give the overall model structure. In the search for an automatic model generating algorithm, it would be extremely useful if the GP methodology could be used to identify these sub-groups. This could potentially enhance the GP algorithm's ability to evolve accurate chemical process models and also help to reveal hidden process knowledge. To achieve this goal, the standard GP algorithm used by McKay et al (1996a) was modified to accommodate the multiple gene model structure. The multiple gene structure was introduced by Altenberg (1994) in an attempt to enhance the learning capabilities of GA and GP algorithms. The work was inspired by the observation that, in nature, genetic information is stored on more than one gene. To demonstrate the feasibility of this new approach, real world examples are used to compare the performance of the algorithm with that of the standard GP algorithm.", notes = "MSword postscript not camptible with unix", size = "7 pages", } @InProceedings{hinchliffe:1996:mcpsm-g, author = "Mark Hinchliffe and Hugo Hiden and Ben McKay and Mark Willis and Ming Tham and Geoffery Barton", title = "Modelling Chemical Process Systems Using a Multi-Gene Genetic Programming Algorithm", booktitle = "Late Breaking Papers at the Genetic Programming 1996 Conference Stanford University July 28-31, 1996", year = "1996", editor = "John R. Koza", pages = "56--65", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California 94305-3079, USA", month = "28--31 " # jul, publisher = "Stanford Bookstore", ISBN = "0-18-201031-7", keywords = "genetic algorithms, genetic programming", broken = "http://lorien.ncl.ac.uk/sorg/paper7.ps", abstract = "In this contribution a multi-gene Genetic Programming (Gp) Algorithm is used to evolve input output models of chemical process systems. Three case studies are used to demonstrate the performance of the method when compared to a standard GP algorithm. A statistical analysis procedure is used to aid in the assessment of the results and suggest the number of independent runs required to obtain a successful result. It is concluded that the multi-gene algorithm provides superior performance, as partitioning the problem into sub-groups incorporates basic heuristic knowledge of the search space.", notes = "GP-96LB MSword .ps file not compatible with unix The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670", } @InProceedings{hinchliffe:1998:cpsmumoGP, author = "Mark Hinchliffe and Mark Willis and Ming Tham", title = "Chemical Process Sytems Modelling Using Multi-objective Genetic Programming", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "134--139", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, MOGP", ISBN = "1-55860-548-7", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1998/hinchliffe_1998_cpsmumoGP.pdf", notes = "GP-98", } @InProceedings{hinchliffe:1999:DCPMUMBFGPA, author = "Mark Hinchliffe and Mark Willis and Ming Tham", title = "Dynamic Chemical Process Modelling Using a Multiple Basis Function Genetic Programming Algorithm", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1782", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, real world applications, poster papers, NARMAX", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-746.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-746.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @PhdThesis{hinchliffe:thesis, author = "Mark P. Hinchliffe", title = "Dynamic Modelling Using Genetic Programming", school = "School of Chemical Engineering and Advanced Materials, University of Newcastle upon Tyne", year = "2001", address = "UK", month = sep, keywords = "genetic algorithms, genetic programming, MOGA, MOGP, SOGP", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/hinchliffe:Thesis.pdf", broken = "http://www.ncl.ac.uk/ceam/postgrad/pg-theses.htm", URL = "http://ethos.bl.uk/OrderDetails.do?did=5&uin=uk.bl.ethos.391407", size = "205 pages", abstract = "Genetic programming (GP) is an evolutionary algorithm that attempts to evolve solutions to a problem by using concepts taken from the naturally occurring evolutionary process. This thesis introduces the concepts of GP model development by applying the technique to steady-state model evolution. A variation of the algorithm known as the multiple basis function GP (MBF-GP) algorithm is described and its performance compared with the standard algorithm. Results show that the MBF-GP algorithm requires significantly less computational effort to evolve models of comparable accuracy to the standard algorithm. The steady-state algorithm is then modified to enable the evolution of dynamic process models. Three case studies are used to demonstrate algorithm performance and show how the MBF-GP algorithm produces performance benefits similar to those observed in the steady-state modelling work. A comparison with neural networks reveals that GP is able to match the accuracy of the network predictions but is more expensive computationally. However, a significant advantage of the GP algorithm is that it can automatically evolve the time history of model terms required to account for process characteristics such as the system time delay. The model development process is not simply a case of reducing the error between the predicted and actual process output. The parallel nature of GP means that it is ideally suited to solving multi-objective problems. The MBF-GP algorithm is modified to incorporate a Pareto based ranking scheme that allows models to be compared using multiple performance criteria. The ranking scheme allows preference information in the form of goals and priorities to be specified in order to guide the search towards the desired region of the search space. Two case studies are used to demonstrate the performance of this technique. The first example uses the multi-objective algorithm to improve the parsimony of the evolved model structures. The second example demonstrates how a set residual correlation tests can be combined and used as an additional performance measure. In each case, the multi-objective algorithm performs significantly better than the single objective version. In addition, the inclusion of preference information overcomes some of the difficulties associated with conventional Pareto ranking and produces a greater number of acceptable solutions.", notes = "{"}the results do not provide sufficient evidence to suggest that GP will become as widely used as neural network modelling techniques.{"} page 160. uk.bl.ethos.391407", } @InProceedings{oai:CiteSeerPSU:263745, author = "Mark Hinchliffe and Mark Willis and Ming Tham and Gary Montague", title = "Dynamic Chemical Process Modelling Using a Multiple Basis Function Genetic Programming Algorithm", booktitle = "Nineteenth IASTED International Conference, Modelling, Identification and Control", year = "2000", address = "Innsbruck, Austria", month = feb # " 14-17", keywords = "genetic algorithms, genetic programming, Modelling, Neural Networks, Identification", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:263745", rights = "unrestricted", URL = "http://citeseer.ist.psu.edu/rd/13718071%2C263745%2C1%2C0.25%2CDownload/http://citeseer.ist.psu.edu/cache/papers/cs/12773/http:zSzzSzwww.iasted.comzSzconferenceszSz2000zSzaustriazSzabstractszSz306-089.pdf/dynamic-chemical-process-modelling.pdf", URL = "http://citeseer.ist.psu.edu/263745.html", size = "2 pages", notes = "cited in \cite{hinchliffe:thesis}", } @InProceedings{hinchliffe:2002:IFAC, author = "M. Hinchliffe and M. Willis", title = "Dynamic Modelling Using Genetic Programming", booktitle = "Proceedings of the 15th IFAC World Congress", year = "2002", editor = "Luis Basanez and Juan A. {de la Puente}", pages = "441--441", address = "Barcelona, Spain", organisation = "The international federation of automatic control", publisher = "Elsevier", keywords = "genetic algorithms, genetic programming, dynamic modelling, multi-objective optimisation", URL = "http://www.ifac-papersonline.net/Detailed/26074.html", DOI = "doi:10.3182/20020721-6-ES-1901.00443", abstract = "In this contribution we demonstrate how a Single Objective Genetic Programming (SOGP) and a Multi-Objective Genetic Programming (MOGP) algorithm can be used to evolve accurate input-output models of dynamic processes. Having described the algorithms, two case studies are used to compare their performance with that of Filter-Based Neural Networks (FBNNs). For the examples given, the models generated using GP have comparable prediction performance to the FBNN. However, performance with respect to additional modelling criteria can be improved using the MOGP algorithm.", notes = "cited in \cite{hinchliffe:thesis}", } @Article{Hinchliffe:2003:CCE, author = "Mark P. Hinchliffe and Mark J. Willis", title = "Dynamic systems modelling using genetic programming", journal = "Computers \& Chemical Engineering", year = "2003", volume = "27", pages = "1841--1854", number = "12", owner = "wlangdon", keywords = "genetic algorithms, genetic programming, Neural networks, Dynamic modelling, Multi-objective", ISSN = "0098-1354", URL = "http://www.sciencedirect.com/science/article/B6TFT-49MDYGW-2/2/742bcc7f22240c7a0381027aa5ff7e73", DOI = "doi:10.1016/j.compchemeng.2003.06.001", abstract = "In this contribution genetic programming (GP) is used to evolve dynamic process models. An innovative feature of the GP algorithm is its ability to automatically discover the appropriate time history of model terms required to build an accurate model. Two case studies are used to compare the performance of the GP algorithm with that of filter-based neural networks (FBNNs). Although the models generated using GP have comparable prediction performance to the FBNN models, a disadvantage is that they required greater computational effort to develop. However, we show that a major benefit of the GP approach is that additional model performance criteria can be included during the model development process. The parallel nature of GP means that it can evolve a set of candidate solutions with varying levels of performance in each objective. Although any combination of model performance criteria could be used as objectives within a multi-objective GP (MOGP) framework, the correlation tests outlined by Billings and Voon (Int. J. Control 44 (1986) 235) were used in this work.", } @InProceedings{conf/ivcnz/HindmarshAZ12, author = "Samuel Hindmarsh and Peter Andreae and Mengjie Zhang", title = "Genetic programming for improving image descriptors generated using the scale-invariant feature transform", booktitle = "Image and Vision Computing New Zealand, IVCNZ, 2012", year = "2012", editor = "Brendan McCane and Steven Mills and Jeremiah D. Deng", pages = "85--90", address = "Dunedin, New Zealand", month = nov # " 26-28", publisher = "ACM", keywords = "genetic algorithms, genetic programming, SIFT, object recognition", isbn13 = "978-1-4503-1473-2", URL = "http://dl.acm.org/citation.cfm?id=2425836", DOI = "doi:10.1145/2425836.2425855", acmid = "2425855", bibdate = "2013-01-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ivcnz/ivcnz2012.html#HindmarshAZ12", size = "6 pages", abstract = "Object recognition is an important task in the computer vision field as it has many applications, including optical character recognition and facial recognition. However, many existing methods have demonstrated relatively poor performance in all but the most simple cases. Scale-invariant feature transform (SIFT) features attempt to alleviate issues surrounding complex examples involving variances in scale, rotation and illumination, but suffer, potentially, from the way the algorithm describes the key points it detects in images. Genetic programming (GP) is used for the first time in an attempt to find the optimal way of describing the image keypoints extracted by the SIFT algorithm. Training and testing results show that the fittest program from a GP search can improve on the standard SIFT descriptors after only a few generations of a small population. While early results may not yet show major improvements over standard SIFT features, they do open the door for further research and experimentation.", notes = "IVCNZ", } @InProceedings{Hingston:2011:RTwC, author = "Philip Hingston and Mike Preuss", title = "Red Teaming with Coevolution", pages = "1155--1163", booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary Computation", year = "2011", editor = "Alice E. Smith", month = "5-8 " # jun, address = "New Orleans, USA", organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, red teaming, coevolution, Coevolutionary systems, Evolutionary simulation-based optimization, Real-world applications", ISBN = "0-7803-8515-2", DOI = "doi:10.1109/CEC.2011.5949747", size = "9 pages", abstract = "we present a coevolutionary algorithm designed to be used as a computational tool to assist in red teaming studies. In these applications, analysts seek to understand the strategic and tactical options available to each side in a conflict situation. Combining scenario simulations with a coevolutionary search of parameter space is an approach that has many attractions. We argue that red teaming applications are sufficiently different from many others where coevolution is used so that specially designed algorithms can bring advantages. We illustrate by presenting a new algorithm that simultaneously evolves strong strategies along with dangerous counter-strategies. We test the new algorithm on two example problems: an abstract problem with some difficult characteristics; and a practical red teaming scenario. Experiments show that the new algorithm is able to solve the abstract problem well, and that it is able to provide useful insights on the red teaming scenario.", notes = "CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.", } @InProceedings{hintze:2018:GPTP, author = "Arend Hintze and Jory Schossau and Clifford Bohm", title = "The Evolutionary Buffet Method", booktitle = "Genetic Programming Theory and Practice XVI", year = "2018", editor = "Wolfgang Banzhaf and Lee Spector and Leigh Sheneman", pages = "17--36", address = "Ann Arbor, USA", month = "17-20 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-030-04734-4", URL = "http://link.springer.com/chapter/10.1007/978-3-030-04735-1_2", DOI = "doi:10.1007/978-3-030-04735-1_2", abstract = "Within the field of Genetic Algorithms (GA) and Artificial Intelligence (AI) a variety computational substrates with the power to find solutions to a large variety of problems have been described. Research has specialized on different computational substrates that each excel in different problem domains. For example, Artificial Neural Networks (ANN) (Russell et al., Artificial intelligence: a modern approach, vol 2. Prentice Hall, Upper Saddle River, 2003) have proven effective at classification, Genetic Programs (by which we mean mathematical tree-based genetic programming and will abbreviate with GP) (Koza, Stat Comput 4:87-112, 1994) are often used to find complex equations to fit data, Neuro Evolution of Augmenting Topologies (NEAT) (Stanley and Miikkulainen, Evolut Comput 10:99-127, 2002) is good at robotics control problems (Cully et al., Nature 521:503, 2015), and Markov Brains (MB) (Edlund et al., PLoS Comput Biol 7:e1002,236, 2011; Marstaller et al., Neural Comput 25:2079-2107, 2013; Hintze et al., Markov brains: a technical introduction. arXiv:1709.05601, 2017) are used to test hypotheses about evolutionary behavior (Olson et al., J R Soc Interf 10:20130,305, 2013) (among many other examples). Given the wide range of problems and vast number of computational substrates practitioners of GA and AI face the difficulty that every new problem requires an assessment to find an appropriate computational substrates and specific parameter tuning to achieve optimal results.", } @InProceedings{hirasawa:2001:cgnpgp, author = "Kotaro Hirasawa and M. Okubo and J. Hu and J. Murata", title = "Comparison between Genetic Network Programming (GNP) and Genetic Programming (GP)", booktitle = "Proceedings of the 2001 Congress on Evolutionary Computation CEC2001", year = "2001", pages = "1276--1282", address = "COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "27-30 " # may, organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, genetic programming Network, Evolution, Ant behaviors, ant behaviour simulation, bloat, complicated programs, evolutionary computation, evolutionary method, genetic algorithm, genome, real world problems, searching efficiency, string structure, tree structure, behavioural sciences computing, biology computing, tree data structures, trees (mathematics), zoology,", ISBN = "0-7803-6658-1", DOI = "doi:10.1109/CEC.2001.934337", abstract = "Recently, many methods of evolutionary computation such as genetic algorithm (GA) and genetic programming (GP) have been developed as a basic tool for modelling and optimising of complex systems. Generally speaking, GA has the genome of a string structure, while the genome in GP is the tree structure. Therefore, GP is suitable for constructing complicated programs, which can be applied to many real world problems. However, GP might sometimes be difficult to search for a solution because of its bloat. A novel evolutionary method named Genetic Network Programming (GNP), whose genome is a network structure is proposed to overcome the low searching efficiency of GP and is applied to the problem of the evolution of ant behaviour in order to study the effectiveness of GNP. In addition, the comparison of the performances between GNP and GP is carried out in simulations on ant behaviors", notes = "CEC-2001 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 01TH8546C, Library of Congress Number = . GNP directed graph: judgement, time delay, processing nodes. Network genome. subnet swapping crossover. Ant pheremone square 32 by 32 grid world, food gathering.", } @PhdThesis{DBLP:phd/ethos/Hirayama07, author = "Yoshikazu Hirayama", title = "Evolutionary algorithms for practical sensor fault tolerant control", school = "School of Physics, Engineering and Technology, Department of Electronics, University of York", year = "2007", address = "UK", month = sep, keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, CGP, PID controller, LM3524DN, Simulink simulation, PCB boards, PWM H-bridge circuits", timestamp = "Tue, 05 Apr 2022 10:59:08 +0200", biburl = "https://dblp.org/rec/phd/ethos/Hirayama07.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://etheses.whiterose.ac.uk/id/eprint/9938/", URL = "https://etheses.whiterose.ac.uk/id/eprint/9938/15/contents.txt", URL = "https://etheses.whiterose.ac.uk/id/eprint/9938/10/485096.pdf", size = "221 pages", abstract = "The Shaky Hand is a multi-input, multi-output laboratory demonstrator which is modeled on a village fete game. In the original, the aim is to guide, by hand, a wire loop along a wire which has been bent to form a meandering track, with out touching the loop to the wire. In the original game, touching the hand-held loop against the wire track sets off a loud warning bell and the player loses. The thesis presents the research work associated with the quest for practical solutions to a generic problem: the correct operation of a fallible system. The work covers three distinct areas: modeling of the demonstrator, design and construction of a physical system, and evolution of algorithms for control of the demonstrator in practice in the presence of sensor faults, using Cartesian Genetic Programming (CGP). The third area forms the core of the thesis. The key challenges in creating the virtual environment to train for generic sensor fault tolerant algorithms are considered and addressed. The evolved algorithms are analysed and then verified using the demonstrator in practice. The practical results showed that sensor fault tolerant control was successfully achieved.", notes = "Control Systems Laboratory, Intelligent Systems Group p14 1-15 movies https://etheses.whiterose.ac.uk/id/eprint/9938/15/contents.txt Also known as \cite{wreo9938} oai:etheses.whiterose.ac.uk:9938 uk.bl.ethos.485096 Supervisor: Tim Clarke", } @InProceedings{Hirayama:2008:gecco, author = "Yoshikazu Hirayama and Tim Clarke and Julian Francis Miller", title = "Fault tolerant control using Cartesian genetic programming", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "1523--1530", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1523.pdf", DOI = "doi:10.1145/1389095.1389389", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, cartesian genetic programming, Fault Tolerance robotics, Real-World application", size = "8 pages", abstract = "The paper focuses on the evolution of algorithms for control of a machine in the presence of sensor faults, using Cartesian Genetic Programming. The key challenges in creating training sets and a fitness function that encourage a general solution are discussed. The evolved algorithms are analysed and discussed. It was found that highly novel, mathematically elegant and hitherto unknown solutions were found.", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389389}", } @InProceedings{Hirotani:2021:IWCIA, author = "Daisuke Hirotani and Tomohiro Hayashida and Ichiro Nishizaki and Shinya Sekizaki and Ibuki Maeda", title = "An Evolutionary Method of Computation for Dynamic Scheduling Problems with Periodic Demand", booktitle = "2021 IEEE 12th International Workshop on Computational Intelligence and Applications (IWCIA)", year = "2021", abstract = "Dynamic scheduling for irregularly arriving jobs is considered. In the real world, demands often change for some reason suddenly. In a previous paper (Eguchi et al., 2006), the optimal schedule was determined by using a neural network. That method was based on existing dispatching rules that determined the job order sequence. Here, a new method using genetic programing is proposed, in which new dispatching rules are generated. By generating a new rule, performance can be increased. Also, in the real world, job arrivals vary periodically depending on the season or month. By using past data, scheduling can be done effectively. Therefore, this paper proposes a new parallel genetic programming introducing long-term memories to use past data. The results of numerical experiments indicate the effectiveness of the proposed method.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IWCIA52852.2021.9626045", ISSN = "1883-3977", month = nov, notes = "Also known as \cite{9626045}", } @InProceedings{Hiroyasu:2010:CISP, author = "Tomoyuki Hiroyasu and Sosuke Fujita and Akihito Watanabe and Mitsunori Miki and Maki Ogura and Manabu Fukumoto", title = "Comparison of GP and SAP in the image-processing filter construction using pathology images", booktitle = "3rd International Congress on Image and Signal Processing (CISP 2010)", year = "2010", month = "16-18 " # oct, volume = "2", pages = "904--908", abstract = "In this paper, programming methods of constructing filters for choosing target images from pathology images are discussed. Automatic construction of these filters would be very useful in the medical field. Image processing filters can be expressed as tree topology operations. Genetic Programming (GP) is an evolutionary computation algorithm that can design tree topology operations. Simulated Annealing Programming (SAP) is also an emergent algorithm that can create tree topology operations. These two algorithms, GP and SAP, were applied to construct Image Processing Filters and the characteristics of these two algorithms were compared. The results indicated that GP has strong search capability for finding the global optimum solution. However, in the latter part of the search, the diversity of solutions is lost and the program size becomes large. This can be avoided by removing introns. It is assumed that filters developed by GP have strong robustness for other images. On the other hand, SAP requires many iterations to find the optimum but the program size is small. Filters developed by SAP are relatively weak from the viewpoint of robustness for other images.", keywords = "genetic algorithms, genetic programming, GP, SAP, image processing filter construction, medical image processing, pathology images, simulated annealing programming, medical image processing, simulated annealing", DOI = "doi:10.1109/CISP.2010.5646895", notes = "'GP can derive the best solution with less evaluation time than SAP.' Also known as \cite{5646895}", } @InProceedings{Hiroyasu:2012:SCIS, author = "Tomoyuki Hiroyasu and Sakito Nunokawa and Hiroaki Yamaguchi and Noriko Koizumi and Naoki Okumura and Hisatake Yokouchi", booktitle = "Soft Computing and Intelligent Systems (SCIS) and 13th International Symposium on Advanced Intelligent Systems (ISIS), 2012 Joint 6th International Conference on", title = "Algorithms for Automatic Extraction of Feature Values of Corneal Endothelial Cells using Genetic Programming", year = "2012", pages = "1388--1392", address = "Kobe, Japan", month = nov # " 20-24", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SCIS-ISIS.2012.6505152", size = "5 page", abstract = "In cornea tissue engineering, a researcher measures cell density and a form, in order to check the status of a cultivated cell. In this paper, these features values of cells are extracted automatically from corneal endothelial cell images. In the proposed method, genetic programing (GP) is used to construct image filters which can detect cell regions from corneal endothelial cells images. After detecting cell regions, feature values of cells such as density, the number of hexagon cells, and cell sizes are derived. To discuss the effectiveness of the proposed algorithm, the algorithm is applied to 16 sheets of corneal endothelial cells images. The cell region detection process was compared with the results of the Watershed filter which is one of the existing region division filters. From the results, it is confirmed that the filters which can extract cell regions from eight sheets of images with low error compared with the Watershed filter were constructed by GP. At the same time, it is also confirmed that the feature values of cells are detected successfully from five sheets of images.", notes = "Also known as \cite{6505152}", } @InProceedings{Hiroyasu:2013:SMC, author = "Tomoyuki Hiroyasu and Shunsuke Sekiya and Sakito Nunokawa and Noriko Koizumi and Naoki Okumura and Utako Yamamoto", booktitle = "IEEE International Conference on Systems, Man, and Cybernetics (SMC 2013)", title = "Extracting Rules for Cell Segmentation in Corneal Endothelial Cell Images Using GP", year = "2013", month = oct, pages = "1811--1816", keywords = "genetic algorithms, genetic programming, cell segmentation, rule", DOI = "doi:10.1109/SMC.2013.305", abstract = "In tissue engineering of the corneal endothelium, extracting feature values of cultured cells from cell images helps us to automatically judge whether they are transplantable. To extract feature values, accurate image processing for cell segmentation is needed. We previously proposed a method that constructs a tree-structural image-processing filter by automatically combining known image-processing filters. In this paper, we propose a more accurate method that can be applied to images in which statistics differ in different regions. The proposed method prepares two types of nodes. One type of node represents known image-processing filters, and the other represents conditional branches, which determine the divergent direction using the statistics of the cell images. Moreover, the proposed method optimises their combination by using genetic programming (GP). The proposed method is compared with the existing method using GP and specialist software for analysing cell images. The results show that the proposed method has superior accuracy.", notes = "Also known as \cite{6722065}", } @InProceedings{Hiroyasu:2014:CIDM, author = "Tomoyuki Hiroyasu and Toshihide Shiraishi and Tomoya Yoshida and Utako Yamamoto", booktitle = "IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2014)", title = "A Feature Transformation Method using Genetic Programming for Two-Class Classification", year = "2014", month = dec, pages = "234--240", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CIDM.2014.7008673", size = "7 pages", abstract = "In this paper, a feature transformation method for two-class classification using genetic programming (GP) is proposed. GP derives a transformation formula to improve the classification accuracy of Support Vector Machine, SVM. In this paper, we propose a weight function to evaluate converted feature space and the proposed function is used to evaluate the function of GP. In the proposed function, the ideal two-class distribution of items is assumed and the distance between the actual and ideal distributions is calculated. The weight is imposed to these distances. To examine the effectiveness of the proposed function, a numerical experiment was performed. In the experiment, as the result, the classification accuracy of the proposed method showed the better result than that of the existing method.", notes = "Fac. of Life & Med. Sci., Doshisha Univ., Kyotanabe, Japan Also known as \cite{7008673}", } @InProceedings{GPandIPDpaper1999Hirsch, author = "Laurence Hirsch and Masoud Saeedi", title = "Modelling exchange using the prisoner's dilemma and genetic programming", booktitle = "Proceedings of the Computer Society of Iran Computing Conference", year = "1999", editor = "Rasool Jalili", address = "Sharif University of Technology, Tehran, Iran", month = "26-28 " # jan, keywords = "genetic algorithms, genetic programming", URL = "http://shura.shu.ac.uk/id/eprint/3809", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/GPandIPDpaper1999Hirsch.pdf", size = "8 pages", abstract = "In this paper we show how exchange, co-operation and other complex strategies found in nature can be modelled using the prisoners dilemma game and genetic programming. We are able to produce and evolve different strategies represented by computer programs that can play the prisoners' dilemma against a set of predefined strategies or against other programs in the population (co-evolution). Although the game is simple the number of possible strategies for playing it is huge. Genetic programming provides an efficient search mechanism capable of identifying and propagating strategies that do well in a particular environment. Our implementation provides a distinct advantage over previous investigations into the prisoner's dilemma using genetic algorithms. In particular strategies can be based upon the entire history of a game at any point, rather than on recent moves only. We incorporate the use of list data structures as terminals and provide list-searching capability in the function set so that potentially large volumes of data can be used by the evolved programs.", notes = "CSICC 98 http://persia.org/Conferences/conf3/cp.html", } @InProceedings{hirsch:2004:eurogp, author = "Laurence Hirsch and Masoud Saeedi and Robin Hirsch", title = "Evolving Text Classifiers with Genetic Programming", booktitle = "Genetic Programming 7th European Conference, EuroGP 2004, Proceedings", year = "2004", editor = "Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule", volume = "3003", series = "LNCS", pages = "309--317", address = "Coimbra, Portugal", publisher_address = "Berlin", month = "5-7 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-21346-5", DOI = "doi:10.1007/978-3-540-24650-3_29", abstract = "We describe a method for using Genetic Programming (GP) to evolve document classifiers. GPs create regular expression type specifications consisting of particular sequences and patterns of N-Grams (character strings) and acquire fitness by producing expressions, which match documents in a particular category but do not match documents in any other category. Libraries of N-Gram patterns have been evolved against sets of pre-categorised training documents and are used to discriminate between new texts. We describe a basic set of functions and terminals and provide results from a categorisation task using the 20 Newsgroup data.", notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004", } @InProceedings{eurogp:HirschSH05, author = "Laurence Hirsch and Masoud Saeedi and Robin Hirsch", editor = "Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. {van Hemert} and Marco Tomassini", title = "Evolving Rules for Document Classification", booktitle = "Proceedings of the 8th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3447", year = "2005", address = "Lausanne, Switzerland", month = "30 " # mar # " - 1 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-25436-6", pages = "85--95", DOI = "doi:10.1007/978-3-540-31989-4_8", DOI = "doi:10.1007/b107383", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "We describe a novel method for using Genetic Programming to create compact classification rules based on combinations of N-Grams (character strings). Genetic programs acquire fitness by producing rules that are effective classifiers in terms of precision and recall when evaluated against a set of training documents. We describe a set of functions and terminals and provide results from a classification task using the Reuters 21578 dataset. We also suggest that because the induced rules are meaningful to a human analyst they may have a number of other uses beyond classification and provide a basis for text mining applications.", notes = "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005", } @Article{journals/aai/HirschSH05, title = "Evolving Text Classification Rules with Genetic Programming", author = "Laurence Hirsch and Masoud Saeedi and Robin Hirsch", journal = "Applied Artificial Intelligence", year = "2005", number = "7", volume = "19", pages = "659--676", month = aug, bibdate = "2005-12-01", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/aai/aai19.html#HirschSH05", keywords = "genetic algorithms, genetic programming", URL = "http://www.journalsonline.tandf.co.uk/openurl.asp?genre=article&issn=0883-9514&volume=19&issue=7&spage=659", DOI = "doi:10.1080/08839510590967307", abstract = "We describe a novel method for using genetic programming to create compact classification rules using combinations of N-grams (character strings). Genetic programs acquire fitness by producing rules that are effective classifiers in terms of precision and recall when evaluated against a set of training documents. We describe a set of functions and terminals and provide results from a classification task using the Reuters 21578 dataset. We also suggest that the rules may have a number of other uses beyond classification and provide a basis for text mining applications.", } @InProceedings{1277279, author = "Laurence Hirsch and Robin Hirsch and Masoud Saeedi", title = "Evolving Lucene search queries for text classification", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "1604--1611", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1604.pdf", DOI = "doi:10.1145/1276958.1277279", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, apache lucene, text classification", abstract = "We describe a method for generating accurate, compact, human understandable text classifiers. Text datasets are indexed using Apache Lucene and Genetic Programs are used to construct Lucene search queries. Genetic programs acquire fitness by producing queries that are effective binary classifiers for a particular category when evaluated against a set of training documents. We describe a set of functions and terminals and provide results from classification tasks.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @InProceedings{Hirsch:2010:cec, author = "Laurie Hirsch", title = "Evolved Apache Lucene SpanFirst queries are good text classifiers", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-6910-9", abstract = "Human readable text classifiers have a number of advantages over classifiers based on complex and opaque mathematical models. For some time now search queries or rules have been used for classification purposes, either constructed manually or automatically. We have performed experiments using genetic algorithms to evolve text classifiers in search query format with the combined objective of classifier accuracy and classifier readability. We have found that a small set of disjunct Lucene SpanFirst queries effectively meet both goals. This kind of query evaluates to true for a document if a particular word occurs within the first N words of a document. Previously researched classifiers based on queries using combinations of words connected with OR, AND and NOT were found to be generally less accurate and (arguably) less readable. The approach is evaluated using standard test sets Reuters-21578 and Ohsumed and compared against several classification algorithms.", DOI = "doi:10.1109/CEC.2010.5585955", notes = "WCCI 2010. Also known as \cite{5585955}", } @Article{hirsh:2000:GP, author = "Haym Hirsh and Wolfgang Banzhaf and John R. Koza and Conor Ryan and Lee Spector and Christian Jacob", title = "Genetic Programming", journal = "IEEE Intelligent Systems", year = "2000", volume = "15", number = "3", pages = "74--84", month = may # "-" # jun, keywords = "genetic algorithms, genetic programming, artificial computer code evolution, machine intelligence, automatic programming, arbitrary computational processes", ISSN = "1094-7167", URL = "http://ieeexplore.ieee.org/iel5/5254/18363/00846288.pdf", DOI = "doi:10.1109/5254.846288", size = "11 pages", abstract = "The paper presents essays on genetic programming which involve topics such as: the artificial evolution of computer code, human-competitive machine intelligence by means of genetic programming, GP as automatic programming, GP application, the evolution of arbitrary computational processes and the art of genetic programming.", notes = "Collection of essays by each author with introduction by Hirsh. See \cite{banzhaf:2000:IS}, \cite{koza:2000:IS}, \cite{ryan:2000:IS}, \cite{spector:2000:IS}, \cite{jacob:2000:IS}.", } @InProceedings{Hiruma:2011:EaERTG, title = "Evolving an Effective Robot Tour Guide", author = "Hideru Hiruma and Alex Fukunaga and Kazuki Komiya and Hitoshi Iba", pages = "137--144", booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary Computation", year = "2011", editor = "Alice E. Smith", month = "5-8 " # jun, address = "New Orleans, USA", organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, evolutionary robotics, exhibit space, exhibition, hand-coded controllers, mobile robots, museum, robot controllers, robot tour guide, visitor guiding, mobile robots, service robots", DOI = "doi:10.1109/CEC.2011.5949610", abstract = "Guiding visitors through an exhibit space such as a museum is an important, early application for mobile robots, and commercial robots designed for this purpose have become available. We consider the problem of using a single mobile robot to simultaneously direct multiple groups of visitors through a museum or exhibition, and formulate an objective function for this task. We show that an evolutionary robotics approach using a simple, low-fidelity simulator and genetic programming can automatically generate robot controllers which can perform this task better than hand-coded controllers as well as humans in both simulation and on a real robot.", notes = "CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.", } @Article{hiruta2021tom, author = "Yusuke Hiruta and Kei Nishihara and Yuji Koguma and Masakazu Fujii and Masaya Nakata", title = "Automated construction of Transferable loading algorithm with Cartesian Genetic Programming", journal = "IPSJ Transactions on Mathematical Modeling and its Applications", year = "2020", number = "10", pages = "1--6", month = dec, note = "in Japanese", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", ISSN = "2188-8833", URL = "http://id.nii.ac.jp/1001/00208645", URL = "https://ipsj.ixsq.nii.ac.jp/ej/?action=repository_uri&item_id=208747&file_id=1&file_no=1", size = "6 pages", abstract = "Google translate: we propose an automatic generation technology of a transferable loading algorithm using Cartesian genetic programming (CGP) under a defined objective function. In the proposed method, multiple selection rules are defined as criteria for selecting from stacking candidates, and a model that outputs the execution order is constructed by CGP. In the simulation experiment using transfer to a similar problem, it is shown that the proposed method can derive the same performance as the baseline. This is significant in showing the possibility that the automatically generated loading algorithm can be transferred to similar problems without additional evaluation by humans.", notes = "Also known as \cite{weko_208747_1} Yokohama National University. 2020-MPS-131 SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.", } @Article{Hiruta:2022:IEEEAccess, author = "Yusuke Hiruta and Kei Nishihara and Yuji Koguma and Masakazu Fujii and Masaya Nakata", journal = "IEEE Access", title = "Automatic Construction of Loading Algorithms With Interactive Genetic Programming", year = "2022", volume = "10", pages = "125167--125180", abstract = "The design of a freight loading pattern is often conducted by skilled workers, who handle unquantifiable objectives and/or preferences. Our previous study presented an automatic construction technique for loading algorithms using genetic programming-based hyper-heuristics; however, this technique is only applicable to fully quantifiable loading problems. Thus, the approach described in this paper integrates an interactive framework with users into our previous technique to automatically construct algorithms that derive loading patterns adapted to user objectives and/or preferences. Thus, once a loading algorithm has been derived with user interactions, it can be reused to obtain the preferred loading patterns on other problems without any additional interactions. Experimental results show that the proposed algorithm can produce loading algorithms adapted for user preferences under a limit of 50 human interactions. Further, we also show that the derived loading algorithms can be applicable to different loading situations without any additional user interactions. Thus, these observations suggest the benefit of our approaches in reducing the burden placed on skilled workers for practical LPD tasks.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ACCESS.2022.3225543", ISSN = "2169-3536", notes = "Also known as \cite{9966559}", } @InProceedings{Hlavac:2016:SCSP, author = "Vladimir Hlavac", title = "A program searching for a functional dependence using genetic programming with coefficient adjustment", booktitle = "2016 Smart Cities Symposium Prague (SCSP)", year = "2016", month = "26-27 " # may, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SCSP.2016.7501014", abstract = "When modelling many traffic problems, it is necessary to find the functional dependence of the output of two input variables. This task can be solved by a neural network, by using some spline interpolation or polynomials, etc. These approaches can produce a model, but its internal description is unreadable and its transfer to another program can be difficult. Therefore, a program to determine this functional dependence using genetic programming has been developed. The result is prepared in such a way that it can be transferred into a source code of another program, or copied to an MS Excel sheet. The program reads data available as triplets, [[x, y], z], and looks for their functional interdependencies by using a selected set of elementary functions and a vector of multiplicative constants. The input data do not have to meet any additional conditions. They can be defined on measured intervals, or even as individual points. For a successful outcome, the only condition is to have a sufficient amount of data. For some functions, the level of noise has to be determined in order to make the model complete. In this case, noise characteristics can be evaluated from the results of the program.", notes = "Also known as \cite{7501014} Department of Applied Informatics in Transportation, Czech Technical University in Prague, Prague, Czech Republic", } @InProceedings{hlavac:2019:RASC, author = "Vladimir Hlavac", title = "Accelerated Genetic Programming", booktitle = "MENDEL 2017, Recent Advances in Soft Computing", year = "2017", editor = "Radek Matousek", volume = "837", series = "AISC", pages = "118--126", address = "Brno, Czech Republic", month = jun # " 20-22", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Symbolic regression, Exponencionated gradient descent, Constant evaluation", isbn13 = "978-3-319-97887-1", URL = "http://link.springer.com/chapter/10.1007/978-3-319-97888-8_9", DOI = "doi:10.1007/978-3-319-97888-8_9", abstract = "Symbolic regression by the genetic programming is one of the options for obtaining a mathematical model for known data of output dependencies on inputs. Compared to neural networks (MLP), they can find a model in the form of a relatively simple mathematical relationship. The disadvantage is their computational difficulty. The following text describes several algorithm adjustments to enable acceleration and wider usage of the genetic programming. The performance of the resulting program was verified by several test functions containing several percent of the noise. The results are presented in graphs. The application is available at www.zpp.wz.cz/g.", notes = "Published 2018", } @InProceedings{Hmida:2016:SmartWorld, author = "Hmida Hmida and Sana {Ben Hamida} and Amel Borgi and Marta Rukoz", booktitle = "2016 Intl IEEE Conferences on Ubiquitous Intelligence Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld)", title = "Hierarchical Data Topology Based Selection for Large Scale Learning", year = "2016", pages = "1221--1226", abstract = "The amount of available data for data mining, knowledge discovery continues to grow very fast with the era of Big Data. Genetic Programming algorithms (GP), that are efficient machine learning techniques, are face up to a new challenge that is to deal with the mass of the provided data. Active Sampling, already used for Active Learning, might be a good solution to improve the Evolutionary Algorithms (EA) training from very big data sets. This paper investigates the adaptation of Topology Based Selection (TBS) to face massive learning datasets by means of Hierarchical Sampling. We propose to combine the Random Subset Selection (RSS) with the TBS to create the RSS-TBS method. Two variants are implemented, applied to solve the KDD intrusion detection problem. They are compared to the original RSS, TBS techniques. The experimental results show that the important computational cost generated by original TBS when applied to large datasets can be lightened with the Hierarchical Sampling.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0186", month = jul, notes = "Also known as \cite{7816982}", } @InProceedings{conf/inns/HmidaHBR16, author = "Hmida Hmida and Sana {Ben Hamida} and Amel Borgi and Marta Rukoz", title = "Sampling Methods in Genetic Programming Learners from Large Datasets: A Comparative Study", year = "2016", volume = "529", keywords = "genetic algorithms, genetic programming", bibdate = "2017-05-24", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/inns/inns2016.html#HmidaHBR16", booktitle = "INNS Conference on Big Data", editor = "Plamen Angelov and Yannis Manolopoulos and Lazaros S. Iliadis and Asim Roy and Marley M. B. R. Vellasco", isbn13 = "978-3-319-47897-5", pages = "50--60", series = "Advances in Intelligent Systems and Computing", DOI = "doi:10.1007/978-3-319-47898-2_6", } @InProceedings{Hmida:2019:ICDS, author = "Hmida Hmida and Sana Ben Hamida and Amel Borgi and Marta Rukoz", booktitle = "2019 Third International Conference on Intelligent Computing in Data Sciences (ICDS)", title = "A new adaptive sampling approach for Genetic Programming", year = "2019", abstract = "Genetic Programming (GP) is afflicted by an excessive computation time that is more exacerbated with data intensive problems. This issue has been addressed with different approaches such as sampling techniques or distributed implementations. In this paper, we focus on dynamic sampling algorithms that mostly give to GP learner a new sample each generation. In so doing, individuals do not have enough time to extract the hidden knowledge. We propose adaptive sampling which is half-way between static and dynamic methods. It is a flexible approach applicable to any dynamic sampling. We implemented some variants based on controlling re-sampling frequency that we experimented to solve KDD intrusion detection problem with GP. The experimental study demonstrates how it preserves the power of dynamic sampling with possible improvements in learning time and quality for some sampling algorithms. This work opens many new relevant extension paths.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICDS47004.2019.8942353", month = oct, notes = "Also known as \cite{8942353}", } @Article{HMIDA2018302, author = "Hmida Hmida and Sana {Ben Hamida} and Amel Borgi and Marta Rukoz", title = "Scale Genetic Programming for large Data Sets: Case of {Higgs} Bosons Classification", journal = "Procedia Computer Science", year = "2018", volume = "126", pages = "302--311", note = "Knowledge-Based and Intelligent Information and Engineering Systems: Proceedings of the 22nd International Conference, KES-2018, Belgrade, Serbia", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Active Sampling, Higgs Bosons Classification, large dataset, Machine Learning", ISSN = "1877-0509", URL = "http://www.sciencedirect.com/science/article/pii/S1877050918312407", DOI = "doi:10.1016/j.procs.2018.07.264", abstract = "Extract knowledge and significant information from very large data sets is a main topic in Data Science, bringing the interest of researchers in machine learning field. Several machine learning techniques have proven effective to deal with massive data like Deep Neuronal Networks. Evolutionary algorithms are considered not well suitable for such problems because of their relatively high computational cost. This work is an attempt to prove that, with some extensions, evolutionary algorithms could be an interesting solution to learn from very large data sets. We propose the use of the Cartesian Genetic Programming (CGP) as meta-heuristic approach to learn from the Higgs big data set. CGP is extended with an active sampling technique in order to help the algorithm to deal with the mass of the provided data. The proposed method is able to take up the challenge of dealing with the complete benchmark data set of 11 million events and produces satisfactory preliminary results.", } @InProceedings{DBLP:conf/bis/HmidaHBR19, author = "Hmida Hmida and Sana Ben Hamida and Amel Borgi and Marta Rukoz", editor = "Witold Abramowicz and Rafael Corchuelo", title = "Genetic Programming over Spark for {Higgs} Boson Classification", booktitle = "Business Information Systems - 22nd International Conference, {BIS} 2019, Seville, Spain, June 26-28, 2019, Proceedings, Part {I}", series = "Lecture Notes in Business Information Processing", volume = "353", pages = "300--312", publisher = "Springer", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/978-3-030-20485-3_23", DOI = "doi:10.1007/978-3-030-20485-3_23", timestamp = "Fri, 27 Dec 2019 00:00:00 +0100", biburl = "https://dblp.org/rec/conf/bis/HmidaHBR19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @PhdThesis{DBLP:phd/hal/Hmida19, author = "Hmida Hmida", title = "Extending Genetic Programming for supervised learning from very large datasets (Big data)", titletranslation = "Extension des Programmes Genetiques pour l'apprentissage supervise a partir de tres larges Bases de Donnees (Big data)", school = "Universite Paris sciences et lettres et Universite de Tunis El Manar", year = "2019", address = "France", month = "23 " # oct, keywords = "genetic algorithms, genetic programming, big data, classification, training set sampling, adaptive sampling, spark, programmation genetique, classification, echantillonnage de la base d'apprentissage, echantillonnage adaptatif", number = "2019PSLED047", hal_id = "tel-03220655", hal_version = "v1", bibsource = "OAI-PMH server at api.archives-ouvertes.fr", identifier = "NNT: 2019PSLED047; tel-03220655", language = "fr", oai = "oai:HAL:tel-03220655v1", rights = "info:eu-repo/semantics/OpenAccess", URL = "https://tel.archives-ouvertes.fr/tel-03220655/document", URL = "https://tel.archives-ouvertes.fr/tel-03220655/file/2019PSLED047.pdf", URL = "https://tel.archives-ouvertes.fr/tel-03220655", timestamp = "Tue, 25 May 2021 01:00:00 +0200", biburl = "https://dblp.org/rec/phd/hal/Hmida19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", size = "192 pages", abstract = "In this thesis, we investigate the adaptation of GP to overcome the data Volume hurdle in Big Data problems. GP is a well-established meta-heuristic for classification problems but is impaired with its computing cost. First, we conduct an extensive review enriched with an experimental comparative study of training set sampling algorithms used for GP. Then, based on the previous study results, we propose some extensions based on hierarchical sampling. The latter combines active sampling algorithms on several levels and has proven to be an appropriate solution for sampling techniques that cannot deal with large datatsets (like TBS) and for applying GP to a Big Data problem as Higgs Boson classification. Moreover, we formulate a new sampling approach called adaptive sampling, based on controlling sampling frequency depending on learning process and through fixed, determinist and adaptive control schemes. Finally, we present how an existing GP implementation (DEAP) can be adapted by distributing evaluations on a Spark cluster. Then, we demonstrate how this implementation can be run on tiny clusters by sampling.Experiments show the great benefits of using Spark as parallelisation technology for GP.", resume = "Dans cette these, nous etudions l'adaptation des Programmes Genetiques (GP) pour surmonter l'obstacle du volume de donnees dans les problemes Big Data. GP est une meta-heuristique qui a fait ses preuves pour les problemes de classification. Neanmoins, son cout de calcul est un frein a son utilisation avec les larges bases d'apprentissage. Tout d'abord, nous effectuons une revue approfondie enrichie par une etude comparative experimentale des algorithmes d'echantillonnage utilises avec GP. Puis, a partir des resultats de l'etude precedente, nous proposons quelques extensions basees sur l'echantillonnage hierarchique. Ce dernier combine des algorithmes d'echantillonnage actif a plusieurs niveaux et s'est prouve une solution appropriee pour mettre a l'echelle certaines techniques comme TBS et pour appliquer GP a un probleme Big Data (cas de la classification des bosons de Higgs). Par ailleurs, nous formulons une nouvelle approche d'echantillonnage appelee echantillonnage adaptatif, basee sur le controle de la frequence d'echantillonnage en fonction du processus d'apprentissage, selon les schemas fixe, deterministe et adaptatif. Enfin, nous presentons comment transformer une implementation GP existante (DEAP) en distribuant les evaluations sur un cluster Spark. Nous demontrons comment cette implementation peut etre executee sur des clusters a nombre de n{\oe}uds reduit grace a l'echantillonnage. Les experiences montrent les grands avantages de l'utilisation de Spark pour la parallelisation de GP.{"}", notes = "In French Also known as \cite{hmida:tel-03220655}", } @InCollection{ho:1994:gqo, author = "Alex Ho and George Lumpkin", title = "The Genetic Query Optimizer", booktitle = "Genetic Algorithms at Stanford 1994", year = "1994", editor = "John R. Koza", pages = "67--76", address = "Stanford, California, 94305-3079 USA", month = dec, organisation = "Stanford University", publisher = "Stanford Bookstore", keywords = "genetic algorithms, Oracle Corporation, Relational Database Query", ISBN = "0-18-187263-3", abstract = "{"}For complex queries, we find that the genetic algorithm produces more efficient query plans in a running time comparable to that of conventional methods{"}.", notes = "This volume contains 20 papers written and submitted by students describing their term projects for the course {"}Genetic Algorithms and Genetic Programming{"} (Computer Science 426) at Stanford University offered during the fall quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs426.html", } @InProceedings{Ho:2009:ispimrc, author = "Lester T. W. Ho and Imran Ashraf and Holger Claussen", title = "Evolving femtocell coverage optimization algorithms using genetic programming", booktitle = "IEEE 20th International Symposium on Personal, Indoor and Mobile Radio Communications", year = "2009", month = sep, pages = "2132--2136", abstract = "The use of a group of femtocells to jointly provide coverage in an enterprise environment introduces several challenges in the introduction of self-configuration and self-optimisation capabilities required for plug-and-play styles of deployment. In this paper, an approach to automatically derive a distributed algorithm to dynamically optimise the coverage of a femtocell group using genetic programming is described. The resulting evolved algorithm showed the ability to optimize the coverage well, and is able to offer increased overall network capacity compared with a fixed coverage femtocell deployment.", keywords = "genetic algorithms, genetic programming, distributed algorithm, enterprise environment, femtocell coverage optimization, self-configuration capability, self-optimisation capability, cellular radio", DOI = "doi:10.1109/PIMRC.2009.5450062", notes = "Bell Labs., Alcatel-Lucent, Swindon, UK. Also known as \cite{5450062}", } @InProceedings{Ho:2013:PIMRC, author = "Lester Ho and Holger Claussen and Davide Cherubini", title = "Online Evolution of Femtocell Coverage Algorithms Using Genetic Programming", booktitle = "24th IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC 2013)", year = "2013", pages = "3033--3038", address = "London, UK", month = "8-11 " # sep, keywords = "genetic algorithms, genetic programming, femtocell, coverage optimization, online genetic programming, model building", isbn13 = "978-1-4673-6235-1", ISSN = "2166-9570", DOI = "doi:10.1109/PIMRC.2013.6666667", size = "6 pages", abstract = "The wide adoption of smart-phones has resulted in an exponential increase in the demand for wireless data. To address this problem, operators have started deploying large numbers of small cells. In order to operate such small cell network cost-effectively they need to be able to intelligently optimise their configuration, which can be achieved by applying machine learning techniques such as genetic programming. The use of genetic programming has previously been used to derive joint coverage algorithms for a group of enterprise femtocells. However, the evolution of the algorithms was performed in an offline manner, on a pre-defined simulation model of the deployment scenario. In this paper, an approach to perform the evolution in an on-line manner using an automated model building process is presented. The model building process uses network traces as inputs to create a hierarchical Markov model that is shown to be able to capture the behaviour of the femtocell network well. It is shown that the resulting environment model can effectively drive the on-line evolution of coverage optimisation algorithms.", notes = "Also known as \cite{6666667} Bell Laboratories, Alcatel-Lucent, Blanchardstown Business and Technology Park, Dublin 15, Ireland", } @InProceedings{ho-ka-pl-14a, author = "Nam Ho and Paul Kaufmann and Marco Platzner", title = "Towards Self-Adaptive Caches: a Run-Time Reconfigurable Multi-Core Infrastructure", booktitle = "International Conference on Evolvable Systems, ICES 2014", year = "2014", pages = "31--37", month = "9-12 " # dec, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, EHW", DOI = "doi:10.1109/ICES.2014.7008719", size = "7 page", abstract = "This paper presents the first steps towards the implementation of an evolvable and self-adaptable processor cache. The implemented system consists of a run-time reconfigurable memory-to-cache address mapping engine embedded into the split level one cache of a Leon3 SPARC processor as well as of an measurement infrastructure able to profile microarchitectural and custom logic events based on the standard Linux performance measurement interface perf_event. The implementation shows, how reconfiguration of the very basic processor properties, and fine granular profiling of custom logic and integer unit events can be realised and meaningfully used to create an adaptable multi-core embedded system.", notes = "EvoCache Xilinx FPGA. Also known as \cite{7008719}", } @InProceedings{ho-ah-ka-15a, author = "Nam Ho and Abdullah Fathi Ahmed and Paul Kaufmann and Marco Platzner", title = "Microarchitectural Optimization by Means of Reconfigurable and Evolvable Cache Mappings", booktitle = "2015 NASA/ESA Conference on Adaptive Hardware and Systems (AHS)", year = "2015", editor = "Giovanni Beltrame", address = "Montreal, Quebec, Canada", month = "15-18 " # jun, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, genetic improvement, Cartesian Genetic Programming, EHW", isbn13 = "978-1-4673-7502-3", DOI = "doi:10.1109/AHS.2015.7231178", size = "7 pages", abstract = "Physical limits are pushing chip manufacturer towards multi- and many-core architectures to maintain the progress of computing power. This trend has also emphasized reconfigurable computing, which enables for even higher parallelization degrees. Reconfigurable computing is often used together with a conventional processor to accelerate highly specific applications. However, exploiting dynamically reconfigurable systems for microarchitectural optimization is a novel research area. This paper presents for the first time an FPGA-based implementation of a processor that can reconfigure and adapt its own memory-to-cache address mapping function at runtime by means of dynamic reconfiguration and nature-inspired optimization. In experiments we can achieve up to 7.8percent better execution times compared to a processor with a conventional cache mapping function.", notes = "http://www.polymtl.ca/ahs2015/en/conference/index.php LEON3, Virtex 6 FPGA, EvoCache. SHA, QSORT, DUKSTRA and FFT MiBench workloads. Also known as \cite{7231178} Phd thesis 2018 doi:10.17619/UNIPB/1-376 FPGA-based Reconfigurable Cache Mapping Schemes: Design and Optimization", } @InProceedings{ho:1999:AEGMEA, author = "Shinn-Ying Ho and Xiao-I Chang", title = "An Efficient Generalized Multiobjective Evolutionary Algorithm", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "871--878", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "evolution strategies and evolutionary programming", ISBN = "1-55860-611-4", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{ho:1999:IGANICUOA, author = "Shinn-Ying Ho and Li-Sun Shu and Hung-Ming Chen", title = "Intelligent Genetic Algorithm with a New Intelligent Crossover Using Orthogonal Arrays", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "289--296", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{ho:1999:SLKBPPUIGA, author = "Shinn-Ying Ho and Hung-Ming Chen and Li-Sun Shu", title = "Solving Large Knowledge Base Partitioning Problems Using an Intelligent Genetic Algorithm", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1567--1572", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "real world applications", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-747NEW.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-747NEW.pdf", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Article{ho:2006:biosystems, author = "Shinn-Ying Ho and Chih-Hung Hsieh and Hung-Ming Chen and Hui-Ling Huang", title = "Interpretable gene expression classifier with an accurate and compact fuzzy rule base for microarray data analysis", journal = "Biosystems", year = "2006", volume = "85", number = "3", pages = "165--176", month = sep, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1016/j.biosystems.2006.01.002", abstract = "An accurate classifier with linguistic interpretability using a small number of relevant genes is beneficial to microarray data analysis and development of inexpensive diagnostic tests. Several frequently used techniques for designing classifiers of microarray data, such as support vector machine, neural networks, k-nearest neighbour, and logistic regression model, suffer from low interpretabilities. This paper proposes an interpretable gene expression classifier (named iGEC) with an accurate and compact fuzzy rule base for microarray data analysis. The design of iGEC has three objectives to be simultaneously optimised: maximal classification accuracy, minimal number of rules, and minimal number of used genes. An {"}intelligent{"} genetic algorithm IGA is used to efficiently solve the design problem with a large number of tuning parameters. The performance of iGEC is evaluated using eight commonly-used data sets. It is shown that iGEC has an accurate, concise, and interpretable rule base (1.1 rules per class) on average in terms of test classification accuracy (87.9percent), rule number (3.9), and used gene number (5.0). Moreover, iGEC not only has better performance than the existing fuzzy rule-based classifier in terms of the above-mentioned objectives, but also is more accurate than some existing non-rule-based classifiers.", notes = "PMID: 16490299 [PubMed - in process]", } @InProceedings{Nguyen:2001:ADFA-csc, author = "X. H. Nguyen and R. I. (Bob) McKay", booktitle = "Post-graduate ADFA Conference on Computer Science", address = "Canberra, Australia", notes = "Refereed Regional and National Conference and Workshop Papers", pages = "93--100", title = "A Framework for Tree-adjunct Grammar Guided Genetic Programming", year = "2001", keywords = "genetic algorithms, genetic programming", URL = "http://sc.snu.ac.kr/PAPERS/TAG3P.pdf", size = "11 pages", abstract = "In this paper we propose the framework for a grammar-guided genetic programming system called Tree-Adjunct Grammar Guided Genetic Programming (TAGGGP). Some intuitively promising aspects of the model compared with other grammar-guided evolutionary methods are also highlighted.", } @InProceedings{hoai:2001:AJWIES, author = "N. X. Hoai", title = "Solving the Symbolic Regression with Tree-Adjunct Grammar Guided Genetic Programming: The Preliminary Results", booktitle = "Australasia-Japan Workshop on Intelligent and Evolutionary Systems", year = "2001", editor = "Nikola Kasabov and Peter Whigham", address = "University of Otago, Dunedin, New Zealand", month = "19-21st " # nov, keywords = "genetic algorithms, genetic programming", notes = "broken Nov 2012 http://divcom.otago.ac.nz/infosci/KEL/conferences/IESWorkshop/default.htm", } @InProceedings{hoai:2001:HIS, title = "Solving Trignometric Identities with Tree Adjunct Grammar Guided Genetic Programming", author = "N. X. Hoai", editor = "Ajith Abraham and Mario Koppen", booktitle = "2001 International Workshop on Hybrid Intelligent Systems", series = "LNCS", pages = "339--352", publisher = "Springer-Verlag", address = "Adelaide, Australia", publisher_address = "Berlin", month = "11-12 " # dec, year = "2001", email = "x.nguyen@student.adfa.edu.au", broken = "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-7908-1480-6", URL = "http://www.amazon.com/Hybrid-Information-Systems-Ajith-Abraham/dp/3790814806/ref=sr_1_8?s=books&ie=UTF8&qid=1326475568&sr=1-8", ISBN = "3-7908-1480-6", keywords = "genetic algorithms, genetic programming, Grammar Guided Genetic Progrogramming, Tree-Adjunct Grammars, Trigonometric Identity Discovery", abstract = "Tree-adjunct grammar guided genetic programming (TAG3P) (Hoai and McKay 2001) is a grammar guided genetic programming system that uses context-free grammars along with tree-adjunct grammars as means to set language bias for the genetic programming system. In this paper, we show the result of TAG3P on the problem of discovering trigonometric identities, one of the benchmark problems in genetic programming (Koza 1992). The results show that although TAG3P did successfully discover all three popular trigonometric identities of the trigonometric function cos(2x), namely, sin(2x+p /2), sin(p /2 -2x) and 1-2sin 2 (x), it had a tendency to converge towards the first two identities.", notes = "HIS01", } @Article{Nguyen:2001:AJIIPS, author = "X. H. Nguyen and R. I. (Bob) McKay and D. L. Essam", journal = "The Australian Journal of Intelligent Information Processing Systems", number = "3/4", pages = "114--121", title = "Solving the Symbolic Regression Problem with Tree-Adjunct Grammar Guided Genetic Programming: The Comparative Results", URL = "http://sc.snu.ac.kr/PAPERS/xuanetal.pdf", volume = "7", year = "2001", keywords = "genetic algorithms, genetic programming", size = "6 pages", abstract = "In this paper, we show some experimental results of tree-adjunct grammar guided genetic programming [6] (TAG3P) on the symbolic regression problem, a benchmark problem in genetic programming. We compare the results with genetic programming [9] (GP) and grammar guided genetic programming [14] (GGGP). The results show that TAG3P significantly outperforms GP and GGGP on the target functions attempted in terms of probability of success. Moreover, TAG3P still performed well when the structural complexity of the target function was scaled up.", notes = "See also \cite{hoai:2002:stsrpwtgggptcr}", } @InProceedings{hoai:2002:EuroGP, title = "Some Experimental Results with Tree Adjunct Grammar Guided Genetic Programming", author = "Nguyen Xuan Hoai and R. I. McKay and D. Essam", editor = "James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi", booktitle = "Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002", volume = "2278", series = "LNCS", pages = "228--237", publisher = "Springer-Verlag", address = "Kinsale, Ireland", publisher_address = "Berlin", month = "3-5 " # apr, year = "2002", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-43378-3", DOI = "doi:10.1007/3-540-45984-7_22", abstract = "Tree-adjunct grammar guided genetic programming (TAG3P) [5] is a grammar guided genetic programming system that uses context -free grammars along with tree-adjunct grammars as means to set language bias for the genetic programming system. In this paper, we show the experimental results of TAG3P on two problems: symbolic regression and trigonometric identity discovery. The results show that TAG3P works well on those problems.", notes = "EuroGP'2002, part of \cite{lutton:2002:GP}", } @InProceedings{Nguyen:2002:ICCCS, publisher_address = "Piscataway, NJ, USA", author = "X. H. Nguyen and R. I. (Bob) McKay and D. L. Essam", booktitle = "IEEE International Conference on Communications, Circuits and Systems", address = "Chengdu, China", month = jul, notes = "Refereed International Conference Papers", pages = "1113--1117", publisher = "IEEE Press", title = "Can Tree Adjunct Grammar Guided Genetic Programming be Good at Finding a Needle in a Haystack? A Case Study", URL = "http://sc.snu.ac.kr/PAPERS/hoaietal.pdf", volume = "2", year = "2002", keywords = "genetic algorithms, genetic programming", } @InProceedings{hoai:2002:stsrpwtgggptcr, author = "N. X. Hoai and R. I. McKay and D. Essam and R. Chau", title = "Solving the Symbolic Regression Problem with Tree-Adjunct Grammar Guided Genetic Programming: The Comparative Results", booktitle = "Proceedings of the 2002 Congress on Evolutionary Computation CEC2002", editor = "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and Mark Shackleton", pages = "1326--1331", year = "2002", publisher = "IEEE Press", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", organisation = "IEEE Neural Network Council (NNC), Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", ISBN = "0-7803-7278-6", month = "12-17 " # may, notes = "CEC 2002 - A joint meeting of the IEEE, the Evolutionary Programming Society, and the IEE. Held in connection with the World Congress on Computational Intelligence (WCCI 2002)", keywords = "genetic algorithms, genetic programming, TAG3P, performance, structural complexity scaling, success probability, symbolic regression problem, target functions, tree-adjunct grammar-guided genetic programming, context-free grammars, functions, problem solving, programming, software performance evaluation, statistical analysis, symbol manipulation, trees (mathematics)", DOI = "doi:10.1109/CEC.2002.1004435", abstract = "In this paper, we show some experimental results of tree-adjunct grammar-guided genetic programming (TAG3P) on the symbolic regression problem, a benchmark problem in genetic programming. We compare the results with genetic programming (GP) and grammar-guided genetic programming (GGGP). The results show that TAG3P significantly outperforms GP and GGGP on the target functions attempted in terms of the probability of success. Moreover, TAG3P still performed well when the structural complexity of the target function was scaled up", } @InProceedings{hoai:2002:SEAL, author = "Nguyen Xuan Hoai and Yin Shan and Robert Ian McKay", title = "Is Ambiguity Useful or Problematic for Grammar Guided Genetic Programming?", booktitle = "Procedings of the 4th Asia-Pacific Conference on Simulated Evolution And Learning (SEAL'02)", year = "2002", editor = "Lipo Wang and Kay Chen Tan and Takeshi Furuhashi and Jong-Hwan Kim and Xin Yao", pages = "449--454", address = "Orchid Country Club, Singapore", month = "18-22 " # nov, keywords = "genetic algorithms, genetic programming", ISBN = "981-04-7522-5", URL = "http://www.cs.adfa.edu.au/~shanyin/publications/ambiguity.pdf", URL = "http://citeseer.ist.psu.edu/545311.html", URL = "http://sc.snu.ac.kr/PAPERS/ambiguity.pdf", abstract = "In [2] Antonisse made a conjecture that unambiguous grammars are better candidates for grammar-guided genetic learning. In this paper, we empirically show that it is not always the case, especially when the structural ambiguity is boosted by semantic redundancies in the grammar. We also show that the search space (or genotype space) of grammar guided genetic programming (GGGP) is truly tree sets rather than string sets of formalisms.", notes = "SEAL 2002 see http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.200.6410&rep=rep1&type=pdf", notes = "Refereed International Conference Papers", } @InProceedings{hoai03, author = "Nguyen Xuan Hoai and R. I. McKay and H. A. Abbass", title = "Tree Adjoining Grammars, Language Bias, and Genetic Programming", booktitle = "Genetic Programming, Proceedings of EuroGP'2003", year = "2003", editor = "Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa", volume = "2610", series = "LNCS", pages = "335--344", address = "Essex", publisher_address = "Berlin", month = "14-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-00971-X", URL = "http://www.cs.adfa.edu.au/~abbass/publications/hardcopies/TAG3P-EuroGp-03.pdf", DOI = "doi:10.1007/3-540-36599-0_31", abstract = "In this paper, we introduce a new grammar guided genetic programming system called tree-adjoining grammar guided genetic programming (TAG3P+), where tree-adjoining grammars (TAGs) are used as means to set language bias for genetic programming. We show that the capability of TAGs in handling context-sensitive information and categories can be useful to set a language bias that cannot be specified in grammar guided genetic programming. Moreover, we bias the genetic operators to preserve the language bias during the evolutionary process. The results pace the way towards a better understanding of the importance of bias in genetic programming.", notes = "EuroGP'2003 held in conjunction with EvoWorkshops 2003", } @InCollection{Nguyen:2004:IISA, address = "Berlin, Germany", author = "X. H. Nguyen and R. I. (Bob) McKay and D. L. Essam", booktitle = "Innovations in Intelligent Systems and Applications", editor = "A. Abraham and L. Jain and B. J. {van der Zwaag}", ISBN = "3-540-20265-X", isbn13 = "9783540202653", month = jan, notes = "Book Chapter", pages = "221--236", publisher = "Springer-Verlag", series = "Springer Studies in Fuzziness and Soft Computing", title = "Finding Trigonometric Identities with Tree Adjunct Grammar Guided Genetic Programming", URL = "http://sc.snu.ac.kr/PAPERS/trigonometry.pdf", url1 = "http://www.springer.com/west/home/engineering?SGWID=4-175-22-13888495-detailsPage=ppmmedia|toc", volume = "140", year = "2004", keywords = "genetic algorithms, genetic programming", size = "18 pages", abstract = "Introduction. Genetic programming (GP) may be seen as a machine learning method, which induces a population of computer programs by evolutionary means (Banzhaf et al. 1998). Genetic programming has been used successfully in generating computer programs for solving a number of problems in a wide range of areas. In (Hoai and McKay 2001), we proposed a framework for a grammar-guided genetic programming system called Tree-Adjunct Grammar Guided Genetic Programming (TAG3P), which uses tree-adjunct grammars along with a context-free grammar to set language bias in genetic programming. The use of tree-adjunct grammars can be seen as a process of building context-free grammar guided programs in the two dimensional space. In this chapter, we show some results of TAG3P on the trigonometric identity discovery problem. The organisation of the remainder of the chapter is as follows. In section 2, we give a brief overview of genetic programming, grammar guided genetic programming, tree-adjunct grammars and TAG3P. The problem of finding trigonometric identities will be given in section 3. Section 4 contains the experiment and results of TAG3P on that problem. The nature of search space is empirically analysed and the bias by selective adjunction is introduced. The last section contains conclusion and future work.", } @InProceedings{nguyen:2004:eurogp, author = "Nguyen Xuan Hoai and R. I. (Bob) McKay and Daryl Essam and Hussein Abbass", title = "Toward an Alternative Comparison between Different Genetic Programming Systems", booktitle = "Genetic Programming 7th European Conference, EuroGP 2004, Proceedings", year = "2004", editor = "Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule", volume = "3003", series = "LNCS", pages = "67--77", address = "Coimbra, Portugal", publisher_address = "Berlin", month = "5-7 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-21346-5", DOI = "doi:10.1007/978-3-540-24650-3_7", abstract = "We use multi-objective techniques to compare different genetic programming systems, permitting our comparison to concentrate on the effect of representation and separate out the effects of different search space sizes and search algorithms. Experimental results are given, comparing the performance and search behaviour of Tree Adjoining Grammar Guided Genetic Programming (TAG3P) and Standard Genetic Programming (GP) on some standard problems.", notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004", } @InProceedings{nguyen:2004:aiotroiadoitagggp, title = "An Investigation on the Roles of Insertion and Deletion Operators in Tree Adjoining Grammar Guided Genetic Programming", author = "Nguyen Xuan Hoai and Robert Ian McKay", pages = "472--477", booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary Computation", year = "2004", publisher = "IEEE Press", month = "20-23 " # jun, address = "Portland, Oregon", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Theory of evolutionary algorithms", DOI = "doi:10.1109/CEC.2004.1330894", size = "6 pages", abstract = "We investigate the roles of insertion and deletion as mutation operators and as local search operators in a Tree Adjoining Grammar Guided Genetic Programming (TAG3P) system [13]. The results show that, on three standard problems, these operators work better as mutation operators than the more standard sub-tree mutation originally used in [13, 14]. Moreover, for some problems, insetion and deletion can also act effectively as local search operators, allowing TAG3P to solve problems with very small population sizes.", notes = "RI Mc Kay CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.", } @InProceedings{hoai:sts:gecco2004, author = "Nguyen Xuan Hoai and R. I. McKay", title = "Softening the Structural Difficulty in Genetic Programming with TAG-Based Representation and Insertion/Deletion Operators", booktitle = "Genetic and Evolutionary Computation -- GECCO-2004, Part II", year = "2004", editor = "Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell", series = "Lecture Notes in Computer Science", pages = "605--616", address = "Seattle, WA, USA", publisher_address = "Heidelberg", month = "26-30 " # jun, organisation = "ISGEC", publisher = "Springer-Verlag", volume = "3103", ISBN = "3-540-22343-6", ISSN = "0302-9743", DOI = "doi:10.1007/b98645", size = "12", keywords = "genetic algorithms, genetic programming", notes = "GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)", } @InProceedings{Nguyen:2004:APCSEL, author = "Xuan Hoai Nguyen and R. I. (Bob) McKay and D. L. Essam and H. A. Abbass", booktitle = "2004 Asia-Pacific Conference on Simulated Evolution and Learning", address = "Busan, Korea", month = oct, notes = "Refereed International Conference Papers", title = "Genetic Transposition in Tree-Adjoining Grammar Guided Genetic Programming: the Relocation Operator", URL = "http://sc.snu.ac.kr/PAPERS/SEAL2004.pdf", year = "2004", keywords = "genetic algorithms, genetic programming", size = "6 pages", abstract = "We empirically investigate the use of relocation operator as a local search operator, in combination with genetic search, in a Tree Adjoining Grammar Guided Genetic Programming system (TAG3P). The results show that, on all the problems we tried, the use of the relocation operator as a local search operator in TAG3P outperforms TAG3P using purely crossover and mutation, and also outperforms standard genetic programming (GP). Moreover, it manages to solve problems with very small population sizes.", } @PhdThesis{hoai_thesis, author = "Nguyen Xuan Hoai", title = "A Flexible Representation for Genetic Programming from Natural Language Processing", school = "Australian Defence force Academy, University of New South Wales", year = "2004", address = "Australia", month = dec, keywords = "genetic algorithms, genetic programming, grammar-guided, genotype space, natural language processing, phenotype space, tree adjoining grammars (TAGs)", URL = "http://handle.unsw.edu.au/1959.4/38750", URL = "http://www.library.unsw.edu.au/~thesis/adt-ADFA/uploads/approved/adt-ADFA20051024.152230/public/01front.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/hoai_thesis.tar.gz", size = "262 pages", abstract = "This thesis principally addresses some problems in genetic programming (GP) and grammar-guided genetic programming (GGGP) arising from the lack of operators able to make small and bounded changes on both genotype and phenotype space. It proposes a new and flexible representation for genetic programming, using a state-of-the-art formalism from natural language processing, Tree Adjoining Grammars (TAGs). It demonstrates that the new TAG-based representation possesses two important properties: non-fixed arity and locality. The former facilitates the design of new operators, including some which are bio-inspired, and others able to make small and bounded changes. The latter ensures that bounded changes in genotype space are reflected in bounded changes in phenotype space. With these two properties, the thesis shows how some well-known difficulties in standard GP and GGGP tree-based representations can be solved in the new representation. These difficulties have been previously attributed to the treebased nature of the representations; since TAG representation is also tree-based, it has enabled a more precise delineation of the causes of the difficulties. Building on the new representation, a new grammar guided GP system known as TAG3P has been developed, and shown to be competitive with other GP and GGGP systems. A new schema theorem, explaining the behaviour of TAG3P on syntactically constrained domains, is derived. Finally, the thesis proposes a new method for understanding performance differences between GP representations requiring different ways to bound the search space, eliminating the effects of the bounds through multi-objective approaches.", notes = "separate files", } @InProceedings{eurogp:HoaiMEH05, author = "Nguyen Xuan Hoai and Robert I. McKay and Daryl Essam and Hoang Tuan Hao", editor = "Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. {van Hemert} and Marco Tomassini", title = "Genetic Transposition in Tree-Adjoining Grammar Guided Genetic Programming: The Duplication Operator", booktitle = "Proceedings of the 8th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3447", year = "2005", address = "Lausanne, Switzerland", month = "30 " # mar # " - 1 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-25436-6", pages = "108--119", DOI = "doi:10.1007/978-3-540-31989-4_10", DOI = "doi:10.1007/b107383", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "We empirically investigate the use of dual duplication/truncation operators both as mutation operators and as generic local search operators, in combination with genetic search in a tree adjoining grammar guided genetic programming system (TAG3P). The results show that, on the problems tried, duplication/truncation works well as a mutation operator but not reliably when the complexity of the problem was scaled up. When using these dual operators as a generic local search operator, however, it helped TAG3P not only to solve the problems reliably but also cope well with scalability in problem complexity. Moreover, it managed to solve problems with very small population sizes.", notes = "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005", } @Article{HBE:IEETEC:06, title = "Representation and Structural Difficulty in Genetic Programming", author = "Nguyen Xuan Hoai and R. I. (Bob) McKay and Daryl Essam", journal = "IEEE Transactions on Evolutionary Computation", year = "2006", volume = "10", number = "2", pages = "157--166", month = apr, keywords = "genetic algorithms, genetic programming, Deletion, insertion, operator, representation, structural difficulty", URL = "http://sc.snu.ac.kr/courses/2006/fall/pg/aai/GP/nguyen/Structdiff.pdf", DOI = "doi:10.1109/TEVC.2006.871252", size = "10 pages", abstract = "Standard tree-based genetic programming suffers from a structural difficulty problem in that it is unable to search effectively for solutions requiring very full or very narrow trees. This deficiency has been variously explained as a consequence of restrictions imposed by the tree structure or as a result of the numerical distribution of tree shapes. We show that by using a different tree-based representation and local (insertion and deletion) structural modification operators, that this problem can be almost eliminated even with trivial (stochastic hill-climbing) search methods, thus eliminating the above explanations. We argue, instead, that structural difficulty is a consequence of the large step size of the operators in standard genetic programming, which is itself a consequence of the fixed-arity property embodied in its representation.", } @Article{HOANG:2017:Measurement, author = "Nhat-Duc Hoang and Chun-Tao Chen and Kuo-Wei Liao", title = "Prediction of chloride diffusion in cement mortar using Multi-Gene Genetic Programming and Multivariate Adaptive Regression Splines", journal = "Measurement", volume = "112", pages = "141--149", year = "2017", keywords = "genetic algorithms, genetic programming, Chloride diffusion, Cement mortar, Machine learning, Modeling equation, Construction material", ISSN = "0263-2241", DOI = "doi:10.1016/j.measurement.2017.08.031", URL = "http://www.sciencedirect.com/science/article/pii/S0263224117305365", abstract = "Chloride-induced damage of coastal concrete structure leads to serious structural deterioration. Thus, chloride content in concrete is a crucial parameter for determining the corrosion state. This study aims at establishing machine learning models for chloride diffusion prediction with the u of the Multi-Gene Genetic Programming (MGGP) and Multivariate Adaptive Regression Splines (MARS). MGGP and MARS are well-established methods to construct predictive modeling equations from experimental data. These modeling equations can be used to express the relationship between the chloride ion diffusion in concrete and its influencing factors. Moreover, a data set, which contains 132 cement mortar specimens, has been collected for this study to train and verify the machine learning approaches. The prediction results of MGGP and MARS are compared with those of the Artificial Neural Network and Least Squares Support Vector Regression. Notably, MARS demonstrates the best prediction performance with the Root Mean Squared Error (RMSE)=0.70 and the coefficient of determination (R2)=0.91", } @Article{Hoang:2018:NatHaz, title = "Spatial prediction of rainfall-induced shallow landslides using gene expression programming integrated with GIS: a case study in Vietnam", author = "Nhat-Duc Hoang and Dieu Tien Bui", publisher = "springer", journal = "Natural Hazards", year = "2018", volume = "92", pages = "1871--1887", keywords = "genetic algorithms, genetic programming, gene expression programming, shallow landslide, rainfall-induced, geographical information system, artificial intelligence", bibsource = "OAI-PMH server at oai.repec.org", identifier = "RePEc:spr:nathaz:v:92:y:2018:i:3:d:10.1007_s11069-018-3286-z", oai = "oai:RePEc:spr:nathaz:v:92:y:2018:i:3:d:10.1007_s11069-018-3286-z", URL = "http://link.springer.com/10.1007/s11069-018-3286-z", DOI = "doi:10.1007/s11069-018-3286-z", abstract = "Shallow landslide represents one of the most devastating morphodynamic processes that bring about great destruction to human life and infrastructure. Landslide spatial prediction can significantly help government agencies in land use and mitigation measure planning. Nevertheless, landslide spatial modeling remains a very challenging problem due to its inherent complexity. This study proposes an integration of geographical information system (GIS) and gene expression programming (GEP) for predicting rainfall-induced shallow landslide occurrences in Son La Province, Vietnam. A landslide inventory map has been constructed based on historical landslide locations. Furthermore, a dataset which features 12 influencing factors is collected using GIS technology. Based on the GEP algorithm and the collected dataset, an empirical model for spatial prediction of the shallow landslide has been established by means of natural selection. The predictive capability of the model has been verified by the area under the curve calculation. Experimental results point out that the newly proposed approach is a promising tool for shallow landslide prediction.", } @Article{hoang:2022:Mathematics, author = "Nhat-Duc Hoang", title = "Machine Learning-Based Estimation of the Compressive Strength of Self-Compacting Concrete: A Multi-Dataset Study", journal = "Mathematics", year = "2022", volume = "10", number = "20", pages = "Article No. 3771", keywords = "genetic algorithms, genetic programming", ISSN = "2227-7390", URL = "https://www.mdpi.com/2227-7390/10/20/3771", DOI = "doi:10.3390/math10203771", abstract = "This paper aims at performing a comparative study to investigate the predictive capability of machine learning (ML) models used for estimating the compressive strength of self-compacting concrete (SCC). Seven prominent ML models, including deep neural network regression (DNNR), extreme gradient boosting machine (XGBoost), gradient boosting machine (GBM), adaptive boosting machine (AdaBoost), support vector regression (SVR), Levenberg–Marquardt artificial neural network (LM-ANN), and genetic programming (GP), are employed. Four experimental datasets, compiled in previous studies, are used to construct the ML-based methods. The models’ generalisation capabilities are reliably evaluated by 20 independent runs. Experimental results point out the superiority of the DNNR, which has excelled other models in three out of four datasets. The XGBoost is the second-best model, which has gained the first rank in one dataset. The outcomes point out the great potential of the used ML approaches in modelling the compressive strength of SCC. In more details, the coefficient of determination (R2) surpasses 0.8 and the mean absolute percentage error (MAPE) is always below 15percent for all datasets. The best results of R2 and MAPE are 0.93 and 7.2percent, respectively.", notes = "also known as \cite{math10203771}", } @MastersThesis{Hoang:mastersthesis, author = "Tuan-Hao Hoang", title = "Representation and Data Preparation Issues in Ecological Time-Series Modeling using Genetic Programming", school = "School of Computer Science University College, University of New South Wales, Australian Defence Force Academy", year = "2004", type = "Master S.c of Information Technology", month = nov, note = "Under the co-supervision of Daryl Essam and R.I. McKay (2004). School of IT and EE, University of New South Wales, ADFA, Canberra, Australia", keywords = "genetic algorithms, genetic programming, TAG3P", URL = "http://seal.tst.adfa.edu.au/~z3106820/publications/masthesis.pdf", size = "42 pages", abstract = "Many important ecological datasets are collected irregularly over time. In view of the fact that many time series modelling techniques require regularly spaced intervals, one common approach is to interpolate the data, and then build a model from the interpolated data. However, this may cause negative effects on the performance of models built on the interpolated data. This thesis has two aims, the first is to investigate the extent of those effect, by comparing models built on the original sample data (the irregular dataset of the phytoplankton in Lake Kasumigaura), and on interpolated data, whilst the second is to examine the effect of representation on modelling systems, in particular the differences between context-free and tree-adjoining grammar models.", } @InProceedings{Hao:2004:aspgp, author = "Hoang Tuan Hao and Nguyen Xuan Hoai and Robert I McKay", title = "Does it Matter Where you Start? A Comparison of Two Initialisation Strategies for Grammar Guided Genetic Programming", booktitle = "Proceedings of The Second Asian-Pacific Workshop on Genetic Programming", year = "2004", editor = "R I Mckay and Sung-Bae Cho", address = "Cairns, Australia", month = "6-7 " # dec, keywords = "genetic algorithms, genetic programming, GGGP, TAG, TAG3P", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.76.7772", URL = "http://sc.snu.ac.kr/PAPERS/Initialisation_comparison.pdf", size = "14 pages", abstract = "In this paper, we experimentally show that the initialisation process is very important for Grammar Guided Genetic Programming (GGGP). In particular, using different initialization strategies (algorithms) can lead to very different overall results with GGGP. We also show that on the problems tried, the initialisation algorithm from Tree Adjoining Grammar Guided Genetic Programming (TAG3P) helps GGGP improve its performance compared with the use of the standard initialisation algorithm proposed in [10, 11].", notes = "broken http://sc.snu.ac.kr/~aspgp/aspgp04/programme.html", } @InCollection{hoang:2005:GPTP, author = "Tuan Hao Hoang and Nguyen Xuan Hoai and R. I. (Bob) McKay and Daryl Essam", title = "The Importance of Local Search: A Grammar Based Approach to Environmental Time Series Modelling", booktitle = "Genetic Programming Theory and Practice {III}", year = "2005", editor = "Tina Yu and Rick L. Riolo and Bill Worzel", volume = "9", series = "Genetic Programming", chapter = "11", pages = "159--175", address = "Ann Arbor", month = "12-14 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, local search, insertion, deletion, grammar guided, tree adjoining grammar, ecological modelling, time series", ISBN = "0-387-28110-X", DOI = "doi:10.1007/0-387-28111-8_11", size = "17 pages", abstract = "Standard Genetic Programming operators are highly disruptive, with the concomitant risk that it may be difficult to converge to an optimal structure. The Tree Adjoining Grammar (TAG) formalism provides a more flexible Genetic Programming tree representation which supports a wide range of operators while retaining the advantages of tree-based representation. In particular, minimal-change point insertion and deletion operators may be defined. Previous work has shown that point insertion and deletion, used as local search operators, can dramatically reduce search effort in a range of standard problems. Here, we evaluate the effect of local search with these operators on a real-World ecological time series modelling problem. For the same search effort, TAG-based GP with the local search operators generates solutions with significantly lower training set error. The results are equivocal on test set error, local search generating larger individuals which generalise only a little better than the less accurate solutions given by the original algorithm.", notes = "part of \cite{yu:2005:GPTP} Published Jan 2006 after the workshop", } @InProceedings{1144141, author = "Tuan-Hao Hoang and Nguyen Xuan Hoai and Nguyen Thi Hien and R I McKay and Daryl Essam", title = "{ORDERTREE}: a new test problem for genetic programming", booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation", year = "2006", editor = "Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos {Coello Coello} and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens", volume = "1", ISBN = "1-59593-186-4", pages = "807--814", address = "Seattle, Washington, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p807.pdf", DOI = "doi:10.1145/1143997.1144141", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "8-12 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, benchmark problems, graph and tree search strategies, languages, problem difficulty, theory", acmid = "1144141", size = "8 pages", abstract = "In this paper, we describe a new test problem for genetic programming (GP), ORDERTREE. We argue that it is a natural analogue of ONEMAX, a popular GA test problem, and that it also avoids some of the known weaknesses of other benchmark problems for Genetic Programming. Through experiments, we show that the difficulty of the problem can be tuned not only by increasing the size of the problem, but also by increasing the non-linearity in the fitness structure.", notes = "GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM Order Number 910060", } @InProceedings{Hoang:2006:CEC, author = "Tuan-Hao Hoang and Daryl Essam and R. I. (Bob) McKay and Xuan Hoai Nguyen", title = "Solving Symbolic Regression Problems using Incremental Evaluation in Genetic Programming", booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary Computation", year = "2006", pages = "7487--7494", address = "Vancouver", month = "16-21 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, TAG, SYM_GP, building block, polynomial symbolic regression, Fourier series", ISBN = "0-7803-9487-9", URL = "http://seal.tst.adfa.edu.au/~z3106820/publications/cec2006.devtag.pdf", DOI = "doi:10.1109/CEC.2006.1688570", size = "8 pages", abstract = "we show some experimental results using Incremental Evaluation with Tree Adjoining Grammar Guided Genetic Programming (DEVTAG) on two symbolic regression problems, a benchmark polynomial fitting problem in genetic programming, and a Fourier series problem (saw-tooth problem). In our pilot study, we compare results with standard Genetic Programming (GP) and the original Tree Adjoining Grammar Guided Genetic Programming (TAG3P). Our results on the two problems are good, outperforming both standard GP and the original TAG3P.", notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE.", } @InProceedings{Hao:2006:ASPGP, title = "Developmental evaluation in genetic programming: A TAG-based framework", author = "Tuan-Hao Hoang and Daryl Essam and R. I. McKay and Xuan Hoai Nguyen", booktitle = "Proceedings of the Third Asian-Pacific workshop on Genetic Programming", year = "2006", editor = "The Long Pham and Hai Khoi Le and Xuan Hoai Nguyen", pages = "86--97", ISSN = "18590209", address = "Military Technical Academy, Hanoi, VietNam", keywords = "genetic algorithms, genetic programming", URL = "http://gpbib.cs.ucl.ac.uk/aspgp06/haodtag3p_new.pdf", size = "12 pages", abstract = "We build on our previous feasibility studies [16, 17], which demonstrated the impact of evaluation during development in the DEVTAG system, and here present a full-fledged developmental system DTAG3P, with developmental evaluation, based on Tree-Adjoining Grammars (TAG). While DEVTAG used only a trivial developmental process, DTAG3P uses L-systems to encode TAG derivation trees, the L-systems permitting a full developmental process. DEVTAG was previously shown to dramatically out-perform standard Genetic Programming (GP) on some structured families of problems; here, we examine DTAG3P's performance on one of these families, and find a further major increment in performance over DEVTAG. DTAG3P achieves this despite dispensing with two extra control parameters which it was necessary to introduce into DEVTAG.", notes = "broken march 2020 http://www.aspgp.org", } @InProceedings{Hoang:2007:ISICA, publisher_address = "Wuhan, China", author = "Tuan-Hao Hoang and Daryl Essam and Robert Ian (Bob) McKay and Xuan Hoai Nguyen", booktitle = "Proceedings of the 2007 International Symposium on Intelligent Computation and Applications (ISICA)", address = "Wuhan, China", month = sep # " 21-23", notes = "Accepted, Refereed International Conference Papers", publisher = "China University of Geosciences Press", title = "Building on Success in Genetic Programming:Adaptive Variation \& Developmental Evaluation", URL = "http://sc.snu.ac.kr/PAPERS/dtag.pdf", year = "2007", keywords = "genetic algorithms, genetic programming", } @InProceedings{conf/isica/HoangEMH07, author = "Tuan Hao Hoang and Daryl Essam and Bob McKay and Nguyen Xuan Hoai", title = "Building on Success in Genetic Programming: Adaptive Variation and Developmental Evaluation", booktitle = "Proceedings of the Second International Symposium on Computation and Intelligence, ISICA 2007", year = "2007", editor = "Lishan Kang and Yong Liu and Sanyou Y. Zeng", volume = "4683", series = "Lecture Notes in Computer Science", pages = "137--146", address = "Wuhan, China", month = sep # " 21-23", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Developmental, Incremental Learning, Adaptive Mutation", isbn13 = "978-3-540-74580-8", DOI = "doi:10.1007/978-3-540-74581-5_15", size = "10 pages", abstract = "We investigate a developmental tree-adjoining grammar guided genetic programming system (DTAG3P+), in which genetic operator application rates are adapted during evolution. We previously showed developmental evaluation could promote structured solutions and improve performance in symbolic regression problems. However testing on parity problems revealed an unanticipated problem, that good building blocks for early developmental stages might be lost in later stages of evolution. The adaptive variation rate in DTAG3P plus preserves good building blocks found in early search for later stages. It gives both good performance on small k-parity problems, and good scaling to large problems.", bibdate = "2007-08-31", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/isica/isica2007.html#HoangEMH07", } @InProceedings{HoaMck07, author = "Tuan-Hao Hoang and R. McKay and D. Essam and Xuan Hoai Nguyen", title = "Developmental Evaluation in Genetic Programming: A Position Paper", booktitle = "Frontiers in the Convergence of Bioscience and Information Technologies, FBIT 2007", year = "2007", pages = "773--778", address = "Jeju City, Korea", month = "11-13 " # oct, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, grammars, trees (mathematics), L-systems, code duplication, code replication, developmental evaluation, developmental tree adjoining grammar guided GP, modularity selection, structural regularity, tree adjoining grammar guided derivation trees", isbn13 = "978-0-7695-2999-8", URL = "http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=4524062&arnumber=4524205&count=165&index=142", DOI = "doi:10.1109/FBIT.2007.104", abstract = "Standard genetic programming genotypes are generally highly disorganised and poorly structured, with little code replication. This is also true of existing developmental genetic programming systems, which exploit regularity by using procedures, functional modules, or macros and parameters passing. By contrast, in biological developmental evolution, nature works through code duplication to generate modularity, regularity and hierarchy. Previous developmental approaches have only one level of evaluation for each individual - an approach which limits the advantages of modularity to the species rather than the individual, and hence inhibits selection of modularity. We argued that evaluation during development is necessary for structural regularity to emerge. To confirm the benefits of developmental evaluation and the contribution of code duplication to nature, our new developmental process uses a new representation. Developmental tree adjoining grammar guided GP (DTAG3P) uses L-systems to encode tree adjoining grammar guided (TAG) derivation trees, and has been investigated. We have demonstrated scalable solutions to difficult families of problems, and have evidence that this performance is linked to the generation and exploitation of structural regularities in the solutions.", notes = "FBIT 2007: http://ieeexplore.ieee.org/servlet/opac?punumber=4524061", } @Article{Hoang:2008:IJKBIES, author = "Tuan-Hao Hoang and Daryl Essam and R. I. (Bob) McKay and Nguyen Xuan Hoai", title = "Developmental evaluation in Genetic Programming: The {TAG}-based frame work", journal = "International Journal of Knowledge-Based and Intelligent Engineering Systems", year = "2008", volume = "12", number = "1", pages = "69--82", keywords = "genetic algorithms, genetic programming", ISSN = "1327-2314", publisher = "IOS Press", URL = "http://content.iospress.com/articles/international-journal-of-knowledge-based-and-intelligent-engineering-systems/kes00142", DOI = "doi:10.3233/KES-2008-12106", size = "14 pages", abstract = "We build on our previous feasibility studies [18,20], which demonstrated the impact of evaluation during development in the DEVTAG system, and here present a full-fledged developmental system - Developmental Tree Adjoining Grammar Guided Genetic Programming (DTAG3P) with developmental evaluation, based on Tree-Adjoining Grammars (TAG). While DEVTAG used only a trivial developmental process, DTAG3P uses L-systems to encode TAG derivation trees, because the L-systems permit a full developmental process. DEVTAG was previously shown to dramatically out-perform standard Genetic Programming (GP) on some structured families of problems; here, we examine DTAG3P's performance on these families, and find a further major increment in performance over DEVTAG. DTAG3P achieves this despite dispensing with two extra control parameters which were necessary with DEVTAG.", notes = "KES", } @InProceedings{DBLP:conf/ices/HoangMEN08, author = "Tuan Hao Hoang and R. I. (Bob) McKay and Daryl Essam and Nguyen Xuan Hoai", title = "Learning General Solutions through Multiple Evaluations during Development", booktitle = "Proceedings of the 8th International Conference Evolvable Systems: From Biology to Hardware, ICES 2008", year = "2008", editor = "Gregory Hornby and Luk{\'a}s Sekanina and Pauline C. Haddow", series = "Lecture Notes in Computer Science", volume = "5216", pages = "201--212", address = "Prague, Czech Republic", month = sep # " 21-24", publisher = "Springer", bibsource = "DBLP, http://dblp.uni-trier.de", keywords = "genetic algorithms, genetic programming, Developmental Genetic Programming, Hyper-heuristics, Generalisation Overfitting, Parsimony", isbn13 = "978-3-540-85856-0", DOI = "doi:10.1007/978-3-540-85857-7_18", abstract = "In this paper, we investigate whether performing multiple evaluations during development, a technique we call Evolutionary Developmental Evaluation (EDE), could help developmental Genetic Programming (GP) evolve general solutions, solving not only the original (training) problem, but also unseen similar problems (with higher degrees of complexity). The hypothesis is tested on two families of regression problems, and the experimental results support the hypothesis.", } @PhdThesis{Hoang:thesis, author = "Tuan-Hao Hoang", title = "Evolutionary Developmental Evaluation : the Interplay between Evolution and Development", school = "Information Technology \& Electrical Engineering, Australian Defence Force Academy, University of New South Wales", year = "2008", address = "Australia", month = dec, keywords = "genetic algorithms, genetic programming, Evolutionary Development Evaluation (EDE), Development Tree Adjoining Grammar Guided Genetic Programming (DTAG3P), Evolutionary computation, Developmental biology, Developmental genetics", broken = "http://trove.nla.gov.au/version/49711933", URL = "http://handle.unsw.edu.au/1959.4/44870", URL = "http://unsworks.unsw.edu.au/fapi/datastream/unsworks:8166/SOURCE01.pdf", size = "338 pages", language = "English", abstract = "This thesis was inspired by the difficulties of artificial evolutionary systems in finding elegant and well structured, regular solutions. That is that the solutions found are usually highly disorganised, poorly structured and exhibit limited re-use, resulting in bloat and other problems. This is also true of previous developmental evolutionary systems, where structural regularity emerges only by chance. We hypothesise that these problems might be ameliorated by incorporating repeated evaluations on increasingly difficult problems in the course of a developmental process. This thesis introduces a new technique for learning complex problems from a family of structured increasingly difficult problems, Evolutionary Developmental Evaluation (EDE). This approach appears to give more structured, scalable and regular solutions to such families of problems than previous methods. In addition, the thesis proposes some bio-inspired components that are required by developmental evolutionary systems to take full advantage of this approach. The key part of this is the developmental process, in combination with a varying fitness function evaluated at multiple stages of development, generates selective pressure toward generalisation. This also means that parsimony in structure is selected for without any direct parsimony pressure. As a result, the system encourages the emergence of modularity and structural regularity in solutions. In this thesis, a new genetic developmental system called Developmental Tree Adjoining Grammar Guided Genetic Programming (DTAG3P), is implemented, embodying the requirements above. It is tested on a range of benchmark problems. The results indicate that the method generates more regularly-structured solutions than the competing methods. As a result, the system is able to scale, at least on the problem classes tested, to very complex instances the system encourages the emergence of modularity and structural regularity in solutions. In this thesis, a new genetic developmental system called Developmental Tree Adjoining Grammar Guided Genetic Programming (DTAG3P), is implemented, embodying the requirements above. It is tested on a range of benchmark problems. The results indicate that the method generates more regularly-structured solutions than competing methods. As a result, the system is able to scale, at least on the problem classes tested, to very complex problem instances.", notes = "oai:unsworks.unsw.edu.au:unsworks:8166 Supervisor: Daryl Essam", } @Article{Hoang:2011:ieeeTEC, author = "Tuan-Hao Hoang and R. I. McKay and Daryl Essam and Nguyen Xuan Hoai", title = "On Synergistic Interactions Between Evolution, Development and Layered Learning", journal = "IEEE Transactions on Evolutionary Computation", year = "2011", volume = "15", number = "3", pages = "287--312", month = jun, keywords = "genetic algorithms, genetic programming, animal development, biological evolution, development learning, evolution learning, evolutionary developmental evaluation, learning theory perspective, lifelong layered learning, plant development, tree-adjoining grammar guided genetic programming, biology, learning systems", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2011.2150752", size = "26 pages", abstract = "We investigate interactions between evolution, development and lifelong layered learning in a combination we call evolutionary developmental evaluation (EDE), using a specific implementation, developmental tree-adjoining grammar guided genetic programming (GP). The approach is consistent with the process of biological evolution and development in higher animals and plants, and is justifiable from the perspective of learning theory. In experiments, the combination is synergistic, outperforming algorithms using only some of these mechanisms. It is able to solve GP problems that lie well beyond the scaling capabilities of standard GP. The solutions it finds are simple, succinct, and highly structured. We conclude this paper with a number of proposals for further extension of EDE systems.", notes = "DTAG3P, TAG3P, tree adjoined grammar (TAG), symbolic regression, k-parity, ordertree Also known as \cite{5898401}", } @Article{Hoang:2025:compstruc, author = "Vu Ngoc Viet Hoang and Pham Trung Thanh", title = "Analytical and {ANN-based} approaches for free vibration and nonlinear transient analysis of {FG-GOEAM} toroidal shell segments", journal = "Computer and Structures", year = "2025", volume = "309", pages = "107676", keywords = "genetic algorithms, genetic programming, Nonlinear dynamics, Galerkin technique, Artificial neural network, ANN, FG-GOEAM toroidal shell segments, Non-homogeneous Winkler-Pasternak foundation", ISSN = "0045-7949", URL = "https://www.sciencedirect.com/science/article/pii/S0045794925000343", DOI = "doi:10.1016/j.compstruc.2025.107676", abstract = "Objectiv", } @InProceedings{hocaoglu:1998:, author = "Cem Hocaoglu and Arthur C. Sanderson", title = "Multi-dimensional Path Planning Evolutionary Computation using Evolutionary Computation", booktitle = "Proceedings of the 1998 IEEE World Congress on Computational Intelligence", year = "1998", pages = "165--170", address = "Anchorage, Alaska, USA", month = "5-9 " # may, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, amplifiers, analog circuit design, circuit evolution, computational circuits, embryonic circuit elimination, filters, knowledge representation, minimal domain knowledge, problem-specific knowledge, analogue circuits, circuit CAD, circuit optimisation, intelligent design assistants, knowledge representation, programming", ISBN = "0-7803-4869-9", file = "c029.pdf", DOI = "doi:10.1109/ICEC.1998.699495", size = "6 pages", abstract = "This paper describes a flexible and efficient multi-dimensional path planning algorithm based on evolutionary computation concepts. A novel iterative multi-resolution path representation is used as a basis for the GA coding. The use of a multi-resolution path representation can reduce the expected search length for the path planning problem. If a successful path is found early in the search hierarchy (at a low level of resolution), then further expansion of that portion of the path search is not necessary. This advantage is mapped into the encoded search space and adjusts the string length accordingly. The algorithm is flexible; it handles multi-dimensional path planning problems, accommodates different optimization criteria and changes in these criteria, and it uses domain specific knowledge for making decisions. In the evolutionary path planner, the individual candidates are evaluated with respect to the workspace so that computation of the configuration space is not required. The algorithm can be applied for planning paths for mobile robots, assembly, pianomovers problems and articulated manipulators. The effectiveness of the algorithm is demonstrated on a number of multi-dimensional path planning problems.", notes = "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE World Congress on Computational Intelligence", } @Article{journals/ijsinnov/HochinSTN14, author = "Teruhisa Hochin and Tatsuya Saigo and Shinji Tamura and Hiroki Nomiya", title = "Generation of Concurrency Control Program by Extending Functions in Genetic Programming", journal = "International Journal of Software Innovation", year = "2014", number = "4", volume = "2", pages = "13--27", keywords = "genetic algorithms, genetic programming", bibdate = "2015-12-18", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ijsinnov/ijsinnov2.html#HochinSTN14", URL = "http://dx.doi.org/10.4018/ijsi.2014100102", abstract = "This paper tries to generate an appropriate concurrency control program by using genetic programming (GP). Although two variables have been introduced for generating concurrency control programs, these made program generation difficult because of the explosion of combination. By limiting the usage of variables to one of two variables, an appropriate program could be generated. This method, however, could not create all of concurrency control programs. This paper extends the variable to bring more information than before for creating any concurrency control programs. It is experimentally shown that an appropriate concurrency control program can successfully be generated by extending the variable.", } @InCollection{hochmuth:2003:OGEPTS, author = "Gregor Hochmuth", title = "On the Genetic Evolution of a Perfect Tic-Tac-Toe Strategy", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2003", year = "2003", editor = "John R. Koza", pages = "75--82", address = "Stanford, California, 94305-3079 USA", month = "4 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms", URL = "http://www.genetic-programming.org/sp2003/Hochmuth.pdf", notes = "part of \cite{koza:2003:gagp}", } @InProceedings{Hodan:2020:GECCO, author = "David Hodan and Vojtech Mrazek and Zdenek Vasicek", title = "Semantically-Oriented Mutation Operator in Cartesian Genetic Programming for Evolutionary Circuit Design", year = "2020", editor = "Carlos Artemio {Coello Coello} and Arturo Hernandez Aguirre and Josu Ceberio Uribe and Mario Garza Fabre and Gregorio {Toscano Pulido} and Katya Rodriguez-Vazquez and Elizabeth Wanner and Nadarajen Veerapen and Efren Mezura Montes and Richard Allmendinger and Hugo Terashima Marin and Markus Wagner and Thomas Bartz-Beielstein and Bogdan Filipic and Heike Trautmann and Ke Tang and John Koza and Erik Goodman and William B. Langdon and Miguel Nicolau and Christine Zarges and Vanessa Volz and Tea Tusar and Boris Naujoks and Peter A. N. Bosman and Darrell Whitley and Christine Solnon and Marde Helbig and Stephane Doncieux and Dennis G. Wilson and Francisco {Fernandez de Vega} and Luis Paquete and Francisco Chicano and Bing Xue and Jaume Bacardit and Sanaz Mostaghim and Jonathan Fieldsend and Oliver Schuetze and Dirk Arnold and Gabriela Ochoa and Carlos Segura and Carlos Cotta and Michael Emmerich and Mengjie Zhang and Robin Purshouse and Tapabrata Ray and Justyna Petke and Fuyuki Ishikawa and Johannes Lengler and Frank Neumann", isbn13 = "9781450371285", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377930.3390188", DOI = "doi:10.1145/3377930.3390188", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference", pages = "940--948", size = "9 pages", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, semantic operator, semantic mutation, evolutionary circuit design", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", abstract = "Despite many successful applications, Cartesian Genetic Programming (CGP) suffers from limited scalability, especially when used for evolutionary circuit design. Considering the multiplier design problem, for example, the 5 by 5-bit multiplier represents the most complex circuit evolved from a randomly generated initial population. The efficiency of CGP highly depends on the performance of the point mutation operator, however, this operator is purely stochastic. This contrasts with the recent developments in Genetic Programming (GP), where advanced informed approaches such as semantic-aware operators are incorporated to improve the search space exploration capability of GP. ee propose a semantically-oriented mutation operator (SOMO) suitable for the evolutionary design of combinational circuits. SOMO uses semantics to determine the best value for each mutated gene. Compared to the common CGP and its variants as well as the recent versions of Semantic GP, the proposed method converges on common Boolean benchmarks substantially faster while keeping the phenotype size relatively small. The successfully evolved instances presented in this paper include 10-bit parity, 10+10-bit adder and 5x5-bit multiplier. The most complex circuits were evolved in less than one hour with a single-thread implementation running on a common CPU.", notes = "Nominated for Best Paper. SOMO Also known as \cite{10.1145/3377930.3390188} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @Article{Hodan:GPEM, author = "David Hodan and Vojtech Mrazek and Zdenek Vasicek", title = "Semantically‑oriented mutation operator in cartesian genetic programming for evolutionary circuit design", journal = "Genetic Programming and Evolvable Machines", year = "2021", volume = "22", number = "4", pages = "539--572", month = dec, note = "Special Issue: Highlights of Genetic Programming 2020 Events", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, evolvable hardware, Semantic operator, Semantic mutation, Evolutionary circuit design", ISSN = "1389-2576", URL = "https://rdcu.be/cyKFV", DOI = "doi:10.1007/s10710-021-09416-6", size = "34 pages", abstract = "Cartesian genetic programming (CGP) represents the most efficient method for the evolution of digital circuits. Despite many successful applications, however, CGP suffers from limited scalability, especially when used for evolutionary circuit design, i.e. design of circuits from a randomly initialized population. Considering the multiplier design problem, for example, the 5 by 5-bit multiplier represents the most complex circuit designed by the evolution from scratch. The efficiency of CGP highly depends on the performance of the point mutation operator, however, this operator is purely stochastic. This contrasts with the recent developments in genetic programming (GP), where advanced informed approaches such as semantic-aware operators are incorporated to improve the search space exploration capability of GP. we propose a semantically-oriented mutation operator (SOMOk) suitable for the evolutionary design of combinational circuits. In contrast to standard point mutation modifying the values of the mutated genes randomly, the proposed operator uses semantics to determine the best value for each mutated gene. Compared to the common CGP and its variants, the proposed method converges on common Boolean benchmarks substantially faster while keeping the phenotype size relatively small. The successfully evolved instances presented in this paper include 10-bit parity, 10 + 10-bit adder and 5 by 5-bit multiplier. The most complex circuits were evolved in less than one hour with a single-thread implementation running on a common CPU.", notes = "Faculty of Information Technology, IT4Innovations Centre of Excellence, Brno University of Technology, Brno, Czech Republic", } @InCollection{Hodjat:2012:GPTP, author = "Babak Hodjat and Hormoz Shahrzad", title = "Introducing an Age-Varying Fitness Estimation Function", booktitle = "Genetic Programming Theory and Practice X", year = "2012", series = "Genetic and Evolutionary Computation", editor = "Rick Riolo and Ekaterina Vladislavleva and Marylyn D. Ritchie and Jason H. Moore", publisher = "Springer", chapter = "5", pages = "59--71", address = "Ann Arbor, USA", month = "12-14 " # may, keywords = "genetic algorithms, genetic programming, Evolutionary Computation, Fitness Functions, Distribution, Large Data", isbn13 = "978-1-4614-6845-5", URL = "http://dx.doi.org/10.1007/978-1-4614-6846-2_5", DOI = "doi:10.1007/978-1-4614-6846-2_5", abstract = "We present a method for estimating fitness functions that are computationally expensive for an exact evaluation. The proposed estimation method applies a number of partial evaluations based on incomplete information or uncertainties. We show how this method can yield results that are close to similar methods where fitness is measured over the entire dataset, but at a fraction of the speed or memory usage, and in a parallelisable manner. We describe our experience in applying this method to a real world application in the form of evolving equity trading strategies.", notes = "part of \cite{Riolo:2012:GPTP} published after the workshop in 2013", } @InCollection{Hodjat:2013:GPTP, author = "Babak Hodjat and Erik Hemberg and Hormoz Shahrzad and Una-May O'Reilly", title = "Maintenance of a Long Running Distributed Genetic Programming System for Solving Problems Requiring Big Data", booktitle = "Genetic Programming Theory and Practice XI", year = "2013", series = "Genetic and Evolutionary Computation", editor = "Rick Riolo and Jason H. Moore and Mark Kotanchek", publisher = "Springer", chapter = "4", pages = "65--83", address = "Ann Arbor, USA", month = "9-11 " # may, keywords = "genetic algorithms, genetic programming, Learning classifier system, Cloud scale, Distributed, Big data", isbn13 = "978-1-4939-0374-0", DOI = "doi:10.1007/978-1-4939-0375-7_4", abstract = "We describe a system, ECStar, that outstrips many scaling aspects of extant genetic programming systems. One instance in the domain of financial strategies has executed for extended durations (months to years) on nodes distributed around the globe. ECStar system instances are almost never stopped and restarted, though they are resource elastic. Instead they are interactively redirected to different parts of the problem space and updated with up-to-date learning. Their non-reproducibility (i.e. single play of the tape process) due to their complexity makes them similar to real biological systems. In this contribution we focus upon how ECStar introduces a provocative, important, new paradigm for GP by its sheer size and complexity. ECStar's scale, volunteer compute nodes and distributed hub-and-spoke design have implications on how a multi-node instance is managed. We describe the set up, deployment, operation and update of an instance of such a large, distributed and long running system. Moreover, we outline how ECStar is designed to allow manual guidance and re-alignment of its evolutionary search trajectory.", notes = " Part of \cite{Riolo:2013:GPTP} published after the workshop in 2013", } @InProceedings{Hodjat:2015:GPTP, author = "Babak Hodjat and Jormoz Shahrzad", title = "Symbolic {nPool}: Massively Distributed Simultaneous Evolution and Cross-Validation in {EC-Star}", booktitle = "Genetic Programming Theory and Practice XIII", year = "2015", editor = "Rick Riolo and William P. Worzel and M. Kotanchek and A. Kordon", series = "Genetic and Evolutionary Computation", pages = "79--90", address = "Ann Arbor, USA", month = "14-16 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Evolutionary computation, Distributed processing, Machine learning, Cross-validation", isbn13 = "978-3-319-34223-8", URL = "http://www.springer.com/us/book/9783319342214", DOI = "doi:10.1007/978-3-319-34223-8_5", abstract = "We introduce a cross-validation algorithm called nPool that can be applied in a distributed fashion. Unlike classic k-fold cross-validation, the data segments are mutually exclusive, and training takes place only on one segment. This system is well suited to run in concert with the EC-Star distributed Evolutionary system, cross-validating solution candidates during a run. The system is tested with different numbers of validation segments using a real-world problem of classifying ICU blood-pressure time series.", notes = " Part of \cite{Riolo:2015:GPTP} Published after the workshop in 2016", } @InProceedings{Hodjat:2016:GPTP, author = "Babak Hodjat and Hormoz Shahrzad and Risto Miikkulainen and Lawrence Murray and Chris Holmes", title = "{PRETSL}: Distributed Probabilistic Rule Evolution for Time-Series Classification", booktitle = "Genetic Programming Theory and Practice XIV", year = "2016", editor = "Rick Riolo and Bill Worzel and Brian Goldman and Bill Tozier", pages = "139--148", address = "Ann Arbor, USA", month = "19-21 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Evolutionary Computation, Probabilistic Rule-sets, Distributed Processing, Time Series Classification", isbn13 = "978-3-319-97087-5", URL = "http://nn.cs.utexas.edu/?hodjat:gptp16", URL = "http://nn.cs.utexas.edu/downloads/papers/hodjat.gptp16.pdf", URL = "https://www.springer.com/us/book/9783319970875", DOI = "doi:10.1007/978-3-319-97088-2_9", size = "12 pages", abstract = "The distributed evolutionary computation platform EC-Star is extended in this paper to probabilistic classifiers. This extension, called PRETSL, allows the distributed age-layered evolution of probabilistic rule sets, which in turn makes more fine-grained decisions possible. The method is tested on 20 UCI data problems, as well as a larger dataset of arterial blood pressure waveforms. The Results show consistent improvement in all cases compared to binary classification rule-sets. Probabilistic rule evolution is thus a promising approach to difficult classification tasks and particularly well suited for time-series classification.", notes = " Part of \cite{Tozier:2016:GPTP} tpublished after the workshop", } @InProceedings{hodjat:alife22, author = "Babak Hodjat and Hormoz Shahrzad and Risto Miikkulainen", title = "{DIAS}: A Domain-Independent Alife-Based Problem-Solving System", booktitle = "Proceedings of the 2022 Conference on Artificial Life", year = "2022", editor = "Silvia Holler and Richard Loeffler and Stuart Bartlett", pages = "214--222", month = jul # " 18-22", organisation = "ISAL", publisher = "MIT Press", note = "32", keywords = "genetic algorithms, genetic programming", URL = "http://nn.cs.utexas.edu/?hodjat:alife22", URL = "https://direct.mit.edu/isal/proceedings-pdf/isal/34/32/2035327/isal_a_00514.pdf", DOI = "doi:10.1162/isal_a_00514", abstract = "A domain-independent problem-solving system based on principles of Artificial Life is introduced. In this system, DIAS, the input and output dimensions of the domain are laid out in a spatial medium. A population of actors, each seeing only part of this medium, solves problems collectively in it. The process is independent of the domain and can be implemented through different kinds of actors. Through a set of experiments on various problem domains, DIAS is shown able to solve problems with different dimensionality and complexity, to require no hyperparameter tuning for new problems, and to exhibit lifelong learning, i.e. adapt rapidly to run-time changes in the problem domain, and do it better than a standard non-collective approach. DIAS therefore demonstrates a role for Alife in building scalable, general, and adaptive problem-solving systems.", notes = "held virtually due to the ongoing COVID-19 pandemic. https://alife.org/conference/alife-2022/", } @InProceedings{hoehn:1999:PCMGAAII, author = "Theodore P. Hoehn and Chrisila C. Pettey", title = "Parental and Cyclic-Rate Mutation in Genetic Algorithms: An Initial Investigation", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "297--304", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-383.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-383.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Hofer:2013:ISSREW, author = "Birgit Hofer and Franz Wotawa", booktitle = "IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW 2013)", title = "Mutation-based spreadsheet debugging", year = "2013", month = "4-7 " # nov, pages = "132--137", keywords = "genetic algorithms, genetic programming, SBSE, Fault Correction, Automated Debugging, Mutation", DOI = "doi:10.1109/ISSREW.2013.6688892", abstract = "Spreadsheets are the most prominent example of end-user programming. Unfortunately, spreadsheets often contain faults. Spreadsheets can be very complex and can contain several thousand formula. Therefore, debugging of spreadsheets can be a frustrating job. In this paper, we explain how genetic programming can be used to automatically debug spreadsheets. Therefore, we adapt an automatic repair approach from the software debugging domain to the spreadsheet domain. In an initial empirical evaluation, we show that genetic programming can be used to debug spreadsheets: For more than 55percent of the spreadsheets, genetic programming is able to find a repair.", notes = "Also known as \cite{6688892}", } @PhdThesis{oai:etd.ohiolink.edu:wright1133882117, title = "Pattern Recognition via Machine Learning with Genetic Decision-Programming", author = "Carl C. Hoff", year = "2005", school = "Department of Computer Science and Engineering, Wright State University", address = "USA", bibsource = "OAI-PMH server at www.ohiolink.edu", language = "English", oai = "oai:etd.ohiolink.edu:wright1133882117", rights = "unrestricted; Copyright information available at the source archive", keywords = "genetic algorithms, genetic programming, Computer Science (0984), Pattern Recognition, Machine Learning, Evolutionary Computation, Genetic Decision-Programming", URL = "http://rave.ohiolink.edu/etdc/view?acc_num=wright1133882117.pdf", URL = "http://rave.ohiolink.edu/etdc/view?acc_num=wright1133882117", size = "179 pages", abstract = "In the intersection of pattern recognition, machine learning, and evolutionary computation is a new search technique by which computers might program themselves. That technique is called genetic decision-programming. A computer can gain the ability to distinguish among the things that it needs to recognise by using genetic decision-programming for pattern discovery and concept learning. Those patterns and concepts can be easily encoded in the spines of a decision program (tree or diagram). A spine consists of two parts: (1) the test-outcome pairs along a path from the program's root to any of its leaves and (2) the conclusion in that leaf. The test-outcome pairs specify a pattern and the conclusion identifies the corresponding concept. Genetic decision-programming combines and extends discrete decision theory with the principles of genetics and natural selection. The resulting algorithm searches for those decision programs that best satisfy some user-defined criteria. Each program mates problem decompositions with subproblem solutions, and consists of overlapping spines. Those spines are manipulated by three context-sensitive operators. The context defines a subproblem and is determined by the operator's point of application within a decision program. Macro-mutation generates a new solution for that context; mini-mutation restructures the existing solution for that context; and spine crossover inserts another program's solution for that context. Those solutions are encoded in the spines. Thus the operators recompose, restructure and recombine spines as the search technique evolves a population of decision programs to satisfy the user-defined criteria. Genetic decision-programming overcomes the difficulties encountered when evolving decision programs with genetic programming techniques that rely on subtree crossover. Those impractical techniques require too much memory and computation. Subtree crossover exchanges random subtrees of broken spines without regard for context. Meaning is lost. In contrast, the spine crossover of genetic decision-programming crosses entire spines and uses them in context. Meaning is retained. This means that genetic decision-programming can be applied to practical problems. In an experiment, it consistently gave very good results without the variability from problem to problem of other more conventional decision-tree construction techniques.", } @InCollection{hoffman:1999:UGADCDHCEP, author = "Don Hoffman", title = "Using Genetic Algorithms for Data Compression: Discovering Huffman Codes as Efficiently as Possible", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1999", year = "1999", editor = "John R. Koza", pages = "58--67", address = "Stanford, California, 94305-3079 USA", month = "15 " # mar, publisher = "Stanford Bookstore", keywords = "genetic algorithms", notes = "part of \cite{koza:1999:GAGPs}", } @InProceedings{hoffmann:1998:itfcES, author = "Frank Hoffmann", title = "Incremental Tuning of Fuzzy Controllers by Means of an Evolution Strategy", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "843--851", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "Evolutionary Strategies", ISBN = "1-55860-548-7", notes = "GP-98", } @Article{Hoffmann:2001:IS, author = "Frank Hoffmann and Oliver Nelles", title = "Genetic programming for model selection of TSK-fuzzy systems", journal = "Information Sciences", year = "2001", volume = "136", number = "1-4", pages = "7--28", month = aug, keywords = "genetic algorithms, genetic programming, Fuzzy modeling, Neuro-fuzzy system", URL = "http://www.sciencedirect.com/science/article/B6V0C-43DDW06-2/1/69cfc0ce8977ebea74cb8cec74efa722", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/22985/http:zSzzSzwww.nada.kth.sezSz~hoffmannzSzjis2001.pdf/genetic-programming-for-model.pdf", URL = "http://citeseer.ist.psu.edu/459134.html", size = "22 pages", ISSN = "0020-0255", DOI = "doi:10.1016/S0020-0255(01)00139-6", abstract = "This paper compares a genetic programming (GP) approach with a greedy partition algorithm (LOLIMOT) for structure identification of local linear neuro-fuzzy models. The crisp linear conclusion part of a Takagi-Sugeno-Kang (TSK) fuzzy rule describes the underlying model in the local region specified in the premise. The objective of structure identification is to identify an optimal partition of the input space into Gaussian, axis-orthogonal fuzzy sets. The linear parameters in the rule consequent are then estimated by means of a local weighted least-squares algorithm. LOLIMOT is an incremental tree-construction algorithm that partitions the input space by axis-orthogonal splits. In each iteration it greedily adds the new model that minimizes the classification error. GP performs a global search for the optimal partition tree and is therefore able to backtrack in case of sub-optimal intermediate split decisions. We compare the performance of both methods for function approximation of a highly non-linear two-dimensional test function and an engine characteristic map.", } @Article{hoffmann:2004:GPEM, author = "James P. Hoffmann and Christopher D. Ellingwood and Osei M. Bonsu and Daniel E. Bentil", title = "Ecological Model Selection via Evolutionary Computation and Information Theory", journal = "Genetic Programming and Evolvable Machines", year = "2004", volume = "5", number = "2", pages = "229--241", month = jun, keywords = "genetic algorithms, genetic programming, model selection, parsimony, complexity-based fitness, variable-length representation", ISSN = "1389-2576", DOI = "doi:10.1023/B:GENP.0000023690.71330.42", abstract = "an evolutionary algorithm-based approach to model selection and demonstrates its effectiveness in using the information content of ecological data to choose the correct model structure. Experiments with a modified genetic algorithm are described that combine parsimony with a novel gene regulation mechanism. This combination creates evolvable switches that implement functional variable-length genomes in the GA that allow for simultaneous model selection and parameter fitting. In effect, the GA orchestrates a competition among a community of models. Parsimony is implemented via the Akaike Information Criterion, and gene regulation uses a modulo function to overload the gene values and create an evolvable binary switch. The approach is shown to successfully specify the correct model structure in experiments with a nested set of polynomial test models and complex biological simulation models, even when Gaussian noise is added to the data.", notes = "Special Issue on Biological Applications of Genetic and Evolutionary Computation Guest Editor(s): Wolfgang Banzhaf , James Foster (1) Botany, University of Vermont, Burlington, VT, 05405-0086 (2) Mathematics & Statistics, University of Vermont, Burlington, VT, 05401-0086-3357", } @InProceedings{Hofmann:2015:evoMusArt, author = "David M. Hofmann", title = "A Genetic Programming Approach to Generating Musical Compositions", booktitle = "4th International Conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design", year = "2015", editor = "Colin Johnson and Adrian Carballal and Joao Correia", series = "LNCS", volume = "9027", publisher = "Springer", pages = "89--100", address = "Copenhagen", month = "8-10 " # apr, organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-16500-4", DOI = "doi:10.1007/978-3-319-16498-4_9", abstract = "Evolutionary algorithms have frequently been applied in the field of computer-generated art. In this paper, a novel approach in the domain of automated music composition is proposed. It is inspired by genetic programming and uses a tree-based domain model of compositions. The model represents musical pieces as a set of constraints changing over time, forming musical contexts allowing to compose, reuse and reshape musical fragments. The system implements a multi-objective optimisation aiming for statistical measures and structural features of evolved models. Furthermore a correspondent domain-specific computer language is introduced used to transform domain models to a comprehensive, human-readable text representation and vice versa. The language is also suitable to limit the search space of the evolution and as a composition language for human composers.", notes = "EvoMusArt2015 held in conjunction with EuroGP'2015, EvoCOP2015 and EvoApplications2015. http://www.evostar.org/2015/cfp_evomusart.php", } @InProceedings{faucris.112822204, author = "Johannes Hofmann and Dietmar Fey", title = "Fast Evolutionary Algorithms: Comparing High Performance Capabilities of {CPUs} and {GPUs}", booktitle = "Mitteilungen - Gesellschaft fuer Informatik e.V.", year = "2013", volume = "1", pages = "15--24", address = "Erlangen, Germany", edition = "1", keywords = "genetic algorithms, genetic programming, GPU, SIMD, AVX, PRNG", URL = "https://users.ece.cmu.edu/~franzf/papers/hpec09-lrb.pdf", size = "10 pages", abstract = "We use Evolutionary Algorithms (EAs) to evaluate different aspects of high performance computing on CPUs and GPUs. EAs have the distinct property of being made up of parts that behave rather differently from each other, and display different requirements for the underlying hardware as well as software. We can use these motives to answer crucial questions for each platform: How do we make best use of the hardware using manual optimization? Which platform offers the better software libraries to perform standard operations such as sorting? Which platform has the higher net floating-point performance and bandwidth? We draw the conclusion that GPUs are able to outperform CPUs in all categories; thus, considering time-to-solution, EAs should be run on GPUs whenever possible", faupublication = "yes", peerreviewed = "Yes", notes = "Brief mention of GP UnivIS-Import:2015-04-16:Pub.2013.tech.IMMD.IMMD3.fastev BibTeX export based on data in FAU CRIS: https://cris.fau.de/ For any questions please write to cris-support@fau.de", } @InProceedings{DBLP:conf/ecai/HofmannS10, author = "Martin Hofmann and Ute Schmid", title = "Data-Driven Detection of Recursive Program Schemes", booktitle = "Proceedings of the 19th European Conference on Artificial Intelligence, ECAI 2010", year = "2010", volume = "215", series = "Frontiers in Artificial Intelligence and Applications", pages = "1063--1064", address = "Lisbon, Portugal", month = aug # " 16-20", publisher = "IOS Press", keywords = "ILP, IGOR2", isbn13 = "978-1-60750-605-8", URL = "http://ebooks.iospress.nl/publication/5965", DOI = "doi:10.3233/978-1-60750-606-5-1063", size = "2 pages", abstract = "We present an extension to a current approach to inductive programming (IGOR2), that is, learning (recursive) programs from incomplete specifications such as input/outout examples. IGOR2 uses an analytical, example-driven strategy for generalization. We extend the set of IGOR2's refinement operators by a further operator, identification of higher-order schemes, and can show that this extension does improve speed as well as scope", notes = "Not GP", } @InProceedings{hofmeyr:1999:IDAAIS, author = "Steven A. Hofmeyr and Stephanie Forrest", title = "Immunity by Design: An Artificial Immune System", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1289--1296", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "artificial life, adaptive behavior and agents", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-039.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-039.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{hogan:2013:EuroGP, author = "Damien Hogan and Tom Arbuckle and Conor Ryan", title = "How Early and with How Little Data? Using Genetic Programing to Evolve Endurance Classifiers for {MLC NAND} Flash Memory", booktitle = "Proceedings of the 16th European Conference on Genetic Programming, EuroGP 2013", year = "2013", month = "3-5 " # apr, editor = "Krzysztof Krawiec and Alberto Moraglio and Ting Hu and A. Sima Uyar and Bin Hu", series = "LNCS", volume = "7831", publisher = "Springer Verlag", address = "Vienna, Austria", pages = "253--264", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, Binary Classifier, Flash Memory", isbn13 = "978-3-642-37206-3", DOI = "doi:10.1007/978-3-642-37207-0_22", size = "12 pages", abstract = "Despite having a multi-billion dollar market and many operational advantages, Flash memory suffers from a serious drawback, that is, the gradual degradation of its storage locations through use. Manufacturers currently have no method to predict how long they will function correctly, resulting in extremely conservative longevity specifications being placed on Flash devices. We leverage the fact that the durations of two crucial Flash operations, program and erase, change as the chips age. Their timings, recorded at intervals early in chips' working lifetimes, are used to predict whether storage locations will function correctly after given numbers of operations. We examine how early and with how little data such predictions can be made. Genetic Programming, employing the timings as inputs, is used to evolve binary classifiers that achieve up to a mean of 97.88percent correct classification. This technique displays huge potential for real-world application, with resulting savings for manufacturers.", notes = "Part of \cite{Krawiec:2013:GP} EuroGP'2013 held in conjunction with EvoCOP2013, EvoBIO2013, EvoMusArt2013 and EvoApplications2013", } @InProceedings{Hogan:2013:GECCO, author = "Damien Hogan and Tom Arbuckle and Conor Ryan", title = "Estimating MLC NAND flash endurance: a genetic programming based symbolic regression application", booktitle = "GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference", year = "2013", editor = "Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi", isbn13 = "978-1-4503-1963-8", pages = "1285--1292", keywords = "genetic algorithms, genetic programming", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2463372.2463537", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "NAND Flash memory is a multi-billion dollar industry which is projected to continue to show significant growth until at least 2017. Devices such as smart-phones, tablets and Solid State Drives use NAND Flash since it has numerous advantages over Hard Disk Drives including better performance, lower power consumption, and lower weight. However, storage locations within Flash devices have a limited working lifetime, as they slowly degrade through use, eventually becoming unreliable and failing. The number of times a location can be programmed is termed its endurance, and can vary significantly, even between locations within the same device. There is currently no technique available to predict endurance, resulting in manufacturers placing extremely conservative specifications on their Flash devices. We perform symbolic regression using Genetic Programming to estimate the endurance of storage locations, based only on the duration of program and erase operations recorded from them. We show that the quality of estimations for a device can be refined and improved as the device continues to be used, and investigate a number of different approaches to deal with the significant variations in the endurance of storage locations. Results show this technique's huge potential for real-world application.", notes = "Also known as \cite{2463537} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)", } @InProceedings{Hogan:2012:ieeeCIS, author = "Damien Hogan and Tom Arbuckle and Conor Ryan", title = "Evolving a storage block endurance classifier for Flash memory: A trial implementation", booktitle = "11th IEEE International Conference on Cybernetic Intelligent Systems (CIS 2012)", year = "2012", month = "22-23 " # aug, pages = "12--17", address = "Limerick", keywords = "genetic algorithms, genetic programming, Testing", DOI = "doi:10.1109/CIS.2013.6782154", abstract = "Solid State Drives (SSDs) have a number of significant advantages over traditional Hard Disk Drives (HDDs) but are currently far more expensive and have smaller capacities. These drives are based on NAND Flash memory devices, which have limited working lives. The number of times locations in such devices can be successfully programmed before they become unreliable is termed their endurance. There is currently no way to estimate accurately when a location within a Flash device will fail, so manufacturers give extremely conservative guarantees about the number of program operations their chips can endure. This paper describes a trial implementation of Genetic Programming (GP) used to evolve a Binary Classifier that predicts whether storage blocks within Flash memory devices will still be functioning correctly beyond some predefined number of cycles. The classifier is supplied with only the measured program and erase times from a relatively early point in the lifetime of a block. Using the relationships between these times, the system can accurately predict whether the block will continue to function satisfactorily up to a required number of cycles. Experiments on test sets comprised of unseen data show that our classifier obtains up to an average of 95percent accuracy across 30 runs.", notes = "Also known as \cite{6782154}", } @PhdThesis{Hogan_2013_genetics, author = "Damien Hogan", title = "Genetic programming based predictions and estimations for the endurance and retention of {NAND} flash memory devices", school = "University of Limerick", year = "2013", address = "Ireland", month = nov, keywords = "genetic algorithms, genetic programming", URL = "http://hdl.handle.net/10344/4875", URL = "https://ulir.ul.ie/bitstream/handle/10344/4875/Hogan_2013_genetics.pdf", size = "246 pages", abstract = "The central hypothesis of this thesis is that it is possible to use a supervised machine learning technique, Genetic Programming (GP), to make accurate predictions and estimations regarding the endurance and retention of multi-level cell NAND Flash Memory devices. The retention of storage locations, or blocks, within these devices is the length of time for which they successfully retain their data, while their endurance is the number of times they can be programmed and erased prior to failure. Manufacturers currently place conservative specifications on their devices since there is no technique available to quickly determine the actual endurance and retention capabilities of blocks within them. An extensive empirical evaluation of a number of MLC NAND Flash devices is completed, identifying features for use with GP, before expressions are evolved to make predictions and estimations regarding the retention and endurance of blocks. The empirical evaluation highlights the large variations in performance between blocks in different devices of the same specification, and even between blocks within the same device. As well as building a data set for later use with GP, the durations of program and erase operations are identified as features with which to make endurance predictions and estimations, while a relationship between block location and endurance is also established. GP is employed to evolve binary classification expressions, referred to as retention period classifiers, to predict whether blocks will correctly retain their data for a specified length of time. Following this, endurance classifiers are evolved to predict whether blocks will successfully complete a predefined number of cycles. Finally, symbolic regression expressions are evolved, building on the earlier experiments, to estimate the actual number of cycles each block will complete prior to failure and are referred to as endurance estimators.", notes = "Supervisor: Conor Ryan", } @PhdThesis{FrederikHogenboomPhDThesis, author = "Frederik Pieter Hogenboom", title = "Automated detection of financial events in news text", title_nl = "Automatische detectie van financiele gebeurtenissen in nieuwsberichten", school = "Erasmus University Rotterdam", year = "2014", address = "Netherlands", month = "11 " # dec, keywords = "genetic algorithms, genetic programming", isbn13 = "978-90-5892-386-8", language = "English", URL = "https://personal.eur.nl/frasincar/theses/FrederikHogenboomPhDThesis.pdf", URL = "https://research.tue.nl/en/publications/automated-detection-of-financial-events-in-news-text", size = "248 pages", notes = "SIKS Dissertation Series No. 2014-41 Supervisors: Prof.dr.ir. Uzay Kaymak Prof.dr. Franciska M.G. de Jong 72188920625843d59396d1fd73602b69", } @TechReport{holden:1998:RN1, author = "S. Holden", title = "Several Things all Genetic Programmers Should Know About Machine Learning", institution = "Computer Science, University College, London", year = "1998", type = "Research Note", number = "RN/98/1", month = jan, notes = "6 Jan 2003. It exists only as a half-finished draft I'm afraid", size = "0 pages", } @InProceedings{Holena:2011:GECCOcomp, author = "Martin Holena and David Linke and Lukas Bajer", title = "Case study: constraint handling in evolutionary optimization of catalytic materials", booktitle = "GECCO 2011 Evolutionary computation techniques for constraint handling", year = "2011", editor = "Carlos Artemio Coello Coello and Dara Curran and Thomas Jansen", isbn13 = "978-1-4503-0690-4", keywords = "genetic algorithms, genetic programming", pages = "333--340", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001858.2002015", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "The paper presents a case study in an industrially important application domain the optimization of catalytic materials. Though evolutionary algorithms are the by far most frequent approach to optimization tasks in that domain, they are challenged by mixing continuous and discrete variables, and especially by a large number of constraints. The paper describes the various kinds of encountered constraints, and explains constraint handling in GENACAT, one of evolutionary optimization systems developed specifically for catalyst optimization. In particular, it is shown that the interplay between cardinality constraints and linear equality and inequality constraints allows GENACAT to efficienlty determine the set of feasible solutions, and to split the original optimization task into a sequence of discrete and continuous optimization. Finally, the genetic operations employed in the discrete optimization are sketched, among which crossover is based on an assumption about the importance of the choice of sets of continuous variables in the cardinality constraints.", notes = "Also known as \cite{2002015} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @InProceedings{eurogp07:holladay, author = "Kenneth Holladay and Kay Robbins and Jeffery {von Ronne}", title = "FIFTH: A Stack Based GP Language for Vector Processing", editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar", booktitle = "Proceedings of the 10th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "4445", year = "2007", address = "Valencia, Spain", month = "11-13 " # apr, pages = "102--113", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-71602-5", isbn13 = "978-3-540-71602-0", DOI = "doi:10.1007/978-3-540-71605-1_10", abstract = "FIFTH, a new stack-based genetic programming language, efficiently expresses solutions to a large class of feature recognition problems. This problem class includes mining time-series data, classification of multivariate data, image segmentation, and digital signal processing (DSP). FIFTH is based on FORTH principles. Key features of FIFTH are a single data stack for all data types and support for vectors and matrices as single stack elements. We demonstrate that the language characteristics allow simple and elegant representation of signal processing algorithms while maintaining the rules necessary to automatically evolve stack correct and control flow correct programs. FIFTH supports all essential program architecture constructs such as automatically defined functions, loops, branches, and variable storage. An XML configuration file provides easy selection from a rich set of operators, including domain specific functions such as the Fourier transform (FFT). The fully-distributed FIFTH environment (GPE5) uses CORBA for its underlying process communication.", notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007", } @InProceedings{Holladay:2007:icdsp, title = "Evolution of Signal Processing Algorithms using Vector Based Genetic Programming", author = "K. L. Holladay and K. A. Robbins", booktitle = "15th International Conference on Digital Signal Processing", year = "2007", editor = "S. Sanei and J. A. Chambers and J. McWhlrter and Y. Hicks and A. G. Constantinlides", pages = "503--506", address = "Cardiff, UK", month = "1-4 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, signal classification, FIFTH, vector based genetic programming language, signal classification problem, signal processing algorithm, symbol rate estimation", ISBN = "1-4244-0881-4", DOI = "doi:10.1109/ICDSP.2007.4288629", size = "4 pages", abstract = "This paper demonstrates that FIFTH, a new vector-based genetic programming (GP) language, can automatically derive very effective signal processing algorithms directly from signal data. Using symbol rate estimation as an example, we compare the performance of a standard algorithm against an evolved algorithm. The evolved algorithm uses a novel approach in developing a symbol transition feature vector and achieves an impressive 97.7% overall accuracy in the defined problem domain, far exceeding the performance of the standard algorithm. These results suggest that vector based GP approaches could be useful in developing more expressive features for a large class of signal processing and classification problems.", notes = "P1333 p506 GP human competitive against DPDT Also known as \cite{4288629}", } @InProceedings{DBLP:conf/gecco/Holladay09, author = "Kenneth Holladay", title = "Characterizing the genetic programming environment for fifth (GPE5) on a high performance computing cluster", booktitle = "GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation", year = "2009", editor = "Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano {van Hemert} and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique Alba", pages = "1363--1370", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1569901.1570084", abstract = "Solving complex, real-world problems with genetic programming (GP) can require extensive computing resources. However, the highly parallel nature of GP facilitates using a large number of resources simultaneously, which can significantly reduce the elapsed wall clock time per GP run. This paper explores the performance characteristics of an MPI version of the Genetic Programming Environment for FIFTH (GPE5) on a high performance computing cluster. The implementation is based on the island model with each node running the GP algorithm asynchronously. In particular, we examine the effect of several configurable properties of the system including the ratio of migration to crossover, the migration cycle of programs between nodes, and the number of processors used. The problems employed in the study were selected from the fields of symbolic regression, finite algebra, and digital signal processing.", notes = "GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009). ACM Order Number 910092.", } @InProceedings{Holladay:2011:GECCOcomp, author = "Kenneth L. Holladay and John Marshall Sharp and Marc Janssens", title = "Automatic pyrolysis mass loss modeling from thermo-gravimetric analysis data using genetic programming", booktitle = "3rd symbolic regression and modeling workshop for GECCO 2011", year = "2011", editor = "Steven Gustafson and Ekaterina Vladislavleva", isbn13 = "978-1-4503-0690-4", keywords = "genetic algorithms, genetic programming", pages = "655--662", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001858.2002063", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Modelling to predict flame spread and fire growth is an active area of research in Fire Safety Engineering. A significant limitation to current approaches has been the lack of thermophysical material properties necessary for the simplified pyrolysis models ... Mode ling to predict flame spread and fire growth is an active area of research in Fire Safety Engineering. A significant limitation to current approaches has been the lack of thermophysical material properties necessary for the simplified pyrolysis models embedded within the models. Researchers have worked to derive physical properties such as density, specific heat capacity, and thermal conductivity from data obtained using bench-scale fire tests such as Thermo-Gravimetric Analysis (TGA). While Genetic Algorithms (GA) have been successfully used to solve for constants in empirical models, it has been shown that the resulting parameters are not valid individually as material properties, especially for complex materials such as wood. This paper describes an alternate approach using Genetic Programming (GP) to automatically derive a mass loss model directly from TGA data.", notes = "Also known as \cite{2002063} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @InProceedings{oai:CiteSeerPSU:279876, author = "Gordon S. Hollingworth and Steve L. Smith and Andy M. Tyrrell", title = "Design of Highly Parallel Edge Detection Nodes Using Evolutionary Techniques", booktitle = "Proceedings of the Seventh Euromicro Workshop on Parallel and Distributed Processing, PDP '99", year = "1999", pages = "35--42", address = "Funchal", month = "3-5 " # feb, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/13853/http:zSzzSzwww.amp.york.ac.ukzSzexternalzSzmediazSzcalzSzbio-inspzSzpublicationszSzgsh-pdp99.pdf/hollingworth99design.pdf", URL = "http://citeseer.ist.psu.edu/279876.html", citeseer-references = "oai:CiteSeerPSU:60383; oai:CiteSeerPSU:15766; oai:CiteSeerPSU:92024; oai:CiteSeerPSU:39781", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:279876", rights = "unrestricted", abstract = "This paper considers the application of bio-inspired systems in the design of a novel and highly parallel image processing tool to detect edges within conventional grey-scale images. The aim of the work is to implement a new image processing architecture through evolvable hardware that is able to adapt according to the particular images encountered. The simulation of such a system through the use of evolutionary algorithms and genetic programming is demonstrated for the conventional image processing operation of edge detection. Results are presented for this system and evaluated with respect to a conventional Sobel edge detector", } @InProceedings{oai:CiteSeerPSU:280684, author = "Gordon S. Hollingworth and Andy M. Tyrrell and Steve L. Smith", title = "Simulation of Evolvable Hardware to Solve Low Level Image Processing Tasks", booktitle = "Evolutionary Image Analysis, Signal Processing and Telecommunications: First European Workshop, EvoIASP'99 and EuroEcTel'99", year = "1999", editor = "Riccardo Poli and Hans-Michael Voigt and Stefano Cagnoni and Dave Corne and George D. Smith and Terence C. Fogarty", volume = "1596", series = "LNCS", pages = "46--58", address = "Goteborg, Sweden", publisher_address = "Berlin", month = "28 " # may, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-65837-8", DOI = "doi:10.1007/10704703_4", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/13853/http:zSzzSzwww.amp.york.ac.ukzSzexternalzSzmediazSzcalzSzbio-inspzSzpublicationszSzgsh-evoiasp99.pdf/hollingworth99simulation.pdf", URL = "http://citeseer.ist.psu.edu/280684.html", citeseer-references = "\cite{oai:CiteSeerPSU:279876}; oai:CiteSeerPSU:212034; oai:CiteSeerPSU:60383; oai:CiteSeerPSU:92024; oai:CiteSeerPSU:15766; oai:CiteSeerPSU:39781", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:280684", rights = "unrestricted", abstract = "The long term goal of the work described in this paper is the development of a bio-inspired system, employing evolvable hardware, that adapts according to the needs of the environment in which it is deployed. The application described here is the design of a novel and highly parallel image processing tool to detect edges within a wide range of conventional grey-scale images. We discuss the simulation of such a system based on a genetic programming paradigm, using a simple binary logic tree to implement the genetic string coding. The results acquired from the simulation are compared with those obtained from the application of a conventional Sobel edge detector, and although rudimentary, show the great potential of such bio-inspired systems.", } @Article{Hollis:2006:BRM, author = "Geoff Hollis and Chris Westbury", title = "{NUANCE}: Naturalistic University of Alberta Nonlinear Correlation Explorer", journal = "Behavior Research Methods", year = "2006", volume = "38", number = "1", pages = "8--23", month = feb, keywords = "genetic algorithms, genetic programming, health, cigarette consumption, Word Frequency, NLP", ISSN = "1554-3528", URL = "https://link.springer.com/content/pdf/10.3758/BF03192745.pdf", URL = "https://pubmed.ncbi.nlm.nih.gov/16817509/", DOI = "doi:10.3758/bf03192745", size = "16 pages", abstract = "we describe the Naturalistic University of Alberta Nonlinear Correlation Explorer (NUANCE), a computer program for data exploration and analysis. NUANCE is specialized for finding nonlinear relations between any number of predictors and a dependent value to be predicted. It searches the space of possible relations between the predictors and the dependent value by using natural selection to evolve equations that maximize the correlation between their output and the dependent value. In this article, we introduce the program, describe how to use it, and provide illustrative examples. NUANCE is written in Java, which runs on most computer platforms. We have contributed NUANCE to the archival Web site of the Psychonomic Society (www.psychonomic.org/archive), from which it may be freely downloaded.", notes = "University of Alberta, Edmonton, Alberta, Canada", } @TechReport{holmes:1995:odin, author = "Paul Holmes", title = "The Odin Genetic Programming System", institution = "Computer Studies, Napier University", year = "1995", type = "Tech Report", number = "RR-95-3", address = "Craiglockhart, 216 Colinton Road, Edinburgh, EH14 1DJ, UK", keywords = "genetic algorithms, genetic programming", broken = "ftp://ftp.dcs.napier.ac.uk/pub/papers/rr-95-3.ps", URL = "http://citeseer.ist.psu.edu/holmes95odin.html", abstract = "A new paradigm for Genetic Programming (GP) is proposed. In the new paradigm the genetic representation is separated from the tree structure of the program with a layer of abstraction, and it is argued that this will allow more efficient evolution of large programs. A GP system which can evolve Turing-complete programs has been developed and is presented. Emphasis is placed on the evolution of real-time functional programs which handle input and output using lazy streams. http://docs.dcs.napier.ac.uk/DOCS/GET/holmes95a/document.html", notes = "Fixed length chromosome, 8 bytes per line of code, Initial population seeded by individual written by user in Odin and compiled to Runes. Functional language, naturally recursive. Domiance bits used to arbitrate order iff conflict between which function to apply. Destructive translocation of genes (desctructive as fixed length) 8byte code interpretted by G-Machine (Antoni Diller) cf Peyton Jones. Standard GA (D-Genesis) crossover and mutation (does it respect opcodes and their boundaries?) Fitness function similarity of output (which may be list of some data type) with user supplied data (ie user also specifies functional language style type of output) page 49 {"}Its [Odin's] relative effectiveness remains to be tested.{"}", size = "56 pages", } @InProceedings{holmes:1996:fllc, author = "Paul Holmes and Peter J. Barclay", title = "Functional Languages on Linear Chromosomes", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "427", address = "Stanford University, CA, USA", publisher = "MIT Press", ISBN = "0-262-61127-9", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap66.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", size = "1 page", notes = "GP-96. See also \cite{holmes:1995:odin}", } @InProceedings{holmes:1998:dnricslr2pubr, author = "John H. Holmes", title = "Differential Negative Reinforcement Improves Classifier System Learning Rate in Two-Class Problems with Unequal Base Rates", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "635--642", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, classifiers, ROC", ISBN = "1-55860-548-7", URL = "http://cceb.med.upenn.edu/holmes/gp98.ps.gz", size = "8 pages", abstract = "The effect of biasing negative reinforcement levels on learning rate and classification accuracy in a learning classifier system (LCS) was investigated. Simulation data at five prevalences (base rates) were used to train and test the LCS. Erroneous decisions made by the LCS during training were punished differentially according to type: false positive (FP) or false negative (FN), across a range of four FP:FN ratios. Training performance was assessed by learning rate, determined from the number of iterations required to reach 95% of the maximum area under the receiver operating characteristic (ROC) curve obtained during learning. Learning rates were compared across the three biased ratios with those obtained at the unbiased ratio. Classification performance of the LCS at testing was evaluated by means of the area under the ROC curve. During learning, differences were found between the biased and unbiased penalty schemes, but only at unequal base rates. A linear relationship between bias level and base rate was suggested. With unequal base rates, biasing the FP:FN ratio improved the learning rate. Little effect was observed on testing the LCS with novel cases.", notes = "GP-98. My version of ghostview barfs with gp98.ps.gz AUC=probability classifier is correct on postive-negative test (Green and Swets, 1966). Wilcoxon statistic (Hanley and McNeil, 1982).", } @InProceedings{holmes:1999:ELCSPITDTALMT, author = "John H. Holmes", title = "Evaluating Learning Classifier System Performance In Two-Choice Decision Tasks: An LCS Metric Toolkit", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "789", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-389.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-389.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Holzinger:2010:gecco, author = "Emily Rose Holzinger and Carrie C. Buchanan and Scott M. Dudek and Eric C. Torstenson and Stephen D. Turner and Marylyn D. Ritchie", title = "Initialization parameter sweep in ATHENA: optimizing neural networks for detecting gene-gene interactions in the presence of small main effects", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "203--210", keywords = "genetic algorithms, genetic programming, grammatical evolution, Bioinformatics, computational, systems and synthetic biology", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830483.1830519", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Recent advances in genotyping technology have led to the generation of an enormous quantity of genetic data. Traditional methods of statistical analysis have proved insufficient in extracting all of the information about the genetic components of common, complex human diseases. A contributing factor to the problem of analysis is that amongst the small main effects of each single gene on disease susceptibility, there are non-linear, gene-gene interactions that can be difficult for traditional, parametric analyses to detect. In addition, exhaustively searching all multi-locus combinations has proved computationally impractical. Novel strategies for analysis have been developed to address these issues. The Analysis Tool for Heritable and Environmental Network Associations (ATHENA) is an analytical tool that incorporates grammatical evolution neural networks (GENN) to detect interactions among genetic factors. Initial parameters define how the evolutionary process will be implemented. This research addresses how different parameter settings affect detection of disease models involving interactions. In the current study, we iterate over multiple parameter values to determine which combinations appear optimal for detecting interactions in simulated data for multiple genetic models. Our results indicate that the factors that have the greatest influence on detection are: input variable encoding, population size, and parallel computation.", notes = "Carrie C Buchanan's 2013 PhD http://etd.library.vanderbilt.edu/available/etd-11272013-142349/ not GP Also known as \cite{1830519} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @InProceedings{holzinger:evobio12, author = "Emily R. Holzinger and Scott M. Dudek and Alex T. Frase and Brooke Fridley and Prabhakar Chalise and Marylyn D. Ritchie", title = "Comparison of methods for meta-dimensional data analysis using in silico and biological data sets", booktitle = "10th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, {EvoBIO 2012}", year = "2012", month = "11-13 " # apr, editor = "Mario Giacobini and Leonardo Vanneschi and William S. Bush", series = "LNCS", volume = "7246", publisher = "Springer Verlag", address = "Malaga, Spain", pages = "134--143", organisation = "EvoStar", isbn13 = "978-3-642-29065-7", DOI = "doi:10.1007/978-3-642-29066-4_12", size = "10 pages", keywords = "genetic algorithms, genetic programming, grammatical evolution, GENN, Systems biology, neural networks, evolutionary computation, data integration, human genetics", abstract = "Recent technological innovations have catalysed the generation of a massive amount of data at various levels of biological regulation, including DNA, RNA and protein. Due to the complex nature of biology, the underlying model may only be discovered by integrating different types of high-throughput data to perform a 'meta-dimensional' analysis. For this study, we used simulated gene expression and genotype data to compare three methods that show potential for integrating different types of data in order to generate models that predict a given phenotype: the Analysis Tool for Heritable and Environmental Network Associations (ATHENA), Random Jungle (RJ), and Lasso. Based on our results, we applied RJ and ATHENA sequentially to a biological data set that consisted of genome-wide genotypes and gene expression levels from lymphoblastoid cell lines (LCLs) to predict cytotoxicity. The best model consisted of two SNPs and two gene expression variables with an r-squared value of 0.32.", notes = "Part of \cite{Giacobini:2012:EvoBio} EvoBio'2012 held in conjunction with EuroGP2012, EvoCOP2012, EvoMusArt2012 and EvoApplications2012", affiliation = "Center for Human Genetics Research, Vanderbilt University, Nashville, TN, USA", } @PhdThesis{Holzinger:Thesis, author = "Emily Rose Holzinger", title = "Development, Optimization, and Application of a Meta-Dimensional Analysis Pipeline Using in Silico and Natural Data Sets", school = "Human Genetics, Vanderbilt University", year = "2013", address = "Nashville, TN, USA", month = "10 " # may, keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Biostatistics; Genetics", broken = "http://gradworks.umi.com/35/74/3574009.html", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Holzinger_Thesis.pdf", URL = "https://search.proquest.com/docview/1444626517/4BBFCADE1E4F4C51PQ/11", size = "178 pages", abstract = "For this project, we develop, optimise, and implement a novel analytical pipeline that combines a tree-based variable selection method with an evolutionary computation modelling method. The purpose of this pipeline is to integrate high-throughput data from different levels of biological regulation to identify meta-dimensional models that predict a given outcome. We suggest that by integrating different types of data we will identify aspects of the genetic architecture that would have been missed by single variable and/or single data type study designs. The development process consisted of rigorous performance testing, method comparisons, and parameter optimisations using in silico and biological data sets. Next, we applied the analysis pipeline to a data set with SNP genotypes, gene expression variables, and quantitative low-density lipoprotein cholesterol (LDL-C) trait outcomes. Using our meta-dimensional analysis pipeline, we were able to generate multi-variable models that explain a proportion of the inter-individual variation in LDL-C traits. Additionally, we were able to map these genetic variants to biological units and pathways that would not have been identified with single data type analysis.", notes = "includes some evaluations of multiple evolutionary approaches, including evolved neural networks and symbolic regression. Supervisor: Marylyn Ritchie ProQuest Dissertations Publishing, 2013. 3574009", } @Article{Homaifar1995, author = "Abdollah Homaifar and Ed McCormick", title = "Simultaneous Design of Membership Functions and Rule Sets for Fuzzy Controllers Using Genetic Algorithms", journal = "IEEE Transactions on Fuzzy Systems", volume = "3", number = "2", year = "1995", pages = "129--139", month = may, keywords = "genetic algorithms, fuzzy control, control system synthesis, membership function design, fuzzy controllers, high-performance membership functions, simultaneous design procedure, rule set design, cart controller, truck controller", ISSN = "1063-6706", URL = "http://ieeexplore.ieee.org/iel4/91/8807/00388168.pdf?isNumber=8807", size = "11 pages", abstract = "This paper examines the applicability of genetic algorithms (GA's) in the simultaneous design of membership functions and rule sets for fuzzy logic controllers. Previous work using genetic algorithms has focused on the development of rule sets or high performance membership functions; however, the interdependence between these two components suggests a simultaneous design procedure would be a more appropriate methodology. When GA's have been used to develop both, it has been done serially, e.g., design the membership functions and then use them in the design of the rule set. This, however, means that the membership functions were optimized for the initial rule set and not the rule set designed subsequently. GA's are fully capable of creating complete fuzzy controllers given the equations of motion of the system, eliminating the need for human input in the design loop. This new method has been applied to two problems, a cart controller and a truck controller. Beyond the development of these controllers, we also examine the design of a robust controller for the cart problem and its ability to overcome faulty rules.", } @InProceedings{Homaifar:1999:CIRA, author = "Abdollah Homaifar and Daryl Battle and Edward Tunstel", title = "Soft computing-based design and control for mobile robot path tracking", booktitle = "Computational Intelligence in Robotics and Automation, CIRA '99. Proceedings. 1999 IEEE International Symposium on", year = "1999", pages = "35--40", month = "8-9 " # nov, keywords = "genetic algorithms, genetic programming, evolutionary computation, soft computing-based design, mobile robot, robot path tracking, evolutionary algorithms, Darwinian concepts, automatic learning, nonlinear mappings, genetic programming, fuzzy control rules, autonomous vehicle, steering control problem, membership functions, rule bases, robustness, sensor measurement noise, nominal forward velocity", ISBN = "0-7803-5806-6", URL = "http://ieeexplore.ieee.org/iel5/6589/17587/00809943.pdf?isNumber=17587", DOI = "doi:10.1109/CIRA.1999.809943", size = "6 pages", abstract = "A variety of evolutionary algorithms, operating according to Darwinian concepts, have been proposed to approximately solve problems of common engineering applications. Increasingly common applications involve automatic learning of nonlinear mappings that govern the behavior of control systems. In many cases where robot control is of primary concern, the systems used to demonstrate the effectiveness of evolutionary algorithms often do not represent practical robotic systems. In this paper, genetic programming (GP) is the evolutionary strategy of interest. It is applied to learn fuzzy control rules for a practical autonomous vehicle steering control problem, namely, path tracking. GP handles the simultaneous evolution of membership functions and rule bases for the fuzzy path tracker. As a matter of practicality, robustness of the genetically evolved fuzzy controller is demonstrated by examining the effects of sensor measurement noise and an increase in the robot's nominal forward velocity.", notes = "CIRA'99 http://web.nps.navy.mil/~yun/cira99/", } @Article{Homaifar:2000:IJKBIES, author = "Abdollah Homaifar and D. Battle and E. Tunstel and G. Dozier", title = "Genetic Programming Design of Fuzzy Controllers for Mobile Robot Path Tracking", journal = "International Journal of Knowledge-Based Intelligent Engineering Systems", year = "2000", volume = "4", number = "1", pages = "33--52", month = jan, keywords = "genetic algorithms, genetic programming", abstract = "Genetic programming (GP) is an evolutionary strategy that attempts to deal with the notion of how computers can learn to solve problems without being explicitly programmed. It has been demonstrated that GP, under the influence of Darwinian concepts, could genetically breed computer programs to approximately solve problems in a variety of applications. One primary example is its application to the problem of automatically learning nonlinear mappings that govern the behavior of control systems. It is demonstrated here that GP can formulate such nonlinear maps in the form of fuzzy control rules, which yield comparable or better performance than one derived through manual design using trial-and-error. The objective is to address the efficient implementation of GP for the discovery of knowledge bases intended for use in fuzzy logic controller applications. Efficiency is achieved with a C programming language implementation of GP, which is applied to a mobile robot steering control problem. Robot path following performance is compared to results obtained using an existing GP implementation in the LISP programming language. It is demonstrated that the C implementation has a definite advantage with regard to computational speed of evolution. In this work, we have extended the application of GP to handle simultaneous evolution of membership functions and rule bases for the same control problem. Furthermore, GP is used to handle selection of fuzzy t-norms. It is concluded that simultaneous evolution of rule bases and membership functions with t-norm selection results in enhanced performance of the evolved controllers. Finally, the robustness characteristics of the genetically evolved fuzzy controllers are investigated by examining the effects of sensor measurement noise and an increase in the robot's nominal forward velocity.", notes = "Nov 2012 IJKBIES web site not listing stuff before 2004", } @InProceedings{hondo:1996:srrs, author = "Naohiro Hondo and Hitoshi Iba and Yukinori Kakazu", title = "Sharing and Refinement for Reusable Subroutines of Genetic Programming", booktitle = "Proceedings of the 1996 {IEEE} International Conference on Evolutionary Computation", year = "1996", volume = "1", pages = "565--570", address = "Nagoya, Japan", month = "20-22 " # may, organisation = "IEEE Neural Network Council", keywords = "genetic algorithms, genetic programming, COAST,efficiency, reusable subroutines, subroutine library, subroutine refinement, subroutine sharing, wall-following problem, software libraries, software performance evaluation, software reusability, subroutines", ISBN = "0-7803-2902-3", DOI = "doi:10.1109/ICEC.1996.542661", size = "6 pages", abstract = "Presents a new approach to genetic programming (GP). The aim of this study is to indicate an approach to make GP fit for practical use. The objective of our study originates in the fact that human-created programs tend to be divided into subroutines that are reused frequently. In traditional GP, the program is structured as a single sequence. Moreover, there is no room to reuse the subroutines in traditional GP. There have been a few techniques proposed for dividing such programs into subroutines, which attempt to discover certain subroutines. However, the reusability of genetic programs has not yet been discussed. In this paper, we propose an approach for reusability. The proposed method has a library for keeping the subroutines in order to share and reuse them. We make use of the wall-following problem to indicate the efficiency of the method experimentally", notes = "ICEC-96", } @InProceedings{hondo:1996:COASTgp96, author = "Naohiro Hondo and Hitoshi Iba and Yukinori Kakazu", title = "COAST: An Approach to Robustness and Reusability in Genetic Programming", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "429", address = "Stanford University, CA, USA", publisher = "MIT Press", size = "1 page", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap68.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96", } @InProceedings{hondo:1996:rGPrl, author = "Naohiro Hondo and Hitoshi Iba and Yukinori Kakazu", title = "Robust GP in Robot Learning", booktitle = "Parallel Problem Solving from Nature IV, Proceedings of the International Conference on Evolutionary Computation", year = "1996", editor = "Hans-Michael Voigt and Werner Ebeling and Ingo Rechenberg and Hans-Paul Schwefel", series = "LNCS", volume = "1141", pages = "751--760", address = "Berlin, Germany", publisher_address = "Heidelberg, Germany", month = "22-26 " # sep, publisher = "Springer Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-61723-X", DOI = "doi:10.1007/3-540-61723-X_1038", size = "10 pages", abstract = "This paper presents a new approach to Genetic Programming (i.e. GP). Our goal is to realise robustness by means of the automatic discovery of functions. In traditional GP, techniques have been proposed which attempt to discover certain subroutines for the sake of improved efficiency. So far, however, the robustness of GP has not yet been discussed in terms of knowledge acquisition. We propose an approach for robustness named COAST, which has a library for storing certain subroutines for reuse. We make use of the Wall Following Problem to illustrate the efficiency of this method.", notes = "http://lautaro.fb10.tu-berlin.de/ppsniv.html PPSN4 COAST, Wall following problem", affiliation = "Hokkaido University Complex Systems Engineering, Division of Systems and Information Engineering N-13 W-8, Sapporo 060 Hokkaido Japan N-13 W-8, Sapporo 060 Hokkaido Japan", } @InProceedings{hondo:1998:mapssrc, author = "Naohiro Hondo and Koji Nishikawa and Hiroshi Yokoi and Yukinori Kakazu", title = "Multi-Agent Programming System for Starfish Robot Control", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "140--145", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-548-7", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1998/hondo_1998_mapssrc.pdf", size = "6 pages", abstract = "no distinct brain, reciprocal nerve network, control mechanism MAP", notes = "GP-98", } @InProceedings{Hong:2006:IDETC/CIE, author = "G. Hong and L. Hu and D. Xue and Y. L. Tu and Y. L. Xiong", title = "Integrated Optimal Product Design and Process Planning for One-of-a-Kind Production", booktitle = "26th Computers and Information in Engineering Conference", year = "2006", address = "Philadelphia, Pennsylvania, USA", month = sep # " 10-13", publisher = "ASME", keywords = "genetic algorithms, genetic programming", isbn_bad = "0-7918-4257-8", DOI = "doi:10.1115/DETC2006-99325", abstract = "This research addresses the issues to identify the optimal product configuration and its parameters based on the requirements of customers on performance and costs of products in one-of-a-kind production (OKP) environment. In this work, variations of product configurations and parameters in an OKP product family are modelled by an AND-OR tree and parameters of the nodes in this tree. Different product configurations with different parameters are evaluated by performance and cost measures. These evaluation measures are converted into comparable customer satisfaction indices using the non-linear relations between the evaluation measures and the customer satisfaction indices. The optimal product configuration and its parameters with the maximum overall customer satisfaction index are identified by genetic programming and constrained Optimization.", notes = "Gang Hong, University of Calgary, Calgary, AB, Canada", } @InProceedings{Hong:2008:IDETC/CIE, author = "G. Hong and P. R. Dean and W. Yang and Y. L. Tu and D. Xue", title = "Integrated Optimal Product Design and Process Planning for One-of-a-Kind Production", booktitle = "28th Computers and Information in Engineering Conference IDETC/CIE2008", year = "2008", volume = "3", pages = "111--120", address = "Brooklyn, New York, USA", month = aug # " 3-6", publisher = "ASME", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1115/DETC2008-49141", abstract = "One-of-a-kind production (OKP) is a new manufacturing paradigm to produce customised products based on requirements of individual customers while maintaining the quality and efficiency of mass production. In this research, an integrated optimal product design and process planning approach is developed to satisfy customer requirements considering design and manufacturing constraints. In this work, a hybrid AND-OR graph is introduced to model the variations of design configurations/parameters and manufacturing processes/parameters in generic product family. Since different design configurations and parameters can be created from the same customer requirements, and each design can be further achieved through alternative manufacturing processes and parameters, co-evolutionary genetic programming and numerical Optimization are employed to identify the optimal product design configuration/parameters and manufacturing process/parameters. An industrial case study to identify the optimal design configuration/parameters and manufacturing process/parameters of custom window products in a local company is introduced to demonstrate the effectiveness of the developed method.", notes = "Gang Hong, University of Calgary, Calgary, AB, Canada", } @PhdThesis{GangHong:thesis, author = "Gang Hong", title = "Research on Product Design and Manufacture for One-of-a-Kind Production", school = "Department of Mechanical and Manufacturing Engineering, University of Calgary", year = "2009", address = "Canada", month = "16 " # mar, keywords = "genetic algorithms, genetic programming", URL = "http://schulich.ucalgary.ca/mechanical/files/mechanical/Gang%20Hong-PhD%20Abstract.pdf", abstract = "To keep competitive advantages in today's global marketplace, many companies, especially the small and medium enterprises, have been embracing a production strategy, named one-of-a-kind production (OKP), which aims at satisfying individual customer requirements while maintaining the efficiency and quality of mass production. This thesis work contributes to a further understanding of one-of-a-kind production by addressing the following three objectives to improve the productivity in OKP companies: (1) customer information should be incorporated in the product modelling scheme; (2) design variations and manufacturing variations should be well integrated, and (3) the concurrent optimal custom product design and manufacturing should be quickly identified based on the individual customer requirements and manufacturing constraints. In this thesis work, a customer-driven product modeling scheme is introduced to incorporate customer information into OKP product family modeling. Through this modeling scheme, relations between customer categories and product categories are explored to facilitate the optimisation process to quickly identify the custom product. In order to provide products in a cost-effective way in addition to satisfying individual customer needs, a hybrid modelling scheme is introduced to model design variations and manufacturing variations in an integrated environment. Based on the hybrid modelling scheme, a new multi-level optimisation method is developed to identify the optimal custom product design and its optimal manufacturing process, where co-evolutionary programming is used for configuration design and numerical search is carried out for parameter design. Two prototype systems are developed to illustrate the effectiveness of the introduced methodologies.", notes = "Gang Tony Hong", } @Article{Hong:2008:IJPR, author = "G. Hong and L. Hu and D. Xue and Y. L. Tu and Y. L. Xiong", title = "Identification of the optimal product configuration and parameters based on individual customer requirements on performance and costs in one-of-a-kind production", journal = "International Journal of Production Research", year = "2008", volume = "46", number = "12", pages = "3297--3326", publisher = "Taylor \& Francis", keywords = "genetic algorithms, genetic programming, One-of-a-kind production (OKP), Optimization, Customer requirements", ISSN = "1366-588X", DOI = "doi:10.1080/00207540601099274", size = "29 pages", abstract = "One-of-a-kind production (OKP) aims at manufacturing products based on the requirements from individual customers while maintaining the high quality and efficiency of mass production. This research addresses the issues in identifying the optimal product configuration and its parameters based on individual customer requirements on performance and costs of products. In this work, variations of product configurations and parameters in an OKP product family are modelled by an AND-OR tree and parameters of the nodes in this tree. Different product configurations with different parameters are evaluated by performance and cost measures. These evaluation measures are converted into comparable customer satisfaction indices using the non-linear relations between the evaluation measures and the customer satisfaction indices. The optimal product configuration and its parameters with the maximum overall customer satisfaction index are identified by genetic programming and constrained optimisation. A case study to identify the optimal configuration and its parameters of window products in an industrial company is used to demonstrate the effectiveness of the introduced approach.", notes = "Official Journal of the International Foundation for Production Research (IFPR)", } @Article{Hong2010270, author = "Gang Hong and Deyi Xue and Yiliu Tu", title = "Rapid identification of the optimal product configuration and its parameters based on customer-centric product modeling for one-of-a-kind production", journal = "Computers in Industry", volume = "61", number = "3", pages = "270--279", year = "2010", month = apr, keywords = "genetic algorithms, genetic programming, One-of-a-kind production, Customer-centric product modelling, Pattern recognition, Rough set, Optimisation", ISSN = "0166-3615", DOI = "doi:10.1016/j.compind.2009.09.006", URL = "http://people.ucalgary.ca/~dxue/journal/COMIND2010.pdf", broken = "http://www.sciencedirect.com/science/article/B6V2D-4XHC68M-2/2/3d71e33179122a81965181a637daea9e", abstract = "One-of-a-kind production (OKP) aims at manufacturing products based on the individual customer requirements while maintaining the high quality and efficiency of mass production. This paper presents a customer-centric product modelling scheme to model OKP product families by considering the relations between customer needs and OKP products. In this modeling scheme, an OKP product family is modelled by an AND-OR tree. In order to investigate the relations between customer needs and OKP products, data mining techniques are employed to achieve knowledge from the historical data. First, OKP products and customer requirements are grouped into product patterns and customer patterns, respectively, using a fuzzy pattern clustering method. Then, hybrid attribute reduction is carried out based on rough set theory to remove the irrelevant attributes for each product pattern. Finally, the relationships between product patterns and customer patterns are obtained. Based on the achieved knowledge, the different patterns of OKP products are modeled by different sub-AND-OR trees trimmed from the original AND-OR tree. Since only partial product descriptions in a product family are used to identify the optimal custom product based on customer requirements, the efficiency of custom product identification process can be improved considerably.", } @InCollection{hong:1999:DIRUGP, author = "Hong S. Hong", title = "Digbital Image Restoration Using Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1999", year = "1999", editor = "John R. Koza", pages = "68--75", address = "Stanford, California, 94305-3079 USA", month = "15 " # mar, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:1999:GAGPs}", } @InProceedings{Hong:aspgp03, author = "Jin-Hyuk Hong and Sung-Bae Cho", title = "Effective Rule Discovery Using Genetic Programming for DNA Microarray Analysis", booktitle = "Proceedings of The First Asian-Pacific Workshop on Genetic Programming", year = "2003", editor = "Sung-Bae Cho and Nguyen Xuan Hoai and Yin Shan", pages = "53--61", address = "Rydges (lakeside) Hotel, Canberra, Australia", month = "8 " # dec, keywords = "genetic algorithms, genetic programming", ISBN = "0-9751724-0-9", notes = "\cite{aspgp03}", } @InProceedings{conf/mdai/HongC05, title = "Cancer Prediction Using Diversity-Based Ensemble Genetic Programming", author = "Jin-Hyuk Hong and Sung-Bae Cho", year = "2005", pages = "294--304", editor = "Vicenc Torra and Yasuo Narukawa and Sadaaki Miyamoto", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3558", booktitle = "Modeling Decisions for Artificial Intelligence, Second International Conference, MDAI 2005, Proceedings", address = "Tsukuba, Japan", month = jul # " 25-27", bibdate = "2005-07-18", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/mdai/mdai2005.html#HongC05", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-27871-0", DOI = "doi:10.1007/11526018_29", abstract = "Combining a set of classifiers has often been exploited to improve the classification performance. Accurate as well as diverse base classifiers are prerequisite to construct a good ensemble classifier. Therefore, estimating diversity among classifiers has been widely investigated. This paper presents an ensemble approach that combines a set of diverse rules obtained by genetic programming. Genetic programming generates interpretable classification rules, and diversity among them is directly estimated. Finally, several diverse rules are combined by a fusion method to generate a final decision. The proposed method has been applied to cancer classification using gene expression profiles, which is one of the important issues in bioinformatics. Experiments on several popular cancer datasets have demonstrated the usability of the method. High performance of the proposed method has been obtained, and the accuracy has increased by diversity among the base classification rules.", } @InProceedings{hong:1999:SAMCMO, author = "Tzung-Pei Hong and Hong-Shung Wang and Wei-Chou Chen", title = "Simultaneously Applying Multiple Crossover and Mutation Operators", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "790", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/ga305.ps", notes = "Information management dept. I-Shou University GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{hong:2004:eurogp, author = "Jin-Hyuk Hong and Sung Bae Cho", title = "Lymphoma Cancer Classification Using Genetic Programming with SNR Features", booktitle = "Genetic Programming 7th European Conference, EuroGP 2004, Proceedings", year = "2004", editor = "Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule", volume = "3003", series = "LNCS", pages = "78--88", address = "Coimbra, Portugal", publisher_address = "Berlin", month = "5-7 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-21346-5", DOI = "doi:10.1007/978-3-540-24650-3_8", abstract = "Lymphoma cancer classification with DNA microarray data is one of important problems in bioinformatics. Many machine learning techniques have been applied to the problem and produced valuable results. However the medical field requires not only a high-accuracy classifier, but also the in-depth analysis and understanding of classification rules obtained. Since gene expression data have thousands of features, it is nearly impossible to represent and understand their complex relationships directly. We adopt the SNR (Signal-to-Noise Ratio) feature selection to reduce the dimensionality of the data, and then use genetic programming to generate cancer classification rules with the features. In the experimental results on Lymphoma cancer dataset, the proposed method yielded 96.6% test accuracy in average, and an excellent arithmetic classification rule set that classifies all the samples correctly is discovered by the proposed method.", notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004", } @InProceedings{Hong:2004:aspgp, author = "Jin-Hyuk Hong and Sung-Bae Cho", title = "Ensemble Genetic Programming for Classifying Gene Expression Data", booktitle = "Proceedings of The Second Asian-Pacific Workshop on Genetic Programming", year = "2004", editor = "R I Mckay and Sung-Bae Cho", address = "Cairns, Australia", month = "6-7 " # dec, keywords = "genetic algorithms, genetic programming", URL = "http://sclab.yonsei.ac.kr/publications/Papers/IC/ASPGP04_Final.pdf", size = "12 pages", abstract = "Ensemble is a representative technique for improving classification performance by combining a set of classifiers. It is required to maintain the diversity among base classifiers for effective ensemble. Conventional ensemble approaches construct various classifiers by estimating the similarity on the output patterns of them, and combine them with several fusion methods. Since they measure the similarity indirectly, it is restricted to evaluate the precise diversity among base classifiers. In this paper, we propose an ensemble method that estimates the similarity between classification rules by matching in representation-level. A set of comprehensive and precise rules is obtained by genetic programming. After evaluating the diversity, a fusion method makes the final decision with a subset of diverse classification rules. The proposed method is applied to cancer classification using gene expression profiles, which requires high accuracy and reliability. Especially, the experiments on popular cancer datasets have demonstrated the usefulness of the proposed method.", notes = "broken http://sc.snu.ac.kr/~aspgp/aspgp04/programme.html", } @Article{Hong:Tco:06, author = "Jin-Hyuk Hong and Sung-Bae Cho", title = "The classification of cancer based on {DNA} microarray data that uses diverse ensemble genetic programming", journal = "Artificial Intelligence In Medicine", year = "2006", volume = "36", number = "1", pages = "43--58", month = jan, keywords = "genetic algorithms, genetic programming, Ensemble, Diversity, Classification", DOI = "doi:10.1016/j.artmed.2005.06.002", abstract = "Object The classification of cancer based on gene expression data is one of the most important procedures in bioinformatics. In order to obtain highly accurate results, ensemble approaches have been applied when classifying DNA microarray data. Diversity is very important in these ensemble approaches, but it is difficult to apply conventional diversity measures when there are only a few training samples available. Key issues that need to be addressed under such circumstances are the development of a new ensemble approach that can enhance the successful classification of these datasets. Materials and methods An effective ensemble approach that does use diversity in genetic programming is proposed. This diversity is measured by comparing the structure of the classification rules instead of output-based diversity estimating. Results Experiments performed on common gene expression datasets (such as lymphoma cancer dataset, lung cancer dataset and ovarian cancer dataset) demonstrate the performance of the proposed method in relation to the conventional approaches. Conclusion Diversity measured by comparing the structure of the classification rules obtained by genetic programming is useful to improve the performance of the ensemble classifier.", } @InProceedings{Hong:2006:ICONIP, author = "Jin-hyuk Hong and Sungsoo Lim and Sung-bae Cho", title = "Language Learning for the Autonomous Mental Development of Conversational Agents", booktitle = "13th International Conference on Neural Information Processing, ICONIP 2006, Part III", year = "2006", editor = "Irwin King and Jun Wang and Lai-Wan Chan and DeLiang Wang", volume = "4234", series = "Lecture Notes in Computer Science", pages = "892--0899", address = "Hong Kong", month = oct # " 3-6", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-46484-6", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.457.234", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.457.234", URL = "http://sclab.yonsei.ac.kr/publications/Papers/LNCS/ICONIP06_JHHong2.pdf", DOI = "doi:10.1007/11893295_98", abstract = "Since the manual construction of our knowledge-base has several crucial limitations when applied to intelligent systems, mental development has been investigated in recent years. Autonomous mental development is a new paradigm for developing autonomous machines, which are adaptive and flexible to the environment. Language development, a kind of mental development, is an important aspect of intelligent conversational agents. In this paper, we propose an intelligent conversational agent and its language development mechanism by putting together five promising techniques; Bayesian networks, pattern matching, finite state machines, templates, and genetic programming. Knowledge acquisition implemented by finite state machines and templates, and language learning by genetic programming are developed for language development. Several illustrations and usability tests show the usefulness of the proposed developmental conversational agent.", } @Article{Hong:2007:TEC, title = "Autonomous Language Development Using Dialogue-Act Templates and Genetic Programming", author = "Jin-Hyuk Hong and Sungsoo Lim and Sung-Bae Cho", journal = "IEEE Transactions on Evolutionary Computation", year = "2007", volume = "11", number = "2", pages = "213--225", month = apr, keywords = "genetic algorithms, genetic programming, belief networks, finite state machines, knowledge acquisition, pattern matching, software agents, Bayesian networks, autonomous language development, autonomous machines, autonomous mental development, behavioural patterns, dialogue-act templates, finite-state machines, genetic programming, intelligent conversational agents, knowledge acquisition, knowledge bases, pattern matching", DOI = "doi:10.1109/TEVC.2006.890265", ISSN = "1089-778X", abstract = "In recent years, the concept of autonomous mental development (AMD) has been applied to the construction of artificial systems such as conversational agents, in order to resolve some of the difficulties involved in the manual definition of their knowledge bases and behavioural patterns. AMD is a new paradigm for developing autonomous machines, which are adaptive and flexible to the environment. Language development, a kind of mental development, is an important aspect of intelligent conversational agents. we propose an intelligent conversational agent and its language development mechanism by putting together five promising techniques: Bayesian networks, pattern matching, finite-state machines, templates, and genetic programming (GP). Knowledge acquisition implemented by finite-state machines and templates, and language learning by GP are used for language development. Several illustrations and usability tests show the usefulness of the proposed developmental conversational agent", } @InProceedings{hong:2013:EuroGP, author = "Libin Hong and John Woodward and Jingpeng Li and Ender Ozcan", title = "Automated Design of Probability Distributions as Mutation Operators for Evolutionary Programming Using Genetic Programming", booktitle = "Proceedings of the 16th European Conference on Genetic Programming, EuroGP 2013", year = "2013", month = "3-5 " # apr, editor = "Krzysztof Krawiec and Alberto Moraglio and Ting Hu and A. Sima Uyar and Bin Hu", series = "LNCS", volume = "7831", publisher = "Springer Verlag", address = "Vienna, Austria", pages = "85--96", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, Evolutionary Programming, Function Optimisation, Machine Learning, Meta-learning, Hyper-heuristics, Automatic Design.", isbn13 = "978-3-642-37206-3", DOI = "doi:10.1007/978-3-642-37207-0_8", abstract = "The mutation operator is the only source of variation in Evolutionary Programming. In the past these have been human nominated and included the Gaussian, Cauchy, and the Levy distributions. We automatically design mutation operators (probability distributions) using Genetic Programming. This is done by using a standard Gaussian random number generator as the terminal set and basic arithmetic operators as the function set. In other words, an arbitrary random number generator is a function of a randomly (Gaussian) generated number passed through an arbitrary function generated by Genetic Programming. Rather than engaging in the futile attempt to develop mutation operators for arbitrary benchmark functions (which is a consequence of the No Free Lunch theorems), we consider tailoring mutation operators for particular function classes. We draw functions from a function class (a probability distribution over a set of functions). The mutation probability distribution is trained on a set of function instances drawn from a given function class. It is then tested on a separate independent test set of function instances to confirm that the evolved probability distribution has indeed generalized to the function class. Initial results are highly encouraging: on each of the ten function classes the probability distributions generated using Genetic Programming outperform both the Gaussian and Cauchy distributions.", notes = "Part of \cite{Krawiec:2013:GP} EuroGP'2013 held in conjunction with EvoCOP2013, EvoBIO2013, EvoMusArt2013 and EvoApplications2013", } @InProceedings{Hong:2016:GECCO, author = "Libin Hong", title = "Automatically Designing More General Mutation Operators of Evolutionary Programming for Groups of Function Classes Using a Hyper-Heuristic", booktitle = "GECCO '16: Proceedings of the 2016 Annual Conference on Genetic and Evolutionary Computation", year = "2016", editor = "Tobias Friedrich", pages = "725--732", keywords = "genetic algorithms, genetic programming", month = "20-24 " # jul, organisation = "SIGEVO", address = "Denver, USA", publisher = "ACM", publisher_address = "New York, NY, USA", isbn13 = "978-1-4503-4206-3", DOI = "doi:10.1145/2908812.2908958", abstract = "In this study we use Genetic Programming (GP) as an offline hyper-heuristic to evolve a mutation operator for Evolutionary Programming. This is done using the Gaussian and uniform distributions as the terminal set, and arithmetic operators as the function set. The mutation operators are automatically designed for a specific function class. The contribution of this paper is to show that a GP can not only automatically design a mutation operator for Evolutionary Programming (EP) on functions generated from a specific function class, but also can design more general mutation operators on functions generated from groups of function classes. In addition, the automatically designed mutation operators also show good performance on new functions generated from a specific function class or a group of function classes.", notes = "University of Nottingham GECCO-2016 A Recombination of the 25th International Conference on Genetic Algorithms (ICGA-2016) and the 21st Annual Genetic Programming Conference (GP-2016)", } @Article{Hong:2018:ASC, author = "Libin Hong and John H. Drake and John R. Woodward and Ender Ozcan", title = "A hyper-heuristic approach to automated generation of mutation operators for evolutionary programming", journal = "Applied Soft Computing", year = "2018", volume = "62", pages = "162--175", month = jan, keywords = "genetic algorithms, genetic programming, Evolutionary programming, Automatic design, Hyper-heuristics, Continuous optimisation", ISSN = "1568-4946", URL = "http://www.sciencedirect.com/science/article/pii/S1568494617306051", DOI = "doi:10.1016/j.asoc.2017.10.002", size = "14 pages", abstract = "Evolutionary programming can solve black-box function optimisation problems by evolving a population of numerical vectors. The variation component in the evolutionary process is supplied by a mutation operator, which is typically a Gaussian, Cauchy, or Levy probability distribution. In this paper, we use genetic programming to automatically generate mutation operators for an evolutionary programming system, testing the proposed approach over a set of function classes, which represent a source of functions. The empirical results over a set of benchmark function classes illustrate that genetic programming can evolve mutation operators which generalise well from the training set to the test set on each function class. The proposed method is able to outperform existing human designed mutation operators with statistical significance in most cases, with competitive results observed for the rest.", notes = "hyperheuristic. Supplement C. Also known as \cite{HONG2018162}", } @PhdThesis{Hong:thesis, author = "Libin Hong", title = "Hyper-heuristic approaches to automatically designing heuristics as mutation operators for evolutionary programming on function classes", school = "University of Nottingham", year = "2018", address = "UK", keywords = "genetic algorithms, genetic programming", language = "en", bibsource = "OAI-PMH server at eprints.nottingham.ac.uk", oai = "oai:eprints.nottingham.ac.uk:52348", URL = "http://eprints.nottingham.ac.uk/52348/", URL = "http://eprints.nottingham.ac.uk/52348/1/THESIS_LATEST_12JUNE2018.pdf", broken = "https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.757549", abstract = "A hyper-heuristic is a search method or learning mechanism for selecting or generating heuristics to solve computational search problems. Researchers classify hyper-heuristics according to the source of feedback during learning: Online learning hyper-heuristics learn while solving a given instance of a problem; Offline learning hyper-heuristics learn from a set of training instances, a method that can generalise to unseen instances. Genetic programming (GP) can be considered a specialization of the more widely known genetic algorithms (GAs) where each individual is a computer program. GP automatically generates computer programs to solve specified tasks. It is a method of searching a space of computer programs. GP can be used as a kind of hyper-heuristic to be a learning algorithm when it uses some feedback from the search process. Our research mainly uses genetic programming as offline hyper-heuristic approach to automatically design various heuristics for evolutionary programming.", notes = "Supervisors: John Cartlidge, Ender Ozcan, Ruibin Bai uk.bl.ethos.757549 ISNI: 0000 0004 7430 3661", } @Article{Hong:CIS, author = "Libin Hong and John R. Woodward and Ender Ozcan and Fuchang Liu", title = "Hyper-heuristic approach: automatically designing adaptive mutation operators for evolutionary programming", journal = "Complex \& Intelligent Systems", year = "2021", volume = "7", number = "6", pages = "3135--3163", month = dec, keywords = "genetic algorithms, genetic programming, Hyper-heuristic, Evolutionary programming, Adaptive mutation", ISSN = "2198-6053", URL = "https://rdcu.be/cxGCh", DOI = "doi:10.1007/s40747-021-00507-6", size = "29 pages", abstract = "Genetic programming (GP) automatically designs programs. Evolutionary programming (EP) is a real-valued global optimisation method. EP uses a probability distribution as a mutation operator, such as Gaussian, Cauchy, or Levy distribution.This study proposes a hyper-heuristic approach that employs GP to automatically design different mutation operators for EP. At each generation, the EP algorithm can adaptively explore the search space according to historical information. The experimental results demonstrate that the EP with adaptive mutation operators, designed by the proposed hyper-heuristics,exhibits improved performance over other EP versions (both manually and automatically designed). Many researchers in evolutionary computation advocate adaptive search operators (which do adapt over time) over non-adaptive operators (which do not alter over time). The core motive of this study is that we can automatically design adaptive mutation operators that out perform automatically designed non-adaptive mutation operators.", notes = "Hangzhou Normal University, Hangzhou, China", } @InProceedings{Hong:2024:CAI, author = "Yuanzi Hong and Wei-Li Liu and Jinghui Zhong and Peng Liang and Jianhua Guo and Chunying Li", title = "Archive-based Cooperative Coevolution Genetic Programming for Workflow Scheduling", booktitle = "2024 IEEE Conference on Artificial Intelligence (CAI)", year = "2024", pages = "216--219", month = jun, keywords = "genetic algorithms, genetic programming, Training, Processor scheduling, Heuristic algorithms, Sociology, Scheduling, Computational efficiency, Workflow scheduling, cooperative coevolution", DOI = "doi:10.1109/CAI59869.2024.00047", abstract = "Workflow Scheduling Problem (WSP) is a well-known combinatorial optimisation problem, which requires allocating tasks to available computing resources to maximize system efficiency, performance, or to meet specific requirements of service quality. The cooperative coevolution hyper-heuristic method based on genetic programming is a promising approach for addressing the WSP, attracting growing attention from researchers. However, this approach still faces the challenge of individual selection bias in the fitness evaluation when coevoluting two sub-populations. To address the above issue, this paper proposes an Archive-based Cooperative Coevolution GP (A-CCGP), which leverages an archive population to improve the quality of fitness evaluation. In addition, an adaptive mechanism is proposed to dynamically adjust the training set during the evolution to reduce the computational cost of fitness evaluation. Experimental results have validated the effectiveness of the proposed A-CCGP algorithm, in comparison with several state-of-the-art algorithms.", notes = "Also known as \cite{10605573} Guangdong Polytechnic Normal University, Guangzhou, China", } @Article{Hong:2002:JH, author = "Yoon-Seok Hong and Michael R. Rosen", title = "Identification of an urban fractured-rock aquifer dynamics using an evolutionary self-organizing modelling", journal = "Journal of Hydrology", year = "2002", volume = "259", pages = "89--104", number = "1-4", keywords = "genetic algorithms, genetic programming", ISSN = "0022-1694", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6V6C-44KPK1K-4/2/cc33fdeeff7d3869ee62940e37e3e133", DOI = "doi:10.1016/S0022-1694(01)00587-X", abstract = "An urban fractured-rock aquifer system, where disposal of storm water is via 'soak holes' drilled directly into the top of fractured-rock basalt, has a highly dynamic nature where theories or knowledge to generate the model are still incomplete and insufficient. Therefore, formulating an accurate mechanistic model, usually based on first principles (physical and chemical laws, mass balance, and diffusion and transport, etc.), requires time- and money-consuming tasks. Instead of a human developing the mechanistic-based model, this paper presents an approach to automatic model evolution in genetic programming (GP) to model dynamic behaviour of groundwater level fluctuations affected by storm water infiltration. This GP evolves mathematical models automatically that have an understandable structure using function tree representation by methods of natural selection ('survival of the fittest') through genetic operators (reproduction, crossover, and mutation). The simulation results have shown that GP is not only capable of predicting the groundwater level fluctuation due to storm water infiltration but also provides insight into the dynamic behaviour of a partially known urban fractured-rock aquifer system by allowing knowledge extraction of the evolved models. Our results show that GP can work as a cost-effective modelling tool, enabling us to create prototype models quickly and inexpensively and assists us in developing accurate models in less time, even if we have limited experience and incomplete knowledge for an urban fractured-rock aquifer system affected by storm water infiltration.", } @InProceedings{hong:2003:gecco:workshop, title = "Automatic Model Induction of a Biological Waste Water Treatment Process using Context-Free Grammar Genetic Programming", author = "Yoon-Seok Hong", pages = "146--149", booktitle = "{GECCO 2003}: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference", editor = "Alwyn M. Barry", year = "2003", month = "11 " # jul, publisher = "AAAI", address = "Chigaco", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025", notes = "Bird-of-a-feather Workshops, GECCO-2003. A joint meeting of the twelth International Conference on Genetic Algorithms (ICGA-2003) and the eigth Annual Genetic Programming Conference (GP-2003) part of barry:2003:GECCO:workshop", keywords = "genetic algorithms, genetic programming", } @Article{Hong:2003:WR, author = "Yoon-Seok Hong and Rao Bhamidimarri", title = "Evolutionary self-organising modelling of a municipal wastewater treatment plant", journal = "Water Research", year = "2003", volume = "37", pages = "1199--1212", number = "6", abstract = "Building predictive models for highly time varying and complex multivariable aspects of the wastewater treatment plant is important both for understanding the dynamics of this complex system, and in the development of optimal control support and management schemes. genetic programming as a self-organising modelling tool, to model dynamic performance of municipal activated-sludge wastewater treatment plants. Genetic programming evolves several process models automatically based on methods of natural selection ('survival of the fittest'), that could predict the dynamics of MLSS and suspended solids in the effluent. The predictive accuracy of the genetic programming approach was compared with a nonlinear state-space model with neural network and a well-known IAWQ ASM2. The genetic programming system evolved some models that were an improvement over the neural network and ASM2 and showed that the transparency of the model evolved may allow inferences about underlying processes to be made. This work demonstrates that dynamic nonlinear processes in the wastewater treatment plant may be successfully modelled through the use of evolutionary model induction algorithms in GP technique. Further, our results show that genetic programming can work as a cost-effective intelligent modelling tool, enabling us to create prototype process models quickly and inexpensively instead of an engineer developing the process model.", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6V73-47XW9PY-5/2/5581df84c89448cc706b69488765c7e1", keywords = "genetic algorithms, genetic programming, Municipal wastewater treatment plant, Self-organising modelling, Model evolution, Neural network, ASM2", DOI = "doi:10.1016/S0043-1354(02)00493-1", notes = "PMID: 12598184", } @Article{Hong:2005:WRR, author = "Yoon-Seok Timothy Hong and Paul A. White and David M. Scott", title = "Automatic rainfall recharge model induction by evolutionary computational intelligence", journal = "Water Resources Research", year = "2005", volume = "41", number = "W08422", email = "T.Hong@gns.cri.nz", keywords = "genetic algorithms, genetic programming, automatic rainfall recharge model induction, Canterbury Plains, evolutionary computational intelligence, New Zealand, soil moisture balance model, 0555 Computational Geophysics: Neural networks, fuzzy logic, machine learning; 1805 Hydrology: Computational hydrology; 1816 Hydrology: Estimation and forecasting; 1829 Hydrology: Groundwater hydrology; 1847 Hydrology: Modelling", URL = "http://www.agu.org/pubs/crossref/2005/2004WR003577.shtml", DOI = "doi:10.1029/2004WR003577", abstract = "Genetic programming (GP) is used to develop models of rainfall recharge from observations of rainfall recharge and rainfall, calculated potential evapotranspiration (PET) and soil profile available water (PAW) at four sites over a 4 year period in Canterbury, New Zealand. This work demonstrates that the automatic model induction method is a useful development in modeling rainfall recharge. The five best performing models evolved by genetic programming show a highly nonlinear relationship between rainfall recharge and the independent variables. These models are dominated by a positive correlation with rainfall, a negative correlation with the square of PET, and a negative correlation with PAW. The best performing GP models are more reliable than a soil water balance model at predicting rainfall recharge when rainfall recharge is observed in the late spring, summer, and early autumn periods. The 'best' GP model provides estimates of cumulative sums of rainfall recharge that are closer than a soil water balance model to observations at all four sites.", } @Article{Hong:2007:ASCE, author = "Yoon-Seok Timothy Hong and Byeong-Cheon Paik", title = "Evolutionary Multivariate Dynamic Process Model Induction for a Biological Nutrient Removal Process", journal = "Journal of Environmental Engineering", year = "2007", volume = "12", month = dec, pages = "1126--1135", email = "hongt@lsbu.ac.uk", publisher = "ASCE", keywords = "genetic algorithms, genetic programming, Grammar-based genetic programming, wastewater treatment process", ISSN = "0733-9372", DOI = "doi:10.1061/(ASCE)0733-9372(2007)133:12(1126)", size = "10 pages", abstract = "This paper proposes an automatic process model induction system using an evolutionary computational intelligence, called grammar-based genetic programming, that is specially designed to automatically discover multivariate dynamic process models that best fit observed process data. This automatic process model induction system combines an evolutionary self-organising system of genetic programming paradigm with various mathematical functions for a multivariate nonlinear model evolution using a grammar system via the mechanism of genetics and natural selection. The results demonstrate how the automatic process model induction system based on grammar-based genetic programming can be used to develop accurate and relatively cost-effective multivariate dynamic process models for the full-scale biological nutrient removal process. Multivariate dynamic process models are derived automatically in the form of understandable mathematical formulas that enable engineers to extract important knowledge hidden in the data and develop better operation and control strategies.", } @Article{Hong:2012:SERRA, author = "Yoon-Seok Timothy Hong and Byeong-Cheon Paik", title = "Inference model derivation with a pattern analysis for predicting the risk of microbial pollution in a sewer system", journal = "Stochastic Environmental Research and Risk Assessment", year = "2012", volume = "26", number = "5", pages = "695--707", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Fecal coliform bacteria, Water quality modelling, Multivariate inference model derivation, Neural network-based pattern analysis, Self-Organising Feature Maps, Evolutionary process model induction system, Grammar-based genetic programming", ISSN = "1436-3240", DOI = "doi:10.1007/s00477-011-0538-9", size = "13 pages", abstract = "Developing a mathematical model for predicting fecal coliform bacteria concentration is very important because it can provide a basis for water quality management decisions that can minimise microbial pollution risk to the public. This paper introduces a hybrid modelling methodology which is a combined use of a neural network-based pattern analysis and an evolutionary process model induction system. The neural network-based pattern analysis technique is applied to extract knowledge on inter-relationships between fecal coliform concentrations and other measurable variables in a sewer system. Based on the result of neural network-based pattern analysis, an evolutionary process model induction system is used to derive mathematical inference models that can predict fecal coliform bacteria concentration from easily measurable variables instead of directly measuring fecal coliform bacteria concentration in a sewer system. The neural network-based pattern analysis extracts that temperature and ammonia concentration are the most important driving forces leading to an increase in fecal coliform bacteria concentration in the sewer system at Paraparaumu City, New Zealand. Fecal coliform bacteria concentration is also positively correlated with dissolved phosphorus and inversely with flow rate. The multivariate inference models that are able to predict fecal coliform bacteria concentration are successfully derived as functions of flow rate, temperature, ammonia, and dissolved phosphorus in the form of understandable mathematical formulae using the evolutionary process model induction system, even if a priori mathematical knowledge of the dynamic nature of fecal coliform bacteria is poor. The multivariate inference models evolved by the evolutionary process model induction system produce a slightly better performance than the multi-layer perceptron neural network model.", affiliation = "Department of Urban Engineering, London South Bank University, 103 Borough Road, London, SE1 0AA UK", } @InProceedings{Hongbo:2007:ICEMI, author = "Yuan Hongbo and Cai Zhenjiang and Cheng Man and Gao liai", title = "Study on Camera Calibration for Binocular Vision Based on Genetic programming", booktitle = "8th International Conference on Electronic Measurement and Instruments, ICEMI '07", year = "2007", pages = "3--890--3--893", address = "Xian, China", month = aug # " 16-" # jul # " 18 ?????", publisher = "IEEE", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-1136-8", DOI = "doi:10.1109/ICEMI.2007.4351060", abstract = "In view of the camera calibration existent questiones, on the basis of Stereo Vision, a new method of camera calibration for binocular vision based on genetic programming is proposed. It is used to learn the relationships between the image information and the 3D information. For two-cameras system, the complicated relation between the cameras is established by training the genetic programming without the parameters of the cameras calibrated. It neither requires an accurate mathematical model nor needs any prior knowledge about the parameters. The 3D information of target is achieved from genetic programming output. The results of the experiment showed that this method was more accurate with traditional visual calibration methods.", notes = "Mechanical and electricity of College Agriculture university of Hebei, Baoding, 071001 China", } @InProceedings{Hoock:2010:ThRaSH, author = "J.-B. Hoock and O. Teytaud", title = "Racing-Based Genetic Programming", booktitle = "4th Workshop on Theory of Randomized Search Heuristics, ThRaSH'2010", year = "2010", editor = "Anne Auger and Benjamin Doerr and Thomas Jansen and Per Kristian Lehre and Frank Neumann and Pietro S. Oliveto and Carsten Witt", address = "Paris", month = mar # " 24-25", keywords = "genetic algorithms, genetic programming", URL = "http://trsh2010.gforge.inria.fr/abstracts/04Hoock.pdf", size = "1 page", notes = "Multiple Simultaneous Hypothesis Testing (MSHT) effect. racing algorithms Co-located JET meeting at Universite Pierre et Marie Curie", } @InProceedings{Hoock:2010:EuroGP, author = "Jean-Baptiste Hoock and Olivier Teytaud", title = "Bandit-Based Genetic Programming", booktitle = "Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010", year = "2010", editor = "Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar", volume = "6021", series = "LNCS", pages = "268--277", address = "Istanbul", month = "7-9 " # apr, organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming, MoGo", isbn13 = "978-3-642-12147-0", DOI = "doi:10.1007/978-3-642-12148-7_23", abstract = "We consider the validation of randomly generated patterns in a Monte-Carlo Tree Search program. Our bandit-based genetic programming (BGP) algorithm, with proved mathematical properties, outperformed a highly optimized handcrafted module of a well-known computer-Go program with several world records in the game of Go.", notes = "Bernstein Races, BGP Part of \cite{Esparcia-Alcazar:2010:GP} EuroGP'2010 held in conjunction with EvoCOP2010 EvoBIO2010 and EvoApplications2010", } @InProceedings{Hoock:2011:EA, author = "Jean-Baptiste Hoock and Olivier Teytaud", title = "Progress Rate in Noisy Genetic Programming for Choosing lambda", booktitle = "Artificial Evolution", year = "2011", editor = "Jin-Kao Hao and Pierrick Legrand and Pierre Collet and Nicolas Monmarch and Evelyne Lutton and Marc Schoenauer", pages = "494--505", address = "Angers, France", month = "24-26 " # oct, organisation = "Association Evolution Artificielle", keywords = "genetic algorithms, genetic programming, game theory", URL = "http://www.info.univ-angers.fr/ea2011/doc/EA2011_ProceedingsWeb.pdf", URL = "http://hal.inria.fr/inria-00622150", URL = "http://hal.inria.fr/docs/00/62/21/50/PDF/ea2011.pdf", size = "12 pages", abstract = "Recently, it has been proposed to use Bernstein races for implementing non-regression testing in noisy genetic programming. We study the population size of such a (1+lambda) evolutionary algorithm applied to a noisy fitness function optimisation by a progress rate analysis and experiment it on a policy search application.", notes = " See also Chapter 4 in \cite{Hoock:thesis} 1 TAO (Inria), LRI, UMR 8623(CNRS - Univ. Paris-Sud), bat 490 Univ. Paris-Sud 91405 Orsay, France, 2 Dept. of Computer Science and Information Engineering, National University of Tainan, Taiwan EA'11", language = "ENG", oai = "oai:hal.inria.fr:inria-00622150", } @PhdThesis{Hoock:thesis, author = "Jean-Baptiste Hoock", title = "Contributions to Simulation-based High-dimensional Sequential Decision Making", year = "2013", school = "Universit{\'e} Paris Sud - Paris XI", address = "France", month = apr # "~10", keywords = "genetic algorithms, genetic programming, GP, computer science/other, informatique/autre, Monte Carlo tree search, MCTS, learning from simulations, high-dimensional sequential decision making, games, planning, Markov decision process, MoGo, MASH", bibsource = "OAI-PMH server at hal.archives-ouvertes.fr", identifier = "2013PA112053", language = "English", oai = "oai:tel.archives-ouvertes.fr:tel-00912338", URL = "http://tel.archives-ouvertes.fr/tel-00912338", URL = "http://tel.archives-ouvertes.fr/docs/00/91/23/38/PDF/VA2_HOOCK_Jean-Baptitste_10042013_SYNTHESE_ANNEXE.pdf", URL = "http://tel.archives-ouvertes.fr/docs/00/91/23/38/PDF/VA2_HOOCK_Jean-Baptiste_10042013.pdf", size = "238 pages", abstract = "My thesis is entitled Contributions to Simulation-based High-dimensional Sequential Decision Making. The context of the thesis is about games, planning and Markov Decision Processes. An agent interacts with its environment by successively making decisions. The agent starts from an initial state until a final state in which the agent can not make decision anymore. At each time-step, the agent receives an observation of the state of the environment. From this observation and its knowledge, the agent makes a decision which modifies the state of the environment. Then, the agent receives a reward and a new observation. The goal is to maximise the sum of rewards obtained during a simulation from an initial state to a final state. The policy of the agent is the function which, from the history of observations, returns a decision. We work in a context where (i) the number of states is huge, (ii) reward carries little information, (iii) the probability to reach quickly a good final state is weak and (iv) prior knowledge is either nonexistent or hardly exploitable. Both applications described in this thesis present these constraints : the game of Go and a 3D simulator of the European project MASH (Massive Sets of Heuristics). In order to take a satisfying decision in this context, several solutions are brought : 1. Simulating with the compromise exploration/exploitation (MCTS) 2. Reducing the complexity by local solving (GoldenEye) 3. Building a policy which improves itself (RBGP) 4. Learning prior knowledge (CluVo+GMCTS) Monte-Carlo Tree Search (MCTS) is the state of the art for the game of Go. From a model of the environment, MCTS builds incrementally and asymmetrically a tree of possible futures by performing Monte-Carlo simulations. The tree starts from the current observation of the agent. The agent switches between the exploration of the model and the exploitation of decisions which statistically give a good cumulative reward. We discuss 2 ways for improving MCTS : the parallelisation and the addition of prior knowledge. The parallelisation does not solve some weaknesses of MCTS; in particular some local problems remain challenges. We propose an algorithm (GoldenEye) which is composed of 2 parts : detection of a local problem and then its resolution. The algorithm of resolution reuses some concepts of MCTS and it solves difficult problems of a classical database. The addition of prior knowledge by hand is laborious and boring. We propose a method called Racing-based Genetic Programming (RBGP) in order to add automatically prior knowledge. The strong point is that RBGP rigorously validates the addition of a prior knowledge and RBGP can be used for building a policy (instead of only optimising an algorithm). In some applications such as MASH, simulations are too expensive in time and there is no prior knowledge and no model of the environment; therefore Monte-Carlo Tree Search can not be used. So that MCTS becomes usable in this context, we propose a method for learning prior knowledge (CluVo). Then we use pieces of prior knowledge for improving the rapidity of learning of the agent and for building a model, too. We use from this model an adapted version of Monte-Carlo Tree Search (GMCTS). This method solves difficult problems of MASH and gives good results in an application to a word game.", notes = "p116 noisy GP progress rate. Hoeffding and Bernstein bounds Supervisor: Olivier Teytaud", } @InProceedings{DBLP:conf/webi/HoogendoornBR19, author = "Mark Hoogendoorn and Ward {van Breda} and Jeroen Ruwaard", editor = "Payam M. Barnaghi and Georg Gottlob and Yannis Manolopoulos and Theodoros Tzouramanis and Athena Vakali", title = "{GP-HD:} Using Genetic Programming to Generate Dynamical Systems Models for Health Care", booktitle = "2019 {IEEE/WIC/ACM} International Conference on Web Intelligence, {WI} 2019, Thessaloniki, Greece, October 14-17, 2019", pages = "1--8", publisher = "{ACM}", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1145/3350546.3352494", DOI = "doi:10.1145/3350546.3352494", URL = "http://arxiv.org/abs/1904.05815", timestamp = "Thu, 05 Dec 2019 15:05:16 +0100", biburl = "https://dblp.org/rec/conf/webi/HoogendoornBR19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", notes = "See also arXiv abs/1904.05815 \cite{DBLP:journals/corr/abs-1904-05815}", } @InProceedings{hooper:1996:iarGPes, author = "Dale Hooper and Nicholas S. Flann", title = "Improving the Accuracy and Robustness of Genetic Programming through Expression Simplification", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "428", address = "Stanford University, CA, USA", publisher = "MIT Press", ISBN = "0-262-61127-9", URL = "http://digital.cs.usu.edu/~flann/gp.pdf", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap67.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", size = "1 page", notes = "GP-96. Occam's razor, bloat, introns, 200 edit rules", } @InProceedings{Hooper:1997:rhc, author = "Dale C. Hooper and Nicholas S. Flann and Stephanie R. Fuller", title = "Recombinative Hill-Climbing: A Stronger Search Method for Genetic Programming", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming", pages = "174--179", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1997/Hooper_1997_rhc.pdf", size = "6 pages", notes = "GP-97 Artificial Ant Santa Fe trail (only 400 time steps p177), Symbolic regression pi*x**2+ex+x**0.5 {"}RHC is shown to run about ten times faster than traditional GP for the same population size{"} p175 NB only on symbolic regression. Optional program simplification (cites \cite{hooper:1996:iarGPes}) 0.5% mutation. Overfitting.", } @TechReport{Hoos:2018:AutoAI, author = "Holger H. Hoos", title = "Automated Artificial Intelligence (AutoAI)", institution = "ADA Research Group, Leiden Institute of Advanced Computer Science (LIACS), Universiteit Leiden", year = "2018", number = "TR-2018-1", address = "The Netherlands", month = "24 " # dec, keywords = "genetic algorithms, genetic programming", URL = "https://ada.liacs.nl/auto-ai/vision-paper.pdf", size = "12 pages", abstract = "While there has been research on artificial intelligence (AI) for at least 50 years, we are now standing on the threshold of an AI revolution, a transformational change whose effects may surpass that of the industrial revolution in the first half of the 19th century. There are multiple reasons why AI is rapidly gaining traction now. Firstly, much of our infrastructure is already controlled by computers; so deploying AI systems is technologically quite straightforward. Secondly, in many situations, there is now easy access to large amounts of data, which can be used as a basis for customising AI systems using machine learning. Thirdly, due to tremendous improvements not only in computer hardware, but also in AI algorithms, advanced AI systems can now be deployed broadly and at low cost. As a result, AI systems are poised to fundamentally change the way we live and work. AI is quickly becoming a major driver of innovation, growth and competitiveness, and is bound to play a crucial role in addressing the challenges we face individually and as societies. However, high-quality AI systems require considerable expertise to build, maintain and operate. For the foreseeable future, AI expertise will be a limiting factor in the broad deployment of AI systems, and, unless managed very carefully, this will lead to uneven access and increasing inequality. It is also likely to cause the wide-spread use of low-quality AI systems, developed without the proper expertise. Here, we propose to address this problem using AI methods, specifically, automated algorithm design, machine learning and optimisation techniques, to help build and deploy the next generation of AI systems. This gives rise to an approach we refer to as automated artificial intelligence (AutoAI). Ultimately, research on AutoAI aims to make it possible for people who benefit from AI to develop, deploy and maintain AI systems that are performant, robust and predictable, without requiring deep and highly specialised AI expertise. AutoAI will thus dramatically broaden access to high-quality AI systems.", notes = "Mention of GP, Genetic Improvement, SBSE", } @InProceedings{Hoover:2011:GECCOcomp, author = "Kristopher Hoover and Rachel Marceau and Tyndall Harris and Nicholas Hardison and David Reif and Alison Motsinger-Reif", title = "Optimization of grammatical evolution decision trees", booktitle = "GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0690-4", keywords = "genetic algorithms, genetic programming, grammatical evolution, Bioinformatics, computational, systems, and synthetic biology: Poster", pages = "35--36", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001858.2001879", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "The detection of gene-gene and gene-environment interactions in genetic association studies presents a difficult computational and statistical challenge, especially as advances in genotyping technology have rapidly expanded the number of potential genetic predictors in such studies. The scale of these studies makes exhaustive search approaches infeasible, inspiring the application of evolutionary computation algorithms to perform variable selection and build classification models. Recently, an application of grammatical evolution to evolve decision trees (GEDT) has been introduced for detecting interaction models. Initial results were promising, but relied on arbitrary parameter choices for the evolutionary process. In the current study, we present the results of a parameter sweep evaluating the power of GEDT and show that improved parameter choices improves the performance of the method. The results of these experiments are important for the continued optimisation, evaluation, and comparison of this and related methods, and for proper application in real data.", notes = "Also known as \cite{2001879} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @InProceedings{Hoover:2012:GECCOcomp, author = "Kristopher Hoover and Rachel Marceau and Tyndall Harris and David Reif and Alison Motsinger-Reif", title = "A comparison of GE optimized neural networks and decision trees", booktitle = "GECCO 2012 Graduate Students Workshop", year = "2012", editor = "Alison Motsinger-Reif", isbn13 = "978-1-4503-1178-6", keywords = "genetic algorithms, genetic programming, grammatical evolution", pages = "611--614", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330784.2330885", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Grammatical evolution neural networks (GENN) is a commonly used method at identifying difficult to detect gene-gene and gene-environment interactions. It has been shown to be an effective tool in the prediction of common diseases using single nucleotide polymorphisms (SNPs). However, GENN lacks interpretability because it is a black box model. Therefore, grammatical evolution of decision trees (GEDT) is being considered as an alternative, as decision trees are easily interpretable for clinicians. Previously, the most effective parameters for GEDT and GENN were found using parameter sweeps. Since GEDT is much more intuitive and easy to understand, it becomes important to compare its predictive power to that of GENN. We show that it is not as effective as GENN at detecting disease causing polymorphisms especially in more difficult to detect models, but this power trade off may be worth it for interpretability.", notes = "Also known as \cite{2330885} Distributed at GECCO-2012. ACM Order Number 910122.", } @InProceedings{Horibe:2021:EuroGP, author = "Kazuya Horibe and Kathryn Walker and Sebastian Risi", title = "Regenerating Soft Robots through Neural Cellular Automata", booktitle = "EuroGP 2021: Proceedings of the 24th European Conference on Genetic Programming", year = "2021", editor = "Ting Hu and Nuno Lourenco and Eric Medvet", series = "LNCS", volume = "12691", publisher = "Springer Verlag", address = "Virtual Event", pages = "36--50", month = "7-9 " # apr, organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Regeneration, soft robots, neural cellular automata, NCA, CA, ANN, damage recovering, evosoro", isbn13 = "978-3-030-72811-3", URL = "https://arxiv.org/abs/2102.02579", DOI = "doi:10.1007/978-3-030-72812-0_3", code_url = "http://github.com/KazuyaHoribe/RegeneratingSoftRobots", size = "12 pages", abstract = "Morphological regeneration is an important feature that highlights the environmental adaptive capacity of biological systems. Lack of this regenerative capacity significantly limits the resilience of machines and the environments they can operate in. To aid in addressing this gap, we develop an approach for simulated soft robots to regrow parts of their morphology when being damaged. Although numerical simulations using soft robots have played an important role in their design, evolving soft robots with regenerative capabilities have so far received comparable little attention. Here we propose a model for soft robots that regenerate through a neural cellular automata. Importantly, this approach only relies on local cell information to regrow damaged components, opening interesting possibilities for physical regenerable soft robots in the future. Our approach allows simulated soft robots that are damaged to partially regenerate their original morphology through local cell interactions alone and regain some of their ability to locomote. These results take a step towards equipping artificial systems with regenerative capacities and could potentially allow for more robust operations in a variety of situations and environments.", notes = "Sea slug regenerate body from head in 9 days vox??? simulation engine. feedforward ANN and Recurrent ANN LSTM. fitness = distance traveled by centre of mass. Evolve growth rule for 2D and 3D CA. Eg biped and S-type, L-type, zigzag. pop=100, 300 gens. One fixed cut (damage) applied. Such,2017,arXiv. Locomotion limited recovery of locomotion after regrowth after damage. Like Hopfield (ANN) networks. http://www.evostar.org/2021/eurogp/ Part of \cite{Hu:2021:GP} EuroGP'2021 held in conjunction with EvoCOP2021, EvoMusArt2021 and EvoApplications2021", } @Article{Horibe:2022:GPEM, author = "Kazuya Horibe and Kathryn Walker and Rasmus Berg Palm and Shyam Sudhakaran and Sebastian Risi", title = "Severe damage recovery in evolving soft robots through differentiable programming", journal = "Genetic Programming and Evolvable Machines", year = "2022", volume = "23", number = "3", pages = "405--426", month = sep, note = "Special Issue: Highlights of Genetic Programming 2021 Events", keywords = "genetic algorithms, genetic programming, Regeneration, Soft robots, Neural cellular automata, Damage recovering", ISSN = "1389-2576", URL = "https://rdcu.be/cPBBH", DOI = "doi:10.1007/s10710-022-09433-z", abstract = "Biological systems are very robust to morphological damage, but artificial systems (robots) are currently not. In this paper we present a system based on neural cellular automata, in which moving robots are evolved and then given the ability to regenerate their morphology from damage through gradient-based training. Our approach thus combines the benefits of evolution to discover a wide range of different robot morphologies, with the efficiency of supervised training for robustness through differentiable update rules. The resulting neural cellular automata are able to grow virtual robots capable of regaining more than 80percent of their functionality, even after severe types of morphological damage.", notes = " Department of Systems Innovation, Osaka University, 1-3 Machikaneyama, Toyonaka, 560-8531, Osaka, Japan", } @InProceedings{Horii:2014:IIAIAAI, author = "F. Horii and T. Hochin and H. Nomiya", booktitle = "3rd International Conference on Advanced Applied Informatics (IIAIAAI 2014)", title = "Improvement of the Success Rate of Automatic Generation of Procedural Programs with Variable Initialization Using Genetic Programming", year = "2014", month = aug, pages = "699--704", abstract = "Genetic Programming (GP), a method of evolutionary computation, is used in producing a variety of programs. In order to generate a procedural program, handling variables is required. It increases the number of combinations of generated programs. This paper proposes a method including the automatic initialisation of variables and decreasing the number of combinations of them. For this propose, two major revisions are introduced. One is the introduction of new parameters, the maximum depth and the minimum depth of the height of a program tree. These make programs easy to have a specific structure. The other is the addition of genetic operations. These are for avoiding convergence of programs. Owing to these revisions, it is possible to improve the success rate of the generation of program that includes all of requirement.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IIAI-AAI.2014.144", notes = "Also known as \cite{6913388}", } @InProceedings{horn:1996:nnclCS, author = "Jeffrey Horn and David E. Goldberg", title = "Natural Niching for Evolving Cooperative Classifiers", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "Classifier Systems, Genetic Algorithms", pages = "553--564", address = "Stanford University, CA, USA", publisher = "MIT Press", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap90.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96 Classifier paper", } @InProceedings{horn:1999:CCBNGA, author = "Jeffrey Horn", title = "Controlling the Cooperative-Competitive Boundary in Niched Genetic Algorithms", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "305--312", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/Ga-830.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/Ga-830.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{hornby:1999:AEGSQR, author = "G. S. Hornby and M. Fujita and S. Takamura and T. Yamamoto and O. Hanagata", title = "Autonomous Evolution of Gaits with the Sony Quadruped Robot", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1297--1304", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "artificial life, adaptive behavior and agents, robotics, evolutionary robotics, locomotion", ISBN = "1-55860-611-4", URL = "http://www.demo.cs.brandeis.edu/papers/hornby_gecco99_sony.pdf", URL = "http://www.demo.cs.brandeis.edu/papers/hornby_gecco99_sony.ps.gz", URL = "http://www.demo.cs.brandeis.edu/papers/hornby_gecco99_sony.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-011.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-011.ps", abstract = "A trend in robotics is towards legged robots. One of the issues with legged robots is the development of gaits. Typically gaits are developed manually. In this paper we report our results of autonomous evolution of dynamic gaits for the Sony Quadruped Robot. Fitness is determined using the robot's digital camera and infrared sensors. Using this system we evolve faster dynamic gaits than previously manually developed", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{hornby:1999:DTCPW, author = "Gregory S. Hornby and Brian Mirtich", title = "Diffuse versus True Coevolution in a Physics-based World", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1305--1312", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "artificial life, adaptive behavior and agents, co-evolution, pursuer-evader, neural networks", ISBN = "1-55860-611-4", URL = "http://www.demo.cs.brandeis.edu/papers/hornby_gecco99_merl.pdf", URL = "http://www.demo.cs.brandeis.edu/papers/hornby_gecco99_merl.ps.gz", URL = "http://www.demo.cs.brandeis.edu/papers/hornby_gecco99_merl.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-025.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-025.ps", abstract = "We compare two types of coevolutionary tournaments, true and diffuse, in contests using a general-purpose, physics-based simulator. Previous work in coevolving agents has used true coevolution and found that populations tend to enter mediocre states. One hypothesis for alleviating these problems is to use diffuse coevolution. Our results show that agents evaluated with diffuse tournaments are more generalized than those evaluated with true tournaments.", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{hornby:2001:taggepd, author = "Gregory S. Hornby and Jordan B. Pollack", title = "The Advantages of Generative Grammatical Encodings for Physical Design", booktitle = "Proceedings of the 2001 Congress on Evolutionary Computation CEC2001", year = "2001", pages = "600--607", address = "COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "27-30 " # may, organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, lindenmayer system, L-systems, generative encoding, design, automatic design creation, engineering problems, evolutionary algorithms, evolved table designs, fitness, generative grammatical encodings, generative specifications, manufacture, physical design, rapid prototyping equipment, CAD, encoding, evolutionary computation, grammars, rapid prototyping (industrial)", ISBN = "0-7803-6658-1", URL = "http://www.demo.cs.brandeis.edu/papers/hornby_cec01.pdf", URL = "http://www.demo.cs.brandeis.edu/papers/hornby_cec01.ps", DOI = "doi:10.1109/CEC.2001.934446", size = "8 pages", abstract = "One of the applications of evolutionary algorithms is the automatic creation of designs. For evolutionary techniques to scale to the complexities necessary for actual engineering problems, it has been argued that generative systems, where the genotype is an algorithm for constructing the final design, should be used as the encoding. We describe a system for creating generative specifications by combining Lindenmayer systems with evolutionary algorithms and apply it to the problem of generating table designs. Designs evolved by our system reach an order of magnitude more parts than previous generative systems. Comparing it against a non-generative encoding we find that the generative system produces designs with higher fitness and is faster than the non-generative system. Finally, we demonstrate the ability of our system to go from design to manufacture by constructing evolved table designs using rapid prototyping equipment.", notes = "CEC-2001 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 01TH8546C, Library of Congress Number = The project page for this work is at: http://www.demo.cs.brandeis.edu/pr/evo_design/evo_design.html", } @InProceedings{Hornby:2001:ICRA, author = "Gregory S. Hornby and Hod Lipson and Jordan B. Pollack", title = "Evolution of Generative Design Systems for Modular Physical Robots", booktitle = "IEEE International Conference on Robotics and Automation", year = "2001", keywords = "genetic algorithms, genetic programming, L-systems, generative encoding, design, robotics, P0L", URL = "http://www.demo.cs.brandeis.edu/papers/hornby_icra01.pdf", URL = "http://www.demo.cs.brandeis.edu/papers/hornby_icra01.ps", size = "6 pages", abstract = "Recent research has demonstrated the ability for automatic design of the morphology and control of real physical robots using techniques inspired by biological evolution. The main criticism of the evolutionary design approach, however, is that it is doubtful whether it will reach the high complexities necessary for practical engineering. Here we claim that for automatic design systems to scale in complexity the designs they produce must be made of re-used modules. Our approach is based on the use of a generative design grammar subject to an evolutionary process. Unlike a direct encoding of a design, a generative design specification can re-use components, giving it the ability to create more complex modules from simpler ones. Re-used modules are also valuable for improved efficiency in testing and construction. We describe a system for creating generative specifications capable of hierarchical modularity by combining Lindenmayer systems with evolutionary algorithms. Using this system we demonstrate for the first time a generative system for physical, modular, 2D locomoting robots and their controllers.", notes = "The project page for this work is at: http://www.demo.cs.brandeis.edu/pr/evo_design/evo_design.html", } @InProceedings{hornby:2001:GECCO, title = "Body-Brain Co-evolution Using L-systems as a Generative Encoding", author = "Gregory S. Hornby and Jordan B. Pollack", pages = "868--875", year = "2001", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)", editor = "Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon and Edmund Burke", address = "San Francisco, California, USA", publisher_address = "San Francisco, CA 94104, USA", month = "7-11 " # jul, keywords = "genetic algorithms, genetic programming, artificial life, adaptive behaviour, agents, L-systems, Lindenmayer grammar, generative encoding, ANN", ISBN = "1-55860-774-9", URL = "http://www.demo.cs.brandeis.edu/papers/hornby_gecco01.pdf", URL = "http://www.demo.cs.brandeis.edu/papers/hornby_gecco01.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco2001/d07.pdf", size = "8 pages", abstract = "We co-evolve the morphology and controller of artificial creatures using two integrated generative processes. L-systems are used as the common generative encoding for both body and brain. Combining the languages of both into a single L-system allows for linkage between the genotype of the controller and the parts of the morphology that it controls. Creatures evolved by this system are more complex than previous work, having an order of magnitude more parts and a higher degree of regularity.", notes = "A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of \cite{spector:2001:GECCO} The project page for this work is at: http://www.demo.cs.brandeis.edu/pr/evo_design/evo_design.html and the source code can be downloaded from here.", } @Article{hornby.cag.01, author = "Gregory S. Hornby and Jordan B. Pollack", title = "Evolving {L}-Systems To Generate Virtual Creatures", journal = "Computers and Graphics", volume = "25", number = "6", year = "2001", pages = "1041--1048", month = dec, note = "Artificial Life", keywords = "genetic algorithms, genetic programming, animation, artificial life, representation, intelligent agents, Lindenmayer systems (L-systems)", publisher = "Elsevier", ISSN = "0097-8493", URL = "http://www.demo.cs.brandeis.edu/papers/hornby_cag01.pdf", URL = "http://www.demo.cs.brandeis.edu/papers/hornby_cag01.ps", DOI = "doi:10.1016/S0097-8493(01)00157-1", abstract = "Virtual creatures play an increasingly important role in computer graphics as special effects and background characters. The artificial evolution of such creatures potentially offers some relief from the difficult and time consuming task of specifying morphologies and behaviours. But, while artificial life techniques have been used to create a variety of virtual creatures, previous work has not scaled beyond creatures with 50 components and the most recent work has generated creatures that are unnatural looking. Here we describe a system that uses Lindenmayer systems (L-systems) as the encoding of an evolutionary algorithm (EA) for creating virtual creatures. Creatures evolved by this system have hundreds of parts, and the use of an L-system as the encoding results in creatures with a more natural look.", notes = "The project page for this work is at: http://www.demo.cs.brandeis.edu/pr/evo_design/evo_design.html", } @Article{Hornby:2002:AL, author = "Gregory S. Hornby and Jordan B. Pollack", title = "Creating High-Level Components with a Generative Representation for Body-Brain Evolution", journal = "Artificial Life", year = "2002", volume = "8", number = "3", pages = "223--246", month = "Summer", email = "hornby@email.arc.nasa.gov", keywords = "genetic algorithms, genetic programming, Body-brain evolution, generative representations, representation, Lindenmayer systems, L-systems", ISSN = "1064-5462", URL = "http://www.demo.cs.brandeis.edu/papers/hornby_alife02.pdf", URL = "http://ic.arc.nasa.gov/people/hornby/genre/genre.html", URL = "http://mitpress.mit.edu/journals/pdf/alife_8_3_223_0.pdf", DOI = "doi:10.1162/106454602320991837", size = "30 pages", size = "25 pages", abstract = "One of the main limitations of scalability in body-brain evolution systems is the representation chosen for encoding creatures. This paper defines a class of representations called generative representations, which are identified by their ability to reuse elements of the genotype in the translation to the phenotype. This paper presents an example of a generative representation for the concurrent evolution of the morphology and neural controller of simulated robots, and also introduces GENRE, an evolutionary system for evolving designs using this representation. Applying GENRE to the task of evolving robots for locomotion and comparing it against a non-generative (direct) representation shows that the generative representation system rapidly produces robots with significantly greater fitness. Analyzing these results shows that the generative representation system achieves better performance by capturing useful bias from the design space and by allowing viable large scale mutations in the phenotype. Generative representations thereby enable the encapsulation, coordination, and reuse of assemblies of parts.", notes = "The project page for this work is at: http://www.demo.cs.brandeis.edu/pr/evo_design/evo_design.html Managed to get two entries for this paper. Combined them (ie also known as \cite{hornby_alife02}. April 2008.", notes = "genetic variations are repeated if offspring fitness<0.1 parent", } @PhdThesis{hornby_phd03, author = "Gregory Scott Hornby", title = "Generative Representations for Evolutionary Design Automation", school = "Brandeis University, Dept. of Computer Science", year = "2003", address = "Boston, MA, USA", month = feb, email = "hornby@email.arc.nasa.gov", keywords = "genetic algorithms, genetic programming, generative representation, evolutionary design", URL = "http://www.demo.cs.brandeis.edu/papers/long.html#hornby_phd", broken = "http://ic.arc.nasa.gov/people/hornby/genre/genre.html", URL = "http://www.demo.cs.brandeis.edu/papers/hornby_phd.pdf", code_url = "http://www.demo.cs.brandeis.edu/pr/evo_design/evo_design.html#genre_source", size = "242 pages", abstract = "In this thesis the class of generative representations is defined and it is shown that this class of representations improves the scalability of evolutionary design systems by automatically learning inductive bias of the design problem thereby capturing design dependencies and better enabling search of large design spaces. First, properties of representations are identified as: combination, control-flow, and abstraction. Using these properties, representations are classified as non-generative, or generative. Whereas non-generative representations use elements of encoded artifacts at most once in translation from encoding to actual artifact, generative representations have the ability to reuse parts of the data structure for encoding artifacts through control-flow (using iteration) and/or abstraction (using labelled procedures). Unlike non-generative representations, which do not scale with design complexity because they cannot capture design dependencies in their structure, it is argued that evolution with generative representations can better scale with design complexity because of their ability to hierarchically create assemblies of modules for reuse, thereby enabling better search of large design spaces. Second, GENRE, an evolutionary design system using a generative representation, is described. Using this system, a non-generative and a generative representation are compared on four classes of designs: three-dimensional static structures constructed from voxels; neural networks; actuated robots controlled by oscillator networks; and neural network controlled robots. Results from evolving designs in these substrates show that the evolutionary design system is capable of finding solutions of higher fitness with the generative representation than with the non-generative representation. This improved performance is shown to be a result of the generative representation's ability to capture intrinsic properties of the search space and its ability to reuse parts of the encoding in constructing designs. By capturing design dependencies in its structure, variation operators are more likely to be successful with a generative representation than with a non-generative representation. Second, reuse of data elements in encoded designs improves the ability of an evolutionary algorithm to search large design spaces.", notes = "Fri, 10 Sep 2004 01:13:34 EDT genetic_programming@yahoogroups.com GENREv1.1b source http://www.demo.cs.brandeis.edu/pr/evo_design/evo_design.html#genre_source", } @InProceedings{hornby:2003:aaaiS, author = "Gregory S. Hornby", title = "Creating Complex Building Blocks through Generative Representations", booktitle = "Computational Synthesis: From Basic Building Blocks to High Level Functionality: Papers from the 2003 AAAI Spring Symposium", year = "2003", editor = "Hod Lipson and Erik K. Antonsson and John R. Koza", series = "AAAI technical report SS-03-02", pages = "98--105", address = "Stanford, California, USA", publisher = "AAAI Press", keywords = "genetic algorithms, genetic programming", ISBN = "1-57735-179-7", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/30633/http:zSzzSzic.arc.nasa.govzSzpeoplezSzhornbyzSzpaperszSzhornby_ascs03.pdf/hornby03creating.pdf", URL = "http://citeseer.ist.psu.edu/693104.html", URL = "http://www.aaai.org/Press/Reports/Symposia/Spring/ss-03-02.html", URL = "http://ic.arc.nasa.gov/people/hornby/papers/abstracts.html#pollack_alife01", abstract = "One of the main limitations for the functional scalability of computer automated design systems is the representation used for encoding designs. Using computer programs as an analogy, representations can be thought of as having the properties of combination, control-flow and abstraction. We define generative representations as those which have the ability to reuse elements in an encoding through either iteration or abstraction and argue that reuse improves functional scalability by allowing the representation to construct building-blocks and capture design dependencies. Next we describe GENRE, an evolutionary design system for evolving a variety of different types of designs. Using this system we compare the generative representation against a non-generative representation on evolving tables and robots and show that designs evolved with the generative representation have higher fitness than designs created with the non-generative representation. Further, we show that designs evolved with the generative representation are constructed in a modular way through the reuse of discovered building blocks.", notes = "TR SS-03-02", } @InProceedings{hornby:2003:gecco, author = "Gregory S. Hornby", title = "Generative Representations for Evolving Families of Designs", booktitle = "Genetic and Evolutionary Computation -- GECCO-2003", editor = "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller", year = "2003", pages = "1678--1689", address = "Chicago", publisher_address = "Berlin", month = "12-16 " # jul, volume = "2724", series = "LNCS", ISBN = "3-540-40603-4", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, parametric Lindenmayer systems, evolving neural networks, ANN", URL = "http://ic.arc.nasa.gov/people/hornby/papers/hornby_gecco03.pdf", broken = "http://ic.arc.nasa.gov/people/hornby/papers/abstracts.html#hornby_gecco03", DOI = "doi:10.1007/3-540-45110-2_61", abstract = "Since typical evolutionary design systems encode only a single artifact with each individual, each time the objective changes a new set of individuals must be evolved. When this objective varies in a way that can be parameterized, a more general method is to use a representation in which a single individual encodes an entire class of artifacts. In addition to saving time by preventing the need for multiple evolutionary runs, the evolution of parameter-controlled designs can create families of artifacts with the same style and a reuse of parts between members of the family. In this paper an evolutionary design system is described which uses a generative representation to encode families of designs. Because a generative representation is an algorithmic encoding of a design, its input parameters are a way to control aspects of the design it generates. By evaluating individuals multiple times with different input parameters the evolutionary design system creates individuals in which the input parameter controls specific aspects of a design. This system is demonstrated on two design substrates: neural-networks which solve the 3/5/7-parity problem and three-dimensional tables of varying heights.", notes = "GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)", } @Article{hornby:2003:tRA, author = "Gregory S. Hornby and Hod Lipson and Jordan B. Pollack", title = "Generative Representations for the Automated Design of Modular Physical Robots", journal = "IEEE transactions on Robotics and Automation", year = "2003", volume = "19", number = "4", pages = "709--713", month = aug, keywords = "genetic algorithms, genetic programming, Design automation, evolutionary robotics, generative representations, Lindenmayer systems", ISSN = "1042-296X", URL = "http://ccsl.mae.cornell.edu/papers/ITRA03_Hornby.pdf", URL = "http://ieeexplore.ieee.org/iel5/70/27428/01220719.pdf?isnumber=27428&arnumber=1220719", DOI = "doi:10.1109/TRA.2003.814502", size = "17 pages", abstract = "The field of evolutionary robotics has demonstrated the ability to automatically design the morphology and controller of simple physical robots through synthetic evolutionary processes. However, it is not clear if variation-based search processes can attain the complexity of design necessary for practical engineering of robots. Here, we demonstrate an automatic design system that produces complex robots by exploiting the principles of regularity, modularity, hierarchy, and reuse. These techniques are already established principles of scaling in engineering design and have been observed in nature, but have not been broadly used in artificial evolution. We gain these advantages through the use of a generative representation, which combines a programmatic representation with an algorithmic process that compiles the representation into a detailed construction plan. This approach is shown to have two benefits: it can reuse components in regular and hierarchical ways, providing a systematic way to create more complex modules from simpler ones; and the evolved representations can capture intrinsic properties of the design space, so that variations in the representations move through the design space more effectively than equivalent-sized changes in a nongenerative representation. Using this system, we demonstrate for the first time the evolution and construction of modular, three-dimensional, physically moving robots, comprising many more components than previous work on body-brain evolution.", notes = "INSPEC Accession Number: 7719817", } @InProceedings{Hornby:SwT:gecco2004, author = "Gregory S. Hornby", title = "Shortcomings with Tree-Structured Edge Encodings for Neural Networks", booktitle = "Genetic and Evolutionary Computation -- GECCO-2004, Part II", year = "2004", editor = "Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell", series = "Lecture Notes in Computer Science", pages = "495--506", address = "Seattle, WA, USA", publisher_address = "Heidelberg", month = "26-30 " # jun, organisation = "ISGEC", publisher = "Springer-Verlag", volume = "3103", ISBN = "3-540-22343-6", ISSN = "0302-9743", DOI = "doi:10.1007/b98645", URL = "http://ic.arc.nasa.gov/people/hornby/papers/hornby_gecco04.ps", size = "12 pages", keywords = "genetic algorithms, genetic programming, neural networks, graphs, representation", abstract = "In evolutionary algorithms a common method for encoding neural networks is to use a tree-structured assembly procedure for constructing them. Since node operators have difficulties in specifying edge weights and these operators are execution-order dependent, an alternative is to use edge operators. Here we identify three problems with edge operators: in the initialisation phase most randomly created genotypes produce an incorrect number of inputs and outputs; variation operators can easily change the number of input/output (I/O) units; and units have a connectivity bias based on their order of creation. Instead of creating I/O nodes as part of the construction process we propose using parameterised operators to connect to pre-existing I/O units. Results from experiments show that these parameterized operators greatly improve the probability of creating and maintaining networks with the correct number of I/O units, remove the connectivity bias with I/O units and produce better controllers for a goal-scoring task.", notes = "GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004) football", } @Article{Hornby:2004:EPb, author = "Gregory S. Hornby", title = "Functional Scalability through Generative Representations: the Evolution of Table Designs", journal = "Environment and Planning B: Planning and Design", year = "2004", volume = "31", number = "4", pages = "569--587", month = jul, keywords = "genetic algorithms, genetic programming, representation, evolutionary design", ISSN = "0265-8135", URL = "http://www0.arc.nasa.gov/publications/pdf/0814.pdf", URL = "http://ti.arc.nasa.gov/people/hornby/papers/abstracts.html#hornby_epb04", URL = "http://www.envplan.com/epb/abstracts/b31/b3015.html", abstract = "One of the main limitations for the functional scalability of automated design systems is the representation used for encoding designs. I argue that generative representations, those which are capable of reusing elements of the encoded design in the translation to the actual artifact, are better suited for automated design because reuse of building blocks captures some design dependencies and improves the ability to make large changes in design space. To support this argument I compare a generative and a nongenerative representation on a table-design problem and find that designs evolved with the generative representation have higher fitness and a more regular structure. Additionally the generative representation was found to capture better the height dependency between table legs and also produced a wider range of table designs.", } @InProceedings{hornby:2004:ALwks, author = "Gregory S. Hornby", title = "Properties of Artifact Representations for Evolutionary Design", booktitle = "Workshop and Tutorial Proceedings Ninth International Conference on the Simulation and Synthesis of Living Systems(Alife {XI})", year = "2004", editor = "Mark Bedau and Phil Husbands and Tim Hutton and Sanjeev Kumar and Hideaki Sizuki", pages = "-", address = "Boston, Massachusetts", month = "12 " # sep, note = "Self-organisation and development in artificial and natural systems workshop.", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/S.Kumar/hornby.pdf", size = "4 pages", notes = "ALIFE9 http://www.cs.ucl.ac.uk/staff/S.Kumar/sodans.htm ", } @InProceedings{1068297, author = "Gregory S. Hornby", title = "Measuring, enabling and comparing modularity, regularity and hierarchy in evolutionary design", booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation", year = "2005", editor = "Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and Edwin D. {de Jong} and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler", volume = "2", ISBN = "1-59593-010-8", pages = "1729--1736", address = "Washington DC, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2005/docs/p1729.pdf", DOI = "doi:10.1145/1068009.1068297", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "25-29 " # jun, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, evolutionary algorithm, computer-automated design, design, open-ended design, evolutionary design, representations", notes = "GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM Order Number 910052", } @InProceedings{1144142, author = "Gregory S. Hornby", title = "{ALPS}: the age-layered population structure for reducing the problem of premature convergence", booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation", year = "2006", editor = "Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos {Coello Coello} and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens", volume = "1", ISBN = "1-59593-186-4", pages = "815--822", address = "Seattle, Washington, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p815.pdf", DOI = "doi:10.1145/1143997.1144142", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "8-12 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, age, computer-automated design, evolutionary algorithm, open-ended design, premature convergence, reliability", size = "8 pages", abstract = "To reduce the problem of premature convergence we define a new method for measuring an individual's age and propose the Age-Layered Population Structure (ALPS). This new measure of age measures how long the genetic material has been evolving in the population: offspring start with an age of 1 plus the age of their oldest parent instead of starting with an age of 0 as with traditional measures of age. ALPS differs from a typical evolutionary algorithm (EA) by segregating individuals into different age-layers by their age and by regularly introducing new, randomly generated individuals in the youngest layer. The introduction of randomly generated individuals at regular intervals results in an EA that is never completely converged and is always exploring new parts of the fitness landscape. By using age to restrict competition and breeding, younger individuals are able to develop without being dominated by older ones. Analysis of the search behaviour of ALPS finds that the offspring of individuals that are randomly generated mid-way through a run are able to move the population out of mediocre local-optima to better parts of the fitness landscape. In comparison against a traditional EA, a multi-start EA and two other EAs with diversity maintenance schemes we find that ALPS produces significantly better designs with a higher reliability than the other EAs.", notes = "GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM Order Number 910060", } @InProceedings{Hornby:2006:space, author = "Gregory Hornby and Al Globus and Derek Linden and Jason Lohn", title = "Automated Antenna Design with Evolutionary Algorithms", booktitle = "AIAA SPACE Forum, Space 2006", year = "2006", address = "San Jose, California, USA", month = "19-21 " # sep, publisher = "American Institute of Aeronautics and Astronautics", keywords = "genetic algorithms, genetic programming, EHW, Evolutionary Algorithm, Communications Antenna, Space Technology, Tracking and Data Relay Satellites, S Band, Satellites, NASA Goddard Space Flight Center, New Millennium Program, Earth Magnetosphere, Spacecraft Components", isbn13 = "978-1-62410-049-9", broken = "http://citeseerx.ist.psu.edu/viewdoc/download?doi:10.1.1.102.9841&rep=rep1&type=pdf", URL = "https://ntrs.nasa.gov/api/citations/20060024675/downloads/20060024675.pdf", URL = "https://arc.aiaa.org/doi/pdf/10.2514/6.2006-7242", DOI = "doi:10.2514/6.2006-7242", size = "8 pages", abstract = "Whereas the current practice of designing antennas by hand is severely limited because it is both time and labour intensive and requires a significant amount of domain knowledge, evolutionary algorithms can be used to search the design space and automatically find novel antenna designs that are more effective than would otherwise be developed. Here we present automated antenna design and optimization methods based on evolutionary algorithms. We have evolved efficient antennas for a variety of aerospace applications and here we describe one proof-of-concept study and one project that produced flight antennas that flew on NASA Space Technology 5 (ST5) mission.", notes = "Tracking and Data Relay Satellites TDRS-C antenna University of California, Santa Cruz. AIAA 2006-7242 Aerospace Research Central https://arc.aiaa.org/doi/book/10.2514/MSPACE06", } @Article{Hornby:2006:GPEM, author = "Gregory S. Hornby", title = "Shortcomings with using edge encodings to represent graph structures", journal = "Genetic Programming and Evolvable Machines", year = "2006", volume = "7", number = "3", pages = "231--252", month = oct, keywords = "genetic algorithms, genetic programming, Circuits, Graphs, Neural networks, Representations, CEEL, PEEL, ANN", ISSN = "1389-2576", URL = "http://ic.arc.nasa.gov/publications/pdf/1212.pdf", DOI = "doi:10.1007/s10710-006-9007-5", abstract = "There are various representations for encoding graph structures, such as artificial neural networks (ANNs) and circuits, each with its own strengths and weaknesses. Here we analyse edge encodings and show that they produce graphs with a node creation order connectivity bias (NCOCB). Additionally, depending on how input/ output (I/O) nodes are handled, it can be difficult to generate ANNs with the correct number of I/O nodes. We compare two edge encoding languages, one which explicitly creates I/O nodes and one which connects to pre-existing I/O nodes with parameterised connection operators. Results from experiments show that these parameterized operators greatly improve the probability of creating and maintaining networks with the correct number of I/O nodes, remove the connectivity bias with I/O nodes and produce better ANNs. These results suggest that evolution with a representation which does not have the NCOCB will produce better performing ANNs. Finally we close with a discussion on which directions hold the most promise for future work in developing better representations for graph structures.", notes = "3-parity. goal scoring robot", } @Article{Hornby:2007:GPEM, author = "Gregory S. Hornby and Sanjeev Kumar and Christian Jacob", title = "Editorial introduction to the special issue on developmental systems", journal = "Genetic Programming and Evolvable Machines", year = "2007", volume = "8", number = "2", pages = "111--113", month = jun, note = "Special issue on developmental systems", keywords = "genetic algorithms, genetic programming, evolvable hardware", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-007-9026-x", size = "3 pages", } @InCollection{Hornby:2007:GPTP, author = "Gregory S. Hornby", title = "Improving the Scalability of Generative Representations", booktitle = "Genetic Programming Theory and Practice {V}", year = "2007", editor = "Rick L. Riolo and Terence Soule and Bill Worzel", series = "Genetic and Evolutionary Computation", chapter = "8", pages = "127--144", address = "Ann Arbor", month = "17-19" # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-0-387-76308-8", DOI = "doi:10.1007/978-0-387-76308-8_8", size = "17 pages", abstract = "With the recent examples of the human-competitiveness of evolutionary design systems, it is not of interest to scale them up to produce more sophisticated designs. Here we argue that for computer-automated design systems to scale to producing more sophisticated results they must be able to produce designs with greater structure and organisation. By structure and organization we mean the characteristics of modularity, reuse and hierarchy (MR&H), characteristics that are found both in man-made and natural designs. We claim that these characteristics are enabled by implementing the attributes of combination, control-flow and abstraction in the representation, and define metrics for measuring MR&H and define two measures of overall structure and organisation by combining the measures of MR&H. To demonstrate the merit of our complexity measures, we use an evolutionary algorithm to evolve solutions to different sizes for a table design problem, and compare the structure and organisation scores of the best tables against existing complexity measures. We find that our measures better correlate with the complexity of good designs than do others, which supports our claim that MR&H are important components of complexity. We also compare evolution using five representations with different combinations of MR&H, and find that the best designs are achieved when all three of these attributes are present. The results of this second set of experiments demonstrate that implementing representations with MR&H can greatly improve search performance.", notes = "part of \cite{Riolo:2007:GPTP} Published 2008", affiliation = "NASA Ames Research Center U. C. Santa Cruz, Mail Stop 269-3 Moffett Field CA 94035", } @InProceedings{Hornby:2007:cec, author = "Gregory S. Hornby", title = "Measuring Complexity by Measuring Structure and Organization", booktitle = "2007 IEEE Congress on Evolutionary Computation", year = "2007", editor = "Dipti Srinivasan and Lipo Wang", pages = "2017--2024", address = "Singapore", month = "25-28 " # sep, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "1-4244-1340-0", file = "1518.pdf", keywords = "genetic algorithms, genetic programming, L-system, GENRE, ALPS", DOI = "doi:10.1109/CEC.2007.4424721", size = "8 pages", abstract = "Necessary for furthering the development of more powerful evolutionary design systems, capable of scaling to evolving more sophisticated and complex artifacts, is the ability to meaningfully and objectively compare these systems by applying complexity measures to the artifacts they evolve. Previously we have proposed measures of modularity, reuse and hierarchy (MR&H), here we compare these measures to ones from the fields of Complexity, Systems Engineering and Computer Programming. In addition, we propose several ways of combining the MR&H measures into a single measure of structure and organization. We compare all of these measures empirically as well as on three sample objects and find that the best measures of complexity are two of the proposed measures of structure and organization.", notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C 3d table from cubes", } @InProceedings{DBLP:conf/ices/HornbyKL08, author = "Gregory Hornby and William F. Kraus and Jason D. Lohn", title = "Evolving MEMS Resonator Designs for Fabrication", booktitle = "Proceedings of the 8th International Conference Evolvable Systems: From Biology to Hardware, ICES 2008", year = "2008", editor = "Gregory Hornby and Luk{\'a}s Sekanina and Pauline C. Haddow", series = "Lecture Notes in Computer Science", volume = "5216", pages = "213--224", address = "Prague, Czech Republic", month = sep # " 21-24", publisher = "Springer", bibsource = "DBLP, http://dblp.uni-trier.de", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-85856-0", URL = "http://idesign.ucsc.edu/pubs.html", DOI = "doi:10.1007/978-3-540-85857-7_19", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "Because of their small size and high reliability, microelectromechanical (MEMS) devices have the potential to revolution many areas of engineering. As with conventionally-sized engineering design, there is likely to be a demand for the automated design of MEMS devices. Here we present our work in using an evolutionary algorithm and generative representation to automatically create designs for a MEMS meandering resonator and describe what is involved in having these designs fabricated. To produce designs that are likely to transfer to reality, we give two ways to modify evaluation of designs: using fabrication noise, differences between the actual dimensions of the design and the design blueprint, which has helped us in our work in evolving antennas and robots; and including prestress, to model the warping that occurs during the extreme heat of fabrication. We have had the best evolved designs fabricated with a commercial MEMS fabrication process and are currently in the process of testing designs to verify how closely the actual devices compare to simulation performance.", } @InCollection{Hornby:2009:GPTP, author = "Gregory S. Hornby", title = "A Steady-State Version of the Age-Layered Population Structure EA", booktitle = "Genetic Programming Theory and Practice {VII}", year = "2009", editor = "Rick L. Riolo and Una-May O'Reilly and Trent McConaghy", series = "Genetic and Evolutionary Computation", address = "Ann Arbor", month = "14-16 " # may, publisher = "Springer", chapter = "6", pages = "87--102", keywords = "genetic algorithms, genetic programming, Age, Evolutionary Design, Genetic Programming, Metaheuristic, Premature Convergence", isbn13 = "978-1-4419-1653-2", DOI = "doi:10.1007/978-1-4419-1626-6_6", abstract = "The Age-Layered Population Structure (ALPS) paradigm is a novel meta heuristic for overcoming premature convergence by running multiple instances of a search algorithm simultaneously. When the ALPS paradigm was first introduced it was combined with a generational Evolutionary Algorithm (EA) and the ALPS-EA was shown to work significantly better than a basic EA. Here we describe a version of ALPS with a steady-state EA, which is well suited for use in situations in which the synchronisation constraints of a generational model are not desired. To demonstrate the effectiveness of our version of ALPS we compare it against a basic steady-state EA (BEA) in two test problems and find that it outperforms the BEA in both cases.", notes = "part of \cite{Riolo:2009:GPTP}", } @Article{Hornby:2011:EC, author = "Gregory. S. Hornby and Jason D. Lohn and Derek S. Linden", title = "Computer-Automated Evolution of an X-Band Antenna for NASA's Space Technology 5 Mission", journal = "Evolutionary Computation", year = "2011", volume = "19", number = "1", pages = "1--23", month = "Spring", keywords = "genetic algorithms, genetic programming, Antenna, automated design, computational design, evolutionary design, generative representation, spacecraft", ISSN = "1063-6560", DOI = "doi:10.1162/EVCO_a_00005", size = "23 pages", abstract = "Whereas the current practise of designing antennas by hand is severely limited because it is both time and labour intensive and requires a significant amount of domain knowledge, evolutionary algorithms can be used to search the design space and automatically find novel antenna designs that are more effective than would otherwise be developed. Here we present our work in using evolutionary algorithms to automatically design an X-band antenna for NASA's Space Technology 5 (ST5) spacecraft. Two evolutionary algorithms were used: the first uses a vector of real-valued parameters and the second uses a tree-structured generative representation for constructing the antenna. The highest-performance antennas from both algorithms were fabricated and tested and both outperformed a hand-designed antenna produced by the antenna contractor for the mission. Subsequent changes to the spacecraft orbit resulted in a change in requirements for the spacecraft antenna. By adjusting our fitness function we were able to rapidly evolve a new set of antennas for this mission in less than a month. One of these new antenna designs was built, tested, and approved for deployment on the three ST5 spacecraft, which were successfully launched into space on 22 March 2006. This evolved antenna design is the first computer-evolved antenna to be deployed for any application and is the first computer-evolved hardware in space.", notes = "GP and GA approaches to 2 problems. NASA flew GP and traditional QHA microwave aerials in 2006. 20 gauge wire. VSWR part of multiplicative fitness (3 multi-objective components. Randomised to simulate manufacturing errors. Take _worse_ fitness in order to evolve robust designs)", } @Misc{horner-class, author = "Helmut Horner", title = "A C++ Class Library for Genetic Programming: The Vienna University of Economics Genetic Programming Kernel", howpublished = "citeseer", year = "1996", month = "29 " # may, keywords = "genetic algorithms, genetic programming, evolutionary strategies, machine learning", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.39.2713", size = "69 pages", abstract = "This article gives a brief introduction in a variant of genetic programming (namely simple genetic algorithms over k-bounded context-free languages) and presents the most important genetic operators. A C++ class-library for genetic programming with context-free languages - the Vienna University of Economics Genetic Programming Kernel - is presented within this article. This program is flexible and includes the most important genetic operators. It is able to interpret every grammar in its Backus-NaurForm provided it is available in a file. In addition, this article deals with the problems of search-space-size calculations in connection with depth-bounded derivation trees.", notes = "Only appears to be available via citeseer (oct 2001)", } @InProceedings{horng:1999:A, author = "Jorng-Tzong Horng and Yu-Jan Chang and Cheng-Yen Kao", title = "Applying evolutionary algorithms to materialized view selection in a data warehouse", booktitle = "Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference", year = "1999", editor = "Scott Brave and Annie S. Wu", pages = "107--115", address = "Orlando, Florida, USA", month = "13 " # jul, keywords = "Genetic Algorithms", notes = "GECCO-99LB", } @InProceedings{horng:1999:R, author = "Jorng-Tzong Horng and Chien-Chin Chen and Cheng-Yen Kao", title = "Resolution of quadratic assignment problems using an evolutionary algorithm", booktitle = "Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference", year = "1999", editor = "Scott Brave and Annie S. Wu", pages = "116--124", address = "Orlando, Florida, USA", month = "13 " # jul, keywords = "Genetic Algorithms, Evolutionary Strategies", notes = "GECCO-99LB", } @Article{HOSEINGHOLI:2023:asej, author = "Pegah Hoseingholi and Ramtin Moeini", title = "Pipe failure prediction of wastewater network using genetic programming: Proposing three approaches", journal = "Ain Shams Engineering Journal", volume = "14", number = "5", pages = "101958", year = "2023", ISSN = "2090-4479", DOI = "doi:10.1016/j.asej.2022.101958", URL = "https://www.sciencedirect.com/science/article/pii/S2090447922002696", keywords = "genetic algorithms, genetic programming, Wastewater network, Pipe failure prediction, Number of failure, Artificial neural network", abstract = "Finding critical points of the wastewater network by rebuilding the infrastructure is cheaper than repairing it after occurring failure. This task can be done by using predictive approaches. Therefore, in this study, a new method is proposed to predict the number of pipe failures per length of wastewater network. For this purpose, genetic programming (GP) is used to predict the pipe failure of sewer network in Isfahan region 2 using the data from year 2014 to 2017.The obtained results are compared with the results of corresponding artificial neural network (ANN) model. For this purpose, three different approaches are proposed. In the first approach named GA-CLU-T, the number of pipe failures is predicted using all data. However, in the second ones named GA-CLU-Y, the models are created and trained using the data of year 2014 and the obtained model is used to predict the number of pipe failure for other years in future. Finally, the third ones named GA-CLU-R is proposed to determine the number of pipe failures in other regions. Here, two different models are proposed for each approaches using GP method. The result shows that the best RMSE (R2) values of first, second and third approaches for test data set are 0.00316 (0.966), 0.00074 (0.996) and 0.00075 (0.997), respectively. The results show that the result accuracy of GP models is better than the corresponding ANN models", } @InProceedings{Hosic:2014:COMPSACW, author = "Jasenko Hosic and Daniel R. Tauritz and Samuel A. Mulder", title = "Evolving Decision Trees for the Categorization of Software", booktitle = "Proceedings of the 38th IEEE Annual Computers, Software and Applications Conference Workshops (COMPSACW '14)", year = "2014", pages = "337--342", address = "Vasteras", month = "21-25 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, program understanding, SBSE, software categorisation, decision trees", DOI = "doi:10.1109/COMPSACW.2014.59", size = "6 pages", abstract = "Current manual techniques of static reverse engineering are inefficient at providing semantic program understanding. We have developed an automated method to categorise applications in order to quickly determine pertinent characteristics. Prior work in this area has had some success, but a major strength of our approach is that it produces heuristics that can be reused for quick analysis of new data. Our method relies on a genetic programming algorithm to evolve decision trees which can be used to categorise software. The terminals, or leaf nodes, within the trees each contain values based on selected features from one of several attributes: system calls, byte n-grams, opcode n-grams, cyclomatic complexity, and bonding. The evolved decision trees are reusable and achieve average accuracies above 95percent when categorising programs based on compiler origin and versions. Developing new decision trees simply requires more labelled datasets and potentially different feature selection algorithms for other attributes, depending on the data being classified.", notes = "Dept. of Comput. Sci., Missouri Univ. of Sci. & Technol., Rolla, MO, USA Also known as \cite{6903152}", } @InProceedings{Hosic:2015:WCICSS, author = "Jasenko Hosic and Jereme Lamps and Derek H. Hart", booktitle = "2015 World Congress on Industrial Control Systems Security (WCICSS)", title = "Evolving decision trees to detect anomalies in recurrent ICS networks", year = "2015", pages = "50--57", abstract = "Researchers have previously attempted to apply machine learning techniques to network anomaly detection problems. Due to the staggering amount of variety that can occur in normal networks, as well as the difficulty in capturing realistic data sets for supervised learning or testing, the results have often been underwhelming. These challenges are far less pronounced when considering industrial control system (ICS) networks. The recurrent nature of these networks results in less noise and more consistent patterns for a machine learning algorithm to recognise. We propose a method of evolving decision trees through genetic programming (GP) in order to detect network anomalies, such as device outages. Our approach extracts over a dozen features from network packet captures and netflows, normalizes them, and relates them in decision trees using fuzzy logic operators. We used the trees to detect three specific network events from three different points on the network across a statistically significant number of runs and achieved 100percent accuracy on five of the nine experiments. When the trees attempted to detect more challenging events at points of presence further from the occurrence, the accuracy averaged to above 98percent. On cases where the trees were many hops away and not enough information was available, the accuracy dipped to roughly 50percent, or that of a random search. Using our method, all of the evolutionary cycles of the GP algorithm are computed a-priori, allowing the best resultant trees to be deployed as semi-real-time sensors with little overhead. In order for the trees to perform optimally, buffered packets and flows need to be ingested at twenty minute intervals.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/WCICSS.2015.7420323", month = dec, notes = "Sandia National Laboratories, Albuquerque, New Mexico 87123, United States Also known as \cite{7420323}", } @Article{HOSSEINHASHEMI:2023:est, author = "Somayeh Hosseinhashemi and Yibo Zhang and Christoph Thon and Carsten Schilde", title = "Process insights with physics-inspired data-driven modeling- example of battery electrode processing", journal = "Journal of Energy Storage", volume = "73", pages = "109046", year = "2023", ISSN = "2352-152X", DOI = "doi:10.1016/j.est.2023.109046", URL = "https://www.sciencedirect.com/science/article/pii/S2352152X23024441", keywords = "genetic algorithms, genetic programming, Physics-inspired data-driven modeling, Dimensional analysis, ANN, Deep neural networks, Hybrid modeling, Gray-box technique", abstract = "Lithium-ion batteries are essential for a wide range of applications due to their high energy density and rechargeability. However, their production and performance improvement often rely on time-consuming and expensive experiments. Typically, simulation analyzes is used to build process models to optimize battery performance and lifetime. However, simulations cannot always take into account the limitations of the manufacturing process. As a result, the process parameters determined by such a model remain largely theoretical. For the efficient production of lithium-ion batteries, an understanding of the relationships between physical quantities is essential to meet optimized performance and safety standards. However, these relationships are quite complex, making it difficult for traditional methods to determine the physical model. Alternatively, artificial intelligence methods can be beneficial. This work uses a novel gray-box modeling technique that incorporates physical knowledge and empirical data. To achieve this goal, we combine a deep neural network with a genetic algorithm to determine the existing physical relationships within the data and then estimate the final model for the system", } @Article{journals/soco/HosseiniHG14, title = "Detecting nonlinear interrelation patterns among process variables using genetic programming", author = "Amir Hossein Hosseini and Sajid Hussain and Hossam A. Gabbar", journal = "Soft Comput", year = "2014", number = "7", volume = "18", pages = "1283--1292", keywords = "genetic algorithms, genetic programming", bibdate = "2014-06-11", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/soco/soco18.html#HosseiniHG14", URL = "http://dx.doi.org/10.1007/s00500-013-1142-3", } @Article{journals/nca/HosseiniG12, author = "Seyyed Soheil {Sadat Hosseini} and Amir Hossein Gandomi", title = "Short-term load forecasting of power systems by gene expression programming", journal = "Neural Computing and Applications", year = "2012", number = "2", volume = "21", pages = "377--389", keywords = "genetic algorithms, genetic programming, gene expression programming", ISSN = "0941-0643", DOI = "doi:10.1007/s00521-010-0444-y", size = "Special Issue on Theory and applications of swarm intelligence", abstract = "Short-term load forecasting is a popular topic in the electric power industry due to its essentiality in energy system planning and operation. Load forecasting is important in deregulated power systems since an improvement of a few percentages in the prediction accuracy will bring benefits worth of millions of dollars. In this study, a promising variant of genetic programming, namely gene expression programming (GEP), is used to improve the accuracy and enhance the robustness of load forecasting results. With the use of the GEP technique, accurate relationships were obtained to correlate the peak and total loads to average, maximum and lowest temperatures of day. The presented model is applied to forecast short-term load using the actual data from a North American electric utility. A multiple least squares regression analysis was performed using the same variables and same data sets to benchmark the GEP models. For more verification, a subsequent parametric study was also carried out. The observed agreement between the predicted and measured peak and total load values indicates that the proposed correlations are capable of effectively forecasting the short-term load. The GEP-based formulae are relatively short, simple and particularly valuable for providing an analysis tool accessible to practising engineers.", affiliation = "Tafresh University, Tafresh, Iran", bibdate = "2012-02-24", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/nca/nca21.html#HosseiniG12", } @InCollection{Hosseini:2015:hbgpa, author = "Seyyed Soheil Sadat Hosseini and Alireza Nemati", title = "Application of Genetic Programming for Electrical Engineering Predictive Modeling: A Review", booktitle = "Handbook of Genetic Programming Applications", publisher = "Springer", year = "2015", editor = "Amir H. Gandomi and Amir H. Alavi and Conor Ryan", chapter = "6", pages = "141--154", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-20882-4", DOI = "doi:10.1007/978-3-319-20883-1_6", abstract = "The purpose of having computers automatically resolve problems is essential for machine learning, artificial intelligence and a wide area covered by what Turing called machine intelligence. Genetic programming (GP) is an adaptable and strong evolutionary algorithm with some features that can be very priceless and adequate to get computers automatically to address problems starting from a high-level statement of what to do. Using the concept from natural evolution, GP begins from an ooze of random computer programs and improve them progressively through processes of mutation and sexual recombination until solutions appear. All this without the user needing to know or determine the form or structure of solutions in advance. GP has produced a plethora of human-competitive results and applications, involving novel scientific discoveries and patent-able inventions. The goal of this paper is to give an introduction to the quickly developing field of GP. We begin with a gentle introduction to the basic representation, initialization and operators used in GP, completed by a step by step description of their use and application. Then, we progress to explain the diversity of alternative representations for programs and more advanced specializations of GP. Despite the fact that this paper has been written with beginners and practitioners in mind, for completeness we also provide an outline of the theoretical aspect available to date for GP.", notes = "Author Affiliations: Department of Electrical Engineering and Computer Science, University of Toledo, Toledo, OH, 43606, USA", } @Article{HOSSEINI:2019:SDEE, author = "Seied Ahmad Hosseini and Amir Tavana and Seyed Mohamad Abdolahi and Saber Darvishmaslak", title = "Prediction of blast induced ground vibrations in quarry sites: a comparison of {GP, RSM and MARS}", journal = "Soil Dynamics and Earthquake Engineering", volume = "119", pages = "118--129", year = "2019", keywords = "genetic algorithms, genetic programming, Response surface methodology, Multivariate adaptive regression splines, Peak particle velocity, Prediction", ISSN = "0267-7261", DOI = "doi:10.1016/j.soildyn.2019.01.011", URL = "http://www.sciencedirect.com/science/article/pii/S0267726118309205", abstract = "Among the side effects caused by the blast, ground vibration (GV) is the most important one and can make serious damages to the surrounding structures. According to many scholars, the peak particle velocity (PPV) is one of the main indicators for determining the extent of blasta induced GVs. Recently, following the rapid growth of soft computing approaches, researchers have tried to use these new techniques. This paper aims to explore three methods of soft computing including genetic programming (GP), response surface methodology (RSM), and multivariate adaptive regression splines (MARS) to predict the PPV values. For this purpose, a dataset of 200 published data including PPV, distance from the blasting face (D), and charge weight per delay (W) was used. The data have been recorded using blast seismograph, during the blast-induced earthquake triggered at 10 quarry sites in Ibadan and Abeokuta areas, Nigeria (https://doi.org/10.1016/j.dib.2018.04.103). The coefficient of determination for the MARS model as a most accurate model built in this research based on overall data results (R2 = 0.81), compared with the most accurate empirical equations presented in the research literature, namely general predictor model (R2 = 0.78), had a variation equal to 0.02. This variation for the root mean of squared error (RMSE), mean of absolute deviation (MAD), and mean of absolute percent error (MAPE) values were equal to 0.85, 0.25, and 0.38, respectively. In addition, the sensitivity analysis using cosine amplitude method (CAM) showed that the influence of each D and W parameters on PPV values based on developed models by this paper was more similar with the influence of these parameters based on the actual values, compared to empirical models. Finally, the parametric studies to investigate the behavior of various developed models were done to survey the changes to the values of the two variables D and W", } @Article{HOSSEINI:2020:IJR, author = "S. H. Hosseini and M. A. Moradkhani and M. Valizadeh and Alireza Zendehboudi and M. Olazar", title = "A general heat transfer correlation for flow condensation in single port mini and macro channels using genetic programming", journal = "International Journal of Refrigeration", volume = "119", pages = "376--389", year = "2020", ISSN = "0140-7007", DOI = "doi:10.1016/j.ijrefrig.2020.06.021", URL = "http://www.sciencedirect.com/science/article/pii/S0140700720302760", keywords = "genetic algorithms, genetic programming, Two-phase flow, Mini channel, Heat transfer coefficient, Condensation, Programmation genetique, Ecoulement diphasique, Mini-canal, Coefficient de transfert de chaleur, Condensation", abstract = "A new general explicit correlation is proposed to predict the heat transfer coefficient of fluids condensing in conventional and mini channels. The expression has been developed by correlating the Numix number with Remix, Prmix, phase density ratio, Pres, WeGT, and FrL using genetic programming for the two-phase flow. The model has been validated with a big dataset consisting of 6521 data samples, covering a wide range of fluids used in refrigeration and heat pump industries, cross-sectional geometries (different diameters), mass fluxes, and saturation temperatures. The new generalized correlation fits the wide range of data points used with an average relative error of 17.82 percent. The same database has been used to compare predictions of eight correlations available in the literature, but they failed to give a reasonable estimation of the present experimental results", } @Article{HOSSEINI:2020:EB, author = "S. H. Hosseini and M. Valizadeh and Alireza Zendehboudi and Mengjie Song", title = "General correlation for frost thermal conductivity on parallel surface channels", journal = "Energy and Buildings", volume = "225", pages = "110282", year = "2020", ISSN = "0378-7788", DOI = "doi:10.1016/j.enbuild.2020.110282", URL = "http://www.sciencedirect.com/science/article/pii/S0378778819336163", keywords = "genetic algorithms, genetic programming, Frost thermal conductivity, Empirical correlation, Heat exchanger", abstract = "Growth of frost layer on cold fins of tube-fin heat exchangers leads to an increase in the pressure drop and a decrease in the frost thermal conductivity and thereby the heat transfer rate. There is a lack of a general model in the literature for estimating the frost thermal conductivity on parallel plate channels, including almost all parameters affecting this factor. In this study, for the first time, the general explicit semi-empirical correlations consist of dimensionless parameters are developed, which apply to parallel surface channels. The dimensionless input parameters include the wall temperature, air temperature, air velocity, frost porosity, relative humidity, specific heat of moist air, latent heat of sublimation, and operating time. The comparative results indicate that the best correlation predicts data points with an coefficient of determination, average absolute relative error, and relative root mean square error equal 0.9921, 2.755percent, and 3.713percent, respectively. Other available published correlations present higher deviations using the same dataset. Furthermore, to provide a good insight into this study, a sensitivity analysis is carried out employing the validated model. It is shown that the effective thermal conductivity of the frost layer is not only a function of frost density but also depends on a group of dimensionless parameters. It is observed that the thermal conductivity of the frost layer increases with the increase in the Reynolds number, Fourier number, air humidity, and it decreases with the increase in the dimensionless temperature, modified Jakob number, and porosity", } @Article{HOSSEINI:2020:ICHMT, author = "S. H. Hosseini and M. A. Moradkhani and Mirza M. Shah and M. Edalati", title = "General equation for flow condensation heat transfer coefficient in different orientations of helical coils of smooth tubes using genetic programming", journal = "International Communications in Heat and Mass Transfer", volume = "119", pages = "104916", year = "2020", ISSN = "0735-1933", DOI = "doi:10.1016/j.icheatmasstransfer.2020.104916", URL = "https://www.sciencedirect.com/science/article/pii/S0735193320304449", keywords = "genetic algorithms, genetic programming, Coiled tubes, Two-phase flow, Heat transfer coefficient, Condensation, Correlation", abstract = "There are several experimental studies on heat transfer during condensation in coiled tubes. But there is no well-verified method for calculating of heat transfer coefficient. In this study, a general non-linear correlation for estimation of heat transfer coefficient during flow condensation in different orientations of smooth coiled tubes is proposed. The correlation has been obtained by correlating the Nusselt number with two-phase Reynolds number, reduced pressure, Froude number, tube to coil diameter ratio, and inclination angle of coil axis to horizontal using Genetic programming (GP). This model has been validated with 503 experimental data points from 9 sources, which include different tube diameters, coil diameters, inclination angles, orientations, working fluids, mass fluxes and saturation temperatures. The new correlation predicts experimental data points with an excellent value of average absolute relative deviation (AARD) of 9.20percent. The same database is also compared to 9 available correlations for straight and coiled tubes. Their deviations are significantly higher than the present correlation. In addition, impact of each input parameter on heat transfer coefficient in coiled tubes has been discussed", } @Article{HOSSEINI:2021:IJR, author = "S. H. Hosseini and M. A. Moradkhani and M. Valizadeh and G. Ahmadi", title = "Applying genetic programming in estimation of frost layer thickness on horizontal and vertical plates at ultra-low temperature", journal = "International Journal of Refrigeration", volume = "125", pages = "113--121", year = "2021", ISSN = "0140-7007", DOI = "doi:10.1016/j.ijrefrig.2020.12.035", URL = "https://www.sciencedirect.com/science/article/pii/S0140700720305296", keywords = "genetic algorithms, genetic programming, Frost layer thickness, Smart model, Cryogenic condition, Horizontal and vertical plates, Epaisseur de la couche de givre, Modele intelligent, Condition cryogenique, Plaques verticales et horizontales", abstract = "In this study, the intelligent method of genetic programming (GP) was used for developing predictive models for estimating the frost layer thickness under natural and forced convection on ultra-low temperature surfaces. The affecting dimensionless parameters were used as GP input variables, and realistic empirical correlations were developed for estimating the frost thickness under different conditions. The coefficient of determination of 0.9731, 0.9812, and 0.9906, and average absolute relative error of 6.52percent, 11.65percent, and 2.87percent, were obtained by the developed models for natural convection on vertical plates, natural convection on horizontal plates, and forced convection on horizontal plates, respectively. The physical trends of the developed models were evaluated by comparing the model predictions with the experimental data for different operating conditions, and reasonable agreements were obtained. The same experimental database was also compared to some existing correlations for ordinary-low temperature surfaces, but they failed to provide reasonable estimates for the data", } @Article{hosseini:2022:Processes, author = "Seyyed Hossein Hosseini and Mohamed Arselene Ayari and Amith Khandakar and Mohammad Amin Moradkhani and Mehdi Jowkar and Mohammad Panahi and Goodarz Ahmadi and Jafar Tavoosi", title = "Robust and General Model to Forecast the Heat Transfer Coefficient for Flow Condensation in Multi Port {Mini/Micro-Channels}", journal = "Processes", year = "2022", volume = "10", number = "2", keywords = "genetic algorithms, genetic programming", ISSN = "2227-9717", URL = "https://www.mdpi.com/2227-9717/10/2/243", DOI = "doi:10.3390/pr10020243", abstract = "A general correlation for predicting the two-phase heat transfer coefficient (HTC) during condensation inside multi-port mini/micro-channels was presented. The model was obtained by correlating the two-phase multiplier, φtp with affecting parameters using the genetic programming (GP) method. An extensive database containing 3503 experimental data samples was gathered from 21 different sources, including a broad range of operating parameters. The newly obtained correlation fits the broad range of measured data analysed with an average absolute relative deviation (AARD) of 16.87percent and estimates 84.73percent of analysed data points with a relative error of less than 30percent. Evaluation of previous correlations was also conducted using the same database. They showed the AARD values ranging from 36.94percent to 191.19percent. However, the GP model provides more accurate results, AARD lower than 17percent, by considering the surface tension effects. Finally, the effect of various operating parameters on the HTC was studied using the proposed correlation.", notes = "also known as \cite{pr10020243}", } @Article{Hosseini:2025:rineng, author = "Seyed Hossein Hosseini and Ramtin Moeini", title = "Correlation-based reliability index equipped with machine learning methods to complete the groundwater level gaps", journal = "Results in Engineering", year = "2025", volume = "25", pages = "104146", keywords = "genetic algorithms, genetic programming, Groundwater level, Missing data, Completing GWL data gaps, Clustering, Machine learning method, Correlation-based reliability index, ANN", ISSN = "2590-1230", URL = "https://www.sciencedirect.com/science/article/pii/S2590123025002348", DOI = "doi:10.1016/j.rineng.2025.104146", abstract = "Completing gaps in groundwater level (GWL) data is critical for reliable hydrological analysis and modelling. The irregularities and missing values often present in GWL measurements necessitate robust methods for infilling. In this study, an innovative approach is proposed to reconstruct the GWL time series by addressing data gaps. Historical GWL data spanning 20 years from 40 observation wells in the Lenjanat aquifer, Isfahan Province, Iran, are used. Three clustering methods-K-means, Fuzzy C-means (FCM), and Self-Organising Map (SOM)-are applied to group the wells, and a novel metric, the Correlation-based Reliability Index (CRI), is introduced to identify the most suitable clustering method for GWL prediction. The selected cluster is analysed using four machine learning and hybrid models, including support vector regression (SVR) with linear, polynomial, and radial basis function (RBF) kernels, genetic programming (GP), artificial neural networks (ANN), and a hybrid model combining SVR with the whale optimisation algorithm (WOA) (WOA-SVR). Results reveals that the SVR model with an RBF kernel outperformed its counterparts with linear and polynomial kernels, while the hybrid WOA-SVR model shows superior performance compared to other models. The hybrid approach significantly improves the accuracy of GWL gap completion. The WOA-SVR model leads to average R2, RMSE, MAPE, and NS values of 0.81 (0.74), 0.55 m (0.95 m), 0.02 percent (0.04 percent), and 0.80 (0.72) for the training (test and validation) data, respectively. The results demonstrates that the proposed approach, integrating clustering, CRI, and machine learning models, can effectively fill GWL gaps and provide clean data for further hydrological modelling and groundwater management", } @Article{HosseiniMonazzah:2017:CI, author = "Asal {Hosseini Monazzah} and Hesam Pouraliakbar and Mohammad Reza Jandaghi and Reza Bagheri and Seyed Morteza Seyed Reihani", title = "Influence of interfacial adhesion on the damage tolerance of Al6061/SiCp laminated composites", journal = "Ceramics International", volume = "43", number = "2", pages = "2632--2643", year = "2017", ISSN = "0272-8842", DOI = "doi:10.1016/j.ceramint.2016.11.074", URL = "http://www.sciencedirect.com/science/article/pii/S0272884216320831", abstract = "In this study, lamination as extrinsic mechanism was considered to enhance damage tolerance of three-layer Al6061-5percentvol. SiCp/Al1050/Al6061-5percentvol. SiCp composites. To fabricate laminates of dissimilar interfacial adhesion, different rolling strains were applied during hot roll-bonding. The discrepancy in interfacial strength of laminates was examined by shear test while toughness values were studied using three-point bending test. It was revealed that both interfacial adhesion and damage tolerance were influenced by rolling strain. Interfacial bonding played the major role in the energy absorption during fracture which was quantified as initiation, propagation and total toughness. The results declared that improving the interfacial adhesion elevated the energy consumed for emergence and growth of debonded area. Five different models based on genetic programming have been proposed in order to predict the toughness of composites. Also, corresponding mathematical correlations of introduced models were exhibited. To construct the models, experimental data were randomly divided and used as training and testing sets. The data used as inputs were comprised of five independent parameters such as {"}SiCp volume content{"}, {"}average SiCp volume in bulk laminates{"}, {"}specimen thickness{"}, {"}rolling strain{"} and experimented {"}shear strength{"}. The training and testing results were in good agreement and revealed strong capability for predicting the toughness of laminates.", keywords = "genetic algorithms, genetic programming, Aluminum matrix composite, Laminates, Toughness, Damage tolerance, Delamination, Modeling", } @Article{HOSSEINKARIMIDARVANJOOGHI:2024:crbiot, author = "Mohammad {Hossein Karimi Darvanjooghi} and Usman T. Khan and Sara Magdouli and Satinder {Kaur Brar}", title = "Biooxidation of refractory sulfide-bearing ore using feroplasma acidophilum: Efficiency assessment and machine learning based prediction", journal = "Current Research in Biotechnology", volume = "7", pages = "100179", year = "2024", ISSN = "2590-2628", DOI = "doi:10.1016/j.crbiot.2024.100179", URL = "https://www.sciencedirect.com/science/article/pii/S2590262824000054", keywords = "genetic algorithms, genetic programming, Gold recovery, Biooxidation, Experimental data, Mchine learning, Artificial neural network model", abstract = "The adhesive properties of microorganisms on the surface of minerals play an important role in the biooxidation efficiency of sulfidic refractory gold ores. In this research, the simultaneous effects of monosaccharides, ore content, pyrite content, and time on the activity and growth rate of Ferroplasma acidiphilum-from native Acid Mine Drainage (AMD)- was investigated during biooxidization alongside finding the best machine learning approach for the prediction of process efficiency using the independent variables. The results revealed that the optimum condition for reaching the highest pyrite dissolution (~75 percent) is 15 days of operating time, pyrite content of 7.2 wtpercent, and ore content of 5 wtpercent, pH of 1.47, and D-+-sucrose, D-+-galactose, and D-+-fructose concentrations of 0.52, 0.09, and 0.12 wtpercent, respectively. The results of the model comparison indicated that the Artificial Neural Network (ANN) model was able to predict the experimental results of this study with acceptable accuracy and better than Genetic Programming (GP) and Polynomial Regression informed by Response Surface Methodology (PR-RSM) from experimental data. Finally, the results showed that the change in D-+-fructose and D-+-galactose concentration has no significant effect on ferric ions concentration and pyrite dissolution content, while the influence of alteration in D-+-sucrose concentration is significantly high", } @Article{Hosseinnia:2025:Structures, author = "Amirali Hosseinnia and Mohammadreza Noori Sichani and Babak Enami Alamdari and Pariya Aghelizadeh and Amirehsan Teimortashlu", title = "Machine learning formulation for predicting concrete carbonation depth: A sustainability analysis and optimal mixture design", journal = "Structures", year = "2025", volume = "76", pages = "109036", keywords = "genetic algorithms, genetic programming, Machine learning (ML), Carbonation depth formulation, Feature selection, Multi-gene genetic programming (MGGP), Multi-criteria decision-making (MCDM), Grey relational analysis (GRA), ANN", ISSN = "2352-0124", URL = "https://www.sciencedirect.com/science/article/pii/S2352012425008501", DOI = "doi:10.1016/j.istruc.2025.109036", abstract = "Predicting the Carbonation Depth (CD) of concrete is crucial for evaluating its long-term durability and resistance to environmental degradation, particularly in structures exposed to Carbon Dioxide (CO2). Such phenomenon leads to corrosion of embedded reinforcement and structural deterioration. In this study, Multi-Gene Genetic Programming (MGGP) and Random Forest (RF), as Machine Learning (ML) techniques, were used to predict the CD of various concrete samples containing fly ash, an eco-friendly partial substitute for cement, using a dataset of 198 concrete mixtures. The dataset consists of the content of cement, fine and coarse aggregates, fly ash, water, superplasticizer, and their respective testing conditions, including relative humidity, CO2 concentration, and the cycle duration of the carbonation test. However, identifying the most relevant variables to enhance prediction accuracy is a challenging and complex task. Therefore, to tackle this challenge, the Pareto Envelope-based Selection Algorithm II (PESA-II) was integrated with Artificial Neural Networks (ANNs) to form a multi-objective optimisation approach. This novel feature selection method efficiently identifies the most important variables, leading to improved predictive accuracy. Although recent studies have used various ML algorithms to predict carbonation depth, none of them have generated a predictive formula for such variable. In this regard, the MGGP method was selected due to its efficiency to generate such formula. This formula demonstrated high accuracy, achieving an R-squared value of 0.91 on the testing set and 0.92 on the training set, indicating the capability of this method. Finally, the Grey Relational Analysis (GRA) method was employed as a Multi-Criteria Decision-Making (MCDM) tool to identify the optimal concrete mixture, considering both cost efficiency and environmental impact, including CO2 emissions and energy consumption associated with the production of each concrete ingredient", notes = "Department of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, Iran", } @InProceedings{hou_2005_iscas, author = "Hao-Sheng Hou and Shoou-Jinn Chang and Yan-Kuin Su", title = "Economical passive filter synthesis using genetic programming based on tree representation", booktitle = "Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS)", year = "2005", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Passive Filter Synthesis, Circuit Representation", URL = "http://www.ncku.edu.tw/~acadserv/abroad/94q2-10a.pdf", URL = "http://www.epapers.org//iscas2005/ESR/paper_details.php?PHPSESSID=3b6735b25d9602780e3827e15b1ee196&paper_id=4103", abstract = "we propose a tree representation for RLC circuits. Genetic programming based on the tree representation is described and applied to passive filter synthesis problems. In addition, a way to minimize the size of synthesized circuits is presented. The results show that the proposed method can effectively generate not only compliant but also economical passive filters.", notes = "National Cheng Kung University, Taiwan ROC", } @Article{hou_2005_IEICE, author = "Hao-Sheng Hou and Shoou-Jinn Chang and Yan-Kuin Su", title = "Practical Passive Filter Synthesis Using Genetic Programming", journal = "IEICE Transactions on Electronics", year = "2005", volume = "E88-C", number = "6", pages = "1180--1185", keywords = "genetic algorithms, genetic programming, passive filter synthesis, frequency-dependent component", URL = "http://ietele.oxfordjournals.org/cgi/reprint/E88-C/6/1180", DOI = "doi:10.1093/ietele/e88-c.6.1180", size = "6 pages", abstract = "proposes a genetic programming method to synthesise passive filter circuits. This method allows both the circuit topology and the component values to be evolved simultaneously. Experiments show that this method is fast and capable of generating circuits which are more economical than those generated by traditional design approaches. In addition, we take into account practical design considerations at high-frequency applications, where the component values are frequency-dependent and restricted to some discrete values. Experimental results show that our method can effectively generate not only compliant but also economical circuits for practical design tasks.", notes = "Special Section on Analog Circuit and Device Technologies -- Papers -- CAD", } @PhdThesis{hou:thesis, author = "Jia-Li Hou", title = "Constructing Static and Dynamic Investment Strategy Portfolios by Genetic Programming", school = "Information Management, National Central University", year = "2008", type = "Doctoral Dissertation", address = "Taiwan", month = "8 " # jan, keywords = "genetic algorithms, genetic programming, Portfolio, Artificial Intelligence, Capital Allocation, Investment Strategy, Linear Capital Allocation, Non-Linear Capital Allocation", URL = "http://ir.lib.ncu.edu.tw/handle/987654321/13036", URL = "http://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=90443001", broken = "http://thesis.lib.ncu.edu.tw/ETD-db/ETD-search/view_etd?URN=90443001", size = "117 pages", abstract = "The study comes up with a framework of portfolio, dividing investment issues into four quadrants based on two dimensions: capital allocation frequency and allocation approach. In allocation approach, there are linear and non-linear. In capital allocation frequency selection approach, there are static and dynamic allocation approaches. In the framework, static allocation, based on the assumption that if investment duration is identical, is to complete capital allocation selection at the beginning of duration; dynamic allocation, based on the assumption that each investment period is different, is to allocate capital when needed. In traditional financial area, investment portfolios are linear and static investment issue, which is take all investment duration are the same, and to buy in at the beginning of period, therefore, invest decision is to directly allocate capital on multiple investment objectives by static allocation, in order to gain the greatest profit or minimize the risk probability.[Huang, 2008; Li, 2008] And reconsidering investment decision for next duration at the end of duration. The framework of the research takes investment strategy as investment objectives. The research is to make pairs of investment objectives and transaction rules, and allocate capital on investment strategies rather on investment objectives directly. And the research comes up a solution of non-linear capital allocation approach, including planning a capital allocation tree by soft computing and genetic algorithms, calculating every capital weight on every investment strategies, and providing static and dynamic capital frequency strategies. The research takes 30 stocks in Dow Jones Industrial Average of U.S. stock market textbook academic researches and 9 technical indexes which are commonly used in investment markets to comprise 81 simple transaction rules and constitute 2,430 investment strategies which are planned by genetic algorithms. And experiment test of research is based on 1999 to 2006 stock market data, the outcome of experiment shows that static and dynamic and non-linear portfolios gains greater profit and smaller probability of risk, comparing to buy-in strategy.", notes = "Language zh-TW.Big5 Chinese. Locked for two years. Feb 2013 available.", } @InProceedings{Hou:2012:RACSIE, author = "Jin-jun Hou", title = "Seed Selection Genetic Programming and Its Implementation in {Matlab}", booktitle = "Recent Advances in Computer Science and Information Engineering", year = "2012", editor = "Zhihong Qianand Lei Cao and Weilian Su and Tingkai Wang and Huamin Yang", volume = "129", series = "LNEE", pages = "753--759", publisher = "Springer", note = "Results of the 2011 2nd World Congress on Computer Science and Information Engineering (CSIE 2011) held at 17-19 June 2011 (Part and 20-22 September 2011 (Part 2) at Changchun International Conference Exhibition Center Hotel, Changchun, China", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-642-25778-0_106", DOI = "doi:10.1007/978-3-642-25778-0_106", abstract = "Some defects of the Genetic Programming had been point out first in this paper. To overcome these defects, we proposed the Seed Selection genetic algorithm. And the algorithmis implemented in the environment ofMatlab. The numerical results show that the algorithm is effective and rapidly convergent. Furthermore, it can assure the evolution algorithm can not run into local minimizer.", notes = "School of Mathematics and Computational Science, Hunan University of Science and Technology, Xiangtan, P.R. China", } @InCollection{houlette:1998:ECGPCFP, author = "Ryan Houlette", title = "Evolving Communication using Genetic Programming in the Central-Place Foraging Problem", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1998", year = "1998", editor = "John R. Koza", pages = "29--38", address = "Stanford, California, 94305-3079 USA", month = "17 " # mar, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-212568-8", notes = "part of \cite{koza:1998:GAGPs}", } @Article{Houshmand:2014:GPEM, author = "Mahboobeh Houshmand and Morteza {Saheb Zamani} and Mehdi Sedighi and Monireh Houshmand", title = "GA-based approach to find the stabilizers of a given sub-space", journal = "Genetic Programming and Evolvable Machines", year = "2015", volume = "16", number = "1", pages = "57--71", month = mar, keywords = "genetic algorithms, Pauli matrices, Quantum information, Stabiliser formalism", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-014-9219-z", size = "15 pages", abstract = "Stabilizer formalism is a powerful framework for understanding a wide class of operations in quantum information. This formalism is a framework where multiple qubit states and sub-spaces are described in a compact way in terms of operators under which they are invariant. In stabiliser formalism, one focuses the members of Pauli groups which have the stabilising property of a given sub-space. Therefore, finding the Pauli stabilisers of a given sub-space in an efficient way is of great interest. In this paper, this problem is addressed in the field of quantum information theory. We present a two-phase algorithm to solve the problem whose order of complexity is considerably smaller than the common solution. In the first phase, a genetic algorithm is run. The results obtained by this algorithm are the matrices that can potentially be the Pauli stabilizers of the given sub-space. Then an analytical approach is applied to find the correct answers among the results of the first phase. Experimental results show that speed-ups are remarkable as compared to the common solution.", notes = "Author Affiliations: 1. Quantum Design Automation Lab, Department of Computer Engineering and Information Technology, Amirkabir University of Technology, Tehran, Iran 2. Department of Electrical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran", } @InProceedings{Houshmand:2009:ICIS, author = "Monireh Houshmand and Saied Hosseini Khayat and Razie Rezaei", title = "Genetic algorithm based logic optimization for multi-output majority gate-based nano-electronic circuits", booktitle = "IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009", year = "2009", volume = "1", pages = "584--588", address = "Shanghai", month = "20-22 " # nov, keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-4754-1", DOI = "doi:10.1109/ICICISYS.2009.5357775", size = "5 pages", abstract = "The majority-gate and the inverter-gate together make a universal set of Boolean primitives in quantum-dot cellular automata (QCA) circuits. An important step in designing QCA circuits is reducing the number of required primitives to implement a given Boolean function. This paper presents a method to reduce the number of primitive gates in a multi-output Boolean circuit. It extends the previous methodology based on genetic algorithm for converting sum of product expressions into a reduced number of QCA primitive gates in a single-output Boolean circuit. Simulation results show that the proposed method is able to reduce the number of primitive gates.", notes = "Also known as \cite{5357775}", } @Article{Hoverstad:2010:GPEM, author = "Boye Annfelt Hoverstad", title = "Simdist: a distribution system for easy parallelization of evolutionary computation", journal = "Genetic Programming and Evolvable Machines", year = "2010", volume = "11", number = "2", pages = "185--203", month = jun, keywords = "genetic algorithms, Distributed computing, Program development", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-009-9100-7", size = "19 pages", abstract = "This article introduces Simdist, a software tool for parallel execution of evolutionary algorithms (EAs) in a master-slave configuration on cluster architectures. Clusters have become a cost-effective parallel solution, and the potential computational capabilities are phenomenal. However, the transition from traditional R&D on a personal computer to parallel development and deployment can be a major step. Simdist simplifies this transition considerably, by separating the task of distributing data across the cluster network from the actual EA-related processing performed on the master and slave nodes. Simdist is constructed in the vein of traditional Unix command line tools; it runs in a separate process and communicates with EA child processes via standard input and output. As a result, Simdist is oblivious to the programming language(s) used in the EA, and the EA is similarly oblivious to the internals of Simdist.", notes = "http://simdist.sourceforge.net.", } @Article{How:2010:QF, author = "Janice How and Martin Ling and Peter Verhoeven", title = "Does size matter? A genetic programming approach to technical trading", journal = "Quantitative Finance", year = "2010", volume = "10", number = "2", pages = "130--140", keywords = "genetic algorithms, genetic programming", ISSN = "1469-7696", URL = "http://www.informaworld.com/smpp/title~db=all~content=g918916776", DOI = "doi:10.1080/14697680902773629", notes = "School of Economics and Finance, Queensland University of Technology, Brisbane, Queenland 4001, Australia b General Electric, Auckland, New Zealand", } @InProceedings{howard:1998:wGPpde, author = "Daniel Howard", title = "Why Genetic Programming for solution of partial differential equations?", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "66--66", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "genetic algorithms, genetic programming", size = "2 pages", notes = "GPquick GP-98LB", } @InProceedings{howard:1998:tdSARiGP, author = "Daniel Howard and Simon C. Roberts and Richard Brankin", title = "Target Detection in SAR Imagery by Genetic Programming", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "67--75", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "genetic algorithms, genetic programming", size = "9 pages", notes = "see \cite{Howard:1999:AES} GP-98LB", } @InProceedings{howard:1999:esdsSARi, author = "Daniel Howard and Simon C. Roberts and Richard Brankin", title = "Evolution of Ship Detectors for Satellite {SAR} Imagery", booktitle = "Genetic Programming, Proceedings of EuroGP'99", year = "1999", editor = "Riccardo Poli and Peter Nordin and William B. Langdon and Terence C. Fogarty", volume = "1598", series = "LNCS", pages = "135--148", address = "Goteborg, Sweden", publisher_address = "Berlin", month = "26-27 " # may, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-65899-8", DOI = "doi:10.1007/3-540-48885-5_11", abstract = "A two-stage evolution scheme is proposed to obtain an object detector for an image analysis task, and is applied to the problem of ship detection by inspection of the SAR images taken by satellites. The scheme: (1) affords practical evolution times, (2) is structured to discover fast automatic detectors, (3) can produce small detectors that shed light into the nature of the detection. Detectors compare favourably in accuracy to those obtained using a SOM neural network.", notes = "EuroGP'99, part of \cite{poli:1999:GP}", } @InProceedings{howard:1999:EuroGEN, author = "Daniel Howard and Simon C. Roberts", title = "Evolving object detectors for infrared imagery: a comparison of texture analysis against simple statistics", booktitle = "Evolutionary Algorithms in Engineering and Computer Science", year = "1999", editor = "Kaisa Miettinen and Marko M. Makela and Pekka Neittaanmaki and Jacques Periaux", pages = "79--86", address = "Jyvaskyla, Finland", publisher_address = "Chichester, UK", month = "30 " # may # " - 3 " # jun, publisher = "John Wiley \& Sons", keywords = "genetic algorithms, genetic programming", isbn13 = "978-0-471-99902-7", URL = "http://www.mit.jyu.fi/eurogen99/papers/howard.ps", notes = "EUROGEN'99 http://eu.wiley.com/WileyCDA/WileyTitle/productCd-0471999024.html Multi-stage GP terminals based on fourier transforms found to be (marginally?) better than those based on simple stats (mean, standard deviation). Looking for parked cars from 300 feet.", } @InProceedings{howard:1999:ASGPSIA, author = "Daniel Howard and Simon C. Roberts", title = "A Staged Genetic Programming Strategy for Image Analysis", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1047--1052", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-461.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-461.pdf", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Article{Howard:1999:AES, author = "Daniel Howard and Simon C. Roberts and Richard Brankin", title = "Target detection in SAR imagery by genetic programming", journal = "Advances in Engineering Software", volume = "30", pages = "303--311", year = "1999", number = "5", month = may, keywords = "genetic algorithms, genetic programming", ISSN = "0965-9978", DOI = "doi:10.1016/S0965-9978(98)00093-3", URL = "http://www.sciencedirect.com/science/article/B6V1P-3W1XV4H-1/1/6e7aee809f33757d0326c62a21824411", abstract = "The automatic detection of ships in low-resolution synthetic aperture radar (SAR) imagery is investigated in this article. The detector design objectives are to maximise detection accuracy across multiple images, to minimise the computational effort during image processing, and to minimise the effort during the design stage. The results of an extensive numerical study show that a novel approach, using genetic programming (GP), successfully evolves detectors which satisfy the earlier objectives. Each detector represents an algebraic formula and thus the principles of detection can be discovered and reused. This is a major advantage over artificial intelligence techniques which use more complicated representations, e.g. neural networks.", } @InProceedings{howard:2000:EMRRID, author = "Daniel Howard and Simon C. Roberts", title = "Evolution of Mesh Refinement Rules for Impact Dynamics", booktitle = "Proceedings of the 2000 Congress on Evolutionary Computation CEC00", year = "2000", pages = "1297--1303", address = "La Jolla Marriott Hotel La Jolla, California, USA", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "6-9 " # jul, organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, novel applications, impact (mechanical), evolutionary computation, learning (artificial intelligence), mechanical engineering computing, partial differential equations, mesh refinement rule evolution, impact dynamics, rule learning, adaptive mesh refinement, mesh cells, material densities, high speed impact, spherical ball, metal plate", ISBN = "0-7803-6375-2", DOI = "doi:10.1109/CEC.2000.870801", abstract = "Genetic programming (GP) was used in an experiment to investigate the possibility of learning rules that trigger adaptive mesh refinement. GP detected mesh cells that required refinement by evolving a formula involving cell quantities such as material densities. Various cell variable combinations were investigated in order to identify the optimal ones for indicating mesh refinement. The problem studied was the high speed impact of a spherical ball on a metal plate.", notes = "CEC-2000 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 00TH8512, Library of Congress Number = 00-018644", } @InProceedings{howard:2001:gecco, title = "Genetic Programming solution of the convection-diffusion equation", author = "Daniel Howard and Simon C. Roberts", pages = "34--41", year = "2001", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)", editor = "Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon and Edmund Burke", address = "San Francisco, California, USA", publisher_address = "San Francisco, CA 94104, USA", month = "7-11 " # jul, keywords = "genetic algorithms, genetic programming, convection-diffusion, differential equations, WRM, FEM, numerical method", ISBN = "1-55860-774-9", URL = "http://gpbib.cs.ucl.ac.uk/gecco2001/d01.pdf", size = "8 pages", abstract = "A version of Genetic Programming (GP) is proposed for the solution of the steady-state convection-diffusion equation which neither requires sampling points to evaluate fitness nor application of the chain rule to GP trees for obtaining the derivatives. The method is successfully applied to the equation in one space dimension.", notes = "GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of \cite{spector:2001:GECCO} linear one dimensional second order partial differential equation, comparison of GP with known analytic solution. Evolve single polynomial approximation. Fitness based on analytical integration of differentials of polynomial. Polynomial is phenotype created by GP ADD, BACK WRITE functions on variable length vector of polynomial co-efficients. read and write memory (two cells). Peclet numbers. p37 GP with ADFs 'did not significantly improve performance'. Weighted residues method, WRM. p39 'This method cannot be recommended'", } @InProceedings{Howard11:2002:EvoWorkshops, author = "Daniel Howard and Simon C. Roberts", title = "The Prediction of Journey Times on Motorways using Genetic Programming", booktitle = "Applications of Evolutionary Computing, Proceedings of EvoWorkshops2002: EvoCOP, EvoIASP, EvoSTim/EvoPLAN", year = "2002", editor = "Stefano Cagnoni and Jens Gottlieb and Emma Hart and Martin Middendorf and G{"}unther Raidl", volume = "2279", series = "LNCS", pages = "210--221", address = "Kinsale, Ireland", publisher_address = "Berlin", month = "3-4 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, evolutionary computation, applications, MIDAS, London orbital motorway M25", ISBN = "3-540-43432-1", DOI = "doi:10.1007/3-540-46004-7_22", size = "12 pages", abstract = "Considered is the problem of reliably predicting motorway journey times for the purpose of providing accurate information to drivers. This proof of concept experiment investigates: (a) the practicalities of using a Genetic Programming (GP) method to model/forecast motorway journey times; and (b) different ways of obtaining a journey time predictor. Predictions are compared with known times and are also judged against a collection of naive prediction formulae. A journey time formula discovered by GP is analysed to determine its structure, demonstrating that GP can indeed discover compact formulae for different traffic situations and associated insights. GP's felxibility allows it to self-determine the required level of modelling complexity.", notes = "EvoWorkshops2002, part of cagnoni:2002:ews Counter clockwise (ie south bound) between junction 15 (M4) and Junction 11 (Chertsey) Sepetember 1999. (Covered by variable speed limits)", } @InProceedings{Howard13:2002:EvoWorkshops, author = "Daniel Howard and Simon C. Roberts and Conor Ryan", title = "The Boru Data Crawler for Object Detection Tasks in Machine Vision", booktitle = "Applications of Evolutionary Computing, Proceedings of EvoWorkshops2002: EvoCOP, EvoIASP, EvoSTim/EvoPLAN", year = "2002", editor = "Stefano Cagnoni and Jens Gottlieb and Emma Hart and Martin Middendorf and G{"}unther Raidl", volume = "2279", series = "LNCS", pages = "222--232", address = "Kinsale, Ireland", publisher_address = "Berlin", month = "3-4 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, evolutionary computation, applications", ISBN = "3-540-43432-1", DOI = "doi:10.1007/3-540-46004-7_23", size = "11 pages", abstract = "A 'data crawler' is allowed to meander around an image deciding what it considers to be interesting and laying down flags in areas where its interest has been aroused. These flags can be analysed statistically as if the image was being viewed from afar to achieve object recognition. The guidance program for the crawler, the program which excites it to deposit a flag and how the flags are combined statistically, are driven by an evolutionary process which has as objective the minimisation of misses and false alarms. The crawler is represented by a tree-based Genetic Programming (GP) method with fixed architecture Automatically Defined Functions (ADFs). The crawler was used as a post-processor to the object detection obtained by a Staged GP method, and it managed to appreciably reduce the number of false alarms on a real-world application of vehicle detection in infrared imagery.", notes = "EvoWorkshops2002, part of cagnoni:2002:ews READMEM WRITEMEM working memory. Mark decisions branch. Flags. Second results branch. Looking for cars ", } @InProceedings{howard2:2002:gecco, author = "Daniel Howard and Simon C. Roberts", title = "Application Of Genetic Programming To Motorway Traffic Modelling", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "1097--1104", address = "New York", publisher_address = "San Francisco, CA 94104, USA", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "genetic algorithms, genetic programming, real world applications, forecasting, incident detection, motorway traffic modelling, time series prediction, MIDAS, M25 london orbital motorway, 2 September 1999", ISBN = "1-55860-878-8", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/RWA305.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/RWA305.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-20.pdf", size = "8 pages", notes = "GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)", } @InProceedings{howard:2002:gecco, author = "Daniel Howard and Simon C. Roberts and Conor Ryan", title = "Machine Vision: Exploring Context With Genetic Programming", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "756--763", address = "New York", publisher_address = "San Francisco, CA 94104, USA", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "genetic algorithms, genetic programming, automatically defined functions, data crawler, image analysis, machine vision, target detection", ISBN = "1-55860-878-8", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/GP303.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/GP303.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf", notes = "GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)", } @InProceedings{Howard:evowks03, author = "Daniel Howard and Karl Benson", title = "Promoter Prediction with a {GP}-Automaton", booktitle = "Applications of Evolutionary Computing, EvoWorkshops2003: Evo{BIO}, Evo{COP}, Evo{IASP}, Evo{MUSART}, Evo{ROB}, Evo{STIM}", year = "2003", editor = "G{\"u}nther R. Raidl and Stefano Cagnoni and Juan Jes\'us Romero Cardalda and David W. Corne and Jens Gottlieb and Agn\`es Guillot and Emma Hart and Colin G. Johnson and Elena Marchiori and Jean-Arcady Meyer and Martin Middendorf", volume = "2611", series = "LNCS", pages = "44--53", address = "University of Essex, England, UK", publisher_address = "Berlin", month = "14-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, evolutionary computation, applications", isbn13 = "978-3-540-00976-4", DOI = "doi:10.1007/3-540-36605-9_5", abstract = "A GP-automaton evolves motif sequences for its states; it moves the point of motif application at transition time using an integer that is stored and evolved in the transition; and it combines motif matches via logical functions that it also stores and evolves in each transition. This scheme learns to predict promoters in human genome. The experiments reported use 5-fold cross validation.", notes = "EvoWorkshops2003", } @Article{howard:2003:JDS, author = "Daniel Howard", title = "Innovating with Automatic Programming", journal = "Journal of Defence Science", year = "2003", volume = "8", number = "2", pages = "76--82", month = may, keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/howard_2003_JDS.pdf", broken = "http://www.iwm.org.uk/collections/item/object/1506000374", size = "7 pages", abstract = "Automatic programming... Darwinian inspiration and biological analogues... solve many civil and military problems", notes = "pixel fusion tirrs. midas linda London orbital motorway M25 Article on automatic programming and its connection with Darwin and biological analogues. It asks how bio-inspired is automatic programming; explores the context of identification; and looks at target orientation and the automated discovery of target features. Aug 2022 https://www.worldcat.org/title/journal-of-defence-science/oclc/55683758 says OCLC Number: 55683758", } @InProceedings{howard:2003:gecco, author = "Daniel Howard and Karl Benson", title = "Evolutionary Computation Method for Promoter Site Prediction in {DNA}", booktitle = "Genetic and Evolutionary Computation -- GECCO-2003", editor = "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller", year = "2003", pages = "1690--1701", address = "Chicago", publisher_address = "Berlin", month = "12-16 " # jul, volume = "2724", series = "LNCS", ISBN = "3-540-40603-4", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1007/3-540-45110-2_62", abstract = "develops an evolutionary method that learns inductively to recognize the makeup and the position of very short consensus sequences, which are a typical feature of promoters in eukaryotic genomes. This class of method can be used to discover candidate promoter sequences in primary sequence data. If further developed, it has the potential to discover genes which are regulated together.", notes = "GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)", } @InCollection{howard:2003:GPTP, author = "Daniel Howard", title = "Modularization by Multi-Run Frequency Driven Subtree Encapsulation", booktitle = "Genetic Programming Theory and Practice", publisher = "Kluwer", year = "2003", editor = "Rick L. Riolo and Bill Worzel", chapter = "10", pages = "155--171", keywords = "genetic algorithms, genetic programming, Modularization, Subtree Encapsulation, Multi-run, ADF, Subtree Database, Subtree Frequency, Parity Problem", ISBN = "1-4020-7581-2", URL = "http://www.springer.com/computer/ai/book/978-1-4020-7581-0", DOI = "doi:10.1007/978-1-4419-8983-3_10", abstract = "In tree-based Genetic Programming, subtrees which represent potentially useful sub-solutions can be encapsulated in order to protect them and aid their proliferation throughout the population. This paper investigates implementing this as a multi-run method. A two-stage encapsulation scheme based on subtree survival and frequency is compared against Automatically Defined Functions in fixed and evolved architectures and standard Genetic Programming for solving a Parity problem.", notes = "Part of \cite{RioloWorzel:2003}", size = "18 pages", } @Article{Howard:2003:CIB, author = "Daniel Howard and Karl Benson", title = "Evolutionary computation method for pattern recognition of cis-acting sites", journal = "Biosystems", year = "2003", volume = "72", number = "1-2", pages = "19--27", month = nov, note = "Special Issue on Computational Intelligence in Bioinformatics", keywords = "genetic algorithms, genetic programming, Finite State Automata, DNA, human genome, promoter, evolutionary computation, bioinformatics", ISSN = "0303-2647", DOI = "doi:10.1016/S0303-2647(03)00132-1", broken = "http://www.sciencedirect.com/science/article/B6T2K-49NRT53-1/2/a695c769043ab9105da3bb6cf90fe774", URL = "http://www.ncbi.nlm.nih.gov/PubMed/", abstract = "This paper develops an evolutionary method that learns inductively to recognize the makeup and the position of very short consensus sequences, cis-acting sites, which are a typical feature of promoters in genomes. The method combines a Finite State Automata (FSA) and Genetic Programming (GP) to discover candidate promoter sequences in primary sequence data. An experiment measures the success of the method for promoter prediction in the human genome. This class of method can take large base pair jumps and this may enable it to process very long genomic sequences to discover gene specific cis-acting sites, and genes which are regulated together.", notes = "PMID: 14642656", } @InProceedings{Howard:2004:ICKBIIESC, author = "Daniel Howard", title = "Top Down Modelling with Genetic Programming", booktitle = "Proceedings of the 8th International Conference on Knowledge-Based Intelligent Information and Engineering Systems Conference, KES 2004, Part III", year = "2004", editor = "Mircea Gh. Negoita and Robert J. Howlett and Lakhmi C. Jain", series = "Lecture Notes in Artificial Intelligence", publisher = "Springer", keywords = "genetic algorithms, genetic programming, top down modelling", volume = "3215", pages = "217--223", month = sep # " 20-25", ISBN = "3-540-23205-2", DOI = "doi:10.1007/b100916", size = "7 pages", abstract = "explores the connection between top down modelling and the artificial intelligence (AI) technique of Genetic Programming (GP). It provides examples to illustrate how the author and colleagues took advantage of this connection to solve real world problems. Following this account, the paper speculates about how GP may be developed further to meet more challenging real world problems. It calls for novel applications of GP to quantify a top down design in order to make rapid progress with the understanding of organisations.", } @InCollection{howard:2004:GPTP, author = "Daniel Howard and Simon C. Roberts", title = "Incident Detection on Highways", booktitle = "Genetic Programming Theory and Practice {II}", year = "2004", editor = "Una-May O'Reilly and Tina Yu and Rick L. Riolo and Bill Worzel", chapter = "16", pages = "263--282", address = "Ann Arbor", month = "13-15 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, automatic incident detection, freeway, motorway, highways, traffic management, control office, low flow, high speed, occupancy, reversing vehicles, roadworks, HIOCC, California Algorithm, MIDAS, LINDA", ISBN = "0-387-23253-2", DOI = "doi:10.1007/0-387-23254-0_16", abstract = "This chapter discusses the development of the Low-occupancy INcident Detection Algorithm (LINDA) that detects night-time motorway incidents. LINDA is undergoing testing on live data and deployment on the M5, M6 and other motorways in the United Kingdom. It was developed by the authors using Genetic Programming.", notes = "part of \cite{oreilly:2004:GPTP2}", } @Misc{BDS-TR-2005-001, author = "Daniel Howard and Joseph Kolibal", title = "Solution of differential equations with Genetic Programming and the Stochastic Bernstein Interpolation", institution = "Biocomputing-Developmental Systems Group, University of Limerick", year = "2005", number = "BDS-TR-2005-001", address = "Ireland", month = jun # " 19", keywords = "genetic algorithms, genetic programming", URL = "http://www.genetic-programming.org/hc2005/bds.pdf", abstract = "This report introduces a method for the solution of the Convection-Diffusion equations (CDE) that combines Genetic Programming with Stochastic Bernstein Interpolation. Significantly, it is being used to solve a problem that has resisted analysis for a long time using other methods. Although the method in this report solves the one-dimensional CDE which has also been solved analytically and optimally, our strategy of combining the Stochastic Bernstein Interpolation method with GP allows for the method to extend to higher dimensions, and thus it shows how to construct GP based methods for solving a range of computational problems in multiple dimensions which have hitherto resisted numerical solution.", size = "37 pages", notes = "Honorable Mention 2005 HUMIES GECCO-2005", } @Article{howard:2006:PRL, author = "Daniel Howard and Simon C. Roberts and Conor Ryan", title = "Pragmatic Genetic Programming strategy for the problem of vehicle detection in airborne reconnaissance", journal = "Pattern Recognition Letters", year = "2006", volume = "27", number = "11", pages = "1275--1288", month = aug, note = "Evolutionary Computer Vision and Image Understanding", keywords = "genetic algorithms, genetic programming, Object detection, Method of stages, Reconnaissance, Discrete Fourier transform, Vehicle detection, Machine vision", DOI = "doi:10.1016/j.patrec.2005.07.025", abstract = "A Genetic Programming (GP) method uses multiple runs, data decomposition stages, to evolve a hierarchical set of vehicle detectors for the automated inspection of infrared line scan imagery that has been obtained by a low flying aircraft. The performance on the scheme using two different sets of GP terminals (all are rotationally invariant statistics of pixel data) is compared on 10 images. The discrete Fourier transform set is found to be marginally superior to the simpler statistics set that includes an edge detector. An analysis of detector formulae provides insight on vehicle detection principles. In addition, a promising family of algorithms that take advantage of the GP method's ability to prescribe an advantageous solution architecture is developed as a post-processor. These algorithms selectively reduce false alarms by exploring context, and determine the amount of contextual information that is required for this task.", } @InProceedings{conf/rskt/Howard07, author = "Daniel Howard", title = "Multiple Solutions by Means of Genetic Programming: {A} Collision Avoidance Example", booktitle = "Proceedings of the Second International Conference on Rough Sets and Knowledge Technology, RSKT 2007", year = "2007", editor = "Jingtao Yao and Pawan Lingras and Wei-Zhi Wu and Marcin S. Szczuka and Nick Cercone and Dominik Slezak", volume = "4481", series = "Lecture Notes in Computer Science", pages = "508--517", address = "Toronto, Canada", month = may # " 14-16", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Multiple Solutions", isbn13 = "978-3-540-72457-5", DOI = "doi:10.1007/978-3-540-72458-2_63", size = "10 pages", abstract = "Seldom is it practical to completely automate the discovery of the Pareto Frontier by genetic programming (GP). It is not only difficult to identify all of the optimization parameters a-priori but it is hard to construct functions that properly evaluate parameters. For instance, the ease of manufacture of a particular antenna can be determined but coming up with a function to judge this on all manner of GP-discovered antenna designs is impractical. This suggests using GP to discover many diverse solutions at a particular point in the space of requirements that are quantifiable, only a-posteriori (after the run) to manually test how each solution fares over the less tangible requirements e.g. ease of manufacture. Multiple solutions can also suggest requirements that are missing. A new toy problem involving collision avoidance is introduced to research how GP may discover a diverse set of multiple solutions to a single problem. It illustrates how emergent concepts (linguistic labels) rather than distance measures can cluster the GP generated multiple solutions for their meaningful separation and evaluation.", notes = "railway track, two train speeds, GP sets the points", bibdate = "2007-07-05", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/rskt/rskt2007.html#Howard07", } @Article{Howard:2008:JBB, author = "Daniel Howard and Simon C. Roberts and Conor Ryan and Adrian Brezulianu", title = "Textural Classification of Mammographic Parenchymal Patterns with the SONNET Selforganizing Neural Network", journal = "Journal of Biomedicine and Biotechnology", year = "2008", volume = "2008", pages = "526343", month = jul # " 22", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1155/2008/526343", abstract = "In nationwide mammography screening, thousands of mammography examinations must be processed. Each consists of two standard views of each breast, and each mammogram must be visually examined by an experienced radiologist to assess it for any anomalies. The ability to detect an anomaly in mammographic texture is important to successful outcomes in mammography screening and, in this study, a large number of mammograms were digitized with a highly accurate scanner; and textural features were derived from the mammograms as input data to a SONNET self organizing neural network. The paper discusses how SONNET was used to produce a taxonomic organization of the mammography archive in an unsupervised manner. This process is subject to certain choices of SONNET parameters, in these numerical experiments using the craniocaudal view, and typically produced O(10), for example, 39 mammogram classes, by analysis of features from O(103) mammogram images. The mammogram taxonomy captured typical subtleties to discriminate mammograms, and it is submitted that this may be exploited to aid the detection of mammographic anomalies, for example, by acting as a preprocessing stage to simplify the task for a computational detection scheme, or by ordering mammography examinations by mammogram taxonomic class prior to screening in order to encourage more successful visual examination during screening. The resulting taxonomy may help train screening radiologists and conceivably help to settle legal cases concerning a mammography screening examination because the taxonomy can reveal the frequency of mammographic patterns in a population.", notes = "PMID: {"}As the [previously evolved] data crawler has been developed for target detection in imagery, it is highly transferable to the problem of lesion detection in mammograms. The crawler could scrutinize mammogram areas which possess the greatest asymmetry and thus focus on candidate lesions. The evolutionary approach allows the crawler to discover its own multiscale features which best locate lesions.{"}", } @InProceedings{Howard:2009:bliss, author = "Daniel Howard", title = "A Method of Project Evaluation and Review Technique (PERT) Optimization by Means of Genetic Programming", booktitle = "2009 Symposium on Bio-inspired Learning and Intelligent Systems for Security, BLISS '09", year = "2009", month = aug, pages = "132--135", keywords = "genetic algorithms, genetic programming, PERT optimization, project control, project evaluation and review technique, scheduling problems, PERT, project management, scheduling", abstract = "Genetic Programming is applied to solve scheduling problems. The resulting tool simulates the PERT method of project control, and Genetic Programming provides multiple acceptable solutions. This tool has a wide application in the management of large and complex projects. It is a bio-inspired means to obtain solution in many disparate areas of activity such as for computer gaming, and when a complex system needs to be understood and executed properly as in many types of security operation.", DOI = "doi:10.1109/BLISS.2009.12", notes = "Also known as \cite{5376803}", } @Article{Howard:2011:SC, title = "Genetic programming of the stochastic interpolation framework: convection-diffusion equation", author = "Daniel Howard and Adrian Brezulianu and Joseph Kolibal", journal = "Soft Computing", year = "2011", number = "1", volume = "15", pages = "71--78", month = jan, bibdate = "2011-02-19", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/soco/soco15.html#HowardBK11", keywords = "genetic algorithms, genetic programming", ISSN = "1432-7643", URL = "https://rdcu.be/cAMsI", URL = "http://link.springer.com/article/10.1007%2Fs00500-009-0520-3", DOI = "doi:10.1007/s00500-009-0520-3", size = "8 pages", abstract = "The stochastic interpolation (SI) framework of function recovery from input data comprises a de-convolution step followed by a convolution step with row stochastic matrices generated by a mollifier, such as a probability density function. The choice of a mollifier and of how it gets weighted, offers unprecedented flexibility to vary both the interpolation character and the extent of influence of neighbouring data values. In this respect, a soft computing method such as a genetic algorithm or heuristic method may assist applications that model complex or unknown relationships between data by tuning the parameters, functional and component choices inherent in SI. Alternatively or additionally, the input data itself can be reverse engineered to recover a function that satisfies properties, as illustrated in this paper with a genetic programming scheme that enables SI to recover the analytical solution to a two-point boundary value convection-diffusion differential equation. If further developed, this nascent solution method could serve as an alternative to the weighted residual methods, as these are known to have inherent mathematical difficulties.", affiliation = "Howard Science Limited, 24 Sunrise, Malvern, WR142NJ UK", } @Article{Howard:2011a:SC, author = "Daniel Howard and Adrian Brezulianu", title = "Capturing expert knowledge of mesh refinement in numerical methods of impact analysis by means of genetic programming", journal = "Soft Computing", year = "2011", volume = "15", number = "1", pages = "103--110", month = jan, keywords = "genetic algorithms, genetic programming", publisher = "Springer Berlin / Heidelberg", ISSN = "1432-7643", URL = "https://rdcu.be/cAMtA", DOI = "doi:10.1007/s00500-010-0684-x", abstract = "The mesh refinement decisions of an experienced user of high-velocity impact numerical approximation finite differences computations are discovered as a set of comprehensible rules by means of Genetic Programming. These rules that could automatically trigger adaptive mesh refinement to mimic the expert user, detect mesh cells that require refinement by evolving a formula involving cell quantities such as material densities. Various cell variable combinations are investigated in order to identify the optimal ones for indicating mesh refinement. A high-velocity impact phenomena example of a tungsten ball that strikes a steel plate illustrates this methodology.", affiliation = "Howard Science Limited, 24 Sunrise, Malvern, WR14 2NJ UK", } @InProceedings{Howard:2011:ICHIT, author = "Daniel Howard and Conor Ryan and J. J. Collins", title = "Attribute Grammar Genetic Programming Algorithm for Automatic Code Parallelization", booktitle = "Proceedings of the 5th International Conference on Convergence and Hybrid Information Technology, ICHIT 2011", year = "2011", editor = "Geuk Lee and Daniel Howard and Dominik Slezak", volume = "6935", series = "Lecture Notes in Computer Science", pages = "250--257", address = "Daejeon, Korea", month = sep # " 22-24", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, genetic improvement, Context Free Grammar, Attribute Grammar, Parallel Computing, Automatic Parallelisation, Evolutionary Computation, SBSE", isbn13 = "978-3-642-24081-2", DOI = "doi:10.1007/978-3-642-24082-9_31", abstract = "A method is presented for evolving individuals that use an Attribute Grammar (AG) in a generative way. AGs are considerably more flexible and powerful than the closed , context free grammars normally employed by GP. Rather than evolving derivation trees as in most approaches, we employ a two step process that first generates a vector of real numbers using standard GP, before using the vector to produce a parse tree. As the parse tree is being produced, the choices in the grammar depend on the attributes being input to the current node of the parse tree. The motivation is automatic parallelisation or the discovery of a re-factoring of a sequential code or equivalent parallel code that satisfies certain performance gains when implemented on a target parallel computing platform such as a multicore processor. An illustrative and a computed example demonstrate this methodology.", affiliation = "Howard Science Limited, 24 Sunrise, Malvern, WR14 2NJ United Kingdom", } @InProceedings{conf/ichit/HowardC12, author = "Daniel Howard and J. J. Collins", title = "Grammatical Genetic Programming: Application in Automatic Code Parallelization", booktitle = "6th International Conference Convergence and Hybrid Information Technology, ICHIT 2012", year = "2012", editor = "Geuk Lee and Daniel Howard and Jeong Jin Kang and Dominik Slezak", volume = "7425", series = "Lecture Notes in Computer Science", pages = "217--223", address = "Daejeon, Korea", month = aug # " 23-25", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Parallel Computing, Automatic Parallelisation, Grammatical Genetic Programming, Evolutionary Computation, Parallel Compilers, Artificial Intelligence", isbn13 = "978-3-642-32644-8", DOI = "doi:10.1007/978-3-642-32645-5_28", bibdate = "2012-08-30", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ichit/ichit2012-1.html#HowardC12", abstract = "This novel algorithm uses standard Genetic Programming (GP) to evolve a grammar. It is applied to the automatic parallelisation of sequential software. Alternative parallel schedules are generated for a computational resource constrained illustrative example demonstrating the power of the methodology.", notes = "GGP tree GP. Cited by \cite{Howard:2018:iCMLDE}", } @InProceedings{conf/ichit/HowardR12, author = "Daniel Howard and Conor Ryan", title = "Testing a Novel Attribute Grammar Genetic Programming Algorithm", booktitle = "6th International Conference Convergence and Hybrid Information Technology, ICHIT 2012", year = "2012", editor = "Geuk Lee and Daniel Howard and Jeong Jin Kang and Dominik Slezak", volume = "7425", series = "Lecture Notes in Computer Science", pages = "224--231", address = "Daejeon, Korea", month = aug # " 23-25", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Attribute Grammar, Parallel Computing, Automatic Parallelisation, Evolutionary Computation, epigenetic diseases,stem cells", isbn13 = "978-3-642-32644-8", DOI = "doi:10.1007/978-3-642-32645-5_29", bibdate = "2012-08-30", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ichit/ichit2012-1.html#HowardR12", size = "8 pages", abstract = "A novel algorithm uses standard Genetic Programming (GP) to evolve an Attribute Grammar (AG) and this is tested on a problem with known solution in automatic code parallelisation. Standard GP first generates a vector of real numbers and its elements are in turn applied to the grammar. As the parse tree is being produced the choices in the grammar depend on the attributes being input to the current node of the parse tree. Experiments reveal different levels of success at finding solutions to different versions of the test problem. It is speculated that the novel method may find a role in computational medicine in stem cell research and in the modelling of epigenetic disease.", notes = "Tree GP generates variable length list of floats each in the range 0.0 to 0.99999 which is used to navigate through an attribute grammar which is then used to define how programming 40 instructions are scheduled on 4 CPUs.", } @InProceedings{Howard:2018:iCMLDE, author = "Daniel Howard", title = "A Tunable Deceptive Problem to Challenge Genetic and Evolutionary Computation and Other {A.I.}", booktitle = "2018 International Conference on Machine Learning and Data Engineering (iCMLDE)", year = "2018", pages = "160--162", address = "Sydney, Australia", month = "3-7 " # dec, organisation = "Western Sydney University", keywords = "genetic algorithms, genetic programming, attribute grammar genetic programming, benchmark problem, deceptive problem, Evolutionary Computation, AI, solution landscape, heuristic method, tunable problem, analytical solution, toy problem", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/icmlde_2018/Howard_2018_iCMLDE.pdf", URL = "https://www.researchgate.net/publication/330474606_A_Tunable_Deceptive_Problem_to_Challenge_Genetic_and_Evolutionary_Computation_and_Other_AI", DOI = "doi:10.1109/iCMLDE.2018.00038", size = "3 pages", abstract = "A deceptive problem with known analytical solution is introduced. Arguably its solution search landscape is such that heuristic methods will find it difficult to search for the solution. The problem is tunable offering a test bed by which to examine the performance of different methods of heuristic and evolutionary search.", notes = "p160 'A sequential computer program consisting of a set of instructions with some inter-dependencies between instructions is to be run on a parallel computer. No instruction or underlying algorithm is modified but the instructions must be distributed optimally among a potentially unlimited number of parallel processors, respecting the dependencies, such that the program is run in minimum time, essentially carries out the same computation and outputs the similar results as its sequential version.' Cites \cite{conf/ichit/HowardC12} http://www.icmlde.net.au/IndustrialTrack.aspx also known as \cite{8614021}", } @InProceedings{Howard:2018:iCMLDEb, author = "Daniel Howard and Mark A. Edwards", title = "Explainable {A.I.}: The Promise of Genetic Programming Multi-run Subtree Encapsulation", booktitle = "2018 International Conference on Machine Learning and Data Engineering (iCMLDE)", year = "2018", pages = "158--159", address = "Sydney, Australia", month = "3-7 " # dec, organisation = "Western Sydney University", keywords = "genetic algorithms, genetic programming, Explainable Artificial Intelligence, AI, XAI, Evolutionary Computation, modularization, Subtree Encapsulation, Automatically Defined Functions, ADF, Software Evolution, white box, black box, expression simplification, Deep Learning, Artificial Neural Networks, Multirun Subtree Encapsulation, subtree database", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/icmlde_2018/Howard_2018_iCMLDEb.pdf", URL = "https://www.researchgate.net/publication/330475457_Evomorph_Morphological_Modularization_in_AI_for_Machine_Vision_Inspired_by_Embryology", DOI = "doi:10.1109/iCMLDE.2018.00037", size = "2 pages", abstract = "Deep Learning and other Artificial Neural Network based solutions are rarely transparent, and white-box solutions are often called for. This paper explains how Multirun Subtree Encapsulation can provide equivalent white box solutions to facilitate Explainable Artificial Intelligence.", notes = "Howard Science Ltd, Malvern, UK Gone Sep 2021 http://www.icmlde.net.au/IndustrialTrack.aspx Also known as \cite{8614020}", } @InProceedings{Howard:2018:iCMLDEc, author = "Daniel Howard", title = "Evomorph: Morphological Modularization in {A.I.} for Machine Vision Inspired by Embryology", booktitle = "2018 International Conference on Machine Learning and Data Engineering (iCMLDE)", year = "2018", pages = "163--166", address = "Sydney, Australia", month = "3-7 " # dec, organisation = "Western Sydney University", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, embryology, modularization, machine vision, image analysis, object detection, classification, template matching, pattern matching, Artificial Intelligence, Evolutionary Computation, code re-use", isbn13 = "978-1-7281-0405-8", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/icmlde_2018/Howard_2018_iCMLDEc.pdf", URL = "https://www.researchgate.net/publication/330472647_Explainable_AI_The_Promise_of_Genetic_Programming_Multi-run_Subtree_Encapsulation", DOI = "doi:10.1109/iCMLDE.2018.00039", size = "4 pages", abstract = "Nature likely implements modularization in multicellular developmental biology using the chemical context of the cell, cell division generational distance, and genetic triggers. Inspired in this, Evomorph is a proposed heuristic method of Artificial Intelligence that pairs these concepts with Evolutionary Computation. It is described here as a flexible template matching for object detection in Machine Vision.", notes = "Also known as \cite{8614022} gone 2023 http://www.icmlde.net.au/IndustrialTrack.aspx Howard Science Ltd. Malvern, UK.", } @TechReport{ITLAB-TR-2020-02, author = "Daniel Howard", title = "Genetic Programming visitation scheduling in lockdown with partial infection model that leverages information from {COVID-19} testing", institution = "ITLab, Inha University", year = "2020", number = "ITLAB-TR-2020-02", address = "Room 1301, HITECH Building, 100, Inha-ro, Nam-gu, Incheon, South Korea", month = "3 " # jun, keywords = "genetic algorithms, genetic programming, Software as a Service, SaaS, Corona pandemic", broken = "http://itlab.inha.ac.kr/#tr", broken = "https://drive.google.com/file/d/1u9Ti5p7w_4-uA2o3l-CTslnSLCdlxQxO/view", broken = "https://www.human-competitive.org/sites/default/files/howard_0.txt", URL = "https://www.human-competitive.org/sites/default/files/replacementhoward_0.pdf", broken = "http://www.human-competitive.org/sites/default/files/humies2020_d_howard_entry.mp4", size = "37 pages", abstract = "This report introduces a computational methodology to minimize infection opportunities for people suffering some degree of lockdown in response to a pandemic. Persons use their mobile phone or computational device to request trips to places of need or of their interest. An artificial intelligence methodology which uses Genetic Programming studies all requests and responds with granted time allocations for such visits that minimize the overall risks of infection, hospitalization and death of people. A number of alternatives for this computation are presented as well as the results of numerical experiments involving over 200 people of various ages. In particular, a model of partial infection is developed and implemented to address the real world situation whereby COVID-19 testing indicates risks of infection for members of a taxonomic class - for example, age groups, exploiting such information for the aforementioned purpose.", notes = "See also \cite{howard2020genetic} 2020 HUMIES finalist.", } @Misc{howard2020genetic, author = "Daniel Howard", title = "Genetic Programming visitation scheduling solution can deliver a less austere {COVID-19} pandemic population lockdown", howpublished = "arXiv", year = "2020", month = "22 " # jun, keywords = "genetic algorithms, genetic programming", URL = "https://arxiv.org/abs/2006.10748", broken = "http://www.human-competitive.org/sites/default/files/humies2020_d_howard_entry.mp4", eprint = "2006.10748", archiveprefix = "arXiv", primaryclass = "cs.NE", size = "41 pages", abstract = "A computational methodology is introduced to minimize infection opportunities for people suffering some degree of lockdown in response to a pandemic, as is the 2020 COVID-19 pandemic. Persons use their mobile phone or computational device to request trips to places of their need or interest indicating a rough time of day: morning, afternoon, night or any time when they would like to undertake these outings as well as the desired place to visit. An artificial intelligence methodology which is a variant of Genetic Programming studies all requests and responds with specific time allocations for such visits that minimize the overall risks of infection, hospitalization and death of people. A number of alternatives for this computation are presented and results of numerical experiments involving over 230 people of various ages and background health levels in over 1700 visits that take place over three consecutive days. A novel partial infection model is introduced to discuss these proof of concept solutions which are compared to round robin uninformed time scheduling for visits to places. The computations indicate vast improvements with far fewer dead and hospitalized. These auger well for a more realistic study using accurate infection models with the view to test deployment in the real world. The input that drives the infection model is the degree of infection by taxonomic class, such as the information that may arise from population testing for COVID-19 or, alternatively, any contamination model. The taxonomy class assumed in the computations is the likely level of infection by age group.", notes = "See also \cite{ITLAB-TR-2020-02} 2020 HUMIES finalist.", } @InCollection{Howard:2010:GECma, author = "David M. Howard and Andy M. Tyrrell and Crispin Cooper", title = "Towards an Alternative to Magnetic Resonance Imaging for Vocal Tract Shape Measurement Using the Principles of Evolution", booktitle = "Genetic and Evolutionary Computation: Medical Applications", publisher = "John Wiley and Sons, Ltd", year = "2010", editor = "Stephen L. Smith and Stefano Cagnoni", chapter = "6.2", pages = "191--207", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, towards alternative to magnetic resonance imaging - for vocal tract shape measurement using principles of evolution, electronic voice synthesis - applications, in highly intelligible speech output, physical modelling synthesis techniques - used successfully for electronic music synthesis, fMRI data acquisition - hampered by practical factors, principles of evolution - new computational paradigm, finding oral tract cross-sectional areas, method, calculating shape of oral tract - and extension of linear predictive coding (LPC), recording the target vowels, bio-inspired computing - genetic evolution, as computational tool in application areas, target vowels, for experiments - those uttered with flat intonation contour, physical modelling, using digital waveguide mesh - appropriate engine for technique", isbn13 = "9780470748138", DOI = "doi:10.1002/9780470973134.ch11", abstract = "In this work, a revised form of Implicit Context Representation Cartesian Genetic Programming is used in the development of a diagnostic tool for the assessment of patients with neurological dysfunction such as Alzheimer's disease. Specifically, visuo-spatial ability is assessed by analysing subjects' digitised responses to a simple figure copying task using a conventional test environment. The algorithm was trained to distinguish between classes of visuo-spatial ability based on responses to the figure copying test by 7-11 year old children in which visuo-spatial ability is at varying stages of maturity. Results from receiver operating characteristic (ROC) analysis are presented for the training and subsequent testing of the algorithm and demonstrate this technique has the potential to form the basis of an objective assessment of visuo-spatial ability.", } @InProceedings{howard:2012:EuroGP, author = "Gerard David Howard and Larry Bull and Andrew Adamatzky", title = "Cartesian Genetic Programming for Memristive Logic Circuits", booktitle = "Proceedings of the 15th European Conference on Genetic Programming, EuroGP 2012", year = "2012", month = "11-13 " # apr, editor = "Alberto Moraglio and Sara Silva and Krzysztof Krawiec and Penousal Machado and Carlos Cotta", series = "LNCS", volume = "7244", publisher = "Springer Verlag", address = "Malaga, Spain", pages = "37--48", organisation = "EvoStar", isbn13 = "978-3-642-29138-8", DOI = "doi:10.1007/978-3-642-29139-5_4", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, Self-adaptation, Nanotechnology, Boolean logic, Memristors, Robotics", abstract = "In this paper memristive logic circuits are evolved using Cartesian Genetic Programming. Graphs comprised of implication logic (IMP) nodes are compared to more ubiquitous NAND circuitry on a number of logic circuit problems and a robotic control task. Self-adaptive search parameters are used to provide each graph with autonomy with respect to its relative mutation rates. Results demonstrate that, although NAND-logic graphs are easier to evolve, IMP graphs carry benefits in terms of (i) numbers of memristors required (ii) the time required to process the graphs.", notes = "Part of \cite{Moraglio:2012:GP} EuroGP'2012 held in conjunction with EvoCOP2012 EvoBIO2012, EvoMusArt2012 and EvoApplications2012", } @Article{howard:1995:GA-P, author = "Les M. Howard and Donna J. D'Angelo", title = "The {GA--P}: A Genetic Algorithm and Genetic Programming hybrid", journal = "IEEE Expert", year = "1995", volume = "10", number = "3", pages = "11--15", month = jun, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/64.393137", size = "5 pages", abstract = "The GA-P performs symbolic regression by combining the traditional genetic algorithm's function optimization strength with the genetic-programming paradigm to evolve complex mathematical expressions capable of handling numeric and symbolic data. This technique should provide new insights into poorly understood data relationships. Discovering relationships has been a task troubling researchers since the dawn of modern science. Discovering relationships between sets of data is laborious and error prone, and it is highly subject to researcher bias. Because many of today's research problems are more complex than those of the past, it is increasingly important that robust data analysis methods be available to researchers. For a data analysis method to be most useful, it must meet at least three criteria: good predictive ability, insight into the inner workings of the system being analyzed, and unbiased results. Historically, researchers deduced relationships solely by examining the data--a difficult task if the relationship is complex, if many variables are involved, or if the data are noisy (as often occurs in real-world problems).", abstract = "Moreover, the examination is easily influenced by the researcher's desires and expectations. Statistical methods were among the first tools developed to help a researcher find the relationships of observed facts. Statistical methods are often based on such assumptions as these: (1) the data are normally distributed, (2) the equation relating the data is of a specific form (for example, linear, quadratic, or polynomial), and (3) the variables are independent. If the problem meets these assumptions, statistics are a valuable tool for providing static descriptors. But real-world problems seldom meet these criteria. Neural networks, an artificial intelligence technique, are not limited by these assumptions. They serve as strong predictive models that can uncover complex relationships, but they give little insight into the underlying mechanisms that describe a relationship. However, two other nonstatistical AI techniques, genetic algorithms and genetic programming, are more robust methods of exploring complex solution spaces. Independently, they have had some success at revealing the mechanisms relating data items. Recently, genetic algorithms, which use the principles of evolution through natural selection to solve problems, have established themselves as a powerful search and optimization technique. Most GAs are linear (the structure of an individual is a flat bit string). The basic GA proceeds as follows: 1. Create a population of random individuals, in which each individual represents a possible solution to the problem at hand. 2. Evaluate each individual's fitness--its ability to solve the specified problem. 3. Select individual population members to be parents. 4. Produce children by recombining parent material via crossover and mutation, and add them to the population. 5. Evaluate the children's fitness. 6. Repeat steps 3-5 until a solution with the desired fitness goal is obtained. GAs have been used for everything from multiple-fault diagnosis to medical-image registration. They have shown themselves to be a superior tool for developing rule-based systems, capable of gleaning knowledge from data inaccessible to statistical methods. Goldberg thoroughly discusses genetic algorithms and their use as a problem-solving and function optimization technique. Goldberg and Forrest give additional examples. Although linear GAs are adept at developing rule-based systems, they cannot develop equations. A recent addition to the evolutionary domain is genetic programming, which uses an evolutionary approach to generate symbolic expressions and perform symbolic regressions. However, the genetic-programming method of performing symbolic regressions has some limitations. It can modify only the structure of an expression, not its contents, which is generated by the implementation program when the genetic programming starts. In performing symbolic regressions, genetic programming cannot deal with nonnumeric variables. It also tends to produce convoluted equations because it cannot modify the coefficients it uses (for example, a genetic program might use (2.523+2.523)/2.523 to represent the number 2). We have developed a method combining the known strengths of traditional genetic algorithms with the new field of genetic programming to produce a superior tool for performing symbolic regressions. We call this tool the genetic algorithm-program, or the GA-P.", notes = "University of Georgia. IEEE Expert Special Track on Evolutionary Programming (P. J. Angeline editor) \cite{angeline:1995:er}", } @InProceedings{Howell:gecco06lbp, author = "Abraham L. Howell and Roy T. R. McGrann and Richard R. Eckert and Hiroki Sayama and Eileen Way", title = "Using RFID and a Low Cost Robot to Evolve Foraging Behavior", booktitle = "Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2006)}", year = "2006", month = "8-12 " # jul, editor = "J{\"{o}}rn Grahl", address = "Seattle, WA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006etc/papers/lbp131.pdf", notes = "Distributed on CD-ROM at GECCO-2006", keywords = "genetic algorithms, genetic programming", abstract = "The process of developing genetic algorithms, genetic programs or training neural networks is a time consuming task. When the target device is an autonomous mobile robot, this development is often performed using software simulation. Software simulations are a cost effective tool and provide researchers with the ability to test out multiple algorithms quickly and efficiently. However, the end result is that the optimised algorithm(s) must be implemented and tested on an actual robot to evaluate performance in the real world. Significant cost can be associated with this final step. In this paper we propose to leverage Radio Frequency Identification (RFID) and a low-cost RFID capable mobile robot with the intent of creating basic foraging behaviour. Additionally, we will present experimental results that demonstrate the effectiveness of using Genetic Programming (GP) and a low-cost RFID capable robot to create foraging behaviour by presenting our experimental results.", } @InProceedings{4720346, author = "Abraham L. Howell and Roy T. R. McGrann and Richard R. Eckert", title = "Teaching concepts in fuzzy logic using low cost robots, PDAs, and custom software", booktitle = "38th Annual Frontiers in Education Conference, FIE 2008", year = "2008", month = oct, pages = "T3H-7--T3H-11", keywords = "GUI, PDA, artificial intelligence, bioengineering course, classical control theory, custom software, desktop computers, fuzzy logic libraries, low cost robots, machine learning, neural networks, personal digital assistant, robotics courses, software modules, control engineering education, educational courses, fuzzy logic, graphical user interfaces, learning (artificial intelligence), notebook computers, robots", DOI = "doi:10.1109/FIE.2008.4720346", ISSN = "0190-5848", notes = "not on GP", } @InProceedings{Howlett:2010:AISB, author = "Andrew Howlett and Simon Colton and Cameron Browne", title = "Evolving Pixel Shaders for the Prototype Video Game Subversion", booktitle = "The Thirty Sixth Annual Convention of the Society for the Study of Artificial Intelligence and Simulation of Behaviour (AISB'10)", year = "2010", address = "De Montfort University, Leicester, UK", month = "30th " # mar, note = "AI \& Games Symposium", keywords = "genetic algorithms, genetic programming, GPU, OpenGL GLSL", URL = "http://www.doc.ic.ac.uk/~sgc/papers/howlett_aisb10.pdf", size = "6 pages", abstract = "Pixel shaders can be used to create a variety of visual effects in 3D environments, far more efficiently than if produced using the standard graphics pipeline. For such efficiency reasons, pixel shaders are commonly used in video game rendering, to add artistic or other visual effects. We investigate the automated creation of novel shader programs for rendering scenes in the Subversion virtual game world, with a view to providing the player with a visually richer and more diverse 3D environment. We show how shader programs based on the OpenGL shading language may be represented in a hierarchical tree form. This representation admits an evolutionary approach to shader creation, and we show how the application of genetic programming techniques can lead to the evolution of new and interesting shaders. We harness this for an approach where the user supplies details of a fitness function for the overall look of the city environment. We experimented with a number of different fitness function setups in order to produce some preliminary results about this approach. While generally successful in the creation of novel and visually interesting shading effects with little effort, we find some drawbacks to the approach and suggest methods for improvement.", notes = "Colour as RGBA. http://www.aisb.org.uk/convention/aisb10/AISB2010.html http://staffwww.dcs.shef.ac.uk/people/D.Romano/AISB10/program.html www.introversion.co.uk/subversion", } @InProceedings{howley:1996:GPsam, author = "Brian Howley", title = "Genetic Programming of Near-Minimum-Time Spacecraft Attitude Maneuvers", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "98--106", address = "Stanford University, CA, USA", publisher = "MIT Press", size = "9 pages", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap12.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96 see also \cite{howley:1996:samAIAA}", } @InProceedings{howley:1996:samAIAA, author = "Brian Howley", title = "Genetic Programming of Spacecraft Attitude Maneuvers Under Reaction Wheel Control", booktitle = "AIAA Guidance Navigation and Control Conference", year = "1996", month = "29--31 " # jul, keywords = "genetic algorithms, genetic programming", address = "San Diego, CA, USA", publisher_address = "1801 Alexander Bell Crive, Suite 500, Reston, VA 22091, USA", organisation = "American Institute of Aeronautics and Astronautics", broken = "http://www.aiaa.org/content.cfm?pageid=406&gTable=mtgpaper&gID=10524", DOI = "doi:10.2514/6.1996-3849", size = "11 pages", abstract = "A general solution for maneuvers with non-zero initial and final rates was not found, however, the GP solution out performs a hand crafted solution to the problem", notes = "Lockheed Martin Missiles and Space, Sunnyvale, CA AIAA 1996-3849 see also \cite{howley:1996:GPsam}", } @InProceedings{Howley:1997:GPps, author = "Brian Howley", title = "Genetic Programming and Parametric Sensitivity: a Case Study In Dynamic Control of a Two Link Manipulator", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming", pages = "180--185", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", broken = "http://cdr.stanford.edu/~bhowley/AAAIGP97.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.40.9214", abstract = "Minimum time control of a two link manipulator is used to investigate the sensitivity of genetic programming solutions to parametric design changes. Two methods of reducing sensitivity are considered. An aggregate fitness method in which results from multiple fitness cases are combined into a single fitness measure, and a bimodal selection method in which male and female parents are selected on the basis of fitness' derived from different parameter values. Results are preliminary. The genetically derived solutions perform poorly compared to numerical solutions. The poor performance may be due to an insufficiently large population. Population size was limited by simulation run time concerns.", notes = "GP-97", } @InCollection{howley:1995:GPNMTSAM, author = "Brian Howley", title = "Genetic Programming of Near Minimum Time Spacecraft Attitude Maneuvers", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1995", year = "1995", editor = "John R. Koza", pages = "96--106", address = "Stanford, California, 94305-3079 USA", month = "11 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-195720-5", notes = "part of \cite{koza:1995:gagp}", } @Article{DBLP:journals/air/HowleyM05, author = "Tom Howley and Michael G. Madden", title = "The Genetic Kernel Support Vector Machine: Description and Evaluation", journal = "Artificial Intelligence Review", volume = "24", number = "3-4", year = "2005", pages = "379--395", bibsource = "DBLP, http://dblp.uni-trier.de", keywords = "genetic algorithms, genetic programming, classification, genetic Kernel SVM, Mercer Kernel, model selection, support vector machine", ISSN = "0269-2821", DOI = "doi:10.1007/s10462-005-9009-3", abstract = "The Support Vector Machine (SVM) has emerged in recent years as a popular approach to the classification of data. One problem that faces the user of an SVM is how to choose a kernel and the specific parameters for that kernel. Applications of an SVM therefore require a search for the optimum settings for a particular problem. This paper proposes a classification technique, which we call the Genetic Kernel SVM (GK SVM), that uses Genetic Programming to evolve a kernel for a SVM classifier. Results of initial experiments with the proposed technique are presented. These results are compared with those of a standard SVM classifier using the Polynomial, RBF and Sigmoid kernel with various parameter settings", } @InProceedings{Hrbacek:2013:ECAL, author = "Radek Hrbacek and Michaela Sikulova", title = "Coevolutionary Cartesian Genetic Programming in {FPGA}", booktitle = "Advances in Artificial Life, ECAL 2013", year = "2013", editor = "Pietro Lio and Orazio Miglino and Giuseppe Nicosia and Stefano Nolfi and Mario Pavone", series = "Complex Adaptive Systems", pages = "431--438", address = "Taormina, Italy", month = sep # " 2-6", publisher = "MIT Press", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, EHW", isbn13 = "978-0-262-31709-2", DOI = "doi:10.7551/978-0-262-31709-2-ch062", size = "8 pages", abstract = "In this paper, a hardware platform for coevolutionary cartesian genetic programming is proposed. The proposed two population coevolutionary algorithm involves the implementation of search algorithms in two MicroBlaze soft processors (one for each population) interconnected by the AXI bus in Xilinx Virtex 6 FPGA. Candidate programs are evaluated in a domain-specific virtual reconfigurable circuit incorporated into custom MicroBlaze peripheral. Experimental results in the task of evolutionary image filter design show that we can achieve significant speed-up (up to 58) in comparison with highly optimised software implementation.", notes = "http://www.dmi.unict.it/ecal2013/ http://mitpress.mit.edu/books/advances-artificial-life-ecal-2013 ECAL-2013", } @InProceedings{Hrbacek:2014:GECCO, author = "Radek Hrbacek and Lukas Sekanina", title = "Towards highly optimized cartesian genetic programming: from sequential via {SIMD} and thread to massive parallel implementation", booktitle = "GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation", year = "2014", editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges", isbn13 = "978-1-4503-2662-9", pages = "1015--1022", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Parallel Computing, SIMD, AVX, Cluster, Combinational Circuit Design", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "https://www.fit.vut.cz/research/publication/10512", URL = "https://www.fit.vut.cz/research/publication-file/10512/p1015-hrbacek.pdf", URL = "http://doi.acm.org/10.1145/2576768.2598343", DOI = "doi:10.1145/2576768.2598343", publisher = "ACM", publisher_address = "New York, NY, USA", size = "8 pages", abstract = "Most implementations of Cartesian genetic programming (CGP) which can be found in the literature are sequential. However, solving complex design problems by means of genetic programming requires parallel implementations of search methods and fitness functions. This paper deals with the design of highly optimized implementations of CGP and their detailed evaluation in the task of evolutionary circuit design. Several sequential implementations of CGP have been analyzed and the effect of various additional optimizations has been investigated. Furthermore, the parallelism at the instruction, data, thread and process level has been applied in order to take advantage of modern processor architectures and computer clusters. Combinational adders and multipliers have been chosen to give a performance comparison with state of the art methods.", notes = "Included in \cite{Hrbacek:thesis} p1016 '128 or even 256 test vectors ... SSE or AVX' POPCNT. Compiled chromosome. combinational adder design, combinational multiplier. function set: {BUF, NOT, AND, OR, XOR, NAND, NOR, XNOR}. mutation, no crossover. 10000 generations, or 100000 generations (pop=5). p1019 '2 x 8-core Intel E5-2670, 128 GB RAM, 2 x 600 GB 15 k scratch hard disks, connected by gigabit Ethernet and Infiniband links' p1021 'massively parallel CGP exploiting tens of islands' Also known as \cite{2598343} \cite{FITPUB10512} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)", } @InProceedings{Hrbacek:2014:PPSN, author = "Radek Hrbacek and Vaclav Dvorak", title = "Bent Function Synthesis by Means of Cartesian Genetic Programming", booktitle = "13th International Conference on Parallel Problem Solving from Nature", year = "2014", editor = "Thomas Bartz-Beielstein and Juergen Branke and Bogdan Filipic and Jim Smith", publisher = "Springer", isbn13 = "978-3-319-10761-5", pages = "414--423", series = "Lecture Notes in Computer Science", address = "Ljubljana, Slovenia", month = "13-17 " # sep, volume = "8672", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", DOI = "doi:10.1007/978-3-319-10762-2_41", abstract = "In this paper, a new approach to synthesise bent Boolean functions by means of Cartesian Genetic Programming (CGP) is proposed. Bent functions have important applications in cryptography due to their high nonlinearity. However, they are very rare and their discovery using conventional brute force methods is not efficient enough. We show that by using CGP we can routinely design bent functions of up to 16 variables. The evolutionary approach exploits parallelism in both the fitness calculation and the search algorithm.", notes = "Winner Bronze at 11th Annual Humies Awards 2014 Vancouver, British Columbia HUMIES http://www.genetic-programming.org/hc2014/Radek-Text.txt Included in \cite{Hrbacek:thesis} PPSN-XIII", } @InProceedings{DBLP:conf/memics/Hrbacek14, author = "Radek Hrbacek", title = "Bent Functions Synthesis on {Intel Xeon Phi} Coprocessor", booktitle = "9th International Doctoral Workshop Mathematical and Engineering Methods in Computer Science, {MEMICS} 2014", year = "2014", editor = "Petr Hlineny and Zdenek Dvorak and Jiri Jaros and Jan Kofron and Jan Korenek and Petr Matula and Karel Pala", series = "Lecture Notes in Computer Science", volume = "8934", pages = "88--99", address = "Telc, Czech Republic", month = oct # " 17-19", publisher = "Springer", note = "Revised Selected Papers", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", isbn13 = "978-3-319-14896-0", timestamp = "Thu, 12 Sep 2019 08:30:28 +0200", biburl = "https://dblp.org/rec/conf/memics/Hrbacek14.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://doi.org/10.1007/978-3-319-14896-0_8", DOI = "doi:10.1007/978-3-319-14896-0_8", abstract = "A new approach to synthesize bent Boolean functions by means of Cartesian Genetic Programming (CGP) has been proposed recently. Bent functions have important applications in cryptography due to their high nonlinearity. However, they are very rare and their discovery using conventional brute force methods is not efficient enough. In this paper, a new parallel implementation is proposed and the performance is evaluated on the Intel Xeon Phi Coprocessor.", notes = "Included in \cite{Hrbacek:thesis}", } @InProceedings{Hrbacek:2015:GECCO, author = "Radek Hrbacek", title = "Parallel Multi-Objective Evolutionary Design of Approximate Circuits", booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation", year = "2015", editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr", isbn13 = "978-1-4503-3472-3", pages = "687--694", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, Evolutionary Multiobjective Optimization", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739480.2754785", DOI = "doi:10.1145/2739480.2754785", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Evolutionary design of digital circuits has been well established in recent years. Besides correct functionality, the demands placed on current circuits include the area of the circuit and its power consumption. By relaxing the functionality requirement, one can obtain more efficient circuits in terms of the area or power consumption at the cost of an error introduced to the output of the circuit. As a result, a variety of trade-offs between error and efficiency can be found. In this paper, a multi-objective evolutionary algorithm for the design of approximate digital circuits is proposed. The scalability of the evolutionary design has been recently improved using parallel implementation of the fitness function and by employing spatially structured evolutionary algorithms. The proposed multi-objective approach uses Cartesian Genetic Programming for the circuit representation and a modified NSGA-II algorithm. Multiple isolated islands are evolving in parallel and the populations are periodically merged and new populations are distributed across the islands. The method is evaluated in the task of approximate arithmetical circuits design.", notes = "Included in \cite{Hrbacek:thesis} Also known as \cite{2754785} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)", } @InProceedings{Hrbacek:2016:DTIS, author = "Radek Hrbacek and Vojtech Mrazek and Zdenek Vasicek", title = "Automatic design of approximate circuits by means of multi-objective evolutionary algorithms", booktitle = "2016 International Conference on Design and Technology of Integrated Systems in Nanoscale Era (DTIS)", year = "2016", pages = "239--244", address = "Istanbul Sehir University", month = apr, keywords = "genetic algorithms, genetic programming, cartesian genetic programming", isbn13 = "978-1-5090-0335-8", DOI = "doi:10.1109/DTIS.2016.7483885", abstract = "Recently, power efficiency has become the most important parameter of many real circuits. At the same time, a wide range of applications capable of tolerating imperfections has spread out especially in multimedia. Approximate computing, an emerging paradigm, takes advantage of relaxed functional requirements to make computer systems more efficient in terms of energy consumption, speed or complexity. As a result, a variety of trade-offs between error and efficiency can be found. In this paper, a design method based on a multi-objective evolutionary algorithm is proposed. For a given circuit, the method is able to produce a set of Pareto optimal solutions in terms of the error, power consumption and delay. The proposed design method uses Cartesian Genetic Programming for the circuit representation and a modified NSGA-II algorithm for design space exploration. The method is used to design Pareto optimal approximate versions of arithmetic circuits such as multipliers and adders.", notes = "Included in \cite{Hrbacek:thesis} Also known as \cite{7483885}", } @PhdThesis{Hrbacek:thesis, author = "Radek Hrbacek", title = "Automated Multi-Objective Parallel Evolutionary Circuit Design and Approximation", school = "Department of Computer Systems, Faculty of Information Tech-nology, Brno University of Technology", year = "2017", address = "Brno, Czech Republic", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, EHW, Approximate Computing, Approximate Circuits, Digital Circuits, Evolutionary Algorithms,Evolutionary Design, Multi-Objective Optimization", URL = "https://theses.cz/id/vj3yes/781.pdf", size = "112 pages", abstract = "Recently, energy efficiency has become one of the most important properties of computing platforms, especially because of limited power supply capacity of battery-power devices and very high consumption of growing data centers and cloud infrastructure. At the same time,in an increasing number of applications users are able to tolerate inaccurate or incorrect computations to a certain extent due to the imperfections of human senses, statistical nature of data processing, noisy input data etc. Approximate computing, an emerging paradigm in computer engineering, takes advantage of relaxed functionality requirements to make computer systems more efficient in terms of energy consumption, computing performance or complexity. Error resilient applications can achieve significant savings while still serving their purpose with the same or a slightly degraded quality. Even though new design methods for approximate computing are emerging, there is alack of methods for automated approximate HW/SW design offering a rich set of compromise solutions. Conventional methods often produce solutions that are far from an optimum. Evolutionary algorithms have been shown to bring innovative solutions to complex design and optimization problems. However, these methods suffer from several problems,such as the scalability or a high number of fitness evaluations needed to evolve competitive results. Finally, existing methods are usually single-objective whilst multi-objective approach is more suitable in the case of approximate computing. In this thesis, a new automated multi-objective parallel evolutionary algorithm for circuit design and approximation is proposed. The method is based on Cartesian Genetic Programming. In order to improve the scalability of the algorithm, a brand new highly parallel implementation was proposed. The principles of the NSGA-II algorithm were used to provide the multiobjective design and approximation capability. The performance of the implementation was evaluated in multiple different applications,in particular (approximate) combinational arithmetic circuits design, bent Boolean functions discovery and approximate logic circuits for TMR schema. In these cases, important improvements with respect to the state of the art were obtained.", notes = "Comprises \cite{Hrbacek:2014:GECCO} \cite{Hrbacek:2014:PPSN} \cite{DBLP:conf/memics/Hrbacek14} \cite{Hrbacek:2015:GECCO} \cite{Hrbacek:2016:DTIS} \cite{Sanchez-Clemente:2016:ieeeTReliability} \cite{7926993}", } @InProceedings{hrbek:2020:SEPIS, author = "Vaclav Hrbek and Jan Merta", title = "Searching the Hyper-heuristic for the Traveling Salesman Problem with Time Windows by Genetic Programming", booktitle = "Software Engineering Perspectives in Intelligent Systems", year = "2020", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-030-63322-6_81", DOI = "doi:10.1007/978-3-030-63322-6_81", } @Book{Hrnjica:book, author = "Bahrudin Hrnjica and Ali {Danandeh Mehr}", title = "Optimized Genetic Programming Applications: Emerging Research and Opportunities", publisher = "IGI Global", year = "2018", keywords = "genetic algorithms, genetic programming, Gene Expression Programming", isbn13 = "9781522560050", URL = "https://www.igi-global.com/book/optimized-genetic-programming-applications/195404", DOI = "doi:10.4018/978-1-5225-6005-0", size = "310 pages", abstract = "Chapter 1 Fundamentals of Genetic Programming (pages 1-47) Chapter 2 Genetic Programming as Supervised Machine Learning Algorithm (pages 48-101) Chapter 3 Different Approaches in Genetic Programming (pages 102-130) Chapter 4 Computer Implementation of Genetic Programming (pages 132-182) tree-based genetic programming in C# programming language Chapter 5 GPdotNET Open Source Software for Running Genetic Programming (pages 183-242) Chapter 6 Genetic Programming Applications in Solving Engineering Problems (pages 243-279)", } @InProceedings{Hrvatic:2024:EA, author = "Josip Hrvatic and Marko Durasevic and Francisco Javier {Gil Gala} and Domagoj Jakobovic", title = "Can Mutations Replace Local Search? Studying the Effect of Repeated Genetic Programming Operators in the Unrelated Machines Environment", booktitle = "Artificial Evolution, EA-2024", year = "2024", editor = "Pierrick Legrand", address = "Bordeaux, France", month = oct # " 29-31", keywords = "genetic algorithms, genetic programming: Poster", notes = "EA2024", } @InProceedings{Hrytsyshyn:2007:CADSM, author = "Yarema Hrytsyshyn and Rostyslav Kryvyy and Sergiy Tkatchenko", title = "Genetic Programming For Solving Cutting Problem", booktitle = "9th International Conference on the Experience of Designing and Applications or CAD Systems in Microelectronics, CADSM '07", year = "2007", pages = "280--282", address = "Polyana, Ukraine", month = "20-24 " # feb, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, CAD system, arbitrary shape platforms, automated arbitrary shape object arrangement, material cutting task, optimal cutting problem, CAD/CAM, cutting", DOI = "doi:10.1109/CADSM.2007.4297550", size = "3 pages", abstract = "This paper described the functioning of genetic algorithm for the automated arranging the arbitrary shape objects on the arbitrary shape platforms. The set of criteria for determination the sequence of selecting templates and platforms for arranging and also a set of criteria for selecting the optimum arranging of single template are suggested. The genetic algorithm for the selecting criteria manipulation and choice of necessary decisions is developed.", notes = "Yarema Hrytsyshyn a CAD/CAM Department, Lviv Polytechnic National University, 12, S. Bandery Str., Lviv, 79013, UKRAINE, E-mail: hrytsyshyn@gmail.com Rostyslav Kryvyy a CAD/CAM Department, Lviv Polytechnic National University, 12, S. Bandery Str., Lviv, 79013, UKRAINE, E-mail: rostyslav.kryvyy@gmail.com Sergiy Tkatchenko a CAD/CAM Department, Lviv Polytechnic National University, 12, S. Bandery Str., Lviv, 79013, UKRAINE8) Also known as \cite{4297550}", } @Article{Hsu:2011:ESA, author = "Chih-Ming Hsu", title = "A hybrid procedure for stock price prediction by integrating self-organizing map and genetic programming", journal = "Expert Systems with Applications", year = "2011", volume = "38", number = "11", pages = "14026--14036", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2011.04.210", URL = "http://www.sciencedirect.com/science/article/B6V03-52T13T7-7/2/c2626c201c0da6cbc20628185936eaf3", keywords = "genetic algorithms, genetic programming, Stock price prediction, Self-organising map", size = "11 pages", abstract = "Stock price prediction is a very important financial topic, and is considered a challenging task and worthy of the considerable attention received from both researchers and practitioners. Stock price series have properties of high volatility, complexity, dynamics and turbulence, thus the implicit relationship between the stock price and predictors is quite dynamic. Hence, it is difficult to tackle the stock price prediction problems effectively by using only single soft computing technique. This study hybridises a self-organizing map (SOM) neural network and genetic programming (GP) to develop an integrated procedure, namely, the SOM-GP procedure, in order to resolve problems inherent in stock price predictions. The SOM neural network is used to divide the sample data into several clusters, in such a manner that the objects within each cluster possess similar properties to each other, but differ from the objects in other clusters. The GP technique is applied to construct a mathematical prediction model that describes the functional relationship between technical indicators and the closing price of each cluster formed in the SOM neural network. The feasibility and effectiveness of the proposed hybrid SOM-GP prediction procedure are demonstrated through experiments aimed at predicting the finance and insurance sub-index of TAIEX (Taiwan stock exchange capitalisation weighted stock index). Experimental results show that the proposed SOM-GP prediction procedure can be considered a feasible and effective tool for stock price predictions, as based on the overall prediction performance indices. Furthermore, it is found that the frequent and alternating rise and fall, as well as the range of daily closing prices during the period, significantly increase the difficulties of predicting.", } @Article{journals/ijsysc/Hsu12, author = "Chih-Ming Hsu", title = "Applying genetic programming and ant colony optimisation to improve the geometric design of a reflector", journal = "International Journal of Systems Science", year = "2012", number = "5", volume = "43", pages = "972--986", month = may, keywords = "genetic algorithms, genetic programming, light-emitting diode, reflector, ant colony optimisation, multi-response parameter design", ISSN = "0020-7721", DOI = "doi:10.1080/00207721.2010.547627", size = "15 pages", abstract = "The lighting performance of an LED (light-emitting diode) flash is significantly influenced by the geometric form of a reflector. Previously, design engineers have usually determined the geometric design of a reflector according to the principles of optics and their own experience. Some real reflectors have then been created to verify the feasibility and performance of a certain geometric design. This, however, is a costly and time-consuming procedure. Furthermore, the geometric design of a reflector cannot be proved to be actually optimal. This study proposes a systematic approach based on genetic programming (GP) and ant colony optimisation (ACO), called the GP-ACO procedure, to improve the geometric design of a reflector. A case study is used to demonstrate the feasibility and effectiveness of the proposed optimisation procedure. The results show that all the crucial quality characteristics of an LED flash fulfil the required specifications; thus, the optimal geometric parameter settings of the reflector obtained can be directly applied to mass production. Consequently, the proposed GP-ACO procedure can be considered an effective method for resolving general multi-response parameter design problems", notes = "Department of Business Administration, Minghsin University of Science and Technology, 1 Hsin Hsin Road, Hsin Feng, Hsin Chu, 30401 Taiwan", bibdate = "2012-03-05", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ijsysc/ijsysc43.html#Hsu12", } @Article{Hsu20122933, author = "Chih-Ming Hsu", title = "Improving the lighting performance of a 3535 packaged hi-power {LED} using genetic programming, quality loss functions and particle swarm optimization", journal = "Applied Soft Computing", year = "2012", volume = "12", number = "9", pages = "2933--2947", keywords = "genetic algorithms, genetic programming, Light-emitting diode, Lighting performance, Taguchi quality loss functions, Particle swarm optimization, PSO, Multi-response parameter design", ISSN = "1568-4946", URL = "http://www.sciencedirect.com/science/article/pii/S1568494612002165", DOI = "doi:10.1016/j.asoc.2012.04.023", size = "15 pages", abstract = "The lighting performance of a 3535 packaged hi-power LED (light-emitting diode) is mainly influenced by its geometric design and the refractive properties of its materials. In the past, engineers often determined the settings of the geometric parameters and selected the refractive properties of the materials through a trial-and-error process based on the principles of optics and their own experience. This procedure was costly and time-consuming, and its use did not ensure that the settings of the design parameters were optimal. Therefore, this study proposed a hybrid approach based on genetic programming (GP), Taguchi quality loss functions, and particle swarm optimisation (PSO) to solve the multi-response parameter design problems. The feasibility and effectiveness of the proposed approach was demonstrated by a case study on improving the lighting performance of an LED. The confirmation results showed that all of the key quality characteristics of an LED fulfil the required specifications, and the comparison found that the proposed hybrid approach outperforms the traditional Taguchi method in solving this multi-response parameter design problem. The proposed hybrid approach can be extended to solve parameter design problems with multiple responses in various application fields.", } @Article{journals/ijsysc/Hsu14, title = "An integrated portfolio optimisation procedure based on data envelopment analysis, artificial bee colony algorithm and genetic programming", author = "Chih-Ming Hsu", journal = "Int. J. Systems Science", year = "2014", number = "12", volume = "45", pages = "2645--2664", keywords = "genetic algorithms, genetic programming", bibdate = "2014-08-22", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ijsysc/ijsysc45.html#Hsu14", URL = "http://dx.doi.org/10.1080/00207721.2013.775388", } @InProceedings{Hsu:2015:IMECS, author = "Chih-Ming Hsu and Ying-Chi Fu and Yu-Chun Liu and Chun-Yi Peng", title = "Forecasting the Prices of TAIEX Options by Using Genetic Programming and Support Vector Regression", booktitle = "Proceedings of the International MultiConference of Engineers and Computer Scientists, IMECS 2015", year = "2015", editor = "S. I. Ao and Oscar Castillo and Craig Douglas and David Dagan Feng and Jeong-A Lee", volume = "1", pages = "57--62", address = "Hong Kong", month = "18-20 " # mar, publisher = "International Association of Engineers", keywords = "genetic algorithms, genetic programming, options, support vector regression, SVM, Black-Scholes model", volume = "2215", isbn13 = "978-988-19253-2-9", ISSN = "2078-0958; 2078-0966", bibsource = "OAI-PMH server at doaj.org", issue = "1", language = "English", oai = "oai:doaj.org/article:0d2a7c2d6b6843f498c74cdee8ea0d64", URL = "http://www.iaeng.org/publication/IMECS2015/IMECS2015_pp57-62.pdf", size = "6 pages", abstract = "The Black-Scholes (B-S) model is the traditional tool for giving a theoretical estimate of the price of European-style options. However, the basic assumptions on the assets and market made in the B-S model are ideal. Furthermore, a lot of factors which might affect the prices of options have not been considered in the B-S model. In this study, the genetic programming (GP) and support vector regression (SVR) are applied to forecast the prices of stock options by using the six basic factors in the B-S model and the other factors, such as the opening and closing prices, highest and lowest prices, trading volume, open interest etc., as the predictors. The performance of GP and SVR forecasting models are also compared to the B-S pricing model. The feasibility and effectiveness of the proposed approach are demonstrated by forecasting the closing prices of Taiwan Stock Exchange Capitalization Weighted Stock Index Options (TAIEX Options) from April 1, 2010 to March 29, 2013.", notes = "http://www.iaeng.org/publication/IMECS2015/ Department of Business Administration, Minghsin University of Science and Technology, Hsinfong, Hsinchu 304, Taiwan", } @InProceedings{conf/icnc/HsuCKWC09, title = "Estimating Strength of Concrete Using a Grammatical Evolution", author = "Hsun-Hsin Hsu and Li Chen and Chang-Huan Kou and Tai-Sheng Wang and Sing-Han Chen", booktitle = "Fifth International Conference on Natural Computation, 2009. ICNC '09", year = "2009", editor = "Haiying Wang and Kay Soon Low and Kexin Wei and Junqing Sun", month = "14-16 " # aug, address = "Tianjian, China", publisher = "IEEE Computer Society", isbn13 = "978-0-7695-3736-8", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", bibdate = "2010-01-21", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icnc/icnc2009-3.html#RaoWY09", pages = "134--138", DOI = "doi:10.1109/ICNC.2009.492", abstract = "The main purpose of this paper is to propose an incorporating a grammatical evolution (GE) into the genetic algorithm (GA), called GEGA, and apply it to estimate the compressive strength of high-performance concrete (HPC). The GE, an evolutionary programming type system, automatically discovers complex relationships between significant factors and the strength of HPC in a more transparent way to enhance our understanding of the mechanisms. A GA was used afterward with GE to optimize the appropriate function type and associated coefficients using over 1,000 examples for which experimental data were available. The results show that this novel model, GEGA, can obtain a highly nonlinear mathematical equation which outperforms than the traditional multiple regression analysis (RA) with lower estimating errors for predicting the compressive strength of HPC.", } @InProceedings{hsu:2024:CEC, author = "Tzu-Hao Hsu and Chi-Hsien Chang and Tian-Li Yu", title = "Program Synthesis on Single-Layer Loop Behavior in Pure Functional Programming", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Synthesizers, Redundancy, Search problems, Feature extraction, Market research, Maintenance, Program Synthesis", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10612128", abstract = "Program synthesis (PS) is a field devoted to auto-matically generating computer programs from high-level specifications, and genetic programming (GP) is one commonly-used way to achieve PS. PushGP, operating on a stack-based language, is considered as a state-of-the-art program synthesiser among GPs, while another research trend foucus on the grammar-based languages due to the readability and the ease of maintenance. In this paper, we propose the repetitive structure genetic programming (RSGP), a new grammar-based program synthesiser under the pure functional programming paradigm. RSGP defines a recursive function to simulate the single-layer loop behaviour and leverages the minimum redundancy maximum relevance $(\text{mRMR})$ feature selection with the Pearson correlation coefficient (PCC) to select the capable and diverse programs for the next generation. The experiment results show that RSGP outperforms PushGP, CBGP, and HOTGP in terms of the number of successful programs on CountOdds and LastIndexofZero from PSBl, Luhn from PSB2, and 3 out of 4 designed problems. Additionally, the ablation study indicates that using $\text{mRMR}$ with PCC does encourage proper problem decomposition with the trade-off of diminishing the search ability within a similar neighbourhood. RSGP uses an adaptation mechanism to balance the trade-off to automatically fit the needs of different problems.", notes = "also known as \cite{10612128} WCCI 2024", } @InProceedings{hsu:1999:GAASLDM, author = "William H. Hsu and William M. Pottenger and Michael Welge and Jie Wu and Ting-Hao Yang", title = "Genetic Algorithms for Attribute Synthesis in Large-Scale Data Mining", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1783", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "real world applications, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-754.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-754.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99) See also freitas:1999:AAGR Freitas {"}Data Mining with Evolutionary Algorithms{"} AAAI tech report WS-99-06", } @InProceedings{hsu:2001:waptmaoGP, author = "William H. Hsu and Steven M. Gustafson", title = "Wrappers for Automatic Parameter Tuning in Multi-Agent Optimization by Genetic Programming", booktitle = "IJCAI-2001 Workshop on Wrappers for Performance Enhancement in Knowledge Discovery in Databases (KDD)", year = "2001", address = "Seattle, Washington, USA", month = "4 " # aug, keywords = "genetic algorithms, genetic programming, robotic soccer", abstract = "We present an adaptation of the standard genetic program (GP) to hierarchically decomposable, multi-agent learning problems. To break down a problem that requires cooperation of multiple agents, we use the team objective function to derive a simpler, intermediate objective function for pairs of cooperating agents. We apply GP to optimize first for the intermediate, then for the team objective function, using the final population from the earlier GP as the initial seed population for the next. This layered learning approach facilitates the discovery of primitive behaviors that can be reused and adapted towards complex objectives based on a shared team goal. We use this method to evolve agents to play a subproblem of robotic soccer (keep-away soccer). Finally, we show how layered learning GP evolves better agents than standard GP, including GP with automatically defined functions, and how the problem decomposition results in a significant learning-speed increase.", notes = "broken sep 2018 http://www.kddresearch.org/KDD/Workshops/IJCAI-2001/ Paper from author 19 Jul 2001. Also available as GECCO'2001 late breaking paper \cite{hsu:2001:gpllmt}. Coaching, seeding, LLGP, keep-away soccer (minimize number of turnovers), MAS, RoboCup, passing agents and keep-away soccer agents, ADF. Simple GP and ADFGP trained with one shot fitness function, ie not layered. Popsize 2000 'yeilded good results'. Luke's ECJ. SoccerServer, TeamBots. See also \cite{gustafson:mastersthesis}", } @InProceedings{hsu:2001:gpllmt, author = "William H. Hsu and Steven M. Gustafson", title = "Genetic Programming for Layered Learning of Multi-Agent Tasks", booktitle = "2001 Genetic and Evolutionary Computation Conference Late Breaking Papers", year = "2001", editor = "Erik D. Goodman", pages = "176--182", address = "San Francisco, California, USA", month = "9-11 " # jul, keywords = "genetic algorithms, genetic programming, soccer, RoboCup", URL = "http://www.cs.nott.ac.uk/~smg/research/publications/gecco-2001.ps", URL = "http://www.cs.nott.ac.uk/~smg/research/publications/gecco-2001.pdf", size = "7 pages", abstract = "We present an adaptation of the standard genetic program (GP) to hierarchically decomposable, multi-agent learning problems. To break down a problem that requires cooperation of multiple agents, we use the team objective function to derive a simpler, intermediate objective function for pairs of cooperating agents. We apply GP to optimize first for the intermediate, then for the team objective function, using the final population from the earlier GP as the initial seed population for the next. This layered learning approach facilitates the discovery of primitive behaviours that can be reused and adapted towards complex objectives based on a shared team goal. We use this method to evolve agents to play a subproblem of robotic soccer (keep-away soccer). Finally, we show how layered learning GP evolves better agents than standard GP, including GP with automatically defined functions, and how the problem decomposition results in a significant learning-speed increase.", notes = "GECCO-2001LB. Luke's ECJ, teambots. See also \cite{gustafson:mastersthesis}", } @InProceedings{hsu3:2002:gecco, author = "William H. Hsu and Steven M. Gustafson", title = "Genetic Programming And Multi-agent Layered Learning By Reinforcements", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "764--771", address = "New York", publisher_address = "San Francisco, CA 94104, USA", URL = "http://www.cs.nott.ac.uk/~smg/research/publications/gecco-llgp-2002.pdf", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "genetic algorithms, genetic programming, Layered learning GP, LLGP, MAS, robot football, soccer", ISBN = "1-55860-878-8", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/GP004.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf", size = "8 pages", abstract = "We present an adaptation of the standard genetic program (GP) to hierarchically decomposable, multi-agent learning problems. To break down a problem that requires cooperation of multiple agents, we use the team objective function to derive a simpler, intermediate objective function for pairs of cooperating agents. We apply GP to optimise first for the intermediate, then for the team objective function, using the final population from the earlier GP as the initial seed population for the next. This layered learning approach facilitates the discovery of primitive behaviours that can be reused and adapted towards complex objectives based on a shared team goal. We use this method to evolve agents to play a subproblem of robotic soccer (keep-away soccer). Finally, we show how layered learning GP evolves better agents than standard GP, including GP with automatically defined functions, and how the problem decomposition results in a significant learning-speed increase.", notes = "GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002) Nominated for best at GECCO award", } @InProceedings{hsu:2004:lbp, author = "William H. Hsu and Scott J. Harmon and Edwin Rodriguez and Christopher Zhong", title = "Empirical Comparison of Incremental Reuse Strategies in Genetic Programming for Keep-Away Soccer", booktitle = "Late Breaking Papers at the 2004 Genetic and Evolutionary Computation Conference", year = "2004", editor = "Maarten Keijzer", address = "Seattle, Washington, USA", month = "26 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://gpbib.cs.ucl.ac.uk/gecco2004/LBP010.pdf", abstract = "Easy missions approaches to machine learning seek to synthesise solutions for complex tasks from those for simpler ones. In genetic programming, this has been achieved by identifying goals and fitness functions for subproblems of the overall problem. Solutions evolved for these subproblems are then reused to speed up learning, either as automatically defined functions (ADFs) or by seeding a new GP population. Previous positive results using both approaches for learning in multi-agent systems (MAS) showed that incremental reuse using easy missions achieves comparable or better overall fitness than monolithic simple GP. A key unresolved issue dealt with hybrid reuse using ADF plus easy missions. Results in the keep-away soccer domain (a test bed for MAS learning) were also inconclusive on whether compactness inducing reuse helped or hurt overall agent performance. In this paper, we compare monolithic (simple GP and GP with ADFs) and easy missions reuse to two types of GP learning systems with incremental reuse: GP/ADF hybrids with easy missions and single-mission incremental ADFs. As hypothesised, pure easy missions reuse achieves results competitive with the best hybrid approaches in this domain. We interpret this finding and suggest a theoretical approach to characterising incremental reuse and code growth.", notes = "Part of \cite{keijzer:2004:GECCO:lbp}", } @Article{hu:2016:NH, author = "HaiBo Hu", title = "Rainstorm flash flood risk assessment using genetic programming: a case study of risk zoning in Beijing", journal = "Natural Hazards", year = "2016", volume = "83", number = "1", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s11069-016-2325-x", DOI = "doi:10.1007/s11069-016-2325-x", } @InProceedings{hu:2002:thfcmfpea, author = "Jianjun Hu and Erik D. Goodman", title = "The Hierarchical Fair Competition (HFC) Model for Parallel Evolutionary Algorithms", booktitle = "Proceedings of the 2002 Congress on Evolutionary Computation CEC2002", editor = "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and Mark Shackleton", pages = "49--54", year = "2002", publisher = "IEEE Press", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", organisation = "IEEE Neural Network Council (NNC), Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", ISBN = "0-7803-7278-6", URL = "http://garage.cse.msu.edu/papers/GARAGe02-05-01.pdf", DOI = "doi:10.1109/CEC.2002.1006208", month = "12-17 " # may, notes = "CEC 2002 - A joint meeting of the IEEE, the Evolutionary Programming Society, and the IEE. Held in connection with the World Congress on Computational Intelligence (WCCI 2002)", keywords = "genetic algorithms, genetic programming, HFC model, biology, evolutionary computation, fitness-based admission threshold, hierarchical fair competition model, higher-fitness subpopulations, low-fitness subpopulations, parallel evolutionary algorithms, premature convergence, society, stratified competition, biology, convergence, evolutionary computation, parallel algorithms", abstract = "The HFC model for evolutionary computation is inspired by the stratified competition often seen in society and biology. Subpopulations are stratified by fitness. Individuals move from low-fitness subpopulations to higher-fitness subpopulations if and only if they exceed the fitness-based admission threshold of the receiving subpopulation, but not of a higher one. HFC's balanced exploration and exploitation, while avoiding premature convergence, is shown on a genetic programming example.", } @InProceedings{hu2:2002:gecco, author = "Jianjun Hu and Kisung Seo and Shaobo Li and Zhun Fan and Ronald C. Rosenberg and Erik D. Goodman", title = "Structure Fitness Sharing ({SFS}) For Evolutionary Design By Genetic Programming", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "780--787", address = "New York", publisher_address = "San Francisco, CA 94104, USA", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "genetic algorithms, genetic programming, evolutionary design, fitness sharing, mechatronic system, premature convergence, topology and parameter search", ISBN = "1-55860-878-8", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/GP186.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf", notes = "GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)", } @InProceedings{hu:2002:gecco, author = "Jianjun Hu and Erik D. Goodman and Kisung Seo and Min Pei", title = "Adaptive Hierarchical Fair Competition ({AHFC}) Model For Parallel Evolutionary Algorithms", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "772--779", address = "New York", publisher_address = "San Francisco, CA 94104, USA", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "genetic algorithms, genetic programming, adaptive evolutionary algorithm, fair competition principle, hierarchical topology, parallel evolutionary algorithms, premature convergence", ISBN = "1-55860-878-8", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/GP195.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf", size = "8 pages", abstract = "The HFC model for parallel evolutionary computation is inspired by the stratified competition often seen in society and biology. Subpopulations are stratified by fitness. Individuals move from low-fitness to higher-fitness subpopulations if and only if they exceed the fitness-based admission threshold of the receiving subpopulation, but not of a higher one. The HFC model implements several critical features of a competent parallel evolutionary computation model, simultaneously and naturally, allowing rapid exploitation while impeding premature convergence. The AHFC model is an adaptive version of HFC, extending it by allowing the admission thresholds of fitness levels to be determined dynamically by the evolution process itself. The effectiveness of the Adaptive HFC model is compared with the HFC model on a genetic programming-based evolutionary synthesis example.", notes = "GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)", } @InProceedings{Jianjun-Hu:2003:AAAI, author = "Jianjun Hu and Erik D. Goodman and Kisung Seo and Zhun Fan and Ronald C. Rosenberg", title = "{HFC:} A Continuing {EA} Framework for Scalable Evolutionary Synthesis", booktitle = "Proceedings of the 2003 {AAAI} Spring Symposium - Computational Synthesis: From Basic Building Blocks to High Level Functionality", year = "2003", pages = "106--113", address = "Stanford, California", publisher_address = "445 Burgess Drive. Menlo park, CA, 94025, USA", publisher = "AAAI press", month = "24" # Mar, organisation = "AAAI", email = "hujianju@msu.edu, goodman@egr.msu.edu", keywords = "genetic algorithms, genetic programming, scalability, sustainability, HFC", URL = "http://www-rcf.usc.edu/~jianjunh/paper/stanford_hfc.pdf", abstract = "The scalability of evolutionary synthesis is impeded by its characteristic discrete landscape with high multimodality. It is also impaired by the convergent nature of conventional EAs. A generic framework, called Hierarchical Fair Competition (HFC), is proposed for formulation of continuing evolutionary algorithms. This framework features a hierarchical organisation of individuals by different fitness levels. By maintaining repositories of intermediate-fitness individuals and ensuring a continuous supply of raw genetic material into an environment in which it can be exploited, HFC is able to transform the convergent nature of current EAs into a sustainable evolutionary search framework. It is also well suited for the special demands of scalable evolutionary synthesis. An analog circuit synthesis problem, the eigenvalue placement problem, is used as an illustrative case study.", } @InCollection{Jianjun-Hu:2003:GPTP, author = "Jianjun Hu and Erik D. Goodman and Kisung Seo", title = "Continuous Hierarchical Fair Competition Model for Sustainable Innovation in Genetic Programming", booktitle = "Genetic Programming Theory and Practice", publisher = "Kluwer", year = "2003", editor = "Rick L. Riolo and Bill Worzel", chapter = "6", pages = "81--98", keywords = "genetic algorithms, genetic programming, sustainable innovation, HFC, fair competition principle", ISBN = "1-4020-7581-2", URL = "http://www.springer.com/computer/ai/book/978-1-4020-7581-0", DOI = "doi:10.1007/978-1-4419-8983-3_6", abstract = "Lack of sustainable search capability of genetic programming has severely constrained its application to more complex problems. A new evolutionary algorithm model named the continuous hierarchical fair competition (CHFC) model is proposed to improve the capability of sustainable innovation for single population genetic programming. It is devised by extracting the fundamental principles underlying sustainable biological and societal processes originally proposed in the multi-population HFC model. The hierarchical elitism, breeding probability distribution and individual distribution control over the whole fitness range enable CHFC to achieve sustainable evolution while enjoying flexible control of an evolutionary search process. Experimental results demonstrate its capability to do robust sustainable search and avoid the aging problem typical in genetic programming.", notes = "Part of \cite{RioloWorzel:2003}", size = "pages", } @InCollection{hu:2004:GPTP, author = "Jianjun Hu and Erik Goodman", title = "Topological Synthesis of Robust Dynamic Systems by Sustainable Genetic Programming", booktitle = "Genetic Programming Theory and Practice {II}", year = "2004", editor = "Una-May O'Reilly and Tina Yu and Rick L. Riolo and Bill Worzel", chapter = "9", pages = "143--157", address = "Ann Arbor", month = "13-15 " # may, publisher = "Springer", note = "pages missing", keywords = "genetic algorithms, genetic programming, sustainable genetic programming, automated synthesis, dynamic systems, robust design, bond graphs, analog filter", ISBN = "0-387-23253-2", DOI = "doi:10.1007/0-387-23254-0_9", abstract = "Traditional robust design constitutes only one step in the detailed design stage, where parameters of a design solution are tuned to improve the robustness of the system. This chapter proposes that robust design should start from the conceptual design stage and genetic programming-based open-ended topology search can be used for automated synthesis of robust systems. Combined with a bond graph-based dynamic system synthesis methodology, an improved sustainable genetic programming technique - quick hierarchical fair competition (QHFC)- is used to evolve robust high-pass analog filters. It is shown that topological innovation by genetic programming can be used to improve the robustness of evolved design solutions with respect to both parameter perturbations and topology faults.", notes = "part of \cite{oreilly:2004:GPTP2}", } @InProceedings{hu:2004:wapcbgp, title = "Wireless Access Point Configuration by Genetic Programming", author = "Jianjun Hu and Erik Goodman", pages = "1178--1184", booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary Computation", year = "2004", publisher = "IEEE Press", month = "20-23 " # jun, address = "Portland, Oregon", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Evolutionary design \& evolvable hardware, Real-world applications, Combinatorial \& numerical optimization, STGP", URL = "http://www-rcf.usc.edu/~jianjunh/paper/cec2004_wireless.pdf", language = "en", oai = "oai:CiteSeerXPSU:10.1.1.134.6950", DOI = "doi:10.1109/CEC.2004.1330995", abstract = "Wireless access point configuration problem in wireless LAN deployment can be formulated as a non-linear optimization problem with variable number of parameters. In this paper, a strongly-typed genetic programming is applied to solve an abstract version of this problem successfully. It is argued that this problem can be used as a potential benchmark problem for evaluating techniques and investigating issues in strongly typed genetic programming, topologically open-ended synthesis by genetic programming, and simultaneous topological and parametric search", notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE. \cite{cordella:evocop05} claims to outperform this", } @InProceedings{jianjunHu:2004:ACC, author = "Jianjun Hu and Erik Goodman and Ronald Rosenberg", title = "Topological search in automated mechatronic system synthesis using bond graphs and genetic programming", booktitle = "Proceedings of American Control Conference ACC 2004", year = "2004", volume = "6", pages = "5628--5634", month = jun # " 30-" # jul # " 2", organisation = "American Control Conference", address = "Boston, MA, USA", email = "hujianju@msu.edu", keywords = "genetic algorithms, genetic programming, bond graphs, control system synthesis, eigenvalues and eigenfunctions, inverse problems, mechatronics, search problems, automated mechatronic system synthesis, bond graphs, eigenvalue placement problem, encoding, inverse problem, open ended topology search, population seeding, scalable benchmark problem", ISBN = "0-7803-8335-4", URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1384751", abstract = "We have introduced a well-defined scalable benchmark problem - the eigenvalue placement problem - to investigate scalability issues in automated topology synthesis of mechatronic systems based on bond graphs and genetic programming. This classical inverse problem shares characteristics with many other system synthesis problems, such as electric circuit and controller synthesis, in terms of epistasis and multi-modality of the search space. Critical issues of open-ended topology search by genetic programming are investigated, including encoding, population seeding, scalability and evolvability. For the eigenvalue problems, we have found there exists a correlation between structure and function that is important for efficient topology search. Standard genetic programming has been used to solve up to 20-eigen-value problems, finding the target system of bush topology out of 823,065 possibilities with only 29506 topology evaluations.", notes = "Also known as \cite{1384751}", } @PhdThesis{JianjunHu:thesis, author = "Jianjun Hu", title = "Sustainable Evolutionary Algorithms and Scalable Evolutionary Synthesis of Dynamic Systems", school = "Michigan State University", year = "2004", address = "East Lancing, Michigan, 48823, USA", month = "18 " # aug, keywords = "genetic algorithms, genetic programming, HFC", URL = "http://www-rcf.usc.edu/~jianjunh/paper/Hu_thesis_print.pdf", size = "269 pages", abstract = "This dissertation concerns the principles and techniques for scalable evolutionary computation to achieve better solutions for larger problems with more computational resources. It suggests that many of the limitations of existent evolutionary algorithms, such as premature convergence, stagnation, loss of diversity, lack of reliability and efficiency, are derived from the fundamental convergent evolution model, the oversimplified {"}survival of the fittest{"} Darwinian evolution model. Within this model, the higher the fitness the population achieves, the more the search capability is lost. This is also the case for many other conventional search techniques. The main result of this dissertation is the introduction of a novel sustainable evolution model, the Hierarchical Fair Competition (HFC) model, and corresponding five sustainable evolutionary algorithms (EA) for evolutionary search. By maintaining individuals in hierarchically organized fitness levels and keeping evolution going at all fitness levels, HFC transforms the conventional convergent evolutionary computation model into a sustainable search framework by ensuring a continuous supply and incorporation of low-level building blocks and by culturing and maintaining building blocks of intermediate levels with its assembly-line structure. By reducing the selection pressure within each fitness level while maintaining the global selection pressure to help ensure exploitation of good building blocks found, HFC provides a good solution to the explore vs. exploitation dilemma, which implies its wide applications in other search, optimization, and machine learning problems and algorithms. The second theme of this dissertation is an examination of the fundamental principles and related techniques for achieving scalable evolutionary synthesis. It first presents a survey of related research on principles for handling complexity in artificially designed and naturally evolved systems, including modularity, reuse, development, and context evolution. Limitations of current genetic programming based evolutionary synthesis paradigm are discussed and future research directions are outlined. Within this context, this dissertation investigates two critical issues in topologically open-ended evolutionary synthesis, using bond-graph-based dynamic system synthesis as benchmark problems. For the issue of balanced topology and parameter search in evolutionary synthesis, an effective technique named Structure Fitness Sharing (SFS) is proposed to maintain topology search capability. For the representation issue in evolutionary synthesis, or more specifically the function set design problem of genetic programming, two modular set approaches are proposed to investigate the relationship between representation, evolvability, and scalability.", notes = "Related research http://www.egr.msu.edu/~hujianju/HFC http://www.egr.msu.edu/~hujianju/gpbg", } @InCollection{hu:2005:GPTP, author = "Jianjun Hu and Ronald C. Rosenberg and Erik D. Goodman", title = "Domain Specificity of Genetic Programming based Automated Synthesis: a Case Study with Synthesis of Mechanical Vibration Absorbers", booktitle = "Genetic Programming Theory and Practice {III}", year = "2005", editor = "Tina Yu and Rick L. Riolo and Bill Worzel", volume = "9", series = "Genetic Programming", chapter = "18", pages = "275--290", address = "Ann Arbor", month = "12-14 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Automated synthesis, passive vibration absorber, bond graphs, mechatronic systems, domain knowledge", ISBN = "0-387-28110-X", DOI = "doi:10.1007/0-387-28111-8_18", size = "16 pages", abstract = "Genetic programming has proved its potential for automated synthesis of a variety of engineering systems such as electrical, control, and mechanical systems. Given any of these application domains, a set of generic GP functions can be developed for its synthesis. In this chapter, however, we illustrate that while a generic GP system can often be used to prove a concept, realistic or industrial automated synthesis often requires domain-specific GP configuration, especially of the GP function sets. As a case study, it is shown how the open-ended topology search capability of GP readily exploits _loopholes_ in a generic bond-graph-based GP function set and evolves high-performance but unrealistic mechanical vibration absorbers, even though the bond graphs would be readily implementable in, for example, the electrical domain. The preliminary attempt to constrain evolved topologies to only those that would be readily implementable in the mechanical domain was not sufficiently restrictive.", notes = "part of \cite{yu:2005:GPTP} Published Jan 2006 after the workshop", } @Article{hu:2005:EC, author = "Jianjun Hu and Erik Goodman and Kisung Seo and Zhun Fan and Rondal Rosenberg", title = "The Hierarchical Fair Competition Framework for Sustainable Evolutionary Algorithms", journal = "Evolutionary Computation", year = "2005", volume = "13", number = "2", pages = "241--277", month = "Summer", keywords = "genetic algorithms, genetic programming, sustainable evolutionary algorithms, building blocks, premature convergence, diversity, fair competition, hierarchical problem solving", ISSN = "1063-6560", publisher = "MIT Press", broken = "http://www.ingentaconnect.com/content/mitpress/evco/2005/00000013/00000002/art00005", DOI = "doi:10.1162/1063656054088530", size = "37 pages", abstract = "Many current Evolutionary Algorithms (EAs) suffer from a tendency to converge prematurely or stagnate without progress for complex problems. This may be due to the loss of or failure to discover certain valuable genetic material or the loss of the capability to discover new genetic material before convergence has limited the algorithm's ability to search widely. In this paper, the Hierarchical Fair Competition (HFC) model, including several variants, is proposed as a generic framework for sustainable evolutionary search by transforming the convergent nature of the current EA framework into a non-convergent search process. That is, the structure of HFC does not allow the convergence of the population to the vicinity of any set of optimal or locally optimal solutions. The sustainable search capability of HFC is achieved by ensuring a continuous supply and the incorporation of genetic material in a hierarchical manner, and by culturing and maintaining, but continually renewing, populations of individuals of intermediate fitness levels. HFC employs an assembly-line structure in which subpopulations are hierarchically organised into different fitness levels, reducing the selection pressure within each subpopulation while maintaining the global selection pressure to help ensure the exploitation of the good genetic material found. Three EAs based on the HFC principle are tested - two on the even-10-parity genetic programming benchmark problem and a real-world analog circuit synthesis problem, and another on the HIFF genetic algorithm (GA) benchmark problem. The significant gain in robustness, scalability and efficiency by HFC, with little additional computing effort, and its tolerance of small population sizes, demonstrates its effectiveness on these problems and shows promise of its potential for improving other existing EAs for difficult problems. A paradigm shift from that of most EAs is proposed: rather than trying to escape from local optima or delay convergence at a local optimum, HFC allows the emergence of new optima continually in a bottom-up manner, maintaining low local selection pressure at all fitness levels, while fostering exploitation of high-fitness individuals through promotion to higher levels.", notes = "http://mitpress.mit.edu/catalog/item/default.asp?ttype=4&tid=25", } @InProceedings{1068283, author = "Jianjun Hu and Xiwei Zhong and Erik D. Goodman", title = "Open-ended robust design of analog filters using genetic programming", booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation", year = "2005", editor = "Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and Edwin D. {de Jong} and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler", volume = "2", ISBN = "1-59593-010-8", pages = "1619--1626", address = "Washington DC, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2005/docs/p1619.pdf", DOI = "doi:10.1145/1068009.1068283", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "25-29 " # jun, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, analog filter synthesis, automated design, bond graph, design, robust design", notes = "GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM Order Number 910052", } @InCollection{hu:2007:ECdue, author = "Jianjun Hu and Shaobo Li and Erik D. Goodman", title = "Evolutionary Robust Design of Analog Filters Using Genetic Programming", booktitle = "Evolutionary Computation in Dynamic and Uncertain Environments", publisher = "Springer", year = "2007", editor = "Shengxiang Yang and Yew-Soon Ong and Yaochu Jin", volume = "51", series = "Studies in Computational Intelligence", pages = "479--496", chapter = "21", email = "hujianju@gmail.com", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-49772-1", DOI = "doi:10.1007/978-3-540-49774-5_21", abstract = "This chapter proposes a robust design approach that exploits the open ended topological synthesis capability of genetic programming (GP) to evolve robust low pass and high pass analog filters. Compared with a traditional robust design approach based on genetic algorithms (GAs), the open-ended topology search based on genetic programming and bond graph modeling (GPBG) is shown to be able to evolve more robust filters with respect to parameter perturbations than what was achieved through parameter tuning alone, for the test problems.", notes = "http://www.cse.sc.edu/~jianjunh/", } @Article{hu:2008:AIEDAM, author = "Jianjun Hu and Erik D. Goodman and Shaobo Li and Ronald Rosenberg", title = "Automated Synthesis of Mechanical Vibration Absorbers Using Genetic Programming", journal = "Artificial Intelligence for Engineering Design, Analysis and Manufacturing", year = "2008", volume = "22", number = "3", pages = "207--217", keywords = "genetic algorithms, genetic programming, Automated Design, Bond Graphs, Conceptual Design, Evolutionary Design", URL = "http://journals.cambridge.org/action/displayAbstract;jsessionid=7665C0F109E52E12771D5DFCBD27C245.tomcat1?fromPage=online&aid=1903160", DOI = "doi:10.1017/S0890060408000140", size = "11 pages", abstract = "Conceptual innovation in mechanical engineering design has been extremely challenging compared to the wide applications of automated design systems in digital circuits. This paper presents an automated methodology for open-ended synthesis of mechanical vibration shock absorbers based on genetic programming and bond graphs. It is shown that our automated design system can automatically evolve passive vibration absorber that have performance equal to or better than the standard passive vibration absorbers invented in 1911. A variety of other vibration absorbers with competitive performance are also evolved automatically using a desktop PC in less than 10 h.", notes = "AIEDAM also known as \cite{CambridgeJournals:1903160} and \cite{DBLP:journals/aiedam/HuGLR08}", } @InCollection{hu:2008:DbE, author = "Jianjun Hu and Zhun Fan and Jiachuan Wang and Shaobo Li and Kisung Seo and Xiangdong Peng and Janis Terpenny and Ronald Rosenberg and Erik Goodman", title = "GPBG: A Framework for Evolutionary Design of Multi-domain Engineering Systems Using Genetic Programming and Bond Graphs", booktitle = "Design by Evolution", publisher = "Springer", year = "2008", editor = "Philip F. Hingston and Luigi C. Barone and Zbigniew Michalewicz", series = "Natural Computing Series", chapter = "14", pages = "319--345", address = "Berlin, Heidelberg", email = "hujianju@gmail.com", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-74109-1", DOI = "doi:10.1007/978-3-540-74111-4_18", abstract = "Current engineering design is a multi-step process proceeding from conceptual design to detailed design and to evaluation and testing. It is estimated that 60percent of design decisions and most innovation occur in the conceptual design stage, which may include conceptual design of function, operating principles, layout, shape, and structure. However, few computational tools are available to help designers to explore the design space and stimulate the product innovation process. As a result, product innovation is strongly constrained by the designer's ingenuity and experience, and a systematic approach to product innovation is strongly needed.", } @InProceedings{Hu:2010:ICCET, author = "Jiaojiao Hu and Mei Xie", title = "Fingerprint classification based on genetic programming", booktitle = "2nd International Conference on Computer Engineering and Technology (ICCET), 2010", year = "2010", month = "16-18 " # apr, volume = "6", pages = "V6--193--V6--196", abstract = "In this paper, we present a novel algorithm for fingerprint classification. This algorithm classifies a fingerprint image into one of the five classes: Arch, Left loop, Right loop, Whorl, and Tented arch. Initially, preprocessing of fingerprint images is carried out to enhance the image. Then we use genetic programming (GP) to generate new features from the original dataset without prior knowledge. Finally we can classify the fingerprint through a combination of BP network and SVM classifiers, which can not only supplement their advantages, but also improve the computation efficiency. We experiment this algorithm on database from FVC2004. For the five-class problem, a classification accuracy of 93.6percent without any reject, and classification accuracy of 96.2percent with a 15percent reject rate. For the four-class problem (arch and tented arch combined into one class), classification error can be reduced to 3.6percent with only 7.2percent reject rate.", keywords = "genetic algorithms, genetic programming, BP network, FVC2004, SVM classifier, fingerprint classification, four-class problem, image classification, backpropagation, fingerprint identification, image classification, neural nets, support vector machines", DOI = "doi:10.1109/ICCET.2010.5486315", notes = "School of Electronic Engineering, University of Electronic Science and Technology of China Chengdu, China. Also known as \cite{5486315}", } @InProceedings{Hu:2015:CCDC, author = "Qin Hu and Aisong Qin and Qinghua Zhang and Guoxi Sun and Longqiu Shao", booktitle = "The 27th Chinese Control and Decision Conference (2015 CCDC)", title = "Application of an information fusion method to compound fault diagnosis of rotating machinery", year = "2015", pages = "3859--3864", abstract = "Aiming at how to use the multiple fault features information synthetically to improve accuracy of compound fault diagnosis, an information fusion method based on the weighted evidence theory was proposed to effectively diagnose compound faults of rotating machinery. Firstly multiple fault features were extracted by the genetic programming. Each fault feature was separately used to act as evidence and the initial diagnosis accuracy was regarded as the weight coefficient of the evidence. Then through the negative selection algorithm, the diagnosis ability of the local diagnosis was advanced and an impersonal means of obtaining basic probability assignment was given. Finally the fusion result was obtained by using the weighted evidence theory into the decision-making information fusion for the preliminary result. By comparing the diagnosis results with other artificial intelligence algorithm, experiment result indicates that using multiple weighted evidences fusion can improve the diagnostic accuracy of compound fault.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CCDC.2015.7162598", ISSN = "1948-9439", month = may, notes = "Also known as \cite{7162598}", } @InProceedings{conf/smc/HuG19, author = "Junfei Hu and Wenxuan Guo", title = "Flexibility Analysis in Waste-to-Energy Systems based on Decision Rules and Gene Expression Programming", publisher = "IEEE", year = "2019", pages = "988--993", booktitle = "2019 {IEEE} International Conference on Systems, Man and Cybernetics, SMC", address = "Bari, Italy", month = oct # " 6-9", keywords = "genetic algorithms, genetic programming, gene expression programming", isbn13 = "978-1-7281-4569-3", bibdate = "2019-12-06", DOI = "doi:10.1109/SMC.2019.8914659", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/smc/smc2019.html#HuG19", } @Article{journals/cor/HuGP20, author = "Junfei Hu and Peng Guo and Kim-Leng Poh", title = "Generating decision rules for flexible capacity expansion problem through gene expression programming", journal = "Computers \& Operations Research", year = "2020", volume = "122", pages = "105003", month = oct, keywords = "genetic algorithms, genetic programming, gene expression programming", bibdate = "2020-07-17", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/cor/cor122.html#HuGP20", DOI = "doi:10.1016/j.cor.2020.105003", } @Article{HU201890, author = "Nan Hu and Jinghui Zhong and Joey Tianyi Zhou and Suiping Zhou and Wentong Cai and Christopher Monterola", title = "Guide them through: An automatic crowd control framework using multi-objective genetic programming", journal = "Applied Soft Computing", year = "2018", volume = "66", pages = "90--103", month = may, keywords = "genetic algorithms, genetic programming, Crowd modelling and simulation, Crowd control, Multi-objective optimisation", ISSN = "1568-4946", URL = "http://eprints.mdx.ac.uk/23685/", URL = "http://www.sciencedirect.com/science/article/pii/S1568494618300437", DOI = "doi:10.1016/j.asoc.2018.01.037", size = "14 pages", abstract = "We propose an automatic crowd control framework based on multi-objective optimisation of strategy space using genetic programming. In particular, based on the sensed local crowd densities at different segments, our framework is capable of generating control strategies that guide the individuals on when and where to slow down for optimal overall crowd flow in real-time, quantitatively measured by multiple objectives such as shorter travel time and less congestion along the path. The resulting Pareto-front allows selection of resilient and efficient crowd control strategies in different situations. We first chose a benchmark scenario as used in [1] to test the proposed method. Results show that our method is capable of finding control strategies that are not only quantitatively measured better, but also well aligned with domain experts recommendations on effective crowd control such as slower is faster and asymmetric control. We further applied the proposed framework in actual event planning with approximately 400 participants navigating through a multi-story building. In comparison with the baseline crowd models that do no employ control strategies or just use some hard-coded rules, the proposed framework achieves a shorter travel time and a significantly lower (20percent) congestion along critical segments of the path.", } @TechReport{MUN-CS-2008-04, author = "Ting Hu and Wolfgang Banzhaf", title = "Evolvability and Acceleration in Evolutionary Computation", institution = "Department of Computer Science, Memorial University of Newfoundland", year = "2008", number = "2008-04", address = "St. John's, NL, Canada A1B 3X5", month = oct, keywords = "genetic algorithms, genetic programming", URL = "http://www.mun.ca/computerscience/research/MUN-CS-2008-04.pdf", abstract = "Biological and artificial evolutionary systems can possess varying degrees of evolvability and different rates of evolution. Such quantities can be affected by various factors. Here, we review some evolutionary mechanisms and discuss new developments in biology that may improve evolvability or accelerate evolution in artificial systems. Biological notions are discussed to the degree they correspond to notions in evolutionary computation. We hope the findings put forward here can be used to design computational models of evolution that exhibit significant gains in evolvability and evolutionary speed.", notes = "cited by \cite{Weise:2011:ieeeTEC}", size = "72 pages", } @InProceedings{Hu:2008:gecco, author = "Ting Hu and Wolfgang Banzhaf", title = "Measuring rate of evolution in genetic programming using amino acid to synonymous substitution ratio ka/ks", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "1337--1338", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1337.pdf", DOI = "doi:10.1145/1389095.1389352", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, ka/ks Ratio, Rate of evolution: Poster", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389352}", } @InProceedings{Hu:2008:PPSN, author = "Ting Hu and Wolfgang Banzhaf", title = "Nonsynonymous to Synonymous Substitution Ratio ka/ks: Measurement for Rate of Evolution in Evolutionary Computation", booktitle = "Parallel Problem Solving from Nature - PPSN X", year = "2008", editor = "Gunter Rudolph and Thomas Jansen and Simon Lucas and Carlo Poloni and Nicola Beume", volume = "5199", series = "LNCS", pages = "448--457", address = "Dortmund", month = "13-17 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-87699-5", DOI = "doi:10.1007/978-3-540-87700-4_45", size = "pages", abstract = "Measuring fitness progression using numeric quantification in an Evolutionary Computation (EC) system may not be sufficient to capture the rate of evolution precisely. In this paper, we define the rate of evolution R e in an EC system based on the rate of efficient genetic variations being accepted by the EC population. This definition is motivated by the measurement of amino acid to synonymous substitution ratio k a/k s in biology, which has been widely accepted to measure the rate of gene sequence evolution. Experimental applications to investigate the effects of four major configuration parameters on our rate of evolution measurement show that R e well reflects how evolution proceeds underneath fitness development and provides some insights into the effectiveness of EC parameters in evolution acceleration.", notes = "PPSN X", } @InProceedings{Hu:2009:eurogp, author = "Ting Hu and Wolfgang Banzhaf", title = "The Role of Population Size in Rate of Evolution in Genetic Programming", booktitle = "Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009", year = "2009", editor = "Leonardo Vanneschi and Steven Gustafson and Alberto Moraglio and Ivanoe {De Falco} and Marc Ebner", volume = "5481", series = "LNCS", pages = "85--96", address = "Tuebingen", month = apr # " 15-17", organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-01180-1", DOI = "doi:10.1007/978-3-642-01181-8_8", notes = "Part of \cite{conf/eurogp/2009} EuroGP'2009 held in conjunction with EvoCOP2009, EvoBIO2009 and EvoWorkshops2009", } @InProceedings{DBLP:conf/gecco/HuB09, author = "Ting Hu and Wolfgang Banzhaf", title = "Neutrality and variability: two sides of evolvability in linear genetic programming", booktitle = "GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation", year = "2009", editor = "Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano {van Hemert} and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique Alba", pages = "963--970", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1569901.1570033", abstract = "The notion of evolvability has been put forward to describe the {"}core mechanism{"} of natural and artificial evolution. Recently, studies have revealed the influence of the environment upon a system's evolvability. In this contribution, we study the evolvability of a system in various environmental situations. We consider neutrality and variability as two sides of evolvability. The former makes a system tolerant to mutations and provides a hidden staging ground for future phenotypic changes. The latter produces explorative variations yielding phenotypic improvements. Which of the two dominates is influenced by the environment. We adopt two tools for this study of evolvability: 1) the rate of adaptive evolution, which captures the observable adaptive variations driven by evolvability; and 2) the variability of individuals, which measures the potential of an individual to vary functionally. We apply these tools to a Linear Genetic Programming system and observe that evolvability is able to exploit its two sides in different environmental situations.", notes = "GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009). ACM Order Number 910092.", } @Article{hu:2010:jaea, author = "Ting Hu and Wolfgang Banzhaf", title = "Evolvability and Speed of Evolutionary Algorithms in Light of Recent Developments in Biology", journal = "Journal of Artificial Evolution and Applications", year = "2010", volume = "2010", pages = "Article ID 568375", note = "Review Article", keywords = "genetic algorithms, genetic programming", URL = "https://www.hindawi.com/journals/jaea/2010/568375/", DOI = "doi:10.1155/2010/568375", size = "28 pages", abstract = "Biological and artificial evolutionary systems exhibit varying degrees of evolvability and different rates of evolution. Such quantities can be affected by various factors. Here, we review some evolutionary mechanisms and discuss new developments in biology that can potentially improve evolvability or accelerate evolution in artificial systems. Biological notions are discussed to the degree they correspond to notions in Evolutionary Computation. We hope that the findings put forward here can be used to design computational models of evolution that produce significant gains in evolvability and evolutionary speed.", notes = "'Evolvability, as the capability to generate adaptation by producing fitter offspring via evolutionary operations,...'", } @PhdThesis{TingHu:thesis, author = "Ting Hu", title = "Evolvability and Rate of Evolution in Evolutionary Computation", school = "Department of Computer Science, Memorial University of Newfoundland", year = "2010", address = "ST. John's, Newfoundland, Canada", month = May, keywords = "genetic algorithms, genetic programming", URL = "http://www.mun.ca/computerscience/graduate/thesis_TingHU.pdf", size = "173 pages", abstract = "Evolvability has emerged as a research topic in both natural and computational evolution. It is a notion put forward to investigate the fundamental mechanisms that enable a system to evolve. A number of hypotheses have been proposed in modern biological research based on the examination of various mechanisms in the biosphere for their contribution to evolvability. Therefore, it is intriguing to try to transfer new discoveries from Biology to and test them in Evolutionary Computation (EC) systems, so that computational models would be improved and a better understanding of general evolutional mechanisms is achieved. Rate of evolution comes in different flavors in natural and computational evolution. Specifically, we distinguish the rate of fitness progression from that of genetic substitutions. The former is a common concept in EC since the ability to explicitly quantify the fitness of an evolutionary individual is one of the most important differences between computational systems and natural systems. Within the biological research community, the definition of rate of evolution varies, depending on the objects being examined such as gene sequences, proteins, tissues, etc. For instance, molecular biologists tend to use the rate of genetic substitutions to quantify how fast evolution proceeds at the genetic level. This concept of rate of evolution focuses on the evolutionary dynamics underlying fitness development, due to the inability to mathematically define fitness in a natural system. In EC, the rate of genetic substitutions suggests an unconventional and potentially powerful method to measure the rate of evolution by accessing lower levels of evolutionary dynamics. Central to this thesis is our new definition of rate of evolution in EC. We transfer the method of measurement of the rate of genetic substitutions from molecular biology to EC. The implementation in a Genetic Programming (GP) system shows that such measurements can indeed be performed and reflect well how evolution proceeds. Below the level of fitness development it provides observables at the genetic level of a GP population during evolution. We apply this measurement method to investigate the effects of four major configuration parameters in EC, i.e., mutation rate, crossover rate, tournament selection size, and population size, and show that some insights can be gained into the effectiveness of these parameters with respect to evolution acceleration. Further, we observe that population size plays an important role in determining the rate of evolution. We formulate a new indicator based on this rate of evolution measurement to adjust population size dynamically during evolution. Such a strategy can stabilise the rate of genetic substitutions and effectively improve the performance of a GP system over fixed-size populations. This rate of evolution measure also provides an avenue to study evolvability, since it captures how the two sides of evolvability, i.e., variability and neutrality, interact and cooperate with each other during evolution. We show that evolvability can be better understood in the light of this interplay and how this can be used to generate adaptive phenotypic variation via harnessing random genetic variation. The rate of evolution measure and the adaptive population size scheme are further transferred to a Genetic Algorithm (GA) to solve a real world application problem - the wireless network planning problem. Computer simulation of such an application proves that the adaptive population size scheme is able to improve a GA's performance against conventional fixed population size algorithms.", notes = "http://www.mun.ca/computerscience/graduate/grad_thesis.php", } @Article{hu:2010:GPEM, author = "Ting Hu and Simon Harding and Wolfgang Banzhaf", title = "Variable population size and evolution acceleration: a case study with a parallel evolutionary algorithm", journal = "Genetic Programming and Evolvable Machines", year = "2010", volume = "11", number = "2", pages = "205--225", month = jun, keywords = "genetic algorithms, genetic programming, Variable population size, Population bottleneck, Evolution acceleration, Parallel computing, GPU", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-010-9105-2", size = "21 pages", abstract = "With current developments of parallel and distributed computing, evolutionary algorithms have benefited considerably from parallelization techniques. Besides improved computation efficiency, parallelization may bring about innovation to many aspects of evolutionary algorithms. In this article, we focus on the effect of variable population size on accelerating evolution in the context of a parallel evolutionary algorithm. In nature it is observed that dramatic variations of population size have considerable impact on evolution. Interestingly, the property of variable population size here arises implicitly and naturally from the algorithm rather than through intentional design. To investigate the effect of variable population size in such a parallel algorithm, evolution dynamics, including fitness progression and population diversity variation, are analyzed. Further, this parallel algorithm is compared to a conventional fixed-population-size genetic algorithm. We observe that the dramatic changes in population size allow evolution to accelerate.", notes = "Not GP, preparation for it? 'a simulation of the APEA algorithm', compiled, CUDA, OneMax problem, Spears multi-model problem. 'Individuals are encoded as binary strings for both problems'", } @InProceedings{hu:2011:EuroGP, author = "Ting Hu and Joshua Payne and Jason Moore and Wolfgang Banzhaf", title = "Robustness, Evolvability, and Accessibility in Linear Genetic Programming", booktitle = "Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011", year = "2011", month = "27-29 " # apr, editor = "Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado", series = "LNCS", volume = "6621", publisher = "Springer Verlag", address = "Turin, Italy", pages = "13--24", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-20406-7", DOI = "doi:10.1007/978-3-642-20407-4_2", abstract = "Whether neutrality has positive or negative effects on evolutionary search is a contentious topic, with reported experimental results supporting both sides of the debate. Most existing studies use performance statistics, e.g. success rate or search efficiency, to investigate if neutrality, either embedded or artificially added, can benefit an evolutionary algorithm. Here, we argue that understanding the influence of neutrality on evolutionary optimisation requires an understanding of the interplay between robustness and evolvability at the genotypic and phenotypic scales. As a concrete example, we consider a simple linear genetic programming system that is amenable to exhaustive enumeration, and allows for the full characterisation of these properties. We adopt statistical measurements from RNA systems to quantify robustness and evolvability at both genotypic and phenotypic levels. Using an ensemble of random walks, we demonstrate that the benefit of neutrality crucially depends upon its phenotypic distribution.", notes = "Part of \cite{Silva:2011:GP} EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011", } @Article{Hu:2012:GPEM, author = "Ting Hu and Joshua Payne and Wolfgang Banzhaf and Jason Moore", title = "Evolutionary dynamics on multiple scales: a quantitative analysis of the interplay between genotype, phenotype, and fitness in linear genetic programming", journal = "Genetic Programming and Evolvable Machines", year = "2012", volume = "13", number = "3", pages = "305--337", month = sep, note = "Special issue on selected papers from the 2011 European conference on genetic programming", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Accessibility, Coreness, Evolvability, Genotype-phenotype map, Phenotype-fitness map, Networks, Neutrality, Redundancy, Robustness", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-012-9159-4", size = "33 pages", abstract = "Redundancy is a ubiquitous feature of genetic programming (GP), with many-to-one mappings commonly observed between genotype and phenotype, and between phenotype and fitness. If a representation is redundant, then neutral mutations are possible. A mutation is phenotypically-neutral if its application to a genotype does not lead to a change in phenotype. A mutation is fitness-neutral if its application to a genotype does not lead to a change in fitness. Whether such neutrality has any benefit for GP remains a contentious topic, with reported experimental results supporting both sides of the debate. Most existing studies use performance statistics, such as success rate or search efficiency, to investigate the utility of neutrality in GP. Here, we take a different tack and use a measure of robustness to quantify the neutrality associated with each genotype, phenotype, and fitness value. We argue that understanding the influence of neutrality on GP requires an understanding of the distributions of robustness at these three levels, and of the interplay between robustness, evolvability, and accessibility amongst genotypes, phenotypes, and fitness values. As a concrete example, we consider a simple linear genetic programming system that is amenable to exhaustive enumeration and allows for the full characterisation of these quantities, which we then relate to the dynamical properties of simple mutation-based evolutionary processes. Our results demonstrate that it is not only the distribution of robustness amongst phenotypes that affects evolutionary search, but also (1) the distributions of robustness at the genotypic and fitness levels and (2) the mutational biases that exist amongst genotypes, phenotypes, and fitness values. Of crucial importance is the relationship between the robustness of a genotype and its mutational bias toward other phenotypes.", notes = "EuroGP 2011 \cite{Silva:2011:GP}", affiliation = "Computational Genetics Laboratory, Dartmouth Medical School, Hanover, NH, USA", } @InProceedings{hu:2013:EuroGP, author = "Ting Hu and Wolfgang Banzhaf and Jason H. Moore", title = "Robustness and Evolvability of Recombination in Linear Genetic Programming", booktitle = "Proceedings of the 16th European Conference on Genetic Programming, EuroGP 2013", year = "2013", month = "3-5 " # apr, editor = "Krzysztof Krawiec and Alberto Moraglio and Ting Hu and A. Sima Uyar and Bin Hu", series = "LNCS", volume = "7831", publisher = "Springer Verlag", address = "Vienna, Austria", pages = "97--108", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, Robustness, Evolvability, Accessibility, Neutrality, Recombination", isbn13 = "978-3-642-37206-3", DOI = "doi:10.1007/978-3-642-37207-0_9", abstract = "The effect of neutrality on evolutionary search is known to be crucially dependent on the distribution of genotypes over phenotypes. Quantitatively characterising robustness and evolvability in genotype and phenotype spaces greatly helps to understand the influence of neutrality on Genetic Programming. Most existing robustness and evolvability studies focus on mutations with a lack of investigation of recombination operations. Here, we extend a previously proposed quantitative approach of measuring mutational robustness and evolvability in Linear GP. By considering a simple LGP system that has a compact representation and enumerable genotype and phenotype spaces, we quantitatively characterise the robustness and evolvability of recombination at the phenotypic level. In this simple yet representative LGP system, we show that recombinational properties are correlated with mutational properties. Using a population evolution experiment, we demonstrate that recombination significantly accelerates the evolutionary search process and particularly promotes robust phenotypes that innovative phenotypic explorations.", notes = "Part of \cite{Krawiec:2013:GP} EuroGP'2013 held in conjunction with EvoCOP2013, EvoBIO2013, EvoMusArt2013 and EvoApplications2013", } @InProceedings{Hu:2014:PPSN, author = "Ting Hu and Wolfgang Banzhaf and Jason Moore", title = "Population Exploration on Genotype Networks in Genetic Programming", booktitle = "13th International Conference on Parallel Problem Solving from Nature", year = "2014", editor = "Thomas Bartz-Beielstein and Juergen Branke and Bogdan Filipic and Jim Smith", publisher = "Springer", isbn13 = "978-3-319-10761-5", pages = "424--333", series = "Lecture Notes in Computer Science", address = "Ljubljana, Slovenia", month = "13-17 " # sep, volume = "8672", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1007/978-3-319-10762-2_42", abstract = "Redundant genotype-to-phenotype mappings are pervasive in evolutionary computation. Such redundancy allows populations to expand in neutral genotypic regions where mutations to a genotype do not alter the phenotypic outcome. Genotype networks have been proposed as a useful framework to characterise the distribution of neutrality among genotypes and phenotypes. In this study, we examine a simple Genetic Programming model that has a finite and compact genotype space by characterising its genotype networks. We study the topology of individual genotype networks underlying unique phenotypes, investigate the genotypic properties as vertices in genotype networks, and discuss the correlation of these network properties with robustness and evolvability. Using GP simulations of a population, we demonstrate how an evolutionary population diffuses on genotype networks.", notes = "PPSN-XIII", } @Article{Hu:2014:Alife, author = "Ting Hu and Wolfgang Banzhaf and Jason H. Moore", title = "The effects of recombination on phenotypic exploration and robustness in evolution", journal = "Artificial Life", year = "2014", month = oct, volume = "20", number = "4", pages = "457--470", note = "Ten thousandth GP entry in the genetic programming bibliography", keywords = "genetic algorithms, genetic programming, Recombination, epistasis, evolvability, genotype network, robustness", ISSN = "1064-5462", URL = "http://web.cs.mun.ca/~banzhaf/papers/ALIFE2014.pdf", DOI = "doi:10.1162/ARTL_a_00145", size = "14 pages", abstract = "Recombination is a commonly used genetic operator in artificial and computational evolutionary systems. It has been empirically shown to be essential for evolutionary processes. However, little has been done to analyse the effects of recombination on quantitative genotypic and phenotypic properties. The majority of studies only consider mutation, mainly due to the more serious consequences of recombination in reorganising entire genomes. Here we adopt methods from evolutionary biology to analyse a simple, yet representative, genetic programming method, linear genetic programming. We demonstrate that recombination has less disruptive effects on phenotype than mutation, that it accelerates novel phenotypic exploration, and that it particularly promotes robust phenotypes and evolves genotypic robustness and synergistic epistasis. Our results corroborate an explanation for the prevalence of recombination in complex living organisms, and helps elucidate a better understanding of the evolutionary mechanisms involved in the design of complex artificial evolutionary systems and intelligent algorithms.", notes = "Also known as \cite{6926028}", } @InProceedings{Hu:2016:GPTP, author = "Ting Hu and Wolfgang Banzhaf", title = "Neutrality, Robustness, and Evolvability in Genetic Programming", booktitle = "Genetic Programming Theory and Practice XIV", year = "2016", editor = "Rick Riolo and Bill Worzel and Brian Goldman and Bill Tozier", pages = "101--117", address = "Ann Arbor, USA", month = "19-21 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Linear Genetic Programming, Robustness, Evolvability, Neutrality, Redundancy, Genotype-to-phenotype mapping, Genotype network, Phenotype network", isbn13 = "978-3-319-97087-5", URL = "http://www.cs.mun.ca/~banzhaf/papers/GPTP_2016_Hu_2017.pdf", URL = "https://www.springer.com/us/book/9783319970875", DOI = "doi:10.1007/978-3-319-97088-2_7", size = "16 pages", abstract = "Redundant mapping from genotype to phenotype is common in evolutionary algorithms, especially in genetic programming (GP). Such a redundancy can lead to neutrality, where mutations to a genotype may not alter its phenotypic outcome. The effects of neutrality can be better understood by quantitatively analysing its two observed properties, i.e., robustness and evolvability. In this study, we examine a compact Linear GP algorithm, characterize its entire genotype, phenotype, and fitness networks, and quantitatively measure robustness and evolvability at the genotypic, phenotypic, and fitness levels. We investigate the relationship of robustness and evolvability at those different levels. We use an ensemble of random walks and hill climbs to study how robustness and evolvability and the structure of genotypic, phenotypic, and fitness networks influence the evolutionary search process.", notes = " Part of \cite{Tozier:2016:GPTP} to be published after the workshop", } @InProceedings{Hu:2016:GECCO, author = "Ting Hu and Wolfgang Banzhaf", title = "Quantitative Analysis of Evolvability using Vertex Centralities in Phenotype Network", booktitle = "GECCO '16: Proceedings of the 2016 Annual Conference on Genetic and Evolutionary Computation", year = "2016", editor = "Tobias Friedrich", pages = "733--740", note = "Nominated for best paper", keywords = "genetic algorithms, genetic programming", month = "20-24 " # jul, organisation = "SIGEVO", address = "Denver, USA", publisher = "ACM", publisher_address = "New York, NY, USA", isbn13 = "978-1-4503-4206-3", DOI = "doi:10.1145/2908812.2908940", abstract = "In an evolutionary system, robustness describes the resilience to mutational and environmental changes, whereas evolvability captures the capability of generating novel and adaptive phenotypes. The research literature has not seen an effective quantification of phenotypic evolvability able to predict the evolutionary potential of the search for novel phenotypes. In this study, we propose to characterize the mutational potential among different phenotypes using the phenotype network, where vertices are phenotypes and edges represent mutational connections between them. In the framework of such a network, we quantitatively analyse the evolvability of phenotypes by exploring a number of vertex centrality measures commonly used in complex networks. In our simulation studies we use a Linear Genetic Programming system and a population of random walkers. Our results suggest that the weighted eigenvector centrality serves as the best estimator of phenotypic evolvability.", notes = "Memorial University GECCO-2016 A Recombination of the 25th International Conference on Genetic Algorithms (ICGA-2016) and the 21st Annual Genetic Programming Conference (GP-2016)", } @InProceedings{Hu:2018:EuroGP, author = "Ting Hu and Karoliina Oksanen and Weidong Zhang and Edward Randell and Andrew Furey and Guangju Zhai", title = "Analyzing Feature Importance for Metabolomics using Genetic Programming", booktitle = "EuroGP 2018: Proceedings of the 21st European Conference on Genetic Programming", year = "2018", month = "4-6 " # apr, editor = "Mauro Castelli and Lukas Sekanina and Mengjie Zhang and Stefano Cagnoni and Pablo Garcia-Sanchez", series = "LNCS", volume = "10781", publisher = "Springer Verlag", address = "Parma, Italy", pages = "68--83", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-77552-4", DOI = "doi:10.1007/978-3-319-77553-1_5", abstract = "The emerging and fast-developing field of metabolomics examines the abundance of small-molecule metabolites in body fluids to study the cellular processes related to how the human body responds to genetic and environmental perturbations. Considering the complexity of metabolism, metabolites and their represented cellular processes can correlate and synergistically contribute to a phenotypic status. Genetic programming (GP) provides advanced analytical instruments for the investigation of multifactorial causes of metabolic diseases. In this article, we analysed a population-based metabolomics dataset on osteoarthritis (OA) and developed a Linear GP (LGP) algorithm to search classification models that can best predict the disease outcome, as well as to identify the most important metabolic markers associated with the disease. The LGP algorithm was able to evolve prediction models with high accuracies especially with a more focused search using a reduced feature set that only includes potentially relevant metabolites. We also identified a set of key metabolic markers that may improve our understanding of the biochemistry and pathogenesis of the disease.", notes = "Part of \cite{Castelli:2018:GP} EuroGP'2018 held in conjunction with EvoCOP2018, EvoMusArt2018 and EvoApplications2018", } @InProceedings{Hu:2019:EuroGP, author = "Ting Hu and Marco Tomassini and Wolfgang Banzhaf", title = "Complex Network Analysis of a Genetic Programming Phenotype Network", booktitle = "EuroGP 2019: Proceedings of the 22nd European Conference on Genetic Programming", year = "2019", month = "24-26 " # apr, editor = "Lukas Sekanina and Ting Hu and Nuno Lourenco", series = "LNCS", volume = "11451", publisher = "Springer Verlag", address = "Leipzig, Germany", pages = "49--63", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-030-16669-4", URL = "https://www.springer.com/us/book/9783030166694", DOI = "doi:10.1007/978-3-030-16670-0_4", size = "16 pages", abstract = "The genotype-to-phenotype mapping plays an essential role in the design of an evolutionary algorithm. Since variation occurs at the genotypic level but fitness is evaluated at the phenotypic level, this mapping determines how variations are effectively translated into quality improvements. We numerically study the redundant genotype-to-phenotype mapping of a simple Boolean linear genetic programming system. In particular, we investigate the resulting phenotypic network using tools of complex network analysis. The analysis yields a number of interesting statistics of this network, considered both as a directed as well as an undirected graph. We show by numerical simulation that less redundant phenotypes are more difficult to find as targets of a search than others that have much more genotypic abundance. We connect this observation with the fact that hard to find phenotypes tend to belong to small and almost isolated clusters in the phenotypic network.", notes = "http://www.evostar.org/2019/cfp_eurogp.php#abstracts Part of \cite{Sekanina:2019:GP} EuroGP'2019 held in conjunction with EvoCOP2019, EvoMusArt2019 and EvoApplications2019", } @InProceedings{Hu:2019:GPTP, author = "Ting Hu", title = "Can Genetic Programming Perform Explainable Machine Learning for Bioinformatics?", booktitle = "Genetic Programming Theory and Practice XVII", year = "2019", editor = "Wolfgang Banzhaf and Erik Goodman and Leigh Sheneman and Leonardo Trujillo and Bill Worzel", pages = "63--77", address = "East Lansing, MI, USA", month = "16-19 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-030-39957-3", DOI = "doi:10.1007/978-3-030-39958-0_4", abstract = "Although proven powerful in making predictions and finding patterns, machine learning algorithms often struggle to provide explanations and translational knowledge when applied to many problems, especially in biomedical sciences. This is often resulted by the highly complex structure employed by machine learning algorithms to represent and model the relationship of the predictors and the response. The prediction accuracy is increased at the cost of having a black-box model that is not amenable for interpretation. Genetic programming may provide a potential solution to explainable machine learning for bioinformatics where learned knowledge and patterns can be translated to clinical actions. In this study, we employed an LGP algorithm for a bioinformatics classification problem. We developed feature selection analysis methods and aimed at explaining which features are influential in the prediction, and whether such an influence is through individual effects or synergistic effects of combining with other features.", notes = "Part of \cite{Banzhaf:2019:GPTP}, published after the workshop", } @Article{Hu:2019:sigevolution, author = "Ting Hu and Lukas Sekanina", title = "{EuroGP} 2019 Panel Discussion: What is the Killer Application of {GP}?", journal = "SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation", year = "2019", volume = "12", number = "2", pages = "3--7", month = aug, keywords = "genetic algorithms, genetic programming, robotics, SBSE, APR, SapFix", ISSN = "1931-8499", URL = "https://evolution.sigevo.org/issues/SIGEVOlution1202.pdf", DOI = "doi:10.1145/3357514.3357515", acmid = "3357515", size = "4.3 pages", abstract = "Moderator: Ting Hu, Memorial University, Canada Panelists: Gusz Eiben, Vrije Universiteit Amsterdam, the Netherlands Given the fact that evolution can produce intelligence, it is plausible that Artificial Evolution can produce Artificial Intelligence. Gabriela Ochoa, University of Stirling, Scotland GP has already proved to be a powerful problem-solving and design tool in many domains, so there are already several killer-applications that are part of our everyday lives! CGP \cite{Lones:2017:JMS} James Foster, University of Idaho, USA GP is much more likely to be used under the hood. I teach computers to program themselves. Risto Miikkulainen, University of Texas at Austin and Cognizant, USA GP/EC is a creative approach to AI.", } @Article{Hu:GPEM:gene-phen, author = "Ting Hu and Marco Tomassini and Wolfgang Banzhaf", title = "A network perspective on genotype-phenotype mapping in genetic programming", journal = "Genetic Programming and Evolvable Machines", year = "2020", volume = "21", number = "3", pages = "375--397", month = sep, note = "Special Issue: Highlights of Genetic Programming 2019 Events", keywords = "genetic algorithms, genetic programming, linear genetic programming, Evolvability, Genotype phenotype map, Networks, Neutrality, Redundancy, Robustness", ISSN = "1389-2576", URL = "https://rdcu.be/cGPa2", DOI = "doi:10.1007/s10710-020-09379-0", size = "23 pages", abstract = "Genotype phenotype mapping plays an essential role in the design of an evolutionary algorithm. Variation occurs at the genotypic level but fitness is evaluated at the phenotypic level, therefore, this mapping determines if and how variations are effectively translated into quality improvements. In evolutionary algorithms, this mapping has often been observed as highly redundant, i.e., multiple genotypes can map to the same phenotype, as well as heterogeneous, i.e., some phenotypes are represented by a large number of genotypes while some phenotypes only have few. We numerically study the redundant genotype-phenotype mapping of a simple Boolean linear genetic programming system and quantify the mutational connections among phenotypes using tools of complex network analysis. The analysis yields several interesting statistics of the phenotype network. We show the evidence and provide explanations for the observation that some phenotypes are much more difficult to find as the target of a search than others. Our study provides a quantitative analysis framework to better understand the genotype-phenotype map, and the results may be used to inspire algorithm design that allows the search of a difficult target to be more effective.", } @Article{editorial:GPEM:H2019, author = "Ting Hu and Miguel Nicolau and Lukas Sekanina", title = "Special issue on highlights of genetic programming 2019 events", journal = "Genetic Programming and Evolvable Machines", year = "2020", volume = "21", number = "3", pages = "283--285", month = sep, note = "Guest Editorial", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-020-09385-2", size = "3 pages", abstract = "EuroGP'2019 and GECCO-2019 GP track \cite{Kocnova:GPEM:resynthesis}, \cite{Atkinson:GPEM:H2019}, \cite{Helmuth:GPEM:lexi}, \cite{Hu:GPEM:gene-phen}, \cite{Lensen:GPEM:H2019}, \cite{LaCava:GPEM}", } @Proceedings{Hu:2020:GP, title = "EuroGP 2020: Proceedings of the 23rd European Conference on Genetic Programming", year = "2020", editor = "Ting Hu and Nuno Lourenco and Eric Medvet", volume = "12101", series = "LNCS", address = "Seville, Spain", month = "15-17 " # apr, organisation = "EvoStar, Species", publisher = "Springer Verlag", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-030-44093-0", DOI = "doi:10.1007/978-3-030-44094-7", size = "x+295 pages", notes = "http://www.evostar.org/2020/cfp_eurogp.php EuroGP'2020 held in conjunction with EvoCOP2020, EvoMusArt2020 and EvoApplications2020", } @Proceedings{Hu:2021:GP, title = "EuroGP 2021: Proceedings of the 24th European Conference on Genetic Programming", year = "2021", editor = "Ting Hu and Nuno Lourenco and Eric Medvet", series = "LNCS", volume = "12691", address = "Virtual Event", month = "7-9 " # apr, organisation = "EvoStar, Species", publisher = "Springer Verlag", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-030-72811-3", URL = "https://www.springer.com/gp/book/9783030728113", DOI = "doi:10.1007/978-3-030-72812-0", size = "289 pages", } @InProceedings{Hu:2022:GPTP, author = "Ting Hu", title = "Genetic Programming for Interpretable and Explainable Machine Learning", booktitle = "Genetic Programming Theory and Practice XIX", year = "2022", editor = "Leonardo Trujillo and Stephan M. Winkler and Sara Silva and Wolfgang Banzhaf", series = "Genetic and Evolutionary Computation", pages = "81--90", address = "Ann Arbor, USA", month = jun # " 2-4", publisher = "Springer", keywords = "genetic algorithms, genetic programming, XAI", isbn13 = "978-981-19-8459-4", URL = "https://drive.google.com/file/d/1--GmAFCPNfOHtvOWQr5p3Fzabn5CL5Nj/view?pli=1", DOI = "doi:10.1007/978-981-19-8460-0_4", size = "ten pages", abstract = "Increasing demand for human understanding of machine decision-making is deemed crucial for machine learning (ML) methodology development and further applications. It has inspired the emerging research field of interpretable and explainable ML/AI. Techniques have been developed to either provide additional explanations to a trained ML model or learn innately compact and understandable models. Genetic programming (GP), as a powerful learning instrument, holds great potential in interpretable and explainable learning. In this chapter, we first discuss concepts and popular methods in interpretable and explainable ML, and review research using GP for interpretability and explainability. We then introduce our previously proposed GP-based framework for interpretable and explainable learning applied to bioinformatics.", notes = "Part of \cite{Banzhaf:2022:GPTP} published after the workshop in 2023 School of Computing, Queen's University, Kingston, ON, Canada", } @Misc{hu:2022:pstnLGP, author = "Ting Hu and Gabriela Ochoa and Wolfgang Banzhaf", title = "Phenotype Search Trajectory Networks for Linear Genetic Programming", howpublished = "ArXiv", year = "2022", month = "15 " # nov, keywords = "genetic algorithms, genetic programming, Neutral networks, Genotype-to-phenotype mapping, Al-gorithm modeling, Algorithm analysis, Search trajectories, Complexnetworks, Visualisation, Kolmogorov complexity, Populations and Evolution (q-bio.PE), Artificial Intelligence (cs.AI), FOS: Biological sciences, FOS: Biological sciences, FOS: Computer and information sciences, FOS: Computer and information sciences", URL = "https://arxiv.org/abs/2211.08516", DOI = "doi:10.48550/ARXIV.2211.08516", size = "16 pages", abstract = "Genotype-to-phenotype mappings translate genotypic variations such as mutations into phenotypic changes. Neutrality is the observation that some mutations do not lead to phenotypic changes. Studying the search trajectories in genotypic and phenotypic spaces, especially through neutral mutations, helps us to better understand the progression of evolution and its algorithmic behaviour. we visualise the search trajectories of a genetic programming system as graph-based models, where nodes are genotypes/phenotypes and edges represent their mutational transitions. We also quantitatively measure the characteristics of phenotypes including their genotypic abundance (the requirement for neutrality) and Kolmogorov complexity. We connect these quantified metrics with search trajectory visualisations, and find that more complex phenotypes are under-represented by fewer genotypes and are harder for evolution to discover. Less complex phenotypes, on the other hand, are over-represented by genotypes, are easier to find, and frequently serve as stepping-stones for evolution.", copyright = "Creative Commons Attribution 4.0 International", } @InProceedings{Hu:2023:EuroGP, author = "Ting Hu and Gabriela Ochoa and Wolfgang Banzhaf", title = "Phenotype Search Trajectory Networks for Linear Genetic Programming", booktitle = "EuroGP 2023: Proceedings of the 26th European Conference on Genetic Programming", year = "2023", month = "12-14 " # apr, editor = "Gisele Pappa and Mario Giacobini and Zdenek Vasicek", series = "LNCS", volume = "13986", publisher = "Springer Verlag", address = "Brno, Czech Republic", pages = "52--67", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Neutral networks, Genotype-to-phenotype mapping, Algorithm modeling, Algorithm analysis, Search trajectories, Complex networks, Visualisation, Kolmogorov complexity", isbn13 = "978-3-031-29572-0", URL = "https://rdcu.be/c8UPb", DOI = "doi:10.1007/978-3-031-29573-7_4", size = "16 pages", abstract = "we visualise the search trajectories of a genetic programming system as graph-based models, where nodes are genotypes/phenotypes and edges represent their mutational transitions. We also quantitatively measure the characteristics of phenotypes including their genotypic abundance (the requirement for neutrality) and Kolmogorov complexity. We connect these quantified metrics with search trajectory visualisations, and find that more complex phenotypes are under-represented by fewer genotypes and are harder for evolution to discover. Less complex phenotypes, on the other hand, are over-represented by genotypes, are easier to find, and frequently serve as stepping-stones for evolution.", notes = "STN R code https://github.com/gabro8a/STNs Part of \cite{Pappa:2023:GP} EuroGP'2023 held in conjunction with EvoCOP2023, EvoMusArt2023 and EvoApplications2023", } @Proceedings{Hu:2023:GPTP, title = "Genetic Programming Theory and Practice XX", year = "2023", editor = "Stephan Winkler and Leonardo Trujillo and Charles Ofria and Ting Hu", series = "Genetic and Evolutionary Computation", address = "Michigan State University, USA", month = jun # " 1-3", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-981-99-8412-1", URL = "https://link.springer.com/book/9789819984121", DOI = "doi:10.1007/978-981-99-8413-8", size = "xv 337 pages", abstract = "Chapters: \cite{Affenzeller:2023:GPTP} x, \cite{Baeck:2023:GPTP} x, \cite{Banzhaf:2023:GPTP} 4, \cite{Card:2023:GPTP} x, \cite{Carja:2023:GPTP} x, \cite{deFranca:2023:GPTP} 14, \cite{Dolson:2023:GPTP} 15, \cite{Foster:2023:GPTP} x, \cite{Haider:2023:GPTP} 12, \cite{Haut:2023:GPTP} 3, \cite{Hidalgo:2023:GPTP} 6, \cite{Hussain:2023:GPTP} 16, \cite{Lalejini:2023:GPTP} 13, \cite{Lehman:2023:GPTP} 10, \cite{McPhee:2023:GPTP} 5, \cite{Medvet:2023:GPTP} 11, \cite{Moreno:2023:GPTP} 7, \cite{O'Reilly:2023:GPTP} 2, \cite{Ribeiro:2023:GPTP} 1, \cite{Sipper:2023:GPTP} 8, \cite{Soros:2023:GPTP} x, \cite{Spector:2023:GPTP} 9, x not in published book", notes = " published after the workshop in 2024", } @Article{HU:2023:jmgm, author = "Wenguang Hu and Lei Zhang", title = "First-principles, machine learning and symbolic regression modelling for organic molecule adsorption on two-dimensional {CaO} surface", journal = "Journal of Molecular Graphics and Modelling", volume = "124", pages = "108530", year = "2023", ISSN = "1093-3263", DOI = "doi:10.1016/j.jmgm.2023.108530", URL = "https://www.sciencedirect.com/science/article/pii/S1093326323001286", keywords = "genetic algorithms, genetic programming, Machine learning, Symbolic regression, Two-dimensional, Adsorption, Data-driven", abstract = "Data-driven methods are receiving significant attention in recent years for chemical and materials researches; however, more works should be done to leverage the new paradigm to model and analyze the adsorption of the organic molecules on low-dimensional surfaces beyond using the traditional simulation methods. In this manuscript, we employ machine learning and symbolic regression method coupled with DFT calculations to investigate the adsorption of atmospheric organic molecules on a low-dimensional metal oxide mineral system. The starting dataset consisting of the atomic structures of the organic/metal oxide interfaces are obtained via the density functional theory (DFT) calculation and different machine learning algorithms are compared, with the random forest algorithm achieving high accuracies for the target output. The feature ranking step identifies that the polarizability and bond type of the organic adsorbates are the key descriptors for the adsorption energy output. In addition, the symbolic regression coupled with genetic programming automatically identifies a series of hybrid new descriptors displaying improved relevance with the target output, suggesting the viability of symbolic regression to complement the traditional machine learning techniques for the descriptor design and fast modeling purposes. This manuscript provides a framework for effectively modeling and analyzing the adsorption of the organic molecules on low-dimensional surfaces via comprehensive data-driven approaches", } @Article{DBLP:journals/digearth/HuDSFNNZ20, author = "Xuke Hu and Lei Ding and Jianga Shang and Hongchao Fan and Tessio Novack and Alexey Noskov and Alexander Zipf", title = "Data-driven approach to learning salience models of indoor landmarks by using genetic programming", journal = "Int. J. Digit. Earth", volume = "13", number = "11", pages = "1230--1257", year = "2020", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1080/17538947.2019.1701109", DOI = "doi:10.1080/17538947.2019.1701109", timestamp = "Tue, 23 Mar 2021 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/digearth/HuDSFNNZ20.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{HU:2020:CC, author = "Ying Hu and Guang Cheng and Yongning Tang and Feng Wang3", title = "A practical design of hash functions for {IPv6} using multi-objective genetic programming", journal = "Computer Communications", volume = "162", pages = "160--168", year = "2020", ISSN = "0140-3664", DOI = "doi:10.1016/j.comcom.2020.08.013", URL = "http://www.sciencedirect.com/science/article/pii/S0140366420318983", keywords = "genetic algorithms, genetic programming, Hash function, Multi-objective optimization, Network measurement", abstract = "Hash functions are widely used in high-speed network traffic measurement. A hash function of high quality is supposed to meet the requirements of collision free and fast execution. Existing works have already developed methods to generate hash functions for IPv4 data, while IPv6 data with much longer addresses and different data characteristics may decline the effectiveness of those methods. In this paper, we present a practical design of hash functions for IPv6 measurement, based on the entropy analysis of IPv6 network data and an automated method of multi-objective genetic programming (GP). Considering our specific application of hash functions, we use three fitness functions as the optimization objectives, including active flow estimation, uniformity and seed avalanche effect, among which the active flow estimation is the main objective as the specific measurement task. In implementation of multi-objective GP, we adopted a strategy to limit the hash functions to shorter execution time than other hash functions by advanced experimental investigation. Experiments were conducted to construct hash functions for WIDE IPv6 network data. The results show that our generated hash functions have high usability on different evaluation criteria. It indicates that our generated hash functions are superior in active flow estimation and execution time and could compete with state of art hash functions in terms of uniformity and generating independent hash values for data structures like Bloom Filter", } @InProceedings{Hu:2006:GeoCLEF, author = "You-Heng Hu and Linlin Ge", title = "The University of New South Wales at {GeoCLEF 2006}", booktitle = "7th Workshop of the Cross-Language Evaluation Forum, CLEF 2006", year = "2006", editor = "Carol Peters and Paul Clough and Fredric C. Gey and Jussi Karlgren and Bernardo Magnini and Douglas W. Oard and Maarten {de Rijke} and Maximilian Stempfhuber", volume = "4730", series = "LNCS", pages = "905--912", address = "Alicante, Spain", month = sep # " 20-22", publisher = "Springer", note = "Revised Selected Papers", keywords = "genetic algorithms, genetic programming, geographic information retrieval, geographic knowledge base, geo-textual indexing", isbn13 = "978-3-540-74999-8", annote = "The Pennsylvania State University CiteSeerX Archives", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.521.389", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.521.389", URL = "http://clef.isti.cnr.it/2006/working_notes/workingnotes2006/HuCLEF2006.pdf", DOI = "doi:10.1007/978-3-540-74999-8_115", abstract = "This paper describes our participation in the GeoCLEF monolingual English task of the Cross Language Evaluation Forum 2006. The main objective of this study is to evaluate the retrieve performance of our geographic information retrieval system. The system consists of four modules: the geographic knowledge base that provides information about important geographic entities around the world and relationships between them; the indexing module that creates and maintains textual and geographic indices for document collections; the document retrieval module that uses the Boolean model to retrieve documents that meet both textual and geographic criteria; and the ranking module that ranks retrieved results based on ranking functions learnt using Genetic Programming. Experiments results show that the geographic knowledge base, the indexing module and the retrieval module are useful for geographic information retrieval tasks, but the proposed ranking function learning method doesn't work well.", notes = "http://ir.shef.ac.uk/geoclef/2006/ Evaluation of Multilingual and Multi-modal Information Retrieval (GIR). Published 2007", } @InProceedings{hu:1998:GPci, author = "Yuh-Jyh Hu", title = "A Genetic Programming Approach to Constructive Induction", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "146--151", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-548-7", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1998/hu_1998_GPci.pdf", notes = "GP-98", } @InProceedings{hu:1998:bdGP, author = "Yuh-Jyh Hu", title = "Biopattern Discovery by Genetic Programming", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "152--157", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-548-7", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1998/hu_1998_bdGP.pdf", size = "6 pages", notes = "GP-98. Cited by \cite{ross:2001:gecco} . PROSITE pattern language. GPPD. ambigious positions (Arikawa et al., 1992). greedy method to refine the patterns. Function set=(wildcard gap X(i) and X(i,j) ?). Gaps always separate terminals? Terminals=(all legal symbols, eg amino acids,nucleotides and symbol indexings).", } @InProceedings{Hu:2000:GECCO, author = "Yuh-Jyh Hu", title = "Global Gene Expression Analysis with Genetic Programming", pages = "753", year = "2000", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)", editor = "Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer", address = "Las Vegas, Nevada, USA", publisher_address = "San Francisco, CA 94104, USA", month = "10-12 " # jul, keywords = "genetic algorithms, genetic programming, Poster", ISBN = "1-55860-708-0", URL = "http://gpbib.cs.ucl.ac.uk/gecco2000/RW010.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco2000/RW010.ps", notes = "A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of \cite{whitley:2000:GECCO}", } @Article{Yuh-JyhHu:2002:NAR, author = "Yuh-Jyh Hu", title = "Prediction of consensus structural motifs in a family of coregulated RNA sequences", journal = "Nucleic Acids Research", year = "2002", volume = "30", number = "17", pages = "3886--3893", keywords = "genetic algorithms, genetic programming", broken = "http://www.ingentaconnect.com/content/oup/nar/2002/00000030/00000017/art03886", URL = "http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=137409.pdf", URL = "http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=137409", DOI = "doi:10.1093/nar/gkg521", size = "8 pages", abstract = "Given a set of homologous or functionally related RNA sequences, the consensus motifs may represent the binding sites of RNA regulatory proteins. Unlike DNA motifs, RNA motifs are more conserved in structures than in sequences. Knowing the structural motifs can help us gain a deeper insight of the regulation activities. There have been various studies of RNA secondary structure prediction, but most of them are not focused on finding motifs from sets of functionally related sequences. Although recent research shows some new approaches to RNA motif finding, they are limited to finding relatively simple structures, e.g. stemloops. In this paper, we propose a novel genetic programming approach to RNA secondary structure prediction. It is capable of finding more complex structures than stem-loops. To demonstrate the performance of our new approach as well as to keep the consistency of our comparative study, we first tested it on the same data sets previously used to verify the current prediction systems. To show the flexibility of our new approach, we also tested it on a data set that contains pseudo knot motifs which most current systems cannot identify. A web-based user interface of the prediction system is set up at http://bioinfo.cis.nctu.edu.tw/service/gprm/.", notes = "PMID: 12202774 p3887 negative examples randomly generated. fitness=F-score. pop=1000, 50gens. Tournament=2 (pop culled to 50percent???). virus 3'-UTR. Matthews correlation coefficient. GP fairly insensitive to crossover and mutation rates. GPRM", } @Article{Yuh-JyhHu:2003:NAR, author = "Yuh-Jyh Hu", title = "{GPRM}: a genetic programming approach to finding common {RNA} secondary structure elements", journal = "Nucleic Acids Research", year = "2003", volume = "31", number = "13", pages = "3446--3449", month = "1 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=168928.pdf", URL = "http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=168928", DOI = "doi:10.1093/nar/gkg521", URL = "http://hdl.handle.net/11536/27741", size = "4 pages", abstract = "RNA molecules play an important role in many biological activities. Knowing its secondary structure can help us better understand the molecule's ability to function. The methods for RNA structure determination have traditionally been implemented through biochemical, biophysical and phylogenetic analyses. As the advance of computer technology, an increasing number of computational approaches have recently been developed. They have different goals and apply various algorithms. For example, some focus on secondary structure prediction for a single sequence; some aim at finding a global alignment of multiple sequences. Some predict the structure based on free energy minimisation; some make comparative sequence analyses to determine the structure. In this paper, we describe how to correctly use GPRM, a genetic programming approach to finding common secondary structure elements in a set of unaligned coregulated or homologous RNA sequences.", notes = "GPRM can be accessed at http://bioinfo.cis.nctu.edu.tw/service/gprm/ Computer and Information Science Department, National Chiao Tung University, 1001 Ta Hsueh Rd, Hsinchu, Taiwan *Tel: +886 35731795; Fax: +886 35721490; Email: yhu@cis.nctu.edu.tw PMID: 12824343 Cited by \cite{Kawaguchi:2005:NAR}", } @Article{HU:2023:powtec, author = "Mingjian Hu and Yin Wang and Yewei Li and Ziyi Pang and Yubin Ren", title = "Development of drag force model for predicting the flow behavior of porous media based on genetic programming", journal = "Powder Technology", year = "2023", volume = "413", pages = "118041", month = jan, keywords = "genetic algorithms, genetic programming, Seepage, Drag force model, Porous media, Pressure drop", ISSN = "0032-5910", DOI = "doi:10.1016/j.powtec.2022.118041", URL = "https://www.sciencedirect.com/science/article/pii/S0032591022009226", size = "13 pages", abstract = "Seepage in soils is a phenomenon related to the interaction between solid particles and fluid phase. The present study develops a drag force model by focusing on the voidage function using a genetic programing (GP) procedure. A systematic laboratory seepage tests was carried out on porous media with different materials by a self-made seepage apparatus. Based on the database obtained by the numerous seepage tests, the drag force model was developed with the aid of symbolic regression in genetic program. The results indicate that the developed drag force model by GP method composed of a constant and four gene items has satisfied performance in predicting the drag behavior of particles, which is attributed to the GP's advantages on optimizing both the parameters and structure of the model. Among the influencing factors, the gradation coefficient, porosity, and shape coefficient have a significant effect on the seepage characteristics of the porous media. The proposed model in this study could be used to analyze the flow characteristics of porous media in the field of geotechnical and ocean engineering", notes = "State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan, Hubei 430071, China", } @InProceedings{Hu:2023:ICACI, author = "Xiao-Min Hu and Yu-Hui Duan and Min Li and Ying Zeng", booktitle = "2023 15th International Conference on Advanced Computational Intelligence (ICACI)", title = "Hyper-Heuristic Algorithm for Urban Traffic Flow Optimization", year = "2023", abstract = "Traffic flow assignment optimisation is a core issue in the field of intelligent transportation. The goal of this problem is to find suitable routes for all travel needs and improve the overall efficiency of the transportation network. This paper proposes a city traffic flow optimisation method based on hyper-heuristic. This method uses terminal sets and function sets designed according to the characteristics of urban road networks to construct hyper-heuristic strategies and simulate them on small-scale road networks to test the optimisation effects. The hyper-heuristic strategy formulates the current optimal route for each vehicle on the road network and uses Genetic Programming (GP) for iterative training. The average traveling time at the end of each simulation serves as the evaluation value for GP, and finally iteratively outputs the best strategy for simulation and test on larger-scale urban road networks. Tests on different sizes and regions of road networks show that using GP iterative training can improve the traffic efficiency of urban road networks with hyper-heuristic strategies.", keywords = "genetic algorithms, genetic programming, Training, Roads, Heuristic algorithms, Urban areas, Transportation, Optimisation methods, Traffic flow assignment, hyper heuristic, intelligent transportation", DOI = "doi:10.1109/ICACI58115.2023.10146154", month = may, notes = "Also known as \cite{10146154}", } @Article{Huang:2007:ESA, author = "Cheng-Lung Huang and Mu-Chen Chen and Chieh-Jen Wang", title = "Credit scoring with a data mining approach based on support vector machines", journal = "Expert Systems with Applications", year = "2007", volume = "33", number = "4", pages = "847--856", month = nov, keywords = "genetic algorithms, genetic programming, SVM, Support vector machine, Credit scoring, Neural networks, ANN, Decision tree, Data mining, Classification", ISSN = "0957-4174", URL = "http://nlg.csie.ntu.edu.tw/~cjwang/paper/Credit%20Card%20Scoring%20with%20a%20Data%20Mining%20Approach%20Based%20on%20Support%20Vector%20Machine.pdf", DOI = "doi:10.1016/j.eswa.2006.07.007", size = "10 pages", abstract = "The credit card industry has been growing rapidly recently, and thus huge numbers of consumers credit data are collected by the credit department of the bank. The credit scoring manager often evaluates the consumers credit with intuitive experience. However, with the support of the credit classification model, the manager can accurately evaluate the applicant's credit score. Support Vector Machine (SVM) classification is currently an active research area and successfully solves classification problems in many domains. This study used three strategies to construct the hybrid SVM-based credit scoring models to evaluate the applicant's credit score from the applicants input features. Two credit datasets in UCI database are selected as the experimental data to demonstrate the accuracy of the SVM classifier. Compared with neural networks, genetic programming, and decision tree classifiers, the SVM classifier achieved an identical classificatory accuracy with relatively few input features. Additionally, combining genetic algorithms with SVM classifier, the proposed hybrid GA-SVM strategy can simultaneously perform feature selection task and model parameters optimisation. Experimental results show that SVM is a promising addition to the existing data mining methods.", notes = "UCI dataset", } @InProceedings{Huang:2009:IEEE, author = "Chia-Hui Huang and Han-Ying Kao", title = "An effective linear approximation method for geometric programming problems", booktitle = "IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2009", year = "2009", month = dec, pages = "1743--1747", abstract = "A geometric program (GP) is a type of mathematical optimisation problem characterised by objective and constraint functions, where", keywords = "geometric programming, constraint functions, effective linear approximation method, geometric programming problems, mathematical optimisation problem, objective functions, posynomial form, approximation theory", DOI = "doi:10.1109/IEEM.2009.5373154", notes = "Not GP. Also known as \cite{5373154}", } @InProceedings{huang2:2001:GECCO, title = "Independent Sampling Genetic Algorithms", author = "Chien-Feng Huang", pages = "367--374", year = "2001", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)", editor = "Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon and Edmund Burke", address = "San Francisco, California, USA", publisher_address = "San Francisco, CA 94104, USA", month = "7-11 " # jul, keywords = "genetic algorithms, independent sampling genetic algorithms, idealized genetic algorithms, building block detecting strategy, mate selection, Royal Road functions, bounded deception problem", ISBN = "1-55860-774-9", URL = "http://www.c3.lanl.gov/~cfhuang/reprints/ISGA_033101.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco2001/d03a.pdf", abstract = "Premature convergence is the loss of diversity in the population that has long been recognised as one crucial factor that hinders the efficacy of crossover. We propose a strategy for independent sampling of building blocks in order to nicely implement implicit parallelism. Based on this methodology, we developed a modified version of GA: independent sampling genetic algorithms (ISGAs). Simply stated, each individual independently samples candidate schemata and creates population diversity in the first phase; subsequently we allow individuals to actively select their mates for reproduction. We will present experimental results on two benchmark problems, {"}Royal Road{"} functions of 64-bits and bounded deception of 30-bits, to show how the performance of GAs can be improved through the proposed approach.", notes = "A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of \cite{spector:2001:GECCO}", } @InProceedings{Huang:2003:gecco, author = "Chien-Feng Huang", title = "Using an Immune System Model to Explore Mate Selection in Genetic Algorithms", booktitle = "Genetic and Evolutionary Computation -- GECCO-2003", editor = "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller", year = "2003", pages = "1041--1052", address = "Chicago", publisher_address = "Berlin", month = "12-16 " # jul, volume = "2723", series = "LNCS", ISBN = "3-540-40602-6", publisher = "Springer-Verlag", email = "cfhuang@lanl.gov", keywords = "Genetic Algorithms, AIS, immune system, mate selection", DOI = "doi:10.1007/3-540-45105-6_114", abstract = "When Genetic Algorithms (GAs) are employed in multimodal function optimization, engineering and machine learning, identifying multiple peaks and maintaining subpopulations of the search space are two central themes. In this paper, an immune system model is adopted to develop a framework for exploring the role of mate selection in GAs with respect to these two issues. The experimental results reported in the paper will shed more light into how mate selection schemes compare to traditional selection schemes. In particular, we show that dissimilar mating is beneficial in identifying multiple peaks, yet harmful in maintaining subpopulations of the search space.", notes = "GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eights Annual Genetic Programming Conference (GP-2003)", } @InProceedings{C-FHuang:2003:CEC1, author = "Chien-Feng Huang and Luis M. Rocha", title = "Exploration of {RNA} Editing and Design of Robust Genetic Algorithms", booktitle = "Proceedings of the 2003 Congress on Evolutionary Computation CEC2003", editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon", pages = "2799--2806", year = "2003", publisher = "IEEE Press", address = "Canberra", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "8-12 " # dec, organisation = "IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", email = "cfhuang@lanl.gov rocha@lanl.gov", keywords = "genetic algorithms", ISBN = "0-7803-7804-0", size = "8 pages", abstract = "This paper presents our computational methodology using Genetic Algorithms (GA) for exploring the nature of RNA editing. These models are constructed using several genetic editing characteristics that are gleaned from the RNA editing system as observed in several organisms. We have expanded the traditional Genetic Algorithm with artificial editing mechanisms as proposed by (Rocha, 1997). The incorporation of editing mechanisms provides a means for artificial agents with genetic descriptions to gain greater phenotypic plasticity, which may be environmentally regulated. Our first implementations of these ideas have shed some light into the evolutionary implications of RNA editing. Based on these understandings, we demonstrate how to select proper RNA editors for designing more robust GAs, and the results will show promising applications to real-world problems. We expect that the framework proposed will both facilitate determining the evolutionary role of RNA editing in biology, and advance the current state of research in Genetic Algorithms.", notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.", } @InProceedings{C-FHuang:2003:CEC2, author = "Chien-Feng Huang", title = "The Role of Crossover in an Immunity Based Genetic Algorithm for Multimodal Function Optimization", booktitle = "Proceedings of the 2003 Congress on Evolutionary Computation CEC2003", editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon", pages = "2807--2814", year = "2003", publisher = "IEEE Press", address = "Canberra", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "8-12 " # dec, organisation = "IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", email = "cfhuang@lanl.gov", keywords = "genetic algorithms, mate selection, immune systems", ISBN = "0-7803-7804-0", size = "8 pages", abstract = "When Genetic Algorithms are employed in multimodal function optimization, identifying multiple peaks and maintaining subpopulations of the search space are two central themes. In this paper, we use an immune system model to explore the role of crossover in GAs with respect to these two issues. The experimental results reported here will shed more light into how crossover affects the GA's search power in the context of multimodal function optimization. We will also show that an adaptive crossover strategy successfully achieves the two goals simultaneously. These results on the effects of crossover are a step toward a deeper understanding of how GAs work, and thus how to design more robust GAs for solving multimodal optimization problems.", notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.", } @InProceedings{Huang:IMECS:fir, author = "Ching-Ya Huang and Shih-Yen Tsai and Te-Jen Su", title = "FIR Equalizer using Genetic Programming", booktitle = "Proceedings of the International MultiConference of Engineers and Computer Scientists, IMECS 2008", year = "2008", volume = "II", pages = "1440--1443", address = "Hong Kong", month = "19-21 " # mar, keywords = "genetic algorithms, genetic programming, Finite Impulse Response equalizer", isbn13 = "978-988-17012-1-3", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.3713", URL = "http://www.iaeng.org/publication/IMECS2008/IMECS2008_pp1440-1443.pdf", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", contributor = "CiteSeerX", language = "en", oai = "oai:CiteSeerXPSU:10.1.1.149.3713", abstract = "The main duty of communication systems is to assure to provide adequate message interchange, through a certain channel, between a transmitter and a receiver. The distortion takes place in the process of transmitting message, and it usually leads to severe degradation. Consequently we need a device named equalizer filters to recover the desired information from the received signal. In this paper, a FIR equalizer based on the GP approach to recover the transmitted signal is proposed. In addition, the equalizer coefficient will be estimated by the GP algorithm.", } @InProceedings{Huang:2017:SICE, author = "FangWei Huang and Ivan Tanev and Katsunori Shimohara", booktitle = "2017 56th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE)", title = "Emergence of collective escaping strategies of empathie caribou agents with swarming behavior implemented in wolf-caribou predator-prey problem", year = "2017", pages = "1634--1637", abstract = "We investigate whether socio-psychological aspects - such as empathy and grouping (swarming) - implemented in caribou agents improves the efficiency of the simulated evolution (via genetic programming) of their escaping behaviour or the effectiveness of such a behaviour in the wolf-caribou predator prey pursuit problem (WCP). The latter comprises a team of inferior caribou agents attempting to escape from a single yet superior (in terms of sensory abilities, raw speed, and maximum energy) wolf agent in a simulated two-dimensional infinite toroidal world. We experimentally verified the survival value of empathy in that it improves both the efficiency of evolution of the escaping behaviour and the effectiveness of such a behaviour. Also, we concluded that swarming facilitates a faster evolution of caribou agents while preserving the effectiveness of their evolved behaviour.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.23919/SICE.2017.8105643", month = sep, notes = "Also known as \cite{8105643}", } @InProceedings{Huang:2007:cec, author = "Haoming Huang and Michel Pasquier and Chai Quek", title = "HiCEFS - A Hierarchical Coevolutionary Approach for the Dynamic Generation of Fuzzy System", booktitle = "2007 IEEE Congress on Evolutionary Computation", year = "2007", editor = "Dipti Srinivasan and Lipo Wang", pages = "3426--3433", address = "Singapore", month = "25-28 " # sep, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "1-4244-1340-0", file = "1911.pdf", DOI = "doi:10.1109/CEC.2007.4424915", abstract = "A novel hierarchical coevolutionary approach called HiCEFS for the dynamic generation of a fuzzy system from data is presented. This paper is focused on using the proposed hierarchical coevolutionary approach to generate a form of generic membership function (MF) called Irregular Shaped Membership Function (ISMF). This approach divides the ISMFs generation task into several subtasks of finding ISMFs for each input, which are co-evolved in separate genetic populations. The approach is able automatically allocate proper number of accurate ISMFs to fully represent the data distribution. Experimental results show that the fuzzy systems adopting the ISMFs generated by the proposed approach generally outperform those derived by the previous work both in accuracy and structure compactness and compare favourably against other well known systems.", notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C", } @PhdThesis{Haoming_Huang:thesis, author = "Haoming Huang", title = "Coevolutionary synthesis of fuzzy decision support systems", school = "School of Computer Engineering, Nanyang Technological University", year = "2009", address = "Singapore 639798", URL = "http://repository.ntu.edu.sg/handle/10356/19087", URL = "http://repository.ntu.edu.sg/bitstream/10356/19087/1/Haoming-Final%20Thesis%20v1.5.6-for%20print.pdf", abstract = "Many essential applications in finance, medicine, engineering, and science require increasingly complex decision-making capabilities. There is accordingly a growing demand for decision support systems (DSSs) to assist humans in their tasks. To provide accurate and reliable decision support, a DSS needs not only to be robust in the face of the uncertainty but also to model the decision-making logic in a form that is understandable. Compared with other machine learning methods, fuzzy rule-based systems possess the merits of providing strong approximate reasoning in the presence of imprecise data while representing domain knowledge as a set of interpretable semantic rules. Using them to realise DSSs is thus a most suitable approach yielding powerful fuzzy decision support systems (FDSSs). However, the synthesis of an optimal FDSS with well-balanced accuracy and interpretability is an arduous task. Experience shows that it is very difficult for human experts to manually design its two most important components, the fuzzy membership functions and fuzzy rule base, which directly affect system performance. Ad-hoc architectures, which must be redesigned anew for every application, and improperly chosen parameters typically introduce unwanted biases and unavoidably result in suboptimal systems. Ideally, the decision-making logic should therefore be induced automatically from example and further optimised for the problem at hand. To achieve this goal, a generic approach is needed that can automatically synthesise an accurate and interpretable FDSS, while requiring minimal or no human effort.", notes = "Not GP? Centre for Computational Intelligence, Supervisor: Michel B Pasquier (SCE), URLs broken June 2010", } @Article{HUANG:2021:MM, author = "Heqing Huang and Xiaohui Xu and Chunling Tang", title = "Design of parallel computing system for embedded network distributed load tasks", journal = "Microprocessors and Microsystems", volume = "83", pages = "104017", year = "2021", ISSN = "0141-9331", DOI = "doi:10.1016/j.micpro.2021.104017", URL = "https://www.sciencedirect.com/science/article/pii/S0141933121001903", keywords = "genetic algorithms, genetic programming, Data centers, Parallel computer, Parallel computation, Hardware, Software, Embedded network", abstract = "Parallel computing is a type of computational construction in which multiple processors perform multiple small calculations at once and a whole large and complex set of problems. Dynamic simulation and real-world data modeling are required to achieve a similar level of parallel computation are critical. Co-calculation provides integration and saves time and money. Parallel computation can only be arranged for complex large data sets and his administration. Parallel computers have been used to solve various isolation and continuous optimization problems. Mechanisms such as single level, linear optimization and branch and internal point systems are not restricted, and genetic programming is often used in parallel and effectively. Embedded systems are generally distributed and often face changing demands over time. That said, existing methods that are obsolete or invalid at the time of compilation are unpredictable by classifying optimal computing tasks as the best use of existing resources for Hardware (HW) and Software (SW). Here, investigate a different idiosyncratic algorithm to balance the load of online HW / SW segmentation. Once there are modifications to suit the computing needs, the system must assign dynamic tasks and become necessary when performing tasks with local hardware or software sources and other nodes. The results obtained show that the proposed method significantly shares the load between different nodes and significantly reduces the allowable task's worst response time", } @InProceedings{huang:1999:AESSSSP, author = "Hsien-Da Huang and Jih Tsung Yang and Shu Fong Shen and Jorng-Tzong Horng", title = "An Evolution Strategy to Solve Sports Scheduling Problems", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "943", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "evolution strategies and evolutionary programming, poster papers", ISBN = "1-55860-611-4", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Huang:2009:WISM, author = "Jiangtao Huang and Chuang Deng", title = "A Novel Multiclass Classification Method with Gene Expression Programming", booktitle = "International Conference on Web Information Systems and Mining, WISM 2009", year = "2009", month = nov, pages = "139--143", keywords = "genetic algorithms, genetic programming, computer programs, data mining, eigenvalue centroid, eigenvalue power function, gene expression programming, genotype-phenotype genetic algorithm, linear chromosomes, machine learning algorithms, multiclass classification method, data mining, eigenvalues and eigenfunctions, learning (artificial intelligence)", DOI = "doi:10.1109/WISM.2009.36", abstract = "Classification is one of the fundamental tasks of data mining, and many machine learning algorithms are inherently designed for binary (two-class) decision problems. Gene expression programming (GEP) is a genotype/phenotype genetic algorithm that evolves computer programs of different sizes and shapes (expression trees) encoded in linear chromosomes of fixed length. In this paper, we propose a novel method for multiclass classification by using GEP, a new hybrid of genetic algorithms (GAs) and genetic programming (GP). Different to the common method of formulating a multiclass classification problem as multiple two-class problems, we construct a novel multiclass classification by using eigenvalue centroid of each class and eigenvalue-power function. Experimental results on two real data sets demonstrate that method is able to achieve a preferable solution.", notes = "Also known as \cite{5369449}", } @Article{Huang:Tgp:06, author = "Jih-Jeng Huang and Gwo-Hshiung Tzeng and Chorng-Shyong Ong", title = "Two-stage genetic programming {(2SGP)} for the credit scoring model", journal = "Applied Mathematics and Computation", year = "2006", volume = "174", number = "2", pages = "1039--1053", month = "15 " # mar, keywords = "genetic algorithms, genetic programming, Credit scoring model, Artificial neural network (ANN), Decision trees, Rough sets, Two-stage genetic programming (2SGP)", URL = "http://www.scorto.ru/downloads/Two-stage%20genetic%20programming%20(2SGP)%20for%20the%20credit%20scoring%20model.pdf", DOI = "doi:10.1016/j.amc.2005.05.027", abstract = "Credit scoring models have been widely studied in the areas of statistics, machine learning, and artificial intelligence (AI). Many novel approaches such as artificial neural networks (ANNs), rough sets, or decision trees have been proposed to increase the accuracy of credit scoring models. Since an improvement in accuracy of a fraction of a percent might translate into significant savings, a more sophisticated model should be proposed for significantly improving the accuracy of the credit scoring models. In this paper, two-stage genetic programming (2SGP) is proposed to deal with the credit scoring problem by incorporating the advantages of the IF-THEN rules and the discriminant function. On the basis of the numerical results, we can conclude that 2SGP can provide the better accuracy than other models.", } @InProceedings{Huang:2015:ieeeBigData, author = "Jih-Jeng Huang", booktitle = "2015 IEEE International Congress on Big Data", title = "Two Steps Genetic Programming for Big Data - Perspective of Distributed and High-Dimensional Data", year = "2015", pages = "753--756", abstract = "The term big data has been the most popular topic in recent years in practice, academe and government for realizing the value of data. Then, many information technologies and software are proposed to deal with big data, such as Hadoop, NoSQL databases, and cloud computing. However, these tools can only help us to store, manage, search, and control data rather than extract knowledge from big data. The only way to mine the nugget from big data is to have the ability to analyse them. The characteristics of complexity of big data, e.g., Volume and variety make traditional data mining algorithms invalid. In this paper, we deal with big data by solving distributed and high-dimensional problems. We propose a novel algorithm to effectively extract knowledge from big data. According to the empirical study, the propose method can handle big data soundly.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/BigDataCongress.2015.125", ISSN = "2379-7703", month = jun, notes = "Also known as \cite{7207309}", } @InProceedings{Huang:2015:CEC, author = "Jilin Huang and Ivan Tanev and Katsunori Shimohara", title = "Evolutionary Development of Electronic Stability Program for a Simulated Car in TORCS Environment", booktitle = "Proceedings of 2015 IEEE Congress on Evolutionary Computation (CEC 2015)", year = "2015", editor = "Yadahiko Murata", pages = "1474--1481", address = "Sendai, Japan", month = "25-28 " # may, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-7491-7", DOI = "doi:10.1109/CEC.2015.7257062", abstract = "We propose an approach of applying genetic programming (GP) for automated development of electronic stability program (ESP) of a car, realistically simulated in The Open Source Racing Car Simulator (TORCS). ESP facilitates the yaw rotation of an unstable (e.g., understeering or oversteering) car in slippery road conditions by applying asymmetric braking forces to its wheels. In the proposed approach, the amount of ESP-induced braking force is evolved - via GP - as an algebraic function of the parameters, pertinent to the state of the car, and their derivatives. The experimental results suggest that, compared to the car without ESP, the best evolved ESP offers a superior controllability - in terms of both (i) a smaller deviation from the ideal trajectory and (ii) faster average speed on a given, snowy test track. Presented work could be viewed as step towards the verification of the feasibility of GP for automated development of ESP. Also, we hope that the ESP, as a contributed new functionality of TORCS, would enrich the experience of gamers by adding an enhanced controllability of their cars in challenging road conditions.", notes = "1025 hrs 15347 CEC2015", } @InProceedings{Huang:2015:CIG, author = "Jilin Huang and Ivan Tanev and Katsunori Shimohara", title = "Evolving a General Electronic Stability Program for Car Simulated in TORCS", booktitle = "Proceedings of the IEEE Conference on Computational Intelligence and Games (CIG-2015)", year = "2015", editor = "Shi-Jim Yen and Tristan Cazenave and Philip Hingston", pages = "446--453", address = "Tainan, Taiwan", month = aug # " 31-" # sep # " 2", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, electronic stability program, evolutionary design, TORCS", DOI = "doi:10.1109/CIG.2015.7317955", size = "8 pages", abstract = "We present an approach of evolving (via Genetic Programming, GP) the electronic stability program (ESP) of a car, realistically simulated in The Open Racing Car Simulator (TORCS). ESP is intended to assist the yaw rotation of an unstable (e.g., either understeering or oversteering) car in low-grip, slippery road conditions by applying a carefully-timed asymmetrical braking forces to its wheels. In the proposed approach, the amount of ESP-induced brake force is represented as an evolvable (via GP) algebraic function (brake force function BFF) of the values of parameters, pertinent to the state of the car, and their derivatives. In order to obtain a general BFF, i.e., a function that result in a handling of the car, that is better than that of non ESP car, for a wide range of conditions, we evaluate the evolving BFF in several fitness cases representing different combinations of surface conditions and speeds of the car. The experimental results indicate that, compared to the car without ESP, the best evolved BFF of ESP offers a superior controllability - in terms of both (i) a smaller deviation from the ideal trajectory and (ii) faster average speed on a wide range of track conditions (icy, snowy, rainy and dry) and travelling speeds. Presented work could be viewed as an attempt to contribute a new functionality in TORCS that might enrich the experience of gamers by the enhanced controllability of their cars in slippery road conditions. Also, the results could be seen as a step towards the verification of the feasibility of applying GP for automated, evolutionary development of ESP.", notes = "Nice pictures of understeer and oversteer. XPG \cite{Tanev:2003:AROB} 11:20 http://cig2015.nctu.edu.tw/program Graduate School of Science and Engineering, Doshisha University, Kyoto, Japan", } @InProceedings{Huang:2019:CEC, author = "Kuan-Chun Huang and Yu-Wei Wen and Chuan-Kang Ting", title = "Enhancing k-Nearest Neighbors through Learning Transformation Functions by Genetic Programming", booktitle = "2019 IEEE Congress on Evolutionary Computation (CEC)", year = "2019", pages = "1891--1897", abstract = "The k-nearest neighbours algorithm (kNN) is renowned for solving classification tasks. The notion of kNN is to seek similar data instances in the dataset as prediction reference, for which the similarity between instances is ordinarily measured by Euclidean distance. Recently, some studies propose problem-tailored distance metrics to improve the classification performance of kNN. In this paper, we use genetic programming to learn the transformation function, which interprets the relationship of two data instances into a scalar differential. The differential of data pairs indicates the dissimilarity between two instances. This study considers two forms of transformation functions. Experimental results show the transform functions learned by GP can effectively enhance the performance of kNN.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2019.8790163", month = jun, notes = "Also known as \cite{8790163}", } @Article{huang:2024:Molecules, author = "Lulu Huang and Wen Liu and Xiaopeng Xing", title = "Adsorption of {O2} on the Preferred {-O-Au} Sites of Small Gold Oxide Clusters: Charge-dependent Interaction and Activation", journal = "Molecules", year = "2024", volume = "29", number = "7", pages = "Article No. 1645", keywords = "genetic algorithms, genetic programming", ISSN = "1420-3049", URL = "https://www.mdpi.com/1420-3049/29/7/1645", DOI = "doi:10.3390/molecules29071645", abstract = "Decades of research have illuminated the significant roles of gold/gold oxide clusters in small molecule catalytic oxidation. However, many fundamental questions, such as the actual sites to adsorb and activate O2 and the impact of charge, remain unanswered. Here, we have used an improved genetic algorithm program coupled with the DFT method to systematically search for the structures of Au1-5Ox-/+/0 (x = 1-4) and calculated binding interactions between Au1-5Ox-/+/0 (x = 1-2) and O2, aiming to determine the active sites and to elucidate the impact of different charge states in gold oxide systems. The results revealed that the reactivity of all three kinds of small gold oxide clusters toward O2 is strongly site-dependent, with clusters featuring an -O-Au site exhibiting a preference for adsorption. The charges on small gold oxide clusters significantly impact the interaction strength and the activation degree of adsorbed O2: in the case of anionic cluster, the interaction between O2 and the -O-Au sites leads to a chemical reaction involving electron transfer, thereby significantly activating O2; in neutral and cationic clusters, the adsorption of O2 on their -O-Au sites can be viewed as an electrostatic interaction. Pointedly, for cationic clusters, the highly concentrated positive charge on the Au atom of the -O-Au sites can strongly adsorb but hardly activate the adsorbed O2. These results have certain reference points for understanding the gold oxide interfaces and the improved catalytic oxidation performance of gold-based systems in the presence of atomic oxygen species.", notes = "also known as \cite{molecules29071645}", } @Article{Huang:2018:IEEEAccess, author = "Sai Huang and Yizhou Jiang and Xiaoqi Qin and Yue Gao and Zhiyong Feng and Ping Zhang", journal = "IEEE Access", title = "Automatic Modulation Classification of Overlapped Sources Using Multi-Gene Genetic Programming With Structural Risk Minimization Principle", year = "2018", volume = "6", pages = "48827--48839", abstract = "As the spectrum environment becomes increasingly crowded and complicated, primary users may be interfered by secondary users and other illegal users. Automatic modulation classification (AMC) of a single source cannot recognize the overlapped sources. Consequently, the AMC of overlapped sources attracts much attention. In this paper, we propose a genetic programming-based modulation classification method for overlapped sources (GPOS). The proposed GPOS consists of two stages, the training stage, and the classification stage. In the training stage, multi-gene genetic programming (MGP)-based feature engineering transforms sample estimates of cumulants into highly discriminative MGP-features iteratively, until optimal MGP-features (OMGP-features) are obtained, where the structural risk minimization principle (SRMP) is employed to evaluate the classification performance of MGP-features and train the classifier. Moreover, a self-adaptive genetic operation is designed to accelerate the feature engineering process. In the classification stage, the classification decision is made by the trained classifier using the OMGP-features. Through simulation results, we demonstrate that the proposed scheme outperforms other existing methods in terms of classification performance and robustness in case of varying power ratios and fading channel.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ACCESS.2018.2868224", ISSN = "2169-3536", notes = "Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China Also known as \cite{8452898}", } @Article{Huang:2025:jrmge, author = "Shuai Huang and Jian Zhou", title = "An enhanced stability evaluation system for entry-type excavations: Utilizing a hybrid bagging-{SVM} model, {GP} and kriging techniques", journal = "Journal of Rock Mechanics and Geotechnical Engineering", year = "2025", volume = "17", number = "4", pages = "2360--2373", keywords = "genetic algorithms, genetic programming, Entry-type excavations, Critical span graph, Stability evaluation, Machine learning, Support vector machine", ISSN = "1674-7755", URL = "https://www.sciencedirect.com/science/article/pii/S1674775524003068", DOI = "doi:10.1016/j.jrmge.2024.05.024", abstract = "In underground mining, especially in entry-type excavations, the instability of surrounding rock structures can lead to incalculable losses. As a crucial tool for stability analysis in entry-type excavations, the critical span graph must be updated to meet more stringent engineering requirements. Given this, this study introduces the support vector machine (SVM), along with multiple ensemble (bagging, adaptive boosting, and stacking) and optimisation (Harris hawks optimisation (HHO), cuckoo search (CS)) techniques, to overcome the limitations of the traditional methods. The analysis indicates that the hybrid model combining SVM, bagging, and CS strategies has a good prediction performance, and its test accuracy reaches 0.86. Furthermore, the partition scheme of the critical span graph is adjusted based on the CS-BSVM model and 399 cases. Compared with previous empirical or semi-empirical methods, the new model overcomes the interference of subjective factors and possesses higher interpretability. Since relying solely on one technology cannot ensure prediction credibility, this study further introduces genetic programming (GP) and kriging interpolation techniques. The explicit expressions derived through GP can offer the stability probability value, and the kriging technique can provide interpolated definitions for two new subclasses. Finally, a prediction platform is developed based on the above three approaches, which can rapidly provide engineering feedback", } @InProceedings{huang:2022:ANCFSKD, author = "WeiHong Huang and Pei He and ZhengHeng Yan and HaoYu Wu", title = "An Efficient {MRI} Impulse Noise Multi-stage Hybrid Filter Based on Cartesian Genetic Programming", booktitle = "Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery", year = "2022", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", URL = "http://link.springer.com/chapter/10.1007/978-3-030-89698-0_11", DOI = "doi:10.1007/978-3-030-89698-0_11", } @InProceedings{bias_code_review_FSE20, author = "Yu Huang and Kevin Leach and Zohreh Sharafi and Nicholas McKay and Tyler Santander and Westley Weimer", title = "Biases and Differences in Code Review using Medical Imaging and Eye-Tracking: Genders, Humans, and Machines", booktitle = "Proceedings of the 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2020", year = "2020", editor = "Myra Cohen and Thomas Zimmermann", pages = "456--468", address = "Virtual Event, USA", month = "8--13 " # nov, publisher = "ACM", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, APR, automatic program repair, code review, sex bias, fMRI, gender, eye-tracking, automation", isbn13 = "9781450370431", URL = "https://2020.esec-fse.org/details/fse-2020-papers/114/Biases-and-Differences-in-Code-Review-using-Medical-Imaging-and-Eye-Tracking-Genders", URL = "http://www-personal.umich.edu/~yhhy/index_files/bias_code_review_FSE20.pdf", URL = "https://doi.org/10.1145/3368089.3409681", DOI = "doi:10.1145/3368089.3409681", video_url = "https://youtu.be/Z_fKVMkd2Dk", size = "13 pages", abstract = "Code review is a critical step in modern software quality assurance,yet it is vulnerable to human biases. Previous studies have clarified the extent of the problem, particularly regarding biases against the authors of code, but no consensus understanding has emerged.Advances in medical imaging are increasingly applied to software engineering, supporting grounded neurobiological explorations of computing activities, including the review, reading, and writing of source code. In this paper, we present the results of a controlled experiment using both medical imaging and also eye tracking to investigate the neurological correlates of biases and differences between genders of humans and machines (e.g., automated program repair tools) in code review. We find that men and women conduct code reviews differently, in ways that are measurable and supported by behaviour, eye-tracking and medical imaging data. We also find biases in how humans review code as a function of its apparent author, when controlling for code quality. In addition to advancing our fundamental understanding of how cognitive biases relate to the code review process, the results may inform subsequent training and tool design to reduce bias", notes = "Not on GP, but human bias against machine generated code may be of interest. Human (37 students) bias against machine generated code patches. Data: https://web.eecs.umich.edu/~weimerw/fmri.html Thu 12 Nov 2020 01:35 - 01:36 at Virtual room 1 - Community. Univ. of Michigan Ann Arbor, MI, USA", } @InProceedings{Huang:2021:GI, author = "Yu Huang and Hammad Ahmad and Stephanie Forrest and Westley Weimer", title = "Applying Automated Program Repair to Dataflow Programming Languages", booktitle = "GI @ ICSE 2021", year = "2021", month = "30 " # may, editor = "Justyna Petke and Bobby R. Bruce and Yu Huang and Aymeric Blot and Westley Weimer and W. B. Langdon", publisher = "IEEE", address = "internet", pages = "21--22", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, Automated program repair, dataflow programming languages, parallelism, Verilog, HDL, TensorFlow, fault localisation", isbn13 = "978-1-6654-4466-8/21", video_url = "https://www.youtube.com/watch?v=-GHGgOtWp7o&list=PLI8fiFpB7BoKDaxvS7SQp0iA7fN7rrvDD&index=17", video_url = "https://www.youtube.com/watch?v=EEhK3SAvGws&list=PLI8fiFpB7BoKDaxvS7SQp0iA7fN7rrvDD&index=18", video_url = "https://www.youtube.com/watch?v=3a7nqROKlZI&list=PLXTjhGKkSnI-se7uQneCX-pEDiDrQ7TIS&index=1", DOI = "doi:10.1109/GI52543.2021.00013", size = "2 pages", abstract = "Dataflow programming languages are used in a variety of settings, and defects in their programs can have serious consequences. However, prior work in automated program repair (APR) emphasizes control flow over dataflow languages. We identify three impediments to the use of APR in dataflow programming, parallelism, state, and evaluation, and highlight opportunities for overcoming them.", notes = "p21 'reduce the maintenance cost' Video 3a7nqROKlZI Yu Huang. Discussion: 3:10 chair Aymeric Blot. Yu Huang, Hammad Ahmad, Stephanie Forrest 4:09 Q: Myra B. Cohen fault localisation in dataflow programs. 5:35 Q: W. B. Langdon, A: errors common and expensive in dataflow programs 6:25 Q: Bobby R. Bruce, mutations specific to dataflow languages 7:58 Q: Aymeric Blot evalution using symbolic execution part of \cite{Petke:2021:ICSEworkshop} http://geneticimprovementofsoftware.com/events/icse2021.html", } @Article{HUANG:2018:IJHE, author = "Yuhao Huang and Akhil Garg and Saeed Asghari and Xiongbin Peng and My Loan Phung Le", title = "Robust model for optimization of forming process for metallic bipolar plates of cleaner energy production system", journal = "International Journal of Hydrogen Energy", volume = "43", number = "1", pages = "341--353", year = "2018", keywords = "genetic algorithms, genetic programming, Proton-exchange membrane fuel cell(PEMFC), Rubber pad forming(RPF), Genetic programming(GP), Factorial design method", ISSN = "0360-3199", DOI = "doi:10.1016/j.ijhydene.2017.11.043", URL = "http://www.sciencedirect.com/science/article/pii/S0360319917343604", abstract = "Energy production systems such as proton-exchange membrane fuel cell (PEMFC) has a promising future in the cleaner energy market due to zero emissions. Rubber pad forming (RPF) process of metallic bipolar plates of PEMFCs is gaining attention among the researchers. Studies based on design of experiments have been conducted to find the crucial parameters of the forming process. These methods are based on the assumptions of the model structure, correlated residuals, etc., which can cause uncertainty in estimation ability of the model on unseen data. Therefore, the present study focuses on the design of robust models of these parameters for PEMFCs using an optimization approach of genetic programming (GP). The inputs from the experiments considered in GP are radius, the friction coefficient, the filling factor and the minimum thickness. Experiments on PEMFCs validates the performance of the GP models. Further, the relationships between the two inputs and the three outputs for PEMFCs are generated as well as the contributions of each input to each of the output. Optimization of the models generated by GP can further determine the forming quality of metallic bipolar plates of PEMFCs by an appropriate setting of the two inputs", } @Article{HUANG:2018:Measurement, author = "Yuhao Huang and Liang Gao and Zhang Yi and Kang Tai and P. Kalita and Paweena Prapainainar and Akhil Garg", title = "An application of evolutionary system identification algorithm in modelling of energy production system", journal = "Measurement", volume = "114", pages = "122--131", year = "2018", keywords = "genetic algorithms, genetic programming, System identification, Modelling methods, Fuel cell, Energy system", ISSN = "0263-2241", DOI = "doi:10.1016/j.measurement.2017.09.009", URL = "http://www.sciencedirect.com/science/article/pii/S0263224117305742", abstract = "The present work introduces the literature review on System Identification (SI) by classifying it into several fields. The review summarizes the need of evolutionary SI method that automates the model structure selection and its parameter evaluation based on only the system data. In this context, the evolutionary SI approach of genetic programming (GP) is applied in modelling and optimization of cleaner energy system such as direct methanol fuel cell. The functional response of the power density of the fuel cell with respect to input conditions is selected based on the minimum training error. Further, an experimental data is used to validate the robustness of the formulated GP model. The analysis based on 2-D and 3-D parametric procedure is further conducted to reveals insights into functioning of the fuel cell. The Pareto front obtained from optimization of model reveals that the operating temperature of 64.5 degree C, methanol flow rate of 28.04mL/min and methanol concentration of 0.29M are the optimum settings for achieving the maximum power density of 7.36mW/cm2 for DMFC", } @Article{Huang:2018:ieeeTEC, author = "Zhengwen Huang and Maozhen Li and Christos Chousidis and Ali Mousavi and Changjun Jiang", title = "Schema Theory Based Data Engineering in Gene Expression Programming for Big Data Analytics", journal = "IEEE Transactions on Evolutionary Computation", year = "2018", volume = "22", number = "5", pages = "792--804", month = oct, keywords = "genetic algorithms, genetic programming, Gene expression programming, data engineering, big data analytic, parallelization and segmentation", ISSN = "1089-778X", URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8187687", DOI = "doi:10.1109/TEVC.2017.2771445", size = "14 pages", abstract = "Gene expression programming (GEP) is a data driven evolutionary technique that well suits for correlation mining. Parallel GEPs are proposed to speed up the evolution process using a cluster of computers or a computer with multiple CPU cores. However, the generation structure of chromosomes and the size of input data are two issues that tend to be neglected when speeding up GEP in evolution. To fill the research gap, this paper proposes three guiding principles to elaborate the computation nature of GEP in evolution based on an analysis of GEP schema theory. As a result, a novel data engineered GEP is developed which follows closely the generation structure of chromosomes in parallelization and considers the input data size in segmentation. Experimental results on two data sets with complementary features show that the data engineered GEP speeds up the evolution process significantly without loss of accuracy in data correlation mining. Based on the experimental tests, a computation model of the data engineered GEP is further developed to demonstrate its high scalability in dealing with potential big data using a large number of CPU cores.", notes = "also known as \cite{8187687}", } @Article{journals/tetci/HuangLMDW19, title = "{EGEP}: An Event Tracker Enhanced Gene Expression Programming for Data Driven System Engineering Problems", author = "Zhengwen Huang and Maozhen Li and Alireza Mousavi and Morad Danishvar and Zidong Wang", journal = "IEEE Transactions on Emerging Topics in Computational Intelligence", year = "2019", number = "2", volume = "3", pages = "117--126", month = apr, keywords = "genetic algorithms, genetic programming, gene expression programming, schema theory,event tracker, data driven system engineering, Z-fact0r", bibdate = "2020-07-14", DOI = "doi:10.1109/TETCI.2018.2864724", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/tetci/tetci3.html#HuangLMDW19", size = "10 pages", abstract = "Gene expression programming (GEP) is a data driven evolutionary technique that is well suited to correlation mining of system components. With the rapid development of industry 4.0, the number of components in a complex industrial system has increased significantly with a high complexity of correlations. As a result, a major challenge in employing GEP to solve system engineering problems lies in computation efficiency of the evolution process. To address this challenge, this paper presents EGEP, an event tracker enhanced GEP, which filters irrelevant system components to ensure the evolution process to converge quickly. Furthermore, we introduce three theorems to mathematically validate the effectiveness of EGEP based on a GEP schema theory. Experiment results also confirm that EGEP outperforms the GEP with a shorter computation time in an evolution.", } @InProceedings{Huang:2012:ICML, author = "Zhi-Qian Huang and Wing W. Y. Ng", booktitle = "Proceedings of the 2012 International Conference on Machine Learning and Cybernetics, ICML 2012", title = "Empirical estimation of functional relationships between {Q} value of the {L-GEM} and training data using genetic programming", year = "2012", volume = "1", pages = "341--348", month = "15-17 " # jul, address = "Xian", size = "8 pages", abstract = "The Localised Generalisation Error Model (L-GEM) provides a practical framework for evaluating generalisation capability of a learning machine , e.g. neural network. The Q value of the L-GEM controls the coverage of unseen samples under evaluation. Owing to the nonlinear and real unknown relationship of unseen samples and their generalisation error, different Q values yield different L-GEM values. In this paper, we adopt an evolutionary procedure based on genetic programming and artificial datasets to estimate functional relationship between Q values and statistics of training samples. In this first empirical study, a simple training samples generated from two two-dimensional Gaussian distribution is adopted. Resulting formulae provide hints to select optimal Q value for given classification problems.", keywords = "genetic algorithms, genetic programming, Gaussian distribution, generalisation (artificial intelligence), learning (artificial intelligence), pattern classification, 2D Gaussian distribution, L-GEM, Q value, artificial dataset, classification problems, empirical estimation, evolutionary procedure, functional relationship, generalisation error, localized generalisation error model, machine learning, statistics, training data sample, Abstracts, Programming, Localised Generalisation Error Model, Q-neighbourhood", DOI = "doi:10.1109/ICMLC.2012.6358937", ISSN = "2160-133X", notes = "Also known as \cite{6358937}", } @InProceedings{Huang:2018:GECCOcomp, author = "Zhixing Huang and Jinghui Zhong and Weili Liu and Zhou Wu", title = "Multi-population genetic programming with adaptively weighted building blocks for symbolic regression", booktitle = "GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2018", editor = "Carlos Cotta and Tapabrata Ray and Hisao Ishibuchi and Shigeru Obayashi and Bogdan Filipic and Thomas Bartz-Beielstein and Grant Dick and Masaharu Munetomo and Silvino {Fernandez Alzueta} and Thomas Stuetzle and Pablo Valledor Pellicer and Manuel Lopez-Ibanez and Daniel R. Tauritz and Pietro S. Oliveto and Thomas Weise and Borys Wrobel and Ales Zamuda and Anne Auger and Julien Bect and Dimo Brockhoff and Nikolaus Hansen and Rodolphe {Le Riche} and Victor Picheny and Bilel Derbel and Ke Li and Hui Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Stephane Doncieux and Richard Duro and Joshua Auerbach and Harold {de Vladar} and Antonio J. Fernandez-Leiva and JJ Merelo and Pedro A. Castillo-Valdivieso and David Camacho-Fernandez and Francisco {Chavez de la O} and Ozgur Akman and Khulood Alyahya and Juergen Branke and Kevin Doherty and Jonathan Fieldsend and Giuseppe Carlo Marano and Nikos D. Lagaros and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Boris Naujoks and Vanessa Volz and Tea Tusar and Pascal Kerschke and Riyad Alshammari and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and John R. Woodward and Shin Yoo and John McCall and Nayat Sanchez-Pi and Luis Marti and Danilo Vasconcellos and Masaya Nakata and Anthony Stein and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Ivanoe {De Falco} and Antonio {Della Cioppa} and Ernesto Tarantino and Umberto Scafuri and P. G. M. Baltus and Giovanni Iacca and Ahmed Hallawa and Anil Yaman and Alma Rahat and Handing Wang and Yaochu Jin and David Walker and Richard Everson and Akira Oyama and Koji Shimoyama and Hemant Kumar and Kazuhisa Chiba and Pramudita Satria Palar", isbn13 = "978-1-4503-5764-7", pages = "266--267", address = "Kyoto, Japan", DOI = "doi:10.1145/3205651.3205673", publisher = "ACM", publisher_address = "New York, NY, USA", month = "15-19 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", abstract = "Genetic programming(GP) is a powerful tool to solve Symbolic Regression that requires finding mathematic formula to fit the given observed data. However, existing GPs construct solutions based on building blocks (i.e., the terminal and function set) defined by users in an ad-hoc manner. The search efficacy of GP could be degraded significantly when the size of the building blocks increases. To solve the above problem, this paper proposes a multi-population GP framework with adaptively weighted building blocks. The key idea is to divide the whole population into multiple sub-populations with building blocks with different weights. During the evolution, the weights of building blocks in the sub-populations are adaptively adjusted so that important building blocks can have larger weights and higher selection probabilities to construct solutions. The proposed framework is tested on a set of benchmark problems, and the experimental results have demonstrated the efficacy of the proposed method.", notes = "Also known as \cite{3205673} GECCO-2018 A Recombination of the 27th International Conference on Genetic Algorithms (ICGA-2018) and the 23rd Annual Genetic Programming Conference (GP-2018)", } @InProceedings{Huang:2019:SSCI, author = "Zhixing Huang and Chengyu Lu and Jinghui Zhong", booktitle = "2019 IEEE Symposium Series on Computational Intelligence (SSCI)", title = "A Multi-Objective Hyper-Heuristic for Unmanned Aerial Vehicle Data Collection in Wireless Sensor Networks", year = "2019", pages = "1614--1621", abstract = "Monitoring dangerous regions is one of the most important applications of wireless sensor networks. Limited by the danger of monitoring regions and the battery power of sensors, unmanned aerial vehicles (UAVs) are often used to collect data in such applications. How to properly schedule the movement of UAVs to efficiently collect data is still a challenging problem to be solved. In this paper, we formulate the UAV scheduling problem as a multi-objective optimization problem and design a genetic programming based hyper-heuristic framework to solve the problem. The simulation results show that our method can provide very promising performance in comparison with several state-of-the-art methods.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SSCI44817.2019.9002862", month = dec, notes = "Also known as \cite{9002862}", } @Article{DBLP:journals/soco/HuangZFMC20, author = "Zhixing Huang and Jinghui Zhong and Liang Feng and Yi Mei and Wentong Cai", title = "A fast parallel genetic programming framework with adaptively weighted primitives for symbolic regression", journal = "Soft Comput.", volume = "24", number = "10", pages = "7523--7539", year = "2020", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/s00500-019-04379-4", DOI = "doi:10.1007/s00500-019-04379-4", timestamp = "Mon, 15 Jun 2020 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/soco/HuangZFMC20.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{DBLP:conf/ssci/HuangMZ21, author = "Zhixing Huang and Yi Mei and Mengjie Zhang", title = "Investigation of Linear Genetic Programming for Dynamic Job Shop Scheduling", booktitle = "{IEEE} Symposium Series on Computational Intelligence, {SSCI} 2021, Orlando, FL, USA, December 5-7, 2021", publisher = "{IEEE}", year = "2021", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1109/SSCI50451.2021.9660091", DOI = "doi:10.1109/SSCI50451.2021.9660091", timestamp = "Thu, 03 Feb 2022 00:00:00 +0100", biburl = "https://dblp.org/rec/conf/ssci/HuangMZ21.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{Huang:2022:EuroGP, author = "Zhixing Huang and Fangfang Zhang and Yi Mei and Mengjie Zhang", title = "An Investigation of Multitask Linear Genetic Programming for Dynamic Job Shop Scheduling", booktitle = "EuroGP 2022: Proceedings of the 25th European Conference on Genetic Programming", year = "2022", editor = "Eric Medvet and Gisele Pappa and Bing Xue", series = "LNCS", volume = "13223", publisher = "Springer Verlag", address = "Madrid, Spain", pages = "162--178", month = "20-22 " # apr, organisation = "EvoStar, Species", note = "Best paper", keywords = "genetic algorithms, genetic programming, Linear genetic programming, Multitask, Hyper-heuristic, Dynamic job shop scheduling", isbn13 = "978-3-031-02055-1", DOI = "doi:10.1007/978-3-031-02056-8_11", abstract = "Dynamic job shop scheduling has a wide range of applications in reality such as order picking in warehouse. Using genetic programming to design scheduling heuristics for dynamic job shop scheduling problems becomes increasingly common. In recent years, multitask genetic programming-based hyper-heuristic methods have been developed to solve similar dynamic scheduling problem scenarios simultaneously. However, all of the existing studies focus on the tree-based genetic programming. In this paper, we investigate the use of linear genetic programming, which has some advantages over tree-based genetic programming in designing multitask methods, such as building block reusing. Specifically, this paper makes a preliminary investigation on several issues of multitask linear genetic programming. The experiments show that the linear genetic programming within multitask frameworks have a significantly better performance than solving tasks separately, by sharing useful building blocks.", notes = "http://www.evostar.org/2022/eurogp/ Part of \cite{Medvet:2022:GP} EuroGP'2022 held inconjunction with EvoApplications2022 EvoCOP2022 EvoMusArt2022", } @InProceedings{huang:2022:GECCO2, author = "Zhixing Huang and Yi Mei and Fangfang Zhang and Mengjie Zhang", title = "Graph-based Linear Genetic Programming: A Case Study of Dynamic Scheduling", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference", year = "2022", editor = "Alma Rahat and Jonathan Fieldsend and Markus Wagner and Sara Tari and Nelishia Pillay and Irene Moser and Aldeida Aleti and Ales Zamuda and Ahmed Kheiri and Erik Hemberg and Christopher Cleghorn and Chao-li Sun and Georgios Yannakakis and Nicolas Bredeche and Gabriela Ochoa and Bilel Derbel and Gisele L. Pappa and Sebastian Risi and Laetitia Jourdan and Hiroyuki Sato and Petr Posik and Ofer Shir and Renato Tinos and John Woodward and Malcolm Heywood and Elizabeth Wanner and Leonardo Trujillo and Domagoj Jakobovic and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Inmaculada Medina-Bulo and Slim Bechikh and Andrew M. Sutton and Pietro Simone Oliveto", pages = "955--963", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, hyper-heuristic, directed acyclic graph, linear genetic programming, building block, intron, dynamic job shop scheduling, DJSS", isbn13 = "978-1-4503-9237-2", DOI = "doi:10.1145/3512290.3528730", video_url = "https://vimeo.com/723510274", size = "9 pages", abstract = "Linear genetic programming (LGP) has been successfully applied to various problems such as classification, symbolic regression and hyper-heuristics for automatic heuristic design. In contrast with the traditional tree-based genetic programming (TGP), LGP uses a sequence of instructions to represent an individual (program), and the data is carried by registers. A common issue of LGP is that LGP is susceptible to introns (i.e., instructions with no effect to the program output), which limits the effectiveness of traditional genetic operators. To address these issues, we propose a new graph-based LGP system. Specifically, graph-based LGP uses graph-based crossover and graph-based mutation to produce offspring. The graph-based crossover operator firstly converts each LGP parent to a directed acyclic graph (DAG), and then swaps the sub-graphs between the DAGs. The graph-based mutation selectively modify the connections in DAGs based on the height of sub graphs. To verify the effectiveness of the new graph-based genetic operators, we take the dynamic job shop scheduling as a case study, which has shown to be a challenging problem for LGP. The experimental results show that the LGP with the new graph-based genetic operators can obtain better scheduling heuristics than the LGP with the traditional operators and TGP.", notes = "p955 'introns are in the sub-graphs that are isolated from the main DAG.' 'often destruct useful building blocks (i.e., topological structures of effective instructions).' genetic operators 'select only the instructions contributing to the final program output for modification. Specifically, the graph-based crossover operator selects a sub-graph from the DAGs corresponding to both parents and swaps them. The graph-based mutation mutates registers (i.e.,connection in DAGs) based on the height of sub-graphs.' p957 crossover 'swaps the two sub-graphs to generate two offspring'. Graph-based Mutation 'tend to obtain a wider DAG with more parallel branches', 'increase ... large sub-graphs and reuse larger building blocks'. GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @InProceedings{Huang:2022:SSCI, author = "Zhixing Huang and Yi Mei and Fangfang Zhang and Mengjie Zhang", title = "A Further Investigation to Improve Linear Genetic Programming in Dynamic Job Shop Scheduling", year = "2022", booktitle = "2022 IEEE Symposium Series on Computational Intelligence (SSCI)", pages = "496--503", month = dec, keywords = "genetic algorithms, genetic programming, Training, Job shop scheduling, Sensitivity, Dynamic scheduling, Real-time systems, Dispatching, Linear Genetic Programming, Dynamic Job Shop Scheduling, Hyper Heuristics", DOI = "doi:10.1109/SSCI51031.2022.10022208", notes = "Also known as \cite{10022208}", abstract = "Dynamic Job Shop Scheduling (DJSS) is an important problem with many real-world applications. Genetic programming is a promising technique to solve DJSS, which automatically evolves dispatching rules to make real-time scheduling decisions in dynamic environments. Linear Genetic Programming (LGP) is a notable variant of genetic programming methods. Compared with Tree-based Genetic Programming (TGP), LGP has high flexibility of reusing building blocks and easy control of bloat effect. Due to these advantages, LGP has been successfully applied to various domains such as classification and symbolic regression. However, for solving DJSS, the most commonly used GP method is TGP. It is interesting to see whether LGP can perform well, or even outperform TGP in the DJSS domain. Applying LGP as a hyper-heuristic method to solve DJSS problems is still in its infancy. An existing study has investigated some basic design issues (e.g., parameter sensitivity and training and test performance) of LGP. However, that study lacks a comprehensive investigation on the number of generations and different genetic operator rates, and misses the investigation on register initialization strategy of LGP. To have a more comprehensive investigation, this paper investigates different generations, genetic operator rates, and register initialization strategies of LGP for solving DJSS. A further comparison with TGP is also conducted. The results show that sufficient evolution generations and initializing registers by diverse features are important for LGP to have a superior performance.", } @Article{9810862, author = "Zhixing Huang and Yi Mei and Jinghui Zhong", title = "Semantic Linear Genetic Programming for Symbolic Regression", journal = "IEEE Transactions on Cybernetics", year = "2024", volume = "54", number = "2", pages = "1321--1334", month = feb, keywords = "genetic algorithms, genetic programming, Semantics, Genetics, Syntactics, Biological cells, Regression analysis, mutate-and-divide propagation (MDP), symbolic regression (SR)", DOI = "doi:10.1109/TCYB.2022.3181461", abstract = "Symbolic regression (SR) is an important problem with many applications, such as automatic programming tasks and data mining. Genetic programming (GP) is a commonly used technique for SR. In the past decade, a branch of GP that uses the program behaviour to guide the search, called semantic GP (SGP), has achieved great success in solving SR problems. However, existing SGP methods only focus on the tree-based chromosome representation and usually encounter the bloat issue and unsatisfactory generalisation ability. To address these issues, we propose a new semantic linear GP (SLGP) algorithm. In SLGP, we design a new chromosome representation to encode the programs and semantic information in a linear fashion. To use the semantic information more effectively, we further propose a novel semantic genetic operator, namely, mutate-and-divide propagation, to recursively propagate the semantic error within the linear program. The empirical results show that the proposed method has better training and test errors than the state-of-the-art algorithms in solving SR problems and can achieve a much smaller program size.", } @InProceedings{Huang:2023:GECCO, author = "Zhixing Huang and Yi Mei and Fangfang Zhang and Mengjie Zhang", title = "{Grammar-Guided} Linear Genetic Programming for Dynamic Job Shop Scheduling", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "1137--1145", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, linear genetic programming, grammar, dynamic job shop scheduling", isbn13 = "9798400701191", DOI = "doi:10.1145/3583131.3590394", size = "9 pages", abstract = "Dispatching rules are commonly used to make instant decisions in dynamic scheduling problems. Linear genetic programming (LGP) is one of the effective methods to design dispatching rules automatically. However, the effectiveness and efficiency of LGP methods are limited due to the large search space. Exploring the entire search space of programs is inefficient for LGP since a large number of programs might contain redundant blocks and might be inconsistent with domain knowledge, which would further limit the effectiveness of the produced LGP models. To improve the performance of LGP in dynamic job shop scheduling problems, this paper proposes a grammar-guided LGP to make LGP focus more on promising programs. Our dynamic job shop scheduling simulation results show that the proposed grammar-guided LGP has better training efficiency than basic LGP, and can produce solutions with good explanations. Further analyses show that grammar-guided LGP significantly improves the overall test effectiveness when the number of LGP registers increases.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @Article{Huang:ieeeTEC, author = "Zhixing Huang and Yi Mei and Fangfang Zhang and Mengjie Zhang", title = "Multitask Linear Genetic Programming with Shared Individuals and its Application to Dynamic Job Shop Scheduling", journal = "IEEE Transactions on Evolutionary Computation", year = "2024", volume = "28", number = "6", pages = "1546--1560", month = dec, keywords = "genetic algorithms, genetic programming, genetic Multitask optimization, Linear genetic programming, Directed acyclic graph, Dynamic job shop scheduling", ISSN = "1089-778X", URL = "https://ieeexplore.ieee.org/document/10090245", DOI = "doi:10.1109/TEVC.2023.3263871", size = "15 pages", notes = "also known as \cite{10090245}", } @Article{Huang:ieeeTEC2, author = "Zhixing Huang and Yi Mei and Fangfang Zhang and Mengjie Zhang", title = "Toward Evolving Dispatching Rules With Flow Control Operations By Grammar-Guided Linear Genetic Programming", journal = "IEEE Transactions on Evolutionary Computation", note = "Accepted for future publication", keywords = "genetic algorithms, genetic programming, Dispatching, Job shop scheduling, Grammar, Dynamic scheduling, Aerospace electronics, Process control, Grammar-guided Linear Genetic Programmin, Flow Control Operations, Hyper Heuristics", ISSN = "1089-778X", URL = "https://ieeexplore.ieee.org/document/10398533", DOI = "doi:10.1109/TEVC.2024.3353207", size = "15 pages", notes = "also known as \cite{10398533}", } @Article{Huang:2024:GPEM, author = "Zhixing Huang and Yi Mei and Fangfang Zhang and Mengjie Zhang and Wolfgang Banzhaf", title = "Bridging directed acyclic graphs to linear representations in linear genetic programming: a case study of dynamic scheduling", journal = "Genetic Programming and Evolvable Machines", year = "2024", volume = "25", pages = "Article no 5", note = "Online first", keywords = "genetic algorithms, genetic programming, Linear genetic programming, Directed acyclic graph, Genetic operator, Dynamic job shop scheduling", ISSN = "1389-2576", URL = "https://rdcu.be/dw3ha", DOI = "doi:10.1007/s10710-023-09478-8", size = "33 pages", } @Article{Huang:ieeeTEC3, author = "Zhixing Huang and Yi Mei and Fangfang Zhang and Mengjie Zhang and Wolfgang Banzhaf", title = "Fitness Landscape Optimization Makes Stochastic Symbolic Search By Genetic Programming Easier", journal = "IEEE Transactions on Evolutionary Computation", note = "Early Access", keywords = "genetic algorithms, genetic programming, Search problems, Optimization, Symbols, Phenotypes, Genotypes, Stochastic processes, Genetic operators, Robustness, Evolutionary computation, Fitness landscape, neighborhood structure, stochastic symbolic search", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2024.3525006", notes = "also known as \cite{10819486}", } @Article{RISI5715Ana, author = "Ana Maria {Huayna Duenas}", title = "Revision de las tecnicas existentes en Programacion Genetica para evolucionar las subrutinas", journal = "Revista de investigacion de Sistemas e Informatica", volume = "10", number = "1", year = "2014", pages = "65--73", keywords = "genetic algorithms, genetic programming, evolution of subroutines, ADF", ISSN = "1816-3823", URL = "http://revistasinvestigacion.unmsm.edu.pe/index.php/sistem/article/view/5715", DOI = "doi:10.15381/risi.v10i1.5715", abstract = "The genetic programming (GP) is a machine learning technique based on the evolution of computer programs by a genetic algorithm. The version, called ADF (automatic function definition) to reuse a subroutine several times within the same individual. However, there is the possibility that the same subroutine may be reused by several individuals from the same population. There are several systems that, in principle, allow subroutines discover valid for many individuals in a population. One of the most advanced is the DLGP dynamic network. This work aims to develop the State of Art of PG varied techniques lo evolve existing subroutines", abstract = "La programacion genetica (PG) es una tecnica de aprendizaje automatico que se basa en la evolucion de programas de ordenador mediante un algoritmo genetico. La version, denominadaADF (definicion automatica de funciones) permite reutilizar una subrutina varias veces dentro de un mismo individuo. Sin embargo, existe la posibilidad de que la misma subrutina pueda ser reaprovechada por varios individuos de la misma poblacion. Existen varios sistemas que, en principio, permiten descubrir subrutinas validas para muchos individuos de una poblacion. Uno de los mas avanzados es el de red dinamica DLGP. Este trabajo tiene como proposito desarrollar el Estado de Arte de las variadas tecnicas existentes en PG para evolucionar subrutinas", notes = "In spanish", } @InProceedings{Hubball:2007:ICSC, author = "D. Hubball and M. Chen and P. W. Grant and D. Cosker", title = "Evolutionary Morphing for Facial Aging Simulation", booktitle = "International Crime Science Conference (ICSC 2007)", year = "2007", address = "UCL, London", month = "16 " # jul, keywords = "genetic algorithms, genetic programming, artificial intelligence, problem solving, control methods and search, computer graphics, picture image generation, methodology and techniques, image processing and computer vision:, reconstruction, image metamorphosis, morphing, warping, nonuniform radial basis functions, facial aging, face modelling, evolutionary computing, data-driven modelling", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.205.6838", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.205.6838", abstract = "Aging has considerable effects on the appearance of the human face and is difficult to simulate using a universally-applicable global model. In this paper, we present a data-driven framework for facial age progression (and regression) automatically in conjunction with a database of facial images. We build parametrised local models for face modelling, age-transformation and image warping based on a subset of imagery data selected according to an input image and associated metadata. In order to obtain a person-specific mapping in the model space from an encoded face description to an encoded age-transformation, we employed genetic programming to automatically evolve a solution by learning from example transformations in the selected subset. In order to capture various factors that determine the influence of feature points, we developed a new image warping algorithm based on non-uniform radial basis functions (NURBFs). A genetic algorithm was used to handle the large parameter space associated with NURBFs. With evolutionary computing, our approach is able to infer from the input and the database the most appropriate models to be used for transforming the input face. We compared our data-driven approach with the traditional global model approach. The noticeable improvement in terms of the resemblance between the output images and the actual target images (which are unknown to the process) demonstrated the effectiveness and usability of this new approach.", notes = "Daniel Hubball and Min Chen and Phil W. Grant and Darren Cosker See also technical report CSR 6-2006 http://www.cs.swansea.ac.uk/reports/yr2006/CSR6-2006.pdf http://www.ucl.ac.uk/scs/events/crime-science-conf/icsc-2007", } @Article{Hubball:2008:CGF, author = "Daniel Hubball and Min Chen and Phil W. Grant", title = "Image-based Aging Using Evolutionary Computing", journal = "Computer Graphics Forum", year = "2008", volume = "27", number = "2", pages = "607--616", note = "EUROGRAPHICS 2008 / G. Drettakis and R. Scopigno (Guest Editors)", keywords = "genetic algorithms, genetic programming, I.3.3 Computer Graphics, Picture/Image Generation; I.3.6 Computer Graphics, Methodology and Techniques; I.2.8 Artificial Intelligence, Problem Solving, Control Methods and Search", ISSN = "1467-8659", publisher = "Blackwell Publishing Ltd", URL = "http://dx.doi.org/10.1111/j.1467-8659.2008.01158.x", DOI = "doi:10.1111/j.1467-8659.2008.01158.x", abstract = "Ageing has considerable visual effects on the human face and is difficult to simulate using a universally-applicable global model. In this paper, we focus on the hypothesis that the patterns of age progression (and regression) are related to the face concerned, as the latter implicitly captures the characteristics of gender, ethnic origin, and age group, as well as possibly the person-specific development patterns of the individual. We use a data-driven framework for automatic image-based facial transformation in conjunction with a database of facial images. We build a novel parametrised model for encoding age-transformation in addition with the traditional model for face description. We use evolutionary computing to learn the relationship between the two models. To support this work, we also developed a new image warping algorithm based on non-uniform radial basis functions (NURBFs). Evolutionary computing was also used to handle the large parameter space associated with NURBFs. In comparison with several different methods, it consistently provides the best results against the ground truth.", notes = "Also known as \cite{CGF:CGF1158}", } @InProceedings{hue:2024:CEC, author = "Tran Thi Hue and Bao Nguyen and Quoc Nguyen and Pham Phu Manh and Nguyen Khanh Phuong and Huynh Thi Thanh Binh and Dang Quang Thang", title = "The Vehicle Routing Problem with Drones and Flexibility Demands", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, Power demand, Soft sensors, Vehicle routing, Transportation, Benchmark testing, Mixed integer linear programming, Proposals, drone, routing, split delivery, flexibility demand, profit, genetic algorithm", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10611902", abstract = "In recent years, the Vehicle Routing Problem, with its variants handling complex realistic constraints, continues to be a key area of research, reflecting its enduring importance in modernized societies. Nevertheless, less attention has been paid to the variant with the flexibility of customer demands. This paper introduces the problem of addressing the integration of drones into the existing transportation system to maximize the profit of distributing freight during a limited working time by allowing flexibility in the delivered quantity. We propose a Mixed Integer Linear Programming (MILP) formulation and a genetic algorithm (GA) to solve the problem. To assess the proposed algorithm, we create the benchmark instances of the problem from published data sources. Experimental results on the instances show a significantly better performance of our proposal than MILP. Moreover, we evaluate our approach by comparing its performance with existing solutions in published closely related works.", notes = "also known as \cite{10611902} Is this GP? WCCI 2024", } @InProceedings{Huelsbergen:1996:tsemli, author = "Lorenz Huelsbergen", title = "Toward Simulated Evolution of Machine-Language Iteration", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "315--320", address = "Stanford University, CA, USA", publisher = "MIT Press", URL = "http://cm.bell-labs.com/cm/cs/who/lorenz/papers/gp96.pdf", URL = "http://cm.bell-labs.com/cm/cs/who/lorenz/papers/gp96.ps", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap41.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", size = "6 pages", notes = "GP-96 Cites \cite{koza:book}, \cite{icnn93:kinnear}, \cite{brave:1996:aigp2}. Virtual register machine (VRM) Finnegan SML/NJ. p 316 {"}GP can automatically synthesis multiplication routines.{"} {"}All (GP) solutions discovered to date are general{"}. 12 instructions. Pop=1024, 2000 GP runs versus 1000000000 random programs.", } @InProceedings{Huelsbergen:1997:lrsemlp, author = "Lorenz Huelsbergen", title = "Learning Recursive Sequences via Evolution of Machine-Language Programs", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming, MLGP", pages = "186--194", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", URL = "http://bell-labs.co/who/lorenz/papers/gp97.pdf", URL = "http://cm.bell-labs.com/cm/cs/who/lorenz/papers/gp97.ps", size = "9 pages", abstract = "We use directed search techniques in the space of computer programs to learn recursive sequences of positive integers. Specifically, the integer sequences of squares, x^2; cubes, x^3; factorial, x!; and Fibonacci numbers are studied. Given a small finite prefix of a sequence, we show that three directed searches, machine-language genetic programming with crossover, exhaustive iterative hill climbing, and a hybrid (crossover and hill climbing), can automatically discover programs that exactly reproduce the finite target prefix and, moreover, that correctly produce the remaining sequence up to the under lying machine precision. Our machine-language representation is generic, it contains instructions for arithmetic, register manipulation and comparison, and control flow. We also introduce an output instruction that allows variable-length sequences as result values. Importantly, this representation does not contain recursive operators; recursion, when needed, is automatically synthesised from primitive instructions. For a fixed set of search parameters (e.g., instruction set, program size, fitness criteria), we compare the frequencies of the three directed search techniques on the four sequence problems. For this parameter set, an evolutionary-based search always out performs exhaustive hill climbing as well as undirected random search. Since only the prefix of the target sequence is variable in our experiments, we posit that this approach to sequence induction is potentially quite general.", notes = "GP-97. Comparison with random search", } @InProceedings{huelsbergen:1998:fgsppemlr, author = "Lorenz Huelsbergen", title = "Finding General Solutions to the Parity Problem by Evolving Machine-Language Representations", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "158--166", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", email = "lorenz@research.bell-labs.com", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-548-7", URL = "http://cm.bell-labs.com/cm/cs/who/lorenz/papers/gp98.ps", notes = "GP-98", } @InProceedings{huelsbergen:2005:CEC, author = "Lorenz Huelsbergen", title = "Fast Evolution of Custom Machine Representations", booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary Computation", year = "2005", editor = "David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L. Gwenn Volkert and Dan Ashlock and Marc Schoenauer", volume = "1", pages = "97--104", address = "Edinburgh, UK", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "2-5 " # sep, organisation = "IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9363-5", URL = "http://netlib.bell-labs.com/who/lorenz/papers/huelsbergen-cec2005.pdf", DOI = "doi:10.1109/CEC.2005.1554672", abstract = "Described are new approaches for evaluating computer program representations for use in automated search methodologies such as the evolutionary design of software. Previously, program representations have been either evaluated directly on raw hardware, providing high speed but little control and flexibility; or, programs were interpreted by a software interpreter which can incorporate much flexibility into a program's evaluation, but does so at a large cost in time due to interpretation overheads. Here we bridge this gap by providing intermediate compilation techniques for machine representations that approach the speed of running raw bits directly on hardware, but that have all the flexibility and control of custom instruction sets. In particular, we describe two compilation techniques: the first uses just-in-time compilation to convert a custom instruction sequence to machine code; the second compiles an instruction set specification into a specialised interpreter which incurs only small overheads for instruction decoding. We show that both techniques can provide manyfold speedups over direct interpretation while retaining the expressiveness of custom representations.", notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS. 'complex control structures such as loops and recursion'. 'branches...are always realtive to the program counter' (target address fix_offset macro used after crossover). JIT. 'MIPS instruction set architecture as the target native machine'. Exact number of instrauctions interprestted can be controlled (ie limited) via termchk_macro.", } @InProceedings{Hughes:2016:GECCOcomp, author = "James Alexander Hughes and Mark Daley", title = "Finding Nonlinear Relationships in {fMRI} Time Series with Symbolic Regression", booktitle = "GECCO '16 Companion: Proceedings of the Companion Publication of the 2016 Annual Conference on Genetic and Evolutionary Computation", year = "2016", editor = "Tobias Friedrich and Frank Neumann and Andrew M. Sutton and Martin Middendorf and Xiaodong Li and Emma Hart and Mengjie Zhang and Youhei Akimoto and Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and Daniele Loiacono and Julian Togelius and Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and Faustino Gomez and Carlos M. Fonseca and Heike Trautmann and Alberto Moraglio and William F. Punch and Krzysztof Krawiec and Zdenek Vasicek and Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and Boris Naujoks and Enrique Alba and Gabriela Ochoa and Simon Poulding and Dirk Sudholt and Timo Koetzing", pages = "101--102", keywords = "genetic algorithms, genetic programming: Poster", month = "20-24 " # jul, organisation = "SIGEVO", address = "Denver, USA", publisher = "ACM", publisher_address = "New York, NY, USA", isbn13 = "978-1-4503-4323-7", DOI = "doi:10.1145/2908961.2909021", abstract = "The brain is an intrinsically nonlinear system, yet the dominant methods used to generate network models of functional connectivity from fMRI data use linear methods. Although these approaches have been used successfully, they are limited in that they can find only linear relations within a system we know to be nonlinear. This study employs a highly specialized genetic programming system which incorporates multiple enhancements to perform symbolic regression, a type of regression analysis that searches for declarative mathematical expressions to describe relationships in observed data. Publicly available fMRI data from the Human Connectome Project were segmented into meaningful regions of interest and highly nonlinear mathematical expressions describing functional connectivity were generated. These nonlinear expressions exceed the explanatory power of traditional linear models and allow for more accurate investigation of the underlying physiological connectivities.", notes = "Distributed at GECCO-2016.", } @InProceedings{Hughes:2016:CEC, author = "James Alexander Hughes and Joseph Alexander Brown and Adil Mehmood Khan", title = "Smartphone Gait Fingerprinting Models via Genetic Programming", booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016)", year = "2016", editor = "Yew-Soon Ong", pages = "408--415", address = "Vancouver", month = "24-29 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Symbolic Regression, Mathematical Model, Human Walking Models, Gait", isbn13 = "978-1-5090-0623-6", DOI = "doi:10.1109/CEC.2016.7743823", abstract = "The idea of using the gait of a walking person asa biometric identification method has been seen in a number of proposed authentication methods, yet previous works focus on the addition of other authentication methods along with the gait, or have required a stationary sensor attached to the hip of the user. This paper uses a Genetic Programming model in order to act as an identifier of gait fingerprints from two users sampled from the accelerometer in a commercially available phone. With the phone freely placed within a pocket, users moved without a fixed protocol at a normal, nonuniform pace. This design of data collection more closely matches the real world applications of such a method. The highly specialized Genetic Programming system with multiple modular enhancements was implemented to perform symbolic regression. The system was demonstrated to be robust to noise and was able to effectively model each dataset with high accuracy. It was also determined that a model could be generated for a subject's whole dataset from only a single step's worth of data. Top models were applied to other subject's data in order to evaluate the uniqueness of these mathematical models.", notes = "CEC2016 WCCI2016", } @InProceedings{Hughes:2017:ieeeCIBCB, author = "James Alexander Hughes and Ethan C. Jackson and Mark Daley", booktitle = "2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)", title = "Modelling intracranial pressure with noninvasive physiological measures", year = "2017", abstract = "Patients who suffered a traumatic brain injury (TBI) require special care, and physicians often monitor intercranial pressure (ICP) as it can greatly aid in management. Although monitoring ICP can be critical, it requires neurosurgery, which presents additional significant risk. Monitoring ICP also aids in clinical situations beyond TBI, however the risk of neurosurgery can prevent physicians from gathering the data. The need for surgery may be eliminated if ICP could be accurately inferred using noninvasive physiological measures. Genetic programming (GP) and linear regression were used to develop nonlinear and linear mathematical models describing the relationships between intercranial pressure and a collection of physiological measurements from noninvasive instruments. Nonlinear models of ICP were generated that not only fit the subjects they were trained on, but generalised well across other subjects. The nonlinear models were analysed and provided insight into the studied underlying system which led to the creation of additional models. The new models were developed with a refined search, and were more accurate and general. It was also found that the relations between the features could be explained effectively with a simple linear model after GP refined the search.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CIBCB.2017.8058525", month = aug, notes = "Also known as \cite{8058525}", } @InProceedings{Hughes:2018:CIBCB, author = "James Alexander Hughes and Joseph Alexander Brown and Adil Mehmood Khan and Asad Masood Khattak and Mark Daley", booktitle = "2018 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)", title = "Analysis of symbolic models of biometrie data and their use for action and user identification", year = "2018", month = "30 " # may # "-2 " # jun, address = "St. Louis, MO, USA", keywords = "genetic algorithms, genetic programming, Biomechanics, Gait Recognition, Kinetics, Symbolic Regression, Smartwatch, Identification", DOI = "doi:10.1109/CIBCB.2018.8404969", size = "8 pages", abstract = "Smart devices are becoming an extension of ourselves that contain sensitive information and are often targeted for theft. The development of an intelligent and reliable means of user identification and authentication is critical. Not only can the development of user models performing tasks be used for user and task identification, but systems can also notify individuals if there is a potential health concern. The construction of an idealized model of human locomotion may give medical care providers a better understanding of individual differences and guide therapy and treatment. Data was gathered from a smart watch worn by six subjects performing five different tasks and Genetic Programming was used to perform symbolic regression - a model free, nonlinear type of regression analysis. Symbolic regression was applied to smartwatch data and a collection of nonlinear closed form symbolic mathematical models were generated. Not only did these models fit the data well, but they provided insight into the underlying system. With only 5 seconds of unseen data, the models could classify which subjects were performing which task with 83.9percent accuracy when chance was only 3.33percent.", notes = "INSPEC Accession Number: 17898966 University of Western Ontario, London, Ontario, Canada Also known as \cite{8404969}", } @InProceedings{Hughes:2019:CEC, author = "James Hughes and Mark Daley", title = "Generating Nonlinear Models of Functional Connectivity from Functional Magnetic Resonance Imaging Data with Genetic Programming", booktitle = "2019 IEEE Congress on Evolutionary Computation, CEC 2019", year = "2019", editor = "Carlos A. Coello Coello", pages = "3252--3261", month = "10-13 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Computational Neuroscience, Functional Connectivity, Functional Magnetic Resonance Imaging, Symbolic Regression", isbn13 = "978-1-7281-2152-6", DOI = "doi:10.1109/CEC.2019.8790120", size = "8 pages", abstract = "The brain is a nonlinear computational system; however, most methods employed in finding functional connectivity models with functional magnetic resonance imaging (fMRI) data produce strictly linear models - models incapable of truly describing the underlying system. Genetic programming is used to develop non-linear models of functional connectivity from fMRI data. The study builds on previous work and observes that non linear models contain relationships not found by traditional linear methods. When compared to linear models, the nonlinear models contained fewer regions of interest and were never significantly worse when applied to data the models were fit to. Nonlinear models could generalize to unseen data from the same subject better than traditional linear models (intra-subject). Nonlinear models could not generalize to unseen data recorded from other subjects (intersubject) as well as the linear models, and reasons for this are discussed. This study presents the problem that many, manifestly different models in both operators and features, can effectively describe the system with acceptable metrics.", notes = "also known as \cite{8790120}, IEEE Catalog Number: CFP19ICE-ART", } @Article{Hughes:2019:JBHI, author = "James Hughes and Sheridan Houghten and Joseph Alexander Brown", journal = "IEEE Journal of Biomedical and Health Informatics", title = "Models of Parkinson's Disease Patient Gait", year = "2019", abstract = "Parkinson's Disease is a disorder with diagnostic symptoms that include a change to a walking gait. The disease is problematic to diagnose. An objective method of monitoring the gait of a patient is required to ensure the effectiveness of diagnosis and treatments. We examine the suitability of Extreme Gradient Boosting (XGBoost) and Artificial Neural Network (ANN) Models compared to Symbolic Regression (SR) using genetic programming that was demonstrated to be successful in previous works on gait. The XGBoost and ANN models are found to out-perform SR, but the SR model is more human explainable.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/JBHI.2019.2961808", ISSN = "2168-2208", notes = "Also known as \cite{8939380}", } @InProceedings{Hughes:2019:CIBCB, author = "James Alexander Hughes and Sheridan Houghten and Joseph Alexander Brown", title = "Descriptive Symbolic Models of Gaits from {Parkinson's} Disease Patients", booktitle = "2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)", year = "2019", abstract = "Parkinson's disease (PD) is a degenerative disorder of the central nervous system that has many debilitating symptoms which affect the patient's motor system and can cause significant changes in their gait. By using genetic programming, we aim to develop descriptive symbolic nonlinear models of PD patient gait from time series data recorded from pressure sensors under subjects' feet. When compared to popular types of linear regression (OLS and LASSO), the nonlinear models fit their data better and generalize to unseen data significantly better. It was found that models developed for healthy control subjects generalized to other control subjects well, however the models trained on subjects with PD did not generalize well to other PD patients, which complicates the issue of being able to detect the progression of the disease. It is suspected that health care professionals can have difficulty classifying PD due to a lack of accurate data from patient reports; having individually trained models for active monitoring of patients would help in effectively diagnosing PD.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CIBCB.2019.8791459", month = jul, notes = "Also known as \cite{8791459}", } @InProceedings{Hughes:2019:CEC2, author = "James Alexander Hughes and Joseph {Alexander Brown} and Adil Mehmood Khan and Asad {Masood Khattak} and Mark Daley", booktitle = "2019 IEEE Congress on Evolutionary Computation (CEC)", title = "User and Task Identification of Smartwatch Data with an Ensemble of Nonlinear Symbolic Models", year = "2019", pages = "2506--2513", abstract = "Smart devices are becoming more universally adopted and can be used to track and model user activity and monitor for abnormalities. Deviations from what is expected may indicate that a fall is imminent or that an injury has been sustained. Healthcare practitioners can use descriptive models of human kinematics as a tool to monitor patient recovery. This work extends previous work which generated descriptive nonlinear symbolic models of human kinematics with genetic programming. Previously, linear models were developed and compared to the nonlinear models. Although the linear models fit the data well, they were significantly worse than the nonlinear models. In this phase of the project, ensembles of nonlinear models were created to more accurately fit and classify data. Different model selection strategies for the ensembles were investigated. As one would expect, ensembles of models were significantly better than a single model classifier. It was also observed that, although more models in the ensemble yielded better results, only 2 models were required to obtain significantly better results. It was also observed that a random model selection strategy for the ensembles produced competitive results when compared to a more rigorous model selection strategy.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2019.8790249", month = jun, notes = "Mathematics, Statistics, and Computer Science, St. Francis Xavier University, Antigonish, Nova Scotia, Canada Also known as \cite{8790249}", } @InProceedings{Hughes:2020:CEC, author = "James Alexander Hughes and Sheridan Houghten and Joseph Alexander Brown", booktitle = "2020 IEEE Congress on Evolutionary Computation (CEC)", title = "Gait Model Analysis of {Parkinson's} Disease Patients under Cognitive Load", year = "2020", editor = "Yaochu Jin", month = "19-24 " # jul, keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-7281-6929-3", DOI = "doi:10.1109/CEC48606.2020.9185621", abstract = "Parkinson's disease is a neurodegenerative disease that affects close to 10 million with various symptoms including tremors and changes in gait. Observing differences or changes in an individual's manifestations of gait may provide a mechanism to identify Parkinson's disease and understand specific changes. In this study, time series data from both Control subjects and Parkinson's disease patients was modeled with symbolic regression and extreme gradient boosting. Model effectiveness was analyzed along with the differences in the models between modeling strategies, between Control subjects and Parkinson's disease patients, and between normal walking and walking while under a cognitive load. Both modelling strategies were found to effective. The symbolic regression models were more easily interpreted, while extreme gradient boosting had higher overall accuracy. Interpretation of the models identified certain characteristics that distinguished Control subjects from Parkinson's disease patients and normal walking conditions from walking while under a cognitive load.", notes = "Also known as \cite{9185621}", } @InProceedings{Hughes:2020:CIBCB, author = "James Alexander Hughes and Ryan E. R. Reid and Sheridan Houghten and Ross E. Andersen", title = "Using Genetic Programming to Investigate a Novel Model of Resting Energy Expenditure for Bariatric Surgery Patients", booktitle = "2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)", year = "2020", abstract = "Traditionally, models developed to estimate resting energy expenditure (REE) in the bariatric population have been limited to linear modelling based on data from `normal' or `overweight' individuals not `obese'. This type of modelling can be restrictive and yield functions which poorly estimate this important physiological outcome.Linear and nonlinear models of REE for individuals after bariatric surgery are developed with linear regression and symbolic regression via genetic programming. Features not traditionally used in REE modelling were also incorporated and analyzed and genetic programming's intrinsic feature selection was used as a measure of feature importance. A collection of effective new linear and nonlinear models were generated. The linear models generated outperformed the nonlinear on testing data, although the nonlinear models fit the training data better. Ultimately, the newly developed linear models showed an improvement over existing models and the feature importance analysis suggested that the typically used features (age, weight, and height) were the most important.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CIBCB48159.2020.9277696", month = oct, notes = "Also known as \cite{9277696}", } @PhdThesis{Hughes:thesis, author = "Martin Hughes", title = "Metaheuristics for black-box robust optimisation problems", school = "Management Science, Lancaster University", year = "2020", address = "Bailrigg, Lancaster, United Kingdom", month = may, keywords = "genetic algorithms, genetic programming, Grammar-Guided Genetic Programming, PSO, Largest Empty Hypersphere Metaheuristic, LEH", broken = "https://www.research.lancs.ac.uk/portal/en/publications/metaheuristics-for-blackbox-robust-optimisation-problems.html", URL = "https://eprints.lancs.ac.uk/id/eprint/145732/1/2020hughesphd.pdf", DOI = "doi:10.17635/lancaster/thesis/1036", size = "176 pages", abstract = "Our interest is in the development of algorithms capable of tackling robust black-box optimisation problems, where the number of model runs is limited. When a desired solution cannot be implemented exactly (implementation uncertainty) the aim is to find a robust one. Here that is to find a point in the decision variable space such that the worst solution from within an uncertainty region around that point still performs well. This thesis comprises three research papers. One has been published, one accepted for publication, and one submitted for publication. We initially develop a single-solution based approach, largest empty hypersphere (LEH), which identifies poor performing points in the decision variable space and repeatedly moves to the centre of the region devoid of all such points. Building on this we develop population based approaches using a particle swarm optimisation (PSO) framework. This combines elements of the LEH approach, a local descent directions (d.d.) approach for robust problems, and a series of novel features. Finally we employ an automatic generation of algorithms technique, genetic programming (GP), to evolve a population of PSO based heuristics for robust problems. We generate algorithmic sub-components, the design rules by which they are combined to form complete heuristics, and an evolutionary GP framework. The best performing heuristics are identified. With the development of each heuristic we perform experimental testing against comparator approaches on a suite of robust test problems of dimension between 2D and 100D. Performance is shown to improve with each new heuristic. Furthermore the generation of large numbers of heuristics in the GP process enables an assessment of the best performing sub-components. This can be used to indicate the desirable features of an effective heuristic for tackling the problem under consideration. Good performance is observed for the following characteristics: inner maximisation by random sampling, a small number of inner points, particle level stopping conditions, a small swarm size, a Global topology, and particle movement using a baseline inertia formulation augmented by LEH and d.d. capabilities.", notes = "Supervisors: Marc Goerigk and Vikam Dokka and Mike Wright", } @Article{HUGHES:2021:COR, author = "Martin Hughes and Marc Goerigk and Trivikram Dokka", title = "Automatic generation of algorithms for robust optimisation problems using Grammar-Guided Genetic Programming", journal = "Computer \& Operations Research", volume = "133", pages = "105364", year = "2021", ISSN = "0305-0548", DOI = "doi:10.1016/j.cor.2021.105364", URL = "https://www.sciencedirect.com/science/article/pii/S0305054821001398", keywords = "genetic algorithms, genetic programming, Robust optimisation, Implementation uncertainty, Metaheuristics, Global optimisation", abstract = "We develop algorithms capable of tackling robust black-box optimisation problems, where the number of model runs is limited. When a desired solution cannot be implemented exactly the aim is to find a robust one, where the worst case in an uncertainty neighbourhood around a solution still performs well. To investigate improved methods we employ an automatic generation of algorithms approach: Grammar-Guided Genetic Programming. We develop algorithmic building blocks in a Particle Swarm Optimisation framework, define the rules for constructing heuristics from these components, and evolve populations of search algorithms for robust problems. Our algorithmic building blocks combine elements of existing techniques and new features, resulting in the investigation of a novel heuristic solution space. We obtain algorithms which improve upon the current state of the art. We also analyse the component level breakdowns of the populations of algorithms developed against their performance, to identify high-performing heuristic components for robust problems", } @InProceedings{Hugosson:2007:pliks, author = "Jonatan Hugosson and Erik Hemberg and Anthony Brabazon and Michael O'Neill", title = "An investigation of the mutation operator using different representations in Grammatical Evolution", booktitle = "2nd International Symposium {"}Advances in Artificial Intelligence and Applications{"}", year = "2007", volume = "2", pages = "409--419", address = "Wisla, Poland", month = oct # " 15-17", keywords = "genetic algorithms, genetic programming, grammatical evolution", ISSN = "1896 7094", URL = "http://www.proceedings2007.imcsit.org/pliks/45.pdf", abstract = "Grammatical evolution (GE) is a form of grammar-based genetic programming. A particular feature of GE is that it adopts a distinction between the genotype and phenotype similar to that which exists in nature by using a grammar to map between the genotype and phenotype. This study seeks to extend our understanding of GE by examining the impact of different genotypic representations in order to determine whether certain representations, and associated diversity-generation operators, improve GE's efficiency and effectiveness. Four mutation operators using two different representations, binary and gray code representation respectively, are investigated. The differing combinations of representation and mutation operator are tested on three benchmark problems. The results provides support for the continued use of the standard genotypic integer representation as the alternative representations do not exhibit higher locality nor better GE performance. The results raise the question as to whether higher locality in GE actually improves GE performance.", } @Article{Hugosson2009, author = "Jonatan Hugosson and Erik Hemberg and Anthony Brabazon and Michael O'Neill", title = "Genotype representations in grammatical evolution", journal = "Applied Soft Computing", volume = "10", number = "1", pages = "36--43", year = "2010", month = jan, keywords = "genetic algorithms, genetic programming, Grammatical evolution, Representation", DOI = "doi:10.1016/j.asoc.2009.05.003", URL = "http://www.sciencedirect.com/science/article/B6W86-4WGK6J4-1/2/69a04787be7085909d54edcef2d4d45a", abstract = "Grammatical evolution (GE) is a form of grammar-based genetic programming. A particular feature of GE is that it adopts a distinction between the genotype and phenotype similar to that which exists in nature by using a grammar to map between the genotype and phenotype. Two variants of genotype representation are found in the literature, namely, binary and integer forms. For the first time we analyse and compare these two representations to determine if one has a performance advantage over the other. As such this study seeks to extend our understanding of GE by examining the impact of different genotypic representations in order to determine whether certain representations, and associated diversity-generation operators, improve GE's efficiency and effectiveness. Four mutation operators using two different representations, binary and gray code representation, are investigated. The differing combinations of representation and mutation operator are tested on three benchmark problems. The results provide support for the use of an integer-based genotypic representation as the alternative representations do not exhibit better performance, and the integer representation provides a statistically significant advantage on one of the three benchmarks. In addition, a novel wrapping operator for the binary and gray code representations is examined, and it is found that across the three problems examined there is no general trend to recommend the adoption of an alternative wrapping operator. The results also back up earlier findings which support the adoption of wrapping.", } @InCollection{hui:2003:UGPPTFSP, author = "Anthony Hui", title = "Using Genetic Programming to Perform Time-Series Forecasting of Stock Prices", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2003", year = "2003", editor = "John R. Koza", pages = "83--90", address = "Stanford, California, 94305-3079 USA", month = "4 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", URL = "http://www.genetic-programming.org/sp2003/Hui.pdf", notes = "part of \cite{koza:2003:gagp}", } @InProceedings{Huk:2015:ieeeCYBCONF, author = "Maciej Huk and Jan Kwiatkowski and Dariusz Konieczny and Michal Kedziora and Jolanta Mizera-Pietraszko", booktitle = "2nd IEEE International Conference on Cybernetics (CYBCONF)", title = "Context-sensitive text mining with fitness leveling Genetic Algorithm", year = "2015", pages = "342--347", abstract = "Contextual processing is a great challenge for information retrieval study - the most approved techniques include scanning content of HTML web pages, user supported metadata analysis, automatic inference grounded on knowledge base, or content-oriented digital documents analysis. We propose a meta-heuristic by making use of Genetic Algorithms for Contextual Search (GACS) built on genetic programming (GP) and custom fitness levelling function to optimise contextual queries in exact search that represents unstructured phrases generated by the user. Our findings show that the queries built with GACS can significantly optimise the retrieval process.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CYBConf.2015.7175957", month = jun, notes = "Department of Computer Science, Wroclaw University of Technology, Poland Also known as \cite{7175957}", } @InProceedings{hulka:2024:ICAISC, author = "Tomas Hulka and Radomil Matousek and Ladislav Dobrovsky and Jakub Kudela and Ondrej Hojny", title = "Cartesian Genetic Programming with a Modified Selection Operator for Combinational Circuit Design: Arithmetic Multipliers and Adders", booktitle = "Artificial Intelligence and Soft Computing", year = "2024", address = "Zakopane, Poland", month = jun # " 16-20", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", URL = "https://link.springer.com/chapter/10.1007/978-3-031-84356-3_5", DOI = "doi:10.1007/978-3-031-84356-3_5", } @InProceedings{Hulse:1997:dgpj, author = "Paul Hulse and Richard Gerber and Jenanne Price", title = "Distributed Genetic Programming In {Java}", booktitle = "Late Breaking Papers at the 1997 Genetic Programming Conference", year = "1997", editor = "John R. Koza", pages = "81--86", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California, 94305-3079, USA", month = "13--16 " # jul, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-206995-8", notes = "GP-97LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670 TARA", } @PhdThesis{Hulse:thesis, author = "Paul Hulse", title = "A study of topical applications of genetic programming and genetic algorithms in physical and engineering systems", school = "University of Salford", year = "1999", address = "Manchester, UK", keywords = "genetic algorithms, genetic programming", URL = "https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.391313", URL = "http://classify.oclc.org/classify2/ClassifyDemo?owi=12290264", notes = "ISNI: 0000 0001 3584 2070 uk.bl.ethos.391313", } @Article{Huml:2013:BMRI, author = "Marlene Huml and Rene Silye and Gerald Zauner and Stephan Hutterer and Kurt Schilcher", title = "Brain Tumor Classification Using {AFM} in Combination with Data Mining Techniques", journal = "BioMed Research International", year = "2013", month = aug # "~25", pages = "Article ID 176519", keywords = "genetic algorithms, genetic programming, GP", bibsource = "OAI-PMH server at www.ncbi.nlm.nih.gov", language = "en", publisher = "Hindawi Publishing Corporation", oai = "oai:pubmedcentral.nih.gov:3766995", URL = "http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3766995", URL = "http://dx.doi.org/10.1155/2013/176519", size = "11 pages", abstract = "Although classification of astrocytic tumours is standardised by the WHO grading system, which is mainly based on microscopy-derived, histomorphological features, there is great inter-observer variability. The main causes are thought to be the complexity of morphological details varying from tumour to tumour and from patient to patient, variations in the technical histopathological procedures like staining protocols, and finally the individual experience of the diagnosing pathologist. Thus, to raise astrocytoma grading to a more objective standard, this paper proposes a methodology based on atomic force microscopy (AFM) derived images made from histopathological samples in combination with data mining techniques. By comparing AFM images with corresponding light microscopy images of the same area, the progressive formation of cavities due to cell necrosis was identified as a typical morphological marker for a computer-assisted analysis. Using genetic programming as a tool for feature analysis, a best model was created that achieved 94.74percent classification accuracy in distinguishing grade II tumours from grade IV ones. While using modern image analysis techniques, AFM may become an important tool in astrocytic tumour diagnosis. By this way patients suffering from grade II tumours are identified unambiguously, having a less risk for malignant transformation. They would benefit from early adjuvant therapies.", } @Article{Hung:2006:NA, author = "Chun-Min Hung and Yueh-Min Huang and Ming-Shi Chang", title = "Alignment using genetic programming with causal trees for identification of protein functions", journal = "Nonlinear Analysis", year = "2006", volume = "65", number = "5", pages = "1070--1093", month = "1 " # sep, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1016/j.na.2005.09.048", abstract = "A hybrid evolutionary model is used to propose a hierarchical homology of protein sequences to identify protein functions systematically. The proposed model offers considerable potentials, considering the inconsistency of existing methods for predicting novel proteins. Because some novel proteins might align without meaningful conserved domains, maximising the score of sequence alignment is not the best criterion for predicting protein functions. This work presents a decision model that can minimise the cost of making a decision for predicting protein functions using the hierarchical homologies. Particularly, the model has three characteristics: (i) it is a hybrid evolutionary model with multiple fitness functions that uses genetic programming to predict protein functions on a distantly related protein family, (ii) it incorporates modified robust point matching to accurately compare all feature points using the moment invariant and thin-plate spline theorems, and (iii) the hierarchical homologies holding up a novel protein sequence in the form of a causal tree can effectively demonstrate the relationship between proteins. This work describes the comparisons of nucleocapsid proteins from the putative polyprotein SARS virus and other coronaviruses in other hosts using the model.", notes = "Hybrid Systems and Applications", } @InProceedings{Hung:2010:IEEM, author = "Ching-Tsung Hung and Shih-Huang Chen", title = "A comparison of three forecasting methods to establish a flexible pavement serviceability index", booktitle = "2010 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)", year = "2010", month = dec, pages = "926--929", abstract = "Since 1960, the pavement serviceability index has supported the efforts of engineers who make decisions concerning maintenance strategies. The data of pavement surfaces do not belong to a normal distribution. Because the data violate the basic assumptions of linear regression, the pavement serviceability index is not suitable for regression modelling. Many kinds of prediction models with non-statistical foundations have been developed in recent years. To establish a flexible pavement serviceability index, this paper considers a fuzzy regression model, a support vector machine and a genetic programming. Our support vector machine has the highest predictive accuracy of the three methods in this study. The support vector machine uses a hyperplane transform to process interactions among pavement variables.", keywords = "genetic algorithms, genetic programming, flexible pavement serviceability index, forecasting method, fuzzy regression model, hyperplane transform, linear regression, maintenance strategy, normal distribution, pavement surfaces data, regression modeling, support vector machine, fuzzy set theory, maintenance engineering, normal distribution, regression analysis, roads, structural engineering, support vector machines", DOI = "doi:10.1109/IEEM.2010.5674216", ISSN = "2157-3611", notes = "Also known as \cite{5674216}", } @InProceedings{Hung:2013:iFUZZY, author = "Lung-Hsuan Hung and Chih-Hung Wu", booktitle = "International Conference on Fuzzy Theory and Its Applications (iFUZZY 2013)", title = "Load prediction of virtual machine servers using genetic expression programming", year = "2013", month = dec, pages = "402--406", abstract = "Virtualisation is a key technology for cloud-computing, which creates various types of virtual computing resources on physical machines. A centre of virtual machine (VM) servers manages different load situations of servers and adjusts flexibly the consumptions of physical resources to achieve better cost-performance efficiency. One of the key problems in the management of VM servers (VMSs) is load prediction with which decisions for load-balance as well as other management issues can be engaged. This study employs genetic expression programming (GEP) for deriving regression models of load of VMSs. GEP regression models are white-boxes that have visible structures and can be modified and integrated with other VM management mechanisms. Data representing the types of VM resources, VM loads, etc., are collected for training GEP models. With the GEP models, one can predict the work load of VMSs so that precise decisions of load-balance can be made. The experimental results show that GEP can generate precise models for load prediction of VMSs than other methods.", keywords = "genetic algorithms, genetic programming, genetic expression programming", DOI = "doi:10.1109/iFuzzy.2013.6825473", notes = "Also known as \cite{6825473}", } @Article{Hung:2021:ACC, author = "Lung-Hsuan Hung and Chih-Hung Wu and Chiung-Hui Tsai and Hsiang-Cheh Huang", journal = "IEEE Access", title = "Migration-Based Load Balance of Virtual Machine Servers in Cloud Computing by Load Prediction Using Genetic-Based Methods", year = "2021", volume = "9", pages = "49760--49773", abstract = "This paper presents a two-stage genetic mechanism for the migration-based load balance of virtual machine hosts (VMHs) in cloud computing. Previous methods usually assume this issue as a job-assignment optimisation problem and only consider the current VMHs' loads; however, without considering loads of VMHs after balancing, these methods can only gain limited effectiveness in real environments. In this study, two genetic-based methods are integrated and presented. First, performance models of virtual machines (VMs) are extracted from their creating parameters and corresponding performance measured in a cloud computing environment. The gene expression programming (GEP) is applied for generating symbolic regression models that describe the performance of VMs and are used for predicting loads of VMHs after load-balance. Secondly, with the VMH loads estimated by GEP, the genetic algorithm considers the current and the future loads of VMHs and decides an optimal VM-VMH assignment for migrating VMs and performing load-balance. The performance of the proposed methods is evaluated in a real cloud-computing environment, Jnet, wherein these methods are implemented as a centralized load balancing mechanism. The experimental results show that our method outperforms previous methods, such as heuristics and statistics regression.", keywords = "genetic algorithms, genetic programming, gene expression programming, Cloud computing, Load management, Hardware, Virtualization, Servers, Computational modelling, Virtual machine monitors, Cloud computing, virtualization, load balancing, migration", DOI = "doi:10.1109/ACCESS.2021.3065170", ISSN = "2169-3536", notes = "Also known as \cite{9374470}", } @InProceedings{Hunt:2010:ACAI, author = "Rachel Hunt and Mark Johnston and Will N. Browne and Mengjie Zhang", title = "Sampling Methods in Genetic Programming for Classification with Unbalanced Data", booktitle = "Australasian Conference on Artificial Intelligence", year = "2010", editor = "Jiuyong Li", volume = "6464", series = "Lecture Notes in Computer Science", pages = "273--282", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-17431-5", DOI = "doi:10.1007/978-3-642-17432-2_28", size = "10 pages", bibdate = "2010-11-30", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ausai/ausai2010.html#HuntJBZ10", abstract = "This work investigates the use of sampling methods in Genetic Programming (GP) to improve the classification accuracy in binary classification problems in which the datasets have a class imbalance. Class imbalance occurs when there are more data instances in one class than the other. As a consequence of this imbalance, when overall classification rate is used as the fitness function, as in standard GP approaches, the result is often biased towards the majority class, at the expense of poor minority class accuracy. We establish that the variation in training performance introduced by sampling examples from the training set is no worse than the variation between GP runs already accepted. Results also show that the use of sampling methods during training can improve minority class classification accuracy and the robustness of classifiers evolved, giving performance on the test set better than that of those classifiers which made up the training set Pareto front.", affiliation = "School of Mathematics, Statistics and Operations Research, Victoria University of Wellington, P.O. Box 600, Wellington, New Zealand", } @InProceedings{conf/ausai/HuntJZ11, author = "Rachel Hunt and Mark Johnston and Mengjie Zhang", title = "Improving Robustness of Multiple-Objective Genetic Programming for Object Detection", booktitle = "Proceedings of the 24th Australasian Joint Conference Advances in Artificial Intelligence (AI 2011)", year = "2011", editor = "Dianhui Wang and Mark Reynolds", volume = "7106", series = "Lecture Notes in Computer Science", pages = "311--320", address = "Perth, Australia", month = dec # " 5-8", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-25831-2", DOI = "doi:10.1007/978-3-642-25832-9_32", size = "10 pages", abstract = "Object detection in images is inherently imbalanced and prone to overfitting on the training set. This work investigates the use of a validation set and sampling methods in Multi-Objective Genetic Programming (MOGP) to improve the effectiveness and robustness of object detection in images. Results show that sampling methods decrease run times substantially and increase robustness of detectors at higher detection rates, and that a combination of validation together with sampling improves upon a validation-only approach in effectiveness and efficiency.", bibdate = "2011-12-02", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ausai/ausai2011.html#HuntJZ11", affiliation = "School of Mathematics, Statistics and Operations Research, Victoria University of Wellington, PO Box 600, Wellington, New Zealand", } @InProceedings{Hunt:2012:CEC, title = "Scalability Analysis of Genetic Programming Classifiers", author = "Rachel Hunt and Kourosh Neshatian and Mengjie Zhang", pages = "509--516", booktitle = "Proceedings of the 2012 IEEE Congress on Evolutionary Computation", year = "2012", editor = "Xiaodong Li", month = "10-15 " # jun, DOI = "doi:10.1109/CEC.2012.6256520", address = "Brisbane, Australia", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Complex Networks and Evolutionary Computation", abstract = "Genetic programming (GP) has been used extensively for classification due to its flexibility, interpretability and implicit feature manipulation. There are also disadvantages to the use of GP for classification, including computational cost, bloating and parameter determination. This work analyses how GP-based classifier learning scales with respect to the number of examples in the classification training data set as the number of examples grows, and with respect to the number of features in the classification training data set as the number of features grows. The scalability of GP with respect to the number of examples is studied analytically. The results show that GP scales very well (in linear or close to linear order) with the number of examples in the data set and the upper bound on testing error decreases. The scalability of GP with respect to the number of features is tested experimentally, with results showing that the computations increase exponentially with the number of features.", notes = "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the EPS and the IET.", } @InProceedings{Hunt:2012:SEAL, author = "Rachel Hunt and Kourosh Neshatian and Mengjie Zhang", title = "A Genetic Programming Approach to Hyper-Heuristic Feature Selection", booktitle = "The Ninth International Conference on Simulated Evolution And Learning, SEAL 2012", year = "2012", editor = "Lam Thu Bui and Yew-Soon Ong and Nguyen Xuan Hoai and Hisao Ishibuchi and Ponnuthurai Nagaratnam Suganthan", volume = "7673", series = "Lecture Notes in Computer Science", pages = "320--330", address = "Vietnam", month = dec # " 16-19", organisation = "Faculty of Information Technology, Le Quy Don Technical University", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-34858-7", DOI = "doi:10.1007/978-3-642-34859-4_32", size = "11 pages", abstract = "Feature selection is the task of finding a subset of original features which is as small as possible yet still sufficiently describes the target concepts. Feature selection has been approached through both heuristic and meta-heuristic approaches. Hyper-heuristics are search methods for choosing or generating heuristics or components of heuristics, to solve a range of optimisation problems. This paper proposes a genetic-programming-based hyper-heuristic approach to feature selection. The proposed method evolves new heuristics using some basic components (building blocks). The evolved heuristics act as new search algorithms that can search the space of subsets of features. The classification performance (accuracy) of classifiers are improved by using small subsets of features found by evolved heuristics.", } @InProceedings{Hunt:2014:CEC, title = "Evolving Machine-Specific Dispatching Rules for a Two-Machine Job Shop using Genetic Programming", author = "Rachel Hunt and Mark Johnston and Mengjie Zhang", pages = "618--625", booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary Computation", year = "2014", month = "6-11 " # jul, editor = "Carlos A. {Coello Coello}", address = "Beijing, China", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Evolutionary Computation for Planning and Scheduling", DOI = "doi:10.1109/CEC.2014.6900655", abstract = "Job Shop Scheduling (JSS) involves determining a schedule for processing jobs on machines to optimise some measure of delivery speed or customer satisfaction. We investigate a genetic programming based hyper-heuristic (GPHH) approach to evolving dispatching rules for a two-machine job shop in both static and dynamic environments. In the static case the proposed GPHH method can represent and discover optimal dispatching rules. In the dynamic case we investigate two representations (using a single rule at both machines and evolving a specialised rule for each machine) and the effect of changing the training problem instances throughout evolution. Results show that relative performance of these methods is dependent on the testing instances.", notes = "WCCI2014", } @InProceedings{Hunt:2014:GECCO, author = "Rachel Hunt and Mark Johnston and Mengjie Zhang", title = "Evolving {"}less-myopic{"} scheduling rules for dynamic job shop scheduling with genetic programming", booktitle = "GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation", year = "2014", editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges", isbn13 = "978-1-4503-2662-9", pages = "927--934", keywords = "genetic algorithms, genetic programming", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2576768.2598224", DOI = "doi:10.1145/2576768.2598224", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Job Shop Scheduling (JSS) is a complex real-world problem aiming to optimise a measure of delivery speed or customer satisfaction by determining a schedule for processing jobs on machines. A major disadvantage of using a dispatching rule (DR) approach to solving JSS problems is their lack of a global perspective of the current and potential future state of the shop. We investigate a genetic programming based hyper-heuristic (GPHH) approach to develop less-myopic DRs for dynamic JSS. Results show that in the dynamic ten machine job shop, incorporating features of the state of the wider shop, and the stage of a job's journey through the shop, improves the mean performance, and decreases the standard deviation of performance of the best evolved rules.", notes = "Also known as \cite{2598224} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)", } @InProceedings{Hunt:2015:evoCOP, author = "Rachel Hunt and Mark Johnston and Mengjie Zhang", title = "Using Local Search to Evaluate Dispatching Rules in Dynamic Job Shop Scheduling", booktitle = "The 15th European Conference on Evolutionary Computation in Combinatorial Optimisation", year = "2015", editor = "Gabriela Ochoa and Francisco Chicano", series = "LNCS", volume = "9026", publisher = "Springer", pages = "222--233", address = "Copenhagen", month = "8-10 " # apr, organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-16500-4", DOI = "doi:10.1007/978-3-319-16468-7_19", abstract = "Improving scheduling methods in manufacturing environments such as job shops offers the potential to increase throughput, decrease costs, and therefore increase profit. This makes scheduling an important aspect in the manufacturing industry. Job shop scheduling has been widely studied in the academic literature because of its real-world applicability and difficult nature. Dispatching rules are the most common means of scheduling in dynamic environments. We use genetic programming to search the space of potential dispatching rules. Dispatching rules are often short-sighted as they make one instantaneous decision at each decision point. We incorporate local search into the evaluation of dispatching rules to assess the quality of decisions made by dispatching rules and encourage the dispatching rules to make good local decisions for effective overall performance. Results show that the inclusion of local search in evaluation led to the evolution of DRs which make better decisions over the local time horizon, and attain lower TWT. The advantages of using local search as a tie-breaking mechanism are not so pronounced.", notes = "EvoCOP2015 held in conjunction with EuroGP'2015, EvoMusArt2015 and EvoApplications2015 http://www.evostar.org/2015/cfp_evocop.php", } @PhdThesis{DBLP:phd/basesearch/Hunt16, author = "Rachel Hunt", title = "Genetic Programming Hyper-heuristics for Job Shop Scheduling", school = "Victoria University of Wellington, New Zealand", year = "2016", keywords = "genetic algorithms, genetic programming", URL = "http://hdl.handle.net/10063/5219", timestamp = "Sat, 05 Nov 2022 00:00:00 +0100", biburl = "https://dblp.org/rec/phd/basesearch/Hunt16.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{hunter:2002:ECAI, author = "Andrew Hunter", title = "Using Multiobjective Genetic Programming to Infer Logistic Polynomial Regression Models", booktitle = "15th European Conference on Artificial Intelligence", year = "2002", editor = "Frank {Van Harmelen}", pages = "193--197", address = "Lyon, France", month = "21-26 " # jul, organisation = "ECCAI and AFAI", publisher = "IOS Press", keywords = "genetic algorithms, genetic programming", URL = "http://frontiersinai.com/ecai/ecai2002/pdf/p0193.pdf", size = "5 pages", abstract = "In designing non-linear classifiers, there are important trade-offs to be made between predictive accuracy and model comprehensibility or complexity. We introduce the use of Genetic Programming to generate logistic polynomial models, a relatively comprehensible non-linear parametric model; describe an efficient twostage algorithm consisting of GP structure design and Quasi-Newton coefficient setting; demonstrate that Niched Pareto Multiobjective Genetic Programming can be used to discover a range of classifiers with different complexity versus 'performance' trade-offs; introduce a technique to integrate a new 'ROC (Receiver Operating Characteristic) dominance' concept into the multiobjective setting; and suggest some modifications to the Niched Pareto GA for use in Genetic Programming. The technique successfully generates classifiers with diverse complexity and performance characteristics.", notes = "ECAI 2002 http://frontiersinai.com/ecai/ecai2002/index.html broken Dec 2012 http://ecai2002.univ-lyon1.fr/show_en.pl?page=en/program/ecai.html PAIS 2012 Boi Faltings", } @InProceedings{Huo:2007:ICMA, author = "Limin Huo and Xinqiao Fan and Yunfang Xie and Jinliang Yin", title = "Short-Term Load Forecasting Based on the Method of Genetic Programming", booktitle = "International Conference on Mechatronics and Automation, ICMA 2007", year = "2007", pages = "839--843", address = "Harbin, China", month = "5-8 " # aug, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-0828-3", DOI = "doi:10.1109/ICMA.2007.4303654", abstract = "The algorithm of Genetic Programming is described and applied to short-term load forecasting. For the fault in history load data, the load samples are filtered and processed generally before using, and then the load series of the same time point but different days are chosen as the training sets. According to the complex expressive capacity of Genetic Programming, the future short-term load model of different time point is forecasted by time-sharing. This method of Genetic Programming can find out relevant elements to electric load data automatically, so the artificial errors in forecasting can be avoided effectively. And the future load value of each time point can be calculated with the corresponding model created. Finally, it proves that the method of Genetic Programming in short-term load forecasting is better through out comparison between the results forecasted by Genetic Programming and time series.", notes = "Department of Mechanical and Electronic Engineering, Agricultural University of Hebei, Baoding 071001, China.", } @InProceedings{Huo:2008:ICICTA, author = "Limin Huo and Jinliang Yin and Yao Yu and Liguo Zhang", title = "Distribution Network Reconfiguration Based on Load Forecasting", booktitle = "International Conference on Intelligent Computation Technology and Automation, ICICTA 2008", year = "2008", month = oct, volume = "1", pages = "1039--1043", keywords = "genetic algorithms, genetic programming, decision making, distribution network reconfiguration, line loss calculation data, load forecasting, partheno-genetic programming algorithm, distribution networks, load forecasting", DOI = "doi:10.1109/ICICTA.2008.206", abstract = "Line loss calculation data adopted in the previous distribution network reconfiguration was historical load data or real-time data. And that reduced the realistic significance of distribution network reconfiguration. A new method is presented. At first forecast the load, then apply the load data forecasted to the line loss calculation. By do so the decision can be made in advance that if the distribution network reconfiguration is needed at some time of the future. Load forecasting adopted genetic programming algorithm (GP). Distribution network reconfiguration adopted partheno-genetic algorithm (PGA). And the partheno-genetic algorithm was improved according to the features of the distribution network reconfiguration.", notes = "Also known as \cite{4659648}", } @InProceedings{conf/ausai/HuoXSZ17, author = "Jiatong Huo and Bing Xue and Lin Shang and Mengjie Zhang", title = "Genetic Programming for Multi-objective Test Data Generation in Search Based Software Testing", year = "2017", booktitle = "AI 2017: Advances in Artificial Intelligence - 30th Australasian Joint Conference, Melbourne, VIC, Australia, August 19-20, 2017, Proceedings", publisher = "Springer", volume = "10400", editor = "Wei Peng and Damminda Alahakoon and Xiaodong Li", pages = "169--181", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming, SBSE", bibdate = "2017-07-14", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ausai/ausai2017.html#HuoXSZ17", isbn13 = "978-3-319-63003-8", DOI = "doi:10.1007/978-3-319-63004-5_14", } @InProceedings{Huppe:2017:ICSE-C, author = "Samuel Huppe and Mohamed Aymen Saied and Houari Sahraoui", booktitle = "2017 IEEE/ACM 39th International Conference on Software Engineering Companion (ICSE-C)", title = "Mining Complex Temporal {API} Usage Patterns: An Evolutionary Approach", year = "2017", pages = "274--276", month = may, keywords = "genetic algorithms, genetic programming, SBSE, API documentation, API usage pattern Linear temporal logic", DOI = "doi:10.1109/ICSE-C.2017.147", size = "2.2 pages", abstract = "Learning to use existing or new software libraries is a difficult task for software developers, which would impede their productivity. Much existing work has provided different techniques to mine API usage patterns from client programs in order to help developers on understanding and using existing libraries. However, these techniques produce incomplete patterns, i.e., without temporal properties, or simple ones. In this paper, we propose a new formulation of the problem of API temporal pattern mining and a new approach to solve it. Indeed, we learn complex temporal patterns using a genetic programming approach. Our preliminary results show that across a considerable variability of client programs, our approach has been able to infer non-trivial patterns that reflect informative temporal properties.", notes = "Also known as \cite{7965328}", } @InProceedings{Hurta:2022:EuroGP, author = "Martin Hurta and Michaela Drahosova and Lukas Sekanina and Stephen L. Smith and Jane E. Alty", title = "Evolutionary Design of Reduced Precision {Levodopa}-Induced {Dyskinesia} Classifiers", booktitle = "EuroGP 2022: Proceedings of the 25th European Conference on Genetic Programming", year = "2022", editor = "Eric Medvet and Gisele Pappa and Bing Xue", series = "LNCS", volume = "13223", publisher = "Springer Verlag", address = "Madrid, Spain", pages = "85--101", month = "20-22 " # apr, organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, Coevolution, Adaptive size fitness predictors, Energy-efficient, Hardware-oriented, Fixed-point arithmetic, Levodopa-induced dyskinesia, Parkinson disease", isbn13 = "978-3-031-02055-1", DOI = "doi:10.1007/978-3-031-02056-8_6", abstract = "http://www.fit.vutbr.cz/~jarosjir/SUPSY/index.html.en Parkinson's disease (PD) belongs among the most common neurological conditions, with PD's symptoms often treated with the dopamine-replacement drug levodopa. The right dosage is essential in order to suppress PD's symptoms and, at the same time to avoid the drug's troublesome side effects, including involuntary and often violent muscle spasms, called dyskinesia. A small low-power solution that could be implemented directly into a home wearable device would enable long-term continuous monitoring of Parkinson's disease patients in their homes and allow clinicians accurate assessment of patients condition and the advised adjustment of levodopa dosage. The presentation will show my current progress in solving this challenge using Cartesian genetic programming with adaptive size fitness predictors.", notes = "http://www.evostar.org/2022/eurogp/ Part of \cite{Medvet:2022:GP} EuroGP'2022 held inconjunction with EvoApplications2022 EvoCOP2022 EvoMusArt2022", } @InProceedings{Hurta:2023:BIBM, author = "Martin Hurta and Jana Schwarzerova and Thomas Naegele and Wolfram Weckwerth and Valentine Provaznik and Lukas Sekanina", booktitle = "2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)", title = "Utilizing Genetic Programming to Enhance Polygenic Risk Score Calculation", year = "2023", pages = "3782--3787", abstract = "The polygenic risk score has proven to be a valuable tool for assessing an individual's genetic predisposition to phenotype (disease) within biomedicine in recent years. However, traditional regression-based methods for polygenic risk scores calculation have limitations that can impede their accuracy and predictive power. This study introduces an innovative approach to enhance polygenic risk scores calculation through the application of genetic programming. By harnessing the power of genetic programming, we aim to overcome the limitations of traditional regression techniques and improve the accuracy of polygenic risk scores predictions. Specifically, we showed that a polygenic risk score generated through Cartesian genetic programming yielded comparable or even more robust statistical distinctions between groups that we evaluated within three independent case studies.", keywords = "genetic algorithms, genetic programming, Evolution (biology), Plants (biology), Sociology, Medical services, Data models, Polygenic risk score, Genetic Variations, Computational biology", DOI = "doi:10.1109/BIBM58861.2023.10385615", ISSN = "2156-1133", month = dec, notes = "Also known as \cite{10385615}", } @InProceedings{Hurta:2023:DATE, author = "Martin Hurta and Vojtech Mrazek and Michaela Drahosova and Lukas Sekanina", booktitle = "2023 Design, Automation \& Test in Europe Conference \& Exhibition (DATE)", title = "{ADEE-LID:} Automated Design of Energy-Efficient Hardware Accelerators for Levodopa-Induced Dyskinesia Classifiers", year = "2023", abstract = "Levodopa, a drug used to treat symptoms of Parkinson's disease, is connected to side effects known as Levodopa-induced dyskinesia (LID). LID is difficult to classify during a physician's visit. A wearable device allowing long-term and continuous classification would significantly help with dosage adjustments. This paper deals with an automated design of energy-efficient hardware accelerators for such LID classifiers. The proposed accelerator consists of a feature extractor and a classifier co-designed using genetic programming. Improvements are achieved by introducing a variable bit width for arithmetic operators, eliminating redundant registers, and using precise energy consumption estimation for Pareto front creation. Evolved solutions reduce energy consumption while maintaining classification accuracy comparable to the state of the art.", keywords = "genetic algorithms, genetic programming, EHW, Energy consumption, Wearable computers, Estimation, Medical services, Feature extraction, levodopa-induced dyskinesia, energy efficiency, hardware accelerator", DOI = "doi:10.23919/DATE56975.2023.10137079", ISSN = "1558-1101", month = apr, notes = "Also known as \cite{10137079}", } @InProceedings{Hurta:2023:DDECS, author = "Martin Hurta and Vojtech Mrazek and Michaela Drahosova and Lukas Sekanina", booktitle = "2023 26th International Symposium on Design and Diagnostics of Electronic Circuits and Systems (DDECS)", title = "{MODEE-LID:} Multiobjective Design of Energy-Efficient Hardware Accelerators for Levodopa-Induced Dyskinesia Classifiers", year = "2023", pages = "155--160", abstract = "Taking levodopa, a drug used to treat symptoms of Parkinson's disease, is often connected with severe side effects, known as Levodopa-induced dyskinesia (LID). It can fluctuate in severity throughout the day and thus is difficult to classify during a short period of a physician's visit. A low-power wearable classifier enabling long-term and continuous LID classification would thus significantly help with LID detection and dosage adjustment. This paper deals with an automated design of energy-efficient hardware accelerators of LID classifiers that can be implemented in wearable devices. The accelerator consists of a feature extractor and a classification circuit co-designed using genetic programming (GP). We also introduce and evaluate a fast and accurate energy consumption estimation method for the target architecture of considered classifiers. In a multiobjective design scenario, GP evolves solutions showing the best trade-offs between accuracy and energy. Compared to the state-of-the-art solutions, the proposed method leads to classifiers showing a comparable accuracy while the energy consumption is reduced by 49 percent.", keywords = "genetic algorithms, genetic programming, Drugs, Energy consumption, Wearable computers, Estimation, Feature extraction, Energy efficiency, levodopa-induced dyskinesia, energy efficient, hardware accelerator, multiobjective design", DOI = "doi:10.1109/DDECS57882.2023.10139399", ISSN = "2473-2117", month = may, notes = "Also known as \cite{10139399}", } @InProceedings{hurta:2025:EuroGP, author = "Martin Hurta and Anna Ovesna and Vojtech Mrazek and Lukas Sekanina", title = "{Multi-Objective} Evolutionary Design of Explainable {EEG} Classifier", booktitle = "European Conference on Genetic Programming, EuroGP 2025", year = "2025", editor = "Bing Xue and Luca Manzoni and Illya Bakurov", volume = "15609", series = "LNCS", pages = "52--67", address = "Trieste", month = "23-25 " # apr, publisher = "Springer Nature", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, Multi-objective design, Classification, Explainability, Co-evolution, EEG", isbn13 = "978-3-031-89990-4", URL = "https://www.fit.vut.cz/research/publication/13198", URL = "https://rdcu.be/ejgBG", DOI = "doi:10.1007/978-3-031-89991-1_4", abstract = "Deep neural networks (DNNs) have achieved impressive results in many fields. However, the use of black-box solutions based on DNNs in medical applications poses challenges, as understanding the rationale behind decisions is crucial for application in healthcare. For those reasons, we propose a new method for the evolutionary multi-objective design (MOD) of small and potentially explainable EEG (Electroen-cephalography) signal classifiers. We evaluate a combination of genetic algorithm (GA) for feature selection with multiple algorithms for the automated design of the classifier, including Support Vector Machine, k-Nearest Neighbors, and Naive Bayes. To further improve the classification quality and obtain less complex solutions, we compare three different MOD scenarios targeting the accuracy, specificity, sensitivity, and the number of used features. In addition, we evaluate the use of Cartesian Genetic Programming (CGP) as a way to achieve smaller and more interpretable solutions and combine it with the compositional co-evolution of selected features to improve computational requirements and find solutions in a reasonable time. The proposed methods are experimentally evaluated on tasks of alcohol use disorder and major depressive disorder classification. Experimental results show that newly proposed MOD scenarios lead to significantly better trade-offs between the accuracy and the number of features compared to the state-of-the-art method employing the NSGA-II algorithm. The proposed co-evolution of features (evolved by GA) and classifier (evolved by CGP) allowed the design of small and potentially explainable solutions and led to 20-100 times faster convergence than the baseline CGP-based approach.", notes = "also known as \cite{paper_48_explainable_eeg_classifier} Part of \cite{Xue:2025:GP} EuroGP'2025 held in conjunction with EvoCOP2025, EvoMusArt2025 and EvoApplications2025", } @InProceedings{Husa:2017:GECCO, author = "Jakub Husa and Roland Dobai", title = "Designing Bent {Boolean} Functions with Parallelized Linear Genetic Programming", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4939-0", address = "Berlin, Germany", pages = "1825--1832", size = "8 pages", URL = "https://www.fit.vut.cz/research/publication/11402/.cs", URL = "http://doi.acm.org/10.1145/3067695.3084220", DOI = "doi:10.1145/3067695.3084220", acmid = "3084220", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, bent functions, boolean functions, cryptography, island model, linear genetic programming, nonlinearity", month = "15-19 " # jul, abstract = "Bent Boolean functions are cryptographic primitives essential for the safety of cryptographic algorithms, providing a degree of non-linearity to otherwise linear systems. The maximum possible non-linearity of a Boolean function is limited by the number of its inputs, and as technology advances, functions with higher number of inputs are required in order to guarantee a level of security demanded in many modern applications. Genetic programming has been successfully used to discover new larger bent Boolean functions in the past. This paper proposes the use of linear genetic programming for this purpose. It shows that this approach is suitable for designing of bent Boolean functions larger than those designed using other approaches, and explores the influence of multiple evolutionary parameters on the evolution runtime. Parallelized implementation of the proposed approach is used to search for new, larger bent functions, and the results are compared with other related work. The results show that linear genetic programming copes better with growing number of function inputs than genetic programming, and is able to create significantly larger bent functions in comparable time.", notes = "Also known as \cite{Husa:2017:DBB:3067695.3084220} \cite{FITPUB11402} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @InProceedings{Husa:2018:EuroGP, author = "Jakub Husa and Roman Kalkreuth", title = "A Comparative Study on Crossover in Cartesian Genetic Programming", booktitle = "EuroGP 2018: Proceedings of the 21st European Conference on Genetic Programming", year = "2018", month = "4-6 " # apr, editor = "Mauro Castelli and Lukas Sekanina and Mengjie Zhang and Stefano Cagnoni and Pablo Garcia-Sanchez", series = "LNCS", volume = "10781", publisher = "Springer Verlag", address = "Parma, Italy", pages = "203--219", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming: Poster", isbn13 = "978-3-319-77552-4", DOI = "doi:10.1007/978-3-319-77553-1_13", abstract = "Cartesian Genetic Programming is often used with mutation as the sole genetic operator. Compared to the comprehensive and detailed knowledge about the effect and use of mutation in CGP, the use of crossover has been less investigated and studied. In this paper, we present a comparative study of previously proposed crossover techniques for Cartesian Genetic Programming. This work also includes the proposal of a new crossover technique which swaps block of the CGP phenotype between two selected parents. The experiments of our study open a new perspective on comparative studies on crossover in CGP and its challenges. Our results show that it is possible for a crossover operator to outperform the standard (1+lambda) strategy on a limited number of tasks. The question of finding a universal crossover operator in CGP remains open.", notes = "Part of \cite{Castelli:2018:GP} EuroGP'2018 held in conjunction with EvoCOP2018, EvoMusArt2018 and EvoApplications2018", } @InProceedings{Husa:2019:EuroGP, author = "Jakub Husa", title = "Comparison of Genetic Programming Methods on Design of Cryptographic {Boolean} Functions", booktitle = "EuroGP 2019: Proceedings of the 22nd European Conference on Genetic Programming", year = "2019", month = "24-26 " # apr, editor = "Lukas Sekanina and Ting Hu and Nuno Lourenco", series = "LNCS", volume = "11451", publisher = "Springer Verlag", address = "Leipzig, Germany", pages = "228--244", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Cartesian Genetic programming: Poster", isbn13 = "978-3-030-16669-4", URL = "https://www.springer.com/us/book/9783030166694", DOI = "doi:10.1007/978-3-030-16670-0_15", size = "16 pages", abstract = "The ever-increasing need for information security requires a constant refinement of contemporary ciphers. One of these are stream ciphers which secure data by using a pseudo-randomly generated binary sequence. Generating a cryptographically secure sequence is not an easy task and requires a Boolean function possessing multiple cryptographic properties. One of the most successful ways of designing these functions is genetic programming. In this paper, we present a comparative study of three genetic programming methods, tree-based, Cartesian and linear, on the task of generating Boolean functions with an even number of inputs possessing good values of nonlinearity, balancedness, correlation immunity, and algebraic degree. Our results provide a comprehensive overview of how genetic programming methods compare when designing functions of different sizes, and we show that linear genetic programming, which has not been used for design of some of these functions before, is the best at dealing with increasing number of inputs, and creates desired functions with better reliability than the commonly used methods.", notes = "http://www.evostar.org/2019/cfp_eurogp.php#abstracts Part of \cite{Sekanina:2019:GP} EuroGP'2019 held in conjunction with EvoCOP2019, EvoMusArt2019 and EvoApplications2019", } @InProceedings{Husa:2019:GECCOcomp, author = "Jakub Husa", title = "Designing correlation immune boolean functions with minimal hamming weight using various genetic programming methods", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", isbn13 = "978-1-4503-6748-6", pages = "342--343", address = "Prague, Czech Republic", DOI = "doi:10.1145/3319619.3321925", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{3321925} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InProceedings{Husa:2020:CEC, author = "Jakub Husa and Lukas Sekanina", booktitle = "2020 IEEE Congress on Evolutionary Computation (CEC)", title = "Evolving Cryptographic Boolean Functions with Minimal Multiplicative Complexity", year = "2020", editor = "Yaochu Jin", month = "19-24 " # jul, keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-7281-6929-3", DOI = "doi:10.1109/CEC48606.2020.9185517", abstract = "The multiplicative complexity (MC) is a cryptographic criterion that describes the vulnerability of a Boolean function to certain algebraic attacks, and in many important cryptographic applications also determines the computational cost. In this paper, we use Cartesian genetic programming to find various types of cryptographic Boolean functions, improve their implementation to achieve the minimal MC, and examine how difficult these optimized functions are to find in comparison to functions than only need to satisfy some base cryptographic criteria. To provide a comparison with other state-of-the-art optimization approaches, we also use our method to improve the implementation of several generic benchmark circuits. Our results provide new upper limits on MC of certain functions, show that our approach is competitive, and also that finding functions with an implementation that has better MC is not mutually exclusive with improving other performance criteria.", notes = "Also known as \cite{9185517}", } @Article{Husa:2024:GPEM, author = "Jakub Husa and Lukas Sekanina", title = "Semantic mutation operator for a fast and efficient design of bent Boolean functions", journal = "Genetic Programming and Evolvable Machines", year = "2024", volume = "25", pages = "Article number: 3", note = "Online first", keywords = "genetic algorithms, genetic programming, Nonlinearity, Bent Boolean functions, Heuristic optimization, Semantic mutation", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-023-09476-w", abstract = "Boolean functions are important cryptographic primitives with extensive use in symmetric cryptography. These functions need to possess various properties, such as nonlinearity to be useful. The main limiting factor of the quality of a Boolean function is the number of its input variables, which has to be sufficiently large. The contemporary design methods either scale poorly or are able to create only a small subset of all functions with the desired properties. This necessitates the development of new and more efficient ways of Boolean function design. we propose a new semantic mutation operator for the design of bent Boolean functions via genetic programming. The principle of the proposed operator lies in evaluating the function's nonlinearity in detail to purposely avoid mutations that could be disruptive and taking advantage of the fact that the nonlinearity of a Boolean function is invariant under all affine transformations. To assess the efficiency of this operator, we experiment with three distinct variants of genetic programming and compare its performance to three other commonly used non-semantic mutation operators. The detailed experimental evaluation proved that the proposed semantic mutation operator is not only significantly more efficient in terms of evaluations required by genetic programming but also nearly three times faster than the second-best operator when designing bent functions with 12 inputs and almost six times faster for functions with 20 inputs.", notes = "Faculty of Information Technology, Brno University of Technology, Božetěchova 2/1, Brno, 612 00, Czech Republic", } @InProceedings{Hussain:2000:GECCO, author = "Daniar Hussain and Steven Malliaris", title = "Evolutionary Techniques Applied to Hashing: An efficient data retrieval method", pages = "760", year = "2000", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)", editor = "Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer", address = "Las Vegas, Nevada, USA", publisher_address = "San Francisco, CA 94104, USA", month = "10-12 " # jul, keywords = "genetic algorithms, genetic programming, genetic improvement, hashing: poster", ISBN = "1-55860-708-0", broken = "http://www.insanemath.com/hash/", URL = "http://gpbib.cs.ucl.ac.uk/gecco2000/RW054.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco2000/RW054.ps", size = "1 page", abstract = "Hashing is an efficient method for storage and retrieval of large amounts of data. Presented here is an evolutionary algorithm to locate efficient hashing functions for specific data sets by sampling and evolving from the set of polynomials. Functions derived in this way show consistently better performance than other common hashing methods, and indicate the power of evolutionary algorithms in search and retrieval.", notes = "Evolves better hash function. A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of \cite{whitley:2000:GECCO}", } @InCollection{Hussain:2015:hbgpa, author = "Mohammed S. Hussain and Alireza Ahangar-asr and Youliang Chen and Akbar A. Javadi", title = "A New Evolutionary Approach to Geotechnical and Geo-Environmental Modelling", booktitle = "Handbook of Genetic Programming Applications", publisher = "Springer", year = "2015", editor = "Amir H. Gandomi and Amir H. Alavi and Conor Ryan", chapter = "19", pages = "483--499", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-20882-4", DOI = "doi:10.1007/978-3-319-20883-1_19", abstract = "In many cases, models based on certain laws of physics can be developed to describe the behaviour of physical systems. However, in case of more complex phenomena with less known or understood contributing parameters or variables the physics-based modelling techniques may not be applicable. Evolutionary Polynomial Regression (EPR) offers a new way of rendering models, in the form of easily interpretable polynomial equations, explicitly expressing the relationship between contributing parameters of a system of complex nature, and the behaviour of the system. EPR is a recently developed hybrid regression method that provides symbolic expressions for models and works with formulae based on pseudo-polynomial expressions. In this chapter the application of EPR to two important geotechnical and geo-environmental engineering systems is presented. These systems include thermo-mechanical behaviour of unsaturated soils and optimisation of performance of an aquifer system subjected to seawater intrusion. Comparisons are made between the EPR model predictions and the actual measured or synthetic data. The results show that the proposed methodology is able to develop highly accurate models with excellent capability of reflecting the real and expected physical effects of the contributing parameters on the performance of the systems. Merits and advantages of the suggested methodology are highlighted.", } @PhdThesis{Hussain:thesis, author = "Mohammed Salih Hussain", title = "Numerical Simulation and Effective Management of Saltwater Intrusion in Coastal Aquifers", school = "University of Exeter", year = "2015", address = "UK", month = oct, URL = "https://ore.exeter.ac.uk/repository/bitstream/handle/10871/19239/HussainM.pdf", URL = "https://ore.exeter.ac.uk/repository/handle/10871/19239", size = "253 pages", notes = "NotGP? Superisors Akbar Javadi and Diego Gomez MSH July 2018 at University of Duhok", } @InProceedings{Hussain:1998:IJCNN, author = "Talib S. Hussain and Roger A. Browse", title = "Network generating attribute grammar encoding", booktitle = "1998 IEEE International Joint Conference on Neural Networks Proceedings", year = "1998", volume = "1", pages = "431--436", address = "Anchorage, Alaska, USA", month = "5-9 " # may, organisation = "IEEE", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, ANN", URL = "https://drive.google.com/open?id=1h9ps1puk7iCDtbimTBWJ-mPbHfK1wm0H", URL = "https://research.cs.queensu.ca/home/browse/Publications/Talib_Browse/1998_ijcnn98paper.pdf", DOI = "doi:10.1109/IJCNN.1998.682305", size = "6 pages", abstract = "The development and theoretical analysis of neural network architectures may be improved with the availability of techniques which allow the systematic representation and generation of classes of architectures. Recent work on the genetic optimization of neural networks has led to new ideas on how to encode neural network architectures abstractly as grammars. Extending this approach, we have devised an encoding system that uses an attribute grammar in which the evaluation of both synthesised and inherited attributes within a generated parse tree provides the details of the connectivity of the network. Comparison with cellular encoding and the geometry-oriented variation of cellular encoding suggests that attribute grammar encoding is simpler, easier to use, and has more potential as a technique for effectively generating neural networks.", notes = "also known as \cite{682305} IJCNN 98 Held In Conjunction With WCCI-98 --- 1998 IEEE World Congress on Computational Intelligence (Cat. No.98CH36227)", } @InProceedings{hussain:1998:bpage, author = "Talib S. Hussain and Roger A. Browse", title = "Basic Properties of Attribute Grammar Encoding", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "76 and 256", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "genetic algorithms, genetic programming, grammar, ANN, NGAGE", URL = "https://drive.google.com/open?id=1IieS0krLvyr3nM2E7icqfzJ0mU9qx1mP", URL = "http://openmap.bbn.com/~thussain/publications/1998_gp98paper.pdf", size = "1+1 page", notes = "NGAGE 'uses an attribute grammar to specify a neural network', 'a sequence of production rules represented in a tree, is used as the genetic representation'. Gruau cellular encoding \cite{gruau:1995:admnn}. GP-98LB, GP-98PhD Student Workshop", } @Misc{oai:CiteSeerPSU:397503, title = "Genetic Encoding of Neural Networks using Attribute Grammars", author = "Talib S. Hussain and Roger A. Browse", year = "1998", booktitle = "CITO Researcher Retreat", address = "Hamilton, Ontario, Canada", month = may # " 12-14", keywords = "genetic algorithms, genetic programming", URL = "https://drive.google.com/open?id=1B-Z1_wugHke42n4XqbyTg6xPc8jR0OFM", URL = "http://www.cs.queensu.ca/RPL/Publications/Talib_Browse/1998_cito98paper.ps.gz", URL = "http://citeseer.ist.psu.edu/397503.html", citeseer-isreferencedby = "oai:CiteSeerPSU:54190", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:397503", size = "4 pages", abstract = "The discovery of good neural network solutions to complex problems may be facilitated through the use of evolutionary computation techniques, such as genetic algorithms or genetic programming. One key issue in the development of any system which will evolve neural networks is how and what information about a neural network will be encoded in the genetic description that will be manipulated by the evolutionary processes. Several approaches have been taken to this encoding problem, including direct, structural, parametric, and grammatical encoding. We present a new grammatical encoding technique in which an attribute grammar is used to represent a class of neural networks. We propose that the resulting encoding offers several improvements over existing approaches.", } @InProceedings{oai:CiteSeerPSU:393107, author = "Talib S. Hussain and Roger A. Browse", title = "Attribute Grammars for Genetic Representations of Neural Networks and Syntactic Constraints of Genetic Programming", year = "1998", booktitle = "AIVIG'98 Workshop on Evolutionary Computation. Held at the 12 Canadian Conference on Artificial Intelligence", address = "Vancouver, Canada", month = "17 " # jun, keywords = "genetic algorithms, genetic programming, grammar", URL = "https://drive.google.com/open?id=1Dg1-Xc2Mg6L2UyzkH4hUq65Ivccn17n7", URL = "http://www.cs.queensu.ca/RPL/Publications/Talib_Browse/1998_aivigi98workshop.ps.gz", URL = "http://citeseer.ist.psu.edu/393107.html", size = "3 pages", citeseer-isreferencedby = "oai:CiteSeerPSU:53251", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:393107", rights = "unrestricted", size = "3 pages", abstract = "this paper, we give a broad overview of our research into attribute grammar representations, from the basic and known capabilities, to the current ideas being addressed, to the future directions of our research.", } @InProceedings{hussain:1999:W, author = "Talib S. Hussain", title = "Workshop on advanced grammar techniques within genetic programming and evolutionary computation", booktitle = "Advanced Grammar Techniques Within Genetic Programming and Evolutionary Computation", year = "1999", editor = "Talib S. Hussain", pages = "72", address = "Orlando, Florida, USA", month = "13 " # jul, keywords = "genetic algorithms, genetic programming, grammar, ANN", URL = "http://openmap.bbn.com/~thussain/publications/1999_gecco99bofworkshop.pdf", notes = "GECCO-99WKS Part of wu:1999:GECCOWKS", } @InProceedings{hussain:1999:G, author = "Talib S. Hussain and Roger A. Browse", title = "Genetic Operators with Dynamic Biases that Operate on Attribute Grammar Representations of Neural Networks", booktitle = "Advanced Grammar Techniques Within Genetic Programming and Evolutionary Computation", year = "1999", editor = "Talib S. Hussain", pages = "83--86", address = "Orlando, Florida, USA", month = "13 " # jul, keywords = "genetic algorithms, genetic programming, grammar, ANN", URL = "https://drive.google.com/open?id=1Bv4in0ph5WxUvX8FXBATioVD7MsWwX2u", size = "4 pages", abstract = "Grammar-based representations of neural networks have shown promise in advancing the study of the evolutionary optimization of neural networks (Yao, 1993; Gruau, 1995; Hussain and Browse, 1998). Our research on the Network Generating Attribute Grammar Encoding (NGAGE) technique has demonstrated that attribute grammars may be used successfully in representing and exploring a space of neural networks (Browse, Hussain and Smillie, 1999). In addition to offering the capability of representing a wide variety of neural network models, NGAGE also offers the potential of designing meaningful dynamic genetic operators. In this paper, we present two reproduction operators that perform a biased offspring creation, and use knowledge of the grammar representation to adapt those biases in response to fitness measurements.", notes = "GECCO-99WKS Part of wu:1999:GECCOWKS", } @PhdThesis{Talib.Hussain:thesis, author = "Talib Sajad Hussain", title = "Attribute Grammar Encoding of the Structure and Behaviour of Artificial Neural Networks", school = "School of Computing, Queen's University", year = "2003", address = "Kinston, Ontario, Canada", month = aug, keywords = "genetic algorithms, genetic programming, ANN", URL = "https://drive.google.com/file/d/1OGQbBSfLF2IiCuBP62ra6Z_aJQpLTHls/view?pli=1", size = "403 pages", abstract = "Current techniques for the abstract representation of complex artificial neural network architectures are limited in the variety and types of neural network characteristics that may be represented. The Network Generated Attribute Grammar Encoding (NGAGE) technique is introduced to address these limitations. NGAGE uses an attribute grammar to explicitly represent both topological and behavioural properties of a neural network, and uses a common neural interpreter to generate functional neural networks from a derivation of the grammar. Grammars that represent a wide variety of current and novel neural network architectures are presented. Together, these grammars demonstrate that the NGAGE technique has greater representation flexibility than current approaches. A novel evolutionary algorithm, the Probabilistic Context-Free Grammar Genetic Programming (PCFG-GP), is introduced to enable a constrained evolutionary search of the space of context-free parse trees generated by an attribute grammar. Experimental results demonstrating the search behaviour of the PCFG-GP algorithm are presented. The NGAGE technique is shown to be a valuable tool for the representation and exploration of novel and existing neural network architectures.", notes = "Supervisor: Roger A. Browse", } @InCollection{DBLP:series/sci/Hussain11, author = "Talib S. Hussain", title = "A Meta-Model Perspective and Attribute Grammar Approach to Facilitating the Development of Novel Neural Network Models", booktitle = "Meta-Learning in Computational Intelligence", publisher = "Springer", year = "2011", editor = "Norbert Jankowski and Wlodzislaw Duch and Krzysztof Grabczewski", volume = "358", series = "Studies in Computational Intelligence", pages = "245--272", keywords = "genetic algorithms, genetic programming, NGAGE, GNML", timestamp = "Tue, 16 May 2017 14:24:34 +0200", biburl = "https://dblp.org/rec/series/sci/Hussain11.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://drive.google.com/file/d/1TPQ4NG5fJhl2b7Gj9ikfQevyLaXVsZPD/view", DOI = "doi:10.1007/978-3-642-20980-2_8", size = "28 pages", abstract = "There is a need for methods and tools that facilitate the systematic exploration of novel artificial neural network models. While significant progress has been made in developing concise artificial neural networks that implement basic models of neural activation, connectivity and plasticity, limited success has been attained in creating neural networks that integrate multiple diverse models to produce highly complex neural systems. From a problem-solving perspective, there is a need for effective methods for combining different neural-network-based learning systems in order to solve complex problems. Different models may be more appropriate for solving different subproblems, and robust, systematic methods for combining those models may lead to more powerful machine learning systems. From a neuroscience modelling perspective, there is a need for effective methods for integrating different models to produce more robust models of the brain. These needs may be met through the development of meta-model languages that represent diverse neural models and the interactions between different neural elements. A meta-model language based on attribute grammars, the Network Generating Attribute Grammar Encoding, is presented, and its capability for facilitating automated search of complex combinations of neural components from different models is discussed.", notes = "Raytheon BBN Technologies, Cambridge, MA, USA", } @InProceedings{Hussain:2023:GPTP, author = "Talib S. Hussain", title = "Let's Evolve Intelligence, Not Solutions", booktitle = "Genetic Programming Theory and Practice XX", year = "2023", editor = "Stephan Winkler and Leonardo Trujillo and Charles Ofria and Ting Hu", series = "Genetic and Evolutionary Computation", pages = "303--333", address = "Michigan State University, USA", month = jun # " 1-3", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-981-99-8412-1", URL = "https://drive.google.com/file/d/1i71SvtvfXlGP53627JqSj8Qb6XHNv7Ff/view", DOI = "doi:10.1007/978-981-99-8413-8_16", size = "30 pages", abstract = "Modern methodologies across the disparate fields of artificial intelligence, including neural networks, evolutionary computation and machine learning, suffer from some limiting assumptions and perspectives that perhaps fundamentally prevent us from pursuing the creation of strong, or at least strongish, AI. This position paper offers several contrarian posits, namely that it is impossible to engineer intelligence, that there is no Occam's Razor for intelligence, that intelligence must be grounded and transferable, and that intelligence must be intrinsically self-reinforcing. Based on these, a new re-framing is discussed of the worlds, drivers, models and processes needed to support the creation of strongish AI. Key elements include the need for an intelligence function, the value of increasing the complexity of the world and drivers over time, and the importance of composable intelligence and processes. Some notations for this new framing are provided, musings on revisiting reproducibility in the context of intelligence are discussed and some preliminary thoughts for how to pursue these ideas using genetic programming for example are offered. Let's move together towards a common methodology for creating quantifiable, grounded intelligence capabilities that are shareable across different efforts and AI techniques, and work collectively to create robust artificial general intelligences.", notes = " Part of \cite{Hu:2023:GPTP} published after the workshop in 2024", } @InProceedings{CSTN-190, author = "Alwyn V. Husselmann and K. A. Hawick", title = "Visualisation of Combinatorial Program Space and Related Metrics", booktitle = "Proc. 12th International Conference on Information and Knowledge Engineering (IKE'13)", year = "2013", address = "Las Vegas, USA", month = "22-25 " # jul, publisher = "WorldComp", keywords = "genetic algorithms, genetic programming, combinatorial information, knowledge engineering, visualisation, optimisation, PSO", owner = "kahawick", timestamp = "2013.03.19", URL = "http://worldcomp-proceedings.com/proc/p2013/IKE3096.pdf", size = "7 pages", abstract = "Searching a large knowledge or information space for optimal regions demands sophisticated algorithms, and sometimes unusual hybrids or combined algorithms. Choosing the best algorithm often requires obtaining a good intuitive or visual understanding of its properties and progress through a space. Visualisation in combinatorial optimisers is more challenging than visualising parametric optimizers. Each problem in combinatorial optimisation is qualitative and has a very different objective, whereas parametric optimizers are quantitative and can be visualised almost trivially. We present a method for visualising abstract syntax trees in an interactive manner, as well as some certain enhancements for evolutionary algorithms. We also discuss the use of this in improving the convergence performance of a Geometric Particle Swarm Optimiser.", notes = "See also Technical Report CSTN-190 Computer Science, Massey University, North Shore 102-904, Auckland, New Zealand", } @InProceedings{CSTN-192, author = "Alwyn V. Husselmann and K. A. Hawick", title = "Geometric Optimisation using {Karva} for Graphical Processing Units", booktitle = "Proc. 15th International Conference on Artificial Intelligence (ICAI'13)", year = "2013", editor = "Hamid R. Arabnia and David de la Fuente and Elena B. Kozerenko and Peter M. LaMonica and Raymond A. Liuzzi and Jose A. Olivas and Todd Waskiewicz", volume = "I", pages = "225--231", address = "Las Vegas, USA", month = "22-25 " # jul, publisher = "WorldComp", keywords = "genetic algorithms, genetic programming, gene expression programming, GPU, CUDA, geometric, parallel computing, SMIT, particle swarm, PSO, GPSO Santa Fe Ant Trail", timestamp = "2013.03.19", ISBN = "1-60132-246-1", URL = "https://www.researchgate.net/publication/266261192_Geometric_Optimisation_using_Karva_for_Graphical_Processing_Units", URL = "http://worldcomp-proceedings.com/proc/p2013/ICA2335.pdf", size = "7 pages", abstract = "Population-based evolutionary algorithms continue to play an important role in artificially intelligent systems, but can not always easily use parallel computation. We have combined a geometric (any-space) particle swarm optimisation algorithm with use of Ferreira Karva language of gene expression programming to produce a hybrid that can accelerate the genetic operators and which can rapidly attain a good solution. We show how Graphical Processing Units (GPUs) can be exploited for this. While the geometric particle swarm optimiser is not markedly faster that genetic programming, we show it does attain good solutions faster, which is important for the problems discussed when the fitness function is inordinately expensive to compute.", notes = "See also technical Report: CSTN-191, Computer Science, Massey University, Auckland, New Zealand http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=28299©ownerid=5571", } @PhdThesis{Husselmann:thesis, author = "Alwyn Visser Husselmann", title = "Data-parallel structural optimisation in agent-based modelling", school = "Computer Science at Massey University", year = "2014", address = "Albany, New Zealand", month = may, keywords = "genetic algorithms, genetic programming, GPU, Karva, MOLPSO", URL = "http://hdl.handle.net/10179/6219", URL = "https://mro.massey.ac.nz/bitstream/handle/10179/6219/02_whole.pdf", size = "228 pages", abstract = "Agent-based modeling (ABM) is particularly suitable for aiding analysis and producing insight in a range of domains where systems have constituent entities which are autonomous, interactive and situated. Decentralised control and irregular communication patterns among these make such models difficult to simulate and even more so to understand. However, the value in this methodology lies in its ability to formulate systems naturally, not only generating the desired macroscopic phenomena, but doing so in an elegant manner. With these advantages, ABM has been enjoying widespread and sustained increasing use. It is then reasonable to seek advances in the field of ABM which would improve productivity, comparability, and ease of implementation. Much work has been done towards these, notably in terms of design methodology, reporting, languages and optimisation. Three issues which remain despite these efforts concern the efficient construction, performance and calibration of agent-based models. Constructing a model involves selecting agents, their attributes, behaviours, interaction rules, and environment, but it also demands a certain level of programming ability. This learning curve stymies research effort from disciplines unrelated to computer science. It is also not clear that one methodology and software package is suitable for all circumstances. Domain-specific languages (DSLs) make development much simpler for their application area. Agent-based model simulation sometimes suffer tremendously from performance issues. Models of situations such as algal cultivation, international markets and pedestrians in dense urban areas invariably suffer from poor scaling. This puts large system sizes and temporally distant states out of reach. The advent of scientific programming on graphical processing units (GPUs) now provides inexpensive high performance, giving hope in this area. It is also important to calibrate such models. More interestingly, the problem of calibrating model structure is given particular emphasis. This ambitious task is difficult for a number of reasons, and is investigated with considerable thought in this work. In summary, the research shows that appropriate use of data-parallelism by multi-stage programming in a simple domain-specific language affords high performance, extensibility and ease of use which is capable of effective automatic model structure optimisation.", } @Article{Husselmann:2015:AISBq, author = "Alwyn Husselmann", title = "About the Cover", journal = "AISB Quarterly", year = "2015", number = "142", month = oct, keywords = "genetic algorithms, genetic programming", URL = "https://aisb.org.uk/wp-content/uploads/2019/09/AISBQ142.pdf", size = "1 page", abstract = "Cover art work", notes = "The newsletter of the society for the study of artificial intelligence and simulation of behaviour", } @InProceedings{hussian:2000:mwmugp, author = "Abo El-Abbass Hussian and Alaa Sheta and Mahmoud Kamel and Mohamed Telbaney and Ashraf Abdelwahab", title = "Modeling of a Winding Machine Using Genetic Programming", booktitle = "Proceedings of the 2000 Congress on Evolutionary Computation CEC00", year = "2000", pages = "398--402", address = "La Jolla Marriott Hotel La Jolla, California, USA", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "6-9 " # jul, organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, control system design, ARMA, autoregressive moving average model, data sets, experiments, industrial process, winding machine modelling, winding process dynamics, autoregressive moving average processes, industrial plants, winding (process)", ISBN = "0-7803-6375-2", DOI = "doi:10.1109/CEC.2000.870323", abstract = "In this paper, we present a new method for modeling the dynamics of a winding process using genetic programming and compare it with traditional modeling approaches. Data sets collected from an actual industrial process was used throughout the experiments. Three models were developed to describe the dynamics of the winding process. Experimental results are presented and discussed.", notes = "CEC-2000 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 00TH8512, Library of Congress Number = 00-018644", } @InProceedings{hutter:2024:CEC, author = "David Hutter and Michael Hellwig and Thomas Steinberger", title = "An Interior-point Genetic Algorithm with Restarts for Flexible Job Shop Scheduling Problems", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, Job shop scheduling, Uncertainty, Processor scheduling, Sociology, Production, Evolutionary computation, Complexity theory, Production Planning, Flexible Job Shop Scheduling", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10611934", abstract = "The Flexible Job Shop Problem (FJSSP) represents an extension of the Job Shop Scheduling Problem (JSSP), in which the sequence of operations on assigned machines has to be optimised. In the FJSSP, operations can be processed by different machines, with different processing times on each machine type. The paper proposes a Genetic Algorithm (GA) variant that uses an encoding capable to be applied to as general FJSSP instances as possible and variation principles designed to maintain a population of feasible candidate solutions over the whole search process. Additionally, the GA parameter setting for each FJSSP instance is derived from problem-specific attributes, such as the production environments flexibility and the variety of duration values, and a restart scheme that accounts for the dissimilarity of the initial population is introduced to avoid premature convergence. The GA results are compared to best- known solutions in literature as well as to the solutions of the commercial solver GUROBI being applied to a Mixed-Integer Linear Programming (MILP) representation of the FJSSP. In addition to demonstrating the decent results of the developed GA on 402 recognised FJSSP benchmark instances, the paper investigates the influence of different aspects of problem complexity on the relative GA performance to assert the GAs viability for more complex FJSSP variants Including uncertainties. Regardless of the machine flexibility of a FJSSP instance, the experimental results suggest that both approaches tie on smaller and medium sized problem instances. With increasing complexity in problem characteristics like problem size, flexibility, and duration variety, the GA gains advantage over the MILP/GUROBI approach. The observation substantiates the expectation that the proposed GA is well suited for practical (more realistic) scheduling problems with additional requirements like worker flexibility or uncertainty effects.", notes = "also known as \cite{10611934} Is this GP? WCCI 2024", } @InProceedings{Hutterer:2012:CIBCB, author = "Stephan Hutterer and Gerald Zauner and Marlene Huml and Rene Silye and Kurt Schilcher", title = "Data mining techniques for {AFM-} based tumor classification", booktitle = "IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2012)", year = "2012", month = "9-12 " # may, pages = "105--111", size = "7 pages", abstract = "The present paper deals with the application of atomic force microscopy (AFM) as a tool for morphological characterisation of histological brain tumour samples. Data mining techniques will be applied for automatic identification of brain tumour tissues based on AFM images by means of classifying grade II and IV tumours. The rapid advancement of AFM in recent years turned it into a valuable and useful tool to determine the topography of surface nanoscale structures with high precision. Therefore, it is used in a variety of applications in life science, materials science, electrochemistry, polymer science, biophysics, nanotechnology, and biotechnology. Minkowski functionals are used (in particular the Euler-Poincare characteristic) as a feature descriptor to characterise global geometric structures in images related to the topology of the AFM image. In order to improve classification accuracy on the one hand, but to infer interpretable information from AFM images for domain experts on the other hand, feature analysis and reduction will be applied. From a data mining point of view, Genetic Programming will be introduced as a sophisticated method for both feature analysis and reduction as well as for producing highly accurate and interpretable models. Support Vector Machines will be used for comparison reasons when talking about reachable model accuracy.", keywords = "genetic algorithms, genetic programming, AFM-based tumour classification, Euler-Poincare characteristics, Minkowski functionals, atomic force microscopy, automatic identification, biophysics, biotechnology, brain tumour tissues, data mining techniques, electrochemistry, feature analysis, feature descriptor, feature reduction, global geometric structures, histological brain tumour samples, life science, materials science, morphological characterisation, nanotechnology, polymer science, support vector machines, surface nanoscale structure topography, Poincare mapping, atomic force microscopy, brain, data mining, electrochemistry, feature extraction, image classification, medical image processing, nanomedicine, support vector machines, surface morphology, surface topography, tumours", DOI = "doi:10.1109/CIBCB.2012.6217218", notes = "Also known as \cite{6217218}", } @InProceedings{Hutterer:2013:GECCOcomp, author = "Stephan Hutterer and Stefan Vonolfen and Michael Affenzeller", title = "Genetic programming enabled evolution of control policies for dynamic stochastic optimal power flow", booktitle = "GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion", year = "2013", editor = "Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A. N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks", isbn13 = "978-1-4503-1964-5", keywords = "genetic algorithms, genetic programming", pages = "1529--1536", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2464576.2482732", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "The optimal power flow (OPF) is one of the central Optimization problems in power grid engineering, building an essential tool for numerous control as well as planning issues. Methods for solving the OPF that mainly treat steady-state situations have been studied extensively, ignoring uncertainties of system variables as well as their volatile behaviour. While both the economical as well as well as technical importance of accurate control is high, especially for power flow control in dynamic and uncertain power systems, methods are needed that provide (near-) optimal actions quickly, eliminating issues on convergence speed or robustness of the Optimization. This paper shows an approximate policy-based control approach where optimal actions are derived from policies that are learnt offline, but that later provide quick and accurate control actions in volatile situations. These policies are evolved using genetic programming, where multiple and interdependent policies are learnt synchronously with simulation-based Optimization. Finally, an approach is available for learning fast and robust power flow control policies suitable to highly dynamic power systems such as smart electric grids.", notes = "Also known as \cite{2482732} Distributed at GECCO-2013.", } @Article{3071, author = "S. Hutterer and M. Affenzeller", title = "Probabilistic Electric Vehicle Charging Optimized With Genetic Algorithms and a Two- Stage Sampling Scheme", journal = "International Journal of Energy Optimization and Engineering (IJEOE)", year = "2013", volume = "2", number = "3", pages = "1--15", month = oct, DOI = "doi:10.4018/ijeoe.2013070101", URL = "https://www.igi-global.com/article/probabilistic-electric-vehicle-charging-optimized-with-genetic-algorithms-and-a-two-stage-sampling-scheme/93097", keywords = "genetic algorithms, genetic programming", } @InProceedings{3264, author = "S. Hutterer and M. Affenzeller and F. Auinger", title = "Evolutionary Computation Enabled Controlled Charging for E-Mobility Aggregators", booktitle = "Proceedings of the IEEE Symposium on Computational Intelligence Applications in Smart Grid", year = "2013", address = "Singapur, Singapore", month = apr, DOI = "doi:10.1109/CIASG.2013.6611507", URL = "https://ieeexplore.ieee.org/document/6611507/", keywords = "genetic algorithms, genetic programming", } @InProceedings{Huynh:2016:GECCOcomp, author = "Quang Nhat Huynh and Hemant Kumar Singh and Tapabrata Ray", title = "A Semantics based Symbolic Regression Framework for Mining Explicit and Implicit Equations from Data", booktitle = "GECCO '16 Companion: Proceedings of the Companion Publication of the 2016 Annual Conference on Genetic and Evolutionary Computation", year = "2016", editor = "Tobias Friedrich and Frank Neumann and Andrew M. Sutton and Martin Middendorf and Xiaodong Li and Emma Hart and Mengjie Zhang and Youhei Akimoto and Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and Daniele Loiacono and Julian Togelius and Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and Faustino Gomez and Carlos M. Fonseca and Heike Trautmann and Alberto Moraglio and William F. Punch and Krzysztof Krawiec and Zdenek Vasicek and Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and Boris Naujoks and Enrique Alba and Gabriela Ochoa and Simon Poulding and Dirk Sudholt and Timo Koetzing", pages = "103--104", month = "20-24 " # jul, keywords = "genetic algorithms, genetic programming: Poster", organisation = "SIGEVO", address = "Denver, USA", publisher = "ACM", publisher_address = "New York, NY, USA", isbn13 = "978-1-4503-4323-7", DOI = "doi:10.1145/2908961.2908989", abstract = "Symbolic Regression (SR) is commonly used to identify relationships among variables and responses in a data in the form of analytical, preferably compact expressions. Genetic Programming (GP) is one of the common ways to perform SR. Such relationships could be represented using explicit or implicit expressions, of which the former has been more extensively studied in literature. Some of the key challenges that face SR are bloat, loss of diversity, and accurate determination of coefficients. More recently, semantics and multi-objective formulations have been suggested as potential tools to build more intelligence in the search process. However, studies along both these directions have been in isolation and applied only to selected components of SR so far. In this paper, we intend to build a framework that integrates semantics deeper into more components of SR. The framework could be operated in traditional single objective as well as multi-objective mode and is capable of dealing with both explicit and implicit functions. The constituent modules use semantics for compaction of expressions, maintaining diversity by identifying unique individuals, crossover and local exploitation. A comparison of obtained results with those from existing semantics-based and multi-objective approach demonstrates the advantages of the proposed framework.", notes = "Distributed at GECCO-2016.", } @InProceedings{Huynh:2016:SSCI, author = "Quang Nhat Huynh and Hemant Kumar Singh and Tapabrata Ray", booktitle = "2016 IEEE Symposium Series on Computational Intelligence (SSCI)", title = "Improving Symbolic Regression through a semantics-driven framework", year = "2016", abstract = "The process of identifying analytical relationships among variables and responses in observed data is commonly referred to as Symbolic Regression (SR). Genetic Programming is one of the commonly used approaches for SR, which operates by evolving expressions. Such relationships could be explicit or implicit in nature, of which the former has been more extensively studied in literature. Even though extensive studies have been done in SR, the fundamental challenges such as bloat, loss of diversity and accurate determination of coefficients still persist. Recently, semantics and multi-objective formulation have been suggested as potential tools to alleviate these issues by building more intelligence in the search process. However, studies along both these directions have been in isolation and applied only to selected components of SR so far. In this paper, we intend to build a framework that integrates semantics deeper into more components of SR. The framework could be operated in conventional single objective as well as multi-objective mode and is capable of dealing with both explicit and implicit functions. Semantics are used in the proposed framework for improving compactness and diversity of expressions, crossover and local exploitation. Numerical experiments are presented on a set of benchmark problems to demonstrate the strengths of the proposed approach.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SSCI.2016.7849941", month = dec, notes = "Also known as \cite{7849941}", } @Article{Huynh:ieeeTEC, author = "Quang Nhat Huynh and Shelvin Chand and Hemant Kumar Singh and Tapabrata Ray", title = "Genetic Programming with Mixed Integer Linear Programming Based Library Search", journal = "IEEE Transactions on Evolutionary Computation", year = "2018", volume = "22", number = "5", pages = "733--747", month = oct, keywords = "genetic algorithms, genetic programming, Mixed Integer Linear Programming, MLIP, Semantic Backpropagation, SB, Library of Sub-expressions", ISSN = "1089-778X", URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8364611", DOI = "doi:10.1109/TEVC.2018.2840056", size = "15 pages", abstract = "Genetic programming (GP) is one of the commonly used tools for symbolic regression. In the field of GP, the use of semantics and an external library of sub-expressions for designing better search operators has recently gained significant attention. A notable example is semantic back-propagation, which has demonstrated an ability to obtain expressions with extremely small prediction errors. However, these expressions often tend to be long and difficult to interpret, which may restrict their applicability in real-life problems. In this paper, we propose a GP framework that includes two key elements, a new library construction scheme and a novel semantic operator based on mixed-integer linear programming (MILP). The proposed library construction scheme maintains diverse sub-expressions and keeps the library size in check by imposing an upper limit. The proposed semantic operator constructs new expressions by effectively combining a given number of sub-expressions from the library. These improvements have been integrated in a bi-objective GP framework with random desired operator (RDO), which attempts to simultaneously reduce the complexity and improve the fitness of the evolving expressions. The contributions of individual components are studied in detail using fifteen benchmarks. It is observed that the use of the proposed scheme with RDO leads to shorter expressions without sacrificing accuracy of approximation. The addition of MILP further improves the results for certain types of problems.", notes = "also known as \cite{8364611}", } @InProceedings{Huynh:2022:SSCI, author = "Quang Huynh and Hemant Singh and Tapabrata Ray and Akira Oyama", booktitle = "2022 IEEE Symposium Series on Computational Intelligence (SSCI)", title = "Improved Genetic Programming for Symbolic Regression: Case Studies on Practical Applications", year = "2022", pages = "1135--1142", abstract = "Genetic Programming (GP), especially Semantic GP (SGP), has shown significant potential in solving numerical benchmarks in Symbolic Regression (SR) domain in recent years. However, its application on real-world problems has been less explored due to the large sizes of the resulting expressions, which are prone to over-fitting and are difficult to interpret. In this paper, we propose a method that incorporates customization for real-world data sets based on a combination of two operators of GP. The first operator uses the concept of Semantic Backpropagation, a noteworthy method in SGP, to create short expressions which are highly correlated with the outputs. The second operator makes use of Mixed Integer Linear Programming (MILP) to combine short expressions into the overall expression with good accuracy. The proposed approach is tested on one synthetic data set and two practical applications which are challenging for conventional GP. The experimental results are very promising, with further scope of improvement.", keywords = "genetic algorithms, genetic programming, Backpropagation, Sensitivity analysis, Semantics, Benchmark testing, Mixed integer linear programming, Computational intelligence", DOI = "doi:10.1109/SSCI51031.2022.10022279", month = dec, notes = "Also known as \cite{10022279}", } @InProceedings{Hvatov:2020:GECCOcomp, author = "Alexander Hvatov and Mikhail Maslyaev", title = "The Data-Driven Physical-Based Equations Discovery Using Evolutionary Approach", year = "2020", editor = "Richard Allmendinger and Hugo Terashima Marin and Efren Mezura Montes and Thomas Bartz-Beielstein and Bogdan Filipic and Ke Tang and David Howard and Emma Hart and Gusz Eiben and Tome Eftimov and William {La Cava} and Boris Naujoks and Pietro Oliveto and Vanessa Volz and Thomas Weise and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Rui Wang and Ran Cheng and Guohua Wu and Miqing Li and Hisao Ishibuchi and Jonathan Fieldsend and Ozgur Akman and Khulood Alyahya and Juergen Branke and John R. Woodward and Daniel R. Tauritz and Marco Baioletti and Josu Ceberio Uribe and John McCall and Alfredo Milani and Stefan Wagner and Michael Affenzeller and Bradley Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and Nayat Sanchez-Pi and Luis Marti and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and Matthew Johns and Nick Ross and Ed Keedwell and Herman Mahmoud and David Walker and Anthony Stein and Masaya Nakata and David Paetzel and Neil Vaughan and Stephen Smith and Stefano Cagnoni and Robert M. Patton and Ivanoe {De Falco} and Antonio {Della Cioppa} and Umberto Scafuri and Ernesto Tarantino and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Erik Hemberg and Riyad Alshammari and Tokunbo Makanju and Fuijimino-shi and Ivan Zelinka and Swagatam Das and Ponnuthurai Nagaratnam and Roman Senkerik", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion", pages = "129--130", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, data-driven models, generic programming, PDE discovery, equation discovery, sparse regression", isbn13 = "9781450371278", URL = "http://www.human-competitive.org/sites/default/files/entry_0.txt", URL = "http://www.human-competitive.org/sites/default/files/gecco2020final.pdf", URL = "https://arxiv.org/abs/2004.01680", URL = "https://doi.org/10.1145/3377929.3389943", DOI = "doi:10.1145/3377929.3389943", size = "2 pages", abstract = "The modern machine learning methods allow one to obtain the data-driven models in various ways. However, the more complex the model is, the harder it is to interpret. In the paper, we describe the algorithm for the mathematical equations discovery from the given observations data. The algorithm combines genetic programming with the sparse regression. This algorithm allows obtaining different forms of the resulting models. As an example, it could be used for governing analytical equation discovery as well as for partial differential equations (PDE) discovery. The main idea is to collect a bag of the building blocks (it may be simple functions or their derivatives of arbitrary order) and consequently take them from the bag to create combinations, which will represent terms of the final equation. The selected terms pass to the evolutionary algorithm, which is used to evolve the selection. The evolutionary steps are combined with the sparse regression to pick only the significant terms. As a result, we obtain a short and interpretable expression that describes the physical process that lies beyond the data. In the paper, two examples of the algorithm application are described: the PDE discovery for the metocean processes and the function discovery for the acoustics.", notes = "Entered 2021 HUMIES Also known as \cite{10.1145/3377929.3389943} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @InProceedings{hwang:2003:ICICS, author = "Wen-Jyi Hwang and Chien-Min Ou and Rui-Chuan Lin and Wen-Wei Hu", title = "Genetic Programming for Robust Video Transmission", booktitle = "International Conference on Informatics, Cybernetics, and Systems, ICICS 2003", year = "2003", editor = "Xuemin Chen", address = "I-Shou University, Kaohsiung, Taiwan", month = dec # " 14-16", organisation = "I-Shou University, IEEE Taipei Section", keywords = "genetic algorithms, genetic programming", notes = "Broken Jan 2013 http://www.isu.edu.tw/icics2003/ 15-Dec-03 Session C(2):Multimedia Processing National Taiwan Normal University, Ching-Yun University, Chung Yuan Christian University", } @Article{Hwang:2004:N, author = "Wen-Jyi Hwang and Chien-Min Ou and Rui-Chuan Lin and Wen-Wei Hu", title = "Layered video transmission based on genetic programming for lossy channels", journal = "Neurocomputing", year = "2004", volume = "57", pages = "361--372", owner = "wlangdon", note = "New Aspects in Neurocomputing: 10th European Symposium on Artificial Neural Networks 2002", keywords = "genetic algorithms, genetic programming, Genetic algorithm, Video transmission, Wavelet transform", ISSN = "0925-2312", URL = "http://www.sciencedirect.com/science/article/B6V10-4BJ23B3-1/2/4d871f85b5d703962a9dd8745bac3672", DOI = "doi:10.1016/j.neucom.2003.10.013", abstract = "This paper presents a novel robust layered video transmission design algorithm for noisy channels. In the algorithm, the 3D SPIHT coding technique is used to encode the video sequences for the transmission of each layer. A new error protection allocation scheme based on genetic programming is then employed to determine the degree of protection for each layer so that the average distortion of the reconstructed images after transmission can be minimised. Simulation results show that, subject to the same amount of redundancy bits for error protection, the new algorithm outperforms other existing algorithms where equal-protection schemes are adopted.", } @InProceedings{DBLP:conf/gecco/HydeBK09, author = "Matthew R. Hyde and Edmund K. Burke and Graham Kendall", title = "Evolving human-competitive reusable 2D strip packing heuristics", booktitle = "GECCO-2009 Workshop on Automated heuristic design: crossing the chasm for search methods", year = "2009", editor = "Anna I. Esparcia and Ying-ping Chen and Gabriela Ochoa and Ender Ozcan and Marc Schoenauer and Anne Auger and Hans-Georg Beyer and Nikolaus Hansen and Steffen Finck and Raymond Ros and Darrell Whitley and Garnett Wilson and Simon Harding and W. B. Langdon and Man Leung Wong and Laurence D. Merkle and Frank W. Moore and Sevan G. Ficici and William Rand and Rick Riolo and Nawwaf Kharma and William R. Buckley and Julian Miller and Kenneth Stanley and Jaume Bacardit and Will Browne and Jan Drugowitsch and Nicola Beume and Mike Preuss and Stephen L. Smith and Stefano Cagnoni and Jim DeLeo and Alexandru Floares and Aaron Baughman and Steven Gustafson and Maarten Keijzer and Arthur Kordon and Clare Bates Congdon and Laurence D. Merkle and Frank W. Moore", pages = "2189--2192", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1570256.1570299", abstract = "This extended abstract presents preliminary work on reusable automatically generated heuristics for the 2D strip packing problem. It builds on our previous work, where the heuristics were not shown to be reusable. The best constructive heuristic for this problem in the literature is 'best-fit', and the motivation of this work is to obtain heuristics which are comparable to the performance of this heuristic.", notes = "Distributed on CD-ROM at GECCO-2009. ACM Order Number 910092.", } @InProceedings{Hyde:2010:cec, author = "Edmund K. Burke and Matthew R. Hyde and Graham Kendall", title = "Providing a memory mechanism to enhance the evolutionary design of heuristics", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-6910-9", abstract = "Genetic programming approaches have previously been employed in the literature to evolve heuristics for various combinatorial optimisation problems. This paper presents a hyper-heuristic genetic programming methodology to evolve more sophisticated one dimensional bin packing heuristics than have been evolved previously. The heuristics have access to a memory, which allows them to make decisions with some knowledge of their potential future impact. In contrast to previously evolved heuristics for this problem, we show that these heuristics evolve to draw upon this memory in order to facilitate better planning, and improved packings. This fundamental difference enables an evolved heuristic to represent a dynamic packing strategy rather than a fixed packing strategy. A heuristic can change its behaviour depending on the characteristics of the pieces it has seen before, because it has evolved to draw upon its experience.", DOI = "doi:10.1109/CEC.2010.5586388", notes = "WCCI 2010. Also known as \cite{5586388}", } @Article{Hyde:2011:ieeeTEC, author = "Edmund K. Burke and Matthew Hyde and Graham Kendall and John Woodward", title = "A Genetic Programming Hyper-Heuristic Approach for Evolving 2-D Strip Packing Heuristics", journal = "IEEE Transactions on Evolutionary Computation", year = "2010", volume = "14", number = "6", pages = "942--958", month = dec, keywords = "genetic algorithms, genetic programming, volutionary computation, evolving 2D strip packing heuristics, genetic programming hyper heuristic approach, search methodologies, computational complexity, search problems", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2010.2041061", URL = "http://results.ref.ac.uk/Submissions/Output/3290828", abstract = "We present a genetic programming (GP) system to evolve reusable heuristics for the 2-D strip packing problem. The evolved heuristics are constructive, and decide both which piece to pack next and where to place that piece, given the current partial solution. This paper contributes to a growing research area that represents a paradigm shift in search methodologies. Instead of using evolutionary computation to search a space of solutions, we employ it to search a space of heuristics for the problem. A key motivation is to investigate methods to automate the heuristic design process. It has been stated in the literature that humans are very good at identifying good building blocks for solution methods. However, the task of intelligently searching through all of the potential combinations of these components is better suited to a computer. With such tools at their disposal, heuristic designers are then free to commit more of their time to the creative process of determining good components, while the computer takes on some of the design process by intelligently combining these components. This paper shows that a GP hyper-heuristic can be employed to automatically generate human competitive heuristics in a very-well studied problem domain.", notes = "Entered for 2011 HUMIES GECCO 2011 also known as \cite{5491153}", uk_research_excellence_2014 = "This represents the first attempt to use a computer to design new constructive packing methods for rectangular stock cutting. It can automatically produce constructive heuristics which are often better than human-created methods. This methodology is having a major impact in this field by providing the foundations for fundamentally new directions in the automatic design of effective algorithms by computer. The results in this paper provided some of the foundation blocks and signposts for a new major EPSRC programme grant (EP/J017515/1) of pounds6.8M between UCL, Stirling, York and Birmingham, started in 2012.", } @PhdThesis{Hyde:thesis, author = "Matthew Hyde", title = "A genetic programming hyper-heuristic approach to automated packing", school = "School of Computer Science, University of Nottingham", year = "2010", address = "UK", month = mar, keywords = "genetic algorithms, genetic programming, Electronic computers, Computer science, memory", URL = "http://etheses.nottingham.ac.uk/1625/1/mvh_corrected_thesis.pdf", URL = "http://etheses.nottingham.ac.uk/1625/", URL = "http://ethos.bl.uk/OrderDetails.do?did=28&uin=uk.bl.ethos.523511", size = "226 pages", bibsource = "OAI-PMH server at etheses.nottingham.ac.uk", oai = "oai:etheses.nottingham.ac.uk:1625", abstract = "This thesis presents a programme of research which investigated a genetic programming hyper-heuristic methodology to automate the heuristic design process for one, two and three dimensional packing problems. Traditionally, heuristic search methodologies operate on a space of potential solutions to a problem. In contrast, a hyper-heuristic is a heuristic which searches a space of heuristics, rather than a solution space directly. The majority of hyper-heuristic research papers, so far, have involved selecting a heuristic, or sequence of heuristics, from a set predefined by the practitioner. Less well studied are hyper-heuristics which can create new heuristics, from a set of potential components. This thesis presents a genetic programming hyper-heuristic which makes it possible to automatically generate heuristics for a wide variety of packing problems. The genetic programming algorithm creates heuristics by intelligently combining components. The evolved heuristics are shown to be highly competitive with human created heuristics. The methodology is first applied to one dimensional bin packing, where the evolved heuristics are analysed to determine their quality, specialisation, robustness, and scalability. Importantly, it is shown that these heuristics are able to be reused on unseen problems. The methodology is then applied to the two dimensional packing problem to determine if automatic heuristic generation is possible for this domain. The three dimensional bin packing and knapsack problems are then addressed. It is shown that the genetic programming hyper-heuristic methodology can evolve human competitive heuristics, for the one, two, and three dimensional cases of both of these problems. No change of parameters or code is required between runs. This represents the first packing algorithm in the literature able to claim human competitive results in such a wide variety of packing domains.", notes = "uk.bl.ethos.523511", } @Article{Hyde:2011:EC, author = "Edmund K. Burke and Matthew R. Hyde and Graham Kendall and John Woodward", title = "Automating the Packing Heuristic Design Process with Genetic Programming", journal = "Evolutionary Computation", year = "2012", volume = "20", number = "1", pages = "63--89", month = "Spring", keywords = "genetic algorithms, genetic programming, evolutionary design, cutting and packing, hyper-heuristicsn", ISSN = "1063-6560", DOI = "doi:10.1162/EVCO_a_00044", URL = "http://results.ref.ac.uk/Submissions/Output/944156", size = "25 pages", abstract = "The literature shows that one, two and three dimensional bin packing and knapsack packing are difficult problems in Operational Research. Many techniques, including exact, heuristic, and metaheuristic approaches, have been investigated to solve these problems and it is often not clear which method to use when presented with a new instance. This paper presents an approach which is motivated by the goal of building computer systems which can design heuristic methods. The overall aim is to explore the possibilities for automating the heuristic design process. We present a genetic programming system to automatically generate a good quality heuristic for each instance. It is not necessary to change the methodology depending on the problem type (one, two or three dimensional knapsack and bin packing problems), and it therefore has a level of generality unmatched by other systems in the literature. We carry out an extensive suite of experiments and compare with the best human designed heuristics in the literature. Note that our heuristic design methodology uses the same parameters for all the experiments. The contribution of this paper is to present a more general packing methodology than those currently available, and to show that, by using this methodology, it is possible for a computer system to design heuristics which are competitive with the human designed heuristics from the literature. This represents the first packing algorithm in the literature able to claim human competitive results in such a wide variety of packing domains.", uk_research_excellence_2014 = "This paper describes how to automatically develop 3D packing heuristics, which is relevant in many industrial areas where 3D physical objects need to be packed optimally into a confined space. This is the first attempt at automatically developing 3D packing heuristics. The results in this paper provided some of the foundation blocks and signposts for a new major EPSRC programme grant (EP/J017515/1) of pounds6.8M between UCL, Stirling, York and Birmingham, which started in 2012. This research has led to Woodward organising a workshop on the automated design of algorithms at the 2013 GECCO (Genetic and Evolutionary Computation Conference).", } @Article{Hyde:2013:JORS, author = "M. R. Hyde and E. K. Burke and G. Kendall", title = "Automated code generation by local search", journal = "Journal of the Operational Research Society", year = "2013", volume = "64", number = "12", pages = "1725--1741", month = dec, keywords = "genetic algorithms, genetic programming, heuristics, local search", publisher = "Palgrave Macmillan", ISSN = "0160-5682", URL = "http://dx.doi.org/10.1057/jors.2012.149", DOI = "doi:10.1057/jors.2012.149", size = "17 pages", abstract = "There are many successful evolutionary computation techniques for automatic program generation, with the best known, perhaps, being genetic programming. Genetic programming has obtained human competitive results, even infringing on patented inventions. The majority of the scientific literature on automatic program generation employs such population-based search approaches, to allow a computer system to search a space of programs. In this paper, we present an alternative approach based on local search. There are many local search methodologies that allow successful search of a solution space, based on maintaining a single incumbent solution and searching its neighbourhood. However, use of these methodologies in searching a space of programs has not yet been systematically investigated. The contribution of this paper is to show that a local search of programs can be more successful at automatic program generation than current nature inspired evolutionary computation methodologies.", notes = "Also known as \cite{Hyde2013}", } @InProceedings{ga96aHyotyniemi, author = "Heikki Hy{\"o}tyniemi and Heikki Koivo", title = "Genes, codes, and dynamic systems", pages = "225--232", year = "1996", editor = "Jarmo T. Alander", booktitle = "Proceedings of the Second Nordic Workshop on Genetic Algorithms and their Applications (2NWGA)", series = "Proceedings of the University of Vaasa, Nro. 13", publisher = "University of Vaasa", address = "Vaasa (Finland)", month = "19.-23.~" # aug, organisation = "Finnish Artificial Intelligence Society", URL = "ftp://ftp.uwasa.fi/cs/2NWGA/Hyotyniemi.ps.Z", notes = "Hyotyniemi.ps.Z contains only the first few pages. About expressing Turing Machines as recurrent neural networks. However these do not appear to be evolve. Apparently an abstract of \cite{hyotyniemi:1996:STeP}", } @InProceedings{hyotyniemi:1996:STeP, author = "Heikki Hy{\"o}tyniemi", title = "Turing Machines are Recurrent Neural Networks", booktitle = "Proceedings of STeP'96", year = "1996", editor = "Jarmo Alander and Timo Honkela and Matti Jakobsson", pages = "13--24", publisher = "Finnish Artificial Intelligence Society", URL = "http://www.uwasa.fi/stes/step96/step96/hyotyniemi1/", URL = "http://www.hut.fi/~hhyotyni/HH1/HH1.ps", abstract = "Any algebraically computable function can be expressed as a recurrent neural network structure consisting of identical computing elements (or, equivalently, as a nonlinear discrete-time system of the form , where is a simple `cut' function). A constructive proof is presented in this paper.", } @InProceedings{Iannone:2021:ICPC, author = "Emanuele Iannone and Dario {Di Nucci} and Antonino Sabetta and Andrea {De Lucia}", title = "Toward Automated Exploit Generation for Known Vulnerabilities in Open-Source Libraries", booktitle = "2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC)", year = "2021", pages = "396--400", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, SBSE, SIEGE, EVOSUITE, Exploit Generation, Security Testing, Software Vulnerabilities", DOI = "doi:10.1109/ICPC52881.2021.00046", size = "5 pages", abstract = "Modern software applications, including commercial ones, extensively use Open-Source Software (OSS) components,accounting for 90 percent of software products on the market. This has serious security implications, mainly because developers rely on non-updated versions of libraries affected by software vulnerabilities. Several tools have been developed to help developers detect these vulnerable libraries and assess and mitigate their impact. The most advanced tools apply sophisticated reachability analyses to achieve high accuracy; however, they need additional data (inparticular, concrete execution traces, such as those obtained by running a test suite) that is not always readily available. we propose SIEGE, a novel automatic exploit generation approach based on genetic algorithms, which generates test cases that execute the methods in a library known to contain a vulnerability. These test cases represent precious, concrete evidence that the vulnerable code can indeed be reached; they are also useful for security researchers to better understand how the vulnerability could be exploited in practice. This technique has been implemented as an extension of EVOSUITE and applied on set of 11 vulnerabilities exhibited by widely used OSS JAVA libraries. Our initial findings show promising results that deserve to be assessed further in larger-scale empirical studies.", notes = "SeSa Lab - University of Salerno, Fisciano, Italy", } @InProceedings{Iba:1993:elpbsc, author = "Hitoshi Iba and Hugo {de Garis} and Tetsuya Higuchi", title = "Evolutionary learning of predatory behaviors based on structured classifiers", booktitle = "From Animals to Animats 2: Proceedings of the Second International Conference on Simulation of Adaptive Behavior", year = "1993", editor = "Jean-Arcady Meyer and Herbert L. Roitblat and Stewart W. Wilson", pages = "356--363", publisher = "MIT Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-262-63149-0", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Iba_1993_elpbsc.pdf", URL = "http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=3971", URL = "http://citeseerx.ist.psu.edu/showciting?cid=38619", size = "8 pages", notes = "SAB'92 http://www.isab.org/confs/sab92.php", } @TechReport{Iba:1993:sipsGA, author = "Hitoshi Iba and Hugo {de Garis} and Taisuke Sato", title = "Solving identification problems by structured genetic algorithms", institution = "Electrotechnical Laboratory", year = "1993", type = "Technical report", number = "ETL-TR-93-17", address = "1-1-4 Umezono, Tsukuba-city, Ibaraki, 305, Japan", month = "10 " # aug, keywords = "genetic algorithms, genetic programming, system identification, GMDH (group method of Data handling), structured genetic algorithms", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Iba_1993_sipsGA.pdf", abstract = "stroganoff minimum description length", notes = "This paper is based on our earlier results presented at ICGA93 \cite{icga93:iba}", size = "26 pages Iba:1993:sipsGA.pdf missing figure 9", } @TechReport{etl-tr-93-25, author = "Hitoshi Iba and Tatsuya Niwa and Taisuke Sato", title = "Evolutionary Learning of {Boolean} Concepts: An empirical Study", institution = "Electrotechnical Laboratory", year = "1993", number = "ETL-TR-93-25", address = "1-1-4 Umezono, Tsukuba-city, Ibaraki, 305, Japan", month = "18 " # oct, keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/etl-tr-93-25.pdf", size = "6 pages", } @InProceedings{icga93:iba, author = "Hitoshi Iba and Takio Karita and Hugo {de Garis} and Taisuke Sato", title = "System Identification Using Structured Genetic Algorithms", year = "1993", booktitle = "Proceedings of the 5th International Conference on Genetic Algorithms, ICGA-93", editor = "Stephanie Forrest", publisher = "Morgan Kaufmann", address = "University of Illinois at Urbana-Champaign", month = "17-21 " # jul, keywords = "genetic algorithms, genetic programming", pages = "279--286", size = "8 pages", notes = "Hierarchical tree GA, used for learning sequence of multiple variables and then predicting, STOGANOFF. See also \cite{Iba:1993:sipsGA}", } @InCollection{kinnear:iba, title = "Genetic Programming Using a Minimum Description Length Principle", author = "Hitoshi Iba and Hugo {de Garis} and Taisuke Sato", booktitle = "Advances in Genetic Programming", publisher = "MIT Press", editor = "Kenneth E. {Kinnear, Jr.}", year = "1994", pages = "265--284", chapter = "12", keywords = "genetic algorithms, genetic programming, MDL, GMDH, symbolic regression, STROGANOFF", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/etl-tr-93-15.pdf", URL = "http://citeseer.ist.psu.edu/327857.html", URL = "http://www.amazon.co.uk/Advances-Genetic-Programming-Complex-Adaptive/dp/0262111888", URL = "http://cognet.mit.edu/sites/default/files/books/9780262277181/pdfs/9780262277181_chap12.pdf", DOI = "doi:10.7551/mitpress/1108.003.0017", size = "15 pages", abstract = "This paper introduces a Minimum Description Length (MDL) principle to define fitness functions in Genetic Programming (GP). In traditional (Koza-style) GP, the size of trees was usually controlled by user-defined parameters, such as the maximum number of nodes and maximum tree depth. Large tree sizes meant that the time necessary to measure their fitnesses often dominated total processing time. To overcome this difficulty, we introduce a method for controlling tree growth, which uses an MDL principle. Initially we choose a decision tree representation for the GP chromosomes, and then show how an MDL principle can be used to define GP fitness functions. Thereafter we apply the MDL-based fitness functions to some practical problems. Using our implemented system STROGANOFF, we show how MDL-based fitness functions can be applied successfully to problems of pattern recognitions. The results demonstrate that our approach is superior to usual neural networks in terms of generalization of learning", notes = "Describes MDL; Work on both decision trees and GMDH symbolic regression trees (STROGANOFF). Nature of trees (ie never worse than component trees) more important than MDL? Part of \cite{kinnear:book}", } @InProceedings{Iba:1992:mlslsGA, author = "Hitoshi Iba and Taisuke Sato", title = "Meta-level strategy learning for GA based on structured representation", booktitle = "Proceedings of the Second Pacific Rim International Conference on Artificial Intelligence", year = "1992", editor = "Jin-Hyung Kim", pages = "548--554", address = "Seoul, Korea", month = "15-18 " # sep, organisation = "Center for Artificial Intelligence Research, Kaist", broken_isbn = "89-85368-00-093560", keywords = "genetic algorithms, genetic programming", size = "7 pages", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Iba_1992_mlslsGA.pdf", notes = "ETL-TR92-12 http://www.pricai.org/pricai-92.html", } @TechReport{Iba:1992:eSsp, author = "H. Iba and T. Sato", title = "Extension of STROGANOFF for symbolic problems", institution = "Electrotechnical Laboratory", year = "1992", type = "Technical report", number = "ETL-TR-94-1", address = "1-1-4 Umezono, Tsukuba-city, Ibaraki, 305, Japan", keywords = "genetic algorithms, genetic programming", } @InProceedings{DBLP:conf/ijcai/IbaHGS93, author = "Hitoshi Iba and Tetsuya Higuchi and Hugo {de Garis} and Taisuke Sato", title = "Evolutionary Learning Strategy using Bug-Based Search", booktitle = "Proceedings of the 13th International Joint Conference on Artificial Intelligence", year = "1993", bibsource = "DBLP, http://dblp.uni-trier.de", editor = "Ruzena Bajcsy", volume = "1", pages = "960--966", address = "Chambery, France", month = aug # " 28 - " # sep # " 3", publisher = "Morgan Kaufmann", keywords = "genetic algorithms", ISBN = "1-55860-300-X", URL = "http://ijcai.org/Past%20Proceedings/IJCAI-93-VOL2/PDF/018.pdf", notes = "IJCAI", } @InProceedings{Iba:1994:siGP, author = "Hitoshi Iba and Taisuke Sato and Hugo {de Garis}", title = "System identification approach to genetic programming", booktitle = "Proceedings of the 1994 IEEE World Congress on Computational Intelligence", year = "1994", pages = "401--406", volume = "1", address = "Orlando, Florida, USA", month = "27-29 " # jun, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Boolean concept formation, STROGANOFF, adaptive program, adaptive search, local parameter tuning mechanism, minimum description length-based selection criterion, multiple node types, multiple regression analysis, nonlinear function fitting, nonnumerical reasoning, numerical problems, statistical search, structured representation, symbolic reasoning, symbolic regression problems, system identification, tree pruning, tree structures, Boolean functions, identification, search problems, statistical analysis, symbol manipulation, trees (mathematics), tuning", size = "6 pages", DOI = "doi:10.1109/ICEC.1994.349917", abstract = "Introduces a new approach to genetic programming (GP), based on a system identification technique, which integrates a GP-based adaptive search of tree structures and a local parameter tuning mechanism employing a statistical search. In Proc. 5th Int. Joint Conf. on Genetic Algorithms (1993), we introduced our adaptive program called STROGANOFF (STructured Representation On Genetic Algorithms for NOnlinear Function Fitting), which integrated a multiple regression analysis method and a GA-based search strategy. The effectiveness of STROGANOFF was demonstrated by solving several system identification (numerical) problems. This paper extends STROGANOFF to symbolic (non-numerical) reasoning, by introducing multiple types of nodes, using a modified minimum description length (MDL) based selection criterion, and a pruning of the resultant trees. The effectiveness of this system-identification approach to GP is demonstrated by successful application to Boolean concept formation and to symbolic regression problems", } @TechReport{Iba:1994:GPlHC, author = "Hitoshi Iba and Taisuke Sato", title = "Genetic Programming with Local Hill-Climbing", institution = "Electrotechnical Laboratory", year = "1994", number = "ETL-TR-94-4", address = "1-1-4 Umezono, Tsukuba-city, Ibaraki, 305, Japan", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Iba_1994_GPlHC.pdf", notes = "Also published in PPSN-94, see \cite{iba:1994:GPlHCppsn3} ", size = "16 pages", } @InProceedings{iba:1994:GPlHCppsn3, author = "Hitoshi Iba and Hugo {de Garis} and Taisuke Sato", title = "Genetic Programming with Local Hill-Climbing", booktitle = "Parallel Problem Solving from Nature III", year = "1994", editor = "Yuval Davidor and Hans-Paul Schwefel and Reinhard M{\"a}nner", series = "LNCS", volume = "866", pages = "334--343", address = "Jerusalem", publisher_address = "Berlin, Germany", month = "9-14 " # oct, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-58484-6", URL = "https://rdcu.be/diflR", URL = "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-58484-6", DOI = "doi:10.1007/3-540-58484-6_274", size = "10 pages", abstract = "This paper proposes a new approach to Genetic Programming (GP). In traditional GP, recombination can cause frequent disruption of building-blocks or mutation can cause abrupt changes in the semantics. To overcome these difficulties, we supplement traditional GP with a recovery mechanism of disrupted building-blocks. More precisely, we integrate the structural search of traditional GP with a local hill-climbing search, using a relabeling procedure. This integration allows us to extend GP for Boolean and numerical problems. We demonstrate the superior effectiveness of our approach with experiments in Boolean concept formation and symbolic regression.", notes = "'We demonstrate the superior effectiveness of GP+local Hill Climbing with experiments in Boolean concept formation and symbolic regression'. Boolean GP combines GP with Adaptive Logic Network trees. Combination can evove to cope with time varying fitness functions. Numerical GP combines GP with GMDH (Group Method of Data Handling, Ivakhnenko) PPSN3 see also technical note \cite{Iba:1994:GPlHC}", } @Book{iba:1994:GA, author = "Hitoshi Iba", title = "Introduction to Genetic Algorithms", publisher = "Ohm-sha", year = "1994", keywords = "genetic algorithms", notes = "in Japanese", size = "pages", } @InProceedings{iba:1995:nGPsi, author = "Hitoshi Iba and Taisuke Sato and Hugo {de Garis}", title = "Numerical Genetic Programming for System Identification", booktitle = "Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications", year = "1995", editor = "Justinian P. Rosca", pages = "64--75", address = "Tahoe City, California, USA", month = "9 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/iba_1995_nGPsi.pdf", size = "12 pages", notes = "This paper based on earlier results \cite{icga93:iba} \cite{Iba:1994:siGP} and ETL-TR-94-20 1994 (submitted to ICEC-95, see \cite{iba:1885:rgn} part of \cite{rosca:1995:ml}", } @InProceedings{Iba:1995:tdpGP, author = "Hitoshi Iba and Hugo {de Garis} and Taisuke Sato", title = "Temporal Data Processing Using Genetic Programming", booktitle = "Genetic Algorithms: Proceedings of the Sixth International Conference (ICGA95)", year = "1995", editor = "Larry J. Eshelman", pages = "279--286", address = "Pittsburgh, PA, USA", publisher_address = "San Francisco, CA, USA", month = "15-19 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-370-0", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/iba_1995_tdpgp.pdf", size = "8 pages", abstract = "This paper reports an extension of STROGANOFF called R-STROGANOFF which uses special memory terminal nodes to provide a form of recurrancy to process time ordered events. All functions are polynomials (quadratics in the examples), terminals are either inputs or memories. Each memory terminals hold the value of a function node on the previous time step. The coeffients of the polynomials are learnt by trying to match the training data using a 'Generalised Error Proporgation Algorithm'. This is determinstic. Seems like STROGANOFF's (but different?), time sequence based, based on back-propagation. The coefficients are recalculated each generation (assuming tree has changed). Fitness function used 'minimum description length' (MDL). Quadratic coefficients mya be limited to 0<=x<=1 to avoid divergence. Examples: 2 step 0-1 oscilator, 4 Tomita languages (on binary alphabet). Tree could be converted to finite state automata, which was more general than tree, ie works in all cases including those not in the training set. On the tomita languages problems 'R-STROGANOFF works almost as well as (the best) best recurrent networks' ", } @InProceedings{iba:1885:rgn, author = "Hitoshi Iba and Taisuke Sato and Hugo {de Garis}", title = "Recombination Guidance for Numerical Genetic Programming", booktitle = "1995 IEEE Conference on Evolutionary Computation", year = "1995", volume = "1", pages = "97--102", address = "Perth, Australia", publisher_address = "Piscataway, NJ, USA", month = "29 " # nov # " - 1 " # dec, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, adaptive estimation, computer vision, numerical analysis, search problems, statistical analysis, time series, STROGANOFF, adaptive program, adaptive recombination mechanism, chaotic time series prediction, genetic algorithm-based search strategy, genetic program recombination, multiple regression analysis method, nonlinear function fitting, numerical genetic programming, structured representation, system identification problems", ISBN = "0-7803-2759-4", DOI = "doi:10.1109/ICEC.1995.489292", size = "6 pages", abstract = "In our earlier papers, we introduced our adaptive program called STROGANOFF (i.e. STructured Representation On Genetic Algorithms for Non-linear Function Fitting), which integrated a multiple regression analysis method and a GA-based search strategy. The effectiveness of STROGANOFF was demonstrated by solving several system identification problems. This paper proposes an {"}adaptive recombination{"} mechanism for STROGANOFF. Our intention is to exploit already built structures by 'adaptive recombination', in which GP recombination is guided by a certain measure. The effectiveness of our approach is shown by the experiment in predicting a chaotic time series. Thereafter we describe real-world applications of STROGANOFF to computer vision.", notes = "ICEC-95 Editors not given by IEEE, Organisers David Fogel and Chris deSilva. Broken Jan 2017 conference details at http://ciips.ee.uwa.edu.au/~dorota/icnn95.html Female face outline. Stately home Hursley house windows.", } @InCollection{iba:1996:aigp2, author = "Hitoshi Iba and Hugo {de Garis}", title = "Extending Genetic Programming with Recombinative Guidance", booktitle = "Advances in Genetic Programming 2", publisher = "MIT Press", year = "1996", editor = "Peter J. Angeline and K. E. {Kinnear, Jr.}", pages = "69--88", chapter = "4", address = "Cambridge, MA, USA", keywords = "genetic algorithms, genetic programming", ISBN = "0-262-01158-1", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6277499", DOI = "doi:10.7551/mitpress/1109.003.0008", size = "20 pages", abstract = "This chapter introduces a recombinative guidance mechanism for GP (Genetic Programming), and shows the effectiveness of our approach using various experiments. Traditional GP blindly combines subtrees, by applying crossover operations. This blind replacement, in general, can often disrupt beneficial building-blocks in tree structures. Randomly chosen crossover points ignore the semantics of the parent trees. Our goal is to exploit already built structures by adaptive recombination, in which GP recombination is guided by ``S-value'' measures. We present various S-value definitions, and show that the performance depends upon the definition.", } @InProceedings{iba:1996:ecma, author = "Hitoshi Iba", title = "Emergent Cooperation for Multiple Agents using Genetic Programming", booktitle = "Late Breaking Papers at the Genetic Programming 1996 Conference Stanford University July 28-31, 1996", year = "1996", editor = "John R. Koza", pages = "66--74", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California 94305-3079, USA", month = "28--31 " # jul, publisher = "Stanford Bookstore", ISBN = "0-18-201031-7", keywords = "genetic algorithms, genetic programming", notes = "GP-96LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670 see also \cite{iba:1996:ecmaPPSN}", } @TechReport{iba:1995:rtgTR, author = "Hitoshi Iba", title = "Random Tree Generation for Genetic Programming", institution = "ElectroTechnical Laboratory (ETL)", year = "1995", number = "ETL-TR-95-35", address = "1-1-4 Umezono, Tsukuba-city, Ibaraki, 305, Japan", month = "14 " # nov, keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/iba_1995_rtgTR.pdf", size = "24 pages", } @InProceedings{iba:1996:rtg, author = "Hitoshi Iba", title = "Random Tree Generation for Genetic Programming", booktitle = "Late Breaking Papers at the Genetic Programming 1996 Conference Stanford University July 28-31, 1996", year = "1996", editor = "John R. Koza", pages = "75--82", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California 94305-3079, USA", month = "28--31 " # jul, publisher = "Stanford Bookstore", ISBN = "0-18-201031-7", keywords = "genetic algorithms, genetic programming", notes = "GP-96LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670", } @InProceedings{iba:1996:rtgGP, author = "Hitoshi Iba", title = "Random Tree Generation for Genetic Programming", booktitle = "Parallel Problem Solving from Nature IV, Proceedings of the International Conference on Evolutionary Computation", year = "1996", editor = "Hans-Michael Voigt and Werner Ebeling and Ingo Rechenberg and Hans-Paul Schwefel", series = "LNCS", volume = "1141", pages = "144--153", address = "Berlin, Germany", publisher_address = "Heidelberg, Germany", month = "22-26 " # sep, publisher = "Springer Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-61723-X", DOI = "doi:10.1007/3-540-61723-X_978", size = "10 pages", abstract = "This paper introduces a random tree generation algorithm for GP (Genetic Programming). Generating random trees is an essential part of GP. However, the recursive method commonly used in GP does not necessarily generate random trees, i.e the standard GP initialisation procedure does not sample the space of possible initial trees uniformly. This paper proposes a truly random tree generation procedure for GP. Our approach is grounded upon a bijection method, i.e., a 1-1 correspondence between a tree with n nodes and some simple word composed by letters x and y. We show how to use this correspondence to generate a GP tree and how GP search is improved by using this randomness", notes = " PPSN4 bijection, tree_by_dyck Demonstrated on Mackey-Glass compared to 'grow' method (not ramped half-and-half)", affiliation = "Electrotechnical Laboratory (ETL) Machine Inference Section 1-1-4 Umezono, Tsukuba Science City 305 Ibaraki Japan 1-1-4 Umezono, Tsukuba Science City 305 Ibaraki Japan", } @InProceedings{iba:1996:ecmaPPSN, author = "Hitoshi Iba", title = "Emergent Cooperation for Multiple Agents Using Genetic Programming", booktitle = "Parallel Problem Solving from Nature IV, Proceedings of the International Conference on Evolutionary Computation", year = "1996", editor = "Hans-Michael Voigt and Werner Ebeling and Ingo Rechenberg and Hans-Paul Schwefel", series = "LNCS", volume = "1141", pages = "32--41", address = "Berlin, Germany", publisher_address = "Heidelberg, Germany", month = "22-26 " # sep, publisher = "Springer Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-61723-X", DOI = "doi:10.1007/3-540-61723-X_967", size = "10 pages", abstract = "This paper presents the emergence of the cooperative behaviour for the multiple agents by means of Genetic Programming (GP). Our experimental domain is the Tile World, a multi-agent test bed [Pollack90]. The world consists of a simulated robot agent and a simulated environment which is both dynamic and unpredictable. For the purpose of evolving the cooperative behavior, we propose three types of strategies, i.e, homogeneous breeding, heterogeneous breeding, and co-evolutionary breeding. The effectiveness of these three types of GP-based multi-agent learning is discussed with comparative experiments.", notes = "http://lautaro.fb10.tu-berlin.de/ppsniv.html PPSN4 Comparison of homogeneous, heterogeneous and co-evolutionary breeding on 'Tile world' simulated environment problem.", affiliation = "Electrotechnical Laboratory (ETL) Machine Inference Section 1-1-4 Umezono, Tsukuba Science City 305 Ibaraki Japan 1-1-4 Umezono, Tsukuba Science City 305 Ibaraki Japan", } @Book{iba:1996:GP, author = "Hitoshi Iba", title = "Genetic Programming", publisher = "Tokyo Denki University Press", year = "1996", keywords = "genetic algorithms, genetic programming", notes = "in Japanese", size = "pages", } @InProceedings{iba:1997:eca, author = "Hitoshi Iba and Tishihide Nozoe and Kanji Ueda", title = "Evolving Communicating Agents based on Genetic Programming", booktitle = "Proceedings of the 1997 {IEEE} International Conference on Evolutionary Computation", year = "1997", pages = "297--302", address = "Indianapolis, IN, USA", publisher_address = "Piscataway, NJ, USA", month = "13-16 " # apr, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Artificial intelligence, Cloning, Intelligent agent, Laboratories, Learning, Multiagent systems, Robot kinematics, Robustness, Testing, cooperative systems, digital simulation, games of skill, intelligent control, learning (artificial intelligence), linear programming, software agents, GP based multi agent learning, co-evolutionary breeding strategy, communicating agents, comparative experiments, cooperative behaviour, evolving communicating agents, multi agent test bed, pursuit game, simulated environment, simulated robot agents", ISBN = "0-7803-3949-5", DOI = "doi:10.1109/ICEC.1997.592321", size = "6 pages", abstract = "The paper presents the emergence of the cooperative behavior for communicating agents by means of genetic programming (GP). Our experimental domain is the pursuit game, a multi agent test bed. The world consists of simulated robot agents and a simulated environment which is both dynamic and unpredictable. For the purpose of evolving the cooperative behavior, we use the co-evolutionary breeding strategy. We confirm the emergence of cooperation via communication. The effectiveness of GP based multi agent learning is discussed with comparative experiments", notes = "ICEC-97", } @InProceedings{iba:1997:malrntGP, author = "Hitoshi Iba", title = "Multiple-Agent Learning for a Robot Navigation Task by Genetic Programming", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming", pages = "195--200", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", ISBN = "1-55860-483-9", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1997/iba_1997_malrntGP.pdf", size = "6 pages", abstract = "emergence cooperative multiple agents dynamic and unpredictable homogeneous breeding heterogeneous coevolution", notes = "GP-97", } @Unpublished{iba:1997:cfevlr, author = "Hitoshi Iba", title = "Complexity-based Fitness Evaluation for Variable Length Representation", editor = "Wolfgang Banzhaf and Inman Harvey and Hitoshi Iba and William Langdon and Una-May O'Reilly and Justinian Rosca and Byoung-Tak Zhang", note = "Position paper at the Workshop on Evolutionary Computation with Variable Size Representation at ICGA-97", month = "20 " # jul, year = "1997", address = "East Lansing, MI, USA", keywords = "genetic algorithms, genetic programming, bloat, variable size representation", URL = "http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/16452/http:zSzzSzwww.miv.t.u-tokyo.ac.jpzSz~ibazSztmpzSzagp94.pdf/iba94genetic.pdf", URL = "http://citeseer.ist.psu.edu/327857.html", abstract = "This paper introduces a Minimum Description Length (MDL) principle to define fitness functions in Genetic Programming (GP). In traditional (Koza-style) GP, the size of trees was usually controlled by user-defined parameters, such as the maximum number of nodes and maximum tree depth. Large tree sizes meant that the time necessary to measure their fitnesses often dominated total processing time. To overcome this difficulty, we introduce a method for controlling tree growth, which uses an...", notes = "http://web.archive.org/web/19971014081458/http://www.ai.mit.edu/people/unamay/icga-ws.html ", size = "3 pages", } @InCollection{Iba:1997:HEC, author = "Hitoshi Iba", title = "Complexity-based fitness evaluation", booktitle = "Handbook of Evolutionary Computation", publisher = "Oxford University Press", publisher_2 = "Institute of Physics Publishing", year = "1997", editor = "Thomas Baeck and David B. Fogel and Zbigniew Michalewicz", chapter = "section C4.4", keywords = "genetic algorithms, genetic programming", ISBN = "0-7503-0392-1", URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.375.6494.pdf", DOI = "doi:10.1201/9781420050387.ptc", size = "8 pages", abstract = "This section describes the complexity-based fitness evaluation for evolutionary algorithms. We first introduce and compare the leading competing model selection criteria, namely, an MDL (minimum-description-length) principle, the AIC (Akaike information criterion), an MML (minimum-message-length) principle, the PLS (predictive least-squares) measure, cross-validation, and the maximum-entropy principle. Then we give an illustrative example to show the effectiveness of the complexity-based fitness by experimenting with evolving decision trees using genetic programming (GP). Thereafter, we describe various research on complexity-based fitness evaluation, that is, controlling genetic algorithm or GP search strategies by means of the MDL criterion.", } @InCollection{iba:1997:HECa, author = "Hitoshi Iba", title = "Identification", booktitle = "Handbook of Evolutionary Computation", publisher = "Oxford University Press", publisher_2 = "Institute of Physics Publishing", year = "1997", editor = "Thomas Baeck and David B. Fogel and Zbigniew Michalewicz", chapter = "section F1.4", keywords = "genetic algorithms, genetic programming, stroganoff, gmdh", ISBN = "0-7503-0392-1", URL = "http://www.crcnetbase.com/isbn/9780750308953", URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.375.6494.pdf", size = "4 pages", abstract = "System identification techniques are applied in many fields in order to model and predict the behaviours of unknown systems given input-output data. Their practical application domains include pattern recognition, time-series prediction, Boolean function generation, and symbolic regression. Many evolutionary computation approaches have been tested in solving these problems. This section addresses brief summaries of these approaches, and compares them with alternative traditional approaches such as the group method of data handling.", } @InCollection{iba:1997:HECb, author = "Hitoshi Iba", title = "System identification using structured genetic algorithms", booktitle = "Handbook of Evolutionary Computation", publisher = "Oxford University Press", publisher_2 = "Institute of Physics Publishing", year = "1997", editor = "Thomas Baeck and David B. Fogel and Zbigniew Michalewicz", chapter = "section G1.4", keywords = "genetic algorithms, genetic programming, stroganoff, gmdh, sgpc version 1.1", ISBN = "0-7503-0392-1", URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.375.6494.pdf", broken = "doi:10.1201/9781420050387.ptg", URL = "https://www.amazon.com/Handbook-Evolutionary-Computation-Computational-Intelligence/dp/0750303921", size = "11 pages", abstract = "This case study describes a new approach to system identification problems based on genetic programming (GP), and presents an adaptive system called STROGANOFF (structured representation on genetic algorithms for nonlinear function fitting). STROGANOFF integrates an adaptive search and a statistical method called group method of data handling (GMDH). More precisely, STROGANOFF consists of two processes: (i) the evolution of structured representations using a traditional genetic algorithm and (ii) the fitting of parameters of the nodes with a multiple-regression analysis. The fitness evaluation is based on a minimum-description-length (MDL) criterion. Our approach builds a bridge from traditional GP to a more powerful search strategy. In other words, we introduce a new approach to GP, by supplementing it with a local hill climbing. The approach is successfully applied to a time-series prediction.", } @InProceedings{iba:1998:marlGP, author = "Hitoshi Iba", title = "Multi-Agent Reinforcement Learning with Genetic Programming", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "167--172", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-548-7", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1998/iba_1998_marlGP.pdf", notes = "GP-98", } @Article{Iba:1998:ISJ, author = "Hitoshi Iba", title = "Evolutionary learning of communicating agents", journal = "Information Sciences", year = "1998", volume = "108", number = "1-4", pages = "181--205", month = jul, keywords = "genetic algorithms, genetic programming, Multi-agent system, Distributed artificial intelligence", ISSN = "0020-0255", DOI = "doi:10.1016/S0020-0255(97)10055-X", URL = "http://www.sciencedirect.com/science/article/B6V0C-3TKS65B-F/2/ecac160ea272b4818c97d3aab09527d4", abstract = "This paper presents the emergence of the cooperative behavior for communicating agents by means of Genetic Programming (GP). Our experimental domains are the pursuit game and the robot navigation task. We conduct experiments with the evolution of the communicating agents and show the effectiveness of the emergent communication in terms of the robustness of generated GP programs. The performance of GP-based multi-agent learning is discussed with comparative experiments by using different breeding strategies, i.e., homogenous breeding and heterogeneous breeding.", notes = "Information Sciences http://www.elsevier.com/inca/publications/store/5/0/5/7/3/0/505730.pub.htt", } @InCollection{iba:1999:aigp3, author = "Hitoshi Iba", title = "Evolving Multiple Agents by Genetic Programming", booktitle = "Advances in Genetic Programming 3", publisher = "MIT Press", year = "1999", editor = "Lee Spector and William B. Langdon and Una-May O'Reilly and Peter J. Angeline", chapter = "19", pages = "447--466", address = "Cambridge, MA, USA", month = jun, keywords = "genetic algorithms, genetic programming, QGP", ISBN = "0-262-19423-6", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/aigp3/ch19.pdf", DOI = "doi:10.7551/mitpress/1110.003.0024", abstract = "On the emergence of the cooperative behaviour for multiple agents by means of Genetic Programming (GP). Our experimental domains are multi-agent test beds, i.e., the robot navigation task and the Tile World. The world consists of a simulated robot agent and a simulated environment which is both dynamic and unpredictable. In our previous paper, we proposed three types of strategies, i.e, homogeneous breeding, heterogeneous breeding, and co-evolutionary breeding, for the purpose of evolving the cooperative behavior. We use the heterogeneous breeding in this paper. The previous Q-learning approach commonly used for the multi-agent task has the difficulty with the combinatorial explosion for many agents. This is because the state space for Q-table is so huge for the practical computer resources. We show how successfully GP-based multi-agent learning is applied to multi-agent tasks and compare the performance with Q-learning by experiments. Thereafter, we conduct experiments with the evolution of the communicating agents. The communication is an essential factor for the emergence of cooperation. This is because a collaborative agent must be able to handle situations in which conflicts arise and must be capable of negotiating with other agents to reach an agreement. The effectiveness of the emergent communication is empirically shown in terms of the robustness of generated GP programs.", notes = "AiGP3 See http://cognet.mit.edu", } @Book{iba:1999:EC, author = "Hitoshi Iba", title = "Evolutionary Computing", publisher = "Tokyo University Press", year = "1999", keywords = "genetic algorithms, genetic programming", notes = "in Japanese", } @InProceedings{iba:1999:BBBGP, author = "Hitoshi Iba", title = "Bagging, Boosting, and Bloating in Genetic Programming", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1053--1060", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, classifier ensembles", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-407.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-407.ps", size = "8 pages", abstract = "We present an extension of GP (Genetic Programming) by means of resampling techniques, i.e., Bagging and Boosting. These methods both manipulate the training data in order to improve the learning algorithm. In theory they can significantly reduce the error of any weak learning algorithm by repeatedly running it. This paper extends GP by dividing a whole population into a set of sub-populations, each of which is evolvable by using the Bagging and Boosting methods. The effectiveness of our approach is shown by experiments. The performance is discussed by the comparison with the traditional GP in view of the bloating effect.", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99) 10 Subpopulations each has its own training data (produced using the boosting or bagging methods. Best of each subpopulation has vote in final result. Do we actually need subpopulations, could not the whole algorithm be split into T entirely separate GP runs? SGPC1.1 p1054 {"}controlling the bloating effect is closely related to the performance improvement...{"} noisy cos(2x)=1-sin(x)**2, Mackey-Glass chaotic time series, 6MUX, symbolic regression, nikkei225 Description of boosting weight adjustment algorithm (p1054) seems to be wrong? p1056 BagGP, BoostGP > GP, BagGP=BoostGP But only in the case of noisy cos(2x) does difference (table 2) seem big. Mention of DSS and PADO. p1059 Says Bagging and Boosting yield lower bloat. (does not explain why) Little supporting data (Fig 5). Boosting v co-evolution", } @InProceedings{iba:1999:UGPPFD, author = "Hitoshi Iba and Takashi Sasaki", title = "Using Genetic Programming to Predict Financial Data", booktitle = "Proceedings of the Congress on Evolutionary Computation", year = "1999", editor = "Peter J. Angeline and Zbyszek Michalewicz and Marc Schoenauer and Xin Yao and Ali Zalzala", volume = "1", pages = "244--251", address = "Mayflower Hotel, Washington D.C., USA", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "6-9 " # jul, organisation = "Congress on Evolutionary Computation, IEEE / Neural Networks Council, Evolutionary Programming Society, Galesia, IEE", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, time series, Japanese stock market, bankruptcy prediction, best stock choosing, financial data prediction, financial forecasting, fraud detection, high profit, investment, neural networks, portfolio optimization, price data prediction, scheduling, time-series prediction, evolutionary computation, financial data processing, investment, neural nets, stock markets", ISBN = "0-7803-5536-9 (softbound)", ISBN = "0-7803-5537-7 (Microfiche)", DOI = "doi:10.1109/CEC.1999.781932", abstract = "This paper presents the application of genetic programming (GP) to the prediction of price data in the Japanese stock market. The goal of this task is to choose the best stocks when making an investment and to decide when and how many stocks to sell or buy. There have been several applications of genetic algorithms (GAs) to financial problems, such as portfolio optimisation, bankruptcy prediction, financial forecasting, fraud detection and scheduling. GP has also been applied to many problems in time-series prediction. However, relatively few studies have been made for the purpose of predicting stock market data by means of GP. This paper describes how successfully GP is applied to predicting stock data so as to gain a high profit. Comparative experiments are conducted with neural networks to show the effectiveness of the GP-based approach", notes = "CEC-99 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. Library of Congress Number = 99-61143", } @InProceedings{iba:2000:CEF, author = "Hitoshi Iba and Nikolay Nikolaev", title = "Financial data prediction by means of genetic programming", booktitle = "Computing in Economics and Finance", year = "2000", address = "Universitat Pompeu Fabra, Barcelona, Spain", month = "6-8 " # jul, keywords = "genetic algorithms, genetic programming", broken = "http://enginy.upf.es/SCE/papers/paper330.ps.gz", URL = "http://EconPapers.repec.org/RePEc:sce:scecf0:z101", notes = "broken Sep 2018 http://enginy.upf.es/SCE/index2.html http://ideas.repec.org/p/sce/scecf0/z101.html number Z101 Also known as \cite{RePEc:sce:scecf0:z101}", } @InProceedings{Iba:2000:GECCO, author = "Hitoshi Iba and Makoto Terao", title = "Controlling Effective Introns for Multi-Agent Learning by Genetic Programming", pages = "419--426", year = "2000", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)", editor = "Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer", address = "Las Vegas, Nevada, USA", publisher_address = "San Francisco, CA 94104, USA", month = "10-12 " # jul, keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-708-0", URL = "http://gpbib.cs.ucl.ac.uk/gecco2000/GP191.pdf", URL = "http://citeseer.ist.psu.edu/478951.html", abstract = "This paper presents the emergence of the cooperative behavior for multiple agents by means of Genetic Programming (GP). For the purpose of evolving the e#ective cooperative behavior, we propose a controlling strategy of introns, which are non-executed code segments dependent upon the situation. The traditional approach to removing introns was able to cope with only a part of syntactically defined introns, which excluded other frequent types of introns. The validness of our approach is discussed with comparative experiments with robot simulation tasks, i.e., a navigation problem and an escape problem.", notes = "A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of \cite{whitley:2000:GECCO}", } @InProceedings{iba:2000:gppmfds, author = "Hitoshi Iba and Nikolay Nikolaev", title = "Genetic Programming Polynomial Models of Financial Data Series", booktitle = "Proceedings of the 2000 Congress on Evolutionary Computation CEC00", year = "2000", pages = "1459--1466", volume = "2", address = "La Jolla Marriott Hotel, La Jolla, California, USA", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "6-9 " # jul, organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, time series, stroganoff, GP system, Tokyo Stock Exchange data, data transformations, economical measures, financial data series, fitness function, functional models, polynomial models, predictive models, profit increase, profitable polynomials, series preprocessing, stock market analysis, traditional GP, data handling, financial data processing, polynomials, series (mathematics), stock markets", ISBN = "0-7803-6375-2", DOI = "doi:10.1109/CEC.2000.870826", abstract = "The problem of identifying the trend in financial data series in order to forecast them for profit increase is addressed using genetic programming (GP). We enhance a GP system that searches for polynomial models of financial data series and relate it to a traditional GP manipulating functional models. Two of the key issues in the development are: 1) preprocessing of the series which includes data transformations and embedding; and 2) design of a proper fitness function that navigates the search by favouring parsimonious and predictive models. The two GP systems are applied for stock market analysis, and examined with real Tokyo Stock Exchange data. Using statistical and economical measures to estimate the results, we show that the GP could evolve profitable polynomials", notes = "CEC-2000 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 00TH8512, Library of Congress Number = 00-018644", } @InProceedings{iba:2001:SCA, author = "Hitoshi Iba and Makoto Terao", title = "Controlling Effective Introns for Multi-Agent Learning by Means of Genetic Programming", booktitle = "Soft Computing Agents", year = "2001", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-7908-1815-4_3", DOI = "doi:10.1007/978-3-7908-1815-4_3", } @InProceedings{iba:2002:gecco, author = "Hitoshi Iba and Erina Sakamoto", title = "Inference Of Differential Equation Models By Genetic Programming", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "788--795", address = "New York", publisher_address = "San Francisco, CA 94104, USA", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "genetic algorithms, genetic programming, bioinformatics, differential equation, E-cell, genome informatics, Lotka-Volterra model, S-systems", ISBN = "1-55860-878-8", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/GP042.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/GP042.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf", abstract = "An evolutionary method for identifying a causal model from the observed time series data. We use a system of ordinary differential equations (ODEs) as the causal model. This approach is well known to be useful for the practical application, e.g., bioinformatics, chemical reaction models, controlling theory etc. To explore the search space more effectively in the course of evolution, the right-hand sides of ODEs are inferred by Genetic Programming (GP) and the least mean square (LMS) method is used along with the ordinary GP. We apply our method to several target tasks and empirically show how successfully GP infers the systems of ODEs.", notes = "GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)", } @Article{Iba:2008:IS, author = "Hitoshi Iba", title = "Inference of differential equation models by genetic programming", journal = "Information Sciences", year = "2008", volume = "178", number = "23", pages = "4453--4468", month = "1 " # dec, note = "Special Section: Genetic and Evolutionary Computing", keywords = "genetic algorithms, genetic programming, Ordinary differential equations, Genome informatics", ISSN = "0020-0255", DOI = "doi:10.1016/j.ins.2008.07.029", size = "16 pages", abstract = "This paper describes an evolutionary method for identifying a causal model from the observed time-series data. We use a system of ordinary differential equations (ODEs) as the causal model. This approach is known to be useful for practical applications, e.g., bioinformatics, chemical reaction models, control theory, etc. To explore the search space more effectively in the course of evolution, the right-hand sides of ODEs are inferred by genetic programming (GP) and the least mean square (LMS) method is used along with the ordinary GP. We apply our method to several target tasks and empirically show how successfully GP infers the systems of ODEs. We also describe an extension of the approach to the inference of differential equation systems with transcendental functions.", notes = "The reaction between formaldehyde and carbamide in the aqueous solution gives methylol urea which continues to react with carbamide and form methylene urea. GP with LMS. Forced vibration with damping. ODE. Penalty against bloat. S-expression: power-law exponents for terminal set. MDL. Fourth order Runge-Kutta. Numerical overflow -> poor fitness -> weeded out. Synthetic data. E-CELL SE, Michaelis-Menten law. Levenberg-Marquardt Is genotype {"}repaired{"} or just phenotype? p4467 considers possibility that there is more than one solution.", } @Book{Iba:2009:AGPML, author = "Hitoshi Iba and Yoshihiko Hasegawa and Topon Kumar Paul", title = "Applied Genetic Programming and Machine Learning", publisher = "CRC", year = "2009", series = "CRC Complex and Enterprise Systems Engineering", keywords = "genetic algorithms, genetic programming", ISBN = "1-4398-0369-2", URL = "http://www.crcpress.com/product/isbn/9781439803691", abstract = "Reflecting rapidly developing concepts and newly emerging paradigms in intelligent machines, this text is the first to integrate genetic programming and machine learning techniques to solve diverse real-world tasks.These tasks include financial data prediction, day-trading rule development; and bio-marker selection. Written by a leading authority, this text will teach readers how to use machine learning techniques, make learning operators that efficiently sample a search space, navigate the search process through the design of objective fitness functions, and examine the search performance of the evolutionary system. All source codes and GUIs are available for download from the author's website.", notes = "Book reviews in \cite{Veeramachaneni:2011:GPEM} \cite{journals/mima/Osman12}. Introduction. Evolutionary computation. Genetic Programming. Hybrid Genetic Programming and GMDH System. Principles of STROGANOFF. Classification by Ensemble of Genetic Programming Rules. Probabilistic Program Evolution with Estimation of Distribution. Other Related Methods. Discussion. Conclusion. Appendix A: STROGANOFF system overviews. Appendix B: MVGPC system overviews.", size = "349 pages", } @InCollection{Iba:2009:GPGMDH, author = "Hitoshi Iba", title = "Hybrid Genetic Programming and {GMDH} System: {STROGANOFF}", booktitle = "Hybrid Self-Organizing Modeling Systems", publisher = "Springer", year = "2009", editor = "Godfrey C. Onwubolu", volume = "211", series = "Studies in Computational Intelligence", pages = "27--98", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-01530-4", URL = "https://doi.org/10.1007/978-3-642-01530-4_2", DOI = "doi:10.1007/978-3-642-01530-4_2", abstract = "This chapter introduces a new approach to Genetic Programming (GP), based on GMDH-based technique, which integrates a GP-based adaptive search of tree structures, and a local parameter tuning mechanism employing statistical search. The GP is supplemented with a local hill climbing search, using a parameter tuning procedure. More precisely, we integrate the structural search of traditional GP with a multiple regression analysis method and establish our adaptive program called STROGANOFF (i.e. STructured Representation On Genetic Algorithms for NOnlinear Function Fitting). The fitness evaluation is based on a Minimum Description Length (MDL) criterion, which effectively controls the tree growth in GP. Its effectiveness is demonstrated by solving several system identification (numerical) problems and comparing the performance of STROGANOFF with traditional GP and another standard technique. The effectiveness of this numerical approach to GP is demonstrated by successful application to computational finances.", } @InCollection{Iba:2010:GPTP, author = "Hitoshi Iba and Claus Aranha", title = "Composition of Music and Financial Strategies via Genetic Programming", booktitle = "Genetic Programming Theory and Practice VIII", year = "2010", editor = "Rick Riolo and Trent McConaghy and Ekaterina Vladislavleva", series = "Genetic and Evolutionary Computation", volume = "8", address = "Ann Arbor, USA", month = "20-22 " # may, publisher = "Springer", chapter = "13", pages = "211--226", keywords = "genetic algorithms, genetic programming, IEC, portfolio optimization, music composition, memetic algorithms, interactive genetic programming", isbn13 = "978-1-4419-7746-5", URL = "http://www.springer.com/computer/ai/book/978-1-4419-7746-5", DOI = "doi:10.1007/978-1-4419-7747-2_13", abstract = "We present two applications of genetic programming to real world problems: musical composition and financial portfolio optimization. In each of these applications, a specialized genome representation is used in order to break the problem down into smaller instances and put them back together. Results showing the applicability of the approaches are presented.", notes = "part of \cite{Riolo:2010:GPTP}", } @Book{Iba:2018:book, author = "Hitoshi Iba", title = "Evolutionary Approach to Machine Learning and Deep Neural Networks", subtitle = "Neuro-Evolution and Gene Regulatory Networks", publisher = "Springer", year = "2018", keywords = "genetic algorithms, genetic programming, neuroevolution, NEAT, L-system, Hyperneat, CPPN, CNN", isbn13 = "978-981-13-0199-5", URL = "https://www.springer.com/us/book/9789811301995", size = "XIII+245 pages", notes = "Reviewed by \cite{Vidnerova:GPEM}", } @InProceedings{Iba:2018:ieeeSMC, author = "Hitoshi Iba and Ji Feng and Hossein {Izadi Rad}", booktitle = "2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)", title = "GP-RVM: Genetic Programing-Based Symbolic Regression Using Relevance Vector Machine", year = "2018", pages = "255--262", abstract = "This paper proposes a hybrid basis function construction method (GP-RVM) for Symbolic Regression problem, which combines an extended version of Genetic Programming called Kaizen Programming and Relevance Vector Machine to evolve an optimal set of basis functions. Different from traditional evolutionary algorithms where a single individual is a complete solution, our method proposes a solution based on linear combination of basis functions built from individuals during the evolving process. RVM which is a sparse Bayesian kernel method selects suitable functions to constitute the basis. RVM determines the posterior weight of a function by evaluating its quality and sparsity. The solution produced by GP-RVM is a sparse Bayesian linear model of the coefficients of many non-linear functions. Our hybrid approach is focused on nonlinear white-box models selecting the right combination of functions to build robust predictions without prior knowledge about data. Experimental results show that GP-RVM outperforms conventional methods, which suggest that it is an efficient and accurate technique for solving SR. The computational complexity of GP-RVM scales in O(M3), where M is the number of functions in the basis set and is typically much smaller than the number N of training patterns.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SMC.2018.00054", ISSN = "2577-1655", month = oct, notes = "Also known as \cite{8616049}", } @Article{Ibadulla:2017:PCS, author = "S. I. Ibadulla and E. Yu Shmalko and K. K. Daurenbekov", title = "The Comparison of Genetic Programming and Variational Genetic Programming for a Control Synthesis Problem on the Model Predator-victim", journal = "Procedia Computer Science", volume = "103", pages = "155--161", year = "2017", note = "\{XII\} International Symposium Intelligent Systems 2016, \{INTELS\} 2016, 5-7 October 2016, Moscow, Russia", ISSN = "1877-0509", DOI = "doi:10.1016/j.procs.2017.01.041", URL = "http://www.sciencedirect.com/science/article/pii/S187705091730042X", abstract = "The work is devoted to the comparison of two methods of symbolic regression, a method of genetic programming and a variational method of genetic programming. The comparison is made on the basis of the computing experiment, which solved a problem of control system synthesis for a model of nonlinear control object, describing the interaction of the two systems of predator and victim. For the purity of the experiment the genetic algorithms parameters in the both methods were Identical. For variational genetic programming there was selected a trivial basic solution in the form of the sum of input variable products for custom settings. This basic solution is always chosen in the case of the absence of meaningful task analysis. The comparison of methods for the speed of solving the problem and for the quality of the achieved control is made.", keywords = "genetic algorithms, genetic programming, synthesis of control system, the method of variations of the basis solutions", } @InProceedings{ibarra:2002:EuroGP, title = "Transformation of Equational Specification by Means of Genetic Programming", author = "Aitor Ibarra and J. Lanchares and J. Mendias and J. I. Hidalgo and R. Hermida", editor = "James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi", booktitle = "Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002", volume = "2278", series = "LNCS", pages = "248--257", publisher = "Springer-Verlag", address = "Kinsale, Ireland", publisher_address = "Berlin", month = "3-5 " # apr, year = "2002", keywords = "genetic algorithms, genetic programming, FRESH", ISBN = "3-540-43378-3", DOI = "doi:10.1007/3-540-45984-7_24", abstract = "High Level Synthesis (HLS) is a designing methodology aimed to the synthesis of hardware devices from behavioural specifications. One of the techniques used in HLS is formal verification. In this work we present an evolutionary algorithm in order to optimize circuit equational specifications by means of a special type of genetic operator. We have named this operator algebraic mutation, carried out with the help of the equations that Formal Verification Synthesis offers. This work can be classified within the development of an automatic tool of Formal Verification Synthesis by using genetic techniques. We have applied this technique to a simple circuit equational specification and to a much more complex algebraic equation. In the first case our algorithm simplifies the equation until the best specification is found and in the second a solution improving the former is always obtained.", notes = "EuroGP'2002, part of \cite{lutton:2002:GP} Algebraic mutation. No crossover. gpcc++ 0.5.2 Two example equations simplified. Pop size 4. 60 percent improvement. ", } @InProceedings{Ibarra-Vazquez:2021:ECADA, author = "Gerardo Ibarra-Vazquez and Gustavo Olague and Cesar Puente and Mariana Chan-Ley and Carlos Soubervielle-Montalvo", title = "Automated Design of Accurate and Robust Image Classifiers with Brain Programming", booktitle = "11th Workshop on Evolutionary Computation for the Automated Design of Algorithms (ECADA)", year = "2021", editor = "Manuel Lopez-Ibanez and Daniel R. Tauritz and John R. Woodward", address = "internet", series = "GECCO '21", month = jul # " 10-14", organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, ANN, Secure, Face Recognition, Art Media Categorization, Adversarial Attacks, Convolutional Neural Networks, Brain Programming", URL = "http://www.human-competitive.org/sites/default/files/olague-humies2021-final_0.txt", URL = "http://www.human-competitive.org/sites/default/files/olague-geccow2021.pdf", DOI = "doi:10.1145/3449726.3463179", size = "9 pages", abstract = "Foster the mechanical design of artificial vision requires a delicate balance between high-level analytical methods and the discovery through metaheuristics of near-optimal functions working towards complex visual problems. Evolutionary computation and swarm intelligence have developed strategies that automatically design meaningful deep convolutional neural network architectures to create better image classifiers. However, these architectures have not surpassed hand-craft models working with outdated problems with datasets of icon images. Nowadays, recent concerns about deep convolutional neural networks to adversarial attacks in the form of modifications to the input image can manipulate their output to make them untrustworthy. Brain programming is a hyper-heuristic whose aim is to work at a higher level of abstraction to develop automatically artificial visual cortex algorithms for a problem domain like image classification. Our primary goal is to employ brain programming to design an artificial visual cortex to produce accurate and robust image classifiers in two problems. We analyze the final models designed by brain programming with the assumption of fooling the system using two adversarial attacks. In both experiments, brain programming constructed artificial brain models capable of competing with hand-crafted deep convolutional neural networks without any influence in the predictions when an adversarial attack is present.", notes = "Entered 2021 HUMIES https://bonsai.auburn.edu/ecada/GECCO2021/index.html GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)", } @Article{IBARRAVAZQUEZ:2022:swevo, author = "Gerardo Ibarra-Vazquez and Gustavo Olague and Mariana Chan-Ley and Cesar Puente and Carlos Soubervielle-Montalvo", title = "Brain programming is immune to adversarial attacks: Towards accurate and robust image classification using symbolic learning", journal = "Swarm and Evolutionary Computation", volume = "71", pages = "101059", year = "2022", ISSN = "2210-6502", DOI = "doi:10.1016/j.swevo.2022.101059", URL = "https://www.sciencedirect.com/science/article/pii/S2210650222000311", keywords = "genetic algorithms, genetic programming, Brain programming, Adversarial attacks, Image classification, Art media categorization", abstract = "In recent years, the security concerns about the vulnerability of deep convolutional neural networks to adversarial attacks in slight modifications to the input image almost invisible to human vision make their predictions untrustworthy. Therefore, it is necessary to provide robustness to adversarial examples with an accurate score when developing a new classifier. In this work, we perform a comparative study of the effects of these attacks on the complex problem of art media categorization, which involves a sophisticated analysis of features to classify a fine collection of artworks. We tested a prevailing bag of visual words approach from computer vision, four deep convolutional neural networks (AlexNet, VGG, ResNet, ResNet101), and brain programming. The results showed that brain programming predictions' change in accuracy was below 2percent using adversarial examples from the fast gradient sign method. With a multiple-pixel attack, brain programming obtained four out of seven classes without changes and the rest with a maximum error of 4percent. Finally, brain programming got four categories without changes using adversarial patches and for the remaining three classes with an accuracy variation of 1percent. The statistical analysis confirmed that brain programming predictions' confidence was not significantly different for each pair of clean and adversarial examples in every experiment. These results prove brain programming's robustness against adversarial examples compared to deep convolutional neural networks and the computer vision method for the art media categorization problem", } @InProceedings{DBLP:conf/iwann/IbiasG019, author = "Alfredo Ibias and David Grinan and Manuel Nunez", editor = "Ignacio Rojas and Gonzalo Joya and Andreu Catala", title = "{GPTSG:} {A} Genetic Programming Test Suite Generator Using Information Theory Measures", booktitle = "Advances in Computational Intelligence - 15th International Work-Conference on Artificial Neural Networks, {IWANN} 2019, Gran Canaria, Spain, June 12-14, 2019, Proceedings, Part {I}", series = "Lecture Notes in Computer Science", volume = "11506", pages = "716--728", publisher = "Springer", year = "2019", keywords = "genetic algorithms, genetic programming, SBSE", URL = "https://doi.org/10.1007/978-3-030-20521-8_59", DOI = "doi:10.1007/978-3-030-20521-8_59", timestamp = "Fri, 05 Jul 2019 01:00:00 +0200", biburl = "https://dblp.org/rec/conf/iwann/IbiasG019.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{Ibias:2021:CEC, author = "Alfredo Ibias and Pablo Vazquez-Gomis and Miguel Benito-Parejo", booktitle = "2021 IEEE Congress on Evolutionary Computation (CEC)", title = "Coverage-Based Grammar-Guided Genetic Programming Generation of Test Suites", year = "2021", editor = "Yew-Soon Ong", pages = "2411--2418", address = "Krakow, Poland", month = "28 " # jun # "-1 " # jul, keywords = "genetic algorithms, genetic programming, SBSE, Software testing, Software algorithms, Evolutionary computation, Software, Software reliability, Genetic communication, Coverage, Software Testing", isbn13 = "978-1-7281-8393-0", DOI = "doi:10.1109/CEC45853.2021.9504969", abstract = "Software testing is fundamental to ensure the reliability of software. To properly test software, it is critical to generate test suites with high fault finding ability. We propose a new method to generate such test suites: a coverage-based grammar-guide genetic programming algorithm. This evolutionary computation based method allows us to generate test suites that conform with respect to a specification of the system under test using the coverage of such test suites as a guide. We considered scenarios for both black-box testing and white-box testing, depending on the different criteria we work with at each situation. Our experiments show that our proposed method outperforms other baseline methods, both in performance and execution time.", notes = "Also known as \cite{9504969} DTRS research group, Universidad Complutense de Madrid, Madrid, Spain", } @PhdThesis{Ibias-Martinez:thesis, author = "Alfredo {Ibias Martinez}", title = "Applications of information theory and artificial intelligence to software testing", school = "Facultad de Informatica, Universidad Complutense de Madrid", year = "2021", address = "Spain", month = dec, keywords = "genetic algorithms, genetic programming, SBSE, ACO, Artificial intelligent, computer Algorithms, computer software, Software Testing, Information Theory, Artificial Intelligence, Evolutionary Algorithms, Machine Learning, Failed Error Propagation (FEP), Test Case Generation, Software Product Lines, Mutation Testing", URL = "https://eprints.ucm.es/id/eprint/74119/", URL = "https://books.google.co.uk/books?id=Np0yzwEACAAJ", URL = "https://eprints.ucm.es/id/eprint/74119/1/T43374.pdf", size = "276 pages", abstract = "Software Testing is a critical field for the software industry, as it has the main tools used to ensure the reliability of the produced software. Currently, more than 50percent of the time and resources for creating a software product are diverted to testing tasks, from unit testing to system testing. Moreover, there is a huge interest into automatising this field, as software gets bigger and the amount of required testing increases. However, Software Testing is not only an industry oriented field; it is also a really interesting field with a noble goal (improving the reliability of software systems) that at the same time is full of problems to solve. Therefore, it leaves space for imagination to dream and try to address such problems through the application of tools from other fields. In this thesis, such fields are Information Theory and Artificial Intelligence. Information Theory is a field with a strong mathematical basis. Its main goal is to measure the information of a string based on the commonality of its components. Artificial Intelligence is an algorithmic field that tries to approximate solutions for exponentially complex problems. Both fields are full of tools and methodologies that could help addressing some of the problems that Software Testing arise. Moreover, although both fields can seem disparate, with tools that would be better fitted to solve different kinds of problems, in fact that is not always the case. Along the research carried out during this thesis we found multiple situations where the use of tools from Information Theory improves an Artificial Intelligence-based solution and vice versa. Actually, these synergies make this thesis a compact work more than a compilation of methods. The main goal of this thesis is, therefore, to address different problems from the Software Testing field and devise ways of solving (or approximate a solution for) such problems using tools and results coming from the Information Theory and Artificial Intelligence fields. Specifically, this thesis addresses the Failed Error Propagation (FEP) problem, the test case generation problem, the Integration Testing of Software Product Lines (SPLs) problem, and the selection of hard-to-kill mutants for Mutation Testing problem. These four problems are addressed from different perspectives, looking for the best method to try to solve each of them. This way, for the test case generation problem we propose both an evolutionary method based on a Grammar-Guided Genetic Programming Algorithm and an Information Theory-based measure (initially developed to choose between test cases) to guide such algorithm, with the goal of generating test cases with high fault finding capability. This is one of those cases where both fields join forces to obtain really good solutions. Additionally, we develop a Grammar Guided Genetic Programming Algorithm to generate test cases guided by coverage metrics, with the goal of increasing the coverability of the produced test cases. For the Failed Error Propagation problem our work focuses on the use of Information Theory based measures to address it. Specifically, we focus on a previously proposed information theoretic measure called Squeeziness that measures the likelihood of FEP in a System Under Test (SUT), and we adapt it to work in a black-box scenario, in a non-deterministic one, and even to work with notions of entropy different from the original Shannon's entropy. Additionally, we develop a tool to automatically compute this last version. It is inside this tool where another case of these two fields helping each other can be found: we implement an Artificial Neural Network to automatically estimate the best notion of entropy to use for the given SUT. In another line of work, our research to address the selection of hard-to-kill-mutants problem delves in the idea of using swarm intelligence to solve a complex problem. Specifically, with the goal of reducing the amount of useful mutants, we develop a swarm intelligence algorithm, inspired in the Particle Swarm Optimisation one, to decide which mutants are the harder-to-kill ones. Finally, in order to solve the Integration Testing of SPLs problem we use an Ant Colony Optimisation algorithm to select features either with a low testing cost or with a high probability of being requested. The goal is to simplify the testing processes through the reduction of the number of feature combinations needed to test an SPL. The outcomes of all these proposals are relevant, improve the state-of-the-art and set new precedents for future work. Moreover, they open newlines of work for further development of the proposals and for improving the obtained solutions. Thus, this thesis makes its humble contribution to the aforementioned fields, for the enjoyment of whoever find it interesting.", notes = "also known as \cite{martinez2022applications} In english Supervisor: Manuel Nunez Garcia", } @Article{IBIAS:2022:jss, author = "Alfredo Ibias", title = "Using mutual information to test from Finite State Machines: Test suite generation", journal = "Journal of Systems and Software", volume = "192", pages = "111391", year = "2022", ISSN = "0164-1212", DOI = "doi:10.1016/j.jss.2022.111391", URL = "https://www.sciencedirect.com/science/article/pii/S0164121222001108", keywords = "genetic algorithms, genetic programming, Formal approaches to testing, Information Theory, Mutual information, Finite State Machines", abstract = "Mutual Information is an information theoretic measure designed to quantify the amount of similarity between two random variables ranging over two sets. In recent work we have use it as a base for a measure, called Biased Mutual Information, to guide the selection of a test suite among different possibilities. In this paper, we adapt this concept and show how it can be used to address the problem of generating a test suite with high fault finding capability, in a black-box scenario and following a maximise diversity approach. Additionally, we present a new Grammar-Guided Genetic Programming Algorithm that uses Biased Mutual Information to guide the generation of such test suites. Our experimental results clearly show the potential value of our measure when used to generate test suites. Moreover, they show that our measure is better in guiding test generation than current state-of-the-art measures, like Test Set Diameter (TSDm) measures. Additionally, we compared our proposal with classical completeness-oriented methods, like the H-Method and the Transition Tour method, and found that our proposal produces smaller test suites with high enough fault finding capability. Therefore, our methodology is preferable in an scenario where a compromise is necessary between fault detection and execution time", } @InProceedings{Ibrahim:2011:ISSNIP, author = "Mohd Faisal Ibrahim and Bradley Alexander", title = "Evolving a Path Planner for A Multi-Robot Exploration System Using Grammatical Evolution", booktitle = "Seventh International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2011", year = "2011", editor = "D. Nandagopal and M. Palaniswami", pages = "590--595", address = "Adelaide, Australia", month = dec # " 6-9", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", isbn13 = "978-1-4577-0675-2", DOI = "doi:10.1109/ISSNIP.2011.6146624", size = "6 pages", abstract = "Area exploration and mapping with teams of robots is a challenging application. As the complexity of this application increases so does the challenge of designing effective coordinated control. One potential solution to this problem is to explore some relevant parts of the design space automatically. In this paper, we present an approach which uses Grammatical Evolution to design a control function for coordinated path planning of teams of mobile robots. Simulation results are promising with evolved control functions showing performance better than handwritten control in term of amount of explored area.", notes = "School of Computer Science, The University of Adelaide SA 5005, Australia http://www.issnip.org/2011/ Also known as \cite{6146624}", } @InProceedings{conf/iros/IbrahimA13, author = "Mohd Faisal Ibrahim and Bradley James Alexander", title = "Evolving decision-making functions in an autonomous robotic exploration strategy using grammatical evolution", booktitle = "IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2013)", publisher = "IEEE", year = "2013", month = nov, pages = "4340--4346", keywords = "genetic algorithms, genetic programming, grammatical evolution, control engineering computing, evolutionary computation, path planning, robots, automatic derivation, autonomous robotic exploration, autonomous robotic mapping, control software, decision-making function, ground based exploration platform, navigational control, navigational task, scoring function, collision avoidance, grammar, mobile robots, navigation, power capacitors, power demand", bibdate = "2014-01-10", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/iros/iros2013.html#IbrahimA13", URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6679723", DOI = "doi:10.1109/IROS.2013.6696979", ISSN = "2153-0858", abstract = "Customising navigational control for autonomous robotic mapping platforms is still a challenging task. Control software must simultaneously maximise the area explored whilst maintaining safety and working within the constraints of the platform. Scoring functions to assess navigational options are typically written by hand and manually refined. As navigational tasks become more complex this manual approach is unlikely to yield the best results. In this paper we explore the automatic derivation of a scoring function for a ground based exploration platform. We show that it is possible to derive the entire structure of a scoring function and that allowing structure to evolve yields significant performance advantages over the evolution of embedded constants alone.", notes = "also known as \cite{6696979}", } @InCollection{Ichimura:2010:SOM, title = "A Knowledge Acquisition Method of Judgment Rules for Spam {E-mail} by using Self Organizing Map and Automatically Defined Groups by Genetic Programming", author = "Takumi Ichimura and Kazuya Mera and Akira Hara", booktitle = "Self-Organizing Maps", publisher = "InTech", year = "2010", editor = "George K Matsopoulos", chapter = "24", month = apr, keywords = "genetic algorithms, genetic programming", isbn13 = "978-953-307-074-2", bibsource = "OAI-PMH server at www.intechopen.com", language = "eng", oai = "oai:intechopen.com:10468", URL = "http://www.intechopen.com/articles/show/title/a-knowledge-acquisition-method-of-judgment-rules-for-spam-e-mail-by-using-self-organizing-map-and-au", DOI = "doi:10.5772/9177", abstract = "In this paper, we propose a classification method for Spam E-mail based on the results of SpamAssassin. This method can learn patterns of Ham and Spam E-mails. First, SOM can classify many E-mails into the some categories. In this phase, we can see the characters of current received Spam E-mails. Second, ADG can extract the correct judgement rules of Hams misjudged as Spams. However, there are a few cases of Spam misjudged as Ham. In this experiment, ADG makes an over fitting to the characters of Hams. We have met the problems according to the limitation of classification capability by SOM and explosive search in GP using many nodes as shown in T1 Therefore, we improve the proposed method", notes = "http://www.intechopen.com/books/show/title/self-organizing-maps", size = "14 pages", } @InProceedings{ichise:1998:ilpGP, author = "R. Ichise", title = "Inductive Logic Programming and Genetic Programming", booktitle = "13th European Conference on Artificial Intelligence", year = "1998", editor = "Henri Prade", address = "Brighton", month = "23-28 " # aug, publisher = "John Wiley and Sons", keywords = "genetic algorithms, genetic programming", ISBN = "0-471-98431-0", URL = "http://www.amazon.co.uk/ECAI-Proceedings-Conference-Artificial-Intelligence/dp/0471984310", notes = "ECAI-98 young researcher paper http://www.informatik.uni-trier.de/~ley/db/conf/ecai/ecai98.html See also \cite{Ichise:1999:JJSAI}", } @Article{Ichise:1999:JJSAI, author = "Ryutaro Ichise and Masayuki Numao", title = "Inductive Learning with Inductive Logic Programming and Genetic Programming", journal = "Journal of Japanese Society for Artificial Intelligence", year = "1999", volume = "14", number = "2", pages = "307--314", month = mar, keywords = "genetic algorithms, genetic programming, ILP", ISSN = "0912-8085", broken = "http://sciencelinks.jp/j-east/article/199911/000019991199A0325870.php", URL = "http://www.ai-gakkai.or.jp/en/vol14_no2/", abstract = "Two approaches to inducing a concept represented in first order logic are inductive logic programming(ILP) and genetic programming(GP). In ILP, concept learning can be considered as a search in the space specified by the background knowledge, and in which the goal concept is represented by Horn clauses. On the other hand, in GP, the search space is specified by terminal and nonterminal symbols, and the goal is represented generally by S-expressions. These two approaches are very similar in terms of their methods and goals, yet their combination in previous work is rare. In this paper, we propose a method that synthesises the inductive logic programming and genetic programming approaches. The concept behind this approach is to combine the search method of GP, that is, Genetic Algorithm, with the type and mode methods of ILP. We have implemented a system called SYNGIP (SYNthesized system with Genetic programming and Inductive logic Programming) based on the method. Experimental results show that the proposed method can be used to treat, in the same way, learning from training examples that do not have discrete classes, and learning from both positive and negative training examples. Moreover, the proposed method constitutes a novel solution to the closure problem and provides a new bias for concept learning. (author abst.)", notes = "Language=Japanese Journal Code=X0330A Accession number=99A0325870 Tokyo Inst. of Technology, Graduate School", } @InProceedings{Icke:2010:geccocomp, author = "Ilknur Icke and Andrew Rosenberg", title = "Dimensionality reduction using symbolic regression", booktitle = "GECCO 2010 Late breaking abstracts", year = "2010", editor = "Daniel Tauritz", isbn13 = "978-1-4503-0073-5", keywords = "genetic algorithms, genetic programming", pages = "2085--2086", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830761.1830874", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "In this paper, we propose a symbolic regression approach for data visualisation that is suited for classification tasks. Our algorithm seeks a visually and semantically interpretable lower dimensional representation of the given dataset that would increase classifier accuracy as well. This simultaneous identification of easily interpretable dimensionality reduction and improved classification accuracy relieves the user of the burden of experimenting with the many combinations of classification and dimensionality reduction techniques", notes = "Flubber, ECJ, WEKA, UCI wisconsin breast, leptographsus crabs. Compare with PCA, MDS and random projections. no significant improvement. Also known as \cite{1830874} Distributed on CD-ROM at GECCO-2010. ACM Order Number 910102.", } @InProceedings{Icke:2010:WiML, author = "Ilknur Icke and Andrew Rosenberg", title = "Multi-Objective Genetic Programming Projection Pursuit for Exploratory Data Modeling", booktitle = "Workshop for Women in Machine Learning", year = "2010", editor = "Diane Oyen", address = "Canada", month = "6 " # dec, keywords = "genetic algorithms, genetic programming, MOG3P", URL = "http://arxiv.org/abs/1010.1888", URL = "http://pami.uwaterloo.ca/~ealee/wiml/2010/program/WiML2010_IlknurIcke.pdf", size = "2 pages", abstract = "For classification problems, feature extraction is a crucial process which aims to find a suitable data representation that increases the performance of the machine learning algorithm. According to the curse of dimensionality theorem, the number of samples needed for a classification task increases exponentially as the number of dimensions (variables, features) increases. On the other hand, it is costly to collect, store and process data. Moreover, irrelevant and redundant features might hinder classifier performance. In exploratory analysis settings, high dimensionality prevents the users from exploring the data visually. Feature extraction is a two-step process: feature construction and feature selection. Feature construction creates new features based on the original features and feature selection is the process of selecting the best features as in filter, wrapper and embedded methods. In this work, we focus on feature construction methods that aim to decrease data dimensionality for visualisation tasks. Various linear (such as principal components analysis (PCA), multiple discriminants analysis (MDA), exploratory projection pursuit) and non-linear (such as multidimensional scaling (MDS), manifold learning, kernel PCA/LDA, evolutionary constructive induction) techniques have been proposed for dimensionality reduction. Our algorithm is an adaptive feature extraction method which consists of evolutionary constructive induction for feature construction and a hybrid filter/wrapper method for feature selection.", notes = "WiML 2010 http://pami.uwaterloo.ca/~ealee/wiml/2010/index.php", } @InProceedings{icke:2011:EuroGP, author = "Ilknur Icke and Andrew Rosenberg", title = "Multi-Objective Genetic Programming for Visual Analytics", booktitle = "Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011", year = "2011", month = "27-29 " # apr, editor = "Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado", series = "LNCS", volume = "6621", publisher = "Springer Verlag", address = "Turin, Italy", pages = "322--334", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming: poster", DOI = "doi:10.1007/978-3-642-20407-4_28", abstract = "Visual analytics is a human-machine collaboration to data modelling where extraction of the most informative features plays an important role. Although feature extraction is a multi-objective task, the traditional algorithms either only consider one objective or aggregate the objectives into one scalar criterion to optimise. In this paper, we propose a Pareto-based multi-objective approach to feature extraction for visual analytics applied to data classification problems. We identify classifiability, visual interpretability and semantic interpretability as the three equally important objectives for feature extraction in classification problems and define various measures to quantify these objectives. Our results on a number of benchmark datasets show consistent improvement compared to three standard dimensionality reduction techniques. We also argue that exploration of the multiple Pareto-optimal models provide more insight about the classification problem as opposed to a single optimal solution.", notes = "Part of \cite{Silva:2011:GP} EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011", } @PhdThesis{Icke:thesis, author = "Ilknur Icke", title = "Multi-objective genetic programming for data visualization and classification", school = "Computer Science, City University of New York", year = "2011", address = "USA", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-267-01062-9", URL = "http://files.matlabsite.com/docs/thesis/th930929283.pdf", broken = "http://www.gc.cuny.edu/GC-Header/Alumni/Alumni-Dissertations-and-Theses.aspx?page=3&program=Computer+Science&searchterm=genetic%20programming&sortby=author", broken = "https://onesearch.cuny.edu/permalink/f/68hr40/CUNY_ALEPH007152073", URL = "https://www.proquest.com/openview/3dfdfa4e004ad61dabb9f6ce5c3ba68f", URL = "http://dl.acm.org/citation.cfm?id=2395668", size = "239 pages", abstract = "The process of knowledge discovery lies on a continuum ranging between the human driven (manual exploration) approaches to fully automatic data mining methods. As a hybrid approach, the emerging field of visual analytics aims to facilitate human-machine collaborative decision making by providing automated analysis of data via interactive visualizations. One area of interest in visual analytics is to develop data transformation methods that support visualization and analysis. In this thesis, we develop an evolutionary computing based multi-objective dimensionality reduction method for visual data classification. The algorithm is called Genetic Programming Projection Pursuit (G3P) where genetic programming is used in order to automatically create visualizations of higher dimensional labeled datasets which are assessed in terms of discriminative power and interpretability. We consider two forms of interpretability of the visualizations: clearly separated and compact class structures along with easily interpretable data transformation expressions relating the original data attributes to the visualization axes. The G3P algorithm incorporates a number of automated measures of interpretability that were found to be in alignment with human judgement through a user study we conducted. On a number of data mining problems, we show that G3P generates a large number of data transformations that are better than those generated by a number of dimensionality reduction methods such as the principal components analysis (PCA), multiple discriminants analysis (MDA) and targeted projection pursuit (TPP) in terms of discriminative power and interpretability.", notes = "UMI Number: 3481647 Supervisor Andrew Rosenberg", } @InCollection{Icke:2013:GPTP, author = "Ilknur Icke and Nicholas A. Allgaier and Christopher M. Danforth and Robert A. Whelan and Hugh P. Garavan and Joshua C. Bongard", title = "A Deterministic and Symbolic Regression Hybrid Applied to Resting-State {fMRI} Data", booktitle = "Genetic Programming Theory and Practice XI", year = "2013", series = "Genetic and Evolutionary Computation", editor = "Rick Riolo and Jason H. Moore and Mark Kotanchek", publisher = "Springer", chapter = "9", pages = "155--173", address = "Ann Arbor, USA", month = "9-11 " # may, keywords = "genetic algorithms, genetic programming, Symbolic regression, Hybrid algorithm, Regularisation, Resting-state fMRI", isbn13 = "978-1-4939-0374-0", oai = "oai:CiteSeerX.psu:10.1.1.368.634", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.368.634", URL = "http://www.cs.uvm.edu/~jbongard/papers/2013_GPTP_Icke.pdf", DOI = "doi:10.1007/978-1-4939-0375-7_9", abstract = "Symbolic regression (SR) is one the most popular applications of genetic programming (GP) and an attractive alternative to the standard deterministic regression approaches due to its flexibility in generating free-form mathematical models from observed data without any domain knowledge. However, GP suffers from various issues hindering the applicability of the technique to real-life problems. In this paper, we show that a hybrid deterministic regression (DR)/genetic programming based symbolic regression (GP-SR) algorithm outperforms GP-SR alone on a brain imaging dataset.", notes = " Part of \cite{Riolo:2013:GPTP} published after the workshop in 2013", } @InProceedings{Icke:2013:CEC, article_id = "1512", author = "Ilknur Icke and Joshua Bongard", title = "Modeling Hierarchy Using Symbolic Regression", booktitle = "2013 IEEE Conference on Evolutionary Computation", volume = "1", year = "2013", month = jun # " 20-23", editor = "Luis Gerardo {de la Fraga}", pages = "2980--2987", address = "Cancun, Mexico", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-0453-2", DOI = "doi:10.1109/CEC.2013.6557932", notes = "CEC 2013 - A joint meeting of the IEEE, the EPS and the IET.", } @InProceedings{Icke:2013:CECa, article_id = "1743", author = "Ilknur Icke and Joshua Bongard", title = "Improving Genetic Programming Based Symbolic Regression Using Deterministic Machine Learning", booktitle = "2013 IEEE Conference on Evolutionary Computation", volume = "1", year = "2013", month = jun # " 20-23", editor = "Luis Gerardo {de la Fraga}", pages = "1763--1770", address = "Cancun, Mexico", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-0453-2", DOI = "doi:10.1109/CEC.2013.6557774", notes = "CEC 2013 - A joint meeting of the IEEE, the EPS and the IET.", } @InProceedings{Idris:2012:SMC, author = "Adnan Idris and Asifullah Khan and Yeon Soo Lee", booktitle = "IEEE International Conference on Systems, Man, and Cybernetics (SMC 2012)", title = "Genetic Programming and Adaboosting based churn prediction for Telecom", year = "2012", pages = "1328--1332", month = oct # " 14-17", address = "Seoul, Korea", DOI = "doi:10.1109/ICSMC.2012.6377917", size = "5 pages", abstract = "Churn prediction model guides the customer relationship management to retain the customers who are expected to quit. In recent times, a number of tree based ensemble classifiers are used to model the churn prediction in telecom. These models predict the churners quite satisfactorily; however, there is a considerable margin of improvement. In telecom, the enormous size, imbalanced nature, and high dimensionality of the training dataset mainly cause the classification algorithms to suffer in accurately predicting the churners. In this paper, we use Genetic Programming (GP) based approach for modelling the challenging problem of churn prediction in telecom. Adaboost style boosting is used to evolve a number of programs per class. Finally, the predictions are made with the resulting programs using the higher output, from a weighted sum of the outputs of programs per class. The prediction accuracy is evaluated using 10 fold cross validation on standard telecom datasets and a 0.89 score of area under the curve is observed. We hope that such an efficient churn prediction approach might be significantly beneficial for the competitive telecom industry.", keywords = "genetic algorithms, genetic programming, customer relationship management, learning (artificial intelligence), pattern classification, telecommunication computing, telecommunication industry, trees (mathematics), GP based approach, adaboosting based churn prediction, churn prediction model, classification algorithms, customer relationship management, prediction accuracy, telecom datasets, telecom industry, training dataset, tree based ensemble classifiers, Accuracy, Boosting, Prediction algorithms, Predictive models, Sociology, Telecommunications, Training, Adaboost, churn prediction, cross validation, prediction accuracy, telecom", notes = "Also known as \cite{6377917}", } @Article{IFTIKHAR:2023:jmrt, author = "Bawar Iftikhar and Sophia {C. Alih} and Mohammadreza Vafaei and Muhammad Faisal Javed and Mujahid Ali and Yaser Gamil and Muhammad Faisal Rehman", title = "A machine learning-based genetic programming approach for the sustainable production of plastic sand paver blocks", journal = "Journal of Materials Research and Technology", volume = "25", pages = "5705--5719", year = "2023", ISSN = "2238-7854", DOI = "doi:10.1016/j.jmrt.2023.07.034", URL = "https://www.sciencedirect.com/science/article/pii/S2238785423015636", keywords = "genetic algorithms, genetic programming, Plastic waste, Gene expression programming, Plastic sand paver blocks, Sustainable, Compressive strength, Mathematical expression", abstract = "Plastic sand paver blocks (PSPB) provide a sustainable alternative by reprocessing plastic waste and decreasing reliance on environmentally hazardous materials such as concrete. They promote waste management and environmentally favorable building practices. This paper presents a novel method for estimating the compressive strength (CS) of plastic sand paver blocks based on gene expression programming (GEP) techniques. The database collected from the experimental work comprises 135 compressive strength results. Seven input parameters were involved in predicting the CS of PSPB, namely, plastic, sand, sand size, fiber percentage, fibre length, fibre diameter, and tensile strength of the fibre. Simplified mathematical expressions were used to figure out the CS. The results of GEP formulations showed that they were better in line with the experimental data, with R2 values for CS of 0.89 (training) and 0.88 (testing). The models' performance was evaluated using sensitivity analysis and statistical checks. The statistical evaluations show that the actual and predicted values are closer together, which lends credence to the GEP model's capacity to forecast PSPB CS. The sensitivity analysis showed that sand size and fibre percentage contribute more than 50percent of the CS in PSPB. In addition, the results demonstrate that the proposed models are accurate and have a robust capacity for generalization and prediction. This research can improve environmental protection and economic benefit by enhancing the reuse of PSPB in producing green ecosystems", } @InProceedings{igel:98, author = "Christian Igel", title = "Causality of Hierarchical Variable Length Representations", booktitle = "Proceedings of the 1998 IEEE World Congress on Computational Intelligence", year = "1998", pages = "324--329", address = "Anchorage, Alaska, USA", month = "5-9 " # may, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, coding, hierarchical variable-length representations, problem difficulty, program tree representations, quantitative probabilistic causality measure, search space metric, statistical fitness landscape analysis, strong causality, tree edit distance, probability, program control structures, programming theory, tree searching", ISBN = "0-7803-4869-9", URL = "http://www.neuroinformatik.ruhr-uni-bochum.de/PEOPLE/igel/CoHVLR.ps.gz", DOI = "doi:10.1109/ICEC.1998.699753", size = "6 pages", abstract = "In this paper, the strong causality of program tree representations is considered. A quantitative, probabilistic causality measure is used in contrast to statistical fitness landscape analysis methods. Although it fails to rank different problems according to their difficulty, it is helpful for choosing the right coding for a given task. The investigation uses a metric on the search space called the tree edit distance. Different ways to define such a measure are discussed.", notes = "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE World Congress on Computational Intelligence", } @InCollection{igel:1999:aigp3, author = "Christian Igel and Kumar Chellapilla", title = "Fitness Distributions: Tools for Designing Efficient Evolutionary Computations", booktitle = "Advances in Genetic Programming 3", publisher = "MIT Press", year = "1999", editor = "Lee Spector and William B. Langdon and Una-May O'Reilly and Peter J. Angeline", chapter = "9", pages = "191--216", address = "Cambridge, MA, USA", month = jun, keywords = "genetic algorithms, genetic programming", ISBN = "0-262-19423-6", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/aigp3/ch09.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.141.867", DOI = "doi:10.7551/mitpress/1110.003.0013", abstract = "Fitness distributions are employed as tools for understanding the effects of variation operators in Genetic Programming. Eleven operators are analysed on four common benchmark problems by estimating generation dependent features of the fitness distributions, e.g. the probability of improvement and the expected average fitness change.", notes = "AiGP3 See http://cognet.mit.edu", } @InProceedings{igel:1999:UFDIELS, author = "Christian Igel and Martin Kreutz", title = "Using Fitness Distributions to Improve the Evolution of Learning Structures", booktitle = "Proceedings of the Congress on Evolutionary Computation", year = "1999", editor = "Peter J. Angeline and Zbyszek Michalewicz and Marc Schoenauer and Xin Yao and Ali Zalzala", volume = "3", pages = "1902--1909", address = "Mayflower Hotel, Washington D.C., USA", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "6-9 " # jul, organisation = "Congress on Evolutionary Computation, IEEE / Neural Networks Council, Evolutionary Programming Society, Galesia, IEE", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, fitness distributions, density estimation, gradient-based operators, absolute benefit, coefficient adaptation, density estimation models, fitness distributions, fitness space, fitness trajectory analysis, gradient based operators, gradient information, information theory based measure, learning structure evolution, offline analysis, online operator adaptation, information theory, learning (artificial intelligence), probability", ISBN = "0-7803-5536-9 (softbound)", ISBN = "0-7803-5537-7 (Microfiche)", URL = "http://www.neuroinformatik.ruhr-uni-bochum.de/ini/PEOPLE/igel/UFDtItEoLS.ps.gz", URL = "http://citeseer.ist.psu.edu/294668.html", DOI = "doi:10.1109/CEC.1999.785505", abstract = "the absolute benefit, a measure of improvement in the fitness space, is derived from the viewpoint of fitness distribution and fitness trajectory analysis. It is used for online operator-adaptation, where the optimisation of density estimation models serves as an example. A new information theory based measure is proposed to judge the accuracy of the evolved models. Further, the absolute benefit is applied to offline analysis of new gradient based operators used for coefficient adaptation in genetic programming. An efficient method to calculate the gradient information is presented.", notes = "CEC-99 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. Library of Congress Number = 99-61143", } @InProceedings{igel:1999:IIDDGCFGP, author = "Christian Igel and Kumar Chellapilla", title = "Investigating the Influence of Depth and Degree of Genotypic Change on Fitness in Genetic Programming", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1061--1068", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-422.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-422.ps", abstract = "In this paper we investigate the influence of (a) the amount of variation generated in the genotype and (b) the depth of application of variation operators on the offspring fitness in genetic programming. Simulation results on three common test problems indicate that for certain features of the fitness distribution the location of the variation may play as important a role as the choice of the applied operators.", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99) Errata: We thought of binary trees when the second paragraph on the second page (i.e. 1062) was written...", } @Article{Igel:2003:NC, author = "Christian Igel and Marc Toussaint", title = "Neutrality and Self-Adaptation", journal = "Natural Computing", year = "2003", volume = "2", number = "2", URL = "http://www.neuroinformatik.ruhr-uni-bochum.de/PEOPLE/igel/NaSA.pdf", URL = "http://ipsapp009.kluweronline.com/content/getfile/5030/5/1/abstract.htm", DOI = "doi:10.1023/A:1024906105255", pages = "117--132", keywords = "genetic algorithms, genetic programming, evolutionary computation, genotype-phenotype mapping, neutrality, No-Free-Lunch theorem, redundancy, self-adaptation", abstract = "Neutral genotype-phenotype mappings can be observed in natural evolution and are often used in evolutionary computation. In this article, important aspects of such encodings are analysed. First, it is shown that in the absence of external control neutrality allows a variation of the search distribution independent of phenotypic changes. In particular, neutrality is necessary for self-adaptation, which is used in a variety of algorithms from all main paradigms of evolutionary computation to increase efficiency. Second, the average number of fitness evaluations needed to find a desirable (e.g., optimally adapted) genotype depending on the number of desirable genotypes and the cardinality of the genotype space is derived. It turns out that this number increases only marginally when neutrality is added to an encoding presuming that the fraction of desirable genotypes stays constant and that the number of these genotypes is not too small.", notes = "Article ID: 5126729", } @Proceedings{Igel:2014:GECCO, title = "GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation", year = "2014", editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges", address = "Vancouver, BC, Canada", publisher_address = "New York, NY, USA", month = "12-16 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, ant colony optimization and swarm intelligence, artificial immune systems, artificial life, robotics, and evolvable hardware, biological and biomedical applications, digital entertainment technologies and arts, estimation of distribution algorithms, evolution strategies and evolutionary programming, evolutionary combinatorial optimization and metaheuristics, evolutionary machine learning, evolutionary multiobjective optimization, generative and developmental systems, integrative genetic and evolutionary computation, parallel evolutionary systems, real world applications, search based software engineering, self-* search, theory", isbn13 = "978-1-4503-2662-9", URL = "http://dl.acm.org/citation.cfm?id=2576768", DOI = "doi:10.1145/2576768", notes = "GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)", } @InProceedings{conf/saicsit/IgwePR13, author = "Kevin Igwe and Nelishia Pillay and Christopher Rae", title = "Solving the 8-Puzzle Problem Using Genetic Programming", booktitle = "Proceedings of the South African Institute for Computer Scientists and Information Technologists Conference, SAICSIT'13", year = "2013", editor = "John McNeill and Karen L. Bradshaw and Philip Machanick and Mosiuoa Tsietsi", pages = "64--67", address = "East London, South Africa", month = oct # " 7-9", publisher = "ACM", keywords = "genetic algorithms, genetic programming, game, Algorithms, Performance, Experimentation", bibdate = "2013-09-28", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/saicsit/saicsit2013.html#IgwePR13", isbn13 = "978-1-4503-2112-9", URL = "http://dl.acm.org/citation.cfm?id=2513456", URL = "http://doi.acm.org/10.1145/2513456.2513492", DOI = "doi:10.1145/2513456.2513492", acmid = "2513492", size = "4 pages", abstract = "The 8-puzzle problem is a classic artificial intelligence problem which has been well-researched. The research in this domain has focused on evaluating traditional search methods such as the breadth-first search and the A* algorithm and deriving and testing various heuristics for use with informed searches to solve the 8-puzzle problem. The study presented in this paper evaluates a machine learning technique, namely genetic programming, as means of solving the 8-puzzle problem. The genetic programming algorithm uses the grow method to create an initial population which is iteratively refined using tournament selection to choose parents which the reproduction, mutation and crossover operators are applied to, thereby producing successive generations. The edit operator has been used to exert parsimony pressure in order to reduce the size of solution trees and hence the number of moves to solve a problem instance. The genetic programming system was successfully applied to 20 problem instances of differing difficulty, producing solutions to all 20 problems. Furthermore, for a majority of the problems the solutions produced solve the problem instance using the known minimum number of moves.", notes = "Tree GP. Comparison between: depth first, breadth first, A* (A-star) and GP. http://www.ict.ru.ac.za/saicsit2013/ Also known as \cite{Igwe:2013:SPU:2513456.2513492}", } @InProceedings{Igwe:2013:WICT, author = "Kevin Igwe and Nelishia Pillay", title = "Automatic Programming Using Genetic Programming", booktitle = "Proceedings of the 2013 Third World Congress on Information and Communication Technologies (WICT 2013)", year = "2013", editor = "Long Thanh Ngo and Ajith Abraham and Lam Thu Bui and Emilio Corchado and Choo Yun-Huoy and Kun Ma", pages = "337--342", address = "Hanoi, Vietnam", month = "15-18 " # dec, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Automatic programming, incremental learning, modularisation", isbn13 = "978-1-4799-3230-6", URL = "http://www.mirlabs.net/wict13/proceedings/html/paper91.xml", URL = "http://www.mirlabs.net/wict13/proceedings/pdf/paper91.pdf", URL = "http://www.titan.cs.unp.ac.za/~nelishiap/uploads/5.pdf", DOI = "doi:10.1109/WICT.2013.7113158", size = "6 pages", abstract = "Genetic programming (GP) is an evolutionary algorithm which explores a program space rather than a solution space which is typical of other evolutionary algorithms such as genetic algorithms. GP finds solutions to problems by evolving a program, which when implemented will produce a solution. This paper investigates the use of genetic programming for automatic programming. The paper focuses on the procedural/imperative programming paradigm. More specifically the evolution of programs using memory, conditional and iterative programming constructs is investigated. An internal representation language is defined in which to evolve programs. The generational GP algorithm was implemented using the grow method to create the initial population, tournament selection to choose parents and reproduction, crossover and mutation for regeneration purposes. The paper also presents a form of incremental learning which facilitates modularisation. The GP approach to automatic programming was tested on ten programming problems that are usually presented to novice programmers in a first year procedural programming course of an undergraduate degree in Computer Science. The GP approach evolved solutions for all ten problems, with incremental learning needed in two instances to produce a solution.", notes = "memory, if-then-else, while, blockn, combn. Encapsulation. Factorial, square, swap, reverse, sum, complex root, vowel, vat, salary. Netbeans JDK 1.7.2_25 IEEE Catalogue Number: CFP1368R-POD http://www.mirlabs.net/wict13/proceedings/index.html Also known as \cite{7113158}", } @InProceedings{Igwe:2014:PRASA, author = "Kevin Igwe and Nelishia Pillay", title = "A Comparative Study of Genetic Programming and Grammatical Evolution for Evolving Data Structures", booktitle = "Proceedings of the 2014 PRASA, RobMech and AfLaT International Joint Symposium", year = "2014", editor = "Martin Puttkammer and Roald Eiselen", pages = "115--121", address = "Cape Town, South Africa", month = "27-28 " # nov, publisher = "Pattern Recognition Association of South Africa (PRASA)", keywords = "genetic algorithms, genetic programming, grammatical evolution, algorithm induction, automatic programming", isbn13 = "978-0-620-62617-0", URL = "http://www.prasa.org/proceedings/2014/prasa2014-20.pdf", size = "7 pages", abstract = "The research presented in the paper forms part of a larger initiative aimed at automatic algorithm induction using machine learning. This paper compares the performance of two machine learning techniques, namely, genetic programming and a variation of genetic programming, grammatical evolution, for automatic algorithm induction. The application domain used to evaluate both the approaches is the induction of data structure algorithms. Genetic programming is an evolutionary algorithm that searches a program space for an algorithm/program which when executed will provide a solution to the problem at hand. Grammatical evolution is a variation of genetic programming which provides a more flexible encoding, thereby eliminating the sufficiency and closure requirement imposed by genetic programming. The paper firstly extends previous work on genetic programming for evolving data structures, providing an alternative genetic programming solution to the problem. A grammatical evolution solution to the problem is then presented. This is the first application of grammatical evolution to this domain and for the simultaneous induction of algorithms. The performance of these approaches in inducing algorithms for the stack and queue data structures are compared.", notes = "broken July 2023 http://www.prasa.org/proceedings/2014/", } @InProceedings{Igwe:2015:NaBIC, title = "A Study of Genetic Programming and Grammatical Evolution for Automatic Object-Oriented Programming: {A} Focus on the List Data Structure", author = "Kevin Igwe and Nelishia Pillay", booktitle = "Advances in Nature and Biologically Inspired Computing: Proceedings of the 7th World Congress on Nature and Biologically Inspired Computing (NaBIC2015)", publisher = "Springer", editor = "Nelishia Pillay and Andries P. Engelbrecht and Ajith Abraham and Mathys C. du Plessis and Vaclav Snasel and Azah Kamilah Muda", year = "2015", volume = "419", series = "Advances in Intelligent Systems and Computing", pages = "151--163", address = "Pietermaritzburg, South Africa", month = dec # " 01-03", keywords = "genetic algorithms, genetic programming, grammatical evolution object-oriented programming, grammar, ADF, OOGE, GOOGE, GE", isbn13 = "978-3-319-27400-3", DOI = "doi:10.1007/978-3-319-27400-3_14", abstract = "Automatic programming is a concept which until today has not been fully achieved using evolutionary algorithms. Despite much research in this field, a lot of the concepts remain unexplored. The current study is part of ongoing research aimed at using evolutionary algorithms for automatic programming. The performance of two evolutionary algorithms, namely, genetic programming and grammatical evolution are compared for automatic object-oriented programming. Genetic programming is an evolutionary algorithm which searches a program space for a solution program. A program generated by genetic programming is executed to yield a solution to the problem at hand. Grammatical evolution is a variation of genetic programming which adopts a genotype-phenotype distinction and uses grammars to map from a genotypic space to a phenotypic (program) space. The study implements and tests the abilities of these approaches as well as a further variation of genetic programming, namely, object-oriented genetic programming, for automatic object-oriented programming. The application domain used to evaluate these approaches is the generation of abstract data types, specifically the class for the list data structure. The study also compares the performance of the algorithms when human programmer problem domain knowledge is incorporated and when such knowledge is not incorporated. The results show that grammatical evolution performs better than genetic programming and object-oriented genetic programming, with object-oriented genetic programming outperforming genetic programming. Future work will focus on evolution of programs that use the evolved classes.", } @PhdThesis{Igwe_Kevin_Chizoba_2023, author = "Igwe Kevin Chizoba", title = "A co-evolutionary approach to data-driven agent-based modelling: Simulating the Virtual Interaction APPLication experiments", school = "Social Psychology Discipline, School of Applied Human Sciences, University of KwaZulu-Natal", year = "2023", address = "South Africa", month = jan, URL = "https://ukzn-dspace.ukzn.ac.za/handle/10413/21607", URL = "https://ukzn-dspace.ukzn.ac.za/bitstream/handle/10413/21607/Igwe_Kevin_Chizoba_2023.pdf", size = "176 pages", abstract = "The dynamics of social interactions are barely captured by the traditional methods of research in social psychology, vis-a-vis, interviews, surveyed data and experiments. To capture the dynamics of social interactions, researchers adopt computer-mediated experiments and agent-based simulations (a). These methods have been efficiently applied to game theories. While strategic games such as the prisoner dilemma and GO have optimal outcomes, interactive social exchanges can have obscure and multiple conflicting objectives (fairness, selfishness, group bias) whose relative importance evolves in interaction. Discovering and understanding the mechanisms underlying these objectives become even more difficult when there is little or no information about the interacting individual(s). This study describes this as an information-scarce interactive social exchange context. This study, therefore, forms part of a larger initiative on developing efficient simulations of social interaction in an information-scarce interactive social exchange context. First, this dissertation develops a context for and justifies the importance of simulation in an information-scarce interactive social exchange context (Chapter 2). It then performs a literature review of the studies that have developed a computational model and simulation in this context (Chapter 3). Next, the dissertation develops a co-evolutionary data-driven model and simulates exchange behaviour in an information-scarce context (Chapter 4). To benchmark the data-driven model, this dissertation develops a rule-based model. Furthermore, it creates agents that use the rule-based model, integrates them into Virtual Interaction APPLication (VIAPPL) and tests their usefulness in predicting and influencing exchange decisions. Precisely, it measures the agent's ability in reducing in-group bias during interaction in an information-scarce context (Chapter 5). Likewise, it creates machine learning (adaptive) agents that use the data-drivel model, and tests them in a similar experimental context. These chapters were written independently; thus, their objectives, methods and results are discussed in each chapter. Finally, the study presents a general conclusion (Chapter 6).", notes = "Student No: 212553209 not GP? Supervisor: Kevin Durrheim", } @InProceedings{iima:1999:GALSPEWPP, author = "Hitoshi Iima and Nobuo Sannomiya", title = "Genetic Algorithm for a Large-Scale Scheduling Problem in an Electric Wire Production Process", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1784", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "real world applications, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-707.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-707.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Iio:2008:SICE, author = "Takamasa Iio and Ivan Tanev and Katsunori Shimohara", title = "Evolutionary adaptive behavior in noisy multi-agent system", booktitle = "SICE Annual Conference", year = "2008", month = "20-22 " # aug, address = "Japan", pages = "1506--1509", keywords = "genetic algorithms, genetic programming, environmental information, evolutionary adaptive behavior, multi-agent system, perceptual noise, multi-agent systems", DOI = "doi:10.1109/SICE.2008.4654898", abstract = "In this paper, we discuss a relationship between perceptual noise and fitness of agents in a multi-agent system. In multi-agent system, agents perceive environmental information and act based on this information. Therefore, in case that the perceptual information contains some noise, a cooperative behavior of agents is more challenging and the resulting fitness of the agents is inferior. In order to develop a behavior of the agents that is robust to the perception noise, we evolved the behavior of the agents in noisy environment. As a result, the evolved behavior, obtained in a noisy environment is superior (in terms of robustness) than that evolved in noiseless environment.", notes = "Also known as \cite{4654898}", } @InProceedings{IJntemaconf/wise/IJntemaHFV14, author = "Wouter IJntema and Frederik Hogenboom and Flavius Frasincar and Damir Vandic", title = "A Genetic Programming Approach for Learning Semantic Information Extraction Rules from News", bibdate = "2014-09-14", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/wise/wise2014-1.html#IJntemaHFV14", booktitle = "Web Information Systems Engineering - {WISE} 2014 - 15th International Conference, Thessaloniki, Greece, October 12-14, 2014, Proceedings, Part {I}", publisher = "Springer", year = "2014", volume = "8786", editor = "Boualem Benatallah and Azer Bestavros and Yannis Manolopoulos and Athena Vakali and Yanchun Zhang", isbn13 = "978-3-319-11748-5", pages = "418--432", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming", URL = "http://dx.doi.org/10.1007/978-3-319-11749-2", } @PhdThesis{ijspeert:thesis, author = "Auke Jan Ijspeert", title = "Design of artificial neural oscillatory circuits for the control of lamprey- and salamander-like locomotion using evolutionary algorithms", school = "Department of Artificial Intelligence, University of Edinburgh", year = "1998", address = "UK", keywords = "genetic algorithms, artificial life, CPG", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/ftp.io.com/papers/ijspeert", size = "200+ pages", abstract = "This dissertation investigates the evolutionary design of oscillatory artificial neural networks for the control of animal-like locomotion. It is inspired by the neural organisation of locomotor circuitries in vertebrates, and explores in particular the control of undulatory swimming and walking. The difficulty with designing such controllers is to find mechanisms which can transform commands concerning the direction and the speed of motion into the multiple rhythmic signals sent to the multiple actuators typically involved in animal-like locomotion. In vertebrates, such control mechanisms are provided by central pattern generators which are neural circuits capable of producing the patterns of oscillations necessary for locomotion without oscillatory input from higher control centres or from sensory feedback. This thesis explores the space of possible neural configurations for the control of undulatory locomotion, and addresses the problem of how biologically plausible neural controllers can be automatically generated. Evolutionary algorithms are used to design connectionist models of central pattern generators for the motion of simulated lampreys and salamanders. This work is inspired by Ekeberg's neuronal and mechanical simulation of the lamprey [Ekeberg 93]. The first part of the thesis consists of developing alternative neural controllers for a similar mechanical simulation. Using a genetic algorithm and an incremental approach, a variety of controllers other than the biological configuration are successfully developed which can control swimming with at least the same efficiency. The same method is then used to generate synaptic weights for a controller which has the observed biological connectivity in order to illustrate how the genetic algorithm could be used for developing neurobiological models. Biologically plausible controllers are evolved which better fit physiological observations than Ekeberg's hand-crafted model. Finally, in collaboration with Jerome Kodjabachian, swimming controllers are designed using a developmental encoding scheme, in which developmental programs are evolved which determine how neurons divide and get connected to each other on a two-dimensional substrate. The second part of this dissertation examines the control of salamander-like swimming and trotting. Salamanders swim like lampreys but, on the ground, they switch to a trotting gait in which the trunk performs a standing wave with the nodes at the girdles. Little is known about the locomotion circuitry of the salamander, but neurobiologists have hypothesised that it is based on a lamprey-like organisation. A mechanical simulation of a salamander-like animat is developed, and neural controllers capable of exhibiting the two types of gaits are evolved. The controllers are made of two neural oscillators projecting to the limb motoneurons and to lamprey-like trunk circuitry. By modulating the tonic input applied to the networks, the type of gait, the speed and the direction of motion can be varied. By developing neural controllers for lamprey- and salamander-like locomotion, this thesis provides insights into the biological control of undulatory swimming and walking, and shows how evolutionary algorithms can be used for developing neurobiological models and for generating neural controllers for locomotion. Such a method could potentially be used for designing controllers for swimming or walking robots, for instance.", } @Article{oai:CiteSeerPSU:317384, author = "Auke Jan Ijspeert and Jerome Kodjabachian", title = "Evolution and Development of a Central Pattern Generator for the Swimming of a Lamprey", journal = "Artificial Life", year = "1999", volume = "5", number = "3", pages = "247--269", month = "Summer", keywords = "genetic algorithms, genetic programming, neural control, developmental encoding, SGOCE, simulation, central pattern generator, CPG, swimming, lamprey", DOI = "doi:10.1162/106454699568773", abstract = "This article describes the design of neural control architectures for locomotion using an evolutionary approach. Inspired by the central pattern generators found in animals, we develop neural controllers that can produce the patterns of oscillations necessary for the swimming of a simulated lamprey. This work is inspired by Ekeberg's neuronal and mechanical model of a lamprey [11] and follows experiments in which swimming controllers were evolved using a simple encoding scheme [25, 26]. Here, controllers are developed using an evolutionary algorithm based on the SGOCE encoding [31, 32] in which a genetic programming approach is used to evolve developmental programs that encode the growing of a dynamical neural network. The developmental programs determine how neurons located on a two-dimensional substrate produce new cells through cellular division and how they form efferent or afferent interconnections. Swimming controllers are generated when the growing networks eventually create connections to the muscles located on both sides of the rectangular substrate. These muscles are part of a two-dimensional mechanical simulation of the body of the lamprey in interaction with water. The motivation of this article is to develop a method for the design of control mechanisms for animal-like locomotion. Such a locomotion is characterized by a large number of actuators, a rhythmic activity, and the fact that efficient motion is only obtained when the actuators are well coordinated. The task of the control mechanism is therefore to transform commands concerning the speed and direction of motion into the signals sent to the multiple actuators. We define a fitness function, based on several simulations of the controller with different commands settings, that rewards the capacity of modulating the speed and the direction of swimming in response to simple, varying input signals. Central pattern generators are thus evolved capable of producing the relatively complex patterns of oscillations necessary for swimming. The best solutions generate traveling waves of neural activity, and propagate, similarly to the swimming of a real lamprey, undulations of the body from head to tail propelling the lamprey forward through water. By simply varying the amplitude of two input signals, the speed and the direction of swimming can be modulated.", notes = "http://alife.tuke.sk/projekty/abstract/abstract99.html#a34 Also available as University of Edinburgh Technical report IngentaPDF version crashes my acrobat reader", } @Article{Ikeda00, author = "Yoshikazu Ikeda and Shozo Tokinaga", title = "Approximation of Chaotic Dynamics by Using Smaller Number of Data Based upon the Genetic Programming and Its Applications", journal = "IEICE Transactions on fundamentals of electronics, communications and computer sciences", volume = "E83A", number = "8", pages = "1599--1607", year = "2000", keywords = "genetic algorithms, genetic programming, nonlinear dynamics, system identification, Nonlinear Signal Processing, chaotic dynamics, economics,identification,prediction", organisation = "The Institute of Electronics, Information and Communication Engineers. JAPAN", publisher = "Oxford University Press", ISSN = "0916-8524", ISSN = "0916-8508", URL = "http://search.ieice.org/bin/summary.php?id=e83-a_8_1599&category=A&year=2000&lang=E&abst=", URL = "http://search.ieice.org/bin/summary.php?id=e83-a_8_1599", URL = "https://ci.nii.ac.jp/naid/10008989573/en/", broken = "http://www.ee.psu.ac.th/ieice/2000/pdf/e83-a_8_1599.pdf", abstract = "This paper deals with the identification of system equation of the chaotic dynamics by using smaller number of data based upon the genetic programming (GP). The problem to estimate the system equation from the chaotic data is important to analyze the structure of dynamics in the fields such as the business and economics. Especially, for the prediction of chaotic dynamics, if the number of data is restricted, we can not use conventional numerical method such as the linear-reconstruction of attractors and the prediction by using the neural networks. In this paper we use an efficient method to identify the system equation by using the GP. In the GP, the performance (fitness) of each individual is defined as the inversion of the root mean square error of the spectrum obtained by the original and predicted time series to suppress the effect of the initial value of variables. Conventional GA (Genetic Algorithm) is combined to optimize the constants in equations and to select the primitives in the GP representation. By selecting a pair of individuals having higher fitness, the crossover operation is applied to generate new individuals. The crossover operation used here means the replacement of a part of tree in individual A by a part of tree in individual B. To avoid the meaningless genetic operation, the validity of prefix representation of the subtree to be embedded to the other tree is probed by using the stack count. These newly generated individuals replace old individuals with lower fitness. The mutation operation is also used to avoid the convergence to the local minimum. In the simulation study, the identification method is applied at first to the well known chaotic dynamics such as the Logistic map and the Henon map. Then, the method is applied to the identification of the chaotic data of various time series by using one dimensional and higher dimensional system. The result shows better prediction than conventional ones in cases where the number of data is small.", } @Article{journals/ieicet/IkedaT07, author = "Yoshikazu Ikeda and Shozo Tokinaga", title = "Analysis of Price Changes in Artificial Double Auction Markets Consisting of Multi-Agents Using Genetic Programming for Learning and Its Applications", journal = "IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences", year = "2007", volume = "90-A", number = "10", pages = "2203--2211", keywords = "genetic algorithms, genetic programming, artificial double auction market, multi-agents, electricity market, control of chaos", ISSN = "0916-8508", DOI = "doi:10.1093/ietfec/e90-a.10.2203", abstract = "In this paper, we show the analysis of price changes in artificial double auction markets consisting of multi-agents who learn from past experiences based on the Genetic Programming (GP) and its applications. For simplicity, we focus on the double auction in an electricity market. Agents in the market are allowed to buy or sell items (electricity) depending on the prediction of situations. Each agent has a pool of individuals (decision functions) represented in tree structures to decide bid price by using the past result of auctions. A fitness of each individual is defined by using successful bids and a capacity usage of production units for a production of items, and agents improve their individuals based on the GP to get higher return in coming auctions. In simulation studies, changes of bid prices and returns of bidders are discussed depending on demand curves of customers and the weight between an average profit obtained by successful bids and the capacity usage rate of production units. The validation of simulation studies is examined by comparing results with classical models and price changes in real double auction markets. Since bid prices bear relatively large changes, we apply an approximate method for a control by forcing agents stabilize the changes in bid prices. As a result, we see the stabilization scheme of bid prices in double auction markets is not realistic, then it is concluded that the market contains substantial instability.", bibdate = "2008-01-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ieicet/ieicet90a.html#IkedaT07", } @Article{journals/ieicet/IkedaT07a, author = "Yoshikazu Ikeda and Shozo Tokinaga", title = "Multi-Fractality Analysis of Time Series in Artificial Stock Market Generated by Multi-Agent Systems Based on the Genetic Programming and Its Applications", journal = "IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences", year = "2007", volume = "90-A", number = "10", pages = "2212--2222", keywords = "genetic algorithms, genetic programming, multi-fractal, artificial stock market, multi-agent-based modeling", ISSN = "0916-8508", DOI = "doi:10.1093/ietfec/e90-a.10.2212", abstract = "There are several methods for generating multi-fractal time series, but the origin of the multi-fractality is not discussed so far. This paper deals with the multi-fractality analysis of time series in an artificial stock market generated by multi-agent systems based on the Genetic Programming (GP) and its applications to feature extractions. Cognitive behaviors of agents are modeled by using the GP to introduce the co-evolutionary (social) learning as well as the individual learning. We assume five types of agents, in which a part of the agents prefer forecast equations or forecast rules to support their decision making, and another type of the agents select decisions at random like a speculator. The agents using forecast equations and rules usually use their own knowledge base, but some of them use their public (common) knowledge base to improve trading decisions. For checking the multi-fractality we use an extended method based on the continuous time wavelet transform. Then, it is shown that the time series of the artificial stock price reveals as a multi-fractal signal. We mainly focus on the proportion of the agents of each type. To examine the role of agents of each type, we classify six cases by changing the composition of agents of types. As a result, in several cases we find strict multi-fractality in artificial stock prices, and we see the relationship between the realizability (reproducibility) of multi-fractality and the system parameters. By applying a prediction method for mono-fractal time series as counterparts, features of the multi-fractal time series are extracted. As a result, we examine and find the origin of multi-fractal processes in artificial stock prices.", bibdate = "2008-01-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ieicet/ieicet90a.html#IkedaT07a", } @InProceedings{PDPTA96b, author = "I. M. Ikram", title = "An occam Library for Genetic Programming on Transputer Networks", booktitle = "Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications", year = "1996", editor = "Hamid R. Arabnia", pages = "1186--1189", address = "Sunnyvale, California", month = "9-11 " # aug, publisher = "CSREA", keywords = "genetic algorithms, genetic programming, occam, Transputers", ISBN = "0-9648666-4-1", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "This paper describes the contents of a library of occam procedures used to implement parallel versions of the Genetic Programming (GP) machine learning paradigm. GP attempts to evolve solutions to machine learning problems, in the form of trees encoding programs or expressions. As occam lacks recursion and both higher order functions and function pointers, the implementation of a generic tree evaluation procedure for trees containing arbitrary functions is not trivial. We present a concurrent algorithm used to alleviate this problem.", notes = "http://www.cs.cmu.edu/~scandal/conf/pro-PDPTA-960809.txt Ismail Ikram broken Sep 2018 http://cs.ru.ac.za/homes/g93i0527/", } @InProceedings{ilakovac:1996:GANNrsvp, author = "Tin Ilakovac and Zeljka Perkovic and Strahil Ristov", title = "The Use of Genetic Algorithms in the Optimization of Competitive Neural Networks which Resolve the Stuck Vectors Problem", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "Genetic Algorithms", pages = "499", address = "Stanford University, CA, USA", publisher = "MIT Press", size = "1 page", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap82.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96 GA paper", } @InProceedings{Ilampari:2024:ICSCSA, author = "M. Ilampari and M. Amina Begum and N. Mahesh Babu and S. Gopi and M. Mythreyee and Shanmugavel Deivasigamani", title = "Enhancing Lifetime in Wireless Sensor Networks through Image Processing-Based Clustering with Genetic Algorithm Routing", booktitle = "2024 4th International Conference on Soft Computing for Security Applications (ICSCSA)", year = "2024", pages = "248--255", month = sep, keywords = "genetic algorithms, genetic programming, Wireless sensor networks, Protocols, Image processing, Disaster management, Routing, Energy efficiency, Environmental monitoring, Security, Reliability, WSN, Clustering, Network Longevity", DOI = "doi:10.1109/ICSCSA64454.2024.00046", abstract = "Wireless Sensor Networks (WSNs) are a very important technology widely used in diverse application domains of environmental monitoring, disaster management, health care, industrial automation etc. These are networks in which spatially distributed sensor nodes monitor physical or environmental conditions and communications data are processed through a base station. Here, we introduced a novel method for enhancing the efficiency and lifetime of WSNs by using the WSN to process images data and genetic programming (GA) to find the best route. A system model was developed to balance a network consisting of homogenous sensor nodes placed randomly in a particular geographical area, representing realistic scenarios. Each sensor node has limited energy resources, hence the operation of the network was partitioned into clustering phase and routing phase to optimal use of energy. To improve both the accuracy and efficiency of clustering, the genetic algorithm then identified the optimal path through these clusters using the best combination of pivot nodes for transmission. The proposed method applies image processing techniques to process sensor data, preprocesses sensor data, and then improves the classification process, clustering results, and the genetic algorithm obtains the more energy-efficient paths. Our extensive evaluation results prove substantial optimisations in network performance, energy efficiency and lifetime over known protocols. This method is expected to be effective in such applications as environmental monitoring, disaster management, and other applications that will use reliable and sustainable WSNs.", notes = "Also known as \cite{10917996}", } @InProceedings{iles:2002:RDS, author = "Michael Iles and Dwight Deugo", title = "A search for routing strategies in a peer-to-peer network using genetic programming", booktitle = "Proceedings 21st IEEE Symposium on Reliable Distributed Systems", year = "2002", pages = "341--346", month = "13-16 " # oct, keywords = "genetic algorithms, genetic programming, computer networks, discrete event simulation, learning (artificial intelligence), protocols, telecommunication network routing, Gnutella protocol, machine learning techniques, resource location optimization, routing strategies, simulated peer-to-peer network, traffic flow scenarios", ISSN = "1060-9857", DOI = "doi:10.1109/RELDIS.2002.1180207", abstract = "Results taken from a simulated peer-to-peer network are described, in which genetic programming is used to evolve routing strategies that optimise resource location in various traffic flow scenarios. In all cases the evolved strategies result in more numerous resource locations than a pure, non-adaptive peer-to-peer protocol such as the Gnutella protocol. The resulting evolved strategies are described, and empirical validation of the Gnutella protocol is given via both its creation through machine-learning techniques, and through the analysis of real-world constants used in the protocol.", notes = "Inspec Accession Number: 7516795. Carleton Univ., Ottawa, Ont., Canada", } @Article{Ilgin2010563, title = "Environmentally conscious manufacturing and product recovery (ECMPRO): A review of the state of the art", journal = "Journal of Environmental Management", volume = "91", number = "3", pages = "563--591", year = "2010", ISSN = "0301-4797", DOI = "doi:10.1016/j.jenvman.2009.09.037", URL = "http://www.sciencedirect.com/science/article/B6WJ7-4XHC6JT-5/2/d21573d2beec024e5b27fd2fdb11b653", author = "Mehmet Ali Ilgin and Surendra M. Gupta", keywords = "genetic algorithms, genetic programming, Closed-loop supply chains, Disassembly, Environmentally conscious manufacturing, Environmentally conscious product design, Product recovery, Remanufacturing, Reverse logistics", abstract = "Gungor and Gupta [1999, Issues in environmentally conscious manufacturing and product recovery: a survey. Computers and Industrial Engineering, 36(4), 811-853] presented an important review of the development of research in Environmentally Conscious Manufacturing and Product Recovery (ECMPRO) and provided a state of the art survey of published work. However, that survey covered most papers published through 1998. Since then, a lot of activity has taken place in EMCPRO and several areas have become richer. Many new areas also have emerged. In this paper we primarily discuss the evolution of ECMPRO that has taken place in the last decade and discuss the new areas that have come into focus during this time. After presenting some background information, the paper systematically investigates the literature by classifying over 540 published references into four major categories, viz., environmentally conscious product design, reverse and closed-loop supply chains, remanufacturing, and disassembly. Finally, we conclude by summarising the evolution of ECMPRO over the past decade together with the avenues for future research.", notes = "survey", } @PhdThesis{ilich:2000:thesis, author = "Nesa Ilich", title = "A Strongly Feasible Evolution Program for non-linear optimization of Network Flows", school = "Department of Civil and Geological Sciences, University of Manitoba", year = "2000", address = "Winnipeg, Canada", month = oct, email = "NIlich@mail.com", keywords = "genetic algorithms, genetic programming, Evolution Programs, Network Flows, Non-Linear Constraints", URL = "http://mspace.lib.umanitoba.ca/bitstream/1993/1759/1/NQ57510.pdf", size = "163 pages", abstract = "This thesis describes the main features of a Strongly Feasible Evolution Program (SFEP) for solving network flow programs that can be non-linear both in the constraints and in the objective function. The approach is a hybrid of a network flow algorithm and an evolution program. Network flow theory is used to help conduct the search exclusively within the feasible region, while progress towards optimal points in the search space is achieved using evolution programming mechanisms such as recombination and mutation. The solution procedure is based on a recombination operator in which all parents in a small mating pool have equal chance of contributing their genetic material to an offspring. When an offspring is created with better fitness value than that of the worst parent, the worst parent is discarded from the mating pool while the offspring is placed in it. The main contributions are in the massive parallel initialization procedure which creates only feasible solutions with simple heuristic rules that increase chances of creating solutions with good fitness values for the initial mating pool, and the gene therapy procedure which fixes {"}defective genes{"} ensuring that the offspring resulting from recombination is always feasible. Both procedures use the properties of network flows. Tests were conducted on a number of previously published transportation problems with 49 and 100 decision variables, and on two problems involving water resources networks with complex non-linear constraints with up to 1500 variables. Convergence to equal or better solutions was achieved with often less than one tenth of the previous computational efforts.", notes = "Bighorn/Brazeau hydro power. Brantas river basin in east Java. ", } @Article{Ilie:2017:gmd, title = "Reverse engineering model structures for soil and ecosystem respiration: the potential of gene expression programming", author = "Iulia Ilie and Peter Dittrich and Nuno Carvalhais and Martin Jung and Andreas Heinemeyer and Micro Migliavacca and James I. L. Morison and Sebastian Sippel and Jens-Arne Subke and Matthew Wilkinson and Miguel D. Mahecha", journal = "Geoscientific Model Development", year = "2017", volume = "10", pages = "3519--3545", month = sep # "~25", keywords = "genetic algorithms, genetic programming, gene expression programming", ISSN = "1991-959X", bibsource = "OAI-PMH server at eprints.whiterose.ac.uk", format = "text", identifier = "Ilie, Iulia, Dittrich, Peter, Carvalhais, Nuno et al. (8 more authors) (2017) Reverse engineering model structures for soil and ecosystem respiration: the potential of gene expression programming. Geoscientific Model Development. gmd-2016-242. ISSN 1991-959X", oai = "oai:eprints.whiterose.ac.uk:120841", type = "PeerReviewed", URL = "http://eprints.whiterose.ac.uk/120841/1/GMD_Ilie_et_al_2016_finalAccepted.pdf", URL = "http://eprints.whiterose.ac.uk/120841/", DOI = "doi:10.5194/gmd-2016-242", size = "27 pages", abstract = "Accurate model representation of land-atmosphere carbon fluxes is essential for climate projections. However, the exact responses of carbon cycle processes to climatic drivers often remain uncertain. Presently, knowledge derived from experiments, complemented with a steadily evolving body of mechanistic theory provides the main basis for developing such models. The strongly increasing availability of measurements may facilitate new ways of identifying suitable model structures using machine learning. Here, we explore the potential of gene expression programming (GEP) to derive relevant model formulations based solely on the signals present in data by automatically applying various mathematical transformations to potential predictors and repeatedly evolving the resulting model structures. In contrast to most other machine learning regression techniques, the GEP approach generates readable models that allow for prediction and possibly for interpretation. Our study is based on two cases: artificially generated data and real observations. Simulations based on artificial data show that GEP is successful in identifying prescribed functions with the prediction capacity of the models comparable to four state-of-the-art machine learning methods (Random Forests, Support Vector Machines, Artificial Neural Networks, and Kernel Ridge Regressions). Based on real observations we explore the responses of the different components of terrestrial respiration at an oak forest in south-east England. We find that the GEP retrieved models are often better in prediction than some established respiration models. Based on their structures, we find previously unconsidered exponential dependencies of respiration on seasonal ecosystem carbon assimilation and water dynamics. We noticed that the GEP models are only partly portable across respiration components; the identification of a general terrestrial respiration model possibly prevented by equifinality issues. Overall, GEP is a promising tool for uncovering new model structures for terrestrial ecology in the data rich era, complementing more traditional modelling approaches.", notes = "also known as \cite{oai:eprints.whiterose.ac.uk:120841}", } @PhdThesis{dissIuliaIlie, author = "Iulia Ilie", title = "{CMAGEP}: a new method for automatic model discovery from data and its application to terrestrial ecosystem carbon exchange fluxes", school = "Friedrich-Schiller-Universitaet, Jena", year = "2019", address = "Germany", month = "6 " # sep, keywords = "genetic algorithms, genetic programming, gene expression programming, Equifinality, CMA-ES, CMAGEP, methane transport in the arctic, fluxnet", URL = "https://nbn-resolving.org/urn:nbn:de:gbv:27-dbt-20191108-110917-008", URL = "https://www.db-thueringen.de/receive/dbt_mods_00039800", URL = "https://www.db-thueringen.de/servlets/MCRFileNodeServlet/dbt_derivate_00045705/dissIuliaIlie.pdf", URL = "http://uri.gbv.de/document/gvk:ppn:1681505169", DOI = "doi:10.22032/dbt.39800", ISSN = "00045705", bibsource = "OAI-PMH server at www.db-thueringen.de", contributor = "Peter Dittrich and Miguel Mahecha", format = "193 Seiten", language = "english", oai = "oai:www.db-thueringen.de:dbt_mods_00039800", rights = "https://creativecommons.org/licenses/by-nc/4.0/>; info:eu-repo/semantics/openAccess", size = "193 pages", abstract = "Accurately representing and understanding the dynamics driving the global carbon cycle are of strong significance for the study of the Earth System as well as for reliable climate change projections. Model development in the biogeochemistry field traditionally relies on empirical studies and on already established theoretical foundations. With increased data availability, model development in the field of biogeochemistry has started to open more to the use of machine learning approaches for helping to validate and calibrate the existing model formulations. However, the validity of the studied model structures are not often debated. This thesis introduces a novel framework for modeling biogeochemistry fluxes by using symbolic regression approaches to automatically generate interpretable mathematical models. The thesis starts by first illustrating the potential of gene expression programming (GEP) to discover interesting models as mathematical formulas based entirely on real time series data measured at a single monitoring site. The GEP discovered models perform better predictions than already established models in the ecology community. Further, the GEP models have the advantage of being represented as mathematical formulas that can be used similarly to natural laws from the ecology community. Still, the complexity of GEP models makes it difficult to really interpret the described model dynamics. To tackle model complexity GEP is extended with CMA-ES for performing local parameter optimisations in the evolution process. The resulting algorithm is CMAGEP, a novel system that is a GEP and ES hybrid approach capable of delivering more accurate and more compact solutions compared to standard GEP. Generating compact solutions means that CMAGEP discovers mathematical models that can be more easily interpretable, and that can be more easily combined with already established knowledge. CMAGEP is successfully used for modelling various carbon fluxes; first it helps discover non-linear dynamics in the carbon cycle at an Arctic site and produce a very compact solution, and secondly, it reveals interesting and relevant patterns in the underlying processes determining the global terrestrial carbon exchanges. Considering the important results shown in this extensive interdisciplinary study it becomes clear that by introducing the new CMAGEP system, an important contribution was made to the field of symbolic regression by giving deserved attention to the often neglected aspect of interpretability. Furthermore, the application of CMAGEP in a symbolic regression framework to model terrestrial carbon fluxes helped build novel knowledge in the ecology field, giving this approach a significant potential for other future applications.", zusammenfassung = "Die Dynamik, die den globalen Kohlenstoffkreislauf antreibt, genau darzustellen und zu verstehen, ist von grosser Bedeutung fur das Studium des Erdsystems und fur zuverlassige Prognosen zum Klimawandel. Die Modellentwicklung in der Biogeochemie beruht traditionell auf empirischen Studien und auf bereits etablierten theoretischen Grundlagen. Mit zunehmender Datenverfugbarkeit hat die Modellentwicklung auf dem Gebiet der Biogeochemie begonnen, sich mehr fur den Einsatz von Methoden des maschinellen Lernens zu offnen, um die bestehenden Modellformulierungen zu validieren und zu kalibrieren. Die Validitat der untersuchten Modellstrukturen wird jedoch nicht oft diskutiert. Diese Arbeit stellt einen neuartigen Rahmen fur die Modellierung von biogeochemischen Flussen vor, indem mithilfe von symbolischen Regressions ansatzen interpretierbare mathematische Modelle automatisch generiert werden. Die Arbeit beginnt damit, zunachst das Potenzial der Gene Expression Programming (GEP) aufzuzeigen, um interessante Modelle als mathematische Formeln automatisch aus Echtzeit-Zeitseriendaten abzuleiten, die an nur einem Ort gemessen worden sind. Das GEP hat dabei Modelle generiert, die eine bessere Performanz als bereits etablierte Modelle der Okologie-Community aufweisen. Ferner haben die erzeugten Modelle den Vorteil, dass sie als mathematische Formeln reprasentiert werden, die den Formeln der Okologie-Community ahnlich sind. Allerdings macht die Komplexitat der GEP-Modelle es schwierig, die beschriebene Modelldynamik zu interpretieren. Im nachsten Schritt der Arbeit wurde GEP um eine lokale Parameteroptimierung mittels der CMA-ES erweitert. Das resultierende CMAGEP System ist ein GEP- und ES-Hybridansatz, der Losungen liefert, die im Vergleich zu Standard GEP Kohlenstoffflusse sowohl genauer als auch auch kompakter beschreiben. Die Generierung von kompakten Losungen bedeutet, dass mathematische Modelle entdeckt werden, die leichter interpretiert werden konnen und die sich einfacher mit bereits etabliertem Wissen kombinieren lassen. Im Anschluss wird CMAGEP erfolgreich zur Modellierung von unterschiedlichen Kohlenstoffflussen verwendet; Erstens hilft es, nichtlineare Dynamiken im Kohlenstoffkreislauf an einem arktischen Standort zu entdecken und eine sehr kompakte Losung zu erzeugen, und zweitens offenbart es interessante und relevante Muster in den zugrunde liegenden Prozessen, die den globalen terrestrischen Kohlenstoffaustausch bestimmen. Betrachtet man die wichtigen Ergebnisse dieser umfangreichen interdisziplinaren Studie, so wird deutlich, dass mit der Einfuhrung des neuen CMAGEP Systems ein wichtiger Beitrag zum Bereich der symbolischen Regression mit dem oft vernachlassigten aber bedeutsamen Aspekt der Interpretierbarkeit geleistet wurde. Daruber hinaus trug die Anwendung von CMAGEP zur Modellierung terrestrischer Kohlenstoffflusse dazu bei, neues Wissen auf dem Gebiet der Okologie aufzubauen, was diesem Ansatz ein signifikantes Potenzial fur andere zukunftige Anwendungen verleiht.", notes = "Supervisor: Miguel Mahecha", } @InProceedings{Illanes:2021:GI, author = "Vicente Illanes and Alexandre Bergel", title = "Generating Objected-Oriented Source Code Using Genetic Programming", booktitle = "GI @ ICSE 2021", year = "2021", month = "30 " # may, editor = "Justyna Petke and Bobby R. Bruce and Yu Huang and Aymeric Blot and Westley Weimer and W. B. Langdon", publisher = "IEEE", address = "internet", pages = "45--50", keywords = "genetic algorithms, genetic programming, genetic improvement, OOP, Pharo, Spy profiling framework, AST", isbn13 = "978-1-6654-4466-8/21", bibdate = "2021-08-03", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/gi-ws/gi-ws2021.html#IllanesB21 ", URL = "https://geneticimprovementofsoftware.com/paper_pdfs/gi2021icse/illanes_gi-icse_2021.pdf", URL = "http://bergel.eu/MyPapers/Illa21a-GP.pdf", video_url = "https://www.youtube.com/watch?v=PMPm-jpdg6U&list=PLI8fiFpB7BoKDaxvS7SQp0iA7fN7rrvDD&index=19", video_url = "https://www.youtube.com/watch?v=0KOJqrYrgCc&list=PLI8fiFpB7BoKDaxvS7SQp0iA7fN7rrvDD&index=20", video_url = "https://www.youtube.com/watch?v=oOxhBdc8FT4&list=PLXTjhGKkSnI-se7uQneCX-pEDiDrQ7TIS&index=5", DOI = "doi:10.1109/GI52543.2021.00019", size = "6 pages", abstract = "Using machine learning to generate source code is an active and highly important research area. In particular,it has been shown that genetic programming (GP) efficiently contributes to software repair. However, most of the published advances on applying GP to generate source code are limited to the C programming language, a statically-typed procedural language. As a consequence, applying GP to object-oriented and dynamically-typed languages may represent a significant opportunity. explores the use of genetic programming to generate objected-oriented source code in a dynamically-typed setting. We found that GP is able to produce missing one-line statements with a precision of 51 percent. Our preliminary results contributes to the state of the art by indicating that GP maybe effectively employed to generate source code for dynamically-typed object-oriented applications.", notes = "ISCLab, Department of Computer Science (DCC), University of Chile video oOxhBdc8FT4 Alexandre Bergel 16:32 Discussion Chair Justyna Petke A: Alexandre Bergel + Vicente Illanes 16:35 Q: Westley Weimer 17:30 Justyna Petke, Pharo, A: Pharo very simple message passing leads to small modules. Applications of Pharo and support environment good. 19:34 Q: more complex methods 21:32 Q: Bobby R. Bruce, what features should a GI friendly language have? A: Importance of names in programming context (cf. two channels in software engineering) 23:27 Q: W. B. Langdon short modules helping? A: yes, small methods in Pharo help. Also known as \cite{conf/gi-ws/IllanesB21} part of \cite{Petke:2021:ICSEworkshop} http://geneticimprovementofsoftware.com/events/icse2021.html", } @InProceedings{Illanes:2024:BIP, author = "Ana Illanes and Lizbeth Rodriguez-Rolon and Stephanie Esquivel and Luis Fernando Rivera and Ioshua Pena and Claudia N. Sanchez", title = "Generation of Hyperspectral Indices for Non-Invasive Crop Property Analysis Through Genetic Programming", booktitle = "2024 IEEE 6th International Conference on BioInspired Processing (BIP)", year = "2024", month = dec, keywords = "genetic algorithms, genetic programming, Correlation coefficient, Satellites, Correlation, Codes, Crops, Vegetation mapping, Food products, Proposals, Hyperspectral imaging, Hyperspectral Indices, Crop Internal Properties", DOI = "doi:10.1109/BIP63158.2024.10885374", abstract = "Nowadays, the use of hyperspectral images in the agroindustry is growing because it represents a non-invasive methodology for identifying the internal properties of food products. Hyperspectral indices are common in satellite and aerial images for identifying vegetation, water, etcetera. This document proposes a methodology for generating hyperspectral indices on a minor scale to identify dry matter in crops based on Genetic Programming. We use the correlation coefficients Pearson and Kendall as fitness functions. We use the datasets of four crops: apple, broccoli, leek, and mushroom. The results of the experiments confirm that our proposal can generate simple and understanding hyperspectral indices that correlate with dry matter by combining only two or four hyperspectral bands. Our code is available online.", notes = "Also known as \cite{10885374} Massachusetts Institute of Technology, Cambridge, USA", } @InProceedings{Illetskova:2017:ieeeSSCI, author = "Marketa Illetskova and Alex R. Bertels and Joshua M. Tuggle and Adam Harter and Samuel Richter and Daniel R. Tauritz and Samuel Mulder and Denis Bueno and Michelle Leger and William M. Siever", booktitle = "2017 IEEE Symposium Series on Computational Intelligence (SSCI)", title = "Improving performance of {CDCL SAT} solvers by automated design of variable selection heuristics", year = "2017", address = "Honolulu, Hawaii, U.S.A.", month = nov # " 27-" # dec # " 1", keywords = "genetic algorithms, genetic programming, Hyper-heuristics, ADSSEC", DOI = "doi:10.1109/SSCI.2017.8280953", size = "8 pages", abstract = "Many real-world engineering and science problems can be mapped to Boolean satisfiability problems (SAT). CDCL SAT solvers are among the most efficient solvers. Previous work showed that instances derived from a particular problem class exhibit a unique underlying structure which impacts the effectiveness of a solver's variable selection scheme. Thus, customizing the variable scoring heuristic of a solver to a particular problem class can significantly enhance the solver's performance; however, manually performing such customization is very labour intensive. This paper presents a system for automating the design of variable scoring heuristics for CDCL solvers, making it feasible to tailor solvers to arbitrary problem classes. Experimental results are provided demonstrating that this system, which evolves variable scoring heuristics using an asynchronous parallel hyper-heuristics approach employing genetic programming, has the potential to create more efficient solvers for particular problem classes.", notes = "ibm-18 cactus plot. MiniSat for the unif-k5. Also known as \cite{8280953}", } @InProceedings{Illetskova:2019:GECCOcomp, author = "Marketa Illetskova and Islam Elnabarawy and Leonardo Enzo Brito {da Silva} and Daniel R. Tauritz and Donald C. {Wunsch, II}", title = "Nested {Monte Carlo} search expression discovery for the automated design of fuzzy {ART} category choice functions", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", isbn13 = "978-1-4503-6748-6", pages = "171--172", address = "Prague, Czech Republic", DOI = "doi:10.1145/3319619.3322050", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{3322050} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @Article{ilyas:2022:Polymers, author = "Israr Ilyas and Adeel Zafar and Muhammad Talal Afzal and Muhammad Faisal Javed and Raid Alrowais and Fadi Althoey and Abdeliazim Mustafa Mohamed and Abdullah Mohamed and Nikolai Ivanovich Vatin", title = "Advanced Machine Learning Modeling Approach for Prediction of Compressive Strength of {FRP} Confined Concrete Using Multiphysics Genetic Expression Programming", journal = "Polymers", year = "2022", volume = "14", number = "9", pages = "Article No. 1789", keywords = "genetic algorithms, genetic programming, gene expression programming", ISSN = "2073-4360", URL = "https://www.mdpi.com/2073-4360/14/9/1789", DOI = "doi:10.3390/polym14091789", abstract = "The purpose of this article is to demonstrate the potential of gene expression programming (GEP) in anticipating the compressive strength of circular CFRP confined concrete columns. A new GEP model has been developed based on a credible and extensive database of 828 data points to date. Numerous analyses were carried out to evaluate and validate the presented model by comparing them with those presented previously by different researchers along with external validation comparison. In comparison to other artificial intelligence (AI) techniques, such as Artificial Neural Networks (ANN) and the adaptive neuro-fuzzy interface system (ANFIS), only GEP has the capability and robustness to provide output in the form of a simple mathematical relationship that is easy to use. The developed GEP model is also compared with linear and nonlinear regression models to evaluate the performance. Afterwards, a detailed parametric and sensitivity analysis confirms the generalised nature of the newly established model. Sensitivity analysis results indicate the performance of the model by evaluating the relative contribution of explanatory variables involved in development. Moreover, the Taylor diagram is also established to visualize how the proposed model outperformed other existing models in terms of accuracy, efficiency, and being closer to the target. Lastly, the criteria of external validation were also fulfilled by the GEP model much better than other conventional models. These findings show that the presented model effectively forecasts the confined strength of circular concrete columns significantly better than the previously established conventional regression-based models.", notes = "also known as \cite{polym14091789}", } @InProceedings{Im:2019:GECCOcomp, author = "Carl Im and Erik Hemberg", title = "On the use of context sensitive grammars in grammatical evolution for legal non-compliance detection", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", isbn13 = "978-1-4503-6748-6", pages = "371--372", address = "Prague, Czech Republic", DOI = "doi:10.1145/3319619.3322038", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, grammatical evolution", notes = "Also known as \cite{3322038} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @Article{im:2021:ND, author = "Jinwoo Im and Calogero B. Rizzo and Felipe P. J. {de Barros} and Sami F. Masri", title = "Application of genetic programming for model-free identification of nonlinear multi-physics systems", journal = "Nonlinear Dynamics", year = "2021", volume = "104", number = "2", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s11071-021-06335-0", DOI = "doi:10.1007/s11071-021-06335-0", } @InProceedings{Imada:2008:geccocomp, author = "Janine H. Imada and Brian J. Ross", title = "Using feature-based fitness evaluation in symbolic regression with added noise", year = "2008", editor = "Marc Ebner and Mike Cattolico and Jano {van Hemert} and Steven Gustafson and Laurence D. Merkle and Frank W. Moore and Clare Bates Congdon and Christopher D. Clack and Frank W. Moore and William Rand and Sevan G. Ficici and Rick Riolo and Jaume Bacardit and Ester Bernado-Mansilla and Martin V. Butz and Stephen L. Smith and Stefano Cagnoni and Mark Hauschild and Martin Pelikan and Kumara Sastry", isbn13 = "978-1-60558-131-6", booktitle = "GECCO-2008 Late-Breaking Papers", pages = "2153--2158", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p2153.pdf", DOI = "doi:10.1145/1388969.1389039", organisation = "SIGEvo", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, noisy signals, symbolic regression", size = "5 pages", abstract = "Symbolic regression is a popular genetic programming (GP) application. Typically, the fitness function for this task is based on a sum-of-errors, involving the values of the dependent variable directly calculated from the candidate expression. While this approach is extremely successful in many instances, its performance can deteriorate in the presence of noise. In this paper, a feature-based fitness function is considered, in which the fitness scores are determined by comparing the statistical features of the sequence of values, rather than the actual values themselves. The set of features used in the fitness evaluation are customized according to the target, and are drawn from a wide set of features capable of characterizing a variety of behaviours. Experiments examining the performance of the feature-based and standard fitness functions are carried out for non-oscillating and oscillating targets in a GP system which introduces noise during the evaluation of candidate expressions. Results show strength in the feature-based fitness function, especially for the oscillating target.", notes = "Distributed on CD-ROM at GECCO-2008 ACM Order Number 910081. Also known as \cite{1389039}", } @MastersThesis{Imada:mastersthesis, author = "Janine Imada", title = "Evolutionary synthesis of stochastic gene network models using feature-based search spaces", school = "Department of Computer Science, Brock University", year = "2009", type = "M.Sc. Computer Science", address = "St. Catharines, Ontario, Canada", month = "28 " # jan, keywords = "genetic algorithms, genetic programming", URL = "http://dr.library.brocku.ca/bitstream/handle/10464/2853/Brock_Imada_Janine_2009.pdf", URL = "http://hdl.handle.net/10464/2853", size = "138 pages", abstract = "A feature-based fitness function is applied in a genetic programming system to synthesise stochastic gene regulatory network models whose behaviour is defined by a time course of protein expression levels. Typically, when targeting time series data, the fitness function is based on a sum-of-errors involving the values of the fluctuating signal. While this approach is successful in many instances, its performance can deteriorate in the presence of noise. This thesis explores a fitness measure determined from a set of statistical features characterising the time series' sequence of values, rather than the actual values themselves. Through a series of experiments involving symbolic regression with added noise and gene regulatory network models based on the stochastic pi-calculus, it is shown to successfully target oscillating and non-oscillating signals. This practical and versatile fitness function offers an alternate approach, worthy of consideration for use in algorithms that evaluate noisy or stochastic behaviour.", notes = "cited by \cite{Ross:2011:GPEM}", } @Article{Imada:2011:NGC, author = "Janine Imada and Brian J. Ross", title = "Evolutionary Synthesis of Stochastic Gene Network Models Using Feature-based Search Spaces", journal = "New Generation Computing", publisher = "Ohmsha, Ltd. and Springer", year = "2011", pages = "365--390", volume = "29", issue = "4", month = oct, keywords = "genetic algorithms, genetic programming, Stochastic, Statistical Features, Gene Regulatory Networks, Time Series", ISSN = "0288-3635", DOI = "doi:10.1007/s00354-009-0115-7", size = "26 pages", abstract = "A feature-based fitness function is applied in a genetic programming system to synthesise stochastic gene regulatory network models whose behaviour is defined by a time course of protein expression levels. Typically, when targeting time series data, the fitness function is based on a sum-of-errors involving the values of the fluctuating signal. While this approach is successful in many instances, its performance can deteriorate in the presence of noise and/or stochastic behaviour. This paper explores a fitness measure determined from a set of statistical features characterising the time series' sequence of values, rather than the actual values themselves. Through a series of experiments involving modular gene regulatory network models based on the stochastic pi-calculus, it is shown to successfully target oscillating and non-oscillating signals. This practical and versatile fitness function offers an alternate approach, worthy of consideration for use in algorithms that evaluate noisy or stochastic behaviour.", affiliation = "Brock University, 500 Glenridge Ave., St. Catharines, ON, Canada L2S 3A1", } @InProceedings{imae:2003:amcdfnsbogpjcua, author = "Joe Imae and Nobuyuki Ohtsuki and Yoshiteru Kikuchi and Tomoaki Kobayashi", title = "A minimax control design for nonlinear systems based on genetic programming: Jung's collective unconscious approach", booktitle = "Proceedings of the 2003 Congress on Evolutionary Computation CEC2003", editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon", pages = "1702--1707", year = "2003", publisher = "IEEE Press", address = "Canberra", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "8-12 " # dec, organisation = "IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", keywords = "genetic algorithms, genetic programming, Control design, Control systems, Design methodology, Differential equations, Minimax techniques, Nonlinear control systems, Nonlinear systems, Optimal control, Partial differential equations, minimax techniques, nonlinear control systems, Jung collective unconscious, difficulty-free design, minimax control problem, minimax controller design, minimisation process, nonlinear systems", ISBN = "0-7803-7804-0", DOI = "doi:10.1109/CEC.2003.1299878", abstract = "When it comes to the minimax controller design, it would be extremely difficult to obtain such controllers in the nonlinear situations. One of the reasons is that the minimax controller should be robust against any kind of disturbances in the nonlinear situations. In this paper, we propose a difficulty-free design method of minimax control problems. First, based on the genetic programming and Jung's collective unconscious, this paper presents a very simple design technique to solve the minimax control problems, where the minimax controller may be constructed only paying attention to the minimisation process. It would be surprising that the maximization process is not needed in the construction of minimax controllers. Then, some simulations are given to demonstrate the usefulness of the proposed design technique with the identification problem, and minimax control problems.", notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.", } @InProceedings{imae:2003:ancsdbohevadgpa, author = "Joe Imae and Yoshiteru Kikuchi and Nobuyuki Ohtsuki and Tomoaki Kobayashi", title = "A nonlinear control system design based on {HJB/HJI/FBI} equations via a differential genetic programming approach", booktitle = "Proceedings of the 2003 Congress on Evolutionary Computation CEC2003", editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon", pages = "763--769", year = "2003", publisher = "IEEE Press", address = "Canberra", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "8-12 " # dec, organisation = "IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", keywords = "genetic algorithms, genetic programming, Automatic control, Control systems, Design methodology, Differential equations, Nonlinear control systems, Nonlinear equations, Nonlinear systems, Optimal control, Robust control, control system synthesis, differentiation, nonlinear control systems, optimal control, robust control, Francis-Byrnes-Isidori equations, Hamilton-Jacobi-Bellman equations, Hamilton-Jacobi-Isaacs equations, differential genetic programming, nonlinear control system design, optimal controllers, robust controllers", ISBN = "0-7803-7804-0", DOI = "doi:10.1109/CEC.2003.1299744", abstract = "Based on the differential genetic programming, a new design method is proposed for optimal and/or robust controllers of nonlinear systems. First we introduce a new type of the genetic programming (GP), so-called differential GP (DGP), combining GP with an automatic differentiation scheme, which could solve Hamilton-Jacobi-Bellman (HJB) / Hamilton-Jacobi-Isaacs (HJI) / Francis-Byrnes-Isidori (FBI) equations. Lastly, the effectiveness of a DGP based design method is demonstrated through some design examples of nonlinear systems.", notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.", } @InProceedings{Imae:2008:SICE, author = "Joe Imae and Yasuhiko Morita and Guisheng Zhai and Tomoaki Kobayashi", title = "An evolutionary approach to identification problems with incomplete output data", booktitle = "SICE Annual Conference", year = "2008", month = "20-22 " # aug, address = "Japan", pages = "2262--2265", keywords = "genetic algorithms, genetic programming, evolutionary algorithm, nonlinear system identification problems, identification, nonlinear control systems", DOI = "doi:10.1109/SICE.2008.4655041", abstract = "In this paper, we consider nonlinear system identification problems in the case where output data is incomplete. We propose an identification method based on an evolutionary algorithm, which is a fusion of a genetic algorithm (GA) and genetic programming (GP), and illustrate the effectiveness of the proposed method through a simulation and an experiment with a cart.", notes = "Also known as \cite{4655041}", } @InProceedings{Imae:2010:WAC, author = "Joe Imae and Yasuhiko Morita and Guisheng Zhai and Tomoaki Kobayashi", title = "A GP-based Design Method for Nonlinear Control Systems using Differential Flatness", booktitle = "World Automation Congress (WAC), 2010", year = "2010", address = "Kobe, Japan", month = "19-23 " # sep, publisher = "TSI Press", keywords = "genetic algorithms, genetic programming, GP-based design method, MIMO systems, decoupling process, differential flatness theory, nonlinear control systems, MIMO systems, control system synthesis, nonlinear control systems", isbn13 = "978-1-4244-9673-0", ISSN = "2154-4824", broken = "http://confconnect.info/confconnect/ocs/index.php?conference=wac&schedConf=WAC2010&page=paper&op=view&path[]=92", URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5665445", size = "7 pages", abstract = "In this paper, we propose a practical and systematic approach to the control design method for MIMO systems based on flatness theory. The proposed approach focuses on the emergent ability of genetic programming and the decoupling ability of Descusse and Moog's algorithm. The former could generate nonlinear functions as the flat outputs, and the latter could construct dynamic controllers through the decoupling process. Some simulations are carried out to show the effectiveness of the proposed approach.", notes = "Submarine. broken http://confconnect.info/confconnect/ocs/index.php?conference=wac&schedConf=WAC2010&page=schedConf&op=presentations Osaka Prefecture Univ., Sakai, Japan. Also known as \cite{5665445}", } @InProceedings{imai-aldeia:2024:GECCO, author = "Guilherme Seidyo {Imai Aldeia} and Fabricio Olivetti {De Franca} and William G. {La Cava}", title = "Inexact Simplification of Symbolic Regression Expressions with Locality-sensitive Hashing", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference", year = "2024", editor = "Ting Hu and Aniko Ekart and Julia Handl and Xiaodong Li and Markus Wagner and Mario Garza-Fabre and Kate Smith-Miles and Richard Allmendinger and Ying Bi and Grant Dick and Amir H Gandomi and Marcella Scoczynski Ribeiro Martins and Hirad Assimi and Nadarajen Veerapen and Yuan Sun and Mario Andres Munyoz and Ahmed Kheiri and Nguyen Su and Dhananjay Thiruvady and Andy Song and Frank Neumann and Carla Silva", pages = "896--904", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, locality sensitive hashing, simplification, symbolic regression", isbn13 = "979-8-4007-0494-9", DOI = "doi:10.1145/3638529.3654147", size = "9 pages", abstract = "Symbolic regression (SR) searches for parametric models that accurately fit a dataset, prioritizing simplicity and interpretability. Despite this secondary objective, studies point out that the models are often overly complex due to redundant operations, introns, and bloat that arise during the iterative process, and can hinder the search with repeated exploration of bloated segments. Applying a fast heuristic algebraic simplification may not fully simplify the expression and exact methods can be infeasible depending on size or complexity of the expressions. We propose a novel agnostic simplification and bloat control for SR employing an efficient memoization with locality-sensitive hashing (LHS). The idea is that expressions and their sub-expressions traversed during the iterative simplification process are stored in a dictionary using LHS, enabling efficient retrieval of similar structures. We iterate through the expression, replacing subtrees with others of same hash if they result in a smaller expression. Empirical results shows that applying this simplification during evolution performs equal or better than without simplification in minimization of error, significantly reducing the number of nonlinear functions. This technique can learn simplification rules that work in general or for a specific problem, and improves convergence while reducing model complexity.", notes = "GECCO-2024 GP A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{imai-aldeia:2024:GECCO2, author = "Guilherme Seidyo {Imai Aldeia} and Fabricio Olivetti {De Franca} and William G. {La Cava}", title = "Minimum variance threshold for epsilon-lexicase selection", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference", year = "2024", editor = "Ting Hu and Aniko Ekart and Julia Handl and Xiaodong Li and Markus Wagner and Mario Garza-Fabre and Kate Smith-Miles and Richard Allmendinger and Ying Bi and Grant Dick and Amir H Gandomi and Marcella Scoczynski Ribeiro Martins and Hirad Assimi and Nadarajen Veerapen and Yuan Sun and Mario Andres Munyoz and Ahmed Kheiri and Nguyen Su and Dhananjay Thiruvady and Andy Song and Frank Neumann and Carla Silva", pages = "905--913", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, lexicase selection, minimum variance", isbn13 = "979-8-4007-0494-9", DOI = "doi:10.1145/3638529.3654149", size = "9 pages", abstract = "Parent selection plays an important role in evolutionary algorithms, and many strategies exist to select the parent pool before breeding the next generation. Methods often rely on average error over the entire dataset as a criterion to select the parents, which can lead to an information loss due to aggregation of all test cases. Under epsilon-lexicase selection, the population goes to a selection pool that is iteratively reduced by using each test individually, discarding individuals with an error higher than the elite error plus the median absolute deviation (MAD) of errors for that particular test case. In an attempt to better capture differences in performance of individuals on cases, we propose a new criteria that splits errors into two partitions that minimize the total variance within partitions. Our method was embedded into the FEAT symbolic regression algorithm, and evaluated with the SRBench framework, containing 122 black-box synthetic and real-world regression problems. The empirical results show a better performance of our approach compared to traditional epsilon-lexicase selection in the real-world datasets while showing equivalent performance on the synthetic dataset.", notes = "GECCO-2024 GP A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{Imamura:2000:eh, author = "Kosuke Imamura and James A. Foster and Axel W. Krings", title = "The Test Vector Problem and Limitations to Evolving Digital Circuits", booktitle = "The Second NASA/DoD workshop on Evolvable Hardware", year = "2000", editor = "Jason Lohn and Adrian Stoica and Didier Keymeulen", pages = "75--80", address = "Palo Alto, California", publisher_address = "1730 Massachusetts Avenue, N.W., Washington, DC, 20036-1992, USA", month = "13-15 " # jul, organisation = "Jet Propulsion Laboratory, California Institute of Technology", publisher = "IEEE Computer Society", keywords = "genetic algorithms, logic design, logic testing, VLSI, evolutionary techniques, evolving digital circuits, test vector generation problem, test vector problem, truth table", ISBN = "0-7695-0762-X", abstract = "Evolvable Hardware (EHW) has been proposed as a new technique to design complex systems. Often, complex systems turn out to be very difficult to evolve. The problem is that a general strategy is too difficult for the evolution process to discover directly. This paper proposes a new approach that performs incremental evolution in two directions: from complex system to sub-systems and from subsystems back to complex system. In this approach, incremental evolution gradually decomposes a complex problem into some sub-tasks. In a second step, we gradually make the tasks more challenging and general. Our approach automatically discovers the sub-tasks, their sequence as well as circuit layout dimensions. Our method is tested in a digital circuit domain and compared to direct evolution. We show that our bidirectional incremental approach can handle more complex, harder tasks and evolve them more effectively, then direct evolution.", notes = "EH2000 http://ic.arc.nasa.gov/projects/eh2000/", } @TechReport{imamura:2001:geccoTR, author = "Kosuke Imamura and James A. Foster", title = "Fault-Tolerant Computing with N-Version Genetic Programming", institution = "Initiative for Bioinformatics and Evolutionary STudies (IBEST), Computer Science Department, University of Idaho", year = "2001", address = "Moscow, ID 83844-1010, USA", note = "Submitted to Genetic and Evolutionary Computing Conference (GECCO 2001)", keywords = "genetic algorithms, genetic programming", URL = "http://people.ibest.uidaho.edu/~foster/Papers/7386.pdf", abstract = "Software reliability is an increasingly important issue today. Yet, reliability of genetic programming has not been studied fully. A genetic program to be deployed is often the one which performs the best on sample tests. One of the techniques to improve reliability is N-version programming. Our question is whether N-version genetic programming (NVGP) improves reliability over a single version. We applied NVGP to a path prediction problem, and compared the performance with a single version. Statistics from the experiment suggests that NVGP is a viable method to increase reliability.", notes = "see \cite{imamura:2001:gecco}", size = "7 pages", } @InProceedings{imamura:2001:gecco, title = "Fault-Tolerant Computing with N-Version Genetic Programming", author = "Kosuke Imamura and James A. Foster", pages = "178", year = "2001", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)", editor = "Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon and Edmund Burke", address = "San Francisco, California, USA", publisher_address = "San Francisco, CA 94104, USA", month = "7-11 " # jul, keywords = "genetic algorithms, genetic programming: Poster, Fault-Tolerant N-Version Genetic Programming", ISBN = "1-55860-774-9", URL = "http://gpbib.cs.ucl.ac.uk/gecco2001/d02.pdf", notes = "See \cite{imamura:2001:geccoTR} GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of \cite{spector:2001:GECCO}", } @InProceedings{imamura:2002:EuroGP, title = "{$N$}-version Genetic Programming via Fault Masking", author = "Kosuke Imamura and Robert B. Heckendorn and Terence Soule and James A. Foster", editor = "James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi", booktitle = "Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002", volume = "2278", series = "LNCS", pages = "172--181", publisher = "Springer-Verlag", address = "Kinsale, Ireland", publisher_address = "Berlin", month = "3-5 " # apr, year = "2002", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-43378-3", DOI = "doi:10.1007/3-540-45984-7_17", abstract = "We introduce a new method, N-Version Genetic Programming (NVGP), for building fault tolerant software by building an ensemble of automatically generated modules in such a way as to maximize their collective fault masking ability. The ensemble itself is an example of n-version modular redundancy for fault tolerance, where the output of the ensemble is the most frequent output of n independent modules. By maximising collective fault masking, NVGP approaches the fault tolerance expected from n version modular redundancy with independent faults in component modules. The ensemble comprises individual modules from a large pool generated with genetic programming, using operators that increase the diversity of the population. Our experimental test problem classified promoter regions in Escherichia coli DNA sequences. For this problem, NVGP reduced the number and variance of errors over single modules produced by GP, with statistical significance.", notes = "EuroGP'2002, part of \cite{lutton:2002:GP}, UCI ML e.coli benchmark (balanced training 35 positives, 35 negatives). beowulf. 2-gram (16 possible). linear gp (MIPS like). max length 80. 4 read/write registers (memory). 5 crossover types. Inversion (!). 2 mutation operators, tournament fitness=correlation coefficient. 40 isolated islands (demes) each 100 individuals. ensemble = composition from (randomly chosen) island. ensemble is qualified if number of its errors <= number of errors expected if its components were _independent_ 14% to 58% improvement in error rate for ensemble (of 30) compared to single GP (pop 100). ", } @InProceedings{imamura:2002:gecco, author = "Kosuke Imamura and Robert B. Heckendorn and Terence Soule and James A. Foster", title = "Abstention Reduces Errors--decision Abstaining {N}-version Genetic Programming", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "796--803", address = "New York", publisher_address = "San Francisco, CA 94104, USA", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-878-8", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/GP169.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/GP169.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf", URL = "http://dl.acm.org/citation.cfm?id=646205.682481", acmid = "682481", abstract = "Optimal fault masking N-Version Genetic Programming (NVGP) is a technique for building fault tolerant software via ensemble of automatically generated modules in such a way as to maximise their collective fault masking ability. Decision Abstaining N-Version Genetic Programming is NVGP that abstains from decision-making, when there is no decisive vote among the modules to make a decision. A special course of action may be taken for an abstained instance. We found that decision abstention contributed to error reduction in our experimental Escherichia coli DNA promoter sequence classification problem. Though decision abstention may reduce errors, high abstention rate makes the system of little use. This paper investigates the trade-off between abstention rate and error reduction.", notes = "GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)", } @InProceedings{imamura:2002:gecco:workshop, title = "Abstention Reduces Errors - Decision Abstaining {N-version} Genetic Programming", author = "Kosuke Imamura", pages = "284--287", booktitle = "Graduate Student Workshop", editor = "Sean Luke and Conor Ryan and Una-May O'Reilly", year = "2002", month = "8 " # jul, publisher = "AAAI", address = "New York", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025", keywords = "genetic algorithms, genetic programming", notes = "Bird-of-a-feather Workshops, GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002) part of barry:2002:GECCO:workshop", } @PhdThesis{Imamura:thesis, author = "Kosuke Imamura", title = "{N}-Version Genetic Programming: A Probabilistically Optimal Ensemble Approach", school = "Department of Computer Science, University of Idaho", year = "2002", address = "Moscow, ID, USA", month = "6 " # dec, keywords = "genetic algorithms, genetic programming, genetic improvement, NVGP", broken = "https://www.uidaho.edu/engr/departments/cs/research/theses", URL = "https://alliance-uidaho.primo.exlibrisgroup.com/discovery/fulldisplay?docid=alma9971206801851&context=L&vid=01ALLIANCE_UID:UID&lang=en&search_scope=DN_and_CI&adaptor=Local%20Search%20Engine&tab=Everything&query=any,contains,Imamura&mode=advanced&pfilter=rtype,exact,dissertations,AND", URL = "http://search.proquest.com/docview/288080102", size = "96 pages", abstract = "This research provides a method to enhance accuracy and reduce performance fluctuation of programs produced by genetic programming by combining individual evolved programs into robust ensembles. More effective ensembles have fewer correlated faulty outputs. Therefore, current ensemble techniques focus on diversity pressures to reduce correlated faults among the ensemble members. However, whether or not an optimal ensemble is formed through these pressures is unknown, simply because ensemble optimality is undefined. We define the behavioural diversity of an ensemble of imperfect programs as the degree to which the ensemble failure rate deviates from what one would expect if fault occurrences were statistically independent. Given this metric, we form an ensemble by selecting individuals that exhibit this diversity from a large pool of evolved programs and combining their outputs into a single ensemble output. Classification or prediction problems benefit the most from this research. We have validated our approach by showing statistically significant improvements when applied to a DNA segment classification problem.", notes = "also known as \cite{alma9971206801851} Supervisor: James A. Foster UMI Microform 3080258 E-coli promoter recognition", } @Article{imamura:2003:GPEM, author = "Kosuke Imamura and Terence Soule and Robert B. Heckendorn and James A. Foster", title = "Behavioral Diversity and a Probabilistically Optimal {GP} Ensemble", journal = "Genetic Programming and Evolvable Machines", year = "2003", volume = "4", number = "3", pages = "235--253", month = sep, keywords = "genetic algorithms, genetic programming, N-version programming, classification, ensemble, diversity", ISSN = "1389-2576", DOI = "doi:10.1023/A:1025124423708", abstract = "We propose N-version Genetic Programming (NVGP) as an ensemble method to enhance accuracy and reduce performance fluctuation of programs produced by genetic programming. Diversity is essential for forming successful ensembles. NVGP quantifies behavioural diversity of ensemble members and defines NVGP optimal as an ensemble that has independent fault occurrences among its members. We observed significant accuracy improvement by NVGP optimal ensembles when applied to a DNA segment classification problem.", notes = "Article ID: 5141123 CJMP CJMPI p243 five different types of crossover NVGP", } @PhdThesis{Imen:thesis, author = "Sanaz Imen", title = "Drinking Water Infrastructure Assessment with Teleconnection Signals, Satellite Data Fusion and Mining", school = "Civil Engineering, University of Central Florida", year = "2015", address = "USA", keywords = "genetic algorithms, genetic programming, grammatical evolution, Water quality, water quantity, remote sensing, data fusion, nowcasting, forecasting, lake mead", URL = "http://purl.fcla.edu/fcla/etd/40-Sanaz_Imen-Dissertation-After_Changes_From_Final_Format_Check.pdf", URL = "http://purl.fcla.edu/fcla/etd/CFE0005632", size = "162 pages", abstract = "Adjustment of the drinking water treatment process as a simultaneous response to climate variations and water quality impact has been a grand challenge in water resource management in recent years. This desired and preferred capability depends on timely and quantitative knowledge to monitor the quality and availability of water. This issue is of great importance for the largest reservoir in the United States, Lake Mead, which is located in the proximity of a big metropolitan region - Las Vegas, Nevada. The water quality in Lake Mead is impaired by forest fires, soil erosion, and land use changes in nearby watersheds and waste water effluents from the Las Vegas Wash. In addition, more than a decade of drought has caused a sharp drop by about 100 feet in the elevation of Lake Mead. These hydrological processes in the drought event led to the increased concentration of total organic carbon (TOC) and total suspended solids (TSS) in the lake. TOC in surface water is known as a precursor of disinfection by-products in drinking water, and high TSS concentration in source water is a threat leading to possible clogging in the water treatment process. Since Lake Mead is a principal source of drinking water for over 25 million people, high concentrations of TOC and TSS may have a potential health impact. Therefore, it is crucial to develop an early warning system which is able to support rapid forecasting of water quality and availability. In this study, the creation of the nowcasting water quality model with satellite remote sensing technologies lays down the foundation for monitoring TSS and TOC, on a near real-time basis. Yet the novelty of this study lies in the development of a forecasting model to predict TOC and TSS values with the aid of remote sensing technologies on a daily basis. The forecasting process is aided by an iterative scheme via updating the daily satellite imagery in concert with retrieving the long-term memory from the past states with the aid of non-linear autoregressive neural network with external input on a rolling basis onward. To account for the potential impact of long-term hydrological droughts, telecommunication signals were included on a seasonal basis in the Upper Colorado River basin which provides 97percent of the inflow into Lake Mead. Identification of teleconnection patterns at a local scale is challenging, largely due to the coexistence of non-stationary and non-linear signals embedded within the ocean-atmosphere system. Empirical mode decomposition as well as wavelet analysis are used to extract the intrinsic trend and the dominant oscillation of the sea surface temperature (SST) and precipitation time series. After finding possible associations between the dominant oscillation of seasonal precipitation and global SST through lagged correlation analysis, the statistically significant index regions in the oceans are extracted. With these characterized associations, individual contribution of these SST forcing regions that are linked to the related precipitation responses are further quantified through the use of the extreme learning machine. Results indicate that the non-leading SST regions also contribute saliently to the terrestrial precipitation variability compared to some of the known leading SST regions and confirm the capability of predicting the hydrological drought events one season ahead of time. With such an integrated advancement, an early warning system can be constructed to bridge the current gap in source water monitoring for water supply.", notes = "Public - Allow Worldwide Access Supervisor: Ni-bin Chang", } @InProceedings{Inacio:2016:SSCI, author = "Tiago Inacio and Rolando Miragaia and Gustavo Reis and Carlos Grilo and Francisco Fernandez", booktitle = "2016 IEEE Symposium Series on Computational Intelligence (SSCI)", title = "Cartesian genetic programming applied to pitch estimation of piano notes", year = "2016", abstract = "Pitch Estimation, also known as Fundamental Frequency (F0) estimation, has been a popular research topic for many years, and is still investigated nowadays. This paper presents a novel approach to the problem of Pitch Estimation, using Cartesian Genetic Programming (CGP). We take advantage of evolutionary algorithms, in particular CGP, to evolve mathematical functions that act as classifiers. These classifiers are used to identify piano notes' pitches in an audio signal. For a first approach, the obtained results are very promising: our error rate outperforms two of three state-of-the-art pitch estimators.", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", DOI = "doi:10.1109/SSCI.2016.7850046", month = dec, notes = "Masters see http://hdl.handle.net/10400.8/2341 https://iconline.ipleiria.pt/handle/10400.8/2341 Also known as \cite{7850046}", } @PhdThesis{oai:xtcat.oclc.org:OCLCNo/ocm43628471, title = "On synchronized evolution of the network of automata", author = "Yoshiyuki Inagaki", year = "1999", description = "Thesis (Ph. D., Social Science)--University of California, Irvine, 1999.; Includes bibliographical references (leaves 97-98).", oai = "oai:xtcat.oclc.org:OCLCNo/ocm43628471", school = "Social Science, University of California, Irvine", address = "USA", keywords = "genetic algorithms, genetic programming, Computer science, Sequential machine theory, Artificial intelligence", URL = "https://uci.primo.exlibrisgroup.com/discovery/fulldisplay?docid=alma991023376589704701&context=L&vid=01CDL_IRV_INST:UCI&search_scope=MyInst_and_CI&tab=Everything&lang=en", URL = "http://search.proquest.com/docview/304498722", size = "112 pages", abstract = "One of the tasks in machine learning is to build a device which guesses each next input symbol of a sequence as it takes one input symbol from the sequence. We studied new approaches to this task. We suggest that deterministic finite automata, DFA , are good building blocks for this device together with genetic algorithms, GA , which let these automata {"}evolve{"} to guess each next input symbol of the sequence. Moreover, we studied the way to combine these highly fit automata so that the network of them would compensate for each other's weakness and guess better than any single automaton can. We studied the simplest approaches to combine automata: building trees of automata with special purpose automata, which may be called switch-boards . These switch-board automata are located on the internal nodes of the tree, take an input symbol from the input sequence just like other automata do, and guess which subtree will make a right guess on each next input symbol. Genetic algorithms again play a crucial role in searching for switch-board automata. We studied various ways of growing trees of automata and tested them on sample input sequences, mainly note pitches, note duration, and up/down notes of Bach's Fugue. The test results show that DFAs together with GAs seem to be very effective for this type of pattern learning task. Besides this main finding, the tests revealed several interesting things. For example, the sequence of the note pitches is more predictable than the sequence of up/down notes. This is counter intuitive. Larger alphabets mean larger numbers of possible configurations of automata. This implies a larger search space for genetic algorithms; therefore, the algorithms should have difficulty finding automata which fit the tasks. However, the tree devices built to predict the note pitches often outperform those built to predict the up/down notes even though the size of the input alphabet for the former is 8 and that for the latter is 2. This suggests the following: The genetic search is so powerful and effective that if there are good solutions in its search space, it will find one when it works with a large enough population for a large enough number of generations. Therefore, if the search fails to find a good solution, the search space almost certainly does not contain one.", notes = "OCLC : (OCoLC)43628471 UCI Langson library LD 791.9.S56 1999 UMI 9940706 Supervisor Louis Narens See also \cite{inagaki:2002:TEC}", } @Article{inagaki:2002:TEC, author = "Yoshiyuki Inagaki", title = "On Synchronized Evolution of the Network of Automata", journal = "IEEE Transactions on Evolutionary Computation", year = "2002", volume = "6", number = "2", pages = "147--158", month = apr, keywords = "genetic algorithms, genetic programming, Evolutionary programming, finite automaton, sequence prediction problem, DFA, FSM, DT, music", ISSN = "1089-778X", URL = "http://ieeexplore.ieee.org/iel5/4235/21497/00996014.pdf?tp=&arnumber=996014&isnumber=21497&arSt=147&ared=158&arAuthor=Inagaki%2C+Y.%3B", DOI = "doi:10.1109/4235.996014", size = "12 pages", abstract = "One of the tasks in machine learning is to build a device that predicts each next input symbol of a sequence as it takes one input symbol from the sequence. We studied new approaches to this task. We suggest that deterministic finite automata (DFA) are good building blocks for this device together with genetic algorithms (GAs), which let these automata evolve to predict each next input symbol of the sequence. Moreover, we studied how to combine these highly fit automata so that a network of them would compensate for each others weaknesses and predict better than any single automaton.We studied the simplest approaches to combine automata: building trees of automata with special-purpose automata, which may be called switchboards. These switchboard automata are located on the internal nodes of the tree, take an input symbol from the input sequence just as do other automata, and predict which subtree will make a correct prediction on each next input symbol. GAs again play a crucial role in searching for switchboard automata. We studied various ways of growing trees of automata and tested them on sample input sequences, mainly note pitches, note duration, and up/down notes of Bach s Fugue IX. The test results show that DFAs together with GAs seem to be very effective for this type of pattern learning task.", notes = "1089-778X(02)04103-6 crossover combining 2 tree roots with switchboard. p150 bit vector scores. Clock DFA (overfitting?????). Time series prediction (stock market).", } @InProceedings{Indrakala:2016:ICETETS, author = "S. Indrakala and T. Chitrakalarani", booktitle = "2016 International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS)", title = "Comparative study of an intelligent dynamic approaches in predicting exchange rate", year = "2016", abstract = "The objective of the projected paper is to do study, development in an intelligent dynamic methods to expect the financial goods. For financial shop expectation different methods like Rough Set, Genetic Programming with Boosting Technique, Best Replacement Optimisation (BRO), and Genetic Programming with Rough Set and BRO with Rough Set are used. These models tested with five datasets representing different sectors in S&P 50 stock market and used to predict daily stock prices. Results presented in this paper showed that the proposed BRO-RS model have quick convergence rate at early stages of the iterations. BRO-RS model achieved better accuracy than compared models in price and trend prediction.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICETETS.2016.7603125", month = feb, notes = "Also known as \cite{7603125}", } @InProceedings{Indri:2022:EuroGP, author = "Patrick Indri and Alberto Bartoli and Eric Medvet and Laura Nenzi", title = "One-Shot Learning of Ensembles of Temporal Logic Formulas for Anomaly Detection in Cyber-Physical Systems", booktitle = "EuroGP 2022: Proceedings of the 25th European Conference on Genetic Programming", year = "2022", editor = "Eric Medvet and Gisele Pappa and Bing Xue", series = "LNCS", volume = "13223", publisher = "Springer Verlag", address = "Madrid, Spain", pages = "34--50", month = "20-22 " # apr, organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Ensemble learning, Grammar Guided Genetic Programming, Specification mining", isbn13 = "978-3-031-02055-1", URL = "https://medvet.inginf.units.it/publications/2022-c-ibmn-one/", DOI = "doi:10.1007/978-3-031-02056-8_3", code_url = "https://github.com/pindri/OneShot-ensemble-learning-anomaly-detection-MTS", size = "16 pages", abstract = "Cyber-Physical Systems (CPS) are prevalent in critical infrastructures and a prime target for cyber-attacks. Multivariate time series data generated by sensors and actuators of a CPS can be monitored for detecting cyber-attacks that introduce anomalies in those data. We use Signal Temporal Logic (STL) formulas to tightly describe the normal behavior of a CPS, identifying data instances that do not satisfy the formulas as anomalies. We learn an ensemble of STL formulas based on observed data, without any specific knowledge of the CPS being monitored. We propose an algorithm based on Grammar-Guided Genetic Programming (G3P) that learns the ensemble automatically in a single evolutionary run. We test the effectiveness of our data-driven proposal on two real-world datasets, finding that the proposed one-shot algorithm provides good detection performance.", notes = "http://www.evostar.org/2022/eurogp/ Part of \cite{Medvet:2022:GP} EuroGP'2022 held inconjunction with EvoApplications2022 EvoCOP2022 EvoMusArt2022", } @Article{INES:2017:PCS, author = "Gasmi Ines and Soui Makram and Chouchane Mabrouka and Abed Mourad", title = "Evaluation of Mobile Interfaces as an Optimization Problem", journal = "Procedia Computer Science", volume = "112", pages = "235--248", year = "2017", note = "Knowledge-Based and Intelligent Information and Engineering Systems: Proceedings of the 21st International Conference, KES-20176-8 September 2017, Marseille, France", ISSN = "1877-0509", DOI = "doi:10.1016/j.procs.2017.08.234", URL = "http://www.sciencedirect.com/science/article/pii/S1877050917316393", abstract = "Mobile applications are more and more present everywhere (at home, at work, in public places, etc.). Many academic and industrial studies are conducted about design methods and tools for mobile user interface generation. However, the evaluation of such interfaces is object of relatively few propositions and studies in the literature. The existing evaluation methods are widely based on a questionnaire, survey, eye tracking, etc. to assess mobile interface. These methods are time-consuming, error-prone task. In fact, one of the widely used methods to assess quality of MUI is using detection rules. But, the manual definition of these methods is still a difficult task. In this context, we define a method that generates evaluation rules for assessing the quality of mobile interfaces. To this end, we consider the generation of evaluation rules as a mono-objective technique problem where the goal is to find the best rules maximizing the quality of mobile interfaces. We evaluate our approach on four mobile applications. This study was designed around the android mobile devices. The obtained results confirm the efficiency of our technique with an average of more than 70percent of precision and recall", } @InProceedings{conf/eurocast/InfuhrR11, author = "Johannes Infuehr and Guenther R. Raidl", title = "Automatic Generation of 2-AntWars Players with Genetic Programming", booktitle = "13th International Conference on Computer Aided Systems Theory, EUROCAST 2011", year = "2011", editor = "Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia", volume = "6927", series = "Lecture Notes in Computer Science", pages = "248--255", address = "Las Palmas de Gran Canaria, Spain", month = feb # " 6-11", publisher = "Springer", keywords = "genetic algorithms, genetic programming, automatic strategy creation, strongly typed genetic programming, game rule evaluation", isbn13 = "978-3-642-27548-7", DOI = "doi:10.1007/978-3-642-27549-4_32", size = "8 pages", abstract = "In this work, we show how Genetic Programming can be used to create game playing strategies for 2-AntWars, a deterministic turn-based two player game with local information. We evaluate the created strategies against fixed, human created strategies as well as in a coevolutionary setting, where both players evolve simultaneously. We show that genetic programming is able to create competent players which can beat the static playing strategies, sometimes even in a creative way. Both mutation and crossover are shown to be essential for creating superior game playing strategies.", notes = "published 2012", } @InProceedings{ingalalli:2014:EuroGP, author = "Vijay Ingalalli and Sara Silva and Mauro Castelli and Leonardo Vanneschi", title = "A Multi-dimensional Genetic Programming Approach for Multi-class Classification Problems", booktitle = "17th European Conference on Genetic Programming", year = "2014", editor = "Miguel Nicolau and Krzysztof Krawiec and Malcolm I. Heywood and Mauro Castelli and Pablo Garcia-Sanchez and Juan J. Merelo and Victor M. {Rivas Santos} and Kevin Sim", series = "LNCS", volume = "8599", publisher = "Springer", pages = "48--60", address = "Granada, Spain", month = "23-25 " # apr, organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-662-44302-6", DOI = "doi:10.1007/978-3-662-44303-3_5", abstract = "Classification problems are of profound interest for the machine learning community as well as to an array of application fields. However, multi-class classification problems can be very complex, in particular when the number of classes is high. Although very successful in so many applications, GP was never regarded as a good method to perform multi-class classification. In this work, we present a novel algorithm for tree based GP, that incorporates some ideas on the representation of the solution space in higher dimensions. This idea lays some foundations on addressing multi-class classification problems using GP, which may lead to further research in this direction. We test the new approach on a large set of benchmark problems from several different sources, and observe its competitiveness against the most successful state-of-the-art classifiers.", notes = "Part of \cite{Nicolau:2014:GP} EuroGP'2014 held in conjunction with EvoCOP2014, EvoBIO2014, EvoMusArt2014 and EvoApplications2014", } @Misc{EasyChair:7821, author = "Leon Ingelse and Guilherme Espada and Alcides Fonseca", title = "Benchmarking Individual Representation in Grammar-Guided Genetic Programming", howpublished = "EasyChair Preprint no. 7821", year = "2022", month = "20 " # apr, keywords = "genetic algorithms, genetic programming, Grammatical Evolution, derivation trees, Grammar-Guided GP", URL = "https://easychair.org/publications/preprint/wqrb", broken = "https://wwwww.easychair.org/publications/preprint_download/wqrb", URL = "https://arxiv.org/abs/2208.00555", size = "5 pages", abstract = "Grammar-Guided Genetic Programming (GGGP) has two main flavors, Context-Free Grammar GP (CFG-GP) and Grammatical Evolution (GE). GE enjoys multiple benefits, leading to being the most widely-used approach. However, GE also suffers from disadvantages. we first review the established advantages and disadvantages of both GE and CFG-GP. Then, we identify three new advantages of CFG-GP over GE: direct evaluation, in-node storage, and deduplication. We conclude that there is further need for studying the performance of CFG-GP and GE.", notes = "See also 'Evostar 2022 Late breaking abstract' A.M. Mora and A.I. Esparcia-Alcazar (editors) arxiv.org/abs/2208.00555", } @InProceedings{Ingelse:2023:EuroGP, author = "Leon Ingelse and Alcides Fonseca", title = "Domain-Aware Feature Learning with Grammar-Guided Genetic Programming", booktitle = "EuroGP 2023: Proceedings of the 26th European Conference on Genetic Programming", year = "2023", month = "12-14 " # apr, editor = "Gisele Pappa and Mario Giacobini and Zdenek Vasicek", series = "LNCS", volume = "13986", publisher = "Springer Verlag", address = "Brno, Czech Republic", pages = "227--243", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, interpretability, Domain-aware feature learning, Historical-data aggregation, Grammar-guided genetic programming: Poster", isbn13 = "978-3-031-29572-0", URL = "https://rdcu.be/c8U0S", DOI = "doi:10.1007/978-3-031-29573-7_15", size = "17 pages", abstract = "Feature Learning (FL) is key to well-performing machine learning models. However, the most popular FL methods lack interpretability, which is becoming a critical requirement of Machine Learning. We propose to incorporate information from the problem domain in the structure of programs on top of the existing M3GP approach. This technique, named Domain-Knowledge M3GP, works by defining the possible feature transformations using a grammar through Grammar-Guided Genetic Programming. While requiring the user to specify the domain knowledge, this approach has the advantage of limiting the search space, excluding programs that make no sense to humans. We extend this approach with the possibility of introducing complex, aggregating queries over historic data. This extension allows to expand the search space to include relevant programs that were not possible before. We evaluate our methods on performance and interpretability in 6 use cases, showing promising results in both areas. We conclude that performance and interpretability of FL methods can benefit from domain-knowledge incorporation and aggregation, and give guidelines on when to use them.", notes = "Part of \cite{Pappa:2023:GP} EuroGP'2023 held in conjunction with EvoCOP2023, EvoMusArt2023 and EvoApplications2023", } @InProceedings{ingelse:2023:GEWS2023, author = "Leon Ingelse and Jose-Ignacio Hidalgo and Jose Manuel Colmenar and Nuno Lourenco and Alcides Fonseca", title = "Comparing Individual Representations in {Grammar-Guided} Genetic Programming for Glucose Prediction in People with Diabetes", booktitle = "Grammatical Evolution Workshop - 25 years of GE", year = "2023", editor = "Conor Ryan and Mahsa Mahdinejad and Aidan Murphy", pages = "2013--2021", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, grammatical evolution, individual representations, grammar-guided genetic programming, symbolic regression", isbn13 = "9798400701191", DOI = "doi:10.1145/3583133.3596315", size = "9 pages", abstract = "The representation of individuals in Genetic Programming (GP) has a large impact on the evolutionary process. In this work, we investigate the evolutionary process of three Grammar-Guided GP (GGGP) methods, Context-Free Grammars GP (CFG-GP), Grammatical Evolution (GE) and Structured Grammatical Evolution (SGE), in the context of the complex, real-world problem of predicting the glucose level of people with diabetes two hours ahead of time. Our analysis differs from previous analyses by (1) comparing all three methods on a complex benchmark, (2) implementing the methods in the same framework, allowing a fairer comparison, and (3) analyzing the evolutionary process outside of performance. We conclude that representation choice is more impactful with a higher maximum depth, and that CFG-GP better explores the search space for deeper trees, achieving better results. Furthermore, we find that CFG-GP relies more on feature construction, whereas GE and SGE rely more on feature selection. Finally, we altered the GGGP methods in two ways: using e-lexicase selection, which solved the overfitting problem of CFG-GP; and with a penalization of complex trees, to create more interpretable trees. Combining epsilon-lexicase selection with CFG-GP performed best.", notes = "See also \cite{Ingelse:2025:GPEM} GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @Article{Ingelse:2025:GPEM, author = "Leon Ingelse and J. Ignacio Hidalgo and J. Manuel Colmenar and Nuno Lourenco and Alcides Fonseca", title = "A comparison of representations in grammar‑guided genetic programming in the context of glucose prediction in people with diabetes", journal = "Genetic Programming and Evolvable Machines", year = "2025", volume = "26", pages = "Article no 5", note = "Online first", keywords = "genetic algorithms, genetic programming, Grammar-guided genetic programming, Individual representations, Symbolic regression, Initialization methods", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-024-09502-5", size = "25 pages", notes = "LASIGE, Faculdade de Ciencias da Universidade de Lisboa, Lisbon, Portugal", } @InProceedings{inhasz:2010:MBRST, author = "Rafael Inhasz and Julio Michael Stern", title = "Emergent Semiotics in Genetic Programming and the Self-Adaptive Semantic Crossover", booktitle = "Model-Based Reasoning in Science and Technology", year = "2010", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-642-15223-8_21", DOI = "doi:10.1007/978-3-642-15223-8_21", } @PhdThesis{Innes:thesis, author = "Andrew Innes", title = "Genetic Programing for Cephalometric Landmark Detection", school = "School of Aerospace, Mechanical and Manufacturing Engineering, RMIT University", year = "2007", address = "Victoria, Australia", month = "29 " # aug, keywords = "genetic algorithms, genetic programming", URL = "http://adt.lib.rmit.edu.au/adt/uploads/approved/adt-VIT20080221.123310/public/02whole.pdf", size = "265 pages", abstract = "The domain of medical imaging analysis has burgeoned in recent years due to the availability and affordability of digital radiographic imaging equipment and associated algorithms and, as such, there has been significant activity in the automation of the medical diagnostic process. One such process, cephalometric analysis, is manually intensive and it can take an experienced orthodontist thirty minutes to analyse one radiology image. This thesis describes an approach, based on genetic programming, neural networks and machine learning, to automate this process. A cephalometric analysis involves locating a number of points in an X-ray and determining the linear and angular relationships between them. If the points can be located accurately enough, the rest of the analysis is straightforward. The investigative steps undertaken were as follows: Firstly, a previously published method, which was claimed to be domain independent, was implemented and tested on a selection of landmarks, ranging from easy to very difficult. These included the menton, upper lip, incisal upper incisor, nose tip and sella landmarks. The method used pixel values, and pixel statistics (mean and standard deviation) of pre-determined regions as inputs to a genetic programming detector. This approach proved unsatisfactory and the second part of the investigation focused on alternative handcrafted features sets and fitness measures. This proved to be much more successful and the third part of the investigation involved using pulse coupled neural networks to replace the handcrafted features with learned ones. The fourth and final stage involved an analysis of the evolved programs to determine whether reasonable algorithms had been evolved and not just random artefacts learnt from the training images. A significant finding from the investigative steps was that the new domain independent approach, using pulse coupled neural networks and genetic programming to evolve programs,ii was as good as or even better than one using the handcrafted features. The advantage of this finding is that little domain knowledge is required, thus obviating the requirement to manually generate handcrafted features. The investigation revealed that some of the easy landmarks could be found with 100percent accuracy while the accuracy of finding the most difficult ones was around 78percent. An extensive analysis of evolved programs revealed underlying regularities that were captured during the evolutionary process. Even though the evolutionary process took different routes and a diverse range of programs was evolved, many of the programs with an acceptable detection rate implemented algorithms with similar characteristics. The major outcome of this work is that the method described in this thesis could be used as the basis of an automated system. The orthodontist would be required to manually correct a few errors before completing the analysis.", } @InProceedings{inokuchi:2024:CEC, author = "Yuto Inokuchi and Pedro Henrique Gonzalez and Israel Mendonca and Masayoshi Aritsugi", title = "Optimization of Node Reduction Using {BRKGA} for {GNN-Based} Traffic Speed Forecasting", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, Training, Accuracy, Graph convolutional networks, Roads, Noise, Layout, Evolutionary computation, evolutionary algorithms, graph convolution, optimization, traffic speed forecasting", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10612119", abstract = "While Spatio-Temporal Graph Convolutional Networks (STGCNs) are an effective method for traffic speed fore-casting, their training and inference tend to be time-consuming. In this paper, we aim to refine these networks by strategically reducing their number of nodes, thereby boosting computational efficiency. The nodes in these graphs represent data observed for road segments, and by analysing the interconnections and layout of the graph, we can identify nodes with minimal contribution to overall performance. Removing these nodes can potentially decrease computation time while maintaining the prediction accuracy. We employ the Biased Random-Key Genetic Algorithm (BRKGA) to identify a good set of nodes for removal, based on a predefined percentage reduction of the original graph size (e.g., retaining 95 percent of the original graph). We evaluate different graph size configurations, ranging from 95 percent to 70 percent node retention, to determine the least impactful node set performance. Our experiments on three real-world datasets reveal that reducing nodes can decrease computation time by up to 29percent, and as a byproduct of removing noise, even improve the prediction accuracy.", notes = "also known as \cite{10612119} Is this GP? WCCI 2024", } @InProceedings{his03:Inoue, author = "Y. Inoue and T. Tohge and H. Iba", year = "2003", title = "Cooperative Transportation by Humanoid Robots: Learning to Correct Positioning", editor = "Ajith Abraham and Mario K{\"o}ppen and Katrin Franke", chapter = "Real-World Applications", series = "Frontiers in Artificial Intelligence and Applications Vol. 104", pages = "1124--1134", booktitle = "Design and Application of Hybrid Intelligent Systems", publisher = "IOS Press Amsterdam, Berlin, Oxford, Tokyo, Washington D.C.", address = "Melbourne", month = dec, keywords = "genetic algorithms, genetic programming", ISBN = "1-58603-394-8", URL = "http://www.iba.k.u-tokyo.ac.jp/papers/2003/inoueHIS2003.pdf", size = "10 pages", abstract = "In this paper, we describe a cooperative transportation problem with two humanoid robots and introduce a machine learning approach to solving the problem. The difficulty of the task lies on the fact that each position shifts with the other's while they are moving. Therefore, it is necessary to correct the position in a realtime manner. However, it is difficult to generate such an action in consideration of the physical formula.We empirically show how successful the humanoid robot HOAP-1's cooperate with each other for the sake of the transportation as a result of Q-learning.", notes = "part of his03:book", } @InCollection{Inoue:2004:EMTP, author = "Yutaka Inoue and Takahiro Tohge and Hitoshi Iba", title = "Learning for Cooperative Transportation by Autonomous Humanoid Robots", year = "2004", booktitle = "Evolvable Machines: Theory \& Practice", pages = "3--20", volume = "161", series = "Studies in Fuzziness and Soft Computing", chapter = "1", editor = "Nadia Nedjah and Luiza {de Macedo Mourelle}", publisher = "Springer", address = "Berlin", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-22905-1", URL = "http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-175-22-33980449-0,00.html", notes = "Springer says published in 2005 but available Nov 2004", } @InProceedings{Ioannides:2010:HVC, author = "Charalambos Ioannides and Geoff Barrett and Kerstin I. Eder", title = "Feedback-Based Coverage Directed Test Generation: An Industrial Evaluation", booktitle = "Hardware and Software: Verification and Testing", year = "2010", editor = "Sharon Barner and Ian Harris and Daniel Kroening and Orna Raz", volume = "6504", series = "Lecture Notes in Computer Science", pages = "112--128", address = "Haifa, Israel", month = "4-7 " # oct, publisher = "Springer", keywords = "genetic algorithms, genetic programming, SBSE, microprocessor verification, MicroGP, coverage directed test generation", isbn13 = "978-3-642-19582-2", URL = "http://hdl.handle.net/1983/1740", DOI = "doi:10.1007/978-3-642-19583-9_13", size = "17 pages", abstract = "Although there are quite a few approaches to Coverage Directed test Generation aided by Machine Learning which have been applied successfully to small and medium size digital designs, it is not clear how they would scale on more elaborate industrial-level designs. This paper evaluates one of these techniques, called MicroGP, on a fully fledged industrial design. The results indicate relative success evidenced by a good level of code coverage achieved with reasonably compact tests when compared to traditional test generation approaches. However, there is scope for improvement especially with respect to the diversity of the tests evolved.", notes = "FirePath, FireDrill, DSC, ASC, BIRTH p126 'MicroGP has proven to be a tool that can effectively guide a test generator such as FireDrill in producing compact single tests that achieve near optimal coverage.' Published 2011", affiliation = "Industrial Doctorate Centre in Systems, University of Bristol, Queen's Building, University Walk, Bristol, BS8 1TR UK", bibsource = "OAI-PMH server at rose.bris.ac.uk", contributor = "Sharon Barner and Ian Harris and Daniel Kroening and Orna Raz and Israel IBM Research Labs", description = "Author's own version of a paper published by Springer. The original publication is available at broken www.springerlink.com; EPSRC", identifier = "9783642195822 (print), 9783642195839 (online); 03029743 (print), 16113349 (online)", language = "en", oai = "oai:rose.bris.ac.uk:1983/1740", type = "Conference contribution", } @InProceedings{Iokibe:1997:WSC2, author = "Tadashi Iokibe", title = "Industrial Application of Chaos Engineering", booktitle = "Soft Computing in Engineering Design and Manufacturing", year = "1997", editor = "P. K. Chawdhry and R. Roy and R. K. Pant", pages = "141--150", publisher_address = "Godalming, GU7 3DJ, UK", month = "23-27 " # jun, publisher = "Springer-Verlag London", keywords = "Chaos engineering, Deterministic non-linear short-term prediction, Fault diagnosis, Deterministic, system Stochastic process", ISBN = "3-540-76214-0", DOI = "doi:10.1007/978-1-4471-0427-8_2", size = "10 pages", abstract = "Recently, the study of chaos is attracting attention, and a wide range of academic fields is actively involved. On the other hand, Aihara proposed the term chaos engineering to describe the application of chaos theory for engineering purposes, and its possibilities have been demonstrated. Examples of applications reported so far include Oil Fan Heaters (Sanyo Electric Co., Ltd.), Air-conditioners and Dish Washing Dryers (Matsushita Electric Industrial Co., Ltd.), Washing Machines (Goldstar Co., Ltd.; Korea) and other home appliances and Application to Health Care (Computer Convenience). However, industrially, there has been only one application which is the Tap Water Demand Prediction (Meidensha Corporation). This paper first reviews the history of chaos research. Next, deterministic chaos is described. Time series forecasting and fault diagnosis are discussed as prospective industrial applications, and the related methodology is explained using practical examples.", notes = "Not GP. Introduction/survey/a few examples. Fuzzy. A bit like Mutation testing applied to physical world of consumer goods products??? WSC2 Second On-line World Conference on Soft Computing in Engineering Design and Manufacturing", } @InProceedings{Iovino:2021:ICRA, author = "Matteo Iovino and Jonathan Styrud and Pietro Falco and Christian Smith", title = "Learning Behavior Trees with Genetic Programming in Unpredictable Environments", booktitle = "2021 IEEE International Conference on Robotics and Automation (ICRA)", year = "2021", pages = "4591--4597", abstract = "Modern industrial applications require robots to operate in unpredictable environments, and programs to be created with a minimal effort, to accommodate frequent changes to the task. Here, we show that genetic programming can be effectively used to learn the structure of a behavior tree (BT) to solve a robotic task in an unpredictable environment. We propose to use a simple simulator for learning, and demonstrate that the learned BTs can solve the same task in a realistic simulator, converging without the need for task specific heuristics, making our method appealing for real robotic applications.", keywords = "genetic algorithms, genetic programming, Automation, Service robots, Conferences, Task analysis, Behavior Trees, Mobile Manipulation", DOI = "doi:10.1109/ICRA48506.2021.9562088", ISSN = "2577-087X", month = may, notes = "Also known as \cite{9562088}", } @InProceedings{Iovino:2023:CASE, author = "Matteo Iovino and Jonathan Styrud and Pietro Falco and Christian Smith", booktitle = "2023 IEEE 19th International Conference on Automation Science and Engineering (CASE)", title = "A Framework for Learning Behavior Trees in Collaborative Robotic Applications", year = "2023", abstract = "In modern industrial collaborative robotic applications, it is desirable to create robot programs automatically, intuitively, and time-efficiently. Moreover, robots need to be controlled by reactive policies to face the unpredictability of the environment they operate in. In this paper we propose a framework that combines a method that learns Behaviour Trees (BTs) from demonstration with a method that evolves them with Genetic Programming (GP) for collaborative robotic applications. The main contribution of this paper is to show that by combining the two learning methods we obtain a method that allows non-expert users to semi-automatically, time-efficiently, and interactively generate BTs. We validate the framework with a series of manipulation experiments. The BT is fully learnt in simulation and then transferred to a real collaborative robot.", keywords = "genetic algorithms, genetic programming, Learning systems, Computer aided software engineering, Automation, Service robots, Collaboration, Behavioural sciences, Behaviour Trees, Learning from Demonstration, Collaborative Robotics", DOI = "doi:10.1109/CASE56687.2023.10260363", ISSN = "2161-8089", month = aug, notes = "Also known as \cite{10260363}", } @InProceedings{Iqbal:2011:GECCOcomp, author = "Muhammad Iqbal and Mengjie Zhang and Will Browne", title = "Automatically defined functions for learning classifier systems", booktitle = "Fourteenth international workshop on learning classifier systems", year = "2011", editor = "Daniele Loiacono and Albert Orriols-Puig and Ryan Urbanowicz", isbn13 = "978-1-4503-0690-4", keywords = "genetic algorithms, genetic programming, 20mux", pages = "375--382", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001858.2002022", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "This work introduces automatically defined functions (ADFs) for learning classifier systems (LCS). ADFs had been successfully implemented in genetic programming (GP)for various domain problems such as multiplexer and even-odd parity, but they have never been attempted in LCS research field before. ADFs in GP contract program trees and shorten training times whilst providing resilience to destructive genetic operators. We have implemented ADFs in Wilson's accuracy based LCS, known as XCS [14]. This initial investigation of ADFs in LCS shows that the multiple genotypes to a phenotype issue in feature rich encodings disables the subsumption deletion function. The additional methods and increased search space also leads to much longer training times. This is compensated by the ADFs containing useful knowledge, such as the importance of the address bits in the multiplexer problem. The ADFs also create masks that autonomously subdivide the search space into areas of interest and uniquely, areas of not interest. The next stage of this work is to implement simplification methods and then determine methods by which ADFs can facilitate scaling for more complex problems within the same problem domain.", notes = "Also known as \cite{2002022} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @Article{Iqbal:2013:SC, author = "Muhammad Iqbal and Will N. Browne and Mengjie Zhang", title = "Evolving optimum populations with XCS classifier systems", journal = "Soft Computing", year = "2013", volume = "17", number = "3", pages = "503--518", month = mar, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Learning classifier systems, XCS, Optimal populations, Scalability, Code fragments, Action consistency", ISSN = "1432-7643", DOI = "doi:10.1007/s00500-012-0922-5", language = "English", size = "16 pages", abstract = "The main goal of the research xdirection is to extract building blocks of knowledge from a problem domain. Once extracted successfully, these building blocks are to be used in learning more complex problems of the domain, in an effort to produce a scalable learning classifier system (LCS). However, whilst current LCS (and other evolutionary computation techniques) discover good rules, they also create sub-optimum rules. Therefore, it is difficult to separate good building blocks of information from others without extensive post-processing. In order to provide richness in the LCS alphabet, code fragments similar to tree expressions in genetic programming are adopted. The accuracy-based XCS concept is used as it aims to produce maximally general and accurate classifiers, albeit the rule base requires condensation (compaction) to remove spurious classifiers. Serendipitously, this work on scalability of LCS produces compact rule sets that can be easily converted to the optimum population. The main contribution of this work is the ability to clearly separate the optimum rules from others without the need for expensive post-processing for the first time in LCS. This paper identifies that consistency of action in rich alphabets guides LCS to optimum rule sets.", notes = "XCS with code fragmented action", } @Article{Iqbal:2013:ieeeTEC, author = "Muhammad Iqbal and Will N. Browne and Mengjie Zhang", title = "Reusing Building Blocks of Extracted Knowledge to Solve Complex, Large-Scale Boolean Problems", journal = "IEEE Transactions on Evolutionary Computation", year = "2014", volume = "18", number = "4", pages = "465--480", month = aug, keywords = "genetic algorithms, genetic programming, XCS, Learning Classifier Systems, Layered Learning, Scalability, Building Blocks, Code Fragments", ISSN = "1089-778X", URL = "http://homepages.ecs.vuw.ac.nz/~mengjie/papers/", URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=06595603", DOI = "doi:10.1109/TEVC.2013.2281537", size = "16 pages", abstract = "Evolutionary computation techniques have had limited capabilities in solving large-scale problems due to the large search space demanding large memory and much longer training times. In the work presented here, a genetic programming like rich encoding scheme has been constructed to identify building blocks of knowledge in a learning classifier system. The fitter building blocks from the learning system trained against smaller problems have been used in a higher complexity problem in the domain in order to achieve scalable learning. The proposed system has been examined and evaluated on four different Boolean problem domains, i.e. multiplexer, majority-on, carry, and even-parity problems. The major contribution of this work is to successfully extract useful building blocks from smaller problems and reuse them to learn more complex, large-scale problems in the domain, e.g. 135-bits multiplexer problem, where the number of possible instances is 2**135 = 4.0 10**40, is solved by reusing the extracted knowledge from the learnt lower level solutions in the domain. Autonomous scaling is, for the first time, shown to be possible in learning classifier systems. It improves effectiveness and reduces the number of training instances required in large problems, but requires more time due to its sequential build-up of knowledge.", notes = "Entered for 2013 HUMIES GECCO 2013 ", } @InProceedings{Iqbal:2013:GECCOcomp, author = "Muhammad Iqbal and Will N. Browne and Mengjie Zhang", title = "Comparison of two methods for computing action values in XCS with code-fragment actions", booktitle = "GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion", year = "2013", editor = "Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A. N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks", isbn13 = "978-1-4503-1964-5", keywords = "genetic algorithms, genetic programming", pages = "1235--1242", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2464576.2482702", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "XCS is a learning classifier system that uses accuracy-based fitness to learn a problem. Commonly, a classifier rule in XCS is encoded using a ternary alphabet based condition and a numeric action. Previously, we implemented a code-fragment action based XCS, called XCSCFA, where the typically used numeric action was replaced by a genetic programming like tree-expression. In XCSCFA, the action value in a classifier was computed by loading the terminal symbols in the action-tree with the corresponding binary values in the condition of the classifier rule. This enabled accurate, general and compact rule sets to be simply produced. The main contribution of this work is to investigate an intuitive way, i.e. using the environmental instance, to compute the action value in XCSCFA, instead of the condition of the classifier rule. The methods will be compared in five different Boolean problem domains, i.e. multiplexer, even-parity, majority-on, design verification, and carry problems. The environmental instance based XCSCFA approach had better classification performance than standard XCS as well as classifier condition based XCSCFA and solved all the problems experimented here. In addition it produced more general and compact classifier rules in the final solution. However, classifier condition based XCSCFA has the advantage of producing the optimal classifiers such that they are clearly separated from the sub-optimal ones in certain domains.", notes = "Also known as \cite{2482702} Distributed at GECCO-2013.", } @InProceedings{Iqbal:2013:GECCO, author = "Muhammad Iqbal and Will N. Browne and Mengjie Zhang", title = "Extending learning classifier system with cyclic graphs for scalability on complex, large-scale {Boolean} problems", booktitle = "GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference", year = "2013", editor = "Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi", isbn13 = "978-1-4503-1963-8", pages = "1045--1052", keywords = "genetic algorithms, genetic programming", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2463372.2463500", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Evolutionary computational techniques have had limited capabilities in solving large-scale problems, due to the large search space demanding large memory and much longer training time. Recently work has begun on autonomously reusing learnt building blocks of knowledge to scale from low dimensional problems to large-scale ones. An XCS-based classifier system has been shown to be scalable, through the addition of tree-like code fragments, to a limit beyond standard learning classifier systems. Self-modifying Cartesian genetic programming (SMCGP) can provide general solutions to a number of problems, but the obtained solutions for large-scale problems are not easily interpretable. A limitation in both techniques is the lack of a cyclic representation, which is inherent in finite state machines. Hence this work introduces a state-machine based encoding scheme into scalable XCS, for the first time, in an attempt to develop a general scalable classifier system producing easily interpretable classifier rules. The proposed system has been tested on four different Boolean problem domains, i.e. even-parity, majority-on, carry, and multiplexer problems. The proposed approach outperformed standard XCS in three of the four problem domains. In addition, the evolved machines provide general solutions to the even-parity and carry problems that are easily interpretable as compared with the solutions obtained using SMCGP.", notes = "Also known as \cite{2463500} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)", } @Article{Iqbal:2013:EI, author = "Muhammad Iqbal and Will N. Browne and Mengjie Zhang", title = "Learning complex, overlapping and niche imbalance {Boolean} problems using {XCS-based} classifier systems", journal = "Evolutionary Intelligence", year = "2013", volume = "6", number = "2", pages = "73--91", keywords = "genetic algorithms, genetic programming, Learning classifier systems, XCS, XCSCFA, Code fragments, Overlapping problems, Niche imbalance", ISSN = "1864-5909", publisher = "Springer", URL = "http://dx.doi.org/10.1007/s12065-013-0091-1", DOI = "doi:10.1007/s12065-013-0091-1", size = "19 pages", abstract = "XCS is an accuracy-based learning classifier system, which has been successfully applied to learn various classification and function approximation problems. Recently, it has been reported that XCS cannot learn overlapping and niche imbalance problems using the typical experimental setup. This paper describes two approaches to learn these complex problems: firstly, tune the parameters and adjust the methods of standard XCS specifically for such problems. Secondly, apply an advanced variation of XCS. Specifically, we developed previously an XCS with code-fragment actions, named XCSCFA, which has a more flexible genetic programming like encoding and explicit state-action mapping through computed actions. This approach is examined and compared with standard XCS on six complex Boolean datasets, which include overlapping and niche imbalance problems. The results indicate that to learn overlapping and niche imbalance problems using XCS, it is beneficial to either deactivate action set subsumption or use a relatively high subsumption threshold and a small error threshold. The XCSCFA approach successfully solved the tested complex, overlapping and niche imbalance problems without parameter tuning, because of the rich alphabet, inconsistent actions and especially the redundancy provided by the code-fragment actions. The major contribution of the work presented here is overcoming the identified problem in the wide-spread XCS technique.", } @PhdThesis{ECS980094535, author = "Muhammad Iqbal", title = "Improving the Scalability of XCS-Based Learning Classifier Systems", year = "2014", school = "Victoria University", address = "New Zealand", keywords = "XCS", URL = "https://ecs.victoria.ac.nz/cgi-bin/publications?rm=details&id=980094535", URL = "http://hdl.handle.net/10063/3225", size = "269 pages", notes = "Supervisors Will Browne and Prof Mengjie Zhang", } @Article{Iqbal:2015:SC, author = "Muhammad Iqbal and Will N. Browne and Mengjie Zhang", title = "Improving genetic search in XCS-based classifier systems through understanding the evolvability of classifier rules", journal = "Soft Computing", year = "2015", volume = "19", number = "7", pages = "1863--1880", month = jul, keywords = "genetic algorithms, genetic programming, Learning classifier systems, XCS, XCSCFA, Evolvability", ISSN = "1432-7643", DOI = "doi:10.1007/s00500-014-1369-7", size = "18 pages", abstract = "Learning classifier systems (LCSs), an established evolutionary computation technique, are over 30 years old with much empirical testing and foundations of theoretical understanding. XCS is a well-tested LCS model that generates optimal (i.e., maximally general and accurate) classifier rules in the final solution. Previous work has hypothesised the evolution mechanisms in XCS by identifying the bounds of learning and population requirements. However, no work has shown exactly how an optimum rule is evolved or especially identifies whether the methods within an LCS are being effectively. In this paper, we introduce a method to trace the evolution of classifier rules generated in an XCS-based classifier system. Specifically, we introduce the concept of a family tree, termed parent-tree, for each individual classifier rule generated in the system during training, which describes the whole generational process for that classifier. Experiments are conducted on two sample Boolean problem domains, i.e., multiplexer and count ones problems using two XCS-based systems, i.e., standard XCS and XCS with code-fragment actions. The analysis of parent-trees reveals, for the first time in XCS, that no matter how specific or general the initial classifiers are, all the optimal classifiers are converged through the mechanism be specific then generalize near the final stages of evolution. Populations where the initial classifiers were slightly more specific than the known ideal specificity in the target solutions evolve faster than either very specific, ideal or more general starting classifier populations. Consequently introducing the flip mutation method and reverting the conventional wisdom back to apply rule discovery in the match set has demonstrated benefits in binary classification problems, which has implications in using XCS for knowledge discovery tasks. It is further concluded that XCS does not directly all relevant information or all breeding strategies to evolve the optimum solution, indicating areas for performance and efficiency improvement in XCS-based systems.", } @InProceedings{conf/pakdd/IqbalXZ16, author = "Muhammad Iqbal and Bing Xue and Mengjie Zhang", title = "Reusing Extracted Knowledge in Genetic Programming to Solve Complex Texture Image Classification Problems", bibdate = "2016-04-13", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/pakdd/pakdd2016-2.html#IqbalXZ16", booktitle = "Advances in Knowledge Discovery and Data Mining - 20th Pacific-Asia Conference, {PAKDD} 2016, Auckland, New Zealand, April 19-22, 2016, Proceedings, Part {II}", publisher = "Springer", year = "2016", volume = "9652", editor = "James Bailey and Latifur Khan and Takashi Washio and Gillian Dobbie and Joshua Zhexue Huang and Ruili Wang", isbn13 = "978-3-319-31749-6", pages = "117--129", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming", URL = "http://dx.doi.org/10.1007/978-3-319-31750-2", } @InProceedings{Iqbal:2016:CEC, author = "Muhammad Iqbal and Mengjie Zhang and Bing Xue", title = "Improving Classification on Images by Extracting and Transferring Knowledge in Genetic Programming", booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016)", year = "2016", editor = "Yew-Soon Ong", pages = "3582--3589", address = "Vancouver", month = "24-29 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-5090-0623-6", DOI = "doi:10.1109/CEC.2016.7744243", abstract = "Genetic programming (GP) is a well established evolutionary computation technique that automatically generates a computer program to solve a given problem. GP has been successfully used to solve optimization, symbolic regression and classification problems. Transfer learning in GP has been investigated to learn various Boolean and symbolic regression problems. However, there has been not much work on transfer learning in GP for image classification problems. In this paper, we propose a new technique to use transfer learning in GP to learn image classification problems. The developed method has been compared with the baseline GP method on three image classification benchmarks. The obtained results indicate that transfer learning has significantly improved the classification accuracy in learning various rotated and noisy versions of the tested image classification problems.", notes = "WCCI2016", } @Article{Iqbal:xd:ieeeTEC, author = "Muhammad Iqbal and Bing Xue and Harith Al-Sahaf and Mengjie Zhang", title = "Cross-Domain Reuse of Extracted Knowledge in Genetic Programming for Image Classification", journal = "IEEE Transactions on Evolutionary Computation", year = "2017", volume = "21", number = "4", pages = "569--587", month = aug, keywords = "genetic algorithms, genetic programming, Code Fragments, Image Classification, Knowledge Extraction, Building Blocks", ISSN = "1089-778X", URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7833127", DOI = "doi:10.1109/TEVC.2017.2657556", size = "19 pages", abstract = "Genetic programming (GP) is a well-known evolutionary computation technique, which has been successfully used to solve various problems, such as optimisation, image analysis and classification. Transfer learning is a type of machine learning approach that can be used to solve complex tasks. Transfer learning has been introduced to genetic programming to solve complex Boolean and symbolic regression problems with some promise. However, the use of transfer learning with genetic programming has not been investigated to address complex image classification tasks with noise and rotations, where GP cannot achieve satisfactory performance, but GP with transfer learning may improve the performance. In this paper, we propose a novel approach based on transfer learning and genetic programming to solve complex image classification problems by extracting and reusing blocks of knowledge/information, which are automatically discovered from similar as well as different image classification tasks during the evolutionary process. The proposed approach is evaluated on three texture data sets and three office data sets of image classification benchmarks, and achieves better classification performance than the state-of-the-art image classification algorithm. Further analysis on the evolved solutions/trees shows that the proposed approach with transfer learning can successfully discover and reuse knowledge/information extracted from similar or different problems to improve its performance on complex image classification problems.", notes = "also known as \cite{7833127}", } @Article{iqbal:SC, author = "Muhammad Iqbal and Harith Al-Sahaf and Bing Xue and Mengjie Zhang", title = "Genetic programming with transfer learning for texture image classification", journal = "Soft Computing", year = "2019", volume = "23", number = "23", pages = "12859--12871", month = dec, keywords = "genetic algorithms, genetic programming, Transfer learning, Image classification, Code fragments, Evolutionary computation", ISSN = "1432-7643", URL = "http://link.springer.com/article/10.1007/s00500-019-03843-5", DOI = "doi:10.1007/s00500-019-03843-5", size = "13 pages", abstract = "Genetic programming (GP) represents a well-known and widely used evolutionary computation technique that has shown promising results in optimisation, classification, and symbolic regression problems. However, similar to many other techniques, the performance of GP deteriorates for solving highly complex tasks. Transfer learning can improve the learning ability of GP, which can be seen from previous research on including, but not limited to, symbolic regression and Boolean problems. However, using transfer learning to tackle image-related, specifically, image classification, problems in GP is limited. This paper aims at proposing a new method for employing transfer learning in GP to extract and transfer knowledge in order to tackle complex texture image classification problems. To assess the improvement gained from using the extracted knowledge, the proposed method is examined and compared against the baseline GP method and a state-of-the-art method on three publicly available and commonly used texture image classification datasets. The obtained results indicate that the reuse of the extracted knowledge from an image dataset has significant impact on improving the performance in learning different rotated versions of the same dataset, as well as other related image datasets. Further, it is found that the proposed approach in the very first generation of the evolutionary process produces better classification accuracy than the final classification accuracy obtained by the baseline method after 50 generations.", } @InProceedings{Irani:1997:gaoijt, author = "Zahir Irani and Amir Shari", title = "Genetic Algorithm Optimization of Investment Justification Theory", booktitle = "Late Breaking Papers at the 1997 Genetic Programming Conference", year = "1997", editor = "John R. Koza", pages = "87--92", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California, 94305-3079, USA", month = "13--16 " # jul, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-206995-8", notes = "GP-97LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670", } @InProceedings{irani:1998:rpeIea, author = "Zahir Irani and Amir M. Sharif", title = "A Revised Perspective on the Evaluation of IT/IS Investments using an Evolutionary Approach", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "77--83", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "genetic algorithms, genetic programming", URL = "http://bura.brunel.ac.uk/handle/2438/4257", URL = "http://bura.brunel.ac.uk/bitstream/2438/4257/1/GP98%20-%20GA-ISE.pdf", size = "6.1 pages", abstract = "On-going research into the evaluation of Information Technology (IT) / Information Systems (IS) projects has shown that aerospace and supply chain industries are needing to address the issue of effective project investment in order to gain technological and competitive advantage. The evaluative nature of the justification process requires a mapping of interrelated quantities to be optimised. Earlier work by the authors (Irani and Sharif 1997) has presented a theoretical functional model that describes these relationships in turn. By applying a fuzzy mapping to these variables, the optimisation of intangible relationships in the form of a Genetic Algorithm (GA) is proposed as a method for investment justification. This paper revises and reviews these key concepts and provides a recapitulation of this optimisation problem in terms of long-term strategy options and cost implications", notes = "GP-98LB", } @InProceedings{Irfan:2010:ICET, author = "Muhammad Irfan and Qaiser Habib and Ghulam M. Hassan and Khawaja M. Yahya and Samira Hayat", title = "Combinational digital circuit synthesis using Cartesian Genetic Programming from a NAND gate template", booktitle = "6th International Conference on Emerging Technologies (ICET 2010)", year = "2010", month = oct, pages = "343--347", abstract = "Evolutionary synthesis of combinational digital circuits is a promising research area and many a success has been achieved in this field. This paper presents a new technique for the synthesis of combinational circuits by using Cartesian Genetic Programming (CGP) and uniform NAND gate based templates. Using a uniform gate template implies an ease in the fabrication process but in some instances, the number of gates required may increase which can be optimised by CGP. The mutation operator has been used for achieving convergence. A 2-bit multiplier and 4-bit odd parity generator circuits have been evolved for experimentation and comparison to previous results. The results obtained are compared to earlier work done in the same field. Moreover, the relationship of evolution time (in terms of number of generations) to the population size has been established and analysed.", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, NAND gate template, combinational digital circuit synthesis, evolutionary synthesis, mutation operator, NAND circuits, combinational circuits, network synthesis", DOI = "doi:10.1109/ICET.2010.5638462", notes = "University of Engineering & Technology Peshawar, Pakistan. Also known as \cite{5638462}", } @InProceedings{Isaka:1997:esfife, author = "Satoru Isaka", title = "An Empirical Study of Facial Image Feature Extraction by Genetic Programming", booktitle = "Late Breaking Papers at the 1997 Genetic Programming Conference", year = "1997", editor = "John R. Koza", pages = "93--99", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California, 94305-3079, USA", month = "13--16 " # jul, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-206995-8", notes = "GP-97LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670", } @InProceedings{conf/semweb/IseleB11, title = "Learning linkage rules using genetic programming", author = "Robert Isele and Christian Bizer", booktitle = "Proceedings of the 6th International Workshop on Ontology Matching", editor = "Pavel Shvaiko and Jerome Euzenat and Tom Heath and Christoph Quix and Ming Mao and Isabel F. Cruz", year = "2011", volume = "814", series = "CEUR Workshop Proceedings", address = "Bonn, Germany", month = oct # " 24", publisher = "CEUR-WS.org", keywords = "genetic algorithms, genetic programming, linked data, link discovery, duplicate detection, deduplication, record linkage", URL = "http://ceur-ws.org/Vol-814/om2011_Tpaper2.pdf", URL = "http://ceur-ws.org/Vol-814", size = "12 pages", abstract = "An important problem in Linked Data is the discovery of links between entities which identify the same real world object. These links are often generated based on manually written linkage rules which specify the condition which must be fulfilled for two entities in order to be interlinked. In this paper, we present an approach to automatically generate linkage rules from a set of reference links. Our approach is based on genetic programming and has been implemented in the Silk Link Discovery Framework. It is capable of generating complex linkage rules which compare multiple properties of the entities and employ data transformations in order to normalise their values. Experimental results show that it outperforms a genetic programming approach for record deduplication recently presented by Carvalho et. al. In tests with linkage rules that have been created for our research projects our approach learnt rules which achieve a similar accuracy than the original human-created linkage rule.", notes = "http://www4.wiwiss.fu-berlin.de/bizer/silk/", bibdate = "2012-02-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/semweb/om2011.html#IseleB11", } @Article{p1638:robertisele:vldb2012, author = "Robert Isele and Christian Bizer", title = "Learning Expressive Linkage Rules using Genetic Programming", journal = "Proceedings of the VLDB Endowment", year = "2012", volume = "5", number = "11", pages = "1638--1649", month = jul, editor = "Ahmet Sacan and Nesime Tatbul", keywords = "genetic algorithms, genetic programming, VLDB", URL = "http://vldb.org/pvldb/vol5/p1638_robertisele_vldb2012.pdf", URL = "http://www.vldb.org/pvldb/vol5.html", URL = "http://arxiv.org/abs/1208.0291", URL = "http://arxiv.org/pdf/1208.0291v1", size = "12 page", abstract = "A central problem in data integration and data cleansing is to find entities in different data sources that describe the same real-world object. Many existing methods for identifying such entities rely on explicit linkage rules which specify the conditions that entities must fulfil in order to be considered to describe the same real-world object. In this paper, we present the GenLink algorithm for learning expressive linkage rules from a set of existing reference links using genetic programming. The algorithm is capable of generating linkage rules which select discriminative properties for comparison, apply chains of data transformations to normalise property values, choose appropriate distance measures and thresholds and combine the results of multiple comparisons using non-linear aggregation functions. Our experiments show that the GenLink algorithm outperforms the state-of-the-art genetic programming approach to learning linkage rules recently presented by Carvalho et. al. and is capable of learning linkage rules which achieve a similar accuracy as human written rules for the same problem.", notes = "Articles from this volume were invited to present their results at The 38th International Conference on Very Large Data Bases, August 27th 31st 2012, Istanbul, Turkey.", bibsource = "OAI-PMH server at export.arxiv.org", oai = "oai:arXiv.org:1208.0291", } @PhdThesis{Isele_Dissertation, author = "Robert Isele", title = "Learning Expressive Linkage Rules for Entity Matching using Genetic Programming", school = "Mannheim", year = "2013", address = "Germany", month = "10 " # jul, keywords = "genetic algorithms, genetic programming, Entity Matching, Record Linkage, Data Integration, Linkage Rules, Active Learning", URL = "https://ub-madoc.bib.uni-mannheim.de/33418/", URL = "https://ub-madoc.bib.uni-mannheim.de/33418/1/Isele_Dissertation.pdf", size = "224 pages", abstract = "A central problem in data integration and data cleansing is to identify pairs of entities in data sets that describe the same real-world object. Many existing methods for matching entities rely on explicit linkage rules, which specify how two entities are compared for equivalence. Unfortunately, writing accurate linkage rules by hand is a non-trivial problem that requires detailed knowledge of the involved data sets. Another important issue is the efficient execution of linkage rules. In this thesis, we propose a set of novel methods that cover the complete entity matching workflow from the generation of linkage rules using genetic programming algorithms to their efficient execution on distributed systems. First, we propose a supervised learning algorithm that is capable of generating linkage rules from a gold standard consisting of set of entity pairs that have been labelled as duplicates or non-duplicates. We show that the introduced algorithm outperforms previously proposed entity matching approaches including the state-of-the-art genetic programming approach by de Carvalho et al. and is capable of learning linkage rules that achieve a similar accuracy than the human written rule for the same problem. In order to also cover use cases for which no gold standard is available, we propose a complementary active learning algorithm that generates a gold standard interactively by asking the user to confirm or decline the equivalence of a small number of entity pairs. In the experimental evaluation, labelling at most 50 link candidates was necessary in order to match the performance that is achieved by the supervised GenLink algorithm on the entire gold standard. Finally, we propose an efficient execution work flow that can be run on cluster of multiple machines. The execution workflow employs a novel multidimensional indexing method that allows the efficient execution of learnt linkage rules by reducing the number of required comparisons significantly.", notes = "Supervised by Professor Bizer and Professor Stuckenschmidt. Broken Feb 2019 http://dws.informatik.uni-mannheim.de/en/news/detail/news/2013/09/02/congratulations-to-robert-isele-for-receiving-his-doctoral-degree/ Woody Allen in DBpedia and Freebase", } @Article{Isele:2013:WSSSAWWW, author = "Robert Isele and Christian Bizer", title = "Active learning of expressive linkage rules using genetic programming", journal = "Web Semantics: Science, Services and Agents on the World Wide Web", volume = "23", month = dec, pages = "2--15", year = "2013", note = "Data Linking", ISSN = "1570-8268", DOI = "doi:10.1016/j.websem.2013.06.001", URL = "http://www.sciencedirect.com/science/article/pii/S1570826813000231", keywords = "genetic algorithms, genetic programming, Entity matching, Duplicate detection, Active learning, Linkage rules, ActiveGenLink", } @InProceedings{Isherwood:2023:WSC, author = "Alex Isherwood and Matthew Koehler and David Slater", booktitle = "2023 Winter Simulation Conference (WSC)", title = "Using Evolutionary Model Discovery to Develop Robust Policies", year = "2023", pages = "130--137", abstract = "Agent-based models can be a powerful tool for evaluating the impact of policy decisions on a population. However, analyses are traditionally beholden to one set of rules hypothesized at the conception of the model. Modellers must make assumptions of agent behaviour that are not necessarily governed by data and the actual behaviour of the true population can thusly vary. Evolutionary model discovery (EMD) seeks to provide a solution to this problem by leveraging genetic algorithms and genetic programming to explore the plausible set of rules that can explain agent behaviour. Here we describe an initial use of the EMD system to develop robust policies in a resource constrained environment. In this instance, we extend the NetLogo implementation of the Epstein Rebellion model of civil violence as a sample problem. We use the EMD framework to generate 23 plausible populations and then develop policy responses for the government that are robust across the plausible populations.", keywords = "genetic algorithms, genetic programming, Sociology, Government, Data models, Behavioural sciences, Space exploration, Statistics, Tuning", DOI = "doi:10.1109/WSC60868.2023.10407233", ISSN = "1558-4305", month = dec, notes = "Also known as \cite{10407233}", } @InProceedings{ishida:2002:gmsgpidm, author = "Celso Yoshikazu Ishida and Aurora Trinidad Ramirez Pozo", title = "{GPSQL} Miner: {SQL}-Grammar Genetic Programming in Data Mining", booktitle = "Proceedings of the 2002 Congress on Evolutionary Computation CEC2002", editor = "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and Mark Shackleton", pages = "1226--1231", year = "2002", publisher = "IEEE Press", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", organisation = "IEEE Neural Network Council (NNC), Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", ISBN = "0-7803-7278-6", month = "12-17 " # may, keywords = "genetic algorithms, genetic programming, SQL, GPSQL Miner, SQL-grammar genetic programming, data mining, relational databases, grammars", DOI = "doi:10.1109/CEC.2002.1004418", size = "6 pages", abstract = "The present work describes GPSQL Miner, a Genetic Programming system for mining relational databases. This system uses Grammar Genetic Programming for classification task and one of its main features is the representation of the classifiers. The system uses SQL grammar, which facilitates the evaluation process, once the data are in relational databases. The tool was tested with some databases and the results were compared with other algorithms. These first experiments had shown promising results for the classification task.", notes = "BNF grammar based Derivation tree. UCI iris, Monk1, Monk2, Monk 3. LOGENPRO CEC 2002 - A joint meeting of the IEEE, the Evolutionary Programming Society, and the IEE. Held in connection with the World Congress on Computational Intelligence (WCCI 2002)", } @InProceedings{Ishikawa:2014:ICEM, author = "Kota Ishikawa and Wataru Kitagawa and Takaharu Takeshita", title = "Shape Optimization of Flux Barriers in IPMSM by using Polygon Model Method with GP", booktitle = "International Conference on Electrical Machines (ICEM 2014)", year = "2014", month = sep, pages = "1403--1408", keywords = "genetic algorithms, genetic programming, Finite element method, Shape optimization, Polygon model method, Interior, permanent magnet synchronous motor", DOI = "doi:10.1109/ICELMACH.2014.6960365", size = "6 pages", abstract = "Recently, one of the problems is high efficiency for the electromagnetic machinery like a motor. This paper presents a new method of shape optimisation. The target is flux barriers in the interior permanent magnetic synchronous motor (IPMSM) which is adopted as the benchmark model in IEE of Japan. Authors use the polygon model method with genetic programming (GP) by the two-dimensional finite element method (2D-FEM). The purpose is the investigation of shape design of flux barriers to improve the electromagnetic characteristics. In a conventional method as a size optimisation, its design parameters are limited in most cases. However, the proposed method is the shape optimisation by the tree structure. This method has more freedom for design parameters because the tree structure is possible to express every shape design.", notes = "Also known as \cite{6960365}", } @Article{Ishino:2002:AEI, author = "Yoko Ishino and Yan Jin", title = "Estimate design intent: a multiple genetic programming and multivariate analysis based approach", journal = "Advanced Engineering Informatics", year = "2002", volume = "16", pages = "107--125", number = "2", keywords = "genetic algorithms, genetic programming, Design process, Design intent, Multivariate analysis", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6X1X-45XR6TT-3/2/d9b1ec675457ba42091348338705293d", ISSN = "1474-0346", DOI = "doi:10.1016/S1474-0346(01)00005-2", abstract = "Understanding design intent of designers is important for managing design quality, achieving coherent integration of design solutions, and transferring design knowledge. This paper focuses on automatically estimating design intent, represented as a summation of weighted functions, based on the operational and product-specific information monitored through design processes. This estimated design intent provides a basis for us to identify the evaluation tendency of designers' ways of doing design. To represent and estimate the design intent, we introduced a staged design evaluation model as a general yet powerful model of design decision-making process, and developed a methodology for estimation of design intent (MEDI) as a reasoning method. MEDI is composed of two basic algorithms. One is our newly introduced multiple genetic programming (MGP) and the other is statistical multivariate analysis including principal component analysis and multivariate regression. The characteristics of MEDI are; (1) principal component analysis provides approximate evaluation of how much preferable a specific product model is, assuming the final product model (or design) is the most preferable one; (2) MGP enables us to simultaneously estimate both structure of target performance functions and the approximate values of their weights for a domain of design problems; and (3) multivariate regression readjusts the approximate weights obtained by MGP into more accurate ones for specific design problems within the domain. Our framework and methods have been successfully tested in a case study of designing a double-reduction gear system.", } @InProceedings{Ishiwaka:2020:ICHMS, author = "Yuko Ishiwaka and Kazutaka Izumi and Tomohiro Yoshida and Gaku Yasui", title = "Wordoids: Boid Based Personalized Word Clustering System in Dark Side Ternary Stars", booktitle = "2020 IEEE International Conference on Human-Machine Systems (ICHMS)", year = "2020", abstract = "Personalized systems are required in many domains. However, gathering training data for personalization from individuals, as is necessary with deep learning, is a difficult and time-consuming task. With our proposed method, less or no training data is required to adapt to individuals' preferences, even when they shift over time. We introduce a potential field based method {"}Dark Side Ternary Stars{"} which has three components, GAGPL, Wordoids, and EGO. In this paper, we focus on two of them, {"}Wordoids{"}, which adopt extends Boids algorithms to perform individualized classification of keywords by topic and improved our previous work {"}GAGPL{"}, which calculates the individualized semantic orientation of sentences by using learned words per topic. As experimental results, we applied this method to news articles about Japanese professional baseball and we show that our method can obtain individualized semantic orientations and summaries of the article per individual.", keywords = "genetic algorithms, genetic programming, Semantics, Training data, Force, Mathematical model, Boids, Wordoids, Personalized Word Distance, GAGPL", DOI = "doi:10.1109/ICHMS49158.2020.9209540", month = sep, notes = "Also known as \cite{9209540}", } @InProceedings{Iskander:2022:AP-S, author = "Magdy F. Iskander", booktitle = "2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI)", title = "Genetic Programming for Advanced Metamaterial Design: A Legacy of a Perfectionist", year = "2022", pages = "855--856", abstract = "In this presentation we celebrate Professor Tapan Sarkar's legacy and outstanding contributions to the electromagnetic community by drawing parallelisms between his innovative and longstanding contributions to the electromagnetic technologies.", keywords = "genetic algorithms, genetic programming, Conferences, Parallel processing, Metamaterials, Electromagnetics", DOI = "doi:10.1109/AP-S/USNC-URSI47032.2022.9886990", ISSN = "1947-1491", month = jul, notes = "Also known as \cite{9886990}", } @InProceedings{DBLP:conf/ijcci/IslamKG18, author = "Mohiul Islam and Nawwaf N. Kharma and Peter Grogono", editor = "Christophe Sabourin and Juan Julian Merelo Guervos and Alejandro Linares-Barranco and Kurosh Madani and Kevin Warwick", title = "Expansion: {A} Novel Mutation Operator for Genetic Programming", booktitle = "Proceedings of the 10th International Joint Conference on Computational Intelligence, {IJCCI} 2018, Seville, Spain, September 18-20, 2018", pages = "55--66", publisher = "SciTePress", year = "2018", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.5220/0006927800550066", DOI = "doi:10.5220/0006927800550066", timestamp = "Thu, 26 Sep 2019 16:43:57 +0200", biburl = "https://dblp.org/rec/conf/ijcci/IslamKG18.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{ISLAM:2020:ASC, author = "Mohiul Islam and Nawwaf Kharma and Peter Grogono", title = "Mutation operators for Genetic Programming using Monte Carlo Tree Search", journal = "Applied Soft Computing", pages = "106717", year = "2020", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2020.106717", URL = "http://www.sciencedirect.com/science/article/pii/S1568494620306554", keywords = "genetic algorithms, genetic programming, Evolutionary computation, Computational intelligence, Program synthesis, Monte Carlo Simulation, Monte Carlo Tree Search, Symbolic regression, Expansion, Reduction", abstract = "Expansion is a novel mutation operator for Genetic Programming (GP). It uses Monte Carlo simulation to repeatedly expand and evaluate programs using unit instructions, which extends the search beyond the immediate - often misleading - horizon of offspring programs. To evaluate expansion, a standard Koza-style tree-based representation is used and a comparison is carried out between expansion and sub-tree crossover as well as point mutation. Using a diverse set of benchmark symbolic regression problems, we prove that expansion provides for better fitness performance than point mutation, when included with crossover. Expansion also provides a significant boost to fitness when compared to GP using crossover only, with similar or lower levels of program bloat. Despite expansion's success in improving evolutionary performance, it does not eliminate the problem of program bloat. In response, an analogous genetic operator, reduction, is proposed and tested for its ability to keep a check on program size. We conclude that the best fitness can be achieved by including these three operators in GP: crossover, point mutation and expansion", } @InProceedings{Islam:alife22, author = "Mohiul Islam and Nawwaf Kharma and Peter Grogono", title = "String: a programming language for the evolution of ribozymes in a new computational protocell model", booktitle = "Proceedings of the 2022 Conference on Artificial Life", year = "2022", editor = "Silvia Holler and Richard Loeffler and Stuart Bartlett", pages = "362--370", month = jul # " 18-22", organisation = "ISAL", publisher = "MIT Press", note = "54", keywords = "genetic algorithms, genetic programming", URL = "https://direct.mit.edu/isal/proceedings-pdf/isal/34/54/2035323/isal_a_00538.pdf", DOI = "doi:10.1162/isal_a_00538", abstract = "String is a new computer language designed specifically for the implementation of 'ribozymes', the active entities within a new (highly simplified) model of protocellular life. The purpose of the model (which is presented here, only in outline) is the study of the abstract nature of simple cellular life and its relationship to computation. This model contains passive and active entities; passive entities are data and active ones are executable data (or programs). All programs in our model are written or evolved in String. In this paper, we describe String and provide examples of both hand-written and evolved String programs belonging to different functional categories needed for cellular operation (e.g., mass transporter, information transporter, transformer, replicator and translator). Results from the evolutionary runs are presented and discussed, where almost all ribozymes reached their optimum fitness.", notes = "held virtually due to the ongoing COVID-19 pandemic. https://alife.org/conference/alife-2022/", } @Article{Islam:2022:JCEM, author = "Muhammad Saiful Islam and Saeed Reza Mohandes and Amir Mahdiyar and Alireza Fallahpour and Ayokunle Olubunmi Olanipekun", title = "A Coupled Genetic Programming Monte Carlo Simulation-Based Model for Cost Overrun Prediction of Thermal Power Plant Projects", journal = "Journal of Construction Engineering and Management", year = "2022", volume = "148", number = "8", pages = "04022073", keywords = "genetic algorithms, genetic programming", URL = "https://ascelibrary.org/doi/abs/10.1061/%28ASCE%29CO.1943-7862.0002327", DOI = "doi:10.1061/(ASCE)CO.1943-7862.0002327", size = "14 pages", abstract = "Globally, power projects are prone to cost overrun projects. Within the body of knowledge, previous studies have paid less attention to predicting the cost overruns to assist contingency cost planning. Particularly, in thermal power plant projects (TPPPs), the enormous risks involved in their delivery undermine the accuracy of cost overrun prediction. To prevent cost overrun in thermal power plant projects, these risks need to be accounted for by employing sophisticated cost overrun prediction techniques. This study aims to develop a hybrid predictive-probabilistic-based model (HPPM) that integrates a genetic programming technique with Monte Carlo simulation (MCS). The HPPM was proposed based on the data collected from TPPPs in Bangladesh. Also, the sensitivity of the HPPM was examined to identify the critical risks in cost overruns simulation. The simulation outcomes show that 40.48percent of a projects initial estimated budget was the most probable to cost overrun, while the maximum cost overrun will not exceed 75percent with 90percent confidence. Practically, the analysis will sensitize project managers to emphasize thermal plants budget accuracy not only at the initial project delivery phase but throughout the project life cycle. Theoretically, the HPPM could be employed for cost overrun prediction in other types of power plant projects.", notes = "School of Engineering and Technology, Central Queensland Univ., Melbourne Campus, VIC3000, Australia. American Society of Civil Engineers", } @Article{Islam2012, author = "Tanvir Islam and Miguel A. Rico-Ramirez and Dawei Han", title = "Tree-based Genetic Programming Approach to Infer Microphysical Parameters of the DSDs from the Polarization Diversity Measurements", journal = "Computer \& Geosciences", volume = "48", pages = "20--30", year = "2012", ISSN = "0098-3004", DOI = "doi:10.1016/j.cageo.2012.05.028", URL = "http://www.sciencedirect.com/science/article/pii/S0098300412001847?v=s5", keywords = "genetic algorithms, genetic programming, Drop size distribution (DSD) retrievals, Polarimetric radar, Dual polarisation radar, Disdrometer raindrop spectra, Precipitation microphysics, Shape and size parameters", abstract = "The use of polarisation diversity measurements to infer the microphysical parametrisation has remained an active goal in the radar remote sensing community. In view of this, the tree-based genetic programming (GP) as a novel approach has been presented for retrieving the governing microphysical parameters of a normalised gamma drop size distribution model- D0 (median drop diameter), Nw (concentration parameter), and ?\mu (shape parameter) from the polarisation diversity measurements. A large number of raindrop spectra acquired from a Joss-Waldvogel disdrometer has been used to develop the GP models, relating the microphysical parameters to the T-matrix scattering simulated polarization measurements. Several functional formulations retrieving the microphysical parameters-D0 [f(ZDR), f(ZH, ZDR)], log10Nw [f(ZH, D0), f(ZH, ZDR, D0), and ?\mu f(ZDR, D0), f(ZH, ZDR, D0)], where ZH represents reflectivity and ZDR represents differential reflectivity, have been investigated, and applied to a S-band polarimetric radar (CAMRA) for evaluation. It has been shown that the GP model retrieved microphysical parameters from the polarisation measurements are in a reasonable agreement with disdrometer observations. The calculated root mean squared errors (RMSE) are noted as 0.23-0.25 mm for D0, 0.74-0.85 for log10Nw (Nw in mm-1 mm-3), and 3.30-3.36 for ?. The GP model based microphysical retrieval procedure is further compared with a physically based constrained gamma model for D0 and log10Nw estimates. The close agreement of the retrieval results between the GP and the constrained gamma models support the suitability of the proposed genetic programming approach to infer microphysical parametrisation.", } @Article{Islam:2012:ASR, author = "Tanvir Islam and Miguel A. Rico-Ramirez and Dawei Han and Prashant K. Srivastava", title = "Using {S-band} dual polarized radar for convective/stratiform rain indexing and the correspondence with {AMSR-E GSFC} profiling algorithm", journal = "Advances in Space Research", volume = "50", number = "10", pages = "1383--1390", year = "2012", month = "15 " # nov, keywords = "genetic algorithms, genetic programming, Polarimetric radar, AMSR-E GPROF rain type, Clouds and precipitation types, Separation and classification, Drop size distributions (DSD), Passive microwave sensors", ISSN = "0273-1177", DOI = "doi:10.1016/j.asr.2012.07.011", URL = "http://www.sciencedirect.com/science/article/pii/S0273117712004693", abstract = "The separation of rain types in convective and stratiform regimes has long been a goal in microwave remote sensing of precipitation research. In this essence, a dual polarised radar based indexing scheme that provides information on convective and stratiform (C/S) rain regimes has been presented in correspondence with advanced microwave scanning radiometer - earth observing system (AMSR-E) GSFC profiling algorithm estimate of convective rain percentage. The dual polarized radar based C/S indexing scheme first retrieves the normalised gamma drop size distribution parameters, median volume drop diameter (D0) and concentration parameter (Nw), from dual polarized radar measurements ZH and ZDR, representing reflectivity and differential reflectivity respectively, by means of the genetic programming approach. Next, the C/S rain index is calculated based on the formulation of an empirical relation in Nw-D0 domain. The scheme has been inspected and applied on measurements from the S-band Chilbolton dual polarised radar. A considerable number of {"}coincident{"} cases from the radar and the AMSR-E observations are investigated. It has been revealed that the dual polarised radar based C/S rain indexing is in a similar pattern with the AMSR-E GSFC profiling algorithm estimate of convective rain percentage. Generally, as C/S rain index value increases, which signifies a stratiform to convective trend, the AMSR-E convective rain percentage also increases.", } @PhdThesis{Islam:thesis, author = "Tanvir Islam", title = "Advances in numerical analysis of precipitation remote sensing with polarimetric radar", school = "Civil Engineering, University of Bristol", year = "2012", address = "UK", note = "University Prize for Best Thesis in Faculty of Engineering in 2012-13", keywords = "genetic algorithms, genetic programming, polarimetric radar, dual polarisation radar, microphysics of precipitation, drop size distribution (DSD), clutter and anomalous propagation identification, attenuation correction, rainfall estimators, microphysical DSD retrievals, melting layer and bright band detection, hydrometeor classification", broken = "http://www.bristol.ac.uk/engineering/graduate/commendations/", URL = "http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.574418", abstract = "Since the early use of ground radar for precipitation detection in post-world war II, the radar has evolved on its own in precipitation remote sensing research and applications. The recent advances in radar remote sensing is, the development of polarimetric radar, also known as dual polarization radar, which has the capability of transmitting electromagnetic spectra in both horizontal (H) and vertical (V) polarisation states, thus providing additional information of the target precipitation particles by measuring polarimetric signatures, the reflectivity factor at H polarisation (ZH) , differential reflectivity (ZDR) , differential propagation phase (Delta Phi DP) , specific differential phase (KDP) , cross-correlation coefficient (PHV) and linear depolarization ratio (LDR). In commensurate with new era in precipitation remote sensing, this thesis explores the potential of polarimetric radar on the improvements in precipitation remote sensing in the UK context. All major area of the improvements aided by the polarimetry and polarimetric signatures are addressed. These include the clutter and anomalous propagation identification, attenuation signal correction, polarimetric rainfall estimators, drop size distribution retrievals, bright band/melting layer recognition and hydrometeor classification. Several novel approaches and investigations dealing with the polarimetric improvements are scrutinised and proposed in terms of numerical analysis, while some of them employ artificial intelligence (AI) techniques. Key original contributions in synergy with polarimetric radar signatures on precipitation remote sensing are: 1) long-term disdrometer DSD analysis to support the development of polarimetry based algorithms and models, 2) the use of several AI techniques such as support vector machine, artificial neural network, decision tree, and nearest neighbour system for clutter identification, 3) the sensitivity associated with total differential propagation phase constraint (delta phi DP) on ZH correction for attenuation, 4) the exploration of polarimetric rainfall estimators [R(ZH, ZDR, Knp)] for rainfall estimation, 5) a genetic programming approach for drop size distribution retrievals [Do(ZH, ZDR) , Nw(ZH, ZDR, Do), mu(ZH, ZDR, Do)], and its use for convective/stratiform rain indexing, and 6) a fuzzy logic based system for automatic melting layer/bright band recognition and hydrometeor classification as well as appraisal with a numerical weather prediction (NWP) model and radio soundings observations. In fact, the radar polarimetry has been proved not only to improve data quality and precipitation estimation, but also characterising the precipitation particles, thus has a great potential on fostering the precipitation remote sensing research and applications.", notes = "EThOS Persistent ID: uk.bl.ethos.574418", } @Article{Ismail:2009:IJCSIS, title = "Genetic Programming Framework for Fingerprint Matching", author = "Ismail A. Ismail and Nabawia A. ElRamly and Mohammed A. Abd-ElWahid and Passent M. ElKafrawy and Mohammed M. Nasef", journal = "International Journal of Computer Science and Information Security", year = "2009", ISSN = "19475500", publisher = "LJS Publisher and IJCSIS Press", bibsource = "OAI-PMH server at www.doaj.org", oai = "oai:doaj-articles:2bfab83f03215701df9aa2b73d4725d9", keywords = "genetic algorithms, genetic programming, Fingerprint matching, minutiae points", URL = "http://arxiv.org/abs/0912.1017", URL = "http://sites.google.com/site/ijcsis/vol-6-no-2-november-2009", broken = "http://www.doaj.org/doaj?func=openurl\&genre=article\&issn=19475500\&date=2009\&volume=6\&issue=2\&spage=316", URL = "http://arxiv.org/abs/0912.1017", abstract = "A fingerprint matching is a very difficult problem. Minutiae-based-matching is the most popular and widely used technique for fingerprint matching. The minutiae points considered in automatic identification systems are based normally on termination and bifurcation points. In this paper we propose a new technique for fingerprint matching using minutiae points and genetic programming. The goal of this paper is extracting the mathematical formula that defines the minutiae points. .", } @Misc{Ismail:2015, author = "Siti Afiqah Ismail and Jason Teo Tze Wi", title = "Evolution of an adaptive mathematics learning game for lower primary students", year = "2015", keywords = "genetic algorithms, genetic programming", URL = "http://www.ums.edu.my/fki/index.php/en/evolution-of-an-adaptive-mathematics-learning-game-for-lower-primary-students", URL = "http://www.ums.edu.my/fki/files/EVOLUTION_OF_AN_ADAPTIVE_MATHEMATICS_LEARNING_GAME_FOR_LOWER_PRIMARY_STUDENTS_new.pdf", size = "6 pages", abstract = "The newly coined term courseware was actually derived from the words course and software. The courseware that is available nowadays has been added with the adaptiveness values. These adaptive elements have been implemented by researchers in various ways. Some are using fuzzy, neural-network or even metaheuristics to implement the adaptive elements in to their courseware systems. By using these approaches, they apply the adaptiveness by optimizing the learning path. In this research, the learning path will be optimized based on the learners' understanding level of the concept being learnt. This approach is commonly known as personalization. In this project, the Evolutionary Algorithm approach is selected as the optimization method. The EA used in this project is Genetic Programming. Instead of evolving the separate representations to the solution, Genetic Programming evolves the solution itself. Genetic Programming usually evolves computer programs instead of evolving the solution representations found in Genetic Algorithms. Nonetheless, the process of Genetic Programming is still similar to Genetic Algorithms. Apart from implementing GP into the learning system, this research uses the basic user interface design for designing an interface of the mathematics learning game. Since the main audience of the game is young children, some interface design elements especially suited for young children have to be taken into account. In this research, 4 experiments had been conducted to test the algorithms implemented. In comparison, experiment 2 yielded better results compared to other experiments. In experiment 2, the level was set to be fixed, while in the other experiments, the level changing parameter is set to be random. In experiments 1, 3 and 4, the findings show that the random changing level is unpredictable. Some level jumps are too high and some level jumps are too low. In general, the overall outcomes of this research demonstrate that EAs can be a viable approach in terms of implementing adaptive courseware at least in the realms of teaching mathematics to young children.", notes = " ...new.pdf gives rough outline of table of contents See also: THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENT FOR THE DEGREE OF BACHELOR OF COMPUTER SCIENCE (SOFTWARE ENGINEERING) WITH HONOURS", } @Article{ISMAIL:2023:engstruct, author = "Mohamed K. Ismail and Basem H. AbdelAleem and Assem A. A. Hassan and Wael El-Dakhakhni", title = "Prediction of tapered steel plate girders shear strength using multigene genetic programming", journal = "Engineering Structures", volume = "295", pages = "116806", year = "2023", ISSN = "0141-0296", DOI = "doi:10.1016/j.engstruct.2023.116806", URL = "https://www.sciencedirect.com/science/article/pii/S014102962301221X", keywords = "genetic algorithms, genetic programming, Data-driven models, Multi-gene genetic programming, Ultimate shear strength, Sensitivity analyses, Tapered web panel", abstract = "Tapered steel plate girders have been widely used in the construction of bridges and heavy industry structures. However, predicting their structural behavior, particularly in shear, is challenging due to their non-prismatic sections and ill-understood influencing factors. Therefore, simplified analytical and classic regression techniques may not be able to capture the underlying nonlinear relationships controlling the shear behavior of tapered plate girders. In this study, multigene genetic programming (MGGP) was used to explore such complex relationships, and subsequently develop robust predictive expressions for the tapered steel plate girders shear strength. Attributed to the lack of a large experimental dataset, a nonlinear finite element model (FEM) was first developed and validated against available experimental results in literature. The FEM was subsequently employed to generate a matrix of 211 numerical results to augment 200 more FEM results compiled from previous studies, to cover a wider range of design parameters. The entire dataset was then used in the training and testing of the MGGP predictive expressions. The prediction accuracy of the developed expressions was evaluated against that of other existing expressions. The results showed that the adopted MGGP approach, guided be mechanics understanding, produced elegant predictive expressions with high level of accuracy and generalizability compared to other existing ones examined herein. As such, the developed expressions present an efficient prediction tool that can be adopted by design standards for estimating the ultimate shear strength of tapered steel girders. Finally, reliability analysis is performed on the developed expressions to introduce strength reduction factors in order to satisfy target design conservatism", } @Article{Ismail:2023:MCR, author = "Mohamed K. Ismail and Ahmed Yosri and Wael El-Dakhakhni", title = "Evolutionary computing-based models for predicting seismic shear strength of {RC} columns", journal = "Magazine of Concrete Research", year = "2023", volume = "76", number = "3", pages = "124--143", keywords = "genetic algorithms, genetic programming, artificial intelligence, columns, concrete structures, shear strength, structural design", ISSN = "1751-763X", URL = "https://www.sciencedirect.com/science/article/pii/S1751763X23000648", DOI = "doi:10.1680/jmacr.23.00043", abstract = "A number of regression-based models have been proposed to quantify the seismic shear strength of reinforced concrete (RC) columns. However, most of these models suffer from a high degree of uncertainty as a result of the limited datasets used in their development and/or the classic approaches used to capture the non-linear interrelationships between the shear strength and influencing factors. To address these issues, in this work, the power of multi-gene genetic programming (MGGP), guided by mechanics, was harnessed to identify the primary influencing factors and subsequently develop efficient shear capacity prediction models for rectangular and circular RC columns. Comprehensive published datasets for the shear strength of cyclically loaded RC columns were compiled and employed to develop the MGGP-based models. The efficiency of the developed models was assessed and their performance was also compared with that of other relevant prediction models. The results showed that the developed mechanics-guided MGGP approach produced more accurate and consistent prediction models to describe the complex shear behaviour of RC columns under cyclic loading than the models available in the relevant design standards and literature", } @InProceedings{ito:1995:pd, author = "Akira Ito and Hiroyuki Yano", title = "The Emergence of Cooperation in a Society of Autonomous Agents -- The Prisoner's Dilemma Game Under the Disclosure of Contract Histories --", booktitle = "ICMAS-95 Proceedings First International Conference on Multi-Agent Systems", year = "1995", editor = "Victor Lesser", pages = "201--208", address = "San Francisco, California, USA", month = "12--14 " # jun, publisher = "AAAI Press/MIT Press", keywords = "multi-agent", ISBN = "0-262-62102-9", notes = "Society of agents play PD against each other according to inherited strategy. Strategies are specified by (possibly recursive) programs written in a small language. Programs are mutated but no crossover.", } @InProceedings{ito:1996:rrpgGP, author = "Takuya Ito and Hitoshi Iba and Masayuki Kimura", title = "Robustness of Robot Programs Generated by Genetic Programming", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", address = "Stanford University, CA, USA", publisher = "MIT Press", note = "321--326", ISBN = "0-262-61127-9", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap42.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", size = "6 pages", notes = "GP-96. Moderatly complex robot simulation", } @InProceedings{ito:1998:nddx, author = "Takuya Ito and Hitoshi Iba and Satoshi Sato", title = "Non-Destructive Depth-Dependent Crossover for Genetic Programming", booktitle = "Proceedings of the First European Workshop on Genetic Programming", year = "1998", editor = "Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer and Terence C. Fogarty", volume = "1391", series = "LNCS", pages = "71--82", address = "Paris", publisher_address = "Berlin", month = "14-15 " # apr, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-64360-5", DOI = "doi:10.1007/BFb0055929", abstract = "In our previous paper [Ito et al., 1998], a depth-dependent crossover was proposed for GP. The purpose was to solve the difficulty of the blind application of the normal crossover, i.e., building blocks are broken unexpectedly. In the depth-dependent crossover, the depth selection ratio was varied according to the depth of a node. However, the depth-dependent crossover did not work very effectively as generated programs became larger. To overcome this, we introduce a non-destructive depth-dependent crossover, in which each offspring is kept only if its fitness is better than that of its parent. We compare GP performance with the depth-dependent crossover and that with the non-destructive depth-dependent crossover to show the effectiveness of our approach. Our experimental results clarify that the non-destructive depth-dependent crossover produces smaller programs than the depth-dependent crossover.", notes = "EuroGP'98. Santa Fe artificial ant", affiliation = "Japan Advanced Institute of Science and Technology School of Information Science 1-1 Asahidai 923-12 Tatsunokuchi, Nomi, Ishikawa Japan", } @InProceedings{ito:1998:ddx, author = "Takuya Ito and Hitoshi Iba and Satoshi Sato", title = "Depth-Dependent Crossover for Genetic Programming", booktitle = "Proceedings of the 1998 IEEE World Congress on Computational Intelligence", year = "1998", pages = "775--780", address = "Anchorage, Alaska, USA", month = "5-9 " # may, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, blind application, building blocks, crossover operator, depth-dependent crossover, effective partial programs, encapsulation, node depth, selection operator, variable depth selection ratio, mathematical operators, programming, software performance evaluation", ISBN = "0-7803-4869-9", file = "c135.pdf", DOI = "doi:10.1109/ICEC.1998.700150", size = "6 pages", abstract = "It is known that selection and crossover operators contribute to generate solutions in GP. Traditionally, crossover points are selected randomly by a normal (canonical) crossover. However, the traditional method has several difficulties that building blocks (i.e. effective partial programs) are broken because of blind application of the normal crossover. This paper proposes a depth-dependent crossover for GP, in which the depth selection ratio is varied according to the depth of a node. This proposed method is to accumulate building blocks via the encapsulation of the depth-dependent crossover. We compare GP performance with the depth-dependent crossover and that with the normal crossover. Our experimental results clarify that the superiority of the proposed crossover to the normal.", notes = "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE World Congress on Computational Intelligence", } @InCollection{ito:1999:aigp3, author = "Takuya Ito and Hitoshi Iba and Satoshi Sato", title = "A Self-Tuning Mechanism for Depth-Dependent Crossover", booktitle = "Advances in Genetic Programming 3", publisher = "MIT Press", year = "1999", editor = "Lee Spector and William B. Langdon and Una-May O'Reilly and Peter J. Angeline", chapter = "16", pages = "377--399", address = "Cambridge, MA, USA", month = jun, keywords = "genetic algorithms, genetic programming", ISBN = "0-262-19423-6", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/aigp3/ch16.pdf", DOI = "doi:10.7551/mitpress/1110.003.0021", abstract = "There are three genetic operators: crossover, mutation and reproduction in Genetic Programming (GP). Among these genetic operators, the crossover operator mainly contributes to searching for a solution program. Therefore, we aim at improving the program generation by extending the crossover operator. The normal crossover selects crossover points randomly and destroys building blocks. We think that building blocks can be protected by swapping larger substructures. In our former work, we proposed a depth-dependent crossover. The depth-dependent crossover protected building blocks and constructed larger building blocks easily by swapping shallower nodes. However, there was problem-dependent characteristics on the depth-dependent crossover, because the depth selection probability was fixed for all nodes in a tree. To solve this difficulty, we propose a self-tuning mechanism for the depth selection probability. We call this type of crossover a {"}self-tuning depth-dependent crossover{"}. We compare GP performances of the selftuning depthdependent crossover with performances of the original depth-dependent crossover. Our experimental results clarify the superiority of the self tuning depth dependent crossover.", notes = "AiGP3 See http://cognet.mit.edu 11 mux, santa fe ant, 4-even parity, simulated robot", } @PhdThesis{TakuyaIto:thesis, author = "Takuya Ito", title = "Efficient program generation by genetic programming", school = "School of Information Science, Japan Advanced Instutute of Science and Technology", year = "1999", address = "Ishikawa, Japan", month = mar, keywords = "genetic algorithms, genetic programming", URL = "http://hdl.handle.net/10119/876", URL = "https://dspace.jaist.ac.jp/dspace/bitstream/10119/876/3/608paper.pdf", size = "79 pages", abstract = "Genetic Programming (GP) can generate computer programs automatically without any explicit knowledge for target programs (solution programs). The solution programs are generated by means of selection and some genetic operators. However, GP has a difficulty, which it often takes too much time to generate solution programs. This may be a critical problem when GP generates large scale programs. The goal of this work is to generate computer programs efficiently by means of the framework of GP. 'Efficient' means to reduce the number of generations which is necessary to generate solution programs. To realise this goal, this work improves a genetic operator of GP. There are three genetic operators for GP, crossover, mutation and reproduction. Among these genetic operators, crossover mainly contributes to searching for solution programs. Therefore, this work improves crossover. The normal crossover selects a crossover point randomly so that it breaks building blocks (i.e., effective small program which contributes to improving fitness performance) due to its blind application. To solve this problem, this work proposes four new crossovers The first crossover is a 'depth-dependent crossover' and the second crossover is a 'revised depth-dependent crossover'. 'Depth-dependent' means that node selection probability is determined by the depth of the tree structure. On these crossovers, shallower nodes are more often selected, and deeper nodes are selected rarely. The building blocks can be protected by swapping shallower nodes. The third crossover is a 'non-destructive depth-dependent crossover', which is a combination of the depth-dependent crossover and a 'non-destructive crossover'. 'Nondestructive' means that offsprings of crossover are kept only if their fitness are better than fitness of their parents. This crossover is proposed to solve the program size problem of the depth-dependent crossover. The fourth crossover is a 'self-tuning depth-dependent crossover'. On this crossover, each individual of the population has a different depth selection probability and depth selection probability of a selected individual is copied to the next generation. This crossover is proposed to enhance the applicability of the depth-dependent crossover for various GP problems. This work compares GP performances (i.e., fitness value and the size of generated programs) of the normal crossover with performances of these four crossovers using standard GP problems and an original robot problem. These experimental results clarify that the superiority of the proposed crossovers to the normal crossover. Furthermore, this work discusses the building block hypothesis, which explains how crossover searches solution programs with a survey of previous works and these experimental results.", notes = "Supervisor Satoshi Sato", } @InCollection{ito:2000:RMUGA, author = "Choshu Ito", title = "RF-LDMOSFET Modeling Using Genetic Algorithms", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "221--227", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms", notes = "part of \cite{koza:2000:gagp}", } @InCollection{ito:2003:SRCWCEBGP, author = "Keith Ito", title = "Simple Robots in a Complex World: Collaborative Exploration Behavior using Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2003", year = "2003", editor = "John R. Koza", pages = "91--99", address = "Stanford, California, 94305-3079 USA", month = "4 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", URL = "http://www.genetic-programming.org/sp2003/Ito.pdf", notes = "part of \cite{koza:2003:gagp}", } @InProceedings{conf/icaart/ItoTI14, author = "Takashi Ito and Kenichi Takahashi and Michimasa Inaba", title = "Experiments Assessing Learning of Agent Behavior using Genetic Programming with Multiple Trees", pages = "264--271", booktitle = "{ICAART} 2014 - Proceedings of the 6th International Conference on Agents and Artificial Intelligence, Volume 1, {ESEO}, Angers, Loire Valley, France, 6-8 March, 2014", publisher = "SciTePress", year = "2014", editor = "Beatrice Duval and H. Jaap van den Herik and Stephane Loiseau and Joaquim Filipe", keywords = "genetic algorithms, genetic programming", isbn13 = "978-989-758-015-4", bibdate = "2014-09-22", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icaart/icaart2014-1.html#ItoTI14", URL = "http://dx.doi.org/10.5220/0004751402640271", } @Article{journals/jcp/ItoTI16, author = "Takashi Ito and Kenichi Takahashi and Michimasa Inaba", title = "Extension of Genetic Programming with Multiple Trees for Agent Learning", journal = "Journal of Computers", year = "2016", number = "4", volume = "11", pages = "329--340", keywords = "genetic algorithms, genetic programming, Autonomous agent, conditional probability, island model", bibdate = "2016-06-09", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/jcp/jcp11.html#ItoTI16", ISSN = "1796-203X", URL = "http://www.jcomputers.us/index.php?m=content&c=index&a=show&catid=179&id=2649", URL = "http://www.jcomputers.us/vol11/jcp1104-07.pdf", DOI = "doi:10.17706/jcp.11.4.329-340", size = "12 pages", abstract = "This paper proposes an extension of genetic programming (GP) with multiple trees. In order to improve the performance, GP with control node (GPCN) and its three kinds of modification have been proposed. In GPCN, an individual consists of several trees which have the number P of executions. In previous work, the two kinds of modification, the conditional probability and the cross-cultural island model are employed. This paper proposes two methods: the new island model that combines the conditional probability with two islands in the cross-cultural island model and a method exchanges multiple trees in an individual in a suitable order. Experiments are conducted to show the performance in the garbage collection problem and the Santa Fe Trail problem.", } @Article{Ito:2016:PCS, author = "Takashi Ito and Kenichi Takahashi and Michimasa Inaba", title = "Obtaining Repetitive Actions for Genetic Programming with Multiple Trees", journal = "Procedia Computer Science", volume = "96", pages = "120--128", year = "2016", note = "Knowledge-Based and Intelligent Information and Engineering Systems: Proceedings of the 20th International Conference KES-2016", ISSN = "1877-0509", DOI = "doi:10.1016/j.procs.2016.08.111", URL = "http://www.sciencedirect.com/science/article/pii/S1877050916319123", abstract = "This paper proposes a method to improve genetic programming with multiple trees (GPCN). An individual in GPCN comprises multiple trees, and each tree has a number P that indicates the number of repetitive actions based on the tree. In previous work, a method for updating the number P has been proposed to obtain P suitable to the tree in evolution. However, in the method efficiency becomes worse as the range of P becomes wider. In order to solve the problem, in this study, two methods are proposed: inheriting the number P of a tree from an excellent individual and using mutation for preventing the number P from being into a local optimum. Additionally, a method to eliminate trees consisting of a single terminal node is proposed.", keywords = "genetic algorithms, genetic programming, autonomous agent, garbage collection problem, evolutionary learning, multiple trees.", } @InProceedings{Ito:2021:IMCOM, author = "Takashi Ito", title = "Search Method of Number of Trees for Genetic Programming with Multiple Trees", booktitle = "2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM)", year = "2021", abstract = "Genetic programming (GP), which is an evolutionary computational method, is known to be effective for agent problems because individuals are represented by a tree structure. As an extension method, GP with control nodes (GP_CN ) has been proposed. Because one individual has multiple tree structures, GP_CN can efficiently evolve and obtain highly readable behavioral rules. However, the number of trees suitable for each problem has to be manually adjusted in advance and cannot be easily applied various problems. In the previous study, a method for automatically determining the number of trees have proposed. However, because the method of the previous study changes the fitness function and uses a special population, it cannot be combined with the extension methods to improve the evolution performance. In this study, a method for searching for the appropriate number of trees using three islands is proposed. The proposed method divides the population into three islands, but because the genetic operations and the fitness function of each island are not changed, it can be combined with the existing extension methods. In the experiments, they are compared these using two benchmark problems.", keywords = "genetic algorithms, genetic programming, Search methods, Sociology, Benchmark testing, Information management, Statistics, Genetic Approach, Autonomous Agent, Multiple Trees", DOI = "doi:10.1109/IMCOM51814.2021.9377427", month = jan, notes = "Also known as \cite{9377427}", } @InProceedings{Ito:2023:IMCOM, author = "Takashi Ito", booktitle = "2023 17th International Conference on Ubiquitous Information Management and Communication (IMCOM)", title = "Improved Evolution Performance for Genetic Programming with Method to Search Numbers of Trees", year = "2023", abstract = "In evolution, Genetic programming (GP) is proposed to obtain suitable action rules for a target problem. Because action rules are expressed in a tree structure, their meaning is easily understandable. In addition, GP with multiple tree structures has been proposed for agent learning, and a method has been proposed to decide the number of multiple trees that must be set to individuals for each target problem during evolution. In this study, we focused on an algorithm to search for the suitable number of multiple trees in evolution and introduced a method for generating individuals with conditional probability to improve performance.", keywords = "genetic algorithms, genetic programming, Sociology, Information management, Statistics, Evolutionary Computation, Autonomous Agent, Multiple Action Rules", DOI = "doi:10.1109/IMCOM56909.2023.10035600", month = jan, notes = "Also known as \cite{10035600}", } @InProceedings{Ito:2021:GCCE, author = "Yusaku Ito and Hironori Washizaki and Kazunori Sakamoto and Yoshiaki Fukazawa", title = "Evaluating Partial Correctness of Programs in Automated Program Repair", booktitle = "2021 IEEE 10th Global Conference on Consumer Electronics (GCCE)", year = "2021", pages = "742--743", abstract = "Genetic programming-based automated program repair is actively studied as a bug fixing method. The existing methods evaluates randomly generated solution candidates using the success rate of test suites. However, the candidates are sometimes evaluated inaccurately. This study proposes a method to more appropriately judge the correctness of program candidates. The proposed method verifies the correctness of the intermediate calculation process using statements to check the predicted conditions for internal variables. In an experiment involving the Defects4J dataset, the execution time was reduced in 15 of the 23 bugs.", keywords = "genetic algorithms, genetic programming, genetic improvement, APR", DOI = "doi:10.1109/GCCE53005.2021.9621861", ISSN = "2378-8143", month = oct, notes = "Also known as \cite{9621861}", } @InProceedings{ivan:1998:aplspGP, author = "Laur Ivan", title = "Automatic Parallelization of Loops in Sequential Programs using Genetic Programming", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "84 and 257", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1998/ryan_1998_aplspGP.pdf", size = "1+1 page", notes = "Paragen OCCAM GP-98LB, GP-98PhD Student Workshop", } @InProceedings{Ivanchik:2023:CEC, author = "Elizaveta Ivanchik and Alexander Hvatov", title = "Directed differential equation discovery using modified mutation and cross-over operators", booktitle = "2023 IEEE Congress on Evolutionary Computation (CEC)", year = "2023", editor = "Gui DeSouza and Gary Yen", address = "Chicago, USA", month = "1-5 " # jul, keywords = "genetic algorithms, genetic programming, equation discovery, evolutionary algorithm, knowledge-based algorithm, directed evolution", isbn13 = "979-8-3503-1458-8/23/", URL = "https://human-competitive.org/sites/default/files/entry_hvatov.txt", URL = "https://human-competitive.org/sites/default/files/directed_differential_equation_discovery_using_modified_mutation_and_cross-over_operators.pdf", URL = "https://arxiv.org/abs/2308.04996", DOI = "doi:10.1109/CEC53210.2023.10254047", code_url = "https://github.com/ITMO-NSS-team/CEC_2023_knowledge", size = "10 pages", abstract = "The discovery of equations with knowledge of the process origin is a tempting prospect. However, most equation discovery tools rely on gradient methods, which offer limited control over parameters. An alternative approach is the evolutionary equation discovery, which allows modification of almost every optimization stage. we examine the modifications that can be introduced into the evolutionary operators of the equation discovery algorithm, taking inspiration from directed evolution techniques employed in fields such as chemistry and biology. The resulting approach, dubbed directed equation discovery, demonstrates a greater ability to converge towards accurate solutions than the conventional method. To support our findings, we present experiments based on Burgers wave, and Korteweg-de Vries equations.", notes = "Entered 2023 HUMIES See also arXiv \cite{ivanchik2023directeddifferentialequationdiscovery} CEC2023", } @PhdThesis{Ivask:thesis, author = "Eero Ivask", title = "Digital Test in WEB-Based Environment", school = "Computer Engineering and Diagnostics, Department of Computer Engineering, Tallinn University of Technology", year = "2006", address = "Estonia", month = "5 " # jul, keywords = "genetic algorithms, genetic programming, SBSE, digital electronics, digital integrated circuits, very large scale integration,y digital testing, defects, fault simulation, computerized simulation, computer modelling, web-based environment, Internet, software", isbn13 = "9985596366", ISSN = "1406-4731", URL = "https://digikogu.taltech.ee/en/Download/e1d292d3-a351-49f1-bee4-abd9ec1fd7b7", URL = "https://digikogu.taltech.ee/en/Item/501c2593-1799-4946-a5f6-fa6a569fe80c", URL = "https://digikogu.taltech.ee/en/Download/e1d292d3-a351-49f1-bee4-abd9ec1fd7b7.pdf", size = "127 pages", abstract = "Current thesis presents an Internet based collaborative framework for digital testing using genetic algorithms for test generation software modules. Genetic algorithms are proposed in order to overcome complexity of the test generation problem for modern digital integrated circuits. Issues of hierarchical fault simulation and defect oriented fault simulation for test quality analysis are discussed as simulation is critical issue in genetic test generation. Digital test design flow begins with behavioural level VHDL description. Suitable flow chart like input format is extracted from source VHDL and fed into academical high-level synthesis tool xTractor. Subsequently generation of decision diagram models for test generation tools follows. Current thesis also addresses issues of collaborative design and test. Universal state-of-the-art collaborative platform MOSCITO is described and possibilities of its use for digital design and test flow are analysed and suitable strategies for work flow integration with existing test tools are proposed. In addition, necessary enhancements are proposed in order to use the MOSCITO system in firewall-protected environments. Finally, based on earlier studies and experience, the new completely http protocol based environment for remote tool usage is proposed. New platform has three-tier architecture using mostly Java applets as front-end, servlets on Tomcat as middleware and MySql as physical back end database server.", notes = "In English supervisor: Raimund-Johannes Ubar", } @InProceedings{Ivert:2015:CEC, author = "Annica Ivert and Claus Aranha and Hitoshi Iba", title = "Feature Selection and Classification Using Ensembles of Genetic Programs and Within-class and Between-class Permutations", booktitle = "Proceedings of 2015 IEEE Congress on Evolutionary Computation (CEC 2015)", year = "2015", editor = "Yadahiko Murata", pages = "1121--1128", address = "Sendai, Japan", month = "25-28 " # may, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-7491-7", DOI = "doi:10.1109/CEC.2015.7257015", abstract = "Many feature selection methods are based on the assumption that important features are highly correlated with their corresponding classes, but mainly uncorrelated with each other. Often, this assumption can help eliminate redundancies and produce good predictors using only a small subset of features. However, when the predictability depends on interactions between features, such methods will fail to produce satisfactory results. In this paper a method that can find important features, both independently and dependently discriminative, is introduced. This method works by performing two different types of permutation tests that classify each of the features as either irrelevant, independently predictive or dependently predictive. It was evaluated using a classifier based on an ensemble of genetic programs. The attributes chosen by the permutation tests were shown to yield classifiers at least as good as the ones obtained when all attributes were used during training - and often better. The proposed method also fared well when compared to other attribute selection methods such as RELIEFF and CFS. Furthermore, the ability to determine whether an attribute was independently or dependently predictive was confirmed using artificial datasets with known dependencies.", notes = "1005 hrs 15256 CEC2015", } @InProceedings{iwashita:2002:imgwiaadc, author = "Makoto Iwashita and Hitoshi Iba", title = "Island Model GP with Immigrants Aging and Depth-Dependent Crossover", booktitle = "Proceedings of the 2002 Congress on Evolutionary Computation CEC2002", editor = "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and Mark Shackleton", pages = "267--272", year = "2002", publisher = "IEEE Press", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", organisation = "IEEE Neural Network Council (NNC), Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", ISBN = "0-7803-7278-6", month = "12-17 " # may, notes = "Dec 2012 ieee xplor says 1st author is Washita CEC 2002 - A joint meeting of the IEEE, the Evolutionary Programming Society, and the IEE. Held in connection with the World Congress on Computational Intelligence (WCCI 2002)", keywords = "genetic algorithms, genetic programming, Deme, Migration, aborigine, depth-dependent crossover, genetic operator, global search strategies, immigrant aging, island model GP, island model genetic programming, local search strategies, evolutionary computation, mathematical operators, search problems", DOI = "doi:10.1109/CEC.2002.1006245", abstract = "This paper proposes a new method for island model GP. The proposed method applies a traditional genetic operator to an aborigine and a depth-dependent crossover to the immigrants according to their ages, which show how long they survive in the island.This method can provide both local and global search strategies. The experimental results have shown that our approach works effectively.", } @Article{Izabela:2023:GPEM, author = "Rejer Izabela and Lorenz Krzysztof", title = "{GAAMmf}: genetic algorithm with aggressive mutation and decreasing feature set for feature selection", journal = "Genetic Programming and Evolvable Machines", year = "2023", volume = "24", number = "2", pages = "Article number: 10", month = dec, note = "Online first", keywords = "genetic algorithms, Feature selection, Aggressive mutation, Holland, NSGA2, AAM", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-023-09458-y", size = "25 pages", abstract = "a modified version of a genetic algorithm with aggressive mutation (GAAM), one of the genetic algorithms (GAs) used for feature selection. The modification proposed in this study expands the original GAAM's capabilities by allowing not only feature selection but also feature reduction. To obtain this effect, we applied the concept of ranks used in the non-dominated sorting genetic algorithm (NSGA) and the concept of penalty term used in the Holland genetic algorithm. With those two concepts, we managed to balance the importance of two competing criteria in the GAAM fitness function: classification accuracy and the feature subset's size. To assess the algorithm's effectiveness, we evaluated it on eleven datasets with different characteristics and compared the results with eight reference methods: GAAM, Melting GAAM, Holland GA with a penalty term, NSGA-II, Correlation-based Feature Selection, Lasso, Sequential Forward Selection, and IniPG (an algorithm for particle swarm optimisation). The main conclusion drawn from this study is that the genetic algorithm with aggressive mutation and decreasing feature set (GAAMmf) introduced in this paper returned feature sets with a significantly smaller number of features than almost all reference methods. Furthermore, GAAMmf outperformed most of the methods in terms of classification accuracy (except the original GAAM). In contrast to Holland GA and NSGA-II, GAAMmf was able to perform the feature reduction task for all datasets, regardless of the initial number of features", notes = "Faculty of Computer Science, West Pomeranian University of Technology in Szczecin, Zolnierska 49, Szczecin 71-210, Szczecin, Poland", } @InProceedings{Izadi:2010:ICCSIT, author = "Ashkan Izadi and Vic Ciesielski", title = "An exploration of genetic programming for non-photorealistic animations", booktitle = "3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT 2010)", year = "2010", month = "9-11 " # jul, volume = "9", pages = "255--259", abstract = "In this paper we present a new technique for non photo-realistic rendering by using genetic programming. Our technique produces aesthetically pleasing animations in which a subject gradually emerges from a random collection of brushstrokes. We employ triangular brushstrokes with three different possibilities of strokes drawing on the canvas. The animations are evaluated by using a numerical measure of similarity to a target image and a qualitative evaluation of aesthetic characteristics by an artist. We provide many facilities to the artists to control the rendered images and create desirable animations.", keywords = "genetic algorithms, genetic programming, aesthetic characteristics, aesthetically pleasing animations, non-photorealistic animation, non-photorealistic rendering, triangular brushstrokes, computer animation, rendering (computer graphics)", DOI = "doi:10.1109/ICCSIT.2010.5563645", notes = "Also known as \cite{5563645}", } @Article{IZADI:2021:PT, author = "Ahad Izadi and Elham Kashani and Ali Mohebbi", title = "Combining 10 meta-heuristic algorithms, {CFD}, {DOE}, {MGGP} and {PROMETHEE} {II} for optimizing Stairmand cyclone separator", journal = "Powder Technology", volume = "382", pages = "70--84", year = "2021", ISSN = "0032-5910", DOI = "doi:10.1016/j.powtec.2020.12.056", URL = "https://www.sciencedirect.com/science/article/pii/S0032591020312237", keywords = "genetic algorithms, genetic programming, Gas cyclone separator, CFD simulation, Multi-gene genetic programming, Multi-objective optimization, DOE, PROMETHEE II", abstract = "Gas cyclone separators have been widely used in different industries. In this study, to find the best geometrical ratios of Stairmand cyclone separator, computational fluid dynamics (CFD), design of experiments (DOE), multi-gene genetic programming (MGGP), and ten meta-heuristic algorithms were combined. Six geometrical dimensions of the gas cyclone separator including inlet height and width, vortex finder length and its diameter, cylinder height and cone-tip diameter were optimized. The obtained models from MGGP were optimized by ten meta-heuristic algorithms and non-dominated Pareto fronts were analyzed using six unary and binary metrics and PROMETHEE II as a decision making method. According to the optimization results, multi-objective Particle Swarm Optimization (MOPSO) showed the best performance and generated more preferred designs than Stairmand design compared to other algorithms. These preferred designs increased the collection efficiency within 0.36 to 6percent and decreased the pressure drop within 3.3 to 27.5percent compared to the Stairmand", } @Article{Izadmehr:2016:JNGSE, author = "Mojtaba Izadmehr and Reza Shams and Mohammad Hossein Ghazanfari", title = "New correlations for predicting pure and impure natural gas viscosity", journal = "Journal of Natural Gas Science and Engineering", year = "2016", volume = "30", pages = "364--378", month = mar, ISSN = "1875-5100", URL = "http://www.sciencedirect.com/science/article/pii/S1875510016300713", DOI = "doi:10.1016/j.jngse.2016.02.026", keywords = "genetic algorithms, genetic programming, Pure/impure natural gas viscosity, New correlations, Empirical models, Design of experiments, Leverage value statistics", abstract = "Accurate determination of natural gas viscosity is important for successful design of production, transportation, and gas storage systems. However, most of available models/correlations suffer from complexity, robustness, and inadequate accuracy especially when wide range of pressure and temperature is applied. Present study illustrates development of two novel models for predicting natural gas viscosity for pure natural gas (CH4) as well as natural gas containing impurities. For this purpose, 6484 data points have been gathered and analysed from the open literature covering wide range of pressure, temperature, and specific gravity levels, temperature ranges from -262.39 to 620.33 degree F (109.6 to 600 K), pressure ranges from 1.4508 to 29,000 psi (0.0100-199.94801 MPa), and gas specific gravity ranges from 0.553 to 1.5741. Sensitivity analysis on the collected data points through design of experiments algorithm showed that pseudo reduced pressure and pseudo reduced temperature are the most effective parameters as the inputs of the models. The Leverage Value Statistics is applied and doubtful data points are determined. The average absolute relative error and the coefficient of determination of the proposed models for predicting pure/impure natural gas viscosity on a wide range of conditions are 5.67percent and 1.87percent, 0.9826 and 0.9953, respectively. Reliable accuracy of proposed models in comparison to eight commonly used correlations makes them attractive for possible implementing in natural gas simulation/modelling applications.", notes = "Department of Chemical and Petroleum Engineering, Sharif University of Technology, Tehran, Iran", } @Article{Izadyar:2015:EB, author = "Nima Izadyar and Hwai Chyuan Ong and Shahaboddin Shamshirband and Hossein Ghadamian and Chong Wen Tong", title = "Intelligent forecasting of residential heating demand for the District Heating System based on the monthly overall natural gas consumption", journal = "Energy and Buildings", volume = "104", pages = "208--214", year = "2015", ISSN = "0378-7788", DOI = "doi:10.1016/j.enbuild.2015.07.006", URL = "http://www.sciencedirect.com/science/article/pii/S0378778815301225", abstract = "In this study, the residential heating demand of a case study (Baharestan town, Karaj) in Iran was forecasted based on the monthly natural gas consumption data and monthly average of the ambient temperature. Three various methods containing Extreme Learning Machine (ELM), artificial neural networks (ANNs) and genetic programming (GP) were employed to forecast residential heating demand of the case study and the results of these methods were compared after validating via real data. Actually, the main goal of the current study is to obtain the most accurate technique among these 3 common methods in this context. Validation of the forecasting results reveals that the important progress can be achieved in terms of accuracy by the ELM method in comparison with ANN and GP. Moreover, obtained results indicate that developed ELM models can be used with confidence for further work on formulating novel model predictive strategy for residential heating demand for the DHS. The outputs reveal that the new procedure can have a suitable performance in major cases and can be learned more rapid compare with other common learning algorithms.", keywords = "genetic algorithms, genetic programming, Residential natural gas demand, District Heating System (DHS), Estimation, Computational models, Energy consumption", } @Article{Izadyar:2015:Energy, author = "Nima Izadyar and Hossein Ghadamian and Hwai Chyuan Ong and Zeinab moghadam and Chong Wen Tong and Shahaboddin Shamshirband", title = "Appraisal of the support vector machine to forecast residential heating demand for the District Heating System based on the monthly overall natural gas consumption", journal = "Energy", volume = "93, Part 2", pages = "1558--1567", year = "2015", ISSN = "0360-5442", DOI = "doi:10.1016/j.energy.2015.10.015", URL = "http://www.sciencedirect.com/science/article/pii/S0360544215013791", abstract = "DHS (District Heating System) is one of the most efficient technologies which has been used to meet residential thermal demand. In this study, the most accurate forecasting of the residential heating demand is investigated via soft computing method. The objective of this study is to obtain the most accurate prediction of the residential heating consumption to employ forecasting result for designing optimum DHS system as a possible substitute of a pipeline natural gas in BAHARESTAN Town. For this purpose, three Support Vector Machine (SVM) models namely SVM coupled with the discrete wavelet transform (SVM-Wavelet), the firefly algorithm (SVM-FFA) and using the radial basis function (SVM-RBF) were analysed. The estimation and prediction results of these models were compared with two other soft computing methods (ANN (Artificial Neural Network) and GP (Genetic programming)) by using three statistical indicators i.e. RMSE (root means square error), coefficient of determination (R2) and Pearson coefficient (r). Based on the experimental outputs, the SVM-Wavelet method can lead to slightly accurate forecasting of the monthly overall natural gas demand.", keywords = "genetic algorithms, genetic programming, Residential natural gas demand, DHS (District heating system), Estimation, Wavelet and firefly algorithms (FFAs), SVM (Support vector machine)", } @InProceedings{Izumi:2006:CEC, author = "Yoshihiro Izumi and Tokiyo Yamaguchi and Shingo Mabu and Kotaro Hirasawa and Jingle Hu", title = "Trading Rules on the Stock Markets using Genetic Network Programming with Candlestick Chart", booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary Computation", year = "2006", editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas", pages = "8531--8536", address = "Vancouver", month = "16-21 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Genetic Network Programming, GNP", ISBN = "0-7803-9487-9", DOI = "doi:10.1109/CEC.2006.1688600", size = "6 pages", abstract = "A new evolutionary method named Genetic Network Programming, GNP has been proposed. GNP represents its solutions as directed graph structures which have some useful features inherently. For example, GNP has the implicit memory function which memorises the past action sequences of agents, and GNP can re-use nodes repeatedly in the network flow, so very compact graph structures can be made. In this paper, buying /selling model for stock market using GNP with Candlestick Chart has been proposed and its effectiveness is confirmed by simulations.", notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D", } @InProceedings{Izzo:2017:EuroGP, author = "Dario Izzo and Francesco Biscani and Alessio Mereta", title = "Differentiable Genetic Programming", booktitle = "EuroGP 2017: Proceedings of the 20th European Conference on Genetic Programming", year = "2017", month = "19-21 " # apr, editor = "Mauro Castelli and James McDermott and Lukas Sekanina", series = "LNCS", volume = "10196", publisher = "Springer Verlag", address = "Amsterdam", pages = "35--51", organisation = "species", note = "Nominated for best paper", keywords = "genetic algorithms, genetic programming, truncated Taylor polynomials, machine learning, symbolic regression, back-propagation", isbn13 = "978-3-319-55695-6", URL = "https://arxiv.org/abs/1611.04766", DOI = "doi:10.1007/978-3-319-55696-3_3", code_url = "https://darioizzo.github.io/dcgp/", abstract = "We introduce the use of high order automatic differentiation, implemented via the algebra of truncated Taylor polynomials, in genetic programming. Using the Cartesian Genetic Programming encoding we obtain a high-order Taylor representation of the program output that is then used to back-propagate errors during learning. The resulting machine learning framework is called differentiable Cartesian Genetic Programming (dCGP). In the context of symbolic regression, dCGP offers a new approach to the long unsolved problem of constant representation in GP expressions. On several problems of increasing complexity we find that dCGP is able to find the exact form of the symbolic expression as well as the constants values. We also demonstrate the use of dCGP to solve a large class of differential equations and to find prime integrals of dynamical systems, presenting, in both cases, results that confirm the efficacy of our approach.", notes = "see also https://arxiv.org/abs/1611.04766 Part of \cite{Castelli:2017:GP} EuroGP'2017 held inconjunction with EvoCOP2017, EvoMusArt2017 and EvoApplications2017", } @Article{DBLP:journals/jossw/IzzoB20, author = "Dario Izzo and Francesco Biscani", title = "dcgp: Differentiable Cartesian Genetic Programming made easy", journal = "J. Open Source Softw.", volume = "5", number = "51", pages = "2290", year = "2020", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", URL = "https://doi.org/10.21105/joss.02290", DOI = "doi:10.21105/joss.02290", timestamp = "Tue, 01 Dec 2020 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/jossw/IzzoB20.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{Jabeen:2010:ICICA, author = "Fouzia Jabeen and Zahoor Jan and Arfan Jaffar and Anwar M. Mirza", title = "Energy Based Coefficient Selection for Digital Watermarking in Wavelet Domain", booktitle = "Proceedings of the International Conference on Information Computing and Applications, ICICA 2010. Part II", year = "2010", editor = "Rongbo Zhu and Yanchun Zhang and Baoxiang Liu and Chunfeng Liu", volume = "106", series = "Communications in Computer and Information Science", pages = "260--267", address = "Tangshan, China", month = oct # " 15-18", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Discrete wavelet transform (DWT), imperceptibility, robustness, perceptual mask, luminance, contrast, noise visibility function", isbn13 = "978-3-642-16338-8", DOI = "doi:10.1007/978-3-642-16339-5_34", size = "8 pages", abstract = "Ownership protection and authorisation of digital multimedia is of paramount importance. The availability of powerful tools for editing, loss less copying and transmission of digital multimedia (such as images) has compounded the problem. Image watermarking is an effective solution for the problem of authentication and protection of copyrighted image content. In this paper Discrete Wavelet Transform (DWT) based watermarking technique is proposed in which mean energy of the each of 32x32 block in the CH and CV subbands is calculated and range of coefficients that exceed the mean energy of the corresponding block are selected for watermark embedding. Watson Perceptual Distortion Control Model is considered to keep the Perceptual quality of the image. Genetic Programming (GP) delivers optimum watermarking level for the selected coefficients. Results show negligible difference between original and watermarked image demonstrating key feature of imperceptibility. The technique proves to be effective against a set of malicious attacks.", } @Article{Jabeen:2013:IJICA, author = "Fouzia Jabeen and Zahoor Jan and Farhana Jahangir", title = "Energy-Based Coefficient Selection for Digital Watermarking in Wavelet Domain", journal = "International Journal of Innovative Computing and Applications", year = "2013", volume = "5", number = "1", pages = "18--25", note = "Special Issue on: Innovative Computing in Image Processing and Applications", keywords = "genetic algorithms, genetic programming, Discrete Wavelet Transform (DWT), Perceptual mask, Imperceptibility, Robustness, Luminance, Contrast, Noise visibility Function", ISSN = "1751-648X", DOI = "doi:10.1504/IJICA.2013.052352", abstract = "The massive spreading of broadband networks and new developments in digital technology has made owner-ship protection and authorisation of digital multimedia a very important issue. The reason is the availability of powerful tools for editing, lossless copying and transmission of digital multimedia such as images. Image watermarking is now an effective solution for the problem of authentication and protection of copyrighted image content. In this paper Discrete Wavelet Transform (DWT) based watermarking technique is proposed in which mean energy of the each of 32x32 block in the CH and CV subbands is calculated and range of coefficients that exceed the mean energy of the block are selected for watermark embedding. Watson Perceptual Distortion Control Model is considered to keep the Perceptual quality of the image and Genetic Programming (GP) is used to provide optimum watermarking level for the selected coefficients. The results show that there is almost no difference between original and watermarked image demonstrating key feature of imperceptibility. The technique has been tested and proves to be effective against a set of malicious attacks.", notes = "Acceptance Date: 22 Nov 2011 IJICA http://www.inderscience.com/jhome.php?jcode=ijica Department of Computer Science, Frontier Women University, Peshawar 25000, Pakistan Department of Computer Science, Abdul Wali Khan University, Noshera Mardan Road, Mardan 23200, Pakistan Frontier Women University, Naz Cinema Road, Opposite Qila Bala Hisar, Peshawar City 25000, Pakistan See also \cite{Jabeen:2010:ICICA}", } @Article{Jabeen:2010:IJEST, author = "Hajira Jabeen and Abdul Rauf Baig", title = "Review of Classification Using Genetic Programming", journal = "International Journal of Engineering Science and Technology", year = "2010", volume = "2", number = "2", pages = "94--103", month = feb, keywords = "genetic algorithms, genetic programming, Data Classification, Survey, Taxonomy", ISSN = "0975-5462", URL = "http://www.ijest.info/abstract.php?file=10-02-02-06", URL = "http://www.ijest.info/docs/IJEST10-02-02-06.pdf", size = "10 pages", abstract = "Genetic programming (GP) is a powerful evolutionary algorithm introduced to evolve computer programs automatically. It is a domain independent, stochastic method with an important ability to represent programs of arbitrary size and shape. Its flexible nature has attracted numerous researchers in data mining community to use GP for classification. In this paper we have reviewed and analyzed tree based GP classification methods and propose taxonomy of these methods. We have also discussed various strengths and weaknesses of the technique and provide a framework to optimize the task of GP based classification.", notes = "National University of Computer and Emerging Sciences, Islamabad, Pakistan", } @InProceedings{DBLP:conf/nicso/JabeenB10, author = "Hajira Jabeen and Abdul Rauf Baig", title = "Particle Swarm Optimization Based Tuning of Genetic Programming Evolved Classifier Expressions", booktitle = "Nature Inspired Cooperative Strategies for Optimization, NICSO 2010", editor = "Juan Ram{\'o}n Gonz{\'a}lez and David A. Pelta and Carlos Cruz and Germ{\'a}n Terrazas and Natalio Krasnogor", series = "Studies in Computational Intelligence", volume = "284", year = "2010", pages = "385--397", address = "Granada, Spain", month = may # " 12-14", publisher = "Springer", keywords = "genetic algorithms, genetic programming, PSO", isbn13 = "978-3-642-12537-9", DOI = "doi:10.1007/978-3-642-12538-6_32", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "Genetic Programming (GP) has recently emerged as an effective technique for classifier evolution. One specific type of GP classifiers is arithmetic classifier expression trees. In this paper we propose a novel method of tuning these arithmetic classifiers using Particle Swarm Optimization (PSO) technique. A set of weights are introduced into the bottom layer of evolved GP classifier expression tree, associated with each terminal node. These weights are initialized with random values and optimized using PSO. The proposed tuning method is found efficient in increasing performance of GP classifiers with lesser computational cost as compared to GP evolution for longer number of generations. We have conducted a series of experiments over datasets taken from UCI ML repository. Our proposed technique has been found successful in increasing the accuracy of classifiers in much lesser number of function evaluations.", notes = "NICSO", } @PhdThesis{Jabeen:thesis, author = "Hajira Jabeen", title = "Advancements in Genetic Programming for Data Classification", school = "National University of Computer and Emerging Sciences Islamabad", year = "2010", address = "Pakistan", month = aug, keywords = "genetic algorithms, genetic programming", URL = "http://prr.hec.gov.pk/Thesis/717S.pdf", URL = "http://prr.hec.gov.pk/jspui/handle/123456789//1058", broken = "http://eprints.hec.gov.pk/6983/", size = "130 pages", abstract = "This thesis aims to advance the state of the art in data classification using Genetic programming (GP). GP is an evolutionary algorithm that has several outstanding features making it ideal for complex problems like data classification. However, it suffers from a few limitations that reduce its significance. This thesis targets at proposing optimal solutions to these GP limitations.The problems covered in this thesis are: 1. Increase in GP tree complexity during evolution that results in long training time. 2. Lack of convergence to a single (optimal) solution. 3. Lack of methodology to handle mixed data-type without type transformation. 4. Search of a better method for multi-class classification. Through this work, we have proposed a method which achieves significant reduction in bloat for classification task. Moreover, we have presented a Particle Swarm Optimisation based hybrid approach to increase performance of GP evolved classifiers.The approach offers better performance in less computational effort. Another approach introduces a new two layered paradigm for mixed type data classification with an added feature that uses data in its original form instead of any transformation or pre-processing.The last but not the least contribution is an efficient binary encoding method for multi-class classification problems. The method involves smaller number of GP evolutions, reducing the computation and suffers from fewer conflicts yielding better results. All of the proposed methods have been tested and our experiments conclude the efficiency of proposed approaches.", notes = "ID Code: 6983 Supervisor: Dr. Abdul Rauf Baig prr.hec.gov.pk broken September 2021 try http://173.208.131.244:9060", } @InProceedings{DBLP:conf/kes/JabeenB10, author = "Hajira Jabeen and Abdul Rauf Baig", title = "CLONAL-GP Framework for Artificial Immune System Inspired Genetic Programming for Classification", booktitle = "14th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2010, Part I", year = "2010", editor = "Rossitza Setchi and Ivan Jordanov and Robert J. Howlett and Lakhmi C. Jain", series = "Lecture Notes in Computer Science", volume = "6276", pages = "61--68", address = "Cardiff", month = sep # " 8-10", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-15386-0", DOI = "doi:10.1007/978-3-642-15387-7_10", abstract = "This paper presents a novel framework for artificial immune system (AIS) inspired evolution in Genetic Programming (GP). A typical GP system uses the reproduction operators mimicking the phenomena of natural evolution to search for efficient classifiers. The proposed framework uses AIS inspired clonal selection algorithm to evolve classifiers using GP. The clonal selection principle states that, in human immune system, high affinity cells that recognise the invading antigens are selected to proliferate. Furthermore, these cells undergo hyper mutation and receptor editing for maturation. In this paper, we propose a computational implementation of the clonal selection principle. The motivation for using non-Darwinian evolution includes avoidance of bloat, training time reduction and simpler classifiers. We have performed empirical analysis of proposed framework over a benchmark dataset from UCI repository. The CLONAL-GP is contrasted with two variants of GP based classification mechanisms and results are found encouraging.", notes = "KES (1)", bibsource = "DBLP, http://dblp.uni-trier.de", } @InProceedings{conf/hais/JabeenB10, title = "A Framework for Optimization of Genetic Programming Evolved Classifier Expressions Using Particle Swarm Optimization", author = "Hajira Jabeen and Abdul Rauf Baig", booktitle = "Hybrid Artificial Intelligence Systems, 5th International Conference, {HAIS} 2010, San Sebasti{\'a}n, Spain, June 23-25, 2010. Proceedings, Part {I}", publisher = "Springer", year = "2010", volume = "6076", editor = "Manuel Gra{\~n}a Romay and Emilio Corchado and M. Teresa Garc{\'i}a-Sebast{\'i}an", isbn13 = "978-3-642-13768-6", pages = "56--63", series = "Lecture Notes in Computer Science", URL = "http://link.springer.com/chapter/10.1007%2F978-3-642-13769-3_7", DOI = "doi:10.1007/978-3-642-13769-3_7", keywords = "genetic algorithms, genetic programming", abstract = "Genetic Programming has emerged as an efficient algorithm for classification. It offers several prominent features like transparency, flexibility and efficient data modelling ability. However, GP requires long training times and suffers from increase in average population size during evolution. The aim of this paper is to introduce a framework to increase the accuracy of classifiers by performing a PSO based optimisation approach. The proposed hybrid framework has been found efficient in increasing the accuracy of classifiers (expressed in the form of binary expression trees) in comparatively lesser number of function evaluations. The technique has been tested using five datasets from the UCI ML repository and found efficient.", bibdate = "2010-06-25", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/hais/hais2010-1.html#JabeenB10", } @InProceedings{conf/icic/JabeenB11, author = "Hajira Jabeen and Abdul Rauf Baig", title = "Lazy Learning for Multi-class Classification Using Genetic Programming", booktitle = "7th International Conference on Advanced Intelligent Computing Theories and Applications, with Aspects of Artificial Intelligence (ICIC 2011)", year = "2011", editor = "De-Shuang Huang and Yong Gan and Phalguni Gupta and M. Michael Gromiha", volume = "6839", series = "Lecture Notes in Computer Science", pages = "177--182", address = "Zhengzhou, China", month = aug # " 11-14", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-25943-2", DOI = "doi:10.1007/978-3-642-25944-9_23", size = "6 pages", abstract = "In this paper we have proposed a lazy learning mechanism for multiclass classification using genetic programming. This method is an improvement of traditional binary decomposition method for multiclass classification. We train classifiers for individual classes for a certain number of generations. Individual trained classifiers for each class are combined in a single chromosome. A population of such chromosomes is created and evolved further. This method suppresses the conflicting situations common in binary decomposition method. The proposed lazy learning method has performed better than traditional binary decomposition method over five benchmark datasets taken from UCI ML repository.", notes = "Revised Selected Papers. Published 2012", affiliation = "Iqra University, 5 H-9/1, Islamabad, Pakistan", bibdate = "2012-01-05", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icic/icic2011-2.html#JabeenB11", } @Article{Jabeen2010, author = "Hajira Jabeen and Abdul Rauf Baig", title = "DepthLimited crossover in GP for classifier evolution", journal = "Computers in Human Behavior", year = "2011", volume = "27", number = "5", pages = "1475--1481", month = sep, keywords = "genetic algorithms, genetic programming, Crossover, Depth Limited, Bloat, Classification, Data mining", ISSN = "0747-5632", URL = "http://www.sciencedirect.com/science/article/B6VDC-51FWRJY-1/2/813b60cff35fd1e0399e95fb3fa246be", DOI = "doi:10.1016/j.chb.2010.10.011", size = "7 pages", abstract = "Genetic Programming (GP) provides a novel way of classification with key features like transparency, flexibility and versatility. Presence of these properties makes GP a powerful tool for classifier evolution. However, GP suffers from code bloat, which is highly undesirable in case of classifier evolution. In this paper, we have proposed an operator named DepthLimited crossover. The proposed crossover does not let trees increase in complexity while maintaining diversity and efficient search during evolution. We have compared performance of traditional GP with DepthLimited crossover GP, on data classification problems and found that DepthLimited crossover technique provides compatible results without expanding the search space beyond initial limits. The proposed technique is found efficient in terms of classification accuracy, reduced complexity of population and simplicity of evolved classifiers.", } @Article{Jabeen2012416, author = "Hajira Jabeen and Abdul Rauf Baig", title = "Two layered Genetic Programming for mixed-attribute data classification", journal = "Applied Soft Computing", volume = "12", number = "1", pages = "416--422", year = "2012", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2011.08.029", URL = "http://www.sciencedirect.com/science/article/pii/S1568494611003127", keywords = "genetic algorithms, genetic programming, Classification, Mixed attribute data, Mixed type data classification, Classifier", abstract = "The important problem of data classification spans numerous real life applications. The classification problem has been tackled by using Genetic Programming in many successful ways. Most approaches focus on classification of only one type of data. However, most of the real-world data contain a mixture of categorical and continuous attributes. In this paper, we present an approach to classify mixed attribute data using Two Layered Genetic Programming (L2GP). The presented approach does not transform data into any other type and combines the properties of arithmetic expressions (using numerical data) and logical expressions (using categorical data). The outer layer contains logical functions and some nodes. These nodes contain the inner layer and are either logical or arithmetic expressions. Logical expressions give their Boolean output to the outer tree. The arithmetic expressions give a real value as their output. Positive real value is considered true and a negative value is considered false. These outputs of inner layers are used to evaluate the outer layer which determines the classification decision. The proposed classification technique has been applied on various heterogeneous data classification problems and found successful.", } @Article{Jabeen:2012:ijicic, author = "Hajira Jabeen and Abdul Rauf Baig", title = "GPSO: A Framework for Optimization of Genetic Programming Classifier Expressions for Binary Classification Using Particle Swarm Optimization", journal = "International journal of innovative computing, information and control", year = "2012", volume = "8", number = "1 A", pages = "233--242", month = jan, keywords = "genetic algorithms, genetic programming, classification, particle swarm optimisation, optimisation, expressions", ISSN = "1349-418X", publisher = "ICIC international", URL = "http://www.ijicic.org/ijicic-10-06097.pdf", size = "10 pages", abstract = "Genetic Programming (GP) is an emerging classification tool known for its flexibility, robustness and lucidity. However, GP suffers from a few limitations like long training time, bloat and lack of convergence. In this paper, we have proposed a hybrid technique that overcomes these drawbacks by improving the performance of GP evolved classifiers using Particle Swarm Optimisation (PSO). This hybrid classification technique is a two-step process. In the first phase, we have used GP for evolution of arithmetic classifier expressions (ACE). In the second phase, we add weights to these expressions and optimise them using PSO. We have compared the performance of proposed frame- work (GPSO) with the GP classification technique over twelve benchmark data sets. The results conclude that the proposed optimisation strategy outperforms GP with respect to classification accuracy and less computation.", } @Article{Jabeen:2013:Neurocomputing, author = "Hajira Jabeen and Abdul Rauf Baig", title = "Two-stage learning for multi-class classification using genetic programming", journal = "Neurocomputing", volume = "116", month = "20 " # sep, pages = "311--316", year = "2013", note = "Advanced Theory and Methodology in Intelligent Computing Selected Papers from the Seventh International Conference on Intelligent Computing (ICIC 2011).", keywords = "genetic algorithms, genetic programming, Classification, Classifier, Expression, Rule, Algorithm", ISSN = "0925-2312", DOI = "doi:10.1016/j.neucom.2012.01.048", URL = "https://hajirajabeen.github.io/publications/NEUCOM.pdf", URL = "http://www.sciencedirect.com/science/article/pii/S0925231212007308", DOI = "doi:10.1016/j.neucom.2012.01.048", size = "6 pages", abstract = "This paper introduces a two-stage strategy for multi-class classification problems. The proposed technique is an advancement of tradition binary decomposition method. In the first stage, the classifiers are trained for each class versus the remaining classes. A modified fitness value is used to select good discriminators for the imbalanced data. In the second stage, the classifiers are integrated and treated as a single chromosome that can classify any of the classes from the dataset. A population of such classifier-chromosomes is created from good classifiers (for individual classes) of the first phase. This population is evolved further, with a fitness that combines accuracy and conflicts. The proposed method encourages the classifier combination with good discrimination among all classes and less conflicts. The two-stage learning has been tested on several benchmark datasets and results are found encouraging.", } @InProceedings{Jabeen:2020:CEC, author = "Hajira Jabeen and Jonas Weinz and Jens Lehmann", title = "{AutoChef:} Automated Generation of Cooking Recipes", booktitle = "2020 IEEE Congress on Evolutionary Computation (CEC)", year = "2020", editor = "Yaochu Jin", month = "19-24 " # jul, keywords = "genetic algorithms, genetic programming, Dairy products, Heating systems, Symmetric matrices, Art, Data mining, Machine learning algorithms, Generators", isbn13 = "978-1-7281-6929-3", URL = "http://vigir.missouri.edu/~gdesouza/Research/Conference_CDs/IEEE_WCCI_2020/CEC/Papers/E-24487.pdf", URL = "http://www.human-competitive.org/sites/default/files/autochef-humies.txt", URL = "http://www.human-competitive.org/sites/default/files/autochef-final.pdf", DOI = "doi:10.1109/CEC48606.2020.9185605", broken = "https://the-cake-is-a-lie.net/gogs/jonas/Autochef", size = "7 pages", abstract = "Cooking is an endeavour unique to humans. It is mainly considered an art requiring culinary intuition acquired through practice. The preparation of food is a complex and subjective process that makes it challenging to determine underlying rules for automation. In this paper, we present AutoChef, the first open-source autonomous recipe generator. AutoChef extracts the data from existing recipes using natural language processing, learns the combination of ingredients, preparation actions and cooking instructions, and autonomously generates the recipes. Furthermore, AutoChef uses Genetic Programming to represent and evolve the recipes. The fitness of recipes is designed to evaluate the combination of ingredients, actions and cooking-processes learned from the existing recipe data. Finally, the resulting recipes are translated back into text format and evaluated by human experts.", notes = "Entered 2021 HUMIES Also known as \cite{9185605}", } @InProceedings{Jabez:2012:ICSEMA, author = "J. Jabez and G. S. A. Mala", booktitle = "International Conference on Software Engineering and Mobile Application Modelling and Development (ICSEMA 2012)", title = "A study on genetic-fuzzy based automatic intrusion detection on network datasets", year = "2012", month = dec, abstract = "The intrusion detection aims at distinguishing the attack data and the normal data from the network pattern database. It is an indispensable part of the information security system. Due to the variety of network data behaviours and the rapid development of attack fashions, it is necessary to develop a fast machine-learning-based intrusion detection algorithm with high detection rates and low false-alarm rates. In this correspondence, we propose a novel fuzzy method with genetic for detecting intrusion data from the network database. Genetic algorithm is an evolutionary optimisation technique, which uses Directed graph structures instead of strings in genetic algorithm or trees in genetic programming, which leads to enhancing the representation ability with a compact programs derived from the reusability of nodes in a graph structure. By combining fuzzy set theory with Genetic proposes a new method that can deal with a mixed of database that contains both discrete and continuous attributes and also extract many important association rules to contribute and to enhance the Intrusion data detections ability. Therefore, the proposed method is flexible and can be applied for both misuse and anomaly detection in data-intrusion-detection problems. Also the incomplete database will include some of the missing data in some tuples and however, the proposed methods by applying some rules to extract these tuples. The Genetic-Fuzzy presents a data Intrusion Detection Systems for recovering data. It also include following steps in Genetic-Fuzzy rules: Process data model as a mathematical representation for Normal data.; Improving the process data model which improves the Model of normal data and it should represent the underlying truth of normal Data.; Uses cluster centres or centroids and use distances away from the centroids and co", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1049/ic.2012.0135", notes = "Also known as \cite{6549299}", } @InProceedings{jackson:2004:eurogp2, author = "David Jackson", title = "A Practical Approach to Evolving Concurrent Programs", booktitle = "Genetic Programming 7th European Conference, EuroGP 2004, Proceedings", year = "2004", editor = "Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule", volume = "3003", series = "LNCS", pages = "89--100", address = "Coimbra, Portugal", publisher_address = "Berlin", month = "5-7 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-21346-8", DOI = "doi:10.1007/978-3-540-24650-3_9", abstract = "Although much research has been devoted to devising genetic programming systems that are capable of running the evolutionary process in parallel, thereby improving execution speed, comparatively little effort has been expended on evolving programs which are themselves inherently concurrent. A suggested reason for this is that the vast number of parallel execution paths that are open to exploration during the fitness evaluation of population members renders evolutionary computation prohibitively expensive. We have therefore investigated the potential for minimising this expense by using a far more limited exploration of the execution state space to guide evolution. The approach, involving the definition of sets of schedulings to enable a variety of execution interleavings to be specified, has been applied to the classic dining philosophers problem, and has been found to evolve solutions that are as good as those created by human programmers", notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004", } @InProceedings{jackson:2004:eurogp, author = "David Jackson", title = "Automatic Synthesis of Instruction Decode Logic by Genetic Programming", booktitle = "Genetic Programming 7th European Conference, EuroGP 2004, Proceedings", year = "2004", editor = "Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule", volume = "3003", series = "LNCS", pages = "318--327", address = "Coimbra, Portugal", publisher_address = "Berlin", month = "5-7 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, evolvable hardware: Poster", ISBN = "3-540-21346-5", DOI = "doi:10.1007/978-3-540-24650-3_30", abstract = "On many modern computers, the processor control unit is microprogrammed rather than built directly in hardware. One of the tasks of the microcode is to decode machine-level instructions: for each such instruction, it must be ensured that control-flow is directed to the appropriate microprogram for emulating it. We have investigated the use of genetic programming for evolving this instruction decode logic. Success is highly dependent on the number of opcodes in the instruction set and their relationship to the conditional branch and shift instructions offered on the micro architecture, but experimental results are promising.", notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004", } @InProceedings{eurogp:Jackson05, author = "David Jackson", editor = "Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. {van Hemert} and Marco Tomassini", title = "Evolving Defence Strategies by Genetic Programming", booktitle = "Proceedings of the 8th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3447", year = "2005", address = "Lausanne, Switzerland", month = "30 " # mar # " - 1 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-25436-6", pages = "281--290", DOI = "doi:10.1007/978-3-540-31989-4_25", DOI = "doi:10.1007/b107383", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "Computer games and simulations are commonly used as a basis for analysing and developing battlefield strategies. Such strategies are usually programmed explicitly, but it is also possible to generate them automatically via the use of evolutionary programming techniques. We focus in particular on the use of genetic programming to evolve strategies for a single defender facing multiple simultaneous attacks. By expressing the problem domain in the form of a {"}Space Invaders{"} game, we show that it is possible to evolve winning strategies for an increasingly complex sequence of scenarios.", notes = "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005", } @InProceedings{1068291, author = "David Jackson", title = "Parsing and translation of expressions by genetic programming", booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation", year = "2005", editor = "Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and Edwin D. {de Jong} and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler", volume = "2", ISBN = "1-59593-010-8", pages = "1681--1688", address = "Washington DC, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2005/docs/p1681.pdf", DOI = "doi:10.1145/1068009.1068291", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "25-29 " # jun, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, application, experimentation, software tools", abstract = "We have investigated the potential for using genetic programming to evolve compiler parsing and translation routines for processing arithmetic and logical expressions as they are used in a typical programming language. Parsing and translation are important and complex real-world problems for which evolved solutions must make use of a range of programming constructs. The exercise also tests the ability of genetic programming to evolve extensive and appropriate use of abstract data types namely, stacks. Experimentation suggests that the evolution of such code is achievable, provided that program function and terminal sets are judiciously chosen.", notes = "GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM Order Number 910052", } @InProceedings{1068299, author = "David Jackson", title = "Dormant program nodes and the efficiency of genetic programming", booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation", year = "2005", editor = "Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and Edwin D. {de Jong} and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler", volume = "2", ISBN = "1-59593-010-8", pages = "1745--1751", address = "Washington DC, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2005/docs/p1745.pdf", DOI = "doi:10.1145/1068009.1068299", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "25-29 " # jun, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, dormant node, efficiency, experimentation, fitness preserving crossover, intron, performance", size = "7 pages", abstract = "In genetic programming, there is a tendency for individuals in a population to accumulate fragments of code, often called introns, which are redundant in the fitness evaluation of those individuals. Crossover at the sites of certain classes of intron cannot produce a different fitness in the offspring, but the cost of identifying such sites may be high. We have therefore focused our attention on one particular class of non-contributory node that can be easily identified without sophisticated analysis. Experimentation shows that, for certain problem types, the presence of such dormant nodes can be extensive. We have therefore devised a technique that can use this information to reduce the number of fitness evaluations performed, leading to substantial savings in execution time without affecting the results obtained.", notes = "GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM Order Number 910052", } @InProceedings{jackson:2005:CEC, author = "David Jackson", title = "Fitness Evaluation Avoidance in {Boolean} GP Problems", booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary Computation", year = "2005", editor = "David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L. Gwenn Volkert and Dan Ashlock and Marc Schoenauer", volume = "3", pages = "2530--2536", address = "Edinburgh, UK", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "2-5 " # sep, organisation = "IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9363-5", DOI = "doi:10.1109/CEC.2005.1555011", size = "7 pages", abstract = "A technique has been devised which, via consideration of the program nodes executed during fitness evaluation, allows a genetic programming system to determine many instances in which invocation of the fitness function can be avoided. The nature of Boolean logic problems renders them of particular interest as a focus of study for the application of this technique, and experimental evidence shows that significant speed-ups in execution time can be achieved when evolving solutions to these problems.", notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS. Santa Fe Ant, 6-mux, 5-parity. Visit tree. ~2 fold speed up. 2.8GHz pentium 500 6-mux 50-gens runs 6-10mins in total. AND, OR, NOT, IF", } @Article{Jackson:2005:TEC, title = "Evolution of Processor microcode", author = "David Jackson", journal = "IEEE Transactions on Evolutionary Computation", year = "2005", volume = "9", number = "1", pages = "44--54", month = feb, keywords = "genetic algorithms, genetic programming, firmware, microcomputers, microprogramming computer processor, evolutionary computing technique, genetic programming system, machine code, microprogrammed system, processor microcode", DOI = "doi:10.1109/TEVC.2004.837922", ISSN = "1089-778X", abstract = "The control unit of many modern computer processors is implemented using microcode. Because of its low level and high complexity, writing microcode that is not only correct but efficient is extremely challenging. An interesting question is whether evolutionary computing techniques could be used to generate microprograms that are of the necessary quality. To answer this, a genetic programming system has been built that evolves microprograms for an architecture that incorporates many of the features common to real microprogrammed systems. Fitness is assessed via simulated execution to determine whether candidate solutions effect the correct machine state changes. The system has been used to evolve microprograms that emulate a range of machine code instructions, of varying complexity. It has been found that, provided appropriate evolutionary guidance is extracted from operational specifications of those instructions, the approach is largely successful in generating solutions that are both correct and optimal.", } @InProceedings{eurogp07:jackson, author = "David Jackson and Adrian P. Gibbons", title = "Layered Learning in {Boolean} GP Problems", editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar", booktitle = "Proceedings of the 10th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "4445", year = "2007", address = "Valencia, Spain", month = "11-13 " # apr, pages = "148--159", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-71602-5", isbn13 = "978-3-540-71602-0", DOI = "doi:10.1007/978-3-540-71605-1_14", abstract = "Layered learning is a decomposition and reuse technique that has proved to be effective in the evolutionary solution of difficult problems. Although previous work has integrated it with genetic programming (GP), much of the application of that research has been in relation to multi-agent systems. In extending this work, we have applied it to more conventional GP problems, specifically those involving Boolean logic. We have identified two approaches which, unlike previous methods, do not require prior understanding of a problem's functional decomposition into sub-goals. Experimentation indicates that although one of the two approaches offers little advantage, the other leads to solution-finding performance significantly surpassing that of both conventional GP systems and those which incorporate automatically defined functions.", notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007", } @InProceedings{1277280, author = "David Jackson", title = "Hierarchical genetic programming based on test input subsets", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "1612--1619", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1612.pdf", DOI = "doi:10.1145/1276958.1277280", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, decomposition, hierarchical GP, program architecture", abstract = "Crucial to the more widespread use of evolutionary computation techniques is the ability to scale up to handle complex problems. In the field of genetic programming, a number of decomposition and reuse techniques have been devised to address this. As an alternative to the more commonly employed encapsulation methods, we propose an approach based on the division of test input cases into subsets, each dealt with by an independently evolved code segment. Two program architectures are suggested for this hierarchical approach, and experimentation demonstrates that they offer substantial performance improvements over more established methods. Difficult problems such as even-10 parity are readily solved with small population sizes.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @InProceedings{conf/eurogp/Jackson08a, title = "The Performance of a Selection Architecture for Genetic Programming", author = "David Jackson", bibdate = "2008-04-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/eurogp/eurogp2008.html#Jackson08a", booktitle = "Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008", address = "Naples", month = "26-28 " # mar, publisher = "Springer", year = "2008", volume = "4971", editor = "Michael O'Neill and Leonardo Vanneschi and Steven Gustafson and Anna Isabel {Esparcia Alcazar} and Ivanoe {De Falco} and Antonio {Della Cioppa} and Ernesto Tarantino", isbn13 = "978-3-540-78670-2", pages = "170--181", series = "Lecture Notes in Computer Science", DOI = "doi:10.1007/978-3-540-78671-9_15", keywords = "genetic algorithms, genetic programming", notes = "Part of \cite{conf/eurogp/2008} EuroGP'2008 held in conjunction with EvoCOP2008, EvoBIO2008 and EvoWorkshops2008", } @InProceedings{conf/eurogp/Jackson08, title = "Partitioned Incremental Evolution of Hardware Using Genetic Programming", author = "David Jackson", bibdate = "2008-04-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/eurogp/eurogp2008.html#Jackson08", booktitle = "Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008", address = "Naples", month = "26-28 " # mar, publisher = "Springer", year = "2008", volume = "4971", editor = "Michael O'Neill and Leonardo Vanneschi and Steven Gustafson and Anna Isabel {Esparcia Alcazar} and Ivanoe {De Falco} and Antonio {Della Cioppa} and Ernesto Tarantino", isbn13 = "978-3-540-78670-2", pages = "86--97", series = "Lecture Notes in Computer Science", DOI = "doi:10.1007/978-3-540-78671-9_8", keywords = "genetic algorithms, genetic programming", notes = "Part of \cite{conf/eurogp/2008} EuroGP'2008 held in conjunction with EvoCOP2008, EvoBIO2008 and EvoWorkshops2008", } @InProceedings{Jackson:2008:PPSN, author = "David Jackson", title = "The Generalisation Ability of a Selection Architecture for Genetic Programming", booktitle = "Parallel Problem Solving from Nature - PPSN X", year = "2008", editor = "Gunter Rudolph and Thomas Jansen and Simon Lucas and Carlo Poloni and Nicola Beume", volume = "5199", series = "LNCS", pages = "468--477", address = "Dortmund", month = "13-17 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-87699-5", DOI = "doi:10.1007/978-3-540-87700-4_47", abstract = "As an alternative to various existing approaches to incorporating modular decomposition and reuse in genetic programming (GP), we have proposed a new method for hierarchical evolution. Based on a division of the problem's test case inputs into subsets, it employs a program structure that we refer to as a selection architecture. Although the performance of GP systems based on this architecture has been shown to be superior to that of conventional systems, the nature of evolved programs is radically different, leading to speculation as to how well such programs may generalise to deal with previously unseen inputs. We have therefore performed additional experimentation to evaluate the approach's generalisation ability, and have found that it seems to stand up well against standard GP in this regard.", notes = "PPSN X", } @InProceedings{Jackson:2009:eurogp, author = "David Jackson", title = "Behavioural Diversity and Filtering in GP Navigation Problems", booktitle = "Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009", year = "2009", editor = "Leonardo Vanneschi and Steven Gustafson and Alberto Moraglio and Ivanoe {De Falco} and Marc Ebner", volume = "5481", series = "LNCS", pages = "256--267", address = "Tuebingen", month = apr # " 15-17", organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming, poster", isbn13 = "978-3-642-01180-1", DOI = "doi:10.1007/978-3-642-01181-8_22", notes = "Part of \cite{conf/eurogp/2009} EuroGP'2009 held in conjunction with EvoCOP2009, EvoBIO2009 and EvoWorkshops2009", } @InProceedings{Jackson:2009:cec, author = "David Jackson", title = "Self-Adaptive Focusing of Evolutionary Effort in Hierarchical Genetic Programming", booktitle = "2009 IEEE Congress on Evolutionary Computation", year = "2009", editor = "Andy Tyrrell", pages = "1821--1828", address = "Trondheim, Norway", month = "18-21 " # may, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-2959-2", file = "P462.pdf", DOI = "doi:10.1109/CEC.2009.4983162", size = "8 pages", abstract = "In an attempt to address the scaling up of genetic programming to handle complex problems, we have proposed a hierarchical approach in which programs are formed from independently evolved code fragments, each of which is responsible for handling a subset of the test input cases. Although this approach offers substantial performance advantages in comparison to more conventional systems, the programs it evolves exhibit some undesirable properties for certain problem domains. We therefore propose the introduction of a self adaptive mechanism that allows the system dynamically to focus evolutionary effort on the program components most in need. Experimentation reveals that not only does this technique lead to better-behaved programs, it also gives rise to further significant performance improvements.", keywords = "genetic algorithms, genetic programming", notes = "even 4-parity, 5-parity, majority on, symbolic regression 4x**4 -3x**3 +2x**2 -x. Refers to \cite{roberts:2001:EuroGP}. CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR", } @Article{Jackson:2009:GPEM, author = "David Jackson", title = "The identification and exploitation of dormancy in genetic programming", journal = "Genetic Programming and Evolvable Machines", year = "2010", volume = "11", number = "1", pages = "89--121", month = mar, keywords = "genetic algorithms, genetic programming, Introns, Efficiency, Performance, Simplification", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-009-9086-1", abstract = "In genetic programming, introns, fragments of code which do not contribute to the fitness of individuals, are usually viewed negatively, and much research has been undertaken into ways of minimising their occurrence or effects. However, identification and removal of introns is often computationally expensive and sometimes intractable. We have therefore focused our attention on one particular class of intron, which we refer to as dormant nodes. Mechanisms for locating such nodes are cheap to implement, and reveal that the presence of dormancy can be extensive. Once identified, dormancy can be exploited in at least three ways: improving execution efficiency, improving solution-finding performance, and simplifying program code. Experimentation shows that the gains to be had in all three cases can be significant.", notes = "artificial ant Santa Fe trail, Maze navigation, Space Invaders arcade game, parsing arithmetic and logical expressions into postfix (Reverse Polish, RPN) 6-multiplexer, Even-4, parity", } @InProceedings{Jackson:2010:EuroGP, author = "David Jackson", title = "Phenotypic Diversity in Initial Genetic Programming Populations", booktitle = "Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010", year = "2010", editor = "Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar", volume = "6021", series = "LNCS", pages = "98--109", address = "Istanbul", month = "7-9 " # apr, organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-12147-0", DOI = "doi:10.1007/978-3-642-12148-7_9", abstract = "A key factor in the success or otherwise of a genetic programming population in evolving towards a solution is the extent of diversity amongst its members. Diversity may be viewed in genotypic (structural) or in phenotypic (behavioural) terms, but the latter has received less attention. We propose a method for measuring phenotypic diversity in terms of the run-time behaviour of programs. We describe how this is applicable to a range of problem domains and show how the promotion of such diversity in initial genetic programming populations can have a substantial impact on solution-finding performance.", notes = "Part of \cite{Esparcia-Alcazar:2010:GP} EuroGP'2010 held in conjunction with EvoCOP2010 EvoBIO2010 and EvoApplications2010", } @InProceedings{Jackson:2010:PPSN, author = "David Jackson", title = "Promoting Phenotypic Diversity in Genetic Programming", booktitle = "PPSN 2010 11th International Conference on Parallel Problem Solving From Nature", year = "2010", editor = "Robert Schaefer and Carlos Cotta and Joanna Kolodziej and Guenter Rudolph", publisher = "Springer", pages = "472--481", series = "Lecture Notes in Computer Science", address = "Krakow, Poland", month = "11-15 " # sep, volume = "6239", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1007/978-3-642-15871-1_48", abstract = "Population diversity is generally seen as playing a crucial role in the ability of evolutionary computation techniques to discover solutions. In genetic programming, diversity metrics are usually based on structural properties of individual program trees, but are also sometimes based on the spread of fitness values in the population. We explore the use of a further interpretation of diversity, in which differences are measured in terms of the behaviour of programs when executed. Although earlier work has shown that improving behavioural diversity in initial GP populations can have a marked beneficial effect on performance, further analysis reveals that lack of behavioural diversity is a problem throughout whole runs, even when other diversity levels are high. To address this, we enhance phenotypic diversity via modifications to the crossover operator, and show that this can lead to additional performance improvements.", affiliation = "Dept. of Computer Science, University of Liverpool, Liverpool, L69 3BX United Kingdom", } @InProceedings{Jackson:2011:GECCO, author = "David Jackson", title = "Mutation as a diversity enhancing mechanism in genetic programming", booktitle = "GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0557-0", pages = "1371--1378", keywords = "genetic algorithms, genetic programming", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001576.2001761", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "In various evolutionary computing algorithms, mutation operators are employed as a means of preserving diversity of populations. In genetic programming (GP), by contrast, mutation tends to be viewed as offering little benefit, to the extent that it is often not implemented in GP systems. We investigate the role of mutation in GP, and attempt to answer questions regarding its effectiveness as a means for enhancing diversity, and the consequent effects of any such diversity promotion on the solution finding performance of the algorithm. We find that mutation can be beneficial for GP, but subject to the proviso that it be tailored to enhance particular forms of diversity.", notes = "Santa Fe Ant, 600 steps, diversity of path. Mux, 4-even-parity, polynomial (diversity = 32 floats). Also known as \cite{2001761} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)", } @InProceedings{jackson:2012:EuroGP, author = "David Jackson", title = "A New, Node-Focused Model for Genetic Programming", booktitle = "Proceedings of the 15th European Conference on Genetic Programming, EuroGP 2012", year = "2012", month = "11-13 " # apr, editor = "Alberto Moraglio and Sara Silva and Krzysztof Krawiec and Penousal Machado and Carlos Cotta", series = "LNCS", volume = "7244", publisher = "Springer Verlag", address = "Malaga, Spain", pages = "49--60", organisation = "EvoStar", isbn13 = "978-3-642-29138-8", DOI = "doi:10.1007/978-3-642-29139-5_5", size = "12 pages", keywords = "genetic algorithms, genetic programming, Graph-based representation", abstract = "We introduce Single Node Genetic Programming (SNGP), a new graph-based model for genetic programming in which every individual in the population consists of a single program node. Function operands are other individuals, meaning that the graph structure is imposed externally on the population as a whole, rather than existing within its members. Evolution is via a hill-climbing mechanism using a single reversible operator. Experimental results indicate substantial improvements over conventional GP in terms of solution rates, efficiency and program sizes.", notes = " SNGP, 6-mux, parity, symbolic regression. Cited by \cite{kubalik2017enhanced} Part of \cite{Moraglio:2012:GP} EuroGP'2012 held in conjunction with EvoCOP2012 EvoBIO2012, EvoMusArt2012 and EvoApplications2012", } @InProceedings{conf/ppsn/Jackson12, author = "David Jackson", title = "Single Node Genetic Programming on Problems with Side Effects", booktitle = "Parallel Problem Solving from Nature, PPSN XII (part 1)", year = "2012", editor = "Carlos A. {Coello Coello} and Vincenzo Cutello and Kalyanmoy Deb and Stephanie Forrest and Giuseppe Nicosia and Mario Pavone", volume = "7491", series = "Lecture Notes in Computer Science", pages = "327--336", address = "Taormina, Italy", month = sep # " 1-5", publisher = "Springer", keywords = "genetic algorithms, genetic programming, SNGP", isbn13 = "978-3-642-32936-4", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1007/978-3-642-32937-1_33", size = "10 pages", abstract = "Single Node Genetic Programming (SNGP) offers a new approach to GP in which every member of the population consists of just a single program node. Operands are formed from other members of the population, and evolution is driven by a hill-climbing approach using a single reversible operator. When the functions being used in the problem are free from side effects, it is possible to make use of a form of dynamic programming, which provides huge efficiency gains. In this research we turn our attention to the use of SNGP when the solution of problems relies on the presence of side effects. We demonstrate that SNGP can still be superior to conventional GP, and examine the role of evolutionary strategies in achieving this.", notes = "Cited by \cite{kubalik2017enhanced} Santa Fe artificial ant, Maze, Parse (Reverse Polish) stack", affiliation = "Dept. of Computer Science, University of Liverpool, Liverpool, L69 3BX United Kingdom", } @InProceedings{Jackson:2018:CIBCB, author = "Ethan C. Jackson and ames Alexander Hughes and Mark Daley", booktitle = "2018 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)", title = "On the generalizability of linear and non-linear region of interest-based multivariate regression models for fMRI data", year = "2018", abstract = "In contrast to conventional, univariate analysis, various types of multivariate analysis have been applied to functional magnetic resonance imaging (fMRI) data. In this paper, we compare two contemporary approaches for multivariate regression on task-based fMRI data: linear regression with ridge regularization and non-linear symbolic regression using genetic programming. The data for this project is representative of a contemporary fMRI experimental design for visual stimuli. Linear and non-linear models were generated for 10 subjects, with another 4 withheld for validation. Model quality is evaluated by comparing R scores (Pearson product-moment correlation) in various contexts, including single run self-fit, within-subject generalization, and between-subject generalization. Propensity for modelling strategies to overfit is estimated using a separate resting state scan. Results suggest that neither method is objectively or inherently better than the other.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CIBCB.2018.8404973", month = may, notes = "Also known as \cite{8404973}", } @PhdThesis{Jackson:thesis, author = "Ethan C. Jackson", title = "Algebraic Neural Architecture Representation, Evolutionary Neural Architecture Search, and Novelty Search in Deep Reinforcement Learning", school = "Computer Science, The University of Western Ontario", year = "2019", address = "Canada", month = "6-3", keywords = "genetic algorithms, genetic programming, ANN, Artificial neural networks, deep learning, reinforcement learning, algebraic methods, novelty search, neural architecture search", URL = "https://ir.lib.uwo.ca/etd/6510", size = "93 pages", abstract = "Evolutionary algorithms have recently re-emerged as powerful tools for machine learning and artificial intelligence, especially when combined with advances in deep learning developed over the last decade. In contrast to the use of fixed architectures and rigid learning algorithms, we leveraged the open-endedness of evolutionary algorithms to make both theoretical and methodological contributions to deep reinforcement learning. This thesis explores and develops two major areas at the intersection of evolutionary algorithms and deep reinforcement learning: generative network architectures and behaviour-based optimization. Over three distinct contributions, both theoretical and experimental methods were applied to deliver a novel mathematical framework and experimental method for generative, modular neural network architecture search for reinforcement learning, and a generalized formulation of a behaviour- based optimization framework for reinforcement learning called novelty search. Experimental results indicate that both alternative, behaviour-based optimization and neural architecture search can each be used to improve learning in the popular Atari 2600 benchmark compared to DQN, a popular gradient-based method. These results are in-line with related work demonstrating that strictly gradient-free methods are competitive with gradient-based reinforcement learning. These contributions, together with other successful combinations of evolutionary algorithms and deep learning, demonstrate that alternative architectures and learning algorithms to those conventionally used in deep learning should be seriously investigated in an effort to drive progress in artificial intelligence. Summary for Lay Audience Artificial neural networks (ANNs) have become popular tools for implementing many kinds of machine learning and artificially intelligent systems. While popular, there are many outstanding questions about how ANNs should be structured, and how they should be trained. Of particular interest is the branch of machine learning called reinforcement learning, which focuses on training artificial agents to perform complex, sequential tasks, like playing video games or navigating a maze. In this thesis, three contributions to research at the intersection of ANNs and reinforcement learning are presented. First, a mathematical language that generalizes multiple contemporary ways of describing neural network organization, second, an evolutionary algorithm that uses this mathematical language to help define an algorithm for machine learning with ANNs in which the network's architecture can be modified during training by the algorithm, and third, a related algorithm that experiments with an alternative method to training ANNs for reinforcement learning called novelty search, which promotes behavioural diversity over greedy reward seeking behaviour. Experimental results indicate that evolutionary algorithms, a form of random search guided by evolutionary principles of selection pressure, are competitive alternatives to conventional deep learning algorithms such as error back propagation. Results also show that architectural mutability. The ability for network architectures to change automatically during training. Can dramatically improve learning performance in games over contemporary methods.", notes = "Supervisor Mark Daley mentions GP and CGP", } @InCollection{jackson:2001:CES, author = "Helen Jackson", title = "Toward a Symbiotic Coevolutionary Approach to Architecture", booktitle = "Creative Evolutionary Systems", publisher = "Morgan Kaufmann", year = "2001", editor = "Peter J. Bentley and David W. Corne", chapter = "11", pages = "299--313", month = jul, keywords = "genetic algorithms, genetic programming, coevolution, lindenmayer systems", ISBN = "1-55860-673-4", DOI = "doi:10.1016/B978-155860673-9/50049-5", URL = "http://www.sciencedirect.com/science/article/B85XH-4P615HB-Y/2/e89b8cfc99c3d25e0cb0177455fa539c", abstract = "This chapter builds on earlier work using genetic programming (GP) and a Lindenmayer system (L-system) representation within the sphere of generative architectural design. L-systems are explained briefly and two contrasting embryology strategies are outlined. Artificial selection is discussed, and the wide divergence of opinion as to what might constitute an architectural configuration illustrated. Examples of successful single-goal evolution are presented, with the space syntax measure of integration investigated as a generic identifier of architectural form. Dual-and multigoal evolution are considered within the context of the architectural design discipline. It is suggested that an appropriate response to the complex nature of architectural organisms is the development of a symbiotic coevolutionary metaphor where interwoven systems within architecture are viewed as mutual species. The classification of these species leads toward a more architecture-specific genetic code. An outline of future work intended to develop such a representation begins with the identification of a naive architectural form representation and summarizes a gradual process for the refinement of this representation into a genuinely useful encoding of architectural form.", notes = "generic fitness function. spatial embryology Part of \cite{Bentley:2002:bookCES}", } @InProceedings{Jackson:2020:CEC, author = "Jericho Jackson and Yi Mei", booktitle = "2020 IEEE Congress on Evolutionary Computation (CEC)", title = "Genetic Programming Hyper-heuristic with Cluster Awareness for Stochastic Team Orienteering Problem with Time Windows", year = "2020", editor = "Yaochu Jin", month = "19-24 " # jul, keywords = "genetic algorithms, genetic programming, Clustering algorithms, Schedules, Stochastic processes, Real-time systems, Heuristic algorithms, Decision making", isbn13 = "978-1-7281-6929-3", DOI = "doi:10.1109/CEC48606.2020.9185911", abstract = "This paper looks at the stochastic Team orienteering Problem with Time Windows, a well-known problem that models the Personalised Tourist Trip Design Problem. Due to the nature of randomness such as real-time delays, the traditional optimisation approaches are not effective in solving the stochastic problem variant. In this case, genetic programming hyper-heuristics (GPHH) are promising techniques for automatically learning heuristics to make real-time decisions to effectively handle the stochastic environment, however, they still have limitations as the decision making policies use short-sighted information. In this paper, we propose to incorporate global information into the GPHH solution, with a constructed terminal feature based on cluster information to be used by the GPHH, as well as a clustering-aware solution generation process. The experimental studies showed that the newly designed cluster-based feature gave an improvement over the standard GPHH solution. This suggests that incorporating cluster information can be beneficial. Although the clustering-aware solution generation process did not achieve satisfactory performance, the further analysis showed that it could lead to improved performance under certain condition. Overall we demonstrate the effectiveness of using clustering as a global information to enhance the performance of GPHH.", notes = "Also known as \cite{9185911}", } @InProceedings{jacob:1994:glp, author = "Christian Jacob", title = "Genetic L-System Programming", booktitle = "Parallel Problem Solving from Nature III", year = "1994", editor = "Yuval Davidor and Hans-Paul Schwefel and Reinhard M{\"a}nner", series = "LNCS", volume = "866", pages = "334--343", address = "Jerusalem", publisher_address = "Berlin, Germany", month = "9-14 " # oct, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-58484-6", URL = "http://www2.informatik.uni-erlangen.de/IMMD-II/Persons/jacob/Publications/GeneticLSystemProgramming.ps.gz", DOI = "doi:10.1007/3-540-58484-6_277", size = "10 pages", abstract = "We present the Genetic L-System Programming (GLP) paradigm for evolutionary creation and development of parallel rewrite systems (L-systems, Lindenmayer-systems) which provide a commonly used formalism to describe developmental processes of natural organisms. The L-system paradigm will be extended for the purpose of describing time- and context-dependent formation of formal data structures representing rewrite rules or computer programs (expressions). With GLP two methods gleaned from nature are combined: simulated evolution and simulated structure formation. A prototypical GLP system implementation is described. Controlled evolution of complex structures is exemplified by the development of tree structures generated by the movement of a 3D-turtle.", notes = "GLP combines simulated evolution and simulated structure formation (based on Lindenmayer systems) PPSN3 L-systems difficult for human programmers to use, presents simple example where L-system is evolved using a GP. Initial population created from pool of pre-defined patterns (subtrees, building blocks?) rather than GP functions or terminals. Such patterns and genetic operators have a rank (like a fitness) which is used to bias the choice of pattern. A pattern is a gramtical rule and specifies (a number of possible) types for each of its arguments. Genetic operators include copying templates (sub trees?) into the pattern pool (or genetic library).", } @InProceedings{jacob:1996:GPls, author = "Christian Jacob", title = "Evolving Evolution Programs: Genetic Programming and L-Systems", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "107--115", address = "Stanford University, CA, USA", publisher = "MIT Press", URL = "http://pages.cpsc.ucalgary.ca/~jacob/HomeCJ/Christian's%20Home%20Page/Publications/A016B70E-EF02-4BBD-A39A-E9AF3EECBA19_files/GP-96-ArtFlowers-1.pdf", size = "9 pages", abstract = "Parallel rewrite systems in the form of string based L-systems are used for modelling and visualising growth processes of artificial plants. It is demonstrated how to use evolutionary algorithms for inferring L-systems encoding structures with characteristic properties. We describe our Mathematica based genetic programming system Evolvica , present an L-system encoding via expressions, and explain how to generate, modify and breed L-systems through simulated evolution techniques. Extensions of genetic programming operators and expression generation methods strongly relying on templates and pattern matching are shown by example.", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap13.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96", } @InProceedings{jacob:1996:epe, author = "Christian Jacob", title = "Evolution Programs Evolved", booktitle = "Parallel Problem Solving from Nature IV, Proceedings of the International Conference on Evolutionary Computation", year = "1996", editor = "Hans-Michael Voigt and Werner Ebeling and Ingo Rechenberg and Hans-Paul Schwefel", series = "LNCS", volume = "1141", pages = "42--51", address = "Berlin, Germany", publisher_address = "Heidelberg, Germany", month = "22-26 " # sep, publisher = "Springer Verlag", keywords = "genetic algorithms, genetic programming, L-Systems, Growth Grammars, morphogenesis", ISBN = "3-540-61723-X", URL = "http://pages.cpsc.ucalgary.ca/~jacob/Publications/PPSN-96-EvolutionPrograms.pdf", DOI = "doi:10.1007/3-540-61723-X_968", size = "10 pages", abstract = "Growth grammars in the form of parallel rewrite systems (L-systems) are used to model morphogenetic processes of plant structures. With the help of evolutionary programming techniques developmental programs are bred which encode plants that exhibit characteristic growth patterns advantageous in competitive environments. Program evolution is demonstrated on the basis of extended genetic programming on symbolic expressions with genetic operators and expression generation strongly relying on templates and pattern matching.", notes = "http://lautaro.fb10.tu-berlin.de/ppsniv.html PPSN4 '3.2 Stochastic generation of L-system encodings by template' Lindenmayer rewrite grammars", affiliation = "University of Erlangen-Nuernberg Lehrstuhl fuer Programmiersprachen Martensstr. 3 D-91058 Erlangen Germany Martensstr. 3 D-91058 Erlangen Germany", } @PhdThesis{jacob:thesis, author = "Christian Jacob", title = "{MathEvolvica} - Simulated Evolution of Development Programs in Nature", school = "Arbeitsberichte des Instituts fur Mathematische Maschinen und Datenverarbeitung (IMMD), Informatik, Band 28(10), Erlangen", year = "1995", address = "Germany", keywords = "genetic algorithms, genetic programming", URL = "http://katalog.tub.tu-harburg.de/Record/191683663", size = "VIII, 232 S", notes = "In German. MathEvolvica: simulierte Evolution von Entwicklungsprogrammen der Natur ", } @Book{jacob:1997:deutsch, author = "Christian Jacob", title = "Principia Evolvica -- Simulierte Evolution mit Mathematica", publisher = "dpunkt.verlag", year = "1997", address = "Heidelberg, Germany", month = aug, note = "In German", keywords = "genetic algorithms, genetic programming", ISBN = "3-920993-48-9", URL = "http://www.amazon.de/Principia-Evolvica-Simulierte-Evolution-Mathematica/dp/3920993489", URL = "http://library.wolfram.com/infocenter/TechNotes/282/", notes = "The book has 712 pages and comes with a CD that contains a lot of Mathematica notebooks with explanatory text, graphics, and animations. I just started some web pages to make part of this material available: http://www2.informatik.uni-erlangen.de/~jacob/Evolvica/EA-Mathematica.html The publishers web site is: http://www.dpunkt.de For English translation see \cite{jacob:2001:iecm}", size = "712 pages", } @InProceedings{jacob:1999:L, author = "Christian Jacob", title = "Lindenmayer systems and growth program evolution", booktitle = "Advanced Grammar Techniques Within Genetic Programming and Evolutionary Computation", year = "1999", editor = "Talib S. Hussain", pages = "76--79", address = "Orlando, Florida, USA", month = "13 " # jul, keywords = "genetic algorithms, genetic programming", notes = "GECCO-99WKS Part of wu:1999:GECCOWKS", } @Article{jacob:1999:CPC, author = "Christian Jacob", title = "Computer Physics Communications", journal = "Evolution and coevolution of developmental programs", year = "1999", volume = "121-122", pages = "46--50", month = sep # "-" # oct, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1016/S0010-4655(99)00277-5", abstract = "The developmental processes of single organisms, such as growth and structure formation, can be described by parallel rewrite systems in the form of Lindenmayer systems, which also allow one to generate geometrical structures in 3D space using turtle interpretation. We present examples of L-systems for growth programs of plant-like structures. Evolution-based programming techniques are applied to design L-systems by Genetic L-system Programming (GLP), demonstrating how developmental programs for plants, exhibiting specific morphogenetic properties can be interactively bred or automatically evolved. Finally, we demonstrate coevolutionary effects among plant populations consisting of different species, interacting with each other, competing for resources like sunlight and nutrients, and evolving successful reproduction strategies in their specific environments.", notes = "Proceedings of the Europhysics Conference on Computational Physics CCP 1998", } @Book{jacob:2001:iecm, author = "Christian Jacob", title = "Illustrating Evolutionary Computation with Mathematica", publisher = "Morgan Kaufmann", year = "2001", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-637-8", URL = "http://www.amazon.com/Illustrating-Evolutionary-Computation-Mathematica-Intelligence/dp/1558606378/ref=sr_1_1?ie=UTF8&s=books&qid=1266160160&sr=1-1", DOI = "doi:10.1016/B978-155860637-1/50020-5", DOI = "doi:10.1016/B978-155860637-1/50021-7", notes = "Sciencedirect doi refer to chapters 6 and 8", abstract = "An essential capacity of intelligence is the ability to learn. An artificially intelligent system that could learn would not have to be programmed for every eventuality; it could adapt to its changing environment and conditions just as biological systems do. Illustrating Evolutionary Computation with Mathematica introduces evolutionary computation to the technically savvy reader who wishes to explore this fascinating and increasingly important field. Unique among books on evolutionary computation, the book also explores the application of evolution to developmental processes in nature, such as the growth processes in cells and plants. If you are a newcomer to the evolutionary computation field, an engineer, a programmer, or even a biologist wanting to learn how to model the evolution and coevolution of plants, this book will provide you with a visually rich and engaging account of this complex subject. Features: Introduces the major mechanisms of biological evolution. Demonstrates many fascinating aspects of evolution in nature with simple, yet illustrative examples. Explains each of the major branches of evolutionary computation: genetic algorithms, genetic programming, evolutionary programming, and evolution strategies. Demonstrates the programming of computers by evolutionary principles using Evolvica, a genetic programming system designed by the author. Shows in detail how to evolve developmental programs modeled by cellular automata and Lindenmayer systems. Provides Mathematica notebooks on the Web that include all the programs in the book and supporting animations, movies, and graphics. Christian Jacob is assistant professor in the Department of Computer Science at the University of Calgary. His areas of interest include evolutionary algorithms, Lindenmayer systems, ecosystems modeling, distributed computing, alternative programming paradigms, biocomputing, and bioinformatics. He is the author of the German edition of this book, Principia Evolvica Simulierte Evolution mit Mathematica \cite{jacob:1997:deutsch} Part 1: Fascinating Evolution Part 2: Evolutionary Computation Part 3: If Darwin was a Programmer Part 4: Evolution of Developmental Programs", notes = "English version of \cite{jacob:1997:deutsch}", size = "578 pages", } @Article{jacob:2000:IS, author = "Christian Jacob", title = "The art of genetic programming", journal = "IEEE Intelligent Systems", year = "2000", volume = "15", number = "3", pages = "83--84", month = may # "-" # jun, keywords = "genetic algorithms, genetic programming, epigenesis, genotype-phenotype mappings, development, Gruau's embryonic technique, cellular automata, The Art of Genes, evo-computer", ISSN = "1094-7167", URL = "http://ieeexplore.ieee.org/iel5/5254/18363/00846288.pdf", DOI = "doi:10.1109/5254.846288", size = "2 pages", notes = "part of \cite{hirsh:2000:GP}", } @InCollection{jacob:2005:GPTP, author = "Christian Jacob and Ian Burleigh", title = "Genetic Programming inside a Cell", booktitle = "Genetic Programming Theory and Practice {III}", year = "2005", editor = "Tina Yu and Rick L. Riolo and Bill Worzel", volume = "9", series = "Genetic Programming", chapter = "13", pages = "191--206", address = "Ann Arbor", month = "12-14 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Agent-based Biological Modelling, agent, Gene Regulatory System, gene regulation, Lactose Operon, Bioinformatics, Simulation, Swarm Intelligence, Self-Organisation", ISBN = "0-387-28110-X", DOI = "doi:10.1007/0-387-28111-8_13", size = "16 pages", abstract = "Gene Regulation and Self-Organization: Inspirations from Genetic Programming in vivo We present an agent-based, 3D model of the lactose (lac) operon, a gene regulatory system in the bacterium E. coli. The lac operon is a prime example of a _real genetic programming_ system, which has been studied extensively and lends itself to rigorous mathematical analysis and computational simulations. We suggest natural gene regulatory systems, as observed within E. coli, to serve as testbeds for future in silico genetic programming systems.", notes = "part of \cite{yu:2005:GPTP} Published Jan 2006 after the workshop", } @Proceedings{DBLP:conf/icaris/2005, editor = "Christian Jacob and Marcin L. Pilat and Peter J. Bentley and Jonathan Timmis", title = "4th International Conference on Artificial Immune Systems: ICARIS 2005", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3627", year = "2005", address = "Banff, Alberta, Canada", month = aug # " 14-17", ISBN = "3-540-28175-4", bibsource = "DBLP, http://dblp.uni-trier.de", } @InProceedings{jacobsen-grocott:2017:CEC, author = "Josiah Jacobsen-Grocott and Yi Mei and Gang Chen2 and Mengjie Zhang", booktitle = "2017 IEEE Congress on Evolutionary Computation (CEC)", title = "Evolving heuristics for Dynamic Vehicle Routing with Time Windows using genetic programming", year = "2017", editor = "Jose A. Lozano", pages = "1948--1955", address = "Donostia, San Sebastian, Spain", month = "5-8 " # jun, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, combinatorial mathematics, mathematical programming, scheduling, vehicle routing, combinatorial optimisation problem, dynamic vehicle routing problem, evolutionary algorithms, evolving heuristics, genetic programming-based hyper-heuristic, manually designed heuristics, meta-algorithm, optimisation methods, scheduling horizon, static problems, time windows, Optimization, Real-time systems, Time factors, Vehicle dynamics", isbn13 = "978-1-5090-4601-0", URL = "https://homepages.ecs.vuw.ac.nz/~yimei/papers/CEC17-Josiah.pdf", DOI = "doi:10.1109/CEC.2017.7969539", abstract = "Dynamic vehicle routing problem with time windows is an important combinatorial optimisation problem in many real-world applications. The most challenging part of the problem is to make real-time decisions (i.e. whether to accept the newly arrived service requests or not) during the execution of the routes. It is hardly applicable to use the optimisation methods such as mathematical programming and evolutionary algorithms that are competitive for static problems, since they are usually time-consuming, and cannot give real-time responses. In this paper, we consider solving this problem using heuristics. A heuristic gradually builds a solution by adding the requests to the end of the route one by one. This way, it can take advantage of the latest information when making the next decision, and give immediate response. In this paper, we propose a meta-algorithm to generate a solution given any heuristic. The meta-algorithm maintains a set of routes throughout the scheduling horizon. Whenever a new request arrives, it tries to re-generate new routes to include the new request by the heuristic. It accepts the new request if successful, and reject otherwise. Then we manually designed several heuristics, and proposed a genetic programming-based hyper-heuristic to automatically evolve heuristics. The results showed that the heuristics evolved by genetic programming significantly outperformed the manually designed heuristics.", notes = "IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969539}", } @InProceedings{Jaewuk:2014:HIC, author = "Koo Jaewuk and Shin Yonghyun and Sangho Lee and Juneseok Choi", title = "Application Of Data Mining For Reverse Osmosis Process In Seawater Desalination", booktitle = "11th International Conference on Hydroinformatics", year = "2014", pages = "Paper 443", address = "New York, USA", month = aug # " 17-21", organisation = "IAHR/IWA Joint Committee on Hydroinformatics", keywords = "genetic algorithms, genetic programming", isbn13 = "978-0-692-28129-1", URL = "http://academicworks.cuny.edu/cc_conf_hic/443/", URL = "http://academicworks.cuny.edu/cgi/viewcontent.cgi?article=1442&context=cc_conf_hic.pdf", broken = "http://www.hic2014.org/proceedings/bitstream/handle/123456789/1682/1785.pdf", size = "4 pages", abstract = "Reverse osmosis (RO) membrane process has been considered a promising technology for water treatment and desalination. However, it is difficult to predict the performance of pilot- or full-scale RO systems because numerous factors are involved in RO performance, including variations in feed water (quantity, quality, temperature, etc), membrane fouling, and time-dependent changes (deteriorations). Accordingly, this study intended to develop a practical approach for the analysis of operation data in pilot-scale reverse osmosis (RO) processes. Novel techniques such as artificial neural network (ANN) and genetic programming (GP) technique were applied to correlate key operating parameters and RO permeability statistically. The ANN and GP models were trained using a set of experimental data from a RO pilot plant with a capacity of 1000 cubic meters per day and then used to predict its performance. The comparison of the ANN and GP model calculations with the experiment results revealed that the models were useful for analysing and classifying the performance of pilot-scale RO systems. The models were also applied for an in-depth analysis of RO system performance under dynamic conditions.", notes = "Order of within authors' names unclear, alternative: Jaewuk Koo and Yonghyun Shin and Sangho Lee and Juneseok Choi. Broken June 2021 http://www.hic2014.org/xmlui/", } @Article{jafari:2020:NRR, author = "Hamideh Jafari and Taher Rajaee and Ozgur Kisi", title = "Improved Water Quality Prediction with Hybrid {Wavelet-Genetic} Programming Model and Shannon Entropy", journal = "Natural Resources Research", year = "2020", volume = "29", number = "6", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s11053-020-09702-7", DOI = "doi:10.1007/s11053-020-09702-7", } @Article{jafari:2018:JSST, author = "Mehrdad Mahdavi Jafari and Gholam Reza Khayati", title = "Prediction of hydroxyapatite crystallite size prepared by sol-gel route: gene expression programming approach", journal = "Journal of Sol-Gel Science and Technology", year = "2018", volume = "86", number = "1", keywords = "genetic algorithms, genetic programming, gene expression programming", URL = "http://link.springer.com/article/10.1007/s10971-018-4601-6", DOI = "doi:10.1007/s10971-018-4601-6", } @Article{Jafarian20101523, author = "Yaser Jafarian and Elnaz Kermani and Mohammad H. Baziar", title = "Empirical predictive model for the (v max)/(a max) ratio of strong ground motions using genetic programming", journal = "Computer \& Geosciences", volume = "36", number = "12", pages = "1523--1531", year = "2010", month = dec, ISSN = "0098-3004", DOI = "doi:10.1016/j.cageo.2010.07.002", URL = "http://www.sciencedirect.com/science/article/B6V7D-517YN79-1/2/f812ef6b3ddb0cdd20c12efbec9c4b09", keywords = "genetic algorithms, genetic programming, Earthquake, Predictive model, vmax/amax ratio, Frequency content", abstract = "Earthquake-induced deformation of structures is strongly influenced by the frequency content of input motion. Nevertheless, state-of-the-practice studies commonly use the intensity measures such as peak ground acceleration (PGA), which are not frequency dependent. The vmax/amax ratio of strong ground motions can be used in seismic hazard studies as a parameter that captures the influence of frequency content. In the present study, genetic programming (GP) is employed to develop a new empirical predictive equation for the vmax/amax ratio of the shallow crustal strong ground motions recorded at free field sites. The proposed model is a function of earthquake magnitude, closest distance from source to site (Rclstd), faulting mechanism, and average shear wave velocity over the top 30 m of site (Vs30). A wide-ranging database of strong ground motion released by Pacific Earthquake Engineering Research Center (PEER) was used. It is demonstrated that residuals of the final equation show insignificant bias against the variations of the predictive parameters. The results indicate that vmax/amax increases through increasing earthquake magnitude and source-to-site distance while magnitude dependency is considerably more than distance dependency. In addition, the proposed model predicts higher (v max)/(a max) ratio at softer sites that possess higher fundamental periods. Consequently, as an instance for the application of the proposed model, its reasonable performance in liquefaction potential assessment of sands and silty sands is presented.", notes = "See also \cite{Kermani:2009:IJCE}", } @InProceedings{oai:CiteSeerPSU:315959, title = "A Genetic Programming Approach To The Space Layout Planning Problem", author = "Romuald Jagielski and John S. Gero", booktitle = "CAAD Futures 97", year = "1997", editor = "Richard Junge", pages = "875--884", address = "Technical University Munich, Germany", month = "4-6 " # aug, publisher = "Kluwer Academic Publishers", keywords = "genetic algorithms, genetic programming", ISBN = "0-7923-4726-9", URL = "http://people.arch.usyd.edu.au/~john/publications/1997/97JagielskiGeroCAADFutur.pdf", URL = "http://citeseer.ist.psu.edu/315959.html", DOI = "doi:10.1007/978-94-011-5576-2_67", citeseer-references = "oai:CiteSeerPSU:99515; oai:CiteSeerPSU:212034; oai:CiteSeerPSU:283749", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:315959", rights = "unrestricted", size = "10 pages", abstract = "The space layout planning problem belongs to the class of NP-hard problems with a wide range of practical applications. Many algorithms have been developed in the past, however recently evolutionary techniques have emerged as an alternative approach to their solution. In this paper, a genetic programming approach, one variation of evolutionary computation, is discussed. A representation of the space layout planning problem suitable for genetic programming is presented along with some implementation details and results.", notes = "http://www.caadfutures.arch.tue.nl/proceedings_97.htm Department of Architectural and Design Science, University of Sydney, NSW 2006, Australia", } @InProceedings{Jagielski:2000:GPP, author = "Romuald Jagielski", title = "Genetic Programming Prediction of Solar Activity", booktitle = "Intelligent Data Engineering and Automated Learning - IDEAL 2000: Data Mining, Financial Engineering, and Intelligent Agents", editor = "Kwong Sak Leung and Lai-Wan Chan and Helen Meng", year = "2000", series = "Lecture Notes in Computer Science", volume = "1983", pages = "199--205", address = "Shatin, N.T., Hong Kong, China", month = "13-15 " # dec, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-41450-9", CODEN = "LNCSD9", ISSN = "0302-9743", bibdate = "Tue Sep 10 19:08:58 MDT 2002", DOI = "doi:10.1007/3-540-44491-2_30", acknowledgement = ack-nhfb, size = "7 pages", abstract = "For many practical applications, such as planning for satellite orbits and space missions, it is important to estimate the future values of the sunspot numbers. There have been numerous methods used for this particular case of time series prediction, including recently neural networks. In this paper we present genetic programming technique employed to sunspot series prediction. The paper investigates practical solutions and heuristics for an effective choice of parameters and functions of genetic programming. The results obtained expect the maximum in the current cycle of the smoothed series monthly sunspot numbers is $164 \pm 20$, and $162 \pm 20$ for the next cycle maximum, at the 95% level of confidence. These results are discussed and compared with other predictions.", } @InProceedings{Jahan:2020:ACSOS-C, author = "Sharmin Jahan and Ian Riley and Charles Walter and Rose F. Gamble", title = "Extending Context Awareness by Anticipating Uncertainty with Enki and Darjeeling", booktitle = "2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)", year = "2020", pages = "170--175", abstract = "A self-adaptive system (SAS) requires automated planning that alters its behavior to properly operate in dynamic environments. To select a successful adaptation, the SAS must be context aware, which includes knowledge about a system's internal and environmental conditions, strategies to monitor conditions, and the capability to reason over an adaptation's relevance to its current conditions. Operational and environmental conditions are subject to foreseeable sources of uncertainty. Processes should be embedded in the SAS that generate data across a diverse set of conditions to investigate such sources and anticipate their conditions. Enki is a technology that applies a genetic algorithm to generate scenarios with diverse conditions. These scenarios should be further investigated to configure adaptations that address unexpected system behavior and failures. Darjeeling, an automated program repair tool can accept generated scenarios as input and apply genetic programming to generate patches from failed tests. Our prior work created a framework to evaluate patches by assessing their risk of requirements violation and their degree of security compliance confidence. In this paper, we incorporate these third-party tools, Enki and Darjeeling, into our framework that employs a MAPE-K loop of a previous assessed example system to extend its context awareness and increases automated capabilities.", keywords = "genetic algorithms, genetic programming, Context-aware services, Uncertainty, Synthetic aperture sonar, Fuels, Monitoring, Security, Tools, context awareness, uncertainty, self-adaptive systems", DOI = "doi:10.1109/ACSOS-C51401.2020.00051", month = aug, notes = "Also known as \cite{9196337}", } @Article{Jain:2008:OOEJ, author = "Pooja Jain and M. C. Deo", title = "Artificial Intelligence Tools to Forecast Ocean Waves in Real Time", journal = "The Open Ocean Engineering Journal", year = "2008", volume = "1", pages = "13--20", keywords = "genetic algorithms, genetic programming", ISSN = "1874-835X", DOI = "doi:10.2174/1874835X00801010013", size = "8 pages", abstract = "Prediction of wind generated ocean waves over short lead times of the order of some hours or days is helpful in carrying out any operation in the sea such as repairs of structures or laying of submarine pipelines. This paper discusses an application of different artificial intelligent tools for this purpose. The physical domain where the wave forecasting is made belongs to the western part of the Indian coastline in Arabian Sea. The tools used are artificial neural networks, genetic programming and model trees. Station specific forecasts are made at those locations where wave data are continuously observed. A time series forecasting scheme is employed. Based on a sequence of preceding observations forecasts are made over lead times of 3 hr to 72 hr. Large differences in the accuracy of the forecasts were not seen when alternative forecasting tools were employed and hence the user is free to use any one of them as per her convenience and confidence. A graphical user interface has been developed that operates on the received wave height data from the field and produces the forecasts and further makes them accessible to any user located anywhere in the world.", notes = "Department of Civil Engineering, IIT Bombay", } @Article{Jain201126, author = "Pooja Jain and M. C. Deo and G. Latha and V. Rajendran", title = "Real time wave forecasting using wind time history and numerical model", journal = "Ocean Modelling", volume = "36", number = "1-2", pages = "26--39", year = "2011", ISSN = "1463-5003", DOI = "doi:10.1016/j.ocemod.2010.07.006", URL = "http://www.sciencedirect.com/science/article/B6VPS-50XCY8V-1/2/535abd8afbb53832e8278b7eaf4d3932", keywords = "genetic algorithms, genetic programming, Artificial neural networks, Model trees, Wave prediction, Numerical wave prediction", abstract = "Operational activities in the ocean like planning for structural repairs or fishing expeditions require real time prediction of waves over typical time duration of say a few hours. Such predictions can be made by using a numerical model or a time series model employing continuously recorded waves. This paper presents another option to do so and it is based on a different time series approach in which the input is in the form of preceding wind speed and wind direction observations. This would be useful for those stations where the costly wave buoys are not deployed and instead only meteorological buoys measuring wind are moored. The technique employs alternative artificial intelligence approaches of an artificial neural network (ANN), genetic programming (GP) and model tree (MT) to carry out the time series modelling of wind to obtain waves. Wind observations at four offshore sites along the east coast of India were used. For calibration purpose the wave data was generated using a numerical model. The predicted waves obtained using the proposed time series models when compared with the numerically generated waves showed good resemblance in terms of the selected error criteria. Large differences across the chosen techniques of ANN, GP, MT were not noticed. Wave hindcasting at the same time step and the predictions over shorter lead times were better than the predictions over longer lead times. The proposed method is a cost effective and convenient option when a site-specific information is desired.", } @InProceedings{jaiswal:2017:CEC, author = "Satish Kumar Jaiswal and Hitoshi Iba", booktitle = "2017 IEEE Congress on Evolutionary Computation (CEC)", title = "Coevolution of mapping functions for linear SVM", year = "2017", editor = "Jose A. Lozano", pages = "2225--2232", address = "Donostia, San Sebastian, Spain", publisher = "IEEE", isbn13 = "978-1-5090-4601-0", abstract = "A linear SVM scales linearly with the size of a dataset, and hence is very desirable as a classifier for large datasets. However, it is not able to classify a dataset having a nonlinear decision boundary between the classes unless the dataset has been transformed by some mapping function so that the decision boundary becomes linear or it is a good approximation to a linear boundary. Often these mapping functions may result in a dataset with very large dimension or even infinite dimension. To avoid the curse of dimensionality, kernel functions are used as mapping functions. However, a kernel SVM has quadratic time complexity, and hence does not scale very well with large datasets. Moreover, the choice of a kernel function and its parameter optimization are arduous tasks. Therefore, a replacement of kernel function with an explicit mapping function is desirable in the case of large datasets. In this paper, we propose a novel co-evolutionary approach to find an explicit mapping function. We use GA to evolve an n-tuple of GP trees as a mapping function, and GP to evolve each individual GP tree. The dataset is then transformed using the found mapping function so that a linear SVM can be used. Besides the fact that the proposed algorithm allows us to use a fast linear SVM, the results also show that the proposed algorithm outperforms the kernel trick and even performs as good as the kernel trick combined with feature selection.", keywords = "genetic algorithms, genetic programming, computational complexity, feature selection, pattern classification, support vector machines, trees (mathematics), GA, GP tree n-tuple evolution, coevolutionary approach, dataset classifier, explicit mapping function, infinite dimension, kernel functions, linear SVM, mapping function coevolution, nonlinear decision boundary, parameter optimization, quadratic time complexity, Kernel, Optimization, Sociology, Statistics, Symbiosis, Vegetation, co-evolutionary algorithm, feature extraction, feature map, genetic algorithm, mapping function", isbn13 = "978-1-5090-4601-0", DOI = "doi:10.1109/CEC.2017.7969574", month = "5-8 " # jun, notes = "IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969574}", } @InProceedings{Jaiyeola:2015:LENFI, author = "Adesoji Tunbosun Jaiyeola", title = "Modelling Streamflow-Sediment Relationship Using Genetic Programming", booktitle = "Advances in Energy and Environmental Science and Engineering", year = "2015", editor = "Aida Bulucea", volume = "41", series = "Energy, Environmental and Structural Engineering Series", pages = "124--129", address = "Michigan State University, East Lansing, MI, USA", month = sep # " 20-22", publisher = "WSEAS", keywords = "genetic algorithms, genetic programming, Streamflow, suspended sediment, GPdotNET, data-driven modelling", isbn13 = "978-1-61804-338-2", URL = "http://www.wseas.us/e-library/conferences/2015/Michigan/LENFI/LENFI-18.pdf", size = "6 pages", abstract = "The presence of sediment in a river or reservoir is detrimental to the operation and management of water resources because it affects the design, planning and management of any water resource. Hence it is important to accurately estimate the quantity of sediment flowing in a river or been transported into a reservoir. The process of measuring the quantity of sediment in a river manually or using automatic sampling device is labour intensive, expensive and time consuming. In this study a data-driven approach, genetic programming techniques is used to develop an explicit model that accurately captures the relationship between streamflow and suspended sediment. The accuracy of the developed models was evaluated using Root Mean Square Error (RMSE) and Determination Coefficient (R2). The results show that GP is capable of modelling streamflow sediment process accurately with R-squared value of 0.999 and RMS errors of 0.032 during the validation phase.", notes = "Mangosuthu University of Technology, Durban", } @MastersThesis{Jaiyeola:masters, author = "Adesoji Tunbosun Jaiyeola", title = "Estimation of suspended sediment yield flowing into {Inanda Dam} using genetic programming", school = "Durban University of Technology", year = "2016", type = "Master of Engineering", address = "Durban, South Africa", month = dec, keywords = "genetic algorithms, genetic programming", URL = "http://hdl.handle.net/10321/1495", URL = "http://ir.dut.ac.za/bitstream/handle/10321/1495/JAIYEOLA_2016.pdf", size = "175 pages", abstract = "Reservoirs are designed to specific volume called the dead storage to be able to withstand the quantity of particles in the rivers flowing into it during its design period called its economic life. Therefore, accurate calculation of the quantities of sediment being transported is of great significance in environment engineering, hydroelectric equipment longevity, river aesthetics, pollution and channel navigability. In this study different input combination of monthly upstream suspended sediment concentration and upstream flow dataset for Inanda Dam for 15 years was used to develop a model for each month of the year. The predictive abilities of each of the developed model to predict the quantity of suspended sediment flowing into Inanda Dam were also compared with those of the corresponding developed Sediment Rating Curves using two evaluation criteria - Determination of Coefficient (R 2 ) and Root-Mean-Square Error (RMSE). The results from this study show that a genetic programming approach can be used to accurately predict the relationship between the streamflow and the suspended sediment load flowing into Inanda Dam. The twelve developed monthly genetic programming (GP)...", notes = "KwaZulu-Natal advisor Josiah Adeyemo", } @Article{JAKLINOVIC:2021:ESA, author = "Kristijan Jaklinovic and Marko Durasevic and Domagoj Jakobovic", title = "Designing dispatching rules with genetic programming for the unrelated machines environment with constraints", journal = "Expert Systems with Applications", volume = "172", pages = "114548", year = "2021", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2020.114548", URL = "https://www.sciencedirect.com/science/article/pii/S0957417420311921", keywords = "genetic algorithms, genetic programming, Scheduling, Unrelated machines environment, Constraints, Dispatching rules, Apparent tardiness cost", abstract = "Scheduling problems constitute an important part in many everyday systems, where a variety of constraints have to be met to ensure the feasibility of schedules. These problems are often dynamic, meaning that changes occur during the execution of the system. In such cases, the methods of choice are dispatching rules (DRs), simple methods that construct the schedule by determining the next decision which needs to be performed. Designing DRs for every possible problem variant is unfeasible. Therefore, the attention has shifted towards automatic generation of DRs using different methods, most notably genetic programming (GP), which demonstrated its superiority over manually designed rules. Since many real world applications of scheduling problems include various constraints, it is required to create high quality DRs even when different constraints are considered. However, most studies focused on problems without additional constraints or only considered them briefly. The goal of this study is to examine the potential of GP to construct DRs for problems with constraints. This is achieved primarily by adapting the schedule generation scheme used in automatically designed DRs. Also, to provide GP with a better overview of the problem, a set of supplementary terminal nodes is proposed. The results show that automatically generated DRs obtain better performance than several manually designed DRs adapted for problems with constraints. Using additional terminals resulted in the construction of better DRs for some constraints, which shows that their usefulness depends on the considered constraint type. Therefore, automatically generating DRs for problems with constraints presents a better alternative than adapting existing manually designed DRs. This finding is important as it shows the capability of GP to construct high quality DRs for more complicated problems, which is useful for real world situations where a number of constraints can be present", } @InProceedings{jakobi:1998:rsete, author = "Nick Jakobi and Phil Husbands and Tom Smith", title = "Robot Space Exploration by Trial and Error", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "807--815", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "Evolutionary Robotics", ISBN = "1-55860-548-7", notes = "GP-98", } @PhdThesis{Jakobovic:thesis, author = "Domagoj Jakobovic", title = "Scheduling based on adaptive rules", title_cr = "Rasporedivanje zasnovano na prilagodljivim pravilima", school = "Department of Electronics, Microelectronics, Computer and Intelligent Systems, University of Zagreb", year = "2005", address = "Croatia", month = "7 " # dec, keywords = "genetic algorithms, genetic programming, Computing, Data processing, scheduling heuristics, priority scheduling, adaptive genetic operators", URL = "https://dr.nsk.hr/en/islandora/object/fer%3A5430", URL = "https://urn.nsk.hr/urn:nbn:hr:168:435819", URL = "https://www.zemris.fer.hr/~yeti/doktorat/doktorat.pdf", size = "143 pages", abstract = "In this work the problem of devising an appropriate scheduling policy for different environments is addressed. The methodology which uses genetic programming to evolve scheduling heuristics is described. The scheduling heuristics are developed in the form of scheduling rules which define dynamic priorities for the elements in the system. Scheduling rules for different environments are devised using genetic programming: one machine, parallel proportional machines, unrelated machines and job shop environment. Scheduling algorithms are defined with two components: one component represents meta-algorithm which operates in scheduling environment, and the other represents an appropriate scheduling policy which derives job or machine priorities. The scheduling policy is evolved with genetic programming. For each scheduling environment a set of learning and a set of evaluation scheduling instances is defined. Devised algorithms are compared with existing algorithms in each environment. The evolved algorithms exhibit similar or better efficiency in all cases, and a significant improvement is achieved in scheduling environments where there are no fitting algorithms. Additionally, a method for evaluation of terminals in genetic programming solution and adaptive probabilities for genetic operators crossover and mutation are devised. The adaptive methods increase the probability of finding a good solution and may speed up the evolution process.", abstract = "U radu se promatra problem definiranja prikladnih postupaka rasporedivanja za razlicita okruzenja s obzirom na uvjete rasporedivanja i zadane kriterije. Predlaze se metodologija izvodenja algoritama rasporedivanja uz pomoc genetskog programiranja. Algoritmi rasporedivanja poprimaju oblik pravila u kojima se elementima u sustavu dodjeljuje prioritet na temelju kojega se aktivnosti pridruzuju sredstvima. Koristeci genetsko programiranje, izvode se pravila za razlicita okruzenja rasporedivanja: rasporedivanje na jednom stroju, na paralelnim jednolikim strojevima, nesrodnim strojevima te u okruzenju proizvoljne obrade. Za pojedino okruzenje postupak rasporedivanja definiran je u dva dijela: jedan dio predstavlja meta-algoritam koji koristi prioritete elemenata u sustavu kako bi pridruzivao aktivnosti sredstvima, a drugi dio predstavlja funkciju koja odreduje prioritete elemenata. Prioritetna funkcija dobiva se primjenom genetskog programiranja. Za svako okruzenje definirani su skupovi ispitnih primjera za ucenje i ocjenu, a predlozeni algoritmi usporedeni su sa postojecim algoritmima rasporedivanja. Algoritmi rasporedivanja izvedeni uz pomoc genetskog programiranja pokazuju slicnu ili bolju ucinkovitost u usporedbi s postojecim algoritmima, a znacajnu prednost ostvaruju u okolinama rasporedivanja za koje ne postoje prikladni postupci rasporedivanja. U radu je takoder opisan postupak vrednovanja podatkovnih elemenata rješenja genetskog programiranja te postupak prilagodbe primjene genetskih operatora krizanja i mutacije. Predlozeni postupci prilagodbe olakšavaju pronalazenje kvalitetnog rješenja i povecavaju uspješnost evolucijskog procesa.", notes = "Language croatian Universal decimal classification (UDC) 004 URN:NBN urn:nbn:hr:168:435819 Mentor Leo Budin", } @InProceedings{eurogp06:JkobovicBudin, author = "Domagoj Jakobovic and Leo Budin", title = "Dynamic Scheduling with Genetic Programming", editor = "Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art", booktitle = "Proceedings of the 9th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3905", year = "2006", address = "Budapest, Hungary", month = "10 - 12 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-33143-3", pages = "73--84", URL = "http://www.zemris.fer.hr/~yeti/download/EuroGP_2006.pdf", DOI = "doi:10.1007/11729976_7", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "This paper investigates the use of genetic programming in automatic synthesis of scheduling heuristics. The applied scheduling technique is priority scheduling, where the next state of the system is determined based on priority values of certain system elements. The evolved solutions are compared with existing scheduling heuristics for single machine dynamic problem and job shop scheduling with bottleneck estimation.", notes = "Part of \cite{collet:2006:GP} EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006", } @InProceedings{eurogp07:Jakobovic, author = "Domagoj Jakobovic and Leonardo Jelenkovic and Leo Budin", title = "Genetic Programming Heuristics for Multiple Machine Scheduling", editor = "Marc Ebner and Michael O'Neill and Aniko Ekart and Leonardo Vanneschi and Anna Isabel Esparcia-Alcazar", booktitle = "Proceedings of the 10th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "4445", year = "2007", address = "Valencia, Spain", month = "11-13 " # apr, pages = "321--330", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-71602-5", isbn13 = "978-3-540-71602-0", DOI = "doi:10.1007/978-3-540-71605-1_30", abstract = "In this paper we present a method for creating scheduling heuristics for parallel proportional machine scheduling environment and arbitrary performance criteria. Genetic programming is used to synthesise the priority function which, coupled with an appropriate meta-algorithm for a given environment, forms the priority scheduling heuristic. We show that the procedures derived in this way can perform similarly or better than existing algorithms. Additionally, this approach may be particularly useful for those combinations of scheduling environment and criteria for which there are no adequate scheduling algorithms.", notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007", } @Misc{DBLP:journals/corr/abs-2004-11300, author = "Domagoj Jakobovic and Luca Manzoni and Luca Mariot and Stjepan Picek", title = "{CoInGP}: Convolutional Inpainting with Genetic Programming", howpublished = "arXiv", volume = "abs/2004.11300", year = "2020", keywords = "genetic algorithms, genetic programming", URL = "https://arxiv.org/abs/2004.11300", archiveprefix = "arXiv", eprint = "2004.11300", timestamp = "Tue, 28 Apr 2020 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/corr/abs-2004-11300.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{Jakobovic20122781, author = "Domagoj Jakobovic and Kristina Marasovic", title = "Evolving priority scheduling heuristics with genetic programming", journal = "Applied Soft Computing", volume = "12", number = "9", pages = "2781--2789", year = "2012", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2012.03.065", URL = "http://www.sciencedirect.com/science/article/pii/S1568494612001780", keywords = "genetic algorithms, genetic programming, Priority scheduling, Scheduling heuristics", abstract = "This paper investigates the use of genetic programming in automated synthesis of scheduling heuristics for an arbitrary performance measure. Genetic programming is used to evolve the priority function, which determines the priority values of certain system elements (jobs, machines). The priority function is used within an appropriate meta-algorithm for a given environment, which forms the priority scheduling heuristic. The evolved solutions are compared with existing scheduling heuristics and found to perform similarly to or better than existing algorithms. We intend to show that this approach is particularly useful for combinations of scheduling environments and performance measures for which no adequate scheduling algorithms exist.", } @InProceedings{Jakobovic:2021:GECCO, author = "Domagoj Jakobovic and Luca Manzoni and Luca Mariot and Stjepan Picek and Mauro Castelli", title = "{CoInGP}: Convolutional Inpainting with Genetic Programming", booktitle = "Proceedings of the 2021 Genetic and Evolutionary Computation Conference", year = "2021", editor = "Francisco Chicano and Alberto Tonda and Krzysztof Krawiec and Marde Helbig and Christopher W. Cleghorn and Dennis G. Wilson and Georgios Yannakakis and Luis Paquete and Gabriela Ochoa and Jaume Bacardit and Christian Gagne and Sanaz Mostaghim and Laetitia Jourdan and Oliver Schuetze and Petr Posik and Carlos Segura and Renato Tinos and Carlos Cotta and Malcolm Heywood and Mengjie Zhang and Leonardo Trujillo and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Fuyuki Ishikawa and Inmaculada Medina-Bulo and Frank Neumann and Andrew M. Sutton", pages = "795--803", address = "internet", series = "GECCO '21", month = jul # " 10-14", organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, Convolution, Supervised learning, Prediction, Images, Inpainting", isbn13 = "9781450383509", URL = "http://www.human-competitive.org/sites/default/files/mariot_0.txt", URL = "http://www.human-competitive.org/sites/default/files/jmmpc_coingp.pdf", DOI = "doi:10.1145/3449639.3459346", size = "9 pages", abstract = "We investigate the use of Genetic Programming (GP) as a convolutional predictor for missing pixels in images. The training phase is performed by sweeping a sliding window over an image, where the pixels on the border represent the inputs of a GP tree. The output of the tree is taken as the predicted value for the central pixel. We consider two topologies for the sliding window, namely the Moore and the Von Neumann neighbourhood. The best GP tree scoring the lowest prediction error over the training set is then used to predict the pixels in the test set. We experimentally assess our approach through two experiments. In the first one, we train a GP tree over a subset of 1000 complete images from the MNIST dataset. The results show that GP can learn the distribution of the pixels with respect to a simple baseline predictor, with no significant differences observed between the two neighborhoods. In the second experiment, we train a GP convolutional predictor on two degraded images, removing around 20 percent of their pixels. In this case, we observe that the Moore neighborhood works better, although the Von Neumann neighborhood allows for a larger training set.", notes = "Entered 2021 HUMIES University of Zagreb, Croatia GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)", } @Article{jakobovic:2023:Algorithms, author = "Domagoj Jakobovic and Marko Durasevic and Karla Brkic and Juraj Fosin and Tonci Caric and Davor Davidovic", title = "Evolving Dispatching Rules for Dynamic Vehicle Routing with Genetic Programming", journal = "Algorithms", year = "2023", volume = "16", number = "6", pages = "Article No. 285", keywords = "genetic algorithms, genetic programming", ISSN = "1999-4893", URL = "https://www.mdpi.com/1999-4893/16/6/285", DOI = "doi:10.3390/a16060285", abstract = "Many real-world applications of the vehicle routing problem (VRP) are arising today, which range from physical resource planning to virtual resource management in the cloud computing domain. A common trait of these applications is usually the large scale size of problem instances, which require fast algorithms to generate solutions of acceptable quality. The basis for many VRP approaches is a heuristic which builds a candidate solution that may subsequently be improved by a local search procedure. Since there are many variants of the basic VRP model, specialised algorithms must be devised that take into account specific constraints and user-defined objective measures. Another factor is that the scheduling process may be carried out in dynamic conditions, where future information may be uncertain or unavailable or may be subject to change. When all of this is considered, there is a need for customised heuristics, devised for a specific problem variant, that could be used in highly dynamic environments. In this paper, we use genetic programming (GP) to evolve a suitable dispatching rule to build solutions for different objectives and classes of VRP problems, applicable in both dynamic and stochastic conditions. The results show great potential, since this method may be used for different problem classes and user-defined performance objectives.", notes = "also known as \cite{a16060285}", } @Article{JAKOBOVIC:2024:softx, author = "Domagoj Jakobovic and Marko Durasevic and Stjepan Picek and Bruno Gasperov", title = "{ECF:} A C++ framework for evolutionary computation", journal = "SoftwareX", year = "2024", volume = "27", pages = "101640", keywords = "genetic algorithms, genetic programming, Evolutionary computation, C++, Artificial intelligence, Metaheuristics", ISSN = "2352-7110", URL = "https://www.sciencedirect.com/science/article/pii/S2352711024000116", DOI = "doi:10.1016/j.softx.2024.101640", code_url = "http://ecf.zemris.fer.hr/html/index.html", code_url = "https://github.com/ElsevierSoftwareX/SOFTX-D-23-00449", size = "7 pages", abstract = "Metaheuristics have been shown to be efficient techniques for addressing a wide range of complex optimization problems. Developing flexible, reliable, and efficient frameworks for evolutionary computation metaheuristics is of great importance. With this in mind, ECF - Evolutionary Computation Framework, a versatile open-source framework for evolutionary computation written in C++, was developed. In addition to a wide range of efficiently implemented algorithms, it offers a variety of genotypes, parallelism with MPI, plug-and-play components, predefined problems, a configurable environment, as well as seamless integration between its components. By combining user-friendliness and customizability, ECF caters to both novice users and experienced practitioners. Its versatility has been demonstrated through extensive applications to various continuous and combinatorial optimization problems. This paper delves into the framework's key features, provides practical usage examples, highlights the impact of ECF, and outlines the plans for its future development", } @Article{Jakobovic:2024:GPEM, author = "Domagoj Jakobovic and Eric Medvet and Gisele L. Pappa and Leonardo Trujillo", title = "Introduction to special issue on highlights of genetic programming 2022 events", journal = "Genetic Programming and Evolvable Machines", year = "2024", volume = "25", pages = "Article no 1", note = "Editorial", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dtbUn", DOI = "doi:10.1007/s10710-023-09475-x", size = "3 pages", } @InProceedings{jakubeci:2016:TSAF, author = "Martin Jakubeci and Michal Gregus", title = "Search and Evaluation of Stock Ranking Rules Using Internet Activity Time Series and Multiobjective Genetic Programming", booktitle = "Time Series Analysis and Forecasting", year = "2016", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-319-28725-6_14", DOI = "doi:10.1007/978-3-319-28725-6_14", } @InProceedings{Jakubik:2020:ICETA, author = "M. Jakubik and P. Pocta", title = "Parametric audio quality estimation models for broadcasting systems and web-casting applications based on the Genetic Programming", booktitle = "2020 18th International Conference on Emerging eLearning Technologies and Applications (ICETA)", year = "2020", pages = "219--225", month = nov, keywords = "genetic algorithms, genetic programming, Performance evaluation, Electronic learning, Education, Estimation, Broadcasting, Parametric statistics", DOI = "doi:10.1109/ICETA51985.2020.9379251", abstract = "The COVID-19 pandemic has been one of the biggest disruptions to education that the world has ever experienced, affecting the most of the world student population. Many countries turned to online based distance education to ensure that learning never stops. As a consequence, throughout the globe there has been an increasing trend among the students to use different broadcasting systems and web-casting applications for the purpose of online learning. However, the video or audio quality that these various applications offer will be the key factor for their acceptance, i.e. whether or not the students will be willing to use those systems for online learning. Therefore, a machine learning technique, i.e. Genetic Programming, is used in this work for the purpose of assessing audio quality using an objective approach. A design and performance evaluation of the parametric models estimating the audio quality perceived by the end user of broadcasting systems and web-casting applications are presented in this paper. To estimate the quality of audio broadcasting systems and web-casting applications, a set of parameters influencing the quality is used as an input for the developed parametric quality estimation models. The results obtained by the developed parametric audio quality estimation models have validated Genetic Programming as a powerful technique, providing a good accuracy and generalization capabilities. This makes it a possible candidate for the estimation of audio quality perceived by the end user in the context of the broadcasting systems and web-casting applications.", notes = "Also known as \cite{9379251}", } @InProceedings{Jakubik:2021:RADIOELEKTRONIKA, author = "Martin Jakubik and Peter Pocta", title = "Estimating the Perceived Audio Quality Based on Multigene Symbolic Regression for Broadcasting Systems and Web-Casting Applications", booktitle = "2021 31st International Conference Radioelektronika (RADIOELEKTRONIKA)", year = "2021", month = "19-21 " # apr, address = "Brno, Czech Republic", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-6654-1474-6", DOI = "doi:10.1109/RADIOELEKTRONIKA52220.2021.9420201", size = "5 pages", abstract = "In these challenging times of pandemic, people are increasingly using various broadcasting systems and webcasting applications. For this reason, the importance of evaluating the perceived quality from the perspective of the end user of these applications is also growing. In this paper we present a design and performance evaluation of parametric models estimating the audio quality perceived by the end users of broadcasting systems and web-casting applications. We used a concept of symbolic regression (SR) by Multi-Gene Genetic Programming (MGGP). Symbolic regression (SR) is used to discover mathematical expressions of functions that are multigene in nature, i.e. linear combinations of the input variables. Multigene symbolic regression was validated as an effective method by the results obtained by the designed parametric audio quality estimation models, providing good accuracy and generalisation capabilities.", notes = "Also known as \cite{9420201}", } @InProceedings{Filho:2017:AGS:3131151.3131152, author = "Helson L. {Jakubovski Filho} and Jackson A. {Prado Lima} and Silvia R. Vergilio", title = "Automatic Generation of Search-Based Algorithms Applied to the Feature Testing of Software Product Lines", booktitle = "Proceedings of the 31st Brazilian Symposium on Software Engineering, SBES-2017", year = "2017", editor = "Jose Carlos Maldonado and Fabiano {Cutigi Ferrari} and Uira Kulesza and Tayana {Uchoa Conte}", pages = "114--123", address = "Fortaleza, CE, Brazil", month = sep # " 20-22", organisation = "Brazilian Computer Society", publisher = "ACM", keywords = "genetic algorithms, genetic programming, grammatical evolution, NSGA-II, SBSE, SPL, Hyper-Heuristics, Search-Based Software Engineering, Software Product Line Testing", isbn13 = "978-1-4503-5326-7", acmid = "3131152", DOI = "doi:10.1145/3131151.3131152", size = "10 pages", abstract = "The selection of products for the variability testing of Feature Models (FMs) is a complex task impacted by many factors. To solve this problem, Multi-Objective Evolutionary Algorithms (MOEAs) have been successfully used in the field known as Search-Based Software Engineering (SBSE). However, the design of a search-based approach is not an easy task for the software engineer, who can find some difficulties such as: the choice and configuration of the best MOEAs, the choice of the best search operators to be implemented, and so on. In addition to this, existing approaches are dependent on the problem domain and do not allow reuse. In this way the use of Hyper-Heuristic (HH) can help to obtain more generic and reusable search-based approaches, and because of this is considered a trend in the SBSE field. Following this trend and to contribute to reduce the software engineer's efforts, this work explores the use of a hyper-heuristic for automatic generation of MOEAs to select test products from the FM", notes = "http://www.lia.ufc.br/~cbsoft2017/en/xxxi-sbes/sbes-cfp/ Department of Computer Science, Federal University of Parana, Curitiba, Parana, Brazil", } @Article{JALAL:2021:TG, author = "Fazal E. Jalal and Yongfu Xu and Mudassir Iqbal and Babak Jamhiri and Muhammad Faisal Javed", title = "Predicting the compaction characteristics of expansive soils using two genetic programming-based algorithms", journal = "Transportation Geotechnics", volume = "30", pages = "100608", year = "2021", ISSN = "2214-3912", DOI = "doi:10.1016/j.trgeo.2021.100608", URL = "https://www.sciencedirect.com/science/article/pii/S2214391221000982", keywords = "genetic algorithms, genetic programming, Expansive soil, Gene expression programming, Multi expression programming, Maximum dry density, Optimum moisture content", abstract = "In this study, gene expression programming (GEP) and multi gene expression programming (MEP) are used to formulate new prediction models for determining the compaction parameters (rhodmax and wopt) of expansive soils. A total of 195 datasets with five input parameters (i.e., clay fraction CF, plastic limit wP, plasticity index IP, specific gravity Gs, maximum dry density rhodmax), and two output variables rhodmax and wopt are collected from the literature comprising 119 internationally published research articles to develop the GEP and MEP models. Simplified mathematical expressions were derived for these models to determine the rhodmax and wopt of expansive soils. The performance of the models was tested using mean absolute error (MAE), root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), and correlation coefficient (R). Sensitivity and parametric analyses were also performed on the GEP and MEP models. Additionally, external validation of the models was also verified using commonly recognized statistical criteria. It is clear from the results that the GEP and MEP methods accurately characterize the compaction characteristics of expansive soils resulting in reasonable prediction performance, however, GEP model yielded relatively better performance. Also, the proposed predictive models were compared with previously available empirical models and they exhibited robust and superior performance. Moreover, the rhodmax model provided significantly improved results as compared to the wopt prediction model in the case of GEP, and vice versa in the MEP model. It is therefore recommended that the proposed GP based models can reliably be used for determining the compaction parameters of expansive soils which effectively reduces the time-consuming and laborious testing, hence attaining sustainability in the field of geo-environmental engineering", } @Article{jalal:2024:ESI, author = "Fazal E. Jalal and Xiaohua Bao and Maher Omar", title = "Predictive Genetic Programming Approaches for Swell-Shrink Soil Compaction", journal = "Earth Science Informatics", year = "2024", keywords = "genetic algorithms, genetic programming", URL = "https://link.springer.com/article/10.1007/s12145-024-01482-5", DOI = "doi:10.1007/s12145-024-01482-5", } @Article{journals/nca/JalalRPT13, author = "Mostafa Jalal and Ali Akbar Ramezanianpour and Ali R. Pouladkhan and Payman Tedro", title = "Application of genetic programming ({GP}) and {ANFIS} for strength enhancement modeling of {CFRP}-retrofitted concrete cylinders", journal = "Neural Computing and Applications", year = "2013", number = "2", volume = "23", pages = "455--470", note = "See Retraction Note \cite{jalal:2021:NCA}", keywords = "genetic algorithms, genetic programming, GP, Soft computing, ANFIS, Artificial neural network (ANN), Concrete cylinder, CFRP composites", bibdate = "2013-07-24", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/nca/nca23.html#JalalRPT13", URL = "http://dx.doi.org/10.1007/s00521-012-0941-2", size = "16 pages", abstract = "Soft computing modelling of strength enhancement of concrete cylinders retrofitted by carbon-fibre reinforced polymer (CFRP) composites using adaptive neuro-fuzzy inference system (ANFIS) and genetic programming has been carried out in the present work. A comparative study has also been presented using artificial neural network, multiple regression and some existing empirical models. The proposed models are based on experimental results collected from literature. The models represent the ultimate strength of concrete cylinders after CFRP confinement that is in terms of diameter and height of the cylindrical specimen, ultimate circumferential strain in the CFRP jacket, elastic modulus of CFRP, unconfined concrete strength and total thickness of CFRP layer used. The results obtained from different models are presented and compared among which the ANFIS models are considered to be the most accurate so far and quite satisfactory as compared to the experimental results.", } @Article{JALAL:2020:CBM, author = "Mostafa Jalal and Zachary Grasley and Charles Gurganus and Jeffrey W. Bullard", title = "Experimental investigation and comparative machine-learning prediction of strength behavior of optimized recycled rubber concrete", journal = "Construction and Building Materials", volume = "256", pages = "119478", year = "2020", ISSN = "0950-0618", DOI = "doi:10.1016/j.conbuildmat.2020.119478", URL = "http://www.sciencedirect.com/science/article/pii/S0950061820314835", keywords = "genetic algorithms, genetic programming, Recycled rubber concrete, NDT, Compressive strength, Formulation-based prediction models, Machine-learning techniques, ANN, ANFIS, GP, SVM", abstract = "In the present paper, the design of optimized rubber concrete composite containing silica fume (SF) and zeolite (ZE) was undertaken using the literature, and the properties were assessed through destructive and non-destructive (NDT) methods. In order to optimize the rubberized cement composite, the optimum tradeoff between compressive strength as the main objective and rubber content, as well as the optimum fractions of the admixtures were taken into account. Main tests including workability, compressive strength, elastic modulus, and ultrasonic tests were carried out to fully assess the effects of rubber, ZE, SF, curing, and age on the rubberized composite behavior. Primary and secondary wave velocities, i.e. Vp and Vs were determined from ultrasonic test to characterize different mixtures. Static modulus results obtained from NDT were compared, and it was found that NDT results were in very good agreement with those of destructive test results. Moreover, the dynamic elastic modulus determined from compression and shear wave velocities (Vp, Vs) conforming to ASTM were compared with those estimated from six different relationships including BS, EN and ACI relationships along with other well-known equations available in the literature. In order to predict the compressive strength of the rubberized cement composite as a function of the influencing variables, a comprehensive comparative modeling was performed and different predictive models were developed using regressions and machine-learning (ML) techniques, i.e. nonlinear multi-variable regression (NMVR), Artificial neural network (ANN), genetic programming (GP), adaptive neuro-fuzzy inference system (ANFIS), and support-vector machine (SVM). Closed- form formulations were derived for NMVR, ANN, and GP models, and parametric study was conducted for ML models. Performance criteria such as root mean squared error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2) were used to compare the models' performance. It was found that SVM outperformed the other models with the highest R2 and the lowest RMSE equal to 0.989 and 1.393, respectively", } @Article{jalal:2021:NCA, author = "Mostafa Jalal and Ali A. Ramezanianpour and Ali R. Pouladkhan and Payman Tedro", title = "Retraction Note to: Application of genetic programming {(GP)} and {ANFIS} for strength enhancement modeling of {CFRP-retrofitted} concrete cylinders", journal = "Neural Computing and Applications", year = "2021", volume = "33", number = "18", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s00521-021-06174-5", DOI = "doi:10.1007/s00521-021-06174-5", abstract = "See \cite{journals/nca/JalalRPT13}", } @Article{JALALI:2019:SM, author = "S. K. Jalali and M. J. Beigrezaee and S. Hayati", title = "How does porosity affect the free vibration of single-layered graphene sheets?", journal = "Superlattices and Microstructures", volume = "128", pages = "221--242", year = "2019", keywords = "genetic algorithms, genetic programming, Porous graphene, Vibration, Molecular structural mechanics, Nonlocal theory of elasticity, Neural network", ISSN = "0749-6036", DOI = "doi:10.1016/j.spmi.2019.01.023", URL = "http://www.sciencedirect.com/science/article/pii/S0749603618323723", abstract = "This paper aims to investigate the influence of porosity and length size on the free vibration of single-layered graphene sheets (SLGSs). Frequency analysis is performed using a finite element based molecular structural mechanics (MSM) approach mimicking the SLGSs as frame-like structures constructed out of the beam elements. Defining a porous unit cell, 320 SLGSs with different arrangements and values of porosities and various length sizes ranging from 4 to 32a nm are considered. Results reveal that increasing porosity as well as length size both decrease the natural frequencies of SLGSs, significantly. To improve the applicability of the results, a nonlocal small scale parameter introduced by the analytical solutions for vibration of nanoplates in the literature is calibrated in such a way that the obtained frequencies by MSM match the analytical solutions based on the nonlocal theory of elasticity. Both neural network and genetic programming processes are successfully implemented for the calibration. The proposed calibrated parameter can be easily applied to evaluate the natural frequencies of SLGSs for certain values of porosities and length sizes", } @InProceedings{Jalali:2019:ICIT, author = "Seyed Mohammad Jafar Jalali and Abbas Khosravi and Roohallah Alizadehsani and Syed Moshfeq Salaken and Parham Mohsenzadeh Kebria and Rishi Puri and Saeid Nahavandi", title = "Parsimonious Evolutionary-based Model Development for Detecting Artery Disease", booktitle = "2019 IEEE International Conference on Industrial Technology (ICIT)", year = "2019", pages = "800--805", month = feb, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICIT.2019.8755107", ISSN = "2643-2978", abstract = "Coronary artery disease (CAD) is the most common cardiovascular condition. It often leads to a heart attack causing millions of deaths worldwide. Its accurate prediction using data mining techniques could reduce treatment risks and costs and save million lives. Motivated by these, this study proposes a framework for developing parsimonious models for CAD detection. A novel feature selection method called weight by Support Vector Machine is first applied to identify most informative features for model development. Then two evolutionary-based models called genetic programming expression (GEP) and genetic algorithm-emotional neural network (GA-ENN) are implemented for CAD prediction. Obtained results indicate that the GEP models outperform GA-ENN models and achieve the state of the art accuracy of 9percent. Such a precise model could be used as an assistive tool for medical diagnosis as well as training purposes.", notes = " Institute for Intelligent Systems Research and Innovations (IISRI), Deakin University, Australia Also known as \cite{8755107}", } @Article{Jaloun:2011:IJWMN, author = "Mohammed Jaloun and Zouhair Guennoun and Adnane Elasri", title = "Use of Genetic Algorithm in the Optimisation of the {LTE} Deployment", journal = "International Journal of Wireless \& Mobile Networks", year = "2011", volume = "3", number = "3", pages = "42--49", month = jun, keywords = "genetic algorithms, genetic programming, LTE, rf optimisation, antenna, genetic algorithm, wireless", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.208.5126", URL = "http://airccse.org/journal/jwmn/0611wmn04.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.208.5126", DOI = "doi:10.5121/ijwmn.2011.3304", abstract = "The purpose of this paper is to evaluate LTE deployment and to optimise RF parameters that include subchannel power, antenna down tilt, azimuth and beam-width. An integer optimising based on genetic programming is developed by evaluating the signal-to-interference plus noise ratio. The simulation uses a static model based on an OFDMA module designed for a Long Term Evolution (LTE) network from 3GPP [TR36.942]. The site location and initial antenna parameters are taken from real GSM network already optimised for coverage. Our analysis shows that the LTE network performance could be increased by more than 45percent by adjusting both cells power and antenna parameters.", } @Article{journals/ijsysc/JamaliKGN16, author = "Ali Jamali and E. Khaleghi and I. Gholaminezhad and Nader Nariman-Zadeh", title = "Modelling and prediction of complex non-linear processes by using Pareto multi-objective genetic programming", journal = "Int. J. Systems Science", year = "2016", number = "7", volume = "47", keywords = "genetic algorithms, genetic programming", bibdate = "2016-01-18", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ijsysc/ijsysc47.html#JamaliKGN16", pages = "1675--1688", URL = "http://dx.doi.org/10.1080/00207721.2014.945983", } @Article{journals/jim/JamaliKGNGJ17, author = "Ali Jamali and E. Khaleghi and I. Gholaminezhad and Nader Nariman-Zadeh and B. Gholaminia and A. Jamal-Omidi", title = "Multi-objective genetic programming approach for robust modeling of complex manufacturing processes having probabilistic uncertainty in experimental data", journal = "J. Intelligent Manufacturing", year = "2017", volume = "28", number = "1", pages = "149--163", keywords = "genetic algorithms, genetic programming", bibdate = "2017-05-28", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/jim/jim28.html#JamaliKGNGJ17", DOI = "doi:10.1007/s10845-014-0967-7", } @Article{JAMALI:2019:DCN, author = "Shahram Jamali and Amin Badirzadeh and Mina Soltani Siapoush", title = "On the use of the genetic programming for balanced load distribution in software-defined networks", journal = "Digital Communications and Networks", volume = "5", number = "4", pages = "288--296", year = "2019", ISSN = "2352-8648", DOI = "doi:10.1016/j.dcan.2019.10.002", URL = "http://www.sciencedirect.com/science/article/pii/S235286481830261X", keywords = "genetic algorithms, genetic programming, Software-defined networking, OpenFlow, Mininet, OpenDaylight, Load balancing", abstract = "As a new networking paradigm, Software-Defined Networking (SDN)enables us to cope with the limitations of traditional networks. SDN uses a controller that has a global view of the network and switch devices which act as packet forwarding hardware, known as {"}OpenFlow switches{"}. Since load balancing service is essential to distribute workload across servers in data centers, we propose an effective load balancing scheme in SDN, using a genetic programming approach, called Genetic Programming based Load Balancing (GPLB). We formulate the problem to find a path: 1) with the best bottleneck switch which has the lowest capacity within bottleneck switches of each path, 2) with the shortest path, and 3) requiring the less possible operations. For the purpose of choosing the real-time least loaded path, GPLB immediately calculates the integrated load of paths based on the information that receives from the SDN controller. Hence, in this design, the controller sends the load information of each path to the load balancing algorithm periodically and then the load balancing algorithm returns a least loaded path to the controller. In this paper, we use the Mininet emulator and the OpenDaylight controller to evaluate the effectiveness of the GPLB. The simulative study of the GPLB shows that there is a big improvement in performance metrics and the latency and the jitter are minimized. The GPLB also has the maximum throughput in comparison with related works and has performed better in the heavy traffic situation. The results show that our model stands smartly while not increasing further overhead", } @Article{JAMEI:2020:ICHMT, author = "Mehdi Jamei and Rashid Pourrajab and Iman Ahmadianfar and Aminreza Noghrehabadi", title = "Accurate prediction of thermal conductivity of ethylene glycol-based hybrid nanofluids using artificial intelligence techniques", journal = "International Communications in Heat and Mass Transfer", volume = "116", pages = "104624", year = "2020", ISSN = "0735-1933", DOI = "doi:10.1016/j.icheatmasstransfer.2020.104624", URL = "http://www.sciencedirect.com/science/article/pii/S0735193320301512", keywords = "genetic algorithms, genetic programming, Hybrid nanofluid, Thermal conductivity, Volume fraction, Model tree", abstract = "Accurate prediction of thermal conductivity of hybrid nanofluids is very important for industries such as microelectronics and cooling applications that heavily rely on the heat transfer. Many experimental investigations are conducted aiming at developing correlations to predict the relative thermal conductivity of hybrid nanofluids. However, the proposed correlations are limited to specific types of hybrid nanofluids. In this research, for the first time three soft computing techniques namely, Genetic programming (GP), Model tree (MT) and Multi linear regression (MLR) models, were developed and used to accurately predict the thermal conductivity of various ethylene glycol (EG)-based hybrid nanofluids. A total of 275 datasets from literature were collected and divided into the testing and training groups. The results obtained from the proposed approaches were compared with a number of performance metrics and empirical correlations. The performance criteria indicated that the GP model for the test dataset (R = 0.950, RMSE = 0.0225) had the best prediction performance for the relative thermal conductivity of hybrid nanofluids in comparison to MT (R = 0.928, RMSE =0.0301) and MLR (R = 0.787, RMSE =0.050), respectively. Sensitivity analysis showed that the nanoparticle volume fraction (R = 0.445, SI = 0.0667) was the most influential factor among all model input parameters", } @Article{JAMEI:2020:PASMA, author = "Mehdi Jamei and Iman Ahmadianfar", title = "A rigorous model for prediction of viscosity of oil-based hybrid nanofluids", journal = "Physica A: Statistical Mechanics and its Applications", volume = "556", pages = "124827", year = "2020", ISSN = "0378-4371", DOI = "doi:10.1016/j.physa.2020.124827", URL = "http://www.sciencedirect.com/science/article/pii/S0378437120304283", keywords = "genetic algorithms, genetic programming, Relative viscosity, Oil-based hybrid nanofluids, Artificial intelligence, Multigene genetic programming, Gene expression programming", abstract = "Oil-based hybrid nanofluids play an important role in heat transfer in cooling systems and lubrication. Therefore, various experimental investigations are conducted to estimate their viscosity. However, such measurements can be carried out on limited types of oil-based hybrid nanofluids and often are time consuming and expensive. The main objective of this paper is to develop a rigorous data-driven method based on an advanced genetic programming (GP) called multigene genetic programming (MGGP) to predict the viscosity of Newtonian oil-based hybrid nanofluids which has not previously been used in this area. A comparative analysis was performed using the gene expression programming (GEP), multi-variate linear regression (MLR) methods and various correlations. 679 experimental data points with different nanoparticles and oil-based fluids were collected from literature to develop the Artificial Intelligent (AI) models. The new approach showed superior performance in estimating of the relative viscosity of oil-based hybrid nanofluids in comparison with all correlations methods. Furthermore, the MGGP results for the test dataset (R=0.991, RMSE=0.05, PI=0.643) were more accurate than those obtained from the GEP (R=0.975, RMSE=0.083, PI=0.696) and MLR (R=0.912, RMSE =0.153, PI=1), respectively. The sensitivity analysis was also performed demonstrating that the volume fraction (PIs=0.849, DV1=10.079percent), temperature (PIs=0.463, DV2=9.966percent) and nanoparticles size (PIs=0.420, DV3=6.092percent) are the most significant factors in assessing relative viscosity, respectively", } @Article{JAMEI:2020:JH, author = "Mehdi Jamei and Iman Ahmadianfar and Xuefeng Chu and Zaher Mundher Yaseen", title = "Prediction of surface water total dissolved solids using hybridized wavelet-multigene genetic programming: New approach", journal = "Journal of Hydrology", volume = "589", pages = "125335", year = "2020", ISSN = "0022-1694", DOI = "doi:10.1016/j.jhydrol.2020.125335", URL = "http://www.sciencedirect.com/science/article/pii/S0022169420307952", keywords = "genetic algorithms, genetic programming, Water quality, Total dissolved solids, Wavelet-multigene genetic programming, Wavelet analysis, River engineering", abstract = "Total dissolved solids (TDS) are recognized as an essential indicator of surface water quality. The current research investigates the potential of a novel computer aid approach based on the hybridization of wavelet pre-processing with multigene genetic programming (W-MGGP) for monthly TDS prediction at the Sefid Rud River in Northern Iran. 20-year historical monthly river flow (Q) and TDS data measured at the Astaneh station were used for the model training and testing. The employed time series data were decomposed into several sub-series using three mother wavelets (i.e., Daubechies4 (db4), biorthogonal (bior6.8), and discrete meyer (dmey)) to assess appropriate combinations of the time series and their lag times, which were further used for prediction process. The W-MGGP model was compared against the wavelet-gene expression programming (W-GEP), stand-alone MGGP, and GEP models. Results were evaluated using several performance metrics including root mean square error (RMSE), correlation coefficient (R), and Nash-Sutcliffe efficiency (NSE). Modeling results indicated that W-MGGP and W-GEP provided a superior prediction capacity for the TDS in comparison with the other stand-alone artificial intelligence (AI) models. The discrete meyer method exhibited the best performance in time series data decomposition as a pre-processing approach. The proposed W-MGGP model based on the dmey mother wavelet attained the best statistical metrics (R = 0.942, RMSE = 90.383, and NSE = 0.862). The research findings demonstrated the hybridization of the wavelet pre-processing approach with MGGP predictive model for the TDS simulation", } @Article{JAMEI:2021:IJHMT, author = "Mehdi Jamei and Ismail Adewale Olumegbon and Masoud Karbasi and Iman Ahmadianfar and Amin Asadi and Mehdi Mosharaf-Dehkordi", title = "On the Thermal Conductivity Assessment of Oil-Based Hybrid Nanofluids using Extended Kalman Filter integrated with feed-forward neural network", journal = "International Journal of Heat and Mass Transfer", volume = "172", pages = "121159", year = "2021", ISSN = "0017-9310", DOI = "doi:10.1016/j.ijheatmasstransfer.2021.121159", URL = "https://www.sciencedirect.com/science/article/pii/S0017931021002623", keywords = "genetic algorithms, genetic programming, Nanofluids, thermal conductivity, oil-based hybrid nanofluids, Kalman filter, response surface methodology", abstract = "Regarding their ability to enhance conventional thermal oils' thermophysical properties, oil-based hybrid nanofluids have recently been widely investigated by researchers, especially on lubrication and cooling application in the automotive industry. Thermal conductivity is one of the most crucial thermophysical properties of oil-based hybrid nanofluids, which has been studied in a minimal case of studies on the specific types of them. In this research, for the first time, a comprehensive data-intelligence analysis performed on 400 gathered data points of various types of oil-based hybrid nanofluids using a novel hybrid machine learning approach; the Extended Kalman Filter-Neural network (EKF-ANN). The genetic programming (GP) and response surface methodology (RSM) approaches were examined to appraise the main paradigm. In this research, the best subset regression analysis, as a novel feature selection scheme, was provided for finding the best input parameter among all existing predictive variables (the volume fraction, temperature, thermal conductivity of the base fluid, mean diameter, and bulk density of nanoparticles). The provided models were examined using several statistical metrics, graphical tools and trends, and sensitivity analysis. The results assessment indicated that the EKF-ANN in terms of (R = 0.9738, RMSE = 0.0071 W/m.K, and KGE = 0.9630) validation phase outperformed the RSM (R = 0.9671, RMSE = 0.0079 W/m.K, and KGE = 0.9593) and GP (R = 0.9465, RMSE = 0.010 W/m.K, and KGE = 0.9273), for accurate estimation of the thermal conductivity of oil-based hybrid nanofluids", } @Article{JAMEI:2023:engappai, author = "Mehdi Jamei and Mumtaz Ali and Masoud Karbasi and Ekta Sharma and Mozhdeh Jamei and Xuefeng Chu and Zaher Mundher Yaseen", title = "A high dimensional features-based cascaded forward neural network coupled with {MVMD} and Boruta-{GBDT} for multi-step ahead forecasting of surface soil moisture", journal = "Engineering Applications of Artificial Intelligence", volume = "120", pages = "105895", year = "2023", ISSN = "0952-1976", DOI = "doi:10.1016/j.engappai.2023.105895", URL = "https://www.sciencedirect.com/science/article/pii/S0952197623000799", keywords = "genetic algorithms, genetic programming, Surface soil moisture forecasting, Microwave remote sensing, SMAP, Cascaded forward neural network, Bidirectional gated recurrent unit, Boruta-GBDT, Multivariate variational model decomposition", abstract = "The objective of this study is to develop a novel multi-level pre-processing framework and apply it for multi-step (one and seven days ahead) daily forecasting of Surface soil moisture (SSM) based on the NASA's Soil Moisture Active Passive (SMAP)-satellite datasets in arid and semi-arid regions of Iran. The framework consists of the Boruta gradient boosting decision tree (Boruta-GBDT) feature selection integrated with the multivariate variational mode decomposition (MVMD) and advanced machine learning (ML) models including bidirectional gated recurrent unit (Bi-GRU), cascaded forward neural network (CFNN), adaptive boosting (AdaBoost), genetic programming (GP), and classical multilayer perceptron neural network (MLP). For this purpose, effective geophysical soil moisture predictors for two arid stations of Khosrowshah and Neyshabur were first filtered among 21 daily input signals from 2015 to 2020 by using the Boruta-GBDT feature selection. The selected signals were then decomposed using the MVMD scheme. In the last pre-processing stage, the most relevant sub-sequences from a large pool in previous process were filtered using the Boruta-GBDT scheme aiming to reduce the computation and enhance the accuracy, before feeding the ML approaches. The comparison of the results from the five hybrid and standalone counterpart models in term of standardized RMSE improvement (SRMSEI) revealed that MV MD-BG-CFNN for SSM(T+1)| 27.13percent and SSM (T+7)| 43.55percent at Khosrowshah station and SSM(T+1)| 21.16percent and SSM (T+7)| 30.10percent at Neyshabur station outperformed the other hybrid frameworks, followed by MV MD-BG-Bi-GRU, MV MD-BG-Adaboost, MV MD-BG-GP, and MV MD-BG-MLP. The accurately forecasted SSM data help improve irrigation scheduling, which is of significant importance in water use efficiency and food security", } @Article{JAMEI:2022:apenergy, author = "Mehdi Jamei and Mumtaz Ali and Masoud Karbasi and Yong Xiang and Iman Ahmadianfar and Zaher Mundher Yaseen", title = "Designing a Multi-Stage Expert System for daily ocean wave energy forecasting: A multivariate data decomposition-based approach", journal = "Applied Energy", volume = "326", pages = "119925", year = "2022", ISSN = "0306-2619", DOI = "doi:10.1016/j.apenergy.2022.119925", URL = "https://www.sciencedirect.com/science/article/pii/S0306261922011825", keywords = "genetic algorithms, genetic programming, Wave energy, Multivariate variational decomposition, Boruta-extreme gradient boosting, Cascaded forward neural network, LSSVM, MGGP", abstract = "Accurate forecasting of the wave energy is crucial and has significant potential because every wave meter possesses an energy amount ranging from 30 to 40 kW along the shore. By harnessing, it does not produce toxic gases, which is a better alternative to the energies that use fossil fuels. In this research, a multi-stage Multivariate Variational Mode Decomposition (MVMD) integrated with Boruta-Extreme Gradient Boosting (BXGB) feature selection and Cascaded Forward Neural Network (CFNN) (i.e., MVMD-BXGB-CFNN) is proposed to forecast daily ocean wave energy in the regions of Queensland State, Australia. The modelling outcomes were benchmarked via three other robust intelligence-based alternatives comprised of Multigene Genetic Programming (MGGP), Least Square Support Machine (LSSVM), and Gradient Boosted Decision Tree (GBDT) models hybridized with MVMD and BXGB (i.e., MVMD-BXGB-MGGP, MVMD-BXGB-LSSVM, and MVMD-BXGB-GBDT), and their counterpart standalone CFNN, GBDT, LSSVM, and MGGP models. To develop the multi-step hybrid intelligent systems, first, the primary input signals were simultaneously decomposed into intrinsic mode functions (IMFs) and residual components using the MVMD pre-processing technique. Next, the significant lags at the t-1 and t-2 timescales computed using the cross-correlation function were imposed on the decomposed components and further filtered by the BXGB feature selection to identify the best IMFs and reduce the computational cost and enhance the accuracy. Finally, the filtered IMFs were incorporated into the machine learning (ML) models to forecast the wave energy. Forecasting performance of all the provided models (hybrid and counterpart standalone ones) was evaluated during the testing phase by several well-known metrics, infographic tools, and diagnostic analysis. The results showed that the MVMD-BXGB-CFNN technique, as a capable expert system, outperformed the other hybrid and counterpart standalone methods and has an adequate degree of reliability to forecast the daily wave energy in coastal regions", } @Article{jamei:2022:AS, author = "Mehdi Jamei and Ahmed Salih Mohammed and Iman Ahmadianfar and Mohanad Muayad Sabri Sabri and Masoud Karbasi and Mahdi Hasanipanah", title = "Predicting Rock Brittleness Using a Robust Evolutionary Programming Paradigm and Regression-Based Feature Selection Model", journal = "Applied Sciences", year = "2022", volume = "12", number = "14", pages = "Article No. 7101", keywords = "genetic algorithms, genetic programming", ISSN = "2076-3417", URL = "https://www.mdpi.com/2076-3417/12/14/7101", DOI = "doi:10.3390/app12147101", abstract = "Brittleness plays an important role in assessing the stability of the surrounding rock mass in deep underground projects. To this end, the present study deals with developing a robust evolutionary programming paradigm known as linear genetic programming (LGP) for estimating the brittleness index (BI). In addition, the bootstrap aggregate (Bagged) regression tree (BRT) and two efficient lazy machine learning approaches, namely local weighted linear regression (LWLR) and KStar approach, were examined to validate the LGP model. To the best of our knowledge, this is the first attempt to estimate the BI through the LGP model. A tunneling project in Pahang state, Malaysia, was investigated, and the requirement datasets were measured to construct the proposed models. According to the results from the testing phase, the LGP model yielded the best statistical indicators (R = 0.9529, RMSE = 0.4838, and IA = 0.9744) for modelling BI, followed by LWLR (R = 0.9490, RMSE = 0.6607, and IA = 0.9400), BRT (R = 0.9433, RMSE = 0.6875, and IA = 0.9324), and KStar (R = 0.9310, RMSE = 0.7933, and IA = 0.9095), respectively. In addition, the sensitivity analysis demonstrated that the dry density factor demonstrated the most effective prediction of BI.", notes = "also known as \cite{app12147101}", } @Article{Jamiolahmadi:2015:IFAC-PapersOnLine, author = "Saeed Jamiolahmadi and Ahmad Barari", title = "A Genetic Programming Approach to Model Detailed Surface Integrity of Additive Manufacturing Parts", journal = "IFAC-PapersOnLine", volume = "48", number = "3", pages = "2339--2344", year = "2015", note = "15th IFAC Symposium on Information Control Problems in Manufacturing INCOM 2015", ISSN = "2405-8963", DOI = "doi:10.1016/j.ifacol.2015.06.437", URL = "http://www.sciencedirect.com/science/article/pii/S240589631500676X", abstract = "Surface integrity is a crucial issue that needs to be improved in the additive manufactured products. Precise evaluation of surface integrity demands a detailed understanding of the surface behaviour. Optical surface and roughness measurement sensors only provide information of the discrete points measured from the manufactured surface without the details of the surface topography. Throughout this paper, a methodology is developed to approximate the surface behavior. This work employs a Genetic Programming approach to assess the relation between the position of the measured points and their corresponding roughness. The resulting function would assist to reconstruct the surface three dimensional topography. To validate the process, actual case study on an additive manufactured part is examined for the surface integrity.", keywords = "genetic algorithms, genetic programming, Surface integrity, Surface roughness, Additive manufacturing, Surface function", } @Article{Jamiolahmadi:2016:Measurement, author = "Saeed Jamiolahmadi and Ahmad Barari", title = "Study of detailed deviation zone considering coordinate metrology uncertainty", journal = "Measurement", year = "2016", ISSN = "0263-2241", DOI = "doi:10.1016/j.measurement.2016.12.032", URL = "http://www.sciencedirect.com/science/article/pii/S0263224116307308", abstract = "The detailed Deviation Zone Evaluation (DZE) based on the measurement of the discrete points is a crucial task in coordinate metrology. The knowledge of detailed deviation zone is necessary for any form of intelligent dynamic sampling approach in coordinate metrology or any downstream manufacturing process. Developing the desired knowledge of the deviation zone using only a finite set of the data points always needs a set of efficient interpolation and extrapolation techniques. These methods are selected based on the nature of the perusing pattern of the geometric deviation. The objective of this work is to study the efficiency of a DZE approach for the various combinations of the manufacturing errors and coordinate metrology accuracies. The first employed DZE method is governed by a Laplace equation to estimate the geometric deviations and a Finite Difference scheme is used to iteratively solve the problem. The other DZE method uses a metaheuristic approach based on Genetic Programming. Several cases of surfaces manufactured by various levels of fabrication errors and also different types of metrology systems are studied and the convergence of the employed methodologies are analyzed. It is shown how efficient the DZE solutions are to reduce the uncertainty of the resulting deviation zone based on the number of points acquired during the measurement process. The DZE solutions are successful to minimize the number of the required inspected points which directly reduces the cost and the time of inspection. The results show a great improvement in reliability of deviation zone evaluation process.", keywords = "genetic algorithms, genetic programming, Deviation zone evaluation, Coordinate metrology, Finite Difference Method, Manufacturing accuracy, Measurement uncertainty", } @Article{Jamshidi:2001:AMC, author = "Mohammad Jamshidi", title = "Autonomous control of complex systems: robotic applications", journal = "Applied Mathematics and Computation", volume = "120", pages = "15--29", year = "2001", number = "1-3", month = "10 " # may, keywords = "genetic algorithms, genetic programming, Autonomy, Control systems, Complex systems, Robotics, Behavior control", URL = "http://www.sciencedirect.com/science/article/B6TY8-42RVSF8-3/1/d9087f02589b85a2c6ef556307f7c0a8", DOI = "doi:10.1016/S0096-3003(99)00285-4", size = "15 pages", abstract = "One of the biggest challenges of any control paradigm is being able to handle large complex systems under unforeseen uncertainties. A system may be called complex here if its dimension (order) is too high and its model (if available) is nonlinear, interconnected, and information on the system is uncertain such that classical techniques cannot easily handle the problem. Soft computing, a collection of fuzzy logic, neuro-computing, genetic algorithms and genetic programming, has proven to be a powerful tool for adding autonomy to many complex systems. For such systems the size soft computing control architecture will be nearly infinite. Examples of complex systems are power networks, national air traffic control system, an integrated manufacturing plant, etc. In this paper a new rule base reduction approach is suggested to manage large inference engines. Notions of rule hierarchy and sensor data fusion are introduced and combined to achieve desirable goals. New paradigms using soft computing approaches are used to design autonomous controllers for a number of robotic applications at the ACE Center are also presented briefly.", } @Book{Jamshidi:2002:rcsGA, author = "Mo Jamshidi and Renato A. Krohling and Leandro {dos S. Coelho} and Peter J. Fleming", title = "Robust Control Systems with Genetic Algorithms", publisher = "CRC Press", year = "2002", month = "14 " # oct, keywords = "genetic algorithms, genetic programming", isbn13 = "978-0-8493-1251-9", URL = "http://www.routledge.com/books/details/9780849312519/", abstract = "In recent years, new paradigms have emerged to replace-or augment-the traditional, mathematically based approaches to optimisation. The most powerful of these are genetic algorithms (GA), inspired by natural selection, and genetic programming, an extension of GAs based on the optimization of symbolic codes.", notes = "Mostly GA? Some stuff on GP? Series Editor: Robert H. Bishop", size = "232 pages", } @InProceedings{Jan:2010:ICET, author = "Zahoor Jan and Arfan Jaffar and Fauzia Jabeen and Azhar Rauf", title = "Watermarking scheme based on wavelet transform, genetic programming and Watson perceptual distortion control model for JPEG2000", booktitle = "6th International Conference on Emerging Technologies (ICET 2010)", year = "2010", month = "18-19 " # oct, pages = "128--133", abstract = "Embedding of the digital watermark in an electronic document proves to be a viable solution for the protection of copyright and for authentication. In this paper we proposed a watermarking scheme based on wavelet transform, genetic programming (GP) and Watson distortion control model for JPEG2000. To select the coefficients for watermark embedding image is first divided into 32x32 blocks. Discrete Wavelet Transform DWT of each block is obtained. Coefficients in LH, HL and HH subbands of each 32 by 32 block are selected based on the Just Noticeable Difference (JND). Watermark is embedded by carefully chosen watermarking level. Choice of watermarking level is very important. The two important properties robustness and imperceptibility depends on good choice of watermarking level. GP is used to obtain mathematical function representing optimum watermarking level. The proposed scheme is tested and gives a good compromise between the robustness and imperceptibly.", keywords = "genetic algorithms, genetic programming, JPEG2000 image, Watson perceptual distortion control, authentication mechanism, copyright protection, digital watermarking scheme, discrete wavelet transform, electronic document, just noticeable difference, copyright, discrete wavelet transforms, image coding, message authentication, watermarking", DOI = "doi:10.1109/ICET.2010.5638368", notes = "Dept. of Comput. Sci., Nat. Univ. of Comput. & Emerging Sci., Islamabad, Pakistan Also known as \cite{5638368}, \cite{Jabeen:2013:IJICA}", } @PhdThesis{Jan:thesis, author = "Zahoor Jan", title = "Intelligent Image Watermarking using Genetic Programming", school = "Department Of Computer Science, National University Of Computer and Emerging Sciences, Islamabad", year = "2011", address = "Pakistan", month = jul, keywords = "genetic algorithms, genetic programming", URL = "http://eprints.hec.gov.pk/7540/1/1016S.htm", URL = "http://prr.hec.gov.pk/Thesis/1016S.pdf", size = "128 pages", abstract = "Multimedia applications are becoming increasingly significant in modern world.The mushroom growth of multimedia data of these applications, particularly over the web has increased the demand for protection of copyright.Digital watermarking is much more acceptable as a solution to the problem of copyright protection and authentication of multimedia data while working in a networked environment.In this thesis a DWT based watermarking scheme is proposed. Wavelet transform is used because it has a number of advantages over other transforms, such as DCT. It has multi-resolution hierarchical characteristics, and lower resolution embedding and detection which are computationally inexpensive. The presentation of the image because of the hierarchical multi-resolution properties of the transformation is well-suited for applications where the multimedia data is transmitted regularly, as such in the application of video systems, or applications in real time. Wavelet transform is closer to HVS contrast to DCT. For this reason, the range of artifacts introduced by wavelet is less infuriating as compared to DCT. For better imperceptibility, the watermarking technique should support a vision model which integrates various masking effects of the Human Visual System (HVS), to embed watermark in an invisible manner. For HVS we have used Watson's Perceptual Model of JPEG2000. The basic aim of perceptual coding is, to conceal the watermark below the detection threshold.This can be obtained by making use of the HVS and JND threshold.The watermarking technique based on this model resists all types of common signal processing operations and many geometric attacks but unfortunately was not resistant against rotation. Keeping in mind this we explored Morton scanning. Morton scanning is used to frequency wise arrange the coefficients to resist geometric attacks. We have used Genetic Programming (GP) in order to make an optimum trade off between imperceptibility and robustness by choosing an optimum watermarking level for each coefficient of the cover image. In addition to the suitable watermarking strength, the selection of best block size is also necessary for superior perceptual shaping functions.To achieve this goal we have trained and used GP to pick the best block size to tailor the watermark in a manner such that it can survive all kinds of intentional and unintentional attacks.Extensive experiments have been carried out, to demonstrate the strong robustness and imperceptibility of the proposed technique over the existing approaches.", notes = "Supervisor: Anwar Majid Mirza", } @InProceedings{Janairo:2022:IEMTRONICS, author = "Adrian Genevie Janairo and Jonah Jahara Baun and Ronnie Concepcion and R-Jay Relano and Kate Francisco and Mike Louie Enriquez and Argel Bandala and Ryan Rhay Vicerra and Melchizedek Alipio and Elmer P. Dadios", booktitle = "2022 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS)", title = "Optimization of Subsurface Imaging Antenna Capacitance through Geometry Modeling using Archimedes, Lichtenberg and Henry Gas Solubility Metaheuristics", year = "2022", abstract = "Capacitive resistivity subsurface imaging of roads operating at very low frequency is susceptible to antenna characteristic capacitance dynamics that may cause unwanted signal reflection, coupling, and unfavorable effect on reception sensitivity. Antennas are conventionally modeled using a complex and repetitive default mathematical method that is prone to human error and discrete results. To address this emerging challenge, this study has developed a new technique for plate-wire antenna capacitance optimization through equatorial dipole-dipole antenna geometry modeling using genetic programming (GP) integrated with metaheuristic methods, namely Archimedes optimization algorithm (AOA), Lichtenberg algorithm (LA), and Henry gas solubility optimization (HGSO). GP was used to construct the antenna capacitance fitness function based on 241 combinations of wire antenna radius and elevation, and dipole plate elevation, length, width, and thickness measurements. Minimization of antenna capacitance (approaching 1 nF) to achieve quasi-static condition was performed using GP-AOA, GP-LA, and GP-HGSO. The 3 metaheuristic-based antennas were 3D-modeled using Altair Feko and compared from the default antenna's electrical features. It was found that even with the smallest dipole geometry, hybrid GP-LA antenna model exhibited the most practical outputs at 5 kHz with correct directional propagation based on its radiation pattern, a realistic receiver voltage of -8.86 dBV which is close to the default model, and a high-power efficiency of 99.92percent. While hybrid GP-AOA and GP-HGSO resulted in indirect coupled transceiver systems with unsuitable antenna characteristic capacitance inducing anomalous receiver voltages. The experimental results prove the validity of the developed technique for more accurate determination of optimal antenna geometry.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IEMTRONICS55184.2022.9795789", month = jun, notes = "Also known as \cite{9795789}", } @Article{DBLP:journals/jaciii/JanairoBCLCVBD23, author = "Adrian Genevie G. Janairo and Jonah Jahara G. Baun and Johndel Garrison Chan and Joseph Aristotle R. {De Leon} and Ronnie S. {Concepcion, II} and Ryan Rhay P. Vicerra and Argel A. Bandala and Elmer P. Dadios", title = "{MeterGPX}: A Smart Multimeter Embedded with Multigene Genetic Programming Model for Multiarray Antenna Transmitter", journal = "J. Adv. Comput. Intell. Intell. Informatics", volume = "27", number = "1", pages = "19--26", year = "2023", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.20965/jaciii.2023.p0019", DOI = "doi:10.20965/jaciii.2023.p0019", timestamp = "Sat, 25 Feb 2023 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/jaciii/JanairoBCLCVBD23.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{janeczko:2000:AAE, author = "Cesar Janeczko and Heitor S. Lopes", title = "A genetic approach to ARMA filter synthesis for EEG signal simulation", booktitle = "Proceedings of the 2000 Congress on Evolutionary Computation CEC00", year = "2000", pages = "373--378", volume = "1", address = "La Jolla Marriott Hotel La Jolla, California, USA", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "6-9 " # jul, organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", publisher = "IEEE Press", keywords = "genetic algorithms, ARMA, filter, EEG, image/ signal processing, ARMA filter synthesis, EEG signal simulation, alpha waves, autoregressive moving average filter, background activity, computational simulation, electroencephalographic signal, white noise, autoregressive moving average processes, electroencephalography, filtering theory, medical signal processing, white noise", ISBN = "0-7803-6375-2", URL = "http://www.cpgei.cefetpr.br/~hslopes/publicacoes/2000/cec2000b.zip", DOI = "doi:10.1109/CEC.2000.870319", abstract = "This paper describes the computational simulation of an electroencephalographic (EEG) signal (background activity, alpha waves) by filtering a white noise with an ARMA (Autoregressive Moving Average) filter. The filter coefficients were obtained interactively using genetic algorithms, comparing the spectrum of a real and a simulated signal. Results demonstrate the feasibility of the technique", notes = "CEC-2000 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 00TH8512, Library of Congress Number = 00-018644", } @Article{Janeiro:2013:ieeeTIM, author = "Fernando M. Janeiro and Jose Santos and Pedro M. Ramos", title = "Gene Expression Programming in Sensor Characterization: Numerical Results and Experimental Validation", journal = "IEEE Transactions on Instrumentation and Measurement", year = "2013", number = "5", volume = "62", pages = "1373--1381", month = may, keywords = "genetic algorithms, genetic programming, gene expression programming", ISSN = "0018-9456", DOI = "doi:10.1109/TIM.2012.2224275", size = "9 pages", abstract = "In this paper, impedance spectroscopy, gene expression programming (GEP), and genetic algorithms are combined to perform sensor characterisation. The process presented is useful when there is no knowledge of the sensor equivalent circuit, and a set of impedance responses can be obtained for different measurand values. These responses are used by the algorithm to determine a suitable equivalent circuit and choose a circuit component that describes the measurand values. From this component, interpolation is used to infer the measurand value from the measured frequency responses. Improvements on the application of GEP to impedance characterisation are presented. The method is validated through its application to numerical results of a humidity sensor and measurement results of a viscosity sensor.", notes = "Also known as \cite{6353575}", } @InProceedings{Jang:2006:ASPGP, title = "Automated construction of diagnosis rules from DNA samples", author = "Ha-Young Jang and Byoung-Tak Zhang", booktitle = "Proceedings of the Third Asian-Pacific workshop on Genetic Programming", year = "2006", editor = "The Long Pham and Hai Khoi Le and Xuan Hoai Nguyen", pages = "47--56", ISSN = "18590209", address = "Military Technical Academy, Hanoi, VietNam", keywords = "genetic algorithms, genetic programming", URL = "http://gpbib.cs.ucl.ac.uk/aspgp06/ASPGP06_Jang_revised.pdf", size = "10 page", abstract = "We propose a molecular computing algorithm for constructing diagnosis rules from blood sample automatically. Different to disease diagnosis based on microarray, proposed method can make a diagnosis without statistical analysis of sample. Every operator in the proposed method can be implemented with conventional wet-lab techniques such as Polymerase Chain Reaction (PCR), hybridisation and affinity separation. Tested on a real disease data, simulation results show not only the feasibility of proposed method but also the possibility of biological information processing. The use of huge population in molecular evolutionary algorithm also can give various insights to evolutionary computation.", notes = "broken march 2020 http://www.aspgp.org", } @Article{JANIGA:2018:Fuel, author = "Damian Janiga and Robert Czarnota and Jerzy Stopa and Pawe Wojnarowski", title = "Huff and puff process optimization in micro scale by coupling laboratory experiment and numerical simulation", journal = "Fuel", volume = "224", pages = "289--301", year = "2018", keywords = "genetic algorithms, genetic programming, Huff and puff, Enhanced oil recovery, Particle swarm optimization", ISSN = "0016-2361", DOI = "doi:10.1016/j.fuel.2018.03.085", URL = "http://www.sciencedirect.com/science/article/pii/S0016236118304940", abstract = "Huff and Puff Enhanced Oil Recovery method can be regarded as promising process to increase oil production rates from developed field. Worldwide experiences in the application for an industrial-scale of this technology has been extensively discussed for heavy oil and tight oil production, however, field unique does not guarantee success for technology transfer to different site. In this way reservoir simulation is used as a first approximation of the project efficiency. However, numerical simulation requires representative data from laboratory experiments. Furthermore, huff-and-puff should be considered as complex problem, where influences from injection rates, soaking time and production rates can not be neglected. On the other side, conducting laboratory investigations are expensive and time-consuming, therefore, these researches should provide the most valuable information. In the presented methodology, laboratory experiments were conjuncted with the numerical representation of a core sample, to generate trustworthy models which were used for the process optimization. The optimal huff-n-puff operational design was computed using a stochastic population-based particle swarm optimization (PSO) method. As a consequence of high computational cost of a single full physic numerical run, the genetic programming as a novel tool for the huff-and-puff process optimization was successfully implemented. The comparison of the optimized results between genetic programming data-drive model and the full-physic numerical run revealed the right approximation and significant computing time reduction", } @Article{janikow:1996:CGP, author = "Cezary Z. Janikow", title = "A Methodology for Processing Problem Constraints in Genetic Programming", journal = "Computers and Mathematics with Applications", year = "1996", volume = "32", number = "8", pages = "97--113", month = oct, keywords = "genetic algorithms, genetic programming, lil-gp", URL = "http://www.cs.umsl.edu/~janikow/psdocs/cgp.CMwA.ps", broken = "http://www.sciencedirect.com/science/article/B6TYJ-41GX54B-9/1/5a1e263b86597ce90a6eec429c357ce5", URL = "http://citeseer.ist.psu.edu/326996.html", ISSN = "0898-1221", DOI = "doi:10.1016/0898-1221(96)00170-8", size = "17 pages", abstract = "Search mechanisms of artificial intelligence combine two elements: representation, which determines the search space, and a search mechanism, which actually explores the space. Unfortunately, many searches may explore redundant and/or invalid solutions. Genetic programming refers to a class of evolutionary algorithms based on genetic algorithms, but using a parameterized representation in the form of trees. These algorithms perform searches based on simulation of nature. They face the same problems of redundant/invalid subspaces. These problems have just recently been addressed in a systematic manner. This paper presents a methodology devised for the public domain genetic programming tool lil-gp. This methodology uses data typing and semantic information to constrain the representation space so that only valid, and possibly unique, solutions will be explored. The user enters problem-specific constraints, which are transformed into a normal set. This set is checked for feasibility, and subsequently, it is used to limit the space being explored. The constraints can determine valid, possibly unique spaces. Moreover, they can also be used to exclude subspaces the user considers uninteresting, using some problem-specific knowledge. A simple example is followed thoroughly to illustrate the constraint language, transformations, and the normal set. Experiments with Boolean 11-multiplexer illustrate practical applications of the method to limit redundant space exploration by using problem-specific knowledge.", notes = "http://laplace.cs.umsl.edu/~janikow/cgp-lilgp/ CGP uses GP [Koza] to evolve programs (or trees in general). It extends GP by allowing syntactic and sematical constraints on function calls (the constraints can be weighted rather than strict), plus function overloading. In future releases, evolution of representation (i.e., constraints), ADFs, and recursive functions are planned. lil-gp comparison of solving 11-multiplexor problem nine different ways with different type systems. Some tighter (than Koza) type systems (eg different address and data bits, different function sets) are worse than Koza GP and some are better. Problem dependant reasons for this suggested. Comparison with GIL. STGP", } @InProceedings{janikow:1998:pcCGP2.1, author = "Cezary Z. Janikow and Scott DeWeese", title = "Processing Constraints in Genetic Programming with {CGP2.1}", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "173--180", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, CGP, 11-Multiplexer, DNF-constrained", ISBN = "1-55860-548-7", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1998/janikow_1998_pcCGP2.1.pdf", size = "8 pages", notes = "GP-98", } @InProceedings{janikow:1999:C, author = "Cezary Z. Janikow", title = "Constrained genetic programming", booktitle = "Advanced Grammar Techniques Within Genetic Programming and Evolutionary Computation", year = "1999", editor = "Talib S. Hussain", pages = "80--82", address = "Orlando, Florida, USA", month = "13 " # jul, keywords = "genetic algorithms, genetic programming", notes = "GECCO-99WKS Part of wu:1999:GECCOWKS", } @InProceedings{janikow:2003:ANNIE, author = "Cezary Z. Janikow and Rahul A Deshpande", title = "Adaptation of Representation in Genetic Programming", booktitle = "Smart Engineering System Design: Neural Networks, Fuzzy Logic, Evolutionary Programming, Complex Systems, and Artificial Life (ANNIE'2003)", editor = "Cihan H. Dagli and Anna L. Buczak and Joydeep Ghosh and Mark J. Embrechts and Okan Ersoy", pages = "45--50", publisher = "ASME Press", month = "2-5 " # nov, year = "2003", keywords = "genetic algorithms, genetic programming", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.585.5050", URL = "http://www.cs.umsl.edu/~janikow/publications/2003/Adaptation%20of%20Representation%20in%20Genetic%20Programming/ams2003.pdf", size = "6 pages", abstract = "This paper discusses our initial work on automatically adapting Genetic Programming (GP) representation. We present here two independent techniques: AMS and ACE. Both techniques are based on Constrained GP (CGP), which uses mutation set methodology to prune the representation space according to some context-specific constraints. The ASM technique monitors the performance of local context heuristics when used in mutation/crossover, during GP evolution, and dynamically modifies the heuristics. The ACE technique iterates complete CGP runs and then uses the distribution information from the best solutions to adjust the heuristics for the next iteration. As the results indicate, GP is able to gain substantial performance improvements as well as learn qualitative heuristics.", notes = "broken http://web.mst.edu/~annie/annie03_Final/welcome.htm", } @InCollection{janikow:2004:GPTP, author = "Cezary Z. Janikow", title = "{ACGP}: Adaptable Constrained Genetic Programming", booktitle = "Genetic Programming Theory and Practice {II}", year = "2004", editor = "Una-May O'Reilly and Tina Yu and Rick L. Riolo and Bill Worzel", chapter = "12", pages = "191--206", address = "Ann Arbor, MI, USA", month = "13-15 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, representation, learning, adaptation, heuristics", ISBN = "0-387-23253-2", URL = "http://www.umsl.edu/cmpsci/about/People/Faculty/CezaryJanikow/untitled%20folder/ACGP.pdf", DOI = "doi:10.1007/0-387-23254-0_12", abstract = "Genetic Programming requires that all functions/terminals (tree labels) be given a priori. In the absence of specific information about the solution, the user is often forced to provide a large set, thus enlarging the search space often resulting in reducing the search efficiency. Moreover, based on heuristics, syntactic constraints, or data typing, a given subtree may be undesired or invalid in a given context. Typed Genetic Programming methods give users the power to specify some rules for valid tree construction, and thus to prune the otherwise unconstrained representation in which Genetic Programming operates. However, in general, the user may not be aware of the best representation space to solve a particular problem. Moreover, some information may be in the form of weak heuristics. In this work, we present a methodology, which automatically adapts the representation for solving a particular problem, by extracting and using such heuristics. Even though many specific techniques can be implemented in the methodology, in this paper we use information on local first-order (parent-child) distributions of the functions and terminals. The heuristics are extracted from the population by observing their distribution in better individuals. The methodology is illustrated and validated using a number of experiments with the 11-multiplexer. Moreover, some preliminary empirical results linking population size and the sampling rate are also given.", notes = "part of \cite{oreilly:2004:GPTP2}", } @InProceedings{janikow:ari:gecco2004, author = "Cezary Z. Janikow", title = "Adapting Representation in Genetic Programming", booktitle = "Genetic and Evolutionary Computation -- GECCO-2004, Part II", year = "2004", editor = "Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell", series = "Lecture Notes in Computer Science", pages = "507--518", address = "Seattle, WA, USA", publisher_address = "Heidelberg", month = "26-30 " # jun, organisation = "ISGEC", publisher = "Springer-Verlag", volume = "3103", ISBN = "3-540-22343-6", ISSN = "0302-9743", DOI = "doi:10.1007/b98645", size = "12", keywords = "genetic algorithms, genetic programming", notes = "GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)", } @InProceedings{janikow:2004:mod:czjan, author = "Cezary Z. Janikow", title = "{ACGP} is a new method to explore regularity", editor = "R. Poli and S. Cagnoni and M. Keijzer and E. Costa and F. Pereira and G. Raidl and S. C. Upton and D. Goldberg and H. Lipson and E. {de Jong} and J. Koza and H. Suzuki and H. Sawai and I. Parmee and M. Pelikan and K. Sastry and D. Thierens and W. Stolzmann and P. L. Lanzi and S. W. Wilson and M. O'Neill and C. Ryan and T. Yu and J. F. Miller and I. Garibay and G. Holifield and A. S. Wu and T. Riopka and M. M. Meysenburg and A. W. Wright and N. Richter and J. H. Moore and M. D. Ritchie and L. Davis and R. Roy and M. Jakiela", booktitle = "GECCO 2004 Workshop Proceedings", year = "2004", month = "26-30 " # jun, address = "Seattle, Washington, USA", keywords = "genetic algorithms, genetic programming, Adaptable Constrained GP", URL = "http://gpbib.cs.ucl.ac.uk/gecco2004/WMOD006.pdf", notes = "GECCO-2004WKS Distributed on CD-ROM at GECCO-2004", } @TechReport{janikow:2004:NASA, author = "Cezary Z. Janikow", title = "Adaptable Constrained Genetic Programming: Extensions and Applications", institution = "NASA", year = "2005", type = "Summer Faculty Fellowship Program 2004", number = "Volumes 1 and 2, Page: 11-1 - 11-7", month = "1 " # aug, keywords = "genetic algorithms, genetic programming, ACGP2.1", URL = "http://hdl.handle.net/2060/20050202032", URL = "https://ntrs.nasa.gov/api/citations/20050202032/downloads/20050202032.pdf", size = "7 pages", abstract = "An evolutionary algorithm applies evolution-based principles to problem solving. To solve a problem, the user defines the space of potential solutions, the representation space. Sample solutions are encoded in a chromosome-like structure. The algorithm maintains a population of such samples, which undergo simulated evolution by means of mutation, crossover, and survival of the fittest principles. Genetic Programming (GP) uses tree-like chromosomes, providing very rich representation suitable for many problems of interest. GP has been successfully applied to a number of practical problems such as learning Boolean functions and designing hardware circuits. To apply GP to a problem, the user needs to define the actual representation space, by defining the atomic functions and terminals labeling the actual trees. The sufficiency principle requires that the label set be sufficient to build the desired solution trees. The closure principle allows the labels to mix in any arity-consistent manner. To satisfy both principles, the user is often forced to provide a large label set, with ad hoc interpretations or penalties to deal with undesired local contexts. This unfortunately enlarges the actual representation space, and thus usually slows down the search. In the past few years, three different methodologies have been proposed to allow the user to alleviate the closure principle by providing means to define, and to process, constraints on mixing the labels in the trees. Last summer we proposed a new methodology to further alleviate the problem by discovering local heuristics for building quality solution trees. A pilot system was implemented last summer and tested throughout the year. This summer we have implemented a new revision, and produced a User's Manual so that the pilot system can be made available to other practitioners and researchers. We have also designed, and partly implemented, a larger system capable of dealing with much more powerful heuristics.", notes = "Broken Aug 2018 http://www.sti.nasa.gov/scan/rss99-01.html Document ID: 20050202032 Report #: None Sales Agency: CASI Hardcopy A02 No Copyright Source: Missouri Univ. (Saint Louis, MO, United States)", } @InProceedings{1068293, author = "Cezary Z. Janikow and Christopher J. Mann", title = "{CGP} visits the {Santa Fe} trail: effects of heuristics on {GP}", booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation", year = "2005", editor = "Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and Edwin D. {de Jong} and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler", volume = "2", ISBN = "1-59593-010-8", pages = "1697--1704", address = "Washington DC, USA", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "25-29 " # jun, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Adaptable Constrained Genetic Programming, evolutionary computation, design, experimentation, heuristics", URL = "http://gpbib.cs.ucl.ac.uk/gecco2005/docs/p1697.pdf", DOI = "doi:10.1145/1068009.1068293", code_url = "http://www.cs.umsl.edu/~janikow/cgp-lilgp/", size = "8 pages", abstract = "GP uses trees to represent chromosomes. The user defines the representation space by defining the set of functions and terminals to label the nodes in the trees, and GP searches the space. Previous research and experimentation show that the choice of the function/terminal set, choice of the initial population, and some other explicit and implicit design factors have great influence on both the quality and the speed of the evolution. Such heuristics are valuable simply because they improve GP's performance, or because they enforce some desired properties on the solutions. In this paper, we evaluate the effect of heuristics on GP solving the Santa Fe trail. We concentrate on improving the solution quality, but we also look at efficiency. Various heuristics are tried and mixed by hand, while evaluated with the help of the CGP system. Results show that some heuristics result in very substantial performance improvements, that complex heuristics are usually not decomposable, and that the heuristics generalize to apply to other similar problems, but the applicability reduces with the complexity of the heuristics and the dissimilarity of the new problem to the old one. We also compare such user-mixed heuristics with those generated by the ACGP system which automatically extracts heuristics improving GP performance.", notes = "http://www.cs.umsl.edu/~janikow/cgp-lilgp/ GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM Order Number 910052", } @InProceedings{janikow:gecco05ws, author = "Cezary Z. Janikow", title = "Adaptable Representation in {GP}", booktitle = "Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program", year = "2005", month = "25-29 " # jun, editor = "Franz Rothlauf and Misty Blowers and J{\"u}rgen Branke and Stefano Cagnoni and Ivan I. Garibay and Ozlem Garibay and J{\"o}rn Grahl and Gregory Hornby and Edwin D. {de Jong} and Tim Kovacs and Sanjeev Kumar and Claudio F. Lima and Xavier Llor{\`a} and Fernando Lobo and Laurence D. Merkle and Julian Miller and Jason H. Moore and Michael O'Neill and Martin Pelikan and Terry P. Riopka and Marylyn D. Ritchie and Kumara Sastry and Stephen L. Smith and Hal Stringer and Keiki Takadama and Marc Toussaint and Stephen C. Upton and Alden H. Wright", publisher = "ACM Press", address = "Washington, D.C., USA", pages = "327--331", keywords = "genetic algorithms, genetic programming, ACGP, Heuristics, Representation", URL = "http://gpbib.cs.ucl.ac.uk/gecco2005wks/papers/0327.pdf", DOI = "doi:10.1145/1102256.1102329", size = "5 pages", abstract = "Genetic Programming uses trees to represent chromosomes. The user defines the representation space by defining the set of functions and terminals to label the nodes in the trees. The sufficiency principle requires that the set be sufficient to label the desired solution trees, often forcing the user to enlarge the set, thus also enlarging the search space. Structure-preserving crossover, STGP, CGP, and CFG-based GP give the user the power to reduce the space by specifying rules for valid tree construction, based on types, syntax, and heuristics. These rules in effect change the representation. However, in general the user may not be aware of the best representation, including heuristics, to solve a particular problem. Last year, ACGP methodology was introduced for extracting local problem-specific heuristics, that is for learning a local model of the problem domain. ACGP discovers representation, in the space of probabilistic representations, one that improves the search itself and that provides the user with heuristics about the domain. We discuss and illustrate the probabilistic representation.", notes = "Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006", } @InProceedings{1274017, author = "Cezary Z. Janikow", title = "Evolving problem heuristics with on-line {ACGP}", booktitle = "Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2007)}", year = "2007", month = "7-11 " # jul, editor = "Peter A. N. Bosman", isbn13 = "978-1-59593-698-1", pages = "2503--2508", address = "London, United Kingdom", keywords = "genetic algorithms, genetic programming, heuristics, machine learning, STGP, artificial ant", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p2503.pdf", DOI = "doi:10.1145/1274000.1274017", publisher = "ACM Press", publisher_address = "New York, NY, USA", size = "6 pages", abstract = "Genetic Programming uses trees to represent chromosomes. The user defines the representation space by defining the set of functions and terminals to label the nodes in the trees. The sufficiency principle requires that the set be sufficient to label the desired solution trees, often forcing the user to enlarge the set, thus also enlarging the search space. Structure-preserving crossover, STGP, CGP, and CFG-based GP give the user the power to reduce the space by specifying rules for valid tree construction: types, syntax, and heuristics. However, in general the user may not be aware of the best representation space, including heuristics, to solve a particular problem. Recently, the ACGP methodology for extracting problem-specific heuristics, and thus for learning model of the problem domain, was introduced with preliminary off-line results. This paper overviews ACGP, pointing out its strength and limitations in the off-line mode. It then introduces a new on-line model, for learning while solving a problem, illustrated with experiments involving the multiplexer and the Santa Fe trail.", notes = "Distributed on CD-ROM at GECCO-2007 ACM Order No. 910071", } @InProceedings{Janikow:2011:GECCOcomp, author = "Cezary Z. Janikow and John Aleshunas and Mark W. Hauschild", title = "Second order heuristics in {ACGP}", booktitle = "Optimization by building and using probabilistic models (OBUPM-2011)", year = "2011", editor = "Mark Hauschild and Martin Pelikan", publisher = "ACM", publisher_address = "New York, NY, USA", pages = "671--678", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", keywords = "genetic algorithms, genetic programming, Heuristics, Search Space", isbn13 = "978-1-4503-0690-4", URL = "http://umsl.edu/cmpsci/about/People/Faculty/CezaryJanikow/folder%20two/secondorder.pdf", DOI = "doi:10.1145/2001858.2002066", size = "8 pages", abstract = "Genetic Programming explores the problem search space by means of operators and selection. Mutation and crossover operators apply uniformly, while selection is the driving force for the search. Constrained GP changes the uniform exploration to pruned non-uniform, skipping some subspaces and giving preferences to others, according to some heuristics. Adaptable Constrained GP is a methodology for discovery of such useful heuristics. Both methodologies have previously demonstrated their surprising capabilities using only first-order (parent-child) heuristics. Recently, they have been extended to second-order (parent-children) heuristics. This paper describes the second-order processing, and illustrates the usefulness and efficiency of this approach using a simple problem specifically constructed to exhibit strong second-order structure.", notes = "University of Missouri-St Louis St. Louis, MO 63121 Also known as \cite{2002066} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @InProceedings{Janikow:2013:CEC, article_id = "1632", author = "Cezary Janikow and John Aleshunas", title = "Impact of Commutative and Non-commutative Functions on Symbolic Regression with {ACGP}", booktitle = "2013 IEEE Conference on Evolutionary Computation", volume = "1", year = "2013", month = jun # " 20-23", editor = "Luis Gerardo {de la Fraga}", pages = "2290--2297", address = "Cancun, Mexico", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-0453-2", DOI = "doi:10.1109/CEC.2013.6557842", notes = "CEC 2013 - A joint meeting of the IEEE, the EPS and the IET.", } @InCollection{kinnear:jannink, author = "Jan Jannink", title = "Cracking and Co-Evolving Randomizers", booktitle = "Advances in Genetic Programming", publisher = "MIT Press", editor = "Kenneth E. {Kinnear, Jr.}", year = "1994", chapter = "20", pages = "425--443", keywords = "genetic algorithms, genetic programming, memory", URL = "http://infolab.stanford.edu/pub/jannink/gp.ps", URL = "http://www.amazon.co.uk/Advances-Genetic-Programming-Complex-Adaptive/dp/0262111888", URL = "http://cognet.mit.edu/sites/default/files/books/9780262277181/pdfs/9780262277181_chap20.pdf", DOI = "doi:10.7551/mitpress/1108.003.0026", size = "19 pages", abstract = "Although pseudo-random number generator or randomizers are of great importance in the domain of simulating real world phenomena, it is difficult to construct functions which satisfy the many criteria, such as uniform distribution, which good randomizers possess. It is computationally expensive perform the statistical analysis required to establish their quality. Moreover, no current method of analysis can guarantee quality, since even the question of what constitutes the set of criteria defining randomness remains open. ...", notes = "Uses protect mod. But doesnt give details.Uses Teller's READ and WRITE Uses IFGEN macro to evolve two separate functions in same tree Ref Kolmogorov (1965) = on density of information packing Ref James (1990) = on very long (10**170) sequence random numbers Initilises so store[x]=x+1 rather than zero. Part of \cite{kinnear:book}", } @Article{JANOUT:2023:procs, author = "Hannah Janout and Thomas Paier and Carina Ringelhahn and Michael Heckmann and Andreas Haghofer and Gabriel Kronberger and Stephan Winkler", title = "Identification of Surrogate Models for the Prediction of Degrees of Freedom within a Tolerance Chain", journal = "Procedia Computer Science", volume = "217", pages = "796--805", year = "2023", note = "4th International Conference on Industry 4.0 and Smart Manufacturing", ISSN = "1877-0509", DOI = "doi:10.1016/j.procs.2022.12.276", URL = "https://www.sciencedirect.com/science/article/pii/S1877050922023547", keywords = "genetic algorithms, genetic programming, Machine Learning, Surrogate Model, Gradient Boosted Tree, Neural Network, Robust Design, Tolerance Analysis, Symbolic Regression", abstract = "The computation of assembly tolerance information is necessary to fulfill robust design requirements. This assembly is computationally costly, with current calculations taking several hours. We aim to identify surrogate models for predicting degrees of freedom within a tolerance chain based on point connections between assembly components. Thus, replacing part of the current computation workflow and consequently reduce computation time. We use manufacturing tolerances set by norms and industrial standards to identifly these surrogate models, which define all relevant features and resulting output variables. We use black-box modeling methods (artificial neural networks and gradient boosted trees), as well as white-box modeling (symbolic regression by genetic programming). We see that these three models can reliably predict the degrees of freedom of a tolerance chain with high accuracy (R2 > 0.99)", } @PhdThesis{Jansen:thesis, author = "Sebastian Jansen", title = "Testing market imperfections via genetic programming", school = "Institut fur Financial Management, Universitaet Hohenheim", year = "2011", type = "Dr. oec", address = "Bonn, Germany", month = "17 " # mar, keywords = "genetic algorithms, genetic programming, Market Efficiency, Excess Returns", URL = "http://opus.ub.uni-hohenheim.de/volltexte/2011/588/", URL = "http://opus.ub.uni-hohenheim.de/volltexte/2011/588/pdf/Dissertation_Testing_Market_Imperfections_via_Genetic_Programming_Sebastian_Jansen.pdf", size = "161 pages", abstract = "The thesis checks the validity of the efficient markets hypothesis focusing on stock markets. Technical trading rules are generated by using an evolutionary optimisation algorithm (Genetic Programming) based on training samples. The trading rules are subsequently applied to data samples unknown to the algorithm beforehand. The benchmark strategy consists of a classic buy-and-hold strategy in the DAX and the Hang Seng. The trading rules generally fail at consistently beating the benchmark thus indicating that market efficiency holds.", abstract = "Gegenstand der Dissertation ist die Uberpruefung von Markteffizienz auf Aktienmaerkten. Hierzu werden technische Handelsregeln mit Hilfe eines evolutionaeren Optimierungsalgorithmus (Genetic Programming) anhand von Trainingsdaten erlernt und anschliessend auf eine unbekannte Zeitreihe angewandt. Als Benchmark dient eine klassische buy-and-hold Strategie im DAX und Hang Seng. Es zeigt sich, dass die mittels Genetic Programming generierten Handelsstrategien den Benchmark auf risikoadjustierter Basis nicht durchgaengig schlagen konnen und somit die These effizienter Maerkte fuer den DAX und den Hang Seng gueltig ist.", notes = "in English. Supervisor Prof. Dr. Hans-Peter Burghof", } @Article{Janson:2006:GPEM, author = "Stefan Janson and Martin Middendorf", title = "A hierarchical particle swarm optimizer for noisy and dynamic environments", journal = "Genetic Programming and Evolvable Machines", year = "2006", volume = "7", number = "4", pages = "329--354", month = dec, keywords = "Particle Swarm Optimization, PSO, Noisy functions, Dynamic functions", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-006-9014-6", size = "26 pages", abstract = "New Particle Swarm Optimisation (PSO) methods for dynamic and noisy function optimisation are studied in this paper. The new methods are based on the hierarchical PSO (H-PSO) and a new type of H-PSO algorithm, called Partitioned Hierarchical PSO (PH-PSO). PH-PSO maintains a hierarchy of particles that is partitioned into several sub-swarms for a limited number of generations after a change of the environment occurred. Different methods for determining the best time when to rejoin the sub-swarms and how to handle the topmost sub-swarm are discussed. A standard method for metaheuristics to cope with noise is to use function re-evaluations. To reduce the number of necessary re-evaluations a new method is proposed here which uses the hierarchy to find a subset of particles for which re-evaluations are particularly important. In addition, a new method to detect changes of the optimization function in the presence of noise is presented. It differs from conventional detection methods because it does not require additional function evaluations. Instead it relies on observations of changes that occur within the swarm hierarchy. The new algorithms are compared experimentally on different dynamic and noisy benchmark functions with a variant of standard PSO and H-PSO that are both provided with a change detection and response method.", } @PhdThesis{Janssen:thesis, author = "Kristel Josephina Matthea Janssen", title = "Improvements in clinical prediction research", school = "Utrecht, Universiteit Utrecht, Faculteit Geneeskunde", year = "2007", address = "Holland", keywords = "genetic algorithms, genetic programming, clinical prediction research, prediction models, derivation, (external) validation, updating, logistic regression, penalised maximum likelihood estimation, genetic programming, missing values, multiple imputation", URL = "http://igitur-archive.library.uu.nl/dissertations/2007-1206-200929/full.pdf", broken = "http://igitur-archive.library.uu.nl/dissertations/2007-1206-200929/UUindex.html", URL = "https://hdl.handle.net/1874/24678", isbn13 = "978-90-393-4668-6", size = "160 pages", abstract = "This thesis aims to improve methods of clinical prediction research. In clinical prediction research, patient characteristics, test results and disease characteristics are often combined in so-called prediction models to estimate the risk that a disease or outcome is present (diagnosis) or will occur (prognosis). This thesis focuses on the derivation, validation, updating, and application of prediction models. Dealing with missing values is an under appreciated aspect in medical research. Three methods were compared that can handle missing predictor values when a prediction model is derived (complete case analysis, dropping the predictor with missing values and multiple imputation). Multiple imputation outperformed both other methods in terms of bias, coverage of the 90percent confidence interval, and the discriminative ability. Similarly, six methods were compared that can handle missing predictor values when a physician applies a prediction model for an individual patient with missing predictor values. Multiple imputation proved to be best capable of improving the predictive performance of the prediction model, compared to imputation of the value zero, mean imputation, subgroup mean imputation, and applying a submodel consisting of only the observed predictors. Many prediction models are derived with dichotomous logistic regression analysis. Alternative methods are logistic regression with inherent shrinkage by penalised maximum likelihood estimation (PMLE) and genetic programming (a novel and promising search method that may improve the selection of predictors). The effect of four derivation methods was compared, namely logistic regression, logistic regression with a single shrinkage factor, logistic regression with inherent shrinkage by PMLE, and genetic programming. The performance measures of the four models were only slightly different, and the 95percent confidence intervals of the areas mostly overlapped. The choice between these derivation methods should be based on the characteristics of the data and situation at hand. The predictive performance of most derived prediction models is decreased when tested in new patients. Therefore, before a prediction model can be applied in daily clinical practice, it needs to be tested (i.e. externally validated) in new patients. However, when the predictive performance is disappointing in the validation data set, the original prediction model is frequently rejected and the researchers simply pursue to build their own (new) prediction model on the data of their patients, thereby neglecting the prior information that is captured in previous studies. The alternative is to update existing prediction models. The updated models combine the information that is captured in the original model with the information of the new patients. As a result, updated models are adjusted to the new patients and thus based on data of the original and new patients, potentially increasing their generalisability. We show the effect of these updating methods with empirical data, and give recommendations for its application. This thesis ends with an overview of the promises and pitfalls of using electronic patient records (EPR) as a basis for prediction research to enhance patient care, and vice versa. The EPR are medical records in digital format that facilitate storage and retrieval of data on patient care. Though the primary aim of the EPR is to aid patient care it creates highly attractive opportunities for prediction research.", notes = "NBN URN:NBN:NL:UI:10-1874-24678", } @Article{Janssen2012404, author = "Kristel J. M. Janssen and Ivar Siccama and Yvonne Vergouwe and Hendrik Koffijberg and T. P. A. Debray and Maarten Keijzer and Diederick E. Grobbee and Karel G. M. Moons", title = "Development and validation of clinical prediction models: Marginal differences between logistic regression, penalized maximum likelihood estimation, and genetic programming", journal = "Journal of Clinical Epidemiology", volume = "65", number = "4", pages = "404--412", year = "2012", ISSN = "0895-4356", DOI = "doi:10.1016/j.jclinepi.2011.08.011", URL = "http://www.sciencedirect.com/science/article/pii/S0895435611002708", keywords = "genetic algorithms, genetic programming, Prediction model, Logistic regression, Penalised maximum likelihood estimation", abstract = "Objective Many prediction models are developed by multivariable logistic regression. However, there are several alternative methods to develop prediction models. We compared the accuracy of a model that predicts the presence of deep venous thrombosis (DVT) when developed by four different methods. Study Design and Setting We used the data of 2,086 primary care patients suspected of DVT, which included 21 candidate predictors. The cohort was split into a derivation set (1,668 patients, 329 with DVT) and a validation set (418 patients, 86 with DVT). Also, 100 cross-validations were conducted in the full cohort. The models were developed by logistic regression, logistic regression with shrinkage by bootstrapping techniques, logistic regression with shrinkage by penalised maximum likelihood estimation, and genetic programming. The accuracy of the models was tested by assessing discrimination and calibration. Results There were only marginal differences in the discrimination and calibration of the models in the validation set and cross-validations. Conclusion The accuracy measures of the models developed by the four different methods were only slightly different, and the 95percent confidence intervals were mostly overlapped. We have shown that models with good predictive accuracy are most likely developed by sensible modelling strategies rather than by complex development methods.", } @InProceedings{IMECE2005-79416-R3, author = "David Japikse and Oleg Dubitsky and Kerry N. Oliphant and Robert J. Pelton and Daniel Maynes and Jamin Bitter", title = "Multi-variable, high order, performance Models (2005C)", booktitle = "2005 ASME International Mechanical Engineering Congress \& Exposition", year = "2005", pages = "513--521", pages = "IMECE2005--79416", address = "Orlando, Florida, USA", month = nov # " 5-11", organisation = "ASME", keywords = "genetic algorithms, genetic programming, genetic expression programming, Numerical, Modeling, Turbomachinery, Statistics", ISBN = "0-7918-4219-3", URL = "http://www.conceptsnrec.com/pdf/IMECE2005-79416-R3.pdf", DOI = "doi:10.1115/IMECE2005-79416", size = "9 pages", abstract = "In the course of developing advanced data processing and advanced performance models, as presented in companion papers, a number of basic scientific and mathematical questions arose. This paper deals with questions such as uniqueness, convergence, statistical accuracy, training, and evaluation methodologies. The process of bringing together large data sets and using them, with outside data supplementation, is considered in detail. After these questions are focused carefully, emphasis is placed on how the new models, based on highly refined data processing, can best be used in the design world. The impact of this work on designs of the future is discussed. It is expected that this methodology will allow many designers to move well beyond contemporary design practices.", notes = "See also \cite{IMECE2005-79414R3} Concepts NREC White River Jct., Vermont, 05001 USA. Brigham Young University", } @InProceedings{DBLP:conf/oois/JarilloSPR01, author = "Gabriel Jarillo and Giancarlo Succi and Witold Pedrycz and Marek Reformat", title = "Analysis of Software Engineering Data Using Computational Intelligence Techniques", booktitle = "7th International Conference on Object Oriented Information Systems, OOIS'2001", year = "2001", editor = "Yingxu Wang and Shushma Patel and Ronald Johnston", pages = "133--142", address = "Calgary, Canada", month = "27-29 " # aug, publisher = "Springer", bibsource = "DBLP, http://dblp.uni-trier.de", keywords = "genetic algorithms, genetic programming, SBSE", isbn13 = "9781852335465", URL = "http://www.springer.com/computer/database+management+%26+information+retrieval/book/978-1-85233-546-5", URL = "http://www.inf.unibz.it/~gsucci/publications/images/analysisofsoftwareengineeringdatausingcomputationalsoftwaretechniques.pdf", size = "10 pages", abstract = "The accurate estimation of software development effort has major implications for the management of software development in the industry. Underestimates lead to time pressures that may compromise full functional development and thorough testing of the software product. On the other hand, overestimates can result in over allocation of development resources and personnel [7]. Many models for effort estimation have been developed during the past years; some of them use parametric methods with some degree of success, other kind of methods belonging to the computational intelligence family, such as Neural Networks (NN), have been also studied in this field showing more accurate estimations, and finally the Genetic programming (GP) techniques are being considered as promising tools for the prediction of effort estimation. Organizations are wandering how they can predict the quality of their software before it is used. Generally there are tree approaches to do so [1]: 1. - Predicting the number of defects in the system. 2. - Estimating the reliability of the system in terms of time and failure. 3. - Understanding the impact of the design and testing processes on defect counts and failure densities. Knowing the quality of the software allows the organization to estimate the amount of resources to be invested on its maintenance. Software maintenance is a factor that consumes most of the resources in many software organizations [2], therefore its worth it to be able to characterize, assess and predict defects in the software at early stages of its development in order to reduce maintenance costs. Maintenance involves activities such as correcting errors, maintaining software, and adapting software to deal with new environment requirements [2].", notes = "http://enel.ucalgary.ca/oois2001/programme.html", } @Article{jarmatz:2024:fbp, author = "Niklas Jarmatz and Wolfgang Augustin and Stephan Scholl and Alberto Tonda and Guillaume Delaplace", title = "Development of a soft sensor for fouling prediction in pipe fittings using the example of particulate deposition from suspension flow", journal = "Food and Bioproducts Processing", year = "2024", volume = "145", pages = "116--127", keywords = "genetic algorithms, genetic programming, PySR, Food processing, Fouling, Cleaning, Sustainability, Machine learning, Dimensional analysis", ISSN = "0960-3085", URL = "https://www.sciencedirect.com/science/article/pii/S0960308524000245", DOI = "doi:10.1016/j.fbp.2024.02.009", code_url = "https://github.com/albertotonda/soft-sensor-pipe-fouling", abstract = "Fouling is the unwanted accumulation of material on a processing surface which is an especially problematic issue in the food industry. Characterising or predicting fouling through traditional methods or models is a challenge due to the complexity of fouling mechanisms. Machine learning (ML) techniques can overcome this challenge by creating models for prediction directly from experimental data. Unfortunately, the results can be hard to interpret depending on the algorithm. Here, a soft sensor is generated from an extensive data set to predict the fouling of a model particle material system. This is performed inside two different pipe fittings, an inaccessible and accessible fitting (e.g., for sensor measurements). Additionally, dimensional analysis (DA) is conducted to identify the correlations responsible for fouling while keeping descriptors with physical meaning. The resulting dimensionless numbers (DNs) are further processed by three ML algorithms: linear regression (LR), symbolic regression (SR), and random forest (RF). The soft sensor generated using a RF outperformed the other two regressors for the dimensional (Q2=0.90 p/m 0.08) and for the dimensionless data (Q2=0.88 p/m 0.09). The parameter time and particle mass fraction were determined to be most influential. Furthermore, seven DNs were obtained allowing a reduced experimental design.", notes = "also known as \cite{JARMATZ2024116}", } @InProceedings{Jarraya:2015:CEC, author = "Yosra Jarraya and Souhir Bouaziz and Adel M. Alimi and Ajith Abraham", booktitle = "2015 IEEE Congress on Evolutionary Computation (CEC)", title = "Evolutionary multi-objective optimization for evolving hierarchical fuzzy system", year = "2015", pages = "3163--3170", isbn13 = "978-1-4799-7491-7", abstract = "In this paper, a Multi-Objective Extended Genetic Programming (MOEGP) algorithm is developed to evolve the structure of the Hierarchical Flexible Beta Fuzzy System (HFBFS). The proposed algorithm allows finding the best representation of the hierarchical fuzzy system while trying to attain the desired balance of accuracy/interpretability. Furthermore, the free parameters (Beta membership functions and the consequent parts of rules) encoded in the best structure are tuned by applying the hybrid Bacterial Foraging Optimisation Algorithm (the hybrid BFOA). The proposed methodology interleaves both MOEGP and the hybrid BFOA for the structure and the parameter optimisation respectively until a satisfactory HFBFS is found. The performance of the approach is evaluated using several classification datasets with low and high input dimensions. Results prove the superiority of our method as compared with other existing works.", keywords = "genetic algorithms, genetic programming, MOGP", DOI = "doi:10.1109/CEC.2015.7257284", ISSN = "1089-778X", month = may, notes = "Res. Groups in Intell. Machines (REGIM), Univ. of Sfax, Sfax, Tunisia Also known as \cite{7257284}", } @InProceedings{Jarraya:2016:SMC, author = "Yosra Jarraya and Souhir Bouaziz and Adel M. Alimi and Ajith Abraham", booktitle = "2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)", title = "Evolutionary hierarchical fuzzy modeling of Interval Type-2 Beta Fuzzy Systems", year = "2016", pages = "003481--003486", abstract = "The automated evolutionary design of an optimal hierarchical fuzzy system combined with the use of Interval Type-2 Fuzzy Systems and the Beta basis function is considered in this study. The resulted proposed system is named the Hierarchical interval Type-2 Beta Fuzzy System (HT2BFS). For the learning process, two main optimisations steps are considered. The first one executes the structure learning of the HT2BFS by the Extended Genetic Programming (EGP) algorithm allowing the generation of an optimal architecture. In the second step, the Opposite-based Particle Swarm Optimisation (OPSO) algorithm is employed for the adjustment of parameters existing in the best obtained architecture. The two optimisation algorithms are interleaved until an optimal HT2BFS is generated. Experiments on some time-series forecasting problems were performed and prove the effectiveness of the proposed system.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SMC.2016.7844772", month = oct, notes = "Also known as \cite{7844772}", } @InProceedings{Jarraya:2017:FUZZ-IEEE, author = "Yosra Jarraya and Souhir Bouaziz and Adel M. Alimi", booktitle = "2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)", title = "Evolutionary multi-objective based hierarchical interval type-2 beta fuzzy system for classification problems", year = "2017", abstract = "This study addresses evolutionary structure optimisation and parameter tuning processes for evolving a proposed Hierarchical interval Type-2 Beta Fuzzy System (HT2BFS). The structure learning phase is performed in a multi-objective context by applying the Multi-Objective Extended Genetic Programming (MOEGP) algorithm. This phase aims to obtain a near-optimal structure of HT2BFS taking into account the optimisation of two objectives, which are the accuracy maximization and the number of rules minimization. Moreover, a second parameter tuning phase is also performed in order to refine the parameters of the obtained near-optimal structure by applying the PSO-based Update Memory for Improved Harmony Search (PSOUM-IHS) algorithm. The system's performance is validated through two classification problems. Results prove the efficiency of the proposed approach.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/FUZZ-IEEE.2017.8015617", month = jul, notes = "Also known as \cite{8015617}", } @InProceedings{ga96aJaske, annote = "*on,*FIN,genetic programming,astronomy /sunspots,time series sunspots", author = "Harri J{\"a}ske", title = "One-step-ahead prediction of sunspots with genetic programming", pages = "79--88", year = "1996", editor = "Jarmo T. Alander", booktitle = "Proceedings of the Second Nordic Workshop on Genetic Algorithms and their Applications (2NWGA)", series = "Proceedings of the University of Vaasa, Nro. 13", publisher = "University of Vaasa", address = "Vaasa (Finland)", month = "19.-23.~" # aug, organisation = "Finnish Artificial Intelligence Society", keywords = "genetic algorithms, genetic programming, time series prediction , sunspots", URL = "ftp://ftp.uwasa.fi/cs/2NWGA/Jaske.ps.Z", broken = "http://www.uwasa.fi/cs/publications/2NWGA/node70.html#SECTION04700000000000000000", abstract = "Timeinvariant nonlinear one-step-ahead prediction models were developed by genetic programming. As a test case benchmark sunspot series was used. Functional form and numerical parameters of the models were optimized. The generalisation ability, i.e. final suitability, of the predictors was assessed through crossvalidation. The results were compared to those of threshold autoregression and neural network -based predictors of the sunspot benchmarks found in literature. Standard GP-approach is shown not to be sufficient to solve this prediction problem as well as the methods in comparison do.", notes = "lil-gp, non standard GP parameters? {"}evolved models might not be numerically stable{"} page 86 ", } @InProceedings{Jaske:1997:crGP, author = "Harri Jaske", title = "On code reuse in genetic programming", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming", pages = "201--206", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1997/Jaske_1997_crGP.pdf", size = "6 pages", notes = "GP-97", } @InProceedings{jaskowski:evows07, author = "Wojciech Jaskowski and Krzysztof Krawiec and Bartosz Wieloch", title = "Learning and Recognition of Hand-drawn Shapes using Generative Genetic Programming", booktitle = "Applications of Evolutionary Computing, EvoWorkshops2007: {EvoCOMNET}, {EvoFIN}, {EvoIASP}, {EvoInteraction}, {EvoMUSART}, {EvoSTOC}, {EvoTransLog}", year = "2007", month = "11-13 " # apr, editor = "Mario Giacobini and Anthony Brabazon and Stefano Cagnoni and Gianni A. {Di Caro} and Rolf Drechsler and Muddassar Farooq and Andreas Fink and Evelyne Lutton and Penousal Machado and Stefan Minner and Michael O'Neill and Juan Romero and Franz Rothlauf and Giovanni Squillero and Hideyuki Takagi and A. Sima Uyar and Shengxiang Yang", series = "LNCS", volume = "4448", publisher = "Springer Verlag", address = "Valencia, Spain", pages = "281--290", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-71804-8", DOI = "doi:10.1007/978-3-540-71805-5_31", abstract = "We describe a novel method of evolutionary visual learning that uses generative approach for assessing learner's ability to recognise image contents. Each learner, implemented as a genetic programming individual, processes visual primitives that represent local salient features derived from a raw input raster image. In response to that input, the learner produces partial reproduction of the input image, and is evaluated according to the quality of that reproduction. We present the method in detail and verify it experimentally on the real-world task of recognition of hand-drawn shapes.", notes = "EvoWorkshops2007", } @InProceedings{1277281, author = "Wojciech Jaskowski and Krzysztof Krawiec and Bartosz Wieloch", title = "Genetic programming for cross-task knowledge sharing", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "1620--1627", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1620.pdf", DOI = "doi:10.1145/1276958.1277281", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, knowledge sharing, multitask learning, representation", abstract = "We consider multi-task learning of visual concepts within genetic programming (GP) framework. The proposed method evolves a population of GP individuals, with each of them composed of several GP trees that process visual primitives derived from input images. The two main trees are delegated to solving two different visual tasks and are allowed to share knowledge with each other by calling the remaining GP trees (sub-functions) included in the same individual. The method is applied to the visual learning task of recognising simple shapes, using generative approach based on visual primitives, introduced in [17]. We compare this approach to a reference method devoid of knowledge sharing, and conclude that in the worst case cross-task learning performs equally well, and in many cases it leads to significant performance improvements in one or both solved tasks.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @InProceedings{1277318, author = "Wojciech Jaskowski and Krzysztof Krawiec and Bartosz Wieloch", title = "Knowledge reuse in genetic programming applied to visual learning", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "1790--1797", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1790.pdf", DOI = "doi:10.1145/1276958.1277318", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Genetics-Based Machine Learning, knowledge reuse, pattern recognition", abstract = "We propose a method of knowledge reuse for an ensemble of genetic programming-based learners solving a visual learning task. First, we introduce a visual learning method that uses genetic programming individuals to represent hypotheses. Individuals-hypotheses process image representation composed of visual primitives derived from the training images that contain objects to be recognised. The process of recognition is generative, i.e., an individual is supposed to restore the shape of the processed object by drawing its reproduction on a separate canvas. This canonical method is extended with a knowledge reuse mechanism that allows a learner to import genetic material from hypotheses that evolved for the other decision classes (object classes). We compare the performance of the extended approach to the basic method on a real-world tasks of handwritten character recognition, and conclude that knowledge reuse leads to significant convergence speedup and, more importantly, significantly reduces the risk of overfitting.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @InProceedings{Jaskowski:2007:PL, author = "Wojciech Jaskowski and Krzysztof Krawiec and Bartosz Wieloch", title = "Evolutionary Learning with Cross-Class Knowledge Reuse for Handwritten Character Recognition", booktitle = "proceedings of the Planning to learn workshop, PlanLearn-07", year = "2007", address = "Warsaw, Poland", month = sep # " 17", keywords = "genetic algorithms, genetic programming", URL = "http://www.ecmlpkdd2007.org/CD/workshops/PlanLearn/WS_PlanLearn_p2/WS_PlanLearn_p2.pdf", abstract = "We propose a learning algorithm that reuses knowledge acquired in past learning sessions to improve its performance on a new learning task. The method concerns visual learning and uses genetic programming to represent hypotheses, each of them being a procedure that processes visual primitives derived from the training images. The process of recognition is generative, i.e., a procedure is supposed to restore the shape of the processed object by drawing its reproduction on a separate canvas. This basic method is extended with a knowledge reuse mechanism that allows learners to import genetic material from hypotheses that evolved for the other decision classes (object classes). We compare both methods on a task of handwritten character recognition, and conclude that knowledge reuse leads to significant improvement of classification accuracy and reduces the risk of overfitting.", notes = "Institute of Computing Science, Poznan University of Technology Piotrowo 2, 60965 Pozna, Poland", } @InProceedings{conf/eurogp/JaskowskiKW08, title = "Winning Ant Wars: Evolving a Human-Competitive Game Strategy Using Fitnessless Selection", author = "Wojciech Jaskowski and Krzysztof Krawiec and Bartosz Wieloch", bibdate = "2008-04-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/eurogp/eurogp2008.html#JaskowskiKW08", booktitle = "Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008", address = "Naples", month = "26-28 " # mar, publisher = "Springer", year = "2008", volume = "4971", editor = "Michael O'Neill and Leonardo Vanneschi and Steven Gustafson and Anna Isabel {Esparcia Alcazar} and Ivanoe {De Falco} and Antonio {Della Cioppa} and Ernesto Tarantino", isbn13 = "978-3-540-78670-2", pages = "13--24", series = "Lecture Notes in Computer Science", DOI = "doi:10.1007/978-3-540-78671-9_2", keywords = "genetic algorithms, genetic programming", notes = "Part of \cite{conf/eurogp/2008} EuroGP'2008 held in conjunction with EvoCOP2008, EvoBIO2008 and EvoWorkshops2008", } @InProceedings{Jaskowski:2008:geccocomp, author = "Wojciech Jaskowski and Krzysztof Krawiec and Bartosz Wieloch", title = "Multi-task code reuse in genetic programming", year = "2008", editor = "Marc Ebner and Mike Cattolico and Jano {van Hemert} and Steven Gustafson and Laurence D. Merkle and Frank W. Moore and Clare Bates Congdon and Christopher D. Clack and Frank W. Moore and William Rand and Sevan G. Ficici and Rick Riolo and Jaume Bacardit and Ester Bernado-Mansilla and Martin V. Butz and Stephen L. Smith and Stefano Cagnoni and Mark Hauschild and Martin Pelikan and Kumara Sastry", isbn13 = "978-1-60558-131-6", booktitle = "GECCO-2008 Late-Breaking Papers", pages = "2159--2164", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p2159.pdf", DOI = "doi:10.1145/1388969.1389040", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, code Reuse, multi-task learning", notes = "Distributed on CD-ROM at GECCO-2008 ACM Order Number 910081. Also known as \cite{1389040}", } @Article{Jaskowski:2008:GPEM, author = "Wojciech Jaskowski and Krzysztof Krawiec and Bartosz Wieloch", title = "Evolving strategy for a probabilistic game of imperfect information using genetic programming", journal = "Genetic Programming and Evolvable Machines", year = "2008", volume = "9", number = "4", pages = "281--294", month = dec, keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-008-9062-1", abstract = "We provide the complete record of methodology that let us evolve BrilliAnt, the winner of the Ant Wars contest. Ant Wars contestants are virtual ants collecting food on a grid board in the presence of a competing ant. BrilliAnt has been evolved through a competitive one-population coevolution using genetic programming and fitness less selection. In this paper, we detail the evolutionary setup that lead to BrilliAnt's emergence, assess its direct and indirect human-competitiveness, and describe the behavioural patterns observed in its strategy.", } @Article{Jaskowski:2008:EC, author = "Wojciech Jaskowski and Krzysztof Krawiec and Bartosz Wieloch", title = "Multitask Visual Learning Using Genetic Programming", journal = "Evolutionary Computation", year = "2008", volume = "16", number = "4", pages = "439--459", month = "Winter", keywords = "genetic algorithms, genetic programming", ISSN = "1063-6560", DOI = "doi:10.1162/evco.2008.16.4.439", abstract = "We propose a multi-task learning method of visual concepts within the genetic programming (GP) framework. Each GP individual is composed of several trees that process visual primitives derived from input images. Two trees solve two different visual tasks and are allowed to share knowledge with each other by commonly calling the remaining GP trees (sub functions) included in the same individual. The performance of a particular tree is measured by its ability to reproduce the shapes contained in the training images. We apply this method to visual learning tasks of recognizing simple shapes and compare it to a reference method. The experimental verification demonstrates that such multitask learning often leads to performance improvements in one or both solved tasks, without extra computational effort.", notes = "Part of special issue on Evolutionary Computer Vision \cite{Cagnoni:2008:EC}", } @InCollection{Jaskowski:2009:EIASP, author = "Wojciech Jaskowski and Krzysztof Krawiec and Bartosz Wieloch", title = "Genetic Programming for Generative Learning and Recognition of Hand-Drawn Shapes", booktitle = "Evolutionary Image Analysis and Signal Processing", publisher = "Springer", year = "2009", editor = "Stefano Cagnoni", volume = "213", series = "Studies in Computational Intelligence", pages = "73--90", address = "Berlin / Heidelberg", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-01635-6", ISSN = "1860-949X", DOI = "doi:10.1007/978-3-642-01636-3_5", abstract = "We propose a novel method of evolutionary visual learning that uses a generative approach to assess the learner's ability to recognise image contents. Each learner, implemented as a genetic programming (GP) individual, processes visual primitives that represent local salient features derived from the input image. The learner analyses the visual primitives, which involves mostly their grouping and selection, eventually producing a hierarchy of visual primitives build upon the input image. Based on that it provides partial reproduction of the shapes of the analysed objects and is evaluated according to the quality of that reproduction.We present the method in detail and verify it experimentally on the real-world task of recognition of hand-drawn shapes. In particular, we show how GP individuals trained on examples from different decision classes can be combined to build a complete multiclass recognition system. We compare such recognition systems to reference methods, showing that our generative learning approach provides similar results. This chapter also contains detailed analysis of processing carried out by an exemplary individual.", notes = "Institute of Computing Science, Poznan University of Technology,Poland EvoISAP, EvoNET, EvoStar", } @PhdThesis{jaskowski11algorithms, author = "Wojciech Jaskowski", title = "Algorithms for Test-Based Problems", school = "Institute of Computing Science, Poznan University of Technology", year = "2011", address = "Poznan, Poland", month = may, keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.put.poznan.pl/wjaskowski/pub/papers/jaskowski11algorithms.pdf", size = "193 pages", abstract = "Problems in which some elementary entities interact with each other are common in computational intelligence. This scenario, typical for coevolving artificial-life agents, learning strategies for games, and machine learning from examples, can be formalised as a test-based problem and conveniently embedded in the common conceptual framework of coevolution. In test-based problems candidate solutions are evaluated on a number of test cases such as agents, opponents or examples. Although coevolutionary algorithms proved successful in some applications, they also turned out to have hard to predict dynamics and fail to sustain progress during a run, thus being unable to obtain competitive solutions for many test-based problems. It has been recently shown that one of the reasons why coevolutionary algorithms demonstrate such undesired behaviour is the aggregation of results of interactions between individuals representing candidate solutions and tests, which typically leads to characterising the performance of an individual by a single scalar value. In order to remedy this situation, in the thesis, we make an attempt to get around the problem of aggregation using two methods. First, we introduce Fitnessless Coevolution, a method for symmetrical test-based problems. Fitness-less Coevolution plays games between individuals to settle tournaments in the selection phase and skips the typical phase of evaluation and the aggregation of results connected with it. The selection operator applies a single-elimination tournament to a randomly drawn group of individuals, and the winner of the final round becomes the result of selection. Therefore, Fitnessless Coevolution does not involve explicit fitness measure and no aggregation of interaction results is required. We prove that, under a condition of transitivity of the payoff matrix, the dynamics of Fitnessless Coevolution is identical to that of the traditional evolutionary algorithm. The experimental results, obtained on a diversified group of problems, demonstrate that Fitnessless Coevolution is able to produce solutions that are equally good or better than solutions obtained using fitness-based one-population coevolution with different selection methods. In a case study, we provide the complete record of methodology that let us evolve BrilliAnt, the winner of the Ant Wars contest. We detail the coevolutionary setup that lead to BrilliAnt's emergence, assess its direct and indirect human-competitiveness, and describe the behavioural patterns observed in its strategy.", abstract = "Second, we study the consequences of the fact that the problem of aggregation of interaction results may be got around by regarding every test of a test-based problem as a separate objective, and the whole problem as a multi-objective optimisation task. Research on reducing the number of objectives while preserving the relations between candidate solutions and tests led to the notions of underlying objectives and internal problem structure, which can be formalized as a coordinate system that spatially arranges candidate solutions and tests. The coordinate system that spans the minimal number of axes determines the so-called dimension of a problem and, being an inherent property of every test-based problem, is of particular interest. We investigate in-depth the formalism of coordinate system and its properties, relate them to the properties of partially ordered sets, and design an exact algorithm for finding a minimal coordinate system. We also prove that this problem is NP-hard and come up with a heuristic which is superior to the best algorithm proposed so far. Finally, we apply the algorithms to several benchmark problems to demonstrate that the dimension of a problem is typically much lower than the number of tests. Our work suggest that for at least some classes of test-based problems, the dimension of a problem may be proportional to the logarithm of number of tests. Based on the above-described theoretical results, we propose a novel coevolutionary archive method founded on the concept of coordinate systems, called Coordinate System Archive (COSA), and compare it to two state-of-the-art archive methods, IPCA and LAPCA. Using two different objective performance measures, we find out that COSA is superior to these methods on a class of artificial test-based problems.", notes = "Adviser: Krzysztof Krawiec", } @Article{journals/amcs/JaskowskiKW14, title = "Cross-task code reuse in genetic programming applied to visual learning", author = "Wojciech Jaskowski and Krzysztof Krawiec and Bartosz Wieloch", journal = "Applied Mathematics and Computer Science", year = "2014", number = "1", volume = "24", pages = "183--197", keywords = "genetic algorithms, genetic programming, code reuse, knowledge sharing, visual learning, multi-task learning, optical character recognition", bibdate = "2014-05-05", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/amcs/amcs24.html#JaskowskiKW14", URL = "http://dx.doi.org/10.2478/amcs-2014-0014", abstract = "We propose a method that enables effective code reuse between evolutionary runs that solve a set of related visual learning tasks. We start with introducing a visual learning approach that uses genetic programming individuals to recognise objects. The process of recognition is generative, i.e., requires the learner to restore the shape of the processed object. This method is extended with a code reuse mechanism by introducing a crossbreeding operator that allows importing the genetic material from other evolutionary runs. In the experimental part, we compare the performance of the extended approach to the basic method on a real-world task of handwritten character recognition, and conclude that code reuse leads to better results in terms of fitness and recognition accuracy. Detailed analysis of the crossbred genetic material shows also that code reuse is most profitable when the recognised objects exhibit visual similarity", } @Article{Javadi:2006:CG, author = "Akbar A. Javadi and Mohammad Rezania and Mohaddeseh {Mousavi Nezhad}", title = "Evaluation of liquefaction induced lateral displacements using genetic programming", journal = "Computers and Geotechnics", year = "2006", volume = "33", number = "4-5", pages = "222--233", month = jun # "-" # jul, keywords = "genetic algorithms, genetic programming, Geotechnical models, Soil liquefaction, Earthquake, Evolutionary computation, Evolutionary programming, Lateral displacement", ISSN = "0266352X", DOI = "doi:10.1016/j.compgeo.2006.05.001", size = "12 pages", abstract = "Determination of liquefaction induced lateral displacements during earthquake is a complex geotechnical engineering problem due to the complex and heterogeneous nature of the soils and the participation of a large number of factors involved. In this paper, a new approach is presented, based on genetic programming (GP), for determination of liquefaction induced lateral spreading. The GP models are trained and validated using a database of SPT-based case histories. Separate models are presented to estimate lateral displacements for free face and for gently sloping ground conditions. It is shown that the GP models are able to learn, with a very high accuracy, the complex relationship between lateral spreading and its contributing factors in the form of a function. The attained function can then be used to generalise the learning to predict liquefaction induced lateral spreading for new cases not used in the construction of the model. The results of the developed GP models are compared with those of a commonly used multi linear regression (MLR) model and the advantages of the proposed GP model over the conventional method are highlighted.", notes = "a Department of Engineering, University of Exeter, Exeter EX4 4QF, Devon, UK b Department of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran", } @InProceedings{Javadi:2010:ICCCBE, author = "Akbar A. Javadi and Asaad Faramarzi and Alireza Ahangar-Asr and Moura Mehravar", title = "Finite element analysis of three dimensional shallow foundation using artificial intelligence based constitutive model", booktitle = "Proceedings of the International Conference on Computing in Civil and Building Engineering", year = "2010", editor = "W. Tizani", pages = "421", address = "Nottingham, UK", month = "30 " # jun # "-2 " # jul, publisher = "Nottingham University Press", note = "Paper 211", keywords = "genetic algorithms, genetic programming, constitutive modelling, evolutionary computation, data mining, finite element", isbn13 = "978-1-907284-60-1", URL = "http://www.engineering.nottingham.ac.uk/icccbe/proceedings/pdf/pf211.pdf", URL = "http://www.engineering.nottingham.ac.uk/icccbe/proceedings/html/211.htm", size = "6 pages", abstract = "In this paper, a new approach is presented for constitutive modelling of materials in finite element analysis. The proposed approach provides a unified framework for modelling of complex materials using evolutionary polynomial regression (EPR). A procedure is presented for construction of EPR-based constitutive model (EPRCM) and its integration in finite element procedure. The main advantage of EPRCM over conventional and neural network-based constitutive models is that it provides the optimum structure for the material constitutive model representation as well as its parameters, directly from raw experimental (or field) data. It can learn nonlinear and complex material behaviour without any prior assumption on the constitutive relationships. The proposed algorithm provides a transparent relationship for the constitutive material model that can readily be incorporated in a finite element model. The developed EPRCM-based finite element model is used to analyse a 3D shallow foundation and the results are compared with conventional methods. It is shown that the proposed approach provides an efficient alternative to conventional constitutive modelling in finite element analysis.", notes = "Uses GA to evolve polynomial model icccbe2010 http://www.engineering.nottingham.ac.uk/icccbe/proceedings/html/home.htm", } @InProceedings{JBCMS2004GPGI, author = "Faizad Javed and Barrett R. Bryant and M. Crepinsek and Marjan Mernik and Alan Sprague", title = "Context-free grammar induction using genetic programming", booktitle = "ACM-SE 42: Proceedings of the 42nd annual Southeast regional conference", year = "2004", ISBN = "1-58113-870-9", pages = "404--405", address = "Huntsville, Alabama", URL = "http://portal.acm.org/ft_gateway.cfm?id=986635&type=pdf&coll=GUIDE&dl=GUIDE&CFID=59883361&CFTOKEN=89203485", DOI = "doi:10.1145/986537.986635", publisher = "ACM Press", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming", abstract = "While grammar inference is used in areas like natural language acquisition, syntactic pattern recognition, etc., its application to the programming language problem domain has been limited. We propose a new application area for grammar induction which intends to make domain-specific language development easier and finds a second application in renovation tools for legacy systems. The genetic programming approach is used for grammatical inference. Our earlier work used grammar-specific heuristic operators in tandem with non-random construction of the initial grammar population and succeeded in inducing small grammars.", notes = "\onlineAvailable{http://doi.acm.org/10.1145/986537.986635}{2007-09-09} Also known as \cite{986635}", } @InProceedings{conf/mlmta/JavedMBS07, author = "Faizan Javed and Marjan Mernik and Barrett R. Bryant and Alan Sprague", title = "{GenInc:} An Incremental Context-Free Grammar Learning Algorithm for Domain-Specific Language Development", booktitle = "Proceedings of the 2007 International Conference on Machine Learning; Models, Technologies {\&} Applications, MLMTA 2007", year = "2007", editor = "Hamid R. Arabnia and Matthias Dehmer and Frank Emmert-Streib and Mary Qu Yang", pages = "118--124", address = "Las Vegas Nevada, USA", month = jun # " 25-28", publisher = "CSREA Press", keywords = "genetic algorithms, genetic programming, Grammar Inference, Domain-Specific Languages, Incremental Learning", isbn13 = "1-60132-027-2", URL = "http://www.cis.uab.edu/softcom/GrammarInference/publications/mlmta2007.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.145.7594", bibsource = "DBLP, http://dblp.uni-trier.de", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", contributor = "CiteSeerX", language = "en", oai = "oai:CiteSeerXPSU:10.1.1.145.7594", abstract = "While grammar inference (or grammar induction) has found extensive application in the areas of robotics, computational biology, speech and pattern recognition, its application to problems in programming language and software engineering domains has been limited. We have found a new application area for grammar inference which intends to make domain specific language development easier for domain experts not well versed in programming language design, and finds a second application in construction of renovation tools for legacy software systems. As a continuation of our previous efforts to infer context-free grammars (CFGs) for domain-specific languages which previously involved a genetic-programming based CFG inference system, we discuss improvements made to an incremental learning algorithm, called GenInc, for inferring context-free grammars with a core focus on facilitating domain-specific language development. We elaborate on the enhancements made to GenInc in the form of new operators, and conclude by discussing the results of applying GenInc to domain-specific languages.", } @PhdThesis{JavedFaizan, author = "Faizan Javed", title = "Techniques for Context-Free Grammar Induction and Applications", school = "Computer and Information Sciences, University of Alabama-Birmingham", year = "2007", address = "Birmingham, Alabama, USA", month = "5 " # dec, keywords = "genetic algorithms, genetic programming, MARS, GenParse", URL = "http://www.cis.uab.edu/softcom/dissertations/JavedFaizan.pdf", broken = "https://www.cis.uab.edu/softcom/dissertations.php", size = "183 pages", abstract = "Grammar Inference is the process of learning a grammar from examples, either positive (i.e., the grammar generates the string) and/or negative (i.e., the grammar does not generate the string). Although grammar inference has been successfully applied to many diverse domains such as speech recognition and robotics, its application to software engineering has been limited. This research investigates the applicability of grammar inference to software engineering and programming language development challenge problems, where grammar inference offers an innovative solution to the problem, while remaining tractable and within the scope of that problem. Specifically, the following challenges are addressed in this research: 1. Recovery of a metamodel from instance models: Within the area of domain-specific modelling (DSM), instance models may evolve independently of the original metamodel resulting in metamodel drift, an inconsistency between the instance model and the associated metamodel such that the instance model may no longer be loaded into the modeling tool. Although prior work has focused on the problem of schema evolution, no previous work addresses the problem of recovering a lost metamodel from instance models. A contribution of this research is the MetAmodel Recovery System (MARS) that uses grammar inference in concert with a host of complementary technologies and tools to address the metamodel drift problem. 2. Recovery of domain-specific language (DSL) specifications from example DSL programs: An open problem in DSL development is a need for reducing the time needed to learn language development tools by incorporating support for the description-by-example (DBE) paradigm of language specifications like syntax. This part of the dissertation focuses on recovering specifications of imperative, explicitly Turing-complete and context-free DSLs. A contribution of this research is GenInc, an unsupervised incremental CFG learning algorithm that allows further progress towards inferring DSLs and finds a second application in recovery of legacy DSLs. The research described in this dissertation makes the following contributions: i) A metamodel recovery tool for DSM environments, ii) Easier development of DSLs for domain experts, and iii) Advances in grammar inference algorithms that may also have new applications in other areas of computer sciences (e.g., bioinformatics).", notes = "See also \cite{Javed:book} https://www.sigsoft.org/dissertations.html http://www.cis.uab.edu/node/1971 http://genealogy.math.ndsu.nodak.edu/id.php?id=123516 Advisor: Barrett Richard Bryant", } @Book{Javed:book, author = "Faizan Javed", title = "Techniques for Context-Free Grammar Induction and Applications: Application of novel inference algorithms to software maintenance problems", publisher = "VDM Verlag Dr. Mueller", year = "2009", month = "6 " # jan, keywords = "genetic algorithms, genetic programming", isbn13 = "978-3639110104", URL = "https://search.worldcat.org/title/724911304", URL = "https://www.amazon.com/dp/3639110102", size = "188 pages", abstract = "Grammar Inference is the process of learning a grammar from examples, either positive (i.e., the grammar generates the string) and/or negative (i.e., the grammar does not generate the string). Although grammar inference has been successfully applied to many diverse domains such as speech recognition and robotics, its application to software engineering has been limited. This book provides an overview of the area and discusses the following applications of grammar inference: 1) Recovery of a metamodel from instance models: the MetAmodel Recovery System (MARS), a system that uses grammar inference in concert with a host of complementary technologies and tools to address the metamodel drift problem. 2) Recovery of domain-specific language (DSL) specifications from example DSL programs: GenInc, an unsupervised incremental CFG learning algorithm that allows further progress towards inferring DSLs and finds a second application in recovery of legacy DSLs. This book is directed at researchers and software developers interested in learning about the exciting field of grammar inference and its applications to software maintenance issues.", notes = "See also \cite{JavedFaizan} ASIN ‏ : ‎ 3639110102, OCLC Number 724911304", } @Article{Javed:2022:DMKD, author = "Noman Javed and Fernand Gobet and Peter Lane", title = "Simplification of genetic programs: a literature survey", journal = "Data Mining and Knowledge Discovery", year = "2022", volume = "36", number = "4", pages = "1279--1300", month = jul, note = "Special Issue on Explainable and Interpretable Machine Learning and Data Mining", keywords = "genetic algorithms, genetic programming, Simplification, Bloat control, Explainability, XAI, Genetically Evolving Models in Science, GEMS", ISSN = "1384-5810", URL = "http://eprints.lse.ac.uk/114852/", URL = "https://uhra.herts.ac.uk/id/eprint/9655/", URL = "https://rdcu.be/cUozw", DOI = "doi:10.1007/s10618-022-00830-7", size = "22 pages", abstract = "Genetic programming (GP), a widely used evolutionary computing technique, suffers from bloat—the problem of excessive growth in individuals sizes. As a result, its ability to efficiently explore complex search spaces reduces. The resulting solutions are less robust and generalisable. Moreover, it is difficult to understand and explain models which contain bloat. This phenomenon is well researched, primarily from the angle of controlling bloat: instead, our focus in this paper is to review the literature from an explainability point of view, by looking at how simplification can make GP models more explainable by reducing their sizes. Simplification is a code editing technique whose primary purpose is to make GP models more explainable. However, it can offer bloat control as an additional benefit when implemented and applied with caution. Researchers have proposed several simplification techniques and adopted various strategies to implement them. We organise the literature along multiple axes to identify the relative strengths and weaknesses of simplification techniques and to identify emerging trends and areas for future exploration. We highlight design and integration challenges and propose several avenues for research. One of them is to consider simplification as a standalone operator, rather than an extension of the standard crossover or mutation operators. Its role is then more clearly complementary to other GP operators, and it can be integrated as an optional feature into an existing GP setup. Another proposed avenue is to explore complexity measures in simplification. So far, size is the most discussed measure, with only two pieces of prior work pointing out the benefits of using time as a measure when controlling bloat.", notes = "CPNSS, London School of Economics and Political Science, London, UK", } @InProceedings{Javed:2023:AISB, author = "Noman Javed and Angelo Pirrone and Laura Bartlett and Peter Lane and Fernand Gobet", title = "Trust in cognitive models: understandability and computational reliabilism", booktitle = "AISB 2023 convention proceedings. The Society for the Study of Artificial Intelligence and Simulation Behaviour", year = "2023", editor = "Berndt Mueller", pages = "43--50", address = "Swansea, UK", month = "13-14 " # apr, keywords = "genetic algorithms, genetic programming, trust, Computational reliabilism, Understandability", isbn13 = "978-1-908187-85-7", URL = "http://eprints.lse.ac.uk/id/eprint/118805", URL = "https://aisb.org.uk/wp-content/uploads/2023/05/aisb2023.pdf", size = "8 pages", abstract = "The realm of knowledge production, once considered a solely human endeavour, has transformed with the rising prominence of artificial intelligence. AI not only generates new forms of knowledge but also plays a substantial role in scientific discovery. This development raises a fundamental question: can we trust knowledge generated by AI systems? Cognitive modelling, a field at the intersection between psychology and computer science that aims to comprehend human behaviour under various experimental conditions, underscores the importance of trust. To address this concern, we identified understandability and computational reliabilism as two essential aspects of trustworthiness in cognitive modelling. This paper delves into both dimensions of trust, taking as case study a system for semi-automatically generating cognitive models. These models evolved interactively as computer programs using genetic programming. The selection of genetic programming, coupled with simplification algorithms, aims to create understandable cognitive models. To discuss reliability, we adopted computational reliabilism and demonstrate how our test-driven software development methodology instils reliability in the model generation process and the models themselves.", notes = "Genetically Evolving Models in Science GEMS European Research Council ERC-2018-ADG-835002 https://aisb.org.uk/aisb-convention-2023-non-members/", } @Article{javed:2020:Crystals, author = "Muhammad Faisal Javed and Furqan Farooq and Shazim Ali Memon and Arslan Akbar and Mohsin Ali Khan and Fahid Aslam and Rayed Alyousef and Hisham Alabduljabbar and Sardar Kashif Ur Rehman", title = "New Prediction Model for the Ultimate Axial Capacity of {Concrete-Filled} Steel Tubes: An Evolutionary Approach", journal = "Crystals", year = "2020", volume = "10", number = "9", keywords = "genetic algorithms, genetic programming, gene expression programming", ISSN = "2073-4352", URL = "https://www.mdpi.com/2073-4352/10/9/741", DOI = "doi:10.3390/cryst10090741", abstract = "The complication linked with the prediction of the ultimate capacity of concrete-filled steel tubes (CFST) short circular columns reveals a need for conducting an in-depth structural behavioural analyses of this member subjected to axial-load only. The distinguishing feature of gene expression programming (GEP) has been used for establishing a prediction model for the axial behaviour of long CFST. The proposed equation correlates the ultimate axial capacity of long circular CFST with depth, thickness, yield strength of steel, the compressive strength of concrete and the length of the CFST, without need for conducting any expensive and laborious experiments. A comprehensive CFST short circular column under an axial load was obtained from extensive literature to build the proposed models, and subsequently implemented for verification purposes. This model consists of extensive database literature and is comprised of 227 data samples. External validations were carried out using several statistical criteria recommended by researchers. The developed GEP model demonstrated superior performance to the available design methods for AS5100.6, EC4, AISC, BS, DBJ and AIJ design codes. The proposed design equations can be reliably used for pre-design purposes—or may be used as a fast check for deterministic solutions.", notes = "also known as \cite{cryst10090741}", } @InProceedings{Javed:2021:SAC, author = "Noman Javed and Fernand R. Gobet", title = "On-the-Fly Simplification of Genetic Programming Models", booktitle = "Proceedings of the 36th Annual ACM Symposium on Applied Computing, SAC 2021", year = "2021", series = "SAC '21", pages = "464--471", address = "Virtual Event, Republic of Korea", publisher = "Association for Computing Machinery", keywords = "genetic algorithms, genetic programming, simplification, evolutionary computing", isbn13 = "9781450381048", URL = "https://doi.org/10.1145/3412841.3441926", DOI = "doi:10.1145/3412841.3441926", abstract = "The last decade has seen amazing performance improvements in deep learning. However, the black-box nature of this approach makes it difficult to provide explanations of the generated models. In some fields such as psychology and neuroscience, this limitation in explainability and interpretability is an important issue. Approaches such as genetic programming are well positioned to take the lead in these fields because of their inherent white box nature. Genetic programming, inspired by Darwinian theory of evolution, is a population-based search technique capable of exploring a high-dimensional search space intelligently and discovering multiple solutions. However, it is prone to generate very large solutions, a phenomenon often called bloat. The bloated solutions are not easily understandable. we propose two techniques for simplifying the generated models. Both techniques are tested by generating models for a well-known psychology experiment. The validity of these techniques is further tested by applying them to a symbolic regression problem. Several population dynamics are studied to make sure that these techniques are not compromising diversity, an important measure for finding better solutions. The results indicate that the two techniques can be both applied independently and simultaneously and that they are capable of finding solutions at par with those generated by the standard GP algorithm, but with significantly reduced program size. There was no loss in diversity nor reduction in overall fitness. In fact, in some experiments, the two techniques even improved fitness.", notes = "London School of Economics and Political Science", } @InProceedings{Javed:2024:CogMI, author = "Noman Javed and Dmitry Bennett and Laura K. Bartlett and Peter C. R. Lane and Fernand Gobet", title = "Evolving Cognitive Models: A Novel Approach to Verbal Learning", booktitle = "2024 IEEE 6th International Conference on Cognitive Machine Intelligence (CogMI)", year = "2024", pages = "226--233", month = oct, keywords = "genetic algorithms, genetic programming, Computational modelling, Data models, Cognition, Cognitive science, Machine intelligence, chunking, evolution, GEMS, LTM, STM, CHREST", DOI = "doi:10.1109/CogMI62246.2024.00037", abstract = "A common goal in cognitive science involves explaining/predicting human performance in experimental settings. This study proposes a single GEMS computational scientific discovery framework that automatically generates multiple models for verbal learning simulations. GEMS achieves this by combining simple and complex cognitive mechanisms with genetic programming. This approach evolves populations of interpretable cognitive agents, with each agent learning by chunking and incorporating long-term memory (LTM) and short-term memory (STM) stores, as well as attention and perceptual mechanisms. The models simulate two different verbal learning tasks: the first investigates the effect of prior knowledge on the learning rate of stimulus-response (S-R) pairs and the second examines how backward recall is affected by the similarity of the stimuli. The models produced by GEMS are compared to both human data and EPAM - a different verbal learning model that uses hand-crafted task-specific strategies. The models automatically evolved by GEMS produced good fit to the human data in both studies, improving on EPAM's measures of fit by almost a factor of three on some of the pattern recall conditions. These findings offer further support to the mechanisms proposed by chunking theory (Simon, 1974), connect them to the evolutionary approach, and make further inroads towards a Unified Theory of Cognition (Newell, 1990).", notes = "Also known as \cite{10835591}", } @InProceedings{Javed:2015:FIT, author = "Syed Gibran Javed and Abdul Majid and Nabeela Kausar", booktitle = "13th International Conference on Frontiers of Information Technology (FIT)", title = "Combining Robust Statistical and {1D} Laplacian Operators Using Genetic Programming to Detect and Remove Impulse Noise from Images", year = "2015", pages = "18--23", abstract = "In this paper, genetic programming (GP) based intelligent scheme is proposed for the denoising of digital images from impulse noise. Mixed impulse noise model which comprises a mixture of both salt & pepper, and uniform impulse noise, is considered. The proposed scheme works in two stages. First stage detects impulse noise in the image through a novel single-stage GP detector which is based on the extraction of robust statistical features and convolution of corrupted image with 1D Laplacian operators. The second stage consists of a GP based estimator that removes the noise by estimating the pixel value. This estimator approximates the pixel value by calculating the statistical features in the neighbourhood of noise-free pixels. The idea of developing a single-stage detector and estimator is very effective in the removal of impulse noise. The proposed approach is tested on a variety of standard images and its comparison with other relevant techniques show that the performance of the proposed approach is better.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/FIT.2015.15", month = dec, notes = "Also known as \cite{7420969}", } @Article{Javed:2015:MTA, author = "Syed Gibran Javed and Abdul Majid and Anwar M. Mirza and Asifullah Khan", title = "Multi-Denoising based Impulse Noise Removal from Images using Robust Statistical Features and Genetic Programming", journal = "Multimedia Tools and Applications", year = "2016", volume = "75", number = "10", pages = "5887--5916", month = may, keywords = "genetic algorithms, genetic programming, Image denoising, Noise detection, Mixed impulse noise, Salt and pepper noise, Impulse burst noise, Statistical features, Robust outlyingness ratio", publisher = "Springer", ISSN = "1380-7501", language = "English", URL = "http://dx.doi.org/10.1007/s11042-015-2554-0", DOI = "doi:10.1007/s11042-015-2554-0", size = "30 pages", abstract = "Recently, several interesting computational intelligence based image denoising techniques have been reported for the removal of either salt and pepper or uniform impulse noise. However, to the best of our knowledge, the difficult challenge of developing a multi denoising method that can remove mixed-impulse noise, uniform impulse, salt and pepper, and impulse-burst noise, has not been reported so far. In this regard, we propose a new noise removal approach called INDE-GP for the removal of multi types of impulse noises. The proposed approach consists of two stages: noise detection stage and removal stage. At first, the impulse noise is localized by a single stage GP detector that exploits various information-rich, rank-ordered and robust statistical features for detection. Next the noise is removed only from the detected noisy pixels by single stage GP estimator. This estimator is developed by exploiting the global learning capability of GP and local statistical measures of noise-free pixels present in the neighbourhood of noisy pixels. The experimental results and comparative analysis with existing denoising techniques show that multi denoising performance of the proposed INDE-GP approach is better both quantitative and qualitative ways.", notes = "Affiliated with Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences (PIEAS) The on-line version of this article contains supplementary material, which is available to authorized users.", } @Article{journals/cogcom/JavedMAK16, author = "Syed Gibran Javed and Abdul Majid and Safdar Ali and Nabeela Kausar", title = "A Bio-inspired Parallel-Framework Based Multi-gene Genetic Programming Approach to Denoise Biomedical Images", journal = "Cognitive Computation", year = "2016", number = "4", volume = "8", keywords = "genetic algorithms, genetic programming", bibdate = "2017-05-18", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/cogcom/cogcom8.html#JavedMAK16", pages = "776--793", DOI = "doi:10.1007/s12559-016-9416-6", } @Article{javed:2018:MTaA, author = "Syed Gibran Javed and Abdul Majid and Yeon Soo Lee", title = "Developing a bio-inspired multi-gene genetic programming based intelligent estimator to reduce speckle noise from ultrasound images", journal = "Multimedia Tools and Applications", year = "2018", volume = "77", number = "12", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s11042-017-5139-2", DOI = "doi:10.1007/s11042-017-5139-2", } @InProceedings{jayawardena:2005:MODSIM, author = "A. W. Jayawardena and N. Muttil and T. M. K. G. Fernando", title = "Rainfall-Runoff Modelling Using Genetic Programming", booktitle = "International Congress on Modelling and Simulation, MODSIM 2005", year = "2005", editor = "Andre Zerger and Robert M. Argent", pages = "1841--1847", month = dec, organisation = "Modelling and Simulation Society of Australia and New Zealand", keywords = "genetic algorithms, genetic programming, rainfall-runoff modelling, data-driven models, evolutionary algorithms", isbn13 = "0-9758400-2-9", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.375.6188", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.375.6188", URL = "http://www.mssanz.org.au/modsim05/papers/jayawardena.pdf", size = "7 pages", abstract = "The problem of accurately determining river flows from rainfall, evaporation and other factors, occupies an important place in hydrology. The rainfall-runoff process is believed to be highly non-linear, time varying, spatially distributed and not easily described by simple models. Practitioners in water resources have embraced data-driven modelling approaches enthusiastically, as they are perceived to overcome some of the difficulties associated with physics-based approaches. Such approaches have proved to be an effective and efficient way to model the rainfall runoff process in situations where enough data on physical characteristics of catchment is not available or when it is essential to predict the flow in the shortest possible time to enable sufficient time for notification and evacuation procedures. In the recent past, an evolutionary based data driven modelling approach, genetic programming (GP) has been used for rainfall-runoff modelling. In this study, GP has been applied for predicting the runoff from three catchments -- a small steeply sloped catchment in Hong Kong (Hok Tau catchment) and two relatively bigger catchments", notes = "https://www.mssanz.org.au/modsim05/ University of Hong Kong", } @Article{Jayawardena:2006:JHE, author = "A. W. Jayawardena and N. Muttil and J. H. W. Lee", title = "Comparative Analysis of Data-Driven and GIS-Based Conceptual Rainfall-Runoff Model", journal = "Journal of Hydrologic Engineering", year = "2006", volume = "11", number = "1", pages = "1--11", month = jan # "/" # feb, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1061/(ASCE)1084-0699(2006)11:1(1))", abstract = "Modelling of the rainfall-runoff process is important in hydrology. Historically, researchers relied on conventional deterministic modeling techniques based either on the physics of the underlying processes, or on the conceptual systems which may or may not mimic the underlying processes. This study investigates the suitability of a conceptual technique along with a data-driven technique, to model the rainfall-runoff process. The conceptual technique used is based on the Xinanjiang model coupled with geographic information system (GIS) for runoff routing and the data-driven model is based on genetic programming (GP), which was used for rainfall-runoff modelling in the recent past. To verify GP's capability, a simple example with a known relation from fluid mechanics is considered first. For a small, steep-sloped catchment in Hong Kong, it was found that the conceptual model outperformed the data-driven model and provided a better representation of the rainfall-runoff process in general, and better prediction of peak discharge, in particular. To demonstrate the potential of GP as a viable data-driven rainfall-runoff model, it is successfully applied to two catchments located in southern China.", notes = "c ASCE", } @Article{Jebari:2013:IJCSI, author = "Khalid jebari and Mohammed Madiafi and Abdelaziz Elmoujahid", title = "An Evolutionary approach for solving {Shrodinger} Equation", journal = "International Journal of Computer Science Issues", year = "2013", volume = "10(6)", number = "2", pages = "168--172", month = nov, keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Shroedinger equation, Evolutionary Computation, Quantum Physics", ISSN = "1694-0814", URL = "https://www.ijcsi.org/papers/IJCSI-10-6-2-168-172.pdf", URL = "https://www.ijcsi.org/articles/An-evolutionary-approach-for-solving-the-schrodinger-equation.php", size = "5 pages", abstract = "The purpose of this paper is to present a method of solving the Shrodinger Equation (SE) by Genetic Algorithms and Grammatical Evolution. The method forms generations of trial solutions expressed in an analytical form. We illustrate the effectiveness of this method providing, for example, the results of its application to a quantum system minimal energy, and we compare these results with those produced by traditional analytical methods.", notes = "See also \cite{oai:arXiv.org:1402.5428} http://www.IJCSI.org LCS Laboratory Faculty of Sciences Rabat Agdal, University Mohammed V, UM5A, Rabat, Morocco", } @Article{Jebari:2013:ICCA, author = "Khalid Jebari and Mohammed Madiafi and Abdelaziz {El Moujahid}", title = "Solving {Poisson} Equation by Genetic Algorithms", journal = "International Journal of Computer Applications", year = "2013", volume = "83", number = "5", pages = "1--6", month = dec, keywords = "genetic algorithms, genetic programming, grammatical evolution, evolutionary computation, Artificial Intelligence, Poisson equation", oai = "oai:arXiv.org:1401.0523", URL = "http://arxiv.org/abs/1401.0523", ISSN = "0975-8887", URL = "http://research.ijcaonline.org/volume83/number5/pxc3892597.pdf", DOI = "doi:10.5120/14441-2597", size = "6 pages", abstract = "This paper deals with a method for solving Poisson Equation (PE) based on genetic algorithms and grammatical evolution. The method forms generations of solutions expressed in an analytical form. Several examples of PE are tested and in most cases the exact solution is recovered. But, when the solution cannot be expressed in an analytical form, our method produces a satisfactory solution with a good level of accuracy", notes = "differential equations. See also http://arxiv.org/abs/1402.5428 An Evolutionary approach for solving Schrodinger Equation \cite{Jebari:2013:IJCSI} Published by Foundation of Computer Science, New York, USA.", } @Misc{oai:arXiv.org:1402.5428, author = "Khalid jebari and Mohammed Madiafi and Abdelaziz Elmoujahid", title = "An Evolutionary approach for solving Shrodinger Equation", note = "Comment: arXiv admin note: substantial text overlap with arXiv:1401.0523", year = "2014", month = feb # "~21", keywords = "genetic algorithms, genetic programming, grammatical evolution, neural and evolutionary computing", bibsource = "OAI-PMH server at export.arxiv.org", oai = "oai:arXiv.org:1402.5428", URL = "http://arxiv.org/abs/1402.5428", abstract = "The purpose of this paper is to present a method of solving the Shroedinger Equation (SE) by Genetic Algorithms and Grammatical Evolution. The method forms generations of trial solutions expressed in an analytical form. We illustrate the effectiveness of this method providing, for example, the results of its application to a quantum system minimal energy, and we compare these results with those produced by traditional analytical methods", notes = "See \cite{Jebari:2013:IJCSI}", } @InProceedings{DBLP:conf/iccci/JedrzejowiczJ10, author = "Joanna Jedrzejowicz and Piotr Jedrzejowicz", title = "Cellular GEP-Induced Classifiers", booktitle = "Second International Conference on Computational Collective Intelligence, Technologies and Applications, ICCCI 2010, Part I", year = "2010", editor = "Jeng-Shyang Pan and Shyi-Ming Chen and Ngoc Thanh Nguyen", volume = "6421", series = "LNCS", pages = "343--352", address = "Kaohsiung, Taiwan", month = nov # " 10-12", keywords = "genetic algorithms, genetic programming, gene expression programming", isbn13 = "978-3-642-16692-1", URL = "https://doi.org/10.1007/978-3-642-16693-8_36", DOI = "doi:10.1007/978-3-642-16693-8_36", timestamp = "Mon, 22 May 2017 17:11:07 +0200", biburl = "https://dblp.org/rec/bib/conf/iccci/JedrzejowiczJ10", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{journals/tcci/JedrzejowiczJ11, author = "Joanna Jedrzejowicz and Piotr Jedrzejowicz", title = "Cellular Gene Expression Programming Classifier Learning", journal = "Transactions on Computational Collective Intelligence {5}", year = "2011", volume = "6910", series = "Lecture Notes in Computer Science", pages = "66--83", editor = "Ngoc Thanh Nguyen", publisher = "Springer", keywords = "genetic algorithms, genetic programming, gene expression programming, cellular evolutionary algorithm, ensemble classifiers", isbn13 = "978-3-642-24015-7", DOI = "doi:10.1007/978-3-642-24016-4_4", size = "18 pages", abstract = "In this paper we propose integrating two collective computational intelligence techniques: gene expression programming and cellular evolutionary algorithms with a view to induce expression trees, which, subsequently, serve as weak classifiers. From these classifiers stronger ensemble classifiers are constructed using majority-voting and boosting techniques. The paper includes the discussion of the validating experiment result confirming high quality of the proposed ensemble classifiers.", notes = "Journal volume 5 but published as book in LNCS 6910 series", affiliation = "Institute of Informatics, Gdansk University, Wita Stwosza 57, 80-952 Gdansk, Poland", } @InProceedings{DBLP:series/sci/JedrzejowiczJ11, author = "Joanna Jedrzejowicz and Piotr Jedrzejowicz", title = "Constructing Ensemble Classifiers from GEP-Induced Expression Trees", booktitle = "Next Generation Data Technologies for Collective Computational Intelligence", year = "2011", editor = "Nik Bessis and Fatos Xhafa", volume = "352", series = "Studies in Computational Intelligence", pages = "167--193", publisher = "Springer", keywords = "genetic algorithms, genetic programming, gene expression programming", isbn13 = "978-3-642-20343-5", URL = "https://doi.org/10.1007/978-3-642-20344-2_7", DOI = "doi:10.1007/978-3-642-20344-2_7", timestamp = "Tue, 16 May 2017 14:24:33 +0200", biburl = "https://dblp.org/rec/bib/series/sci/JedrzejowiczJ11", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{DBLP:journals/eswa/JedrzejowiczJ11, author = "Joanna Jedrzejowicz and Piotr Jedrzejowicz", title = "Experimental evaluation of two new {GEP}-based ensemble classifiers", journal = "Expert Systems with Applications", volume = "38", number = "9", pages = "10932--10939", year = "2011", URL = "https://doi.org/10.1016/j.eswa.2011.02.135", DOI = "doi:10.1016/j.eswa.2011.02.135", timestamp = "Fri, 26 May 2017 22:54:11 +0200", biburl = "https://dblp.org/rec/bib/journals/eswa/JedrzejowiczJ11", bibsource = "dblp computer science bibliography, https://dblp.org", keywords = "genetic algorithms, genetic programming, gene expression programming", } @InProceedings{conf/iccci/JedrzejowiczJ17, author = "Joanna Jedrzejowicz and Piotr Jedrzejowicz", title = "Gene Expression Programming Ensemble for Classifying Big Datasets", booktitle = "Computational Collective Intelligence - 9th International Conference, ICCCI 2017, Nicosia, Cyprus, September 27-29, 2017, Proceedings, Part II", pages = "3--12", series = "Lecture Notes in Computer Science", publisher = "Springer", year = "2017", volume = "10449", editor = "Ngoc Thanh Nguyen and George A. Papadopoulos and Piotr Jedrzejowicz and Bogdan Trawinski and Gottfried Vossen", keywords = "genetic algorithms, genetic programming, gene expression programming", bibdate = "2017-09-18", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/iccci/iccci2017-2.html#JedrzejowiczJ17", isbn13 = "978-3-319-67076-8", DOI = "doi:10.1007/978-3-319-67077-5_1", } @Article{Jedrzejowicz:2018:Complexity, author = "Joanna Jedrzejowicz and Piotr Jedrzejowicz", title = "Incremental Gene Expression Programming Classifier with Metagenes and Data Reduction", journal = "Complexity", year = "2018", pages = "Article ID 6794067", keywords = "genetic algorithms, genetic programming, gene expression programming", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/complexity/complexity2018.html#JedrzejowiczJ18", URL = "http://downloads.hindawi.com/journals/complexity/2018/6794067.pdf", DOI = "doi:10.1155/2018/6794067", size = "12 pages", abstract = "The paper proposes an incremental Gene Expression Programming classifier. Its main features include using two-level ensemble consisting of base classifiers in form of genes and the upper-level classifier in the form of metagene. The approach enables us to deal with big datasets through controlling computation time using data reduction mechanisms. The user can control the number of attributes used to induce base classifiers as well as the number of base classifiers used to induce metagenes. To optimise the parameter setting phase, an approach based on the Orthogonal Experiment Design principles is proposed, allowing for statistical evaluation of the influence of different factors on the classifier performance. In addition, the algorithm is equipped with a simple mechanism for drift detection. A detailed description of the algorithm is followed by the extensive computational experiment. Its results validate the approach. Computational experiment results show that the proposed approach compares favourably with several state-of-the-art incremental classifiers.", notes = "Institute of Informatics, Faculty of Mathematics, Physics and Informatics, University of Gdansk, 80-308 Gdansk, Poland", } @Article{Jedrzejowicz:2019:jifs, author = "Joanna Jedrzejowicz and Piotr Jedrzejowicz", title = "Gene Expression Programming as a data classification tool. A review", journal = "Journal of Intelligent and Fuzzy Systems", year = "2019", number = "1", volume = "36", pages = "91--100", keywords = "genetic algorithms, genetic programming, gene expression programming", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/jifs/jifs36.html#JedrzejowiczJ19", DOI = "doi:10.3233/JIFS-18026", notes = "journals/jifs/JedrzejowiczJ19", } @Article{journals/vjcs/JedrzejowiczJW19, title = "Implementing Gene Expression Programming in the Parallel Environment for Big Datasets' Classification", author = "Joanna Jedrzejowicz and Piotr Jedrzejowicz and Izabela Wierzbowska", journal = "Vietnam J. Computer Science", year = "2019", number = "2", volume = "6", pages = "163--175", keywords = "genetic algorithms, genetic programming, gene expression programming", bibdate = "2019-11-06", DOI = "doi:10.1142/S2196888819500118", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/vjcs/vjcs6.html#JedrzejowiczJW19", } @InProceedings{conf/kesidt/JedrzejowiczJ19, author = "Joanna Jedrzejowicz and Piotr Jedrzejowicz", title = "Gene Expression Programming Classifier with Concept Drift Detection Based on {Fisher} Exact Test", booktitle = "Intelligent Decision Technologies, 2019", year = "2019", editor = "Ireneusz Czarnowski and Robert J. Howlett and Lakhmi C. Jain", volume = "142", series = "Smart Innovation, Systems and Technologies", pages = "203--211", publisher = "Springer", keywords = "genetic algorithms, genetic programming, gene expression programming", isbn13 = "978-981-13-8310-6", bibdate = "2021-04-09", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/kesidt/kesidt2019-1.html#JedrzejowiczJ19", DOI = "doi:10.1007/978-981-13-8311-3_18", abstract = "The paper proposes to use gene expression programming with metagenes as a base classifier integrated with the Fisher exact test drift detector. The approach assumes maintaining during the classification process two windows, recent and older. If the drift is detected, the recent window is used to induce a new classifier with a view to adapt to the drift changes. The idea is validated in the computational experiment where the performance of the GEP-based classifier with Fisher exact test detector is compared with classifiers using Naive Bayes and Hoeffding tree as the base learners.", } @InProceedings{conf/icannga/JedrzejowiczR09, title = "Agent-Based Gene Expression Programming for Solving the RCPSP/max Problem", author = "Piotr Jedrzejowicz and Ewa Ratajczak-Ropel", booktitle = "9th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2009", year = "2009", editor = "Mikko Kolehmainen and Pekka J. Toivanen and Bartlomiej Beliczynski", volume = "5495", series = "Lecture Notes in Computer Science", pages = "203--212", address = "Kuopio, Finland", month = apr # " 23-25", publisher = "Springer", note = "Revised Selected Papers", keywords = "genetic algorithms, genetic programming, gene expression programming", isbn13 = "978-3-642-04920-0", DOI = "doi:10.1007/978-3-642-04921-7", bibdate = "2009-12-16", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icannga/icannga2009.html#JedrzejowiczR09", } @InProceedings{jelasity:1999:TASEC, author = "Mark Jelasity", title = "The Adaptationist Stance and Evolutionary Computation", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1859--1864", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "methodology, pedagogy and philosophy", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/MP-600.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/MP-600.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Article{Jenkins:1997:AAH, author = "Bob Jenkins", title = "Algorithm Alley: Hash Functions", journal = "Dr. Dobb's Journal", year = "1997", volume = "22", number = "9", pages = "107--109, 115--116", month = "1 " # sep, CODEN = "DDJOEB", ISSN = "1044-789X", bibdate = "Fri Apr 30 10:04:44 1999", URL = "http://www.drdobbs.com/database/algorithm-alley/184410284", URL = "http://en.wikipedia.org/wiki/Jenkins_hash_function", notes = "Describes a new hash function which is much better at producing uniform key distributions than others commonly used, yet remains acceptably fast. See \cite{Boyer:1998:AAR} for comparison with a related algorithm. Cited by \cite{Estebanez:PPSN:2006} as human generated", acknowledgement = ack-nhfb, } @Article{Jenkins:2025:compstruct, author = "William Jenkins and Hunter Gross and Kaitlin Detwiler and Gregory Morscher and Craig Przybyla and Jacob Hochhalter", title = "Developing a continuum damage model for ceramic matrix composites using genetic programming based symbolic regression", journal = "Composite Structures", year = "2025", volume = "369", pages = "119274", keywords = "genetic algorithms, genetic programming, Ceramic-matrix composites (CMCs), Computational modeling, Damage mechanics, Mechanical testing", ISSN = "0263-8223", URL = "https://www.sciencedirect.com/science/article/pii/S0263822325004398", DOI = "doi:10.1016/j.compstruct.2025.119274", abstract = "Modeling damage in continuous fiber reinforced ceramic matrix composites (CMCs) needs improvement before CMCs can be widely adopted in critical aerospace applications. While damage mechanisms in CMCs are dependent on the microstructure, we develop a continuum damage model (CDM) that homogenizes governing microstructural mechanisms for application at the engineering scale. The CDM operates on the ply-level, under 2D plane stress assumptions, derived using a thermodynamics-based approach. We employ genetic programming based symbolic regression (GPSR) to learn individual components of the unknown damage model tensor from SiC/SiC tensile testing data. Known elastic material behaviour is separated such that GPSR specifically learns the corresponding damage tensor. Training constraints based on mechanics knowledge are enforced to promote the evolution of models that are physically valid and interpretable. The resulting damage model fits well to training data and provides physical insight into the laminate damage mechanisms and interactions", } @InProceedings{Jensen:2010:gecco, author = "Adam C. Jensen and Betty H. C. Cheng", title = "On the use of genetic programming for automated refactoring and the introduction of design patterns", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "1341--1348", keywords = "genetic algorithms, genetic programming, SBSE, Search-based software engineering", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830483.1830731", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Maintaining an object-oriented design for a piece of software is a difficult, time-consuming task. Prior approaches to automated design refactoring have focused on making small, iterative changes to a given software design. However, such approaches do not take advantage of composition of design changes, thus limiting the richness of the refactoring strategies that they can generate. In order to address this problem, this paper introduces an approach that supports composition of design changes and makes the introduction of design patterns a primary goal of the refactoring process. The proposed approach uses genetic programming and software engineering metrics to identify the most suitable set of refactorings to apply to a software design. We illustrate the efficacy of this approach by applying it to a large set of published models, as well as a real-world case study", notes = "p1343 'Gamma design patterns, including Abstract Factory, Adapter, Bridge, Decorator, Prototype, and Proxy.' Search based refactoring QMOOD. Remodel = (UML design graph,transformation tree). O'Cinneide mini-transformations: abstraction, abstract access, delegation partial abstract, wrapper design patterns. ReMoDD. ECJ. JGraphT, JLog. Large cluster of SuSE linux enterprise server. Prolog. Also known as \cite{1830731} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @Article{JEONG:2021:ES, author = "Hoseong Jeong and Sun-Jin Han and Seung-Ho Choi and Jae-Hyun Kim and Kang Su Kim", title = "Genetic programming approach and data generation for transfer lengths in pretensioned concrete members", journal = "Engineering Structures", volume = "231", pages = "111747", year = "2021", ISSN = "0141-0296", DOI = "doi:10.1016/j.engstruct.2020.111747", URL = "https://www.sciencedirect.com/science/article/pii/S0141029620343480", keywords = "genetic algorithms, genetic programming, Transfer length, Pretensioned concrete, Generative adversarial network, Artificial neural network, Random forest", abstract = "This study aims to derive a practical equation that can predict the transfer length of prestressing strands with the use of genetic programming. Towards this end, a total of 260 transfer length test results were collected from previous studies, and a feature selection procedure was applied to the collected database to extract the key features influencing the transfer length. Based on the five most important features, a practical equation was derived using a genetic programming approach, and the rationality of the proposed equation was verified by comparing it with design codes, existing models, and machine learning models (random forest and artificial neural network). In addition, 1.0 times 104 fake transfer length data that follow the probability distribution of the real data were generated using a generative adversarial network, based on which the prediction performances were visualized and compared in detail. The results showed that the proposed equation exhibited a higher level of accuracy than other existing equations", } @Article{JEONG:2022:advengsoft, author = "Hoseong Jeong and Jae Hyun Kim and Seung-Ho Choi and Seokin Lee and Inwook Heo and Kang Su Kim", title = "Semantic Cluster Operator for Symbolic Regression and Its Applications", journal = "Advances in Engineering Software", volume = "172", pages = "103174", year = "2022", ISSN = "0965-9978", DOI = "doi:10.1016/j.advengsoft.2022.103174", URL = "https://www.sciencedirect.com/science/article/pii/S0965997822000850", keywords = "genetic algorithms, genetic programming, Automatic code derivation, Semantic, Clustering, Iterated local search, Symbolic regression", abstract = "a novel operator, semantic cluster operator, was developed to overcome the low convergence performance of genetic programming in symbolic regression. The main strategy for steep convergence was to narrow search space and scrutinize the narrowed search space using a semantic cluster library. To demonstrate the success of this idea, the computation time and offspring fitness of the operator developed in this paper were compared with those of exhaustive search. The computation time of the operator was approximately 6percent of that of the exhaustive search, and its offspring fitness was in the top 0.5percent among all offspring derived from the exhaustive search. In two application problems, derived models from an algorithm using the operator showed high prediction accuracy comparable to an artificial neural network, random forest, and support vector machine despite its simplicity.", } @Article{jeong:2022:IJCSM, author = "Hoseong Jeong and Seongwoo Ji and Jae Hyun Kim and Seung-Ho Choi and Inwook Heo and Kang Su Kim", title = "Development of Mapping Function to Estimate {Bond-Slip} and Bond Strength of {RC} Beams Using Genetic Programming", journal = "International Journal of Concrete Structures and Materials", year = "2022", volume = "16", number = "1", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1186/s40069-022-00536-6", DOI = "doi:10.1186/s40069-022-00536-6", } @Article{jeongGP1, author = "Kwang-Seuk Jeong and Dong-Kyun Kim and Peter Whigham and Gea-Jae Joo", title = "Modelling Microcystis aeruginosa bloom dynamics in the Nakdong River by means of evolutionary computation and statistical approach", journal = "Ecological Modelling", year = "2003", volume = "161", number = "1-2", pages = "67--78", month = "1 " # mar, keywords = "genetic algorithms, genetic programming, Multivariate linear regression, Microcystis aeruginosa, Algal blooms, Ecological modelling, Nakdong River", URL = "http://www.business.otago.ac.nz/infosci/SIRC/PeterW/Publications/Jeong_EcolMod_V161_Is_1_2_pg67_78.pdf", URL = "http://www.sciencedirect.com/science/article/B6VBS-47VRMKR-4/2/816a2fac74d51d8caefedf1f9c2055b0", DOI = "doi:10.1016/S0304-3800(02)00280-6", size = "12 pages", abstract = "Dynamics of a bloom-forming cyanobacteria (Microcystis aeruginosa) in a eutrophic river?reservoir hybrid system were modelled using a genetic programming (GP) algorithm and multivariate linear regression (MLR). The lower Nakdong River has been influenced by cultural eutrophication since construction of an estuarine barrage in 1987. During 1994?1998, the average concentrations of nutrients and phytoplankton were: NO3-?N, 2.7 mg l-1; NH4+?N, 0.6 mg l-1; PO43-?P, 34.7 g l-1; and chlorophyll a, 50.2 g l-1. Blooms of M. aeruginosa occurred in summers when there were droughts. Using data from 1995 to 1998, GP and MLR were used to construct equation models for predicting the occurrence of M. aeruginosa. Validation of the model was done using data from 1994, a year when there were severe summer blooms. GP model was very successful in predicting the temporal dynamics and magnitude of blooms while MLR resulted rather insufficient predictability. The lower Nakdong River exhibits reservoir-like ecological dynamics rather than riverine, and for this reason a previous river mechanistic model failed to describe uncertainty and complexity. Results of this study suggest that an inductive-empirical approach is more suitable for modelling the dynamics of bloom-forming algal species in a river?reservoir transitional system.", notes = "a Department of Biology, Pusan National University, Jang-Jeon Dong, Gum-Jeong Gu, Busan 609-735, South Korea b Department of Information Science, University of Otago, PO Box 56, Dunedin, New Zealand", } @Article{Jeong20113149, author = "Kwang-Seuk Jeong and Ji-Deok Jang and Dong-Kyun Kim and Gea-Jae Joo", title = "Waterfowls habitat modelling: Simulation of nest site selection for the migratory Little Tern (Sterna albifrons) in the Nakdong estuary", journal = "Ecological Modelling", volume = "222", number = "17", pages = "3149--3156", year = "2011", ISSN = "0304-3800", DOI = "doi:10.1016/j.ecolmodel.2011.05.032", URL = "http://www.sciencedirect.com/science/article/pii/S0304380011003139", keywords = "genetic algorithms, genetic programming, Little Tern Sterna albifrons, Habitat selection pattern, Elevation, Vegetation, Rule-set model", abstract = "This paper aims to find patterns in nest site selection by Little Terns Sterna albifrons, in the Nakdong estuary in South Korea. This estuary is important waterfowl stopover and breeding habitat, located in the middle of the East Asia-Australasian Flyway. The Little Tern is a common species easily observed near the seashore but their number is gradually declining around the world. We investigated their nests and eggs on a barrier islet in the Nakdong estuary during the breeding season (May to June, 2007), and a pattern for the nest site selection was identified using genetic programming (GP). The GP generated a predictive rule-set model for the number of Little Tern nests (training: R2 = 0.48 and test: 0.46). The physical features of average elevation, variation of elevation, plant coverage, and average plant height were estimated to determine the influence on nest numbers for Little Tern. A series of sensitivity analyses stressed that mean elevation and vegetation played an important role in nest distribution for Little Tern. The influence of these two variables could be maximised when elevation changed moderately within the sampled quadrats. The study results are regarded as a good example of applying GP to vertebrate distribution patterning and prediction with several important advantages compared to conventional modelling techniques, and can help establish a management or restoration strategy for the species.", } @InProceedings{Jericho:2020:CEC, author = "Jackson Jericho and Yi Mei", title = "Genetic Programming Hyper-heuristic with Cluster Awareness for Stochastic Team Orienteering Problem with Time Windows", booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC 2020", year = "2020", editor = "Yaochu Jin", pages = "paper id24536", address = "internet", month = "19-24 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-7281-6929-3", DOI = "doi:10.1109/CEC48606.2020.9185911", notes = "https://wcci2020.org/ Victoria University of Wellington, New Zealand", } @InProceedings{Jeyakarthic:2023:ICAIS, author = "M. Jeyakarthic and R. Ramesh", booktitle = "2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)", title = "Genetic Programming with Dynamic Bayesian Network based Credit Risk Assessment Model", year = "2023", pages = "845--850", month = feb, keywords = "genetic algorithms, genetic programming, Heuristic algorithms, Decision making, Organizations, Data models, Bayes methods, Dynamic programming, Credit risk assessment, Credit scoring, Dynamic Bayesian network, Data classification", DOI = "doi:10.1109/ICAIS56108.2023.10073788", abstract = "An accurate credit risk assessment system is essential to a financial organization for its impeccable and proper functioning. Precise predictions of credit risk would enable them to continue their function transparently and gainfully. Since the rate of loan defaults was progressively rising, bank officials find it very difficult to properly evaluate loan requests. Many credit risk analysis methods were used for evaluating credit risk of the customer data. The assessment of the credit risk data results in the decision to grant the loan to the debtor or deny the application of the debtor which can be tough task that includes the deep analysis of the data offered by the customer or the credit data of customer. This study develops a Genetic Programming with Dynamic Bayesian Network based Credit Risk Assessment (GPDBN-CRA) model. The presented GPDBN-CRA model helps the financial institutions in the decision making process of accepting a loan request or not. To do so, the presented GPDBN-CRA model normalizes the customer data as an initial stage. For credit risk evaluation, the presented GPDBN-CRA method applies DBN model to perform classification model. To enhance the assessment performance of the GPDBN-CRA model, the GP technique is applied for hyperparameter tuning process. The experimental validation of the presented GPDBN-CRA method can be tested using customer dataset. The extensive outcomes stated the improved outcomes of the GPDBN-CRA method.", notes = "Also known as \cite{10073788}", } @InProceedings{jezic:1998:GAstitcm, author = "Gordan Jezic and Robert Kostelac and Ignac Lovrek and Vjekoslav Sinkovic", title = "Genetic Algorithms for Scheduling Tasks with Non-negligible Intertask Communication onto Multiprocessors", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "518", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms", notes = "SGA-98", } @InProceedings{Jha:2019:APS/DFD, author = "Animesh Kumar Jha and Bo Yin and Larry K. B. Li", title = "Closed-loop control of thermoacoustic oscillations using genetic programming", booktitle = "72nd Annual Meeting of the American Physical Society Division of Fluid Dynamics (APS/DFD 2019)", year = "2019", volume = "64", number = "13", address = "Seattle, USA", month = "23-26 " # nov, keywords = "genetic algorithms, genetic programming", URL = "http://repository.ust.hk/ir/Record/1783.1-100536", URL = "http://lbdiscover.ust.hk/uresolver?url_ver=Z39.88-2004\&rft_val_fmt=info:ofi/fmt:kev:mtx:journal\&rfr_id=info:sid/HKUST:SPI\&rft.genre=article\&rft.issn=\&rft.volume=\&rft.issue=\&rft.date=2019\&rft.spage=\&rft.aulast=Jha\&rft.aufirst=Animesh\&rft.atitle=Closed-loop+control+of+thermoacoustic+oscillations+using+genetic+programming\&rft.title=Bulletin+of+the+American+Physical+Society", URL = "http://hdl.handle.net/1783.1/100536", URL = "https://meetings.aps.org/Meeting/DFD19/Session/NP05", URL = "https://meetings.aps.org/Meeting/DFD19/Session/NP05.45", abstract = "The use of genetic programming (GP) to discover model-free control laws for nonlinear flow systems has gained considerable traction recently, having been applied for the closed-loop control of recirculation zones behind backward-facing steps, flow separation over sharp edges and turbulent mixing layers. This unsupervised data-driven control strategy has been shown to outperform conventional open-loop forcing, by enabling successful individual control laws to spread their genetic traits from one generation to the next. In this experimental study, we use GP to discover model-free control laws for the suppression of self-excited thermoacoustic oscillations, which are detrimental to combustion systems. We evaluate every individual control law in a given generation on a real-time closed-loop control system equipped with a single sensor (a pressure transducer) and a single actuator (a loudspeaker). We rank the effectiveness of the control laws with a cost function and use a tournament process to breed subsequent generations of control laws. We then benchmark the performance of the final generation against that of open-loop forcing, providing improved control laws for the suppression of self-excited thermoacoustic oscillations.", bibsource = "OAI-PMH server at repository.ust.hk", language = "English", oai = "oai:repository.ust.hk:1783.1-100536", } @InProceedings{Jha:2020:ICACCS, author = "Mayank Jha and Richa Jha", title = "Comparing the Effort Estimated By Different Models", booktitle = "2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS)", year = "2020", pages = "1148--1154", address = "Coimbatore, India", month = "6-7 " # mar, keywords = "genetic algorithms, genetic programming, SBSE, software effort estimation, software testing, Cobb-Douglas function, Neuro Fuzzy Approach, Genetic programming method, STLC", isbn13 = "978-1-7281-5198-4", DOI = "doi:10.1109/ICACCS48705.2020.9074165", size = "7 pages", abstract = "Management of project software starts with a collection of activities referred to as project planning procedure. A companys team must decide the work to be done, the resources to be reorganized and a time from beginning of the calculation until project starts. Following completion of these activities, the program team will set up a set of projects that will assign program development tasks, identify key milestones, identify responsibilities for each task and identify related dependencies among participants that may have a significant impact on progress. There is usually no full precise estimation process, but in this research we have tried to find the best programming methods to find the best estimate of programming. Effort estimation is one of greatest objection of STLC. It is platform for planning, estimating and preparing effort for project. This paper demonstrates model with a purpose of depicting bias variation and an accuracy of the technology of an enterprise test attempt estimates concluding the function of Cobb-Douglas (CDF), Neuro fuzzy approach, and Genetic methods. The purpose of this review is to present an analysis of principles to minimize software costs and to explain how these concepts are applied to general system divisions. We deliver simple algorithms namely-Cobb Douglas, Genetic Algorithms, and Adaptive Neuro Fuzzy Approach to decide which algorithm is best suited to finding the best estimates as accurate as possible. The best outcomes they have been found in Neuro Fuzzy Approach. The Neuro Fuzzy has highest accuracy to be found, but the Genetic Algorithm was better than Fuzzy Logic, the worst compared to Cobb Douglas and Genetic Algorithms.", notes = "Citicorp Services India Limited, Pune, India. Also known as \cite{9074165}", } @Article{jha:2014:SRIN, author = "Rajesh Jha and Prodip Kumar Sen and Nirupam Chakraborti", title = "Multi-Objective Genetic Algorithms and Genetic Programming Models for Minimizing Input Carbon Rates in a Blast Furnace Compared with a Conventional Analytic Approach", journal = "Steel Research International", year = "2014", volume = "85", number = "2", pages = "219--232", month = feb, keywords = "genetic algorithms, genetic programming, Blast furnace, CO2 emission, Si in hot metal, evolutionary algorithms, artificial neural network, multi-objective optimisation, Pareto front, BioGP, EvoNN, modeFRONTIER, KIMEME", ISSN = "1869-344X", DOI = "doi:10.1002/srin.201300074", size = "14 pages", abstract = "Data-driven models were constructed for the Productivity, CO2 emission, and Si content for an operational Blast furnace using evolutionary approaches that involved two recent strategies based upon bi-objective genetic Programming and neural nets evolving through Genetic Algorithms. The models were used to compute the optimum tradeoff between the level of CO2 emission and productivity at different Si levels, using a Predator-Prey Genetic Algorithm, well tested for computing the Pareto-optimality. The results were pitted against some similar calculations performed with commercial software and also compared with the results of thermodynamics-based analytical models.", notes = "Department of Metallurgical and Materials Engineering, Indian Institute of Technology, Kharagpur 721 302, India. Also known as \cite{SRIN:SRIN201300074}", } @Article{Jha:2015:MMP, author = "R. Jha and F. Pettersson and G. S. Dulikravich and H. Saxen and N. Chakrabortic", title = "Evolutionary Design of Nickel-Based Superalloys Using Data-Driven Genetic Algorithms and Related Strategies", journal = "Materials and Manufacturing Processes", year = "2015", volume = "30", number = "4", pages = "488--510", month = apr, email = "rjha001@fiu.edu", keywords = "genetic algorithms, genetic programming, Alloy design, Data-driven modelling, Evolutionary optimisation, Meta-models, Multi-objective optimisation, Phase equilibria, Superalloy", ISSN = "1042-6914", URL = "http://dx.doi.org/10.1080/10426914.2014.984203", DOI = "doi:10.1080/10426914.2014.984203", size = "23 pages", abstract = "Data-driven models were constructed for the mechanical properties of multi-component Ni-based superalloys, based on systematically planned, limited experimental data using a number of evolutionary approaches. Novel alloy design was carried out by optimising two conflicting requirements of maximising tensile stress and time-to-rupture using a genetic algorithm-based multi-objective optimization method. The procedure resulted in a number of optimised alloys having superior properties. The results were corroborated by a rigorous thermodynamic analysis and the alloys found were further classified in terms of their expected levels of hardenabilty, creep, and corrosion resistances along with the two original objectives that were optimised. A number of hitherto unknown alloys with potential superior properties in terms of all the attributes ultimately emerged through these analyses. This work is focused on providing the experimentalists with linear correlations among the design variables and between the design variables and the desired properties, non-linear correlations (qualitative) between the design variables and the desired properties, and a quantitative measure of the effect of design variables on the desired properties. Pareto-optimised predictions obtained from various data-driven approaches were screened for thermodynamic equilibrium. The results were further classified for additional properties.", notes = "Special Issue on Genetic Algorithms Rajesh Jha and Frank Pettersson and George S. Dulikravich and Henrik Saxen and Nirupam Chakraborti", } @Misc{oai:CiteSeerX.psu:10.1.1.299.770, title = "Taiwan Stock Forecasting with the Genetic Programming {$\ast$}", author = "Siao-ming Jhou and Chang-biau Yang and Hung-hsin Chen", year = "2013", month = jul # "~19", keywords = "genetic algorithms, genetic programming, taiwan stock exchange capitalisation weighted stock index, annualised return, feature", abstract = "---In this paper, we propose a model for generating profitable trading strategies for Taiwan stock market. Our model applies the genetic programming (GP) to obtain profitable and stable trading strategies in the training period, and then the strategies are applied to trade the stock in the testing period. The variables for GP include 6 basic information and 25 technical indicators. We perform five experiments on Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) from 2000/9/14 to 2010/5/21. In these experiments, we find that the trading strategies generated by GP with two arithmetic trees have more stable returns. In addition, if we obtain the trading strategies in three historical periods which are the most similar to the current training period, we are able to earn higher return in the testing period. In each experiment, 24 cases are considered. The testing period is rolling updated with the sliding window scheme. The best cumulative return 166.57percent occurs when 545-day training period pairs with 365-day testing period, which is much higher than the buy-and-hold strategy.", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.299.770", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.299.770", URL = "http://par.cse.nsysu.edu.tw/~cbyang/person/publish/c11stock_gp.pdf", notes = "NOT in IEEE xplor or table of contents and another paper has overlapping page numbers", } @Article{Ji201293, author = "He Ji and Wang Songlin and Wu Qinglin and Chen Xiaonan", title = "Douhe Reservoir Flood Forecasting Model Based on Data Mining Technology", journal = "Procedia Environmental Sciences", year = "2012", volume = "12, Part A", pages = "93--98", note = "2011 International Conference of Environmental Science and Engineering", keywords = "genetic algorithms, genetic programming, ANN, Hydrological Forecasting, Data Mining Technology, Artificial Neural Networks", ISSN = "1878-0296", URL = "http://www.sciencedirect.com/science/article/pii/S1878029612002538", DOI = "doi:10.1016/j.proenv.2012.01.252", abstract = "Calculating flood based on rainfall is an important part of hydrological forecast. However, due to the diversity and complexity of factors affecting the relationship between rainfall and runoffs, using the perspective of mechanism to simulate the forming of flood through rainfall is often difficult. In this paper, flood forecast model is constructed based on Artificial Neural Networks (ANN) and Genetic Programming (GP), using actual data to mine the relationship among rainfall, pre rain and net rain, to avoid the flaws of constructing actual mathematical expression in advance, and automatically search for optimal structure. Practice has approved that applying data mining technique on flood forecasting of Douhe Reservoir is able to achieve outstanding results.", notes = "ICESE2011 North China University of Water Resources and Electric Power, Zhengzhou 450011, China", } @InProceedings{Jia:2015:GECCO, author = "Baozhu Jia and Marc Ebner and Christian Schack", title = "A {GP}-based Video Game Player", booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation", year = "2015", editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr", pages = "1047--1053", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, General Video Game Player, Game State Features", isbn13 = "978-1-4503-3472-3", URL = "https://stubber.math-inf.uni-greifswald.de/~ebner/resources/uniG/jiaGPbasedVGP.pdf", URL = "http://doi.acm.org/10.1145/2739480.2754735", DOI = "doi:10.1145/2739480.2754735", size = "7 pages", abstract = "A general video game player is an an agent that can learn to play different video games with no specific domain knowledge. We are working towards developing a GP-based general video game player. Our system currently extracts game state features from screen grabs. This information is then passed on to the game player. Fitness is computed from data obtained directly from the internals of the game simulator. For this paper, we compare three different types of game state features. These features differ in how they describe the position to the nearest object surrounding the player. We have tested our genetic programming game player system on three games: Space Invaders, Frogger and Missile Command. Our results show that a playing strategy for each game can be found efficiently for all three representations.", notes = "Also known as \cite{2754735} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)", } @InProceedings{Jia:2015:CIG, author = "Baozhu Jia and Marc Ebner", title = "A Strongly Typed {GP}-Based Video Game Player", booktitle = "Proceedings of the IEEE Conference on Computational Intelligence and Games (CIG-2015)", year = "2015", editor = "Shi-Jim Yen and Tristan Cazenave and Philip Hingston", pages = "299--305", address = "Tainan, Taiwan", month = aug # " 31-" # sep # " 2", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, STGP, Py-vgdl, AI, MCTS, Atari 2600 Space Invaders, Frogger, Missile Command", isbn13 = "978-1-4799-8622-4", URL = "https://stubber.math-inf.uni-greifswald.de/~ebner/resources/uniG/jiaSTGP-VGP.pdf", DOI = "doi:10.1109/CIG.2015.7317920", size = "7 pages", abstract = "This paper attempts to evolve a general video game player, i.e. an agent which is able to learn to play many different video games with little domain knowledge. Our project uses strongly typed genetic programming as a learning algorithm. Three simple hand-crafted features are chosen to represent the game state. Each feature is a vector which consists of the position and orientation of each game object that is visible on the screen. These feature vectors are handed to the learning algorithm which will output the action the game player will take next. Game knowledge and feature vectors are acquired by processing screen grabs from the game. Three different video games are used to test the algorithm. Experiments show that our algorithm is able to find solutions to play all these three games efficiently.", notes = "ECJ. RGB to grey scale and downscaled screen grab, 3 sets of high level terminals: no difference between them found. Importance of STGP unclear: types seem to be void, boolean and float. 15:30 http://cig2015.nctu.edu.tw/program", } @InProceedings{Jia:2017:EuroGP, author = "Baozhu Jia and Marc Ebner", title = "Evolving Game State Features from Raw Pixels", booktitle = "EuroGP 2017: Proceedings of the 20th European Conference on Genetic Programming", year = "2017", month = "19-21 " # apr, editor = "Mauro Castelli and James McDermott and Lukas Sekanina", series = "LNCS", volume = "10196", publisher = "Springer Verlag", address = "Amsterdam", pages = "52--63", organisation = "species", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-55695-6", DOI = "doi:10.1007/978-3-319-55696-3_4", abstract = "General video game playing is the art of designing artificial intelligence programs that are capable of playing different video games with little domain knowledge. One of the great challenges is how to capture game state features from different video games in a general way. The main contribution of this paper is to apply genetic programming to evolve game state features from raw pixels. A voting method is implemented to determine the actions of the game agent. Three different video games are used to evaluate the effectiveness of the algorithm: Missile Command, Frogger, and Space Invaders. The results show that genetic programming is able to find useful game state features for all three games.", notes = "Part of \cite{Castelli:2017:GP} EuroGP'2017 held inconjunction with EvoCOP2017, EvoMusArt2017 and EvoApplications2017", } @Article{JIA:2020:CI, author = "Bin Jia and Dingjun Hao and Feng Qiao and Xiaoqing Zhou and Yuming Zhang and Mohsen Mesbah and Alireza Fallahpour and Bahman Nasiri-Tabrizi and Tao Wang", title = "Metal-doped bioceramic nanopowders with tunable structural properties aimed at enhancing bone density: Rapid synthesis and modeling", journal = "Ceramics International", year = "2020", ISSN = "0272-8842", DOI = "doi:10.1016/j.ceramint.2020.07.301", URL = "http://www.sciencedirect.com/science/article/pii/S027288422032318X", keywords = "genetic algorithms, genetic programming, Metal-doped bioceramic, Nanoparticles, Rapid mechanosynthesis, Structural features, Cytotoxicity assay, Modeling", abstract = "Metal doped bioceramic nanopowders were prepared by solid-state mechanochemical reactions. Also, genetic programming (GP) and gene expression programming (GEP) models were developed to predict the structural features of the mechanosynthesized nanopowders aimed at developing an innovative solution to enhance bone mineral density. The substitution of Ca2+ with different ions in the apatite structure was confirmed from chemical analysis and structural assessment, where irregular changes in the lattice parameters and unit cell volume were observed due to the replacement of the Ca2+ bivalent cation with monovalent, bivalent or trivalent ions as well as the carbonate ions effects on the apatite lattice. It was found that the crystallite size and micro-strain of the substituted bioceramics were between ~11 and 98 nm and ~0.31-2.49percent, respectively. From the functional group analysis, the intensity of the hydroxyl groups decreased as the dopant content increased. The electron microscopy images showed that both undoped and low-doped samples consist of spheroidal particles in the nano regime, whereas the high-doped specimens exhibited a high propensity to agglomerate. The results of cytotoxicity assays corroborated that appropriate ionic substitution can prevent the toxic effects of Li on Mus musculus fibroblast cells, and thus by increasing dopant concentration up to z = 0.25, cell viability of around 90percent was observed. The results obtained from the modeling demonstrated that both GP and GEP methods are reliable in predicting the structural properties of the synthetic metal-doped bioceramic nanopowders", } @InProceedings{Jia:2008:ICNC, author = "Guangfeng Jia and Yuehui Chen and Qiang Wu", title = "A MEP and IP Based Flexible Neural Tree Model for Exchange Rate Forecasting", booktitle = "Fourth International Conference on Natural Computation, ICNC '08", year = "2008", month = oct, volume = "5", pages = "299--303", keywords = "genetic algorithms, genetic programming, MEP, financial problem, flexible neural tree model, foreign exchange rate forecasting, immune programming, multi expression programming, exchange rates, financial management, neural nets, trees (mathematics)", DOI = "doi:10.1109/ICNC.2008.669", abstract = "Forecasting exchange rate is an important financial problem that is received much more attentions because of its difficulty and practical applications. The problem of prediction of foreign exchange rates by using multi expression programming and immune programming based flexible neural tree (MEPIP-FNT) is presented in this paper. This work is an extension of our previously traditional FNT model which can optimize the architectures and the weights of flexible neuron model respectively. The novel MEPIPFNT model with the underlying immune theories is capable of evolving the architectures and the weights simultaneously. To demonstrate the efficiency of the model, we conduct three different datasets in our forecasting performance analysis.", notes = "Also known as \cite{4667445}", } @InProceedings{Jia:2017:VLSI, author = "Hongyang Jia and Jie Lu and Niraj K. Jha and Naveen Yerma", booktitle = "2017 Symposium on VLSI Circuits", title = "A heterogeneous microprocessor for energy-scalable sensor inference using genetic programming", year = "2017", pages = "C28--C29", abstract = "We present a heterogeneous microprocessor for IoE sensor-inference applications, which achieves programmability required for feature extraction strictly using application data. Acceleration, though key for energy efficiency, poses substantial programmability challenges. These are overcome by exploiting genetic programming (GP) for automatic program synthesis. GP yields highly structured models of computation, enabling: (1) high degree of specialization; (2) systematic mapping of programs to the accelerator; and (3) energy scalability via user-controllable approximation. The microprocessor (130nm) achieves 325times/156times energy reduction, and farther 20x/9x energy scalability, for programmable feature extraction in two medical-sensor applications (seizure/arrhythmia-detection) vs. GP-model execution on CPU. The energy efficiency is 220 GOPS/W, near that of fixed-function accelerators, exceeding typical programmable accelerators.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.23919/VLSIC.2017.8008535", month = jun, notes = "Also known as \cite{8008535}", } @Article{Jia:2018:ieeeJSSC, author = "Hongyang Jia and Naveen Verma", journal = "IEEE Journal of Solid-State Circuits", title = "Exploiting Approximate Feature Extraction via Genetic Programming for Hardware Acceleration in a Heterogeneous Microprocessor", year = "2018", volume = "53", number = "4", pages = "1016--1027", month = apr, keywords = "genetic algorithms, genetic programming, Approximate computation, feature extraction, machine learning, programmable accelerator, sensor inference", ISSN = "0018-9200", URL = "http://www.princeton.edu/~nverma/VermaLabSite/Publications/2018/JiaVerma_JSSC2018.pdf", DOI = "doi:10.1109/JSSC.2017.2787762", size = "12 pages", abstract = "This paper presents a heterogeneous microprocessor for low-energy sensor-inference applications. Hardware acceleration has shown to enable substantial energy-efficiency and throughput gains, but raises significant challenges where programmable computations are required, as in the case of feature extraction. To overcome this, a programmable feature-extraction accelerator (FEA) is presented that exploits genetic programming for automatic program synthesis. This leads to approximate, but highly structured, computations, enabling: 1) a high degree of specialization; 2) systematic mapping of programs to the accelerator; and 3) energy scalability via user-controllable approximation knobs. A microprocessor integrating a CPU with feature-extraction and classification accelerators is prototyped in 130-nm CMOS. Two medical-sensor applications (electroencephalogram-based seizure detection and electrocardiogram-based arrhythmia detection) demonstrate 325times and 156times energy reduction, respectively, for programmable feature extraction implemented on the accelerator versus a CPU-only architecture, and 7.6times and 6.5times energy reduction, respectively, versus a CPU-with-coprocessor architecture. Furthermore, 20times and 9times energy scalability, respectively, is demonstrated via the approximation knobs. The energy-efficiency of the programmable FEA is 220 GOPS/W, near that of fixed-function accelerators in the same technology, exceeding typical programmable accelerators.", notes = "Also known as \cite{8262650}", } @InProceedings{jia:2024:GECCOcomp2, author = "Kaixuan Jia and Jianbin Ma and Xiaoying Gao and Jiaxin Niu", title = "Archive-based multiple feature construction methods using adaptive Genetic Programming", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion", year = "2024", editor = "Ting Hu and Aniko Ekart", pages = "503--506", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, feature construction, adaptive, multiple feature, classification: Poster", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3654239", size = "4 pages", abstract = "The quality of features is an important factor that affects the classification performance of machine learning algorithms. Feature construction based on Genetic Programming (GP) can automatically create more discriminative features, sometimes greatly improving classification performance. However, insufficient information caused by constructing a single feature or constructing only a few features can affect the classification performance of feature construction. In addition, GP may fall into premature convergence, which also affects classification performance. This paper proposes an archive-based multiple feature construction method which uses elite archive strategy to preserve and select effective constructed features, and employs an adaptive strategy for GP to adjust the crossover and mutation probabilities based on fitness values. Experiments on ten datasets show that our proposed archive-based multiple feature construction method without using adaptive GP can significantly improve the classification performance compared with traditional single feature construction method, and the classification performance can be maintained or further improved by adding the adaptive strategy. The comparisons with three state-of-the-art techniques show that our proposed methods can significantly achieve better classification performance.", notes = "GECCO-2024 GP A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @Article{jia:2025:MC, author = "Kaixuan Jia and Fan Zhang and Xiaoying Gao and Jianbin Ma", title = "Archive-based multiple feature construction method using adaptive genetic programming", journal = "Memetic Computing", year = "2025", keywords = "genetic algorithms, genetic programming", URL = "https://link.springer.com/article/10.1007/s12293-025-00453-7", DOI = "doi:10.1007/s12293-025-00453-7", } @InProceedings{Jia:2010:IWCFTA, author = "Qiang Jia and Wallace K. S. Tang", title = "Synthesizing Chaotic Systems with Genetic Programming", booktitle = "2010 International Workshop on Chaos-Fractals Theories and Applications (IWCFTA)", year = "2010", month = "29-31 " # oct, pages = "132--136", abstract = "In this paper, it is to apply genetic programming to explore some new chaotic systems. Based on a tree representation, each function in the state dynamical equation of a chaotic system can be well defined. Thus, through the optimisation process governed by genetic programming, it is demonstrated that some new potential forms can be determined, for which chaotic systems are obtained by having tuning of the coefficients.", keywords = "genetic algorithms, genetic programming, optimisation, state dynamical equation, synthesising chaotic system, tree searching", DOI = "doi:10.1109/IWCFTA.2010.110", notes = "City University of Hong Kong. Also known as \cite{5671295}", } @InProceedings{DBLP:conf/ispass/JiaSM12, author = "Wenhao Jia and Kelly A. Shaw and Margaret Martonosi", title = "Stargazer: Automated regression-based {GPU} design space exploration", booktitle = "2012 IEEE International Symposium on Performance Analysis of Systems \& Software", year = "2012", editor = "Rajeev Balasubramonian and Vijayalakshmi Srinivasan", pages = "2--13", address = "New Brunswick, NJ, USA", month = apr # " 1-3", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, GPU", timestamp = "Wed, 16 Oct 2019 14:14:56 +0200", biburl = "https://dblp.org/rec/conf/ispass/JiaSM12.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "http://jiawenhao.com/stargazer.pdf", DOI = "doi:10.1109/ISPASS.2012.6189201", size = "12 pages", abstract = "Graphics processing units (GPUs) are of increasing interest because they offer massive parallelism for high-throughput computing. While GPUs promise high peak performance, their challenge is a less-familiar programming model with more complex and irregular performance trade-offs than traditional CPUs or CMPs. In particular, modest changes in software or hardware characteristics can lead to large or unpredictable changes in performance. In response to these challenges, our work proposes, evaluates, and offers usage examples of Stargazer 1 , an automated GPU performance exploration framework based on stepwise regression modeling. Stargazer sparsely and randomly samples parameter values from a full GPU design space and simulates these designs. Then, our automated stepwise algorithm uses these sampled simulations to build a performance estimator that identifies the most significant architectural parameters and their interactions. The result is an application-specific performance model which can accurately predict program runtime for any point in the design space. Because very few initial performance samples are required relative to the extremely large design space, our method can drastically reduce simulation time in GPU studies. For example, we used Stargazer to explore a design space of nearly 1 million possibilities by sampling only 300 designs. For 11 GPU applications, we were able to estimate their runtime with less than 1.1 percent average error. In addition, we demonstrate several usage scenarios of Stargazer.", notes = "not GP? breadth-first search GPGPU-Sim benchmark suite and the Rodinia benchmark suite", } @InProceedings{DBLP:conf/IEEEpact/JiaSM13, author = "Wenhao Jia and Kelly A. Shaw and Margaret Martonosi", title = "Starchart: Hardware and software optimization using recursive partitioning regression trees", booktitle = "Proceedings of the 22nd International Conference on Parallel Architectures and Compilation Techniques", year = "2013", editor = "Christian Fensch and Michael F. P. O'Boyle and Andre Seznec and Francois Bodin", pages = "257--267", address = "Edinburgh, United Kingdom", month = sep # " 7-11", publisher = "IEEE Computer Society", keywords = "genetic algorithms, genetic programming, genetic improvement, GPU, SBSE, energy optimisation, auto-tuning, design space exploration, regression tree, decision tree", isbn13 = "978-1-4799-1018-2", timestamp = "Wed, 16 Oct 2019 14:14:52 +0200", biburl = "https://dblp.org/rec/conf/IEEEpact/JiaSM13.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "http://jiawenhao.com/starchart.pdf", DOI = "doi:10.1109/PACT.2013.6618822", size = "11 pages", abstract = "Graphics processing units (GPUs) are in increasingly wide use, but significant hurdles lie in selecting the appropriate algorithms, runtime parameter settings, and hardware configurations to achieve power and performance goals with them. Exploring hardware and software choices requires time-consuming simulations or extensive real-system measurements. While some auto-tuning support has been proposed, it is often narrow in scope and heuristic in operation. This paper proposes and evaluates a statistical analysis technique, Starchart, that partitions the GPU hardware/software tuning space by automatically discerning important inflection points in design parameter values. Unlike prior methods, Starchart can identify the best parameter choices within different regions of the space. Our tool is efficient, evaluating at most 0.3 percent of the tuning space, and often much less, and is robust enough to analyze highly variable real-system measurements, not just simulation. In one case study, we use it to automatically find platform-specific parameter settings that are 6.3 fold faster (for AMD) and 1.3 fold faster (for NVIDIA) than a single general setting. We also show how power-optimized parameter settings can save 47 Watts (26 percent of total GPU power) with little performance loss. Overall, Starchart can serve as a foundation for a range of GPU compiler optimisations, auto-tuners, and programmer tools. Furthermore, because Starchart does not rely on specific GPU features, we expect it to be useful for broader CPU/GPU studies as well.", notes = "Not GP? Parameter Selection", } @Article{DBLP:journals/taco/JiaGSM15, author = "Wenhao Jia and Elba Garza and Kelly A. Shaw and Margaret Martonosi", title = "{GPU} Performance and Power Tuning Using Regression Trees", journal = "ACM Transactions on Architecture and Code Optimization", year = "2015", volume = "12", number = "2", pages = "13:1--13:26", month = jul, keywords = "genetic algorithms, genetic programming, genetic improvement, GPU, SBSE, parallel computing, Design space exploration, GPGPU, statistical modeling, decision tree, Multiple Data Stream Architectures, Multiprocessors, Design, Measurement, Performance, breadth-first search graph algorithm", ISSN = "1544-3566", timestamp = "Wed, 17 Feb 2021 22:02:39 +0100", biburl = "https://dblp.org/rec/journals/taco/JiaGSM15.bib", bibsource = "dblp computer science bibliography, https://dblp.org", DOI = "doi:10.1145/2736287", size = "26 pages", abstract = "GPU performance and power tuning is difficult, requiring extensive user expertise and time-consuming trial and error. To accelerate design tuning, statistical design space exploration methods have been proposed. This article presents Starchart, a novel design space partitioning tool that uses regression trees to approach GPU tuning problems. Improving on prior work, Starchart offers more automation in identifying key design trade-offs and models design subspaces with distinctly different behaviours. Starchart achieves good model accuracy using very few random samples: less than 0.3percent of a given design space; iterative sampling can more quickly target subspaces of interest.", notes = "Not GP? nvidia, AMD, Semiconductor Research Corporation See also {"}Analysis and Optimization Techniques for Massively Parallel Processors{"}, Wenhao Jia, Ph.D. Thesis. Princeton University", } @Article{Jia:ieeeTC, author = "Ya-Hui Jia and Yi Mei and Mengjie Zhang", journal = "IEEE Transactions on Cybernetics", title = "Learning Heuristics With Different Representations for Stochastic Routing", year = "2023", volume = "53", number = "5", pages = "3205--3219", month = may, keywords = "genetic algorithms, genetic programming, artificial neural network, ANN, evolutionary learning, hyperheuristic, stochastic routing, uncertain capacitated arc routing", ISSN = "2168-2275", URL = "https://flyki.github.io/paper/CYB-E-2021-11-3079_Manuscript.pdf", DOI = "doi:10.1109/TCYB.2022.3169210", size = "15 pages", abstract = "Uncertainty is ubiquitous in real-world routing applications. The automated design of the routing policy by hyperheuristic methods is an effective technique to handle the uncertainty and to achieve online routing for dynamic or stochastic routing problems. Currently, the tree representation routing policy evolved by genetic programming is commonly adopted because of the remarkable flexibility. However, numeric representations have never been used. Considering the practicability of the numeric representations and the capability of the numeric optimization methods, in this article, we investigate two numeric representations on a representative stochastic routing problem and uncertain capacitated arc routing problem. Specifically, a linear representation and an artificial neural-network (ANN) representation are implemented and compared with the tree representation to reveal the potential of the numeric representations and the characteristics of their optimization. Experimental results show that the tree representation is the best choice, but on a majority of the test instances, the numeric representations, especially the ANN representation, can provide competitive performance. Further analyses also show that training a good ANN representation policy requires more training data than the tree representation. Finally, a guideline of representation selection is given.", notes = "Also known as \cite{9771076}", } @InProceedings{Jia:2013:CSBSE, author = "Yue Jia and Mark Harman and Bill Langdon", title = "The {GISMOE} Architecture", booktitle = "2nd Chinese Search Based Software Engineering workshop", year = "2013", editor = "Yan Hu and Xiaochen Lai and Zhilei Ren and Jifeng Xuan", address = "Dalian, China", month = "8-9 " # jun, organisation = "Dalian University Of Technology", note = "Invited keynote", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, GISMOE", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Jia_2013_CSBSE.pdf", size = "2 pages", abstract = "The GISMOE research agenda is concerned with optimising programs for non-functional properties such as speed, size, throughput, power consumption and bandwidth can be demanding. GISMOE sets out a vision for a new kind of software development environment inspired by recent results from Search Based Software Engineering (SBSE). Details of the GISMOE research agenda are provided in the extended keynote paper for the 27th IEEE/ACM International Conference on Automated Software Engineering (ASE 2012) \cite{Harman:2012:ASE}. This talk overview is a brief introduction to the approach and a description of the talk about the GISMOE agenda at the 2nd Chinese SBSE workshop in Dalian, 8th and 9th June 2013.", notes = "CSBSE 2013, http://oscar-lab.org/csbse13/keynote.html", } @InProceedings{Jia:2015:gi, author = "Yue Jia and Fan Wu and Mark Harman and Jens Krinke", title = "Genetic Improvement using Higher Order Mutation", booktitle = "Genetic Improvement 2015 Workshop", year = "2015", editor = "William B. Langdon and Justyna Petke and David R. White", pages = "803--804", address = "Madrid", publisher_address = "New York, NY, USA", month = "11-15 " # jul, organisation = "SIGEvo", publisher = "ACM", keywords = "genetic algorithms, genetic programming, Genetic Improvement", isbn13 = "978-1-4503-3488-4", URL = "http://gpbib.cs.ucl.ac.uk/gi2015/genetic_improvement_using_higher_order_mutation.pdf", URL = "http://doi.acm.org/10.1145/2739482.2768417", DOI = "doi:10.1145/2739482.2768417", size = "2 pages", abstract = "This paper presents a brief outline of a higher-order mutation based framework for Genetic Improvement (GI). We argue that search-based higher-order mutation testing can be used to implement a form of genetic programming (GP) to increase the search granularity and testability of GI.", notes = "position paper Slides: http://gpbib.cs.ucl.ac.uk/gi2015/gi_2015_slides/wu/GI_Fan.pdf Also known as \cite{2768417} Distributed at GECCO-2015.", } @InProceedings{jia:2015:gsgp, author = "Yue Jia and Mark Harman and William B. Langdon and Alexandru Marginean", title = "Grow and Serve: Growing {Django} Citation Services Using {SBSE}", booktitle = "SSBSE 2015 Challenge Track", year = "2015", editor = "Shin Yoo and Leandro Minku", volume = "9275", series = "LNCS", pages = "269--275", address = "Bergamo, Italy", month = "5-7 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, GGGP, Phyton", isbn13 = "978-3-319-22182-3", URL = "http://alexandrumarginean.com/grow_and_serve.pdf", DOI = "doi:10.1007/978-3-319-22183-0_22", size = "6 pages", abstract = "We introduce a grow and serve approach to Genetic Improvement (GI) that grows new functionality as a web service running on the Django platform. Using our approach, we successfully grew and released a citation web service. This web service can be invoked by existing applications to introduce a new citation counting feature. We demonstrate that GI can grow genuinely useful code in this way, so we deployed the SBSE-grown web service into widely-used publications repositories, such as the GP bibliography. In the first 24 hours of deployment alone, the service was used to provide GP bibliography citation data 369 times from 29 countries.", notes = "Used with GP bibliography May 5 15:46 to May 11 13:25 http://ssbse.info/2015/?page_id=29", } @Misc{Jia:Picassevo, author = "Yue Jia", title = "Picassevo", howpublished = "Android App", year = "2016", keywords = "genetic algorithms, genetic programming, iPhone, art, Pareto Front", broken = "http://www.cs.ucl.ac.uk/staff/Y.Jia/projects/picassevo/", URL = "https://mobile.twitter.com/picassevo", size = "2 pages", abstract = "Picassevo‏ @picassevo 19 Feb 2016 The #picassevo app (iOS version) is now available in the app store. broken Mar 2022 https://appsto.re/gb/en1Nab.i Yue Jia @YueJ 19 Feb 2016 An image of Bill Langdon generated by the @picassevo app in quick doodle mode! Yue Jia @YueJ 31 May 2015 A picture of YY generated by the new @picassevo using 500 polygons #picassevo", notes = "The app is based on Evo-Bentham. ", } @Misc{Jia:2018:SapFix, author = "Yue Jia and Ke Mao and Mark Harman", title = "Finding and fixing software bugs automatically with {SapFix} and {Sapienz}", howpublished = "Posted on Sep 13, 2018 to AI Research, Developer Tools, Open Source, Production Engineering", year = "2018", month = "13 " # sep, keywords = "genetic algorithms, genetic programming, genetic improvement", URL = "https://bit.ly/3hR2gpy", URL = "https://code.fb.com/developer-tools/finding-and-fixing-software-bugs-automatically-with-sapfix-and-sapienz/", notes = "'Since SapFix is still in development, it isn't being used at the same scale as Sapienz' 'Approximately three quarters of Sapienz reports resulted in fixes by developers.' SapFix 'has successfully generated patches that have been accepted by human reviewers and pushed to production' within Facebook.", } @Article{Jiang:2016:CILS, author = "Dazhi Jiang and Wan-Huan Zhou and Ankit Garg and Akhil Garg", title = "Model development and surface analysis of a bio-chemical process", journal = "Chemometrics and Intelligent Laboratory Systems", volume = "157", pages = "133--139", year = "2016", ISSN = "0169-7439", DOI = "doi:10.1016/j.chemolab.2016.07.010", URL = "http://www.sciencedirect.com/science/article/pii/S0169743916301721", abstract = "Phytoremediation, is a promising biochemical process which has gained wide acceptance in remediating the contaminants from the soil. Phytoremediation process comprises of biochemical mechanisms such as adsorption, transport, accumulation and translocation. State-of-the-art modelling methods used for studying this process in soil are limited to the traditional ones. These methods rely on the assumptions of the model structure and induce ambiguity in its predictive ability. In this context, the Artificial Intelligence approach of Genetic programming (GP) can be applied. However, its performance depends heavily on the architect (objective functions, parameter settings and complexity measures) chosen. Therefore, this present work proposes a comprehensive study comprising of the experimental and numerical one. Firstly, the lead removal efficiency (percent) from the phytoremediation process based on the number of planted spinach, sampling time, root and shoot accumulation of the soil is measured. The numerical modelling procedure comprising of the two architects of GP investigates the role of the two objective functions (SRM and AIC) having two complexity measures: number of nodes and order of polynomial in modelling this process. The performance comparison analysis of the proposed models is conducted based on the three error metrics (RMSE, MAPE and R) and cross-validation. The findings reported that the models formed from GP architect using SRM objective function and order of polynomial as complexity measure performs better with lower size and higher generalization ability than those of AIC based GP models. 2-D and 3-D surface analysis on the selected GP architect suggests that the shoot accumulation influences (non-linearly) the lead removal efficiency the most followed by the number of planted spinach, the root accumulation and the sampling time. The present work will be useful for the experts to accurately determine lead removal efficiency based on the explicit GP model, thus saving the waste of input resources.", keywords = "genetic algorithms, genetic programming, Phytoremediation, Lead removal, Statistical modelling, Biochemical, Cross-validation", } @Article{Jiang:2021:KJCE, author = "Fengyuan Jiang and Sheng Dong", title = "An Integrated Reliability Analysis Model of Sheet Pile Wharfs Based on Virtual Support Beam Model and Artificial Intelligence Algorithm", journal = "KSCE Journal of Civil Engineering", year = "2021", volume = "25", number = "7", pages = "2613--2630", keywords = "genetic algorithms, genetic programming, Sheet pile structures, Reliability analysis, Artificial intelligence models, Engineering mechanics, Coastal engineering", ISSN = "1226-7988", URL = "https://www.sciencedirect.com/science/article/pii/S1226798824016556", DOI = "doi:10.1007/s12205-021-1444-4", abstract = "Sheet pile wharfs are widely used coastal structures with good adaptability for geological conditions. A reliability evaluation is required for safety. Challenges are raised from the complicated solution in both the structural response and reliability. To provide a flexible and efficient approach, an integrated reliability analysis model was proposed: The virtual simply support beam (VSSB) model was developed to estimate the structural response; Based on probabilistic methodologies and artificial intelligence (AI) models, the limit state surface was fitted to search reliability solutions. A case study was used to illustrate the applicability of the proposed model and compare the prediction performance of AI models. An extensive parametric study was performed to investigate the influential factors on structural reliability. Compared with finite element analysis, the VSSB model presented reasonable estimations on structural response. The genetic programming performed best in predicting structural response with an average relative absolute error (RAE) of 0.018. The corresponding integrated models gave the reliability solutions with an average RAE of less than 0.014. Soil parameters in different positions significantly affected the structural stability, that the material qualities should be addressed. This study provides references in engineering safety design and guidelines the reliability analysis framework for complicated structures", } @Article{Jiang:SBSE:intro, author = "He Jiang and Ke Tang and Justyna Petke and Mark Harman", title = "Search Based Software Engineering", journal = "IEEE Computational Intelligence Magazine", year = "2017", volume = "12", number = "2", pages = "23 and 71", month = may, note = "Guest Editorial", keywords = "genetic algorithms, genetic programming, SBSE", ISSN = "1556-603X", URL = "https://discovery.ucl.ac.uk/id/eprint/1555870/1/Harman_Editorial-cim-2017-Feb.12.pdf", DOI = "doi:10.1109/MCI.2017.2670459", size = "2 pages", notes = "Also known as \cite{7895281} Dalian University of Technology, China", } @Article{Jiang1131, author = "Jiuxing Jiang and Jose L. Jorda and Jihong Yu and Laurent A. Baumes and Enrico Mugnaioli and Maria J. Diaz-Cabanas and Ute Kolb and Avelino Corma", title = "Synthesis and Structure Determination of the Hierarchical Meso-Microporous {Zeolite ITQ-43}", journal = "Science", year = "2011", volume = "333", number = "6046", pages = "1131--1134", month = "26 " # aug, keywords = "GPU", publisher = "American Association for the Advancement of Science", ISSN = "0036-8075", URL = "https://science.sciencemag.org/content/333/6046/1131", URL = "https://science.sciencemag.org/content/333/6046/1131.full.pdf", DOI = "doi:10.1126/science.1208652", abstract = "The formation of mesopores in microporous zeolites is generally performed by postsynthesis acid, basic, and steam treatments. The hierarchical pore systems thus formed allow better adsorption, diffusion, and reactivity of these materials. By combining organic and inorganic structure-directing agents and high-throughput methodologies, we were able to synthesise a zeolite with a hierarchical system of micropores and mesopores, with channel openings delimited by 28 tetrahedral atoms. Its complex crystalline structure was solved with the use of automated diffraction tomography.", notes = "is this GP? Supplement https://science.sciencemag.org/content/sci/suppl/2011/08/25/333.6046.1131.DC1/Jiang.SOM.pdf Instituto de Tecnologia Quimica, Universidad Politecnica de Valencia-Consejo Superior de Investigaciones Cientificas, Avenida de los Naranjos s/n, 46022 Valencia, Spain PubMed: 21868673", } @MastersThesis{Jiang:1992:thesis, author = "Mingda Jiang", title = "A hierarchical genetic system for symbolic function identification", school = "University of Montana", year = "1992", keywords = "genetic algorithms, genetic programming", } @InProceedings{Jiang:1993:afis, author = "Mingda Jiang and Alden H. Wright", title = "An adaptive function identification system", booktitle = "Proceedings of the IEEE/ACM Conference on Developing and Managing Intelligent System Projects, Vienna, Virginia, USA", year = "1993", pages = "47--53", month = mar, keywords = "genetic algorithms, genetic programming, Levenberg-Marquardt nonlinear regression algorithm, adaptive function identification system, adaptive system, expression-tree representation, symbolic function identification problem, adaptive systems, learning (artificial intelligence)", DOI = "doi:10.1109/DMISP.1993.248637", size = "7 pages", abstract = "Given data in the form of a collection of (x,y) pairs of real numbers, the symbolic function identification problem is to find a functional model of the form y=f(x) that fits the data. This paper describes an adaptive system for solution of symbolic function identification problems that combines a genetic algorithm and the Levenberg-Marquardt nonlinear regression algorithm. The genetic algorithm uses an expression-tree representation rather than the more usual binary-string representation. Experiments were run with data generated using a wide variety of function models. The system was able to find a function model that closely approximated the data with a very high success rate", notes = "HGSFI, Ultrix, Unidata Inc. Also known as \cite{248637}", } @InProceedings{Jiang:1992:hGPsfi, author = "Mingda Jiang and Alden H. Wright", title = "A Hierarchical Genetic System for Symbolic Function Identification", institution = "University of Montana, Missoula, MT 59812", booktitle = "Proceedings of the 24th Symposium on the Interface: Computing Science and Statistics, College Station, Texas", year = "1992", month = mar, keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.umt.edu/u/wright/papers/hgsfi.ps.gz", URL = "http://citeseer.ist.psu.edu/202012.html", size = "27 pages", abstract = "Given data in the form of a collection of (x,y) pairs of real numbers, the symbolic function identification problem is to find a functional model of the form y = f(x) that fits the data. This paper describes a system for solution of symbolic function identification problems that combines a genetic algorithm and the Levenberg-Marquardt nonlinear regression algorithm. The genetic algorithm uses an expression-tree representation rather than the more usual binary-string representation. Experiments were run with data generated using a wide variety of function models. The system was able to find a function model that closely approximated the data with a very high success rate.", notes = "Also available as technical report, 26 pages. Does Symbolic regression but uses Levenberg-Marquadt statistical technique to adjust parameters to get closer (equivalent of local hill climbing?) Some case GP don't work on. Mentions Permutation but don't say how usefully it is ", } @InProceedings{jiang:1996:aGAidc, author = "J. Jiang and D. Butler", title = "An Adaptive Genetic Algorithm for Image Data Compression", booktitle = "Late Breaking Papers at the Genetic Programming 1996 Conference Stanford University July 28-31, 1996", year = "1996", editor = "John R. Koza", pages = "83--87", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California 94305-3079, USA", month = "28--31 " # jul, publisher = "Stanford Bookstore", ISBN = "0-18-201031-7", keywords = "genetic algorithms, genetic programming", notes = "GP-96LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670", } @InProceedings{Jiang_Xue_2024, author = "Nan Jiang and Yexiang Xue", title = "Racing Control Variable Genetic Programming for Symbolic Regression", booktitle = "Proceedings of the 38th AAAI Conference on Artificial Intelligence", year = "2024", pages = "12901--12909", keywords = "genetic algorithms, genetic programming", URL = "https://ojs.aaai.org/index.php/AAAI/article/view/29187", DOI = "doi:10.1609/aaai.v38i11.29187", size = "9 pages", abstract = "Symbolic regression, ... Control Variable Genetic Programming (CVGP) has been introduced which accelerates the regression process by discovering equations from designed control variable experiments. However, the set of experiments is fixed a-priori in CVGP and we observe that sub-optimal selection of experiment schedules delay the discovery process significantly. To overcome this limitation, we propose Racing Control Variable Genetic Programming (Racing-CVGP), which carries out multiple experiment schedules simultaneously. A selection scheme similar to that used in selecting good symbolic equations in genetic programming is implemented to ensure that promising experiment schedules eventually win over the average ones. The unfavorable schedules are terminated early to save time for the promising ones. We evaluate Racing-CVGP on several synthetic and real-world datasets corresponding to true physics laws. We demonstrate that Racing CVGP outperforms CVGP and a series of symbolic regressors which discover equations from fixed dataset", } @InProceedings{jiang:1998:eaosesMSTsd, author = "Tianzi Jiang", title = "An Evolutionary Approach to Optimal Structuring Element Extraction for MST-Based Shapes Description", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "85--91", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "genetic algorithms, genetic programming", size = "7pages", abstract = "evolutionary tabu search", notes = "GP-98LB", } @InProceedings{Jiang:2014:IIKI, author = "Dazhi Jiang and Liyu Li and Zhun Fan and Jin Liu", booktitle = "2014 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)", title = "Detection of Acute Hypotensive Episodes via Empirical Mode Decomposition and Genetic Programming", year = "2014", pages = "225--228", abstract = "Big data time series in the Intensive Care Unit (ICU) is now touted as a solution to help clinicians to diagnose the case of the physiological disorder and select proper treatment based on this diagnosis. Acute Hypotensive Episodes (AHE) is one of the hemodynamic instabilities with high mortality rate that is frequent among many groups of patients. This study presented a methodology to predict AHE for ICU patients based on big data time series. Empirical Mode Decomposition (EMD) was used to calculate patient's Mean Arterial Pressure (MAP) time series and some features, which are bandwidth of the amplitude modulation, frequency modulation and power of Intrinsic Mode Function (IMF) were extracted. Then, the Genetic Programming (GP) is used to build the classification model for detection of AHE. The methodology was applied in the datasets of the 10th Physio Net and Computers Cardiology Challenge in 2009 and Multi-parameter Intelligent Monitoring for Intensive Care (MIMIC-II). We achieve the accuracy of 83.33percent in the training set and 91.89percent in the testing set of the 2009 challenge's dataset, and the 83.37percent in the training set and 80.64percent in the testing set of the MIMIC-II dataset.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IIKI.2014.53", month = oct, notes = "Also known as \cite{7064034}", } @Article{journals/ijdsn/JiangLHF15, author = "Dazhi Jiang and Liyu Li and Bo Hu and Zhun Fan", title = "An Approach for Prediction of Acute Hypotensive Episodes via the Hilbert-Huang Transform and Multiple Genetic Programming Classifier", journal = "International Journal of Distributed Sensor Networks", year = "2015", volume = "11", number = "8", pages = "354807:1--354807:11", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1155/2015/354807", abstract = "Acute hypotensive episodes (AHEs) are one of the hemodynamic instabilities with high mortality rate that is frequent among many groups of patients. This study presents a methodology to predict AHE for ICU patients based on big data time series. The experimental data we used is mean arterial pressure (MAP), which is transformed from arterial blood pressure (ABP) data. Then, the Hilbert-Huang transform method was used to calculate patient's MAP time series and some features, which are the bandwidth of the amplitude modulation, the frequency modulation, and the power of intrinsic mode function (IMF), were extracted. Finally, the multiple genetic programming (Multi-GP) is used to build the classification models for detection of AHE. The methodology is applied in the datasets of the 10th PhysioNet and Computers Cardiology Challenge in 2009 and Multiparameter Intelligent Monitoring for Intensive Care (MIMIC-II). We achieve the accuracy of 83.33percent in the training set and 91.89percent in the testing set of the 2009 challenge's dataset and the 84.13percent in the training set and 82.41percent in the testing set of the MIMIC-II dataset.", } @Article{DBLP:journals/nc/JiangTHTH21, author = "Dazhi Jiang and Zhihang Tian and Zhihui He and Geng Tu and Ruixiang Huang", title = "A framework for designing of genetic operators automatically based on gene expression programming and differential evolution", journal = "Nat. Comput.", volume = "20", number = "3", pages = "395--411", year = "2021", keywords = "genetic algorithms, genetic programming, gene expression programming", URL = "https://doi.org/10.1007/s11047-020-09830-2", DOI = "doi:10.1007/s11047-020-09830-2", timestamp = "Sat, 11 Sep 2021 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/nc/JiangTHTH21.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{JIANG:2020:EFA, author = "Fengyuan Jiang and Sheng Dong", title = "Collision failure risk analysis of falling object on subsea pipelines based on machine learning scheme", journal = "Engineering Failure Analysis", volume = "114", pages = "104601", year = "2020", ISSN = "1350-6307", DOI = "doi:10.1016/j.engfailanal.2020.104601", URL = "http://www.sciencedirect.com/science/article/pii/S1350630720302855", keywords = "genetic algorithms, genetic programming, Offshore pipelines, Quantitative risk analysis, Machine learning algorithm, Impact loading, Pipe-soil interaction", abstract = "Platform falling object collision on offshore pipelines are catastrophic to the environment and economy. Based on finite element analysis and machine learning algorithms, a quantitative analysis model is proposed to quantify failure risk. To consider the uncertainties and nonlinear effects in the collision events, the Latin Hypercube Sampling technique and the finite element simulation is coupled to draw the sample space. Then four machine learning models are developed and the prediction abilities in the pipeline response are compared. The genetic programming shows the best performance with the relative absolute error of 0.04-0.05, which is integrated into Monte Carlo Simulation to complete the risk analysis. This quantitative analysis model is verified with a method and indicates good consistency and potential in considering nonlinear effects and pipe-soil interactions. Effects of related factors on failure risk are examined, including seabed flexibility, burial depth, acceptable criterion, and sensibility of basic variables. Compared with the method recommended by the Det Norkske Veritas, the proposed model can account for the seabed flexibility effect, and the failure risk declined by 23.6percent. The increase in burial depth affects risk reduction significantly but is limited under a strict criterion. The fitting equations of burial depth and failure probabilities as well as different acceptable criteria are proposed for safety design. Sensibility analysis of the basic variables reveals that the quality of wall thickness and pipeline diameter are important to failure risk", } @Article{jiang:2022:Atmosphere, author = "Hongxun Jiang and Xiaotong Wang and Caihong Sun", title = "Predicting {PM2.5} in the Northeast China Heavy Industrial Zone: A Semi-Supervised Learning with Spatiotemporal Features", journal = "Atmosphere", year = "2022", volume = "13", number = "11", pages = "Article No. 1744", keywords = "genetic algorithms, genetic programming", ISSN = "2073-4433", URL = "https://www.mdpi.com/2073-4433/13/11/1744", DOI = "doi:10.3390/atmos13111744", abstract = "Particulate matter PM2.5 pollution affects the Chinese population, particularly in cities such as Shenyang in northeastern China, which occupies a number of traditional heavy industries. This paper proposes a semi-supervised learning model used for predicting PM2.5 concentrations. The model incorporates rich data from the real world, including 11 air quality monitoring stations in Shenyang and nearby cities. There are three types of data: air monitoring, meteorological data, and spatiotemporal information (such as the spatiotemporal effects of PM2.5 emissions and diffusion across different geographical regions). The model consists of two classifiers: genetic programming (GP) to forecast PM2.5 concentrations and support vector classification (SVC) to predict trends. The experimental results show that the proposed model performs better than baseline models in accuracy, including 3percent to 18percent over a classic multivariate linear regression (MLR), 1percent to 11percent over a multi-layer perceptron neural network (MLP-ANN), and 21percent to 68percent over a support vector regression (SVR). Furthermore, the proposed GP approach provides an intuitive contribution analysis of factors for PM2.5 concentrations. The data of backtracking points adjacent to other monitoring stations are critical in forecasting shorter time intervals (1 h). Wind speeds are more important in longer intervals (6 and 24 h).", notes = "also known as \cite{atmos13111744}", } @Article{Jiang:2015:AEI, author = "Huimin Jiang and C. K. Kwong and K. W. M. Siu and Y. Liu", title = "Rough set and PSO-based {ANFIS} approaches to modeling customer satisfaction for affective product design", journal = "Advanced Engineering Informatics", volume = "29", number = "3", pages = "727--738", year = "2015", ISSN = "1474-0346", DOI = "doi:10.1016/j.aei.2015.07.005", URL = "http://www.sciencedirect.com/science/article/pii/S1474034615000713", abstract = "Facing fierce competition in marketplaces, companies try to determine the optimal settings of design attribute of new products from which the best customer satisfaction can be obtained. To determine the settings, customer satisfaction models relating affective responses of customers to design attributes have to be first developed. Adaptive neuro-fuzzy inference systems (ANFIS) was attempted in previous research and shown to be an effective approach to address the fuzziness of survey data and nonlinearity in modelling customer satisfaction for affective design. However, ANFIS is incapable of modelling the relationships that involve a number of inputs which may cause the failure of the training process of ANFIS and lead to the `out of memory' error. To overcome the limitation, in this paper, rough set (RS) and particle swarm optimization (PSO) based-ANFIS approaches are proposed to model customer satisfaction for affective design and further improve the modeling accuracy. In the approaches, the RS theory is adopted to extract significant design attributes as the inputs of ANFIS and PSO is employed to determine the parameter settings of an ANFIS from which explicit customer satisfaction models with better modeling accuracy can be generated. A case study of affective design of mobile phones is used to illustrate the proposed approaches. The modeling results based on the proposed approaches are compared with those based on ANFIS, fuzzy least-squares regression (FLSR), fuzzy regression (FR), and genetic programming-based fuzzy regression (GP-FR). Results of the training and validation tests show that the proposed approaches perform better than the others in terms of training and validation errors.", keywords = "genetic algorithms, genetic programming, Affective product design, Customer satisfaction, Rough set theory, Particle swarm optimization, ANFIS", } @InProceedings{jiang:2023:ECML-PKDD, author = "Nan Jiang and Yexiang Xue", title = "Symbolic Regression via Control Variable Genetic Programming", booktitle = "Joint European Conference on Machine Learning and Knowledge Discovery in Databases: Research Track", year = "2023", pages = "178--195", address = "Turin, Italy", month = "18-22 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-031-43421-1_11", DOI = "doi:10.1007/978-3-031-43421-1_11", } @InProceedings{jiang:2024:CENet, author = "Qinhui Jiang and Zhili Wang and Xingyu Chen", title = "A Bi-level Particle Swarm Genetic Programming Algorithm for Dependent Task Scheduling in Computing Power Networks", booktitle = "Proceedings of the 14th International Conference on Computer Engineering and Networks", year = "2024", address = "Kashi, China", month = oct # " 18-21", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "https://link.springer.com/chapter/10.1007/978-981-96-4963-1_11", DOI = "doi:10.1007/978-981-96-4963-1_11", } @Article{JIANG:2020:ECM, author = "Yan Jiang and Shuoyu Liu and Ning Zhao and Jingzhou Xin and Bo Wu", title = "Short-term wind speed prediction using time varying filter-based empirical mode decomposition and group method of data handling-based hybrid model", journal = "Energy Conversion and Management", volume = "220", pages = "113076", year = "2020", ISSN = "0196-8904", DOI = "doi:10.1016/j.enconman.2020.113076", URL = "http://www.sciencedirect.com/science/article/pii/S0196890420306208", keywords = "genetic algorithms, genetic programming, Short-term wind speed prediction, Time varying filter-based empirical mode decomposition, Group method of data handling neural network, Nonlinear residuals, Selective prediction", abstract = "The realization of precise and reliable short-term wind speed prediction is extremely essential to wind power development, especially for its integration into traditional grid system. For this purpose, this study develops a novel forecasting method based on time varying filter-based empirical mode decomposition, auto-regressive integrated moving average model and group method of data handling-based hybrid model. This method mainly contains four individual steps for grasping the major behavioral characteristics of wind speed data. The first step adopts time varying filter-based empirical mode decomposition to handle the nonlinearity and nonstationarity of the raw wind speed data by decomposing them into a number of subseries with more stability and regularity. Then, auto-regressive integrated moving average model is applied to depict the linear characteristic hidden in the data. For the above modeling errors (i.e., the nonlinear residuals), the third step employs three nonlinear models with different action mechanisms (i.e., least square support vector machine, genetic programming algorithm and spatio-temporal radial basis function neural network) to systematically capture their complex nonlinear features. Finally, group method of data handling neural network is used to combine these nonlinear models and perform the selective prediction, where the involved models and their weights could be determined automatically. Four groups of the measured wind speed datasets with two different time intervals are used to assess the performance of the proposed method. The experimental results indicate it outperforms the other compared models and may have great potential for the practical application in power system", } @Article{journals/prl/JiangLMQR17, author = "Zhao-Hui Jiang and Tingting Li and Wenfang Min and Zhao Qi and Yuan Rao", title = "Fuzzy c-means clustering based on weights and gene expression programming", journal = "Pattern Recognition Letters", year = "2017", volume = "90", pages = "1--7", keywords = "genetic algorithms, genetic programming, gene expression programming", bibdate = "2017-05-28", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/prl/prl90.html#JiangLMQR17", DOI = "doi:10.1016/j.patrec.2017.02.015", } @InProceedings{Jiang:2016:WPMC, author = "Yizhou Jiang and Sai Huang and Yifan Zhang and Zhiyong Feng", booktitle = "2016 19th International Symposium on Wireless Personal Multimedia Communications (WPMC)", title = "Multi-gene genetic programming based modulation classification using multinomial logistic regression", year = "2016", pages = "352--357", month = "14-16 " # nov, address = "Shenzhen, China", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-5090-5377-3", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=7954505", size = "6 pages", abstract = "Automatic modulation classification (AMC) acts as a critical role in cognitive radio network, which has many civilian and military applications including signal demodulation and interference identification. In this paper, we explore a novel feature based (FB) AMC method using multi-gene genetic programming (MGGP) and multinomial logistic regression (MLR) jointly with spectral correlation features (SCFs). The proposed scheme includes two phases. In the training phase, MGGP generates various mappings to transform SCFs into new features and MLR selects some highly distinctive new features as MGGP-features and the mappings as feature optimisation functions (FOFs). Meanwhile the corresponding MLR based classifier is output. In the classification phase, SCFs are transformed by the FOFs and the trained classifier identifies signal formats with MGGP-features. Compared to traditional FB methods, simulation results demonstrate that our proposed method yields satisfactory performance improvement and achieves robust classification, especially at lower SNR and fewer number of samples.", notes = "Also known as \cite{7954505}", } @InProceedings{jin:2024:CEC, author = "Chenwei Jin and Ruibin Bai and Huayan Zhang", title = "Evolving Priority Rules for Online Yard Crane Scheduling with Incomplete Tasks Data", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Cranes, Uncertainty, Processor scheduling, Loading, Evolutionary computation, Throughput, yard crane, online scheduling, genetic program-ming, priority rules", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10611875", abstract = "In the last decade, the surge in global container port throughput has heightened the need for terminal efficiency. The loading process plays a crucial role in overall port performance. However, the unpredictable arrival of external trucks poses challenges for yard cranes in scheduling both internal loading tasks and external truck tasks simultaneously. Existing approaches on yard crane scheduling, considering uncertain arrivals, typically rely on prior knowledge, which often fails to fully capture the nature of the real-life uncertainties. In response, we propose an online scheduling approach guided by a two-stage decision model, eliminating the need for prior knowledge of uncertain arrival and has the ability to dynamically adapt to different scenarios. In the look-ahead stage, future tasks are filtered dynamically to eliminate undesired tasks, followed by a priority rule guided selection stage, where the task with the highest priority is selected. Genetic Programming (GP) is employed for automated evolution of priority rules without human intervention. Realistic experiments showcase the effectiveness of the proposed dynamic look-ahead method compared to static minimum and maximum look-ahead, as well as the superiority of GP-evolved priority rules compared to manually crafted priority rules in terms of both performance and simplicity. A comprehensive analysis of GP-evolved rules highlights GP's proficiency in problem understanding and rule extraction, comparable to human experts.", notes = "also known as \cite{10611875} WCCI 2024", } @Article{jin:2024:MC, author = "Chenwei Jin and Ruibin Bai and Yuyang Zhou and Xinan Chen and Leshan Tan", title = "Enhancing online yard crane scheduling through a two-stage rollout memetic genetic programming", journal = "Memetic Computing", year = "2024", keywords = "genetic algorithms, genetic programming", URL = "https://link.springer.com/article/10.1007/s12293-024-00424-4", DOI = "doi:10.1007/s12293-024-00424-4", } @InProceedings{eurogp:JinT05, author = "Nanlin Jin and Edward P. K. Tsang", editor = "Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. {van Hemert} and Marco Tomassini", title = "Relative Fitness and Absolute Fitness for Co-evolutionary Systems", booktitle = "Proceedings of the 8th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3447", year = "2005", address = "Lausanne, Switzerland", month = "30 " # mar # " - 1 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-25436-6", pages = "331--340", DOI = "doi:10.1007/978-3-540-31989-4_30", DOI = "doi:10.1007/b107383", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "The commonly adopted fitness which evaluates the performance of individuals in co-evolutionary systems is relative fitness. Relative fitness is a dynamic assessment subject to the other co-evolving population(s). Researchers apparently pay less attention to the use of absolute fitness functions in studying co-evolutionary algorithms than the use of relative fitness functions. One of our aims in this work is to formalise both relative fitness and absolute fitness for co-evolving systems. Another aim is to demonstrate the usage of absolute and relative fitness through a case study. We develop a co-evolutionary system by means of Genetic Programming to discover co-adapted strategies for a Basic Alternating-Offers Bargaining Problem. In this case, the relative fitness essentially drives co-evolution to converge to game-theoretic equilibrium. Whereas the relative fitness alone can not discover the whole view of co-evolutionary progress. The absolute fitness, on the other hand helps us to monitor the development of co-adaptive learning. Having analysed the micro-behaviour of the players' strategies, based on their absolute fitness, we can explain how the co-evolving populations converge to the perfect equilibria.", notes = "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005", } @InProceedings{Jin:2005:CIG, author = "Nanlin Jin and Edward Tsang", title = "Co-evolutionary Strategies for an Alternating-Offer Bargaining Problem", booktitle = "IEEE 2005 Symposium on Computational Intelligence and Games CIG'05", year = "2005", editor = "Graham Kendall and Simon Lucas", pages = "211--217", email = "njin@essex.ac.uk, edward@essex.ac.uk", address = "Essex, UK", month = "4-6 " # apr, organisation = "Computational Intelligence Society", publisher = "IEEE Press", URL = "http://cswww.essex.ac.uk/Research/CSP/finance/papers/JinTsa-Bargaining-Cig2005.pdf", size = "7 pages", keywords = "genetic algorithms, genetic programming, Co-evolution, GP, Bargaining Theory", abstract = "We apply an Evolutionary Algorithm (EA) to solve the Rubinstein's Basic Alternating-Offer Bargaining Problem, and compare our experimental results with its analytic game-theoretic solution. The application of EA employs an alternative set of assumptions on the players' behaviours. Experimental outcomes suggest that the applied co-evolutionary algorithm, one of Evolutionary Algorithms, is able to generate convincing approximations of the theoretic solutions. The major advantages of EA over the game-theoretic analysis are its flexibility and ease of application to variants of Rubinstein Bargaining Problems and complicated bargaining situations for which theoretic solutions are unavailable.", } @InProceedings{NanlinJin:2005:CEC, author = "Nanlin Jin", title = "Equilibrium Selection by Co-evolution for Bargaining Problems under Incomplete Information about Time Preferences", booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary Computation", year = "2005", editor = "David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L. Gwenn Volkert and Dan Ashlock and Marc Schoenauer", volume = "3", pages = "2661--2668", address = "Edinburgh, UK", month = "2-5 " # sep, organization = "IEEE", publisher = "IEEE Press", email = "njin@essex.ac.uk", keywords = "genetic algorithms, genetic programming, co-evolution, game theory", ISBN = "0-7803-9363-5", URL = "http://cswww.essex.ac.uk/Research/CSP/finance/papers/Jin-IncompleteInfo-Cec2005.pdf", DOI = "doi:10.1109/CEC.2005.1555028", size = "8 pages", abstract = "The main purpose of this work is to measure the impact of players' information completeness on the outcomes in dynamic strategic games. We apply Co-evolutionary Algorithms to solve four incomplete information bargaining problems and investigate the experimental outcomes on players' shares from agreements, the efficiency of agreements and the evolutionary time for convergence. Empirical analyses indicate that in the absence of complete information on the counterpart(s)' preferences, co-evolving populations are still able to select equilibriums which are Pareto-efficient and stationary. This property of the co-evolutionary algorithm supports its future applications on complex dynamic games.", notes = "CEC2005 - A joint meeting of the IEEE, the EPS, and the IEE.", } @InProceedings{1144067, author = "Nanlin Jin", title = "Indirect co-evolution for understanding belief in an incomplete information dynamic game", booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation", year = "2006", editor = "Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos {Coello Coello} and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens", volume = "1", ISBN = "1-59593-186-4", pages = "383--384", address = "Seattle, Washington, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p383.pdf", DOI = "doi:10.1145/1143997.1144067", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "8-12 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Coevolution: Poster, belief, concept learning, game theory, heuristic methods, incomplete information, knowledge acquisition", size = "2 pages", abstract = "This study aims to design a new co-evolution algorithm, Mixture Co-evolution which enables modelling of integration and composition of direct co-evolution and it indirect coevolution. This algorithm is applied to investigate properties of players' belief and of information incompleteness in a dynamic game.", notes = "GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM Order Number 910060", } @InProceedings{JinTsang_2006_CEC, author = "Nanlin Jin and Edward Tsang", title = "Co-adaptive Strategies for Sequential Bargaining Problems with Discount Factors and Outside Options", booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary Computation", year = "2006", pages = "7913--7920", address = "Vancouver", month = "6-21 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9487-9", DOI = "doi:10.1109/CEC.2006.1688572", size = "8 pages", abstract = "Bargaining is fundamental in social activities. Game-theoretic methodology has provided theoretic solutions for certain abstract models. Even for a simple model, this method demands substantial human intelligent effort in order to solve game-theoretic equilibriums. The analytic complexity increases rapidly when more elements are included in the models. In our previous work, we have demonstrated how coevolutionary algorithms can be used to find approximations to game-theoretic equilibriums of bargaining models that consider bargaining costs only. In this paper, we study more complicated bargaining models, in which outside option is taken into account besides bargaining cost. Empirical studies demonstrate that evolutionary algorithms are efficient in finding near-perfect solutions. Experimental results reflect the compound effects of discount factors and outside options upon bargaining outcomes. We argue that evolutionary algorithm is a practical tool for generating reasonably good strategies for complicated bargaining models beyond the capability of game theory.", notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. best presentation in session", } @PhdThesis{NanlinJin:Thesis, author = "Nanlin Jin", title = "Constraint-based co-evolutionary genetic programming for bargaining problems", school = "Department of Computer Science, University of Essex", year = "2007", address = "UK", keywords = "genetic algorithms, genetic programming", URL = "http://www.bracil.net/finance/papers/Jin-Bargaining-PhD2007.pdf", size = "236 pages", notes = "Feb 2015 uk.bl.ethos.438140 This thesis is not available from the EThOS service. Please contact the current institution's library directly if you wish to view the thesis.", } @Article{Jin2009924, author = "Nanlin Jin and Edward Tsang and Jin Li", title = "A constraint-guided method with evolutionary algorithms for economic problems", journal = "Applied Soft Computing", volume = "9", number = "3", pages = "924--935", year = "2009", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2008.11.006", URL = "http://www.sciencedirect.com/science/article/B6W86-4V0TCY0-6/2/6b82133b94fa2c3580d4e43064120400", keywords = "genetic algorithms, genetic programming, Constraint satisfaction, Economic problems", abstract = "This paper presents an evolutionary algorithms based constrain-guided method (CGM) that is capable of handling both hard and soft constraints in optimization problems. While searching for constraint-satisfied solutions, the method differentiates candidate solutions by assigning them with different fitness values, enabling favorite solutions to be distinguished more likely and more effectively from unfavoured ones. We illustrate the use of CGM in solving two economic problems with optimization involved: (1) searching equilibriums for bargaining problems; (2) reducing the rate of failure in financial prediction problems. The efficacy of the proposed CGM is analysed and compared with some other computational techniques, including a repair method and a penalty method for the problem (1), a linear classifier and three neural networks for the problem (2), respectively. Our studies here suggest that the evolutionary algorithms based CGM compares favorably against those computational approaches.", } @Article{Jin1996883, author = "Jian-Ping Jin", title = "Alternative RNA Splicing-Generated Cardiac Troponin T Isoform Switching: A Non-Heart-Restricted Genetic Programming Synchronized in Developing Cardiac and Skeletal Muscles", journal = "Biochemical and Biophysical Research Communications", volume = "225", number = "3", pages = "883--889", year = "1996", ISSN = "0006-291X", DOI = "doi:10.1006/bbrc.1996.1267", URL = "http://www.sciencedirect.com/science/article/B6WBK-45N4ST7-14/2/925d3a91d563e35c558593bdd19ba17a", notes = "Not on GP", } @Article{jin:2020:SaMO, author = "Seung-Seop Jin", title = "Compositional kernel learning using tree-based genetic programming for Gaussian process regression", journal = "Structural and Multidisciplinary Optimization", year = "2020", volume = "62", number = "3", keywords = "genetic algorithms, genetic programming, Gaussian processes regression, Compositional kernel learning, Tree-based genetic programming, Surrogate modeling, Reliability analysis", URL = "http://link.springer.com/article/10.1007/s00158-020-02559-7", DOI = "doi:10.1007/s00158-020-02559-7", } @Article{Jin:2025:fishres, author = "Yiheng Jin and Lingcheng Meng and Tao Shi", title = "An effective feature learning approach using genetic programming for crab age classification", journal = "Fisheries Research", year = "2025", volume = "281", pages = "107197", keywords = "genetic algorithms, genetic programming, Age classification, Crab, Feature learning, Genetic programming representation", ISSN = "0165-7836", URL = "https://www.sciencedirect.com/science/article/pii/S0165783624002613", DOI = "doi:10.1016/j.fishres.2024.107197", abstract = "Reliable age estimation plays an important role in managing populations of marine organisms. The extraction and analysis of eyestalks and gastric mill ossicles for determining the age of crabs are difficult and extremely time consuming. In this paper, we propose a novel Genetic Programming (GP) approach to learning high-level features from easily accessible features of crabs, such as length, weight, and sex, for crab age classification. We develop a new representation of GP to extend the width and depth of GP trees, so as to automatically generate a flexible number of high-level features without extensive domain knowledge. With the high-level features and easily accessible features, the new GP approach is subsequently wrapped with classifiers, e.g., Support Vector Machine (SVM), to effectively classify the crab age. The performance of the proposed GP approach is compared with five mainstream machine learning classification algorithms. Experiments show that the high-level features learnt by GP improve the classification accuracy of crab age classification. Moreover, the learnt features have good interpretability", } @Article{ZhanliJin:2005:SMS, author = "Zhanli Jin and Yaowen Yang and Chee Kiong Soh", title = "Application of fuzzy GA for optimal vibration control of smart cylindrical shells", journal = "Smart Materials and Structures", year = "2005", volume = "14", pages = "1250--1264", email = "cywyang@ntu.edu.sg", number = "6", month = dec, keywords = "genetic algorithms, genetic programming", URL = "http://stacks.iop.org/0964-1726/14/1250", DOI = "doi:10.1088/0964-1726/14/6/018", abstract = "a fuzzy-controlled genetic-based optimisation technique for optimal vibration control of cylindrical shell structures incorporating piezoelectric sensor/actuators (S/As) is proposed. The geometric design variables of the piezoelectric patches, including the placement and sizing of the piezoelectric S/As, are processed using fuzzy set theory. The criterion based on the maximisation of energy dissipation is adopted for the geometric optimization. A fuzzy-rule-based system (FRBS) representing expert knowledge and experience is incorporated in a modified genetic algorithm (GA) to control its search process. A fuzzy logic integrated GA is then developed and implemented. The results of three numerical examples, which include a simply supported plate, a simply supported cylindrical shell, and a clamped simply supported plate, provide some meaningful and heuristic conclusions for practical design. The results also show that the proposed fuzzy-controlled GA approach is more effective and efficient than the pure GA method.", notes = "PII: S0964-1726(05)07295-2 School of Civil and Environmental Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore", } @InProceedings{Jinbo:2011:ICEE, author = "Wang Jinbo and Su Bo", booktitle = "E-Business and E-Government (ICEE), 2011 International Conference on", title = "Research on learning behavior of traders in artificial stock market based on genetic algorithm", year = "2011", note = "in chinese", DOI = "doi:10.1109/ICEBEG.2011.5882429", abstract = "In this paper, one kind of artificial stock market which based on genetic algorithm is built. By using statistic theories and methods, learning behaviour of traders in this market is researched. In order to survive in the stock market, traders should learn from each other as new information becoming available and adapt their behaviour accordingly over time. It is the interacting of the adaptive traders causing the complexity of stock market and the abnormal phenomena of the market. Therefore, the conclusions based on this study have the theoretical and realistic significance.", keywords = "genetic algorithms, genetic programming, Banking, Pricing, Stock markets, Time series analysis, Artificial Stock Market, Individual Learning, Social Learning", notes = "Also known as \cite{5882429}", } @InProceedings{Jitkongchuen:2019:DAMT-NCON, author = "Duangjai Jitkongchuen and Eakasit Pacharawongsakda", booktitle = "2019 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT-NCON)", title = "Prediction Heating and Cooling Loads of Building Using Evolutionary Grey Wolf Algorithms", year = "2019", pages = "93--97", abstract = "This paper proposes using evolutionary grey wolf algorithm to predict the heating load (HL) and the cooling load (CL) of buildings. The proposed algorithm was constructed using 768 various residential buildings with eight input variables (relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, glazing area distribution) and two output variables. The experimental results are evaluated by comparative to previous work, geometric semantic genetic programming (GSGP), artificial neural network (ANN), support vector regression (SVR), evolutionary multivariate adaptive regression splines (EMARS), random forests (RF) and multilayer perceptron (MLP). The results prove that the proposed algorithm is competitive compared to the other machine learning algorithms.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ECTI-NCON.2019.8692232", month = jan, notes = "Also known as \cite{8692232}", } @InProceedings{jo:1999:ECAOPPMR, author = "Yong-Gun Jo and Hoon Kang", title = "Evolutionary Cellular Automata for Optimal Path Planning of Mobile Robots", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1443", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "artificial life, adaptive behavior and agents, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-037.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-037.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{DBLP:conf/kdd/JoLG07, author = "Yookyung Jo and Carl Lagoze and C. Lee Giles", title = "Detecting research topics via the correlation between graphs and texts", booktitle = "Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining KDD-2007", year = "2007", editor = "Pavel Berkhin and Rich Caruana and Xindong Wu", pages = "370--379", address = "San Jose, California, USA", month = aug # " 12-15", publisher = "ACM", keywords = "genetic algorithms, genetic programming, Algorithms, Languages, Measurement, topic detection, graph mining, probabilistic measure, citation graphs, correlation of text and links", isbn13 = "978-1-59593-609-7", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1281192.1281234", size = "10 pages", abstract = "In this paper we address the problem of detecting topics in large-scale linked document collections. Recently, topic detection has become a very active area of research due to its utility for information navigation, trend analysis, and high-level description of data. We present a unique approach that uses the correlation between the distribution of a term that represents a topic and the link distribution in the citation graph where the nodes are limited to the documents containing the term. This tight coupling between term and graph analysis is distinguished from other approaches such as those that focus on language models. We develop a topic score measure for each term, using the likelihood ratio of binary hypotheses based on a probabilistic description of graph connectivity. Our approach is based on the intuition that if a term is relevant to a topic, the documents containing the term have denser connectivity than a random selection of documents. We extend our algorithm to detect a topic represented by a set of terms, using the intuition that if the co-occurrence of terms represents a new topic, the citation pattern should exhibit the synergistic effect. We test our algorithm on two electronic research literature collections, arXiv and Citeseer. Our evaluation shows that the approach is effective and reveals some novel aspects of topic detection.", notes = "GP literature used as one example", } @InProceedings{DBLP:conf/ecai/JoedickeKCWVCH20, author = "David Joedicke and Gabriel Kronberger and Jose Manuel Colmenar and Stephan M. Winkler and Jose Manuel Velasco and Sergio Contador and Jose Ignacio Hidalgo", editor = "Kerstin Bach and Razvan C. Bunescu and Cindy Marling and Nirmalie Wiratunga", title = "Analysis of the performance of Genetic Programming on the Blood Glucose Level Prediction Challenge 2020", booktitle = "Proceedings of the 5th International Workshop on Knowledge Discovery in Healthcare Data co-located with 24th European Conference on Artificial Intelligence, KDH@ECAI 2020", series = "CEUR Workshop Proceedings", volume = "2675", pages = "141--145", publisher = "CEUR-WS.org", year = "2020", address = "Santiago de Compostela, Spain and Virtually", month = aug # " 29-30", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Random Forest, ARIMA, GP-OS, GE, MOGE", URL = "http://ceur-ws.org/Vol-2675/paper25.pdf", timestamp = "Wed, 23 Sep 2020 17:50:25 +0200", biburl = "https://dblp.org/rec/conf/ecai/JoedickeKCWVCH20.bib", bibsource = "dblp computer science bibliography, https://dblp.org", size = "5 pages", abstract = "we present results for the Blood Glucose Level Prediction Challenge for the Ohio2020 dataset. We have used four variants of genetic programming to build white-box models for predicting 30 minutes and 60 minutes ahead. The results are compared to classical methods including multi-variate linear regression,random forests, as well as two types of ARIMA models. Notably,we have included future values of bolus and basal into some of the models because we assume that these values can be controlled. Additionally, we have used a convolution filter to smooth the information in the bolus volume feature. We find that overall tree-based GP performs well and better than multivariate linear regression and random forest, while ARIMA models performed worst on the here analysed data.", notes = "Josef Ressel Center for Symbolic Regression, Upper Austria University of Applied Sciences", } @InCollection{joffe:1995:AGASNPP, author = "David Joffe", title = "A Genetic Algorithm for a Stochastic Network Planning Problem", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1995", year = "1995", editor = "John R. Koza", pages = "107--116", address = "Stanford, California, 94305-3079 USA", month = "11 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms", ISBN = "0-18-195720-5", notes = "part of \cite{koza:1995:gagp}", } @MastersThesis{johanson:1997:masters, author = "Brad Johanson", title = "Automated Fitness Raters for GP-Music System", school = "School of Computer Science, University of Birmingham", year = "1997", address = "Birmingham, UK", keywords = "genetic algorithms, genetic programming", URL = "http://graphics.stanford.edu/~bjohanso/gp-music/gp-music-auto-raters.ps.gz", size = "74 pages", } @TechReport{Johanson98, author = "Bradley E Johanson and Riccardo Poli", title = "{GP-Music}: An Interactive Genetic Programming System for Music Generation with Automated Fitness Raters", institution = "University of Birmingham, School of Computer Science", number = "CSRP-98-13", month = may, year = "1998", keywords = "genetic algorithms, genetic programming", email = "bjohanso@stanford.edu, R.Poli@cs.bham.ac.uk", file = "/1998/CSRP-98-13.ps.gz", URL = "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1998/CSRP-98-13.ps.gz", URL = "http://graphics.stanford.edu/~bjohanso/gp-music/tech-report", size = "12 pages", abstract = "In this paper we present the GP-Music System, an interactive system which allows users to evolve short musical sequences using interactive genetic programming, and its extensions aimed at making the system fully automated. The basic GP system works by using a genetic programming algorithm, a small set of functions for creating musical sequences, and a user interface which allows the user to rate individual sequences. With this user interactive technique it was possible to generate pleasant tunes over runs of 20 individuals over 10 generations. As the user is the bottleneck in interactive systems, the system takes rating data from a users run and uses it to train a neural network based automatic rater, or auto rater, which can replace the user in bigger runs. Using this auto rater we were able to make runs of up to 50 generations with 500 individuals per generation. The best of run pieces generated by the auto raters were pleasant but were not, in general, as nice as those generated in user interactive runs.", notes = "See also \cite{johanson:1998:GP-Music}", } @InProceedings{johanson:1998:GP-Music, author = "Brad Johanson and Riccardo Poli", title = "GP-Music: An Interactive Genetic Programming System for Music Generation with Automated Fitness Raters", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "181--186", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, automated music rating", ISBN = "1-55860-548-7", URL = "http://graphics.stanford.edu/~bjohanso/papers/gp98/johanson98gpmusic.pdf", URL = "http://citeseer.ist.psu.edu/336573.html", abstract = "In this paper we present the GP-Music System, an interactive system which allows users to evolve short musical sequences using interactive genetic programming. We also present an extension which uses a neural network to model a users preferences, then stands in for them during the evolutionary process. The use of this `automated fitness rater' allows the system to operate both with and without user interaction.", notes = "GP-98, see also \cite{Johanson98}", } @TechReport{johansson:1996:rfbcGPtr, author = "Stefan J. Johansson", title = "Evolving integer recurrences using genetic programming", institution = "Faculteit der Wiskunde en Informatica, VU Amsterdam", year = "1996", number = "IR 402", address = "Holland", month = "2 " # apr, keywords = "genetic algorithms, genetic programming", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/2226/http:zSzzSzwww.sikt.hk-r.sezSz~soczSzpublicationszSz.zSz1996zSzeirgp.pdf/evolving-integer-recurrences-using.pdf", URL = "http://citeseer.ist.psu.edu/177550.html", abstract = "his report addresses the problem of synthesizing integer recurrences by genetic programming (GP). A number of alternative approaches were proposed and tested by running thousands of experiments. In particular the following aspects were investigated: approaches to base cases, population size, different fitness measures and superiority of GP over random search. Results of the experiments showed that our approach (fixed base cases) is much better than the conventional one (evolved base cases) on...", notes = "Masters Thesis, CWI Shelf mark A-26012 CWI library 2-12-98", size = "38 pages", } @InProceedings{johansson:1996:rfbcGP, author = "Stefan J. Johansson", title = "Recurrences with Fixed Base Cases in Genetic Programming", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "430", address = "Stanford University, CA, USA", publisher = "MIT Press", size = "1 page", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap69.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96", } @InProceedings{DBLP:conf/flairs/JohanssonKN04, author = "Ulf Johansson and Rikard Konig and Lars Niklasson", title = "The Truth is In There - Rule Extraction from Opaque Models Using Genetic Programming", booktitle = "Proceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference", year = "2004", editor = "Valerie Barr and Zdravko Markov", pages = "658--663", address = "Miami Beach, Florida, USA", month = may # " 12-14", publisher = "AAAI Press", keywords = "genetic algorithms, genetic programming, G-REX, symbolic regression trees, decision trees, fuzzy rules, crisp rules", ISBN = "1-57735-201-7", URL = "https://dblp.org/db/conf/flairs/flairs2004.html", URL = "http://www.aaai.org/Papers/FLAIRS/2004/Flairs04-113.pdf", broken = "https://www.aaai.org/Library/FLAIRS/2004/flairs04-113.php", URL = "http://his.diva-portal.org/smash/record.jsf?pid=diva2%3A32473&dswid=-5602", size = "6 pages", abstract = "A common problem when using complicated models for prediction and classification is that the complexity of the model entails that it is hard, or impossible, to interpret. For some scenarios this might not be a limitation, since the priority is the accuracy of the model. In other situations the limitations might be severe, since additional aspects are important to consider; e.g. comprehensibility or scalability of the model. In this study we show how the gap between accuracy and other aspects can be bridged by using a rule extraction method (termed G-REX) based on genetic programming. The extraction method is evaluated against the five criteria accuracy, comprehensibility, fidelity, scalability and generality. It is also shown how G-REX can create novel representation languages; here regression trees and fuzzy rules. The problem used is a data-mining problem from the marketing domain where the impact of advertising is predicted from investment plans. Several experiments, covering both regression and classification tasks, are evaluated. Results show that G-REX in general is capable of extracting both accurate and comprehensible representations, thus allowing high performance also in domains where comprehensibility is of essence.", notes = "Any time rule extraction", bibsource = "DBLP, http://dblp.uni-trier.de", } @InProceedings{Johansson:2006:ICAISC, author = "Ulf Johansson and Tuve Lofstrom and Rikard Konig and Lars Niklasson", title = "Genetically Evolved Trees Representing Ensembles", booktitle = "Proceedings 8th International Conference on Artificial Intelligence and Soft Computing {ICAISC}", year = "2006", pages = "613--622", series = "Lecture Notes on Artificial Intelligence (LNAI)", volume = "4029", publisher = "Springer-Verlag", editor = "Leszek Rutkowski and Ryszard Tadeusiewicz and Lotfi A. Zadeh and Jacek Zurada", address = "Zakopane, Poland", month = jun # " 25-29", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-35748-3", DOI = "doi:10.1007/11785231_64", size = "10 pages", abstract = "We have recently proposed a novel algorithm for ensemble creation called GEMS (Genetic Ensemble Member Selection). GEMS first trains a fixed number of neural networks (here twenty) and then uses genetic programming to combine these networks into an ensemble. The use of genetic programming makes it possible for GEMS to not only consider ensembles of different sizes, but also to use ensembles as intermediate building blocks. In this paper, which is the first extensive study of GEMS, the representation language is extended to include tests partitioning the data, further increasing flexibility. In addition, several micro techniques are applied to reduce overfitting, which appears to be the main problem for this powerful algorithm. The experiments show that GEMS, when evaluated on 15 publicly available data sets, obtains very high accuracy, clearly outperforming both straightforward ensemble designs and standard decision tree algorithms.", } @InProceedings{Johansson:2006:CEC, author = "Ulf Johansson and Tuve Lofstrom and Rikard Konig and Lars Niklasson", title = "Building Neural Network Ensembles using Genetic Programming", booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary Computation", year = "2006", editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas", pages = "2239--2244", address = "Vancouver", month = "6-21 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9487-9", DOI = "doi:10.1109/IJCNN.2006.246836", size = "6 pages", abstract = "In this paper we present and evaluate a novel algorithm for ensemble creation. The main idea of the algorithm is to first independently train a fixed number of neural networks (here ten) and then use genetic programming to combine these networks into an ensemble. The use of genetic programming makes it possible to not only consider ensembles of different sizes, but also to use ensembles as intermediate building blocks. The final result is therefore more correctly described as an ensemble of neural network ensembles. The experiments show that the proposed method, when evaluated on 22 publicly available data sets, obtains very high accuracy, clearly outperforming the other methods evaluated. In this study several micro techniques are used, and we believe that they all contribute to the increased performance. One such micro technique, aimed at reducing overtraining, is the training method, called tombola training, used during genetic evolution. When using tombola training, training data is regularly resampled into new parts, called training groups. Each ensemble is then evaluated on every training group and the actual fitness is determined solely from the result on the hardest part.", notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D", } @InProceedings{Johansson:2007:IJCNN, author = "Ulf Johansson and Rikard Konig and Lars Niklasson", title = "Inconsistency - Friend or Foe", booktitle = "International Joint Conference on Neural Networks, IJCNN 2007", year = "2007", pages = "1383--1388", address = "Orlando, USA", month = "12-17 " # aug, keywords = "genetic algorithms, genetic programming, G-REX tree, consistency criterion, evolutionary algorithms, inconsistency criterion, neural network ensembles, probability estimation, publicly available data sets, regression trees, rule extraction algorithms, data integrity, data mining, estimation theory, evolutionary computation, learning (artificial intelligence), probability, regression analysis", ISSN = "1098-7576", isbn13 = "1-4244-1380-X", DOI = "doi:10.1109/IJCNN.2007.4371160", abstract = "One way of obtaining accurate yet comprehensible models is to extract rules from opaque predictive models. When evaluating rule extraction algorithms, one frequently used criterion is consistency; i.e. the algorithm must produce similar rules every time it is applied to the same problem. Rule extraction algorithms based on evolutionary algorithms are, however, inherently inconsistent, something that is regarded as their main drawback. In this paper, we argue that consistency is an over valued criterion, and that inconsistency can even be beneficial in some situations. The study contains two experiments, both using publicly available data sets, where rules are extracted from neural network ensembles. In the first experiment, it is shown that it is normally possible to extract several different rule sets from an opaque model, all having high and similar accuracy. The implication is that consistency in that perspective is useless; why should one specific rule set be considered superior? Clearly, it should instead be regarded as an advantage to obtain several accurate and comprehensible descriptions of the relationship. In the second experiment, rule extraction is used for probability estimation. More specifically, an ensemble of extracted trees is used in order to obtain probability estimates. Here, it is exactly the inconsistency of the rule extraction algorithm that makes the suggested approach possible.", notes = "Also known as \cite{4371160}", } @PhdThesis{UlfJohansson:thesis, author = "Ulf Johansson", title = "Obtaining Accurate and Comprehensible Data Mining Models - An Evolutionary Approach", school = "Linkoping University, Department of Computer and Information Science", year = "2007", type = "doctoral thesis", address = "SE-581 83, Linkoping, Sweden", number = "1086", keywords = "genetic algorithms, genetic programming, rule extraction, ensembles, data mining, artificial neural networks", URL = "http://hdl.handle.net/2320/2136", URL = "http://bada.hb.se/bitstream/2320/2136/1/AvhandlingFinal.pdf", isbn13 = "978-91-85715-34-3", ISSN = "0345-7524", size = "272 pages", abstract = "When performing predictive data mining, the use of ensembles is claimed to virtually guarantee increased accuracy compared to the use of single models. Unfortunately, the problem of how to maximise ensemble accuracy is far from solved. In particular, the relationship between ensemble diversity and accuracy is not completely understood, making it hard to efficiently use diversity for ensemble creation. Furthermore, most high-accuracy predictive models are opaque, i.e. it is not possible for a human to follow and understand the logic behind a prediction. For some domains, this is unacceptable, since models need to be comprehensible. To obtain comprehensibility, accuracy is often sacrificed by using simpler but transparent models; a trade-off termed the accuracy vs. comprehensibility trade-off. With this trade-off in mind, several researchers have suggested rule extraction algorithms, where opaque models are transformed into comprehensible models, keeping an acceptable accuracy. In this thesis, two novel algorithms based on Genetic Programming are suggested. The first algorithm (GEMS) is used for ensemble creation, and the second (G-REX) is used for rule extraction from opaque models. The main property of GEMS is the ability to combine smaller ensembles and individual models in an almost arbitrary way. Moreover, GEMS can use base models of any kind and the optimisation function is very flexible, easily permitting inclusion of, for instance, diversity measures. In the experimentation, GEMS obtained accuracies higher than both straightforward design choices and published results for Random Forests and AdaBoost. The key quality of G-REX is the inherent ability to explicitly control the accuracy vs. comprehensibility trade-off. Compared to the standard tree inducers C5.0 and CART, and some well-known rule extraction algorithms, rules extracted by G-REX are significantly more accurate and compact. Most importantly, G-REX is thoroughly evaluated and found to meet all relevant evaluation criteria for rule extraction algorithms, thus establishing G-REX as the algorithm to benchmark against.", } @InProceedings{Johansson:2008:cec, author = "Ulf Johansson and Rikard Konig and Tuve Lofstrom and Lars Niklasson", title = "Increasing Rule Extraction Accuracy by Post-Processing GP Trees", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "3005--3010", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1823-7", file = "EC0669.pdf", DOI = "doi:10.1109/CEC.2008.4631203", abstract = "Genetic programming (GP), is a very general and efficient technique, often capable of outperforming more specialised techniques on a variety of tasks. In this paper, we suggest a straightforward novel algorithm for post-processing of GP classification trees. The algorithm iteratively, one node at a time, searches for possible modifications that would result in higher accuracy. More specifically, the algorithm for each split evaluates every possible constant value and chooses the best. With this design, the post-processing algorithm can only increase training accuracy, never decrease it. In this study, we apply the suggested algorithm to GP trees, extracted from neural network ensembles. Experimentation, using 22 UCI datasets, shows that the post-processing results in higher test set accuracies on a large majority of datasets. As a matter of fact, for two setups of three evaluated, the increase in accuracy is statistically significant.", keywords = "genetic algorithms, genetic programming", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @InProceedings{DBLP:conf/dmin/JohanssonKN08, author = "Ulf Johansson and Rikard Konig and Lars Niklasson", title = "Evolving a Locally Optimized Instance Based Learner", booktitle = "The 2008 International Conference on Data Mining", year = "2008", pages = "124--129", address = "Las Vegas, USA", month = jul # " 14-17", publisher = "CSREA Press", keywords = "genetic algorithms, genetic programming, instance-based learner, kNN, classification", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1011.1", URL = "http://bada.hb.se:80/bitstream/2320/4208/2/Johansson%2C%20K%C3%B6nig%2C%20Niklasson%20-%202008%20-%20Evolving%20a%20Locally%20Optimized%20Instance%20Based%20Learner.pdf", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.1011.1", abstract = "Standard kNN suffers from two major deficiencies, both related to the parameter k. First of all, it is well-known that the parameter value k is not only extremely important for the performance, but also very hard to estimate beforehand. In addition, the fact that k is a global constant, totally independent of the particular region in which an instance to be classified falls, makes standard kNN quite blunt. In this paper, we introduce a novel instance-based learner, specifically designed to avoid the two drawbacks mentioned above. The suggested technique, named G-kNN, optimises the number of neighbours to consider for each specific test instance, based on its position in input space; i.e. the algorithm uses several, locally optimised k, instead of just one global. More specifically, G-kNN uses genetic programming to build decision trees, partitioning the input space in regions, where each leaf node (region) contains a kNN classifier with a locally optimised k. In the experimentation, using 27 datasets from the UCI repository, the basic version of G-kNN is shown to significantly outperform standard kNN, with respect to accuracy. Although not evaluated in this study, it should be noted that the flexibility of genetic programming makes sophisticated extensions, like weighted voting and axes scaling, fairly straightforward.", notes = "http://www.dmin-2008.com/programme.htm", } @InProceedings{Johansson:2009:ieeeCIDM, author = "Ulf Johansson and Lars Niklasson", title = "Evolving decision trees using oracle guides", booktitle = "IEEE Symposium on Computational Intelligence and Data Mining, CIDM '09", year = "2009", month = "30 2009-" # apr # " 2", pages = "238--244", keywords = "genetic algorithms, genetic programming, data mining, decision trees, high-accuracy techniques, human inspection, neural network ensemble, opaque models, oracle guides, predictive models, rule extraction, transparent models, data mining, decision trees, neural nets", DOI = "doi:10.1109/CIDM.2009.4938655", abstract = "Some data mining problems require predictive models to be not only accurate but also comprehensible. Comprehensibility enables human inspection and understanding of the model, making it possible to trace why individual predictions are made. Since most high-accuracy techniques produce opaque models, accuracy is, in practice, regularly sacrificed for comprehensibility. One frequently studied technique, often able to reduce this accuracy vs. comprehensibility tradeoff, is rule extraction, i.e., the activity where another, transparent, model is generated from the opaque. In this paper, it is argued that techniques producing transparent models, either directly from the dataset, or from an opaque model, could benefit from using an oracle guide. In the experiments, genetic programming is used to evolve decision trees, and a neural network ensemble is used as the oracle guide. More specifically, the datasets used by the genetic programming when evolving the decision trees, consist of several different combinations of the original training data and 'oracle data', i.e., training or test data instances, together with corresponding predictions from the oracle. In total, seven different ways of combining regular training data with oracle data were evaluated, and the results, obtained on 26 UCI datasets, clearly show that the use of an oracle guide improved the performance. As a matter of fact, trees evolved using training data only had the worst test set accuracy of all setups evaluated. Furthermore, statistical tests show that two setups, both using the oracle guide, produced significantly more accurate trees, compared to the setup using training data only.", notes = "Also known as \cite{4938655}", } @InProceedings{Johansson:2009:cec, author = "Ulf Johansson and Cecilia Sonstrod and Tuve Lofstrom and Rikard Konig", title = "Using Genetic Programming to Obtain Implicit Diversity", booktitle = "2009 IEEE Congress on Evolutionary Computation", year = "2009", editor = "Andy Tyrrell", pages = "2454--2459", address = "Trondheim, Norway", month = "18-21 " # may, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-2959-2", file = "P558.pdf", DOI = "doi:10.1109/CEC.2009.4983248", abstract = "When performing predictive data mining, the use of ensembles is known to increase prediction accuracy, compared to single models. To obtain this higher accuracy, ensembles should be built from base classifiers that are both accurate and diverse. The question of how to balance these two properties in order to maximize ensemble accuracy is, however, far from solved and many different techniques for obtaining ensemble diversity exist. One such technique is bagging, where implicit diversity is introduced by training base classifiers on different subsets of available data instances, thus resulting in less accurate, but diverse base classifiers. In this paper, genetic programming is used as an alternative method to obtain implicit diversity in ensembles by evolving accurate, but different base classifiers in the form of decision trees, thus exploiting the inherent inconsistency of genetic programming. The experiments show that the GP approach outperforms standard bagging of decision trees, obtaining significantly higher ensemble accuracy over 25 UCI datasets. This superior performance stems from base classifiers having both higher average accuracy and more diversity. Implicitly introducing diversity using GP thus works very well, since evolved base classifiers tend to be highly accurate and diverse.", keywords = "genetic algorithms, genetic programming", notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR", } @InProceedings{Johansson:2010:EuroGP, author = "Ulf Johansson and Rikard Konig and Tuve Lofstrom and Lars Niklasson", title = "Using Imaginary Ensembles to Select GP Classifiers", booktitle = "Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010", year = "2010", editor = "Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar", volume = "6021", series = "LNCS", pages = "278--288", address = "Istanbul", month = "7-9 " # apr, organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-12147-0", DOI = "doi:10.1007/978-3-642-12148-7_24", abstract = "When predictive modeling requires comprehensible models, most data miners will use specialized techniques producing rule sets or decision trees. This study, however, shows that genetically evolved decision trees may very well outperform the more specialized techniques. The proposed approach evolves a number of decision trees and then uses one of several suggested selection strategies to pick one specific tree from that pool. The inherent inconsistency of evolution makes it possible to evolve each tree using all data, and still obtain somewhat different models. The main idea is to use these quite accurate and slightly diverse trees to form an imaginary ensemble, which is then used as a guide when selecting one specific tree. Simply put, the tree classifying the largest number of instances identically to the ensemble is chosen. In the experimentation, using 25 UCI data sets, two selection strategies obtained significantly higher accuracy than the standard rule inducer J48.", notes = "BNF grammar, parsimony pressure to lessen bloat, persistence, roulette wheel selection, p287 suggests opaque techniques (ANN, SVM, ensembles) will 'almost always' do better than rule sets or decision trees. Part of \cite{Esparcia-Alcazar:2010:GP} EuroGP'2010 held in conjunction with EvoCOP2010 EvoBIO2010 and EvoApplications2010", } @InProceedings{Johansson:2010:gecco, author = "Ulf Johansson and Rikard Konig and Lars Niklasson", title = "Genetic rule extraction optimizing brier score", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "1007--1014", keywords = "genetic algorithms, genetic programming, Genetics based machine learning", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830483.1830668", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Most highly accurate predictive modelling techniques produce opaque models. When comprehensible models are required, rule extraction is sometimes used to generate a transparent model, based on the opaque. Naturally, the extracted model should be as similar as possible to the opaque. This criterion, called fidelity, is therefore a key part of the optimisation function in most rule extracting algorithms. To the best of our knowledge, all existing rule extraction algorithms targeting fidelity use 0/1 fidelity, i.e., maximise the number of identical classifications. In this paper, we suggests and evaluate a rule extraction algorithm using a more informed fidelity criterion. More specifically, the novel algorithms, which is based on genetic programming, minimises the difference in probability estimates between the extracted and the opaque models, by using the generalised Brier score as fitness function. Experimental results from 26 UCI data sets show that the suggested algorithm obtained considerably higher accuracy and significantly better AUC than both the exact same rule extraction algorithm maximizing 0/1 fidelity, and the standard tree inducer J48. Somewhat surprisingly, rule extraction using the more informed fidelity metric normally resulted in less complex models, making sure that the improved predictive performance was not achieved on the expense of comprehensibility.", notes = "Also known as \cite{1830668} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @InProceedings{Johansson:2011:OTtETA, title = "One Tree to Explain Them All", author = "Ulf Johansson and Cecilia Sonstrod and Tuve Lofstrom", pages = "1444--1451", booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary Computation", year = "2011", editor = "Alice E. Smith", month = "5-8 " # jun, address = "New Orleans, USA", organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Classification, clustering, data analysis and data mining, Learning classifier systems", DOI = "doi:10.1109/CEC.2011.5949785", size = "8 pages", abstract = "Random forest is an often used ensemble technique, renowned for its high predictive performance. Random forests models are, however, due to their sheer complexity inherently opaque, making human interpretation and analysis impossible. This paper presents a method of approximating the random forest with just one decision tree. The approach uses oracle coaching, a recently suggested technique where a weaker but transparent model is generated using combinations of regular training data and test data initially labelled by a strong classifier, called the oracle. In this study, the random forest plays the part of the oracle, while the transparent models are decision trees generated by either the standard tree inducer J48, or by evolving genetic programs. Evaluation on 30 data sets from the UCI repository shows that oracle coaching significantly improves both accuracy and area under ROC curve, compared to using training data only. As a matter of fact, resulting single tree models are as accurate as the random forest, on the specific test instances. Most importantly, this is not achieved by inducing or evolving huge trees having perfect fidelity; a large majority of all trees are instead rather compact and clearly comprehensible. The experiments also show that the evolution outperformed J48, with regard to accuracy, but that this came at the expense of slightly larger trees.", notes = "CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.", } @InProceedings{Johansson:2013:CEC, article_id = "1506", author = "Ulf Johansson and Rikard Konig and Tuve Lofstrom and Henrik Bostrom", title = "Evolved Decision Trees as Conformal Predictors", booktitle = "2013 IEEE Conference on Evolutionary Computation", volume = "1", year = "2013", month = jun # " 20-23", editor = "Luis Gerardo {de la Fraga}", pages = "1794--1801", address = "Cancun, Mexico", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-0453-2", DOI = "doi:10.1109/CEC.2013.6557778", notes = "CEC 2013 - A joint meeting of the IEEE, the EPS and the IET.", } @Article{Johari:2006:JGGE, author = "A. Johari and G. Habibagahi and A. Ghahramani", title = "Prediction of Soil-Water Characteristic Curve Using Genetic Programming", journal = "Journal of Geotechnical and Geoenvironmental Engineering", year = "2006", volume = "132", number = "5", pages = "661--665", month = may, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1061/(ASCE)1090-0241(2006)132:5(661)", abstract = "In this technical note, a genetic programming (GP) approach is employed to predict the soil-water characteristic curve (SWCC) of soils. The GP model requires an input terminal set that consists of initial void ratio, initial gravimetric water content, logarithm of suction normalised with respect to atmospheric air pressure, clay content, and silt content. The output terminal set consists of the gravimetric water content corresponding to the assigned input suction. The function set includes operators such as plus, minus, product, division, and power. Results from pressure plate tests carried out on clay, silty clay, sandy loam, and loam compiled in the SoilVision software were adopted as a database for developing and validating the genetic model. For this purpose, and after data digitisation, GP software (GPLAB) provided by MATLAB was employed for the analysis. Furthermore, GP simulations were compared with the experimental results as well as the models proposed by other investigators. This comparison indicated superior performance of the proposed model for predicting the SWCC.", notes = "c2006 ASCE", } @Article{Johari20111002, author = "A. Johari and G. Habibagahi and A. Ghahramani", title = "Prediction of SWCC using artificial intelligent systems: A comparative study", journal = "Scientia Iranica", volume = "18", number = "5", pages = "1002--1008", year = "2011", ISSN = "1026-3098", DOI = "doi:10.1016/j.scient.2011.09.002", URL = "http://www.sciencedirect.com/science/article/pii/S1026309811001829", keywords = "genetic algorithms, genetic programming, Unsaturated soils, Soil suction, Soil Water Characteristic Curve (SWCC), Geotechnical models, Computer models, Numerical models", abstract = "The significance of the Soil Water Characteristic Curve (SWCC) or soil retention curve in understanding the unsaturated soils behaviour such as shear strength, volume change and permeability has resulted in many attempts for its prediction. In this regard, the authors had previously developed two models, namely. Genetic-Based Neural Network (GBNN) and Genetic Programming (GP). These two models have identical set of input parameters. These parameters include void ratio, initial water content, clay fraction, silt content and logarithm of suction normalised with respect to air pressure. In this paper, performance of these two models is further investigated using additional test data. For this purpose, soil samples from 14 different locations in Shiraz city in the Fars province of Iran are tested and their SWCCs are established, using a pressure plate apparatus. Next, the results are used to demonstrate the suitability of the previously proposed models and to evaluate relative importance of the input parameters. Assessment of the results indicates that predictions from GBNN model have relatively higher accuracy as compared to GP model.", } @InProceedings{john:1999:GARS, author = "Maria John and David Panton and Kevin White", title = "Genetic Algorithm for Regional Surveillance", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1573--1579", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "real world applications", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-712.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-712.pdf", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{john:2022:GECCOcomp, author = "Taran Cyriac John and Muhammad Shabbir Abbasi and Harith Al-Sahaf and Ian Welch", title = "Automatically Evolving Malice Scoring Models through Utilisation of Genetic Programming: A Cooperative Coevolution Approach", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion", year = "2022", editor = "Heike Trautmann and Carola Doerr and Alberto Moraglio and Thomas Bartz-Beielstein and Bogdan Filipic and Marcus Gallagher and Yew-Soon Ong and Abhishek Gupta and Anna V Kononova and Hao Wang and Michael Emmerich and Peter A. N. Bosman and Daniela Zaharie and Fabio Caraffini and Johann Dreo and Anne Auger and Konstantin Dietric and Paul Dufosse and Tobias Glasmachers and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Tea Tusar and Dimo Brockhoff and Tome Eftimov and Pascal Kerschke and Boris Naujoks and Mike Preuss and Vanessa Volz and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Mark Coletti and Catherine (Katie) Schuman and Eric ``Siggy'' Scott and Robert Patton and Paul Wiegand and Jeffrey K. Bassett and Chathika Gunaratne and Tinkle Chugh and Richard Allmendinger and Jussi Hakanen and Daniel Tauritz and John Woodward and Manuel Lopez-Ibanez and John McCall and Jaume Bacardit and Alexander Brownlee and Stefano Cagnoni and Giovanni Iacca and David Walker and Jamal Toutouh and UnaMay O'Reilly and Penousal Machado and Joao Correia and Sergio Nesmachnow and Josu Ceberio and Rafael Villanueva and Ignacio Hidalgo and Francisco {Fernandez de Vega} and Giuseppe Paolo and Alex Coninx and Antoine Cully and Adam Gaier and Stefan Wagner and Michael Affenzeller and Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and William B. Langdon and Justyna Petke and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Paetzel and Alexander Wagner and Michael Heider and Nadarajen Veerapen and Katherine Malan and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Mohammad Nabi Omidvar and Yuan Sun and Ernesto Tarantino and De Falco Ivanoe and Antonio {Della Cioppa} and Scafuri Umberto and John Rieffel and Jean-Baptiste Mouret and Stephane Doncieux and Stefanos Nikolaidis and Julian Togelius and Matthew C. Fontaine and Serban Georgescu and Francisco Chicano and Darrell Whitley and Oleksandr Kyriienko and Denny Dahl and Ofer Shir and Lee Spector and Alma Rahat and Richard Everson and Jonathan Fieldsend and Handing Wang and Yaochu Jin and Erik Hemberg and Marwa A. Elsayed and Michael Kommenda and William {La Cava} and Gabriel Kronberger and Steven Gustafson", pages = "562--565", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, ransomware detection, evolutionary computation, regression", isbn13 = "978-1-4503-9268-6/22/07", DOI = "doi:10.1145/3520304.3529063", abstract = "Quantification of software malignance through the assignment of a malice score based upon a scoring module, is a technique that is present throughout the literature. The majority of these, however, are synthesised using hand-picked features and manual weighting with the expertise of a domain specialist. proposes an automated malice scoring model evolved through genetic programming and cooperative coevolution, which automatically produces an ensemble of symbolic regression functions to assign a malice score to an instance of software data. The experimental results on a publicly available data set show that the proposed method has significantly outperformed the state-of-the-art malice scoring method, and exhibits the best performing model that produces an overall balanced accuracy of 95.80%, correctly classifying 94.21% and 97.39% of unseen malignant and benign instances, respectively.", notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @Article{JOHN:2023:cose, author = "Taran Cyriac John and Muhammad Shabbir Abbasi and Harith Al-Sahaf and Ian Welch and Julian Jang-Jaccard", title = "Evolving malice scoring models for ransomware detection: An automated approach by utilising genetic programming and cooperative coevolution", journal = "Computer \& Security", volume = "129", pages = "103215", year = "2023", ISSN = "0167-4048", DOI = "doi:10.1016/j.cose.2023.103215", URL = "https://www.sciencedirect.com/science/article/pii/S0167404823001256", keywords = "genetic algorithms, genetic programming, Ransomware detection, Evolutionary computation, Symbolic regression, Malice score, Cooperative coevolution", abstract = "Malice scoring is a technique that is present throughout the literature to quantify a software malignance through the assignment of a malice score. However, the majority of existing malice scoring models are synthesised using manually selected features and weights, where a domain specialist is needed. Genetic Programming and cooperative coevolution are used to automatically evolve an ensemble of symbolic regression functions to assign a malice score to an instance of software data. Using a publicly available dataset, the effectiveness of the proposed method is assessed and compared to that of the state-of-the-art malice scoring method. The experimental results show that the proposed method has significantly outperformed the benchmark method and exhibits the best-performing model that produces an overall balanced accuracy of 95.80percent, correctly classifying 94.21percent and 97.39percent of unseen malicious and benign instances, respectively. Furthermore, various aspects of the proposed method and experimental results have been analysed in-depth to provide insight into the evolutionary process and some of the automatically evolved models", } @InProceedings{John:2024:evoapplications, author = "Taran Cyriac John and Qurrat Ul Ain and Harith Al-Sahaf and Mengjie Zhang", title = "Evolving Feature Extraction Models for Melanoma Detection: A Co-operative Co-evolution Approach", booktitle = "27th International Conference, EvoApplications 2024", year = "2024", editor = "Stephen Smith and Joao Correia and Christian Cintrano", series = "LNCS", volume = "14634", publisher = "Springer", address = "Aberystwyth", month = "3-5 " # apr, pages = "413--429", organisation = "EvoStar, Species", note = "Best paper", keywords = "genetic algorithms, genetic programming, Skin Cancer", isbn13 = "978-3-031-56851-0", URL = "https://rdcu.be/dDZ0h", DOI = "doi:10.1007/978-3-031-56852-7_26", notes = "http://www.evostar.org/2024/ EvoApplications2024 held in conjunction with EuroGP'2024, EvoCOP2024 and EvoMusArt2024", } @InProceedings{John:2025:evoapplications, author = "Taran Cyriac John and Qurrat Ul Ain and Harith Al-Sahaf and Mengjie Zhang", title = "Genetic Programming with Co-operative Co-evolution for Feature Manipulation in Basal Cell Carcinoma Identification", booktitle = "28th International Conference, EvoApplications 2025", year = "2025", editor = " Pablo Garcia-Sanchez and Emma Hart and Sarah L. Thomson", series = "LNCS", volume = "15612", pages = "254--269", publisher = "Springer", address = "Trieste, Italy", month = "3-5 " # apr, organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Skin cancer, Tumour, Machine learning", isbn13 = "978-3-031-90062-4", timestamp = "Wed, 07 May 2025 17:49:32 +0200", biburl = "https://dblp.org/rec/conf/evoapps/JohnAAZ25.bib", bibsource = "dblp computer science bibliography, https://dblp.org", DOI = "doi:doi;10.1007/978-3-031-90062-4_16", notes = "DBLP:conf/evoapps/JohnAAZ25 http://www.evostar.org/2025/ EvoApplications2025 held in conjunction with EuroGP'2025, EvoCOP2025 and EvoMusArt2025", } @InCollection{johnson:1995:AAECASESGP, author = "Bryan H. Johnson", title = "An Attempt to Evolve Cooperation Among Separately Evolved Structure in Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1995", year = "1995", editor = "John R. Koza", pages = "117--126", address = "Stanford, California, 94305-3079 USA", month = "11 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-195720-5", notes = "part of \cite{koza:1995:gagp}", } @InProceedings{johnson:2004:eurogp, author = "Clayton M. Johnson and James Farrell", title = "Evolutionary Induction of Grammar Systems for Multi-agent Cooperation", booktitle = "Genetic Programming 7th European Conference, EuroGP 2004, Proceedings", year = "2004", editor = "Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule", volume = "3003", series = "LNCS", pages = "101--112", address = "Coimbra, Portugal", publisher_address = "Berlin", month = "5-7 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-21346-5", DOI = "doi:10.1007/978-3-540-24650-3_10", abstract = "We propose and describe a minimal cooperative problem that captures essential features of cooperative behaviour and permits detailed study of the mechanisms involved. We characterise this problem as one of language generation by cooperating grammars, and present initial results for language induction by pairs of right-linear grammars using grammatically based genetic programming. Populations of cooperating grammar systems were found to induce grammars for regular languages more rapidly than non-cooperating controls. Cooperation also resulted in greater absolute accuracy in the steady state, even though the control performance exceeded that of prior results for the induction of regular languages by a genetic algorithm.", notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004", } @InProceedings{johnson:2002:EuroGP, title = "Deriving genetic programming fitness properties by static analysis", author = "Colin G. Johnson", editor = "James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi", booktitle = "Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002", volume = "2278", series = "LNCS", pages = "298--307", publisher = "Springer-Verlag", address = "Kinsale, Ireland", publisher_address = "Berlin", month = "3-5 " # apr, year = "2002", keywords = "genetic algorithms, genetic programming, grammatical evolution", ISBN = "3-540-43378-3", URL = "http://www.cs.kent.ac.uk/pubs/2002/1351/content.ps", URL = "http://www.cs.ukc.ac.uk/pubs/2002/1351", DOI = "doi:10.1007/3-540-45984-7_29", abstract = "The aim of this paper is to introduce the idea of using static analysis of computer programs as a way of measuring fitness in genetic programming. Such techniques extract information about the programs without explicitly running them, and in particular they infer properties which hold across the whole of the input space of a program. This can be applied to measure fitness, and has a number of advantages over measuring fitness by running members of the population on test cases. The most important advantage is that if a solution is found then it is possible to formally trust that solution to be correct across all inputs. This paper introduces these ideas, discusses various ways in which they could be applied, discusses the type of problems for which they are appropriate, and ends by giving a simple test example and some questions for future research.", notes = "EuroGP'2002, part of \cite{lutton:2002:GP}", } @InProceedings{Johnson:2002:ukci, author = "Colin G. Johnson", booktitle = "The 2002 U.K. Workshop on Computational Intelligence (UKCI'02)", title = "What Can Automatic Programming Learn from Theoretical Computer Science?", year = "2002", editor = "Xin Yao", address = "Birmingham, U.K.", month = "2-4 " # sep, organisation = "eunite", keywords = "genetic algorithms, genetic programming, SBSE", URL = "http://kar.kent.ac.uk/id/eprint/13729", URL = "http://kar.kent.ac.uk/13729/1/WhatColin1.pdf", size = "7 pages", abstract = "This paper considers two (seemingly) radically different perspectives on the construction of software. On one hand, search-based heuristics such as genetic programming. On the other hand, the theories of programming which underpin mathematical program analysis and formal methods. The main part of the paper surveys possible links between these perspectives. In particular the contrast between inductive and deductive approaches to software construction are studied, and various suggestions are made as to how randomised search heuristics can be combined with formal approaches to software construction without compromising the rigorous provability of the results. The aim of the ideas proposed is to improve the efficiency, effectiveness and safety of search-based automatic programming.", notes = "http://www.cs.bham.ac.uk/~jxb/UKCI/program.shtml", } @InProceedings{RASC2002SC2108, author = "Colin G. Johnson", title = "Genetic Programming with Guaranteed Constraints", booktitle = "Proceedings of the 4th International Conference on Recent Advances in Soft Computing", year = "2002", editor = "Ahmad Lotfi and Jon Garibaldi and Robert John", pages = "134--140", address = "Nottingham, United Kingdom", month = dec # " 12-13", publisher = "The Nottingham Trent University", keywords = "genetic algorithms, genetic programming, constraints, safety, mobile robotics, verification", ISBN = "1-84233-076-4", URL = "http://www.cs.kent.ac.uk/pubs/2002/1545/content.pdf", size = "6 pages", abstract = "Genetic programming is a powerful technique for automatically generating program code from a description of the desired functionality. However it is frequently distrusted by users because the programs are generated with reference to a training set, and there is no formal guarantee that the generated programs will operate as intended outside of this training set. This paper describes a way of including constraints into the fitness function of a genetic programming system, so that the evolution is guided towards a solution which satisfies those constraints and so that a check can be made when a solution satisfies those constraints. This is applied to a problem in mobile robotics.", notes = "broken Dec 2024 http://www.rasc2002.info https://irep.ntu.ac.uk/id/eprint/7246", } @InProceedings{johnson03, author = "Colin G. Johnson", title = "Artificial Immune System Programming for Symbolic Regression", booktitle = "Genetic Programming, Proceedings of EuroGP'2003", year = "2003", editor = "Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa", volume = "2610", series = "LNCS", pages = "345--353", address = "Essex", publisher_address = "Berlin", month = "14-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-00971-X", URL = "http://link.springer.com/chapter/10.1007/3-540-36599-0_32", DOI = "doi:10.1007/3-540-36599-0_32", abstract = "Artificial Immune Systems are computational algorithms which take their inspiration from the way in which natural immune systems learn to respond to attacks on an organism. This paper discusses how such a system can be used as an alternative to genetic algorithms as a way of exploring program-space in a system similar to genetic programming. Some experimental results are given for a symbolic regression problem. The paper ends with a discussion of future directions for the use of artificial immune systems in program induction.", notes = "EuroGP'2003 held in conjunction with EvoWorkshops 2003", } @InProceedings{eurogp07:johnson, author = "Colin Johnson", title = "Genetic Programming with Fitness based on Model Checking", editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar", booktitle = "Proceedings of the 10th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "4445", year = "2007", address = "Valencia, Spain", month = "11-13 " # apr, pages = "114--124", keywords = "genetic algorithms, genetic programming, evolution strategy, finite state machine FSM, CFA, AES, temporal logic, computational tree logic CTL, Stuttgart model-checking kit SMV, growth style mutation, SBSE", ISBN = "3-540-71602-5", isbn13 = "978-3-540-71602-0", URL = "https://kar.kent.ac.uk/14594/1/Genetic.pdf", DOI = "doi:10.1007/978-3-540-71605-1_11", abstract = "Model checking is a way of analysing programs and program-like structures to decide whether they satisfy a list of temporal logic statements describing desired behaviour. In this paper we apply this to the fitness checking stage in an evolution strategy for learning finite state machines. We give experimental results consisting of learning the control program for a vending machine.", notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007 No recombination. 30 runs failed to solve coffee and Tea vending problem", } @InProceedings{Johnson:2009:eurogp, author = "Colin Johnson", title = "Genetic Programming Crossover: Does it Cross Over?", booktitle = "Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009", year = "2009", editor = "Leonardo Vanneschi and Steven Gustafson and Alberto Moraglio and Ivanoe {De Falco} and Marc Ebner", volume = "5481", series = "LNCS", pages = "97--108", address = "Tuebingen", month = apr # " 15-17", organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-01180-1", DOI = "doi:10.1007/978-3-642-01181-8_9", notes = "Part of \cite{conf/eurogp/2009} EuroGP'2009 held in conjunction with EvoCOP2009, EvoBIO2009 and EvoWorkshops2009", } @Article{Johnson:2009:IEEECIM, title = "Teaching natural computation", author = "Colin Johnson", journal = "IEEE Computational Intelligence Magazine", year = "2009", month = feb, volume = "4", number = "1", pages = "24--30", keywords = "computer science education, philosophical aspects computational ideas, computer science education, computing education, computing paradigms, natural computational property, philosophical debates, teaching", ISSN = "1556-603X", DOI = "doi:10.1109/MCI.2008.930984", size = "7 pages", abstract = "This paper consists of a discussion of the potential impact on computer science education of regarding computation as a property of the natural world, rather than just a property of artifacts specifically created for the purpose of computing. Such a perspective is becoming increasingly important: new computing paradigms based on the natural computational properties of the world are being created, scientific questions are being answered using computational ideas, and philosophical debates on the nature of computation are being formed. This paper discusses how computing education might react to these developments, goes on to discuss how these ideas can help to define computer science as a discipline, and reflects on our experience at Kent in teaching these subjects.", notes = "Also known as \cite{4762307}", } @Proceedings{Johnson:2014:SMGPwork, title = "Semantic Methods in Genetic Programming", year = "2014", editor = "Colin Johnson and Krzysztof Krawiec and Alberto Moraglio and Michael O'Neill", address = "Ljubljana, Slovenia", month = "13 " # sep, note = "Workshop at Parallel Problem Solving from Nature 2014 conference", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.put.poznan.pl/kkrawiec/smgp/?n=Site.SMGP2014", URL = "http://ppsn2014.ijs.si/?show=workshops#w2", abstract = "Genetic programming (GP), the application of evolutionary computing techniques to the creation of computer programs, has been a key topic in computational intelligence in the last couple of decades. In the last few years a rising topic in GP has been the use of semantic methods. The aim of this is to provide a way of exploring the input-output behaviour of programs, which is ultimately what matters for problem solving. This contrasts with much previous work in GP, where operators transform the program code and the effect on program behaviour is indirect. This new approach has produced substantially better results on a number of problems, both benchmark problems and real-world applications in areas such as pharmacy; and, has been grounded in a body of theory, which also informs algorithm design. All aspects of research related to Semantic Methods in Genetic Programming will be considered, including both theoretical and empirical work.", notes = "Special issue in Genetic Programming and Evolvable Machines March 2016, Volume 17, Issue 1, \cite{ONeill:2016:GPEM} SMGP 2014", } @InProceedings{Johnson:2014:SMGP, author = "Colin G. Johnson and John R. Woodward", title = "Information Theory, Fitness, and Sampling Semantics", booktitle = "Semantic Methods in Genetic Programming", year = "2014", editor = "Colin Johnson and Krzysztof Krawiec and Alberto Moraglio and Michael O'Neill", address = "Ljubljana, Slovenia", month = "13 " # sep, note = "Workshop at Parallel Problem Solving from Nature 2014 conference", keywords = "genetic algorithms, genetic programming", URL = "https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=1bdff27d8e4dbc6321bef2aab06feb13f642b977", URL = "http://www.cs.put.poznan.pl/kkrawiec/smgp2014/uploads/Site/Johnson.pdf", size = "2 pages", notes = "SMGP 2014 http://www.cs.put.poznan.pl/kkrawiec/smgp/?n=Site.SMGP2014", } @InProceedings{Johnson:2015:gi, author = "Colin G. Johnson and John R. Woodward", title = "Fitness as Task-relevant Information Accumulation", booktitle = "Genetic Improvement 2015 Workshop", year = "2015", editor = "William B. Langdon and Justyna Petke and David R. White", pages = "855--856", address = "Madrid", publisher_address = "New York, NY, USA", month = "11-15 " # jul, organisation = "SIGEvo", publisher = "ACM", keywords = "genetic algorithms, genetic programming, Genetic Improvement, SBSE, information gain, model complexity", isbn13 = "978-1-4503-3488-4", URL = "http://gpbib.cs.ucl.ac.uk/gi2015/fitness_as_task-relevant_information_accumulation.pdf", DOI = "doi:10.1145/2739482.2768428", size = "2 pages", abstract = "If you cannot measure it, you cannot improve it. Lord Kelvin Fitness in GP/GI is usually a short-sighted greedy fitness function counting the number of satisfied test cases (or some other score based on error). If GP/GI is to be extended to successfully tackle full software systems, which is the stated domain of Genetic Improvement, with loops, conditional statements and function calls, then this kind of fitness will fail to scale. One alternative approach is to measure the fitness gain in terms of the accumulated information at each executed step of the program. This paper discusses methods for measuring the way in which programs accumulate information relevant to their task as they run, by building measures of this information gain based on information theory and model complexity.", notes = "position paper, slides http://gpbib.cs.ucl.ac.uk/gi2015/gi_2015_slides/johnson ", } @Article{DBLP:journals/ijart/Johnson16, author = "Colin G. Johnson", title = "Fitness in evolutionary art and music: a taxonomy and future prospects", journal = "International Journal of Arts and Technology", year = "2016", volume = "9", number = "1", pages = "4--25", month = "23 " # mar, keywords = "genetic algorithms, genetic programming, evolutionary art, evolutionary music, fitness evaluation, digital art, evolutionary computation, taxonomy, memory, scaffolding, connotation, web search", ISSN = "1754-8853", timestamp = "Tue, 21 Mar 2023 21:15:54 +0100", biburl = "https://dblp.org/rec/journals/ijart/Johnson16.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://kar.kent.ac.uk/id/document/3132950", DOI = "doi:10.1504/IJART.2016.075406", size = "23 pages", abstract = "the idea of fitness in art and music systems that are based on evolutionary computation. A taxonomy is presented of the ways in which fitness is used in such systems, with two dimensions: what the fitness function is applied to, and the basis by which the function is constructed. A large collection of papers are classified using this taxonomy. The paper then discusses a number of ideas that have not been used for fitness evaluation in evolutionary art and which might be valuable in future developments: memory, scaffolding, connotation and web search.", notes = "Cites a few GP papers", } @Article{DBLP:journals/es/Johnson21, author = "Colin G. Johnson", title = "Solving the {Rubik}'s cube with stepwise deep learning", journal = "Expert Systems: The Journal of Knowledge Engineering", year = "2021", volume = "38", number = "3", month = may, keywords = "genetic algorithms, genetic programming, artificial intelligence, evolutionary computation, Learned Guidance Functions, LGF, fitness functions, human-like AI, loss functions, ANN, evolution strategies", ISSN = "0266-4720", DOI = "doi:10.1111/exsy.12665", timestamp = "Tue, 01 Jun 2021 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/es/Johnson21.bib", bibsource = "dblp computer science bibliography, https://dblp.org", size = "13 pages", abstract = "explores a novel technique for learning the fitness function for search algorithms such as evolutionary strategies and hill climbing. The aim of the new technique is to learn a fitness function (called a Learned Guidance Function) from a set of sample solutions to the problem. These functions are learned using a supervised learning approach based on deep neural network learning, that is, neural networks with a number of hidden layers. This is applied to a test problem: unscrambling the Rubik's Cube using evolutionary and hillclimbing algorithms. Comparisons are made with a previous LGF approach based on random forests, with a baseline approach based on traditional error-based fitness, and with other approaches in the literature. This demonstrates how a fitness function can be learned from existing solutions, rather than being provided by the user, increasing the autonomy of AI search processes.", notes = "Keras on TensorFlow, Adam optimizer, Keras deep learning network, dense 9x6 relu, dropout. 50 epochs. Deep LGF + ES p6 'fitness landscape created by a real LGF will still have local minima.' p8 'percentage of successes in the Deep LGF + ES approach is considerably higher than the Random Forest LGF + Hillclimbing' p11 'learning does not generalize from the anticlockwise to the clockwise.' speculation: program code manipulated (GP). interpretable School of Computer Science at the University of Nottingham", } @Article{johnson:2023:GPEM, author = "Colin G. Johnson", title = "New Directions in fitness evaluation: commentary on {Langdon's JAWS30}", journal = "Genetic Programming and Evolvable Machines", year = "2023", volume = "24", number = "2", pages = "Article number: 22", month = dec, note = "Special Issue: Thirtieth Anniversary of Genetic Programming: On the Programming of Computers by Means of Natural Selection", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/drZc8", DOI = "doi:10.1007/s10710-023-09470-2", size = "3 pages", notes = "Response to \cite{langdon:jaws30} Peer commentary editors: Leonardo Vanneschi and Leonardo Trujillo \cite{Vanneschi:2023:GPEM} See also \cite{jaws30_reply}", } @InProceedings{eurogp:JohnsonTS05, author = "Derek M. Johnson and Ankur Teredesai and Robert T. Saltarelli", editor = "Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. {van Hemert} and Marco Tomassini", title = "Genetic Programming in Wireless Sensor Networks", booktitle = "Proceedings of the 8th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3447", year = "2005", address = "Lausanne, Switzerland", month = "30 " # mar # " - 1 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-25436-6", pages = "96--107", URL = "http://www.cs.rit.edu/~amt/pubs/EuroGP05FinalTeredesai.pdf", DOI = "doi:10.1007/978-3-540-31989-4_9", DOI = "doi:10.1007/b107383", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "Wireless sensor networks (WSNs) are becoming increasingly important as they attain greater deployment. New techniques for evolutionary computing (EC) are needed to address these new computing models. This paper describes a novel effort to develop a series of variations to evolutionary computing paradigms such as Genetic Programming to enable their operation within the wireless sensor network. The ability to compute evolutionary algorithms within the WSN has innumerable advantages including, intelligent-sensing, resource optimised communication strategies, intelligent-routing protocol design, novelty detection, etc to name a few. In this paper we first discuss an evolutionary computing algorithm that operates within a distributed wireless sensor network. Such algorithms include continuous evolutionary computing. Continuous evolutionary computing extends the concept of an asynchronous evolutionary cycle where each individual resides and communicates with its immediate neighbours in an asynchronous time-step and exchanges genetic material. We then describe the adaptations required to develop practicable implementations of evolutionary computing algorithms to effectively work in resource constrained environments such as WSNs. Several adaptations including a novel representation scheme, an approximate fitness computation method and a sufficient statistics based data reduction technique lead to the development of a GP implementation that is usable on the low-power, small footprint architectures typical to wireless sensor modes. We demonstrate the utility of our formulations and validate the proposed ideas using a variety of problem sets and describe the results.", notes = "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005", } @InCollection{Johnson:2013:IMSE, author = "Duane D. Johnson", title = "Chapter 14 - Evolutionary Algorithms Applied to Electronic-Structure Informatics: Accelerated Materials Design Using Data Discovery vs. Data Searching", editor = "Krishna Rajan", booktitle = "Informatics for Materials Science and Engineering", publisher = "Butterworth-Heinemann", address = "Oxford", year = "2013", pages = "349--364", isbn13 = "978-0-12-394399-6", DOI = "doi:10.1016/B978-0-12-394399-6.00014-X", URL = "http://www.sciencedirect.com/science/article/pii/B978012394399600014X", abstract = "We exemplify and propose extending the use of genetic programs (GPs) - a genetic algorithm (GA) that evolves computer programs via mechanisms similar to genetics and natural selection - to symbolically regress key functional relationships between materials data, especially from electronic structure. GPs can extract structure-property relations or enable simulations across multiple scales of time and/or length. Uniquely, GP-based regression permits {"}data discovery{"} - finding relevant data and/or extracting correlations (data reduction/data mining) - in contrast to searching for what you know, or you think you know (intuition). First, catalysis-related materials correlations are discussed, where simple electronic-structure-based rules are revealed using well-developed intuition, and then, after introducing the concepts, GP regression is used to obtain (i) a constitutive relation between flow stress and strain rate in aluminium, and (ii) multi-time-scale kinetics for surface alloys. We close with some outlook for a range of applications (materials discovery, excited-state chemistry, and multiscaling) that could rely primarily on density functional theory results.", keywords = "genetic algorithms, genetic programming, Electronic structure, Density functional theory, Evolutionary algorithms, Genetic programs, Informatics", } @Article{johnson:1999:eurogp, author = "Helen Johnson", title = "EuroGP A biologist's persepective", journal = "EvoNEWS", year = "1999", volume = "11", pages = "11", month = "summer", keywords = "genetic algorithms, genetic programming", URL = "http://evonet.lri.fr/evoweb/files/evonews/evonews11.pdf", size = "0.5 page", abstract = "More than 70 people attended EvoWorkshops-99 in Goteborg this May for four days of presentations on the state of the art in evolutionary computing. The event, which brought together the expertise of four EvoNet working groups, promised to be wide ranging and inspirational. Here, some of the participants report back.", } @Article{Johnson:2000:eamGPsir, author = "Helen E. Johnson and Richard J. Gilbert and Michael K. Winson and Royston Goodacre and Aileen R. Smith and Jem J. Rowland and Michael A. Hall and Douglas B. Kell", title = "Explanatory Analysis of the Metabolome Using Genetic Programming of Simple, Interpretable Rules", journal = "Genetic Programming and Evolvable Machines", year = "2000", volume = "1", number = "3", pages = "243--258", month = jul, keywords = "genetic algorithms, genetic programming, metabolome, tomato fruit, salinity, Fourier transform infra-spectroscopy (FTIR), chemometrics", ISSN = "1389-2576", URL = "http://www.biospec.net/pubs/pdfs/Johnson-GPEvolMach2000.pdf", DOI = "doi:10.1023/A:1010014314078", abstract = "Genetic programming, in conjunction with advanced analytical instruments, is a novel tool for the investigation of complex biological systems at the whole-tissue level. In this study, samples from tomato fruit grown hydroponically under both high- and low-salt conditions were analysed using Fourier-transform infrared spectroscopy (FTIR), with the aim of identifying spectral and biochemical features linked to salinity in the growth environment. FTIR spectra of whole tissue extracts are not amenable to direct visual analysis, so numerical modelling methods were used to generate models capable of classifying the samples based on their spectral characteristics. Genetic programming (GP) provided models with a better prediction accuracy to the conventional data modelling methods used, whilst being much easier to interpret in terms of the variables used. Examination of the GP-derived models showed that there were a small number of spectral regions that were consistently being used. In particular, the spectral region containing absorbances potentially due to a cyanide/nitrile functional group was identified as discriminatory. The explanatory power of the GP models enabled a chemical interpretation of the biochemical differences to be proposed. The combination of FTIR and GP is therefore a powerful and novel analytical tool that, in this study, improves our understanding of the biochemistry of salt tolerance in tomato plants.", notes = "Article ID: 264703", } @Article{johnson:2003:mfsst, author = "Helen E. Johnson and David Broadhurst and Royston Goodacre and Aileen R. Smith", title = "Metabolic fingerprinting of salt-stressed tomatoes", journal = "Phytochemistry", year = "2003", volume = "62", number = "6", pages = "919--928", month = mar, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1016/S0031-9422(02)00722-7", abstract = "The aim of this study was to adopt the approach of metabolic fingerprinting through the use of Fourier transform infrared (FT-IR) spectroscopy and chemometrics to study the effect of salinity on tomato fruit. Two varieties of tomato were studied, Edkawy and Simge F1. Salinity treatment significantly reduced the relative growth rate of Simge F1 but had no significant effect on that of Edkawy. In both tomato varieties salt-treatment significantly reduced mean fruit fresh weight and size class but had no significant affect on total fruit number. Marketable yield was however reduced in both varieties due to the occurrence of blossom end rot in response to salinity. Whole fruit flesh extracts from control and salt-grown tomatoes were analysed using FT-IR spectroscopy. Each sample spectrum contained 882 variables, absorbance values at different wavenumbers, making visual analysis difficult and therefore machine learning methods were applied. The unsupervised clustering method, principal component analysis (PCA) showed no discrimination between the control and salt-treated fruit for either variety. The supervised method, discriminant function analysis (DFA) was able to classify control and salt-treated fruit in both varieties. Genetic algorithms (GA) were applied to identify discriminatory regions within the FT-IR spectra important for fruit classification. The GA models were able to classify control and salt-treated fruit with a typical error, when classifying the whole data set, of 9% in Edkawy and 5% in Simge F1. Key regions were identified within the spectra corresponding to nitrile containing compounds and amino radicals. The application of GA enabled the identification of functional groups of potential importance in relation to the response of tomato to salinity.", notes = "PMID: 12590119 See \cite{03_essa_153-163}", } @InProceedings{JJohnson:2000:GECCOlb, author = "Judy Johnson and Soundar Kumara", title = "Coadaptation of Cooperative Players in an Iterated Prisoners Dilemma Game using an XML Based GA", pages = "147--154", booktitle = "Late Breaking Papers at the 2000 Genetic and Evolutionary Computation Conference", year = "2000", editor = "Darrell Whitley", address = "Las Vegas, Nevada, USA", month = "8 " # jul, keywords = "genetic algorithms, genetic programming", notes = "Part of \cite{whitley:2000:GECCOlb}", } @InProceedings{johnson:2002:SEAL, author = "Martin Johnson", title = "Sequence Generation Using Machine Language Evolved by Genetic Programming", booktitle = "Procceedings of the 4th Asia-Pacific Conference on Simulated Evolution And Learning (SEAL'02)", year = "2002", editor = "Lipo Wang and Kay Chen Tan and Takeshi Furuhashi and Jong-Hwan Kim and Xin Yao", pages = "\#1251", address = "Orchid Country Club, Singapore", month = "18-22 " # nov, keywords = "genetic algorithms, genetic programming", ISBN = "981-04-7522-5", URL = "http://www.worldcat.org/title/seal02-proceedings-of-the-4th-asia-pacific-conference-on-simulated-evolution-and-learning-november-18-22-2002-orchid-country-club-singapore/oclc/51951214", abstract = "This paper presents a method for evolving simple machine language programs which generate mathematical sequences. The machine language used is a restricted subset of x86 code and programs are recursive, terminating when a fixed size stack overflows. A program has the use of 4 registers and must write its output into a small section of memory. Programs evolve using a topological neighbourhood. Examples are shown for power and Fibonacci sequences where the system evolves interesting solutions unlike those that would be found be a human programmer. The frequent random replacement of part of the population is investigated as a mechanism for avoiding local minima in the search space.", notes = "SEAL 2002 see http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.200.6410&rep=rep1&type=pdf", } @Misc{oai:CiteSeerX.psu:10.1.1.596.3170, title = "Evolving a Bipedal Robot Controller", author = "Michael Johnson", year = "2004?", keywords = "genetic algorithms, genetic programming", size = "1 page", abstract = "Research activity into developing bipedal humanoid robots has recently been on the increase. Humanoid robots are well suited for navigating environments created for humans, and have the potential to perform well on uneven terrain. Bipedal locomotion is a crucial area of interest, and the problems it presents are not yet fully solved. This poster discusses the simulation of a bipedal robot and the use of Genetic Programming techniques to evolve bipedal locomotion. Genetic Programming is a technique that uses the principles of natural selection to evolve programs. It allows computers to learn to solve problems without being explicitly programmed (Koza, 1992). The aim of the project is to apply Genetic Programming techniques to evolve a robot controller that is able to walk without having to explicitly describe the gait. When evolving a robot controller, it is not practical to use real hardware to test the fitness of individuals. The repeated testing would quickly wear out the hardware. Instead, by using a simulation we can happily subject our", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.596.3170", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.596.3170", } @InProceedings{johnson:1994:EVR, author = "Michael Patrick Johnson and Pattie Maes and Trevor Darrell", title = "Evolving Visual Routines", booktitle = "ARTIFICIAL LIFE IV, Proceedings of the fourth International Workshop on the Synthesis and Simulation of Living Systems", year = "1994", editor = "Rodney A. Brooks and Pattie Maes", pages = "198--209", address = "MIT, Cambridge, MA, USA", month = "6-8 " # jul, publisher = "MIT Press", keywords = "genetic algorithms, genetic programming", URL = "http://pubs.media.mit.edu/pubs/papers/alife-iv.ps.gz", URL = "http://citeseer.ist.psu.edu/402594.html", abstract = "Traditional machine vision assumes that the vision system recovers a complete, labeled description of the world [Marr]. Recently, several researchers have criticized this model and proposed an alternative model which considers perception as a distributed collection of task-specific, task-driven visual routines [Aloimonos, Ullman]. Some of these researchers have argued that in natural living systems these visual routines are the product of natural selection [ramachandran]. So far, researchers have hand-coded task-specific visual routines for actual implementations (e.g. [Chapman]). In this paper we propose an alternative approach in which visual routines for simple tasks are evolved using an artificial evolution approach. We present results from a series of runs on actual camera images, in which simple routines were evolved using Genetic Programming techniques [Koza]. The results obtained are promising: the evolved routines are able to correctly classify up to 93% of the images, which is better than the best algorithm we were able to write by hand.", notes = "alife-4 See also \cite{johnson:1994:EVRAL}", } @MastersThesis{jonhson:1995:mscthesis, author = "Michael Patrick Johnson", title = "Evolving Visual Routines", school = "School or Architecture and Planning, MIT, USA", year = "1995", month = sep, keywords = "genetic algorithms, genetic programming, visual routines, active vision, machine learning", URL = "http://pubs.media.mit.edu/pubs/papers/ms-thesis.ps.gz", URL = "http://citeseer.ist.psu.edu/johnson94evolving.html", size = "117 pages", notes = "Extension of \cite{johnson:1994:EVR} Applies Genetic Programming to the problem of Active Vision", } @Article{johnson:1994:EVRAL, author = "Michael Patrick Johnson and Pattie Maes and Trevor Darrell", title = "Evolving Visual Routines", journal = "Artificial Life", year = "1994", volume = "1", number = "4", pages = "373--389", month = "summer", keywords = "genetic algorithms, genetic programming, active vision, visual routines", DOI = "doi:10.1162/artl.1994.1.4.373", size = "17 pages", abstract = "Traditional machine vision assumes that the vision system recovers a complete, labeled description of the world [10]. Recently, several researchers have criticized this model and proposed an alternative model that considers perception as a distributed collection of task-specific, context-driven visual routines [1,12]. Some of these researchers have argued that in natural living systems these researchers have argued that in natural selection [11]. So far, researchers have hand-coded task-specific visual routines for actual implementations (e.g.,[3]). In this article we propose an alternative approach in which visual routines for simple tasks are created using an artificial evolution approach. We present results from a series of runs on actual camera images, in which simple routines were evolved using genetic programming techniques [7]. The results obtained are promising: The evolved routines are able to process correctly up to 93percent of the test images, which is better than any algorithm we were able to write by hand.", notes = "Extension of \cite{johnson:1994:EVR}", } @Article{johnson:1996:GPadac, author = "R. Colin Johnson", title = "Genetic program auto-designs analog circuits", journal = "Electronic Engineering Times", year = "1996", number = "904", month = "3 " # jun, keywords = "genetic algorithms, genetic programming", URL = "http://www.genetic-programming.com/published/eetimes060396.html", broken = "http://www.eet.com/news/96/hr903.html#genetic", notes = "short On-line publication", } @InCollection{johnson:1999:SPUGATS, author = "Soren Johnson", title = "Swords vs. Plowshares: Using Genetic Algorithms in Turn-Based Strategy", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1999", year = "1999", editor = "John R. Koza", pages = "76--85", address = "Stanford, California, 94305-3079 USA", month = "15 " # mar, publisher = "Stanford Bookstore", keywords = "genetic algorithms", notes = "part of \cite{koza:1999:GAGPs}", } @TechReport{Johnston:tr09-7, author = "Mark Johnston and Thomas Liddle and Mengjie Zhang", title = "A Linear Regression Approach to Numerical Simplification in Tree-Based Genetic Programming", institution = "School of Mathematics Statistics and Operations Research, Victoria University of Wellington", year = "2009", type = "Research report", number = "09-7", address = "New Zealand", month = "14 " # dec, keywords = "genetic algorithms, genetic programming", URL = "http://msor.victoria.ac.nz/twiki/pub/Main/ResearchReportSeries/msor09-07.pdf", abstract = "We propose a novel approach to simplification in tree-based Genetic Programming to combat program bloat, based upon numerical relaxations of algebraic rules.We also separate proposal of simplifications (using linear regression, removing redundant children, and replacing small ranges with a constant) from an acceptance criterion that checks the effect of proposed simplifications on the evaluation of training examples, looking several levels up the tree.We test our simplification method on three classification datasets and conclude that the success of linear regression is data set dependent, that looking further up the tree can catch unwanted bad case simplifications, and that CPU time can be significantly reduced while maintaining classification accuracy on unseen examples.", notes = "Wine, Wisconsin, Coins", size = "38 pages", } @InProceedings{Johnston:2010:EuroGP, author = "Mark Johnston and Thomas Liddle and Mengjie Zhang", title = "A Relaxed Approach to Simplification in Genetic Programming", booktitle = "Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010", year = "2010", editor = "Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar", volume = "6021", series = "LNCS", pages = "110--121", address = "Istanbul", month = "7-9 " # apr, organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-12147-0", DOI = "doi:10.1007/978-3-642-12148-7_10", abstract = "We propose a novel approach to program simplification in tree-based Genetic Programming, based upon numerical relaxations of algebraic rules. We also separate proposal of simplifications from an acceptance criterion that checks the effect of proposed simplifications on the evaluation of training examples, looking several levels up the tree. We test our simplification method on three classification datasets and conclude that the success of linear regression is dataset dependent, that looking further up the tree can catch ineffective simplifications, and that CPU time can be significantly reduced while maintaining classification accuracy on unseen examples.", notes = "Part of \cite{Esparcia-Alcazar:2010:GP} EuroGP'2010 held in conjunction with EvoCOP2010 EvoBIO2010 and EvoApplications2010", } @InProceedings{Johri:2015:ICCCA, author = "Prashant Johri and Arun Kumar and Amba Mishra", booktitle = "2015 International Conference on Computing, Communication Automation (ICCCA)", title = "Review paper on text and audio steganography using GA", year = "2015", pages = "190--192", abstract = "Steganography is used to hide the secret information within a cover media in such a way that the existence of the message could not be noticeable. Here we are considering audio file as cover media and text message as secret information. The secret information is embedded in a cover media as noise as the HAS cannot detect the sound less than 20Hz or greater than 20000Hz. Generally LSB algorithm is used to embed the secret information within a cover media. Here we are using genetic programming to increase the robustness of the data so that the secret data could not be noticeable as far as possible.", keywords = "genetic algorithms, genetic programming, PKE algorithm, LSB, RSA, HAS, HVS", DOI = "doi:10.1109/CCAA.2015.7148403", month = may, notes = "Also known as \cite{7148403}", } @Article{JOMEKIAN:2019:FPE, author = "Abolfazl Jomekian and Seyed Jalil Poormohammadian", title = "Improved prediction of solubility of gases in polymers using an innovative non-equilibrium lattice fluid/Flory-Huggins model", journal = "Fluid Phase Equilibria", volume = "500", pages = "112261", year = "2019", ISSN = "0378-3812", DOI = "doi:10.1016/j.fluid.2019.112261", URL = "http://www.sciencedirect.com/science/article/pii/S037838121930322X", keywords = "genetic algorithms, genetic programming, Polymers, Solubility prediction, Genetic programing, Flory-Huggins model, Non-equilibrium lattice fluid model, Sanchez-Lacombe equation of state", abstract = "A combination of Flory-Huggins and non-equilibrium lattice fluid models are used for predicting the solubility coefficients of CO2, CH4, N2, n-C4H10 and i-C4H10 in low and high-density polyethylene, polysulfone and polycarbonate. The genetic programming has been used to acquire the appropriate function for this model. The solubility coefficients at infinite dilution are calculated based on non-equilibrium lattice fluid theory and the gas-polymer interaction is expressed by the Flory-Huggins interaction parameter. The solubility coefficients at infinite dilution, Flory-Huggins interaction parameter and pressure were selected as terminal sets and some simple mathematical functions and operators such as multiplication, division, summation, power and absolute value were regarded as mathematical function sets. The adjustable parameters of the proposed function were determined for each gas-polymer system based on nonlinear data fitting. The first adjustable model parameter was in the form of constant power and the second was obtained as a variable coefficient in the form of quadratics function of temperature. The results of the presented model demonstrate improved ability to predict the solubility of investigated gases in the considered polymers at high pressures in comparison to non-equilibrium lattice fluid and Sanchez-Lacombe equation of state models. In some cases, the absolute errors between experimental and predicted values of solubility coefficients were below 1percent at the considered conditions", } @MastersThesis{Jones:1991:masters, author = "A. Jones", title = "Writing Programs Using Genetic Algorithms", school = "Department of Computer Science, University of Manchester, United Kingdom", year = "1991", keywords = "genetic algorithms, genetic programming", } @Article{jones:1993:GPreview, author = "Antonia J. Jones", title = "Nature's Way", journal = "Nature", year = "1993", volume = "363", number = "6426", pages = "222", note = "Book Review", keywords = "genetic algorithms, genetic programming", URL = "http://adsabs.harvard.edu/abs/1993Natur.363..222J", DOI = "doi:10.1038/363222a0", size = "1/2 pages", abstract = "Genetic Programming: On the Programming of Computers by Means of Natural Selection. By John R. Koza. MIT Press: 1992.", notes = "review of \cite{koza:book} ", } @Article{Jones:1998:qmpmalssl, author = "Alun Jones and Daniella Young and Janet Taylor and Douglas B. Kell and Jem J Rowland", title = "Quantification of microbial productivity via multi-angle light scattering and supervised learning", journal = "Biotechnology and Bioengineering", year = "1998", volume = "59", number = "2", pages = "131--143", month = "20 " # jul, publisher = "John Wiley and Sons", keywords = "genetic algorithms, genetic programming, chemometrics, light scattering. microbial productivity", ISSN = "0006-3592", DOI = "doi:10.1002/(SICI)1097-0290(19980720)59:2%3C131::AID-BIT1%3E3.0.CO%3B2-I", abstract = "This article describes the use of chemometric methods for prediction of biological parameters of cell suspensions on the basis of their light scattering profiles. Laser light is directed into a vial or flow cell containing media from the suspension. The intensity of the scattered light is recorded at 18 angles. Supervised learning methods are then used to calibrate a model relating the parameter of interest to the intensity values. Using such models opens up the possibility of estimating the biological properties of fermentor broths extremely rapidly (typically every 4 sec), and, using the flow cell, without user interaction. Our work has demonstrated the usefulness of this approach for estimation of yeast cell counts over a wide range of values (10(5)-10(9) cells mL-1), although it was less successful in predicting cell viability in such suspensions.", notes = "PMID: 10099324", } @Misc{percolation-of-the-impact-of-coding-mistakes-through-a-program, author = "Derek Jones", title = "Percolation of the impact of coding mistakes through a program", year = "2023", month = "2 " # apr, keywords = "genetic algorithms, genetic programming, genetic improvement, error analysis, error detection, error recovery", URL = "https://shape-of-code.com/2023/04/02/percolation-of-the-impact-of-coding-mistakes-through-a-program/", code_url = "https://github.com/Derek-Jones/ESEUR-code-data/blob/master/reliability/1611-09187a.R", data_url = "https://www.shape-of-code.com/code-data/gi-expr-tree.tgz", size = "1 page", notes = "many cites including \cite{danglot:hal-01378523} https://stacks.stanford.edu/file/druid:zm955yw2192/Hyungmin_thesis_submit-augmented.pdf \cite{langdon:2022:GECCO2} ", } @InProceedings{Jones:2017:IROS, author = "Dominic Jones and Hongbo Wang and Ali Alazmani and Peter R. Culmer", title = "A soft multi-axial force sensor to assess tissue properties in RealTime", booktitle = "2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)", year = "2017", pages = "5738--5743", address = "Vancouver, Canada", month = "24-28 " # sep, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IROS.2017.8206464", size = "6 pages", abstract = "Objective: This work presents a method for the use of a soft multi-axis force sensor to determine tissue trauma in Minimally Invasive Surgery. Despite recent developments, there is a lack of effective haptic sensing technology employed in instruments for Minimally Invasive Surgery (MIS). There is thus a clear clinical need to increase the provision of haptic feedback and to perform real-time analysis of haptic data to inform the surgical operator. This paper establishes a methodology for the capture of real-time data through use of an inexpensive prototype grasper. Fabricated using soft silicone and 3D printing, the sensor is able to precisely detect compressive and shear forces applied to the grasper face. The sensor is based upon a magnetic soft tactile sensor, using variations in the local magnetic field to determine force. The performance of the sensing element is assessed and a linear response was observed, with a max hysteresis error of 4.1percent of the maximum range of the sensor. To assess the potential of the sensor for surgical sensing, a simulated grasping study was conducted using ex vivo porcine tissue. Two previously established metrics for prediction of tissue trauma were obtained and compared from recorded data. The normalized stress rate (kPa.mm -1 ) of compression and the normalized stress relaxation (Delta rho R) were analysed across repeated grasps. The sensor was able to obtain measures in agreement with previous research, demonstrating future potential for this approach. In summary this work demonstrates that inexpensive soft sensing systems can be used to instrument surgical tools and thus assess properties such as tissue health. This could help reduce surgical error and thus benefit patients.", notes = "p5739 'The system was calibrated using a Genetic Programming algorithm to define a discrete relationship between force and magnetic field' also known as \cite{8206464} School of Mechanical Engineering, University of Leeds", } @InProceedings{jones:1999:Gdec, author = "Eric A. Jones and William T. Joines", title = "Genetic design of electronic circuits", booktitle = "Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference", year = "1999", editor = "Scott Brave and Annie S. Wu", pages = "125--133", address = "Orlando, Florida, USA", month = "13 " # jul, keywords = "genetic algorithms, genetic programming, low-pass filter design, grammatical evolution", notes = "GECCO-99LB. Grammatical Evolution", } @InCollection{jones:2005:GPTP, author = "Lee W. Jones and Sameer H. Al-Sakran and John R. Koza", title = "Automated Design of a Previously Patented Aspherical Optical Lens System by Means of Genetic Programming", booktitle = "Genetic Programming Theory and Practice {III}", year = "2005", editor = "Tina Yu and Rick L. Riolo and Bill Worzel", volume = "9", series = "Genetic Programming", chapter = "3", pages = "33--48", address = "Ann Arbor", month = "12-14 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Automated design, optical lens system, aspherical lenses, developmental process, replication of previously patented invention, human-competitive result, Automated design, replication of previously patented invention", ISBN = "0-387-28110-X", DOI = "doi:10.1007/0-387-28111-8_3", size = "16 pages", abstract = "This chapter describes how genetic programming was used as an invention machine to automatically synthesise a complete design for an aspherical optical lens system (a type of lens system that is especially difficult to design and that offers advantages in terms of cost, weight, size, and performance over traditional spherical systems). The genetically evolved aspherical lens system duplicated the functionality of a recently patented aspherical system. The automatic synthesis was open-ended --- that is, the process did not start from a pre-existing good design and did not pre-specify the number of lenses, which lenses (if any) should be spherical or aspherical, the topological arrangement of the lenses, the numerical parameters of the lenses, or the non-numerical parameters of the lenses. The genetically evolved design is an instance of human-competitive results produced by genetic programming in the field of optical design.", notes = "part of \cite{yu:2005:GPTP} Published Jan 2006 after the workshop", } @InProceedings{1144143, author = "Lee W. Jones and Sameer H. Al-Sakran and John R. Koza", title = "Automated synthesis of a human-competitive solution to the challenge problem of the 2002 international optical design conference by means of genetic programming and a multi-dimensional mutation operation", booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation", year = "2006", editor = "Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos {Coello Coello} and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens", volume = "1", ISBN = "1-59593-186-4", pages = "823--830", address = "Seattle, Washington, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p823.pdf", DOI = "doi:10.1145/1143997.1144143", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "8-12 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, automated design, human-competitive result, International optical design conference, invention machine, mutation operation, optical lens system", notes = "GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM Order Number 910060", } @InProceedings{jonkergouw:2005:icannga, author = "Philip Jonkergouw and Ed Keedwell and Soon-Thiam Khu", title = "Modelling Chlorine Decay in Water Networks with Genetic Programming", pages = "206--209", booktitle = "Adaptive and Natural Computing Algorithms", year = "2005", editor = "Bernardete Ribeiro and Rudof F. Albrecht and Andrej Dobnikar and David W. Pearson and Nigel C. Steele", series = "Springer Computer Series", publisher = "Springer", ISBN = "3-211-24934-6", address = "Coimbra, Portugal", month = "21-23 " # mar, keywords = "genetic algorithms, genetic programming", notes = "http://icannga05.dei.uc.pt/", DOI = "doi:10.1007/3-211-27389-1_49", abstract = "The disinfection of water supplies for domestic consumption is often achieved with the use of chlorine. Aqueous chlorine reacts with many harmful micro-organisms and other aqueous constituents when added to the water supply, which causes the chlorine concentration to decay over time. Up to a certain extent, this decay can be modelled using various decay models that have been developed over the last 50+ years. Assuming an accurate prediction of the chlorine concentration over time, a measured deviation from the values provided by such a decay model could be used as an indicator of harmful (intentional) contamination. However, current chlorine decay models have been based on assumptions that do not allow the modelling of another species, i.e. the species with which chlorine is reacting, thereby limiting their use for modelling the effect of a contaminant on chlorine. This paper investigates the use of genetic programming as a method for developing a mixed second-order chlorine decay model.", } @Article{jonoska:2003:GPEM, author = "Natasha Jonoska and Phiset {Sa-Ardyen} and Nadrian C. Seeman", title = "Computation by Self-assembly of {DNA} Graphs", journal = "Genetic Programming and Evolvable Machines", year = "2003", volume = "4", number = "2", pages = "123--137", month = jun, keywords = "DNA-computing, self-assembly, junction molecules, ligation, 3-SAT, graphs", ISSN = "1389-2576", DOI = "doi:10.1023/A:1023980828489", abstract = "Using three dimensional graph structure and DNA self-assembly we show that theoretically 3-SAT and 3-colourability can be solved in a constant number of laboratory steps. In this assembly, junction molecules and duplex DNA molecules are the basic building blocks. The graphs involved are not necessarily regular, so experimental results of self-assembling non regular graphs using junction molecules as vertices and duplex DNA molecules as edge connections are presented.", notes = "Special Issue on Biomolecular Machines and Artificial Evolution Article ID: 5122741", } @Article{Jonoska:2006:GPEM, author = "Natasa Jonoska", title = "Theoretical and Experimental DNA Computation Published by: Springer-Verlag, Martyn Amos 172 pages, 78 figures, 2005, ISBN-10 3-540-65773-8", journal = "Genetic Programming and Evolvable Machines", year = "2006", volume = "7", number = "3", pages = "287--291", month = oct, note = "Book Review", keywords = "DNA computing", ISSN = "1389-2576", URL = "https://rdcu.be/dR8vE", DOI = "doi:10.1007/s10710-006-9011-9", } @InCollection{jonsson:1996:csb, author = "Per Jonsson and Jonas Barklund", title = "Characterizing Signal Behaviour Using Genetic Programming", booktitle = "Evolutionary Computing", publisher = "Springer-Verlag", year = "1996", editor = "Terence C. Fogarty", number = "1143", series = "Lecture Notes in Computer Science", pages = "62--72", address = "University of Sussex, UK", month = "1-2 " # apr, keywords = "genetic algorithms, genetic programming", ISBN = "3-540-61749-3", DOI = "doi:10.1007/BFb0032773", size = "11 pages", abstract = "Our overall goal is to detect automatically that a signal begins to deviate from its previous behaviours, using no other information than a sequence of samples of the signal. In order to detect such changes we use genetic programming to evolve an expression describing how the signal varies over time. One major difficulty when observing such signals is that they typically contain noise and other disturbances. Such disturbances makes it more difficult to find a useful expression characterising the signal. We have derived a new method that simultaneously evolves a numeral denoting the number of neighbours to use in a moving average of the signal, and an expression characterizing the smoothed signal.", notes = "The post-workshop proceedings of the 1996 AISB workshop on evolutionary computing.", affiliation = "Uppsala University Computing Science Department Box 311 751 05 Uppsala Sweden Box 311 751 05 Uppsala Sweden", } @InProceedings{Jonyer:2006:FLAIRS, author = "Istvan Jonyer and Akiko Himes", title = "Improving Modularity in Genetic Programming Using Graph-Based Data Mining", booktitle = "Proceedings of the Nineteenth International Florida Artificial Intelligence Research Society Conference", year = "2006", editor = "Geoff C. J. Sutcliffe and Randy G. Goebel", pages = "556--561", address = "Melbourne Beach, Florida, USA", month = may # " 11-13", publisher = "American Association for Artificial Intelligence", keywords = "genetic algorithms, genetic programming, Machine Learning and Discovery", URL = "http://www.aaai.org/Papers/FLAIRS/2006/Flairs06-110.pdf", abstract = "We propose to improve the efficiency of genetic programming, a method to automatically evolve computer programs. We use graph-based data mining to identify common aspects of highly fit individuals and modularising them by creating functions out of the subprograms identified. Empirical evaluation on the lawn mower problem shows that our approach is successful in reducing the number of generations needed to find target programs. Even though the graph-based data mining system requires additional processing time, the number of individuals required in a generation can also be greatly reduced, resulting in an overall speed-up.", notes = "cited by \cite{Spector:2011:GECCO} http://www.cs.miami.edu/~geoff/Conferences/FLAIRS-19/Schedule.shtml http://www.aaai.org/Press/Proceedings/flairs06.php", } @InProceedings{Joo:2009:eurogp, author = "Andras Joo", title = "Mining Evolving Learning Algorithms", booktitle = "Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009", year = "2009", editor = "Leonardo Vanneschi and Steven Gustafson and Alberto Moraglio and Ivanoe {De Falco} and Marc Ebner", volume = "5481", series = "LNCS", pages = "73--84", address = "Tuebingen", month = apr # " 15-17", organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-01180-1", DOI = "doi:10.1007/978-3-642-01181-8_7", notes = "Part of \cite{conf/eurogp/2009} EuroGP'2009 held in conjunction with EvoCOP2009, EvoBIO2009 and EvoWorkshops2009", } @InProceedings{DBLP:conf/gecco/JooN09, author = "Andras Joo and Juan Pablo Neirotti", title = "Towards identifying salient patterns in genetic programming individuals", booktitle = "GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation", year = "2009", editor = "Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano {van Hemert} and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique Alba", pages = "1885--1886", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming, Poster", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1569901.1570217", abstract = "A practical method for the offline extraction and analysis of salient patterns from tree-based genetic programming (GP) individuals is proposed. The method is contrasted with Tackett's algorithm [7] and it is shown that relying solely on frequency and fitness profiles for the salient pattern identification can be misleading. To amend Tackett's work a formula for measuring saliency is proposed. A method for separating inert and salient patterns is also discussed.", notes = "GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009). ACM Order Number 910092.", } @PhdThesis{a.m.joo.phd.069952236, author = "Andras Matyas Joo", title = "Towards identifying salient patterns in genetic programming individuals", school = "Aston University", year = "2010", address = "Birmingham, UK", month = jun, keywords = "genetic algorithms, genetic programming, tree mining, data mining, PGA", URL = "http://eprints.aston.ac.uk/13364/", URL = "http://eprints.aston.ac.uk/13364/1/a.m.joo.phd.069952236.pdf", URL = "http://ethos.bl.uk/OrderDetails.do?did=29&uin=uk.bl.ethos.533151", size = "90 pages", abstract = "This thesis addresses the problem of offline identification of salient patterns in genetic programming individuals. It discusses the main issues related to automatic pattern identification systems, namely that these (a) should help in understanding the final solutions of the evolutionary run, (b) should give insight into the course of evolution and (c) should be helpful in optimising future runs. Moreover, it proposes an algorithm, Extended Pattern Growing Algorithm ([E]PGA) to extract, filter and sort the identified patterns so that these fulfill as many as possible of the following criteria: (a) they are representative for the evolutionary run and/or search space, (b) they are human-friendly and (c) their numbers are within reasonable limits. The results are demonstrated on six problems from different domains", notes = "[E]PGA uk.bl.ethos.533151", } @InProceedings{Jordaan:PPSN:2004, author = "Elsa Jordaan and Arthur Kordon and Leo Chiang and Guido Smits", title = "Robust Inferential Sensors based on Ensemble of Predictors generated by Genetic Programming", booktitle = "Parallel Problem Solving from Nature - PPSN VIII", year = "2004", editor = "Xin Yao and Edmund Burke and Jose A. Lozano and Jim Smith and Juan J. Merelo-Guerv\'os and John A. Bullinaria and Jonathan Rowe and Peter Ti\v{n}o Ata Kab\'an and Hans-Paul Schwefel", volume = "3242", pages = "522--531", series = "LNCS", address = "Birmingham, UK", publisher_address = "Berlin", month = "18-22 " # sep, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-23092-0", URL = "https://rdcu.be/dc0jT", DOI = "doi:10.1007/b100601", DOI = "doi:10.1007/978-3-540-30217-9_53", abstract = "Inferential sensors are mathematical models used to predict the quality variables of industrial processes. One factor limiting the widespread use of soft sensors in the process industry is their inability to cope with non-constant noise in the data and process variability. A novel approach for inferential sensors design with increased robustness is proposed in the paper. It is based on three techniques. The first technique increases robustness by using explicit nonlinear functions derived by Genetic Programming. The second technique applies multi-objective model selection on a Pareto-front to guarantee the right balance between accuracy and complexity. The third technique uses ensembles of predictors for more consistent estimates and possible self-assessment capabilities. The increased robustness of the proposed sensor is demonstrated on a number of industrial applications.", notes = "PPSN-VIII", } @InProceedings{Jordaan:PPSN:2006, author = "Elsa Jordaan and Jaap {den Doelder} and Guido Smits", title = "Novel Approach to Develop Rheological Structure-Property Relationships Using Genetic Programming", booktitle = "Parallel Problem Solving from Nature - PPSN IX", year = "2006", editor = "Thomas Philip Runarsson and Hans-Georg Beyer and Edmund Burke and Juan J. Merelo-Guervos and L. Darrell Whitley and Xin Yao", volume = "4193", pages = "322--331", series = "LNCS", address = "Reykjavik, Iceland", publisher_address = "Berlin", month = "9-13 " # sep, publisher = "Springer-Verlag", ISBN = "3-540-38990-3", keywords = "genetic algorithms, genetic programming, rheology, molar mass distribution.", DOI = "doi:10.1007/11844297_33", size = "10 pages", abstract = "Rheological structure-property models play a crucial role in the manufacturing and processing of polymers. Traditionally rheological models are developed by design of experiments that measure a rheological property as a function of the moments of molar mass distributions. These empirical models lack the capacity to apply to a wide range of distributions due the limited availability of experimental data. In recent years fundamental models were developed to satisfy a wider range of distributions, but they are in terms of variables not readily available during processing or manufacturing. Genetic programming can be used to bridge the gap between the practical, but limited, empirical models and the more general, but less practical, fundamental models. This is a novel approach of generating rheological models that are both practical and valid for a wide set of distributions.", notes = "PPSN-IX", } @Article{Jordanous:2022:GPEM, author = "Anna Jordanous", title = "Review: {Machado, Romero and Greenfield} (editors): Artificial intelligence and the arts", journal = "Genetic Programming and Evolvable Machines", year = "2022", volume = "23", number = "4", pages = "583--584", month = dec, keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/cQnPo", DOI = "doi:10.1007/s10710-022-09440-0", size = "2 pages", abstract = "Artificial Intelligence and the Arts, Computational Creativity, Artistic Behavior, and Tools for Creatives, ISBN: 978-3-030-59474-9, Springer, 2021", notes = "School of Computing, University of Kent, Canterbury, Kent, UK", } @InProceedings{me16, author = "K. J{\o}rgensen and B. Elfrink and M. Keijzer and V. Babovic", title = "Analysis of long term morphological changes: A data mining approach", booktitle = "Proceedings of the International Conference on Coastal Engineering", address = "Australia", year = "2000", keywords = "genetic algorithms, genetic programming", } @InProceedings{jorgensen:2024:GECCO, author = "Steven Jorgensen and Giorgia Nadizar and Gloria Pietropolli and Luca Manzoni and Eric Medvet and Una-May O'Reilly and Erik Hemberg", title = "Large Language Model-based Test Case Generation for {GP} Agents", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference", year = "2024", editor = "Ting Hu and Aniko Ekart and Julia Handl and Xiaodong Li and Markus Wagner and Mario Garza-Fabre and Kate Smith-Miles and Richard Allmendinger and Ying Bi and Grant Dick and Amir H Gandomi and Marcella Scoczynski Ribeiro Martins and Hirad Assimi and Nadarajen Veerapen and Yuan Sun and Mario Andres Munyoz and Ahmed Kheiri and Nguyen Su and Dhananjay Thiruvady and Andy Song and Frank Neumann and Carla Silva", pages = "914--923", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, linear GP, large language models, curriculum learning", isbn13 = "979-8-4007-0494-9", DOI = "doi:10.1145/3638529.3654056", size = "10 pages", abstract = "Genetic programming (GP) is a popular problem-solving and optimization technique. However, generating effective test cases for training and evaluating GP programs requires strong domain knowledge. Furthermore, GP programs often prematurely converge on local optima when given excessively difficult problems early in their training. Curriculum learning (CL) has been effective in addressing similar issues across different reinforcement learning (RL) domains, but it requires the manual generation of progressively difficult test cases as well as their careful scheduling. In this work, we leverage the domain knowledge and the strong generative abilities of large language models (LLMs) to generate effective test cases of increasing difficulties and schedule them according to various curricula. We show that by integrating a curriculum scheduler with LLM-generated test cases we can effectively train a GP agent player with environments-based curricula for a single-player game and opponent-based curricula for a multi-player game. Finally, we discuss the benefits and challenges of implementing this method for other problem domains.", notes = "GECCO-2024 GP A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{Joseph:2024:evoapplications, author = "Marshall Joseph and Brian J. Ross", title = "Using Evolution and Deep Learning to Generate Diverse Intelligent Agents", booktitle = "27th International Conference, EvoApplications 2024", year = "2024", editor = "Stephen Smith and Joao Correia and Christian Cintrano", series = "LNCS", volume = "14635", publisher = "Springer", address = "Aberystwyth", month = "3-5 " # apr, pages = "361--375", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, games", isbn13 = "978-3-031-56854-1", URL = "https://rdcu.be/dD0og", DOI = "doi:10.1007/978-3-031-56855-8_22", notes = "http://www.evostar.org/2024/ EvoApplications2024 held in conjunction with EuroGP'2024, EvoCOP2024 and EvoMusArt2024", } @Article{Jothiprakash:2009:ISHjhe, author = "V. Jothiprakash and R. Magar", title = "Soft Computing tools in Rainfall-runoff Modeling", journal = "ISH Journal of Hydraulic Engineering", year = "2009", volume = "15", number = "sup1", pages = "84--96", keywords = "genetic algorithms, genetic programming", publisher = "Taylor \& Francis", organisation = "Indian Society for Hydraulics", DOI = "doi:10.1080/09715010.2009.10514970", abstract = "The use of rainfall-runoff models in the decision making process of water resources planning and management has become increasingly indispensable. Rainfall-runoff modeling in the broad sense started at the end of 19th century and till today there are various types of models based on their mechanism, input data and other modeling requirements. These type of models range from physical, conceptual, empirical models and more sophisticated models like Artificial Neural Network (ANN), Adaptive Neuro Fuzzy Inference System (ANFIS), Genetic Programming (GP), Model Tree (MT), Support Vector Machine (SVM) and recently Chaos theory. The primary aim of this paper is to review the recent works on Rainfall-Runoff modeling using soft computing techniques.", } @Article{Jothiprakash2012293, author = "V. Jothiprakash and R. B. Magar", title = "Multi-time-step ahead daily and hourly intermittent reservoir inflow prediction by artificial intelligent techniques using lumped and distributed data", journal = "Journal of Hydrology", year = "2012", volume = "450-451", month = "11 " # jul, pages = "293--307", month = "11 " # jul, keywords = "genetic algorithms, genetic programming, Time-series models, Cause-effect models, Combined models, Daily and hourly, Lumped and distributed data, Artificial intelligent techniques, ANN, India", ISSN = "0022-1694", URL = "http://www.sciencedirect.com/science/article/pii/S0022169412003459", DOI = "doi:10.1016/j.jhydrol.2012.04.045", abstract = "artificial intelligent (AI) techniques such as artificial neural network (ANN), Adaptive neuro-fuzzy inference system (ANFIS) and Linear genetic programming (LGP) are used to predict daily and hourly multi-time-step ahead intermittent reservoir inflow. To illustrate the applicability of AI techniques, intermittent Koyna river watershed in Maharashtra, India is chosen as a case study. Based on the observed daily and hourly rainfall and reservoir inflow various types of time-series, cause-effect and combined models are developed with lumped and distributed input data. Further, the model performance was evaluated using various performance criteria. From the results, it is found that the performances of LGP models are found to be superior to ANN and ANFIS models especially in predicting the peak inflows for both daily and hourly time-step. A detailed comparison of the overall performance indicated that the combined input model (combination of rainfall and inflow) performed better in both lumped and distributed input data modelling. It was observed that the lumped input data models performed slightly better because; apart from reducing the noise in the data, the better techniques and their training approach, appropriate selection of network architecture, required inputs, and also training-testing ratios of the data set. The slight poor performance of distributed data is due to large variations and lesser number of observed values.", } @InProceedings{Jothiprakash:2012:GII-ISH, author = "V. Jothiprakash and R. B. Magar", title = "Reservoir Inflow Modeling Using Artificial Intelligence. Techniques and its Use in Real Life", booktitle = "2nd Indo-Italian Workshop on Hydraulic Sciences", year = "2012", address = "Venice, Italy", month = jul # " 19-20", keywords = "genetic algorithms, genetic programming, Linear Genetic Programming, LGP, ANN, Koyna, KRIFS", URL = "https://aiktc.ndl.gov.in/items/0de6873f-d20a-4a9a-8966-40ff770fe240/full", URL = "https://aiktc.ndl.gov.in/bitstreams/1d530e34-c790-4df6-bd5e-62d5bb5dfec1/download", URL = "http://hdl.handle.net/123456789/862", size = "12 pages", notes = "LGP validate by dam authorities... field engineers found it very useful ... in use at least one year Rajendra Magar", } @Article{Jothsna:2013:ijetae, author = "Kotha Jothsna and R. V. Krishniah", title = "A Signature-Free Buffer Overflow Attack Blocker Using Genetic Programming", journal = "International Journal of Emerging Technology and Advanced Engineering", year = "2013", volume = "3", number = "2", pages = "640--647", month = feb, keywords = "genetic algorithms, genetic programming, code injection, intrusion detection systems", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.413.8516", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.413.8516", URL = "http://www.ijetae.com/files/Volume3Issue2/IJETAE_0213_109.pdf", ISSN = "2250-2459", size = "8 pages", abstract = "Now days Internet threat takes a blended attack form, targeting individual users to gain control over networks and data. Buffer Overflow which is one of the most occurring security vulnerabilities in Internet services such as such as web service, cloud service etc. Motivated by the observation that buffer overflow attacks typically contain executables whereas legitimate client requests never contain executables in most Internet services. Unlike the previous detection algorithms, a new SigFree uses a Genetic Programming technique that is generic, fast, and hard for exploit code to evade. SigFree blocks attacks by detecting the presence of code, it is a signature free, thus it can block new and unknown buffer overflow attacks; SigFree is also immunised from most attack-side code obfuscation. To do so, we pay particular attention to the formulation of an appropriate fitness function and partnering instruction set. Moreover, by making use of the intron behaviour inherent in the genetic programming paradigm, we are able to explicitly Obfuscate the true intent of the code. All the resulting attacks Defeat the widely used in Intrusion Detection System.", } @Article{Jourdan2009620, author = "L. Jourdan and M. Basseur and E.-G. Talbi", title = "Hybridizing exact methods and metaheuristics: A taxonomy", journal = "European Journal of Operational Research", volume = "199", number = "3", pages = "620--629", year = "2009", ISSN = "0377-2217", DOI = "doi:10.1016/j.ejor.2007.07.035", URL = "http://www.sciencedirect.com/science/article/B6VCT-4S8K9FW-5/2/da4a040e6d29d78527bb46fcab2eeacd", keywords = "genetic algorithms, genetic programming, Taxonomy, Combinatorial optimisation, Metaheuristics, Exact methods", abstract = "The interest about hybrid optimisation methods has grown for the last few years. Indeed, more and more papers about cooperation between heuristics and exact techniques are published. In this paper, we propose to extend an existing taxonomy for hybrid methods involving heuristic approaches in order to consider cooperative schemes between exact methods and metaheuristics. First, we propose some natural approaches for the different schemes of cooperation encountered, and we analyse, for each model, some examples taken from the literature. Then we recall and complement the proposed grammar and provide an annotated bibliography.", } @Article{JOVIC:2018:CEA, author = "Srdjan Jovic and Blagoje Nedeljkovic and Zoran Golubovic and Nikola Kostic", title = "Evolutionary algorithm for reference evapotranspiration analysis", journal = "Computers and Electronics in Agriculture", volume = "150", pages = "1--4", year = "2018", keywords = "genetic algorithms, genetic programming, Evolutionary algorithm, Evapotranspiration, Estimation", ISSN = "0168-1699", DOI = "doi:10.1016/j.compag.2018.04.003", URL = "http://www.sciencedirect.com/science/article/pii/S0168169918303934", abstract = "Evapotranspiration of important indicator for management and planning of water resources. It is essential to analyze the evapotranspiration in order to improve water resources planning. The main goal of the study was to analyze the evapotranspiration based on several input parameters. It is important to estimate the influence of the input parameters on the evapotranspiration. For such a purpose evolutionary algorithm was applied. The algorithm applied in this article has space solution of genetic programs. Therefore this methodology is known as genetic programming. The input parameters in the model are monthly minimum and maximum air temperatures, sunshine hours, actual vapour pressure, minimum and maximum relative humidity and wind speed. Results presented in this study could be used for practical application of water resources planning and management based on the input parameters influence on the evapotranspiration", } @Article{Ju:2024:swevo, author = "Zeliang Ju and Yan Wang and Zhen Quan and Xiang Liu and Zhicheng Ji", title = "Bottleneck alleviation and scheduling optimization of flexible manufacturing system based on information-energy flow model", journal = "Swarm and Evolutionary Computation", year = "2024", volume = "89", pages = "101600", keywords = "genetic algorithms, genetic programming, Flexible job shop scheduling, Generation hyper-heuristics, Bottleneck identification, Bottleneck alleviation, Information flow model", ISSN = "2210-6502", URL = "https://www.sciencedirect.com/science/article/pii/S221065022400138X", DOI = "doi:10.1016/j.swevo.2024.101600", abstract = "The identification and alleviation of bottleneck machines in manufacturing systems are of paramount importance for optimising production decision-making in enterprises. Extensive research has been conducted by numerous scholars on the identification of bottleneck machines; however, there is a relative scarcity of research, particularly in the context of flexible manufacturing systems, regarding how to effectively alleviate the identified bottlenecks. In this paper, a novel hyper-heuristic evolutionary scheduling algorithm with a bottleneck alleviation strategy based on the information-energy flow model (HESA-BA-IEF) is proposed for training dispatching rules and optimising decision-making in flexible job shops with bottleneck machines. HESA-BA-IEF constructs an information-energy flow model for flexible job shops based on complex networks. Through this model, dynamic changes in machine processing status and energy consumption during the scheduling process can be effectively simulated, facilitating the accurate identification of bottleneck machines. Moreover, the algorithm adopts the genetic programming hyper-heuristics and introduces a scheduling mechanism considering the alleviation of bottleneck machines to optimise the makespan and total energy consumption, while simultaneously alleviating bottlenecks. Experiments show that HESA-BA-IEF generates dispatching rules that optimise scheduling and alleviate bottlenecks simultaneously. Furthermore, statistical analysis reveals a 7.71percent reduction in bottleneck metrics of production line due to 30.40percent scheduling rounds with bottleneck alleviation mechanism in effect", } @Article{DBLP:journals/ijimai/JuanWLCYCS21, author = "Chun-Jung Juan and Chen-Shu Wang and Bo-Yi Lee and Shang-Yu Chiang and Chun-Chang Yeh and Der-Yang Cho and Wu-Chung Shen", title = "Integration of Genetic Programming and {TABU} Search Mechanism for Automatic Detection of Magnetic Resonance Imaging in Cervical Spondylosis", journal = "Int. J. Interact. Multim. Artif. Intell.", volume = "6", number = "7", pages = "109", year = "2021", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.9781/ijimai.2021.08.006", DOI = "doi:10.9781/ijimai.2021.08.006", timestamp = "Fri, 12 Nov 2021 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/ijimai/JuanWLCYCS21.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{Juarez-Smith:2016:GECCOcomp, author = "Perla Juarez-Smith and Leonardo Trujillo", title = "Integrating Local Search within {neat-GP}", booktitle = "GECCO '16 Companion: Proceedings of the Companion Publication of the 2016 Annual Conference on Genetic and Evolutionary Computation", year = "2016", editor = "Tobias Friedrich and Frank Neumann and Andrew M. Sutton and Martin Middendorf and Xiaodong Li and Emma Hart and Mengjie Zhang and Youhei Akimoto and Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and Daniele Loiacono and Julian Togelius and Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and Faustino Gomez and Carlos M. Fonseca and Heike Trautmann and Alberto Moraglio and William F. Punch and Krzysztof Krawiec and Zdenek Vasicek and Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and Boris Naujoks and Enrique Alba and Gabriela Ochoa and Simon Poulding and Dirk Sudholt and Timo Koetzing", isbn13 = "978-1-4503-4323-7", pages = "993--996", address = "Denver, Colorado, USA", month = "20-24 " # jul, keywords = "genetic algorithms, genetic programming", organisation = "SIGEVO", DOI = "doi:10.1145/2908961.2931659", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "There are two important limitations of standard tree-based genetic programming (GP). First, GP tends to evolve unnecessarily large programs, what is referred to as bloat. Second, it uses inefficient search operators that operate at the syntax level. The first problem has been the subject of a fair amount of research over the years. Regarding the second problem, one approach is to use alternative search operators, for instance geometric semantic operators. However, another approach is to introduce greedy local search strategies, combining the syntactic search performed by standard GP with local search strategies for solution tuning, which is a simple strategy that has comparatively received much less attention. This work combines a recently proposed bloat-free GP called neat-GP with a local search strategy. One benefit of using a bloat-free GP is that it reduces the size of the parameter space confronted by the local searcher, offsetting some of the added computational cost. The algorithm is validated on a real-world problem with promising results.", notes = "GECCO Student Workshop, Best Paper Award 2nd Place. Distributed at GECCO-2016.", } @Article{Juarez-Smith:GPEM, author = "Perla Juarez-Smith and Leonardo Trujillo and Mario Garcia-Valdez and Francisco {Fernandez de Vega} and Francisco Chavez", title = "Local search in speciation-based bloat control for genetic programming", journal = "Genetic Programming and Evolvable Machines", year = "2019", volume = "20", number = "3", pages = "351--384", month = sep, keywords = "genetic algorithms, genetic programming, Bloat, NEAT, Local search", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-019-09351-7", size = "34 pages", abstract = "This work presents a unique genetic programming (GP) approach that integrates a numerical local search method and a bloat-control mechanism to address some of the main issues with traditional GP. The former provides a directed search operator to work in conjunction with standard syntax operators that perform more exploration in design space, while the latter controls code growth by maintaining program diversity through speciation. The system can produce highly parsimonious solutions, thus reducing the cost of performing the local optimization process. The proposal is extensively evaluated using real-world problems from diverse domains, and the behavior of the search is analyzed from several different perspectives, including how species evolve, the effect of the local search process and the interpretability of the results. Results show that the proposed approach compares favorably with a standard approach, and that the hybrid algorithm can be used as a viable alternative for solving real-world symbolic regression problems.", } @Article{juarez-smith:2019:MCA, author = "Perla Juarez-Smith and Leonardo Trujillo and Mario Garcia-Valdez and Francisco {Fernandez de Vega} and Francisco Chavez", title = "{Pool-Based} Genetic Programming Using Evospace, Local Search and Bloat Control", journal = "Mathematical and Computational Applications", year = "2019", volume = "24", number = "3", keywords = "genetic algorithms, genetic programming", ISSN = "2297-8747", URL = "https://www.mdpi.com/2297-8747/24/3/78", DOI = "doi:10.3390/mca24030078", abstract = "This work presents a unique genetic programming (GP) approach that integrates a numerical local search method and a bloat-control mechanism within a distributed model for evolutionary algorithms known as EvoSpace. The first two elements provide a directed search operator and a way to control the growth of evolved models, while the latter is meant to exploit distributed and cloud-based computing architectures. EvoSpace is a Pool-based Evolutionary Algorithm, and this work is the first time that such a computing model has been used to perform a GP-based search. The proposal was extensively evaluated using real-world problems from diverse domains, and the behaviour of the search was analysed from several different perspectives. The results show that the proposed approach compares favorably with a standard approach, identifying promising aspects and limitations of this initial hybrid system.", notes = "also known as \cite{mca24030078}", } @TechReport{juels:1995:shceGA, author = "Ari Juels and Martin Wattenberg", title = "Stochastic Hillclimbing as a Baseline Method for Evaluating Genetic Algorithms", institution = "Department of Computer Science, University of California at Berkeley", year = "1995", type = "Technical Report", number = "CSD-94-834", address = "USA", month = "18 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://www.eecs.berkeley.edu/Pubs/TechRpts/1994/CSD-94-834.pdf", URL = "http://citeseer.nj.nec.com/juels94stochastic.html", abstract = "We investigate the effectiveness of stochastic hillclimbing as a baseline for evaluating the performance of genetic algorithms (GAs) as combinatorial function optimisers. In particular, we address four problems to which GAs have been applied in the literature: the maximum-cut problem, Koza's 11-multiplexer problem, MDAP (the Multiprocessor Document Allocation Problem), and the jobshop problem. We demonstrate that simple stochastic hill climbing methods are able to achieve results comparable or superior to those obtained by the GAs designed to address these four problems. We further illustrate, in the case of the jobshop problem, how insights obtained in the formulation of a stochastic hill-climbing algorithm can lead to improvements in the encoding used by a GA.", notes = "{"}We demonstate that simple stochastic hillcliming methods are able to achieve results comparable or superior to those obtained by the GAs{"}. 4 GAs one is Koza's 11-multiplexor. citeseer.nj.nec.com/juels94stochastic.html may be slightly different from CSD-94-834", } @InProceedings{juille_icga95, author = "Hugues Juille", title = "Evolution of Non-Deterministic Incremental Algorithms as a New Approach for Search in State Spaces", booktitle = "Genetic Algorithms: Proceedings of the Sixth International Conference (ICGA95)", year = "1995", editor = "Larry J. Eshelman", pages = "351--358", address = "Pittsburgh, PA, USA", publisher_address = "San Francisco, CA, USA", month = "15-19 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, Sorting Networks, Stochastic Search", ISBN = "1-55860-370-0", URL = "http://www.demo.cs.brandeis.edu/papers/icga95.pdf", URL = "http://www.demo.cs.brandeis.edu/papers/icga95.ps", abstract = "Let us call a non-deterministic incremental algorithm one that is able to construct any solution to a combinatorial problem by selecting incrementally an ordered sequence of choices that defines this solution, each choice being made non-deterministically. In that case, the state space can be represented as a tree, and a solution is a path from the root of that tree to a leaf. This paper describes how the simulated evolution of a population of such non-deterministic incremental algorithms offers a new approach for the exploration of a state space, compared to other techniques like Genetic Algorithms (GA), Evolutionary Strategies (ES) or Hill Climbing. In particular, the efficiency of this method, implemented as the Evolving Non-Determinism (END) model, is presented for the sorting network problem, a reference problem that has challenged computer science. Then, we shall show that the END model remedies some drawbacks of these optimization techniques and even outperforms them for this problem. Indeed, some 16-input sorting networks as good as the best known have been built from scratch, and even a 25-year-old result for the 13-input problem has been improved by one comparator.", } @InProceedings{juille:1995:fgSIMD, author = "Hugues Juille and Jordan B. Pollack", title = "Parallel Genetic Programming and Fine-Grained SIMD Architecture", booktitle = "Working Notes for the AAAI Symposium on Genetic Programming", year = "1995", editor = "E. V. Siegel and J. R. Koza", pages = "31--37", address = "MIT, Cambridge, MA, USA", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025, USA", month = "10--12 " # nov, publisher = "AAAI", keywords = "genetic algorithms, genetic programming", URL = "http://www.aaai.org/Papers/Symposia/Fall/1995/FS-95-01/FS95-01-005.pdf", URL = "http://www.cs.brandeis.edu/~hugues/papers/AAAI_GP_95.ps.gz", URL = "http://www.aaai.org/Library/Symposia/Fall/fs95-01.php", size = "7 pages", abstract = "As tile field of Genetic Programming (GP) matures and its breadth of application increases, the need for parallel implementations becomes absolutely necessary. The transputer-based system recently presented by Koza ([8]) is one of the rare such parallel implementations. Until today, no implementation has been proposed for parallel GP using a SIMD architecture, except for a data-parallel approach ([16]), although others have exploited workstation farms and pipelined supercomputers. One reason is certainly the apparent difficulty of dealing with the parallel evaluation of different S-expressions when only a single instruction can be executed at the same time on every processor. The aim of this paper is to present such an implementation of parallel GP on a SIMD system, where each processor can efficiently evaluate a different S-expression. We have implemented this approach on a MasPar MP-2 computer, and will present some timing results. To the extent that SIMD machines, like the MasPar are available to offer cost-effective cycles for scientific experimentation, this is a useful approach.", notes = "AAAI-95f GP. Part of \cite{siegel:1995:aaai-fgp} {\em Telephone:} 415-328-3123 {\em Fax:} 415-321-4457 {\em email} info@aaai.org {\em URL:} http://www.aaai.org/ tic-tak-toe coevolution", } @InCollection{pollack:1996:aigp2, author = "Hugues Juille and Jordan B. Pollack", title = "Massively Parallel Genetic Programming", booktitle = "Advances in Genetic Programming 2", publisher = "MIT Press", year = "1996", editor = "Peter J. Angeline and K. E. {Kinnear, Jr.}", pages = "339--357", chapter = "17", address = "Cambridge, MA, USA", keywords = "genetic algorithms, genetic programming, coevolution, competitive fitness, spirals problem", ISBN = "0-262-01158-1", URL = "http://www.demo.cs.brandeis.edu/papers/gp2.pdf", URL = "http://www.demo.cs.brandeis.edu/papers/gp2.ps.Z", URL = "http://www.demo.cs.brandeis.edu/papers/gp2.ps", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6277531", DOI = "doi:10.7551/mitpress/1109.003.0023", size = "19 pages", abstract = "As the field of Genetic Programming (GP) matures and its breadth of application increases, the need for parallel implementations becomes absolutely necessary. The transputer-based system presented in \cite{andre:1996:aigp2} is one of the rare such parallel implementations. Until today, no implementation has been proposed for parallel GP using a SIMD architecture, except for a data-parallel approach \cite{tufts93}, although others have exploited workstation farms and pipelined supercomputers. One reason is certainly the apparent difficulty of dealing with the parallel evaluation of different S-expressions when only a single instruction can be executed at the same time on every processor. The aim of this chapter is to present such an implementation of parallel GP on a SIMD system, where each processor can efficiently evaluate a different S-expression. We have implemented this approach on a MasPar MP-2 computer, and will present some timing results. To the extent that SIMD machines, like the MasPar are available to offer cost-effective cycles for scientific experimentation, this is a useful approach. ", notes = "tic-tak-toe, intertwined spirals, coevolution", } @InProceedings{juile:1996:dcl, author = "Hugues Juille and Jordan B. Pollack", title = "Dynamics of Co-evolutionary Learning", booktitle = "Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior: From animals to animats 4", year = "1996", editor = "Pattie Maes and Maja J. Mataric and Jean-Arcady Meyer and Jordan Pollack and Stewart W. Wilson", pages = "526--534", address = "Cape Code, USA", publisher_address = "Cambridge, MA, USA", month = "9-13 " # sep, publisher = "MIT Press", keywords = "genetic algorithms, genetic programming, Spirals, Coevolution", ISBN = "0-262-63178-4", URL = "http://www.demo.cs.brandeis.edu/papers/sab96b.pdf", URL = "http://www.demo.cs.brandeis.edu/papers/sab96b.ps.gz", URL = "http://www.demo.cs.brandeis.edu/papers/sab96b.ps", URL = "http://www.cs.brandeis.edu/~hugues/papers/SAB_96.ps.gz", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6291901", size = "9 pages", abstract = "Co-evolutionary learning, which involves the embedding of adaptive learning agents in a fitness environment which dynamically responds to their progress, is a potential solution for many technological chicken and egg problems, and is at the heart of several recent and surprising successes, such as Sim's artificial robot and Tesauro's backgammon player. We recently solved the two spirals problem, a difficult neural network benchmark classification problem, using the genetic programming primitives set up by [ \cite{koza:book} ]. Instead of using absolute fitness, we use a relative fitness [ \cite{icga93:angeline} ] based on a competition for coverage of the data set. As the population reproduces, the fitness function driving the selection changes, and subproblem niches are opened, rather than crowded out. The solutions found by our method have a symbiotic structure which suggests that by holding niches open.", notes = "SAB-96", } @InProceedings{juille:1996:cis, author = "Hugues Juille and Jordan B Pollack", title = "Co-evolving Intertwined Spirals", booktitle = "Evolutionary Programming V: Proceedings of the Fifth Annual Conference on Evolutionary Programming", year = "1996", editor = "Lawrence J. Fogel and Peter J. Angeline and Thomas Baeck", pages = "461--467", address = "San Diego", publisher_address = "Cambridge, MA, USA", month = feb # " 29-" # mar # " 3", publisher = "MIT Press", keywords = "genetic algorithms, genetic programming, Spirals, Coevolution", ISBN = "0-262-06190-2", URL = "http://www.demo.cs.brandeis.edu/papers/ep96.pdf", URL = "http://www.demo.cs.brandeis.edu/papers/ep96.ps.gz", URL = "http://www.demo.cs.brandeis.edu/papers/ep96.ps", URL = "http://www.cs.brandeis.edu/~hugues/papers/EP_96.ps.gz", URL = "http://citeseer.ist.psu.edu/201284.html", abstract = "We recently solved the two spirals problem, a difficult neural network benchmark classification problem, using the genetic programming primitives set up by [ \cite{koza:book} ]. Instead of using absolute fitness, we use a relative fitness based on a competition for coverage of the data set. This is a form of co-evolutionary search because the fitness function changes with the population. Because niches are opened by proportionate reproduction, rather than crowded out, and because of the crossover operator, we find solutions which have a nice modular structure. Our experiments used our Massively Parallel Genetic Programming (MPGP) system running on a SIMD machine of 4096 processors, the Maspar MP-2.", notes = "EP-96 http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=8383 Massively Parallel Genetic Programming MPGP on SIMD machine of 4096 processors, the Maspar MP-2", } @InProceedings{juille:1998:cit:adCAr, author = "Hugues Juille and Jordan B. Pollack", title = "Coevolving the Ideal Trainer: Application to the Discovery of Cellular Automata Rules", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "519--527", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, Cellular Automata", URL = "http://www.demo.cs.brandeis.edu/papers/gp98.pdf", URL = "http://www.demo.cs.brandeis.edu/papers/gp98.ps.gz", URL = "http://www.demo.cs.brandeis.edu/papers/gp98.ps", URL = "http://www.cs.brandeis.edu/~hugues/papers/GP_98.ps.gz", abstract = "Coevolution provides a framework to implement search heuristics that are more elaborate than those driving the exploration of the state space in canonical evolutionary systems. However, some drawbacks have also to be overcome in order to ensure continuous progress on the long term. This paper presents the concept of coevolutionary learning and introduces a search procedure which successfully addresses the underlying impediments in coevolutionary search. The application of this algorithm to the discovery of cellular automata rules for a classification task is described. This work resulted in a significant improvement over previously known best rules for this task.", notes = "SGA-98", } @InProceedings{juille:1998:carig, author = "Hugues Juille and Jordan Pollack", title = "Coevolutionary Arms Race Improves Generalization", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "92--100", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "genetic algorithms, genetic programming", size = "9 pages", notes = "GP-98LB", } @InProceedings{juille:1998:shtsgi, author = "Hugues Juille and Jordan B. Pollack", title = "A Sampling-Based Heuristic for Tree Search Applied to Grammar Induction", booktitle = "Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98) Tenth Conference on Innovative Applications of Artificial Intelligence (IAAI-98)", year = "1998", address = "Madison, Wisconsin, USA", month = "26-30 " # jul, publisher = "AAAI Press Books", keywords = "genetic algorithms, genetic programming, search, massively parallel systems, inductive learning, DFA induction", URL = "http://www.demo.cs.brandeis.edu/papers/aaai98.pdf", URL = "http://www.demo.cs.brandeis.edu/papers/aaai98.ps.gz", URL = "http://www.demo.cs.brandeis.edu/papers/aaai98.ps", URL = "http://www.cs.brandeis.edu/~hugues/papers/AAAI_98.ps.gz", abstract = "In the field of Operation Research and Artificial Intelligence, several stochastic search algorithms have been designed based on the theory of global random search (Zhigljavsky, 1991). Basically, those techniques iteratively sample the search space with respect to a probability distribution which is updated according to the result of previous samples and some predefined strategy. Genetic Algorithms (GAs) (Goldberg, 1989) or Greedy Randomised Adaptive Search Procedures (GRASP) (Feo & Resende, 1995) are two particular instances of this paradigm. we present SAGE, a search algorithm based on the same fundamental mechanisms as those techniques. However, it addresses a class of problems for which it is difficult to design transformation operators to perform local search because of intrinsic constraints in the definition of the problem itself. For those problems, a procedural approach is the natural way to construct solutions, resulting in a state space represented as a tree or a DAG. The aim of this paper is to describe the underlying heuristics used by SAGE to address problems belonging to that class. The performance of SAGE is analysed on the problem of grammar induction and its successful application to problems from the recent Abbadingo DFA learning competition is presented.", } @PhdThesis{hugues_thesis, author = "Hugues Juille", title = "Methods for Statistical Inference: Extending the Evolutionary Computation Paradigm", school = "Department of Computer Science, Brandeis University", year = "1999", address = "USA", month = may, keywords = "genetic algorithms, genetic programming, Coevolutionary Learning, Stochastic Search, Cellular Automata", URL = "http://www.demo.cs.brandeis.edu/papers/hugues_thesis.pdf", size = "221 pages", abstract = "In many instances, Evolutionary Computation (EC) techniques have demonstrated their ability to tackle ill-structured and poorly understood problems against which traditional Artificial Intelligence (AI) search algorithms fail. The principle of operation behind EC techniques can be described as a statistical inference process which implements a sampling-based strategy to gather information about the state space, and then exploits this knowledge for controlling search. However, this statistical inference process is supported by a rigid structure that is an integral part of an EC technique. For instance, {\em schemas} seem to be the basic components that form this structure in the case of Genetic Algorithms (GAs). Therefore, it is important that the encoding of a problem in an EC framework exhibits some regularities that correlate with this underlying structure. Failure to find an appropriate representation prevents the evolutionary algorithm from making accurate decisions. This dissertation introduces new methods that exploit the same principles of operation as those embedded in EC techniques and provide more flexibility for the choice of the structure supporting the statistical inference process. The purpose of those methods is to generalize the EC paradigm, thereby expanding its domain of applications to new classes of problems. Two techniques implementing those methods are described in this work. The first one, named SAGE, extends the sampling-based strategy underlying evolutionary algorithms to perform search in trees and directed acyclic graphs. The second technique considers coevolutionary learning, a paradigm which involves the embedding of adaptive agents in a fitness environment that dynamically responds to their progress. Coevolution is proposed as a framework in which evolving agents would be permanently challenged, eventually resulting in continuous improvement of their performance. After identifying obstacles to continuous progress, the concept of an ``Ideal'' trainer is presented as a paradigm which successfully achieves that goal by maintaining a pressure toward adaptability. The different algorithms discussed in this dissertation have been applied to a variety of difficult problems in learning and combinatorial optimization. Some significant achievements that resulted from those experiments concern: (1) the discovery of new constructions for 13-input sorting networks using fewer comparators than the best known upper bound, (2) an improved procedure for the induction of DFAs from sparse training data which ended up as a co-winner in a grammar inference competition, and (3) the discovery of new cellular automata rules to implement the majority classification task which outperform the best known rules. By describing evolutionary algorithms from the perspective of statistical inference techniques, this research work contributes to a better understanding of the underlying search strategies embedded in EC techniques. In particular, an extensive analysis of the coevolutionary paradigm identifies two fundamental requirements for achieving continuous progress. Search and machine learning are two fields that are closely related. This dissertation emphasises this relationship and demonstrates the relevance of the issue of generalisation in the context of coevolutionary races.", notes = "2 Jan 2013 discussed by Broken March 2021 http://tech.groups.yahoo.com/group/genetic_programming/message/6014 Says coevolution majority CA better than GKL, Das and ABK \cite{andre:1996:GKL} (Table 6.2). GP, eg starting on page 169", } @InProceedings{julstrom:1996:clnra, author = "Bryant A. Julstrom", title = "Contest Length, Noise, and Reciprocal Altruism in the Population of a Genetic Algorithm for the Iterated Prisoner's Dilemma", booktitle = "Late Breaking Papers at the Genetic Programming 1996 Conference Stanford University July 28-31, 1996", year = "1996", editor = "John R. Koza", pages = "88--93", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California 94305-3079, USA", month = "28--31 " # jul, publisher = "Stanford Bookstore", ISBN = "0-18-201031-7", keywords = "genetic algorithms, genetic programming", notes = "GP-96LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670", } @InProceedings{Julstrom:1997:swcga, author = "Bryant A. Julstrom", title = "Strings of Weights as Chromosomes in Genetic Algorithms for the Traveling Salesman Problem", booktitle = "Late Breaking Papers at the 1997 Genetic Programming Conference", year = "1997", editor = "John R. Koza", pages = "100--106", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California, 94305-3079, USA", month = "13--16 " # jul, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-206995-8", notes = "GP-97LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670", } @InProceedings{julstrom:1998:idaitwGATSP, author = "Bryant A. Julstrom", title = "Insertion Decoding Algorithms and Initial Tours in a Weight-Coded GA for TSP", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "528--534", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms", notes = "SGA-98", } @InProceedings{julstrom:1998:mwpwcTSP, author = "Bryant A. Julstrom", title = "The Maximum Weight Parameter in a Weight-Coded GA for TSP", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "101--105", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "genetic algorithms", size = "5 pages", notes = "GP-98LB", } @InProceedings{julstrom:1999:RGEMNBH, author = "Bryant A. Julstrom", title = "Redundant Genetic Encodings May Not Be Harmful", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "791", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/Julstrom_gecco99short.html", URL = "http://condor.stcloudstate.edu/~julstrom/ga_papers/gecco99short.html", abstract = "In a redundant genetic encoding, several distinct chromosomes represent each candidate solution to the target problem. Such an encoding would seem to hinder genetic search by allowing competing representations of the same information. Tests using a GA for the 3-cycle problem (3CP), which seeks to partition n = 3k points in the plane into 3-cycles of minimum total length, indicate that this is not necessarily so.", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{julstrom:1999:CDBL, author = "Bryant A. Julstrom", title = "Comparing Darwinian, Baldwinian, and Lamarckian search in a genetic algorithm for the 4-Cycle problem", booktitle = "Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference", year = "1999", editor = "Scott Brave and Annie S. Wu", pages = "134--138", address = "Orlando, Florida, USA", month = "13 " # jul, keywords = "Genetic Algorithms", notes = "GECCO-99LB", } @InProceedings{julstrom:2002:gecco:lbp, title = "Manipulating Valid Solutions in a Genetic Algorithm for the Bounded-Diameter Minimum Spanning Tree Problem", author = "Bryant A. Julstrom", booktitle = "Late Breaking Papers at the Genetic and Evolutionary Computation Conference ({GECCO-2002})", editor = "Erick Cant{\'u}-Paz", year = "2002", month = jul, pages = "247--254", address = "New York, NY", publisher = "AAAI", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025", keywords = "genetic algorithms, genetic programming", URL = "http://web.stcloudstate.edu/bajulstrom/ga_abstracts/gecco2002lbp.html", abstract = "Given a connected, weighted, undirected graph G and a bound D, the bounded-diameter minimum spanning tree problem seeks a shortest spanning tree on G in which no path between two vertices contains more than D edges. In general, this problem is NP-hard. A greedy heuristic for it imitates Prim's algorithm. Beginning at an arbitrary start vertex, it builds a bounded-diameter spanning tree by repeatedly appending the lowest-weight edge between a vertex in the tree and one not yet connected to it whose inclusion does not violate the diameter bound. A genetic algorithm for the problem encodes candidate bounded-diameter spanning trees as lists of their edges and applies operators based on the greedy heuristic that maintain the diameter bound. In tests on sixteen Euclidean instances of the problem, the genetic algorithm consistently identifies much shorter trees; however, it is slower than the greedy heuristic and becomes infeasible on larger problem instances.", notes = "Late Breaking Papers, {GECCO-2002}. A joint meeting of the eleventh International Conference on Genetic Algorithms ({ICGA-2002}) and the seventh Annual Genetic Programming Conference ({GP-2002}) part of cantu-paz:2002:GECCO:lbp", } @Article{Jun:2021:IJPR, author = "Sungbum Jun and Seokcheon Lee", title = "Learning dispatching rules for single machine scheduling with dynamic arrivals based on decision trees and feature construction", journal = "International Journal of Production Research", year = "2021", volume = "59", number = "9", pages = "2838--2856", keywords = "genetic algorithms, genetic programming, scheduling, single-machine scheduling, decision tree, machine learning, feature con-struction, dispatching rules", ISSN = "00207543", bibsource = "OAI-PMH server at oai.repec.org", identifier = "RePEc:taf:tprsxx:v:59:y:2021:i:9:p:2838-2856", oai = "oai:RePEc:taf:tprsxx:v:59:y:2021:i:9:p:2838-2856", URL = "http://hdl.handle.net/10.1080/00207543.2020.1741716", DOI = "doi:10.1080/00207543.2020.1741716", size = "19 pages", abstract = "In this paper, we address the dynamic single-machine scheduling problem for minimisation of total weighted tardiness by learning of dispatching rules (DRs) from schedules. We propose a decision-tree-based approach called Generation of Rules Automatically with Feature construction and Tree-based learning (GRAFT) in order to extract dispatching rules from existing or good schedules. GRAFT consists of two phases: learning a DR from schedules, and improving the DR with feature-construction-based genetic programming. With respect to the process of learning DRs from schedules, we present an approach for transforming schedules into training data containing underlying scheduling decisions and generating a decision-tree-based DR. Thereafter, the second phase improves the learnt DR by feature-construction-based genetic programming so as to minimise the average total weighted tardiness. We conducted experiments to verify the performance of the proposed approach, and the results showed that it outperforms the existing dispatching rules. Moreover, the proposed algorithm is effective in terms of extracting scheduling insights in such understandable formats as IF--THEN rules from existing schedules and improving DRs by grafting a new branch with a discovered attribute into a decision tree.", notes = "School of Industrial Engineering, Purdue University, West Lafayette, IN, USA", } @InProceedings{conf/tsd/Junczys-Dowmunt12a, author = "Marcin Junczys-Dowmunt", title = "A Genetic Programming Experiment in Natural Language Grammar Engineering", booktitle = "Proceedings of the 15th International Conference on Text, Speech and Dialogue, TSD 2012", year = "2012", editor = "Petr Sojka and Ales Horak and Ivan Kopecek and Karel Pala", volume = "7499", series = "Lecture Notes in Computer Science", pages = "336--344", address = "Brno, Czech Republic", month = sep # " 3-7", publisher = "Springer", keywords = "genetic algorithms, genetic programming, NLP, Shallow parsing natural language grammar engineering, treebank", isbn13 = "978-3-642-32789-6", DOI = "doi:10.1007/978-3-642-32790-2_41", bibdate = "2012-08-11", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/tsd/tsd2012.html#Junczys-Dowmunt12a", size = "9 pages", abstract = "This paper describes an experiment in grammar engineering for a shallow syntactic parser using Genetic Programming and a treebank. The goal of the experiment is to improve the Parseval score of a previously manually created seed grammar. We illustrate the adaptation of the Genetic Programming paradigm to the problem of grammar engineering. The used genetic operators are described. The performance of the evolved grammar after 1,000 generations on an unseen test set is improved by 2.7 points F-score (3.7 points on the training set). Despite the large number of generations no overfitting effect is observed.", notes = "Variable arity genetic operators. French treebank 15000 for training 6000 heldout. max grammar size 25000 nodes", } @InProceedings{Jundt:2019:GECCO, author = "Lia Jundt and Thomas Helmuth", title = "Comparing and combining lexicase selection and novelty search", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference", year = "2019", editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and Anne Auger and Petr Posik and Leslie {Peprez Caceres} and Andrew M. Sutton and Nadarajen Veerapen and Christine Solnon and Andries Engelbrecht and Stephane Doncieux and Sebastian Risi and Penousal Machado and Vanessa Volz and Christian Blum and Francisco Chicano and Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and Jonathan Fieldsend and Jose Antonio Lozano and Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and Robin Purshouse and Thomas Baeck and Justyna Petke and Giuliano Antoniol and Johannes Lengler and Per Kristian Lehre", isbn13 = "978-1-4503-6111-8", pages = "1047--1055", address = "Prague, Czech Republic", DOI = "doi:10.1145/3321707.3321787", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, lexicase selection, novelty search, program synthesis", size = "9 pages", abstract = "Lexicase selection and novelty search, two parent selection methods used in evolutionary computation, emphasise exploring widely in the search space more than traditional methods such as tournament selection. However, lexicase selection is not explicitly driven to select for novelty in the population, and novelty search suffers from lack of direction toward a goal, especially in unconstrained, highly-dimensional spaces. We combine the strengths of lexicase selection and novelty search by creating a novelty score for each test case, and adding those novelty scores to the normal error values used in lexicase selection. We use this new novelty-lexicase selection to solve automatic program synthesis problems, and find it significantly outperforms both novelty search and lexicase selection. Additionally, we find that novelty search has very little success in the problem domain of program synthesis. We explore the effects of each of these methods on population diversity and long-term problem solving performance, and give evidence to support the hypothesis that novelty-lexicase selection resists converging to local optima better than lexicase selection.", notes = "Also known as \cite{3321787} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @Article{Jung1996109, author = "Anthony B. Jung and James P. Bennett", title = "Development of striatal dopaminergic function. I. Pre- and postnatal development of mRNAs and binding sites for striatal D1 (D1a) and D2 (D2a) receptors", journal = "Developmental Brain Research", volume = "94", number = "2", pages = "109--120", year = "1996", ISSN = "0165-3806", DOI = "doi:10.1016/S0165-3806(96)80002-2", URL = "http://www.sciencedirect.com/science/article/B6SYW-47G1W7V-2/2/82536e82898d98ddcc2fef6c92792a86", notes = "Not on GP", } @InProceedings{DBLP:conf/gecco/JungR09, author = "Jae-Yoon Jung and James A. Reggia", title = "Evolving an autonomous agent for non-Markovian reinforcement learning", booktitle = "GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation", year = "2009", editor = "Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano {van Hemert} and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique Alba", pages = "971--978", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1569901.1570034", abstract = "In this paper, we investigate the use of nested evolution in which each step of one evolutionary process involves running a second evolutionary process. We apply this approach to build an evolutionary system for reinforcement learning (RL) problems. Genetic programming based on a descriptive encoding is used to evolve the neural architecture, while an evolution strategy is used to evolve the connection weights. We test this method on a non-Markovian RL problem involving an autonomous foraging agent, finding that the evolved networks significantly outperform a rule-based agent serving as a control. We also demonstrate that nested evolution, partitioning into subpopulations, and crossover operations all act synergistically in improving performance in this context.", notes = "GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009). ACM Order Number 910092.", } @InProceedings{Jung:2010:cec, author = "Tae-min Jung and Young-Seol Lee and Sung-Bae Cho", title = "Mobile interface for adaptive image refinement using interactive evolutionary computing", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-6910-9", abstract = "Due to developing mobile devices and providing services like mobile blogs, people can easily share their thought and experience, at any place and any time. A picture is an important datum to record and share their thought and experience, while we can easily take pictures with a mobile device that has a camera in it. However, the quality is usually poor without image refinement. Many mobile devices provide a simply interface to improve the quality, but require knowledge of predefined filters or image enhancement to control the parameters. It causes the user to feel inconvenient in mobile environments for their real-time editing pictures. In this paper, we propose a novel image enhancement interface in consideration of the accessibility to the mobile environment and various constraints. A usability test with various images has been conducted to show its usefulness, and the proposed interface achieved better performance than the other through the SUS test.", DOI = "doi:10.1109/CEC.2010.5585966", notes = "WCCI 2010. Also known as \cite{5585966}", } @InProceedings{Junges:2011:GECCOcomp, author = "Robert Junges and Franziska Klugl", title = "Evolution for modeling: a genetic programming framework for {SeSAm}", booktitle = "GECCO 2011 Evolutionary computation and multi-agent systems and simulation (ECoMASS) - fifth annual workshop", year = "2011", editor = "William Rand and Forrest Stonedahl", isbn13 = "978-1-4503-0690-4", keywords = "genetic algorithms, genetic programming", pages = "551--558", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001858.2002047", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Developing a valid agent-based simulation model is not always straight forward, but involves a lot of prototyping, testing and analysing until the right low-level behaviour is fully specified and calibrated. Our aim is to replace the try and error search of a modeller by adaptive agents which learn a behaviour that then can serve as a source of inspiration for the modeler. In this contribution, we suggest to use genetic programming as the learning mechanism. For this aim we developed a genetic programming framework integrated into the visual agent-based modeling and simulation tool SeSAm, providing similar easy-to-use functionality.", notes = "Also known as \cite{2002047} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @InProceedings{Junges:2011:FedCSIS, author = "Robert Junges and Franziska Klugl", title = "Modeling agent behavior through online evolutionary and reinforcement learning", booktitle = "2011 Federated Conference on Computer Science and Information Systems (FedCSIS 2011)", year = "2011", month = "18-21 " # sep, pages = "643--650", size = "8 pages", address = "Szczecin", abstract = "The process of creation and validation of an agent-based simulation model requires the modeller to undergo a number of prototyping, testing, analysing and re-designing rounds. The aim is to specify and calibrate the proper low-level agent behaviour that truly produces the intended macro-level phenomena. We assume that this development can be supported by agent learning techniques, specially by generating inspiration about behaviours as starting points for the modeller. In this contribution we address this learning-driven modelling task and compare two methods that are producing decision trees: reinforcement learning with a post-processing step for generalisation and Genetic Programming.", keywords = "genetic algorithms, genetic programming, agent behaviour modelling, agent learning technique, agent-based simulation model, decision tree, generalisation, learning-driven modelling task, macrolevel phenomena, online evolutionary, redesigning round, reinforcement learning, decision trees, learning (artificial intelligence), multi-agent systems", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6078268", notes = "Also known as \cite{6078268}", } @Article{JUNIOR:2021:IS, author = "Airton Bordin Junior and Nadia Felix F. {da Silva} and Thierson Couto Rosa and Celso G. C. Junior", title = "Sentiment analysis with genetic programming", journal = "Information Sciences", year = "2021", volume = "562", pages = "116--135", month = jul, keywords = "genetic algorithms, genetic programming, Sentiment analysis, Lexicon, Classifiers", ISSN = "0020-0255", URL = "https://www.sciencedirect.com/science/article/pii/S0020025521000529", DOI = "doi:10.1016/j.ins.2021.01.025", abstract = "With the advent of online social networks, people became more eager to express and share their opinions and sentiment about all kinds of targets. The overwhelming amount of opinion texts soon attracted the interest of many entities (industry, e-commerce, celebrities, etc.) that were interested in analyzing the sentiment people express about what they produce or communicate. This interest has led to the surge of the sentiment analysis (SA) field. One of the most studied subfields of SA is polarity detection, which is the problem of classifying a text as positive, negative, or neutral. This classification problem is difficult to solve automatically, and many hand-adjusted resources are needed to overcome the difficulties in detecting sentiment from text. These resources include hand-adjusted textual features as well as lexicons. Deciding which resource and which combination of resources are more appropriate to a given scenario is a time-consuming trial-and-error process. Thus, in this work, we propose the use of Genetic Programming (GP) as a tool for automatically choosing, combining, and classifying sentiment from text. We propose a series of functions that allow GP to deal with preprocessing tasks, handcrafted features, and automatic weighting of lexicons for a given training set. Our experiments show that our GP solution is competitive and sometimes better than SVM and superior to naive Bayes, logistic regression, and stochastic gradient descent, which are methods used in SA competitions", notes = "Also known as \cite{JUNIOR2021116}", } @Article{Juster2009, author = "Robert-Paul Juster and Bruce S. McEwen and Sonia J. Lupien", title = "Allostatic load biomarkers of chronic stress and impact on health and cognition", journal = "Neuroscience \& Biobehavioral Reviews", year = "2010", volume = "35", number = "1", pages = "2--16", month = sep, ISSN = "0149-7634", DOI = "doi:10.1016/j.neubiorev.2009.10.002", URL = "http://www.sciencedirect.com/science/article/B6T0J-4XF83T1-1/2/ba6b3d4794b04ffafb547bc67f45f581", keywords = "genetic algorithms, genetic programming, Allostatic load, Chronic stress, Aging, Resilience, Health, Cognition, Biomedicine", abstract = "The allostatic load model expands the stress-disease literature by proposing a temporal cascade of multi-systemic physiological dysregulations that contribute to disease trajectories. By incorporating an allostatic load index representing neuroendocrine, immune, metabolic, and cardiovascular system functioning, numerous studies have demonstrated greater prediction of morbidity and mortality over and beyond traditional detection methods employed in biomedical practice. This article reviews theoretical and empirical work using the allostatic load model vis-a-vis the effects of chronic stress on physical and mental health. Specific risk and protective factors associated with increased allostatic load are elucidated and policies for promoting successful aging are proposed.", notes = "survey", } @InProceedings{Juza:2023:EuroGP, author = "Tadeas Juza and Lukas Sekanina", title = "{GPAM}: Genetic Programming with Associative Memory", booktitle = "EuroGP 2023: Proceedings of the 26th European Conference on Genetic Programming", year = "2023", month = "12-14 " # apr, editor = "Gisele Pappa and Mario Giacobini and Zdenek Vasicek", series = "LNCS", volume = "13986", publisher = "Springer Verlag", address = "Brno, Czech Republic", pages = "68--83", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Associative memory, Neural network, ANN, Weight compression, Symbolic regression", isbn13 = "978-3-031-29572-0", URL = "https://www.fit.vut.cz/research/publication/12860", URL = "https://rdcu.be/c8UPG", DOI = "doi:10.1007/978-3-031-29573-7_5", size = "16 pages", abstract = "We focus on the evolutionary design of programs capable of capturing more randomness and outliers in the input data set than the standard genetic programming (GP)-based methods typically allow. We propose Genetic Programming with Associative Memory (GPAM), a GP-based system for symbolic regression which can use a small associative memory to store various data points to better approximate the original data set. The method is evaluated on five standard benchmarks in which a certain number of data points is replaced by randomly generated values. In another case study, GPAM is used as an on-chip generator capable of approximating the weights for a convolutional neural network (CNN) to reduce the access to an external weight memory. Using Cartesian genetic programming (CGP), we evolved expression-memory pairs that can generate weights of a single CNN layer. If the associative memory contains 10percent of the original weights, the weight generator evolved for a convolutional layer can approximate the original weights such that the CNN using the generated weights shows less than a 1percent drop in the classification accuracy on the MNIST data set.", notes = "Also known as \cite{FITPUB12860} Part of \cite{Pappa:2023:GP} EuroGP'2023 held in conjunction with EvoCOP2023, EvoMusArt2023 and EvoApplications2023", } @Article{KABIRI:2021:ATE, author = "S. Kabiri and M. H. {Khoshgoftar Manesh} and M. Amidpour", title = "Dynamic {R}-Curve analysis and optimization of steam power plant solar repowering", journal = "Applied Thermal Engineering", volume = "195", pages = "117218", year = "2021", ISSN = "1359-4311", DOI = "doi:10.1016/j.applthermaleng.2021.117218", URL = "https://www.sciencedirect.com/science/article/pii/S1359431121006566", keywords = "genetic algorithms, genetic programming, Multi-effect desalination, Multi-generation plant, Multi-stage desalination, Optimization, R-curve, Repowering, Solar collectors, Water cycle algorithm", abstract = "Installing solar collectors to preheat boiler feedwater is one of the most economical methods of repowering steam power plants. The production of freshwater in repowered plants can increase their productivity. The present study aimed at integrating the Bandar Abbas steam power plant's repowered cycles with desalination units and subsequently analyzing the cycles using the R-Curve tool. Three scenarios are were considered for repowering: In the first scenario, parallel collectors were used instead of the low-pressure feedwater heaters, while in the second and third scenarios, parallel solar collectors were used instead of low-pressure feedwater heaters integrated with multi-effect and multi-stage flash desalination units, respectively. The dynamic development of the R-Curve, as well as the use of a combination of artificial intelligence and genetic algorithm programming to optimize the complex cycles of the multi-generation of power, heat, and freshwater, are the most important issues presented in this study. Results show that the Bandar Abbas steam power plant in operation has an R-ratio equal to 1.21 and a cogeneration efficiency of 36.5 percent. In the first scenario of repowering, the R-ratio is equal to 1.21, and in most months, the cogeneration efficiency varies between 35 and 45 percent. In the second and third scenarios, however, cogeneration efficiency is 50 percent at its lowest level. Moreover, with the introduction of the new conceptual graphical curves, it was found that using more solar energy and adding desalination units increase the cogeneration efficiency throughout the year. Optimization of repowered cycles integrated with multi-effect and multi-stage flash desalination units increased freshwater production by 178 and 42 percent, respectively", } @InProceedings{kabliman:2019:ESAFORM, author = "Evgeniya Kabliman and Ana Helena Kolody and Michael Kommenda and Gabriel Kronberger", title = "Prediction of stress-strain curves for aluminium alloys using symbolic regression", booktitle = "Proceedings of the 22nd International ESAFORM Conference on Material Forming", year = "2019", volume = "2113", number = "1", series = "AIP Conference Proceedings", pages = "180009--1--180009--6", month = "07", publisher = "AIP", keywords = "genetic algorithms, genetic programming", isbn13 = "978-0-7354-1847-9", ISSN = "0094-243X", URL = "https://doi.org/10.1063/1.5112747", eprint = "https://pubs.aip.org/aip/acp/article-pdf/doi/10.1063/1.5112747/13181889/180009_1_online.pdf", DOI = "doi:10.1063/1.5112747", size = "6 pages", abstract = "An in-depth understanding of material flow behaviour is crucial for numerical simulation of plastic deformation processes. In present work, we use a Symbolic Regression method in combination with Genetic Programming for modelling flow stress curves. In contrast to classical regression methods that fit parameters to an equation of a given form, symbolic regression searches for both numerical parameters and the equation form simultaneously; therefore, no prior assumption on a flow model is required. This identification process is done by generating and adapting equations iteratively using a genetic algorithm. The constitutive model is derived for two aluminium wrought alloys: a conventional AA6082 and modified Cu-containing AA7000 alloy. The required dataset is created by performing a series of hot compression tests at temperatures between 350 degrees C and 500 degrees C and strain rates from 0.001 to 0.1 using a deformation dilatometer. The measured data, experimental set-up parameters as well as the material process history and its chemical composition are stored in a SQL database using a python script. To correct raw measured data, e.g. minimize the noise, an in-house Flow Stress Analysis Toolkit was used. The obtained results represent a data-driven free-form constitutive model and are compared to a physics-based model, which describes the flow stress in terms of internal state parameters (herein, mean dislocation density). We find that both models reproduce reasonably well the measured data, while for modelling using symbolic regression no prior knowledge on materials behaviour was required.", } @Article{kabliman:2021:apples, author = "Evgeniya Kabliman and Ana Helena Kolody and Johannes Kronsteiner and Michael Kommenda and Gabriel Kronberger", title = "Application of symbolic regression for constitutive modeling of plastic deformation", journal = "Applications in Engineering Science", year = "2021", volume = "6", pages = "100052", month = jun, keywords = "genetic algorithms, genetic programming, Material constitutive equations, Machine learning, Symbolic regression, Data-driven modelling, Physics-based modelling, Finite element analysis", ISSN = "2666-4968", URL = "https://www.sciencedirect.com/science/article/pii/S2666496821000182", DOI = "doi:10.1016/j.apples.2021.100052", size = "11 pages", abstract = "In numerical process simulations, in-depth knowledge about material behaviour during processing in the form of trustworthy material models is crucial. Among the different constitutive models used in the literature one can distinguish a physics-based approach (white-box model), which considers the evolution of material internal state variables, such as mean dislocation density, and data-driven models (grey or even black-box). Typically, parameters in physics-based models such as physical constants or material parameters, are interpretable and have a physical meaning. However, even physics-based models often contain calibration coefficients that are fitted to experimental data. In the present work, we investigate the applicability of symbolic regression for (1) predicting calibration coefficients of a physics-based model and (2) for deriving a constitutive model directly from measurement data. Our goal is to find mathematical expressions, which can be integrated into numerical simulation models. For this purpose, we have chosen symbolic regression to derive the constitutive equations based on data from compression testing with varying process parameters. To validate the derived constitutive models, we have implemented them into a FE solver (herein, LS-DYNA), and calculated the force-displacement curves. The comparison with experiments shows a reasonable agreement for both data-driven and physics-based (with fitted and learned calibration parameters) models.", notes = "Also known as \cite{KABLIMAN2021100052} LKR Light Metals Technologies, Austrian Institute of Technology, Ranshofen, Austria", } @Article{Kabliman:2023:GPEM, author = "Evgeniya Kabliman", title = "Nirupam Chakraborti: Data-Driven Evolutionary Modeling in Materials Technology", journal = "Genetic Programming and Evolvable Machines", year = "2023", volume = "24", number = "2", pages = "Article number: 8", month = dec, note = "Book review: CRC Press, 2023, ISBN: 978-1-032-06173-3", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/df2Zg", DOI = "doi:10.1007/s10710-023-09455-1", size = "2 pages", abstract = "Review of \cite{Chakraborti:book}", notes = "Chair of Materials Engineering of Additive Manufacturing, Department of Materials Engineering, TUM School of Engineering and Design, Technical University of Munich, Boltzmannstrasse 15, 85748, Garching b. Munich, Germany", } @InProceedings{kaboudan:1998:sesrfts, author = "M. A. Kaboudan and M. K. Vance", title = "Statistical Evaluation of Symbolic Regression Forecasting of Time-Series", booktitle = "Proceedings of the International Federation of Automatic Control Symposium on Computation in Economics, Finance and Engineering: Economic Systems", year = "1998", editor = "S. Holly", volume = "31", number = "16", pages = "275--279", address = "Cambridge, UK", month = "29 " # jun # " - 1 " # jul, organisation = "IFAC", keywords = "genetic algorithms, genetic programming, nonlinear dynamics, complexity, artificial intelligence", ISBN = "0-08-043048-1", ISSN = "1474-6670", URL = "http://www.sciencedirect.com/science/article/pii/S1474667017404940", URL = "http://gso.gbv.de/DB=2.1/PPNSET?PPN=318304171", broken = "http://catalog.library.colostate.edu/record=b2366049~S5", DOI = "doi:10.1016/S1474-6670(17)40494-0", size = "5 pages", abstract = "This is an evaluation of the ability of symbolic regression to predict time series. Symbolic regression is an application of genetic programming. Three codes GPCPP, GPQuick, and Vienna University GP Kemel-written in C++ were tested. Six models generated data by linear, nonlinear, and pseudo-random processes, and the three codes were employed to search for the six data generating processes. The results suggest that: (1) complexity and predictability are inversely related, (2) the symbolic regression technique is successful in predicting less complex processes, and (3) all three failed to find a data generating process for pseudo-random data.", notes = "IFAC Symposium. Published for the International Federation of Automatic Control by Pergamon, 2000. Also known as \cite{KABOUDAN1998275}", } @InProceedings{kaboudan:1998:fsrGPC, author = "M. Kaboudan", title = "Forecasting Stock Returns Using Genetic Programming in C++", booktitle = "Proceedings of 11th Annual Florida Artificial Intelligence International Research Symposium", year = "1998", editor = "Diane J. Cook", address = "Sanibel Island, Florida, USA", month = may # " 18-20", publisher = "AAAI Press", keywords = "genetic algorithms, genetic programming", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.532.2726", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.532.2726", URL = "https://www.aaai.org/Papers/FLAIRS/1998/FLAIRS98-014.pdf", URL = "http://aaaipress.org/Papers/FLAIRS/1998/FLAIRS98-014.pdf", ISBN = "1-57735-051-0", size = "5 pages", abstract = "This is an investigation of forecasting stock returns using genetic programming. We first test the hypothesis that genetic programming is equally successful in predicting series produced by data generating processes of different structural complexity. After rejecting the hypothesis, we measure the complexity of thirty-two time series representing four different frequencies of eight stock returns. Then using symbolic regression, it is shown that less complex high frequency data are more predictable than more complex low frequency returns. Although no forecasts are generated here, this investigation provides new insights potentially useful in predicting stock prices.", notes = "FLAIRS-98", } @InProceedings{kaboudan:1998:GPadcns, author = "M. A. Kaboudan", title = "A {GP} Approach to Distinguish Chaotic from Noisy Signals", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "187--191", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-548-7", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1998/kaboudan_1998_GPadcns.pdf", size = "5 pages", abstract = "We propose a measure of the probability of predicting time series based on genetic programming (GP). The measure is important since GP performs well in predicting deterministic time series while fails on predicting random data. Mixed deterministic and random process must then be at least partially predictable. The proposed measure was tested on artificial data with known but different characteristics. Test results are phenomenological evidence suggest that the measure reasonably approximates a series chance of predictability. it potentially helps reduce model search space, forecasting time and cost. and improve prediction results", notes = "GP-98", } @InProceedings{kaboudan:1999:seGP, author = "M. A. Kaboudan", title = "Statistical Evaluation of Genetic Programming", booktitle = "Fifth International Conference: Computing in Economics and Finance", year = "1999", editor = "David A. Belsley and Christopher F. Baum", pages = "148", address = "Boston College, MA, USA", month = "24-26 " # jun, organisation = "Society for Computational Economics", note = "Book of Abstracts", keywords = "genetic algorithms, genetic programming, GP-QUICK", broken = "http://www.lv.psu.edu/mak7/GP-Stat.htm", URL = "http://EconPapers.repec.org/RePEc:sce:scecf9:1031", URL = "http://fmwww.bc.edu/CEF99/doc/pgm.html", size = "1 page", abstract = "A recent advance in genetic computations is the heuristic prediction model (symbolic regression), which have received little statistical scrutiny. Diagnostic checks of genetically evolved models (GEMs) as a forecasting method are therefore essential. This requires assessing the statistical properties of errors produced by GEMs. Since the predicted models and their forecasts are produced artificially by a computer program, little controls the final model specification. However, it is of interest to understand the final specification and to know the statistical characteristics of its errors, particularly if artificially produced models furnish better forecasts than humanly conceived ones. This paper's main concern is the statistical analysis of errors from genetically evolved models. Genetic programming (GP) is one of two computational algorithms for evolving regression models, the other being evolutionary programming (EP). GP-QUICK computer code written in C ++ evolves the regression models for this study. GP-QUICK replicates an original GP program in LISP by Koza. Both are designed to evolve regression models randomly, finding one that replicates the series' data-generating process best. Prediction errors from GP evolved regression models are tested for whiteness (or autocorrelation) and for normality. Well-established diagnostic tools for linear time-series modeling apply also to nonlinear models. Only diagnostic methods using errors without having to replicate the models that produced them are selected and applied to series. This restriction is avoids reproducing the resulting genetically evolved equations. These equations are generated by a random selection mechanism almost impossible to replicate with GP unless the process is deterministic, and they are usually too complex for standard statistical software to reproduce and analyze. The diagnostic methods are selected for their simplicity and speed of execution without sacrificing reliability. This paper contains four other sections. One presents the diagnostic tools to determine the statistical properties of residuals produced by GEMs. Residuals from evolved models representing systems with known characteristics are used to evaluate the statistical performance of GEMs. Another furnishes six data-generating processes representing linear, linear-stochastic, nonlinear, nonlinear-stochastic, and pseudo-random systems for which models are evolved and residuals computed. The final contains those residuals' diagnostics. Diagnostic tools include the Kolmogorov-Smirnov test for whiteness developed by Durbin (1969) in addition to statistical testing of the null hypotheses that the fitted residuals' mean, skewness, and kurtosis are independently equal to zero. Conclusions and future research are given.", notes = "CEF'99 RePEc:sce:scecf9:1031 23 Nov 1999: Our printers barf if given GP-Stat.prn 22 Aug 2004 http://ideas.repec.org/p/sce/scecf9/1031.html CEF number 1031", } @Article{Kaboudan:1999:GPpsp, author = "M. A. Kaboudan", title = "Genetic Programming Prediction of Stock Prices", journal = "Computational Economics", year = "2000", volume = "16", number = "3", pages = "207--236", month = dec, publisher = "Kluwer Academic Publishers", keywords = "genetic algorithms, genetic programming, evolved regression models, stock returns, financial market analysis, nonlinear systems", ISSN = "0927-7099", URL = "https://rdcu.be/c0eEg", DOI = "doi:10.1023/A:1008768404046", size = "30 pages", abstract = "Based on predictions of stock-prices using genetic programming (or GP), a possibly profitable trading strategy is proposed. A metric quantifying the probability that a specific timeseries is GP-predictable is presented first. It is used to show that stock prices are predictable. GP then evolves regression models that produce reasonable one-day-ahead forecasts only. This limited ability led to the development of a single day-trading strategy(SDTS) in which trading decisions are based on GP-forecasts of daily highest and lowest stock prices.SDTS executed for fifty consecutive trading days of six stocks yielded relatively high returns on investment.", } @Article{Kaboudan:1999:mtspGP, author = "M. A. Kaboudan", title = "A Measure of Time Series' Predictability Using Genetic Programming Applied to Stock Returns", journal = "Journal of Forecasting", year = "1999", volume = "18", number = "5", pages = "345--357", month = sep, keywords = "genetic algorithms, genetic programming, model specification, complexity, non-linearity, artificial intelligence forecasting, financial markets", ISSN = "1099-131X", DOI = "doi:10.1002/(SICI)1099-131X(199909)18:5%3C345::AID-FOR744%3E3.0.CO%3B2-7", size = "13 pages", abstract = "Based on the standard genetic programming (GP) paradigm, we introduce a new probability measure of time series' predictability. It is computed as a ratio of two fitness values (SSE) from GP runs. One value belongs to a subject series, while the other belongs to the same series after it is randomly shuffled. Theoretically, the boundaries of the measure are between zero and 100, where zero characterises stochastic processes while 100 typifies predictable ones. To evaluate its performance, we first apply it to experimental data. It is then applied to eight Dow Jones stock returns. This measure may reduce model search space and produce more reliable forecast models.", } @InProceedings{kaboudan:1999:GERMBEF, author = "M. A. Kaboudan", title = "Genetic Evolution of Regression Models for Business and Economic Forecasting", booktitle = "Proceedings of the Congress on Evolutionary Computation", year = "1999", editor = "Peter J. Angeline and Zbyszek Michalewicz and Marc Schoenauer and Xin Yao and Ali Zalzala", volume = "2", pages = "1260--1268", address = "Mayflower Hotel, Washington D.C., USA", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "6-9 " # jul, organisation = "Congress on Evolutionary Computation, IEEE / Neural Networks Council, Evolutionary Programming Society, Galesia, IEE", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, forecasting, business, complex systems, data fitting, economic forecasting, economics researchers, evolutionary computer programs, evolutionary methodology, evolutionary methods, genetic evolution, output files, regression models, scientific research, statistical tests, business data processing, economics, forecasting theory, statistical analysis", ISBN = "0-7803-5536-9 (softbound)", ISBN = "0-7803-5537-7 (Microfiche)", DOI = "doi:10.1109/CEC.1999.782587", abstract = "The paper attempts to bridge the gap between genetic evolution of regression models and their use in business and economic forecasting. With ample evidence of their successful fitting of data from fairly complex systems, a logical next step is to make genetic and evolutionary methods useful and available to business and economics researchers. A few suggestions are made; they describe desirable output files and statistical tests to evaluate results from evolved models which genetic or evolutionary computer programs should produce. These suggestions should invite better ones to popularise use of evolutionary methodology and to benefit scientific research", notes = "CEC-99 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. Library of Congress Number = 99-61143", } @InProceedings{Kaboudan:2000:CEF, author = "M. A. Kaboudan", title = "Evaluation Of Forecasts Produced By Genetically Evolved Models", booktitle = "Computing in Economics and Finance", year = "2000", address = "Universitat Pompeu Fabra, Barcelona, Spain", month = "6-8 " # jul, organisation = "Society for Computational Economics", keywords = "genetic algorithms, genetic programming", URL = "http://fmwww.bc.edu/cef00/papers/paper331.pdf", size = "33 pages", abstract = "Genetic programming (or GP) is a random search technique that emerged in the late 1980s and early 1990s. A formal description of the method was introduced in Koza (1992). GP applies to many optimisation areas. One of them is modelling time series and using those models in forecasting. Unlike other modeling techniques, GP is a computer program that 'searches' for a specification that replicates the dynamic behaviour of observed series. To use GP, one provides operators (such as +, -, *, ?, exp, log, sin, cos, ... etc.) and identifies as many variables thought best to reproduce the dependent variable's dynamics. The program then randomly assembles equations with different specifications by combining some of the provided variables with operators and identifies that specification with the minimum sum of squared errors (or SSE). This process is an iterative evolution of successive generations consisting of thousands of the assembled equations where only the fittest within a generation survive to breed better equations also using random combinations until the best one is found. Clearly from this simple description, the method is based on heuristics and has no theoretical foundation. However, resulting final equations seem to produce reasonably accurate forecasts that compare favourably to forecasts humanly conceived specifications produce. With encouraging results difficult to overlook or ignore, it is important to investigate GP as a forecasting methodology. This paper attempts to evaluate forecasts genetically evolved models (or GEMs) produce for experimental data as well as real world time series.The organisation of this paper in four Sections. Section 1 contains an overview of GEMs. The reader will find lucid explanation of how models are evolved using genetic methodology as well as features found to characterise GEMs as a modeling technique. Section 2 contains descriptions of simulated and real world data and their respective fittest identified GEMs. The MSE and a new alpha-statistic are presented to compare models' performances. Simulated data were chosen to represent processes with different behavioral complexities including linear, linear-stochastic, nonlinear, nonlinear chaotic, and nonlinear-stochastic. Real world data consist of two time series popular in analytical statistics: Canadian lynx data and sunspot numbers. Predictions of historic values of each series (used in generating the fittest model) are also presented there. Forecasts and their evaluations are in Section 3. For each series, single- and multi-step forecasts are evaluated according to the mean squared error, normalised mean squared error, and alpha- statistic. A few concluding remarks are in the discussion in Section 4.", notes = "22 August 2004 http://ideas.repec.org/p/sce/scecf0/331.html CEF number 331", } @Article{kaboudan:2000:gemnfr, author = "M. A. Kaboudan", title = "Genetically evolved models and normality of their fitted residuals", journal = "Journal of Economic Dynamics and Control", year = "2001", volume = "25", number = "11", pages = "1719--1749", month = "1 " # nov, organisation = "Society for Computational Economics", email = "Mahmoud_Kaboudan@Redlands.edu", keywords = "genetic algorithms, genetic programming, Model evaluation, Sunspot numbers, Canadian lynx data", URL = "http://www.sciencedirect.com/science/article/B6V85-43DKSHS-2/1/814779519703b0e20b2ed476f932e7e5", DOI = "doi:10.1016/S0165-1889(00)00004-X", size = "31 pages", abstract = "This paper evaluates performance of genetically evolved models. GPQuick, a genetic programming software written in C++, is used to evolve best-fit regression models for simulated and real world data. Simulated data are twelve time series with different but known dynamical structures. Predicted values from best models are compared with originally simulated data and the residuals are statistically evaluated. The results suggest that genetic programming approximates less complex and less noisy data better than it does more complex and noisy data. GPQuick is then used to evolve models of real world data extracted from Canadian lynx and sunspot numbers.", notes = "JEL Classification: C63; C45; C52. cf. CEF'2000.", } @InProceedings{kaboudan:2001:cfcop, author = "M. A. Kaboudan", title = "Compumetric Forecasting of Crude Oil Prices", booktitle = "Proceedings of the 2001 Congress on Evolutionary Computation CEC2001", year = "2001", pages = "283--287", address = "COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "27-30 " # may, organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, ANN, computer forecasting, crude oil prices, forecasting models, monthly forecasts, random walk type, commodity trading, forecasting theory, neural nets", ISBN = "0-7803-6658-1", DOI = "doi:10.1109/CEC.2001.934402", abstract = "This paper contains short term monthly forecasts of crude oil prices using computerised methods. Compumetric forecasting methods are ones that use computers to identify the underlying model that produces the forecast. Typically, forecasting models are designed or specified by humans rather than machines. Compumetric methods are applied to determine whether models they provide produce reliable forecasts. Forecasts produced by two compumetric methods-genetic programming and artificial neural networks-are compared and evaluated relative to a random walk type of prediction. The results suggest that genetic programming has advantage over random walk predictions while the neural network forecast proved inferior", notes = "CEC-2001 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 01TH8546C, Library of Congress Number =", } @InProceedings{Kaboudan2003365, author = "M. A. Kaboudan", title = "Short-Term Compumetric Forecast of Crude Oil Prices", editor = "R. Neck", booktitle = "Modeling and Control of Economic Systems 2001 -- A Proceedings volume from the 10th IFAC Symposium", publisher = "Elsevier Science Ltd", year = "2003", pages = "365--370", address = "Klagenfurt, Austria", publisher_address = "Oxford", month = "6-8 " # sep, keywords = "genetic algorithms, genetic programming", isbn13 = "978-0-08-043858-0", DOI = "doi:10.1016/B978-008043858-0/50062-0", URL = "http://www.sciencedirect.com/science/article/B86BF-4PF22NC-17/2/96bb656b1958ddb535464abece56273c", abstract = "Forecasting oil prices remains an important empirical issue. This paper compares three forecasts of short-term oil prices using two compumetric methods and naive random walk. Compumetric methods use model specifications generated by computers with limited human intervention. Users are responsible only for selecting the appropriate set of explanatory variables. The compumetric methods employed here are genetic programming and artificial neural networks. The variable to forecast is monthly US imports FOB oil prices. Each method is used to forecast one and three months ahead. The results suggest that neural networks deliver better predictions.", } @InCollection{maboudan:2002:ECEF, author = "M. Kaboudan", title = "GP forecasts of stock prices for profitable trading", booktitle = "Evolutionary Computation in Economics and Finance", publisher = "Physica Verlag", year = "2002", editor = "Shu-Heng Chen", volume = "100", series = "Studies in Fuzziness and Soft Computing", chapter = "19", pages = "359--381", month = "2002", keywords = "genetic algorithms, genetic programming", ISBN = "3-7908-1476-8", DOI = "doi:10.1007/978-3-7908-1784-3_19", abstract = "This chapter documents how GP forecasting of stock prices used to execute a single-day-trading-strategy (or SDTS) improves trading returns. The strategy mandates holding no positions overnight to minimise risk and daily trading decisions are based on forecasts of daily high and low stock prices. For comparison, two methods produce the price forecasts. Genetically evolved models produce one. The other is a naive forecast where today's actual price is used as tomorrow's forecast. Trading decisions tested on a small sample of four stocks over a period of twenty days produced higher returns for decisions based on the GP price forecasts.", } @Article{Kaboudan:2003:COR, author = "M. A. Kaboudan", title = "Forecasting with computer-evolved model specifications: a genetic programming application", journal = "Computers and Operations Research", year = "2003", volume = "30", number = "11", pages = "1661--1681", month = sep, email = "mahmoud_kaboudan@Redlands.edu", keywords = "genetic algorithms, genetic programming, Computational methods, Nonlinear dynamic systems, Time series, Sunspot numbers", URL = "http://www.sciencedirect.com/science/article/B6VC5-47P1N3H-1/2/d89d466d6ed20bb2d2da43b3701f351b", ISSN = "0305-0548", DOI = "doi:10.1016/S0305-0548(02)00098-9", abstract = "This paper uses genetic programming (GP) to evolve model specifications of time series data. GP is a computerized random search optimisation algorithm that assembles equations until it identifies the fittest one. The technique is applied here to artificially simulated data first then to real-world sunspot numbers. One-step-ahead forecasts produced by the fittest of computer-evolved models are evaluated and compared with alternatives. The results suggest that GP may produce reasonable forecasts if their user selects appropriate input variables and comprehends the process investigated. Further, the technique appears promising in forecasting noisy complex series perhaps better than other existing methods. It is suitable for decision makers who set high priority on obtaining accurate forecasts rather than on probing into and approximating the underlying data generating process. This paper contains a brief introduction and an evaluation of the use of genetic programming (GP) in forecasting time series. GP is a computerized random search optimization technique based upon Darwin's theory of evolution. The algorithm is first applied to model and forecast artificially simulated linear and nonlinear time series. Results are used to evaluate the effectiveness of GP as a forecasting technique. It is then applied to model and forecast sunspot numbers--the most frequently analyzed and forecasted series. An autoregressive and a threshold nonlinear dynamical systems to capture the dynamics of the irregular sunspot numbers' cycle were tested using GP. The latter delivered estimated equations yielding the lowest mean square error ever reported for the series. This paper demonstrates that GP's forecasting capabilities depend on the structure and complexity of the process to model. Skills and intuition of GP's user are its limitation.", } @InProceedings{RePEc:sce:scecf3:44, author = "M. A. Kaboudan", title = "Forecasting Demand for Natural Gas Using GP-Econometric Integrated Systems", booktitle = "Computing in Economics and Finance", year = "2003", address = "University of Washington, Seattle, USA", month = "11-13 " # jul, organisation = "Society for Computational Economics", keywords = "genetic algorithms, genetic programming", URL = "http://bulldog2.redlands.edu/fac/mak_kaboudan/cef2003/Kaboudan_Extended_Abstract.pdf", abstract = "genetic programming (GP) is used in econometrics to predict US demand for natural gas using two recursive systems of equations. The first contains econometric models estimated using two-stage-least-squares (2SLS). These deliver estimates of policy-making parameters. The system contains four demand equations representing consuming sectors and an identity for total US. The second is to deliver forecasts of exogenous variables in the first using GP. GP can deliver relatively accurate predictions but its evolved equations are not useful in policy-making. For comparison, ARIMA models are used as input into the 2SLS system to compete with GP. Further, GP demand equations are evolved and used to obtain a different forecast altogether. The two forecasts are then compared with a forecast available from the US Department of Energy (DOE). Econometric and GP models deliver forecasts with different merits. Econometric models are concerned with estimating measures of interactions between a dependent variable and each of the independent variables. They provide for what if scenarios fundamental in policy-making that GP does not. The evolved equations are random combinations of variables and terminals that may not capture interactions between variables. Their forecasts may outperform those available using standard statistical techniques. Therefore, GP may add value to econometric models.", notes = "22 August 2004 http://ideas.repec.org/p/sce/scecf3/44.html CEF 2003", } @InProceedings{RePEc:sce:scecf3:97, author = "M. A. Kaboudan", title = "Genetic Programming Software to Forecast Time Series", booktitle = "Computing in Economics and Finance", year = "2003", address = "University of Washington, Seattle, USA", month = "11-13 " # jul, organisation = "Society for Computational Economics", keywords = "genetic algorithms, genetic programming, TSGP", URL = "http://bulldog2.redlands.edu/fac/mak_kaboudan/cef2003/Kaboudan_Extended_Abstract_2.pdf", URL = "https://ideas.repec.org/p/sce/scecf3/97.html", size = "2 pages", abstract = "Genetic programming (GP) is an optimisation technique useful in forecasting. GP software is available freely on the Internet or can be purchased commercially. Free software demands advanced programming skills, while commercial software may be expensive. This paper introduces TSGP software developed to forecast time series. It is free to download with instructions, works in windows environment, is user-friendly, does not require programming skills, delivers comprehensible output, and reports statistics a time series analyst, statistician, or econometrician finds desirable. This introduction benefits forecasting researchers and practitioners. Genetic programming (GP) emerged in the late 1980s and early 1990s. Koza was first to introduce a formal description of the technique. GP applies to many optimisation areas including modelling time series. Unlike other modelling techniques, GP is a computerised search for specifications that replicate patterns of observed series. Users of GP software provide input files containing mathematical operators and values of variables. The program is designed to randomly assemble specifications of equations until it finds the best one. That equation, its fitted values, residuals, and evaluation statistics are written to output files. Such automated search for specifications makes GP an attractive algorithm. TSGP stands for time series genetic programming. The software is available at HYPERLINK (broken June 2020 http://www.compumetrica.com ). It is an expansion of a code in Koza's 1990 GP book written in LISP that was converted to C by Andy Singleton in 1994. TSGP gets its instructions from a configuration file containing self-reproduction, crossover, and mutation rates, names of input variables, population size, number of generations, minimum threshold error (set at 0.0001), and operators (including standard ones: +, -, *, %, and sqrt, where % is protected division as well as two other sets the user selects from: set 1: sin and cos; set 2: ln and exp). In addition to protected division, the program also contains these protections: If in (x(y), y = 0, then (x/y) = 1. If in y1/2, y < 0, then y1/2 = -| y|1/2.", notes = "22 August 2004 http://ideas.repec.org/p/sce/scecf3/97.html CEF 2003 number 97 CEF 2003 http://depts.washington.edu/sce2003/", } @Unpublished{agb_kaboudan_paper, author = "Mak Kaboudan", title = "Spatiotemporal forecasting of housing prices by use of genetic programming", note = "A paper presented during the 16th Annual Meeting of the Association of Global Business in Cancun Mexico, November 18-21, 2004.", month = nov, year = "2004", email = "Mak_kaboudan@Redlands.edu", keywords = "genetic algorithms, genetic programming", URL = "http://bulldog2.redlands.edu/fac/mak_kaboudan/agb_kaboudan_paper.pdf", abstract = "Complexity of space-time analysis remains a major problem faced by forecasters. Theoretical issues and forecast inaccuracy emanate from specification error, aggregation error, measurement error, and perhaps model complexity. Because such problems are mainly statistical in nature, employing techniques not based on statistical methods is tested here. Two computational techniques (genetic programming and neural networks) are employed to demonstrate their potential. Their forecasts can help deliver sequences of maps of the same geographic region depicting future temporal changes.", notes = "http://falcon.jmu.edu/~damanpfx/ ", } @Article{Kaboudan:2004:ITEM, author = "Mahmoud A. Kaboudan and Qingfeng ``Wilson'' Liu", title = "Forecasting quarterly US demand for natural gas", journal = "Information Technology for Economics and Management", year = "2004", volume = "2", number = "1", email = "Mak_kaboudan@Redlands.edu", keywords = "genetic algorithms, genetic programming", ISSN = "1643-8949", URL = "http://www.item.woiz.polsl.pl/issue2.1/journal2.1.htm", URL = "http://www.item.woiz.polsl.pl/issue2.1/pdf/forecastingquarterlyusdemandfornaturalgas.pdf", size = "14 pages", abstract = "forecasting demand for natural gas in the short run. The method used combines genetic programming with a two-stage least squares (2SLS) regression system of equations. In the system developed, each of US consuming sectors is represented by a regression model. These models quantify each sector's demand elasticity and produce a four-year-ahead forecast of quarterly consumption of gas. Genetic programming (GP) is used here to obtain accurate predictions of exogenous variables to use as inputs into the 2SLS system of equations. GP is a computerised search algorithm that identifies equations that can forecast well. The proposed method delivered interesting nonlinear equations that seem to produce a reasonable forecast.", notes = "http://www.item.woiz.polsl.pl/", } @Misc{Kaboudan:2004:efmaci, author = "Mak Kaboudan", title = "GP Basics / A Measure of Time Series' Predictability Using Genetic Programming", howpublished = "Tutorial at Computational Intelligence in Economics and Finance, Summer Workshop", year = "2004", month = "16 " # aug, address = "Taiwan", keywords = "genetic algorithms, genetic programming, Complexity, Nonlinearity, Artificial intelligence, Search algorithms", URL = "http://www.aiecon.org/conference/efmaci2004/pdf/GP_Basics_paper.pdf", URL = "http://www.aiecon.org/conference/efmaci2004/pdf/GP_Basics_ppt.pdf", size = "24 pages", abstract = "Based on standard genetic programming (GP) paradigm, we introduce a new test of time series predictability. It is an index computed as the ratio of two fitness values from GP runs when searching for a series data generating process. One value belongs to the original series, while the other belongs to the same series after it is randomly shuffled. Theoretically, the index boundaries are between zero and 100, where zero characterizes stochastic processes while 100 typifies predictability. This test helps in reducing model search space and in producing more reliable forecast models.", notes = "Taiwan's National Science Counsel and AI-Econ Research Center", } @Article{Kaboudan:2005:NMNC, author = "Mak Kaboudan", title = "Extended daily exchange rates forecasts using wavelet temporal resolutions", journal = "New Mathematics and Natural Computing", year = "2005", volume = "1", pages = "79--107", number = "1", month = mar, ISSN = "1793-0057", URL = "https://econpapers.repec.org/article/wsinmncxx/v_3a01_3ay_3a2005_3ai_3a01_3an_3as1793005705000056.htm", DOI = "doi:10.1142/S1793005705000056", email = "Mak_kaboudan@Redlands.edu", keywords = "genetic algorithms, genetic programming", abstract = "Applying genetic programming and artificial neural networks to raw as well as wavelet-transformed exchange rate data showed that genetic programming may have good extended forecasting abilities. Although it is well known that most predictions of exchange rates using many alternative techniques could not deliver better forecasts than the random walk model, in this paper employing natural computational strategies to forecast three different exchange rates produced two extended forecasts (that go beyond one-step-ahead) that are better than naive random walk predictions. Sixteen-step-ahead forecasts obtained using genetic programming outperformed the one- and sixteen-step-ahead random walk US dollar/Taiwan dollar exchange rate predictions. Further, sixteen-step-ahead forecasts of the wavelet-transformed US dollar/Japanese Yen exchange rate also using genetic programming outperformed the sixteen-step-ahead random walk predictions of the exchange rate. However, random walk predictions of the US dollar/British pound exchange rate outperformed all forecasts obtained using genetic programming. Random walk predictions of the same three exchange rates employing raw and wavelet-transformed data also outperformed all forecasts obtained using artificial neural networks.", notes = " ", } @InProceedings{wavelets_in_forecasting, author = "Mak Kaboudan", title = "Wavelets in Multi-step-ahead forecasting", year = "2005", booktitle = "The 16th IFAC World Congress", editor = "Pavel Zitek", address = "Prague", month = jul # " 4-8", organisation = "IFAC", publisher = "Elsevier Science Ltd", note = "A paper presented during", email = "Mak_kaboudan@Redlands.edu", keywords = "genetic algorithms, genetic programming, sunspot numbers, Artificial intelligence, Nonlinear systems", ISBN = "0-08-045108-X", URL = "http://bulldog2.redlands.edu/fac/mak_kaboudan/wavelets_in_forecasting.pdf", URL = "http://folk.ntnu.no/skoge/prost/proceedings/ifac2005/Fullpapers/03423.pdf", DOI = "doi:10.3182/20050703-6-CZ-1902.02242", size = "6 pages", abstract = "This paper investigates the possibility of obtaining long-into-the-future reliable forecasts of observed nonlinear cyclical phenomena. Unsmoothed monthly sunspot numbers that are characteristically cyclical with nonlinear dynamics as well as their wavelet-transformed and wavelet-denoised series are forecast through October 2008. The objective is to determine whether modelling wavelet-conversions of a series provides reasonable forecasts. Two computational techniques, neural networks and genetic programming, are used to model the dynamics of the series. Statistical comparison of their ex post forecasts is then used to identify the data set and computational technique to use under the circumstances.", notes = "Additional info from http://www.ifac.cz/ Broken 2024 http://www.ecampus.com/book/008045108X http://folk.ntnu.no/skoge/prost/proceedings/ifac2005/Index.html", } @InProceedings{kaboudanprices3, author = "Mak Kaboudan", title = "Spatiotemporal forecasting of home prices: aGIS application", year = "2005", booktitle = "The 16th IFAC World Congress", editor = "Pavel Zitek", address = "Prague", month = jul # " 4-8", organisation = "IFAC", publisher = "Elsevier Science Ltd", note = "A paper presented during", email = "Mak_kaboudan@Redlands.edu", keywords = "genetic algorithms, genetic programming", URL = "http://bulldog2.redlands.edu/fac/mak_kaboudan/kaboudanprices3.pdf", ISBN = "0-08-045108-X", size = "5 pages", abstract = "Computational techniques may be useful in modelling and forecasting spatiotemporal data. Statistical challenges that emanate from specification error, aggregation error, measurement error, and perhaps model complexity among other problems encourage employing computational techniques. Genetic programming and neural networks are two such techniques that are robust with respect to autocorrelation, multicollinearity, and stationarity problems statistical and econometric methods encounter. These two computational techniques are employed to demonstrate their potential in producing dynamic forecasts of spatial data. Such forecasts can then help produce sequences of maps of the same geographic region depicting future temporal changes.", notes = "Additional info from http://www.ifac.cz/ http://www.ecampus.com/book/008045108X", } @InProceedings{Kaboudan:2005:CIEF, author = "Mak Kaboudan", title = "Computational Forecasting of Two Exchange Rates", booktitle = "The 4th International Workshop on Computational Intelligence in Economics and Finance (CIEF'2005)", year = "2005", editor = "Paul P. Wang", pages = "(CIEF-10)", address = "Marriott City Center, Salt Lake City, Utah, USA", month = jul # " 21-26", email = "Mak_kaboudan@Redlands.edu", keywords = "genetic algorithms, genetic programming, neural networks, wavelets", URL = "http://bulldog2.redlands.edu/fac/mak_kaboudan/kaboudan_cief05.pdf", abstract = "genetic programming and artificial neural networks are employed to forecast two different exchange rates, US dollar/Japanese Yen and US dollar/Taiwan dollar. Extended forecasts (that go beyond one-step-ahead) obtained using the computational techniques were compared with naive random walk predictions of the two exchange rates. Sixteen-step-ahead forecasts obtained using genetic programming outperformed the one- and sixteen-step-ahead random walk US dollar/Taiwan dollar exchange rate predictions. Further, sixteen-step-ahead forecasts of the wavelet-transformed US dollar/Japanese Yen exchange rate also using genetic programming outperformed the sixteen-step-ahead random walk predictions of the exchange rate.", notes = "Broken Dec 2020 http://www.aiecon.org/cief2005/schedule.htm", } @InCollection{Kaboudan:2006:nicem, author = "Mak Kaboudan", title = "Genetic programming for spatiotemporal forecasting of housing prices", booktitle = "Handbook of Research on Nature-Inspired Computing for Economics and Management", editor = "Jean-Philippe Rennard", publisher = "Idea Group Inc.", year = "2007", volume = "II", chapter = "LV", pages = "851--868", address = "1200 E. Colton Ave", email = "Mak_kaboudan@Redlands.edu", keywords = "genetic algorithms, genetic programming, ANN, TSGP, C++,", ISBN = "1-59140-984-5", DOI = "doi:10.4018/978-1-59140-984-7.ch055", abstract = "This chapter compares forecasts of the median neighbourhood prices of residential single-family homes in Cambridge, Massachusetts, using parametric and nonparametric techniques. Prices are measured over time (annually) and over space (by neighborhood). Modelling variables characterised by space and time dynamics is challenging. Multi-dimensional complexities due to specification, aggregation, and measurement errors thwart use of parametric modeling, and nonparametric computational techniques (specifically genetic programming and neural networks) may have the advantage. To demonstrate their efficacy, forecasts of the median prices are first obtained using a standard statistical method: weighted least squares. Genetic programming and neural networks are then used to produce two other forecasts. Variables used in modelling neighbourhood median home prices include economic variables such as neighbourhood median income and mortgage rate, as well as spatial variables that quantify location. Two years out-of-sample forecasts comparisons of median prices suggest that genetic programming may have the edge.", size = "18 pages", } @Article{Kaboudan:2006:JAS, author = "Mak Kaboudan", title = "Computational Forecasting of Wavelet-Converted Monthly Sunspot Numbers", journal = "Journal of Applied Statistics", year = "2006", volume = "33", number = "9", pages = "925--941", month = nov, keywords = "genetic algorithms, genetic programming, Wavelets, thresholding, neural networks, sunspot numbers", ISSN = "0266-4763", DOI = "doi:10.1080/02664760600744215", abstract = "Monthly average sunspot numbers follow irregular cycles with complex nonlinear dynamics. Statistical linear models constructed to forecast them are therefore inappropriate while nonlinear models produce solutions sensitive to initial conditions. Two computational techniques 'neural networks' and 'genetic programming' that have their advantages are applied instead to the monthly numbers and their wavelet-transformed and wavelet-denoised series. The objective is to determine if modelling wavelet-conversions produces better forecasts than those from modeling a series' observed values. Because sunspot numbers are indicators of geomagnetic activity their forecast is important. Geomagnetic storms endanger satellites and disrupt communications and power systems on Earth.", notes = "http://www.tandf.co.uk/journals/titles/02664763.asp", } @Article{Kaboudan:2006:GPEM, author = "Mak Kaboudan", title = "Biologically Inspired Algorithms for Financial Modelling Published by: Springer, A. Brabazon and M. O'Neill, 2006, ISBN 3-540-26252-0, \$85", journal = "Genetic Programming and Evolvable Machines", year = "2006", volume = "7", number = "3", pages = "285--286", month = oct, note = "Book Review", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dR8vR", DOI = "doi:10.1007/s10710-006-9010-x", notes = "review of \cite{Brabazon:2006:BIAS}", } @Article{Kaboudan:2008:NMNC, author = "Mak (Mahmoud) Kaboudan", title = "GP versus GLS Spatial Index Models to Forecast Single-Family Home Prices", journal = "New Mathematics and Natural Computation", year = "2008", volume = "4", number = "2", pages = "143--163", email = "mak_kaboudan@redlands.edu", month = jul, keywords = "genetic algorithms, genetic programming, generalised least squares, hedonic model, spatial index, home prices", DOI = "doi:10.1142/S1793005708001021", abstract = "This paper investigates use of genetic programming regression models to forecast home values. Neighbourhood prices in a city are represented by a quarterly index. Index values are ratios of each local neighborhood to the global city average real price of homes sold. Relative average neighbourhood home attributes, local socioeconomic characteristics, spatial measures, and real mortgage rates explain spatiotemporal variations in the index. To examine efficacy of model estimation, forecasts obtained using genetic programming are compared with those obtained using generalised least squares. Out-of-sample genetic programming predictions of home prices obtained using spatial index models deliver reasonable forecasts of home prices.", } @Article{Kaboudan:2008:JREL, author = "Mak Kaboudan", title = "Genetic Programming Forecasting of Real Estate Prices of Residential Single Family Homes in Southern California", journal = "Journal of Real Estate Literature", year = "2008", volume = "16", number = "2", pages = "217--240", keywords = "genetic algorithms, genetic programming", ISSN = "0927-7544", publisher = "American Real Estate Society", broken = "http://aresjournals.org/doi/abs/10.5555/reli.16.2.a881465823035385", broken = "http://ares.metapress.com/content/a881465823035385/", DOI = "doi:10.1080/10835547.2008.12090227", size = "21 pages", abstract = "Use of an artificial intelligence technique, genetic programming (GP), is introduced here to predict real estate residential single family home prices. GP is a computerised random search technique that can deliver regression-like models. Spatiotemporal model specifications of periodic average neighbourhood prices are implemented to predict individual property prices. Average price variations are explained in terms of changes in home attributes, spatial attributes, and temporal economic variables. Quarterly data (2000-2005) from two cities in Southern California are used to obtain GP and standard statistical models (generalised least square - GLS). Results obtained suggest that forecasts from city neighborhood average price GP equations may have advantage over forecasts from GLS equations and over forecasts from models estimated using city aggregated data.", notes = "house price prediction in US of america http://business.fullerton.edu/finance/jrel/ Jan 2024 Perhaps try https://www.jstor.org/stable/44105045", } @Article{Kaboudan:2009:ijamcs, author = "Mak Kaboudan", title = "A two-stage multi-agent system to predict the unsmoothed monthly sunspot numbers", journal = "International Journal of Mathematics and Computer Sciences", year = "2009", volume = "5", number = "3", pages = "136--143", month = "Summer", email = "Mak_Kaboudan@Redlands.edu", keywords = "genetic algorithms, genetic programming, Computational techniques, discrete wavelet transformations, solar cycle prediction, sunspot numbers", ISSN = "2070-3902", URL = "http://www.waset.org/journals/ijmcs/v5/v5-3-21.pdf", size = "8 pages", abstract = "A multi-agent system is developed here to predict monthly details of the upcoming peak of the 24th solar magnetic cycle. While studies typically predict the timing and magnitude of cycle peaks using annual data, this one uses the unsmoothed monthly sunspot number instead. Monthly numbers display more pronounced fluctuations during periods of strong solar magnetic activity than the annual sunspot numbers. Because strong magnetic activities may cause significant economic damages, predicting monthly variations should provide different and perhaps helpful information for decision-making purposes. The multi-agent system developed here operates in two stages. In the first, it produces twelve predictions of the monthly numbers. In the second, it uses those predictions to deliver a final forecast. Acting as expert agents, genetic programming and neural networks produce the twelve fits and forecasts as well as the final forecast. According to the results obtained, the next peak is predicted to be 156 and is expected to occur in October 2011, with an average of 136 for that year.", } @InCollection{Kaboudan:2011:chen, author = "Mak Kaboudan", title = "A genetic programming/neural network multi-agent system to forecast the {S\&P/Case-Shiller} home price index for {Los Angeles}", publisher = "IGI Global", year = "2011", booktitle = "Multi-Agent Applications with Evolutionary Computation and Biologically Inspired Technologies: Intelligent Techniques for Ubiquity and Optimization", editor = "Shu-Heng Chen and Yasushi Kambayashi and Hiroshi Sato", chapter = "1", pages = "1--18", email = "Mak_kaboudan@Relands.edu", keywords = "genetic algorithms, genetic programming", ISBN = "1-60566-898-2", URL = "http://www.igi-global.com/bookstore/Chapter.aspx?TitleId=46196", DOI = "doi:10.4018/978-1-60566-898-7.ch001", abstract = "Successful decision-making by home-owners, lending institutions, and real estate developers among others is dependent on obtaining reasonable forecasts of residential home prices. For decades, home-price forecasts were produced by agents using academically well-established statistical models. In this chapter, several modelling agents will compete and cooperate to produce a single forecast. A cooperative multi-agent system (MAS) is developed and used to obtain monthly forecasts (April 2008 through March 2010) of the S&P/Case-Shiller home price index for Los Angeles, CA (LXXR). Monthly housing market demand and supply variables including conventional 30-year fixed real mortgage rate, real personal income, cash out loans, homes for sale, change in housing inventory, and construction material price index are used to find different independent models that explain percentage change in LXXR. An agent then combines the forecasts obtained from the different models to obtain a final prediction.", } @Article{Kaboudan:2012:nmnc, author = "Mak Kaboudan", title = "Genetic Programming and Neural Networks Forecasting of monthly sunspot numbers", journal = "New Mathematics and Natural Computation", year = "2012", volume = "8", number = "2", pages = "167--182", month = jul, email = "Mak_Kaboudan@Redlands.edu", keywords = "genetic algorithms, genetic programming, Sunspot numbers, solar cycle 24, neural networks", ISSN = "1793-0057", DOI = "doi:10.1142/S1793005712500020", abstract = "A three-stage computational intelligence strategy is used to forecast the unsmoothed monthly sunspot number. The strategy employs agents that use two computational techniques, genetic programming (GP) and neural networks (NN), in a sequence of three stages. In the first, two agents fit the same set of observed monthly data. One employs GP, while the other employs NN. In the second, residuals (= differences between observed and solution values) from the first stage are fitted employing a different technique. The NN fitted-residuals are added to the GP first-stage solution while the GP fitted-residuals are added to the NN first-stage solution. In the third, outputs from the first and second stages become inputs to use in producing two new solutions that reconcile differences. The fittest third stage solution is then used to forecast 48 monthly sunspot numbers (September 2009 through August 2013). This modelling scheme delivered lower estimation errors at each stage. The next sunspot number peak is predicted to be around the middle of 2012.", notes = "School of Business, University of Redlands, Redlands, CA 92373, USA", } @Article{journals/ijcia/KaboudanC13, author = "Mak Kaboudan and Mark Conover", title = "A Three-Step Combined Genetic Programming and Neural Networks Method of Forecasting the {S\&P/Case-Shiller} Home Price Index", journal = "International Journal of Computational Intelligence and Applications", year = "2013", number = "1", volume = "12", pages = "1350001", month = mar, keywords = "genetic algorithms, genetic programming, Forecasting home prices, neural networks, ANN, case-Shiller index", ISSN = "1469-0268", bibdate = "2013-04-22", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ijcia/ijcia12.html#KaboudanC13", URL = "http://www.worldscientific.com/doi/abs/10.1142/S1469026813500016", DOI = "doi:10.1142/S1469026813500016", abstract = "Forecasts of the San Diego and San Francisco S&P/Case-Shiller Home Price Indices through December 2012 are obtained using a multi-agent system that uses January, 2002 to June, 2011 data. Agents employ genetic programming (GP) and neural networks (NN) in a three-stage process to produce fits and forecasts. First, GP and NN compete to provide independent predictions. In the second stage, they cooperate by fitting the first-stage competitor's residuals. Outputs from the first two stages then become inputs to produce two final GP and NN outputs. The NN output from the third stage using the combined method produces improved forecasts over the 3-stage GP method as well as those produced by either method alone. The proposed methodology serves as an example of how combining more than one estimation/forecasting technique may lead to more accurate forecasts.", } @InProceedings{DBLP:conf/icpr/KadarBS08, author = "Ilan Kadar and Ohad Ben-Shahar and Moshe Sipper", title = "Evolving boundary detectors for natural images via Genetic Programming", booktitle = "19th International Conference on Pattern Recognition, ICPR 2008", year = "2008", month = dec # " 8-11", pages = "1--4", address = "Tampa, Florida, USA", keywords = "genetic algorithms, genetic programming, computer vision, learning (artificial intelligence), boundary detection, boundary detectors, computer vision, filter kernels, human visual system, human-marked boundaries, human-marked boundary maps, learning approach, learning framework, natural images, primate visual system", isbn13 = "978-1-4244-2175-6", DOI = "doi:10.1109/ICPR.2008.4761581", abstract = "Boundary detection constitutes a crucial step in many computer vision tasks. We present a novel learning approach to automatically construct a boundary detector for natural images via Genetic Programming (GP). Our approach aims to use GP as a learning framework for evolving computer programs that are evaluated against human-marked boundary maps, in order to accurately detect and localize boundaries in natural images. Our GP system is unique in that it combines filter kernels that were inspired by models of processing in the early stages of the primate visual system, but makes no assumption about what constitutes a boundary, thus avoiding the need to make ad-hoc intuitive definitions. By testing the evolved boundary detectors on a benchmark set of natural images with associated human-marked boundaries, we show performance to be quantitatively competitive with existing computer-vision approaches. Moreover, we show that our evolved detector provides insights into the mechanisms underlying boundary detection in the human visual system.", notes = "Also known as \cite{4761581}", } @InProceedings{DBLP:conf/gecco/KadarBS09, author = "Ilan Kadar and Ohad Ben-Shahar and Moshe Sipper", title = "Evolution of a local boundary detector for natural images via genetic programming and texture cues", booktitle = "GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation", year = "2009", editor = "Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano {van Hemert} and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique Alba", pages = "1887--1888", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming, Poster", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1569901.1570218", abstract = "Boundary detection constitutes a crucial step in many computer vision tasks. We present a learning approach for automatically constructing high-performance local boundary detectors for natural images via genetic programming (GP). Our GP system is unique in that it combines filter kernels that were inspired by models of processing in the early stages of the primate visual system, but makes no assumptions about what constitutes a boundary, thus avoiding the need to make ad hoc intuitive definitions. By testing our evolved boundary detectors on a highly challenging benchmark set of natural images with associated human-marked boundaries, we show performance that outperforms most existing approaches.", notes = "Phd \cite{Kadar:thesis} GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009). ACM Order Number 910092.", } @PhdThesis{Kadar:thesis, author = "Ilan Kadar", title = "From Perceptual Relations to Scene Gist Recognition", school = "Department of Computer Science, Faculty of Natural Sciences, Ben-Gurion University of the Negev", year = "2013", address = "Israel", month = sep, URL = "http://aranne5.bgu.ac.il/others/KadarIlan3.pdf", size = "127 pages", abstract = "The ability to recognize visual scenes quickly and accurately is highly constructive for both biological and machine vision. Following the seminal demonstrations of the ability of humans to recognize scenes in a fraction of a second, much research has been devoted to understanding its underlying visual process, as well as its computational modelling. In this thesis we take a multidisciplinary approach to explore in depth the role of perceptual relations in scene gist recognition and how they may be exploited for understanding and modeling scene gist recognition. We first introduce a psychophysical paradigm that probes human scene gist recognition, extracts perceptual relations between scene categories, and suggests that these perceptual relations do not always conform the semantic structure between categories. We then investigate the perceptual relations between scene categories in a way that allows us to identify the order of processing of scene categories and to provide a new and solid type of psychophysical evidence for multi-level hierarchy that guides the gist recognition process from general (easy) decisions to specific (and more complicated) ones. Next, We incorporate the obtained perceptual relations into a new computational classification scheme, which takes inter-class relationships into account to obtain better scene gist recognition performance regardless of the particular descriptors with which scenes are represented. We also discuss why the contribution of inter-class perceptual relations is particularly pronounced for under-sampled training sets, and we argue that this mechanism may explain the ability of the human visual system to perform well under similar conditions. Finally, we introduce SceneNet, the first large-scale ontology database for scene understanding that organizes scene categories according to their perceptual relationships and provides a lower dimensional scene representation with perceptually meaningful Euclidean distance.Apart from much better computational results on various large scale scene understanding operations, the SceneNet database facilitates important insights into human scene representation and organization and may serve as a key element in better understanding of this important perceptual capacity", notes = "Is this GP? Supervisor: Prof. Ohad Ben-Shahar", } @Article{Kadlec2009795, author = "Petr Kadlec and Bogdan Gabrys and Sibylle Strandt", title = "Data-driven Soft Sensors in the process industry", journal = "Computers \& Chemical Engineering", volume = "33", number = "4", pages = "795--814", year = "2009", ISSN = "0098-1354", DOI = "doi:10.1016/j.compchemeng.2008.12.012", keywords = "genetic algorithms, genetic programming, Soft Sensors, Process industry, Data-driven models, PCA, ANN", size = "20 pages", abstract = "In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work.", notes = "Survey Smart Technology Research Centre, Computational Intelligence Research Group, Bournemouth University, Poole BH12 5BB, United Kingdom", } @PhdThesis{Kadlec:thesis, author = "Petr Kadlec", title = "On robust and adaptive soft sensors", school = "School of Design, Engineering and Computing, Bournemouth University", year = "2009", address = "UK", month = dec # " 17", keywords = "genetic algorithms, genetic programming", URL = "http://eprints.bournemouth.ac.uk/15907/", URL = "http://eprints.bournemouth.ac.uk/15907/1/PhD_Thesis-finalForOneSidedPrinting.pdf", size = "217 pages", abstract = "In process industries, there is a great demand for additional process information such as the product quality level or the exact process state estimation. At the same time, there is a large amount of process data like temperatures, pressures, etc. measured and stored every moment. This data is mainly measured for process control and monitoring purposes but its potential reaches far beyond these applications. The task of soft sensors is the maximal exploitation of this potential by extracting and transforming the latent information from the data into more useful process knowledge. Theoretically, achieving this goal should be straightforward since the process data as well as the tools for soft sensor development in the form of computational learning methods, are both readily available. However, contrary to this evidence, there are still several obstacles which prevent soft sensors from broader application in the process industry. The identification of the sources of these obstacles and proposing a concept for dealing with them is the general purpose of this work. The proposed solution addressing the issues of current soft sensors is a conceptual architecture for the development of robust and adaptive soft sensing algorithms. The architecture reflects the results of two review studies that were conducted during this project. The first one focuses on the process industry aspects of soft sensor development and application. The main conclusions of this study are that soft sensor development is currently being done in a non-systematic, ad-hoc way which results in a large amount of manual work needed for their development and maintenance. It is also found that a large part of the issues can be related to the process data upon which the soft sensors are built. The second review study dealt with the same topic but this time it was biased towards the machine learning viewpoint. The review focused on the identification of machine learning tools, which support the goals of this work. The machine learning concepts which are considered are: (i) general regression techniques for building of soft sensors; (ii) ensemble methods; (iii) local learning; (iv) meta-learning; and (v) concept drift detection and handling. The proposed architecture arranges the above techniques into a three-level hierarchy, where the actual prediction-making models operate at the bottom level. Their predictions are flexibly merged by applying ensemble methods at the next higher level. Finally from the top level, the underlying algorithm is managed by means of metalearning methods. The architecture has a modular structure that allows new pre-processing, predictive or adaptation methods to be plugged in. Another important property of the architecture is that each of the levels can be equipped with adaptation mechanisms, which aim at prolonging the lifetime of the resulting soft sensors. The relevance of the architecture is demonstrated by means of a complex soft sensing algorithm, which can be seen as its instance. This algorithm provides mechanisms for autonomous selection of data preprocessing and predictive methods and their parameters. It also includes five different adaptation mechanisms, some of which can be applied on a sample-by-sample basis without any requirement to store the on-line data. Other, more complex ones are started only on-demand if the performance of the soft sensor drops below a defined level. The actual soft sensors are built by applying the soft sensing algorithm to three industrial data sets. The different application scenarios aim at the analysis of the fulfillment of the defined goals. It is shown that the soft sensors are able to follow changes in dynamic environment and keep a stable performance level by exploiting the implemented adaptation mechanisms. It is also demonstrated that, although the algorithm is rather complex, it can be applied to develop simple and transparent soft sensors. In another experiment, the soft sensors are built without any manual model selection or parameter tuning, which demonstrates the ability of the algorithm to reduce the effort required for soft sensor development. However, if desirable, the algorithm is at the same time very flexible and provides a number of parameters that can be manually optimised. Evidence of the ability of the algorithm to deploy soft sensors with minimal training data and as such to provide the possibility to save the time consuming and costly training data collection is also given in this work.", notes = "Brief mention of GP, in collaboration with Evonik Industries AG, Germany Supervisor: Prof. Bogdan Gabrys", } @Article{Kadlic:2014:PV, author = "Branislav Kadlic and Ivan Sekaj and Daniel Pernecky", title = "Design of Continuous-time Controllers using Cartesian Genetic Programming", journal = "IFAC Proceedings Volumes", volume = "47", number = "3", pages = "6982--6987", year = "2014", note = "19th IFAC World Congress", keywords = "genetic algorithms, genetic programming, continuous-time control, controller structure design, control performance optimization, non-linear systems", ISSN = "1474-6670", DOI = "doi:10.3182/20140824-6-ZA-1003.00915", URL = "http://www.sciencedirect.com/science/article/pii/S1474667016427114", size = "6 pages", abstract = "An evolutionary computation - based design/optimisation approach using the Cartesian Genetic Programming is proposed for non-linear continuous-time process control. It is a simplification of a more general Genetic Programming - based design, which is powerful, but more computationally demanding. The approach is able to produce effective and non-intuitive controllers in the form of a network of interconnected elementary building blocks, which minimize the defined performance index. Each building block performs mathematical operations between its inputs, next it contains gain and an elementary dynamic part as integrator, derivative or unity gain. The proposed design method is demonstrated on water turbine control design, and the results are compared with the genetic algorithm-based PID controller design.", notes = "Proceedings of the 19th World Congress, The International Federation of Automatic Control, Cape Town, South Africa. August 24-29, 2014 978-3-902823-62-5/2014 IFAC Institute of Control and Industrial Informatics Faculty of Electrical Engineering and Information Technology Slovak University of Technology, Bratislava, Slovak Republic", } @PhdThesis{Kadlic_PhD, author = "Branislav Kadlic", title = "Navrh evolucnych algoritmov pre riadenie procesov", school = "Faculty of Electrical Engineering and Information Technology Slovak University of Technology", year = "2016", address = "Bratislava, Slovak Republic", month = "10 " # aug, keywords = "genetic algorithms, genetic programming, cartesian genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Kadlic_PhD.pdf", URL = "https://www.fei.stuba.sk/buxus/docs/2016/autoreferaty/Kadlic-autoref.pdf", size = "90 pages", abstract = "In slovak", notes = "https://www.fei.stuba.sk/sk/aktuality-a-informacie/doktorandske-studium/autoreferaty-dizertacnych-prac/kybernetika.html?page_id=3911 Supervisor: Ivan Seka", } @Article{Kadu:2013:IJARCET, author = "Shweta R. Kadu and A. D. Gawande and L. K Gautam", title = "Blind Image De-convolution In Surveillance Systems By Genetic Programming", journal = "International Journal of Advanced Research in Computer Engineering \& Technology", year = "2013", volume = "2", number = "4", pages = "1415--1419", month = apr, keywords = "genetic algorithms, genetic programming, image blind de-convolution, maximum likelihood, PSF", ISSN = "22781323", URL = "http://ijarcet.org/?p=338", bibsource = "OAI-PMH server at www.doaj.org", oai = "oai:doaj-articles:e04f9e103f8d2c09e8f86bd16ad4ca73", URL = "http://ijarcet.org/wp-content/uploads/IJARCET-VOL-2-ISSUE-4-1415-1419.pdf", size = "5 pages", abstract = "surveillance systems has an important part as image acquisition and filtering, segmentation, object detection and tracking the object in that image. In blind image de-convolution .most of the methods requires that the PSF and the original image must be irreducible. Blurring is a perturbation due to the imaging system while noise is intrinsic to the detection process. Therefore image de-convolution is basically a post-processing of the detected images aimed to reduce the disturbing effects of blurring and noise. Image de-convolution implies the solution of a linear equation ,but this problem turns out to be ill-posed: the solution may not exist or may not be unique. Moreover, even if a unique solution can be found this solution is strongly perturbed by noise propagation.In this papers we proposed a genetic programming based blind-image de-convolution Blind De-convolution algorithm can be used effectively when of distortion is known. It restores image and Point Spread Function (PSF) simultaneously. This algorithm can be achieved based on Maximum Likelihood Estimation (MLE).", notes = "Shri Pannalal Research Institute of Technology. PDF gives date as Jan 2013", } @InProceedings{1144160, author = "Stefan Kahrs", title = "Genetic programming with primitive recursion", booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation", year = "2006", editor = "Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos {Coello Coello} and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens", volume = "1", ISBN = "1-59593-186-4", pages = "941--942", address = "Seattle, Washington, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p941.pdf", DOI = "doi:10.1145/1143997.1144160", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "8-12 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, grammatical evolution, primitive recursion, program transformation, theory: Poster", abstract = "When Genetic Programming is used to evolve arithmetic functions it often operates by composing them from a fixed collection of elementary operators and applying them to parameters or certain primitive constants. This limits the expressiveness of the programs that can be evolved. It is possible to extend the expressiveness of such an approach significantly without leaving the comfort of terminating programs by including primitive recursion as a control operation.The technique used here was gene expression programming [2], a variation of grammatical evolution [8]. Grammatical evolution avoids the problem of program bloat; its separation of genotype (string of symbols) and phenotype (expression tree) permits to optimise the generated programs without interfering with the evolutionary process.", notes = "GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM Order Number 910060", } @Article{Kahyaoglu200826, author = "Talip Kahyaoglu", title = "Optimization of the pistachio nut roasting process using response surface methodology and gene expression programming", journal = "LWT - Food Science and Technology", volume = "41", number = "1", pages = "26--33", year = "2008", ISSN = "0023-6438", DOI = "doi:10.1016/j.lwt.2007.03.026", broken = "http://www.sciencedirect.com/science/article/B6WMV-4NFXDRG-2/2/af126f2eab53c54caa0cefff78e6558e", month = jan, keywords = "genetic algorithms, genetic programming, Pistachio nut, Roasting, Response surface, Optimization", abstract = "Roasted pistachio nuts are consumed as snack foods and used as ingredients in confectionery, chocolates and ice-cream industries. Response surface methodology (RSM) and Gene Expression Programming (GEP) were used to optimize the roasting process for production of the pistachios in shell, kernel, and ground-kernel forms over a range of temperature (100-180degrees C) and for various times (10-60min). The moisture content and color parameters (L, a, b and yellowness index (YI)) were evaluated during roasting and modeled by RSM and GEP. The moisture content changes of the pistachios during roasting were successfully described by RSM and GEP models. The results showed that the L, a and b values could be used as parameters for the development of the predictive models during roasting of in shell pistachios, but the color of kernel and ground-kernel pistachios could be monitored by measuring only a and a, b values, respectively. The quadratic models developed by RSM adequately described the changes in selected color parameters during roasting. The GEP models were found to be slightly better than RSM models. The response surface of desirability function was used successfully in optimization procedure of pistachio nut roasting.", } @InProceedings{Kaiser:2017:IFAC, author = "Eurika Kaiser and Ruiying Li and Bernd R. Noack", title = "On the control landscape topology", booktitle = "20th IFAC World Congress", year = "2017", editor = "Dimitri Peaucelle", pages = "Paper ThP23.4", address = "Toulouse, France", month = jul # " 9-14", organisation = "International Federation of Automatic Control", keywords = "genetic algorithms, genetic programming, Statistical data analysis, Evolutionary algorithms in control and identification, Knowledge discover (data mining), Information processing and decision support, control of fluid flows and fluids-structures interactions, evolutionary algorithms, machine learning control, proximity map, physics, mechanics of the fluids", bibsource = "OAI-PMH server at api.archives-ouvertes.fr", contributor = "Laboratoire d'Informatique pour la M{\'e}canique et les Sciences de l'Ing{\'e}nieur and Elsevier and Elsevier", identifier = "hal-01856264", language = "en", oai = "oai:HAL:hal-01856264v1", type = "info:eu-repo/semantics/conferenceObject", URL = "http://eurika-kaiser.com/ressources/articles/KaRuNo_2017_IFAC.pdf", URL = "http://www.ifac2017.org/sites/www.ifac2017.org/files/u88/IFAC17_ContentListWeb_4.html", URL = "https://hal.archives-ouvertes.fr/hal-01856264", size = "5 pages", abstract = "Evolutionary algorithms are powerful tools to optimise parameters and structure of control laws. However, these approaches are often very costly, or even prohibitive, for expensive experiments due to long evaluation times and large population sizes. Reducing the learning time, e.g. by decreasing the number of function evaluations, is a challenging problem as it often requires additional knowledge on the objective function and assumptions. We address the need to analyse these algorithms and guide their acceleration through examination of the search space topology and the exploratory and exploitative nature of the genetic operators. We show how this gives insights on the convergence and performance behaviour of Genetic Programming Control for the drag reduction of a car model (Li et al., 2016). Profiling machine learning algorithms, that are very powerful but also more complex to analyse, aids the goal to increase their performance and making them eventually feasible for a wide range of applications.", notes = "Author sometimes given as Ruying Li http://www.ifac2017.org/ oai:HAL:hal-01856264v1,", } @InProceedings{Kakizako:2022:SCIS, author = "Kosuke Kakizako and Yoshiko Hanada", title = "Genetic Programming for Optimizing Behavioral Rules of Agents Mimicking Human Behavior Patterns", booktitle = "2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems (SCIS+ISIS)", year = "2022", month = nov, keywords = "genetic algorithms, genetic programming, Training, Energy consumption, Linear programming, Search problems, Behavioural sciences, Complexity theory, behaviour rule, agent control", DOI = "doi:10.1109/SCISISIS55246.2022.10002152", abstract = "Genetic Programming (GP) is one of the effective methods to automatically generate a structure of behavior rule of agent such as robots. In optimization of a behavior rule of an agent to achieve a task, it is important to generate robust rules that work well in an environment involving slight errors. This paper shows a new approach for generating a flexible behavior rule of agent achieving task in an inaccurate environment. In our approach, we focus on the flexibility of humans' behavior to apply learned knowledge to similar patterns. Here we extend the Santa Fe Trail problem which is one of artificial ant problems, and introduce a degree of imitation of human operations to the objective function. Through the numerical experiments, we show that GP with a new objective function can generate rules that work well in an environment with errors.", notes = "Also known as \cite{10002152}", } @Article{kakoudakis:2018:hydro, author = "Konstantinos Kakoudakis and Raziyeh Farmani and David Butler", title = "Pipeline failure prediction in water distribution networks using weather conditions as explanatory factors", journal = "Journal of Hydroinformatics", year = "2018", volume = "20", number = "5", pages = "1191--1200", month = "1 " # sep, keywords = "genetic algorithms, genetic programming, ANN, XAI, EPR, artificial neural network, evolutionary polynomial regression, pipe failure predictions,rehabilitation, water distribution networks, weather factors", ISSN = "1464-7141", eprint = "https://iwaponline.com/jh/article-pdf/20/5/1191/657152/jh0201191.pdf", URL = "https://doi.org/10.2166/hydro.2018.152", DOI = "doi:10.2166/hydro.2018.152", size = "10 pages", abstract = "This paper examines the impact of weather conditions on pipe failure in water distribution networks using artificial neural network (ANN) and evolutionary polynomial regression (EPR). A number of weather-related factors over 4 consecutive days are the input of the binary ANN model while the output is the occurrence or not of at least a failure during the following 2 days. The model is able to correctly distinguish the majority 87 percent of the days with failure(s). The EPR is employed to predict the annual number of failures. Initially, the network is divided into six clusters based on pipe diameter and age. The last year of the monitoring period is used for testing while the remaining years since the beginning are retained for model development. An EPR model is developed for each cluster based on the relevant training data. The results indicate a strong relationship between the annual number of failures and frequency and intensity of low temperatures. The outputs from the EPR models are used to calculate the failures of the homogeneous groups within each cluster proportionally to their length.", } @PhdThesis{kakoudakis:thesis, author = "Konstantinos Kakoudakis", title = "Pipe failure prediction and impacts assessment in a water distribution network", school = "University of Exeter", year = "2019", address = "UK", month = mar, keywords = "genetic algorithms, genetic programming, EPR, ANN", URL = "http://hdl.handle.net/10871/37593", URL = "https://ore.exeter.ac.uk/repository/handle/10871/37593", URL = "https://ore.exeter.ac.uk/repository/bitstream/handle/10871/37593/KakoudakisK.pdf", size = "190 pages", abstract = "Water distribution networks (WDNs) aim to provide water with desirable quantity, quality and pressure to the consumers. However, in case of pipe failure, which is the cumulative effect of physical, operational and weather-related factors, the WDN might fail to meet these objectives. Rehabilitation and replacement of some components of WDNs, such as pipes, is a common practice to improve the condition of the network to provide an acceptable level of service. The overall aim of this thesis is to predict, long-term, annually and short-term, the pipe failure propensity and assess the impacts of a single pipe failure on the level of service. The long-term and annual predictions facilitate the need for effective capital investment,whereas the short-term predictions have an operational use, enabling the water utilities to adjust the daily allocation and planning of resources to accommodate possible increase in pipe failure. The proposed methodology was implemented to the cast iron (CI) pipes in a UK WDN. The long-term and annual predictions are made using a novel combination of Evolutionary Polynomial Regression (EPR) and K-means clustering. The inclusion of K-means improves the predictions accuracy by using a set of models instead of a single model. The long-term predictive models consider physical factors,while the annual predictions also include weather-related factors. The analysis is conducted on a group level assuming that pipes with similar properties have similar breakage patterns. Soil type is another aggregation criterion since soil properties are associated with the corrosion of metallic pipes. The short-term predictions are based on a novel Artificial Neural Network (ANN) model that predicts the variations above a predefined threshold in the number of failures in the following days. The ANN model uses only existing weather data to make predictions reducing their uncertainty. The cross-validation technique is used to derive an accurate estimate of accuracy of EPR and ANN models by guaranteeing that all observations are used for both training and testing, and each observation is used for testing only once. The impact of pipe failure is assessed considering its duration, the topology of the network, the geographic location of the failed pipe and the time. The performance indicators used are the ratio of unsupplied demand and the number of customers with partial or no supply. Two scenarios are examined assuming that the failure occurs when there is a peak in either pressure or demand. The pressure-deficient conditions are simulated by introducing a sequence of artificial elements to all the demand nodes with pressure less than the required. This thesis proposes a new combination of a group-based method for deriving the failure rate and an individual-pipe method for evaluating the impacts on the level of service. Their conjunction indicates the most critical pipes The long-term approach improves the accuracy of predictions, particularly for the groups with very low or very high failure frequency, considering diameter, age and length. The annual predictions accurately predict the fluctuation of failure frequency and its peak during the examined period. The EPR models indicate a strong direct relationship between low temperatures and failure frequency. The short-term predictions interpret the intra-year variation of failure frequency, with most failures occurring during the coldest months. The exhaustive trials led to the conclusion that the use of four consecutive days as input and the following two days as output results in the highest accuracy. The analysis of the relative significance of each input variable indicates that the variables that capture the intensity of low temperatures are the most influential The outputs of the impact assessment indicate that the failure of most of the pipes in both scenarios (i.e. peak in pressure and demand) would have low impacts (i.e. low ratio of unsupplied demand and small number of affected nodes). This can be explained by the fact that the examined network is a large real-life network,and a single failure of a distribution pipe is likely to cause pressure-deficient conditions in a small part of it,whereas performance elsewhere is mostly satisfactory. Furthermore, the complex structure of the WDN allows them to recover from local pipe failures, exploiting the topological redundancy provided by closed loops, so that the flow could reach a given demand node through alternative paths.", notes = "Supervisors: Raziyeh Farmani and David Butler", } @Article{Kala20123817, author = "Rahul Kala", title = "Multi-robot path planning using co-evolutionary genetic programming", journal = "Expert Systems with Applications", volume = "39", number = "3", pages = "3817--3831", year = "2012", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2011.09.090", URL = "http://www.sciencedirect.com/science/article/pii/S0957417411014138", keywords = "genetic algorithms, genetic programming, Path planning, Motion planning, Mobile robotics, Grammatical evolution, Co-operative evolution, Multi-robot systems", abstract = "Motion planning for multiple mobile robots must ensure the optimality of the path of each and every robot, as well as overall path optimality, which requires cooperation amongst robots. The paper proposes a solution to the problem, considering different source and goal of each robot. Each robot uses a grammar based genetic programming for figuring the optimal path in a maze-like map, while a master evolutionary algorithm caters to the needs of overall path optimality. Co-operation amongst the individual robots' evolutionary algorithms ensures generation of overall optimal paths. The other feature of the algorithm includes local optimisation using memory based lookup where optimal paths between various crosses in map are stored and regularly updated. Feature called wait for robot is used in place of conventionally used priority based techniques. Experiments are carried out with a number of maps, scenarios, and different robotic speeds. Experimental results confirm the usefulness of the algorithm in a variety of scenarios.", } @Article{KALAM:2023:coal, author = "Shams Kalam and Muhammad Arif and Arshad Raza and Najeebullah Lashari and Mohamed Mahmoud", title = "Data-driven modeling to predict adsorption of hydrogen on shale kerogen: Implication for underground hydrogen storage", journal = "International Journal of Coal Geology", volume = "280", pages = "104386", year = "2023", ISSN = "0166-5162", DOI = "doi:10.1016/j.coal.2023.104386", URL = "https://www.sciencedirect.com/science/article/pii/S0166516223002045", keywords = "genetic algorithms, genetic programming, Hydrogen adsorption, Kerogen, Shale gas reservoirs, Machine learning, Data-driven modeling, Underground hydrogen storage", abstract = "The interaction of hydrogen in shale gas formations holds significant interest for long-term subsurface hydrogen storage. Accurately and rapidly predicting hydrogen adsorption in these formations is crucial for assessing underground hydrogen storage potential. Many laboratory experiments and molecular simulations have been conducted to determine hydrogen adsorption. However, laboratory experiments and molecular simulations require complex setups and extensive calculations, which can be time-consuming. Consequently, end-users may prefer quick and accurate prediction of hydrogen adsorption to reduce the experimental and computational burden. This study introduces a novel model for predicting hydrogen adsorption using gradient boosting regression and available molecular simulation data from the literature. The data-driven model predicts hydrogen adsorption on kerogen structures based on pressure, temperature, adsorbed methane, hydrogen-to-carbon ratio, oxygen-to-carbon ratio, and kerogen density. We compared gradient-boosting regression with other machine learning tools, including artificial neural networks, symbolic regression assisted with genetic programming, decision trees, and random forests in terms of their capability to predict H2 adsorption on shale kerogen. A simple mathematical equation based on symbolic regression via genetic programming has also been provided, with training and testing coefficients of determination of 88.4percent and 85.8percent, respectively. However, the digital model created using gradient boosting regression outperformed all other machine learning tools, achieving a coefficient of determination of 99.6percent for training data and 94.6percent for testing data. A sensitivity analysis was also conducted that demonstrates the robustness of the developed model. In the case of kerogen type A, the order of increasing hydrogen adsorption is KIA < KIIA {"}complexity is produced slowly and thus cannot be created without commensurate history of computation{"}. p375 IPD FSA GA without fitness selection {"}no significant increase in compression depth{"}. With normal GA fitness selection {"}average compression depth{"} (ie .gz size) {"}of the ten most fit players generally increases as more generations (computation time) is provided.{"} --p376 {"}even if fitness does not{"} (Is this just FSM bloat? WBL) GP-97", } @InProceedings{eurogp:LauLLLC05, author = "Wai Shing Lau and Gang Li and Kin-Hong Lee and Kwong-Sak Leung and Sin Man Cheang", editor = "Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. {van Hemert} and Marco Tomassini", title = "Multi-logic-Unit Processor: A Combinational Logic Circuit Evaluation Engine for Genetic Parallel Programming", booktitle = "Proceedings of the 8th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3447", year = "2005", address = "Lausanne, Switzerland", month = "30 " # mar # " - 1 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-25436-6", pages = "167--177", DOI = "doi:10.1007/978-3-540-31989-4_15", DOI = "doi:10.1007/b107383", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "Genetic Parallel Programming (GPP) is a novel Genetic Programming paradigm. GPP Logic Circuit Synthesiser (GPPLCS), is a combinational logic circuit learning system based on GPP. The GPPLCS comprises a Multi-Logic-Unit Processor (MLP) which is a hardware processor built on a Field Programmable Gate Array (FPGA). The MLP is designed to speed up the evaluation of genetic parallel programs that represent combinational logic circuits. Four combinational logic circuit problems are presented to show the performance of the hardware-assisted GPPLCS. Experimental results show that the hardware MLP speeds up evolutions over 10 times. For difficult problems such as the 6-bit priority selector and the 6-bit comparator, the speedup ratio can be up to 22.", notes = "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005", } @InProceedings{1144145, author = "Wai Shing Lau and Kin Hong Lee and Kwong Sak Leung", title = "A hybridized genetic parallel programming based logic circuit synthesizer", booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation", year = "2006", editor = "Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos {Coello Coello} and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens", volume = "1", ISBN = "1-59593-186-4", pages = "839--846", address = "Seattle, Washington, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p839.pdf", DOI = "doi:10.1145/1143997.1144145", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "8-12 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, a hybridised genetic parallel programming logic circuit synthesiser, design aids, field programmable gate array, flowMap, genetic parallel programming, look up table, performance and experimentation, technology mapping", notes = "GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM Order Number 910060", } @Article{Laucelli:2007:JH, author = "D. Laucelli and O. Giustolisi and V. Babovic and M. Keijzer", title = "Ensemble modeling approach for rainfall/groundwater balancing", journal = "Journal of Hydroinformatics", year = "2007", volume = "9", number = "2", pages = "95--106", month = mar, publisher = "IWA Publishing", keywords = "genetic algorithms, genetic programming, ensemble modelling, groundwater, hydrology", ISSN = "1464-7141", URL = "http://www.iwaponline.com/jh/009/0095/0090095.pdf", DOI = "doi:10.2166/hydro.2007.102", size = "12 pages", abstract = "This paper introduces an application of machine learning, on real data. It deals with Ensemble Modelling, a simple averaging method for obtaining more reliable approximations using symbolic regression. Considerations on the contribution of bias and variance to the total error, and ensemble methods to reduce errors due to variance, have been tackled together with a specific application of ensemble modeling to hydrological forecasts. This work provides empirical evidence that genetic programming can greatly benefit from this approach in forecasting and simulating physical phenomena. Further considerations have been taken into account, such as the influence of Genetic Programming parameter settings on the model's performance.", notes = "Piana di Brindisi", } @Article{Laucelli:2011:EMS, author = "Daniele Laucelli and Orazio Giustolisi", title = "Scour depth modelling by a multi-objective evolutionary paradigm", journal = "Environmental Modelling \& Software", year = "2011", volume = "26", number = "4", pages = "498--509", keywords = "genetic algorithms, genetic programming, Evolutionary polynomial regression, Evolutionary computation, Regression analysis, Multi-objective optimisation, Local scouring", ISSN = "1364-8152", URL = "http://www.sciencedirect.com/science/article/pii/S1364815210002859", DOI = "doi:10.1016/j.envsoft.2010.10.013", size = "12 pages", abstract = "Local scour modelling is an important issue in environmental engineering in order to prevent degradation of river bed and safeguard the stability of grade-control structures. Many empirical formulations can be retrieved from literature to predict the equilibrium scour depth, which is usually assumed as representative of the phenomenon. These empirical equations have been mostly constructed in some ways by leveraging regression procedures on experimental data, usually laboratory observations (thus from small/medium scale experiments). Laboratory data are more accurate measurements but generally not completely representative of the actual conditions in real-world cases, that are often much more complex than those schematised by the laboratory equipment. This is the main reason why some of the literature expressions were not adequate when used for practical applications in large-scale examples. This work deals with the application of an evolutionary modelling paradigm, named Evolutionary Polynomial Regression (EPR), to such problem. Such a technique was originally presented as a classical approach, used to achieve a single model for each analysis, and has been recently updated by implementing a multi-modelling approach (i.e., to obtain a set of optimal candidate solutions/models) where a multi-objective genetic algorithm is used to get optimal models in terms of parsimony of mathematical expressions vs. fitting to data. A wide database of field and laboratory observations is used for predicting the equilibrium scour depth as a function of a set of variables characterising the flow, the sediments and the dimension of the grade-control structure. Results are discussed considering two regressive models available in literature that have been trained on the same data used for EPR. The proposed modelling paradigm proved to be a useful tool for data analysis and, in the particular case study, able to find feasible explicit models featured by an appreciable generalisation performance.", } @Article{Laucelli:2014:JoH, author = "Daniele Laucelli and Balvant Rajani and Yehuda Kleiner and Orazio Giustolisi", title = "Study on relationships between climate-related covariates and pipe bursts using evolutionary-based modelling", journal = "Journal of Hydroinformatics", year = "2014", volume = "16", number = "4", pages = "743--757", month = "1 " # jul, keywords = "genetic algorithms, genetic programming, data-mining, Evolutionary Polynomial Regression, impact of climate on water main bursts, knowledge discovery, pipe burst modelling", URL = "https://iwaponline.com/jh/article-pdf/16/4/743/387365/743.pdf", DOI = "doi:10.2166/hydro.2013.082", abstract = "Researchers extensively studied external loads since they are widely recognized as significant contributors to water pipe failures. Physical phenomena that affect pipe bursts, such as pipe-environment interactions, are very complex and only partially understood. This paper analyses the possible link between pipe bursts and climate-related factors. Many water utilities observed consistent occurrence of peaks in pipe bursts in some periods of the year, during winter or summer. The paper investigates the relationships between climate data (i.e., temperature and precipitation-related covariates) and pipe bursts recorded during a 24-year period in Scarborough (Ontario, Canada). The Evolutionary Polynomial Regression modelling paradigm is used here. This approach is broader than statistical modelling, implementing a multi-modelling approach, where a multi-objective genetic algorithm is used to get optimal models in terms of parsimony of mathematical expressions vs. fitting to data. The analyses yielded interesting results, in particular for cold seasons, where the discerned models show good accuracy and the most influential explanatory variables are clearly identified. The models discerned for warm seasons show lower accuracy, possibly implying that the overall phenomena that underlay the generation of pipe bursts during warm seasons cannot be thoroughly explained by the available climate-related covariates.", notes = "This content is only available as a PDF.", } @Article{Laucelli:2016:JoH, author = "Daniele Laucelli and Michele Romano and Dragan Savic and Orazio Giustolisi", title = "Detecting anomalies in water distribution networks using {EPR} modelling paradigm", journal = "Journal of Hydroinformatics", year = "2016", volume = "18", number = "3", month = "11 " # may, keywords = "genetic algorithms, genetic programming, data mining, evolutionary polynomial regression, timely burst detection, unreported bursts, water distribution networks", ISSN = "1464-7141", DOI = "doi:10.2166/hydro.2015.113", abstract = "Sustainable management of water distribution networks (WDNs) requires effective exploitation of available data from pressure/flow devices. Water companies collect a large amount of such data, which need to be managed correctly and analysed effectively using appropriate techniques. Furthermore, water companies need to balance the data gathering and handling costs with the benefits of extracting useful information. Recent approaches implementing data mining techniques for analysing pressure/flow data appear very promising, because they can automate mundane tasks involved in data analysis process and efficiently deal with sensor data collected. Furthermore, they rely on empirical observations of a WDN behaviour over time, allowing reproducing/predicting possible future behaviour of the network. This paper investigates the effectiveness of the evolutionary polynomial regression (EPR) paradigm to reproduce the behaviour of a WDN using on-line data recorded by low-cost pressure/flow devices. Using data from a real district metered area, the case study presented shows that by using the EPR paradigm a model can be built which enables the accurate reproduction and prediction of the WDN behaviour over time and detection of flow anomalies due to possible unreported bursts or unknown increase of water withdrawal. Such an EPR model might be integrated into an early warning system to raise alarms when anomalies are detected.", } @InBook{Lauckner:2009:CC, author = "Kurt F. Lauckner and Zenia C. Bahorski", title = "The Computer Continuum", chapter = "12.6 Evolutionary Systems", publisher = "Pearson Custom Publishing", year = "2009", edition = "5th", month = "10 " # sep, keywords = "genetic algorithms, genetic programming", isbn13 = "978-0-558-34516-7", URL = "http://www.mypearsonstore.com/bookstore/product.asp?isbn=0558345166", notes = "Text book. The Genetic Algorithm - Genetic Programming - THE CUTTING EDGE: The Computer as an Inventor", } @InProceedings{Lavangnananda_2004_CIMCA, author = "K. Lavangnananda", title = "A Genetic Programming Approach to Inductive Learning", booktitle = "2004 International Conference on Computational Intelligence for Modelling, Control and Automation - Cimca'2004", year = "2004", editor = "Masoud Mohammadian", pages = "279--290", address = "Gold Coast, Australia", month = "12-14 " # jul, keywords = "genetic algorithms, genetic programming, data mining, evolutionary computation, inductive learning", URL = "http://dummy/Lavangnananda_2004_CIMCA.pdf", size = "12 pages", abstract = "There have been many applications of artificial intelligence data mining recently. One of its many benefits includes the ability to cluster or generate patterns from large amount of data when conventional statistical methods are proven ineffective. One such techniques in data mining is inductive learning. There have been applications of evolutionary computation in inductive learning where genetic algorithms have been employed in chromosomes representation. This paper describes an attempt to use genetic programming in inductive learning. A program known as Genetic Programming for Inductive Learning (GPIL) is described. It uses genetic programming and rectifies the short comings of chromosomes representation in genetic algorithms. The program has been tested on a benchmark data set. It achieved better performance with higher accuracy than previous works on the same data set. The paper also discusses relevant aspects in using genetic programming in inductive learning and suggests directions for future work.", notes = "http://www.ise.canberra.edu.au/ An early version of \cite{Lavangnananda:2006:ieeeMWALS} ", } @InProceedings{Lavangnananda:2006:ieeeMWALS, author = "K. Lavangnananda", title = "Self-adjusting Associative Rules Generator for Classification : An Evolutionary Computation Approach", booktitle = "2006 IEEE Mountain Workshop on Adaptive and Learning Systems", year = "2006", pages = "237--242", address = "Logan, UT, USA", month = "24-26 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", ISBN = "1-4244-0166-6", URL = "http://dummy/Lavangnananda_2006_ieeeMWALS.pdf", DOI = "doi:10.1109/SMCALS.2006.250722", size = "6 pages", abstract = "The problem of generating efficient association rules can seen as search problem since many different sets of rules are possible from a given set of instances. As the application of evolutionary computation in searching is well studied, it is possible to use evolutionary computation in mining for efficient association rules. In this paper, a program known as self-adjusting associative rules generator (SARG) is described. SARG is a data mining program which can generate associative rules for classification. It is an improvement of the data mining program called genetic programming for inductive learning (GPIL). Both use evolutionary computation in inductive learning. The shortcoming of GPIL lies in the operations crossover and selection. These two operations were inflexible and not able to adjust themselves in order to select suitable methods for the task at hand. SARG introduces new method of crossover known as MaxToMin crossover together with a self-adjusting reproduction. It has been tested on several benchmark data sets available in the public domain. Comparison between GPIL and SARG revealed that SARG achieved better performance and was able to classify these data sets with higher accuracy. The paper also discusses relevant aspects of SARG and suggests directions for future work", notes = "Title should have been: {"}Self-adjusting Association Rules Generator for Classification : An Evolutionary Computation Approach{"} INSPEC Accession Number: 9131818 Sch. of Inf. Technol., King Mongkut's Inst. of Technol., Bangkok;", } @Misc{DBLP:journals/corr/abs-2112-03235, author = "Alexander Lavin and Hector Zenil and Brooks Paige and David Krakauer and Justin Gottschlich and Tim Mattson and Anima Anandkumar and Sanjay Choudry and Kamil Rocki and Atilim G{\"{u}}nes Baydin and Carina Prunkl and Olexandr Isayev and Erik Peterson and Peter L. McMahon and Jakob Macke and Kyle Cranmer and Jiaxin Zhang and Haruko M. Wainwright and Adi Hanuka and Manuela Veloso and Samuel Assefa and Stephan Zheng and Avi Pfeffer", title = "Simulation Intelligence: Towards a New Generation of Scientific Methods", howpublished = "arXiv", year = "2021", volume = "abs/2112.03235", month = "6 " # dec, keywords = "genetic algorithms, genetic programming, Simulation, Artificial Intelligence, Machine Learning, Scientific Computing, Physics-infused ML, Inverse Design, Human-Machine Teaming, Optimization, Causality, Complexity, Open-endedness", URL = "https://arxiv.org/abs/2112.03235", eprint = "2112.03235", timestamp = "Mon, 03 Jan 2022 22:03:29 +0100", biburl = "https://dblp.org/rec/journals/corr/abs-2112-03235.bib", bibsource = "dblp computer science bibliography, https://dblp.org", size = "108 pages", abstract = "The original Seven Motifs set forth a roadmap of essential methods for the field of scientific computing, where a motif is an algorithmic method that captures a pattern of computation and data movement. We present the {"}Nine Motifs of Simulation Intelligence{"}, a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence. We call this merger simulation intelligence (SI), for short. We argue the motifs of simulation intelligence are interconnected and interdependent, much like the components within the layers of an operating system. Using this metaphor, we explore the nature of each layer of the simulation intelligence operating system stack (SI-stack) and the motifs therein: (1) Multi-physics and multi-scale modeling; (2) Surrogate modeling and emulation; (3) Simulation-based inference; (4) Causal modeling and inference; (5) Agent-based modeling; (6) Probabilistic programming; (7) Differentiable programming; (8) Open-ended optimization; (9) Machine programming. We believe coordinated efforts between motifs offers immense opportunity to accelerate scientific discovery, from solving inverse problems in synthetic biology and climate science, to directing nuclear energy experiments and predicting emergent behavior in socioeconomic settings. We elaborate on each layer of the SI-stack, detailing the state-of-art methods, presenting examples to highlight challenges and opportunities, and advocating for specific ways to advance the motifs and the synergies from their combinations. Advancing and integrating these technologies can enable a robust and efficient hypothesis-simulation-analysis type of scientific method, which we introduce with several use-cases for human-machine teaming and automated science.", notes = "Institute for Simulation Intelligence. p32 'Generating agent-based model programs from scratch' agent-based modeling (ABM)", } @InProceedings{Lavinas:2018:ieeeSMC, author = "Yuri Lavinas and Claus Aranha and Tetsuya Sakurai and Marcelo Ladeira", booktitle = "2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)", title = "Experimental Analysis of the Tournament Size on Genetic Algorithms", year = "2018", pages = "3647--3653", abstract = "We perform an experimental study about the effect of the tournament size parameter from the Tournament Selection operator. Tournament Selection is a classic operator for Genetic Algorithms and Genetic Programming. It is simple to implement and has only one control parameter, the tournament size. Even though it is commonly used, most practitioners still rely on rules of thumb when choosing the tournament size. For example, almost all works in the past 15 years use a value of 2 for the tournament size, with little reasoning behind that choice. To understand the role of the tournament size, we run a real-valued GA on 24 BBOB problems with 10, 20 and 40 dimensions. We also vary the crossover operator and the generational policy of the GA. For each combination of the above factors we observe how the quality of the final solution changes with the tournament size. Our findings do not support the indiscriminate use of tournament size 2, and recommend a more careful set up of this parameter.", keywords = "genetic algorithms, genetic programming, Sociology, Statistics, Convergence, Benchmark testing, Decision making, Next generation networking", DOI = "doi:10.1109/SMC.2018.00617", ISSN = "2577-1655", month = oct, notes = "Also known as \cite{8616614}", } @InProceedings{lavinas:2023:GGP, author = "Yuri Lavinas and Kevin Cortacero and Sylvain Cussat-Blanc", title = "Evolving Graphs with Cartesian Genetic Programming with Lexicase Selection", booktitle = "Graph-based Genetic Programming", year = "2023", editor = "Roman Kalkreuth and Thomas Baeck and Dennis G. Wilson and Paul Kaufmann and Leo Francoso Dal Piccol Sotto and Timothy Aktinson", pages = "1920--1924", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, evolutionary computation, graph-based methods, lexicase selection", isbn13 = "9798400701191", DOI = "doi:10.1145/3583133.3596402", size = "5 pages", abstract = "The automatic construction of an image filter is a difficult task for which many recent machine-learning methods have been proposed. Cartesian Genetic Programming (CGP) has been effectively used in image-processing tasks by evolving programs with a function set specialized for computer vision. Although standard CGP is able to construct understandable image filter programs, we hypothesize that explicitly using a mechanism to control the size of the generated filter programs would help reduce the size of the final solution while keeping comparable efficacy on a given task. It is indeed central to keep the graph size as contained as possible as it improves our ability to understand them and explain their inner functioning. In this work, we use the Lexicase selection as the mechanism to control the size of the programs during the evolutionary process, by allowing CGP to evolve solutions based on performance and on the size of such solutions. We extend Kartezio, a Cartesian Genetic Programming for computer vision tasks, to generate our programs. We found in our preliminary experiment that CGP with Lexicase selection is able to achieve similar performance to the standard CGP while keeping the size of the solutions smaller.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @PhdThesis{lavinas:thesis, author = "Yuri Cossich Lavinas", title = "Effects of Algorithm Design in the Population Behavior of Multiobjective Evolutionary Algorithms", school = "Graduate School of Science and Technology, Degree Programs in Systems and Information Engineering, University of Tsukuba", year = "2023", address = "Japan", month = mar, keywords = "genetic algorithms", URL = "https://tsukuba.repo.nii.ac.jp/records/2008119", URL = "https://tsukuba.repo.nii.ac.jp/records/2007674/export/bibtex", URL = "https://tsukuba.repo.nii.ac.jp/record/2008119/files/DA010793.pdf", size = "131 pages", abstract = "This work aims to understand the effects of algorithm design on the population behavior of evolutionary algorithms when solving multiobjective optimization problems (MOPs). The performance of multiobjective evolutionary algorithms (MOEAs) varies across problems,making it hard to develop new algorithms or apply existing ones to new problems. Given that the behavior of these algorithms varies it is hard to understand the design choices made in such metaheuristics. Therefore, finding good tools to characterize such behavior dynamics of these algorithms in practice and how these choices are affected by the features of MOPs is an important field of research. Traditionally, one way of characterising the MOEAs is to analyze the final Pareto front, for example, looking at the frequency of solutions and how large is their distribution.As expected, this way of analyzing algorithms may be incomplete mainly because it ignores the decision space, the population characteristics, and the metrics convergence rates. These traditional methods neglect the population dynamics effect during the search progress and how suitable they are, and the interaction effects among the decision variables and the features of the problems. Moreover, these structural features cannot be derived. We propose a complementary analysis method that increments the traditional analysis by studying the convergence performance effects and the search space exploration. This allows us to focus on the changes in anytime behavior of MOEAs. To identify the most influential components of an algorithm and how they affect the objective and search space behavior in different problems, we introduce a methodology that measures the differences in anytime behaviour of variants of an algorithm to alter from the base algorithm by a single component. We use this methodology in two case studies: the first, where we study the algorithm design in terms of how a new component affects the behavior of one commonly used multiobjective evolutionary algorithm; and the second,where we investigate how different multiobjective evolutionary algorithms explore the search space during their execution in relation to the same optimisation problem. Finally, we end our experiments by looking at how different components alter the search and performance efficiency of a given algorithm", notes = "also known as \cite{oai:tsukuba.repo.nii.ac.jp:02007674}", } @InProceedings{Lavinas:2024:GPTP, author = "Yuri Lavinas and Camilo {De La Torre} and Kevin Cortacero and Dennis G. Wilson and Sylvain Cussat-Blanc", title = "Interpretable Genetic Programming Models for Real-World Biomedical Images", booktitle = "Genetic Programming Theory and Practice XXI", year = "2024", editor = "Stephan M. Winkler and Wolfgang Banzhaf and Ting Hu and Alexander Lalejini", series = "Genetic and Evolutionary Computation", pages = "297--319", address = "University of Michigan, USA", month = jun # " 6-8", publisher = "Springer", keywords = "genetic algorithms, genetic programming, XAI", isbn13 = "978-981-96-0076-2", DOI = "doi:10.1007/978-981-96-0077-9_15", notes = "Published in 2025 after the workshop", } @InProceedings{lavinas:2024:CEC, author = "Yuri Lavinas and Nathan Haut and William Punch and Wolfgang Banzhaf and Sylvain Cussat-Blanc", title = "Data Sampling via Active Learning in Cartesian Genetic Programming for Biomedical Data", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, Uncertainty, Shape, Image color analysis, Training data, Bioinformatics, Task analysis, image analysis, image processing, data sampling", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10611879", abstract = "In this contribution, we explore Cartesian Genetic Programming for image analysis of biomedical data. Producing large quantities of human-labeled biomedical data is an expensive task. Here, we introduce a way for CGP to use a small amount of training data, without loss in performance. To define the size of the training data, we use an Active Learning method to direct the algorithm towards informative samples. We examine how sampling a small set of data from the CELLPOSE dataset affects the performance of CGP. We also study the effects of restarting CGP with Active Learning. We found that using several restarts can lead to a more diverse set of the highest-performing solutions with fewer active nodes while maintaining similar performance to standard CGP.", notes = "also known as \cite{10611879} WCCI 2024", } @InProceedings{lavinas:2024:GECCOcomp2, author = "Yuri Lavinas and Nathaniel Haut and William Punch and Wolfgang Banzhaf and Sylvain Cussat-Blanc", title = "Dynamically Sampling biomedical Images For Genetic Programming", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion", year = "2024", editor = "Ting Hu and Aniko Ekart", pages = "515--518", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, image processing, active learning, data sampling: Poster", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3654202", size = "4 pages", abstract = "In this contribution we study how to effectively evolve programs tailored for biomedical image segmentation by using an Active Learning approach in Cartesian Genetic Programming (CGP). Active Learning allows to dynamically select training data by identifying the most informative next image to add to the training set. We study how different metrics for selecting images under active learning impact the searchability of CGP. Our results show that datasets built during evolution with active learning improve the performance of Cartesian GP substantially. In addition, we found that the choice of the particular metric used for selecting which images to add heavily impacts convergence speed. Our work shows that the right choice of the image selection metric positively impacts the effectiveness of the evolutionary algorithm.", notes = "GECCO-2024 GP A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{Lavinas:2024:PPSN, author = "Yuri Lavinas and Nathaniel Haut and William Punch and Wolfgang Banzhaf and Sylvain Cussat-Blanc", title = "Adaptive Sampling of Biomedical Images with Cartesian Genetic Programming", booktitle = "18th International Conference on Parallel Problem Solving from Nature", year = "2024", editor = "Heike Trautmann and Tea Tusar and Penousal Machado and Thomas Baeck", volume = "15148", series = "Lecture Notes in Computer Science", pages = "256--272", address = "University of Applied Sciences Upper Austria, Hagenberg, Austria", month = "13-18 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, biomedical data, data sampling, active learning", isbn13 = "978-3-031-70054-5", URL = "https://rdcu.be/d5mXM", DOI = "doi:10.1007/978-3-031-70055-2_16", notes = "https://ppsn2024.fh-ooe.at/program/", } @Article{Lavington:1999:IST, author = "S. Lavington and N. Dewhurst and E. Wilkins and A. Freitas", title = "Interfacing knowledge discovery algorithms to large database management systems", journal = "Information and Software Technology", volume = "41", pages = "605--617", year = "1999", number = "9", month = "25 " # jun, note = "special issue on data mining", keywords = "genetic algorithms, genetic programming, data mining, KDD primitives, decision trees, client-server", ISSN = "0950-5849", URL = "http://www.sciencedirect.com/science/article/B6V0B-3WN7DYN-8/1/cdabdda09c085c6a4536aa5e116366ee", DOI = "doi:10.1016/S0950-5849(99)00024-5", size = "13 pages", abstract = "The efficient mining of large, commercially credible, databases requires a solution to at least two problems: (a) better integration between existing Knowledge Discovery algorithms and popular DBMS; (b) ability to exploit opportunities for computational speedup such as data parallelism. Both problems need to be addressed in a generic manner, since the stated requirements of end-users cover a range of data mining paradigms, DBMS, and (parallel) platforms. In this paper we present a family of generic, set-based, primitive operations for Knowledge Discovery in Databases (KDD). We show how a number of well-known KDD classification metrics, drawn from paradigms such as Bayesian classifiers, Rule-Induction/Decision Tree algorithms, Instance-Based Learning methods, and Genetic Programming, can all be computed via our generic primitives. We then show how these primitives may be mapped into SQL and, where appropriate, optimised for good performance in respect of practical factors such as client-server communication overheads. We demonstrate how our primitives can support C4.5, a widely-used rule induction system. Performance evaluation figures are presented for commercially available parallel platforms, such as the IBM SP/2.", } @InProceedings{law:1999:G, author = "Kin Lun Law", title = "Generating hard satisfiability problems using genetic programming", booktitle = "Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference", year = "1999", editor = "Scott Brave and Annie S. Wu", pages = "171--174", address = "Orlando, Florida, USA", month = "13 " # jul, keywords = "genetic algorithms, genetic programming", notes = "GECCO-99LB", } @InCollection{law:1999:TRDGP, author = "Ken Law", title = "Traffic Rules Discovery using Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1999", year = "1999", editor = "John R. Koza", pages = "105--114", address = "Stanford, California, 94305-3079 USA", month = "15 " # mar, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:1999:GAGPs}", } @Article{Lawrence01, author = "Steve Lawrence", title = "Free online availability substantially increases a paper's impact", journal = "Nature", year = "2001", volume = "411", number = "6837", pages = "521", month = "31 " # may, keywords = "jrnl, citeseer, www, world wide web, papers, publications, online, citations, cited, impact factor, c2001, c200x, c20xx", ISSN = "0028-0836", URL = "http://www.nature.com/cgi-taf/DynaPage.taf?file=/nature/journal/v411/n6837/full/411521a0_fs.html&filetype=pdf", URL = "http://citeseer.ist.psu.edu/online-nature01/", DOI = "doi:10.1038/35079151", size = "1 page", abstract = "...analysed 119,924 conference articles in computer science and related disciplines, obtained from DBLP (dblp.uni-trier.de). In computer science, conference articles are typically formal publications and are often more prestigious than journal articles, with acceptance rates at some conferences below 10percent. Citation counts and online availability were estimated using ResearchIndex....", notes = "Free online availability of scientific literature offers substantial benefits to science and society. To maximise impact, minimise redundancy and speed scientific progress, authors and publishers should aim to make research easy to access.", } @InProceedings{Laws:2005:WRAC, author = "A. G. Laws and A. Taleb-bendiab and S. J. Wade", title = "Genetically Modified Software: Realizing Viable Autonomic Agency", booktitle = "Innovative Concepts for Autonomic and Agent-Based Systems, WRAC 2005", year = "2005", editor = "Michael G. Hinchey and Patricia Rago and James L. Rash and Christopher A. Rouff and Roy Sterritt and Walt Truszkowski", volume = "3825", series = "Lecture Notes in Computer Science", pages = "184--196", address = "Greenbelt, MD, USA", month = "20-22 " # sep, publisher = "Springer", note = "Revised Papers", keywords = "genetic algorithms, SBSE", isbn13 = "978-3-540-69265-2", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.297.1051", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.297.1051", URL = "http://www.cms.livjm.ac.uk/taleb/Publications/05/AL_WRAC'05.pdf", URL = "http://dx.doi.org/10.1007/11964995_16", DOI = "doi:10.1007/11964995_16", size = "13 pages", abstract = "Inspired by the autonomic aspects of the human central nervous system, the vision of autonomic computing arrived with a fully-formed wish list of characteristics that such systems should exhibit, essentially those self-referential aspects required for effective self-management. Although much progress has been made, a unifying approach or indeed an underlying theoretical foundation to support such work has not as yet emerged. Here, the authors contend that the biologically-inspired managerial cybernetics of Beer's Viable System Model (VSM) provides significant conceptual guidance for the development of a general architecture for the operation and management of such complex, evolving, adaptive systems that arguably extends the concept of autonomic systems to cognitive systems. Consequently, the VSM has been used as the basis of a unifying reference model that provides the blueprint for an extensible intelligent agent architecture that readily scales to a polyarchical agency of autonomic systems. Furthermore and with recourse to the classical cybernetics that underpin the VSM, the authors demonstrate that survival in a changing environment requires that such systems should develop capabilities to identify, specify, develop and deploy appropriately, a repertoire of actions that would guide their adaptation to changing circumstances/environments. The authors then show that the use of Holland's Genetic Algorithms (GAs) can, in specific circumstances, provide a means to provide tailored responses to environmental change and when coupled with the associated Learning Classifier Systems (LCS) approach allow the system to develop an adaptive environmental model of appropriate, optimised responses. Of course, although these approaches appear to offer a profitable route forward, the general applicability and scalability of GAs in particular, are open to question, therefore the paper concludes with some speculation on the possible contributions that associated approaches like genetic programming may have to offer.", notes = "The author suggests this is not GP", } @InProceedings{Lay:1994:GPssdy, author = "Ming-Yi Lay", title = "Application of genetic programming in analyzing multiple steady states of dynamical systems", booktitle = "Proceedings of the 1994 IEEE World Congress on Computational Intelligence", year = "1994", volume = "1", pages = "333--336b", address = "Orlando, Florida, USA", month = "27-29 " # jun, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Hopf bifurcation points, dynamical systems, genetic programming paradigm, multiple steady states, bifurcation, linear programming, search problems", DOI = "doi:10.1109/ICEC.1994.349930", size = "6 pages", abstract = "Multiple steady states are very interesting phenomena in dynamical systems. However, it is hard to analyse these kinds of phenomena directly by using traditional numerical methods. It is shown that the genetic programming paradigm could be used to directly analyze the existence of multiple steady states in dynamical systems and it could even possibly be applied in analysing other kinds of behaviour in dynamical systems, e.g., the Hopf bifurcation points", notes = "Uses GP to search for steady states in a reaction vessel. The equations for the behaviour of the chemicals is known but not how to solve them. GP is able to find to high accuracy (7 figure) the steady states. States are divined by two floating point variables. Each represented within the prog by an effectively independent tree, ie they don't exchange via crossover.", } @InProceedings{Lazarus:2001:timr, author = "Christopher Lazarus and Huosheng Hu", title = "Using Genetic Programming to Evolve Behaviours", booktitle = "TIMR 01 - Towards Intelligent Mobile Robots", year = "2001", editor = "Ulrich Nehmzow and Chris Melhuish", address = "Manchester, UK", publisher_address = "UK", month = "5 " # apr, organisation = "Computer Science, University of Manchester", keywords = "genetic algorithms, genetic programming, Evolutionary Robotics, Wall-following.", URL = "http://apt.cs.manchester.ac.uk/ftp/pub/TR/UMCS-01-4-1.html", URL = "http://apt.cs.manchester.ac.uk/ftp/pub/TR/UMCS-01-4-1-lazarus.ps.Z", URL = "http://cswww.essex.ac.uk/staff/hhu/Papers/TIMR2001_Chris.pdf", size = "8 pages", abstract = "This paper presents the application of genetic programming (GP) to the task of evolving robot behaviours. The domain used here is the well-known wall-following problem. A set of programs were evolved that can successfully perform wall-following behaviours. The experiments involving different wall shapes were designed and implemented to investigate whether the solutions offered by GP are scalable. Experimental results show that GP is able to automatically produce algorithms for wall-following tasks. In addition, more complex wall shapes were introduced to verify that they do not affect our GP implementation detrimentally.", notes = "Aim to target RoboCup, robot football, soccer. Proceedings British Conference on Autonomous Mobile Robotics & Autonomous Systems, 2001 Proceedings available as Technical Report UMCS-01-4-1 of the Computer Science department of the University of Manchester UMCS-01-4-1 Also known as TAROS 2001 http://www.cs.ox.ac.uk/conferences/TAROS2013/past.html", } @PhdThesis{Lazarus:thesis, author = "Christopher Lazarus", title = "Genetic Programming Based Evolution of Multi-Agent Behaviours", school = "School of Computer Science and Electronic Engineering, Essex University", year = "2011", address = "UK", keywords = "genetic algorithms, genetic programming", URL = "http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.549295", size = "556 pages", notes = "uk.bl.ethos.549295 gives name as Christopher Lazareus Supervisor: Huosheng Hu", } @InProceedings{Lazarus:2015:ieeeSSCI, author = "Christopher Lazarus", booktitle = "IEEE Symposium Series on Computational Intelligence", title = "Pareto-Dominance Based MOGP for Evolving Soccer Agents", year = "2015", pages = "280--287", abstract = "Robot behaviour generation is an attractive option to automatically produce robot controllers. Most high-level robot behaviours comprise multiple objectives that may be conflicting with each other. This research describes experiments using two Pareto-dominance based algorithms together with a Multiobjective Genetic Programming (MOGP) framework to evolve high-level robot behaviours using only primitive commands. The performance of hand-coded controllers are compared against controllers evolved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Strength Pareto Evolutionary Algorithm 2 (SPEA2) algorithms. An additional comparison is also performed against controllers evolved using the weighted sum fitness function. The experiment results show that the Pareto dominance based MOGP performed better than the hand-coded and the weighted sum evolved controllers.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SSCI.2015.49", month = dec, notes = "Also known as \cite{7376622}", } @Article{lazebnik:2024:Mathematics, author = "Teddy Lazebnik and Svetlana Bunimovich-Mendrazitsky", title = "More Numerically Accurate Algorithm for Stiff Matrix Exponential", journal = "Mathematics", year = "2024", volume = "12", number = "8", pages = "Article No. 1151", keywords = "genetic algorithms, genetic programming", ISSN = "2227-7390", URL = "https://www.mdpi.com/2227-7390/12/8/1151", DOI = "doi:10.3390/math12081151", abstract = "In this paper, we propose a novel, highly accurate numerical algorithm for matrix exponentials (MEs). The algorithm is based on approximating Putzer's algorithm by analytically solving the ordinary differential equation (ODE)-based coefficients and approximating them. We show that the algorithm outperforms other ME algorithms for stiff matrices for several matrix sizes while keeping the computation and memory consumption asymptotically similar to these algorithms. In addition, we propose a numerical-error- and complexity-optimised decision tree model for efficient ME computation based on machine learning and genetic programming methods. We show that, while there is not one ME algorithm that outperforms the others, one can find a good algorithm for any given matrix according to its properties.", notes = "also known as \cite{math12081151}", } @PhdThesis{Le_Xuan-Bach:thesis, author = "Dinh Xuan Bach Le", title = "Overfitting in Automated Program Repair: Challenges and Solutions", school = "School of Information Systems, Singapore Management University", year = "2018", address = "Singapore", month = jun, keywords = "genetic algorithms, genetic programming, genetic improvement, program repair, APR, Program synthesis, Symbolic execution, Empirical study, SemFix, MaxSMT", URL = "https://ink.library.smu.edu.sg/etd_coll/177/", URL = "https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=1177&context=etd_coll", size = "151 pages", abstract = "discusses the main problem and motivation of this dissertation. It also discusses a quantification of various research issues directly related to the dissertation. A summary of works done will also be presented along with the structure of the dissertation.", notes = "GP only in history of automatic bug fixing. Supervisor: David Lo https://sis.smu.edu.sg/newsletter/28876", } @InProceedings{Le:2019:GECCOcomp, author = "Duc C. Le and Malcolm I. Heywood and Nur Zincir-Heywood", title = "Benchmarking genetic programming in dynamic insider threat detection", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", isbn13 = "978-1-4503-6748-6", pages = "385--386", address = "Prague, Czech Republic", DOI = "doi:10.1145/3319619.3322029", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{3322029} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InProceedings{Le:2019:CNSM, author = "Duc C. Le and Nur Zincir-Heywood", title = "Learning From Evolving Network Data for Dependable Botnet Detection", booktitle = "2019 15th International Conference on Network and Service Management (CNSM)", year = "2019", month = oct, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.23919/CNSM46954.2019.9012710", ISSN = "2165-963X", abstract = "This work presents an emerging problem in real-world applications of machine learning (ML) in cybersecurity, particularly in botnet detection, where the dynamics and the evolution in the deployment environments may render the ML solutions inadequate. We propose an approach to tackle this challenge using Genetic Programming (GP) - an evolutionary computation based approach. Preliminary results show that GP is able to evolve pre-trained classifiers to work under evolved (expanded) feature space conditions. This indicates the potential use of such an approach for botnet detection under non-stationary environments, where much less data and training time are required to obtain a reliable classifier as new network conditions arise.", notes = "Also known as \cite{9012710}", } @InProceedings{Le:2019:SSCI, author = "Duc C. Le and A. Nur Zincir-Heywood and Malcolm I. Heywood", booktitle = "2019 IEEE Symposium Series on Computational Intelligence (SSCI)", title = "Dynamic Insider Threat Detection Based on Adaptable Genetic Programming", year = "2019", pages = "2579--2586", month = dec, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SSCI44817.2019.9003134", abstract = "Different variations in deployment environments of machine learning techniques may affect the performance of the implemented systems. The variations may cause changes in the data for machine learning solutions, such as in the number of classes and the extracted features. This paper investigates the capabilities of Genetic Programming (GP) for malicious insider detection in corporate environments under such changes. Assuming a Linear GP detector, techniques are introduced to allow a previously trained GP population to adapt to different changes in the data. The experiments and evaluation results show promising insider threat detection performances of the techniques in comparison with training machine learning classifiers from scratch. This reduces the amount of data needed and computation requirements for obtaining dependable insider threat detectors under new conditions.", notes = "Also known as \cite{9003134}", } @InProceedings{Le:2016:CEC, author = "Nam Le and Hoai Nguyen Xuan and Anthony Brabazon and Thuong Pham Thi", title = "Complexity measures in Genetic Programming Learning: A Brief Review", booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016)", year = "2016", editor = "Yew-Soon Ong", pages = "2409--2416", address = "Vancouver", month = "24-29 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Model Selection, Vaknik-Chervonenkis dimension, Statistical Machine Learning, Rademacher complexity", isbn13 = "978-1-5090-0623-6", URL = "http://ncra.ucd.ie/papers/complexity_measures_cec2016.pdf", DOI = "doi:10.1109/CEC.2016.7744087", size = "8 pages", abstract = "Model complexity of Genetic Programming (GP) as a learning machine is currently attracting considerable interest from the research community. Here we provide an up-to-date overview of the research concerning complexity measure techniques in GP learning. The scope of this review includes methods based on information theory techniques, such as the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC); plus those based on statistical machine learning theory on generalization error bound, namely, Vapnik-Chervonenkis (VC) theory; and some based on structural complexity. The research contributions from each of these are systematically summarized and compared, allowing us to clearly define existing research challenges, and to highlight promising new research directions. The findings of this review provides valuable insights into the current GP literature and is a good source for anyone who is interested in the research on model complexity and applying statistical learning theory to GP.", notes = "WCCI2016", } @InProceedings{le:2017:CEC, author = "Nam Le and Michael O'Neill and David Fagan and Anthony Brabazon", booktitle = "2017 IEEE Congress on Evolutionary Computation (CEC)", title = "Social Grammatical Evolution with imitation learning for real-valued function estimation", year = "2017", editor = "Jose A. Lozano", pages = "1572--1578", address = "Donostia, San Sebastian, Spain", publisher = "IEEE", month = "5-8 " # jun, keywords = "genetic algorithms, genetic programming, Grammatical Evolution, grammars, regression analysis, social sciences, imitation learning, real-valued function estimation, social grammatical evolution, social learning, symbolic regression, Algorithm design and analysis, Animals, Benchmark testing, Optimization, Sociology, Statistics", isbn13 = "978-1-5090-4601-0", DOI = "doi:10.1109/CEC.2017.7969490", size = "7 pages", abstract = "Drawing on a rich literature concerning social learning in animals, this paper presents a variation of Grammatical Evolution (GE) which incorporates one of the most powerful forms of social learning, namely imitation learning. This replaces the traditional method of communication between individuals in GE - crossover - which is drawn from an evolutionary metaphor. The paper provides an introduction to social learning, describes the proposed variant of GE, and tests on a series of benchmark symbolic regression problems. The results obtained are encouraging, being very competitive when compared with canonical GE. It is noted that the literature on social learning provides a number of useful meta-frameworks which can be used in the design of new search algorithms and to allow us to better understand the strengths and weaknesses of existing algorithms. Future work is indicated in this area.", notes = "IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969490}", } @InProceedings{Le:2014:CISDA, author = "Thi Anh Le and Thi Huong Chu and Quang Uy Nguyen and Xuan Hoai Nguyen", booktitle = "Seventh IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA 2014)", title = "Malware detection using genetic programming", year = "2014", month = "14-17 " # dec, address = "Hanoi, Vietnam", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-5431-5", DOI = "doi:10.1109/CISDA.2014.7035623", size = "6 pages", abstract = "Malware is any software aiming to disrupt computer operation. Malware is also used to gather sensitive information or gain access to private computer systems. This is widely seen as one of the major threats to computer systems nowadays. Traditionally, anti-malware software is based on a signature detection system which keeps updating from the Internet malware database and thus keeping track of known malwares. While this method may be very accurate to detect previously known malwares, it is unable to detect unknown malicious codes. Recently, several machine learning methods have been used for malware detection, achieving remarkable success. In this paper, we propose a method in this strand by using Genetic Programming for detecting malwares. The experiments were conducted with the malwares collected from an updated malware database on the Internet and the results show that Genetic Programming, compared to some other well-known machine learning methods, can produce the best results on both balanced and imbalanced datasets.", notes = "Faculty of IT, Le Quy Don University Hanoi, Vietnam Also known as \cite{7035623}", } @Article{Le:2019:BioInf, author = "Trang T. Le and Weixuan Fu and Jason H. Moore", title = "Scaling tree-based automated machine learning to biomedical big data with a feature set selector", journal = "Bioinformatics", year = "2020", volume = "36", number = "1", pages = "250--256", DOI = "doi:10.1093/bioinformatics/btz470", notes = "Oct 2022 disabled as duplicate of Le:2020:Bioinformatics", } @Misc{Le:2019:TPOT, author = "Trang T. Le", title = "{TPOT}: Where do I start?", howpublished = "www blog", year = "2019", month = nov # " 5", keywords = "genetic algorithms, genetic programming, TPOT, AutoML", URL = "https://trang.page/2019/11/05/tpot-where-do-i-start/", abstract = "Tree-based Pipeline Optimization Tool (TPOT) is an automated machine learning tool that helps the data scientist find the optimal model pipeline for their prediction problem. Using genetic programming (GP), TPOT explores different pipelines (sequences of feature selectors, model classifiers, etc.) and recommends one with optimal cross-validated score after a specified number of generations.", notes = "24 April 2021 http://epistasislab.github.io/tpot/ https://slides.com/trang1618/tpot-cic#/0/1 \cite{Orlenko:2018:PSB}", } @Article{Le:2020:Bioinformatics, author = "Trang T. Le and Weixuan Fu and Jason H. Moore", title = "Scaling tree-based automated machine learning to biomedical big data with a feature set selector", journal = "Bioinformatics", year = "2020", volume = "36", number = "1", pages = "250--256", month = "1 " # jan, keywords = "genetic algorithms, genetic programming, TPOT, Data and text mining", ISSN = "1367-4803", URL = "https://academic.oup.com/bioinformatics/article-pdf/36/1/250/31813758/btz470.pdf", code_url = "https://github.com/EpistasisLab/tpot", code_url = "https://github.com/lelaboratoire/tpot-fss", DOI = "doi:10.1093/bioinformatics/btz470", size = "7 pages", abstract = "Automated machine learning (AutoML) systems are helpful data science assistants designed to scan data for novel features, select appropriate supervised learning models and optimize their parameters. For this purpose, Tree-based Pipeline Optimization Tool (TPOT) was developed using strongly typed genetic programming (GP) to recommend an optimized analysis pipeline for the data scientist prediction problem. However, like other AutoML systems, TPOT may reach computational resource limits when working on big data such as whole-genome expression data. We introduce two new features implemented in TPOT that helps increase the system's scalability: Feature Set Selector (FSS) and Template. FSS provides the option to specify subsets of the features as separate datasets, assuming the signals come from one or more of these specific data subsets. FSS increases TPOT's efficiency in application on big data by slicing the entire dataset into smaller sets of features and allowing GP to select the best subset in the final pipeline. Template enforces type constraints with strongly typed GP and enables the incorporation of FSS at the beginning of each pipeline. Consequently, FSS and Template help reduce TPOT computation time and may provide more interpretable results. Our simulations show TPOT-FSS significantly outperforms a tuned XGBoost model and standard TPOT implementation. We apply TPOT-FSS to real RNA-Seq data from a study of major depressive disorder. Independent of the previous study that identified significant association with depression severity of two modules, TPOT-FSS corroborates that one of the modules is largely predictive of the clinical diagnosis of each individual. Detailed simulation and analysis code needed to reproduce the results in this study is available at https://github.com/lelaboratoire/tpot-fss. Implementation of the new TPOT operators is available at https://github.com/EpistasisLab/tpot. Supplementary data are available at Bioinformatics online.", notes = "See also https://doi.org/10.1101/502484 Also known as cite{10.1093/bioinformatics/btz470}. Also known as cite{le2020scaling}. PMID: 31165141; PMCID: PMC6956793", } @InProceedings{Le:2020:GECCOcomp, author = "Trang T. Le and Weixuan Fu and Jason H. Moore", title = "Large Scale Biomedical Data Analysis with Tree-Based Automated Machine Learning", year = "2020", editor = "Richard Allmendinger and Hugo Terashima Marin and Efren Mezura Montes and Thomas Bartz-Beielstein and Bogdan Filipic and Ke Tang and David Howard and Emma Hart and Gusz Eiben and Tome Eftimov and William {La Cava} and Boris Naujoks and Pietro Oliveto and Vanessa Volz and Thomas Weise and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Rui Wang and Ran Cheng and Guohua Wu and Miqing Li and Hisao Ishibuchi and Jonathan Fieldsend and Ozgur Akman and Khulood Alyahya and Juergen Branke and John R. Woodward and Daniel R. Tauritz and Marco Baioletti and Josu Ceberio Uribe and John McCall and Alfredo Milani and Stefan Wagner and Michael Affenzeller and Bradley Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and Nayat Sanchez-Pi and Luis Marti and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and Matthew Johns and Nick Ross and Ed Keedwell and Herman Mahmoud and David Walker and Anthony Stein and Masaya Nakata and David Paetzel and Neil Vaughan and Stephen Smith and Stefano Cagnoni and Robert M. Patton and Ivanoe {De Falco} and Antonio {Della Cioppa} and Umberto Scafuri and Ernesto Tarantino and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Erik Hemberg and Riyad Alshammari and Tokunbo Makanju and Fuijimino-shi and Ivan Zelinka and Swagatam Das and Ponnuthurai Nagaratnam and Roman Senkerik", isbn13 = "9781450371278", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377929.3397770", DOI = "doi:10.1145/3377929.3397770", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion", pages = "21--22", size = "2 pages", keywords = "genetic algorithms, genetic programming, TPOT, autoML", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", abstract = "Tree-based Pipeline Optimization Tool (TPOT) is an automated machine learning (AutoML) system that recommends optimal pipeline for supervised learning problems by scanning data for novel features, selecting appropriate models and optimizing their parameters. However, like other AutoML systems, TPOT may reach computational resource limits when working on big data such as whole-genome expression data. We develop two novel features for TPOT, Feature Set Selector and Template, which leverage domain knowledge, greatly reduce the computational expense and flexibly extend TPOT's application to biomedical big data analysis.", notes = "Also known as \cite{10.1145/3377929.3397770} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @InProceedings{Le:2015:ieeeISSRE, author = "Xuan-Bach D. Le and Tien-Duy B. Le and David Lo", booktitle = "26th IEEE International Symposium on Software Reliability Engineering (ISSRE)", title = "Should fixing these failures be delegated to automated program repair?", year = "2015", pages = "427--437", month = nov, keywords = "genetic algorithms, genetic programming, genetic improvement, APR, SBSE", DOI = "doi:10.1109/ISSRE.2015.7381836", abstract = "Program repair constitutes one of the major components of software maintenance that usually incurs a significant cost in software production. Automated program repair is supposed to help in reducing the software maintenance cost by automatically fixing software defects. Despite the recent advances in automated software repair, it is still very costly to wait for repair tools to produce valid repairs of defects. This paper addresses the following question: 'Will an automated program repair technique find a repair for a defect within a reasonable time?'. To answer this question, we build an oracle that can predict whether fixing a failure should be delegated to an automated repair technique. If the repair technique is predicted to take too long to produce a repair, the bug fixing process should rather be assigned to a developer or other appropriate techniques available. Our oracle is built for genetic-programming-based automated program repair approaches, which have recently received considerable attention due to their capability to automatically fix real-world bugs. These approaches search for a valid repair over a large number of variants that are syntactically mutated from the original program. At an early stage of running a repair tool, we extract a number of features that are potentially related to the effectiveness of the tool. Leveraging advances in machine learning, we process the values of these features to learn a discriminative model that is able to predict whether continuing a genetic programming search will lead to a repair within a desired time limit. We perform experiments to evaluate the ability of our approach to predict the effectiveness of GenProg, a well-known genetic-programming-based automated program repair approach, in fixing 105 real bugs. Our experiments show that our approach can identify effective cases from ineffective ones (i.e., bugs for which GenProg cannot produce correct fixes after a long period of time) with a precision, recall, F-measure, and AUC of 72percent, 74percent, 73percent, and 76percent respectively.", notes = "Also known as \cite{7381836}", } @InProceedings{Le:2016:ICSME, author = "Xuan-Bach D. Le and Quang Loc Le and David Lo and Claire {Le Goues}", booktitle = "2016 IEEE International Conference on Software Maintenance and Evolution (ICSME)", title = "Enhancing Automated Program Repair with Deductive Verification", year = "2016", pages = "428--432", month = "2-10 " # oct, address = "Raleigh, North Carolina, USA", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, SBSE, Automated Repair, Deductive Verification, Sound Repair", URL = "https://goo.gl/9TS9wo", DOI = "doi:10.1109/ICSME.2016.66", abstract = "Automated program repair (APR) is a challenging process of detecting bugs, localizing buggy code, generating fix candidates and validating the fixes. Effectiveness of program repair methods relies on the generated fix candidates, and the methods used to traverse the space of generated candidates to search for the best ones. Existing approaches generate fix candidates based on either syntactic searches over source code or semantic analysis of specification, e.g., test cases. In this paper, we propose to combine both syntactic and semantic fix candidates to enhance the search space of APR, and provide a function to effectively traverse the search space. We present an automated repair method based on structured specifications, deductive verification and genetic programming. Given a function with its specification, we use a modular verifier to detect bugs and localize both program statements and sub-formulas in the specification that relate to those bugs. While the former are identified as buggy code, the latter are transformed as semantic fix candidates. We additionally generate syntactic fix candidates via various mutation operators. Best candidates, which receives fewer warnings via a static verification, are selected for evolution though genetic programming until we find one satisfying the specification. Another interesting feature of our proposed approach is that we efficiently ensure the soundness of repaired code through modular (or compositional) verification. We implemented our proposal and tested it on C programs taken from the SIR benchmark that are seeded with bugs, achieving promising results.", notes = "ICSME 2016 http://icsme2016.github.io/program/accepted.html Slides https://www.cs.cmu.edu/~clegoues/docs/slides/presentation-DeductiveRepair.pdf Also known as \cite{7816488}", } @InProceedings{Le:2017:SSS, author = "Xuan-Bach D. Le and Duc-Hiep Chu and David Lo and Claire {Le Goues} and Willem Visser", title = "{S3}: Syntax- and Semantic-guided Repair Synthesis via Programming by Examples", booktitle = "Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering, ESEC/FSE 2017", year = "2017", editor = "Eric Bodden and Wilhelm Schaefer", pages = "593--604", address = "Paderborn, Germany", month = "408 " # sep, publisher = "ACM", keywords = "genetic algorithms, genetic programming, genetic improvement, Inductive Synthesis, Program Repair, Programming by Examples, Symbolic Execution", isbn13 = "978-1-4503-5105-8", acmid = "3106309", URL = "https://www.cs.cmu.edu/~clegoues/docs/legoues-esecfse17.pdf", URL = "http://doi.acm.org/10.1145/3106237.3106309", DOI = "doi:10.1145/3106237.3106309", abstract = "A notable class of techniques for automatic program repair is known as semantics-based. Such techniques, e.g., Angelix, infer semantic specifications via symbolic execution, and then use program synthesis to construct new code that satisfies those inferred specifications. However, the obtained specifications are naturally incomplete, leaving the synthesis engine with a difficult task of synthesizing a general solution from a sparse space of many possible solutions that are consistent with the provided specifications but that do not necessarily generalize. We present S3, a new repair synthesis engine that leverages programming-by-examples methodology to synthesize high-quality bug repairs. The novelty in S3 that allows it to tackle the sparse search space to create more general repairs is three-fold: (1) A systematic way to customize and constrain the syntactic search space via a domain-specific language, (2) An efficient enumeration- based search strategy over the constrained search space, and (3) A number of ranking features based on measures of the syntactic and semantic distances between candidate solutions and the original buggy program. We compare S3 repair effectiveness with state-of-the-art synthesis engines Angelix, Enumerative, and CVC4. S3 can successfully and correctly fix at least three times more bugs than the best baseline on datasets of 52 bugs in small programs, and 100 bugs in real-world large programs.", notes = "Is this GP? SYNTH-LIB intro class benchmark https://github.com/Spirals-Team/IntroClassJava \cite{durieux:hal-01272126} Port of Angelix to Java http://esec-fse17.uni-paderborn.de/ Also known as \cite{Le:2017:SSS:3106237.3106309}", } @Article{le:2024:AJSE, author = "Ba-Anh Le and Bao-Viet Tran and Thai-Son Vu and Viet-Hung Vu and Van-Hung Nguyen", title = "Predicting the Compressive Strength of Pervious Cement Concrete based on Fast Genetic Programming Method", journal = "Arabian Journal for Science and Engineering", year = "2024", volume = "49", number = "4", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s13369-023-08396-2", DOI = "doi:10.1007/s13369-023-08396-2", } @Article{Le:2025:trpro, author = "Ba Anh Le and Hoang Quan Nguyen and Bao Viet Tran and Thai Son Vu and Thi Loan Bui", title = "Symbolic regression model for predicting compression strength of steel fiber-reinforced concrete", journal = "Transportation Research Procedia", year = "2025", volume = "85", pages = "94--101", note = "TRPRO SDCAT 2023", keywords = "genetic algorithms, genetic programming, Fiber Reinforced Concrete, Symbolic Regression model", ISSN = "2352-1465", URL = "https://www.sciencedirect.com/science/article/pii/S2352146525001978", DOI = "doi:10.1016/j.trpro.2025.03.138", abstract = "black box' category, failing to provide a transparent formula. This article sets out to achieve the goal of developing a mathematical expression based on genetic programming to predict the compressive strength of fiber-reinforced concrete. A dataset comprising 166 entries, extracted from published journal papers and conference proceedings, has been used to train the model. The results obtained reveal that the formulated equation achieves a relatively high level of accuracy, with an R-squared value of 0.91. This outcome is further benchmarked against alternative machine learning models and demonstrates comparable performance", } @Misc{le2025decouplingrepresentationlearninggenetic, author = "Nam H. Le and Josh Bongard", title = "Decoupling Representation and Learning in Genetic Programming: the LaSER Approach", howpublished = "arXiv 2505.17309", year = "2025", month = "6 " # jun, edition = "v2", keywords = "genetic algorithms, genetic programming, symbolic tree GP, DEAP, LASER, Baldwin Effect, lifetime learning: Linear Regression, Ridge Regression, scikit-learn, Decision Tree, Random Forest, Gradient Boosting, Multi-layer Perceptron, MLP, ANN", archiveprefix = "arXiv", primaryclass = "cs.NE", URL = "https://arxiv.org/abs/2505.17309", size = "15 pages", abstract = "Genetic Programming (GP) has traditionally entangled the evolution of symbolic representations with their performance-based evaluation, often relying solely on raw fitness scores. This tight coupling makes GP solutions more fragile and prone to overfitting, reducing their ability to generalize. we propose LaSER (Latent Semantic Representation Regression) -- a general framework that decouples representation evolution from lifetime learning. At each generation, candidate programs produce features which are passed to an external learner to model the target task. This approach enables any function approximator, from linear models to neural networks, to serve as a lifetime learner, allowing expressive modeling beyond conventional symbolic forms. Here we show for the first time that LaSER can out compete standard GP and GP followed by linear regression when it employs non-linear methods to fit coefficients to GP-generated equations against complex data sets. Further, we explore how LaSER enables the emergence of innate representations, supporting long-standing hypotheses in evolutionary learning such as the Baldwin Effect. By separating the roles of representation and adaptation, LaSER offers a principled and extensible framework for symbolic regression and classification.", notes = "'LaSER evolves programs that act as feature generators' cites \cite{keijzer03} 'semantic vector supports downstream learning' Population size: 100, Generations: 100, Function set: {+, -, *, /, sin, cos, exp, log}, 700 training examples. Learning is non-inheritable. Nguyen benchmark functions + Vladislavleva Suite. perfectly innate, potential Baldwinian assimilation. See also \cite{le:2025:GPTP}", } @InProceedings{Le:2025:GPTP, author = "Nam Le", title = "{LASER}: How Learning Can Guide The Evolution Of Equations", booktitle = "Genetic Programming Theory and Practice XXII", year = "2025", editor = "Wolfgang Banzhaf and Bogdan Burlacu and Stephen Kelly and Alexander Lalejini and Fabricio {Olivetti de Franca}", address = "Michigan State University, USA", month = jun # " 5-7", note = "lightning talk", keywords = "genetic algorithms, genetic programming, LaSER", notes = "See also \cite{le2025decouplingrepresentationlearninggenetic} To be published after the workshop", } @InProceedings{Leach:2019:GI, author = "Kevin Leach and Ryan Dougherty and Chad Spensky and Stephanie Forrest and Westley Weimer", title = "Evolutionary Computation for Improving Malware Analysis", booktitle = "GI-2019, ICSE workshops proceedings", year = "2019", editor = "Justyna Petke and Shin Hwei Tan and William B. Langdon and Westley Weimer", pages = "18--19", address = "Montreal", month = "28 " # may, publisher = "IEEE", note = "Best Presentation", keywords = "genetic algorithms, genetic programming, genetic improvement", isbn13 = "978-1-7281-2268-7", URL = "http://dijkstra.eecs.umich.edu/kleach/malware-gi-19.pdf", URL = "http://geneticimprovementofsoftware.com/paper_pdfs/leach2019evolutionary.pdf", DOI = "DOI:10.1109/GI.2019.00013", size = "2 pages", abstract = "Research in genetic improvement (GI) conventionally focuses on the improvement of software, including the automated repair of bugs and vulnerabilities as well as the refinement of software to increase performance. Eliminating or reducing vulnerabilities using GI has improved the security of benign software, but the growing volume and complexity of malicious software necessitates better analysis techniques that may benefit from a GI-based approach. Rather than focus on the use of GI to improve individual software artefacts, we believe GI can be applied to the tools used to analyse malicious code for its behaviour. First, malware analysis is critical to understanding the damage caused by an attacker, which GI-based bug repair does not currently address. Second, modern malware samples leverage complex vectors for infection that cannot currently be addressed by GI. In this paper, we discuss an application of genetic improvement to the realm of automated malware analysis through the use of variable-strength covering arrays.", notes = "stealthy malware samples Slides: http://geneticimprovementofsoftware.com/slides/leach2019evolutionary_slides.pdf GI-2019 http://geneticimprovementofsoftware.com part of \cite{Petke:2019:ICSEworkshop}", } @MastersThesis{leahy:2005:SPIGS, title = "Social Programming: Investigations in Grammatical Swarm", author = "Finbar Leahy", school = "University of Limerick", year = "2005", type = "Master of Science in Software Engineering", address = "University of Limerick, Ireland", month = "16 " # oct, keywords = "genetic algorithms, genetic programming, grammatical evolution, grammatical swarm", URL = "https://finbarleahy.files.wordpress.com/2010/07/mastersdissertation.pdf", size = "129 pages", language = "en", abstract = "This study details a series of investigations examining a recently introduced form of automatic programming called Social Programming. The Grammatical Swarm algorithm is a form of Social Programming as it uses Particle Swarm Optimisation, a social swarm algorithm, for the automatic construction of computer programs for the optimisation of continuous, non-linear problems. An investigation into the performance effects of two different quality Pseudo-Random Number Generators (PRNG) on the Grammatical Swarm algorithm was examined. The results demonstrate that the choice of PRNG does, in fact, have a small effect of the performance of the Grammatical Swarm, with the more sophisticated PRNG producing better results on two of the four problems analysed. An investigations was conducted into the effects of increasing the size of the particle representations of the Grammatical Swarm algorithm, such that the hard-length vector constraint of all particles in the swarm was doubled from 100 to 200. The results demonstrated that this leads to a significant gain in performance. This thesis also introduces a new variable-length form of the Grammatical Swarm algorithm. Thus, this can be considered a proof of concept study. It examines the possibility of constructing programs using a particles representations which are variable in length and it is referred to as the Variable-Length Grammatical Swarm. This newly developed algorithm extends earlier work on the fixed-length incarnation of Grammatical Swarm, where each individual represents choices of program construction rules, where these rules are specified using a Backus-Naur Form grammar. The results demonstrate that is possible to successfully generate programs programs using a variable-length Particle Swarm Algorithm. This investigation also examines the performance effects of increasing the initialisation size of the variable-length particles. The results demonstrate that the performance of the Variable-Length Grammatical Swarm can be increased by doubling the potential size of the particle representations. Furthermore, the evolution of size in the particle representations is examined. This investigation was conduced in an effort to determine if the the variable- length particles suffered from bloat, which is a common problem in other Evolutionary Algorithms that use variable-length vector representations. No evidence of bloat was found. Based on an overall comparative review of the both the fixed-length and variable-length forms of Grammatical Swarm it is recommended that the simpler fixed-length Grammatical Swarm with particle representation sizes of 200 codons in length be adopted.", notes = "Supervisor: Dr. Michael O'Neill", } @InProceedings{Leather:2009:CGO, author = "Hugh Leather and Edwin Bonilla and Michael O'Boyle", title = "Automatic Feature Generation for Machine Learning Based Optimizing Compilation", booktitle = "2009. International Symposium on Code Generation and Optimization", year = "2009", publisher = "IEEE", address = "Seattle, WA, USA", month = "22-25 " # mar, pages = "81--91", keywords = "genetic algorithms, genetic programming, grammatical evolution, SBSE, Pentium 6, automatic feature generation, compilation, compiler writer, feature generation technique, grammar, loop unrolling, machine learning, predictive modeling, grammars, learning (artificial intelligence), program compilers", isbn13 = "978-0-7695-3576-0", DOI = "doi:10.1109/CGO.2009.21", abstract = "Recent work has shown that machine learning can automate and in some cases outperform hand crafted compiler optimizations. Central to such an approach is that machine learning techniques typically rely upon summaries or features of the program. The quality of these features is critical to the accuracy of the resulting machine learned algorithm; no machine learning method will work well with poorly chosen features. However, due to the size and complexity of programs, theoretically there are an infinite number of potential features to choose from. The compiler writer now has to expend effort in choosing the best features from this space. This paper develops a novel mechanism to automatically find those features which most improve the quality of the machine learned heuristic. The feature space is described by a grammar and is then searched with genetic programming and predictive modeling. We apply this technique to loop unrolling in GCC 4.3.1 and evaluate our approach on a Pentium 6. On a benchmark suite of 57 programs, GCC's hard-coded heuristic achieves only 3percent of the maximum performance available, while a state of the art machine learning approach with hand-coded features obtains 59percent. Our feature generation technique is able to achieve 76percent of the maximum available speedup, outperforming existing approaches.", notes = "p85 'Our search technique is a hybrid between Grammatical Evolution [12] and Genetic Programming [13].' Also known as \cite{4907653}", } @Article{Leather:2014:AFG, author = "Hugh Leather and Edwin Bonilla and Michael O'Boyle", title = "Automatic feature generation for machine learning--based optimising compilation", journal = "ACM Transactions on Architecture and Code Optimization", volume = "11", number = "1", pages = "14:1--14:32", month = feb, year = "2014", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1145/2536688", ISSN = "1544-3566 (print), 1544-3973 (electronic)", bibdate = "Fri Mar 14 17:30:52 MDT 2014", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/taco.bib", abstract = "Recent work has shown that machine learning can automate and in some cases outperform handcrafted compiler optimisations. Central to such an approach is that machine learning techniques typically rely upon summaries or features of the program. The quality of these features is critical to the accuracy of the resulting machine learnt algorithm; no machine learning method will work well with poorly chosen features. However, due to the size and complexity of programs, theoretically there are an infinite number of potential features to choose from. The compiler writer now has to expend effort in choosing the best features from this space. This article develops a novel mechanism to automatically find those features that most improve the quality of the machine learnt heuristic. The feature space is described by a grammar and is then searched with genetic programming and predictive modelling. We apply this technique to loop unrolling in GCC 4.3.1 and evaluate our approach on a Pentium 6. On a benchmark suite of 57 programs, GCCs hard-coded heuristic achieves only 3percent of the maximum performance available, whereas a state-of-the-art machine learning approach with hand-coded features obtains 59percent. Our feature generation technique is able to achieve 76percent of the maximum available speedup, outperforming existing approaches.", acknowledgement = "Nelson H. F. Beebe, University of Utah, Department of Mathematics, 110 LCB, 155 S 1400 E RM 233, Salt Lake City, UT 84112-0090, USA, Tel: +1 801 581 5254, FAX: +1 801 581 4148, e-mail: \path|beebe@math.utah.edu|, \path|beebe@acm.org|, \path|beebe@computer.org| (Internet), URL: \path|http://www.math.utah.edu/~beebe/|", articleno = "14", fjournal = "ACM Transactions on Architecture and Code Optimization (TACO)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J924", doi-url = "http://dx.doi.org/10.1145/2536688", } @Article{LeBaron:2012:GPEM, author = "Blake LeBaron", title = "Alma Lilia Garcia Almanza, and Edward Tsang: Evolutionary applications for financial prediction: classification methods to gather patterns using genetic programming", journal = "Genetic Programming and Evolvable Machines", year = "2012", volume = "13", number = "4", pages = "537--538", month = dec, note = "Book review", publisher = "Springer", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dR8zy", DOI = "doi:10.1007/s10710-012-9168-3", size = "2 pages", notes = "Review of \cite{Garcia-Almanza:book} Reply by Edward Tsang Friday, March 8, 2013 at http://gpemjournal.blogspot.co.uk/2013/03/alma-lilia-garcia-almanza-and-edward.html", affiliation = "International Business School, Brandeis University, Waltham, MA, USA", } @InProceedings{Ledwith:2010:ICES, author = "Ricky D. Ledwith and Julian F. Miller", title = "Introducing Flexibility in Digital Circuit Evolution: Exploiting Undefined Values in Binary Truth Tables", booktitle = "Proceedings of the 9th International Conference Evolvable Systems: From Biology to Hardware, ICES 2010", year = "2010", editor = "Gianluca Tempesti and Andy M. Tyrrell and Julian F. Miller", series = "Lecture Notes in Computer Science", volume = "6274", pages = "25--36", address = "York", month = sep # " 6-8", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Evolutionary Algorithms, Cartesian Genetic Programming (CGP), Evolvable Hardware, Don't Care Logic", isbn13 = "978-3-642-15322-8", DOI = "doi:10.1007/978-3-642-15323-5_3", abstract = "Evolutionary algorithms can be used to evolve novel digital circuit solutions. This paper proposes the use of flexible target truth tables, allowing evolution more freedom where values are undefined. This concept is applied to three test circuits with different distributions of don't care values. Two strategies are introduced for using the undefined output values within the evolutionary algorithm. The use of flexible desired truth tables is shown to significantly improve the success of the algorithm in evolving circuits to perform this function. In addition, we show that this flexibility allows evolution to develop more hardware efficient solutions than using a fully-defined truth table.", affiliation = "Dept. of Electronics, The University of York, York, UK", } @Article{Lee:2020:JLMN, author = "An-Chen Lee and Ruei-Yu Huang and Trong-Doan Nguyen and Chung-Wei Cheng and Mi-Ching Tsai", title = "Laser Powder Bed Fusion of Multilayer Thin-walled Structures Based on Data-driven Model", journal = "JLMN-Journal of Laser Micro/Nanoengineering", year = "2020", volume = "15", number = "1", pages = "38--44", month = jun, keywords = "genetic algorithms, genetic programming, symbolic regression, selective laser melting, SLM, symbolic regressed solution, data-driven model", ISSN = "1880-0688", URL = "http://www.jlps.gr.jp/jlmn/uploads/b8b7209c72a808bfa694af825e6835fed8cec913.pdf", DOI = "doi:10.2961/jlmn.2020.01.2007", size = "7 pages", abstract = "The laser powder bed fusion (LPBF) process has the advantage of directly building metal parts with complex geometries and lattice structures. The lattice structures are usually composed of different thin-walled structures. Therefore, precise control over the shape of structures on the LPBF fabricated lattice structures is important. a symbolic regression solution that describes the relationship between scan track width and process parameters (laser power and scanning velocity) is generated by empirical data. This regression model is then implemented into the control scheme that stabilizes the structure width for single-layer and multilayer thin-walled structures. The experiment results showed that the average errors for the single-layer track with desired width from 120 micrometers to 180 um are all under 5 percent, and the average error for the ten-layered structures with desired width 180 micrometers about 2.2 percent.", notes = "http://www.jlps.gr.jp/jlmn/ Department of Mechanical Engineering, National Chiao Tung University, No. 1001, Ta Hsueh Road, Hsinchu 300, Taiwan", } @Article{Lee:ieeeTEC, author = "Chan Gyu Lee and Sungbum Jun", title = "Feature Extraction With Genetic Programming for Root Cause Identification in Manufacturing With Interpretable Machine Learning", journal = "IEEE Transactions on Evolutionary Computation", note = "Early Access", keywords = "genetic algorithms, genetic programming, Feature extraction, Manufacturing, Closed box, Fault diagnosis, Data models, Predictive models, Decision trees, decision tree, dimensional awareness, feature extraction, interpretability, machine learning", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2024.3388725", notes = "also known as \cite{10499858} Department of Industrial and Systems Engineering, Dongguk University, Seoul, Republic of Korea", } @InProceedings{conf/kes/LeeYC14, title = "Taiwan Stock Investment with Gene Expression Programming", author = "Cheng-Han Lee and Chang-Biau Yang and Hung-Hsin Chen", booktitle = "18th International Conference in Knowledge Based and Intelligent Information and Engineering Systems, {KES} 2014, Gdynia, Poland, 15-17 September 2014", publisher = "Elsevier", year = "2014", volume = "35", editor = "Piotr Jedrzejowicz and Lakhmi C. Jain and Robert J. Howlett and Ireneusz Czarnowski", pages = "137--146", series = "Procedia Computer Science", keywords = "genetic algorithms, genetic programming, gene expression programming, stock investment, majority vote, technical indicator, strategy pool", bibdate = "2014-10-12", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/kes/kes2014.html#LeeYC14", URL = "http://www.sciencedirect.com/science/journal/18770509/35", DOI = "doi:10.1016/j.procs.2014.08.093", abstract = "In this paper, we first find out some good trading strategies from the historical series and apply them in the future. The profitable strategies are trained out by the gene expression programming (GEP), which involves some well-known stock technical indicators as features. Our data set collects the 100 stocks with the top capital from the listed companies in the Taiwan stock market. Accordingly, we build a new series called portfolio index as the investment target. For each trading day, we search for some similar template intervals from the historical data and pick out the pertained trading strategies from the strategy pool. These strategies are validated by the return during a few days before the trading day to check whether each of them is suitable or not. Then these suitable strategies decide the buying or selling consensus signal with the majority vote on the trading day. The training period is from 1996/1/6 to 2012/12/28, and the testing period is from 2000/1/4 to 2012/12/28. Two simulation experiments are performed. In experiment 1, the best average accumulated return is 548.97percent (average annualised return is 15.47percent). In experiment 2, we increase the diversity of trading strategies with more training. The best average accumulated return is increased to 685.31percent (average annualized return is 17.18percent). These two results are much better than that of the buy-and-hold strategy, whose return is 287.00percent.", } @InProceedings{conf/icpr/LeeS16, author = "Chulwoo Lee and Ling Shao", title = "Learning-based single image dehazing via genetic programming", booktitle = "23rd International Conference on Pattern Recognition (ICPR 2016)", year = "2016", pages = "745--750", publisher = "IEEE", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-5090-4847-2", bibdate = "2017-05-24", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icpr/icpr2016.html#LeeS16", URL = "http://ieeexplore.ieee.org/document/7899724/", DOI = "doi:10.1109/ICPR.2016.7899724", abstract = "A genetic programming (GP)-based framework to learn the effective feature representation for image de-hazing is proposed in this work. In GP, an individual program is randomly generated and genetically evolved to achieve the desired goal. To make GP estimate haze in an input image, a set of operators and operands is designed, each of which is a primitive of a GP program. Specifically, we provide four basic features as candidates, and also include function operators to construct sophisticated representations of these features. After the entire GP process finishes, we obtain a near-optimal compact descriptor for haze estimation. Experimental results demonstrate that the proposed algorithm enhances the visual quality of haze-degraded images both objectively and subjectively.", } @Article{Lee:1997:ICHMT, author = "Dong-Gyu Lee and Han-Gon Kim and Won-Pil Baek and Soon Heung Chang", title = "Critical heat flux prediction using genetic programming for water flow in vertical round tubes", journal = "International Communications in Heat and Mass Transfer", year = "1997", volume = "24", pages = "919--929", number = "7", month = nov, abstract = "The genetic programming method is used to develop critical heat flux (CHF) correlations for upward water flow in vertical round tubes under low pressure and low flow conditions. The genetic programming, as a symbolic regression tool, finds both the functional form and fitting coefficients of a correlation without any initial assumptions. Inlet and local condition type correlations are developed based on 414 and 314 CHF data from KAIST CHF data bank, respectively. The inlet condition type correlation shows the rms error of 15.2% and the local condition type one shows the rms errors of 32.7% and 13.2% by the direct substitution method and the heat balance method, respectively. Prediction errors are smaller than or comparable to those for other existing correlations.", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6V3J-3SN6JCK-3/2/6b7c99878889f319da36b00d1c087dfd", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1016/S0735-1933(97)00078-X", } @InProceedings{Dong-WookLee:2000:SMC, author = "Dong-Wook Lee and Chang-Bong Ban and Kwee-Bo Sim and Ho-Sik Seok and Kwang-Ju Lee and Byoung-Tak Zhang", title = "Behavior evolution of autonomous mobile robot using genetic programming based on evolvable hardware", booktitle = "IEEE International Conference on Systems, Man, and Cybernetics", year = "2000", volume = "5", pages = "3835--3840", address = "Nashville, TN, USA", month = "8-11 " # oct, keywords = "genetic algorithms, genetic programming, EHW, autonomous mobile robot, autonomous mobile robots cooperation, behavior evolution, chromosome representation method, context switchable identical block structure, crossover operator, evolutionary strategy, evolvable hardware, genetic tree, hardware implementation method, online adaptive learnable evolvable hardware, tree structured chromosome, evolutionary computation, mobile robots, multi-robot systems, reconfigurable architectures", DOI = "doi:10.1109/ICSMC.2000.886608", abstract = "This paper presents a genetic programming based evolutionary strategy for on-line adaptive learnable evolvable hardware. Genetic programming can be a useful control method for evolvable hardware for its unique tree structured chromosome. However it is difficult to represent the tree structured chromosome in hardware, and it is difficult to use the crossover operator in hardware. Therefore, genetic programming is not as popular as genetic algorithms in the evolvable hardware community in spite of its possible strengths. We propose a chromosome representation method and a hardware implementation method that can be helpful for this situation. Our method uses a context switchable identical block structure to implement a genetic tree in evolvable hardware. We compose an evolutionary strategy for evolvable hardware by combining the proposed method with other research results. The proposed method is applied to the autonomous mobile robots cooperation problem to verify its usefulness", } @InCollection{GeumYongLee:1999:aigp3, author = "Geum Yong Lee", title = "Genetic Recursive Regression for Modeling and Forecasting Real-World Chaotic Time Series", booktitle = "Advances in Genetic Programming 3", publisher = "MIT Press", year = "1999", editor = "Lee Spector and William B. Langdon and Una-May O'Reilly and Peter J. Angeline", chapter = "17", pages = "401--423", address = "Cambridge, MA, USA", month = jun, keywords = "genetic algorithms, genetic programming", ISBN = "0-262-19423-6", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/aigp3/ch17.pdf", language = "en", oai = "oai:CiteSeerXPSU:10.1.1.141.1197", URL = "https://books.google.co.uk/books?id=5Qwbal3AY6oC", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.141.1197", DOI = "doi:10.7551/mitpress/1110.003.0022", size = "23 pages", abstract = "I explore several extensions to genetic programming for applications involving the forecasting of real world chaotic time series. We first used Genetic Symbolic Regression (GSR),which is the standard genetic programming technique applied to the forecasting problem in the same way that it is often applied to symbolic regression problems [ Koza 1992, 1994]. We observed that the performance of GSR depends on the characteristics of the time series, and in particular that it worked better for deterministic time series than it did for stochastic or volatile time series. Taking a hint from this observation, an assumption was made in this study that the dynamics of a time series comprise a deterministic and a stochastic part. By subtracting the model built by GSR for the deterministic part from the original time series, the stochastic part would be obtained as a residual time series. This study noted the possibility that GSR could be used recursively to model the residual time series of rather stochastic dynamics, which may still comprise another deterministic and stochastic part. An algorithm called GRR (Genetic Recursive Regression) has been developed to apply GSR recursively to the sequence of residual time series of stochastic dynamics, giving birth to a sequence of sub-models for deterministic dynamics extractable at each recursive application. At each recursive application and after some termination conditions are met, the submodels become the basis functions for a series-expansion type representation of a model. The numerical coefficients of the model are calculated by the least square method with respect to the predetermined region of the time series data set. When the region includes the latest data set, the model reflects the most recent changes in the dynamics of a time series, thus increasing the forecasting performance. This chapter shows how GRR has been successfully applied to many real world chaotic time series. The results are compared with those from other GSR-like methods and various soft-computing technologies such as neural networks. The results show that GRR saves much computational effort while achieving enhanced forecasting performance for several selected problems.", notes = "AiGP3 See http://cognet.mit.edu citeseerx url broken Aug 2018", } @InProceedings{lee:2006:CAIDCD, author = "Ho Cheong Lee and Ming Xi Tang", title = "Generating stylistically consistent product form designs using interactive evolutionary parametric shape grammars", booktitle = "7th International Conference on Computer-Aided Industrial Design and Conceptual Design, CAIDCD '06", year = "2006", pages = "1--6", address = "Hangzhou", month = "17-19 " # nov, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", ISBN = "1-4244-0684-6", DOI = "doi:10.1109/CAIDCD.2006.329391", abstract = "Interactive grammar based design systems (IGBDS) are capable of generating large numbers of alternative designs. Prior to the application of IGBDS, a set of rules should be defined based on the theoretical theories and practical experiences in designing the objects, or by analysing the existing sets of objects. However, due to the time constraints in developing theoretical theories, gaining the practical skills of experts is of qualitative in nature and stylistically inconsistent designs to be analysed, it is difficult to systematically derive a set of shape grammar rules in generating designs which fulfils specific requirements. To address this problem, this paper presents an IGBDS enhanced by evolutionary computing to evolve a set of grammar rules for product form design. In this research, the forms of a product are analysed to derive shape features in the form of shape grammar (SG) rules. The rules are then encoded as the code scripts of a genetic algorithm (GA) representation in order to generate new shape grammar rules. The parameters in GA representation are modified by genetic programming (GP) which is regulated by control planning strategies named GP-GA-SG. The GP-GA-SG control planning strategy first defines ways on how the control variables in GP should modify the variables in GA representation. The GP-GA-SG control planning strategy then defines ways on how the control variables in GA should modify the variables in SG representation. The SG rules modified by the GA variables define a new combination of shape features for alternative designs. In this way, traditional shape grammar is extended to an interactive context in which generative and evolutionary computing methods are combined. Both product component design as well as product configuration are supported in this framework. In this paper, we describe how this framework is formulated and discuss its potentials in supporting product design, with initial examples showing how the system is intended to work", notes = "INSPEC Accession Number: 9487202 Design Technol. Res. Centre, Hong Kong Polytech. Univ.;", } @Article{journals/aiedam/LeeT09, title = "Evolving product form designs using parametric shape grammars integrated with genetic programming", author = "Ho Cheong Lee and Ming Xi Tang", journal = "Artificial Intelligence for Engineering Design, Analysis and Manufacturing", year = "2009", number = "2", volume = "23", pages = "131--158", publisher = "Cambridge University Press", keywords = "genetic algorithms, genetic programming, Configuration Designs, Evolutionary Shape Grammars, Interactive Grammar-Based Design Systems, Product Designs", DOI = "doi:10.1017/S0890060409000031", bibdate = "2009-05-05", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/aiedam/aiedam23.html#LeeT09", abstract = "The two critical issues related to product design exploration are addressed: the balance between stylistic consistency and innovation, and the control of design process under a great diversity of requirements. To address these two issues, the view of understanding product design exploration is first sought. In this view, the exploration of designs is not only categorized as a problem-solving activity but also as a problem-finding activity. A computational framework is developed based on this view, and it encompasses the belief that these two activities go hand in hand to accomplish the design tasks in an interactive design environment. The framework adopts an integration approach of two key computational techniques, shape grammars and evolutionary computing, for addressing the above two critical issues. For the issues of stylistic consistency, this paper focuses on the computational techniques in balancing the conflicts of stylistic consistency and innovation with shape grammars. For the issues of controlling design process, the practical concerns of monitoring the design process through various activities starting from the preparation works to the implementation of shape grammars have been emphasized in the development of this framework. To evaluate the effectiveness of the framework, the experiments have been set up to reflect the practical situations with which the designers have to deal. The system generates a number of models from scratch with numerical analysis that can be evaluated effectively by the designers. This reduces the designers' time and allows the designers to concentrate their efforts on performing higher level of design activities such as evaluation of designs and making design decisions.", notes = "Camera example. ACIS", } @InProceedings{KHLee:2002:FEA, author = "K. H. Lee and Y. S. Yeun and W. S. Ruy and Y. S. Yang", title = "Polynomial Genetic Programming for Response Surface Modeling", booktitle = "4th International Workshop on Frontiers in Evolutionary Algorithms", year = "2002", editor = "Manuel Grana Romay and Richard Duro", address = "North Carolina, USA", month = "8-14 " # mar, keywords = "genetic algorithms, genetic programming", ISBN = "0-9707890-1-7", notes = "See also \cite{yeun_2005_SMO}. http://www.sc.ehu.es/ccwgrrom/FEA2002/ FEA2002 In conjunction with Sixth Joint Conference on Information Sciences", } @InProceedings{conf/iccsa/LeeYYLO06, title = "Data Analysis and Utilization Method Based on Genetic Programming in Ship Design", author = "Kyung Ho Lee and Yun Seog Yeun and Young Soon Yang and Jang Hyun Lee and June Oh", booktitle = "Computational Science and Its Applications - ICCSA 2006, Part {II}", publisher = "Springer", year = "2006", volume = "3981", editor = "Marina L. Gavrilova and Osvaldo Gervasi and Vipin Kumar and Chih Jeng Kenneth Tan and David Taniar and Antonio Lagan{\`a} and Youngsong Mun and Hyunseung Choo", pages = "1199--1209", series = "Lecture Notes in Computer Science", address = "Glasgow, {UK}", month = may # " 8-11", bibdate = "2006-05-17", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/iccsa/iccsa2006-2.html#LeeYYLO06", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-34072-6", DOI = "doi:10.1007/11751588_127", size = "11 pages", abstract = "Although Korean shipyards have accumulated a great amount of data, they do not have appropriate tools to use the data in practical works. Engineering data contains the experiences and know-how of experts. Data mining technique is useful to extract knowledge or information from the accumulated existing data. This paper presents a machine learning method based on genetic programming (GP), which can be one of the components for the realization of data mining. The paper deals with linear models of GP for regression or approximation problems when the given learning samples are not sufficient.", } @InProceedings{DBLP:conf/ausai/LeeOP06, author = "Kyung Ho Lee and June Oh and Jong Hoon Park", title = "Development of Data Miner for the Ship Design Based on Polynomial Genetic Programming", booktitle = "Australian Conference on Artificial Intelligence", year = "2006", pages = "981--985", editor = "Abdul Sattar and Byeong Ho Kang", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "4304", address = "Hobart, Australia", month = dec # " 4-8", bibsource = "DBLP, http://dblp.uni-trier.de", keywords = "genetic algorithms, genetic programming, PGP, regularisation", ISBN = "3-540-49787-0", DOI = "doi:10.1007/11941439_108", size = "5 pages", abstract = "Engineering data contains the experiences and know-how of experts. Data mining technique is useful to extract knowledge or information from the accumulated existing data. This paper deals with generating optimal polynomials using genetic programming (GP) as the module of Data Miner. The Data Miner for the ship design based on polynomial genetic programming is presented.", } @InProceedings{oai:CiteSeerPSU:454905, author = "Kwang-Ju Lee and Byoung-Tak Zhang", title = "Learning robot behaviors by evolving genetic programs", booktitle = "26th Annual Conference of the IEEE Industrial Electronics Society, IECON", year = "2000", volume = "4", pages = "2867--2872", address = "Nagoya", month = "22-28 " # oct, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", citeseer-isreferencedby = "oai:CiteSeerPSU:67000; oai:CiteSeerPSU:319694; oai:CiteSeerPSU:269370; oai:CiteSeerPSU:265613", citeseer-references = "oai:CiteSeerPSU:454784; oai:CiteSeerPSU:3551; oai:CiteSeerPSU:163604; oai:CiteSeerPSU:160348", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:454905", rights = "unrestricted", URL = "http://bi.snu.ac.kr/Publications/Conferences/International/SEAL00.pdf", URL = "http://citeseer.ist.psu.edu/454905.html", abstract = "A method for evolving behavior-based robot controllers using genetic programming is presented. Due to their hierarchical nature, genetic programs are useful representing high-level knowledge for robot controllers. One drawback is the difficulty of incorporating sensory inputs. To overcome the gap between symbolic representation and direct sensor values, the elements of the function set in genetic programming is implemented as a single-layer perceptron. Each perceptron is composed of sensory input nodes and a decision output node. The robot learns proper behavior rules based on local, limited sensory information without using an internal map. First, it learns how to discriminate the target using single-layer perceptrons. Then, the learned perceptrons are applied to the function nodes of the genetic program tree which represents a robot controller. Experiments have been performed using Khepera robots. The presented method successfully evolved high-level genetic programs that control the robot to find the light source from sensory inputs", } @InCollection{lee:1995:efnGPe, author = "Jack Y. B. Lee and P. C. Wong", title = "The effect of function noise on GP efficiency", booktitle = "Progress in Evolutionary Computation", publisher = "Springer-Verlag", year = "1995", editor = "Xin Yao", volume = "956", series = "Lecture Notes in Artificial Intelligence", pages = "1--16", address = "Heidelberg, Germany", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-60154-8", DOI = "doi:10.1007/3-540-60154-6_43", abstract = "Genetic Programming (GP) has been applied to many problems and there are indications [1,2,3] that GP is potentially useful in evolving algorithms for problem solving. This paper investigates one problem with algorithmic evolution using GP - Function Noise. We show that the performance of GP could be severely degraded even in the presence of minor noise in the GP functions. We investigated two counter noise schemes, Multi-Sampling Function and Multi-Testcases. We show that the Multi-Sampling Function scheme can reduce the effect of noise in a predictable way while the Multi-Test cases scheme evolves radically different program structures to avoid the effect of noise. Essentially, the two schemes lead the GP to evolve into different approaches to solving the same problem.", size = "16 pages", notes = "Artificial ant on Santa Fe Trail with noisy IfFoodAhead GP does poorly even with small amounts of with noise. Sometimes population abandons use of IfFoodAhead entirely (what else could it do?) ", affiliation = "The Chinese University of Hong Kong Advanced Network Systems Laboratory Department of Information Engineering Hongkong Hongkong", } @Unpublished{lee:1996:aigp2, author = "G. Y. Lee", title = "Explicit Models for Chaotic and Noisy Time Series Through the Genetic Recursive Regression", year = "1995", keywords = "genetic algorithms, genetic programming", note = "unpublished", notes = "Draft submitted to Advances in Genetic Programming 2 Peter J. Angeline and K. E. {Kinnear, Jr.} (Eds.) MIT Press, 1996.", } @Article{Lee:2004:Omega, author = "Sang M. Lee and Arben A. Asllani", title = "Job scheduling with dual criteria and sequence-dependent setups: mathematical versus genetic programming", journal = "Omega", year = "2004", volume = "32", pages = "145--153", number = "2", abstract = "Flexibility, speed, and efficiency are major challenges for operations managers in today's knowledge-intensive organisations. Such requirements are converted into three production scheduling criteria: (a) minimise the impact of setup times in flexible production lines when moving from one product to another, (b) minimize number of tardy jobs, and (c) minimize overall production time, or makespan, for a given set of products or services. There is a wide range of solution methodologies for such NP-hard scheduling problems. While mathematical programming models provide optimal solutions, they become too complex to model for large scheduling problems. Simultaneously, heuristic approaches are simpler and very often independent of the problem size, but provide {"}good{"} rather than optimal solutions. This paper proposes and compares two alternative solutions: 0-1 mixed integer linear programming and genetic programming. It also provides guidelines that can be used by practitioners in the process of selecting the appropriate scheduling methodology.", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6VC4-4B42761-1/2/94e937150cd10b51245fedaa40f1d3cc", month = apr # " 2004", keywords = "genetic algorithms, genetic programming, Dual criteria scheduling, Sequence dependent setup times, 0-1 mathematical programming", DOI = "doi:10.1016/j.omega.2003.10.001", } @InProceedings{lee:1996:hGPGAccrb, author = "Wei-Po Lee and John Hallam and Henrik Hautop Lund", title = "A Hybrid {GP/GA} Approach for Co-evolving Controllers and Robot Bodies to Achieve Fitness-Specified Tasks", booktitle = "Proceedings of the 1996 {IEEE} International Conference on Evolutionary Computation", year = "1996", pages = "384--389", address = "Nagoya, Japan", month = "20-22 " # may, organisation = "IEEE Neural Network Council", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Robot control, Robotics and automation, Robot sensing systems, Automatic control, Intelligent robots, Turning, Vehicles, Infrared sensors, Artificial intelligence", ISBN = "0-7803-2902-3", URL = "ftp://ftp.daimi.au.dk/pub/stud/hhl/bodyplan.ps.Z", URL = "http://citeseer.ist.psu.edu/lee96hybrid.html", DOI = "doi:10.1109/ICEC.1996.542394", size = "6 pages", abstract = "Evolutionary approaches have been advocated to automate robot design. Some research work has shown the success of evolving controllers for the robots by genetic approaches. As we can observe, however, not only the controller but also the robot body itself can affect the behaviour of the robot in a robot system. We develop a hybrid GP/GA approach to evolve both controllers and robot bodies to achieve behavior-specified tasks. In order to assess the performance of the developed approach, it is used to evolve a simulated agent, with its own controller and body, to do obstacle avoidance in the simulated environment. Experimental results show the promise of this work. In addition, the importance of co-evolving controllers and robot bodies is analysed and discussed", notes = "ICEC-96 Evolves controller for (simulated?) mobile robot ", } @InProceedings{lee:1997:aGPebpamr, author = "Wei-Po Lee and John Hallam and Henrik Hautop Lund", title = "Applying Genetic Programming to Evolve Behavior Primitives and Arbitrators for Mobile Robots", booktitle = "Proceedings of IEEE 4th International Conference on Evolutionary Computation", year = "1997", volume = "1", pages = "501--506", address = "Indianapolis, USA", month = "13-16 " # apr, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-3949-5", URL = "ftp://ftp.daimi.au.dk/pub/stud/hhl/ebbicec97.ps.Z", URL = "http://www.dai.ed.ac.uk/pub/daidb/papers/rp832.ps.gz", DOI = "doi:10.1109/ICEC.1997.592362", URL = "http://citeseer.ist.psu.edu/lee97applying.html", size = "6 pages", abstract = "The behaviour-based approach has been successfully applied to designing robot control systems. This paper presents our work, based on evolutionary algorithms, to program behavior-based robots automatically. Instead of hand-coding all the behaviour controllers or evolving an entire control system for an overall task, we suggest our approach at the intermediate level: it includes evolving behaviour primitives and behaviour arbitrators for a mobile robot to achieve the specified tasks. To examine the developed approach, we evolve a control system for a moderately complicated box-pushing task as an example. We first evolved the controllers in a simulation and then transferred them to the Khepera miniature robot. Experimental results show the promise of our approach, and the evolved controllers are transferred to the real robot without loss of performance", notes = "Khepera. Division of Informatics, Research Paper #832, Edinburgh University", } @InProceedings{lee:1997:lcrbea, author = "Wei-Po Lee and John Hallam and Henrik Hautop Lund", title = "Learning Complex Robot Behaviours by Evolutionary Approaches", booktitle = "6th European Workshop on Learning Robots, EWLR-6", year = "1997", editor = "Andreas Birk and John Demiris", pages = "42--51", address = "Hotel Metropole, Brighton, UK", month = "1-2 " # aug, keywords = "genetic algorithms, genetic programming", broken = "http://130.203.133.150/showciting;jsessionid=326C58F9018712DAB6892709F2110874?cid=1315116&sort=recent", size = "10 pages", notes = "Task of getting Khepera to push a box to a light source broken up by hand into 4 subtasks. Fitness function etc devised for each task and GP used to evolve code to solve it in simulation. Evolved codes put together and run on real robot. Published as \cite{lee:1997:lcrbeaLNAI}", } @InProceedings{lee:1997:lcrbeaLNAI, author = "Wei-Po Lee and John Hallam and Henrik Hautop Lund", title = "Learning Complex Robot Behaviours by Evolutionary Computing with Task Decomposition", booktitle = "Learning Robots, 6th European Workshop, EWLR-6, Proceedings", year = "1997", editor = "Andreas Birk and John Demiris", series = "LNAI", volume = "1545", pages = "155--172", address = "Hotel Metropole, Brighton, UK", month = "1-2 " # aug, publisher = "Springer Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-65480-1", DOI = "doi:10.1007/3-540-49240-2_11", size = "10 pages", abstract = "Building robots can be a tough job because the designer has to predict the interactions between the robot and the environment as well as to deal with them. One solution to cope the difficulties in designing robots is to adopt learning methods. Evolution-based approaches are a special kind of machine learning method and during the last few years some researchers have shown the advantages of using this kind of approach to automate the design of robots. However, the tasks achieved so far are fairly simple. In this work, we analyse the difficulties of applying evolutionary approaches to learn complex behaviours for mobile robots. And, instead of evolving the controller as a whole, we propose to take the control architecture of a behavior-based system and to learn the separate behaviours and the arbitration by the use of an evolutionary approach. By using the technique of task decomposition, the job of defining fitness functions becomes more straightforward and the tasks become easier to achieve. To assess the performance of the developed approach, we have evolved a control system to achieve an application task of box-pushing as an example. Experimental results show the promise and efficiency of the presented approach.", notes = "Published version may be different from that in proceedings \cite{lee:1997:lcrbea}", } @PhdThesis{Wei-PoLee:thesis, author = "Wei-Po Lee", title = "Evolving Robots: from Simple Behaviours to Complete Systems", school = "Department of Artificial Intelligence. University of Edinburgh", year = "1997", address = "UK", keywords = "genetic algorithms, genetic programming", URL = "http://books.google.co.uk/books/about/Evolving_robots.html?id=uIkKcgAACAAJ&redir_esc=y", URL = "http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.653764", abstract = "Building robots is generally considered difficult, because the designer not only has to predict the interaction between the robot and the environment, but also to deal with ensuing problems. This thesis examines the use of the evolutionary approach in designing robots; the explorations range from evolving simple behaviours for real robots, to complex behaviours (also for real robots), and finally to the complete robot systems - including controllers and body plans. A framework is presented for evolving robot control systems. It includes two components: a task independent Genetic Programming sub-system and a task dependent controller evaluation sub-system. The performance evaluation of each robot controller is done in a simulator to reduce the evaluation time, and then the evolved controllers are downloaded to a real robot for performance verification. In addition, a special representation is designed for the reactive robot controller. It is succinct and can capture well the characteristics of a reactive control system, so that the evolutionary system can efficiently evolve the controllers of the desired behaviours for the robots. The framework has been used to evolve controllers for real robots to achieve a variety of simple tasks successfully, such as obstacle avoidance, safe exploration and box-pushing. A methodology is then proposed to scale up the system to evolve controllers for more complicated tasks. It involves adopting the architecture of a behaviour-based system, and evolving separate behaviour controllers and arbitrators for coordination. This allows robot controllers for more complex skills to be constructed in an incremental manner. Therefore the whole control system becomes easy to evolve; moreover, the resulting control system can be explicitly distributed, understandable to the system designer, and easy to maintain. The methodology has been used to evolve control systems for more complex tasks with good results", notes = "OCLC Number: 607678835", } @Article{Wei-PoLee:1999:ISJ, author = "Wei-Po Lee", title = "Evolving complex robot behaviors", journal = "Information Sciences", year = "1999", volume = "121", number = "1-2", pages = "1--25", email = "wplee@mail.npust.edu.tw", keywords = "genetic algorithms, genetic programming, Evolutionary computing, Computational intelligence, Robot learning, Automatic robot programming", ISSN = "0020-0255", ISSN = "0020-0255", DOI = "doi:10.1016/S0020-0255(99)00078-X", URL = "http://www.sciencedirect.com/science/article/B6V0C-3Y3XPFF-1/2/ddd56f7f1c35319bee24c38eb8db5652", abstract = "Building robots is a tough job because the designer has to predict the interactions between the robot and the environment as well as to deal with them. One solution to such difficulties in designing robots is to adopt learning methods. The evolution-based approach is a special method of machine learning and it has been advocated to automate the design of robots. Yet, the tasks achieved so far are fairly simple. In this work, we first analyze the difficulties of applying evolutionary approaches to synthesize robot controllers for complicated tasks, and then suggest an approach to resolve them. Instead of directly evolving a monolithic control system, we propose to decompose the overall task to fit in the behavior-based control architecture, and then to evolve the separate behavior modules and arbitrators using an evolutionary approach. Consequently, the job of defining fitness functions becomes more straightforward and the tasks easier to achieve. To assess the performance of the developed approach, we evolve a control system to achieve an application task of box-pushing as an example. Experimental results show the promise and efficiency of the presented approach.", notes = "Khepera Information Sciences http://www.elsevier.com/inca/publications/store/5/0/5/7/3/0/505730.pub.htt", } @Article{lee:1999:SC, author = "Wei-Po Lee and John Hallam", title = "Evolving reliable and robust controllers for real robots by genetic programming", journal = "Soft Computing -- A Fusion of Foundations, Methodologies and Applications", year = "1999", volume = "3", number = "2", pages = "63--75", month = sep, keywords = "genetic algorithms, genetic programming", ISSN = "1432-7643", DOI = "doi:10.1007/s005000050054", abstract = "Using Genetic Programming (GP)-based approaches to evolve robot controllers has the advantage of operating variable-size genotype. This is an important feature for evolving robot control systems as it allows complete freedom for the control architecture in respect to the task complexity which is difficult to predict. However, GP-based work in evolving controllers has been questioned in the verification of the performance on real robots, the generalisation of defining primitives, and the computational cost needed. In this paper, we present our GP framework in which a special representation of the robot controller is designed; this representation can capture well the characteristic of a behaviour controller so that our system can efficiently evolve desired robot behaviours by a relatively low computational cost. This system has been successfully used to evolve reliable and robust controllers working on a real robot, for a variety of tasks.", } @Article{Lee2008111, author = "Wei-Po Lee and Kung-Cheng Yang", title = "Applying Intelligent Computing Techniques to Modeling Biological Networks from Expression Data", journal = "Genomics, Proteomics \& Bioinformatics", volume = "6", number = "2", pages = "111--120", year = "2008", ISSN = "1672-0229", DOI = "doi:10.1016/S1672-0229(08)60026-1", URL = "http://www.sciencedirect.com/science/article/B82XM-4TSTVT9-6/2/3622f6428cf373014593567706357973", keywords = "genetic algorithms, genetic programming, reverse engineering, system modeling, recurrent neural network, expression data", abstract = "Constructing biological networks is one of the most important issues in systems biology. However, constructing a network from data manually takes a considerable large amount of time, therefore an automated procedure is advocated. To automate the procedure of network construction, in this work we use two intelligent computing techniques, genetic programming and neural computation, to infer two kinds of network models that use continuous variables. To verify the presented approaches, experiments have been conducted and the preliminary results show that both approaches can be used to infer networks successfully.", } @InProceedings{DBLP:conf/jcis/Lee06a, author = "Wo-Chiang Lee", title = "Genetic Programming Decision Tree for Bankruptcy Prediction", booktitle = "Proceedings of the 2006 Joint Conference on Information Sciences, JCIS 2006", year = "2006", publisher = "Atlantis Press", address = "Kaohsiung, Taiwan, ROC", month = oct # " 8-11", keywords = "genetic algorithms, genetic programming", ISBN = "90-78677-01-5", bibsource = "DBLP, http://dblp.uni-trier.de", broken = "http://www.atlantis-press.com/php/download_paper?id=8", DOI = "doi:10.2991/jcis.2006.8", size = "4 pages", abstract = "In this paper, we apply the CART ,C5.0 , GP decision tree classifiers and compares with logic model and ANN model for Taiwan listed electronic companies bankruptcy prediction. Results reveal that the GP decision tree can outperform all the classifiers either in overall percentage of correct or k-fold cross validation test in out sample. That is to say, GP decision tree model have the highest accuracy and lowest expected misclassification costs. It can provide an efficient alternative to discriminates financial distress problems in Taiwan.", notes = "CIEF-76", } @InProceedings{lee:1999:EPLPD, author = "Chang-Yong Lee and Yoonseon Song", title = "Evolutionary Programming using the Levy Probability Distribution", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "886--893", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "evolution strategies and evolutionary programming", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/ES-203.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{lee:1999:IHPCIRUIGA, author = "Joo-Young Lee and Sung-Bae Cho", title = "Incorporating Human Preference into Content-based Image Retrieval Using Interactive Genetic Algorithm", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1788", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "real world applications, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-749.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-749.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InCollection{lee:2000:FSKTPGA, author = "Miler Lee", title = "Finding Solutions to the Knight's Tour Problem using Genetic Algorithms", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "252--260", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms", notes = "part of \cite{koza:2000:gagp}", } @InProceedings{lee:2001:tspgp, author = "G. Y. Lee", title = "Time Series Perturbation by Genetic Programming", booktitle = "Proceedings of the 2001 Congress on Evolutionary Computation CEC2001", year = "2001", pages = "403--409", address = "COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "27-30 " # may, organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Time Series, Perturbation Theory", ISBN = "0-7803-6658-1", DOI = "doi:10.1109/CEC.2001.934419", abstract = "We present a new algorithm that combines perturbation theory and genetic programming for modelling and forecasting real-world chaotic time series. Both perturbation theory and time series modeling have to build symbolic models for very complex system dynamics. Perturbation theory does not work without a well-defined system equation. Difficulties in modelling time series lie in the fact that we cannot have or assume any system equation. The new algorithm shows how genetic programming can be combined with perturbation theory for time series modelling. Detailed discussions on successful applications to chaotic time series from practically important fields of science and engineering are given. Computational resources were negligible as compared with earlier similar regression studies based on genetic programming. A desktop PC provides sufficient computing power to make the new algorithm very useful for real-world chaotic time series. Especially, it worked very well for deterministic or stationary time series, while stochastic or nonstationary time series needed extended effort, as it should be", notes = "CEC-2001 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 01TH8546C, Library of Congress Number =", } @Article{journals/isci/LeeM17, title = "Adaptive outlier elimination in image registration using genetic programming", author = "Ik Hyun Lee and Muhammad Tariq Mahmood", journal = "Information Sciences", year = "2017", volume = "421", pages = "204--217", keywords = "genetic algorithms, genetic programming, outlier removal, image registration", bibdate = "2017-10-16", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/isci/isci421.html#LeeM17", DOI = "doi:10.1016/j.ins.2017.08.098", abstrct = "In feature-based methods, outlier removal plays an important role in attaining a reasonable accuracy for image registration. In this paper, we propose a genetic programming (GP) based adaptive method for outlier removal. First, features are extracted through the scale-invariant feature transform (SIFT) from the reference and sensed images which were initially matched using Euclidean distance. The classification of feature points into inliers and outliers is done in two stages. In the first stage, feature vectors are computed using various distance and angle information. Feature points are categorized into three groups; inliers, outliers and non-classified feature (NCF) points. In the second stage, a GP-based classifier is developed to classify NCF points into inliers and outliers. The GP-based function takes features as an input feature vector and provides a scalar output by combining features with arithmetic operations. Finally, registration is done by eliminating the outliers. The effectiveness of the proposed outlier removal method is analyzed through the classification and positional accuracy. The experimental results show a considerable improvement in the registration accuracy.", } @InProceedings{Lee:2012:SICE, author = "Jong-Hyun Lee and Chang Wook Ahn", booktitle = "SICE Annual Conference (SICE), 2012 Proceedings of", title = "Evolutionary self-assembling swarm robots using genetic programming", year = "2012", pages = "807--811", month = "20-23 " # aug, address = "Akita, Japan", isbn13 = "978-1-4673-2259-1", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6318552", size = "5 pages", abstract = "Nowadays, self-assembling swarm robots are studied by many researchers because of their advantages such as high efficiency, stable, scalability. However, there are still several problems for applying practical fields in real world. In this paper, we design a new self-assembling swarm robots control algorithm with evolution to overcome the limitations. The main idea is that the oscillators contained in each module are defined by genetic programming.", keywords = "genetic algorithms, genetic programming, mobile robots, multi-robot systems, evolutionary self-assembling swarm robot, oscillator, self-assembling swarm robots control algorithm, Animals, Equations, Legged locomotion, Oscillators, Robot sensing systems, Evolutionary, Self-Assembling, Swarm robotics", notes = "Also known as \cite{6318552}", } @Article{Lee:2013:tswj, author = "Jong-Hyun Lee and Chang Wook Ahn and Jinung An", title = "An Approach to Self-Assembling Swarm Robots Using Multitree Genetic Programming", year = "2013", journal = "The Scientific World Journal", month = jun # "~18", keywords = "genetic algorithms, genetic programming", bibsource = "OAI-PMH server at www.ncbi.nlm.nih.gov", language = "en", oai = "oai:pubmedcentral.nih.gov:3703902", publisher = "Hindawi Publishing Corporation", URL = "http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3703902", URL = "http://www.ncbi.nlm.nih.gov/pubmed/23861655", URL = "http://dx.doi.org/10.1155/2013/593848", abstract = "In recent days, self-assembling swarm robots have been studied by a number of researchers due to their advantages such as high efficiency, stability, and scalability. However, there are still critical issues in applying them to practical problems in the real world. The main objective of this study is to develop a novel self-assembling swarm robot algorithm that overcomes the limitations of existing approaches. To this end, multitree genetic programming is newly designed to efficiently discover a set of patterns necessary to carry out the mission of the self-assembling swarm robots. The obtained patterns are then incorporated into their corresponding robot modules. The computational experiments prove the effectiveness of the proposed approach.", } @InProceedings{conf/bic-ta/LeeAA14, author = "Jong-Hyun Lee and Jinung An and Chang Wook Ahn", title = "An Ensemble Pattern Classification System Based on Multitree Genetic Programming for Improving Intension Pattern Recognition Using Brain Computer Interaction", bibdate = "2014-09-30", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/bic-ta/bic-ta2014.html#LeeAA14", booktitle = "Bio-Inspired Computing - Theories and Applications - 9th International Conference, {BIC}-{TA} 2014, Wuhan, China, October 16-19, 2014. Proceedings", publisher = "Springer", year = "2014", volume = "472", editor = "Linqiang Pan and Gheorghe Paun and Mario J. Perez-Jimenez and Tao Song", isbn13 = "978-3-662-45048-2", pages = "239--246", series = "Communications in Computer and Information Science", keywords = "genetic algorithms, genetic programming", URL = "http://dx.doi.org/10.1007/978-3-662-45049-9", } @Article{Lee:2015:ESA, author = "Jong-Hyun Lee and Javad Rahimipour Anaraki and Chang Wook Ahn and Jinung An", title = "Efficient classification system based on Fuzzy-Rough Feature Selection and Multitree Genetic Programming for intension pattern recognition using brain signal", journal = "Expert Systems with Applications", volume = "42", number = "3", pages = "1644--1651", year = "2015", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2014.09.048", URL = "http://www.sciencedirect.com/science/article/pii/S0957417414006095", size = "8 pages", abstract = "Recently, many researchers have studied in engineering approach to brain activity pattern of conceptual activities of the brain. In this paper we proposed a intension recognition framework (i.e. classification system) for high accuracy which is based on Fuzzy-Rough Feature Selection and Multitree Genetic Programming. The enormous brain signal data measured by fNIRS are reduced by proposed feature selection and extracted the informative features. Also, proposed Multitree Genetic Programming use the remain data to construct the intention recognition model effectively. The performance of proposed classification system is demonstrated and compared with existing classifiers and unreduced dataset. Experimental results show that classification accuracy increases while number of features decreases in proposed system.", keywords = "genetic algorithms, genetic programming, Fuzzy-rough sets, Feature selection, Multitree GP, Brain signal, Intension recognition", } @Article{LEE:2018:OE, author = "Jong-hyun Lee and Sung-soo Kim and Soon-sup Lee and Donghoon Kang and Jae-chul Lee", title = "Prediction of added resistance using genetic programming", journal = "Ocean Engineering", year = "2018", volume = "153", pages = "104--111", month = "1 " # apr, keywords = "genetic algorithms, genetic programming, Hydrodynamic design, Added resistance", ISSN = "0029-8018", URL = "http://www.sciencedirect.com/science/article/pii/S0029801818300970", DOI = "doi:10.1016/j.oceaneng.2018.01.089", size = "8 pages", abstract = "In recent years, the increasing demand for a reduction of carbon emission has made hydrodynamic design and the optimization of hull design more important. For appropriate hydrodynamic design, the added resistance needs to be predicted. However, as existing methods including computer simulations or experiments require considerable amounts of time and money, it is difficult to consider the prediction result at the initial design stage. Therefore, we propose a prediction method that can be used in the initial design stage for predicting the added resistance in waves, thereby contributing to the optimization of hull design and saving time and money. The proposed method is a nonlinear mathematical function and is based on genetic programming. For verification, the predicted results are compared with the experimental results and the strip theory results", notes = "Department of Ocean System Engineering, Institute of Marine Industry, Gyeongsang National University, South Korea", } @Article{lee:2019:Metabolites, author = "Michael Y. Lee and Ting Hu", title = "Computational Methods for the Discovery of Metabolic Markers of Complex Traits", journal = "Metabolites", year = "2019", volume = "9", number = "4", keywords = "genetic algorithms, genetic programming", ISSN = "2218-1989", URL = "https://www.mdpi.com/2218-1989/9/4/66", DOI = "doi:10.3390/metabo9040066", abstract = "Metabolomics uses quantitative analyses of metabolites from tissues or bodily fluids to acquire a functional readout of the physiological state. Complex diseases arise from the influence of multiple factors, such as genetics, environment and lifestyle. Since genes, RNAs and proteins converge onto the terminal downstream metabolome, metabolomics datasets offer a rich source of information in a complex and convoluted presentation. Thus, powerful computational methods capable of deciphering the effects of many upstream influences have become increasingly necessary. In this review, the workflow of metabolic marker discovery is outlined from metabolite extraction to model interpretation and validation. Additionally, current metabolomics research in various complex disease areas is examined to identify gaps and trends in the use of several statistical and computational algorithms. Then, we highlight and discuss three advanced machine-learning algorithms, specifically ensemble learning, artificial neural networks, and genetic programming, that are currently less visible, but are budding with high potential for utility in metabolomics research. With an upward trend in the use of highly-accurate, multivariate models in the metabolomics literature, diagnostic biomarker panels of complex diseases are more recently achieving accuracies approaching or exceeding traditional diagnostic procedures. This review aims to provide an overview of computational methods in metabolomics and promote the use of up-to-date machine-learning and computational methods by metabolomics researchers.", notes = "also known as \cite{metabo9040066}", } @InCollection{lee:2002:EPGIMMA, author = "Peter Lee", title = "Evolving Presentations of Genetic Information: Motivation, Methods, and Analysis", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2002", year = "2002", editor = "John R. Koza", pages = "119--128", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms", URL = "http://www.genetic-programming.org/sp2002/Lee.pdf", notes = "part of \cite{koza:2002:gagp} Positional information on genes => do shorter schema survive? Matlab 6.1.0.450 SUN Blade 2000", } @InProceedings{DBLP:conf/gecco/LeeM09a, author = "Seung-Kyu Lee and Byung Ro Moon", title = "Finding attractive rules in stock markets using a modular genetic programming", booktitle = "GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation", year = "2009", editor = "Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano {van Hemert} and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique Alba", pages = "1933--1934", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming, Poster", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1569901.1570244", abstract = "We propose a new modular genetic programming for finding attractive and statistically sound technical rules. We restrict the problem space using well-known technical rules to discover attractive technical rules. Experimental results show that our modular genetic programming can successfully find unknown attractive technical rules for Korean stock market.", notes = "GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009). ACM Order Number 910092.", } @InProceedings{Lee:2010:gecco, author = "Seung-Kyu Lee and Byung-Ro Moon", title = "A new modular genetic programming for finding attractive technical patterns in stock markets", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "1219--1226", keywords = "genetic algorithms, genetic programming, modular genetic programming, Real world applications", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830483.1830704", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "We propose a new modular genetic programming for finding attractive and statistically sound technical patterns for stock trading. We restrict the problem space to combinations of modules for more effective space search. We carefully prepared the set of modules based on existing studies of technical indicators and our own experience. Our modular genetic programming successfully found unknown attractive technical patterns for the Korean stock market. A trading simulation with the generated patterns by a commercial tool showed significantly higher accumulative returns than the KOSPI index.", notes = "Also known as \cite{1830704} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @Article{Lee2009285, author = "Tae-Mun Lee and Hyunje Oh and Youn-Kyoo Choung and Sanghoun Oh and Moongu Jeon and Joon Ha Kim and Sook Hyun Nam and Sangho Lee", title = "Prediction of membrane fouling in the pilot-scale microfiltration system using genetic programming", journal = "Desalination", volume = "247", number = "1-3", pages = "285--294", year = "2009", month = oct, ISSN = "0011-9164", DOI = "doi:10.1016/j.desal.2008.12.031", URL = "http://www.sciencedirect.com/science/article/B6TFX-4X502WT-11/2/27587f58d0280f3d90f2898992cdab65", keywords = "genetic algorithms, genetic programming, Membrane fouling, Prediction", abstract = "In the recent past, machine learning (ML) techniques such as artificial neural networks (ANN) or genetic algorithm (GA) have been increasingly used to model membrane fouling and performance. In the present study, we select genetic programming (GP) for modeling and prediction of the membrane fouling rate in a pilot-scale drinking water production system. The model used input parameters for operating conditions (flow rate and filtration time) and feed water quality (turbidity, temperature, algae pH). GP was applied to discover the mathematical function for the pattern of the membrane fouling rate. The GP model allows predicting satisfactorily the filtration performances of the pilot plant obtained for different water quality and changing operating conditions. A valuable benefit of GP modeling was that the models did not require underlying descriptions of the physical processes. GP has displayed the potential to evaluate membrane performance as a feed-forward simulator toward an 'intelligent' membrane system.", notes = "Presented at the First IWA Asia Pacific Young Water Professionals Conference, Gwangju, South Korea, December 8-10, 2008", } @InProceedings{DBLP:conf/pldi/LeeHAN18, author = "Woosuk Lee and Kihong Heo and Rajeev Alur and Mayur Naik", title = "Accelerating search-based program synthesis using learned probabilistic models", booktitle = "Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI 2018", year = "2018", editor = "Jeffrey S. Foster and Dan Grossman", pages = "436--449", address = "Philadelphia, PA, USA", month = jun # " 18-22", publisher = "ACM", keywords = "genetic algorithms, genetic programming, SBSE, Synthesis, Domain-specific languages, Statistical methods, Transfer learning", timestamp = "Wed, 23 Jun 2021 15:34:31 +0200", biburl = "https://dblp.org/rec/conf/pldi/LeeHAN18.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "http://psl.hanyang.ac.kr/assets/pdf/pldi18.pdf", URL = "https://doi.org/10.1145/3192366.3192410", DOI = "doi:10.1145/3192366.3192410", code_url = "https://github.com/wslee/euphony", size = "14 pages", abstract = "A key challenge in program synthesis concerns how to efficiently search for the desired program in the space of possible programs. We propose a general approach to accelerate search-based program synthesis by biasing the search towards likely programs. Our approach targets a standard formulation, syntax-guided synthesis (SyGuS), by extending the grammar of possible programs with a probabilistic model dictating the likelihood of each program. We develop a weighted search algorithm to efficiently enumerate programs in order of their likelihood. We also propose a method based on transfer learning that enables to effectively learn a powerful model, called probabilistic higher-order grammar, from known solutions in a domain. We have implemented our approach in a tool called Euphony and evaluate it on SyGuS benchmark problems from a variety of domains. We show that Euphony can learn good models using easily obtainable solutions, and achieves significant performance gains over existing general-purpose as well as domain-specific synthesizers.", notes = "https://www.cis.upenn.edu/~alur/PLDI18.pdf Appendix: 'To find pbest, we adopt a genetic-programming like procedure' p447 'We compare Euphony with FlashFill'", } @Article{Lee2011147, author = "Yi-Shian Lee and Lee-Ing Tong", title = "Forecasting energy consumption using a grey model improved by incorporating genetic programming", journal = "Energy Conversion and Management", volume = "52", number = "1", pages = "147--152", year = "2011", ISSN = "0196-8904", DOI = "doi:10.1016/j.enconman.2010.06.053", URL = "http://www.sciencedirect.com/science/article/B6V2P-50JPRY8-1/2/2a8da744ea8e078b297748c80fb2890c", keywords = "genetic algorithms, genetic programming, Energy consumption, Grey forecasting model", abstract = "Energy consumption is an important economic index, which reflects the industrial development of a city or a country. Forecasting energy consumption by conventional statistical methods usually requires the making of assumptions such as the normal distribution of energy consumption data or on a large sample size. However, the data collected on energy consumption are often very few or non-normal. Since a grey forecasting model, based on grey theory, can be constructed for at least four data points or ambiguity data, it can be adopted to forecast energy consumption. In some cases, however, a grey forecasting model may yield large forecasting errors. To minimise such errors, this study develops an improved grey forecasting model, which combines residual modification with genetic programming sign estimation. Finally, a real case of Chinese energy consumption is considered to demonstrate the effectiveness of the proposed forecasting model.", } @Article{Lee201166, author = "Yi-Shian Lee and Lee-Ing Tong", title = "Forecasting time series using a methodology based on autoregressive integrated moving average and genetic programming", journal = "Knowledge-Based Systems", year = "2011", volume = "24", number = "1", pages = "66--72", month = feb, keywords = "genetic algorithms, genetic programming, ARIMA, Hybrid model, Forecasting, Artificial neural network", ISSN = "0950-7051", DOI = "doi:10.1016/j.knosys.2010.07.006", broken = "http://www.sciencedirect.com/science/article/B6V0P-50JHBSY-1/2/1501a7c1121cbfcf9683f1a0d781806b", abstract = "The autoregressive integrated moving average (ARIMA), which is a conventional statistical method, is employed in many fields to construct models for forecasting time series. Although ARIMA can be adopted to obtain a highly accurate linear forecasting model, it cannot accurately forecast nonlinear time series. Artificial neural network (ANN) can be used to construct more accurate forecasting model than ARIMA for nonlinear time series, but explaining the meaning of the hidden layers of ANN is difficult and, moreover, it does not yield a mathematical equation. This study proposes a hybrid forecasting model for nonlinear time series by combining ARIMA with genetic programming (GP) to improve upon both the ANN and the ARIMA forecasting models. Finally, some real data sets are adopted to demonstrate the effectiveness of the proposed forecasting model.", } @InProceedings{Lee:2012:ICALT, author = "Yi-Shian Lee and Hou-Chiang Tseng and Ju-Ling Chen and Chun-Yi Peng and Tao-Hsing Chang and Yao-Ting Sung", booktitle = "12th IEEE International Conference on Advanced Learning Technologies (ICALT 2012)", title = "Constructing a Novel Chinese Readability Classification Model Using Principal Component Analysis and Genetic Programming", year = "2012", pages = "164--166", keywords = "genetic algorithms, genetic programming, natural language processing, pattern classification, principal component analysis, text analysis, English text, Flesch-Kincaid formula, GP, PCA, multiple linguistic features, novel Chinese readability classification model, principal component analysis, text classification, text readability, Educational institutions, Mathematical model, Predictive models, Principal component analysis, Psychology, Support vector machines, Principal component analysis, Readability, Text analysis component", DOI = "doi:10.1109/ICALT.2012.134", abstract = "The studies of readability aim to measure the level of text difficulty. Although traditional formulae such as the Flesch-Kincaid formula can properly predict text readability, they are only effective for English text. Other formulae with very few features may result in inaccurate text classification. The study takes into account multiple linguistic features, and attempts to increase the level of accuracy in text classification by adopting a new model which integrates Principal Component Analysis (PCA) with Genetic Programming (GP). Empirical data are used to demonstrate the performance of the proposed model.", notes = "Also known as \cite{6268065}", } @Article{Lee:2012:Energies, author = "Yi-Shian Lee and Lee-Ing Tong", title = "Predicting High or Low Transfer Efficiency of Photovoltaic Systems Using a Novel Hybrid Methodology Combining Rough Set Theory, Data Envelopment Analysis and Genetic Programming", journal = "Energies", year = "2012", volume = "5", number = "3", pages = "545--560", publisher = "Molecular Diversity Preservation International", keywords = "genetic algorithms, genetic programming, photovoltaic systems, rough set theory, data envelopment analysis, hybrid model", ISSN = "1996-1073; 19961073", URL = "http://www.mdpi.com/1996-1073/5/3/545/pdf", URL = "http://www.mdpi.com/1996-1073/5/3/545/", broken = "http://www.doaj.org/doaj?func=openurl\&genre=article\&issn=19961073\&date=2012\&volume=5\&issue=3\&spage=545", DOI = "doi:10.3390/en5030545", size = "16 pages", abstract = "Solar energy has become an important energy source in recent years as it generates less pollution than other energies. A photovoltaic (PV) system, which typically has many components, converts solar energy into electrical energy. With the development of advanced engineering technologies, the transfer efficiency of a PV system has been increased from low to high. The combination of components in a PV system influences its transfer efficiency. Therefore, when predicting the transfer efficiency of a PV system, one must consider the relationship among system components. This work accurately predicts whether transfer efficiency of a PV system is high or low using a novel hybrid model that combines rough set theory (RST), data envelopment analysis (DEA), and genetic programming (GP). Finally, real data-set are used to demonstrate the accuracy of the proposed method.", bibsource = "OAI-PMH server at www.doaj.org", language = "eng", oai = "oai:doaj-articles:601889dc955f0d7b09d556498b97d8da", } @Article{Lee2012251, author = "Yi-Shian Lee and Lee-Ing Tong", title = "Forecasting nonlinear time series of energy consumption using a hybrid dynamic model", journal = "Applied Energy", volume = "94", pages = "251--256", year = "2012", ISSN = "0306-2619", DOI = "doi:10.1016/j.apenergy.2012.01.063", URL = "http://www.sciencedirect.com/science/article/pii/S0306261912000694", keywords = "genetic algorithms, genetic programming, Energy consumption, Grey forecasting model, Hybrid dynamic approach", abstract = "Energy consumption is an important index of the economic development of a country. Rapid changes in industry and the economy strongly affect energy consumption. Although traditional statistical approaches yield accurate forecasts of energy consumption, they may suffer from several limitations such as the need for large data sets and the assumption of a linear formula. This work describes a novel hybrid dynamic approach that combines a dynamic grey model with genetic programming to forecast energy consumption. This proposed approach is used to forecast energy consumption because of its excellent accuracy, applicability to cases with limited data sets and ease of computability using mathematical software. Two case studies of energy consumption demonstrate the reliability of the proposed model. Computational results indicate that the proposed approach outperforms other models in forecasting energy consumption.", } @Article{Lee:2014:CEA, author = "Yi-Shian Lee and Wan-Yu Liu", title = "Forecasting value of agricultural imports using a novel two-stage hybrid model", journal = "Computers and Electronics in Agriculture", volume = "104", pages = "71--83", year = "2014", ISSN = "0168-1699", DOI = "doi:10.1016/j.compag.2014.03.011", URL = "http://www.sciencedirect.com/science/article/pii/S0168169914000817", keywords = "genetic algorithms, genetic programming, Value of agricultural imports, GM(1,1), Residual signs, Residual series", abstract = "Agricultural imports are becoming increasingly important in terms of their impact on economic development. An accurate model must be developed for forecasting the value of agricultural imports since rapid changes in industry and economic policy affect the value of agricultural imports. Conventionally, the ARIMA model has been used to forecast the value of agricultural imports, but it generally requires a large sample size and several statistical assumptions. Some studies have applied nonlinear methods such as the GM(1,1) and improved GM(1,1) models, yet neglected the importance of enhancing the accuracy of residual signs and residual series. Therefore, this study develops a novel two-stage forecasting model that combines the GM(1,1) model with genetic programming to accurately forecast the value of agricultural imports. Moreover, accuracy of the proposed model is demonstrated based on two agricultural imports data sets from the Taiwan and USA.", } @InProceedings{Leehter:GPB:cec2006, author = "Yao Leehter and Lin Chin-Chin", title = "Genetic Programming Based Multichannel Identification of Nonlinear Systems by Volterra Filters", booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary Computation", year = "2006", editor = "Gary G. Yen and Simon M. Lucas and Gary Fogel and Graham Kendall and Ralf Salomon and Byoung-Tak Zhang and Carlos A. Coello Coello and Thomas Philip Runarsson", pages = "2864--2871", address = "Vancouver, BC, Canada", month = "16-21 " # jul, publisher = "IEEE Press", ISBN = "0-7803-9487-9", URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=11108", DOI = "doi:10.1109/CEC.2006.1688669", keywords = "genetic algorithms, genetic programming", abstract = "Genetic Programming (GP) is used to search the optimal structure of Volterra filter in this paper. The Volterra filter with high order and large memories contains great amount of cross product terms. In stead of applying GP to search all cross products, GP is used to search a smaller set of primary signals which evolve to the whole set of cross products. With GP's optimisation capability, the important primary signals and the associated cross products of input signals attributing most to the outputs will be chosen while the primary signals and their associated cross products of input signals which are trivial to the outputs will be excluded from the possible candidate primary signals.", notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D", } @InProceedings{Leeke:2021:DASC, author = "Matthew Leeke", booktitle = "2021 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)", title = "Reducing Model Complexity and Cost in the Generation of Efficient Error Detection Mechanisms", year = "2021", pages = "26--34", abstract = "The design and location of error detection mechanisms (EDMs) is fundamental to the design of a dependable software system. The application of machine learning algorithms to fault injection data has been shown to be an effective approach for the generation of efficient EDMs. However, the complexity of the generated models and initial cost of generation represent barriers to the adoption of the approach. Addressing these challenges directly, this paper demonstrates that genetic programming can be used as an approach to reduce the complexity of the models generated and obviate the computational cost associated with the sampling and refinement stages of EDM generation. More specifically, it is shown that (i) genetic programming can be used to project the instance space of fault injection data sets into a space more amenable to learning, (ii) machine learning algorithms can be applied to the resultant projection to permit the generation of efficient EDMs with reduced model complexity, and (iii) the cost of generating efficient EDMs can be reduced by the approach because it obviates the need for data set sampling methods and model refinement.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/DASC-PICom-CBDCom-CyberSciTech52372.2021.00020", month = oct, notes = "Also known as \cite{9730167} See also Phd: http://webcat.warwick.ac.uk/record=b2581851~S1 http://wrap.warwick.ac.uk/52394/1/WRAP_THESIS_Leeke_2011.pdf", } @PhdThesis{Leemans:thesis, author = "Vasco Leemans", title = "Modelling local order book dynamics in financial markets", school = "Judge Business School, Faculty of Business and Management, Cambridge University", year = "2008", type = "PhD", URL = "https://www.repository.cam.ac.uk/handle/1810/252052", notes = "This thesis is not available on this repository until the author agrees to make it public... Supervisor Prof M Dempster http://www.cfr.statslab.cam.ac.uk/people/alumni.html#2008", } @InProceedings{lefley:2003:gecco, author = "Martin Lefley and Martin J. Shepperd", title = "Using Genetic Programming to Improve Software Effort Estimation Based on General Data Sets", booktitle = "Genetic and Evolutionary Computation -- GECCO-2003", editor = "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller", year = "2003", pages = "2477--2487", address = "Chicago", publisher_address = "Berlin", month = "12-16 " # jul, volume = "2724", series = "LNCS", ISBN = "3-540-40603-4", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, Search Based Software Engineering", DOI = "doi:10.1007/3-540-45110-2_151", abstract = "various techniques including genetic programming, with public data sets, to attempt to model and hence estimate software project effort. The main research question is whether genetic programs can offer `better' solution search using public domain metrics rather than company specific ones. Unlike most previous research, a realistic approach is taken, whereby predictions are made on the basis of the data available at a given date. Experiments are reported, designed to assess the accuracy of estimates made using data within and beyond a specific company. This research also offers insights into genetic programming's performance, relative to alternative methods, as a problem solver in this domain. The results do not find a clear winner but, for this data, GP performs consistently well, but is harder to configure and produces more complex models. The evidence here agrees with other researchers that companies would do well to base estimates on in house data rather than incorporating public data sets. The complexity of the GP must be weighed against the small increases in accuracy to decide whether to use it as part of any effort prediction estimation.", notes = "GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)", } @InProceedings{Lefticaru:2009:BCI, author = "Raluca Lefticaru and Florentin Ipate and Cristina Tudose", title = "Automated Model Design Using Genetic Algorithms and Model Checking", booktitle = "Fourth Balkan Conference in Informatics, BCI '09", year = "2009", month = sep, pages = "79--84", keywords = "genetic algorithms, automated model design, computer program evolution, finite state based models, metaheuristic search algorithms, model checking, software engineering activities, software testing, complete computer programs, formal specification, formal verification, search problems, software performance evaluation", DOI = "doi:10.1109/BCI.2009.15", abstract = "In recent years there has been a growing interest in applying metaheuristic search algorithms in model-checking. On the other hand, model checking has been used far less in other software engineering activities, such as model design and software testing. In this paper we propose an automated model design strategy, by integrating genetic algorithms (used for model generation) with model checking (used to evaluate the fitness, which takes into account the satisfied/unsatisfied specifications). Genetic programming is the process of evolving computer programs, by using a fitness value determined by the program's ability to perform a given computational task. This evaluation is based on the output produced by the program for a set of training input samples. The consequence is that the evolved program can function well for the sample set used for training, but there is no guarantee that the program will behave properly for every possible input. Instead of training samples, in this paper we use a model checker, which verifies if the generated model satisfies the specifications. This approach is empirically evaluated for the generation of finite state-based models. Furthermore, the previous fitness function proposed in the literature, that takes into account only the number of unsatisfied specifications, presents plateaux and so does not offer a good guidance for the search. This paper proposes and evaluates the performance of a number of new fitness functions, which, by taking also into account the counterexamples provided by the model checker, improve the success rate of the genetic algorithm.", notes = "Not GP. temporal logic formula:-( LTL, SMV specification, NuSMV traffic lights 14 integer genes, search space 279936, multiple possible solutions. JGAP pop=20, 20 gens, 1pt crossover or UXO. PWR safety injection, category injection, one solution in whole search space 221184 Vehicle, one solution in search space 2**40 (10^12) Also known as \cite{5359333}", } @PhdThesis{leger:1999:thesis, author = "Chris Leger", title = "Automated Synthesis and Optimisation of Robot Configurations: An Evolutionary Approach", school = "The Robotics Institute, Carnegie Mellon University", year = "1999", address = "Pittsbugh, PA 15213, USA", month = "9 " # dec, note = "CMU-RI-TR-99-43", keywords = "genetic algorithms, genetic programming, Darwin2K", URL = "http://www.ri.cmu.edu/pub_files/pub2/leger_patrick__chris__1999_1/leger_patrick__chris__1999_1.ps.gz", URL = "http://www.ri.cmu.edu/pub_files/pub2/leger_patrick__chris__1999_1/leger_patrick__chris__1999_1.pdf", size = "234 pages", abstract = "Robot configuration design is hampered by the lack of established, well-known design rules, and designers cannot easily grasp the space of possible designs and the impact of all design variables on a robot's performance. Realistically, a human can only design and evaluate several candidate configurations, though there may be thousands of competitive designs that should be investigated. In contrast, an automated approach to configuration synthesis can create tens of thousands of designs and measure the performance of each one without relying on previous experience or design rules. This thesis creates Darwin2K, an extensible, automated system for robot configuration synthesis. This research focuses on the development of synthesis capabilities required for many robot design problems: a flexible and effective synthesis algorithm, useful simulation capabilities, appropriate representation of robots and their properties, and the ability to accomodate application-specific synthesis needs. Darwin2K can synthesize and optimize kinematics, dynamics, structural geometry, actuator selection, and task and control parameters for a wide range of robots. Darwin2K uses an evolutionary algorithm to synthesize robots, and uses two new multi-objective selection procedures that are applicable to other evolutionary design domains. The evolutionary algorithm can effectively optimize multiple performance objectives while satisfying multiple performance constraints, and can generate a range of solutions representing different trade-offs between objectives. Darwin2K uses a novel representation for robot configurations called the parameterized module configuration graph, enabling efficient and extensible synthesis of mobile robots, of single, multiple and bifurcating manipulators, and of robots with either modular or monolithic construction. Task-specific simulation is used to provide the synthesis algorithm with performance measurements for each robot. Darwin2K can automatically derive dynamic equations for each robot it simulates, enabling dynamic simulation to be used during synthesis for the first time. Darwin2K also includes a variety of simulation components, including Jacobian and PID controllers, algorithms for estimating link deflection and for detecting collisions; modules for robot links, joints (including actuator models), tools, and bases (fixed and mobile); and metrics such as task coverage, task completion time, end effector error, actuator saturation, and link deflection. A significant component of the system is its extensible object-oriented software architecture, which allows new simulation capabilities and robot modules to be added without impacting the synthesis algorithm. The architecture also encourages re-use of existing toolkit components, allowing task-specific simulators to be quickly constructed. Darwin2K's synthesis algorithm, simulation capabilities, and extensible architecture combine to allow synthesis of robots for a wide range of tasks. Results are presented for nearly 150 synthesis experiments for six different applications, including synthesis of a free-flying 22-DOF robot with multiple manipulators and a walking machine for zero-gravity truss walking. The synthesis system and results represent a significant advance in the state-of-the-art in automated synthesis for robotics.", } @InProceedings{DBLP:conf/sigsoft/GouesFW10, author = "Claire {Le Goues} and Stephanie Forrest and Westley Weimer", title = "The case for software evolution", booktitle = "Proceedings of the FSE/SDP workshop on Future of software engineering research, FoSER'10", year = "2010", editor = "Gruia-Catalin Roman and Kevin J. Sullivan", pages = "205--210", address = "Santa Fe, New Mexico, USA", publisher_address = "New York, NY, USA", month = nov # " 7-11", organisation = "ACM SIGSOFT", publisher = "ACM", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, sbse, evolutionary computation, program repair, software engineering", isbn13 = "978-1-4503-0427-6", URL = "http://www.cs.virginia.edu/~weimer/p/p205-legoues.pdf", DOI = "doi:10.1145/1882362.1882406", size = "5 pages", acmid = "1882406", abstract = "Many software systems exceed our human ability to comprehend and manage, and they continue to contain unacceptable errors. This is an unintended consequence of Moore's Law, which has led to increases in system size, complexity, and interconnectedness. Yet, software is still primarily created, modified, and maintained by humans. The interactions among heterogeneous programs, machines and human operators has reached a level of complexity rivalling that of some biological ecosystems. By viewing software as an evolving complex system, researchers could incorporate biologically inspired mechanisms and employ the quantitative analysis methods of evolutionary biology. This approach could improve our understanding and analysis of software; it could lead to robust methods for automatically writing, debugging and improving code; and it could improve predictions about functional and structural transitions as scale increases. In the short term, an evolutionary perspective challenges several research assumptions, enabling advances in error detection, correction, and prevention.", bibsource = "DBLP, http://dblp.uni-trier.de", } @Article{DBLP:journals/tse/GouesNFW12, author = "Claire {Le Goues} and ThanhVu Nguyen and Stephanie Forrest and Westley Weimer", title = "{GenProg}: A Generic Method for Automatic Software Repair", year = "2012", journal = "IEEE Transactions on Software Engineering", volume = "38", number = "1", pages = "54--72", month = jan # "-" # feb, keywords = "genetic algorithms, genetic programming, genetic improvement, APR, SBSE, Automatic programming, corrections, testing and debugging", bibsource = "DBLP, http://dblp.uni-trier.de", URL = "http://www.cs.virginia.edu/~weimer/p/weimer-tse2012-genprog.pdf", DOI = "doi:10.1109/TSE.2011.104", code_url = "https://squareslab.github.io/genprog-code/", size = "19 pages", abstract = "This paper describes GenProg, an automated method for repairing defects in off-the-shelf, legacy programs without formal specifications, program annotations, or special coding practices. GenProg uses an extended form of genetic programming to evolve a program variant that retains required functionality but is not susceptible to a given defect, using existing test suites to encode both the defect and required functionality. Structural differencing algorithms and delta debugging reduce the difference between this variant and the original program to a minimal repair. We describe the algorithm and report experimental results of its success on 16 programs totalling 1.25 million lines of C code and 120000 lines of module code, spanning eight classes of defects, in 357 seconds, on average. We analyse the generated repairs qualitatively and quantitatively to demonstrate that the process efficiently produces evolved programs that repair the defect, are not fragile input memorisations, and do not lead to serious degradation in functionality.", notes = "Software maintenance 90percent of cost of software project. Auto bug fix. localise which C source statements genetic operators act on. CIL abstract syntax tree AST and weighted linear path. Always correct syntax, but may not compile. deterministic bugs. delta debugging. 100 trials. Very small number of positive and negative test cases. xdiff converted to diff patch format. Crossover and mutation, pop=40, gen=10. page62 pmut<0.12. Fig 9 linear scaling. Online repair, www web http lighttpd examples. SPIKE black box fuzz testing. Cited by \cite{Tan:ICSE:2015}", } @InProceedings{LeGoues:2012:ICSE, author = "Claire {Le Goues} and Michael Dewey-Vogt and Stephanie Forrest and Westley Weimer", title = "A Systematic Study of Automated Program Repair: Fixing 55 out of 105 bugs for \$8 Each", booktitle = "34th International Conference on Software Engineering (ICSE 2012)", year = "2012", editor = "Martin Glinz", pages = "3--13", address = "Zurich", month = jun # " 2-9", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, GenProg, algorithmic improvement, automated program repair, cloud computing resource, defect repair, grounded human-competitive cost measurement, off-the-shelf C program, open-source program, program bug, real-world program, repair cost, systematic evaluation, C language, cloud computing, program debugging, public domain software, software cost estimation, software maintenance", ISSN = "0270-5257", URL = "http://dijkstra.cs.virginia.edu/genprog/papers/weimer-icse2012-genprog-preprint.pdf", code_url = "https://squareslab.github.io/genprog-code/", DOI = "doi:10.1109/ICSE.2012.6227211", size = "11 pages", abstract = "There are more bugs in real-world programs than human programmers can realistically address. This paper evaluates two research questions: What fraction of bugs can be repaired automatically? and How much does it cost to repair a bug automatically? In previous work, we presented GenProg, which uses genetic programming to repair defects in off-the-shelf C programs. To answer these questions, we: (1) propose novel algorithmic improvements to GenProg that allow it to scale to large programs and find repairs 68percent more often, (2) exploit GenProg's inherent parallelism using cloud computing resources to provide grounded, human competitive cost measurements, and (3) generate a large, indicative benchmark set to use for systematic evaluations. We evaluate GenProg on 105 defects from 8 open-source programs totalling 5.1 million lines of code and involving 10,193 test cases. GenProg automatically repairs 55 of those 105 defects. To our knowledge, this evaluation is the largest available of its kind, and is often two orders of magnitude larger than previous work in terms of code or test suite size or defect count. Public cloud computing prices allow our 105 runs to be reproduced for 403 USA dollars; a successful repair completes in 96 minutes and costs $7.32, on average.", notes = "GenProg >> ClearView, AutoFix-E, AFix. Bug bounties, Tarsnap. Chrome is ordered list of AST edit operations. Delete, insert, swap, uniform crossover. 10 GP runs (population 40, <= ten generations, <12 hours) in parallel on Amazon EC2 (c1.medium, 1.7Gbyte RAM) cloud. Less than 10percent of children fail to compile. fbc, gmp, gzip, libtiff, lighttpd, php, Python, wireshark approx 5.1 million lines of C code. Human difficulty of bugfix != GP difficulty? See also \cite{Fry:2012:ISSTA} http://www.ifi.uzh.ch/icse2012/ Bronze winner 2012 HUMIES GECCO 2012. cited by \cite{Nguyen:2013:ICSE} \cite{Qi:2015:APP:2771783.2771791} \cite{DBLP:journals/corr/DurieuxMMSX15} Also known as \cite{6227211}", } @InProceedings{LeGoues:2012:GECCO, author = "Claire {Le Goues} and Westley Weimer and Stephanie Forrest", title = "Representations and operators for improving evolutionary software repair", booktitle = "GECCO '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference", year = "2012", editor = "Terry Soule and Anne Auger and Jason Moore and David Pelta and Christine Solnon and Mike Preuss and Alan Dorin and Yew-Soon Ong and Christian Blum and Dario Landa Silva and Frank Neumann and Tina Yu and Aniko Ekart and Wil Browne and Tim Kovacs and Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and Giovanni Squillero and Nicolas Bredeche and Stephen L. Smith and Alison Motsinger-Rei and Jose Lozano and Martin Pelikan and Silja Meyer-Nienber and Christian Igel and Greg Hornby and Rene Doursat and Steve Gustafson and Gustavo Olague and Shin Yoo and John Clark and Gabriela Ochoa and Gisele Pappa and Fernando Lobo and Daniel Tauritz and Jurgen Branke and Kalyanmoy Deb", isbn13 = "978-1-4503-1177-9", pages = "959--966", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, SBSE, Software Engineering, Testing and Debugging, Artificial Intelligence, Search, Algorithms, Representation, crossover, mutation, search-based software engineering, software repair, GenProg, bug fixing", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", URL = "http://www.clairelegoues.com.s3-website-us-east-1.amazonaws.com/docs/genprog-gecco2012-preprint.pdf", DOI = "doi:10.1145/2330163.2330296", publisher = "ACM", publisher_address = "New York, NY, USA", size = "8 pages", abstract = "Evolutionary computation is a promising technique for automating time-consuming and expensive software maintenance tasks, including bug repair. The success of this approach, however, depends at least partially on the choice of representation, fitness function, and operators. Previous work on evolutionary software repair has employed different approaches, but they have not yet been evaluated in depth. This paper investigates representation and operator choices for source-level evolutionary program repair in the GenProg framework [17], focusing on: (1) representation of individual variants, (2) crossover design, (3) mutation operators, and (4) search space definition. We evaluate empirically on a dataset comprising 8 C programs totalling over 5.1 million lines of code and containing 105 reproducible, human-confirmed defects. Our results provide concrete suggestions for operator and representation design choices for evolutionary program repair. When augmented to incorporate these suggestions, GenProg repairs 5 additional bugs (60 vs. 55 out of 105), with a decrease in repair time of 17--43percent for the more difficult repair searches.", notes = "Bronze winner 2012 HUMIES GECCO 2012. Also known as \cite{2330296} GECCO-2012 A joint meeting of the twenty first international conference on genetic algorithms (ICGA-2012) and the seventeenth annual genetic programming conference (GP-2012)", } @PhdThesis{LeGoues:thesis, author = "Claire {Le Goues}", title = "Automatic Program Repair Using Genetic Programming", school = "Faculty of the School of Engineering and Applied Science, University of Virginia", year = "2013", address = "USA", month = may, keywords = "genetic algorithms, genetic programming, genetic improvement, APR, SBSE, automatic program repair, software engineering, software defects, GenProg", URL = "http://www.cs.virginia.edu/~weimer/students/claire-phd.pdf", URL = "https://doi.org/10.18130/V3KZ3C", size = "146 pages", abstract = "Software quality is an urgent problem. There are so many bugs in industrial program source code that mature software projects are known to ship with both known and unknown bugs [1], and the number of outstanding defects typically exceeds the resources available to address them [2]. This has become a pressing economic problem whose costs in the United States can be measured in the billions of dollars annually [3]. A dominant reason that software defects are so expensive is that fixing them remains a manual process. The process of identifying, triaging, reproducing, and localising a particular bug, coupled with the task of understanding the underlying error, identifying a set of code changes that address it correctly, and then verifying those changes, costs both time [4] and money, and the cost of repairing a defect can increase by orders of magnitude as development progresses [5]. As a result, many defects, including critical security defects [6], remain unaddressed for long periods of time [7]. Moreover, humans are error-prone, and many human fixes are imperfect, in that they are either incorrect or lead to crashes, hangs, corruption, or security problems [8]. As a result, defect repair has become a major component of software maintenance, which in turn consumes up to 90% of the total lifecycle cost of a given piece of software [9]. Although considerable research attention has been paid to supporting various aspects of the manual debugging process [10, 11], and also to preempting or dynamically addressing particular classes of vulnerabilities, such as buffer overruns [12, 13], there exist virtually no previous automated solutions that address the synthesis of patches for general bugs as they are reported in real-world software. The primary contribution of this dissertation is GenProg, one of the very first automatic solutions designed to help alleviate the manual bug repair burden by automatically and generically patching bugs in deployed and legacy software. GenProg uses a novel genetic programming algorithm, guided by test cases and domain-specific operators, to affect scalable, expressive, and high quality automated repair. We present experimental evidence to substantiate our claims that GenProg can repair multiple types of bugs in multiple types of programs, and that it can repair a large proportion of the bugs that human developers address in practice (that it is expressive); that it scales to real-world system sizes (that it is scalable); and that it produces repairs that are of sufficiently high quality. Over the course of this evaluation, we contribute new benchmark sets of real bugs in real open-source software and novel experimental frameworks for quantitatively evaluating an automated repair technique. We also contribute a novel characterisation of the automated repair search space, and provide analysis both of that space and of the performance and scaling behaviour of our technique. General automated software repair was unheard of in 2009. In 2013, it has its own multi-paper sessions in top tier software engineering conferences. The research area shows no signs of slowing down. This dissertation's description of GenProg provides a detailed report on the state of the art for early automated software repair efforts.", notes = "Supervisor Wesley R. Weimer", } @Article{legouesWFSQJO2013, author = "Claire {Le Goues} and Stephanie Forrest and Westley Weimer", title = "Current challenges in automatic software repair", journal = "Software Quality Journal", year = "2013", volume = "21", issue = "3", pages = "421--443", month = sep, keywords = "genetic algorithms, genetic programming, genetic improvement, APR, SBSE, GenProg, automatic program repair, software engineering, evolutionary computation", ISSN = "0963-9314", publisher = "Springer", URL = "http://dx.doi.org/10.1007/s11219-013-9208-0", URL = "http://www.cs.cmu.edu/~clegoues/docs/legoues-sqjo13.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.368.1034", DOI = "doi:10.1007/s11219-013-9208-0", language = "English", size = "23 pages", abstract = "The abundance of defects in existing software systems is unsustainable. Addressing them is a dominant cost of software maintenance, which in turn dominates the life cycle cost of a system. Recent research has made significant progress on the problem of automatic program repair, using techniques such as evolutionary computation, instrumentation and run-time monitoring, and sound synthesis with respect to a specification. This article serves three purposes. First, we review current work on evolutionary computation approaches, focusing on GenProg, which uses genetic programming to evolve a patch to a particular bug. We summarize algorithmic improvements and recent experimental results. Second, we review related work in the rapidly growing subfield of automatic program repair. Finally, we outline important open research challenges that we believe should guide future research in the area.", notes = "total 5139000 lines of code", } @Article{LeGoues:2015:TSE, author = "Claire {Le Goues} and Neal Holtschulte and Edward K. Smith and Yuriy Brun and Premkumar Devanbu and Stephanie Forrest and Westley Weimer", title = "The {ManyBugs} and {IntroClass} Benchmarks for Automated Repair of {C} Programs", journal = "IEEE Transactions on Software Engineering", year = "2015", volume = "41", number = "12", pages = "1236--1256", month = dec, keywords = "bugfixing, SBSE", ISSN = "0098-5589", DOI = "doi:10.1109/TSE.2015.2454513", abstract = "The field of automated software repair lacks a set of common benchmark problems. Although benchmark sets are used widely throughout computer science, existing benchmarks are not easily adapted to the problem of automatic defect repair, which has several special requirements. Most important of these is the need for benchmark programs with reproducible, important defects and a deterministic method for assessing if those defects have been repaired. This article details the need for a new set of benchmarks, outlines requirements, and then presents two datasets, ManyBugs and IntroClass, consisting between them of 1,183 defects in 15 C programs. Each dataset is designed to support the comparative evaluation of automatic repair algorithms asking a variety of experimental questions. The datasets have empirically defined guarantees of reproducibility and benchmark quality, and each study object is categorized to facilitate qualitative evaluation and comparisons by category of bug or program. The article presents baseline experimental results on both datasets for three existing repair methods, GenProg, AE, and TrpAutoRepair, to reduce the burden on researchers who adopt these datasets for their own comparative evaluations.", notes = "Not GP? For Java see \cite{durieux:hal-01272126}. Also known as \cite{7153570}.", } @InProceedings{LeGoues:2018:GI, author = "Claire {Le Goues}", title = "Evolving Software Quality", booktitle = "GI-2018, ICSE workshops proceedings", year = "2018", editor = "Justyna Petke and Kathryn Stolee and William B. Langdon and Westley Weimer", pages = "xiii", address = "Gothenburg, Sweden", month = "2 " # jun, publisher = "ACM", publisher_address = "New York, NY, USA", note = "Invited Keynote", keywords = "genetic algorithms, genetic programming, genetic improvement", isbn13 = "978-1-4503-5753-1", broken = "http://geneticimprovementofsoftware.com/keynote/", URL = "http://www.cs.ucl.ac.uk/staff/w.langdon/icse2018/GI_2018_FM_to_chair_04-26-18.pdf", size = "0.1 pages", abstract = "Genetic Improvement describes a class of search-based techniques that automatically improve software along a a variety of quality dimensions. In this talk, I will survey the research advances that have made possible the significant recent progress in this field. I will focus on the ongoing research opportunities that lie in genetic software improvement, with an especial focus on the challenges of confidently reasoning about, measuring, and assuring the quality of automatically and constantly evolving artefacts.", notes = "GI-2018 http://geneticimprovementofsoftware.com/events/papers#icse2018 part of \cite{Petke:2018:ICSEworkshop}", } @Article{cacm2019_program_repair, author = "Claire {Le Goues} and Michael Pradel and Abhik Roychoudhury", title = "Automated Program Repair", journal = "Communications of the ACM", year = "2019", volume = "62", number = "12", pages = "56--65", month = dec, keywords = "genetic algorithms, genetic programming, genetic improvement, APR, Bugs, Symbolic Execution, Learning", ISSN = "0001-0782", URL = "http://software-lab.org/publications/cacm2019_program_repair.pdf", DOI = "doi:10.1145/3318162", video_url = "https://youtu.be/MGpJEa8makw", size = "10 pages", abstract = "Automated program repair can greatly relieve programmers from the burden of manually fixing the ever increasing number of programming mistakes. At the same time, achieving such a goal involves solving technical challenges in scalability, patch quality, and integration into developer work flows. This article presents an overview of the state-of-the-art tools and techniques in automated program repair. We also take a forward looking view of the area by presenting emerging and potential use cases for program repair, such as on-line programming education and patching of security vulnerabilities.", notes = "Key Insights Automated program repair is an emerging and exciting field of research that allows for automated rectification of software errors and vulnerabilities. The uses of automated program repair can be myriad, such as improving programmer productivity, automated fixing of security vulnerabilities, self-healing software for autonomous devices, and automatically generating hints for solving programming assignments. Automated repair can benefit from various techniques: intelligent navigation over a search space of program edits, symbolic reasoning to synthesize suitable code fragments, and techniques that learn from existing code and patches. See also \cite{Rinard:2020:ACM}", } @Article{LeGoues:2025:TSE, author = "Claire {Le Goues} and ThanhVu Nguyen and Stephanie Forrest and Westley Weimer", title = "The Evolution of Automated Software Repair", journal = "IEEE Transactions on Software Engineering", year = "2025", volume = "51", number = "03", pages = "870--873", month = mar, keywords = "genetic algorithms, genetic programming, APR, automatic programming, corrections, testing, debugging, evolutionary computation, Maintenance engineering, Computer bugs, Software, Codes, Standards, Manuals", ISSN = "1939-3520", URL = "https://doi.ieeecomputersociety.org/10.1109/TSE.2025.3533309", DOI = "doi:10.1109/TSE.2025.3533309", size = "4 pages", abstract = "GenProg implemented a novel method for automatically evolving patches to repair test suite failures in legacy C programs. It combined insights from genetic programming and software engineering. Many of the original design decisions in GenProg were ultimately less important than its impact as an existence proof. In particular, it demonstrated that useful patches for non-trivial bugs and programs could be generated automatically. Since the original publication, research in automated program repair has expanded to consider and evaluate many new methods, contexts and defects. As code synthesis and debugging techniques based on machine learning have become popular, it is informative to consider how views on perennial issues in program repair have changed, or remained static, over time. This retrospective discusses the issues of repair quality (including the role of tests), use cases for automated repairs (including the role of humans), and why these approaches work at all.", notes = "also known as \cite{10854421} Cites \cite{DBLP:journals/cacm/WidderG24}", } @Article{Legrand:2007:GPEM, author = "Pierrick Legrand and Claire Bourgeois-Republique and Vincent Pean and Esther Harboun-Cohen and Jacques Levy-Vehel and Bruno Frachet and Evelyne Lutton and Pierre Collet", title = "Interactive evolution for cochlear implants fitting", journal = "Genetic Programming and Evolvable Machines", year = "2007", volume = "8", number = "4", pages = "319--354", month = dec, note = "special issue on medical applications of Genetic and Evolutionary Computation", keywords = "Interactive evolution, ES, Cochlear implants fitting, Signal processing, Classification, HEVEA project", ISSN = "1389-2576", URL = "http://evelyne.lutton.free.fr/Papers/207_GPEMmainSOUMIS.pdf", DOI = "doi:10.1007/s10710-007-9048-4", size = "36 pages", abstract = "Cochlear implants (CI) are devices that become more and more sophisticated and adapted to the need of patients, but at the same time they become more and more difficult to parameterise. After a deaf patient has been surgically implanted, a specialised medical practitioner has to spend hours during months to precisely fit the implant to the patient. This process is a complex one implying two intertwined tasks: the practitioner has to tune the parameters of the device (optimisation) while the patient's brain needs to adapt to the new data he receives (learning). This paper presents a study that intends to make the implant more adaptable to environment (auditive ecology) and to simplify the process of fitting. Real experiments on volunteer implanted patients are presented, that show the efficiency of interactive evolution for this purpose.", notes = "EASEA, GALib, PDA, IEA also known as \cite{PL-al-2007}", } @Misc{oai:HAL:tel-02429815v1, author = "Pierrick Legrand", title = "Artificial evolution, fractal analysis and applications", school = "Universite de Bordeaux", year = "2019", type = "l'habilitation a diriger des recherches", address = "UMR CNRS 5251, Bordeaux, France", month = "28 " # nov, keywords = "genetic algorithms, genetic programming", identifier = "tel-02429815", language = "en", oai = "oai:HAL:tel-02429815v1", rights = "info:eu-repo/semantics/OpenAccess", URL = "https://hal.inria.fr/tel-02429815/document", URL = "https://hal.inria.fr/tel-02429815/file/HDR_depot_HAL_20200108_biffe.pdf", size = "426 pages", abstract = "This document contains a selection of research works to which I have contributed. It is structured around two themes, artificial evolution and signal regularity analysis and consists of three main parts: Part I: Artificial evolution, Part II: Estimation of signal regularity and Part III: Applications, combination of signal processing, fractal analysis and artificial evolution. In order to set the context and explain the coherence of the rest of the document, this manuscript begins with an introduction, Chapter 1, providing a list of collaborators and of the research projects carried out. Theoretical contributions focus on two areas: evolutionary algorithms and the measurement of signal regularity and are presented in Part I and Part II respectively. These two themes are then exploited and applied to real problems in Part III. Part I, Artificial Evolution, consists of 8 chapters. Chapter 2 contains a brief presentation of various types of evolutionary algorithms (genetic algorithms, evolutionary strategies and genetic programming) and presents some contributions in this area, which will be detailed later in the document. Chapter 3, entitled Prediction of Expected Performance for a Genetic Programming Classifier proposes a method to predict the expected performance for a genetic programming (GP) classifier without having to run the program or sample potential solutions in the research space. For a given classification problem, a pre-processing step to simplify the feature extraction process is proposed. Then the step of extracting the characteristics of the problem is performed. Finally, a PEP (prediction of expected performance) model is used, which takes the characteristics of the problem as input and produces the predicted classification error on the test set as output. To build the PEP model, a supervised learning method with a GP is used. Then, to refine this work, an approach using several PEP models is developed, each now becoming a specialized predictors of expected performance (SPEP) specialized for a particular group of problems. It appears that the PEP and SPEP models were able to accurately predict the performance of a GP-classifier and that the SPEP approach gave the best results. Chapter 4, entitled A comparison of fitness-case sampling methods for genetic programming presents an extensive comparative study of four fitness-case sampling methods, namely: Interleaved Sampling, Random Interleaved Sampling, Lexicase Selection and the proposed Keep-Worst Interleaved Sampling. The algorithms are compared on 11 symbolic regression problems and 11 supervised classification problems, using 10 synthetic benchmarks and 12 real-world datasets. They are evaluated based on test performance, overfitting and average program size, comparing them with a standard GP search. The experimental results suggest that fitness-case sampling methods are particularly useful for difficult real-world symbolic regression problems, improving performance, reducing overfitting and limiting code growth. On the other hand, it seems that fitness-case sampling cannot improve upon GP performance when considering supervised binary classification. Chapter 5, entitled Evolving Genetic Programming Classifiers with Novelty Search, deals with a new and unique approach towards search and optimisation, the Novelty Search (NS), where an explicit objective function is replaced by a measure of solution novelty. This chapter proposes a NS-based GP algorithm for supervised classification. Results show that NS can solve real-world classification tasks, the algorithm is validated on real-world benchmarks for binary and multiclass problems. Moreover, two new versions of the NS algorithm are proposed, Probabilistic NS (PNS) and a variant of Minimal Criteria NS (MCNS). The former models the behaviour of each solution as a random vector and eliminates all of the original NS parameters while reducing the computational overhead of the NS algorithm. The latter uses a standard objective function to constrain and bias the search towards high performance solutions. This chapter also discusses the effects of NS on GP search dynam...", } @Article{Leguizamon:2011:GPEM, author = "Guillermo Leguizamon", title = "{Arthur K. Kordon}: Applying computational intelligence: how to create value, {Springer}, 2009, Hardcover, 459 pages, {ISBN}: 978-3-540-69910-1", journal = "Genetic Programming and Evolvable Machines", year = "2011", volume = "12", number = "1", pages = "85--86", month = mar, note = "Book Review", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dR8AJ", DOI = "doi:10.1007/s10710-010-9120-3", size = "2 pages", notes = "Review of \cite{Kordon:book}", affiliation = "Laboratorio de Investigacion y Desarrollo en Inteligencia Computacional, Universidad Nacional de San Luis, San Luis, Argentina", } @Article{DBLP:journals/informaticaLT/LehirecheR04, author = "Ahmed Lehireche and Abdellatif Rahmoun", title = "Evolving in Real Time a Neural Net Controller of Robot-Arm: Track and Evolve", journal = "Informatica, Lith. Acad. Sci.", year = "2004", volume = "15", number = "1", pages = "63--76", keywords = "genetic algorithms, genetic programming, evolutionary engineering, tracking, real time, neighbourhood hypothesis, artificial intelligence", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.532.6397", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.532.6397", URL = "http://www.mii.lt/Informatica/pdf/INFO539.pdf", URL = "http://content.iospress.com/download/informatica/inf15-1-05?id=informatica%2Finf15-1-05", URL = "http://content.iospress.com/articles/informatica/inf15-1-05", timestamp = "Mon, 18 May 2015 17:23:14 +0200", biburl = "http://dblp.uni-trier.de/rec/bib/journals/informaticaLT/LehirecheR04", bibsource = "dblp computer science bibliography, http://dblp.org", size = "14 pages", abstract = "Evolutionary Engineering (EE) is defined to be the art of using evolutionary algorithms approach such as genetic algorithms to build complex systems. This paper deals with a neural net based system. It analyses ability of genetically trained neural nets to control Simulated robot arm, witch tries to track a moving object. In difference from classical Approaches neural network learning is performed on line, i.e., in real time. Usually systems are built/evolved, i.e., genetically trained separately of their use. That is how it is commonly done. It is a fact that evolution process is heavy on time; that is why Real-Time approach is rarely taken into consideration. The results presented in this paper show that such approach (Real-Time EE) is possible. These successful results are essentially due to the continuity of the target's trajectory. In EE terms, we express this by the Neighbourhood Hypothesis (NH) concept.", notes = "Institute of Mathematics and Informatics, Vilnius", } @InProceedings{Lehman:2010:gecco, author = "Joel Lehman and Kenneth O. Stanley", title = "Efficiently evolving programs through the search for novelty", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "837--844", keywords = "genetic algorithms, genetic programming, premature convergence, program bloat, Artificial Intelligence, Learning, Novelty search", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830483.1830638", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.365.4409", URL = "http://eplex.cs.ucf.edu/papers/lehman_gecco10b.pdf", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "A significant challenge in genetic programming is premature convergence to local optima, which often prevents evolution from solving problems. This paper introduces to genetic programming a method that originated in neuroevolution (i.e. the evolution of artificial neural networks) that circumvents the problem of deceptive local optima. The main idea is to search only for behavioural novelty instead of for higher fitness values. Although such novelty search abandons following the gradient of the fitness function, if such gradients are deceptive they may actually occlude paths through the search space towards the objective. Because there are only so many ways to behave, the search for behavioral novelty is often computationally feasible and differs significantly from random search. Counter intuitively, in both a deceptive maze navigation task and the artificial ant benchmark task, genetic programming with novelty search, which ignores the objective, outperforms traditional genetic programming that directly searches for optimal behaviour. Additionally, novelty search evolves smaller program trees in every variation of the test domains. Novelty search thus appears less susceptible to bloat, another significant problem in genetic programming. The conclusion is that novelty search is a viable new tool for efficiently solving some deceptive problems in genetic programming.", notes = "Maze, artificial ant (Santa Fe, Los Altos). LilGP. p838 'novelty search ... ignores the objective'. p839 'novelty needs to be measured'. k-nearest (k=25) phenotype clustering (cf fitness sharing, hall-of-fame coevolution archive, tabu) Mouret. Fitness = Euclidean distance between time sequences of food collection (fixed length vectors?) Pop=1000, 1000 generations, binary tournaments. Santa Fe Ant Wrong number of time steps. Performance similar to GP on Ant, much better on hardest maze. Also known as \cite{1830638} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @InCollection{Lehman:2011:GPTP, author = "Joel Lehman and Kenneth O. Stanley", title = "Novelty Search and the Problem with Objectives", booktitle = "Genetic Programming Theory and Practice IX", year = "2011", editor = "Rick Riolo and Ekaterina Vladislavleva and Jason H. Moore", series = "Genetic and Evolutionary Computation", address = "Ann Arbor, USA", month = "12-14 " # may, publisher = "Springer", chapter = "3", pages = "37--56", keywords = "genetic algorithms, genetic programming, Novelty search, objective-based search, non-objective search, deception, evolutionary computation", isbn13 = "978-1-4614-1769-9", DOI = "doi:10.1007/978-1-4614-1770-5_3", abstract = "By synthesising a growing body of work in search processes that are not driven by explicit objectives, this paper advances the hypothesis that there is a fundamental problem with the dominant paradigm of objective-based search in evolutionary computation and genetic programming: Most ambitious objectives do not illuminate a path to themselves. That is, the gradient of improvement induced by ambitious objectives tends to lead not to the objective itself but instead to dead end local optima. Indirectly supporting this hypothesis, great discoveries often are not the result of objective-driven search. For example, the major inspiration for both evolutionary computation and genetic programming, natural evolution, innovates through an open-ended process that lacks a final objective. Similarly, large-scale cultural evolutionary processes, such as the evolution of technology, mathematics, and art, lack a unified fixed goal. In addition, direct evidence for this hypothesis is presented from a recently-introduced search algorithm called novelty search. Though ignorant of the ultimate objective of search, in many instances novelty search has counter-intuitively outperformed searching directly for the objective, including a wide variety of randomly-generated problems introduced in an experiment in this chapter. Thus a new understanding is beginning to emerge that suggests that searching for a fixed objective, which is the reigning paradigm in evolutionary computation and even machine learning as a whole, may ultimately limit what can be achieved. Yet the liberating implication of this hypothesis argued in this paper is that by embracing search processes that are not driven by explicit objectives, the breadth and depth of what is reachable through evolutionary methods such as genetic programming may be greatly expanded.", notes = "part of \cite{Riolo:2011:GPTP}", affiliation = "Department of EECS, University of Central Florida, Orlando, Florida, USA", } @Article{Lehman:2011:EC, author = "Joel Lehman and Kenneth O. Stanley", title = "Abandoning Objectives: Evolution through the Search for Novelty Alone", journal = "Evolutionary Computation", year = "2011", volume = "19", number = "2", pages = "189--223", month = "Summer", keywords = "genetic algorithms, genetic programming", ISSN = "1063-6560", DOI = "doi:10.1162/EVCO_a_00025", size = "34 pages", abstract = "In evolutionary computation, the fitness function normally measures progress towards an objective in the search space, effectively acting as an objective function. Through deception, such objective functions may actually prevent the objective from being reached. While methods exist to mitigate deception, they leave the underlying pathology untreated: Objective functions themselves may actively misdirect search towards dead ends. This paper proposes an approach to circumventing deception that also yields a new perspective on open-ended evolution: Instead of either explicitly seeking an objective or modelling natural evolution to capture open-endedness, the idea is to simply search for behavioural novelty. Even in an objective-based problem, such novelty search ignores the objective. Because many points in the search space collapse to a single behavior, the search for novelty is often feasible. Furthermore, because there are only so many simple behaviours, the search for novelty leads to increasing complexity. By decoupling open-ended search from artificial life worlds, the search for novelty is applicable to real world problems. Counterintuitively, in the maze navigation and biped walking tasks in this paper, novelty search significantly outperforms objective-based search, suggesting the strange conclusion that some problems are best solved by methods that ignore the objective. The main lesson is the inherent limitation of the objective-based paradigm and the unexploited opportunity to guide search through other means.", } @PhdThesis{Lehman:thesis, author = "Joel Lehman", title = "Evolution Through the Search for Novelty", school = "Department of Electrical Engineering and Computer Science in the College of Engineering and Computer Science at the University of Central Florida", year = "2012", address = "Orlando, Florida, USA", month = "Summer Term", keywords = "genetic algorithms, genetic programming, Novelty search", URL = "http://joellehman.com/lehman-dissertation.pdf", URL = "http://purl.fcla.edu/fcla/etd/CFE0004398", size = "223 pages", abstract = "I present a new approach to evolutionary search called novelty search, wherein only behavioural novelty is rewarded, thereby abstracting evolution as a search for novel forms. This new approach contrasts with the traditional approach of rewarding progress towards the objective through an objective function. Although they are designed to light a path to the objective, objective functions can instead deceive search into converging to dead ends called local optima. As a significant problem in evolutionary computation, deception has inspired many techniques designed to mitigate it. However, nearly all such methods are still ultimately susceptible to deceptive local optima because they still measure progress with respect to the objective, which this dissertation will show is often a broken compass. Furthermore, although novelty search completely abandons the objective, it counter intuitively often out-performs methods that search directly for the objective in deceptive tasks and can induce evolutionary dynamics closer in spirit to natural evolution. The main contributions are to (1) introduce novelty search, an example of an effective search method that is not guided by actively measuring or encouraging objective progress; (2) validate novelty search by applying it to biped locomotion; (3) demonstrate novelty search's benefits for evolvability (i.e. the ability of an organism to further evolve) in a variety of domains; (4) introduce an extension of novelty search called minimal criteria novelty search that brings a new abstraction of natural evolution to evolutionary computation (i.e. evolution as a search for many ways of meeting the minimal criteria of life); (5) present a second extension of novelty search called novelty search with local competition that abstracts evolution instead as a process driven towards diversity with competition playing a subservient role; and (6) evolve a diversity of functional virtual creatures in a single run as a culminating application of novelty search with local competition. Overall these contributions establish novelty search as an important new research direction for the field of evolutionary computation.", notes = "Major Professor: Kenneth O. Stanley Public - Allow Worldwide Access CFE0004398 Graduation Date 2012-08-01 Release Date 2012-08-15 Chapter 5 on GP, Santa Fe Ant", } @Article{Lehman:2015:GPEM, author = "Joel Lehman", title = "Patricia Vargas, Ezequiel Di Paolo, Inman Harvey, and Phil Husbands (eds), The Horizons of Evolutionary Robotics, The MIT Press, 2014, ISBN: 978-0-262-02676-5, Hardcover book, 302 pages", journal = "Genetic Programming and Evolvable Machines", year = "2015", volume = "16", number = "3", pages = "393--395", month = sep, note = "Book review", keywords = "genetic algorithms", ISSN = "1389-2576", URL = "https://rdcu.be/dR8A2", DOI = "doi:10.1007/s10710-015-9245-5", size = "3 pages", notes = "no mention of GP. p395 'The chapters have been written by foundational researchers, who provide insights into what they believe are the most interesting issues and developments for the future of this rich and ambitious field.'", } @Misc{DBLP:journals/corr/abs-1803-03453, author = "Joel Lehman and Jeff Clune and Dusan Misevic and Christoph Adami and Julie Beaulieu and Peter J. Bentley and Samuel Bernard and Guillaume Beslon and David M. Bryson and Patryk Chrabaszcz and Nick Cheney and Antoine Cully and Stephane Doncieux and Fred C. Dyer and Kai Olav Ellefsen and Robert Feldt and Stephan Fischer and Stephanie Forrest and Antoine Frenoy and Christian Gagne and Leni K. Le Goff and Laura M. Grabowski and Babak Hodjat and Frank Hutter and Laurent Keller and Carole Knibbe and Peter Krcah and Richard E. Lenski and Hod Lipson and Robert MacCurdy and Carlos Maestre and Risto Miikkulainen and Sara Mitri and David E. Moriarty and Jean-Baptiste Mouret and Anh Nguyen and Charles Ofria and Marc Parizeau and David P. Parsons and Robert T. Pennock and William F. Punch and Thomas S. Ray and Marc Schoenauer and Eric Schulte and Karl Sims and Kenneth O. Stanley and Francois Taddei and Danesh Tarapore and Simon Thibault and Westley Weimer and Richard Watson and Jason Yosinksi", title = "The Surprising Creativity of Digital Evolution: {A} Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities", howpublished = "arXiv", year = "2018", month = "14 " # nov, note = "v3", keywords = "genetic algorithms, genetic programming", archiveprefix = "arXiv", eprint = "1803.03453", timestamp = "Wed, 11 Apr 2018 07:46:55 +0200", biburl = "https://dblp.org/rec/bib/journals/corr/abs-1803-03453", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "http://arxiv.org/abs/1803.03453", size = "32 pages", abstract = "Biological evolution provides a creative fount of complex and subtle adaptations, often surprising the scientists who discover them. However, because evolution is an algorithmic process that transcends the substrate in which it occurs, evolution's creativity is not limited to nature. Indeed, many researchers in the field of digital evolution have observed their evolving algorithms and organisms subverting their intentions, exposing unrecognised bugs in their code, producing unexpected adaptations, or exhibiting outcomes uncannily convergent with ones in nature. Such stories routinely reveal creativity by evolution in these digital worlds, but they rarely fit into the standard scientific narrative. Instead they are often treated as mere obstacles to be overcome, rather than results that warrant study in their own right. The stories themselves are traded among researchers through oral tradition, but that mode of information transmission is inefficient and prone to error and outright loss. Moreover, the fact that these stories tend to be shared only among practitioners means that many natural scientists do not realize how interesting and lifelike digital organisms are and how natural their evolution can be. To our knowledge, no collection of such anecdotes has been published before. This paper is the crowd-sourced product of researchers in the fields of artificial life and evolutionary computation who have provided first-hand accounts of such cases. It thus serves as a written, fact-checked collection of scientifically important and even entertaining stories. In doing so we also present here substantial evidence that the existence and importance of evolutionary surprises extends beyond the natural world, and may indeed be a universal property of all complex evolving systems.", notes = "Also known as \cite{Lehman:etal:2018:Surprising} See \cite{Lehman:2020:alife}", } @InProceedings{Lehman:2019:GPTP, author = "Joel Lehman", title = "Evolutionary Computation and {AI} Safety", booktitle = "Genetic Programming Theory and Practice XVII", year = "2019", editor = "Wolfgang Banzhaf and Erik Goodman and Leigh Sheneman and Leonardo Trujillo and Bill Worzel", pages = "181--200", address = "East Lansing, MI, USA", month = "16-19 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-030-39957-3", DOI = "doi:10.1007/978-3-030-39958-0_10", abstract = "Recent developments in artificial intelligence and machine learning have spurred interest in the growing field of AI safety, which studies how to prevent human-harming accidents when deploying AI systems. This paper thus explores the intersection of AI safety with evolutionary computation, to show how safety issues arise in evolutionary computation and how understanding from evolutionary computational and biological evolution can inform the broader study of AI safety.", notes = "Part of \cite{Banzhaf:2019:GPTP}, published after the workshop", } @Article{Lehman:2020:alife, author = "Joel Lehman and Jeff Clune and Dusan Misevic and Christoph Adami and Lee Altenberg and Julie Beaulieu and Peter J. Bentley and Samuel Bernard and Guillaume Beslon and David M. Bryson and Nick Cheney and Patryk Chrabaszcz and Antoine Cully and Stephane Doncieux and Fred C. Dyer and Kai Olav Ellefsen and Robert Feldt and Stephan Fischer and Stephanie Forrest and Antoine Frenoy and Christian Gagne and Leni {Le Goff} and Laura M. Grabowski and Babak Hodjat and Frank Hutter and Laurent Keller and Carole Knibbe and Peter Krcah and Richard E. Lenski and Hod Lipson and Robert MacCurdy and Carlos Maestre and Risto Miikkulainen and Sara Mitri and David E. Moriarty and Jean-Baptiste Mouret and Anh Nguyen and Charles Ofria and Marc Parizeau and David Parsons and Robert T. Pennock and William F. Punch and Thomas S. Ray and Marc Schoenauer and Eric Schulte and Karl Sims and Kenneth O. Stanley and Francois Taddei and Danesh Tarapore and Simon Thibault and Richard Watson and Westley Weimer and Jason Yosinski", title = "The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities", journal = "Artificial Life", year = "2020", volume = "26", number = "2", pages = "274--306", month = "Spring", keywords = "genetic algorithms, genetic programming, Surprise, creativity, digital evolution, experimental evolution, evolutionary computation", ISSN = "1064-5462", DOI = "doi:10.1162/artl_a_00319", size = "33 pages", abstract = "Evolution provides a creative fount of complex and subtle adaptations that often surprise the scientists who discover them. However, the creativity of evolution is not limited to the natural world: Artificial organisms evolving in computational environments have also elicited surprise and wonder from the researchers studying them. The process of evolution is an algorithmic process that transcends the substrate in which it occurs. Indeed, many researchers in the field of digital evolution can provide examples of how their evolving algorithms and organisms have creatively subverted their expectations or intentions, exposed unrecognized bugs in their code, produced unexpectedly adaptations, or engaged in behaviors and outcomes, uncannily convergent with ones found in nature. Such stories routinely reveal surprise and creativity by evolution in these digital worlds, but they rarely fit into the standard scientific narrative. Instead they are often treated as mere obstacles to be overcome, rather than results that warrant study in their own right. Bugs are fixed, experiments are refocused, and one-off surprises are collapsed into a single data point. The stories themselves are traded among researchers through oral tradition, but that mode of information transmission is inefficient and prone to error and outright loss. Moreover, the fact that these stories tend to be shared only among practitioners means that many natural scientists do not realize how interesting and life like digital organisms are and how natural their evolution can be. To our knowledge, no collection of such anecdotes has been published before. This article is the crowd-sourced product of researchers in the fields of artificial life and evolutionary computation who have provided first-hand accounts of such cases. It thus serves as a written, fact-checked collection of scientifically important and even entertaining stories. In doing so we also present here substantial evidence that the existence and importance of evolutionary surprises extends beyond the natural world, and may indeed be a universal property of all complex evolving systems.", } @Misc{Lehman:2022:ELM, author = "Joel Lehman and Jonathan Gordon and Shawn Jain and Kamal Ndousse and Cathy Yeh and Kenneth O. Stanley", title = "Evolution through Large Models", howpublished = "ArXiv", year = "2022", month = "17 " # jun, keywords = "genetic algorithms, genetic programming, genetic improvement, ELM, Open-endedness, reinforcementlearning, automated code generation, APR, GPT-3, GitHub, Python, Seed Source Code", URL = "https://arxiv.org/abs/2206.08896", video_url = "https://y2u.be/8C2K5fk28HI", code_url = "https://github.com/CarperAI/OpenELM", size = "58 pages", abstract = "large language models (LLMs) trained to generate code can vastly improve the effectiveness of mutation operators applied to programs in genetic programming (GP). Because such LLMs benefit from training data that includes sequential changes and modifications, they can approximate likely changes that humans would make. To highlight the breadth of implications of such evolution through large models (ELM), in the main experiment ELM combined with MAP-Elites generates hundreds of thousands of functional examples of Python programs that output working ambulating robots in the Sodarace domain, which the original LLM had never seen in pre-training. These examples then help to bootstrap training a new conditional language model that can output the right walker for a particular terrain. The ability to bootstrap new models that can output appropriate artifacts for a given context in a domain where zero training data was previously available carries implications for open-endedness, deep learning, and reinforcement learning. These implications are explored here in depth in the hope of inspiring new directions of research now opened up by ELM.", notes = "See also \cite{Lehman:2023:hbeml} p2 large language models 'LLM can serve as a highly sophisticated [GP] mutation operator' p7 300000000 parameter diff model 4-Parity, Quadratic, seeded with bugs. QD score. MAP-Elites. Sodaracers two diemnsioal mobile robot invested field. Invention Pipeline. CPPN-based encoding. terrain embedding network, ResNets. Proximal policy optimization (PPO), GAE. DCT OpenAI", } @InProceedings{Lehman:2023:GPTP, author = "Herbie Bradley and Honglu Fan and Theodoros Galanos and Ryan Zhou and Daniel Scott and Joel Lehman", title = "The {OpenELM} Library: Leveraging Progress in Language Models for Novel Evolutionary Algorithms", booktitle = "Genetic Programming Theory and Practice XX", year = "2023", editor = "Stephan Winkler and Leonardo Trujillo and Charles Ofria and Ting Hu", series = "Genetic and Evolutionary Computation", pages = "177--201", address = "Michigan State University, USA", month = jun # " 1-3", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-981-99-8412-1", DOI = "doi:10.1007/978-981-99-8413-8_10", code_url = "https://github.com/CarperAI/OpenELM", abstract = "in recent years, Large Language Models (LLMs) have rapidly progressed in their capabilities in natural language processing (NLP) tasks, which have interestingly grown in scope to include generating computer programs. Indeed, recent studies have demonstrated how LLMs can enable highly proficient genetic programming (GP) algorithms and novel evolutionary algorithms more broadly. Motivated by these opportunities, this paper introduces OpenELM, an open-source Python library for designing evolutionary algorithms that leverage LLMs to intelligently generate variation, as well as to assess fitness and measures of diversity. The library includes implementations of several variation operators, and is designed to accommodate those with limited compute resources, by enabling fast inference, being runnable through hosted notebooks (such as Google Colab), and allowing for API-based LLMs to be used instead of local models run on GPUs. Additionally, OpenELM includes a variety of domain implementations for easy experimentation and adaptation, including several GP domains. The hope is to help researchers easily develop new approaches and applications within the nascent and largely unexplored paradigm of evolutionary algorithms that leverage LLMs.", notes = " https://huggingface.co/CarperAI/ Part of \cite{Hu:2023:GPTP} published after the workshop in 2024", } @InCollection{Lehman:2023:hbeml, author = "Joel Lehman and Jonathan Gordon and Shawn Jain and Kamal Ndousse and Cathy Yeh and Kenneth O. Stanley", title = "Evolution Through Large Models", booktitle = "Handbook of Evolutionary Machine Learning", publisher = "Springer Nature", year = "2023", editor = "Wolfgang Banzhaf and Penousal Machado and Mengjie Zhang", series = "Genetic and Evolutionary Computation (GEVO)", pages = "331--366", address = "Singapore", edition = "1", month = "2 " # nov, keywords = "genetic algorithms, genetic programming, ANN, LLM, ELM, Sodarace mobile robot", isbn13 = "978-981-99-3813-1", ISSN = "1932-0167", URL = "https://openai.com/index/evolution-through-large-models/", DOI = "doi:10.1007/978-981-99-3814-8_11", abstract = "This chapter pursues the insight that large language models (LLMs) trained to generate code can vastly improve the effectiveness of mutation operators applied to programs in genetic programming (GP). Because such LLMs benefit from training data that includes sequential changes and modifications, they can approximate likely changes that humans would make. To highlight the breadth of implications of such evolution through large models (ELM), in Evolution through Large Models the main experiment ELM combined with MAP-Elites generates hundreds of thousands of functional examples of Python programs that output working ambulating robots in the Sodarace domain, which the original LLM had never seen in pretraining. These examples then help to bootstrap training a new conditional language model that can output the right walker for a particular terrain. The ability to bootstrap new models that can output appropriate artifacts for a given context in a domain where zero training data was previously available carries implications for open-endedness, deep learning, and reinforcement learning Learning. These implications are explored here in depth in the hope of inspiring new directions of research now opened up by ELM.", notes = "Also known as \cite{Lehman2024} See also \cite{Lehman:2022:ELM} OpenAI", } @InProceedings{Lehmann:2007:MLDM, author = "Jens Lehmann", title = "Hybrid Learning of Ontology Classes", booktitle = "Proceedings of the 5th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2007", year = "2007", editor = "Petra Perner", volume = "4571", series = "Lecture Notes in Computer Science", pages = "883--898", month = jul # " 18-20", publisher = "Springer", keywords = "genetic algorithms, genetic programming", language = "English", isbn13 = "978-3-540-73498-7", DOI = "doi:10.1007/978-3-540-73499-4_66", size = "16 pages", abstract = "Description logics have emerged as one of the most successful formalisms for knowledge representation and reasoning. They are now widely used as a basis for ontologies in the Semantic Web. To extend and analyse ontologies, automated methods for knowledge acquisition and mining are being sought for. Despite its importance for knowledge engineers, the learning problem in description logics has not been investigated as deeply as its counterpart for logic programs. We propose the novel idea of applying evolutionary inspired methods to solve this task. In particular, we show how Genetic Programming can be applied to the learning problem in description logics and combine it with techniques from Inductive Logic Programming. We base our algorithm on thorough theoretical foundations and present a preliminary evaluation.", } @PhdThesis{jl_2010/phd_thesis, title = "Learning {OWL} Class Expressions", author = "Jens Lehmann", school = "University of Leipzig", year = "2010", address = "Germany", keywords = "genetic algorithms, genetic programming", URL = "http://jens-lehmann.org/files/2010/phd_thesis.pdf", size = "223 pages", abstract = "With the advent of the Semantic Web and Semantic Technologies, ontologies have become one of the most prominent paradigms for knowledge representation and reasoning. The popular ontology language OWL, based on description logics, became a W3C recommendation in 2004 and a standard for modelling ontologies on the Web. In the meantime, many studies and applications using OWL have been reported in research and industrial environments, many of which go beyond Internet usage and employ the power of ontological modelling in other fields such as biology, medicine, software engineering, knowledge management, and cognitive systems. However, recent progress in the field faces a lack of well-structured ontologies with large amounts of instance data due to the fact that engineering such ontologies requires a considerable investment of resources. Nowadays, knowledge bases often provide large volumes of data without sophisticated schemata. Hence, methods for automated schema acquisition and maintenance are sought. Schema acquisition is closely related to solving typical classification problems in machine learning, e.g. the detection of chemical compounds causing cancer. In this work, we investigate both, the underlying machine learning techniques and their application to knowledge acquisition in the Semantic Web. In order to leverage machine-learning approaches for solving these tasks, it is required to develop methods and tools for learning concepts in description logics or, equivalently, class expressions in OWL. In this thesis, it is shown that methods from Inductive Logic Programming (ILP) are applicable to learning in description logic knowledge bases. The results provide foundations for the semi-automatic creation and maintenance of OWL ontologies, in particular in cases when extensional information (i.e. facts, instance data) is abundantly available, while corresponding intensional information (schema) is missing or not expressive enough to allow powerful reasoning over the ontology in a useful way. Such situations often occur when extracting knowledge from different sources, e.g. databases, or in collaborative knowledge engineering scenarios, e.g. using semantic wikis. It can be argued that being able to learn OWL class expressions is a step towards enriching OWL knowledge bases in order to enable powerful reasoning, consistency checking, and improved querying possibilities. In particular, plugins for OWL ontology editors based on learning methods are developed and evaluated in this work. The developed algorithms are not restricted to ontology engineering and can handle other learning problems. Indeed, they lend themselves to generic use in machine learning in the same way as ILP systems do. The main difference, however, is the employed knowledge representation paradigm: ILP traditionally uses logic programs for knowledge representation, whereas this work rests on description logics and OWL. This difference is crucial when considering Semantic Web applications as target use cases, as such applications hinge centrally on the chosen knowledge representation format for knowledge interchange and integration. The work in this thesis can be understood as a broadening of the scope of research and applications of ILP methods. This goal is particularly important since the number of OWL-based systems is already increasing rapidly and can be expected to grow further in the future. The thesis starts by establishing the necessary theoretical basis and continues with the specification of algorithms. It also contains their evaluation and, finally, presents a number of application scenarios. The research contributions of this work are threefold:", abstract = "The first contribution is a complete analysis of desirable properties of refinement operators in description logics. Refinement operators are used to traverse the target search space and are, therefore, a crucial element in many learning algorithms. Their properties (completeness, weak completeness, properness, redundancy, infinity, minimality) indicate whether a refinement operator is suitable for being employed in a learning algorithm. The key research question is which of those properties can be combined. It is shown that there is no ideal, i.e. complete, proper, and finite, refinement operator for expressive description logics, which indicates that learning in description logics is a challenging machine learning task. A number of other new results for different property combinations are also proven. The need for these investigations has already been expressed in several articles prior to this PhD work. The theoretical limitations, which were shown as a result of these investigations, provide clear criteria for the design of refinement operators. In the analysis, as few assumptions as possible were made regarding the used description language. The second contribution is the development of two refinement operators. The first operator supports a wide range of concept constructors and it is shown that it is complete and can be extended to a proper operator. It is the most expressive operator designed for a description language so far. The second operator uses the light-weight language EL and is weakly complete, proper, and finite. It is straight-forward to extend it to an ideal operator, if required. It is the first published ideal refinement operator in description logics. While the two operators differ a lot in their technical details, they both use background knowledge efficiently. The third contribution is the actual learning algorithms using the introduced operators. New redundancy elimination and infinity-handling techniques are introduced in these algorithms. According to the evaluation, the algorithms produce very readable solutions, while their accuracy is competitive with the state-of-the-art in machine learning. Several optimisations for achieving scalability of the introduced algorithms are described, including a knowledge base fragment selection approach, a dedicated reasoning procedure, and a stochastic coverage computation approach. The research contributions are evaluated on benchmark problems and in use cases. Standard statistical measurements such as cross validation and significance tests show that the approaches are very competitive. Furthermore, the ontology engineering case study provides evidence that the described algorithms can solve the target problems in practice. A major outcome of the doctoral work is the DL-Learner framework. It provides the source code for all algorithms and examples as open-source and has been incorporated in other projects.", notes = "Some experiments on hybrid GP. supervisors: Prof. Klaus-Peter F{\"a}hnrich, Dr. S{\"o}ren Auer", } @InProceedings{lei:1999:T, author = "Wang Lei and Jiao Licheng", title = "The immune evolutionary programming and its convergence", booktitle = "Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference", year = "1999", editor = "Scott Brave and Annie S. Wu", pages = "175--183", address = "Orlando, Florida, USA", month = "13 " # jul, keywords = "immune evolutionary programming, antibody, TSP", notes = "GECCO-99LB", } @InProceedings{leier:2003:gecco, author = "Andr{\'e} Leier and Wolfgang Banzhaf", title = "Evolving {Hogg's} Quantum Algorithm Using Linear-Tree {GP}", booktitle = "Genetic and Evolutionary Computation -- GECCO-2003", editor = "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller", year = "2003", pages = "390--400", address = "Chicago", publisher_address = "Berlin", month = "12-16 " # jul, volume = "2723", series = "LNCS", ISBN = "3-540-40602-6", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, DNA, Molecular, and Quantum Computing", DOI = "doi:10.1007/3-540-45105-6_48", abstract = "Intermediate measurements in quantum circuits compare to conditional branchings in programming languages. Due to this, quantum circuits have a natural linear-tree structure. In this paper a Genetic Programming system based on linear-tree genome structures developed for the purpose of automatic quantum circuit design is introduced. It was applied to instances of the 1-SAT problem, resulting in evidently and {"}visibly{"} scalable quantum algorithms, which correspond to Hogg's quantum algorithm.", notes = "GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)", } @InProceedings{Leier:2003:Etssoqp, author = "Andre Leier and Wolfgang Banzhaf", title = "Exploring the search space of quantum programs", booktitle = "Proceedings of the 2003 Congress on Evolutionary Computation CEC2003", editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon", pages = "170--177", year = "2003", publisher = "IEEE Press", address = "Canberra", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "8-12 " # dec, organisation = "IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", volume = "1", keywords = "genetic algorithms, genetic programming, Circuit synthesis, Computer science, Constraint theory, Genetic mutations, Information analysis, Polynomials, Quantum computing, Quantum mechanics, Space exploration, evolutionary computation, quantum computing, search problems, Deutsch-Josza problem, autocorrelation characteristics, evolutionary search, fitness landscapes, information measures, mutation landscapes, quantum circuit evolution, quantum program evolution, search space", ISBN = "0-7803-7804-0", URL = "http://www.cs.mun.ca/~leier/publications/cec03.pdf", DOI = "doi:10.1109/CEC.2003.1299571", size = "8 pages", abstract = "This work is a first study of search spaces and fitness landscapes in the context of quantum program evolution. Considering small instances of the Deutsch-Josza problem as a staring point for explorations of quantum program search spaces, we analyse the structure of mutation landscapes using autocorrelation characteristics and information measures. Our motivation is to obtain insights into the relationship between landscape characteristic and quantum circuit evolution with the aim to improve the efficiency of evolutionary search.", notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE. H,Rx,Ry,NOT,CNOT,Ph quantum gates. autocorrelation, information content, partial information content, ", } @PhdThesis{oai:eldorado:0x0007ce5c, title = "Evolution of Quantum Algorithms using Genetic Programming", author = "Andr{\'e} Leier", school = "Dortmund University", year = "2004", month = jul # "~21", annote = "Fachbereich 4; Universit{\"a}t Dortmund", contributor = "W. Banzhaf and Fachbereich 4 and D. Sieling", language = "ENG", oai = "oai:eldorado:0x0007ce5c", rights = "These documents can be used freely according to copyright laws. They can be printed freely. It is not allowed to distribute them further on.", address = "Germany", keywords = "genetic algorithms, genetic programming, Quantum Computing, Quantum Circuits, Evolutionary Circuit Design", URL = "https://eldorado.uni-dortmund.de/bitstream/2003/2745/1/Leierunt.pdf", URL = "http://hdl.handle.net/2003/2745", size = "199 pages", abstract = "Automatic quantum circuit design is motivated by the difficulties in manual design, because quantum algorithms are highly non-intuitive and practical quantum computer hardware is not yet available. Thus, quantum computers have to be simulated on classical hardware which naturally entails an exponential growth of computational costs and allows only to simulate small quantum systems, i. e., with only few qubits. Huge search spaces render evolutionary approaches nearly unable to achieve breakthrough solutions in the development of new quantum algorithms. Consequently, at present we must be content to evolve essentially already existing (black-box) quantum algorithms. This thesis presents empirical results on the evolution of quantum circuits using genetic programming. For that purpose, a linear and a linear-tree GP system (allowing intermediate measurements) with integrated quantum computer simulator were implemented. Their practicality in evolving quantum circuits is shown in different experiments for 1-SAT (solutions act like Hogg's algorithm) and the Deutsch-Jozsa problem. These experiments confirm that the evolution of quantum circuits is practically feasible only for sufficiently small problem instances. In this context, scalability and the detection of scalability becomes very important. It is shown that scalable quantum circuits are evolvable to a certain degree: a general quantum circuit can be inferred manually from the evolved solutions for small instances of the given problem. Besides, further experiments indicate that 're-evolution' is effective for the evolution of scalable quantum circuits. With this method the start population of a problem instance is inoculated with evolved solutions for a smaller problem instance. Furthermore, investigations of fitness landscapes and selection strategies are made, with the aim of improving the efficiency of evolutionary search. A notable result is that using the crossover operator damages rather than benefits evolution of quantum circuits.", abstract = "Quantenalgorithmen sind hochgradig unintuitiv und einsetzbare Quantenrechner sind (noch) nicht verf{\"{u}}gbar. Dies erschwert den manuellen Entwurf von Quantenalgorithmen und motiviert die Suche nach Techniken zum computerunterst{\"{u}}tzten bzw. automatischen Entwurf. Simulationen von Quantenschaltkreisen (QS) auf konventionellen Rechnern sind aber leider sehr rechenintensiv. Aufgrund der (in der Anzahl der Qubits) exponentiell anwachsenden Kosten ist nur eine Simulation kleiner Quantensysteme (mit wenig Qubits) akzeptabel. Zudem sind die Suchr{\"{a}}ume quasi beliebig gro\ss, worin wohl auch begr{\"{u}}ndet liegt, warum der evolution{\"{a}}re Ansatz bislang nicht zu einem Durchbruch in der Entwicklung neuer Quantenalgorithmen f{\"{u}}hrte. Zum gegenw{\"{a}}rtigen Zeitpunkt muss man sich daher mit der Evolution bekannter (black-box) Quantenalgorithmen begn{\"{u}}gen. Die vorliegende Arbeit pr{\"{a}}sentiert empirische Ergebnisse zur Evolution von QS mit Hilfe des Genetischen Programmierens. F{\"{u}}r die Experimente wurde ein effizienter Quantensimulator entwickelt, der in einem umgebenden GP-System zum Einsatz kommt. Dabei wurden zun{\"{a}}chst linear-tree (erlaubt Zwischenmessungen), sp{\"{a}}ter auch rein lineare Genom-Strukturen f{\"{u}}r die Programmrepr{\"{a}}sentation verwendet. Die Evolvierbarkeit von QS wird an Hand von Experimenten f{\"{u}}r kleine Probleminstanzen des 1-SAT Problems und des Deutsch-Jozsa Problems gezeigt. Die Experimente best{\"{a}}tigen, dass die Evolution von QS nur f{\"{u}}r gen{\"{u}}gend kleine Probleminstanzen praktisch machbar ist. Vor diesem Hintergrund ist gerade die Skalierbarkeit von QS besonders wichtig. Es wird gezeigt, dass skalierbare QS bis zu einem gewissen Grad evolviert werden konnen. Dabei wird ein allgemeiner Schaltkreis von den evolvierten Losungen f{\"{u}}r sehr kleine Probleminstanzen abgeleitet. Die Methode der 'Vorevolution', so belegen weitere Experimente, ist f{\"{u}}r die Evolution skalierbarer QS wirksam einsetzbar. Bei dieser Methode werden der Startpopulation einer Probleminstanz bereits evolvierte Losungen einer kleineren Probleminstanz 'eingeimpft'. Ferner werden Fitnesslandschaften untersucht und ein Vergleich von Selektionsstrategien angestellt, mit dem Ziel, durch diese Erkenntnisse zu einer Effizienzsteigerung der evolution{\"{a}}ren Suche zu gelangen. Dabei ist ein beachtenswertes Resultat, dass die Verwendung eines Crossover Operators der Evolution von QS eher schadet, als ihr n{\"{u}}tzt.", } @InProceedings{eurogp06:LeierKuoBanzhafBurrage, author = "Andr\'{e} Leier and P. Dwight Kuo and Wolfgang Banzhaf and Kevin Burrage", title = "Evolving noisy oscillatory dynamics in genetic regulatory networks", editor = "Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art", booktitle = "Proceedings of the 9th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3905", year = "2006", address = "Budapest, Hungary", month = "10 - 12 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-33143-3", pages = "290--299", URL = "http://www.cs.mun.ca/~banzhaf/papers/eurogp06.pdf", DOI = "doi:10.1007/11729976_26", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "We introduce a genetic programming (GP) approach for evolving genetic networks that demonstrate desired dynamics when simulated as a discrete stochastic process. Our representation of genetic networks is based on a biochemical reaction model including key elements such as transcription, translation and post-translational modifications. The stochastic, reaction-based GP system is similar but not identical with algorithmic chemistries. We evolved genetic networks with noisy oscillatory dynamics. The results show the practicality of evolving particular dynamics in gene regulatory networks when modelled with intrinsic noise.", notes = "Part of \cite{collet:2006:GP} EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006", } @Article{Leier:2007:ACS, author = "Andre Leier and Dwight Kuo and Wolfgang Banzhaf", title = "Analysis of preferential network motif generation in an artificial regulatory network model created by duplication and divergence", journal = "Advances in Complex Systems", year = "2007", volume = "10", number = "2", pages = "155--172", month = jun, keywords = "genetic algorithms, genetic programming, Gene duplication, network motif, gene regulatory networks, artificial regulatory networks", ISSN = "0219-5259", DOI = "doi:10.1142/S0219525907000994", abstract = "Previous studies on network topology of artificial gene regulatory networks created by whole genome duplication and divergence processes show subgraph distributions similar to gene regulatory networks found in nature. In particular, certain network motifs are prominent in both types of networks. In this contribution, we analyze how duplication and divergence processes influence network topology and preferential generation of network motifs. We show that in the artificial model such preference originates from a stronger preservation of protein than regulatory sites by duplication and divergence. If these results can be transferred to regulatory networks in nature, we can infer that after duplication the paralogous transcription factor binding site is less likely to be preserved than the corresponding paralogous protein.", notes = "Biological Systems", } @Article{Leier:2014:GPEM, author = "Andre Leier", title = "Emergence in simulated evolution", journal = "Genetic Programming and Evolvable Machines", year = "2014", volume = "15", number = "1", pages = "79--81", month = mar, keywords = "genetic algorithms, genetic programming, Emergence, Evolution, Multiscalarity, Stochasticity", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-013-9201-1", size = "3 pages", abstract = "Wolfgang Banzhaf's essay elegantly shows how emergence can be observed within the genetic programming (GP) framework. His work provides inspiration to employ GP for investigating emerging phenomena in biological evolution. This commentary attempts to further stimulate such development towards a more realistic GP framework by incorporating features observed in natural evolution. The examples discussed are multiscalarity, lower-level stochasticity, and co-evolution of repair or protection mechanisms.", notes = "\cite{Banzhaf:2014:GPEM}", } @InProceedings{Leitao:2013:GECCO, author = "Antonio Leitao and Penousal Machado", title = "Self-adaptive mate choice for cluster geometry optimization", booktitle = "GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference", year = "2013", editor = "Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi", isbn13 = "978-1-4503-1963-8", pages = "957--964", keywords = "genetic algorithms, genetic programming", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2463372.2463494", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Sexual Selection through Mate Choice has, over the past few decades, attracted the attention of researchers from various fields. They have gathered numerous supporting evidence, establishing Mate Choice as a major driving force of evolution, capable of shaping complex traits and behaviours. Despite its wide acceptance and relevance across various research fields, the impact of Mate Choice in Evolutionary Computation is still far from understood, both regarding performance and behaviour. In this study we describe a nature-inspired self-adaptive mate choice model, relying on a Genetic Programming representation tailored for the optimisation of Morse clusters, a relevant and widely accepted problem for benchmarking new algorithms, which provides a set of hard test instances. The model is coupled with a state-of-the-art hybrid steady-state approach and both its performance and behaviour are assessed with a particular interest on the replacement strategy's acceptance rate and diversity handling.", notes = "Also known as \cite{2463494} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)", } @InCollection{Leitao:2015:hbgpa, author = "Antonio Leitao and Penousal Machado", title = "Mate Choice in Evolutionary Computation", booktitle = "Handbook of Genetic Programming Applications", publisher = "Springer", year = "2015", editor = "Amir H. Gandomi and Amir H. Alavi and Conor Ryan", chapter = "7", pages = "155--177", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-20882-4", DOI = "doi:10.1007/978-3-319-20883-1_7", abstract = "Darwin considered two major theories that account for the evolution of species. Natural Selection was described as the result of competition within or between species affecting its individuals relative survival ability, while Sexual Selection was described as the result of competition within species affecting its individuals relative rate of reproduction. This theory emerged from Darwin's necessity to explain complex ornamentation and behaviour that while being costly to maintain, bring no apparent survival advantages to individuals. Mate Choice is one of the processes described by Darwin's theory of Sexual Selection as responsible for the emergence of a wide range of characteristics such as the peacock's tail, bright coloration in different species, certain bird singing or extravagant courtship behaviours. As the theory attracted more and more researchers, the role of Mate Choice has been extensively discussed and backed up by supporting evidence, showing how a force which adapts individuals not to their habitat but to each other can have a strong impact on the evolution of species. While Mate Choice is highly regarded in many research fields, its role in Evolutionary Computation (EC) is still far from being explored and understood. Following Darwin's ideas on Mate Choice, as well as Fisher's contributions regarding the heritability of mating preferences, we propose computational models of Mate Choice, which follow three key rules: individuals choose their mating partners based on their perception mechanisms and mating preferences; mating preferences are heritable the same way as any other trait; Mate Choice introduces its own selection pressure but is subjected to selection pressure itself. The use of self-adaptive methods allows individuals to encode their own mating preferences, use them to evaluate mating candidates and pass preferences on to future generations. Self-adaptive Mate Choice also allows evaluation functions to adapt to the problem at hand as well as to the individuals in the population. In this study we show how Genetic Programming (GP) can be used to represent and evolve mating preferences. In our approach the genotype of each individual is composed of two chromosomes encoding: (1) a candidate solution to the problem at hand (2) a mating partner evaluation function. During the reproduction step of the algorithm, the first parent is chosen based on fitness, as in conventional EC approaches; the mating partner evaluation function encoded on the genotype of this individual is then used to evaluate its potential partners and choose a second parent. Being part of the genotype, the evaluation functions are subjected to evolution and there is an evolutionary pressure to evolve adequate mate evaluation functions. We analyze and discuss the impact of this approach on the evolutionary process, showing how valuable and innovative mate evaluation functions, which would unlikely be designed by humans, arise. We also explain how GP non-terminal and terminal sets can be defined in order to allow the representation of mate selection functions. Finally, we show how self-adaptive Mate Choice can be applied in both academic and real world applications, having achieved encouraging results in both cases. Future venues of research are also proposed such as applications on dynamic environments or multi-objective problems.", } @Article{LEITAOJUNIOR:2020:IST, author = "Plinio S. Leitao-Junior and Diogo M. Freitas and Silvia R. Vergilio and Celso G. Camilo-Junior and Rachel Harrison", title = "Search-based fault localisation: A systematic mapping study", journal = "Information and Software Technology", volume = "123", pages = "106295", year = "2020", ISSN = "0950-5849", DOI = "doi:10.1016/j.infsof.2020.106295", URL = "http://www.sciencedirect.com/science/article/pii/S0950584920300458", keywords = "genetic algorithms, genetic programming, SBSE, Meta-heuristic algorithms, Search-based fault localisation, Systematic mapping", abstract = "Software Fault Localisation (FL) refers to finding faulty software elements related to failures produced as a result of test case execution. This is a laborious and time consuming task. To allow FL automation search-based algorithms have been successfully applied in the field of Search-Based Fault Localisation (SBFL). However, there is no study mapping the SBFL field to the best of our knowledge and we believe that such a map is important to promote new advances in this field. Objective To present the results of a mapping study on SBFL, by characterising the proposed methods, identifying sources of used information, adopted evaluation functions, applied algorithms and elements regarding reported experiments. Method Our mapping followed a defined process and a search protocol. The conducted analysis considers different dimensions and categories related to the main characteristics of SBFL methods. Results All methods are grounded on the coverage spectra category. Overall the methods search for solutions related to suspiciousness formulae to identify possible faulty code elements. Most studies use evolutionary algorithms, mainly Genetic Programming, by using a single-objective function. There is little investigation of real-and-multiple-fault scenarios, and the subjects are mostly written in C and Java. No consensus was observed on how to apply the evaluation metrics. Conclusions Search-based fault localisation has seen a rise in interest in the past few years and the number of studies has been growing. We identified some research opportunities such as exploring new sources of fault data, exploring multi-objective algorithms, analysing benchmarks according to some classes of faults, as well as, the use of a unique definition for evaluation measures.", } @InProceedings{Leite:2023:EuroGP, author = "Alessandro Leite and Marc Schoenauer", title = "Memetic Semantic Genetic Programming for Symbolic Regression", booktitle = "EuroGP 2023: Proceedings of the 26th European Conference on Genetic Programming", year = "2023", month = "12-14 " # apr, editor = "Gisele Pappa and Mario Giacobini and Zdenek Vasicek", series = "LNCS", volume = "13986", publisher = "Springer Verlag", address = "Brno, Czech Republic", pages = "198--212", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Memetic Semantic, Symbolic Regression", isbn13 = "978-3-031-29572-0", URL = "https://rdcu.be/c8UZJ", DOI = "doi:10.1007/978-3-031-29573-7_13", size = "15 pages", abstract = "a new memetic semantic algorithm for symbolic regression (SR). While memetic computation offers a way to encode domain knowledge into a population-based process, semantic-based algorithms allow one to improve them locally to achieve a desired output. Hence, combining memetic and semantic enables us to (a) enhance the exploration and exploitation features of genetic programming (GP) and (b) discover short symbolic expressions that are easy to understand and interpret without losing the expressivity characteristics of symbolic regression. Experimental results show that our proposed memetic semantic algorithm can outperform traditional evolutionary and non-evolutionary methods on several real-world symbolic regression problems, paving a new direction to handle both the bloating and generalization endeavors of genetic programming.", notes = "Part of \cite{Pappa:2023:GP} EuroGP'2023 held in conjunction with EvoCOP2023, EvoMusArt2023 and EvoApplications2023", } @Article{leite:2025:GPEM, author = "Alessandro Leite and Marc Schoenauer", title = "Memetic semantic boosting for symbolic regression", journal = "Genetic Programming and Evolvable Machines", year = "2025", volume = "26", pages = "Article no 11", note = "Online first", keywords = "genetic algorithms, genetic programming, Gradient boosting, Memetic semantic, Symbolic regression", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-024-09506-1", size = "24 pages", abstract = "This paper introduces a novel approach called semantic boosting regression (SBR), leveraging the principles of boosting algorithms in symbolic regression using a Memetic Semantic GP for Symbolic Regression (MSGP) algorithm as weak learners. Memetic computation facilitates the integration of domain knowledge into a population-based approach, and semantic-based algorithms enhance local improvements to achieve targeted outputs. The fusion of memetic and semantic approaches allows us to augment the exploration and exploitation capabilities inherent in Genetic Programming (GP) and identify concise symbolic expressions that maintain interpretability without compromising the expressive power of symbolic regression. Our approach echoes the boosting algorithm characteristic, where weak learners (e.g., MSGP) are sequentially improved upon, focusing on correcting previous errors and continuously enhancing overall performance. This iterative strategy, intrinsic to boosting methods, is adeptly adapted to our SBR model. Experimental results demonstrate that our memetic-semantic approach has equal or better performance when compared to state-of-the-art evolutionary-based techniques when addressing real-world symbolic regression challenges. This advancement helps tackle the bloating issue in GP and significantly improves generalization capabilities. However, akin to classic boosting algorithms, one limitation of our approach is the increased computational cost due to the sequential training of boosting learners.", notes = "TAU, Inria Saclay, LISN, Paris-Saclay University, Gif-sur-Yvette, France", } @InProceedings{leitner2012marsterrain, author = "J. Leitner and S. Harding and A. Forster and J. Schmidhuber", title = "Mars Terrain Image Classification using Cartesian Genetic Programming", booktitle = "11th International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS)", year = "2012", address = "Turin, Italy", month = "4-6 " # sep, organisation = "European Space Agency", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", URL = "http://juxi.net/papers/isairas2012leitner.pdf", size = "8 pages", abstract = "Automatically classifying terrain such as rocks, sand and gravel from images is a challenging machine vision problem. In addition to human designed approaches, a great deal of progress has been made using machine learning techniques to perform classification from images. In this work, we demonstrate the first known use of Cartesian Genetic Programming (CGP) to this problem. Our CGP for Image Processing (CGP-IP) system quickly learns classifiers and detectors for certain terrain types. The learnt program outperforms currently used techniques for classification tasks performed on a panorama image collected by the Mars Exploration Rover Spirit.", date-added = "2012-05-28 16:31:09 +0200", date-modified = "2012-05-28 16:31:09 +0200", notes = "fitness = MCC, classify rocks, sand and gravel from Mars. Broken July 2020 http://robotics.estec.esa.int/i-SAIRAS/?q=node/6 http://robotics.estec.esa.int/i-SAIRAS/isairas2012/i-SAIRAS2012.pdf", } @InProceedings{Leitner:2012:ICDL, author = "Juergen Leitner and Pramod Chandrashekhariah and Simon Harding and Mikhail Frank and Gabriele Spina and Alexander Forster and Jochen Triesch and Juergen Schmidhuber", booktitle = "IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL 2012)", title = "Autonomous learning of robust visual object detection and identification on a humanoid", year = "2012", DOI = "doi:10.1109/DevLrn.2012.6400826", abstract = "In this work we introduce a technique for a humanoid robot to autonomously learn the representations of objects within its visual environment. Our approach involves an attention mechanism in association with feature based segmentation that explores the environment and provides object samples for training. These samples are learnt for further object identification using Cartesian Genetic Programming (CGP). The learnt identification is able to provide robust and fast segmentation of the objects, without using features. We showcase our system and its performance on the iCub humanoid robot.", keywords = "genetic algorithms, genetic programming, humanoid robots, image segmentation, object detection, robot vision, Cartesian genetic programming, autonomous learning, feature based segmentation, iCub humanoid robot, object identification, object segmentation, robust visual object detection, visual environment, Feature extraction, Image segmentation, Object segmentation, Robots, Robustness, Training, Visualisation", notes = "Also known as \cite{6400826}", } @Article{Leitner:2012:IJARS, author = "Juergen Leitner and Simon Harding and Mikhail Frank and Alexander Forster and Jurgen Schmidhuber", title = "Learning Spatial Object Localization from Vision on a Humanoid Robot", journal = "International Journal of Advanced Robotic Systems", year = "2012", volume = "9", publisher = "InTech", keywords = "genetic algorithms, genetic programming, spatial Perception, Computer Vision, Machine Learning, Humanoid Robotics, Object Localisation", ISSN = "1729-8806", bibsource = "OAI-PMH server at www.doaj.org", language = "eng", oai = "oai:doaj-articles:31e24b4d43ea5ad01dffb4748d976920", URL = "http://www.intechopen.com/journals/international_journal_of_advanced_robotic_systems/learning-spatial-object-localization-from-vision-on-a-humanoid-robot", DOI = "doi:10.5772/54657", abstract = "We present a combined machine learning and computer vision approach for robots to localise objects. It allows our iCub humanoid to quickly learn to provide accurate 3D position estimates (in the centimetre range) of objects seen. Biologically inspired approaches, such as Artificial Neural Networks (ANN) and Genetic Programming (GP), are trained to provide these position estimates using the two cameras and the joint encoder readings. No camera calibration or explicit knowledge of the robot{'}s kinematic model is needed. We find that ANN and GP are not just faster and have lower complexity than traditional techniques, but also learn without the need for extensive calibration procedures. In addition, the approach is localising objects robustly, when placed in the robot's workspace at arbitrary positions, even while the robot is moving its torso, head and eyes.", } @InProceedings{Leitner:2013:CEC, article_id = "1075", author = "Jurgen Leitner and Simon Harding and Mikhail Frank and Alexander Forster and Jurgen Schmidhuber", title = "Humanoid Learns to Detect Its Own Hands", booktitle = "2013 IEEE Conference on Evolutionary Computation", volume = "1", year = "2013", month = jun # " 20-23", editor = "Luis Gerardo {de la Fraga}", pages = "1411--1418", address = "Cancun, Mexico", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", isbn13 = "978-1-4799-0453-2", DOI = "doi:10.1109/CEC.2013.6557729", notes = "CEC 2013 - A joint meeting of the IEEE, the EPS and the IET.", } @InProceedings{oai:CiteSeerX.psu:10.1.1.297.4992, title = "Towards Spatial Perception: Learning to Locate Objects From Vision", author = "Juergen Leitner and Simon Harding and Mikhail Frank and Alexander Foerster and Juergen Schmidhuber", booktitle = "Proceedings of the Post-Graduate Conference on Robotics and Development of Cognition", year = "2012", editor = "Joanna Szufnarowska", pages = "20--23", address = "Lausanne, Switzerland", month = "10-12 " # sep, keywords = "genetic algorithms, genetic programming, spatial understanding, object localisation, humanoid robot, neural network", size = "4 pages", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.297.4992", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.297.4992", URL = "http://www.idsia.ch/~alexander/2012/9/RobotDoc2012.pdf", abstract = "Our humanoid robot learns to provide position estimates of objects placed on a table, even while the robot is moving its torso, head and eyes (cm range accuracy). These estimates are provided by trained artificial neural networks (ANN) and a Cartesian genetic programming (GP) method, based solely on the inputs from the two cameras and the joint encoder positions. No prior camera calibration and kinematic model is used. We find that ANN and GP are both able to localise localise objects robustly even while the robot is moving, with an accuracy comparable to current techniques used on the iCub.", notes = "Postgraduate Conference on Robotics and Development of Cognition RobotDoC-PhD http://www.eucognition.org/eucog-wiki/Postgraduate_Conference_on_Robotics_and_Development_of_Cognition_RobotDoC-PhD See \cite{Leitner:2013:BICA}. Also known as \cite{Leitneretal2012} http://people.idsia.ch/~alexander/2012/9/citation.bib", } @Article{Leitner:2013:BICA, author = "Juergen Leitner and Simon Harding and Pramod Chandrashekhariah and Mikhail Frank and Alexander Foerster and Jochen Triesch and Juergen Schmidhuber", title = "Learning visual object detection and localisation using icVision", journal = "Biologically Inspired Cognitive Architectures", year = "2013", volume = "5", pages = "29--41", month = jul, note = "Extended versions of selected papers from the Third Annual Meeting of the BICA Society (BICA 2012)", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Humanoid robots, iCub, cartesianController, Computer vision, Perception, Cognition, Learning, Object detection", ISSN = "2212-683X", URL = "http://www.sciencedirect.com/science/article/pii/S2212683X13000443", DOI = "doi:10.1016/j.bica.2013.05.009", size = "13 pages", abstract = "Building artificial agents and robots that can act in an intelligent way is one of the main research goals in artificial intelligence and robotics. Yet it is still hard to integrate functional cognitive processes into these systems. We present a framework combining computer vision and machine learning for the learning of object recognition in humanoid robots. A biologically inspired, bottom-up architecture is introduced to facilitate visual perception and cognitive robotics research. It aims to mimic processes in the human brain performing visual cognition tasks. A number of experiments with this icVision framework are described. We showcase both detection and identification in the image plane (2D), using machine learning. In addition we show how a biologically inspired attention mechanism allows for fully autonomous learning of visual object representations. Furthermore localising the detected objects in 3D space is presented, which in turn can be used to create a model of the environment.", notes = "a Dalle Molle Institute for Artificial Intelligence (IDSIA)/SUPSI/USI, Manno-Lugano, Switzerland b Machine Intelligence Ltd., South Zeal, United Kingdom c Frankfurt Institute of Advanced Studies (FIAS), Frankfurt am Main, Germany Also known as \cite{Leitner201329}", } @InProceedings{Leitner:2014:IJCNN, author = "J. Leitner and A. Foerster and J. Schmidhuber", booktitle = "International Joint Conference on Neural Networks (IJCNN 2014)", title = "Improving robot vision models for object detection through interaction", year = "2014", month = jul, pages = "3355--3362", abstract = "We propose a method for learning specific object representations that can be applied (and reused) in visual detection and identification tasks. A machine learning technique called Cartesian Genetic Programming (CGP) is used to create these models based on a series of images. Our research investigates how manipulation actions might allow for the development of better visual models and therefore better robot vision. This paper describes how visual object representations can be learnt and improved by performing object manipulation actions, such as, poke, push and pick-up with a humanoid robot. The improvement can be measured and allows for the robot to select and perform the `right' action, i.e. the action with the best possible improvement of the detector.", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", DOI = "doi:10.1109/IJCNN.2014.6889556", notes = "Also known as \cite{6889556}", } @PhdThesis{Leitner:thesis, author = "Juergen Leitner", title = "Towards adaptive and autonomous humanoid robots: From Vision to Actions", school = "Faculty of Informatics of the Universita della Svizzera Italiana", year = "2014", address = "Switzerland", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", bibsource = "OAI-PMH server at doc.rero.ch", language = "english", oai = "oai:doc.rero.ch:20151106093403-UA", URL = "http://doc.rero.ch/record/257528", URL = "http://doc.rero.ch/record/257528/files/2014INFO020.pdf", size = "225 pages", abstract = "Although robotics research has seen advances over the last decades robots are still not in widespread use outside industrial applications. Yet a range of proposed scenarios have robots working together, helping and coexisting with humans in daily life. In all these a clear need to deal with a more unstructured, changing environment arises. I herein present a system that aims to overcome the limitations of highly complex robotic systems, in terms of autonomy and adaptation. The main focus of research is to investigate the use of visual feedback for improving reaching and grasping capabilities of complex robots. To facilitate this a combined integration of computer vision and machine learning techniques is employed. From a robot vision point of view the combination of domain knowledge from both imaging processing and machine learning techniques, can expand the capabilities of robots. I present a novel framework called Cartesian Genetic Programming for Image Processing (CGP-IP). CGP-IP can be trained to detect objects in the incoming camera streams and successfully demonstrated on many different problem domains. The approach requires only a few training images (it was tested with 5 to 10 images per experiment) is fast, scalable and robust yet requires very small training sets. Additionally, it can generate human readable programs that can be further customized and tuned. While CGP-IP is a supervised-learning technique, I show an integration on the iCub, that allows for the autonomous learning of object detection and identification. Finally this dissertation includes two proof-of-concepts that integrate the motion and action sides. First, reactive reaching and grasping is shown. It allows the robot to avoid obstacles detected in the visual stream, while reaching for the intended target object. Furthermore the integration enables us to use the robot in non-static environments, i.e. the reaching is adapted on-the- fly from the visual feedback received, e.g. when an obstacle is moved into the trajectory. The second integration highlights the capabilities of these frameworks, by improving the visual detection by performing object manipulation actions.", notes = "Supervisor Juergen Schmidhuber and Alexander Forster", } @Article{Leitner:2016:GPEM, author = "Juergen Leitner", title = "Malachy Eaton: Evolutionary humanoid robotics", journal = "Genetic Programming and Evolvable Machines", year = "2016", volume = "17", number = "1", pages = "75--76", month = mar, note = "Book review", keywords = "robotics", ISSN = "1389-2576", URL = "https://rdcu.be/dR8BE", DOI = "doi:10.1007/s10710-015-9256-2", size = "2 pages", notes = "Springer, 2015, ISBN 978-3-662-44598-3 'I highly recommend it for young researchers starting out in the field of EHR...'", } @InProceedings{1144162, author = "Michal Lemczyk and Malcolm Heywood", title = "Pareto-coevolutionary genetic programming classifier", booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation", year = "2006", editor = "Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos {Coello Coello} and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens", volume = "1", ISBN = "1-59593-186-4", pages = "945--946", address = "Seattle, Washington, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p945.pdf", DOI = "doi:10.1145/1143997.1144162", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "8-12 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming: Poster, coevolution, evolutionary computation, parameter learning, subset selection, supervised learning", notes = "GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM Order Number 910060", } @InProceedings{eurogp07:lemczyk, author = "Michal Lemczyk and Malcolm I. Heywood", title = "Training Binary GP Classifiers Efficiently: a Pareto-coevolutionary Approach", editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar", booktitle = "Proceedings of the 10th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "4445", year = "2007", address = "Valencia, Spain", month = "11-13 " # apr, pages = "229--240", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-71602-5", isbn13 = "978-3-540-71602-0", DOI = "doi:10.1007/978-3-540-71605-1_21", abstract = "The conversion and extension of the Incremental Pareto-Coevolution Archive algorithm (IPCA) into the domain of Genetic Programming classification is presented. In particular, the coevolutionary aspect of the IPCA algorithm is used to simultaneously evolve a subset of the training data that provides distinctions between candidate classifiers. Empirical results indicate that such a scheme significantly reduces the computational overhead of fitness evaluation on large binary classification data sets. Moreover, unlike the performance of GP classifiers trained using alternative subset selection algorithms, the proposed Pareto-coevolutionary approach is able to match or better the classification performance of GP trained over all training exemplars. Finally, problem decomposition appears as a natural consequence of assuming a Pareto model for coevolution. In order to make use of this property a voting scheme is used to integrate the results of all classifiers from the Pareto front, post training.", notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007", } @InProceedings{AleLem02, author = "Alexandre Lemieux and Christian Gagn\'e and Marc Parizeau", title = "Genetical Engineering of Handwriting Representations", booktitle = "Eighth International Workshop on Frontiers in Handwriting Recognition 2002 (IWFHR 2002)", year = "2002", pages = "145--150", address = "Niagara-on-the-Lake, Ontario, Canada", month = aug # " 6-8", keywords = "genetic algorithms, genetic programming, handwriting recognition, feature extraction, fuzzy set theory, handwritten character recognition, pattern classification, Unipen database, character frame decomposition, feature sets, floating regions, fuzzy operators, fuzzy-regional representation, handwriting representations, handwritten character recognition, region base representation,", URL = "http://www.gel.ulaval.ca/~cgagne/pubs/iwfhr02.pdf", broken = "http://www.computer.org/proceedings/iwfhr/1692/16920145abs.htm", URL = "http://vision.gel.ulaval.ca/en/publications/Id_40/PublDetails.php", DOI = "doi:10.1109/IWFHR.2002.1030900", URL = "http://citeseer.ist.psu.edu/509026.html", abstract = "This paper presents experiments with genetically engineered feature sets for recognition of on-line handwritten characters. These representations stem from a nondescript decomposition of the character frame into a set of rectangular regions, possibly overlapping, each represented by a vector of 7 fuzzy variables. Efficient new feature sets are automatically discovered using genetic programming techniques. Recognition experiments conducted on isolated digits of the Unipen database yield improvements of more than 3percent over a previously manually designed representation where region positions and sizes were fixed.", } @InProceedings{lenaerts:1998:GPfavdp, author = "Tom Lenaerts and Bernard Manderick", title = "Building a Genetic Programming Framework: The Added-Value of Design Patterns", booktitle = "Proceedings of the First European Workshop on Genetic Programming", year = "1998", editor = "Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer and Terence C. Fogarty", volume = "1391", series = "LNCS", pages = "196--208", address = "Paris", publisher_address = "Berlin", month = "14-15 " # apr, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-64360-5", DOI = "doi:10.1007/BFb0055939", abstract = "A large body of public domain software exists which addresses standard implementations of the Genetic Programming paradigm. Nevertheless researchers are frequently confronted with the lack of flexibility and reusability of the tools when for instance one wants to alter the genotype representation or the overall behavior of the evolutionary process. This paper addresses the construction of a object-oriented Genetic Programming framework using on design patterns to increase its flexibility and reusability.", notes = "EuroGP'98", affiliation = "Vrije Universiteit Brussel Adaptive Systems Group, Department of Computer Science Pleinlaan 2 1050 Brussels Belgium Pleinlaan 2 1050 Brussels Belgium", } @Article{Lenahan:2006:sigevo, author = "Jack Lenahan", title = "The Synthesis of Evolutionary Algorithms and Quantum Computing", journal = "SIGEVOlution", year = "2006", volume = "1", number = "3", pages = "36--39", month = sep, keywords = "genetic algorithms, genetic programming", URL = "http://www.sigevolution.org/2006/03/issue.pdf", URL = "https://dl.acm.org/action/showFmPdf?doi=10.1145%2F1181964", abstract = "The purpose of this letter is to describe the beneficial relationship between evolutionary and quantum computational models. Evolutionary computation has proved successful in achieving human competitive results [1] in varied disciplines including the evolution of quantum algorithms. Similarly, applying quantum computing models to evolutionary computation has also been shown to exceed the capabilities of traditional evolutionary algorithms in selected cases. The emerging fusion of evolutionary computation with quantum computing models is simply elegant.", notes = "July 2024 https://evolution.sigevo.org/issues/SIGEVOlution0103.pdf Staff Scientist, Imagine-One Corporation", } @InProceedings{Lenartowicz:2016:GECCOcomp, author = "Sebastian Lenartowicz and Mark Wineberg", title = "{mpEAd:} Multi-Population EA Diagrams", booktitle = "GECCO '16 Companion: Proceedings of the Companion Publication of the 2016 Annual Conference on Genetic and Evolutionary Computation", year = "2016", editor = "Tobias Friedrich and Frank Neumann and Andrew M. Sutton and Martin Middendorf and Xiaodong Li and Emma Hart and Mengjie Zhang and Youhei Akimoto and Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and Daniele Loiacono and Julian Togelius and Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and Faustino Gomez and Carlos M. Fonseca and Heike Trautmann and Alberto Moraglio and William F. Punch and Krzysztof Krawiec and Zdenek Vasicek and Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and Boris Naujoks and Enrique Alba and Gabriela Ochoa and Simon Poulding and Dirk Sudholt and Timo Koetzing", pages = "93--94", month = "20-24 " # jul, organisation = "SIGEVO", address = "Denver, USA", publisher = "ACM", keywords = "genetic algorithms, genetic programming: Poster", publisher_address = "New York, NY, USA", isbn13 = "978-1-4503-4323-7", DOI = "doi:10.1145/2908961.2909011", abstract = "Multi-population evolutionary algorithms are, by nature, highly complex and difficult to describe. Even two populations working in concert (or opposition) present a myriad of potential configurations that are often difficult to relate using text alone. Little effort has been made, however, to depict these kinds of systems, relying solely on the simple structural connections (related using ad hoc diagrams) between populations and often leaving out crucial details. In this paper, we propose a notation and accompanying formalism for consistently and powerfully depicting these structures and the relationships within them in an intuitive and consistent way.", notes = "Distributed at GECCO-2016.", } @Article{Lenehan:2012:cancer, author = "Peter F. Lenehan and Lisa A. Boardman and Douglas Riegert-Johnson and Giovanni {De Petris} and David W. Fry and Jeanne Ohrnberger and Eugene R. Heyman and Brigitte Gerard and Arpit A. Almal and William P. Worzel", title = "Generation and external validation of a tumor-derived 5-gene prognostic signature for recurrence of lymph node-negative, invasive colorectal carcinoma", journal = "Cancer", year = "2012", volume = "118", number = "21", pages = "5234--5244", month = "1 " # nov, keywords = "genetic algorithms, genetic programming, colorectal cancer, gene expression signatures, machine learning, recurrence, reverse transcriptase-polymerase chain reaction, prognosis, validation studies, sensitivity and specificity, colonic polyps.", ISSN = "1097-0142", organization = "American Cancer Society", DOI = "doi:10.1002/cncr.27628", size = "11 pages", abstract = "BACKGROUND: One in 4 patients with lymph node-negative, invasive colorectal carcinoma (CRC) develops recurrent disease after undergoing curative surgery, and most die of advanced disease. Predicting which patients will develop a recurrence is a significantly growing, unmet medical need. METHODS: Archival formalin-fixed, paraffin-embedded (FFPE) primary adenocarcinoma tissues obtained at surgery were retrieved from 74 patients with CRC (15 with stage I disease and 59 with stage II disease) for Training/Test Sets. In addition, FFPE tissues were retrieved from 49 patients with stage I CRC and 215 patients with stage II colon cancer for an External Validation (EV) Set (n=264) from 18 hospitals in 4 countries. No patients had received neoadjuvant/adjuvant therapy. Proprietary genetic programming analysis of expression profiles for 225 prespecified tumour genes was used to create a 36-month recurrence risk signature. RESULTS: Using reverse transcriptase-polymerase chain reaction, a 5-gene rule correctly classified 62 of 92 recurrent patients and 87 of 172 nonrecurrent patients in the EV Set (sensitivity, 0.67; specificity, 0.51). High-risk patients had a greater probability of 36-month recurrence (42percent) than low-risk patients (26percent; hazard ratio, 1.80; 95percent confidence interval, 1.19-2.71; P=0.007; Cox regression) independent of T-classification, the number of lymph nodes examined, histologic grade/subtype, anatomic location, age, sex, or race. The rule outperformed (P=0.021) current National Comprehensive Cancer Network Guidelines (hazard ratio, 0.897). The same rule also differentiated the risk of recurrence (hazard ratio, 1.63; P=0.031) in a subset of patients from the EV Set who had stage I/II colon cancer only (n=251). CONCLUSIONS: the 5-gene rule (OncoDefender-CRC) is the first molecular prognostic that has been validated in both stage I CRC and stage II colon cancer. It outperforms standard clinicopathologic prognostic criteria and obviates the need to retrieve >= 12 lymph nodes for accurate prognostication. It identifies those patients most likely to develop recurrent disease within 3 years after curative surgery and, thus, those most likely to benefit from adjuvant treatment.", notes = "PMID: PMC3532613 http://onlinelibrary.wiley.com/journal/10.1002/%28ISSN%291097-0142", } @Article{Lennartsson:2004:EURASIP, author = "David Lennartsson and Peter Nordin", title = "A Genetic Programming Method for the Identification of Signal Peptides and Prediction of Their Cleavage Sites", journal = "EURASIP Journal on Advances in Signal Processing", year = "2004", volume = "2004", number = "1", pages = "138--145", month = jan, keywords = "genetic algorithms, genetic programming, linear genetic programming, demes, parallel sub-populations, signal peptides, bioinformatics, programmatic motif, artificial neural networks, cleavage site", ISSN = "1687-6180", publisher = "Hindawi Publishing Corporation", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.398.1015", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.398.1015", URL = "http://asp.eurasipjournals.com/content/pdf/1687-6180-2004-153697.pdf", DOI = "doi:10.1155/S1110865704309108", size = "8 pages", abstract = "A novel approach to signal peptide identification is presented. We use an evolutionary algorithm for automatic evolution of classification programs, so-called programmatic motifs. The variant of evolutionary algorithm used is called genetic programming where a population of solution candidates in the form of full computer programs is evolved, based on training examples consisting of signal peptide sequences. The method is compared with a previous work using artificial neural network (ANN) approaches. Some advantages compared to ANNs are noted. The programmatic motif can perform computational tasks beyond that of feed-forward neural networks and has also other advantages such as readability. The best motif evolved was analysed and shown to detect the h-region of the signal peptide. A powerful parallel computer cluster was used for the experiment.", notes = "Saida Medical AB, Stena Center 1A, Goteborg SE-412 92, Sweden", } @InProceedings{Lensberg:1997:GPeibuku, author = "Terje Lensberg", title = "A Genetic Programming Experiment on Investment Behavior under Knightian Uncertainty", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming", pages = "231--239", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1997/Lensberg_1997_GPeibuku.pdf", size = "9 pages", notes = "See \cite{Lensberg:1999:JEDC} GP-97", } @Article{Lensberg:1999:JEDC, author = "Terje Lensberg", title = "Investment behavior under Knightian uncertainty - An evolutionary approach", journal = "Journal of Economic Dynamics and Control", year = "1999", volume = "23", pages = "1587--1604", number = "9-10", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6V85-3Y9RKX5-G/2/6c6369b7934fdea4d1937c49a35ada38", keywords = "genetic algorithms, genetic programming, Knightian uncertainty, Bayesian rationality", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.205.7821", URL = "http://sci2s.ugr.es/keel/pdf/specific/articulo/science2_31.pdf", DOI = "doi:10.1016/S0165-1889(98)00085-2", abstract = "The as if view of economic rationality defends the profit maximisation hypothesis by pointing out that only those firms who act as if they maximise profits can survive in the long run. Recently, the problem of arriving at a logically consistent definition of rational behaviour in games has shown that one must sometimes study explicitly the evolutionary processes that form the basis of this view. The purpose of this paper is to investigate the usefulness of genetic programming as a tool for generating hypotheses about rational behavior in situations where explicit maximization is not well defined. We use an investment decision problem with Knightian uncertainty as a borderline test case, and show that when the artificial agents receive the same information about the unknown probability distributions, they develop behaviour rules as if they were expected utility maximisers with Bayesian learning rules.", notes = "JEL classification codes: B41; C63; D83", } @Article{Lensberg:2005:EJOR, author = "Terje Lensberg and Aasmund Eilifsen and Thomas E. McKee", title = "Bankruptcy theory development and classification via genetic programming", journal = "European Journal of Operational Research", year = "2006", volume = "169", pages = "677--697", number = "2", abstract = "Bankruptcy is a highly significant worldwide problem with high social costs. Traditional bankruptcy risk models have been criticised for falling short with respect to bankruptcy theory building due to either modelling assumptions or model complexity. Genetic programming minimises the amount of a priori structure that is associated with traditional functional forms and statistical selection procedures, but still produces easily understandable and implementable models. Genetic programming was used to analyse 28 potential bankruptcy variables found to be significant in multiple prior research studies, including 10 fraud risk factors. Data was taken from a sample of 422 bankrupt and non-bankrupt Norwegian companies for the period 1993-1998. Six variables were determined to be significant. A genetic programming model was developed for the six variables from an expanded sample of 1136 bankrupt and non-bankrupt Norwegian companies. The model was 81% accurate on a validation sample, slightly better than prior genetic programming research on US public companies, and statistically significantly better than the 77% accuracy of a traditional logit model developed using the same variables and data. The most significant variable in the final model was the prior auditor opinion, thus validating the information value of the auditor's report. The model provides insight into the complex interaction of bankruptcy related factors, especially the effect of company size. The results suggest that accounting information, including the auditor's evaluation of it, is more important for larger than smaller firms. It also suggests that for small firms the most important information is liquidity and non-accounting information. The genetic programming model relationships developed in this study also support prior bankruptcy research, including the finding that company size decreases bankruptcy risk when profits are positive. It also confirms that very high profit levels are associated with increased bankruptcy risk even for large companies an association that may be reflecting the potential for management to be {"}Cooking the Books{"}.", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6VCT-4D5P6FY-8/2/b08574948226f93f16a6013ffef1cd19", month = "1 " # mar, keywords = "genetic algorithms, genetic programming, Going concern, Bankruptcy, Fraud risk", DOI = "doi:10.1016/j.ejor.2004.06.013", } @Article{Lensberg:2007:RoF, author = "Terje Lensberg and Klaus Reiner Schenk-Hoppe", title = "On the Evolution of Investment Strategies and the Kelly Rule A Darwinian Approach", journal = "Review of Finance", year = "2007", volume = "11", number = "1", pages = "25--50", keywords = "genetic algorithms, genetic programming, Evolutionary finance, portfolio choice, Behavioral Asset Pricing Investment Strategies and Anomalies", ISSN = "1572-3097", URL = "http://www.univie.ac.at/rof/papers/pdf/Lensberg-Schenk-Hoppe_Kelly%20Rule.pdf", URL = "http://www.nccr-finrisk.unizh.ch/media/pdf/RoF07_Vol11_pages25_50.pdf", DOI = "doi:10.1093/rof/rfm003", size = "26 pages", abstract = "This paper complements theoretical studies on the Kelly rule in evolutionary finance by studying a Darwinian model of selection and reproduction in which the diversity of investment strategies is maintained through genetic programming.We find that investment strategies which optimise long-term performance can emerge in markets populated by unsophisticated investors. Regardless whether the market is complete or incomplete and whether states are i.i.d. or Markov, the Kelly rule is obtained as the asymptotic outcome. With price-dependent rather than just state-dependent investment strategies, the market portfolio plays an important role as a protection against severe losses in volatile markets", notes = "http://www.revfin.org/", } @Article{Lensberg:2013:QFL, author = "Terje Lensberg and Klaus Reiner Schenk-Hoppe", title = "Hedging without sweat: a genetic programming approach", journal = "Quantitative Finance Letters", year = "2013", volume = "1", pages = "41--46", keywords = "genetic algorithms, genetic programming, Hedging, Transaction costs, Closed-form approximations", publisher = "Taylor \& Francis", DOI = "doi:10.1080/21649502.2013.813166", size = "6 page", abstract = "Hedging in the presence of transaction costs leads to complex optimisation problems. These problems typically lack closed-form solutions, and their implementation relies on numerical methods that provide hedging strategies for specific parameter values. In this paper, we use a genetic programming algorithm to derive explicit formulae for near-optimal hedging strategies under nonlinear transaction costs. The strategies are valid over a large range of parameter values and require no information about the structure of the optimal hedging strategy.", notes = "Author affiliations NHH Norwegian School of Economics, Norway University of Leeds, UK", } @Article{Lensberg2015103, author = "Terje Lensberg and Klaus Reiner Schenk-Hoppe and Dan Ladley", title = "Costs and benefits of financial regulation: Short-selling bans and transaction taxes", journal = "Journal of Banking \& Finance", year = "2015", volume = "51", pages = "103--118", month = feb, keywords = "genetic algorithms, genetic programming, Financial regulation, Portfolio management, Market microstructure", ISSN = "0378-4266", URL = "http://www.sciencedirect.com/science/article/pii/S0378426614003458", DOI = "doi:10.1016/j.jbankfin.2014.10.014", size = "16 pages", abstract = "We quantify the effects of financial regulation in an equilibrium model with delegated portfolio management. Fund managers trade stocks and bonds in an order-driven market, subject to transaction taxes and constraints on short-selling and leverage. Results are obtained on the equilibrium properties of portfolio choice, trading activity, market quality and price dynamics under the different regulations. We find that these measures are neither as beneficial as some politicians believe nor as damaging as many practitioners fear", } @Article{Lensberg2021, author = "Terje Lensberg and Klaus Reiner Schenk-Hoppe", title = "Cold play: Learning across bimatrix games", journal = "Journal of Economic Behavior \& Organization", year = "2021", volume = "185", pages = "419--441", month = may, keywords = "genetic algorithms, genetic programming, One-shot games, Solution concepts, Evolutionary stability", ISSN = "0167-2681", URL = "https://www.sciencedirect.com/science/article/pii/S0167268121000949", URL = "https://gplab.nhh.no/gamesolver.php", DOI = "doi:10.1016/j.jebo.2021.02.027", abstract = "We study one-shot play in the set of all bimatrix games by a large population of agents. The agents never see the same game twice, but they can learn across games by developing solution concepts that tell them how to play new games. Each agents individual solution concept is represented by a computer program, and natural selection is applied to derive a stochastically stable solution concept. Our aim is to develop a theory predicting how experienced agents would play in one-shot games.", } @InProceedings{Lensen:2015:CEC, author = "Andrew Lensen and Harith Al-Sahaf and Mengjie Zhang and Brijesh Verma", title = "Genetic Programming for Algae Detection in River Images", booktitle = "Proceedings of 2015 IEEE Congress on Evolutionary Computation (CEC 2015)", year = "2015", editor = "Yadahiko Murata", pages = "2468--2475", address = "Sendai, Japan", month = "25-28 " # may, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-7491-7", DOI = "doi:10.1109/CEC.2015.7257191", abstract = "Genetic Programming (GP) has been applied to a wide range of image analysis tasks including many real-world segmentation problems. This paper introduces a new biological application of detecting Phormidium algae in rivers of New Zealand using raw images captured from the air. In this paper, we propose a GP method to the task of algae detection. The proposed method synthesises a set of image operators and adopts a simple thresholding approach to segmenting an image into algae and non-algae regions. Furthermore, the introduced method operates directly on raw pixel values with no human assistance required. The method is tested across seven different images from different rivers. The results show good success on detecting areas of algae much more efficiently than traditional manual techniques. Furthermore, the result achieved by the proposed method is comparable to the hand-crafted ground truth with a F-measure fitness value of 0.64 (where 0 is best, 1 is worst) on average on the test set. Issues such as illumination, reflection and waves are discussed.", notes = "1645 hrs 15398 CEC2015", } @InProceedings{Lensen:2015:IVCNZ, author = "Andrew Lensen and Harith Al-Sahaf and Mengjie Zhang and Bing Xue", booktitle = "2015 International Conference on Image and Vision Computing New Zealand (IVCNZ)", title = "A hybrid Genetic Programming approach to feature detection and image classification", year = "2015", abstract = "Image classification is a crucial task in Computer Vision. Feature detection represents a key component of the image classification process, which aims at detecting a set of important features that have the potential to facilitate the classification task. In this paper, we propose a Genetic Programming (GP) approach to image feature detection. The proposed method uses the Speeded Up Robust Features (SURF) method to extract features from regions automatically selected by GP, and adopts a wrapper approach combined with a voting scheme to perform image classification. The proposed approach is evaluated using three datasets of increasing difficulty, and is compared to five popularly used machine learning methods: Support Vector Machines, Random Forest, Naive Bayes, Decision Trees, and Adaptive Boosting. The experimental results show the proposed approach has achieved comparable or better performance than the five existing methods on all three datasets, and reveal its capability to automatically detect good regions from a large image from which good features are automatically constructed.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IVCNZ.2015.7761564", month = nov, notes = "Also known as \cite{7761564}", } @InProceedings{Lensen:2016:EuroGP, author = "Andrew Lensen and Harith Al-Sahaf and Mengjie Zhang and Bing Xue", title = "Genetic Programming for Region Detection, Feature Extraction, Feature Construction and Classification in Image Data", booktitle = "EuroGP 2016: Proceedings of the 19th European Conference on Genetic Programming", year = "2016", month = "30 " # mar # "--1 " # apr, editor = "Malcolm I. Heywood and James McDermott and Mauro Castelli and Ernesto Costa and Kevin Sim", series = "LNCS", volume = "9594", publisher = "Springer Verlag", address = "Porto, Portugal", pages = "51--67", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, , Image Classification, Feature Extraction, Feature Construction", isbn13 = "978-3-319-30668-1", DOI = "doi:10.1007/978-3-319-30668-1_4", abstract = "Image analysis is a key area in the computer vision domain that has many applications. Genetic Programming (GP) has been successfully applied to this area extensively, with promising results. High-level features extracted from methods such as Speeded Up Robust Features (SURF) and Histogram of Oriented Gradients (HoG) are commonly used for object detection with machine learning techniques. However, GP techniques are not often used with these methods, despite being applied extensively to image analysis problems. Combining the training process of GP with the powerful features extracted by SURF or HoG has the potential to improve the performance by generating high-level, domain-tailored features. This paper proposes a new GP method that automatically detects different regions of an image, extracts HoG features from those regions, and simultaneously evolves a classifier for image classification. By extending an existing GP region selection approach to incorporate the HoG algorithm, we present a novel way of using high-level features with GP for image classification. The ability of GP to explore a large search space in an efficient manner allows all stages of the new method to be optimised simultaneously, unlike in existing approaches. The new approach is applied across a range of datasets, with promising results when compared to a variety of well-known machine learning techniques. Some high-performing GP individuals are analysed to give insight into how GP can effectively be used with high-level features for image classification. ", notes = "Part of \cite{Heywood:2016:GP} EuroGP'2016 held in conjunction with EvoCOP2016, EvoMusArt2016 and EvoApplications2016", } @InProceedings{Lensen:2017:GECCO, author = "Andrew Lensen and Bing Xue and Mengjie Zhang", title = "{GPGC}: Genetic Programming for Automatic Clustering Using a Flexible Non-hyper-spherical Graph-based Approach", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4920-8", address = "Berlin, Germany", pages = "449--456", size = "8 pages", URL = "http://doi.acm.org/10.1145/3071178.3071222", DOI = "doi:10.1145/3071178.3071222", acmid = "3071222", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, automatic clustering, cluster analysis, evolutionary computation, feature construction, graph-based clustering", month = "15-19 " # jul, abstract = "Genetic programming (GP) has been shown to be very effective for performing data mining tasks. Despite this, it has seen relatively little use in clustering. In this work, we introduce a new GP approach for performing graph-based (GPGC) non-hyper-spherical clustering where the number of clusters is not required to be set in advance. The proposed GPGC approach is compared with a number of well known methods on a large number of data sets with a wide variety of shapes and sizes. Our results show that GPGC is the most generalisable of the tested methods, achieving good performance across all datasets. GPGC significantly outperforms all existing methods on the hardest ellipsoidal datasets, without needing the user to pre-define the number of clusters. To our knowledge, this is the first work which proposes using GP for graph-based clustering.", notes = "Also known as \cite{Lensen:2017:GGP:3071178.3071222} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @InProceedings{Lensen:2017:GECCOa, author = "Andrew Lensen and Bing Xue and Mengjie Zhang", title = "Improving K-means Clustering with Genetic Programming for Feature Construction", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4939-0", address = "Berlin, Germany", pages = "237--238", size = "2 pages", URL = "http://doi.acm.org/10.1145/3067695.3075962", DOI = "doi:10.1145/3067695.3075962", acmid = "3075962", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, cluster analysis, evolutionary computation, feature construction, k-means", month = "15-19 " # jul, abstract = "k-means is one of the most commonly used clustering algorithms in data mining. Despite this, it has a number of fundamental limitations which prevent it from performing effectively on large or otherwise difficult datasets. A common technique to improve performance of data mining algorithms is feature construction, a technique which combines features together to produce more powerful constructed features that can improve the performance of a given algorithm. Genetic Programming (GP) has been used for feature construction very successfully, due to its program-like structure. This paper proposes two representations for using GP to perform feature construction to improve the performance of k-means, using a wrapper approach. Our results show significant improvements in performance compared to k-means using all original features across six difficult datasets.", notes = "Also known as \cite{Lensen:2017:IKM:3067695.3075962} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @InProceedings{lensen2017new, author = "Andrew Lensen and Bing Xue and Mengjie Zhang", title = "New Representations in Genetic Programming for Feature Construction in k-Means Clustering", booktitle = "Proceedings of the 11th International Conference on Simulated Evolution and Learning, SEAL-2017", year = "2017", editor = "Yuhui Shi and Kay Chen Tan and Mengjie Zhang and Ke Tang and Xiaodong Li and Qingfu Zhang and Ying Tan and Martin Middendorf and Yaochu Jin", volume = "10593", series = "Lecture Notes in Computer Science", pages = "543--555", address = "Shenzhen, China", month = nov # " 10-13", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Cluster analysis, Feature construction, k-means, Evolutionary computation", isbn13 = "978-3-319-68759-9", URL = "https://doi.org/10.1007/978-3-319-68759-9_44", DOI = "doi:10.1007/978-3-319-68759-9_44", abstract = "k-means is one of the fundamental and most well-known algorithms in data mining. It has been widely used in clustering tasks, but suffers from a number of limitations on large or complex datasets. Genetic Programming (GP) has been used to improve performance of data mining algorithms by performing feature construction the process of combining multiple attributes (features) of a dataset together to produce more powerful constructed features. In this paper, we propose novel representations for using GP to perform feature construction to improve the clustering performance of the k-means algorithm. Our experiments show significant performance improvement compared to k-means across a variety of difficult datasets. Several GP programs are also analysed to provide insight into how feature construction is able to improve clustering performance.", } @InProceedings{Lensen:2018:EuroGP, author = "Andrew Lensen and Bing Xue and Mengjie Zhang", title = "Generating Redundant Features with Unsupervised Multi-Tree Genetic Programming", booktitle = "EuroGP 2018: Proceedings of the 21st European Conference on Genetic Programming", year = "2018", month = "4-6 " # apr, editor = "Mauro Castelli and Lukas Sekanina and Mengjie Zhang and Stefano Cagnoni and Pablo Garcia-Sanchez", series = "LNCS", volume = "10781", publisher = "Springer Verlag", address = "Parma, Italy", pages = "84--100", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-77552-4", DOI = "doi:10.1007/978-3-319-77553-1_6", abstract = "Recently, feature selection has become an increasingly important area of research due to the surge in high-dimensional datasets in all areas of modern life. A plethora of feature selection algorithms have been proposed, but it is difficult to truly analyse the quality of a given algorithm. Ideally, an algorithm would be evaluated by measuring how well it removes known bad features. Acquiring datasets with such features is inherently difficult, and so a common technique is to add synthetic bad features to an existing dataset. While adding noisy features is an easy task, it is very difficult to automatically add complex, redundant features. This work proposes one of the first approaches to generating redundant features, using a novel genetic programming approach. Initial experiments show that our proposed method can automatically create difficult, redundant features which have the potential to be used for creating high-quality feature selection benchmark datasets.", notes = "Part of \cite{Castelli:2018:GP} EuroGP'2018 held in conjunction with EvoCOP2018, EvoMusArt2018 and EvoApplications2018", } @InProceedings{Lensen:2018:GECCO, author = "Andrew Lensen and Bing Xue and Mengjie Zhang", title = "Automatically evolving difficult benchmark feature selection datasets with genetic programming", booktitle = "GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference", year = "2018", editor = "Hernan Aguirre and Keiki Takadama and Hisashi Handa and Arnaud Liefooghe and Tomohiro Yoshikawa and Andrew M. Sutton and Satoshi Ono and Francisco Chicano and Shinichi Shirakawa and Zdenek Vasicek and Roderich Gross and Andries Engelbrecht and Emma Hart and Sebastian Risi and Ekart Aniko and Julian Togelius and Sebastien Verel and Christian Blum and Will Browne and Yusuke Nojima and Tea Tusar and Qingfu Zhang and Nikolaus Hansen and Jose Antonio Lozano and Dirk Thierens and Tian-Li Yu and Juergen Branke and Yaochu Jin and Sara Silva and Hitoshi Iba and Anna I Esparcia-Alcazar and Thomas Bartz-Beielstein and Federica Sarro and Giuliano Antoniol and Anne Auger and Per Kristian Lehre", isbn13 = "978-1-4503-5618-3", pages = "458--465", address = "Kyoto, Japan", DOI = "doi:10.1145/3205455.3205552", publisher = "ACM", publisher_address = "New York, NY, USA", month = "15-19 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", abstract = "There has been a wealth of feature selection algorithms proposed in recent years, each of which claims superior performance in turn. A wide range of datasets have been used to compare these algorithms, each with different characteristics and quantities of redundant and noisy features. Hence, it is very difficult to comprehensively and fairly compare these feature selection methods in order to find which are most robust and effective. In this work, we examine using Genetic Programming to automatically synthesise redundant features for augmenting existing datasets in order to more scientifically test feature selection performance. We develop a method for producing complex multi-variate redundancies, and present a novel and intuitive approach to ensuring a range of redundancy relationships are automatically created. The application of these augmented datasets to well-established feature selection algorithms shows a number of interesting and useful results and suggests promising directions for future research in this area.", notes = "Also known as \cite{3205552} GECCO-2018 A Recombination of the 27th International Conference on Genetic Algorithms (ICGA-2018) and the 23rd Annual Genetic Programming Conference (GP-2018)", } @InProceedings{Lensen:2019:EuroGP, author = "Andrew Lensen and Bing Xue and Mengjie Zhang", title = "Can Genetic Programming Do Manifold Learning Too?", booktitle = "EuroGP 2019: Proceedings of the 22nd European Conference on Genetic Programming", year = "2019", month = "24-26 " # apr, editor = "Lukas Sekanina and Ting Hu and Nuno Lourenco", series = "LNCS", volume = "11451", publisher = "Springer Verlag", address = "Leipzig, Germany", pages = "114--130", organisation = "EvoStar, Species", note = "Best paper", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-030-16669-4", URL = "https://www.springer.com/us/book/9783030166694", DOI = "doi:10.1007/978-3-030-16670-0_8", size = "16 pages", abstract = "Exploratory data analysis is a fundamental aspect of knowledge discovery that aims to find the main characteristics of a dataset. Dimensionality reduction, such as manifold learning, is often used to reduce the number of features in a dataset to a manageable level for human interpretation. Despite this, most manifold learning techniques do not explain anything about the original features nor the true characteristics of a dataset. In this paper, we propose a genetic programming approach to manifold learning called GP-MaL which evolves functional mappings from a high-dimensional space to a lower dimensional space through the use of interpretable trees. We show that GP-MaL is competitive with existing manifold learning algorithms, while producing models that can be interpreted and re-used on unseen data. A number of promising future directions of research are found in the process.", notes = "http://www.evostar.org/2019/cfp_eurogp.php#abstracts Part of \cite{Sekanina:2019:GP} EuroGP'2019 held in conjunction with EvoCOP2019, EvoMusArt2019 and EvoApplications2019", } @PhdThesis{Lensen:thesis, author = "Andrew Lensen", title = "Evolutionary Feature Manipulation in Unsupervised Learning", school = "Computer Science, Victoria University of Wellington", year = "2019", address = "New Zealand", keywords = "genetic algorithms, genetic programming", URL = "http://hdl.handle.net/10063/8617", URL = "https://researcharchive.vuw.ac.nz/xmlui/bitstream/handle/10063/8617/thesis_access.pdf", size = "300 pages", abstract = "Unsupervised learning is a fundamental category of machine learning that works on data for which no pre-existing labels are available. Unlike in supervised learning, which has such labels, methods that perform unsupervised learning must discover intrinsic patterns within data. The size and complexity of data has increased substantially in recent years, which has necessitated the creation of new techniques for reducing the complexity and dimensionality of data in order to allow humans to understand the knowledge contained within data. This is particularly problematic in unsupervised learning, as the number of possible patterns in a dataset grows exponentially with regard to the number of dimensions. Feature manipulation techniques such as feature selection (FS) and feature construction (FC) are often used in these situations. FS automatically selects the most valuable features (attributes) in a dataset, whereas FC constructs new, more powerful and meaningful features that provide a lower-dimensional space. Evolutionary computation (EC) approaches have become increasingly recognised for their potential to provide high-quality solutions to data mining problems in a reasonable amount of computational time. Unlike other popular techniques such as neural networks, EC methods have global search ability without needing gradient information, which makes them much more flexible and applicable to a wider range of problems. EC approaches have shown significant potential in feature manipulation tasks with methods such as Particle Swarm Optimisation (PSO) commonly used for FS, and Genetic Programming (GP) for FC. The use of EC for feature manipulation has, until now, been predominantly restricted to supervised learning problems. This is a notable gap in the research: if unsupervised learning is even more sensitive to high-dimensionality, then why is EC-based feature manipulation not used for unsupervised learning problems? This thesis provides the first comprehensive investigation into the use of evolutionary feature manipulation for unsupervised learning tasks. It clearly shows the ability of evolutionary feature manipulation to improve both the performance of algorithms and interpretability of solutions in unsupervised learning tasks. A variety of tasks are investigated, including the well-established task of clustering, as well as more recent unsupervised learning problems, such as benchmark dataset creation and manifold learning. This thesis proposes a new PSO-based approach to performing simultaneous FS and clustering. A number of improvements to the state-of-the-art are made, including the introduction of a new medoid-based representation and an improved fitness function. A sophisticated three-stage algorithm, which takes advantage of heuristic techniques to determine the number of clusters and to fine-tune clustering performance is also developed. Empirical evaluation on a range of clustering problems demonstrates a decrease in the number of features used, while also improving the clustering performance. This thesis also introduces two innovative approaches to performing wrapper-based FC in clustering tasks using GP. An initial approach where constructed features are directly provided to the k-means clustering algorithm demonstrates the clear strength of GP-based FC for improving clustering results. A more advanced method is proposed that uses the functional nature of GP-based FC to evolve more specific, concise, and understandable similarity functions for use in clustering algorithms. These similarity functions provide clear improvements in performance and can be easily interpreted by machine learning practitioners. This thesis demonstrates the ability of evolutionary feature manipulation to solve unsupervised learning tasks that traditional methods have struggled with. The synthesis of benchmark datasets has long been a technique used for evaluating machine learning techniques, but this research is the first to present an approach that automatically creates diverse and challenging redundant features for a given dataset. This thesis introduces a GP-based FC approach that creates difficult benchmark datasets for evaluating FS algorithms. It also makes the intriguing discovery that using a mutual information-based fitness function with GP has the potential to be used to improve supervised learning tasks even when the labels are not used. Manifold learning is an approach to dimensionality reduction that aims to reduce dimensionality by discovering the inherent lower-dimensional structure of a dataset. While state-of-the-art manifold learning approaches show impressive performance in reducing data dimensionality, they do so at the cost of removing the ability for humans to understand the data in terms of the original features. By using a GP-based approach, this thesis proposes new methods that can perform interpretable manifold learning, which provides deep insight into patterns in the data. These four contributions clearly support the hypothesis that evolutionary feature manipulation has untapped potential in unsupervised learning. This thesis demonstrates that EC-based feature manipulation can be successfully applied to a variety of unsupervised learning tasks with clear improvements in both performance and interpretability. A plethora of future research directions in this area are also discovered, which we hope will lead to further valuable findings in this area.", } @Article{Lensen:EC, author = "Andrew Lensen and Bing Xue and Mengjie Zhang", title = "Genetic Programming for Evolving Similarity Functions for Clustering: Representations and Analysis", journal = "Evolutionary Computation", year = "2020", volume = "28", number = "4", pages = "531--561", note = "Winter", keywords = "genetic algorithms, genetic programming, Cluster analysis, automatic clustering, similarity function, feature selection, feature construction", ISSN = "1063-6560", DOI = "doi:10.1162/evco_a_00264", size = "29 pages", abstract = "Clustering is a difficult and widely-studied data mining task, with many varieties of clustering algorithms proposed in the literature. Nearly all algorithms use a similarity measure such as a distance metric (e.g. Euclidean distance) to decide which instances to assign to the same cluster. These similarity measures are generally pre-defined and cannot be easily tailored to the properties of a particular dataset, which leads to limitations in the quality and the interpretability of the clusters produced. In this paper, we propose a new approach to automatically evolving similarity functions for a given clustering algorithm by using genetic programming. We introduce a new genetic programming-based method which automatically selects a small subset of features (feature selection) and then combines them using a variety of functions (feature construction) to produce dynamic and flexible similarity functions that are specifically designed for a given dataset. We demonstrate how the evolved s", notes = "Evolutionary Computation Research Group, Victoria University of Wellington,Wellington 6140, New Zealand", } @Article{Lensen:GPEM:H2019, author = "Andrew Lensen and Mengjie Zhang and Bing Xue", title = "Multi-objective genetic programming for manifold learning: balancing quality and dimensionality", journal = "Genetic Programming and Evolvable Machines", year = "2020", volume = "21", number = "3", pages = "399--431", month = sep, note = "Special Issue: Highlights of Genetic Programming 2019 Events", keywords = "genetic algorithms, genetic programming, Manifold learning, Dimensionality reduction, Feature construction", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-020-09375-4", size = "33 pages", abstract = "Manifold learning techniques have become increasingly valuable as data continues to grow in size. By discovering a lower-dimensional representation (embedding) of the structure of a dataset, manifold learning algorithms can substantially reduce the dimensionality of a dataset while preserving as much information as possible. However, state-of-the-art manifold learning algorithms are opaque in how they perform this transformation. Understanding the way in which the embedding relates to the original high-dimensional space is critical in exploratory data analysis. We previously proposed a Genetic Programming method that performed manifold learning by evolving mappings that are transparent and interpretable. This method required the dimensionality of the embedding to be known a priori, which makes it hard to use when little is known about a dataset. In this paper, we substantially extend our previous work, by introducing a multi-objective approach that automatically balances the competing...", } @Article{Lensen:ieeeCYB, author = "Andrew Lensen and Bing Xue and Mengjie Zhang", title = "Genetic Programming for Evolving a Front of Interpretable Models for Data Visualization", journal = "IEEE Transactions on Cybernetics", year = "2021", volume = "51", number = "11", pages = "5468--5482", month = nov, keywords = "genetic algorithms, genetic programming, Data Visualisation", DOI = "doi:10.1109/TCYB.2020.2970198", URL = "https://arxiv.org/abs/2001.09578", ISSN = "2168-2275", abstract = "Data visualization is a key tool in data mining for understanding big datasets. Many visualization methods have been proposed, including the well-regarded state-of-the-art method t-distributed stochastic neighbor embedding. However, the most powerful visualization methods have a significant limitation: the manner in which they create their visualization from the original features of the dataset is completely opaque. Many domains require an understanding of the data in terms of the original features; there is hence a need for powerful visualization methods which use understandable models. In this article, we propose a genetic programming (GP) approach called GP-tSNE for evolving interpretable mappings from the dataset to high-quality visualizations. A multiobjective approach is designed that produces a variety of visualizations in a single run which gives different tradeoffs between visual quality and model complexity. Testing against baseline methods on a variety of datasets shows the clear potential of GP-tSNE to allow deeper insight into data than that provided by existing visualization methods. We further highlight the benefits of a multiobjective approach through an in-depth analysis of a candidate front, which shows how multiple models can be analyzed jointly to give increased insight into the dataset.", notes = "See also arXiv abs/2001.09578 \cite{DBLP:journals/corr/abs-2001-09578} Also known as \cite{9007046}", } @InProceedings{Lensen:2021:EuroGP, author = "Andrew Lensen", title = "Mining Feature Relationships in Data", booktitle = "EuroGP 2021: Proceedings of the 24th European Conference on Genetic Programming", year = "2021", editor = "Ting Hu and Nuno Lourenco and Eric Medvet", series = "LNCS", volume = "12691", publisher = "Springer Verlag", address = "Virtual Event", pages = "247--262", month = "7-9 " # apr, organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Association Rule Mining, Feature Relationships, Feature Construction, Feature Analysis, Unsupervised Learning: Poster", isbn13 = "978-3-030-72811-3", URL = "https://arxiv.org/abs/2102.01355", DOI = "doi:10.1007/978-3-030-72812-0_16", size = "16 pages", abstract = "When faced with a new dataset, most practitioners begin by performing exploratory data analysis to discover interesting patterns and characteristics within data. Techniques such as association rule mining are commonly applied to uncover relationships between features (attributes) of the data. However, association rules are primarily designed for use on binary or categorical data, due to their use of rule-based machine learning. A large proportion of real-world data is continuous in nature, and discretisation of such data leads to inaccurate and less informative association rules. In this paper, we propose an alternative approach called feature relationship mining (FRM), which uses a genetic programming approach to automatically discover symbolic relationships between continuous or categorical features in data. To the best of our knowledge, our proposed approach is the first such symbolic approach with the goal of explicitly discovering relationships between features. Empirical testing on a variety of real-world datasets shows the proposed method is able to find high-quality, simple feature relationships which can be easily interpreted and which provide clear and non-trivial insight into data.", notes = "Crossover within limited number of species. Parsimony pressure: tree size penalty. UCI wine dataset Flavonoids v Proanthocyanidins. http://www.evostar.org/2021/eurogp/ Part of \cite{Hu:2021:GP} EuroGP'2021 held in conjunction with EvoCOP2021, EvoMusArt2021 and EvoApplications2021", } @Article{Lensen:ieeeTEC, author = "Andrew Lensen and Bing Xue and Mengjie Zhang", title = "Genetic Programming for Manifold Learning: Preserving Local Topology", journal = "IEEE Transactions on Evolutionary Computation", year = "2022", volume = "26", number = "4", pages = "661--675", month = aug, keywords = "genetic algorithms, genetic programming, Manifold Learning, Genetic Programming, Feature Selection, Feature Construction, Evolutionary Learning", ISSN = "1089-778X", URL = "https://www.andrewlensen.com/files/lensen2021genetic.pdf", URL = "https://arxiv.org/abs/2108.09914", DOI = "doi:10.1109/TEVC.2021.3106672", size = "15 pages", abstract = "Manifold learning methods are an invaluable tool in todays world of increasingly huge datasets. Manifold learning algorithms can discover a much lower-dimensional representation (embedding) of a high-dimensional dataset through non-linear transformations that preserve the most important structure of the original data. State-of-the-art manifold learning methods directly optimise an embedding without mapping between the original space and the discovered embedded space. This makes interpretability, a key requirement in exploratory data analysis, nearly impossible. Recently, genetic programming has emerged as a very promising approach to manifold learning by evolving functional mappings from the original space to an embedding. However, genetic programming-based manifold learning has struggled to match the performance of other approaches. In this work, we propose a new approach to using genetic programming for manifold learning, which preserves local topology. This is expected to significantly improve performance on tasks where local neighbourhood structure (topology) is paramount. We compare our proposed approach with various baseline manifold learning methods and find that it often outperforms other methods, including a clear improvement over previous genetic programming approaches. These results are particularly promising, given the potential interpretability and reusability of the evolved mappings.", notes = "Evolutionary Computation Research Group (ECRG), Victoria University of Wellington, Wellington 6140, New Zealand also known as \cite{9520652}", } @Misc{lensertan, author = "Scott R Lenser and Desney S Tan", title = "Genetic Algorithms for Synthesizing Data Value Predictors", howpublished = "CMU Computer Science 15740: Computer Architecture Final Project", year = "1999", month = "Fall", keywords = "genetic algorithms, genetic programming, data value prediction, instruction-level parallelism, speculative execution, pipeline branch prediction, GAlib, PVM, parallel GP, DEC Alpha", URL = "http://www.cs.cmu.edu/~slenser/ca_project", URL = "http://www.cs.cmu.edu/~slenser/ca_project/lensertan.pdf", size = "8 pages", abstract = "As processor architectures increase their reliance on speculative parallel execution of sequential programs, the importance of not only what instructions to execute, but also how to resolve data dependence has increased. Data dependences present a major hurdle to the amount of instruction-level parallelism that can be exploited. Datavalue prediction is a technique that by passes these dependences by speculating on the outcomes of producer instructions, allowing consumer instructions to execute in parallel. The goal of our project is to explore the application of genetic algorithms (GAs) to the design of value prediction hardware.", notes = "two tree GP (64 bit prediction: 1 bit use or not prediction) BLUT and LUT lookup tables. Crossover of graphs. SPEC95 (CINT95) benchmark, Digital western lab's ATOM. Cited by \cite{krauss:2020:EuroGP}", } @InCollection{lent:1994:trade, author = "Brian Lent", title = "Evolution of Trade Strategies using Genetic Algorithms and Genetic Programming", booktitle = "Genetic Algorithms at Stanford 1994", year = "1994", editor = "John R. Koza", pages = "87--98", address = "Stanford, California, 94305-3079 USA", month = dec, organisation = "Stanford University", publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "This volume contains 20 papers written and submitted by students describing their term projects for the course {"}Genetic Algorithms and Genetic Programming{"} (Computer Science 426) at Stanford University offered during the fall quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs426.html", } @InProceedings{Leonard:2015:GECCO, author = "Philip Leonard and David Jackson", title = "Efficient Evolution of High Entropy RNGs Using Single Node Genetic Programming", booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation", year = "2015", editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr", isbn13 = "978-1-4503-3472-3", pages = "1071--1078", keywords = "genetic algorithms, genetic programming", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739480.2754820", DOI = "doi:10.1145/2739480.2754820", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Random Number Generators are an important aspect of many modern day software systems, cryptographic protocols and modelling techniques. To be more accurate, it is Pseudo Random Number Generators (PRNGs) that are more commonly used over their expensive, and less practical hardware based counterparts. Given that PRNGs rely on some deterministic algorithm (typically a Linear Congruential Generator) we can leverage Shannon's theory of information as our fitness function in order to generate these algorithms by evolutionary means. In this paper we compare traditional Genetic Programming (GP) against its graph based implementation, Single Node Genetic Programming (SNGP), for this task. We show that with SNGPs unique program structure and use of dynamic programming, it is possible to obtain smaller, higher entropy PRNGs, over six times faster and produced at a solution rate twice that achieved using Koza's standard GP model. We also show that the PRNGs obtained from evolutionary methods produce higher entropy outputs than other widely used PRNGs and Hardware RNGs (specifically recordings of atmospheric noise), as well as surpassing them in a variety of other statistical tests presented in the NIST RNG test suite.", notes = "Also known as \cite{2754820} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)", } @Article{Leong:2018:JMCE, author = "Hsiao Yun Leong and Dominic Ek Leong Ong and Jay G. Sanjayan and Sze Miang Kueh", title = "Effects of significant variables on compressive strength of soil-fly ash geopolymer: variable analytical approach based on neural networks and genetic programming", journal = "Journal of Materials in Civil Engineering", year = "2018", volume = "30", number = "7", month = jul, keywords = "genetic algorithms, genetic programming, Polymer, Compressive strength, Neural networks, Soil analysis, Synthetic materials, Soil strength, Soil compression, Ashes", publisher = "American Society of Civil Engineers", ISSN = "0899-1561", URL = "http://hdl.handle.net/1959.3/443163", URL = "https://researchbank.swinburne.edu.au/items/a778b74d-b507-44cd-a0fd-5f94c2f1cc37/1/", DOI = "doi:10.1061/(ASCE)MT.1943-5533.0002246", abstract = "The identification of significant input variables to the output provides very useful information for mix design for soil-fly ash geopolymer in order to obtain the optimum compressive strength. The importance of input variables to the output of soil-fly ash geopolymer is quantified by Garson algorithm and connection weights approach in an artificial neural networks (ANN) model, whereas model analysis and fitness method are used in a genetic programming (GP) model. The former approaches in the ANN model use the connection weights among the input, hidden, and output layers to evaluate the importance of the input variables. The latter methods in the GP model assess the frequency of variables used in the model and the value of fitness for the evaluation. The assessment results identify the percentages of fly ash, water, and soil as important input variables to the output. The percentage of hydroxide and the ratios of silicate to hydroxide and alkali activator to ash are ranked as less important input variables. The positive or negative relationships between these input variables and the output demonstrate a very significant influence on the strength development of soil-fly ash geopolymer, showing a positive or negative effect on the compressive strength.", notes = "Faculty of Engineering, Science and Computing, Research Centre for Sustainable Technologies, Swinburne Univ. of Technology Sarawak Campus, 93350 Kuching, Sarawak, Malaysia", } @InCollection{leong:1998:GSRC, author = "Kian Fai Leong", title = "Genetically Solving a Rubik's Cube", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1998", year = "1998", editor = "John R. Koza", pages = "58--67", address = "Stanford, California, 94305-3079 USA", month = "17 " # mar, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-212568-8", notes = "part of \cite{koza:1998:GAGPs}", } @InProceedings{Leong:2013:ICCSCE, author = "Leow Chin Leong and Gan Kim Soon and Tan Tse Guan and Chin Kim On and Rayner Alfred and Patricia Anthony", title = "Self-synthesized controllers for tower defense game using genetic programming", booktitle = "IEEE International Conference on Control System, Computing and Engineering (ICCSCE 2013)", year = "2013", month = nov, pages = "487--492", keywords = "genetic algorithms, genetic programming, artificial intelligence, computer games, Artificial Neural Network, ANN, Tower Defence (TD) Game, Feed-forward Neural Network, FFNN, Elman-Recurrent Neural Network, ERNN", DOI = "doi:10.1109/ICCSCE.2013.6720014", abstract = "In this paper, we describe the results of implementing Genetic Programming (GP) using two different Artificial Neural Networks (ANN) topologies in a customised Tower Defence (TD) games. The ANNs used are (1) Feed-forward Neural Network (FFNN) and (2) Elman-Recurrent Neural Network (ERNN). TD game is one of the strategy game genres. Players are required to build towers in order to prevent the creeps from reaching their bases. Lives will be deducted if any creeps manage to reach the base. In this research, a map will be designed. The AI method used will self-synthesise and analyse the level of difficulty of the designed map. The GP acts as a tuner of the weights in ANNs. The ANNs will act as players to block the creeps from reaching the base. The map will then be evaluated by the ANNs in the testing phase. Our findings showed that GP works well with ERNN compared to GP with FFNN.", notes = "Also known as \cite{6720014}", } @InProceedings{lerena:1999:CMC, author = "Patricio Lerena and Michele Courant", title = "Complexity in Mate Choice", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1446", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "artificial life, adaptive behaviour and agents, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-034.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-034.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Leroux:2014:GECCOcomp, author = "Claris Leroux and Fernando E. B. Otero and Colin G. Johnson", title = "A genetic programming problem definition language code generator for the epochX framework", booktitle = "GECCO 2014 Workshop on Evolutionary Computation Software Systems (EvoSoft)", year = "2014", editor = "Stefan Wagner and Michael Affenzeller", isbn13 = "978-1-4503-2881-4", keywords = "genetic algorithms, genetic programming", pages = "1149--1154", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2598394.2605691", DOI = "doi:10.1145/2598394.2605691", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "There are many different genetic programming (GP) frameworks that can be used to implement algorithms to solve a particular optimisation problem. In order to use a framework, users need to become familiar with a large numbers of source code before actually implementing the algorithm, adding a learning overhead. In some cases, this can prevent users from trying out different frameworks. This paper discusses the implementation of a code generator in the EpochX framework to facilitate the implementation of GP algorithms. The code generator is based on the GP definition language (GPDL), which is a framework-independent language that can be used to specify GP problems.", notes = "Also known as \cite{2605691} Distributed at GECCO-2014.", } @InProceedings{leser:2024:GECCOcomp, author = "Patrick Leser and Geoffrey Bomarito and Gabriel Kronberger and Fabricio {Olivetti De Franca}", title = "Comparing Methods for Estimating Marginal Likelihood in Symbolic Regression", booktitle = "Symbolic Regression", year = "2024", editor = "William {La Cava} and Steven Gustafson", pages = "2058--2066", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, symbolic regression, model selection, equation learning, marginal likelihood", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3664142", size = "9 pages", abstract = "Marginal likelihood has been proposed as a genetic programming-based symbolic regression (GPSR) fitness metric to prevent overly complex expressions and overfitting, particularly when data is limited and noisy. Here, two particular methods for estimating marginal likelihood - the Laplace approximation and sequential Monte Carlo - are studied with a focus on tradeoffs between accuracy and computational efficiency. The comparison focuses on practical challenges in the context of two sets of example problems. First, the methods are compared on handcrafted expressions exhibiting nonlinearity and multimodality in their respective posterior distributions. Next, the methods are compared on a real-world set of equations produced by GPSR using training data from a well-known symbolic regression benchmark. A key finding is that there are potentially significant differences between the methods that, for example, could lead to conflicting selection of expressions within a GPSR implementation. However, it is concluded that there are scenarios where either method could be preferred over the other based on accuracy or computational budget. Algorithmic improvements for both methods as well as future areas of study are discussed.", notes = "GECCO-2024 SymReg A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InCollection{lesko:1999:BBWHEMWWUGP, author = "Jim Lesko", title = "Building a Better Wumpuus Hunter: Evaluating Memory in the World of the Wumpus Using Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1999", year = "1999", editor = "John R. Koza", pages = "115--121", address = "Stanford, California, 94305-3079 USA", month = "15 " # mar, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:1999:GAGPs}", } @InProceedings{Letia:2012:AQTR, author = "Tiberiu S. Letia and Mihai Hulea and Octavian Cuibus", title = "Controller synthesis method for Discrete Event Systems", booktitle = "IEEE International Conference on Automation Quality and Testing Robotics (AQTR 2012)", year = "2012", month = "24-27 " # may, pages = "85--90", keywords = "genetic algorithms, genetic programming, Lisp descriptions, controller synthesis method, deadlocks, delay time Petri nets, discrete event systems, time Petri net language, time Petri nets models, Petri nets, control system synthesis, discrete event systems", DOI = "doi:10.1109/AQTR.2012.6237680", size = "6 pages", abstract = "Many applications contain plants that are Discrete Events Systems (DES). They have to be controlled such that DES fulfils some specifications like: avoid the deadlocks, reach or avoid the reaching of some given states, execute or avoid the execution of some given sequences of events, execute cyclically sequences of events with the shortest periods, etc. In the current study the plants are modelled by Delay Time Petri Nets (DTPN) and the controllers are Time Petri Nets (TPN) models. The controllers can be described by a particular Time Petri Net Language (TPNL). The TPNL descriptions can be transformed into Lisp descriptions. The latest are used by a Genetic Programming (GP) method for the controller synthesis such that DES meets most accurately the system requirements.", notes = "Also known as \cite{6237680}", } @InProceedings{conf/rita/LetiaC13, author = "Tiberiu Letia and Octavian Cuibus", title = "Automatic Linear Robot Control Synthesis Using Genetic Programming", publisher = "Springer", year = "2013", volume = "274", keywords = "genetic algorithms, genetic programming", bibdate = "2014-05-05", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/rita/rita2013.html#LetiaC13", booktitle = "RiTA", editor = "Jong-Hwan Kim and Eric T. Matson and Hyun Myung and Peter Xu and Fakhri Karray", isbn13 = "978-3-319-05581-7", pages = "601--618", series = "Advances in Intelligent Systems and Computing", URL = "http://dx.doi.org/10.1007/978-3-319-05582-4", } @InProceedings{Letia:2014:ICSTCC, author = "Tiberiu S. Letia and Ors Kilyen", booktitle = "18th International Conference System Theory, Control and Computing (ICSTCC 2014)", title = "Enhancing the Time Petri Nets for Automatic Hybrid Control Synthesis", year = "2014", month = oct, pages = "621--626", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICSTCC.2014.6982486", abstract = "Hybrid control refers to applications having a discrete event part and a continuous one that involves the interactions of different model types. Their control synthesis is difficult due to the fact that they contain models that belong to different approaches. In the current study the Time Petri Nets (TPNs) are used for discrete event controlled part model. For the continuous plant model is used a discrete time system (DTS) and the Fuzzy Logic Control (FLC) for its control. A new proposed method links the TPN models with FLC models. The formal Time Petri Net based Language (TPNL) (used for TPNs description) is enhanced to comprise the FLC functions and thus the entire hybrid control system can be described. The hybrid control descriptions constructed with the extended TPNL are transformed into trees that are further processed by Genetic Programming for control structure synthesis. The controller's parameters are improved by Genetic Algorithm (GA). The evolutionary individuals are organised in species taking into account the isomorphic distance between them. The species evolution implemented by GP is alternated by its adaptation performed by GA. New methods are used for the bloat control leading to a higher speed of solution search.", notes = "Dept. of Autom., Tech. Univ. of Cluj-Napoca, Cluj-Napoca, Romania Also known as \cite{6982486}", } @InProceedings{Letia:2015:ieeeICCP, author = "Tiberiu S. Letia and Attila O. Kilyen", booktitle = "2015 IEEE International Conference on Intelligent Computer Communication and Processing (ICCP)", title = "Evolutionary synthesis of hybrid controllers", year = "2015", pages = "133--140", abstract = "Automatic synthesis of control systems for hybrid systems is the main goal of the current approach. Such applications are found in the cyber-physical systems. These systems usually include some hardware components endowed with sensors and actuators that integrate software components. Each of the software components provides verified control competences. The research had the goal to conceive a method capable to automatically synthesise the software that implements specified competences and join them to compose the necessary hybrid control system. Enhanced Time Petri Nets (ETPNs) are used to model the different control components that react accordingly to expected competences. The ETPNs can model concurrent structures as well as their implementation behaviours. These models can be described by an ETPN based language (ETPNL). The capability descriptions are transformed into Lisp expressions that allow the use of Genetic Programming (GP) to guide the software evolution according to some performance criteria. The evolutionary system is used to perform the controller adaptations to their specific environments. For the general purpose, the control system is composed of Discrete Event Controller (DEC) and/or Discrete Time Controller (DTC), each of them providing different kinds of competences. The control system synthesis of an electric hydro power plant composed of a lake and two generators is used to show the application of this approach.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICCP.2015.7312618", month = sep, notes = "Also known as \cite{7312618}", } @Article{Letia:2013:PV, author = "T. S. Letia and O. Cuibus and M. Hulea and R. Miron", title = "Automatic Control Synthesis of Hydro-Power Systems", journal = "IFAC Proceedings Volumes", volume = "46", number = "6", pages = "119--124", year = "2013", note = "2nd IFAC Workshop on Convergence of Information Technologies and Control Methods with Power Systems", ISSN = "1474-6670", DOI = "doi:10.3182/20130522-3-RO-4035.00027", URL = "http://www.sciencedirect.com/science/article/pii/S1474667016341945", abstract = "The control of a set of hydro-power plants with or without reservoirs is taken into account. Even if the water input flows vary significantly, the lake levels have to be constrained and the power generation should be preferentially provided during the demanded peak hours. Each power station has assigned a local controller capable to communicate with its neighbour controllers. The proposed genetic programming method automatically generates a distributed Time Petri Net (TPN) that is capable to minimize the lake level variations and to update the decisions to start the generator(s) with the input flow levels. The cooperation of neighbour controllers improves the local control decisions taking into account the neighbour decisions and so reducing the daily generator start number.", keywords = "genetic algorithms, genetic programming, Petri net, control system synthesis, hydroelectric system, power system", } @TechReport{vuw-CS-TR-04-12, author = "Malcolm Lett and Mengjie Zhang", title = "New Fitness Functions in Genetic Programming for Object Detection", institution = "Computer Science, Victoria University of Wellington", year = "2004", number = "CS-TR-04-12", address = "New Zealand", keywords = "genetic algorithms, genetic programming", URL = "http://www.mcs.vuw.ac.nz/comp/Publications/CS-TR-04-12.abs.html", URL = "http://www.mcs.vuw.ac.nz/comp/Publications/archive/CS-TR-04/CS-TR-04-12.ps.gz", URL = "http://www.mcs.vuw.ac.nz/comp/Publications/archive/CS-TR-04/CS-TR-04-12.pdf", abstract = "Object detection is an important field of research in computer vision which genetic programming has been applied to recently. This paper describes two new fitness functions in genetic programming for object detection. Both fitness functions are based on recall and precision of genetic programs. The first is a tolerance based fitness function and the second is a weighted fitness function. The merits and effectiveness of the two fitness function are discussed. The two fitness functions are examined and compared on three object detection problems of increasing difficulty. The results suggest that both fitness functions perform very well on the relatively easy problem, the weighted fitness function outperforms the tolerance based fitness function on the relatively difficult problems.", } @InProceedings{LettZhang:04:ivcnz, author = "Malcolm Lett and Mengjie Zhang", title = "New Fitness Functions in Genetic Programming for Object Detection", booktitle = "Proceeding of Image and Vision Computing International Conference", year = "2004", editor = "David Pairman and Heather North and Stephen McNeill", pages = "441--446", month = nov, publisher = "Lincoln, Landcare Research", address = "Akaroa, New Zealand", keywords = "genetic algorithms, genetic programming, object detection, object localisation, fitness function", URL = "http://www.worldcat.org/title/proceedings-image-and-vision-computing-new-zealand-2004-the-gaiety-hall-akaroa-new-zealand-21-23-november-2004/oclc/156245877", size = "6 pages", abstract = "Object detection is an important field of research in computer vision which genetic programming has been applied to recently. This paper describes two new fitness functions in genetic programming for object detection. Both fitness functions are based on recall and precision of genetic programs. The first is a tolerance based fitness function and the second is a weighted fitness function. The merits and effectiveness of the two fitness function are discussed. The two fitness functions are examined and compared on three object detection problems of increasing dificulty. The results suggest that both fitness functions perform very well on the relatively easy problem, the weighted fitness function outperforms the tolerance based fitness function on the relatively dificult problems.", notes = "see also \cite{vuw-CS-TR-04-12} \cite{Lett:BSc} www.elec.canterbury.ac.nz/ivcnz/Programme.pdf (URL broken Mar 2018) Fri, 02 Jun 2006 17:03:20 +0800 IVCNZ", } @Misc{Lett:BSc, author = "Malcolm Lett", title = "Improving Training Performance of Genetic Programming for Object Detection", howpublished = "Undergraduate project", school = "Computer Science, Victoria University of Wellington", year = "2004", address = "Wellington, New Zealand", month = oct # " 23", keywords = "genetic algorithms, genetic programming", URL = "http://homepages.ecs.vuw.ac.nz/~mengjie/students/malcolm.pdf", size = "66 pages", abstract = "Object detection has become an important research topic within computer science. Databases of images need to be searched, security images and speed camera images need image processing to search for various information, and there is an increased desire that computers should be able to recognise human faces and determine who they are. Genetic programming has been used for these sorts of tasks with varying success, however object detection is still a difficult task and can require long training times. This project investigates the task of finding the accurate positions of objects. From this investigation, two new fitness functions are devised which are competitive with existing methods in terms of detection rate, false alarm rate and time to evolve the solution programs when applied to data sets of increasing difficulty. Also produced from this investigation are guidelines for the types of data which should be trained on for object detection.", notes = "Submitted in partial fulfilment of the requirements for Bachelor of Science with Honours in Computer Science. Supervisor: Dr. Mengjie Zhang", } @InProceedings{Leung:2002:ICAIET, author = "Kwong Sak Leung and Kin Hong Lee and Sin Man Cheang", title = "Genetic Parallel Programming - Evolving Linear Machine Codes on a Multiple-ALU Processor", booktitle = "Proceedings of International Conference on Artificial Intelligence in Engineering and Technology - ICAIET 2002", year = "2002", editor = "Sazali Yaacob and R. Nagarajan and Ali Chekima", pages = "207--213", month = "17-18 " # jun, address = "Sabah, Malaysia", publisher_address = "Sabah, Malaysia", organisation = "School of Engineering and Information Technology, Universiti Malaysia Sabah", publisher = "Universiti Malaysia Sabah", keywords = "genetic algorithms, genetic programming", ISBN = "983-2188-92-X", abstract = "Genetic Programming (GP) is a robust method in Evolutionary Computation. There are two main streams in GP, namely, Tree-based GP (TGP) and Linear GP (LGP). TGP evolves programs represented in tree structure. LGP evolves sequential programs directly. LGP suffers from inflexibility while TGP suffers from inefficiency. This paper proposes a novel framework of an integrated system called Genetic Parallel Programming (GPP) for evolving optimal parallel programs by LGP. The core of the GPP consists of a Multi-ALU Processor (MAP) and an Evolution Engine (EE). The MAP uses Multiple Instruction streams Multiple Data streams (MIMD) architecture. The EE uses a two-phase evolutionary approach and a new GP operation to swap sub-instructions in a parallel program. Three experiments (i.e. Cubic function, Sextic function and Artificial Ant - Santa Fe Trail) are given as examples to show that GPP could discover novel parallel programs that fully use the processor's parallelism. The GPP opens up an entire new opportunity for solving problems with appropriate parallel architecture and learning optimal programs/algorithms automatically.", notes = "http://books.google.co.uk/books?id=ejBaPgAACAAJ", } @InProceedings{leung:2002:epmpfamp, author = "Kwong Sak Leung and Kin Hong Lee and Sin Man Cheang", title = "Evolving Parallel Machine Programs for a {Multi-ALU} Processor", booktitle = "Proceedings of the 2002 Congress on Evolutionary Computation CEC2002", editor = "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and Mark Shackleton", pages = "1703--1708", year = "2002", publisher = "IEEE Press", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", organisation = "IEEE Neural Network Council (NNC), Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", ISBN = "0-7803-7278-6", month = "12-17 " # may, notes = "CEC 2002 - A joint meeting of the IEEE, the Evolutionary Programming Society, and the IEE. Held in connection with the World Congress on Computational Intelligence (WCCI 2002)", keywords = "genetic algorithms, genetic programming, Factorial sequence, Fibonacci sequence, Sextic function, experiments, genetic parallel programming, linear genetic programming, multi-ALU processor, optimal parallel program evolution, parallel machine programs, program execution speed optimisation, two-phase evolution, instruction sets, multiprocessing systems, parallel programming", DOI = "doi:10.1109/CEC.2002.1004499", abstract = "This paper proposes a novel genetic parallel programming (GPP) paradigm for evolving optimal parallel programs running on a multi-ALU processor by linear genetic programming. GPP uses a two-phase evolution approach. It evolves completely correct solution programs in the first phase. Then it optimises execution speeds of solution programs in the second phase. Besides, GPP also employs a new genetic operation that swaps sub-instructions of a solution program. Three experiments (Sextic, Fibonacci and Factorial) are given as examples to show that GPP could discover novel parallel programs that fully use the processor's parallelism", } @InProceedings{leung03, author = "Kwong Sak Leung and Kin Hong Lee and Sin Man Cheang", title = "Parallel Programs are More Evolvable than Sequential Programs", booktitle = "Genetic Programming, Proceedings of EuroGP'2003", year = "2003", editor = "Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa", volume = "2610", series = "LNCS", pages = "107--118", address = "Essex", publisher_address = "Berlin", month = "14-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-00971-X", DOI = "doi:10.1007/3-540-36599-0_10", abstract = "This paper presents a novel phenomenon of the Genetic Parallel Programming (GPP) paradigm - the GPP accelerating phenomenon. GPP is a novel Linear Genetic Programming representation for evolving parallel programs running on a Multi-ALU Processor (MAP). We carried out a series of experiments on GPP with different number of ALUs. We observed that parallel programs are more evolvable than sequential programs. For example, in the Fibonacci sequence regression experiment, evolving a 1-ALU sequential program requires 51 times on average of the computational effort of an 8-ALU parallel program. This paper presents three benchmark problems to show that the GPP can accelerate evolution of parallel programs. Due to the accelerating evolution phenomenon of GPP over sequential program evolution, we could increase the normal GP's evolution efficiency by evolving a parallel program by GPP and if there is a need, the evolved parallel program can be translated into a sequential program so that it can run on conventional hardware.", notes = "EuroGP'2003 held in conjunction with EvoWorkshops 2003", } @InProceedings{levenick:1999:SI, author = "James R. Levenick", title = "Swappers: Introns promote flexibility, diversity and invention", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "361--368", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/swappers.ps", URL = "http://www.willamette.edu/~levenick/swappers.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{conf/amw/LevinH10, title = "Using Genetic Programming to Evaluate the Impact of Social Network Analysis in Author Name Disambiguation", author = "Felipe Hoppe Levin and Carlos A. Heuser", booktitle = "Proceedings of the 4th Alberto Mendelzon International Workshop on Foundations of Data Management, Buenos Aires, Argentina, May 17-20, 2010", publisher = "CEUR-WS.org", year = "2010", volume = "619", editor = "Alberto H. F. Laender and Laks V. S. Lakshmanan", series = "CEUR Workshop Proceedings", URL = "http://ceur-ws.org/Vol-619/paper2.pdf", keywords = "genetic algorithms, genetic programming", bibdate = "2010-08-30", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/amw/amw2010.html#LevinH10", } @InProceedings{Levine:evowks03, author = "John Levine and David Humphreys", title = "Learning Action Strategies for Planning Domains using Genetic Programming", booktitle = "Applications of Evolutionary Computing, EvoWorkshops2003: Evo{BIO}, Evo{COP}, Evo{IASP}, Evo{MUSART}, Evo{ROB}, Evo{STIM}", year = "2003", editor = "G{\"u}nther R. Raidl and Stefano Cagnoni and Juan Jes\'us Romero Cardalda and David W. Corne and Jens Gottlieb and Agn\`es Guillot and Emma Hart and Colin G. Johnson and Elena Marchiori and Jean-Arcady Meyer and Martin Middendorf", volume = "2611", series = "LNCS", pages = "684--695", address = "University of Essex, England, UK", publisher_address = "Berlin", month = "14-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, evolutionary computation, applications, L2Plan, planning, EvoCK, blocksworld, STRIPS, PDDL, Briefcase Domain", isbn13 = "978-3-540-00976-4", URL = "http://www.cis.strath.ac.uk/~johnl/papers/levine-evostim03.pdf", URL = "http://citeseer.ist.psu.edu/569259.html", DOI = "doi:10.1007/3-540-36605-9_62", size = "13 pages", abstract = "There are many different approaches to solving planning problems, one of which is the use of domain specific control knowledge to help guide a domain independent search algorithm. This paper presents L2Plan which represents this control knowledge as an ordered set of control rules, called a policy, and learns using genetic programming. The genetic program's crossover and mutation operators are augmented by a simple local search. L2Plan was tested on both the blocks world and briefcase domains. In both domains, L2Plan was able to produce policies that solved all the test problems and which outperformed the hand-coded policies written by the authors.", notes = "learn to plan. Prodigy4.0, Planning as a Tree Search. Policy Restricted Planning. Evolution of Policies. Single Point Rule Level Crossover. Rule Addition Mutation. EvoWorkshops2003", } @InCollection{levitt:1995:TGAGS, author = "Jeremy R. Levitt", title = "The Genetic Algorithm applied to Gate Sizing", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1995", year = "1995", editor = "John R. Koza", pages = "191--198", address = "Stanford, California, 94305-3079 USA", month = "11 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms", ISBN = "0-18-195720-5", notes = "part of \cite{koza:1995:gagp}", } @Book{Levy:2005:ru, author = "David Levy", title = "Robots Unlimited: Life in a Virtual Age", publisher = "A K Peters/CRC Press", year = "2005", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1568812397", URL = "http://www.amazon.co.uk/Robots-Unlimited-Life-Virtual-Age/dp/1568812396", abstract = "Consider this: Robots will one day be able to write poetry and prose so touching that it will make men weep; compose dozens or even hundreds of symphonies that will rival the work of Mozart; judge a court case with absolute impartiality and fairness; or even converse with the natural ease of your best friend. Robots will one day be so life-like that a human could fall in love and marry one. Thought provoking and controversial? Certainly. Far-fetched? Not at all. David Levy presents the history of Artificial Intelligence, considers recent developments, and speculates about the future of AI. A complete bibliography is available here.", notes = "Several mentions of GP", size = "466 pages", } @InProceedings{Lew:2010:HFES, author = "Roger Lew and Brian P. Dyre and Terence Soule and Stuart A. Ragsdale and Steffen Werner", title = "Assessing Mental Workload from Skin Conductance and Pupillometry using Wavelets and Genetic Programming", booktitle = "Proceedings of the Human Factors and Ergonomics Society Annual Meeting", year = "2010", volume = "54", number = "3", pages = "254--258", address = "San Francisco, USA", month = "27 " # sep # " - 1 " # oct, organisation = "Human Factors and Ergonomics Society", publisher = "Sage", keywords = "genetic algorithms, genetic programming, augmented cognition", isbn13 = "978-0-945289-37-1", DOI = "doi:10.1177/154193121005400315", abstract = "An essential component of augmented cognition (AC) is developing robust methods of extracting reliable and meaningful information from physiological measures in real-time. To evaluate the potential of skin conductance (SC) and pupil diameter (PD) measures, we used a dual-axis pursuit tracking task where the control mappings repeatedly and abruptly rotated 90degree throughout the trials to provide an immediate and obvious challenge to proper system control. Using these data, a model-building technique novel to these measures, genetic programming (GP) with scaled symbolic regression and Age Layered Populations (ALPS), was compared to traditional linear discriminant analysis (LDA) for predicting tracking error and control-mapping state. When compared with traditional linear modelling approaches, symbolic regression better predicted both tracking error and control mapping state. Furthermore, the estimates obtained from symbolic regression were less noisy and more robust.", notes = "1 Department of Psychology and Communication Studies. 2 Department of Computer Science University of Idaho Moscow, ID http://hcibib.org/bibtoc.cgi?file=ftp/HFES10*", } @Article{Lew:2006:MSSP, author = "T. L. Lew and A. B. Spencer and F. Scarpa and K. Worden and A. Rutherford and F. Hemez", title = "Identification of response surface models using genetic programming", journal = "Mechanical Systems and Signal Processing", year = "2006", volume = "20", number = "8", pages = "1819--1831", month = nov, keywords = "genetic algorithms, genetic programming, Surrogate/replacement model, Response surface models, Symbolic regression", DOI = "doi:10.1016/j.ymssp.2005.12.003", abstract = "There is a move in modern research in Structural Dynamics towards analysing the inherent uncertainty in a given problem. This may be quantifying or fusing uncertainty models, or can be propagation of uncertainty through a system or calculation. If the system of interest is represented by, e.g. a large Finite Element (FE) model the large number of computations involved can rule out many approaches due to the expense of carrying out many runs. One way of circumnavigating this problem is to replace the true system by an approximate surrogate/replacement model, which is fast-running compared to the original. In traditional approaches using response surfaces a simple least-squares multinomial model is often adopted. The objective of this paper is to extend the class of possible models considerably by carrying out a general symbolic regression using a Genetic Programming approach. The approach is demonstrated on both univariate and multivariate problems with both computational and experimental data.", } @InProceedings{Lewin:2005:IFAC, title = "Applications in IC Manufacturing", author = "Daniel R. Lewin and Sivan Lachman-shalem and Benyamin Grosman", booktitle = "16th IFAC World Congress", year = "2005", editor = "Pavel Zitek", address = "Prague, Czech Republic", month = jul # " 4-8", keywords = "genetic algorithms, genetic programming, integrated circuit manufacturing, process systems engineering, model-based control, process monitoring, yield enhancement", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.468.1497", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.468.1497", URL = "http://www.nt.ntnu.no/users/skoge/prost/proceedings/ifac2005/Fullpapers/03538.pdf", size = "6 pages", abstract = "The manufacture of integrated circuits is driven by a demand for faster calculation capabilities and lower costs, which will require the development of a new generation of manufacturing tools to increase yield productivity, spearheaded by improved measurement devices and advanced process control. The objectives of this paper are review of the challenges in two main PSE areas: process monitoring and process control. PSE solutions appropriate for these challenges involve harnessing multivariate statistics, automated modelling approaches like genetic programming, and multivariable model-based control. The paper is illustrated with several example applications, all tested in", notes = "http://www.nt.ntnu.no/users/skoge/prost/proceedings/ifac2005/Index.html", } @Article{Lewin:2006:CEP, author = "Daniel R. Lewin and Sivan Lachman-Shalem and Benyamin Grosman", title = "The role of process system engineering (PSE) in integrated circuit (IC) manufacturing", journal = "Control Engineering Practice", year = "2006", volume = "15", number = "7", pages = "793--802", month = jul, note = "Special Issue on Award Winning Applications, 2005 IFAC World Congress", keywords = "genetic algorithms, genetic programming, Integrated circuit manufacturing, Process systems engineering, Model-based control, Process monitoring, Yield enhancement", DOI = "doi:10.1016/j.conengprac.2006.04.003", abstract = "The manufacture of integrated circuits is driven by a demand for faster calculation capabilities and lower costs, which will require the development of a new generation of manufacturing tools to increase yield productivity, spearheaded by improved measurement devices and advanced process control. The objectives of this paper are to review of the challenges in applying two areas of expertise in process systems engineering (PSE), namely process monitoring and control, and to motivate more academics working in PSE to get actively involved. PSE solutions appropriate for these challenges involve harnessing multivariate statistics, automated modelling approaches like genetic programming, and multivariable model-based control. The paper is illustrated with several example applications, all tested in fabrication facilities in Israel.", } @InProceedings{lewis:evows04, author = "Matthew Lewis", title = "Aesthetic Video Filter Evolution in an Interactive Real-time Framework", booktitle = "Applications of Evolutionary Computing, EvoWorkshops2004: {EvoBIO}, {EvoCOMNET}, {EvoHOT}, {EvoIASP}, {EvoMUSART}, {EvoSTOC}", year = "2004", month = "5-7 " # apr, editor = "Guenther R. Raidl and Stefano Cagnoni and Jurgen Branke and David W. Corne and Rolf Drechsler and Yaochu Jin and Colin R. Johnson and Penousal Machado and Elena Marchiori and Franz Rothlauf and George D. Smith and Giovanni Squillero", series = "LNCS", volume = "3005", address = "Coimbra, Portugal", publisher = "Springer Verlag", publisher_address = "Berlin", pages = "409--418", keywords = "genetic algorithms, genetic programming, evolutionary computation", ISBN = "3-540-21378-3", URL = "http://accad.osu.edu/research/interactive_performance_htmls/video.pdf", DOI = "doi:10.1007/978-3-540-24653-4_42", abstract = "A data-flow network-based interactive evolutionary design framework is presented which will provide a testbed for the development and exploration of a diverse range of visual artistic design spaces. The domain of real-time layered video filters is focused on as the primary example. The system supports both real-time video streams and prerecorded video. Issues of stylistic signature, GA vs. GP-based approaches, rapid tuning of fitness distributions, and desirable traits of generic evolutionary design systems are discussed.", notes = "EvoWorkshops2004", } @InProceedings{lewis:1992:gpnnwr, author = "M. Anthony Lewis and Andrew H. Fagg and Alan Solidum", title = "Genetic Programming Approach to the Construction of a Neural Network Control of a Walking Robot", booktitle = "Proceedings of the 1992 IEEE InternationalConference on Robotics and Automation", year = "1992", pages = "2618--2623", address = "Nice, France", month = may, organisation = "IEEE", publisher = "Electronica Bks", keywords = "genetic algorithms", notes = "NOT a Koza style GP but a conventional 65 bit binary string using Genesis. Evolves ANN controller for real 6 legged robot, 2 stages first uses human to score 2 neuron controller as ocsillator, when 50% of pop can do this nest stage evolves 6 such oscilators together to control robot. All runs produced tripod gait eventually, intermediate states wave gait and marked tendancy to walk backwards.", } @InProceedings{Lewis:2006:VTC, author = "Tim Lewis and Neil Fanning and Gary Clemo", title = "Enhancing IEEE802.11 DCF using Genetic Programming", booktitle = "IEEE 63rd Vehicular Technology Conference, VTC 2006-Spring", year = "2006", volume = "3", pages = "1261--1265", address = "Melbourne, Australia", month = "7-10 " # may, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9392-9", DOI = "doi:10.1109/VETECS.2006.1683037", abstract = "This paper introduces a method of designing optimised MAC layer algorithms using genetic programming. By evolving entire algorithmic behaviour rather than optimising a set of values to tune a parameterised design, a much wider space of behaviour can be explored automatically. This technique is illustrated using the variation of contention window size that is part of the distributed coordination function of 802.11. When applied to the example of a variable sized network under saturated load this approach produces expressions that comfortably outperform the standard 802.11b behaviour. Also, despite being automatically generated, these solutions achieve the throughput performance of the best enhancements to this aspect of the protocol", notes = "Res. Lab., Toshiba Telecommun., Bristol", } @InProceedings{DBLP:conf/gecco/LewisH09, author = "Tim Lewis and Russell J. Haines", title = "Formal verification to enhance evolution of protocols", booktitle = "GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation", year = "2009", editor = "Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano {van Hemert} and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique Alba", pages = "1889--1890", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming, Poster", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1569901.1570219", abstract = "This paper describes a combined evolutionary system whereby formal correctness properties are used to augment a standard functional fitness score. This system was applied to the problem of evolving the receive side of an alternating bit protocol, represented by a Petrinet. The fitness function combined a test for freedom from deadlock in addition to a functional scoring system. The efficiency gain produced nets of equal functional fitness requiring approximately one third of the number of evaluations required when functional tests were used alone. This result has wider applicability in any genetic programming evolution where formal correctness tests of the algorithms can be carried out.", notes = "Toshiba Telecommunications Laboratory, Bristol, United Kingdom GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009). ACM Order Number 910092.", } @InProceedings{Lewis:2008:gecco, author = "Tony E. Lewis and George D. Magoulas", title = "TREAD: A new genetic programming representation aimed at research of long term complexity growth", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "1339--1340", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1339.pdf", DOI = "doi:10.1145/1389095.1389353", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, artificial intelligence, representations: Poster, TREAD", abstract = "Several forms of computer program (or representation) have been proposed for Genetic Programming (GP) systems to evolve, such as linear, tree based or graph based. Typically, GP representations are highly effective during the initial search phases of evolution but stagnate before deep levels of complexity are acquired. A new representation, TREAD, is proposed to combine aspects of flow of execution and flow of data systems. The distinguishing features of TREAD are designed for researching improvements to the long term acquisition of novel features in GP (at the expense of the speed of the initial search if necessary). TREAD is validated on a symbolic regression problem and is found to be capable of successfully developing solutions through artificial evolution.", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389353} PADO \cite{teller:1995:PADO}. Data flow, flow of execution. PDGP.", } @InProceedings{DBLP:conf/gecco/LewisM09, author = "Tony E. Lewis and George D. Magoulas", title = "Strategies to minimise the total run time of cyclic graph based genetic programming with GPUs", booktitle = "GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation", year = "2009", editor = "Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano {van Hemert} and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique Alba", pages = "1379--1386", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming, cyclic cartesian genetic programming, GPU, deme, parallel interpretter", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1569901.1570086", URL = "http://results.ref.ac.uk/Submissions/Output/457444", size = "8 pages", abstract = "In this paper, we describe our work to investigate how much cyclic graph based Genetic Programming (GP) can be accelerated on one machine using currently available mid-range Graphics Processing Units (GPUs). Cyclic graphs pose different problems for evaluation than do trees and we describe how our CUDA based, {"}population parallel{"} evaluator tackles these problems. Previous similar work has focused on the evaluation alone. Unfortunately large reductions in the evaluation time do not necessarily translate to similar reductions in the total run time because the time spent on other tasks becomes more significant. We show that this problem can be tackled by having the GPU execute in parallel with the Central Processing Unit (CPU) and with memory transfers. We also demonstrate that it is possible to use a second graphics card to further improve the acceleration of one machine. These additional techniques are able to reduce the total run time of the GPU system by up to 2.83 times. The combined architecture completes a full cyclic GP run 434.61 times faster than the single-core CPU equivalent. This involves evaluating at an average rate of 3.85 billion GP operations per second over the course of the whole run.", notes = "twin GPU and dual CPU, superclocked GeForce 8800 GT, CUDA 2.0 GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009). ACM Order Number 910092.", uk_research_excellence_2014 = "This paper opened up a new research direction in accelerating Genetic Programming (GP) on GPUs. It won a best paper award at GECCO'09.", } @InProceedings{Lewis:2010:cec, author = "Tony E. Lewis and George D. Magoulas", title = "Tweaking a tower of blocks leads to a TMBL: Pursuing long term fitness growth in program evolution", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", pages = "4465--4472", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, TMBL, evolutionary computation, genetic programming, long term fitness growth, program evolution, tower of blocks, tweaking mutation behaviour learning, behavioural sciences computing, biology computing", isbn13 = "978-1-4244-6910-9", DOI = "doi:10.1109/CEC.2010.5586375", abstract = "If a population of programs evolved not for a few hundred generations but for a few hundred thousand or more, could it generate more interesting behaviours and tackle more complex problems? We begin to investigate this question by introducing Tweaking Mutation Behaviour Learning (TMBL), a form of evolutionary computation designed to meet this challenge. Whereas Genetic Programming (GP) typically involves creating a large pool of initial solutions and then shuffling them (with crossover and mutation) over relatively few generations, TMBL focuses on the cumulative acquisition of small adaptive mutations over many generations. In particular, we aim to reduce limits on long term fitness growth by encouraging tweaks: changes which affect behaviour without ruining the existing functionality. We use this notion to construct a standard representation for TMBL. We then experimentally compare TMBL against linear GP and tree-based GP and find that TMBL shows strong signs of being more conducive to the long term growth of fitness.", DOI = "doi:10.1109/CEC.2010.5586375", notes = "WCCI 2010. Also known as \cite{5586375}", } @InProceedings{Lewis:2011:CIGPU, author = "Tony E. Lewis and George D. Magoulas", title = "Identifying Similarities in TMBL Programs with Alignment to Quicken Their Compilation for GPUs", booktitle = "GECCO 2011 Computational intelligence on consumer games and graphics hardware (CIGPU)", year = "2011", editor = "Simon Harding and W. B. Langdon and Man Leung Wong and Garnett Wilson and Tony Lewis", isbn13 = "978-1-4503-0690-4", keywords = "genetic algorithms, genetic programming, Artificial Intelligence, Automatic Programming, program synthesis, Performance, Tweaking Mutation Behaviour Learning (TMBL), Alignment, Graphics Card, Graphics Processing Unit, GPU, CUDA", pages = "447--454", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin", publisher = "ACM", publisher_address = "New York, NY, USA", DOI = "doi:10.1145/2001858.2002032", size = "8 pages", abstract = "The most impressive accelerations of Genetic Programming (GP) using the Graphics Processing Unit (GPU) have been achieved by dynamically compiling new GPU code for each batch of individuals to be evaluated. This approach suffers an overhead in compilation time. We aim to reduce this penalty by pre-processing the individuals to identify and draw out their similarities, hence reducing duplication in compilation work. We use this approach with Tweaking Mutation Behaviour Learning (TMBL), a form focused on long term fitness growth. For individuals of 300 instructions, the technique is found to reduce compilation time 4.817 times whilst only reducing evaluation speed by 3.656percent.", notes = "wk307c-lewis.pdf Computational Intelligence on Consumer Games and Graphics Hardware Also known as \cite{2002032} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @InProceedings{Lewis:2011:CIGPU2, author = "Tony E. Lewis and George D. Magoulas", title = "{TMBL} Kernels for {CUDA GPUs} Compile Faster Using {PTX}", booktitle = "GECCO 2011 Computational intelligence on consumer games and graphics hardware (CIGPU)", year = "2011", editor = "Simon Harding and W. B. Langdon and Man Leung Wong and Garnett Wilson and Tony Lewis", isbn13 = "978-1-4503-0690-4", keywords = "genetic algorithms, genetic programming, GPU, Artificial Intelligence, Automatic Programming, program synthesis, Performance, Tweaking Mutation Behaviour Learning (TMBL), Parallel Thread EXecution (PTX), Graphics Card, Graphics Processing Unit, GPU, CUDA", pages = "455--462", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", publisher = "ACM", publisher_address = "New York, NY, USA", DOI = "doi:10.1145/2001858.2002033", size = "8 pages", abstract = "Many of the most effective attempts to harness the power of the Graphics Processing Unit (GPU) to accelerate Genetic Programming (GP) have dynamically compiled code for individuals as they are to be evaluated. This approach executes very quickly on the GPU but is slow to compile, hence only vast data-sets fully reap its rewards. To reduce compilation time, we generate and compile code in the lower-level language PTX. We investigate this in the context of implementing Tweaking Mutation Behaviour Learning (TMBL) on the GPU. We find that for programs of 300 instructions, using PTX reduces the compile time 5.861 times and even increases the evaluation speed by 23.029percent", notes = "wk308d-lewis.pdf Computational Intelligence on Consumer Games and Graphics Hardware Also known as \cite{2002033} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @PhdThesis{Lewis:thesis, author = "Tony Lewis", title = "Accelerating Genetic Programming Using Graphics Processing Units", school = "Department of Computer Science and Information Systems Birkbeck, University of London", year = "2011", address = "UK", keywords = "genetic algorithms, genetic programming, GPU, GPGPU, TMBL, CUDA, PTX, nVidia, Cartesian Genetic Programming, cyclic CGP, UML", URL = "http://www.dcs.bbk.ac.uk/research/recentphds.php", URL = "http://www.dcs.bbk.ac.uk/research/recentphds/tonylewis.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.298.5163", size = "241 pages", abstract = "Evolution through natural selection offers the possibility of automatically generating functionally complex solutions to a wide range of problems. Methods such as Genetic Programming (GP) show the promise of this approach but tend to stagnate after relatively few generations. To research this issue, execution speed must be substantially improved. This thesis presents work to accelerate the execution of such methods. The work uses the Graphics Processing Unit (GPU) to target the evaluation of individuals since this is the most time-consuming part of the run. Two models have been emerging for this: dynamically compiling each new generation of individuals for the GPU or using a single GPU interpreter, to which successive groups of individuals can be sent. Using the latter model, a GPU interpreter is constructed to implement cyclic GP, an advanced form of GP that imposes several challenging implementation issues which are addressed. Accelerating the evaluation using the GPU is only part of the story. The next part of the work interleaves CPU and GPU computation to keep both chips as busy as possible with the tasks to which they are best suited and then to recruit multiple GPUs and CPU cores to further accelerate the run. Using the former model, a compiling system is constructed and this is used to investigate two methods to overcome the primary difficulty with the approach: long compilation times. That system implements Tweaking Mutation Behaviour Learning (TMBL), a form focused on long term fitness growth and overcoming the previously mentioned stagnation issues. Further work optimises two CPU tasks highlighted by profiling: tournament selection and individual copying. These techniques are highly effective and permit much shorter run-times. This clears the way for research into stimulating long term fitness growth and hence for tackling new, complex problems.", notes = "supervisor, George Magoulas", } @InProceedings{Li:2010:SICE, author = "Bing Li and Shingo Mabu and Kotaro Hirasawa", title = "Automatic program generation with genetic network programming using subroutines", booktitle = "Proceedings of SICE Annual Conference 2010", year = "2010", address = "Taipei, Taiwan", month = "18-21 " # aug, pages = "3089--3094", organisation = "SICE", keywords = "genetic algorithms, genetic programming, automatic program generation, evolutionary algorithm, genetic network programming, genotype phenotype mapping technology, graph based structure, subroutine program, automatic programming, performance evaluation, subroutines", isbn13 = "978-1-4244-7642-8", URL = "http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5602565", abstract = "Genetic Network Programming with Automatic Program Generation (GNP-APG) is an evolutionary algorithm to generate programs. Genotype-phenotype mapping technology is introduced in this algorithm to create legal programs. With the help of graph-based structures of Genetic Network Programming (GNP), GNP-APG can efficiently generate robust programs to cope with problems. In this paper, the extended algorithm of GNP-APG is proposed which can create a hierarchy program, in other words, a program which contains a main function and subroutines. The proposed method works like Automatic Defined Functions (ADFs) in Genetic Programming (GP). By using subroutines, a complex program can be decomposed to several simple programs which are obtained more easily. Moreover, these subroutines might be called many times, which results in reducing the size of the program significantly. In simulations, different tile-worlds between the training phase and testing phase are used for performance evaluations and the results shows that GNP-APG with subroutines (GNP-APGsr) could have better performances than GNP-APG.", notes = "tile world. Grad. Sch. of Inf., Production & Syst., Waseda Univ., Fukuoka, Japan. Also known as \cite{5602565}", } @InProceedings{Li:2010:ieeeSMC, author = "Bing Li and Shingo Mabu and Kotaro Hirasawa", title = "Tile-world - A case study of Genetic Network Programming with automatic program generation", booktitle = "IEEE International Conference on Systems Man and Cybernetics (SMC 2010)", year = "2010", month = "10-13 " # oct, pages = "2708--2715", address = "Istanbul, Turkey", keywords = "genetic algorithms, genetic programming, Tile-world, automatic program generation, data mining, elevator control system, evolutionary algorithm, genetic network programming, genotype-phenotype mapping process, graph-based structure, performance evaluation, automatic programming, data mining, lifts", ISSN = "1062-922X", iabn13 = "978-1-4244-6586-6", DOI = "doi:10.1109/ICSMC.2010.5641793", abstract = "Genetic Network Programming (GNP) is a novel evolutionary algorithm. It has graph-based structures which is extended from Genetic Algorithm (GA) and Genetic Programming (GP). Up to now, GNP has been applied to many research fields such as data mining and elevator control systems. On the other hand, automatic program generation is a way to obtain a program without explicitly programming it, and Genetic Programming is the traditional paradigm in this field. Drawn from the inspiration of GP, GNP for Automatic Program Generation (GNP-APG) has been proposed. GNP-APG is applied to the Tile-world, which is a famous test bed with dynamic and uncertain characteristics. GNP-APG uses a kind of genotype-phenotype mapping process to create program. The procedure of the program generation based on evolution is demonstrated in this paper. In simulations, different tile-worlds between the training phase and the testing phase are used for performance evaluations and the results shows that GNP-APG could have better performances than the conventional GNP methods.", notes = "Also known as \cite{5641793} Grad. Sch. of Inf., Production & Syst., Waseda Univ., Fukuoka, Japan.", } @InProceedings{Li:2011:VSGNPwBD, title = "Variable Size Genetic Network Programming with Binomial Distribution", author = "Bing Li and Xianneng Li and Shingo Mabu and Kotaro Hirasawa", pages = "973--980", booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary Computation", year = "2011", editor = "Alice E. Smith", month = "5-8 " # jun, address = "New Orleans, USA", organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, genetic network programming, Algorithms:", DOI = "doi:10.1109/CEC.2011.5949723", abstract = "This paper proposes a different type of Genetic Network Programming (GNP) -- Variable Size Genetic Network Programming (GNPvs) with Binomial Distribution. In contrast to the individuals with fixed size in Standard GNP, GNPvs will change the size of the individuals and obtain the optimal size of them during evolution. The proposed method defines a new type of crossover to implement the new feature of GNP. The new crossover will select the number of nodes to move from each parent GNP to another parent GNP. The probability of selecting the number of nodes to move satisfies the binomial probability distribution. The proposed method can keep the effectiveness of crossover and improve the performance of GNP. In order to verify the performance of the proposed method, a well-known benchmark problem - - Tile-world is used in the simulations. The simulation results show the effectiveness of the proposed method.", notes = "CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.", } @InProceedings{Li:2012:CECa, title = "Towards Automatic Discovery and Reuse of Subroutines in Variable Size Genetic Network Programming", author = "Bing Li and Xianneng Li and Shingo Mabu and Kotaro Hirasawa", pages = "485--492", booktitle = "Proceedings of the 2012 IEEE Congress on Evolutionary Computation", year = "2012", editor = "Xiaodong Li", month = "10-15 " # jun, DOI = "doi:10.1109/CEC.2012.6256512", size = "8 pages", address = "Brisbane, Australia", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Genetic Network Programming, Algorithms", abstract = "This paper presents an algorithm to discover and reuse subroutines in Variable Size Genetic Network Programming (GNPvs) called Subroutine embedded GNPvs (SGNPvs). GNPvs is a general type of GNP, which has a direct graph representation with changeable size. In order to improve the performance of GNPvs, SGNPvs has been proposed, in which a subroutine mechanism has been introduced to GNPvs by module acquisition. In SGNPvs, useful subgraphs are extracted and reused for individuals. Through extracting new subroutines to replace the old subroutines, SGNPvs can evolve the subroutines as well as evolve the individuals. The simulation results verify the performance of SGNPvs on a well-known dynamic multi-agent test bed -- Tileworld.", notes = "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the EPS and the IET.", } @Article{Li:2013:TEEEb, author = "Bing Li and Xianneng Li and Shingo Mabu and Kotaro Hirasawa", title = "Evolving graph-based chromosome by means of variable size genetic network programming with binomial distribution", journal = "IEEJ Transactions on Electrical and Electronic Engineering", year = "2013", volume = "8", number = "4", pages = "348--356", month = jul, publisher = "Wiley", keywords = "genetic algorithms, genetic programming, variable size, genetic network programming, crossover, binomial distribution, Tileworld", ISSN = "1931-4981", DOI = "doi:10.1002/tee.21865", size = "9 pages", abstract = "Genetic network programming (GNP) is a graph-based evolutionary algorithm with fixed size, which has been proven to solve complicated problems efficiently and effectively. In this paper, variable size genetic network programming (GNPvs) with binomial distribution has been proposed, which will change the size of the individuals and obtain their optimal size during evolution. The proposed method will select the number of nodes to move from one parent GNP to another parent GNP during crossover to implement the new feature of GNP. The probability of selecting the number of nodes to move satisfies a binomial distribution. The proposed method can keep the effectiveness of crossover, improve the performance of GNP, and find the optimal size of the individuals. The well-known testbed Tileworld is used to show the numerical results in the simulations.", } @InProceedings{Li:2013:SMC, author = "Bing Li and Shanqing Yu and Kotaro Hirasawa", title = "Usage of Frequently Used Node in Variable Size Genetic Network Programming", booktitle = "IEEE International Conference on Systems, Man, and Cybernetics (SMC 2013)", year = "2013", month = oct, pages = "174--179", keywords = "genetic algorithms, genetic programming, Genetic Network Programming, Variable size, Generalisation, Tile-world", DOI = "doi:10.1109/SMC.2013.37", abstract = "This paper describes a kind of replacement mechanism which improves the generalisation ability of Variable Size Genetic Network Programming (GNPvs). GNPvs is an extension of Genetic Network Programming (GNP), which has changeable number of nodes. Inspired by the theory of Evolution by Gene Duplication, the non-frequently used nodes are replaced with the frequently used nodes in the proposed method, which can make the individual survive under the selection pressure as usual, but eventually might accumulate mutations that produce new features of individuals for adapting new environments. The effectiveness of the proposed method is verified by comparing with the performance of GNPvs and GNP on a well-known dynamic multi-agent test bed - Tile world.", notes = "Also known as \cite{6721790}", } @PhdThesis{BingLi:thesis, author = "Bing Li", title = "Study on Genetic Network Programming with Variable Size Structure and Genotype/Phenotype Mapping Mechanism", school = "Graduate School of Information, Production and Systems, Waseda University", year = "2013", address = "Japan", month = jun, keywords = "genetic algorithms, genetic programming, GNP, Genotype/Phenotype", URL = "http://hdl.handle.net/2065/44360", URL = "https://waseda.repo.nii.ac.jp/?action=repository_uri&item_id=16739&file_id=20&file_no=3", URL = "http://hdl.handle.net/2065/44360/Honbun-6415.pdf", URL = "https://dl.ndl.go.jp/info:ndljp/pid/8977235", size = "124 pages", abstract = "In the research field of Artificial Intelligence, Evolutionary Algorithms (EAs) are subset of important optimization technologies, which are inspired by the Darwin theory of evolution. EAs are kinds of effective algorithms for solving very large search space problems with less prior knowledge and human intervention. Starting from the 1950s, a lot of EAs are developed, such as Evolution Strategies (ES), Genetic Algorithm (GA), Genetic Programming (GP) and Evolution Programming (EP). They have been successfully applied to many fields such as engineering, biology, economics, marketing, robotics, physics, chemistry, education and so on. After investigating the benefits and shortcoming of GA and GP, Genetic Network Programming (GNP) was proposed around 2000. The directed graph structure of GNP extends the chromosome representation of strings in GA and trees in GP, which makes it have high expression ability with relevant small size of individuals, and consequently GNP has the better performance than other evolutionary algorithms. Nowadays, GNP is not only used to solve benchmark problems but also applied to many real world applications such as elevator supervisory control systems, stock market prediction, datamining and traffic prediction. Since GNP was proposed, many methods have been developed to improve the performance of GNP such as combining GNP with reinforcement learning, introducing symbiotic learning in GNP, upgrading the structure of GNP by defining macro node and rule accumulation. Although these methods have been proved to improve the performance of GNP by combing some other machine learning methods, some useful prior knowledge of biology: variable length of gene, evolution by gene duplication and genotype-phenotype mapping, are not well considered. Therefore, in this research, two kinds of methods and their extensions have been proposed to improve the performance including the expression and generalization ability of GNP by upgrading the structure of GNP using the above theories, and to solve two problems of GNP, i.e., the node size of GNP is fixed and an individual is a solution. One of the methods is Variable Size Genetic Network Program (GNPvs) and its extension GNPvs with Replacement (GNPvs-R), which simulates the variable length of gene and gene duplication, and solve the problem that the node size of GNP is fixed. The other is Genetic Network Programming for Automatic Program Generation with Mapping Mechanism (GNP-APGm) and its extension Subroutine embedded GNP-APGm (GNPsr-APGm), which implements the genotype-phenotype mapping process, and solve the problem that an individual is a solution. The above methods are verified on the tileworld benchmark problem. The simulation results shows these proposed methods increase the performance of GNP exactly.", notes = "info:ndljp/pid/8977235 supervisor: Hirasawa", } @InProceedings{conf/atal/LiYSM09, author = "Boyang Li and Han Yu and Zhiqi Shen and Chunyan Miao", title = "Evolutionary organizational search", booktitle = "8th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2009)", year = "2009", editor = "Carles Sierra and Cristiano Castelfranchi and Keith S. Decker and Jaime Sim{\~a}o Sichman", volume = "2", pages = "1329--1330", address = "Budapest, Hungary", month = may # " 10-15", publisher = "IFAAMAS", note = "Extended Abstract", keywords = "genetic algorithms, genetic programming, Poster, Experimental, Systems, Biologically-Inspired Approaches, Organizational Planning, Multi-Agent Systems", isbn13 = "978-0-9817381-7-8", bibsource = "DBLP, http://dblp.uni-trier.de", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", bib_url = "https://dblp.org/rec/conf/atal/LiYSM09.html?view=bibtex", contributor = "CiteSeerX", language = "en", oai = "oai:CiteSeerXPSU:10.1.1.149.4762", URL = "http://www.ifaamas.org/Proceedings/aamas09/pdf/02_Extended_Abstract/D_SP_0876.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.4762", URL = "https://dl.acm.org/doi/10.5555/1558109.1558277", size = "2 pages", abstract = "In this paper, we proposed Evolutionary Organizational Search (EOS), an optimization method for the organizational control of multi-agent systems (MASs) based on genetic programming (GP). EOS adds to the existing armory a metaheuristic extension, which is capable of efficient search and less vulnerable to stalling at local optima than greedy methods due to its stochastic nature. EOS employs a flexible genotype which can be applied to a wide range of tree-shaped organizational forms. EOS also considers special constraints of MASs. A novel mutation operator, the redistribution operator, was proposed. Experiments optimizing an information retrieval system illustrated the adaptation of solutions generated by EOS to environmental changes.", notes = "broken May 2022 Poster: http://www3.ntu.edu.sg/home/BYLI/paper/aamas_poster.pdf", } @InProceedings{Li:2015:WI-IAT, author = "Boyang Li and Han Yu and Zhiqi Shen and Lizhen Cui and Victor R. Lesser", booktitle = "2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)", title = "An Evolutionary Framework for Multi-agent Organizations", year = "2015", volume = "2", pages = "35--38", abstract = "The organizational design of a multi-agent system (MAS) is important for its efficiency, adaptability and robustness. However, finding suitable organizational structures for different MASs is a challenging problem. In this paper, we propose a Framework of Evolutionary Optimisation for Agent Organizations (FEVOR) based on Genetic Programming for optimising tree-structured MASs. FEVOR employs a flexible representation of organizations and may be applied to a wide range of organizational forms such as pure hierarchies, holarchies, and federations. Compared to existing work, FEVOR is capable of efficient quantitative search and less vulnerable to stalling at local optima due to its non-greedy nature. Extensive experiments for optimising an information retrieval system have been conducted to demonstrate the advantages of FEVOR in generating suitable MAS organizations for adaptive environments.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/WI-IAT.2015.45", month = dec, notes = "Georgia Inst. of Technol., Atlanta, GA, USA Also known as \cite{7397312}", } @Article{LI:2023:jenvman, author = "Chunyan Li and Dongchao Guo and Yan Dang and Dezhi Sun and Pengsong Li", title = "Application of artificial intelligence-based methods in bioelectrochemical systems: Recent progress and future perspectives", journal = "Journal of Environmental Management", volume = "344", pages = "118502", year = "2023", ISSN = "0301-4797", DOI = "doi:10.1016/j.jenvman.2023.118502", URL = "https://www.sciencedirect.com/science/article/pii/S0301479723012902", keywords = "genetic algorithms, genetic programming, Artificial intelligence, Machine learning, Bioelectrochemical system, Microbial fuel cell, Microbial electrolysis cell", abstract = "Bioelectrochemical Systems (BESs) leverage microbial metabolic processes to either produce electricity by degrading organic matter or consume electricity to assist metabolism, and can be used for various applications such as energy production, wastewater treatment, and bioremediation. Given the intricate mechanisms of BESs, the application of artificial intelligence (AI)-based methods have been proposed to enhance the performance of BESs due to their capability to identify patterns and gain insights through data analysis. This review focuses on the analysis and comparison of AI algorithms commonly used in BESs, including artificial neural network (ANN), genetic programming (GP), fuzzy logic (FL), support vector regression (SVR), and adaptive neural fuzzy inference system (ANFIS). These algorithms have different features, such as ANN's simple network structure, GP's use in the training process, FL's human-like thought process, SVR's high prediction accuracy and robustness, and ANFIS's combination of ANN and FL features. The AI-based methods have been applied in BESs to predict microbial communities, products or substrates, and reactor performance, which can provide valuable information and improve system efficiency. Limitations of AI-based methods for predicting and optimizing BESs and recommendations for future development are also discussed. This review demonstrates the potential of AI-based methods in optimizing BESs and provides valuable information for the future development of this field", } @InProceedings{Li2:2008:gecco, author = "Cuimin Li and Tomoyuki Hiroyasu and Mitsunori Miki", title = "Stress-based crossover operator for structure topology optimization using small population size and variable length chromosome", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "1341--1342", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1341.pdf", DOI = "doi:10.1145/1389095.1389354", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, stress-based crossover, structural topology optimisation, Poster", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389354}", } @Article{li:2021:AbdomRadiol, author = "Dan Li and Rong Hu and Huizhou Li and Yeyu Cai and Paul J Zhang and Jing Wu and Chengzhang Zhu and Harrison X Bai", title = "Performance of automatic machine learning versus radiologists in the evaluation of endometrium on computed tomography", journal = "Abdominal radiology", year = "2021", volume = "46", number = "11", pages = "5316--5324", month = nov, keywords = "genetic algorithms, genetic programming, TPOT, Endometrium, Female, Humans, Machine Learning, Radiologists, Retrospective Studies, Tomography, X-Ray Computed, Automatic machine learning, Computed tomography, Endometrial cancer, Radiomics, Pelvis", ISSN = "2366-0058", DOI = "doi:10.1007/s00261-021-03210-9", abstract = "PURPOSE: In this study, we developed radiomic models that use a combination of imaging features and clinical variables to distinguish endometrial cancer (EC) from normal endometrium on routine computed tomography (CT). METHODS: A total of 926 patients consisting of 416 endometrial cancer (EC) and 510 normal endometrium were included. The CT images of these patients were segmented manually, and divided into training, validation, testing and external testing sets. Non-texture and texture features of these images with endometrium or uterus as region of interest were extracted. The clinical feature age was also included in the feature set. Feature selection and machine learning classifier were applied to normalized feature set. This manual optimized combination was then compared with the best pipeline exported by Tree-Based Pipeline Optimization Tool (TPOT) on testing and external testing set. The performances of these machine learning pipelines were compared to that of radiologists. RESULTS: The manual expert optimized pipeline using the reliefF feature selection method and Bagging classifier on the external testing set achieved a test ROC AUC of 0.73, accuracy of 0.73 (95percent CI 0.62-0.82), sensitivity of 0.64 (95percent CI 0.45-0.79), and specificity of 0.78 (95percent CI 0.65-0.87), while TPOT achieved a test ROC AUC of 0.79, accuracy of 0.80 (95percent CI 0.70-0.87), sensitivity of 0.61 (95percent CI 0.43-0.77), and specificity of 0.90 (95percent CI 0.78-0.96). When compared to average radiologist performance, the TPOT achieved higher test accuracy (0.80 vs. 0.49, p < 0.001) and specificity (0.90 vs. 0.51, p < 0.001), with comparable sensitivity (0.61 vs. 0.46, p = 0.130). CONCLUSION: Our results demonstrate that automatic machine learning can distinguish EC from normal endometrium on routine CT imaging with higher accuracy and specificity than radiologists.", notes = "PMID: 34286371", } @Article{Li:2022:IEEEAccess, author = "Dongcheng Li and W. Eric Wong and Mingyong Jian and Yi Geng and Matthew Chau", journal = "IEEE Access", title = "Improving Search-Based Automatic Program Repair With Neural Machine Translation", year = "2022", volume = "10", pages = "51167--51175", abstract = "The challenge of automatically repairing bugs in programs to reduce debugging expenses and increase program quality is known as automated program repair. To overcome this issue, test-suite-based repair techniques use a specified test suite as an oracle and alter the input faulty program to pass the full test suite. GenProg is a well-known example of this kind of repair, in which genetic programming is used to reorder the statements already present in the faulty program. However, recent practical experiments suggest that GenProg's performance, notably for Java, is not sufficient. Improved program dependability necessitates the use of automatic program repair techniques. Template-based program repair techniques have recently been combined with search-based techniques to solve program issues automatically. Although intriguing, it has two fundamental drawbacks: Its search space often lacks the correct solution, and the technique disregards program expertise, such as precise code language. Compared with the template-based program repair approach, existing neural-machine-translation-based approaches are not limited by these constraints due to their ability to learn and generate new solutions. We propose an approach that combines a search-based automatic program repair technique with a neural-machine-translation-based approach. More specifically, we use both redundancy assumption and sequence-to-sequence learning of correct patches as the source for potential fix statements that feed into a multiobjective evolutionary search algorithm to find test-suite-adequate patches. In this work, a novel framework called ARJANMT is introduced for automatically repairing Java programs. Two sets of controlled experiments are conducted on 410 bugs from two benchmarks to investigate the repairability and correctness of our proposed framework. A comparison between state-of-the-art automatic program repair frameworks is made. The experimental results indicate that combining those two types of repair techniques (search-based and neural-machine-translation-based) produces better results or fixes bugs that they previously were unable to fix individually.", keywords = "genetic algorithms, genetic programming, genetic improvment, APR", DOI = "doi:10.1109/ACCESS.2022.3164780", ISSN = "2169-3536", notes = "Also known as \cite{9749095}", } @Article{Li:2016:ieeeASE, author = "Dongni Li and Rongxin Zhan and Dan Zheng and Miao Li and Ikou Kaku", journal = "IEEE Transactions on Automation Science and Engineering", title = "A Hybrid Evolutionary Hyper-Heuristic Approach for Intercell Scheduling Considering Transportation Capacity", year = "2016", volume = "13", number = "2", pages = "1072--1089", abstract = "The problem of intercell scheduling considering transportation capacity with the objective of minimizing total weighted tardiness is addressed in this paper, which in nature is the coordination of production and transportation. Since it is a practical decision-making problem with high complexity and large problem instances, a hybrid evolutionary hyper-heuristic (HEH) approach, which combines heuristic generation and heuristic selection, is developed in this paper. In order to increase the diversity and effectiveness of heuristic rules, genetic programming is used to automatically generate new rules based on the attributes of parts, machines, and vehicles. The new rules are added to the candidate rule set, and a rule selection genetic algorithm is developed to choose appropriate rules for machines and vehicles. Finally, scheduling solutions are obtained using the selected rules. A comparative evaluation is conducted, with some state-of-the-art hyper-heuristic approaches which lack some of the strategies proposed in HEH, with a meta-heuristic approach that is suitable for large scale scheduling problems, and with adaptations of some well-known heuristic rules. Computational results show that the new rules generated in HEH have similarities to the best-performing human-made rules, but are more effective due to the evolutionary processes in HEH. Moreover, the HEH approach has advantages over other approaches in both computational efficiency and solution quality, and is especially suitable for problems with large instance sizes.", keywords = "genetic algorithms, genetic programming, Job shop scheduling, Processor scheduling, Search problems, Vehicles, Discrete event systems, manufacturing automation, scheduling, transportation", DOI = "doi:10.1109/TASE.2015.2470080", ISSN = "1545-5955", month = apr, notes = "Also known as \cite{7270346}", } @Article{Li:2019:ASE, author = "Dongni Li and Rongxin Zhan and Shaofeng Du and Xuhui Wu and Shuai Wang", journal = "IEEE Transactions on Automation Science and Engineering", title = "Automatic Design of Intercell Scheduling Heuristics", year = "2019", volume = "16", number = "4", pages = "1907--1921", month = oct, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/TASE.2019.2895369", ISSN = "1558-3783", abstract = "Though intercell scheduling problems have been studied in the literature, extant algorithms can hardly come into play in practice. This is because of two reasons: 1) transportation among cells, which is important for practical intercell scheduling, has not been adequately considered and 2) the problem size is large in practice, which may lead to intolerable computation efficiency. The motivation of this paper is to automatically design intercell scheduling heuristics that are suitable for practical application. A genetic programming algorithm with a pretraining strategy (GP-PS) is proposed. Production within cells and transportation among cells are simultaneously considered, and a cooperative coevolutionary framework is designed. To evolve better heuristics, a PS is developed. A speedup strategy is designed to accelerate the evolutionary process. Comparative experiments are conducted with other GP-based algorithms, speedup strategies, and with some state-of-the-art intercell scheduling algorithms. GP-PS is also put into use in a large manufacturing enterprise of China. Computational experiments and application results both verify the effectiveness of GP-PS.", notes = "Also known as \cite{8671727}", } @InProceedings{Li:2018:SmartWorld, author = "Dongrui Li and Yongliang Chen", booktitle = "2018 IEEE SmartWorld, Ubiquitous Intelligence Computing, Advanced Trusted Computing, Scalable Computing Communications, Cloud Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)", title = "An Efficient Ant Colony Programming Approach", year = "2018", pages = "1438--1443", abstract = "In this paper, a novel ant colony optimisation for linear imperative programming (ACOP) is proposed to improve the efficiency and accuracy of automating the design of computer programs. Different from existing linear genetic programming (LGP), the evolution of ACOP is based on cooperation of artificial ants. In ACOP, each solution is a sequence of instructions. The ants treat elements (registers or operators) in instructions as nodes in the construction graph. An ant chooses its next element according to the amount of pheromone deposited during the generation of a solution. The performance of ACOP is tested on twelve benchmark symbolic regression problems. Experimental results show that ACOP can perform better or competitive in comparison with two well-known genetic programming variants.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SmartWorld.2018.00249", month = oct, notes = "South China University of Technology Also known as \cite{8560227}", } @Article{DBLP:journals/tomacs/LiZ20, author = "Dongrui Li and Jinghui Zhong", title = "Dimensionally Aware Multi-Objective Genetic Programming for Automatic Crowd Behavior Modeling", journal = "{ACM} Trans. Model. Comput. Simul.", volume = "30", number = "3", pages = "19:1--19:24", year = "2020", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1145/3391407", DOI = "doi:10.1145/3391407", timestamp = "Tue, 04 Aug 2020 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/tomacs/LiZ20.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{RASC200491, author = "Gang Li and Kwong Sak Leung and Kin Hong Lee", title = "{IMGP} - A Novel Instruction Matrix based Genetic Programming", booktitle = "Proceedings of the 5th International Conference on Recent Advances in Soft Computing", year = "2004", editor = "Ahmad Lotfi", pages = "403--409", address = "Nottingham, United Kingdom", month = dec # " 16-18", publisher = "Nottingham Trent University", keywords = "genetic algorithms, genetic programming", ISBN = "1-84233-110-8", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.136.3418", URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.136.3418.pdf", size = "7 pages", abstract = "This paper proposes a new architecture for tree-based genetic programming, which evolves the schemata directly. Rather than gathering individuals to form the population, the architecture extracts individuals from an instruction matrix, which keeps the schemata information. It also uses fixed length hs-expressions to represent trees in any shape. In order to manipulate the instruction matrix and the hs-expression, new genetic operators are exploited as well as the new fitness evaluation function. The experimental results verify that it produces results much better than those of the canonical genetic programming in the problems tested in this paper.", notes = "Broken Jan 2013 http://www.rasc2004.info", } @InProceedings{eurogp:LiLL05, author = "Gang Li and Kin-Hong Lee and Kwong-Sak Leung", editor = "Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. {van Hemert} and Marco Tomassini", title = "Evolve Schema Directly Using Instruction Matrix Based Genetic Programming", booktitle = "Proceedings of the 8th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3447", year = "2005", address = "Lausanne, Switzerland", month = "30 " # mar # " - 1 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-25436-6", pages = "271--280", DOI = "doi:10.1007/978-3-540-31989-4_24", DOI = "doi:10.1007/b107383", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "This paper proposes a new architecture for tree-based genetic programming to evolve schemata directly. It uses fixed length hs-expressions to represent program trees in any shape, keeps schemata information in an instruction matrix, and extracts individuals from it. In order to manipulate the instruction matrix and the hs-expression, new genetic operators and a new fitness evaluation function are developed. The experimental results verify that its results are much better than those of the canonical genetic programming on the problems tested in this paper.", notes = "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005", } @InProceedings{conf/isica/LiLL07, author = "Gang Li and Kin Hong Lee and Kwong-Sak Leung", title = "Using Instruction Matrix Based Genetic Programming to Evolve Programs", booktitle = "Proceedings of the Second International Symposium on Computation and Intelligence, ISICA 2007", year = "2007", editor = "Lishan Kang and Yong Liu and Sanyou Y. Zeng", volume = "4683", series = "Lecture Notes in Computer Science", pages = "631--640", address = "Wuhan, China", month = sep # " 21-23", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Instruction Matrix based Genetic Programming", isbn13 = "978-3-540-74580-8", DOI = "doi:10.1007/978-3-540-74581-5_69", size = "10 pages", abstract = "In Genetic Programming (GP), evolving tree nodes separately would be an ideal approach to reduce the huge solution space of GP. We use Instruction Matrix based Genetic Programming (IMGP) to evolve tree nodes separately while taking into account their interdependencies in the form of subtrees. IMGP uses an Instruction Matrix (IM) to maintain the statistical data of tree nodes and subtrees. IMGP extracts program trees from IM, and updates IM with the information of the extracted program trees. The experiments have verified that the results of IMGP are better than those the related GP algorithms in terms of the qualities of the solutions and the number of program evaluations.", notes = "'no explicit population' cites \cite{shan:2004:gmpe} \cite{Shan:2003:Pewel} assumes full binary tree (hs-expression) so every part of tree has a known grid position. (Limits three height) 'like context preserving crossover' \cite{Dhaeseleer:1994:cpcGP} IMGP matrix shuffle. 6-parity also with sin,cos,exp,rlog. Max. Symbolic regression like PIPE? Fitness of primitive by grid position (rather than by neighbours in sub-tree).", bibdate = "2007-08-31", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/isica/isica2007.html#LiLL07", } @Article{Li:2008:ieeeTSMCB, author = "Gang Li and Jin Feng Wang and Kin Hong Lee and Kwong-Sak Leung", title = "Instruction-Matrix-Based Genetic Programming", journal = "IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics", year = "2008", month = aug, volume = "38", number = "4", pages = "1036--1049", keywords = "genetic algorithms, genetic programming, benchmark classification problems, condition matrix, instruction-matrix-based genetic programming, multiclass classification problems, program trees, rule learning, tree nodes, feature extraction, learning (artificial intelligence), matrix algebra, pattern classification, trees (mathematics), Algorithms, Artificial Intelligence, Computer Simulation, Feedback, Models, Genetic, Models, Theoretical, Pattern Recognition, Automated, Programming, Linear, Systems Theory", DOI = "doi:10.1109/TSMCB.2008.922054", ISSN = "1083-4419", abstract = "In genetic programming (GP), evolving tree nodes separately would reduce the huge solution space. However, tree nodes are highly interdependent with respect to their fitness. In this paper, we propose a new GP framework, namely, instruction-matrix (IM)-based GP (IMGP), to handle their interactions. IMGP maintains an IM to evolve tree nodes and subtrees separately. IMGP extracts program trees from an IM and updates the IM with the information of the extracted program trees. As the IM actually keeps most of the information of the schemata of GP and evolves the schemata directly, IMGP is effective and efficient. Our experimental results on benchmark problems have verified that IMGP is not only better than those of canonical GP in terms of the qualities of the solutions and the number of program evaluations, but they are also better than some of the related GP algorithms. IMGP can also be used to evolve programs for classification problems. The classifiers obtained have higher classification accuracies than four other GP classification algorithms on four benchmark classification problems. The testing errors are also comparable to or better than those obtained with well-known classifiers. Furthermore, an extended version, called condition matrix for rule learning, has been used successfully to handle multiclass classification problems.", notes = "Also known as \cite{4510842}", } @InProceedings{conf/swarm/LiZ10, title = "Bottom-Up Tree Evaluation in Tree-Based Genetic Programming", author = "Geng Li and Xiao-Jun Zeng", booktitle = "Advances in Swarm Intelligence, First International Conference, {ICSI} 2010, Beijing, China, June 12-15, 2010, Proceedings, Part {I}", publisher = "Springer", year = "2010", volume = "6145", editor = "Ying Tan and Yuhui Shi and Kay Chen Tan", isbn13 = "978-3-642-13494-4", pages = "513--522", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming", URL = "http://dx.doi.org/10.1007/978-3-642-13495-1", DOI = "doi:10.1007/978-3-642-13495-1_63", abstract = "In tree-based genetic programming (GP) performance optimisation, the primary optimization target is the process of fitness evaluation. This is because fitness evaluation takes most of execution time in GP. Standard fitness evaluation uses the top-down tree evaluation algorithm. Top-down tree evaluation evaluates program tree from the root to the leaf of the tree. The algorithm reflects the nature of computer program execution and hence it is the most widely used tree evaluation algorithm. In this paper, we identify a scenario in tree evaluation where top-down evaluation is costly and less effective. We then propose a new tree evaluation algorithm called bottom-up tree evaluation explicitly addressing the problem identified. Both theoretical analysis and practical experiments are performed to compare the performance of bottom-up tree evaluation and top-down tree evaluation. It is found that bottom-up tree evaluation algorithm outperforms standard top-down tree evaluation when the program tree depth is small.", bibdate = "2010-06-10", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/swarm/icsi2010-1.html#LiZ10", } @InProceedings{Li:2011:GECCO, author = "Geng Li and Xiao-Jun Zeng", title = "Genetic programming with a norm-referenced fitness function", booktitle = "GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0557-0", pages = "1323--1330", keywords = "genetic algorithms, genetic programming", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001576.2001755", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Not presented", notes = "Also known as \cite{2001755} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)", } @PhdThesis{Geng.Li:thesis, author = "Geng Li", title = "Tuning Genetic Programming Performance via Bloating Control and a Dynamic Fitness Function Approach", school = "Computer Science, University of Manchester", year = "2013", address = "UK", keywords = "genetic algorithms, genetic programming", URL = "https://www.escholar.manchester.ac.uk/api/datastream?publicationPid=uk-ac-man-scw:211199&datastreamId=FULL-TEXT.PDF", URL = "https://www.escholar.manchester.ac.uk/uk-ac-man-scw:211199", URL = "http://ethos.bl.uk/OrderDetails.do?did=58&uin=uk.bl.ethos.607007", size = "171 pages", abstract = "Inspired by Darwin's natural selection, genetic programming is an evolutionary computation technique which searches for computer programs best solving an optimisation problem. The ability of GP to perform structural optimization at the same time of parameter optimisation makes it uniquely suitable to solve more complex optimisation problems, in which the structure of the solution is not known a priori. But, as GP is applied to increasingly difficult problems, the efficiency of the algorithm has been severely limited by bloating. Previous studies of bloating suggest that bloating can be resolved either directly by delaying the growth in depth and size, or indirectly by making GP to find optimal solutions faster. This thesis explores both options in order to improve the scalability and the capacity of GP algorithm. It tackles the former by firstly systematically analysing the effect of bloating using a mathematical tool developed called activation rate. It then proposes depth difference hypothesis as a new cause of bloating and investigates depth constraint crossover as a new bloating control method, which is able to give very competitive control over bloating without affecting the exploration of fitter individuals. This thesis explores the second option by developing norm-referenced fitness function, which dynamically determines the individual's fitness based on not only how well it performs, but also the population's average performance as well. It is shown both theoretically and empirically that, norm-referenced fitness is able to significantly improve GP performance over the standard GP setup.", notes = "Supervisor: Xiao-Jun Zeng uk.bl.ethos.607007 Manchester eScholar ID: uk-ac-man-scw:211199", } @InProceedings{Li:2015:CEC, author = "Haibing Li and Man-Leung Wong", title = "Financial Fraud Detection by using Grammar-based Multi-objective Genetic Programming with ensemble learning", booktitle = "Proceedings of 2015 IEEE Congress on Evolutionary Computation (CEC 2015)", year = "2015", editor = "Yadahiko Murata", pages = "1113--1120", address = "Sendai, Japan", month = "25-28 " # may, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-7491-7", URL = "https://scholars.ln.edu.hk/en/publications/financial-fraud-detection-by-using-grammar-based-multi-objective-/fingerprints/", DOI = "doi:10.1109/CEC.2015.7257014", mphil_url = "https://commons.ln.edu.hk/cds_etd/13/", size = "8 pages", abstract = "Financial fraud is a criminal act, which violates the law, rules or policy to gain unauthorized financial benefit. The major consequences are loss of billions of dollars each year, investor confidence or corporate reputation. A study area called Financial Fraud Detection (FFD) is obligatory, in order to prevent the destructive results caused by financial fraud. In this study, we propose a new method based on Grammar-based Genetic Programming (GBGP), multi-objectives optimization and ensemble learning for solving FFD problems. We comprehensively compare the proposed method with Logistic Regression (LR), Neural Networks (NNs), Support Vector Machine (SVM), Bayesian Networks (BNs), Decision Trees (DTs), AdaBoost, Bagging and LogitBoost on four FFD datasets. The experimental results showed the effectiveness of the new approach in the given FFD problems including two real-life problems. The major implications and significances of the study can concretely generalize for two points. First, it evaluates a number of data mining techniques by the given real-life classification problems. Second, it suggests a new method based on GBGP, NSGA-II and ensemble learning.", notes = "See also mphil Lingnan University 0945 hrs 15244 CEC2015", } @InCollection{Li:2021:mhfms, author = "Haibing Li and Man-Leung Wong", title = "Grammar-Based Multi-objective Genetic Programming with Token Competition and Its Applications in Financial Fraud Detection", booktitle = "Metaheuristics for Finding Multiple Solutions", publisher = "Springer", year = "2021", editor = "Mike Preuss and Michael G. Epitropakis and Xiaodong Li and Jonathan E. Fieldsend", series = "Natural Computing Series", pages = "259--285", keywords = "genetic algorithms, genetic programming, Grammar-based genetic programming, Token competition, Financial fraud detection, Multi-objective optimization", isbn13 = "978-3-030-79552-8", ISSN = "1619-7127", URL = "https://scholars.ln.edu.hk/en/publications/grammar-based-multi-objective-genetic-programming-with-token-comp", DOI = "doi:10.1007/978-3-030-79553-5_11", abstract = "we propose a new approach based on Grammar-based Genetic Programming (GBGP), token competition, multi-objective optimization, and ensemble learning for solving Financial Fraud Detection (FFD) problems. Token competition is a niching technique to maintain diversity among individuals. It can be used to adjust the objective values of each individual, and the individuals with similar objective values but different meanings are separated. Financial fraud is a serious problem that often produces destructive results in the world and it is exacerbating swiftly in many countries. It refers to many activities including credit card fraud, money laundering, insurance fraud, corporate fraud, etc. The major consequences of financial fraud are loss of billions of dollars each year, investor confidence, and corporate reputation. Therefore, a research area called FFD is obligatory, in order to prevent the destructive results caused by financial fraud. We comprehensively compare the proposed approach with Logistic Regression, Neural Networks, Support Vector Machine, Bayesian Networks, Decision Trees, AdaBoost, Bagging, and LogitBoost on four FFD datasets including two real-life datasets. The experimental results showed the effectiveness of the new approach. It outperforms existing data mining methods in different aspects.", notes = "https://link.springer.com/book/10.1007/978-3-030-79553-5", } @Article{journals/jise/LiLZH12, author = "Haifeng Li and Minyan Lu and Min Zeng and Bai-Qiao Huang", title = "A Non-Parametric Software Reliability Modeling Approach by Using Gene Expression Programming", journal = "Journal of Information Science and Engineering", year = "2012", volume = "28", number = "6", pages = "1145--1160", month = nov, keywords = "genetic algorithms, genetic programming, Gene Expression Programming, SBSE, software reliability modelling, non-parametric model, machine learning, software reliability", bibdate = "2012-10-31", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/jise/jise28.html#LiLZH12", URL = "http://www.iis.sinica.edu.tw/page/jise/2012/201211_10.html", size = "16 pages", abstract = "Software reliability growth models (SRGMs) are very important for estimating and predicting software reliability. However, because the assumptions of traditional parametric SRGMs (PSRMs) are usually not consistent with the real conditions, the prediction accuracy of PSRMs are hence not very satisfying in most cases. In contrast to PSRMs, the non-parametric SRGMs (NPSRMs) which use machine learning (ML) techniques, such as artificial neural networks (ANN), support vector machine (SVM) and genetic programming (GP), for reliability modelling can provide better prediction results across various projects. Gene Expression Programming (GEP) which is a new evolutionary algorithm based on Genetic algorithm (GA) and GP, has been acknowledged as a powerful ML and widely used in the field of data mining. Thus, we apply GEP into non-parametric software reliability modelling in this paper due to its unique and pretty characters, such as genetic encoding method, translation process of chromosomes. This new GEP-based modelling approach considers some important characters of reliability modelling in several main components of GEP, i.e. function set, terminal criteria, fitness function, and then obtains the final NPSRM (GEP-NPSRM) by training on failure data. Finally, on several real failure data-sets based on time or coverage, four case studies are proposed by respectively comparing GEP-NPSRM with several representative PSRMs, NPSRMs based on ANN, SVM and GP in the form of fitting and prediction power which show that compared with the comparison models, the GEP-NPSRM provides a significantly better power of reliability fitting and prediction. In other words, the GEP is promising and effective for reliability modelling. So far as we know, it is the first time that GEP is applied into constructing NPSRM.", } @Article{Li:2015:Neurocomputing, author = "Haiyuan Li and Hongxing Wei and Jiangyang Xiao and Tianmiao Wang", title = "Co-evolution framework of swarm self-assembly robots", journal = "Neurocomputing", volume = "148", pages = "112--121", year = "2015", ISSN = "0925-2312", DOI = "doi:10.1016/j.neucom.2012.10.047", URL = "http://www.sciencedirect.com/science/article/pii/S0925231214009394", abstract = "In this paper, we present a co-evolution framework of configuration and control for swarm self-assembly robots, Sambots, in changing environments. The framework can generate different patterns composed of a set of Sambot robots to adapt to the uncertainties in complex environments. Sambot robots are able to autonomously aggregate and disaggregate into a multi-robot organism. To obtain the optimal pattern for the organism, the configuration and control of locomoting co-evolve by means of genetic programming. To finish self-adaptive tasks, we imply a unified locomotion control model based on Central Pattern Generators (CPGs). In addition, taking modular assembly modes into consideration, a mixed genotype is used, which encodes the configuration and control. Specialised genetic operators are designed to maintain the evolution in the simulation environment. By using an orderly method of evaluation, we can select some resulting patterns of better performance. Simulation experiments demonstrate that the proposed system is effective and robust in simultaneously constructing the adaptive structure and locomotion pattern. The algorithmic research and application analysis bring about deeper insight into swarm intelligence and evolutionary robotics.", keywords = "genetic algorithms, genetic programming, Co-evolution, Swarm robot", } @Article{Li:2017:ieeeTSMC, author = "Hao Li and Yangjian Ji and Liang Chen and Roger Jianxin Jiao", journal = "IEEE Transactions on Systems, Man, and Cybernetics: Systems", title = "Bi-Level Coordinated Configuration Optimization for Product-Service System Modular Design", year = "2017", volume = "47", number = "3", pages = "537--554", abstract = "Product-service systems (PSSs) deploy a selection of products and services in order to cope with diverse markets, so as to achieve a higher profit than would be possible by offering physical products alone. Modular design inherently contributes to the sustainability performance of PSS by material and resource reuse through the configuration of physical product and service modules. PSS configuration design is enacted through service configuration in line with product configuration; this entails two separate yet coordinated optimization problems, enabling customer satisfaction through service configuration and manufacturers sales profits through product configuration, respectively. Traditional multiobjective optimization approaches assume that the conflicting goals between customers and manufacturers can be aggregated into one single objective function through cooperative protocols, such as a weighted sum; in practice, this scarcely holds true. Consistent with game-theory decision-making, it is necessary to leverage the concerns of customers and manufacturers within a coherent framework of equilibrium solutions. This paper proposes a bi-level coordinated optimization framework to support PSS configuration design. An upper-level optimization problem is formulated for service configuration to act as a leader in the achievement of customer satisfaction, and a lower-level optimization problem is formulated for product configuration to act as a follower in an effort to enhance sales profits. Coordination between the upper and lower levels coincides with the tradeoffs underlying the conflicting goals that exist between customers and manufacturers. A constrained genetic algorithm is developed to solve the bi-level optimization model, and a case study of transformer PSS configuration design is reported to illustrate the feasibility and potential of bi-level coordinated configuration.", keywords = "genetic algorithms, genetic programming, Bi-level programming, configuration design optimization, modular design, product-service systems (PSSs)", DOI = "doi:10.1109/TSMC.2015.2507407", ISSN = "2168-2216", month = mar, notes = "Also known as \cite{7364287}", } @Article{LI:2022:watres, author = "Haochen Li and John Sansalone", title = "Implementing machine learning to optimize the cost-benefit of urban water clarifier geometrics", journal = "Water Research", year = "2022", volume = "220", pages = "118685", keywords = "genetic algorithms, genetic programming, Water treatment, Green infrastructure, OpenFOAM, PyTorch, Dakota", ISSN = "0043-1354", URL = "https://www.sciencedirect.com/science/article/pii/S0043135422006388", DOI = "doi:10.1016/j.watres.2022.118685", size = "35 pages", abstract = "Clarification basins are ubiquitous water treatment units applied across urban water systems. Diverse applications include stormwater systems, stabilization lagoons, equalization, storage and green infrastructure. Residence time (RT), surface overflow rate (SOR) and the Storm Water Management Model (SWMM) are readily implemented but are not formulated to optimize basin geometrics because transport dynamics remain unresolved. As a result, basin design yields high costs from hundreds of thousands to tens of million USD. Basin optimization and retrofits can benefit from more robust and efficient tools. More advanced methods such as computational fluid dynamics (CFD), while demonstrating benefits for resolving transport, can be complex and computationally expensive for routine applications. To provide stakeholders with an efficient and robust tool, this study develops a novel optimization framework for basin geometrics with machine learning (ML). This framework (1) leverages high-performance computing (HPC) and the predictive capability of CFD to provide artificial neural network (ANN) development and (2) integrates a trained ANN model with a hybrid evolutionary-gradient-based optimization algorithm through the ANN automatic differentiation (AD) functionality. ANN model results for particulate matter (PM) clarification demonstrate high predictive capability with a coefficient of determination (R2) of 0.998 on the test dataset. The ANN model for total PM clarification of three (3) heterodisperse particle size distributions (PSDs) also illustrates good performance (R2>0.986). The proposed framework was implemented for a basin and watershed loading conditions in Florida (USA), the ML basin designs yield substantially improved cost-effectiveness compared to common designs (square and circular basins) and RT-based design for all PSDs tested. To meet a presumptive regulatory criteria of 80percent PM separation (widely adopted in the USA), the ML framework yields 4.7X to 8X lower cost than the common basin designs tested. Compared to the RT-based design, the ML design yields 5.6X to 83.5X cost reduction as a function of the finer, medium, and coarser PSDs. Furthermore, the proposed framework benefits from ANN's high computational efficiency. Optimization of basin geometrics is performed in minutes on a laptop using the framework. The framework is a promising adjuvant tool for cost-effective and sustainable basin implementation across urban water systems", notes = "PMID: 35671685 Department of Civil and Environmental Engineering, University of Tennessee, Knoxville, Tennessee 37996, USA", } @Article{Li:2021:PoF, author = "Haochen Li2 and Yaomin Zhao and Jianchun Wang and Richard D. Sandberg", title = "Data-driven model development for large-eddy simulation of turbulence using gene-expression programing", journal = "Physics of Fluids", year = "2021", volume = "33", number = "12", month = dec, note = "Special Collection: Artificial Intelligence in Fluid Mechanics", keywords = "genetic algorithms, genetic programming, gene expression programming", DOI = "doi:10.1063/5.0076693", abstract = "We apply the gene-expression programing (GEP) method to develop subgrid-scale models for large-eddy simulations (LESs) of turbulence. The GEP model is trained based on Galilean invariants and tensor basis functions, and the training data are from direct numerical simulation (DNS) of incompressible isotropic turbulence. The model trained with GEP has been explicitly tested, showing that the GEP model can not only provide high correlation coefficients in a priori tests but also show great agreement with filtered DNS data when applied to LES. Compared to commonly used models like the dynamic Smagorinsky model and the dynamic mixed model, the GEP model provides significantly improved results on turbulence statistics and flow structures. Based on an analysis of the explicitly given model equation, the enhanced predictions are related to the fact that the GEP model is less dissipative and that it introduces high-order terms closely related to vorticity distribution. Furthermore, the GEP model with the explicit equation is straightforward to be applied in LESs, and its additional computational cost is substantially smaller than that of models trained with artificial neural networks with similar levels of predictive accuracies in a posteriori tests.", notes = "HEDPS, Center for Applied Physics and Technology, and College of Engineering, Peking University, Beijing 100871, China", } @InProceedings{Li:2009:APPEEC, author = "Hongyan Li and Shan Jiang and Xinhua Bao", title = "Application of Genetic Programming to Identifying Water-Level and Storage-Capacity Curve of the Xingxingshao Reservoir", booktitle = "Asia-Pacific Power and Energy Engineering Conference, APPEEC 2009", year = "2009", month = mar, pages = "1--3", keywords = "genetic algorithms, genetic programming, Xingxingshao reservoir, reservoir flood routing programming, storage-capacity curve fitting, water-level curve, curve fitting, geophysics computing, level measurement, reservoirs", DOI = "doi:10.1109/APPEEC.2009.4918182", abstract = "Water-level and storage-capacity curve (WSC) fitting is the foundation and key link of reservoir flood routing programming. Also, its precision directly determines the accuracy of flood routing. In this paper, based on the measured hydrological data, the correlations of water-level and storage-capacity are identified using genetic programming (GP), and the equations of water-level and storage-capacity curve are established. Then, the research results are applied to the feasibility study to enhance the flood limit level of Xingxingshao reservoir. And the results indicate that, compared to the measured data, the water-level and storage-capacity curve identified by GP has a more satisfied accuracy, which provides a fundamental guarantee for the accurate flood routing.", notes = "Also known as \cite{4918182}", } @InProceedings{Li:2018:ICIC, author = "Hongyan Li and Yuzhong Peng and Chuyan Deng and Yonghua Pan and Daoqing Gong and Hao Zhang", title = "Multicellular Gene Expression Programming-Based Hybrid Model for Precipitation Prediction Coupled with {EMD}", booktitle = "Intelligent Computing Theories and Application - 14th International Conference, ICIC 2018, Wuhan, China, August 15-18, 2018, Proceedings, Part I", publisher = "Springer", year = "2018", volume = "10954", editor = "De-Shuang Huang and Vitoantonio Bevilacqua and Prashan Premaratne and Phalguni Gupta", pages = "207--218", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming, gene expression programming, Empirical Mode Decomposition, Precipitation modeling, Precipitation prediction, Time series prediction", isbn13 = "978-3-319-95929-0", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icic/icic2018-1.html#LiPDPGZ18", DOI = "doi:10.1007/978-3-319-95930-6_20", notes = "conf/icic/LiPDPGZ18", } @Misc{journals/corr/abs-1906-08852, title = "A Hybrid Precipitation Prediction Method based on Multicellular Gene Expression Programming", author = "Hongyan Li and Yuzhong Peng and Chuyan Deng and Yonghua Pan and Daoqing Gong and Hao Zhang", howpublished = "arXiv", year = "2019", volume = "abs/1906.08852", keywords = "genetic algorithms, genetic programming, gene expression programming", URL = "http://arxiv.org/abs/1906.08852", bibdate = "2019-06-24", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/corr/corr1906.html#abs-1906-08852", } @Article{LI:2020:swarm, author = "Haoran Li and Fazhi He and Yilin Chen and Jinkun Luo", title = "Multi-objective self-organizing optimization for constrained sparse array synthesis", journal = "Swarm and Evolutionary Computation", volume = "58", pages = "100743", year = "2020", ISSN = "2210-6502", DOI = "doi:10.1016/j.swevo.2020.100743", URL = "http://www.sciencedirect.com/science/article/pii/S2210650220303965", keywords = "genetic algorithms, genetic programming, MOEAs, Sparse plane array", abstract = "Sparse span array is a critical communication technology for detecting microwave signal, yet it is difficult to simultaneously satisfy both reducing antenna elements' number and maintaining maximum side lobe level. Towards this problem, we propose a multi-objective optimization approach for self-organizing limited-area sparse span array, termed MOSSA. Overall, a uniform framework of multi-objective sparse span array is proposed. Specially, two objectives, number of selected antenna and peak side lobe level, are established for exploring the optimal array distribution in the framework. Based on the framework, for the problem of global-optimum array distribution, we propose a multi-objective particle swarm optimization searching pattern and design a MOSSA algorithm; Furthermore, for the problem of flexibly-adjusted self-organizing array structure, we present a multi-objective genetic programming searching pattern and design a MOSSA-gp algorithm. Moreover, a limited-region mode supplements to the framework. Finally, combination decision strategy assists users to screen out suitable solutions under the guidance of fuzzy-range indexes and then select the optimal solution by a triangle-approximating approach based on minimum Manhattan distance. Numerous experiments demonstrate that the proposed MOSSA outperforms other state-of-the-art algorithms in terms of both antenna elements' number and maximum side lobe level", } @Article{LI:2022:jreng, author = "Haoran Li and Lev Khazanovich", title = "Multi-gene genetic programming extension of {AASHTO M-E} for design of low-volume concrete pavements", journal = "Journal of Road Engineering", year = "2022", volume = "2", number = "3", pages = "252--266", month = sep, keywords = "genetic algorithms, genetic programming, Mechanistic-empirical pavement design guide, Low-volume roads, Concrete pavement, Transverse cracking, Joint faulting, Multi-gene genetic programming (MGGP)", ISSN = "2097-0498", DOI = "doi:10.1016/j.jreng.2022.08.002", URL = "https://www.sciencedirect.com/science/article/pii/S2097049822000464", size = "15 pages", abstract = "The American Association of State Highway and Transportation Officials Mechanistic-Empirical Pavement Design Guide (AASHTO M-E) offers an opportunity to design more economical and sustainable high-volume rigid pavements compared to conventional design guidelines. It is achieved through optimizing pavement structural and thickness design under specified climate and traffic conditions using advanced M-E principles, thereby minimizing economic costs and environmental impact. However, the implementation of AASHTO M-E design for low-volume concrete pavements using AASHTOWare Pavement ME Design (Pavement ME) software is often overly conservative. This is because Pavement ME specifies the minimum design thickness of concrete slab as 152.4mm (6 in.). This paper introduces a novel extension of the AASHTO M-E framework for the design of low-volume joint plain concrete pavements (JPCPs) without modification of Pavement ME. It uses multi-gene genetic programming (MGGP)-based computational models to obtain rapid solutions for JPCP damage accumulation and long-term performance analyses. The developed MGGP models simulate the fatigue damage and differential energy accumulations. This permits the prediction of transverse cracking and joint faulting for a wide range of design input parameters and axle spectrum. The developed MGGP-based models match Pavement ME-predicted cracking and faulting for rigid pavements with conventional concrete slab thicknesses and enable rational extrapolation of performance prediction for thinner JPCPs. This paper demonstrates how the developed computational model enables sustainable low-volume pavement design using optimized ME solutions for Pittsburgh, PA, conditions", } @InProceedings{Li:2009:CiSE, author = "Huang Li and Lixin Ding", title = "Research on Two Stage Evolutionary Modeling Based on Gene Expression Programming", booktitle = "International Conference on Computational Intelligence and Software Engineering, CiSE 2009", year = "2009", month = dec, abstract = "Gene expression programming is presented here for two stage evolutionary modelling. It uses character linear chromosomes composed of genes which encode expression trees. This feature which is different from existing algorithms allows the algorithm to perform with high efficiency when dealing with the same problem. And then, the analysis of convergence of this algorithm is mentioned. Finally, a numeric experiment is given to verify the efficiency of this algorithm. The results show that multiple highly precise ordinary differential equations (ODEs) model can be found out, and its predict values surprisingly coincide with the exact solutions.", keywords = "genetic algorithms, genetic programming, gene expression programming, convergence analysis, expression tree encoding, linear chromosomes, ordinary differential equations model, two stage evolutionary modelling, convergence, differential equations, linear programming, trees (mathematics)", DOI = "doi:10.1109/CISE.2009.5365685", notes = "Also known as \cite{5365685}", } @InProceedings{Li:2022:SEAMS, author = "Jia Li and Shiva Nejati and Mehrdad Sabetzadeh", title = "Learning Self-adaptations for {IoT} Networks: A Genetic Programming Approach", booktitle = "2022 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)", year = "2022", pages = "13--24", month = may # " 18-23", address = "Pittsburgh, PA, USA", keywords = "genetic algorithms, genetic programming, Time-frequency analysis, Adaptive systems, Packet loss, Computer architecture, Internet of Things, IoT, Standards", ISSN = "2157-2321", URL = "https://arxiv.org/abs/2205.04352", DOI = "doi:10.1145/3524844.3528053", code_url = "https://github.com/JiaLi123456/GenAdapt", size = "12 pages", abstract = "Internet of Things (IoT) is a pivotal technology in application domains that require connectivity and interoperability between large numbers of devices. IoT systems predominantly use a software-defined network (SDN) architecture as their core communication backbone. This architecture offers several advantages, including the flexibility to make IoT networks self-adaptive through software programmability. In general, self-adaptation solutions need to periodically monitor, reason about, and adapt a running system. The adaptation step involves generating an adaptation strategy and applying it to the running system whenever an anomaly arises. In this paper, we argue that, rather than generating individual adaptation strategies, the goal should be to adapt the logic / code of the running system in such a way that the system itself would learn how to steer clear of future anomalies, without triggering self-adaptation too frequently. We instantiate and empirically assess this idea in the context of IoT networks. Specifically, using genetic programming (GP), we propose a self-adaptation solution that continuously learns and updates the control constructs in the data-forwarding logic of SDN-based IoT networks. Our evaluation, performed using open-source synthetic and industrial data, indicates that, compared to a baseline adaptation technique that attempts to generate individual adaptations, our GP-based approach is more effective in resolving network congestion, and further, reduces the frequency of adaptation interventions over time. In addition, we compare our approach against a standard data-forwarding algorithm from the network literature, demonstrating that our approach significantly reduces packet loss.", notes = "Also known as \cite{9799836} \cite{li2022learningselfadaptationsiotnetworks} MAPE-K, DICES, GenAdapt, OSPF, SDN https://github.com/sednalab/IoTECS", } @Misc{DBLP:journals/corr/abs-2306-00316, author = "Jia Li and Shiva Nejati and Mehrdad Sabetzadeh", title = "Using Genetic Programming to Build Self-Adaptivity into Software-Defined Networks", howpublished = "arXiv", volume = "abs/2306.00316", year = "2023", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.48550/arXiv.2306.00316", DOI = "doi:10.48550/ARXIV.2306.00316", eprinttype = "arXiv", eprint = "2306.00316", timestamp = "Mon, 15 Apr 2024 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/corr/abs-2306-00316.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{DBLP:journals/taas/LiNS24, author = "Jia Li and Shiva Nejati and Mehrdad Sabetzadeh", title = "Using Genetic Programming to Build Self-Adaptivity into Software-Defined Networks", journal = "{ACM} Trans. Auton. Adapt. Syst.", volume = "19", number = "1", pages = "2:1--2:35", year = "2024", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1145/3616496", DOI = "doi:10.1145/3616496", timestamp = "Mon, 15 Apr 2024 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/taas/LiNS24.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{Li:2023:SSCI, author = "Jiarui Li and Ran Ji and Cheng'ao Li and Xiaoying Yang and Jiayi Li and Yiran Li and Xihan Xiong and Yutong Fang and Shusheng Ding and Tianxiang Cui", booktitle = "2023 IEEE Symposium Series on Computational Intelligence (SSCI)", title = "Prediction of Flight Arrival Delay Time Using U.S. Bureau of Transportation Statistics", year = "2023", pages = "603--608", abstract = "According to the data from the Bureau of Transportation Statistics (BTS), the number of passengers and flights has been increasing year by year. However, flight delay has become a pervasive problem in the United States in recent years due to various factors, including human factors such as security regulations, as well as natural factors such as bad weather. Flight delay not only affects the profits of airlines but also affects the satisfaction of passengers. Therefore, a model that can predict the arrival time of airplanes needs to be developed. Machine learning methods have been widely applied to prediction problems. a variety of machine learning and computational intelligence methods, including linear regression, decision tree (DT), random forest (RF), gradient boosting (GB), gaussian regression models and genetic programming were trained on the U.S. Department of Transportation's (DOT) BTS dataset. The results show that genetic programming performs best and can be used to predict the arrival time of the U.S. flights in advance, which is beneficial for airlines and passengers to make timely decisions.", keywords = "genetic algorithms, genetic programming, Computational modelling, Atmospheric modelling, Transportation, US Department of Transportation, Regulation, Delays, Big data, Air flight, Airport, Delay, Machine learning, Computational intelligence, Prediction, Regression", DOI = "doi:10.1109/SSCI52147.2023.10371912", ISSN = "2472-8322", month = dec, notes = "Also known as \cite{10371912}", } @InProceedings{FLAIRS99-019, title = "Linear programming", author = "Jin Li and Edward P. K. Tsang", booktitle = "Proceedings of Twelth International Florida Artificial Intelligence Research Society Conference", year = "1999", publisher = "AAAI Press", keywords = "genetic algorithms, genetic programming", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.538.6697", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.538.6697", URL = "https://www.aaai.org/Papers/FLAIRS/1999/FLAIRS99-019.pdf", size = "5 pages", abstract = "Recent studies in finance domain suggest that technical analysis may have merit to predictability of stock. Technical rules are widely used for market assessment and timing. For example, moving average rules are used to make buy or sell decisions at each day. In this paper, to explore the potential prediction power of technical analysis, we present a genetic programming based system FGP (Financial Genetic Programming), which specialises in taking some well known technical rules and adapting them to prediction problems. FGP uses the power of genetic programming to generate decision trees through efficient combination of technical rules with self-adjusted thresholds. The generated rules are more suitable for the prediction problem at hand. FGP was tested extensively on historical DJIA (Dow Jones Industrial Average) index data through a specific prediction problem. Preliminary results show that it outperforms commonly used, non-adaptive, individual technical rules with respect to prediction accuracy and average annualised rate of return over two different out-of-sample test periods (three and a half year in each period).", notes = "FLAIRS-99", } @InProceedings{li:1999:IDMUFACS, author = "Jin Li and Edward P. K. Tsang", title = "Investment Decision Making Using FGP: A Case Study", booktitle = "Proceedings of the Congress on Evolutionary Computation", year = "1999", editor = "Peter J. Angeline and Zbyszek Michalewicz and Marc Schoenauer and Xin Yao and Ali Zalzala", volume = "2", pages = "1253--1259", address = "Mayflower Hotel, Washington D.C., USA", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "6-9 " # jul, organisation = "Congress on Evolutionary Computation, IEEE / Neural Networks Council, Evolutionary Programming Society, Galesia, IEE", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, forecasting, Dow Jones Industrial Average, FGP, Financial Genetic Programming, case study, daily closing price, financial investment decision making, future prices, genetic generated decision trees, genetic programming based forecasting system, inflation rate, moving average, price-earning ratio, self-adjusted thresholds, share prices, shorter term investment decisions, technical analysis rules, technical rules, trading range breakout, decision support systems, decision trees, forecasting theory, investment, stock markets", ISBN = "0-7803-5536-9 (softbound)", ISBN = "0-7803-5537-7 (Microfiche)", URL = "http://www.cs.bham.ac.uk/~jxl/cercialink/web/publication/CEC99.pdf", URL = "ftp://ftp.essex.ac.uk/pub/csp/LiTsa-C45-Cec99.ps", URL = "http://citeseer.ist.psu.edu/237547.html", DOI = "doi:10.1109/CEC.1999.782584", abstract = "Financial investment decision making is extremely difficult due to the complexity of the domain. Many factors could influence the change of share prices. FGP (Financial Genetic Programming) is a genetic programming based forecasting system, which is designed to help users evaluate the impact of factors and explore their interactions in relation to future prices. Users channel into FGP factors which they believe are relevant to the prediction. Examples of such factors may include fundamental factors such as 'price-earning ratio', 'inflation rate' and/or technical factors such as '5-days moving average', '63-days trading range breakout', etc. FGP uses the power of genetic generated decision trees through technical rules with self-adjusted thresholds. In earlier papers, we have reported how FGP used well-known technical analysis rules to make investment decisions (E.P.K. Tsang et al., 1998; J. Li and E.P.K. Tsang, 1999). The paper tests the versatility of FGP by testing it on shorter term investment decisions. To evaluate FGP more thoroughly, we also compare it with C4.5, a well known machine learning classifier system. We used six and a half years' daily closing price of the Dow Jones Industrial Average (DJIA) index for training and over three and half years' data for testing, and obtained favourable results for FGP", notes = "CEC-99 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. Library of Congress Number = 99-61143", } @Misc{li:1999:FAGPTFP, author = "Jin Li", title = "FGP: A Genetic Programming Tool for Financial Prediction", booktitle = "GECCO-99 Student Workshop", year = "1999", editor = "Una-May O'Reilly", pages = "374", address = "Orlando, Florida, USA", month = "13 " # jul, keywords = "genetic algorithms, genetic programming, stock market, prediction", broken = "http://privatewww.essex.ac.uk/~jli/GPTool.htm", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/li_1999_FAGPTFP.pdf", size = "1 page", notes = "GECCO-99WKS Part of wu:1999:GECCOWKS", } @InProceedings{JinLi:2000:CEF, author = "Jin Li and Edward P. K. Tsang", title = "Reducing Failures in Investment Recommendations using Genetic Programming", booktitle = "Computing in Economics and Finance", year = "2000", address = "Universitat Pompeu Fabra, Barcelona, Spain", month = "6-8 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.bham.ac.uk/~jxl/cercialink/web/publication/ReducingFailures.pdf", URL = "http://cswww.essex.ac.uk/CSP/finance/papers/LiTsa-LowRF-Cef2000.ps", URL = "http://ideas.repec.org/p/sce/scecf0/332.html", abstract = "FGP (Financial Genetic Programming) is a genetic programming based system that specialises in financial forecasting. In the past, we have reported that FGP-1 (the first version of FGP) is capable of producing accurate predictions in a variety of data sets. It can accurately predict whether a required rate of return can be achieved within a user-specified period. This paper reports further development of FGP, which is motivated by realistic needs as described below: a recommendation {"}not to invest{"} is often less interesting than a recommendation {"}to invest{"}. The former leads to no action. If it is wrong, the user loses an investment opportunity, which may not be serious if other investment opportunities are available. On the other hand, a recommendation to invest leads to commitment of funds. If it is wrong, the user fails to achieve the target rate of return. Our objective is to reduce the rate of failure when FGP recommends to invest. In this paper, we present a method of tuning the rate of failure by FGP to reflect the user's preference. This is achieved by introducing a novel constraint-directed fitness function to FGP. The new system, FGP-2, was extensively tested on historical Dow Jones Industrial Average (DJIA) Index. Trained with data from a seven-and-a-half-years period, decision trees generated by FGP-2 were tested on data from a three-and-a-half-years out-of-sample period. Results confirmed that one can tune the rate of failure by adjusting a constraint parameter in FGP-2. Lower failure rate can be achieved at the cost of missing opportunities, but without affecting the overall accuracy of the system. The decision trees generated were further analysed over three sub-periods with down trend, side-way trend and up trend, respectively. Consistent results were achieved. This shows the robustness of FGP-2. We believe there is scope to generalise the constrained fitness function method to other applications.", notes = "http://enginy.upf.es/SCE/index2.html", } @PhdThesis{JinLi:thesis, author = "Jin Li", title = "{FGP}: A genetic programming based tool for financial forecasting", school = "Department of Computer Science, University of Essex", year = "2000", address = "UK", month = "7 " # oct, keywords = "genetic algorithms, genetic programming, Artificial intelligence, Finance, Taxation, Banking", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.170.5643.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.170.5643", broken = "http://ethos.bl.uk/OrderDetails.do?did=20&uin=uk.bl.ethos.343550", size = "190 pages", abstract = "Computers-aided financial forecasting has been made possible following continuous increase in machine power at reduced price, increasingly easy access to financial information, and advances in artificial intelligence (AI) techniques. In this thesis, we present a genetic programming based machine-learning tool called FGP (Financial Genetic Programming). We apply FGP to financial forecasting. Two versions of FGP, namely, FGP-1 and FGP-2, have been designed and implemented to address two research goals that we set. FGP-1 is intended to improve prediction accuracy over the predictions given. FGP-2 is aimed at improving prediction precision. Predictions are available to users from different sources. We investigate whether FGP-1 has the capability of improving on them by combining them together. Based on the experiments presented in this thesis, we conclude that FGP-1 is capable of improving the given predictions in terms of prediction accuracy. This partly attributes the capability of FGP-1 of finding positive interactions between the predictions given. However, caution should be excised for choosing its parameters in such applications. Improving prediction precision is highly demanded in financial forecasting. Our investigation is based on a set of specific prediction problems: to predict whether a required rate of return can be achieved within a user-specified period. In order to improve prediction precision, without affecting the overall prediction accuracy much, we invent a novel constrained fitness function, which is used by FGP-2. The effectiveness of FGP-2 is demonstrated and analysed in a variety of prediction tasks and data sets. The constrained fitness function provides the user with a handle to improve prediction precision at the price of missing opportunities. This thesis reports the utility of FGP applications to financial forecasting to a certain extent. As a tool, FGP aims to help users make the best use of information available to them. It may assist the user to make more reliable decisions that would otherwise not be achieved without it.", notes = "Oct 2024 www.bl.uk This item is not currently available. Some of the services we normally offer for collection items are currently unavailable due to a cyber incident. Please revisit this item in the future when our services are fully restored. 343550 : This item is currently unavailable. Feb 2015 uk.bl.ethos.343550 This thesis is not available from the EThOS service. Please contact the current institution's library directly if you wish to view the thesis.", } @InProceedings{Li:PPSN:2004a, author = "Jin Li and Xin Yao and Colin Frayn and Habib G. Khosroshahi and Somak Raychaudhury", title = "An Evolutionary Approach to Modeling Radial Brightness Distributions in Elliptical Galaxies", booktitle = "Parallel Problem Solving from Nature - PPSN VIII", year = "2004", editor = "Xin Yao and Edmund Burke and Jose A. Lozano and Jim Smith and Juan J. Merelo-Guerv\'os and John A. Bullinaria and Jonathan Rowe and Peter Ti\v{n}o Ata Kab\'an and Hans-Paul Schwefel", volume = "3242", pages = "591--601", series = "LNCS", address = "Birmingham, UK", publisher_address = "Berlin", month = "18-22 " # sep, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-23092-0", URL = "http://www.cs.bham.ac.uk/~xin/papers/Li_ppsn314.pdf", URL = "https://rdcu.be/dc0ix", DOI = "doi:10.1007/978-3-540-30217-9_60", DOI = "doi:10.1007/b100601", abstract = "A reasonably good description of the luminosity profiles of galaxies is needed as it serves as a guide towards understanding the process of galaxy formation and evolution. To obtain a radial brightness profile model of a galaxy, the way varies both in terms of the exact mathematical form of the function used and in terms of the algorithm used for parameters fitting for the function given. Traditionally, one builds such a model by means of fitting parameters for a functional form assumed beforehand. As a result, such a model depends crucially on the assumed functional form. In this paper we propose an approach that enables one to build profile models from data directly without assuming a functional form in advance by using evolutionary computation. This evolutionary approach consists of two major steps that serve two goals. The first step applies the technique of genetic programming with the aim of finding a promising functional form, whereas the second step takes advantage of the power of evolutionary programming with the aim of fitting parameters for functional forms found at the first step. The proposed evolutionary approach has been applied to modelling 18 elliptical galaxies profiles and its preliminary results are reported.", notes = "PPSN-VIII", } @InProceedings{li:2005:CECj, author = "Jin Li and Xiaoli Li and Xin Yao", title = "Cost-Sensitive Classification with Genetic Programming", booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary Computation", year = "2005", editor = "David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L. Gwenn Volkert and Dan Ashlock and Marc Schoenauer", volume = "3", pages = "2114--2121", address = "Edinburgh, UK", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "2-5 " # sep, organisation = "IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9363-5", URL = "http://www.cs.bham.ac.uk/~xin/papers/LiLiYaoCEC05.pdf", DOI = "doi:10.1109/CEC.2005.1554956", size = "8 pages", abstract = "Cost-sensitive classification is an attractive topic in data mining. Although genetic programming (GP) technique has been applied to general classification, to our knowledge, it has not been exploited to address cost-sensitive classification in the literature, where the costs of misclassification errors are non-uniform. To investigate the applicability of GP to cost-sensitive classification, this paper first reviews the existing methods of cost-sensitive classification in data mining. We then apply GP to address cost-sensitive classification by means of two methods through: a) manipulating training data, and b) modifying the learning algorithm. In particular, a constrained genetic programming (CGP), a GP based cost-sensitive classifier, has been introduced in this study. CGP is capable of building decision trees to minimise not only the expected number of errors, but also the expected misclassification costs through a novel constraint fitness function. CGP has been tested on the heart disease dataset and the German credit dataset from the UCI repository. Its efficacy with respect to cost has been demonstrated by comparisons with noncost-sensitive learning methods and cost-sensitive learning methods in terms of the costs.", notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS.", } @InProceedings{Li:2006:CEC, author = "Jin Li and Sope Taiwo", title = "Enhancing Financial Decision Making Using Multi-Objective Financial Genetic Programming", booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary Computation", year = "2006", editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas", pages = "7935--7942", address = "Vancouver", month = "16-21 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9487-9", URL = "http://www.cs.bham.ac.uk/~jxl/cercialink/web/publication/MOFGP-JinSope.pdf", DOI = "doi:10.1109/CEC.2006.1688575", size = "8 pages", abstract = "a multi-objective genetic programming based financial forecasting system, MOFGP. MOFGP is built upon our previous decision-making tool, FGP (Financial Genetic Programming) [1]-[5]. By taking advantage of the techniques of multi-objective evolutionary algorithms (MOEAs), MOFGP enhances FGP in a number of ways. Firstly, MOFGP is faster in obtaining the same quantity of diverse forecasting models optimised with respect to multiple conflicting objectives. This is attributed to the inherent property of MOEAs, i.e., a set of Pareto front solutions can be obtained in a single execution of its algorithm. Secondly, MOFGP is friendlier and simpler from the user's perspective. It is friendlier because it eliminates a number of user-supplied parameters previously required by FGP. Consequently, it becomes simpler as the user no longer needs to have a priori domain knowledge required for the proper use of those parameters. Finally, compared with FGP, which exploits a canonical single-objective approach to tackle a multi-criterion financial forecasting problem, MOFGP demonstrates the above advantages without seriously sacrificing its forecasting performance, although it suffers from an inadequate generalisation capability over the test data in this study. Given its strengths and weaknesses, MOFGP could be employed as a useful starting investigative tool for financial decision making.", notes = "Also known as \cite{1688575} NB Dec 2023 IEEE xplor gives page numbers 2171-2178 WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D", } @Article{Jin_GP_Wavelet, author = "Jin Li and Zhu Shi and Xiaoli Li", title = "Genetic Programming with Wavelet-Based Indicators for Financial Forecasting", journal = "Transactions of the Institute of Measurement and Control", year = "2006", volume = "28", number = "3", pages = "285--297", month = aug, keywords = "genetic algorithms, genetic programming, wavelet analysis, financial forecasting", URL = "http://www.cs.bham.ac.uk/~jxl/cercialink/web/publication/Jin_GP_Wavelet.pdf", URL = "http://tim.sagepub.com/content/vol28/issue3/", DOI = "doi:10.1191/0142331206tim177oa", size = "13 pages", abstract = "Wavelet analysis, as a promising technique, has been used to approach numerous problems in science and engineering. Recent years have witnessed its novel application in economic and finance. This paper is to investigate whether features (or indicators) extracted using the wavelet analysis technique could improve financial forecasting by means of Financial Genetic Programming (FGP), a genetic programming based forecasting tool (i.e., Li, 2001). More specifically, to predict whether Down Jones Industrial Average (DJIA) Index will rise by 2.2 percent or more within the next 21 trading days, we first extract some indicators based on wavelet coefficients of the DJIA time series using a discrete wavelet transform; we then feed FGP with those wavelet-based indicators to generate decision trees and make predictions. By comparison with the prediction performance of our previous study (i.e., Li and Tsang, 2000), it is suggested that wavelet analysis be capable of bringing in promising indicators, and improving the forecasting performance of FGP.", } @Article{Li:2021:A, author = "Jinhui Li2 and Yunfeng Dong", title = "Multi-Granularity Genetic Programming Optimization Method for Satellite System Topology and Parameter", journal = "IEEE Access", year = "2021", volume = "9", pages = "89958--89971", abstract = "Granular computing is usually considered as a representative method for solving complex problems, which can be solved quickly through freely switching among different granular models. In this paper, a genetic programming method based on the concept of granular computing is proposed to provide an efficient solution for optimizing the topology and parameters of a satellite system simultaneously. According to the coupling relationship of multiple physical fields, the multi-granularity description method of the satellite system scheme is defined and a multi-granularity digital satellite model is constructed. The genetic programming method is improved according to the principle of falsity preserving in granular computing. The concept and calculation method of granular risk factor are proposed to allow different individuals of the current population to switch among different granularities. The convergence difficulty caused by the complexity, hugeness, and high integration of satellites is effectively alleviated. The application to design and optimize an earth observation satellite proves the effectiveness of the proposed method.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ACCESS.2021.3091307", ISSN = "2169-3536", notes = "Also known as \cite{9461756} School of Astronautics, Beihang University, Beijing 100191, China", } @InCollection{Li:2004:EMTP, author = "Ju Hui Li and Meng Hiot Lim and Qi Cao", title = "Evolvable Fuzzy Hardware for Real-time Embedded Control in Packet Switching", year = "2004", booktitle = "Evolvable Machines: Theory \& Practice", pages = "205--227", volume = "161", series = "Studies in Fuzziness and Soft Computing", chapter = "9", editor = "Nadia Nedjah and Luiza {de Macedo Mourelle}", publisher = "Springer", address = "Berlin", keywords = "genetic algorithms, evolvable hardware", ISBN = "3-540-22905-1", URL = "http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-175-22-33980449-0,00.html", notes = "Springer says published in 2005 but available Nov 2004", } @TechReport{competition_level_code_generation_with_alphacode, author = "Yujia Li and David Choi and Junyoung Chung and Nate Kushman and Julian Schrittwieser and Remi Leblond and Tom Eccles and James Keeling and Felix Gimeno and Agustin Dal Lago and Thomas Hubert and Peter Choy and Cyprien de Masson d'Autume and Igor Babuschkin and Xinyun Chen and Po-Sen Huang and Johannes Welbl and Sven Gowal and Alexey Cherepanov and James Molloy and Daniel J. Mankowitz and Esme Sutherland Robson and Pushmeet Kohli and Nando de Freitas and Koray Kavukcuoglu and Oriol Vinyals", title = "Competition-Level Code Generation with {AlphaCode}", institution = "Google DeepMind", year = "2022", month = "2 " # feb, keywords = "genetic algorithms, genetic programming, C++, C#, Go, Java, JavaScript, Lua, PHP, Python, Ruby, Rust, Scala, TypeScript, GitHub, Codeforces, Description2Code, CodeNet, Jax, Haiku, TPUv4 (GPU), bloat16, SentencePiece tokenize, AdamW, Tempering regularization, GOLD, Clustering", URL = "https://storage.googleapis.com/deepmind-media/AlphaCode/competition_level_code_generation_with_alphacode.pdf", URL = "https://arxiv.org/abs/2203.07814", code_url = "https://github.com/deepmind/code_contests", size = "73 pages", abstract = "Programming is a powerful and ubiquitous problem-solving tool. Developing systems that can assist programmers or even generate programs independently could make programming more productive and accessible, yet so far incorporating innovations in AI has proven challenging. Recent large-scale language models have demonstrated an impressive ability to generate code, and are now able to complete simple programming tasks. However, these models still perform poorly when evaluated on more complex, unseen problems that require problem-solving skills beyond simply translating instructions into code. For example, competitive programming problems which require an understanding of algorithms and complex natural language remain extremely challenging. To address this gap, we introduce AlphaCode, a system for code generation that can create novel solutions to these problems that require deeper reasoning. Evaluated on recent programming competitions on the Codeforces platform, Alpha-Code achieved on average a ranking of top 54.3 percent in programming competitions with more than 5000 participants. We found that three key components were critical to achieve good and reliable performance: (1) an extensive and clean competitive programming dataset for training and evaluation, (2) large and efficient-to-sample transformer-based architectures, and (3) large-scale model sampling to explore the search space, followed by filtering based on program behavior to a small set of submissions.", notes = "NOT GP See also Li et al.,Science 378, 1092--1097 (2022) 9 December 2022 DOI: 10.1126/science.abq115 pre-training dataset 715.1 GB of code p7 'Fine-tuning the model on a dedicated competitive programming dataset is critical for performance' 'high-quality test cases are not readily available' 'slow positives where correct but algorithmically inefficient' 'We reduced the false positive rates of our dataset by generating additional test cases, created by mutating existing test inputs' sequence-to-sequence, eg NLP to Java, encoder(1536 tokens)-decoder(768 tokens) transformer architecture. AlphaCode 41B 41.1B parameters. CodeContests: train on both ok and bad answers. 'perhaps because there are many ways solutions can be incorrect while correct solutions tend to behave the same and so are grouped into larger clusters' Anna Karenina (by Leo Tolstoy). p16 'Solve rates scale log-linearly with more samples.' 'Solve rates scale log-linearly with more compute.' p21 'boilerplate code for reading and parsing the input data format, rather than key logic for solving problems'. p22 'AlphaCode generates approximately the same amount of dead code as humans.' p29 'Improving human readable code generation' p30 'no knowledge of coding is required to create software.' 'Interpretability makes code generation safer for real-world environments and for fairer machine learning.' 'Generalization.' 'outdated APIs' 'required hundreds of petaFLOPS days' p31 'Coding capabilities could lead to [Artificial Intelligence] systems that can recursively write and improve themselves, rapidly leading to more and more advanced [AI] systems.' ", } @Article{Li:2007:RCIM, author = "L. Li and G. Q. Huang and Stephen T. Newman", title = "Interweaving genetic programming and genetic algorithm for structural and parametric optimization in adaptive platform product customization", journal = "Robotics and Computer-Integrated Manufacturing", year = "2007", volume = "23", number = "6", pages = "650--658", month = dec, note = "16th International Conference on Flexible Automation and Intelligent Manufacturing", keywords = "genetic algorithms, genetic programming, Product platform, Platform product customisation, GBOM, Evolutionary algorithm", DOI = "doi:10.1016/j.rcim.2007.02.014", abstract = "An adaptive product platform offers high customisability for generating feasible product variants for customer requirements. Customisation takes place not only to product platform structure but also to its relevant parameters. Structural and parametric optimisation processes are interwoven with each other to achieve the total optimality. This paper presents an evolutionary method dealing with interwoven structural and parametric optimisation of adaptive platform product customisation. The method combines genetic programming and genetic algorithm for handling structural and parametric optimization, respectively. Efficient genetic representation and operation schemes are carefully adapted. While designing these schemes, features specific to structural and parameter customisation are considered for the simplification of platform product management. The experimental results show that the performance of the proposed algorithm outperforms that of the tandem evolutionary algorithm in which a genetic algorithm for parametric optimisation is totally nested in a genetic programming for structural optimisation.", } @Article{GA-SVM_optgenesubset, author = "Li Li and Wei Jiang and Xia Li and Kathy L. Moser and Zheng Guo and Lei Du and Qiuju Wang and Eric J. Topol and Qing Wang and Shaoqi Rao", title = "A robust hybrid between genetic algorithm and support vector machine for extracting an optimal feature gene subset", journal = "Genomics", year = "2005", volume = "85", number = "1", pages = "16--23", month = jan, keywords = "genetic algorithms, genetic programming, Feature gene selection, Support vector machine, DNA Microarray", DOI = "doi:10.1016/j.ygeno.2004.09.007", abstract = "Development of a robust and efficient approach for extracting useful information from microarray data continues to be a significant and challenging task. Microarray data are characterised by a high dimension, high signal-to-noise ratio, and high correlations between genes, but with a relatively small sample size. Current methods for dimensional reduction can further be improved for the scenario of the presence of a single (or a few) high influential gene(s) in which its effect in the feature subset would prohibit inclusion of other important genes. We have formalised a robust gene selection approach based on a hybrid between genetic algorithm and support vector machine. The major goal of this hybridisation was to exploit fully their respective merits (e.g., robustness to the size of solution space and capability of handling a very large dimension of feature genes) for identification of key feature genes (or molecular signatures) for a complex biological phenotype. We have applied the approach to the microarray data of diffuse large B cell lymphoma to demonstrate its behaviours and properties for mining the high-dimension data of genome-wide gene expression profiles. The resulting classifier(s) (the optimal gene subset(s)) has achieved the highest accuracy (99percent) for prediction of independent microarray samples in comparisons with marginal filters and a hybrid between genetic algorithm and K nearest neighbours.", notes = "department of Bioinformatics, Harbin Medical University, Harbin 150086, People's Republic of China college of Biological Science and Technology, Tongji University, Shanghai 200092, People's Republic of China department of Computer Science, Harbin Institute of Technology, Harbin 150080, People's Republic of China department of Medicine, Institute of Human Genetics, University of Minnesota, Minneapolis?St. Paul, MN 55455, USA department of Otorhinolaryngology/Head and Neck Surgery, Institute of Otolaryngology, Chinese PLA General Hospital, Beijing 100853, People's Republic of China department of Cardiovascular Medicine and Department of Molecular Cardiology, The Cleveland Clinic Foundation, Cleveland, OH 44195, USA http://www.elsevier.com/wps/find/journaldescription.cws_home/622838/description#description ", } @Article{li:2014:WRM, author = "Liping Li and Pan Liu and David E. Rheinheimer and Chao Deng and Yanlai Zhou", title = "Identifying Explicit Formulation of Operating Rules for {Multi-Reservoir} Systems Using Genetic Programming", journal = "Water Resources Management", year = "2014", volume = "28", number = "6", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s11269-014-0563-9", DOI = "doi:10.1007/s11269-014-0563-9", } @InProceedings{li:2005:CTHPCA, author = "Kangshun Li and Zhangxin Chen and Yuanxiang Li and Aimin Zhou", title = "An Application of Genetic Programming to Economic Forecasting", booktitle = "Current Trends in High Performance Computing and Its Applications", year = "2005", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/3-540-27912-1_7", DOI = "doi:10.1007/3-540-27912-1_7", } @InProceedings{Li6:2008:cec, author = "Kangshun Li and Weifeng Pan and Wensheng Zhang and Zhangxin Chen", title = "Automatic Modeling of a Novel Gene Expression Programming Based on Statistical Analysis and Critical Velocity", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "169--173", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, gene expression programming", isbn13 = "978-1-4244-1823-7", file = "EC0167.pdf", DOI = "doi:10.1109/CEC.2008.4630794", abstract = "The basic principle of GEP is briefly introduced. And considering the defects of classic GEP such as lack of variety, the problem of convergence and blind searching without learning mechanism, a novel GEP based on statistical analysis and stagnancy velocity is proposed (called AMACGEP). It mainly has the following characteristics: First, improve the initial population by statistic analysis of repeated bodies. Second, introduce the concept of stagnancy velocity to adjust the searching space, evolution velocity, the diversity of individuals and the accuracy of prediction. Third, introduce dynamic mutation operator to improve the diversity of individuals and the velocity of convergence. Compared with other methods like traditional methods, methods of neural network, classic GEP and other improved GEPs in automatic modelling of complex function, the simulation results show that the AMACGEP set up by this paper is better.", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @InProceedings{Li8:2008:cec, author = "Kangshun Li and Jiusheng Liang and Wensheng Zhang and Feng Wang", title = "A New Method of Evolving Digital Circuit Based on Gene Expression Programming", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "905--908", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1823-7", file = "EC0231.pdf", DOI = "doi:10.1109/CEC.2008.4630903", abstract = "Evolutionary Hardware (EHW) is a new focus in recent research work. The new method of design hardware is combined evolution algorithm with programmable logic device. Optimization digital circuit is a main domain of EHW. The algebra way and Karnaugh map way are the traditionary methods, but they will meet trouble with the large scale ones to get optimisation structure of circuit. This paper proposes a new method (GEP) to optimise the complex digital circuit and designs a new function fitness. The experiments demonstrate the GEP is not only fast convergence but also optimisation large circuit. It conquers the slow convergence even not convergence of the traditionary method. The GEP algorithm is simpler and more efficient than the traditional ones.", keywords = "genetic algorithms, genetic programming, gene expression programming", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @Article{journals/swevo/LiCLHX18, author = "Kangshun Li and Yan Chen and Wei Li and Jun He and Yu Xue", title = "Improved gene expression programming to solve the inverse problem for ordinary differential equations", journal = "Swarm and Evolutionary Computation", year = "2018", volume = "38", pages = "231--239", keywords = "genetic algorithms, genetic programming, gene expression programming", DOI = "doi:10.1016/j.swevo.2017.07.005", bibdate = "2018-01-04", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/swevo/swevo38.html#LiCLHX18", } @Article{LI:2019:swarm, author = "Ke-Sen Li and Han-Rui Wang and Kun-Hong Liu", title = "A novel Error-Correcting Output Codes algorithm based on genetic programming", journal = "Swarm and Evolutionary Computation", volume = "50", pages = "100564", year = "2019", ISSN = "2210-6502", DOI = "doi:10.1016/j.swevo.2019.100564", URL = "http://www.sciencedirect.com/science/article/pii/S2210650219302044", keywords = "genetic algorithms, genetic programming, Error-Correcting Output Codes (ECOC), Genetic programming (GP), Multiclass classification, Genetic operators", abstract = "Error-Correcting Output Codes (ECOC) is widely used in the field of multiclass classification. As an optimal codematrix is key to the performance of an ECOC algorithm, this paper proposes a genetic programming (GP) based ECOC algorithm (GP-ECOC). In the design of individual of our GP, each terminal node represents a class, and nonterminal nodes combine the classes in their child nodes. In this way, an individual is a class combination tree, and represents an ECOC codematrix. A legality checking process is embedded in our algorithm to check each codematrix, so as to ensure each codematrix satisfying ECOC constraints. Those violating the constraints will be corrected by a proposed Guided Mutation operator. Before fitness evaluation, a local optimization algorithm is proposed to append new columns for tough classes, so as to improve the generalization ability of each individual and accelerate the evolutionary speed. In this way, our GP can evolve optimal codematrices through the evolutionary process. Experiments show that compared with other ensemble algorithms, our algorithm can achieve stable and high performances with relatively small ensemble scales on various UCI data sets. To the best of our knowledge, it is the first time that GP has been applied to implement the ECOC encoding algorithm. Our Python code is available at https://github.com/samuellees/gpecoc", } @Article{LiangminLi:2004:ZRB, author = "Liangmin Li and Liangsheng Qu", title = "Fault Detection Based on Genetic Programming and Support Vector Machines", journal = "Journal of Xi'an Jiaotong University", year = "2004", volume = "38", number = "3", month = mar, keywords = "genetic algorithms, genetic programming, fault detection, support vector machines, SVM, rolling bearing", broken = "http://unit.xjtu.edu.cn/xb/zrb/04/0403/xbe0405.html", URL = "http://en.cnki.com.cn/Article_en/CJFDTotal-XAJT200403005.htm", abstract = "A new classification model based on genetic programming and support vector machine for machine fault diagnosis was proposed.In this model,genetic programming constructs and selects features from original feature set.The selected features form input feature set of support vector machines.Then multi-class support vector machine is applied to detect abnormal cases from normal ones.Experiments of rolling bearings fault detection are carried out to test the performance of this model.Practical results show that the compound features generated by genetic programming possess better recognition ability than the initial time domain features do.The classification ability of multi-class support vector machine is improved after feature extraction and selection.", notes = "http://unit.xjtu.edu.cn/xb/zrb/ School of Mechanical Engineering,Xi'an Jiaotong University,Xi'an 710049,China", } @Article{LI:2021:ML, author = "Maohua Li and Mohsen Mesbah and Alireza Fallahpour and Bahman Nasiri-Tabrizi and Baoyu Liu", title = "Mechanical strength estimation of ultrafine-grained magnesium implant by neural-based predictive machine learning", journal = "Materials Letters", volume = "305", pages = "130627", year = "2021", ISSN = "0167-577X", DOI = "doi:10.1016/j.matlet.2021.130627", URL = "https://www.sciencedirect.com/science/article/pii/S0167577X21013240", keywords = "genetic algorithms, genetic programming, Biomaterials, Metal forming and shaping, Mechanical properties, Simulation and modeling", abstract = "The relation between severe plastic deformation (SPD) and the mechanical behavior of the biodegradable magnesium (Mg) implants is not clearly understood yet. Thus, the present study aims to provide, for the first time, a framework for modeling the mechanical features of the ultrafine-grained (UFG) biodegradable Mg-based implant. First, an adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM) were employed to determine relationships between SPD parameters, including the kind of metal forming process, the number of the pass, and temperature of the procedure based on the restricted training dataset. Second, gene expression programming (GEP) and genetic programming (GP) were then used to further verify the estimation capability of neural-based predictive machine learning techniques. Comparison of estimation results with real data confirmed that both ANFIS and SVM-based models had high accuracy for predicting the mechanical behavior of UFG Mg alloys for fracture fixation and orthopedic implants", } @InProceedings{Li:2014:CASE, author = "Miao Li and Hong Zheng and Dongni Li and Xianwen Meng", booktitle = "IEEE International Conference on Automation Science and Engineering (CASE 2014)", title = "An intercell scheduling approach considering transportation capacity", year = "2014", month = aug, pages = "594--599", abstract = "Intercell scheduling disrupts the cellular manufacturing philosophy of creating independent cells, but is essential for enterprises to reduce costs. Since intercell scheduling is in nature the coordination of intercell production and intercell transportation, the intercell scheduling problem is considered with transportation constraints in this paper. Hyper-heuristics are known for their computational efficiency but are lack in effectiveness since the candidate heuristic rules are usually manually set in advance. In this paper, a hybrid evolution-based hyper-heuristic algorithm is developed for the addressed intercell scheduling problem considering transportation capability. In order to improve the effectiveness of hyper-heuristics, genetic programming is introduced to generate new heuristic rules automatically based on the information of machines or vehicles, thus expanding the set of the candidate rules, and then, a rule selection genetic algorithm is developed to select appropriate rules from the obtained rule set, for the machines and vehicles, respectively. Finally, the scheduling solutions are generated according to the selected rules. The contribution of this work lies in (a) intercell transportation is considered in the intercell scheduling problem, and (b) heuristic generation is adopted in advance of the heuristic selection, constructing a more effective hyper-heuristic with both computation efficiency and optimisation performance.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CoASE.2014.6899387", notes = "Also known as \cite{6899387}", } @Article{li:1992:irkc, author = "Ming Li and Paul M. B. Vitanyi", title = "Inductive Reasoning and Kolmogorov Complexity", journal = "Journal of Computer and System Sciences", publisher = "Academic Press", year = "1992", volume = "44", number = "2", pages = "343--384", month = apr, notes = "April's issue was devoted to proceedings of the fourth annual conference on Structure in Complexity Theory, IEEE Computer Society, held in University or Oregon, 19-22 June 1989.", } @TechReport{oai:CiteSeerPSU:387590, title = "Computational Machine Learning in Theory and Praxis", author = "Ming Li", abstract = "In the last few decades a computational approach to machine learning has emerged based on paradigms from recursion theory and the theory of computation. Such ideas include learning in the limit, learning by enumeration, and probably approximately correct (pac) learning. These models usually are not suitable in practical situations. In contrast, statistics based inference methods have enjoyed a long and distinguished career. Currently, Bayesian reasoning in various forms, minimum message length (MML) and minimum description length (MDL), are widely applied approaches. They are the tools to use with particular machine learning praxis such as simulated annealing, genetic algorithms, genetic programming, artificial neural networks, and the like. These statistical inference methods select the hypothesis which minimizes the sum of the length of the description of the hypothesis (also called `model') and the length of the description of the data relative to the hypothesis. It app...", citeseer-isreferencedby = "oai:CiteSeerPSU:52132; oai:CiteSeerPSU:560308; oai:CiteSeerPSU:359110; oai:CiteSeerPSU:561730; oai:CiteSeerPSU:530322", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:387590", rights = "unrestricted", URL = "http://citeseer.ist.psu.edu/387590.html", URL = "http://www.neurocolt.com/abs/1995/../../tech_reps/1995/nc-tr-95-052.ps.gz", year = "1995", type = "NeuroCOLT technical report series", number = "NC-TR-95-052", institution = "Royal Holloway and Bedford New College, University of London", address = "Surrey, UK", month = sep, keywords = "ML", notes = "not a CP paper", size = "20 pages", } @InProceedings{LI:2019:SPAWDA, author = "Nuo Li and Hao Chen and Jian-qiang Han", title = "Application of Multigene Genetic Programming for Estimating Elastic Modulus of Reservoir Rocks", booktitle = "2019 13th Symposium on Piezoelectrcity, Acoustic Waves and Device Applications (SPAWDA)", year = "2019", month = "11-14 " # jan, publisher = "IEEE", address = "Harbin, China", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-7281-0614-4", DOI = "doi:10.1109/SPAWDA.2019.8681879", abstract = "Based on the Multigene genetic programming (MGGP) method, the static elastic moduli (Young's moduli) of reservoir sandstones are estimated with the bulk densities, porosities and P-wave velocities data. A analytical expression for static moduli is obtained by evolution according to the method, and the static moduli are calculated by the expression. Then the calculation results are compared with those obtained by experimental and empirical methods. It shows that MGGP method is more accurate than the traditional empirical relation between dynamic and static parameters. Furthermore, MGGP requires less input parameters. For example, S-wave velocities are not necessary. So the method reduce the error induced by the inaccurate or missing of S-wave velocities.", notes = "University of Chinese Academy of Sciences, Beijing, China Also known as \cite{8681879}", } @InProceedings{Li:2009:ieeeIC-BNMT, author = "Piji Li and Jun Ma", title = "Learning to rank for web image retrieval based on genetic programming", booktitle = "2nd IEEE International Conference on Broadband Network Multimedia Technology, IC-BNMT '09", year = "2009", month = oct, pages = "137--142", keywords = "genetic algorithms, genetic programming, WIRank, Web image retrieval, graph theory, image-based feature, information retrieval system, link structure analysis, ranking, temporal information, text information, Internet, graph theory, image retrieval, text analysis", DOI = "doi:10.1109/ICBNMT.2009.5348465", abstract = "Ranking is a crucial task in information retrieval systems. This paper proposes a novel ranking model named WIRank, which employs a layered genetic programming architecture to automatically generate an effective ranking function, by combining various types of evidences in Web image retrieval, including text information, image-based features and link structure analysis. This paper also introduces a new significant feature to represent images: Temporal information, which is rarely used in the current information retrieval systems and applications. The experimental results show that the proposed algorithms are capable of learning effective ranking functions for Web image retrieval. Significant improvement in relevancy obtained, in comparison to some other well-known ranking techniques, in terms of MAP, NDCG@n and D@n.", notes = "Also known as \cite{5348465}", } @Article{li:2024:SR, author = "Qi Li and Norshaliza Kamaruddin and Siti Sophiayati Yuhaniz and Hamdan Amer Ali Al-Jaifi", title = "Forecasting stock prices changes using long-short term memory neural network with symbolic genetic programming", journal = "Scientific Reports", year = "2024", volume = "14", number = "1", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1038/s41598-023-50783-0", DOI = "doi:10.1038/s41598-023-50783-0", } @Article{Li:2024:tws, author = "Qi Li2 and Vu Ngoc Viet Hoang and Peng Shi and Jian Yang and Ferruh Turan", title = "Nonlinear dynamics and chaos of functionally graded graphene origami-enabled auxetic metamaterials doubly curved shells with bi-directionally stepped thickness in thermal environment", journal = "Thin-Walled Structures", year = "2024", volume = "205", pages = "112420", keywords = "genetic algorithms, genetic programming, Dynamic and chaotic responses, Bi-directionally stepped shells, Functionally graded graphene origami-enabled auxetic metamaterials, Galerkin's technique, Thermal environment", ISSN = "0263-8231", URL = "https://www.sciencedirect.com/science/article/pii/S0263823124008619", DOI = "doi:10.1016/j.tws.2024.112420", abstract = "This paper presents the dynamic and chaotic responses of functionally graded graphene origami (GOri)-enabled auxetic metamaterials (GOEAMs) doubly curved shells featuring stepped thickness profiles in thermal environment. A novel analytical framework is introduced to investigate geometric variations and material distributions, highlighting their crucial roles. Specifically, the investigation delves into structural variations in shell thickness, characterised by abrupt changes in uni- or bi-directional orientations, encompassing both single and double stepped thickness profiles. The five geometric configurations of the stepped structures encompass plates, cylindrical shells, spherical shells, hyperbolic paraboloid shells, and elliptical paraboloid shells. The FG-GOEAM structures, comprising multiple layers with varied GOri distributions across their thickness, are scrutinized using genetic programming-assisted micromechanical models. Central to the present approach is the formulation of nonlinear kinematic relationships using Reddy's third-order shear deformation theory alongside von Karman's nonlinear geometric assumptions, with equations of motion being solved using Galerkin's technique. Notably, the enhanced model is developed to address non-continuous thickness variation through integral calculus operations, enhancing computational efficiency and obviating the need for complex algorithms. To verify the accuracy of the proposed method, the obtained results are compared with those from published literature. The study thoroughly investigates the influence of material properties, thermal conditions, and geometric parameters on the free vibration and nonlinear behaviours of the structures. Key findings include: The shell stiffness of the functionally graded (FG) shell (X-WGr) surpasses that of the homogeneous shell (U-WGr). The transitions from periodic to chaotic states of the stepped structures are discerned through the analysis of the time history response, the phase plane illustrating the deflection-velocity relationship, and the Poincare map. Increasing the thickness ratios and GOri content while reducing the folding degree of GOri results in a significant increase in the fundamental frequency and critical load, along with a simultaneous decrease in vibrational amplitudes", notes = "School of Intelligent Manufacturing, Huanghuai University, Zhumadian, Henan 463000, China", } @InProceedings{li:2024:GECCOcomp3, author = "Qiaoman Li and Xiaoying Gao and Wenyang Meng and Jianbin Ma", title = "Multi-objective multi-population Genetic Programming for feature selection and classification to high-dimensional data", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion", year = "2024", editor = "Ting Hu and Aniko Ekart", pages = "519--522", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, high-dimensional data, multi-objective, multi-population: Poster", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3654234", size = "4 pages", abstract = "Classification for high-dimensional data is a challenging task due to a great number of redundant and irrelevant features. Genetic Programming (GP) has its built-in feature selection characteristics and is suitable for processing high-dimensional data. However, if the number of features in GP individual is not restricted, the bloat phenomenon will occur, which affects the generalization of the training model. Moreover, GP is easy to fall into local search in the evolutionary process. In this paper, a multi-objective multi-population GP classifier construction method is proposed, which uses a multipopulation co-evolution strategy to divide the evolving population into the main and auxiliary populations with different evolutionary strategies and different evaluation criteria, and employs a multiobjective strategy to ensure classification performance and restrict the number of features at the same time. Experiments on seven high-dimensional datasets show that our proposed multi-population co-evolution strategy and multi-objective strategy are all effective to improve the classification performance of GP classifiers. Comparisons with three state-of-the-art GP classifier construction methods show that our proposed methods achieve better or comparable classification performance on most cases.", notes = "GECCO-2024 GP A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{Li:2004:ICPR, author = "Qingyong Li and Hong Hu and Zhongzhi Shi", title = "Semantic feature extraction using genetic programming in image retrieval", booktitle = "Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004", year = "2004", volume = "1", pages = "648--651", month = aug, keywords = "genetic algorithms, genetic programming, content-based retrieval, feature extraction, image retrieval, image texture, visual perception Tamura texture model, content based image retrieval, human visual perception, linguistic expression, semantic feature extraction, texture extraction, visual feature extraction", DOI = "doi:10.1109/ICPR.2004.1334248", size = "4 pages", abstract = "One of the big hurdles facing current content-based image retrieval (CBIR) is the semantic gap between the low-level visual features and the high-level semantic features. We proposed an approach to describe and extract the global texture semantic features. According to the Tamura texture model, we use the linguistic variable to describe the texture semantics, so it becomes possible to depict the image in linguistic expression such as coarse, fine. We use genetic programming to simulate the human visual perception and extract the semantic features value. Our experiments show that the semantic features have good accordance with the human perception, and also have good retrieval performance. In some extent, our approach bridges the semantic gap in CBIR.", notes = "also known as \cite{1334248}", } @InProceedings{Li:2009:cec, author = "Qu Li and Min Yao and Weihong Wang and Xiaohong Cheng", title = "Dynamic Split-Point Selection Method for Decision Tree Evolved by Gene Expression Programming", booktitle = "2009 IEEE Congress on Evolutionary Computation", year = "2009", editor = "Andy Tyrrell", pages = "736--740", address = "Trondheim, Norway", month = "18-21 " # may, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, gene expression programming, C4.5, classification accuracy, decision tree, dynamic split-point selection method, evolutionary computation theory, heuristic method, optimal split points, tree splitting, data handling, decision trees", isbn13 = "978-1-4244-2959-2", file = "P196.pdf", DOI = "doi:10.1109/CEC.2009.4983018", abstract = "Gene Expression Programming(GEP) is a kind of heuristic method based on evolutionary computation theory. GEP has been used to evolve parsimonious decision tree with high accuracy comparable to C4.5. However, the basic GEPDT do not distinguish different attributes, whose boundaries are usually quite different. The basic GEPDT often fails to find optimal split points for some branches and thus handicapped the learning tasks. In this paper, we proposed a simple but effective Split-point Selection Method for GEP evolved decision tree to improve the performance of tree splitting and classification accuracy. Results show that our method can find better generalized ability rules and it is especially suitable for difficult problems with many attributes in different boundaries.", notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR. Also known as \cite{4983018}", } @Article{LI:2024:eswa, author = "Lubo Li and Haohua Zhang and Sijun Bai", title = "A multi-surrogate genetic programming hyper-heuristic algorithm for the manufacturing project scheduling problem with setup times under dynamic and interference environments", journal = "Expert Systems with Applications", year = "2024", volume = "250", pages = "123854", month = "15 " # sep, keywords = "genetic algorithms, genetic programming, Multi-surrogate, Project scheduling, Setup time, Dynamic and interference environments", ISSN = "0957-4174", URL = "https://www.sciencedirect.com/science/article/pii/S0957417424007206", DOI = "doi:10.1016/j.eswa.2024.123854", abstract = "In this study, in order to cope with the uncertain environments and space interference scenarios encountered in the production process for a class of manufacturing projects, we propose a novel manufacturing project scheduling problem with setup times under dynamic and interference environments (MPSPST-DIE), and design a novel multi-surrogate genetic programming hyper-heuristic (HH-MGP) algorithm to address it. Firstly, MPSPST-DIE is required to make decisions on the activity schedule, resource setup and space allocation. Therefore, we modify the traditional resource based policy class that only contains the activity schedule and simulate the entire scheduling process. Secondly, a new hyper-heuristic genetic programming algorithm is designed to automatically evolve activity rules, resource setup rules and space allocation rules simultaneously. Moreover, the multi-surrogate is devised to improve the performance of the basic genetic programming (GP) algorithm. In addition, a new evolutionary learning mechanism is embedded in the multi-surrogate. Different surrogates learn from each other to complement each other's strengths. Finally, numerical instances of the MPSPST-DIE are generated by configuring specific parameters of spatial resources and extensive numerical experiments are performed. At the same time, we implement the Taguchi experiment for the sensitivity analysis of parameters. The comparative analysis between the HH-MGP and traditional rules is performed. Further, the performance comparison between the multi-surrogate and other surrogates is also conducted. The experimental results show that the evolved rule of the HH-MGP performs better than the traditional rules for the MPSPST-DIE. The performance of the multi-surrogate model added to the GP algorithm are generally better than the single-surrogate model and no-surrogate", } @Article{Li:2025:cor, author = "Lubo Li and Jingwen Zhang and Haohua Zhang and Roel Leus", title = "A novel hyper-heuristic based on surrogate genetic programming for the three-dimensional spatial resource-constrained project scheduling problem under uncertain environments", journal = "Computer and Operations Research", year = "2025", volume = "179", pages = "107013", keywords = "genetic algorithms, genetic programming, Project schedule, 3D spatial resource, Fitness function surrogate, Random forest technique", ISSN = "0305-0548", URL = "https://www.sciencedirect.com/science/article/pii/S0305054825000413", DOI = "doi:10.1016/j.cor.2025.107013", abstract = "For a class of large and complex engineering projects with limited construction sites, three-dimensional (3D) spatial resources usually become a bottleneck that hinders their smooth implementation. A project schedule is easily disturbed by space conflicts and uncertain environments if these factors are not considered in advance. Firstly, we extend the traditional resource-constrained project scheduling problem (RCPSP) by considering 3D spatial resource constraints under uncertain environments, and propose a new three-dimensional spatial resource-constrained project scheduling problem with stochastic activity durations (3D-sRCPSPSAD). The activity schedule and the space allocation need to be decided simultaneously, so we design the first-fit and the best-fit strategies, and integrate them into the traditional resource-based policy to schedule activities and allocate 3D space. Secondly, a novel hyper-heuristic based on surrogate genetic programming (HH-SGP) is designed to evolve rules automatically for the 3D-sRCPSPSAD. The main goal of the surrogate model in HH-SGP is to construct an approximate model of the fitness function based on the random forest technique. Therefore, it can be used as an efficient alternative to the more expensive fitness function in the evolutionary process. More importantly, the weak elitism mechanism and other modified techniques are designed to improve the performance of HH-SGP. Thirdly, we configure the parameters of 3D spatial resources and generate numerical instances. Finally, from the aspects of solution quality and stability, we verify the efficiency, quality and convergence rate of HH-SGP under different uncertain environments. The effectiveness of the surrogate model, and the performance of the first-fit and the best-fit strategies are also analysed through extensive numerical experiments. The results indicate that our designed HH-SGP algorithm performs better than traditional heuristics for the 3D-sRCPSPSAD, and the performance of the fitness function surrogate model in HH-SGP is generally better than without it. This study can also help project practitioners schedule activities and allocate spatial resources more effectively under various uncertain scenarios", } @InProceedings{Li:2009:CIRA, author = "Maolin Li and Lin Liang and Sunan Wang and Xiaohu Li", title = "Feature generation in fault diagnosis based on immune programming", booktitle = "2009 IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA)", year = "2009", month = "15-18 " # dec, pages = "183--187", abstract = "In the symptom feature discovery, genetic programming has the shortage of premature convergence. So a new feature generation method based on immune programming is put forward. The new features are constructed by polynomial expressions of the original features. And then, with the immune operators such as antibody representation and mutation of tree-like structure, affinity function defined by classification performance of every individual, the clonal selection optimal algorithm is adopted to search the best feature that has excellent classification performance. The experiments of sound signal for gasoline engine show that, due to the diversity of antibodies is maintained by clonal selection principle, the best compound feature founded by immune programming has better classification ability than feature optimism by genetic programming.", keywords = "genetic algorithms, genetic programming, affinity function, antibody representation, clonal selection optimal algorithm, fault diagnosis, feature generation, immune programming, polynomial expressions, premature convergence, symptom feature discovery, tree-like structure, fault diagnosis, pattern recognition, polynomials", DOI = "doi:10.1109/CIRA.2009.5423210", notes = "Sch. of Mech. Eng. & the Eng. Workshop, Xi'an Jiaotong Univ., Xi'an, China. Also known as \cite{5423210}", } @InProceedings{Li:2009:ICACC, author = "Miao Li and Jian Zhang and Ze-lin Hu and Yuan Yuan1 and Lu-jiu Li", title = "An Algorithm of Fertilization Model Fitting Based On Mixed Intelligent Computation", booktitle = "International Conference on Advanced Computer Control, ICACC '09", year = "2009", month = "22-24 " # jan, pages = "425--429", abstract = "During the process of pluralistic fertilisation model construction, the unreasonable ratio of nitrogen, phosphorus and kalium easily results in the deviation of fertilising model. This paper has proposed an adaptive algorithm of fertilisation model fitting based mixed intelligent computing of GP/GA, and solved the issue of structure and parameters optimisation of adaptive fertilisation model. This algorithm has carried out the research of applying control factors to adjust the parameters of fitting function, the appropriate ratio of nitrogen, phosphorus and kalium is regarded as control factors of heuristic search to adjust models, on the basis of history test data dynamical models are generated, and the optimisation and correction of models based appropriate ratio of nutrients are achieved.", keywords = "genetic algorithms, genetic programming, adaptive algorithm, fertilisation model fitting, kalium, mixed intelligent computation, nitrogen, phosphorus, pluralistic fertilisation model construction, CAD, fertilisers, nitrogen, phosphorus", DOI = "doi:10.1109/ICACC.2009.153", notes = "Also known as \cite{4777379}", } @InProceedings{Li:2005:MULTIMEDIA, author = "Rui Li and Bir Bhanu and Anlei Dong", title = "Coevolutionary feature synthesized {EM} algorithm for image retrieval", booktitle = "Proceedings of the 13th Annual ACM International Conference on Multimedia, MULTIMEDIA '05", year = "2005", pages = "696--705", address = "Singapore", publisher = "ACM", keywords = "genetic algorithms, genetic programming, coevolutionary feature synthesis, content-based image retrieval, expectation maximization algorithm, semi-supervised learning", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.654.3189", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", isbn13 = "1-59593-044-2", acmid = "1101304", URL = "http://doi.acm.org/10.1145/1101149.1101304", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.654.3189", DOI = "doi:10.1145/1101149.1101304", abstract = "As a commonly used unsupervised learning algorithm in Content-Based Image Retrieval (CBIR), Expectation-Maximization (EM) algorithm has several limitations, including the curse of dimensionality and the convergence at a local maximum. In this article, we propose a novel learning approach, namely Coevolutionary Feature Synthesized Expectation-Maximization (CFS-EM), to address the above problems. The CFS-EM is a hybrid of coevolutionary genetic programming (CGP) and EM algorithm applied on partially labelled data. CFS-EM is especially suitable for image retrieval because the images can be searched in the synthesised low-dimensional feature space, while a kernel-based method has to make classification computation in the original high-dimensional space. Experiments on real image databases show that CFS-EM outperforms Radial Basis Function Support Vector Machine (RBF-SVM), CGP, Discriminant-EM (D-EM) and Transductive-SVM (TSVM) in the sense of classification performance and it is computationally more efficient than RBF-SVM in the query phase.", notes = "See \cite{Li:2008:TOMM}", } @Article{Li:2008:TOMM, author = "Rui Li and Bir Bhanu and Anlei Dong", title = "Feature Synthesized EM Algorithm for Image Retrieval", journal = "ACM Transactions on Multimedia Computing, Communications, and Applications", year = "2008", volume = "4", number = "2", pages = "10:1--10:24", month = may, keywords = "genetic algorithms, genetic programming, Coevolutionary feature synthesis, content-based image retrieval, expectation maximization, semi-supervised learning", ISSN = "1551-6857", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.654.3189", URL = "http://vislab.ucr.edu/PUBLICATIONS/pubs/Journal%20and%20Conference%20Papers/after10-1-1997/Journals/2008/Feature%20synthesized%20EM%20algorithm%20for%20image%20retrieval08.pdf", URL = "http://doi.acm.org/10.1145/1352012.1352014", DOI = "doi:10.1145/1352012.1352014", acmid = "1352014", publisher = "ACM", abstract = "As a commonly used unsupervised learning algorithm in Content-Based Image Retrieval (CBIR), Expectation-Maximization (EM) algorithm has several limitations, including the curse of dimensionality and the convergence at a local maximum. In this article, we propose a novel learning approach, namely Coevolutionary Feature Synthesised Expectation-Maximization (CFS-EM), to address the above problems. The CFS-EM is a hybrid of coevolutionary genetic programming (CGP) and EM algorithm applied on partially labelled data. CFS-EM is especially suitable for image retrieval because the images can be searched in the synthesized low-dimensional feature space, while a kernel-based method has to make classification computation in the original high-dimensional space. Experiments on real image databases show that CFS-EM outperforms Radial Basis Function Support Vector Machine (RBF-SVM), CGP, Discriminant-EM (D-EM) and Transductive-SVM (TSVM) in the sense of classification performance and it is computationally more efficient than RBF-SVM in the query phase.", notes = "Replaces \cite{Li:2005:MULTIMEDIA} Also known as \cite{Li:2008:FSE:1352012.1352014}", } @Article{Li:2017:QMQM, author = "Rui Li and Unai Alvarez-Rodriguez and Lucas Lamata and Enrique Solano", title = "Approximate Quantum Adders with Genetic Algorithms: An {IBM} Quantum Experience", journal = "Quantum Measurements and Quantum Metrology", year = "2017", volume = "4", number = "1", pages = "1--7", keywords = "genetic algorithms, genetic programming, Quantum Information, Quantum Algorithms", URL = "https://arxiv.org/pdf/1611.07851", URL = "https://www.degruyter.com/view/j/qmetro.2017.4.issue-1/qmetro-2017-0001/qmetro-2017-0001.xml", DOI = "doi:10.1515/qmetro-2017-0001", size = "7 pages", abstract = "It has been proven that quantum adders are forbidden by the laws of quantum mechanics. We analyse theoretical proposals for the implementation of approximate quantum adders and optimize them by means of genetic algorithms, improving previous protocols in terms of efficiency and fidelity. Furthermore, we experimentally realize a suitable approximate quantum adder with the cloud quantum computing facilities provided by IBM Quantum Experience. The development of approximate quantum adders enhances the toolbox of quantum information protocols, paving the way for novel applications in quantum technologies.", } @InProceedings{Li:2017:AAAF, author = "Ruying Li and Bernd R. Noack and Laurent Cordier and Jacques Boree and Fabien Harambat", title = "Machine learning control of the turbulent wake past {3D} bluff body", booktitle = "3AF International Conference on Applied Aerodynamics, AAAF", year = "2017", address = "Ecole Centrale de Lyon, France", month = mar # " 27-29", organisation = "French Aeronautics and Space Society 3AF", keywords = "genetic algorithms, genetic programming, machine learning control, drag reduction, car, physics, mechanics of the fluids", bibsource = "OAI-PMH server at api.archives-ouvertes.fr", contributor = "Laboratoire d'Informatique pour la M{\'e}canique et les Sciences de l'Ing{\'e}nieur and AAAF and AAAF", identifier = "hal-01856273", language = "en", oai = "oai:HAL:hal-01856273v1", URL = "https://hal.archives-ouvertes.fr/hal-01856273", abstract = "We investigate experimentally a novel model-free control strategy, called Machine Learning Control (MLC), for aerodynamic drag reduction of a 3D bluff body. Fluidic actuation is applied at the blunt trailing edge of the body combined with a curved deflection surface. The impact of actuation on the flow is monitored with base pressure sensors. The applied model-free control strategy detects and exploits nonlinear actuation mechanisms in an unsupervised manner with the aim of minimising the drag. Key enabler is linear genetic programming as simple and efficient framework for systems with multiple inputs (actuators) and multiple outputs (sensors). The ansatz of control laws include periodic forcing, multi-frequency forcing and sensor-based feedback control. Approximately 33percent base pressure recovery is achieved by the optimal control law for a turbulent flow at Re_H {$\approx$} 3 {$\times$} 10 5 based on body height.", notes = "http://3af-aerodynamics2017.com/ oai:HAL:hal-01856273v1 Also known as \cite{li:hal-01856273} Contributor : Limsi Publications ", } @Misc{oai:arXiv.org:1609.02505, author = "Ruiying Li and Bernd R. Noack and Laurent Cordier and Jacques Boree and Fabien Harambat and Eurika Kaiser and Thomas Duriez", title = "Drag reduction of a car model by linear genetic programming control", note = "Comment: 39 pages, 23 figures", year = "2016", month = sep # "~08", keywords = "genetic algorithms, genetic programming, physics - fluid dynamics", bibsource = "OAI-PMH server at export.arxiv.org", oai = "oai:arXiv.org:1609.02505", URL = "http://arxiv.org/abs/1609.02505", abstract = "We investigate open- and closed-loop active control for aerodynamic drag reduction of a car model. Turbulent flow around a blunt-edged Ahmed body is examined at $Re_{H}\approx3\times10^{5}$ based on body height. The actuation is performed with pulsed jets at all trailing edges combined with a Coanda deflection surface. The flow is monitored with pressure sensors distributed at the rear side. We apply a model-free control strategy building on Dracopoulos \& Kent (Neural Comput. \& Applic., vol. 6, 1997, pp. 214-228) and Gautier et al. (J. Fluid Mech., vol. 770, 2015, pp. 442-457). The optimised control laws comprise periodic forcing, multi-frequency forcing and sensor-based feedback including also time-history information feedback and combination thereof. Key enabler is linear genetic programming as simple and efficient framework for multiple inputs (actuators) and multiple outputs (sensors). The proposed linear genetic programming control can select the best open- or closed-loop control in an unsupervised manner. Approximately 33percent base pressure recovery associated with 22percent drag reduction is achieved in all considered classes of control laws. Intriguingly, the feedback actuation emulates periodic high-frequency forcing by selecting one pressure sensor in the optimal control law. Our control strategy is, in principle, applicable to all multiple actuators and sensors experiments.", notes = "see \cite{Li:2017:expfluids}", } @InProceedings{Li:2017:IFAC, author = "Ruiying Li and Bernd R. Noack and Laurent Cordier and Jacques Boree and Fabien Harambat", title = "Machine learning control for drag reduction of a car model in experiment", booktitle = "20th IFAC World Congress", year = "2017", editor = "Dimitri Peaucelle", pages = "Paper ThP23.1", address = "Toulouse, France", month = jul # " 9-14", organisation = "International Federation of Automatic Control", keywords = "genetic algorithms, genetic programming, evolutionary algorithms, real-time control, machine learning control, drag reduction, car, physics, PHYS, MECA, MEFL, mechanics of the fluids", identifier = "hal-01856274", language = "en", oai = "oai:HAL:hal-01856274v1", type = "info:eu-repo/semantics/conferenceObject", URL = "https://hal.archives-ouvertes.fr/hal-01856274", URL = "http://www.ifac2017.org/sites/www.ifac2017.org/files/u88/IFAC17_ContentListWeb_4.html", abstract = "We investigate experimentally a novel model-free in-time control strategy, called Machine Learning Control (MLC), for aerodynamic drag reduction of a car model. Fluidic actuation is applied at the trailing edge of a blunt-edged Ahmed body combined with a curved deflection surface. The impact of actuation on the flow is monitored with base pressure sensors.Based on the idea of genetic programming, the applied model-free control strategy detects and exploits nonlinear actuation mechanisms in an unsupervised manner with the aim of minimising the drag. Key enabler is linear genetic programming as simple and efficient framework for multiple inputs (actuators) and multiple outputs (sensors). The optimised control laws comprise periodic forcing, multi-frequency forcing and sensor-based feedback control. Approximately 33 percent base pressure recovery associated with 22 percent drag reduction is achieved by the optimal control law for a turbulent flow at Reynolds number 300000 based on body height.", notes = "Author sometimes given as Ruying Li http://www.ifac2017.org/ oai:HAL:hal-01856274v1, Contributor : Limsi Publications ", } @Article{Li:2017:expfluids, author = "Ruiying Li and Bernd R. Noack and Laurent Cordier and Jacques Boree and Fabien Harambat", title = "Drag reduction of a car model by linear genetic programming control", journal = "Experiments in Fluids", year = "2017", volume = "58", number = "8", pages = "103", month = aug, keywords = "genetic algorithms, genetic programming", ISSN = "1432-1114", DOI = "doi:10.1007/s00348-017-2382-2", size = "20 pages", abstract = "We investigate open- and closed-loop active control for aerodynamic drag reduction of a car model. Turbulent flow around a blunt-edged Ahmed body is examined at based on body height. The actuation is performed with pulsed jets at all trailing edges (multiple inputs) combined with a Coanda deflection surface. The flow is monitored with 16 pressure sensors distributed at the rear side (multiple outputs). We apply a recently developed model-free control strategy building on genetic programming in Dracopoulos and Kent (Neural Comput Appl 6:214--228, 1997) and Gautier et al. (J Fluid Mech 770:424--441, 2015). The optimized control laws comprise periodic forcing, multi-frequency forcing and sensor-based feedback including also time-history information feedback and combinations thereof. Key enabler is linear genetic programming (LGP) as powerful regression technique for optimizing the multiple-input multiple-output control laws. The proposed LGP control can select the best open- or closed-loop control in an unsupervised manner. Approximately 33percent base pressure recovery associated with 22percent drag reduction is achieved in all considered classes of control laws. Intriguingly, the feedback actuation emulates periodic high-frequency forcing. In addition, the control identified automatically the only sensor which listens to high-frequency flow components with good signal to noise ratio. Our control strategy is, in principle, applicable to all multiple actuators and sensors experiments.", notes = "Does not have real page numbers, treat 103 as an article id?", } @PhdThesis{2017ESMA0014_li, author = "Ruiying Li", title = "Aerodynamic Drag Reduction of a Square-Back Car Model Using Linear Genetic Programming and Physic-Based Control", titletranslation = "R{\'e}duction de la tra{\^i}n{\'e}e a{\'e}rodynamique d'un v{\'e}hicule {\`a} culot droit en utilisant un contr{\^o}le bas{\'e} sur la programmation g{\'e}n{\'e}tique lin{\'e}aire et sur la physique", school = "ISAE-ENSMA Ecole Nationale Superieure de Mecanique et d'Aerotechique", year = "2017", address = "Poitiers, France", month = "13 " # dec, keywords = "genetic algorithms, genetic programming, linear genetic programming control, lgpc-3 aerodynamic drag, wake, flow control, feedback control", bibsource = "OAI-PMH server at api.archives-ouvertes.fr", language = "en", oai = "oai:HAL:tel-01685306v1", URL = "https://tel.archives-ouvertes.fr/tel-01685306", URL = "https://tel.archives-ouvertes.fr/tel-01685306/document", URL = "https://tel.archives-ouvertes.fr/tel-01685306/file/2017ESMA0014_li.pdf", URL = "http://www.theses.fr/2017ESMA0014", publisher = "HAL CCSD", identifier = "NNT : 2017ESMA0014; tel-01685306", size = "170 pages", abstract = "The thesis aims to develop effective active flow control strategies for aerodynamic drag reduction of road vehicles.We experimentally examine the effects of fluidic actuation on the wake past a simplified square-back car model.The actuation is performed with pulsed jets at trailing edges and the flow is monitored with 16 pressure sensors distributed at the rear side. We address the challenging nonlinear turbulence control---which is often beyond the capabilities of model-oriented approach---by developing a simple yet powerful model-free control strategy: the data-driven linear genetic programming control (LGPC). This method explores and exploits strongly nonlinear dynamics in an unsupervised manner with no or little prior knowledge about the system. The control problem is to find a control logic which optimises a given cost function by employing linear genetic programming as an easy and simple regression solver in a high-dimensional control search space. In particular, the present work advances and generalises the previous studies of genetic programming control by comprising multi-frequency forcing, sensor-based feedback including also time-history information feedback and combinations thereof in the control search space. The performance of LGPC is successfully demonstrated on the drag control experiments of the car model where the investigated turbulent wake exhibits a spanwise symmetry and a wall-normal asymmetry. Approximately 33percent base pressure recovery associated with 22percent drag reduction is achieved in all considered classes of control laws. The consumed actuation energy accounts for only 30percent of the aerodynamic power saving. In this research, we also study the turbulent wakes having a lateral asymmetry: an intermittent bi-modal wake at zero yaw and an asymmetric wake at a moderate yaw angle of 5 degree. For the bimodal wake exhibiting are flectional symmetry-breaking, a physics-based opposition feedback control is inferred from the previous open loop control tests. The controller successfully suppresses the bi-modality of the wake and renders a symmetrized wake with a concomitant drag reduction. For the asymmetric wake at yaw, we infer from the single-frequency forcing results a bi-frequency control at the windward edge comprising two frequencies having one order of magnitude difference. This bi-frequency actuation combines the favourable effects of fluidic boat-tailing and balance control of the shear layers. Importantly, LGPC is also applied to this yawed situation and converges to the same bi-frequency actuation. The control strategies proposed in the present study open promising new paths for the control of drag reduction in more complex conditions such as the varying oncoming velocity and wind gust.", resume = "Le but de la th{\`e}se est de d{\'e}velopper des strat{\'e}gies de contr{\^o}le efficaces pour la r{\'e}duction de la train{\'e}e a{\'e}rodynamique des v{\'e}hicules terrestres. Nous examinons exp{\'e}rimentalement les effets d{'}un for{\c c}age fluidique sur le sillage d{'}un mod{\`e}le de v{\'e}hicule simplifi{\'e} {\`a} culot droit. Le for{\c c}age est effectu{\'e} par des jets puls{\'e}s aux ar{\^e}tes et16 capteurs de pression r{\'e}partis {\`a} la surface arri{\`e}re permettent d{'}estimer la tra{\^i}n{\'e}e instantan{\'e}e. Nous abordons le probl{\`e}me difficile du contr{\^o}le de l{'}{\'e}coulement turbulent non lin{\'e}aire---qui est souvent au-del{\`a} des capacit{\'e}s de la mod{\'e}lisation r{\'e}duite---par le d{\'e}veloppement d'une strat{\'e}gie de contr{\^o}le sans mod{\`e}le: le contr{\^o}le via la programmation g{\'e}n{\'e}tique lin{\'e}aire (LGPC) dirig{\'e} par les donn{\'e}es. Cette m{\'e}thode explore et exploite la dynamique fortement non lin{\'e}aire d'une mani{\`e}re non supervis{\'e}e avec pas ou peu de connaissances ant{\'e}rieures sur le syst{\`e}me.Le probl{\`e}me est de trouver une logique de contr{\^o}le qui optimise une fonction de co{\^u}t donn{\'e}e. Cette optimisation est r{\'e}alis{\'e}e par la programmation g{\'e}n{\'e}tique lin{\'e}aire comme un solveur de r{\'e}gression simple dans un espace de recherche de grande dimension. En particulier, cette recherche fait progresser et g{\'e}n{\'e}ralise les {\'e}tudes ant{\'e}rieures sur le contr{\^o}le via la programmation g{\'e}n{\'e}tique en incluant le for{\c c}age multi-fr{\'e}quences, le signal des capteurs,l{'}historique des informations temporelles et leurs combinaisons dans l'espace de recherche de contr{\^o}le. La performance de LGPC est d{\'e}montr{\'e}e avec succ{\`e}s sur les exp{\'e}riences de contr{\^o}le de tra{\^i}n{\'e}e du mod{\`e}le de v{\'e}hicule simplifi{\'e} o{\`u} le sillage turbulent pr{\'e}sente une sym{\'e}trie lat{\'e}rale et une asym{\'e}trie normale {\`a} la paroi. Environ 33percent de r{\'e}cup{\'e}ration de pression au culot associ{\'e}e {\`a} 22percent de r{\'e}duction de train{\'e}e est obtenue dans toutes les classes de loisde contr{\^o}le consid{\'e}r{\'e}es. L'{\'e}nergie consomm{\'e}e du for{\c c}age ne repr{\'e}sente que 30percent de l'{\'e}nergie a{\'e}rodynamique r{\'e}cup{\'e}r{\'e}e. Dans ce travail, nous {\'e}tudions {\'e}galement les sillages turbulents ayant une asym{\'e}trie lat{\'e}rale: un sillage intermittent et bi-modal {\`a} d{\'e}rapage nul et un sillage asym{\'e}trique avec un angle de d{\'e}rapage mod{\'e}r{\'e} de 5 degr{\'e}s.Pour le sillage intermittent, un contr{\^o}le de r{\'e}troaction en opposition bas{\'e} sur la physique est d{\'e}duit {\`a} partir des essais pr{\'e}c{\'e}dents de contr{\^o}le en boucle ouverte. Le contr{\^o}leur supprime avec succ{\`e}s la bi-modalit{\'e} du sillage et rend le sillage sym{\'e}trique avec une r{\'e}duction de tra{\^i}n{\'e}e concomitante. Pour le sillage asym{\'e}trique en d{\'e}rapage,nous construisons un contr{\^o}le bi-fr{\'e}quence {\`a} l{'}ar{\^e}te au vent {\`a} partir des r{\'e}sultats de for{\c c}age {\`a} fr{\'e}quence unique. Ce for{\c c}age bi-fr{\'e}quentiel comprend deux fr{\'e}quences ayant une diff{\'e}rence d'un ordre de grandeur. Il combine les effets favorables de la vectorisation du sillage et le contr{\^o}le de l'{\'e}quilibre des couches de cisaillement. Il est important de noter que la strat{\'e}gie LGPC est {\'e}galement appliqu{\'e} {\`a} cette situation en d{\'e}rapage et converge vers le m{\^e}me for{\c c}age bi-fr{\'e}quentiel. Les strat{\'e}gies de contr{\^o}le propos{\'e}es dans cette {\'e}tude ouvrent de nouveaux chemins prometteurs pour le contr{\^o}le de la r{\'e}duction de la tra{\^i}n{\'e}e dans des conditions plus complexes de vitesse amont variable ou de rafale.", notes = "In english/ also known as \cite{oai:HAL:tel-01685306v1} Supvervisors: Jacques Boree and Bernd R. Noack and Laurent Cordier PSA-Peugeot Citroen ENSMA", } @Article{Li:2018:AM, author = "Ruiying Li and Bernd R. Noack and Laurent Cordier and Jacques Boree", title = "Linear genetic programming control for strongly nonlinear dynamics with frequency crosstalk", journal = "Archives of Mechanics", year = "2018", volume = "70", number = "6", pages = "505--534", publisher = "HAL CCSD", month = jan # "~08", keywords = "genetic algorithms, genetic programming, flow control, nonlinear dynamics, turbulent wake, physics, mechanics, mechanics of the fluids, materials and structures in mechanics, engineering sciences, acoustics, automatic, electromagnetism, reactive fluid environment, electric power, thermics, vibrations", URL = "https://hal.archives-ouvertes.fr/hal-02290373", DOI = "doi:10.24423/aom.3000", abstract = "We advance Genetic Programming Control (GPC) for turbulence flow control application building on the pioneering work of [1]. GPC is a recently proposed model-free control framework which explores and exploits strongly nonlinear dynamics in an unsupervised manner. The assumed plant has multiple actuators and sensors and its performance is measured by a cost function. The control problem is to find a control logic which optimises the given cost function. The corresponding regression problem for the control law is solved by employing linear genetic programming as an easy and simple regression solver in a high-dimensional control search space. This search space comprises open-loop actuation, sensor-based feedback and combinations thereof --- thus generalizing former GPC studies [2, 3]. This new methodology is denoted as linear genetic programming control (LGPC). The focus of this study is the frequency crosstalk between unforced, unstable oscillation and the actuation at different frequencies. LGPC is first applied to the stabilization of a forced nonlinearly coupled three-oscillator model comprising open- and closed-loop frequency crosstalk mechanisms. LGPC performance is then demonstrated in a turbulence control experiment, achieving 22 percent drag reduction for a simplified car model. In both cases, LGPC identifies the best nonlinear control achieving the optimal performance by exploiting frequency crosstalk. Our control strategy is suited to complex control problems with multiple actuators and sensors featuring nonlinear actuation dynamics. Significant further performance enhancement is envisioned in the more general field of machine learning control [4].", annote = "Turbulence Incompressible et Controle (TIC ) ; Departement Fluides, Thermique et Combustion (FTC) ; Institut Pprime (PPRIME) ; Universite de Poitiers-Centre National de la Recherche Scientifique (CNRS)-ENSMA-Universite de Poitiers-Centre National de la Recherche Scientifique (CNRS)-ENSMA-Institut Pprime (PPRIME) ; Universite de Poitiers-Centre National de la Recherche Scientifique (CNRS)-ENSMA-Universite de Poitiers-Centre National de la Recherche Scientifique (CNRS)-ENSMA; Acoustique, Aerodynamique, Turbulence (2AT ) ; Departement Fluides, Thermique et Combustion (FTC) ; Institut Pprime (PPRIME) ; Universite de Poitiers-Centre National de la Recherche Scientifique (CNRS)-ENSMA-Universite de Poitiers-Centre National de la Recherche Scientifique (CNRS)-ENSMA-Institut Pprime (PPRIME) ; Universite de Poitiers-Centre National de la Recherche Scientifique (CNRS)-ENSMA-Universite de Poitiers-Centre National de la Recherche Scientifique (CNRS)-ENSMA", bibsource = "OAI-PMH server at api.archives-ouvertes.fr", contributor = "Turbulence Incompressible et Controle and Aerodynamique Acoustique, Turbulence", description = "International audience", identifier = "hal-02290373", language = "en", oai = "oai:HAL:hal-02290373v1", } @InCollection{Li:2013:GPTP, author = "Ruowang Li and Emily R. Holzinger and Scott M. Dudek and Marylyn D. Ritchie", title = "Evaluation of Parameter Contribution to Neural Network Size and Fitness in ATHENA for Genetic Analysis", booktitle = "Genetic Programming Theory and Practice XI", year = "2013", series = "Genetic and Evolutionary Computation", editor = "Rick Riolo and Jason H. Moore and Mark Kotanchek", publisher = "Springer", chapter = "12", pages = "211--224", address = "Ann Arbor, USA", month = "9-11 " # may, keywords = "genetic algorithms, genetic programming, Grammatical evolution, Neural networks, Data mining, Human genetics, Systems biology, XOR model", isbn13 = "978-1-4939-0374-0", DOI = "doi:10.1007/978-1-4939-0375-7_12", abstract = "The vast amount of available genomics data provides us an unprecedented ability to survey the entire genome and search for the genetic determinants of complex diseases. Until now, Genome-wide association studies have been the predominant method to associate DNA variations to disease traits. GWAS have successfully uncovered many genetic variants associated with complex diseases when the effect loci are strongly associated with the trait. However, methods for studying interaction effects among multiple loci are still lacking. Established machine learning methods such as the grammatical evolution neural networks (GENN) can be adapted to help us uncover the missing interaction effects that are not captured by GWAS studies. We used an implementation of GENN distributed in the software package ATHENA (Analysis Tool for Heritable and Environmental Network Associations) to investigate the effects of multiple GENN parameters and data noise levels on model detection and network structure. We concluded that the models produced by GENN were greatly affected by algorithm parameters and data noise levels. We also produced complex, multi-layer networks that were not produced in the previous study. In summary, GENN can produce complex, multi-layered networks when the data require it for higher fitness and when the parameter settings allow for a wide search of the complex model space.", notes = " Part of \cite{Riolo:2013:GPTP} published after the workshop in 2013", } @Article{Li:2016:bdm, author = "Ruowang Li and Scott M. Dudek and Dokyoon Kim and Molly A. Hall and Yuki Bradford and Peggy L. Peissig and Murray H. Brilliant and James G. Linneman and Catherine A. McCarty and Le Bao and Marylyn D. Ritchie", title = "Identification of genetic interaction networks via an evolutionary algorithm evolved {Bayesian} network", journal = "BioData Mining", year = "2016", volume = "9", number = "1", pages = "18", month = "10 " # may, keywords = "genetic algorithms, genetic programming, Grammatical Evolution Bayesian Network (GEBN)", ISSN = "1756-0381", DOI = "doi:10.1186/s13040-016-0094-4", URL = "https://doi.org/10.1186/s13040-016-0094-4", language = "en", oai = "oai:biomedcentral.com:s13040-016-0094-4", URL = "http://www.biodatamining.org/content/9/1/18", abstract = "The future of medicine is moving towards the phase of precision medicine, with the goal to prevent and treat diseases by taking inter-individual variability into account. A large part of the variability lies in our genetic makeup. With the fast paced improvement of high-throughput methods for genome sequencing, a tremendous amount of genetics data have already been generated. The next hurdle for precision medicine is to have sufficient computational tools for analysing large sets of data. Genome-Wide Association Studies (GWAS) have been the primary method to assess the relationship between single nucleotide polymorphisms (SNPs) and disease traits. While GWAS is sufficient in finding individual SNPs with strong main effects, it does not capture potential interactions among multiple SNPs. In many traits, a large proportion of variation remain unexplained by using main effects alone, leaving the door open for exploring the role of genetic interactions. However, identifying genetic interactions in large-scale genomics data poses a challenge even for modern computing.", } @InProceedings{Li:2006:iccis, author = "Shaobo Li and Jianjun Hu", title = "Evolving Vibration Absorbers Based on Genetic Programming and Bond Graphs", booktitle = "2006 International Conference on Computational Intelligence and Security", year = "2006", volume = "1", pages = "202--207", address = "Guangzhou", month = nov, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", ISBN = "1-4244-0605-6", DOI = "doi:10.1109/ICCIAS.2006.294122", abstract = "Conceptual innovation in mechanical engineering design has been extremely challenging compared to the wide applications of automated design systems in digital circuits. This paper presents an automated methodology for open-ended synthesis of mechanical vibration absorbers based on genetic programming and bond graphs. It is shown that our automated design system can automatically evolve passive vibration absorbers that are close to or better than the standard passive vibration absorbers invented in 1912. A variety of other vibration absorbers with competitive performance are also evolved automatically using a desktop PC in less than 10 hours", notes = "CAD/CIMS Inst., Guizhou Univ., Guiyang", } @InProceedings{Li:2008:WiCOM, author = "Shaobo Li and Guanci Yang and Qingsheng Xie", title = "Automatic Design Method of Dynamic Systems Based on Hungarian Algorithm and Genetic Programming", booktitle = "4th International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM '08", year = "2008", month = oct, pages = "1--4", keywords = "genetic algorithms, genetic programming, Hungarian algorithm, automatic design method, bond graphs, dynamic systems, fitness definition, graph theory, telecommunication network topology", DOI = "doi:10.1109/WiCom.2008.552", abstract = "This paper summarizes the present research status of automated design method for dynamic systems, investigates efficient method of fitness definition for automated design method of dynamic systems based on bond graphs and genetic programming. The automated design method based on Hungarian algorithm and genetic programming (HAGP) is proposed, and the statistic results of domain independent - an eigenvalues -placement design problem, which is tested for some sample target sets of eigenvalues, strongly shows the search capability of HAGP is good enough to make feasible automated design and obtain high-quality, well evolutionary solutions with less computational efforts, rapid speed in convergence compared to other state-of art algorithms.", notes = "Also known as \cite{4678460}", } @InProceedings{Li:2008:IITAW, author = "Shaobo Li and Jianning Qiang and Juanjuan Wang and Gao Jie", title = "Synthesis of Analog Filter Based on Genetic Program and Network Topology Transformation", booktitle = "International Symposium on Intelligent Information Technology Application Workshops, IITAW '08", year = "2008", month = dec, pages = "1033--1036", abstract = "This paper briefly introduces GA and GP and succeeds in designing analog filter with the aid of GP, expressing electro-circuit by woking use of tree structure, then proceeding genetic operation. In the process of evolution, network transformation is involved in so that parameter could advance select optimization. Finally, this paper sets forth a concrete example of the design in which the procedures and the outcome are listed clearly and the designing is simulated by Dyloma 7.0 dynamic simulating software.", keywords = "genetic algorithms, genetic programming, analog filter design synthesis, electro-circuit optimization, genetic program, network topology transformation, tree structure, analogue circuits, circuit optimisation, filters, network topology", DOI = "doi:10.1109/IITA.Workshops.2008.270", notes = "Also known as \cite{4732114}", } @Book{Li:2009:book, author = "Shaobo Li and Jianjun Hu", title = "Genetic Programming and Creative Design of Mechatronic Systems", publisher = "China Machine Press", year = "2009", email = "hujianju@gmail.com", keywords = "genetic algorithms, genetic programming, bond graphs, evolutionary design", isbn13 = "9787111254157", URL = "http://product.dangdang.com/product.aspx?product_id=20470877", abstract = "Mechatronic product design is a multi-domain design problem, which is different from the common design of mechanical, electronic or hydraulic systems in isolation. In a multi-domain system, there are many energy conversion behaviors among energy sources of different types. The designer often faces the challenge of proposing an innovative solution that satisfies a multitude of design objectives and constraints as perfectly as practical. Traditionally, innovative design of mechatronic products relies on engineers who explore and accumulate experience over a long period of time, and is incremental in nature. In 2005, the authors were funded by the National Natural Science of China under Grant 50575047 to investigate Creative Design of Mechatronic Systems Based on Genetic Programming. This book, which arises from that study and preceding work by the authors and their colleagues, covers several fields such as sustainable evolutionary algorithms, bond graph theory, modeling and simulation of bond graphs, genetic programming, scalable evolutionary synthesis of dynamic system, and related concepts. An innovative design method for mechatronic products, based on genetic programming and simulation of bond graphs, is presented. In this method, the model of a mechatronic product is expressed as a bond graph and genetic programming is used to search for the best individual (representing the best system) in the design space. A unique advantage of evolutionary synthesis is its capability to produce innovative designs, starting from a nearly blank sheet of paper, rather than relying solely on expert knowledge and thereby, sometimes, not discovering solutions that are radically different from those already existing. Evolutionary synthesis can find an innovative solution by searching in an open-ended design space, subject only to the constraints imposed by the designer based on the real requirements for the system. This book is the first monograph on creative design of mechatronic products using genetic programming and bond graphs published in China. It includes 11 chapters. Chapter 1 introduces the background of the research, methods and techniques for creative design of mechatronic systems, and the objectives of the research; Chapter 2 mainly describes two evolutionary algorithms GA and GP; Chapter 3 describes a sustainable GA; Chapter 4 presents sustainable GP, including a sustainable evolutionary model based on the HFC concept; Chapter 5 describes sustainable SA (Simulated Annealing) based on the HFC model; Chapter 6 introduces basic knowledge such as the theory of bond graphs and system modeling and simulation with bond graphs, including modular modeling of mechatronic systems using bond graphs; Chapter 7 concerns evolutionary synthesis based on bond graphs and GP, describing basic and advanced methods; Chapter 8 introduces GP-based design using Open BEAGLE, which includes basic grammar, a class library, and program design; Chapter 9 is an analysis of an example evolutionary synthesis of an analog circuit; Chapter 10 presents an example of evolutionary synthesis of a vibration absorber; and Chapter 11 concludes the book with an analysis of an example of evolutionary synthesis of a MEMS system.", abstract = "The research in this book spans several fields, including large-scale scientific computation, simulation of mechatronic and control systems, computational intelligence and genetic programming technology, automated design, and parameter optimization. By employing genetic programming and simulation of dynamic systems in a bond graph expression, this book provides a systematic exposition of automated design of hybrid mechatronic systems that include mechanical, electronic and control systems. The research results in this book include a systematic method for creative design of modern mechatronic products. Based on GP, the hybrid topology algorithm, mixing bond graphs with control block diagrams and circuit diagrams, has many advantages, such as searching structures in an open-ended way and simultaneously searching for the optimal parameters of the components of the structure. This hybrid searching method breaks through the restrictions of the classical parametric optimization of designs based on GA. And it also implements automated design of complicated systems. This hybrid search algorithm can evolve some complicated mechatronic products with designs that are superior to those done by human designers, in areas such as circuits, controllers, vibration absorbers, etc. The design theory, methods, and prototype systems for evolutionary computation based on GP can also potentially improve design practice for other types of engineering systems. The important advances described in this book give it profound academic value and application significance. The content of this book is easy to understand. It can be used as a guide for creative design theory and practice. It also will be a practical toolbox for students in mechanical engineering, computer science and related majors. Of course, it will also be a good choice as teaching reference book for postgraduate students or doctoral students. I enthusiastically recommend it for study by persons interested in studying the automated design of mechatronic systems. Erik D. Goodman Professor and Design Coordinator, Electrical and Computer Engineering Professor, Mechanical Engineering Michigan State University Vice President for Technology, Red Cedar Technology, Inc. Founding Chair, ACM Special Interest Group on Genetic and Evolutionary Computation", } @InProceedings{Li:2009:ISCID, author = "Shaobo Li and Weijie Pan and Guanci Yang and Linna Chen", title = "Optimization of 3G Wireless Network Using Genetic Programming", booktitle = "Second International Symposium on Computational Intelligence and Design, ISCID '09", year = "2009", month = dec, volume = "2", pages = "131--134", address = "Changsha, China", keywords = "genetic algorithms, genetic programming, 3G wireless network, automated optimization design, base station configuration plans, evolutional topological operators, network design, 3G mobile communication", DOI = "doi:10.1109/ISCID.2009.181", abstract = "We proposed a genetic programming (GP) based method for automated optimization design of base station configuration plans of 3G wireless networks. This method aims to address the disadvantages of the current methods on wireless network optimization and to satisfy new requirements for network design. Evolutional topological operators and terminal set are designed for GP the Prim is used to guide the evolutionary design. The result of topological graph shows that the algorithm can balance topology and parameter search, and it works well for 3G network optimization.", notes = "Also known as \cite{5368796}", } @Article{li:2018:Algorithms, author = "Shaobo Li and Wang Zou and Jianjun Hu", title = "A Novel Evolutionary Algorithm for Designing Robust Analog Filters", journal = "Algorithms", year = "2018", volume = "11", number = "3", keywords = "genetic algorithms, genetic programming", ISSN = "1999-4893", URL = "https://www.mdpi.com/1999-4893/11/3/26", DOI = "doi:10.3390/a11030026", abstract = "Designing robust circuits that withstand environmental perturbation and device degradation is critical for many applications. Traditional robust circuit design is mainly done by tuning parameters to improve system robustness. However, the topological structure of a system may set a limit on the robustness achievable through parameter tuning. This paper proposes a new evolutionary algorithm for robust design that exploits the open-ended topological search capability of genetic programming (GP) coupled with bond graph modelling. We applied our GP-based robust design (GPRD) algorithm to evolve robust lowpass and highpass analog filters. Compared with a traditional robust design approach based on a state-of-the-art real-parameter genetic algorithm (GA), our GPRD algorithm with a fitness criterion rewarding robustness, with respect to parameter perturbations, can evolve more robust filters than what was achieved through parameter tuning alone. We also find that inappropriate GA tuning may mislead the search process and that multiple-simulation and perturbed fitness evaluation methods for evolving robustness have complementary behaviours with no absolute advantage of one over the other.", notes = "also known as \cite{a11030026}", } @InProceedings{Li:2011:ICICTA, author = "Shengen Li and Xiaofei Niu and Peiqi Li and Lin Wang", title = "Generating New Features Using Genetic Programming to Detect Link Spam", booktitle = "2011 International Conference on Intelligent Computation Technology and Automation (ICICTA)", year = "2011", month = mar, volume = "1", pages = "135--138", abstract = "Link spam techniques can enable some pages to achieve higher-than-deserved rankings in the results of a search engine. They negatively affect the quality of search results. Classification methods can detect link spam. For classification problem, features play an important role. This paper proposes to derive new features using genetic programming from existing link-based features and use the new features as the inputs to SVM and GP classifiers for the identification of link spam. Experiments on WEBSPAM-UK2006 show that the classification results of the classifiers that use 10 newly generated features are much better than those of the classifiers that use original 41 link-based features and equivalent to those of the classifiers that use 138 transformed link-based features. The newly generated features can improve the link spam classification performance.", keywords = "genetic algorithms, genetic programming, GP classifier, SVM, WEBSPAM-UK2006, classification method, link spam detection, link-based feature generation, search engine, search result quality, Internet, feature extraction, information retrieval, pattern classification, search engines, support vector machines", DOI = "doi:10.1109/ICICTA.2011.41", notes = "Also known as \cite{5750574}", } @Article{LI:2022:JH, author = "Shicheng Li and Qiancheng Xie and James Yang", title = "Daily suspended sediment forecast by an integrated dynamic neural network", journal = "Journal of Hydrology", volume = "604", pages = "127258", year = "2022", ISSN = "0022-1694", DOI = "doi:10.1016/j.jhydrol.2021.127258", URL = "https://www.sciencedirect.com/science/article/pii/S0022169421013081", keywords = "genetic algorithms, genetic programming, River suspended sediment, Wavelet transformation, Multigene genetic programing, Multilayer perceptron neural network, INARX", abstract = "Suspended sediment is of importance in river and dam engineering. Due to its high nonlinearity and stochasticity, sediment prediction by conventional methods is a challenging task. Consequently, this paper establishes a new hybrid model for an improved forecast of suspended sediment concentration (SSC). It is a nonlinear autoregressive network with exogenous inputs (NARX) integrated with a data pre-processing framework (thereafter INARX). In this model, wavelet transformation (WT) is used for time series decomposition and multigene genetic programing (MGGP) for details scaling. The two incorporated modules improve time and frequency domain analysis, allowing the network to unveil the embedded characteristics and capture the non-stationarity. At a hydrological station on the upper reaches of the Yangtze River, the records of daily water stage, flow discharge and suspended sediment are collected and refer to a nine-year period during 2004-2012. The data are used to evaluate the models. Several wavelets are explored, showing that the Coif3 leads to the most accurate prediction. Compared to the sediment rating curve (SRC), the conventional MGGP, multilayer perceptron neural network (MLPNN) and NARX, the INARX demonstrates the best forecast performance. Its mean coefficient of determination (CD) increases by 7.7percent-38.6percent and the root mean squared error (RMSE) reduces by 15.1percent-54.5percent. The INARX with the Coif3 wavelet is further evaluated for flood events and multistep forecasts. Under flood conditions, the model generates satisfactory results, with CD > 0.83 and 84.7percent of the simulated data falling within the plus-minus0.1 kg/m3 error. For the multistep forecast, at a one-week lead time, the network also yields predictions with acceptable accuracy (mean CD = 0.78). The model performance deteriorates if the lead time becomes larger. The established framework is robust and reliable for real-time and multistep SSC forecasts and provides reference for time series modeling, e.g. streamflow, river temperature and salinity", } @Article{Li:2022:jwama, author = "Shicheng Li and James Yang", title = "A hybrid gene expression programming model for discharge prediction", journal = "Proceedings of the Institution of Civil Engineers - Water Management", year = "2022", volume = "176", number = "5", pages = "223--234", keywords = "genetic algorithms, genetic programming, gene expression programming, data-driven modelling, flow measurement, grey relational analysis, hydraulics, hydrodynamics, mathematical modelling, simulated annealing, waterways, canals", ISSN = "1741-7589", URL = "https://www.sciencedirect.com/science/article/pii/S1741758922000026", DOI = "doi:10.1680/jwama.21.00037", abstract = "The head-discharge relationship of an overflow weir is a prerequisite for flow measurement. Conventionally, it is determined by regression methods. With machine learning techniques, data-driven modelling becomes an alternative. However, a standalone model may be inadequate to generate satisfactory results, particularly for a complex system. With the intention of improving the performance of standard gene expression programming (GEP), a hybrid evolutionary scheme is proposed, which is coupled with grey system theory and probabilistic technique. As a gene filter, grey relational analysis (GRA) eliminates noise and simulated annealing (SA) reduces overfitting by optimising the gene weights. The proposed GEP-based model was developed and validated using experimental data of a submerged pivot weir. Compared with standalone GEP, the GRA-GEP-SA model was found to generate more accurate results. Its coefficients of determination and correlation were improved by 3.6percent and 1.7percent, respectively. The root mean square error was lowered by 24.8percent, which is significant. The number of datasets with an error of less than 10percent and 20percent was increased by 15percent and 12percent, respectively. The proposed approach outperforms classic genetic programming and shows a comparative error level with the empirical formula. The hybrid procedure also provides a reference for applications in other hydraulic issues", } @Article{Li:2024:jenvman, author = "Shicheng Li and Xin Ma", title = "A new modeling approach for microplastic drag and settling velocity", journal = "Journal of Environmental Management", year = "2024", volume = "372", pages = "123375", keywords = "genetic algorithms, genetic programming, Microplastic, Drag coefficient, Terminal velocity, Multigene genetic programming, Pareto optimization", ISSN = "0301-4797", URL = "https://www.sciencedirect.com/science/article/pii/S0301479724033619", DOI = "doi:10.1016/j.jenvman.2024.123375", abstract = "Understanding microplastics' (MPs') transport and settling behaviours in aquatic environments is crucial for devising effective management strategies. This study contributes a novel modelling framework to develop accurate and interpretable drag and velocity models for MPs using machine learning techniques. It achieves faster model creation and improved accuracy than traditional methods like theoretical analysis and data fitting. The framework demonstrates high predictive accuracy across different MP types (1D, 2D, 3D, and mixed), with a coefficient of determination CD = 0.86-0.95 for the drag models and CD = 0.92-0.95 for the velocity models. Compared with best-performing empirical approaches, the new drag models exhibit an average reduction in root mean square error (RMSE) by 59percent and mean absolute error (MAE) by 62percent. Similarly, the velocity models show a mean decrease in RMSE and MAE by 27percent and 25percent, respectively. Moreover, the framework outperforms commonly used symbolic regression methods, reducing errors by 18percent-27percent. The sensitivity analysis reveals that the relative density difference and the dimensionless diameter are essential for predicting the settling of all MP types, while the effective shape parameters vary across different MP categories. By providing accurate predictions of MPs' settling dynamics, this study offers insights for developing targeted mitigation strategies to reduce MPs' environmental impacts", } @PhdThesis{Shuai_Li:thesis, author = "Shuai Li2", title = "Learning to Rank with Click Models: From Online Algorithms to Offline Evaluations", school = "The Chinese University of Hong Kong", year = "2019", address = "Hong Kong", month = sep, keywords = "genetic algorithms, genetic programming", URL = "https://shuaili8.github.io/thesis_slides.pdf", URL = "https://search.proquest.com/openview/f766c3629df5a5b7dbff7a3420141f33/1?pq-origsite=gscholar&cbl=2026366&diss=y", size = "193 pages", abstract = "we consider online learning to rank with feedback from specific cascade click model to more robust general click model and offline evaluation of ranking policies with click models. First we propose contextual combinatorial cascading bandits. At each time step a learning agent is given a set of contextual information, then selects a list of items, and observes stochastic outcomes of a prefix in the selected items by cascading click feedback. The cascade model assumes users check the list from the first item to the last and stops at the first satisfying one. We consider position discounts in the list order, so that the agents reward is discounted depending on the position of the clicked item. Our setting generalizes existing studies with contextual information, position discounts,and a more general reward function. We design an algorithm with proven sublinear regret bound and the experiments on synthetic and real datasets demonstrate the advantage of involving contextual information and position discounts. Next we employ the techniques of online clustering of bandits to improve the recommendation qualities under cascade click feedback. We consider a new setting of online clustering of contextual cascading bandits, an on-line learning problem where the underlying cluster structure over users is unknown and needs to be learned under cascade feedback. The last work corresponds to the degenerate case of only one cluster and our general regret bound in this degenerate case also improves the previous results. The experiments on both synthetic and real data demonstrate the advantage of incorporating online clustering structure. Since the existing online clustering methods only consider uniform distribution over users and are not very efficient in separating dissimilar users. We consider a general setting of online clustering of bandits by allowing non-uniform distribution over user frequencies together with a more efficient algorithm which uses sets to represent structures. We provide a regret bound for the new algorithm which is free of the minimal frequency over users. The experiments on both synthetic and real datasets consistently show the advantage of our new algorithm over existing methods. Next we introduce a new model for online learning to rank in which the click probability factors into an examination and attractiveness function and the attractiveness function is a linear function of a feature vector and an unknown parameter. Only relatively mild assumptions are made on the examination function. The new click model covers many common click models including cascade model and position-based model. We bring up a novel recursive ranking algorithm for this setup and prove its regret depends on the feature dimension instead of the number of items, which allows the new algorithm to handle a large number of items. When reduced to the orthogonal case, the regret of the algorithm improves on the state-of-the-art. Last we consider to evaluate new ranking policies offline and optimize them before they are deployed. This is an important problem in practice. We address this problem by proposing evaluation algorithms for estimating the expected number of clicks on ranked lists from historical logged data. The existing algorithms are not guaranteed to be statistically efficient in our problem because the number of recommended lists can grow exponentially with their length. To overcome this challenge, we use the click models to construct estimators that learn statistically efficiently. We analyze our estimators and prove that they are more efficient than the estimators that do not use the structure of the click model, under the assumption that the click model holds. We evaluate our estimators in a series of experiments on a real-world dataset and show that they consistently outperform prior estimators.", notes = "ProQuest Dissertations Publishing, 27784073 Supervisor: Kwong-Sak Leung", } @InProceedings{Li:2010:ICMA, author = "Shuguang Li and Jianping Yuan and Jianjun Luo and Weihua Ma", title = "Satellite attitude control through evolving a neural network", booktitle = "2010 International Conference on Mechatronics and Automation (ICMA)", year = "2010", month = "4-7 " # aug, pages = "553--559", address = "Xi'an, China", abstract = "We propose a pure topological recurrent network controller for satellite attitude control, which has random binary connections in hidden layer, and all hidden neurons are activated by sinusoidal functions. A direct graph encoding method and four genetic operators are implemented for using genetic programming to train this controller. Moreover, a simulated small satellite which equipped with three reaction wheels was developed, then this simulator was employed to test the controller and training method for a given simple attitude adjusting mission. The experimental results reveal that this controller has the simplicity, usability and potentials for satellite attitude control through evolutionary learning.", keywords = "genetic algorithms, genetic programming, direct graph encoding method, evolutionary learning, genetic operator, neural network, pure topological recurrent network controller, satellite attitude control, sinusoidal function, training method, artificial satellites, attitude control, directed graphs, encoding, neurocontrollers, recurrent neural nets", DOI = "doi:10.1109/ICMA.2010.5588493", ISSN = "2152-7431", notes = "Also known as \cite{5588493}", } @InProceedings{Li:2010:BioRob, author = "Shuguang Li and Jianping Yuan and Xiaokui Yue and Jianjun Luo", title = "The binary-weights neural network for robot control", booktitle = "3rd IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob 2010", year = "2010", month = "26-29 " # sep, pages = "765--770", abstract = "We propose a pure topological recurrent networks controller, which has random binary connections in hidden layer, and all hidden neurons are activated by sinusoidal functions. A direct graph encoding method and four genetic operators are implemented for using genetic programming to train this controller. Firstly, its feasibility and efficiency were validated by a pair of function approximation experiments, the results show that through evolutionary learning, this novel RNN controller can handle nonlinear problems as well as common RNN even without adjustable weights. Moreover, a simulated mobile robot was equipped with this controller, and the robot was navigated around obstacles toward a goal in physical simulation environments; during tests, this robot exhibited four successful behaviours just by topological evolving on the simple controller. This experiment reveals that this controller has the simplicity, usability and potential for robot control, it then raises the hope for further works in exploring network motifs from high level controllers.", keywords = "genetic algorithms, genetic programming, binary-weight neural network, direct graph encoding, evolutionary learning, genetic operator, navigation, nonlinear problems, obstacle avoidance, random binary connection, robot control, simulated mobile robot, sinusoidal functions, topological recurrent artificial neural network controller, collision avoidance, encoding, learning (artificial intelligence), mobile robots, neurocontrollers, random functions, recurrent neural nets", DOI = "doi:10.1109/BIOROB.2010.5626893", ISSN = "2155-1774", notes = "is this GP? Northwestern Polytech. Univ., Xi'an, China. Also known as \cite{5626893}", } @InProceedings{Li:2022:GI, author = "Shuyue Stella Li and Hannah Peeler and Andrew N. Sloss and Kenneth N. Reid and Wolfgang Banzhaf", title = "Genetic Improvement in the {Shackleton} Framework for Optimizing {LLVM} Pass Sequences", booktitle = "GI @ GECCO 2022", year = "2022", editor = "Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and W. B. Langdon and Justyna Petke", pages = "1938--1939", address = "Boston, USA", publisher_address = "New York, NY, USA", month = "9 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", note = "{Winner Best Presentation}", keywords = "genetic algorithms, genetic programming, genetic improvement, linear genetic programming, Compilers, Evolutionary Algorithms, Compiler Optimization, Parameter Tuning, Metaheuristics, Shackleton-GI", isbn13 = "978-1-4503-9268-6/22/07", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/gecco2022/gi2022/papers/Li_2022_GI.pdf", URL = "https://arxiv.org/abs/2204.13261", DOI = "doi:10.1145/3520304.3534000", slides_url = "http://geneticimprovementofsoftware.com/slides/gi2022gecco/li-genetic-improvement-in-the-shackleton-gi-gecco-22.pdf", code_url = "https://github.com/ARM-software/Shackleton-Framework", video_url = "https://www.youtube.com/watch?v=20l_d3UuDPU&list=PLI8fiFpB7BoIHgl5CsdtjfWvHlE5N6pje&index=3", size = "2 pages", abstract = "Genetic Improvement is a search technique that aims to improve a given acceptable solution to a problem. we present the novel use of genetic improvement to find problem-specific optimized LLVM Pass sequences. We develop a Pass-level edit representation in the linear genetic programming framework, Shackleton, to evolve the modifications to be applied to the default optimization Pass sequences. Our GI-evolved solution has a mean of 3.7percent runtime improvement compared to the default LLVM optimisation level -O3 which targets runtime. The proposed GI method provides an automatic way to find a problem-specific optimization sequence that improves upon a general solution without any expert domain knowledge. we discuss the advantages and limitations of the GI feature in the Shackleton Framework and present our results.", notes = "http://geneticimprovementofsoftware.com/events/gecco2022 Backtrack Algorithm for the Subset Sum Problem (SSP) GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @InProceedings{Li:2008:ICNC, author = "Taiyong Li and Changjie Tang and Jiang Wu and Xuzhong Wei and Chuan Li and Shucheng Dai and Jun Zhu", title = "GEP-NFM: Nested Function Mining Based on Gene Expression Programming", booktitle = "Fourth International Conference on Natural Computation, ICNC '08", year = "2008", month = oct, volume = "6", pages = "283--287", abstract = "Mining the interesting functions from the large scale data sets is an important task in KDD. Traditional gene expression programming (GEP) is a useful tool to discover functions. However, it cannot mine very complex functions. To resolve this problem, a novel method of function mining is proposed in this paper. The main contributions of this paper include: (1) analysing the limitations of function mining based on traditional GEP, (2) proposing a nested function mining method based on GEP (GEP-NFM), and (3) experimental results suggest that the performance of GEP-NFM is better than that of the existing GEP-ADF. Averagely, compared with traditional GEP-ADF, the successful rate of GEP-NFM increases 20percent and the number of evolving generations decrease 25percent.", keywords = "genetic algorithms, genetic programming, gene expression programming, data mining, function discovery, knowledge discovery, machine learning, nested function mining, data mining, learning (artificial intelligence)", DOI = "doi:10.1109/ICNC.2008.640", notes = "Also known as \cite{4667846}", } @InProceedings{TaiyongLi:2009:ICNC, title = "Gene Expression Programming without Reduplicate Individuals", author = "Taiyong Li and Changjie Tang and Ting He and Jiang Wu and Wenbing Qin", booktitle = "Fifth International Conference on Natural Computation, 2009. ICNC '09", year = "2009", editor = "Haiying Wang and Kay Soon Low and Kexin Wei and Junqing Sun", month = "14-16 " # aug, pages = "249--253", address = "Tianjian, China", publisher = "IEEE Computer Society", isbn13 = "978-0-7695-3736-8", bibdate = "2010-01-22", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icnc/icnc2009-4.html#LiTHWQ09", keywords = "genetic algorithms, genetic programming, gene expression programming", DOI = "doi:10.1109/ICNC.2009.406", } @InProceedings{Li:2009:ICCSIT, author = "Taiyong Li and Tiangang Dong and Jiang Wu and Ting He", title = "Function mining based on gene Expression Programming and Particle Swarm Optimization", booktitle = "2nd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2009", year = "2009", month = aug, pages = "99--103", abstract = "Gene expression programming (GEP) is a powerful tool widely used in function mining. However, it is difficult for GEP to generate appropriate numeric constants for function mining. In this paper, a novel approach of creating numeric constants, GEPPSO, was proposed, which embedded particle swarm optimization (PSO) into GEP. In the approach, the evolutionary process was divided into 2 phases: in the first phase, GEP focused on optimising the structure of function expression, and in the second one, PSO focused on optimising the constant parameters. The experimental results on function mining problems show that the performance of GEPPSO is better than that of the existing GEP random numerical constants algorithm (GEP-RNC).", keywords = "genetic algorithms, genetic programming, gene expression programming, GEP, PSO, evolutionary process, function mining, particle swarm optimisation, random numerical constants algorithm, data mining, particle swarm optimisation", DOI = "doi:10.1109/ICCSIT.2009.5234621", notes = "Also known as \cite{5234621}", } @Article{Li:2006:JIM, author = "Te-Sheng Li and Cheng-Lung Huang and Zong-Yuan Wu", title = "Data Mining using Genetic Programming for Construction of a Semiconductor Manufacturing Yield Rate Prediction System", journal = "Journal of Intelligent Manufacturing", year = "2006", volume = "17", number = "3", pages = "355--361", month = jun, keywords = "genetic algorithms, genetic programming, Data mining, Feature selection, Yield prediction, Semiconductor manufacturing", DOI = "doi:10.1007/s10845-005-0008-7", abstract = "he complexity of semiconductor manufacturing is increasing due to the smaller feature sizes, greater number of layers, and existing process reentry characteristics. As a result, it is difficult to manage and clarify responsibility for low yields in specific products. This paper presents a comprehensive data mining method for predicting and classifying the product yields in semiconductor manufacturing processes. A genetic programming (GP) approach, capable of constructing a yield prediction system and performing automatic discovery of the significant factors that might cause low yield, is presented. Comparison with the results then is performed using a decision tree induction algorithm. Moreover, this research illustrates the robustness and effectiveness of this method using a well-known DRAM fab's real data set, with discussion of the results.", } @Article{Li:2025:frl, author = "Wenjuan Li and Xinghua Liu and Junqi Su and Tianxiang Cui", title = "Advancing financial risk management: A transparent framework for effective fraud detection", journal = "Finance Research Letters", year = "2025", volume = "75", pages = "106865", keywords = "genetic algorithms, genetic programming, Financial fraud detection, Financial indicators, Explainable model", ISSN = "1544-6123", URL = "https://www.sciencedirect.com/science/article/pii/S1544612325001308", DOI = "doi:10.1016/j.frl.2025.106865", abstract = "Total Liabilities/Operating Costs' and outperforms traditional and machine learning models in detecting fraud, as demonstrated through analysis of data from the CSMAR database of Chinese publicly listed companies", } @Article{Li:2007:IJRMMS, author = "Wen-Xiu Li and Lan-Fang Dai and Xiao-Bing Hou and Wen Lei", title = "Fuzzy genetic programming method for analysis of ground movements due to underground mining", journal = "International Journal of Rock Mechanics and Mining Sciences", year = "2007", volume = "44", number = "6", pages = "954--961", month = sep, keywords = "genetic algorithms, genetic programming, Fuzzy measures, Underground mining, Ground surface movement", DOI = "doi:10.1016/j.ijrmms.2007.02.003", abstract = "The prediction of ground surface movements is an important problem in rock and soil mechanics in the excavation activities especially the coal and metal mining. Based on results of the statistical analysis of a large amount of measured data in underground excavation engineering, the fuzzy genetic programming method (FGPM) of ground surface movements is given by using the theory of fuzzy probability measures and genetic programming (GP). And genetic programming approach is proposed to determine the parameter of ground surface movements due to underground mining of coal in this paper. Genetic programming is trained by used practical mining induced surface movement data. The agreement of the theoretical results with the field measurements shows that the FGPM is satisfactory and the formulae obtained are valid and thus can be effectively used for predicting the ground surface movements due to underground mining, especially the mining of coal and metal.", notes = "College of Machinery and Civil Engineering, Hebei University, Baoding 071002, PR China Research Institute of Geotechnical Engineering, Hebei University, Baoding 071002, PR China", } @Article{Li:2012:TUST, author = "Wen-Xiu Li and Ji-Fei Li and Qi Wang and Yin Xia and Zhan-Hua Ji", title = "SMT-GP method of prediction for ground subsidence due to tunneling in mountainous areas", journal = "Tunnelling and Underground Space Technology", volume = "32", month = nov, pages = "198--211", year = "2012", keywords = "genetic algorithms, genetic programming, Tunneling, Ground subsidence, Engineering parameters", ISSN = "0886-7798", DOI = "doi:10.1016/j.tust.2012.06.012", URL = "http://www.sciencedirect.com/science/article/pii/S0886779812001228", size = "14 pages", abstract = "This paper introduces a new analysis method - stochastic medium technique (SMT) combined with genetic programming (GP) in the prediction of ground subsidence due to tunnelling in mountainous areas. The methodology involves the use of stochastic medium theory to generate theory models and to predict ground subsidence due to tunnelling in mountainous areas. The parameters in the theory models which are optimised by genetic programming. The use of the integrated methodology is demonstrated via a case study in the prediction of ground subsidence due to tunnelling in mountainous areas in Hebei, North China. The results show that the integrated stochastic medium technique - genetic programming (SMT-GP) gives the smallest error on the ground subsidence data when compared to traditional finite element method. The SMT-GP method is expected to provide a significant improvement when the ground subsidence data come from mountainous areas. The agreement of the theoretical results with the field measurements shows that the SMT-GP is satisfactory and the models and SMT-GP method proposed are valid and thus can be effectively used for predicting the ground surface subsidence due to tunneling engineering in mountainous areas and urban areas.", } @InProceedings{Li:2022:DSIT, author = "Xiaobin Li and Chunli Xie and Shuqin Wang and Xiehua Zhang", title = "Programming Course Student Performance Prediction based on Feature Construction", booktitle = "2022 5th International Conference on Data Science and Information Technology (DSIT)", year = "2022", month = "22-24 " # jul, address = "Shanghai, China", keywords = "genetic algorithms, genetic programming, Source coding, Education, Decision making, Data science, Information technology, Student Performance Prediction, Feature Construction", isbn13 = "978-1-6654-9869-2", DOI = "doi:10.1109/DSIT55514.2022.9943955", size = "6 pages", abstract = "Programming course has become an important basic course for the cultivation of college students computing thinking ability. Student performance prediction is very useful for assisting teachers teaching, promoting students learning and providing management decision-making suggestions. In the blended teaching environment, the students homework and unit exam data collected by the online system are used to predict the students performance at the end of the semester. we propose genetic programming to construct the features of the collected data, generate more features that can be used for performance prediction, and finally predict the final exam. we improve the accuracy of course performance prediction. (The datasets, source code and experimental result can be downloaded from https://gitee.com/wb0817002/spr)", notes = "Also known as \cite{9943955}", } @Article{LI:2023:ijrmhm, author = "Xiangyue Li and Dexin Zhu and Kunming Pan and Hong-Hui Wu and Yongpeng Ren and Can Hu and Shuaikai Zhao", title = "Exploring interpretable features of hardness for intermetallic compounds prepared by spark plasma sintering", journal = "International Journal of Refractory Metals and Hard Materials", volume = "117", pages = "106386", year = "2023", ISSN = "0263-4368", DOI = "doi:10.1016/j.ijrmhm.2023.106386", URL = "https://www.sciencedirect.com/science/article/pii/S026343682300286X", keywords = "genetic algorithms, genetic programming, Intermetallic compounds, Vickers hardness, Machine learning, Symbolic regression, XAI", abstract = "Intermetallic compounds, known for their excellent hardness, conductivity, and strength, have significant applications in aerospace and automotive industries. Hardness is a crucial mechanical property in the development and optimization of intermetallic compounds (IMCs), and meanwhile, spark plasma sintering (SPS) serves as a prevalent technique for preparing IMCs. In this study, a dataset of Vickers hardness of binary intermetallic compounds prepared by SPS and potential feature sets influencing the target performance (HV) were collected. Three machine-learning strategies were developed and comprehensively evaluated. The first strategy focuses on processing parameters and compositions, the second incorporates physical properties in addition to the features considered in the first strategy, and the third one employs a combined feature engineering based on the second strategy. The third strategy, which includes three screened features through a rigorous feature engineering process, achieves the highest predictive accuracy. Subsequently, a symbolic regression (SR) model based on genetic programming (GP) was employed to develop a physically interpretable formula linking the target performance with the selected features. The findings of this study are of significance for developing high-performance intermetallic compounds", } @PhdThesis{DBLP:phd/de/Li2003, author = "Xuhui Li", title = "Dynamische Kompensation von nichtlinearen Verzerrungen mit genetischer Programmierung", school = "Universitaet der Bundeswehr", year = "2003", address = "Munich, Germany", keywords = "genetic algorithms, genetic programming", publisher = "Shaker", ISBN = "3-8322-1922-6", URN = "urn:nbn:de:bvb:706-679", timestamp = "Wed, 07 Dec 2016 14:16:48 +0100", biburl = "http://dblp.org/rec/bib/phd/de/Li2003", bibsource = "dblp computer science bibliography, http://dblp.org", URL = "http://ub.unibw-muenchen.de/dissertationen/ediss/li-xuhui/inhalt.pdf", URL = "https://opac.unibw.de/search?bvnr=BV021974600", URL = "http://www.unibw.de/unibib/medienserver/node?id=85278", URL = "https://www.amazon.de/Kompensation-nichtlinearen-Verzerrungen-genetischer-Programmierung/dp/3832219226", size = "pages", language = "German", notes = "Deutsch, Taschenbuch. Cited by \cite{Xie:2004:fusee} http://ub.unibw-muenchen.de/ down Oct 2017", } @InProceedings{li:1999:MGARPP, author = "Y. Li and K. F. Man and K. S. Tang", title = "Multiobjective Genetic Algorithm for Rolling-Horizon Production Planning", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1789", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "real world applications, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-708.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-708.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Li16:2008:cec, author = "Yamin Li and Jinru Ma and Qiuxia Zhao", title = "Two Improvements in Genetic Programming for Image Classification", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "2492--2497", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1823-7", file = "EC0573.pdf", DOI = "doi:10.1109/CEC.2008.4631132", abstract = "A new classification algorithm for multi-image classification in genetic programming (GP) is introduced, which is the centred dynamic class boundary determination with quick-decreasing power value of arithmetic progression. In the classifier learning process using GP for multi-image classification, different sets of power values are tested to achieve a more suitable range of margin values for the improvement of the accuracy of the classifiers. In the second development, the program size is introduced into the fitness function to control the size of program growth during the evolutionary learning process. The approach is examined on a Chinese character image data set and a grass leaves data set, both of which have four or more classes. The experimental results show that while dealing with complicated problems of multi-image classification, the new approach can be used for more accurate classification and work better than the previous algorithms of either static or dynamic class boundary determination. With the fitness function, the size of the programs in the population can be controlled effectively and shortened considerably during evolution. Thus, the readability of the programs could be seemingly improved.", keywords = "genetic algorithms, genetic programming", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @InProceedings{Li:2008:ICMLC, author = "Ya-Min Li and Jin-Ru Ma and Li-Juan Cui and Qiu-Xia Zhao", title = "On the control of the growth of program sizes in the course of classifier evolution in image classification in genetic programming", booktitle = "International Conference on Machine Learning and Cybernetics", year = "2008", month = jul, volume = "2", pages = "976--980", keywords = "genetic algorithms, genetic programming, classifier evolution, image classification, program size growth control, image classification", DOI = "doi:10.1109/ICMLC.2008.4620546", abstract = "The main indicators of the performance of an image classifier include classification accuracy, classification efficiency and evolution efficiency, whereas the sizes of the programs involved in genetic programming stand as one of the major factors that influence the performance of the classifier. Some effective means are introduced in this paper for the control of program sizes, which is done through scientific construction of fitness functions of the classifier in image classification in genetic programming. Tests showed that, the growth of program sizes were effectively put under control during the course of evolution, which, in turn, greatly improved the general performance of the image classifier in genetic programming.", notes = "Artificial Intelligence Research Center, Agricultural University of Hebei, Baoding 071001, China Also known as \cite{4620546}", } @InProceedings{Li:2015:EITT, author = "Yaqin Li and Cao Yuan and Cong Zhang and Shigao Li and Kaiqiong Sun and Xuan Wang", booktitle = "2015 International Conference of Educational Innovation through Technology (EITT)", title = "A Novel Approximation Algorithm Based on Genetic Programming in Digital Learning Environment", year = "2015", pages = "33--36", abstract = "With the development of information and the integration of media, it has great practical significance and research value to build a digital learning environment based on the complicated electronic circuit. However, the complicated electronic circuit in real-time need a complex and expensive technology. In order to overcome the high cost and technology, an approach was proposed for simplifying generation by approximating the excitations with rectangular pulses, triangular pulses and cosine waves which can be implemented with a moderate cost in analogical electronics. In this work, we improved a novel approach based on genetic programming, The differences between theoretical excitation signals and the approximation driving pulses, related to their excitation effects, were minimised by genetic programming. From these results, the accuracy of simulation can be improved by the new approach, the difference between theoretical complicated digital signals and the new approach is reduced. A trade off is obtained between the costs of implementation of digital processing in digital learning environments.", keywords = "genetic algorithms, genetic programming, Biological cells, Electronic circuits, Encoding, Evolutionary computation, Optimisation, approximation algorithm, digital learning environment", DOI = "doi:10.1109/EITT.2015.13", month = oct, notes = "Also known as \cite{7446142}", } @InCollection{Li2003922, author = "Yugang Li and Fangyu Han and Shiqing Zheng and Shuguang Xiang and Xinshun Tan", title = "An automatic approach to design water utilization network", editor = "Bingzhen Chen and Arthur W. Westerberg", booktitle = "Process Systems Engineering 2003, 8th International Symposium on Process Systems Engineering", publisher = "Elsevier", year = "2003", volume = "15, Part 2", pages = "922--927", series = "Computer Aided Chemical Engineering", note = "Process Systems Engineering 2003, 8th International Symposium on Process Systems Engineering, China. Edited by: Bingzhen Chen and Arthur W. Westerberg, ISBN: 9780444514042", ISSN = "1570-7946", DOI = "doi:10.1016/S1570-7946(03)80425-X", URL = "http://www.sciencedirect.com/science/article/B8G5G-4P40D5S-1C/2/0893b2e39b14ef5b2d394d10c926f0b0", keywords = "genetic algorithms, genetic programming, Water network, Wastewater reuse", abstract = "This paper presents an automatic approach for the design of water network(WUN). In this work, water network design is formulated as an optimisation problem of network configuration and design variables, where both operating and investment cost are optimised simultaneously. Genetic Programming(GP) was used to solve the optimization problem. The encode mode to represent WUN configuration has been studied. Several general unit operations have been selected, such as column, splitter, mix, regenerate, etc., which constitute the GP function set. The primary advantage of this approach is the automatic search for potential promising alternatives without any pre-defined superstructures.", } @InProceedings{LiCie04, author = "Xiang Li and Vic Ciesielski", title = "Using Loops in Genetic Programming for a Two Class Binary Image Classification Problem", booktitle = "AI 2004: Advances in Artificial Intelligence: Proceedings of the 17th Australian Joint Conference on Artificial Intelligence", year = "2004", editor = "Geoffrey I. Webb and Xinghuo Yu", volume = "3339", series = "Lecture Notes in Computer Science", pages = "898--909", address = "Cairns, Australia", month = dec # " 4-6", publisher = "Springer", keywords = "genetic algorithms, genetic programming, image classification, classification problem", ISBN = "3-540-24059-4", DOI = "doi:10.1007/b104336", abstract = "Loops are rarely used in genetic programming (GP), because they lead to massive computation due to the increase in the size of the search space. We have investigated the use of loops with restricted semantics for a problem in which there are natural repetitive elements, that of distinguishing two classes of images. Using our formulation, programs with loops were successfully evolved and performed much better than programs without loops. Our results suggest that loops can successfully used in genetic programming in situations where domain knowledge is available to provide some restrictions on loop semantics.", } @InProceedings{LiCieWork04, author = "Vic Ciesielski and Xiang Li", title = "Analysis of genetic programming runs", booktitle = "Proceedings of The Second Asian-Pacific Workshop on Genetic Programming", year = "2004", editor = "R I Mckay and Sung-Bae Cho", address = "Cairns, Australia", month = "6-7 " # dec, keywords = "genetic algorithms, genetic programming, analysis of runs", URL = "http://goanna.cs.rmit.edu.au/~xiali/pub/ai04.vc.pdf", size = "15 pages", abstract = "We have analysed runs of 12 different genetic programming problems. Some of the problems are the `toy' problems used in generic programming research and some are significant real world applications. We have generated log files of the runs and looked for recurring and unusual patterns and whether there are any differences between the toy problems and the real world problems. The major finding is that some programs are being evaluated many times. In the real-world problems 30-78per cent of the time was spent on reevaluating programs that had already been evaluated. For problems where the evaluation function is expensive significant savings are possible if evaluated programs are cached. A surprising finding was that, for two of the real world problems, a very large number of the evaluations were of 1-node programs.", notes = "broken http://sc.snu.ac.kr/~aspgp/aspgp04/programme.html", } @InProceedings{li:2005:CECx, author = "Xiang Li and Vic Ciesielski", title = "An Analysis of Explicit Loops in Genetic Programming", booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary Computation", year = "2005", editor = "David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L. Gwenn Volkert and Dan Ashlock and Marc Schoenauer", volume = "3", pages = "2522--2529", address = "Edinburgh, UK", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "2-5 " # sep, organisation = "IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, iteration, forloops, modified ant, ADL, STGP", ISBN = "0-7803-9363-5", DOI = "doi:10.1109/CEC.2005.1555010", abstract = "we analyse the reasons why evolving programs with a restricted form of loops is superior to evolving programs without loops for two problems which have underlying repetitive characteristics - a visit every- square problem and a modified Santa Fe ant problem. We show that in the case of loops there is a larger number of solutions with smaller tree sizes. We show that the computational patterns captured in the bodies of the loops are reflective of repeating patterns in the domain. We show that the increased computational cost of evaluating an individual can be controlled by domain knowledge.", notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS.", } @PhdThesis{XiangLi:thesis, author = "Xiang Li", title = "Utilising Restricted For-Loops in Genetic Programming", school = "Department of Computer Science, RMIT", year = "2007", address = "Australia", month = "28 " # feb, keywords = "genetic algorithms, genetic programming", URL = "http://goanna.cs.rmit.edu.au/~vc/papers/li-phd.pdf", URL = "https://researchrepository.rmit.edu.au/discovery/fulldisplay?docid=alma9921861226701341&context=L&vid=61RMIT_INST:ResearchRepository&lang=en&search_scope=ResearchETD&adaptor=Local%20Search%20Engine&tab=Research&query=any,contains,An%20Analysis%20of%20Explicit%20Loops%20in%20Genetic%20Programming&offset=0", URL = "http://researchbank.rmit.edu.au/view/rmit:6317", size = "223 pages", abstract = "Genetic programming is an approach that uses the power of evolution to allow computers to evolve programs with little human involvement. It has demonstrated its usefulness in solving many experimental problems as well as many real world problems. However, it suffers from weaknesses in using repetitions effectively. While loops are natural components of most programming languages and appear in every reasonably-sized application, they are rarely used in genetic programming. Extending the power of genetic programming by encouraging more use of loops will bridge the gap between the current state-of-the-art in programs evolved with genetic programming and those written by humans, and improve this automatic programming method. The goal of the work is to investigate a number of restricted looping constructs in which infinite loops are not possible and to determine whether any significant benefits can be obtained with these restricted loops. Possible benefits include: Solving problems which cannot be solved without loops, evolving smaller sized solutions which can be more easily understood by human programmers and solving existing problems quicker by using fewer evaluations. In this thesis, a number of explicit restricted loop formats were formulated and tested on the Santa Fe ant problem, a modified ant problem, a sorting problem, a visit-every-square problem and a difficult object classification problem. A maximum number of iterations based on domain knowledge was used to avoid the infinite iteration problem. The experimental results showed that these explicit loops can be successfully used in genetic programming. The evolutionary process can decide when, where and how to use them. Runs with these loops tended to generate smaller sized solutions in fewer evaluations. Solutions with loops were found to some problems that could not be solved without loops. From these experimental problems, the modified ant problem and the visit-every-square problem were selected to analyse differences between using and not using loops with respect to the search spaces, the patterns captured by genetic programming and the sensitivity to changes in the maximum number of iterations on CPU time. The analysis of the search spaces found that there were more fitter programs within a limited tree depth for programs with loops. To solve the same problem without loops required a larger tree depth and this exponentially increases the number of possible programs and may decrease the chance of finding a good solution. The analysis of the patterns captured found that runs with loops captured repetitive patterns of the problem domain and repeated them to improve the fitness. The analysis of the effect of different values of maximum number of iterations showed that CPU time per evaluation increased as the maximum number of iterations increased. However, solutions were found in fewer evaluations. There was a large range of values for maximum number of iterations for which the overall CPU time was lower. Good choices for maximum number of iterations could be found from domain knowledge. Overall, the results and analysis have established that there are significant benefits in using loops in genetic programming. Restricted loops can avoid the difficulties of evolving consistent programs and the infinite iterations problem. Researchers and practioners of genetic programming should not be afraid of loops.", notes = "Jan 2021 http://researchbank.rmit.edu.au/view/rmit:6317 broken, given replacement (researchrepository.rmit.edu.au/) flaky", } @InProceedings{Li:2009:ICCAS-SICE, author = "Xianneng Li and Shingo Mabu and Huiyu Zhou and Kaoru Shimada and Kotaro Hirasawa", title = "Genetic Network Programming with Estimation of Distribution Algorithms and its application to association rule mining for traffic prediction", booktitle = "ICCAS-SICE, 2009", year = "2009", month = "18-21 " # aug, address = "Fukuoka", pages = "3457--3462", publisher = "IEEE", isbn13 = "978-4-9077-6433-3", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5334374", size = "6 pages", abstract = "In this paper, a novel evolutionary paradigm combining Genetic Network Programming (GNP) and Estimation of Distribution Algorithms (EDAs) is proposed and used to find important association rules in time-related applications, especially in traffic prediction. GNP is one of the evolutionary optimisation algorithms, which uses directed-graph structures. EDAs is a novel algorithm, where the new population of individuals is produced from a probabilistic distribution estimated from the selected individuals from the previous generation. This model replaces random crossover and mutation to generate offspring. Instead of generating the candidate association rules using conventional GNP, the proposed method can obtain a large number of important association rules more effectively. The purpose of this paper is to compare the proposed method with conventional GNP in traffic prediction systems in terms of the number of rules obtained.", keywords = "genetic algorithms, genetic programming, genetic network programming, association rule mining, association rules, directed graph structures, estimation of distribution algorithms, evolutionary optimisation algorithm, evolutionary paradigm, probabilistic distribution, traffic prediction, data mining, directed graphs, probability", notes = "Also known as \cite{5334374}", } @InProceedings{Li:2010:cec, author = "Xianneng Li and Shingo Mabu and Huiyu Zhou and Kaoru Shimada and Kotaro Hirasawa", title = "Genetic Network Programming with Estimation of Distribution Algorithms for class association rule mining in traffic prediction", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Genetic Network Programming", isbn13 = "978-1-4244-6910-9", abstract = "As an extension of Genetic Algorithm (GA) and Genetic Programming (GP), a new approach named Genetic Network Programming (GNP) has been proposed in the evolutionary computation field. GNP uses multiple reusable nodes to construct directed-graph structures to represent its solutions. Recently, many research has clarified that GNP can work well in data mining area. In this paper, a novel evolutionary paradigm named GNP with Estimation of Distribution Algorithms (GNP-EDAs) is proposed and used to solve traffic prediction problems using class association rule mining. In GNP-EDAs, a probabilistic model is constructed by estimating the probability distribution from the selected elite individuals of the previous generation to replace the conventional genetic operators, such as crossover and mutation. The probabilistic model is capable of enhancing the evolution to achieve the ultimate objective. In this paper, two methods are proposed based on extracting the probabilistic information on the node connections and node transitions of GNP-EDAs to construct the probabilistic model. A comparative study of the proposed paradigm and the conventional GNP is made to solve the traffic prediction problems using class association rule mining. The simulation results showed that GNP-EDAs can extract the class association rules more effectively, when the number of the candidate class association rules increases. And the classification accuracy of the proposed method shows good results in traffic prediction systems.", DOI = "doi:10.1109/CEC.2010.5586456", notes = "WCCI 2010. Also known as \cite{5586456}", } @Article{Li:2010:TJSEC, author = "Xianneng Li and Shingo Mabu and Kotaro Hirasawa", title = "Towards the maintenance of population diversity: A hybrid genetic network programming", journal = "Transaction of the Japanese Society for Evolutionary Computation", year = "2010", volume = "1", number = "1", pages = "89--101", month = "12", email = "sennou@asagi.waseda.jp", keywords = "genetic algorithms, genetic programming, genetic network programming, probabilistic model building evolutionary algorithm, PMBEA, estimation of distribution algorithm, EDA, GNP, probabilistic model building genetic network programming, PMBGNP, diversity maintenance", ISSN = "2185-7385", URL = "http://www.jpnsec.org/online_journal/1_1/1_89.pdf", size = "13 pages", abstract = "Some researchers have investigated that the diversity loss will significantly decrease the performance of Probabilistic Model Building Genetic Algorithm (PMBGA), especially under large search space, leading to the premature convergence and local optimum. However, few work has been done on the diversity maintenance in the Probabilistic Model Building Evolutionary Algorithms (PMBEAs) with more complex chromosome structures, such as tree structure based Probabilistic Model Building Genetic Programming (PMBGP) and graph structure based Probabilistic Model Building Genetic Network Programming (PMBGNP). For the PMBEAs with more complex chromosome structures, the required sample size is usually much larger than that of binary structure based PMBGA. Therefore, these algorithms usually become much more sensitive to the population diversity. In order to obtain enough population diversity, the large population size is needed, which is not the best way. the maintenance of the population diversity is studied in PMBGNP, which is a kind of PMBEA, but has its unique characteristics because of its directed graph structure. This paper proposed a hybrid PMBGNP algorithm to maintain the population diversity to avoid the premature convergence and local optimum, and presented a theoretical analysis of the diversity loss in PMBGA, PMBGP and PMBGNP. Two techniques have been proposed for the diversity maintenance when the population size is set at not large values, which are multiple probability vectors and genetic operators. The proposed algorithm is applied and evaluated in a kind of autonomous robot, Khepera robot. The simulation study demonstrates that the proposed hybrid PMBGNP is often able to achieve a better performance than the conventional algorithms.", } @InProceedings{Li:2011:ANEoDAUGCRaRL, title = "A Novel Estimation of Distribution Algorithm Using Graph-based Chromosome Representation and Reinforcement Learning", author = "Xianneng Li and Bing Li and Shingo Mabu and Kotaro Hirasawa", pages = "37--44", booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary Computation", year = "2011", editor = "Alice E. Smith", month = "5-8 " # jun, address = "New Orleans, USA", organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, EDA, estimation of distribution algorithms, probabilistic model building genetic network programming", DOI = "doi:10.1109/CEC.2011.5949595", abstract = "This paper proposed a novel EDA, where a directed graph network is used to represent its chromosome. In the proposed algorithm, a probabilistic model is constructed from the promising individuals of the current generation using reinforcement learning, and used to produce the new population. The node connection probability is studied to develop the probabilistic model, therefore pairwise interactions can be demonstrated to identify and recombine building blocks in the proposed algorithm. The proposed algorithm is applied to a problem of agent control, i.e., autonomous robot control. The experimental results show the superiority of the proposed algorithm comparing with the conventional algorithms.", notes = "CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.", } @InProceedings{XiannengLi:2011:GECCO, author = "Xianneng Li and Shingo Mabu and Kotaro Hirasawa", title = "Use of infeasible individuals in probabilistic model building genetic network programming", booktitle = "GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0557-0", pages = "601--608", keywords = "genetic algorithms, genetic programming, genetic network programming, Estimation of distribution algorithms", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001576.2001659", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Classical EDAs generally use truncation selection to estimate the distribution of the feasible (good) individuals while ignoring the infeasible (bad) ones. However, various research in EAs reported that the infeasible individuals may affect and help the problem solving. This paper proposed a new method to use the infeasible individuals by studying the sub-structures rather than the entire individual structures to solve Reinforcement Learning (RL) problems, which generally factorise their entire solutions to the sequences of state-action pairs. This work was studied in a recent graph-based EDA named Probabilistic Model Building Genetic Network Programming (PMBGNP) which can solve RL problems successfully. The effectiveness of this work is verified in a RL problem, i.e., robot control, comparing with some other related work.", notes = "Also known as \cite{2001659} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)", } @InProceedings{Li:2012:CECc, title = "A Continuous Estimation of Distribution Algorithm by Evolving Graph Structures Using Reinforcement Learning", author = "Xianneng Li and Bing Li and Shingo Mabu and Kotaro Hirasawa", pages = "2097--2104", booktitle = "Proceedings of the 2012 IEEE Congress on Evolutionary Computation", year = "2012", editor = "Xiaodong Li", month = "10-15 " # jun, DOI = "doi:10.1109/CEC.2012.6256481", address = "Brisbane, Australia", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Genetic Network Programming, Estimation of distribution algorithms, Adaptive dynamic programming and reinforcement learning, Representation and operators", abstract = "A novel graph-based Estimation of Distribution Algorithm (EDA) named Probabilistic Model Building Genetic Network Programming (PMBGNP) has been proposed. Inspired by classical EDAs, PMBGNP memorises the current best individuals and uses them to estimate a distribution for the generation of the new population. However, PMBGNP can evolve compact programs by representing its solutions as graph structures. Therefore, it can solve a range of problems different from conventional ones in EDA literature, such as data mining and Reinforcement Learning (RL) problems. This paper extends PMBGNP from discrete to continuous search space, which is named PMBGNP-AC. Besides evolving the node connections to determine the optimal graph structures using conventional PMBGNP, Gaussian distribution is used for the distribution of continuous variables of nodes. The mean value mu and standard deviation sigma are constructed like those of classical continuous Population-based incremental learning (PBILc). However, a RL technique, i.e., Actor-Critic (AC), is designed to update the parameters (mu and sigma). AC allows us to calculate the Temporal-Difference (TD) error to evaluate whether the selection of the continuous value is better or worse than expected. This scalar reinforcement signal can decide whether the tendency to select this continuous value should be strengthened or weakened, allowing us to determine the shape of the probability density functions of the Gaussian distribution. The proposed algorithm is applied to a RL problem, i.e., autonomous robot control, where the robot's wheel speeds and sensor values are continuous. The experimental results show the superiority of PMBGNP-AC comparing with the conventional algorithms.", notes = "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the EPS and the IET.", } @PhdThesis{XiannengLi:thesis, author = "Xianneng Li", title = "Study on Probabilistic Model Building Genetic Network Programming", school = "Waseda University", year = "2013", address = "Japan", month = jan, keywords = "genetic algorithms, genetic programming, estimation of distribution algorithm, genetic network programming", URL = "http://hdl.handle.net/2065/40062", URL = "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/40062/1/Honbun-6149.pdf", URL = "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/40062/2/Shinsa-6149.pdf", URL = "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/40062/3/Gaiyo-6149.pdf", size = "128 pages", abstract = "Estimation of Distribution Algorithm (EDA) is one of the most important branches in Evolutionary Computation (EC). Different from the conventional Evolutionary Algorithms (EAs) which use stochastic ways to simulate the biological genetic operators, i.e., crossover and mutation, for new population generation, EDA constructs a probabilistic model using the techniques of statistics and machine learning to estimate the probability distribution of the current population, and samples the model to generate the new population. By explicitly estimating and recombining the good partial solutions of the population, EDA has been successfully proven to outperform conventional EAs by avoiding the premature convergence and speeding up the evolution process in many problems. The primary objective of this thesis is to propose a novel paradigm of EDA named Probabilistic Model Building Genetic Network Programming (PMBGNP), where the directed graph structure of a novel graph-based EA called Genetic Network Programming (GNP) is used to represent its individuals. Different from most of the current EDAs proposed in string structure based Genetic Algorithm (GA) and tree structure based Genetic Programming (GP), the distinguished graph structure allows PMBGNP to ensure higher expression ability. As a result, a sort of problems can be explored and solved efficiently and effectively comparing with the conventional research in EDA literature. To achieve this objective, contributions of this thesis are presented on the following two aspects: algorithm part and application part. From the perspective of algorithm part, first, the thesis proposes the high-level PMBGNP to use Maximum Likelihood Estimation (MLE) to model the probability distribution of the promising individuals. PMBGNP is empirically studied to show the capability of speeding up the evolution efficiency by the estimation of probability distribution. Second, the thesis addresses the issue of population diversity loss by theoretical comparison with classical EDAs, and proposes a hybrid algorithm to maintain the population diversity of PMBGNP. Third, the integration of PMBGNP and Reinforcement Learning (RL) is studied. Inspired by behaviourist psychology, RL concerns with reinforcing the growth of the individuals by learning their experiences. The learning knowledge formulated by Q values can be approximated and incorporated into the probabilistic modelling of PMBGNP to improve the performance by constructing a more accurate model. Finally, PMBGNP is extended from discrete optimisation problems to continuous optimization problems. From the viewpoint of application part, most of the current studies in EDA are carried out in the benchmark problems of GA and GP, such as function optimisation and symbolic regression. Therefore, to accomplish one of the essential challenges of EDA for novel applications, the thesis applies PMBGNP to two novel applications of EDA, including data mining and the problems of controlling the agents' behaviour. By comparing with the other state-of-the-art algorithms, PMBGNP is testified to be capable of achieving better performances.", } @Article{Li:2013:TEEE, author = "Xianneng Li and Shingo Mabu and Kotaro Hirasawa", title = "An extended probabilistic model building genetic network programming using both of good and bad individuals", journal = "IEEJ Transactions on Electrical and Electronic Engineering", year = "2013", volume = "8", number = "4", pages = "339--347", month = jul, publisher = "Wiley", keywords = "genetic algorithms, genetic programming, probabilistic modelling, estimation of distribution algorithms (EDAs), bad individuals, reinforcement learning, probabilistic model building genetic network programming", ISSN = "1931-4981", DOI = "doi:10.1002/tee.21864", size = "9 pages", abstract = "Classical estimation of distribution algorithms (EDAs) generally use truncation selection to estimate the distribution of the good individuals while ignoring the bad ones. However, various researches in evolutionary algorithms (EAs) have reported that the bad individuals may affect and help solving the problem. This paper proposes a new method to use the bad individuals by studying the substructures rather than the entire individual structures to solve reinforcement learning (RL) problems, which generally factorise their entire solutions to the sequences of state-action pairs. This work was studied in a recent graph-based EDA named probabilistic model building genetic network programming (PMBGNP), which could solve RL problems successfully, to propose an extended PMBGNP. The effectiveness of this work is verified in an RL problem, namely robot control. Compared to other related work, results show that the proposed method can significantly speed up the evolution efficiency.", } @InProceedings{Li:2013:GECCOcompb, author = "Xianneng Li and Kotaro Hirasawa", title = "Extended rule-based genetic network programming", booktitle = "GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion", year = "2013", editor = "Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A. N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks", isbn13 = "978-1-4503-1964-5", keywords = "genetic algorithms, genetic programming, genetic network programming", pages = "155--156", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", URL = "http://doi.acm.org/10.1145/2464576.2464655", DOI = "doi:10.1145/2464576.2464655", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Recent advances in rule-based systems, i.e., Learning Classifier Systems (LCSs), have shown their sequential decision-making ability with a generalisation property. In this paper, a novel LCS named eXtended rule-based Genetic Network Programming (XrGNP) is proposed. Different from most of the current LCSs, the rules are represented and discovered through a graph-based evolutionary algorithm GNP, which consequently has the distinct expression ability to model and evolve the if-then decision-making rules. Experiments on a benchmark multi-step problem (so-called Reinforcement Learning problem) demonstrate its effectiveness.", notes = "Also known as \cite{2464655} Distributed at GECCO-2013.", } @InProceedings{Li:2013:ieeeSMC, author = "Xianneng Li and Kotaro Hirasawa", title = "A Learning Classifier System Based on Genetic Network Programming", booktitle = "2013 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2013)", year = "2013", pages = "1323--1328", address = "Manchester", month = "13-16 " # oct, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, genetic network programming, learning classifier systems, niching, fitness sharing, reinforcement learning", DOI = "doi:10.1109/SMC.2013.229", size = "6 pages", abstract = "Recent advances in Learning Classifier Systems (LCSs) have shown their sequential decision-making ability with a generalisation property. In this paper, a novel LCS named extended rule-based Genetic Network Programming (XrGNP) is proposed. Different from most of the current LCSs, the rules are represented and discovered through a graph-based evolutionary algorithm GNP, which consequently has the distinct expression ability to model and evolve the decision-making rules. XrGNP is described in details in which its unique features are explicitly mapped. Experiments on benchmark and real-world multi-step problems demonstrate the effectiveness of XrGNP.", notes = "Also known as \cite{6721982}", } @Book{XiannengLi:book, author = "Xianneng Li", title = "Study on Probabilistic Model Building Genetic Network Programming", publisher = "Waseda University", year = "2013", volume = "92", series = "Waseda University monograph", address = "Japan", month = oct, keywords = "genetic algorithms, genetic programming, Genetic Network Programming", isbn13 = "978-4-657-13515-5", URL = "http://iss.ndl.go.jp/books/R100000002-I025080437-00", size = "124 pages", notes = "Language English, see \cite{XiannengLi:thesis}", } @InProceedings{Li:2013:ICONIP, author = "Xianneng Li and Wen He and Kotaro Hirasawa", title = "Genetic Network Programming with Simplified Genetic Operators", booktitle = "International Conference on Neural Information Processing, ICONIP 2013, Part II", year = "2013", editor = "Minho Lee and Akira Hirose and Zeng-Guang Hou and RheeMan Kil", volume = "8227", series = "Lecture Notes in Computer Science", pages = "51--58", address = "Daegu, Korea", month = nov # " 3-7", publisher = "Springer", keywords = "genetic algorithms, genetic programming, genetic network programming, directed graph, transition by necessity, invalid evolution", isbn13 = "978-3-642-42041-2", URL = "http://link.springer.com/chapter/10.1007/978-3-642-42042-9_7", DOI = "doi:10.1007/978-3-642-42042-9_7", size = "8 pages", abstract = "Recently, a novel type of evolutionary algorithms (EAs), called Genetic Network Programming (GNP), has been proposed. Inspired by the complex human brain structures, GNP develops a distinguished directed graph structure for its individual representations, consequently showing an excellent expressive ability for modelling a range of complex problems. This paper is dedicated to reveal GNP's unique features. Accordingly, simplified genetic operators are proposed to highlight such features of GNP, reduce its computational effort and provide better results. Experimental results are presented to confirm its effectiveness over original GNP and several state-of-the-art algorithms.", } @Article{Li:2014:ieeeTEC, author = "Xianneng Li and Shingo Mabu and Kotaro Hirasawa", title = "A Novel Graph-Based Estimation of the Distribution Algorithm and its Extension Using Reinforcement Learning", journal = "IEEE Transactions on Evolutionary Computation", year = "2014", volume = "18", number = "1", pages = "98--113", month = feb, keywords = "genetic algorithms, genetic programming, genetic network programming, Agent control, estimation of distribution algorithm (EDA), GNP, graph structure, reinforcement learning (RL)", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2013.2238240", size = "16 pages", abstract = "In recent years, numerous studies have drawn the success of estimation of distribution algorithms (EDAs) to avoid the frequent breakage of building blocks of the conventional stochastic genetic operators-based evolutionary algorithms (EAs). In this paper, a novel graph-based EDA called probabilistic model building genetic network programming (PMBGNP) is proposed. Using the distinguished graph (network) structure of a graph-based EA called genetic network programming (GNP), PMBGNP ensures higher expression ability than the conventional EDAs to solve some specific problems. Furthermore, an extended algorithm called reinforced PMBGNP is proposed to combine PMBGNP and reinforcement learning to enhance the performance in terms of fitness values, search speed, and reliability. The proposed algorithms are applied to solve the problems of controlling the agents' behaviour. Two problems are selected to demonstrate the effectiveness of the proposed algorithms, including the benchmark one, i.e., the Tileworld system, and a real mobile robot control.", notes = "also known as \cite{6408015}", } @InProceedings{Li:2014:CECc, title = "Creating Stock Trading Rules Using Graph-Based Estimation of Distribution Algorithm", author = "Xianneng Li and Wen He and Kotaro Hirasawa", pages = "731--738", booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary Computation", year = "2014", month = "6-11 " # jul, editor = "Carlos A. {Coello Coello}", address = "Beijing, China", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, genetic network programming, Evolutionary Algorithms with Statistical and Machine Learning Techniques, Estimation of distribution algorithms", DOI = "doi:10.1109/CEC.2014.6900421", abstract = "Though there are numerous approaches developed currently, exploring the practical applications of estimation of distribution algorithm (EDA) has been reported to be one of the most important challenges in this field. This paper is dedicated to extend EDA to solve one of the most active research problems, stock trading, which has been rarely revealed in the EDA literature. A recent proposed graph-based EDA called reinforced probabilistic model building genetic network programming (RPMBGNP) is investigated to create stock trading rules. With its distinguished directed graph-based individual structure and the reinforcement learning-based probabilistic modelling, we demonstrate the effectiveness of RPMBGNP for the stock trading task through real-market stock data, where much higher profits are obtained than traditional non-EDA models.", notes = "WCCI2014", } @InProceedings{Li:2014:CECd, title = "Learning and Evolution of Genetic Network Programming with Knowledge Transfer", author = "Xianneng Li and Wen He and Kotaro Hirasawa", pages = "798--805", booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary Computation", year = "2014", month = "6-11 " # jul, editor = "Carlos A. {Coello Coello}", address = "Beijing, China", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, genetic network programming, Representation and operators, Adaptive dynamic programming and reinforcement learning", DOI = "doi:10.1109/CEC.2014.6900315", abstract = "Traditional evolutionary algorithms (EAs) generally starts evolution from scratch, in other words, randomly. However, this is computationally consuming, and can easily cause the instability of evolution. In order to solve the above problems, this paper describes a new method to improve the evolution efficiency of a recently proposed graph-based EA genetic network programming (GNP) by introducing knowledge transfer ability. The basic concept of the proposed method, named GNP-KT, arises from two steps: First, it formulates the knowledge by discovering abstract decision-making rules from source domains in a learning classifier system (LCS) aspect; Second, the knowledge is adaptively reused as advice when applying GNP to a target domain. A reinforcement learning (RL)-based method is proposed to automatically transfer knowledge from source domain to target domain, which eventually allows GNP-KT to result in better initial performance and final fitness values. The experimental results in a real mobile robot control problem confirm the superiority of GNP-KT over traditional methods.", notes = "WCCI2014", } @InProceedings{Li:2014:CECk, title = "Adaptive Genetic Network Programming", author = "Xianneng Li and Wen He and Kotaro Hirasawa", pages = "1808--1815", booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary Computation", year = "2014", month = "6-11 " # jul, editor = "Carlos A. {Coello Coello}", address = "Beijing, China", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Genetic Network Programming, Self-adaptation in evolutionary computation, Intelligent systems applications", DOI = "doi:10.1109/CEC.2014.6900290", abstract = "Genetic Network Programming (GNP) is derived from Genetic Algorithm (GA) and Genetic Programming (GP), which applies evolution theory to evolve a population of directed graph to model complex systems. It has been shown that GNP can solve typical control problems, as well as many real-world problems. However, studying GNP is mainly focused on the specific aspect, while the fundamental characteristics that ensure the success of GNP are rarely investigated in the previous research. This paper reveals an important feature of GNP, reusability of nodes, to efficiently identify and formulate the building blocks of evolution. Accordingly, adaptive GNP is developed which self-adapts both crossover and mutation probabilities of each search variable to circumstances. The adaptation allows the automatic adjustment of evolution bias toward the frequently reused nodes in high-quality individuals. The adaptive GNP is compared with traditional GNP in a benchmark control testbed to evaluate its superiority.", notes = "WCCI2014", } @InProceedings{Li:2014:ICISDA, author = "Xianneng Li and Guangfei Yang and Kotaro Hirasawa", booktitle = "14th International Conference on Intelligent Systems Design and Applications", title = "Evolving directed graphs with artificial bee colony algorithm", year = "2014", pages = "89--94", abstract = "Artificial bee colony (ABC) algorithm is a relatively new optimisation technique that simulates the intelligent foraging behaviour of honey bee swarms. It has been applied to several optimisation domains to show its efficient evolution ability. In this paper, ABC algorithm is applied for the first time to evolve a directed graph chromosome structure, which derived from a recent graph-based evolutionary algorithm called genetic network programming (GNP). Consequently, it is explored to new application domains which can be efficiently modelled by the directed graph of GNP. In this work, a problem of controlling the agents's behaviour under a well known benchmark test bed called Tileworld are solved using the ABC-based evolution strategy. Its performance is compared with several very well-known methods for evolving computer programs, including standard GNP with crossover/mutation, genetic programming (GP) and reinforcement learning (RL).", keywords = "genetic algorithms, genetic programming, genetic network programming", DOI = "doi:10.1109/ISDA.2014.7066282", ISSN = "2164-7143", month = nov, notes = "Also known as \cite{7066282}", } @Article{Li:2015:ASC, author = "Xianneng Li and Kotaro Hirasawa", title = "Continuous probabilistic model building genetic network programming using reinforcement learning", journal = "Applied Soft Computing", year = "2015", volume = "27", number = "Supplement C", pages = "457--467", keywords = "genetic algorithms, genetic programming, genetic network programming, Estimation of distribution algorithm, Probabilistic model building, Continuous optimization, Reinforcement learning", ISSN = "1568-4946", URL = "http://www.sciencedirect.com/science/article/pii/S156849461400533X", DOI = "doi:10.1016/j.asoc.2014.10.023", abstract = "Recently, a novel probabilistic model-building evolutionary algorithm (so called estimation of distribution algorithm, or EDA), named probabilistic model building genetic network programming (PMBGNP), has been proposed. PMBGNP uses graph structures for its individual representation, which shows higher expression ability than the classical EDAs. Hence, it extends EDAs to solve a range of problems, such as data mining and agent control. This paper is dedicated to propose a continuous version of PMBGNP for continuous optimization in agent control problems. Different from the other continuous EDAs, the proposed algorithm evolves the continuous variables by reinforcement learning (RL). We compare the performance with several state-of-the-art algorithms on a real mobile robot control problem. The results show that the proposed algorithm outperforms the others with statistically significant differences.", } @Article{DBLP:journals/access/LiYY18, author = "Xianneng Li and Huiyan Yang and Meihua Yang", title = "Revisiting Genetic Network Programming {(GNP):} Towards the Simplified Genetic Operators", journal = "{IEEE} Access", volume = "6", pages = "43274--43289", year = "2018", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1109/ACCESS.2018.2864253", DOI = "doi:10.1109/ACCESS.2018.2864253", timestamp = "Fri, 14 Sep 2018 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/access/LiYY18.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{DBLP:journals/eswa/LiYW18, author = "Xianneng Li and Meihua Yang and Shizhe Wu", title = "Niching genetic network programming with rule accumulation for decision making: An evolutionary rule-based approach", journal = "Expert Systems with Applications", volume = "114", pages = "374--387", year = "2018", month = "30 " # dec, keywords = "genetic algorithms, genetic programming, Decision making, Genetic network programming, Learning classifier system, Niching, Reinforcement learning, Rule accumulation", ISSN = "0957-4174", URL = "https://doi.org/10.1016/j.eswa.2018.07.041", DOI = "doi:10.1016/j.eswa.2018.07.041", timestamp = "Tue, 28 Aug 2018 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/eswa/LiYW18.bib", bibsource = "dblp computer science bibliography, https://dblp.org", size = "14 pages", abstract = "As one of the most important research branches of evolutionary computation (EC), learning classifier system (LCS) is dedicated to discover decision making classifiers (IF-THEN type rules) via evolution and learning. Recent advances in LCS have shown distinguished generalization property over traditional approaches. In this paper, a novel LCS named niching genetic network programming with rule accumulation (nGNP-RA) is proposed. The unique features of the proposal arise from the following three points: First, it uses an advanced graph-based EC named GNP as the rule generator, resulting higher knowledge representation ability than traditional genetic algorithm (GA)-based LCSs; Second, a novel niching mechanism is developed in GNP to encourage the discovery of high-quality diverse rules; Third, a novel reinforcement learning (RL)-based mechanism is embedded to assign accurate credits to the discovered rules. To verify the effectiveness and robustness of nGNP-RA over traditional systems, two decision making testbeds are applied, including the benchmark tileworld problem and the real mobile robot control application.", notes = "Faculty of Management and Economics, Dalian University of Technology, No. 2 Linggong Road, Ganjingzi, Dalian 116024, China", } @Article{Li:2008:GPEM, author = "Xiaodong Li and Wenjian Luo and Xin Yao", title = "Theoretical foundations of evolutionary computation", journal = "Genetic Programming and Evolvable Machines", year = "2008", volume = "9", number = "2", pages = "107--108", month = jun, note = "Special Issue on Theoretical foundations of evolutionary computation", keywords = "genetic algorithms", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-007-9047-5", size = "2 pages", notes = "Editorial SEAL-2006", } @Article{Li:2013:CMMM, author = "Xiaoou Li and Yuning Yan and Wenshi Wei", title = "Identifying Patients with Poststroke Mild Cognitive Impairment by Pattern Recognition of Working Memory Load-Related {ERP}", journal = "Computational and Mathematical Methods in Medicine", year = "2013", pages = "Article ID 658501", month = oct # "~23", keywords = "genetic algorithms, genetic programming, GP, SVM", bibsource = "OAI-PMH server at www.ncbi.nlm.nih.gov", language = "en", oai = "oai:pubmedcentral.nih.gov:3819888", publisher = "Hindawi Publishing Corporation", URL = "http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3819888", URL = "http://dx.doi.org/10.1155/2013/658501", size = "10 pages", abstract = "The early detection of subjects with probable cognitive deficits is crucial for effective appliance of treatment strategies. This paper explored a methodology used to discriminate between evoked related potential signals of stroke patients and their matched control subjects in a visual working memory paradigm. The proposed algorithm, which combined independent component analysis and orthogonal empirical mode decomposition, was applied to extract independent sources. Four types of target stimulus features including P300 peak latency, P300 peak amplitude, root mean square, and theta frequency band power were chosen. Evolutionary multiple kernel support vector machine (EMK-SVM) based on genetic programming was investigated to classify stroke patients and healthy controls. Based on 5-fold cross-validation runs, EMK-SVM provided better classification performance compared with other state-of-the-art algorithms. Comparing stroke patients with healthy controls using the proposed algorithm, we achieved the maximum classification accuracies of 91.76percent and 82.23percent for 0-back and 1-back tasks, respectively. Overall, the experimental results showed that the proposed method was effective. The approach in this study may eventually lead to a reliable tool for identifying suitable brain impairment candidates and assessing cognitive function.", } @InProceedings{li:2004:lbp, author = "Xin Li and Chi Zhou and Peter C. Nelson and Thomas M. Tirpak", title = "Investigation of Constant Creation Techniques in the Context of Gene Expression Programming", booktitle = "Late Breaking Papers at the 2004 Genetic and Evolutionary Computation Conference", year = "2004", editor = "Maarten Keijzer", address = "Seattle, Washington, USA", month = "26 " # jul, keywords = "genetic algorithms, genetic programming, GEP", URL = "http://gpbib.cs.ucl.ac.uk/gecco2004/LBP023.pdf", URL = "http://www.cs.uic.edu/~xli1/papers/GEPConstantCreation(GECCO04_LBP).pdf", abstract = "Gene Expression Programming (GEP) is a new technique of Genetic Programming (GP) that implements a linear genotype representation. It uses fixed-length chromosomes to represent expression trees of different shapes and sizes, which results in unconstrained search of the genome space while still ensuring validity of the programs output. However, GEP has some difficulty in discovering suitable function structures because the genetic operators are more disruptive than traditional tree-based GP. One possible remedy is to specifically assist the algorithm in discovering useful numeric constants. In this paper, the effectiveness of several constant creation techniques for GEP has been investigated through two symbolic regression benchmark problems. Our experimental results show that constant creation methods applied to the whole population for selected generations perform better than methods that are applied only to the best individuals. The proposed tune-up process for the entire population can significantly improve the average fitness of the best solutions.", notes = "Part of \cite{keijzer:2004:GECCO:lbp}", } @InProceedings{Li:gecco05lbp, author = "Xin Li and Chi Zhou and Weimin Xiao and Peter C. Nelson", title = "Prefix Gene Expression Programming", booktitle = "Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}", year = "2005", month = "25-29 " # jun, editor = "Franz Rothlauf", address = "Washington, D.C., USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2005lbp/papers/85-li.pdf", URL = "http://www.cs.uic.edu/~xli1/papers/PGEP_GECCOLateBreaking05_XLi.pdf", keywords = "genetic algorithms, genetic programming, gene expression programming, Novel representations, algorithm design, theory, Polish notation, genotype-phenotype mapping mechanism, schema theorem", size = "7 pages", abstract = "Gene Expression Programming (GEP) is a powerful evolutionary method derived from Genetic Programming (GP) for model learning and knowledge discovery. However, when dealing with complex problems, its genotype under Karva notation does not allow hierarchical composition of the solution, which impairs the efficiency of the algorithm. We propose a new representation scheme based on prefix notation that overcomes the original GEP's drawbacks. The resulted algorithm is called Prefix GEP (P-GEP). The major advantages with P-GEP include the natural hierarchy in forming the solutions and more protective genetic operations for substructure components. An artificial symbolic regression problem and a set of benchmark classification problems from UCI machine learning repository have been tested to demonstrate the applicability of P-GEP. The results show that P-GEP follows a faster fitness convergence curve and the rules generated from P-GEP consistently achieve better average classification accuracy compared with GEP", notes = "Distributed on CD-ROM at GECCO-2005", } @InProceedings{Substructures(ICMLA05)_XLi, author = "Xin Li and Chi Zhou and Weimin Xiao and Peter C. Nelson", title = "Direct Evolution of Hierarchical Solutions with Self-Emergent Substructures", booktitle = "The Fourth International Conference on Machine Learning and Applications (ICMLA'05)", year = "2005", pages = "337--342", address = "Los Angeles, California", month = dec # " 15-17", publisher = "IEEE press", keywords = "genetic algorithms, genetic programming, Prefix Gene Expression Programming", URL = "http://www.cs.uic.edu/~xli1/papers/Substructures(ICMLA05)_XLi.pdf", abstract = "Linear genotype representation and modularity have continuously received extensive attention from the Genetic Programming (GP) community. The advantages of a linear genotype include a convenient and efficient implementation scheme. However, most existing techniques using a linear genotype follow the imperative programming language paradigm and a direct hierarchical composition for the functionality of the solution is under achieved. Our work is based on Prefix Gene Expression Programming (P-GEP), a new GP method featured by a prefix notation based linear genotype representation. Since P-GEP uses a functional language paradigm, its framework results in natural self emergence of substructures as functional components during the evolution. We propose to preserve and use potentially useful emergent substructures via a dynamic substructure library, empowering the algorithm to focus the search on a higher level of the solution structure. Preliminary experiments on the benchmark regression problems have shown the effectiveness of this approach.", notes = "cited by \cite{Spector:2011:GECCO} http://www.cs.csubak.edu/~icmla/icmla05/CFP_Program.html ", } @InProceedings{conf/aaai/Li05, author = "Xin Li", title = "Self-Emergence of Structures in Gene Expression Programming", year = "2005", editor = "Manuela M. Veloso and Subbarao Kambhampati", booktitle = "Proceedings, The Twentieth National Conference on Artificial Intelligence and the Seventeenth Innovative Applications of Artificial Intelligence Conference", address = "Pittsburgh, Pennsylvania, USA", pages = "1650--1651", publisher = "AAAI Press AAAI Press / The MIT Press", month = jul # " 9-13", note = "The Tenth AAAI/SIGART Doctoral Consortium", keywords = "genetic algorithms, genetic programming, Gene Expression Programming", bibdate = "2005-09-01", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/aaai/aaai2005.html#Li05", ISBN = "1-57735-236-X", URL = "http://www.cs.uic.edu/~xli1/papers/AAAI053LiX.pdf", size = "2 pages", abstract = "This thesis work aims at improving the problem solving ability of the Gene Expression Programming (GEP) algorithm to fulfill complex data mining tasks by preserving evolutionary process. The main contributions include the investigation of the constant creation techniques for promoting good functional structures emergent in the evolution, analysis of the limitation with the current implementation scheme of GEP,", } @InProceedings{Li:2006:ICMLA, author = "Xin Li and Chi Zhou and Weimin Xiao and Peter C. Nelson", title = "Introducing Emergent Loose Modules into the Learning Process of a Linear Genetic Programming System", booktitle = "5th International Conference on Machine Learning and Applications, ICMLA '06", year = "2006", pages = "219--224", address = "Orlando, USA", month = dec, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, gene expression programming", ISBN = "0-7695-2735-3", DOI = "doi:10.1109/ICMLA.2006.31", size = "6 pages", abstract = "Modularity and building blocks have drawn attention from the genetic programming (GP) community for a long time. The results are usually twofold: a hierarchical evolution with adequate building block reuse can accelerate the learning process, but rigidly defined and excessively employed modules may also counteract the expected advantages by confining the reachable search space. In this work, we introduce the concept of emergent loose modules based on a new linear GP system, prefix gene expression programming (P-GEP), in an attempt to balance between the stochastic exploration and the hierarchical construction for the optimal solutions. Emergent loose modules are dynamically produced by the evolution, and are reusable as sub-functions in later generations. The proposed technique is fully illustrated with a simple symbolic regression problem. The initial experimental results suggest it is a flexible approach in identifying the evolved regularity and the emergent loose modules are critical in composing the best solutions", notes = "fixed sized linear genome Dept. of Comput. Sci., Illinois Univ., Chicago, IL", } @PhdThesis{XinLu:thesis, author = "Xin Li", title = "Self-emergence of structures in gene expression programming", school = "University of Illinois at Chicago", year = "2006", address = "USA", month = may # " 5", keywords = "genetic algorithms, genetic programming, gene expression programming", URL = "http://search.proquest.com/docview/304947984", size = "133 pages", abstract = "Data mining tasks are pivotal for the improvement of manufacturing and design processes. However, some of the hidden patterns or relationships among the data are very complex, which cannot be easily detected by traditional data mining techniques. Several example industrial applications include cell phone reliability drop testing, call failure detection, wave filter design, and simulations, etc. Gene Expression Programming (GEP) was recently developed to address this challenge in data analysis and knowledge discovery. Being an evolutionary computation method, GEP distinguishes itself by searching the global optimum through a population of candidate solutions in parallel and being able to produce solutions of any possible form with minimum requirements of pre-knowledge. Although quite flexible, the algorithm still has limited performance with respect to complex problems since structure related information about evolving solutions is overlooked during its execution. This research aims to improve the problem solving ability of the GEP algorithm for complex data mining tasks by preserving and using the self-emergence of structures during its evolutionary process. An incremental approach has been pursued to achieve the proposed research goal, including the investigation of the constant creation methods in GEP, for identifying and promoting good solution structures; the design of a new genotype representation, namely, Prefix Gene Expression Programming (P-GEP), for establishing a solution structure preserving evolutionary process; and the introduction and implementation of self-emergent structures in P-GEP, for speeding up the learning process by reusing some evolved useful structural components and hence decomposing the complexity of the target solutions. Benchmark testing and theoretical analysis have both demonstrated that this line of work successfully assists the evolutionary process in advocating solutions with good functional structures, and finding meaningful building blocks to hierarchically form the final solutions following a faster fitness convergence curve, especially when applied to structurally complex problems. In general, more accurate solutions, higher success rates, and more compact solution structures have been achieved compared to the original GEP algorithm and other traditional methods.", notes = "OCLC Number: 82267944 http://en.scientificcommons.org/xin_li http://gradworks.umi.com/32/33/3233164.html Adviser Peter C. Nelson School UNIVERSITY OF ILLINOIS AT CHICAGO Source DAI/B 67-09, p. , Dec 2006 Source Type Dissertation Subjects Computer science Publication Number 3233164 Paypal/Ebay, San Jose, CA, USA", } @Article{li:2008:IJAMT, author = "X. Y. Li and X. Y. Shao and L. Gao", title = "Optimization of flexible process planning by genetic programming", journal = "The International Journal of Advanced Manufacturing Technology", year = "2008", volume = "38", number = "1-2", pages = "143--153", month = jul, keywords = "genetic algorithms, genetic programming, flexible process planning, Process plans selection, Optimization", ISSN = "0268-3768", DOI = "doi:10.1007/s00170-007-1069-x", size = "11 pages", abstract = "The traditional manufacturing system research literature generally assumed that there was only one feasible process plan for each job. This implied that there was no flexibility considered in the process plan. But, in the modern manufacturing system, most jobs may have a large number of flexible process plans. So, flexible process plans selection in a manufacturing environment has become a crucial problem. In this paper, a new method using an evolutionary algorithm, called genetic programming (GP), is presented to optimize flexible process planning. The flexible process plans and the mathematical model of flexible process planning have been described, and a network representation is adopted to describe the flexibility of process plans. To satisfy GP, it is very important to convert the network to a tree. The efficient genetic representations and operator schemes also have been considered. Case studies have been used to test the algorithm, and the comparison has been made for this approach and genetic algorithm (GA), which is another popular evolutionary approach to indicate the adaptability and superiority of the GP-based approach. The experimental results show that the proposed method is promising and very effective in the optimization research of flexible process planning.", notes = "The State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China", } @InProceedings{li:2020:EMIPPS, author = "Xinyu Li and Liang Gao", title = "Improved Genetic Programming for Process Planning", booktitle = "Effective Methods for Integrated Process Planning and Scheduling", year = "2020", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-662-55305-3_4", DOI = "doi:10.1007/978-3-662-55305-3_4", } @Article{Li:2024:TST, author = "Xixing Li and Qingqing Zhao and Hongtao Tang and Xing Guo and Mengzhen Zhuang and Yibing Li and Xi Vincent Wang", title = "Flexible Job Shop Composite Dispatching Rule Mining Approach Based on an Improved Genetic Programming Algorithm", journal = "Tsinghua Science and Technology", year = "2024", volume = "29", number = "5", pages = "1390--1408", month = oct, keywords = "genetic algorithms, genetic programming, Job shop scheduling, Sociology, Binary trees, Deep reinforcement learning, Dispatching, Encoding, flexible job shop scheduling, composite dispatching rule, improved genetic programming algorithm", ISSN = "1007-0214", DOI = "doi:10.26599/TST.2023.9010141", abstract = "To obtain a suitable scheduling scheme in an effective time range, the minimum completion time is taken as the objective of Flexible Job Shop scheduling Problems (FJSP) with different scales, and Composite Dispatching Rules (CDRs) are applied to generate feasible solutions. Firstly, the binary tree coding method is adopted, and the constructed function set is normalised. Secondly, a CDR mining approach based on an Improved Genetic Programming Algorithm (IGPA) is designed. Two population initialization methods are introduced to enrich the initial population, and a superior and inferior population separation strategy is designed to improve the global search ability of the algorithm. At the same time, two individual mutation methods are introduced to improve the algorithm's local search ability, to achieve the balance between global search and local search. In addition, the effectiveness of the IGPA and the superiority of CDRs are verified through comparative analysis. Finally, Deep Reinforcement Learning (DRL) is employed to solve the FJSP by incorporating the CDRs as the action set, the selection times are counted to further verify the superiority of CDRs.", notes = "Also known as \cite{10517917}", } @Article{Li:2024:egyr, author = "Xuebin Li and Zhao Jin and Shengqun Li and Daiwei Yu and Jun Zhang and Wenjin Zhang", title = "Li-ion battery state of health prediction through metaheuristic algorithms and genetic programming", journal = "Energy Reports", year = "2024", volume = "12", pages = "368--380", keywords = "genetic algorithms, genetic programming, Lithium-ion battery, State of health (SOH), Multiobjective grey wolf optimization (MOGWO), Genetic programming (GP), Decision making", ISSN = "2352-4847", URL = "https://www.sciencedirect.com/science/article/pii/S2352484724003937", DOI = "doi:10.1016/j.egyr.2024.06.038", abstract = "Predicting the State of Health (SOH) of the Lithium-ion battery with higher accuracy and reduced cost is a challenging and crucial task for ensuring its reliability and safety. To achieve this, a two-stage prediction framework is proposed to find a concise expression of SOH using the health features of the battery through metaheuristic algorithms and genetic programming (GP).In Stage-I, three conflicting objectives are considered concurrently: the root-mean-square error (RMSE) of SOH prediction, the health features selected, and the expressional complexity of the battery capacity. The wrapper structure is used for SOH prediction, where a binary multi-objective grey wolf optimisation (Binary MOGWO) algorithm is employed to select features and generate the Pareto set. Genetic programming is used to calculate the SOH value using symbolic regression models. In Stage-II, the final compromise solution is filtered from the Pareto set through decision-making approaches. The relationships between selected features and the capacity of the battery are investigated. The NASA Prognostics Center of Excellence battery dataset is chosen to verify the effectiveness of the proposed framework. The simulation results show that the features found through the framework can provide SOH predictions in the form of symbolic equations with higher accuracy, lower cost, and reduced complexity", } @InProceedings{Li:2012:EvoMUSART, author = "Yang Li and Changjun Hu and Ming Chen and Jingyuan Hu", title = "Investigating Aesthetic Features to Model Human Preference in Evolutionary Art", booktitle = "Proceedings of the 1st International Conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design, EvoMUSART 2012", year = "2012", month = "11-13 " # apr, editor = "Penousal Machado and Juan Romero and Adrian Carballal", series = "LNCS", volume = "7247", publisher = "Springer Verlag", address = "Malaga, Spain", pages = "153--164", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, Aesthetic learning, evolutionary art, interactive evolutionary computation, computational aesthetics", isbn13 = "978-3-642-29141-8", DOI = "doi:10.1007/978-3-642-29142-5_14", abstract = "In this paper we investigate aesthetic features in learning aesthetic judgements in an evolutionary art system. We evolve genetic art with our evolutionary art system, BioEAS, by using genetic programming and an aesthetic learning model. The model is built by learning both phenotype and genotype features, which we extracted from internal evolutionary images and external real world paintings, which could lead to more interesting paths. By learning aesthetic judgment and applying the knowledge to evolve aesthetical images, the model helps user to automate the process of evolutionary process. Several independent experimental results show that our system is efficient to reduce user fatigue in evolving art.", notes = "Part of \cite{Machado:2012:EvoMusArt_proc} EvoMUSART'2012 held in conjunction with EuroGP2012, EvoCOP2012, EvoBIO2012 and EvoApplications2012", } @Article{Li:2013:GPEM, author = "Yang Li and Changjun Hu and Leandro L. Minku and Haolei Zuo", title = "Learning aesthetic judgements in evolutionary art systems", journal = "Genetic Programming and Evolvable Machines", year = "2013", volume = "14", number = "3", pages = "315--337", month = sep, note = "Special issue on biologically inspired music, sound, art and design", keywords = "genetic algorithms, genetic programming, Evolutionary art, Interactive evolutionary computation, IEC, Image complexity, Fractal compression", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-013-9188-7", language = "English", size = "23 pages", abstract = "Learning aesthetic judgements is essential for reducing users' fatigue in evolutionary art systems. Although judging beauty is a highly subjective task, we consider that certain features are important to please users. In this paper, we introduce an adaptive model to learn aesthetic judgements in the task of interactive evolutionary art. Following previous work, we explore a collection of aesthetic measurements based on aesthetic principles. We then reduce them to a relevant subset by feature selection, and build the model by learning the features extracted from previous interactions. To apply a more accurate model, multi-layer perceptron and C4.5 decision tree classifiers are compared. In order to test the efficacy of the approach, an evolutionary art system is built by adopting this model, which analyses the user's aesthetic judgements and approximates their implicit aesthetic intentions in the subsequent generations. We first tested these aesthetic measurements on different artworks from our selected artists. Then, a series of experiments were performed by a group of users to validate the adaptive learning model. The study reveals that different features are useful for identifying different patterns, but not all are relevant for the description of artists' styles. Our results show that the use of the learning model in evolutionary art systems is sound and promising for predicting users' preferences", } @Article{Li:2025:cnsns, author = "Yang Li2 and Shengyuan Xu and Jinqiao Duan", title = "An evolutionary approach for discovering non-Gaussian stochastic dynamical systems based on nonlocal Kramers-Moyal formulas", journal = "Communications in Nonlinear Science and Numerical Simulation", year = "2025", volume = "145", pages = "108751", keywords = "genetic algorithms, genetic programming, Nonlocal Kramers-Moyal formulas, Stochastic dynamical systems, Evolutionary symbolic sparse regression method, Levy motion", ISSN = "1007-5704", URL = "https://www.sciencedirect.com/science/article/pii/S1007570425001625", DOI = "doi:10.1016/j.cnsns.2025.108751", abstract = "Discovering explicit governing equations of stochastic dynamical systems with both (Gaussian) Brownian noise and (non-Gaussian) Levy noise from data is challenging due to the possible intricate functional forms and the inherent complexity of Levy motion. This research endeavors to develop an evolutionary symbolic sparse regression (ESSR) approach to extract non-Gaussian stochastic dynamical systems from sample path data, based on nonlocal Kramers-Moyal formulas, genetic programming, and sparse regression. Specifically, genetic programming is employed to generate a diverse array of candidate functions, sparse regression is used to learn the coefficients associated with these candidates, and the nonlocal Kramers-Moyal formulas serve as the foundation for constructing the fitness measure in genetic programming and the loss function in sparse regression. The efficacy and capabilities of this approach are demonstrated through its application to several illustrative models. This approach stands out as a powerful tool for deciphering non-Gaussian stochastic dynamics from available datasets, suggesting a wide range of applications across various fields", } @Article{Li:2025:inffus, author = "Yanjie Li and Jingyi Liu and Min Wu and Lina Yu and Weijun Li and Xin Ning and Wenqiang Li and Meilan Hao and Yusong Deng and Shu Wei", title = "{MMSR:} Symbolic regression is a multi-modal information fusion task", journal = "Information Fusion", year = "2025", volume = "114", pages = "102681", keywords = "genetic algorithms, genetic programming, Symbolic regression, Multi-modal, Information fusion, Contrastive learning, Modal alignment", ISSN = "1566-2535", URL = "https://www.sciencedirect.com/science/article/pii/S1566253524004597", DOI = "doi:10.1016/j.inffus.2024.102681", abstract = "Mathematical formulas are the crystallization of human wisdom in exploring the laws of nature for thousands of years. Describing the complex laws of nature with a concise mathematical formula is a constant pursuit of scientists and a great challenge for artificial intelligence. This field is called symbolic regression (SR). Symbolic regression was originally formulated as a combinatorial optimisation problem, and Genetic Programming (GP) and Reinforcement Learning algorithms were used to solve it. However, GP is sensitive to hyperparameters, and these two types of algorithms are inefficient. To solve this problem, researchers treat the mapping from data to expressions as a translation problem. And the corresponding large-scale pre-trained model is introduced. However, the data and expression skeletons do not have very clear word correspondences as the two languages do. Instead, they are more like two modalities (e.g., image and text). Therefore, in this paper, we proposed MMSR. The SR problem is solved as a pure multi-modal problem, and contrastive learning is also introduced in the training process for modal alignment to facilitate later modal feature fusion. It is worth noting that to better promote the modal feature fusion, we adopt the strategy of training contrastive learning loss and other losses at the same time, which only needs one-step training, instead of training contrastive learning loss first and then training other losses. Because our experiments prove training together can make the feature extraction module and feature fusion module wearing-in better. Experimental results show that compared with multiple large-scale pre-training baselines, MMSR achieves the most advanced results on multiple mainstream datasets including SRBench. Our code is open source at https://github.com/1716757342/MMSR", } @InProceedings{Li:2012:CECb, title = "Phase Transition and New Fitness Function Based Genetic Inductive Logic Programming Algorithm", author = "Yanjuan Li and Maozu Guo", pages = "956--963", booktitle = "Proceedings of the 2012 IEEE Congress on Evolutionary Computation", year = "2012", editor = "Xiaodong Li", month = "10-15 " # jun, DOI = "doi:10.1109/CEC.2012.6256626", size = "8 pages", address = "Brisbane, Australia", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Learning classifier systems, machine learning, inductive logic programming, genetic inductive logic programming", abstract = "A new genetic inductive logic programming (GILP for short) algorithm named PT- NFF-GILP (Phase Transition and New Fitness Function based Genetic Inductive Logic Programming) is proposed in this paper. Based on phase transition of the covering test, PT-NFF-GILP randomly generates initial population in phase transition region instead of the whole space of candidate clauses. Moreover, a new fitness function, which not only considers the number of examples covered by rules, but also considers the ratio of the examples covered by rules to the training examples, is defined in PT-NFF-GILP. The new fitness function measures the quality of first-order rules more precisely, and enhances the search performance of algorithm. Experiments on ten learning problems show that: 1) the new method of generating initial population can effectively reduce iteration number and enhance predictive accuracy of GILP algorithm; 2) the new fitness function measures the quality of first-order rules more precisely and avoids generating over-specific hypothesis; 3) The performance of PT-NFF-GILP is better than other algorithms compared with it, such as G-NET, KFOIL and NFOIL.", notes = "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the EPS and the IET.", } @InProceedings{Li:2015:ISCID, author = "Yao Li and Xuehua Zhang and Hongyu Liu", booktitle = "2015 8th International Symposium on Computational Intelligence and Design (ISCID)", title = "Evolutionary Design of a Second-Order Butterworth Lowpass Filter", year = "2015", volume = "2", pages = "532--535", month = "12-13 " # dec, address = "Hangzhou, China", keywords = "genetic algorithms, genetic programming, Band-pass filters, Filtering theory, Filtering algorithms, Sociology, Statistics, Design methodology, Matched filters, lowpass filter, evolutionary design, simulation experiment", isbn13 = "978-1-4673-9586-1", DOI = "doi:10.1109/ISCID.2015.126", size = "4 pages", abstract = "The traditional design method of filter had too much human intervention, a very tedious design process. Precision of parameters was lower for the designed filter, bandpass edge of attenuation characteristics was not enough steep and filtering quality was lower. In order to overcome these shortcomings, an evolutionary design method for an analogue filter is presented in the paper, according to basic principles of evolutionary design for circuit. The method is structure of a second-order Butterworth low-pass filter is devised by genetic programming. Optimisation design of parameters for the filter is realized by improved adaptive genetic algorithm. Performance indexes for the filter are analysed by simulation software. Design results have been simulated and verified. Experiment shows that values of evolutionary parameters are in line with theoretical values exceedingly for the filter, and simulation results are satisfying.", notes = "Also known as \cite{7469190} Physics School Beihua University Jilin City, China", } @Article{li:2025:CIS, author = "Jiayi Li and Fan Zhang and Jianbin Ma", title = "A new representation in genetic programming with hybrid feature ranking criterion for high-dimensional feature selection", journal = "Complex \& Intelligent Systems", year = "2025", keywords = "genetic algorithms, genetic programming", URL = "https://link.springer.com/article/10.1007/s40747-025-01784-1", DOI = "doi:10.1007/s40747-025-01784-1", } @InProceedings{Li:2021:GECCOcomp, author = "Yiming Li and Lin Shang", title = "{Re-ID BUFF}: An Enhanced Similarity Measurement Based on Genetic Programming for Person Re-identification", booktitle = "Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion", year = "2021", editor = "Francisco Chicano and Alberto Tonda and Krzysztof Krawiec and Marde Helbig and Christopher W. Cleghorn and Dennis G. Wilson and Georgios Yannakakis and Luis Paquete and Gabriela Ochoa and Jaume Bacardit and Christian Gagne and Sanaz Mostaghim and Laetitia Jourdan and Oliver Schuetze and Petr Posik and Carlos Segura and Renato Tinos and Carlos Cotta and Malcolm Heywood and Mengjie Zhang and Leonardo Trujillo and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Fuyuki Ishikawa and Inmaculada Medina-Bulo and Frank Neumann and Andrew M. Sutton", pages = "255--256", address = "internet", series = "GECCO '21", month = jul # " 10-14", organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, Person reidentification,: Poster", isbn13 = "978-1-4503-8351-6", DOI = "doi:10.1145/3449726.3459432", size = "2 pages", abstract = "Person re-identification (re-ID) is a fundamental link to ensure successful cross-camera tracking. Its overall process is divided into two stages, feature extraction and matching. In the matching stage,the simple Euclid measurement has limitations in score due to the lack of consideration of the vector directions. The majority of recent contributions focus on the use of neighborhood information of the query image, which makes the calculation process more complex. In addition, these similarity measurements are predefined and therefore evaluated. In this paper, we propose a re-ID similarity measurement based on Genetic Programming (GP). The similarity measurement formula is automatically evolved and constructed through the optimization using specific function set, terminal set and fitness function targeting re-ID. For the training process of GP,we propose the feature triplet-dataset like the triplet-loss based on the existing re-ID datasets. We conduct sufficient experiments on three benchmark datasets, comparing with features of different quality and various measurements. The proposed method has a better effect than the Euclid measurement and achieves a general improvement on the mAP and rank-1 of combination with other metrics. Therefore, our method can be used as a gain buff to enhance the score of other methods on the original basis.", notes = "Nanjing University GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)", } @InProceedings{Li:2017:SEAL, author = "Ying Li and Zhixing Huang and Jinghui Zhong and Liang Feng", title = "Genetic Programming for Lifetime Maximization in Wireless Sensor Networks with a Mobile Sink", booktitle = "Proceedings of the 11th International Conference on Simulated Evolution and Learning, SEAL-2017", year = "2017", editor = "Yuhui Shi and Kay Chen Tan and Mengjie Zhang and Ke Tang and Xiaodong Li and Qingfu Zhang and Ying Tan and Martin Middendorf and Yaochu Jin", volume = "10593", series = "Lecture Notes in Computer Science", pages = "774--785", address = "Shenzhen, China", month = nov # " 10-13", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-68759-9", URL = "https://doi.org/10.1007/978-3-319-68759-9_63", DOI = "doi:10.1007/978-3-319-68759-9_63", abstract = "Maximizing the lifetime of Wireless Sensor Network (WSN) with a mobile sink is a challenging and important problem that has attracted increasing research attentions. In the literature, heuristic based approaches have been proposed to solve the problem, such as the Greedy Maximum Residual Energy (GMRE) based method. However, existing heuristic based approaches highly rely on expert knowledge, which makes them inconvenient for practical applications. Taking this cue, in this paper, we propose an automatic method to construct heuristic for sink routing based on Genetic Programming (GP) approach. Empirical study shows that the proposed method can generate promising heuristics that achieve superior performance against existing methods with respect to the global lifetime of WSN.", } @Article{JITCS-V2-N1-5, author = "Yinxing Li and Ning Li", title = "A Genetic Programming Framework for Topic Discovery from Online Digital Library", journal = "International Journal of Information Technology and Computer Science", year = "2010", volume = "2", number = "1", pages = "32--39", month = nov, keywords = "genetic algorithms, non-linear matrix factorisation, web-click data, convex optimisation, interior point method", ISSN = "20749007", URL = "http://www.mecs-press.org/ijitcs/ijitcs-v2-n1/v2n1-5.html", broken = "http://www.doaj.org/doaj?func=openurl\&genre=article\&issn=20749007\&date=2010\&volume=2\&issue=1\&spage=32", publisher = "MECS Publisher", size = "8 pages", abstract = "Various topic extraction techniques for digital libraries have been proposed over the past decade. Generally the topic extraction system requires a large number of features and complicated lexical analysis. While these features and analysis are effective to represent the statistical characteristics of the document, they didn't capture the high level semantics. In this paper, we present a new approach for topic extraction. Our approach combines user's click stream data with traditional lexical analysis. From our point of view, the user's click stream directly reflects human understanding of the high-level semantics in the document. Furthermore, a simple, yet effective, piece-wise linear model for topic evolution is proposed. We apply genetic algorithm to estimate the model and extract topics. Experiments on the set of US congress digital library documents demonstrate that our approach achieves better accuracy for the topic extraction than traditional methods.", notes = "Appears to be GA rather than GP", bibsource = "OAI-PMH server at www.doaj.org", oai = "oai:doaj-articles:a40f73041434b1d26363e11463913670", } @Article{LI:2022:RCM, author = "Yuxin Li and Wenbin Gu and Minghai Yuan and Yaming Tang", title = "Real-time data-driven dynamic scheduling for flexible job shop with insufficient transportation resources using hybrid deep {Q} network", journal = "Robotics and Computer-Integrated Manufacturing", volume = "74", pages = "102283", year = "2022", ISSN = "0736-5845", DOI = "doi:10.1016/j.rcim.2021.102283", URL = "https://www.sciencedirect.com/science/article/pii/S0736584521001630", keywords = "genetic algorithms, genetic programming, Flexible job shop scheduling, Insufficient transportation resources, Hybrid deep Q network, Multiobjective optimization, Dynamic scheduling", abstract = "With the extensive application of automated guided vehicles in manufacturing system, production scheduling considering limited transportation resources becomes a difficult problem. At the same time, the real manufacturing system is prone to various disturbance events, which increase the complexity and uncertainty of shop floor. To this end, this paper addresses the dynamic flexible job shop scheduling problem with insufficient transportation resources (DFJSP-ITR) to minimize the makespan and total energy consumption. As a sequential decision-making problem, DFJSP-ITR can be modeled as a Markov decision process where the agent should determine the scheduling object and allocation of resources at each decision point. So this paper adopts deep reinforcement learning to solve DFJSP-ITR. In this paper, the multiobjective optimization model of DFJSP-ITR is established. Then, in order to make agent learn to choose the appropriate rule based on the production state at each decision point, a hybrid deep Q network (HDQN) is developed for this problem, which combines deep Q network with three extensions. Moreover, the shop floor state model is established at first, and then the decision point, generic state features, genetic-programming-based action space and reward function are designed. Based on these contents, the training method using HDQN and the strategy for facing new job insertions and machine breakdowns are proposed. Finally, comprehensive experiments are conducted, and the results show that HDQN has superiority and generality compared with current optimization-based approaches, and can effectively deal with disturbance events and unseen situations through learning", } @Article{Li:2025:TSMC, author = "Yuxin Li and Qingzheng Wang and Xinyu Li and Liang Gao and Ling Fu and Yanbin Yu and Wei Zhou", title = "Real-Time Scheduling for Flexible Job Shop With {AGVs} Using Multiagent Reinforcement Learning and Efficient Action Decoding", journal = "IEEE Transactions on Systems, Man, and Cybernetics: Systems", year = "2025", volume = "55", number = "3", pages = "2120--2132", month = mar, keywords = "genetic algorithms, genetic programming, Job shop scheduling, Dynamic scheduling, Real-time systems, Production, Logistics, Conferences, Costs, Vehicle dynamics, Reinforcement learning, Metaheuristics, Automated guided vehicle (AGV), disturbance events, flexible job shop, multiagent reinforcement learning (MARL)", ISSN = "2168-2232", DOI = "doi:10.1109/TSMC.2024.3520381", abstract = "The application of automated guided vehicle (AGV) greatly improves the production efficiency of workshop. However, machine flexibility and limited logistics equipment increase the complexity of collaborative scheduling, and frequent dynamic events bring uncertainty. Therefore, this article proposes a real-time scheduling method for dynamic flexible job shop scheduling problem with AGVs using multiagent reinforcement learning (MARL). Specifically, a real-time scheduling framework is proposed in which a multiagent scheduling architecture is designed for achieving task selection, machine allocation and AGV allocation. Then, an action space and an efficient action decoding algorithm are proposed, which enable agents to explore in the high-quality solution space and improve the learning efficiency. In addition, a state space with generalisation, a reward function considering machine idle time and a strategy for handling four disturbance events are designed to minimise the total tardiness cost. Comparison experiments show that the proposed method outperforms the priority dispatching rules, genetic programming and four popular reinforcement learning (RL)-based methods, with performance improvements mostly exceeding 10percent. Furthermore, experiments considering four disturbance events demonstrate that the proposed method has strong robustness, and it can provide appropriate scheme for uncertain manufacturing system.", notes = "Also known as \cite{10829984}", } @Article{Li:2025:aei, author = "Yuxin Li and Xinyu Li and Liang Gao", title = "Real-time scheduling for production-logistics collaborative environment using multi-agent deep reinforcement learning", journal = "Advanced Engineering Informatics", year = "2025", volume = "65", pages = "103216", keywords = "genetic algorithms, genetic programming, Multi-agent deep reinforcement learning, Production-logistics collaborative, Real-time scheduling, Large-scale order", ISSN = "1474-0346", URL = "https://www.sciencedirect.com/science/article/pii/S1474034625001090", DOI = "doi:10.1016/j.aei.2025.103216", abstract = "With the extensive application of automated guided vehicle (AGV), production-logistics collaborative scheduling problem (PLCSP) becomes challenging for enterprises. Meanwhile, large-scale order and dynamic events bring more complexity and uncertainty. At present, deep reinforcement learning (DRL) has emerged as a promising scheduling approach. Therefore, this paper proposes a real-time scheduling method based on multi-agent DRL for PLCSP with dynamic job arrivals to minimise the total weighted tardiness. Specifically, a novel scheduling framework is designed in which a new logistics task release moment is given to reserve lots of AGV preparation time and avoid unnecessary premature decisions. Then, a training algorithm based on multi-agent proximal policy optimisation is proposed to achieve job filtering, job selection and AGV selection. The action space and action space pruning strategy are designed for each agent to ensure the sufficient exploration and reduce the learning difficulty. Moreover, three state spaces with serial relationship and a reward function considering job classification are proposed. Experiments on 120 instances show that the proposed method has superiority and generality compared with scheduling rules and genetic programming, as well as three popular DRL-based methods, and the performance improvement mostly exceeds 10percent. Furthermore, a real-world case is studied to show that the proposed method is applicable to solve the complex production-logistics collaborative scheduling problems", } @Article{Li:2025:jmsy, author = "Yuxin Li and Qihao Liu and Xinyu Li and Liang Gao", title = "Manufacturing resource-based self-organizing scheduling using multi-agent system and deep reinforcement learning", journal = "Journal of Manufacturing Systems", year = "2025", volume = "79", pages = "179--198", keywords = "genetic algorithms, genetic programming, Multi-agent deep reinforcement learning, Multi-agent system, Production-logistics, Self-organizing scheduling, Disturbance events", ISSN = "0278-6125", URL = "https://www.sciencedirect.com/science/article/pii/S0278612525000123", DOI = "doi:10.1016/j.jmsy.2025.01.004", abstract = "Enterprises are vigorously developing smart factories to meet the approaching mass customization. As a promising control paradigm for smart factories, the self-organising scheduling mode can build networked manufacturing things. Compared to the global control of traditional scheduling methods, its decentralized control can provide stronger dynamic response and self-regulation capabilities. Therefore, this paper proposes a self-organising scheduling method based on multi-agent system (MAS) and deep reinforcement learning (DRL) for smart factory. Firstly, a novel MAS with partially decentralized control architecture is established, where the manufacturing resources and cloud are constructed as agents. Then, unlike traditional methods, a self-organising negotiation mechanism based on contract network protocol is designed for production-logistics collaboration. Considering problem domain knowledge, logistics task bidding of automated guided vehicle agents is based on heuristics, and processing task bidding of machine agents is based on multi-agent DRL. It can ensure the timely delivery of orders, rapid logistics process and efficient production. Finally, machine agents embedded with DRL adopt the centralized training and decentralized execution framework. An action space based on three priorities is designed to ensure the correct bidding of each machine agent and reasonable auction of processing tasks. Experimental results show that compared with scheduling rules, genetic programming and three DRL methods, the proposed method achieves better scheduling performance through reasonable competition of heterogeneous resource agents, and can effectively handle new job arrivals and machine breakdowns", } @Article{Li:2025:rcim, author = "Yuxin Li and Xinyu Li and Liang Gao and Zhibing Lu", title = "Multi-agent deep reinforcement learning for dynamic reconfigurable shop scheduling considering batch processing and worker cooperation", journal = "Robotics and Computer-Integrated Manufacturing", year = "2025", volume = "91", pages = "102834", keywords = "genetic algorithms, genetic programming, Reconfigurable workshop, Multi-agent deep reinforcement learning, Dynamic scheduling, Batch processing, Worker cooperation", ISSN = "0736-5845", URL = "https://www.sciencedirect.com/science/article/pii/S0736584524001212", DOI = "doi:10.1016/j.rcim.2024.102834", abstract = "Reconfigurable manufacturing system is considered as a promising next-generation manufacturing paradigm. However, limited equipment and complex product processes add additional coupled scheduling problems, including resource allocation, batch processing and worker cooperation. Meanwhile, dynamic events bring uncertainty. Traditional scheduling methods are difficult to obtain good solutions quickly. To this end, this paper proposes a multi-agent deep reinforcement learning (DRL) based method for dynamic reconfigurable shop scheduling problem considering batch processing and worker cooperation to minimise the total tardiness cost. Specifically, a dual-agent DRL-based scheduling framework is first designed. Then, a multi-agent DRL-based training algorithm is developed, where two high-quality end-to-end action spaces are designed using rule adjustment, and an estimated tardiness cost driven reward function is proposed for order-level scheduling problem. Moreover, a multi-resource allocation heuristics is designed for the reasonable assignment of equipment and workers, and a batch processing rule is designed to determine the action of manufacturing cell based on workshop state. Finally, a strategy is proposed for handling new order arrivals, equipment breakdown and job reworks. Experimental results on 140 instances show that the proposed method is superior to scheduling rules, genetic programming, and two popular DRL-based methods, and can effectively deal with various disturbance events. Furthermore, a real-world assembly and debugging workshop case is studied to show that the proposed method is applicable to solve the complex reconfigurable shop scheduling problems", } @Misc{Li:2018:arxiv, author = "Zhe Li and Xuehan Xiong and Zhou Ren and Ning Zhang and Xiaoyu Wang and Tianbao Yang", title = "An Aggressive Genetic Programming Approach for Searching Neural Network Structure Under Computational Constraints", howpublished = "arXiv", year = "2018", month = "3 " # jun, keywords = "genetic algorithms, genetic programming, ANN", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/corr/corr1806.html#abs-1806-00851", URL = "http://arxiv.org/abs/1806.00851", size = "16 pages", abstract = "Recently, there emerged revived interests of designing automatic programs (e.g., using genetic/evolutionary algorithms) to optimise the structure of Convolutional Neural Networks (CNNs) for a specific task. The challenge in designing such programs lies in how to balance between large search space of the network structures and high computational costs. Existing works either impose strong restrictions on the search space or use enormous computing resources. In this paper, we study how to design a genetic programming approach for optimising the structure of a CNN for a given task under limited computational resources yet without imposing strong restrictions on the search space. To reduce the computational costs, we propose two general strategies that are observed to be helpful: (i) aggressively selecting strongest individuals for survival and reproduction, and killing weaker individuals at a very early age; (ii) increasing mutation frequency to encourage diversity and faster evolution. The combined strategy with additional optimisation techniques allows us to explore a large search space but with affordable computational costs. Our results on standard benchmark datasets (MNIST, SVHN, CIFAR-10, CIFAR-100) are competitive to similar approaches with significantly reduced computational costs.", notes = "The University of Iowa journals/corr/abs-1806-00851", } @InProceedings{Li:2020:BIBM, author = "Zhuang Li and Jie He and Xiaotong Zhang and Huadong Fu and Jingyan Qin", title = "Toward high accuracy and visualization: An interpretable feature extraction method based on genetic programming and non-overlap degree", booktitle = "2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)", year = "2020", pages = "299--304", abstract = "Genetic programming (GP) has shown promising results in interpretable feature extraction, but few works considered both classification accuracy and data visualization as objectives. Evaluating the extracted features based on the combination of accuracy measures and visualization measures can help to achieve the two objectives simultaneously. However, the exploitation of improper visualization measures and combination methods will decrease the classification accuracy. In this paper, a novel feature extraction method based on GP and non-overlap degree is proposed to extract interpretable features for high accuracy and visualization. And a novel function that maximizes the product of the accuracy of a linear classifier and the non-overlap degree is proposed to evaluate the extracted features. The proposed method, named GP-ANO, is compared with other methods on five medical datasets by six common machine learning methods. The experimental results demonstrate that the GP-ANO method outperforms other compared methods in terms of both classification accuracy and data visualization.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/BIBM49941.2020.9313182", month = dec, notes = "Also known as \cite{9313182}", } @InProceedings{Li:2021:BIBM, author = "Zhuang Li and Jingyan Qin and Haiyan Gong and Xiaotong Zhang and Yadong Wan", title = "Enhancing the generalization of feature construction using genetic programming for imbalanced data with augmented non-overlap degree", booktitle = "2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)", year = "2021", pages = "960--965", month = dec, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/BIBM52615.2021.9669863", abstract = "Genetic programming (GP) has a significant achievement in feature construction and non-overlap degree can help to improve the generalization ability of GP based feature construction. However, the non-overlap degree is biased towards the majority class. In this paper, a novel GP based feature construction method with augmented non-overlap degree is proposed to enhance the generalization ability for imbalanced data. And the constructed features are evaluated by a novel function based on the combination of the area under the ROC curve metric and the augmented non-overlap degree. The generalization performance is evaluated not only by a particular classification algorithm, but also by six widely used classification algorithms. The experiments conducted on five imbalanced biomedical datasets with different imbalance rates show that the proposed GP-AANO method can achieve superior generalization performance for classification.", notes = "Also known as \cite{9669863}", } @Article{Liang:2009:GPEM, author = "Houjun Liang and Wenjian Luo and Xufa Wang", title = "A three-step decomposition method for the evolutionary design of sequential logic circuits", journal = "Genetic Programming and Evolvable Machines", year = "2009", volume = "10", number = "3", pages = "231--262", month = sep, keywords = "evolvable hardware,Adaptive system, Evolutionary computation, Sequential circuit Decomposition, EHW", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-009-9083-4", size = "32 pages", abstract = "Evolvable hardware (EHW) refers to an automatic circuit design approach, which employs evolutionary algorithms (EAs) to generate the configurations of the programmable devices. The scalability is one of the main obstacles preventing EHW from being applied to real-world applications. Several techniques have been proposed to overcome the scalability problem. One of them is to decompose the whole circuit into several small evolvable sub-circuits. However, current techniques for scalability are mainly used to evolve combinational logic circuits. In this paper, in order to decompose a sequential logic circuit, the state decomposition, output decomposition and input decomposition are united as a threestep decomposition method (3SD). A novel extrinsic EHW system, namely 3SD-ES, which combines the 3SD method with the (l, k) ES (evolution strategy), is proposed, and is used for the evolutionary designing of larger sequential logic circuits. The proposed extrinsic EHW system is tested extensively on sequential logic circuits taken from the Microelectronics Center of North Carolina (MCNC) benchmark library. The results demonstrate that 3SD-ES has much better performance in terms of scalability. It enables the evolutionary designing of larger sequential circuits than have ever been evolved before.", } @Article{LIANG:2020:ASC, author = "Jiayu Liang and Yuxin Liu and Yu Xue", title = "Preference-driven {Pareto} front exploitation for bloat control in genetic programming", journal = "Applied Soft Computing", year = "2020", volume = "92", pages = "106254", month = jul, keywords = "genetic algorithms, genetic programming, Preference, Pareto front, Multi-objective optimization, Bloat control", ISSN = "1568-4946", URL = "http://www.sciencedirect.com/science/article/pii/S1568494620301940", DOI = "doi:10.1016/j.asoc.2020.106254", size = "18 pages", abstract = "As one of evolutionary algorithms (EAs), genetic programming (GP) has been applied in a wide range of areas, e.g. bioinformatics and robotics. Different from other EAs, GP can represent problems with variable length (e.g. trees), which makes it more flexible in evolving solutions, yet leads to a serious problem, bloat. It can cause evolving redundant parts and slowing down search. Multi-objective techniques are popularly used for reducing bloat in GP (termed as MOGP). Specifically, MOGP methods evolve trade-off solutions of all objectives, which constitute the so-called Pareto front. Then users select solutions on the front based on their preference for specific tasks. However, existing MOGP methods rarely consider users preference during evolution, which wastes computation power and time to search for useless solutions and cannot generate fine-grained interested regions on the Pareto front. Therefore, this paper investigates introducing users' preference to guide multi-objective techniques to focus on the interested regions on Pareto front during evolution. Specifically, Pareto dominance is an important notion in multi-objective techniques for comparing two solutions. We design two preference-driven Pareto dominance mechanisms, scPd (static constraint Pareto dominance) and dcPd (dynamic constraint Pareto dominance), which are introduced in a base multi-objective technique and then are incorporated with GP respectively to form two new bloat control MOGP methods, i.e. scPd_MOGP and dcPd_MOGP. They are tested on benchmark symbolic regression tasks comparing with GP, two existing bloat control methods (i.e. a parsimony GP method (pGP) and a standard multi-objective GP method (sMOGP)), and four popularly-used symbolic regression methods. Results show that the proposed methods can reduce bloat in GP and outperform pGP in bloat control, and comparison with sMOGP shows that they can search front regions based on users preference where the solutions have better functionality, yet relatively larger sizes. In addition, compared with four popularly-used symbolic regression methods, scPd_MOGP is generally better; while dcPd_MOGP achieves varied results, yet it performs better or similar to the reference methods on the majority of the given test functions. Moreover, comparison between the two proposed methods suggests that the constraint in the Pareto dominance of scPd_MOGP is more relaxed than that of dcPd_MOGP", } @Article{LIANG:2020:IS, author = "Jiayu Liang and Yu Xue and Jianming Wang", title = "Bi-objective memetic {GP} with dispersion-keeping {Pareto} evaluation for real-world regression", journal = "Information Sciences", volume = "539", pages = "16--35", year = "2020", ISSN = "0020-0255", DOI = "doi:10.1016/j.ins.2020.05.136", URL = "http://www.sciencedirect.com/science/article/pii/S0020025520305636", keywords = "genetic algorithms, genetic programming, Memetic algorithm, Bi-objective GP, Local search, Real-world regression", abstract = "Regression tasks aim to determine accurate and simple relationship expressions between variables, which can be regarded as bi-objective optimization problems. As GP (genetic programming) can use expression trees as representation, it is popularly-used for regression. Introducing multi-objective techniques into GP enables it to solve bi-objective tasks, and the success of memetic algorithms show the importance of local search in improving GP. However, existing memetic GP methods are mainly single-objective, in which the local search operators cannot be applied in multi-objective optimization. Moreover, the popularly-used solution evaluation mechanism (Pareto local search) in existing multi-objective memetic methods cannot assure solution dispersion. To handle these problems, a dispersion-keeping Pareto evaluation (DkPE) mechanism is proposed, based on which new crossover and mutation operators adaptive to bi-objective GP are designed. In addition, two base bi-objective GP methods (NSGP (non-dominated sorting GP) and SPGP (strength Pareto GP)) are developed. Applying the new operators in them respectively forms two bi-objective memetic GP methods (MNSGP (memetic NSGP) and MSPGP (memetic SPGP)). Results show that MNSGP and MSPGP outperform NSGP and SPGP respectively, which reflects that DkPE based crossover/mutation increase the performance of NSGP and SPGP. Moreover, solutions evolved by MNSGP outperform reference GP and non-GP based methods", } @Article{LIANG:2020:EAAI, author = "Jiayu Liang and Yu Xue and Jianming Wang", title = "Genetic programming based feature construction methods for foreground object segmentation", journal = "Engineering Applications of Artificial Intelligence", volume = "89", pages = "103334", year = "2020", ISSN = "0952-1976", DOI = "doi:10.1016/j.engappai.2019.103334", URL = "http://www.sciencedirect.com/science/article/pii/S0952197619302799", keywords = "genetic algorithms, genetic programming, Feature construction, Foreground object segmentation, Bloat control", abstract = "Foreground object segmentation is a crucial preprocessing step for many high-level computer vision tasks, e.g. object recognition. It is still challenging to achieve accurate segmentation, especially for complex images (e.g. with high variations). Feature construction can help to improve the segmentation performance by extracting more distinctive features for foreground/background regions from the original features. However, commonly-used feature construction methods (e.g. principle component analysis) often involve certain assumptions/constraints, and the constructed features cannot be interpreted. To address these problems, genetic programming (GP) is employed in this paper, which is a well-suited feature construction technique. The aim of this work is to design new feature construction methods using GP, and analyse/compare popular GP-based feature construction methods for foreground object segmentation, especially on complex image datasets with high variations. Specifically, one new feature construction method that incorporates the subtree technique in GP is designed, which can construct multiple features simultaneously (called SubtMFC, Subtree Multiple Feature Construction). Moreover, a parsimony pressure technique is introduced to improve SubtMFC for bloat control (a common issue for GP-based methods), which forms the method, PSubtMFC (Parsimony SubtMFC). In addition, comparison of popular GP-based feature construction methods for foreground object segmentation is conducted for the first time. Results show that SubtMFC achieves better or similar performance compared with three reference methods. In addition, compared with SubtMFC that does not control bloat, PSubtMFC can significantly reduce the solution size while maintain similar performance in the segmentation accuracy. The GP-based feature construction framework is further extended for feature representation based knowledge transfer, which can handle the problem of the scare labelled training data. Moreover, after GP is thoroughly investigated on benchmark datasets with one type of foreground objects (i.e. the Weizmann horse dataset and Pascal aeroplane dataset), it is considered whether the GP methods can perform well on datasets containing multiple types of foreground objects. Compared with three other well-performing GP-based feature construction methods, the proposed method achieves better or comparable results for the given segmentation tasks. In addition, this paper thoroughly compares/analyses popular GP-based feature construction methods for complex figure-ground segmentation for the first time. Moreover, further analyses on the input features frequently used by the GP-evolved feature construction functions reflect the effectiveness of the extracted high-level features", } @Article{DBLP:journals/soco/LiangWWW20, author = "Jiayu Liang and Jixiang Wen and Zhe Wang and Jianming Wang", title = "Evolving semantic object segmentation methods automatically by genetic programming from images and image processing operators", journal = "Soft Comput.", volume = "24", number = "17", pages = "12887--12900", year = "2020", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/s00500-020-04713-1", DOI = "doi:10.1007/s00500-020-04713-1", timestamp = "Fri, 07 Aug 2020 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/soco/LiangWWW20.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{DBLP:journals/npl/LiangX21, author = "Jiayu Liang and Yu Xue", title = "Multi-Objective Memetic Algorithms with Tree-Based Genetic Programming and Local Search for Symbolic Regression", journal = "Neural Process. Lett.", volume = "53", number = "3", pages = "2197--2219", year = "2021", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/s11063-021-10497-8", DOI = "doi:10.1007/s11063-021-10497-8", timestamp = "Fri, 04 Jun 2021 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/npl/LiangX21.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{Liang:AI, author = "Jiayu Liang and Yu Xue", title = "Bloat-aware {GP}-based methods with bloat quantification", journal = "Applied Intelligence", year = "2022", volume = "52", pages = "4211--4225", keywords = "genetic algorithms, genetic programming, bloat quantification, Parsimony Pressure, Multi-objective optimization", ISSN = "0924-669X", URL = "https://rdcu.be/cw94T", DOI = "doi:10.1007/s10489-021-02245-1", size = "15 pages", abstract = "Genetic programming (GP) solves optimization problems by simulating the evolution procedure in nature. It has a serious problem termed as bloat, which can cost memory, hamper effective breeding and slow down the evolution process. However, there are only a limited number of works to quantify bloat directly, and existing techniques use the solution size/complexity as an indirect indicator for bloat control. Therefore, a new bloat quantification measure is designed in this work, based on which three bloat aware GP methods are proposed. Specifically, the bloat quantification measure is incorporated with two parsimony pressure techniques and a multiobjective technique respectively, termed as GPLTSb (GP Lexicographic Tournament Selection bloat), GPPTSb (GP Proportional Tournament Selection bloat), and MOGPb (Multi-objective GP bloat). Unlike the existing bloat control methods, the bloat-aware methods apply the bloat values directly for bloat control. The proposed methods are tested on benchmark symbolic regression tasks, and are compared with GP, existing bloat control methods and four widely used regression methods. Results show that MOGPb is effective for bloat control with the solution size reduced obviously; while GPLTSb and GPPTSb can also reduce bloat in GP with the solution size reduced slightly. In addition, compared with GP and existing bloat control methods, the proposed methods evolve solutions with similar/better regression performance. Moreover, the evolved solutions of proposed methods can outperform most reference regression methods for the given tasks consistently.", notes = "Tianjin Key Laboratory of Autonomous Intelligent Technology and System, Tiangong University, Tianjin 300387, China", } @Article{Liang:2024:GPEM, author = "Jiayu Liang and Hanqi Cao and Yaxin Lu and Mingming Su", title = "Architecture search of accurate and lightweight {CNNs} using genetic algorithm", journal = "Genetic Programming and Evolvable Machines", year = "2024", volume = "25", pages = "Article no 13", note = "Online first", keywords = "genetic algorithms, genetic programming, ANN, CNN architecture search, Evolutionary methods, Lightweight architecture, Image classification", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-024-09484-4", abstract = "Convolutional neural networks (CNNs) are popularly-used in various AI fields, yet the design of CNN architectures heavily depends on domain expertise. Evolutionary neural architecture search (ENAS) methods can search for neural architectures automatically using evolutionary computation algorithms, e.g. genetic algorithm. However, most existing ENAS methods solely focus on the network accuracy, which leads to large-sized networks to be evolved and huge cost in computation resources and search time. Even though there are ENAS works using multi-objective techniques to optimise time/resource-consuming. two new ENAS methods are designed, which aim to evolve both accurate and lightweight CNN architectures efficiently using genetic algorithm (GA). They are termed as GACNN WS (GA CNN Weighted Sum) and GACNN_LE (GA CNN Local Elitism) respectively. Specifically, GACNN_WS designs a weighted-sum fitness of two ...", notes = "Is this GP? Tianjin Key Laboratory of Autonomous Intelligent Technology and System, Tiangong University, Tianjin, 300387, China", } @Article{Liang:2024:GPEMa, author = "Jiayu Liang and Yaxin Lu and Mingming Su", title = "{LSTM} architecture and hyperparameter search by hybrid {GA} for air pollution prediction", journal = "Genetic Programming and Evolvable Machines", year = "2024", volume = "25", number = "2", pages = "Article no 20", month = dec, keywords = "genetic algorithms, Hga-lstm, Air pollution prediction, ANN, LSTM model search, GA-optimized LSTM, Hybrid GA", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-024-09493-3", abstract = "Air pollution prediction is a process of predicting the levels of air pollutants in a specific area over a given period. Since LSTM (Long Short-Term Memory) networks are particularly effective in capturing long-term dependencies and patterns in sequential data, they are widely-used for air pollution prediction. However, designing appropriate LSTM architectures and hyperparameters for given tasks can be challenging, which are normally determined by users in existing LSTM-based methods. Note that Genetic Algorithm (GA) is an effective optimization technique, and local search in augmenting the global search ability of GA has been proved, which is rarely considered by existing GA-optimzied LSTM methods. In this work, simultaneous LSTM architecture and hyperparameter search based on GA and local search techniques is investigated for air pollution prediction. Specifically, a new LSTM model search method is designed, termed as HGA-LSTM. HGA is a hybrid GA, which is proposed by integrating GA with local search adaptively. Based on HGA, HGA-LSTM is developed to search for LSTM models with simultaneous LSTM architecture and hyperparameter optimization. In HGA-LSTM, a new crossover is designed to be adaptive to the variable-length representation of LSTM models. The proposed HGA-LSTM is compared with widely-used LSTM-based and nonLSTM-based prediction methods on UCI (University of California Irvine) datasets for air pollution prediction. Results show that HGA-LSTM is generally better than both types of reference methods with its evolved LSTM models achieving lower mean square/absolute errors. Moreover, compared with a baseline method (a GA without local search), HGA-LSTM converges to lower error values, which reflects that HGA has better search ability than GA.", notes = "Is this GP? Tianjin Key Laboratory of Autonomous Intelligent Technology and System, Tiangong University, Tianjin, 300387, China", } @Article{Jing-Liang:2023:bbad, author = "Jing Liang and Zong-Wei Li and Ze-Ning Sun and Ying Bi and Han Cheng and Tao Zeng2 and Wei-Feng Guo", title = "Latent space search based multimodal optimization with personalized edge-network biomarker for multi-purpose early disease prediction", journal = "Briefings in Bioinformatics", year = "2023", volume = "24", number = "6", pages = "bbad364", month = oct, keywords = "genetic algorithms, genetic programming, MMPDENB-RBM, multimodal optimization, latent space research, personalised dynamic edge-network biomarkers, multi-purpose disease prediction, drug targets", ISSN = "1477-4054", eprint = "https://academic.oup.com/bib/article-pdf/24/6/bbad364/52107912/bbad364.pdf", URL = "https://doi.org/10.1093/bib/bbad364", DOI = "doi:10.1093/bib/bbad364", size = "17 pages", abstract = "Considering that cancer is resulting from the comutation of several essential genes of individual patients, researchers have begun to focus on identifying personalized edge-network biomarkers (PEBs) using personalized edge-network analysis for clinical practice. However, most of existing methods ignored the optimization of PEBs when multimodal biomarkers exist in multi-purpose early disease prediction (MPEDP). To solve this problem, this study proposes a novel model (MMPDENB-RBM) that combines personalized dynamic edge-network biomarkers (PDENB) theory, multimodal optimization strategy and latent space search scheme to identify biomarkers with different configurations of PDENB modules (i.e. to effectively identify multimodal PDENBs). The application to the three largest cancer omics datasets from The Cancer Genome Atlas database (i.e. breast invasive carcinoma, lung squamous cell carcinoma and lung adenocarcinoma) showed that the MMPDENB-RBM model could more effectively predict critical cancer state compared with other advanced methods. And, our model had better convergence, diversity and multimodal property as well as effective optimization ability compared with the other state-of-art methods. Particularly, multimodal PDENBs identified were more enriched with different functional biomarkers simultaneously, such as tissue-specific synthetic lethality edge-biomarkers including cancer driver genes and disease marker genes. Importantly, as our aim, these multimodal biomarkers can perform diverse biological and biomedical significances for drug target screen, survival risk assessment and novel biomedical sight as the expected multi-purpose of personalized early disease prediction. In summary, the present study provides multimodal property of PDENBs, especially the therapeutic biomarkers with more biological significances, which can help with MPEDP of individual cancer patients.", notes = " State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 7510060, China.", } @InProceedings{liang:2024:CEC, author = "Jing Liang and Zexuan Yang and Tuo Zhang and Ying Bi", title = "A Two-Stage Approach Using Genetic Algorithm and Genetic Programming for Remote Sensing Crop Classification", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Support vector machines, Precision agriculture, Crops, Training data, Evolutionary computation, Feature extraction, Genetic Algorithm, Feature Construction, Feature Selection, Crop Classification", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10612210", abstract = "Crop classification is an important task in remote sensing image analysis. To effectively classify crops, it is necessary to extract or obtain a set of effective features from raw pixels. However, existing methods have several limitations, including poor interpretability of the learnt models and the requirements of sufficient training data and domain expertise. To address this, this paper develops a two-stage approach using Genetic Algorithm (GA) and Genetic Programming (GP) to automatically learn a feature set that can effectively classify crops using remote sensing images. In the first stage of the new approach, a GP method is applied to automatically construct a set of high-level features by evolving tree-based solutions. In the second stage, a GA method is employed to select a small subset of features from the constructed features and the original features by removing redundant ones. The performance of the new approach is evaluated on three datasets in two scenarios, i.e., classifying four main crop types and all crop types, respectively. The results demonstrate that the new approach achieves more accurate crop classification compared with eight competitive methods.", notes = "also known as \cite{10612210} WCCI 2024", } @Article{Liang:2024:GNC, author = "Jing Liang and Yaxin Chang and Ying Bi and Caitong Yue and Boyang Qu and Mengnan Liu", title = "A New Road Extraction Method from Satellite Images Using Genetic Programming", journal = "Guidance, Navigation and Control", year = "2024", volume = "4", number = "2", pages = "2450009", month = "31 " # may, keywords = "genetic algorithms, genetic programming, ANN, Road extraction, feature construction", organisation = "CSAA, Technical Committee on Guidance, Navigation and Control, Chinese Society of Aeronautics and Astronautics", publisher = "World Scientific Publishing Co", ISSN = "2737-4807", URL = "https://www.worldscientific.com/doi/pdf/10.1142/S2737480724500092", DOI = "doi:10.1142/S2737480724500092", size = "31 pages", abstract = "Extracting roads from satellite images is an important task in the field of computer vision with a wide range of applications. However, efficient road extraction from satellite images remains a complex challenge due to issues such as data labeling and the diversity of road features. Existing methods often struggle to balance accuracy, robustness, and interpretability. Genetic programming (GP) is based on a flexible and interpretable structure that is robust and does not require a large amount of data support. We position the road extraction problem as a binary semantic segmentation task and introduce GP algorithms. First, an approach for extracting pixel neighborhood features is proposed, and features from multiple images in the DeepGlobe road extraction dataset are extracted. Then, an advanced feature construction method based on GP is employed. Finally, these advanced features are used for training classifier and classification to achieve road extraction. We have validated the effectiveness of the approach on the Deep-Globe road extraction dataset. The results demonstrated that the proposed approach exhibits superior performance compared to traditional classification methods and multilayer perceptron (MLP) in terms of accuracy, generalization, and interpretability. This study provides a valuable reference for the integration of GP into the domain of road extraction from satellite images, showcasing their potential to enhance the accuracy and efficiency.", notes = "School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China", } @Article{Jing-Liang:TGRS, author = "Jing Liang and Zexuan Yang and Ying Bi and Boyang Qu and Mengnan Liu and Bing Xue and Mengjie Zhang", title = "A Multi-Tree Genetic Programming-based Feature Construction Approach to Crop Classification Using Hyperspectral Images", journal = "IEEE Transactions on Geoscience and Remote Sensing", year = "2024", volume = "62", pages = "1--17", keywords = "genetic algorithms, genetic programming, Crops, Feature extraction, Hyperspectral imaging, Statistics, Sociology, Deep learning, Accuracy, Crop Classification, Feature Construction", ISSN = "0196-2892", DOI = "doi:10.1109/TGRS.2024.3415773", size = "17 pages", notes = "also known as \cite{10570344} School of Electrical and Information En-gineering, Zhengzhou University, Zhengzhou, China", } @Article{Jing-Liang:ieeeTEC, author = "Jing Liang and Zhuo Hu and Ying Bi and Han Cheng and Kunjie Yu and Cai-Tong Yue and Xianfang Wang and Wei-Feng Guo", title = "A Survey on Evolutionary Computation for Identifying Biomarkers of Complex Disease", journal = "IEEE Transactions on Evolutionary Computation", note = "Accepted for future publication", keywords = "genetic algorithms, genetic programming, Diseases, Biomarkers, Drugs, Biology, Genomics, Bioinformatics, Databases, Evolutionary Computation, Disease Biomarkers, Omics Data, Precision Medicine", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2024.3414442", size = "22 pages", notes = "also known as \cite{10558779} ", } @Article{Jing-Liang:ieeeTEC2, author = "Jing Liang and Junting Yang and Caitong Yue and Ying Bi and Kunjie Yu and Boyang Qu and Yuyang Zhang and Mengmeng Li", title = "A Joint-Encoding Evolutionary Algorithm for Multimodal Multiobjective Feature Selection in Classification", journal = "IEEE Transactions on Evolutionary Computation", note = "Early Access", keywords = "genetic algorithms, Encoding, Feature extraction, Optimization, Search problems, Costs, Clustering algorithms, Classification algorithms, Accuracy, Shape, Feature selection, multimodal multiobjective optimization, niche, classification", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2025.3529977", abstract = "In multiobjective feature selection, different feature subsets with the same number of selected features can achieve identical classification accuracy, meaning that it is a multimodal optimization problem. To effectively search for multimodal feature subsets within the vast search spaces of high-dimensional datasets, it is crucial to adopt reasonable encoding and search methods. Generally, applying a uniform evolutionary operator based on a single encoding method across the entire feature space is inefficient and prone to falling into local optima. To address the above issues, this paper proposes a multimodal multiobjective feature selection method based on a joint encoding mechanism that combines discrete encoding and continuous encoding. It provides new perspectives to solve the high-dimensional feature selection problem from encoding methods to search operators. First, the search space is divided into a discrete encoding region and a continuous encoding region based on the knee points of feature importance ranking curve. A tailored initialization strategy is used to obtain the initial population for joint encoding. Second, an adaptive niche strategy based on three priorities is proposed, which ensures the similarity of individuals within a niche and the difference between niches. In addition, different search operators are cooperated with the two encoding strategies respectively to achieve effective and efficient search. The experimental results on 24 datasets show that the proposed algorithm achieves a better classification performance than the state-of-the-art feature selection methods.", notes = "also known as \cite{10843352} School of Electrical and Information Engineering Zhengzhou University Zhengzhou, China", } @InProceedings{Liang:2025:ICEAAI, author = "Jing Liang and Wenjing Li and Yahui Jia and Ying Bi", title = "A Multitask Genetic Programming Approach with {ANew} Individual Representation to Symbolic Regression", booktitle = "2025 International Conference on Electrical Automation and Artificial Intelligence (ICEAAI)", year = "2025", pages = "1301--1306", month = jan, keywords = "genetic algorithms, genetic programming, Automation, Heuristic algorithms, Benchmark testing, Multitasking, Mathematical models, Artificial intelligence, Regression tree analysis, Knowledge transfer, symbolic regression, evolutionary multitask", DOI = "doi:10.1109/ICEAAI64185.2025.10956846", abstract = "Symbolic regression is a task aimed at auto-matically discovering and constructing mathematical models from data that describe the complex relationships between inputs and outputs. Evolutionary multitask methods have demonstrated powerful capabilities across various domains, but research in the field of symbolic regression remains relatively limited. To further explore the potential of evo-lutionary multi task algorithms in symbolic regression, this paper proposes a multitask Genetic Programming (GP) approach with a new individual representation (IRMTGP), which encodes shared knowledge as a shared tree, explicitly facilitating knowledge transfer between tasks. Specifically, in IRMTGP, each task's solution is composed of a shared tree and an unshared tree, where the shared tree captures common knowledge across tasks, and the unshared tree focuses on learning the characteristics of individual tasks. Furthermore, to learn the optimal shared and unshared trees, a new fitness function and evolutionary process are designed. Comparing IRMTGP with GP and Multifactorial GP on six multi task problems composed of four symbolic regression benchmarks, IRMTGP achieves smaller regression errors and better generalisation capabilities in most cases.", notes = "Also known as \cite{10956846}", } @Article{LIANG:2023:jobe, author = "Shixue Liang and Yuanxie Shen and Xiangling Gao and Yiqing Cai and Zhengyu Fei", title = "Symbolic machine learning improved {MCFT} model for punching shear resistance of {FRP-reinforced} concrete slabs", journal = "Journal of Building Engineering", volume = "69", pages = "106257", year = "2023", ISSN = "2352-7102", DOI = "doi:10.1016/j.jobe.2023.106257", URL = "https://www.sciencedirect.com/science/article/pii/S2352710223004369", keywords = "genetic algorithms, genetic programming, FRP-Reinforced concrete slab, Punching shear resistance, Modified compression field theory, Machine learning", abstract = "Fiber reinforced polymer (FRP)-reinforced concrete slabs, an extension of reinforced concrete (RC) slabs leveraged for resisting environment corrosion, are susceptible to punching shear failure due to the lower elasticity modulus of FRP reinforcement. To estimate the punching shear resistance accurately, there are two types of models (e.g., white box and black-box models) proposed based on theoretical derivations and machine learning methods. However, these two types of models are considered as independent of each other. In this study, a hybrid model (e.g., grey-box model) derived from modified compression field theory (MCFT) is proposed by this paper, in which the performance is improved by a machine-learning-aided approach (genetic programming). In order to exploit the performance of machine learning, a database containing 154 experimental data is established and used for fitting the correction equations. Iterating the population containing 300 tree-based individuals in 300 times, a correction equation with simple format is obtained, which performs well in performance improvement of the basic model derived from MCFT. Herein, the influential factors involved in the correction equation comply with the sorting in order of the importance quantified by extreme gradient boosting (XGBoost) and shapley additive explanation (SHAP). Combining the correction equation with the basic model derived from MCFT, a symbolic regression MCFT (SR-MCFT) model is established, which performs better prediction performance than other five empirical models", } @Article{Liang:2006:Omega, author = "Wen-Yau Liang and Chun-Che Huang", title = "A hybrid approach to constrained evolutionary computing: Case of product synthesis", journal = "Omega", year = "2008", volume = "36", number = "6", pages = "1072--1085", month = dec, note = "A Special Issue Dedicated to the 2008 Beijing Olympic Games", keywords = "genetic algorithms, Evolutionary computing, Rough set, Product synthesis", DOI = "doi:10.1016/j.omega.2006.06.001", abstract = "Evolutionary computing (EC) is comprised of techniques involving evolutionary programming, evolution strategies, genetic algorithms (GA), and genetic programming. It has been widely used to solve optimisation problems for large scale and complex systems. However, when insufficient knowledge is incorporated, EC is less efficient in terms of searching for an optimal solution. In addition, the GA employed in previous literature is modelled to solve one problem exactly. The GA needs to be redesigned, at a cost, for it to be applied to another problem. Due to these two reasons, this paper develops a generic GA incorporating knowledge extracted from the rough set theory. The advantages of the proposed solution approach include: (i) solving problems that can be decomposed into functional requirements, and (ii) improving the performance of the GA by reducing the domain range of initial population and constraining crossover using the rough set theory. The solution approach is exemplified by solving the problem of product synthesis, where there is a conflict between performance and cost. Manufacturing or assembling a product of high performance and quality at a low cost is critical for a company to maximise its advantages. Based on our experimental results, this approach has shown great promise and has reduced costs when the GA is in processing.", } @InProceedings{Liang:2023:ICTAI, author = "Xinjie Liang and Wen Song and Pengfei Wei", booktitle = "2023 IEEE 35th International Conference on Tools with Artificial Intelligence (ICTAI)", title = "Dynamic Job Shop Scheduling via Deep Reinforcement Learning", year = "2023", pages = "369--376", abstract = "Recently, deep reinforcement learning (DRL) is shown to be promising in learning dispatching rules end-to-end for complex scheduling problems. However, most research is limited to deterministic problems. In this paper, we focus on the dynamic job-shop scheduling problem (DJSP), which is a complex dynamic optimisation problem under uncertainty. We propose a DRL based method to learn dispatching policies for DJSP. Unlike existing DRL based dynamic scheduling methods that use a fixed number of dispatching rules as actions, our decision-making framework directly selects legitimate jobs, which is able to break the limitations imposed by priority dispatching rules. We design two training methods, including a gradient based algorithm with dense rewards, and an evolutionary strategy with sparse rewards. Extensive experiments show that our DRL method can learn high-quality DJSP dispatching policies, and can significantly outperform a state-of-the-art Genetic Programming (GP) based dispatching rule learning method.", keywords = "genetic algorithms, genetic programming, Deep learning, Training, Learning systems, Job shop scheduling, Uncertainty, Heuristic algorithms, Reinforcement learning, Deep Reinforcement Learning, Dynamic Job Shop Scheduling Problem, Evolutionary Strategy", DOI = "doi:10.1109/ICTAI59109.2023.00060", ISSN = "2375-0197", month = nov, notes = "Also known as \cite{10356485}", } @InProceedings{Liang:2019:ISPA, author = "Yifan Liang and Chang Liu and Hanrui Wang and Kunhong Liu", booktitle = "2019 IEEE Intl Conf on Parallel Distributed Processing with Applications, Big Data Cloud Computing, Sustainable Computing Communications, Social Computing Networking (ISPA/BDCloud/SocialCom/SustainCom)", title = "The Research of Ternary Error-Correcting Output Codes Based on Genetic Programming", year = "2019", pages = "831--837", month = dec, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ISPA-BDCloud-SustainCom-SocialCom48970.2019.00123", abstract = "Error-Correcting Output Codes (ECOC) provides an effective solution for the multiclass classification problem by decomposing a multiclass problem into a set of binary class problems. In an ECOC algorithm, the design of coding matrix is the key to its performance. In this paper, we propose a Genetic Programming (GP) based ECOC algorithm, aiming to produce optimal coding matrices through the evolutionary process. In our GP, each terminal node denotes a column in the coding matrix, and each nonterminal node represents an operator, which combines the columns represented by its terminal nodes. In this way, an individual is interpreted as a coding matrix, and a set of operators are proposed to exchange information between column pairs, so as to produce new columns. Feature selection methods are also integrated into the terminal nodes, so that individuals are dynamically assigned to optimal feature subspaces for diverse classification problems. With evolutionary operators, offspring with high discriminant capability would be produced in the evolution. Our experiments compare our algorithm with other 7 classic ECOC algorithms with the deployment of diverse basic classifiers based on a set of UCI data sets, and results prove the superiority and robustness of our algorithm.", notes = "Also known as \cite{9047376}", } @InProceedings{Liang:2014:SEAL, author = "Yuyu Liang and Mengjie Zhang and Will N. Browne", title = "Image Segmentation: A Survey of Methods Based on Evolutionary Computation", booktitle = "Proceedings 10th International Conference on Simulated Evolution and Learning, SEAL 2014", year = "2014", editor = "Grant Dick and Will N. Browne and Peter Whigham and Mengjie Zhang and Lam Thu Bui and Hisao Ishibuchi and Yaochu Jin and Xiaodong Li and Yuhui Shi and Pramod Singh and Kay Chen Tan and Ke Tang", volume = "8886", series = "Lecture Notes in Computer Science", pages = "847--859", address = "Dunedin, New Zealand", month = dec # " 15-18", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Evolutionary Computer Vision, Image Segmentation, Evolutionary Computation", isbn13 = "978-3-319-13562-5", DOI = "doi:10.1007/978-3-319-13563-2_71", abstract = "Image segmentation is mainly used as a preprocessing step in problems of image processing and computer vision. Its performance has a great influence on subsequent tasks. Evolutionary Computation (EC) techniques have been introduced to the area of image segmentation due to their high search capacity. However, there are rarely comprehensive surveys on EC based image segmentation methods, which can enable researchers to get a quick understanding of this area and compare the existing methods. Therefore, this paper provides an overview of EC based image segmentation methods, and discusses the remaining issues in this area. It is observed that among all EC techniques, four of them (genetic algorithms, genetic programming, differential equation and partial swarm optimization) are more frequently used and GAs are the most popular technique. It is noted that low generalization capacity and computational complexity are two common problems in EC techniques applied to image segmentation.", } @InProceedings{Liang:2015:evoApplications, author = "Yuyu Liang and Mengjie Zhang and Will Browne", title = "A Supervised Figure-ground Segmentation Method using Genetic Programming", booktitle = "18th European Conference on the Applications of Evolutionary Computation", year = "2015", editor = "Antonio M. Mora and Giovanni Squillero", series = "LNCS", volume = "9028", publisher = "Springer", pages = "491--503", address = "Copenhagen", month = "8-10 " # apr, organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, Image segmentation, Raw pixel values, Grayscale statistics", isbn13 = "978-3-319-16548-6", DOI = "doi:10.1007/978-3-319-16549-3_40", abstract = "Figure-ground segmentation is an important preprocessing phase in many computer vision applications. As different classes of objects require specific segmentation rules, supervised (or top-down) methods, which learn from prior knowledge of objects, are suitable for figure-ground segmentation. However, existing top-down methods, such as model-based and fragment-based ones, involve a lot of human work. As genetic programming (GP) can evolve computer programs to solve problems automatically, it requires less human work. Moreover, since GP contains little human bias, it is possible for GP-evolved methods to obtain better results than human constructed approaches. This paper develops a supervised GP-based segmentation system. Three kinds of simple features, including raw pixel values, six dimension and eleven dimension grayscale statistics, are employed to evolve image segmentors. The evolved segmentors are tested on images from four databases with increasing difficulty, and results are compared with four conventional techniques including thresholding, region growing, clustering, and active contour models. The results show that GP-evolved segmentors perform better than the four traditional methods with consistently good results on both simple and complex images.", notes = "EvoIASP EvoApplications2015 held in conjunction with EuroGP'2015, EvoCOP2015 and EvoMusArt2015 http://www.evostar.org/2015/cfp_evoapps.php", } @InProceedings{conf/acal/LiangZB16, author = "Yuyu Liang and Mengjie Zhang and Will N. Browne", title = "Multi-objective Genetic Programming for Figure-Ground Image Segmentation", bibdate = "2016-01-25", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/acal/acalci2016.html#LiangZB16", booktitle = "Artificial Life and Computational Intelligence - Second Australasian Conference, {ACALCI} 2016, Canberra, {ACT}, Australia, February 2-5, 2016, Proceedings", publisher = "Springer", year = "2016", volume = "9592", editor = "Tapabrata Ray and Ruhul A. Sarker and Xiaodong Li", isbn13 = "978-3-319-28269-5", pages = "134--146", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming", URL = "http://dx.doi.org/10.1007/978-3-319-28270-1", DOI = "doi:10.1007/978-3-319-28270-1_12", } @InProceedings{Liang:2016:CEC, author = "Yuyu Liang and Mengjie Zhang and Will N. Browne", title = "Figure-ground Image Segmentation using Genetic Programming and Feature Selection", booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016)", year = "2016", editor = "Yew-Soon Ong", pages = "3839--3846", address = "Vancouver", month = "24-29 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-5090-0623-6", DOI = "doi:10.1109/CEC.2016.7744276", abstract = "Figure-ground segmentation is an essential but difficult preprocessing step for many computer vision and image preprocessing tasks, such as object recognition. One challenge is to separate objects from backgrounds on images with high variations (e.g. in object shapes), which requires both effective feature sets and powerful segmentors. This paper develops a GP based segmentation method, which transforms segmentation tasks into pixel classification based problems. To control the complexity of evolved solutions, parsimony pressure is introduced in GP. Tested on two datasets with high variations (the Weizmann and Pascal datasets), the proposed method achieves similar performance in F1 score with much simpler solutions, compared with a reference GP based method that does not consider solution complexity. Moreover, it is the first time that the occurrence rates of the features used by the evolved solutions are studied to conduct feature selection for figure-ground segmentation. Compared with the whole feature set using traditional classifier based segmentation methods, the selected feature subsets can improve the segmentation performance. Moreover, analyses on the evolved solutions reveal how they function and why specific features are selected.", notes = "WCCI2016", } @InProceedings{conf/acalci/LiangZB17, author = "Yuyu Liang and Mengjie Zhang and Will N. Browne", title = "Wrapper Feature Construction for Figure-Ground Image Segmentation Using Genetic Programming", bibdate = "2017-05-26", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/acalci/acalci2017.html#LiangZB17", booktitle = "Artificial Life and Computational Intelligence - Third Australasian Conference, {ACALCI} 2017, Geelong, {VIC}, Australia, January 31 - February 2, 2017, Proceedings", year = "2017", volume = "10142", editor = "Markus Wagner and Xiaodong Li and Tim Hendtlass", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-51690-5", pages = "111--123", series = "Lecture Notes in Computer Science", DOI = "doi:10.1007/978-3-319-51691-2_10", } @Article{Liang:2017:ASC, author = "Yuyu Liang and Mengjie Zhang and Will N. Browne", title = "Genetic programming for evolving figure-ground segmentors from multiple features", journal = "Applied Soft Computing", volume = "51", pages = "83--95", year = "2017", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2016.07.055", URL = "http://www.sciencedirect.com/science/article/pii/S156849461630391X", abstract = "Figure-ground segmentation is a crucial preprocessing step for many image processing and computer vision tasks. Since different object classes need specific segmentation rules, the top-down approach, which learns from the object information, is more suitable to solve segmentation problems than the bottom-up approach. A problem faced by most existing top-down methods is that they require much human work/intervention, meanwhile introducing human bias. As genetic programming (GP) does not require users to specify the structure of solutions, we apply it to evolve segmentors that can conduct the figure-ground segmentation automatically and accurately. This paper aims to determine what kind of image information is necessary for GP to evolve capable segmentors (especially for images with high variations, e.g. varied object shapes or cluttered backgrounds). Therefore, seven different terminal sets are exploited to evolve segmentors, and images from four datasets (bitmap, Brodatz texture, Weizmann and Pascal databases), which are increasingly difficult for segmentation tasks, are selected for testing. Results show that the proposed GP based method can be successfully applied to diverse types of images. In addition, intensity based features are not sufficient for complex images, whereas features containing spectral and statistical information are necessary. Compared with four widely-used segmentation techniques, our method obtains consistently better segmentation performance.", keywords = "genetic algorithms, genetic programming, Figure-ground segmentation, Intensity based features, Gabor features", } @Article{Liang:2017:EAAI, author = "Yuyu Liang and Mengjie Zhang and Will N. Browne", title = "Image feature selection using genetic programming for figure-ground segmentation", journal = "Engineering Applications of Artificial Intelligence", year = "2017", volume = "62", pages = "96--108", month = jun, keywords = "genetic algorithms, genetic programming, Figure-ground segmentation, Feature selection, Multi-objective methods", ISSN = "0952-1976", URL = "http://www.sciencedirect.com/science/article/pii/S0952197617300544", DOI = "doi:10.1016/j.engappai.2017.03.009", abstract = "Figure-ground segmentation is the process of separating regions of interest from unimportant background. One challenge is to segment images with high variations (e.g. containing a cluttered background), which requires effective feature sets to capture the distinguishing information between objects and backgrounds. Feature selection is necessary to remove noisy/redundant features from those extracted by image descriptors. As a powerful search algorithm, genetic programming (GP) is employed for the first time to build feature selection methods that aims to improve the segmentation performance of standard classification techniques. Both single-objective and multi-objective GP techniques are investigated, based on which three novel feature selection methods are proposed. Specifically, one method is single-objective, called PGP-FS (parsimony GP feature selection); while the other two are multi-objective, named nondominated sorting GP feature selection (NSGP-FS) and strength Pareto GP feature selection (SPGP-FS). The feature subsets produced by the three proposed methods, two standard sequential selection algorithms, and the original feature set are tested via standard classification algorithms on two datasets with high variations (the Weizmann and Pascal datasets). The results show that the two multi-objective methods (NSGP-FS and SPGP-FS) can produce feature subsets that lead to solutions achieving better segmentation performance with lower numbers of features than the sequential algorithms and the original feature set based on standard classifiers for given segmentation tasks. In contrast, PGP-FS produces results that are not consistent for different classifiers. This indicates that the proposed multi-objective methods can help standard classifiers improve the segmentation performance while reducing the processing time. Moreover, compared with SPGP-FS, NSGP-FS is equally capable of producing effective feature subsets, yet is better at keeping diverse solutions.", notes = "Also known as \cite{LIANG201796}", } @InProceedings{Liang:2017:GECCO, author = "Yuyu Liang and Mengjie Zhang and Will N. Browne", title = "Learning Figure-ground Image Segmentors by Genetic Programming", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4939-0", address = "Berlin, Germany", pages = "239--240", size = "2 pages", URL = "http://doi.acm.org/10.1145/3067695.3075989", DOI = "doi:10.1145/3067695.3075989", acmid = "3075989", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, figure-ground segmentation, postprocessing, preprocessing", month = "15-19 " # jul, abstract = "Figure-ground segmentation is an important image processing task that genetic programming (GP) has been successfully introduced to solve. However, existing GP methods use a homogeneous mixture of preprocessing and post processing operators for segmentation. This can result in inappropriate operators being connected, leading to poor performance and unnecessary operations in solutions. To address this issue, two new methods are designed to enable GP to conduct image preprocessing, binarisation and postprocessing separately. Specifically, the two methods introduce a strongly-typed representation (StronglyGP) and a two-stage evolution (TwostageGP) in GP respectively Results show that StronglyGP can evolve effective segmentors for the given complex segmentation tasks. However, TwostageGP currently performs poorly, which is likely caused by over fitting, which will be addressed in future work.", notes = "Also known as \cite{Liang:2017:LFI:3067695.3075989} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @InProceedings{liang:2017:IES, author = "Yuyu Liang and Mengjie Zhang and Will N. Browne", title = "Feature Construction Using Genetic Programming for Figure-Ground Image Segmentation", booktitle = "Intelligent and Evolutionary Systems", year = "2017", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-319-49049-6_17", DOI = "doi:10.1007/978-3-319-49049-6_17", } @PhdThesis{Liang:thesis, author = "Yuyu Liang", title = "Genetic Programming for Supervised Figure-ground Image Segmentation", school = "Computer Science, Victoria University of Wellington", year = "2018", address = "New Zealand", keywords = "genetic algorithms, genetic programming", URL = "http://hdl.handle.net/10063/6923", URL = "http://researcharchive.vuw.ac.nz/bitstream/handle/10063/6923/thesis_access.pdf", size = "266 pages", abstract = "Figure-ground segmentation is a process of separating regions of interest from unimportant backgrounds. It is essential to various applications in computer vision and image processing, e.g. object tracking and image editing, as they are only interested in certain regions of an image and use figure-ground segmentation as a pre-processing step. Traditional figure-ground segmentation methods often require heavy human workload (e.g. ground truth labelling), and/or rely heavily on human guidance (e.g. locating an initial model), accordingly cannot easily adapt to diverse image domains. Evolutionary computation (EC) is a family of algorithms for global optimisation, which are inspired by biological evolution. As an EC technique, genetic programming (GP) can evolve algorithms automatically for complex problems without predefining solution models. Compared with other EC techniques, GP is more flexible as it can use complex and variable length representations (e.g. trees) of candidate solutions. It is hypothesised that this flexibility of GP makes it possible to evolve better solutions than those designed by experts. However, there have been limited attempts at applying GP to figure ground segmentation. In this thesis, GP is enabled to successfully address figure-ground segmentation through evolving well performing segmentors and generating effective features. The objectives are to investigate various image features as inputs of GP, develop multiobjective approaches, develop feature selection/construction methods, and conduct further evaluations of the proposed GP methods. The following new methods have been developed. Effective terminal sets of GP are investigated for figureground segmentation, covering three general types of image features, i.e. colour/brightness, texture and shape features. Results show that texture features are more effective than intensities and shape features as they are discriminative for different materials that foreground and background regions normally belong to (e.g. metal or wood). Two new multi-objective GP methods are proposed to evolve figure-ground segmentors, aiming at producing solutions balanced between the segmentation performance and solution complexity. Compared with a reference method that does not consider complexity and a parsimony pressure based method (a popular bloat control technique), the proposed methods can significantly reduce the solution size while achieving similar segmentation performance based on the Mann-Whitney U-Test at the significance level 5percent. GP is introduced for the first time to conduct feature selection for figure-ground segmentation tasks, aiming to maximise the segmentation performance and minimise the number of selected features. The proposed methods produce feature subsets that lead to solutions achieving better segmentation performance with lower features than those of two benchmark methods (i.e. sequential forward selection and sequential backward selection) and the original full feature set. This is due to GP's high search ability and higher likelihood of finding the global optima. GP is introduced for the first time to construct high level features from primitive image features, which aims to improve the image segmentation performance, especially on complex images. By considering linear/nonlinear interactions of the original features, the proposed methods construct fewer features that achieve better segmentation performance than the original full feature set. This investigation has shown that GP is suited for figure-ground image segmentation for the following reasons. Firstly, the proposed methods can evolve segmenters with useful class characteristic patterns to segment various types of objects. Secondly, the segmentors evolved from one type of foreground object can generalise well on similar objects. Thirdly, both the selected and constructed features of the proposed GP methods are more effective than original features, with the selected/constructed features being better for subsequent tasks. Finally, compared with other segmentation techniques, the major strengths of GP are that it does not require pre-defined problem models, and can be easily adapted to diverse image domains without major parameter tuning or human intervention.", } @Article{DBLP:journals/nca/LiangZB19, author = "Yuyu Liang and Mengjie Zhang and Will N. Browne", title = "Figure-ground image segmentation using feature-based multi-objective genetic programming techniques", journal = "Neural Comput. Appl.", volume = "31", number = "7", pages = "3075--3094", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/s00521-017-3253-8", DOI = "doi:10.1007/s00521-017-3253-8", timestamp = "Thu, 10 Sep 2020 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/nca/LiangZB19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{Liang:2014:NaBIC, author = "Zhifeng Liang and Bo Yang and Lin Wang and Xiaoqiang Zhang and Nana He and Ajith Abraham", title = "Extracting Three-Dimensional Cellular Automaton for Cement Microstructure Development using Gene Expression Programming", booktitle = "Sixth World Congress on Nature and Biologically Inspired Computing", year = "2014", editor = "Ana Maria Madureira and Ajith Abraham and Emilio Corchado and Leonilde Varela and Azah Kamilah Muda and Choo yun Huoy", pages = "41--46", address = "Porto, Portugal", month = "30 " # jul # " - 1 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Gene Expression Programming: Poster", isbn13 = "978-1-4799-5937-2/14", DOI = "doi:10.1109/NaBIC.2014.6921851", abstract = "A three-dimensional CA model for the simulation of Portland cement microstructure development has been developed in this paper. The Gene Expression Programming (GEP) algorithm is employed as the learning algorithm to evolve the transition rule reversely from the microstructure development characteristic data due to hydration reactions. The characteristic data is extracted from 8-bit gray images that based on the processing of real cement acquired by Micro Computed Tomography (micro-CT) technology. Starting with initial micro-CT image, cement microstructure evolution images of 28 days is constructed through CA rule discovered by GEP. The experimental results show that this model with the CA rule designed by GEP has higher agreement between the model predictions and experimental measurements for degree of hydration than other models. Furthermore, this model still has good generalisation ability when changing the water-cement ratio and chemical composition.", notes = "NaBIC 2014 http://www.mirlabs.net/nabic14/", } @PhdThesis{Liao:thesis, author = "Benjamin Penyang Liao", title = "Goal-Directed Portfolio Insurance Strategies", school = "Department of Information Management, National Central University, NSYSU", year = "2006", address = "Taiwan", month = jun, keywords = "genetic algorithms, genetic programming, forest genetic programming, GDPI, implicit piecewise linear GDPI strategy, piecewise nonlinear GDPI strategy, piecewise linear GDPI strategy, goal-directed strategy, Portfolio insurance strategy", URL = "http://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=87443004", URL = "http://thesis.lib.ncu.edu.tw/ETD-db/ETD-search-c/getfile?URN=87443004&filename=87443004.pdf", size = "120 pages", abstract = "Traditional portfolio insurance (PI) strategy such as constant proportion portfolio insurance (CPPI) only considers the floor constraint but not the goal aspect. There seems to be two contradictory risk-attitudes according to different studies: low wealth risk aversion and high wealth risk aversion. Although low wealth risk aversion can be explained by the CPPI strategy, high wealth risk aversion can not be explained by CPPI. We argue that these contradictions can be explained from two perspectives: the portfolio insurance perspective and the goal-directed perspective. This study proposes a goal-directed (GD) strategy to express an investor's goal-directed trading behaviour and combines this floor-less GD strategy with the goal-less CPPI strategy to form a piecewise linear goal-directed CPPI (GDCPPI) strategy. The piecewise linear GDCPPI strategy shows that there is a wealth position M at the intersection of the GD strategy and CPPI strategy. This M position guides investors to apply CPPI strategy or GD strategy depending on whether the current wealth is less than or greater than M respectively. In addition, we extend the piecewise linear GDCPPI strategy to a piecewise nonlinear GDCPPI strategy. Moreover, we extend the piecewise GDCPPI strategy to the piecewise GDTIPP strategy by applying the time invariant portfolio protection (TIPP) idea, which allows variable floor and goal comparing to the constant floor and goal for piecewise GDCPPI strategy. Therefore, piecewise GDCPPI strategy and piecewise GDTIPP strategy are two special cases of piecewise goal-directed portfolio insurance (GDPI) strategies. When building the piecewise nonlinear GDPI strategies, it is difficult to preassign an explicit $M$ value when the structures of nonlinear PI strategies and nonlinear GD strategies are uncertain. To solve this problem, we then apply the minimum function to build the piecewise nonlinear GDPI strategies, which these strategies still apply the $M$ concept but operate it in an implicit way. Also, the piecewise linear GDPI strategies can attain the same effect by applying the minimum function to form implicit piecewise linear GDPI strategies. This study performs some experiments to justify our propositions for piecewise GDPI strategies: there are nonlinear GDPI strategies that can outperform the linear GDPI strategies and there are some data-driven techniques that can find better linear GDPI strategies than the solutions found by Brownian technique. The GA and forest genetic programming (GP) are two data-drive techniques applied in this study. This study applies genetic algorithm (GA) technique to find better piecewise linear GDPI strategy parameters than those under Brownian motion assumption. This study adapts traditional GP to a forest GP in order to generate piecewise nonlinear GDPI strategies. The statistical tests show that the GP strategy outperforms the GA strategy which in turn outperforms the Brownian strategy. These statistical tests therefore justify our propositions.", } @InProceedings{Liao:2013:PHM, author = "Linxia Liao and Radu Pavel", booktitle = "IEEE Conference on Prognostics and Health Management (PHM 2013)", title = "Machinery time to failure prediction - Case study and lesson learned for a spindle bearing application", year = "2013", month = "24-27 " # jun, keywords = "genetic algorithms, genetic programming, spindle bearing, time to failure, predictive analytics", DOI = "doi:10.1109/ICPHM.2013.6621416", abstract = "One of the important roles of prognostics health management (PHM) is to predict the time to failure of a system in order to avoid unexpected downtime and optimise maintenance activities. Although many attempts to predict time to failure have been reported in the literature, there are still challenges related to data availability and methodology. In addition, there is significant variation from case to case due to complexity of system usage and failure modes. This paper reveals various aspects related to such challenges experienced while applying a novel predictive technology to a spindle test-bed. The goal was to evaluate the ability of the technology to predict the remaining useful life of a bearing with seeded faults. Testing has been conducted to reveal the effectiveness of signal processing, health modelling and prediction techniques. While conducting the evaluation tests, besides some well-known bearing failure modes, an unusual case was recorded. This atypical bearing failure mode created a new challenge for the predictive technology being investigated, which prompted the development of an advanced feature discovering methodology using genetic programming. This new methodology and the technology evaluation results obtained for both the well-known and the atypical failure modes will be discussed in the paper. In addition, the paper will describe the test-bed and instrumentation approach, the data acquisition system and the experimental design for testing and validation of the technology.", notes = "Also known as \cite{6621416}", } @Article{Liao:2014:ieeeIE, author = "Linxia Liao", journal = "IEEE Transactions on Industrial Electronics", title = "Discovering Prognostic Features Using Genetic Programming in Remaining Useful Life Prediction", year = "2014", month = may, volume = "61", number = "5", pages = "2464--2472", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/TIE.2013.2270212", ISSN = "0278-0046", abstract = "In prognostics approaches, features (e.g., vibration level, root mean square or outputs from signal processing techniques) extracted from the measurement (e.g., vibration, current, and pressure, etc.) are often used or modelled as an indicator to the equipment's health condition. When faults are detected or when increasing/decreasing trends are shown in the health indicator, prediction algorithms are applied to extrapolate the future behaviour and predict remaining useful life (RUL). However, it is difficult to make an accurate prediction if the trend of the health indicator is not obvious through the entire life cycle or if the trend is only shown right before a failure occurs. The challenge lies in whether an advanced feature (e.g., a mathematical combination of a group of the extracted features) can be found to clearly present/correlate with the fault progression. A genetic programming method is proposed to address the challenge of automatically discovering advanced feature(s), which can well capture the fault progression, from the measurement or extracted features in the purpose of RUL prediction.", notes = "Also known as \cite{6544227}", } @InProceedings{Liao:2021:CEC, author = "Lushen Liao and Adam Kotaro Pindur and Hitoshi Iba", booktitle = "2021 IEEE Congress on Evolutionary Computation (CEC)", title = "Genetic Programming with Random Binary Decomposition for Multi-Class Classification Problems", year = "2021", editor = "Yew-Soon Ong", pages = "564--571", address = "Krakow, Poland", month = "28 " # jun # "-1 " # jul, keywords = "genetic algorithms, genetic programming, Evolutionary computation, Benchmark testing, Feature extraction, Task analysis, multiclass classification, binary decomposition, feature extraction, feature synthesis", isbn13 = "978-1-7281-8393-0", DOI = "doi:10.1109/CEC45853.2021.9504967", abstract = "This paper introduces a new Genetic Programming (GP) based classification framework for multiclass classification problems. The proposed framework uses a binary decomposition-based GP method to extract new features to enhance the performance of classifiers in the multiclass classification task. We firstly introduce a random binary decomposition method that uses a part-vs-part strategy to decompose the multiclass problems which increase the number of binary problems that can be decomposed from a multiclass problem. Then the details of combining GP with this binary decomposition method for feature extraction are explained. Finally, we compare our method to several popular ML methods and traditional GP methods in a broad set of benchmark problems. The outcome shows the performance of classifiers is enhanced for multi-class classification tasks when combined with this technique. The effect of applying this framework to different classifiers and large real-world data set is also explored. The results suggest the effectiveness and universality of our method.", notes = "Also known as \cite{9504967}", } @InProceedings{Liao:2022:ICONIP, author = "Xiao-Cheng Liao and Wen-Jin Qiu and Feng-Feng Wei and Wei-Neng Chen", title = "Combining Traffic Assignment and Traffic Signal Control for Online Traffic Flow Optimization", booktitle = "29th International Conference on Neural Information Processing, ICONIP 2022", year = "2022", editor = "Mohammad Tanveer and Sonali Agarwal and Seiichi Ozawa and Asif Ekbal and Adam Jatowt", volume = "1793", series = "CCIS", pages = "150--163", address = "Virtual Event", month = nov # " 22-26", publisher = "Springer Nature", keywords = "genetic algorithms, genetic programming", isbn13 = "978-981-99-1645-0", DOI = "doi:10.1007/978-981-99-1645-0_13", abstract = "With the continuous development of urbanization, traffic congestion has become a key problem that plagues many large cities around the world. As new information technologies like the Internet of Things and the mobile Internet develop, the interconnection between vehicles and road facilities provides a new mechanism to improve transportation efficiency. In this paper, we adopt the mechanism of vehicle-road coordination, and propose a new dynamic traffic flow optimization approach that combines the traffic assignment method and traffic signal control method together. For traffic assignment, a gene expression programming (GEP) based online navigation algorithm is proposed to generate a generalized navigation rule for the vehicles on the road network. Each vehicle can dynamically select an appropriate route for itself through the navigation rule based on its own states and information about the nearby road network. For traffic signal control, the Maximum Throughput Control (MTC) method is adopted. MTC checks the states of the intersections periodically and greedily takes the action that maximum the throughput of the intersections. By combining these two methods, the vehicle-road coordination mechanism can significantly improve the efficiency of city traffic flow optimization. The experimental results yielded based on the CityFlow simulator verify the effectiveness of the proposed approach.", notes = "South China University of Technology, Guangzhou, China", } @Misc{DBLP:journals/corr/abs-2403-17328, author = "Xiao-Cheng Liao and Yi Mei and Mengjie Zhang", title = "Learning Traffic Signal Control via Genetic Programming", howpublished = "arXiv", volume = "abs/2403.17328", year = "2024", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.48550/arXiv.2403.17328", DOI = "doi:10.48550/ARXIV.2403.17328", eprinttype = "arXiv", eprint = "2403.17328", timestamp = "Fri, 12 Apr 2024 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/corr/abs-2403-17328.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{Liao:2024:ETCI, author = "Xiao-Cheng Liao and Wei-Neng Chen and Ya-Hui Jia and Wen-Jin Qiu", journal = "IEEE Transactions on Emerging Topics in Computational Intelligence", title = "Towards Scalable Dynamic Traffic Assignment With Streaming Agents: A Decentralized Control Approach Using Genetic Programming", year = "2024", volume = "8", number = "1", pages = "942--955", abstract = "Traffic assignment is of great importance in real life from foot traffic assignment of a building to vehicle traffic assignment of a city. With the rapid increase of the number of agents and the size of the traffic network, the problem becomes more and more challenging nowadays. To solve large-scale efficient dynamic traffic assignment, this article proposes a decentralized control approach to achieving this. First, we transform the traffic assignment problem into a routing rule generation problem by designing a corresponding simulation-based optimisation framework that can evaluate the performance of routing rules. Then, we propose a genetic programming hyper-heuristic algorithm to generate the optimal routing rule. During execution, agents can collect environmental information and plan their paths when moving according to the generated rule. In this way, the proposed method can handle the dynamically changing traffic flow efficiently instead of pre-planing the whole path for each agent. The proposed method is verified on both synthetic networks and real-world networks in terms of sensitivity, generality, and scalability. The experimental results demonstrate that our method is effective in urban-scale traffic networks and outperforms the compared algorithms.", keywords = "genetic algorithms, genetic programming, Biological cells, Heuristic algorithms, Transportation, Tail, Symbols, Navigation, Adaptation models, routing, traffic assignment", DOI = "doi:10.1109/TETCI.2023.3296671", ISSN = "2471-285X", month = feb, notes = "Also known as \cite{10197148}", } @InProceedings{Liao:2024:GECCO, author = "Xiao-Cheng Liao and Yi Mei and Mengjie Zhang", title = "Learning Traffic Signal Control via Genetic Programming", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference", year = "2024", editor = "Ting Hu and Aniko Ekart and Julia Handl and Xiaodong Li and Markus Wagner and Mario Garza-Fabre and Kate Smith-Miles and Richard Allmendinger and Ying Bi and Grant Dick and Amir H Gandomi and Marcella Scoczynski Ribeiro Martins and Hirad Assimi and Nadarajen Veerapen and Yuan Sun and Mario Andres Munyoz and Ahmed Kheiri and Nguyen Su and Dhananjay Thiruvady and Andy Song and Frank Neumann and Carla Silva", pages = "924--932", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", note = "Best Paper Award", keywords = "genetic algorithms, genetic programming, Planning and scheduling, Traffic signal control, traffic light control, signalized intersection, deep reinforcement learning, transportation", isbn13 = "979-8-4007-0494-9", URL = "https://human-competitive.org/sites/default/files/entryform_3.txt", URL = "https://human-competitive.org/sites/default/files/paper_2.pdf", DOI = "doi:10.1145/3638529.3654037", slides_url = "https://human-competitive.org/sites/default/files/meiyi_acm-pr-form.forhumies2024.pdf", video_url = "https://mediaspace.msu.edu/media/Yi+Mei+Humies+presentation/1_z0alwzho", size = "9 pages", abstract = "The control of traffic signals is crucial for improving transportation efficiency. Recently, learning-based methods, especially Deep Reinforcement Learning (DRL), garnered substantial success in the quest for more efficient traffic signal control strategies. However, the design of rewards in DRL highly demands domain knowledge to converge to an effective policy, and the final policy also presents difficulties in terms of explainability. In this work, a new learning-based method for signal control in complex intersections is proposed. In our approach, we design a concept of phase urgency for each signal phase. During signal transitions, the traffic light control strategy selects the next phase to be activated based on the phase urgency. We then proposed to represent the urgency function as an explainable tree structure. The urgency function can calculate the phase urgency for a specific phase based on the current road conditions. Genetic programming is adopted to perform gradient-free optimization of the urgency function. We test our algorithm on multiple public traffic signal control datasets. The experimental results indicate that the tree-shaped urgency function evolved by genetic programming outperforms the baselines, including a state-of-the-art method in the transportation field and a well-known DRL-based method.", notes = "HUMIES 2024 FINALIST also known as \cite{liao:2024:GECCO} GECCO-2024 GP A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @Article{Liao:CSS, author = "Xiao-Cheng Liao and Ya-Hui Jia and Xiao-Min Hu and Wei-Neng Chen", journal = "IEEE Transactions on Computational Social Systems", title = "Uncertain Commuters Assignment Through Genetic Programming Hyper-Heuristic", year = "2024", volume = "11", number = "2", pages = "2606--2619", month = apr, note = "Early access", keywords = "genetic algorithms, genetic programming, Roads, Transportation, Vehicle dynamics, Delays, Heuristic algorithms, Real-time systems, Planning, routing, traffic assignment", DOI = "doi:10.1109/TCSS.2023.3265727", ISSN = "2329-924X", abstract = "Traffic assignment problem (TAP) is of great significance for promoting the development of smart city and society. It usually focuses on the deterministic or predictable traffic demand and the vehicle traffic assignment. However, in the real world, traffic demand is usually unpredictable, especially the foot traffic assignment inside buildings such as shopping malls and subway stations. In this work, we consider the dynamic version of TAP, where uncertain commuters keep entering the traffic network constantly. These dynamically arriving commuters bring new challenges to this problem where planning paths for each commuter in advance is incompetent. To address this problem, we propose a genetic programming (GP) hyper-heuristic method to assign uncertain commuters in real-time. Specifically, a low-level heuristic rule called reactive assignment strategy (RAS) is proposed and is evolved by the proposed method. All commuters obey the same strategy to route themselves based on their local observations in a traffic network. Through training based on a designed heuristic template, all commuters will have the ability to find their appropriate paths in real-time to maximize the throughput of the traffic network. This decentralized control mechanism can address dynamically arriving commuters more efficiently than centralized control mechanisms. The experimental results show that our method significantly outperforms the state-of-the-art methods and the evolved RAS has a certain generalisation ability.", notes = "Also known as \cite{10106053}", } @Article{Liao:2025:TAI, author = "Xiao-Cheng Liao and Xiao-Min Hu and Xiang-Ling Chen and Yi Mei and Ya-Hui Jia and Wei-Neng Chen", title = "Learning to Communicate Among Agents for Large-Scale Dynamic Path Planning With Genetic Programming Hyperheuristic", journal = "IEEE Transactions on Artificial Intelligence", year = "2025", volume = "6", number = "5", pages = "1269--1283", month = may, keywords = "genetic algorithms, genetic programming, Roads, Protocols, Vehicle dynamics, Artificial intelligence, Path planning, Routing, Learning systems, Vehicle routing, Transportation, Communication, dynamic path planning, genetic programming (GP), hyperheuristic", ISSN = "2691-4581", DOI = "doi:10.1109/TAI.2024.3522861", abstract = "Genetic programming hyperheuristic (GPHH) has recently become a promising methodology for large-scale dynamic path planning (LDPP) since it can produce reusable heuristics rather than disposable solutions. However, in this methodology, the extracted local and decentralized heuristic for agents that lack a global systemic view sometimes may be problematic. Therefore, a new challenge is to strike a balance between conciseness to improve generalisation ability and incorporation of more global information to obtain better performance. In this work, we target the LDPP problem and propose a communication learning mechanism (ComLGP) for GPHH to address the above difficulties. In ComLGP, a communication function is introduced to serve as a communication protocol and exist in the form of an extra terminal in GPHH. Compared to the classic terminals which are fixed in genetic programing, this communication function undergoes optimisation along with the evolutionary process of GPHH. In this way, the communication function can be learnt which enables agents to communicate without a predefined communication protocol. Then, a caching and lazy updating mechanism for ComLGP is presented to accelerate the calculation of communication content. Last, we verified our method on 22 scenarios including two real world road networks. The experimental results demonstrate that the proposed ComLGP can successfully learn to communicate. Although in the absence of any manually designed communication features, ComLGP is capable of achieving performance competitive to the state-of-the-art method that employs a predefined communication protocol and outperforms the remaining compared methods in most scenarios.", notes = "Also known as \cite{10816321}", } @InProceedings{liao:2024:EAIC, author = "Yuefeng Liao and Zhiqiang Wang and Wenjing Li and Ying Bi", title = "A Genetic Programming Approach to Energy Storage Battery State of Charge Estimation", booktitle = "Proceedings of the 1st Electrical Artificial Intelligence Conference, Volume 3", year = "2024", address = "Nanjing, China", month = "6-8 " # dec, publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "https://link.springer.com/chapter/10.1007/978-981-96-4067-6_40", DOI = "doi:10.1007/978-981-96-4067-6_40", } @Article{Liaw:ETCI, author = "Rung-Tzuo Liaw and Yu-Wei Wen", journal = "IEEE Transactions on Emerging Topics in Computational Intelligence", title = "Ensemble Learning Through Evolutionary Multitasking: A Formulation and Case Study", year = "2024", volume = "8", number = "4", pages = "3081--3094", month = aug, keywords = "genetic algorithms, genetic programming, Task analysis, Optimisation, Symbiosis, Multitasking, Statistics, Sociology, Knowledge transfer, Evolutionary multitasking, dynamic multitask optimisation, online ensemble learning, evolutionary machine learning, evolutionary instance selection", ISSN = "2471-285X", DOI = "doi:10.1109/TETCI.2024.3369949", abstract = "Evolutionary machine learning has drawn much attentions on solving data-driven learning problem in the past decades, where classification is a major branch of data-driven learning problem. To improve the quality of obtained classifier, ensemble is a simple yet powerful strategy. However, gathering classifiers for ensemble requires multiple runs of learning process which bring additional cost at evaluation on the data. This study proposes an innovative framework for ensemble learning through evolutionary multitasking, i.e., the evolutionary multitasking for ensemble learning (EMTEL). There are four main features in the EMTEL. First, the EMTEL formulates a classification problem as a dynamic multitask optimisation problem. Second, the EMTEL uses evolutionary multitasking to resolve the dynamic multitask optimisation problem for better convergence through the synergy of common properties hidden in the tasks. Third, the EMTEL incorporates evolutionary instance selection for saving the cost at evaluation. Finally, the EMTEL formulates the ensemble learning problem as a numerical optimisation problem and proposes an online ensemble aggregation approach to simultaneously select appropriate ensemble candidates from learning history and optimise ensemble weights for aggregating predictions. A case study is investigated by integrating two state-of-the-art methods for evolutionary multitasking and evolutionary instance selection respectively, i.e., the symbiosis in biocoenosis optimisation and cooperative evolutionary learning and instance selection. For online ensemble aggregation, this study adopts the well-known covariance matrix adaptation evolution strategy. Experiments validate the effectiveness of the EMTEL over conventional and advanced evolutionary machine learning algorithms, including genetic programming, self-learning gene expression programming, and multi-dimensional genetic programming. Experimental results show that the proposed framework ameliorates state-of-the-art methods, and the improvements on quality for multiclass classification are at 8.48percent at least and 56.35percent at most in relation to the macro F-score. For convergence speed, the speedups achieved by the proposed framework are 7.85 at least and 100.53 at most on multiclass classification.", notes = "Also known as \cite{10463524}", } @PhdThesis{Dissertation_Libuschewski, author = "Pascal Libuschewski", title = "Exploration of cyber-physical systems for {GPGPU} computer vision-based detection of biological viruses", school = "LS07, Fakultaet fuer Informatik der Technischen Universitaet Dortmund", year = "2017", address = "Dortmund, Germany", month = "22 " # mar, keywords = "genetic algorithms, genetic programming, JGAP, GPU, Design space exploration, DSE, Virus detection, Biological viruses, Medical image processing, Computer vision, GPGPU, GPU, Cyber-physical systems, Energy-aware Multi-objective Optimization, Embedded systems, Mobile sensor", URL = "https://eldorado.tu-dortmund.de/bitstream/2003/35929/1/Dissertation_Libuschewski.pdf", URL = "http://hdl.handle.net/2003/35929", URL = "https://eldorado.tu-dortmund.de/handle/2003/35929", DOI = "doi:10.17877/DE290R-17952", size = "290 pages", abstract = "This work presents a method for a computer vision-based detection of biological viruses in PAMONO sensor images and, related to this, methods to explore cyber-physical systems such as those consisting of the PAMONO sensor, the detection software, and processing hardware. The focus is especially on an exploration of Graphics Processing Units (GPU) hardware for General-Purpose computing on Graphics Processing Units (GPGPU) software and the targeted systems are high performance servers, desktop systems, mobile systems, and hand-held systems. The first problem that is addressed and solved in this work is to automatically detect biological viruses in PAMONO sensor images. PAMONO is short for Plasmon Assisted Microscopy Of Nano-sized Objects. The images from the PAMONO sensor are very challenging to process. The signal magnitude and spatial extension from attaching viruses is small, and it is not visible to the human eye on raw sensor images. Compared to the signal, the noise magnitude in the images is large, resulting in a small Signal-to-Noise Ratio (SNR). With the VirusDetectionCL method for a computer vision-based detection of viruses, presented in this work, an automatic detection and counting of individual viruses in PAMONO sensor images has been made possible. A data set of 4000 images can be evaluated in less than three minutes, whereas a manual evaluation by an expert can take up to two days. As the most important result, sensor signals with a median SNR of two can be handled. This enables the detection of particles down to 100 nm. The VirusDetectionCL method has been realized as a GPGPU software. The PAMONO sensor, the detection software, and the processing hardware form a so called cyber-physical system. For different PAMONO scenarios, e.g., using the PAMONO sensor in laboratories, hospitals, airports, and in mobile scenarios, one or more cyber-physical systems need to be explored. Depending on the particular use case, the demands toward the cyber-physical system differ. This leads to the second problem for which a solution is presented in this work: how can existing software with several degrees of freedom be automatically mapped to a selection of hardware architectures with several hardware configurations to fulfil the demands to the system? Answering this question is a difficult task. Especially, when several possibly conflicting objectives, e.g., quality of the results, energy consumption, and execution time have to be optimized. An extensive exploration of different software and hardware configurations is expensive and time-consuming. Sometimes it is not even possible, e.g., if the desired architecture is not yet available on the market or the design space is too big to be explored manually in reasonable time. A Pareto optimal selection of software parameters, hardware architectures, and hardware configurations has to be found. To achieve this, three parameter and design space exploration methods have been developed. These are named SOG-PSE, SOG-DSE, and MOGEA-DSE. MOGEA-DSE is the most advanced method of these three. It enables a multi-objective, energy-aware, measurement-based or simulation-based exploration of cyber-physical systems. This can be done in a hardware/software co-design manner. In addition, offloading of tasks to a server and approximate computing can be taken into account. With the simulation-based exploration, systems that do not exist can be explored. This is useful if a system should be equipped, e.g., with the next generation of GPUs. Such an exploration can reveal bottlenecks of the existing software before new GPUs are bought. With MOGEA-DSE the overall goal, to develop a method to automatically explore suitable cyber-physical systems for different PAMONO scenarios, could be achieved. As a result, a rapid, reliable detection and counting of viruses in PAMONO sensor data using high-performance, desktop, laptop, down to hand-held systems has been made possible. The fact that this could be achieved even for a small, hand-held device is the most important result of MOGEA-DSE. With the automatic parameter and design space exploration 84% energy could be saved on the hand-held device compared to a baseline measurement. At the same time, a speed-up of four and an F-1 quality score of 0.995 could be obtained. The speedup enables live processing of the sensor data on the embedded system with a very high detection quality. With this result, viruses can be detected and counted on a mobile, hand-held device in less than three minutes and with real-time visualization of results. This opens up completely new possibilities for biological virus detection that were not possible before.", notes = "page 123 Figure 7.3 Supervisors Prof. Dr. Mueller and Prof. Dr. Marwedel In English", } @InProceedings{Lichocki:2009:EvoGAMES, title = "Evolving Teams of Cooperating Agents for Real-Time Strategy Game", author = "Pawel Lichocki and Krzysztof Krawiec and Wojciech Jaskowski", year = "2009", booktitle = "EvoGAMES", series = "Lecture Notes in Computer Science", editor = "Mario Giacobini and Anthony Brabazon and Stefano Cagnoni and Gianni Di Caro and Aniko Ekart and Anna Esparcia-Alcazar and Muddassar Farooq and Andreas Fink and Penousal Machado", volume = "5484", pages = "333--342", publisher = "Springer", keywords = "genetic algorithms, genetic programming, real-time strategy games, artificial intelligence", DOI = "doi:10.1007/978-3-642-01129-0_37", bibsource = "OAI-PMH server at infoscience.epfl.ch", language = "en", oai = "oai:infoscience.epfl.ch:147850", abstract = "We apply gene expression programing to evolve a player for a real-time strategy (RTS) video game. The paper describes the game, evolutionary encoding of strategies and the technical implementation of experimental framework. In the experimental part, we compare two setups that differ with respect to the used approach of task decomposition. One of the setups turns out to be able to evolve an effective strategy, while the other leads to more sophisticated yet inferior solutions. We discuss both the quantitative results and the behavioural patterns observed in the evolved strategies.", affiliation = "Poznan Supercomputing and Networking Centre Poznan Poland", } @Article{Lichocki:2012:ieeeTEC, author = "Pawel Lichocki and Steffen Wischmann and Laurent Keller and Dario Floreano", title = "Evolving team compositions by agent swapping", journal = "IEEE Transactions on Evolutionary Computation", year = "2013", volume = "17", number = "2", pages = "282--298", month = apr, keywords = "genetic algorithms, genetic programming, Multiagent systems, cooperation, crossover, evolutionary computation, team composition, team optimisation", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2012.2191292", size = "18 pages", abstract = "Optimising collective behaviour in multiagent systems requires algorithms to find not only appropriate individual behaviors but also a suitable composition of agents within a team. Over the last two decades, evolutionary methods have been shown to be a promising approach for the design of agents and their compositions into teams. The choice of a crossover operator that facilitates the evolution of optimal team composition is recognised to be crucial, but so far it has never been thoroughly quantified. Here we highlight the limitations of two different crossover operators that exchange entire agents between teams: restricted agent swapping that exchanges only corresponding agents between teams and free agent swapping that allows an arbitrary exchange of agents. Our results show that restricted agent swapping suffers from premature convergence, whereas free agent swapping entails insufficient convergence. Consequently, in both cases the exploration and exploitation aspects of the evolutionary algorithm are not well balanced resulting in the evolution of suboptimal team compositions. To overcome this problem we propose to combine the two methods. Our approach first applies free agent swapping to explore the search space and then restricted agent swapping to exploit it. This mixed approach turns out to be a much more efficient strategy for the evolution of team compositions compared to either strategy alone. Our results suggest that such a mixed agent swapping algorithm should always be preferred whenever the optimal composition of individuals in a multiagent system is unknown.", notes = "also known as \cite{6171841}", } @PhdThesis{Lichocki:thesis, author = "Pawel Lichocki", title = "Evolution of division of labor in artificial societies", school = "Ecole Polytechnique Federale de Lausanne", year = "2013", address = "Switzerland", month = "22 " # mar, keywords = "genetic algorithms, genetic programming, artificial evolution, multi-agent systems, cooperation, division of labour, specialisation, team composition, task allocation, response thresholds, artificial neural networks, simulations, evolutionary computation, selection method, crossover. Evolution artificielle, systemes multi-agents, cooporation, division du travail, specialisation, composition des equipes, allocation des taches, seuil de raponse, reseaux neuronaux artificiels, simulations, algorithmes evolutionnaires, methode de selection, enjambement", URL = "http://infoscience.epfl.ch/record/184959/files/EPFL_TH5687.pdf", size = "162 pages", abstract = "Natural and artificial societies often divide the workload between specialised members. For example, an ant worker may preferentially perform one of many tasks such as brood rearing, foraging and nest maintenance. A robot from a rescue team may specialise in search, obstacle removal, or transportation. Such division of labour is considered crucial for efficient operation of multi-agent systems and has been studied from two perspectives. First, scientists address the how question seeking for mechanical explanations of division of labour. The focus has been put on behavioural and environmental factors and on task allocation algorithms leading to specialisation. Second, scientists address the why question uncovering the origins of division of labour. The focus has been put on evolutionary pressures and optimisation procedures giving rise to specialisation. Studies have usually addressed one of these two questions in isolation, but for a full understanding of division of labour the explanation of the origins of specific mechanisms is necessary. Here, we rise to this challenge and study three major transitions related to division of labour. By means of theoretical analyses and evolutionary simulations, we construct a pathway from the occurrence of cooperation, through fixed castes, up to dynamic task allocation. First, we study conditions favouring the evolution of cooperation, as it opens the doors for the potentially following specialisation. We demonstrate that these conditions are sensitive to the mechanisms of intra-specific selection (or selection methods). Next, we take an engineering perspective and we study division of labour at the genetic level in teams of artificial agents. We devise efficient algorithms to evolve fixed assignments of agents to castes (or team compositions). To this end, we propose a novel technique that exchanges agents between teams, which greatly eases the search for the optimal composition. Finally, we take a biological perspective and we study division of labour at the behavioural level in simulated ant colonies. We quantify the efficiency of task allocation algorithms, which have been used to explain specialisation in social insects. We show that these algorithms fail to induce precise reallocation of the workforce in response to changes in the environment. We overcome this issue by modelling task allocation with artificial neural networks, which lead to near optimal colony performance. Overall, this work contributes both to biology and to engineering. We shed light on the evolution of cooperation and division of labour in social insects, and we show how to efficiently optimise teams of artificial agents. We resolve the encountered methodological issues and demonstrate the power of evolutionary simulations to address biological questions and to tackle engineering problems.", notes = "Is this GP? EPFL Presentee le 22 mars 2013 Suisse THESE NO 5687 (2013) acceptee sur proposition du jury: Prof. A. Billard, presidente du jury Prof. D. Floreano, Prof. L. Keller, directeurs de these Prof. M.-O. Hongler, rapporteur Prof. L. Lehmann, rapporteur", } @InProceedings{lichodzijewski:2004:cgmfdm, author = "Peter Lichodzijewski and Nur Zincir-Heywood and Malcolm Heywood", title = "Cascaded GP Models for Data Mining", pages = "2258--2264", booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary Computation", year = "2004", publisher = "IEEE Press", month = "20-23 " # jun, address = "Portland, Oregon", ISBN = "0-7803-8515-2", URL = "http://flame.cs.dal.ca/~piotr/01331178.pdf", DOI = "doi:10.1109/CEC.2004.1331178", keywords = "genetic algorithms, genetic programming", abstract = "The Cascade Architecture for incremental learning is demonstrated within the context of Genetic Programming. Such a scheme provides the basis for building steadily more complex models until a desired degree of accuracy is reached. The architecture is demonstrated for several data mining datasets. Efficient training on standard computing platforms is retained through the use of the RSS-DSS algorithm for stochastically sampling datasets in proportion to exemplar 'difficulty' and 'age'. Finally, the ensuing empirical study provides the basis for recommending the utility of sum square cost functions in the datasets considered.", notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.", } @InProceedings{lichodzijewski:2005:CEC, author = "Peter Lichodzijewski and Malcolm I. Heywood and A. Nur Zincir-Heywood", title = "CasGP: Building Cascaded Hierarchical Models Using Niching", booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary Computation", year = "2005", editor = "David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L. Gwenn Volkert and Dan Ashlock and Marc Schoenauer", volume = "2", pages = "1180--1187", address = "Edinburgh, UK", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "2-5 " # sep, organisation = "IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, RSS, DSS, C4.5, boosting, naive Bayes", ISBN = "0-7803-9363-5", URL = "http://flame.cs.dal.ca/~piotr/01554824.pdf", DOI = "doi:10.1109/CEC.2005.1554824", size = "8 pages", abstract = "A Cascaded model is introduced for mining large datasets using Genetic Programming without recourse to specialist hardware. Such an algorithm satisfies the seeming conflicting requirements of scalability and accuracy on large datasets by incrementally building GP classifiers through the use of a hierarchical Dynamic Subset Selection algorithm. Models are built incrementally with each layer of the cascade receiving as input the original feature vector, plus the output from the previous layer(s). In order to encourage each layer to explicitly solve new aspects of the problem a combination of Sum Square Error and Niching is used. Thus, previous layers of the model are considered a niche, and the cost function is a shared error metric.", notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS.", } @InProceedings{eurogp07:lichodzijewski, author = "Peter Lichodzijewski and Malcolm I. Heywood", title = "GP Classifier Problem Decomposition Using First-Price and Second-Price Auctions", editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar", booktitle = "Proceedings of the 10th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "4445", year = "2007", address = "Valencia, Spain", month = "11-13 " # apr, pages = "137--147", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-71602-5", isbn13 = "978-3-540-71602-0", DOI = "doi:10.1007/978-3-540-71605-1_13", abstract = "This work details an auction-based model for problem decomposition in Genetic Programming classification. The approach builds on the population-based methodology of Genetic Programming to evolve individuals that bid high for patterns that they can correctly classify. The model returns a set of individuals that decompose the problem by way of this bidding process and is directly applicable to multi-class domains. An investigation of two auction types emphasises the effect of auction design on the properties of the resulting solution. The work demonstrates that auctions are an effective mechanism for problem decomposition in classification problems and that Genetic Programming is an effective means of evolving the underlying bidding behaviour.", notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007", } @InProceedings{1277058, author = "Peter Lichodzijewski and Malcolm I. Heywood", title = "Pareto-coevolutionary genetic programming for problem decomposition in multi-class classification", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "1", isbn13 = "978-1-59593-697-4", pages = "464--471", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p464.pdf", DOI = "doi:10.1145/1276958.1277058", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Coevolution, problem decomposition, subset selection, supervised learning, training efficiency", abstract = "A bid-based approach for coevolving Genetic Programming classifiers is presented. The approach Co-evolves a population of learners that decompose the instance space by way of their aggregate bidding behaviour. To reduce computation overhead, a small, relevant, subset of training exemplars is (competitively) coevolved alongside the learners. The approach solves multi-class problems using a single population and is evaluated on three large datasets. It is found to be competitive, especially compared to classifier systems, while significantly reducing the computation overhead associated with training.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @InProceedings{Lichodzijewski:2008:gecco, author = "Peter Lichodzijewski and Malcolm I. Heywood", title = "Managing team-based problem solving with symbiotic bid-based genetic programming", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "363--370", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p363.pdf", DOI = "doi:10.1145/1389095.1389162", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, active learning, classification, coevolution, efficiency, problem decomposition, supervised learning, teaming", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389162}", } @Article{Lichodzijewski:2008:GPEM, author = "Peter Lichodzijewski and Malcolm I. Heywood", title = "Coevolutionary bid-based genetic programming for problem decomposition in classification", journal = "Genetic Programming and Evolvable Machines", year = "2008", volume = "9", number = "4", pages = "331--365", month = dec, keywords = "genetic algorithms, genetic programming, Coevolution, Problem decomposition, Teaming, Classification, SVM", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-008-9067-9", abstract = "In this work a cooperative, bid-based, model for problem decomposition is proposed with application to discrete action domains such as classification. This represents a significant departure from models where each individual constructs a direct input-outcome map, for example, from the set of exemplars to the set of class labels as is typical under the classification domain. In contrast, the proposed model focuses on learning a bidding strategy based on the exemplar feature vectors; each individual is associated with a single discrete action and the individual with the maximum bid wins the right to suggest its action. Thus, the number of individuals associated with each action is a function of the intra-action bidding behaviour. Credit assignment is designed to reward correct but unique bidding strategies relative to the target actions. An advantage of the model over other teaming methods is its ability to automatically determine the number of and interaction between cooperative team members. The resulting model shares several traits with learning classifier systems and as such both approaches are benchmarked on nine large classification problems. Moreover, both of the evolutionary models are compared against the deterministic Support Vector Machine classification algorithm. Performance assessment considers the computational, classification, and complexity characteristics of the resulting solutions. The bid-based model is found to provide simple yet effective solutions that are robust to wide variations in the class representation. Support Vector Machines and classifier systems tend to perform better under balanced datasets albeit resulting in black-box solutions.", } @InProceedings{Lichodzijewski:2010:gecco, author = "Peter Lichodzijewski and Malcolm I. Heywood", title = "Symbiosis, complexification and simplicity under GP", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "853--860", keywords = "genetic algorithms, genetic programming", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830483.1830640", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Models of Genetic Programming (GP) frequently reflect a neo-Darwinian view to evolution in which inheritance is based on a process of gradual refinement and the resulting solutions take the form of single monolithic programs. Conversely, introducing an explicitly symbiotic model of inheritance makes a divide-and-conquer metaphor for problem decomposition central to evolution. Benchmarking gradualist versus symbiotic models of evolution under a common evolutionary framework illustrates that not only does symbiosis result in more accurate solutions, but the solutions are also much simpler in terms of instruction and attribute count over a wide range of classification problem domains.", notes = "Also known as \cite{1830640} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @InCollection{Lichodzijewski:2010:GPTP, author = "Peter Lichodzijewski and Malcolm Heywood", title = "The {Rubik Cube} and {GP} Temporal Sequence Learning: An Initial Study", booktitle = "Genetic Programming Theory and Practice VIII", year = "2010", editor = "Rick Riolo and Trent McConaghy and Ekaterina Vladislavleva", series = "Genetic and Evolutionary Computation", volume = "8", address = "Ann Arbor, USA", month = "20-22 " # may, publisher = "Springer", chapter = "3", pages = "35--54", keywords = "genetic algorithms, genetic programming, bid-based cooperative behaviours, problem decomposition, Rubik cube, symbiotic coevolution, temporal sequence learning", isbn13 = "978-1-4419-7746-5", URL = "http://www.springer.com/computer/ai/book/978-1-4419-7746-5", DOI = "doi:10.1007/978-1-4419-7747-2_3", abstract = "The 3 by 3 Rubik cube represents a potential benchmark for temporal sequence learning under a discrete application domain with multiple actions. Challenging aspects of the problem domain include the large state space and a requirement to learn invariances relative to the specific colours present the latter element of the domain making it difficult to evolve individuals that learn macro-moves relative tomultiple cube configurations. An initial study is presented in thiswork to investigate the utility ofGenetic Programming capable of layered learning and problem decomposition. The resulting solutions are tested on 5000 test cubes, of which specific individuals are able to solve up to 350 (7 percent) cube configurations and population wide behaviours are capable of solving up to 1200 (24 percent) of the test cube configurations. It is noted that the design options for generic fitness functions are such that users are likely to face either reward functions that are very expensive to evaluate or functions that are very deceptive. Addressing this might well imply that domain knowledge is explicitly used to decompose the task to avoid these challenges. This would augment the described generic approach currently employed for Layered learning, problem decomposition.", notes = "part of \cite{Riolo:2010:GPTP}", } @PhdThesis{Lichodzijewski_Peter.pdf, title = "A Symbiotic Bid-Based Framework for Problem Decomposition using Genetic Programming", author = "Peter Lichodzijewski", school = "Dalhousie University", year = "2011", address = "Halifax, Canada", month = "22 " # feb, keywords = "genetic algorithms, genetic programming, problem decomposition, symbiosis, Coevolution, Machine Learning", bibsource = "OAI-PMH server at amican.webapps1.lac-bac.gc.ca", language = "en", oai = "oai:collectionscanada.gc.ca:NSHD.ca#10222/13260", URL = "http://hdl.handle.net/10222/13260", URL = "http://dalspace.library.dal.ca/bitstream/handle/10222/13260/Lichodzijewski_Peter.pdf", size = "330 pages", abstract = "This thesis investigates the use of symbiosis as an evolutionary metaphor for problem decomposition using Genetic Programming. It begins by drawing a connection between lateral problem decomposition, in which peers with similar capabilities coordinate their actions, and vertical problem decomposition, whereby solution subcomponents are organised into increasingly complex units of organisation. Furthermore, the two types of problem decomposition are associated respectively with context learning and layered learning. The thesis then proposes the Symbiotic Bid-Based framework modelled after a three-staged process of symbiosis abstracted from biological evolution. As such, it is argued, the approach has the capacity for both types of problem decomposition. Three principles capture the essence of the proposed framework. First, a bid-based approach to context learning is used to separate the issues of `what to do' and `when to do it'. Whereas the former issue refers to the problem-specific actions, e.g., class label predictions, the latter refers to a bidding behaviour that identifies a set of problem conditions. In this work, Genetic Programming is used to evolve the bids casting the method in a non-traditional role as programs no longer represent complete solutions. Second, the proposed framework relies on symbiosis as the primary mechanism of inheritance driving evolution, where this is in contrast to the crossover operator often encountered in Evolutionary Computation. Under this evolutionary metaphor, a set of symbionts, each representing a solution subcomponent in terms of a bid-action pair, is compartmentalised inside a host. Communication between symbionts is realised through their collective bidding behaviour, thus, their cooperation is directly supported by the bid-based approach to context learning. Third, assuming that challenging tasks where problem decomposition is likely to play a key role will often involve large state spaces, the proposed framework includes a dynamic evaluation function that explicitly models the interaction between candidate solutions and training cases. As such, the computational overhead incurred during training under the proposed framework does not depend on the size of the problem state space. An approach to model building, the Symbiotic Bid-Based framework is first evaluated on a set of real-world classification problems which include problems with multi-class labels, unbalanced distributions, and large attribute counts. The evaluation includes a comparison against Support Vector Machines and AdaBoost. Under temporal sequence learning, the proposed framework is evaluated on the truck reversal and Rubik's Cube tasks, and in the former case, it is compared with the Neuroevolution of Augmenting Topologies algorithm. Under both problems, it is demonstrated that the increased capacity for problem decomposition under the proposed approach results in improved performance, with solutions employing vertical problem decomposition under temporal sequence learning proving to be especially effective.", notes = "broken Nov 2023 http://www.cs.dal.ca/news/presentations/2011-02-22-symbiotic-bid-based-framework-problem-decomposition-using-genetic-prog", } @Misc{DBLP:journals/corr/abs-2202-01490, author = "Sherlock A. Licorish and Markus Wagner", title = "On the Utility of Marrying {GIN} and {PMD} for Improving {Stack Overflow} Code Snippets", howpublished = "ArXiv", year = "2022", month = "3 " # feb, keywords = "genetic algorithms, genetic programming, Genetic improvement, static analysis, Hybridisation", timestamp = "Thu, 17 Feb 2022 16:43:17 +0100", biburl = "https://dblp.org/rec/journals/corr/abs-2202-01490.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://arxiv.org/abs/2202.01490", size = "5 pages", abstract = "Software developers are increasingly dependent on question and answer portals and blogs for coding solutions. While such interfaces provide useful information, there are concerns that code hosted here is often incorrect, insecure or incomplete. Previous work indeed detected a range of faults in code provided on Stack Overflow through the use of static analysis. Static analysis may go a far way towards quickly establishing the health of software code available online. In addition, mechanisms that enable rapid automated program improvement may then enhance such code. Accordingly, we present this proof of concept. We use the PMD static analysis tool to detect performance faults for a sample of Stack Overflow Java code snippets, before performing mutations on these snippets using GIN. We then re-analyse the performance faults in these snippets after the GIN mutations. GIN RandomSampler was used to perform 17986 unique line and statement patches on 3034 snippets where PMD violations were removed from 770 patched versions. Our outcomes indicate that static analysis techniques may be combined with automated program improvement methods to enhance publicly available code with very little resource requirements. We discuss our planned research agenda in this regard.", notes = "Cited by \cite{Licorish:2022:GI}", } @InProceedings{Licorish:2022:GI, author = "Sherlock A. Licorish and Markus Wagner", title = "Dissecting Copy/Delete/Replace/Swap mutations: Insights from a {GIN} Case Study", booktitle = "GI @ GECCO 2022", year = "2022", editor = "Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and W. B. Langdon and Justyna Petke", pages = "1940--1945", address = "Boston, USA", publisher_address = "New York, NY, USA", month = "9 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, Automated static analysis, SBSE, Search-based software engineering, Source Code Analyzer Project, PMD, Java, GIN", isbn13 = "978-1-4503-9268-6/22/07", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/gecco2022/gi2022/papers/Licorish_2022_GI.pdf", DOI = "doi:10.1145/3520304.3533970", slides_url = "http://geneticimprovementofsoftware.com/slides/gi2022gecco/wagner-dissecting-copy-delete-replace-swap-gi-gecco-22.pdf", video_url = "https://www.youtube.com/watch?v=CI5sT3L_-CI&list=PLI8fiFpB7BoIHgl5CsdtjfWvHlE5N6pje&index=9", size = "6 pages", abstract = "Research studies are increasingly critical of publicly available code due to evidence of faults. This has led researchers to explore ways to improve such code, with static analysis and genetic code improvement previously singled out. Previous work has evaluated the feasibility of these techniques, using PMD and GIN for enhancing Stack Overflow code snippets. Results reported in this regard pointed to the potential of these techniques, especially in terms of GIN removal of PMD performance faults from 58 programs. We use a contextual lens to explore these mutations in this study, to evaluate the promise of these techniques. The outcomes show that while the programs were syntactically correct after GIN mutations, many of GIN mutations changed the semantics of the code, rendering its purpose questionable. However, certain code mutations tend to retain code semantics more than others. In addition, GINs mutations at times affected PMDs parsing ability, potentially increasing false negatives. Overall, while these approaches may prove useful, full utility may not be claimed at this time. For enhancing the outcomes of these approaches, we outline multiple strategies.", notes = "cites \cite{DBLP:journals/corr/abs-2202-01490} they report 'GIN mutations removed all' violations Human study by both authors. https://pmd.github.io/ https://pmd.github.io/latest/pmd_rules_java.html Highly used Stack Overflow snips from 2014, 2015 and 2106. http://geneticimprovementofsoftware.com/events/gecco2022 Table 1: efficient Java code 'almost all fixing mutations remove the offending code and thus change the semantics' 'removing offending code can be an effective program repair strategy.' GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @InProceedings{Liddle:2010:cec, author = "Thomas Liddle and Mark Johnston and Mengjie Zhang", title = "Multi-Objective Genetic Programming for object detection", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-6910-9", abstract = "In object detection, the goals of successfully discriminating between different kinds of objects (object classification) and accurately identifying the positions of all objects of interest in a large image (object localisation) are potentially in conflict. We propose a Multi-Objective Genetic Programming (MOGP) approach to the task of providing a decision-maker with a diverse set of alternative object detection programs that balance between high detection rate and low false-alarm rate. Experiments on two datasets, simple shapes and photographs of coins, show that it is difficult for a Single-Objective GP (SOGP) system (which weights the multiple objectives a priori) to evolve effective object detectors, but that an MOGP system is able to evolve a range of effective object detectors more efficiently.", DOI = "doi:10.1109/CEC.2010.5586072", notes = "WCCI 2010. Also known as \cite{5586072}", } @Article{Lim:2006:GPEM, author = "Dudy Lim and Yew-Soon Ong and Yaochu Jin and Bernhard Sendhoff and Bu Sung Lee", title = "Inverse multi-objective robust evolutionary design", journal = "Genetic Programming and Evolvable Machines", year = "2006", volume = "7", number = "4", pages = "383--404", month = dec, keywords = "Evolutionary algorithms, Robust design optimisation, Design optimisation in the presence of uncertainty", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-006-9013-7", size = "22 pages", abstract = "we present an Inverse Multi-Objective Robust Evolutionary (IMORE) design methodology that handles the presence of uncertainty without making assumptions about the uncertainty structure. We model the clustering of uncertain events in families of nested sets using a multi-level optimisation search. To reduce the high computational costs of the proposed methodology we proposed schemes for (1) adapting the step-size in estimating the uncertainty, and (2) trimming down the number of calls to the objective function in the nested search. Both offline and online adaptation strategies are considered in conjunction with the IMORE design algorithm. Design of Experiments (DOE) approaches further reduce the number of objective function calls in the online adaptive IMORE algorithm. Empirical studies conducted on a series of test functions having diverse complexities show that the proposed algorithms converge to a set of Pareto-optimal design solutions with non-dominated nominal and robustness performances efficiently.", notes = "p390 'one dimensional Michalewicz 2 function'", } @InProceedings{IkSooLim:1998:imgphvGP, author = "Ik Soo Lim and Daniel Thalmann", title = "Indexed Memory as a Generic Protocol for Handling Vectors of Data in Genetic Programming", booktitle = "Fifth International Conference on Parallel Problem Solving from Nature", year = "1998", editor = "Agoston E. Eiben and Thomas Back and Marc Schoenauer and Hans-Paul Schwefel", volume = "1498", series = "LNCS", pages = "325--334", address = "Amsterdam", publisher_address = "Berlin", month = "27-30 " # sep, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-65078-4", DOI = "doi:10.1007/BFb0056875", size = "10 pages", abstract = "Indexed memory is used as a generic protocol for handling vectors of data in genetic programming. Using this simple method, a single program can generate many outputs. It eliminates the complexity of maintaining different trees for each desired parameter and avoid problem-specific function calls for handling the vectors. This allows a single set of programming language primitives applicable to wider range of problems. For a test case, the technique is appliedto evolution of behavioural control programs for a simulated 2d vehicle in a corridor following problem.", notes = "PPSN-V", affiliation = "LIG, Department of Computer Science, Swiss Federal Institute of Technology (EPFL), CH-1015 Lausanne, Switzerland", } @InProceedings{lim:1999:HNBBEGHB, author = "Ik Soo Lim and Daniel Thalmann", title = "How Not to Be a Black-Box: Evolution and Genetic-Engineering of High-Level Behaviours", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1329--1335", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, artificial life, adaptive behavior and agents", ISBN = "1-55860-611-4", URL = "http://ligwww.epfl.ch/~thalmann/papers.dir/GECCO99.pdf", URL = "http://citeseer.ist.psu.edu/242132.html", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-001.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-001.pdf", abstract = "In spite of many success stories in various domains, Genetic Algorithm and Genetic Programming still suffer from some significant pitfalls. Those evolved programs often lack of some important properties such as robustness, comprehensibility, transparency, modifiability and usability of domain knowledge easily available. We attempt to resolve these problems, at least in evolving high-level behaviours, by adopting a technique of conditions-and-behaviours originally used for minimizing the learning space in reinforcement learning. We experimentally validate the approach on a foraging task.", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99). computer animations, foraging task, non-transparency", } @Article{Lim:2016:CS, author = "Jian C. Lim and Murat Karakus and Togay Ozbakkaloglu", title = "Evaluation of ultimate conditions of FRP-confined concrete columns using genetic programming", journal = "Computer \& Structures", volume = "162", pages = "28--37", year = "2016", ISSN = "0045-7949", DOI = "doi:10.1016/j.compstruc.2015.09.005", URL = "http://www.sciencedirect.com/science/article/pii/S0045794915002643", abstract = "A large database consisting of 832 axial compression tests results of fibre reinforced polymer (FRP)-confined concrete specimens was assembled. Using the test database, existing conventional and evolutionary algorithm models developed for FRP-confined concrete were then assessed. New genetic programming (GP) models for predicting the ultimate condition of FRP-confined concrete were developed. The predictions of the proposed models suggest that more accurate results can be achieved in explaining and formulating the ultimate condition of FRP-confined concretes by GP. The model assessment also illustrates the influences of the size of the databases and the selected parameters used in the GP models.", keywords = "genetic algorithms, genetic programming, Fiber reinforced polymer (FRP), Confinement, Concrete, Compressive strength, Ultimate axial strain", } @InProceedings{Lim:2016:SSBSE, author = "Jin-Suk Lim and Shin Yoo", title = "Field Report: Applying {Monte Carlo Tree Search} for Program Synthesis", booktitle = "Proceedings of the 8th International Symposium on Search Based Software Engineering, SSBSE 2016", year = "2016", editor = "Federica Sarro and Kalyanmoy Deb", volume = "9962", series = "LNCS", pages = "304--310", address = "Raleigh, North Carolina, USA", month = "8-10 " # oct, publisher = "Springer", keywords = "genetic algorithms, genetic programming, genetic improvement, MCTS, PSB1", isbn13 = "978-3-319-47106-8", URL = "https://coinse.github.io/projects/mctsps/", URL = "https://rdcu.be/dqazC", DOI = "doi:10.1007/978-3-319-47106-8_27", size = "7 pages", abstract = "Program synthesis aims to automatically generate an executable segment of code that satisfies a given set of criteria. Genetic programming has been widely studied for program synthesis. However, it has drawbacks such as code bloats and the difficulty in finer control over the growth of programs. This paper explores the possibility of applying Monte Carlo Tree Search (MCTS) technique to general purpose program synthesis. The exploratory study applies MCTS to synthesis of six small benchmarks using Java Bytecode instructions, and compares the results to those of genetic programming. The paper discusses the major challenges and outlines the future work.", notes = "cites \cite{Helmuth:2015:GECCO} Web page (Aug 2016) says 'MCTS performs comparably to GP'. co-located with ICSME-2016 gismo", } @InProceedings{Lim:2020:SSBSE:RENE, author = "Mingyi Lim and Giovani Guizzo and Justyna Petke", title = "Impact of Test Suite Coverage on Overfitting in Genetic Improvement of Software", booktitle = "12th International Symposium on Search Based Software Engineering SSBSE 2020", year = "2020", editor = "Juan Pablo Galeotti and Bonita Sharif", series = "LNCS", volume = "12420", pages = "188--203", address = "Bari, Italy", month = "7-8 " # oct, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Genetic Improvement, SBSE, GIN, EvoSuite", isbn13 = "978-3-030-59761-0", URL = "http://www.cs.ucl.ac.uk/staff/J.Petke/papers/Lim_2020_SSBSE_RENE.pdf", video_url = "https://www.youtube.com/watch?v=018oszfebqg", DOI = "doi:10.1007/978-3-030-59762-7_14", size = "16 pages", abstract = "Genetic Improvement (GI) uses automated search to improve existing software. It can be used to improve runtime, energy consumption, fix bugs, and any other software property, provided that such property can be encoded into a fitness function. GI usually relies on testing to check whether the changes disrupt the intended functionality of the software, which makes test suites important artefacts for the overall success of GI. The objective of this work is to establish which characteristics of the test suites correlate with the effectiveness of GI. We hypothesise that different test suite properties may have different levels of correlation to the ratio between overfitting and non-overfitting patches generated by the GI algorithm. In order to test our hypothesis, we perform a set of experiments with automatically generated test suites using EvoSuite and 4 popular coverage criteria. We used these test suites as input to a GI process and collected the patches generated throughout such a process. We find that while test suite coverage has an impact on the ability of GI to produce correct patches, with branch coverage leading to least overfitting, the overfitting rate was still significant. We also compared automatically generated tests with manual, developer-written ones and found that while manual tests had lower coverage, the GI runs with manual tests led to less overfitting than in the case of automatically generated tests. Finally, we did not observe enough statistically significant correlations between the coverage metrics and overfitting ratios of patches, i.e., the coverage of test suites cannot be used as a linear predictor for the level of overfitting of the generated patches.", notes = " Replications and Negative Results line, branch, conditional branch, and weak mutation coverage (ie how many mutants does the test suite detect). Replication package code https://github.com/justynapt/ssbse2020RENE python3, java, R, MacOS GI & EvoSuite to reduce runtime of Java programs. output diversity metric. Triangle and nine sort programs, 14 to 52 lines of Java code. Gin PatchAnalyser held out test suite. 420000 patches generated and tested in 16 hours. 'GI can indeed find valid and non-overfitting patches' 'manually created test suites generate patches that overfit significantly less than patches generated with automatically generated test suites.' With EvoSuite 'test suite (coverage) measures seem to have no bearing on how good a test suite is for the purpose of applying genetic improvement' http://ssbse2020.di.uniba.it/accepted-papers/", } @InProceedings{Lim:2011:GECCO, author = "Soo Ling Lim and Peter J. Bentley", title = "Evolving Relationships between Social Networks and Stakeholder Involvement in Software Projects", booktitle = "GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0557-0", pages = "1899--1906", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, SBSE, Social network analysis, search-based software engineering", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001576.2001831", publisher = "ACM", publisher_address = "New York, NY, USA", URL = "http://soolinglim.files.wordpress.com/2010/07/fp551-lim.pdf", size = "8 pages", abstract = "Software projects often fail because stakeholder communication and involvement are inadequate. This paper proposes a novel method to understand project social networks and their corresponding stakeholder involvement. The method uses five types of model social network, which represent various types of stakeholder activity in a project. It exploits evolutionary computation to correlate the social network of a real software project against each model. Experiments show that the real project most resembles the rational model where stakeholders who are more highly connected in the social network are more involved in the project.", notes = "Also known as \cite{2001831} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)", } @InProceedings{Lim:2004:aspgp, author = "Sungsoo Lim and Kyoung-Min Kim and Jin-Hyuk Hong and Sung-Bae Cho", title = "Interactive Genetic Programming for the Sentence Generation of Dialog-Based Travel Planning System", booktitle = "Proceedings of The Second Asian-Pacific Workshop on Genetic Programming", year = "2004", editor = "R I Mckay and Sung-Bae Cho", address = "Cairns, Australia", month = "6-7 " # dec, keywords = "genetic algorithms, genetic programming", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.540.4801", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.540.4801", URL = "http://sclab.yonsei.ac.kr/publications/Papers/IC/ASPGP2004.pdf", size = "13 pages", abstract = "As dialogue systems have been widely investigated, the research on natural language generation in dialogue has aroused interest. Contrary to conventional dialogue systems that reply to the user with a set of predefined answers, a newly developed dialogue system generates them dynamically and trains answers to support more flexible and customised dialogues with humans. The paper proposes an evolutionary method for generating sentences using genetic programming. Sentence plan trees, which stand for the sentence structure, are adopted as the representation of genetic programming. With interactive evolution process with the user, a set of customized sentence structures is obtained. The proposed method applies to a dialogue-based travel planning system and the usability test demonstrates the usefulness of the proposed method.", notes = "broken http://sc.snu.ac.kr/~aspgp/aspgp04/programme.html", } @InProceedings{conf/mdai/LimC05, title = "Language Generation for Conversational Agent by Evolution of Plan Trees with Genetic Programming", author = "Sung-Soo Lim and Sung-Bae Cho", year = "2005", pages = "305--315", editor = "Vicenc Torra and Yasuo Narukawa and Sadaaki Miyamoto", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3558", booktitle = "Modeling Decisions for Artificial Intelligence, Second International Conference, MDAI 2005, Proceedings", address = "Tsukuba, Japan", month = jul # " 25-27", bibdate = "2005-07-18", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/mdai/mdai2005.html#HongC05", keywords = "genetic algorithms, genetic programming, Dialogue system, Natural language generation, Interactive genetic programming, Sentence plan tree", ISBN = "3-540-27871-0", DOI = "doi:10.1007/11526018_30", size = "11 pages", abstract = "As dialogue systems are widely demanded, the research on natural language generation in dialogue has raised interest. Contrary to conventional dialogue systems that reply to the user with a set of predefined answers, a newly developed dialogue system generates them dynamically and trains answers to support more flexible and customised dialogues with humans. The paper proposes an evolutionary method for generating sentences using interactive genetic programming. Sentence plan trees, which stand for the sentence structure, are adopted as the representation of genetic programming. With interactive evolution process with the user, a set of customised sentence structures is obtained. The proposed method applies to a dialogue-based travel planning system and the usability test demonstrates the usefulness of the proposed method", } @TechReport{RC24442, author = "Yow Tzu Lim and Pau Chen Cheng and Pankaj Rohatgi and John Andrew Clark", title = "Policy Evolution with Genetic Programming", institution = "IBM", year = "2007", type = "IBM Research Report", number = "RC24442 (W0711-220)", address = "Yorktown Heights, NY, USA", month = "28 " # nov, keywords = "genetic algorithms, genetic programming", URL = "http://domino.watson.ibm.com/library/CyberDig.nsf/papers/.../RC24442.pdf", size = "18 pages", notes = "University of York", } @InProceedings{Lim:2008:cec, author = "Yow Tzu Lim and Pau Chen Cheng and John Andrew Clark and Pankaj Rohatgi", title = "Policy Evolution with Genetic Programming: A Comparison of Three Approaches", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "1792--1800", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1823-7", file = "EC0442.pdf", DOI = "doi:10.1109/CEC.2008.4631032", abstract = "In the early days a policy was a set of simple rules with a clear intuitive motivation that could be formalised to good effect. However the world is now much more complex. Subtle risk decisions may often need to be made and people are not always adept at expressing rationale for what they do. Previous research has demonstrated that Genetic Programming can be used to infer statements of policies from examples of decisions made [1]. This allows a policy that may not formally have been documented to be discovered automatically, or an underlying set of requirements to be extracted by interpreting user decisions to posed ``what if'' scenarios. This study compares the performance of three different approaches in using Genetic Programming to infer security policies from decision examples made, namely symbolic regression, IF-THEN rules inference and fuzzy membership functions inference. The fuzzy membership functions inference approach is found to have the best performance in terms of accuracy. Also, the fuzzification and de-fuzzification methods are found to be strongly correlated; incompatibility between them can have strong negative impact to the performance.", keywords = "genetic algorithms, genetic programming", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @InProceedings{Lim:2008:gecco, author = "Yow Tzu Lim and Pau Chen Cheng and Pankaj Rohatgi and John Andrew Clark", title = "MLS security policy evolution with genetic programming", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "1571--1578", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1571.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.145.6020", DOI = "doi:10.1145/1389095.1389395", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, MLS, policy inference, security policy, Real-World application", oai = "oai:CiteSeerXPSU:10.1.1.145.6020", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389395}", } @InProceedings{DBLP:conf/seal/LimCCR08, author = "Yow Tzu Lim and Pau-Chen Cheng and John Andrew Clark and Pankaj Rohatgi", title = "Policy Evolution with Grammatical Evolution", booktitle = "Proceedings of the 7th International Conference on Simulated Evolution And Learning (SEAL '08)", year = "2008", editor = "Xiaodong Li and Michael Kirley and Mengjie Zhang and David G. Green and Victor Ciesielski and Hussein A. Abbass and Zbigniew Michalewicz and Tim Hendtlass and Kalyanmoy Deb and Kay Chen Tan and J{\"u}rgen Branke and Yuhui Shi", volume = "5361", series = "Lecture Notes in Computer Science", pages = "71--80", address = "Melbourne, Australia", month = dec # " 7-10", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", isbn13 = "978-3-540-89693-7", DOI = "doi:10.1007/978-3-540-89694-4_8", abstract = "Security policies are becoming more sophisticated. Operational forces will often be faced with making tricky risk decisions and policies must be flexible enough to allow appropriate actions to be facilitated. Access requests are no longer simple subject access object matters. There is often a great deal of context to be taken into account. Most security work is couched in terms of risk management, but the benefits of actions will need to be taken into account too. In some cases it may not be clear what the policy should be. People are often better at dealing with specific examples than producing general rules. In this paper we investigate the use of Grammatical Evolution (GE) to attempt to infer Fuzzy MLS policy from decision examples. This approach couches policy inference as a search for a policy that is most consistent with the supplied examples set. The results show this approach is promising.", bibsource = "DBLP, http://dblp.uni-trier.de", } @PhdThesis{Lim:thesis, author = "Yow Tzu Lim", title = "Evolving Security Policies", school = "Computer Science, University of York", year = "2010", address = "UK", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", URL = "http://etheses.whiterose.ac.uk/id/eprint/1612", URL = "http://etheses.whiterose.ac.uk/1612/3/thesis.pdf", size = "212 pages", abstract = "As computer system size and complexity grow, formulating effective policies require more sophistication. There are many risk factors that need to be considered, some of which may be in conflict. Inevitably, unpredictable circumstances that demand decisions will arise during operation. In some cases an automated response may be imperative; in other cases these may be ill-advised. Manual decisions are often made that override the current policy and serve effectively to redefine it. This matter is further complicated in highly dynamic operational environments like mobile ad-hoc networks, in which the risk factors may be changing continually. Thus, security policies must be able to change and adapt to the operational needs. This study investigates the potential of evolutionary algorithms as a tool in determining the optimal security policies that suit such environments. This thesis reviews some fundamental concepts in related domains. It presents three applications of evolutionary algorithms in solving problems that are of direct relevance. These include the inference of security policies from decision examples, the dynamic adaptation of security policies, and the optimisation of security policies for a specific set of missions. The results show that the inference approaches based on evolutionary algorithms are very promising. The thesis concludes with an evaluation of the work done, the extent to which the work justifies the thesis hypothesis and some possible directions on how evolutionary algorithms can be applied to address a wider range of relevant problems in the domain of concern.", notes = "Supervisor John A. Clark", } @InProceedings{DBLP:conf/mod/LimaBB19, author = "Leandro S. Lima and Heder S. Bernardino and Helio J. C. Barbosa", editor = "Giuseppe Nicosia and Panos M. Pardalos and Renato Umeton and Giovanni Giuffrida and Vincenzo Sciacca", title = "Designing Combinational Circuits Using a Multi-objective Cartesian Genetic Programming with Adaptive Population Size", booktitle = "Machine Learning, Optimization, and Data Science - 5th International Conference, {LOD} 2019, Siena, Italy, September 10-13, 2019, Proceedings", series = "Lecture Notes in Computer Science", volume = "11943", pages = "592--604", publisher = "Springer", year = "2019", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", URL = "https://doi.org/10.1007/978-3-030-37599-7_49", DOI = "doi:10.1007/978-3-030-37599-7_49", timestamp = "Fri, 10 Jan 2020 14:31:49 +0100", biburl = "https://dblp.org/rec/conf/mod/LimaBB19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{Lima:2019:GECCOcomp, author = "Ricardo H. R. Lima and Aurora T. R. Pozo", title = "Evolving convolutional neural networks through grammatical evolution", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", isbn13 = "978-1-4503-6748-6", pages = "179--180", address = "Prague, Czech Republic", DOI = "doi:10.1145/3319619.3322058", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, grammatical evolution", notes = "Also known as \cite{3322058} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InProceedings{conf/bracis/LimaPS19, author = "Ricardo Henrique Remes {de Lima} and Aurora T. R. Pozo and Roberto Santana", title = "Automatic Design of Convolutional Neural Networks using Grammatical Evolution", publisher = "IEEE", year = "2019", booktitle = "8th Brazilian Conference on Intelligent Systems, BRACIS", pages = "329--334", month = oct # " 15-18", address = "Salvador, Brazil", keywords = "genetic algorithms, genetic programming, grammatical evolution, ANN", URL = "https://ieeexplore.ieee.org/xpl/conhome/8910170/proceeding", bibdate = "2020-01-09", isbn13 = "978-1-7281-4253-1", DOI = "doi:10.1109/BRACIS.2019.00065", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/bracis/bracis2019.html#LimaPS19", } @InProceedings{Lima:2020:CEC, author = "Ricardo Henrique Remes Lima and Aurora Pozo and Alexander Mendiburu and Roberto Santana", title = "A Symmetric grammar approach for designing segmentation models", booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC 2020", year = "2020", editor = "Yaochu Jin", pages = "paper id24460", address = "internet", month = "19-24 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-7281-6929-3", DOI = "doi:10.1109/CEC48606.2020.9185760", abstract = "Image segmentation is a relevant problem in computer vision present in multiple application domains. One of the most used methods for image segmentation is U-net, a type of convolutional network with additional constraints in its architecture. Studies regarding the U-net usually rely on well-known architectures, which leads to a narrow exploration of the possibilities, and possibly impacting the performance. Genetic Programming approaches have become increasingly popular for designing neural networks due to studies where the generated models were able to achieve results comparable to humans. These approaches can evolve the structure at different levels of abstraction, reducing the need for a specialist. In this paper, we propose the use of Grammatical Evolution for evolving U-net architectures. We propose a mirror grammar, which is capable of generating a variety of flexible U-nets that better explores the search space. We show that the proposed grammar can capture the complex constraints that define the U-nets and achieve comparable results in terms of accuracy, on a benchmark of segmentation problems of varying difficulty.", notes = "https://wcci2020.org/ Federal University of Parana, Brazil; University of the Basque Country UPV/EHU, Spain. Also known as \cite{9185760}", } @InProceedings{Lima:2021:EuroGP, author = "Ricardo Henrique Remes {de Lima} and Aurora Pozo and Alexander Mendiburu and Roberto Santana", title = "Automatic Design of Deep Neural Networks Applied to Image Segmentation Problems", booktitle = "EuroGP 2021: Proceedings of the 24th European Conference on Genetic Programming", year = "2021", editor = "Ting Hu and Nuno Lourenco and Eric Medvet", series = "LNCS", volume = "12691", publisher = "Springer Verlag", address = "Virtual Event", pages = "98--113", month = "7-9 " # apr, organisation = "EvoStar, Species", note = "Best paper candidate", keywords = "genetic algorithms, genetic programming, Grammatical evolution, DSGE, Neural architecture search, Deep learning, Edge detection, ANN", isbn13 = "978-3-030-72811-3", DOI = "doi:10.1007/978-3-030-72812-0_7", abstract = "A U-Net is a convolutional neural network mainly used for image segmentation domains such as medical image analysis. As other deep neural networks, the U-Net architecture influences the efficiency and accuracy of the network. We propose the use of a grammar-based evolutionary algorithm for the automatic design of deep neural networks for image segmentation tasks. The approach used is called Dynamic Structured Grammatical Evolution (DSGE), which employs a grammar to define the building blocks that are used to compose the networks, as well as the rules that help build them. We perform a set of experiments on the BSDS500 and ISBI12 datasets, designing networks tuned to image segmentation and edge detection. Subsequently, by using image similarity metrics, the results of our best performing networks are compared with the original U-Net. The results show that the proposed approach is able to design a network that is less complex in the number of trainable parameters, while also achieving slightly better results than the U-Net with a more consistent training.", notes = "See also \cite{Lima:2022:GPEM} Image segmentation, binary, multi-objects. GE to define structure of feedforward ANN (U-Net). Grammar to define U-Net structure. Automatic addition of matching up (on right of U) for each level on left of U. DSGE restricted crossover cut points. http://www.evostar.org/2021/eurogp/ Part of \cite{Hu:2021:GP} EuroGP'2021 held in conjunction with EvoCOP2021, EvoMusArt2021 and EvoApplications2021", } @Article{Lima:2022:GPEM, author = "Ricardo H. R. Lima and Dimmy Magalhaes and Aurora Pozo and Alexander Mendiburu and Roberto Santana", title = "A grammar-based {GP} approach applied to the design of deep neural networks", journal = "Genetic Programming and Evolvable Machines", year = "2022", volume = "23", number = "3", pages = "427--452", month = sep, note = "Special Issue: Highlights of Genetic Programming 2021 Events", keywords = "genetic algorithms, genetic programming, Grammatical evolution, ANN, Evolutionary algorithms, Automatic design, Deep neural networks", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-022-09432-0", abstract = "Deep Learning has been very successful in automating the feature engineering process, widely applied for various tasks, such as speech recognition, classification, segmentation of images, time-series forecasting, among others. Deep neural networks (DNNs) incorporate the power to learn patterns through data, following an end-to-end fashion and expand the applicability in real world problems, since less pre-processing is necessary. With the fast growth in both scale and complexity, a new challenge has emerged regarding the design and configuration of DNNs. we present a study on applying an evolutionary grammar-based genetic programming algorithm (GP) as a unified approach to the design of DNNs. Evolutionary approaches have been growing in popularity for this subject as Neuroevolution is studied more. We validate our approach in three different applications: the design of Convolutional Neural Networks for image classification, Graph Neural Networks for text classification, and U-Nets for image segmentation. The results show that evolutionary grammar-based GP can efficiently generate different DNN architectures, adapted to each problem, employing choices that differ from what is usually seen in networks designed by hand. This approach has shown a lot of promise regarding the design of architectures, reaching competitive results with their counterparts.", notes = "Department of Computer Science, Federal University of Parana, Curitiba, Parana, Brazil", } @InProceedings{DBLP:conf/wcre/LimaSLCMF16, author = "Luis Gabriel Lima and Francisco Soares-Neto and Paulo Lieuthier and Fernando Castor and Gilberto Melfe and Joao Paulo Fernandes", title = "Haskell in Green Land: Analyzing the Energy Behavior of a Purely Functional Language", booktitle = "IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering, SANER 2016", year = "2016", volume = "1", pages = "517--528", address = "Suita, Osaka, Japan", month = mar # " 14-18", publisher = "IEEE Computer Society", keywords = "genetic improvement, Refactoring", URL = "http://green-haskell.github.io/papers/saner2016.pdf", timestamp = "Wed, 16 Oct 2019 14:14:53 +0200", biburl = "https://dblp.org/rec/conf/wcre/LimaSLCMF16.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://doi.org/10.1109/SANER.2016.85", DOI = "doi:10.1109/SANER.2016.85", size = "12 pages", abstract = "Recent work has studied the effect that factors such as code obfuscation, refactorings and data types have on energy efficiency. we attempt to shed light on the energy behaviour of programs written in a lazy purely functional language, Haskell. We have conducted two empirical studies to analyze the energy efficiency of Haskell programs from two different perspectives: strictness and concurrency. Our experimental space exploration comprises more than 2000 configurations and 20000 executions. We found out that small changes can make a big difference in terms of energy consumption. For example, in one of our benchmarks, under a specific configuration, choosing one data sharing primitive (MVar) over another (TMVar) can yield 60 percent energy savings. In another benchmark, the latter primitive can yield up to 30 percent energy savings over the former. Thus, tools that support developers in quickly refactoring a program to switch between different primitives can be of great help if energy is a concern. In addition, the relationship between energy consumption and performance is not always clear. In sequential benchmarks, high performance is an accurate proxy for low energy consumption. However, for one of our concurrent benchmarks, the variants with the best performance also exhibited the worst energy consumption. To support developers in better understanding this complex relationship, we have extended two existing performance analysis tools to also collect and present data about energy consumption.", notes = "Exhaustive search (ie not GP). concurrent testing. Criterion benchmarking library. http://green-haskell.github.io/ 2x10-core Intel Xeon E5-2660 v2 processors. Energy monitoring with Intel's RAPL. GHC profiler. Edison library of data structures. 'simple refactorings such as switching between thread management constructs can have considerable impact on energy usage' Informatics Center Federal University of Pernambuco (UFPE), Recife, Brazil", } @Article{journals/eswa/CamposOR16, title = "Optimization of neural networks through grammatical evolution and a genetic algorithm", author = "Lidio Mauro {Lima de Campos} and Roberto Celio {Lima de Oliveira} and Mauro Roisenberg", journal = "Expert Syst. Appl", year = "2016", volume = "56", keywords = "genetic algorithms, genetic programming, grammatical evolution", bibdate = "2016-05-14", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/eswa/eswa56.html#CamposOR16", pages = "368--384", URL = "http://dx.doi.org/10.1016/j.eswa.2016.03.012", } @InProceedings{Limon:2015:ROPEC, author = "Mauricio Garcia Limon and Hugo Jair Escalante and Eduardo Morales and Luis Villasenor Pineda", booktitle = "2015 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)", title = "Class-specific feature generation for {1NN} through genetic programming", year = "2015", abstract = "This paper introduces a genetic program for class-specific feature extraction for 1NN. Under the proposed method a new feature space is generated for each class in the problem under analysis. Where feature spaces are build by merging the initial features with a genetic program that aims at maximizing classification accuracy of a 1NN classifier. We compare the performance of our method to both, classical-standard techniques (e.g., PCA, LDA) and to solutions based on evolutionary algorithms. Experimental results reveal our method outperforms alternative solutions in a wide variety of data sets.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ROPEC.2015.7395158", month = nov, notes = "Also known as \cite{7395158}", } @Article{lin:2025:MC, author = "Bo-Cheng Lin and Yi Mei and Mengjie Zhang", title = "Automated design of state transition rules in ant colony optimization by genetic programming: a comprehensive investigation", journal = "Memetic Computing", year = "2025", keywords = "genetic algorithms, genetic programming", URL = "https://link.springer.com/article/10.1007/s12293-025-00435-9", DOI = "doi:10.1007/s12293-025-00435-9", } @MastersThesis{PAPER44, author = "Cheng-Han Lin", title = "Application of Genetic Programming to Fuzzy Modeling and the Schedule of Direct Load Control", school = "Electrical Engineering, National Taipei University of Technology", year = "2007", type = "master's degree", address = "Taiwan", keywords = "genetic algorithms, genetic programming, Fuzzy Neural Network,Fuzzy Modeling, Direct Load Control", URL = "http://hdl.handle.net/11296/72juw6", URL = "http://itlab.ee.ntut.edu.tw/web_2011/papers/PAPER44.HTM", URL = "http://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi/login?o=dnclcdr&s=id=%22095TIT05442060%22.&searchmode=basic", abstract = "Based on a good searching ability of structure, Genetic Programming is designed to search the structural solution and to use crossover mechanism of tree structure to get the better structure. In this thesis, we will use the performance of Genetic Programming to solve two optimal questions. (1).Application of Genetic Programming to fuzzy modeling: This study is concerned with a general methodology of identification of fuzzy models. Unlike other numeric models, fuzzy models operate at a level of information granules (fuzzy sets), and this aspect brings up an important requirement of design on about the transparency of the model. (2).Application of Genetic Programming to the schedule of direct load control: Based on the searching ability of Genetic Programming, we can find an optimal control strategy to reduce the peak load.", } @InProceedings{lin:2015:APSIES, author = "Chiao-Jou Lin and Rung-Tzuo Liaw and Chien-Chih Liao and Chuan-Kang Ting", title = "Considering Reputation in the Selection Strategy of Genetic Programming", booktitle = "Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems - Volume 2", year = "2015", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-319-13356-0_42", DOI = "doi:10.1007/978-3-319-13356-0_42", } @Article{lin:2018:Energies, author = "Chun-Cheng Lin and Rou-Xuan He and Wan-Yu Liu", title = "Considering Multiple Factors to Forecast {CO2} Emissions: A Hybrid Multivariable Grey Forecasting and Genetic Programming Approach", journal = "Energies", year = "2018", volume = "11", number = "12", keywords = "genetic algorithms, genetic programming", ISSN = "1996-1073", URL = "https://www.mdpi.com/1996-1073/11/12/3432", DOI = "doi:10.3390/en11123432", abstract = "Development of technology and economy is often accompanied by surging usage of fossil fuels. Global warming could speed up air pollution and cause floods and droughts, not only affecting the safety of human beings, but also causing drastic economic changes. Therefore, the trend of carbon dioxide emissions and the factors affecting growth of emissions have drawn a lot of attention in all countries in the world. Related studies have investigated many factors that affect carbon emissions such as fuel consumption, transport emissions, and national population. However, most of previous studies on forecasting carbon emissions hardly considered more than two factors. In addition, conventional statistical methods of forecasting carbon emissions usually require some assumptions and limitations such as normal distribution and large dataset. Consequently, this study proposes a two-stage forecasting approach consisting of multivariable grey forecasting model and genetic programming. The multivariable grey forecasting model at the first stage enjoys the advantage of introducing multiple factors into the forecasting model, and can accurately make prediction with only four or more samples. However, grey forecasting may perform worse when the data is nonlinear. To overcome this problem, the second stage is to adopt genetic programming to establish the error correction model to reduce the prediction error. To evaluating performance of the proposed approach, the carbon dioxide emissions in Taiwan from 2000 to 2015 are forecasted and analysed. Experimental comparison on various combinations of multiple factors shows that the proposed forecasting approach has higher accuracy than previous approaches.", notes = "also known as \cite{en11123432}", } @Article{Lin:2020:ESA, author = "Jian Lin and Lei Zhu and Kaizhou Gao", title = "A genetic programming hyper-heuristic approach for the multi-skill resource constrained project scheduling problem", journal = "Expert Systems with Applications", year = "2020", volume = "140", pages = "112915", month = feb, keywords = "genetic algorithms, genetic programming, Hyper-heuristic, Multi-skill, Project scheduling", ISSN = "0957-4174", URL = "http://www.sciencedirect.com/science/article/pii/S0957417419306335", DOI = "doi:10.1016/j.eswa.2019.112915", abstract = "Multi-skill resource-constrained project scheduling problem (MS-RCPSP) is one of the most investigated problems in operations research and management science. In this paper, a genetic programming hyper-heuristic (GP-HH) algorithm is proposed to address the MS-RCPSP. Firstly, a single task sequence vector is used to encode solution, and a repair-based decoding scheme is proposed to generate feasible schedules. Secondly, ten simple heuristic rules are designed to construct a set of low-level heuristics. Thirdly, genetic programming is used as a high-level strategy which can manage the low-level heuristics on the heuristic domain flexibly. In addition, the design-of-experiment (DOE) method is employed to investigate the effect of parameters setting. Finally, the performance of GP-HH is evaluated on the intelligent multi-objective project scheduling environment (iMOPSE) benchmark dataset consisting of 36 instances. Computational comparisons between GP-HH and the state-of-the-art algorithms indicate the superiority of the proposed GP-HH in computing feasible solutions to the problem.", notes = "Also known as \cite{LIN2020112915}", } @InProceedings{Lin:2002:FBC, author = "Jung-Yi Lin and Been-Chian Chien and Tzung-Pei Hong", title = "A Function-Based Classifier Learning Scheme Using Genetic Programming", booktitle = "Advances in Knowledge Discovery and Data Mining : 6th Pacific-Asia Conference, PAKDD 2002", editor = "M.-S. Chen and P. S. Yu and B. Liu", year = "2002", volume = "2336", pages = "92--103", series = "Lecture Notes in Computer Science", address = "Taipel, Taiwan", publisher_address = "Heidelberg", month = "6-8 " # may, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-43704-8", CODEN = "LNCSD9", ISSN = "0302-9743", bibdate = "Tue Sep 10 19:09:39 MDT 2002", DOI = "doi:10.1007/3-540-47887-6_9", acknowledgement = ack-nhfb, size = "12 pages", abstract = "Classification is an important research topic in knowledge discovery and data mining. Many different classifiers have been motivated and developed of late years. In this paper, we propose an effective scheme for learning multi-category classifiers based on genetic programming. For a $k$-class classification problem, a training strategy called adaptive incremental learning strategy and a new fitness function are used to generate $k$ discriminant functions. We urge the discriminant functions to map the domains of training data into a specified interval, and thus data will be assigned into one of the classes by the values of functions. Furthermore, a $Z$-value measure is developed for resolving the conflicts. The experimental results show that the proposed GP-based classification learning approach is effective and performs a high accuracy of classification.", notes = "http://arbor.ee.ntu.edu.tw/pakdd02/ chinese version http://www.bohr.idv.tw/chinese/pdf/B013.pdf", } @Article{Lin:2007:ESA, author = "Jung-Yi Lin and Hao-Ren Ke and Been-Chian Chien and Wei-Pang Yang", title = "Classifier design with feature selection and feature extraction using layered genetic programming", journal = "Expert Systems with Applications", year = "2007", volume = "34", number = "2", pages = "1384--1393", month = feb, keywords = "genetic algorithms, genetic programming, Feature generation, Feature selection, Pattern classification, Multi-population genetic programming, Layered genetic programming", DOI = "doi:10.1016/j.eswa.2007.01.006", abstract = "This paper proposes a novel method called FLGP to construct a classifier device of capability in feature selection and feature extraction. FLGP is developed with layered genetic programming that is a kind of the multiple-population genetic programming. Populations advance to an optimal discriminant function to divide data into two classes. Two methods of feature selection are proposed. New features extracted by certain layer are used to be the training set of next layer's populations. Experiments on several well-known datasets are made to demonstrate performance of FLGP.", notes = "a Department of Computer Science, National Chiao Tung University, 1001 Ta Hsueh Road, HsinChu 300, Taiwan b Library and Institute of Information Management, National Chiao Tung University, Taiwan c Department of Computer Science and Information Engineering, National University of Tainan, Taiwan d Department of Information Management, National Dong Hwa University, Taiwan", } @Article{Lin20072211, author = "Jung-Yi Lin and Hao-Ren Ke and Been-Chian Chien and Wei-Pang Yang", title = "Designing a classifier by a layered multi-population genetic programming approach", journal = "Pattern Recognition", volume = "40", number = "8", pages = "2211--2225", year = "2007", note = "Part Special Issue on Visual Information Processing", ISSN = "0031-3203", DOI = "DOI:10.1016/j.patcog.2007.01.003", URL = "http://www.sciencedirect.com/science/article/B6V14-4MVVSM4-5/2/2085e138e1b34ae21d5e76438ae3fc70", keywords = "genetic algorithms, genetic programming, Classification, Evolutionary computation, Multi-population genetic programming", abstract = "This paper proposes a method called layered genetic programming (LAGEP) to construct a classifier based on multi-population genetic programming (MGP). LAGEP employs layer architecture to arrange multiple populations. A layer is composed of a number of populations. The results of populations are discriminant functions. These functions transform the training set to construct a new training set. The successive layer uses the new training set to obtain better discriminant functions. Moreover, because the functions generated by each layer will be composed to a long discriminant function, which is the result of LAGEP, every layer can evolve with short individuals. For each population, we propose an adaptive mutation rate tuning method to increase the mutation rate based on fitness values and remaining generations. Several experiments are conducted with different settings of LAGEP and several real-world medical problems. Experiment results show that LAGEP achieves comparable accuracy to single population GP in much less time.", } @PhdThesis{Jung-Yi_Lin:thesis, author = "Jung-Yi Lin", title = "Layered Multi-Population Genetic Programming And Its Applications", school = "Computer Science, National Chiao Tung University (NCTU)", year = "2007", address = "HsinChu, Taiwan", month = jul, keywords = "genetic algorithms, genetic programming, multi-population genetic programming, classification, classifier design, feature selection, feature construction, evolutionary computation", URL = "http://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi/ccd=CYyGVt/result#result", URL = "http://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi?o=dnclcdr&s=id=%22095NCTU5394045%22.&searchmode=basic", URL = "http://hdl.handle.net/11536/53780", URL = "http://ir.nctu.edu.tw/bitstream/11536/53780/1/381501.pdf", size = "106 pages", abstract = "This study focuses on a proposed method based on genetic programming (GP). Genetic programming is a prominent technique of evolutionary computation (EC). It mimics the evolution mechanism of biological environment to determine optimal solutions for given training instances. Many researchers have been devoted to enhance effectiveness and efficiency of genetic programming. The applications of the proposed method include classification and feature processing. Classification problems play an important role in the development of knowledge engineering. Hidden relations that can be used as a basis for classification are often unclear and not easily elucidated. Thus, many machine learning algorithms have arisen to solve such problems. Feature selection and feature generation are two important techniques dealing with features. Feature selection is capable of removing useless, irrelevant, redundant, and noisy features. Feature generation generates new useful features that could improve classification accuracy. In this study we propose a layered multi-population genetic programming method to solve classification problems. The proposed method that can complete feature selection and feature construction simultaneously is also proposed. The layered multipopulation genetic programming method employs layer architecture to arrange multiple populations. A layer is composed of a number of populations. Each population evolves to generate a discriminant function. A set of discriminant functions generated by one layer will be integrated and be transformed by the successive layer. To improve the learning performance, an adaptive mutation probability tuning method is proposed. Moreover, a statistical-based method is proposed to solve multi-category classification problems. Several experiments on classical classification problems and real-world medical problems are conducted using different configurations. Experimental results show that the proposed methods are accurate and effective.", notes = "In english. Supervisor: Dr. Wei-Pang Yang, Dr. Been-Chian Chien 312 004D:2 96-3 003639188", } @InProceedings{DBLP:conf/gecco/Lin09a, author = "Jung-Yi Lin", title = "Cancer classification using microarray and layered architecture genetic programming", booktitle = "GECCO-2009 Late-Breaking Papers", year = "2009", editor = "Anna I. Esparcia and Ying-ping Chen and Gabriela Ochoa and Ender Ozcan and Marc Schoenauer and Anne Auger and Hans-Georg Beyer and Nikolaus Hansen and Steffen Finck and Raymond Ros and Darrell Whitley and Garnett Wilson and Simon Harding and W. B. Langdon and Man Leung Wong and Laurence D. Merkle and Frank W. Moore and Sevan G. Ficici and William Rand and Rick Riolo and Nawwaf Kharma and William R. Buckley and Julian Miller and Kenneth Stanley and Jaume Bacardit and Will Browne and Jan Drugowitsch and Nicola Beume and Mike Preuss and Stephen L. Smith and Stefano Cagnoni and Jim DeLeo and Alexandru Floares and Aaron Baughman and Steven Gustafson and Maarten Keijzer and Arthur Kordon and Clare Bates Congdon and Laurence D. Merkle and Frank W. Moore", pages = "2085--2090", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1570256.1570281", abstract = "An important problem of cancer diagnosis and treatment is to distinguish tumors from malignant or benign. Classifying tumors correctly leads us to target specific therapies properly to maximizing efficiency and reducing toxicity. Through the microarray technology, it is possible that monitoring expression in cells for numerous of genes simultaneously. Therefore we are allowed to use potential information hidden in the gene expression data to build a more accurate and more reliable classification model on tumor samples. In this paper we intend to investigate a new approach for cancer classification using genetic programming and microarray gene expression profiles. The layered architecture genetic programming (LAGEP) is applied to build the classification model. Some typical cancer gene expression datasets are validated to demonstrate the classification accuracy of the proposed model.", notes = "Distributed on CD-ROM at GECCO-2009. ACM Order Number 910092.", } @InProceedings{Lin:2010:ICS, author = "Jung Yi Lin", title = "Fitness enhancement of layered architecture genetic programming", booktitle = "2010 International Computer Symposium (ICS)", year = "2010", month = "16-18 " # dec, pages = "700--704", abstract = "Layered architecture genetic programming (LAGEP) has been applied on variety classification problems. It organises populations as layers. Populations in different layers evolve with different training sets. Individuals produced by populations of layer Li transform training instances into new ones. Populations in Li+1 then evolve with the new training set instead of evolve with the original given training set. Each population in Li produces one feature for the new training instances. New training instances could have fewer features and are easier to be classified. Such mechanism makes consecutive layer gain better fitness value than preceding layers do. At this paper, we intend to analyse the enhancement of fitness value over all layers. We conduct experiments with a high-dimensional gene expression dataset to show the fitness enhancement.", keywords = "genetic algorithms, genetic programming, classification problems, fitness enhancement, high dimensional gene expression dataset, layered architecture genetic programming, pattern classification", DOI = "doi:10.1109/COMPSYM.2010.5685423", notes = "Also known as \cite{5685423}", } @InProceedings{Lin:2012:CyberneticsCom, author = "Jung Yi Lin and Jen-Yuan Yeh and Chao Chung Liu", booktitle = "IEEE International Conference on Computational Intelligence and Cybernetics (CyberneticsCom 2012)", title = "Learning to rank for information retrieval using layered multi-population genetic programming", year = "2012", pages = "45--49", DOI = "doi:10.1109/CyberneticsCom.2012.6381614", size = "5 pages", abstract = "To determine which documents are relevant and which are not to the user query is one central problem broadly studied in the field of information retrieval (IR). Learning to rank for information retrieval (LR4IR), which leverages supervised learning-based methods to address the problem, aims to produce a ranking model automatically for defining a proper sequential order of related documents according to the given query. The ranking model is employed to determine the relationship degree between one document and the user query, based on which a ranking of query-related documents could be produced. In this paper we proposed an improved RankGP algorithm using multi-layered multi-population genetic programming to obtain a ranking function, trained from collections of IR results with relevance judgements. In essence, the generated ranking function is consisted of a set of IR evidences (or features) and particular predefined GP operators. The proposed method is capable of generating complex functions through evolving small populations. LETOR 4.0 was used to evaluate the effectiveness of the proposed method and the results showed that the method is competitive with RankSVM and AdaRank.", keywords = "genetic algorithms, genetic programming, document handling, learning (artificial intelligence), query processing, AdaRank, GP operator, LETOR 4.0, LR4IR, RankGP algorithm, RankSVM, learning to rank for information retrieval, miltilayered multipopulation genetic programming, query-related document, ranking model, relevance judgment, supervised learning, support vector machines, user query, Feature extraction, Information retrieval, Machine learning, Sociology, Statistics, Training, Vectors, Learning to rank for Information Retrieval, evolutionary computation, ranking function", notes = "Also known as \cite{6381614}", } @InProceedings{Lin:2012:ICMLC, author = "Jung Yi Lin and Jen-Yuan Yeh and Chao-Chung Liu", booktitle = "International Conference on Machine Learning and Cybernetics (ICMLC 2012)", title = "Applying layered multi-population genetic programming on learning to rank for information retrieval", year = "2012", volume = "5", pages = "1754--1759", size = "6 pages", abstract = "Information retrieval (IR) returns a relative ranking of documents with respect to a user query. Learning to rank for information retrieval (LR4IR) employs supervised learning techniques to address this problem, and it aims to produce a ranking model automatically for defining a proper sequential order of related documents based on the query. The ranking model determines the relationship degree between documents and the query. In this paper an improved version of RankGP is proposed. It uses layered multi-population genetic programming to obtain a ranking function which consists of a set of IR evidences and particular predefined operators. The proposed method is capable to generate complex functions through evolving small populations. In this paper, LETOR 4.0 was used to evaluate the effectiveness of the proposed method and the results showed that the method is competitive with other LR4IR Algorithms.", keywords = "genetic algorithms, genetic programming, document handling, learning (artificial intelligence), query processing, LETOR 4.0, LR4IR, RankGP, document ranking, layered multipopulation genetic programming, learning to rank for information retrieval, ranking function, supervised learning techniques, user query, Abstracts, Programming, Sociology, Statistics, Evolutionary computation, Learning to rank for Information Retrieval, Ranking function", DOI = "doi:10.1109/ICMLC.2012.6359640", ISSN = "2160-133X", notes = "Also known as \cite{6359640}", } @InProceedings{Lin:2012:ICGEC, author = "Jung Yi Lin and Ming Chih Tung and Chia Hui Chang and Chao Chung Liu and Ju Fu Peng", booktitle = "Genetic and Evolutionary Computing (ICGEC), 2012 Sixth International Conference on", title = "Position Correction on Consumer-Grade GPS Using Genetic Programming", year = "2012", pages = "200--203", DOI = "doi:10.1109/ICGEC.2012.120", abstract = "Consumer-grade global positioning system (GPS) becomes common and popular in these days because of its low cost along with acceptable accuracy. A GPS receiver cannot always obtain precise position because it affected by errors from either satellites or the receiver itself. Many researchers proposed effective approaches to improve positioning accuracy of GPS receivers. In this paper, we propose a method based on the concept of differential correction using two consumer-grade GPS receivers and genetic programming (GP). The proposed method generates a correction function through GPS information gathered by GPS receivers and a known position. Any GPS receiver which transfers NMEA (National Marine Electronics Association) sentence information can be used for the proposed method. the method could be implemented on various GPS-embedded devices without modifying hardware components.", keywords = "genetic algorithms, genetic programming, Global Positioning System, radio receivers, GP, NMEA sentence information, National Marine Electronics Association sentence information, consumer-grade GPS receiver, hardware components, position correction, satellites, Accuracy, Global Positioning System, Receivers, Satellite broadcasting, Satellites, Training, Global Positioning System (GPS), evolutionary computation", notes = "Also known as \cite{6457257}", } @InProceedings{Lin:2012:SCIS, author = "Jung Yi Lin and Chia Hui Chang and Ju Fu Peng and Ming Chih Tung and Chao Chung Liu", booktitle = "Soft Computing and Intelligent Systems (SCIS) and 13th International Symposium on Advanced Intelligent Systems (ISIS), 2012 Joint 6th International Conference on", title = "Evolving GPS position correction function using genetic programming", year = "2012", pages = "136--140", keywords = "genetic algorithms, genetic programming, Global Positioning System (GPS), Position Correction, evolutionary computation", DOI = "doi:10.1109/SCIS-ISIS.2012.6505080", size = "5 pages", abstract = "Many mobile devices embeds Global positioning system (GPS) to enable tour navigation or path programming. Those low-cost consumer-grade GPS receivers usually do not have high accuracy so that a position correction algorithm is therefore necessary. This paper proposed a correction technique using genetic programming. This technique requires only two GPS receivers and a known position. Using position information gathered by the receivers we are capable of predicting the correct position. The proposed technique can be implemented without modifying hardware devices or settings. The algorithm generates a correction function constructed by features of NMEA (national Marine Electronics Association) sentences, which is a standard format and is common in most GPS receivers. Experiments are conducted to demonstrate performance of the proposed technique. Positioning error could be reduced significantly.", notes = "Also known as \cite{6505080}", } @InProceedings{lin:2013:AISA, author = "Jung Yi Lin and Ming Chih Tung and Chia Hui Chang and Chao Chung Liu and Ju Fu Peng", title = "A New {GPS} Position Correction Method Based on Genetic Programming", booktitle = "Advances in Intelligent Systems and Applications - Volume 1", year = "2013", volume = "20", series = "SIST", pages = "177--185", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-35452-6", URL = "http://link.springer.com/chapter/10.1007/978-3-642-35452-6_20", DOI = "doi:10.1007/978-3-642-35452-6_20", abstract = "More and more mobile devices equipped with global positioning system (GPS) are helpful in tour navigation. However, such consumer-grade GPS receivers usually have low accuracy in positioning and require position correction algorithms. In this paper, we proposed an evolutionary computation based technique to correct a GPS receiver with another GPS receiver and a known reference point. The proposed technique can be implemented without changing any hardware. It generates a correction function from given NMEA (national Marine Electronics Association) information. Using such function to derive correct position information could be efficient. Experiments are conducted to demonstrate performance of the proposed technique. Positioning error could be reduced from in the order of 10 m to in the order of 1 m.", } @Article{LIN:2023:cscm, author = "Lang Lin and Jinjun Xu and Jialiang Yuan and Yong Yu", title = "Compressive strength and elastic modulus of {RBAC:} An analysis of existing data and an artificial intelligence based prediction", journal = "Case Studies in Construction Materials", volume = "18", pages = "e02184", year = "2023", ISSN = "2214-5095", DOI = "doi:10.1016/j.cscm.2023.e02184", URL = "https://www.sciencedirect.com/science/article/pii/S2214509523003649", keywords = "genetic algorithms, genetic programming, Recycled brick aggregate concrete (RBAC), Compressive strength, Elastic modulus, Artificial neural network, ANN, Multigene genetic programming", abstract = "In recent years crushing waste brick to produce recycled brick aggregates (RBAs) has become a viable solution for reducing environmental pollution and addressing the natural resource shortage in civil engineering. To promote the widespread use of the recycled brick aggregate concrete (RBAC) in construction, this study analyzes existing test results on the attributes of RBAs and the compressive mechanical behaviors of RBAC. The review results indicate significant differences and variabilities in the characteristics of RBAs compared to natural coarse aggregates and recycled concrete coarse aggregates. RBAs have the highest absorption capacity and crushing index among the three aggregates, leading to changes in the compressive failure mechanism and a decline in the mechanical properties of RBAC. Additionally, it is also observed that existing formulas do not adequately account for the deterioration of the compressive mechanical properties of RBAC. To tackle this problem, artificial intelligence (AI) approaches including artificial neural network and multigene genetic programming are used to develop precise models for predicting the compressive strength and elastic modulus of RBAC. It is found that RBAC's these two mechanical indexes are mainly influenced by the standard strength of cement paste, water-to-cement ratio, sand-to-aggregate mass ratio, RBA replacement ratio and mass-weighted water absorption ratio of coarse aggregates. The AI models developed in this study accurately capture the trends of these factors and offer desirable predictive results", } @InProceedings{Lin:2023:ICDMW, author = "Mingqian Lin and Lin Shang and Xiaoying Gao", booktitle = "2023 IEEE International Conference on Data Mining Workshops (ICDMW)", title = "Enhancing Interpretability in {AI-Generated} Image Detection with Genetic Programming", year = "2023", pages = "371--378", address = "Shanghai, China", month = "1-4 " # dec, keywords = "genetic algorithms, genetic programming, Image forensics, Decision making, Detectors, Reliability, Task analysis, Image classification, AI-generated image detection, Interpretability, Transparency", isbn13 = "979-8-3503-8165-8", ISSN = "2375-9259", DOI = "doi:10.1109/ICDMW60847.2023.00053", abstract = "IGC can produce realistic AI-generated images that challenge human perception. Detecting AI-generated content is critical, which has prompted the technology to tell apart real images from the generated ones. However, the existing methods, such as CNND, LGrad, lack interpretability. Unlike traditional image classification, it is crucial to know why the image can be considered as AI-generated. We introduce a novel AI-generated image detector based on genetic programming (GP), prioritizing both interpretability and classification accuracy. This application of GP in this context emphasizes the need for interpretability in AI-generated content identification. Our GP-based approach not only achieves competitive classification accuracy but also provides transparent decision-making processes, bridging the interpretability gap. This method enhances trust and understanding in the AI-generated image detection process. Through extensive experiments, we highlight the potential of GP-based detectors for this unique task. This research contributes to improving the transparency and reliability of AI-generated image detection, holding implications for computer vision and image forensics. Our work emphasizes the pivotal role of interpretability in distinguishing AI-generated content and offers insights into the inner workings of such models and also achieves a good generation ability.", notes = "Also known as \cite{10411549}", } @Misc{pingchen_lin_paper, author = "Pin-Chen (P. C.) Lin and Jiah-Shing Chen", title = "FuzzyTree Crossover for Multi-Valued Stock Valuation", howpublished = "Tutorial at Computational Intelligence in Economics and Finance, Summer Workshop", year = "2004", month = aug, keywords = "genetic algorithms, genetic programming, Stock Valuation, Intrinsic Value, Multi-Value, Fuzzy Number", URL = "http://www.aiecon.org/conference/efmaci2004/pdf/pingchen_lin_paper.pdf", size = "15 pages", abstract = "Stock valuation is very important for fundamental investors to select undervalue stocks to earn excess profit. However, it may be difficult to use stock valuation results because different models generate different estimates on the same stock. This suggests that the value of a stock should be multi-valued rather than single-valued. We therefore develop a multi-valued stock valuation model based on fuzzy genetic programming. In our fuzzy GP model, the value of a stock is represented as a fuzzy expression tree whose terminal nodes are allowed to be fuzzy numbers. There is little literature available on the crossover operator for our fuzzy trees except the vanilla subtree crossover. This study generalizes the subtree crossover to design a new crossover operator for the fuzzy trees. Since the stock value is estimated by a fuzzy expression tree which calculates to a fuzzy number, the stock value becomes multi-valued. In addition, the resulting fuzzy stock value induces a natural trading strategy which can readily be executed and evaluated. Experimental results indicate that the FuzzyTree crossover is more effective than subtree crossover in terms of expression tree complexity and run time. Second, shorter training periods produce better ROI. It indicates long-term financial statement may distort the intrinsic value of a stock. Finally, the return of multi-valued fuzzy trading strategy is better than that of single-valued and Buy-and-Hold strategy. We suggest that more attention should be put on the multi-valued stock valuation approach.", notes = "Pin-Chen (P.C.) Lin = Ping-Chen Lin", } @Article{Lin:2007:IS, author = "Ping-Chen Lin and Jiah-Shing Chen", title = "FuzzyTree crossover for multi-valued stock valuation", journal = "Information Sciences", year = "2007", volume = "177", number = "5", pages = "1193--1203", month = "1 " # mar, note = "Including: The 3rd International Workshop on Computational Intelligence in Economics and Finance (CIEF'2003)", keywords = "genetic algorithms, genetic programming, Multi-valued stock valuation, Intrinsic value, Fuzzy number", DOI = "doi:10.1016/j.ins.2006.08.017", abstract = "Stock valuation is very important for fundamental investors in order to select undervalued stocks so as to earn excess profits. However, it may be difficult to use stock valuation results, because different models generate different estimates for the same stock. This suggests that the value of a stock should be multi-valued rather than single-valued. We therefore develop a multi-valued stock valuation model based on fuzzy genetic programming (GP). In our fuzzy GP model the value of a stock is represented as a fuzzy expression tree whose terminal nodes are allowed to be fuzzy numbers. There is scant literature available on the crossover operator for our fuzzy trees, except for the vanilla subtree crossover. This study generalises the subtree crossover in order to design a new crossover operator for the fuzzy trees. Since the stock value is estimated by a fuzzy expression tree which calculates to a fuzzy number, the stock value becomes multi-valued. In addition, the resulting fuzzy stock value induces a natural trading strategy which can readily be executed and evaluated. These experimental results indicate that the fuzzy tree (FuzzyTree) crossover is more effective than a subtree (SubTree) crossover in terms of expression tree complexity and run time. Secondly, shorter training periods produce a better return of investment (ROI), indicating that long-term financial statements may distort the intrinsic value of a stock. Finally, the return of a multi-valued fuzzy trading strategy is better than that of single-valued and buy-and-hold strategies.", } @InProceedings{lin:1999:IPOSMGA, author = "Wen-Yang Lin", title = "Improving Parallel Ordering of Sparse Matrices using Genetic Algorithms", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1790", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "real world applications, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-776.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-776.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Article{Lin:2017:Vacuum, author = "Y. C. Lin and Fu-Qi Nong and Xiao-Min Chen and Dong-Dong Chen and Ming-Song Chen", title = "Microstructural evolution and constitutive models to predict hot deformation behaviors of a nickel-based superalloy", journal = "Vacuum", volume = "137", pages = "104--114", year = "2017", ISSN = "0042-207X", DOI = "doi:10.1016/j.vacuum.2016.12.022", URL = "http://www.sciencedirect.com/science/article/pii/S0042207X16308041", abstract = "To investigate the hot deformation behaviors of a nickel-based superalloy, the hot compressive tests are conducted at the deformation temperature range of 920-1040 degreeC and strain rate range of 0.001-1s-1. It is found that the effects of strain rate and deformation temperature on the grain boundary maps are significant. An almost competed dynamic recrystallization (DRX) microstructure occurs at relatively low strain rates. However, the increased strain rate easily leads to the uneven microstructures. The DRX degree notably increases with the increase of deformation temperature, because the high temperature enhances the grain boundary migration mobility and facilitates the nucleation and growth of DRX grains. Based on the experimental results, multi-gene genetic programming (MGGP), artificial neural network (ANN) and Arrhenius type phenomenological models are established to predict the flow stress. Due to the obvious over-fitting problem of MGGP model, a Hannan-Quinn information criterion based MGGP (HQC-MGGP) approach is proposed. The performances of MGGP, HQC-MGGP, ANN and phenomenological models are compared. It is found that HQC-MGGP model has the best performance to predict the flow stress under the experimental conditions. Therefore, HQC-MGGP model is accurate and reliable in describing the hot deformation behaviors of the studied nickel-based superalloy.", keywords = "genetic algorithms, genetic programming, Alloy, Hot deformation, Grain boundary map, Constitutive model", } @Article{Lin:2021:NatureComm, author = "Yajuan Lin and Carly Moreno and Adrian Marchetti and Hugh Ducklow and Oscar Schofield and Erwan Delage and Michael Meredith and Zuchuan Li and Damien Eveillard and Samuel Chaffron and Nicolas Cassar", title = "Decline in plankton diversity and carbon flux with reduced sea ice extent along the Western Antarctic Peninsula", journal = "Nature Communications", year = "2021", volume = "12", pages = "Article number: 4948", keywords = "genetic algorithms, genetic programming, matlab, R", publisher = "HAL CCSD; Nature Publishing Group", ISSN = "2041-1723", bibsource = "OAI-PMH server at api.archives-ouvertes.fr", identifier = "hal-03389081", language = "en", oai = "oai:HAL:hal-03389081v1", URL = "https://hal.archives-ouvertes.fr/hal-03389081", URL = "https://hal.archives-ouvertes.fr/hal-03389081/document", URL = "https://www.nature.com/articles/s41467-021-25235-w.pdf", DOI = "doi:10.1038/s41467-021-25235-w", code_url = "https://github.com/nicolascassar/WGCNA-Analyses", code_url = "https://github.com/nicolascassar/O2Ar_calculations", size = "9 pages", abstract = "Since the middle of the past century, the Western Antarctic Peninsula has warmed rapidly with a significant loss of sea ice but the impacts on plankton biodiversity and carbon cycling remain an open question. Here, using a 5-year dataset of eukaryotic plankton DNA metabarcoding, we assess changes in biodiversity and net community production in this region. Our results show that sea-ice extent is a dominant factor influencing eukaryotic plankton community composition, biodiversity, and net community production. Species richness and evenness decline with an increase in sea surface temperature (SST). In regions with low SST and shallow mixed layers, the community was dominated by a diverse assemblage of diatoms and dinoflagellates. Conversely, less diverse plankton assemblages were observed in waters with higher SST and/or deep mixed layers when sea ice extent was lower. A genetic programming machine-learning model explained up to 80percent of the net community production variability at the Western Antarctic Peninsula. Among the biological explanatory variables, the sea-ice environment associated plankton assemblage is the best predictor of net community production. We conclude that eukaryotic plankton diversity and carbon cycling at the Western Antarctic Peninsula are strongly linked to sea-ice conditions.", } @PhdThesis{Yingqiang_Lin:thesis, author = "Yingqiang Lin", title = "Feature synthesis and analysis by evolutionary computation for object detection and recognition", school = "University of California, Riverside", year = "2003", address = "USA", month = jun, keywords = "genetic algorithms, genetic programming, coevolution, Applied sciences, Object detection, Minimum description length, Computer science", URL = "http://dl.acm.org/citation.cfm?id=979049", URL = "http://search.proquest.com/docview/305342764", size = "168 pages", abstract = "This dissertation investigates evolutionary computational techniques such as genetic programming (GP), coevolutionary genetic programming (CGP) and genetic algorithm (GA) to automate the synthesis and analysis of object detection and recognition systems. First, this dissertation shows the efficacy of GP and CGP in synthesizing effective composite operators and composite features from domain-independent primitive image processing operations and primitive features for object detection and recognition. Based on GP and CGP's ability of synthesizing effective features from simple features not specifically designed for a particular kind of imagery, the cost of building object detection and recognition systems is lowered and the flexibility of the systems is increased. More importantly, it shows that a large amount of unconventional features are explored by GP and CGP and these unconventional features yield exceptionally good detection and recognition performances in some cases, overcoming the human experts' limitation of considering only a small number of conventional features. Second, smart crossover, smart mutation and a new fitness function based on minimum description length (MDL) principle are designed to improve the efficiency of genetic programming. Smart crossover and smart mutation are designed to identify and keep the effective components of composite operators from being disrupted and a MDL-based fitness function is proposed to address the well-known code bloat problem of GP without imposing severe restriction on the GP search. Compared to normal GP, smart GP algorithm with smart crossover, smart mutation and a MDL-based fitness function finds effective composite operators more quickly and the composite operators learned by smart GP algorithm have smaller size, greatly reducing both the computational expense during testing and the possibility of overfitting during training. Finally, a new MDL-based fitness function is proposed to improve the genetic algorithm's performance on feature selection for object detection and recognition. The MDL-based fitness function incorporates the number of features selected into the fitness evaluation process and prevents GA from selecting a large number of features to overfit the training data. The goal is to select a small set of features with good discrimination performances on both training and unseen testing data to reduce the possibility of overfitting the training data during training and the computational burden during testing.", notes = "SAR image, region of interest. Paved road, lake, tank t72, river, grass, brdm2, d7, t62, zil, zsu Supervisor Bir Bhanu Senior Research Engineer, Trend Micro Inc UMI Number: 3096772 ProQuest Order No. 3096772 OCLC: 53984756", } @Article{bb38973, author = "Yingqiang Lin and Bir Bhanu", title = "Object Detection via Feature Synthesis Using {MDL}-Based Genetic Programming", journal = "IEEE Transactions on Systems, Man and Cybernetics, Part B", volume = "35", year = "2005", number = "3", month = jun, pages = "538--547", bibsource = "http://iris.usc.edu/Vision-Notes/bibliography/pattern650.html#TT36418", keywords = "genetic algorithms, genetic programming, Feature learning, minimum description length (MDL), primitive feature image, primitive operator, synthetic aperture radar (SAR) image", ISSN = "1083-4419", URL = "http://ieeexplore.ieee.org/iel5/3477/30862/01430837.pdf", DOI = "doi:10.1109/TSMCB.2005.846656", size = "10 pages", abstract = "we use genetic programming (GP) to synthesise composite operators and composite features from combinations of primitive operations and primitive features for object detection. The motivation for using GP is to overcome the human experts' limitations of focusing only on conventional combinations of primitive image processing operations in the feature synthesis. GP attempts many unconventional combinations that in some cases yield exceptionally good results. To improve the efficiency of GP and prevent its well-known code bloat problem without imposing severe restriction on the GP search, we design a new fitness function based on minimum description length principle to incorporate both the pixel labelling error and the size of a composite operator into the fitness evaluation process. To further improve the efficiency of GP, smart crossover, smart mutation and a public library ideas are incorporated to identify and keep the effective components of composite operators. Our experiments, which are performed on selected training regions of a training image to reduce the training time, show that compared to normal GP, our GP algorithm finds effective composite operators more quickly and the learned composite operators can be applied to the whole training image and other similar testing images. Also, compared to a traditional region-of-interest extraction algorithm, the composite operators learned by GP are more effective and efficient for object detection.", } @Article{Lin:2005:tSMC, title = "Evolutionary feature synthesis for object recognition", author = "Yingqiang Lin and Bir Bhanu", journal = "IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews", year = "2005", volume = "35", number = "2", pages = "156--171", month = may, keywords = "genetic algorithms, genetic programming, feature extraction, object recognition, radar imaging, synthetic aperture radar, SAR images, coevolutionary genetic programming approach, domain-independent primitive operator, evolutionary feature synthesis, human experts, object recognition, real synthetic aperture radar, vehicle recognition", DOI = "doi:10.1109/TSMCC.2004.841912", ISSN = "1094-6977", abstract = "Features represent the characteristics of objects and selecting or synthesising effective composite features are the key to the performance of object recognition. In this paper, we propose a coevolutionary genetic programming (CGP) approach to learn composite features for object recognition. The knowledge about the problem domain is incorporated in primitive features that are used in the synthesis of composite features by CGP using domain-independent primitive operators. The motivation for using CGP is to overcome the limitations of human experts who consider only a small number of conventional combinations of primitive features during synthesis. CGP, on the other hand, can try a very large number of unconventional combinations and these unconventional combinations yield exceptionally good results in some cases. Our experimental results with real synthetic aperture radar (SAR) images show that CGP can discover good composite features to distinguish objects from clutter and to distinguish among objects belonging to several classes. The comparison with other classical classification algorithms is favourable to the CGP-based approach proposed in this paper.", } @InProceedings{Lin:2010:ICMLC, author = "Yi-Shen Lin and Xiao-Ting Liang", title = "Gene expression programming with parallel hybrid model", booktitle = "2010 International Conference on Machine Learning and Cybernetics (ICMLC)", year = "2010", month = jul, volume = "5", pages = "2406--2409", abstract = "In this paper we discussed a hybrid parallel and distributed model and their relationships of diversity phenomenon. We study the synchronous and asynchronous version of the island-model in GEP algorithm. The experiments that we have performed have allowed us to find an interesting link between the subpopulations and the parameters setting to GEP.", keywords = "genetic algorithms, genetic programming, gene expression programming, GEP algorithm, distributed model, island-model, parallel hybrid model, evolutionary computation, mathematical programming, parallel processing", DOI = "doi:10.1109/ICMLC.2010.5580717", notes = "Coll. of Inf., South China Agric. Univ., Guangzhou, China Also known as \cite{5580717}", } @InProceedings{conf/cikm/LinLZX14, author = "Yuan Lin and Hongfei Lin and Ping Zhang and Bo Xu", title = "{GPQ}: Directly Optimizing {Q}-measure based on Genetic Programming", booktitle = "Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, CIKM 2014", publisher = "ACM", year = "2014", editor = "Jianzhong Li and Xiaoyang Sean Wang and Minos N. Garofalakis and Ian Soboroff and Torsten Suel and Min Wang", address = "Shanghai, China", month = nov # " 3-7", pages = "1859--1862", keywords = "genetic algorithms, genetic programming, information retrieval, learning to rank, q-measure", isbn13 = "978-1-4503-2598-1", bibdate = "2014-11-07", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/cikm/cikm2014.html#LinLZX14", URL = "http://dl.acm.org/citation.cfm?id=2661829", DOI = "doi:10.1145/2661829.2661932", acmid = "2661932", abstract = "Ranking plays an important role in information retrieval system. In recent years, a kind of research named learning to rank becomes more and more popular, which applies machine learning technology to solve ranking problems. Lots of ranking models belonged to learning to rank have been proposed, such as Regression, RankNet, and ListNet. Inspired by this, we proposed a novel learning to rank algorithm named GPQ in this paper, in which genetic programming was employed to directly optimize Q-measure evaluation metric. Experimental results on OHSUMED benchmark dataset indicated that our method GPQ could be competitive with Ranking SVM, SVMMAP and ListNet, and improve the ranking accuracies.", } @InProceedings{Linard:2019:DSD, author = "Alexis Linard and Joost {van Pinxten}", booktitle = "2019 22nd Euromicro Conference on Digital System Design (DSD)", title = "An Application of Hyper-Heuristics to Flexible Manufacturing Systems", year = "2019", pages = "343--350", abstract = "Optimizing the productivity of Flexible Manufacturing Systems requires online scheduling to ensure that the timing constraints due to complex interactions between modules are satisfied. This work focuses on optimizing a ranking metric such that the online scheduler locally (i.e., per product) chooses an option that yields the highest productivity in the long term. In this paper, we focus on the scheduling of a re-entrant Flexible Manufacturing System, more specifically a Large Scale Printer capable of printing hundreds of sheets per minute. The system requires an online scheduler that determines for each sheet when it should enter the system, be printed for the first time, and when it should return for its second print. We have applied genetic programming, a hyper-heuristic, to heuristically find good ranking metrics that can be used in an online scheduling heuristic. The results show that metrics can be tuned for different job types, to increase the productivity of such systems. Our methods achieved a significant reduction in the jobs' makespan.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/DSD.2019.00057", month = aug, notes = "Also known as \cite{8875227}", } @InProceedings{Linares-Vasquez:2015:FSE, author = "Mario Linares-Vasquez and Gabriele Bavota and Carlos Eduardo Bernal Cardenas and Rocco Oliveto and Massimiliano {Di Penta} and Denys Poshyvanyk", title = "Optimizing Energy Consumption of GUIs in Android Apps: A Multi-objective Approach", booktitle = "Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering, ESEC/FSE 2015", year = "2015", pages = "143--154", address = "Bergamo, Italy", publisher = "ACM", keywords = "genetic algorithms, NSGA-II, Empirical Study, Energy consumption, Mobile applications", isbn13 = "978-1-4503-3675-8", URL = "http://doi.acm.org/10.1145/2786805.2786847", DOI = "doi:10.1145/2786805.2786847", acmid = "2786847", abstract = "The wide diffusion of mobile devices has motivated research towards optimizing energy consumption of software systems, including apps, targeting such devices. Besides efforts aimed at dealing with various kinds of energy bugs, the adoption of Organic Light-Emitting Diode (OLED) screens has motivated research towards reducing energy consumption by choosing an appropriate colour palette. Whilst past research in this area aimed at optimizing energy while keeping an acceptable level of contrast, this paper proposes an approach, named GEMMA (Gui Energy Multi-objective optiMization for Android apps), for generating colour palettes using a multi- objective optimization technique, which produces colour solutions optimizing energy consumption and contrast while using consistent colours with respect to the original colour palette. An empirical evaluation that we performed on 25 Android apps demonstrates not only significant improvements in terms of the three different objectives, but also confirmed that in most cases users still perceived the choices of colors as attractive. Finally, for several apps we interviewed the original developers, who in some cases expressed the intent to adopt the proposed choice of color palette, whereas in other cases pointed out directions for future improvements", notes = "cited by \cite{Haraldsson:thesis}", } @InProceedings{4541423, author = "D. Linaro and M. Storace", title = "A method based on a genetic algorithm to find PWL approximations of multivariate nonlinear functions", booktitle = "Circuits and Systems, 2008. ISCAS 2008. IEEE International Symposium on", year = "2008", month = may, pages = "336--339", keywords = "genetic algorithms, Hodgkin-Huxley neuron model, circuit, multivariate continuous nonlinear functions, piecewise-linear approximations, nonlinear network analysis, piecewise linear techniques", DOI = "doi:10.1109/ISCAS.2008.4541423", notes = "Not on GP", } @InProceedings{lindblad:2002:emioiugh, author = "Fredrik Lindblad and Peter Nordin and Krister Wolff", title = "Evolving {3D} model interpretation of images using graphics hardware", booktitle = "Proceedings of the 2002 Congress on Evolutionary Computation CEC2002", editor = "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and Mark Shackleton", pages = "225--230", year = "2002", publisher = "IEEE Press", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", organisation = "IEEE Neural Network Council (NNC), Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", ISBN = "0-7803-7278-6", month = "12-17 " # may, notes = "CEC 2002 - A joint meeting of the IEEE, the Evolutionary Programming Society, and the IEE. Held in connection with the World Congress on Computational Intelligence (WCCI 2002)", keywords = "genetic algorithms, genetic programming, machine vision, GPU", URL = "http://fy.chalmers.se/~wolff/LNW_wcci02.pdf", DOI = "doi:10.1109/CEC.2002.1006238", abstract = "We present a novel approach for 3d-scene interpretation with numerous applications, for instance in robotics. The models are rendered using 3d graphics hardware and DirectX. Both artificial and real images were used to test the system. More than one target image can be used, allowing stereoscopic vision. These experiments present results of interesting generalisation.", } @Article{Linden:2007:biosystems, author = "Ricardo Linden and Amit Bhaya", title = "Evolving fuzzy rules to model gene expression", journal = "Biosystems", year = "2007", volume = "88", number = "1-2", pages = "76--91", month = mar, keywords = "genetic algorithms, genetic programming, Fuzzy logic, Microarrays, Reverse engineering, Gene regulatory network", DOI = "doi:10.1016/j.biosystems.2006.04.006", abstract = "This paper develops an algorithm that extracts explanatory rules from microarray data, which we treat as time series, using genetic programming (GP) and fuzzy logic. Reverse polish notation is used (RPN) to describe the rules and to facilitate the GP approach. The algorithm also allows for the insertion of prior knowledge, making it possible to find sets of rules that include the relationships between genes already known. The algorithm proposed is applied to problems arising in the construction of gene regulatory networks, using two different sets of real data from biological experiments on the Arabidopsis thaliana cold response and the rat central nervous system, respectively. The results show that the proposed technique can fit data to a pre-defined precision even in situations where the data set has thousands of features but only a limited number of points in time are available, a situation in which traditional statistical alternatives encounter difficulties, due to the scarcity of time points.", } @InProceedings{lindhorst:1998:rGAsmtpm, author = "Gwenda Lindhorst", title = "Relational Genetic Algorithms: With application to Surface Mount Technology Placement Machines", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "543--550", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms", notes = "SGA-98", } @InProceedings{Ling:2022:Communications, author = "Zhen Ling and Gui Xiao and Wenjia Wu and Xiaodan Gu and Ming Yang and Xinwen Fu", booktitle = "IEEE INFOCOM 2022 - IEEE Conference on Computer Communications", title = "Towards an Efficient Defense against Deep Learning based Website Fingerprinting", year = "2022", pages = "310--319", abstract = "Website fingerprinting (WF) attacks allow an attacker to eavesdrop on the encrypted network traffic between a victim and an anonymous communication system so as to infer the real destination websites visited by a victim. Recently, the deep learning (DL) based WF attacks are proposed to extract high level features by DL algorithms to achieve better performance than that of the traditional WF attacks and defeat the existing defense techniques. To mitigate this issue, we propose a-genetic-programming-based variant cover traffic search technique to generate defense strategies for effectively injecting dummy Tor cells into the raw Tor traffic. We randomly perform mutation operations on labeled original traffic traces by injecting dummy Tor cells into the traces to derive variant cover traffic. A high level feature distance based fitness function is designed to improve the mutation rate to discover successful variant traffic traces that can fool the DL-based WF classifiers. Then the dummy Tor cell injection patterns in the successful variant traces are extracted as defense strategies that can be applied to the Tor traffic. Extensive experiments demonstrate that we can introduce 8.percent of bandwidth overhead to significantly decrease the accuracy rate below 0.percent in the realistic open-world setting.", keywords = "genetic algorithms, genetic programming, Deep learning, Privacy, Computational modeling, Sociology, Bandwidth, Telecommunication traffic, Fingerprint recognition, Anonymous communication systems, website fingerprinting, cover traffic", DOI = "doi:10.1109/INFOCOM48880.2022.9796685", ISSN = "2641-9874", month = may, notes = "Also known as \cite{9796685}", } @Article{oai:arXiv.org:hep-ex/0503007, title = "Application of Genetic Programming to High Energy Physics Event Selection", author = "J. M. Link and P. M. Yager and J. C. Anjos and I. Bediaga and C. Castromonte and C. Gobel and A. A. Machado and J. Magnin and A. Massafferri and J. M. {de Miranda} and I. M. Pepe and E. Polycarpo and A. C. {dos Reis} and S. Carrillo and E. Casimiro and E. Cuautle and A. Sanchez-Hernandez and C. Uribe and F. Vazquez and L. Agostino and L. Cinquini and J. P. Cumalat and B. O'Reilly and I. Segoni and K. Stenson and J. N. Butler and H. W. K. Cheung and G. Chiodini and I. Gaines and P. H. Garbincius and L. A. Garren and E. Gottschalk and P. H. Kasper and A. E. Kreymer and R. Kutschke and M. Wang and L. Benussi and M. Bertani and S. Bianco and F. L. Fabbri and S. Pacetti and A. Zallo and M. Reyes and C. Cawlfield and D. Y. Kim and A. Rahimi and J. Wiss and R. Gardner and A. Kryemadhi and Y. S. Chung and J. S. Kang and B. R. Ko and J. W. Kwak and K. B. Lee and K. Cho and H. Park and G. Alimonti and S. Barberis and M. Boschini and A. Cerutti and P. D'Angelo and M. DiCorato and P. Dini and L. Edera and S. Erba and P. Inzani and F. Leveraro and S. Malvezzi and D. Menasce and M. Mezzadri and L. Moroni and D. Pedrini and C. Pontoglio and F. Prelz and M. Rovere and S. Sala and T. F. {Davenport III} and V. Arena and G. Boca and G. Bonomi and G. Gianini and G. Liguori and D. {Lopes Pegna} and M. M. Merlo and D. Pantea and S. P. Ratti and C. Riccardi and P. Vitulo and H. Hernandez and A. M. Lopez and H. Mendez and A. Paris and J. Quinones and J. E. Ramirez and Y. Zhang and J. R. Wilson and T. Handler and R. Mitchell and D. Engh and M. Hosack and W. E. Johns and E. Luiggi and J. E. Moore and M. Nehring and P. D. Sheldon and E. W. Vaandering and M. Webster and M. Sheaff", year = "2005", volume = "A551", pages = "504--527", journal = "Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment", number = "2-3", month = "11 " # oct, note = "The FOCUS Collaboration", keywords = "genetic algorithms, genetic programming, Event selection, Classification", bibsource = "OAI-PMH server at arXiv.org", identifier = "Nucl.Instrum.Meth. A551 (2005) 504-527", oai = "oai:arXiv.org:hep-ex/0503007", URL = "http://docdb.fnal.gov/FOCUS/DocDB/0000/000005/011/gp_method.pdf", URL = "http://arxiv.org/abs/hep-ex/0503007", DOI = "doi:10.1016/j.nima.2005.05.069", size = "39 pages", abstract = "We review genetic programming principles, their application to FOCUS data samples, and use the method to study the doubly Cabibbo suppressed decay D+ -> K+ pi+ pi- relative to its Cabibbo favoured counterpart, D+ -> K- pi+ pi+. We find that this technique is able to improve upon more traditional analysis methods. To our knowledge, this is the first application of the genetic programming technique to High Energy Physics data.", notes = "lilgp, PACS: 02.50.Sk, 07.05.Kf, 13.25.Ft, Journal-ref: Nucl.Instrum.Meth. A551 (2005) 504-527 see \cite{Link:2005ym}", } @Article{Link:2005ym, author = "J. M. Link and P. M. Yager and J. C. Anjos and I. Bediaga and C. Castromonte and A. A. Machado and J. Magnin and A. Massafferri and J. M. {de Miranda} and I. M. Pepe and E. Polycarpo and A. C. {dos Reis} and S. Carrillo and E. Casimiro and E. Cuautle and A. Sanchez-Hernandez and C. Uribe and F. Vazquez and L. Agostino and L. Cinquini and J. P. Cumalat and B. O'Reilly and I. Segoni and K. Stenson and J. N. Butler and H. W. K. Cheung and G. Chiodini and I. Gaines and P. H. Garbincius and L. A. Garren and E. Gottschalk and P. H. Kasper and A. E. Kreymer and R. Kutschke and M. Wang and L. Benussi and M. Bertani and S. Bianco and F. L. Fabbri and S. Pacetti and A. Zallo and M. Reyes and C. Cawlfield and D. Y. Kim and A. Rahimi and J. Wiss and R. Gardner and A. Kryemadhi and Y. S. Chung and J. S. Kang and B. R. Ko and J. W. Kwak and K. B. Lee and K. Cho and H. Park and G. Alimonti and S. Barberis and M. Boschini and A. Cerutti and P. D'Angelo and M. DiCorato and P. Dini and L. Edera and S. Erba and P. Inzani and F. Leveraro and S. Malvezzi and D. Menasce and M. Mezzadri and L. Moroni and D. Pedrini and C. Pontoglio and F. Prelz and M. Rovere and S. Sala and T. F. {Davenport III} and V. Arena and G. Boca and G. Bonomi and Gabriele Gianini and G. Liguori and D. {Lopes Pegna} and M. M. Merlo and D. Pantea and S. P. Ratti and C. Riccardi and P. Vitulo and C. Gobel and H. Hernandez and A. M. Lopez and H. Mendez and A. Paris and J. Quinones and J. E. Ramirez and Y. Zhang and J. R. Wilson and T. Handler and R. Mitchell and D. Engh and M. Hosack and W. E. Johns and E. Luiggi and J. E. Moore and M. Nehring and P. D. Sheldon and E. W. Vaandering and M. Webster and M. Sheaff", collaboration = "FOCUS", title = "Search for Lambda/c+ --> p K+ pi- and D/s+ --> K+ K+ pi- using genetic programming event selection", journal = "Physics Letters B", volume = "B624", year = "2005", pages = "166--172", number = "3-4", month = "29 " # sep, eprint = "hep-ex/0507103", slaccitation = "%%CITATION = HEP-EX 0507103;%%", keywords = "genetic algorithms, genetic programming", URL = "http://arxiv.org/pdf/hep-ex/0507103", DOI = "doi:10.1016/j.physletb.2005.08.032", size = "10 pages", abstract = "We apply a genetic programming technique to search for the doubly Cabibbo suppressed decays \lambda +c to pK+p- and D+s to K+K+p-. We normalise these decays to their Cabibbo favoured partners and find BR(\lambda to pK+p-)/BR(\lambda to pK-p+) = (0.05 \pm 0.26 \pm 0.02)% and BR(D+s to K+K+p-)/BR(D to K-K+p+) = (0.52 \pm 0.17 \pm 0.11) percent where the first errors are statistical and the second are systematic. Expressed as 90 percent confidence levels (CL), we find < 0.46 percent and < 0.78 percent respectively. This is the first successful use of genetic programming in a high energy physics data analysis.", notes = "http://www-focus.fnal.gov/authors.html for additional author information. PACS: 13.25.Ft; 13.30.Eg see \cite{oai:arXiv.org:hep-ex/0503007}", } @InProceedings{Linkola:2018:evoMusArt, author = "Simo Linkola and Otto Hantula", title = "On Collaborator Selection in Creative Agent Societies: An Evolutionary Art Case Study", booktitle = "7th International Conference on Computational Intelligence in Music, Sound, Art and Design, EvoMUSART 2018", year = "2018", editor = "Juan Romero and Antonios Liapis and Aniko Ekart", series = "LNCS", volume = "10783", publisher = "Springer", pages = "206--222", address = "Parma, Italy", month = "4-6 " # apr, organisation = "Species", keywords = "genetic algorithms, genetic programming, Computational social creativity, Evolutionary art, Collaboration, Learning from experience", isbn13 = "978-3-319-77582-1", DOI = "doi:10.1007/978-3-319-77583-8_14", abstract = "We study how artistically creative agents may learn to select favourable collaboration partners. We consider a society of creative agents with varying skills and aesthetic preferences able to interact with each other by exchanging artefacts or through collaboration. The agents exhibit interaction awareness by modelling their peers and make decisions about collaboration based on the learned peer models. To test the peer models, we devise an experimental collaboration process for evolutionary art, where two agents create an artifact by evolving the same artifact set in turns. In an empirical evaluation, we focus on how effective peer models are in selecting collaboration partners and compare the results to a baseline where agents select collaboration partners randomly.We observe that peer models guide the agents to more beneficial collaborations.", notes = "EvoMusArt2018 held in conjunction with EuroGP'2018 EvoCOP2018 and EvoApplications2018 http://www.evostar.org/2018/cfp_evomusart.php", } @InProceedings{Linsbauer:2014:SSBSE, author = "Lukas Linsbauer and Roberto Erick Lopez-Herrejon and Alexander Egyed", title = "Feature Model Synthesis with Genetic Programming", booktitle = "Proceedings of the 6th International Symposium, on Search-Based Software Engineering, SSBSE 2014", year = "2014", editor = "Claire {Le Goues} and Shin Yoo", volume = "8636", series = "LNCS", pages = "153--167", address = "Fortaleza, Brazil", month = "26-29 " # aug, publisher = "Springer", keywords = "genetic algorithms, genetic programming, SBSE, Feature Models, Feature Set, Reverse Engineering, Software Product Lines, Variability Modelling", isbn13 = "978-3-319-09939-2", URL = "http://www.springer.com/computer/swe/book/978-3-319-09939-2", DOI = "doi:10.1007/978-3-319-09940-8_11", size = "15 pages", abstract = "Search-Based Software Engineering (SBSE) is successful on several stages of the software development life cycle. It has also been applied to different challenges in the context of Software Product Lines (SPLs) like generating minimal test suites. When reverse engineering SPLs from legacy software an important challenge is the reverse engineering of variability, often expressed in the form of Feature Models (FMs). The synthesis of FMs has been studied with techniques such as Genetic Algorithms. In this paper we explore the use of Genetic Programming for this task. We sketch our general workflow, the GP pipeline employed, and its evolutionary operators. We report our experience in synthesising feature models from sets of feature combinations for 17 representative feature models, and analyse the results using standard information retrieval metrics.", } @InProceedings{Liong2001777, author = "Shie-Yui Liong and V. T. Van Nguyen and Tirtha Raj Gautam and Loong Wee", title = "Alternative well calibrated rainfall-runoff model: Genetic programming scheme", booktitle = "Urban Drainage Modeling", year = "2001", editor = "R W Brashear and C Maksimovic and R W Brashear and C Maksimovic", pages = "777--787", address = "Orlando, Florida, USA", month = may # " 20-24", publisher = "American Society of Civil Engineers", keywords = "genetic algorithms, genetic programming, Catchments, Computer simulation, Mathematical models, Optimisation, Runoff, Storms, Weather forecasting, Rainfall runoff model, Storm water management model, Rain gauges", isbn13 = "978-0-7844-0583-3", URL = "http://ascelibrary.org/doi/abs/10.1061/40583%28275%2973", DOI = "doi:10.1061/40583(275)73", size = "11 pages", abstract = "Genetic Programming (GP) has been explored as a flow forecasting tool. A catchment in Singapore with a drainage area of about 6 km2 is used for this case study. GP was trained to simulate runoff from a conceptual rainfall-runoff model, Storm Water Management Model (SWMM), which was first calibrated using Shuffled Complex Evolution (SCE) algorithm. Four storms of different intensities and durations are used for training and verification of the GP models. The results show that the runoff prediction accuracy of genetic programming based tool, measured in terms of root mean square error and correlation coefficient, is reasonably high. Thus, GP coupled with a robust optimisation scheme such as SCE is a viable complementary tool to traditional conceptual rainfall-runoff models.", notes = "GPKernel Babovic", affiliation = "Department of Civil Engineering, National University of Singapore, 10 Kent Ridge Crescent, Singapore 119260, Singapore", correspondence_address1 = "Liong, S.-Y.; Department of Civil Engineering, National University of Singapore, 10 Kent Ridge Crescent, Singapore 119260, Singapore; email: cvelsy@nus.edu.sg", language = "English", document_type = "Conference Paper", } @Article{me22, title = "Genetic Programming: A New Paradigm in Rainfall Runoff Modeling", author = "Shie-Yui Liong and Tirtha Raj Gautam and Soon Thiam Khu and Vladan Babovic and Maarten Keijzer and Nitin Muttil", journal = "Journal of American Water Resources Association", year = "2002", volume = "38", number = "3", pages = "705--718", month = jun, keywords = "genetic algorithms, genetic programming, Rainfall-runoff relationships, Runoff forecasting, Rainfall-runoff models, Algorithms, Singapore, Upper Bukit Timah catchment", DOI = "doi:10.1111/j.1752-1688.2002.tb00991.x", size = "14 pages", abstract = "Genetic Programming (GP) is a domain-independent evolutionary programming technique that evolves computer programs to solve, or approximately solve, problems. To verify GP's capability, a simple example with known relation in the area of symbolic regression, is considered first. GP is then used as a flow forecasting tool. A catchment in Singapore with a drainage area of about 6 km2 is considered in this study. Six storms of different intensities and durations are used to train GP and then verify the trained GP. Analysis of the GP induced rainfall and runoff relationship shows that the cause and effect relationship between rainfall and runoff is consistent with the hydrologic process. The result shows that the runoff prediction accuracy of symbolic regression based models, measured in terms of root mean square error and correlation coefficient, is reasonably high. Thus, GP induced rainfall runoff relationships can be a viable alternative to traditional rainfall runoff models.", notes = "AWRA Paper Number 00146", } @InProceedings{Liou:2019:GPGPU, author = "Jhe-Yu Liou and Stephanie Forrest and Carole-Jean Wu", title = "Uncovering Performance Opportunities by Relaxing Program Semantics of {GPGPU} Kernels", booktitle = "12th Workshop on General Purpose Processing Using GPU (GPGPU 2019) @ ASPLOS 2019", year = "2019", editor = "Adwait Jog and Onur Kayiran and Ashutosh Pattnaik", address = "Providence, RI, USA", month = "13-17 " # apr, keywords = "genetic algorithms, genetic programming, genetic improvement, GPU, GEVO, LLVM-IR, GEVO-APPROX, MNIST, a9a, SVM, NVIDIA Tesla P1000 GPU", URL = "https://asplos-conference.org/2019/index.html@p=824.html", URL = "https://asplos-conference.org/2019/wp-content/uploads/2019/04/asplos19waci-liou-forrest-wu.pdf", size = "2 pages", notes = "See also \cite{Liou:2019:GI}, \cite{Liou:2020:ACMtaco} https://insight-archlab.github.io/gpgpu.html", } @InProceedings{Liou:2019:GI, author = "Jhe-Yu Liou and Stephanie Forrest and Carole-Jean Wu", title = "Genetic Improvement of {GPU} Code", booktitle = "GI-2019, ICSE workshops proceedings", year = "2019", editor = "Justyna Petke and Shin Hwei Tan and William B. Langdon and Westley Weimer", pages = "20--27", address = "Montreal", month = "28 " # may, publisher = "IEEE", note = "{Best Paper}", keywords = "genetic algorithms, genetic programming, genetic improvement, Multi-objective Evolutionary Computation, GPU code optimization, LLVM Intermediate Representation, GPU, LLVM, CUDA, nvidia, GEVO, NSGA-II, ML, MNIST, Epistatis", isbn13 = "978-1-7281-2268-7", URL = "http://gpbib.cs.ucl.ac.uk/gi2019/Liou_2019_GI.pdf", URL = "http://geneticimprovementofsoftware.com/paper_pdfs/liou2019genetic.pdf", URL = "https://dl.acm.org/citation.cfm?id=3339020.3339025", DOI = "doi:10.1109/GI.2019.00014", code_url = "https://github.com/Xtra-Computing/thundersvm", acmid = "3339025", size = "8 pages", abstract = "As the programming stack and tool support for GPU have matured, GPUs have become accessible to programmers who often lack domain-specific knowledge of the underlying architecture and fail to fully leverage the GPU's computation power. This paper presents GEVO (Gpu EVOlution), a tool for automatically tuning the performance of GPU kernels in the LLVM representation to meet desired criteria. GEVO uses population-based search to find edits to programs compiled to LLVM-IR that improve performance on desired criteria and retain required functionality. GEVO extends earlier GI work by operating directly on the LLVM-IR without custom representations or other manual interventions. We demonstrate that GEVO improves runtime on NVIDIA Tesla P100 for many programs in the Rodinia benchmark suite and a supervised machine learning code, ThunderSVM. For the Rodinia benchmark, GEVO improves GPU kernel runtime performance by an average of 13.87percent and as much as 43percent over the fully compiler-optimized baseline. If the kernel output accuracy is relaxed to tolerate 1percent error, GEVO can find kernel variants that outperform the baseline version by an average of 15.47percent. For ThunderSVM, GEVO reduces entire model training time by 50percent and 24.8percent, for MNIST handwriting recognition dataset and a9a income prediction, where the accuracy of trained model are improved by 0.17percent and 0.04percent respectively.", notes = "Jhe-Yu (Jerry) Liou GPGPU code https://github.com/Xtra-Computing/thundersvm Slides: http://geneticimprovementofsoftware.com/slides/liou2019genetic_slides.pdf Mutation of LLVM IR intermiedate representation (copy, delete, move, replace, swap instructions/operands) of nVidia CUDA GPU kernel device code often breaks LLVM syntax and so mutation requires repair. Crossover uses mutation's patches to LLVM-IR representation. Crossover randomises patch order so 2 parent patches are split between two offspring, cf uniform crossover, possibly in a new order. GEVO runtime two days. Benchmarks Rodinia and ThunderSVM. Epistatis: Observed 3 key mutations, introducing 0.3 error rate individually, but only incurring 0.1 error rate when combined. Sub-optimal individual can be served as the stepping stone for better optimization combination. This implies error tolerance can be used for circumventing and reaching other program spaces. Examples of optimsation. Eg of LU decomposition where evolution removes redundant store. Arizona State University GI-2019 http://geneticimprovementofsoftware.com part of \cite{Petke:2019:ICSEworkshop}", } @InProceedings{Liou:2020:GECCOcomp, author = "Jhe-Yu Liou and Xiaodong Wang and Stephanie Forrest and Carole-Jean Wu", title = "{GEVO-ML}: A Proposal for Optimizing {ML} Code with Evolutionary Computation", year = "2020", editor = "Richard Allmendinger and Hugo Terashima Marin and Efren Mezura Montes and Thomas Bartz-Beielstein and Bogdan Filipic and Ke Tang and David Howard and Emma Hart and Gusz Eiben and Tome Eftimov and William {La Cava} and Boris Naujoks and Pietro Oliveto and Vanessa Volz and Thomas Weise and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Rui Wang and Ran Cheng and Guohua Wu and Miqing Li and Hisao Ishibuchi and Jonathan Fieldsend and Ozgur Akman and Khulood Alyahya and Juergen Branke and John R. Woodward and Daniel R. Tauritz and Marco Baioletti and Josu Ceberio Uribe and John McCall and Alfredo Milani and Stefan Wagner and Michael Affenzeller and Bradley Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and Nayat Sanchez-Pi and Luis Marti and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and Matthew Johns and Nick Ross and Ed Keedwell and Herman Mahmoud and David Walker and Anthony Stein and Masaya Nakata and David Paetzel and Neil Vaughan and Stephen Smith and Stefano Cagnoni and Robert M. Patton and Ivanoe {De Falco} and Antonio {Della Cioppa} and Umberto Scafuri and Ernesto Tarantino and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Erik Hemberg and Riyad Alshammari and Tokunbo Makanju and Fuijimino-shi and Ivan Zelinka and Swagatam Das and Ponnuthurai Nagaratnam and Roman Senkerik", isbn13 = "9781450371278", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377929.3398139", DOI = "doi:10.1145/3377929.3398139", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion", pages = "1849--1856", size = "8 pages", keywords = "genetic algorithms, genetic programming, grammatical evolution, genetic improvement, GPU, machine learning, multi-objective evolutionary computation", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", abstract = "Parallel accelerators, such as GPUs, are a key enabler of large-scale Machine Learning (ML) applications. However, programmers often lack detailed knowledge of the underlying architecture and fail to fully leverage their computational power. This paper proposes GEVO-ML, a tool for automatically discovering optimization opportunities and tuning the performance of ML kernels. GEVO-ML extends earlier work on GEVO (Gpu optimization using EVOlutionary computation) by focusing directly on ML frameworks, intermediate languages, and target architectures. It retains the multi-objective evolutionary search developed for GEVO, which searches for edits to GPU code compiled to LLVM-IR and improves performance on desired criteria while retaining required functionality. In earlier work, we studied some ML workloads in GPU settings and found that GEVO could improve kernel speeds by factors ranging from 1.7X to 2.9X, even with access to only a small portion of the overall ML framework. This workshop paper examines the limitations and constraints of GEVO for ML workloads and discusses our GEVO-ML design, which we are currently implementing.", notes = "Also known as \cite{10.1145/3377929.3398139} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @Article{Liou:2020:ACMtaco, author = "Jhe-Yu Liou and Xiaodong Wang and Stephanie Forrest and Carole-Jean Wu", title = "{GEVO: GPU} Code Optimization Using Evolutionary Computation", journal = "ACM Transactions on Architecture and Code Optimization", year = "2020", volume = "17", number = "4", pages = "Article 33", month = dec, keywords = "genetic algorithms, genetic programming, Genetic improvement, DEAP, GPGPU, SIMT, CUDA, LLVM intermediate representation, LLVM IR, PTX, GPU code optimization, approximate computing, NSGA-II, MOGA, MOGI, multi-objective evolutionary computation, Non-dominated sorting, Heuristic function construction, Support Vector Machine (SVM), ThunderSVM, Stochastic Gradient Descent, momentumSGD, Caffe2, ResNet, Rodinia, MNIST, income prediction a9a", ISSN = "1544-3566", publisher = "Association for Computing Machinery", DOI = "doi:10.1145/3418055", code_url = "https://github.com/lioujheyu/cuda_evolve", size = "28 pages", abstract = "GPUs are a key enabler of the revolution in machine learning and high-performance computing, functioning as de facto co-processors to accelerate large-scale computation. As the programming stack and tool support have matured, GPUs have also become accessible to programmers, who may lack detailed knowledge of the underlying architecture and fail to fully leverage the GPU's computation power. GEVO (Gpu optimization using EVOlutionary computation) is a tool for automatically discovering optimization opportunities and tuning the performance of GPU kernels in the LLVM representation. GEVO uses population-based search to find edits to GPU code compiled to LLVM-IR and improves performance on desired criteria while retaining required functionality. We demonstrate that GEVO improves the execution time of general-purpose GPU programs and machine learning (ML) models on NVIDIA Tesla P100. For the Rodinia benchmarks, GEVO improves GPU kernel runtime performance by an average of 49.48percent and by as much as 412percent over the fully compiler-optimized baseline. If kernel output accuracy is relaxed to tolerate up to 1percent error, GEVO can find kernel variants that outperform the baseline by an average of 51.08percent. For the ML workloads, GEVO achieves kernel performance improvement for SVM on the MNIST handwriting recognition (3.24) and the a9a income prediction (2.93 fold) datasets with no loss of model accuracy. GEVO achieves 1.79 fold kernel performance improvement on image classification using ResNet18/CIFAR-10, with less than 1percent model accuracy reduction.", notes = "strongly typed Mutate-Copy LLVM-IR. list of mutations. 'crossover using the patch representation'. 'both representations' ??? 'fitness function as multi-objective (GEVO-mO) = min(time,error)'. 'held-out test cases'. 25% elitism, 75% tournament selection (population=256, generation 12..80+). LLVM-IR type based repair, does it use semantics too? 'GEVO uses dominator analysis to eliminate values ...' 'GEVO implements one-point messy crossover,which combines shuffle [17] and variable-length [54] crossover operations' (produces two children). 'our mutations are relatively expensive' 'The acceptance rate (ie child is ok) of crossover is as high as 80%, because each individual mutation has already been validated. DEAP \cite{DeRainville:2012:GECCOcomp}. Intel Xeon E5-2640 CPU with 40 cores, 256 GB plus NVIDIA Tesla P100 GPU with 16 GB GPU memory, Ubuntu 16.04 with NVIDIA CUDA 10.1. 'nvprof introduced no overhead to kernel execution time 'each input set contains from tens of thousands to millions of input values' 'ML kernel variants are rejected if the training error exceeds the error achieved from the original kernel by the 1% threshold.' B+Tree kernel improved five fold Remove CUDA syncthread() 'As part of a wide deployment, additional post hoc methods, such as test-case generation or program analysis, could be employed to double-check that specific optimizations are indeed safe under the relevant use cases.' 'Removing Redundant Store.' Replaces with a temporary register. 'GEVO removes dead code'. 'Removing Redundant Load.' 'Loop Perforation.' 'Memoization.' GEVO-mO compensatory evolution 'highlights the strength of a population-based search' 'the best kernel variant would not be found if a tighter error bound had been enforced from the beginning.' 'incomplete LU factorization'. 'sequential minimal optimization', 'perhaps by avoiding over fitting'. 'injecting inline-assembly in LLVM-IR' found to be unnecessary here. 'some programs for which GEVO was unable to find improvement' 'defeat some power side channel attacks.' Arizona State University, Tempe, USA", } @Misc{Liou:2022:arxiv, author = "Jhe-Yu Liou and Muaaz Awan and Steven Hofmeyr and Stephanie Forrest and Carole-Jean Wu", title = "Understanding the Power of Evolutionary Computation for {GPU} Code Optimization", howpublished = "arXiv", year = "2022", month = "25 " # aug, keywords = "genetic algorithms, genetic programming, grammatical evolution, GPU, GPGPU, GEVO, epistasis, LLVM Clang, LLVM-IR, nVida nvcc, PTX, Backus Normal Form, BNF, ADEPT, bioinformatics sequence alignment, SIMCoV, COVID viral spread, SBSE, Smith-Waterman algorithm, Software Engineering (cs.SE), Distributed, Parallel, and Cluster Computing (cs.DC), Performance (cs.PF), FOS: Computer and information sciences, FOS: Computer and information sciences", copyright = "Creative Commons Attribution 4.0 International", URL = "https://arxiv.org/abs/2208.12350", DOI = "doi:10.48550/ARXIV.2208.12350", code_url = "https://github.com/lioujheyu/gevo", size = "12 pages", abstract = "Achieving high performance for GPU codes requires developers to have significant knowledge in parallel programming and GPU architectures, and in-depth understanding of the application. This combination makes it challenging to find performance optimisations for GPU-based applications, especially in scientific computing. This paper shows that significant speedups can be achieved on two quite different scientific workloads using the tool, GEVO, to improve performance over human-optimized GPU code. GEVO uses evolutionary computation to find code edits that improve the runtime of a multiple sequence alignment kernel and a SARS-CoV-2 simulation by 28.9percent and 29percent respectively. Further, when GEVO begins with an early, unoptimized version of the sequence alignment program, it finds an impressive 30 times speedup -- a performance improvement similar to that of the hand-tuned version. This work presents an in-depth analysis of the discovered optimizations, revealing that the primary sources of improvement vary across applications; that most of the optimizations generalize across GPU architectures; and that several of the most important optimizations involve significant code interdependencies. The results showcase the potential of automated program optimization tools to help reduce the optimization burden for scientific computing developers and enhance performance portability for domain-specific accelerators.", notes = "p19 'demonstrates the excellent potential ... GEVO to augment [human] developer efforts to optimize GPU codes' 'code edits' p1 '6.7 million CPU hours .. (NERSC) Cori Supercomputer' nVidia P100, 1080Ti, V100: 3584, 3585, 5120 GPU cores. p2 'up to 17 [mutations] contribute significantly to the optimization.' Fig 1 (GEVO) population of 256 LLVM IR edits, selection, crossover, mutation, (approx) 300 or 130 generations, 7 or 2 days. => NVPTX => PTX => fitness evaluation. mutation = copy, delete, move, replace, swap, replace the operand. 'strengthening a programmer understanding of system performance improvement opportunities.' p3 stochastic simulation of 2D model of Lung epithelial cells and human immune system. p4 'Over 90 percent of the GPU kernel runtime is spent moving T cells and spreading virus and inflammatory signals.' 'Fixing the random seed removes most of the stochasticity, but not all.' race condition (ie dont care...) Three cases: with 1097, 1707, 1712 LLVM-IR instructions. Validation not the same as training test (more onerous). p5 variation between runs. 'some performance optimizations are GPU architecture-dependent.' Post evolution 'Edit Minimization' removal of edits that speed up by less than 1 percent (reduce 1394 to 12) but twelve interact episatically with each other. 'code is robust against so many mutations while preserving required functionality.' Fig 8: progressive improvement in performance (elite = 4/256 of population) even though genes interact. p9 ' The developers were surprised that EC could synthesize code modifications with such large performance improvements' 'EC-driven optimization does not necessarily preserve exact program semantics, which is both a strengthand a limitation.' p10 'There is no golden rule for finding optimal performance on GPUs.' 'EC can automate this search for counter-intuitive optimizations'", } @InProceedings{Liou:2022:IISWC, author = "Jhe-Yu Liou and Muaaz Awan and Steven Hofmeyr and Stephanie Forrest and Carole-Jean Wu", title = "Understanding the Power of Evolutionary Computation for {GPU} Code Optimization", booktitle = "2022 IEEE International Symposium on Workload Characterization (IISWC)", year = "2022", pages = "185--198", address = "Austin, TX, USA", month = "6-8 " # nov, keywords = "genetic algorithms, genetic programming, genetic improvement, grammatical evolution, automated program optimization, CUDA, GPU, GEVO, Clang, C++, LLVM IR, SBSE, codes, runtime, scientific computing, computational modeling, search methods, graphics processing units, ADEPT-V1 Smith-Waterman, nvidie P100, nvcc, PTX, computer architecture, epistasisi", isbn13 = "978-1-6654-8798-6", DOI = "doi:10.1109/IISWC55918.2022.00025", abstract = "Achieving high performance for GPU codes requires developers to have significant knowledge in parallel programming and GPU architectures, and in-depth understanding of the application. This combination makes it challenging to find performance optimizations for GPU-based applications, especially in scientific computing. This paper shows that significant speedups can be achieved on two quite different scientific workloads using the tool, GEVO, to improve performance over human-optimized GPU code. GEVO uses evolutionary computation to find code edits that improve the runtime of a multiple sequence alignment kernel and a SARS-CoV-2 simulation by 28.9 percent and 29 percent respectively. Further, when GEVO begins with an early, unoptimized version of the sequence alignment program, it finds an impressive 30 times speedup performance improvement similar to that of the hand-tuned version. This work presents an in-depth analysis of the discovered optimizations, revealing that the primary sources of improvement vary across applications; that most of the optimizations generalize across GPU architectures; and that several of the most important optimizations involve significant code interdependencies. The results showcase the potential of automated program optimization tools to help reduce the optimization burden for scientific computing developers and enhance performance portability for domain-specific accelerators.", notes = "See \cite{Liou:2022:arxiv}. Also known as \cite{9975391} GEVO mutate LLVM-IR using copy, delete, move, replace, or swap then compile LLVM-IR to PTX and run on GPU by fitness function. pop=256 (elite 4) 80% Crossover 30% mutation. Run for 2 or 7 days. 'modified GEVO's mutation operator to encode the source code location information' 'ADEPT-V1 [hand optimised] executes approximately 20-30 times faster than ADEPT-V0'. 3 kernels approx 1700 lines of LLVM-IR Slides: http://www.iiswc.org/iiswc2022/IISWC2022_46.pdf", } @Article{Liou:TELO, author = "Jhe-Yu Liou and Muaaz Awan and Kirtus Leyba and Petr Sulc and Steven Hofmeyr and Carole-Jean Wu and Stephanie Forrest", title = "Evolving to find optimizations humans miss: using evolutionary computation to improve {GPU} code for bioinformatics applications", journal = "ACM Transactions on Evolutionary Learning and Optimization", year = "2024", volume = "4", number = "4", articleno = "21", month = dec, keywords = "genetic algorithms, genetic programming, Genetic improvement, GPU, Evolutionary programming, Bioinformatics, GEVO, ADEPT, SIMCoV, Coronavirus, oxDNA, LLVM-IR, nvidia P100 1080Ti V100, NERSC Cori Supercomputer, CUDA 11.4", ISSN = "2688-299X", publisher = "Association for Computing Machinery", URL = "https://doi.org/10.1145/3703920", DOI = "doi:10.1145/3703920", size = "28 pages", abstract = "GPUs are used in many settings to accelerate large-scale scientific computation, including simulation, computational biology, and molecular dynamics. However, optimizing codes to run efficiently on GPUs requires developers to have both detailed understanding of the application logic and significant knowledge of parallel programming and GPU architectures. This paper shows that an automated GPU program optimization tool, GEVO, can leverage evolutionary computation to find code edits that reduce the runtime of three important applications, multiple sequence alignment, agent-based simulation and molecular dynamics codes, by 28.9%, 29%, and 17.8% respectively. The paper presents an in-depth analysis of the discovered optimizations, revealing that (1) several of the most important optimizations involve significant epistasis, (2) the primary sources of improvement are application-specific, and (3) many of the optimizations generalize across GPU architectures. In general, the discovered optimizations are not straightforward even for a GPU human expert, showcasing the potential of automated program optimization tools to both reduce the optimization burden for human domain experts and provide new insights for GPU experts.", notes = "https://dlnext.acm.org/journal/telo", } @InProceedings{lipinski:2024:GECCOcomp, author = "Piotr Lipinski and Klaudia Balcer", title = "Explaining Session-based Recommendations using Grammatical Evolution", booktitle = "Evolutionary Computation and Explainable AI", year = "2024", editor = "John McCall and Jaume Bacardit and Giovanni Iacca", pages = "1590--1597", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, grammatical evolution, explainable artificial intelligence, XAI, evolutionary algorithms, recommender systems, session-based recommender systems, latent vector representations", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3664156", size = "8 pages", abstract = "This paper concerns explaining session-based recommendations using Grammatical Evolution. A session-based recommender system processes a given sequence of products browsed by a user and suggests the most relevant next product to display to the user. State-of-the-art session-based recommender systems are often a type of deep learning black box, so explaining their results is a challenge.In this paper, we propose an approach with a grammatical expression that provides explanations of recommendations generated by session-based recommender systems as well as an evolutionary algorithm, GE-XAI-SBRS, based on Grammatical Evolution, with its own initialization and crossover operators, to construct such a grammatical expression. Our approach uses latent product representations, so-called vector embeddings, generated by the recommender systems and providing some additional knowledge on dependencies between products.Computational experiments on the YooChoose dataset being one of the most popular session-based benchmarks, and the recommendations generated by the Target Attentive Graph Neural Network (TAGNN) model confirm the usefulness of the proposed approach, the efficiency of the proposed algorithm and outperforming the regular GE algorithm in the task under consideration.", notes = "GECCO-2024 ECXAI A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{Lipitakis:2019:CSCI, author = "Anastasia-Dimitra Lipitakis and Evangelia A. E. C. Lipitakis", title = "Computational Science and Intelligence Programming in Business with Genetic, Generic Algorithms and {AI} Software Products: An Adaptive Algorithmic Approach", booktitle = "2019 International Conference on Computational Science and Computational Intelligence (CSCI)", year = "2019", pages = "289--295", abstract = "In this research study the topic of Artificial Intelligence and Business is examined. AI methodologies and their usage in several organizations in various applications concerning staffing, careers etc. and hiring predictions are discussed. The concept of genetic and generic algorithms with their several important aspects are discussed and various related applications in a wide spectrum of topics are presented. Various genetic programming implementations with the corresponding related software products are given and several AI software packages, genetic algorithmic methodologies and related applications are presented.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CSCI49370.2019.00058", month = dec, notes = "Also known as \cite{9071213}", } @Article{Lipson:2000:admrl, author = "Hod Lipson and Jordan B. Pollack", title = "Automatic design and manufacture of robotic lifeforms", journal = "Nature", year = "2000", number = "406", pages = "974--978", month = "31 " # aug, keywords = "genetic algorithms, genetic programming, evolutionary programming, evolutionstrategies", URL = "http://demo.cs.brandeis.edu/pr/golem/download/AutomaticDesign.pdf", URL = "https://rdcu.be/dJy9v", broken = "http://www.nature.com/cgi-taf/DynaPage.taf?file=/nature/journal/v406/n6799/full/406974a0_fs.html&content_filetype=pdf", DOI = "doi:10.1038/35023115", size = "5 pages", abstract = "Biological life is in control of its own means of reproduction, which generally involves complex, autocatalysing chemical reactions. But this autonomy of design and manufacture has not yet been realized artificially. Robots are still laboriously designed and constructed by teams of human engineers, usually at considerable expense. Few robots are available because these costs must be absorbed through mass production, which is justified only for toys, weapons and industrial systems such as automatic teller machines. Here we report the results of a combined computational and experimental approach in which simple electromechanical systems are evolved through simulations from basic building blocks (bars, actuators and artificial neurons); the 'fittest' machines (defined by their locomotive ability) are then fabricated robotically using rapid manufacturing technology. We thus achieve autonomy of design and construction using evolution in a 'limited universe' physical simulation coupled to automatic fabrication.", notes = "Note I have filed as GP even though the authors state they are not using GP (their genetic search uses only mutation) however (as far as I can tell) the representation of the genome is variable length. Nice mpeg videos online at https://www.nature.com/articles/35023115", } @Article{Lipson_et_al-2002-Evolution, author = "Hod Lipson and Jordan B. Pollack and Nam P. Suh", title = "On the Origin of Modular Variation", journal = "Evolution", year = "2002", volume = "56", number = "8", pages = "1549--1556", month = aug, keywords = "genetic algorithms", ISSN = "1558-5646", DOI = "doi:10.1111/j.0014-3820.2002.tb01466.x", size = "8 pages", abstract = "We study the dynamics of modularization in a minimal substrate. A module is a functional unit relatively separable from its surrounding structure. Although it is known that modularity is useful both for robustness and for evolvability (Wagner 1996), there is no quantitative model describing how such modularity might originally emerge. Here we suggest, using simple computer simulations, that modularity arises spontaneously in evolutionary systems in response to variation, and that the amount of modular separation is logarithmically proportional to the rate of variation. Consequently, we predict that modular architectures would appear in correlation with high environmental change rates. Because this quantitative model does not require any special substrate to occur, it may also shed light on the origin of modular variation in nature. This observed relationship also indicates that modular design is a generic phenomenon that might be applicable to other fields, such as engineering: Engineering design methods based on evolutionary simulation would benefit from evolving to variable, rather than stationary, fitness criteria, as a weak and problem-independent method for inducing modularity.", notes = "Cited by \cite{Yu:2007:ECAL} 8 by 8 matrices", } @InProceedings{lipson:2004:lbp, author = "Hod Lipson", title = "How to Draw a Straight Line Using a {GP}: Benchmarking Evolutionary Design Against 19th Century Kinematic Synthesis", booktitle = "Late Breaking Papers at the 2004 Genetic and Evolutionary Computation Conference", year = "2004", editor = "Maarten Keijzer", address = "Seattle, Washington, USA", month = "26 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://gpbib.cs.ucl.ac.uk/gecco2004/LBP063.pdf", abstract = "This paper discusses the application of genetic programming to the synthesis of compound 2D kinematic mechanisms, and benchmarks the results against one of the classical kinematic challenges of 19th century mechanical design. Considerations for selecting a representation for mechanism design are presented, and a number of human-competitive inventions are shown.", notes = "Part of \cite{keijzer:2004:GECCO:lbp}", } @Article{DBLP:journals/aiedam/Lipson08, author = "Hod Lipson", title = "Evolutionary synthesis of kinematic mechanisms", journal = "Artificial Intelligence for Engineering Design, Analysis and Manufacturing", volume = "22", number = "3", year = "2008", pages = "195--205", DOI = "doi:10.1017/S0890060408000139", bibsource = "DBLP, http://dblp.uni-trier.de", keywords = "genetic algorithms, genetic programming, Automated Synthesis, Design Automation, Kinematic Mechanisms, Robotics", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.375.5254", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.375.5254", URL = "http://creativemachines.cornell.edu/sites/default/files/AIEDAM08_Lipson.pdf", size = "11 pages", abstract = "This paper discusses the application of genetic programming to the synthesis of compound two-dimensional kinematic mechanisms, and benchmarks the results against one of the classical kinematic challenges of 19th century mechanical design. Considerations for selecting a representation for mechanism design are presented, and a number of human-competitive inventions are shown.", } @Book{Lipson:book, author = "Hod Lipson and Melba Kurman", title = "Fabricated: The New World of {3D} Printing", publisher = "John Wiley and Sons, Inc.", year = "2013", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-118-35063-8", URL = "http://www.amazon.com/Fabricated-The-New-World-Printing-ebook/dp/B00B9V5W34", URL = "https://www.wiley.com/en-gb/Fabricated%3A+The+New+World+of+3D+Printing-p-9781118350638", abstract = "Fabricated tells the story of 3D printers, humble manufacturing machines that are bursting out of the factory and into homes, businesses, schools, kitchens, hospitals, even the fashion catwalk. The magic happens when you plug a 3D printer into today's mind-boggling digital technologies. Add to that the...", notes = "There is quite a bit of GP especially in the new CAD chapter.", size = "320 pages", } @InProceedings{Liskowski:2015:GECCOcomp, author = "Pawel Liskowski and Krzysztof Krawiec and Thomas Helmuth and Lee Spector", title = "Comparison of Semantic-aware Selection Methods in Genetic Programming", booktitle = "GECCO 2015 Semantic Methods in Genetic Programming (SMGP'15) Workshop", year = "2015", editor = "Colin Johnson and Krzysztof Krawiec and Alberto Moraglio and Michael O'Neill", isbn13 = "978-1-4503-3488-4", keywords = "genetic algorithms, genetic programming, Semantic Methods in (SMGP'15) Workshop", pages = "1301--1307", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739482.2768505", DOI = "doi:10.1145/2739482.2768505", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "This study investigates the performance of several semantic-aware selection methods for genetic programming (GP). In particular, we consider methods that do not rely on complete GP semantics (i.e., a tuple of outputs produced by a program for fitness cases (tests)), but on binary outcome vectors that only state whether a given test has been passed by a program or not. This allows us to relate to test-based problems commonly considered in the domain of coevolutionary algorithms and, in prospect, to address a wider range of practical problems, in particular the problems where desired program output is unknown (e.g., evolving GP controllers). The selection methods considered in the paper include implicit fitness sharing (ifs), discovery of derived objectives (doc), lexicase selection (lex), as well as a hybrid of the latter two. These techniques, together with a few variants, are experimentally compared to each other and to conventional GP on a battery of discrete benchmark problems. The outcomes indicate superior performance of lex and ifs, with some variants of doc showing certain potential.", notes = "Also known as \cite{2768505} Distributed at GECCO-2015.", } @InProceedings{Liskowski:2016:GECCO, author = "Pawel Liskowski and Krzysztof Krawiec", title = "Non-negative Matrix Factorization for Unsupervised Derivation of Search Objectives in Genetic Programming", booktitle = "GECCO '16: Proceedings of the 2016 Annual Conference on Genetic and Evolutionary Computation", year = "2016", editor = "Tobias Friedrich", pages = "749--756", keywords = "genetic algorithms, genetic programming", month = "20-24 " # jul, organisation = "SIGEVO", address = "Denver, USA", publisher = "ACM", publisher_address = "New York, NY, USA", isbn13 = "978-1-4503-4206-3", DOI = "doi:10.1145/2908812.2908888", abstract = "In genetic programming (GP), the outcomes of the evaluation phase in an evolutionary loop can be represented as an interaction matrix, with rows corresponding to programs in a population, columns corresponding to tests that define a program synthesis task, and ones and zeroes signalling respectively passing a test and failing to do so. The conventional fitness, equivalent to a row sum in that matrix, only crudely reflects program's compliance with desired output, and recent contributions in semantic and behavioural GP point to alternative, multifaceted characterizations that facilitate navigation in the search space. In this paper, we propose DOF, a method that uses the popular machine learning technique of non-negative matrix factorization to heuristically derive a low number of underlying objectives from an interaction matrix. The resulting objectives redefine the original single-objective synthesis problem as a multiobjective optimization problem, and we posit that such characterization fosters diversification of search directions while maintaining useful search gradient. The comparative experiment conducted on 15 problems from discrete domains confirms this claim: DOF outperforms the conventional GP and GP equipped with an alternative method of derivation of search objectives on success rate and convergence speed.", notes = "Institute of Computing Science Poznan University of Technology GECCO-2016 A Recombination of the 25th International Conference on Genetic Algorithms (ICGA-2016) and the 21st Annual Genetic Programming Conference (GP-2016)", } @InProceedings{Liskowski:2016:EuroGP, author = "Pawel Liskowski and Krzysztof Krawiec", title = "Surrogate Fitness via Factorization of Interaction Matrix", booktitle = "EuroGP 2016: Proceedings of the 19th European Conference on Genetic Programming", year = "2016", month = "30 " # mar # "--1 " # apr, editor = "Malcolm I. Heywood and James McDermott and Mauro Castelli and Ernesto Costa and Kevin Sim", series = "LNCS", volume = "9594", publisher = "Springer Verlag", address = "Porto, Portugal", pages = "68--82", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, test-based problem, recommender systems, machine learning, surrogate fitness", isbn13 = "978-3-319-30668-1", DOI = "doi:10.1007/978-3-319-30668-1_5", abstract = "We propose \mname, a method that reduces the number of required interactions between programs and tests in genetic programming. \mname performs factorization of the matrix of the outcomes of interactions between the programs in a working population and the tests. Crucially, that factorization is applied to matrix that is only partially filled with interaction outcomes, i.e., sparse. The reconstructed approximate interaction matrix is then used to calculate the fitness of programs. In empirical comparison to several reference methods in categorical domains, \mname attains higher success rate of synthesizing correct programs within a given computational budget.", notes = "Part of \cite{Heywood:2016:GP} EuroGP'2016 held in conjunction with EvoCOP2016, EvoMusArt2016 and EvoApplications2016", } @InProceedings{Liskowski:2017:GECCO, author = "Pawel Liskowski and Krzysztof Krawiec", title = "Discovery of Search Objectives in Continuous Domains", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4920-8", address = "Berlin, Germany", pages = "969--976", size = "8 pages", URL = "http://doi.acm.org/10.1145/3071178.3071344", DOI = "doi:10.1145/3071178.3071344", acmid = "3071344", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, machine learning, multiobjective optimization, nonnegative matrix factorization", month = "15-19 " # jul, abstract = "In genetic programming (GP), the outcomes of the evaluation phase can be represented as an interaction matrix, with rows corresponding to programs in a population and columns corresponding to tests that define a program synthesis task. Recent contributions on Discovery of Objectives via Clustering (DOC) and Discovery of Objectives by Factorization of interaction matrix (DOF) show that informative characterizations of programs can be automatically derived from interaction matrices in discrete domains and used as search objectives in multidimensional setting. In this paper, we propose analogous methods for continuous domains and compare them with conventional GP that uses tournament selection, Age-Fitness Pareto Optimization, and GP with epsilon-lexicase selection. Experiments show that the proposed methods are effective for symbolic regression, systematically producing better-fitting models than the two former baselines, and surpassing epsilon-lexicase selection on some problems. We also investigate the hybrids of the proposed approach with the baselines, concluding that hybridization of DOC with epsilon-lexicase leads to the best overall results.", notes = "Also known as \cite{Liskowski:2017:DSO:3071178.3071344} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @Article{Liskowski:2017:EC, author = "Pawel Liskowski and Krzysztof Krawiec", title = "Online Discovery of Search Objectives for Test-Based Problems", journal = "Evolutionary Computation", year = "2017", volume = "25", number = "3", pages = "375--406", month = "Fall", keywords = "genetic algorithms, genetic programming, Coevolution, test-based problems, multi-objective evolutionary computation, search driver", ISSN = "1063-6560", URL = "https://doi.org/10.1162/evco_a_00179", DOI = "doi:10.1162/evco_a_00179", size = "32 pages", abstract = "In test-based problems, commonly approached with competitive coevolutionary algorithms, the fitness of a candidate solution is determined by the outcomes of its interactions with multiple tests. Usually, fitness is a scalar aggregate of interaction outcomes, and as such imposes a complete order on the candidate solutions. However, passing different tests may require unrelated skills, and candidate solutions may vary with respect to such capabilities. In this study, we provide theoretical evidence that scalar fitness, inherently incapable of capturing such differences, is likely to lead to premature convergence. To mitigate this problem, we propose disco, a method that automatically identifies the groups of tests for which the candidate solutions behave similarly and define the above skills. Each such group gives rise to a derived objective, and these objectives together guide the search algorithm in multi-objective fashion. When applied to several well-known test-based problems, the proposed approach significantly outperforms the conventional two-population coevolution. This opens the door to efficient and generic countermeasures to premature convergence for both coevolutionary and evolutionary algorithms applied to problems featuring aggregating fitness functions.", notes = "PMID: 26953882", } @PhdThesis{LiskowskiPhd2018, author = "Pawel Liskowski", title = "Heuristic Algorithms for Discovery of Search Objectives in Test-based Problems", school = "Poznan University of Technology", year = "2018", address = "Poznan, Poland", note = "Honourable mention in the competition for the 2018 Polish Artificial Intelligence Society Award for the Best Ph.D. Dissertation in Artificial Intelligence", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.put.poznan.pl/pliskowski/pub/phdthesis.pdf", size = "235 pages", notes = "http://www.pssi.agh.edu.pl/en:start honorable mention http://fc.put.poznan.pl/drupal/en/aktualnosci/dr-pawe-liskowski-otrzyma-wyr-nienie-w-konkursie-polskiego-stowarzyszenia-sztucznej-inte Supervisor: Krzysztof Krawiec", } @InProceedings{Liskowski:2018:GECCOa, author = "Pawel Liskowski and Iwo Bladek and Krzysztof Krawiec", title = "Neuro-guided genetic programming: prioritizing evolutionary search with neural networks", booktitle = "GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference", year = "2018", editor = "Hernan Aguirre and Keiki Takadama and Hisashi Handa and Arnaud Liefooghe and Tomohiro Yoshikawa and Andrew M. Sutton and Satoshi Ono and Francisco Chicano and Shinichi Shirakawa and Zdenek Vasicek and Roderich Gross and Andries Engelbrecht and Emma Hart and Sebastian Risi and Ekart Aniko and Julian Togelius and Sebastien Verel and Christian Blum and Will Browne and Yusuke Nojima and Tea Tusar and Qingfu Zhang and Nikolaus Hansen and Jose Antonio Lozano and Dirk Thierens and Tian-Li Yu and Juergen Branke and Yaochu Jin and Sara Silva and Hitoshi Iba and Anna I Esparcia-Alcazar and Thomas Bartz-Beielstein and Federica Sarro and Giuliano Antoniol and Anne Auger and Per Kristian Lehre", isbn13 = "978-1-4503-5618-3", pages = "1143--1150", address = "Kyoto, Japan", DOI = "doi:10.1145/3205455.3205629", publisher = "ACM", publisher_address = "New York, NY, USA", month = "15-19 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, ANN", abstract = "When search operators in genetic programming (GP) insert new instructions into programs, they usually draw them uniformly from the available instruction set. Preferring some instructions to others would require additional domain knowledge, which is typically unavailable. However, it has been recently demonstrated that the likelihoods of instructions occurrence in a program can be reasonably well estimated from its input-output behaviour using a neural network. We exploit this idea to bias the choice of instructions used by search operators in GP. Given a large sample of programs and their input-output behaviours, a neural network is trained to predict the presence of individual instructions. When applied to a new program synthesis task, the network is first queried on the set of examples that define the task, and the obtained probabilities determine the frequencies of using instructions in initialization and mutation operators. This priming leads to significant improvements of the odds of successful synthesis on a range of benchmarks.", notes = "Also known as \cite{3205629} GECCO-2018 A Recombination of the 27th International Conference on Genetic Algorithms (ICGA-2018) and the 23rd Annual Genetic Programming Conference (GP-2018)", } @InProceedings{Liskowski:2020:GECCO, author = "Pawel Liskowski and Krzysztof Krawiec and Nihat Engin Toklu and Jerry Swan", title = "Program Synthesis as Latent Continuous Optimization: Evolutionary Search in Neural Embeddings", year = "2020", editor = "Carlos Artemio {Coello Coello} and Arturo Hernandez Aguirre and Josu Ceberio Uribe and Mario Garza Fabre and Gregorio {Toscano Pulido} and Katya Rodriguez-Vazquez and Elizabeth Wanner and Nadarajen Veerapen and Efren Mezura Montes and Richard Allmendinger and Hugo Terashima Marin and Markus Wagner and Thomas Bartz-Beielstein and Bogdan Filipic and Heike Trautmann and Ke Tang and John Koza and Erik Goodman and William B. Langdon and Miguel Nicolau and Christine Zarges and Vanessa Volz and Tea Tusar and Boris Naujoks and Peter A. N. Bosman and Darrell Whitley and Christine Solnon and Marde Helbig and Stephane Doncieux and Dennis G. Wilson and Francisco {Fernandez de Vega} and Luis Paquete and Francisco Chicano and Bing Xue and Jaume Bacardit and Sanaz Mostaghim and Jonathan Fieldsend and Oliver Schuetze and Dirk Arnold and Gabriela Ochoa and Carlos Segura and Carlos Cotta and Michael Emmerich and Mengjie Zhang and Robin Purshouse and Tapabrata Ray and Justyna Petke and Fuyuki Ishikawa and Johannes Lengler and Frank Neumann", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference", pages = "359--367", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, autoencoders, deep learning, embedding, program synthesis", isbn13 = "9781450371285", URL = "https://doi.org/10.1145/3377930.3390213", DOI = "doi:10.1145/3377930.3390213", size = "9 pages", abstract = "In optimization and machine learning, the divide between discrete and continuous problems and methods is deep and persistent. We attempt to remove this distinction by training neural network autoencoders that embed discrete candidate solutions in continuous latent spaces. This allows us to take advantage of state-of-the-art continuous optimization methods for solving discrete optimisation problems, and mitigates certain challenges in discrete optimization, such as design of bias-free search operators. In the experimental part, we consider program synthesis as the special case of combinatorial optimization. We train an autoencoder network on a large sample of programs in a problem-agnostic, unsupervised manner, and then use it with an evolutionary continuous optimization algorithm (CMA-ES) to map the points from the latent space to programs. We propose also a variant in which semantically similar programs are more likely to have similar embeddings. Assessment on a range of benchmarks in two domains indicates the viability of this approach and the usefulness of involving program semantics.", notes = "Also known as \cite{10.1145/3377930.3390213} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @InProceedings{Liskowski:2020:PPSN, author = "Pawel Liskowski and Krzysztof Krawiec and Nihat Engin Toklu", title = "Neuromemetic Evolutionary Optimization", booktitle = "16th International Conference on Parallel Problem Solving from Nature, Part I", year = "2020", editor = "Thomas Baeck and Mike Preuss and Andre Deutz and Hao Wang2 and Carola Doerr and Michael Emmerich and Heike Trautmann", volume = "12269", series = "LNCS", pages = "623--636", address = "Leiden, Holland", month = "7-9 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, ANN, Optimization, Neural networks, Program synthesis", isbn13 = "978-3-030-58111-4", URL = "https://doi.org/10.1007/978-3-030-58112-1_43", DOI = "doi:10.1007/978-3-030-58112-1_43", abstract = "Discrete and combinatorial optimization can be notoriously difficult due to complex and rugged characteristics of the objective function. We address this challenge by mapping the search process to a continuous space using recurrent neural networks. Alongside with an evolutionary run, we learn three mappings: from the original search space to a continuous Cartesian latent space, from that latent space back to the search space, and from the latent space to the search objective. We elicit gradient from that last network and use it to perform moves in the latent space, and apply this Neuromemetic Evolutionary Optimization (NEO) to evolutionary synthesis of programs. Evaluation on a range of benchmarks suggests that NEO significantly outperforms conventional genetic programming.", notes = "PPSN XVI PPSN2020", } @InProceedings{Lissovoi:2018:AAAI, author = "Andrei Lissovoi and Pietro S. Oliveto", title = "On the Time and Space Complexity of Genetic Programming for Evolving Boolean Conjunctions", booktitle = "The Thirty-Second AAAI Conference on Artificial Intelligence", year = "2018", editor = "S. A. McIlraith and K. Q. Weinberger", pages = "1363--1370", address = "New Orleans, Louisiana USA", publisher_address = "Palo Alto, California USA", month = feb # " 2-7", publisher = "AAAI", keywords = "genetic algorithms, genetic programming, Runtime analysis, Computational complexity", isbn13 = "978-1-57735-800-8", URL = "https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16145", URL = "https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16145/15830.pdf", size = "8 pages", abstract = "Genetic Programming (GP) is a general purpose bio-inspired meta-heuristic for the evolution of computer programs. In contrast to the several successful applications, there is little understanding of the working principles behind GP. In this paper we present a performance analysis that sheds light on the behaviour of simple GP systems for evolving conjunctions of n variables (AND_n). The analysis of a random local search GP system with minimal terminal and function sets reveals the relationship between the number of iterations and the expected error of the evolved program on the complete training set. Afterwards we consider a more realistic GP system equipped with a global mutation operator and prove that it can efficiently solve AND_n by producing programs of linear size that fit a training set to optimality and with high probability generalise well. Additionally, we consider more general problems which extend the terminal set with undesired variables or negated variables. In the presence of undesired variables, we prove that, if non-strict selection is used, then the algorithm fits the complete training set efficiently while the strict selection algorithm may fail with high probability unless the substitution operator is switched off. In the presence of negations, we show that while the algorithms fail to fit the complete training set, the constructed solutions generalise well. Finally, from a problem hardness perspective, we reveal the existence of small training sets that allow the evolution of the exact conjunctions even in the presence of negations or of undesired variables.", notes = "https://aaai.org/Library/AAAI/aaai18contents.php https://eprints.whiterose.ac.uk/133966/ The University of Sheffield Also known as \cite{wrro133966}", } @Misc{Lissovoi:2018:arxiv, author = "Andrei Lissovoi and Pietro Simone Oliveto", title = "Computational Complexity Analysis of Genetic Programming", howpublished = "arXiv", year = "2018", month = "11 " # nov, keywords = "genetic algorithms, genetic programming", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/corr/corr1811.html#abs-1811-04465", URL = "http://arxiv.org/abs/1811.04465", abstract = "Genetic Programming (GP) is an evolutionary computation technique to solve problems in an automated, domain-independent way. Rather than identifying the optimum of a function as in more traditional evolutionary optimisation, the aim of GP is to evolve computer programs with a given functionality. A population of programs is evolved using variation operators inspired by Darwinian evolution (crossover and mutation) and natural selection principles to guide the search process towards better programs. While many GP applications have produced human competitive results, the theoretical understanding of what problem characteristics and algorithm properties allow GP to be effective is comparatively limited. Compared to traditional evolutionary algorithms for function optimisation, GP applications are further complicated by two additional factors: the variable length representation of candidate programs, and the difficulty of evaluating their quality efficiently. Such difficulties considerably impact the runtime analysis of GP where space complexity also comes into play. As a result initial complexity analyses of GP focused on restricted settings such as evolving trees with given structures or estimating the quality of solutions using only a small polynomial number of input/output examples. However, the first runtime analyses concerning GP applications for evolving proper functions with defined input/output behaviour have recently appeared. In this chapter, we present an overview of the state-of-the-art.", notes = "journals/corr/abs-1811-04465", } @Article{Lissovoi:2019:JAIR, author = "Andrei Lissovoi and Pietro S. Oliveto", title = "On the Time and Space Complexity of Genetic Programming for Evolving Boolean Conjunctions", journal = "Journal of Artificial Intelligence Research", year = "2019", volume = "66", pages = "655--689", keywords = "genetic algorithms, genetic programming, Boolean, theory", URL = "https://eprints.whiterose.ac.uk/154159/1/11821-Article%20%28PDF%29-22425-1-10-20191112.pdf", URL = "https://www.jair.org/index.php/jair/article/view/11821/26536", DOI = "doi:10.1613/jair.1.11821", size = "35 pages", abstract = "Genetic programming (GP) is a general purpose bio-inspired meta-heuristic for the evolution of computer programs. In contrast to the several successful applications, there is little understanding of the working principles behind GP. In this paper we present a performance analysis that sheds light on the behaviour of simple GP systems for evolving conjunctions of n variables (AND_n). The analysis of a random local search GP system with minimal terminal and function sets reveals the relationship between the number of iterations and the progress the GP makes toward finding the target function. Afterwards we consider a more realistic GP system equipped with a global mutation operator and prove that it can efficiently solve ANDn by producing programs of linear size that fit a training set to optimality and with high probability generalise well. Additionally, we consider more general problems which extend the terminal set with undesired variables or negated variables. In the presence of undesired variables, we prove that, if non-strict selection is used, then the algorithm fits the complete training set efficiently while the strict selection algorithm may fail with high probability unless the substitution operator is switched off. If negations are allowed, we show that while the algorithms fail to fit the complete training set, the constructed solutions generalise well. Finally, from a problem hardness perspective, we reveal the existence of small training sets that allow the evolution of the exact conjunctions even with access to negations or undesired variables.", notes = "Department of Computer Science, University of Sheffield, Sheffield S1 4DP, United Kingdom", } @InCollection{Lissovoi:2019:theoryEC, author = "Andrei Lissovoi and Pietro S. Oliveto", title = "Computational Complexity Analysis of Genetic Programming", booktitle = "Theory of Evolutionary Computation", publisher = "Springer Nature", year = "2020", editor = "Benjamin Doerr and Frank Neumann", series = "Natural Computing Series", chapter = "11", pages = "475--518", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-030-29413-7", URL = "https://arxiv.org/abs/1811.04465", URL = "https://doi.org/10.1007/978-3-030-29414-4_11", DOI = "doi:10.1007/978-3-030-29414-4_11", size = "41 pages", abstract = "Genetic programming (GP) is an evolutionary computation technique to solve problems in an automated, domain-independent way. Rather than identifying the optimum of a function as in more traditional evolutionary optimization, the aim of GP is to evolve computer programs with a given functionality. While many GP applications have produced human competitive results, the theoretical understanding of what problem characteristics and algorithm properties allow GP to be effective is comparatively limited. Compared with traditional evolutionary algorithms for function optimization, GP applications are further complicated by two additional factors: the variable-length representation of candidate programs, and the difficulty of evaluating their quality efficiently. Such difficulties considerably impact the runtime analysis of GP, where space complexity also comes into play. As a result, initial complexity analyses of GP have focused on restricted settings such as the evolution of trees with given structures or the estimation of solution quality using only a small polynomial number of input/output examples. However, the first computational complexity analyses of GP for evolving proper functions with defined input/output behavior have recently appeared. we present an overview of the state of the art.", notes = "lissovoi2019computational, HVL-Prime (1+1)-GP RLS-GP, order, majority, bloat control. Sorting, inv, las, ham, exc, run, max. PAC Learning, poly, parity, xor. CGP. Geometric Semantic Genetic Programming (GSGP). 2/3-SuperMajority, Identification Department of Computer Science, University of Sheffield, UK", } @InProceedings{Littman:1994:mp, author = "Michael L. Littman", title = "Memoryless Policies: Theoretical limitations and practical results", booktitle = "Simulation of Adaptive Behaviour (SAB-94)", year = "1994", pages = "238--245", organisation = "Brown University / Bellcore", keywords = "genetic algorithms", size = "8 pages", notes = "Discusses designing agents to solve completely known problems. Agents 1) are entriely reactive or 2) finite state machines (1 bit memory). Determinstic. Proof given: satisfactory determinsistic memory less agent is NP complete problem. So too is design of optimal agent. Presents method which _may_ be able to find optimal solution in polynomial time. Shown producing optimal or near optimal agents in almost all runs on three differnt problems.", } @InProceedings{Litvinenko:2005:IEEEIDAACSTA, author = "V. I. Litvinenko and P. I. Bidyuk and J. N. Bardachov and V. G. Sherstjuk and A. A. Fefelov", title = "Combining Clonal Selection Algorithm and Gene Expression Programming for Time Series Prediction", booktitle = "Proceedings of the Third Workshop 2005 IEEE Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications", year = "2005", pages = "133--138", address = "Sofia, Bulgaria", month = "5-7 " # sep, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Gene Expression Programming, artificial immune systems, clonal selection algorithm, time series", DOI = "doi:10.1109/IDAACS.2005.282956", size = "6 pages", abstract = "Dynamic system identification algorithm is developed using the basic mechanisms of clonal selection and idea of a new evolutionary computing paradigm - gene expression programming. On the basis of the algorithm developed a computer based system is proposed for making decisions relevant to forecasting of single variable and multivariate time series. The results of computing experiments achieved with the system developed show high quality of short and medium period forecasts.", } @InProceedings{Litvinenko:IWIM:2007, author = "V. I. Litvinenko and P. I. Bidjuk and J. N Bardachov and A. A. Fefelov and V. G. Sherstjuk", title = "The Combined Immune Algorithm Based on Clonal Selection", booktitle = "International Workshop on Inductive Modelling, IWIM 2007", year = "2007", editor = "Jan Drchal and Jan Koutnik", address = "Prague", month = "22-26 " # sep, organisation = "Czech Technical University in Prague", keywords = "genetic algorithms, genetic programming, gene expression programming", isbn13 = "978-80-01-03881-9", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.383.8141", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.383.8141", URL = "http://www.gmdh.net/articles/iwim/IWIM_30.pdf", size = "7 pages", abstract = "A dynamic system identification algorithm is developed using the basic mechanisms of clonal selection and an idea of a new evolutionary computing paradigm -- gene expression programming. On the basis of the algorithm developed a computer based system is proposed for making decisions relevant to forecasting of a single variable and multivariate time series. The results of computing experiments achieved with the system developed show high quality of short and medium period forecasts.", notes = "National Technical University of Ukraine KPI", } @InProceedings{Liu:2022:ISCAS, author = "Bo Liu2 and Xuetao Wang and Renyuan Zhang and Anfeng Xue and Ziyu Wang and Haige Wu and Hao Cai", booktitle = "2022 IEEE International Symposium on Circuits and Systems (ISCAS)", title = "A Low Power {DNN-based} Speech Recognition Processor with Precision Recoverable Approximate Computing", year = "2022", pages = "2102--2106", abstract = "This paper proposes a low power speech recognition processor based on an optimized DNN with precision recoverable approximate computing. In order to accelerate and improve energy of DNN, an approximate multiplier based on cartesian genetic programming with weight pre-classification and mismatch compensation is proposed. A partial retraining scheme based on approximate noise is proposed to recover the accuracy loss caused by approximate computing. Experimental results show that the proposed approximate multiplier reduces power consumption by 42.percent, and the partial retraining scheme can recover accuracy of 3.0percent~4.3percent. Implemented under 22nm, the proposed processor can support the recognition of 10 keywords under different noise types and signal-to-noise ratios (5dB~clean), while the recognition accuracy is 83.3percent ~89.8percent and power consumption is 8.6μ W.", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming", DOI = "doi:10.1109/ISCAS48785.2022.9937896", ISSN = "2158-1525", month = may, notes = "Also known as \cite{9937896}", } @Article{LIU:2023:compeleceng, author = "Bo Liu2 and Renyuan Zhang and Qiao Shen and Zeju Li and Na Xie and Yuanhao Wang and Chonghang Xie and Hao Cai", title = "{W-AMA:} Weight-aware Approximate Multiplication Architecture for neural processing", journal = "Computers and Electrical Engineering", volume = "111", pages = "108921", year = "2023", ISSN = "0045-7906", DOI = "doi:10.1016/j.compeleceng.2023.108921", URL = "https://www.sciencedirect.com/science/article/pii/S0045790623003452", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Approximate computing, Deep Neural Network, ANN, Hardware accelerator, Average Hessian trace", abstract = "This paper presents the Weight-aware Approximate Multiplication Architecture (W-AMA) for Deep Neural Networks (DNNs). Considering the Gaussian-like weight distribution, it deploys an accuracy-configurable computing component to improve the computational efficiency. Two techniques for effectively integrating the W-AMA into DNN accelerator are presented: (1) A Cartesian Genetic Programming (CGP) based approximate multiplier is designed and selectable to compute the Least Significant Bit (LSB) for a higher accuracy mode. The Reward-Penalty-Coefficient (RPC) is proposed to achieve the internal-compensation. (2) The Hessian-Aware-Approximation (HAA) method is used for hybrid approximate modes cross-layer mapping. Based on the W-AMA, an energy-efficient DNN accelerator is proposed and evaluated on 28 nm technology. It can achieve the energy efficiency of 9.6 TOPS/W, and the computational energy efficiency can be improved by 1.5times compared with the standard units, with an 0.52percent accuracy loss on CIFAR-10 using ResNet-18", } @Article{LIU:2022:actamat, author = "Chang Liu and Suyue Yuan and Jinwoo Im and Felipe P. J. {de Barros} and Sami F. Masri and Paulo S. Branicio", title = "Mechanical properties, failure mechanisms, and scaling laws of bicontinuous nanoporous metallic glasses", journal = "Acta Materialia", volume = "239", pages = "118255", year = "2022", ISSN = "1359-6454", DOI = "doi:10.1016/j.actamat.2022.118255", URL = "https://www.sciencedirect.com/science/article/pii/S1359645422006358", keywords = "genetic algorithms, genetic programming, Nanoporous metallic glass, Mechanical behavior, Scaling laws, Bicontinuous nanoporous", abstract = "Molecular dynamics simulations are employed to study the mechanical properties of nanoporous CuxZr1-x metallic glasses (MGs) with five different compositions, x = 0.28, 0.36, 0.50, 0.64, and 0.72, and porosity in the range 0.1 < ? < 0.7. Results from tensile loading simulations indicate a strong dependence of Young's modulus, E, and Ultimate Tensile Strength (UTS) on porosity and composition. By increasing the porosity from phi = 0.1 to phi = 0.7, the topology of the nanoporous MG shifts from closed cell to open-cell bicontinuous. The change in nanoporous topology enables a brittle-to-ductile transition in deformation and failure mechanisms from a single critical shear band to necking and rupture of ligaments. Genetic Programming (GP) is employed to find scaling laws for E and UTS as a function of porosity and composition. A comparison of the GP-derived scaling laws against existing relationships shows that the GP method is able to uncover expressions that can predict accurately both the values of E and UTS in the whole range of porosity and compositions considered", } @InCollection{liu:2000:DGSDGUGA, author = "David Liu", title = "Development of Game-Playing Strategies in a Darwinistic Game Using Genetic Algorithms", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "261--268", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms", notes = "part of \cite{koza:2000:gagp}", } @Misc{Liu:2022:edMOGPsr, author = "Dazhuang Liu and Marco Virgolin and Tanja Alderliesten and Peter A. N. Bosman", title = "Evolvability Degeneration in Multi-Objective Genetic Programming for Symbolic Regression", howpublished = "ArXiv", year = "2022", month = "14 " # feb, keywords = "genetic algorithms, genetic programming, Symbolic regression, multi-objective optimization, MOGP, evolvability", URL = "https://arxiv.org/abs/2202.06983", size = "16 pages", abstract = "Genetic programming (GP) is one of the best approaches today to discover symbolic regression models. To find models that trade off accuracy and complexity, the non-dominated sorting genetic algorithm II (NSGA-II) is widely used. Unfortunately, it has been shown that NSGA-II can be inefficient: in early generations, low-complexity models over-replicate and take over most of the population. Consequently, studies have proposed different approaches to promote diversity. Here, we study the root of this problem, in order to design a superior approach. We find that the over-replication of low complexity-models is due to a lack of evolvability, i.e., the inability to produce offspring with improved accuracy. We therefore extend NSGA-II to track, over time, the evolvability of models of different levels of complexity. With this information, we limit how many models of each complexity level are allowed to survive the generation. We compare this new version of NSGA-II, evoNSGA-II, with the use of seven existing multi-objective GP approaches on ten widely-used data sets, and find that evoNSGA-II is equal or superior to using these approaches in almost all comparisons. Furthermore, our results confirm that evoNSGA-II behaves as intended: models that are more evolvable form the majority of the population.", } @InProceedings{Liu:2022:GECCO, author = "Dazhuang Liu and Marco Virgolin and Tanja Alderliesten and Peter Bosman", title = "Evolvability Degeneration in {Multi-Objective} Genetic Programming for Symbolic Regression", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference", year = "2022", editor = "Alma Rahat and Jonathan Fieldsend and Markus Wagner and Sara Tari and Nelishia Pillay and Irene Moser and Aldeida Aleti and Ales Zamuda and Ahmed Kheiri and Erik Hemberg and Christopher Cleghorn and Chao-li Sun and Georgios Yannakakis and Nicolas Bredeche and Gabriela Ochoa and Bilel Derbel and Gisele L. Pappa and Sebastian Risi and Laetitia Jourdan and Hiroyuki Sato and Petr Posik and Ofer Shir and Renato Tinos and John Woodward and Malcolm Heywood and Elizabeth Wanner and Leonardo Trujillo and Domagoj Jakobovic and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Inmaculada Medina-Bulo and Slim Bechikh and Andrew M. Sutton and Pietro Simone Oliveto", pages = "973--981", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", note = "Best Paper GP Track", keywords = "genetic algorithms, genetic programming, evolvability, multi-objective optimization, symbolic regression", isbn13 = "978-1-4503-9237-2", DOI = "doi:10.1145/3512290.3528787", code_url = "https://github.com/dzhliu/evoNSGA-II", abstract = "Genetic programming (GP) is one of the best approaches today to discover symbolic regression models. To find models that trade off accuracy and complexity, the non-dominated sorting genetic algorithm II (NSGA-II) is widely used. Unfortunately, it has been shown that NSGA-II can be inefficient: in early generations, low-complexity models over-replicate and take over most of the population. Consequently, studies have proposed different approaches to promote diversity. Here, we study the root of this problem, in order to design a superior approach. We find that the over-replication of low complexity-models is due to a lack of evolvability, i.e., the inability to produce offspring with improved accuracy. We therefore extend NSGA-II to track, over time, the evolvability of models of different levels of complexity. With this information, we limit how many models of each complexity level are allowed to survive the generation. We compare this new version of NSGA-II, evoNSGA-II, with the use of seven existing multi-objective GP approaches on ten widely-used data sets, and find that evoNSGA-II is equal or superior to using these approaches in almost all comparisons. Furthermore, our results confirm that evoNSGA-II behaves as intended: models that are more evolvable form the majority of the population. Code: https://github.com/dzhliu/evoNSGA-II", notes = "see also \cite{Liu:2022:edMOGPsr} GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @Article{doi:10.1080/0305215X.2015.1125262, author = "D. Liu and H. Lohse-Busch and V. Toropov and C. Huehne and U. Armani", title = "Detailed design of a lattice composite fuselage structure by a mixed optimization method", journal = "Engineering Optimization", year = "2016", volume = "48", number = "10", pages = "1707--1720", keywords = "genetic algorithms, genetic programming, aircraft fuselage design, topology optimization, lattice structure, metamodel, parametric optimization, Metamodel building, Parameterised finite element model, DoE", publisher = "Taylor \& Francis", ISSN = "0305-215X", URL = "https://ueaeprints.uea.ac.uk/id/eprint/56158/1/Detailed_design_of_a_lattice_composite_fuselage_structure_by_a_mixed_optimization_method_by_D_Liu_final.pdf", DOI = "doi:10.1080/0305215X.2015.1125262", size = "14 pages", abstract = "a procedure for designing a lattice fuselage barrel is developed. It comprises three stages: first, topology optimisation of an aircraft fuselage barrel is performed with respect to weight and structural performance to obtain the conceptual design. The interpretation of the optimal result is given to demonstrate the development of this new lattice airframe concept for the fuselage barrel. Subsequently, parametric optimization of the lattice aircraft fuselage barrel is carried out using genetic algorithms on meta-models generated with genetic programming from a 101-point optimal Latin hypercube design of experiments. The optimal design is achieved in terms of weight savings subject to stability, global stiffness and strain requirements, and then verified by the fine mesh finite element simulation of the lattice fuselage barrel. Finally, a practical design of the composite skin complying with the aircraft industry lay-up rules is presented. It is concluded that the mixed optimization method, combining topology optimization with the global metamodel-based approach, allows the problem to be solved with sufficient accuracy and provides the designers with a wealth of information on the structural behaviour of the novel anisogrid composite fuselage design.", notes = "Faculty of Science, University of East Anglia, Norwich, UK", } @InProceedings{Liu:2010:cec, author = "Fang Liu and Antoaneta Serguieva and Paresh Date", title = "A mixed-game and co-evolutionary genetic programming agent-based model of financial contagion", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-6910-9", abstract = "Over the past two decades, financial market crises with similar features have occurred in different regions of the world. Unstable cross-market linkages during financial crises are referred to as financial contagion. We simulate the transmission of financial crises in the context of a model of market participants adopting various strategies; this allows testing for financial contagion under alternative scenarios. Using a comprehensive approach, we develop an agent-based multinational model and investigate the reasons for contagion. Our model comprises four types of traders: noise, herd, game, and technical traders respectively. Different types of traders use different computational strategies to make buy, sell, or hold decisions. Although contagion has been extensively investigated in the financial literature, it has not yet been studied through computational intelligence techniques. Our simulations shed light on parameter values and characteristics which can be exploited to detect contagion at an earlier stage, hence recognising financial crises with the potential to destabilise cross-market linkages. In the real world, such information would be extremely valuable to develop appropriate risk management strategies.", DOI = "doi:10.1109/CEC.2010.5586243", notes = "WCCI 2010. Also known as \cite{5586243}", } @PhdThesis{Fang_Liu:thesis, author = "Fang Liu", title = "Nature inspired computational intelligence for financial contagion modelling", school = "College of Business, Arts and Social Sciences, Brunel University", year = "2014", address = "UK", month = "22 " # feb, keywords = "genetic algorithms, genetic programming, FGP, PSO, Particle swarm optimization, Independence, Copula, Game theory", URL = "http://bura.brunel.ac.uk/bitstream/2438/8208/1/FulltextThesis.pdf", URL = "http://bura.brunel.ac.uk/handle/2438/8208", size = "190 pages", abstract = "Financial contagion refers to a scenario in which small shocks, which initially affect only a few financial institutions or a particular region of the economy, spread to the rest of the financial sector and other countries whose economies were previously healthy. This resembles the 'transmission' of a medical disease. Financial contagion happens both at domestic level and international level. At domestic level, usually the failure of a domestic bank or financial intermediary triggers transmission by defaulting on inter-bank liabilities, selling assets in a fire sale, and undermining confidence in similar banks. An example of this phenomenon is the failure of Lehman Brothers and the subsequent turmoil in the US financial markets. International financial contagion happens in both advanced economies and developing economies, and is the transmission of financial crises across financial markets. Within the current globalise financial system, with large volumes of cash flow and cross-regional operations of large banks and hedge funds, financial contagion usually happens simultaneously among both domestic institutions and across countries. There is no conclusive definition of financial contagion, most research papers study contagion by analysing the change in the variance-covariance matrix during the period of market turmoil. King and Wadhwani (1990) first test the correlations between the US, UK and Japan, during the US stock market crash of 1987. Boyer (1997) finds significant increases in correlation during financial crises, and reinforces a definition of financial contagion as a correlation changing during the crash period. Forbes and Rigobon (2002) give a definition of financial contagion. In their work, the term interdependence is used as the alternative to contagion. They claim that for the period they study, there is no contagion but only interdependence. Interdependence leads to common price movements during periods both of stability and turmoil. In the past two decades, many studies (e.g. Kaminsky et at., 1998; Kaminsky 1999) have developed early warning systems focused on the origins of financial crises rather than on financial contagion. Further authors (e.g. Forbes and Rigobon, 2002; Caporale et al, 2005), on the other hand, have focused on studying contagion or interdependence. In this thesis, an overall mechanism is proposed that simulates characteristics of propagating crisis through contagion. Within that scope, a new co-evolutionary market model is developed, where some of the technical traders change their behaviour during crisis to transform into herd traders making their decisions based on market sentiment rather than underlying strategies or factors. The thesis focuses on the transformation of market interdependence into contagion and on the contagion effects. The author first build a multi-national platform to allow different type of players to trade implementing their own rules and considering information from the domestic and a foreign market. Traders strategies and the performance of the simulated domestic market are trained using historical prices on both markets, and optimizing artificial market's parameters through immune - particle swarm optimization techniques (I-PSO). The author also introduces a mechanism contributing to the transformation of technical into herd traders. A generalized auto-regressive conditional heteroscedasticity - copula (GARCH-copula) is further applied to calculate the tail dependence between the affected market and the origin of the crisis, and that parameter is used in the fitness function for selecting the best solutions within the evolving population of possible model parameters, and therefore in the optimization criteria for contagion simulation. The overall model is also applied in predictive mode, where the author optimize in the pre-crisis period using data from the domestic market and the crisis-origin foreign market, and predict in the crisis period using data from the foreign market and predicting the affected domestic market.", notes = "Mention of GP Supervisor Antoaneta Serguieva and P.Date", } @InProceedings{Liu:2010:CSO, author = "Guiquan Liu and Xiufang Jiang and Lingyun Wen", title = "A Clustering System for Gene Expression Data Based upon Genetic Programming and the HS-Model", booktitle = "Third International Joint Conference on Computational Science and Optimization (CSO)", year = "2010", month = "28-31 " # may, volume = "1", pages = "238--241", abstract = "Cluster analysis is a major method to study gene function and gene regulation information for there is a lack of prior knowledge for gene data. Many clustering methods existed at present usually need manual operations or pre-determined parameters, which are difficult for gene data. Besides, gene data possess their own characteristics, such as large scale, high-dimension, and noise. Therefore, a systematic clustering algorithm should be proposed to effectively deal with gene data. In this paper, a novel genetic programming (GP) clustering system for gene data based on hierarchical statistical model (HS-model) is proposed. And an appropriate fitness function is also proposed in this system. This clustering system can largely eliminate the infection of data scale and dimension. The proposed GP clustering system is applied to cluster the whole intact yeast gene data without dimensionality reduction. The experimental results indicate that the algorithm is highly efficient and can effectively deal with missing values in gene dataset.", keywords = "genetic algorithms, genetic programming, hierarchical statistical", DOI = "doi:10.1109/CSO.2010.116", notes = "Key Laboratory of Software in Computing and Communication, Anhui Province School of Computer Science and Technology University of Science and Technology of China, Hefei, Anhui 230027, China Also known as \cite{5532998}", } @InProceedings{liu:2005:EH, author = "Heng Liu and Julian F. Miller and Andy M. Tyrrell", title = "Intrinsic Evolvable Hardware Implementation of a Robust Biological Development Model for Digital Systems", booktitle = "Proceedings of the 2005 NASA/DoD Conference on Evolvable Hardware", year = "2005", editor = "Jason Lohn and David Gwaltney and Gregory Hornby and Ricardo Zebulum and Didier Keymeulen and Adrian Stoica", pages = "87--92", address = "Washington, DC, USA", month = "29 " # jun # "-1 " # jul, publisher = "IEEE Press", publisher_address = "IEEE Service Center 445 Hoes Lane Asia P.O. Box 1331 Piscataway, NJ 08855-1331", organisation = "NASA, DoD", keywords = "genetic algorithms, genetic programming, EHW", ISBN = "0-7695-2399-4", DOI = "doi:10.1109/EH.2005.32", abstract = "An intrinsic evolvable hardware platform was realized to accelerate the evolutionary search process of a biologically inspired developmental model targeted at off the shelf FPGA implementation. The model has the capability of exhibiting very large transient fault-tolerance. The evolved circuits make up a digital {"}organism{"} from identical cells which only differ in internal states. Organisms implementing a 2-bit multiplier were evolved that can {"}recover{"} from almost any kinds of transient faults. This paper focuses on the design concerns and details of the evolvable hardware system, including the digital organism/cell and the intrinsic FPGA-based evolvable hardware platform.", notes = "EH2005 IEEE Computer Society Order Number P2399", } @Article{LIU:2019:CIE, author = "Hongqi Liu and Hai Lin2 and Xuchu Jiang and Xinyong Mao and Quanxin Liu and Bin Li", title = "Estimation of mass matrix in machine tool's weak components research by using symbolic regression", journal = "Computer \& Industrial Engineering", volume = "127", pages = "998--1011", year = "2019", keywords = "genetic algorithms, genetic programming, Weak component research, Modal mass matrix, Mass matrix, Genetic programming algorithm, Symbolic regression algorithm", ISSN = "0360-8352", DOI = "doi:10.1016/j.cie.2018.11.033", URL = "http://www.sciencedirect.com/science/article/pii/S0360835218305734", abstract = "Modal mass is the important dynamic parameter in weak component research of machine tool structure and also for its control design and load design. Modal mass matrix is defined as the multiplication of mass matrix of a machine tool and its corresponding modal shape matrix. Currently, the big problem is that the mass matrix is hard to get for the calculation of modal mass matrix. Traditional method such as the finite element method cannot acquire the mass matrix well because the overall mass matrix of complex systems cannot be given by experience and the mass matrix in finite element analysis is so large that the computer hard disk will be blasted. In addition to finite element method, the UMM method is used commonly but the noise contained in the mode of the data processing is mixed into the mass matrix, resulting in the inaccurate result and even failure in severe cases. So, there is an urgent need for a method of directly obtaining the mass matrix based on a general equation of multi-degree-of-freedom vibration system from a data source, and then used to calculate the modal mass. In this paper, Genetic programming algorithm (GP) in symbolic regression as an evolution computation method is used to search out the equation expression structure and its coefficients among a group of variances including displacement, velocity, acceleration and external excitation force. And the mass matrix is contained in the equations' coefficients. In addition, its performance is compared with LRA method and PSO method", } @InProceedings{liu:2003:gecco, author = "Hongwei Liu and Hitoshi Iba", title = "Multi-agent Learning of Heterogeneous Robots by Evolutionary Subsumption", booktitle = "Genetic and Evolutionary Computation -- GECCO-2003", editor = "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller", year = "2003", pages = "1715--1728", address = "Chicago", publisher_address = "Berlin", month = "12-16 " # jul, volume = "2724", series = "LNCS", ISBN = "3-540-40603-4", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", URL = "http://www.iba.k.u-tokyo.ac.jp/papers/2003/lhwGECCO2003.pdf", DOI = "doi:10.1007/3-540-45110-2_64", abstract = "Many multi-robot systems are heterogeneous cooperative systems, systems consisting of different species of robots cooperating with each other to achieve a common goal. This paper presents the emergence of cooperative behaviors of heterogeneous robots by means of GP. Since directly using GP to generate a controller for complex behaviors is inefficient and intractable, especially in the domain of multi-robot systems, we propose an approach called Evolutionary Subsumption, which applies GP to subsumption architecture. We test our approach in an {"}eye{"}-{"}hand{"} cooperation problem. By comparing our approach with direct GP and artificial neural network (ANN) approaches, our experimental results show that ours is more efficient in emergence of complex behaviors.", notes = "GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)", } @InProceedings{liu:2004:ahafahrc, title = "A Hierarchical Approach for Adaptive Humanoid Robot Control", author = "Hongwei Liu and Hitoshi Iba", pages = "1546--1553", booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary Computation", year = "2004", publisher = "IEEE Press", month = "20-23 " # jun, address = "Portland, Oregon", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Real-world applications, Evolutionary design \& evolvable hardware, CBR", URL = "http://www.iba.k.u-tokyo.ac.jp/papers/2004/lhwCEC2004.pdf", DOI = "doi:10.1109/CEC.2004.1331080", abstract = "The key idea in our approach is to extract control rules with GP in simplified simulation and get a prototype of the control program then interpret and interpolate it with CBR in the real world environments. Accordingly, our proposed approach consists of two stages: the evolution stage and the adaptation stage. In the first stage, the prototype of the control program is evolved based on abstract primitive behaviors in a highly simplified simulation. In the second stage, the best control program is applied to a physical robot thereby adapting it to the real world environments by using CBR.", notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.", } @InProceedings{Liu:2004:aspgp, author = "Hongwei Liu and Hitoshi Iba", title = "An Evolution-Adapation Approach of Genetic Programming for Programming Humanoid Robot", booktitle = "Proceedings of The Second Asian-Pacific Workshop on Genetic Programming", year = "2004", editor = "R I Mckay and Sung-Bae Cho", address = "Cairns, Australia", month = "6-7 " # dec, keywords = "genetic algorithms, genetic programming", notes = "Broken Sep 2018 http://sc.snu.ac.kr/~aspgp/aspgp04/programme.html", } @InProceedings{liu:hrp:gecco2004, author = "Hongwei Liu and Hitoshi Iba", title = "Humanoid Robot Programming Based on CBR Augmented GP", booktitle = "Genetic and Evolutionary Computation -- GECCO-2004, Part II", year = "2004", editor = "Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell", series = "Lecture Notes in Computer Science", pages = "708--709", address = "Seattle, WA, USA", publisher_address = "Heidelberg", month = "26-30 " # jun, organisation = "ISGEC", publisher = "Springer-Verlag", volume = "3103", ISBN = "3-540-22343-6", ISSN = "0302-9743", DOI = "doi:10.1007/b98645", URL = "http://www.iba.k.u-tokyo.ac.jp/papers/2004/lhwGECCO2004.pdf", size = "2", keywords = "genetic algorithms, genetic programming, Poster", notes = "GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)", } @InProceedings{Liu:2004:WOR:HLiu, author = "Heng Liu and Julian F. Miller and Andy M. Tyrrell", title = "An Intrinsic Robust Transient Fault-Tolerant Developmental Model for Digital Systems", editor = "R. Poli and S. Cagnoni and M. Keijzer and E. Costa and F. Pereira and G. Raidl and S. C. Upton and D. Goldberg and H. Lipson and E. {de Jong} and J. Koza and H. Suzuki and H. Sawai and I. Parmee and M. Pelikan and K. Sastry and D. Thierens and W. Stolzmann and P. L. Lanzi and S. W. Wilson and M. O'Neill and C. Ryan and T. Yu and J. F. Miller and I. Garibay and G. Holifield and A. S. Wu and T. Riopka and M. M. Meysenburg and A. W. Wright and N. Richter and J. H. Moore and M. D. Ritchie and L. Davis and R. Roy and M. Jakiela", booktitle = "GECCO 2004 Workshop Proceedings", year = "2004", month = "26-30 " # jun, address = "Seattle, Washington, USA", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", abstract = "A biologically inspired developmental model targeted at hardware implementation (off-shelf FPGA) is proposed which exhibits extremely robust transient fault-tolerant capability: in the software simulation of the experimental application. In a 6x6 cell French Flag, some individuals were discovered using evolution that have the ability to {"}recover{"} themselves from almost any kinds of transient faults, even in the worst case of only one {"}live{"} cell remaining. All cells in this model have identical genotype (physical structures), and only differ in internal states.", URL = "http://gpbib.cs.ucl.ac.uk/gecco2004/WWOR005.pdf", notes = "Distributed on CD-ROM at GECCO-2004", } @PhdThesis{HengLiu:thesis, author = "Heng Liu", title = "Biological Development model for the design of Robust Digital System", school = "Electronic Engineering, York University", year = "2008", address = "UK", month = sep, keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, Fault-tolerance, Evolvable hardware, FPGA, Development principle, Multicellular organism, Evolutionary algorithm, French Flag, Multiplier, Digital circuit, Autonomous Robot Controller", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/HengLiu_thesis.pdf", size = "180 pages", abstract = "This thesis presents a biologically-inspired developmental model for the design of digital circuits. Circuits have been evolved that exhibit the ability to self-repair and correct transient faults to recover correct functionality. The method devised gives no explicit coordinate information to the evolved cell circuits. The method presented has been implemented fully in electronic hardware. This allowed developmental circuits to be evolved considerably more quickly than in software simulation. The methods presented have been applied to produce a self-repairing two bit multiplier and an autonomous robot controller circuit. Results are presented that shows that after introduction of faults, both circuits can autonomously recover correct functionality.", notes = "Liu Heng created a hardware model based on my developmental 'French flag' work. He introduced an execution unit in each cell, whose code was evolved (written in CGP), together with developmental code (also in CGP). He was able to evolve a self-repairing 2-bit parallel multiplier and also robot controllers, which recovered autonomously fater damage. EO, Xilinx XCV1000, Celoxia RC1000, PLX PCI9080, multiplier, Kiki robot Supervised by Julian Francis Miller and Andy Tyrrell", } @Article{journals/entropy/LiuJJ16, author = "Hongguang Liu and Ping Ji and Jian Jin2", title = "Intra-Day Trading System Design Based on the Integrated Model of Wavelet De-Noise and Genetic Programming", journal = "Entropy", year = "2016", number = "12", volume = "18", pages = "435", keywords = "genetic algorithms, genetic programming, intra-day trading, wavelet de-noise, technical analysis, CSI 300 index", bibdate = "2017-05-26", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/entropy/entropy18.html#LiuJJ16", DOI = "doi:10.3390/e18120435", size = "16 pages", abstract = "Technical analysis has been proved to be capable of exploiting short-term fluctuations in financial markets. Recent results indicate that the market timing approach beats many traditional buy-and-hold approaches in most of the short-term trading periods. Genetic programming (GP) was used to generate short-term trade rules on the stock markets during the last few decades. However, few of the related studies on the analysis of financial time series with genetic programming considered the non-stationary and noisy characteristics of the time series. In this paper, to de-noise the original financial time series and to search profitable trading rules, an integrated method is proposed based on the Wavelet Threshold (WT) method and GP. Since relevant information that affects the movement of the time series is assumed to be fully digested during the market closed periods, to avoid the jumping points of the daily or monthly data, in this paper, intra-day high-frequency time series are used to fully exploit the short-term forecasting advantage of technical analysis. To validate the proposed integrated approach, an empirical study is conducted based on the China Securities Index (CSI) 300 futures in the emerging China Financial Futures Exchange (CFFEX) market. The analysis outcomes show that the wavelet de-noise approach outperforms many comparative models", notes = "Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China", } @PhdThesis{Hongguang_Liu:thesis, author = "Hongguang Liu", title = "Multi-frequency analysis for high frequency trading", school = "Dept. of Industrial and Systems Engineering, Hong Kong Polytechnic University", year = "2016", type = "Nov", address = "Hong Kong", keywords = "genetic algorithms, genetic programming, ANN, MLP, Wavelet MLP, NARX, WMLP, Investment analysis, Mathematical models, Portfolio management, Mathematical models, Stocks, Prices, Mathematical models", URL = "http://theses.lib.polyu.edu.hk/handle/200/8985", URL = "http://library.polyu.edu.hk/record=99102195", broken = "http://theses.lib.polyu.edu.hk/bitstream/handle/200/8985/991021952843203411.pdf", URL = "https://theses.lib.polyu.edu.hk/bitstream/200/8985/1/991021952843203411.pdf", size = "xiv, 186 pages", abstract = "High-Frequency Trading (HFT) in financial markets has been making media headlines. The 2010 Flash Crash in the US and the 2013 Everbright Securities incident in China showed its dramatic impacts on the markets. However, as a relatively new phenomenon, most of the discussion on HFT is not backed by solid academic research. At the same time, current academic research on high-frequency trading focuses on its afterward influences, the motivation and the trading logic behind the HFT is rarely explored. Basically, there are two kinds of HFT, the first kind of HFT takes advantage of _time_, the most advanced computers are placed right next to the exchanges to reduce the time delay of the receiving of market data and the execution of trading orders that aiming to capture a very small fraction of the profit on every trade. The second kind of HFT is conducted based on the analysis of the historical data of the related financial time series. This thesis focuses on the study of the second kind of HFT. Multiple methods can be used in the design of the second kind of HFT. In this research, multi-frequency analysis and wavelet are combined with technical indicators and modern machine learning tools. Forecasting of the directions of the financial time series is crucial in the design of such kind of HFT systems, many economic and technical models and indicators have been built in the past, however, most of the past research merely analyse the data in time domain, the frequency domain of the HFT is rarely explored. This research focuses on the multi-frequency predictions of the short-term movements of the financial time series and the design of the trading systems based on the forecast. HFT systems based on moving averages and a simple trend following system are developed to set benchmarks for the multi-frequency related systems. An experiment on the performance of two-frequency ARIMA model is also conducted to show the prediction power of the multi-frequency analysis, as time series in different resolutions may convey different information on its characteristics, the empirical results indicated that multi-frequency could improve the forecast performance. After that, an intra-day trading system is designed based on the Genetic Programming (GP) and technical analysis, wavelet de-noise is introduced to improve the performance of the GP based system, the system with wavelet de-noise showed best performance in the empirical test. To explore the nonlinear relationship, artificial neural network (ANN) is applied in the prediction of the financial time series. Both Nonlinear Auto-regressive with eXogenous (NARX) and wavelet based Multi-layer perceptron models are used in the forecasting of the intra-day high-frequency time series, based on which, HFT systems are developed. To test the performance of the HFT systems, the China index futures is selected as the experiment asset. Based on the experiments in this thesis, the HMA trading system shows the best performance among the tested moving averages trading systems; the two-frequency ARIMA beats the traditional single frequency models; the GP systems trained using the wavelet de-noised data outperforms the GP systems trained using the original data, and the hard-threshold denoise method provides the best out-of-sample trading performance; the WMLP based trading model outperforms the NARX model in the out-of-sample trading test.", } @MastersThesis{Jia-Hong_Liu:thesis, author = "Jia-Hong Liu", title = "Portfolio Investment Based on Neural Networks", school = "Department of Computer Science and Engineering, National Sun Yat-sen University", year = "2018", address = "Kaohsiung, Taiwan", month = jul # "~21", note = "Master Thesis", keywords = "genetic algorithms, genetic programming, gene expression programming, neural network, ANN, stock investment, convolutional neural network, portfolio", language = "en", contributor = "Shih-Chung Chen and Chiou-Yi Hor and Chien-Feng Huang and Chang-Biau Yang", oai = "oai:NSYSU:etd-0621118-142449", rights = "user_define; Copyright information available at source archive", URL = "https://ethesys.lis.nsysu.edu.tw/ETD-db/ETD-search-c/view_etd?URN=etd-0621118-142449", URL = "http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/getfile?URN=etd-0621118-142449&filename=etd-0621118-142449.pdf", size = "56 pages", abstract = "In this thesis, we combine the trading signals generated by the gene expression programming (GEP) method of Lee et al. and the portfolio generated by convolutional neural network (CNN) structure of Jiang et al. to form a stock investment method with portfolio management. The method of Jiang et al. focuses on the investment of the cryptocurrency. We change the invested target of Jiang et al. from cryptocurrency to stocks. We recompute the weights of the portfolio when the method of Lee et al. generates a trading signal (buy or sell). To test our method, we choose 213 stocks which always exist during 1995/1/5 to 2017/12/29 on stock market in Taiwan. Our training period starts from 1995/1/5. We perform the trading from 2002/1/2 until 2017/12/29. There are three cases in our experiments: Trading 100 stocks with the 100-stock features, trading 100 stocks with the 213-stock features, and trading 213 stocks with the 213-stock features. The annualized returns for the three cases are 25.00percent, 26.52percent and 27.32percent, respectively. Our method is better than the buy-and-hold 12.36percent for 100 stocks, and 12.21percent for 213 stocks. Our method is also better than the method of Lee et al. without portfolio management 12.94percent for 100 stocks, and 12.67percent for 213 stocks.", notes = "NSYSU Supervisor: Chang-Biau Yang", } @InProceedings{Liu:2020:CEC, author = "Jiandong Liu and Ruibin Bai and Zheng Lu and Peiming Ge and Uwe Aickelin and Daoyun Liu", title = "Data-Driven Regular Expressions Evolution for Medical Text Classification Using Genetic Programming", booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC 2020", year = "2020", editor = "Yaochu Jin", pages = "paper id24564", address = "internet", month = "19-24 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-7281-6929-3", DOI = "doi:10.1109/CEC48606.2020.9185500", abstract = "In medical fields, text classification is one of the most important tasks that can significantly reduce human work-load through structured information digitization and intelligent decision support. Despite the popularity of learning-based text classification techniques, it is hard for human to understand or manually fine-tune the classification for better precision and recall, due to the black box nature of learning. This study proposes a novel regular expression-based text classification method making use of genetic programming (GP) approaches to evolve regular expressions that can classify a given medical text inquiry with satisfaction. Given a seed population of regular expressions (randomly initialized or manually constructed by experts), our method evolves a population of regular expressions, using a novel regular expression syntax and a series of carefully chosen reproduction operators. Our method is evaluated with real-life medical text inquiries from an online health care provider and shows promising performance. More importantly, our method generates classifiers that can be fully understood, checked and updated by medical doctors, which are fundamentally crucial for medical related practices.", notes = "https://wcci2020.org/ University of Nottingham Ningbo China, China; Ping An Health Cloud Company Limited China, China; University of Melbourne, Australia. Also known as \cite{9185500}", } @Article{Liu2007284, author = "Jie Liu and Yaxiong Xie and Ryan Cooper and Danica M. K. Ducharme and Raymond Tennant and Bhalchandra A. Diwan and Michael P. Waalkes", title = "Transplacental exposure to inorganic arsenic at a hepatocarcinogenic dose induces fetal gene expression changes in mice indicative of aberrant estrogen signaling and disrupted steroid metabolism", journal = "Toxicology and Applied Pharmacology", volume = "220", number = "3", pages = "284--291", year = "2007", ISSN = "0041-008X", DOI = "doi:10.1016/j.taap.2007.01.018", URL = "http://www.sciencedirect.com/science/article/B6WXH-4N0HJBH-4/2/17b4a380d5a6ecedeb7db7df525f7fb9", notes = "Not on GP", } @InProceedings{Liu:2008:ieeeIAS, author = "J. Liu and S. Ghafari and W. Wang and F. Golnaraghi and F. Ismail", title = "Bearing Fault Diagnostics Based on Reconstructed Features", booktitle = "IEEE Industry Applications Society Annual Meeting, IAS '08", year = "2008", month = oct, pages = "1--7", keywords = "genetic algorithms, genetic programming, bearing condition monitoring, bearing fault diagnostic technique, fault diagnostic reliability, feature reconstruction, modified kurtosis ratio, one-scale wavelet analysis, condition monitoring, fault diagnosis, feature extraction, image reconstruction, machine bearings, wavelet transforms", DOI = "doi:10.1109/08IAS.2008.173", ISSN = "0197-2618", abstract = "Rolling-element bearings are widely used in various mechanical and electrical systems. A reliable bearing fault diagnostic technique is critically needed in industries to recognize a bearing fault at its early stage so as to prevent system's performance degradation and malfunction. In this work, a genetic programming based feature reconstruction approach is proposed for bearing fault diagnostics. A new fitness measure is proposed to improve the GP operations in feature formulation. The original features are from the modified kurtosis ratio and the one-scale wavelet analysis. Investigation results show that the proposed method is an effective feature formulation tool; the reconstructed features are more robust against the variations in bearing geometry and operating conditions. The corresponding fault diagnostic reliability can be enhanced significantly. As a result, this work provides a promising technique and tool for bearing condition monitoring for real-world applications.", notes = "Also known as \cite{4658961}", } @Article{DBLP:journals/robotica/LiuZ04, author = "Jiming Liu and Shiwu Zhang", title = "Multi-phase Sumo Maneuver Learning", journal = "Robotica", volume = "22", number = "1", year = "2004", pages = "61--75", keywords = "genetic algorithms, genetic programming, Multi-phase genetic programming (MPGP), Autonomous robots, Sumo tasks, Maneuver learning, Evolutionary robotics", DOI = "doi:10.1017/S0263574703005356", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "In this paper, we demonstrate a multi-phase genetic programming (MPGP) approach to an autonomous robot learning task, where a sumo wrestling robot is required to execute specialised pushing manoeuvres in response to different opponents' postures. The sumo robot used has a very simple, minimalist hardware configuration. This example differs from the earlier studies in evolutionary robotics in that the former is carried out on-line during the performance of a robot, whereas the latter is concerned with the evolution of a controller in a simulated environment based on extended genetic algorithms. As illustrated in several sumo maneuver learning experiments, strategic manoeuvres with respect to some possible changes in the shape and size of an opponent can readily emerge from the on-line MPGP learning sessions.", notes = "See also \cite{LIU:2004:IJPRAI} Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong (P.R. of China). Department of Precision Machinery & Precision Instrumentation, University of Science and Technology of China (P.R. of China)", } @Article{LIU:2004:IJPRAI, author = "Jiming Liu and Shiwu Zhang", title = "Multiphase Genetic Programming: A case Study in Sumo Maneuver Evolution", journal = "International Journal of Pattern Recognition and Artificial Intelligence", year = "2004", volume = "18", number = "4", pages = "665--684", keywords = "genetic algorithms, genetic programming, Multiphase genetic programming (MPGP), sumo manoeuvre evolution, adaptive behaviour", DOI = "doi:10.1142/S0218001404003319", abstract = "In this paper, we describe a new evolutionary computation approach, called multiphase genetic programming (MPGP). The special features of this approach lie in its variable-granularity representations of chromosomes and their corresponding genetic operations. In the paper, we provide an overview of the MPGP approach as well as details on how the sumo manoeuvre evolution experiments are carried out and how the MPGP-based case study differs from others.", notes = "IJPRAI Partial results presented in this paper have been published in Jiming Liu and Shiwu Zhang, Multi-phase sumo maneuver learning, Robotica 22 (2004) 61-75, \cite{DBLP:journals/robotica/LiuZ04}", } @InProceedings{DBLP:conf/seal/LiuFZ08, author = "Jing Liu and Wenlong Fu and Weicai Zhong", title = "Hybrid Genetic Programming for Optimal Approximation of High Order and Sparse Linear Systems", booktitle = "Proceedings of the 7th International Conference on Simulated Evolution And Learning (SEAL '08)", year = "2008", editor = "Xiaodong Li and Michael Kirley and Mengjie Zhang and David G. Green and Victor Ciesielski and Hussein A. Abbass and Zbigniew Michalewicz and Tim Hendtlass and Kalyanmoy Deb and Kay Chen Tan and J{\"u}rgen Branke and Yuhui Shi", volume = "5361", series = "Lecture Notes in Computer Science", pages = "462--472", address = "Melbourne, Australia", month = dec # " 7-10", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-89693-7", DOI = "doi:10.1007/978-3-540-89694-4_47", abstract = "A Hybrid Genetic Programming (HGP) algorithm is proposed for optimal approximation of high order and sparse linear systems. With the intrinsic property of linear systems in mind, an individual in HGP is designed as an organization that consists of two cells. The nodes of the cells include a function and a terminal. All GP operators are designed based on organizations. In the experiments, three kinds of linear system approximation problems, namely stable, unstable, and high order and sparse linear systems, are used to test the performance of HGP. The experimental results show that HGP obtained a good performance in solving high order and sparse linear systems.", bibsource = "DBLP, http://dblp.uni-trier.de", notes = "Institute of Intelligent Information Processing, Xidian University Xi'an, China", } @InProceedings{Liu:2010:ICECE, author = "Jing Liu2 and Aiguo Wu", title = "Modeling Gene Regulatory Network Based on Genetic Programming", booktitle = "2010 International Conference on Electrical and Control Engineering (ICECE)", year = "2010", month = jun, pages = "5341--5344", abstract = "The purpose to establish the gene regulatory network model is to study the interaction relationship between genes on the system level, and thus to understand the essentials of creatures activity. Currently, the mainstream methods for modelling either prerequire the regulatory relationship or are not able to demonstrate the dynamics of the regulatory network. This paper proposes a model based on differential equation, to study gene regulatory network using Genetic Programming. This method is able to adjust to continuously external changes; search for the regulatory models suitable for the experiment data using genetic operators; and realise the prediction for the random regulatory relationship between genes. Based on the experiment, and compared with linear differential equation model, the calculation result is better suitable for the experiment data. This method is sufficient for the gene regulatory network structure reconstruction.", keywords = "genetic algorithms, genetic programming, gene regulatory network modelling, gene regulatory network structure reconstruction, linear differential equation model, biology, linear differential equations", DOI = "doi:10.1109/iCECE.2010.1296", notes = "In chinese. Also known as \cite{5630756}", } @InProceedings{Liu:2019:SANER, author = "Kui Liu and Anil Koyuncu and Dongsun Kim and Tegawende F. Bissyande", title = "{AVATAR:} Fixing Semantic Bugs with Fix Patterns of Static Analysis Violations", booktitle = "2019 IEEE 26th International Conference on Software Analysis, Evolution and Reengineering (SANER)", year = "2019", pages = "456--467", month = "24-27 " # feb, address = "Hangzhou, China", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, Automated program repair, static analysis, fix pattern", isbn13 = "978-1-7281-0591-8", ISSN = "1534-5351", URL = "https://arxiv.org/abs/1812.07270", DOI = "doi:10.1109/SANER.2019.8667970", code_url = "https://github.com/SerVal-DTF/AVATAR", size = "12 pages", abstract = "Fix pattern-based patch generation is a promising direction in Automated Program Repair (APR). Notably, it has been demonstrated to produce more acceptable and correct patches than the patches obtained with mutation operators through genetic programming. The performance of pattern-based APR systems, however, depends on the fix ingredients mined from fix changes in development histories. Unfortunately, collecting a reliable set of bug fixes in repositories can be challenging. In this paper, we propose to investigate the possibility in an APR scenario of leveraging code changes that address violations by static bug detection tools. To that end, we build the AVATAR APR system, which exploits fix patterns of static analysis violations as ingredients for patch generation. Evaluated on the Defects4J benchmark, we show that, assuming a perfect localization of faults, AVATAR can generate correct patches to fix 34/39 bugs. We further find that AVATAR yields performance metrics that are comparable to that of the closely-related approaches in the literature. While AVATAR outperforms many of the state-of-the-art pattern-based APR systems, it is mostly complementary to current approaches. Overall, our study highlights the relevance of static bug finding tools as indirect contributors of fix ingredients for addressing code defects identified with functional test cases.", notes = "Also known as \cite{8667970}", } @InProceedings{Liu:2019:ISSTA, author = "Kui Liu and Anil Koyuncu and Dongsun Kim and Tegawende F. Bissyande", title = "{TBar}: Revisiting Template-Based Automated Program Repair", booktitle = "Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis, ISSTA 2019", year = "2019", pages = "31--42", address = "Beijing, China", month = "15-19 " # jul, keywords = "genetic algorithms, genetic programming, genetic improvement, APR, Automated program repair, SBSE, empirical assessment, fix pattern, template-based, stochastic mutation-based or synthesis-based APR, Software and its engineering, Software verification and validation, Software defect analysis, Software testing and debugging", isbn13 = "9781450362245", URL = "https://arxiv.org/abs/1903.08409", DOI = "doi:10.1145/3293882.3330577", code_url = "https://github.com/SerVal-DTF/TBar", code_url = "https://doi.org/10.5281/zenodo.3237378", size = "12 pages", abstract = "We revisit the performance of template-based APR to build comprehensive knowledge about the effectiveness of fix patterns, and to highlight the importance of complementary steps such as fault localization or donor code retrieval. To that end, we first investigate the literature to collect, summarize and label recurrently-used fix patterns. Based on the investigation, we build TBar, a straightforward APR tool that systematically attempts to apply these fix patterns to program bugs. We thoroughly evaluate TBar on the Defects4J benchmark. In particular, we assess the actual qualitative and quantitative diversity of fix patterns, as well as their effectiveness in yielding plausible or correct patches. Eventually, we find that, assuming a perfect fault localization, TBar correctly/plausibly fixes 74/101 bugs. Replicating a standard and practical pipeline of APR assessment, we demonstrate that TBar correctly fixes 43 bugs from Defects4J, an unprecedented performance in the literature (including all approaches, i.e., template-based, stochastic mutation-based or synthesis-based APR).", notes = "not GP? University of Luxembourg, Luxembourg", } @PhdThesis{Kui_Liu:thesis, author = "Kui Liu", title = "Deep Pattern Mining for Program Repair", school = "Interdisciplinary Centre for Security, Reliability and Trust (SNT), University of Luxembourg", year = "2019", address = "Luxembourg", month = "18 " # dec, keywords = "genetic algorithms, genetic programming, genetic improvement, APR, Security, Reliability and Trust, Program repair, fix pattern, pattern mining, fault localization, inconsistent method name", URL = "https://orbilu.uni.lu/bitstream/10993/41348/1/thesis.pdf", URL = "http://hdl.handle.net/10993/41348", size = "173 pages", abstract = "Error-free software is a myth. Debugging thus accounts for a significant portion of software maintenance and absorbs a large part of software cost. In particular, the manual task of fixing bugs is tedious, error- prone and time-consuming. In the last decade, automatic bug-fixing, also referred to as automated program repair (APR) has boomed as a promising endeavour of software engineering towards alleviating developers burden. Several potentially promising techniques have been proposed making APR an increasingly prominent topic in both the research and practice communities. In production, APR will drastically reduce time-to-fix delays and limit downtime. In a development cycle, APR can help suggest changes to accelerate debugging. As an emergent domain, however, program repair has many open problems that the community is still exploring. Our work contributes to this momentum on two angles: the repair of programs for functionality bugs, and the repair of programs for method naming issues. The thesis starts with highlighting findings on key empirical studies that we have performed to inform future repair approaches. Then, we focus on template-based program repair scenarios and explore deep learning models for inferring accurate and relevant patterns. Finally, we integrate these patterns into APR pipelines, which yield the state of the art repair tools. The dissertation includes the following contributions: Real-world Patch Study: Existing APR studies have shown that the state-of-the-art techniques in automated repair tend to generate patches only for a small number of bugs even with quality issues (e.g., incorrect behaviour and nonsensical changes). To improve APR techniques, the community should deepen its knowledge on repair actions from real-world patches since most of the techniques rely on patches written by human developers. However, previous investigations on real-world patches are limited to statement level that is not sufficiently fine-grained to build this knowledge. This dissertation starts with deepening this knowledge via a systematic and fine-grained study of real-world Java program bug fixes. Fault Localization Impact: Existing test-suite-based APR systems are highly dependent on the performance of the fault localization (FL) technique that is the process of the widely studied APR pipeline. However, APR systems generally focus on the patch generation, but tend to use similar but different strategies for fault localization. To assess the impact of FL on APR, we identify and investigate a practical bias caused by the FL step in a repair pipeline. We propose to highlight the different FL configurations used in the literature, and their impact on APR systems when applied to the real bugs. Then, we explore the performance variations that can be achieved by tweaking the FL step. Fix Pattern Mining: Fix patterns (a.k.a. fix templates) have been studied in various APR scenarios. Particularly, fix patterns have been widely used in different APR systems. To date, fix pattern mining is mainly studied in three ways: manually summarisation, transformation inferring and code change action statistics. In this dissertation, we explore mining fix patterns for static bugs leveraging deep learning and clustering algorithms. Avatar: Fix pattern based patch generation is a promising direction in the APR community. Notably, it has been demonstrated to produce more acceptable and correct patches than the patches obtained with mutation operators through genetic programming. The performance of fix pattern based APR systems, however, depends on the fix ingredients mined from commit changes in development histories. Unfortunately, collecting a reliable set of bug fixes in repositories can be challenging. We propose to investigate the possibility in an APR scenario of leveraging code changes that address violations by static bug detection tools. To that end, we build the Avatar APR system, which exploits fix patterns of static analysis violations as ingredients for patch generation. TBar: Fix patterns are widely used in patch generation of APR, however, the repair performance of a single fix pattern is not studied. We revisit the performance of template-based APR to build comprehensive knowledge about the effectiveness of fix patterns, and to highlight the importance of complementary steps such as fault localization or donor code retrieval. To that end, we first investigate the literature to collect, summarize and label recurrently-used fix patterns. Based on the investigation, we build TBar, a straightforward APR tool that systematically attempts to apply these fix patterns to program bugs. We thoroughly evaluate TBar on the Defects4J benchmark. In particular, we assess the actual qualitative and quantitative diversity of fix patterns, as well as their effectiveness in yielding plausible or correct patches. Debugging Method Names: Except the issues about semantic/static bugs in programs, we note that how to debug inconsistent method names automatically is important to improve program quality. In this dissertation, we propose a deep learning based approach to spotting and refactoring inconsistent method names in programs.", notes = "Language : English Supervisor Dr. Yves Le Traon", } @Article{Liu:2009:B, author = "Kun-Hong Liu and Chun-Gui Xu", title = "A genetic programming-based approach to the classification of multiclass microarray datasets", journal = "Bioinformatics", year = "2009", volume = "25", number = "3", pages = "331--337", keywords = "genetic algorithms, genetic programming, lung cancer", DOI = "doi:10.1093/bioinformatics/btn644", size = "7 pages", abstract = "MOTIVATION: Feature selection approaches have been widely applied to deal with the small sample size problem in the analysis of micro-array datasets. For the multiclass problem, the proposed methods are based on the idea of selecting a gene subset to distinguish all classes. However, it will be more effective to solve a multiclass problem by splitting it into a set of two-class problems and solving each problem with a respective classification system. RESULTS: We propose a genetic programming (GP)-based approach to analyze multiclass microarray datasets. Unlike the traditional GP, the individual proposed in this article consists of a set of small-scale ensembles, named as sub-ensemble (denoted by SE). Each SE consists of a set of trees. In application, a multiclass problem is divided into a set of two-class problems, each of which is tackled by a SE first. The SEs tackling the respective two-class problems are combined to construct a GP individual, so each individual can deal with a multiclass problem directly. Effective methods are proposed to solve the problems arising in the fusion of SEs, and a greedy algorithm is designed to keep high diversity in SEs. This GP is tested in five datasets. The results show that the proposed method effectively implements the feature selection and classification tasks.", notes = "multi-tree (cf ADF) individual, one tree per class. Supplementary data are available at Bioinformatics online. School of Software, Xiamen University, Xiamen, Fujian, 361005, China PMID: 19088122 [PubMed - indexed for MEDLINE]", } @InProceedings{conf/icic/LiuTXZ14, author = "KunHong Liu and MuChenxuan Tong and ShuTong Xie and ZhiHao Zeng", title = "Fusing Decision Trees Based on Genetic Programming for Classification of Microarray Datasets", booktitle = "Intelligent Computing Methodologies - 10th International Conference, {ICIC} 2014, Taiyuan, China, August 3-6, 2014. Proceedings", publisher = "Springer", year = "2014", volume = "8589", editor = "De-Shuang Huang and Kang-Hyun Jo and Ling Wang", isbn13 = "978-3-319-09338-3", pages = "126--134", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming", bibdate = "2014-07-07", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icic/icic2014-2.html#LiuTXZ14", URL = "http://dx.doi.org/10.1007/978-3-319-09339-0", } @Article{Liu:2015:CMMM, author = "Kun-Hong Liu and Muchenxuan Tong and Shu-Tong Xie and Vincent To Yee Ng", title = "Genetic Programming Based Ensemble System for Microarray Data Classification", journal = "Computational and Mathematical Methods in Medicine", year = "2015", volume = "2015", pages = "Article ID 193406", keywords = "genetic algorithms, genetic programming", publisher = "Hindawi Publishing Corporation", bibsource = "OAI-PMH server at www.ncbi.nlm.nih.gov", language = "en", oai = "oai:pubmedcentral.nih.gov:4355811", rights = "Copyright 2015 Kun-Hong Liu et al.; This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.", URL = "http://www.ncbi.nlm.nih.gov/pmc/articles/PMC", ISSN = "1748-670X", URL = "http://downloads.hindawi.com/journals/cmmm/2015/193406.pdf", DOI = "doi:10.1155/2015/193406", size = "12 pages", abstract = "Recently, more and more machine learning techniques have been applied to microarray data analysis. The aim of this study is to propose a genetic programming (GP) based new ensemble system (named GPES), which can be used to effectively classify different types of cancers. Decision trees are deployed as base classifiers in this ensemble framework with three operators: Min, Max, and Average. Each individual of the GP is an ensemble system, and they become more and more accurate in the evolutionary process. The feature selection technique and balanced subsampling technique are applied to increase the diversity in each ensemble system. The final ensemble committee is selected by a forward search algorithm, which is shown to be capable of fitting data automatically. The performance of GPES is evaluated using five binary class and six multiclass microarray datasets, and results show that the algorithm can achieve better results in most cases compared with some other ensemble systems. By using elaborate base classifiers or applying other sampling techniques, the performance of GPES may be further improved.", } @Article{Liu:2019:APJOR, author = "Lingxuan Liu and Leyuan Shi", title = "Simulation Optimization on Complex Job Shop Scheduling with Non-Identical Job Sizes", journal = "Asia-Pacific Journal of Operational Research", year = "2019", volume = "36", number = "5", pages = "1950026--1950026--26", month = oct # " 3", keywords = "genetic algorithms, genetic programming, complex job shop scheduling, non-identical job sizes, stochastic simulation, nested partition", publisher = "World Scientific Publishing Co. and Operational Research Society of Singapore", bibsource = "OAI-PMH server at oai.repec.org", identifier = "RePEc:wsi:apjorx:v:36:y:2019:i:05:n:s021759591950026x", oai = "oai:RePEc:wsi:apjorx:v:36:y:2019:i:05:n:s021759591950026x", URL = "http://www.worldscientific.com/doi/abs/10.1142/S021759591950026X", DOI = "doi:10.1142/S021759591950026X", size = "26 pages", abstract = "This paper addresses the complex job shop scheduling problem with the consideration of non-identical job sizes. By simultaneously considering practical constraints of sequence dependent setup times, incompatible job families and job dependent batch processing time, we formulate this problem into a simulation optimisation problem based on the disjunctive graph representation. In order to find scheduling policies that minimise the expectation of mean weighted tardiness, we propose a genetic programming based hyper heuristic to generate efficient dispatching rules. And then, based on the nested partition framework together with the optimal computing budget allocation technique, a hybrid rule selection algorithm is proposed for searching machine group specified rule combinations. Numerical results show that the proposed algorithms outperform benchmark algorithms in both solution quality and robustness.", } @Article{liu:2022:Symmetry, author = "Lingxuan Liu and Leyuan Shi", title = "Automatic Design of Efficient Heuristics for Two-Stage Hybrid Flow Shop Scheduling", journal = "Symmetry", year = "2022", volume = "14", number = "4", pages = "Article No. 632", keywords = "genetic algorithms, genetic programming", ISSN = "2073-8994", URL = "https://www.mdpi.com/2073-8994/14/4/632", DOI = "doi:10.3390/sym14040632", abstract = "This paper addresses the two-stage hybrid flow shop scheduling problem with a batch processor in the first stage and a discrete processor in the second stage. Incompatible job families and limited buffer size are considered. This hybrid flow shop configuration commonly appears in manufacturing operations and the batch processor is always the bottleneck which breaks the symmetry of processing time. Since making a real-time high-quality schedule is challenging, we focus on the automatic design of efficient heuristics for this two-stage problem based on the genetic programming method. We develop a hyper-heuristic approach to automate the tedious trial-and-error design process of heuristics. The goal is to generate efficient dispatching rules for identifying complete schedules to minimise the total completion time. A genetic programming with cooperative co-evolution approach is proposed to evolve the schedule policy automatically. Numerical results demonstrate that the proposed approach outperforms both the constructive heuristic and meta-heuristic algorithms, and is capable of producing high-quality schedules within seconds.", notes = "also known as \cite{sym14040632}", } @Article{Liu2009S126, author = "Long Liu and Jun Sun and Miao Wang and Guocheng Du and Jian Chen", title = "Modeling and optimization of mixing performance for enhanced hyaluronic acid production by Streptococcus zooepidemicus using genetic programming coupling quantum-behaved particle swarm optimization algorithm", journal = "Journal of Bioscience and Bioengineering", volume = "108", number = "Supplement 1", pages = "S126--S126", year = "2009", note = "APBioChEC2009", keywords = "genetic algorithms, genetic programming, GP-QPSO", ISSN = "1389-1723", DOI = "doi:10.1016/j.jbiosc.2009.08.368", URL = "http://www.sciencedirect.com/science/article/B6VSD-4XHM1DM-DH/2/c3e7c20090de04d58b8f66e53d63b264", size = "0.3 pages", } @InProceedings{Liu:2010:ICNC, author = "Mengwei Liu and Xia Li and Tao Liu and Dan Li and Zheng Lin", title = "A gene expression programming algorithm for multiobjective site-search problem", booktitle = "Sixth International Conference on Natural Computation (ICNC 2010)", year = "2010", month = "10-12 " # aug, volume = "1", pages = "14--18", keywords = "genetic algorithms, genetic programming, gene expression programming, bohachevsky function, MOP2 function, pareto-front, shubert function, expression trees, geographical information system, linear coding method, multiobjective site-search problem, simple strings coding strategy, spatial analysis problem, pareto optimisation, geographic information systems, trees (mathematics)", DOI = "doi:10.1109/ICNC.2010.5582975", abstract = "Multiobjective site selection is a class complicated spatial analysis problem which can hardly be solved with traditional methods of Geographical Information System (GIS). In this paper we described an approach based on the gene expression programming (GEP) algorithm, with which the multiobjective site-search problems can be resolved. The validity of this method is verified by using MOP2 function, Bohachevsky function and Shubert function. By the comparison with genetic algorithms, it is concluded that the proposed GEP method using the expression trees/simple strings coding strategy can generate more approximate Pareto-front than the GAs using the linear coding method. This proposed model is finally applied to facilities optimal location search in Guangzhou.", notes = "Also known as \cite{5582975}", } @InProceedings{conf/icnc/LiuZYPY13, author = "Mengwei Liu and Guanghong Zeng and Guohui Yuan and Yabo Pei and Zili Yang", title = "Classifying remote sensing image using Gene Expression Programming algorithms", booktitle = "Ninth International Conference on Natural Computation, ICNC 2013", publisher = "IEEE", year = "2013", pages = "423--427", month = jul, keywords = "genetic algorithms, genetic programming, gene expression programming", bibdate = "2014-05-21", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icnc/icnc2013.html#LiuZYPY13", DOI = "doi:10.1109/ICNC.2013.6818013", abstract = "Gene Expression programming (GEP) is a new algorithm of evolutionary computation, which possesses much more powerful abilities of parallel computation and global search to resolve complex problems. This paper constructs the GEP classification model and carries out experiments with Landsat Thematic Mapper(TM) image of Dongguan city in 1997. The result shows that the GEP classification model has good classification accuracy and demonstrates effectiveness in remote sensing image classification.", notes = "Also known as \cite{6818013}", } @Article{LIU:2020:AWR, author = "Meng-Yang Liu and Wen-Xin Huai and Zhong-Hua Yang and Yu-Hong Zeng", title = "A genetic programming-based model for drag coefficient of emergent vegetation in open channel flows", journal = "Advances in Water Resources", volume = "140", pages = "103582", year = "2020", ISSN = "0309-1708", DOI = "doi:10.1016/j.advwatres.2020.103582", URL = "http://www.sciencedirect.com/science/article/pii/S0309170819302222", keywords = "genetic algorithms, genetic programming, Drag coefficient, Vegetated flows, Estimating drag, Cylinder arrays", abstract = "The estimation of drag exerted by vegetation is of great interest because of its importance in assessing the impact of vegetation on the hydrodynamic processes in aquatic environments. In the current research, genetic programming (GP), a machine learning (ML) technique based on natural selection, was adopted to search for a robust relationship between the bulk drag coefficient (Cd) for arrays of rigid circular cylinders representing emergent vegetation with blockage ratio (ψ), vegetation density (lambda) and pore Reynolds number (Rep) based on published data. We use a data set covering a wide range of each parameter involved to cover all possible dependencies. A new predictor, which shares the same form with the Ergun-derived formula, was obtained without any pre-specified forms before searching. The dependence of the two parameters in Ergun equation on vegetation characteristics was also estimated by GP. This new Cd predictor for emergent vegetation with a relatively concise form exhibits a considerable improvement in terms of prediction ability relative to existing predictors", } @Article{Liu:2009:IJMS, title = "Current Mathematical Methods Used in {QSAR}/{QSPR} Studies", author = "Peixun Liu and Wei Long", journal = "International Journal of Molecular Sciences", publisher = "Molecular Diversity Preservation International", year = "2009", ISSN = "1422-0067; 14220067", bibsource = "OAI-PMH server at www.doaj.org", language = "eng", oai = "oai:doaj-articles:a634e99c5db7a3846db7d582ee717285", keywords = "genetic algorithms, genetic programming, QSAR, QSPR, Mathematical methods, Regression, Algorithm", URL = "http://www.mdpi.com/1422-0067/10/5/1978/pdf", DOI = "doi:10.3390/ijms10051978", URL = "http://www.mdpi.com/1422-0067/10/5/1978/", broken = "http://www.doaj.org/doaj?func=openurl\&genre=article\&issn=14220067\&date=2009\&volume=10\&issue=5\&spage=1978", size = "21 pages", abstract = "This paper gives an overview of the mathematical methods currently used in quantitative structure-activity/property relationship (QASR/QSPR) studies. Recently, the mathematical methods applied to the regression of QASR/QSPR models are developing very fast, and new methods, such as Gene Expression Programming (GEP), Project Pursuit Regression (PPR) and Local Lazy Regression (LLR) have appeared on the QASR/QSPR stage. At the same time, the earlier methods, including Multiple Linear Regression (MLR), Partial Least Squares (PLS), Neural Networks (NN), Support Vector Machine (SVM) and so on, are being upgraded to improve their performance in QASR/QSPR studies. These new and upgraded methods and algorithms are described in detail, and their advantages and disadvantages are evaluated and discussed, to show their application potential in QASR/QSPR studies in the future.", } @InProceedings{QinghuaLiu:1998:sbDNAc:1sp, author = "Qinghua Liu and Anthony G. Frutos and Liman Wang and Andrew J. Thiel and Susan D. Gillmor and Todd Strother and Anne E. Condon and Robert M. Corn and Max G. Lagally and Lloyd M. Smith", title = "Progress Toward Demonstration of a Surface Based DNA Computation: a One Word Approach to Solve a Model Satisfiability Problem", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "709--717", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "DNA Computing", ISBN = "1-55860-548-7", notes = "GP-98", } @InCollection{liu:1998:SRCUGP, author = "Richard Liu", title = "Solving the Rubik's Cube Using Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1998", year = "1998", editor = "John R. Koza", pages = "68--73", address = "Stanford, California, 94305-3079 USA", month = "17 " # mar, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-212568-8", notes = "part of \cite{koza:1998:GAGPs}", } @InProceedings{conf/seal/LiuLLJ10, title = "A Population Diversity-Oriented Gene Expression Programming for Function Finding", author = "Ruochen Liu and Qifeng Lei and Jing Liu and Licheng Jiao", booktitle = "8th International Conference on Simulated Evolution and Learning (SEAL 2010)", year = "2010", volume = "6457", editor = "Kalyanmoy Deb and Arnab Bhattacharya and Nirupam Chakraborti and Partha Chakroborty and Swagatam Das and Joydeep Dutta and Santosh K. Gupta and Ashu Jain and Varun Aggarwal and J{\"u}rgen Branke and Sushil J. Louis and Kay Chen Tan", series = "Lecture Notes in Computer Science", pages = "215--219", address = "Kanpur, India", month = dec # " 1-4", publisher = "Springer", keywords = "genetic algorithms, genetic programming, gene expression programming", bibdate = "2010-12-01", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/seal/seal2010.html#LiuLLJ10", isbn13 = "978-3-642-17297-7", DOI = "doi:10.1007/978-3-642-17298-4", abstract = "Gene expression programming (GEP) is a novel evolutionary algorithm, which combines the advantages of simple genetic algorithm (SGA) and genetic programming (GP). Owing to its special structure of linear encoding and nonlinear decoding, GEP has been applied in various fields such as function finding and data classification. In this paper, we propose a modified GEP (Mod-GEP), in which, two strategies including population updating and population pruning are used to increase the diversity of population. Mod-GEP is applied into two practical function finding problems, the results show that Mod-GEP can get a more satisfactory solution than that of GP, GEP and GEP based on statistical analysis and stagnancy (AMACGEP", } @InProceedings{conf/iconip/LiuXL17, author = "Ruochen Liu and Guan Xia and Jianxia Li", title = "Shape-Based Image Retrieval Based on Improved Genetic Programming", booktitle = "24th International Conference on Neural Information Processing, ICONIP 2017, Part IV", year = "2017", editor = "Derong Liu and Shengli Xie and Yuanqing Li and Dongbin Zhao and El-Sayed M. El-Alfy", volume = "10637", series = "Lecture Notes in Computer Science", pages = "212--220", address = "Guangzhou, China", month = nov # " 14-18", publisher = "Springer", keywords = "genetic algorithms, genetic programming, two-stage genetic programming, image retrieval, special rule for generation of individual tree", isbn13 = "978-3-319-70092-2", bibdate = "2017-11-17", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/iconip/iconip2017-4.html#LiuXL17", DOI = "doi:10.1007/978-3-319-70093-9_22", size = "9 pages", abstract = "Two-stage genetic programming algorithm based on a novel coding strategy (NTGP) is proposed in this paper, in which the generation of individual tree is not random but according to a special rule. This rule assigns each function operator a weight and the assignments of these weights based on the frequencies of function operators in good individuals. The greater weight of a function is, the more possibly it will be selected. By using the new coding strategy, the image feature database can be rebuilt. For two-stage genetic programming algorithm, in the first stage, the feature weight vector is obtained, GP is used to construct new features for the next step. While in the second stage, GP is used to induce an image matching function based on the features provided by the first stage. Based on these models, one can retrieve target images from the image database with much better performance. Three benchmark problems are used to validate performance of the proposed algorithm. Experimental results demonstrate that the proposed algorithm can obtain better performance.", } @Article{DBLP:journals/tinstmc/LiuAY18, author = "Ruochen Liu and Lijia An and Xin Yu", title = "A new two-stage genetic programming classification algorithm and its applications", journal = "Trans. Inst. Meas. Control", volume = "40", number = "8", pages = "2560--2578", year = "2018", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1177/0142331217707362", DOI = "doi:10.1177/0142331217707362", timestamp = "Thu, 18 Mar 2021 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/tinstmc/LiuAY18.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{Liu:2008:CCDC, author = "Shu-an Liu and Qing Wang and Shuai Lv", title = "Application of Genetic Programming in credit scoring", booktitle = "Chinese Control and Decision Conference, CCDC 2008", year = "2008", month = jul, pages = "1106--1110", keywords = "genetic algorithms, genetic programming, coding structure, combinatorial optimization problem, credit scoring, genetic programming algorithm, human-computer interactions, information value, combinatorial mathematics, finance", DOI = "doi:10.1109/CCDC.2008.4597485", abstract = "Derived characteristics are usually regarded as important index in credit scoring, however, only some derived characteristics in common sense can be obtained with analytical methods. In this paper, the selection of derived characteristics is considered as a combinatorial optimization problem of mathematical symbols and original characteristics. To solve the problem, a Genetic Programming algorithm is proposed, where the coding structure is in a tree form and the objective is expressed by Information Value (IV). A procedure of human-computer interactions are designed to choose the derived characteristics with practical significance from the better results obtained by the Genetic Programming algorithm. Furthermore, an improved model is proposed based on linear discriminate analysis, where derived characteristics are included The simulation experiments show that the results is satisfactory, the proposed models are of competitive discrimination.", notes = "Also known as \cite{4597485}", } @InProceedings{Liu:2010:ieeeICCI, author = "Taiyang Liu and Shicheng Wang and Zhiguo Liu and Haibo Min and Renbing Li", title = "Multi-kernel SVM based star pattern recognition for Celestial Navigation", booktitle = "9th IEEE International Conference on Cognitive Informatics (ICCI 2010)", year = "2010", month = "7-9 " # jul, pages = "748--753", abstract = "This paper presents a combination of intelligent learning algorithm, the Support Vector Machine, and the recognition of star pattern in Celestial Navigation. Considering the star pattern recognition's character, noticing the advantages of SVM in learning competence, the paper proposes a solution to star pattern recognition with multi-kernel SVM. A multi-kernel algorithm bases on Genetic Programming is designed. Topics of multi-kernel function generation are cited in detail, and a star pattern recognition routine including an Indexing + recognition scheme, feature vector definition and generation, SVM training realisation are designed and realised.", keywords = "genetic algorithms, genetic programming, SVM training realisation, celestial navigation, feature vector definition, indexing scheme, intelligent learning algorithm, multikernel SVM algorithm, multikernel function generation, star pattern recognition character, support vector machine, astronomical image processing, image matching, image recognition, indexing, learning (artificial intelligence), navigation, support vector machines", DOI = "doi:10.1109/COGINF.2010.5599814", notes = "Also known as \cite{5599814}", } @Article{Liu:2020:ACC, author = "Tonglin Liu and Hengzhe Zhang and Hu Zhang and Aimin Zhou", title = "Information Fusion in Offspring Generation: A Case Study in Gene Expression Programming", journal = "IEEE Access", year = "2020", volume = "8", pages = "74782--74792", DOI = "doi:10.1109/ACCESS.2020.2988587", ISSN = "2169-3536", abstract = "Gene expression programming (GEP), which is a variant of genetic programming (GP) with a fixed-length linear model, has been applied in many domains. Typically, GEP uses genetic operators to generate offspring. In recent years, the estimation of distribution algorithm (EDA) has also been proven to be efficient for offspring generation. Genetic operators such as crossover and mutation generate offspring from an implicit model by using the individual information. By contrast, EDA operators generate offspring from an explicit model by using the population distribution information. Since both the individual and population distribution information are useful in offspring generation, it is natural to hybrid EDA and genetic operators to improve the search efficiency. To this end, we propose a hybrid offspring generation strategy for GEP by using a univariate categorical distribution based EDA operator and its original genetic operators. To evaluate the performance of the new hybrid algorithm, we apply the algorithm to ten regression tasks using various parameters and strategies. The experimental results demonstrate that the new algorithm is a promising approach for solving regression problems efficiently. The GEP with hybrid operators outperforms the original GEP that uses genetic operators on eight out of ten benchmark datasets.", keywords = "genetic algorithms, genetic programming", notes = "Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory, Beijing Electro-Mechanical Engineering Institute, Beijing, China Also known as \cite{9069874}", } @InProceedings{10.1109/IPDPS.2005.2, author = "Weiguo Liu and Bertil Schmidt", title = "A Case Study on Pattern-Based Systems for High Performance Computational Biology", year = "2005", booktitle = "19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 7", pages = "197b", publisher = "IEEE Computer Society", keywords = "genetic algorithms, genetic programming", URL = "http://www.hicomb.org/papers/HICOMB2005-04.pdf", DOI = "doi:10.1109/IPDPS.2005.2", size = "8 pages", abstract = "Computational biology research is now faced with the burgeoning number of genome data. The rigorous postprocessing of this data requires an increased role for high performance computing (HPC). Because the development of HPC applications for computational biology problems is much more complex than the corresponding sequential applications, existing traditional programming techniques have demonstrated their inadequacy. Many high level programming techniques, such as skeleton and pattern based programming, have therefore been designed to provide users new ways to get HPC applications without much effort. However, most of them remain absent from the mainstream practice for computational biology. In this paper, we present a new parallel pattern-based system prototype for computational biology. The underlying programming techniques are based on generic programming, a programming technique suited for the generic representation of abstract concepts. This allows the system to be built in a generic way at application level and thus provides good extensibility and flexibility. We show how this system can be used to develop HPC applications for popular computational biology algorithms and lead to significant runtime savings on distributed memory architectures.", } @PhdThesis{Weiguo_Liu:thesis, author = "Weiguo Liu", title = "Parallel and distributed algorithms for computational biology", school = "School of Computer Engineering, Nanyang Technological University", year = "2007", address = "Singapore", keywords = "genetic algorithms, genetic programming", URL = "http://hdl.handle.net/10356/2474", abstract = "Computational biology research is now faced with the burgeoning number of genome data. The rigorous postprocessing of this data requires an increased role for high performance computing (HPC). Because the development of HPC applications for computational biology problems is much more complex than the corresponding sequential applications, existing traditional programming techniques have demonstrated their inadequacy. Many high level programming techniques, such as skeleton and pattern-based programming, have therefore been designed to provide users new ways to get HPC applications without much effort. However, most of them remain absent from the mainstream practice for computational biology. In this paper, we present a new parallel pattern-based system prototype for computational biology. The underlying programming techniques are based on generic programming, a programming technique suited for the generic representation of abstract concepts. This allows the system to be built in a generic way at application level and, thus, provides good extensibility and flexibility. We show how this system can be used to develop HPC applications for popular computational biology algorithms and lead to significant run time savings on distributed memory architectures.", notes = "Supervisor: Bertil Schmidt", } @Article{Liu:2006:ITIS, author = "Weiguo Liu and Bertil Schmidt", title = "Mapping of Hierarchical Parallel Genetic Algorithms for Protein Folding onto Computational Grids", journal = "IEICE Transactions on Information and Systems", year = "2006", volume = "E89-D", number = "2", pages = "589--596", email = "liuweiguo@pmail.ntu.edu.sg", keywords = "genetic algorithms, genetic programming, protein folding, HP lattice models, hierarchical parallel genetic algorithms, computational grids, generic programming", ISSN = "0916-8532", DOI = "doi:10.1093/ietisy/e89-d.2.589", abstract = "Genetic algorithms are a general problem-solving technique that has been widely used in computational biology. In this paper, we present a framework to map hierarchical parallel genetic algorithms for protein folding problems onto computational grids. By using this framework, the two level communication parts of hierarchical parallel genetic algorithms are separated. Thus both parts of the algorithm can evolve independently. This permits users to experiment with alternative communication models on different levels conveniently. The underlying programming techniques are based on generic programming, a programming technique suited for the generic representation of abstract concepts. This allows the framework to be built in a generic way at application level and thus provides good extensibility and flexibility. Experiments show that it can lead to significant runtime savings on PC clusters and computational grids.", notes = "Special Section on Parallel/Distributed Computing and Networking -- Papers -- Grid Computing Copyright 2005 IEICE", } @InProceedings{liu:1997:ehoadtcATM, author = "Weixin Liu and Masahiro Murakawa and Tetsuya Higuchi", title = "Evolvable Hardware for On-line Adaptive Traffic Control in ATM Networks", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "Evolvable Hardware", pages = "504--509", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", notes = "GP-97", } @InProceedings{Liu:2008:ITME, author = "Xiyu Liu and Yinghong Ma and Hong Liu and Jianping Zhang", title = "A differential evolutionary architecture for artificial neural trees with applications to medical data mining", booktitle = "IEEE International Symposium on IT in Medicine and Education, ITME 2008", year = "2008", month = dec, pages = "761--767", keywords = "genetic algorithms, genetic programming, artificial neural tree structure, differential evolutionary architecture, medical data mining, numbering scheme, partial ordering-based multidimensional fitness function, data mining, evolutionary computation, medical computing, neural nets, trees (mathematics)", DOI = "doi:10.1109/ITME.2008.4743969", abstract = "This paper presents an evolutionary structure for neural trees by differential evolution. For a neural tree a structure tree and weights tree are defined. A partial ordering based multi-dimensional fitness function is applied to measure the energy of the system. Different to traditional evolution method of genetic programming, a new evolution technique based on differential evolution is proposed. A numbering scheme is designed for basic operations of tree structure. Finally we present an experimental framework for the tree evolution. Application frameworks are given in medical data mining and analysis.", notes = "Popolation of ANN with single root output. Also known as \cite{4743969}", } @InProceedings{conf/icic/LiuEP07, author = "Yanchao Liu and John English and Edward A. Pohl", title = "Application of Gene Expression Programming in the Reliability of Consecutive-k-out-of-n: {F} Systems with Identical Component Reliabilities", booktitle = "Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques, Third International Conference on Intelligent Computing, {ICIC} 2007", year = "2007", editor = "De-Shuang Huang and Laurent Heutte and Marco Loog", volume = "2", series = "Communications in Computer and Information Science", pages = "217--224", address = "Qingdao, China", month = aug # " 21-24", publisher = "Springer", keywords = "genetic algorithms, genetic programming, gene expression programming", isbn13 = "978-3-540-74281-4", DOI = "doi:10.1007/978-3-540-74282-1_25", size = "8 pages", abstract = "This paper presents a GEP-based simulation - data mining approach for obtaining closed-form reliability formulas of consecutive-k-out-of-n: F systems with identical component reliabilities. This work proves to be GEP's first exploration into the reliability realm and also provides a new perspective for the reliability community to solve for complex reliability formulas. Experimentation has shown the feasibility and effectiveness of the proposed framework, although further revisions and developments must be made to the model in order to solve larger scale problems.", notes = "Roller Conveyor Systems", bibdate = "2008-09-11", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icic/icic2007-3.html#LiuEP07", } @InProceedings{Liu:2011:EuroGP, author = "Yang Liu and Gianluca Tempesti and James A. Walker and Jon Timmis and Andrew M. Tyrrell and Paul Bremner", title = "A Self-Scaling Instruction Generator Using Cartesian Genetic Programming", booktitle = "Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011", year = "2011", month = "27-29 " # apr, editor = "Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado", series = "LNCS", volume = "6621", publisher = "Springer Verlag", address = "Turin, Italy", pages = "298--309", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, cartesian genetic programming: poster", isbn13 = "978-3-642-20406-7", DOI = "doi:10.1007/978-3-642-20407-4_26", abstract = "In the past decades, a number of genetic programming techniques have been developed to evolve machine instructions. However, these approaches typically suffer from a lack of scalability that seriously impairs their applicability to real-world scenarios. In this paper, a novel self-scaling instruction generation method is introduced, which tries to overcome the scalability issue by using Cartesian Genetic Programming. In the proposed method, a dual-layer network architecture is created: one layer is used to evolve a series of instructions while the other is dedicated to the generation of loop control parameters.", notes = "Part of \cite{Silva:2011:GP} EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011", } @InProceedings{Liu:2001:HASE, author = "Yi Liu and Taghi M. Khoshgoftaar", title = "Genetic programming model for software quality classification", booktitle = "Sixth IEEE International Symposium on High Assurance Systems Engineering, HASE'01", year = "2001", pages = "127--136", address = "Boco Raton, FL, USA", month = oct # " 22-24", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, classification, evolutionary computation, software qualitygenetic programming, quality classification, software engineering, software metrics, software quality, SBSE", ISBN = "0-7695-1275-5", DOI = "doi:10.1109/HASE.2001.966814", size = "10 pages", abstract = "We apply genetic programming techniques to build a software quality classification model based on the metrics of software modules. The model we built attempts to distinguish the fault-prone modules from non-fault-prone modules using genetic programming (GP). These GP experiments were conducted with a random subset selection for GP in order to avoid overfitting. We then use the whole fit data set as the validation data set to select the best model. We demonstrate through two case studies that the GP technique can achieve good results. Also, we compared GP modeling with logistic regression modeling to verify the usefulness of GP", notes = "Also known as \cite{966814} INSPEC Accession Number:7107475 p126 {"}VLWA{"} C++ {"}over 27.5 million lines of code{"}. Logistic Regression LRM", } @InProceedings{liu2:2003:gecco, author = "Yi Liu and Taghi M. Khoshgoftaar", title = "Building Decision Tree Software Quality Classification Models Using Genetic Programming", booktitle = "Genetic and Evolutionary Computation -- GECCO-2003", editor = "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller", year = "2003", pages = "1808--1809", address = "Chicago", publisher_address = "Berlin", month = "12-16 " # jul, volume = "2724", series = "LNCS", ISBN = "3-540-40603-4", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, poster", DOI = "doi:10.1007/3-540-45110-2_75", abstract = "Predicting the quality of software modules prior to testing or system operations allows a focused software quality improvement endeavor. Decision trees are very attractive for classification problems, because of their comprehensibility and white box modeling features. However, optimizing the classification accuracy and the tree size is a difficult problem, and to our knowledge very few studies have addressed the issue. This paper presents an automated and simplified genetic programming (GP) based decision tree modeling technique for calibrating software quality classification models. The proposed technique is based on multi-objective optimization using strongly typed GP. Two fitness functions are used to optimize the classification accuracy and tree size of the classification models calibrated for a real-world high-assurance software system. The performances of the classification models are compared with those obtained by standard GP. It is shown that the GP-based decision tree technique yielded better classification models.", notes = "GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)", } @PhdThesis{YiLiu:thesis, author = "Yi Liu", title = "Software Reliability Engineering with Genetic Programming", school = "Computer Science, Florida Atlantic University", year = "2003", address = "Boca Raton, Florida, USA", month = aug, keywords = "genetic algorithms, genetic programming, SBSE", URL = "http://search.proquest.com/docview/305323504", broken = "http://digitool.fcla.edu/dtl_publish/25/12047.html", size = "243 pages", isbn13 = "978-0-496-42656-0", abstract = "Software reliability engineering plays a vital role in managing and controlling software quality. As an important method of software reliability engineering, software quality estimation modelling is useful in defining a cost-effective strategy to achieve a reliable software system. By predicting the faults in a software system, the software quality models can identify high-risk modules, and thus, these high-risk modules can be targeted for reliability enhancements. Strictly speaking, software quality modeling not only aims at lowering the misclassification rate, but also takes into account the costs of different misclassifications and the available resources of a project. As a new search-based algorithm, Genetic Programming (GP) can build a model without assuming the size, shape, or structure of a model. It can flexibly tailor the fitness functions to the objectives chosen by the customers. Moreover, it can optimise several objectives simultaneously in the modelling process, and thus, a set of multi-objective optimisation solutions can be obtained. This research focuses on building software quality estimation models using GP. Several GP-based models of predicting the class membership of each software module and ranking the modules by a quality factor were proposed. The first model of categorising the modules into fault-prone or not fault-prone was proposed by considering the distinguished features of the software quality classification task and GP. The second model provided quality-based ranking information for fault-prone modules. A decision tree-based software classification model was also proposed by considering accuracy and simplicity simultaneously. This new technique provides a new multi-objective optimization algorithm to build decision trees for real-world engineering problems, in which several trade-off objectives usually have to be taken into account at the same time. The fourth model was built to find multi-objective optimisation solutions by considering both the expected cost of misclassification and available resources. Also, a new goal-oriented technique of building module-order models was proposed by directly optimizing several goals chosen by project analysts. The issues of GP , bloating and overfitting, were also addressed in our research. Data were collected from three industrial projects, and applied to validate the performance of the models. Results indicate that our proposed methods can achieve useful performance results. Moreover, some proposed methods can simultaneously optimize several different objectives of a software project management team.", notes = "www.fau.edu/dsr/researchnews0903.pdf page 6 Major Professor: Taghi M. Khoshgoftaar UMI 3095028", } @InProceedings{liu:2004:rogp, author = "Yi Liu and Taghi Khoshgoftaar", title = "Reducing overfitting in genetic programming models for software quality classification", booktitle = "Proceedings of the Eighth IEEE Symposium on International High Assurance Systems Engineering", year = "2004", month = "25-26 " # mar, pages = "56--65", address = "Tampa, Florida, USA", keywords = "genetic algorithms, genetic programming", ISSN = "1530-2059", DOI = "doi:10.1109/HASE.2004.1281730", DOI = "doi:10.1109/HASE.2004.1281730", size = "10 pages", abstract = "A high-assurance system is largely dependent on the quality of its underlying software. Software quality models can provide timely estimations of software quality, allowing the detection and correction of faults prior to operations. A software metrics-based quality prediction model may depict overfitting, which occurs when a prediction model has good accuracy on the training data but relatively poor accuracy on the test data. In this paper, we present an approach to address the overfitting problem in the context of software quality classification models based on genetic programming (GP). The overfitting problem has not been addressed in depth for GP-based models. The general aim of classifying software modules as fault-prone (fp) and not fault-prone (nfp) is to aid software management in expending its limited resources toward improving only the fp modules. The presence of overfitting in such a software quality model affects its practical usefulness, because management is interested in good performance of the model when applied to unseen data, i.e., generalisation performance. In the process of building GP-based software quality classification models for a high-assurance telecommunications system, we observed that the GP models were prone to overfitting. We use a random sampling technique to reduce overfitting in our GP models. The approach has been found by many researchers as an effective method for reducing the time of a GP run. However, in our study we use random sampling to reduce overfitting with the aim of improving the generalization capability of our GP models. A case study of an industrial high-assurance software system is used to demonstrate the effectiveness of the random sampling technique.", notes = "HASE 2004", } @InProceedings{conf/iri/LiuKY06, title = "Developing an effective validation strategy for genetic programming models based on multiple datasets", author = "Yi Liu and Taghi M. Khoshgoftaar and Jenq-Foung Yao", year = "2006", booktitle = "2006 IEEE International Conference on Information Reuse and Integration", pages = "232--237", address = "Waikoloa Village, HI, USA", month = sep, publisher = "IEEE", bibdate = "2006-11-14", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/iri/iri2006.html#LiuKY06", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IRI.2006.252418", abstract = "Genetic programming (GP) is a parallel searching technique where many solutions can be obtained simultaneously in the searching process. However, when applied to real-world classification tasks, some of the obtained solutions may have poor predictive performances. One of the reasons is that these solutions only match the shape of the training dataset, failing to learn and generalise the patterns hidden in the dataset. Therefore, unexpected poor results are obtained when the solutions are applied to the test dataset. This paper addresses how to remove the solutions which will have unacceptable performances on the test dataset. The proposed method in this paper applies a multi-dataset validation phase as a filter in GP-based classification tasks. By comparing our proposed method with a standard GP classifier based on the datasets from seven different NASA software projects, we demonstrate that the multi-dataset validation is effective, and can significantly improve the performance of GP-based software quality classification models", notes = "http://ieeexplore.ieee.org/servlet/opac?punumber=4018442", } @Article{Liu:2010:ieeeTSE, author = "Yi (Cathy) Liu and Taghi M. Khoshgoftaar and Naeem Seliya", title = "Evolutionary Optimization of Software Quality Modeling with Multiple Repositories", journal = "IEEE Transactions on Software Engineering", year = "2010", month = nov # "/" # dec, volume = "36", number = "6", pages = "852--864", keywords = "genetic algorithms, genetic programming, sbse, baseline classifier, evolutionary optimisation, machine learner, multiple software project repository, robust software quality model, search-based software quality model, software data set, software measurement data repository, software metrics, software quality modelling, validation classifier, validation-and-voting classifier, software management, software metrics, software quality", ISSN = "0098-5589", DOI = "doi:10.1109/TSE.2010.51", abstract = "A novel search-based approach to software quality modelling with multiple software project repositories is presented. Training a software quality model with only one software measurement and defect data set may not effectively encapsulate quality trends of the development organisation. The inclusion of additional software projects during the training process can provide a cross-project perspective on software quality modelling and prediction. The genetic-programming-based approach includes three strategies for modeling with multiple software projects: Baseline Classifier, Validation Classifier, and Validation-and-Voting Classifier. The latter is shown to provide better generalisation and more robust software quality models. This is based on a case study of software metrics and defect data from seven real-world systems. A second case study considers 17 different (nonevolutionary) machine learners for modelling with multiple software data sets. Both case studies use a similar majority-voting approach for predicting fault-proneness class of program modules. It is shown that the total cost of misclassification of the search-based software quality models is consistently lower than those of the non-search-based models. This study provides clear guidance to practitioners interested in exploiting their organization's software measurement data repositories for improved software quality modelling.", notes = "Also known as \cite{5467094}", } @InProceedings{BMVC.26.18, author = "Li Liu and Ling Shao and Peter Rockett", title = "Genetic Programming-Evolved Spatio-Temporal Descriptor for Human Action Recognition", year = "2012", booktitle = "Proceedings of the British Machine Vision Conference, BMVC 2012", editors = "Richard Bowden and John P. Collomosse and Krystian Mikolajczyk", pages = "18.1--18.12", address = "Surrey, UK", month = sep # " 3-7", publisher = "BMVA Press", keywords = "genetic algorithms, genetic programming", ISBN = "1-901725-46-4", bibdate = "2013-04-24", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/bmvc/bmvc2012.html#LiuSR12", URL = "http://www.bmva.org/bmvc/2012/BMVC/paper018/paper018.pdf", URL = "http://www.bmva.org/bmvc/2012/BMVC/paper018/index.html", DOI = "doi:10.5244/C.26.18", size = "12 pages", abstract = "The potential value of human action recognition has led to it becoming one of the most active research subjects in computer vision. In this paper, we propose a novel method to automatically generate low-level spatio-temporal descriptors showing good performance, for high-level human-action recognition tasks. We address this as an optimisation problem using genetic programming (GP), an evolutionary method, which produces the descriptor by combining a set of primitive 3D operators. As far as we are aware, this is the first report of using GP for evolving spatio-temporal descriptors for action recognition. In our evolutionary architecture, the average cross-validation classification error calculated using the support-vector machine (SVM) classifier is used as the GP fitness function. We run GP on a mixed dataset combining the KTH and the Weizmann datasets to obtain a promising feature-descriptor solution for action recognition. To demonstrate generalisable, the best descriptor generated so far by GP has also been tested on the IXMAS dataset leading to better accuracies compared with some previous hand-crafted descriptors", notes = "Also known as \cite{conf/bmvc/LiuSR12}", } @InProceedings{Liu:2013:ieeeFG, author = "Li Liu and Ling Shao", title = "Synthesis of spatio-temporal descriptors for dynamic hand gesture recognition using genetic programming", booktitle = "10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG 2013)", year = "2013", month = "22-26 " # apr, keywords = "genetic algorithms, genetic programming, gesture recognition, learning (artificial intelligence), Cambridge hand gesture dataset, Northwestern University hand gesture dataset, automatic gesture recognition, domain-independent optimisation, dynamic hand gesture recognition, evolutionary method, machine learnt spatio-temporal descriptors, Accuracy, Feature extraction, Gabor filters, Gesture recognition, Support vector machines, Training", DOI = "doi:10.1109/FG.2013.6553765", abstract = "Automatic gesture recognition has received much attention due to its potential in various applications. In this paper, we successfully apply an evolutionary method-genetic programming (GP) to synthesise machine learnt spatio-temporal descriptors for automatic gesture recognition instead of using hand-crafted descriptors. In our architecture, a set of primitive low-level 3D operators are first randomly assembled as tree-based combinations, which are further evolved generation-by-generation through the GP system, and finally a well performed combination will be selected as the best descriptor for high-level gesture recognition. To the best of our knowledge, this is the first report of using GP to evolve spatio-temporal descriptors for gesture recognition. We address this as a domain-independent optimisation issue and evaluate our proposed method, respectively, on two public dynamic gesture datasets: Cambridge hand gesture dataset and Northwestern University hand gesture dataset to demonstrate its generalizability. The experimental results manifest that our GP-evolved descriptors can achieve better recognition accuracies than state-of-the-art hand-crafted techniques.", notes = "Also known as \cite{6553765}", } @InProceedings{conf/mm/LiuSL13, author = "Li Liu and Ling Shao and Xuelong Li", title = "Building holistic descriptors for scene recognition: a multi-objective genetic programming approach", booktitle = "Proceedings of the 21st ACM international conference on Multimedia", year = "2013", editor = "Alejandro Jaimes and Nicu Sebe and Nozha Boujemaa and Daniel Gatica-Perez and David A. Shamma and Marcel Worring and Roger Zimmermann", publisher = "ACM", pages = "997--1006", address = "Barcelona, Spain", month = oct # " 21-25", keywords = "genetic algorithms, genetic programming", bibdate = "2013-11-14", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/mm/mm2013.html#LiuSL13", isbn13 = "978-1-4503-2404-5", URL = "http://dl.acm.org/citation.cfm?id=2502081", URL = "http://doi.acm.org/10.1145/2502081.2502095", DOI = "doi:10.1145/2502081.2502095", acmid = "2502095", size = "10 pages", abstract = "Real-world scene recognition has been one of the most challenging research topics in computer vision, due to the tremendous intra-class variability and the wide range of scene categories. In this paper, we successfully apply an evolutionary methodology to automatically synthesise domain-adaptive holistic descriptors for the task of scene recognition, instead of using hand-tuned descriptors. We address this as an optimisation problem by using multi-objective genetic programming (MOGP). Specifically, a set of primitive operators and filters are first randomly assembled in the MOGP framework as tree-based combinations, which are then evaluated by two objective fitness criteria i.e., the classification error and the tree complexity. Finally, the best-so-far solution selected by MOGP is regarded as the (near-)optimal feature descriptor for scene recognition. We have evaluated our approach on three realistic scene datasets: MIT urban and nature, SUN and UIUC Sport. Experimental results consistently show that our MOGP-generated descriptors achieve significantly higher recognition accuracies compared with state-of-the-art hand-crafted and machine-learnt features.", } @InProceedings{Liu:2013:IJCAI, author = "Li Liu and Ling Shao", title = "Learning Discriminative Representations from {RGB-D} Video Data", booktitle = "Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence", year = "2013", pages = "1493--1500", address = "Beijing, China", publisher = "AAAI Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-57735-633-2", URL = "http://dl.acm.org/citation.cfm?id=2540128.2540343", acmid = "2540343", oai = "oai:CiteSeerX.psu:10.1.1.417.4472", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.417.4472", URL = "http://ijcai.org/papers13/Papers/IJCAI13-223.pdf", size = "8 pages", abstract = "Recently, the low-cost Microsoft Kinect sensor, which can capture real-time high-resolution RGB and depth visual information, has attracted increasing attentions for a wide range of applications in computer vision. Existing techniques extract hand-tuned features from the RGB and the depth data separately and heuristically fuse them, which would not fully exploit the complementarity of both data sources. In this paper, we introduce an adaptive learning methodology to automatically extract (holistic) spatio-temporal features, simultaneously fusing the RGB and depth information, from RGB-D video data for visual recognition tasks. We address this as an optimisation problem using our proposed restricted graph-based genetic programming (RGGP) approach, in which a group of primitive 3D operators are first randomly assembled as graph-based combinations and then evolved generation by generation by evaluating on a set of RGB-D video samples. Finally the best-performed combination is selected as the (near-)optimal representation for a pre-defined task. The proposed method is systematically evaluated on a new hand gesture dataset, SKIG, that we collected ourselves and the public MSR Daily Activity 3D dataset, respectively. Extensive experimental results show that our approach leads to significant advantages compared with state-of-the-art hand-crafted and machine-learnt features.", } @PhdThesis{thesis_liuli, author = "Li Liu", title = "Learning Discriminative Feature Representations for Visual Categorization", school = "Electronic and Electrical Engineering, The University of Sheffield", year = "2015", address = "UK", month = feb, keywords = "genetic algorithms, genetic programming", URL = "http://etheses.whiterose.ac.uk/8239/1/thesis_liuli.pdf", URL = "http://etheses.whiterose.ac.uk/8239/", size = "180 pages", abstract = "Learning discriminative feature representations has attracted a great deal of attention due to its potential value and wide usage in a variety of areas, such as image/video recognition and retrieval, human activities analysis, intelligent surveillance and human-computer interaction. In this thesis we first introduce a new boosted key-frame selection scheme for action recognition. Specifically, we propose to select a subset of key poses for the representation of each action via AdaBoost and a new classifier, namely WLNBNN, is then developed for final classification. The experimental results of the proposed method are 0.6percent - 13.2percent better than previous work. After that, a domain-adaptive learning approach based on multiobjective genetic programming (MOGP) has been developed for image classification. In this method, a set of primitive 2-D operators are randomly combined to construct feature descriptors through the MOGP evolving and then evaluated by two objective fitness criteria, i.e., the classification error and the tree complexity. Later, the (near-)optimal feature descriptor can be obtained. The proposed approach can achieve 0.9percent ∼ 25.9percent better performance compared with state-of-the-art methods. Moreover, effective dimensionality reduction algorithms have also been widely used for obtaining better representations. In this thesis, we have proposed a novel linear unsupervised algorithm, termed Discriminative Partition Sparsity Analysis (DPSA), explicitly considering different probabilistic distributions that exist over the data points, simultaneously preserving the natural locality relationship among the data. All these above methods have been systematically evaluated on several public datasets, showing their accurate and robust performance (0.44percent - 6.69percent better than the previous) for action and image categorization. Targeting efficient image classification , we also introduce a novel unsupervised framework termed evolutionary compact embedding (ECE) which can automatically learn the task-specific binary hash codes. It is regarded as an optimization algorithm which combines the genetic programming (GP) and a boosting trick. The experimental results manifest ECE significantly outperform others by 1.58percent - 2.19percent for classification tasks. In addition, a supervised framework, bilinear local feature hashing (BLFH), has also been proposed to learn highly discriminative binary codes on the local descriptors for large-scale image similarity search. We address it as a nonconvex optimization problem to seek orthogonal projection matrices for hashing, which can successfully preserve the pairwise similarity between different local features and simultaneously take image-to-class (I2C) distances into consideration. BLFH produces outstanding results (0.017percent - 0.149percent better) compared to the state-of-the-art hashing techniques.", } @Article{Liu:2015:Cybernetics, author = "Li Liu and Ling Shao and Xuelong Li and Ke Lu", journal = "IEEE Transactions on Cybernetics", title = "Learning Spatio-Temporal Representations for Action Recognition: A Genetic Programming Approach", year = "2016", volume = "46", number = "1", pages = "158--170", keywords = "genetic algorithms, genetic programming, Action recognition, feature extraction, feature learning, spatio-temporal descriptors", DOI = "doi:10.1109/TCYB.2015.2399172", ISSN = "2168-2267", abstract = "Extracting discriminative and robust features from video sequences is the first and most critical step in human action recognition. In this paper, instead of using handcrafted features, we automatically learn spatio-temporal motion features for action recognition. This is achieved via an evolutionary method, i.e., genetic programming (GP), which evolves the motion feature descriptor on a population of primitive 3D operators (e.g., 3D-Gabor and wavelet). In this way, the scale and shift invariant features can be effectively extracted from both colour and optical flow sequences. We intend to learn data adaptive descriptors for different datasets with multiple layers, which makes fully use of the knowledge to mimic the physical structure of the human visual cortex for action recognition and simultaneously reduce the GP searching space to effectively accelerate the convergence of optimal solutions. In our evolutionary architecture, the average cross-validation classification error, which is calculated by an support-vector-machine classifier on the training set, is adopted as the evaluation criterion for the GP fitness function. After the entire evolution procedure finishes, the best-so-far solution selected by GP is regarded as the (near-)optimal action descriptor obtained. The GP-evolving feature extraction method is evaluated on four popular action datasets, namely KTH, HMDB51, UCF YouTube, and Hollywood2. Experimental results show that our method significantly outperforms other types of features, either hand-designed or machine-learnt.", notes = "Also known as \cite{7042326}", } @Article{Liu:2015:ieeeNNLS, author = "Li Liu and Ling Shao", journal = "IEEE Transactions on Neural Networks and Learning Systems", title = "Sequential Compact Code Learning for Unsupervised Image Hashing", year = "2015", keywords = "genetic algorithms, genetic programming", ISSN = "2162-237X", DOI = "doi:10.1109/TNNLS.2015.2495345", abstract = "Effective hashing for large-scale image databases is a popular research area, attracting much attention in computer vision and visual information retrieval. Several recent methods attempt to learn either graph embedding or semantic coding for fast and accurate applications. In this paper, a novel unsupervised framework, termed evolutionary compact embedding (ECE), is introduced to automatically learn the task-specific binary hash codes. It can be regarded as an optimisation algorithm that combines the genetic programming (GP) and a boosting trick. In our architecture, each bit of ECE is iteratively computed using a weak binary classification function, which is generated through GP evolving by jointly minimizing its empirical risk with the AdaBoost strategy on a training set. We address this as greedy optimisation by embedding high-dimensional data points into a similarity-preserved Hamming space with a low dimension. We systematically evaluate ECE on two data sets, SIFT 1M and GIST 1M, showing the effectiveness and the accuracy of our method for a large-scale similarity search.", notes = "Also known as \cite{7323857}", } @Article{Liu:2014:IS, author = "Li Liu and Ling Shao and Xuelong Li", title = "Evolutionary compact embedding for large-scale image classification", journal = "Information Sciences", volume = "316", pages = "567--581", year = "2015", month = "20 " # sep, keywords = "genetic algorithms, genetic programming, Dimensionality reduction, Large-scale image classification, Evolutionary compact embedding, AdaBoost", ISSN = "0020-0255", DOI = "doi:10.1016/j.ins.2014.06.030", URL = "http://www.sciencedirect.com/science/article/pii/S0020025514006586", abstract = "Effective dimensionality reduction is a classical research area for many large-scale analysis tasks in computer vision. Several recent methods attempt to learn either graph embedding or binary hashing for fast and accurate applications. In this paper, we propose a novel framework to automatically learn the task-specific compact coding, called evolutionary compact embedding (ECE), which can be regarded as an optimisation algorithm combining genetic programming (GP) and a boosting trick. As an evolutionary computation methodology, GP can solve problems inspired by natural evolution without any prior knowledge of the solutions. In our evolutionary architecture, each bit of ECE is iteratively computed using a binary classification function, which is generated through GP evolving by jointly minimising its empirical risk with the AdaBoost strategy on a training set. We address this as greedy optimisation leading to small Hamming distances for similar samples and large distances for dissimilar samples. We then evaluate ECE on four image datasets: USPS digital hand-writing, CMU PIE face, CIFAR-10 tiny image and SUN397 scene, showing the accurate and robust performance of our method for large-scale image classification.", } @Article{LIU:2021:JCP, author = "Meng-Yang Liu and Wen-Xin Huai and Bin Chen", title = "Predicting the effective diffusivity across the sediment-water interface in rivers", journal = "Journal of Cleaner Production", volume = "292", pages = "126085", year = "2021", ISSN = "0959-6526", DOI = "doi:10.1016/j.jclepro.2021.126085", URL = "https://www.sciencedirect.com/science/article/pii/S095965262100305X", keywords = "genetic algorithms, genetic programming, Effective diffusivity, Hyporheic exchange, Solute transport, Sediment-water interface", abstract = "Hyporheic exchange directly controls and regulates the transport of nutrients, heat, and organic matter across the sediment-water interface (SWI), thereby affecting the biochemical processes in rivers, which is critical for maintaining the health of aquatic ecosystems. The interface exchange is controlled by multiple processes, including physical, chemical, and biological processes, which can be modeled by the effective diffusion model using an effective diffusion coefficient, Deff, to quantify the hyporheic exchange rate. In this study, genetic programming (GP), a machine learning (ML) technique based on natural selection, is adopted to search for a robust relationship between the effective diffusion coefficient and surface flow conditions, bedforms, and sediment characteristics on the basis of published broad interfacial mass exchange flux measurements. By using a data set covering a wide range of environmental condition parameters, the effective diffusion coefficient prediction models for the SWI with and without bedforms are developed. Results show that the dimensionless effective diffusion coefficient is not only related to the permeability Reynolds number, ReK, but also to the channel Reynolds number, Re. Compared with the flat bed, ReK has a greater effect on the hyporheic exchange when bedforms present at the SWI by affecting the pumping advection strength. The new Deff predictor with a relatively concise form exhibits considerable improvements with regard to prediction ability and is physically sound relative to the existing predictors", } @Article{liu:CIS, author = "Qingqing Liu and Xianpeng Wang and Yao Wang and Xiangman Song", title = "Evolutionary convolutional neural network for image classification based on multi-objective genetic programming with leader-follower mechanism", journal = "Complex \& Intelligent Systems", year = "2023", volume = "9", pages = "3211--3228", month = jun, keywords = "genetic algorithms, genetic programming, Evolutionary neural architecture search, ANN, CNN, LF-MOGP, Multi-objective genetic programming, MOGP, Leader-follower mechanism", URL = "http://link.springer.com/article/10.1007/s40747-022-00919-y", URL = "https://rdcu.be/d4p0u", DOI = "doi:10.1007/s40747-022-00919-y", size = "18 pages", notes = "National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Northeastern University, Shenyang 110819, China", } @Article{liu:2021:CIS, author = "Wei-Li Liu and Jiaquan Yang and Jinghui Zhong and Shibin Wang", title = "Genetic programming with separability detection for symbolic regression", journal = "Complex \& Intelligent Systems", year = "2021", volume = "7", number = "3", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s40747-020-00240-6", DOI = "doi:10.1007/s40747-020-00240-6", } @InProceedings{Liu:2024:PPSN, author = "Saining Liu and Joao Guilherme {Cavalcanti Costa} and Yi Mei and Mengjie Zhang", title = "{GPGLS}: Genetic Programming Guided Local Search for Large-Scale Vehicle Routing Problems", booktitle = "18th International Conference on Parallel Problem Solving from Nature", year = "2024", editor = "Heike Trautmann and Tea Tusar and Penousal Machado and Thomas Baeck", volume = "15148", series = "Lecture Notes in Computer Science", pages = "36--51", address = "University of Applied Sciences Upper Austria, Hagenberg, Austria", month = "13-18 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Large-scale vehicle routing problem, Guided local search, Utility function", isbn13 = "978-3-031-70054-5", URL = "https://rdcu.be/d5mX7", DOI = "doi:10.1007/978-3-031-70055-2_3", notes = "https://ppsn2024.fh-ooe.at/program/", } @Article{liu:2019:WRM, author = "Suning Liu and Haiyun Shi", title = "A Recursive Approach to {Long-Term} Prediction of Monthly Precipitation Using Genetic Programming", journal = "Water Resources Management", year = "2019", volume = "33", number = "3", pages = "1103--1121", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s11269-018-2169-0", DOI = "doi:10.1007/s11269-018-2169-0", abstract = "Precipitation is regarded as the basic component of the global hydrological cycle. This study develops a recursive approach to long-term prediction of monthly precipitation using genetic programming (GP), taking the Three-River Headwaters Region (TRHR) in China as the study area. The daily precipitation data recorded at 29 meteorological stations during 1961 thru 2014 are collected, among which the data during 1961 to 2000 are for calibration and the remaining data are for validation. To develop this approach, first, the preliminary estimations of annual precipitation are computed based on a statistical method. Second, the percentage of the monthly precipitation for each month of a year is calculated as the mean monthly precipitation divided by the mean annual precipitation during the study period, and then the preliminary estimation of monthly precipitation for each month of a year is obtained. Third, since GP can be used to improve the prediction results through establishing the relationship of the observations with the preliminary estimations at the past and current times, it is adopted to improve the preliminary estimations. The calibration and validation results reveal that the recursive approach involving GP can provide the more accurate predictions of monthly precipitation. Finally, this approach is used to predict the monthly precipitation over the TRHR till 2050. Overall, the proposed method and the obtained results will enhance our understanding and facilitate future studies regarding the long-term prediction of precipitation in such regions.", } @Article{Liu:2015:Energy, author = "Yan Liu and Jian Yang and Jing-yu Wang and Xu-gang Ding and Zhi-long Cheng and Qiu-wang Wang", title = "Prediction, parametric analysis and bi-objective optimization of waste heat utilization in sinter cooling bed using evolutionary algorithm", journal = "Energy", volume = "90, Part 1", pages = "24--35", year = "2015", ISSN = "0360-5442", DOI = "doi:10.1016/j.energy.2015.05.120", URL = "http://www.sciencedirect.com/science/article/pii/S0360544215007422", abstract = "Based on our previous work, the AEGs (annual energy gains) could be obtained on energy and exergy analysis for a sinter cooling bed. In the present study, a method synthesizing both economic cost and energy benefit aspects of the sinter cooling bed is proposed. Firstly, the GP (genetic programming) is employed to derive accurate correlations between the AEGs and operational parameters. Then, the economic cost model is established to evaluate effects of operational and economic parameters on the EAOC (equivalent annual operational cost). Finally, bi-objective optimization of the sinter cooling bed is performed to achieve the optimal operational conditions from both waste heat use and economic cost aspects using NSGA-II (non-dominated sorting genetic algorithm-II). In order to maximize the AEGs and minimize the EAOC, the EAOC and the AEGs based on the first and second laws of thermodynamics are selected as two objective functions. A Pareto frontier obtained shows that an increase in the AEGs can increase the EAOC of the sinter cooling bed. Under the given operational conditions, the optimum solutions with their corresponding decision variables are obtained. After considering both two Pareto frontiers curves, a set of suggested operational parameters for the decision-makers is also obtained.", keywords = "genetic algorithms, genetic programming, Sinter cooling bed, Waste heat recovery, Bi-objective optimization", } @Article{Liu:2003:JIT, author = "Yan Liu2 and Zhao-feng Geng", title = "Three-dimensional Garment Computer Aided Intelligent Design", journal = "Journal of Industrial Textiles", year = "2003", volume = "33", number = "1", pages = "43--54", month = jul, keywords = "genetic algorithms, genetic programming, garment design, 3d modelling, artificial intelligence", ISSN = "1528-0837", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.1030.2669", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1030.2669", URL = "http://jit.sagepub.com/content/33/1/43.full.pdf", URL = "http://journals.sagepub.com/toc/jitc/33/1", DOI = "doi:10.1177/1528083703035505", size = "12 pages", abstract = "This paper proposes to use expert systems and the technique of 3D modelling to realize intelligent design of 3D garments. The advantages of this new approach, compared with conventional ones, are discussed. The new approach includes several steps: First, set up the 3D garment prototype by using the method developed in this paper. Next study the relationship between the parameters of the 3D garment prototype and different garment styles. Based on the relationship, the algorithms and production rules for transferring style requirements to the parameter values of the garment prototype are developed. As such, the knowledge base can be constructed, and the intelligent design system of the 3D garment style is built. Using the system, various 3D garment styles can be designed automatically to satisfy various style requirements.", notes = "College of Information Science and Technology Dong Hua University Shanghai, 200051, China", } @Article{Liu:2017:CJE, author = "Yaoping Liu and Ning Wu and Xiaoqiang Zhang and Fang Zhou and Fen Ge", journal = "Chinese Journal of Electronics", title = "A Compact Implementation of {AES S-Box} Using Evolutionary Algorithm", year = "2017", volume = "26", number = "4", pages = "688--695", month = jul, keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, Advanced encryption standard (AES), Composite field arithmetic (CFA), S-Box, Evolutionary algorithm (EA)", DOI = "doi:10.1049/cje.2016.08.021", ISSN = "1022-4653", size = "8 pages", abstract = "S-Box based on Composite field arithmetic (CFA) technology is optimised by Genetic algorithm (GA) and Cartesian genetic programming (CGP) model for reducing the hardware complexity. After using the CFA technique to map Multiplicative inverse (MI) over GF(28) into composite field GF((24)2), the compact MI circuit over GF(24) is selected from 100 evolved circuits, and same design method is applied to the compact multiplication circuit over GF(22). Compared with the direct implementations, the areas of optimised circuits of MI over GF(24) and multiplication over GF((22)2) are reduced by 66percent and 57.69percent, respectively. The area reductions for MI over GF(28) and the whole of S-Box are up to 59.23percent and 56.14percent, separately. In 180nm 1.8V COMS technology, compared to previous works, the S-Box proposed in this paper has the minimum area and minimum power, which are 11.27percent and 6.65percent smaller than that of the smallest area S-Box, respectively.", notes = "ollege of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China. Also known as \cite{8013562}", } @InProceedings{Liu:2016:CEC, author = "Yi Liu2 and Muhammad Iqbal and Isidro Alvarez and Will N. Browne", title = "Integration of Code-Fragment based Learning Classifier Systems for Multiple Domain Perception and Learning", booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016)", year = "2016", editor = "Yew-Soon Ong", pages = "2177--2184", address = "Vancouver", month = "24-29 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-5090-0623-6", DOI = "doi:10.1109/CEC.2016.7744057", abstract = "It has been shown that identifying building blocks of knowledge and then reusing them to solve complex problems is a practical and useful endeavour. Previous work made it possible to solve various, until then, intractable tasks. However, the individual algorithms targeted one specific problem type, e.g. scalable problems or domains with repeating patterns. The question that arises is: Can the disparate techniques be combined into a single approach to solve more complex problems that span several domains or that may be unknown to the agent? The first stage in developing such a system is to be able to recognise domains from unidentified input stimuli and identify the approaches best suited to them. The novel work here aims to realise this primary stage by combining several code-fragment (CF) based XCS systems. The stimulus and its guiding effect, will be instrumental in helping the agent decide which of its stored systems is the most capable of solving the problem, or if there is a conflict between possible solutions. Importantly, the agent will be capable of determining if the current problem is entirely new, in which case it spawns a training agent to produce a tractable solution to store and reuse. The proposed technique relies on the proven benefits in scalability of CF based systems and furthers the body of knowledge by tackling unknown problems (to the agent). The main contribution of this research is that a system of proven CF techniques is used for the first time. We show that by using the new CF system, it is possible to identify an unknown problem and to arrive at a viable solution.", notes = "WCCI2016", } @InProceedings{Liu:2018:evoApplications, author = "Yi Liu2 and Will N. Browne and Bing Xue", title = "Adapting Bagging and Boosting to Learning Classifier Systems", booktitle = "21st International Conference on the Applications of Evolutionary Computation, EvoIASP 2018", year = "2018", editor = "Stefano Cagnoni and Mengjie Zhang", series = "LNCS", volume = "10784", publisher = "Springer", pages = "405--420", address = "Parma, Italy", month = "4-6 " # apr, organisation = "Species", keywords = "genetic algorithms, genetic programming, Learning classifier systems, Multiple domain learning, Ensemble learning", isbn13 = "978-3-319-77537-1", DOI = "doi:10.1007/978-3-319-77538-8_28", abstract = "Learning Classifier Systems (LCSs) have demonstrated their classification capability by employing a population of polymorphic rules in addressing numerous benchmark problems. However, although the produced solution is often accurate, the alternative ways to represent the data in a single population obscure the underlying patterns of a problem. Moreover, once a population is dominated by over-general rules, the system will sink into the local optimal trap. To grant a problem's patterns more transparency, the redundant rules and optimal rules need to be distinguished. Therefore, the bagging method is introduced to LCSs with the aim to reduce the variance associated with redundant rules. A novel rule reduction method is proposed to reduce the rules' polymorphism in a problem. This is tested with complex binary problems with typical epistatic, over-lapping niches, niche-imbalance, and specific-addiction properties at various scales. The results show the successful highlighting of the patterns for all the tested problems, which have been addressed successfully. Moreover, by combining the boosting method with LCSs, the hybrid system could adjust previously defective solutions such that they now represent the correct classification of data.", notes = "EvoApplications2018 held in conjunction with EuroGP'2018 EvoCOP2018 and EvoMusArt2018 http://www.evostar.org/2018/cfp_evoapps.php", } @Article{LIU:2021:CS, author = "Yiding Liu and Zewen Gu and Darren J. Hughes and Jianqiao Ye and Xiaonan Hou", title = "Understanding mixed mode ratio of adhesively bonded joints using genetic programming ({GP)}", journal = "Composite Structures", volume = "258", pages = "113389", year = "2021", ISSN = "0263-8223", DOI = "doi:10.1016/j.compstruct.2020.113389", URL = "https://www.sciencedirect.com/science/article/pii/S0263822320333158", keywords = "genetic algorithms, genetic programming, Adhesively bonded joints, Mixed mode ratio, Finite element analysis, Latin Hypercube Sampling, Strain Energy Release Rate", abstract = "Adhesively bonding has been increasingly used for numerous industrial applications to meet the high demand for lightweight and safer structures. Debonding of adhesively bonded joints is a typical mixed mode failure process. It is highly depended on the interactional effects of material properties and geometric definitions of the constituents, which is very complicated. The existing studies in identifying fracture modes of joints based on either experiments or finite element analysis are often prohibitively time and computational expensive. This paper proposed an innovate method by combining Finite Element Analysis (FEA), Latin Hypercube Sampling (LHS) and Genetic Programming (GP) to understand the effect of the physical attributes on the fracture modes of adhesively single lap joints. A dataset of 150 adhesive joint samples has been generated using LHS, including different combinations of adherend and adhesive's material properties and thicknesses. The mixed mode ratios of the 150 samples are calculated using Strain Energy Release Rate (SERR) outputs embedded in Linear Elastic Fracture Mechanics (LEFM), which has been validated by experimental tests. Finally, a GP model is developed and trained to provide an extracted explicit expression used for evaluating the early-state failure modes of the adhesively bonded joints against the design variables", } @Article{Yun_Liu:ieeeTEC, author = "Yun Liu and Fangfang Zhang and Yanan Sun and Mengjie Zhang", title = "Evolutionary Trainer-Based Deep Q-Network for Dynamic Flexible Job Shop Scheduling", journal = "IEEE Transactions on Evolutionary Computation", note = "Accepted for future publication", keywords = "genetic algorithms, genetic programming, Training, Dispatching, Heuristic algorithms, Dynamic scheduling, Job shop scheduling, Feature extraction, Dynamic flexible job shop scheduling, deep Q-network, evolutionary algorithm, dispatching rules", ISSN = "1089-778X", URL = "https://github.com/fangfang-zhang/fangfang-zhang.github.io/blob/main/files/2024-Evolutionary_Trainer-Based_Deep_Q-Network_for_DFJSS.pdf", URL = "https://ieeexplore.ieee.org/document/10439992", code_url = "https://github.com/liuyun0314/ETDQN", DOI = "doi:10.1109/TEVC.2024.3367181", size = "15 pages", notes = "Also known as \cite{10439992} p10 'Comparison with GPRule1 and GPRule2 reveals that ETDQN still achieves the best results...'", } @InProceedings{Liu:2017:GECCOb, author = "Yuxin Liu and Yi Mei and Mengjie Zhang and Zili Zhang", title = "Automated Heuristic Design Using Genetic Programming Hyper-heuristic for Uncertain Capacitated Arc Routing Problem", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4920-8", address = "Berlin, Germany", pages = "290--297", size = "8 pages", URL = "http://doi.acm.org/10.1145/3071178.3071185", DOI = "doi:10.1145/3071178.3071185", acmid = "3071185", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, hyper-heuristic, uncertain capacitated arc routing problem", month = "15-19 " # jul, abstract = "Uncertain Capacitated Arc Routing Problem (UCARP) is a variant of the well-known CARP. It considers a variety of stochastic factors to reflect the reality where the exact information such as the actual task demand and accessibilities of edges are unknown in advance. Existing works focus on obtaining a robust solution beforehand. However, it is also important to design effective heuristics to adjust the solution in real time. In this paper, we develop a new Genetic Programming-based Hyper-Heuristic (GPHH) for automated heuristic design for UCARP. A novel effective meta-algorithm is designed carefully to address the failures caused by the environment change. In addition, it employs domain knowledge to filter some infeasible candidate tasks for the heuristic function. The experimental results show that the proposed GPHH significantly outperforms the existing GPHH methods and manually designed heuristics. Moreover, we find that eliminating the infeasible and distant tasks in advance can reduce much noise and improve the efficacy of the evolved heuristics. In addition, it is found that simply adding a slack factor to the expected task demand may not improve the performance of the GPHH.", notes = "Also known as \cite{Liu:2017:AHD:3071178.3071185} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @Article{Liu:EC:00256, author = "Yuxin Liu and Yi Mei and Mengjie Zhang and Zili Zhang", title = "A Predictive-Reactive Approach with Genetic Programming and Cooperative Co-evolution for Uncertain Capacitated Arc Routing Problem", journal = "Evolutionary Computation", year = "2020", volume = "28", number = "2", pages = "289--316", month = "Summer", keywords = "genetic algorithms, genetic programming, Capacitated arc routing problem, cooperative co-evolution, hyper-heuristics", ISSN = "1063-6560", DOI = "doi:10.1162/evco_a_00256", size = "25 pages", abstract = "The uncertain capacitated arc routing problem is of great significance for its wide applications in the real world. In uncertain capacitated arc routing problem, variables such as task demands and travel costs are realised in real time. This may cause the predefined solution to become ineffective and/or infeasible. There are two main challenges in solving this problem. One is to obtain a high-quality and robust baseline task sequence, and the other is to design an effective recourse policy to adjust the baseline task sequence when it becomes infeasible and/or ineffective during the execution. Existing studies typically only tackle one challenge (the other being addressed using a naive strategy). No existing work optimises the baseline task sequence and recourse policy simultaneously. To fill this gap, we propose a novel proactive-reactive approach, which represents a solution as a baseline task sequence and a recourse policy. The two components are optimised under a cooperative co-evol", notes = "Supplemental Material https://www.mitpressjournals.org/doi/suppl/10.1162/evco_a_00256 Also know as \cite{DBLP:journals/ec/LiuMZZ20}", } @Article{DBLP:journals/connection/LiuMHG22, author = "Guopeng Liu and Jianbin Ma and Tongle Hu and Xiaoying Gao", title = "A feature selection method with feature ranking using genetic programming", journal = "Connect. Sci.", volume = "34", number = "1", pages = "1146--1168", year = "2022", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1080/09540091.2022.2049702", DOI = "doi:10.1080/09540091.2022.2049702", timestamp = "Fri, 29 Apr 2022 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/connection/LiuMHG22.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{Liu:2025:neunet, author = "Jingyi Liu and Weijun Li and Lina Yu and Min Wu and Wenqiang Li and Yanjie Li and Meilan Hao", title = "Mathematical expression exploration with graph representation and generative graph neural network", journal = "Neural Networks", year = "2025", volume = "187", pages = "107405", keywords = "genetic algorithms, genetic programming, Symbolic regression, Directed acyclic graph, Graph neural network, ANN, Reinforcement learning", ISSN = "0893-6080", URL = "https://www.sciencedirect.com/science/article/pii/S0893608025002849", DOI = "doi:10.1016/j.neunet.2025.107405", abstract = "Symbolic Regression (SR) methods in tree representations have exhibited commendable outcomes across Genetic Programming (GP) and deep learning search paradigms. Nonetheless, the tree representation of mathematical expressions occasionally embodies redundant substructures. Representing expressions as computation graphs is more succinct and intuitive through graph representation. Despite its adoption in evolutionary strategies within SR, deep learning paradigms remain under-explored. Acknowledging the profound advancements of deep learning in tree-centric SR approaches, we advocate for addressing SR tasks using the Directed Acyclic Graph (DAG) representation of mathematical expressions, complemented by a generative graph neural network. We name the proposed method as Graph-based Deep Symbolic Regression (GraphDSR). We vectorize node types and employ an adjacent matrix to delineate connections. The graph neural networks craft the DAG incrementally, sampling node types and graph connections conditioned on previous DAG at every step. During each sample step, the valid check is implemented to avoid meaningless sampling, and four domain-agnostic constraints are adopted to further streamline the search. This process culminates once a coherent expression emerges. Constants undergo optimisation by SGD and BFGS algorithms, and rewards refine the graph neural network through reinforcement learning. A comprehensive evaluation across 110 benchmarks underscores the potency of our approach", } @Article{Liu:2025:ijmecsci, author = "Jinlong Liu and Jiahui Liu and Kang Gao and Iman Mohagheghian and Wei Fan and Jie Yang and Zhangming Wu", title = "A bioinspired gradient curved auxetic honeycombs with enhanced energy absorption", journal = "International Journal of Mechanical Sciences", year = "2025", volume = "291-292", pages = "110189", keywords = "genetic algorithms, genetic programming, Auxetic, Bionics design, Energy absorption, Arc-Curved Honeycomb, Fractal self-similar structure, Genetic Programming-Symbolic regression", ISSN = "0020-7403", URL = "https://www.sciencedirect.com/science/article/pii/S0020740325002759", DOI = "doi:10.1016/j.ijmecsci.2025.110189", abstract = "Traditional auxetic honeycombs often exhibit reduced energy absorption capabilities due to global instability arising from shear band formation, significantly limiting their practical applications. To address this limitation, this study presents the Auxetic Arc-Curved Honeycomb with a novel Bioinspired Layering Gradient (BLG-AACH). The innovative gradient design of the BLG-AACH is inspired by the dense exterior and sparse interior characteristics of biological tissues across multiple scales, using fractal self-similar structure to achieve this biological trait. The BLG-AACH facilitates induced deformation in the intermediate layers, thereby preventing overall global buckling and significantly enhancing energy absorption properties. The compressive behaviour of the BLG-AACH was investigated through both experimental testing and finite element modelling. The results demonstrate that the BLG-AACH structure maintains a stable concave folding deformation mode and exhibits multi-level energy absorption capabilities. Its specific energy absorption and total energy absorption are 5.81 and 10.74 times greater than those of the homogeneous AACH, respectively, outperforming other layered configurations. Moreover, the BLG-AACH is highly programmable, enabling the adjustment of mechanical properties such as initial stiffness, plateau stress, and specific energy absorption by varying parameters like cell angle and cell wall thickness. Additionally, Genetic Programming-Symbolic Regression (GP-SR) was innovatively employed to derive a compact and scalable formula for calculating the specific energy absorption of the BLG-AACH, achieving an impressive R2 value of 0.99. These findings provide a novel paradigm for enhancing the energy absorption performance and its calculation in auxetic honeycombs", } @InProceedings{liu:2024:CEC, author = "Lin Liu and Yuan Yuan", title = "Effective Training of {PINNs} by Combining {CMA-ES} with Gradient Descent", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Training, Memetics, Neural networks, Evolutionary computation, Pareto optimization, Prediction algorithms, Linear programming, Physics-informed neural networks, evolutionary algorithm, gradient descent, memetic algorithm, multi-objective optimization", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10611964", abstract = "Physics-Informed Neural Networks (PINNs) have recently received increasing attention, however, optimising the loss function of PINNs is notoriously difficult, where the landscape of the loss function is often highly non-convex and rugged. Local optimisation methods based on gradient information can converge quickly but are prone to being trapped in local minima for training PINNs. Evolutionary algorithms (EAs) are well known for the global search ability, which can help escape from local minima. It has been reported in the literature that EAs show some advantages over gradient-based methods in training PINNs. Inspired by the Memetic Algorithm, we combine global-search based EAs and local-search based batch gradient descent in order to make the best of both word. In addition, since the PINN loss function is composed of multiple terms, balancing these terms is also a challenging issue. Therefore, we also attempt to combine EAs with multiple-gradient descent algorithm for multi-objective optimisation. Our experiments provide strong evidence for the superiority of the above algorithms.", notes = "also known as \cite{10611964} WCCI 2024", } @InProceedings{Liu:2024:GEn-CITy, author = "Linfeng Liu and Filbert H. Juwono and W. K. Wong and Erick Purwanto and Tingyan Jin and Yuyang Zhen", title = "Sodium-ion battery cycle life prediction using machine learning", booktitle = "International Conference on Green Energy, Computing and Intelligent Technology 2024 (GEn-CITy 2024)", year = "2024", volume = "2024", pages = "151--155", month = dec, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1049/icp.2025.0249", abstract = "In this study, we explore the classification and prediction capabilities of three models-Genetic Programming (GP), Logistic Regression (LR), and the Kolmogorov-Arnold Network (KAN)-on the task of sodium-ion battery life prediction. By leveraging a dataset composed of multiple battery characteristics, we aim to determine the remaining power of sodium-ion batteries using these machine learning models. The KAN model, being a novel approach, demonstrates superior performance across various metrics, including accuracy, precision, recall, and F1 score, when compared to the other two models. This highlights the potential of KAN as a robust model for complex classification tasks in the field of battery life prediction.", notes = "Also known as \cite{10915731}", } @InProceedings{Liu:2023:ICTIS, author = "Qing Liu and Zhuofeng Tang and Zhe Cong and Lei Wang", booktitle = "2023 7th International Conference on Transportation Information and Safety (ICTIS)", title = "Optimization of ship command and dispatch in the waters between the three gorges dams based on {GP} algorithm", year = "2023", abstract = "At present, the backlog of ships passing through the Three Gorges has become the norm, and the difference between the passing capacity of the Three Gorges-Gezhouba locks and the passing capacity of the waters between the two dams has led to a reduction in the efficiency of ship navigation through the locks and an increase in the difficulty of control. In view of the above problems, this paper constructs an optimisation model for the control and command of formation ship groups under the existing ship navigation rules, taking into account the safety distance to be maintained between ship groups, the continuity of navigation and other realistic constraints, with the objective of minimizing the imbalance between navigation time and delay time. Based on the characteristics that the ship navigation process is similar to the processing operation in the production plant, a heuristic scheduling rule set and Genetic Programming (GP) algorithm are established for the solution. The research results show that the method of determining the navigation order of vessel groups by generating composite dispatching rules (CDR) with GP algorithm can reduce the navigation time of vessels crossing the gate in the waters between two dams and effectively improve the fairness of scheduling, while meeting the requirements of vessel navigation rules and safety.", keywords = "genetic algorithms, genetic programming, Job shop scheduling, Navigation, Dams, Transportation, Logic gates, Dispatching, Safety, Three Gorges-Gezhouba Dams, Ship command and dispatch, Optimisation model, Scheduling Rules", DOI = "doi:10.1109/ICTIS60134.2023.10243931", ISSN = "2832-899X", month = aug, notes = "Also known as \cite{10243931}", } @Article{liu:2024:CIS, author = "Ruochen Liu and Haoyuan Lv and Ping Yang and Rongfang Wang", title = "A multi-task genetic programming approach for online multi-objective container placement in heterogeneous cluster", journal = "Complex \& Intelligent Systems", year = "2024", keywords = "genetic algorithms, genetic programming", URL = "https://link.springer.com/article/10.1007/s40747-024-01605-x", DOI = "doi:10.1007/s40747-024-01605-x", } @Article{Liu:2024:swevo, author = "Wei-Li Liu and Jinghui Zhong and Peng Liang and Jianhua Guo and Huimin Zhao and Jun Zhang", title = "Towards explainable traffic signal control for urban networks through genetic programming", journal = "Swarm and Evolutionary Computation", year = "2024", volume = "88", pages = "101588", keywords = "genetic algorithms, genetic programming, Symbolic regression, Traffic signal control", ISSN = "2210-6502", URL = "https://www.sciencedirect.com/science/article/pii/S2210650224001263", DOI = "doi:10.1016/j.swevo.2024.101588", abstract = "The increasing number of vehicles in urban areas draws significant attention to traffic signal control (TSC), which can enhance the efficiency of the entire network by properly switching the phases of each signalized intersection. Fixed and max-pressure methods are commonly used in TSC systems owing to their high simplicity and good interpretability, but they respectively lack dynamic adaptability and automatic rule generation, possibly leading to low solution accuracy in complicated traffic environments. Meanwhile, meta-heuristic and black-box learning methods meet challenges in practice such as extensive computational time and poor interpretability. To this end, this paper proposes a new TSC method based on Genetic Programming (GP) to generate descriptive score rules automatically for switching phases of all signalized intersections in an urban transportation network. In the proposed method, switching phases of each signalized intersection type is formulated as a symbolic regression problem, and effective primitives are defined to facilitate GP to solve the problem. Experiments have been conducted on both synthetic and real-world networks. The results have validated the effectiveness of our proposed GP based method compared to several state-of-the-art TSC methods in terms of accuracy and interpretability", } @Article{Liu:2024:cscm, author = "Weizheng Liu and Guiyong Liu and Xiaolin Zhu", title = "Applicability of machine learning algorithms in predicting chloride diffusion in concrete: Modeling, evaluation, and feature analysis", journal = "Case Studies in Construction Materials", year = "2024", volume = "21", pages = "e03573", keywords = "genetic algorithms, genetic programming, Concrete Durability, Machine Learning Algorithm, Chloride Diffusion Coefficient, Model Evaluation, Feature Analysis, ANN", ISSN = "2214-5095", URL = "https://www.sciencedirect.com/science/article/pii/S2214509524007241", DOI = "doi:10.1016/j.cscm.2024.e03573", abstract = "The resistance to chloride diffusion is one of the most crucial durable properties of concrete. However, traditional methods to evaluate this property are time-consuming and inefficient. In this research, backpropagation-artificial neural network (BP-ANN), support vector regression (SVR), genetic programming (GP), extreme gradient boost (XGBoost), and random forest (RF) models were optimised using particle swarm optimisation (PSO) to predict the chloride diffusion coefficient of concretes containing silica fume. A database was also compiled, consisting of various features related to materials composition, curing, and exposure conditions. Statistical assessments were made to evaluate the predictive efficacy of every model. In addition, the distribution of errors and the consistency of each model were scrutinized. The findings indicate that the XGBoost model outperformed the standard models, achieving an R2 value of 0.9382 and an MSE of 3.0162. The models' predictive precision was notably enhanced following their integration with PSO. The PSO algorithm can also decrease the occurrence of significant error points in the predicted values and enhance the consistency of predictive performance across the range of experimental data. Finally, the PSO-XGBoost demonstrated the best comprehensive performance and proved to be the most efficient among the other PSO-synthesised (PSOS) models", } @Article{Liu:2024:ins, author = "Xiang Liu and Yan Wang and Shan Liu and Zhicheng Ji and Shan He", title = "{MIFuGP:} Boolean network inference from multivariate time series using fuzzy genetic programming", journal = "Information Sciences", year = "2024", volume = "680", pages = "121129", keywords = "genetic algorithms, genetic programming, Boolean network inference, Multivariate time series, Fuzzy logic control, Mutual information", ISSN = "0020-0255", URL = "https://www.sciencedirect.com/science/article/pii/S0020025524010430", DOI = "doi:10.1016/j.ins.2024.121129", abstract = "Boolean network inference is essential for gaining insights into gene regulatory networks through multivariate gene expression time series. However, most existing algorithms cannot accurately reconstruct large-scale Boolean networks due to the complex and diverse relationships among genes and the overfitting problem. To address these problems, a novel inference algorithm using a mutual information-based fuzzy genetic programming approach (MIFuGP) is proposed to infer large-scale Boolean networks accurately. To represent complex regulatory relationships in Boolean networks, MIFuGP encodes Boolean functions as syntax tree programs. Taking the dependency between genes into account, MIFuGP fully extracts the mutual information from the syntax trees to alleviate the bloat problem. MIFuGP also provides a novel fitness function to make full use of state-transitions and topology information, together with a fuzzy logic control strategy to reduce the overfitting problem. Extensive experiments validate that MIFuGP significantly outperforms state-of-the-art algorithms on both real-world gene regulatory networks and artificial Boolean networks", } @Article{LIU:2023:combustflame, author = "Yao Liu and Jianguo Tan and Hao Li and Yi Hou and Dongdong Zhang and Bernd R. Noack", title = "Simultaneous control of combustion instabilities and {NOx} emissions in a lean premixed flame using linear genetic programming", journal = "Combustion and Flame", volume = "251", pages = "112716", year = "2023", ISSN = "0010-2180", DOI = "doi:10.1016/j.combustflame.2023.112716", URL = "https://www.sciencedirect.com/science/article/pii/S0010218023001013", keywords = "genetic algorithms, genetic programming, Machine learning, Linear genetic programming, Active control, Combustion instabilities, NO emissions", abstract = "Combustion instabilities have been a plaguing challenge in lean-conditioned propulsion systems. An open-loop control system was developed using machine learning to suppress pressure fluctuations and NOx emissions simultaneously. The open-loop control is realized by regulating the solenoid valve to modulate the methane supply. Control laws comprising the multi-frequency forcing are generated via the linear genetic programming (LGP), before being converted into square waves with different frequencies and duty cycles to activate the solenoid valve. The cost function is intended to evaluate and rank individuals of each generation, so as to select candidates for evolution. Optimized periodic forcing (OPF) with different duty cycles (d) and frequencies (fP) is set to provide a comparison with the superiority of multi-frequency forcing of LGP. Three stages of pressure oscillations and NOx emissions have been found as d increases from 0.5 to 1.0: high level, transition, and low level, revealing the transition of the combustion mode. After ten generations of development, the pressure amplitude and NOx emissions are reduced by 67.1percent and 36.9percent under the optimal control law identified by LGP, respectively. The flame structure images and Rayleigh index maps indicate that the convective movement of the flame, which may be the key factor driving combustion instabilities, can be suppressed by the optimal control law. Furthermore, the proximity graph of the similarity between control laws is introduced to depict the machine learning process, with the steepest descent lines visualizing its ridgeline topology. With the evolution process, individuals are found moving closer to the top right-hand corner of the map, and two main search pathways gradually become clear", } @InProceedings{liu:2024:CEC2, author = "Yifan Liu and Ying Tan", title = "Improved Discrete Fireworks Algorithm for Large Scale Knapsack Problem", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, Resistance, Learning systems, Fireworks algorithm, Technological innovation, Evolutionary computation, Complexity theory, Optimization, Knapsack Problem, Swarm Intelligence, Large Scale Optimization", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10612146", abstract = "The Knapsack Problem (KP) is a renowned combinatorial optimisation challenge, recognised for its NP-hard complexity. This characteristic renders large-scale KP instances resistant to resolution through exact algorithms. In our research, we introduce an enhanced version of the discrete fireworks algorithm, specifically tailored to address various large-scale KP instances. This advancement involves a re-encoding of the original fireworks algorithm and its adaptation into a discrete format, thereby equipping it to effectively tackle discrete KP challenges. Furthermore, we have integrated a probability mechanism and a historical information learning mechanism into the algorithm. These innovations enable the algorithm to assimilate and use historical data, significantly augmenting its capability to explore solutions. This enhancement not only accelerates the algorithm's convergence but also improves its proficiency in identifying optimal solutions. Additionally, we have developed a dual-population strategy to optimise the balance between exploration and exploitation performances of the algorithm. Comparative experimental analyses demonstrate that our modified algorithm exhibits rapid convergence, successfully avoids entrapment in local optima, and efficiently resolves large-scale KPs within a reasonable time, outperforming other evolutionary algorithms.", notes = "also known as \cite{10612146} Is this GP? WCCI 2024", } @InProceedings{Liventsev:2021:GECCOcomp, author = "Vadim Liventsev and Aki Harma and Milan Petkovic", title = "Neurogenetic Programming Framework for Explainable Reinforcement Learning", booktitle = "Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion", year = "2021", editor = "Francisco Chicano and Alberto Tonda and Krzysztof Krawiec and Marde Helbig and Christopher W. Cleghorn and Dennis G. Wilson and Georgios Yannakakis and Luis Paquete and Gabriela Ochoa and Jaume Bacardit and Christian Gagne and Sanaz Mostaghim and Laetitia Jourdan and Oliver Schuetze and Petr Posik and Carlos Segura and Renato Tinos and Carlos Cotta and Malcolm Heywood and Mengjie Zhang and Leonardo Trujillo and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Fuyuki Ishikawa and Inmaculada Medina-Bulo and Frank Neumann and Andrew M. Sutton", pages = "329--330", address = "internet", series = "GECCO '21", month = jul # " 10-14", organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, SBSE, ANN, LSTM, GRU, Reinforcement Learning, Program Synthesis: Poster", isbn13 = "978-1-4503-8351-6", DOI = "doi:10.1145/3449726.3459537", size = "2 pages", abstract = "Automatic programming, the task of generating computer programs compliant with a specification without a human developer, is usually tackled either via genetic programming methods based on mutation and recombination of programs, or via neural language models. We propose a novel method that combines both approaches using a concept of a virtual neuro-genetic programmer, or scrum team. We demonstrate its ability to provide performant and explainable solutions for various OpenAI Gym tasks, as well as inject expert knowledge into the otherwise data-driven search for solutions.", notes = "Technical University of Eindhoven, Philips Research GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)", } @InProceedings{Liventsev:2023:GECCO, author = "Vadim Liventsev and Anastasiia Grishina and Aki Harma and Leon Moonen", title = "Fully Autonomous Programming with Large Language Models", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "1146--1155", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, SBSE, software design engineering, Computing methodologies, ANN, Model development and analysis, Search methodologies, automatic programming, large language models, program repair", isbn13 = "979-8-4007-0119-1/23/07", URL = "https://human-competitive.org/sites/default/files/grishinaentry.txt", URL = "https://human-competitive.org/sites/default/files/fullyautonomousprogrammingwithlargelanguagemodels.pdf", DOI = "doi:10.1145/3583131.3590481", size = "10 pages", abstract = "Current approaches to program synthesis with Large Language Models (LLMs) exhibit a near miss syndrome: they tend to generate programs that semantically resemble the correct answer (as measured by text similarity metrics or human evaluation), but achieve a low or even zero accuracy as measured by unit tests due to small imperfections, such as the wrong input or output format. This calls for an approach known as Synthesize, Execute, Debug (SED), whereby a draft of the solution is generated first, followed by a program repair phase addressing the failed tests. To effectively apply this approach to instruction-driven LLMs, one needs to determine which prompts perform best as instructions for LLMs, as well as strike a balance between repairing unsuccessful programs and replacing them with newly generated ones. We explore these trade-offs empirically, comparing replace-focused, repair-focused, and hybrid debug strategies, as well as different template-based and model-bas", notes = "Finalist 2023 HUMIES GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @InCollection{lo:1998:GMSASSARL, author = "Paul C. K. Lo", title = "Genetically-Evolved Mastermind Strategy: A Self-Simplifying Symbolic Approach to Reinforcement Learning", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1998", year = "1998", editor = "John R. Koza", pages = "74--83", address = "Stanford, California, 94305-3079 USA", month = "17 " # mar, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-212568-8", notes = "part of \cite{koza:1998:GAGPs}", } @InCollection{lo:1999:AGASBPP, author = "Lawrence K. Lo", title = "A Genetic Algorithm to Solve the 2-D Bin Packing Problem", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1999", year = "1999", editor = "John R. Koza", pages = "122--130", address = "Stanford, California, 94305-3079 USA", month = "15 " # mar, publisher = "Stanford Bookstore", keywords = "genetic algorithms", notes = "part of \cite{koza:1999:GAGPs}", } @InProceedings{Lo:2010:gecco, author = "Leung-Yau Lo and Tak-Ming Chan and Kin-Hong Lee and Kwong-Sak Leung", title = "Challenges rising from learning motif evaluation functions using genetic programming", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "171--178", keywords = "genetic algorithms, genetic programming, Bioinformatics, computational, systems and synthetic biology", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830483.1830515", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Motif discovery is an important Bioinformatics problem for deciphering gene regulation. Numerous sequence-based approaches have been proposed employing human specialist motif models (evaluation functions), but performance is so unsatisfactory on benchmarks that the underlying information seems to have already been exploited and have doomed. However, we have found that even a simple modified representation still achieves considerably high performance on a challenging benchmark, implying potential for sequence-based motif discovery. Thus we raise the problem of learning motif evaluation functions. We employ Genetic programming (GP) which has the potential to evolve human competitive models. We take advantage of the terminal set containing specialist-model-like components and have tried three fitness functions. Results exhibit both great challenges and potentials. No models learnt can perform universally well on the challenging benchmark, where one reason may be the data appropriateness for sequence-based motif discovery. However, when applied on different widely-tested datasets, the same models achieve comparable performance to existing approaches based on specialist models. The study calls for further novel GP to learn different levels of effective evaluation models from strict to loose ones on exploiting sequence information for motif discovery, namely quantitative functions, cardinal rankings, and learning feasibility classifications.", notes = "Also known as \cite{1830515} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @InProceedings{Lobao:2016:CEC, author = "Waldir J. A. Lobao and Douglas Mota Dias and Marco Aurelio C. Pacheco", title = "Genetic Programming and Automatic Differentiation Algorithms Applied to the Solution of Ordinary and Partial Differential Equation", booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016)", year = "2016", editor = "Yew-Soon Ong", pages = "5286--5292", address = "Vancouver", month = "24-29 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-5090-0623-6", DOI = "doi:10.1109/CEC.2016.7748362", abstract = "This paper investigates the potential of evolutionary algorithms, developed by the combination of genetic programming (GP) and automatic differentiation methods (AD), in determining analytic solutions to ordinary and partial differential equations (ODE and PDE). In turn, AD is a set of techniques based on the mechanical application of the chain rule to numerically evaluate the derivative of a function specified by a computer program. The AD method has a fundamental role in this work since it calculates the exact values of the derivatives of a function for a given set of input values while numerical differentiation methods introduce unacceptable round-off errors in the discretization process. With this purpose, and using the Matlab programming environment, we developed several algorithms (namely GPAD) and addressed problems of different kinds of differential equations. The results are promising, with exact solutions obtained for most of the addressed problems, which include equations where not even commercial systems could find a symbolic solution. These results empirically indicate that GPAD can be an efficient and robust methodology to find analytic solutions for ODE and PDE.", notes = "WCCI2016", } @InProceedings{lobo:1998:cillGA, author = "Fernando G. Lobo and Kalyanmoy Deb and David E. Goldberg and Georges R. Harik and Liwei Wang", title = "Compressed Introns in a Linkage Learning Genetic Algorithm", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "551--558", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms", notes = "SGA-98", } @InProceedings{lobo:1998:spbdGA, author = "Fernando Lobo", title = "Solving Problems of Bounded Difficulty Using Genetic Algorithms", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "134", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "genetic algorithms", size = "1 page", notes = "GP-98LB", } @InCollection{Lobos2019, author = "Claudio Sanhueza Lobos and Natalie Jane {de Vries} and Mario Inostroza-Ponta and Regina Berretta and Pablo Moscato", title = "Visualizing Products and Consumers: A Gestalt Theory Inspired Method", booktitle = "Business and Consumer Analytics: New Ideas", publisher = "Springer International Publishing", year = "2019", editor = "Pablo Moscato and Natalie Jane {de Vries}", chapter = "16", pages = "661--689", keywords = "genetic algorithms, genetic programming, Memetic algorithm, Customer Churn, Effective visualizations, Quadratic assignment problem", isbn13 = "978-3-030-06222-4", URL = "http://hdl.handle.net/1959.13/1460520", DOI = "doi:10.1007/978-3-030-06222-4_16", abstract = "Motivated by the ability that visualisations have for explaining complex relationships, we revisit an alternative and efficient algorithm for visualizing relationships between objects. QAPgrid was proposed to solve the problem of allocating objects in a grid. The algorithm uses as its mathematical model the NP-hardNP-hard Quadratic assignment problem quadratic assignment Assignment Problem. We implemented an efficient AlgorithmmemeticMemetic algorithmMemetic Algorithm for solving the layout optimization problem. The algorithm has been previously tested on a variety of datasets with good results. In this chapter, we explore the algorithm's potential for analysing social networks. In particular, we examined the collaboration network created around the artificial world of the MarveluniverseMarvel Universe comic books. We show how the algorithm can generate accurate and informative visualizations for analysing complex graphs. Furthermore, to demonstrate an alternative use of the algorithm, we analyse and visualize products (wines) and customers (telecom clients). In doing so, we show how the algorithm is suitable for the analysis of different types of objects organized as a network.", notes = "also known as \cite{DBLP:books/sp/19/LobosVIBM19}", } @PhdThesis{Sanhueza:thesis, author = "Claudio Sanhueza", title = "Scalable and Efficient Multi-Objective Optimization Algorithms for Visual Data Exploration", school = "School of Electrical Engineering and Computing, The University of Newcastle", year = "2019", address = "Australia", month = mar, keywords = "genetic algorithms, PasMoQAP, mQAPViz", URL = "https://nova.newcastle.edu.au/vital/access/services/Download/uon:34770/ATTACHMENT01", abstract_url = "https://nova.newcastle.edu.au/vital/access/services/Download/uon:34770/ATTACHMENT02", size = "210 pages", notes = " Supervisor: Prof. Pablo Moscato Co-Supervisor: Prof. Regina Berretta", } @Article{lodding:2004:queue, author = "Kenneth N. Lodding", title = "Hitchhiker's Guide to Biomorphic Software", journal = "ACM Queue", year = "2004", volume = "2", number = "4", pages = "66--75", month = jun, keywords = "genetic algorithms, genetic programming", URL = "http://queue.acm.org/detail.cfm?id=1016985", DOI = "doi:10.1145/1016978.1016985", size = "10 pages", abstract = "The natural world may be the inspiration we need for solving our computer problems. While it is certainly true that {"}the map is not the territory,{"} most visitors to a foreign country do prefer to take with them at least a guidebook to help locate themselves as they begin their explorations. That is the intent of this article. Although there will not be enough time to visit all the major tourist sites, with a little effort and using the information in the article as signposts, the intrepid explorer can easily find numerous other, interesting paths to explore.", notes = "yet another useless new name:-( cites why biology. Boids (cf eg \cite{Reynolds:1994:sab}), ants (Marco Dorigo), particle swarm (PSO). Notes scale up problem. p74 {"}evolve{"} the program. ", } @InProceedings{DBLP:conf/isda/LoeblR18, author = "Jaroslav Loebl and Viera Rozinajova", editor = "Ajith Abraham and Aswani Kumar Cherukuri and Patricia Melin and Niketa Gandhi", title = "Continuous Cartesian Genetic Programming with Particle Swarm Optimization", booktitle = "Intelligent Systems Design and Applications - 18th International Conference on Intelligent Systems Design and Applications, {ISDA} 2018, Vellore, India, December 6-8, 2018, Volume 2", series = "Advances in Intelligent Systems and Computing", volume = "941", pages = "985--995", publisher = "Springer", year = "2018", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/978-3-030-16660-1_96", DOI = "doi:10.1007/978-3-030-16660-1_96", timestamp = "Thu, 23 May 2019 12:53:30 +0200", biburl = "https://dblp.org/rec/conf/isda/LoeblR18.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{Loeckx:2009:EMC, author = "Johan Loeckx and Thomas Deman and T. McConaghy and G. G. E. Gielen", title = "A novel {EMI-immune} Current Mirror Topology obtained by Genetic Evolution", booktitle = "20th International Zurich Symposium on Electromagnetic Compatibility", year = "2009", editor = "R. Vahldieck", address = "Switzerland", month = jan # " 13", organisation = "ETH", note = "Topical Session: EMC Design, Optimization, Modeling and Simulation of Automotive Components", keywords = "genetic algorithms, genetic programming", notes = "http://www.emc-zurich.ch broken Oct 2015 http://www.emc-zurich.ch/emc09/Descriptions/Top-Para-Tue.pdf Topical Session on Tuesday, January 13, 13:00-18:00 EMC Design, Optimisation, Modelling and Simulation of Automotive Components. Organisers Thomas Steinecke Robert Weigel See also \cite{Loeckx:2009:EL}", } @Article{Loeckx:2009:EL, author = "J. Loeckx and T. Deman and T. McConaghy and G. Gielen", title = "{EMI}-immune analogue circuit generated through genetic evolution", journal = "Electronics Letters", year = "2009", volume = "45", number = "4", pages = "199--200", month = "12 " # feb, keywords = "genetic algorithms, genetic programming, EHW, MOJITO", publisher = "The Institution of Engineering and Technology", ISSN = "0013-5194", DOI = "doi:10.1049/el:20092828", size = "2 pages", abstract = "Specifications are getting higher while environmental circumstances become harsher. Electromagnetic immunity is one of the challenges that future IC designers have to face. A new methodology is presented that allows optimisation of analogue circuits towards their specification while simultaneously evolving them towards a high electromagnetic immunity. Results are illustrated on a current mirror structure, resulting in a new EMI-immune topology.", notes = "Similar to Gruau embryology \cite{Gruau93}? Also known as \cite{4784305}", } @InProceedings{Loginov:evoapps13, author = "Alexander Loginov and Malcolm I. Heywood", title = "On the Utility of Trading Criteria Based Retraining in Forex Markets", booktitle = "Applications of Evolutionary Computing, EvoApplications 2013: EvoCOMNET, EvoCOMPLEX, EvoENERGY, EvoFIN, EvoGAMES, EvoIASP, EvoINDUSTRY, EvoNUM, EvoPAR, EvoRISK, EvoROBOT, EvoSTOC", year = "2013", month = "3-5 " # apr, editor = "Anna I. Esparcia-Alcazar and Antonio Della Cioppa and Ivanoe {De Falco} and Ernesto Tarantino and Carlos Cotta and Robert Schaefer and Konrad Diwold and Kyrre Glette and Andrea Tettamanzi and Alexandros Agapitos and Paolo Burrelli and J. J. Merelo and Stefano Cagnoni and Mengjie Zhang and Neil Urquhart and Kevin Sim and Aniko Ekart and Francisco {Fernandez de Vega} and Sara Silva and Evert Haasdijk and Gusz Eiben and Anabela Simoes and Philipp Rohlfshagen", series = "LNCS", volume = "7835", publisher = "Springer Verlag", address = "Vienna", publisher_address = "Berlin", pages = "192--202", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, Coevolution, non-stationary, FX, Forex, Currency", isbn13 = "978-3-642-37191-2", DOI = "doi:10.1007/978-3-642-37192-9_20", size = "11 pages", abstract = "This research investigates the ability of genetic programming (GP) to build profitable trading strategies for the Foreign Exchange Market (FX) of three major currency pairs (EURUSD, USDCHF and EURCHF) using one hour prices from 2008 to 2011. We recognise that such environments are likely to be non-stationary. Thus, we do not require a single training partition to capture all likely future behaviours. We address this by detecting poor trading behaviours and use this to trigger retraining. In addition the task of evolving good technical indicators (TI) and the rules for deploying trading actions is explicitly separated. Thus, separate GP populations are used to coevolve TI and trading behaviours under a mutualistic symbiotic association. The results of 100 simulations demonstrate that an adaptive retraining algorithm significantly outperforms a single-strategy approach (population evolved once) and generates profitable solutions with a high probability.", notes = " EvoApplications2013 held in conjunction with EuroGP2013, EvoCOP2013, EvoBio'2013 and EvoMusArt2013", } @InProceedings{Loginov:2013:GECCO, author = "Alexander Loginov and Malcolm I. Heywood", title = "On the impact of streaming interface heuristics on {GP} trading agents: an {FX} benchmarking study", booktitle = "GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference", year = "2013", editor = "Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi", isbn13 = "978-1-4503-1963-8", pages = "1341--1348", keywords = "genetic algorithms, genetic programming", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2463372.2463522", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Most research into frameworks for evolving trading agents emphasise aspects associated with the evolution of technical indicators and decision trees / rules. One of the factors that drives the development of such frameworks is the non-stationary, streaming nature of the task. However, it is the heuristics used to interface the evolutionary framework to the streaming data which potentially have most impact on the quality of the resulting trading agents. We demonstrate that including a validation partition has a significant impact on determining the overall success of the trading agents. Moreover, rather than conduct evolution on a continuous basis, only retraining when changes in trading quality are detected also yields significant advantages. Neither of these heuristics are widely recognised by research in evolving trading agent frameworks, although both are relatively easy to add to current frameworks. Benchmarking over a 3 year period of the EURUSD foreign exchange supports these findings.", notes = "Also known as \cite{2463522} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)", } @InProceedings{Loginov:2015:CEC, author = "Alexander Loginov and Garnett Wilson and Malcolm Heywood", title = "Better Trade Exits for Foreign Exchange Currency Trading using {FXGP}", booktitle = "Proceedings of 2015 IEEE Congress on Evolutionary Computation (CEC 2015)", year = "2015", editor = "Yadahiko Murata", pages = "2510--2517", address = "Sendai, Japan", month = "25-28 " # may, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Sociology, Statistics, Resistance, Training, Immune system, Market research, Decision trees", isbn13 = "978-1-4799-7491-7", URL = "https://web.cs.dal.ca/~mheywood/OpenAccess/open-loginov15.pdf", DOI = "doi:10.1109/CEC.2015.7257197", size = "8 pages", abstract = "Retracement is the tendency of markets to move between upper resistance and lower support price levels. Human traders frequently make use of visual tools to help identify these resistance and support levels so that they can by used in their trading decisions. These decision can be put into trading strategies composed of rules designed to mitigate losses after a trade is started, often called stop loss orders, or to take profit at a near optimal time, often called take profit orders. However, identifying such resistance and support levels is notoriously difficult given market volatility. Indeed, the levels need recalculating on a continuous basis, and only hold to an approximate degree. In this work we describe an approach for evolving buy-stay-sell currency trading rules using genetic programming. These rules are explicitly linked to technical indicators that incorporate features characterizing retracement. Benchmarking is then performed using the most recent three years of data from the EURUSD foreign exchange market with three different methods of identifying retracement based on moving average, pivot points and Fibonacci ratios. Investment strategies employing Fibonacci ratios and found to provide superior performance among the strategies examined.", notes = "1010 hrs 15174 CEC2015", } @InProceedings{Loginov:2016:IJCNN, author = "Alexander Loginov and Malcolm I. Heywood and Garnett Wilson", title = "Benchmarking a Coevolutionary Streaming Classifier under the Individual Household Electric Power Consumption Dataset", booktitle = "2016 International Joint Conference on Neural Networks (IJCNN)", year = "2016", pages = "2834--2841", address = "Vancouver", month = "24-29 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IJCNN.2016.7727557", abstract = "The application of genetic programming (GP) to streaming data analysis appears, on the face of it, to be a less than obvious choice. If nothing else, the (perceived) computational cost of model building under GP would preclude its application to tasks with non-stationary properties. Conversely, there is a rich history of applying GP to various tasks associated with trading agent design for currency and stock markets. In this work, we investigate the utility of a coevolutionary framework originally proposed for trading agent design to the related streaming data task of predicting individual household electric power consumption. In addition, we address several benchmarking issues, such as effective preprocessing of stream data using a candlestick representation originally developed for financial market analysis, and quantification of performance using a novel area under the curve style metric for streaming data. The computational cost of evolving GP solutions is demonstrated to be suitable for real-time operation under this task and shown to provide classification performance competitive with current established methods for streaming data classification. Finally, we note that the individual household electric power consumption dataset is more flexible than the more widely used electricity utility prediction dataset, because it supports benchmarking at multiple temporal time scales.", notes = "WCCI2016", } @Article{LOGINOV:2020:TNAJEF, author = "Alexander Loginov and Malcolm Heywood", title = "On the different impacts of fixed versus floating bid-ask spreads on an automated intraday stock trading", journal = "The North American Journal of Economics and Finance", volume = "54", pages = "101247", year = "2020", ISSN = "1062-9408", DOI = "doi:10.1016/j.najef.2020.101247", URL = "http://www.sciencedirect.com/science/article/pii/S1062940820301443", keywords = "genetic algorithms, genetic programming, Stock, Hidden cost, NASDAQ, Bid-ask spread, Intraday", abstract = "Trading or transaction costs are one of the most important attributes of any trading system and can be divided into two major groups: explicit (visible) and implicit (hidden). In this paper, we investigate the impact of the bid-ask spreads, a form of hidden cost, on the results of backtesting (and, therefore, the potential impact on real-time trading) of an automated trading system based on genetic programming. We concentrate on the nature (fixed or floating) of bid-ask spreads (hereafter `spread') and demonstrate that the effectiveness of an automated trading system more significantly degrades in the case of floating spreads compared to fixed spreads. We investigate four fixed spreads (one, two, five and ten pips) and a floating spread with a median value of two pips and demonstrate that the floating spread with a mean value of 0.02 USD results in significantly worse performance than a fixed spread of 0.1 USD. `Floating spreads' in this paper is a term used for market-determined continuously changing bid-ask spreads", } @PhdThesis{Loginov:thesis, author = "Alexander Loginov", title = "On increasing the scope of Genetic Programming trading agents", school = "Dalhousie University", year = "2020", address = "Halifax, Nova Scotia Canada", month = jun, keywords = "genetic algorithms, genetic programming, FXGP, automated trading", URL = "https://web.cs.dal.ca/~mheywood/Thesis/PhD.html", URL = "http://hdl.handle.net/10222/79491", URL = "https://dalspace.library.dal.ca/bitstream/handle/10222/79491/Loginov-Alexander-PhD-CS-June-2020.pdf", size = "206 pages", abstract = "This research investigates the potential for widening the scope of Genetic Programming (GP) trading agents beyond constructing decision trees for buy-hold-sell decisions. First, both technical indicators (temporal feature construction) and decision trees (action selection) are co-evolved under the machine learning paradigm of GP with the benefit of setting Stop-Loss and Take-Profit orders using retracement levels demonstrated. GP trading agents are then used to design trading portfolios under a frequent intra-day trading scenario. Such a scenario implies that transaction costs have a more significant impact on profitability and investment decisions can be revised frequently. Furthermore, existing long term portfolio selection algorithms cannot guarantee optimal asset selection for intraday trading, thus motivating a different approach to asset selection. The proposed algorithm identifies a subset of assets to trade in the next day and generates buy-hold-sell decisions for each selected asset in real-time. A benchmarking comparison of ranking heuristics is conducted with the popular Kelly Criterion, and a strong preference for the proposed Moving Sharpe ratio demonstrated. Moreover, the evolved portfolios perform significantly better than any of the comparator methods (buy-and-hold strategy, investment in the full set of 86 stocks, portfolios built from random stock selection and Kelly Criterion). Transaction costs (explicit and implicit or hidden) are important, yet often overlooked, attributes of any trading system. The impact of hidden costs (bid-ask spread) is investigated. The nature of bid-ask spreads (fixed or floating) is demonstrated to be important for the effectiveness of the automated trading system and a floating spread is shown to have a more significant impact than a fixed spread. Finally, the proposed GP framework was assessed on non-financial streaming data. This is significant because it provides the basis for comparing the proposed GP framework to alternative machine learning methods specifically designed to operate under a prequential model of evaluation. The GP framework is shown to provide classification performance competitive with currently established methods for streaming classification, and thus its general effectiveness.", notes = "'A Fibonacci Retracement is a popular tool among technical traders' supervisor: Malcolm Heywood", } @Article{Loginov:GPEM, author = "Alexander Loginov and Malcolm Heywood and Garnett Wilson", title = "Stock selection heuristics for performing frequent intraday trading with genetic programming", journal = "Genetic Programming and Evolvable Machines", year = "2021", volume = "22", number = "1", pages = "35--72", month = mar, keywords = "genetic algorithms, genetic programming, Stock, Trading, Intraday, Portfolio", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-020-09390-5", abstract = "Intraday trading attempts to obtain a profit from the microstructure implicit in price data. Intraday trading implies many more transactions per stock compared to long term buy-and-hold strategies. As a consequence, transaction costs will have a more significant impact on the profitability. Furthermore, the application of existing long term portfolio selection algorithms for intraday trading cannot guarantee optimal stock selection. This implies that intraday trading strategies may require a different approach to stock selection for daily portfolios. In this work, we assume a symbiotic genetic programming framework that simultaneously coevolves the decision trees and technical indicators to generate trading signals. We generalize this approach to identify specific stocks for intraday trading using stock ranking heuristics: Moving Sharpe ratio and a Moving Average of Daily Returns. Specifically, the trading scenario adopted by this work assumes that a bag of available stocks exist. Our agent then has to both identify which subset of stocks to trade in the next trading day, and the specific buy-hold-sell decisions for each selected stock during real-time trading for the duration of the intraday period. A benchmarking comparison of the proposed ranking heuristics with stock selection performed using the well known Kelly Criterion is conducted and a strong preference for the proposed Moving Sharpe ratio demonstrated. Moreover, portfolios ranked by both the Moving Sharpe ratio and a Moving Average of Daily Returns perform significantly better than any of the comparator methods (buy-and-hold strategy, investment in the full set of 86 stocks, portfolios built from random stock selection and Kelly Criterion).", notes = "Faculty of Computer Science, Dalhousie University, 6050 University Avenue, Halifax, NS, Canada", } @Proceedings{lohn:2000:eh, title = "The Second NASA/DoD Workshop on Evolvable Hardware", year = "2000", editor = "Jason Lohn and Adrian Stoica and Didier Keymeulen", address = "Palo Alto, California", publisher_address = "1730 Massachusetts Avenue, N.W., Washington, DC, 20036-1992, USA", month = "13-15 " # jul, organisation = "Jet Propulsion Laboratory, California Institute of Technology", publisher = "IEEE Computer Society", keywords = "genetic algorithms, evolvable hardware", ISBN = "0-7695-0762-X", URL = "http://ic-www.arc.nasa.gov/ic/eh2000/", notes = "http://csdl.computer.org/comp/proceedings/eh/2000/0762/00/0762toc.htm", } @InProceedings{lohn:ess, title = "Evolvable Systems for Space Applications", author = "Jason Lohn and James Crawford and Al Globus and Gregory Hornby and William Kraus and Gregory Larchev and Anna Pryor and Deepak Srivastava", booktitle = "International Conference on Space Mission Challenges for Information Technology (SMC-IT)", address = "Pasadena, CA, USA", month = jul, year = "2003", URL = "http://people.nas.nasa.gov/~globus/home.html", keywords = "genetic algorithms", } @InCollection{lohn:2004:GPTP, author = "Jason Lohn and Gregory Hornby and Derek Linden", title = "An Evolved Antenna for Deployment on Nasa's Space Technology 5 Mission", booktitle = "Genetic Programming Theory and Practice {II}", year = "2004", editor = "Una-May O'Reilly and Tina Yu and Rick L. Riolo and Bill Worzel", chapter = "18", pages = "301--315", address = "Ann Arbor", month = "13-15 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, evolvable hardware, design, computational design, antenna, wire antenna, spacecraft, evolutionary computation", ISBN = "0-387-23253-2", DOI = "doi:10.1007/0-387-23254-0_18", abstract = "We present an evolved X-band antenna design and flight prototype currently on schedule to be deployed on NASA's Space Technology 5 (ST5) spacecraft. Current methods of designing and optimising antennas by hand are time and labour intensive, limit complexity, and require significant expertise and experience. Evolutionary design techniques can overcome these limitations by searching the design space and automatically finding effective solutions that would ordinarily not be found. The ST5 antenna was evolved to meet a challenging set of mission requirements, most notably the combination of wide beam width for a circularly-polarised wave and wide bandwidth. Two evolutionary algorithms were used: one used a genetic algorithm style representation that did not allow branching in the antenna arms; the second used a genetic programming style tree-structured representation that allowed branching in the antenna arms. The highest performance antennas from both algorithms were fabricated and tested, and both yielded very similar performance. Both antennas were comparable in performance to a hand-designed antenna produced by the antenna contractor for the mission, and so we consider them examples of human-competitive performance by evolutionary algorithms. As of this writing, one of our evolved antenna prototypes is undergoing flight qualification testing. If successful, the resulting antenna would represent the first evolved hardware in space, and the first deployed evolved antenna.", affiliation = "NASA Ames Research Center USA", notes = "part of \cite{oreilly:2004:GPTP2}", } @InCollection{lohn:2005:GPTP, author = "Jason D. Lohn and Gregory S. Hornby and Derek S. Linden", title = "Rapid Re-evolution of an {X}-Band Antenna for {NASA's} Space Technology 5 Mission", booktitle = "Genetic Programming Theory and Practice {III}", year = "2005", editor = "Tina Yu and Rick L. Riolo and Bill Worzel", volume = "9", series = "Genetic Programming", chapter = "5", pages = "65--78", address = "Ann Arbor", month = "12-14 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, design, computational design, evolutionary design, antenna, spacecraft", ISBN = "0-387-28110-X", DOI = "doi:10.1007/0-387-28111-8_5", size = "14 pages", abstract = "One of the challenges in engineering design is adapting a set of created designs to a change in requirements. Previously we presented two four-arm, symmetric, evolved antennas for NASA's Space Technology 5 mission. However, the mission's orbital vehicle was changed, putting it into a much lower earth orbit, changing the specifications for the mission. With minimal changes to our evolutionary system, mostly in the fitness function, we were able to evolve antennas for the new mission requirements and, within one month of this change, two new antennas were designed and prototyped. Both antennas were tested and both had acceptable performance compared with the new specifications. This rapid response shows that evolutionary design processes are able to accommodate new requirements quickly and with minimal human effort.", notes = "part of \cite{yu:2005:GPTP} Published Jan 2006 after the workshop", } @Article{Lohn:2006:iCIm, author = "Jason D. Lohn and Gregory S. Hornby", title = "Evolvable Hardware Using Evolutionary Computation to Design and Optimize Hardware Systems", journal = "IEEE Computational Intelligence Magazine", year = "2006", volume = "1", number = "1", pages = "19--27", month = feb, keywords = "genetic algorithms, genetic programming, EHW", ISSN = "1556-603X", DOI = "doi:10.1109/MCI.2006.1597058", abstract = "Evolvable hardware lies at the intersection of evolutionary computation and physical design. Through the use of evolutionary computation methods, the field seeks to develop a variety of technologies that enable automatic design, adaptation, and reconfiguration of electrical and mechanical hardware systems in ways that outperform conventional techniques. This article surveys evolvable hardware with emphasis on some of the latest developments, many of which deliver performance exceeding traditional methods. As such, the field of evolvable hardware is just now starting to emerge from the research laboratory and into mainstream hardware applications.", notes = "Mention of several applications of GP to EHW", } @Article{DBLP:journals/aiedam/LohnHL08, author = "Jason D. Lohn and Gregory Hornby and Derek S. Linden", title = "Human-competitive evolved antennas", journal = "Artificial Intelligence for Engineering Design, Analysis and Manufacturing", volume = "22", number = "3", year = "2008", pages = "235--247", DOI = "doi:10.1017/S0890060408000164", bibsource = "DBLP, http://dblp.uni-trier.de", keywords = "genetic algorithms, genetic programming, Antenna, Computational Design Design, Evolutionary Computation, Spacecraft, Wire Antenna", size = "13 pages", abstract = "We present a case study showing a human-competitive design of an evolved antenna that was deployed on a NASA spacecraft in 2006. We were fortunate to develop our antennas in parallel with another group using traditional design methodologies. This allowed us to demonstrate that our techniques were human-competitive because our automatically designed antenna could be directly compared to a human-designed antenna. The antennas described below were evolved to meet a challenging set of mission requirements, most notably the combination of wide beamwidth for a circularly polarized wave and wide bandwidth. Two evolutionary algorithms were used in the development process: one used a genetic algorithm style representation that did not allow branching in the antenna arms; the second used a genetic programming style tree-structured representation that allowed branching in the antenna arms. The highest performance antennas from both algorithms were fabricated and tested, and both yielded very similar performance. Both antennas were comparable in performance to a hand-designed antenna produced by the antenna contractor for the mission, and so we consider them examples of human-competitive performance by evolutionary algorithms. Our design was approved for flight, and three copies of it were successfully flown on NASA's Space Technology 5 mission between March 22 and June 30, 2006. These evolved antennas represent the first evolved hardware in space and the first evolved antennas to be deployed.", notes = "* better coverage * significantly higher efficiency * fewer parts: lower cost, increased reliability, easier manufacture * naturally matched to 50 Ohms * faster design time * rapid redesign accomplished at a small cost and in a short time frame", } @Article{Lohn:2011:GPEM, author = "Jason D. Lohn and Jonathan M. Becker and Derek S. Linden", title = "An evolved anti-jamming adaptive beamforming network", journal = "Genetic Programming and Evolvable Machines", year = "2011", volume = "12", number = "3", pages = "217--234", month = sep, note = "Special Issue Title: Evolvable Hardware Challenges", keywords = "genetic algorithms, evolvable hardware, Antenna, Beamforming, Anti-jamming", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-011-9134-5", size = "18 pages", abstract = "Interference in wireless networks is undesirable, whether it is due to unintentional or malicious causes. Adaptive beamforming is a spatial filtering technique that can prevent jammers from disrupting wireless networks. This paper presents an evolvable hardware (EH) application in which an evolutionary algorithm (EA) is used to configure an adaptive beamformer to achieve two goals: (1) steering nulls towards jamming signals and (2) directing gain in the direction of the desired signal. This is the first demonstration of an EA-configured adaptive beamformer to counter a jamming system. Simulation results show that the EA is able to thwart up to three jamming signals. The results suggest that EH is a promising approach towards wireless network security.", } @InProceedings{Lohpetch:2009:NaBIC, author = "Dome Lohpetch and David Corne", title = "Discovering effective technical trading rules with genetic programming: towards robustly outperforming buy-and-hold", booktitle = "World Congress on Nature Biologically Inspired Computing, NaBIC 2009", year = "2009", month = dec, pages = "439--444", keywords = "genetic algorithms, genetic programming, effective trading rules, financial applications, fitness function, profitable rules, research tool, stocks, technical trading rules, financial management, profitability, stock markets", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.457.845", URL = "https://www.macs.hw.ac.uk/~dwcorne/lohpetchnabic.pdf", DOI = "doi:10.1109/NABIC.2009.5393324", size = "6 pages", abstract = "Genetic programming is now a common research tool in financial applications. One classic line of exploration is their use to find effective trading rules for individual stocks or for groups of stocks (such as an index). The classic work in this area (Allen and Karjaleinen, 1999) found profitable rules, but which did not outperform a straightforward buy and hold strategy. Several later works report similar outcomes, while a small number of works achieve out-performance of buy and hold, but prove difficult to replicate. We focus here on indicating clearly how the performance in one such study (Becker and Seshadri, 2003) was replicated, and we carry out additional investigations which point towards guidelines for generating results that robustly outperform buy-and-hold. These guidelines relate to strategies for organizing the training dataset, and aspects of the fitness function.", notes = "Also known as \cite{5393324}", } @InProceedings{Lohpetch:2010:EvoFIN, author = "Dome Lohpetch and David Corne", title = "Outperforming Buy-and-Hold with Evolved Technical Trading Rules: Daily, Weekly and Monthly Trading", booktitle = "EvoFIN", year = "2010", editor = "Cecilia {Di Chio} and Anthony Brabazon and Gianni A. {Di Caro} and Marc Ebner and Muddassar Farooq and Andreas Fink and Jorn Grahl and Gary Greenfield and Penousal Machado and Michael O'Neill and Ernesto Tarantino and Neil Urquhart", volume = "6025", series = "LNCS", pages = "171--181", address = "Istanbul", month = "7-9 " # apr, organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-12241-5", DOI = "doi:10.1007/978-3-642-12242-2_18", abstract = "Genetic programming (GP) is increasingly popular as a research tool for applications in finance and economics. One thread in this area is the use of GP to discover effective technical trading rules. In a seminal article, Allen & Karjalainen (1999) used GP to find rules that were profitable, but were nevertheless outperformed by the simple buy and hold trading strategy. Many succeeding attempts have reported similar findings. There are a small handful of cases in which such work has managed to find rules that outperform buy-and-hold, but these have tended to be difficult to replicate. Recently, however, Lohpetch & Corne (2009) investigated work by Becker & Seshadri (2003), which showed out performance of buy-and-hold. In turn, Becker & Seshadri's work had made several modifications to Allen & Karjalainen's work, including the adoption of monthly rather than daily trading. Lohpetch et al (2009) provided a replicable account of this, and also showed how further modifications enabled fairly reliable out performance of buy-and-hold. It remained unclear, however, whether adoption of monthly trading is necessary to achieve robust out performance of buy-and-hold. Here we investigate and compare each of daily, weekly and monthly trading; we find that outperformance of buy-and-hold can be achieved even for daily trading, but as we move from monthly to daily trading the performance of evolved rules becomes increasingly dependent on prevailing market conditions.", notes = "EvoFIN'2010 held in conjunction with EuroGP'2010 EvoCOP2010 EvoBIO2010", } @InProceedings{Lohpetch:2011:MAfFTMOS, title = "Multiobjective Algorithms for Financial Trading Multiobjective Out-trades Single-Objective", author = "Dome Lohpetch and David Corne", pages = "192--199", booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary Computation", year = "2011", editor = "Alice E. Smith", month = "5-8 " # jun, address = "New Orleans, USA", organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, buy and hold strategy, economics, finance, financial trading, frequent trading decision, infrequent trading strategy, multiobjective algorithm, multiobjective out-trades single-objective, multiobjective strategy, financial management", DOI = "doi:10.1109/CEC.2011.5949618", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.477.3567", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.477.3567", URL = "http://www.macs.hw.ac.uk/~dwcorne/dldccec11.pdf", size = "8 pages", abstract = "Genetic programming (GP) is increasingly investigated in finance and economics. One area of study is its use to discover effective rules for technical trading in the context of a portfolio of equities (or an index). Early work used GP to find rules that were profitable, but were outperformed by the simple buy and hold strategy. Attempts since then report similar findings, except a handful of cases where GP has been found to outperform BH. Recent work has clarified that robust out performance of BH depends on, mainly, the adoption of a relatively infrequent trading strategy (e.g. monthly), as well as a range of other factors. Here we add a comprehensive study of multiobjective approaches to this investigation, and find that multiobjective strategies provide even more robustness in outperforming BH, even in the context of more frequent (e.g. weekly) trading decisions.", notes = "CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.", } @PhdThesis{Lohpetch:thesis, author = "Dome Lohpetch", title = "Evolutionary algorithms for financial trading", school = "Mathematical and Computer Sciences, Heriot-Watt University", year = "2011", address = "UK", month = nov, keywords = "genetic algorithms, genetic programming, grammatical evolution", URL = "http://www.ros.hw.ac.uk/bitstream/handle/10399/2510/LohpetchD_1111_macs.pdf", URL = "http://hdl.handle.net/10399/2510", size = "273 pages", abstract = "Genetic programming (GP) is increasingly popular as a research tool for applications in finance and economics. One thread in this area is the use of GP to discover effective technical trading rules. In a seminal article, Allen & Karjalainen (1999) used GP to find rules that were profitable, but were nevertheless outperformed by the simple buy and hold trading strategy. Many succeeding attempts have reported similar findings. This represents a clear example of a significant open issue in the field of GP, namely, generalization in GP [78]. The issue of generalisation is that GP solutions may not be general enough, resulting in poor performance on unseen data. There are a small handful of cases in which such work has managed to find rules that outperform buy and hold, but these have tended to be difficult to replicate. Among previous studies, work by Becker & Seshadri (2003) was the most promising one, which showed outperformance of buy-and-hold. In turn, Becker & Seshadri's work had made several modifications to Allen & Karjalainen's work, including the adoption of monthly rather than daily trading. This thesis provides a replicable account of Becker & Seshadri's study, and also shows how further modifications enabled fairly reliable outperformance of buy-and-hold, including the use of a train/test/validate methodology [41] to evolve trading rules with good properties of generalization, and the use of a dynamic form of GP [109] to improve the performance of the algorithm in dynamic environments like financial markets. In addition, we investigate and compare each of daily, weekly and monthly trading; we find that outperformance of buy-and-hold can be achieved even for daily trading, but as we move from monthly to daily trading the performance of evolved rules becomes increasingly dependent on prevailing market conditions. This has clarified that robust outperformance of B&H depends on, mainly, the adoption of a relatively infrequent trading strategy (e.g. monthly), as well as a range of factors that amount to sound engineering of the GP grammar and the validation strategy. Moreover, v we also add a comprehensive study of multiobjective approaches to this investigation with assumption from that, and find that multiobjective strategies provide even more robustness in outperforming B&H, even in the context of more frequent (e.g. weekly) trading decisions. Last, inspired by a number of beneficial aspects of grammatical evolution (GE) and reports on the successful performance of various kinds of its applications, we introduce new approach for (GE) with a new suite of operators resulting in an improvement on GE search compared with standard GE. An empirical test of this new GE approach on various kind of test problems, including financial trading, is provided in this thesis as well.", notes = "Supervisor David Wolfe Corne", } @Article{lohr:2002:PCM, author = "Steve Lohr", title = "The Programming Gene", journal = "PC Magazine", year = "2002", month = "3 " # sep, keywords = "genetic algorithms, genetic programming", URL = "http://www.pcmag.com/article2/0,4149,429437,00.asp", abstract = "As part of a series on future technologies, surveys genetic programming and related research.", notes = "Oct 2016 pcmag.com increasingly spam", } @InProceedings{Armani_2013, author = "H. Lohse-Busch and C. Huehne and D. Liu and V. V. Toropov and U. Armani", title = "Parametric Optimization of a Lattice Aircraft Fuselage Barrel Using Metamodels Built with Genetic Programming", booktitle = "Proceedings of the Fourteenth International Conference on Civil, Structural and Environmental Engineering Computing", year = "2013", editor = "B. H. V. Topping and P. Ivanyi", pages = "Paper 230", address = "Cagliari, Italy", publisher_address = "Stirlingshire, UK", publisher = "Civil-Comp Press", keywords = "genetic algorithms, genetic programming, composite structure, anisogrid design, finite element simulation, metamodel", URL = "http://www.ctresources.info/ccp/paper.html?id=7563", DOI = "doi:10.4203/ccp.102.230", abstract = "In the EU FP7 collaborative research programme ALaSCA (Advanced Lattice Structures for Composite Airframes), the novel design of an anisogrid composite fuselage section has been optimized using topology optimization with respect to weight and structural performance. According to the concept of an extended uniform LATIN hypercube design of numerical experiments (DOE), a 101-point DOE has been developed. Each data point represents a set of the geometric fuselage barrel parameters, which are simulated using finite element (FE) method. Using these training data sets, the global metamodels have been built as explicit expressions of the design parameters using genetic programming (GP). This was followed by the parametric optimization of the fuselage barrel by genetic algorithm (GA) to obtain the best design configuration in terms of weight savings subject to stability, strength and strain requirements. The optimal solution has been verified using the finite element simulation of the lattice fuselage barrel and the true structural responses have been compared to those provided by the metamodels. It is concluded that the use of the global metamodel-based approach has enabled the solution of this optimization problem with sufficient accuracy as well as provided the designers with a wealth of information on the structural behaviour of the novel anisogrid design of a composite fuselage.", } @Article{Loiacono_2012_sigevolution, author = "Daniele Loiacono", title = "GECCO-2013 Competitions", journal = "SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation", year = "2012", volume = "6", number = "2", pages = "27--28", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Robocode", ISSN = "1931-8499", URL = "http://www.sigevolution.org/issues/pdf/SIGEVOlution0602.pdf", DOI = "doi:10.1145/2597453.2597456", size = "1.5 pages", notes = "18 Feb 2014 EvoRobocode Competition: The winning robot, SCALPbot, was developed by Robin Harper using Grammatical Evolution together with a spatial co-evolution system.", } @Article{Loiacono:2014:GPEM, author = "Daniele Loiacono", title = "Gene I. Sher: Handbook of neuroevolution through {Erlang}", journal = "Genetic Programming and Evolvable Machines", year = "2014", volume = "15", number = "1", pages = "109--110", month = mar, note = "Book Review", keywords = "genetic algorithms, genetic programming, neuroevolution", ISSN = "1389-2576", URL = "https://rdcu.be/dR8Ck", DOI = "doi:10.1007/s10710-013-9197-6", size = "2 pages", } @Article{Loiacono:2014:GPEMa, author = "Daniele Loiacono and Moshe Sipper", title = "Special issue on GECCO competitions", journal = "Genetic Programming and Evolvable Machines", year = "2014", volume = "15", number = "4", pages = "375--377", month = dec, keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-014-9226-0", size = "3 pages", } @InProceedings{loizides:2001:wsc6, author = "A. Loizides and M. Slater and W. B. Langdon", title = "Measuring Facial Emotional Expressions Using Genetic Programming", booktitle = "Soft Computing and Industry Recent Applications", year = "2001", editor = "Rajkumar Roy and Mario K{\"o}ppen and Seppo Ovaska and Takeshi Furuhashi and Frank Hoffmann", pages = "545--554", month = "10--24 " # sep, publisher = "Springer-Verlag", note = "Published 2002", keywords = "genetic algorithms, genetic programming, data visualisation, symbolic regression", ISBN = "1-85233-539-4", URL = "http://www.cs.ucl.ac.uk/staff/a.loizides/wsc6.pdf", URL = "http://citeseer.ist.psu.edu/474911.html", URL = "https://link.springer.com/book/10.1007/978-1-4471-0123-9", URL = "http://www.amazon.co.uk/Soft-Computing-Industry-Recent-Applications/dp/1852335394", size = "5 pages", abstract = "Genetic Programming techniques can be used to produce regression equations that quantify emotional expressions on a facial model. The formulae give emotional scores based on the position of 25 automatically generated 'landmarks' on the face. The method shown here is an integrated part of a system that maps multidimensional data sets to naturalistic visual structures such as a face", notes = "WSC6 March 2020 Hardcover out of print, available as softcover. ", } @Article{Lombardo:2023:EPJ, author = "Ivano Lombardo and Daniele Dell'Aquila and Brunilde Gnoffo and Luigi Redigolo and Francesco Porto and Marco Russo", title = "Universal Models for Heavy-Ion Fusion Cross Section Above-Barrier", journal = "EPJ Web of Conferences", year = "2023", volume = "290", pages = "Article Number: 02017", keywords = "genetic algorithms, genetic programming, BP", ISSN = "2100-014X", URL = "https://www.epj-conferences.org/articles/epjconf/pdf/2023/16/epjconf_eunpc2023_02017.pdf", DOI = "doi:10.1051/epjconf/202329002017", size = "4 pages", abstract = "The paper discusses a recent re-investigation of a large body of heavy-ion fusion cross section data with the aim of deriving a simple phenomenological model able to describe data from the Coulomb barrier up to the onset of nuclear multifragmentation. To this end, we adopted two complementary approaches: a first universal phenomenological model was derived exploiting a novel artificial intelligence tool for the formal modeling of large datasets. This tool is capable of advanced feature selection and is ideal to drive the discovery process even using traditional methods. A second phenomenological model was derived using a sum-of-difference approach and achieved an unprecedented accuracy in describing above-barrier fusion excitation functions data. Future perspectives and opportunities arising from the present models are also discussed in the text.", notes = "Section P2 Nuclear Structure, Spectroscopy and Dynamics", } @InProceedings{Lombrana-Gonzalez:2007:MAEB, author = "Daniel {Lombrana Gonzalez} and Francisco {Fernandez de Vega}", title = "Estudio experimental del tamano de los individuos en Programacion Genetica", booktitle = "Actas del V Congreso Espa{\~n}ol sobre Metaheur\'{i}sticas, Algoritmos Evolutivos y Bioinspirados ({MAEB}'07)", editor = "Francisco Almeida Rodriguez and Maria Belen Melian Batista and Jose Andres Moreno Perez and Jose Marcos Moreno Vega", publisher = "La Laguna", month = "Febrero", year = "2007", pages = "835--842", address = "Tenerife, Spain", publisher_address = "Tenerife, Spain", keywords = "genetic algorithms, genetic programming", isbn13 = "978-84-690-3470-5", URL = "https://dialnet.unirioja.es/servlet/articulo?codigo=4148370", notes = "MAEB'07 in Spanish", } @InProceedings{Londhe:2008:OCEANS, author = "Shreenivas N. Londhe", title = "Development Of Wave Buoy Network Using Soft Computing Techniques", booktitle = "OCEANS 2008 - MTS/IEEE Kobe Techno-Ocean", year = "2008", month = "8-11 " # apr, pages = "1--8", address = "Kobe, Japan", keywords = "genetic algorithms, genetic programming, AD 2002 to 2004, Artificial Neural Networks, Australia, Canada, Germany, Gulf of Mexico, India, UK, USA, buoy programs, ocean wave buoys network development, ocean wave data measurements, soft computing techniques, stochastic techniques, geophysics computing, neural nets, ocean waves, oceanographic techniques, stochastic processes", DOI = "doi:10.1109/OCEANSKOBE.2008.4530913", abstract = "Wave buoys are perhaps the only reliable source measuring waves continuously for years. This is perhaps the most vital reason for establishment of data buoy programs by various countries like USA (NDBC), Australia, Canada, UK, Germany, India (NDBP) etc. The wave data measurements not only provide real time wave information for Coastal and Ocean related activities but also form wave data base useful for predicting future events using statistical or stochastic techniques. However some times these wave buoys stop functioning either due to malfunctioning instruments or maintenance-related reasons resulting into loss of data. This paper presents use of soft computing techniques like Artificial Neural Networks (ANN) and Genetic Programming (GP) to retrieve this lost data by forming a network of wave buoys in a region. For developing the buoy network common data of hourly significant wave heights at six buoys in the Gulf of Mexico namely 42001, 42003, 42007, 42036, 42039 and 42040 for the years 2002 and 2004 is used. A separate network for each buoy is developed as the 'target buoy' with other 5 buoys as 'input buoys' which can be operated to retrieve lost data at a location. The testing results of both approaches when compared showed superiority of Genetic Programming over Artificial Neural Network as evident by higher correlation coefficient between observed and predicted wave heights in all cases. The wave height plots also pointed out that GP estimates wave heights in extreme events (peaks) more accurately than ANN.", notes = "Also known as \cite{4530913}", } @Article{Londhe20081080, author = "S. N. Londhe", title = "Soft computing approach for real-time estimation of missing wave heights", journal = "Ocean Engineering", year = "2008", volume = "35", number = "11-12", pages = "1080--1089", month = aug, keywords = "genetic algorithms, genetic programming, Water waves, Buoy systems, Soft computing, Artificial Neural Network, Missing data, significant wave heights, SWHs", ISSN = "0029-8018", broken = "http://www.sciencedirect.com/science/article/B6V4F-4SK633V-1/2/22702929635b97a45da2f5fbba866111", DOI = "doi:10.1016/j.oceaneng.2008.05.003", size = "10 pages", abstract = "This paper presents soft computing approach for estimation of missing wave heights at a particular location on a real-time basis using wave heights at other locations. Six such buoy networks are developed in Eastern Gulf of Mexico using soft computing techniques of Artificial Neural Networks (ANN) and Genetic Programming (GP). Wave heights at five stations are used to estimate wave height at the sixth station. Though ANN is now an established tool in time series analysis, use of GP in the field of time series forecasting/analysis particularly in the area of Ocean Engineering is relatively new and needs to be explored further. Both ANN and GP approach perform well in terms of accuracy of estimation as evident from values of various statistical parameters employed. The GP models work better in case of extreme events. Results of both approaches are also compared with the performance of large-scale continuous wave modeling/forecasting system WAVEWATCH III. The models are also applied on real time basis for 3 months in the year 2007. A software is developed using evolved GP codes (C++) as back end with Visual Basic as the Front End tool for real-time application of wave estimation model.", notes = "See also \cite{Alavi20101239} Department of Civil Engineering, Vishwakarma Institute of Information Technology, Survey No. 2/3/4, Kondhwa (Bk), Pune 411048, Maharashtra, India", } @Article{Londhe:2010:HSJ, author = "Shreenivas Londhe and Shrikant Charhate", title = "Comparison of data-driven modelling techniques for river flow forecasting", journal = "Hydrological Sciences Journal", year = "2010", volume = "55", number = "7", pages = "1163--1174", keywords = "genetic algorithms, genetic programming, streamflow, data-driven modelling, artificial neural networks, genetic programming, M5 model trees", ISSN = "02626667", DOI = "doi:10.1080/02626667.2010.512867", size = "12 pages", abstract = "Accurate forecasting of streamflow is essential for the efficient operation of water resources systems. The streamflow process is complex and highly nonlinear. Therefore, researchers try to devise alternative techniques to forecast streamflow with relative ease and reasonable accuracy, although traditional deterministic and conceptual models are available. The present work uses three data-driven techniques, namely artificial neural networks (ANN), genetic programming (GP) and model trees (MT) to forecast river flow one day in advance at two stations in the Narmada catchment of India, and the results are compared. All the models performed reasonably well as far as accuracy of prediction is concerned. It was found that the ANN and MT techniques performed almost equally well, but GP performed better than both these techniques, although only marginally in terms of prediction accuracy in normal and extreme events.", notes = "Department of Civil Engineering, Vishwakarma Institute of Information Technology, Survey no. 2/3/4, Kondhwa (Bk), Pune, MH, 411048, India Department of Civil Engineering, Datta Meghe College of Engineering, Airoli, Navi Mumbai, MH, 400708, India p1172 'Rajghat and Mandaleshwar in the Narmada basin in India. The GP models performed better compared to ANN and MT models, though marginally.' Comparaison de techniques de modelisation conditionnee par les donnees pour la prevision des debits fluviaux", } @InCollection{Londhe:2012:GPnew, author = "Shreenivas N. Londhe and Pradnya R. Dixit", title = "Genetic Programming: A Novel Computing Approach in Modeling Water Flows", booktitle = "Genetic Programming - New Approaches and Successful Applications", publisher = "InTech", year = "2012", editor = "Sebastian Ventura", chapter = "9", pages = "199--224", keywords = "genetic algorithms, genetic programming, ANN, MT", isbn13 = "978-953-51-0809-2", DOI = "doi:10.5772/48179", size = "26 pages", notes = "Missing wave heights USA Caribbean coast line NOAA wavewatch, hurricane Ivan 2004. Modelling river flow Rajghat and Mandaleshwar, Bhopal, India, 1987-1997. Part of \cite{Ventura:2012:GPnew}. Open access book CC BY 3.0 license", } @Article{Londhe:2015:PE, author = "Shreenivas Londhe and Pradnya Dixit and Shweta Narkhede", title = "Application of Geno-wavelet Technique to Improve the Location Specific Wave Forecasts", journal = "Procedia Engineering", volume = "116", pages = "971--978", year = "2015", note = "8th International Conference on Asian and Pacific Coasts (APAC 2015)", ISSN = "1877-7058", DOI = "doi:10.1016/j.proeng.2015.08.388", URL = "http://www.sciencedirect.com/science/article/pii/S1877705815020433", abstract = "Estimation and prediction of the real time information of the oceanographic parameters is of vital importance in India as more than 25percent of the population resides along the coastlines. Information of the significant wave heights is necessary to deal with many oceanographic activities as almost all ocean engineering applications depends on it. Presently Indian National Centre for Ocean Information Services (INCOIS) provides wave forecasts on regional and local level ranging from 3 hours to 7 days ahead using numerical models (www.incois.res.in). It is evident from real time observations that the predicted SWHs by a physics based model vary randomly and have non-linear relationship with observed values due to many reasons. Consequently predicted and actual values deviate significantly from each other with an `error' which has to be removed to cater the needs of safe and secure lives residing along Indian coastline. Present work aims in reducing this error in numerical wave forecast made by INCOIS at Ratnagiri station on the south-west coast of India. For this `error' between forecasted and observed waves at current and previous time steps were taken as input to predict the error at 24 to 48 hr ahead lead time in advance using a Geno-Wavelet Technique. Geno-Wavelet Technique is a combination of Genetic Programming (GP) and Discrete Wavelet Transform (DWT). This predicted error was then added or subtracted from numerical wave forecast to improve the prediction accuracy. It is observed that the numerical model forecast improved considerably when the predicted error was added or subtracted from it. It will add to the usefulness of the wave forecasts given by INCOIS to its stake holders.", keywords = "genetic algorithms, genetic programming, Wave forecasting, Wavelet Transform, Geno-wavelet Technique", } @Article{LONDHE:2021:JBE, author = "S. N. Londhe and P. S. Kulkarni and P. R. Dixit and A. Silva and R. Neves and J. {de Brito}", title = "Predicting carbonation coefficient using Artificial neural networks and genetic programming", journal = "Journal of Building Engineering", volume = "39", pages = "102258", year = "2021", ISSN = "2352-7102", DOI = "doi:10.1016/j.jobe.2021.102258", URL = "https://www.sciencedirect.com/science/article/pii/S2352710221001145", keywords = "genetic algorithms, genetic programming, Concrete carbonation, Durability, Artificial neural networks (ANNs), Genetic programming (GP)", abstract = "Concrete carbonation is considered an important problem in both the Civil Engineering and Materials Science fields. Over time, the properties of concrete change because of the interaction between the material and the environment and, consequently, its durability is affected. Conventionally, concrete carbonation depth at a given time under steady-state conditions can reasonably be estimated using Fick's second law of diffusion. This study addresses the statistical modelling of the concrete carbonation phenomenon, using a large number of results (827 specimens or samples, i.e. 827 is the number of data concerning the measurement of the carbonation coefficient in concrete test specimens), collected in the literature. Artificial Neural Networks (ANNs) and Genetic Programming (GP) were the Soft Computing techniques used to predict the carbonation coefficient, as a function of a set of conditioning factors. These models allow the estimation of the carbonation coefficient and, accordingly, carbonation as a function of the variables considered statistically significant in explaining this phenomenon. The results obtained through Artificial Neural Networks and Genetic Programming were compared with those obtained through Multiple Linear Regression (MLR) (which has been previously used to model the carbonation coefficient of concrete). The results reveal that ANNs and GP models present a better performance when compared with MLR, being able to deal with the nonlinear influence of relative humidity on concrete carbonation, which was the main limitation of MLR in modelling the carbonation coefficient in previous study. ANNs are commonly seen as a black box; in this study, an attempt is made to address this issue through Knowledge Extraction (KE) from trained weights and biases. KE helps to understand the influence of each input on the output and the influences identified by the KE technique are in accordance with general knowledge", } @Article{londhe:2022:AS, author = "Shreenivas Londhe and Preeti Kulkarni and Pradnya Dixit and Ana Silva and Rui Neves and Jorge {de Brito}", title = "Tree Based Approaches for Predicting Concrete Carbonation Coefficient", journal = "Applied Sciences", year = "2022", volume = "12", number = "8", pages = "Article No. 3874", keywords = "genetic algorithms, genetic programming", ISSN = "2076-3417", URL = "https://www.mdpi.com/2076-3417/12/8/3874", DOI = "doi:10.3390/app12083874", abstract = "Carbonation is one of the critical durability issues in reinforced concrete structures in terms of their structural integrity and safety and may cause the fatal deterioration and corrosion of steel reinforcement if ignored. Many researchers have performed a considerable number of studies to predict the carbonation of concrete structures. However, it is still challenging to predict the carbonation depth or carbonation coefficient, as they depend on various factors. Therefore, creating a model that can learn from available data using Data Driven Techniques (DDT) is a step forward in this research field. This study provides new approaches to predict the carbonation coefficient of concrete through Model Tree (MT), Random Forest (RF) and Multi-Gene Genetic Programming (MGGP) approaches. With 827 case studies, the predicted models can be seen as a function of a set of conditioning factors, which are statistically significant in explaining the carbonation mechanism. The results obtained through MT, RF and MGGP were compared with those obtained through Multiple Linear Regression (MLR), Artificial Neural Networks (ANNs) and Genetic Programming (which were previously developed). The results reveal that the MT, RF and MGGP perform better than the previous models. Moreover, the MT technique displays its output in terms of series of equations, RF as multiple trees and MGGP in form of a single equation, which are more user-friendly and applicable in practice.", notes = "also known as \cite{app12083874}", } @InProceedings{Londt:2021:evoapplications, author = "Trevor Londt and Xiaoying Gao and Peter Andreae", title = "Evolving Character-Level {DenseNet} Architectures using Genetic Programming", booktitle = "24th International Conference, EvoApplications 2021", year = "2021", month = "7-9 " # apr, editor = "Pedro Castillo and Juanlu Jimenez-Laredo", series = "LNCS", volume = "12694", publisher = "Springer Verlag", address = "virtual event", pages = "665--680", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, ANN, NLP, Character-level DenseNet, Evolutionary deep learning, Text classification", isbn13 = "978-3-030-72698-0", URL = "https://arxiv.org/abs/2012.02327", DOI = "doi:10.1007/978-3-030-72699-7_42", size = "15 pages", abstract = "DenseNet architectures have demonstrated impressive performance in image classification tasks, but limited research has been conducted on using character-level DenseNet (char-DenseNet) architectures for text classification tasks. It is not clear what DenseNet architectures are optimal for text classification tasks. The iterative task of designing, training and testing of char-DenseNets is an NP-Hard problem that requires expert domain knowledge. Evolutionary deep learning (EDL) has been used to automatically design CNN architectures for the image classification domain, thereby mitigating the need for expert domain knowledge. This study demonstrates the first work on using EDL to evolve char-DenseNet architectures for text classification tasks. A novel genetic programming-based algorithm (GP-Dense) coupled with an indirect-encoding scheme, facilitates the evolution of performant char-DenseNet architectures. The algorithm is evaluated on two popular text datasets, and the best-evolved models are benchmarked against four current state-of-the-art character-level CNN and DenseNet models. Results indicate that the algorithm evolves performant models for both datasets that outperform two of the state-of-the-art models in terms of model accuracy and three of the state-of-the-art models in terms of parameter size.", notes = "http://www.evostar.org/2021/ EvoApplications2021 held in conjunction with EuroGP'2021, EvoCOP2021 and EvoMusArt2021", } @InProceedings{LonTyr01, author = "Michael A. Lones and Andy M. Tyrrell", title = "Enzyme Genetic Programming", booktitle = "Proceedings of the 2001 Congress on Evolutionary Computation, CEC 2001", year = "2001", pages = "1183--1190", month = "27--30 " # may, address = "COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, biomimetic representations, Metabolic Pathways, Evolutionary Electronics", ISBN = "0-7803-6657-3", URL = "http://folk.ntnu.no/lones/lones-cec2001.pdf", DOI = "doi:10.1109/CEC.2001.934325", abstract = "The work reported in this paper follows from the hypothesis that better performance in artificial evolution can be achieved by adhering more closely to the features that make natural evolution effective within biological systems. An important issue in evolutionary computation is the choice of solution representation. Genetic programming, whilst borrowing from biology in the evolutionary axis of behaviour, remains firmly rooted in the artificial domain with its use of a parse tree representation. Following concerns that this approach does not encourage solution evolvability, this paper presents an alternative method modelled upon representations used by biology. Early results are encouraging; demonstrating that the method is competitive when applied to problems in the area of combinatorial circuit design. Whilst too early to gauge its suitability to a more general domain of programming, these results do indicate that the concept of bringing ideas from biological representations to genetic programming is a promising one.", size = "8 pages", notes = "CEC-2001 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 01TH8546C, Library of Congress Number =", } @InProceedings{lones:2001:brgp, author = "Michael A. Lones and Andy M. Tyrrell", title = "Biomimetic Representation in Genetic Programming", booktitle = "Computation in Gene Expression", year = "2001", editor = "Hillol Kargupta", pages = "199--204", address = "San Francisco, California, USA", month = "7 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://folk.ntnu.no/lones/lones-gecco2001a.pdf", abstract = "Biological representations underly biological evolution. Moreover, they are also a product of evolution and consequently well adapted for their purpose. The argument presented in this paper is that the representations of biology are also suitable for representing artificial executable systems in genetic programming and, furthermore, that biomimetic representations could improve both the adaptability and evolvability of GP. To this end a biomimetic approach to GP, enzyme genetic programming, is introduced and its behaviour is analysed when applied to the domain of combinational circuit design.", notes = "GECCO-2001WKS Part of heckendorn:2001:GECCOWKS", } @InProceedings{lones:2001:pgp, author = "Michael A. Lones and Andy M. Tyrrell", title = "Pathways into Genetic Programming", booktitle = "Graduate Student Workshop", year = "2001", editor = "Conor Ryan", pages = "425--428", address = "San Francisco, California, USA", month = "7 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://folk.ntnu.no/lones/lones-gecco2001b.pdf", abstract = "Biochemical pathways are the fundamental structures of biological representations. Biological representations are the fundamental targets of natural evolution. Evolution is the fundamental principle behind genetic programming. Could biological representations be useful to genetic programming? Are biochemical pathways a suitable representation for programs? These are the fundamental questions addressed by this paper.", notes = "GECCO-2001WKS Part of heckendorn:2001:GECCOWKS", } @Article{lones:2002:GPEM, author = "Michael A. Lones and Andy M. Tyrrell", title = "Biomimetic Representation with Genetic Programming Enzyme", journal = "Genetic Programming and Evolvable Machines", year = "2002", volume = "3", number = "2", pages = "193--217", month = jun, keywords = "genetic algorithms, genetic programming, biomimetic representation", ISSN = "1389-2576", DOI = "doi:10.1023/A:1015583926171", URL = "http://link.springer.com/article/10.1023/A%3A1020161122012", DOI = "doi:10.1023/A:1020161122012", abstract = "The standard parse tree representation of genetic programming, while a good choice from a generative viewpoint, does not capture the variational demands of evolution. This paper addresses the issue of whether representations in genetic programming might be improved by mimicry of biological behaviors, particularly those thought to be important in the evolution of metabolic pathways, computational structures of the cell. This issue is broached through a presentation of enzyme genetic programming, a form of genetic programming which uses a biomimetic representation. Evaluation upon problems in combinational logic design does not show any significant performance advantage over other approaches, though does demonstrate a number of interesting behaviors including the preclusion of bloat.", notes = "Special issue on Gene Expression \cite{Kargupta:2002:GPEM} Title of paper should be {"}Biomimetic Representation with Enzyme Genetic Programming{"} Also see paper in WCCI 2002. This article subsumes \cite{LonTyr01}, \cite{lones:2001:brgp} and \cite{lones:2001:pgp} Article ID: 408588 cf. Genetic Programming and Evolvable Machines, 3, 315, 2002 Erratum The Publisher apologizes for a misprint that appeared in Genetic Programming and Evolvable Machines, volume 3, number 2.The correct title of the article by Michael A. Lones and Andy M. Tyrrell, pages 193-217, is 'Biomimetic Representation with Enzyme Genetic Programming'.", } @InProceedings{lones:2002:cabitfmoegp, author = "Michael Lones and Andy Tyrrell", title = "Crossover and Bloat in the Functionality Model of Enzyme Genetic Programming", booktitle = "Proceedings of the 2002 Congress on Evolutionary Computation CEC2002", editor = "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and Mark Shackleton", pages = "986--991", year = "2002", publisher = "IEEE Press", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", organisation = "IEEE Neural Network Council (NNC), Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", ISBN = "0-7803-7278-6", month = "12-17 " # may, notes = "CEC 2002 - A joint meeting of the IEEE, the Evolutionary Programming Society, and the IEE. Held in connection with the World Congress on Computational Intelligence (WCCI 2002)", keywords = "genetic algorithms, genetic programming, crossover, enzyme genetic programming, functionality model, genetic algorithm, genotype evolution, mutation", URL = "http://folk.ntnu.no/lones/lones-cec2002.pdf", URL = "http://citeseer.ist.psu.edu/534034.html", DOI = "doi:10.1109/CEC.2002.1007059", abstract = "The functionality model is a new approach in enzyme genetic programming which enables the evolution of variable length solutions whilst preserving local context. This paper introduces the model and presents an analysis of crossover and the evolution of program size.", } @InProceedings{lones:2003:IPCAT, author = "Michael A. Lones and Andy M. Tyrrell", title = "Modelling Biological Evolvability: Implicit Context and Variation Filtering in Enzyme Genetic Programming", booktitle = "Proceedings of the Fifth International Workshop on Information Processing in Cells and Tissues (IPCAT2003)", year = "2003", editor = "D. Mange and C. Teuscher and M. Holcombe and R. Paton and A. Stauffer and G. Tempesti", month = sep, keywords = "genetic algorithms, genetic programming, evolvability, representation, self-organisation", URL = "http://folk.ntnu.no/lones/lones-ipcat2003.pdf", abstract = "This paper describes recent insights into the role of implicit context within the representations of evolving artifacts and specifically within the program representation used by enzyme genetic programming. Implicit context occurs within self-organising systems where a component's connectivity is both determined implicitly by its own definition and is specified in terms of the behavioural context of other components. This paper argues that implicit context is an important source of evolvability and presents experimental evidence that supports this assertion. In particular, it introduces the notion of variation filtering, suggesting that the use of implicit context within representations leads to meaningful variation filtering whereby inappropriate change is ignored and meaningful change is encouraged during evolution.", notes = "To appear in BioSystems.", } @PhdThesis{lones:thesis, author = "Michael A. Lones", title = "Enzyme Genetic Programming: Modelling Biological Evolvability in Genetic Programming", school = "The University of York", year = "2003", address = "Heslington, York, YO10 5DD, UK", month = sep, keywords = "genetic algorithms, genetic programming, evolvability, representation, self-organisation, biological modelling", URL = "http://www.macs.hw.ac.uk/~ml355/common/thesis/main.html", URL = "http://www-users.york.ac.uk/~mal503/common/thesis/michael_lones_thesis.pdf", URL = "http://ethos.bl.uk/OrderDetails.do?did=8&uin=uk.bl.ethos.399653", size = "200 pages", abstract = "This thesis introduces a new approach to program representation in genetic programming in which interactions between program components are expressed in terms of a component's behaviour rather through its relative position within a representation or through other non-behavioural systems of reference. This approach has the advantage that a component's behaviour is expressed in a way that is independent of any particular program it finds itself within; and thereby overcomes the problem when using conventional program representations whereby program components lose their behavioural context following recombination. More generally, this implicit context representation leads to a process of meaningful variation filtering; whereby inappropriate change induced by variation operators can be wholly or partially ignored. This occurs as a consequence of program behaviours emerging from the self-organisation of program components, ignoring those components which do not fit the contexts declared by the other components within the program. This process results in gradual change within the behaviour of a program during evolution. This thesis also presents results which show that implicit context representation leads to better size evolution characteristics than conventional genetic programming; and that functional redundancy and Lamarckian reinforcement learning both improve evolutionary search, agreeing with previous research by other authors.", notes = "small section on 'homologous crossovers' Gone sep 2023 http://folk.ntnu.no/lones/thesis/c7.html#tth_sEc7.2 These choose crossover points non-randomly according to recognition of genetic homology (bits that look the same). uk.bl.ethos.399653", } @Article{lones::04, author = "Michael A. Lones and Andy M. Tyrrell", title = "Modelling biological evolvability: {I}mplicit context and variation filtering in enzyme genetic programming", journal = "Bio{S}ystems", year = "2004", volume = "76", pages = "229--238", number = "1--3", month = aug # "--" # oct, keywords = "genetic algorithms, genetic programming, Evolvability, Implicit context, Variation filtering", URL = "http://www-users.york.ac.uk/~mal503/common/papers/lones-ipcat2003.pdf", URL = "http://www.sciencedirect.com/science/article/B6T2K-4D09KY2-8/2/cf043c46f0d2f5a9997b5b62067c1f20", DOI = "doi:10.1016/j.biosystems.2004.05.015", abstract = "We describe recent insights into the role of implicit context within the representations of evolving artefacts and specifically within the program representation used by enzyme genetic programming. Implicit context occurs within self-organising systems where a component's connectivity is both determined implicitly by its own definition and is specified in terms of the behavioural context of other components. This paper argues that implicit context is an important source of evolvability and presents experimental evidence that supports this assertion. In particular, it introduces the notion of variation filtering, suggesting that the use of implicit context within representations leads to meaningful variation filtering whereby inappropriate change is ignored and meaningful change is encouraged during evolution.", notes = "Papers presented at the Fifth International Workshop on Information Processing in Cells and Tissues", } @InCollection{lones::04b, author = "Michael A. Lones and Andy M. Tyrrell", title = "Enzyme Genetic Programming", booktitle = "Cellular Computing", publisher = "Oxford University Press", year = "2004", editor = "Martyn Amos", series = "Series in Systems Biology", chapter = "3", pages = "19--42", keywords = "genetic algorithms, genetic programming Adder, Backtracking, Chromosome, Elitism, Fitness, Genetic algorithm, Hash table, LISP, Metabolism, Optimization", ISBN = "0-19-515539-4", URL = "https://www.amazon.com/Cellular-Computing-Bioinformatics-University-Paperback/dp/B00DU80DLU", broken = "http://www.oup.com/us/catalog/general/subject/LifeSciences/GenomicsBioinformatics/?view=usa&sf=toc&ci=9780195155396", DOI = "doi:10.1093/oso/9780195155396.003.0007", abstract = "Programming is a process of optimization; taking a specification, which tells us what we want, and transforming it into an implementation, a program, which causes the target system to do exactly what we want. Conventionally, this optimization is achieved through manual design. However, manual design can be slow and error-prone, and recently there has been increasing interest in automatic programming; using computers to semiautomate the process of refining a specification into an implementation. Genetic programming is a developing approach to automatic programming, which, rather than treating programming as a design process, treats it as a search process. However, the space of possible programs is infinite, and finding the right program requires a powerful search process. Fortunately for us, we are surrounded by a monotonous search process capable of producing viable systems of great complexity: evolution. Evolution is the inspiration behind genetic programming. Genetic programming copies the process and genetic operators of biological evolution but does not take any inspiration from the biological representations to which they are applied. It can be argued that the program representation that genetic programming does use is not well suited to evolution. Biological representations, by comparison, are a product of evolution and, a fact to which this book is testament, describe computational structures. This chapter is about enzyme genetic programming, a form of genetic programming that mimics biological representations in an attempt to improve the evolvability of programs. Although it would be an advantage to have a familiarity with both genetic programming and biological representations, concise introductions to both these subjects are provided. According to modern biological understanding, evolution is solely responsible for the complexity we see in the structure and behavior of biological organisms. Nevertheless, evolution itself is a simple process that can occur in any population of imperfectly replicating entities where the right to replicate is determined by a process of selection. Consequently, given an appropriate model of such an environment, evolution can also occur within computers.", } @InProceedings{Lones:2007:cec, author = "Michael A. Lones and Andy M. Tyrrell", title = "A Co-Evolutionary Framework for Regulatory Motif Discovery", booktitle = "2007 IEEE Congress on Evolutionary Computation", year = "2007", editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 " # sep, pages = "3894--3901", organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "1-4244-1340-0", file = "1436.pdf", DOI = "doi:10.1109/CEC.2007.4424978", size = "8 pages", keywords = "genetic algorithms, genetic programming, biology computing, evolutionary computation, genetics, pattern classification, Boolean classification rules, co-evolutionary framework, co-expressed genes, regulatory motif discovery, sequence classifiers", abstract = "In previous work, we have shown how an evolutionary algorithm with a clustered population can be used to concurrently discover multiple regulatory motifs present within the promoter sequences of co-expressed genes. In this paper, we extend the algorithm by co-evolving a population of Boolean classification rules in parallel with the motif population. Results using synthetic data suggest that this approach allows poorly conserved motifs to be identified in promoter sequences an order of magnitude longer than using population clustering alone, whilst results using muscle-specific promoter data show the algorithm is able to evolve meaningful sequence classifiers in parallel with motifs' suggesting that co-evolution provides a suitable framework for composite motif discovery within eukaryotic sequences.", homepage = "http://www-users.york.ac.uk/~mal503/", notes = "also known as \cite{4424978}. Page numbers from IEEE Xplore 2009. 3896 GP-like, binary tree. CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C", } @InProceedings{Lones:2010:EuroGP, author = "Michael Lones and Andy Tyrrell and Susan Stepney and Leo Caves", title = "Controlling Complex Dynamics with Artificial Biochemical Networks", booktitle = "Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010", year = "2010", editor = "Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar", volume = "6021", series = "LNCS", pages = "159--170", address = "Istanbul", month = "7-9 " # apr, organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-12147-0", DOI = "doi:10.1007/978-3-642-12148-7_14", abstract = "Artificial biochemical networks (ABNs) are computational models inspired by the biochemical networks which underlie the cellular activities of biological organisms. This paper shows how evolved ABNs may be used to control chaotic dynamics in both discrete and continuous dynamical systems, illustrating that ABNs can be used to represent complex computational behaviours within evolutionary algorithms. Our results also show that performance is sensitive to model choice, and suggest that conservation laws play an important role in guiding search.", notes = "Part of \cite{Esparcia-Alcazar:2010:GP} EuroGP'2010 held in conjunction with EvoCOP2010 EvoBIO2010 and EvoApplications2010", } @InProceedings{Lones:2010:CEC, author = "Michael A. Lones and Stephen L. Smith and Andrew T. Harris and Alec S. High and Sheila E. Fisher and D. Alastair Smith and Jennifer Kirkham", title = "Discriminating Normal and Cancerous Thyroid Cell Lines using Implicit Context Representation Cartesian Genetic Programming", booktitle = "2010 IEEE World Congress on Computational Intelligence", year = "2010", editor = "Pilar Sobrevilla", pages = "1945--1950", address = "Barcelona", month = "18-23 " # jul, organisation = "IEEE Computational Intelligence Society", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", isbn13 = "978-1-4244-6910-9", URL = "http://www-users.york.ac.uk/~mal503/common/papers/lones-wcci2010.pdf", DOI = "doi:10.1109/CEC.2010.5586494", size = "6 pages", abstract = "In this paper, we describe a method for discriminating between thyroid cell lines. Five commercial thyroid cell lines were obtained, ranging from non-cancerous to cancerous varieties. Raman spectroscopy was used to interrogate native cell biochemistry. Following suitable normalisation of the data, implicit context representation Cartesian genetic programming was then used to search for classifiers capable of distinguishing between the spectral fingerprints of the different cell lines. The results are promising, producing comprehensible classifiers whose output values correlate with biological aggressiveness.", notes = "WCCI 2010. Also known as \cite{5586494}", } @InCollection{Lones:2010:GECma, author = "Michael A. Lones and Stephen L. Smith", title = "Objective Assessment of Visuo-Spatial Ability Using Implicit Context Representation Cartesian Genetic Programming", booktitle = "Genetic and Evolutionary Computation: Medical Applications", publisher = "John Wiley and Sons, Ltd", year = "2010", editor = "Stephen L. Smith and Stefano Cagnoni", chapter = "6.1", pages = "174--189", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, advanced modelling, diagnosis and treatment using GEC, objective assessment of visuo-spatial ability - using implicit context representation cartesian genetic programming, visuo-spatial ability, person's manipulation - of visual representations and spatial relationships, geometric shape-drawing tasks - evaluating visuo-spatial neglect, implicit context representation cartesian genetic programming (IRCGP), CGP, graph-based genetic programming system - performing well within problem domains, CGP efficacy, attributed to implicit reuse of subexpressions, bottom-up development process - satisfying functionality profiles, receiver operating characteristic (ROC) analysis - measuring evolved classifier's fitness, IRCGP - to identify meaningful patterns of subject movement", isbn13 = "9780470748138", DOI = "doi:10.1002/9780470973134.ch10", abstract = "Functional Magnetic Resonance Imaging (fMRI) is commonly used to measure the shape of the mouth (oral tract), but there are significant disadvantages to its use for measurements during speech or singing. For example, the subject is supine, the local environment is acoustically noisy and the exposure time can be quite long. In this chapter, we describe an experimental method that could replace fMRI for oral tract shape measurement. Oral tracts are evolved for a set of vowels for two adult males using physical modelling 2-dimensional digital waveguide synthesis. Starting with a population of 50 randomly shaped oral tracts, quite close matches to the target natural acoustic outputs were observed after evolution over 50 generations. This was specially the case for phonetically open vowels (e.g. the vowels in cat, cart and caught) as opposed to the results obtained for phonetically close vowels (such as kit, get and coot). It is suggested that this is because of the narrow oral tract constriction associated with close vowels to which special attention needs to be paid in the future and that this technique offers a potential alternative to fMRI for vocal tract shape measurement.", bibsource = "OAI-PMH server at eprints.whiterose.ac.uk", identifier = "Lones, M. A. and Smith, S. L. (2010) Objective Assessment of Visuo-spatial Ability using Implicit Context Representation Cartesian Genetic Programming. In: Genetic and Evolutionary Computation: Medical Applications. Wiley .", oai = "oai:eprints.whiterose.ac.uk:69358", type = "NonPeerReviewed", } @InProceedings{lones2011controlling, author = "Michael A. Lones and Andy M. Tyrrell and Susan Stepney and Leo S. Caves", title = "Controlling legged robots with coupled artificial biochemical networks", booktitle = "Advances in Artificial Life, ECAL", year = "2011", editor = "Rene Doursat", pages = "465--472", address = "Paris", month = aug # " 8-12", organisation = "ISAL", publisher = "MIT Press", keywords = "genetic algorithms, genetic programming", URL = "https://mitpress.mit.edu/sites/default/files/titles/alife/0262297140chap72.pdf", abstract = "Artificial biochemical networks (ABNs) are computational architectures motivated by the organisation of cells and tissues at a biochemical level. In previous work, we have shown how artificial biochemical networks can be used to control trajectories in discrete and continuous dynamical systems. In this work, we extend the approach to the control of a hybrid dynamical system: a legged robot. Taking inspiration from biological cells, in which complex behaviours come about through the interaction of different classes of biochemical network, we develop the notion of a coupled artificial biochemical network, in which an artificial genetic network controls the configuration of an artificial metabolic network. Using a higher-level robotic control task, we show how the coupled network finds solutions which can not be readily expressed using the artificial genetic network or artificial metabolic network alone. Our results also show the important role that non-linear maps can play as a natural source of complex dynamics.", notes = "Broken April 2025 http://www.ecal11.org/", } @Article{Lones:2011:GPEM, author = "Michael Lones", title = "Sean Luke: essentials of metaheuristics", journal = "Genetic Programming and Evolvable Machines", year = "2011", volume = "12", number = "3", pages = "333--334", month = sep, note = "Book review", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dR8Cr", DOI = "doi:10.1007/s10710-011-9139-0", size = "2 pages", notes = "Review of \cite{Luke2009Metaheuristics}", } @InProceedings{Lones:2013:SSCI, author = "Michael A. Lones and Jane E. Alty and Stuart E. Lacy and D. R. Stuart Jamieson and Kate L. Possin and Norbert Schuff and Stephen L. Smith", title = "Evolving classifiers to inform clinical assessment of Parkinson's disease", booktitle = "IEEE Symposium on Computational Intelligence in Healthcare and e-health, CICARE 2013", year = "2013", editor_ssci-2013 = "P. N. Suganthan", editor = "Amir Hussain", pages = "76--82", address = "Singapore", month = "16-19 " # apr, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CICARE.2013.6583072", size = "7 pages", abstract = "We describe the use of a genetic programming system to induce classifiers that can discriminate between Parkinson's disease patients and healthy age-matched controls. The best evolved classifier achieved an AUC of 0.92, which is comparable with clinical diagnosis rates. Compared to previous studies of this nature, we used a relatively large sample of 49 PD patients and 41 controls, allowing us to better capture the wide diversity seen within the Parkinson's population. Classifiers were induced from recordings of these subjects' movements as they carried out repetitive finger tapping, a standard clinical assessment for Parkinson's disease. For ease of interpretability, we used a relatively simple window-based classifier architecture which captures patterns that occur over a single tap cycle. Analysis of window matches suggested the importance of peak closing deceleration as a basis for classification. This was supported by a follow-up analysis of the data set, showing that closing deceleration is more discriminative than features typically used in clinical assessment of finger tapping.", notes = "CICARE 2013 http://www.cs.stir.ac.uk/events/CICARE2013/ also known as \cite{6583072}", } @InProceedings{Lones:2014:GECCOcomp, author = "Michael A. Lones and Jane E. Alty and Phillipa Duggan-Carter and Andrew J. Turner and D. R. Stuart Jamieson and Stephen L. Smith", title = "Classification and characterisation of movement patterns during levodopa therapy for parkinson's disease", booktitle = "GECCO 2014 Workshop on Medical Applications of Genetic and Evolutionary Computation (MedGEC)", year = "2014", editor = "Stephen L. Smith and Stefano Cagnoni and Robert M. Patton", isbn13 = "978-1-4503-2881-4", keywords = "genetic algorithms, genetic programming", pages = "1321--1328", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2598394.2609852", DOI = "doi:10.1145/2598394.2609852", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Parkinson's disease is a chronic neurodegenerative condition that manifests clinically with various movement disorders. These are often treated with the dopamine-replacement drug levodopa. However, the dosage of levodopa must be kept as low as possible in order to avoid the drug's side effects, such as the involuntary, and often violent, muscle spasms called dyskinesia, or levodopa-induced dyskinesia. In this paper, we investigate the use of genetic programming for training classifiers that can monitor the effectiveness of levodopa therapy. In particular, we evolve classifiers that can recognise tremor and dyskinesia, movement states that are indicative of insufficient or excessive doses of levodopa, respectively. The evolved classifiers achieve clinically useful rates of discrimination, with AUC>0.9. We also find that temporal classifiers generally out-perform spectral classifiers. By using classifiers that respond to low-level features of the data, we identify the conserved patterns of movement that are used as a basis for classification, showing how this approach can be used to characterise as well as classify abnormal movement.", notes = "Also known as \cite{2609852} Distributed at GECCO-2014.", } @Article{Lones:2014:ieeeTEC, author = "Michael Adam Lones and Stephen Leslie Smith and Jane Elizabeth Alty and Stuart E. Lacy and Katherine L. Possin and D. R. Stuart Jamieson and Andy M. Tyrrell", title = "Evolving Classifiers to Recognise the Movement Characteristics of Parkinson's Disease Patients", journal = "IEEE Transactions on Evolutionary Computation", year = "2014", volume = "18", number = "4", pages = "559--576", month = aug, keywords = "genetic algorithms, genetic programming, artificial biochemical networks, Automated disease diagnosis, Time series analysis, Classification", ISSN = "1089-778X", URL = "http://www-users.york.ac.uk/~mal503/common/papers/lones-tevc2013-PD.pdf", DOI = "doi:10.1109/TEVC.2013.2281532", size = "18 pages", abstract = "Parkinson's disease is a debilitating neurological condition that affects approximately 1 in 500 people and often leads to severe disability. To improve clinical care, better assessment tools are needed that increase the accuracy of differential diagnosis and disease monitoring. we report how we have used evolutionary algorithms to induce classifiers capable of recognising the movement characteristics of Parkinson's disease patients. These diagnostically-relevant patterns of movement are known to occur over multiple time scales. To capture this, we used two different classifier architectures: sliding-window genetic programming classifiers, which model over-represented local patterns that occur within time series data, and artificial biochemical networks, computational dynamical systems that respond to dynamical patterns occurring over longer time scales. Classifiers were trained and validated using movement recordings of 49 patients and 41 age-matched controls collected during a recent clinical study. By combining classifiers with diverse behaviours, we were able to construct classifier ensembles with diagnostic accuracies in the region of 95percent, comparable to the accuracies achieved by expert clinicians. Further analysis indicated a number of features of diagnostic relevance, including the differential effect of handedness and the over-representation of certain patterns of acceleration.", notes = "Also known as \cite{6600775}", } @Article{Lones:2013:ieeeTEC, author = "Michael A. Lones and Luis A. Fuente and Alexander P. Turner and Leo S. D. Caves and Susan Stepney and Stephen L. Smith and Andy M. Tyrrell", journal = "IEEE Transactions on Evolutionary Computation", title = "Artificial Biochemical Networks: Evolving Dynamical Systems to Control Dynamical Systems", year = "2014", volume = "18", number = "2", pages = "145--166", month = apr, keywords = "genetic algorithms, genetic programming, Biochemical networks, Chaos control, Dynamical systems, Evolutionary robotics", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2013.2243732", size = "22 pages", abstract = "Biological organisms exist within environments in which complex, non-linear dynamics are ubiquitous. They are coupled to these environments via their own complex, dynamical networks of enzyme-mediated reactions, known as biochemical networks. These networks, in turn, control the growth and behaviour of an organism within its environment. In this paper, we consider computational models whose structure and function are motivated by the organisation of biochemical networks. We refer to these as artificial biochemical networks, and show how they can be evolved to control trajectories within three behaviourally diverse complex dynamical systems: the Lorenz system, Chirikovs standard map, and legged robot locomotion. More generally, we consider the notion of evolving dynamical systems to control dynamical systems, and discuss the advantages and disadvantages of using higher order coupling and configurable dynamical modules (in the form of discrete maps) within artificial biochemical networks. We find both approaches to be advantageous in certain situations, though note that the relative trade-offs between different models of artificial biochemical network strongly depend on the type of dynamical systems being controlled.", notes = "Also known as \cite{6423886}", } @InProceedings{lones2015evolving, author = "Michael A. Lones and Stuart E. Lacy and Stephen L. Smith", title = "Evolving Ensembles: What Can We Learn from Biological Mutualisms?", booktitle = "10th International Conference on Information Processing in Cells and Tissues, IPCAT 2015", year = "2015", editor = "Michael Lones and Andy Tyrrell and Stephen Smith and Gary Fogel", volume = "9303", series = "LNCS", pages = "52--60", address = "San Diego, CA, USA", month = sep # " 14-16", publisher = "Springer International Publishing", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-23108-2", DOI = "doi:10.1007/978-3-319-23108-2_5", abstract = "Ensembles are groups of classifiers which cooperate in order to reach a decision. Conventionally, the members of an ensemble are trained sequentially, and typically independently, and are not brought together until the final stages of ensemble generation. In this paper, we discuss the potential benefits of training classifiers together, so that they learn to interact at an early stage of their development. As a potential mechanism for achieving this, we consider the biological concept of mutualism, whereby cooperation emerges over the course of biological evolution. We also discuss potential mechanisms for implementing this approach within an evolutionary algorithm context.", notes = "Affiliated with School of Mathematical and Computer Sciences, Heriot-Watt University", } @InCollection{lones2016computing, author = "Michael A. Lones", title = "Computing with artificial gene regulatory networks", booktitle = "Evolutionary Computation in Gene Regulatory Network Research", year = "2016", editor = "Hitoshi Iba and Nasimul Noman", chapter = "15", pages = "398-", month = mar, publisher = "John Wiley \& Sons", keywords = "genetic algorithms, genetic programming, artificial gene regulatory network, biological gene regulatory network, computational models", isbn13 = "978-1-118-91151-8", URL = "http://eu.wiley.com/WileyCDA/WileyTitle/productCd-1118911512,subjectCd-LSG0.html", DOI = "doi:10.1002/9781119079453.ch15", size = "27 pages", abstract = "Gene regulatory networks (GRNs) are the fundamental mechanisms through which biological organisms control their growth, their dynamical behaviour, their interaction with their environment, and which underlie much of the complexity in the biosphere. This chapter reviews current understanding of artificial gene regulatory network (AGRN), discussing what is known about their computational properties, detailing how they have been applied to computational problems, and speculating about how they may be used in the future. It discusses what is known about biological GRNs, and the implications this has for the design of AGRNs. The chapter presents the different motivations behind the development of AGRN models. It also discusses the modelling decisions that have to be made when developing AGRN models. AGRNs give the opportunity to explore analogous behaviors within a more general setting, which, in turn, might lead to a better understanding of the general properties of GRNs.", } @InProceedings{Lones:2017:GECCO, author = "Michael A. Lones and Jane E. Alty and Jeremy Cosgrove and Stuart Jamieson and Stephen L. Smith", title = "Going Through Directional Changes: Evolving Human Movement Classifiers Using an Event Based Encoding", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4939-0", address = "Berlin, Germany", pages = "1365--1371", size = "7 pages", URL = "http://doi.acm.org/10.1145/3067695.3082490", DOI = "doi:10.1145/3067695.3082490", acmid = "3082490", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, directional changes, dyskinesia, movement analysis, parkinson's disease, time series analysis", month = "15-19 " # jul, abstract = "Directional changes (DC) is an event based encoding for time series data that has become popular in financial analysis, particularly within the evolutionary algorithm community. In this paper, we apply DC to a medical analytics problem, using it to identify and summarise the periods of opposing directional trends present within a set of accelerometry time series recordings. The summarised time series data are then used to train classifiers that can discriminate between different kinds of movement. As a case study, we consider the problem of discriminating the movements of Parkinson's disease patients when they are experiencing a common effect of medication called levodopa-induced dyskinesia. Our results suggest that a DC encoding is competitive against the window-based segmentation and frequency domain encodings that are often used when solving this kind of problem, but offers added benefits in the form of faster training and increased interpretability.", notes = "Also known as \cite{Lones:2017:GTD:3067695.3082490} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @Article{Lones:2017:JMS, author = "Michael A. Lones and Jane E. Alty and Jeremy Cosgrove and Philippa Duggan-Carter and Stuart Jamieson and Rebecca F. Naylor and Andrew J. Turner and Stephen L. Smith", title = "A New Evolutionary Algorithm-Based Home Monitoring Device for {Parkinson's Dyskinesia}", journal = "Journal of Medical Systems", year = "2017", volume = "41", number = "11", pages = "176", month = nov, keywords = "genetic algorithms, genetic programming, Parkinsons disease, Dyskinesia, Home monitoring", URL = "https://doi.org/10.1007/s10916-017-0811-7", ISSN = "1573-689X", URL = "http://www.human-competitive.org/sites/default/files/lones-paper.pdf", DOI = "doi:10.1007/s10916-017-0811-7", size = "8 pages", abstract = "Parkinson's disease (PD) is a neurodegenerative movement disorder. Although there is no cure, symptomatic treatments are available and can significantly improve quality of life. The motor, or movement, features of PD are caused by reduced production of the neurotransmitter dopamine. Dopamine deficiency is most often treated using dopamine replacement therapy. However, this therapy can itself lead to further motor abnormalities referred to as dyskinesia. Dyskinesia consists of involuntary jerking movements and muscle spasms, which can often be violent. To minimise dyskinesia, it is necessary to accurately titrate the amount of medication given and monitor a patient's movements. In this paper, we describe a new home monitoring device that allows dyskinesia to be measured as a patient goes about their daily activities, providing information that can assist clinicians when making changes to medication regimens. The device uses a predictive model of dyskinesia that was trained by an evolutionary algorithm, and achieves AUC>0.9 when discriminating clinically significant dyskinesia.", notes = "2018 HUMIES gold winner. ClearSky Levodopa-induced dyskinesia (LID) Monitor See also doi:10.1016/j.jval.2015.09.682", } @InProceedings{Lones:2019:GECCOcomp, author = "Michael A. Lones", title = "Instruction-level design of local optimisers using push {GP}", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", isbn13 = "978-1-4503-6748-6", pages = "1487--1494", address = "Prague, Czech Republic", DOI = "doi:10.1145/3319619.3326806", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{3326806} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InProceedings{Lones:2020:EuroGP, author = "Michael Lones", title = "Optimising Optimisers with {Push GP}", booktitle = "EuroGP 2020: Proceedings of the 23rd European Conference on Genetic Programming", year = "2020", month = "15-17 " # apr, editor = "Ting Hu and Nuno Lourenco and Eric Medvet", series = "LNCS", volume = "12101", publisher = "Springer Verlag", address = "Seville, Spain", pages = "101--117", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Optimisation, Metaheuristics", isbn13 = "978-3-030-44093-0", URL = "https://arxiv.org/abs/1910.00945", video_url = "https://www.youtube.com/watch?v=pRvH7CdbFDo", DOI = "doi:10.1007/978-3-030-44094-7_7", size = "16 pages", abstract = "This work uses Push GP to automatically design both local and population-based optimisers for continuous-valued problems. The optimisers are trained on a single function optimisation landscape, using random transformations to discourage overfitting. They are then tested for generality on larger versions of the same problem, and on other continuous-valued problems. In most cases, the optimisers generalise well to the larger problems. Surprisingly, some of them also generalise very well to previously unseen problems, outperforming existing general purpose optimisers such as CMA-ES. Analysis of the behaviour of the evolved optimisers indicates a range of interesting optimisation strategies that are not found within conventional optimisers, suggesting that this approach could be useful for discovering novel and effective forms of optimisation in an automated manner.", notes = "http://www.evostar.org/2020/cfp_eurogp.php Part of \cite{Hu:2020:GP} EuroGP'2020 held in conjunction with EvoCOP2020, EvoMusArt2020 and EvoApplications2020", } @Article{Lones:2020p:GPEM, author = "Michael A. Lones", title = "Evolving continuous optimisers from scratch", journal = "Genetic Programming and Evolvable Machines", year = "2021", volume = "22", number = "4", pages = "395--428", month = dec, note = "Special Issue: Highlights of Genetic Programming 2020 Events", keywords = "genetic algorithms, genetic programming, Optimisation, Metaheuristics", ISSN = "1389-2576", URL = "https://rdcu.be/czY2U", DOI = "doi:10.1007/s10710-021-09414-8", size = "34 pages", abstract = "This work uses genetic programming to explore the space of continuous optimisers, with the goal of discovering novel ways of doing optimisation. In order to keep the search space broad, the optimisers are evolved from scratch using Push, a Turing-complete, general-purpose, language. The resulting optimisers are found to be diverse, and explore their optimisation landscapes using a variety of interesting, and sometimes unusual, strategies. Significantly, when applied to problems that were not seen during training, many of the evolved optimisers generalise well, and often outperform existing optimisers. This supports the idea that novel and effective forms of optimisation can be discovered in an automated manner. This paper also shows that pools of evolved optimisers can be hybridised to further increase their generality, leading to optimisers that perform robustly over a broad variety of problem types and sizes.", notes = "supplementary material available at https://doi.org/10.1007/s10710-021-09414-8 School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, Scotland, UK", } @Misc{DBLP:journals/corr/abs-2305-15245, author = "Fu Xing Long and Diederick Vermetten and Anna V. Kononova and Roman Kalkreuth and Kaifeng Yang and Thomas Baeck and Niki {van Stein}", title = "Challenges of {ELA}-guided Function Evolution using Genetic Programming", howpublished = "arXiv", volume = "abs/2305.15245", year = "2023", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.48550/arXiv.2305.15245", DOI = "doi:10.48550/ARXIV.2305.15245", eprinttype = "arXiv", eprint = "2305.15245", timestamp = "Tue, 06 Jun 2023 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/corr/abs-2305-15245.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{DBLP:conf/ijcci/LongVKKYBS23, author = "Fu Xing Long and Diederick Vermetten and Anna V. Kononova and Roman Kalkreuth and Kaifeng Yang and Thomas Baeck and Niki {van Stein}", editor = "Niki {van Stein} and Francesco Marcelloni and H. K. Lam and Marie Cottrell and Joaquim Filipe", title = "Challenges of {ELA}-Guided Function Evolution Using Genetic Programming", booktitle = "Proceedings of the 15th International Joint Conference on Computational Intelligence, {IJCCI} 2023, Rome, Italy, November 13-15, 2023", pages = "119--130", publisher = "{SCITEPRESS}", year = "2023", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.5220/0012206200003595", DOI = "doi:10.5220/0012206200003595", timestamp = "Mon, 05 Feb 2024 00:00:00 +0100", biburl = "https://dblp.org/rec/conf/ijcci/LongVKKYBS23.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{DBLP:conf/ppsn/LongKK22, author = "Xinpeng Long and Michael Kampouridis and Panagiotis Kanellopoulos", title = "Genetic Programming for Combining Directional Changes Indicators in International Stock Markets", booktitle = "Parallel Problem Solving from Nature - PPSN XVII - 17th International Conference, PPSN 2022, Proceedings, Part II", year = "2022", editor = "Guenter Rudolph and Anna V. Kononova and Hernan E. Aguirre and Pascal Kerschke and Gabriela Ochoa and Tea Tusar", volume = "13399", series = "Lecture Notes in Computer Science", pages = "33--47", address = "Dortmund, Germany", month = sep # " 10-14", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Directional changes, Algorithmic trading", timestamp = "Tue, 16 Aug 2022 16:15:42 +0200", biburl = "https://dblp.org/rec/conf/ppsn/LongKK22.bib", bibsource = "dblp computer science bibliography, https://dblp.org", isbn13 = "978-3-031-14720-3", URL = "https://repository.essex.ac.uk/32960/", URL = "https://repository.essex.ac.uk/32960/1/PPSN_2022.pdf", DOI = "doi:10.1007/978-3-031-14721-0_3", size = "14 pages", abstract = "The majority of algorithmic trading studies use data under fixed physical time intervals, such as daily closing prices, which makes the flow of time discontinuous. An alternative approach, namely directional changes (DC), is able to convert physical time interval series into event-based series and allows traders to analyse price movement in a novel way. Previous work on DC has focused on proposing new DC-based indicators, similar to indicators derived from technical analysis. However, very little work has been done in combining these indicators under a trading strategy. Meanwhile, genetic programming (GP) has also demonstrated competitiveness in algorithmic trading, but the performance of GP under the DC framework remains largely unexplored. we present a novel GP that uses DC-based indicators to form trading strategies, namely GP-DC. We evaluate the cumulative return, rate of return, risk, and Sharpe ratio of the GP-DC trading strategies under 33 datasets from 3 international stock markets, and we compare the GP performance to strategies derived under physical time, namely GP-PT, and also to a buy and hold trading strategy. Our results show that the GP-DC is able to outperform both GP-PT and the buy and hold strategy, making DC-based trading strategies a powerful complementary approach for algorithmic trading.", notes = "School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, UK PPSN2022", } @InProceedings{Long:2022:CEC, author = "Xinpeng Long and Michael Kampouridis and Delaram Jarchi", booktitle = "2022 IEEE Congress on Evolutionary Computation (CEC)", title = "An in-depth investigation of genetic programming and nine other machine learning algorithms in a financial forecasting problem", year = "2022", editor = "Carlos A. Coello Coello and Sanaz Mostaghim", address = "Padua, Italy", month = "18-23 " # jul, isbn13 = "978-1-6654-6708-7", abstract = "Machine learning (ML) techniques have shown to be useful in the field of financial forecasting. In particular, genetic programming has been a popular ML algorithm with proven success in improving financial forecasting. Meanwhile, the performance of such ML algorithms depends on a number of factors including data analysis from different markets, data periods, forecasting days ahead, and the transaction cost which have been neglected in most previous studies. Therefore, the focus of this paper is on investigating the effect of such factors. We perform an extensive evaluation of a financial genetic programming-based approach and compare its performance against 9 popular machine learning algorithms and the buy and hold trading strategy. Experiments take place over daily data from 220 datasets from 10 international markets. Results show that genetic programming not only provides profitable results but also outperforms the 9 machine learning algorithms in terms of risk and Sharpe ratio.", keywords = "genetic algorithms, genetic programming, Machine learning algorithms, Data analysis, Costs, Machine learning, Evolutionary computation, Benchmark testing, Machine learning, Financial forecasting, Algorithmic trading", DOI = "doi:10.1109/CEC55065.2022.9870351", notes = "Also known as \cite{9870351}", } @InProceedings{long:2023:CEC, author = "xinpeng long and Michael Kampouridis and Panagiotis Kanellopoulos", title = "Multi-objective optimisation and genetic programming for trading by combining directional changes and technical indicators", booktitle = "2023 IEEE Congress on Evolutionary Computation (CEC)", year = "2023", editor = "Gui DeSouza and Gary Yen", address = "Chicago, USA", month = "1-5 " # jul, keywords = "genetic algorithms, genetic programming, Directional changes, Algorithmic trading, Multi-objective optimisation, technical analysis", isbn13 = "979-8-3503-1459-5", URL = "https://repository.essex.ac.uk/35462/", URL = "https://repository.essex.ac.uk/35462/1/IEEE_CEC_2023_xinpeng.pdf", DOI = "doi:10.1109/CEC53210.2023.10254034", size = "8 pages", abstract = "... we compare the proposed strategy against GP-based single objective optimisation (SOO) and buy-and-hold strategies. Our results show that the proposed approach significantly improves the cumulative return compared to SOO, from 14.29 percent to 62.04 percent, while also outperforming the buy-and-hold strategy.", notes = " School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, United Kingdom CEC2023 https://2023.ieee-cec.org/program-html/", } @PhdThesis{Xinpeng_Long:thesis, author = "Xinpeng Long", title = "Financial Forecasting with the Combination of Physical and Event-based Time using Genetic Programming", school = "School of Computer Science and Electronic Engineering, University of Essex", year = "2024", address = "UK", month = oct, keywords = "genetic algorithms, genetic programming, GP-DC-PT, alpha, apple stock price", URL = "https://repository.essex.ac.uk/40709/1/PhD_Xinpeng__Edited_%20%281%29.pdf", size = "182 pages", abstract = "This thesis explores the application of genetic programming (GP) within the directional changes (DC) framework for algorithmic trading. Traditional algorithmic trading methods rely on datasets with fixed time intervals, such as hourly or daily data, leading to a discontinuous representation of time. DC provides an alternative by transforming these datasets into event-driven sequences, allowing for a unique price analysis approach. The first part of the thesis compares GP with machine learning (ML) algorithms in algorithmic trading, focusing on factors like market data, time periods, forecasting windows, and transaction costs-variables often neglected in previous studies. A comprehensive evaluation of a GP-based financial approach is conducted, comparing it to nine popular ML algorithms and the buy-and-hold strategy, using daily data from 220 datasets across 10 international markets. Results show that GP not only yields profitable results but also outperforms ML algorithms in terms of risk and Sharpe ratio. The second part investigates GP within the DC framework, introducing two novel algorithms: GP-DC, which uses only DC-based indicators, and GP-DC-PT, which combines DC-based and physical-time indicators from technical analysis. Both approaches out- perform non-DC-based GP strategies, technical analysis, and buy-and-hold benchmarks, with GP-DC-PT achieving an average return of over 18 percent, highlighting the advantage of incorporating DC into trading strategies. Finally, the thesis introduces two multi-objective optimization algorithms, MOO2 and MOO3, based on the NSGA-II framework, which optimize two and three fitness functions, respectively, using DC and physical-time indicators. Both MOO2 and MOO3 outperform single-objective methods, with MOO3 showing consistent improvements across all metrics. These findings suggest that incorporating directional changes significantly enhances trading strategies return and risk performance", } @InProceedings{Long:2024:CIFEr, author = "Xinpeng Long and Michael Kampouridis", title = "alpha-dominance two-objective Optimization Genetic Programming for Algorithmic Trading under a Directional Changes Environment", booktitle = "2024 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr)", year = "2024", month = oct, keywords = "genetic algorithms, genetic programming, Event detection, Transforms, Benchmark testing, Stock markets, Optimisation, Computational intelligence, Investment, Convergence, Directional Changes, Algorithmic Trading, Ge-netic Programming, Multi-Objective Optimisation", ISSN = "2640-7701", DOI = "doi:10.1109/CIFEr62890.2024.10772764", abstract = "We present a novel genetic programming (GP) algorithm that combines physical time and event-based time indicators to trade on the stock market. Rather than only using data in fixed intervals (e.g. daily closing prices), we use directional changes to transform physical time into events and allow the GP to make trading decisions based on when significant price movements have occurred. We use a two-objective fitness function, which simultaneously optimises return and risk. To overcome challenges with the convergence ability of the multi-objective GP, we apply an a-dominance strategy, which is able to relax the strict Pareto dominance criteria. We run experiments on 110 stocks from 10 international markets and compare results against a single-objective GP, as well as strategies based on technical analysis indicators and buy-and-hold. Results show that the proposed GP algorithm offers statistically significant improvements when compared to the above benchmarks.", notes = "Also known as \cite{10772764}", } @Article{LONGHITANO:2024:cie, author = "Pedro Dias Longhitano and Christophe Berenguer and Benjamin Echard", title = "Joint electric vehicle routing and battery health management integrating an explicit state of charge model", journal = "Computer \& Industrial Engineering", volume = "188", pages = "109892", year = "2024", ISSN = "0360-8352", DOI = "doi:10.1016/j.cie.2024.109892", URL = "https://www.sciencedirect.com/science/article/pii/S0360835224000135", keywords = "genetic algorithms, genetic programming, Battery health management, Degradation modeling, Electric vehicle routing, Multi-objective optimization", abstract = "Although fleet management has been extensively explored in transportation science, the rise of electromobility imposes several scientific challenges and opportunities. So far, few attempts were made to include battery degradation in the Electric Vehicle Routing Problem (EVRP). To do it realistically, it is necessary to model State of Charge (SoC), however most versions of routing problems use oversimplified SoC models or consider only energy consumption which leads to less robust solutions overall. In this work, a method for estimating battery degradation, which relies on a realistic SoC model is presented and incorporated into a new version of the electric vehicle routing problem. In this version, not only battery degradation is integrated, but also the possibility of limiting different vehicle parameters, such as maximum vehicle speed and acceleration. Due to the extra computational complexity related to the SoC and degradation models, a genetic algorithm capable of solving the aforementioned extended EVRP is presented. Finally, through different numerical experiments, the advantages of the proposed methodology are shown", } @InProceedings{Longshaw:1997:ellg, author = "Tom Longshaw", title = "Evolutionary learning of large Grammars", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "evolutionary programming and evolution strategies", pages = "445", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", notes = "GP-97", } @InProceedings{1068134, author = "Moshe Looks and Ben Goertzel and Cassio Pennachin", title = "Learning Computer Programs with the {Bayesian} Optimization Algorithm", booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation", year = "2005", editor = "Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and Edwin D. {de Jong} and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler", volume = "1", ISBN = "1-59593-010-8", pages = "747--748", address = "Washington DC, USA", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "25-29 " # jun, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, BOA, Estimation of Distribution Algorithms, Poster, design, empirical study, representations", URL = "http://gpbib.cs.ucl.ac.uk/gecco2005/docs/p747.pdf", DOI = "doi:10.1145/1068009.1068134", size = "2 pages", abstract = "We describe an extension of the Bayesian Optimisation Algorithm (BOA), a probabilistic model building genetic algorithm, to the domain of program tree evolution. The new system, BOA programming (BOAP), improves significantly on previous probabilistic model building genetic programming (PMBGP) systems in terms of the articulacy and open-ended flexibility of the models learnt, and hence control over the distribution of instances generated. Innovations include a novel tree representation and a generalised program evaluation scheme.", notes = "Sunspot time series perdiction. GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM Order Number 910052", } @PhdThesis{Looks:thesis, author = "Moshe Looks", title = "Competent Program Evolution", school = "Washington University", year = "2006", type = "Doctor of Science", address = "St. Louis, USA", month = "11 " # dec, keywords = "genetic algorithms, genetic programming", URL = "http://metacog.org/main.pdf", URL = "https://openscholarship.wustl.edu/cse_research/216/", DOI = "doi:10.7936/K7KD1W8C", size = "101 pages", abstract = "Heuristic optimization methods are adaptive when they sample problem solutions based on knowledge of the search space gathered from past sampling. Recently, competent evolutionary optimization methods have been developed that adapt via probabilistic modeling of the search space. However, their effectiveness requires the existence of a compact problem decomposition in terms of prespecified solution parameters. How can we use these techniques to effectively and reliably solve program learning problems, given that program spaces will rarely have compact decompositions? One method is to manually build a problem-specific representation that is more tractable than the general space. But can this process be automated? My thesis is that the properties of programs and program spaces can be leveraged as inductive bias to reduce the burden of manual representation-building, leading to competent program evolution. The central contributions of this dissertation are a synthesis of the requirements for competent program evolution, and the design of a procedure, meta-optimizing semantic evolutionary search (MOSES), that meets these requirements. In support of my thesis, experimental results are provided to analyze and verify the effectiveness of MOSES, demonstrating scalability and real-world applicability.", notes = "http://metacog.org/doc.html Technical Report Number: WUCSE-2006-64", } @InProceedings{1277072, author = "Moshe Looks", title = "Scalable estimation-of-distribution program evolution", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "1", isbn13 = "978-1-59593-697-4", pages = "539--546", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p539.pdf", DOI = "doi:10.1145/1276958.1277072", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Estimation of Distribution Algorithms, empirical Study, heuristics, optimisation, representation", size = "7 pages", abstract = "I present a new estimation-of-distribution approach to program evolution where distributions are not estimated over the entire space of programs. Rather, a novel representation-building procedure that exploits domain knowledge is used to dynamically select program subspaces for estimation over. This leads to a system of demes consisting of alternative representations (i.e. program subspaces) that are maintained simultaneously and managed by the overall system. Meta-optimising semantic evolutionary search (MOSES), a program evolution system based on this approach, is described, and its representation-building subcomponent is analysed in depth. Experimental results are also provided for the overall MOSES procedure that demonstrate good scalability.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071 New initialisation scheme but disappointingly no big performance boost. Parity (AND OR NOT) Mux6 Mux-11. Semantic sampling. C++. Holman Elegant normal form (cf. http://www.patterncraft.com/) ENF Catalan lil-gp.", } @InProceedings{1277283, author = "Moshe Looks", title = "On the behavioral diversity of random programs", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "1636--1642", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1636.pdf", DOI = "doi:10.1145/1276958.1277283", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, empirical Study, heuristics, optimisation, representation", abstract = "Generating a random sampling of program trees with specified function and terminal sets is the initial step of many program evolution systems. I present a theoretical and experimental analysis of the expected distribution of uniformly sampled programs, guided by algorithmic information theory. This analysis demonstrates that increasing the sample size is often an inefficient means of increasing the overall diversity of program behaviours (outputs). A novel sampling scheme (semantic sampling) is proposed that exploits semantics to heuristically increase behavioral diversity. An important property of the scheme is that no calls of the problem-specific fitness function are required. Its effectiveness at increasing behavioural diversity is demonstrated empirically for Boolean formulae. Furthermore, it is found to lead to statistically significant improvements in performance for genetic programming on parity and multiplexer problems.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @InCollection{lopes:1997:GAFLS, author = "Heitor S. Lopes and Mario S. Coutinho and Walter C. Lima", title = "An evolutionary approach to simulate cognitive feedback learning in medical domain", booktitle = "Genetic Algorithms and Fuzzy Logic Systems", publisher = "World Scientific Publishing", year = "1997", editor = "E. Sanchez and T. Shibata and L. A. Zadeh", volume = "7", series = "Advances in Fuzzy Systems - Applications and Theory", pages = "193--207", keywords = "genetic algorithms, medical diagnosis", ISBN = "981-02-2423-0", URL = "http://www.cpgei.cefetpr.br/~hslopes/publicacoes/1997/book97.zip", } @InCollection{lopes:1999:CIA, author = "Heitor S. Lopes", title = "A medical diagnostic system optimization using parallel genetic algorithms", booktitle = "Computational Intelligence and Applications", publisher = "Physica-Verlag", year = "1999", editor = "Piotr S. Szczepaniak", volume = "23", series = "Studies in fuzziness and soft computing", pages = "222--227", keywords = "genetic algorithms, parallel, medical diagnosis", ISBN = "3-7908-1161-0", URL = "http://www.amazon.com/exec/obidos/ASIN/3790811610/qid=/103-1848228-1466238", } @Article{Lopes:2004:AMCS, author = "Heitor S. Lopes and Wagner R. Weinert", title = "EGIPSYS: an Enhanced Gene Expression Programming Approach for Symbolic Regression Problems", journal = "International Journal of Applied Mathematics and Computer Science", year = "2004", volume = "14", number = "3", pages = "375--384", month = sep, note = "Special Issue: Evolutionary Computation", keywords = "genetic algorithms, genetic programming, gene expression programming, evolutionary computation, symbolic regression, mathematical modeling, systems identification", ISSN = "1641-876X", URL = "https://www.amcs.uz.zgora.pl/?action=paper&paper=208", URL = "https://www.amcs.uz.zgora.pl/?action=download&pdf=AMCS_2004_14_3_7.pdf", size = "10 pages", abstract = "This enhanced system, called EGIPSYS, has features specially suited to deal with symbolic regression problems. Amongst the new features implemented in EGIPSYS are: new selection methods, chromosomes of variable length, a new approach to manipulating constants, new genetic operators and an adaptable fitness function. All the proposed improvements were tested separately, and proved to be advantageous over the basic GEP. EGIPSYS was also applied to four difficult identification problems and its performance was compared with a traditional implementation of genetic programming (LilGP). Overall, EGIPSYS was able to obtain consistently better results than the system using genetic programming, finding less complex solutions with less computational effort. The success obtained suggests the adaptation and extension of the system to other classes of problems.", notes = "AMCS University of Zielona Gora Press Centro Federal de Educacao Tecnologica do Parana / CPGEI Av. 7 de setembro, 3165, 80230-901 Curitiba (PR), Brazil March 2014 amc1434.pdf appears to be a different paper", } @Article{Lopes:2007:ASC, author = "Heitor S. Lopes", title = "Genetic programming for epileptic pattern recognition in electroencephalographic signals", journal = "Applied Soft Computing", year = "2007", volume = "7", number = "1", pages = "343--352", month = jan, keywords = "genetic algorithms, genetic programming, Pattern recognition, Epilepsy, EEG", DOI = "doi:10.1016/j.asoc.2005.07.004", size = "10 pages", abstract = "the genetic programming paradigm, in conjunction with pattern recognition principles, can be used to evolve classifiers capable of recognising epileptic patterns in human electroencephalographic signals. The procedure for feature extraction from the raw signal is detailed, as well as the genetic programming system that properly selects the features and evolves the classifiers. Based on the data sets used, two different epileptic patterns were detected: 3 Hz spike-and-slow-wave-complex (SASWC) and spike-or-sharp-wave (SOSW). After training, classifiers for both patterns were tested with unseen instances, and achieved sensibility = 1.00 and specificity = 0.93 for SASWC patterns, and sensibility = 0.94 and specificity = 0.89 for SOSW patterns. Results are very promising and suggest that the methodology presented can be applied to other pattern recognition tasks in complex signals.", } @InProceedings{Lopes:2019:GECCOcomp, author = "Rodolfo Ayala Lopes and Thiago Macedo Gomes and Alan Robert Resende {de Freitas}", title = "A symbolic evolutionary algorithm software platform", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", isbn13 = "978-1-4503-6748-6", pages = "1366--1373", address = "Prague, Czech Republic", DOI = "doi:10.1145/3319619.3326828", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{3326828} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InProceedings{lopes:2011:EuroGP, author = "Rui Lopes and Ernesto Costa", title = "ReNCoDe: A Regulatory Network Computational Device", booktitle = "Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011", year = "2011", month = "27-29 " # apr, editor = "Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado", series = "LNCS", volume = "6621", publisher = "Springer Verlag", address = "Turin, Italy", pages = "142--153", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-20406-7", DOI = "doi:10.1007/978-3-642-20407-4_13", abstract = "In recent years, our biologic understanding was increased with the comprehension of the multitude of regulatory mechanisms that are fundamental in both processes of inheritance and of development, and some researchers advocate the need to explore computationally this new understanding. One of the outcomes was the Artificial Gene Regulatory (ARN) model, first proposed by Wolfgang Banzhaf. In this paper, we use this model as representation for a computational device and introduce new variation operators, showing experimentally that it is effective in solving a set of benchmark problems.", notes = "Part of \cite{Silva:2011:GP} EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011", } @InProceedings{Lopes:2011:GECCO, author = "Rui L. Lopes and Ernesto Costa", title = "Using feedback in a regulatory network computational device", booktitle = "GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0557-0", pages = "1499--1506", keywords = "genetic algorithms, genetic programming, Generative and developmental systems", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001576.2001778", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "The relationship between the genotype and the phenotype in Evolutionary Algorithms (EA) is a recurrent issue among researchers. Based on our current understanding of the multitude of the regulatory mechanisms that are fundamental in both processes of inheritance and of development in natural systems, some researchers start exploring computationally this new insight, including those mechanism in the EA. The Artificial Gene Regulatory (ARN) model, proposed by Wolfgang Banzhaf was one of the first tentatives. Following his seminal work some variants were proposed with increased capabilities. In this paper, we present another modification of this model, consisting in the use the regulatory network as a computational device where feedback edges are used. Using two classical benchmarks, the n-bit parity and the Fibonacci sequence problems, we show experimentally the effectiveness of the proposal.", notes = "Also known as \cite{2001778} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)", } @InProceedings{Lopes:2011:EPIA, author = "Rui L. Lopes and Ernesto Costa", title = "The Squares Problem and a Neutrality Analysis with {ReNCoDe}", booktitle = "Proceedings of the 15th Portuguese conference on Progress in artificial intelligence", year = "2011", editor = "Luis Antunes and H. Sofia Pinto", volume = "7026", series = "Lecture Notes in Computer Science", pages = "182--195", address = "Lisbon", month = "10-13 " # oct, organisation = "FCUL, University of Lisbon", publisher = "Springer", keywords = "genetic algorithms, genetic programming, evolution, network, neutrality, regulation, development", isbn13 = "978-3-642-24768-2", DOI = "doi:10.1007/978-3-642-24769-9_14", size = "14 pages", abstract = "Evolutionary Algorithms (EA) are stochastic search algorithms inspired by the principles of selection and variation posited by the theory of evolution, mimicking in a simple way those mechanisms. In particular, EAs approach differently from nature the genotype - phenotype relationship, and this view is a recurrent issue among researchers. Moreover, in spite of some performance improvements, it is a true fact that biology knowledge has advanced faster than our ability to incorporate novel biological ideas into EAs. Recently, some researchers start exploring computationally our new comprehension about the multitude of the regulatory mechanisms that are fundamental in both processes of inheritance and of development in natural systems, trying to include those mechanism in the EA. One of the first successful proposals is the Artificial Gene Regulatory (ARN) model, by Wolfgang Banzhaf. Soon after some variants of the ARN with increased capabilities were tested. In this paper, we further explore the capabilities of one of those, the Regulatory Network Computational Device, empowering it with feedback connections. The efficacy and efficiency of this alternative is tested experimentally using a typical benchmark problem for recurrent and developmental systems. In order to gain a better understanding about the reasons for the improved quality of the results, we undertake a preliminary study about the role of neutral mutations during the evolutionary process.", notes = "http://epia2011.appia.pt/", affiliation = "Center for Informatics and Systems of the University of Coimbra, Polo II - Pinhal de Marrocos, 3030-290 Coimbra, Portugal", } @Article{Lopes:2012:GPEM, author = "Rui L. Lopes and Ernesto Costa", title = "The Regulatory Network Computational Device", journal = "Genetic Programming and Evolvable Machines", year = "2012", volume = "13", number = "3", pages = "339--375", month = sep, note = "Special issue on selected papers from the 2011 European conference on genetic programming", keywords = "genetic algorithms, genetic programming, Genetic regulatory network, Evolution, Development, Neutrality", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-012-9160-y", size = "37 pages", abstract = "Evolutionary Algorithms (EA) approach the genotype-phenotype relationship differently than does nature, and this discrepancy is a recurrent issue among researchers. Moreover, in spite of some performance improvements, it is a fact that biological knowledge has advanced faster than our ability to incorporate novel biological ideas into EAs. Recently, some researchers have started exploring computationally new comprehension of the multitude of the regulatory mechanisms that are fundamental in both processes of inheritance and of development in natural systems, by trying to include those mechanisms in the EAs. One of the first successful proposals was the Artificial Gene Regulatory Network (ARN) model, by Wolfgang Banzhaf. Soon after some variants of the ARN were tested. we describe one of those, the Regulatory Network Computational Device, demonstrating experimentally its capabilities. The efficacy and efficiency of this alternative is tested experimentally using typical benchmark problems for Genetic Programming (GP) systems. We devise a modified factorial problem to investigate the use of feedback connections and the scalability of the approach. In order to gain a better understanding about the reasons for the improved quality of the results, we undertake a preliminary study about the role of neutral mutations during the evolutionary process.", notes = "N-bit parity, Fibonacci, squares, modified factorial, ReNCoDe symbolic regression, artificial ant \cite{langdon:1998:antspace}, cart centering, neutrality analysis, ARN EuroGP 2011 \cite{Silva:2011:GP}", affiliation = "Center for Informatics and Systems of the University of Coimbra, Polo II, Pinhal de Marrocos, 3030-290 Coimbra, Portugal", } @InProceedings{Lopes:2013:GECCO, author = "Rui L. Lopes and Ernesto Costa", title = "Genetic programming with genetic regulatory networks", booktitle = "GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference", year = "2013", editor = "Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi", isbn13 = "978-1-4503-1963-8", pages = "965--972", keywords = "genetic algorithms, genetic programming, inverted pendulum, artificial art", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2463372.2463488", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Evolutionary Algorithms (EA) approach differently from nature the genotype-phenotype relationship, and this view is a recurrent issue among researchers. Recently, some researchers have started exploring computationally the new comprehension of the multitude of regulatory mechanisms that are fundamental in both processes of inheritance and of development in natural systems, by trying to include those mechanisms in the EAs. One of the first successful proposals was the Artificial Regulatory Network (ARN) model. Soon after some variants of the ARN, including different improvements over the base model, were tested. In this paper, we combine two of those alternatives, demonstrating experimentally how the resulting model can deal with complex problems, including those that have multiple outputs. The efficacy and efficiency of this variant are tested experimentally using two benchmark problems that show how we can evolve a controller or an artificial artist.", notes = "Also known as \cite{2463488} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)", } @InProceedings{Lopes:2013:GECCOa, author = "Rui L. Lopes and Ernesto Costa", title = "{GEARNet}: grammatical evolution with artificial regulatory networks", booktitle = "GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference", year = "2013", editor = "Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi", isbn13 = "978-1-4503-1963-8", pages = "973--980", keywords = "genetic algorithms, genetic programming, grammatical evolution", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2463372.2463490", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "The Central Dogma of Biology states that genes made proteins that made us. This principle has been revised in order to incorporate the role played by a multitude of regulatory mechanisms that are fundamental in both the processes of inheritance and development. Evolutionary Computation algorithms are inspired by the theories of evolution and development, but most of the computational models proposed so far rely on a simple genotype to phenotype mapping. During the last years some researchers advocate the need to explore computationally the new biological understanding and have proposed different gene expression models to be incorporated in the algorithms.Two examples are the Artificial Regulatory Network (ARN) model, first proposed by Wolfgang Banzhaf, and the Grammatical Evolution (GE) model, introduced by Michael O'Neill and Conor Ryan. In this paper, we show how a modified version of the ARN can be combined with the GE approach, in the context of automatic program generation. More precisely, we rely on the ARN to control the gene expression process ending in an ordered set of proteins, and on the GE to build, guided by a grammar, a computational structure from that set. As a proof of concept we apply the hybrid model to two benchmark problems and show that it is effective in solving them.", notes = "Also known as \cite{2463490} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)", } @Article{Lopes:2020:IJPR, author = "Rui L. Lopes and Goncalo Figueira and Pedro Amorim and Bernardo Almada-Lobo", title = "Cooperative coevolution of expressions for {(r,Q)} inventory management policies using genetic programming", journal = "International Journal of Production Research", year = "2020", volume = "58", number = "2", pages = "509--525", keywords = "genetic algorithms, genetic programming", ISSN = "0020-7543", bibsource = "OAI-PMH server at oai.repec.org", identifier = "RePEc:taf:tprsxx:v:58:y:2020:i:2:p:509-525", oai = "oai:RePEc:taf:tprsxx:v:58:y:2020:i:2:p:509-525", URL = "http://hdl.handle.net/10.1080/00207543.2019.1597293", DOI = "doi:10.1080/00207543.2019.1597293", abstract = "There are extensive studies in the literature about the reorder point/order quantity policies for inventory management, also known as $(r, Q) $(r,Q) policies. Over time different algorithms have been proposed to calculate the optimal parameters given the demand characteristics and a fixed cost structure, as well as several heuristics and meta-heuristics that calculate approximations with varying accuracy. We propose a new meta-heuristic that evolves closed-form expressions for both policy parameters simultaneously - Cooperative Coevolutionary Genetic Programming. The implementation used for the experimental work is verified with published results from the optimal algorithm, and a well-known hybrid heuristic. The evolved expressions are compared to those algorithms, and to the expressions of previous Genetic Programming approaches available in the literature. The results outperform the previous closed-form expressions and demonstrate competitiveness against numerical methods, reaching an optimality gap of less than 1 percent, while being two orders of magnitude faster. Moreover, the evolved expressions are compact, have good generalisation capabilities, and present an interesting structure resembling previous heuristics.", } @InProceedings{Lopez:2002:WSC, author = "Antonio M. Lopez and Hilario Lopez and Luciano Sanchez", title = "GA-P based search of structures and parameters of dynamical process models", booktitle = "Advances in Soft Computing - Engineering, Design and Manufacturing", year = "2003", editor = "Jose Benitez and Oscar Cordon and Frank Hoffmann and Rajkumar Roy", pages = "371--380", address = "London", month = sep # " 23 - " # oct # " 4", publisher = "Springer", note = "on line", keywords = "genetic algorithms, genetic programming, GA-P algorithms, System Identification, Hierarchical models", URL = "http://www.di.uniovi.es/~luciano/articulos/alopez_wsc7.pdf", size = "10 pages", abstract = "The most effective approaches for evolutionary identifying dynamical processes depend on iterative trial-error searches in a hierarchical fashion: a new structure is proposed first; then, its set of parameters is numerically determined, and the process is repeated until a model accurate enough is found. Canonical Genetic Programming has been used to automate this search; but its output can be diffcult to interpret. Because of this reason, the use of hierarchical learning methods, that combine GP search of structures with deterministic optimisation algorithms, has been proposed. We will show in this paper that the output of such methods can be further improved with non hierarchical algorithms. In particular, we will show that the use of GA-P improves the interpretability of the models and does a better model search than previous approaches.", notes = "WSC7 http://wsc7.ugr.es Workshop in 2002 but published by Springer in October 2003. Fig. 2. SMOG evolution. Canonical GP is used for structural search and Hooke-Jeeves method is used for parameter tuning. Modeling direct electrical current motor.", } @Article{Lopez:2018:MCA, author = "Roberto Lopez and Luis Carlos {Gonzalez Gurrola} and Leonardo Trujillo and Olanda Prieto and Graciela Ramirez and Antonio Posada and Perla Juarez-Smith and Leticia Mendez", title = "How Am I Driving? Using Genetic Programming to Generate Scoring Functions for Urban Driving Behavior", journal = "Mathematical and Computational Applications", year = "2018", volume = "23", number = "2", pages = "19", month = jun, note = "Special Issue Numerical and Evolutionary Optimization", keywords = "genetic algorithms, genetic programming, driving scoring functions, driving events, risky driving, intelligent transportation systems", ISSN = "2297-8747", URL = "https://www.mdpi.com/2297-8747/23/2", URL = "https://www.mdpi.com/2297-8747/23/2/19/htm", URL = "https://www.mdpi.com/2297-8747/23/2/19/pdf/mca-23-00019.pdf", DOI = "doi:10.3390/mca23020019", size = "13 pages", abstract = "Road traffic injuries are a serious concern in emerging economies. Their death toll and economic impact are shocking, with 9 out of 10 deaths occurring in low or middle-income countries; and road traffic crashes representing 3percent of their gross domestic product. One way to mitigate these issues is to develop technology to effectively assist the driver, perhaps making him more aware about how her (his) decisions influence safety. Following this idea, in this paper we evaluate computational models that can score the behaviour of a driver based on a risky-safety scale. Potential applications of these models include car rental agencies, insurance companies or transportation service providers. In a previous work, we showed that Genetic Programming (GP) was a successful methodology to evolve mathematical functions with the ability to learn how people subjectively score a road trip. The input to this model was a vector of frequencies of risky manoeuvres, which were supposed to be detected in a sensor layer. Moreover, GP was shown, even with statistical significance, to be better than six other Machine Learning strategies, including Neural Networks, Support Vector Regression and a Fuzzy Inference system, among others. A pending task, since then, was to evaluate if a more detailed comparison of different strategies based on GP could improve upon the best GP model. In this work, we evaluate, side by side, scoring functions evolved by three different variants of GP. In the end, the results suggest that two of these strategies are very competitive in terms of accuracy and simplicity, both generating models that could be implemented in current technology that seeks to assist the driver in real-world scenarios.", notes = "journal MCA has no real page numbers", } @Article{Lopez:2018:ieeeSensors, author = "Jesus R. Lopez and Luis C. Gonzalez and Johan Wahlstrom and Manuel {Montes y Gomez} and Leonardo Trujillo and Graciela Ramirez-Alonso", title = "A Genetic Programming Approach for Driving Score Calculation in the Context of Intelligent Transportation Systems", journal = "IEEE Sensors Journal", year = "2018", volume = "18", number = "17", pages = "7183--7192", month = "1 " # sep, keywords = "genetic algorithms, genetic programming, Risky driving, driving profile, driving performance, intelligent transportation systems", ISSN = "1530-437X", DOI = "doi:10.1109/JSEN.2018.2856112", size = "10 pages", abstract = "According to the World Health Organization, recent years have seen a dramatic increase in the number of car accidents worldwide. In an attempt to ameliorate this situation, the automotive and telematics industry has tried to develop technology that can help the drivers make better and safer decisions. One approach is to develop systems that give feedback to the driver by means of a driving score, so that the driver can analyse his driving habits. By considering sensing platforms embedded into either vehicles or smart-phones, this paper models the driving score calculation task as a regression problem. Accordingly, we propose novel scoring functions that are generated through a metaheuristic for automatic program induction called genetic programming (GP). In addition to our proposal, we evaluate six other computational methods, three of which are based on works reported in the literature. Results show that the functions generated by the GP clearly outperform all studied competitors. Moreover...", notes = "also known as \cite{8410904}", } @InProceedings{Lopez:2017:EuroGP, author = "Uriel Lopez and Leonardo Trujillo and Yuliana Martinez and Pierrick Legrand and Enrique Naredo and Sara Silva", title = "{RANSAC-GP}: Dealing with Outliers in Symbolic Regression with Genetic Programming", booktitle = "EuroGP 2017: Proceedings of the 20th European Conference on Genetic Programming", year = "2017", month = "19-21 " # apr, editor = "Mauro Castelli and James McDermott and Lukas Sekanina", series = "LNCS", volume = "10196", publisher = "Springer Verlag", address = "Amsterdam", pages = "114--130", organisation = "species", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-55695-6", DOI = "doi:10.1007/978-3-319-55696-3_8", abstract = "Genetic programming (GP) has been shown to be a powerful tool for automatic modelling and program induction. It is often used to solve difficult symbolic regression tasks, with many examples in real-world domains. However, the robustness of GP-based approaches has not been substantially studied. In particular, the present work deals with the issue of outliers, data in the training set that represent severe errors in the measuring process. In general, a datum is considered an outlier when it sharply deviates from the true behaviour of the system of interest. GP practitioners know that such data points usually bias the search and produce inaccurate models. Therefore, this work presents a hybrid methodology based on the RAndom SAmpling Consensus (RANSAC) algorithm and GP, which we call RANSAC- GP. RANSAC is an approach to deal with outliers in parameter estimation problems, widely used in computer vision and related fields. On the other hand, this work presents the first application of RANSAC to symbolic regression with GP, with impressive results. The proposed algorithm is able to deal with extreme amounts of contamination in the training set, evolving highly accurate models even when the amount of outliers reaches 90percent.", notes = "Part of \cite{Castelli:2017:GP} EuroGP'2017 held inconjunction with EvoCOP2017, EvoMusArt2017 and EvoApplications2017", } @InProceedings{Lopez:2018:PPSN, author = "Uriel Lopez and Leonardo Trujillo and Pierrick Legrand", title = "Filtering Outliers in One Step with Genetic Programming", booktitle = "15th International Conference on Parallel Problem Solving from Nature", year = "2018", editor = "Anne Auger and Carlos M. Fonseca and Nuno Lourenco and Penousal Machado and Luis Paquete and Darrell Whitley", volume = "11101", series = "LNCS", pages = "209--222", address = "Coimbra, Portugal", month = "8-12 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Outliers, Robust regression", isbn13 = "978-3-319-99252-5", URL = "https://www.springer.com/gp/book/9783319992587", DOI = "doi:10.1007/978-3-319-99253-2_17", abstract = "Outliers are one of the most difficult issues when dealing with real-world modelling tasks. Even a small percentage of outliers can impede a learning algorithm's ability to fit a dataset. While robust regression algorithms exist, they fail when a dataset is corrupted by more than 50percent of outliers (breakdown point). In the case of Genetic Programming, robust regression has not been properly studied. In this paper we present a method that works as a filter, removing outliers from the target variable (vertical outliers). The algorithm is simple, it uses a randomly generated population of GP trees to determine which target values should be labelled as outliers. The method is highly efficient. Results show that it can return a clean dataset when contamination reaches as high as 90%, and may be able to handle higher levels of contamination. In this study only synthetic univariate benchmarks are used to evaluate the approach, but it must be stressed that no other approaches can deal with such high levels of outlier contamination while requiring such small computational effort.", notes = "PPSN2018 http://ppsn2018.dei.uc.pt This two-volume set LNCS 11101 and 11102 constitutes the refereed proceedings of the 15th International Conference on Parallel Problem Solving from Nature, PPSN 2018", } @InProceedings{Lopez:2020:GECCO, author = "Uriel Lopez and Leonardo Trujillo and Sara Silva and Leonardo Vanneschi and Pierrick Legrand", title = "Unlabeled Multi-Target Regression with Genetic Programming", year = "2020", editor = "Carlos Artemio {Coello Coello} and Arturo Hernandez Aguirre and Josu Ceberio Uribe and Mario Garza Fabre and Gregorio {Toscano Pulido} and Katya Rodriguez-Vazquez and Elizabeth Wanner and Nadarajen Veerapen and Efren Mezura Montes and Richard Allmendinger and Hugo Terashima Marin and Markus Wagner and Thomas Bartz-Beielstein and Bogdan Filipic and Heike Trautmann and Ke Tang and John Koza and Erik Goodman and William B. Langdon and Miguel Nicolau and Christine Zarges and Vanessa Volz and Tea Tusar and Boris Naujoks and Peter A. N. Bosman and Darrell Whitley and Christine Solnon and Marde Helbig and Stephane Doncieux and Dennis G. Wilson and Francisco {Fernandez de Vega} and Luis Paquete and Francisco Chicano and Bing Xue and Jaume Bacardit and Sanaz Mostaghim and Jonathan Fieldsend and Oliver Schuetze and Dirk Arnold and Gabriela Ochoa and Carlos Segura and Carlos Cotta and Michael Emmerich and Mengjie Zhang and Robin Purshouse and Tapabrata Ray and Justyna Petke and Fuyuki Ishikawa and Johannes Lengler and Frank Neumann", isbn13 = "9781450371285", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377930.3389846", DOI = "doi:10.1145/3377930.3389846", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference", pages = "976--984", size = "9 pages", keywords = "genetic algorithms, genetic programming, unlabeled multi-target regression, multi-target regression, clustering, RANSAC", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", abstract = "Machine Learning (ML) has now become an important and ubiquitous tool in science and engineering, with successful applications in many real-world domains. However, there are still areas in need of improvement, and problems that are still considered difficult with off-the-shelf methods. One such problem is Multi Target Regression (MTR), where the target variable is a multidimensional tuple instead of a scalar value. In this work, we propose a more difficult variant of this problem which we call Unlabeled MTR (uMTR), where the structure of the target space is not given as part of the training data. This version of the problem lies at the intersection of MTR and clustering, an unexplored problem type. Moreover, this work proposes a solution method for uMTR, a hybrid algorithm based on Genetic Programming and RANdom SAmple Consensus (RANSAC). Using a set of benchmark problems, we are able to show that this approach can effectively solve the uMTR problem.", notes = "Also known as \cite{10.1145/3377930.3389846} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @Article{Lopez:2013:KS, author = "Victoria Lopez and Alberto Fernandez and Maria Jose {del Jesus} and Francisco Herrera", title = "A hierarchical genetic fuzzy system based on genetic programming for addressing classification with highly imbalanced and borderline data-sets", journal = "Knowledge-Based Systems", volume = "38", pages = "85--104", year = "2013", note = "Special Issue on Advances in Fuzzy Knowledge Systems: Theory and Application", keywords = "genetic algorithms, genetic programming, Fuzzy rule based classification systems, Hierarchical fuzzy partitions, Genetic rule selection, Tuning, Imbalanced data-sets, Borderline examples", ISSN = "0950-7051", DOI = "doi:10.1016/j.knosys.2012.08.025", URL = "http://www.sciencedirect.com/science/article/pii/S0950705112002596", size = "20 pages", abstract = "Lots of real world applications appear to be a matter of classification with imbalanced data-sets. This problem arises when the number of instances from one class is quite different to the number of instances from the other class. Traditionally, classification algorithms are unable to correctly deal with this issue as they are biased towards the majority class. Therefore, algorithms tend to misclassify the minority class which usually is the most interesting one for the application that is being sorted out. Among the available learning approaches, fuzzy rule-based classification systems have obtained a good behaviour in the scenario of imbalanced data-sets. In this work, we focus on some modifications to further improve the performance of these systems considering the usage of information granulation. Specifically, a positive synergy between data sampling methods and algorithmic modifications is proposed, creating a genetic programming approach that uses linguistic variables in a hierarchical way. These linguistic variables are adapted to the context of the problem with a genetic process that combines rule selection with the adjustment of the lateral position of the labels based on the 2-tuples linguistic model. An experimental study is carried out over highly imbalanced and borderline imbalanced data-sets which is completed by a statistical comparative analysis. The results obtained show that the proposed model outperforms several fuzzy rule based classification systems, including a hierarchical approach and presents a better behavior than the C4.5 decision tree.", } @Article{Lopez:2020:SIM, author = "Luis Fernando de Mingo Lopez and Nuria Gomez Blas and Angel Luis Castellanos Penuela and Juan Bautista Castellanos Penuela", title = "Swarm Intelligence Models: Ant Colony Systems Applied to {BNF} Grammars Rule Derivation", journal = "International Journal of Foundations of Computer Science (IJFCS)", volume = "31", number = "1", pages = "103--116", month = jan, year = "2020", note = "Special Issue: A Collection of Papers in Honour of the 60th Birthday of Victor Mitrana", keywords = "genetic algorithms, genetic programming, grammatical evolution", ISSN = "0129-0541", bibdate = "Fri Jan 31 07:25:10 MST 2020", bibsource = "http://www.math.utah.edu/pub/tex/bib/ijfcs.bib", URL = "http://www.worldscientific.com/loi/ijfcs", URL = "https://www.worldscientific.com/doi/10.1142/S0129054120400079", DOI = "doi:10.1142/S0129054120400079", abstract = "Ant Colony Systems have been widely employed in optimisation issues primarily focused on path finding optimisation, such as Traveling Salesman Problem. The main advantage lies in the choice of the edge to be explored, defined using the idea of pheromone. This article proposes the use of Ant Colony Systems to explore a Backus-Naur form grammar whose elements are solutions to a given problem. Similar studies, without using Ant Colonies, have been used to solve optimisation problems, such as Grammatical Swarm (based on Particle Swarm Optimisation) and Grammatical Evolution (based on Genetic Algorithms). Proposed algorithm opens the way to a new branch of research in Swarm Intelligence, which until now has been almost non-existent, using ant colony algorithms to solve problems described by a grammar.", acknowledgement = "Nelson H. F. Beebe, University of Utah, Department of Mathematics, 110 LCB, 155 S 1400 E RM 233, Salt Lake City, UT 84112-0090, USA, Tel: +1 801 581 5254, FAX: +1 801 581 4148, e-mail: \path|beebe@math.utah.edu|, \path|beebe@acm.org|, \path|beebe@computer.org| (Internet), URL: \path|http://www.math.utah.edu/~beebe/ |", fjournal = "International Journal of Foundations of Computer Science (IJFCS)", journal-URL = "http://www.worldscientific.com/loi/ijfcs", } @Article{LOPEZ-CARMONA:2021:SS, author = "Miguel A. Lopez-Carmona and Alvaro {Paricio Garcia}", title = "{CellEVAC:} An adaptive guidance system for crowd evacuation through behavioral optimization", journal = "Safety Science", volume = "139", pages = "105215", year = "2021", ISSN = "0925-7535", DOI = "doi:10.1016/j.ssci.2021.105215", URL = "https://www.sciencedirect.com/science/article/pii/S0925753521000606", keywords = "genetic algorithms, genetic programming, Crowd evacuation, Behavioral optimization, Exit-choice decisions, Simulation-optimization modeling, Cell-based evacuation, Evacuation safety", abstract = "A critical aspect of crowds' evacuation processes is the dynamism of individual decision making. Identifying optimal strategies at an individual level may improve both evacuation time and safety, which is essential for developing efficient evacuation systems. Here, we investigate how to favor a coordinated group dynamic through optimal exit-choice instructions using behavioral strategy optimization. We propose and evaluate an adaptive guidance system (Cell-based Crowd Evacuation, CellEVAC) that dynamically allocates colors to cells in a cell-based pedestrian positioning infrastructure, to provide efficient exit-choice indications. The operational module of CellEVAC implements an optimized discrete-choice model that integrates the influential factors that would make evacuees adapt their exit choice. To optimize the model, we used a simulation-optimization modeling framework that integrates microscopic pedestrian simulation based on the classical Social Force Model. In the majority of studies, the objective has been to optimize evacuation time. In contrast, we paid particular attention to safety by using Pedestrian Fundamental Diagrams that model the dynamics of the exit gates. CellEVAC has been tested in a simulated real scenario (Madrid Arena) under different external pedestrian flow patterns that simulate complex pedestrian interactions. Results showed that CellEVAC outperforms evacuation processes in which the system is not used, with an exponential improvement as interactions become complex. We compared our system with an existing approach based on Cartesian Genetic Programming. Our system exhibited a better overall performance in terms of safety, evacuation time, and the number of revisions of exit-choice decisions. Further analyses also revealed that Cartesian Genetic Programming generates less natural pedestrian reactions and movements than CellEVAC. The fact that the decision logic module is built upon a behavioral model seems to favor a more natural and effective response. We also found that our proposal has a positive influence on evacuations even for a low compliance rate (40percent)", } @Article{lopez-diez:2003:AFC, author = "E. Consuelo Lopez-Diez and Giorgio Bianchi and Royston Goodacre", title = "Rapid Quantitative Assessment of the Adulteration of Virgin Olive Oils with Hazelnut Oils Using Raman Spectroscopy and Chemometrics", journal = "Journal of Agricultural and Food Chemistry", year = "2003", volume = "51", number = "21", pages = "6145--6150", keywords = "genetic algorithms, genetic programming, Raman spectroscopy, olive oil, hazelnut oil, adulteration, quantification, principal component analysis, partial least-squares regression", DOI = "doi:10.1021/jf034493d", abstract = "The authentication of extra virgin olive oil and its adulteration with lower-priced oils are serious problems in the olive oil industry. In addition to the obvious effect on producer profits, adulteration can also cause severe health and safety problems. A number of techniques, including chromatographic and spectroscopic methods, have recently been employed to assess the purity of olive oils. In this study Raman spectroscopy together with multivariate and evolutionary computational-based methods have been employed to assess the ability of Raman spectroscopy to discriminate between chemically very closely related oils. Additionally, the levels of hazelnut oils used to adulterate extra virgin olive oil were successfully quantified using partial least squares and genetic programming.", } @InProceedings{Lopez-Herrejon:2013:CSMR, author = "Roberto E. Lopez-Herrejon and Alexander Egyed", title = "SBSE4VM: Search Based Software Engineering for Variability Management", booktitle = "17th European Conference on Software Maintenance and Reengineering", year = "2013", pages = "441--444", keywords = "genetic algorithms, genetic programming, SBSE, SPL, Software Product Lines, Feature Orientation, Product Line Evolution, Search Based Software Engineering, Fixing Inconsistencies", URL = "http://ieeexplore.ieee.org/xpl/abstractAuthors.jsp?arnumber=6498506", DOI = "doi:10.1109/CSMR.2013.67", size = "4 pages", abstract = "SBSE4VM is an ongoing Lise Meitner Fellowship project sponsored by the Austrian Science Fund (FWF) that runs for two years. The driving goal of the project is to explore the application of Search Based Software Engineering techniques to reverse engineer, evolve, and fix inconsistencies in systems with variability.", } @Misc{DBLP:journals/corr/Lopez-HerrejonFCLEA14, author = "Roberto E. Lopez-Herrejon and Javier Ferrer and Francisco Chicano and Lukas Linsbauer and Alexander Egyed and Enrique Alba", title = "A Hitchhiker's Guide to Search-Based Software Engineering for Software Product Lines", howpublished = "arXiv", year = "2014", month = "11 " # jun, keywords = "genetic algorithms, genetic programming, SBSE, SPL, Systematic Mapping Study", URL = "http://arxiv.org/abs/1406.2823", bibsource = "DBLP, http://dblp.uni-trier.de", size = "10 pages", abstract = "Search Based Software Engineering (SBSE) is an emerging discipline that focuses on the application of search-based optimisation techniques to software engineering problems. The capacity of SBSE techniques to tackle problems involving large search spaces make their application attractive for Software Product Lines (SPLs). In recent years, several publications have appeared that apply SBSE techniques to SPL problems. In this paper, we present the results of a systematic mapping study of such publications. We identified the stages of the SPL life cycle where SBSE techniques have been used, what case studies have been employed and how they have been analysed. This mapping study revealed potential venues for further research as well as common misunderstanding and pitfalls when applying SBSE techniques that we address by providing a guideline for researchers and practitioners interested in exploiting these techniques.", notes = "Used GP-bib, see also \cite{LopezHerrejon201533}", } @Article{LopezHerrejon201533, author = "Roberto E. Lopez-Herrejon and Lukas Linsbauer and Alexander Egyed", title = "A systematic mapping study of search-based software engineering for software product lines", journal = "Information and Software Technology", year = "2015", volume = "61", pages = "33--51", month = may, keywords = "genetic algorithms, genetic programming, SBSE, SPL, Software Product Lines, Evolutionary algorithm, Metaheuristics", ISSN = "0950-5849", URL = "http://www.sciencedirect.com/science/article/pii/S0950584915000166", DOI = "doi:10.1016/j.infsof.2015.01.008", size = "19 pages", abstract = "Context Search-Based Software Engineering (SBSE) is an emerging discipline that focuses on the application of search-based optimization techniques to software engineering problems. Software Product Lines (SPLs) are families of related software systems whose members are distinguished by the set of features each one provides. SPL development practices have proven benefits such as improved software reuse, better customization, and faster time to market. A typical SPL usually involves a large number of systems and features, a fact that makes them attractive for the application of SBSE techniques which are able to tackle problems that involve large search spaces. Objective The main objective of our work is to identify the quantity and the type of research on the application of SBSE techniques to SPL problems. More concretely, the SBSE techniques that have been used and at what stage of the SPL life cycle, the type of case studies employed and their empirical analysis, and the fora where the research has been published. Method systematic mapping study was conducted with five research questions and assessed 77 publications from 2001, when the term SBSE was coined, until 2014. Results The most common application of SBSE techniques found was testing followed by product configuration, with genetic algorithms and multi-objective evolutionary algorithms being the two most commonly used techniques. Our study identified the need to improve the robustness of the empirical evaluation of existing research, a lack of extensive and robust tool support, and multiple avenues worthy of further investigation. Conclusions Our study attested the great synergy existing between both fields, corroborated the increasing and ongoing interest in research on the subject, and revealed challenging open research questions.", notes = "'It has a few references to GP.'", } @InProceedings{Lopez-Herrejon:2015:gi, author = "Roberto E. Lopez-Herrejon and Lukas Linsbauer and Wesley K. G. Assuncao and Stefan Fischer and Silvia R. Vergilio and Alexander Egyed", title = "Genetic Improvement for Software Product Lines: An Overview and a Roadmap", booktitle = "Genetic Improvement 2015 Workshop", year = "2015", editor = "William B. Langdon and Justyna Petke and David R. White", pages = "823--830", address = "Madrid", publisher_address = "New York, NY, USA", month = "11-15 " # jul, organisation = "SIGEvo", publisher = "ACM", keywords = "genetic algorithms, genetic programming, Genetic Improvement, SPL, evolutionary algorithms, software product lines, variability", isbn13 = "978-1-4503-3488-4", URL = "http://gpbib.cs.ucl.ac.uk/gi2015/genetic_improvement_for_software_product_lines-an_overview_and_a_roadmap.pdf", URL = "http://doi.acm.org/10.1145/2739482.2768422", DOI = "doi:10.1145/2739482.2768422", size = "8 pages", abstract = "Software Product Lines (SPLs) are families of related software systems that provide different combinations of features. Extensive research and application attest to the significant economical and technological benefits of employing SPL practices. However, there are still several challenges that remain open. Salient among them is reverse engineering SPLs from existing variants of software systems and their subsequent evolution. In this paper, we aim at sketching connections between research on these open SPL challenges and ongoing work on Genetic Improvement. Our hope is that by drawing such connections we can spark the interest of both research communities on the exciting synergies at the intersection of these subject areas.", notes = "http://gpbib.cs.ucl.ac.uk/gi2015/gi_2015_slides/asuncao/gi-v7.pdf", } @InProceedings{Lopez-Herrera:2008:ISKE, author = "A. G. Lopez-Herrera and E. Herrera-Viedma and F. Herrera", title = "A Multiobjective Evolutionary Algorithm for Spam {E-mail} Filtering", booktitle = "3rd International Conference on Intelligent System and Knowledge Engineering, ISKE 2008", year = "2008", month = nov, volume = "1", pages = "366--371", keywords = "genetic algorithms, genetic programming, NSGA-II, multiobjective evolutionary algorithm, spam e-mail filtering, unsolicited commercial email, e-mail filters, unsolicited e-mail", DOI = "doi:10.1109/ISKE.2008.4730957", abstract = "Unsolicited commercial email, also known as spam, has been a major problem on the Internet. In this paper a well known multiobjective evolutionary Algorithm, NSGA-II, is first time used for spam e-mail filtering. NSGA-II is adapted to use Genetic Programming components to achieve a set of filtering rules with different profiles.", notes = "Also known as \cite{4730957}", } @Article{LopezHerrera20092192, author = "A. G. Lopez-Herrera and E. Herrera-Viedma and F. Herrera", title = "Applying multi-objective evolutionary algorithms to the automatic learning of extended {Boolean} queries in fuzzy ordinal linguistic information retrieval systems", journal = "Fuzzy Sets and Systems", volume = "160", number = "15", pages = "2192--2205", year = "2009", note = "Special Issue: The Application of Fuzzy Logic and Soft Computing in Information Management", ISSN = "0165-0114", DOI = "doi:10.1016/j.fss.2009.02.013", URL = "http://www.sciencedirect.com/science/article/B6V05-4VPM59B-4/2/21a5a32bf1a659a371ce5c4d320da182", keywords = "genetic algorithms, genetic programming, MOGP, Information retrieval systems, Inductive query by example, Multi-objective evolutionary algorithms, Query learning", size = "14 pages", abstract = "The performance of information retrieval systems (IRSs) is usually measured using two different criteria, precision and recall. Precision is the ratio of the relevant documents retrieved by the IRS in response to a user's query to the total number of documents retrieved, whilst recall is the ratio of the number of relevant documents retrieved to the total number of relevant documents for the user's query that exist in the documentary database. In fuzzy ordinal linguistic IRSs (FOLIRSs), where extended Boolean queries are used, defining the user's queries in a manual way is usually a complex task. In this contribution, our interest is focused on the automatic learning of extended Boolean queries in FOLIRSs by means of multi-objective evolutionary algorithms considering both mentioned performance criteria. We present an analysis of two well-known general-purpose multi-objective evolutionary algorithms to learn extended Boolean queries in FOLIRSs. These evolutionary algorithms are the non-dominated sorting genetic algorithm (NSGA-II) and the strength Pareto evolutionary algorithm (SPEA2).", } @InProceedings{Lopez-Ibanez:2020:GECCOcomp, author = "Manuel Lopez-Ibanez and Thomas Stuetzle", title = "Automated Algorithm Configuration and Design", year = "2020", editor = "Richard Allmendinger and Hugo Terashima Marin and Efren Mezura Montes and Thomas Bartz-Beielstein and Bogdan Filipic and Ke Tang and David Howard and Emma Hart and Gusz Eiben and Tome Eftimov and William {La Cava} and Boris Naujoks and Pietro Oliveto and Vanessa Volz and Thomas Weise and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Rui Wang and Ran Cheng and Guohua Wu and Miqing Li and Hisao Ishibuchi and Jonathan Fieldsend and Ozgur Akman and Khulood Alyahya and Juergen Branke and John R. Woodward and Daniel R. Tauritz and Marco Baioletti and Josu Ceberio Uribe and John McCall and Alfredo Milani and Stefan Wagner and Michael Affenzeller and Bradley Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and Nayat Sanchez-Pi and Luis Marti and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and Matthew Johns and Nick Ross and Ed Keedwell and Herman Mahmoud and David Walker and Anthony Stein and Masaya Nakata and David Paetzel and Neil Vaughan and Stephen Smith and Stefano Cagnoni and Robert M. Patton and Ivanoe {De Falco} and Antonio {Della Cioppa} and Umberto Scafuri and Ernesto Tarantino and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Erik Hemberg and Riyad Alshammari and Tokunbo Makanju and Fuijimino-shi and Ivan Zelinka and Swagatam Das and Ponnuthurai Nagaratnam and Roman Senkerik", isbn13 = "9781450371278", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377929.3389875", DOI = "doi:10.1145/3377929.3389875", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion", pages = "1075--1100", size = "26 pages", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", note = "Tutorial", keywords = "genetic algorithms, genetic programming, grammatical evolution", notes = "Also known as \cite{10.1145/3377929.3389875} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @Article{Lopez-Ibanez:2021:sigevolution, author = "Manuel Lopez-Ibanez", title = "{ACM SIGEVO} Best Dissertation Award 2021", journal = "SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation", year = "2021", volume = "14", number = "3", pages = "5--7", month = oct, keywords = "genetic algorithms, genetic programming", ISSN = "1931-8499", URL = "https://evolution.sigevo.org/issues/HTML/sigevolution-14-3/home.html", URL = "https://dl.acm.org/citation.cfm?id=3357514", DOI = "doi:10.1145/3490676.3490677", abstract = "Winner \cite{DBLP:phd/basesearch/Virgolin20} This dissertation introduces significant innovations in model-based genetic programming (GP) enabling the efficient discovery of compact and accurate solutions, which is key for supporting explainable Artificial Intelligence. In collaboration with medical researchers, the proposed innovations are applied successfully to improve historical 3D-dose reconstruction in radiation oncology which is used to design more effective treatments for cancer patients. This work is a stellar example of the positive real-world impact of Evolutionary Computation. https://sig.sigevo.org/index.html/SIGEVO+Dissertation+Award", notes = "University of Malaga, Spain", } @Article{Lopez-Ibanez:2022:sigevolution, author = "Manuel Lopez-Ibanez", title = "ACM SIGEVO Best Dissertation Award 2022", journal = "SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation", year = "2022", volume = "15", number = "3", month = "Fall", keywords = "genetic algorithms, genetic programming", ISSN = "1931-8499", URL = "https://evolution.sigevo.org/issues/HTML/sigevolution-15-3/index.htm#ACM_SIGEVO_Best_Dissertation_Award_2022", URL = "https://dl.acm.org/action/showFmPdf?doi=10.1145%2F3578482", abstract = "Winner Alexandru Marginean from University College London (UK) \cite{Marginean_10137954_thesis_redacted} This dissertation proposes tools for multi-lingual automated code transplantation based on evolutionary computation to transplant functionality from one piece of software to another one. These methods speed up software development and automate bug fixing. Already 2.9 billion people daily run software that has been automatically repaired by the Sapfix system described in this work. Honorary mention Fangfang Zhang from the Victoria University of Wellington \cite{Fangfang_Zhang:thesis}. Honorary mention: Alexander Hagg from Leiden University.", notes = "https://evolution.sigevo.org/", } @Misc{oai:HAL:hal-01207508v1, author = "Victor R. Lopez-Lopez and Leonardo Trujillo and Pierrick Legrand and Victor H. Diaz-Ramirez", title = "Evaluation of Local Feature Extraction Methods Generated through Genetic Programming on Visual {SLAM}", howpublished = "HAL CCSD", year = "2014", keywords = "genetic algorithms, genetic programming", type = "info:eu-repo/semantics/conferenceObject", URL = "https://hal.inria.fr/hal-01207508", URL = "https://www.researchgate.net/publication/282864073_Evaluation_of_Local_Feature_Extraction_Methods_Generated_through_Genetic_Programming_on_Visual_SLAM", annote = "Instituto Tecnol{\'o}gico de Tijuana [Tijuana]; Universit{\'e} de Bordeaux (UB); Institut de Math{\'e}matiques de Bordeaux (IMB) ; Universit{\'e} Bordeaux Segalen - Bordeaux 2 - Universit{\'e} Sciences et Technologies - Bordeaux 1 - CNRS", bibsource = "OAI-PMH server at api.archives-ouvertes.fr", contributor = "INRIA Bordeaux - Sud-Ouest and INRIA", coverage = "Ixtapa, Mexico", description = "International audience", identifier = "hal-01207508", language = "en", oai = "oai:HAL:hal-01207508v1", source = "Proceedings of the 2014 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC 2014); Proceedings of the 2014 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC 2014), 2014, Ixtapa, Mexico", size = "6 pages", abstract = "The detection and description of locally salient regions is one of the most widely used low-level processes in modern computer vision systems. The general approach relies on the detection of stable and invariant image features, that can be uniquely characterized using compact numerical vectors or descriptors. Many detection and description algorithms have been proposed, most of them derived using different assumptions, problem models or analysis techniques. On the other hand, the current work focuses on detection and description algorithms that were automatically generated using genetic programming (GP), an evolutionary algorithm intended for automatic program induction. In particular, the goal is to determine if these operators are competitive with traditional techniques in a real-world scenario, specifically a vision-based SLAM system. Obtained results indicate that operators that were automatically generated using GP achieve very strong performance, clearly outperforming standard techniques. It seems that the GP-based design process is indeed capable of producing robust and efficient solutions, that can be used as off-the shelf tools for difficult computer vision applications.", notes = "July 2016 NOT in ROPER 2014 proceedings http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?filter%3DAND%28p_IS_Number%3A7036277%29&rowsPerPage=75&pageNumber=1&resultAction=REFINE&resultAction=ROWS_PER_PAGE# http://www.proceedings.com/25242.html also known as \cite{lopezlopez:hal-01207508}", } @InProceedings{Lopez:2016:GI, author = "Victor R. Lopez-Lopez and Leonardo Trujillo and Pierrick Legrand and Gustavo Olague", title = "Genetic Programming: From design to improved implementation", booktitle = "Genetic Improvement 2016 Workshop", year = "2016", editor = "Justyna Petke and David R. White and Westley Weimer", pages = "1147--1154", address = "Denver", publisher_address = "New York, NY, USA", month = jul # " 20-24", organisation = "SIGEvo", publisher = "ACM", keywords = "genetic algorithms, genetic programming, Genetic Improvement, SBSE, computer vision", URL = "http://geneticimprovementofsoftware.com/wp-content/uploads/2016/06/Genetic_Programming_From_Design_to_Improved_Implementation.pdf", DOI = "doi:10.1145/2908961.2931693", size = "8 pages", abstract = "Genetic programming (GP) is an evolutionary-based search paradigm that is well suited to automatically solve difficult design problems. The general principles of GP have been used to evolve mathematical functions, models, image operators, programs, and even antennas and lenses. Since GP evolves the syntax and structure of a solution, the evolutionary process can be carried out in one environment and the solution can then be ported to another. However, given the nature of GP it is common that the evolved designs are unorthodox compared to traditional approaches used in the problem domain. Therefore, efficiently porting, improving or optimizing an evolved design might not be a trivial task. In this work we argue that the same GP principles used to evolve the solution can then be used to optimize a particular new implementation of the design, following the Genetic Improvement approach. In particular, this paper presents a case study where evolved image operators are ported from Matlab to OpenCV, and then the source code is optimized an improved using Genetic Improvement of Software for Multiple Objectives (GISMOE). In the example we show that functional behaviour is maintained (output image) while improving non-functional properties (computation time). Despite the fact that this first example is a simple case, it clearly illustrates the possibilities of using GP principles in two distinct stages of the software development process, from design to improved implementation.", notes = "GPLAB MATLAB, http://www.cs.ucl.ac.uk/staff/ucacbbl/gismo/ http://www.tree-lab.org Fitness from normalized cross correlation and run time on one test case. pop size=10. 21 percent faster by discarding 3 operations GISMOE GECCO-2016 Workshop http://geneticimprovementofsoftware.com/", } @Article{Lopez-Lopez:2016:CyS, author = "Victor R. Lopez-Lopez and Leonardo Trujillo and Pierrick Legrand and Victor H. Diaz-Ramirez and Gustavo Olague", title = "Comparison of Local Feature Extraction Paradigms Applied to Visual SLAM", journal = "Computacion y Sistemas", year = "2016", volume = "20", number = "4", pages = "565--587", note = "Thematic Issue: Research Advances and Applications of Evolutionary Computation", keywords = "genetic algorithms, genetic programming, Local features, composite correlation filter, SLAM", ISSN = "1405-5546", URL = "http://www.cys.cic.ipn.mx/ojs/index.php/CyS/article/view/2500", URL = "http://www.cys.cic.ipn.mx/ojs/index.php/CyS/article/view/2500/2190", DOI = "doi:10.13053/CyS-20-4-2500", size = "23 pages", abstract = "The detection and description of locally salient regions is one of the most widely used low-level processes in modern computer vision systems. The general approach relies on the detection of stable and invariant image features that can be uniquely characterized using compact descriptors. Many detection and description algorithms have been proposed, most of them derived using different assumptions or problem models. This work presents a comparison of different approaches towards the feature extraction problem, namely: (1) standard computer vision techniques, (2) automatic synthesis techniques based on genetic programming (GP), and (3) a new local descriptor based on composite correlation filtering, proposed for the first time in this paper. The considered methods are evaluated on a difficult real-world problem, vision-based simultaneous localization and mapping (SLAM). Using three experimental scenarios, results indicate that the GP-based methods and the correlation filtering techniques outperform widely used computer vision algorithms such as the Harris and Shi-Tomasi detectors and the Speeded Up Robust Features descriptor.", notes = "In english. robot RX-60 with a web-cam", } @InProceedings{Lopez-Lopez:2018:GI5, author = "Victor R. Lopez-Lopez and Leonardo Trujillo and Pierrick Legrand", title = "Novelty Search for software improvement of a {SLAM} system", booktitle = "5th edition of GI @ GECCO 2018", year = "2018", editor = "Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and John R. Woodward and Shin Yoo", pages = "1598--1605", address = "Kyoto, Japan", month = "15-19 " # jul, organisation = "ACM SIGEvo", publisher = "ACM", keywords = "genetic algorithms, genetic programming, genetic improvement, vision for robotics, SLAM, Novelty Search", URL = "http://www.cs.stir.ac.uk/events/gecco-gi-2018/papers/novelty_search_for_software_improvement_of_a_SLAM_system.pdf", DOI = "doi:10.1145/3205651.3208237", size = "8 pages", abstract = "Genetic Improvement (GI) performs a search at the level of source code to find the best variant of a baseline system that improves non-functional properties while maintaining functionality, with noticeable results in several domains. There a many aspects of this general approach that are currently being explored. In particular, this work deals to the way in which the search is guided to efficiently explore the search space of possible software versions in which GI operates. The proposal is to integrate Novelty Search (NS) within the GISMOE GI framework to improve KinectFusion, which is a vision-based Simultaneous Localization and Mapping (SLAM) system that is used for augmented reality, autonomous vehicle navigation, and many other real-world applications. This is one of a small set of works that have successfully combined NS with a GP system, and the first time that it has been used for software improvement. To achieve this, we propose a new behaviour descriptor for SLAM algorithms, based on state-of-the-art benchmarking and present results that show that NS can produce significant improvement gains in a GI setting, when considering execution time and trajectory estimation as the main performance criteria.", notes = "SLAMbench, GPU KinectFusion, CUDA. C++, GISMOE, BNF grammar, 200 generations. KVM, Ubuntu 16.0, GNU Parallel. ICL-NUM videos 2 and 4 are used to train. 'improvements on both ATE and EXT, of 26.3percent and 12.5percent' http://www.cs.stir.ac.uk/events/gecco-gi-2018/cfp.html", } @Article{Lopez-Lopez:2019:SC, author = "Victor R. Lopez-Lopez and Leonardo Trujillo and Pierrick Legrand", title = "Applying Genetic Improvement to a Genetic Programming library in {C++}", journal = "Soft Computing", year = "2019", volume = "23", number = "22", pages = "11593--11609", month = nov, keywords = "genetic algorithms, genetic programming, Genetic Improvement, ADA, GISMOE", ISSN = "1432-7643", URL = "https://www.researchgate.net/publication/328737052_Applying_Genetic_Improvement_to_a_Genetic_Programming_library_in_C", URL = "https://www.researchgate.net/profile/Leonardo_Trujillo/publication/328737052_Applying_Genetic_Improvement_to_a_Genetic_Programming_library_in_C.pdf", URL = "https://hal.inria.fr/hal-01911943", DOI = "doi:10.1007/s00500-018-03705-6", size = "17 pages", abstract = "A young subfield of Evolutionary Computing that has gained the attention of many researchers in recent years is Genetic Improvement. It uses an automated search method that directly modifies the source code or binaries of a software system to find improved versions based on some given criteria. Genetic Improvement has achieved notable results and the acceptance of several research communities, namely software engineering and evolutionary computation. Over the past 10 years there has been core publications on the subject, however, we have identified, to the best of our knowledge, that there is no work on applying Genetic Improvement to a meta-heuristic system. In this work we apply the GI framework called GISMO to the Beagle Puppy library version 0.1 in C++, a Genetic Programming system configured to perform symbolic regression on several benchmark and real-world problems. The objective is to improve the processing time while maintaining a similar or better test-fitness of the best individual produced by the unmodified Genetic Programming search. Results show that GISMO can generate individuals that present an improvement on those two key aspects over some problems, while also reducing the effects of bloat, one of the main issues in Genetic Programming.", notes = "Beagle puppy GP, PAPI timing library. Section 7 'GI to improve GP implementation' Also known as \cite{lopezlopez:hal-01911943}", } @Article{Lopez-Manrique:2018:Energies, author = "Luis M. Lopez-Manrique and E. V. Macias-Melo and O. {May Tzuc} and A. Bassam and K. M. Aguilar-Castro and I. Hernandez-Perez", title = "Assessment of Resource and Forecast Modeling of Wind Speed through An Evolutionary Programming Approach for the North of Tehuantepec Isthmus (Cuauhtemotzin, Mexico)", journal = "Energies", year = "2018", volume = "11", number = "11", month = nov, keywords = "genetic algorithms, genetic programming, wind energy, wind characteristics, artificial intelligence, multi-gene genetic programming, sensitivity analysis, Matlab, GPTIPS", ISSN = "1996-1073", article-number = "3197", URL = "http://www.mdpi.com/1996-1073/11/11/3197", URL = "https://www.mdpi.com/1996-1073/11/11/3197/pdf", DOI = "doi:10.3390/en11113197", size = "22 pages", abstract = "This work studies the characteristics of the wind resource for a location in the north zone of Tehuantepec isthmus. The study was conducted using climatic data from Cuauhtemotzin, Mexico, measured at different altitudes above the ground level. The measured data allowed establishing the profile of wind speeds as well as the analysis of its availability. Analysis results conclude that the behaviour of the wind speed presents a bimodal distribution with dominant northeast wind direction (wind flow of sea-land). In addition, the area was identified as feasible for the use of low speed power wind turbines. On the other hand, the application of a new approach for very short-term wind speed forecast (10 min) applying multi-gene genetic programming and global sensitivity analysis is also presented. Using a computational methodology, an exogenous time series with fast computation time and good accuracy was developed for the forecast of the wind speed. The results presented in this work complement the panorama for the evaluation of the resource in an area recognized worldwide for its vast potential for wind power.", notes = "also known as \cite{en11113197}", } @Article{LOPEZSANTILLAN:2020:IPM, author = "Roberto Lopez-Santillan and Manuel Montes-y-Gomez and Luis Carlos Gonzalez-Gurrola and Graciela Ramirez-Alonso and Olanda Prieto-Ordaz", title = "Richer Document Embeddings for Author Profiling tasks based on a heuristic search", journal = "Information Processing \& Management", year = "2020", volume = "57", number = "4", pages = "102227", month = jul, keywords = "genetic algorithms, genetic programming, Author profiling, Document embeddings, Word embeddings, Weighting scheme", ISSN = "0306-4573", URL = "http://www.sciencedirect.com/science/article/pii/S0306457319306466", DOI = "doi:10.1016/j.ipm.2020.102227", abstract = "In this study we propose a novel method to generate Document Embeddings (DEs) by means of evolving mathematical equations that integrate classical term frequency statistics. To accomplish this, we employed a Genetic Programming (GP) strategy to build competitive formulae to weight custom Word Embeddings (WEs), produced by cutting edge feature extraction techniques (e.g., word2vec, fastText, BERT), and then we create DEs by their weighted averaging. We exhaustively evaluated the proposed method over 9 datasets that are composed of several multilingual social media sources, with the aim to predict personal attributes of authors (e.g., gender, age, personality traits) in 17 tasks. In each dataset we contrast the results obtained by our method against state-of-the-art competitors, placing our approach at the top-quartile in all cases. Furthermore, we introduce a new numerical statistic feature called Relevance Topic Value (rtv), which could be used to enhance the forecasting of characteristics of authors, by numerically describing the topic of a document and the personal use of words by users. Interestingly, based on a frequency analysis of terminals used by GP, rtv turned out to be the most likely feature to appear alone in a single equation, then suggesting its usefulness as a WE weighting scheme", } @Article{Lorandi:2021:TELO, author = "Michela Lorandi and Leonardo Lucio {Custode} and Giovanni Iacca", title = "Genetic Improvement of Routing Protocols for Delay Tolerant Networks", journal = "ACM Transactions on Evolutionary Learning and Optimization", year = "2021", volume = "1", number = "1", articleno = "04", month = may, keywords = "genetic algorithms, genetic programming, genetic improvement, PRoPHET, Ad hoc network, delay tolerant networks, epidemic routing", publisher = "Association for Computing Machinery", address = "New York, NY, USA", ISSN = "2688-299X", URL = "http://www.human-competitive.org/sites/default/files/humies-entry-iacca_0.txt", URL = "http://www.human-competitive.org/sites/default/files/telo-2020-38.r1_proof_fl.pdf", URL = "https://doi.org/10.1145/3453683", DOI = "doi:10.1145/3453683", size = "37 pages", abstract = "Routing plays a fundamental role in network applications, but it is especially challenging in Delay Tolerant Networks (DTNs). These are a kind of mobile ad hoc networks made of, e.g., (possibly, unmanned) vehicles and humans where, despite a lack of continuous connectivity, data must be transmitted while the network conditions change due to the nodes mobility. In these contexts, routing is NP-hard and is usually solved by heuristic store and forward replication-based approaches, where multiple copies of the same message are moved and stored across nodes in the hope that at least one will reach its destination. Still, the existing routing protocols produce relatively low delivery probabilities. Here, we genetically improve two routing protocols widely adopted in DTNs, namely, Epidemic and PRoPHET, in the attempt to optimize their delivery probability. First, we dissect them into their fundamental components, i.e., functionalities such as checking if a node can transfer data, or sending messages to all connections. Then, we apply Genetic Improvement (GI) to manipulate these components as terminal nodes of evolving trees. We apply this methodology, in silico, to six test cases of urban networks made of hundreds of nodes and find that GI produces consistent gains in delivery probability in four cases. We then verify if this improvement entails a worsening of other relevant network metrics, such as latency and buffer time. Finally, we compare the logics of the best evolved protocols with those of the baseline protocols, and we discuss the generalisability of the results across test cases.", notes = "Entered 2021 HUMIES https://dlnext.acm.org/journal/telo", } @InProceedings{Lorandi:2021:TELO:HOP, author = "Michela Lorandi and Leonardo Lucio {Custode} and Giovanni Iacca", title = "Genetic Improvement of Routing Protocols for Delay Tolerant Networks", booktitle = "Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion", series = "GECCO '21", year = "2021", editor = "Carola Doerr", month = "10-14 " # jul, pages = "35--36", organisation = "SIGEVO", address = "Internet", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, genetic improvement, PRoPHET, Ad hoc network, delay tolerant networks, epidemic routing", isbn13 = "978-1-4503-8351-6", DOI = "doi:10.1145/3449726.3462716", size = "2 pages", abstract = "Routing plays a fundamental role in network applications, but it is especially challenging in Delay Tolerant Networks (DTNs). These are a kind of mobile ad hoc networks made of e.g. (possibly, unmanned) vehicles and humans where, despite a lack of continuous connectivity, data must be transmitted while the network conditions change due to the nodes' mobility. In these contexts, routing is NP-hard and is usually solved by heuristic store and forward replication-based approaches, which Improving Assertion Oracles with Evolutionary however produce relatively low delivery probabilities. Here, we genetically improve two routing protocols widely adopted in DTNs, namely Epidemic and PRoPHET, in the attempt to optimize their delivery probability. First, we dissect them into their fundamental components, i.e., functionalities such as checking if a node can transfer data, or sending messages to all connections. Then, we apply Genetic Improvement (GI) to manipulate these components as terminal nodes of evolving trees. We apply this methodology, in silico, to six test cases of urban networks made of hundreds of nodes, and find that GI produces consistent gains in delivery probability in four cases.", notes = "Short version of \cite{Lorandi:2021:TELO} https://dlnext.acm.org/journal/telo", } @InCollection{lorenzen:1998:CEACFCPRGP, author = "Peter J. Lorenzen", title = "Comparing the Evaluation of Antiderivatives of Complex Functions with Cartesian versus Polar Representations via Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1998", year = "1998", editor = "John R. Koza", pages = "84--93", address = "Stanford, California, 94305-3079 USA", month = "17 " # mar, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-212568-8", notes = "part of \cite{koza:1998:GAGPs}", } @InProceedings{Lorway:2021:AIMC, author = "Norah Lorway and Edward J. Powley and Arthur Wilson", title = "Autopia: An {AI} collaborator for live networked computer music performance", booktitle = "2nd Conference on AI Music Creativity (MuMe + CSMC)", year = "2021", editor = "Artemi-Maria Gioti and Gerhard Eckel", address = "online", month = "18-22 " # jul, organisation = "Institute of Electronic Music and Acoustics (KUG, Graz), Austria", keywords = "genetic algorithms, genetic programming, interactive evolution", URL = "https://aimc2021.iem.at/events/paper-session-5/", URL = "https://aimc2021.iem.at/wp-content/uploads/2021/06/AIMC_2021_Lorway_Powley_Wilson.pdf", URL = "https://repository.falmouth.ac.uk/4307/", code_url = "https://github.com/muellmusik/Utopia", size = "10 pages", abstract = "Autopia, designed to participate in collaborative live coding music performances using the Utopia software tool for SuperCollider. This form of human-AI collaboration allows us to explore the implications of mixed-initiative computational creativity from the perspective of live coding. As well as collaboration with human performers, one of our motivations with Autopia is to explore audience collaboration through a gamified mode of interaction, namely voting through a web-based interface accessed by the audience on their smartphones. The results of this are often emergent, chaotic, and surprising to performers and audience a like", notes = "Paper session 5 https://aimc2021.iem.at/ Academy of Music and Theatre Arts, Falmouth University, UK", } @InProceedings{Loseva:2015:ICNC, author = "Elena Loseva and Leonid Lipinsky and Anna Kuklina", booktitle = "11th International Conference on Natural Computation (ICNC)", title = "Eensembles of neural networks with application of multi-objective self-configuring genetic programming in forecasting problems", year = "2015", pages = "686--690", month = aug, keywords = "genetic algorithms, genetic programming, forecasting problems, ANN, ensembles of artificial neural networks, self configuration", DOI = "doi:10.1109/ICNC.2015.7378073", abstract = "The application of multi-criteria self-configuring evolutionary algorithm 'self-configurable' genetic algorithm in forecasting problem using ensembles of neural network models is investigated. The analysis of effectiveness of the proposed method for two types of forecasting problems with different initial settings is made.", notes = "Dept. of Syst. Anal. & Oper. Res., Siberian State Aerosp. Univ., Krasnoyarsk, Russia Also known as \cite{7378073}", } @InCollection{lott:1994:tfar, author = "Christopher G. Lott", title = "Terrain Flattening by Autonomous Robot: {A} Genetic Programming Application", booktitle = "Genetic Algorithms at Stanford 1994", year = "1994", editor = "John R. Koza", pages = "99--109", address = "Stanford, California, 94305-3079 USA", month = dec, organisation = "Stanford University", publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming, agents", ISBN = "0-18-187263-3", notes = "{"}Successfully used GP to ... robot control programs which can transform any random 12 x 12 grid into basically a flat plane.{"} {"}rudimentary cooperation between robots in acheiving the same goal{"}. This volume contains 20 papers written and submitted by students describing their term projects for the course {"}Genetic Algorithms and Genetic Programming{"} (Computer Science 426) at Stanford University offered during the fall quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs426.html", } @InProceedings{Lotz:2010:EvoENVIRONMENT, author = "Marco Lotz and Sara Silva", title = "Application of Genetic Programming Classification in an Industrial Process Resulting in Greenhouse Gas Emission Reductions", booktitle = "EvoENVIRONMENT", year = "2010", editor = "Cecilia {Di Chio} and Anthony Brabazon and Gianni A. {Di Caro} and Marc Ebner and Muddassar Farooq and Andreas Fink and Jorn Grahl and Gary Greenfield and Penousal Machado and Michael O'Neill and Ernesto Tarantino and Neil Urquhart", volume = "6025", series = "LNCS", pages = "131--140", address = "Istanbul", month = "7-9 " # apr, organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-12241-5", DOI = "doi:10.1007/978-3-642-12242-2_14", abstract = "This paper compares Genetic Programming and the Classification and Regression Trees algorithm as data driven modelling techniques on a case study in the ferrous metals and steel industry in South Africa. These industries are responsible for vast amounts of greenhouse gas production, and greenhouse gas emission reduction incentives exist that can fund these abatement technologies. Genetic Programming is used to derive pure classification rule sets, and to derive a regression model used for classification, and both these results are compared to the results obtained by decision trees, regarding accuracy and human interpretability. Considering the overall simplicity of the rule set obtained by Genetic Programming, and the fact that its accuracy was not surpassed by any of the other methods, we consider it to be the best approach, and highlight the advantages of using a rule based classification system. We conclude that Genetic Programming can potentially be used as a process model that reduces greenhouse gas production.", notes = "EvoENVIRONMENT'2010 held in conjunction with EuroGP'2010 EvoCOP2010 EvoBIO2010", } @Article{Lou:2025:Structures, author = "Junbin Lou and Yixuan Li and Qian Feng and Rongqiao Xu", title = "Machine learning models for predicting ultimate bond strength of grouted sleeve connections", journal = "Structures", year = "2025", volume = "72", pages = "108186", keywords = "genetic algorithms, genetic programming, Grouted sleeve connection (GSC), Multi-fidelity modeling, Bond strength prediction, Neural networks, ANN", ISSN = "2352-0124", URL = "https://www.sciencedirect.com/science/article/pii/S2352012424023403", DOI = "doi:10.1016/j.istruc.2024.108186", abstract = "Grouted sleeve connection (GSC) is widely used in prefabricated concrete structures and accurately predicting its ultimate bond strength between rebar and grouting materials of GSC is crucial for ensuring structural safety. In this study, a parameter-sharing residual multi-fidelity neural network (PsRMFNN) is proposed to predict the ultimate bond strength of GSC. For this purpose, a database comprising 209 existing GSC tensile experimental data is established to train and evaluate the PsRMFNN. Comparative analyses are conducted with existing empirical equations, backpropagation neural networks, and residual multi-fidelity neural networks. The PsRMFNN exhibits superior performance, yielding R2 of 0.994, MAE of 6.17kN, RMSE of 12.97kN, and MAPE of 3.55percent across all data. Furthermore, comparative evaluations of the three neural network-based models show that the PsRMFNN accelerates convergence and enhances prediction accuracy. Finally, based on the sensitivity analysis, predictive equations for bond strength are developed using the genetic algorithm and the genetic programming. The outcomes demonstrate that neural network-based models and newly proposed predictive equations can effectively forecast the ultimate bond strength between rebar and grouting materials of GSC", } @InProceedings{louchet:1994:BMVC, author = "Jean Louchet", title = "An evolutionary algorithm for physical motion analysis", booktitle = "British Machine Vision Conference", year = "1994", editor = "Edwin R. Hancock", volume = "2", pages = "701--710", address = "York, UK", month = "13-16 " # sep, publisher = "BMVA Press", keywords = "genetic algorithms, genetic programming", isbn_ = "952 1898 1 X", URL = "http://www.bmva.org/bmvc/1994/bmvc-94-069.pdf", size = "10 pages", notes = "{"}cornerstone{"} paper. BMVC Press http://www.bmva.ac.uk/bmvc/index.html", } @InProceedings{Louchet:EGanim95-4, year = "1995", editor = "Dimitri Terzolpoulos and Daniel Thalmann", publisher = "Springer-Verlag", author = "Jean Louchet and Xavier Provot and David Crochemore", title = "Evolutionary identification of cloth animation models", pages = "44--54", booktitle = "Computer Animation and Simulation '95", series = "LNCS", month = "2-3 " # sep, address = "Maastricht, Netherlands", note = "Proceedings of the Eurographics Workshop", keywords = "genetic algorithms, genetic programming", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/487/http:zSzzSzwww.ensta.frzSz~louchetzSzrecherchezSz95zSzEGW.pdf/louchet95evolutionary.pdf", URL = "http://citeseer.ist.psu.edu/louchet95evolutionary.html", abstract = "This paper presents an application of evolutionary genetic techniques to the identification of internal parameters of a mass-spring physically-based animation model. A physical model of fabrics is first presented. It uses a mass-spring mesh and an inverse dynamics procedure in order to model the non-linear elasticity of fabrics. A method to identify the internal parameters of the model from geometric data is then presented. It is based on a cost function which measures the difference in...", notes = "possible problem with citeseer PDF", } @InProceedings{Louchet:1995:BNT, author = "Jean Louchet and Michael Boccara and David Crochemore and Xavier Provot", title = "Building new tools for synthetic image animation using evolutionary techniques", booktitle = "Artificial Evolution AE'95", year = "1995", editor = "Jean-Marc Alliot and Evelyne Lutton and Edmund Ronald and Marc Schoenauer and Dominique Snyers", volume = "1063", series = "LNCS", pages = "273--286", address = "Brest, France", month = "4-6 " # sep, publisher = "Springer Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-61108-8", DOI = "doi:10.1007/3-540-61108-8_44", size = "14 pages", abstract = "Particle-based models and articulated models are increasingly used in synthetic image animation applications. This paper aims at showing examples of how Evolutionary Algorithms can be used as tools to build realistic physical models for image animation. First, a method to detect regions with rigid 2D motion in image sequences, without solving explicitly the Optical Flow equation, is presented. It is based on the resolution of an equation involving rotation descriptors and first-order image derivatives. An evolutionary technique is used to obtain a raw segmentation based on motion; the result of segmentation is then refined by an accumulation technique in order to determine more accurate rotation centres and deduce articulation points. Second, an evolutionary algorithm designed to identify internal parameters of a mass spring animation model from kinematic data (Physics from Motion) is presented through its application to cloth animation modelling.", bibsource = "DBLP, http://dblp.uni-trier.de", affiliation = "ENSTA Laboratoire d'Electronique et d'Informatique 32 boulevard Victor 75739 Paris cedex 15 France", } @InProceedings{louchet:1996:BMVC, author = "Jean Louchet and Li Jiang", title = "An identification tool to build physical models for virtual reality", booktitle = "Proceedings of the 3rd International Workshop on Image and Signal Processing IWISP96", year = "1996", editor = "B. G. Mertzios and P. Liatsis", pages = "669--672", address = "Manchester, UK", month = "4-7 " # nov, organisation = "UMIST", publisher = "Elsevier", keywords = "genetic algorithms, genetic programming", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/1094/http:zSzzSzwww-syntim.inria.frzSzsyntimzSzresearchzSzlouchetzSzlouchetManchester.pdf/an-identification-tool-to.pdf", URL = "http://citeseer.ist.psu.edu/74697.html", size = "4 pages", abstract = "This paper focuses on the second step. We propose an original method to automatically identify physical models built through using local masses and generalised springs. 1 The Physical Model.", notes = "{"}cornerstone{"} paper. Proceedings IWISP '96 http://www.elsevier.com/inca/publications/store/6/0/0/2/3/2/", } @InProceedings{Louchet:2000:RFIA, author = "J. Louchet", title = "L'algorithme des mouches : une strategie d'evolution individuelle appliquee en stereovision", title_en = "The Fly Algorithm: an Individual Evolutionary Strategy applied to Stereovision", booktitle = "RFIA 2000, Reconnaissance des Formes et Intelligence Artificielle", year = "2000", month = feb, keywords = "genetic algorithms, genetic programming, Parisian GP, 3D vision, Algorithmes genetiques, strategies d'evolution, approche individuelle, vision robotique, detectiond'obstacles", URL = "http://jean.louchet.free.fr/publis/rfia14t.pdf", size = "11 pages", abstract = "This paper presents an Individual Evolutionary Strategy devised for fast image analysis applications. The example problem chosen is obstacle detection using a pair of cameras. The algorithm evolves a population of three-dimensional points (flies) in the cameras fields of view, using a low complexity fitness function giving highest values to flies likely to be on the surfaces of 3-D obstacles. The algorithm uses classical sharing, mutation and crossover operators. The final result is a fraction of the population rather than a single individual. Some test results are presented and potential extensions to real-time image sequence processing,mobile objects tracking and mobile robotics are discussed.", resume = "Cet article presente l'application d'une strategied'evolution individuelle (variante d'algorithme genetique) a la resolution rapide approchee de certains problemes d'analyse de scenes. L'exemple choisi est la detection d'obstacles par stereovision. La population est un ensemble de points (mouches) dans le champdes cameras. La fonction de performance favorise les mouches situees sur les surfaces apparentes des objets. L'algorithme uses des operateurs classiques de partage, mutation et croisement. Le resultat est un ensemble de points de la population, representant les surfaces des objets. Des resultats experiment aux sont presentes ainsi que les extensions en cours a la vision d'un robot mobile et au suivi d'objets en deplacement.", notes = "In French", } @InProceedings{Louchet:2000:INRIA, author = "J. Louchet", title = "introduction to musical acoustics: towards a solfegist robot", booktitle = "INRIA invited conference (Fractales seminar)", year = "2000", address = "Rocquencourt", month = "27th " # apr, note = "invited", keywords = "genetic algorithms, genetic programming", } @InProceedings{louchet:2000:ACIVS, author = "Jean Louchet and Lionel Castillon and Jean-Marie Rocchisant", title = "Evolving flies for stereovision and {3-D} reconstruction in medical imaging", booktitle = "Second International Conference on Advanced concepts for intelligent vision systems ACIVS2000", year = "2000", editor = "Jacques Blanc-Talon and Dan Popescu", address = "Baden-Baden, Germany", month = "3-4 " # aug, keywords = "genetic algorithms, genetic programming", notes = "ACIVS 2000 http://www.etca.fr/CTA/Events/Conf/acivs00.html", } @InProceedings{louchet:2000:ICPR, author = "Jean Louchet", title = "Stereo analysis using individual evolution strategy", booktitle = "15th International conference on pattern recognition", year = "2000", volume = "1", pages = "908--911", address = "Barcelona", month = sep, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICPR.2000.905580", abstract = "This paper presents an individual evolutionary strategy devised for image analysis applications. The example problem chosen is obstacle detection using a pair of cameras. The algorithm evolves a population of three-dimensional points ('flies') in the cameras fields of view, using a low complexity fitness function giving highest values to flies likely to be on the surfaces of 3-D obstacles. The algorithm uses classical sharing, mutation and crossover operators. The result is a fraction of the population rather than a single individual. Some test results are presented and potential extensions to real-time image sequence processing and mobile robotics are discussed.", } @Article{louchet:2001:GPEM, author = "Jean Louchet", title = "Using an Individual Evolution Strategy for Stereovision", journal = "Genetic Programming and Evolvable Machines", year = "2001", volume = "2", number = "2", pages = "101--109", month = jun, keywords = "genetic algorithms, genetic programming, artificial evolution, individual evolution strategy, flies, computer vision, image processing, stereovision, software engineering", ISSN = "1389-2576", broken = "http://ipsapp009.lwwonline.com/content/getfile/4723/5/2/fulltext.pdf", DOI = "doi:10.1023/A:1011544128842", abstract = "The fly algorithm is an individual evolution strategy developed for parameter space exploration in computer vision applications. In the application described, each individual represents a geometrical point in the scene and the population itself is used as a three-dimensional model of the scene. A fitness function containing all pixel-level calculations is introduced to exploit simple optical and geometrical properties and evaluate the relevance of each individual as taking part to the scene representation. Classical evolutionary operators (sharing, mutation, crossover) are used. The combined individual approach and low complexity fitness function allow fast processing. Test results and extensions to real-time image sequence processing, mobile objects tracking and mobile robotics are presented.", notes = "Article ID: 335709", } @InCollection{louchet:2002:ECA, author = "Jean Louchet and Maud Guyon and Marie-Jeanne Lesot and Amine Boumaza", title = "L'algorithme des mouches: apprendre une forme par evolution artificielle, application en vision robotique", booktitle = "Extraction des Connaissances et Apprentissage", publisher = "Hermes", year = "2002", editor = "Claude Lattaud", month = jan, note = "in French", keywords = "genetic algorithms, genetic programming", URL = "http://www.lavoisier.fr/notice/fr2746203600.html", abstract = "To guide a robot by artificial evolution in real time. L'algorithme des mouches dynamiques. Guider un robot par evolution artificielle en temps reel", notes = "Langue : FRANCAIS, See also \cite{louchet:2002:ECAeng},", } @Article{louchet:2002:ECAeng, author = "Jean Louchet and Maud Guyon and Marie-Jeanne Lesot and Amine Boumaza", title = "Dynamic flies: a new pattern recognition tool applied to stereo sequence processing", journal = "Pattern Recognition Letters", year = "2002", volume = "23", number = "1-3", pages = "335--345", month = jan, keywords = "genetic algorithms, genetic programming, Artificial evolution, Pattern recognition, Computer vision, Image processing, Parameter space exploration", DOI = "doi:10.1016/S0167-8655(01)00129-5", abstract = "The {"}fly algorithm{"} is a fast artificial evolution-based technique devised for the exploration of parameter space in pattern recognition applications. In the application described, we evolve a population which constitutes a particle-based three-dimensional representation of the scene. Each individual represents a three-dimensional point in the scene and may be fitted with optional velocity parameters. Evolution is controlled by a fitness function which contains all pixel-level calculations, and uses classical evolutionary operators (sharing, mutation, crossover). The combined individual approach and low complexity fitness function allow fast processing. Test results and an application to mobile robotics are presented.", notes = "Francais voir \cite{louchet:2002:ECA}", } @TechReport{Louchet:2002:DGA, author = "Jean Louchet and Amine Boumaza and Baudoin Coppieters", title = "Detection d'attitude d'un helicoptere en phase d'appontage par evolution artificielle", institution = "DGA", year = "2002", type = "technical report", keywords = "genetic algorithms, genetic programming", } @InProceedings{Louchet:2006:SITIS, author = "J. Louchet and E. Lutton", title = "Parametric and Evolutionary Methods in Image Processing, tutorial", booktitle = "SITIS conference", year = "2006", address = "Hammamet, Tunisia", month = feb, note = "invited", keywords = "genetic algorithms, genetic programming", } @Article{Louchet:2006:TSI, author = "Jean Louchet", title = "Evolution artificielle, optimisation et analyse d'images", journal = "Technique et Science Informatiques", year = "2006", volume = "25", number = "8-9", pages = "1049--1078", keywords = "genetic algorithms, genetic programming", ISSN = "0752-4072", DOI = "doi:10.3166/tsi.25.1049-1078", notes = "Francais", } @InCollection{Louchet:2006:Hermes, author = "Jean Louchet", title = "Modelisation et optimisation en analyse d'images", booktitle = "Modelisation et traitement du signal", publisher = "Hermes", year = "2006", editor = "Patrick Siarry", address = "France", keywords = "genetic algorithms, genetic programming", URL = "https://www.lavoisier.fr/livre/electricite-electronique/optimisation-en-traitement-du-signal-et-de-l-image/siarry/descriptif-9782746214637", } @InCollection{Louchet:2006:Hermes2, author = "Jean Louchet and Pierre Collet", title = "Evolution artificielle et evolution parisienne: applications en traitement de signal et d'images", booktitle = "Modelisation et traitement du signal", publisher = "Hermes", year = "2006", editor = "Patrick Siarry", keywords = "genetic algorithms, genetic programming", URL = "https://www.amazon.fr/Optimisation-traitement-signal-limage-Trait%C3%A9/dp/2746214636", notes = "Traite IC2, serie traitement du signal et de l'image", } @InCollection{Louchet:2006:EURASIP, author = "Jean Louchet", title = "Model-based Image Analysis using Evolutionary Strategies", booktitle = "Genetic and Evolutionary Computation in Image Processing and Computer Vision", publisher = "Hindawi", year = "2006", editor = "Stefano Cagnoni and Evelyne Lutton and Gustavo Olague", series = "EURASIP book series on signal processing and communications", pages = "283--308", keywords = "genetic algorithms, genetic programming", } @InProceedings{Louchet:2007:DAA, author = "J. Louchet", title = "Optimisation Strategies for Modelling and Simulation, tutorial", booktitle = "8th Intl. Workshop on Data Analysis in Astronomy", year = "2007", address = "Erice, Italy", month = apr, note = "invited", keywords = "genetic algorithms, genetic programming", } @InProceedings{Louchet:2009:CIMAT, author = "J. Louchet", title = "Is vision an inverse problem?", booktitle = "CIMAT", year = "2009", address = "Universidad de Guanajuato, Mexico", month = mar, note = "invited conference", keywords = "genetic algorithms, genetic programming", } @InProceedings{Louchet:2009:evows, author = "Jean Louchet and Emmanuel Sapin", title = "Flies Open a Door to {SLAM}", booktitle = "EvoWorkshops 2009", year = "2009", editor = "Mario Giacobini and Anthony Brabazon and Stefano Cagnoni and Gianni A. Di Caro and Anik{\'o} Ek{\'a}rt and Anna Isabel Esparcia-Alc{\'a}zar and Muddassar Farooq and Andreas Fink and Penousal Machado", volume = "5484", series = "Lecture Notes in Computer Science", pages = "385--394", address = "Tuebingen, Germany", month = apr # " 15-17", organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Evolutionary robotics, Fly algorithm, robot vision, Parisian evolution, SLAM, image registration", isbn13 = "978-3-642-01128-3", DOI = "doi:10.1007/978-3-642-01129-0_43", abstract = "The fly algorithm is a real-time evolutionary strategy designed for stereo vision. Previous work has shown how to process stereo image sequences and use an evolving population of flies as a continuously updated representation of the scene for obstacle avoidance in a mobile robot, and the support to collect information about the environment from different sensors. In this paper, we move a step forward and show a way the fly representation may be used by a mobile robot for its own localisation and build a map of its environment (Simultaneous Localisation And Mapping).", } @InCollection{Louchet:2009:Paris12, author = "Jean Louchet", title = "Modelling and optimization in image analysis", booktitle = "Optimization in Signal and Image Processing", publisher = "University of Paris 12", year = "2009", editor = "Patrick Siarry", chapter = "1", address = "France", month = jun, keywords = "genetic algorithms, genetic programming", isbn13 = "9781848210448", } @InProceedings{Louchet:2014:Gent, author = "Jean Louchet", title = "Why use Evolutionary Computation in Image Processing? A practical guide to Evolutionary Image Processing", year = "2014", address = "Universiteit Gent", month = jan, note = "invited conference", keywords = "genetic algorithms, genetic programming", } @TechReport{Louchet:2014:Gent_tr, author = "Jean Louchet", title = "People tracking: introducing evolutionary methods into image processing, Activity Report 2013-2014", institution = "Universiteit Gent", year = "2014", month = mar, keywords = "genetic algorithms, genetic programming", } @InProceedings{loughlin:1999:CGA, author = "Daniel H. Loughlin and S. Ranji Ranjithan", title = "Chance-Constrained Genetic Algorithms", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "369--376", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-381.ps", abstract = "latin square, latin hypercube sampling", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @PhdThesis{Loughran:thesis, author = "Roisin Bernadette Loughran", title = "Music Instrument Identification with Feature Selection Using Evolutionary Methods", school = "University of Limerick", year = "2009", address = "Ireland", month = sep, keywords = "genetic algorithms, genetic programming, feature selection, ANN, PCA, tree visualisation, bloat", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/LoughranThesis.pdf", size = "281 pages", abstract = "Musical instruments may be identified using machine learning methods, but it is not clear which aspects of the sound or features are best used in such methods. Classification experiments using Principal Component Analysis (PCA) and Multi-Layered Perceptrons (MLP) in this thesis and that the addition of extra features may not necessarily be beneficial - optimisation of the features is required. This optimisation is implemented using Evolutionary Computation methods as they have yet to be extensively applied in musical sound analysis. A Genetic Algorithm (GA) with a new instrument-clustering fitness function based on PCA is applied to optimise a set of 95 features for classification with an MLP. With this method, the number of features used to classify an instrument is reduced from 95 to as low as 22 with a classification accuracy reduction of less than 0.3percent. This method is tested against another evolutionary method that has not yet been applied to instrument identification - Genetic Programming (GP). GP is used to evolve a classifier program that can identify unseen samples with an accuracy of 94.3percent using just 14 of the 95 original features. Though not as high as the MLP or the GA-MLP, it is found that the GP is more consistent with its choice of features, offering a possible insight into timbre and the nature of sound recognition. In both EC methods it is found that the first principal component of the envelope of the centroid, a new measure of this feature, is the most important among all 95 features. It is also seen that each classification method performs significantly better when tested with a general set of samples, than with a one-octave sample set common to each instrument. The classifiers are compared to a set of human listening tests on particularly troublesome samples. It is seen that although the GA and GP are accurate at identifying general unseen samples, the human ear performs significantly better than both methods at identifying these difficult samples.", notes = "Supervisors Dr. Jacqueline Walker and Dr. Niall Griffith Matlab MIR toolbox GPLAB", } @InProceedings{Loughran:2012:EvoMUSART, author = "Roisin Loughran and Jacqueline Walker and Michael O'Neill and James McDermott", title = "Genetic Programming for Musical Sound Analysis", booktitle = "Proceedings of the 1st International Conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design, EvoMUSART 2012", year = "2012", month = "11-13 " # apr, editor = "Penousal Machado and Juan Romero and Adrian Carballal", series = "LNCS", volume = "7247", publisher = "Springer Verlag", address = "Malaga, Spain", pages = "176--186", organisation = "EvoStar", isbn13 = "978-3-642-29141-8", DOI = "doi:10.1007/978-3-642-29142-5_16", keywords = "genetic algorithms, genetic programming, Musical Information Retrieval, timbre", abstract = "This study uses Genetic Programming (GP) in developing a classifier to distinguish between five musical instruments. Using only simple arithmetic and Boolean operators with 95 features as terminals, a program is developed that can classify 300 unseen samples with an accuracy of 94percent. The experiment is then run again using only 14 of the most often chosen features. Limiting the features in this way raised the best classification to 94.3percent and the average accuracy from 68.2percent to 75.67percent. This demonstrates that not only can GP be used to create a classifier but it can be used to determine the best features to choose for accurate musical instrument classification, giving an insight into timbre.", notes = "Part of \cite{Machado:2012:EvoMusArt_proc} EvoMUSART'2012 held in conjunction with EuroGP2012, EvoCOP2012, EvoBIO2012 and EvoApplications2012", } @InProceedings{loughran:cec2015, author = "Roisin Loughran and James McDermott and Michael O'Neill", title = "Tonality Driven Piano Compositions with Grammatical Evolution", booktitle = "Proceedings of 2015 IEEE Congress on Evolutionary Computation (CEC 2015)", editor = "Yadahiko Murata", pages = "2168--2175", year = "2015", address = "Sendai, Japan", month = "25-28 " # may, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, grammatical evolution", DOI = "doi:10.1109/CEC.2015.7257152", abstract = "We present a novel method of creating piano melodies with Grammatical Evolution (GE). The system employs a context free grammar in combination with a tonality-driven fitness function to create a population of piano melodies. The grammar is designed to create a variety of styles of musical events within each melody such as runs, arpeggios, turns and chords without any a priori musical information in regards to key or time signature. The fitness of the individuals is calculated as a measure of their tonality defined by a statistical distribution of the pitches in each piece. A number of short compositions are presented demonstrating that our system is capable of creating music that is interesting and unpredictable.", notes = "1110 hrs 15260 CEC2015", } @InProceedings{loughran:smc2015, author = "Roisin Loughran and James McDermott and Michael O'Neill", title = "Grammatical Evolution with Zipf's Law Based Fitness for Melodic Composition", booktitle = "SMC 2015 The 12th Sound and Music Computing Conference", year = "2015", address = "Maynooth, Ireland", editor = "Joseph Timoney", month = "26 " # jul # " - 1 " # aug, organisation = "Music Technology Group in the Departments of Music and Computer Science at Maynooth University, Ireland", keywords = "genetic algorithms, genetic programming, grammatical evolution, Zipf distribution, music", URL = "http://ncra.ucd.ie/papers/smc2015_loughran.pdf", size = "8 pages", abstract = "We present a novel method of composing piano pieces with Grammatical Evolution. A grammar is designed to define a search space for melodies consisting of notes, chords, turns and arpeggios. This space is searched using a fitness function based on the calculation of the Zipfs distribution of a number of pitch and duration attributes within the given melodies. In this way, we can create melodies without specifying a key or time signature. We can then create simple accompanying bass parts to repeat under the melody. This bass part is evolved using a grammar created from the evolved treble line with a fitness based on Zipf's distribution of the harmonic relationship between the treble and bass parts. From an analysis of the system we conclude that the designed grammar and the construction of the compositions from the final population of melodies is more influential on the musicality of the resultant compositions than the use of the Zipf's metrics.", notes = "http://www.maynoothuniversity.ie/smc15/friday.html", } @InProceedings{EvoIasp16Loughranetal, author = "Roisin Loughran and Alexandros Agapitos and Ahmed Kattan and Anthony Brabazon and Michael O'Neill", title = "Speaker Verification on Unbalanced Data with Genetic Programming", booktitle = "19th European Conference on the Applications of Evolutionary Computation", year = "2016", editor = "Giovanni Squillero and Paolo Burelli", series = "Lecture Notes in Computer Science", volume = "9597", pages = "737--753", address = "Porto, Portugal", month = mar # " 30 - " # apr # " 1", organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Speaker verification, Unbalanced data, Feature selection", URL = "http://dx.doi.org/10.1007/978-3-319-31204-0_47", DOI = "doi:10.1007/978-3-319-31204-0_47", abstract = "Automatic Speaker Verification (ASV) is a highly unbalanced binary classification problem, in which any given speaker must be verified against everyone else. We apply Genetic programming (GP) to this problem with the aim of both prediction and inference. We examine the generalisation of evolved programs using a variety of fitness functions and data sampling techniques found in the literature. A significant difference between train and test performance, which can indicate overfitting, is found in the evolutionary runs of all to-be-verified speakers. Nevertheless, in all speakers, the best test performance attained is always superior than just merely predicting the majority class. We examine which features are used in good-generalising individuals. The findings can inform future applications of GP or other machine learning techniques to ASV about the suitability of feature-extraction techniques.", notes = "EvoApplications2016 held in conjunction with EuroGP'2016, EvoCOP2016 and EvoMusArt2016", } @InProceedings{loughran2016grammatical, author = "Roisin Loughran and James McDermott and Michael O'Neill", title = "Grammatical music composition with dissimilarity driven hill climbing", booktitle = "EvoMusArt 2016", year = "2016", editor = "Colin Johnson and Vic Ciesielski and Jo{\~a}o Correia and Penousal Machado", volume = "9596", series = "Lecture Notes in Computer Science", pages = "110--125", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Algorithmic composition, Hill-climbing, Grammar", isbn13 = "978-3-319-31007-7", URL = "https://link.springer.com/chapter/10.1007/978-3-319-31008-4_8", DOI = "doi:10.1007/978-3-319-31008-4_8", abstract = "An algorithmic compositional system that uses hill climbing to create short melodies is presented. A context free grammar maps each section of the resultant individual to a musical segment resulting in a series of MIDI notes described by pitch and duration. The dissimilarity between each pair of segments is measured using a metric based on the pitch contour of the segments. Using a GUI, the user decides how many segments to include and how they are to be distanced from each other. The system performs a hill-climbing search using several mutation operators to create a population of segments the desired distances from each other. A number of melodies composed by the system are presented that demonstrate the algorithm's ability to match the desired targets and the versatility created by the inclusion of the designed grammar.", } @InProceedings{loughran2016popular, author = "Roisin Loughran and Michael O'Neill", title = "The popular critic: Evolving melodies with popularity driven fitness", booktitle = "Proceedings of the 4th international workshop on musical metacreation (MUME 2016)", year = "2016", editor = "Philippe Pasquier and Oliver Bown and Arne Eigenfeldt", address = "Paris", keywords = "genetic algorithms, genetic programming", isbn13 = "978-0-86491-397-5", URL = "http://musicalmetacreation.org/workshops/mume-2016/program/", URL = "http://musicalmetacreation.org/buddydrive/file/loughran_the_popular/", URL = "http://ncra.ucd.ie/papers/The%20Popular%20Critic-%20Evolving%20Melodies%20with%20Popularity%20Driven%20Fitness.pdf", size = "8 pages", abstract = "One of the fundamental challenges in applying evolutionary computation to creative applications such as music composition is in the design of a suitable fitness function. This paper proposes a new method of examining fitness, not from an inherent musical aspect of the individual but from the degree to which a given individual conforms to the popular opinion of its peers. A cyclical system is presented that uses an initial corpus of melodies to evolve a fitness Critic which in turn is used to create a new melody. This new melody is then input into the original corpus to continue the cycle of Critics creating melodies that in turn are used to create Critics. A diversity measure of the changing corpus over evolutionary cycles shows that the corpus becomes less diverse as more of the melodies are created by the system. The system creates melodies in a method that is not random but that is unpredictable to the programmer.", notes = "Full Paper Held at the Seventh International Conference on Computational Creativity, ICCC 2016", } @InProceedings{Loughran:2017:evoMusArt, author = "Roisin Loughran and Michael O'Neill", title = "Clustering Agents for the Evolution of Autonomous Musical Fitness", booktitle = "6th International Conference on Computational Intelligence in Music, Sound, Art and Design", year = "2017", editor = "Joao Correia and Vic Ciesielski and Antonios Liapis", series = "LNCS", volume = "10198", publisher = "Springer", pages = "160--175", address = "Amsterdam", month = "19-21 " # apr, organisation = "Species", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Clustering, Self-adaptive system, Autonomous fitness function", isbn13 = "978-3-319-55750-2", DOI = "doi:10.1007/978-3-319-55750-2_11", abstract = "This paper presents a cyclical system that generates autonomous fitness functions or Agents for evolving short melodies. A grammar is employed to create a corpus of melodies, each of which is composed of a number of segments. A population of Agents are evolved to give numerical judgements on the melodies based on the spacing of these segments. The fitness of an individual Agent is calculated in relation to its clustering of the melodies and how much this clustering correlates with the clustering of the entire Agent population. A preparatory run is used to evolve Agents using 30 melodies of known `clustering'. The full run uses these Agents as the initial population in evolving a new best Agent on a separate corpus of melodies of random distance measures. This evolved Agent is then used in combination with the original melody grammar to create a new melody which replaces one of those from the initial random corpus. This results in a complex adaptive system creating new melodies without any human input after initialisation. This paper describes the behaviour of each phase in the system and presents a number of melodies created by the system.", notes = "EvoMusArt2017 held in conjunction with EuroGP'2017, EvoCOP2017 and EvoApplications2017. http://www.evostar.org/2017/cfp_evomusart.php", } @InProceedings{loughranmy, author = "Roisin Loughran and Michael O'Neill", title = "{`My Little ChucKy'}: Towards Live-coding with Grammatical Evolution", booktitle = "Proceedings of the 5th International Workshop on Musical Metacreation (MUME 2017)", year = "2017", editor = "Philippe Pasquier and Oliver Bown and Arne Eigenfeldt", address = "Georgia Institute of Technology, Atlanta, USA", month = jun # " 19-20", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", isbn13 = "978-1-77287-019-0", URL = "http://musicalmetacreation.org/proceedings/mume-2017/", URL = "http://musicalmetacreation.org/buddydrive/file/loughran/", URL = "https://www.researchgate.net/profile/Roisin_Loughran/publication/317598212_%27My_Little_ChucKy%27_Towards_Live-coding_with_Grammatical_Evolution/links/596777f5aca2728ca67300c9/My-Little-ChucKy-Towards-Live-coding-with-Grammatical-Evolution.pdf", notes = "http://musicalmetacreation.org/workshops/mume-2017/ Held at the Eight International Conference on Computational Creativity, ICCC 2017", } @Article{Loughran2017, author = "Roisin Loughran and Alexandros Agapitos and Ahmed Kattan and Anthony Brabazon and Michael O'Neill", title = "Feature selection for speaker verification using genetic programming", journal = "Evolutionary Intelligence", year = "2017", volume = "10", number = "1-2", pages = "1--21", month = jul, keywords = "genetic algorithms, genetic programming, Speaker verification, Feature selection, Unbalanced data", ISSN = "1864-5917", DOI = "doi:10.1007/s12065-016-0150-5", size = "21 pages", abstract = "We present a study examining feature selection from high performing models evolved using genetic programming (GP) on the problem of automatic speaker verification (ASV). ASV is a highly unbalanced binary classification problem in which a given speaker must be verified against everyone else. We evolve classification models for 10 individual speakers using a variety of fitness functions and data sampling techniques and examine the generalisation of each model on a 1:9 unbalanced set. A significant difference between train and test performance is found which may indicate overfitting in the models. Using only the best generalising models, we examine two methods for selecting the most important features. We compare the performance of a number of tuned machine learning classifiers using the full 275 features and a reduced set of 20 features from both feature selection methods. Results show that using only the top 20 features found in high performing GP programs led to test classifications that are as good as, or better than, those obtained using all data in the majority of experiments undertaken. The classification accuracy between speakers varies considerably across all experiments showing that some speakers are easier to classify than others. This indicates that in such real-world classification problems, the content and quality of the original data has a very high influence on the quality of results obtainable.", } @Article{Loughran:2017:JCMS, author = "Roisin Loughran and Michael O'Neill", title = "Limitations from Assumptions in Generative Music Evaluation", journal = "Journal of Creative Music Systems", year = "2017", volume = "2", number = "1", month = sep, keywords = "genetic algorithms, genetic programming, Autonomous systems, creativity, evaluation, music generation", ISSN = "2399-7656", URL = "http://jcms.org.uk/issues/Vol2Issue1/limitations-from-assumptions/article.html", URL = "http://jcms.org.uk/issues/Vol2Issue1/limitations-from-assumptions/Limitations%20from%20Assumptions%20in%20Generative%20Music%20Evaluation.pdf", size = "31 pages", abstract = "The merit of a given piece of music is difficult to evaluate objectively; the merit of a computational system that creates such a piece of music may be even more so. In this article, we propose that there may be limitations resulting from assumptions made in the evaluation of autonomous compositional or creative systems. The article offers a review of computational creativity, evolutionary compositional methods and current methods of evaluating creativity. We propose that there are potential limitations in the discussion and evaluation of generative systems from two standpoints. First, many systems only consider evaluating the final artefact produced by the system whereas computational creativity is defined as a behaviour exhibited by a system. Second, artefacts tend to be evaluated according to recognised human standards. We propose that while this may be a natural assumption, this focus on human-like or human-based preferences could be limiting the potential and generality of future music generating or creative-AI systems", } @InProceedings{Loughran:2018:AISB, author = "Roisin Loughran and Michael O'Neill", title = "Serendipity in Melodic Self-organising Fitness", booktitle = "Symposium on Cybernetic Serendipity Reimagined", year = "2018", editor = "Joseph Corneli and Colin Johnson and Anna Jordanous and Christian Guckelsberger", pages = "13--20", address = "Liverpool, UK", month = "6 " # apr, organisation = "Artificial Intelligence and Simulation of Behaviour", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, ES in Melody Generation, Serendipitous evolution, Critic-based Fitness", URL = "http://aisb2018.csc.liv.ac.uk/symposia.html#S7", URL = "http://aisb2018.csc.liv.ac.uk/PROCEEDINGS%20AISB2018/Cybernetic%20Serendipity%20Reimagined%20-%20AISB2018.pdf", size = "8 pages", abstract = "Employing Evolutionary Strategies (ES) for subjective tasks such as melody writing causes an immediate problem in determining what to use as a fitness measure. By predefining a measure based on genre, musical rules or human opinion, as has been done in previous studies, we may be prematurely limiting the possibilities obtainable by the system, rendering serendipitous discovery impossible. In this paper, we discuss the development of a system that generates its own self-adaptive fitness measure in response to a corpus of evolved melodies. The system dynamically creates new fitness measures, or Critics, in response to new melodies in a cyclical manner with minimum human intervention. Thus it is a closed loop feedback system that develops its own fitness function through a response to its environment. We propose that the development of such a system could lead to more autonomous creativity and that the use of dynamically changing Critics and melodies could encourage the emergence of serendipitous discovery.", notes = "Evolving the Critic. Second Order Cybernetic system. http://aisb2018.csc.liv.ac.uk/", } @InCollection{Loughran:2018:hbge, author = "Roisin Loughran", title = "Grammatical Evolution and Creativity", booktitle = "Handbook of Grammatical Evolution", publisher = "Springer", year = "2018", editor = "Conor Ryan and Michael O'Neill and J. J. Collins", chapter = "14", pages = "341--366", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", isbn13 = "978-3-319-78716-9", DOI = "doi:10.1007/978-3-319-78717-6_14", abstract = "This paper considers the application of Grammatical Evolution (GE) to the concept of creativity both in theory and through the examination of two applied music generation systems. We discuss previous work on the application of evolutionary strategies to music generation and discuss current issues in the study of creativity and Computational Creativity (CC). In presenting and contrasting the development of two GE music generation systems, we can consider the multi-faceted aspects of creativity and how it may be approached from a computational perspective. The design of any such system is dependent on representation (what is music?) and fitness measure (what makes this music good?). In any aesthetic domain such questions are far from trivial. We conclude that it is vitally important to be clear on the purpose and aim in proposing any such system; systems may be either more generative or more autonomously creative if this is the a priori goal of the proposed experiment. Furthermore, we propose that evolutionary systems, and in particular GE, are highly suitable to the study of creativity as they can offer much scope in representation through grammars while allowing exploration and the possibility of self-adaptivity through the development of novel self-referential fitness measures.", notes = "Part of \cite{Ryan:2018:hbge}", } @Article{Loughran:GPEM20, author = "Roisin Loughran and Michael O'Neill", title = "Evolutionary music: Applying evolutionary computation to the art of creating music", journal = "Genetic Programming and Evolvable Machines", year = "2020", volume = "21", number = "1-2", pages = "55--85", month = jun, note = "Twentieth Anniversary Issue", keywords = "genetic algorithms, genetic programming, Music composition, Evolutionary computation, Computational creativity, Review", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-020-09380-7", size = "31 pages", abstract = "We present a review of the application of Genetic Programming (GP) and other variations of Evolutionary Computation (EC) to the creative art of music composition. Throughout the development of EC methods, since the early 1990s, a small number of researchers have considered aesthetic problems such as the act of composing music alongside other more traditional problem domains. Over the years, interest in these aesthetic or artistic domains has grown significantly. We review the implementation of GP and EC for music composition in terms of the compositional task undertaken, the algorithm used, the representation of the individuals and the fitness measure employed. In these aesthetic studies we note that there are more variations or generalisations in the algorithmic implementation in comparison to traditional GP experiments; even if GP is not explicitly stated, many studies use representations that are distinctly GP-like. We determine that there is no single compositional challenge and no single best evolutionary method with which to approach the act of music composition. We consider autonomous composition as a computationally creative act and investigate the suitability of EC methods to the search for creativity. We conclude that the exploratory nature of evolutionary methods are highly appropriate for a wide variety of compositional tasks and propose that the development and study of GP and EC methods on creative tasks such as music composition should be encouraged.", } @InProceedings{louis:1999:ASSMCIGAT, author = "Sushil J. Louis and Yongmian Zhang", title = "A Sequential Similarity Metric for Case Injected Genetic Algorithms applied to TSPs", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "377--384", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-379.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{louis:1999:IGATSP, author = "Sushil J. Louis and Rilun Tang", title = "Interactive Genetic Algorithms for the Traveling Salesman Problem", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "385--392", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-385.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Lourenco:2012:GECCOcomp, author = "Nuno Lourenco and Francisco Pereira and Ernesto Costa", title = "Evolving evolutionary algorithms", booktitle = "GECCO 2012 2nd Workshop on Evolutionary Computation for the Automated Design of Algorithms", year = "2012", editor = "Gisele L. Pappa and John Woodward and Matthew R. Hyde and Jerry Swan", isbn13 = "978-1-4503-1178-6", keywords = "genetic algorithms, genetic programming", pages = "51--58", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330784.2330794", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "This paper proposes a Grammatical Evolution framework to the automatic design of Evolutionary Algorithms. We define a grammar that has the ability to combine components regularly appearing in existing evolutionary algorithms, aiming to achieve novel and fully functional optimization methods. The problem of the Royal Road Functions is used to assess the capacity of the framework to evolve algorithms. Results show that the computational system is able to evolve simple evolutionary algorithms that can effectively solve Royal Road instances. Moreover, some unusual design solutions, competitive with standard approaches, are also proposed by the grammatical evolution framework.", notes = "Also known as \cite{2330794} Distributed at GECCO-2012. ACM Order Number 910122.", } @InProceedings{Lourenco:2013:GECCO, author = "Nuno Lourenco and Francisco Baptista Pereira and Ernesto Costa", title = "The importance of the learning conditions in hyper-heuristics", booktitle = "GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference", year = "2013", editor = "Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi", isbn13 = "978-1-4503-1963-8", pages = "1525--1532", keywords = "genetic algorithms, genetic programming", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2463372.2463558", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Evolutionary Algorithms are problem solvers inspired by nature. The effectiveness of these methods on a specific task usually depends on a non trivial manual crafting of their main components and settings. Hyper-Heuristics is a recent area of research that aims to overcome this limitation by advocating the automation of the optimisation algorithm design task. In this paper, we describe a Grammatical Evolution framework to automatically design evolutionary algorithms to solve the knapsack problem. We focus our attention on the evaluation of solutions that are iteratively generated by the Hyper-Heuristic. When learning optimisation strategies, the hyper-method must evaluate promising candidates by executing them. However, running an evolutionary algorithm is an expensive task and the computational budget assigned to the evaluation of solutions must be limited. We present a detailed study that analyses the effect of the learning conditions on the optimisation strategies evolved by the Hyper-Heuristic framework. Results show that the computational budget allocation impacts the structure and quality of the learnt architectures. We also present experimental results showing that the best learnt strategies are competitive with state-of-the-art hand designed algorithms in unseen instances of the knapsack problem.", notes = "Also known as \cite{2463558} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)", } @InProceedings{Lourenco:2013:EA, author = "Nuno Lourenco and Francisco B. Pereira and Ernesto Costa", title = "Learning Selection Strategies for Evolutionary Algorithms", booktitle = "Artificial Evolution, EA 2013", year = "2013", editor = "Pierrick Legrand and Marc-Michel Corsini and Jin-Kao Hao and Nicolas Monmarche and Evelyne Lutton and Marc Schoenauer", volume = "8752", series = "Lecture Notes in Computer Science", pre_print_pages = "226--237", pages = "197--208", address = "Bordeaux, France", month = "21-23 " # oct, organisation = "Association Evolution Artificielle", publisher = "Springer", keywords = "genetic algorithms, genetic programming, gramatical evolution, GE-HH, MKP", isbn13 = "9782953926736", isbn13 = "978-3-319-11682-2", URL = "http://ea2013.inria.fr/proceedings.pdf", DOI = "doi:10.1007/978-3-319-11683-9_16", size = "12 pages", abstract = "Hyper-Heuristics is a recent area of research concerned with the automatic design of algorithms. In this paper we propose a grammar-based hyper-heuristic to automate the design of an Evolutionary Algorithm component, namely the parent selection mechanism. More precisely, we present a grammar that defines the number of individuals that should be selected, and how they should be chosen in order to adjust the selective pressure. Knapsack Problems are used to assess the capacity to evolve selection strategies. The results obtained show that the proposed approach is able to evolve general selection methods that are competitive with the ones usually described in the literature.", notes = "Extended BNF grammar by adding ~ (repetition of non-terminals) and & for numeric ranges. Multiple Knapsck problem. Old page numbers for paper 20 in pre-publication ISBN 9782953926736 http://ea2013.inria.fr/", } @PhdThesis{Lourenco:thesis, author = "Nuno Antonio Marques Lourenco", title = "Enhancing Grammar-Based Approaches for the Automatic Design of Algorithms", school = "Department of Informatics Engineering of the Faculty of Sciences and Technology of the University of Coimbra", year = "2015", address = "Portugal", month = sep, keywords = "genetic algorithms, Genetic Programming, Grammatical Evolution, Automatic Design of Algorithms, Evolutionary Algorithms, Evolutionary Computation, Hyper-Heuristics", isbn13 = "979-8-8415-5220-8", URL = "http://hdl.handle.net/10316/29450", URL = "https://estudogeral.uc.pt/handle/10316/29450", URL = "https://www.proquest.com/docview/2018648958", URL = "https://estudogeral.uc.pt/bitstream/10316/29450/1/Enhancing%20Grammar-Based%20Approaches%20for%20the%20Automatic%20Design%20of%20Algorithms.pdf", URL = "https://dl.acm.org/doi/10.5555/AAI29196626", size = "177 pages", abstract = "Evolutionary Algorithms (EA) are stochastic computational methods loosely inspired by the principles of natural selection and genetics. They have been successfully used to solve complex problems in the domains of learning, design and optimization. When using an EA practitioners have to define its main components such as the variation operators, the selection and replacement mechanisms. The performance of an EA can be greatly enhanced if the components are tailored to the specific situation being addressed. These modifications are usually done manually and require a reasonable degree of expertise. In order to ease the use of EAs some researchers have developed methods to automatically design this type of algorithms. Usually, these methods rely on an (meta-) algorithm that combine components and parameters, in order to learn the one that is most suited for the problem being addressed. The area of Hyper-Heuristics (HH) emerges in this context focusing on the development of efficient meta-algorithms. Genetic Programming (GP), specifically the grammar based variants, are commonly used as HH. In this work, we study and analyze the conditions in which Grammatical Evolution (GE) can be enhanced to automatically design EAs. The main contributions can be divided in three aspects. Firstly, we propose an HH framework that relies on GE as the search algorithm. The proposed framework is divided in two complementary phases: Learning and Validation. In Learning the GE engine is used to combine low level components that are specified in a Context Free Grammar. In the second phase, Validation, the best algorithms learned are selected to be applied to scenarios different from the learning, in order to evaluate their generalization capacity. Secondly we study the impact that the learning conditions have in the final structure of the algorithms that are being learned. Moreover, we analyze the relationship between the quality exhibited by the algorithms during learning and their effective optimization ability when used in unseen scenarios. In concrete we analyze if the best strategies discover in learning still have the same good behaviour in validation. Our final contribution addresses some of the limitations exhibited by Grammatical Evolution. The result is a novel representation with an enhanced performance.", notes = "In English SFRH/BD/79649/2011 Order Number:AAI29196626 Cover Image: Evolved using the SGE algorithm and the ideas behind NEvAr \cite{Machado:2002:AI} Knapsack Problem Data Supervisors: Ernesto Jorge Fernandes Costa and Francisco Jose Baptista Pereira", } @InProceedings{Lourenco:2015:EA, author = "Nuno Lourenco and Francisco B. Pereira and Ernesto Costa", title = "{SGE}: A Structured Representation for Grammatical Evolution", booktitle = "Artificial Evolution", year = "2015", editor = "Stephane Bonnevay and Pierrick Legrand and Nicolas Monmarche and Evelyne Lutton and Marc Schoenauer", volume = "9554", series = "LNCS", pages = "136--148", address = "Lyon, France", month = "26-28 " # oct, organisation = "Association Evolution Artificielle", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", isbn13 = "978-3-319-31471-6", DOI = "doi:10.1007/978-3-319-31471-6_11", abstract = "This paper introduces Structured Grammatical Evolution, a new genotypic representation for Grammatical Evolution, where each gene is explicitly linked to a non-terminal of the grammar being used. This one-to-one correspondence ensures that the modification of a gene does not affect the derivation options of other non-terminals, thereby increasing locality. The performance of the new representation is accessed on a set of benchmark problems. The results obtained confirm the effectiveness of the proposed approach, as it is able to outperform standard grammatical evolution on all selected optimization problems", notes = "Published 2016 https://ea2015.inria.fr/ http://www.lifl.fr/EA/", } @Article{Lourenco:2016:GPEM, author = "Nuno Lourenco and Francisco B. Pereira and Ernesto Costa", title = "Unveiling the properties of structured grammatical evolution", journal = "Genetic Programming and Evolvable Machines", year = "2016", volume = "17", number = "3", pages = "251--289", month = sep, keywords = "genetic algorithms, genetic programming, grammatical evolution, locality, Redundancy, Representation", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-015-9262-4", code_url = "https://github.com/nunolourenco/sge3", size = "39 pages", abstract = "Structured grammatical evolution (SGE) is a new genotypic representation for grammatical evolution (GE). It comprises a hierarchical organization of the genes, where each locus is explicitly linked to a non-terminal of the grammar being used. This one-to-one correspondence ensures that the modification of a gene does not affect the derivation options of other non-terminals. We present a comprehensive set of optimization results obtained with problems from three different categories: symbolic regression, path finding, and predictive modelling. In most of the situations SGE outperforms standard GE, confirming the effectiveness of the new representation. To understand the reasons for SGE enhanced performance, we scrutinize its main features. We rely on a set of static measures to model the interactions between the representation and variation operators and assess how they influence the interplay between the genotype-phenotype spaces. The study reveals that the structured organization of SGE promotes an increased locality and is less redundant than standard GE, thus fostering an effective exploration of the search space.", } @InProceedings{Lourenco:2017:EuroGP, author = "Nuno Lourenco and Joaquim Ferrer and Francisco B. Pereira and Ernesto Costa", title = "A Comparative Study of Different Grammar-based Genetic Programming Approaches", booktitle = "EuroGP 2017: Proceedings of the 20th European Conference on Genetic Programming", year = "2017", month = "19-21 " # apr, editor = "Mauro Castelli and James McDermott and Lukas Sekanina", series = "LNCS", volume = "10196", publisher = "Springer Verlag", address = "Amsterdam", pages = "311--325", organisation = "species", keywords = "genetic algorithms, genetic programming, Grammatical Evolution: Poster", isbn13 = "978-3-319-55695-6", DOI = "doi:10.1007/978-3-319-55696-3_20", code_url = "https://github.com/nunolourenco/sge3", abstract = "Grammars are useful formalisms to specify constraints, and not surprisingly, they have attracted the attention of Evolutionary Computation (EC) researchers to enforce problem restrictions. Context-Free-Grammar GP (CFG-GP) established the foundations for the application of grammars in Genetic Programming (GP), whilst Grammatical Evolution (GE) popularised the use of these approaches, becoming one of the most used GP variants. However, studies have shown that GE suffers from issues that have impact on its performance. To minimise these issues, several extensions have been proposed, which made the distinction between GE and CFG-GP less noticeable. Another direction was followed by Structured Grammatical Evolution (SGE) that maintains the separation between genotype and phenotype from GE, but overcomes most of its issues. Our goal is to perform a comparative study between CFG-GP, GE and SGE to examine their relative performance. The results show that in most of the selected benchmarks, CFG-GP and SGE have a similar performance, showing that SGE is a good alternative to GE.", notes = "Part of \cite{Castelli:2017:GP} EuroGP'2017 held inconjunction with EvoCOP2017, EvoMusArt2017 and EvoApplications2017", } @InCollection{Lourenco:2018:hbge, author = "Nuno Lourenco and Filipe Assuncao and Francisco B. Pereira and Ernesto Costa and Penousal Machado", title = "Structured Grammatical Evolution: A Dynamic Approach", booktitle = "Handbook of Grammatical Evolution", publisher = "Springer", year = "2018", editor = "Conor Ryan and Michael O'Neill and J. J. Collins", chapter = "6", pages = "137--161", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", isbn13 = "978-3-319-78716-9", DOI = "doi:10.1007/978-3-319-78717-6_6", abstract = "Grammars have attracted the attention of researchers within the Evolutionary Computation field, specially from the Genetic Programming community. The most successful example of the use of grammars by GP is Grammatical Evolution (GE). In spite of being widely used by practitioners of different fields, GE is not free from drawbacks. The ones that are most commonly pointed out are those linked with redundancy and locality of the representation. To address these limitations Structured Grammatical Evolution (SGE) was proposed, which introduces a one-to-one mapping between the genotype and the non-terminals. In SGE the input grammar must be pre-processed so that recursion is removed, and the maximum number of expansion possibilities for each symbol determined. This has been pointed out as a drawback of SGE and to tackle it we introduce Dynamic Structured Grammatical Evolution (DSGE). In DSGE there is no need to pre-process the grammar, as it is expanded on the fly during the evolutionary process, and thus we only need to define the maximum tree depth. Additionally, it only encodes the integers that are used in the genotype to phenotype mapping, and grows as needed during evolution. Experiments comparing DSGE with SGE show that DSGE performance is never worse than SGE, being statistically superior in a considerable number of the tested problems.", notes = "Part of \cite{Ryan:2018:hbge}", } @InProceedings{Lourenco:2019:GECCO, author = "Nuno Lourenco and J. Manuel Colmenar and J. Ignacio Hidalgo and Oscar Garnica", title = "Structured Grammatical Evolution for Glucose Prediction in Diabetic Patients", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference", year = "2019", editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and Anne Auger and Petr Posik and Leslie {Peprez Caceres} and Andrew M. Sutton and Nadarajen Veerapen and Christine Solnon and Andries Engelbrecht and Stephane Doncieux and Sebastian Risi and Penousal Machado and Vanessa Volz and Christian Blum and Francisco Chicano and Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and Jonathan Fieldsend and Jose Antonio Lozano and Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and Robin Purshouse and Thomas Baeck and Justyna Petke and Giuliano Antoniol and Johannes Lengler and Per Kristian Lehre", isbn13 = "978-1-4503-6111-8", pages = "1250--1257", address = "Prague, Czech Republic", DOI = "doi:10.1145/3321707.3321782", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, grammatical evolution, Structured Grammatical Evolution, Performance.", size = "8 pages", abstract = "Structured grammatical evolution is a recent grammar-based genetic programming variant that tackles the main drawbacks of Grammatical Evolution, by relying on a one-to-one mapping between each gene and a non-terminal symbol of the grammar. It was applied, with success, in previous works with a set of classical benchmarks problems. However, assessing performance on hard real-world problems is still missing. In this paper, we fill in this gap, by analysing the performance of SGE when generating predictive models for the glucose levels of diabetic patients. Our algorithm uses features that take into account the past glucose values, insulin injections, and the amount of carbohydrate ingested by a patient. The results show that SGE can evolve models that can predict the glucose more accurately when compared with previous grammar-based approaches used for the same problem. Additionally, we also show that the models tend to be more robust, since the behaviour in the training and test data is very similar, with a small variance.", notes = "Also known as \cite{3321782} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InProceedings{Lourenco:2020:GECCO, author = "Nuno Lourenco and J. Manuel Colmenar and J. Ignacio Hidalgo and Sancho Salcedo-Sanz", title = "Evolving Energy Demand Estimation Models over Macroeconomic Indicators", year = "2020", editor = "Carlos Artemio {Coello Coello} and Arturo Hernandez Aguirre and Josu Ceberio Uribe and Mario Garza Fabre and Gregorio {Toscano Pulido} and Katya Rodriguez-Vazquez and Elizabeth Wanner and Nadarajen Veerapen and Efren Mezura Montes and Richard Allmendinger and Hugo Terashima Marin and Markus Wagner and Thomas Bartz-Beielstein and Bogdan Filipic and Heike Trautmann and Ke Tang and John Koza and Erik Goodman and William B. Langdon and Miguel Nicolau and Christine Zarges and Vanessa Volz and Tea Tusar and Boris Naujoks and Peter A. N. Bosman and Darrell Whitley and Christine Solnon and Marde Helbig and Stephane Doncieux and Dennis G. Wilson and Francisco {Fernandez de Vega} and Luis Paquete and Francisco Chicano and Bing Xue and Jaume Bacardit and Sanaz Mostaghim and Jonathan Fieldsend and Oliver Schuetze and Dirk Arnold and Gabriela Ochoa and Carlos Segura and Carlos Cotta and Michael Emmerich and Mengjie Zhang and Robin Purshouse and Tapabrata Ray and Justyna Petke and Fuyuki Ishikawa and Johannes Lengler and Frank Neumann", isbn13 = "9781450371285", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377930.3390153", DOI = "doi:10.1145/3377930.3390153", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference", pages = "1143--1149", size = "7 pages", keywords = "genetic algorithms, genetic programming, grammatical evolution, structured grammatical evolution, performance", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", abstract = "Energy is essential for all countries, since it is in the core of social and economic development. Since the industrial revolution, the demand for energy has increased exponentially. It is expected that the energy consumption in the world increases by 50percent by 2030 [17]. As such, managing the demand of energy is of the uttermost importance. The development of tools to model and accurately predict the demand of energy is very important to policy makers. In this paper we propose the use of the Structured Grammatical Evolution (SGE) algorithm to evolve models of energy demand, over macro-economic indicators. The proposed SGE is hybridised with a Differential Evolution approach in order to obtain the parameters of the models evolved which better fit the real energy demand. We have tested the performance of the proposed approach in a problem of total energy demand estimation in Spain, where we show that the SGE is able to generate extremely accurate and robust models for the energy prediction within one year time-horizon.", notes = "Also known as \cite{10.1145/3377930.3390153} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @InProceedings{Lourenco:2024:GPTP, author = "Alexy {de Almeida} and Nuno Lourenco and Penousal Machado", title = "Automatic Design of Autoencoders Using {NeuroEvolution}", booktitle = "Genetic Programming Theory and Practice XXI", year = "2024", editor = "Stephan M. Winkler and Wolfgang Banzhaf and Ting Hu and Alexander Lalejini", series = "Genetic and Evolutionary Computation", pages = "107--132", address = "University of Michigan, USA", month = jun # " 6-8", publisher = "Springer", keywords = "genetic algorithms, genetic programming, ANN", isbn13 = "978-981-96-0076-2", DOI = "doi:10.1007/978-981-96-0077-9_6", notes = "Published in 2025 after the workshop", } @InProceedings{LovCie01, author = "Thomas Loveard and Victor Ciesielski", title = "Representing Classification Problems in Genetic Programming", booktitle = "Proceedings of the Congress on Evolutionary Computation", year = "2001", volume = "2", pages = "1070--1077", address = "COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "27-30 " # may, organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", publisher = "IEEE Press", email = "toml@cs.rmit.edu.au", keywords = "genetic algorithms, genetic programming, Classification", ISBN = "0-7803-6657-3", URL = "http://goanna.cs.rmit.edu.au/~toml/cec2001.ps", DOI = "doi:10.1109/CEC.2001.934310", abstract = "In this paper five alternative methods are proposed to perform multi-class classification tasks using genetic programming. These methods are: Binary decomposition, in which the problem is decomposed into a set of binary problems and standard genetic programming methods are applied; Static range selection, where the set of real values returned by a genetic program is divided into class boundaries using arbitrarily chosen division points; Dynamic range selection in which a subset of training examples are used to determine where, over the set of reals, class boundaries lie; Class enumeration which constructs programs similar in syntactic structure to a decision tree; and evidence accumulation which allows separate branches of the program to add to the certainty of any given class. Results showed that the dynamic range selection method was well suited to the task of multi-class classification and was capable of producing classifiers more accurate than the other methods tried when comparable training times were allowed. Accuracy of the generated classifiers was comparable to alternative approaches over several datasets.", notes = "CEC-2001 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 01TH8546C, Library of Congress Number = . Tested on UCI machine learning testsets. STGP. 5 approaches to multiclass classifications: binary decomposition, static range, dynamic range, class enumeration (additional data type {"}ClassType{"} (cf C4.5), evidence accumulation cf {"}AddToClass{"}, cf Teller", } @InProceedings{loveard:2002:SEAL, author = "Thomas Loveard and Vic Ciesielski", title = "Employing Nominal Attributes in Classification Using Genetic Programming", booktitle = "Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution And Learning (SEAL'02)", year = "2002", editor = "Lipo Wang and Kay Chen Tan and Takeshi Furuhashi and Jong-Hwan Kim and Xin Yao", pages = "487--491", address = "Orchid Country Club, Singapore", month = "18-22 " # nov, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", ISBN = "981-04-7522-5", URL = "http://goanna.cs.rmit.edu.au/~vc/papers/seal02-loveard.pdf", size = "5 pages", abstract = "In this paper methods for performing classification using Genetic Programming (GP) on datasets with nominal attributes are developed and evaluated. The two methods developed included the splitting of GP program execution based upon the value of a nominal attribute (execution branching), and the conversion of a nominal attribute to a continuous or binary attribute (numeric conversion). These two methods of using nominal attributes are tested against six datasets containing either nominal and continuous attributes or nominal only attributes. Results show that the use of the methods developed in this paper allow classifiers trained with GP to perform accurate classification of datasets containing nominal attributes. When compared to other well-known methods of classification the GP method is capable of classifying one of six datasets more accurately than any of the conventional methods tested, and accuracy close to the best achieved method on 3 other datasets.", notes = "SEAL 2002 see http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.200.6410&rep=rep1&type=pdf", } @InProceedings{Loveard:2002:GPC, author = "Thomas Loveard and Vic Ciesielski", title = "Genetic Programming for Classification: An Analysis of Convergence Behaviour", volume = "2557", pages = "309--320", year = "2002", CODEN = "LNCSD9", ISSN = "0302-9743", bibdate = "Sat Nov 30 20:58:15 MST 2002", acknowledgement = ack-nhfb, booktitle = "AI 2002: Advances in Artificial Intelligence : 15th Australian Joint Conference on Artificial Intelligence", editor = "Bob McKay and John Slaney", series = "Lecture Notes in Computer Science", address = "Canberra, Australia", month = "2-6 " # dec, publisher = "Springer", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-00197-2", URL = "http://goanna.cs.rmit.edu.au/~vc/papers/aust-ai02.pdf", DOI = "doi:10.1007/3-540-36187-1_27", abstract = "This paper investigates the unexpected convergence behaviour of genetic Programming (GP) for classification problems. Firstly the paper investigates the relationship between computational effort and attainable classification accuracy. Secondly we attempt to understand why GP classifiers sometimes fail to reach satisfactory levels of accuracy for certain problems regardless of computational effort. The investigation uses an artificially generated dataset for which certain properties are known in advance for the exploration of these areas. Results from this artificial problem show that by increasing computational effort, in the form of larger population sizes and more generations, the probability of success for a run does improve, but that the computational cost far outweighs the rate of this success. Also, some runs, even with very large populations running for many generations, became stagnant and were unable to find an acceptable solution. These results are also reflected in real world classification problems. From analysis of sub-tree components making up successful and unsuccessful programs it was noted that a small number of particular components were almost always present in successful programs, and that these components were often absent from unsuccessful programs. Also a variety of components appeared in unsuccessful programs that were never present in successful ones. Evidence from runs suggests that these components represent paths leading to optimal and sub-optimal branches in the evolutionary search space. Additionally, results suggest that if sub-optimal components (which mirror the concept of deception in genetic algorithms) are relatively greater in number than the optimal components for the problem, then the chances of GP finding a successful solution are reduced.", } @InProceedings{loveard03, author = "Thomas Loveard", title = "Genetic Programming With Meta-Search: Searching For a Successful Population Within The Classification Domain", booktitle = "Genetic Programming, Proceedings of EuroGP'2003", year = "2003", editor = "Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa", volume = "2610", series = "LNCS", pages = "119--129", address = "Essex", publisher_address = "Berlin", month = "14-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-00971-X", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.534.3317", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.534.3317", URL = "http://goanna.cs.rmit.edu.au/~vc/papers/eurogp-03.pdf", DOI = "doi:10.1007/3-540-36599-0_11", abstract = "The genetic programming (GP) search method can often vary greatly in the quality of solution derived from one run to the next. As a result, it is often the case that a number of runs must be performed to ensure that an effective solution is found. This paper introduces several methods which attempt to better use the computational resources spent on performing a number of independent GP runs. Termed meta-search strategies, these methods seek to search the space of evolving GP populations in an attempt to focus computational resources on those populations which are most likely to yield competitive solutions. Two meta-search strategies are introduced and evaluated over a set of classification problems. The meta-search strategies are termed a pyramid search strategy and a population beam search strategy. Additional to these methods, a combined approach using properties of both the pyramid and population beam search methods is evaluated. Over a set of five classification problems, results show that meta-search strategies can substantially improve the accuracy of solutions over those derived by a set of independent GP runs. In particular the combined approach is demonstrated to give more accurate classification performance whilst requiring less time to train than a set of independent GP runs, making this method a promising approach for problems for which multiple GP runs must be performed to ensure a quality solution.", notes = "EuroGP'2003 held in conjunction with EvoWorkshops 2003", } @PhdThesis{Loveard:thesis, author = "Thomas Loveard", title = "Genetic Programming for Classification Learning Problems", school = "Department of Computer Science, Royal Melbourne Institute of Technology, RMIT", year = "2003", address = "Australia", month = "20 " # jan, keywords = "genetic algorithms, genetic programming", URL = "http://goanna.cs.rmit.edu.au/~vc/papers/loveard-phd.pdf", size = "218 pages", notes = "1st supervisor Vic Ciesielski 2nd supervisor: https://titan.csit.rmit.edu.au/~e46507/associates.php", } @InProceedings{LM03, author = "Joern Loviscach and Jennis Meyer-Spradow", title = "Genetic Programming of Vertex Shaders", booktitle = "Proceedings of EuroMedia 2003", year = "2003", editor = "M. Chover and H. Hagen and D. Tost", pages = "29--31", address = "University of Plymouth, Plymouth, United Kingdom", month = apr # " 14-16", keywords = "genetic algorithms, genetic programming, GPU", ISBN = "90-77381-01-5", abstract = "Modern consumer 3-D graphics chips can synthesise procedural textures at a speed comparable to or even better than typical CPUs. We propose genetic programming of vertex shader assembly code for the real-time display and interactive design of procedural video textures and for the approximation and artistic abstraction of given static textures by compact vertex shaders.", notes = "cited by \cite{eurogp:EbnerRA05} https://www.eurosis.org/conf/euromedia/euromedia2003/index.html https://www.eurosis.org/cms/files/ECEC-EUROMEDIA-2003FINPROG.pdf broken Sep 2108 http://85.255.193.245/conf/euromedia/euromedia2003/index.html broken Jan 2013 http://viscg.uni-muenster.de/publications/2003/LM03 Sold out Jan 2013 http://www.eurosis.org/cms/index.php?q=taxonomy/term/58", } @InProceedings{Loviscach:2008:AES, author = "Joern Loviscach", title = "Graphical Control of a Parametric Equalizer", booktitle = "AES", year = "2008", address = "Amsterdam", month = "17-20 " # may, keywords = "genetic algorithms, genetic programming", URL = "http://www.aes.org/e-lib/browse.cfm?elib=14567", abstract = "Graphic equalisers allow the user to define a filter's magnitude response virtually free of restrictions. Parametric equalisers are much more limited. However, they offer some vital advantages over graphic equalizers, such as consuming less computational power and operating minimally invasively with naturally soft magnitude and phase responses. This work aims at combining the best of both worlds: It presents a range of methods to control a digital parametric equaliser graphically through a curve or a collection of anchor points. While the user is editing the graphical input, an optimisation process runs in the background and adjusts the equaliser's parameters to reflect the input. In addition, the number of bands and their type (shelving/peak) can be adjusted automatically to produce a simple solution.", notes = "Paper Number: 7437 AES Convention: 124 (May 2008) slides available on www.j3l7h.de/ ", } @InCollection{lowsky:1999:UCFFCOSIPDG, author = "David Lowsky", title = "Using a Cooperative Fitness Function to Coevolve Optimal Strategies in the Iterated Prisoner's Dilemma Game", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1999", year = "1999", editor = "John R. Koza", pages = "131--139", address = "Stanford, California, 94305-3079 USA", month = "15 " # mar, publisher = "Stanford Bookstore", keywords = "genetic algorithms", notes = "part of \cite{koza:1999:GAGPs}", } @Article{Lozano:2008:IS, author = "Manuel Lozano and Francisco Herrera and Jose Ramon Cano", title = "Replacement strategies to preserve useful diversity in steady-state genetic algorithms", journal = "Information Sciences", year = "2008", volume = "178", number = "23", pages = "4421--4433", month = "1 " # dec, note = "Special Section: Genetic and Evolutionary Computing", keywords = "genetic algorithms", ISSN = "0020-0255", abstract = "In this paper, we propose a replacement strategy for steady-state genetic algorithms that considers two features of the candidate chromosome to be included into the population: a measure of the contribution of diversity to the population and the fitness function. In particular, the proposal tries to replace an individual in the population with worse values for these two features. In this way, the diversity of the population becomes increased and the quality of the solutions gets better, thus preserving high levels of useful diversity. Experimental results show the proposed replacement strategy achieved significant performance for problems with different difficulties, with regards to other replacement strategies presented in the literature. Replacement strategies to preserve useful diversity in steady-state genetic algorithms", notes = "not on GP", } @InCollection{lu:1998:SMPASVSGP, author = "Hui-Ling Lu", title = "Search the Model Parameters of the Articulatory Singing Voice Synthesizer via Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1998", year = "1998", editor = "John R. Koza", pages = "94--100", address = "Stanford, California, 94305-3079 USA", month = "17 " # mar, publisher = "Stanford Bookstore", keywords = "genetic algorithms", ISBN = "0-18-212568-8", notes = "part of \cite{koza:1998:GAGPs}", } @InProceedings{Lu:2006:ieeeiceis, author = "Ji Lu and Tao Li", title = "Computation Process Evolution", booktitle = "2006 IEEE International Conference on Engineering of Intelligent Systems", year = "2006", pages = "1--6", organisation = "Islamabad", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, gene expression programming", ISBN = "1-4244-0456-8", DOI = "doi:10.1109/ICEIS.2006.1703138", abstract = "Unlike other genetic methods which are devoted to optimise the input data, this paper proposes an approach, CPE, aiming at finding the computation process of any problem by only using a few input and output data, consisting of the cases needed to be satisfied and those needed to be avoided. It first encodes the antibody using the method similar to that of gene expression programming (GEP), a new efficient technique of genetic programming (GP) with linear representation. Through the gradual evolution, the affinity between antibody and the non-selves become more and more intense. At the same time, every time after the chromosomes are mutated, the chromosomes should be checked to determine whether the antibody chromosome would match the selves, which are the conditions that should be satisfied. Two kind of experiment are examined in order to test the performance of the approach. The results show that CPE evolves out the data-processing processes which are exactly the same as those from which the experimental input data were generated, and compared with GP and GEP which is currently one of the most efficient genetic methods, CPE experiences shorter evolution process. Most importantly, unlike previous evolutionary methods that only consider increasing fitness, this approach takes into account both the goal (fitness) and the constraints of actual problems, which makes it possible to solve complex real problems using evolutionary computation", notes = "INSPEC Accession Number: 9133110 Dept. of Comput. Sci., Sichuan Univ., Chengdu;", } @InProceedings{Lu:2006:MLC, author = "Jian-Jun Lu and Yun-Ling Liu and Shozo Tokinaga", title = "Nonlinear Modeling for Time Series Based on the Genetic Programming and its Applications", booktitle = "International Conference on Machine Learning and Cybernetics", year = "2006", pages = "2097--2102", address = "Dalian", month = aug, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", ISBN = "1-4244-0061-9", DOI = "doi:10.1109/ICMLC.2006.258350", abstract = "This paper deals with clustering of segments of stock prices by using nonlinear modelling system for time series based on the Genetic Programming (GP). We apply the GP procedure in learning phase of the system where we improve the nonlinear functional forms to approximate the models used to generate time series. The variation of the individuals with relatively high capability in the pool can cope with clustering for various kinds of time series which belong to the same cluster similar to the classifier systems. As an application, we show clustering of artificially generated time series obtained by expanding or shrinking by transformation functions. Then, we apply the system to clustering of 8 kinds of segments of real stock prices.", notes = "Graduate School of Economics, Kyushu University, Fukuoka 812-8581, Japan", } @InProceedings{conf/iccS/LuLT07, author = "Jianjun Lu and Yunling Liu and Shozo Tokinaga", title = "Feature Description Systems for Clusters by Using Logical Rule Generations Based on the Genetic Programming and Its Applications to Data Mining", booktitle = "Proceedings of the 7th International Conference on Computational Science, ICCS 2007, Part {IV}", year = "2007", editor = "Yong Shi and G. Dick {van Albada} and Jack Dongarra and Peter M. A. Sloot", volume = "4490", series = "Lecture Notes in Computer Science", pages = "162--165", address = "Beijing, China", month = may # " 27-30", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-72589-3", DOI = "doi:10.1007/978-3-540-72590-9_23", size = "4 pages", abstract = "This paper deals with the realization of retrieval and feature description systems for clusters by using logical rule generations based on the Genetic Programming (GP). At first, whole data is divided into several clusters and the rules are improved based the GP. The fitness of individuals is defined in proportion to the hits of corresponding logical expression to the samples in targeted cluster c, but also in inversely proportion to the hits outside the cluster c. The GP method is applied to various real world data by showing effective performance compared to conventional methods.", notes = "China Agricultural University, Beijing Graduate School of Economics, Kyushu University, 812-8581, Japan", bibdate = "2007-07-23", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/iccS/iccS2007-4.html#LuLT07", } @Article{journals/cmot/LuT13, title = "Analysis of cluster formations on planer cells based on genetic programming", author = "Jianjun Lu and Shozo Tokinaga", journal = "Computational \& Mathematical Organization Theory", year = "2013", number = "4", volume = "19", pages = "426--445", keywords = "genetic algorithms, genetic programming, Multi-agents, Cellular automaton, Local interaction, Control of agents behaviour", bibdate = "2013-12-06", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/cmot/cmot19.html#LuT13", URL = "http://dx.doi.org/10.1007/s10588-012-9112-3", size = "20 pages", abstract = "This paper offers an analysis of cluster formations on planer cells comprised of multi-agents using local interactions and state transitions based on Genetic Programming (GP) and its applications. First, we illustrate that if the states of agents are allowed to have continuous values, equilibrium is attained on the basis of the fixed-point theorem. We also show that if the agents are restricted to binary states, equilibrium is attained in an asymptotic sense. However, for agents characterised by more than one state, the attainment of equilibrium is not ensured. We examine our results by using a simulation wherein agents learn from past experiences based on GP. Finally, we demonstrate a system comprised of cluster formations on planer cells comprised of artificial agents, and apply this system to the clustering of employees in firms.", } @Article{Lu:2022:RemoteSensing, author = "Miao Lu and Ying Bi and Bing Xue and Qiong Hu and Mengjie Zhang and Yanbing Wei and Peng Yang and Wenbin Wu", title = "Genetic Programming for High-Level Feature Learning in Crop Classification", journal = "Remote Sensing", year = "2022", volume = "14", number = "16", pages = "3982", month = aug, note = "Special Issue Remote Sensing for Mapping Farmland and Agricultural Infrastructure", keywords = "genetic algorithms, genetic programming, crop classification, feature learning, high-level features, genetic programming representation", publisher = "MDPI AG", ISSN = "2072-4292", DOI = "doi:10.3390/rs14163982", size = "18 page", abstract = "Information on crop spatial distribution is essential for agricultural monitoring and food security. Classification with remote-sensing time series images is an effective way to obtain crop distribution maps across time and space. Optimal features are the precondition for crop classification and are critical to the accuracy of crop maps. Although several approaches are available for extracting spectral, temporal, and phenological features for crop identification, these methods depend heavily on domain knowledge and human experiences, adding uncertainty to the final crop classification. This study proposed a novel Genetic Programming (GP) approach to learning high-level features from time series images for crop classification to address this issue. We developed a new representation of GP to extend the GP tree's width and depth to dynamically generate either fixed or flexible informative features without requiring domain knowledge. This new GP approach was wrapped with four classifiers,", notes = "Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China", } @InProceedings{Lu:2011:ICNC, author = "Qiang Lu and Bin Wang", title = "Feature fitness evaluation for symbolic regression via genetic programming", booktitle = "Seventh International Conference on Natural Computation (ICNC 2011)", year = "2011", month = "26-28 " # jul, volume = "2", pages = "1087--1091", address = "Shanghai", size = "5 pages", abstract = "In this paper, feature fitness evaluation method is proposed for accelerating the speed of evolution in symbolic regression. Through analysing the feature of curve or surface which train data represents, vertex and inflection points are extracted from the train data. According to the feature data and diversity of population, the test data for evolution of genetic programming (GP) are generated dynamically. The method was implemented by using GP and genetic expression programming(GEP). Results show that the method in GP, compared with classic GP and GEP, has benefits about efficient of computation, regression performance and avoiding premature convergence.", keywords = "genetic algorithms, genetic programming, gene expression programming, GEP, data representation, feature fitness evaluation, genetic expression programming, inflection points, symbolic regression, vertex points, regression analysis", DOI = "doi:10.1109/ICNC.2011.6022150", ISSN = "2157-9555", notes = "Also known as \cite{6022150}", } @Article{Lu:2016:CIN, title = "Using Genetic Programming with Prior Formula Knowledge to Solve Symbolic Regression Problem", author = "Qiang Lu and Jun Ren and Zhiguang Wang", journal = "Computational Intelligence and Neuroscience", year = "2016", pages = "Article ID 1021378", keywords = "genetic algorithms, genetic programming", publisher = "Hindawi Publishing Corporation", bibsource = "OAI-PMH server at www.ncbi.nlm.nih.gov", identifier = "/pmc/articles/PMC4706865/", language = "en", oai = "oai:pubmedcentral.nih.gov:4706865", rights = "Copyright 2016 Qiang Lu et al.; This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.", URL = "http://dx.doi.org/10.1155/2016/1021378", URL = "http://downloads.hindawi.com/journals/cin/2016/1021378.pdf", size = "18 pages", abstract = "A researcher can infer mathematical expressions of functions quickly by using his professional knowledge (called Prior Knowledge). But the results he finds may be biased and restricted to his research field due to limitation of his knowledge. In contrast, Genetic Programming method can discover fitted mathematical expressions from the huge search space through running evolutionary algorithms. And its results can be generalised to accommodate different fields of knowledge. However, since GP has to search a huge space, its speed of finding the results is rather slow. Therefore, in this paper, a framework of connection between Prior Formula Knowledge and GP (PFK-GP) is proposed to reduce the space of GP searching. The PFK is built based on the Deep Belief Network (DBN) which can identify candidate formulas that are consistent with the features of experimental data. By using these candidate formulas as the seed of a randomly generated population, PFK-GP finds the right formulas quickly by exploring the search space of data features. We have compared PFK-GP with Pareto GP on regression of eight benchmark problems. The experimental results confirm that the PFK-GP can reduce the search space and obtain the significant improvement in the quality of SR.", } @Article{LU:2021:IS, author = "Qiang Lu and Shuo Zhou and Fan Tao and Jake Luo and Zhiguang Wang", title = "Enhancing gene expression programming based on space partition and jump for symbolic regression", journal = "Information Sciences", volume = "547", pages = "553--567", year = "2021", ISSN = "0020-0255", DOI = "doi:10.1016/j.ins.2020.08.061", URL = "https://www.sciencedirect.com/science/article/pii/S0020025520308276", keywords = "genetic algorithms, genetic programming, Symbolic regression, Gene expression programming, Multi-armed bandit, Evolutionary computation", abstract = "When solving a symbolic regression problem, the gene expression programming (GEP) algorithm could fall into a premature convergence which terminates the optimization process too early, and may only reach a poor local optimum. To address the premature convergence problem of GEP, we propose a novel algorithm named SPJ-GEP, which can maintain the GEP population diversity and improve the accuracy of the GEP search by allowing the population to jump efficiently between segmented subspaces. SPJ-GEP first divides the space of mathematical expressions into k subspaces that are mutually exclusive. It then creates a subspace selection method that combines the multi-armed bandit and the a-greedy strategy to choose a jump subspace. In this way, the analysis is made on the population diversity and the range of the number of subspaces. The analysis results show that SPJ-GEP does not significantly increase the computational complexity of time and space than classical GEP methods. Besides, an evaluation is conducted on a set of standard SR benchmarks. The evaluation results show that the proposed SPJ-GEP keeps a higher population diversity and has an enhanced accuracy compared with three baseline GEP methods", } @InProceedings{Lu:2010:ICEE, author = "Shichang Lu and Zhiwei Fan", title = "The Timing Correlation Dimension Study on Regional Economic Growth", booktitle = "2010 International Conference on E-Business and E-Government (ICEE)", year = "2010", month = may, pages = "5339--5342", abstract = "Based on the relevance of fractal theory, taking Liaoning Province as an example to research the time series of regional economic growth, applied GP algorithm to calculate the correlation dimension and observe whether it has fractal characteristics, correlation dimension is a relatively simple extraction fractal dimension method through experimental data by calculating fractal dimension, has a certain operational, the method lays the foundation for predicting the regional economic growth and judging the economic situation.", keywords = "Liaoning Province, experimental data, fractal extraction dimension method, regional economic growth, time series, timing correlation dimension study, correlation methods, economic forecasting, time series", DOI = "doi:10.1109/ICEE.2010.1336", notes = "Not GP but Grassberger and Procaccia. Fac. of Bus., Liaoning Tech. Univ., Huludao, China . Also known as \cite{5592299}", } @InProceedings{lu:2003:dnfoniugp, author = "Wei Lu and Issa Traore", title = "Detecting new forms of network intrusion using genetic programming", booktitle = "Proceedings of the 2003 Congress on Evolutionary Computation CEC2003", editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon", pages = "2165--2172", year = "2003", publisher = "IEEE Press", address = "Canberra", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "8-12 " # dec, organisation = "IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", keywords = "genetic algorithms, genetic programming, Artificial intelligence, Biological cells, Data structures, Databases, Event detection, Genetic mutations, Intrusion detection, Testing, authorisation, telecommunication security, DARPA, crossover, detection rate, dropping condition operators, false alarm rate, false negative rate, false positive rate, genetic operators, intrusion detection systems, mutation, network attacks, network intrusion, reproduction, rule evolution approach, testing dataset, training dataset,", URL = "http://www.isot.ece.uvic.ca/publications/journals/coi-2004.pdf", DOI = "doi:10.1109/CEC.2003.1299940", ISBN = "0-7803-7804-0", abstract = "How to find and detect novel or unknown network attacks is one of the most important objectives in current intrusion detection systems. In this paper, a rule evolution approach based on Genetic Programming (GP) for detecting novel attacks on network is presented and four genetic operators namely reproduction, mutation, crossover and dropping condition operators are used to evolve new rules. New rules are used to detect novel or known network attacks. A training and testing dataset proposed by DARPA is used to evolve and evaluate these new rules. The proof of concept implementation shows that the rule generated by GP has a low false positive rate (FPR), a low false negative rate (FNR) and a high rate of detecting unknown attacks. Moreover, the rule base composed of new rules has high detection rate (DR) with low false alarm rate (FAR).", notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.", } @Article{Lu:2004:CI, author = "Wei Lu and Issa Traore", title = "Detecting New Forms of Network Intrusion Using Genetic Programming", journal = "Computational Intelligence", year = "2004", volume = "20", number = "3", pages = "475--494", month = aug, keywords = "genetic algorithms, genetic programming, network security, intrusion detection, anomaly detection, rule evolution, rule coverage", URL = "https://onlinelibrary.wiley.com/doi/abs/10.1111/j.0824-7935.2004.00247.x", eprint = "https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.0824-7935.2004.00247.x", DOI = "doi:10.1111/j.0824-7935.2004.00247.x", abstract = "How to find and detect novel or unknown network attacks is one of the most important objectives in current intrusion detection systems. In this paper, a rule evolution approach based on Genetic Programming (GP) for detecting novel attacks on networks is presented and four genetic operators, namely reproduction, mutation, crossover, and dropping condition operators, are used to evolve new rules. New rules are used to detect novel or known network attacks. A training and testing dataset proposed by DARPA is used to evolve and evaluate these new rules. The proof of concept implementation shows that a rule generated by GP has a low false positive rate (FPR), a low false negative rate and a high rate of detecting unknown attacks. Moreover, the rule base composed of new rules has high detection rate with low FPR. An alternative to the DARPA evaluation approach is also investigated.", notes = "Also known as \cite{https://doi.org/10.1111/j.0824-7935.2004.00247.x}", } @InProceedings{Lu:2018:ICSE, author = "Yanxin Lu and Swarat Chaudhuri and Chris Jermaine and David Melski", title = "Program Splicing", booktitle = "40th International Conference on Software Engineering", year = "2018", editor = "Marsha Chechik and Mark Harman", pages = "338--349", address = "Gothenburg, Sweden", month = "27 " # may # "-3 " # jun, publisher = "ACM", keywords = "genetic algorithms, genetic programming, SBSE", isbn13 = "978-1-4503-5638", DOI = "doi:10.1145/3180155.3180190", size = "12 pages", abstract = "a synthesis-based approach to programming that can serve as a principled and automated substitute for copying and pasting code from the Internet. It can query a database containing 3.5 million code snippets mined from open-source repositories.", notes = "Java, Comparison with mu Scalpel \cite{Barr:2015:ISSTA} Reading from CSV files. Face Detection. Rice University", } @Article{LU:2023:conbuildmat, author = "Yi Lu and Changhao Xu and Abolfazl Baghbani", title = "Initial state of excavated soil and rock ({ESR)} to influence the stabilisation with cement", journal = "Construction and Building Materials", volume = "400", pages = "132879", year = "2023", ISSN = "0950-0618", DOI = "doi:10.1016/j.conbuildmat.2023.132879", URL = "https://www.sciencedirect.com/science/article/pii/S0950061823025953", keywords = "genetic algorithms, genetic programming, Unconfined compressive strength, Excavated soil and rock, Cement, Artificial intelligence, Recycling, Sustainability, Tunnel construction", abstract = "This paper investigates the initial state of excavated soil and rock (ESR). These initial states include dry density, organic content, water content (Wc), cement content (Cc), liquid index (LI), dry or wet mixing method. Three ESRs collected from tunnelling projects and kaolin were used in this study to compare. The specimens (i.e., 50 mm in diameter and 100 mm in height) were prepared in the laboratory and cured at 7 and 14 days, and then assessed by the unconfined compressive strength (UCS) test. The analysis shows that the ratio of Wc/Cc is the primary factor to obtain different UCS for high LI ESR and a simple equation is proposed for quick prediction. For ESR with a more general LI, predictive equations are also proposed in terms of artificial neural network (ANN) and genetic programming (GP) for 7-days curing time. The results indicate that the both ANN models with Bayesian Regularization (BR) algorithm outperform ANN with Levenberg-Marquardt (LM) and GP model are accurate to predict UCS of mixtures", } @InProceedings{Lu:2008:IMECS, author = "Yueh-Chun Lu and Ming-Hung Chang and Te-Jen Su", title = "Wiener Model Identification using Genetic Programming", booktitle = "Proceedings of the International MultiConference of Engineers and Computer Scientists, IMECS 2008", year = "2008", volume = "II", address = "Hong Kong", month = "19-21 " # mar, keywords = "genetic algorithms, genetic programming, Wiener model, system identification, Akaike information criterion (AIC)", isbn13 = "978-988-17012-1-3", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.148.3976", URL = "http://www.iaeng.org/publication/IMECS2008/IMECS2008_pp1261-1265.pdf", size = "5 pages", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", contributor = "CiteSeerX", language = "en", oai = "oai:CiteSeerXPSU:10.1.1.148.3976", pages = "1261--1265", abstract = "A Wiener model consists of a dynamic linear transfer function in series with a static nonlinear function. We can through the essences of GP, like robustness, domain independence and ability to search for satisfying solutions in solving complicated nonlinear problems, this study hoped that the evolved GP models could have a better applicability and accuracy of evaluations, and easily obtain the correct structure and parameters of the nonlinear function, and number of zeros and poles of the linear transfer function. GP is applied to the determine nonlinearity and unknown parameters in the nonlinear function and linear dynamic system model are estimated by a least square algorithm. The results of numerical studies indicate the usefulness of proposed approach to Wiener model identification.", } @Article{Lu:2018:ieeeTC, author = "Jie Lu and Hongyang Jia and Naveen Verma and Niraj K. Jha", journal = "IEEE Transactions on Computers", title = "Genetic Programming for Energy-Efficient and Energy-Scalable Approximate Feature Computation in Embedded Inference Systems", year = "2018", volume = "67", number = "2", pages = "222--236", month = feb, keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Approximate computing, energy efficiency, error-aware inference, feature extraction, machine learning", DOI = "doi:10.1109/TC.2017.2738642", ISSN = "0018-9340", size = "15 pages", abstract = "With the increasing interest in deploying embedded sensors in a range of applications, there is also interest in deploying embedded inference capabilities. Doing so under the strict and often variable energy constraints of the embedded platforms requires algorithmic, in addition to circuit and architectural, approaches to reducing energy. A broad approach that has recently received considerable attention in the context of inference systems is approximate computing. This stems from the observation that many inference systems exhibit various forms of tolerance to data noise. While some systems have demonstrated significant approximation-versus-energy knobs to exploit this, they have been applicable to specific kernels and architectures; the more generally available knobs have been relatively weak, resulting in large data noise for relatively modest energy savings (e.g., voltage over scaling, bit-precision scaling). In this work, we explore the use of genetic programming (GP) to compute approximate features. Further, we leverage a method that enhances tolerance to feature-data noise through directed retraining of the inference stage. Previous work in GP has shown that it generalises well to enable approximation of a broad range of computations, raising the potential for broad applicability of the proposed approach. The focus on feature extraction is deliberate because they involve diverse, often highly nonlinear, operations, challenging general applicability of energy-reducing approaches. We evaluate the proposed methodologies through two case studies, based on energy modelling of a custom low-power microprocessor with a classification accelerator. The first case study is on electroencephalogram-based seizure detection. We find that the choice of two primitive functions (square root, subtraction) out of seven possible primitive functions (addition, subtraction, multiplication, logarithm, exponential, square root, and square) enables us to approximate feature in 0.41mJ per feature vector (FV), as compared to 4.79mJ per FV required for baseline feature extraction. This represents a feature extraction energy reduction of 11.68 times. The important system-level performance metrics for seizure detection are sensitivity, latency, and number of false alarms per hour. Our set of GP models achieves 100 percent sensitivity, 4.37 second latency, and 0.15 false alarms per hour. The baseline performance is 100 percent sensitivity, 3.84 second latency, and 0.06 false alarms per hour. The second case study is on electrocardiogram-based arrhythmia detection. In this case, just one primitive function (multiplication) suffices to approximate features in 1.13 microJoules per FV, as compared to 11.69 micro-J per FV required for baseline feature extraction. This represents a feature extraction energy reduction of 10.35 times. The important system-level metrics in this case are sensitivity, specificity, and accuracy. Our set of GP models achieves 81.17 percent sensitivity, 80.63 percent specificity, and 81.86 percent accuracy, whereas the baseline a", notes = "Seizure Detection. CHB-MIT database, ECG-based arrhythmia detection. Also known as \cite{8008802}", } @InProceedings{Lu:2019:GECCOcomp, author = "Qiang Lu and Shuo Zhou and Fan Tao and Zhiguang Wang", title = "Space partition based gene expression programming for symbolic regression", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", isbn13 = "978-1-4503-6748-6", pages = "348--349", address = "Prague, Czech Republic", DOI = "doi:10.1145/3319619.3322075", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{3322075} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @Article{Lu:2021:ieeeTEC, author = "Zhichao Lu and Ian Whalen and Yashish Dhebar and Kalyanmoy Deb and Erik Goodman and Wolfgang Banzhaf and Vishnu Bodetti", title = "Multi-Objective Evolutionary Design of Deep Convolutional Neural Networks for Image Classification", journal = "IEEE Transactions on Evolutionary Computation", year = "2021", volume = "25", number = "2", pages = "277--291", month = apr, keywords = "genetic algorithms, genetic programming, ANN, convolutional neural networks, CNN, evolutionary deep learning, GAs, neural architecture search (NAS)", ISSN = "1089-778X", URL = "https://hal.cse.msu.edu/assets/pdfs/papers/2020-tevc-nsganetv1.pdf", URL = "https://arxiv.org/abs/1912.01369", DOI = "doi:10.1109/TEVC.2020.3024708", code_url = "https://github.com/human-analysis/nsganetv1", size = "15/23 pages", abstract = "Convolutional neural networks (CNNs) are the backbones of deep learning paradigms for numerous vision tasks. Early advancements in CNN architectures are primarily driven by human expertise and by elaborate design processes. Recently, neural architecture search was proposed with the aim of automating the network design process and generating task-dependent architectures. While existing approaches have achieved competitive performance in image classification, they are not well suited to problems where the computational budget is limited for two reasons: 1) the obtained architectures are either solely optimized for classification performance, or only for one deployment scenario and 2) the search process requires vast computational resources in most approaches. To overcome these limitations, we propose an evolutionary algorithm for searching neural architectures under multiple objectives, such as classification performance and floating point operations (FLOPs). The proposed method addresses the first shortcoming by populating a set of architectures to approximate the entire Pareto frontier through genetic operations that recombine and modify architectural components progressively. Our approach improves computational efficiency by carefully down-scaling the architectures during the search as well as reinforcing the patterns commonly shared among past successful architectures through Bayesian model learning. The integration of these two main contributions allows an efficient design of architectures that are competitive and in most cases outperform both manually and automatically designed architectures on benchmark image classification datasets: CIFAR, ImageNet, and human chest X-ray. The flexibility provided from simultaneously obtaining multiple architecture choices for different compute requirements further differentiates our approach from other methods in the literature.", notes = "Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824 USA also known as \cite{9201169}", } @InCollection{lubell-doughtie:2003:UGPEGPSNCDS, author = "Peter B. Lubell-Doughtie", title = "Using Genetic Programming to Evolve a General Purpose Sorting Network for Comparable Data Sets", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2003", year = "2003", editor = "John R. Koza", pages = "128--132", address = "Stanford, California, 94305-3079 USA", month = "4 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", URL = "http://www.genetic-programming.org/sp2003/LubellDoughtie.pdf", notes = "part of \cite{koza:2003:gagp}", } @InProceedings{lucas:2002:esmatfges, author = "Simon Lucas", title = "Evolving spring-mass models: a test-bed for graph encoding schemes", booktitle = "Proceedings of the 2002 Congress on Evolutionary Computation CEC2002", year = "2002", editor = "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and Mark Shackleton", pages = "1952--1957", publisher = "IEEE Press", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", organisation = "IEEE Neural Network Council (NNC), Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", month = "12-17 " # may, keywords = "genetic algorithms, genetic programming, evolving spring-mass models, graph encoding schemes, height challenge design problem, performance evaluation, planar graph coding scheme, CAD, computational geometry, evolutionary computation, graph theory", ISBN = "0-7803-7278-6", URL = "http://algoval.essex.ac.uk/rep/springs/cec2002.pdf", DOI = "doi:10.1109/CEC.2002.1004542", size = "6 pages", abstract = "For many interesting design problems the solution is most naturally represented as a type of graph. This paper proposes that the problem of evolving spring-mass models for a set of design challenges makes an excellent test-bed for evaluating the performance of various graph encoding schemes. We describe how the problem is set up, and intro-duce a planar graph coding scheme. Results demonstrate that the planar graph encoding scheme significantly out-performs a simple direct encoding scheme on a height-challenge design problem.", notes = "CEC 2002 - A joint meeting of the IEEE, the Evolutionary Programming Society, and the IEE. Held in connection with the World Congress on Computational Intelligence (WCCI 2002)", } @InProceedings{lucas03, author = "Simon M. Lucas", title = "Evolving Finite State Transducers: Some Initial Explorations", booktitle = "Genetic Programming, Proceedings of EuroGP'2003", year = "2003", editor = "Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa", volume = "2610", series = "LNCS", pages = "130--141", address = "Essex", publisher_address = "Berlin", month = "14-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-00971-X", URL = "http://algoval.essex.ac.uk/rep/fst/EuroFST.pdf", DOI = "doi:10.1007/3-540-36599-0_12", abstract = "Finite state transducers (FSTs) are finite state machines that map strings in a source domain into strings in a target domain. While there are many reports in the literature of evolving general finite state machines, there has been much less work on evolving FSTs. In particular, the fitness functions required for evolving FSTs are generally different to those used for FSMs. This paper considers three string-distance based fitness functions. We compute their fitness distance correlations, and present results on using two of these (Strict and Hamming) to evolve FSTs. We can control the difficulty of the problem by the presence of short strings in the training set, which make the learning problem easier. In the case of the harder problem, the Hamming measure performs best, while the Strict measure performs best on the easier problem.", notes = "EuroGP'2003 held in conjunction with EvoWorkshops 2003", } @InProceedings{lucas:2004:eurogp, author = "Simon Lucas", title = "Exploiting Reflection in Object Oriented Genetic Programming", booktitle = "Genetic Programming 7th European Conference, EuroGP 2004, Proceedings", year = "2004", editor = "Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule", volume = "3003", series = "LNCS", pages = "369--378", address = "Coimbra, Portugal", publisher_address = "Berlin", month = "5-7 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-21346-5", URL = "http://algoval.essex.ac.uk/rep/oogp/ReflectionBasedGP.pdf", DOI = "doi:10.1007/978-3-540-24650-3_35", size = "10 pages", abstract = "Most programs currently written by humans are object-oriented ones. Two of the greatest benefits of object oriented programming are the separation of interface from implementation, and the notion that an object may have state. This paper describes a simple system that enables object-oriented programs to be evolved. The system exploits reflection to automatically discover features about the environment (the existing classes and objects) in which it is to operate. This enables us to evolve object-oriented programs for the given problem domain with the minimum of effort. Currently, we are only evolving method implementations. Future work will explore how we can also evolve interfaces and classes, which should be beneficial to the automatic generation of structured solutions to complex problems. We demonstrate the system with the aid of an evolutionary art example.", notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004", } @InProceedings{lucas-gonzalez:2001:gpsvmpgp, author = "Socrates A. Lucas-Gonzalez and Hugo Terashima-Marin", title = "Generating Programs for Solving Vector and Matrix Problems using Genetic Programming", booktitle = "2001 Genetic and Evolutionary Computation Conference Late Breaking Papers", year = "2001", editor = "Erik D. Goodman", pages = "260--266", address = "San Francisco, California, USA", month = "9-11 " # jul, keywords = "genetic algorithms, genetic programming, GP-BNF", notes = "GECCO-2001LB. Implementation based on \cite{horner-class} GP Kernel. GP-BNF uses C. Element on an array, dot product, adding two matrices, inverse of matrix. iteration (loop) 6 test cases. popsize=100. No details of grammar.", } @InProceedings{luchian:2015:AIAPG, author = "Henri Luchian and Andrei Bautu and Elena Bautu", title = "Genetic Programming Techniques with Applications in the Oil and Gas Industry", booktitle = "Artificial Intelligent Approaches in Petroleum Geosciences", year = "2015", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-319-16531-8_3", DOI = "doi:10.1007/978-3-319-16531-8_3", } @InProceedings{oai:CiteSeerPSU:523944, author = "Alexey Luchko", title = "Genetic Programming Application to One-way Quantum Finite State Automata Generation", booktitle = "Proceedings of International Workshop on Quantum Computation and Learning", year = "2002", editor = "Richard Bonner and Rusins Freivalds", address = "Riga, Latvia", month = "25-26 " # may, keywords = "genetic algorithms, genetic programming", citeseer-isreferencedby = "oai:CiteSeerPSU:84574", citeseer-references = "oai:CiteSeerPSU:163032", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:523944", rights = "unrestricted", URL = "http://www.mdh.se/ima/personal/rbr01/courses/riga02proc/11.pdf", URL = "http://citeseer.ist.psu.edu/523944.html", size = "5 pages", abstract = "In this paper I would like to introduce genetic programming application to one-way quantum finite state automata (QFA) generation.", } @InProceedings{lucier:1998:pofGP, author = "Bradley J. Lucier and Sudhakar Mamillapalli and Jens Palsberg", title = "Program Optimization for Faster Genetic Programming", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "202--207", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-548-7", URL = "http://www.cs.ucla.edu/~palsberg/paper/gp98.pdf", notes = "Better image processing for breast cancer xrays GP-98", } @InProceedings{Ludwig:2010:IHIS, author = "Simone A. Ludwig", title = "Prediction of breast cancer biopsy outcomes using a distributed genetic programming approach", booktitle = "Proceedings of the 1st ACM International Health Informatics Symposium", editor = "Tiffany C. Veinot and {\"U}mit V. {\c C}ataly{\"u}rek and Gang Luo and Henrique Andrade and Neil R. Smalheiser", year = "2010", pages = "694--699", address = "Arlington, Virginia, USA", publisher = "ACM", keywords = "genetic algorithms, genetic programming, benign, cancer recurrence, classification, malignant", isbn13 = "978-1-4503-0030-8", DOI = "doi:10.1145/1882992.1883099", size = "6 pages", acmid = "1883099", abstract = "Worldwide, breast cancer is the second most common type of cancer after lung cancer and the fifth most common cause of cancer death accounting for 519,000 deaths worldwide in 2004. The most effective method for breast cancer screening today is mammography. However, presently predictions of breast biopsies resulting from mammogram interpretation lead to approximately 70percent biopsies with benign outcomes, which are preventable. Therefore, an automatic method is necessary to aid physicians in the prognosis of mammography interpretations. The data set used for this investigation is based on BI-RADS findings. Previous work has achieved good results using a decision tree, an artificial neural networks and a case-based reasoning approach to develop predictive classifiers. This paper uses a distributed genetic programming approach to predict the outcomes of the mammography achieving even better prediction results.", } @InProceedings{Ludwig:2010:KES, title = "Prognosis of Breast Cancer Using Genetic Programming", author = "Simone A. Ludwig and Stefanie Roos", booktitle = "14th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (KES 2010), Part {IV}", year = "2010", editor = "Rossitza Setchi and Ivan Jordanov and Robert J. Howlett and Lakhmi C. Jain", volume = "6279", series = "Lecture Notes in Computer Science", pages = "536--545", address = "Cardiff, UK", month = sep # " 8-10", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-15383-9", DOI = "doi:10.1007/978-3-642-15384-6_57", size = "10 pages", bibdate = "2010-12-02", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/kes/kes2010-4.html#LudwigR10", abstract = "Worldwide, breast cancer is the second most common type of cancer after lung cancer and the fifth most common cause of cancer death. In 2004, breast cancer caused 519,000 deaths worldwide. In order to reduce the cancer deaths and thereby increasing the survival rates an automatic approach is necessary to aid physicians in the prognosis of breast cancer. This paper investigates the prognosis of breast cancer using a machine learning approach, in particular genetic programming, whereas earlier work has approached the prognosis using linear programming. The genetic programming method takes a digitized image of a patient and automatically generates the prediction of the time to recur as well as the disease-free survival time. The breast cancer dataset from the University of California Irvine Machine Learning Repository was used for this study. The evaluation shows that the genetic programming approach outperforms the linear programming approach by 33 percent.", affiliation = "Department of Computer Science, University of Saskatchewan, Canada", } @Article{Ludwig:2010:IJCMAM, author = "Simone A. Ludwig and Stefanie Roos and Monique Frize and Nicole Yu", title = "Medical Outcome Prediction for Intensive Care Unit Patients", journal = "International Journal of Computational Models and Algorithms in Medicine (IJCMAM)", year = "2010", volume = "1", number = "4", pages = "19--30", keywords = "genetic algorithms, genetic programming, Intelligent Technologies", ISSN = "1947-3133", URL = "http://www.irma-international.org/article/medical-outcome-prediction-intensive-care/51668/", DOI = "doi:10.4018/jcmam.2010100102", size = "12", abstract = "The rate of people dying from medical errors in hospitals each year is very high. Errors that frequently occur during the course of providing health care are adverse drug events and improper transfusions, surgical injuries and wrong-site surgery, suicides, restraint-related injuries or death, falls, burns, pressure ulcers, and mistaken patient identities. Medical decision support systems play an increasingly important role in medical practice. By assisting physicians in making clinical decisions, medical decision support systems improve the quality of medical care. Two approaches have been investigated for the prediction of medical outcomes: hours of ventilation and the mortality rate in the adult intensive care unit. The first approach is based on neural networks with the weight-elimination algorithm, and the second is based on genetic programming. Both approaches are compared to commonly used machine learning algorithms. Results show that both algorithms developed score well for the outcomes selected", notes = "Simone A. Ludwig (North Dakota State University, USA), Stefanie Roos (Darmstadt University, Germany), Monique Frize (Carleton University, Canada), and Nicole Yu (Carleton University, Canada)", } @Article{Ludwig:2016:GPEM, author = "Simone A. Ludwig", title = "{Anthony Brabazon}, {Michael O'Neill}, {Sean McGarraghy}: {Natural} computing algorithms {Springer}, 2015, 554 pp, {ISBN}: 978-3-662-43631-8", journal = "Genetic Programming and Evolvable Machines", year = "2016", volume = "17", number = "2", pages = "193--195", month = jun, note = "Book review", keywords = "genetic algorithms, genetic programming, grammatical evolution, evolvable hardware", ISSN = "1389-2576", URL = "https://rdcu.be/dR8CI", DOI = "doi:10.1007/s10710-016-9266-8", size = "3 pages", notes = "Review of \cite{Brabazon:book:NCA}", } @InProceedings{conf/ieaaie/LuengoWBC15, author = "Sergio Luengo and Stephan Winkler and David F. Barrero and Bonifacio Castano", title = "Optimization of Trading Rules for the Spanish Stock Market by Genetic Programming", booktitle = "28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, {IEA}/{AIE} 2015", year = "2015", editor = "Moonis Ali and Young Sig Kwon and Chang-Hwan Lee and Juntae Kim and Yongdai Kim", volume = "9101", series = "Lecture Notes in Computer Science", pages = "623--634", address = "Seoul, South Korea", month = jun # " 10-12", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-19065-5", bibdate = "2015-04-30", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ieaaie/ieaaie2015.html#LuengoWBC15", URL = "https://www.ieaaie2015.org/sub/acceptedpapers.asp", URL = "https://pure.fh-ooe.at/en/publications/optimization-of-trading-rules-for-the-spanish-stock-market-by-gen", DOI = "doi:10.1007/978-3-319-19066-2_60", abstract = "This paper deals with the development of a method for generating input and output signals in the Spanish stock market. It is based on the application of set of simple trading rules optimized by genetic programming. To this aim we use the HeuristicLab software. To evaluate the performance of our method we make a comparison with other traditional methods such as Buy and Hold and Simple Moving Averages Crossover. We study three different market scenarios: bull market, bear market and sideways market. Empirical test series show that market global behavior has a great influence on the results of each method and that strategies based on genetic programming perform best in the sideways market.", notes = "Proceedings published as Current Approaches in Applied Artificial Intelligence, 2015", URL = "http://dx.doi.org/", } @InProceedings{Luerssen05, author = "Martin H. Luerssen", editor = "Vladimir Estivill-Castro", title = "Graph Grammar Encoding and Evolution of Automata Networks", booktitle = "Twenty-Eighth Australasian Computer Science Conference (ACSC2005)", series = "CRPIT", volume = "38", pages = "229--238", publisher = "ACS", address = "Newcastle, Australia", year = "2005", month = jan # "/" # feb, publisher = "Australian Computer Society, Inc.", keywords = "genetic algorithms, genetic programming, graph grammars, neural networks, ANN", ISBN = "1-920682-20-1", URL = "http://crpit.com/confpapers/CRPITV38Luerssen.pdf", size = "4 pages", abstract = "The global dynamics of automata networks (such as neural networks) are a function of their topology and the choice of automata used. Evolutionary methods can be applied to the optimisation of these parameters, but their computational cost is prohibitive unless they operate on a compact representation. Graph grammars provide such a representation by allowing network regularities to be efficiently captured and reused. We present a system for encoding and evolving automata networks as collective hypergraph grammars, and demonstrate its efficacy on the classical problems of symbolic regression and the design of neural network architectures.", notes = "Also known as \cite{CRPITV38P229-238} \cite{DBLP:conf/acsc/Luerssen05} ACSC '05", } @PhdThesis{Luerssen2006, author = "Martin Holger Luerssen", title = "Experimental Investigations into Graph Grammar Evolution", school = "School of Informatics and Engineering, The Flinders University of South Australia", year = "2006", type = "PhD", address = "Adelaide, Australia", month = may # " 29", keywords = "genetic algorithms, genetic programming, embryogeny", URL = "http://theses.flinders.edu.au/uploads/approved/adt-SFU20110328.120915/public/02whole.pdf", URL = "http://theses.flinders.edu.au/public/adt-SFU20110328.120915/index.html", size = "224 pages", abstract = "Artificial and natural instances of networks are ubiquitous, and many problems of practical interest may be formulated as questions about networks. Determining the optimal topology of a network is pertinent to many domains. Evolutionary algorithms constitute a well-established optimisation method, but they scale poorly if applied to the combinatorial explosion of possible network topologies. Generative representation schemes aim to overcome this by facilitating the discovery and reuse of design dependencies and allowing for adaptable exploration strategies. Biological embryogenesis is a strong inspiration for many such schemes, but the associated complexities of modelling lead to impractical simulation times and poor conceptual understanding. Existing research also predominantly focuses on specific design domains such as neural networks. This thesis seeks to define a simple yet universally applicable and scalable method for evolving graphs and networks. A number of contributions are made in this regard. We establish the notion of directly evolving a graph grammar from which a population of networks can be derived. Compact cellular productions that form a hypergraph grammar are optimised by a novel multi-objective evolutionary design system called G/GRADE. A series of empirical investigations are then carried out to gain a better understanding of graph grammar evolution. G/GRADE is applied to four domains: symbolic regression, circuit design, neural networks, and telecommunications. We compare different strategies for composing graphs from randomly mutated productions and examine the relationship between graph grammar diversity and fitness, presenting both the use of phenotypic diversity objectives and an island model to improve this. Additionally, we address the issue of bloat and demonstrate how concepts from swarm intelligence can be applied to production selection and mutation to improve grammatical convergence. The results of this thesis are relevant to evolutionary research into networks and grammars, and the wide applicability and potential of graph grammar evolution is expected to inspire further study.", notes = "http://csem.flinders.edu.au/research/papers/bibtex.html http://www.flinders.edu.au/science_engineering/csem/publications/phd-theses.cfm Supervisor: David M. W. Powers", } @InProceedings{Luerssen:2007:cec, author = "Martin H. Luerssen and David M. W. Powers", title = "Graph Design by Graph Grammar Evolution", booktitle = "2007 IEEE Congress on Evolutionary Computation", year = "2007", editor = "Dipti Srinivasan and Lipo Wang", pages = "386--393", address = "Singapore", month = "25-28 " # sep, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "1-4244-1340-0", file = "1348.pdf", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2007.4424497", abstract = "Determining the optimal topology of a graph is pertinent to many domains, as graphs can be used to model a variety of systems. Evolutionary algorithms constitute a popular optimisation method, but scalability is a concern with larger graph designs. Generative representation schemes, often inspired by biological development, seek to address this by facilitating the discovery and reuse of design dependencies and allowing for adaptable exploration strategies. We present a novel developmental method for optimising graphs that is based on the notion of directly evolving a hypergraph grammar from which a population of graphs can be derived. A multi-objective design system is established and evaluated on problems from three domains: symbolic regression, circuit design, and neural control. The observed performance compares favourably with existing methods, and extensive reuse of subgraphs contributes to the efficient representation of solutions. Constraints can also be placed on the type of explored graph spaces, ranging from tree to pseudograph. We show that more compact solutions are attainable in less constrained spaces, although convergence typically improves with more constrained designs.", notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C is it a GP? Evolution of executable grammar?", } @InProceedings{Luerssen:2007:cec2, author = "Martin H. Luerssen and David M. W. Powers", title = "Evolvability and Redundancy in Shared Grammar Evolution", booktitle = "2007 IEEE Congress on Evolutionary Computation", year = "2007", editor = "Dipti Srinivasan and Lipo Wang", pages = "370--377", address = "Singapore", month = "25-28 " # sep, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "1-4244-1340-0", file = "1364.pdf", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2007.4424495", abstract = "Shared grammar evolution (SGE) is a novel scheme for representing and evolving a population of variable length programs as a shared set of grammatical productions. Productions that fail to contribute to selected solutions can be retained for several generations beyond their last use. The ensuing redundancy and its effects are assessed in this paper on two circuit design tasks associated with random number generation: finding a recurrent circuit with maximum period, and reproducing a De Bruijn counter from a set of seed/output pairs. In both instances, increasing redundancy leads to significantly higher success rates, outperforming comparable increases in population size. The results support previous studies that have shown that representational redundancy can be beneficial to evolutionary search. However, redundancy promotes an increase in further redundancy by encouraging the creation of large offspring, the evaluation of which is computationally costly. This observation should generalise to any unconstrained variablelength representation and therefore represents a notable drawback of redundancy in evolution.", notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C", } @Article{Luerssen:2008:GPEM, author = "Martin H. Luerssen and David M. W. Powers", title = "Evolving encapsulated programs as shared grammars", journal = "Genetic Programming and Evolvable Machines", year = "2008", volume = "9", number = "3", pages = "203--228", month = sep, keywords = "genetic algorithms, genetic programming, Shared grammars, Developmental systems, Encapsulation, Modularity, Memoization", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-008-9061-2", abstract = "Facilitating the discovery and reuse of modular building blocks is generally regarded as the key to achieving better scalability in genetic programming (GP). A precedent for this exists in biology, where complex designs are the product of developmental processes that can also be abstractly modelled as generative grammars. We introduce shared grammar evolution (SGE), which aligns grammatical development with the common application of grammars in GP as a means of establishing declarative bias. Programs are derived from and represented by a global context-free grammar that is transformed and extended according to another, user-defined grammar. Grammatical productions and the subroutines they encapsulate are shared between programs, which enables their reuse without reevaluation and can significantly reduce total evaluation time for large programs and populations. Several variants of SGE employing different strategies for controlling solution size and diversity are tested on classic GP problems. Results compare favourably against GP and newer techniques, with the best results obtained by promoting diversity between derived programs.", } @Book{Luerssen:book, author = "Martin H. Luerssen", title = "Experimental Investigations into Graph Grammar Evolution: A Novel Approach to Evolutionary Design", publisher = "Verlag Dr. Mueller", year = "2009", address = "Saaarbruecken, Germany", month = feb # " 22", keywords = "genetic algorithms, genetic programming", ISBN = "3-639-12328-X", URL = "http://www.amazon.com/Experimental-Investigations-Graph-Grammar-Evolution/dp/363912328X", abstract = "Artificial and natural instances of networks are ubiquitous, and the problem of determining the optimal topology of a network is of practical value to many domains. Evolutionary algorithms constitute a well-established optimisation method, but they scale poorly if applied to the combinatorial explosion of possible network topologies. Generative representation schemes aim to overcome this problem by facilitating the discovery and reuse of design dependencies and allowing for adaptable exploration strategies. This book seeks to define a simple yet universally applicable and scalable method for evolving graphs and networks. A number of contributions are made in this regard. We establish the notion of directly evolving a graph grammar from which a population of networks can be derived. Compact cellular productions that form a hypergraph grammar are optimised by a novel multi-objective evolutionary design system. A series of empirical investigations are then carried out to gain a better understanding of graph grammar evolution.", notes = "See also \cite{Luerssen2006}", size = "224 pages", } @InProceedings{Lugo:2017:GECCO, author = "Anthony Erb Lugo and Dennis Garcia and Erik Hemberg and Una-May O'Reilly", title = "Developing Proactive Defenses for Computer Networks with Coevolutionary Genetic Algorithms", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4939-0", address = "Berlin, Germany", pages = "273--274", publisher = "ACM", publisher_address = "New York, NY, USA", month = "15-19 " # jul, keywords = "genetic algorithms, genetic programming, cybersecurity coevolution, evolutionary algorithms, network", URL = "http://doi.acm.org/10.1145/3067695.3089234", DOI = "doi:10.1145/3067695.3089234", acmid = "3089234", size = "2 pages", abstract = "Our cybersecurity tool, RIVALS, develops adaptive network defence strategies by modelling adversarial network attack and defense behaviour in peer-to-peer networks via coevolutionary algorithms. Currently RIVALS DOS attacks are modestly modeled by the selection of a node that is completely disabled for a resource-limited duration. Defenders have three different network routing protocols. Attack or mission completion and resource cost metrics serve as attacker and defender objectives. This work also includes a description of RIVALS suite of coevolutionary algorithms that explore archiving as a means of maintaining progressive exploration and support the evaluation of different solution concepts. To compare and contrast the effectiveness of each algorithm, we execute simulations on 3 different network topologies. Our experiments show that it is possible to forgo the assurance of monotonically increasing results and still retain high quality results.", notes = "Also known as \cite{Lugo:2017:DPD:3067695.3089234} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @InProceedings{luis:2013:EuroGP, author = "Sweeney Luis and Marcus Vinicius {dos Santos}", title = "On the Evolvability of A Hybrid Ant Colony-Cartesian Genetic Programming Methodology", booktitle = "Proceedings of the 16th European Conference on Genetic Programming, EuroGP 2013", year = "2013", month = "3-5 " # apr, editor = "Krzysztof Krawiec and Alberto Moraglio and Ting Hu and A. Sima Uyar and Bin Hu", series = "LNCS", volume = "7831", publisher = "Springer Verlag", address = "Vienna, Austria", pages = "109--120", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Ant Colonies, Rank-Based Ant System, Hybrid Architectures, Evolvability, Dynamic Environments", isbn13 = "978-3-642-37206-3", DOI = "doi:10.1007/978-3-642-37207-0_10", abstract = "A method that uses Ant Colonies as a Model-based Search to Cartesian Genetic Programming (CGP) to induce computer programs is presented. Candidate problem solutions are encoded using a CGP representation. Ants generate problem solutions guided by pheromone traces of entities and nodes of the CGP representation. The pheromone values are updated based on the paths followed by the best ants, as suggested in the Rank-Based Ant System (AS_rank). To assess the evolvability of the system we applied a modified version of the method introduced in \cite{Evolvability} to measure rate of evolution. Our results show that such method effectively reveals how evolution proceeds under different parameter settings. The proposed hybrid architecture shows high evolvability in a dynamic environment by maintaining a pheromone model that elicits high genotype diversity.", notes = "Part of \cite{Krawiec:2013:GP} EuroGP'2013 held in conjunction with EvoCOP2013, EvoBIO2013, EvoMusArt2013 and EvoApplications2013", } @InCollection{luiz:1994:sppd, author = "Gerald Luiz", title = "Sufficient Parameters for Population Dynamics Simulations with Adaptation", booktitle = "Artificial Life at Stanford 1994", year = "1994", editor = "John R. Koza", pages = "91--98", address = "Stanford, California, 94305-3079 USA", month = jun, organisation = "Stanford University", publisher = "Stanford Bookstore", keywords = "genetic algorithms", ISBN = "0-18-182105-2", notes = "This volume contains 22 papers written and submitted by students describing their term projects for the course in artificial life (Computer Science 425) at Stanford University offered during the spring quarter quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs425.html", } @InProceedings{luke:1996:etc, author = "Sean Luke and Lee Spector", title = "Evolving Teamwork and Coordination with Genetic Programming", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "150--156", address = "Stanford University, CA, USA", publisher = "MIT Press", URL = "http://www.cs.gmu.edu/~sean/papers/cooperation.pdf", URL = "http://www.cs.gmu.edu/~sean/papers/cooperation.ps.gz", size = "9 pages", abstract = "Some problems can be solved only by multi-agent teams. In using genetic programming to produce such teams, one faces several design decisions. First, there are questions of team diversity and of breeding strategy. In one commonly used scheme, teams consist of clones of single individuals; these individuals breed in the normal way and are cloned to form teams during fitness evaluation. In contrast, teams could also consist of distinct individuals. In this case one can either allow free interbreeding between members of different teams, or one can restrict interbreeding in various ways. A second design decision concerns the types of coordination-facilitating mechanisms provided to individual team members; these range from sensors of various sorts to complex communication systems. This paper examines three breeding strategies (clones, free, and restricted) and three coordination mechanisms (none, deictic sensing, and name-based sensing) for evolving teams of agents in the Serengeti world, a simple predator/prey environment. Among the conclusions are the fact that a simple form of restricted interbreeding outperforms free interbreeding in all teams with distinct individuals, and the fact that name-based sensing consistently outperforms deictic sensing.", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap18.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96", } @InProceedings{luke:1996:egnee, author = "Sean Luke and Lee Spector", title = "Evolving Graphs and Networks with Edge Encoding: Preliminary Report", booktitle = "Late Breaking Papers at the Genetic Programming 1996 Conference Stanford University July 28-31, 1996", year = "1996", editor = "John R. Koza", pages = "117--124", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California 94305-3079, USA", month = "28--31 " # jul, publisher = "Stanford Bookstore", ISBN = "0-18-201031-7", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.gmu.edu/~sean/papers/graph-paper.pdf", URL = "http://www.cs.gmu.edu/~sean/papers/graph-paper.ps.gz", URL = "http://citeseer.ist.psu.edu/412757.html", abstract = "We present an alternative to the cellular encoding technique [Gruau 1992] for evolving graph and network structures via genetic programming. The new technique, called edge encoding, uses edge operators rather than the node operators of cellular encoding. While both cellular encoding and edge encoding can produce all possible graphs, the two encodings bias the genetic search process in different ways; each may therefore be most useful for a different set of problems. The problems for which these techniques may be used, and for which we think edge encoding may be particularly useful, include the evolution of recurrent neural networks, finite automata, and graph-based queries to symbolic knowledge bases. In this preliminary report we present a technical description of edge encoding and an initial comparison to cellular encoding. Experimental investigation of the relative merits of these encoding schemes is currently in progress.", notes = "GP-96LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670", } @InProceedings{luke:1997:ccmGP, author = "Sean Luke and Lee Spector", title = "A Comparison of Crossover and Mutation in Genetic Programming", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming", pages = "240--248", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", URL = "http://www.cs.gmu.edu/~sean/papers/comparison/comparison.pdf", URL = "http://www.cs.gmu.edu/~sean/papers/comparison/comparison.ps.gz", size = "9 pages", abstract = "This paper presents a large and systematic body of data on the relative effectiveness of mutation, crossover, and combinations of mutation and crossover in genetic programming (GP). The literature of traditional genetic algorithms contains related studies, but mutation and crossover in GP differ from their traditional counterparts in significant ways. In this paper we present the results from a very large experimental data set, the equivalent of approximately 12,000 typical runs of a GP system, systematically exploring a range of parameter settings. The resulting data may be useful not only for practitioners seeking to optimize parameters for GP runs, but also for theorists exploring issues such as the role of building blocks in GP.", notes = "GP-97. 6-mux, lawn mower, symbolic regression, Santa Fe trail artificial ant. See alse \cite{luke:1998:rcxmGP}. The Gzipped PostScript version (.ps.gz) does not come with figures; to get the figures for the PostScript version, use the figures URLs below", figures = "http://www.cs.gmu.edu/~sean/papers/comparison/figures1-2.ps.gz", figures = "http://www.cs.gmu.edu/~sean/papers/comparison/figures3-4.ps.gz", } @InProceedings{luke:1997:csstcGP, author = "Sean Luke and Charles Hohn and Jonathan Farris and Gary Jackson and James Hendler", title = "Co-evolving Soccer Softbot Team Coordination with Genetic Programming", booktitle = "Proceedings of the First International Workshop on RoboCup, at the International Joint Conference on Artificial Intelligence", year = "1997", address = "Nagoya, Japan", keywords = "genetic algorithms, genetic programming, lil-gp", URL = "http://www.cs.gmu.edu/~sean/papers/robocupc.pdf", URL = "http://www.cs.gmu.edu/~sean/papers/robocupc.ps.gz", size = "14 pages", abstract = "Genetic Programming is a promising new method for automatically generating functions and algorithms through natural selection. In contrast to other learning methods, Genetic Programming's automatic programming makes it a natural approach for developing algorithmic robot behaviors. In this paper we present an overview of how we apply Genetic Programming to behavior-based team coordination in the RoboCup Soccer Server domain. The result is not just a hand-coded soccer algorithm, but a team of softbots which have learned on their own how to play a reasonable game of soccer.", notes = "IJCAI-97 https://www.robocup.org/a_brief_history_of_robocup Given the acknowledged challenges of applying Genetic Programming to robot soccer, we were happy to just show up at Nagoya with an entry in the RoboCup simulation track. However, Maryland's Genetic Programming entry in in fact beat its first two competitors (5-2 against U British Columbia, Canada and 17-0 over Toyohashi University of Science and Technology, Japan) before losing to University of Tokyo (last year's champion, 6-1) and subsequently Tokyo Institute of Technology (16-4) in the single-elimination round. For its research achievement in demonstrating the feasibility of evolutionary computation in a very difficult domain, Maryland's entry also won the RoboCup Scientific Challenge Award. http://ci.etl.go.jp/~noda/soccer/RoboCup97/result.html Part of Email from John Koza Fri, 29 Aug 1997 21:37:50 PDT to genetic-programming@cs.stanford.edu {"}The Maryland entry competed against various hand-written robot controllers (all of which are very good examples of clever human programming) and its success demonstrated, I think, that GP is precisely the right way to create programmers when the task really gets difficult. {"} Too short to give full technical details: STGP, 50ish problem dependant functions. team composed of 2-3 squads of identical players. Each squad 2 trees (used for possetion and non-possetion of ball. 6 or 12 trees per GP indivdual. Co-evolution. lil-gp. Stepped evolution (like seeding?) build squad from good players, team from good squads.", } @InProceedings{luke:1998:rcxmGP, author = "Sean Luke and Lee Spector", title = "A Revised Comparison of Crossover and Mutation in Genetic Programming", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "208--213", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-548-7", URL = "http://www.cs.gmu.edu/~sean/papers/revisedgp98.pdf", URL = "http://www.cs.gmu.edu/~sean/papers/revisedgp98.ps.gz", abstract = "In [Luke and Spector 1997] we presented a comprehensive suite of data comparing GP crossover and point mutation over four domains and a wide range of parameter settings. Unfortunately, the results were marred by statistical flaws. This revision of the study eliminates these flaws, with three times as much the data as the original experiments had. Our results again show that crossover does have some advantage over mutation given the right parameter settings (primarily larger population sizes), though the difference between the two surprisingly small. Further, the results are complex, suggesting that the big picture is more complicated than is commonly believed.", notes = "GP-98 This paper is a revision of a previous paper \cite{luke:1997:ccmGP}, with statistical correction and a considerable new set of data. However, the original also has some data that does not appear here, so you may want to consider getting both. Also: Figures 1 through 4 are separated from the rest of the paper in the Gzipped PostScript version (not the PDF version). The figures are listed in the figure URLs below. Finally: if you downloaded a copy of this paper prior to May 20, 1998, its graphs were wrong; get the revised revised version. :-", figures = "http://www.cs.gmu.edu/~sean/papers/revisedgp98graphs.ps.gz", } @InProceedings{luke:1998:RoboCup97, author = "Sean Luke", title = "Genetic Programming Produced Competitive Soccer Softbot Teams for {RoboCup97}", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "214--222", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-548-7", URL = "http://www.cs.gmu.edu/~sean/papers/robocupgp98.pdf", URL = "http://www.cs.gmu.edu/~sean/papers/robocupgp98.ps.gz", size = "9 pages", abstract = "At RoboCup, teams of autonomous robots or software softbots compete in simulated soccer matches to demonstrate cooperative robotics techniques in a very difficult, real-time, noisy environment. At the IJCAI/RoboCup97 softbot competition, all entries but ours used human-crafted cooperative decision-making behaviors. We instead entered a softbot team whose high-level decision making behaviors had been entirely evolved using genetic programming. Our team won its first two games against human-crafted opponent teams, and received the RoboCup Scientific Challenge Award. This report discusses the issues we faced and the approach we took to use GP to evolve our robot soccer team for this difficult environment.", notes = "GP-98 This paper is similar to an earlier workshop paper \cite{luke:1997:csstcGP}. The key difference being that the workshop paper, which was not for a Genetic Programming audience, is short on experimental details and long on introductions to how GP works. There also exists a short invited paper \cite{luke:1998:sretro} detailing how this experiment could have been improved. Also available is a short sidebar for an AI Magazine article. http://www.genetic-programming.com/hc/lukesoccer.html", } @Article{luke:1998:firsttime, author = "Sean Luke and Shugo Hamahashi and Koji Kyoda and Hiroki Ueda", title = "Biology: See It Again -- for the First Time", journal = "IEEE Intelligent Systems", year = "1998", volume = "13", number = "5", pages = "6--8", month = sep # "/" # oct, keywords = "genetic algorithms, genetic programming, biological modelling, DNA", ISSN = "1094-7167", URL = "http://www.cs.gmu.edu/~sean/papers/biology.pdf", URL = "http://www.cs.gmu.edu/~sean/papers/biology.ps.gz", URL = "https://ieeexplore.ieee.org/document/722341", DOI = "doi:10.1109/5254.722341", size = "3 pages", abstract = "Computer science owes a huge debt to biological systems. The field itself came about largely as an attempt to understand and replicate the function and abilities of the brain, a complex biological creation. From this early lineage has sprung many subfields derived largely from biological metaphors: computer vision, neural networks, evolutionary computation, robotics, multi-agent studies, and much of artificial intelligence. In some areas, the computer has bested its biological counterparts in efficiency and simplicity. But for many domains, even after decades of hard work, the biological {"}real thing{"} is still superior to the artificial algorithms inspired by it.", notes = "Invited Article. Argues for a revisitation of the biological roots behind artificial intelligence and evolutionary computation", } @InProceedings{luke:1998:sretro, author = "Sean Luke", title = "Evolving {SoccerBots:} A Retrospective", booktitle = "Proceedings of the 12th Annual Conference of the Japanese Society for Artificial Intelligence", year = "1998", URL = "http://www.cs.gmu.edu/~sean/papers/robocupShort.pdf", URL = "http://www.cs.gmu.edu/~sean/papers/robocupShort.ps.gz", keywords = "genetic algorithms, genetic programming", abstract = "In the RoboCup97 robot soccer tournament, we entered a team of softbot programs whose player strategies had been entirely learned by computer. Our team beat other human-coded competitors and received the RoboCup97 Scientific Challenge award. This paper discusses our approach, and details various ways that, in retrospect, it could have been improved.", notes = "Invited Article. This short invited paper was meant to complement the more complete GP98 and RoboCup97 papers, and an AI Magazine sidebar, by discussing things that could have been improved from our previous attempt.", } @InProceedings{luke:1999:P, author = "Sean Luke and Shugo Hamahashi and Hiroaki Kitano", title = "``Genetic'' Programming", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1098--1105", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-611-4", URL = "http://www.cs.gmu.edu/~sean/papers/gene-gecco99.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-437.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-437.pdf", URL = "http://www.cs.gmu.edu/~sean/papers/gene-gecco99.ps.gz", abstract = "Much of evolutionary computation was inspired by Mendelian genetics. But modern genetics has since advanced considerably, revealing that genes are not simply parameter settings, but interactive cogs in a complex chemical machine. At the same time, an increasing number of evolutionary computation domains are evolving non-parameterized mechanisms such as neural networks or symbolic computer programs. As such, we think modern biological genetics offers much in helping us understand how to evolve such things. In this paper, we present a gene regulation model for Drosophila melanogaster. We then apply gene regulation to evolve deterministic finite-state automata, and show that our approach does well compared to past examples from the literature.", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Article{luke:2000:2ftcaGP, author = "Sean Luke", title = "Two Fast Tree-Creation Algorithms for Genetic Programming", journal = "IEEE Transactions on Evolutionary Computation", year = "2000", volume = "4", number = "3", pages = "274--283", month = sep, keywords = "genetic algorithms, genetic programming, Population Initialization, Tree Creation, Subtree Mutation, Tree Growth, Introns, Bloat", size = "9 pages", URL = "http://ieeexplore.ieee.org/iel5/4235/18897/00873237.pdf", URL = "http://www.cs.gmu.edu/~sean/papers/treecreation.pdf", URL = "http://www.cs.gmu.edu/~sean/papers/treecreation.ps.gz", URL = "http://citeseer.ist.psu.edu/409667.html", DOI = "doi:10.1109/4235.873237", size = "10 pages", abstract = "Genetic programming is an evolutionary optimization method that produces functional programs to solve a given task. These programs commonly take the form of trees representing LISP s-expressions, and a typical evolutionary run produces a great many of these trees. For this reason, a good tree generation algorithm is very important to genetic programming. This paper presents two new tree-generation algorithms for genetic programming and for strongly-typed genetic programming, a common variant. These algorithms are fast, allow the user to request specific tree sizes, and guarantee probabilities of certain nodes appearing in trees. The paper analyzes these two algorithms and compares them with traditional and recently proposed approaches.", } @InProceedings{luke:2000:cgnci, author = "Sean Luke", title = "Code Growth is Not Caused by Introns", pages = "228--235", booktitle = "Late Breaking Papers at the 2000 Genetic and Evolutionary Computation Conference", year = "2000", editor = "Darrell Whitley", address = "Las Vegas, Nevada, USA", month = "8 " # jul, keywords = "genetic algorithms, genetic programming, bloat, introns, ineffective code", URL = "http://www.cs.gmu.edu/~sean/papers/intronpaper.pdf", URL = "http://www.cs.gmu.edu/~sean/papers/intronpaper.ps.gz", URL = "http://citeseer.ist.psu.edu/300709.html", size = "8 pages", abstract = "Genetic programming trees have a strong tendency to grow rapidly and relatively independent of fitness, a serious flaw which has received considerable attention in the genetic programming literature. Much of this literature has implicated introns, subtree structures with no effect on the an individual's fitness assessment. The propagation of inviable code, a certain kind of intron, has been especially linked to tree growth. However this paper presents evidence which shows that denying inviable code the opportunity to propagate actually increases tree growth. The paper argues that rather than causing tree growth, a rise in inviable code is in fact an expected result of tree growth. Lastly, this paper proposes a more general theory of growth for which introns are merely a symptom.", notes = "Part of \cite{whitley:2000:GECCOlb}", } @PhdThesis{luke:dissertation, author = "Sean Luke", title = "Issues in Scaling Genetic Programming: Breeding Strategies, Tree Generation, and Code Bloat", year = "2000", school = "Department of Computer Science, University of Maryland", address = "College Park, MD 20742, USA", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.gmu.edu/~sean/papers/thesis2p.pdf", URL = "http://www.cs.gmu.edu/~sean/papers/thesis2p.ps.gz", URL = "https://www.cs.umd.edu/community/alumnus/sean-luke", URL = "https://mathgenealogy.org/id.php?id=290938", size = "178 pages", abstract = "Genetic Programming is an evolutionary computation technique which searches for those computer programs that best solve a given problem. As genetic programming is applied to increasingly difficult problems, its effectiveness is hampered by the tendency of candidate program solutions to grow in size independent of any corresponding increases in quality. This bloat in solutions slows the search process, interferes with genetic programming's searching, and ultimately consumes all available memory. The challenge for scaling up genetic programming is to find the best solutions possible before bloat puts a stop to evolution. This can be tackled either by finding better solutions more rapidly, or by taking measures to delay bloat as long as possible. This thesis discusses issues both in speeding the search process and in delaying bloat in order to scale genetic programming to tackle harder problems. It describes evolutionary computation and genetic programming, and details the application of genetic programming to cooperative robot soccer and to language induction. The thesis then compares genetic programming breeding strategies, showing the conditions under which each strategy produces better individuals with less bloating. It then analyzes the tree growth properties of the standard tree generation algorithms used, and proposes new, fast algorithms which give the user better control over tree size. Lastly, it presents evidence which directly contradicts existing bloat theories, and gives a more general theory of code growth, showing that the issue is more complicated than it first appears.", notes = "errata 1. In Algorithm 2 (p. 6), the line P<-P\\[q] should read P<-P\\[s]. 2. Figures 5.2 through 5.5 (p. 38-39) are not in proper evolutionary-time order. The proper order is 5.4, 5.5, 5.2, 5.3. Supervisor: James Hendler", } @InProceedings{Luke1:2001:GECCO, title = "A Survey and Comparison of Tree Generation Algorithms", author = "Sean Luke and Liviu Panait", pages = "81--88", year = "2001", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)", editor = "Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon and Edmund Burke", address = "San Francisco, California, USA", publisher_address = "San Francisco, CA 94104, USA", month = "7-11 " # jul, keywords = "genetic algorithms, genetic programming, tree generation algorithms, initalization", ISBN = "1-55860-774-9", URL = "http://gpbib.cs.ucl.ac.uk/gecco2001/d01.pdf", URL = "https://dl.acm.org/doi/10.5555/2955239.2955250", size = "8 pages", abstract = "This paper discusses and compares five major tree-generation algorithms for genetic programming, and their effects on fitness: RAMPED HALF-AND-HALF, PTC1, PTC2, RANDOM-BRANCH, and UNIFORM. The paper compares the performance of these algorithms on three genetic programming problems (11-Boolean Multiplexer, Artificial Ant, and Symbolic Regression), and discovers that the algorithms do not have a significant impact on fitness. Additional experimentation shows that tree size does have an important impact on fitness, and further that the ideal initial tree size is very different from that used in traditional GP.", notes = "GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of \cite{spector:2001:GECCO}", } @InProceedings{luke2:2001:gecco, title = "When Short Runs Beat Long Runs", author = "Sean Luke", pages = "74--80", year = "2001", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)", editor = "Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon and Edmund Burke", address = "San Francisco, California, USA", publisher_address = "San Francisco, CA 94104, USA", month = "7-11 " # jul, keywords = "genetic algorithms, genetic programming, schedules, restarts, run length, critical points", ISBN = "1-55860-774-9", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1060.4892", URL = "http://gpbib.cs.ucl.ac.uk/gecco2001/d01.pdf", size = "7 pages", abstract = "What will yield the best results: doing one run n generations long or doing m runs n/mgenerations long each? This paper presents a technique-independent analysis which answers this question, and has direct applicability to scheduling and restart theory in evolutionary computation and other stochastic methods. The paper then applies this technique to three problem domains in genetic programming. It discovers that in two of these domains there is a maximal number of generations beyond which it is irrational to plan a run; instead it makes more sense to do multiple shorter runs.", notes = "santafe ant, even-10 parity, symbolic regression GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of \cite{spector:2001:GECCO}", } @InProceedings{luke2:2002:gecco, author = "Sean Luke and Liviu Panait", title = "Lexicographic Parsimony Pressure", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "829--836", address = "New York", publisher_address = "San Francisco, CA 94104, USA", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "genetic algorithms, genetic programming, bloat, parsimony pressure", ISBN = "1-55860-878-8", URL = "http://cs.gmu.edu/~sean/papers/lexicographic.pdf", URL = "http://cs.gmu.edu/~sean/papers/lexicographic.ps.gz", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/GP157.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf", URL = "http://citeseer.ist.psu.edu/535375.html", abstract = "We introduce a technique called lexicographic parsimony pressure, for controlling the significant growth of genetic programming trees during the course of an evolutionary computation run. Lexicographic parsimony pressure modifies selection to prefer smaller trees only when fitnesses are equal (or equal in rank). This technique is simple to implement and is not affected by specific differences in fitness values, but only by their relative ranking. In two experiments we show that lexicographic parsimony pressure reduces tree size while maintaining good fitness values, particularly when coupled with Koza-style maximum tree depth limits.", notes = "GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)", } @InProceedings{luke:2002:gecco, author = "Sean Luke and Liviu Panait", title = "Is The Perfect The Enemy Of The Good?", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "820--828", address = "New York", publisher_address = "San Francisco, CA 94104, USA", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "genetic algorithms, genetic programming, computational effort, cumulative probability of success", ISBN = "1-55860-878-8", URL = "http://cs.gmu.edu/~sean/papers/ideal.ps.gz", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/GP154.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf", URL = "http://citeseer.ist.psu.edu/532114.html", notes = "GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002) Nominated for best at GECCO award", } @InProceedings{luke:ppsn2002:pp411, author = "Sean Luke and Liviu Panait", title = "Fighting Bloat with Nonparametric Parsimony Pressure", booktitle = "Parallel Problem Solving from Nature - PPSN VII", address = "Granada, Spain", month = "7-11 " # sep, pages = "411--421", year = "2002", editor = "Juan J. Merelo-Guervos and Panagiotis Adamidis and Hans-Georg Beyer and Jose-Luis Fernandez-Villacanas and Hans-Paul Schwefel", number = "2439", series = "Lecture Notes in Computer Science, LNCS", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-44139-5", URL = "http://cs.gmu.edu/~sean/papers/parsimony2.pdf", DOI = "doi:10.1007/3-540-45712-7_40", abstract = "Many forms of parsimony pressure are parametric, that is final fitness is a parametric model of the actual size and raw fitness values. The problem with parametric techniques is that they are hard to tune to prevent size from dominating fitness late in the evolutionary run, or to compensate for problem-dependent nonlinearities in the raw fitness function. In this paper we briefly discuss existing bloat-control techniques, then introduce two new kinds of non-parametric parsimony pressure, Direct and Proportional Tournament. As their names suggest, these techniques are based on simple modifications of tournament selection to consider both size and fitness, but not together as a combined parametric equation. We compare the techniques against, and in combination with, the most popular genetic programming bloat-control technique, Koza-style depth limiting, and show that they are effective in limiting size while still maintaining good best-fitness-of-run results.", } @Article{luke:2003:ECJ, author = "Sean Luke", title = "Modification Point Depth and Genome Growth in Genetic Programming", year = "2003", journal = "Evolutionary Computation", volume = "11", number = "1", pages = "67--106", month = "Spring", keywords = "genetic algorithms, genetic programming, Introns, Inviable Code, Code Bloat, Crossover Point", DOI = "doi:10.1162/106365603321829014", abstract = "The evolutionary computation community has shown increasing interest in arbitrary-length representations, particularly in the field of genetic programming. A serious stumbling block to the scalability of such representations has been bloat: uncontrolled genome growth during an evolutionary run. Bloat appears across the evolutionary computation spectrum, but genetic programming has given it by far the most attention. Most genetic programming models explain this phenomenon as a result of the growth of introns, areas in an individual which serve no functional purpose. This paper presents evidence which directly contradicts intron theories as applied to tree-based genetic programming. The paper then uses data drawn from this evidence to propose a new model of genome growth. In this model, bloat in genetic programming is a function of the mean depth of the modification (crossover or mutation) point. Points far from the root are correspondingly less likely to hurt the child's survivability in the next generation. The modication point is in turn strongly correlated to average parent tree size and to removed subtree size, both of which are directly linked to the size of the resulting child.", } @InProceedings{luke:2003:gecco, author = "Sean Luke and Gabriel Catalin Balan and Liviu Panait", title = "Population Implosion in Genetic Programming", booktitle = "Genetic and Evolutionary Computation -- GECCO-2003", editor = "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller", year = "2003", pages = "1729--1739", address = "Chicago", publisher_address = "Berlin", month = "12-16 " # jul, volume = "2724", series = "LNCS", ISBN = "3-540-40603-4", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", URL = "http://cs.gmu.edu/~lpanait/papers/luke03population.pdf", DOI = "doi:10.1007/3-540-45110-2_65", abstract = "With the exception of a small body of adaptive-parameter literature, evolutionary computation has traditionally favored keeping the population size constant through the course of the run. Unfortunately, genetic programming has an aging problem: for various reasons, late in the run the technique become less effective at optimization. Given a fixed number of evaluations, allocating many of them late in the run may thus not be a good strategy. In this paper we experiment with gradually decreasing the population size throughout a genetic programming run, in order to reallocate more evaluations to early generations. Our results show that over four problem domains and three different numbers of evaluations, decreasing the population size is always as good as, and frequently better than, various fixed-sized population strategies.", notes = "GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)", } @InProceedings{1068022, author = "Sean Luke", title = "Evolutionary computation and the c-value paradox", booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation", year = "2005", editor = "Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and Edwin D. {de Jong} and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler", volume = "1", ISBN = "1-59593-010-8", pages = "91--97", address = "Washington DC, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2005/docs/p91.pdf", DOI = "doi:10.1145/1068009.1068022", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "25-29 " # jun, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Artificial Life, Evolutionary Robotics, Adaptive Behaviour, code bloat, code growth, c-value paradox, evolutionary genetics, experimentation, theoretical biology", size = "7 pages", abstract = "The C-value Paradox is the name given in biology to the wide variance in and often very large amount of DNA in eukaryotic genomes and the poor correlation between DNA length and perceived organism complexity. Several hypotheses exist which purport to explain the Paradox. Surprisingly there is a related phenomenon in evolutionary computation, known as code bloat, for which a different set of hypotheses has arisen. This paper describes a new hypothesis for the Cvalue Paradox derived from models of code bloat. The new explanation is that there is a selective bias in preference of genetic events which increase DNA material over those which decrease it. The paper suggests one possible concrete mechanism by which this may occur: deleting strands of DNA is more likely to damage genomic material than migrating or copying strands. The paper also discusses other hypotheses in biology and in evolutionary computation, and provides a simulation example as a proof of concept.", notes = "GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM Order Number 910052", } @Article{Luke:2006:EC, author = "Sean Luke and Liviu Panait", title = "A Comparison of Bloat Control Methods for Genetic Programming", journal = "Evolutionary Computation", year = "2006", volume = "14", number = "3", pages = "309--344", month = "Fall", keywords = "genetic algorithms, genetic programming", ISSN = "1063-6560", DOI = "doi:10.1162/evco.2006.14.3.309", oai = "oai:CiteSeerX.psu:10.1.1.1011.3644", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1011.3644", URL = "http://cognet.mit.edu/system/cogfiles/journalpdfs/evco.2006.14.3.309.pdf", abstract = "Genetic programming has highlighted the problem of bloat, the uncontrolled growth of the average size of an individual in the population. The most common approach to dealing with bloat in tree-based genetic programming individuals is to limit their maximal allowed depth. An alternative to depth limiting is to punish individuals in some way based on excess size, and our experiments have shown that the combination of depth limiting with such a punitive method is generally more effective than either alone. Which such combinations are most effective at reducing bloat? In this article we augment depth limiting with nine bloat control methods and compare them with one another. These methods are chosen from past literature and from techniques of our own devising. testing with four genetic programming problems, we identify where each bloat control method performs well on a per-problem basis, and under what settings various methods are effective independent of problem. We report on the results of these tests, and discover an unexpected winner in the cross-platform category.", } @Book{Luke2009Metaheuristics, author = "Sean Luke", title = "Essentials of Metaheuristics", year = "2009", publisher = "lulu.com", edition = "First", note = "Available at https://cs.gmu.edu/$\sim$sean/book/metaheuristics/", email = "metaheuristics@cs.gmu.edu", keywords = "genetic algorithms, genetic programming, ECJ", isbn13 = "978-0-557-14859-2", URL = "http://cs.gmu.edu/~sean/book/metaheuristics/", URL = "http://www.lulu.com/shop/sean-luke/essentials-of-metaheuristics-second-edition/paperback/product-21080150.html", abstract = "Gradient Ascent/Descent, Newton's Method, Hill-Climbing, Random Search, Hill Climbing with Random Restarts, Steepest Ascent Hill-Climbing (with and without Replacement), (1+1), (1+lambda), and (1, lambda), Simulated Annealing, Tabu Search, Iterated Local Search, Evolution Strategies and Evolutionary Programming, The Genetic Algorithm, Elitism, Steady State GAs, The Tree-Style GP Pipeline, Hybrid Evolutionary and Hill-Climbing ({"}Memetic{"}) Algorithms, Scatter Search, Differential Evolution, Particle Swarm Optimization, Island Models, Master-Slave Fitness Assessment, Spatially-Embedded Models, 1-Population, 2-Population, and N-Population Coevolution Methods, Explicit and Implicit Fitness Sharing, Crowding and Deterministic Crowding, Naieve Multiobjective Optimization, Non-Dominated Sorting (NSGA-II), Pareto Strength Methods (SPEA2), Optimization with Hard Constraints, GRASP, Ant Colony Optimization (AS, ACS), Guided Local Search, Model Fitting by Classification (LEM), Estimation of Distribution Algorithms (PBIL, UMDA, cGA, BOA), Policy Optimization (Q-Learning, SAMUEL, ZCS, XCS), Representation Issues:, Vectors, Direct Encoded Graphs, Trees, Genetic Programming, Strongly-Typed Genetic Programming, Cellular Encoding, Lists, Machine-code Genetic Programming, Grammatical Evolution, Rulesets: State-Action Rules, Production Rules, Bloat, Experimental Methodolgy, Sample Text Problems, Resources, Example Course Syllabi", notes = "Published by Lulu 14 Dec 2010, Reviewed by \cite{Lones:2011:GPEM}", size = "230 pages. Second edition, http://tinyurl.com/essentialslulu ISBN 9781300549628, 242 pages, 21 June 2013 Oct 2015 Essentials.pdf contains version 2.2", } @Manual{Luke:ECJ, title = "The {ECJ} Owner's Manual -- A User Manual for the {ECJ} Evolutionary Computation Library", author = "Sean Luke", year = "2010", edition = "Zeroth Edition, Online Version 0.2", month = oct, organisation = "Department of Computer Science, George Mason University", keywords = "genetic algorithms, genetic programming", URL = "http://cs.gmu.edu/~eclab/projects/ecj/docs/", URL = "http://www.cs.gmu.edu/~eclab/projects/ecj/docs/manual/manual.pdf", size = "206 pages", abstract = "The purpose of this manual is to describe practically every feature of ECJ, an evolutionary computation toolkit. It's not a good choice to learn the system. It's very terse, boring, and long, and not organised as a tutorial but rather as an encyclopedia. Instead, I refer you to ECJ's four tutorials and various other documentation that comes with the system. But when you need to know about some particular feature that ECJ has available, this manual is where to look.", notes = "July 2015 manual.pdf in the bibliography is version 0.2, more recent versions are available via the ECJ web pages", } @InProceedings{lukschandl:1998:1java, author = "Eduard Lukschandl and Magus Holmlund and Eirk Moden", title = "Automatic Evolution of {Java} Bytecode: First experience with the {Java} virtual machine", booktitle = "Late Breaking Papers at EuroGP'98: the First European Workshop on Genetic Programming", year = "1998", editor = "Riccardo Poli and W. B. Langdon and Marc Schoenauer and Terry Fogarty and Wolfgang Banzhaf", pages = "14--16", address = "Paris, France", publisher_address = "School of Computer Science", month = "14-15 " # apr, publisher = "CSRP-98-10, The University of Birmingham, UK", keywords = "genetic algorithms, genetic programming, JVM", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/csrp-98-10.pdf", size = "3 pages", notes = "EuroGP'98LB part of \cite{Poli:1998:egplb}", } @InProceedings{lukschandl:1998:ijbGP, author = "Eduard Lukschandl and Magnus Holmlund and Eric Moden and Mats Nordahl and Peter Nordin", title = "Induction of {Java} Bytecode with Genetic Programming", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "135--142", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "genetic algorithms, genetic programming, Machine Code GP, Java, Java Bytecode", size = "7 pages", notes = "GP-98LB", } @InProceedings{lukschandl:1999:eraJBGP, author = "Eduard Lukschandl and Henrik Borgvall and Lars Nohle and Mats Nordahl and Peter Nordin", title = "Evolving Routing Algorithms with the {JBGP-System}", booktitle = "Evolutionary Image Analysis, Signal Processing and Telecommunications: First European Workshop, EvoIASP'99 and EuroEcTel'99", year = "1999", editor = "Riccardo Poli and Hans-Michael Voigt and Stefano Cagnoni and Dave Corne and George D. Smith and Terence C. Fogarty", volume = "1596", series = "LNCS", pages = "193--202", address = "Goteborg, Sweden", publisher_address = "Berlin", month = "28-29 " # may, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-65837-8", DOI = "doi:10.1007/10704703_16", abstract = "This paper describes work in progress where we apply genetic programming to the problem of finding routing algorithms in telecommunications networks, using a network simulator and the Java Bytecode Genetic Programming System being developed at EHPT lab.", notes = "EvoIASP99'99 and EuroEcTel'99", } @Misc{lukschandl:1999:EBCEUGP, author = "Eduard Lukschandl", title = "Evolving the Behavior of Collaborating Entities Using Genetic Programming", booktitle = "GECCO-99 Student Workshop", year = "1999", editor = "Una-May O'Reilly", pages = "377--378", address = "Orlando, Florida, USA", month = "13 " # jul, keywords = "genetic algorithms, genetic programming, agents, java, telecommunications", broken = "http://www.ai.mit.edu/people/unamay/phd-final/GECCO-99-Student.html", notes = "GECCO-99WKS Part of wu:1999:GECCOWKS", } @InProceedings{lukschandl:2000:DJBGP, author = "Eduard Lukschandl and Henrik Borgvall and Lars Nohle and Mats Nordahl and Peter Nordin", title = "Distributed Java Bytecode Genetic Programming", booktitle = "Genetic Programming, Proceedings of EuroGP'2000", year = "2000", editor = "Riccardo Poli and Wolfgang Banzhaf and William B. Langdon and Julian F. Miller and Peter Nordin and Terence C. Fogarty", volume = "1802", series = "LNCS", pages = "316--325", address = "Edinburgh", publisher_address = "Berlin", month = "15-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-67339-3", DOI = "doi:10.1007/978-3-540-46239-2_24", abstract = "This paper describes a method for evolutionary program induction of binary Java bytecode. Like many other machine code based methods it uses a linear genome. The genetic operators are adapted to the stack architecture and preserve stack depth during crossover. In this work we have extended a previous system to run in a distributed manner on several different physical machines. We call our new system Distributed Java Bytecode Genetic Programming (DJBGP). We use the Voyager package for migration of Java individuals. The system's feasibility is demonstrated on a telecom routing problem.", notes = "EuroGP'2000, part of \cite{poli:2000:GP}", } @InProceedings{Lukschandl:2000:GECCOlb, author = "Eduard Lukschandl and Peter Nordin and Mats Nordahl", title = "Using the Java Method Evolver for Load Balancing in Communication Networks", pages = "236--239", booktitle = "Late Breaking Papers at the 2000 Genetic and Evolutionary Computation Conference", year = "2000", editor = "Darrell Whitley", address = "Las Vegas, Nevada, USA", month = "8 " # jul, keywords = "genetic algorithms, genetic programming", notes = "Part of \cite{whitley:2000:GECCOlb}", } @InCollection{luman:2002:DKAGA, author = "Ron {Luman II}", title = "Dynamic Keystroke Analysis via Genetic Algorithms", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2002", year = "2002", editor = "John R. Koza", pages = "129--138", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms", URL = "http://www.genetic-programming.org/sp2002/Luman.pdf", notes = "part of \cite{koza:2002:gagp}", } @InProceedings{Luna:2010:HAIS, author = "J. M. Luna and J. R. Romero and S. Ventura", title = "Analysis of the Effectiveness of {G3PARM} Algorithm", booktitle = "Proceedings of the 5th International Conference on Hybrid Artificial Intelligence Systems (HAIS 2010) Part II", year = "2010", editor = "Emilio Corchado and Manuel Grana Romay and Alexandre Manhaes Savio", volume = "6077", series = "Lecture Notes in Computer Science", pages = "27--34", address = "San Sebastian, Spain", month = jun # " 23-25", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Association Rules, G3P", isbn13 = "978-3-642-13802-7", DOI = "doi:10.1007/978-3-642-13803-4_4", size = "8 pages", abstract = "This paper presents an evolutionary algorithm using G3P (Grammar Guided Genetic Programming) for mining association rules in different real-world databases. This algorithm, called G3PARM, uses an auxiliary population made up of its best individuals that will then act as parents for the next generation. The individuals are defined through a context-free grammar and it allows us to obtain datatype-generic and valid individuals. We compare our approach to apriori and FP-Growth algorithms and demonstrate that our proposal obtains rules with better support, confidence and coverage of the dataset instances. Finally, a preliminary study is also introduced to compare the scalability of our algorithm. Our experimental studies illustrate that this approach is highly promising for discovering association rules in databases.", } @InProceedings{Luna:2010:cec, author = "Jose Maria Luna and Jose Raul Romero and Sebastian Ventura", title = "G3PARM: A Grammar Guided Genetic Programming algorithm for mining association rules", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, NSGA-II", isbn13 = "978-1-4244-6910-9", abstract = "This paper presents the G3PARM algorithm for mining representative association rules. G3PARM is an evolutionary algorithm that uses G3P (Grammar Guided Genetic Programming) and an auxiliary population made up of its best individuals who will then act as parents for the next generation. Due to the nature of G3P, the G3PARM algorithm allows us to obtain valid individuals by defining them through a context-free grammar and, furthermore, this algorithm is generic with respect to data type. We compare our algorithm to two multiobjective algorithms frequently used in literature and known as NSGA2 (Non dominated Sort Genetic Algorithm) and SPEA2 (Strength Pareto Evolutionary Algorithm) and demonstrate the efficiency of our algorithm in terms of running-time, coverage and average support, providing the user with high representative rules.", DOI = "doi:10.1109/CEC.2010.5586504", notes = "WCCI 2010. Also known as \cite{5586504}", } @InProceedings{Luna:2010:ISDA, author = "Jose Maria Luna and Aurora Ramirez and Jose Raul Romero and Sebastian Ventura", title = "An intruder detection approach based on infrequent rating pattern mining", booktitle = "10th International Conference on Intelligent Systems Design and Applications (ISDA 2010)", year = "2010", month = nov # " 29-" # dec # " 1", pages = "682--688", abstract = "This work presents a novel proposal for incremental intruder detection in collaborative recommender systems. We explore the use of rare association rule mining to reveal the existence of a suspected raid of attackers that would alter the normal behaviour of a rating-based system. In this position paper we have extended our previous G3PARM algorithm, which has already proven to serve as a solid method for extracting frequent association rules. G3PARM is an evolutionary algorithm that uses G3P (Grammar Guided Genetic Programming), which provides expressiveness and flexibility enough to adapt and apply the base context-free grammar to each specific problem or domain. We fully outline, moreover, the complete exploration and detection model, which includes some further post-analysis steps. Finally, as a proof of concept, we validate the scalability, efficiency and accuracy of our proposal showing the results obtained when different malicious intruders want to attack an on line recommender system.", keywords = "genetic algorithms, genetic programming, G3PARM algorithm, association rule mining, collaborative recommender system, context free grammar, evolutionary algorithm, grammar guided genetic programming, incremental intruder detection, infrequent rating pattern mining, context-free grammars, data mining, recommender systems, security of data", DOI = "doi:10.1109/ISDA.2010.5687184", notes = "Also known as \cite{5687184}", } @InProceedings{Luna:2011:NaBIC, author = "Jose Maria Luna and Jose Raul Romero and Sebastian Ventura", title = "Mining and representing rare association rules through the use of genetic programming", booktitle = "Third World Congress on Nature and Biologically Inspired Computing (NaBIC 2011)", year = "2011", month = "19-21 " # oct, pages = "86--91", address = "Salamanca", size = "6 pages", abstract = "Whereas the extraction of frequent patterns has focused the major researches in association rule mining, the requirements of reliable rules that do not frequently appear is taking an increasing interest in a great number of areas. This field has not been explored in depth and most algorithms for mining infrequent association rules follow an exhaustive search methodology, which hampers the extracting process because of the size of the datasets. The importance of discovering patterns that do not frequently appear in a dataset and the promising results obtained when using evolutionary proposals in the field of frequent pattern mining motivates the evolutionary proposal for discovering rare association rules presented in this paper. Here, a context-free grammar is described and applied to adapt individuals to each particular problem or domain. The use of both an evolutionary approach and a context-free grammar reduces the memory requirements and provides the possibility of extracting any kind of rules, respectively. The experimental study shows that this proposal obtains a set of reliable infrequent rules in a short period of time.", keywords = "genetic algorithms, genetic programming, context-free grammar, evolutionary proposals, extracting process, frequent pattern mining, infrequent association rule mining, rare association rule mining, rare association rule representation, search methodology, context-free grammars, data mining", DOI = "doi:10.1109/NaBIC.2011.6089422", notes = "Also known as \cite{6089422}", } @InProceedings{Luna:2012:ISDA, author = "Jose Maria Luna and Jose Raul Romero and Cristobal Romero and Sebastian Ventura", booktitle = "12th International Conference on Intelligent Systems Design and Applications (ISDA 2012)", title = "A genetic programming free-parameter algorithm for mining association rules", year = "2012", pages = "64--69", keywords = "genetic algorithms, genetic programming, context-free grammars, data mining, association rules, context-free grammar, free-parameter grammar-guided genetic programming algorithm, mining association rules, nonexpert users, tree-shape conformant, Association rules, Evolutionary computation, Genetics, Grammar, Prediction algorithms, Sociology, Statistics, Association Rules, Data Mining, Free-Parameters", ISSN = "2164-7143", DOI = "doi:10.1109/ISDA.2012.6416514", abstract = "This paper presents a free-parameter grammar-guided genetic programming algorithm for mining association rules. This algorithm uses a context-free grammar to represent individuals, encoding the solutions in a tree-shape conformant to the grammar, so they are more expressive and flexible. The algorithm here presented has the advantages of using evolutionary algorithms for mining association rules, and it also solves the problem of tuning the huge number of parameters required by these algorithms. The main feature of this algorithm is the small number of parameters required, providing the possibility of discovering association rules in an easy way for non-expert users. We compare our approach to existing evolutionary and exhaustive search algorithms, obtaining important results and overcoming the drawbacks of both exhaustive search and evolutionary algorithms. The experimental stage reveals that this approach discovers frequent and reliable rules without a parameter tuning.", notes = "Also known as \cite{6416514}", } @Article{journals/kais/LunaRV12, author = "Jose Maria Luna and Jose Raul Romero and Sebastian Ventura", title = "Design and behavior study of a grammar-guided genetic programming algorithm for mining association rules", journal = "Knowledge and Information Systems", year = "2012", number = "1", volume = "32", pages = "53--76", month = jul, publisher = "Springer", keywords = "genetic algorithms, genetic programming, association rules, grammar-guided genetic programming, evolutionary algorithms", language = "English", ISSN = "0219-1377", DOI = "doi:10.1007/s10115-011-0419-z", size = "24 pages", abstract = "This paper presents a proposal for the extraction of association rules called G3PARM (Grammar-Guided Genetic Programming for Association Rule Mining) that makes the knowledge extracted more expressive and flexible. This algorithm allows a context-free grammar to be adapted and applied to each specific problem or domain and eliminates the problems raised by discretisation. This proposal keeps the best individuals (those that exceed a certain threshold of support and confidence) obtained with the passing of generations in an auxiliary population of fixed size n . G3PARM obtains solutions within specified time limits and does not require the large amounts of memory that the exhaustive search algorithms in the field of association rules do. Our approach is compared to exhaustive search (Apriori and FP-Growth) and genetic (QuantMiner and ARMGA) algorithms for mining association rules and performs an analysis of the mined rules. Finally, a series of experiments serve to contrast the scalability of our algorithm. The proposal obtains a small set of rules with high support and confidence, over 90 and 99percent respectively. Moreover, the resulting set of rules closely satisfies all the dataset instances. These results illustrate that our proposal is highly promising for the discovery of association rules in different types of datasets.", affiliation = "Department of Computer Science and Numerical Analysis, University of Cordoba, Rabanales Campus, 14071 Cordoba, Spain", bibdate = "2012-07-03", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/kais/kais32.html#LunaRV12", } @InProceedings{luna:2013:EuroGP, author = "Jose M. Luna and Jose R. Romero and Cristobal Romero and Sebastian Ventura", title = "Discovering Subgroups by Means of Genetic Programming", booktitle = "Proceedings of the 16th European Conference on Genetic Programming, EuroGP 2013", year = "2013", month = "3-5 " # apr, editor = "Krzysztof Krawiec and Alberto Moraglio and Ting Hu and A. Sima Uyar and Bin Hu", series = "LNCS", volume = "7831", publisher = "Springer Verlag", address = "Vienna, Austria", pages = "121--132", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, grammar guided genetic programming, Data mining, subgroup discovery", isbn13 = "978-3-642-37206-3", DOI = "doi:10.1007/978-3-642-37207-0_11", abstract = "This paper deals with the problem of discovering subgroups in data by means of a grammar guided genetic programming algorithm, each subgroup including a set of related patterns. The proposed algorithm combines the requirements of discovering comprehensible rules with the ability of mining expressive and flexible solutions thanks to the use of a context-free grammar. A major characteristic of this algorithm is the small number of parameters required, so the mining process is easy for end-users. The algorithm proposed is compared with existing subgroup discovery evolutionary algorithms. The experimental results reveal the excellent behaviour of this algorithm, discovering comprehensible subgroups and behaving better than the other algorithms. The conclusions obtained were reinforced through a series of non-parametric tests.", notes = "Part of \cite{Krawiec:2013:GP} EuroGP'2013 held in conjunction with EvoCOP2013, EvoBIO2013, EvoMusArt2013 and EvoApplications2013", } @Article{Luna:2013:DKE, title = "Grammar-based multi-objective algorithms for mining association rules", author = "J. M. Luna and J. R. Romero and S. Ventura", journal = "Data \& Knowledge Engineering", year = "2013", volume = "86", pages = "19--37", month = jul, keywords = "genetic algorithms, genetic programming, Association rule mining, Data mining, Mining methods and algorithms", ISSN = "0169-023X", DOI = "doi:10.1016/j.datak.2013.01.002", size = "19 pages", abstract = "In association rule mining, the process of extracting relations from a dataset often requires the application of more than one quality measure and, in many cases, such measures involve conflicting objectives. In such a situation, it is more appropriate to attain the optimal trade-off between measures. This paper deals with the association rule mining problem under a multi-objective perspective by proposing grammar guided genetic programming (G3P) models, that enable the extraction of both numerical and nominal association rules in only one single step. The strength of G3P is its ability to restrict the search space and build rules conforming to a given context-free grammar. Thus, the proposals presented in this paper combine the advantages of G3P models with those of multi-objective approaches. Both approaches follow the philosophy of two well-known multi-objective algorithms: the Non-dominated Sort Genetic Algorithm (NSGA-2) and the Strength Pareto Evolutionary Algorithm (SPEA-2). In the experimental stage, we compare both multi-objective algorithms to a single-objective G3P proposal for mining association rules and perform an analysis of the mined rules. The results obtained show that multi-objective proposals obtain very frequent (with support values above 95percent in most cases) and reliable (with confidence values close to 100percent) rules when attaining the optimal trade-off between support and confidence. Furthermore, for the trade-off between support and lift, the multi-objective proposals also produce very interesting and representative rules.", } @PhdThesis{Luna-JM:thesis, author = "Jose Maria Luna Ariza", title = "New Challenges in Association Rule Mining: an Approach Based on Genetic Programming", school = "Department of Computer Science and Artificial Intelligence, University of Granada", year = "2014", type = "Ph.D. in Computer Science", address = "Spain", month = jan, keywords = "genetic algorithms, genetic programming, association rule mining", URL = "http://www.uco.es/grupos/kdis/docs/thesis/2014-JMLuna.pdf", URL = "https://www.uco.es/kdis/research/theses/thesis-jmluna/", size = "224 pages", abstract = "This Doctoral Thesis involves a series of approaches for mining association rules by means of a grammar-guided genetic programming based methodology. The ultimate goal is to provide new algorithms that mine association rules in only one step and in a highly efficient way. The use of grammars enables the exibility of the extracted knowledge to be increased. Grammars also enable obtaining association rules that comprise categorical, quantitative, positive and negative attributes to be mined. Firstly, as for the mining of frequent association rules, a novel grammar-based algorithm, called G3PARM, has been proposed. It is able to discover rules having positive, negative, categorical and quantitative attributes. The evolutionary model is able to perform the mining process in one single step. This PhD Thesis also includes a model for mining rare or infrequent association rules, as well as two multi-objective approaches that optimise two different quality measures at time. Additionally, two novel algorithms that self-adapt their parameters are considered. In this sense, a previous tuning of the parameters would not be required, as they are adjusted depending on the data under study. Finally, the developed methodologies have been applied to the educational field to discover interesting information that could be used to improve the courses. All the algorithms proposed in this Doctoral Thesis have been evaluated in a proper experimental framework, using different types of datasets and comparing their performance against other published methods of proved quality. Results have been verified by applying non-parametric statistical tests, demonstrating the many benefits of using a grammar-based methodology to address the association rule mining problem", notes = "In English. Supervisors Sebastian Ventura and Jose Raul Romero", } @Article{Luna:2013:KAIS, author = "J. M. Luna and J. R. Romero and S. Ventura", title = "On the adaptability of {G3PARM} to the extraction of rare association rules", journal = "Knowledge and Information Systems", year = "2014", volume = "38", number = "2", pages = "391--418", month = feb, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Rare association rules, Grammar-guided genetic programming, Evolutionary computation", ISSN = "0219-1377", URL = "http://dx.doi.org/10.1007/s10115-012-0591-9", DOI = "doi:10.1007/s10115-012-0591-9", language = "English", size = "28 pages", abstract = "To date, association rule mining has mainly focused on the discovery of frequent patterns. Nevertheless, it is often interesting to focus on those that do not frequently occur. Existing algorithms for mining this kind of infrequent patterns are mainly based on exhaustive search methods and can be applied only over categorical domains. In a previous work, the use of grammar-guided genetic programming for the discovery of frequent association rules was introduced, showing that this proposal was competitive in terms of scalability, expressiveness, flexibility and the ability to restrict the search space. The goal of this work is to demonstrate that this proposal is also appropriate for the discovery of rare association rules. This approach allows one to obtain solutions within specified time limits and does not require large amounts of memory, as current algorithms do. It also provides mechanisms to discard noise from the rare association rule set by applying four different and specific fitness functions, which are compared and studied in depth. Finally, this approach is compared with other existing algorithms for mining rare association rules, and an analysis of the mined rules is performed. As a result, this approach mines rare rules in a homogeneous and low execution time. The experimental study shows that this proposal obtains a small and accurate set of rules close to the size specified by the data miner.", notes = "Also known as \cite{014-KAIS-RARE}", } @Article{014-IEEETC-SD, author = "Jose Maria Luna and Jose Raul Romero and Cristobal Romero and Sebastian Ventura", title = "On the Use of Genetic Programming for Mining Comprehensible Rules in Subgroup Discovery", journal = "IEEE Transactions on Cybernetics", year = "2014", volume = "44", number = "12", pages = "2329--2341", month = dec, keywords = "genetic algorithms, genetic programming, Data mining (DM), grammar-guided genetic programming (G3P), subgroup discovery (SD)", ISSN = "2168-2267", DOI = "doi:10.1109/TCYB.2014.2306819", size = "13 pages", abstract = "This paper proposes a novel grammar-guided genetic programming algorithm for subgroup discovery. This algorithm, called comprehensible grammar-based algorithm for subgroup discovery (CGBA-SD), combines the requirements of discovering comprehensible rules with the ability to mine expressive and flexible solutions owing to the use of a context-free grammar. Each rule is represented as a derivation tree that shows a solution described using the language denoted by the grammar. The algorithm includes mechanisms to adapt the diversity of the population by self-adapting the probabilities of recombination and mutation. We compare the approach with existing evolutionary and classic subgroup discovery algorithms. CGBA-SD appears to be a very promising algorithm that discovers comprehensible subgroups and behaves better than other algorithms as measures by complexity, interest, and precision indicate. The results obtained were validated by means of a series of nonparametric tests.", notes = "UCI Also known as \cite{6756991}", } @Article{2014-ICAE-Gaps, author = "Jose Maria Luna and Jose Raul Romero and Cristobal Romero and Sebastian Ventura", title = "Reducing gaps in quantitative association rules: A genetic programming free-parameter algorithm", journal = "Integrated Computer-Aided Engineering", year = "2014", volume = "21", number = "4", pages = "321--337", month = "29 " # sep, keywords = "genetic algorithms, genetic programming, Quantitative association rules, grammar guided genetic programming, evolutionary computation, data mining", ISSN = "1069-2509", publisher = "IOS Press", DOI = "doi:10.3233/ICA-140467", size = "17 pages", abstract = "The extraction of useful information for decision making is a challenge in many different domains. Association rule mining is one of the most important techniques in this field, discovering relationships of interest among patterns. Despite the mining of association rules being an area of great interest for many researchers, the search for well-grouped continuous values is still a challenge, discovering rules that do not comprise patterns which represent unnecessary ranges of values. Existing algorithms for mining association rules in continuous domains are mainly based on a non-deterministic search, requiring a high number of parameters to be optimised. These parameters hinder the mining process, and the algorithms themselves must be known to those data mining experts that want to use them. We therefore present a grammar guided genetic programming algorithm that does not require as many parameters as other existing approaches and enables the discovery of quantitative association rules comprising small-size gaps. The algorithm is verified over a varied set of data, comparing the results to other association rule mining algorithms from several paradigms. Additionally, some resulting rules from different paradigms are analysed, demonstrating the effectiveness of our model for reducing gaps in numerical features.", notes = "Department of Computer Science and Numerical Analysis, University of Cordoba, Albert Einstein Building, Rabanales Campus, Cordoba, Spain. Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Saudi Arabia Kingdom", } @Article{2014-AI-Luna, author = "J. M. Luna and C. Romero and J. R. Romero and S. Ventura", title = "An Evolutionary Algorithm for the Discovery of Rare Class Association Rules in Learning Management Systems", journal = "Applied Intelligence", year = "2015", volume = "42", number = "3", pages = "501--513", month = apr, keywords = "genetic algorithms, genetic programming, Rare association rules, Grammar guided genetic programming, Evolutionary computation, Educational data mining", publisher = "Springer US", language = "English", ISSN = "0924-669X", URL = "http://dx.doi.org/10.1007/s10489-014-0603-4", DOI = "doi:10.1007/s10489-014-0603-4", size = "13 pages", abstract = "Association rule mining, an important data mining technique, has been widely focused on the extraction of frequent patterns. Nevertheless, in some application domains it is interesting to discover patterns that do not frequently occur, even when they are strongly related. More specifically, this type of relation can be very appropriate in e-learning domains due to its intrinsic imbalanced nature. In these domains, the aim is to discover a small but interesting and useful set of rules that could barely be extracted by traditional algorithms founded in exhaustive search-based techniques. In this paper, we propose an evolutionary algorithm for mining rare class association rules when gathering student usage data from a Moodle system. We analyse how the use of different parameters of the algorithm determine the rule characteristics, and provides some illustrative examples of them to show their interpretability and usefulness in e-learning environments. We also compare our approach to other existing algorithms for mining both rare and frequent association rules. Finally, an analysis of the rules mined is presented, which allows information about students' unusual behaviour regarding the achievement of bad or good marks to be discovered.", } @InCollection{Luna:2015:hbgpa, author = "J. M. Luna and A. Cano and S. Ventura", title = "Genetic Programming for Mining Association Rules in Relational Database Environments", booktitle = "Handbook of Genetic Programming Applications", publisher = "Springer", year = "2015", editor = "Amir H. Gandomi and Amir H. Alavi and Conor Ryan", chapter = "17", pages = "431--450", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-20882-4", URL = "http://www.uco.es/users/i52caroa/publicaciones-bib.html#2015-HGPA", DOI = "doi:10.1007/978-3-319-20883-1_17", abstract = "Most approaches for the extraction of association rules look for associations from a dataset in the form of a single table. However, with the growing interest in the storage of information, relational databases comprising a series of relations (tables) and relationships have become essential. We present the first grammar-guided genetic programming approach for mining association rules directly from relational databases. We represent the relational databases as trees by means of genetic programming, preserving the original database structure and enabling rules to be defined in an expressive and very flexible way. The proposed model deals with both positive and negative items, and also with both discrete and quantitative attributes. We exemplify the utility of the proposed approach with an artificial generated database having different characteristics. We also analyse a real case study, discovering interesting students' behaviors from a moodle database.", } @Article{journals/kais/LunaPV16, author = "Jose Maria Luna and Mykola Pechenizkiy and Sebastian Ventura", title = "Mining exceptional relationships with grammar-guided genetic programming", journal = "Knowledge and Information Systems", year = "2016", number = "3", volume = "47", pages = "571--594", keywords = "genetic algorithms, genetic programming, Association rules, Exceptional subgroups", bibdate = "2016-05-13", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/kais/kais47.html#LunaPV16", ISSN = "0219-1377", URL = "http://dx.doi.org/10.1007/s10115-015-0859-y", DOI = "doi:10.1007/s10115-015-0859-y", abstract = "Given a database of records, it might be possible to identify small subsets of data which distribution is exceptionally different from the distribution in the complete set of data records. Finding such interesting relationships, which we call exceptional relationships, in an automated way would allow discovering unusual or exceptional hidden behaviour. In this paper, we formulate the problem of mining exceptional relationships as a special case of exceptional model mining and propose a grammar-guided genetic programming algorithm (MERG3P) that enables the discovery of any exceptional relationships. In particular, MERG3P can work directly not only with categorical, but also with numerical data. In the experimental evaluation, we conduct a case study on mining exceptional relations between well-known and widely used quality measures of association rules, which exceptional behaviour would be of interest to pattern mining experts. For this purpose, we constructed a data set comprising a wide range of values for each considered association rule quality measure, such that possible exceptional relations between measures could be discovered. Thus, besides the actual validation of MERG3P, we found that the Support and Leverage measures in fact are negatively correlated under certain conditions, while in general experts in the field expect these measures to be positively correlated", } @Article{Luna18a, author = "Jose Maria Luna and Mykola Pechenizkiy and Maria Jose {del Jesus} and Sebastian Ventura", title = "Mining Context-Aware Association Rules Using Grammar-Based Genetic Programming", journal = "IEEE Transactions on Cybernetics", year = "2018", volume = "48", number = "11", pages = "3030--3044", month = nov, keywords = "genetic algorithms, genetic programming, Association rules, context awareness, contextual features", publisher = "IEEE", ISSN = "2168-2267", DOI = "doi:10.1109/TCYB.2017.2750919", size = "15 pages", abstract = "Real-world data usually comprise features whose interpretation depends on some contextual information. Such contextual-sensitive features and patterns are of high interest to be discovered and analysed in order to obtain the right meaning. This paper formulates the problem of mining context-aware association rules, which refers to the search for associations between itemsets such that the strength of their implication depends on a contextual feature. For the discovery of this type of associations, a model that restricts the search space and includes syntax constraints by means of a grammar-based genetic programming methodology is proposed. Grammars can be considered as a useful way of introducing subjective knowledge to the pattern mining process as they are highly related to the background knowledge of the user. The performance and usefulness of the proposed approach is examined by considering synthetically generated datasets. A posteriori analysis on different domains is also carried out to demonstrate the utility of this kind of associations. For example, in educational domains, it is essential to identify and understand contextual and context-sensitive factors that affect overall and individual student behaviour and performance. The results of the experiments suggest that the approach is feasible and it automatically identifies interesting context-aware associations from real-world datasets.", notes = "PubMed ID: 28952954 also known as \cite{8049471}", } @InProceedings{Luna:2019:IoTyBDS, author = "Jose Maria Luna and Francisco Padillo and Sebastian Ventura", title = "Associative classification in big data through a {G3P} approach", booktitle = "Proceedings of the 4th International Conference on Internet of Things, Big Data and Security - IoTBDS", year = "2019", pages = "94--102", address = "Heraklion, Crete, Greece", publisher = "SciTePress", keywords = "genetic algorithms, genetic programming, Big Data Mining, Evolutionary Algorithms, Grammar-Based Genetic Programming, Pattern Mining", isbn13 = "978-989-758-369-8", URL = "http://doi.org/10.5220/0007688400940102", DOI = "doi:10.5220/0007688400940102", abstract = "The associative classification field includes really interesting approaches for building reliable classifiers and any of these approaches generally work on four different phases (data discretization, pattern mining, rule mining, and classifier building). This number of phases is a handicap when big datasets are analysed. The aim of this work is to propose a novel evolutionary algorithm for efficiently building associative classifiers in Big Data. The proposed model works in only two phases (a grammar-guided genetic programming framework is performed in each phase): 1) mining reliable association rules; 2) building an accurate classifier by ranking and combining the previously mined rules. The proposal has been implemented on Apache Spark to take advantage of the distributed computing. The experimental analysis was performed on 40 well-known datasets and considering 13 algorithms taken from literature. A series of non-parametric tests has also been carried out to determine statistical differences. Results are quite promising in terms of reliability and efficiency on high-dimensional data.", } @Article{Luna:2020:IEEEAccessA, author = "Jose Maria Luna and Mykola Pechenizkiy and Wouter Duivesteijn and Sebastian Ventura", title = "Exceptional in so Many Ways--Discovering Descriptors That Display Exceptional Behavior on Contrasting Scenarios", journal = "IEEE Access", year = "2020", volume = "8", pages = "200982--200994", month = "30 " # oct, keywords = "genetic algorithms, genetic programming, Evolutionary Algorithms, Grammar-Based Genetic Programming, Metaheuristics, Pattern Mining", ISSN = "2169-3536", URL = "https://ieeexplore.ieee.org/abstract/document/9245545", DOI = "doi:10.1109/ACCESS.2020.3034885", abstract = "The current state of the art in supervised descriptive pattern mining is very good in automatically finding subsets of the dataset at hand that are exceptional in some sense. The most common form, subgroup discovery, generally finds subgroups where a single target variable has an unusual distribution. Exceptional model mining (EMM) typically finds subgroups where a pair of target variables display an unusual interaction. What these methods have in common is that one specific exceptionality is enough to flag up a subgroup as exceptional. This, however, naturally leads to the question: can we also find multiple instances of exceptional behaviour simultaneously in the same subgroup? This paper provides a first, affirmative answer to that question in the form of the SPEC (Subsets of Pairwise Exceptional Correlations) model class for EMM. Given a set of predefined numeric target variables, SPEC will flag up subgroups as interesting if multiple target pairs display an unusual rank correlation. This is a fundamental extension of the EMM toolbox, which comes with additional algorithmic challenges. To address these challenges, we provide a series of algorithmic solutions whose strengths/flaws are empirically analysed.", } @Article{Luna:2020:IEEEAccessB, author = "Jose Maria Luna and Philippe Fournier-Viger and Sebastian Ventura", title = "Extracting User-Centric Knowledge on Two Different Spaces: Concepts and Records", journal = "IEEE Access", year = "2020", volume = "8", pages = "134782--134799", month = "21 " # jul, keywords = "genetic algorithms, genetic programming, Evolutionary Algorithms, Grammar-Based Genetic Programming, Metaheuristics, Pattern Mining", ISSN = "2169-3536", URL = "https://ieeexplore.ieee.org/abstract/document/9145755", DOI = "doi:10.1109/ACCESS.2020.3010852", abstract = "The growing demand for eliciting useful knowledge from data calls for techniques that can discover insights (in the form of patterns) that users need. Methodologies for describing intrinsic and relevant properties of data through the extraction of useful patterns, however, work on fixed input data, and the data representation, therefore, constrains the discovered insights. In this regard, this paper aims at providing foundations to make the descriptive knowledge that is extracted by pattern mining more user-centric by relying on flexible data structures defined on two different perspectives: concepts and data records. In this sense, items in data can be grouped into abstract terms through subjective hierarchies of concepts, whereas data records can also be organized based on the users subjective perspective. A series of easy-to-follow toy examples are considered for each of the two perspectives to demonstrate the usefulness and necessity of the proposed foundations in pattern mining. Finally, aiming at experimentally testing whether classical pattern mining algorithms can be adapted to such flexible data structures, the experimental analysis comprises different methodologies, including exhaustive search, random search, and evolutionary approaches. All these approaches are based on well-known and widely recognized techniques to demonstrate the usefulness of the provided foundations for future research works and more efficient and specifically designed algorithms. Obtained insights demonstrate the importance of working with subjectivity: an item is a type of soda but belongs to a pack, including two or more soda types.", } @Article{lundberg:1999:elvis, author = "Borje Lundberg", title = "Elvis ror pa sig", journal = "Expressen", year = "1999", pages = "17", month = "21 " # aug, note = "Largest circulation swedish newspaper", keywords = "genetic algorithms, genetic programming", broken = "http://www.expressen.se/article.asp?id=21927", notes = "Peter Nordin and Mats Nordahl, Chalmers Unversity of Technology humanoid robot Elvis.", } @Article{Lundh:2007:GPEM, author = "Torbjorn Lundh", title = "Cellular Automaton Modeling of Biological Pattern Formation: Characterization, Applications, and Analysis Authors: Andreas Deutsch and Sabine Dormann, Birkhauser, 2005, XXVI, 334 p., 131 illus., Hardcover. ISBN:0-8176-4281-1, List Price: \$89.95", journal = "Genetic Programming and Evolvable Machines", year = "2007", volume = "8", number = "1", pages = "105--106", month = mar, note = "Book Review", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dR8CM", DOI = "doi:10.1007/s10710-006-9021-7", size = "2 pages", } @Article{Luo2012, author = "Changtong Luo and Shao-Liang Zhang", title = "Parse-matrix evolution for symbolic regression", journal = "Engineering Applications of Artificial Intelligence", year = "2012", volume = "25", number = "6", pages = "1182--1193", ISSN = "0952-1976", DOI = "doi:10.1016/j.engappai.2012.05.015", URL = "http://www.sciencedirect.com/science/article/pii/S0952197612001212", keywords = "genetic algorithms, genetic programming, Data analysis, Symbolic regression, Grammatical evolution, Artificial intelligence, Evolutionary computation", abstract = "Data-driven model is highly desirable for industrial data analysis in case the experimental model structure is unknown or wrong, or the concerned system has changed. Symbolic regression is a useful method to construct the data-driven model (regression equation). Existing algorithms for symbolic regression such as genetic programming and grammatical evolution are difficult to use due to their special target programming language (i.e., LISP) or additional function parsing process. In this paper, a new evolutionary algorithm, parse-matrix evolution (PME), for symbolic regression is proposed. A chromosome in PME is a parse-matrix with integer entries. The mapping process from the chromosome to the regression equation is based on a mapping table. PME can easily be implemented in any programming language and free to control. Furthermore, it does not need any additional function parsing process. Numerical results show that PME can solve the symbolic regression problems effectively.", } @Article{Luo:2015:EAAI, author = "Changtong Luo and Zongmin Hu and Shao-Liang Zhang and Zonglin Jiang", title = "Adaptive space transformation: An invariant based method for predicting aerodynamic coefficients of hypersonic vehicles", journal = "Engineering Applications of Artificial Intelligence", volume = "46, Part A", pages = "93--103", year = "2015", ISSN = "0952-1976", DOI = "doi:10.1016/j.engappai.2015.09.001", URL = "http://www.sciencedirect.com/science/article/pii/S0952197615002018", abstract = "When developing a new hypersonic vehicle, thousands of wind tunnel tests to study its aerodynamic performance are needed. Due to limitations of experimental facilities and/or cost budget, only a part of flight parameters could be replicated. The point to predict might locate outside the convex hull of sample points. This makes it necessary but difficult to predict its aerodynamic coefficients under flight conditions so as to make the vehicle under control and be optimized. Approximation based methods including regression, nonlinear fit, artificial neural network, and support vector machine could predict well within the convex hull (interpolation). But the prediction performance will degenerate very fast as the new point gets away from the convex hull (extrapolation). In this paper, we suggest regarding the prediction not just a mathematical extrapolation, but a mathematics-assisted physical problem, and propose a supervised self-learning scheme, adaptive space transformation (AST), for the prediction. AST tries to automatically detect an underlying invariant relation with the known data under the supervision of physicists. Once the invariant is detected, it will be used for prediction. The result should be valid provided that the physical condition has not essentially changed. The study indicates that AST can predict the aerodynamic coefficient reliably, and is also a promising method for other extrapolation related predictions.", keywords = "genetic algorithms, genetic programming, Aerodynamic coefficient, Data correlation, Scaling parameter, Invariant", } @Article{luo:2023:Mathematics, author = "Jiayi Luo and Cindy Long Yu", title = "The Application of Symbolic Regression on Identifying Implied Volatility Surface", journal = "Mathematics", year = "2023", volume = "11", number = "9", pages = "Article No. 2108", keywords = "genetic algorithms, genetic programming, Finance, FX", ISSN = "2227-7390", URL = "https://www.mdpi.com/2227-7390/11/9/2108", DOI = "doi:10.3390/math11092108", abstract = "One important parameter in the Black-Scholes option pricing model is the implied volatility. Implied volatility surface (IVS) is an important concept in finance that describes the variation of implied volatility across option strike price and time to maturity. Over the last few decades, economists and financialists have long tried to exploit the predictability in the IVS using various parametric models, which require deep understanding of financial practices in the area. In this paper, we explore how a data-driven machine learning method, symbolic regression, performs in identifying the implied volatility surface even without deep financial knowledge. Two different approaches of symbolic regression are explored through a simulation study and an empirical study using a large panel of option data in the United States options market.", notes = "also known as \cite{math11092108}", } @InProceedings{Luo:2021:euro, author = "Jingyu Luo and Mario Vanhoucke and Jose Coelho and Weikang Guo", title = "An efficient genetic programming approach to design priority rules for resource-constrained project scheduling problem", booktitle = "31st European Conference On Operational Research", year = "2021", address = "Athens", month = "11-14 " # jul, keywords = "genetic algorithms, genetic programming", URL = "https://kar.kent.ac.uk/90397/1/EURO21-Conference_e-Handbook_Full_version.pdf", abstract = "In recent years, machine learning techniques, especially genetic programming (GP), have been a powerful approach for automated design of the priority rule-heuristics for the resource-constrained project scheduling problem (RCPSP). However, it requires intensive computing effort, carefully selected training data, and appropriate assessment criteria. This research proposes a GP hyperheuristic method with a duplicate removal technique to create new priority rules that outperform the traditional rules. The experiments have verified the efficiency of the proposed algorithm as compared to the conventional GP approach.Furthermore, the impact of the training data selection and fitness evaluation has also been investigated. The results show that a compact training set can provide good output, and existing evaluation methods are all usable for evolving efficient priority rules. The priority rules designed by the proposed approach are tested on extensive existing datasets and newly generated large projects with more than 1000 activities. To achieve better performance on small-sized projects, we also develop a method to combine rules as efficient ensembles. Computational comparisons between GP-designed rules and traditional priority rules indicate the superiority and generalization capability of the proposed GP algorithm in solving the RCPSP", notes = "See \cite{Luo:2022:ESA} https://euro2021athens.com/", } @Article{Luo:2022:ESA, author = "Jingyu Luo and Mario Vanhoucke and Jose Coelho and Weikang Guo", title = "An efficient genetic programming approach to design priority rules for resource-constrained project scheduling problem", journal = "Expert Systems with Applications", year = "2022", volume = "198", pages = "116753", month = "15 " # jul, keywords = "genetic algorithms, genetic programming, Resource-constrained project scheduling, Priority rules", ISSN = "0957-4174", URL = "https://www.sciencedirect.com/science/article/pii/S0957417422002196", DOI = "doi:10.1016/j.eswa.2022.116753", abstract = "In recent years, machine learning techniques, especially genetic programming (GP), have been a powerful approach for automated design of the priority rule-heuristics for the resource-constrained project scheduling problem (RCPSP). However, it requires intensive computing effort, carefully selected training data and appropriate assessment criteria. This research proposes a GP hyper-heuristic method with a duplicate removal technique to create new priority rules that outperform the traditional rules. The experiments have verified the efficiency of the proposed algorithm as compared to the standard GP approach. Furthermore, the impact of the training data selection and fitness evaluation have also been investigated. The results show that a compact training set can provide good output and existing evaluation methods are all usable for evolving efficient priority rules. The priority rules designed by the proposed approach are tested on extensive existing datasets and newly generated large projects with more than 1000 activities. In order to achieve better performance on small-sized projects, we also develop a method to combine rules as efficient ensembles. Computational comparisons between GP-designed rules and traditional priority rules indicate the superiority and generalization capability of the proposed GP algorithm in solving the RCPSP.", notes = "Also known as \cite{LUO2022116753} \cite{DBLP:journals/eswa/LuoVCG22}", } @Article{LUO:2023:swevo, author = "Jingyu Luo and Mario Vanhoucke and Jose Coelho", title = "Automated design of priority rules for resource-constrained project scheduling problem using surrogate-assisted genetic programming", journal = "Swarm and Evolutionary Computation", volume = "81", pages = "101339", year = "2023", ISSN = "2210-6502", DOI = "doi:10.1016/j.swevo.2023.101339", URL = "https://www.sciencedirect.com/science/article/pii/S2210650223001128", keywords = "genetic algorithms, genetic programming, Resource-constrained project scheduling, Priority rules, Surrogate models", abstract = "In the past few years, the genetic programming approach (GP) has been successfully used by researchers to design priority rules for the resource-constrained project scheduling problem (RCPSP) thanks to its high generalization ability and superior performance. However, one of the main drawbacks of the GP is that the fitness evaluation in the training process often requires a very high computational effort. In order to reduce the runtime of the training process, this research proposed four different surrogate models for the RCPSP. The experiment results have verified the effectiveness and the performance of the proposed surrogate models. It is shown that they achieve similar performance as the original model with the same number of evaluations and better performance with the same runtime. We have also tested the performance of one of our surrogate models with seven different population sizes to show that the selected surrogate model achieves similar performance for each population size as the original model, even when the searching space is sufficiently explored. Furthermore, we have investigated the accuracy of our proposed surrogate models and the size of the rules they designed. The result reveals that all the proposed surrogate models have high accuracy, and sometimes the rules found by them have a smaller size compared with the original model", } @PhdThesis{Linbo_Luo:thesis, author = "Linbo Luo", title = "Crowd Behavior Modeling and Simulation", school = "School of Computer Engineering, Nanyang Technological University", year = "2011", address = "Nanyang Avenue, Singapore 639798", URL = "http://scse.ntu.edu.sg/Research/PHDTheses/Pages/PhDThesis2012.aspx", URL = "https://repository.ntu.edu.sg/handle/10356/57398", size = "191 pages", abstract = "As a collective and highly dynamic social group, human crowd is a fascinating phenomenon in nature. While well-organized crowd activities improve the public's enjoyment of events, uncontrolled crowd may cause accidents and event disasters in many cases. Numerous incidents with large crowd have been recorded in human history, and many of these incidents have led to severe casualties and injuries. How to predict and control the behaviour of a crowd upon various events has become an intriguing and challenging issue faced by many psychologists, sociologists, and computer scientists. However, due to the inherent nature of the crowd events, it is difficult to study the crowd behaviours based on conventional empirical analysis. Recently, computer-based modelling and simulation technologies have emerged to support investigation of the dynamics of crowds. This research follows this trend and aims to develop a generic crowd behaviour modelling framework, which models human-like cognitive processes involved in decision making and action selection. The designed framework aims to serve as a general framework for model developers to construct their behaviour models for different scenarios.", notes = "Supervisor Cai Wentong", } @InProceedings{1068311, author = "Xiao Luo and Malcolm I. Heywood and A. Nur Zincir-Heywood", title = "Evolving recurrent models using linear GP", booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation", year = "2005", editor = "Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and Edwin D. {de Jong} and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler", volume = "2", ISBN = "1-59593-010-8", pages = "1787--1788", address = "Washington DC, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2005/docs/p1787.pdf", DOI = "doi:10.1145/1068009.1068311", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "25-29 " # jun, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Poster, recurrent architectures, experimentation, languages, linear genetic programming", size = "2 pages", abstract = "Turing complete Genetic Programming (GP) models introduce the concept of internal state, and therefore have the capacity for identifying interesting temporal properties. Surprisingly, there is little evidence of the application of such models to problems for prediction. An empirical evaluation is made of a simple recurrent linear GP model over standard prediction problems.", notes = "GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM Order Number 910052 Even Parity problem, sun spot", } @InProceedings{Luo:2006:CEC, author = "Xiao Luo and A. Nur Zincir-Heywood", title = "Evolving Recurrent Linear-GP for Document Classification and Word Tracking", booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary Computation", year = "2006", editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas", pages = "8605--8612", address = "Vancouver", month = "16-21 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9487-9", DOI = "doi:10.1109/CEC.2006.1688611", size = "8 pages", abstract = "we propose a novel document classification system where the recurrent linear Genetic Programming is employed to classify the documents that are represented in encoded word sequences. During this process, word sequences of documents are tracked, frequent patterns are detected and document is classified. We describe the word encoding model and the recurrent linear Genetic Programming based classification mechanism. The performance results on benchmark data set Reuters 21578 show that this system can analyse the temporal sequence patterns of a document and get competitive performance on classification. We expect that it can be easily applied to other application", notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D", } @InProceedings{Luo:2007:ICDE, author = "Xiao Luo and Nur Zincir-Heywood", title = "Incorporating Temporal Information for Document Classification", booktitle = "2007 IEEE 23rd International Conference on Data Engineering Workshop", year = "2007", pages = "780--789", address = "Istanbul, Turkey", month = "17-20 " # apr, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Encoding, Text categorization, Support vector machines, Support vector machine classification, Kernel, Computer science, Pattern analysis, Performance analysis, Text analysis", isbn13 = "978-1-4244-0831-3", DOI = "doi:10.1109/ICDEW.2007.4401067", abstract = "we propose a novel document classification system where the Recurrent Linear Genetic Programming is employed to classify documents that are represented in encoded word sequences by Self Organising feature Maps. The results using different feature selection techniques on Reuters 21578 data set show that the proposed system can analyse the temporal sequence patterns of a document and achieve competitive performance on classification.", notes = "also known as \cite{4401067} See also \cite{oai:CiteSeerX.psu:10.1.1.419.1455}", } @Misc{oai:CiteSeerX.psu:10.1.1.419.1455, title = "Incorporating Temporal Information for Document Classification", author = "Xiao Luo and Nur Zincir-heywood", year = "2007", keywords = "genetic algorithms, genetic programming", abstract = "In this paper, we propose a novel document classification system where the Recurrent Linear Genetic Programming is employed to classify documents that are represented in encoded word sequences by Self Organising feature Maps. The results using different feature selection techniques on Reuters 21578 data set show that the proposed system can analyse the temporal sequence patterns of a document and achieve competitive performance on classification.", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.419.1455", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.419.1455", URL = "http://ccs.njit.edu/inst/source/10TDMM04.pdf", notes = "see also \cite{Luo:2007:ICDE}", } @InProceedings{Luo:2019:IJCNN, author = "Ziqian Luo and Xiangrui Zeng and Zhipeng Bao and Min Xu", booktitle = "2019 International Joint Conference on Neural Networks (IJCNN)", title = "Deep Learning-Based Strategy For Macromolecules Classification with Imbalanced Data from Cellular Electron Cryotomography", year = "2019", abstract = "Deep learning model trained by imbalanced data may not work satisfactorily since it could be determined by major classes and thus may ignore the classes with small amount of data. In this paper, we apply deep learning based imbalanced data classification for the first time to cellular macromolecular complexes captured by Cryo-electron tomography (Cryo-ET). We adopt a range of strategies to cope with imbalanced data, including data sampling, bagging, boosting, Genetic Programming based method and. Particularly, inspired from Inception 3D network, we propose a multi-path CNN model combining focal loss and mixup on the Cryo-ET dataset to expand the dataset, where each path had its best performance corresponding to each type of data and let the network learn the combinations of the paths to improve the classification performance. In addition, extensive experiments have been conducted to show our proposed method is flexible enough to cope with different number of classes by adjusting the number of paths in our multi-path model. To our knowledge, this work is the first application of deep learning methods of dealing with imbalanced data to the internal tissue classification of cell macromolecular complexes, which opened up a new path for cell classification in the field of computational biology.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IJCNN.2019.8851972", ISSN = "2161-4407", month = jul, notes = "Also known as \cite{8851972}", } @InProceedings{luo:2022:GECCO, author = "Yuanzhen Luo and Qiang Lu and Xilei Hu and Jake Luo and Zhiguang Wang", title = "Exploring Hidden Semantics in Neural Networks with Symbolic Regression", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference", year = "2022", editor = "Alma Rahat and Jonathan Fieldsend and Markus Wagner and Sara Tari and Nelishia Pillay and Irene Moser and Aldeida Aleti and Ales Zamuda and Ahmed Kheiri and Erik Hemberg and Christopher Cleghorn and Chao-li Sun and Georgios Yannakakis and Nicolas Bredeche and Gabriela Ochoa and Bilel Derbel and Gisele L. Pappa and Sebastian Risi and Laetitia Jourdan and Hiroyuki Sato and Petr Posik and Ofer Shir and Renato Tinos and John Woodward and Malcolm Heywood and Elizabeth Wanner and Leonardo Trujillo and Domagoj Jakobovic and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Inmaculada Medina-Bulo and Slim Bechikh and Andrew M. Sutton and Pietro Simone Oliveto", pages = "982--990", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, neural network, ANN, symbolic regression, Lime, Maple, SRNet, USDB", isbn13 = "978-1-4503-9237-2", URL = "https://arxiv.org/abs/2204.10529", DOI = "doi:10.1145/3512290.3528758", size = "9 pages", abstract = "Many recent studies focus on developing mechanisms to explain the black-box behaviors of neural networks (NNs). However, little work has been done to extract the potential hidden semantics (mathematical representation) of a neural network. A succinct and explicit mathematical representation of a ANN model could improve the understanding and interpretation of its behaviors. To address this need, we propose a novel symbolic regression method for neural works (called SRNet) to discover the mathematical expressions of a NN. SRNet creates a Cartesian genetic programming (NNCGP) to represent the hidden semantics of a single layer in a NN. It then leverages a multi-chromosome NNCGP to represent hidden semantics of all layers of the NN. The method uses a (1+λ) evolutionary strategy (called MNNCGP-ES) to extract the final mathematical expressions of all layers in the NN. Experiments on 12 symbolic regression benchmarks and 5 classification benchmarks show that SRNet not only can reveal the complex relationships between each layer of a NN but also can extract the mathematical representation of the whole NN. Compared with LIME and MAPLE, SRNet has higher interpolation accuracy and trends to approximate the real model on the practical dataset", notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @InProceedings{Luotsinen:2016:SMC, author = "L. J. Luotsinen and F. Kamrani and P. Hammar and M. Jandel and R. A. Lovlid", booktitle = "2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)", title = "Evolved creative intelligence for computer generated forces", year = "2016", pages = "003063--003070", abstract = "This paper provides an example of using genetic programming for engendering computational creativity in computer generated forces, i.e. simulated entities used to represent own, opponent and neutral forces in military training or decision support applications. We envision that applying computational creativity in the development of computer generated forces may not only reduce development costs but also offer more interesting and challenging training environments. In this work we provide experimental results to strengthen our arguments using a predator/prey game. We show that predator behaviour created by a computer, using genetic programming, surpasses predator behaviour manually programmed by humans and argue that the sparse automatically generated code is unlikely to be generated by a human and therefore can be considered as a good example of computational creativity. Although the experiments are not conducted in a real-world training simulator they provide valuable insight that exemplifies the opportunities and the challenges of computational creativity applied to computer generated forces.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SMC.2016.7844707", month = oct, notes = "Also known as \cite{7844707}", } @InCollection{DBLP:series/sci/LuqueCH06, author = "Maria Luque and Oscar Cordon and Enrique Herrera-Viedma", title = "A Multi-Objective Genetic Algorithm for Learning Linguistic Persistent Queries in Text Retrieval Environments", booktitle = "Multi-Objective Machine Learning", editor = "Yaochu Jin", year = "2006", pages = "601--627", bibsource = "DBLP, http://dblp.uni-trier.de", publisher = "Springer", series = "Studies in Computational Intelligence", volume = "16", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-30676-4", DOI = "doi:10.1007/3-540-33019-4_26", abstract = "Persistent queries are a specific kind of queries used in information retrieval systems to represent a user's long-term standing information need. These queries can present many different structures, being the bag of words that most commonly used. They can be sometimes formulated by the user, although this task is usually difficult for him and the persistent query is then automatically derived from a set of sample documents he provides.", } @InProceedings{Luthi:2007:gecco, author = "Leslie Luthi and Marco Tomassini and Mario Giacobini and William B. Langdon", title = "The Genetic Programming Collaboration Network and its Communities", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "1643--1650", address = "London", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, human factors", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Luthi_2007_gecco.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1643.pdf", DOI = "doi:10.1145/1276958.1277284", size = "8 page", abstract = "Useful information about scientific collaboration structures and patterns can be inferred from computer databases of published papers. The genetic programming bibliography is the most complete reference of papers on GP. In addition to locating publications, it contains coauthor and coeditor relationships from which a more complete picture of the field emerges. We treat these relationships as undirected small world graphs whose study reveals the community structure of the GP collaborative social network. Automatic analysis discovers new communities and highlights new facets of them. The investigation reveals many similarities between GP and coauthorship networks in other scientific fields but also some subtle differences such as a smaller central network component and a high clustering.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071 demo http://www.cs.bham.ac.uk/~wbl/biblio/gp-coauthors/ Also known as 1277284", } @TechReport{lutton:1995:IFS, author = "Evelyne Lutton and Jacques Levy-Vehel and Guillaume Cretin and Philippe Glevarec and Cedric Roll", title = "Mixed {IFS}: Resolution of the Inverse Problem Using Genetic Programming", institution = "Inria", year = "1995", type = "Research Report", number = "No 2631", keywords = "genetic algorithms, genetic programming", URL = "http://hal.inria.fr/inria-00074056/en/", URL = "http://hal.inria.fr/docs/00/07/40/56/PDF/RR-2631.pdf", URL = "http://citeseer.ist.psu.edu/cretin95mixed.html", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.43.286", abstract = "We address here the resolution of the so-called inverse problem for IFS. This problem has already been widely considered, and some studies have been performed for affine IFS, using deterministic or stochastic methods (Simulated Annealing or Genetic Algorithm). When dealing with non affine IFS, the usual techniques do not perform well, except if some a priori hypotheses on the structure of the IFS (number and type functions) are made. In this work, a Genetic Programming method is investigated to solve the "general" inverse problem, which permits to perform at the same time a numeric and a symbolic optimization. The use of "mixed IFS", as we call them, may enlarge the scope of some applications, as for example image compression, because they allow to code a wider range of shapes.", notes = "Mainly in english, abstract also en francaise Use distance masks for deciding how close GP is to target image (part of fitness function). Says {"}The distance images are very efficient{"} [page 12]. Mutation of constants by +/-10% and variables to constants. Notes constants {"}disappear{"} from the population. popsize 20 to 50 and 1000 to 2000 generations [page 10]. GP functions {"}does not resemble the one [used to create] the target images{"} [page 12]. {"}GP algorith, which seems to perform a more efficient search in a large space.{"} [page 16]", size = "17 pages. See also \cite{lutton:1995:IFScs} and \cite{Cretin:al:EA95} http://www-syntim.inria.fr/fractales/fractales-eng.html", } @Article{lutton:1995:IFScs, author = "Evelyne Lutton and Jacques Levy-Vehel and Guillaume Cretin and Philippe Glevarec and Cidric Roll", title = "Mixed {IFS}: Resolution of the Inverse Problem Using Genetic Programming", journal = "Complex Systems", year = "1995", volume = "9", number = "5", pages = "375--398", keywords = "genetic algorithms, genetic programming, fractals", ISSN = "0891-2513", URL = "http://evelyne.lutton.free.fr/Papers/81_RR-2631.pdf", URL = "http://www.complex-systems.com/pdf/09-5-3.pdf", URL = "http://www.complex-systems.com/abstracts/v09_i05_a03.html", size = "24 pages", abstract = "We address here the resolution of the so-called inverse problem for the iterated functions system (IFS). This problem has already been widely considered, and some studies have been performed for the affine IFS, using deterministic or stochastic methods (simulated annealing or genetic algorithm). In dealing with the nonaffine IFS, the usual techniques do not perform well unless some a priori hypotheses on the structure of the IFS (number and type of functions) are made. In this work, a genetic programming method is investigated to solve the ``general'' inverse problem, which allows the simultaneous performance of a numeric and a symbolic optimization. The use of a ``mixed IFS'' may enlarge the scope of some applications, for example, image compression, because it allows a wider range of shapes to be coded.", notes = "Alos known as \cite{Lutton95-CS} See also Inria Research Report No 2631 see also \cite{Cretin:al:EA95} http://www-syntim.inria.fr/fractales/fractales-eng.html", } @TechReport{LCLSF200, author = "E. Lutton and P. Collet and J. Louchet and M. Sebag and C. Fonlupt", title = "Evolution Artificielle", year = "2000", type = "ENSTA lecture notes", month = "mars", keywords = "genetic algorithms, genetic programming", notes = "Aug 2018 ENSTA called ParisTech? http://evelyne.lutton.free.fr/Reports.html#LCLSF200", } @InProceedings{Lutton:2002:ECT, author = "Evelyne Lutton and Pierre Collet and Jean Louchet", title = "{EASEA} Comparisons on Test Functions: {GALib} versus {EO}", booktitle = "Artificial Evolution : 5th International Conference, Evolution Artificielle, EA 2001", year = "2001", editor = "P. Collet and C. Fonlupt and J.-K. Hao and E. Lutton and M. Schoenauer", volume = "2310", series = "LNCS", pages = "219--230", address = "Le Creusot, France", month = oct # " 29-31", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-43544-0", size = "12 pages", CODEN = "LNCSD9", ISSN = "0302-9743", bibdate = "Tue Sep 10 19:09:24 MDT 2002", URL = "http://fractales.inria.fr/evo-lab/EASEAComparisonFinal.ps.gz", DOI = "doi:10.1007/3-540-46033-0_18", acknowledgement = ack-nhfb, abstract = "The EASEA1 language (EAsy Specification of Evolutionary Algorithms) was created in order to allow scientists to concentrate on evolutionary algorithm design rather than implementation. EASEA currently supports two C++ libraries (GALib and EO) and a JAVA library for the DREAM. The aim of this paper is to assess the quality of EASEA-generated code through an extensive test procedure comparing the implementation for EO and GALib of the same test functions.", } @Proceedings{lutton:2002:GP, title = "Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002", year = "2002", editor = "James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi", volume = "2278", series = "LNCS", address = "Kinsale, Ireland", publisher_address = "Berlin", month = "3-5 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-43378-3", DOI = "doi:10.1007/3-540-45984-7", size = "336 pages", notes = "EuroGP'2002", } @InProceedings{Lutton:evowks03, author = "Evelyne Lutton and Emmanuel Cayla and Jonathan Chapuis", title = "{ArtiE-Fract}: The Artist's Viewpoint", booktitle = "Applications of Evolutionary Computing, EvoWorkshops2003: Evo{BIO}, Evo{COP}, Evo{IASP}, Evo{MUSART}, Evo{ROB}, Evo{STIM}", year = "2003", editor = "G{\"u}nther R. Raidl and Stefano Cagnoni and Juan Jes\'us Romero Cardalda and David W. Corne and Jens Gottlieb and Agn\`es Guillot and Emma Hart and Colin G. Johnson and Elena Marchiori and Jean-Arcady Meyer and Martin Middendorf", volume = "2611", series = "LNCS", pages = "510--521", address = "University of Essex, England, UK", publisher_address = "Berlin", month = "14-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, interactive evolutionary computation, IEC, interactive GP, Parisian GP", isbn13 = "978-3-540-00976-4", URL = "http://evelyne.lutton.free.fr/Papers/134_lutton.pdf", URL = "https://evomusart-index.dei.uc.pt/years/2003", DOI = "doi:10.1007/3-540-36605-9_47", size = "12 pages", abstract = "ArtiE-Fract is an interactive evolutionary system designed for artistic exploration of the space of fractal 2D shapes. We report in this paper an experiment performed with an artist, the painter Emmanuel Cayla. The benefit of such a collaboration was twofold: first of all, the system itself has evolved in order to better fit the needs of non-computerscientist users, and second, it has initiated an artistic approach and open up the way to new possible design outputs.", notes = "EvoWorkshops2003 EvoMUSART-2003", } @Article{Lutton05b, author = "Evelyne Lutton", title = "Evolution of Fractal shapes for artists and designers", journal = "International Journal on Artificial Intelligence Tools", year = "2006", volume = "15", number = "4", pages = "651--672", month = aug, note = "Special Issue on Artificial Intelligence in Music and Art", keywords = "genetic algorithms, genetic programming", ISSN = "0218-2130", URL = "http://evelyne.lutton.free.fr/Papers/Lutton-IJAI-Final.pdf", DOI = "doi:10.1142/S0218213006002850", size = "22 pages", abstract = "We analyse in this paper the way randomness is considered and used in ArtiE-Fract. ArtiE-Fract is an interactive software, that allows the user (artist or designer) to explore the space of fractal 2D shapes with help of an interactive genetic programming scheme. The basic components of ArtiE-Fract are first described, then we focus on its use by two artists, illustrated by samples of their works. These real life tests have led us to implement additional components in the software. It seems obvious for the people who use ArtiE-Fract that this system is a versatile tool for creation, especially regarding the specific use of controlled random components.", notes = "Guest Editors: B. Manaris and P. Machado", } @Book{Lutton:book, author = "Evelyne Lutton and Nathalie Perrot and Alberto Tonda", title = "Evolutionary Algorithms for Food Science and Technology", publisher = "Wiley", year = "2016", volume = "7", month = nov, keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-119-13683-5", URL = "https://www.amazon.com/Evolutionary-Algorithms-Technology-Computer-Engineering/dp/1848218133", size = "182 pages", abstract = "Researchers and practitioners in food science and technology routinely face several challenges, related to sparseness and heterogeneity of data, as well as to the uncertainty in the measurements and the introduction of expert knowledge in the models. Evolutionary algorithms (EAs), stochastic optimization techniques loosely inspired by natural selection, can be effectively used to tackle these issues. In this book, we present a selection of case studies where EAs are adopted in real-world food applications, ranging from model learning to sensitivity analysis.", notes = "Modelling expertise on Camembert cheese ripening. Reviewed by \cite{Androutsopoulos:2019:GPEM}", } @Article{Lutton:2017:GPEM, author = "Evelyne Lutton", title = "{Gustavo Olague}: Evolutionary computer vision, the first footprints", journal = "Genetic Programming and Evolvable Machines", year = "2017", volume = "18", number = "4", pages = "509--510", month = dec, note = "Book review", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dR8C5", DOI = "doi:10.1007/s10710-017-9311-2", notes = "Review of \cite{Olague:book}", } @Article{Lutton:2020:sigevolution, author = "Evelyne Lutton", title = "About the Cover", journal = "SIGEVOlution", year = "2020", volume = "12", number = "1", pages = "1", keywords = "genetic algorithms, genetic programming, interactive evolution, IEC, interactive GP, ArtiE-Fract, IFS functions", URL = "https://evolution.sigevo.org/issues/HTML/sigevolution13-1.html", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/SIGEVOlution_vol13_issue_1.pdf", URL = "https://dl.acm.org/action/showFmPdf?doi=10.1145%2F3402220", size = "0.2 pages", notes = "the flying peacock. ArtiE-Fract in \cite{Lutton:evowks03} and \cite{Lutton05b} Further details at: http://evelyne-lutton.fr/ArtiE-Fract.html", } @InProceedings{Lv:2019:IEMDC, author = "Gang Lv and Dihui Zeng and Tong Zhou and Michele Degano", title = "Investigation of Prediction Models for Forces Calculation in Linear Induction Motor with Data-Based System Identification Algorithms", booktitle = "2019 IEEE International Electric Machines Drives Conference (IEMDC)", year = "2019", pages = "1752--1756", abstract = "This paper investigates the prediction models of the thrust, vertical and transversal forces in the linear induction motors (LIMs) with the laterally asymmetric secondary. The models aim at presenting an analytical process for obtaining dynamic estimation model that takes account of the nonlinear effects in the analysis of the motors, e.g. magnetic saturation, end and edge effects. First, a number of simulation results of a prototype machine are generated by means of finite element method (FEM) for different conditions. The results, which mainly contains the values of the thrust, vertical and transverse forces, are classified as a function of the slip-frequency and the secondary displacement and divided into two sets: training set and test set. Different types of the identification algorithms for the prediction model are investigated: linear regression (LR), support vector machines (SVMs), symbolic regression using genetic programming (GP), random forests (RFRs), and artificial neural networks (ANNs), The prediction models with these algorithms are then optimized by the training set, and their accuracy is then validated by the test set. Finally, a discussion on the most optimal algorithm for the prediction model is given.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IEMDC.2019.8785143", month = may, notes = "Also known as \cite{8785143}", } @InProceedings{lv:2006:CM, author = "H. Y. Lv and C. G. Zhou and J. B. Zhou", title = "Genetic programming for maximum-likelihood phylogeny inference", booktitle = "Computational Methods", year = "2006", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-1-4020-3953-9_37", DOI = "doi:10.1007/978-1-4020-3953-9_37", notes = "College of Computer Science and Technology, Jilin University, Changchun, P. R. China", } @InProceedings{Lv:2017:ISCID, author = "Haili Lv and Guozhen Han", booktitle = "2017 10th International Symposium on Computational Intelligence and Design (ISCID)", title = "Research of Assembly Job Shop Scheduling Problem Based on Modified Genetic Programming", year = "2017", volume = "2", pages = "147--151", abstract = "The study of Job Shop Scheduling Problem (JSP) enables effective control of the production process and improves corporate economic profitability. This research extends traditional JSP to include assembly operations, which is called assembly job shop scheduling (AJSSP). AJSSP is often considered multi-objective decision problems just like JSP. This research adopts the commonly used mean total lateness as the objective for optimisation. GP (Genetic Programming) is proposed as the solution approach for AJSSP to obtain composite dispatching rules (CDR). Through experimental computation, the optimised CDR is shown to perform better than traditional simple rules including SPT, JDD and FIFO. Besides, as searching of the optimised rule is based on 85percent machine use, the performance is further tested under 80percent and 90percent load. Results confirm that the combined rule shows perfect robustness.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ISCID.2017.120", month = dec, notes = "Also known as \cite{8283244}", } @InProceedings{Lv:2021:ISCID, author = "Haoran Lv and Bailin Wang and Wenxin Zhang and Tieke Li", booktitle = "2021 14th International Symposium on Computational Intelligence and Design (ISCID)", title = "Genetic Programming-based Heuristic Generation Algorithm for Steelmaking - Continuous Casting Scheduling", year = "2021", pages = "148--151", abstract = "This paper deals with the special mixed flow shop scheduling problem of steelmaking and continuous casting, establishing the SCC production scheduling solution framework based on charge sequencing rules and device assignment rules, and proposing a heuristic generation algorithm of SCC based on GP. The algorithm can automatically generate scheduling heuristic rules suitable for production scenarios. The simulation experiment is carried out with the actual production data of one refining steel mill, The experimental results show that the scheduling rules generated by the heuristic generation algorithm based on GP have better performance than the benchmarking rules in actual production environment, and with the increase of examples, the performance advantage of generated rules are more obvious and stable.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ISCID52796.2021.00042", ISSN = "2473-3547", month = dec, notes = "Also known as \cite{9679299}", } @Article{LV:2024:ijhydene, author = "Qichao Lv and Tongke Zhou and Haimin Zheng and Behnam Amiri-Ramsheh and Fahimeh Hadavimoghaddam and Abdolhossein Hemmati-Sarapardeh and Xiaochen Li and Longxuan Li", title = "Modeling hydrogen solubility in water: Comparison of adaptive boosting support vector regression, gene expression programming, and cubic equations of state", journal = "International Journal of Hydrogen Energy", volume = "57", pages = "637--650", year = "2024", ISSN = "0360-3199", DOI = "doi:10.1016/j.ijhydene.2023.12.227", URL = "https://www.sciencedirect.com/science/article/pii/S036031992306528X", keywords = "genetic algorithms, genetic programming, gene expression programming, Hydrogen solubility, Aqueous solutions, AdaBoost-SVR, Gradient boosting, GEP, Outlier detection", abstract = "Predicting the solubility of hydrogen (H2) in aqueous solutions is crucial for studying reactions of hydrogen in the formation, which also affects the security and optimal design of hydrogen storage. In this research, five robust machine learning (ML) algorithms, namely adaptive boosting decision tree (AdaBoost-DT), adaptive boosting support vector regression (AdaBoost-SVR), gradient boosting decision tree (GB-DT), gradient boosting support vector regression (GB-SVR), and k-nearest neighbors (KNN) and three powerful white-box techniques, namely gene expression programming (GEP), genetic programming (GP), and group method of data handling (GMDH) were developed to accurately predict H2 solubility in pure and saline water systems. To this aim, a widespread databank containing 427 experimental data points was collected, and temperature, pressure, and salt concentration (mSalt) were considered as input variables. The validity and precision of the developed models were assessed using several statistical and graphical tests. Results demonstrate that the AdaBoost-SVR smart model could obtain a superior performance and provides precise predictions with root mean square error (RMSE) of 0.000115 and determination coefficient (R2) of 0.9973. Among the white-box models, the GEP provided the best results with an RMSE of 0.000362 and an R2 of 0.9542. Although the accuracy of GEP is slightly lower than that of AdaBoost-SVR, it offers explicit and simple mathematical formula for calculating H2 solubility, which is the main advantage of white box models. The results also demonstrated that AdaBoost-SVR outperforms cubic equations of state (EOSs) such as Peng-Robinson (PR), Redlich-Kwong (RK), Soave-Redlich-Kwong (SRK), and Zudkevitch-Joffe (ZJ). Besides, trend analysis showed that AdaBoost-SVR model could match actual trends of H2 solubility change versus temperature and pressure. Finally, outlier detection analysis using the Leverage technique indicated that the majority of data points used for modeling (nearly 94 percent) are reliable and placed in the valid zone", } @Article{Ly:2014:ieeeTEC, author = "Daniel Le Ly and Hod Lipson", title = "Optimal Experiment Design for Coevolutionary Active Learning", journal = "IEEE Transactions on Evolutionary Computation", year = "2014", volume = "18", number = "3", pages = "394--404", month = jun, keywords = "genetic algorithms, genetic programming, Active Learning, Competitive Coevolution, Optimal Experiment Design, Shannon Information Criterion", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2013.2281529", size = "11 pages", abstract = "This paper presents a policy for selecting the most informative individuals in a teacher-learner type coevolution. We propose the use of the surprisal of the mean, based on Shannon information theory, which best disambiguates a collection of arbitrary and competing models based solely on their predictions. This policy is demonstrated within an iterative, coevolutionary framework consisting of symbolic regression for model inference and a genetic algorithm for optimal experiment design. Complex, symbolic expressions are reliably inferred using fewer than 32 observations. The policy requires 21percent fewer experiments for model inference compared to baselines and is particularly effective in the presence of noise corruption, local information content as well as high dimensional systems. Furthermore, the policy was applied in a real-world setting to model concrete compression strength, where it was able to achieve 96.1percent of the passive machine learning baseline performance with only 16.6percent of the data.", notes = "also known as \cite{6595614}", } @InProceedings{Ly:2023:IWCIA, author = "Edward Ly and Julian Villegas", booktitle = "2023 IEEE 13th International Workshop on Computational Intelligence and Applications (IWCIA)", title = "Digital Filter Design via Recurrent Cartesian Genetic Programming", year = "2023", pages = "7--12", abstract = "We introduce a method for the automatic program induction of Single-Input/Single-Output (SISO) Infinite Impulse Response (IIR) filters for Digital Signal Processing (DSP) applications. Recurrent Cartesian Genetic Programming (RCGP) evolves a population of DSP programs, represented as directed cyclic/acyclic graphs, to generate a filter whose magnitude response approximates that of a target filter. The Log-Spectral Distance (LSD) is used as a fitness measure to minimise the differences between the magnitude responses of these filters, and the filter with the smallest distance is output. We evaluated our method by generating a number of filters, and found that the accuracy of the generated filters depends on both the order of the target filter and the parameter values set for the RCGP algorithm.", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, Finite impulse response filters, Software algorithms, Sociology, Signal processing algorithms, IIR filters, Digital signal processing, Digital signal processing, Evolutionary algorithms, Filter design", DOI = "doi:10.1109/IWCIA59471.2023.10335891", ISSN = "1883-3977", month = nov, notes = "Also known as \cite{10335891}", } @Article{Ly:2023:SPL, author = "Edward Ly and Julian Villegas", journal = "IEEE Signal Processing Letters", title = "Cartesian Genetic Programming Parameterization in the Context of Audio Synthesis", year = "2023", volume = "30", pages = "1077--1081", abstract = "This letter presents an evaluation of the effects of elitism, recurrence probability, and prior knowledge on the fitness achieved by Cartesian Genetic Programming (CGP) in the context of DSP audio synthesis. Prior knowledge was introduced using a probabilistic learning method where the distribution of nodes in the expected solutions was used to generate and mutate new individuals. Best results were obtained with traditional elitist selection, no recurrence, and when prior knowledge was used for node initialization and mutation. These results suggest that the apparent benefits of recurrence in CGP are context-dependent, and that selecting nodes from a uniform distribution is not always optimal.", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Synthesisers, Additives, Oscillators, Feedback loop, Computer languages, Wheels, Audio synthesis, digital signal processing, evolutionary algorithms", DOI = "doi:10.1109/LSP.2023.3304198", ISSN = "1558-2361", notes = "Also known as \cite{10214306}", } @InProceedings{1068254, author = "Michelle Lyman and Gary Lewandowski", title = "Genetic programming for association rules on card sorting data", booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation", year = "2005", editor = "Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and Edwin D. {de Jong} and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler", volume = "2", ISBN = "1-59593-010-8", pages = "1551--1552", address = "Washington DC, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2005/docs/p1551.pdf", DOI = "doi:10.1145/1068009.1068254", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "25-29 " # jun, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Poster, card sorts, data mining, experimentation", notes = "GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM Order Number 910052", } @InProceedings{conf/edm/LynchAPA08, author = "Collin Lynch and Kevin D. Ashley and Niels Pinkwart and Vincent Aleven", title = "Argument graph classification with Genetic Programming and C4.5", booktitle = "The 1st International Conference on Educational Data Mining 2008", year = "2008", editor = "Ryan Shaun Joazeiro de Baker and Tiffany Barnes and Joseph E. Beck", pages = "137--146", address = "Montreal, Quebec, Canada", month = jun # " 20-21", publisher = "www.educationaldatamining.org", keywords = "genetic algorithms, genetic programming", URL = "http://www.educationaldatamining.org/EDM2008/uploads/proc/14_Lynch_43.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.141.7750", URL = "http://www.cs.cmu.edu/~aleven/Papers/2008/Lynch_ea_EDM2008.pdf", bibsource = "DBLP, http://dblp.uni-trier.de", language = "en", oai = "oai:CiteSeerXPSU:10.1.1.141.7750", abstract = "In well-defined domains there exist well-accepted criteria for detecting good and bad student solutions. Many ITS implement these criteria characterize solutions and to give immediate feedback. While this has been shown to promote learning, it is not always possible in ill-defined domains that typically lack well-accepted criteria. In this paper we report on the induction of classification rules for student solutions in an ill-defined domain. We compare the viability of classifications using statistical measures with classification trees induced via C4.5 and Genetic Programming.", notes = "See also \cite{CollinLynch-Thesis-3-11-2014}", } @PhdThesis{CollinLynch-Thesis-3-11-2014, author = "Collin F. Lynch", title = "The Diagnosticity of Argument Diagrams", school = "University of Pittsburgh", year = "2014", address = "USA", month = jan # " 30", keywords = "feed-forward search, Argumentation, Essay Writing, Argument Diagrams, Graph Analysis, Machine Learning, Ill-Defined Domains, Intelligent Tutoring Systems, Educational Datamining, Multiple Representations.", URL = "http://d-scholarship.pitt.edu/id/eprint/20710", URL = "http://d-scholarship.pitt.edu/20710/", URL = "http://d-scholarship.pitt.edu/20710/1/CollinLynch-Thesis-3-11-2014.pdf", size = "291 pages", abstract = "Can argument diagrams be used to diagnose and predict argument performance? Argumentation is a complex domain with robust and often contradictory theories about the structure and scope of valid arguments. Argumentation is central to advanced problem solving in many domains and is a core feature of day-to-day discourse. Argumentation is quite literally, all around us, and yet is rarely taught explicitly. Novices often have difficulty parsing and constructing arguments particularly in written and verbal form. Such formats obscure key argumentative moves and often mask the strengths and weaknesses of the argument structure with complicated phrasing or simple sophistry. Argument diagrams have a long history in the philosophy of argument and have been seen increased application as instructional tools. Argument diagrams reify important argument structures, avoid the serial limitations of text, and are amenable to automatic processing. This thesis addresses the question posed above. In it I show that diagrammatic models of argument can be used to predict students essay grades and that automatically-induced models can be competitive with human grades. In the course of this analysis I survey analytical tools such as Augmented Graph Grammars that can be applied to formalize argument analysis, and detail a novel Augmented Graph Grammar formalism and implementation used in the study. I also introduce novel machine learning algorithms for regression and tolerance reduction. This work makes contributions to research on Education, Intelligent Tutoring Systems, Machine Learning, Educational Data-mining, Graph Analysis, and online grading.", notes = "Committee Chair Ashley, Kevin Committee Member Aleven, Vincent Committee Member Litman, Diane Committee Member Schunn, Chris", } @InProceedings{Lynch:2016:GECCOcomp, author = "Collin F. Lynch and Linting Xue and Min Chi", title = "Evolving Augmented Graph Grammars for Argument Analysis", booktitle = "GECCO 2016 Companion Volume", year = "2016", editor = "Tobias Friedrich and Frank Neumann and Andrew M. Sutton and Martin Middendorf and Xiaodong Li and Emma Hart and Mengjie Zhang and Youhei Akimoto and Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and Daniele Loiacono and Julian Togelius and Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and Faustino Gomez and Carlos M. Fonseca and Heike Trautmann and Alberto Moraglio and William F. Punch and Krzysztof Krawiec and Zdenek Vasicek and Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and Boris Naujoks and Enrique Alba and Gabriela Ochoa and Simon Poulding and Dirk Sudholt and Timo Koetzing", pages = "65--66", keywords = "genetic algorithms, genetic programming: Poster", month = "20-24 " # jul, organisation = "SIGEVO", address = "Denver, USA", publisher = "ACM", publisher_address = "New York, NY, USA", isbn13 = "978-1-4503-4323-7", DOI = "doi:10.1145/2908961.2908994", abstract = "Augmented Graph Grammars are a robust rule representation for rich graph data. In this paper we present our work on the automatic induction of graph grammars for argument diagrams via EC. We show that EC outperforms the existing grammar induction algorithms gSpan and Subdue on our dataset. We also show that it is possible to augment the standard EC process to harvest a set of diverse rules which can be filtered via a post-hoc Chi-Squared analysis.", notes = "also known as \cite{Lynch:Evolving:2016} Distributed at GECCO-2016.", } @InProceedings{Lynch:2016:EuroGP, author = "David Lynch and Michael Fenton and Stepan Kucera and Holger Claussen and Michael O'Neill", title = "Scheduling in Heterogeneous Networks using Grammar-based Genetic Programming", booktitle = "EuroGP 2016: Proceedings of the 19th European Conference on Genetic Programming", year = "2016", month = "30 " # mar # "--1 " # apr, editor = "Malcolm I. Heywood and James McDermott and Mauro Castelli and Ernesto Costa and Kevin Sim", series = "LNCS", volume = "9594", publisher = "Springer Verlag", address = "Porto, Portugal", pages = "83--98", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-30668-1", DOI = "doi:10.1007/978-3-319-30668-1_6", abstract = "Effective scheduling in Heterogeneous Networks is key to realising the benefits from enhanced Inter-Cell Interference Coordination. In this paper we address the problem using Grammar-based Genetic Programming. Our solution executes on a millisecond timescale so it can track with changing network conditions. Furthermore, the system is trained using only those measurement statistics that are attainable in real networks. Finally, the solution generalises well with respect to dynamic traffic and variable cell placement. Superior results are achieved relative to a benchmark scheme from the literature, illustrating an opportunity for the further use of Genetic Programming in software-defined autonomic wireless communications networks.", notes = "Part of \cite{Heywood:2016:GP} EuroGP'2016 held in conjunction with EvoCOP2016, EvoMusArt2016 and EvoApplications2016", } @InProceedings{Lynch:2016:GECCO, author = "David Lynch and Michael Fenton and Stepan Kucera and Holger Claussen and Michael O'Neill", title = "Evolutionary Learning of Scheduling Heuristics for Heterogeneous Wireless Communications Networks", booktitle = "GECCO '16: Proceedings of the 2016 Annual Conference on Genetic and Evolutionary Computation", year = "2016", editor = "Tobias Friedrich and Frank Neumann and Andrew M. Sutton and Martin Middendorf and Xiaodong Li and Emma Hart and Mengjie Zhang and Youhei Akimoto and Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and Daniele Loiacono and Julian Togelius and Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and Faustino Gomez and Carlos M. Fonseca and Heike Trautmann and Alberto Moraglio and William F. Punch and Krzysztof Krawiec and Zdenek Vasicek and Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and Boris Naujoks and Enrique Alba and Gabriela Ochoa and Simon Poulding and Dirk Sudholt and Timo Koetzing", pages = "949--956", keywords = "genetic algorithms, genetic programming", month = "20-24 " # jul, organisation = "SIGEVO", address = "Denver, USA", publisher = "ACM", publisher_address = "New York, NY, USA", isbn13 = "978-1-4503-4206-3", DOI = "doi:10.1145/2908812.2908903", abstract = "Network operators are struggling to cope with exponentially increasing demand. Capacity can be increased by densifying existing Macro Cell deployments with Small Cells. The resulting two-tiered architecture is known as a Heterogeneous Network or HetNet. Significant inter-tier interference in channel sharing HetNets is managed by resource interleaving in the time domain. A key task in this regard is scheduling User Equipment to receive data at Small Cells. Grammar-based Genetic Programming (GBGP) is employed to evolve models that map measurement reports to schedules on a millisecond timescale. Two different fitness functions based on evaluative and instructive feedback are compared. The former expresses an industry standard utility of downlink rates. Instructive feedback is obtained by computing highly optimised schedules offline using a Genetic Algorithm, which then act as target semantics for evolving models. This paper also compares two schemes for mapping the GBGP parse trees to Boolean schedules. Simulations show that the proposed system outperforms a state of the art benchmark and is within 17percent of the estimated theoretical optimum. The impressive performance of GBGP illustrates an opportunity for the further use of evolutionary techniques in software-defined wireless communications networks.", notes = "GECCO-2016 A Recombination of the 25th International Conference on Genetic Algorithms (ICGA-2016) and the 21st Annual Genetic Programming Conference (GP-2016)", } @InProceedings{Lynch:2017:evoApplications, author = "David Lynch and Michael Fenton and Stepan Kucera and Holger Claussen and Michael O'Neill", title = "Configuring Dynamic Heterogeneous Wireless Communications Networks Using a Customised Genetic Algorithm", booktitle = "20th European Conference on the Applications of Evolutionary Computation", year = "2017", editor = "Giovanni Squillero", series = "LNCS", volume = "10199", publisher = "Springer", pages = "205--220", address = "Amsterdam", month = "19-21 " # apr, organisation = "Species", keywords = "genetic algorithms, Tabu", isbn13 = "978-3-319-55849-3", DOI = "doi:10.1007/978-3-319-55849-3_14", size = "16 pages", abstract = "Wireless traffic is surging due to the prevalence of smart devices, rising demand for multimedia content and the advent of the Internet of Things. Network operators are deploying Small Cells alongside existing Macro Cells in order to satisfy demand during this era of exponential growth. Such Heterogeneous Networks (HetNets) are highly spectrally efficient because both cell tiers transmit using the same scarce and expensive bandwidth. However, load balancing and cross-tier interference issues constrain cell-edge rates in co-channel operation. Capacity can be increased by intelligently configuring Small Cell powers and biases, and the muting cycles of Macro Cells. This paper presents a customised Genetic Algorithm (GA) for reconfiguring HetNets. The GA converges within minutes so tailored settings can be pushed to cells in real time. The proposed GA lifts cell-edge (2.5th percentile) rates by 32percent over a non-adaptive baseline that is used in practice. HetNets are highly dynamic environments. However, customers tend to cluster in hotspots which arise at predictable locations over the course of a typical day. An explicit memory of previously evolved solutions is maintained and used to seed fresh runs. System level simulations show that the 2.5th percentile rates are boosted to 36percent over baseline when prior knowledge is used.", notes = "Does not seem to use either GP or GE EvoApplications2017 held in conjunction with EuroGP'2017, EvoCOP2017 and EvoMusArt2017 http://www.evostar.org/2017/cfp_evoapps.php.", } @InProceedings{Lynch:2018:CEC, author = "David Lynch and David Fagan and Stepan Kucera and Holger Claussen and Michael O'Neill", title = "Managing Quality of Service through Intelligent Scheduling in Heterogeneous Wireless Communications Networks", booktitle = "2018 IEEE Congress on Evolutionary Computation (CEC)", year = "2018", editor = "Marley Vellasco", address = "Rio de Janeiro, Brazil", month = "8-13 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2018.8477871", abstract = "Small Cells are being deployed alongside pre-existing Macro Cells in order to satisfy demand during the current era of exponential growth in mobile traffic. Heterogeneous networks are economical because both cell tiers share the same scarce and expensive spectrum. However, customers at cell edges experience severe cross-tier interference in channel sharing Het-Nets, resulting in poor service quality. Techniques for improving fairness globally have been developed in previous works. In this paper, a novel method for service differentiation at the level of individual customers is proposed. The proposed algorithm redistributes spectrum on a millisecond timescale, so that premium customers experience minimum downlink rates exceeding a target threshold. System level simulations indicate that downlink rate targets of at least 1 [Mbps] are always satisfied under the proposed scheme. By contrast, naive scheduling achieves the 1 [Mbps] target only 83percent of the time. Quality of service can be improved for premium customers without significantly impacting global fairness metrics. Flexible service differentiation will be key to effectively monetizing the next generation of 5G wireless communications networks.", notes = "WCCI2018", } @Article{Lynch:2019:Networking, author = "David Lynch and Michael Fenton and David Fagan and Stepan Kucera and Holger Claussen and Michael O'Neill", journal = "IEEE/ACM Transactions on Networking", title = "Automated Self-Optimization in Heterogeneous Wireless Communications Networks", year = "2019", volume = "27", number = "1", pages = "419--432", keywords = "genetic algorithms, genetic programming, Heterogeneous networks, software defined networking, self-organizing networks", URL = "http://human-competitive.org/sites/default/files/automated_self-optimization_in_heterogeneous_wireless_communications_networks.pdf", DOI = "doi:10.1109/TNET.2018.2890547", ISSN = "1063-6692", month = feb, abstract = "Traditional single-tiered wireless communications networks cannot scale to satisfy exponentially rising demand. Operators are increasing capacity by densifying their existing macro cell deployments with co-channel small cells. However, cross-tier interference and load balancing issues present new optimization challenges in channel sharing heterogeneous networks (HetNets). One-size-fits-all heuristics for allocating resources are highly suboptimal, but designing ad hoc controllers requires significant human expertise and manual fine-tuning. In this paper, a unified, flexible, and fully automated approach for end-to-end optimization in multi-layer HetNets is presented. A hill climbing algorithm is developed for reconfiguring cells in real time in order to track dynamic traffic patterns. Schedulers for allocating spectrum to user equipment are automatically synthesized using grammar-based genetic programming. The proposed methods for configuring the HetNet and scheduling in the time-frequency domain can address ad hoc objective functions. Thus, the operator can flexibly tune the tradeoff between peak rates and fairness. Far cell edge downlink rates are increased by up to 250percent compared with non-adaptive baselines. Alternatively, peak rates are increased by up to 340percent. The experiments illustrate the utility and future potential of natural computing techniques in software-defined wireless communications networks.", notes = "Gold winner 2019 Humies. Slides: http://www.human-competitive.org/sites/default/files/lynch_slides-3.pdf Also known as \cite{8607896}", } @InProceedings{Lynch:2019:GECCO, author = "David Lynch and Takfarinas Saber and Stepan Kucera and Holger Claussen and Michael O'Neill", title = "Evolutionary Learning of Link Allocation Algorithms for {5G} Heterogeneous Wireless Communications Networks", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference", year = "2019", editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and Anne Auger and Petr Posik and Leslie {Peprez Caceres} and Andrew M. Sutton and Nadarajen Veerapen and Christine Solnon and Andries Engelbrecht and Stephane Doncieux and Sebastian Risi and Penousal Machado and Vanessa Volz and Christian Blum and Francisco Chicano and Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and Jonathan Fieldsend and Jose Antonio Lozano and Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and Robin Purshouse and Thomas Baeck and Justyna Petke and Giuliano Antoniol and Johannes Lengler and Per Kristian Lehre", isbn13 = "978-1-4503-6111-8", pages = "1258--1265", address = "Prague, Czech Republic", DOI = "doi:10.1145/3321707.3321853", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, Link Allocation, Scheduling, 5G", size = "8 pages", abstract = "Wireless communications networks are operating at breaking point during an era of relentless traffic growth. Network operators must use scarce and expensive wireless spectrum efficiently in order to satisfy demand. Spectrum on the links between cells and user equipments (users: smartphones, tablets, etc.) frequently becomes congested. Capacity can be increased by transmitting data packets via multiple links. Packets can be routed through multiple Long Term Evolution (LTE) links in existing fourth generation (4G) networks. In future 5G deployments, users will be equipped to receive packets over LTE, WiFi, and millimetre wave links simultaneously. How can we allocate spectrum on links, so that all customers experience an acceptable quality of service? Building effective schedulers for link allocation requires considerable human expertise. We automate the design process through the novel application of evolutionary algorithms. Evolved schedulers boost downlink rates by over 150percent for the worst-performing users, relative to a single-link baseline. The proposed techniques significantly outperform a benchmark algorithm from the literature. The experiments illustrate the promise of evolutionary algorithms as a paradigm for managing 5G software-defined wireless communications networks.", notes = "Also known as \cite{3321853} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InProceedings{Lynch:2020:PPSN, author = "David Lynch and James McDermott and Michael O'Neill", title = "Program Synthesis in a Continuous Space using Grammars and Variational Autoencoders", booktitle = "16th International Conference on Parallel Problem Solving from Nature, Part II", year = "2020", editor = "Thomas Baeck and Mike Preuss and Andre Deutz and Hao Wang2 and Carola Doerr and Michael Emmerich and Heike Trautmann", volume = "12270", series = "LNCS", pages = "33--47", address = "Leiden, Holland", month = "7-9 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, PSB1", isbn13 = "978-3-030-58114-5", DOI = "doi:10.1007/978-3-030-58115-2_3", abstract = "An important but elusive goal of computer scientists is the automatic creation of computer programs given only input and output examples. We present a novel approach to program synthesis based on the combination of grammars, generative neural models, and evolutionary algorithms. Programs are described by sequences of productions sampled from a Backus-Naur form grammar. A sequence-to-sequence Variational Autoencoder (VAE) is trained to embed randomly sampled programs in a continuous space, the VAE encoder maps a sequence of productions (a program) to a point z in the latent space, and the VAE decoder reconstructs the program given z. After the VAE has converged, we can engage the decoder as a generative model that maps locations in the latent space to executable programs. Hence, an Evolutionary Algorithm can be employed to search for a vector z (and its corresponding program) that solves the synthesis task. Experiments on the program synthesis benchmark suite suggest that the proposed approach is competitive with tree-based GP and PushGP. Crucially, code can be synthesised in any programming language.", notes = " PPSN2020", } @InProceedings{conf/iccci/LysekB14, title = "Genetic Programming with Dynamically Regulated Parameters for Generating Program Code", author = "Tomasz Lysek and Mariusz Boryczka", booktitle = "Computational Collective Intelligence. Technologies and Applications - 6th International Conference, {ICCCI} 2014, Seoul, Korea, September 24-26, 2014. Proceedings", publisher = "Springer", year = "2014", volume = "8733", editor = "Dosam Hwang and Jason J. Jung and Ngoc Thanh Nguyen", isbn13 = "978-3-319-11288-6", pages = "363--372", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming", bibdate = "2014-09-08", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/iccci/iccci2014.html#LysekB14", URL = "http://dx.doi.org/10.1007/978-3-319-11289-3", } @InProceedings{lysek:2019:RASC, author = "Jiri Lysek and Jiri Stastny", title = "Grammatical Evolution for Classification into Multiple Classes", booktitle = "Recent Advances in Soft Computing", year = "2019", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", URL = "http://link.springer.com/chapter/10.1007/978-3-319-97888-8_17", DOI = "doi:10.1007/978-3-319-97888-8_17", } @InProceedings{Lyu:2023:SSBSE, author = "Haozhou Lyu and Gregory Gay and Maiko Sakamoto", title = "Developer Views on Software CarbonFootprint and Its Potential for AutomatedReduction", booktitle = "SSBSE 2023", year = "2023", editor = "Paolo Arcaini and Tao Yue and Erik Fredericks", organisers = "Erik Fredericks and Paolo Arcaini and Tao Yue and Rebecca Moussa and Thomas Vogel and Gregory Gay and Max Hort and Bobby R. Bruce and Jose Miguel Rojas and Vali Tawosi", volume = "14415", series = "LNCS", pages = "35--51", address = "San Francisco, USA", month = "8 " # dec, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Genetic Improvement, Carbon Footprint, Energy Consumption, Sustainability", isbn13 = "978-3-031-48795-8", DOI = "doi:10.1007/978-3-031-48796-5_3", size = "17 pages", abstract = "Reducing software carbon footprint could contribute to efforts to avert climate change. Past research indicates that developers lack knowledge on energy consumption and carbon footprint, and existing reduction guidelines are difficult to apply. Therefore, we propose that automated reduction methods should be explored, e.g., through genetic improvement. However, such tools must be voluntarily adopted and regularly used to have an impact. have conducted interviews and a survey (a) to explore developers existing opinions, knowledge, and practices with regard to carbon foot-print and energy consumption, and (b), to identify the requirements that automated reduction tools must meet to ensure adoption.", notes = "See also MSc http://hdl.handle.net/20.500.12380/306723 https://odr.chalmers.se/items/c832b3a9-609d-40af-bc39-b96278686042 co-located with ESEC/FSE 2023. https://conf.researchr.org/home/ssbse-2023", } @Article{Lyytinen:2021:JIT, author = "Kalle Lyytinen and Jeffrey V Nickerson and John L King", title = "Meta-human Systems = Humans + Machines That Learn", journal = "Journal of Information Technology", year = "2021", volume = "36", number = "4", pages = "427--445", month = "1 " # dec, keywords = "genetic algorithms, genetic programming, AI, machine learning, learning theory, technology, work groups, job design, organizational forms, monitoring, embodiment, autonomy", ISSN = "0268-3962", URL = "https://researchwith.stevens.edu/en/publications/metahuman-systems-humans-machines-that-learn", URL = "https://journals.sagepub.com/doi/pdf/10.1177/0268396220915917", DOI = "doi:10.1177/0268396220915917", size = "19 pages", abstract = "Metahuman systems are new, emergent, sociotechnical systems where machines that learn join human learning and create original systemic capabilities. Metahuman systems will change many facets of the way we think about organizations and work. They will push information systems research in new directions that may involve a revision of the field research goals, methods and theorizing. Information systems researchers can look beyond the capabilities and constraints of human learning toward hybrid human/machine learning systems that exhibit major differences in scale, scope and speed. We review how these changes influence organization design and goals. We identify four organizational level generic functions critical to organize meta-human systems properly: delegating, monitoring, cultivating, and reflecting. We show how each function raises new research questions for the field. We conclude by noting that improved understanding of metahuman systems will primarily come from learning-by-doing as information systems scholars try out new forms of hybrid learning in multiple settings to generate novel, generalisable, impactful designs. Such trials will result in improved understanding of metahuman systems. This need for large-scale experimentation will push many scholars out from their comfort zone, because it calls for the revitalization of action research programs that informed the first wave of socio-technical research at the dawn of automating work systems.", } @Article{m:2022:Polymers, author = "Vishweshwaran M and Evangelin Ramani Sujatha", title = "{Beta-Glucan} as a Sustainable Alternative to Stabilize Pavement Subgrade", journal = "Polymers", year = "2022", volume = "14", number = "14", pages = "Article No. 2850", keywords = "genetic algorithms, genetic programming", ISSN = "2073-4360", URL = "https://www.mdpi.com/2073-4360/14/14/2850", DOI = "doi:10.3390/polym14142850", abstract = "Beta glucan (β-Glucan), a polysaccharide biopolymer, is used to improve the subgrade strength of clayey soils in an attempt to advocate a sustainable, carbon-neutral, and eco-friendly stabilizer. A design thickness catalog was developed for a three-layered flexible pavement using 3D finite element analysis (FEA) and layered elastic analysis. The analyses were performed for β-glucan-treated fine-grained soils with varying traffic intensities based on a mechanistic design philosophy conforming to IRC: 37-2018. Genetic programming (GP) was employed to obtain equations governing the rutting and fatigue failure in pavements. Thirty-nine datasets were used in the determination and analysis of critical strains governing the failure of a flexible pavement. Energy-dispersive X-ray spectroscopy (EDS), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), Zetasizer analysis, and pH tests of the β-glucan-treated soil revealed the mechanism of strength improvement of the fine-grained soils. The savings in cost for a 1 km stretch of the pavement were estimated to be 14.3percent.", notes = "also known as \cite{polym14142850}", } @Article{Ma:2019:ACC, author = "Baoshan Ma and Xiangtian Jiao and Fanyu Meng and Fengping Xu and Yao Geng and Rubin Gao and Wei Wang and Yeqing Sun", title = "Identification of Gene Regulatory Networks by Integrating Genetic Programming With Particle Filtering", year = "2019", journal = "IEEE Access", volume = "7", pages = "113760--113770", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ACCESS.2019.2935216", ISSN = "2169-3536", abstract = "Gene regulatory network can help to analyse and understand the underlying regulatory mechanism and the interaction among genes, and it plays a central role in morphogenesis of complex diseases such as cancer. DNA sequencing technology has efficiently produced a large amount of data for constructing gene regulatory networks. However, measured gene expression data usually contain uncertain noise, and inference of gene regulatory network model under non-Gaussian noise is a challenging issue which needs to be addressed. In this study, a joint algorithm integrating genetic programming and particle filter is presented to infer the ordinary differential equations model of gene regulatory network. The strategy uses genetic programming to identify the terms of ordinary differential equations, and applies particle filtering to estimate the parameters corresponding to each term. We systematically discuss the convergence and complexity of the proposed algorithm, and verify the efficiency and effectiveness of the proposed method compared to the existing approaches. Furthermore, we show the utility of our inference algorithm using a real HeLa dataset. In summary, a novel algorithm is proposed to infer the gene regulatory networks under non-Gaussian noise and the results show that this method can achieve more accurate models compared to the existing inference algorithms based on biological datasets.", notes = "College of Information Science and Technology, Dalian Maritime University, Dalian, China Also known as \cite{8798609}", } @InCollection{Ma2008581, author = "Chao Y Ma and Frances V Buontempo and Xue Z Wang", title = "Inductive data mining: Automatic generation of decision trees from data for QSAR modelling and process historical data analysis", editor = "Bertrand Braunschweig and Xavier Joulia", booktitle = "18th European Symposium on Computer Aided Process Engineering", publisher = "Elsevier", year = "2008", volume = "25", pages = "581--586", series = "Computer Aided Chemical Engineering", ISSN = "1570-7946", DOI = "doi:10.1016/S1570-7946(08)80102-2", URL = "http://www.sciencedirect.com/science/article/B8G5G-4TK2DGX-3M/2/2d0cbf83807000db928a8f08986360cf", keywords = "genetic algorithms, genetic programming, inductive data mining, decision trees, QSAR, process historical data analysis", abstract = "A new inductive data mining method for automatic generation of decision trees from data (GPTree) is presented. Compared with other decision tree induction techniques that are based upon recursive partitioning employing greedy searches to choose the best splitting attribute and value at each node therefore will necessarily miss regions of the search space, GPTree can overcome the problem. In addition, the approach is extended to a new method (YAdapt) that models the original continuous endpoint by adaptively finding suitable ranges to describe the endpoints during the tree induction process, removing the need for discretization prior to tree induction and allowing the ordinal nature of the endpoint to be taken into account in the models built. A strategy for further improving the predictive performance for previously unseen data is investigated that uses multiple decisions trees, i.e., a decision forest, and a majority voting strategy to give a prediction (GPForest). The methods were applied to QSAR (quantitative structure--activity relationships) modeling for eco-toxicity prediction of chemicals and to the analysis of a historical database for a wastewater treatment plant.", notes = "See \cite{Ma20091602}", } @Article{Ma20091602, author = "Chao Y. Ma and Xue Z. Wang", title = "Inductive data mining based on genetic programming: Automatic generation of decision trees from data for process historical data analysis", journal = "Computers \& Chemical Engineering", volume = "33", number = "10", pages = "1602--1616", year = "2009", note = "Selected Papers from the 18th European Symposium on Computer Aided Process Engineering (ESCAPE-18)", ISSN = "0098-1354", DOI = "doi:10.1016/j.compchemeng.2009.04.005", URL = "http://www.sciencedirect.com/science/article/B6TFT-4W7420M-3/2/7984765c8dbd5fb91cfbad06b2673cd3", keywords = "genetic algorithms, genetic programming, Process historical data analysis, Decision trees, Decision forest, Wastewater treatment plant, Inductive data mining", abstract = "An inductive data mining algorithm based on genetic programming, GPForest, is introduced for automatic construction of decision trees and applied to the analysis of process historical data. GPForest not only outperforms traditional decision tree generation methods that are based on a greedy search strategy therefore necessarily miss regions of the search space, but more importantly generates multiple trees in each experimental run. In addition, by varying the initial values of parameters, more decision trees can be generated in new experiments. From the multiple decision trees generated, those with high fitness values are selected to form a decision forest. For predictive purpose, the decision forest instead of a single tree is used and a voting strategy is employed which allows the combination of the predictions of all decision trees in the forest in order to generate the final prediction. It was demonstrated that in comparison with decision tree methods in the literature, GPForest gives much improved performance.", notes = "See \cite{Ma2008581}", } @Article{Ma:2011:IJMIC, author = "Chao Y. Ma and Frances V. Buontempo and Xue Z. Wang", title = "Inductive data mining: automatic generation of decision trees from data for QSAR modelling and process historical data analysis", journal = "International Journal of Modelling, Identification and Control", year = "2011", volume = "12", number = "1/2", pages = "101--106", keywords = "genetic algorithms, genetic programming, inductive data mining, decision trees, quantitative structure activity relationships, QSAR, process historical data analysis; wastewater treatment, modelling, eco-toxicity prediction", ISSN = "1746-6180", language = "eng", URL = "http://www.inderscience.com/link.php?id=37837", DOI = "doi:10.1504/IJMIC.2011.037837", publisher = "Inderscience Publishers", bibsource = "OAI-PMH server at www.inderscience.com", rights = "Inderscience Copyright", abstract = "A new inductive data mining method for automatic generation of decision trees from data (GPTree) is presented. Compared with other decision tree induction techniques that are based upon recursive partitioning employing greedy searches to choose the best splitting attribute and value at each node therefore will necessarily miss regions of the search space, GPTree can overcome the problem. In addition, the approach is extended to a new method (YAdapt) that models the original continuous endpoint by adaptively finding suitable ranges to describe the endpoints during the tree induction process, removing the need for discretisation prior to tree induction and allowing the ordinal nature of the endpoint to be taken into account in the models built. A strategy for further improving the predictive performance for previously unseen data is investigated that uses multiple decision trees, i.e., a decision forest, and a majority voting strategy to give predictions (GPForest). The methods were applied to QSAR (quantitative structure -- activity relationships) modelling for eco-toxicity prediction of chemicals and to the analysis of a historical database for a wastewater treatment plant.", } @Article{MA:2023:engstruct, author = "Gao Ma and Yao Wang and Hyeon-Jong Hwang", title = "Genetic programming-based backbone curve model of reinforced concrete walls", journal = "Engineering Structures", volume = "283", pages = "115824", year = "2023", ISSN = "0141-0296", DOI = "doi:10.1016/j.engstruct.2023.115824", URL = "https://www.sciencedirect.com/science/article/pii/S0141029623002389", keywords = "genetic algorithms, genetic programming, Reinforced concrete wall, Machine learning, SHAP, Symbolic regression, Backbone curve", abstract = "Backbone curve, as a nonlinear response analysis method, can be used for performance assessment of residual resistance and performance prediction during the preliminary design of structures. In this study, a backbone curve model of reinforced concrete (RC) walls based on Genetic programming-based symbolic regression (GP-SR) was proposed, which can help to quickly evaluate the bearing capacity and seismic performance of RC walls. Unlike the black-box characteristic of traditional machine learning models, the GP-SR method can give explicit computational equations, which are more interpretable and easier to be used by researchers and engineers. Experimental data of 388 existing RC walls were used for feature selection, model training, and comparison with the modeling method of ASCE 41-17 to verify its effectiveness for modeling the backbone curves of RC walls with four failure modes (i.e., flexure, flexure-shear, shear, and shear-sliding). The results showed that the accuracy of the GP-SR model was better than that of the prediction of ASCE 41-17. Overall, the GP-SR model described well the backbone curves of RC walls with various design conditions", } @Article{MA:2023:engstruct2, author = "Gao Ma and Chunxiong Qin and Hyeon-Jong Hwang and Zhizhan Zhou", title = "Data-driven models for predicting tensile load capacity and failure mode of grouted splice sleeve connection", journal = "Engineering Structures", volume = "289", pages = "116236", year = "2023", ISSN = "0141-0296", DOI = "doi:10.1016/j.engstruct.2023.116236", URL = "https://www.sciencedirect.com/science/article/pii/S014102962300651X", keywords = "genetic algorithms, genetic programming, Grouted splice sleeve connection, Machine learning, Threshold method, Tensile load capacity, Failure mode, Interpretability, XAI", abstract = "Grouted splice sleeve connection (GSSC) is an important connection technology in prefabricated structures, and the tensile load capacity and failure models of GSSC are very important to joint safety. In this study, two data-driven prediction methods (i.e., the machine learning (ML) method, and the threshold method) are proposed to predict the tensile load capacity and failure mode of GSSC. To this end, a database containing 418 existing GSSC experimental data is built. The database is used for eleven ML algorithms (i.e., linear prediction (LP), artificial neural network (ANN), support vector machine (SVM), k-nearest neighbors (KNN), decision tree (DT), random forest (RF), extremely randomized trees (ET), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost)) to establish ML models interpreted by shapley additive explanations (SHAP) and partial dependence plot (PDP). Further, the database is applied to the genetic programming (GP) algorithm to generate a simplified equation for the bond strength between rebar and grouting materials, which is the key mechanical parameter for the threshold method. The results show that both of these methods can effectively predict the tensile load capacity and failure mode of GSSC with various common construction defects, and the predictive performance of ML is slightly greater than that of the threshold method", } @InProceedings{Ma:2022:CASE, author = "Hang Ma and Cheng Zhang and Zhongshun Shi", booktitle = "2022 IEEE 18th International Conference on Automation Science and Engineering (CASE)", title = "A Simulation Optimization-Aided Learning Method for Design Automation of Scheduling Rules", year = "2022", pages = "1992--1997", abstract = "Intelligent manufacturing systems require real-time optimization algorithms for daily operations management. Scheduling rules have been proven to be efficient and commonly used in plenty of practical production scenarios, especially for the large-scale problems. However, almost all the scheduling rules are manually designed, which is time consuming and also results in the large loss of accuracy for complex problems. This paper proposes a new simulation optimization-aided learning method, denoted by SOaL, for design automation of scheduling rules. The proposed SOaL method treats the automated design of scheduling rules as a simulation optimization problem, where we use genetic programming algorithm to guide the rule generation and introduce ranking and selection algorithm to improve the rule evaluation accuracy. Using dynamic job shop scheduling problem as the simulation testbed, numerical results show the superiority of the proposed method.", keywords = "genetic algorithms, genetic programming, Learning systems, Job shop scheduling, Design automation, Machine learning algorithms, Heuristic algorithms, Production", DOI = "doi:10.1109/CASE49997.2022.9926615", ISSN = "2161-8089", month = aug, notes = "Also known as \cite{9926615}", } @Article{journals/tlsdkcs/MaWZ15, author = "Hui Ma and Anqi Wang and Mengjie Zhang", title = "A Hybrid Approach Using Genetic Programming and Greedy Search for {QoS}-Aware Web Service Composition", journal = "Transactions on Large-Scale Data and Knowledge-Centered Systems", bibdate = "2015-02-24", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/tlsdkcs/tlsdkcs18.html#MaWZ15", year = "2015", volume = "8980", editor = "Abdelkader Hameurlain and Josef Kueng and Roland Wagner and Hendrik Decker and Lenka Lhotska and Sebastian Link", isbn13 = "978-3-662-46484-7", pages = "180--205", series = "Lecture Notes in Computer Science", note = "{XVIII} - Special Issue on Database and Expert-Systems Applications", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://dx.doi.org/10.1007/978-3-662-46485-4", DOI = "doi:10.1007/978-3-662-46485-4_7", abstract = "Service compositions build new web services by orchestrating sets of existing web services provided in service repositories. Due to the increasing number of available web services, the search space for finding best service compositions is growing exponentially. Further, there are many available web services that provide identical functionality but differ in their Quality of Service (QoS). Decisions need to be made to determine which services are selected to participate in service compositions with optimized QoS properties. In this paper, a hybrid approach to service composition is proposed that combines the use of genetic programming and random greedy search. The greedy algorithm is used to generate valid and locally optimized individuals to populate the initial generation for genetic programming (GP), and to perform mutation operations during genetic programming. A full experimental evaluation has been carried out using public benchmark test cases with repositories of up to 15000 web services and 31000 properties. The results show good performance in searching for best service compositions, where the number of atomic web services used and the tree depth are used as objectives for minimization. Further, we extend our approach to the more general problem of finding service composition solutions that have near-optimal QoS. Our experimental evaluation demonstrates that our GP-based greedy algorithm enhanced approach can be applied with good performance to the QoS-aware service composition problem.", notes = "Preface The 24th International Conference on Database and Expert Systems Applications (DEXA 2013), with proceedings published as volumes 8055 and 8056 in Springer's Lecture Notes in Computer Science, featured some outstanding keynote presentations and regular articles. As with previous editions of the DEXA conference, the Program Co-chairs of DEXA 2013 invited some of the authors to submit extended papers to a special issue of the Springer journal Transactions on Large-Scale Data- and Knowledge- Centred Systems (TLDKS). Following these invitations, both keynote papers and eight regular articles were submitted. Apart from the keynotes, each submission was carefully assessed by at least two (often more) recognized experts in the respective field. In total, 35 reviews were received, most of them of excellent quality. After two rounds of revisions, five of the eight regular papers were accepted for inclusion in this special issue, in addition to the two keynote papers... Cites \cite{Aversano:2006:IJCSSE} Aversano:2005:WSEC", } @InProceedings{1277397, author = "Irwin Ma and Tony Wong and Thiagas Sankar", title = "Volatility forecasting using time series data mining and evolutionary computation techniques", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "2262--2262", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p2262.pdf", DOI = "doi:10.1145/1276958.1277397", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Real-World Applications: Poster, data mining, economics, financial volatility, forecasting, S&P 100", abstract = "Traditional parametric methods have limited success in estimating and forecasting the volatility of financial securities. Recent advance in evolutionary computation has provided additional tools to conduct data mining effectively. The current work applies the genetic programming in a Time Series Data Mining framework to characterise the S&P100 high frequency data in order to forecast the one step ahead integrated volatility. Results of the experiment have shown to be superior to those derived by the traditional methods.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @Article{MA:2020:KBS, author = "Jianbin Ma and Xiaoying Gao", title = "A filter-based feature construction and feature selection approach for classification using Genetic Programming", journal = "Knowledge-Based Systems", year = "2020", volume = "196", pages = "105806", keywords = "genetic algorithms, genetic programming, Feature construction, Feature selection, Classification", ISSN = "0950-7051", DOI = "doi:10.1016/j.knosys.2020.105806", URL = "http://www.sciencedirect.com/science/article/pii/S095070512030191X", abstract = "Feature construction and feature selection are two common pre-processing methods for classification. Genetic Programming (GP) can be used to solve feature construction and feature selection tasks due to its flexible representation. In this paper, a filter-based multiple feature construction approach using GP named FCM that stores top individuals is proposed, and a filter-based feature selection approach using GP named FS that uses correlation-based evaluation method is employed. A hybrid feature construction and feature selection approach named FCMFS that first constructs multiple features using FCM then selects effective features using FS is proposed. Experiments on nine datasets show that features selected by FS or constructed by FCM are all effective to improve the classification performance comparing with original features, and our proposed FCMFS can maintain the classification performance with smaller number of features comparing with FCM, and can obtain better classification performance with smaller number of features than FS on the majority of the nine datasets. Compared with another feature construction and feature selection approach named FSFCM that first selects features using FS then constructs features using FCM, FCMFS achieves better performance in terms of classification and the smaller number of features. The comparisons with three state-of-art techniques show that our proposed FCMFS approach can achieve better experimental results in most cases", } @Article{MA:2019:ASC, author = "Jianbin Ma and Guifa Teng", title = "A hybrid multiple feature construction approach for classification using Genetic Programming", journal = "Applied Soft Computing", volume = "80", pages = "687--699", year = "2019", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2019.04.039", URL = "http://www.sciencedirect.com/science/article/pii/S1568494619302315", keywords = "genetic algorithms, genetic programming, Feature construction, Hybrid, Multiple feature, Classification", abstract = "The purpose of feature construction is to create new higher-level features from original ones. Genetic Programming (GP) was usually employed to perform feature construction tasks due to its flexible representation. Filter-based approach and wrapper-based approach are two commonly used feature construction approaches according to their different evaluation functions. In this paper, we propose a hybrid feature construction approach using genetic programming (Hybrid-GPFC) that combines filter's fitness function and wrapper's fitness function, and propose a multiple feature construction method that stores top excellent individuals during a single GP run. Experiments on ten datasets show that our proposed multiple feature construction method (Fcm) can achieve better (or equivalent) classification performance than the single feature construction method (Fcs), and our Hybrid-GPFC can obtain better classification performance than filter-based feature construction approaches (Filter-GPFC) and wrapper-based feature construction approaches (Wrapper-GPFC) in most cases. Further investigations on combinations of constructed features and original features show that constructed features augmented with original features do not improve the classification performance comparing with constructed features only. The comparisons with three state-of-art methods show that in majority of cases, our proposed hybrid multiple feature construction approach can achieve better classification performance", } @Article{MA:2020:ASC, author = "Jianbin Ma and Xiaoying Gao", title = "Designing genetic programming classifiers with feature selection and feature construction", journal = "Applied Soft Computing", volume = "97", pages = "106826", year = "2020", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2020.106826", URL = "https://www.sciencedirect.com/science/article/pii/S156849462030764X", keywords = "genetic algorithms, genetic programming, Feature construction, Feature selection, Classifier, Classification", abstract = "Due to the flexibility of Genetic Programming (GP), GP has been used for feature construction, feature selection and classifier construction. In this paper, GP classifiers with feature selection and feature construction are investigated to obtain simple and effective classification rules. During the construction of a GP classifier, irrelevant and redundant features affect the search ability of GP, and make GP easily fall into local optimum. This paper proposes two new GP classifier construction methods to restrict bad impact of irrelevant and redundant features on GP classifier. The first is to use a multiple-objective fitness function that decreases both classification error rate and the number of selected features, which is named as GPMO. The second is to first use a feature selection method, i.e., linear forward selection (LFS) to remove irrelevant and redundant features and then use GPMO to construct classifiers, which is named as FSGPMO. Experiments on twelve datasets show that GPMO and FSGPMO have advantages over GP classifiers with a single-objective fitness function named GPSO in term of classification performance, the number of selected features, time cost and function complexity. The proposed FSGPMO can achieve better classification performance than GPMO on higher dimension datasets, however, FSGPMO may remove potential effective features for GP classifier and achieve much lower classification performance than GPMO on some datasets. Compared with two other GP-based classifiers, GPMO can significantly improve the classification performance. Comparisons with other classification algorithms show that GPMO can achieve better or comparable classification performance on most selected datasets. Our proposed GPMO can achieve better performance than wrapper-based feature construction methods using GP on applications with insufficient instances. Further investigations show that bloat phenomena exists in the process of GP evolution and overfitting phenomena is not obvious. Moreover, the benefits of GP over other machine learning algorithms are discussed", } @Article{MA:2023:swevo, author = "Jianbin Ma and Xiaoying Gao and Ying Li", title = "Multi-generation multi-criteria feature construction using Genetic Programming", journal = "Swarm and Evolutionary Computation", volume = "78", pages = "101285", year = "2023", ISSN = "2210-6502", DOI = "doi:10.1016/j.swevo.2023.101285", URL = "https://www.sciencedirect.com/science/article/pii/S2210650223000585", keywords = "genetic algorithms, genetic programming, Feature construction, Overfitting, Multi-generation, Multi-criteria", abstract = "The purpose of feature construction is to create new high level features from the original features. When Genetic Programming (GP) is applied to wrapper-based feature construction, especially when the samples size is small, GP generally overfits the training set and generalizes poorly with the deepening of evolution. Overfitting has attracted wide attention in some classification models, however, it is not commonly studied in the field of feature construction. In this paper, a Multi-Generation feature construction method (MG) is developed to preserve the solutions produced by multiple generations of GP. A Multi-Criteria feature construction method (MC) is introduced to use a multi-criteria evaluation function to evaluate GP individuals. Combining the above two methods, a Multi-Generation Multi-Criteria feature construction method (MGMC) is proposed. Experiments on fourteen datasets show that the proposed MG and MC methods can improve the classification performance and overcome overfitting problems of traditional feature construction methods in most cases. The combined MGMC method further improves the classification performance and achieves the best results", } @InProceedings{Ma:2024:MLISE, author = "Jianbin Ma and Man Zhu", title = "Genetic Programming for Feature Selection and Construction to High-Dimensional Data", booktitle = "2024 4th International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)", year = "2024", pages = "196--200", month = jun, keywords = "genetic algorithms, genetic programming, Feature extraction, Decision trees, Intelligent systems, Feature Construction, Feature Selection, High-dimensional, Classification", DOI = "doi:10.1109/MLISE62164.2024.10674630", abstract = "Classification on high-dimensional data is a challenging task due to a large number of redundant and irrelevant features. Feature construction using Genetic Programming (GP) is an effective feature processing approach for classification, however, in high-dimensional applications, too many redundant and irrelevant features may reduce GP's search ability and affect the classification performance of feature construction. In this paper, two feature selection and construction approaches to high-dimensional data are proposed. The first is a two-stage feature selection and construction approach named LfsFc, which first uses linear forword feature selection method (Lfs) to reduce the search space of features, and then uses a GP-based multiple feature construction approach (Fc) to construct multiple features. The second is a multi-objective GP-based feature selection and construction approach named MoFc which optimises information gain ratio and the number of selected features, and archives elite individuals as constructed features. Experiments on ten high-dimensional datasets show that LfsFc and MoFc can improve the classification performance compared with Fc and original features in four decision tree classifiers.", notes = "Also known as \cite{10674630}", } @InProceedings{Ma:2012:IBICA, author = "Jingye Ma and Hideyuki Takagi", booktitle = "Innovations in Bio-Inspired Computing and Applications (IBICA), 2012 Third International Conference on", title = "Design of Composite Image Filters Using Interactive Genetic Programming", year = "2012", pages = "274--279", DOI = "doi:10.1109/IBICA.2012.45", abstract = "We combine a method for designing composite image filters with interactive genetic programming (IGP). Human subjective tests are used to comparatively evaluate the IGP-based filter design method to a manual filter design method and the multi-stage filtering feature of a software photo-retouching program. The composite image filter has a tree structure, with its nodes consisting of multiple simple image filters, arithmetic operators, arithmetic functions, constant values, and the pixel value of the input image. Genetic programming (GP) optimises the tree structure based on the visual inspection of the IGP users, i.e. filter designers. Ten filter designers design composite filters using three methods: an IGP-based design method, a manual-based design method, and using the photo-retouching features of a commercial software program to time-sequentially apply ready-made filters. The designers make filters that output images corresponding to the given design concepts - relaxed and violent - based on their visual inspection. Twenty subjects compare the obtained images in pairs and evaluate which image is closer to achieving the give design concept. Wilcoxon signed-rank test demonstrates that the IGP-base filter design method can produce filters that create images with impressions that are closer to the given design concept than the other two methods.", keywords = "genetic algorithms, genetic programming, filtering theory, image processing, IGP-based filter design method, Wilcoxon signed-rank test, arithmetic functions, arithmetic operators, commercial software program, composite image filter design, human subjective tests, interactive genetic programming, manual filter design method, multistage filtering, software photo-retouching program, tree structure, visual inspection, Convergence, Design methodology, Educational institutions, Humans, Manuals, Software, image filter design, image processing, interactive genetic programming", notes = "Also known as \cite{6337677}", } @InProceedings{Ma:2021:GECCOcomp, author = "Jun Ma3 and Fenghui Gao and Shuangrong Liu and Lin Wang", title = "Linear-dependent Multi-interpretation Neuro-Encoded Expression Programming", booktitle = "Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion", year = "2021", editor = "Francisco Chicano and Alberto Tonda and Krzysztof Krawiec and Marde Helbig and Christopher W. Cleghorn and Dennis G. Wilson and Georgios Yannakakis and Luis Paquete and Gabriela Ochoa and Jaume Bacardit and Christian Gagne and Sanaz Mostaghim and Laetitia Jourdan and Oliver Schuetze and Petr Posik and Carlos Segura and Renato Tinos and Carlos Cotta and Malcolm Heywood and Mengjie Zhang and Leonardo Trujillo and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Fuyuki Ishikawa and Inmaculada Medina-Bulo and Frank Neumann and Andrew M. Sutton", pages = "257--258", address = "internet", series = "GECCO '21", month = jul # " 10-14", organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, ANN, Recurrent Neural Network, Neuro-Encoded Expression Programming: Poster", isbn13 = "978-1-4503-8351-6", DOI = "doi:10.1145/3449726.3459498", size = "2 pages", abstract = "Neuro-Encoded Expression Programming (NEEP) implements the continuous coding for the discrete solution through recurrent neural networks (RNNs), and smooths sharpness of the discrete coding.However, the insertion model generating linear coding in NEEP breaks the coherence of linear coding of RNNs, because the resulting symbols tend to be cluttered when RNNs learn the incoherent sequence relationships. Meanwhile, the redundancy phenomenon that different RNNs generate the same code results in that lots of solutions with the same performance exist in the search space, and causes the decrease for search efficiency. To address these problems, the linear-dependent multi-interpretation NEEP (LM-NEEP)is proposed in this research. LM-NEEP tackles the incoherence problem by employing a linear dependence strategy, and the multi-interpretation strategy is adopted to deal with the redundancy problem in search space. The capability of LM-NEEP is estimated on several symbolic regression problems. The experimental results display that the LM-NEEP significantly outperforms NEEP and some classical genetic programming methods.", notes = "University of Jinan, China GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)", } @Article{Ma:2007:ca, author = "Xiaoli Ma and Guifa Teng and Mengjie Zhang", title = "Carbon Potential Using Genetic Programming", journal = "Control and Automation", year = "2007", volume = "23", number = "9", pages = "239--241", keywords = "genetic algorithms, genetic programming, carbon potential, symbolic regression, Correlation forecasting", ISSN = "1008-0570", broken = "doggy http://c.wanfangdata.com.cn/periodical/wjsjxx/2007-9.aspx", abstract = "This article describes the principle and technology of genetic programming. We propose a new approach to the use of genetic programming for Carbon potential problems. This approach does not rely on the problem domain, and does not need data preprocessing either. It can be used as a general method of solving related problems. This approach is of high-accuracy, and low-cost, and is suitable for online testing and controlling of Carbon Potential. In addition, it can produce visible function expressions, and deal with complex nonlinear problems.", notes = " College of Information Science and Technology Agricultural University of Hebei, 071001, China; School of Mathematics, Statistics and Computer Science, Victoria University of Wellington, New Zealand", } @InProceedings{Ma:2023:ICUS, author = "Xinyu Ma and Hai Zhu and Xiaozhou Zhu and Wen Yao", booktitle = "2023 IEEE International Conference on Unmanned Systems (ICUS)", title = "Evolving Behavior Trees for Self-Adaptive Source Searching in Indoor Environments", year = "2023", pages = "1003--1008", abstract = "This paper presents a self-adaptive source searching approach using evolved Behaviour Trees (BTs). Robots often face challenges in making comprehensive decisions during source searching due to difficulties in integrating sensor information and adjusting policies based on varying states and events. Behaviour Trees offer a modular framework for combining perceptions with corresponding actions, enabling robots to make informed decisions across different source searching stages. The proposed approach incorporates strategies from reactive methods and Infotaxis into BT nodes, with the source searching policies evolved through Genetic Programming. To enhance evolution, we introduce a substitution-based synthesis method for initial BTs and a parallel execution approach for fitness evaluation. Experimental results demonstrate that the proposed method outperforms Infotaxis with a 21.8percent increase in success rate and a 35.9percent decrease in average steps, and outperforms the reactive strategies with a 77.7percent increase in success rate and a 46.3percent decrease in average steps. While our method may exhibit lower performance compared to standard Infotaxis in certain wind conditions, it effectively reduces computational complexity without significant performance degradation.", keywords = "genetic algorithms, genetic programming, Degradation, Solid modelling, Robot sensing systems, Data models, Stability analysis, Behavioural sciences, source searching, Behaviour Trees, Infotaxis", DOI = "doi:10.1109/ICUS58632.2023.10318502", ISSN = "2771-7372", month = oct, notes = "Also known as \cite{10318502}", } @InProceedings{Ma:2024:ICNC-FSKD, author = "Yan Ma and Changsheng Zhang and Yan Gao and Ying Guo", title = "A Method Based on Genetic Programming to Automatically Construct Factors for Annual Report Scenarios", booktitle = "2024 20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)", year = "2024", month = jul, keywords = "genetic algorithms, genetic programming, Industries, Prediction algorithms, Knowledge discovery, Boosting, Decision trees, Stock markets, Random forests, Fuzzy systems", DOI = "doi:10.1109/ICNC-FSKD64080.2024.10702271", abstract = "Factors have always played an important role in stock analysis, but they are only effective for specific problems in specific scenarios. Therefore, constructing factors timely and quickly for different scenarios is an urgent problem to be solved. Although some experts have constructed factors, they need to manually construct factors for each scenario, and the construction process consumes time and effort. The annual report is an important and common scenario. It is an important form of information disclosure and financial reporting. Therefore, this paper proposes a method for automatically constructing factors based on genetic programming that combines expert experience for this scenario. It incorporates the knowledge and insights of experts in the field of stock analysis into the process of automati-cally constructing factors, and continuously adjusts and improves the combination of factors through genetic programming to adapt to the needs of the scenario. The effectiveness of this method is verified through empirical analysis and its advantages in specific scenarios are demonstrated.", notes = "Also known as \cite{10702271}", } @Article{ma:2024:JoH, author = "Yikai Ma and Wenjuan Zhang and Juergen Branke", title = "Genetic programming hyper-heuristic for evolving a maintenance policy for wind farms", journal = "Journal of Heuristics", year = "2024", keywords = "genetic algorithms, genetic programming", URL = "https://link.springer.com/article/10.1007/s10732-024-09533-2", DOI = "doi:10.1007/s10732-024-09533-2", } @PhdThesis{Mabrouk:thesis, author = "Emad H. A. Mabrouk", title = "Meta-heuristics Programming and its Applications", school = "Kyoto University", year = "2011", type = "Doctor of Informatics", address = "Kyoto 606-8501, Japan", month = jan, keywords = "genetic algorithms, genetic programming", URL = "http://www-optima.amp.i.kyoto-u.ac.jp/result/doctor.html", URL = "http://www-optima.amp.i.kyoto-u.ac.jp/result/doctordoc/Emad_Thesis.pdf", size = "110 pages", abstract = "The importance of Artificial Intelligence (AI) increases rapidly, and its implementations in real-life applications are widely spread.", } @Article{Mabrouk:2011:ALR, author = "Emad Mabrouk and Julio Cesar Hernandez-Castro and Masao Fukushima", title = "Prime number generation using memetic programming", journal = "Artificial Life and Robotics", year = "2011", volume = "16", number = "1", pages = "53--56", publisher = "Springer", language = "English", keywords = "genetic algorithms, genetic programming, Hybrid evolutionary algorithm, Iterated local search, Memetic programming, Prime number", ISSN = "1433-5298", DOI = "doi:10.1007/s10015-011-0890-3", size = "4 pages", abstract = "For centuries, the study of prime numbers has been regarded as a subject of pure mathematics in number theory. Recently, this vision has changed and the importance of prime numbers has increased rapidly, especially in information technology, e.g., public key cryptography algorithms, hash tables, and pseudo-random number generators. One of the most popular topics to attract attention is to find a formula that maps the set of natural numbers into the set of prime numbers. However, to date there is no known formula that produces all primes. In this article, we use a hybrid evolutionary algorithm, called the memetic programming (MP) algorithm, to generate mathematical formulae that produce distinct primes. Using the MP algorithm, we succeeded in discovering an interesting set of formulas that produce sets of distinct primes.", notes = "This work was presented in part at the 16th International Symposium on Artificial Life and Robotics, Oita, Japan, January 27-29, 2011", } @PhdThesis{Fatma_Abdalla_Mabrouk_thesis, author = "Fatma Abdalla Mabrouk", title = "Database Hiding in Tag Web Using Steganography by Genetic Algorithm", school = "The National Ribat University", year = "2017", address = "Sudan", note = "1438-2017", keywords = "genetic algorithms, genetic programming", URL = "http://repository.ribat.edu.sd/showRepositoryDetails/459", URL = "http://repository.ribat.edu.sd/public/uploads/upload/repository/database%20hiding%20in%20tag%20web%20using%20steganography%20by_1905996734.pdf", size = "152 pages", abstract = "The main goal of this research is to study steganography technique by GA and to design new system known as (SteganoTag), it is one of the new methods of steganography information through hiding database within the saved web pages by using genetic algorithm, without changing the page size, to increase the reliability and confidentiality of the data base system. A sample of database that is designed in XML language has been selected. The main sector of this research is the application of genetic algorithm as a method of data security, it is applied in the field of evolutionary programming in artificial intelligence, as the experimental method is used in the analysis of different types of home pages, these proposed technical HTML tags and its attributes are applied to hide illogical database using the genetic algorithm. This proposed technique considers labels as genes and characteristics as chromosomes. Then the architecture has been detailed and the implementation of the proposed system of hiding information using the software program C sharp and the system has been simulated using different scenarios and a variety of data. The good side of using steganography with a genetic algorithm has been clarified. Finally, the most important findings in this research is that the combination between the science of genetic algorithm and steganography raising the efficiency of the process of masking data in a web page without changing its parameters, and the encryption algorithm enhances the complexity of illegal attempts of steganography removal. Genetic algorithm has the ability to achieve a significant improvement in data security following the same methodology, this collection can be extended to involve the development of other security systems to get safer and reliable systems of database hiding. The high flexibility of HTML can be applied in many other techniques, other non-public languages can be used in the process of database hiding and exploitation of the Internet protocols, and the development of this method by introducing developmental algorithms to increase the efficiency of data hiding. The development of e-mail data process can also be hidden.", notes = "GPdotNET v3 software Supervisor: Mudawi Mukhtar Elmusharaf", } @Article{mabrouk:2022:Electronics, author = "Emad Mabrouk and Yara Raslan and Abdel-Rahman Hedar", title = "Immune System Programming: A Machine Learning Approach Based on Artificial Immune Systems Enhanced by Local Search", journal = "Electronics", year = "2022", volume = "11", number = "7", pages = "Article No. 982", keywords = "genetic algorithms, genetic programming", ISSN = "2079-9292", URL = "https://www.mdpi.com/2079-9292/11/7/982", DOI = "doi:10.3390/electronics11070982", abstract = "The foundation of machine learning is to enable computers to automatically solve certain problems. One of the main tools for achieving this goal is genetic programming (GP), which was developed from the genetic algorithm to expand its scope in machine learning. Although many studies have been conducted on GP, there are many questions about the disruption effect of the main GP breeding operators, i.e., crossover and mutation. Moreover, this method often suffers from high computational costs when implemented in some complex applications. This paper presents the meta-heuristics programming framework to create new practical machine learning tools alternative to the GP method. Furthermore, the immune system programming with local search (ISPLS) algorithm is composed from the proposed framework to enhance the classical artificial immune system algorithm with the tree data structure to deal with machine learning applications. The ISPLS method uses a set of breeding procedures over a tree space with gradual changes in order to surmount the defects of GP, especially the high disruptions of its basic operations. The efficiency of the proposed ISPLS method was proven through several numerical experiments, including promising results for symbolic regression, 6-bit multiplexer and 3-bit even-parity problems.", notes = "also known as \cite{electronics11070982}", } @InProceedings{mabu:2002:olognp, author = "Shingo Mabu and Kotaro Hirasawa and Jinglu Hu and Junichi Murata", title = "Online learning of Genetic Network Programming (GNP)", booktitle = "Proceedings of the 2002 Congress on Evolutionary Computation CEC2002", editor = "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and Mark Shackleton", pages = "321--326", year = "2002", publisher = "IEEE Press", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", organisation = "IEEE Neural Network Council (NNC), Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", ISBN = "0-7803-7278-6", month = "12-17 " # may, notes = "CEC 2002 - A joint meeting of the IEEE, the Evolutionary Programming Society, and the IEE. Held in connection with the World Congress on Computational Intelligence (WCCI 2002)", keywords = "genetic algorithms, genetic programming, Q learning, dynamic environments, evolutionary computation method, genetic network programming, online learning, state transition rules, learning (artificial intelligence)", DOI = "doi:10.1109/CEC.2002.1006254", abstract = "A new evolutionary computation method named Genetic Network Programming (GNP) was proposed recently. In this paper, an online learning method for GNP is proposed. This method uses Q learning to improve its state transition rules so that it can make GNP adapt to the dynamic environments efficiently.", } @InProceedings{Mabu:2003:Gnpwlaefatde, author = "Shingo Mabu and Kotaro Hirasawa and Jinglu Hu", title = "Genetic network programming with learning and evolution for adapting to dynamical environments", booktitle = "Proceedings of the 2003 Congress on Evolutionary Computation CEC2003", editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon", pages = "69--76", year = "2003", publisher = "IEEE Press", address = "Canberra", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "8-12 " # dec, organisation = "IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", ISBN = "0-7803-7804-0", keywords = "genetic algorithms, genetic programming, genetic network programming, Decision making, Dynamic programming, Economic indicators, Evolutionary computation, Learning systems, Optimisation methods, Tree data structures, Tree graphs, learning (artificial intelligence), search problems, dynamical environments, evolutionary algorithm, learning algorithm, network structures, search ability, wide solution space,", DOI = "doi:10.1109/CEC.2003.1299558", abstract = "A new evolutionary algorithm named genetic network programming, GNP has been proposed. GNP represents its solutions as network structures, which can improve the expression and search ability. Since GA, GP, and GNP already proposed are based on evolution and they cannot change their solutions until one generation ends, we propose GNP with learning and evolution in order to adapt to a dynamical environment quickly. Learning algorithm improves search speed for solutions and evolutionary algorithm enables GNP to search wide solution space efficiently.", notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.", } @InProceedings{mabu:2004:lbp, author = "Shingo Mabu and Kotaro Hirasawa and Jinglu Hu", title = "Genetic Network Programming with Reinforcement Learning and its Performance Evaluation", booktitle = "Late Breaking Papers at the 2004 Genetic and Evolutionary Computation Conference", year = "2004", editor = "Maarten Keijzer", address = "Seattle, Washington, USA", month = "26 " # jul, keywords = "genetic algorithms, genetic programming, GNP", URL = "http://gpbib.cs.ucl.ac.uk/gecco2004/LBP036.pdf", abstract = "A new graph-based evolutionary algorithm named 'Genetic Network Programming, GNP' has been proposed. GNP represents its solutions as graph structures, which can improve the expression ability and performance. Since GA, GP and GNP already proposed are based on evolution and they cannot change their solutions until one generation ends, we propose GNP with Reinforcement Learning (GNP with RL) in this paper in order to search solutions quickly. Evolutionary algorithm of GNP makes very compact graph structure which contributes to reducing the size of the Q-table and saving memory. Reinforcement Learning of GNP improves search speed for solutions because it can use the information obtained during task execution.", notes = "Part of \cite{keijzer:2004:GECCO:lbp}", } @InProceedings{Mabu:2006:CECsarsa, author = "Shingo Mabu and Hiroyuki Hatakeyama and Kotaro Hirasawa and Jinglu Hu", title = "Genetic Network Programming with Reinforcement Learning Using Sarsa Algorithm", booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary Computation", year = "2006", editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas", pages = "1570--1576", address = "Vancouver", month = "16-21 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Genetic Network Programming", ISBN = "0-7803-9487-9", DOI = "doi:10.1109/CEC.2006.1688346", size = "7 pages", abstract = "A new graph-based evolutionary algorithm called Genetic Network Programming (GNP) has been proposed. The solutions of GNP are represented as graph structures, which can improve the expression ability and performance. In addition, GNP with Reinforcement Learning (GNP-RL) has been proposed to search for solutions efficiently. GNP-RL can use current information and change its programs during task execution, i.e., online learning. Thus, it has an advantage over evolution-based algorithms in case much information can be obtained during task execution. GNP-RL has a special state-action space and it contributes to reducing the size of the Qtable and learning efficiently. The proposed method is applied to the controller of Khepera simulator and its performance is evaluated.", notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D", } @InProceedings{1277398, author = "Shingo Mabu and Yan Chen and Kotaro Hirasawa and Jinglu Hu", title = "Genetic network programming with actor-critic and its application to stock trading model", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "2263--2263", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p2263.pdf", DOI = "doi:10.1145/1276958.1277398", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Real-World Applications: Poster, reinforcement learning, stock trading model, technical index", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @InProceedings{Mabu:2008:gecco, author = "Shingo Mabu and Yan Chen and Etsushi Ohkawa and Kotaro Hirasawa", title = "Stock trading strategies by genetic network programming with flag nodes", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "1709--1710", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1709.pdf", DOI = "doi:10.1145/1389095.1389421", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, decision making, stock trading model, technical analysis, Real-World application: Poster", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389421}", } @InProceedings{Mabu:2007:cec, author = "Shingo Mabu and Yan Chen and Kotaro Hirasawa and Jinglu Hu", title = "Stock Trading Rules Using Genetic Network Programming with Actor-Critic", booktitle = "2007 IEEE Congress on Evolutionary Computation", year = "2007", editor = "Dipti Srinivasan and Lipo Wang", pages = "508--515", address = "Singapore", month = "25-28 " # sep, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "1-4244-1340-0", file = "1684.pdf", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2007.4424513", abstract = "Genetic Network Programming (GNP) is an evolutionary computation which represents its solutions using graph structures. Since GNP can create quite compact programs and has an implicit memory function, it has been clarified that GNP works well especially in dynamic environments. In this paper, GNP is applied to creating a stock trading model. The first important point is to combine GNP with Actor-Critic which is one of the reinforcement learning algorithms. Evolution-based methods evolve their programs after task execution because they must calculate fitness values, while reinforcement learning can change programs during task execution, therefore the programs can be created efficiently. The second important point is that GNP with Actor-Critic (GNP-AC) can select appropriate technical indexes to judge the buying and selling timing of stocks using Importance Index especially designed for stock trading decision making. In the simulations, the trading model is trained using the stock prices of 20 brands in 2001, 2002 and 2003. Then the generalisation ability is tested using the stock prices in 2004. From the simulation results, it is clarified that the trading rules of GNP-AC obtain higher profits than Buy and Hold method.", notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C", } @InProceedings{Mabu:2009:ieeeSMC, author = "Shingo Mabu and Kotaro Hirasawa", title = "Evolving plural programs by genetic network programming with multi-start nodes", booktitle = "IEEE International Conference on Systems, Man and Cybernetics, SMC 2009", year = "2009", month = oct, pages = "1382--1387", keywords = "genetic algorithms, genetic programming, automatic program generation, directed graph structures, even-n-parity problem, evolutionary computation, genetic network programming, graph-based evolutionary algorithm, mirror symmetry, multistart nodes, performance evaluation, plural programs, automatic programming, directed graphs", DOI = "doi:10.1109/ICSMC.2009.5346275", ISSN = "1062-922X", address = "San Antonio, TX, USA", isbn13 = "978-1-4244-2793-2", abstract = "Automatic program generation is one of the applicable fields of evolutionary computation, and genetic programming (GP) is the typical method for this field. On the other hand, genetic network programming (GNP) has been proposed as an extended algorithm of GP in terms of gene structures. GNP is a graph-based evolutionary algorithm and applied to automatic program generation in this paper. GNP has directed graph structures which have some features inherently such as re-usability of nodes and the fixed number of nodes. These features contribute to creating complicated programs with compact program structures. In this paper, the extended algorithm of GNP is proposed, which can create plural programs simultaneously in one individual by using multi-start nodes. In addition, GNP can evolve the programs in one individual considering the fitness and also its standard deviation in order to evolve the plural programs efficiently. In the simulations, even-n-parity problem and mirror symmetry problem are used for the performance evaluation, and the results show that the proposed method outperforms the original GNP.", notes = "INSPEC Accession Number: 11004402 Also known as \cite{5346275}", } @Article{Mabu:2011:ieeeSMC, author = "Shingo Mabu and Ci Chen and Nannan Lu and Kaoru Shimada and Kotaro Hirasawa", title = "An Intrusion-Detection Model Based on Fuzzy Class-Association-Rule Mining Using Genetic Network Programming", journal = "IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews", year = "2011", month = jan, volume = "41", number = "1", pages = "130--139", abstract = "As the Internet services spread all over the world, many kinds and a large number of security threats are increasing. Therefore, intrusion detection systems, which can effectively detect intrusion accesses, have attracted attention. This paper describes a novel fuzzy class-association-rule mining method based on genetic network programming (GNP) for detecting network intrusions. GNP is an evolutionary optimisation technique, which uses directed graph structures instead of strings in genetic algorithm or trees in genetic programming, which leads to enhancing the representation ability with compact programs derived from the reusability of nodes in a graph structure. By combining fuzzy set theory with GNP, the proposed method can deal with the mixed database that contains both discrete and continuous attributes and also extract many important class-association rules that contribute to enhancing detection ability. Therefore, the proposed method can be flexibly applied to both misuse and anomaly detection in network-intrusion-detection problems. Experimental results with KDD99Cup and DARPA98 databases from MIT Lincoln Laboratory show that the proposed method provides competitively high detection rates compared with other machine-learning techniques and GNP with crisp data mining.", keywords = "genetic algorithms, genetic programming, directed graph structures, fuzzy class-association-rule mining, fuzzy set theory, genetic network programming, intrusion-detection model, data mining, directed graphs, fuzzy set theory, security of data", DOI = "doi:10.1109/TSMCC.2010.2050685", ISSN = "1094-6977", notes = "Also known as \cite{5499108}", } @Article{Mabu20113618, author = "Shingo Mabu and Kotaro Hirasawa", title = "Efficient program generation by evolving graph structures with multi-start nodes", journal = "Applied Soft Computing", volume = "11", number = "4", pages = "3618--3624", year = "2011", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2011.01.033", URL = "http://www.sciencedirect.com/science/article/B6W86-5230PMW-2/2/83938061ebc19cc5a8ad1b3aa41d96c3", keywords = "genetic algorithms, genetic programming, Evolutionary computation, Program generation, Graph structure, Even-n-Parity problem, Mirror Symmetry problem", abstract = "Automatic program generation is one of the applicable fields of evolutionary computation, and Genetic Programming (GP) is the typical method for this field. On the other hand, Genetic Network Programming (GNP) has been proposed as an extended algorithm of GP in terms of gene structures. GNP is a graph-based evolutionary algorithm and applied to automatic program generation in this paper. GNP has directed graph structures which have some features inherently, for example, re-usability of nodes and the small number of nodes. These features contribute to creating complicated programs with compact structures and never cause bloat. In this paper, the extended algorithm of GNP is proposed, which can create plural programs simultaneously in one individual by using multi-start nodes. In addition, GNP can evolve the programs in one individual considering the fitness and also its standard deviation in order to evolve the plural programs efficiently. In the simulations, Even-n-Parity problem and Mirror Symmetry problem are used for the performance evaluation, and the results show that the proposed method outperforms the standard GNP with single start node.", } @InProceedings{Mabu:2013:CEC, article_id = "1137", author = "Shingo Mabu and Kotaro Hirasawa and Masanao Obayashi and Takashi Kuremoto", title = "A Variable Size Mechanism of Distributed Graph Programs for Creating Agent Behaviors", booktitle = "2013 IEEE Conference on Evolutionary Computation", volume = "1", year = "2013", month = jun # " 20-23", editor = "Luis Gerardo {de la Fraga}", pages = "1756--1762", address = "Cancun, Mexico", keywords = "genetic algorithms, genetic programming, GNP", isbn13 = "978-1-4799-0453-2", DOI = "doi:10.1109/CEC.2013.6557773", size = "7 pages", abstract = "Genetic Algorithm (GA) and Genetic Programming (GP) are typical evolutionary algorithms using string and tree structures, respectively, and there have been many studies on the extension of GA and GP. How to represent solutions, e.g., strings, trees, graphs, etc., is one of the important research topics and Genetic Network Programming (GNP) has been proposed as one of the graph-based evolutionary algorithms. GNP represents its solutions using directed graph structures and has been applied to many applications. However, when GNP is applied to complex real world systems, large size of the programs is needed to represent various kinds of control rules. In this case, the efficiency of evolution and the performance of the systems may decrease due to its huge structures. Therefore, distributed GNP has been studied based on the idea of divide and conquer, where the programs are divided into several subprograms and they cooperatively control whole tasks. However, because the previous work divided a program into some subprograms with the same size, it cannot adjust the sizes of the subprograms depending on the problems. Therefore, in this paper, an efficient evolutionary algorithm of variable size distributed GNP is proposed and its performance is evaluated by the tileworld problem that is one of the benchmark problems of multiagent systems in dynamic environments.", notes = "CEC 2013 - A joint meeting of the IEEE, the EPS and the IET.", } @Article{Mabu:2014:ESA, author = "Shingo Mabu and Kotaro Hirasawa and Masanao Obayashi and Takashi Kuremoto", title = "A variable size mechanism of distributed graph programs and its performance evaluation in agent control problems", journal = "Expert Systems with Applications", volume = "41", number = "4, Part 2", pages = "1663--1671", year = "2014", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2013.08.063", URL = "http://www.sciencedirect.com/science/article/pii/S0957417413006842", keywords = "genetic algorithms, genetic programming, Evolutionary computation, Directed graph, Distributed structure, Variable size, Reinforcement learning, Decision making", } @TechReport{maccallum:2002:perlssp, author = "Robert M. MacCallum", title = "Evolving Perl code for protein secondary structure prediction", institution = "Stockholm Bioinformatics Center, Stockholm University", year = "2002", note = "Presented at the PPSN 2002 workshop entitled: Evolutionary and Neural Computation in the BioSciences", keywords = "genetic algorithms, genetic programming, grammar, regular expressions, scan, strict typing", URL = "http://www.sbc.su.se/~maccallr/publications/maccallum_ppsn02_workshop.pdf", URL = "http://www.sbc.su.se/~maccallr/publications/maccallum_ppsn02_talk.pdf", notes = "Extended abstract: http://www.sbc.su.se/~maccallr/publications/maccallum_ppsn02_workshop.pdf ; Workshop presentation : http://www.sbc.su.se/~maccallr/publications/maccallum_ppsn02_talk.pdf mimicking homologous crossover. Soft-max penalty on tree size and execution time, prosite, pop=2000, 60hours", } @InProceedings{maccallum03, author = "Robert M. MacCallum", title = "Introducing a Perl Genetic Programming System: and Can Meta-evolution Solve the Bloat Problem?", booktitle = "Genetic Programming, Proceedings of EuroGP'2003", year = "2003", editor = "Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa", volume = "2610", series = "LNCS", pages = "364--373", address = "Essex", publisher_address = "Berlin", month = "14-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-00971-X", URL = "http://www.sbc.su.se/~maccallr/publications/perlgp_eurogp2003.pdf", DOI = "doi:10.1007/3-540-36599-0_34", abstract = "An open source Perl package for genetic programming, called PerlGP, is presented. The supplied algorithm is strongly typed tree-based GP with homologous crossover. User-defined grammars allow any valid Perl to be evolved, including object oriented code and parameters of the PerlGP system itself. Time trials indicate that PerlGP is around 10 times slower than a C based system on a numerical problem, but this is compensated by the speed and ease of implementing new problems, particularly string-based ones. The effect of per-node, fixed and self-adapting crossover and mutation rates on code growth and fitness is studied. On a pi estimation problem, self-adapting rates give both optimal and compact solutions. The source code and manual can be found at http://perlgp.org. (Broken Sep 2019) See \cite{maccallum:2003:perlgp}", notes = "EuroGP'2003 held in conjunction with EvoWorkshops 2003", } @Manual{maccallum:2003:perlgp, title = "PerlGP - The Manual", author = "Robert M. MacCallum", year = "2003", address = "106 91 Stockholm, Sweden", month = "3 " # feb, organisation = "Stockholm Bioinformatics Center, Stockholm University", note = "never came out in print", keywords = "genetic algorithms, genetic programming, perl", URL = "http://perlgp.org/docs/manual/manual.pdf", broken = "http://perlgp.org/docs/manual/manual/manual.html", size = "69 pages", notes = "see \cite{maccallum03}", } @Article{MacCallum:2004:BI, author = "Robert M. MacCallum", title = "Striped sheets and protein contact prediction", journal = "Bioinformatics", year = "2004", volume = "20", number = "Suppl 1", pages = "I224--I231", month = aug # " 4", keywords = "genetic algorithms, genetic programming, SOM", ISSN = "1460-2059", URL = "http://bioinformatics.oxfordjournals.org/cgi/reprint/20/suppl_1/i224.pdf", DOI = "doi:10.1093/bioinformatics/bth913", size = "8 pages", abstract = "MOTIVATION: Current approaches to contact map prediction in proteins have focused on amino acid conservation and patterns of mutation at sequentially distant positions. This sequence information is poorly understood and very little progress has been made in this area during recent years. RESULTS: In this study, an observation of 'striped' sequence patterns across beta-sheets prompted the development of a new type of contact map predictor. Computer program code was evolved with an evolutionary algorithm (genetic programming) to select residues and residue pairs likely to make contacts based solely on local sequence patterns extracted with the help of self-organising maps. The mean prediction accuracy is 27percent on a validation set of 156 domains up to 400 residues in length, where contacts are separated by at least 8 residues and length/10 pairs are predicted. The retrospective accuracy on a set of 15 CASP5 targets is 27percent and 14percent for length/10 and length/2 predicted pairs, respectively (both using a minimum residue separation of 24). This compares favourably to the equivalent 21percent and 13percent obtained for the best automated contact prediction methods at CASP5. The results suggest that protein architectures impose regularities in local sequence environments. Other sources of information, such as correlated/compensatory mutations, may further improve accuracy. AVAILABILITY: A web-based prediction service is available at http://www.sbc.su.se/~maccallr/contactmaps", notes = "PMID: 15262803 [PubMed - in process] cited by \cite{Latek:2008:BMCsb}", } @Article{MacCallum:2012:PNAS, author = "Robert M. MacCallum and Matthias Mauch and Austin Burt and Armand M. Leroi", title = "Evolution of music by public choice", journal = "Proceedings of the National Academy of Sciences", year = "2012", volume = "109", number = "30", pages = "12081--12086", month = jul # " 24", keywords = "genetic algorithms, genetic programming, interactive evolution, culture, algorithm", DOI = "doi:10.1073/pnas.1203182109", size = "6 pages", abstract = "Music evolves as composers, performers, and consumers favour some musical variants over others. To investigate the role of consumer selection, we constructed a Darwinian music engine consisting of a population of short audio loops that sexually reproduce and mutate. This population evolved for 2513 generations under the selective influence of 6931 consumers who rated the loop aesthetic qualities. We found that the loops quickly evolved into music attributable, in part, to the evolution of aesthetically pleasing chords and rhythms. Later, however, evolution slowed. Applying the Price equation \cite{price:nature}, a general description of evolutionary processes, we found that this stasis was mostly attributable to a decrease in the fidelity of [genetic] transmission. Our experiment shows how cultural dynamics can be explained in terms of competing evolutionary forces.", notes = "open access. pop 100. Overlapping generations. web www internet user interface. Supplementary document, p14, Figure S2 gives context free grammar defining the general structure of [GP] tree-like genome, STGP?? Analysis Darwin Tunes http://darwintunes.org/participate Survival of the funkiest Broken March 2021 http://tech.groups.yahoo.com/group/genetic_programming/message/5878", } @InProceedings{Maceda:2018:jGDR, author = "Guy Yoslan {Cornejo Maceda} and Bernd R. Noack and Francois Lusseyran and Marek Morzynski and Luc Pastur and Nan Deng", title = "Artificial intelligence control applied to drag reduction of the fluidic pinball", booktitle = "Journees du GDR Controle Des Decollements", year = "2018", address = "Toulouse, France", publisher = "HAL CCSD", month = nov # "~01", keywords = "genetic algorithms, genetic programming, AI, artificial intelligence control, fluidic pinball, control, machine learning, physics, mechanics of the fluids", type = "info:eu-repo/semantics/conferenceObject", URL = "https://hal.archives-ouvertes.fr/hal-02387548", abstract = "Feedback turbulence control is at the core of engineering challenges and have to face high-dimensionality, time-delays, strong nonlinearities and frequency crosstalks, making modelling and linear control theory impractical.The aim of this project is a general, model-free, self-learning control strategy to tame/stabilize nonlinear dynamics and real world turbulence in the plant.The control problem is solved as a regression problem thanks to machine learning control (MLC) (Duriez et al. 2016 Springer).MLC is based on genetic programming (GP), it is a biological inspired method that mimickes the Darwinian process of natural selection to learn the control.Focus of current efforts is to understand the learning process, to reduce this learning time and to include real-world imperfections.Our genetic programming control has been demonstrated on a direct numerical simulation of the fluidic pinball taken as a drag reduction benchmark.It consists of three cylinders in a two-dimensional flow where the actuators are the spinning cylinders and feedback is provided by sensors downstream.Despite the simple configuration, the fluidic pinball shares characteristics with real flows such a bifurcations, nonlinear frequency crosstalk and multiple input multiple output (MIMO) control.We carried out a parameter optimisation study on GP and managed to reduce the learning rate by a factor 5 by avoiding the evaluation of redundant control laws.After 1000 evaluations, GP managed to find a non-trivial solution comprising two distinct actuation mechanisms : boat-tailing (open-loop) and phasor control (closed-loop) reducing even more the net drag power (46percent) than the best boat-tailing configuration (43percent).", annote = "Laboratoire d'Informatique pour la Mecanique et les Sciences de l'Ingenieur (LIMSI) ; Universite Paris Saclay (COmUE)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Universite - UFR d'Ingenierie (UFR 919) ; Sorbonne Universite (SU)-Sorbonne Universite (SU)-Universite Paris-Saclay-Universite Paris-Sud - Paris 11 (UP11)", bibsource = "OAI-PMH server at api.archives-ouvertes.fr", contributor = "Laboratoire d'Informatique pour la Mecanique et les Sciences de l'Ingenieur", coverage = "Toulouse, France", identifier = "hal-02387548", language = "en", oai = "oai:HAL:hal-02387548v1", } @InProceedings{Maceda:2018:EFMC, author = "Guy Yoslan {Cornejo Maceda} and Bernd R. Noack and Francois Lusseyran and Marek Morzynski and Luc Pastur and Nan Deng", title = "Taming the fluidic pinball with artificial intelligence control", booktitle = "European Fluid Mechanics Conference", year = "2018", address = "Vienne, Austria", publisher = "HAL CCSD", month = sep # "~01", keywords = "genetic algorithms, genetic programming, AI, artificial intelligence control, fluidic pinball, control, machine learning, drag reduction, physics, mechanics, mechanics of the fluids", type = "info:eu-repo/semantics/conferenceObject", URL = "https://hal.archives-ouvertes.fr/hal-02387544", abstract = "The aim of this work is to develop a generic control strategy for nonlinear dynamics. This strategy is based on genetic programming, a machine learning technique for regression problems, that maps the sensor signals to the actuators in a unsupervised manner.It's a biological inspired method mimicking Darwin's natural selection: through and evolution process it derives a control law minimising a given objective.Genetic programming has been applied to a DNS of a 2D fluidic mechanic system, the fluidic pinball.Several search spaces including control laws built from periodic functions, sensor signals and time-delay sensor signals have been explored.For the fluidic pinball genetic programming managed a 46percent net drag saving, outperforming by 3.3percent the best open-loop control law found with a parametric study.Our contribution has been the acceleration of the learning process by avoiding the evaluations of redundant control laws, thus improving the learning rate by a factor 3.", annote = "Laboratoire d'Informatique pour la Mecanique et les Sciences de l'Ingenieur (LIMSI) ; Universite Paris Saclay (COmUE)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Universite - UFR d'Ingenierie (UFR 919) ; Sorbonne Universite (SU)-Sorbonne Universite (SU)-Universite Paris-Saclay-Universite Paris-Sud - Paris 11 (UP11)", bibsource = "OAI-PMH server at api.archives-ouvertes.fr", contributor = "Laboratoire d'Informatique pour la Mecanique et les Sciences de l'Ingenieur", coverage = "Vienne, Austria", description = "International audience", identifier = "hal-02387544", language = "en", oai = "oai:HAL:hal-02387544v1", } @Article{Maceda:2019:PAMM, author = "Guy Yoslan {Cornejo Maceda} and Bernd R. Noack and Francois Lusseyran and Marek Morzynski and Nan Deng and Luc Pastur", title = "Artificial intelligence control applied to drag reduction of the fluidic pinball", year = "2019", journal = "Proceedings in Applied Mathematics and Mechanics", volume = "19", number = "1", pages = "e201900268", month = nov, note = "Special Issue:90th Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM)", keywords = "genetic algorithms, genetic programming, artificial intelligence control, fluidic pinball, control, machine learning, physics, mechanics, mechanics of the fluids", ISSN = "1617-7061", publisher = "HAL CCSD; John Wiley \& Sons, Inc.", URL = "https://hal.archives-ouvertes.fr/hal-02387482", DOI = "doi:10.1002/pamm.201900268", abstract = "The aim of our work is to advance a self-learning, model-free control method to tame complex nonlinear flows---building on the pioneering work of Dracopoulous. The cornerstone is the formulation of the control problem as a function optimisation problem. The control law is derived by solving a nonsmooth optimisation problem thanks to an artificial intelligence technique, genetic programming (GP). Metaparameter optimisation of the algorithm and complexity penalization have been our main contribution and have been tested on a cluster of three equidistant cylinders immersed in a incoming flow, the fluidic pinball. The means of control is the independent rotation of the cylinders. GP derived a control law associated to each cylinder in order to minimise the net drag power and managed to outperform past open-loop studies with a 46.0 percent net drag power reduction by combining two strategies from literature. This success of MIMO control including sensor history is promising for exploring even more complex dynamics.", ISSN = "1617-7061", annote = "Laboratoire d'Informatique pour la Mecanique et les Sciences de l'Ingenieur (LIMSI) ; Universite Paris Saclay (COmUE)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Universite - UFR d'Ingenierie (UFR 919) ; Sorbonne Universite (SU)-Sorbonne Universite (SU)-Universite Paris-Saclay-Universite Paris-Sud - Paris 11 (UP11)", bibsource = "OAI-PMH server at api.archives-ouvertes.fr", contributor = "Laboratoire d'Informatique pour la Mecanique et les Sciences de l'Ingenieur", description = "International audience", identifier = "hal-02387482", language = "en", oai = "oai:HAL:hal-02387482v1", } @Article{Maceda:2019:GAMM, author = "Guy {Cornejo Maceda} and Bernd Noack and Francois Lusseyran and Nan Deng and Luc Pastur and Marek Morzynski", title = "Artificial intelligence control applied to drag reduction of the fluidic pinball", journal = "Proceedings in Applied Mathematics and Mechanics", year = "2019", pages = "e201900268", month = nov, note = "Special issue: 90th Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM)", keywords = "genetic algorithms, genetic programming, computer science, systems and control, artificial intelligence, AI, engineering sciences, mechanics, fluids mechanics", publisher = "HAL CCSD; Wiley-VCH Verlag", URL = "https://hal.archives-ouvertes.fr/hal-02398649", URL = "https://hal.archives-ouvertes.fr/hal-02398649/file/2019_PAMM_CornejoMaceda_SUBMITTED.pdf", DOI = "doi:10.1002/pamm.201900268", ISSN = "1617-7061", abstract = "The aim of our work is to advance a self-learning, model-free control method to tame complex nonlinear flows-building on the pioneering work of Dracopoulous [1]. The cornerstone is the formulation of the control problem as a function optimisation problem. The control law is derived by solving a nonsmooth optimisation problem thanks to an artificial intelligence technique, genetic programming (GP). Metaparameters optimisation of the algorithm and complexity penalization have been our main contribution and have been tested on a cluster of three equidistant cylinders immersed in a incoming flow, the fluidic pinball. The means of control is the independent rotation of the cylinders. GP derived a control law associated to each cylinder in order to minimise the net drag power and managed to outperform past open-loop studies with a 46.0 percent net drag power reduction by combining two strategies from literature. This success of MIMO control including sensor history is promising for exploring even more complex dynamics.", annote = "Laboratoire d'Informatique pour la Mecanique et les Sciences de l'Ingenieur (LIMSI) ; Universite Paris Saclay (COmUE)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Universite - UFR d'Ingenierie (UFR 919) ; Sorbonne Universite (SU)-Sorbonne Universite (SU)-Universite Paris-Saclay-Universite Paris-Sud - Paris 11 (UP11); Poznan University of Technology", bibsource = "OAI-PMH server at api.archives-ouvertes.fr", contributor = "Laboratoire d'Informatique pour la Mecanique et les Sciences de l'Ingenieur", description = "International audience", identifier = "hal-02398649; DOI: 10.1002/pamm.201900268", language = "en", oai = "oai:HAL:hal-02398649v1", relation = "info:eu-repo/semantics/altIdentifier/doi/10.1002/pamm.201900268", rights = "info:eu-repo/semantics/OpenAccess", } @InProceedings{Maceda:2019:jdf, author = "Guy Y. {Cornejo Maceda} and Bernd R. Noack and Francois Lusseyran and Marek Morzynski and Nan Deng and Luc Pastur", title = "Apprentissage automatique de lois de controle d'ecoulement par intelligence artificielle", year = "2019", booktitle = "Journee de Dynamique des Fluides du Plateau de Saclay", address = "Orsay, France", month = jan # "~01", publisher = "HAL CCSD", keywords = "genetic algorithms, genetic programming, machine learning, control, fluidic pinball, AI, artificial intelligence control, physics, mechanics, mechanics of the fluids", type = "info:eu-repo/semantics/conferenceObject", URL = "https://hal.archives-ouvertes.fr/hal-02263719", abstract = "Le controle d'ecoulement est au coeur de nombreux defis en ingenierie tel que la reduction de la trainee pour les vehicules de transport terrestre ou aerien, l'augmentation de la portance en aeronautique, l'amelioration du melange pour les reactions chimiques pour ne citer que quelques exemples.Le controle des ecoulements par retroaction s'appuyant sur la connaissance de l'etat du systeme, ouvre la possiblite de controle robuste pour ces applications (Brunton \& Noack, 2015 Appl. Mech. Rev. 67, 050801).Ce projet vise a la mise en place d'une strategie de controle generale, sans modele et auto-adaptaptive pour stabiliser/controler les systemes non-lineaires et la turbulence dans des applications concretes, aussi nommee {"}machine learning control{"} (MLC) (Duriez et al. 2016 Springer).Parmi les differentes techniques d'intelligence artificiel, nous explorons la programmation genetique.Elle est inspiree de la biologie et mime le processus de selection naturelle Darwinien pour faire emerger empiriquement une loi de controle efficace.Cette approche est appliquee a un systeme de mecanique des fluides, le pinball fluidique, presentant une dynamique riche et permettant un controle MIMO.Remerciement au projet ASTRID-ANR-17- FLOwCON, Controle d'ecoulements turbulents en boucle fermee par apprentissage automatique", annote = "Laboratoire d'Informatique pour la Mecanique et les Sciences de l'Ingenieur (LIMSI) ; Universite Paris Saclay (COmUE)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Universite - UFR d'Ingenierie (UFR 919) ; Sorbonne Universite (SU)-Sorbonne Universite (SU)-Universite Paris-Saclay-Universite Paris-Sud - Paris 11 (UP11)", bibsource = "OAI-PMH server at api.archives-ouvertes.fr", contributor = "Laboratoire d'Informatique pour la Mecanique et les Sciences de l'Ingenieur", identifier = "hal-02263719", language = "en", oai = "oai:HAL:hal-02263719v1", } @InProceedings{conf/evoW/MacedoCM16, author = "Joao Macedo and Ernesto Costa and Lino Marques", title = "Genetic Programming Algorithms for Dynamic Environments", booktitle = "19th European Conference on Applications of Evolutionary Computation, EvoApplications 2016", year = "2016", editor = "Giovanni Squillero and Paolo Burelli", volume = "9598", series = "Lecture Notes in Computer Science", pages = "280--295", address = "Porto, Portugal", month = mar # " 30 -- " # apr # " 1", organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", bibdate = "2016-03-29", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/evoW/evoappl2016-2.html#MacedoCM16", isbn13 = "978-3-319-31153-1", URL = "http://dx.doi.org/10.1007/978-3-319-31153-1", DOI = "doi:10.1007/978-3-319-31153-1_19", abstract = "Evolutionary algorithms are a family of stochastic search heuristics that include Genetic Algorithms (GA) and Genetic Programming (GP). Both GAs and GPs have been successful in many applications, mainly with static scenarios. However, many real world applications involve dynamic environments (DE). Many work has been made to adapt GAs to DEs, but only a few efforts in adapting GPs for this kind of environments. In this paper we present novel GP algorithms for dynamic environments and study their performance using three dynamic benchmark problems, from the areas of Symbolic Regression, Classification and Path Planning. Furthermore, we apply the best algorithm we found in the navigation of an Erratic Robot through a dynamic Santa Fe Ant Trail and compare its performance to the standard GP algorithm. The results, statistically validated, are very promising.", notes = "EvoApplications2016 held inconjunction with EuroGP'2016, EvoCOP2016 and EvoMUSART 2016", } @InProceedings{Macedo:2016:ICARSC, author = "Joao Macedo and Lino Marques and Ernesto Costa", booktitle = "2016 International Conference on Autonomous Robot Systems and Competitions (ICARSC)", title = "Evolving Neural Networks for Multi-robot Odor Search", year = "2016", pages = "288--293", abstract = "The tasks of odour detection, plume tracking and odour source localization constitute an important, yet complex, real world problem. One possible solution for them is based on the use of a group of mobile robots whose controllers have to be defined. Artificial Neural Networks (ANN) have already been used as controllers, but the task of hand defining their topology and parameters can be very challenging and time consuming. In this paper, we propose an approach to evolve, rather than design, ANN-based controllers. Our approach relies on Genetic Programming (GP), a family of stochastic search procedures loosely inspired by the biological principles of Natural Selection and Genetics. We compare our approach with a classic one, inspired by the chemotaxis behaviour of the E. coli bacteria. Our results show that this approach is able to outperform the chemotaxis in the experiments performed.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICARSC.2016.37", month = may, notes = "Also known as \cite{7781991}", } @InProceedings{Macedo:2017:ieeeICARSC, author = "Joao Macedo and Lino Marques and Ernesto Costa", booktitle = "2017 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)", title = "Robotic odour search: Evolving a robot's brain with Genetic Programming", year = "2017", pages = "91--97", month = apr # " 26-28", address = "Coimbra, Portugal", size = "7 pages", abstract = "This paper addresses the problem of controlling a group of mobile robots to track an odour plume to its source. To perform this task in real environments, it is important that the robots are able to adapt to a changing world, and use the experience gained to improve their performance. We address this task with Genetic Programming to evolve the controllers for the robots. Two evolutionary approaches are proposed and compared to a variant of the Silkworm Moth algorithm, that has been modified to take advantage of multi robot systems. The statistically validated results showed that, in the groups of robots where significant differences were found, the evolved controllers were able to find the odour plume faster and converge to its source better than the Silkworm Moth approach.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICARSC.2017.7964058", notes = "Also known as \cite{7964058}", } @InProceedings{Macedo:2018:EuroGP, author = "Joao Macedo and Carlos M. Fonseca and Ernesto Costa", title = "Geometric Crossover in Syntactic Space", booktitle = "EuroGP 2018: Proceedings of the 21st European Conference on Genetic Programming", year = "2018", month = "4-6 " # apr, editor = "Mauro Castelli and Lukas Sekanina and Mengjie Zhang and Stefano Cagnoni and Pablo Garcia-Sanchez", series = "LNCS", volume = "10781", publisher = "Springer Verlag", address = "Parma, Italy", pages = "237--252", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Geometric operators, Crossover:Poster", isbn13 = "978-3-319-77552-4", URL = "https://old.cisuc.uc.pt/publication/show/5627", DOI = "doi:10.1007/978-3-319-77553-1_15", size = "16 pages", abstract = "This paper presents a geometric crossover operator for Tree-Based Genetic Programming that acts on the syntactic space, where each expression tree is represented in prefix notation. The proposed operator is compared to the standard subtree crossover on a symbolic regression problem, on the Santa Fe Ant Trail and on a classification problem. Statistically validated results show that the individuals produced using this method are significantly smaller than those produced by the subtree crossover, and have similar or better performance in the target tasks.", notes = "Part of \cite{Castelli:2018:GP} EuroGP'2018 held in conjunction with EvoCOP2018, EvoMusArt2018 and EvoApplications2018", } @InProceedings{Macedo:2020:evoapplications, author = "Joao Macedo and Lino Marques and Ernesto Costa", title = "Locating Odour Sources with Geometric Syntactic Genetic Programming", booktitle = "23rd International Conference, EvoApplications 2020", year = "2020", month = "15-17 " # apr, editor = "Pedro A. Castillo and Juan Luis {Jimenez Laredo} and Francisco {Fernandez de Vega}", series = "LNCS", volume = "12104", publisher = "Springer Verlag", address = "Seville, Spain", pages = "212--227", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Evolutionary Robotics, Odour source localisation, Geometric operators", isbn13 = "978-3-030-43721-3", video_url = "https://www.youtube.com/watch?v=ZF-OZNJ4FU8", DOI = "doi:10.1007/978-3-030-43722-0_14", abstract = "Using robots to locate odour sources is an interesting problem with important applications. Many researchers have drawn inspiration from nature to produce robotic methods, whilst others have attempted to automatically create search strategies with Artificial Intelligence techniques. This paper extends Geometric Syntactic Genetic Programming and applies it to automatically produce robotic controllers in the form of behaviour trees. The modification proposed enables Geometric Syntactic Genetic Programming to evolve trees containing multiple symbols per node. The behaviour trees produced by this algorithm are compared to those evolved by a standard Genetic Programming algorithm and to two bio-inspired strategies from the literature, both in simulation and in the real world. The statistically validated results show that the Geometric Syntactic Genetic Programming algorithm is able to produce behaviour trees that outperform the bio-inspired strategies, while being significantly smaller than those evolved by the standard Genetic Programming algorithm. Moreover, that reduction in size does not imply statistically significant differences in the performance of the strategies.", notes = "ISR, Department of Electrical and Computer Engineering, University of Coimbra, Portugal http://www.evostar.org/2020/ EvoApplications2020 held in conjunction with EuroGP'2020, EvoMusArt2020 and EvoCOP2020", } @InProceedings{Macedo:2021:IROS, author = "Joao Macedo and Lino Marques and Ernesto Costa", title = "Evolving Infotaxis for Meandering Environments", booktitle = "2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)", year = "2021", pages = "8431--8436", abstract = "Locating odour sources with mobile robots is a difficult task with many real world applications. Over the years, researchers have devised bio-inspired and cognitive methods to enable mobile robots to fulfil this task. One of the most popular cognitive approaches is Infotaxis, which computes a probability map for the location of the chemical source and, on each time step, moves the robot in the direction that minimises the entropy of that probability map. The main difficulty for applying Infotaxis in the real world is selecting proper values for the parameters of its internal gas dispersion model, as it has been shown that its performance is greatly influenced by the accuracy of said model. This work proposes a Genetic Algorithm for optimising those parameters for specific environments. The proposed method is applied to environments with distinct wind and odour dispersion characteristics and the resulting parameters are compared. Moreover, the performance of Infotaxis is compared to that of reactive search strategies evolved by Geometric Syntactic Genetic Programming. The statistically validated results show that the evolved reactive strategies achieve equivalent success rates to Infotaxis, while being significantly faster. Real world experiments conducted in a controlled wind tunnel validated the simulation results.", keywords = "genetic algorithms, genetic programming, Biological system modelling, Atmospheric modelling, Wind tunnels, Syntactics, Mobile robots, Task analysis", DOI = "doi:10.1109/IROS51168.2021.9636779", ISSN = "2153-0866", month = sep, notes = "Also known as \cite{9636779}", } @InProceedings{Macedo:2018:LA-CCI, author = "Mariana Macedo and Carlos Henrique Macedo {dos Santos} and Eronita Maria Luizines {Van Leijden} and Joao Fausto Lorenzato {de Oliveira} and Fernando Buarque {de Lima Neto} and Hugo Siqueira", booktitle = "2018 IEEE Latin American Conference on Computational Intelligence (LA-CCI)", title = "Hyper-Heuristics Using Genetic Programming to Time Series Forecasting", year = "2018", abstract = "Time series forecasting methods allow companies and researchers to analyse and predict data that change over time, such as stock exchange and climate change. However, because of their complexity and dynamic nature, each type of time series ideally should be modelled using ad-hoc algorithms. To create a more general methodology, we proposed a combination of meta-heuristics, led by Genetic Programming (GP), to enhance the overall prediction ability. GP may not be as popular as the Box & Jenkins methodology for forecasting tasks, but the literature shows appealing outcomes. Swarm intelligence is also a powerful mechanism for searching patterns in large data spaces. Thus, we investigated and proposed a hybrid method using GP together with the Fish School Search (FSS) algorithm, where the latter is used to select optimal parameters for the former. We also used local search techniques for preventing the Genetic Programming to get stuck in local minima, by refining the coefficients on the GP expression. Our proposal was compared to standard autoregressive integrated moving average (ARIMA) model, exponential smoothing (ETS) and standard GP. The proposed method achieved promising results in one-step-ahead predictions and was applied to a well-known time series data library.", keywords = "genetic algorithms, genetic programming, Frequency selective surfaces, Time series analysis, Sociology, Forecasting, Prediction algorithms", DOI = "doi:10.1109/LA-CCI.2018.8625240", month = nov, notes = "University of Pernambuco, Recife-PE, Brazil Also known as \cite{8625240}", } @InProceedings{macedo:2022:GECCO, author = "Joao Macedo and Lino Marques and Ernesto Costa", title = "Hybridizing {Bio-Inspired} Strategies with Infotaxis through Genetic Programming", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference", year = "2022", editor = "Alma Rahat and Jonathan Fieldsend and Markus Wagner and Sara Tari and Nelishia Pillay and Irene Moser and Aldeida Aleti and Ales Zamuda and Ahmed Kheiri and Erik Hemberg and Christopher Cleghorn and Chao-li Sun and Georgios Yannakakis and Nicolas Bredeche and Gabriela Ochoa and Bilel Derbel and Gisele L. Pappa and Sebastian Risi and Laetitia Jourdan and Hiroyuki Sato and Petr Posik and Ofer Shir and Renato Tinos and John Woodward and Malcolm Heywood and Elizabeth Wanner and Leonardo Trujillo and Domagoj Jakobovic and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Inmaculada Medina-Bulo and Slim Bechikh and Andrew M. Sutton and Pietro Simone Oliveto", pages = "95--103", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, Complex Systems, robotics, infotaxis, evolutionary robotics, odour source localisation", isbn13 = "978-1-4503-9237-2", DOI = "doi:10.1145/3512290.3528739", video_url = "https://vimeo.com/723767120", abstract = "Locating odour sources with mobile robots is a difficult task with many applications. Over the years, researchers have devised bio-inspired and cognitive methods to enable mobile robots to fulfil this task. Cognitive approaches are effective in large spaces, but computationally heavy. On the other hand, bio-inspired ones are lightweight, but they are only effective in the presence of frequent stimuli. One of the most popular cognitive approaches is Infotaxis, which iteratively computes a probability map of the source location. Another strand of work uses Genetic Programming to produce complete search strategies from bio-inspired behaviours. This work combines the two approaches by allowing Genetic Programming to evolve search strategies that include infotactic and bio-inspired behaviours. The proposed method is tested in a set of environments with distinct airflow and chemical dispersion patterns. Its performance is compared to that of evolved strategies without infotactic behaviours and to the standard infotaxis approach. The statistically validated results show that the proposed method produces search strategies that have significantly higher success rates, whilst being faster than those produced by any of the original approaches. Moreover, the best evolved strategies are analysed, providing insight into when infotaxis is more beneficial.", notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @InProceedings{machado:1999:bbir, author = "Penousal Machado and Francisco B. Pereira and Amilcar Cardoso and Ernesto Costa", title = "Busy Beaver -- the Influence of Representation", booktitle = "Genetic Programming, Proceedings of EuroGP'99", year = "1999", editor = "Riccardo Poli and Peter Nordin and William B. Langdon and Terence C. Fogarty", volume = "1598", series = "LNCS", pages = "29--38", address = "Goteborg, Sweden", publisher_address = "Berlin", month = "26-27 " # may, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-65899-8", URL = "http://cisuc.dei.uc.pt/ecos/dlfile.php?fn=135_pub_eurogp99.pdf", DOI = "doi:10.1007/3-540-48885-5_3", abstract = "The Busy Beaver is an interesting theoretical problem proposed by Rado in 1962. In this paper we propose an evolutionary approach to this problem. We will focus on the representational issues, proposing alternative ways of codifying and interpreting Turing Machines. These alternative representations take advantage of the existence of equivalent Turing machine sets. The experimental results show that the proposed representations provide improvement over the standard genetic codification.", notes = "EuroGP'99, part of \cite{poli:1999:GP} Penousal Machado won special jury prize. Busy beaver = Turing machine which generates longest pattern of 1s and terminates. Solutions only known for very small Turing machines.", } @InProceedings{oai:CiteSeerPSU:510392, author = "Penousal Machado and Andre Dias and Amilcar Cardoso", title = "{GenCo}: A project report", booktitle = "ISAS 2001 -- International Symposium on Adaptive Systems -- Evolutionary Computation and Probabilistic Graphical Models", year = "2002", editor = "Alberto Ochoa Rodriguez", address = "Havana, Cuba", month = "19-23 " # mar, email = "machado@dei.uc.pt, adias@student.dei.uc.pt, amilcar@dei.uc.pt", keywords = "genetic algorithms, genetic programming, NEvAr", citeseer-isreferencedby = "oai:CiteSeerPSU:80970", citeseer-references = "oai:CiteSeerPSU:276822; \cite{oai:CiteSeerPSU:336117}; oai:CiteSeerPSU:327061; oai:CiteSeerPSU:15714", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:510392", rights = "unrestricted", URL = "http://eden.dei.uc.pt/~machado/research/pdf/2001/ISAS-2001.pdf", URL = "http://citeseer.ist.psu.edu/510392.html", size = "6 pages", abstract = "Genetic Programming involves the evolution of computer programs, which are usually represented by trees composed by functions and terminals. In order to assign fitness, one must evaluate the programs, which is the most time demanding step of GP. In nowadays standard approaches, the evaluation involves an interpretation step. To avoid this step, which significantly slows the algorithm, some researchers evolve, directly, machine code programs. An alternative approach is to build a Genome Compiler, i.e. a system that transforms the individual's trees in machine-code programs and executes this code. Both techniques can bring huge speed improvements. However, these approaches have some shortcomings. In this paper we present GenCo: a research project whose main goal is development of a Genetic Programming Genome Compiler system, that overcomes some of the drawbacks of current approaches, enabling high speed improvements in a wider range of domains. We will also present experimental results in a programmatic compression task, in which GenCo was, on average, 80 times faster than a standard C based GP system.", notes = "'intron detection, optimization and caching' cites \cite{fukunaga:1998:gchpGP} context of the International Conference CIMAF 2001. Not verified LilGP \cite{zonger:1996:lilgp} interpretation step replaced by a compilation step. Lena image compression. Claims in the region of 100000 to 1 million individuals evaluated per second GPops. ", } @InProceedings{oai:CiteSeerPSU:508435, title = "Giving Colour to Images", author = "Penousal Machado and Andre Dias and Nuno Duarte and Amilcar Cardoso", year = "2002", booktitle = "AI and Creativity in Arts and Science", editor = "Amilcar Cardoso and Geraint Wiggins", address = "Imperial College, United Kingdom", month = "2-5 " # apr, organisation = "the Society for the Study of Artificial Intelligence and the Simulation of Behaviour", note = "A symposium as part of AISB'02", keywords = "genetic algorithms, genetic programming", citeseer-isreferencedby = "oai:CiteSeerPSU:80402", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:508435", rights = "unrestricted", URL = "http://eden.dei.uc.pt/%7Emachado/research/pdf/aisb2002.pdf", URL = "http://citeseer.ist.psu.edu/508435.html", size = "7 pages", abstract = "This paper is about the colouring of greyscale images. More specifically, we address the problem of learning to colour greyscale images from a set of examples of true colour ones. We employ Genetic Programming to evolve computer programs that take as input the Lightness channel of the training images and output the Hue channel. The best programs evolved can then be used to give colour to greyscale images. Due to the computational complexity of the learning task, we use a genome compiler system, GenCo, specially suited to image processing tasks.", notes = "http://comma.doc.ic.ac.uk/aisb2002/ http://www.soi.city.ac.uk/~geraint/aisb02/programme.htm", } @InProceedings{oai:CiteSeerPSU:336117, title = "Speeding up Genetic Programming", author = "Penousal Machado and Amilcar Cardoso", booktitle = "Proceedings of the Second International Symposium on Artificial Intelligence, Adaptive Systems (CIMAF - 99)", year = "1999", address = "Havana, Cuba", month = mar # " 22-26", keywords = "genetic algorithms, genetic programming", URL = "http://eden.dei.uc.pt/~machado/research/pdf/1999/cimaf99-fasteval.pdf", URL = "http://eden.dei.uc.pt/~ernesto/EvoCo/papers/papers/1999/cimaf992.htm", URL = "http://citeseer.ist.psu.edu/336117.html", citeseer-isreferencedby = "oai:CiteSeerPSU:41881; oai:CiteSeerPSU:361360; oai:CiteSeerPSU:231399; oai:CiteSeerPSU:560606", citeseer-references = "oai:CiteSeerPSU:276822; oai:CiteSeerPSU:186935", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:336117", rights = "unrestricted", abstract = "One of the major drawbacks of Evolutionary Computation is the need for great computational power. The set of problems that can be solved, in practice, by evolutionary approaches is highly connected with the efficiency of the algorithm. In most Genetic Programming applications the majority of time is spent on the evaluation of the individuals. Accordingly, it is desirable to optimise this step of the process. In this paper we present two approaches through which significant speed improvements can be achieved. The first approach, T-functions, is effective in tasks, such as symbolic regression, that require repeated evaluation of the individuals. The second approach, caching, resorts to the storage of the execution results of individuals' sub-trees, thus avoiding the recalculation of these sub-programs. Caching finds its application when the function set includes complex, time-consuming functions.", } @Article{Machado:2002:AI, author = "Penousal Machado and Amilcar Cardoso", title = "All the Truth About {NEvAr}", journal = "Applied Intelligence", year = "2002", volume = "16", number = "2", pages = "101--118", keywords = "genetic algorithms, genetic programming, evolutionary art, computational creativity", ISSN = "0924-669X", language = "English", URL = "http://dx.doi.org/10.1023/A%3A1013662402341", publisher = "Kluwer Academic Publishers", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.304.9427", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.304.9427", broken = "http://eden.dei.uc.pt/~machado/research/pdf/2002/ijai2002.pdf", DOI = "doi:10.1023/A:1013662402341", abstract = "The use of Evolutionary Computation approaches to generate images has reached a great popularity. This led to the emergence of a new art form -- Evolutionary Art -- and to the proliferation of Evolutionary Art Tools. In this paper, we present an Evolutionary Art Tool, NEvAr, the experimental results achieved, and the work methodology used to generate images. In NEvAr, useful individuals are stored in a database in order to allow their reuse. This database is playing an increasingly important role in the creation of new images, which led us to the development of automatic seeding procedures, also described. The automation of fitness assignment is one of our present research interests. We will, therefore, describe some preliminary results achieved with our current approach to automatic evaluation.", } @InCollection{machado:2004:rdbic, author = "Penousal Machado and Francisco B. Pereira and Jorge Tavares and Ernesto Costa and Amilcar Cardoso", title = "Evolutionary {Turing} machines: The quest for busy beavers", booktitle = "Recent Developments in Biologically Inspired Computing", publisher = "Idea Group Publishing", year = "2004", editor = "Leandro N. {de Castro} and Fernando J. {Von Zuben}", chapter = "2", keywords = "genetic algorithms, genetic programming", ISBN = "1-59140-312-X", URL = "http://www.cisuc.uc.pt/acg/dlfile.php?fn=792_pub_Beaver-chapter-2004.pdf", DOI = "doi:10.4018/978-1-59140-312-8.ch002", abstract = "In this chapter we study the feasibility of using Turing Machines as a model for the evolution of computer programs. To assess this idea we select, as test problem, the Busy Beaver - a well-known theoretical problem of undisputed interest and difficulty proposed by Tibor Rado in 1962. We focus our research on representational issues and on the development of specific genetic operators, proposing alternative ways of encoding and manipulating Turing Machines. The results attained on a comprehensive set of experiments show that the proposed techniques bring significant performance improvements. Moreover, the use of a graph based crossover operator, in conjunction with new representation techniques, allowed us to establish new best candidates for the 6, 7, and 8 states instances of the 4 tuple Busy Beaver problem.", notes = "broken August 2020 http://www.idea-group.com/books/details.asp?id=4376", } @Article{Machado2007818, author = "Penousal Machado and Juan Romero and Antonino Santos and Amilcar Cardoso and Alejandro Pazos", title = "On the development of evolutionary artificial artists", journal = "Computers \& Graphics", volume = "31", number = "6", pages = "818--826", year = "2007", ISSN = "0097-8493", DOI = "DOI:10.1016/j.cag.2007.08.010", URL = "http://www.sciencedirect.com/science/article/B6TYG-4PTMXVB-1/2/0c81ca71ea76186b393615d17177d4de", keywords = "genetic algorithms, genetic programming, Artificial art, Evolutionary computation, Artificial intelligence, Digital art, NEvAr", abstract = "The creation and the evaluation of aesthetic artifacts are tasks related to design, music and art, which are highly interesting from the computational point of view. Nowadays, Artificial Intelligence systems face the challenge of performing tasks that are typically human, highly subjective, and eventually social. The present paper introduces an architecture which is capable of evaluating aesthetic characteristics of artifacts and of creating artifacts that obey certain aesthetic properties. The development methodology and motivation, as well as the results achieved by the various components of the architecture, are described. The potential contributions of this type of systems in the context of digital art are also considered.", } @InProceedings{machado:2011:EuroGP, author = "Penousal Machado and Ant\'{o}nio Leit\~{a}o", title = "Evolving Fitness Functions for Mating Selection", booktitle = "Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011", year = "2011", month = "27-29 " # apr, editor = "Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado", series = "LNCS", volume = "6621", publisher = "Springer Verlag", address = "Turin, Italy", pages = "227--238", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1007/978-3-642-20407-4_20", abstract = "The tailoring of an evolutionary algorithm to a specific problem is typically a time-consuming and complex process. Over the years, several approaches have been proposed for the automatic adaptation of parameters and components of evolutionary algorithms. We focus on the evolution of mating selection fitness functions and use as case study the Circle Packing in Squares problem. Each individual encodes a potential solution for the circle packing problem and a fitness function, which is used to assess the suitability of its potential mating partners. The experimental results show that by evolving mating selection functions it is possible to surpass the results attained with hardcoded fitness functions. Moreover, they also indicate that genetic programming was able to discover mating selection functions that: use the information regarding potential mates in novel and unforeseen ways; outperform the class of mating functions considered by the authors.", notes = "Part of \cite{Silva:2011:GP} EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011", } @InProceedings{machado:2012:EuroGP, author = "Penousal Machado and Joao Correia and Juan Romero", title = "Improving Face Detection", booktitle = "Proceedings of the 15th European Conference on Genetic Programming, EuroGP 2012", year = "2012", month = "11-13 " # apr, editor = "Alberto Moraglio and Sara Silva and Krzysztof Krawiec and Penousal Machado and Carlos Cotta", series = "LNCS", volume = "7244", publisher = "Springer Verlag", address = "Malaga, Spain", pages = "73--84", organisation = "EvoStar", isbn13 = "978-3-642-29138-8", DOI = "doi:10.1007/978-3-642-29139-5_7", keywords = "genetic algorithms, genetic programming, Face detection, Haar cascade", abstract = "A novel Genetic Programming approach for the improvement of the performance of classifier systems through the synthesis of new training instances is presented. The approach relies on the ability of the Genetic Programming engine to identify and exploit shortcomings of classifier systems, and generate instances that are misclassified by them. The addition of these instances to the training set has the potential to improve classifier's performance. The experimental results attained with face detection classifiers are presented and discussed. Overall they indicate the success of the approach.", notes = "Part of \cite{Moraglio:2012:GP} EuroGP'2012 held in conjunction with EvoCOP2012 EvoBIO2012, EvoMusArt2012 and EvoApplications2012", } @Proceedings{Machado:2012:EvoMusArt_proc, title = "Proceedings of the 1st International Conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design, EvoMUSART 2012", year = "2012", editor = "Penousal Machado and Juan Romero and Adrian Carballal", volume = "7247", series = "LNCS", address = "Malaga, Spain", month = "11-13 " # apr, organisation = "EvoStar", publisher = "Springer Verlag", isbn13 = "978-3-642-29141-8", DOI = "doi:10.1007/978-3-642-29142-5", size = "235 pages", notes = "EvoMUSART'2012 held in conjunction with EuroGP2012, EvoCOP2012, EvoBIO2012 and EvoApplications2012", } @InProceedings{Machado:2012:EvoMUSART, author = "Penousal Machado and Joao Correia and Juan Romero", title = "Expression-Based Evolution of Faces", booktitle = "Proceedings of the 1st International Conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design, EvoMUSART 2012", year = "2012", month = "11-13 " # apr, editor = "Penousal Machado and Juan Romero and Adrian Carballal", series = "LNCS", volume = "7247", publisher = "Springer Verlag", address = "Malaga, Spain", pages = "187--198", organisation = "EvoStar", isbn13 = "978-3-642-29141-8", DOI = "doi:10.1007/978-3-642-29142-5_17", keywords = "genetic algorithms, genetic programming, Evolutionary Art, Automatic Fitness Assignment, Face Detection", abstract = "The combination of a classifier system with an evolutionary image generation engine is explored. The framework is instantiated using an off-the-shelf face detection system and a general purpose, expression-based, genetic programming engine. By default, the classifier returns a binary output, which is inadequate to guide evolution. By retrieving information provided by intermediate results of the classification task, it became possible to develop a suitable fitness function. The experimental results show the ability of the system to evolve images that are classified as faces. A subjective analysis also reveals the unexpected nature and artistic potential of the evolved images.", notes = "Part of \cite{Machado:2012:EvoMusArt_proc} EvoMUSART'2012 held in conjunction with EuroGP2012, EvoCOP2012, EvoBIO2012 and EvoApplications2012", } @InProceedings{Machado:2014:GECCO, author = "Penousal Machado and Joao Correia", title = "Semantic aware methods for evolutionary art", booktitle = "GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation", year = "2014", editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges", isbn13 = "978-1-4503-2662-9", pages = "301--308", keywords = "genetic algorithms, genetic programming", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2576768.2598293", DOI = "doi:10.1145/2576768.2598293", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "In the past few years the use of semantic aware crossover and mutation has become a hot topic of research within the Genetic Programming community. Unlike traditional genetic operators that perform syntactic manipulations of programs regardless of their behavior, semantic driven operators promote direct search on the underlying behavioral space. Based on previous work on semantic Genetic Programming and Genetic Morphing, we propose and implement semantic driven crossover and mutation operators for evolutionary art. The experimental results focus on assessing how these operators compare with traditional ones.", notes = "Also known as \cite{2598293} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)", } @Proceedings{Machado:2015:GP, title = "Proceedings of the 18th European Conference on Genetic Programming, {EuroGP 2015}", year = "2015", editor = "Penousal Machado and Malcolm I. Heywood and James McDermott and Mauro Castelli and Pablo Garcia-Sanchez and Paolo Burelli and Sebastian Risi and Kevin Sim", volume = "9025", series = "LNCS", address = "Copenhagen", month = "8-10 " # apr, organisation = "EvoStar", publisher = "Springer Verlag", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-16500-4", DOI = "doi:10.1007/978-3-319-16501-1", size = "243", notes = "http://www.evostar.org/2015/cfp_eurogp.php EuroGP'2015 held in conjunction with EvoCOP2015, EvoMusArt2015 and EvoApplications2015", } @InCollection{Machado:2015:hbgpa, author = "Penousal Machado and Joao Correia and Filipe Assuncao", title = "Graph-Based Evolutionary Art", booktitle = "Handbook of Genetic Programming Applications", publisher = "Springer", year = "2015", editor = "Amir H. Gandomi and Amir H. Alavi and Conor Ryan", chapter = "1", pages = "3--36", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-20882-4", DOI = "doi:10.1007/978-3-319-20883-1_1", abstract = "A graph-based approach for the evolution of Context Free Design Grammars is presented. Each genotype is a directed hierarchical graph and, as such, the evolutionary engine employs graph-based crossover and mutation. We introduce six different fitness functions based on evolutionary art literature and conduct a wide set of experiments. We begin by assessing the adequacy of the system and establishing the experimental parameters. Afterwards, we conduct evolutionary runs using each fitness function individually. Finally, experiments where a combination of these functions is used to assign fitness are performed. Overall, the experimental results show the ability of the system to optimize the considered functions, individually and combined, and to evolve images that have the desired visual characteristics", } @InProceedings{Machado:2022:GPTP, author = "Penousal Machado and Francisco Baeta and Tiago Martins and Joao Correia", title = "{GP}-Based Generative Adversarial Models", booktitle = "Genetic Programming Theory and Practice XIX", year = "2022", editor = "Leonardo Trujillo and Stephan M. Winkler and Sara Silva and Wolfgang Banzhaf", series = "Genetic and Evolutionary Computation", pages = "117--140", address = "Ann Arbor, USA", month = jun # " 2-4", publisher = "Springer", keywords = "genetic algorithms, genetic programming, ANN", isbn13 = "978-981-19-8459-4", DOI = "doi:10.1007/978-981-19-8460-0_6", abstract = "We explore the use of Artificial Neural Network (ANN)-guided Genetic Programming (GP) to generate images that the guiding network classifies as belonging to a specific class. The experimental results demonstrate the ability of GP to perform such a task but also the inadequacy of most of the generated images, which can be considered false positives. Based on these findings and following an approach analogous to Generative Adversarial Networks (GANs), we propose an generative adversarial model where GP replaces the traditional GAN's generator. The experimental results illustrate the advantages of this approach, highlighting the expressive power of GP, its capacity to perform online learning, thus adapting to a dynamic fitness landscape, and its ability to create novel imagery that fits the target classes.", notes = "Part of \cite{Banzhaf:2022:GPTP} published after the workshop in 2023", } @InProceedings{ijcai2024coin, author = "Penousal Machado and Tiago Martins and Joao Correia and Luis {Espirito Santo} and Nuno Lourenco and Joao Cunha and Sergio Rebelo and Pedro Martins and Joao Bicker", title = "From Pixels to Metal: {AI}-Empowered Numismatic Art", booktitle = "Proceedings of the Thrity-Third International Joint Conference on Artificial Intelligence, IJCAI 2024", year = "2024", address = "Jeju, South Korea", month = "3-9 " # aug, publisher = "International Joint Conferences on Artificial Intelligence Organization", note = "Silver Humie award. AI, the Arts and Creativity", keywords = "genetic algorithms, genetic programming, CLIP, ML, DALL-E, aesthetic models, MetaPrompter grammar, ANN", URL = "https://ijcai24.org/ai-arts-creativity-special-track-accepted-papers/#more-ARTS8286", URL = "https://human-competitive.org/sites/default/files/entryform_9.txt", URL = "https://human-competitive.org/sites/default/files/paper_7.pdf", video_url = "https://mediaspace.msu.edu/media/Penousal+Machado+Humies+presentation/1_h33lkjy3", video_url = "https://cdv.dei.uc.pt/2024/coin-expression-visualisation.mp4", size = "9 pages", abstract = "response to a unique challenge presented by the Portuguese National Press-Mint: to use Artificial Intelligence to design a commemorative coin that celebrates the digital world. We explain the process of this coin co-creation, from conceptualisation to production, highlighting the design process, the underlying rationale, key obstacles encountered, and the technical innovations and developments made to meet the challenge. These include the development of an evolutionary art system guided by Contrastive Language Image Pre-training (CLIP) and Machine Learning-based aesthetic models, a system for prompt evolution, and a representation for encoding genotypes in mintable format. This collaboration produced a limited edition 10 euro silver proof coin, with a total of 4000 units minted by the National Press-Mint. The coin was met with enthusiasm, selling out within two months. This work contributes to Computational Creativity, particularly co-creativity, co-design, and digital art, and represents a significant step in using Artificial Intelligence for Numismatics.", notes = " ijcai 2024 paper ARTS8286 8 August https://ijcai24.org/", } @Article{Machado:2024:sigevolution, author = "Penousal Machado and Tiago Martins and Joao Correia and Luis Espirito Santo and Nuno Lourenco and Joao Cunha and Sergio Rebelo and Pedro Martins and Joao Bicker", title = "Designing Coins with Evolutionary Computation", journal = "SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation", year = "2024", volume = "17", number = "2", month = jun, keywords = "genetic algorithms, genetic programming, genotype-phenotype, Alvar Aalt, creativity", ISSN = "1931-8499", URL = "https://evolution.sigevo.org/issues/HTML/sigevolution-17-2/index.html", DOI = "doi:10.1145/3695933.3695934", video_url = "https://cdv.dei.uc.pt/2024/coin-expression-visualisation.mp4", code_url = "https://github.com/cdvetal", abstract = "Can you create a coin themed around the digital world using AI? In recent years, the application of Artificial Intelligence (AI) for creative and artistic endeavours has attracted considerable attention, increasing the opportunities to use AI for many art and design tasks. This paper describes our response to a unique challenge presented by the Portuguese National Press-Mint (INCM): to use AI to design a commemorative coin that celebrates the digital world. We explain the process of this coin co-creation, from conceptualisation to production, highlighting the design process, key obstacles encountered, and technical innovations made to meet the challenge. These include developing an evolutionary art system guided by Contrastive Language-Image Pre-training (CLIP) and Machine Learning (ML)-based aesthetic models, a system for prompt evolution, and a representation for encoding genotypes in mintable format. This collaboration produced a limited edition 10 euro silver proof coin (Figure 1), with a total of 4000 units minted by the National Press-Mint. The coin was met with enthusiasm, selling out within two months. This work contributes to Computational Creativity (CC), particularly co-creativity, co-design, and digital art, and represents a significant step in using AI for Numismatics.", notes = "See also \cite{ijcai2024coin} University of Coimbra, CISUC/LASI, DEI, Coimbra, Portual", } @InProceedings{Machova:2018:DISA, author = "Kristina Machova and Marian Mach", booktitle = "2018 World Symposium on Digital Intelligence for Systems and Machines (DISA)", title = "Genetic Programming in the Authority of a Web Discussion Identification", year = "2018", pages = "245--250", abstract = "The paper focuses on web data mining to solve a problem of estimation of the authoritativeness measure of social web users, particularly contributors to a discourse content of social web discussions. Two methods from machine learning domain were used - regression analysis and genetic programming. Our aim was to find an approximation of the dependency of the authoritativeness value on variables representing parameters of the structure of discourse content. The approximation function can be used for computation of the authoritativeness value of a given user as well as for discrimination of authoritative from non-authoritative contributors to web discussions. This information is important for web users, who search for truthful and reliable information in the process of decision making about important things since the web users would like to be influenced by some credible professionals.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/DISA.2018.8490640", month = aug, notes = "Department of Cybernetics and Artificial Intelligence, Technical University, Letna 9,042 00, Kosice, Slovakia Also known as \cite{8490640}", } @InProceedings{Mackin00, author = "Kenneth J. Mackin and Eiichiro Tazaki", title = "Unsupervised training of {M}ultiobjective {A}gent {C}ommunication using {G}enetic {P}rogramming", booktitle = "Proceedings of the Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technology", volume = "2", pages = "738--741", address = "Brighton, UK", year = "2000", month = "30 " # aug # "-1 " # sep, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, agent communication protocols, agent group behaviour, automatically defined function genetic programming, multiagent systems, multiobjective agent communication, multiobjective genetic programming, software agents, software simulation, unsupervised learning, multi-agent systems, unsupervised learning", URL = "http://www.lania.mx/~ccoello/EMOO/mackin00.pdf.gz", DOI = "doi:10.1109/KES.2000.884152", size = "4 pages", abstract = "Multiagent systems, in which independent software agents interact with each other to achieve common goals, complete distributed tasks concurrently under autonomous control. Agent communication has been shown to be an important factor in coordinating efficient group behavior in agents. Most research on training or evolving group behavior in multiagent systems used predefined agent communication protocols. Designing agent communication becomes a complex problem in dynamic and large-scale systems. The problem is further complicated in a multiobjective scenario. In order to solve this problem, in our previous research we had proposed a method applying genetic programming techniques, in particular automatically defined function genetic programming (ADF-GP), to allow agents to autonomously learn effective agent communication messaging. For this research we take this approach further and combine multiobjective genetic programming in order to adapt the system to a multiobjective environment. In the proposed method separate agent communication protocols are trained for each objective. A software simulation of a multiagent transaction system is used to observe the effectiveness of the proposed method in multiobjective environments", } @Article{mackin:2002:K, author = "Kenneth J. Mackin and Eiichiro Tazaki", title = "Multiagent communication combining genetic programming and pheromone communication", journal = "Kybernetes", year = "2002", volume = "31", number = "6", pages = "827--843", keywords = "genetic algorithms, genetic programming, Cybernetics; Programming, Communications, Electronic Commence", DOI = "doi:10.1108/03684920210432808", abstract = "Multiagent systems, in which independent software agents interact with each other to achieve common goals, complete concurrent distributed tasks under autonomous control. Agent Communication has been shown to be an important factor in coordinating efficient group behavior in agents. Most researches on training or evolving group behavior in multiagent systems used predefined agent communication protocols. Designing agent communication becomes a complex problem in dynamic and large-scale systems. In order to solve this problem, in this paper we propose a new application of existing training methods. By applying Genetic Programming techniques, namely Automatically Defined Function Genetic Programming (ADF-GP), in combination with pheromone communication features, we allowed the agent system to autonomously learn effective agent communication messaging for coordinated group behavior. A software simulation of a multiagent transaction system aiming at e-commerce usage will be used to observe the effectiveness of the proposed method in the targeted environment. Using the proposed method, automatic training of a compact and efficient agent communication protocol for the multiagent system was observed.", } @InProceedings{mac:2018:AJCAI, author = "Jordan MacLachlan and Yi Mei and Juergen Branke and Mengjie Zhang", title = "An Improved Genetic Programming Hyper-Heuristic for the Uncertain Capacitated Arc Routing Problem", booktitle = "Australasian Joint Conference on Artificial Intelligence", year = "2018", editor = "Tanja Mitrovic and Bing Xue and Xiaodong Li", volume = "11320", series = "LNCS", address = "Wellington, New Zealand", month = dec # " 11-14", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Arc routing, Hyper-heuristic", isbn13 = "978-3-030-03990-5", URL = "http://link.springer.com/chapter/10.1007/978-3-030-03991-2_40", DOI = "doi:10.1007/978-3-030-03991-2_40", size = "13 pages", abstract = "This paper uses a Genetic Programming Hyper-Heuristic (GPHH) to evolve routing policies for the Uncertain Capacitated Arc Routing Problem (UCARP). Given a UCARP instance, the GPHH evolves feasible solutions in the form of decision making policies which decide the next task to serve whenever a vehicle completes its current service. Existing GPHH approaches have two drawbacks. First, they tend to generate small routes by routing through the depot and refilling prior to the vehicle being fully loaded. This usually increases the total cost of the solution. Second, existing GPHH approaches cannot control the extra repair cost incurred by a route failure, which may result in higher total cost. To address these issues, this paper proposes a new GPHH algorithm with a new No-Early-Refill filter to prevent generating small routes, and a novel Flood Fill terminal to better handle route failures. Experimental studies show that the newly proposed GPHH algorithm significantly outperforms the existing GPHH approaches on the Ugdb and Uval benchmark datasets. Further analysis has verified the effectiveness of both the new filter and terminal.", } @Article{MacLachlan:ECJ:gphh, author = "Jordan MacLachlan and Yi Mei and Juergen Branke and Mengjie Zhang", title = "Genetic Programming Hyper-Heuristics with Vehicle Collaboration for Uncertain Capacitated Arc Routing Problems", journal = "Evolutionary Computation", year = "2020", volume = "28", number = "4", pages = "563--593", month = "Winter", keywords = "genetic algorithms, genetic programming, Arc Routing, Hyper Heuristic, Stochastic Optimisation", ISSN = "1063-6560", URL = "https://doi.org/10.1162/evco_a_00267", DOI = "doi:10.1162/evco_a_00267", size = "32 pages", abstract = "Due to its direct relevance to post-disaster operations, meter reading and civil refuse collection, the Uncertain Capacitated Arc Routing Problem (UCARP) is an important optimisation problem. Stochastic models are critical to study as they more accurately represent the real-world than their deterministic counterparts. Although there have been extensive studies in solving routing problems under uncertainty, very few have considered UCARP, and none consider collaboration between vehicles to handle the negative effects of uncertainty. This paper proposes a novel Solution Construction Procedure (SCP) that generates solutions to UCARP within a collaborative, multivehicle framework. It consists of two types of collaborative activities: one when a vehicle unexpectedly expends capacity (route failure), and the other during the refill process. Then, we propose a Genetic Programming Hyper-Heuristic (GPHH) algorithm to evolve the routing policy used within the collaborative framework. The experimental stu", notes = "School of Engineering and Computer Science, Victoria University of Wellington,PO Box 600, Wellington 6140, New Zealand", } @InProceedings{MacLachlan:2021:CEC, author = "Jordan MacLachlan and Yi Mei", booktitle = "2021 IEEE Congress on Evolutionary Computation (CEC)", title = "Look-Ahead Genetic Programming for Uncertain Capacitated Arc Routing Problem", year = "2021", editor = "Yew-Soon Ong", pages = "1872--1879", address = "Krakow, Poland", month = "28 " # jun # "-1 " # jul, isbn13 = "978-1-7281-8393-0", abstract = "Genetic Programming Hyper-Heuristic (GPHH) has been successfully applied to evolve routing policies for the Uncertain Capacitated Arc Routing Problem (UCARP). However, the current GPHH approaches have a limitation that they only consider myopic information of the current decision step. In this paper, we proposed incorporating look-ahead information to the decision process of GP-evolved routing policies. We designed a number of potentially promising chains of candidate tasks, and expand the candidate task pool to consider both the single tasks and task chains. This way, the routing policy can consider the look-ahead information incorporated in the considered task chains. The proposed GP with Chain Policies (GPCP) was compared with the standard GPHH on a range of UCARP instances, and the results showed that the task chains can improve the effectiveness of the routing policies sometimes. The better performance of a routing policy largely depends on whether it can balance the selections of single tasks and task chains, and whether it can stick to the whole selected chain rather than only the first task of the chain. In addition, there are some abnormal runs with serious overfitting issue that we will address in our future work.", keywords = "genetic algorithms, genetic programming, Evolutionary computation, Routing, Task analysis, Standards", DOI = "doi:10.1109/CEC45853.2021.9504785", notes = "Also known as \cite{9504785}", } @InProceedings{MacLachlan:2022:CEC, author = "Jordan MacLachlan and Yi Mei and Fangfang Zhang and Mengjie Zhang", booktitle = "2022 IEEE Congress on Evolutionary Computation (CEC)", title = "Genetic Programming for Vehicle Subset Selection in Ambulance Dispatching", year = "2022", editor = "Carlos A. Coello Coello and Sanaz Mostaghim", address = "Padua, Italy", month = "18-23 " # jul, keywords = "genetic algorithms, genetic programming, Decision making, Semantics, Medical services, Evolutionary computation, Dispatching, Real-time systems, Hyper Heuristic, Ambulance Dispatch, Evolutionary Computation", isbn13 = "978-1-6654-6708-7", URL = "https://github.com/fangfang-zhang/fangfang-zhang.github.io/blob/master/files/2022-GP_for_Vehicle_Subset_Selection_in_Ambulance_Dispatching.pdf", DOI = "doi:10.1109/CEC55065.2022.9870323", size = "8 pages", abstract = "Assigning ambulances to emergencies in real-time, ensuring both that patients receive adequate care and that the fleet remains capable of responding to any potential new emergency, is a critical component of any ambulance service. Thus far, most techniques to manage this problem are as convoluted as the problem itself. As such, many real-world medical services resort to using the naive closest-idle rule, whereby the nearest available vehicles are dispatched to serve each new call. This paper explores the feasibility of using a genetic programming hyper heuristic (GPHH) in order to generate intelligible rules of thumb to select which vehicles should attend any given emergency. Such rules, either manually or automatically designed, are evaluated within a novel solution construction procedure which constructs solutions to the ambulance dispatching problem given the parameters of the simulation environment. Experimental results suggest that GPHH is a promising technique to use when approaching the ambulance dispatching problem. Further, a GPHH-evolved rule interpretability allows for detailed semantic analysis into which features of the environment are valuable to the decision making process, allowing for human dispatching agents to make more informed decisions in practice.", notes = "Also known as \cite{9870323}", } @InProceedings{MacLachlan:2023:GECCO, author = "Jordan MacLachlan and Yi Mei and Fangfang Zhang and Mengjie Zhang and Jessica Signal", title = "Learning Emergency Medical Dispatch Policies via Genetic Programming", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "1409--1417", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", note = "Silver 2023 HUMIES", keywords = "genetic algorithms, genetic programming, dynamic optimisation, hyper-heuristic, emergency medical dispatch, simulation", isbn13 = "9798400701191", URL = "https://human-competitive.org/sites/default/files/maclachlan_entry_form.txt", URL = "https://human-competitive.org/sites/default/files/gphhemd_maclacjord.pdf", DOI = "doi:10.1145/3583131.3590434", size = "9 pages", abstract = "Of great value to modern municipalities is the task of emergency medical response in the community. Resource allocation is vital to ensure minimal response times, which we may perform via human experts or automate by maximising ambulance coverage. To combat black-box modelling, we propose a modularised Genetic Programming Hyper Heuristic framework to learn the five key decisions of Emergency Medical Dispatch (EMD) within a reactive decision-making process. We minimise the representational distance between our work and reality by working with our local ambulance service to design a set of heuristics approximating their current decision-making processes and a set of synthetic datasets influenced by existing patterns in practice. Through our modularised framework, we learn each decision independently to identify those most valuable to EMD and learn all five decisions simultaneously, improving performance by 69percent on the largest novel dataset. We analyse the decision-making logic behind several", notes = " GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @InCollection{maclean:2004:GPTP, author = "Duncan MacLean and Eric A. Wollesen and Bill Worzel", title = "Listening to Data: Tuning a Genetic Programming System", booktitle = "Genetic Programming Theory and Practice {II}", year = "2004", editor = "Una-May O'Reilly and Tina Yu and Rick L. Riolo and Bill Worzel", chapter = "15", pages = "245--262", address = "Ann Arbor", month = "13-15 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, classifier, molecular biology, cancer, microarray, genetics", ISBN = "0-387-23253-2", DOI = "doi:10.1007/0-387-23254-0_15", abstract = "Genetic Programming (GP) may be used to model complex data but it must be tuned to get the best results. This process of tuning often gives insights into the data itself. This is discussed using examples from classification problems in molecular biology and the results and rules of thumb developed to tune the GP system are reviewed in light of current GP theory.", notes = "part of \cite{oreilly:2004:GPTP2}", } @MastersThesis{Macret2013aa, author = "Matthieu Michel Jean Macret", title = "Automatic tuning of the {OP-1} synthesizer using a multi-objective genetic algorithm", school = "Communication, Art \& Technology: School of Interactive Arts and Technology, Simon Fraser University", year = "2013", address = "Vancouver, Canada", month = jul # ", 16", keywords = "Genetic Algorithms, genetic programming, DEAP, Artificial Intelligence, Sound Synthesis, Multi-objective Optimization", date-added = "2018-07-31 16:52:16 +0900", date-modified = "2018-07-31 16:53:11 +0900", URL = "http://summit.sfu.ca/item/13452", size = "108 pages", abstract = "Calibrating a sound synthesizer to replicate or approximate a given target sound is a complex and time consuming task for musicians and sound designers. In the case of the OP1, a commercial synthesizer developed by Teenage Engineering, the difficulty is multiple. The OP-1 contains several synthesis engines, effects and low frequency oscillators, which make the parameters search space very large and discontinuous. Furthermore, interactions between parameters are common and the OP-1 is not fully deterministic. We address the problem of automatically calibrating the parameters of the OP-1 to approximate a given target sound. We propose and evaluate a solution to this problem using a multi-objective Non-dominated-Sorting-Genetic-Algorithm-II. We show that our approach makes it possible to handle the problem complexity, and returns a small set of presets that best approximate the target sound while covering the Pareto front of this multi-objective optimization problem.", notes = "Some comparison with GP", } @InProceedings{Macret:2014:GECCO, author = "Matthieu Macret and Philippe Pasquier", title = "Automatic design of sound synthesizers as pure data patches using coevolutionary mixed-typed cartesian genetic programming", booktitle = "GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation", year = "2014", editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges", isbn13 = "978-1-4503-2662-9", pages = "309--316", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2576768.2598303", DOI = "doi:10.1145/2576768.2598303", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "A sound synthesizer can be defined as a program that takes a few input parameters and returns a sound. The general sound synthesis problem could then be formulated as: given a sound (or a set of sounds) what program and set of input parameters can generate that sound (set of sounds)? We propose a novel approach to tackle this problem in which we represent sound synthesisers using Pure Data (Pd), a graphic programming language for digital signal processing. We search the space of possible sound synthesisers using Coevolutionary Mixed-typed Cartesian Genetic Programming (MT-CGP), and the set of input parameters using a standard Genetic Algorithm (GA). The proposed algorithm co-evolves a population of MT-CGP graphs, representing the functional forms of synthesisers, and a population of GA chromosomes, representing their inputs parameters. A fitness function based on the Mel-frequency Cepstral Coefficients (MFCC) evaluates the distance between the target and produced sounds. Our approach is capable of suggesting novel functional forms and input parameters, suitable to approximate a given target sound (and we hope in future iterations a set of sounds). Since the resulting synthesizers are presented as Pd patches, the user can experiment, interact with, and reuse them.", notes = "http://metacreation.net/automatic-puredata-patch-generation/ Music Also known as \cite{2598303} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)", } @Misc{IAT814_MACRET, author = "Matthieu Macret", title = "Information Flocking Applied to Genetic Programming Visualization", keywords = "genetic algorithms, genetic programming, Information Interfaces and Presentation, Distributed Artificial Intelligence, time-varying information visualisation, ALife, artificial life, motion, boids", URL = "https://matthieumacret.com/pdf/IAT814_MACRET.pdf", size = "8 pages", abstract = "Genetic programming is an evolutionary technique used in optimisation problem. Its dynamic nature makes it difficult to visualise. Indeed, hundred of candidate solutions are evolved per generation. There could be relationships between these candidate solutions. Phenomenon such as code growth (bloat) can also happen. Information flocking uses the emergence of motion to visualize the dynamics of varying datasets. It makes possible to observe the data at different levels. we apply information flocking techniques to genetic programming visualization and intend to demonstrate its qualities.", notes = "School of Interactive Arts and Technology, Simon Fraser University, Surrey,B.C., Canada V3T 0A3", } @Article{MADAENI:2020:CRST, author = "Fatemehalsadat Madaeni and Rachid Lhissou and Karem Chokmani and Sebastien Raymond and Yves Gauthier", title = "Ice jam formation, breakup and prediction methods based on hydroclimatic data using artificial intelligence: A review", journal = "Cold Regions Science and Technology", year = "2020", volume = "174", pages = "103032", month = jun, keywords = "genetic algorithms, genetic programming, Forecasting, Ice jam, Modelling, Neural networks, Fuzzy logic", ISSN = "0165-232X", URL = "http://www.sciencedirect.com/science/article/pii/S0165232X18304634", DOI = "doi:10.1016/j.coldregions.2020.103032", size = "37 apges", abstract = "In cold regions, the high occurrence of ice jams results in severe flooding and significant damage caused by a rapid rise in water levels upstream of ice jams. These floods can be critical hydrological and hydraulic events and be a major concern for citizens, authorities, insurance companies and government agencies. In the past twenty years, several studies have been conducted in ice jam modelling and forecasting, and it has been found that predicting ice jam formation and breakup is challenging, due to the complexity of the interactions between the hydroclimatic variables leading to these processes. At this time, several mathematical models have been developed to predict breakup processes. The current methods of breakup prediction are highly empirical and site-specific. The information on the progress of the methods and the variables used to predict the occurrence, severity, and timing of the breakup ice jams still remains limited. This study summarizes the different processes contributing to ice jam formation and breakup, the various existing ice jam prediction models, and their potential and limitations regarding the improvement in ice jam predictions. An overview of the application of artificial neural networks and fuzzy logic systems in ice-related problems is presented. Genetic programming is also explained as a possible mean for ice-related problems. Although genetic programming shows promising results in hydrological modelling, it has not yet been used in ice-related problems. The review of literature highlights that data-driven and machine learning techniques provide promising means in predicting ice jams with better confidence, but more scientific research is needed", notes = "INRS-ETE, Universite du Quebec, 490 rue de la Couronne, Quebec G1K 9A9, Canada", } @Article{madar:2005:IECR, author = "Janos Madar and Janos Abonyi and Ferenc Szeifert", title = "Genetic Programming for the Identification of Nonlinear Input-Output Models", journal = "Industrial and Engineering Chemistry Research", year = "2005", volume = "44", number = "9", pages = "3178--3186", month = apr, keywords = "genetic algorithms, genetic programming, MATLAB, Chemical structure, Algorithms, Mathematical methods, Organic reactions", ISSN = "0888-5885", URL = "http://www.fmt.vein.hu/softcomp/gp/ie049626e.pdf", DOI = "doi:10.1021/ie049626e", code_url = "https://www.mathworks.com/matlabcentral/fileexchange/47197-genetic-programming-matlab-toolbox", size = "9 pages", abstract = "Linear-in-parameters models are quite widespread in process engineering, e.g., nonlinear additive autoregressive models, polynomial ARMA models, etc. This paper proposes a new method for the structure selection of these models. The method uses genetic programming to generate nonlinear input-output models of dynamical systems that are represented in a tree structure. The main idea of the paper is to apply the orthogonal least squares (OLS) algorithm to estimate the contribution of the branches of the tree to the accuracy of the model. This method results in more robust and interpretable models. The proposed approach has been implemented as a freely available MATLAB Toolbox, www.fmt.veim.hu/softcomp. The simulation results show that the developed tool provides an efficient and fast method for determining the order and structure for nonlinear input-output models.", notes = " http://pubs.acs.org/journals/iecred/index.html Broken May 2024 www.fmt.veim.hu/softcomp S0888-5885(04)09626-5 American Chemical Society Department of Process Engineering, University of Veszprem, P.O. Box 158, Veszprem 8201, Hungary", } @PhdThesis{Madar_Janos_theses_hu, author = "Janos Madar", title = "Application of A Priori Knowledge in Chemical Engineering", title_hu = "Az a priori ismeretek alkalmazasa a vegyipari folyamatmernoksegben", school = "School of Chemical Engineering, University of Veszprem", year = "2005", address = "Hungary", keywords = "genetic algorithms, genetic programming", broken = "http://konyvtar.uni-pannon.hu/doktori/2005", URL = "http://konyvtar.uni-pannon.hu/doktori/bovebb_en.php?id=139", URL = "http://konyvtar.uni-pannon.hu/doktori/2005/Madar_Janos_theses_hu.pdf", URL = "http://konyvtar.uni-pannon.hu/doktori/2005/Madar_Janos_theses_en.pdf", size = "128 pages", notes = "Although 7 page summary available in English, most of Madar_Janos_theses_hu.pdf is also in english. Supervised by Dr. Janos Abonyi", } @Misc{DBLP:journals/corr/abs-2403-03397, author = "Paula Maddigan and Andrew Lensen and Bing Xue", title = "Explaining Genetic Programming Trees using Large Language Models", howpublished = "arXiv", volume = "abs/2403.03397", year = "2024", month = "6 " # mar, keywords = "genetic algorithms, genetic programming, Large Language Models, LLM, Explainable AI, XAI, Non-Linear Dimensionality Reduction, ChatGPT, multilingual french/english", URL = "https://doi.org/10.48550/arXiv.2403.03397", DOI = "doi:10.48550/ARXIV.2403.03397", eprinttype = "arXiv", eprint = "2403.03397", timestamp = "Wed, 03 Apr 2024 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/corr/abs-2403-03397.bib", bibsource = "dblp computer science bibliography, https://dblp.org", size = "17 pages", abstract = "Genetic programming (GP) has the potential to generate explainable results, especially when used for dimensionality reduction. In this research, we investigate the potential of leveraging eXplainable AI (XAI) and large language models (LLMs) like ChatGPT to improve the interpretability of GP-based non-linear dimensionality reduction. Our study introduces a novel XAI dashboard named GP4NLDR, the first approach to combine state-of-the-art GP with an LLM-powered chatbot to provide comprehensive, user-centred explanations. We showcase the system's ability to provide intuitive and insightful narratives on high-dimensional data reduction processes through case studies. Our study highlights the importance of prompt engineering in eliciting accurate and pertinent responses from LLMs. We also address important considerations around data privacy, hallucinatory outputs, and the rapid advancements in generative AI. Our findings demonstrate its potential in advancing the explainability of GP algorithms. This opens the door for future research into explaining GP models with LLMs.", notes = "Prompt engineering, Data privacy, Hallucinations, Rapid advancements, chatbot", } @InProceedings{Madhubala:2014:ICRTIT, author = "G. Madhubala and R. Priyadharshini and P. Ranjitham and Santhi Baskaran", booktitle = "International Conference on Recent Trends in Information Technology (ICRTIT 2014)", title = "Nature Inspired Enhanced Data Deduplication for Efficient Cloud Storage", year = "2014", month = apr, keywords = "genetic algorithms, genetic programming, Deduplication, Hashing, Levenshtein Algorithm", DOI = "doi:10.1109/ICRTIT.2014.6996211", size = "6 pages", abstract = "Cloud Computing is the delivery of computing as a service, which is specifically involved with Storage of data, enabling ubiquitous, convenient access to shared resources that are provided to computers and other devices as a utility over a network. Storage, which is considered to be the key attribute, is hindered by the presence of redundant copies of data. Data Deduplication is a specialised technique for data compression and duplicate detection for eliminating duplicate copies of data to make storage efficient. Cloud Service Providers currently employ Hashing technique so as to avoid the presence of redundant copies. Apparently, there are a few major pitfalls which can be vanquished through the employment of a Nature - Inspired, Genetic Programming Approach, for deduplication. Genetic Programming is a systematic, domain - independent programming model making use of the ideologies of biological evolution so as to handle a complicated problem. A Sequence Matching Algorithm and Levenshtein's Algorithm are used for Text Comparison and then Genetic Programming concepts are used to detect the closest match. The performance of these three algorithms and hashing technique are compared. Since bio-inspired concepts, systems and algorithms are found to be more efficient, a Nature-Inspired Approach for data deduplication in cloud storage is implemented.", notes = "Dept. of Inf. Technol., Pondicherry Eng. Coll., Pondicherry, India ; Also known as \cite{6996211}", } @Article{Madic:2022:FUME, author = "Milos Madic and Marin Gostimirovic and Dragan Rodic and Miroslav Radovanovic and Margareta Coteata", title = "Mathematical modelling of the {CO2} laser cutting process using genetic programming", journal = "Facta Universitatis, Series: Mechanical Engineering", year = "2022", volume = "20", number = "3", pages = "665--676", month = dec, keywords = "genetic algorithms, genetic programming, Kerf taper angle, Laser cutting, carbon dioxide", ISSN = "2335-0164", URL = "https://casopisi.junis.ni.ac.rs/index.php/FUMechEng/article/view/6292", URL = "https://casopisi.junis.ni.ac.rs/index.php/FUMechEng/article/viewFile/6292/4460", DOI = "doi:10.22190/FUME210810003M", size = "12 pages", abstract = "The development of mathematical models by using experimental data is of great importance for modelling and optimization of the laser cutting process. Motivated by the lack of research regarding the use of genetic programming (GP) for deriving empirical mathematical models that describe the laser cutting process, the present study discusses the application of GP to the development of a kerf taper angle mathematical model. The aim was to quantify the relationship between three selected input parameters (cutting speed, laser power and assist gas pressure) and kerf taper angle using GP in the CO2 laser cutting of aluminium alloy AlMg3. To obtain the experimental database for the GP model evolution process, a laser cutting experiment was planned as per standard full factorial design where all three selected parameters were varied at three levels. The fit between the experimental and the GP model prediction values of kerf taper angle was found to be appropriate. Finally, by using the derived GP mathematical model, the analysis of the effects of input parameters on the change in kerf taper angle values was performed by generating 3D surface plots.", notes = "Also known as \cite{FUME6292} University of Nis, Republic of Serbia https://casopisi.junis.ni.ac.rs/index.php/FUMechEng/index", } @TechReport{madjidi:1996:3SE, author = "Payam Madjidi", title = "Genetic Programming and analysis of high frequency financial data", institution = "Center for Parallel Computers, Royal Institute of Technology", year = "1996", type = "TRITA-PDC Report", number = "ISRN KTH/PDC/R--96/3--SE", address = "Stockholm, Sweden", keywords = "genetic algorithms, genetic programming", ISSN = "1401-2731", URL = "http://www.pdc.kth.se/~payam/pub/gp.ps", notes = "Also covers implementation in C? In gp.ps pages in reverse order. May have been presented at 2NWGA 1996 ga96NWGA But not mentioned in ftp://ftp.uwasa.fi/cs/2NWGA/main.ps.Z or ftp://garbo.uwasa.fi/cs/2NWGA/2NWGA.bib", } @Article{madokoro:2021:Algorithms, author = "Hirokazu Madokoro and Stephanie Nix and Kazuhito Sato", title = "Automatic Calibration of Piezoelectric {Bed-Leaving} Sensor Signals Using Genetic Network Programming Algorithms", journal = "Algorithms", year = "2021", volume = "14", number = "4", keywords = "genetic algorithms, genetic programming", ISSN = "1999-4893", URL = "https://www.mdpi.com/1999-4893/14/4/117", DOI = "doi:10.3390/a14040117", abstract = "This paper presents a filter generating method that modifies sensor signals using genetic network programming (GNP) for automatic calibration to absorb individual differences. For our earlier study, we developed a prototype that incorporates bed-leaving detection sensors using piezoelectric films and a machine-learning-based behaviour recognition method using counter-propagation networks (CPNs). Our method learns topology and relations between input features and teaching signals. Nevertheless, CPNs have been insufficient to address individual differences in parameters such as weight and height used for bed-learning behaviour recognition. For this study, we actualize automatic calibration of sensor signals for invariance relative to these body parameters. This paper presents two experimentally obtained results from our earlier study. They were obtained using low-accuracy sensor signals. For the preliminary experiment, we optimised the original sensor signals to approximate high-accuracy ideal sensor signals using generated filters. We used fitness to assess differences between the original signal patterns and ideal signal patterns. For application experiments, we used fitness calculated from the recognition accuracy obtained using CPNs. The experimentally obtained results reveal that our method improved the mean accuracies for three datasets.", notes = "also known as \cite{a14040117}", } @InProceedings{Madureira:2018:SBSE, author = "Vinocius Souza Madureira and Thiago Correia Vieira", title = "Coordination of inverse-time overcurrent relays with fuses using genetic algorithm", booktitle = "2018 Simposio Brasileiro de Sistemas Eletricos (SBSE)", year = "2018", address = "Niteroi, Brazil", month = "12-16 " # may, keywords = "genetic algorithms, genetic programming, electrical system protection, coordination, overcurrent relays, fuses", DOI = "doi:10.1109/SBSE.2018.8395627", size = "6 pages", abstract = "This work presents a methodology to obtain discrete adjustments for overcurrent relays and fuse selection using Genetic Algorithm. The purpose is to minimize the protection devices time and therefore reduce the impact caused by short circuits in an electrical installation. In addition, the mathematical formulation presented aims to ensure that the resulting protection system is coordinated and selective. The choice of fuses is made with the aid of Curve Fitting, which makes the results more accurate. The objective function presented penalizes the mismatches, so that the search for the solution does not converge to infeasible solutions. The method is tested in an electrical radial topology system and the results show its effectiveness.", notes = "In Portuguese Also known as \cite{8395627}", } @InProceedings{Maechtle:2025:SBFT, author = "Felix Maechtle and Nils Loose and Jan-Niclas Serr and Jonas Sander and Thomas Eisenbarth", title = "{AutoStub}: Genetic Programming-Based Stub Creation for Symbolic Execution", booktitle = "Proceedings of the 18th ACM/IEEE International Workshop on Search-Based and Fuzz Testing, SBFT 2025", year = "2025", editor = "Gunel Jahangirova and Matteo Biagiola and Vincenzo Riccio", address = "Ottawa", month = "28-29 " # apr, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, grammar guided", URL = "https://www.human-competitive.org/sites/default/files/autostub_humies2025_submission_0.txt", URL = "https://www.human-competitive.org/sites/default/files/autostub-paper_0.pdf", code_url = "https://github.com/UzL-ITS/AutoStub", abstract = "Symbolic execution is a powerful technique for software testing, but suffers from limitations when encountering external functions, such as native methods or third-party libraries. Existing solutions often require additional context, expensive SMT solvers, or manual intervention to approximate these functions through symbolic stubs. we propose a novel approach to automatically generate symbolic stubs for external functions during symbolic execution that leverages Genetic Programming. When the symbolic executor encounters an external function, AutoStub generates training data by executing the function on randomly generated inputs and collecting the outputs. Genetic programming then derives expressions that approximate the behavior of the function, serving as symbolic stubs. These automatically generated stubs allow the symbolic executor to continue the analysis without manual intervention, enabling the exploration of program paths that were previously intractable. We demonstrate that AutoStub can automatically approximate external functions with over 90% accuracy for 55% of the functions evaluated, and can infer language-specific behaviors that reveal edge cases crucial for software testing.", notes = "2025 HUMIES finalist. Also known as \cite{machtle2024autostub} SBFT 2025 https://sbft25.github.io/", } @Article{DBLP:journals/jca/MaedaS07, author = "Ken-ichi Maeda and Chiaki Sakama", title = "Identifying Cellular Automata Rules", journal = "Journal of Cellular Automata", year = "2007", volume = "2", number = "1", pages = "1--20", keywords = "genetic algorithms, genetic programming, Cellular automata, identification problem, decision tree", bibsource = "DBLP, http://dblp.uni-trier.de", URL = "http://www.oldcitypublishing.com/JCA/JCAabstracts/JCA2.1abstracts/JCAv2n1p1-20Maeda.html", URL = "http://www.sys.wakayama-u.ac.jp/~sakama/papers/jca07.pdf", size = "20 pages", abstract = "This paper studies a method for identifying cellular automata rules (CA rules). Given a sequence of CA configurations, we first seek an appropriate neighbourhood of a cell and collect cellular changes of states as evidences. The collected evidences are then classified using a decision tree, which is used for constructing CA transition rules. Conditions for classifying evidences in a decision tree are computed using genetic programming. We perform experiments using several types of CAs and verify that the proposed method successfully identifies correct CA rules.", } @InProceedings{Maeda:2000:GECCO, author = "Yoichiro Maeda and Satomi Kawaguchi", title = "Redundant Node Pruning and Adaptive Search Method for Genetic Programming", pages = "535", year = "2000", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)", editor = "Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer", address = "Las Vegas, Nevada, USA", publisher_address = "San Francisco, CA 94104, USA", month = "10-12 " # jul, keywords = "genetic algorithms, genetic programming, Poster", ISBN = "1-55860-708-0", URL = "http://gpbib.cs.ucl.ac.uk/gecco2000/GP102.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco2000/GP102.ps", notes = "A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of \cite{whitley:2000:GECCO}", } @InProceedings{Maekawa:2011:alife, author = "Tadao Maekawa and Osamu Ueno and Norie Kawai and Emi Nishina and Manabu Honda and Tsutomu Oohashi", title = "Evolutionary acquisition of genetic program for death", booktitle = "Advances in Artificial Life, ECAL 2011: Proceedings of the Eleventh European Conference on the Synthesis and Simulation of Living Systems", year = "2011", editor = "Tom Lenaerts and Mario Giacobini and Hugues Bersini and Paul Bourgine and Marco Dorigo and Rene Doursat", pages = "481--486", address = "Paris", month = "8-12 " # aug, organisation = "International Society of Artificial Life (ISAL)", publisher = "MIT Press", keywords = "genetic algorithms", isbn13 = "978-0-262-29714-1", URL = "http://mitpress.mit.edu/books/chapters/0262297140chap74.pdf", size = "6 pages", abstract = "As part of our research on , we formed the hypothesis that originally immortal terrestrial organisms evolve into ones that are programmed for autonomous death. We then conducted simulation experiments in which we examined this hypothesis using an artificial ecosystem that we designed to refer to a terrestrial ecosystem endowed with Artificial Chemistry (AChem). Our findings suggest that, in the case of a mortal organism appearing among a population of immortal organisms as a mutant which evolutionarily acquires a genetic program for death by means of self-decomposition, this organism and its surviving offspring surpass immortal organisms and eventually prosper with adaptive divergence under various environmental conditions within a certain probability.", notes = "Appears to be on artificial chemistry rather than genetic programming. Broken April 2025 http://www.ecal11.org/ Broken April 2025 Complete Proceedings e-Book Available at: http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=12760", } @InProceedings{Maertens:2017:EuroGP, author = "Marcus Maertens and Fernando Kuipers and Piet {Van Mieghem}", title = "Symbolic Regression on Network Properties", booktitle = "EuroGP 2017: Proceedings of the 20th European Conference on Genetic Programming", year = "2017", month = "19-21 " # apr, editor = "Mauro Castelli and James McDermott and Lukas Sekanina", series = "LNCS", volume = "10196", publisher = "Springer Verlag", address = "Amsterdam", pages = "131--146", organisation = "species", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-55695-6", DOI = "doi:10.1007/978-3-319-55696-3_9", abstract = "Networks are continuously growing in complexity, which creates challenges for determining their most important characteristics. While analytical bounds are often too conservative, the computational effort of algorithmic approaches does not scale well with network size. This work uses Cartesian Genetic Programming for symbolic regression to evolve mathematical equations that relate network properties directly to the eigenvalues of network adjacency and Laplacian matrices. In particular, we show that these eigenvalues are powerful features to evolve approximate equations for the network diameter and the isoperimetric number, which are hard to compute algorithmically. Our experiments indicate a good performance of the evolved equations for several real-world networks and we demonstrate how the generalization power can be influenced by the selection of training networks and feature sets.", notes = "Part of \cite{Castelli:2017:GP} EuroGP'2017 held inconjunction with EvoCOP2017, EvoMusArt2017 and EvoApplications2017", } @PhdThesis{Maertens:thesis, author = "Marcus Maertens", title = "Information Propagation in Complex Networks Structures and Dynamics", school = "Delft University of Technology", year = "2018", address = "Holland", month = "8 " # jan, keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, information propagation, functional brain networks, toxicity, multi-player online games, network epidemics, epidemic spreading model, complex networks, symbolic regression", isbn13 = "978-94-028-0907-7", URL = "https://repository.tudelft.nl/islandora/object/uuid:b1a1ead7-a631-4f05-b9a9-17a1be6e15e1", URL = "https://repository.tudelft.nl/islandora/object/uuid:b1a1ead7-a631-4f05-b9a9-17a1be6e15e1/datastream/OBJ/download", DOI = "doi:10.4233/uuid:b1a1ead7-a631-4f05-b9a9-17a1be6e15e1", size = "150 pages", abstract = "... chapter 6 is a study on the capabilities of symbolic regression for network properties. We develop an automated system based on Genetic Programming which is able to be trained by families of networks to learn the relations between several of their properties. These properties can be features of the networks like the eigenvalues of their adjacency or Laplacian matrices or network metrics like the network diameter or the isoperimetric number. We show that the system can generate approximate formulas for those metrics that often give better results than previously known analytic bounds. The evolved formulae for the network diameter are evaluated on a selection of real-world networks of different origins. The network diameter bounds hop-based information propagation and is thus of high importance for designing network algorithms. A careful selection of training networks and network features is crucial for evolving good approximate formulas for the network diameter and similar properties. ...", notes = "Supervisor prof. dr. ir. P. F. A. Van Mieghem Section 6.2.2 Cartesian Genetic Programming (CGP) http://repository.tudelft.nl/", } @InProceedings{DBLP:conf/pkdd/MaesGW12, author = "Francis Maes and Pierre Geurts and Louis Wehenkel", title = "Embedding Monte Carlo Search of Features in Tree-Based Ensemble Methods", booktitle = "European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2012, Part I", year = "2012", editor = "Peter A. Flach and Tijl {De Bie} and Nello Cristianini", pages = "191--206", volume = "7523", series = "Lecture Notes in Computer Science", bibsource = "DBLP, http://dblp.uni-trier.de", address = "Bristol UK", month = "24-28 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, MCTS, Dimensionality Reduction, Feature Selection and Extraction, Embedded Feature Generation, Monte Carlo Search, Decision Trees, Random Forests, Tree Boosting", isbn13 = "978-3-642-33459-7", DOI = "doi:10.1007/978-3-642-33460-3_18", size = "16 pages", abstract = "Feature generation is the problem of automatically constructing good features for a given target learning problem. While most feature generation algorithms belong either to the filter or to the wrapper approach, this paper focuses on embedded feature generation. We propose a general scheme to embed feature generation in a wide range of tree-based learning algorithms, including single decision trees, random forests and tree boosting. It is based on the formalisation of feature construction as a sequential decision making problem addressed by a tractable Monte Carlo search algorithm coupled with node splitting. This leads to fast algorithms that are applicable to large-scale problems. We empirically analyse the performances of these tree-based learners combined or not with the feature generation capability on several standard datasets.", notes = " Feature engineering - the process of identifying a good set of features for a given learning task. reverse polish notation RPN grammar postfix. vowel, segment, spambase, satellite, pendigits, dig44. Baseline, random, step, look-ahead. tree stumps variance. www.ecmlpkdd2012.net/files/2012/09/ECMLPKDD2012booklet.pdf", } @InProceedings{Maeshiro:1997:gceo, author = "Tetsuya Maeshiro and Masayuki Kimura", title = "Genetic Code as an Evolving Organism", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "Artifical life and evolutionary robotics", pages = "413", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", notes = "GP-97", } @PhdThesis{1570, author = "Esteban Maestre", title = "Modeling instrumental gestures: an analysis/synthesis framework for violin bowing", year = "2009", school = "Universitat Pompeu Fabra", address = "Barcelona, Spain", isbn_x = "B.47390-2009", URL = "http://hdl.handle.net/10803/7562", abstract = "This work presents a methodology for modeling instrumental gestures in excitation-continuous musical instruments. In particular, it approaches bowing control in violin classical performance. Nearly non-intrusive sensing techniques are introduced and applied for accurately acquiring relevant timbre-related bowing control parameter signals and constructing a performance database. By defining a vocabulary of bowing parameter envelopes, the contours of bow velocity, bow pressing force, and bow-bridge distance are modeled as sequences of Bezier cubic curve segments, yielding a robust parametrisation that is well suited for reconstructing original contours with significant fidelity. An analysis/synthesis statistical modeling framework is constructed from a database of parameterised contours of bowing controls, enabling a flexible mapping between score annotations and bowing parameter envelopes. The framework is used for score-based generation of synthetic bowing parameter contours through a bow planning algorithm able to reproduce possible constraints imposed by the finite length of the bow. Rendered bowing control signals are successfully applied to automatic performance by being used for driving offline violin sound generation through two of the most extended techniques: digital waveguide physical modeling, and sample-based synthesis.", notes = "Not on GP? Supervisor: X. Serra", } @Article{MAGALHAES:2023:asoc, author = "Dimmy Magalhaes and Ricardo H. R. Lima and Aurora Pozo", title = "Creating deep neural networks for text classification tasks using grammar genetic programming", journal = "Applied Soft Computing", year = "2023", volume = "135", pages = "110009", month = mar, keywords = "genetic algorithms, genetic programming, grammatical evolution, ANN, Text classification, Evolutionary algorithms, Automatic design, Deep neural networks", ISSN = "1568-4946", URL = "https://www.sciencedirect.com/science/article/pii/S1568494623000273", DOI = "doi:10.1016/j.asoc.2023.110009", code_url = "https://doi.org/10.24433/CO.5469683.v1", abstract = "Text classification is one of the Natural Language Processing (NLP) tasks. Its objective is to label textual elements, such as phrases, queries, paragraphs, and documents. In NLP, several approaches have achieved promising results regarding this task. Deep Learning-based approaches have been widely used in this context, with deep neural networks (DNNs) adding the ability to generate a representation for the data and a learning model. The increasing scale and complexity of DNN architectures was expected, creating new challenges to design and configure the models. we present a study on the application of a grammar-based evolutionary approach to the design of DNNs, using models based on Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Graph Neural Networks (GNNs). We propose different grammars, which were defined to capture the features of each type of network, also proposing some combinations, verifying their impact on the produced designs and performance of the generated models. We create a grammar that is able to generate different networks specialized on text classification, by modification of Grammatical Evolution (GE), and it is composed of three main components: the grammar, mapping, and search engine. Our results offer promising future research directions as they show that the projected architectures have a performance comparable to that of their counterparts but can still be further improved. We were able to improve the results of a manually structured neural network in 8.18percent in the best case", notes = "also known as \cite{MAGALHAES2023110009}", } @InProceedings{Magalhaes-Mendes:2021:SBrT, author = "Leticia {Magalhaes Mendes} and Elloa B. Guedes", title = "Previsao de Casos de {COVID-19} no {Brasil} com Aprendizagem de Maquina Automatizada", booktitle = "XXXIX Simposio Brasileiro de Telecomunicacoes e Processamento de Sinais - SBrT 2021", year = "2021", address = "Fortaleza, CE, Brazil", month = sep # " 26-29", keywords = "genetic algorithms, genetic programming, TPOT, Machine Learning, Time Series, COVID-19, GPU", URL = "https://biblioteca.sbrt.org.br/articlefile/2852.pdf", size = "2 pages", abstract = "we aim at using Automated Machine Learning to forecast new COVID-19 cases in Brazil. By using a Genetic Programming approach combined with Time Series Split cross-validation, it was possible to identify a Linear Vector Support regression with average R-squared of 0.4257 and maximum equal to 0.975, i.e., that improves its performance as more data becomes available", notes = "In Portuguese", } @Article{Magalhaes-Ribeiro:2018:RITA, author = "Igor {Magalhaes Ribeiro} and Carlos Cristiano {Hasenclever Borges} and Bruno Zonovelli {da Silva} and Wagner Arbex", title = "A Genetic Programming Model for Association Studies to Detect Epistasis in Low Heritability Data", journal = "Revista de Informatica Teorica e Aplicada", year = "2018", volume = "25", number = "2", pages = "85--92", keywords = "genetic algorithms, genetic programming, bioinformatics, GWAS, SNP, random forest, computational modeling, mathematical modeling", ISSN = "2175-2745", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/rita/rita25.html#RibeiroBSA18", URL = "https://seer.ufrgs.br/rita/article/view/RITA-VOL-25-NR2-85", URL = "https://seer.ufrgs.br/rita/article/view/RITA-VOL-25-NR2-85/pdf", DOI = "doi:10.22456/2175-2745.79333", size = "8 pages", abstract = "The genome-wide associations studies (GWAS) aims to identify the most influential markers in relation to the phenotype values. One of the substantial challenges is to find a non-linear mapping between genotype and phenotype, also known as epistasis, that usually becomes the process of searching and identifying functional SNPs more complex. Some diseases such as cervical cancer, leukemia and type 2 diabetes have low heritability. The heritability of the sample is directly related to the explanation defined by the genotype, so the lower the heritability the greater the influence of the environmental factors and the less the genotypic explanation. In this work, an algorithm capable of identifying epistatic associations at different levels of heritability is proposed. The developing model is a aplication of genetic programming with a specialized initialization for the initial population consisting of a random forest strategy. The initialization process aims to rank the most important SNPs increasing the probability of their insertion in the initial population of the genetic programming model. The expected behaviour of the presented model for the obtainment of the causal markers intends to be robust in relation to the heritability level. The simulated experiments are case-control type with heritability level of 0.4, 0.3, 0.2 and 0.1 considering scenarios with 100 and 1000 markers. Our approach was compared with the GPAS software and a genetic programming algorithm without the initialization step. The results show that the use of an efficient population initialization method based on ranking strategy is very promising compared to other models.", notes = "https://seer.ufrgs.br/rita/index Federal University of Juiz de Fora, Juiz de Fora, MG, Brazi also known as \cite{journals/rita/RibeiroBSA18}", } @Article{Magdy:GPEM, author = "Walid Magdy", title = "Robert Elliott Smith: Rage Inside the Machine--the prejudice of algorithms, and how to stop the internet making bigots of us all", journal = "Genetic Programming and Evolvable Machines", year = "2022", volume = "23", number = "1", pages = "157--158", month = mar, note = "Book review", keywords = "genetic algorithms, genetic programming, AI, www", ISSN = "1389-2576", URL = "https://rdcu.be/cAfXL", DOI = "doi:10.1007/s10710-021-09420-w", size = "2 pages", notes = "Bloomsbury business, 2019, 344 pp., ISBN 9781472963888", } @InProceedings{Maghazeh:2013:SAMOS, author = "Arian Maghazeh and Unmesh D. Bordoloi and Petru Eles and Zebo Peng", title = "General purpose computing on low-power embedded {GPUs}: Has it come of age?", booktitle = "2013 International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS XIII)", year = "2013", editor = "H. Jeschke", address = "Samos, Greece", month = "15-18 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, GPU, GPGPU, OpenCL, ARM Cortex A9 Vivante GC2000 GPU, Tesla M2050, Intertwined Spiral problem, Rijndael, bitcount , convolution, pattern matching, energy consumption", isbn13 = "978-1-4799-0103-6", DOI = "doi:10.1109/SAMOS.2013.6621099", size = "10 pages", abstract = "In this paper we evaluate the promise held by low-power GPUs for non-graphic workloads that arise in embedded systems. Towards this, we map and implement 5 benchmarks, that find utility in very different application domains, to an embedded GPU. Our results show that apart from accelerated performance, embedded GPUs are promising also because of their energy efficiency which is an important design goal for battery-driven mobile devices. We show that adopting the same optimization strategies as those used for programming high-end GPUs might lead to worse performance on embedded GPUs. This is due to restricted features of embedded GPUs, such as, limited or no user-defined memory, small instruction-set, limited number of registers, among others. We propose techniques to overcome such challenges, e.g., by distributing the workload between GPUs and multi-core CPUs, similar to the spirit of heterogeneous computation.", notes = "Dept. of Comput. & Inf. Sci., Linkopings Univ., Linkopings, Sweden Also known as \cite{6621099}", } @PhdThesis{Maghazeh1268932, author = "Arian Maghazeh", title = "System-Level Design of {GPU}-Based Embedded Systems", school = "Linkoping University, Faculty of Science \& Engineering", year = "2018", address = "Sweden", keywords = "genetic algorithms, genetic programming, GPU, GPGPU, embedded system, heterogeneous computing, system-level design", ISSN = "0345-7524", isbn13 = "9789176851753", series = "Linkoping Studies in Science and Technology. Dissertations", number = "1964", URL = "https://books.google.co.uk/books/about/System_Level_Design_of_GPU_Based_Embedde.html?id=139-DwAAQBAJ&redir_esc=y", URL = "http://liu.diva-portal.org/smash/get/diva2:1268932/FULLTEXT02.pdf", DOI = "doi:10.3384/diss.diva-152469", size = "62 pages", abstract = "Modern embedded systems deploy several hardware accelerators, in a heterogeneous manner, to deliver high-performance computing. Among such devices, graphics processing units (GPUs) have earned a prominent position by virtue of their immense computing power. However, a system design that relies on sheer throughput of GPUs is often incapable of satisfying the strict power- and time-related constraints faced by the embedded systems. This thesis presents several system-level software techniques to optimize the design of GPU-based embedded systems under various graphics and non-graphics applications. As compared to the conventional application-level optimizations, the system-wide view of our proposed techniques brings about several advantages: First, it allows for fully incorporating the limitations and requirements of the various system parts in the design process. Second, it can unveil optimization opportunities through exposing the information flow between the processing components. Third, the techniques are generally applicable to a wide range of applications with similar characteristics. In addition, multiple system-level techniques can be combined together or with application-level techniques to further improve the performance. We begin by studying some of the unique attributes of GPU-based embedded systems and discussing several factors that distinguish the design of these systems from that of the conventional high-end GPU-based systems. We then proceed to develop two techniques that address an important challenge in the design of GPU-based embedded systems from different perspectives. The challenge arises from the fact that GPUs require a large amount of workload to be present at runtime in order to deliver a high throughput. However, for some embedded applications, collecting large batches of input data requires an unacceptable waiting time, prompting a trade-off between throughput and latency. We also develop an optimization technique for GPU-based applications to address the memory bottleneck issue by using the GPU L2 cache to shorten data access time. Moreover, in the area of graphics applications, and in particular with a focus on mobile games, we propose a power management scheme to reduce the GPU power consumption by dynamically adjusting the display resolution, while considering the user's visual perception at various resolutions. We also discuss the collective impact of the proposed techniques in tackling the design challenges of emerging complex systems. The proposed techniques are assessed by real-life experimentations on GPU-based hardware platforms, which demonstrate the superior performance of our approaches as compared to the state-of-the-art techniques.", notes = "Limited mention of GP Supervisors: Zebo Peng, Petru Ion Eles, Unmesh D. Bordoloi", } @InProceedings{Maghoumi:2014:CIMSIVP, author = "M. Maghoumi and B. J. Ross", booktitle = "IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing (CIMSIVP 2014)", title = "A comparison of genetic programming feature extraction languages for image classification", year = "2014", month = dec, abstract = "Visual pattern recognition and classification is a challenging computer vision problem. Genetic programming has been applied towards automatic visual pattern recognition. One of the main factors in evolving effective classifiers is the suitability of the GP language for defining expressions for feature extraction and classification. This research presents a comparative study of a variety of GP languages suitable for classification. Four different languages are examined, which use different selections of image processing operators. One of the languages does block classification, which means that an image is classified as a whole by examining many blocks of pixels within it. The other languages are pixel classifiers, which determine classification for a single pixel. Pixel classifiers are more common in the GP-vision literature. We tested the languages on different instances of Brodatz textures, as well as aerial and camera images. Our results show that the most effective languages are pixel-based ones with spatial operators. However, as is to be expected, the nature of the image will determine the effectiveness of the language used.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CIMSIVP.2014.7013278", notes = "Also known as \cite{7013278}", } @InProceedings{Magnani:2023:WIVACE, author = "Giulia Magnani and Monica Mordonini and Stefano Cagnoni", title = "Hybrid GP/PSO Representation of 1-D Signals in an Autoencoder Fashion", booktitle = "17th Italian Workshop on Artificial Life and Evolutionary Computation", year = "2023", editor = "Marco Villani and Stefano Cagnoni and Roberto Serra", volume = "1977", series = "Communications in Computer and Information Science", pages = "228--238", address = "Venice, Italy", month = sep # " 6-8", publisher = "Springer Nature", note = "revised selected papers", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-031-57430-6", DOI = "doi:10.1007/978-3-031-57430-6_18", abstract = "A popular low-dimensional latent representation that retains as much information as possible about a data set is the one represented by the output of a hidden layer located within a neural net having homomorphic input and output layers, termed autoencoder, trained to produce a copy of the input data as its output. This exploratory paper suggests that reformulating the problem as a GP-based symbolic regression can achieve the same goal. The latent representation, in this case, is obtained as a byproduct of the solution to the problem of finding a parametric equation that represents a model of a family of signals (functions) that share the same equation, differing only for the values of a set of free parameters that appear in their definition. This hypothesis is supported by a simple proof of concept based on the results of symbolic regression of a set of Gaussian functions. A discussion of possible issues that might need to be tackled when the method is applied to more complex real-world data and of the corresponding possible countermeasures concludes the paper.", notes = "Also known as \cite{DBLP:conf/wivace/MagnaniMC23} Published after the workshop in 2024", } @InProceedings{Magni:2013:GPGPU, author = "Alberto Magni and Dominik Grewe and Nick Johnson", title = "Input-aware Auto-tuning for Directive-based GPU Programming", booktitle = "Proceedings of the 6th Workshop on General Purpose Processor Using Graphics Processing Units, GPGPU-6", year = "2013", pages = "66--75", address = "Houston, Texas, USA", publisher = "ACM", keywords = "genetic improvement, nearest neighbour local hill climbing search, Automatic Programming, Concurrent Programming, Parallel Programming, hill climbing, GPU, OpenACC, autotuning", acmid = "2458530", isbn13 = "978-1-4503-2017-7", URL = "http://doi.acm.org/10.1145/2458523.2458530", DOI = "doi:10.1145/2458523.2458530", size = "10 pages", abstract = "The difficulties posed by GPGPU programming and the need to increase productivity have guided research towards directive-based high-level programs for accelerators. This effort has led to the definition of the OpenACC industry standard. It significantly simplifies writing code for graphics engines leaving the programmer the opportunity to tune the application for the target hardware and input. In this paper we address the problem of choosing the best mapping of sequential OpenACC loops to the parallel thread-space for a given program and input size. We show that auto-tuning on mapping parameters can improve performance by up to 4.8x over the default chosen by a state-of-the-art compiler. To reduce the overhead of auto-tuning we introduce a search technique that exploits similarities in behaviour across inputs using a nearest neighbour approach. This dramatically reduces the search for a good mapping (by 97percent compared to random search). Finally we propose a heuristic for stopping the focused search which, averaged across 12 benchmarks and 30 input sizes each, achieves a speedup over the default of 1.26x with only 8 sampling runs.", notes = "not GP? page 67 'We propose an auto-tuning technique for the optimization of OpenACC programs leading to a maximum improvement of 4.8x over a state-of-the-art compiler. We show that exploiting cross-input similarities can dramatically reduce the search space for good configurations leading to vastly reduced search times. We devise a mechanism to determine when to stop the search because further exploration is unlikely to yield better results.' OpenACC compiler PGCC 12.5 64-bit by PGI. Tesla C2050 p73 '4 runs to reach 90percent of the optimal performance' Also known as \cite{Magni:2013:IAD:2458523.2458530}", } @InProceedings{Magnusson:2016:GECCO, author = "Lars Vidar Magnusson and Roland Olsson", title = "Improving the {Canny} Edge Detector Using Automatic Programming: Improving Non-Max Suppression", booktitle = "GECCO '16: Proceedings of the 2016 Annual Conference on Genetic and Evolutionary Computation", year = "2016", editor = "Tobias Friedrich and Frank Neumann and Andrew M. Sutton and Martin Middendorf and Xiaodong Li and Emma Hart and Mengjie Zhang and Youhei Akimoto and Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and Daniele Loiacono and Julian Togelius and Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and Faustino Gomez and Carlos M. Fonseca and Heike Trautmann and Alberto Moraglio and William F. Punch and Krzysztof Krawiec and Zdenek Vasicek and Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and Boris Naujoks and Enrique Alba and Gabriela Ochoa and Simon Poulding and Dirk Sudholt and Timo Koetzing", pages = "461--468", month = "20-24 " # jul, organisation = "SIGEVO", address = "Denver, USA", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, ADATE", isbn13 = "978-1-4503-4206-3", URL = "http://www.it.hiof.no/iaml/magnusson16_gecco.pdf", DOI = "doi:10.1145/2908812.2908926", size = "8 pages", abstract = "we employ automatic programming, a relatively unknown evolutionary computation strategy, to improve the non-max suppression step in the popular Canny edge detector. The new version of the algorithm has been tested on a dataset widely used to benchmark edge detection algorithms. The performance has increased by 1.9percent, and a pairwise student-t comparison with the original algorithm gives a p-value of 6.45 x 10-9. We show that the changes to the algorithm have made it better at detecting weak edges, without increasing the computational complexity or changing the overall design. Previous attempts have been made to improve the filter stage of the Canny algorithm using evolutionary computation, but, to our knowledge, this is the first time it has been used to improve the non-max suppression algorithm. The fact that we have found a heuristic improvement to the algorithm with significantly better performance on a dedicated test set of natural images suggests that our method should be used as a standard part of image analysis platforms, and that our methodology could be used to improve the performance of image analysis algorithms in general.", notes = "GECCO-2016 A Recombination of the 25th International Conference on Genetic Algorithms (ICGA-2016) and the 21st Annual Genetic Programming Conference (GP-2016)", } @InProceedings{Magnusson:2016:ICIVC, author = "Lars Vidar Magnusson and Roland Olsson", title = "Improving the {Canny} edge detector using automatic programming: Improving the filter", booktitle = "2016 International Conference on Image, Vision and Computing (ICIVC)", year = "2016", pages = "36--40", month = "3-5 " # aug, address = "Portsmouth, UK", keywords = "genetic algorithms, genetic programming, ADATE ML, Detectors, Standards, Benchmark testing, image analysis, edge detection, feature extraction, automatic programming", isbn13 = "978-1-5090-3756-8", DOI = "doi:10.1109/ICIVC.2016.7571270", size = "4 pages", abstract = "We have used automatic programming, a machine learning technique related to inductive logic programming and genetic programming, to make the Canny edge detector better at identifying contours in natural images. We present an improved version of the filter used in the first stage of the Canny algorithm. We show that the mean performance of the Canny algorithm with the improved filter on a popular test set of natural images has been improved by 1.4percent. Our result shows that the heuristic design provides a statistically significant increase in performance-without adding extra processing steps or adding additional information. This suggests that the filter should be used as a standard part of image analysis platforms. The inferred heuristic filter exhibits an ability to retain detail without sacrificing noise reduction. This is further evidence that automatic programming is well suited for generating heuristics for image analysis problems.", notes = "Also known as \cite{7571270} Ostfold University College", } @Article{Magnusson:2023:MIS, author = "Lars Vidar Magnusson and J. Roland Olsson and Chau Thi Thuy Tran", title = "Recurrent Neural Networks for Oil Well Event Prediction", journal = "IEEE Intelligent Systems", year = "2023", volume = "38", number = "2", pages = "73--80", month = mar # "-" # apr, keywords = "genetic algorithms, genetic programming, ADATE, ANN", ISSN = "1541-1672", DOI = "doi:10.1109/MIS.2023.3252446", size = "8 pages", abstract = "We have conducted a comparison between three types of recurrent neural networks and their ability to predict anomalies occurring in oil wells using a publicly available dataset. We have included two types of well-known state-of-the-art recurrent neural networks and a new type with neurons evolved specifically for the dataset using automatic programming. We show that the new type of recurrent neuron offers a massive improvement over the state of the art. The overall test accuracy of the new network type is 94.6percent, which is an improvement by 18.3percent, or 14.6 percentage points. We also show that a network with the new neuron performs better than any other solution proposed for the dataset.", notes = "also known as \cite{10058896} Ostfold University College, Department of Computer Science and Communication, NO-1757, Halden, Norway", } @InProceedings{Magsumbol:2021:HNICEM, author = "Jo-Ann V. Magsumbol and Maria Gemel B. Palconit and Lovelyn C. Garcia and Marife A. Rosales and Argel A. Bandala and Elmer P. Dadios", title = "Multigene Genetic Programming Model for Temperature Optimization to Improve Lettuce Quality", booktitle = "2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)", year = "2021", month = "28-30 " # nov, address = "Manila, Philippines", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-6654-0168-5", DOI = "doi:10.1109/HNICEM54116.2021.9731974", abstract = "This paper presents a Multigene Genetic Programming (MGGP) approach in optimizing the temperature of romaine lettuce inside an artificially controlled environment (ACE). In this research, MGGP is used to find the prediction model that will lead to the optimum temperature for growing lettuce crop. The system used a 1000 population using tournament selection with 40 generations. A mutation probability of 0.14 was applied to validate if it is at global optima. When the iterations reached the termination criteria, the system stopped, resulting in the best temperature model for growing lettuce crop. Training and testing of predictions were done. The model developed in this study can be used for the control system of the temperature setting inside the ACE which can provide optimal condition.", notes = "Also known as \cite{9731974}", } @Article{Mahadev:2020:IJERT, author = "Mukesh Mahadev K and G. Gowrishankar", title = "Estimation of Effort in Software Projects using Genetic Programming", journal = "International Journal of Engineering Research \& Technology", year = "2020", volume = "9", number = "7", pages = "1321--1325", month = jul, keywords = "genetic algorithms, genetic programming, SBSE, DEAP", ISSN = "2278-0181", URL = "https://www.ijert.org/research/estimation-of-effort-in-software-projects-using-genetic-programming-IJERTV9IS070478.pdf", DOI = "doi:10.17577/IJERTV9IS070478", size = "5 pages", abstract = "Software effort estimation refers to the estimation of effort that is required in given software project. It starts at the proposal stage and can sometimes continue till the last stages of a software project. Projects normally have a budget, and continual cost estimation is necessary to ensure that spending is in line with the budget. There is need of finding a good model which can establish an accurate relationship between the software a project and cost drivers. It is important for project managers and the researchers working in the domain to explore, analyse and understand the strengths and weaknesses of various software cost estimation methods. We focus on using Genetic Programming for software effort estimation. The implementation involves evolution of individuals for obtaining best results over several generations. Metrics are chosen to evaluate the model based on the literature survey. Standard software engineering datasets are used in this project so that suitability and possible relations that arise could be realized. K-fold validation is used to sum up with more reliable values of evaluation. The design, implementation and result presentation are completed successfully and recorded clearly.", notes = "Datasets: Desharnais, China, Albrecht IJERTV9IS070478 http://www.ijert.org/ Department of CSE, B.M.S College of Engineering, Bengaluru, India", } @InProceedings{Mahajan:2008:ieeeICCI, author = "Anjali Mahajan and M S Ali", title = "Superblock scheduling using genetic programming for embedded systems", booktitle = "7th IEEE International Conference on Cognitive Informatics, ICCI 2008", year = "2008", month = aug, pages = "261--266", keywords = "genetic algorithms, genetic programming, genetic improvement, NP-complete problem, embedded system, instruction scheduling, optimally scheduling instruction, optimized compiler, processor architecture, superblock scheduling, embedded systems, optimising compilers, scheduling", DOI = "doi:10.1109/COGINF.2008.4639177", size = "6 pages", abstract = "Instruction scheduling is an important issue in the compiler optimization for embedded systems. The instruction scheduling problem is mainly solved heuristically since finding an optimal solution requires significant computational resources and, in general, the problem of optimally scheduling instructions is known to be NP-Complete. The development of processors with pipelines and multiple functional units has increased the demands on compiler writers to write complex instruction scheduling algorithms. These algorithms are required to ensure that the most efficient use of resources, i.e. the functional units and pipelines of the processor, is made due to the increased complexity of processor architectures. In this paper, the specific problem of automatically creating instruction scheduling heuristics is addressed.", notes = "http://www.eecs.harvard.edu/hube/research/machsuif.html broken Sep 2014. Also known as \cite{4639177}", } @InProceedings{mahalakshmi:2019:ICTIS, author = "Ramkumar Mahalakshmi and Chandrasekaran Sivapragasam and Sankararajan Vanitha", title = "Comparison of {BOD5} Removal in Water Hyacinth and Duckweed by Genetic Programming", booktitle = "Information and Communication Technology for Intelligent Systems", year = "2018", editor = "Suresh Chandra Satapathy and Amit Joshi", volume = "1", series = "SIST, volume 106", pages = "401--408", address = "Ahmedabad, India", month = apr # " 6-7", publisher = "Springer", keywords = "genetic algorithms, genetic programming", size = "978-981-13-1741-5", URL = "http://link.springer.com/chapter/10.1007/978-981-13-1742-2_39", DOI = "doi:10.1007/978-981-13-1742-2_39", abstract = "In this study, macrophyte-based plants such as duckweed (Lemna Minor) and water hyacinth (Eichhornia Crassipes) are considered for the removal of biochemical oxygen demand (BOD_5) in domestic wastewater. The maximum value of BOD5 removal for duckweed and water hyacinth is almost the same (99 percent). Experiments are conducted in order to get a wide range of data for mathematical modeling. BOD5, retention time (t), and wastewater temperature (Tw) are the parameters considered for modeling, and a comparison is made between the models of these plants. This study reveals that there are similar functionality relationships that exist for both the plants between the parameters BOD5 and retention time on the removal of BOD5. This function is found to be linear. It is also revealed that Tw is also an important parameter as it influences the treatment systems. Genetic programming (GP) based modeling is effective to understand the wetland system by comparing the removal of BOD5.", notes = "Published 2019", } @Book{Mahalakshmi-Malini:book, author = "G. {Mahalakshmi Malini} and R. Sudarmani", title = "Genetic Algorithm based {Canny} distributed edge detection using Xilinx", publisher = "Lambert Academic Publishing", year = "2020", address = "Republic of Moldova, Chisinau-2068, str. A.Russo 15, of.61", month = "25 " # may, keywords = "genetic algorithms, genetic programming, EHW, evolvable hardwarey, image processing", isbn13 = "978-6202563628", URL = "https://www.lap-publishing.com/catalog/details/store/gb/book/978-620-2-56362-8/genetic-algorithm-based-canny-distributed-edge-detection-using-xilinx", URL = "https://www.amazon.co.uk/GENETIC-ALGORITHM-DISTRIBUTED-DETECTION-XILINX/dp/6202563621", size = "108 pages", abstract = "Edge detection plays a vital role in the areas of feature detection and feature extraction. Conventionally, manual operations were performed to set the window size for extracting edge features. Noise rejection and edge localisation are the two contradicting offset in selection of window size. To overcome this drawback genetic programming is proposed to search pixels automatically to construct new edge features for detecting edges in real images. The proposed method avoids the problem of blurring (large window) and noise effect (small window) by selecting the window size. Genetic programming is used for feature detection and feature extraction to produce rich information and improves classification accuracy. The main aim of feature extraction is to reduce the unwanted data and transform the data bit into a reduced set of features. MATLAB and Verilog is used for simulation and implemented on Xilinx virtex-5 FPGA.", } @InProceedings{Mahanipour:2018:CSIEC, author = "Afsaneh Mahanipour and Hossein Nezamabadi-pour and Bahareh Nikpour", booktitle = "2018 3rd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)", title = "Using fuzzy-rough set feature selection for feature construction based on genetic programming", year = "2018", abstract = "Feature construction can improve the classifier's performance by constructing powerful and distinctive features. Genetic programming algorithm is one the automatic programming methods which provides the possibility of constructing mathematical expressions without any predefined format. As we know, all features of a data set are not suitable; therefore, we believe that if all features are used for feature construction, inappropriate and ineffective features may be constructed. Hence, the main purpose of this paper is firstly, selecting the suitable features, before the construction process, and then constructing a new feature using these selected features. To do so, a fuzzy rough quick feature selection technique is employed. For assessment, the proposed method along with 5 other feature construction methods are applied on 6 standard data sets. The obtained results indicate that the proposed method has more ability in constructing more distinctive features compared to competing approaches.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CSIEC.2018.8405407", month = mar, notes = "Also known as \cite{8405407}", } @InProceedings{Mahanipour:2024:DCOSS-IoT, author = "Afsaneh Mahanipour and Hana Khamfroush", title = "Enhancing {IoT} Security: A Novel Feature Engineering Approach for {ML-Based} Intrusion Detection Systems", booktitle = "2024 20th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT)", year = "2024", pages = "548--555", month = apr, keywords = "genetic algorithms, genetic programming, Cloud computing, Costs, Accuracy, Sensitivity, Quantum computing, Image edge detection, System performance, Binary Quantum-inspired Artificial Bee Colony Algorithm, Feature Construction, Feature Selection, Intrusion Detection Systems", ISSN = "2325-2944", DOI = "doi:10.1109/DCOSS-IoT61029.2024.00086", abstract = "The integration of Internet of Things (IoT) applications in our daily lives has led to a surge in data traffic, posing significant security challenges. IoT applications using cloud and edge computing are at higher risk of cyberattacks because of the expanded attack surface from distributed edge and cloud services, the vulnerability of IoT devices, and challenges in managing security across interconnected systems leading to oversights. This led to the rise of ML-based solutions for intrusion detection systems (IDSs), which have proven effective in enhancing network security and defending against diverse threats. However, ML-based IDS in IoT systems encounters challenges, particularly from noisy, redundant, and irrelevant features in varied IoT datasets, potentially impacting its performance. Therefore, reducing such features becomes crucial to enhance system performance and minimise computational costs. This paper focuses on improving the effectiveness of ML-based IDS at the edge level by introducing a novel method to find a balanced trade-off between cost and accuracy through the creation of informative features in a two-tier edge-user IoT environment. A hybrid Binary Quantum-inspired Artificial Bee Colony and Genetic Programming algorithm is used for this purpose. Three IoT intrusion detection datasets, namely NSL-KDD, UNSW-NB15, and BoT-IoT, are used for the evaluation of the proposed approach. Performance analysis is conducted using various evaluation metrics such as accuracy, sensitivity, specificity, and False Positive Rate (FPR) are employed, while the cost of the IDS system is assessed based on computational time. The results are compared with existing methods in the literature, revealing that the IDS performance can be enhanced with fewer features, consequently reducing computational time, through the proposed method. This offers a better performance-cost trade-off for the IDS system.", notes = "Also known as \cite{10621559}", } @Article{MAHANTA:2023:dche, author = "Bashista Kumar Mahanta and Prakash Gupta and Itishree Mohanty and Tapas Kumar Roy and Nirupam Chakraborti", title = "Evolutionary data driven modeling and tri-objective optimization for noisy {BOF} steel making data", journal = "Digital Chemical Engineering", year = "2023", volume = "7", pages = "100094", month = jun, keywords = "genetic algorithms, genetic programming, Optimization, Multi-objective optimization, Pareto optimality, Evolutionary algorithms, Deep learning, Neural network, ANN, Reference vector", ISSN = "2772-5081", URL = "https://www.sciencedirect.com/science/article/pii/S2772508123000121", DOI = "doi:10.1016/j.dche.2023.100094", abstract = "Evolutionary data-driven modeling and optimization play a major role in generating meta models from real-time data. These surrogate models are applied effectively in various industrial operations and processes to predict a more accurate model from the nonlinear and noisy data. In this work, the data collected from a basic oxygen furnace of TATA steel are used in the modeling process by using evolutionary algorithms like evolutionary neural network (EvoNN), bi-objective genetic programming (BioGP), and evolutionary deep neural network (EvoDN2) to generate the meta models. For creating surrogates out In the current scenario of the Indian plants, reduction of phosphorus to an acceptable level, limiting the carbon and controlling the temperature are the basic needs in a basic oxygen furnace (BOF) to produce steels with a suitable composition. This work focused on three essential process parameters, temperature, carbon and phosphorus contents, and created intelligent models using 91 process variables of the operational process. The analysis began with a total of around 17000 operational observations and creating surrogate models out of them is a mammoth task, for which the data-driven evolutionary algorithms were some apt choices. Even there deep learning turned out to be essential and only the EvoDN2 algorithm performed at the expected level. Once the trained models are generated, optimization work was carried on three objectives simultaneously by using a constraint-based reference vector evolutionary algorithm (cRVEA). The optimized results were analysed in multi-dimensional hyperspace, and their effectiveness in BOF steel making is presented in this work", } @InProceedings{mahapatra:2018:NIC, author = "Cosmena Mahapatra", title = "Medical Diagnosing of Canine Diseases Using Genetic Programming and Neural Networks", booktitle = "Nature Inspired Computing", year = "2018", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-981-10-6747-1_22", DOI = "doi:10.1007/978-981-10-6747-1_22", } @PhdThesis{mahboub:tel-00696675, author = "Karim Mahboub", title = "Emotional processes modelling in decision making", title_fr = "Modelisation des processus emotionnel dans la prise de decision", school = "Universite du Havre", year = "2011", address = "France", month = Nov, keywords = "genetic algorithms, genetic programming, ACO, emotion modelling, problem solving, ant colony algorithms, linear genetic programming, mod{\`e}les de l'{\'e}motion, r{\'e}solution de probl{\`e}mes, algorithmes de colonies de fourmis, programmation g{\'e}n{\'e}tique lin{\'e}aire", hal_id = "tel-00696675", hal_version = "v1", URL = "https://tel.archives-ouvertes.fr/tel-00696675", URL = "https://tel.archives-ouvertes.fr/tel-00696675/file/manuscript.pdf", size = "250 pages", abstract = "Emotion is inseparable from cognitive processes and therefore plays a major role in decision making. As a result, it is becoming increasingly important in today's scientific research. The aim of this thesis is to show the advantages of an emotional approach, and to prove that in certain cases computer models equipped with artificial emotions prove to be more efficient than their purely cognitive equivalents. Based on this observation, two emotional models were realised from different study perspectives. They underline the impact of the addition of an emotional dimension in the elaboration of a fast, adaptive and efficient decision. The first developed model uses a graph for strategies representation in order to solve a ten-year-old pupil mathematics exercise called the Cascades problem. Emotion is represented there as weighting values in the graph edges dynamically managed by an ant algorithm. The tests carried out on two versions, one emotional and the other one fully cognitive, show that the use of an emotional model produces a more efficient and adaptive solving. In addition, a second model named GAEA aims at simulating a robot equipped with sensors and effectors and thrown into a prey-predators environment inside which it must survive. Its behaviour is determined by its internal program that evolves thanks to a linear genetic program algorithm manipulating a population of program individuals. Results are promising and indicate that the population produces individuals whose behaviour is more and more adapted, and whose internal activity is analogous to the emergence of relevant emotional reactions.", notes = "In french. Francais", } @Article{Harman:2002:GPEM, author = "Kiarash Mahdavi and Mark Harman", title = "Book Review: {Automatic} Re-Engineering of Software Using Genetic Programming", journal = "Genetic Programming and Evolvable Machines", year = "2002", volume = "3", number = "2", pages = "219--221", month = jun, keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dR8Dn", DOI = "doi:10.1023/A:1015536110241", notes = "Review of \cite{ryan:book} Cites: M.D.Ernst, Jake Cockrell, William G. Griswold and David Notkin, Dynamically discovering likely program invariants to support program evolution, IEEE Transactions on Software Engineering, Vol. 27, No. 2, pp. 1-25, 2001. Kenneth Peter Williams, Evolutionary algorithms for automatic parallelization, PhD Thesis, University of Reading, UK, Department of Computer Science, September 1998 \cite{williams98}. Article ID: 408589", } @Article{SeyyedMahdavi2009, author = "S. J. {Seyyed Mahdavi} and K. Mohammadi", title = "Reliability enhancement of digital combinational circuits based on evolutionary approach", journal = "Microelectronics Reliability", year = "2010", volume = "50", number = "3", pages = "415--423", month = mar, keywords = "genetic algorithms, EHW", ISSN = "0026-2714", DOI = "doi:10.1016/j.microrel.2009.11.016", URL = "http://www.sciencedirect.com/science/article/B6V47-4Y1W8TW-1/2/e36a1a110cf2e893fd2c978d4cb0c0f8", abstract = "Reliability has become an integral part of the system design process, especially for those systems with life-critical applications such as aircraft and spacecraft flight control. The recent rapid growth in demand for highly reliable digital circuits has focused attention on tools and techniques we might use to enhance the reliability of the circuit. In this paper, we present an algorithm to improve the reliability of digital combinational circuits based on evolutionary approach. This method generates a global VHDL file for the selected initial set of components based on inserting multiplexers at the gate inputs of the circuit which helps to perform the simulations in only one session. This simulation framework is combined with single-pass reliability analysis approach to implement the evolutionary algorithm. The search space of the genetic algorithm is limited by the idea of slicing the initial set of components and also circuit partitioning could be used to further overcome the scalability limitations. The framework is applied to a subset of combinational benchmark circuits and our experiments demonstrate that higher reliabilities can be achieved while other factors such as power, speed and area overhead will remain admissible.", notes = "Fixed sized chromosome. GA evolved contents and connectivity?.VHDL", } @Article{Mahdiani:2016:Petroleum, author = "Mohammad Reza Mahdiani and Ghazal Kooti", title = "The most accurate heuristic-based algorithms for estimating the oil formation volume factor", journal = "Petroleum", volume = "2", number = "1", pages = "40--48", year = "2016", ISSN = "2405-6561", DOI = "doi:10.1016/j.petlm.2015.12.001", URL = "http://www.sciencedirect.com/science/article/pii/S240565611600002X", abstract = "There are various types of oils in distinct situations, and it is essential to discover a model for estimating their oil formation volume factors which are necessary for studying and simulating the reservoirs. There are different correlations for estimating this, but most of them have large errors (at least in some points) and cannot be tuned for a specific oil. In this paper, using a wide range of experimental data points, an artificial neural network model (ANN) has been created. In which its internal parameters (number of hidden layers, number of neurons of each layer and forward or backward propagation) are optimized by a genetic algorithm to improve the accuracy of the model. In addition, four genetic programming (GP)-based models have been represented to predict the oil formation volume factor In these models, the accuracy and the simplicity of each equation are surveyed. As well as, the effect of modifying of the internal parameters of the genetic programming (by using some other values for its nodes or changing the tree depth) on the created model. Finally, the ANN and GP models are compared with fifteen other models of the most common previously introduced ones. Results show that the optimized artificial neural network is the most accurate and genetic programming is the most flexible model, which lets the user set its accuracy and simplicity. Results also recommend not adding another operator to the basic operators of the genetic programming.", keywords = "genetic algorithms, genetic programming, Neural network, Modelling", } @InProceedings{MahdiHadi:2013:ICCKE, author = "Reza {Mahdi Hadi} and Saeid Shokri and Peyman Ayubi", booktitle = "3th International eConference on Computer and Knowledge Engineering (ICCKE 2013)", title = "{Urmia Lake} level forecasting using Brain Emotional Learning (BEL)", year = "2013", month = oct, pages = "246--251", keywords = "genetic algorithms, genetic programming, brain emotional learning, forecasting, water level, time series", DOI = "doi:10.1109/ICCKE.2013.6682804", abstract = "This paper has tried to focus on a new approach for water level forecasting of Urmia Lake by using records of past time series and emotional learning. Water level forecasting is important in water resources engineering and management and efficient management of water resources for use. During the past two decades, the approaches artificial intelligence based on the Genetic Programming (GP), Artificial Neural Networks (ANN), fuzzy logic, neuro-fuzzy and statistical method for example ARIMA and recently, chaos theory have been developed. Time series the measurements from tide gauge at Urmia Lake, were used to train emotional learning approach for the period from March 1965 to February 2011. The research indicates that there is a non-linear and complex relationship between water input and variables, therefore anticipation seems to be more difficult to implement it with conventional tools of time series prediction. Simulation results prove that the applied method has prominent capability in forecasting time series. In this paper, various criterion including Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE) have been used.", notes = "Also known as \cite{6682804}", } @PhdThesis{Mahdinejad:thesis, author = "Mahsa Mahdinejad", title = "Optimizing Convolutional Neural Network Segmentation Tasks Using Evolutionary Algorithms", school = "CRT-AI, Faculty of Science and Engineering, Department of Computer Science and Information Systems, University of Limerick", year = "2023", address = "Ireland", month = "7 " # sep, keywords = "genetic algorithms, genetic programming, grammatical evolution, ANN", URL = "https://researchrepository.ul.ie/articles/thesis/Optimizing_convolutional_neural_network_segmentation_tasks_using_evolutionary_algorithms/24948171/1/files/43921806.pdf", size = "136 pages", abstract = "Neuroevolution combines ideas from evolutionary algorithms and artificial neural networks in order to construct intelligent systems. One of its key benefits is its ability to automatically generate complex and powerful neural networks without the assistance of a pre-trained network or a significant amount of data. This thesis investigates the application of neuroevolution to image segmentation in diabetic iris images. Specifically, we examine the ability of evolutionary algorithms to improve the architecture and hyper-parameters of the U-Net convolutional neural network. We begin the thesis using a genetic algorithm to optimize a U-Net's architecture and compare our results with the original U-Net model. We show that the genetic algorithm can improve the performance of the original U-Net. Next, we refine our method by adding more hyperparameters and different architectures into the evolutionary optimisation process. The genetic algorithm was able to find a smaller model than the original U-Net with only a small trade-off in the area under the curve (AUC) metric observed. We next examine different fitness functions to improve computational performance to reduce overfitting. We improved our previous work and also showed that genetic algorithm could find better performance models compared to the manual design ones. We next use the genetic algorithm to optimize the Spatial Attention UNet, which has been shown to be less prone to over-fitting as it puts more emphasis on more important features in the images. The results show smaller models with similar AUC compared to state-of-the-art models. We also improve training time by using custom epoch optimization and reducing duplicated individuals. This reduced training time by more than half, enabling us to use that saved time to find a better model than in previous works. Finally, we change strategy and use grammatical evolution (GE) instead of the genetic algorithm as our evolutionary strategy. We achieve better AUC than the genetic algorithm and evolve a 10 times smaller model with similar accuracy. This thesis shows that evolutionary algorithms successfully improve state-of-the-art neural network models in image segmentation.", notes = " Supervisor: Conor Ryan and Patrick Healy", } @Article{Mahdinejad:2025:GPEM, author = "Mahsa Mahdinejad and Aidan Murphy and Conor Ryan", title = "Introduction to special issue on twenty-five years of grammatical evolution", journal = "Genetic Programming and Evolvable Machines", year = "2025", volume = "26", pages = "Article no 18", keywords = "genetic algorithms, genetic programming, grammatical evolution", ISSN = "1389-2576", URL = "https://rdcu.be/ehjuS", DOI = "doi:10.1007/s10710-025-09512-x", size = "2 pages", } @InProceedings{Mahdizadeh:2013:IFSC, author = "Mahboubeh Mahdizadeh and Mahdi Eftekhari", booktitle = "13th Iranian Conference on Fuzzy Systems (IFSC 2013)", title = "Designing fuzzy imbalanced classifier based on the subtractive clustering and Genetic Programming", year = "2013", month = "27-29 " # aug, keywords = "genetic algorithms, genetic programming, Fuzzy Inference System, Differential Evolution, Subtractive clustering, Multi-Gene Genetic programming", DOI = "doi:10.1109/IFSC.2013.6675611", abstract = "In this paper, a design methodology is proposed for generating a fuzzy rule-based classifier for imbalanced datasets. The classifier is based on Sugeno-type Fuzzy Inference System. It is generated by using of subtractive clustering and Multi-Gene Genetic Programming to obtain fuzzy rules. The subtractive clustering is used for producing the antecedents of rules and Multi-Gene Genetic Programming is employed for generating the functions in the consequence parts of rules. Feature selection is used as an important pre-processing step for dimension reduction. Experiments are performed with 8 datasets from KEEL. The comparison results reveal that the proposed classifier outperforms the other methods.", notes = "Also known as \cite{6675611}", } @Article{Maher:2013:ICA, author = "Rami A. Maher and Mohamed J. Mohamed", title = "An Enhanced Genetic Programming Algorithm for Optimal Controller Design", journal = "Intelligent Control and Automation", year = "2013", volume = "4", number = "1", pages = "94--101", month = feb, publisher = "Scientific Research Publishing", keywords = "genetic algorithms, genetic programming, Optimal Control, Nonlinear Control System", ISSN = "2153-0653", bibsource = "OAI-PMH server at www.doaj.org", language = "eng", oai = "oai:doaj-articles:95df4bd89754cd8857ce4c2c286e788e", URL = "http://www.scirp.org/journal/ica/", URL = "http://www.scirp.org/journal/PaperDownload.aspx?paperID=27848", DOI = "doi:10.4236/ica.2013.41013", size = "8 pages", abstract = "This paper proposes a Genetic Programming based algorithm that can be used to design optimal controllers. The proposed algorithm will be named a Multiple Basis Function Genetic Programming (MBFGP). Herein, the main ideas concerning the initial population, the tree structure, genetic operations, and other proposed non-genetic operations are discussed in details. An optimisation algorithm called numeric constant mutation is embedded to strengthen the search for the optimal solutions. The results of solving the optimal control for linear as well as nonlinear systems show the feasibility and effectiveness of the proposed MBFGP as compared to the optimal solutions which are based on numerical methods. Furthermore, this algorithm enriches the set of suboptimal state feedback controllers to include controllers that have product time-state terms.", } @InProceedings{Maher:2015:MCSI, author = "Rami A. Maher and Mohamed J. Mohamed", booktitle = "Second International Conference on Mathematics and Computers in Sciences and in Industry (MCSI)", title = "Design of a Discrete Deadbeat Controller Based on Block Diagram Oriented Genetic Programming", year = "2015", pages = "105--112", abstract = "This paper proposes a Genetic Programming representation and algorithm that can be used for control system design. The proposed Genetic Programming algorithm named Block Diagram Oriented Genetic Programming BDOGP. The characteristics of the tree structure and the used genetic operations of the proposed algorithm are illustrated here in detail. A new numeric constant mutation operation is added to the algorithm to strengthen the search for optimal parameters of the BDOGP solutions. The proposed BDOGP is used as an automated method to synthesise the block diagram of a deadbeat controller for discrete control system. The important BDOGP steps and remarks, which perform the solution, are presented. The simulation results show that the comparison between the GP solution and the conventional state-space one indicates the validity and accuracy of the solution obtained by BDOGP.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/MCSI.2015.27", month = aug, notes = "Also known as \cite{7423950}", } @Article{Maher:2015:WSEAStsc, author = "Rami A. Maher and Mohamed J. Mohamed", title = "Discrete Controller Synthesis Based on Genetic Programming", journal = "WSEAS Transactions on Systems and Control", year = "2015", volume = "10", number = "81", pages = "763--775", keywords = "genetic algorithms, genetic programming, Block Diagram Oriented Genetic Programming, Deadbeat Controller", ISSN = "2224-2856", URL = "http://wseas.org/wseas/cms.action?id=10192", URL = "http://www.wseas.org/multimedia/journals/control/2015/b605803-429.pdf", size = "13 pages", abstract = "In this paper, an alternative way of a discrete controller synthesis is introduced. The synthesis is based on a proposed genetic programming algorithm, which is named Block Diagram Oriented Genetic Programming BDOGP. The standard GP structure tree is modified in such a way to obtain a complete block diagram of the discrete controller that satisfies a deadbeat response in a closed-loop system. In one framework solution, the algorithm gives both the block diagram topology and the values to the parameters within the controller structure. A new numeric constant mutation operation is added to the algorithm to strengthen the search for optimal parameters of the BDOGP solutions. Two examples are introduced to validate the use of the proposed algorithm, and for the sake of completeness, the state-space approach design of a deadbeat response is introduced briefly. For a servo system, a comparison between the results of the GP and the conventional state-space solutions shows the accuracy of the GP approach. The second example considers a temperature control in an HVAC system.", notes = "Isra University, University of Technology, Amman/Baghdad", } @Article{Maher:2017:ijmcs, author = "Rami A. Maher and Mohammad J. Mohammad", title = "Identification of Nonlinear Discrete Systems Based Enhanced Genetic Programming", journal = "International Journal of Mathematics and Computers in Simulation", year = "2017", volume = "11", pages = "204--210", keywords = "genetic algorithms, genetic programming", ISSN = "1998-0159", URL = "http://www.naun.org/cms.action?id=2826", URL = "http://www.naun.org/cms.action?id=15242", URL = "http://www.naun.org/main/NAUN/mcs/2017/a582002-010.pdf", size = "7 pages", abstract = "This paper introduces the application of the genetic programming to solve the identification problems of nonlinear discrete dynamic systems. The standard GP is enhanced first to be of a Multi basis function structure and then by a general parameter optimization technique to include one of four proposed techniques. The efficiency of finding the numeric constant node is significantly improved as compared to the traditional methods. The simulation procedure includes first a comparison between the enhanced GP by one of the parameter optimization techniques and the standard GP. Then after for six different models, a comparison between the four techniques is performed. The comparison is made in terms of the number of runs require to find the perfect model, and the average number of generations for successful runs. Finally, a complicated nonlinear discrete is selected to show the powerful of the proposed algorithm.", notes = "Rami A. Maher is with the Isra University, Amman, Jordan, while Mohammad J. Mohammad is with the University Of Tech Baghdad Iraq", } @InProceedings{Maher:2017:EECS, author = "Rami A. Maher and Mohammad J. Mohammad", booktitle = "2017 European Conference on Electrical Engineering and Computer Science (EECS)", title = "Identification of Nonlinear Discrete Dynamic Systems Using Enhanced Genetic Programming", year = "2017", pages = "225--229", abstract = "This paper introduces the use of the evolutionary genetic programming to solve the identification problem of nonlinear discrete dynamic systems. The standard GP is enhanced by a general parameter optimization technique to include one of four proposed techniques. The efficiency of finding the numeric constant node is significantly improved as compared to the traditional methods. The simulation procedure includes first a comparison between the enhanced GP by one of the parameter optimization techniques and the standard GP. Then after for six different models, a comparison between the four techniques is performed. The comparison is made in terms of the number of runs require to find the perfect model, and the average number of generations for successful runs.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/EECS.2017.49", month = nov, notes = "Also known as \cite{8412025}", } @Article{DBLP:journals/ijpe/MaheshaG21, author = "Pandit Mahesha and Deepali Gupta", title = "Performance of Genetic Programming-based Software Defect Prediction Models", journal = "Int. J. Perform. Eng.", volume = "17", number = "9", pages = "787", year = "2021", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.23940/ijpe.21.09.p5.787795", DOI = "doi:10.23940/ijpe.21.09.p5.787795", timestamp = "Tue, 26 Oct 2021 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/ijpe/MaheshaG21.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{conf/semcco/MaheswaranK11, author = "Rathinasamy Maheswaran and Rakesh Khosa", title = "Multi Resolution Genetic Programming Approach for Stream Flow Forecasting", booktitle = "Proceedings of the Second International Conference Swarm, Evolutionary, and Memetic Computing (SEMCCO 2011) Part {I}", year = "2011", editor = "Bijaya K. Panigrahi and Ponnuthurai Nagaratnam Suganthan and Swagatam Das and Suresh Chandra Satapathy", volume = "7076", series = "Lecture Notes in Computer Science", pages = "714--722", address = "Visakhapatnam, Andhra Pradesh, India", month = dec # " 19-21", publisher = "Springer", keywords = "genetic algorithms, genetic programming, wavelet analysis, multiscale forecasting, water stream flow", isbn13 = "978-3-642-27171-7", DOI = "doi:10.1007/978-3-642-27172-4_84", size = "9 pages", abstract = "Genetic Programming (GP) is increasingly used as an alternative for Artificial Neural Networks (ANN) in many applications viz. forecasting, classification etc. However, GP models are limited in scope as their application is restricted to stationary systems. This study proposes use of Multi Resolution Genetic Programming (MRGP) based approach as an alternative modelling strategy to treat non-stationaries. The proposed approach is a synthesis of Wavelets based Multi-Resolution Decomposition and Genetic Programming. Wavelet transform is used to decompose the time series at different scales of resolution so that the underlying temporal structures of the original time series become more tractable. Further, Genetic Programming is then applied to capture the underlying process through evolutionary algorithms. In the case study investigated, the MRGP is applied for forecasting one month ahead stream flow in Fraser River, Canada, and its performance compared with the conventional, but scale insensitive, GP model. The results show the MRGP as a promising approach for flow forecasting.", notes = "Fraser river", affiliation = "Department of Civil Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016 India", bibdate = "2011-12-14", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/semcco/semcco2011-1.html#MaheswaranK11", } @InProceedings{Maheta:2015:ICCCI, author = "Hardik H. Maheta and Vipul K. Dabhi", booktitle = "2015 International Conference on Computer Communication and Informatics (ICCCI)", title = "Classification of imbalanced data sets using Multi Objective Genetic Programming", year = "2015", month = "8-10 " # jan, address = "Coimbatore, India", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Imbalanced data sets, Multi Objective Optimization, MOEA, Multi Objective Genetic Programming, Gaussian distribution, Classification", isbn13 = "978-1-4799-6804-6", DOI = "doi:10.1109/ICCCI.2015.7218125", size = "6 pages", abstract = "Classification of imbalanced data set is a challenging problem as it is very difficult to achieve good classification accuracy for each class in case of imbalanced data sets. This problem arises in many real world applications like medical diagnosis of rare medical disease, fraud detection in financial domain, and faulty area detection in network troubleshooting etc. The imbalanced data set consists of small number of instances of minority classes and large number of instances of majority classes. Overall classification accuracy is computed by taking the ratio of correctly classified instances to total number of instances in a data set. For imbalanced data sets, correct classification of minority class instances contribute minimum in improvement of overall classification accuracy as compared to classification of majority class instances. Conventional classification techniques like Artificial Neural Network (ANN), Support Vector Machine (SVM), and Naive Bayes (NB) consider overall classification accuracy of the classifier only and thus evolve biased classifiers in case of imbalanced data set. However, instances of minority classes may contain rare but important information in many real world data sets. Thus, a classification technique that provides good classification accuracy on both minority and majority classes is needed. This paper proposes a combination of Multi Objective Genetic Programming (MOGP) and probability based Gaussian classifier for classification of imbalanced data set. MOGP considers classification accuracy of each class as separate objective and not the overall accuracy as single objective. Gaussian classifier is generative classifier in which distribution of one class never affect the classification of instances of other classes. The proposed methodology is applied on classification of imbalanced data sets from medical, life science, cars, and space science domain. The results suggest that MOGP classifier outperformed other conventional classifiers (ANN, SVM, and NB) on tested imbalanced data sets.", notes = "Also known as \cite{7218125}", } @InProceedings{Mahjoubi:2022:CloudNet, author = "Ayeh Mahjoubi and Karl-Johan Grinnemo and Javid Taheri", booktitle = "2022 IEEE 11th International Conference on Cloud Networking (CloudNet)", title = "An Efficient Simulated Annealing-based Task Scheduling Technique for Task Offloading in a Mobile Edge Architecture", year = "2022", pages = "159--167", abstract = "The Internet of Things (IoT) has emerged as a fundamental cornerstone in the digitalization of industry and society. Still, IoT devices' limited processing and memory capacities pose a problem for conducting complex and time-sensitive computations such as AI-based shop floor monitoring or personalized health tracking on these devices, and offloading to the cloud is not an option due to excessive delays. Edge computing has recently appeared to address the requirements of these IoT applications. This paper formulates the scheduling of tasks between IoT devices, edge servers, and the cloud in a three-layer Mobile Edge Computing (MEC) architecture as a Mixed-Integer Linear Programming (MILP) problem. The paper proposes a simulated annealing-based task scheduling technique and demonstrates that it schedules tasks almost as time-efficient as if the MILP problem had been solved with a mixed integer programming optimization package; however, at a fraction of the cost in terms of CPU, memory, and network resources. Also, the paper demonstrates that the proposed task scheduling technique compares favorably in terms of efficiency, resource consumption, and timeliness with previously proposed techniques based on heuristics, including genetic programming.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CloudNet55617.2022.9978900", ISSN = "2771-5663", month = nov, notes = "Also known as \cite{9978900}", } @InProceedings{eurogp:MahlerRF05, author = "S{\'e}bastien Mahler and Denis Robilliard and Cyril Fonlupt", editor = "Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. {van Hemert} and Marco Tomassini", title = "Tarpeian Bloat Control and Generalization Accuracy", booktitle = "Proceedings of the 8th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3447", year = "2005", address = "Lausanne, Switzerland", month = "30 " # mar # " - 1 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-25436-6", pages = "203--214", DOI = "doi:10.1007/978-3-540-31989-4_18", DOI = "doi:10.1007/b107383", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "In this paper we will focus on machine-learning issues solved with Genetic Programming (GP). Excessive code growth or bloat often happens in GP , greatly slowing down the evolution process. Poli proposed the Tarpeian Control method to reduce bloat, but possible side-effects of this method on the generalisation accuracy of GP hypotheses remained to be tested. In particular, since Tarpeian Control puts a brake on code growth, it could behave as a kind of Occam's razor, promoting shorter hypotheses more able to extend their knowledge to cases apart from any learning steps. To answer this question, we experiment Tarpeian Control with symbolic regression. The results are contrasted, showing that it can either increase or reduce the generalization power of GP hypotheses, depending on the problem at hand. This suggest that a blind use of TC is not safe, but also that a careful parameter setup may be profitable in some cases.", notes = "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005 \cite{poli03}", } @InProceedings{Mahlmann:2011:CIG, author = "Tobias Mahlmann and Julian Togelius and Georgios N. Yannakakis", title = "Modelling and evaluation of complex scenarios with the Strategy Game Description Language", booktitle = "Proceedings of the 2011 IEEE Conference on Computational Intelligence and Games", year = "2011", pages = "174--181", address = "Seoul, South Korea", month = "31 " # aug # " - 3 " # sep, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", URL = "http://cilab.sejong.ac.kr/cig2011/proceedings/CIG2011/papers/paper45.pdf", size = "8 pages", abstract = "The Strategy Game Description Game Language (SGDL) is intended to become a complete description of all aspects of strategy games, including rules, parameters, scenarios, maps, and unit types. Our aim is to be able to model a wide variety of strategy games, simple ones as well as complex commercially available titles. In our previous work [1] we introduced the basic concepts of modelling game rules in a tree structure and evaluating them through simulated playthrough. In this paper we present some additions to the language and discuss and compare three methods to evaluate the quality of a set of game rules in two different scenarios. We find that the proposed evaluation measures are complementary, and depend on the artificial agent used.", } @InProceedings{Mahmood:2024:UKHTC, author = "Ahmed M. Mahmood and Muhammad F. B. Raihan and Timothy Cockerill and Greg DeBoer and Harvey Thompson and Jochen Voss", title = "Thermal Management of Electric Vehicle Using Cold Plate Technology", booktitle = "18th UK Heat Transfer Conference", year = "2024", editor = "Raya AL-Dadah", pages = "UKHTC2024--057", address = "University of Birmingham, UK", month = "9-11 " # sep, organisation = "national heat transfer committee", keywords = "Electric vehicles, Li-ion batteries, Computational fluid dynamics, Machine learning, Multi-objective optimization", URL = "https://more.bham.ac.uk/ukhtc-2024/wp-content/uploads/sites/80/2024/09/UKHTC-2024_paper_57.pdf", size = "3 pages", abstract = "Effective thermal management systems are essential for extending the lifespan and enhancing overall performance of the Lithium Ion (Li-Ion) battery packs used in electric vehicles (EVs). Accordingly, a novel Computational Fluid Dynamics (CFD)-enabled multi-objective optimisation (MOO) approach for thermal management of Li-Ion battery modules using cold plates is proposed. This is used to optimize successfully the mini-channel cold plates geometrical parameters in terms of the key performance metrics: battery maximum temperature, temperature standard deviation and pumping power.", notes = "Not GP 'our surrogate modelling approaches, using Radial Basis Functions (RBFs), Random Forests (RF), Neural Networks (NN), and Gaussian Processes (GP), are compared to identify which approach will be selected as the most efficient for further optimization investigations' https://more.bham.ac.uk/ukhtc-2024/programme/ Northern Technical University, Mosul, Iraq", } @Article{Mahmood20111249, author = "Muhammad Tariq Mahmood and Abdul Majid and Tae-Sun Choi", title = "Optimal depth estimation by combining focus measures using genetic programming", journal = "Information Sciences", volume = "181", number = "7", pages = "1249--1263", year = "2011", ISSN = "0020-0255", DOI = "doi:10.1016/j.ins.2010.11.039", URL = "http://www.sciencedirect.com/science/article/B6V0C-51N22D2-3/2/463bf41cb8ecb1292e814f690c94cf70", keywords = "genetic algorithms, genetic programming, 3D shape recovery, Focus measure, Shape From Focus, Combining focus measures", abstract = "Three-dimensional (3D) shape reconstruction is a fundamental problem in machine vision applications. Shape From Focus (SFF) is one of the passive optical methods for 3D shape recovery that uses degree of focus as a cue to estimate 3D shape. In this approach, usually a single focus measure operator is applied to measure the focus quality of each pixel in the image sequence. However, the applicability of a single focus measure is limited to estimate accurately the depth map for diverse type of real objects. To address this problem, we develop Optimal Composite Depth (OCD) function through genetic programming (GP) for accurate depth estimation. The OCD function is constructed by optimally combining the primary information extracted using one/or more focus measures. The genetically developed composite function is then used to compute the optimal depth map of objects. The performance of the developed nonlinear function is investigated using both the synthetic and the real world image sequences. Experimental results demonstrate that the proposed estimator is more useful in computing accurate depth maps as compared to the existing SFF methods. Moreover, it is found that the heterogeneous function is more effective than homogeneous function.", } @InCollection{Mahmood:2012:dm3dia, author = "Muhammad Tariq Mahmood and Tae-Sun Choi", title = "Combining Focus Measures for Three Dimensional Shape Estimation Using Genetic Programming", booktitle = "Depth Map and {3D} Imaging Applications: Algorithms and Technologies", publisher = "IGI Global", year = "2012", editor = "Aamir Saeed Malik and Tae Sun Choi and Humaira Nisar", chapter = "11", pages = "209--228", keywords = "genetic algorithms, genetic programming", isbn13 = "9781613503263", DOI = "doi:10.4018/978-1-61350-326-3.ch011", abstract = "Three-dimensional (3D) shape reconstruction is a fundamental problem in machine vision applications. Shape from focus (SFF) is one of the passive optical methods for 3D shape recovery, which uses degree of focus as a cue to estimate 3D shape. In this approach, usually a single focus measure operator is applied to measure the focus quality of each pixel in image sequence. However, the applicability of a single focus measure is limited to estimate accurately the depth map for diverse type of real objects. To address this problem, we introduce the development of optimal composite depth (OCD) function through genetic programming (GP) for accurate depth estimation. The OCD function is developed through optimally combining the primary information extracted using one (homogeneous features) or more focus measures (heterogeneous features). The genetically developed composite function is then used to compute the optimal depth map of objects. The performance of this function is investigated using both synthetic and real world image sequences. Experimental results demonstrate that the proposed estimator is more accurate than existing SFF methods. Further, it is found that heterogeneous function is more effective than homogeneous function.", } @Article{Mahmood:2013:EAAI, author = "Muhammad Tariq Mahmood and Abdul Majid and Jongwoo Han and Young Kyu Choi", title = "Genetic programming based blind image deconvolution for surveillancesystems", journal = "Engineering Applications of Artificial Intelligence", volume = "26", number = "3", pages = "1115--1123", year = "2013", keywords = "genetic algorithms, genetic programming, Surveillance systems, Deconvolution, Image restoration, Deblurring", ISSN = "0952-1976", DOI = "doi:10.1016/j.engappai.2012.08.001", URL = "http://www.sciencedirect.com/science/article/pii/S0952197612002023", abstract = "Image acquisition, segmentation, object detection and tracking are essential parts of surveillance systems. Usually, image filtering approaches are employed as preprocessing step to reduce the effect of motion or out-of-focus blur problem. In this paper, we propose genetic programming (GP) based blind-image deconvolution filter. A GP based numerical expression is developed for image restoration which optimally combines and exploits dependencies among features of the blurred image. In order to develop such function, first, a set of feature vectors is formed by considering a small neighbourhood around each pixel. At second stage, the estimator is trained and developed through GP process that automatically selects and combines the useful feature information under a fitness criterion. The developed function is then applied to estimate the image pixel intensity of the degraded images. The performance of filter function is estimated using various degraded image sequences. Our comparative analysis highlight the effectiveness of GP based proposed filter.", } @InProceedings{Mahmoodabadi:2017:GECCO, author = "Reza Gholami Mahmoodabadi and Harald Koestler", title = "Genetic Programming Meets Linear Algebra: How Genetic Programming Can Be Used to Find Improved Iterative Numerical Methods", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4939-0", address = "Berlin, Germany", pages = "1403--1406", size = "4 pages", URL = "http://doi.acm.org/10.1145/3067695.3082502", DOI = "doi:10.1145/3067695.3082502", acmid = "3082502", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, iterative solvers, sparse linear algebra", month = "15-19 " # jul, abstract = "Iterative schemes play central role in solving large scale simulations in science and engineering. Development of such methods over the past few hundreds of years faces inevitable difficulty of manual design. Herein, we report, for the first time, iterative schemes that are automatically evolved by genetic programming (GP) and outperform the well-known iterative methods. To cope with the diversity of the systems of linear equations, the proposed technique is applied on a sparse system in 1D and 2D domains and on a non-sparse asymmetric system. Our proof-of-principle experiments demonstrate GP evolved schemes that converge up to 4 times faster than the conventional Gauss-Seidel scheme. Our work paves the way towards automatic design of efficient iterative solvers for large scale systems of linear equations.", notes = "Also known as \cite{Mahmoodabadi:2017:GPM:3067695.3082502} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @Article{MahmoodAlJuboori:2016:JH, author = "Anas Mahmood Al-Juboori and Aytac Guven", title = "A stepwise model to predict monthly streamflow", journal = "Journal of Hydrology", volume = "543, Part B", pages = "283--292", year = "2016", ISSN = "0022-1694", DOI = "doi:10.1016/j.jhydrol.2016.10.006", URL = "http://www.sciencedirect.com/science/article/pii/S0022169416306382", abstract = "In this study, a stepwise model empowered with genetic programming is developed to predict the monthly flows of Hurman River in Turkey and Diyalah and Lesser Zab Rivers in Iraq. The model divides the monthly flow data to twelve intervals representing the number of months in a year. The flow of a month, t is considered as a function of the antecedent month's flow (t - 1) and it is predicted by multiplying the antecedent monthly flow by a constant value called K. The optimum value of K is obtained by a stepwise procedure which employs Gene Expression Programming (GEP) and Nonlinear Generalized Reduced Gradient Optimization (NGRGO) as alternative to traditional nonlinear regression technique. The degree of determination and root mean squared error are used to evaluate the performance of the proposed models. The results of the proposed model are compared with the conventional Markovian and Auto Regressive Integrated Moving Average (ARIMA) models based on observed monthly flow data. The comparison results based on five different statistic measures show that the proposed stepwise model performed better than Markovian model and ARIMA model. The R2 values of the proposed model range between 0.81 and 0.92 for the three rivers in this study.", keywords = "genetic algorithms, genetic programming, Monthly streamflow, Gene Expression Programming, Generalized Reduced Gradient Optimization, Markovian model, ARIMA", } @Article{MAHMOODPOUR:2021:JPSE, author = "Soran Mahmoodpour and Ehsan Kamari and Mohammad Reza Esfahani and Amir Karimi Mehr", title = "Prediction of cementation factor for low-permeability Iranian carbonate reservoirs using particle swarm optimization-artificial neural network model and genetic programming algorithm", journal = "Journal of Petroleum Science and Engineering", volume = "197", pages = "108102", year = "2021", ISSN = "0920-4105", DOI = "doi:10.1016/j.petrol.2020.108102", URL = "https://www.sciencedirect.com/science/article/pii/S0920410520311566", keywords = "genetic algorithms, genetic programming, The cementation factor of carbonate reservoirs, RCAL data, Hybrid PSO-ANN model, Genetic programming algorithm", abstract = "cementation factor is a crucial parameter that has a significant influence on the estimation of reservoir parameters. Laboratory measurements for cementation factor are available for occasional cases because experimental special core analyses for determination of cementation factor values are expensive and time-consuming. While this factor plays a significant role in determining water saturation, there is no comprehensive and precise relationship for the case of Iranian carbonate reservoirs. In this article, a unique model was used based on a powerful combination of artificial neural network (ANN) and particle swarm optimization (PSO) algorithm to model the cementation factor. In the second phase of simulation, a correlation for the cementation factor was discovered by genetic programming (GP) algorithm. Both the PSO-ANN model and GP algorithm are trained by input variables such as porosity, permeability, and grain density derived from 175 routine core analysis (RCAL) samples of 21 carbonated oil fields. To determine the relative impact of the independent variables on cementation factor the sensitivity analysis was carried out for both models. The comparison between the PSO-ANN model output and the experimental cementation factor data clearly demonstrated that the built model can predict the cementation factor with great precision; the mean square error between the model predictions and the experimental data was less than 0.07. The root mean square error of training and testing data sets for the new developed correlation using GP algorithm were 0.0902 and 0.0727 respectively. Finally, to evaluate the validity and reliability of the developed models, a comparison was implemented between these two models and other empirical models over an external employment data set (21 data point). This comparison revealed that the GP algorithm and PSO-ANN model deliver a higher performance capacity compared to other proposed correlations for predicting cementation exponent", } @Article{Mahmoud:2015:ECSJ, author = "Abeer M. Mahmoud", title = "Genetic Programming Based Position Estimator and Control Model for Tracking Wheeled Robot Curvatures", journal = "Egyptian Computer Science Journal", year = "2015", volume = "39", number = "2", pages = "32--42", month = may, keywords = "genetic algorithms, genetic programming, Wheel robot, Simulation, Estimation", ISSN = "1110-2586", URL = "http://ecsjournal.org/Archive/Volume39/Issue2/2.pdf", size = "11 pages", abstract = "Wheeled mobile robots (WMRs) have a numerous applications and planetary of exploration tasks, all of which require building the suitable dynamic model of the robot. Also, the optimal control model that is intended to guide the robot to accomplish its mission is of a significant need. In addition, it is a certain that achieving a success in the robot controller mission requires an accurate estimation of the robot trajectory for setting the right control parameters and predicting the robot behaviour in different situations. The overall WMR dynamics subject to skidding, wheel slip, regulation control and turning control are formulated and simulated in this paper for a commercial four wheel robot. In addition, the paper proposes a new genetic programming (GP) based control model for tracking curvature trajectory of a four wheels robot. The proposed model(GPCE) achieved a promising refinement in the robot of curvature estimation and simulation hence provided accurate control parameters for trajectory tracking.", notes = "Figure 1: GAIA-1arobot and simulated model http://ecsjournal.org/Default.aspx Computer Science dept., Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt", } @InProceedings{mahouachi:2024:CEC, author = "Safa Mahouachi and Maha Elarbi and Khaled Sethom and Slim Bechikh and Carlos A. Coello Coello", title = "A Bi-Level Evolutionary Model Tree Induction Approach for Regression", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Computational modeling, Evolutionary computation, Machine learning, Numerical models, Regression tree analysis, Genetic operators, Convergence, Model Trees, Induction, Regression, Bi-level optimization, Evolutionary Algorithm", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10611959", abstract = "Supervised machine learning techniques include classification and regression. In regression, the objective is to map a real-valued output to a set of input features. The main challenge that existing methods for regression encounter is how to maintain an accuracy-simplicity balance. Since Regression Trees (RTs) are simple to interpret, many existing works have focused on proposing RT and Model Tree (MT) induction algorithms. MTs are RTs with a linear function at the leaf nodes rather than a numerical value are able to describe the relationship between the inputs and the output. Traditional RT induction algorithms are based on a top-down strategy which often leads to a local optimal solution. Other global approaches based on Evolutionary Algorithms (EAs) have been proposed to induce RTs but they can require an important calculation time which may affect the convergence of the algorithm to the solution. In this paper, we introduce a novel approach called Bi-level Evolutionary Model Tree Induction algorithm for regression, that we call BEMTI, and which is able to induce an MT in a bi-level design using an EA. The upper-level evolves a set of MTs using genetic operators while the lower-level optimises the Linear Models (LMs) at the leaf nodes of each MT in order to fairly and precisely compute their fitness and obtain the optimal MT. The experimental study confirms the outperformance of our BEMTI compared to six existing tree induction algorithms on nineteen datasets.", notes = "also known as \cite{10611959} WCCI 2024", } @InProceedings{1570089, author = "Ogier Maitre and Laurent A. Baumes and Nicolas Lachiche and Avelino Corma and Pierre Collet", title = "Coarse grain parallelization of evolutionary algorithms on GPGPU cards with EASEA", booktitle = "GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation", year = "2009", editor = "Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano {van Hemert} and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique Alba", pages = "1403--1410", address = "Montreal, Qu\'{e}bec, Canada", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SIGEVO", publisher = "ACM", keywords = "genetic algorithms, GPU, Numerical Analysis Optimisation, Performance, Parallelisation, evolutionary computation, GPGPU, Graphic Processing Unit, EASEA", isbn13 = "978-1-60558-325-9", DOI = "doi:10.1145/1569901.1570089", abstract = "This paper presents a straightforward implementation of a standard evolutionary algorithm that evaluates its population in parallel on a GPGPU card. Tests done on a benchmark and a real world problem using an old nVidia 8800GTX card and a newer but not top of the range GTX260 card show a roughly 30x (resp. 100x) speedup for the whole algorithm compared to the same algorithm running on a standard 3.6GHz PC. Knowing that much faster hardware is already available, this opens new horizons to evolutionary computation, as search spaces can now be explored 2 or 3 orders of magnitude faster, depending on the number of used GPGPU cards. Since these cards remains very difficult to program, the knowhow has been integrated into the old EASEA language, that can now output code for GPGPU (-cuda option).", notes = "Not on GP but mentions it GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009). ACM Order Number 910092.", } @InProceedings{Maitre:2010:EuroGP, author = "Ogier Maitre and Pierre Collet and Nicolas Lachiche", title = "Fast Evaluation of GP Trees on GPGPU by Optimizing Hardware Scheduling", booktitle = "Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010", year = "2010", editor = "Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar", volume = "6021", series = "LNCS", pages = "301--312", address = "Istanbul", month = "7-9 " # apr, organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming, GPU", isbn13 = "978-3-642-12147-0", DOI = "doi:10.1007/978-3-642-12148-7_26", abstract = "This paper shows that it is possible to use General Purpose Graphic Processing Unit cards for a fast evaluation of different Genetic Programming trees on as few as 32 fitness cases by using the hardware scheduling of NVIDIA cards. Depending on the function set, observed speedup ranges between x50 and x250 on one half of an NVidia GTX295 GPGPU card, vs a single core of an Intel Quad core Q8200.", notes = "Part of \cite{Esparcia-Alcazar:2010:GP} EuroGP'2010 held in conjunction with EvoCOP2010 EvoBIO2010 and EvoApplications2010", } @InProceedings{Maitre:2010:cec, author = "Ogier Maitre and Stephane Querry and Nicolas Lachiche and Pierre Collet", title = "EASEA Parallelization of Tree-Based Genetic Programming", booktitle = "2010 IEEE World Congress on Computational Intelligence", year = "2010", editor = "Pilar Sobrevilla", pages = "1997--2004", address = "Barcelona", month = "18-23 " # jul, organisation = "IEEE Computational Intelligence Society", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, GPU", isbn13 = "978-1-4244-6910-9", DOI = "doi:10.1109/CEC.2010.5586258", size = "8 pages", abstract = "This paper introduces the implementation of Koza-style tree-based Genetic Programming on General Purpose Graphic Processing Units (GPGPU) using the EASEA language, and shows how a GP algorithm can be easily implemented using EASEA and CUDA. Performance is first discussed on a classical toy problem taken from one of Koza's books and then on a real world problem inspired from aeronautics, that extends the results to difficult problems with large data sets.", notes = "tree GP. nVidia 295 GTX CUDA 2.3 Trigonometric regression (RMS error fitness disabled) cos(2x) Population 128 to 65536. TGPNode. Lyshevski F3A radio-controlled aerobatic aircraft model competitions. 4 control dimensions quaternion. Population 40960, 100 generations, 51000 training values GP operation per second not given. WCCI 2010 - A joint meeting of the IEEE, the INNS, the EPS and the IET. Also known as \cite{5586258}", } @PhdThesis{MAITRE_Ogier_2011, author = "Ogier Maitre", title = "GPGPU for Evolutionary Algorithms", school = "Strasbourg University", year = "2011", address = "France", month = "12 " # dec, keywords = "genetic algorithms, genetic programming, GPU, EASEA", URL = "http://scd-theses.u-strasbg.fr/2456/01/MAITRE_Ogier_2011.pdf", size = "159 pages", notes = "Supervisor Pierre Collet", } @InCollection{Maitre:2013:ecgpu, author = "Ogier Maitre", title = "Genetic Programming on {GPGPU cards} using {EASEA}", booktitle = "Massively Parallel Evolutionary Computation on {GPGPUs}", publisher = "Springer", year = "2013", editor = "Shigeyoshi Tsutsui and Pierre Collet", series = "Natural Computing Series", chapter = "11", pages = "227--248", keywords = "genetic algorithms, genetic programming, GPU", isbn13 = "978-3-642-37958-1", URL = "http://www.springer.com/computer/ai/book/978-3-642-37958-1", DOI = "doi:10.1007/978-3-642-37959-8_11", abstract = "Genetic programming is one of the most powerful evolutionary paradigms because it allows us to optimise not only the parameter space but also the structure of a solution. The search space explored by genetic programming is therefore huge and necessitates a very large computing power which is exactly what GPGPUs can provide. This chapter will show how Koza-like tree-based genetic programming can be efficiently ported onto GPGPU processors.", } @Article{Maity:2009:ISHjhe, author = "Rajib Maity and S. S. Kashid", title = "Hydroclimatological approach for Monthly Streamflow Prediction using Genetic programming", journal = "The Indian Society for Hydraulics journal of Hydraulic Engineering", year = "2009", volume = "15", number = "2", pages = "89--107", keywords = "genetic algorithms, genetic programming", ISSN = "0971-5010", URL = "http://www.tandfonline.com/doi/abs/10.1080/09715010.2009.10514943", DOI = "doi:10.1080/09715010.2009.10514943", size = "19 pages", abstract = "An approach for monthly streamflow prediction is illustrated in this paper using the concept of hydroclimatological association. Rainfall-runoff relationship over a catchment is very complex, which may not be revealed very easily. This is due to the fact that streamflow is significantly influenced by catchment characteristics, land-use pattern, spatial distribution of rainfall, evapotranspiration over the catchment, water retention over the basin, etc. Keeping the other factors more or less constant over a sufficiently small temporal span (say monthly), intensity and spatial distribution of rainfall plays a major role behind the streamflow variation. Oceans happen to be the major source of moisture for the precipitation and the rainfall distribution over the continents is proved to be linked with Sea Surface Temperature (SST) and various large-scale atmospheric circulation patterns across the globe. Thus, the variation of basin-scale streamflow is expected to be influenced by these large-scale climatological factors, which is investigated in this paper for the Narmada River basin. The information of El Nino-Southern Oscillation (ENSO) from the tropical Pacific Ocean and Equatorial Indian Ocean Oscillation (EQUINOO) from the tropical Indian Ocean is investigated 1) for their possible influence behind the monthly streamflow variation of Narmada River at central India and 2) the efficacy of genetic programming (GP), which is an artificial intelligence technique, for the prediction of monthly streamflow through the concept of hydroclimatological approach. The results of the study indicate that GP-derived streamflow forecasting models that use historical average of monthly streamflow and the large-scale atmospheric circulation information, for basin-scale streamflow prediction are quite satisfactory. The coefficient of determination for monthly streamflow in case of Narmada River was found to be 0.921 for training and 0.836 for testing, which is quite promising for such a complex system", notes = "rajib@civil.iitb.ac.in, now at Indian Institute of Technology, Kharagpur (rajib@civil.iitkgp.emet.in) 2. Department of Civil Engg., liT Bombay,", } @InCollection{Maity:2015:hbgpa, author = "Rajib Maity and Kironmala Chanda", title = "Potential of Genetic Programming in Hydroclimatic Prediction of Droughts: An Indian Perspective", booktitle = "Handbook of Genetic Programming Applications", publisher = "Springer", year = "2015", editor = "Amir H. Gandomi and Amir H. Alavi and Conor Ryan", chapter = "15", pages = "381--398", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-20882-4", DOI = "doi:10.1007/978-3-319-20883-1_15", abstract = "Past studies have established the presence of hydroclimatic teleconnection between hydrological variables across the world and large-scale coupled oceanic-atmospheric circulation patterns, such as El Nino-Southern Oscillation (ENSO), Equatorial Indian Ocean Oscillation (EQUINOO), Pacific Decadal Oscillation (PDO), Atlantic Multi-decadal Oscillation (AMO), Indian Ocean Dipole (IOD). For the purpose of modelling hydroclimatic teleconnections, Artificial intelligence (AI) tools including Genetic Programming (GP) have been successfully applied in several studies. In this chapter, we attempt to explore the potential of Linear Genetic Programming (LGP) for the prediction of droughts using the local and global climate inputs in the context of Indian hydroclimatology. The global anomaly fields of five different climate variables, namely Sea Surface Temperature (SST), Surface Pressure (SP), Air Temperature (AT), Wind Speed (WS) and Total Precipitable Water (TPW), are explored during extreme rainfall events (isolated by standardizing monthly rainfall from 1959 to 2010 using an anomaly based index) to identify the Global Climate Pattern (GCP). The GCP for the target area is characterized by 14 variables where each variable is designated by a particular climate variable from a distinct zone on the globe. The potential of a LGP-based approach is explored to extract the climate information hidden in the GCP and to predict the ensuing drought status. The LGP based approach is found to produce reasonably good results. Many of the dry and wet events observed during the last few decades are found to be predicted successfully.", } @InProceedings{1068304, author = "Hammad Majeed and Conor Ryan and R. Muhammad Atif Azad", title = "Evaluating {GP} schema in context", booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation", year = "2005", editor = "Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and Edwin D. {de Jong} and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler", volume = "2", ISBN = "1-59593-010-8", pages = "1773--1774", address = "Washington DC, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2005/docs/p1773.pdf", DOI = "doi:10.1145/1068009.1068304", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "25-29 " # jun, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Poster, module acquisition, schema theory, tree semantics", size = "2 pages", abstract = "We propose a methodology to look at the fitness contributions (semantics) of different schemata in Genetic Programming (GP). We hypothesise that the significance of a schema can be evaluated by calculating its fitness contribution to the total fitness of the trees that contain it, and use our methodology to test this hypothesis. It is shown that this method can also be used to identify schemata that are important in terms of both individual runs and individual problems (that is, schema that will be important across many runs on a particular problem). The usefulness of this study to existing schema theories and its effective use in the detection of introns, in the identification of potentially useful modular functions are also discussed.", notes = "GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM Order Number 910052 See also \cite{majeed:gecco05ws}. Quartic polynomial. ", } @InProceedings{majeed:gecco05ws, author = "Hammad Majeed", title = "A New Approach to Evaluate {GP} Schema in Context", booktitle = "Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program", year = "2005", month = "25-29 " # jun, editor = "Franz Rothlauf and Misty Blowers and J{\"u}rgen Branke and Stefano Cagnoni and Ivan I. Garibay and Ozlem Garibay and J{\"o}rn Grahl and Gregory Hornby and Edwin D. {de Jong} and Tim Kovacs and Sanjeev Kumar and Claudio F. Lima and Xavier Llor{\`a} and Fernando Lobo and Laurence D. Merkle and Julian Miller and Jason H. Moore and Michael O'Neill and Martin Pelikan and Terry P. Riopka and Marylyn D. Ritchie and Kumara Sastry and Stephen L. Smith and Hal Stringer and Keiki Takadama and Marc Toussaint and Stephen C. Upton and Alden H. Wright", publisher = "ACM Press", address = "Washington, D.C., USA", keywords = "genetic algorithms, genetic programming", pages = "378--381", URL = "http://gpbib.cs.ucl.ac.uk/gecco2005wks/papers/0378.pdf", abstract = "Evaluating GP schema in context is considered to be a complex,and, at times impossible, task. The tightly linked nodes of a GP tree is the main reason behind its complexity. We present a new approach to evaluate GP schema in context. It is simple in its implementation with a potential to address well-known GP problems, such as identification of significant schema, dead code (introns) and module acquisition to name a few. It is based on the principle that the contribution of a schema can be evaluated by neutralising the effect of the schema in the tree containing it (container-tree) and then checking its effect on the container-tree's fitness. Its usefulness is empirically demonstrated along with its limitation.", notes = "Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006", } @InProceedings{eurogp06:MajeedRyan, author = "Hammad Majeed and Conor Ryan", title = "A Less Destructive, Context-aware Crossover Operator for {GP}", editor = "Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art", booktitle = "Proceedings of the 9th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3905", year = "2006", address = "Budapest, Hungary", month = "10 - 12 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming, steepest ascent crossover hill climbing", ISBN = "3-540-33143-3", pages = "36--48", DOI = "doi:10.1007/11729976_4", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "Standard GP crossover is widely accepted as being a largely {\em destructive} operator, creating many poor offspring in the search for better ones. One of the major reasons for its destructiveness is its disrespect for the context of swapped subtrees in their respective parent trees when creating offspring. At times, this hampers GP's performance considerably, and results in populations with {\em low} average fitness values. Many attempts have been made to make it a more constructive crossover, mostly by preserving the context of the selected subtree in the offspring. Although successful at preserving context, none of these methods provide the opportunity to discover new and better contexts for exchanged subtrees. We introduce a context-aware crossover operator which operates by identifying all possible contexts for a subtree, and evaluating each of them. The context that produces the highest fitness is used to create a child which is then passed into the next generation. We have tested its performance on many benchmark problems. It has shown better results than the standard GP crossover operator, using either the same number or fewer individual evaluations. Furthermore, the average fitness of populations using this scheme improves considerably, and programs produced in this way are much smaller than those produced using standard crossover.", notes = "Part of \cite{collet:2006:GP} EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006. Variation in operator frequencies from beginning to end of GP run.", } @InCollection{Majeed:2006:GPTP, author = "Hammad Majeed and Conor Ryan", title = "A re-examination of a real world blood flow modeling problem using context-aware crossover", booktitle = "Genetic Programming Theory and Practice {IV}", year = "2006", editor = "Rick L. Riolo and Terence Soule and Bill Worzel", volume = "5", series = "Genetic and Evolutionary Computation", pages = "279--298", address = "Ann Arbor", month = "11-13 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming", ISBN = "0-387-33375-4", DOI = "doi:10.1007/978-0-387-49650-4_17", size = "18 pages", abstract = "This chapter describes context-aware crossover. This is an improved crossover technique for GP which always swaps subtrees into their best possible context in a parent. We show that this style of crossover is considerably more constructive than the standard method, and present several experiments to demonstrate how it operates, and how well it performs, before applying the technique to a real world application, the Blood Flow Modelling Problem.", notes = "part of \cite{Riolo:2006:GPTP} Published Jan 2007 after the workshop", } @InProceedings{1144146, author = "Hammad Majeed and Conor Ryan", title = "Using context-aware crossover to improve the performance of {GP}", booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation", year = "2006", editor = "Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos {Coello Coello} and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens", volume = "1", ISBN = "1-59593-186-4", pages = "847--854", address = "Seattle, Washington, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p847.pdf", DOI = "doi:10.1145/1143997.1144146", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "8-12 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, context, context aware crossover, destructive effects, one point crossover, standard crossover, tree context", size = "8 pages", abstract = "This paper describes the use of a recently introduced crossover operator for GP, context-aware crossover. Given a randomly selected subtree from one parent, context-aware crossover will always find the best location to place the subtree in the other parent. We examine the performance of GP when context-aware crossover is used as an extra crossover operator, and show that standard crossover is far more destructive, and that performance is better when only context-aware crossover is used. There is still a place for standard crossover, however, and results suggest that using standard crossover in the initial part of the run and then switching to context-aware crossover yields the best performance. We show that, across a range of standard GP benchmark problems, context-aware crossover produces a higher best fitness as well as a higher mean fitness, and even manages to solve the 11-bit multiplexer problem without ADFs. Furthermore, the individuals produced this way are much smaller than standard GP, and far fewer individual evaluations are required, so GP achieves a higher fitness by evaluating fewer and smaller individuals.", notes = "GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM Order Number 910060", } @InProceedings{1277285, author = "Hammad Majeed and Conor Ryan", title = "Context-aware mutation: a modular, context aware mutation operator for genetic programming", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "1651--1658", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1651.pdf", DOI = "doi:10.1145/1276958.1277285", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, building blocks, cache, constructive, context, context aware crossover, crossover, fitness, modules", abstract = "This paper introduces a new type of mutation, Context-Aware Mutation, which is inspired by the recently introduced context-aware crossover. Context-Aware mutation operates by replacing existing sub-trees with modules from a previously constructed repository of possibly useful subtrees. We describe an algorithmic way to produce the repository from an initial, exploratory run and test various GP set ups for producing the repository. The results show that when the exploratory run uses context-aware crossover and the main run uses context-aware mutation, not only is the final result significantly better, the overall cost of the runs in terms of individuals evaluated is significantly lower.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @InProceedings{1277286, author = "Hammad Majeed and Conor Ryan", title = "On the constructiveness of context-aware crossover", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "1659--1666", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1659.pdf", DOI = "doi:10.1145/1276958.1277286", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, cache, constructive, context, context aware crossover, crossover, fitness", abstract = "Crossover in Genetic Programming is mostly a destructive operator, generally producing children worse than the parents and occasionally producing those who are better. A recently introduced operator, Context-Aware Crossover, which implicitly discovers the best possible crossover site for a subtree has been shown to consistently attain higher fitnesses while processing fewer individuals. It has been observed that context-aware crossover is similar to Brood Crossover in that multiple children are produced during each crossover event. This paper performs a thorough analysis of these crossover operators and compares the performance of the two and demonstrates that, although they do work similarly, context-aware crossover performs a far better sampling of the search space and thus performs much better. We also demonstrate that context-aware crossover benefits from a speed up of almost an order of magnitude when using a simple and very small cache, which is over two orders of magnitude smaller than caches typically used.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @InProceedings{Majeed:2007:FBIT, author = "H. Majeed and C. Ryan", title = "A New Approach to Calculate the Best Context of a Tree and its Application in Defining a Constructive, Context Aware Crossover for GP", booktitle = "Proceedings of the 2007 International Conference Frontiers in the Convergence of Bioscience and Information Technologies (FBIT 2007)", year = "2007", pages = "765--768", address = "Jeju Island, Korea", month = oct # " 11-13", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-0-7695-2999-8", DOI = "doi:10.1109/FBIT.2007.100", abstract = "Genetic programming (GP) is an evolutionary algorithm that evolves computer programs. Its main recombination operator is standard one point crossover which is generally accepted to be one of GP's weak points, due to its ignorance of the context into which genetic material is placed. This work introduces a new context aware recombination operator called context-aware crossover. It implicitly calculates the best possible context of the subtree-to- be-exchanged in the other parent and places it there. It is tested on a wide range of problems and found quite constructive in general and quite effective on hard problems, in particular. It has also shown the ability to generate quite smaller trees than standard GP without effecting the fitness of a population adversely.", notes = "Comput. Sci. & Inf. Syst., Limerick Univ., Limerick", } @PhdThesis{Majeed:thesis, author = "Hammad Majeed", title = "The Importance of semantic context in tree based GP and its application in defining a less destructive, context aware crossover for GP", school = "University of Limerick", year = "2007", address = "Ireland", month = "20 " # nov, URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Majeed_thesis.pdf", URL = "https://docs.google.com/file/d/0B1TtmH1V-wKmNFJKRFRDRkNPQzQ/edit?usp=sharing", keywords = "genetic algorithms, genetic programming, context aware crossover, destructive crossover", abstract = "This thesis gives an empirical proof of the existence of competitive building blocks in Grammatical Evolution (GE), a grammar based program evolving algorithm. It shows that in GE, rooted and non-rooted building blocks exist and over the period of time rooted building blocks compete with each other to grow in size, while non-rooted building blocks help them to accomplish that. This is an offline study and done in a retrospective manner. We also present a comprehensive study of the importance of semantic context of a sub-tree in tree based systems and introduce a novel context aware evaluation technique for evaluating sub-trees in context. The usefulness of this technique is demonstrated on a benchmark problem. In this work, we introduce a new constructive and context aware crossover for GP, Context-Aware crossover, which works by placing the selected sub- trees in their best possible context in any tree. This is a greedy approach and results in an improved performance. It is tested on a wide range of problems and showed better performance on all the problems except the Uni-Variate and Bi-Variate Polynomial Symbolic Regression problems. Furthermore, the results show that it generates very compact form of trees without adversely affecting their fitness. Finally, we show the usefulness of the context aware evaluation technique in encapsulating useful trees at the end of a run and using them to create a module repository. This repository is later used to improve the performance of the second cascaded run. For the second run, a variant of context-aware crossover is introduced, Context-Aware mutation which works on module repository. The effectiveness of this setup is demonstrated by re-examining a real world blood flow problem and improving the previously published results.", notes = "Supervisor Conor Ryan", } @Article{MAJEED:2021:SEC, author = "Hammad Majeed and Abdul Wali and Mirza Beg", title = "Optimizing genetic programming by exploiting semantic impact of sub trees", journal = "Swarm and Evolutionary Computation", volume = "65", pages = "100923", year = "2021", ISSN = "2210-6502", DOI = "doi:10.1016/j.swevo.2021.100923", URL = "https://www.sciencedirect.com/science/article/pii/S2210650221000845", keywords = "genetic algorithms, genetic programming, Semantic error, Sub tree impact, Crossover, Semantic distance", abstract = "Now-a-days researchers have diverted their attentions towards making stochastic algorithms deterministic. This is to reduce the fruitless exploration during the search process and to give direction to the search process. Lack of locality in the algorithms is the biggest hindrance in achieving this goal. Locality in GP is described as the correlation between the change in genotype and the semantics of its phenotype (solution). In strong locality, neighboring genotype and phenotype correspond to each other in a search space. It is believed that search algorithms exhibiting strong locality perform better than the algorithms with weak locality. Genetic Programming is among the best performing stochastic algorithms for solving challenging problems and is cursed with the same problem. This means, a small change in GP tree may result in a huge change in the behavior of the solution and vice versa. Unfortunately, this stochastic behavior stops GP from achieving its true potential. 30 years of research since GP's inception has not solved this problem and even today it is among the biggest challenges faced by the GP community. In this paper we propose a partial derivative based technique for calculating impact of a sub tree on the output of a GP tree. This information is then used to define an impact aware crossover operator. This operator reduces semantic error of a GP tree by intelligently picking crossover points in the tree. Performance of the GP augmented with the new proposed crossover operator is compared with the state of the art techniques. The proposed technique is found efficient, reliable and outperforms the state of the art algorithms on all the tested problems", } @InProceedings{Majid:2003:INMIC, author = "Abdul Majid and Asifullah Khan and Anwar M. Mirza", title = "Gender classification using discrete cosine transformation: a comparison of different classifiers", booktitle = "Proceedings of the 7th International Multi Topic Conference, INMIC 2003", year = "2003", pages = "59--64", address = "Islamabad", month = "8-9 " # dec, publisher = "IEEE", keywords = "AUROC", DOI = "doi:10.1109/INMIC.2003.1416616", abstract = "We have investigated the problem of gender classification using a library of four hundred standard frontal facial images employing five classifiers, namely k-means, k-nearest neighbours, linear discriminant analysis (LDA), Mahalanobis distance based (MDB) classifiers and our modified KNN classifier. The image data independent discrete cosine transformation (DCT) basis is used for facial feature extraction. Areas under the convex hull (AUCH) of the classifiers are measured by varying the values of threshold for each feature subset in the receiver operating characteristics (ROC) curve. The scalar values of AUCH of the ROC curve increases with increasing number of features. More features yield a better representation of the gender facial image. The overall performance of classifiers is compared with different values of AUCH versus features under different conditions. It has been observed that when the number of features is increased beyond 5, AUCH starts to saturate. Our experimental results demonstrate that modified-KNN performs better than the rest of the conventional classifiers under all conditions. The LDA classifier did not perform well in the DCT domain; however, it gradually improved its performance with increasing number of features.", } @InProceedings{Majid:2004:ICMLA, author = "Abdul Majid and Asifullah Khan and Anwar M. Mirza", title = "Improving Performance of Nearest Neighborhood Classifier Using Genetic Programming", booktitle = "The Third International Conference on Machine Learning and Applications (ICMLA-04)", year = "2004", pages = "469--476", address = "Louisville, KY, USA", month = "16-18 " # dec, organisation = "IEEE/ACM", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICMLA.2004.1383552", size = "8 pages", abstract = "Nearest neighbourhood classifier (kNN) is most widely used in pattern recognition applications. Depending on the selection of voting methodology, the problem of outliers has been encountered in this classifier. Therefore, selection and optimisation of the voting methodology is very important. In this work, we have used Genetic Programming (GP) to improve the performance of nearest neighbour classifier. Instead of using predefined k nearest neighbors, the number of men and women in the first two quartiles in Euclidean space are used for voting. GP is, then, used to evolve an optimal class mapping function that effectively reduces the outliers. The performance of modified nearest neighborhood (ModNN) classifier is then compared with the conventional kNN for gender classification problem. Receiver Operating Characteristics curve and its Area Under the Convex Hull (A UCH) are used as the performance measures. Considering the first three and first five eigen features respectively, ModNN achieves AUCH equal to 0.985 and 0.992 as compared to 0.9693 and 0.9795 of conventional kNN respectively", notes = "Broken Jan 2013 http://www.cs.csubak.edu/~icmla/icmla04/ also known as \cite{1383552}", } @InProceedings{10.1109/ICMLA.2005.42, author = "Abdul Majid and Asifullah Khan and Anwar M. Mirza", title = "Intelligent Combination of Kernels Information for Improved Classification", booktitle = "Fourth International Conference on Machine Learning and Applications (ICMLA'05)", year = "2005", pages = "16--21", address = "Los Angeles", publisher_address = "Los Alamitos, CA, USA", publisher = "IEEE Computer Society", keywords = "genetic algorithms, genetic programming", ISBN = "0-7695-2495-8", DOI = "doi:10.1109/ICMLA.2005.42", abstract = "we are proposing a combination scheme of kernels information of Support Vector Machines (SVMs) for improved classification task using Genetic Programming. In the scheme, first, the predicted information is extracted by SVM through the learning of different kernel functions. GP is then used to develop an Optimal Composite Classifier (OCC) having better performance than individual SVM classifiers. The experimental results demonstrate that OCC is more effective, generalised and robust. Specifically, it attains high margin of improvement at small features. Another side advantage of our GP based intelligent combination scheme is that it automatically incorporates the issues of optimal kernel and model selection to achieve a higher performance prediction model.", notes = "http://ieeexplore.ieee.org/servlet/opac?punumber=10693", } @InProceedings{Majid:2005:INMIC, author = "Abdul Majid and Asifullah Khan and Anwar M. Mirza", title = "Combination of Nearest Neighborhood Classifiers Using Genetic Programming", booktitle = "9th International Multitopic Conference (INMIC 2005)", year = "2005", pages = "1--6", address = "Karachi, Pakistan", month = "23-25 " # dec # " 2005", publisher = "Pakistan Section IEEE", keywords = "genetic algorithms, genetic programming, pattern classification, GP-based intelligent scheme, decision space, nearest neighborhood classifiers, optimal composite classifier, optimal model selection", ISBN = "0-7803-9429-1", DOI = "doi:10.1109/INMIC.2005.334486", abstract = "In this paper, GP based intelligent scheme has been used to develop an optimal composite classifier (OCC) from individual nearest neighbor (NN) classifiers. In the combining scheme, first, the predicted information is extracted from the component classifiers. Then, GP is used to develop OCC having better performance than individual NN classifiers. The experimental results demonstrate that the combined decision space of OCC is more effective. Further, we observed that heterogeneous combination of classifiers has more promising results than their homogenous one. Another side advantage of our GP based intelligent combination scheme is that it automatically incorporates the issues of optimal model selection of NN classifiers to achieve a higher performance prediction model", notes = "Also known as \cite{4133501}", } @Article{Majid:2006:IJHIS, author = "Abdul Majid and Asifullah Khan and Anwar M. Mirza", title = "Combination of support vector machines using genetic programming", journal = "International Journal of Hybrid Intelligent Systems", year = "2006", volume = "3", number = "2", pages = "109--125", month = jun, keywords = "genetic algorithms, genetic programming, Support vector machines, optimal composite classifiers, receiver operating characteristics curves, Area Under the Convex Hull (AUCH), AUROC", ISSN = "1448-5869", URL = "http://content.iospress.com/articles/international-journal-of-hybrid-intelligent-systems/his00026", DOI = "doi:10.3233/HIS-2006-3204", size = "17 pages", abstract = "the combination of support vector machine (SVM) classifiers using Genetic Programming (GP) for gender classification problem. In our scheme, individual SVM classifiers are constructed through the learning of different SVM kernel functions. The predictions of SVM classifiers are then combined using GP to develop Optimal Composite Classifier (OCC). In this way, the combined decision space is more informative and discriminant. OCC has shown improved performance than that of optimised individual SVM classifiers using grid search. Another advantage of our GP combination scheme is that it automatically incorporates the issues of optimal kernel function and model selection to achieve high performance classification model. The classification performance is reported by using Receiver Operating Characteristics (ROC) Curve. Experiments are conducted under various feature sets to show that OCC is more informative and robust as compared to their individual SVM classifiers. Specifically, it attains high margin of improvement for small feature sets.", } @PhdThesis{Majid:thesis, author = "Abdul Majid", title = "Optimization and Combination of Classifiers Using Genetic Programming", school = "Ghulam Ishaq Khan Institute of Engineering Sciences \& Technology", year = "2006", address = "Topi, Swabi, NWFP, Pakistan", month = may, keywords = "genetic algorithms, genetic programming", URL = "https://www.hec.gov.pk/english/services/students/PCD/Pages/PCD.aspx?Paged=TRUE&p_ID=876", broken = "http://bpt.hec.gov.pk/2511/", URL = "http://prr.hec.gov.pk/Thesis/349S.pdf", size = "155 pages", abstract = "The success of pattern classification system depends on the improvement of its classification stage. The work of thesis has investigated the potential of Genetic Programming (GP) search space to optimise the performance of various classification models. In this thesis, two GP approaches are proposed. In the first approach, GP is used to optimize the performance of individual classifiers. The performance of linear classifiers and nearest neighbour classifiers is improved during GP evolution to develop a high performance numeric classifier. In second approach, component classifiers are trained on the input data and their predictions are extracted. GP search space is then used to combine the predictions of component classifiers to develop an optimal composite classifier (OCC). This composite classifier extracts useful information from its component classifiers during evolution process. In this way, the decision space of composite classifier is more informative and discriminant. Effectiveness of GP combination technique is investigated for four different types of classification models including linear classifiers, support vector machines (SVMs) classifiers, statistical classifiers and instance based nearest neighbour classifiers. The successfulness of such composite classifiers is demonstrated by performing various experiments, while using Receiver Operating Characteristics (ROC) curve as the performance measure. It is evident from the experimental results that OCC outperforms its component classifiers. It attains high margin of improvement at small feature sets. Further, it is concluded that classification models developed by heterogeneous combination of classifiers have more promising results than their homogeneous combination. GP optimisation technique automatically caters the selection of suitable component classifiers and model selection. Two main objectives are achieved, while using GP optimisation. First, objective achieved is the development of more optimal classification models. The second one is the enhancement in the GP search strategy itself.", notes = "Serial/PCD.No 875 Item Type: Thesis (PhD) ID Code: 2511 Deposited By: Ch Abdulla fayyaz Chattha Last Modified: 28 Jul 2009 21:16", } @InProceedings{Majid:2010:ieeeICIP, author = "Abdul Majid and Muhammad Tariq Mahmood and Tae-Sun Choi", title = "A novel noise-free pixels based impulse noise filtering", booktitle = "17th IEEE International Conference on Image Processing (ICIP 2010)", year = "2010", month = "26-29 " # sep, pages = "125--128", abstract = "Generally, impulse noise filtering schemes consider all pixels within a large neighbourhood. However, the estimate from all pixels within the neighborhood may not be accurate. Moreover, large window may remove edges and fine details. In contrast to this approach, we propose iterative impulse noise removal scheme that emphasises on few noise-free pixels within a small neighbourhood. This iterative process continues until all noisy pixels are replaced with the estimated values. To estimate the optimal value of noisy pixel, we developed genetic programming (GP) based estimator using noise-free pixels. The estimator is constituent of useful local pixels information. Experimental results show that the proposed scheme is capable of removing impulse noise effectively while preserving the fine details. Especially, our approach has shown effectiveness against high impulse noise density.", keywords = "genetic algorithms, genetic programming, GP based estimator, impulse noise density, impulse noise filtering, iterative impulse noise removal, iterative process, noise-free pixel, filtering theory, image denoising, impulse noise, iterative methods", DOI = "doi:10.1109/ICIP.2010.5651975", ISSN = "1522-4880", notes = "Sch. of Inf. & Mechatron., Gwangju Inst. of Sci. & Technol., Gwangju, South Korea. Also known as \cite{5651975}", } @Article{journals/kais/MajidLMC12, author = "Abdul Majid and Choong-Hwan Lee and M. Tariq Mahmood and Tae-Sun Choi", title = "Impulse noise filtering based on noise-free pixels using genetic programming", journal = "Knowledge and Information Systems", year = "2012", volume = "32", number = "3", pages = "505--526", publisher = "Springer-Verlag", language = "English", keywords = "genetic algorithms, genetic programming, Impulse noise, Image restoration, Noise detection, Noise filtering", ISSN = "0219-1377", DOI = "doi:10.1007/s10115-011-0456-7", bibdate = "2012-08-14", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/kais/kais32.html#MajidLMC12", size = "22 pages", abstract = "Generally, the impulse noise filtering schemes use all pixels within a neighbourhood and increase the size of neighbourhood with the increase in noise density. However, the estimate from all pixels within neighbourhood may not be accurate. Moreover, the larger window may remove edges and fine details as well. In contrast, we propose a novel impulse noise removal scheme that emphasises on few noise-free pixels and small neighbourhood. The proposed scheme searches noise-free pixels within a small neighbourhood. If at least three pixels are not found, then the noisy pixel is left unchanged in current iteration. This iterative process continues until all noisy pixels are replaced with estimated values. In order to estimate the optimal value of the noisy pixel, genetic programming-based estimator is developed. The estimator (function) is composed of useful pixel information and arithmetic functions. Experimental results show that the proposed scheme is capable of removing impulse noise effectively while preserving the fine image details. Especially, our approach has shown effectiveness against high impulse noise density.", } @Article{Majid:2015:AA, author = "Abdul Majid and Safdar Ali", title = "HBC-Evo: predicting human breast cancer by exploiting amino acid sequence-based feature spaces and evolutionary ensemble system", year = "2015", journal = "Amino Acids", volume = "47", number = "1", pages = "217--221", month = jan, keywords = "genetic algorithms, genetic programming, Breast cancer diagnosis, Protein sequences, Amino acids, Physicochemical properties, Evolutionary ensemble system, PSO", language = "English", publisher = "Springer", ISSN = "0939-4451", URL = "http://dx.doi.org/10.1007/s00726-014-1871-3", DOI = "doi:10.1007/s00726-014-1871-3", size = "5 pages", abstract = "We developed genetic programming (GP)-based evolutionary ensemble system for the early diagnosis, prognosis and prediction of human breast cancer. This system has effectively exploited the diversity in feature and decision spaces. First, individual learners are trained in different feature spaces using physicochemical properties of protein amino acids. Their predictions are then stacked to develop the best solution during GP evolution process. Finally, results for HBC-Evo system are obtained with optimal threshold, which is computed using particle swarm optimization. Our novel approach has demonstrated promising results compared to state of the art approaches.", notes = "PMID: 25488423 [PubMed - indexed for MEDLINE]", } @InBook{Majoros:2007:CGP, author = "William H. Majoros", title = "Methods for Computational Gene Prediction", chapter = "10.12", pages = "346--348?", publisher = "Cambridge University Press", year = "2007", keywords = "genetic algorithms, genetic programming", isbn13 = "978-0-521-70694-0", URL = "http://www.cambridge.org/catalogue/catalogue.asp?isbn=9780521706940", } @InProceedings{Makanju:2008:ARES, author = "Adetokunbo Makanju and A. Nur Zincir-Heywood and Evangelos E. Milios", title = "Adaptabilty of a GP Based IDS on Wireless Networks", booktitle = "Third International Conference on Availability, Reliability and Security, ARES 08", year = "2008", month = mar, pages = "310--318", keywords = "genetic algorithms, genetic programming, GP based IDS, Kismet, Snort-Wireless, WiFi networks, data link layer, intrusion detection system, machine learning, wireless networks, learning (artificial intelligence), security of data, wireless LAN", DOI = "doi:10.1109/ARES.2008.50", abstract = "Security and Intrusion detection in WiFi networks is currently an active area of research where WiFi specific Data Link layer attacks are an area of focus; particularly recent work has focused on producing machine learning based IDSs for these WiFi specific attacks. These proposed machine learning based IDSs come in addition to the already deployed signatures which are already in use in conventional intrusion detection systems like Snort-Wireless and Kismet. In this paper, we compare the detection capability of Snort-Wireless and a Genetic Programming (GP) based intrusion detector, based on the ability to adapt to modified attacks, ability to adapt to similar unknown attacks and infrastructure independent detection. Our results show that the GP based detection system is much more robust against modified attacks compared to Snort-Wireless. Moreover, by focusing on the method(s) used in feature preprocessing for presentation to learning algorithms, GP based IDSs can achieve infrastructure independent detection and can adapt to similar unknown attacks too. On the other hand, even though Snort-Wireless is an infrastructure independent detector, it cannot adapt to unknown attacks even if they are similar to others for which it has signatures on.", notes = "Also known as \cite{4529352}", } @Article{makarov:1999:fpes:sfsdGP, author = "Dmitrii E. Makarov and Horia Metiu", title = "Fitting potential-energy surfaces: A search in the function space by directed genetic programming", journal = "Journal of Chemical Physics", year = "1998", volume = "108", number = "2", pages = "590--598", month = "8 " # jan, keywords = "genetic algorithms, genetic programming, potential energy surfaces, ab initio calculations, physics computing", ISSN = "0021-9606", DOI = "doi:10.1063/1.475421", size = "9 pages", abstract = "We propose new procedures by which genetic programming can be used to find the best functional form and the best set of parameters to fit the energies and the energy derivatives provided by ab initio calculations. Our main contribution is a new procedure, which we call a directed genetic search, which is more efficient and more stable than a 'traditional' genetic program.", notes = "See also \cite{makarov:2000:JPCA} ", } @Article{makarov:2000:JPCA, author = "Dmitrii E. Makarov and Horia Metiu", title = "Using Genetic Programming To Solve the {Schrodinger} Equation", journal = "Journal of Physical Chemistry A", year = "2000", volume = "104", number = "37", pages = "8540--8545", month = sep # " 21", keywords = "genetic algorithms, genetic programming, DGP, mathematica, Wave function, Genetics, Excited states, Approximation, Energy", ISSN = "1089-5639", DOI = "doi:10.1021/jp000695q", abstract = "In a recent paper [Makarov, D. E.; Metiu, H. J. Chem. Phys. 1998, 108, 590], \cite{makarov:1999:fpes:sfsdGP} we developed a directed genetic programming approach for finding the best functional form that fits the energies provided by ab initio calculations. In this paper, we use this approach to find the analytic solutions of the time-independent Schrodinger equation. This is achieved by inverting the Schrodinger equation such that the potential is a functional depending on the wave function and the energy. A genetic search is then performed for the values of the energy and the analytic form of the wave function that provide the best fit of the given potential on a chosen grid. A procedure for finding excited states is discussed. We test our method for a one-dimensional anharmonic well, a double well, and a two-dimensional anharmonic oscillator.", notes = "http://pubs.acs.org/journals/jpcafh/index.html directed genetic programming (DGP), monte Carlo, 'straightforward GP...leads to poor results' p8451 DGP adds form of solution? Fset={+,-<*,/} pop=100 G<=250. Ekart well best(?) -1.5576eV, most within 0.5 percent. Even better with Bessel function. Also tried Gaussian. NB 'proper choice of the grid is important' p852. Asymptotic region dominates tunnelling. 'we believe that...more readily find the solution that has the simplest functional form' p8542 (ie the lowest energy eigenstate). Excited states. Harmonic oscillator creation operator. problems with second excited state? Hartree approximation (p8544), separate x and y dimensions, use same bell curve for both x and y. x,y back together? Seeded run? Fset now also includes exp First excited state E=2.534.", } @Article{Makkeasorn:2008:JH, author = "A. Makkeasoyrn and Ni-Bin Chang and Xiaobing Zhou", title = "Short-term Streamflow Forecasting with Global Climate Change Implications - A Comparative Study between Genetic Programming and Neural Network Models", journal = "Journal of Hydrology", volume = "352", number = "3-4", pages = "336--354", year = "2008", ISSN = "0022-1694", DOI = "doi:10.1016/j.jhydrol.2008.01.023", URL = "http://www.sciencedirect.com/science/article/B6V6C-4RRFNK3-2/2/26f7ea5d045a8c5457038f4c4d0b73e5", keywords = "genetic algorithms, genetic programming, ANN, Streamflow forecasting, Neural network, Global climate change, NEXRAD, Sea surface temperature", abstract = "Summary Sustainable water resources management is a critically important priority across the globe. While water scarcity limits the uses of water in many ways, floods may also result in property damages and the loss of life. To more efficiently use the limited amount of water under the changing world or to resourcefully provide adequate time for flood warning, the issues have led us to seek advanced techniques for improving stream flow forecasting on a short-term basis. This study emphasizes the inclusion of sea surface temperature (SST) in addition to the spatio-temporal rainfall distribution via the Next Generation Radar (NEXRAD), meteorological data via local weather stations, and historical stream data via USGS gage stations to collectively forecast discharges in a semi-arid watershed in south Texas. Two types of artificial intelligence models, including genetic programming (GP) and neural network (NN) models, were employed comparatively. Four numerical evaluators were used to evaluate the validity of a suite of forecasting models. Research findings indicate that GP-derived streamflow forecasting models were generally favored in the assessment in which both SST and meteorological data significantly improve the accuracy of forecasting. Among several scenarios, NEXRAD rainfall data were proven its most effectiveness for a 3-day forecast, and SST Gulf-to-Atlantic index shows larger impacts than the SST Gulf-to-Pacific index on the stream-flow forecasts. The most forward looking GP-derived models can even perform a 30-day streamflow forecast ahead of time with an r-square of 0.84 and RMS error 5.4 in our study.", } @Article{Makkeasorn20091069, author = "Ammarin Makkeasorn and Ni-Bin Chang and Jiahong Li", title = "Seasonal change detection of riparian zones with remote sensing images and genetic programming in a semi-arid watershed", journal = "Journal of Environmental Management", volume = "90", number = "2", pages = "1069--1080", year = "2009", ISSN = "0301-4797", DOI = "DOI:10.1016/j.jenvman.2008.04.004", URL = "http://www.sciencedirect.com/science/article/B6WJ7-4SNGRR7-1/2/952c6978ecce3d3e3e5a40f16f9ad11b", keywords = "genetic algorithms, genetic programming, Riparian classification, Soil moisture, RADARSAT-1, LANDSAT, Vegetation index, Ecohydrology", abstract = "Riparian zones are deemed significant due to their interception capability of non-point source impacts and the maintenance of ecosystem integrity region wide. To improve classification and change detection of riparian buffers, this paper developed an evolutionary computational, supervised classification method - the RIparian Classification Algorithm (RICAL) - to conduct the seasonal change detection of riparian zones in a vast semi-arid watershed, South Texas. RICAL uniquely demonstrates an integrative effort to incorporate both vegetation indices and soil moisture images derived from LANDSAT 5 TM and RADARSAT-1 satellite images, respectively. First, an estimation of soil moisture based on RADARSAT-1 Synthetic Aperture Radar (SAR) images was conducted via the first-stage genetic programming (GP) practice. Second, for the statistical analyses and image classification, eight vegetation indices were prepared based on reflectance factors that were calculated as the response of the instrument on LANDSAT. These spectral vegetation indices were then independently used for discriminate analysis along with soil moisture images to classify the riparian zones via the second-stage GP practice. The practical implementation was assessed by a case study in the Choke Canyon Reservoir Watershed (CCRW), South Texas, which is mostly agricultural and range land in a semi-arid coastal environment. To enhance the application potential, a combination of Iterative Self-Organizing Data Analysis Techniques (ISODATA) and maximum likelihood supervised classification was also performed for spectral discrimination and classification of riparian varieties comparatively. Research findings show that the RICAL algorithm may yield around 90percent accuracy based on the unseen ground data. But using different vegetation indices would not significantly improve the final quality of the spectral discrimination and classification. Such practices may lead to the formulation of more effective management strategies for the handling of non-point source pollution, bird habitat monitoring, and grazing and live stock management in the future.", } @Article{Malan2013148, author = "Katherine M. Malan and Andries P. Engelbrecht", title = "A survey of techniques for characterising fitness landscapes and some possible ways forward", journal = "Information Sciences", year = "2013", volume = "241", pages = "148--163", month = aug, keywords = "genetic algorithms, genetic programming, Fitness landscape, Landscape analysis, optimisation problem, problem hardness measure", ISSN = "0020-0255", URL = "http://www.sciencedirect.com/science/article/pii/S0020025513003125", DOI = "doi:10.1016/j.ins.2013.04.015", abstract = "Real-world optimisation problems are often very complex. Metaheuristics have been successful in solving many of these problems, but the difficulty in choosing the best approach can be a huge challenge for practitioners. One approach to this dilemma is to use fitness landscape analysis to better understand problems before deciding on approaches to solving the problems. However, despite extensive research on fitness landscape analysis and a large number of developed techniques, very few techniques are used in practice. This could be because fitness landscape analysis in itself can be complex. In an attempt to make fitness landscape analysis techniques accessible, this paper provides an overview of techniques from the 1980s to the present. Attributes that are important for practical implementation are highlighted and ways of adapting techniques to be more feasible or appropriate are suggested. The survey reveals the wide range of factors that can influence problem difficulty, emphasising the need for a shift in focus away from predicting problem hardness towards measuring characteristics. It is hoped that this survey will invoke renewed interest in the field of understanding complex optimisation problems and ultimately lead to better decision making on the use of appropriate metaheuristics.", notes = "Not on GP, but some mention of GP", } @Article{Malan:2021:Algorithms, author = "Katherine Mary Malan", title = "A Survey of Advances in Landscape Analysis for Optimisation", journal = "Algorithms", year = "2021", volume = "14", pages = "40", keywords = "genetic algorithms, genetic programming, fitness landscape, landscape analysis, violation landscape, error landscape, automated algorithm selection", ISSN = "1999-4893", DOI = "doi:10.3390/a14020040", size = "16 pages", abstract = "Fitness landscapes were proposed in 1932 as an abstract notion for understanding biological evolution and were later used to explain evolutionary algorithm behaviour. The last ten years has seen the field of fitness landscape analysis develop from a largely theoretical idea in evolutionary computation to a practical tool applied in optimisation in general and more recently in machine learning. With this widened scope, new types of landscapes have emerged such as multiobjective landscapes, violation landscapes, dynamic and coupled landscapes and error landscapes. This survey is a follow-up from a 2013 survey on fitness landscapes and includes an additional 11 landscape analysis techniques. The paper also includes a survey on the applications of landscape analysis for understanding complex problems and explaining algorithm behaviour, as well as algorithm performance prediction and automated algorithm configuration and selection. The extensive use of landscape analysis in a broad range of areas highlights the wide applicability of the techniques and the paper discusses some opportunities for further research in this growing field.", notes = "A few mentions of GP Department of Decision Sciences, University of South Africa, Pretoria 0002, South Africa", } @Article{Maldonado:GPEM, author = "Yazmin Maldonado and Ruben Salas and Joel A. Quevedo and Rogelio Valdez and Leonardo Trujillo", title = "{GSGP}-hardware: instantaneous symbolic regression with an {FPGA} implementation of geometric semantic genetic programming", journal = "Genetic Programming and Evolvable Machines", year = "2024", volume = "25", number = "2", pages = "Article no 18", month = dec, keywords = "genetic algorithms, genetic programming, evolvable hardware, VHDL, FPGA, Geometric Semantic Genetic Programming, GSGP", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-024-09491-5", abstract = "more prevalent with the continued development of the Internet-of-Things and the increased need for efficient and distributed learning on the Edge. GSGP-Hardware, a fully pipelined and parallel design of GSGP developed fully using VHDL, for implementation on FPGA devices. Using Vivado AMD-Xilinx for synthesis and simulation, GSGP-Hardware achieves an approximate improvement in efficiency, in terms of run time and GPops/s, of three and four orders of magnitude, respectively, compared with the state-of-the-art GPU implementation. This is a performance increase that has not been achieved by other FPGA-based implementations of genetic programming. This is possible due to the manner in which GSGP evolves a model, and competitive accuracy is achieved by incorporating simple but powerful enhancements to the original GSGP algorithm. GSGP-Hardware allows for instantaneous symbolic regression, opening up new application domains for this powerful variant of genetic programming.", notes = "See MSc M1921052 https://rinacional.tecnm.mx/bitstream/TecNM/6652/1/Tesis%20Ruben%20Dario%20Salas%20Villegas.pdf M20210005 https://rinacional.tecnm.mx/bitstream/TecNM/6647/1/Tesis%20Joel%20Antonio%20Quevedo%20Felix.pdf Posgrado en Ciencias de la Ingenieria Tecnologico Nacional de Mexico/IT de Tijuana, Tijuana, Mexico", } @Article{Maleki-Dizaji2014, author = "Saeedeh Maleki-Dizaji and Jawed Siddiqi and Yasaman Soltan-Zadeh and Fazilatur Rahman", title = "Adaptive information retrieval system via modelling user behaviour", journal = "Journal of Ambient Intelligence and Humanized Computing", year = "2014", volume = "5", number = "1", pages = "105--110", keywords = "genetic algorithms, User information needs modelling, Interactive evolutionary learning, Adaptive information retrieval", ISSN = "1868-5145", URL = "https://doi.org/10.1007/s12652-012-0138-7", DOI = "doi:10.1007/s12652-012-0138-7", size = "6 pages", abstract = "There has been an exponential growth in the volume and variety of information available on the Internet, similarly there has been a significant demand from users for accurate information that matches their interests, however, the two are often incompatible because of the effectiveness of retrieving the exact information the user requires. This paper addresses this problem with an adaptive agent-based modelling approach that relies on evolutionary user-modelling. The proposed information retrieval system learns user needs from user-provided relevance feedback. It is proposed that retrieval effectiveness can be improved by applying computational intelligence techniques for modelling information needs, through interactive reinforcement learning. The method combines qualitative (subjective) user relevance feedback with quantitative (algorithmic) measures of the relevance of retrieved documents. An adaptive information retrieval system is developed whose retrieval effectiveness is evaluated using traditional precision and recall.", notes = "Not GP? Evolved rules represented as bits", } @InProceedings{maley:1999:FSTOE, author = "C. C. Maley", title = "Four Steps Toward Open-Ended Evolution", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1336--1343", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "artificial life, adaptive behavior and agents", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-049.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-049.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Malhotra:2011:GECCOcomp, author = "Abhinav Malhotra and Varun Aggarwal", title = "HIER-HEIR: an evolutionary system with hierarchical representation \&\#38; contextual operators applied to fashion design", booktitle = "GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0690-4", keywords = "genetic algorithms, genetic programming, Real world applications: Poster", pages = "215--216", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001858.2001980", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "There has been considerable interest in using evolutionary algorithms based techniques to design creative systems. However, these techniques are either too 'creative' and violate design constraints of the domain, or, those catering to a limited search space, but operating within design constraints. Our new evolutionary system 'HIER-HEIR', is not only creative(searches a large space effectively), but creates only such designs which are valid with respect to the design domain. Inspired by human design methodology, the representation is a hierarchy of components and the variation is contextual acting at all levels of the hierarchy intelligently, facilitating effective search in the design space with explicit control over exploitation and exploration. We have explained our technique with the metaphor of automatic design of a fashion dress in this paper. The experimental results validate our hypotheses with regard to the system. With regard to previous work, our technique is new both with regard to previously published hierarchical systems and those designed for evolving fashion designs.", notes = "Also known as \cite{2001980} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @InProceedings{Malhotra:2016:ICICM, author = "Gayatri Malhotra", booktitle = "2016 6th International Conference on Information Communication and Management (ICICM)", title = "Cartesian genetic programming approach for embryonic fabric architecture", year = "2016", pages = "285--290", abstract = "The Cartesian genetic programming (CGP) considers grid of nodes to represent a genotype. In the corresponding phenotype each node represents a processing element. The processing element has similar structure as of smallest embryonic fabric element. It can be an embryonic molecule where all molecules combine and create an embryonic cell equivalent to a circuit. The evolutionary algorithm applied to genotypes select the fittest. The evaluated genotype determines the configuration data for embryonic cell. The configuration data generation is automated with this approach. As well this can lead to optimised embryonic fabric structure if optimisation by evolutionary algorithm is used. In this paper the CGP approach is applied to generate configuration data for embryonic fabric. The approach is simulated for 1-bit adder circuit.", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming", DOI = "doi:10.1109/INFOCOMAN.2016.7784259", month = oct, notes = "Also known as \cite{7784259}", } @InProceedings{malhotra:2019:IBCA, author = "Gayatri Malhotra and V. Lekshmi and S. Sudhakar and S. Udupa", title = "Implementation of Threshold Comparator Using Cartesian Genetic Programming on Embryonic Fabric", booktitle = "Innovations in Bio-Inspired Computing and Applications", year = "2019", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", URL = "http://link.springer.com/chapter/10.1007/978-3-030-16681-6_10", DOI = "doi:10.1007/978-3-030-16681-6_10", } @InProceedings{Malhotra:2021:CONECCT, author = "Gayatri Malhotra and Punithavathi Duraiswamy and J. K. Kishore", title = "Evolving Embryonic Cell for Combinational Circuits using Cartesian Genetic Programming", booktitle = "2021 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)", year = "2021", abstract = "This research aims to explore the possibility to implement concepts of embryonics with potential of self-repair mechanism. As the field of embryonics (embryo electronics) is based on multi-cellular architecture, the concept of growth from single embryo cell into complete organism can be used for fault-tolerant digital circuit design. This paper proposes a novel embryonic fabric and cell architecture that can configure itself as per the circuit requirement. It consists of an embryonic architecture where the configuration data (genome data) is in the form of Cartesian Genetic Programming (CGP). A customized Evolutionary Algorithm (EA) is designed to generate an optimized CGP data for the circuit under design. The CGP data configuration provides the better control at node or gate level in case of circuit fault. The configuration data size in CGP form does not increase linearly with more number of inputs and outputs as in the case of conventional Look Up Table (LUT) form. The embryonic cell architecture proposed is demonstrated for adder and comparator cells. A 4-bit adder is designed using four 1-bit adder cells and a 8-bit comparator is designed using four 2-bit comparator cells by employing cloning mechanism. A 4-bit adder needs 2^8 bits in LUT form of configuration data, while 45 bits are needed in CGP form. Similarly a 8-bit comparator needs 2^16 bits in LUT form, while 108 bits are needed in CGP configuration data form. The transfer of signals between cells is through embryonic switch boxes. The design is simulated and tested using Verilog,", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", DOI = "doi:10.1109/CONECCT52877.2021.9622686", ISSN = "2766-2101", month = jul, notes = "Also known as \cite{9622686}", } @InProceedings{conf/icse/MalhotraK14, title = "A new metric for predicting software change using gene expression programming", author = "Ruchika Malhotra and Megha Khanna", year = "2014", pages = "8--14", booktitle = "Proceedings of the 5th International Workshop on Emerging Trends in Software Metrics, {WETS}o{M} 2014, Hyderabad, India, June 3, 2014", publisher = "ACM", editor = "Steve Counsell and Michele Marchesi and Corrado Aaron Visaggio and Hongyu Zhang and Radhika Venkatasubramanyam", keywords = "genetic algorithms, genetic programming, gene expression programming", bibdate = "2014-06-09", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icse/wetsom2014.html#MalhotraK14", isbn13 = "978-1-4503-2854-8", URL = "http://dl.acm.org/citation.cfm?id=2593868", DOI = "doi:10.1145/2593868.2593870", } @Article{Malhotra:2020:IETsoftware, author = "Ruchika Malhotra and Megha Khanna", title = "Threats to validity in search-based predictive modelling for software engineering", journal = "IET Software", year = "2020", volume = "12", number = "4", pages = "293--305", month = aug, keywords = "genetic algorithms, genetic programming, SBSE, Special Section: Search-Based Software Engineering", ISSN = "1751-8806", DOI = "doi:10.1049/iet-sen.2018.5143", size = "13 pages", abstract = "A number of studies in the literature have developed effective models to address prediction tasks related to a software product such as estimating its development effort, or its change/defect proneness. These predictions are critical as they help in identifying weak areas of a software product and thus guide software project managers in effective allocation of project resources to these weak parts. Such practices assure good quality software products. Recently, the use of search-based approaches (SBAs) for developing software prediction models (SPMs) has been successfully explored by a number of researchers. However, in order to develop effective and practical SPMs it is imperative to analyse various sources of threats. This study extensively reviews 93 primary studies, which use SBAs for developing SPMs of four commonly used software attributes (effort, defect-proneness, maintainability and change-proneness) in order to discuss and identify the various sources of threats while using these approaches for SPMs. The study also lists various actions that may be taken in order to minimise these threats. Furthermore, best practice examples in literature and the year-wise trends of threats indicating the most common threats missed by researchers are provided to help academicians and practitioners in designing effective studies for developing SPMs using SBAs.", notes = "The Institution of Engineering and Technology. Discipline of Software Engineering, Department of Computer Science and Engineering, Delhi Technological University, Delhi, India", } @InProceedings{Malhotra:2017:cloud, author = "Shweta Malhotra and Vikram Bali and K. K. Paliwal", booktitle = "2017 7th International Conference on Cloud Computing, Data Science Engineering - Confluence", title = "Genetic programming and {K}-nearest neighbour classifier based intrusion detection model", year = "2017", pages = "42--46", abstract = "In computer networks, Intrusion Detection has become a major concern. In network security, various traditional techniques like intrusion prevention, cryptography and user authentication are unable to detect establishment of novel attacks. An intrusion detection system is helpful in detecting an unusual intruder which cracks into the system or genuine user mistreating the system. Intrusion Detection System continually runs in the background and when any suspicious or obtrusive event occurs then it warns the user. To implement these systems various researchers introduced numerous machine learning techniques like Decision Trees, Support Vector Machines, Artificial Neural Networks, Linear Genetic Programming, Genetic Algorithms, Fuzzy Inference Systems, Rule Based Approach and their ensemble approaches with the intent to predict the data either normal or abnormal. In this paper genetic programming with K-Nearest Neighbour classifier is proposed so as to build an efficient Intrusion Detection Model. Optimal feature selection task is performed by genetic programming whereas the data mining classifier which performs the classification process is K-Nearest Neighbour. The main aim of genetic programming is to aid K-Nearest Neighbour. The experimental result shows that the validation accuracy for detecting attacks is 99.6percent.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CONFLUENCE.2017.7943121", month = jan, notes = "PDF odd Also known as \cite{7943121}", } @Article{Malik:2020:EACFM, author = "Anurag Malik and Anil Kumar and Sungwon Kim and Mahsa H. Kashani and Vahid Karimi and Ahmad Sharafati and Mohammad Ali Ghorbani and Nadhir Al-Ansari and Sinan Q. Salih and Zaher Mundher Yaseen and Kwok-Wing Chau", title = "Modeling monthly pan evaporation process over the Indian central Himalayas: application of multiple learning artificial intelligence model", journal = "Engineering Applications of Computational Fluid Mechanics", year = "2020", volume = "14", number = "1", pages = "323--338", keywords = "genetic algorithms, genetic programming, water evaporation, multiple model strategy, gamma test, Asia, Indian central Himalayas, meteorological variables, geotechnical engineering, geoteknik", publisher = "Taylor \& Francis", ISSN = "1994-2060", URL = "http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-77534", DOI = "doi:10.1080/19942060.2020.1715845", abstract = "The potential of several predictive models including multiple model-artificial neural network (MM-ANN), multivariate adaptive regression spline (MARS), support vector machine (SVM), multi-gene genetic programming (MGGP), and M5Tree were assessed to simulate the pan evaporation in monthly scale (EPm) at two stations (e.g. Ranichauri and Pantnagar) in India. Monthly~climatological information were~used~for~simulating the pan evaporation. The utmost effective input-variables for the MM-ANN, MGGP, MARS, SVM, and M5Tree were determined using the Gamma test (GT). The predictive models were compared to each other using several statistical criteria (e.g. mean absolute percentage error (MAPE), Willmotts Index of agreement (WI), root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), and Legate and McCabe Index (LM)) and visual inspection. The results showed that the MM-ANN-1 and MGGP-1 models (NSE, WI, LM, RMSE, MAPE are 0.954, 0.988, 0.801, 0.536 mm/month, 9.988 percent at Pantnagar station, and 0.911, 0.975, 0.724, and 0.364 mm/month, 12.297percent at Ranichauri station, respectively) with input variables equal to six were more successful than the other techniques during testing period to simulate the monthly pan evaporation at both Ranichauri and Pantnagar stations. Thus, the results of proposed MM-ANN-1 and MGGP-1 models will help to the local stakeholders in terms of water resources management.", bibsource = "OAI-PMH server at www.diva-portal.org", identifier = "doi:10.1080/19942060.2020.1715845; Scopus 2-s2.0-85079246577", language = "eng", oai = "oai:DiVA.org:ltu-77534", rights = "info:eu-repo/semantics/openAccess", } @Article{journals/ijcat/MalikK08, title = "Verifying experiment for automated design of mechatronic systems using Bond-Graph modelling and simulation and genetic programming", author = "Muhammad Afzaal Malik and Saheeb Ahmed Kayani", journal = "International Journal of Computer Applications in Technology", year = "2008", number = "3", volume = "32", bibdate = "2008-11-13", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ijcat/ijcat32.html#MalikK08", pages = "173--180", DOI = "doi:10.1504/IJCAT.2008.020952", keywords = "genetic algorithms, genetic programming, automated design, unified design, bond graphs, object-oriented modelling, simulation, mechatronics, multi-domain systems, mechatronic system design", abstract = "All modern dynamic engineering systems can be characterised as mechatronic systems. The multi-domain nature of a mechatronic system makes it difficult to model using a single modelling technique over the whole system as varying sets of system variables are required. Bond-Graphs offer an advanced object-oriented modelling and simulation technique. They are domain independent allowing straight forward and efficient model composition, classification and analysis. Bond-Graph model of the mechatronic system can be directly simulated on a digital computer using simulation software such as 20-Sim and Modelica graphically or manipulated mathematically to yield state equations using a simplified set of power and energy variables. The simulation scheme can be augmented to synthesise designs for mechatronic systems using genetic programming as a tool for open-ended search. This research paper presents results of an experiment conducted to verify a unified approach developed by combining Bond-Graph modelling and simulation with genetic programming for automated mechatronic system design. A comprehensive review of various aspects of the physical modelling paradigm along with the concept and development of automated design and the methodology is also included.", notes = "IJCAT", } @InProceedings{malinchik:2004:ieda, title = "Interactive Exploratory Data Analysis", author = "Sergey Malinchik and Belinda Orme and Joseph Rothermich and Eric Bonabeau", pages = "1098--1104", booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary Computation", year = "2004", publisher = "IEEE Press", month = "20-23 " # jun, address = "Portland, Oregon", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Real-world applications", DOI = "doi:10.1109/CEC.2004.1330984", abstract = "We illustrate with two simple examples how Interactive Evolutionary Computation (IEC) can be applied to Exploratory Data Analysis (EDA). IEC is valuable in an EDA context because the objective function is by definition either unknown a priori or difficult to formalize. In the first example IEC is used to evolve the {"}true{"} metric of attribute space. The goal here is to evolve the attribute space distance function until {"}interesting{"} features of the data are revealed when a clustering algorithm is applied. In a second example, we show how a user can interactively evolve an auditory display of cluster data. In this example, we use IEC with Genetic Programming to evolve a mapping of data to sound for sonifying qualities of data clusters.", notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.", } @InProceedings{Malini:2017:AEEICB, author = "N. Malini and M. Pushpa", booktitle = "2017 Third International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB)", title = "Analysis on credit card fraud identification techniques based on {KNN} and outlier detection", year = "2017", pages = "255--258", month = feb, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/AEEICB.2017.7972424", abstract = "Popular payment mode accepted both off line and online is credit card that provides cashless transaction. It is easy, convenient and trendy to make payments and other transactions. Credit card fraud is also growing along with the development in technology. It can also be said that economic fraud is drastically increasing in the global communication improvement. It is being recorded every year that the loss due to these fraudulent acts is billions of dollars. These activities are carried out so elegantly so it is similar to genuine transactions. Hence simple pattern related techniques and other less complex methods are really not going to work. Having an efficient method of fraud detection has become a need for all banks in order to minimise chaos and bring order in place. There are several techniques like Machine learning, Genetic Programming, fuzzy logic, sequence alignment, etc are used for detecting credit card fraudulent transactions. Along with these techniques, KNN algorithm and outlier detection methods are implemented to optimise the best solution for the fraud detection problem. These approaches are proved to minimise the false alarm rates and increase the fraud detection rate. Any of these methods can be implemented on bank credit card fraud detection system, to detect and prevent the fraudulent transaction.", notes = "Also known as \cite{7972424}", } @Misc{maliukov2024evolving, author = "Irina Maliukov and Gera Weiss and Oded Margalit and Achiya Elyasaf", title = "Evolving Assembly Code in an Adversarial Environment", howpublished = "ArXiv", year = "2024", month = "28 " # mar, note = "To appear GECCO 2024", keywords = "genetic algorithms, genetic programming, ANN, LLM, assembly, code generation, cyber-security, CodeGuru Xtreme", eprint = "2403.19489", archiveprefix = "arXiv", primaryclass = "cs.NE", URL = "https://arxiv.org/abs/2403.19489", size = "9 pages", abstract = "we evolve assembly code for the CodeGuru competition. The competitions goal is to create a survivor, an assembly program that runs the longest in shared memory, by resisting attacks from adversary survivors and finding their weaknesses. For evolving top-notch solvers, we specify a Backus Normal Form (BNF) for the assembly language and synthesise the code from scratch using Genetic Programming (GP). We evaluate the survivors by running CodeGuru games against human-written winning survivors. Our evolved programs found weaknesses in the programs they were trained against and used them. In addition, we compare our approach with a Large-Language Model, demonstrating that the latter cannot generate a survivor that can win at any competition. This work has important applications for cyber-security, as we use evolution to detect weaknesses in survivors. The assembly BNF is domain-independent; thus, by modifying the fitness function, it can detect code weaknesses and help fix them. Finally, the CodeGuru competition offers a novel platform for analysing GP and code evolution in adversarial environments. To support further research in this direction, we provide a thorough qualitative analysis of the evolved survivors and the weaknesses found.", notes = "See \cite{Maliukov:2024:GECCOcomp}", } @InProceedings{Maliukov:2024:GECCOcomp, author = "Irina Maliukov and Gera Weiss and Oded Margalit and Achiya Elyasaf", title = "Evolving Assembly Code in an Adversarial Environment", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion", year = "2024", editor = "Dominik Sobania and Aymeric Blot", pages = "723--726", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, Search-Based Software Engineering, SBSE, assembly, code generation, cyber-security, CodeGuru Xtreme, ANN:poster", isbn13 = "979-8-4007-0495-6", URL = "https://human-competitive.org/sites/default/files/elyasafentry.txt", URL = "https://human-competitive.org/sites/default/files/elyasafpaper.pdf", URL = "https://arxiv.org/html/2403.19489v1", DOI = "doi:10.1145/3638530.3654209", size = "4 pages", size = "9 pages", abstract = "we evolve assembly code for the CodeGuru competition. The competition goal is to create a survivor, an assembly program that runs the longest in shared memory, by resisting attacks from adversary survivors and finding their weaknesses. For evolving top-notch solvers, we specify a Backus Normal Form (BNF) for the assembly language and synthesise the code from scratch using Genetic Programming (GP). We evaluate the survivors by running CodeGuru games against human-written winning survivors. Our evolved programs found weaknesses in the programs they were trained against and used them. In addition, we compare our approach with a Large-Language Model, LLM, demonstrating that the latter cannot generate a survivor that can win at any competition. This work has important applications for cyber-security, as we use evolution to detect weaknesses in survivors. The assembly BNF is domain-independent; thus, by modifying the fitness function, it can detect code weaknesses and help fix them. Finally, the CodeGuru competition offers a novel platform for analyzing GP and code evolution in adversarial environments. To support further research in this direction, we provide a thorough qualitative analysis of the evolved survivors and the weaknesses found.", notes = "Entered 2024 HUMIES See \cite{maliukov2024evolving} also known as \cite{maliukov:2024:GECCOcomp} Ben-Gurion University of the Negev, Beer Sheva, Israel GECCO-2024 SBSE A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{Mallick:2008:ICSSSM, author = "Devayan Mallick and Vincent C. S. Lee and Yew Soon Ong", title = "An empirical study of Genetic Programming generated trading rules in computerized stock trading service system", booktitle = "International Conference on Service Systems and Service Management", year = "2008", month = "30 " # jun # " 2008-2 " # jul # " 2008", pages = "1--6", keywords = "genetic algorithms, genetic programming, computerized stock trading service system, financial market, genetic programming based trading rules, statistical analysis, stock market, electronic trading, statistical analysis, stock markets", address = "Melbourne, Australia", isbn13 = "978-1-4244-1671-4", DOI = "doi:10.1109/ICSSSM.2008.4598507", abstract = "Technical analysis is aimed at devising trading rules capable of exploiting short-term fluctuations on the financial markets. The application of genetic programming (GP) as a means to automatically generate such trading rules on the stock markets has been studied. Computational results, based on historical pricing and transaction volume data, are reported for the thirty component stocks of the Dow Jones Industrial Average index. Statistical evidence shows that for the stocks that were studied, the use of GP based trading rules ensures a positive dollar return in all market scenarios. The performance of the GP based trading rules was also evaluated against the performance of the popularly used MACD technical indicator. In general, GP based trading rules offer greater returns over the simple buy and hold approach than the MACD trading signal.", notes = "Also known as \cite{4598507}", } @InProceedings{MallBent99, author = "Hugh Mallinson and Peter Bentley", title = "Evolving Fuzzy Rules for Pattern Classification", booktitle = "Computational Integration for Modelling, Control and Automation '99", year = "1999", editor = "Masoud Mohammadian", volume = "55", series = "Concurrent Systems Engineering Series", pages = "184--191", address = "Hotel Marriott, Vienna, Austria", publisher_address = "Amsterdam, The Netherlands", month = "17-19 " # feb, publisher = "IOS Press", keywords = "genetic algorithms, genetic programming, fuzzy classification, control, modelling", ISBN = "90-5199-473-7", isbn13 = "9789051994742", publicationstatus = "published", URL = "http://www.cs.ucl.ac.uk/staff/P.Bentley/MABEC1.pdf", URL = "http://iospress.nl.master.com/texis/master/redir/?u=http%3A//www.iospress.nl/html/9789051994742.php", size = "8 pages", abstract = "This paper describes the use of a Hybrid Fuzzy-Genetic Programming system to discover patterns in large databases. It does this by evolving a series of variablelength fuzzy rules which generalise from a training set of labelled classes. Numerous novel techniques, including the use of genotypes in Genetic Programming, two new genetic crossover operators, and the processes of Modal Evolution, Modal Reevolution and Nested Evolutionary Search are described. Experimental results show that the system is able to classify data from the Wisconsin Breast Cancer database correctly 95% of the time.", notes = "CIMCA'99 http://www.gscit.monash.edu.au/conferences/cimca99/ UCI Wisconsin Breast Cancer", } @Article{DBLP:journals/soco/MallipeddiGSAJ21, author = "Rammohan Mallipeddi and Iman Gholaminezhad and Mohammad S. Saeedi and Hirad Assimi and Ali Jamali", title = "Robust controller design for systems with probabilistic uncertain parameters using multi-objective genetic programming", journal = "Soft Comput.", volume = "25", number = "1", pages = "233--249", year = "2021", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/s00500-020-05133-x", DOI = "doi:10.1007/s00500-020-05133-x", timestamp = "Thu, 21 Jan 2021 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/soco/MallipeddiGSAJ21.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InCollection{malmer:1994:hive, author = "Daniel Malmer", title = "Hive: Development of a Language Among Artificial Life Forms", booktitle = "Artificial Life at Stanford 1994", year = "1994", editor = "John R. Koza", pages = "99--107", address = "Stanford, California, 94305-3079 USA", month = jun, organisation = "Stanford University", publisher = "Stanford Bookstore", keywords = "genetic algorithms, Finite State Machine, Agents, Communication", ISBN = "0-18-182105-2", notes = "Bees, Genesys This volume contains 22 papers written and submitted by students describing their term projects for the course in artificial life (Computer Science 425) at Stanford University offered during the spring quarter quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs425.html", } @Misc{DBLP:journals/corr/abs-1012-0841, author = "Pekka Malo and Pyry-Antti Siitari and Ankur Sinha", title = "Automated Query Learning with Wikipedia and Genetic Programming", journal = "CoRR", volume = "abs/1012.0841", year = "2010", bibsource = "DBLP, http://dblp.uni-trier.de", howpublished = "arXiv", keywords = "genetic algorithms, genetic programming, Wikipedia, Information retrieval, Genetic programming, Query learning, Automatic indexing, Concept recognition SVM, C4.5,coevolution", URL = "http://arxiv.org/abs/1012.0841", size = "44 pages", abstract = "Most of the existing information retrieval systems are based on bag of words model and are not equipped with common world knowledge. Work has been done towards improving the efficiency of such systems by using intelligent algorithms to generate search queries, however, not much research has been done in the direction of incorporating human-and-society level knowledge in the queries. This paper is one of the first attempts where such information is incorporated into the search queries using Wikipedia semantics. The paper presents an essential shift from conventional token based queries to concept based queries, leading to an enhanced efficiency of information retrieval systems. To efficiently handle the automated query learning problem, we propose Wikipedia-based Evolutionary Semantics (Wiki-ES) framework where concept based queries are learnt using a co-evolving evolutionary procedure. Learning concept based queries using an intelligent evolutionary procedure yields significant improvement in performance which is shown through an extensive study using Reuters newswire documents. Comparison of the proposed framework is performed with other information retrieval systems. Concept based approach has also been implemented on other information retrieval systems to justify the effectiveness of a transition from token based queries to concept based queries.", notes = "Inductive Query By Example (IQBE),TREC-11 dataset with Reuters RCV1 corpus, Wiki as alternative to Cyc, ngrams wikifier and a named-entity recognizer (NER).Conditional Random Fields (CRF)-based classifier using several individuals, JGAP, java weka, best on {"}F-score{"} See \cite{Malo:2013:AI}", } @Article{Malo:2013:AI, author = "Pekka Malo and Pyry Siitari and Ankur Sinha", title = "Automated query learning with Wikipedia and genetic programming", journal = "Artificial Intelligence", year = "2013", volume = "194", pages = "86--110", month = jan, note = "Special issue on Artificial Intelligence, Wikipedia and Semi-Structured Resources", keywords = "genetic algorithms, genetic programming, Wikipedia, Concept recognition, Information filtering, Automatic indexing, Query definition", ISSN = "0004-3702", URL = "http://www.sciencedirect.com/science/article/pii/S0004370212000768", DOI = "doi:10.1016/j.artint.2012.06.006", size = "25 pages", abstract = "Most of the existing information retrieval systems are based on bag-of-words model and are not equipped with common world knowledge. Work has been done towards improving the efficiency of such systems by using intelligent algorithms to generate search queries, however, not much research has been done in the direction of incorporating human-and-society level knowledge in the queries. This paper is one of the first attempts where such information is incorporated into the search queries using Wikipedia semantics. The paper presents Wikipedia-based Evolutionary Semantics (Wiki-ES) framework for generating concept based queries using a set of relevance statements provided by the user. The query learning is handled by a co-evolving genetic programming procedure. To evaluate the proposed framework, the system is compared to a bag-of-words based genetic programming framework as well as to a number of alternative document filtering techniques. The results obtained using Reuters newswire documents are encouraging. In particular, the injection of Wikipedia semantics into a GP-algorithm leads to improvement in average recall and precision, when compared to a similar system without human knowledge. A further comparison against other document filtering frameworks suggests that the proposed GP-method also performs well when compared with systems that do not rely on query-expression learning.", notes = "Aalto University, School of Economics. See also \cite{DBLP:journals/corr/abs-1012-0841}. Also known as \cite{Malo201386} ", } @InProceedings{malolepszy:2000:MIE, author = "Andrzej Malolepszy and Edward Kacki and T Dogdanik", title = "Application of genetic programming for the differential diagnosis of acid-base and anion gap disorders", booktitle = "Medical Infobahn for Europe", year = "2000", editor = "Arie Hasman", pages = "388--392", publisher = "IOS Press", keywords = "genetic algorithms, genetic programming", ISBN = "1-58603-063-9", URL = "http://books.google.co.uk/books/about/Medical_Infobahn_for_Europe.html?id=MwrHQ1-Qg1IC&redir_esc=y", URL = "http://www.amazon.co.uk/Medical-Infobahn-Studies-Technology-Informatics/dp/1586030639/ref=sr_1_1", notes = "Also referenced as: Studies in Health Technol Inform. 2000, 77, 388-92. PMID: 11187580 From Book News, Inc. This (Medical Infobahn for Europe) huge collection of more than 250 papers represents the 2000 Medical Informatics Europe Congress, held in Hanover, Germany, and hosted by the German Association for Medical Informatics, Biometry, and Epidemiology. Medical informatics is an interdisciplinary field of applied research that also involves the use of information technologies in medical research, health economics, and health system sciences. A few of the subject areas covered are better and faster documentation, health information systems, modeling and simulation, electronic prescribing, workflow, security, robotics, and telemedicine. Indexed by author but not by subject.Book News, Inc., Portland, OR", } @InProceedings{Mambrini:2013:foga, author = "Alberto Moraglio and Andrea Mambrini and Luca Manzoni", title = "Runtime Analysis of Mutation-Based Geometric Semantic Genetic Programming on Boolean Functions", booktitle = "Foundations of Genetic Algorithms", year = "2013", editor = "Frank Neumann and Kenneth {De Jong}", pages = "119--132", address = "Adelaide, Australia", month = "16-20 " # jan, organisation = "SigEvo", publisher = "ACM", keywords = "genetic algorithms, genetic programming, Boolean functions, geometric crossover, runtime analysis, semantics", isbn13 = "978-1-4503-1990-4", URL = "http://www.cs.bham.ac.uk/~axm322/pdf/gsgp_foga13.pdf", URL = "http://doi.acm.org/10.1145/2460239.2460251", DOI = "doi:10.1145/2460239.2460251", acmid = "2460251", size = "13 pages", abstract = "Geometric Semantic Genetic Programming (GSGP) is a recently introduced form of Genetic Programming (GP), rooted in a geometric theory of representations, that searches directly the semantic space of functions/programs, rather than the space of their syntactic representations (e.g., trees) as in traditional GP. Remarkably, the fitness landscape seen by GSGP is always, for any domain and for any problem, unimodal with a linear slope by construction. This has two important consequences: (i) it makes the search for the optimum much easier than for traditional GP; (ii) it opens the way to analyse theoretically in a easy manner the optimisation time of GSGP in a general setting. The run time analysis of GP has been very hard to tackle, and only simplified forms of GP on specific, unrealistic problems have been studied so far. We present a runtime analysis of GSGP with various types of mutations on the class of all Boolean functions.", notes = "Note change of author ordering. Also known as \cite{Moraglio:2013:RAM:2460239.2460251}. Jan 2013 gsgp_foga13.pdf is preprint http://www.sigevo.org/foga-2013/index.html", } @InProceedings{Mambrini:2013:CEC, article_id = "1697", author = "Andrea Mambrini and Luca Manzoni and Alberto Moraglio", title = "Theory-Laden Design of Mutation-Based Geometric Semantic Genetic Programming for Learning Classification Trees", booktitle = "2013 IEEE Conference on Evolutionary Computation", volume = "1", year = "2013", month = jun # " 20-23", editor = "Luis Gerardo {de la Fraga}", pages = "416--423", address = "Cancun, Mexico", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-0453-2", DOI = "doi:10.1109/CEC.2013.6557599", size = "8 pages", abstract = "Geometric Semantic Genetic Programming (GSGP) is a recently introduced framework to design domain-specific search operators for Genetic Programming (GP) to search directly the semantic space of functions. The fitness landscape seen by GSGP is always - for any domain and for any problem - unimodal with a constant slope by construction. This makes the search for the optimum much easier than for traditional GP, and it opens the way to analyse theoretically in a easy manner the optimisation time of GSGP in a general setting. We design and analyse a mutation-based GSGP for the class of all classification tree learning problems, which is a classic GP application domain.", notes = "CEC 2013 - A joint meeting of the IEEE, the EPS and the IET.", } @InProceedings{Mambrini:2014:GECCOcomp, author = "Andrea Mambrini and Luca Manzoni", title = "A comparison between geometric semantic GP and cartesian GP for boolean functions learning", booktitle = "GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion", year = "2014", editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges", isbn13 = "978-1-4503-2881-4", keywords = "genetic algorithms, genetic programming: Poster", pages = "143--144", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2598394.2598475", DOI = "doi:10.1145/2598394.2598475", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Geometric Semantic Genetic Programming (GSGP) is a recently defined form of Genetic Programming (GP) that has shown promising results on single output Boolean problems when compared with standard tree-based GP. In this paper we compare GSGP with Cartesian GP (CGP) on comprehensive set of Boolean benchmarks, consisting of both single and multiple outputs Boolean problems. The results obtained show that GSGP outperforms also CGP, confirming the efficacy of GSGP in solving Boolean problems.", notes = "Also known as \cite{2598475} Distributed at GECCO-2014.", } @InProceedings{Mambrini:2014:SMGP, author = "Andrea Mambrini and Yang Yu2 and Xin Yao", title = "A framework for measuring the generalization ability of Geometric Semantic Genetic Programming (GSGP) for Black-Box Boolean Functions Learning", booktitle = "Semantic Methods in Genetic Programming", year = "2014", editor = "Colin Johnson and Krzysztof Krawiec and Alberto Moraglio and Michael O'Neill", address = "Ljubljana, Slovenia", month = "13 " # sep, note = "Workshop at Parallel Problem Solving from Nature 2014 conference", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.put.poznan.pl/kkrawiec/smgp2014/uploads/Site/Mambrini.pdf", size = "2 pages", abstract = "Moraglio et al. proposed GSGP operators for learning Boolean functions [1]. The work provides upper bounds of the expected time for the algorithm to t the training set but it doesn't give any guarantees on how the learnt functions will evolve on unseen input. In this work we provide a framework to analyse GSGP as learning tool. This can be used to obtain lower bounds on the generalisation error of the Boolean functions evolved by the algorithm.", notes = "SMGP 2014 http://www.cs.put.poznan.pl/kkrawiec/smgp/?n=Site.SMGP2014", } @PhdThesis{Mambrini15PhD, author = "Andrea Mambrini", title = "Theory grounded design of genetic programming and parallel evolutionary algorithms", school = "School of Computer Science, University of Birmingham", year = "2015", address = "UK", month = apr, keywords = "genetic algorithms, genetic programming, Geometric Semantic genetic programming", URL = "http://etheses.bham.ac.uk/5928/", URL = "http://etheses.bham.ac.uk/5928/1/Mambrini15PhD.pdf", size = "162 pages", abstract = "Evolutionary algorithms (EAs) have been successfully applied to many problems and applications. Their success comes from being general purpose, which means that the same EA can be used to solve different problems. Despite that, many factors can affect the behaviour and the performance of an EA and it has been proven that there isn't a particular EA which can solve efficiently any problem. This opens to the issue of understanding how different design choices can affect the performance of an EA and how to efficiently design and tune one. This thesis has two main objectives. On the one hand we will advance the theoretical understanding of evolutionary algorithms, particularly focusing on Genetic Programming and Parallel Evolutionary algorithms. We will do that trying to understand how different design choices affect the performance of the algorithms and providing rigorously proven bounds of the running time for different designs. This novel knowledge, built upon previous work on the theoretical foundation of EAs, will then help for the second objective of the thesis, which is to provide theory grounded design for Parallel Evolutionary Algorithms and Genetic Programming. This will consist in being inspired by the analysis of the algorithms to produce provably good algorithm designs.", notes = "ID Code: 5928 Supervisor: Xin Yao", } @InProceedings{Mambrini:2016:EuroGP, author = "Andrea Mambrini and Pietro S. Oliveto", title = "On the Analysis of Simple Genetic Programming for Evolving Boolean Functions", booktitle = "EuroGP 2016: Proceedings of the 19th European Conference on Genetic Programming", year = "2016", month = "30 " # mar # "--1 " # apr, editor = "Malcolm I. Heywood and James McDermott and Mauro Castelli and Ernesto Costa and Kevin Sim", series = "LNCS", volume = "9594", publisher = "Springer Verlag", address = "Porto, Portugal", pages = "99--114", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-30668-1", DOI = "doi:10.1007/978-3-319-30668-1_7", abstract = "This work presents a first step towards a systematic time and space complexity analysis of genetic programming (GP) for evolving functions with desired input/output behaviour. Two simple GP algorithms, called (1+1) GP and (1+1) GP*, equipped with minimal function (F) and terminal (L) sets are considered for evolving two standard classes of Boolean functions. It is rigorously proved that both algorithms are efficient for the easy problem of evolving conjunctions of Boolean variables with the minimal sets. However, if an extra function (i.e. NOT) is added to F, then the algorithms require at least exponential time to evolve the conjunction of $n$ variables. On the other hand, it is proved that both algorithms fail at evolving the difficult parity function in polynomial time with probability at least exponentially close to $1$. Concerning generalisation, it is shown how the quality of the evolved conjunctions depends on the size of the training set $s$ while the evolved exclusive disjunctions generalize equally badly independent of $s$.", notes = "Part of \cite{Heywood:2016:GP} EuroGP'2016 held in conjunction with EvoCOP2016, EvoMusArt2016 and EvoApplications2016", } @InProceedings{mamontov:2022:SaC, author = "Danila Mamontov and Wolfgang Minker and Alexey Karpov", title = "{Self-Configuring} Genetic Programming Feature Generation in Affect Recognition Tasks", booktitle = "Speech and Computer", year = "2022", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-031-20980-2_40", DOI = "doi:10.1007/978-3-031-20980-2_40", } @Article{Manahov:2013:PASMA, author = "Viktor Manahov and Robert Hudson", title = "Herd behaviour experimental testing in laboratory artificial stock market settings. Behavioural foundations of stylised facts of financial returns", journal = "Physica A: Statistical Mechanics and its Applications", year = "2013", volume = "392", number = "19", pages = "4351--4372", keywords = "genetic algorithms, genetic programming, STGP, Agent-based modelling, Artificial stock market, Herd behaviour stylised facts, Efficient market hypothesis", ISSN = "0378-4371", DOI = "doi:10.1016/j.physa.2013.05.029", URL = "http://www.sciencedirect.com/science/article/pii/S0378437113004524", abstract = "Many scholars express concerns that herding behaviour causes excess volatility, destabilises financial markets, and increases the likelihood of systemic risk. We use a special form of the Strongly Typed Genetic Programming (STGP) technique to evolve a stock market divided into two groups-a small subset of artificial agents called Best Agents and a main cohort of agents named All Agents. The Best Agents perform best in term of the trailing return of a wealth moving average. We then investigate whether herding behaviour can arise when agents trade Dow Jones, General Electric, and IBM stocks in four different artificial stock markets. This paper uses real historical quotes of the three financial instruments to analyse the behavioural foundations of stylised facts such as leptokurtosis, non-IIDness, and volatility clustering. We found evidence of more herding in a group of stocks than in individual stocks, but the magnitude of herding does not contribute to the mispricing of assets in the long run. Our findings suggest that the price formation process caused by the collective behaviour of the entire market exhibit less herding and is more efficient than the segmented market populated by a small subset of agents. Hence, greater genetic diversity leads to greater consistency with fundamental values and market efficiency.", } @PhdThesis{Manahov:thesis, author = "Viktor Manahov", title = "An investigation of the behaviour of financial markets using agent-based computational models", school = "Newcastle University Business School", year = "2014", address = "UK", month = may, keywords = "genetic algorithms, genetic programming, STGP", URL = "https://core.ac.uk/download/pdf/153778852.pdf", size = "312 pages", abstract = "This thesis aims to investigate the behaviour of financial markets by using agent-based computational models. By using a special adaptive form of the Strongly Typed Genetic Programming (STGP)-based learning algorithm and real historical data of stocks, indices and currency pairs I analysed various stylized facts of financial returns, market efficiency and stock market forecasts. This thesis also sought to discuss the following: 1) The appearance of herding in financial markets and the behavioural foundations of stylised facts of financial returns; 2) The implications of trader cognitive abilities for stock market properties; 3) The relationship between market efficiency and market adaptability; 4) The development of profitable stockmarket forecasts and the price-volume relationship; 5) High frequency trading, technical analysis and market efficiency. The main findings and contributions suggest that:1) The magnitude of herding behaviour does not contribute to the mispricing of assets in the long run; 2) Individual rationality and market structure are equally important in market performance; 3) Stock market dynamics are better explained by the evolutionary process associated with the Adaptive Market Hypothesis; 4) The STGP technique significantly outperforms traditional forecasting methods such as Box-Jenkins and Holt-Winters; 5) The dynamic relationship between price and volume revealed inconclusive forecasting picture; 6) There is no definite answers as to whether high frequency trading is harmful or beneficial to market efficiency.", notes = " Supervisor: Robert Hudson Co-supervisor: Dr. Bartosz Gebka", } @Article{Manahov:2014:JIFMIM, author = "Viktor Manahov and Robert Hudson and Bartosz Gebka", title = "Does high frequency trading affect technical analysis and market efficiency? And if so, how?", journal = "Journal of International Financial Markets, Institutions and Money", year = "2014", volume = "28", pages = "131--157", keywords = "genetic algorithms, genetic programming, Technical trading rules, Exchange rate", ISSN = "1042-4431", URL = "http://www.sciencedirect.com/science/article/pii/S1042443113000954", DOI = "doi:10.1016/j.intfin.2013.11.002", size = "27 pages", abstract = "In this paper we investigate how high frequency trading affects technical analysis and market efficiency in the foreign exchange (FX) market by using a special adaptive form of the Strongly Typed Genetic Programming (STGP)-based learning algorithm. We use this approach for real one-minute high frequency data of the most traded currency pairs worldwide: EUR/USD, USD/JPY, GBP/USD, AUD/USD, USD/CHF, and USD/CAD. The STGP performance is compared with that of parametric and non-parametric models and validated by two formal empirical tests. We perform in-sample and out-of-sample comparisons between all models on the basis of forecast performance and investment return. Furthermore, our paper shows the relative strength of these models with respect to the actual trading profit generated by their forecasts. Empirical experiments suggest that the STGP forecasting technique significantly outperforms the traditional econometric models. We find evidence that the excess returns are both statistically and economically significant, even when appropriate transaction costs are taken into account. We also find evidence that HFT has a beneficial role in the price discovery process.", } @Article{Manahov:2014:ESA, author = "Viktor Manahov and Robert Hudson", title = "A note on the relationship between market efficiency and adaptability - New evidence from artificial stock markets", journal = "Expert Systems with Applications", volume = "41", number = "16", pages = "7436--7454", year = "2014", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2014.06.004", URL = "http://www.sciencedirect.com/science/article/pii/S0957417414003406", abstract = "We developed various artificial stock markets populated with different numbers of traders using a special adaptive form of the Strongly Typed Genetic Programming (STGP)-based learning algorithm. We then applied the STGP technique to historical data from three indices - the FTSE 100, S&P 500, and Russell 3000 - to investigate the formation of stock market dynamics and market efficiency. We used several econometric techniques to investigate the emergent properties of the stock markets. We have found that the introduction of increased heterogeneity and greater genetic diversity leads to higher market efficiency in terms of the Efficient Market Hypothesis (EMH), demonstrating that market efficiency does not necessarily correlate with rationality assumptions. We have also found that stock market dynamics and nonlinearity are better explained by the evolutionary process associated with the Adaptive Market Hypothesis (AMH), because different trader populations behave as an efficient adaptive system evolving over time. Hence, market efficiency exists simultaneously with the need for adaptive flexibility. Our empirical results, generated by a reduced number of boundedly rational traders in six of the stock markets, for each of the three financial instruments do not support the allocational efficiency of markets, indicating the possible need for governmental or regulatory intervention in stock markets in some circumstances.", keywords = "genetic algorithms, genetic programming, Efficient Market Hypothesis, Adaptive Market Hypothesis, Agent-based modelling, Artificial stock markets", } @Article{Manahov:2014:JIFMIM2, author = "Viktor Manahov and Robert Hudson and Philip Linsley", title = "New evidence about the profitability of small and large stocks and the role of volume obtained using Strongly Typed Genetic Programming", journal = "Journal of International Financial Markets, Institutions and Money", volume = "33", pages = "299--316", year = "2014", ISSN = "1042-4431", DOI = "doi:10.1016/j.intfin.2014.08.007", URL = "http://www.sciencedirect.com/science/article/pii/S1042443114001115", abstract = "We employ a special adaptive form of the Strongly Typed Genetic Programming (STGP)-based learning algorithm to develop trading rules based on a survival of the fittest principle. Employing returns data for the Russell 1000, Russell 2000 and Russell 3000 indices the STGP method produces greater returns compared to random walk benchmark forecasts, and the forecasting models are statistically significant in respect of their predictive effectiveness for all three indices both in- and out-of-sample. Using one-step-ahead STGP models to investigate the differences in return patterns between small and large stocks we demonstrate the superiority of models developed for small-cap stocks over those developed for large-cap stocks, indicating that small stocks are more predictable. We also investigate the relationship between trading volume and returns, and find that trading volume has negligible predictive strength, implying it is not advantageous to develop volume-based trading strategies.", keywords = "genetic algorithms, genetic programming, Forecasting and simulation, Small Stocks, Agent-based modelling, Artificial stock market, Capital asset pricing model, Efficiency", } @Article{Manahov:2015:JIFMIM, author = "Viktor Manahov and Robert Hudson and Hafiz Hoque", title = "Return predictability and the `wisdom of crowds': Genetic Programming trading algorithms, the Marginal Trader Hypothesis and the Hayek Hypothesis", journal = "Journal of International Financial Markets, Institutions and Money", year = "2015", volume = "7", pages = "85--98", keywords = "genetic algorithms, genetic programming, Forecasting and simulation, Agent-based modelling, Artificial stock market, Marginal Trader Hypothesis, Hayek Hypothesis", ISSN = "1042-4431", URL = "https://cronfa.swan.ac.uk/Record/cronfa59966", URL = "http://www.sciencedirect.com/science/article/pii/S1042443115000268", DOI = "doi:10.1016/j.intfin.2015.02.009", abstract = "We develop profitable stock market forecasts for a number of financial instruments and portfolios using a special adaptive form of the Strongly Typed Genetic Programming (STGP)-based trading algorithm. The STGP-based trading algorithm produces one-day-ahead return forecasts for groups of artificial traders with different levels of intelligence and different group sizes. The performance of the algorithm is compared with a number of benchmark forecasts and these comparisons clearly demonstrate the short-term superiority of the STGP-based method in many circumstances. Subsequently we provide detailed analysis of the impact of trader cognitive abilities and trader numbers on the accuracy of forecasting rules which allows us to conduct new experimental tests of the Marginal Trader and the Hayek Hypotheses. We find little support for the Marginal Trader Hypothesis but some evidence for the Hayek Hypothesis.", } @Article{Manahov:2016:IRFA, author = "Viktor Manahov", title = "A note on the relationship between high-frequency trading and latency arbitrage", journal = "International Review of Financial Analysis", year = "2016", volume = "47", pages = "281--296", keywords = "genetic algorithms, genetic programming, Agent-based modelling, High frequency trading, Algorithmic trading, Market regulation, Market efficiency", ISSN = "1057-5219", URL = "http://www.sciencedirect.com/science/article/pii/S1057521916301090", URL = "https://eprints.whiterose.ac.uk/117919/1/A_note_on_the_relationship_between_high_frequency_trading.pdf", DOI = "doi:10.1016/j.irfa.2016.06.014", abstract = "We develop three artificial stock markets populated with two types of market participants - HFT scalpers and aggressive high frequency traders (HFTrs). We simulate real-life trading at the millisecond interval by applying Strongly Typed Genetic Programming (STGP) to real-time data from Cisco Systems, Intel and Microsoft. We observe that HFT scalpers are able to calculate NASDAQ NBBO (National Best Bid and Offer) at least 1.5 ms ahead of the NASDAQ SIP (Security Information Processor), resulting in a large number of latency arbitrage opportunities. We also demonstrate that market efficiency is negatively affected by the latency arbitrage activity of HFT scalpers, with no countervailing benefit in volatility or any other measured variable. To improve market quality, and eliminate the socially wasteful arms race for speed, we propose batch auctions in every 70 ms of trading.", } @Article{journals/anor/Manahov18, title = "The rise of the machines in commodities markets: new evidence obtained using Strongly Typed Genetic Programming", author = "Viktor Manahov", journal = "Annals of Operations Research", year = "2018", number = "1-2", volume = "260", pages = "321--352", keywords = "genetic algorithms, genetic programming, STGP", ISSN = "0254-5330", bibdate = "2018-01-09", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/anor/anor260.html#Manahov18", DOI = "doi:10.1007/s10479-016-2286-1", } @Article{Manahov:2019:ijec, author = "Viktor Manahov and Hanxiong Zhang", title = "Forecasting Financial Markets Using High-Frequency Trading Data: Examination with Strongly Typed Genetic Programming", journal = "International Journal of Electronic Commerce", year = "2019", volume = "23", number = "1", pages = "12--32", keywords = "genetic algorithms, genetic programming, STGP, evolutionary computation, artificial intelligence, high-frequency trading, algorithmic trading, big data analytics, financial econometrics", publisher = "Routledge", ISSN = "1086-4415", bibsource = "OAI-PMH server at eprints.lincoln.ac.uk", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ijecommerce/ijecommerce23.html#ManahovZ19", language = "en", oai = "oai:eprints.lincoln.ac.uk:32097", URL = "http://eprints.lincoln.ac.uk/32097/", URL = "http://eprints.lincoln.ac.uk/32097/1/Forecasting%20Financial%20Markets%20Using%20HighFrequency%20Trading%20Data%20Examination%20with%20Strongly%20Typed%20Genetic%20Programming.docx", DOI = "doi:10.1080/10864415.2018.1512271", abstract = "Market regulators around the world are still debating whether or not high-frequency trading (HFT) plays a positive or negative role in market quality. We develop an artificial futures market populated with high-frequency traders (HFTs) and institutional traders using Strongly Typed Genetic Programming (STGP) trading algorithm. We simulate real-life futures trading at the millisecond timeframe by applying STGP to E-Mini S\&P 500 data stamped at the millisecond interval. A direct forecasting comparison between HFTs and institutional traders indicate the superiority of the former. We observe that the negative implications of high-frequency trading in futures markets can be mitigated by introducing a minimum resting trading period of less than 50 milliseconds. Overall, we contribute to the e-commerce literature by showing that minimum resting trading order period of less than 50 milliseconds could lead to HFTs facing a queuing risk resulting in a less harmful market quality effect. One practical implication of our study is that we demonstrate that market regulators and/or e-commerce practitioners can apply artificial intelligence tools such as STGP to conduct trading behaviour-based profiling. This can be used to detect the occurrence of new HFT strategies and examine their impact on the futures market.", notes = "Also known as \cite{journals/ijecommerce/ManahovZ19}", } @Article{Manahov:2019:IJFE, author = "Viktor Manahov and Robert Hudson and Andrew Urquhart", title = "High-frequency trading from an evolutionary perspective: financial markets as adaptive systems", journal = "International Journal of Finance \& Economics", year = "2019", volume = "24", number = "2", pages = "943--962", month = apr, keywords = "genetic algorithms, genetic programming, STGP, SVR, LASSO, kalman filter, adaptive market hypothesis, efficient market hypothesis, evolutionary computation, high-frequency trading, market efficiency", language = "en; English", oai = "oai:eprints.soton.ac.uk:426151", URL = "https://onlinelibrary.wiley.com/doi/epdf/10.1002/ijfe.1700", URL = "https://eprints.soton.ac.uk/426151/", URL = "https://centaur.reading.ac.uk/79183/1/HudsonManahovUrquhart2018.pdf", DOI = "doi:10.1002/ijfe.1700", size = "20 pages", abstract = "The recent rapid growth of algorithmic high-frequency trading strategies makes it a very interesting time to revisit the long-standing debates about the efficiency of stock prices and the best way to model the actions of market participants. To evaluate the evolution of stock price predictability at the millisecond time frame and to examine whether it is consistent with the newly formed adaptive market hypothesis, we develop three artificial stock markets using a strongly typed genetic programming (STGP) trading algorithm. We simulate real-life trading by applying STGP to millisecond data of the three highest capitalized stocks: Apple, Exxon Mobil, and Google and observe that profit opportunities at the millisecond time frame are better modelled through an evolutionary process involving natural selection, adaptation, learning, and dynamic evolution than by using conventional analytical techniques. We use combinations of forecasting techniques as benchmarks to demonstrate that different heuristics enable artificial traders to be ecologically rational, making adaptive decisions that combine forecasting accuracy with speed.", notes = "Russell 1000 and Russell 2000", } @Article{MANAHOV:2021:IRFA, author = "Viktor Manahov and Andrew Urquhart", title = "The efficiency of Bitcoin: A strongly typed genetic programming approach to smart electronic Bitcoin markets", journal = "International Review of Financial Analysis", year = "2021", volume = "73", pages = "101629", keywords = "genetic algorithms, genetic programming, STGP, Artificial intelligence, AI, Smart electronic markets, Bitcoin trading, Cryptocurrencies, Evolutionary computation, Market efficiency", ISSN = "1057-5219", URL = "https://centaur.reading.ac.uk/93822/", URL = "https://centaur.reading.ac.uk/93822/1/The%20Efficiency%20of%20Bitcoin-revised.pdf", URL = "https://ideas.repec.org/a/eee/finana/v73y2021ics1057521920302726.html", URL = "https://www.sciencedirect.com/science/article/pii/S1057521920302726", DOI = "doi:10.1016/j.irfa.2020.101629", abstract = "Cryptocurrencies have gained a lot of attention since Bitcoin was first proposed by Satoshi Nakamoto in 2008, highlighting the potential to play a significant role in e-commerce. However, relatively little is known about cryptocurrencies, their price behaviour, how quickly they incorporate new information and their corresponding market efficiency. To extend the current literature in this area, we develop four smart electronic Bitcoin markets populated with different types of traders using a special adaptive form of the Strongly Typed Genetic Programming (STGP)-based learning algorithm. We apply the STGP technique to historical data of Bitcoin at the one-minute and five-minute frequencies to investigate the formation of Bitcoin market dynamics and market efficiency. Through a plethora of robust testing procedures, we find that both Bitcoin markets populated by high-frequency traders (HFTs) are efficient at the one-minute frequency but inefficient at the five-minute frequency. This finding supports the argument that at the one-minute frequency investors are able to incorporate new information in a fast and rationale manner and not suffer from the noise associated with the five-minute frequency. We also contribute to the e-commerce literature by demonstrating that zero-intelligence traders cannot reach market efficiency, therefore providing evidence against the hypothesis of Hayek (1945; 1968). One practical implication of this study is that we demonstrate that e-commerce practitioners can apply artificial intelligence tools such as STGP to conduct behaviour-based market profiling", } @Article{Manazir:2019:ACMcsurveys, author = "Abdul Manazir and Khalid Raza", title = "Recent Developments in Cartesian Genetic Programming and Its Variants", journal = "ACM Computing Surveys", year = "2019", volume = "51", number = "6", pages = "122:1--122:29", articleno = "122", month = jan, keywords = "genetic algorithms, genetic programming, cartesian genetic programming, bloat, evolutionary computing, machine learning", ISSN = "0360-0300", acmid = "3275518", publisher = "ACM", URL = "http://doi.acm.org/10.1145/3275518", DOI = "doi:10.1145/3275518", size = "29 pages", abstract = "Cartesian Genetic Programming (CGP) is a variant of Genetic Programming with several advantages. During the last one and a half decades, CGP has been further extended to several other forms with lots of promising advantages and applications. This article formally discusses the classical form of CGP and its six different variants proposed so far, which include Embedded CGP, Self-Modifying CGP, Recurrent CGP, Mixed-Type CGP, Balanced CGP, and Differential CGP. Also, this article makes a comparison among these variants in terms of population representations, various constraints in representation, operators and functions applied, and algorithms used. Further, future work directions and open problems in the area have been discussed.", notes = "Jamia Millia Islamia, New Delhi, India Manazir:2019:RDC:3303862.3275518 and journals/csur/ManazirR19", } @InProceedings{Manazir:2022:ICECET, author = "Abdul Manazir and Khalid Raza", booktitle = "2022 International Conference on Electrical, Computer and Energy Technologies (ICECET)", title = "p{CGP:} A Parallel Implementation of Cartesian Genetic Program-ming for Combinatorial Circuit Design and Time-Series Prediction", year = "2022", abstract = "Cartesian Genetic Programming (CGP), a variant of genetic programming, is computationally extensive where its runtime is dominated by fitness evaluation, especially when the genotypes are complex. In order to speed up the execution, multiple offspring can be evaluated in parallel on multiple processing units. This paper presents a parallel implementation of CGP, called pCGP, using a multiple-threading scheme. The performance of pCGP is tested on two different standard benchmark datasets: i) classical digital circuit design problem, and ii) time-series dataset of monthly total sunspot number. The results show a significant reduction in the mean evolution time of parallel multi-threaded implementation over the serial single-threaded version of standard CGP. Hence, pCGP can be used for faster convergence in a complex problem having a large search space.", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming", DOI = "doi:10.1109/ICECET55527.2022.9872630", month = jul, notes = "Also known as \cite{9872630}", } @InProceedings{manazir:2022:ISDA, author = "Abdul Manazir and Khalid Raza", title = "Comparative Evaluation of Genetic Operators in Cartesian Genetic Programming", booktitle = "Intelligent Systems Design and Applications", year = "2022", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", URL = "http://link.springer.com/chapter/10.1007/978-3-030-96308-8_71", DOI = "doi:10.1007/978-3-030-96308-8_71", } @Article{mandal:2015:EP, author = "S. Mandal and S. S. Mahapatra and S. Adhikari and R. K. Patel", title = "Modeling of Arsenic {(III)} Removal by Evolutionary Genetic Programming and Least Square Support Vector Machine Models", journal = "Environmental Processes", year = "2015", volume = "2", number = "1", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s40710-014-0050-6", DOI = "doi:10.1007/s40710-014-0050-6", notes = "Sangeeta Adhikari PhD research? https://www.lap-publishing.com/catalog/details/store/gb/book/978-3-659-89845-7/nanostructured-wo3-for-electrochromic-and-photocatalytic-applications?locale=tr", } @PhdThesis{Mandli:thesis, author = "Aravinda Reddy Mandli", title = "An Application Of Cybernetic Principles To The Modeling And Optimization Of Bioreactors", school = "Chemical Engineering, Division of Mechanical Sciences, Indian Institute of Science", year = "2017", address = "Bangalore, India", keywords = "genetic algorithms, genetic programming, biochemical engineering, bioreactors, fed-batch bioreactors, cybernetic models, cybernetics, cybernetic modeling, microbial products, bioreactor operation, microbial growth, bioreactor optimisation, cybernetic variables, bioreactor trajectory", bibsource = "OAI-PMH server at etd.ncsi.iisc.ernet.in", contributor = "Jayant M Modak", language = "en_US", oai = "oai:etd.ncsi.iisc.ernet.in:2005/2640", URL = "https://etd.iisc.ac.in/handle/2005/2640", broken = "http://hdl.handle.net/2005/2640", URL = "http://etd.ncsi.iisc.ernet.in/abstracts/3444/G26691-Abs.pdf", broken = "http://etd.ncsi.iisc.ernet.in/bitstream/2005/2640/1/G26691.pdf", abstract = "The word cybernetics has its roots in the Greek word kybernetes or steers-man and was coined by Norbert Wiener in 1948 to describe the science of control and communication, in the animal and the machine. The discipline focuses on the way various complex systems (animals/machines) steer towards/maintain their goals using information, models and control actions in the face of various disturbances. For a given animal/machine, cybernetics considers all the possible behaviours that the animal/machine can exhibit and then enquires about the constraints that result in a particular behaviour. The thesis focuses on the application of principles of cybernetics to the modelling and optimisation of bioreactors and lies at the interface of systems engineering and biology. Specifically, it lies at the interface of control theory and the growth behaviour exhibited by microorganisms. The hypothesis of the present work is that the principles and tools of control theory can give novel insights into the growth behaviour of microorganisms and that the growth behaviour exhibited by microorganisms can in turn provide insights for the development of principles and tools of control theory. Mathematical models for the growth of microorganisms such as stoichiometric, optimal and cybernetic assume that microorganisms have evolved to become optimal with respect to certain cellular goals or objectives. Typical cellular goals used in the literature are the maximization of instantaneous/short term objectives such biomass yield, instantaneous growth rate, instantaneous ATP production rate etc. Since microorganisms live in a dynamic world, it is expected that the microorganisms have evolved towards maximising long term goals. In the literature, it is often assumed that the maximization of a short term cellular goal results in the maximization of the long term cellular goal. However, in the systems engineering literature, it has long been recognised that the maximization of a short term goal does not necessarily result in the maximization of the long term goal. For example, maximization of product production in a fed-batch bioreactor involves two separate phases: a first phase in which the growth of microorganisms is maximised and a second phase in which the production of product is maximised. An analogous situation arises when the bacterium E. coli passes through the digestive tract of mammals wherein it first encounters the sugar lactose in the proximal portions and the sugar maltose in the distal portions. Mitchell et al. (2009) have experimentally shown that when E. coli encounters the sugar lactose, it expresses the genes of maltose operons anticipatorily which reduces its growth rate on lactose. This regulatory strategy of E. coli has been termed asymmetric anticipatory regulation (AAR) and is shown to be beneficial for long term cellular fitness by Mitchell et al. (2009). The cybernetic modelling framework for the growth of microorganisms, developed by Ramakrishna and co-workers, is extended in the present thesis for modelling the AAR strategy of E. coli. The developed model accurately captures the experimental observations of the AAR phenomenon, reveals the inherent advantages of the cybernetic modelling framework over other frameworks in explaining the AAR phenomenon, while at the same time suggesting a scope for the generalisation of the cybernetic framework. As cybernetics is interested in all the possible behaviours that a machine (which is, in the present case, microorganism) can exhibit, a rigorous analysis of the optimal dynamic growth behaviour of microorganisms under various constraints is carried out next using the methods of optimal control theory. An optimal control problem is formulated using a generalised version of the unstructured Monod model with the objective of maximization of cellular concentration at a fixed final time. Optimal control analysis of the above problem reveals that the long term objective of maximization of cellular concentration at a final time is equivalent to maximization of instantaneous growth rate for the growth of microorganisms under various constraints in a two substrate batch environment. In addition, reformulation of the above optimal control problem together with its necessary conditions of optimality reveals the existence of generalised governing dynamic equations of the structured cybernetic modelling framework. The dynamic behaviour of the generalised equations of the cybernetic modelling framework is analysed further to gain insights into the growth of microorganisms. For growth of microorganisms on a single growth limiting carbon substrate, the analysis reveals that the cybernetic model exhibits linear growth behaviour, similar to that of the unstructured Contois model at high cellular concentrations, under appropriate constraints. During the growth of microorganisms on multiple substitutable substrates, the analysis reveals the existence of simple correlations that quantitatively predict the mixed substrate maximum specific growth rate from single substrate maximum specific growth rates during simultaneous consumption of the substrates in several cases. Further analysis of the cybernetic model of the growth of S. cerevisiae on the mixture of glucose and galactose reveals that S. cerevisiae exhibits sub-optimal dynamic growth with a long diauxic lag phase and suggests the possibility for S. cerevisiae to grow optimally with a significantly reduced diauxic lag period. Since cybernetics is interested in understanding the constraints under which a particular machine (microorganism) exhibits a particular behaviour, a methodology is then developed for inferring the internal constraints experienced by the microorganisms from experimental data. The methodology is used for inferring the internal constraints experienced by E. coli during its growth on the mixture of glycerol and lactose. An interesting question in the study of the growth behaviour of microorganisms concerns the objective that the microorganisms optimise. Several studies aim to determine these cellular objectives experimentally. A similar question that is relevant to the optimisation of fed-batch bioreactors is what are the objectives that are to be optimised by the feed flow rate in various time intervals for the optimisation of a final objective? It was mentioned previously that the maximization of product production in a fed-batch bioreactor involves maximization of growth of microorganisms first and the maximization of product production later. However, such guidelines can only be stated for relatively simple bioreactor optimisation problems and no such guidelines exist for sufficiently complex problems. For complex problems, the answer to the above question requires the formulation and solution of a genetic programming problem which can be quite challenging. An alternative numerical solution methodology is developed in the present thesis to address the above question. The solution methodology involves the specification of bioreactor objectives in terms of the bioreactor trajectory in the state space of substrate concentration-volume. The equivalent control law of the sliding mode control technique is used for finding the inlet feed ow rate that tracks the bioreactor trajectory accurately. The search for the best bioreactor trajectory is carried out using the stochastic search technique genetic algorithm. The effectiveness of the developed solution methodology in determining the optimal bioreactor trajectory is demonstrated using three challenging bioreactor optimisation problems.", notes = "Supervisor Jayant M Modak also known as \cite{oai:etd.ncsi.iisc.ernet.in:2005/2640}", } @Article{Mandrioli:TSE, author = "Claudio Mandrioli and Seung Yeob Shin and Domenico Bianculli and Lionel Briand", title = "Testing {CPS} with Design Assumptions-Based Metamorphic Relations and Genetic Programming", journal = "IEEE Transactions on Software Engineering", keywords = "genetic algorithms, genetic programming, Testing, Drones, Software, Shape, Control theory, Software algorithms, Sensors, Linearity, Steady-state, Mathematical models", ISSN = "1939-3520", DOI = "doi:10.1109/TSE.2025.3563121", abstract = "...To address the test case generation and oracle problems, researchers have proposed metamorphic testing, which is based on the study of relations across tests, i.e., metamorphic relations (MRs). we define MRs based on the design assumptions and explore combinations of these MRs using genetic programming to generate CPS test cases. This enables the generation of CPS input traces with potentially arbitrary shapes, together with associated expected output traces. We use the deviation from the expected output traces to guide the generation of input traces that falsify the MRs. Our experiment results show that the MR-falsification provides engineers with new information, helping them identify passed and failed test cases. Furthermore, we show that the generation of traces that falsify the MRs is a non-trivial problem, which cannot be addressed with a random generation approach but is successfully addressed by our approach based on genetic search.", notes = "Also known as \cite{10976605}", } @Article{DBLP:journals/bmcbi/ManduchiFRRM20, author = "Elisabetta Manduchi and Weixuan Fu and Joseph D. Romano and Stefano Ruberto and Jason H. Moore", title = "Embedding covariate adjustments in tree-based automated machine learning for biomedical big data analyses", journal = "BMC Bioinformatics", year = "2020", volume = "21", pages = "Article number: 430", month = "1 " # oct, keywords = "genetic algorithms, genetic programming, TPOT, AutoML, Covariate adjustment, Pathways, Feature importance", ISSN = "1471-2105", URL = "https://doi.org/10.1101/2020.08.24.265116", DOI = "doi:10.1186/s12859-020-03755-4", abstract = "Background A typical task in bioinformatics consists of identifying which features are associated with a target outcome of interest and building a predictive model. Automated machine learning (AutoML) systems such as the Tree-based Pipeline Optimization Tool (TPOT) constitute an appealing approach to this end. However, in biomedical data, there are often baseline characteristics of the subjects in a study or batch effects that need to be adjusted for in order to better isolate the effects of the features of interest on the target. Thus, the ability to perform covariate adjustments becomes particularly important for applications of AutoML to biomedical big data analysis. Results We developed an approach to adjust for covariates affecting features and/or target in TPOT. Our approach is based on regressing out the covariates in a manner that avoids 'leakage' during the cross-validation training procedure. We describe applications of this approach to toxicogenomics and schizophrenia gene expression data sets. The TPOT extensions discussed in this work are available at https://github.com/EpistasisLab/tpot/tree/v0.11.1-resAdj. Conclusions In this work, we address an important need in the context of AutoML, which is particularly crucial for applications to bioinformatics and medical informatics, namely covariate adjustments. To this end we present a substantial extension of TPOT, a genetic programming based AutoML approach. We show the utility of this extension by applications to large toxicogenomics and differential gene expression data. The method is generally applicable in many other scenarios from the biomedical field.", } @Article{manduchi:2021:tcbb, author = "Elisabetta Manduchi and Trang Le and Weixuan Fu and Jason H Moore", title = "Genetic analysis of coronary artery disease using tree-based automated machine learning informed by biology-based feature selection", journal = "IEEE/ACM transactions on computational biology and bioinformatics", year = "2022", volume = "19", number = "3", pages = "1379--1386", month = jul # " 26", keywords = "genetic algorithms, genetic programming, TPOT", ISSN = "1557-9964", DOI = "doi:10.1109/TCBB.2021.3099068", abstract = "Machine Learning (ML) approaches are increasingly being used in biomedical applications. Important challenges of ML include choosing the right algorithm and tuning the parameters for optimal performance. Automated ML (AutoML) methods, such as Tree-based Pipeline Optimization Tool (TPOT), have been developed to take some of the guesswork out of ML thus making this technology available to users from more diverse backgrounds. The goals of this study were to assess applicability of TPOT to genomics and to identify combinations of single nucleotide polymorphisms (SNPs) associated with coronary artery disease (CAD), with a focus on genes with high likelihood of being good CAD drug targets. We leveraged public functional genomic resources to group SNPs into biologically meaningful sets to be selected by TPOT. We applied this strategy to data from the UK Biobank, detecting a strikingly recurrent signal stemming from a group of 28 SNPs. Importance analysis of these SNPs uncovered functional relevance of the top SNPs to genes whose association with CAD is supported in the literature and other resources. Furthermore, we employed game-theory based metrics to study SNP contributions to individual-level TPOT predictions and discover distinct clusters of well-predicted CAD cases. The latter indicates a promising approach towards precision medicine.", notes = "Also known as \cite{9495156}. PMID: 34310318", } @InProceedings{Mane:2020:CINTI, author = "Nikhil Mane and Anjali Verma and Arti Arya", title = "A Pragmatic Optimal Approach for Detection of Cyber Attacks using Genetic Programming", booktitle = "2020 IEEE 20th International Symposium on Computational Intelligence and Informatics (CINTI)", year = "2020", pages = "71--76", abstract = "Cyber-attacks are becoming an increasing threat to people and daily businesses regularly. Attackers have also been evolving their strategies and methods with time. Every attack carried out has the potential to exploit the system on a large scale. Various Artificial Intelligence (AI) algorithms are used to defend such vulnerabilities. This paper analyzes a novel attack and extracts attackers' intrusion scenarios. Evolutionary Computation Techniques have been remarkably used in the field of cybersecurity. This paper particularly discusses the Distributed Denial Of Service (DDoS) attack. The effect of this attack ranges from a disturbance of an elementary service to causing major threats to critical services. In recent times these attacks have become more intricate and carry a significant threat. Therefore, there is a necessity for an intelligent Intrusion Detection System (IDS) to recognize attacks. In this study, work is carried on the latest dataset called Modern DDoS. This paper comprises of comparing the results of six established classification techniques: Random Forest, Naive Bayes, Stochastic Gradient Descent, Decision Trees, Logistic Regression, and K-Nearest Neighbour (KNN) with the proposed Genetic Programming model. The results show that the proposed Genetic Programming model has better accuracy when compared to various existing methods.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CINTI51262.2020.9305844", ISSN = "2471-9269", month = nov, notes = "Also known as \cite{9305844}", } @InProceedings{Mane:2024:ICACITE, author = "Sulakshana Bhausaheb Mane and S. Thangalakshmi and R. Umamageswari and C Praveenkumar and Asala Riyadh Sarhan and Omar Muhsin", title = "The use of {SVM} and {AR} Methods for allotment of Load Scheduling and Energy Management in {SPS}", booktitle = "2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE)", year = "2024", pages = "16--20", month = may, keywords = "genetic algorithms, genetic programming, Support vector machines, Photovoltaic systems, Accuracy, Computational modelling, Predictive models, Prediction algorithms, Robustness, solar power forecasting, additve Regression, support vector machine, Hybrid model", DOI = "doi:10.1109/ICACITE60783.2024.10616972", abstract = "Integration of photovoltaic (PV) systems into energy grids to meet the solar energy consumption requires high-performance predictive algorithms Although genetic process (GP) based additive regression (AR) models so provide answers to nonlinear prediction problems For the information Another hybrid model is proposed that combines the SR with a support vector machine (SVM) architecture. The hybrid model seeks to improve accuracy and robustness by using real-world climate data including solar radiation and historical PV power estimates. Using the Elastic Net and Extreme Boosting methods, the hybrid AR-SVM algorithm makes fast feature selection by eliminating redundant inputs. Stability and reliability are ensured by maintaining the hyper-criteria during the training and testing phases. The model reduces the computational complexity and improves accuracy by using fewer layers and neurons. The improved prediction accuracy is demonstrated by simulation results, which show a significant reduction in the absolute error (MAE) and root mean squared error (RMSE) The improvement in the $\mathbf{R} 2$ evaluation measure confirms the power of the model has to recognise the underlying pattern emphasis. By preventing the AR from reaching a local minimum with the contribution of the SVM branch, the hybrid model exhibits high robustness to forecast errors, and shows superiority in PV power forecasting applications.", notes = "Also known as \cite{10616972} Department of Computer Engineering, Bharati Vidyapeeth College of Engineering, Navi Mumbai", } @InProceedings{Manfrini:2014:CEC, title = "Optimization of Combinational Logic Circuits Through Decomposition of Truth Table and Evolution of Sub-Circuits", author = "Francisco Manfrini and Helio Barbosa and Heder Bernardino", booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary Computation", year = "2014", pages = "945--950", month = "6-11 " # jul, editor = "Carlos A. {Coello Coello}", address = "Beijing, China", ISBN = "0-7803-8515-2", keywords = "Genetic algorithms, genetic programming, Evolvable hardware and software", DOI = "doi:10.1109/CEC.2014.6900565", abstract = "In this work, a genetic algorithm was used to design combinational logic circuits (CLCs), with the goal of minimising the number of logic elements in the circuit. A new coding for circuits is proposed using a multiplexer (MUX) at the output of the circuit. This MUX divides the truth table into two distinct parts, with the evolution occurring in three sub-circuits connected to the control input and the two data inputs of the MUX. The methodology presented was tested with some benchmark circuits. The results were compared with those obtained using traditional design methods, as well as the results found in other articles, which used different heuristics to design CLCs.", notes = "also known as \cite{6900565}", } @InProceedings{Manfrini:2016:GECCOcomp, author = "Francisco A. L. Manfrini and Heder S. Bernardino and Helio J. C. Barbosa", title = "On Heuristics for Seeding the Initial Population of Cartesian Genetic Programming Applied to Combinational Logic Circuits", booktitle = "GECCO '16 Companion: Proceedings of the Companion Publication of the 2016 Annual Conference on Genetic and Evolutionary Computation", year = "2016", editor = "Tobias Friedrich and Frank Neumann and Andrew M. Sutton and Martin Middendorf and Xiaodong Li and Emma Hart and Mengjie Zhang and Youhei Akimoto and Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and Daniele Loiacono and Julian Togelius and Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and Faustino Gomez and Carlos M. Fonseca and Heike Trautmann and Alberto Moraglio and William F. Punch and Krzysztof Krawiec and Zdenek Vasicek and Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and Boris Naujoks and Enrique Alba and Gabriela Ochoa and Simon Poulding and Dirk Sudholt and Timo Koetzing", pages = "105--106", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming: Poster", month = "20-24 " # jul, organisation = "SIGEVO", address = "Denver, USA", publisher = "ACM", publisher_address = "New York, NY, USA", isbn13 = "978-1-4503-4323-7", DOI = "doi:10.1145/2908961.2909031", abstract = "The design of circuits is an important research field and the corresponding optimization problems are complex and computationally expensive. Here, a Cartesian Genetic Programming (CGP) technique was used to design combinational logic circuits. Several configurations were tested for seeding the initial population. First, the number of rows, columns, and levels-back were varied. In addition, the initial population was generated using only NAND gates. These configurations were compared with results from the literature in four benchmark circuits, where in all instances it was possible to find that some seeding configurations contributed beneficially to the evolutionary process, allowing CGP to find a solution employing a lower number of fitness evaluations. Finally, the variation of the number of nodes of the individuals during the search was also analysed and the results showed that there is a correlation between the topology of the initial population and the region of the search space which is explored.", notes = "Distributed at GECCO-2016.", } @InProceedings{Manfrini:2016:PPSN, author = "Francisco A. L. Manfrini and Heder S. Bernardino and Helio J. C. Barbosa", title = "A Novel Efficient Mutation for Evolutionary Design of Combinational Logic Circuits", booktitle = "14th International Conference on Parallel Problem Solving from Nature", year = "2016", editor = "Julia Handl and Emma Hart and Peter R. Lewis and Manuel Lopez-Ibanez and Gabriela Ochoa and Ben Paechter", volume = "9921", series = "LNCS", address = "Edinburgh", month = "17-21 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, Point mutation operator, Circuit design, Combinational circuits", isbn13 = "978-3-319-45823-6", DOI = "doi:10.1007/978-3-319-45823-6_62", size = "10 pages", abstract = "In this paper we investigate evolutionary mechanisms and propose a new mutation operator for the evolutionary design of Combinational Logic Circuits (CLCs). Understanding the root causes of evolutionary success is critical to improving existing techniques. Our focus is two-fold: to analyse beneficial mutations in Cartesian Genetic Programming, and to create an efficient mutation operator for digital CLC design. In the experiments performed the mutation proposed is better than or equivalent to traditional mutation.", notes = "PPSN2016", } @Book{mange:1998:bicm, editor = "Daniel Mange and Marco Tomassini", title = "Bio-Inspired Computing Machines", publisher = "Presses Polytechniques et Universitaires Romandes", year = "1998", ISBN = "2-88074-371-0", URL = "http://lslwww.epfl.ch/pages/publications/books/1998_1/contents.html", abstract = "This volume, written by experts in the field, gives a modern, rigorous and unified presentation of the application of biological concepts to the design of novel computing machines and algorithms. While science has as its fundamental goal the understanding of Nature, the engineering disciplines attempt to use this knowledge to the ultimate benefit of Mankind. Over the past few decades this gap has narrowed to some extent. A growing group of scientists has begun engineering artificial worlds to test and probe their theories, while engineers have turned to Nature, seeking inspiration in its workings to construct novel systems. The organization of living beings is a powerful source of ideas for computer scientists and engineers. This book studies the construction of machines and algorithms based on natural processes: biological evolution, which gives rise to genetic algorithms, cellular development, which leads to self-replicating and self-repairing machines, and the nervous system in living beings, which serves as the underlying motivation for artificial learning systems, such as neural networks.", notes = "Contents An Introduction to Bio-Inspired Machines - An Introduction to Digital Systems - An Introduction to Cellular Automata - Evolutionary Algorithms and their Applications - Programming Cellular Machines by Cellular Programming - Multiplexer-Based Cells - Demultiplexer-Based Cells - Binary Decision Machine-Based Cells - Self-Repairing Molecules and Cells - L-hardware: Modeling and Implementing Cellular Development - Using L-systems - Artificial Neural Networks: Algorithms and Hardware Implementation - Evolution and Learning in Autonomous Robotic Agents - Bibliography - Index. Reviewed in \cite{greenwood:2001:bicm}", size = "384 pages", } @InProceedings{Manh:2016:ICWITS, author = "Linh Ho Manh and Jennifer Rayno and Magdy F. Iskander and Marcelo H. Kobayashi", booktitle = "2016 IEEE/ACES International Conference on Wireless Information Technology and Systems (ICWITS) and Applied Computational Electromagnetics (ACES)", title = "Hybrid genetic programming with modified conjugate direction search for 3D metamaterial design", year = "2016", abstract = "Hybridization of Genetic Programming and global low-level optimiser, namely Genetic Algorithm was previously developed. With the aim of improving computational efficiency, conjugate direction search method is modified and proposed as a new low-level optimiser for upper level Genetic Programming. In order to demonstrate computational efficiency, Genetic Programming and two low-level optimisers are employed to design broadband, low frequency ground plane (225 MHz-450 MHz). When applying optimisers on a single processor, preliminary results show that optimised unit cell found by new low level optimiser has a better reflection magnitude (at least 0.8 over the frequency range) than the one found by Genetic Algorithm (as low as 0.25 over the frequency band). Details of proposed method will be presented and discussed in the paper.", keywords = "genetic algorithms, genetic programming, conjugate direction search, artificial magnetic conductor, antenna ground plane", DOI = "doi:10.1109/ROPACES.2016.7465382", month = mar, notes = "PhD 17-Dec-2014 Computational Intelligence for Electromagnetic Drives https://www.politesi.polimi.it/handle/10589/98503 Also known as \cite{7465382}", } @Article{DBLP:journals/nature/MankowitzMZGSPL23, author = "Daniel J. Mankowitz and Andrea Michi and Anton Zhernov and Marco Gelmi and Marco Selvi and Cosmin Paduraru and Edouard Leurent and Shariq Iqbal and Jean{-}Baptiste Lespiau and Alex Ahern and Thomas K{\"{o}}ppe and Kevin Millikin and Stephen Gaffney and Sophie Elster and Jackson Broshear and Chris Gamble and Kieran Milan and Robert Tung and Minjae Hwang and A. Taylan Cemgil and Mohammadamin Barekatain and Yujia Li and Amol Mandhane and Thomas Hubert and Julian Schrittwieser and Demis Hassabis and Pushmeet Kohli and Martin A. Riedmiller and Oriol Vinyals and David Silver", title = "Faster sorting algorithms discovered using deep reinforcement learning", journal = "Nature", year = "2023", volume = "618", pages = "257--263", month = "8 " # jun, keywords = "genetic improvement, neural network, ANN, deep reinforcement learning, Transformers, Monte Carlo tree search, MCTS, stochastic search optimization, AssemblyGame, AlphaDev, sort3, sort4, sort5, VarSort3, VarSort4, VarSort5, VarInt, x86 assembly instructions, ARMv8, Intel Skylake, AMD Zen 2 CPU, LLVM libc++, JAX, computer science, Software", timestamp = "Sat, 08 Jun 2024 13:15:45 +0200", biburl = "https://dblp.org/rec/journals/nature/MankowitzMZGSPL23.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://rdcu.be/dKZtL", DOI = "doi:10.1038/s41586-023-06004-9", code_url = "https://github.com/google-deepmind/alphadev", size = "17 pages", abstract = "Fundamental algorithms such as sorting or hashing are used trillions of times on any given day1. As demand for computation grows, it has become critical for these algorithms to be as performant as possible. Whereas remarkable progress has been achieved in the past2, making further improvements on the efficiency of these routines has proved challenging for both human scientists and computational approaches. Here we show how artificial intelligence can go beyond the current state of the art by discovering hitherto unknown routines. To realize this, we formulated the task of finding a better sorting routine as a single-player game. We then trained a new deep reinforcement learning agent, AlphaDev, to play this game. AlphaDev discovered small sorting algorithms from scratch that outperformed previously known human benchmarks. These algorithms have been integrated into the LLVM standard C++ sort library3. This change to this part of the sort library represents the replacement of a component with an algorithm that has been automatically discovered using reinforcement learning. We also present results in extra domains, showcasing the generality of the approach.", notes = "p257 'small sort algorithm: (1) the fixed sort and (2) the variable sort' p258 'AlphaDev builds an assembly program from scratch' Says each search move uses MCTS/ANN to 'append a legal assembly instruction' ANN weights updated when results of complete sort are known 'N test sequences' => 'N outputs' optimising length of assembly code rather than run time? but says (2) 'actual latency of the algorithm' p260 'measures the actual latency of the programs by taking the fifth percentile of latency measurements across 100 different machines' AlphaDev-S Stochastic Superoptimization \cite{Schkufza:thesis} Search space only 12000000? Seeding AlphaDev-S can sometimes do better than AlphaDev. p262 'being called trillions of times every day'", } @InProceedings{manning:2013:evobio, author = "Timmy Manning and Paul Walsh", title = "Improving the Performance of {CGPANN} for Breast Cancer Diagnosis using Crossover and Radial Basis Functions", booktitle = "11th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, {EvoBIO 2013}", year = "2013", editor = "Leonardo Vanneschi and William S. Bush and Mario Giacobini", month = apr # " 3-5", series = "LNCS", volume = "7833", publisher = "Springer Verlag", organisation = "EvoStar", address = "Vienna, Austria", pages = "165--176", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", isbn13 = "978-3-642-37188-2", DOI = "doi:10.1007/978-3-642-37189-9_15", abstract = "Recently published evaluations of the topology and weight evolving artificial neural network algorithm Cartesian genetic programming evolved artificial neural networks (CGPANN) have suggested it as a potentially powerful tool for bioinformatics problems. In this paper we provide an overview of the CGPANN algorithm and a brief case study of its application to the Wisconsin breast cancer diagnosis problem. Following from this, we introduce and evaluate the use of RBF kernels and crossover to CGPANN as a means of increasing performance and consistency.", } @InProceedings{Manognya:2009:ICICS, author = "Jandhyala Seetha Manognya and A/P Lipo Wang", title = "Gene expression programming for induction of finite transducer", booktitle = "7th International Conference on Information, Communications and Signal Processing, ICICS 2009", year = "2009", month = dec, abstract = "This paper presents an alternative method for solving the problem of finite transducers using gene expression programming (GEP). Each individual in the GEP system represents a Mealy machine with outputs for each state. Be means of roulette-wheel sampling, individuals are chosen for the next generation and are put through a series of genetic operators which seek to change the mark-up of the individual to better fit the selection environment/ fitness sets. The system was tested with five problems to show its effectiveness and success at solving all of those problems.", keywords = "genetic algorithms, genetic programming, Gene expression programming, Mealy machine, finite transducer induction, gene expression programming, roulette-wheel sampling, finite automata", DOI = "doi:10.1109/ICICS.2009.5397573", notes = "Also known as \cite{5397573}", } @InCollection{Manos:2008:ECP, author = "Steven Manos and Peter J. Bentley", title = "Evolving Microstructured Optical Fibres", booktitle = "Evolutionary Computation in Practice", publisher = "Springer", year = "2008", editor = "Tina Yu and David Davis and Cem Baydar and Rajkumar Roy", volume = "88", series = "Studies in Computational Intelligence", chapter = "5", pages = "87--124", keywords = "genetic algorithms, genetic programming, embryogeny", isbn13 = "978-3-540-75770-2", DOI = "doi:10.1007/978-3-540-75771-9_5", abstract = "Optical fibres are not only one of the major components of modern optical communications systems, but are also used in other areas such as sensing, medicine and optical filtering. Silica microstructured optical fibres are a type of optical fibre where microscopic holes within the fibre result in highly tailorable optical properties, which are not possible in traditional fibres. Microstructured fibres manufactured from polymer, instead of silica, are a relatively recent development in optical fibre technology, and support a wide variety of microstructure fibre geometries, when compared to the more commonly used silica. In order to meet the automated design requirements for such complex fibres, a representation was developed which can describe radially symmetric microstructured fibres of different complexities; from simple hexagonal designs with very few holes, to large arrays of hundreds of holes. This chapter presents a genetic algorithm which uses an embryogeny representation, or a growth phase, to convert a design from its genetic encoding (genotype) to the microstructured fibre (phenotype). The work demonstrates the application of variable-complexity, evolutionary design approaches to photonic design. The inclusion of real-world constraints within the embryogeny aids in the manufacture of designs, resulting in the physical construction and experimental characterisation of both single-mode and high bandwidth multi-mode microstructured fibres, where some GA-designed fibres are currently being patented.", notes = "Part of \cite{TinaYu:2008:book}", } @InProceedings{conf/iwinac/ManriqueMRR05, title = "Grammar Based Crossover Operator in Genetic Programming", author = "Daniel Manrique and Fernando Marquez and Juan Rios and Alfonso Rodriguez-Paton", year = "2005", booktitle = "Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach: First International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2005, Proceedings, Part II", pages = "252--261", editor = "Jos{\'e} Mira and Jos{\'e} R. {\'A}lvarez", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3562", address = "Las Palmas, Canary Islands, Spain", month = jun # " 15-18", bibdate = "2005-06-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/iwinac/iwinac2005-2.html#ManriqueMRR05", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-26319-5", DOI = "doi:10.1007/11499305_26", size = "10 pages", abstract = "This paper introduces a new crossover operator for the genetic programming (GP)paradigm, the grammar-based crossover (GBX). This operator works with any grammar-guided genetic programming system. GBX has three important features: it prevents the growth of tree-based GP individuals (a phenomenon known as code bloat), it provides a satisfactory trade-off between the search space exploration and the exploitation capabilities by preserving the context in which subtrees appear in the parent trees and, finally, it takes advantage of the main feature of ambiguous grammars, namely, that there is more than one derivation tree for some sentences (solutions). These features give GBX a high convergence speed and low probability of getting trapped in local optima, as shown throughout the comparison of the results achieved by GBX with other relevant crossover operators in two experiments: a laboratory problem and a real-world task: breast cancer prognosis.", notes = "http://www.iwinac.uned.es/iwinac2005/", } @InCollection{reference/ai/ManriqueRR09, title = "Grammar-Guided Genetic Programming", author = "Daniel Manrique and Juan Rios and Alfonso Rodriguez-Paton", booktitle = "Encyclopedia of Artificial Intelligence", publisher = "IGI Global", year = "2009", editor = "Juan R. Rabu{\~n}al and Julian Dorado and Alejandro Pazos", chapter = "114", pages = "767--773", keywords = "genetic algorithms, genetic programming", isbn13 = "9781599048499", DOI = "doi:10.4018/978-1-59904-849-9.ch114", DOI = "doi:10.4018/978-1-59904-849-9", URL = "http://www.igi-global.com/bookstore/titledetails.aspx?TitleId=343", bibdate = "2011-01-18", bibsource = "DBLP, http://dblp.uni-trier.de/db/reference/ai/ai2009.html#ManriqueRR09", abstract = "Evolutionary computation (EC) is the study of computational systems that borrow ideas from and are inspired by natural evolution and adaptation (Yao & Xu, 2006, pp. 1-18). EC covers a number of techniques based on evolutionary processes and natural selection: evolutionary strategies, genetic algorithms and genetic programming (Keedwell & Narayanan, 2005). Evolutionary strategies are an approach for efficiently solving certain continuous problems, yielding good results for some parametric problems in real domains. Compared with genetic algorithms, evolutionary strategies run more exploratory searches and are a good option when applied to relatively unknown parametric problems. Genetic algorithms emulate the evolutionary process that takes place in nature. Individuals compete for survival by adapting as best they can to the environmental conditions. Crossovers between individuals, mutations and deaths are all part of this process of adaptation. By substituting the natural environment for the problem to be solved, we get a computationally cheap method that is capable of dealing with any problem, provided we know how to determine individuals' fitness (Manrique, 2001). Genetic programming is an extension of genetic algorithms (Couchet, Manrique, Rios & Rodriguez-Paton, 2006). Its aim is to build computer programs that are not expressly designed and programmed by a human being. It can be said to be an optimisation technique whose search space is composed of all possible computer programs for solving a particular problem. Genetic programming's key advantage over genetic algorithms is that it can handle individuals (computer programs) of different lengths. Grammar-guided genetic programming (GGGP) is an extension of traditional GP systems (Whigham, 1995, pp. 33-41). The difference lies in the fact that they employ context-free grammars (CFG) that generate all the possible solutions to a given problem as sentences, establishing this way the formal definition of the syntactic problem constraints, and use the derivation trees for each sentence to encode these solutions (Dounias, Tsakonas, Jantzen, Axer, Bjerregard & von Keyserlingk, D. 2002, pp. 494-500). The use of this type of syntactic formalisms helps to solve the so-called closure problem (Whigham, 1996). To achieve closure valid individuals (points that belong to the search space) should always be generated. As the generation of invalid individuals slows down convergence speed a great deal, solving this problem will very much improve the GP search capability. The basic operator directly affecting the closure problem is crossover: crossing two (or any) valid individuals should generate a valid offspring. Similarly, this is the operator that has the biggest impact on the process of convergence towards the optimum solution. Therefore, this article reviews the most important crossover operators employed in GP and GGGP, highlighting the weaknesses existing nowadays in this area of research. We also propose a GGGP system. This system incorporates the original idea of employing ambiguous CFG to overcome these weaknesses, thereby increasing convergence speed and reducing the likelihood of trapping in local optima. Comparative results are shown to empirically corroborate our claims.", notes = "Cited by \cite{Krauss:2020:GI} 3 Volumes. Inteligencia Artificial, Facultad de Informatica, UPM, Spain", } @InProceedings{Mansilla:2009:ICCAS-SICE, author = "Johanna Mansilla and Shingo Mabu and Lu Yu and Kotaro Hirasawa", title = "Adaptive Controller for Double-Deck Elevator System using Genetic Network Programming", booktitle = "ICCAS-SICE, 2009", year = "2009", month = "18-21 " # aug, address = "Fukuoka", pages = "3870--3873", publisher = "IEEE", isbn13 = "978-4-9077-6433-3", URL = "http://ieeexplore.ieee.org/search/srchabstract.jsp?tp=&arnumber=5332931", size = "4 pages", abstract = "In this paper, an improved approach is proposed based on an updating strategy using Genetic Network Programming (GNP) for the controller of Double-Deck Elevator System (DDES). Since the elevator controller has to deal with constant changes of its environment, our approach is proposed to deal with better the environment changes, resulting in a reduction of the waiting time and increasing the transportation capacity. This updating looks forward to contributing to the efficient adjustment of the system periodically according to the gained system information. The performance of the proposed method is evaluated by comparison with the conventional GNP method, which does not update the controller. By this evaluation, the enhancement of our model is confirmed.", keywords = "genetic algorithms, genetic programming, adaptive controller, double-deck elevator system, genetic network programming, transportation capacity, adaptive control, lifts", notes = "Also known as \cite{5332931}", } @PhdThesis{Manson:thesis, author = "Steven Michael Manson", title = "Integrated assessment and projection of land-use and land-cover change in the southern Yucatan peninsular region of Mexico", school = "Department of Geography, Clark University,", year = "2002", address = "Worcester, Massachusetts USA", month = "1 " # nov, keywords = "genetic algorithms, genetic programming", URL = "http://search.proquest.com/docview/252123649", size = "328 pages", abstract = "This dissertation presents a spatio-temporal land-change model that represents decision making in the context of socio-economic and ecological forces for the southern Yucatan peninsular region of Mexico. It integrates decision-making theories and considers land-use and land-cover change modelling techniques. It then describes a modeling framework that supports agent-based modelling and cellular modelling of land-change, the SYPR Integrated Assessment. This application focuses on the use of genetic programming to represent decision making.", notes = "Supervisor: B. L. Turner, II UMI Microform 3064036", } @Article{Manson:2005:AEE, author = "Steven M. Manson", title = "Agent-based modeling and genetic programming for modeling land change in the Southern Yucatan Peninsular Region of Mexico", journal = "Agriculture, Ecosystems and Environment", year = "2005", volume = "111", number = "1-4", pages = "47--62", month = "1 " # dec, keywords = "genetic algorithms, genetic programming, Agent-based model, Genetic program, Land-use and land-cover change, Multicriteria evaluation, Symbolic regression", DOI = "doi:10.1016/j.agee.2005.04.024", abstract = "Land-use and land-cover change research increasingly takes the form of integrated land-change science, the explicit joining of ecological, social and information sciences. Traditional interdisciplinary methods are buttressed by new ones stemming from computational intelligence research and the complexity sciences. Several of these genetic programming, cellular modelling and agent-based modeling are applied to land change in the Southern Yucatan Peninsular Region (SYPR) of Mexico through the SYPR Integrated Assessment (SYPRIA). This work illustrates how computational intelligence techniques, such as genetic programming, can be used to model decision making in the context of human environment relationships. This application also contributes to methodological innovations in multicriteria evaluation and modeling of coupled human environment systems. This effort also demonstrates the importance of considering both social and environmental drivers of land change, particularly with respect to the decision making of change agents within the context of key socioeconomic and political drivers, particularly as channelled through market institutions and land tenure, and ecological factors, especially characteristics of land-use and land-cover such as state, history and fragmentation. SYPRIA demonstrates the utility of modelling methods based in computational intelligence and the complexity sciences in helping understand the decision making of land-change agents as a function of both social and environment drivers.", } @Article{Manson:2006:CEUS, author = "Steven Manson", title = "Land use in the southern Yucatan peninsular region of Mexico: Scenarios of population and institutional change", journal = "Computers, Environment and Urban Systems", year = "2006", volume = "30", number = "3", pages = "230--253", month = may, keywords = "genetic algorithms, genetic programming, Agent-based model, Genetic program, Land-use and land-cover change, Multicriteria evaluation, Symbolic regression", DOI = "doi:10.1016/j.compenvurbsys.2005.01.009", abstract = "Land-use and land-cover change, human activity that results in altered land-use systems and surface features, defines the environmental and socioeconomic sustainability of communities around the globe. It is a key response to global environmental change in addition to being both a key cause and medium of this change. This article examines an application of the Southern Yucatan Peninsular Region Integrated Assessment (SYPRIA), a scenario-based spatially explicit model designed to examine and project land use in Mexico. SYPRIA combines Geographic Information Systems (GIS) with agent-based modelling, cellular modeling, and genetic programming. The application examined here explores the effects on land-use and land-cover projections of scenarios that rely on varying assumptions pertaining to population growth, land-use trends, role of agrarian technology, and effects of resource institutions. This work also highlights the importance of understanding the many factors influencing land use, particularly population, different production systems, and the contextual nature of resource institutions in determining the nature of land use.", } @Article{Manson:2006:IJGIS, author = "S. M. Manson", title = "Bounded rationality in agent-based models: experiments with evolutionary programs", journal = "International Journal of Geographical Information Science", year = "2006", volume = "20", number = "9", pages = "991--1012", month = oct, keywords = "genetic algorithms, genetic programming, agent-based model, bounded rationality, evolutionary programs, land change", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.465.8323", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.465.8323", broken = "http://leg.ufpr.br/~pedro/papers/ijgis/manson_bounded_rationality_06.pdf", URL = "http://earth.clarku.edu/lcluc/pubs/Modeling/II-15_Manson_2006_.pdf", URL = "http://dx.doi.org/10.1080/13658810600830566", DOI = "doi:10.1080/13658810600830566", size = "22 pages", abstract = "This paper examines the use of evolutionary programming in agent-based modelling to implement the theory of bounded rationality. Evolutionary programming, which draws on Darwinian analogues of computing to create software programs, is a readily accepted means for solving complex computational problems. Evolutionary programming is also increasingly used to develop problem-solving strategies in accordance with bounded rationality, which addresses features of human decision-making such as cognitive limits, learning, and innovation. There remain many unanswered methodological and conceptual questions about the linkages between bounded rationality and evolutionary programming. This paper reports on how changing parameters in one variant of evolutionary programming, genetic programming, affects the representation of bounded rationality in software agents. Of particular interest are: the ability of agents to solve problems; limits to the complexity of agent strategies; the computational resources with which agents create, maintain, or expand strategies; and the extent to which agents balance exploration of new strategies and exploitation of old strategies.", } @Article{Mansoor:2017:SQJ, author = "Usman Mansoor and Marouane Kessentini and Bruce R. Maxim and Kalyanmoy Deb", title = "Multi-objective code-smells detection using good and bad design examples", journal = "Software Quality Journal", year = "2017", volume = "25", number = "2", pages = "529--552", month = jun, keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, Search-based software engineering, Software maintenance, Software metrics, NSGA-2", ISSN = "1573-1367", DOI = "doi:10.1007/s11219-016-9309-7", abstract = "Code-smells are identified, in general, by using a set of detection rules. These rules are manually defined to identify the key symptoms that characterize a code-smell using combinations of mainly quantitative (metrics), structural, and/or lexical information. We propose in this work to consider the problem of code-smell detection as a multi-objective problem where examples of code-smells and well-designed code are used to generate detection rules. To this end, we use multi-objective genetic programming (MOGP) to find the best combination of metrics that maximizes the detection of code-smell examples and minimizes the detection of well-designed code examples. We evaluated our proposal on seven large open-source systems and found that, on average, most of the different five code-smell types were detected with an average of 87percent of precision and 92percent of recall. Statistical analysis of our experiments over 51 runs shows that MOGP performed significantly better than state-of-the-art code-smell detectors.", } @PhdThesis{Mansoor:thesis, author = "Usman Mansoor", title = "Handling High-Level Model Changes Using Search Based Software Engineering", school = "Information Systems Engineering, College of Engineering and Computer Science, University of Michigan-Dearborn", year = "2017", address = "USA", keywords = "genetic algorithms, genetic programming, SBSE, Software Engineering, Model Merging, Model-Driven Engineering, MDE, Multi-Objective Optimization, Refactoring, Defect Detection, NSGA-II, MOGP, NLP, MOPSO, code-smells, model-smells", URL = "https://hdl.handle.net/2027.42/136077", URL = "https://deepblue.lib.umich.edu/handle/2027.42/136077", URL = "http://deepblue.lib.umich.edu/bitstream/2027.42/136077/1/Usman%20Mansoor%20Final.pdf", size = "169 pages", abstract = "Model-Driven Engineering (MDE) considers models as first-class artifacts during the software life-cycle. The number of available tools, techniques, and approaches for MDE is increasing as its use gains traction in driving quality, and controlling cost in evolution of large software systems. Software models, defined as code abstractions, are iteratively refined, restructured, and evolved. This is due to many reasons such as fixing defects in design, reflecting changes in requirements, and modifying a design to enhance existing features. In this work, we focus on four main problems related to the evolution of software models: 1) the detection of applied model changes, 2) merging parallel evolved models, 3) detection of design defects in merged model, and 4) the recommendation of new changes to fix defects in software models. Regarding the first contribution, a-posteriori multi-objective change detection approach has been proposed for evolved models. The changes are expressed in terms of atomic and composite re-factoring operations. The majority of existing approaches detects atomic changes but do not adequately address composite changes which mask atomic operations in intermediate models. For the second contribution, several approaches exist to construct a merged model by incorporating all non-conflicting operations of evolved models. Conflicts arise when the application of one operation disables the applicability of another one. The essence of the problem is to identify and prioritise conflicting operations based on importance and context, a gap in existing approaches. This work proposes a multi-objective formulation of model merging that aims to maximize the number of successfully applied merged operations. For the third and fourth contributions, the majority of existing works focuses on refactoring at source code level, and does not exploit the benefits of software design optimization at model level. However, refactoring at model level is inherently more challenging due to difficulty in assessing the potential impact on structural and behavioural features of the software system. This requires analysis of class and activity diagrams to appraise the overall system quality, feasibility, and inter-diagram consistency. This work focuses on designing, implementing, and evaluating a multi-objective refactoring framework for detection and fixing of design defects in software models.", notes = "p144 'fixing of design defects by analysing class and activity diagrams'. shotgun surgery Supervisor: Marouane Kessentini", } @Article{Mansourvar:2015:plosone, author = "Marjan Mansourvar and Shahaboddin Shamshirband and Ram Gopal Raj and Roshan Gunalan and Iman Mazinani", title = "An Automated System for Skeletal Maturity Assessment by Extreme Learning Machines", journal = "PLoS ONE", year = "2015", volume = "10", number = "9", month = sep # " 24", keywords = "genetic algorithms, genetic programming", publisher = "Public Library of Science", bibsource = "OAI-PMH server at www.ncbi.nlm.nih.gov", language = "en", oai = "oai:pubmedcentral.nih.gov:4581666", rights = "http://creativecommons.org/licenses/by/4.0/; This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited", URL = "http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4581666/", URL = "http://www.ncbi.nlm.nih.gov/pubmed/26402795", URL = "http://dx.doi.org/10.1371/journal.pone.0138493", DOI = "doi:10.1371/journal.pone.0138493", size = "14 pages", abstract = "Assessing skeletal age is a subjective and tedious examination process. Hence, automated assessment methods have been developed to replace manual evaluation in medical applications. In this study, a new fully automated method based on content-based image retrieval and using extreme learning machines (ELM) is designed and adapted to assess skeletal maturity. The main novelty of this approach is it overcomes the segmentation problem as suffered by existing systems. The estimation results of ELM models are compared with those of genetic programming (GP) and artificial neural networks (ANNs) models. The experimental results signify improvement in assessment accuracy over GP and ANN, while generalisation capability is possible with the ELM approach. Moreover, the results are indicated that the ELM model developed can be used confidently in further work on formulating novel models of skeletal age assessment strategies. According to the experimental results, the new presented method has the capacity to learn many hundreds of times faster than traditional learning methods and it has sufficient overall performance in many aspects. It has conclusively been found that applying ELM is particularly promising as an alternative method for evaluating skeletal age.", } @InProceedings{Mansuri:2012:WOCN, author = "Anwar Mohd Mansuri and Deepali Kelkar and Roopesh Kumar and Prithviraj Singh Rathore and Amit Jain", booktitle = "Wireless and Optical Communications Networks (WOCN), 2012 Ninth International Conference on", title = "Multiclass classifier designing by Modified Crossover and Point Mutation technique using genetic programming", year = "2012", size = "7 pages", abstract = "A Multiclass classifier is an approach for designing classifiers for an m-class (m>=2) problem using genetic programming (GP). In this paper we proposed two methods named Modified Crossover Method and a Point Mutation method. In Point Mutation technique we are generating the two child from single parent and selecting the one child on the basis of fitness and also applying the elitism on the child so that the mutation operation does not reduce the fitness of the individual and in Stepwise Crossover we select the two child for the next generation on the basis of size, depth and fitness along with elitism on each step from the six child which is generated during crossover. To demonstrate our approach we have designed a Multiclass Classifier using GP by taking few benchmark datasets. The results obtained show that by applying Modified crossover together with Point Mutation improves the performance of the classifier.", keywords = "genetic algorithms, genetic programming, pattern classification, GP, classifier performance, m-class problem, modified crossover, multiclass classifier design, point mutation technique, stepwise crossover, Computers, Iris, Next generation networking, Sociology, Statistics, Training, Classifier, Modified Crossover, Point Mutation", DOI = "doi:10.1109/WOCN.2012.6335563", ISSN = "2151-7681", notes = "Also known as \cite{6335563}", } @Article{Mansuri:2013:ijarcs, author = "Anwar Mohd Mansuri and Deepali Kelkar", title = "Generate classifier for Genetic Programming of Multicategory Pattern Classification Using Multiclass Microarray Datasets", journal = "International Journal of Advanced Research in Computer Science", year = "2013", keywords = "genetic algorithms, genetic programming, microarray, classifier, mutation, crossover", ISSN = "0976-5697", bibsource = "OAI-PMH server at www.doaj.org", oai = "oai:doaj-articles:8fe02df864e6a4d6368faf194ea13abd", URL = "http://www.ijarcs.info/Mansuri:2013:ijarcs.pdf", size = "4 pages", abstract = "In this paper a multiclass classification problem solving technique based on genetic programming is presented. This paper explores the feasibility of applying genetic programming (GP) to multicategory pattern classification. GP can discover relationships among observed data and express them mathematically Feature selection approaches have been widely applied to deal with the small sample size problem in the analysis of microarray datasets. Multiclass problem, the proposed methods are based on the idea of selecting a gene subset to distinguish all classes. However, it will be more effective to solve a multiclass problem by splitting it into a set of two- class problems and solving each problem with a respective classification system, Data mining deals with the problem of discovering novel and interesting knowledge from large amount of data. The results obtained show that by applying Modified crossover together with Point Mutation improves the performance of the classifier. A comparison with the results achieved by other techniques on a classical benchmark set is carried out.", } @InProceedings{conf/cosecivi/ManteconCGLRS17, author = "Hector Laria Mantecon and Jorge Sanchez Cremades and Jose Miguel Tajuelo Garrigos and Jorge Vieira Luna and Carlos Cervigon Ruckauer and Antonio A. Sanchez-Ruiz", title = "A {Pac-Man} bot based on grammatical evolution", booktitle = "Proceedings of the 4th Congreso de la Sociedad Espanola para las Ciencias del Videojuego, CoSECiVi 2017", year = "2017", editor = "David Camacho and Marco Antonio Gomez-Martin and Pedro Antonio Gonzalez-Calero", volume = "1957", series = "CEUR Workshop Proceedings", pages = "118--130", address = "Barcelona, Spain", month = jun # " 30", publisher = "CEUR-WS.org", keywords = "genetic algorithms, genetic programming, grammatical evolution, computer game, multi-objective optimisation, decision trees, Pac-Man", bibdate = "2017-10-23", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/cosecivi/cosecivi2017.html#ManteconCGLRS17", URL = "http://ceur-ws.org/Vol-1957", URL = "http://nbn-resolving.de/urn:nbn:de:0074-1957-8", URL = "http://ceur-ws.org/Vol-1957/CoSeCiVi17_paper_12.pdf", size = "12 pages", abstract = "In this article, we propose the development of a bot for playing the video game Ms. Pac-Man vs. Ghosts using a grammatical evolution based evolutionary algorithm. This technique evolves programs that are evaluated by executing them in the game. The program encodes the strategy that the bot plays and is obtained through the derivation of grammar rules in a particular order, which is defined by the algorithm. We experimented with two different grammars: The first one includes high-level actions and the second one involves medium-level actions. Both grammars include state providers. To make the evolutionary process more efficient, we perform a series of optimisations on the evolutionary algorithm, including parallelization of the fitness evaluation and multi-objective optimisation. Experimental results using the two grammars and two different ghost controllers are presented. We report better results with our bots than the baseline controllers and other controllers based on grammatical evolution.", notes = "urn:nbn:de:0074-1957-8", } @PhdThesis{Manukyan:thesis, author = "Narine Manukyan", title = "Analysis and Modeling of Quality Improvement on Clinical Fitness Landscapes", school = "University of Vermont", year = "2014", address = "USA", month = may, keywords = "genetic algorithms, genetic programming, NK, NM Landscapes, GAMET", URL = "https://scholarworks.uvm.edu/graddis/253", size = "168 pages", abstract = "Widespread unexplained variations in clinical practices and patient outcomes, together with rapidly growing availability of data, suggest major opportunities for improving the quality of medical care. One way that healthcare practitioners try to do that is by participating in organized healthcare quality improvement collaboratives (QICs). In QICs, teams of practitioners from different hospitals exchange information on clinical practices, with the aim of improving health outcomes at their own institutions. However, what works in one hospital may not work in others with different local contexts, due to non-linear interactions among various demographics, treatments, and practices. I.e., the clinical landscape is a complex socio-technical system that is difficult to search. In this dissertation we develop methods for analysis and modelling of complex systems, and apply them to the problem of healthcare improvement. Searching clinical landscapes is a multi-objective dynamic problem, as hospitals simultaneously optimize for multiple patient outcomes. We first discuss a general method we developed for finding which changes in features may be associated with various changes in outcomes at different points in time with different delays in affect. This method correctly inferred interactions on synthetic data, however the complexity and incompleteness of the real hospital dataset available to us limited the usefulness of this approach. We then discuss an agent-based model (ABM) of QICs to show that teams comprising individuals from similar institutions outperform those from more diverse institutions, under nearly all conditions, and that this advantage increases with the complexity of the landscape and the level of noise in assessing performance. We present data from a network of real hospitals that provides encouraging evidence of a high degree of similarity in clinical practices among hospitals working together in QIC teams. Based on model outcomes, we propose a secure virtual collaboration system that would allow hospitals to efficiently identify potentially better practices in use at other institutions similar to theirs, without any institutions having to sacrifice the privacy of their own data. To model the search for quality improvement in clinical fitness landscapes, we need benchmark landscapes with tunable feature interactions. NK landscapes have been the classic benchmarks for modeling landscapes with epistatic interactions, but the ruggedness is only tunable in discrete jumps. Walsh polynomials are more finely tunable than NK landscapes, but are only defined on binary alphabets and, in general, have unknown global maximum and minimum. We define a different subset of interaction models that we dub as NM landscapes. NM landscapes are shown to have smoothly tunable ruggedness and difficulty and known location and value of global maxima. With additional constraints, we can also determine the location and value of the global minima. The proposed NM landscapes can be used with alphabets of any arity, from binary to real-valued, without changing the complexity of the landscape. NM landscapes are thus useful models for simulating clinical landscapes with binary or real decision variables and varying number of interactions. NM landscapes permit proper normalization of fitnesses so that search results can be fairly averaged over different random landscapes with the same parameters, and fairly compared between landscapes with different parameters. In future work we plan to use NM landscapes as benchmarks for testing various algorithms that can discover epistatic interactions in real world datasets.", notes = " Supervisor: Margaret J. Eppstein", } @InProceedings{Manzi:2020:CEC, author = "Matteo Manzi and Massimiliano Vasile", title = "Discovering Unmodeled Components in Astrodynamics with Symbolic Regression", booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC 2020", year = "2020", editor = "Yaochu Jin", pages = "paper id24551", address = "internet", month = "19-24 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, symbolic regression, astrodynamics", isbn13 = "978-1-7281-6929-3", URL = "https://pureportal.strath.ac.uk/en/publications/discovering-unmodeled-components-in-astrodynamics-with-symbolic-r", URL = "https://pure.strath.ac.uk/ws/portalfiles/portal/103166942/Manzi_Vasile_IEEE_WCCI_2020_Discovering_unmodeled_components_in_astrodynamics.pdf", DOI = "doi:10.1109/CEC48606.2020.9185534", abstract = "The paper explores the use of symbolic regression to discover missing parts of the dynamics of space objects from tracking data. The starting assumption is that the differential equations governing the motion of an observable object are incomplete and do not allow a correct prediction of the future state of that object. Symbolic regression, making use of Genetic Programming (GP), coupled with a sensitivity analysis-based parameter estimation, is proposed to reconstruct the missing parts of the dynamic equations from sparse measurements of position and velocity. Furthermore, the paper explores the effect of uncertainty in tracking measurements on the ability of GP to recover the correct structure of the dynamic equations. The paper presents a simple, yet representative, example of incomplete orbital dynamics to test the use of symbolic regression.", notes = "https://wcci2020.org/ University of Strathclyde, United Kingdom. Also known as \cite{9185534}", } @Article{Manzoni:2013:GPEM, author = "Luca Manzoni and Mauro Castelli and Leonardo Vanneschi", title = "A new genetic programming framework based on reaction systems", journal = "Genetic Programming and Evolvable Machines", year = "2013", volume = "14", number = "4", pages = "457--471", month = dec, keywords = "genetic algorithms, genetic programming, Reaction systems, Evolutionary computation", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-013-9184-y", size = "15 pages", abstract = "This paper presents a new genetic programming framework called Evolutionary Reaction Systems. It is based on a recently defined computational formalism, inspired by chemical reactions, called Reaction Systems, and it has several properties that distinguish it from other existing genetic programming frameworks, making it interesting and worthy of investigation. For instance, it allows us to express complex constructs in a simple and intuitive way, and it lightens the final user from the task of defining the set of primitive functions used to build up the evolved programs. Given that Evolutionary Reaction Systems is new and it has small similarities with other existing genetic programming frameworks, a first phase of this work is dedicated to a study of some important parameters and their influence on the algorithm's performance. Successively, we use the best parameter setting found to compare Evolutionary Reaction Systems with other well established machine learning methods, including standard tree-based genetic programming. The presented results show that Evolutionary Reaction Systems are competitive with, and in some cases even better than, the other studied methods on a wide set of benchmarks.", } @InProceedings{Manzoni:2020:GECCO, author = "Luca Manzoni and Domagoj Jakobovic and Luca Mariot and Stjepan Picek and Mauro Castelli", title = "Towards an Evolutionary-Based Approach for Natural Language Processing", year = "2020", editor = "Carlos Artemio {Coello Coello} and Arturo Hernandez Aguirre and Josu Ceberio Uribe and Mario Garza Fabre and Gregorio {Toscano Pulido} and Katya Rodriguez-Vazquez and Elizabeth Wanner and Nadarajen Veerapen and Efren Mezura Montes and Richard Allmendinger and Hugo Terashima Marin and Markus Wagner and Thomas Bartz-Beielstein and Bogdan Filipic and Heike Trautmann and Ke Tang and John Koza and Erik Goodman and William B. Langdon and Miguel Nicolau and Christine Zarges and Vanessa Volz and Tea Tusar and Boris Naujoks and Peter A. N. Bosman and Darrell Whitley and Christine Solnon and Marde Helbig and Stephane Doncieux and Dennis G. Wilson and Francisco {Fernandez de Vega} and Luis Paquete and Francisco Chicano and Bing Xue and Jaume Bacardit and Sanaz Mostaghim and Jonathan Fieldsend and Oliver Schuetze and Dirk Arnold and Gabriela Ochoa and Carlos Segura and Carlos Cotta and Michael Emmerich and Mengjie Zhang and Robin Purshouse and Tapabrata Ray and Justyna Petke and Fuyuki Ishikawa and Johannes Lengler and Frank Neumann", isbn13 = "9781450371285", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377930.3390248", DOI = "doi:10.1145/3377930.3390248", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference", pages = "985--993", size = "9 pages", keywords = "genetic algorithms, genetic programming, NLP, natural language processing, next word prediction", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", abstract = "Tasks related to Natural Language Processing (NLP) have recently been the focus of a large research endeavor by the machine learning community. The increased interest in this area is mainly due to the success of deep learning methods. Genetic Programming (GP), however, was not under the spotlight with respect to NLP tasks. Here, we propose a first proof-of-concept that combines GP with the well established NLP tool word2vec for the next word prediction task. The main idea is that, once words have been moved into a vector space, traditional GP operators can successfully work on vectors, thus producing meaningful words as the output. To assess the suitability of this approach, we perform an experimental evaluation on a set of existing newspaper headlines. Individuals resulting from this (pre-)training phase can be employed as the initial population in other NLP tasks, like sentence generation, which will be the focus of future investigations, possibly employing adversarial co-evolutionary approaches.", notes = "next word prediction. Word2Vec (cosine) vectors as inputs (leafs) for GP tree. GP tree returns vector, look it up, finess = mean cosine. Million News headline (newspapers 2003--2017) > 6 words in sentence, 267292 examples, train on ~2700. Dimensionality of vec2word embedding 10..100. Best of 30 GP runs. Also known as \cite{10.1145/3377930.3390248} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @Article{Manzoni:2020:ieeeTEC, author = "Luca Manzoni and Alberto Bartoli and Mauro Castelli and Ivo Goncalves and Eric Medvet", title = "Specializing Context-Free Grammars With a (1 + 1)-{EA}", journal = "IEEE Transactions on Evolutionary Computation", year = "2020", volume = "24", number = "5", pages = "960--973", month = oct, keywords = "genetic algorithms, genetic programming, grammatical evolution,Grammar design, runtime analysis", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2020.2983664", size = "14 pages", abstract = "Context-free grammars are useful tools for modeling the solution space of problems that can be solved by optimization algorithms. For a given solution space, there exists an infinite number of grammars defining that space, and there are clues that changing the grammar may impact the effectiveness of the optimization. In this article, we investigate theoretically and experimentally the possibility of specializing a grammar in a problem, that is, of systematically improving the quality of the grammar for the given problem. To this end, we define the quality of a grammar for a problem in terms of the average fitness of the candidate solutions generated using that grammar. Theoretically, we demonstrate the following findings: 1) that a simple mutation operator employed in a (1 + 1)-EA setting can be used to specialize a grammar in a problem without changing the solution space defined by the grammar and 2) that three grammars of equal quality for a grammar-based version of the ONEMAX problem greatly vary in how they can be specialized with that (1 + 1)-EA, as the expected time required to obtain the same improvement in quality can vary exponentially among grammars. Then, experimentally, we validate the theoretical findings and extend them to other problems, grammars, and a more general version of the mutation operator.", notes = "Dipartimento di Matematica e Geoscienze, University of Trieste, 34127 Trieste, Italy. INSPEC Accession Number: 19992322 Also known as \cite{9047973}", } @Article{Marandi:2012:IJGS, author = "Seyed Morteza Marandi and Seyed Mahmood {VaeziNejad} and Elyas Khavari", title = "Prediction of Concrete Faced Rock Fill Dams Settlements Using Genetic Programming Algorithm", journal = "International Journal of Geosciences", year = "2012", volume = "3", number = "3", pages = "601--609", publisher = "Scientific Research Publishing", keywords = "genetic algorithms, genetic programming, concrete faced rock-fill dams, settlement, finite element model", ISSN = "21568359", URL = "http://www.scirp.org/journal/PaperDownload.aspx?DOI=10.4236/ijg.2012.33060", broken = "http://www.doaj.org/doaj?func=openurl\&genre=article\&issn=21568359\&date=2012\&volume=03\&issue=03\&spage=601", DOI = "doi:10.4236/ijg.2012.33060", size = "9 pages", abstract = "In the present study a Genetic Programing model (GP) proposed for the prediction of relative crest settlement of concrete faced rock fill dams. To this end information of 30 large dams constructed in seven countries across the world is gathered with their reported settlements. The results showed that the GP model is able to estimate the dam settlement properly based on four properties, void ratio of dam's body (e), height (H), vertical deformation modulus (E$_{v}$) and shape factor (Sc) of the dam. For verification of the model applicability, obtained results compared with other research methods such as Clements' formula and the finite element model. The comparison showed that in all cases the GP model led to be more accurate than those of performed in literature. Also a proper compatibility between the GP model and the finite element model was perceived.", bibsource = "OAI-PMH server at www.doaj.org", language = "eng", oai = "oai:doaj-articles:a837bace82189b96000d4a2e20908f7b", } @Article{DBLP:journals/jccee/MarascoFGCM21, author = "Sebastiano Marasco and Alessandra Fiore and Rita Greco and Gian Paolo Cimellaro and Giuseppe Carlo Marano", title = "Evolutionary Polynomial Regression Algorithm Enhanced with a Robust Formulation: Application to Shear Strength Prediction of {RC} Beams without Stirrups", journal = "Journal of Computing in Civil Engineering", year = "2021", volume = "35", number = "6", pages = "04021017", keywords = "genetic algorithms, genetic programming, EPR", timestamp = "Mon, 01 May 2023 13:01:52 +0200", biburl = "https://dblp.org/rec/journals/jccee/MarascoFGCM21.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29CP.1943-5487.0000985", DOI = "doi:10.1061/(ASCE)CP.1943-5487.0000985", size = "15 pages", abstract = "Many classes of engineering problems focus on the process of calibrating mathematical models using observed data. The enormous progress of scientific computation and data-mining techniques has allowed the search for accurate mathematical models from experimental data using algorithms. Among them, the evolutionary polynomial regression (EPR) is an artificial intelligence (AI) technique that merges genetic algorithms (GAs) and regression techniques such as ordinary least square (OLS). This paper presents a robust and well-conditioned EPR technique to remove potential outliers and leverage points included in any biased data set. This hybrid approach combines bisquare, Huber, and Cauchy robust multivariate techniques with GAs and the Akaike weight-based method to assess the optimal polynomial model while limiting the impact of the data bias. The robust techniques will define the parameters, the GAs will determine the exponents, and the Akaike weight-based method will evaluate the relative importance of each observed variable of the proposed model. As a case study, a shear strength data set of RC beams without stirrups is used to compare the standard EPR algorithm with the new proposed hybrid methodology. Furthermore, the optimal robust model is compared with different benchmark formulations to highlight its accuracy and consistency. The proposed hybrid technique can be adopted as a mathematical tool for many engineering problems, providing an unbiased prediction of the observed variable. Furthermore, the shear strength equation that provides the best compromise between accuracy and complexity allows its potential use in many engineering practices and building codes.", notes = "Is this GP?", } @InProceedings{5364892, author = "F. Marcelloni and M. Vecchio", title = "A Multi-objective Evolutionary Approach to Data Compression in Wireless Sensor Networks", booktitle = "Intelligent Systems Design and Applications, 2009. ISDA '09. Ninth International Conference on", year = "2009", month = "30 2009-" # dec # " 2", pages = "402--407", keywords = "genetic algorithms, data compression, data reception, data transmission, differential pulse code modulation scheme, energy efficiency, multiobjective evolutionary algorithm, radio communication, sensor nodes, wireless sensor networks, data communication, data compression, pulse code modulation, wireless sensor networks", DOI = "doi:10.1109/ISDA.2009.101", notes = "Not on GP. Also known as \cite{5364892}", } @InProceedings{marchesi:1997:deeGP, author = "Bruno Marchesi and Alvaro Luiz Stelle and Heitor Silverio Lopes", title = "Detection of Epileptic Events using Genetic Programming", booktitle = "Proceedings of the 19th Annual International Conference of the IEEE and Engineering in Medicine and Biology Society", year = "1997", volume = "3", pages = "1198--1201", address = "Chicago, IL. USA", month = oct # " 30 - " # nov # " 2", organisation = "IEEE", keywords = "genetic algorithms, genetic programming, signal processing, EEG, 3 Hz, Darwinian survival and reproduction, EEG signals, automatic detection, complexes recognition, epileptic events detection, genetic algorithm, ictal period, pattern recognition, spike-and-slow-wave complexes, training features, typical absences, visually classified frames, electroencephalography, evolutionary computation, learning (artificial intelligence), medical expert systems, medical signal processing, pattern classification", ISBN = "0-7803-4262-3", file = "embs98.pdf", DOI = "doi:10.1109/IEMBS.1997.756577", size = "4 pages", abstract = "This paper presents a method using genetic programming for automatic detection of 3 Hz spike-and-slow- wave complexes, that are a characteristic of typical absences, in electroencephalogram (EEG) signals. Training features are extracted from 1s EEG frames, randomly chosen from pre-recorded files. The frames are visually classified as spike-and-slow-wave complexes (SASWC) or non-spike- and-slow-wave complexes (NSASWC). Genetic programming techniques are then applied to these data to build a program capable of recognising such complexes.", notes = " ", } @InProceedings{Marchetti:2020:IJCNN, author = "Francesco Marchetti and Edmondo Minisci and Annalisa Riccardi", title = "Towards Intelligent Control via Genetic Programming", booktitle = "2020 International Joint Conference on Neural Networks (IJCNN)", year = "2020", abstract = "In this paper an initial approach to Intelligent Control (IC) using Genetic Programming (GP) for access to space applications is presented. GP can be employed successfully to design a controller even for complex systems, where classical controllers fail because of the high nonlinearity of the systems. The main property of GP, that is its ability to autonomously create explicit mathematical equations starting from a very poor knowledge of the considered plant, or just data, can be exploited for a vast range of applications. Here, GP has been used to design the control law in an Intelligent Control framework for a modified version of the Goddard Rocket problem in 3 different failure scenarios, where the approach to IC consists in an online re-evaluation of the control law using GP when a considerably big change in the environment or in the plant happens. The presented results are then used to highlight the potential benefits of the method, as well as aspects that will need further developments.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IJCNN48605.2020.9207694", ISSN = "2161-4407", month = jul, notes = "Also known as \cite{9207694}", } @InProceedings{Marchetti:2020:BIOMA, author = "Francesco Marchetti and Edmondo Minisci", title = "A Hybrid Neural Network-Genetic Programming Intelligent Control Approach", booktitle = "Bioinspired Optimization Methods and Their Applications. BIOMA 2020", year = "2020", editor = "Bogdan Filipic and Edmondo Minisci and Massimiliano Vasile", volume = "12438", series = "Lecture Notes in Computer Science", pages = "240--254", address = "Brussels, Belgium", month = "19-20 " # nov, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Intelligent Control, Neural Networks, ANN, optimal control, space transportation system, Electronic computers. Computer science, Theoretical Computer Science, Aerospace Engineering", isbn13 = "978-3-030-63709-5", URL = "https://strathprints.strath.ac.uk/74956/", DOI = "doi:10.1007/978-3-030-63710-1_19", abstract = "The proposed work aims to introduce a novel approach to Intelligent Control (IC), based on the combined use of Genetic Programming (GP) and feedforward Neural Network (NN). Both techniques have been successfully used in the literature for regression and control applications, but, while a NN creates a black box model, GP allows for a greater interpretability of the created model, which is a key feature in control applications. The main idea behind the hybrid approach proposed in this paper is to combine the speed and flexibility of a NN with the interpretability of GP. Moreover, to improve the robustness of the GP control law against unforeseen environmental changes, a new selection and crossover mechanisms, called Inclusive Tournament and Inclusive Crossover, are also introduced. The proposed IC approach is tested on the guidance control of a space transportation system and results, showing the potentialities for real applications, are shown and discussed.", notes = "also known as \cite{strathprints74956}", } @InProceedings{Marchetti:2021:EuroGP, author = "Francesco Marchetti and Edmondo Minisci", title = "Inclusive Genetic Programming", booktitle = "EuroGP 2021: Proceedings of the 24th European Conference on Genetic Programming", year = "2021", editor = "Ting Hu and Nuno Lourenco and Eric Medvet", series = "LNCS", volume = "12691", publisher = "Springer Verlag", address = "Virtual Event", pages = "51--65", month = "7-9 " # apr, organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Population diversity, Entropy, Benchmarks, Symbolic regression", isbn13 = "978-3-030-72811-3", DOI = "doi:10.1007/978-3-030-72812-0_4", abstract = "The promotion and maintenance of the population diversity in a Genetic Programming (GP) algorithm was proved to be an important part of the evolutionary process. Such diversity maintenance improves the exploration capabilities of the GP algorithm, which as a consequence improves the quality of the found solutions by avoiding local optima. This paper aims to further investigate and prove the efficacy of a GP heuristic proposed in a previous work: the Inclusive Genetic Programming (IGP). Such heuristic can be classified as a niching technique, which performs the evolutionary operations like crossover, mutation and selection by considering the individuals belonging to different niches in order to maintain and exploit a certain degree of diversity in the population, instead of evolving the niches separately to find different local optima. A comparison between a standard formulation of GP and the IGP is carried out on nine different benchmarks coming from synthetic and real world data. The obtained results highlight how the greater diversity in the population, measured in terms of entropy, leads to better results on both training and test data, showing that an improvement on the generalization capabilities is also achieved.", notes = "ICE Strathclyde. IGP niching. Size of tree GP genome v fitness, demes mu plus lambda. Hall of fame. Python DEAP github smart-ml. Pop=300. depth<=15, size<=30. Entropy measure of population diversity. http://www.evostar.org/2021/eurogp/ Part of \cite{Hu:2021:GP} EuroGP'2021 held in conjunction with EvoCOP2021, EvoMusArt2021 and EvoApplications2021", } @Article{Marchetti:2021:math, author = "Francesco Marchetti and Edmondo Minisci", title = "Genetic Programming Guidance Control System for a Reentry Vehicle under Uncertainties", journal = "Mathematics", year = "2021", volume = "9", number = "16", article-number = "1868", month = "6 " # aug, keywords = "genetic algorithms, genetic programming, IGP, FESTIP-FSS5 RLV, evolutionary optimization, space vehicle, control, differential evolution, reusable launch vehicle", ISSN = "2227-7390", publisher = "MDPI", URL = "https://www.mdpi.com/2227-7390/9/16/1868", DOI = "doi:10.3390/math9161868", size = "19 pages", abstract = "As technology improves, the complexity of controlled systems increases as well. Alongside it, these systems need to face new challenges, which are made available by this technology advancement. To overcome these challenges, the incorporation of AI into control systems is changing its status, from being just an experiment made in academia, towards a necessity. Several methods to perform this integration of AI into control systems have been considered in the past. In this work, an approach involving GP to produce, offline, a control law for a reentry vehicle in the presence of uncertainties on the environment and plant models is studied, implemented and tested. The results show the robustness of the proposed approach, which is capable of producing a control law of a complex nonlinear system in the presence of big uncertainties. This research aims to describe and analyze the effectiveness of a control approach to generate a nonlinear control law for a highly nonlinear system in an automated way. Such an approach would benefit the control practitioners by providing an alternative to classical control approaches, without having to rely on linearisation techniques.", notes = "Also known as \cite{math9161868}. Intelligent Computational Engineering Laboratory (ICE-Lab), University of Strathclyde, Glasgow G11XJ, UK", } @Article{MARCHETTI:2024:asoc, author = "Francesco Marchetti and Gloria Pietropolli and Federico Julian {Camerota Verdu} and Mauro Castelli and Edmondo Minisci", title = "Automatic design of interpretable control laws through parametrized Genetic Programming with adjoint state method gradient evaluation", journal = "Applied Soft Computing", pages = "111654", year = "2024", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2024.111654", URL = "https://www.sciencedirect.com/science/article/pii/S1568494624004289", keywords = "genetic algorithms, genetic programming, Gradient descent, Adjoint state method, Control, XAI", abstract = "This work investigates the application of a Local Search (LS) enhanced Genetic Programming (GP) algorithm to the control scheme's design task. The combination of LS and GP aims to produce an interpretable control law as similar as possible to the optimal control scheme reference. Inclusive Genetic Programming (IGP), a GP heuristic capable of promoting and maintaining the population diversity, is chosen as the GP algorithm since it proved successful on the considered task. IGP is enhanced with the Operators Gradient Descent (OPGD) approach, which consists of embedding learnable parameters into the GP individuals. These parameters are optimized during and after the evolutionary process. Moreover, the OPGD approach is combined with the adjoint state method to evaluate the gradient of the objective function. The original OPGD was formulated by relying on the backpropagation technique for the gradient's evaluation, which is impractical in an optimization problem involving a dynamical system because of scalability and numerical errors. On the other hand, the adjoint method allows for overcoming this issue. Two experiments are formulated to test the proposed approach, named Operator Gradient Descent - Inclusive Genetic Programming (OPGD-IGP): the design of a Proportional-Derivative (PD) control law for a harmonic oscillator and the design of a Linear Quadratic Regulator (LQR) control law for an inverted pendulum on a cart. OPGD-IGP proved successful in both experiments, being capable of autonomously designing an interpretable control law similar to the optimal ones, both in terms of shape and control gains", } @InProceedings{marchetti:2024:CEC, author = "Francesco Marchetti and Mauro Castelli and Illya Bakurov and Leonardo Vanneschi", title = "Full Inclusive Genetic Programming", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Phenotypes, Sociology, Evolutionary computation, Benchmark testing, Space exploration, Maintenance, Population's Diversity, Symbolic Regression, PMLB Benchmarks, Population Initialization", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10611808", abstract = "This manuscript presents an improved version of the Inclusive Genetic Programming (IGP) algorithm. The IGP was developed to promote and maintain the population's genotypic diversity and showed superior performance compared to standard Genetic Programming (GP). In this work, two modifications to the IGP are proposed: first, the diversity promotion and maintenance mechanism is enhanced with information from the phenotype of the individuals rather than only the genotype; second, the Evolutionary Demes Despeciation Algorithm - V2 (EDDA-V2) is used to initialize the population. The phenotype is considered to differentiate the individuals also according to their behaviour rather than only their structure, while EDDA-V2 is employed to start the evolution with a simultaneously diverse and fit population, contrary to traditional initialization techniques. The algorithms incorporating these improvements are called Full Inclusive Genetic Programming (FIGP) and FIGP _E, respectively with and without the EDDA-V2 initialization. The experimental results, performed over eight benchmarks and considering six algorithms, demonstrate the superior performance of FIGP and FIGP _E in comparison to other GP formulations. Moreover, the EDDA-V2 initialization allows for a significant reduction of the computational time.", notes = "also known as \cite{10611808} WCCI 2024", } @InProceedings{marchiori:1999:AFGAHP, author = "Elena Marchiori and Claudio Rossi", title = "A Flipping Genetic Algorithm for Hard 3-SAT Problems", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "393--400", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/ga-858.ps.gz", URL = "http://www.cs.vu.nl/~elena/ga-858.ps.gz", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Marco:2012:GECCO, author = "David Marco and Carron Shankland and David Cairns", title = "Evolving Bio-PEPA process algebra models using genetic programming", booktitle = "GECCO '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference", year = "2012", editor = "Terry Soule and Anne Auger and Jason Moore and David Pelta and Christine Solnon and Mike Preuss and Alan Dorin and Yew-Soon Ong and Christian Blum and Dario Landa Silva and Frank Neumann and Tina Yu and Aniko Ekart and Will Browne and Tim Kovacs and Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and Giovanni Squillero and Nicolas Bredeche and Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and Martin Pelikan and Silja Meyer-Nienberg and Christian Igel and Greg Hornby and Rene Doursat and Steve Gustafson and Gustavo Olague and Shin Yoo and John Clark and Gabriela Ochoa and Gisele Pappa and Fernando Lobo and Daniel Tauritz and Jurgen Branke and Kalyanmoy Deb", isbn13 = "978-1-4503-1177-9", pages = "177--184", keywords = "genetic algorithms, genetic programming, bioinformatics, computational, systems and synthetic biology", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330163.2330189", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "This paper presents initial results of applying a Genetic Programming (GP) approach to the evolution of process algebra models defined in Bio-PEPA. An incomplete model of a system is provided together with target behaviour. GP is then used to evolve new definitions that complete the model while ensuring a good fit to target data. Our results show that a set of effective models can be developed with this approach that can either be used directly or further refined using a modeller's domain knowledge. Such an approach can greatly reduce the time taken to develop new models, enabling a modeller to focus on the subtler modelling aspects of the problem domain. Although the work presented here concerns the modelling of biological systems, the approach is generally applicable to systems for which appropriate target behaviour can be captured and that can be formalised as a set of communicating processes.", notes = "Also known as \cite{2330189} GECCO-2012 A joint meeting of the twenty first international conference on genetic algorithms (ICGA-2012) and the seventeenth annual genetic programming conference (GP-2012)", } @InProceedings{marconi:1998:hpGAfckg, author = "Jamie Marconi and James A. Foster", title = "A Hard Problem for Genetic Algorithms: Finding Cliques in Keller Graphs", booktitle = "Proceedings of the 1998 IEEE World Congress on Computational Intelligence", year = "1998", pages = "650--655", address = "Anchorage, Alaska, USA", month = "5-9 " # may, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Keller conjecture, Keller graphs, maximum clique, hardness, complexity, edge density, hard problem, hybrid genetic algorithm, maximum clique finding, small diameter, uniformity, computational complexity, graph theory", ISBN = "0-7803-4869-9", file = "c112.pdf", DOI = "doi:10.1109/ICEC.1998.700116", size = "6 pages", abstract = "We present evidence that finding the maximum clique in Keller graphs is an example of a family of problems which are both natural and inherently difficult for genetic algorithms. Specifically, we employ a hybrid genetic algorithm to find the largest clique in Keller graphs. We present theoretical reasons why this problem is likely to be particularly hard for this family of graphs. Our results confirm this suspicion. We then discuss several characteristics of this graph family which confound genetic algorithms: its uniformity, edge density and small diameter.", notes = "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE World Congress on Computational Intelligence", } @InProceedings{maree:2018:AISC, author = "Armand Maree and Marius Riekert and Marde Helbig", title = "Deriving Functions for Pareto Optimal Fronts Using Genetic Programming", booktitle = "Artificial Intelligence and Soft Computing", year = "2018", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-319-91253-0_43", DOI = "doi:10.1007/978-3-319-91253-0_43", } @InProceedings{marek:2002:gecco:lbp, title = "Learning Visual Feature Detectors for Obstacle Avoidance Using Genetic Programming", author = "Andrew J. Marek and William D. Smart and Martin C. Martin", booktitle = "Late Breaking Papers at the Genetic and Evolutionary Computation Conference ({GECCO-2002})", editor = "Erick Cant{\'u}-Paz", year = "2002", month = jul, pages = "330--336", address = "New York, NY", publisher = "AAAI", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025", URL = "http://www.martincmartin.com/papers/LearingVisualFeatureDetectorsForObstAvoidGP_GECCO2002Marek.pdf", URL = "http://citeseer.ist.psu.edu/563994.html", keywords = "genetic algorithms, genetic programming", size = "7 pages", abstract = "In this paper, we use Genetic Programming (GP) techniques to learn visual feature detectors for a mobile robot navigation task. We provide results from a number of environments, each with different characteristics, and draw conclusions about the performance and nature of the training and testing data. We explore the utility of seeding the initial population with a previously evolved individual, and compare this to previous work, where a hand-coded individual was successfully used as an initial seed. Our experiments exhibited a peculiar bi-modality in final performance. An individual either performed very well or very poorly, with nothing in between. We analyse this phenomenon, and offer suggestions as to its cause, and how to alleviate the problem. We explore the utility of seeding the initial population with a previously evolved individual, and compare this to previous work, where a hand-coded individual was successfully used as an initial seed.", notes = "Late Breaking Papers, {GECCO-2002}. A joint meeting of the eleventh International Conference on Genetic Algorithms ({ICGA-2002}) and the seventh Annual Genetic Programming Conference ({GP-2002}) part of cantu-paz:2002:GECCO:lbp Suggests negative impact of seed in initial population p335", } @MastersThesis{Marek:mastersthesis, author = "Andrew J. Marek", title = "Learning Feature Detectors Using Genetic Programming With Multiple Sensors", school = "Sever Institute, Dept. of Computer Science and Engineering, Washington University in St. Louis", year = "2004", number = "WUCSE-2004-22", type = "Master of Science", address = "Saint Louis, Missouri, USA", month = may, keywords = "genetic algorithms, genetic programming", URL = "http://cse.wustl.edu/Research/Pages/search-technical-reports.aspx", URL = "http://cse.wustl.edu/Research/Lists/Technical%20Reports/Attachments/594/345_thesis-main.pdf", size = "58 pages", abstract = "In this thesis, we describe the use of Genetic Programming (GP) to learn obstacle detectors to be used for obstacle avoidance on a mobile robot. The first group of experiments focus on learning visual feature detectors for this task. We provide experimental results across a number of different environments, each with different characteristics, and draw conclusions about the performance of the learned feature detector and the training data used to learn such detectors. We also explore the utility of seeding the initial population with previously evolved individuals and subtrees, and discuss the performance of the resulting individuals. We then include sensory data from a laser range-finder and a camera and discuss the performance of resulting individuals as we use just laser data, just image data, and both in combination.", notes = "Drew. Robot computer vision, Laser Range-finder, Open Beagle", } @Article{marenbach:1995:at, author = "Peter Marenbach and Kurt Dirk Bettenhausen and Bernd Cuno", journal = "at -- Automatisierungstechnik", number = "6", pages = "277--288", title = "{Selbstorganisierende Generierung strukturierter Prozemodelle}", volume = "43", keywords = "genetic algorithms, genetic programming, Selbstorganisierende Modellbildung", year = "1995", email = "mali@rt.e-technik.tu-darmstadt.de", notes = "In German", } @TechReport{marenbach:1995:tr01, author = "Peter Marenbach", address = "Landgraf-Georg-Str.~4, D-64283 Darmstadt, Germany", institution = "FG Regelsystemtheorie \& Robotik, TH Darmstadt", title = "{Status und Perspektiven der strukturierten Modellbildung mit Hilfe Genetischer Algorithmen}", year = "1995", size = "26 pages", keywords = "genetic algorithms, genetic programming, Selbstorganisierende Modellbildung", broken = "http://www.rt.e-technik.tu-darmstadt.de/~mali/GP/trsmog9501.ps.gz", email = "mali@rt.e-technik.tu-darmstadt.de", notes = "In German", } @InProceedings{marenbach:1996:spdpm, author = "Peter Marenbach and Kurt D. Bettenhausen and Stephan Freyer", title = "Signal Path Oriented Approach for Generation of Dynamic Process Models", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming, process engineering, modelling, SMOG", pages = "327--332", address = "Stanford University, CA, USA", publisher = "MIT Press", URL = "http://www.rtr.tu-darmstadt.de/fileadmin/literature/rst_96_11.ps.gz", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap43.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", size = "6 pages", abstract = "This paper discusses our tool for automatic generation of structured models for complex dynamic processes by means of genetic programming. In contrast to other techniques which use genetic programming to find an appropriate arithmetic expression in order to describe the input-output behaviour of a process, this tool is based on a block oriented approach with a transparent description of signal paths. A short survey on other techniques for computer based system identification is given and the basic concept of SMOG (Structured MOdel Generator) is described. Furthermore latest extensions of the system are presented in detail, including automatic defined sub-models and qualitative fitness criteria.", notes = "GP-96 onject oriented GP OOGP", } @InProceedings{marenbach:1997:Evicnf, author = "P. Marenbach and M. Brown", title = "Evolutionary versus inductive construction of neurofuzzy systems for bioprocess modelling", booktitle = "Second International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, GALESIA", year = "1997", editor = "Ali Zalzala", address = "University of Strathclyde, Glasgow, UK", publisher_address = "Savoy Place, London WC2R 0BL, UK", month = "1-4 " # sep, publisher = "Institution of Electrical Engineers", keywords = "genetic algorithms, genetic programming", ISBN = "0-85296-693-8", URL = "http://www.rtr.tu-darmstadt.de/fileadmin/literature/rst_97_11.pdf", URL = "http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?tp=&arnumber=681045", DOI = "doi:10.1049/cp:19971200", size = "6 pages", abstract = "The control and optimization of biotechnological processes is a complex task of industrial relevance, due to the growing importance attached to biotechnology. Therefore, there is an increasing use of intelligent data analysis methods for the development and optimization of bioprocess modelling and control. Since a clear understanding of the underlying physics does not exist, nonlinear learning systems, which can accurately model exemplar data sets and explain their behaviour to the designer, are an attractive approach. This paper investigates applying neurofuzzy construction algorithms to this problem and in particular compares a Genetic Programming structuring approach with a more conventional forwards inductive learning-type algorithm. It is shown that for simple problems, the inductive learning technique generally outperforms the Genetic Programming, although for large complex problems, the latter may prove beneficial.", notes = "GALESIA'97", } @Article{Marenbach1998, author = "Peter Marenbach", title = "Using Prior Knowledge and Obtaining Process Insight in Data Based Modelling of Bioprocesses", journal = "System Analysis Modelling Simulation", year = "1998", howpublished = "Overseas Publishers association", volume = "31", number = "1-2", month = jan, pages = "39--59", note = "Special issue on automatic model generation", keywords = "genetic algorithms, genetic programming, biotechnology, bioprocesses, data based modelling, SMOG", URL = "https://www.sciencebase.gov/catalog/item/50536091e4b097cd4fcd67f7", broken = "http://www.rt.e-technik.tu-darmstadt.de/~mali/GP/publications.html#SAMS98", URL = "http://dl.acm.org/citation.cfm?id=289662&CFID=140128841&CFTOKEN=57960727", ISSN = "0232-9298", acmid = "289662", publisher = "Gordon and Breach Science Publishers, Inc.", address = "Newark, NJ, USA", abstract = "In biotechnology, as is many other fields of technology, the development of a appropriate process model is one of the most important engineering tasks. Data driven modelling becomes an attractive approach whenever analytical modelling is difficult or too time consuming. Disadvantages of the often used artificial neural networks are their missing transparency and the difficulty to integrate prior knowledge. The paper at hand gives an overview of several common modelling techniques with focus on their application to bioprocesses and presents a novel modelling technique that uses genetic programming for the construction and refinement of transparent structured models.", notes = " Reprints available from P. Marenbach.", } @Article{Marenbachetal1997, author = "Peter Marenbach and Kurt D. Bettenhausen and Stephan Freyer and Ulrich Nieken and Hans Rettenmaier", title = "Data-driven Structured Modelling of a Biotechnological Fed-batch Fermentation by Means of Genetic Programming", journal = "Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering", year = "1997", volume = "211", number = "5", pages = "325--332", month = "1 " # aug, keywords = "genetic algorithms, genetic programming, biotechnology, modelling, system identification, fermentation processes, SMOG", ISSN = "0959-6518", broken = "http://www.rt.e-technik.tu-darmstadt.de/~mali/GP/publications.html#MEP97", URL = "http://pii.sagepub.com/content/211/5/325.abstract", DOI = "doi:10.1243/0959651971539858", size = "8 pages", abstract = "This paper describes an approach for data-driven generation of structured models of complex and unknown processes by means of genetic programming. The basic approach which is used to generate and to modify symbolic model descriptions represented as block diagrams is introduced and an application for modelling of an industrial biotechnological fed-batch fermentation process is presented.", notes = " This article is an extended version of the conference paper presented at GALESIA '95 (see \cite{bettenhausen:1995:sombbffGP} ). It presents a view more details and is, since it is extended, probably easier to understand. Reprints available from P. Marenbach doi: flakey Sep 2016 ok Apr 2024", } @InCollection{MarenbachFreyerRST09/98, booktitle = "Industrielle Anwendung Evolution{\"a}rer Algorithmen", editor = "S. Hafner", publisher = "R.\ Oldenbourg Verlag", title = "Generierung von Modellen biotechnologischer Prozesse", pages = "91--102", author = "Peter Marenbach and Stephan Freyer", year = "1998", keywords = "genetic algorithms, genetic programming, bioprocess, modelling, SMOG", broken = "http://www.rt.e-technik.tu-darmstadt.de/LIT", email = "pmarenbach@gmx.net", notes = "In German", } @PhdThesis{Marenbach:thesis, author = "Peter Marenbach", title = "Rechnergest{\"{u}}tzte Methoden zur interaktiven Modellierung biotechnologischer Prozesse", school = "TU Darmstadt", year = "1999", type = "Dissertation, Berichte aus der Automatisierungstechnik", address = "Aachen, Germany", month = oct, publisher = "Shaker Verlag", ISBN = "3-8265-6574-6", email = "pmarenbach@gmx.net", keywords = "genetic algorithms, genetic programming, Automatisierungstechnik, Modellbildung, Evolutionare Algorithmen, Biotechnologie, Neuro-Fuzzy-Systeme, datengetriebene Modellbildung", URL = "http://www.shaker.de/de/content/catalogue/index.asp?ISBN=978-3-8265-6574-8", URL = "http://tubiblio.ulb.tu-darmstadt.de/1611/", isbn13 = "978-3-8265-6574-8", notes = "In German Also known as \cite{tubiblio1611}", } @InProceedings{margetts:2001:EuroGP, author = "Steve Margetts and Antonia J. Jones", title = "An Adaptive Mapping for Developmental Genetic Programming", booktitle = "Genetic Programming, Proceedings of EuroGP'2001", year = "2001", editor = "Julian F. Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon", volume = "2038", series = "LNCS", pages = "97--107", address = "Lake Como, Italy", publisher_address = "Berlin", month = "18-20 " # apr, organisation = "EvoNET", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, Developmental Genetic Programming, Adaptive Genotype to Phenotype Mappings, MAX Problem", ISBN = "3-540-41899-7", DOI = "doi:10.1007/3-540-45355-5_9", size = "11 pages", abstract = "In this article we introduce a general framework for constructing an adaptive genotype-to-phenotype mapping, and apply it to developmental genetic programming. In this preliminary investigation, we run a series of comparative experiments on a simple test problem. Our results show that the adaptive algorithm is able to outperform its non-adaptive counterpart.", notes = "EuroGP'2001, part of \cite{miller:2001:gp}", } @PhdThesis{margetts:thesis, author = "Stephen Margetts", title = "Adaptive Genotype to Phenotype Mappings for Evolutionary Algorithms", school = "Department of Computer Science, Cardiff University", year = "2001", address = "UK", month = may, keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.cf.ac.uk/user/Antonia.J.Jones/Theses/SMargettsThesis.pdf", size = "251 pages", abstract = "This thesis investigates the notion of an adaptive genotype to phenotype mapping in an evolutionary algorithm. We start by defining an abstract framework for evolutionary search which highlights the similarities and differences between evolutionary algorithms. Studying this framework leads us to the idea that an evolutionary algorithm can be specified without defining the structures which make up its genotype and phenotype. Such algorithms are useful as they can be applied to many different problem domains with very little modification. To compare and test this type of algorithm, we construct an abstract problem generator which can be used to create test problems for a wide variety of phenotypes. To help us compare different evolutionary algorithms, we also develop a number of statistics which enable us to efficiently extract useful information from a running evolutionary algorithm. We then use the abstract framework to identify an interesting possibility for an adaptive evolutionary algorithm: an algorithm which has an adaptive mapping function from genotype to phenotype. We develop a general framework for this concept which involves the co-evolution of a population of mappings with a population of structures to which the mappings are applied. To test this idea, we implement an adaptive genetic algorithm and an adaptive genetic programming system. We evaluate these adaptive algorithms by running comparative experiments against several standard evolutionary algorithms, using both artificially generated and real world problems. Although the improvements are perhaps are not as impressive as we might have hoped, we are able to show that our adaptive algorithms are at least as effective as their non-adaptive counterparts.", notes = "1.7 Contributions of this Work This thesis makes five main contributions: 1. Showing that we can construct generic evolutionary algorithms (chapters 3 and 6). Such algorithms are specified without identifying the structures to be used as genotypes and phenotypes, and so can be used in a wide range of problem domains. 2. Demonstrating that the mapping between the genotype and phenotype affects performance of an evolutionary algorithm (chapter 6). We develop the notion of a phlegmatic genotype to phenotype mapping, which constrains the mapping such that changes to a genotype generate similar changes to its corresponding phenotype. This minimises the impact of the mapping on the performance of the algorithm. 3. Motivating and investigating evolutionary algorithms with adaptive genotype to phenotype mappings (chapters 6 and 7). We develop a generic model for constructing evolutionary algorithms with adaptive genotype to phenotype mappings. We demonstrate this model using genetic algorithms for real-valued function optimisation, and with developmental genetic programming. 4. Developing a framework for the generation of optimisation problems (chapter 4). This framework is easily adapted to wide variety of common representations such as lists, strings, trees and graphs. The user of the problem generator specifies the position and fitness of the optima in the search landscape, and so can control the difficulty of the problem. In addition, as the optima are known explicitly, a solution can be assessed for quality directly. 5. Developing population statistics based on near neighbour distances (chapter 5). By calculating a small number of near neighbours of an evolving population, we can measure how the population is distributed in the search space. This allows us to determine the diversity of a population directly, instead of using indirect measures based on fitness. p194 chaotic time series: The Henon Map p202 Huffman encoding p218 Future Work p219 Problem Generation for Genetic Programming p221 Phlegmatic Mappings for Genetic Programming", } @InProceedings{P1120535113, author = "M. H. Marghny and I. E. El-Semman", title = "Extracting Logical Classification Rules With Gene Expression Programming: Microarray Case Study", booktitle = "CGST International Conference on Artificial Intelligence and Machine Learning (AIML-05)", year = "2005", editor = "H. Elmahdy", pages = "11--16", address = "Cairo, Egypt", month = "19-21 " # dec, keywords = "genetic algorithms, genetic programming, Gene Expression Programming, Data mining, classification rules, microarray", URL = "https://www.researchgate.net/publication/246155015_Extracting_Logical_Classification_Rules_With_Gene_Expression_Programming_Microarray_Case_Study", URL = "http://www.icgst.com/paper.aspx?pid=P1120535113", URL = "http://www.icgst.com/AIML05/papers/P1120535113.pdf", size = "6 pages", abstract = "The benefits of finding trends in large volumes of data has driven to the development of data mining technology for over a decade. This paper presents an evolutionary approach for data mining based on enhanced version of gene expression programming (GEP). We enhance the original GEP technique by using a logical operators instead of mathematical ones to represent the chromosome validity evaluation, which results in unconstrained search of the genome space while still ensuring validity of the program's output, it has been demonstrated that GEP greatly surpasses the traditional tree-based GP for its simplicity, high efficiency, solution compactness and comprehensibility.", notes = "Title etc. updated Dec 2022 http://www.icgst.com/AIML05/conference/index.html aiml2005@icgst.com Dept. of Computer Science, Faculty of Computers and Information, Assuit University, Egypt", } @InProceedings{P1120535114, author = "M. H. Marghny and I. E. El-Semman", title = "Extracting fuzzy classification rules with gene expression programming", booktitle = "CGST International Conference on Artificial Intelligence and Machine Learning (AIML-05)", year = "2005", editor = "H. Elmahdy", pages = "17--22", address = "Cairo, Egypt", month = "19-21 " # dec, keywords = "genetic algorithms, genetic programming, Gene Expression Programming, data mining, fuzzy classification rules, logic operators", URL = "http://www.icgst.com/paper.aspx?pid=P1120535114", URL = "http://www.icgst.com/AIML05/papers/P1120535114.pdf", size = "6 pages", abstract = "In essence, data mining consists of extracting knowledge from data. This paper proposes an evolutionary system for discovering fuzzy classification rules. Fuzzy logic is useful for data mining especially in the case for performing classification task. Three methods were used to extract fuzzy classification rules using Evolutionary Algorithms: (1) genetic selection small number of large number of fuzzy candidate rules, (2) genetic reduction of genetic space, selection fuzzy rules from large the candidate rules, (3) genetic learning of fuzzy classification rules. In this paper, we propose a new gene expression programming (GEP) algorithm for discovering logical fuzzy classification rules, the proposed method has been tested and the results are comparable with other techniques include Genetic Programming (GP)", notes = "Title etc. updated Dec 2022 http://www.icgst.com/AIML05/conference/index.html aiml2005@icgst.com P1120535114.pdf format odd, perhaps try alternative PDF viewers. Dept. of Computer Science, Faculty of Computers and Information, Assuit University, Egypt", } @InProceedings{Marginean:2015:SSBSE, author = "Alexandru Marginean and Earl T. Barr and Mark Harman and Yue Jia", title = "Automated Transplantation of Call Graph and Layout Features into {Kate}", booktitle = "SSBSE", year = "2015", editor = "Yvan Labiche and Marcio Barros", volume = "9275", series = "LNCS", pages = "262--268", address = "Bergamo, Italy", month = sep # " 5-7", publisher = "Springer", note = "Winner Gold HUMIE", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE", isbn13 = "978-3-319-22182-3", URL = "http://crest.cs.ucl.ac.uk/autotransplantation/", slide_url = "http://ssbse.org/2015/wp-content/uploads/Slides_Automated_Transplantation_of_Call_Graph_and_Layout_Features_into_Kate.pdf", URL = "http://alexandrumarginean.com/autotransplantation-ssbse.pdf", DOI = "doi:10.1007/978-3-319-22183-0_21", abstract = "We report the automated transplantation of two features currently missing from Kate: call graph generation and automatic layout for C programs, which have been requested by users on the Kate development forum. Our approach uses a lightweight annotation system with Search Based techniques augmented by static analysis for automated transplantation. The results are promising: on average, our tool requires 101 minutes of standard desktop machine time to transplant the call graph feature, and 31 min to transplant the layout feature. We repeated each experiment 20 times and validated the resulting transplants using unit, regression and acceptance test suites. In 34 of 40 experiments conducted our search-based autotransplantation tool, muScalpel, was able to successfully transplant the new functionality, passing all tests.", notes = "Back ward code slicing. Tool, experiments, data sets available at http://crest.cs.ucl.ac.uk/autotransplantation http://ssbse.info/2015", } @InProceedings{Marginean:2019:ICSE, author = "Alexandru Marginean and Johannes Bader and Satish Chandra and Mark Harman and Yue Jia and Ke Mao and Alexander Mols and Andrew Scott", title = "{SapFix}: Automated End-to-End Repair at Scale", booktitle = "41st International Conference on Software Engineering", year = "2019", editor = "Joanne M. Atlee and Tevfik Bultan", pages = "269--278", address = "Montreal", month = "25-31 " # may, publisher = "ACM", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, facebook, automatic bug repair, Automated Program Repair, APR, continuous integration, Sapienz, Infer, FiFiVerify, eclipse JDK", URL = "https://2019.icse-conferences.org/details/icse-2019-Software-Engineering-in-Practice/9/SapFix-Automated-End-to-End-Repair-at-Scale", URL = "https://research.fb.com/publications/sapfix-automated-end-to-end-repair-at-scale/", DOI = "doi:10.1109/ICSE-SEIP.2019.00039", size = "10 pages", abstract = "We report our experience with, Sapfix: the first deployment of automated end-to-end fault fixing, from test case design through to deployed repairs in production code. We have used Sapfix at Facebook to repair 6 production systems, each consisting of tens of millions of lines of code.", notes = "'Definitely felt like a living in the future moment when it sent me the diff to review. Super cool!' 'End-to-end automated repair can work at scale in industrial practice' 'Sociology: Developers may prefer to clone-and-own proposed [Sapfix] fixes' 'Automated Explanations' Software Engineering in Practice (ICSE-SEIP) Also known as \cite{8804442}", } @PhdThesis{Marginean_10137954_thesis_redacted, author = "Alexandru Marginean", title = "Automated Software Transplantation", school = "University College London", year = "2021", address = "UK", month = "8 " # nov, note = "{ACM SIGEVO Award for the best dissertation of the year}", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, Autotransplantation, Automated program repair, APR, SAPFIX, muSCALPEL", URL = "https://discovery.ucl.ac.uk/id/eprint/10137954/", URL = "https://discovery.ucl.ac.uk/id/eprint/10137954/1/Marginean_10137954_thesis_redacted.pdf", size = "240 pages", abstract = "Automated program repair has excited researchers for more than a decade, yet it has yet to find full scale deployment in industry. We report our experience with SAPFIX: the first deployment of automated end-to-end fault fixing, from test case design through to deployed repairs in production code. We have used SAPFIX at Facebook to repair 6 production systems, each consisting of tens of millions of lines of code, and which are collectively used by hundreds of millions of people worldwide. In its first three months of operation,SAPFIX produced 55 repair candidates for 57 crashes reported to SAPFIX, of which 27 have been deem as correct by developers and 14 have been landed into production automatically by SAPFIX. SAPFIX has thus demonstrated the potential of the search-based repair research agenda by deploying, to hundreds of millions of users world wide, software systems that have been automatically tested and repaired. Automated software transplantation (autotransplantation) is a form of automated software engineering, where we use search based software engineering to be able to automatically move a functionality of interest from a donor program that implements it into a host program that lacks it. Autotransplantation is a kind of automated program repair where we repair the host program by augmenting it with the missing functionality. Automated software transplantation would open many exciting avenues for software development: suppose we could auto-transplant code from one system into another, entirely unrelated, system, potentially written in a different programming language. Being able to do so might greatly enhance the software engineering practice, while reducing the costs. Automated software transplantation manifests in two different flavours: monolingual, when the languages of the host and donor programs is the same, or multilingual when the languages differ. This thesis introduces a theory of automated software transplantation, and two algorithms implemented in two tools that achieve this: uSCALPEL for monolingual software transplantation and rSCALPEL for multilingual software transplantation. Leveraging lightweight annotation, program analysis identifies an organ (interesting behaviour to transplant); testing validates that the organ exhibits the desired behavior during its extraction and after its implantation into a host. We report encouraging results: in 14 of 17 monolingual transplantation experiments involving 6 donors and 4 hosts, popular real-world systems, we successfully autotransplanted 6 new functionalities; and in 10 out of 10 multlingual transplantation experiments involving 10 donors and 10 hosts, popular real-world systems written in 4 different programming languages, we successfully autotransplanted 10 new functionalities. That is, we have passed all the test suites that validates the new functionalities behaviour and the fact that the initial program behaviour is preserved. Additionally, we have manually checked the behaviour exercised by the organ. Autotransplantation is also very useful: in just 26 hours computation time we successfully autotransplanted the H.264 video encoding functionality from the x264 system to the VLC media player, a task that is currently done manually by the developers of VLC, since 12 years ago. We autotransplanted call graph generation and indentation for C programs into Kate, (a popular KDE based test editor used as an IDE by a lot of C developers) two features currently missing from Kate, but requested by the users of Kate. Autotransplantation is also efficient: the total run-time across 15 monolingual transplants is 5 hours and a half; the total runtime across 10 multilingual transplants is 33 hours", } @InProceedings{Margraf:2017:ICMLA, author = "Andreas Margraf and Anthony Stein and Leonhard Engstler and Steffen Geinitz and Joerg Haehner", booktitle = "2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)", title = "An Evolutionary Learning Approach to Self-configuring Image Pipelines in the Context of Carbon Fiber Fault Detection", year = "2017", pages = "147--154", abstract = "Carbon fibre reinforced plastics (CFRP) play a key role for the production of lightweight structures. Simultaneously, online quality inspection of CFRP becomes more important, especially for environments with high safety standards. In this context, vision systems aim to find defects of different shape, size, contour and orientation. Little effort, however, has been made in detecting defect areas in images taken from the surface of carbon fibres. A common approach for segmenting filament defects are edge detection and thresholding. With every change of material and process adjustments, the filter parameters have to be adapted. In this paper, we propose a cartesian genetic programming (CGP) approach to semi-automatically select the best parameters. This strategy saves time for parameter identification while at the same time increases precision. A test run on randomly selected samples shows how the approach can substantially improve detection reliability.", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming", DOI = "doi:10.1109/ICMLA.2017.0-165", month = dec, notes = "Also known as \cite{8260627}", } @InProceedings{Margraf:2023:ACSOS, author = "Andreas Margraf and Henning Cui and Anthony Stein and Joerg Hahner", booktitle = "2023 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS)", title = "Evolving Processing Pipelines for Industrial Imaging with Cartesian Genetic Programming", year = "2023", pages = "133--138", abstract = "The reconfiguration of machine vision systems heavily depends on the collection and availability of large datasets, rendering them inflexible and vulnerable to even minor changes in the data. This paper proposes a refinement of Miller's Cartesian Genetic Programming methodology, aimed at generating filter pipelines for image processing tasks. The approach is based on CGP-IP, but specifically adapted for image processing in industrial monitoring applications. The suggested method allows for retraining of filter pipelines using small datasets; this concept of self-adaptivity renders high-precision machine vision more resilient to faulty machine settings or changes in the environment and provides compact programs. A dependency graph is introduced to rule out invalid pipeline solutions. Furthermore, we suggest to not only generate pipelines from scratch, but store and reapply previous solutions and re-adjust filter parameters. Our modifications are designed to increase the likelihood of early convergence and improvement in the fitness indicators. This form of self-adaptivity allows for a more resource-efficient configuration of image filter pipelines with small datasets.", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Machine vision, Pipelines, Imaging, Rendering (computer graphics), Task analysis, Distributed computing, image filters, monitoring, segmentation", DOI = "doi:10.1109/ACSOS58161.2023.00031", month = sep, notes = "Also known as \cite{10336022}", } @InProceedings{DBLP:conf/ijcci/MargrafCBH23, author = "Andreas Margraf and Henning Cui and Stefan Baumann and Joerg Haehner", editor = "Niki {van Stein} and Francesco Marcelloni and H. K. Lam and Marie Cottrell and Joaquim Filipe", title = "Filter Evolution Using Cartesian Genetic Programming for Time Series Anomaly Detection", booktitle = "Proceedings of the 15th International Joint Conference on Computational Intelligence, {IJCCI} 2023, Rome, Italy, November 13-15, 2023", pages = "300--307", publisher = "{SCITEPRESS}", year = "2023", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", URL = "https://doi.org/10.5220/0012210700003595", DOI = "doi:10.5220/0012210700003595", timestamp = "Fri, 08 Dec 2023 00:00:00 +0100", biburl = "https://dblp.org/rec/conf/ijcci/MargrafCBH23.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{Maria-Simoes:2023:EuroGP, author = "Jose Maria Simoes and Nuno Lourenco and Penousal Machado", title = "All You Need Is Sex for Diversity", booktitle = "EuroGP 2023: Proceedings of the 26th European Conference on Genetic Programming", year = "2023", month = "12-14 " # apr, editor = "Gisele Pappa and Mario Giacobini and Zdenek Vasicek", series = "LNCS", volume = "13986", publisher = "Springer Verlag", address = "Brno, Czech Republic", pages = "276--291", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Diversity, Sexual Selection, Mate Choice, Mating Preferences: Poster", isbn13 = "978-3-031-29572-0", URL = "https://rdcu.be/c8U1x", DOI = "doi:10.1007/978-3-031-29573-7_18", size = "16 pages", abstract = "Maintaining genetic diversity as a means to avoid premature convergence is critical in Genetic Programming. Several approaches have been proposed to achieve this, with some focusing on the mating phase from coupling dissimilar solutions to some form of self-adaptive selection mechanism. In nature, genetic diversity can be the consequence of many different factors, but when considering reproduction Sexual Selection can have an impact on promoting variety within a species. Specifically, Mate Choice often results in different selective pressures between sexes, which in turn may trigger evolutionary differences among them. Although some mechanisms of Sexual Selection have been applied to Genetic Programming in the past, the literature is scarce when it comes to mate choice. Recently, a way of modelling mating preferences by ideal mate representations was proposed, achieving good results when compared to a standard approach. These mating preferences evolve freely in a self-adaptive fashion,", notes = "Part of \cite{Pappa:2023:GP} EuroGP'2023 held in conjunction with EvoCOP2023, EvoMusArt2023 and EvoApplications2023", } @InProceedings{Mariani:2016:GECCO, author = "Thaina Mariani and Giovani Guizzo and Silvia Regina Vergilio and Aurora Trinidad Ramirez Pozo", title = "Grammatical Evolution for the Multi-Objective Integration and Test Order Problem", booktitle = "GECCO '16: Proceedings of the 2016 Annual Conference on Genetic and Evolutionary Computation", year = "2016", editor = "Tobias Friedrich", pages = "1069--1076", keywords = "genetic algorithms, genetic programming, grammatical evolution, SBSE, search based software engineering, multi-objective, hyper-heuristic, evolutionary algorithm", month = "20-24 " # jul, organisation = "SIGEVO", address = "Denver, USA", publisher = "ACM", publisher_address = "New York, NY, USA", isbn13 = "978-1-4503-4206-3", DOI = "doi:10.1145/2908812.2908816", abstract = "Search techniques have been successfully applied for solving different software testing problems. However, choosing, implementing and configuring a search technique can be hard tasks. To reduce efforts spent in such tasks, this paper presents an offline hyper-heuristic named GEMOITO, based on Grammatical Evolution (GE). The goal is to automatically generate a Multi-Objective Evolutionary Algorithm (MOEA) to solve the Integration and Test Order (ITO) problem. The MOEAs are distinguished by components and parameters values, described by a grammar. The proposed hyper-heuristic is compared to conventional MOEAs and to a selection hyper-heuristic used in related work. Results show that GEMOITO can generate MOEAs that are statistically better or equivalent to the compared algorithms.", notes = "Federal University of Parana GECCO-2016 A Recombination of the 25th International Conference on Genetic Algorithms (ICGA-2016) and the 21st Annual Genetic Programming Conference (GP-2016)", } @Article{Mariani:2017:IST, author = "Thaina Mariani and Silvia Regina Vergilio", title = "A systematic review on search-based refactoring", journal = "Information and Software Technology", year = "2017", volume = "83", pages = "14--34", month = mar, keywords = "genetic algorithms, genetic programming, SBSE, Search-based software engineering, Refactoring, Evolutionary algorithms", ISSN = "0950-5849", DOI = "doi:10.1016/j.infsof.2016.11.009", URL = "http://www.sciencedirect.com/science/article/pii/S0950584916303779", size = "21 pages", abstract = "To find the best sequence of refactorings to be applied in a software artefact is an optimization problem that can be solved using search techniques, in the field called Search-Based Refactoring (SBR). Over the last years, the field has gained importance, and many SBR approaches have appeared, arousing research interest. Objective: The objective of this paper is to provide an overview of existing SBR approaches, by presenting their common characteristics, and to identify trends and research opportunities. Method: A systematic review was conducted following a plan that includes the definition of research questions, selection criteria, a search string, and selection of search engines. 71 primary studies were selected, published in the last sixteen years. They were classified considering dimensions related to the main SBR elements, such as addressed artefacts, encoding, search technique, used metrics, available tools, and conducted evaluation. Results: Some results show that code is the most addressed artifact, and evolutionary algorithms are the most employed search technique. Furthermore, most times, the generated solution is a sequence of refactorings. In this respect, the refactorings considered are usually the ones of the Fowler's Catalogue. Some trends and opportunities for future research include the use of models as artefacts, the use of many objectives, the study of the bad smells effect, and the use of hyper-heuristics. Conclusions: We have found many SBR approaches, most of them published recently. The approaches are presented, analysed, and grouped following a classification scheme. The paper contributes to the SBR field as we identify a range of possibilities that serve as a basis to motivate future researches.", notes = "Brief mention of GP. Cites \cite{Jensen:2010:gecco}, \cite{langdon:2009:gecco3}", } @InProceedings{mariano:1999:MAAMOOP, author = "Carlos E. Mariano and Eduardo Morales M.", title = "MOAQ an Ant-Q Algorithm for Multiple Objective Optimization Problems", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "894--901", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "evolution strategies and evolutionary programming", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/Mariano_gecco99.ps.gz", URL = "http://dns1.mor.itesm.mx/~emorales/Papers/gecco99.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{marin:1999:EOTED, author = "Jesus Marin and Ricard V. Sole", title = "Evolutionary Optimization Through Extinction Dynamics", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1344--1349", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "artificial life, adaptive behavior and agents", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-036.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-036.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Article{marinakis_pap_2009, title = "Pap smear diagnosis using a hybrid intelligent scheme focusing on genetic algorithm based feature selection and nearest neighbor classification", volume = "39", ISSN = "0010-4825", URL = "http://www.sciencedirect.com/science/article/pii/S0010482508001674", DOI = "doi:10.1016/j.compbiomed.2008.11.006", abstract = "The term pap-smear refers to samples of human cells stained by the so-called Papanicolaou method. The purpose of the Papanicolaou method is to diagnose pre-cancerous cell changes before they progress to invasive carcinoma. In this paper a metaheuristic algorithm is proposed in order to classify the cells. Two databases are used, constructed in different times by expert MDs, consisting of 917 and 500 images of pap smear cells, respectively. Each cell is described by 20 numerical features, and the cells fall into 7 classes but a minimal requirement is to separate normal from abnormal cells, which is a 2 class problem. For finding the best possible performing feature subset selection problem, an effective genetic algorithm scheme is proposed. This algorithmic scheme is combined with a number of nearest neighbour based classifiers. Results show that classification accuracy generally outperforms other previously applied intelligent approaches.", number = "1", journal = "Computers in Biology and Medicine", author = "Yannis Marinakis and Georgios Dounias and Jan Jantzen", month = jan, year = "2009", keywords = "genetic algorithms, genetic programming, artificial intelligence and medical diagnosis, data mining, feature selection problem, nearest neighbor based classifiers, Pap-smear classification", pages = "69--78", } @InProceedings{Marinescu:2018:SYNASC, author = "Alexandru-Ion Marinescu and Anca Andreica", title = "Evolving Mathematical Formulas using {LINQ} Expression Trees and Direct Applications to Credit Scoring", booktitle = "2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)", year = "2018", pages = "409--416", month = sep, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SYNASC.2018.00069", abstract = "Credit scoring is a well established and scrutinized domain within the artificial intelligence field of research and has direct implications in the functioning of financial institutions, by evaluating the risk of approving loans for different clients, which may or may not reimburse them in due time. It is the clients who fail to repay their debt that we are interested in predicting, which makes it a much more difficult task, since they form only a small minority of the total client count. From an input-output perspective, the problem can be stated as: given a set of client properties, such as age, marital status, loan duration, one must yield a 0-1 response variable, with 0 meaning {"}good{"} and 1, {"}bad{"} clients. Many techniques with high accuracy exist, such as artificial neural networks, but they behave as black box units. We add to this whole context the constraint that the output must be a concrete, tractable mathematical formula, which provides significant added value for a financial analyst. To this end, we present a means for evolving mathematical formulas using genetic programming coupled with Language Integrated Query expression trees, a feature present in the C# programming language.", notes = "Also known as \cite{8750761}", } @InProceedings{Marinescu:2014:NCA, author = "Cristina Marinescu", title = "How Good is Genetic Programming at Predicting Changes and Defects?", booktitle = "11th Workshop on Natural Computing and Applications", year = "2014", editor = "Siby Abraham and Catalin Stoean", pages = "544--548", address = "Timisoara, Romania", month = sep # " 22-25", keywords = "genetic algorithms, genetic programming, SBSE, source code, changes, defects, metrics, software repositories, empirical software engineering", DOI = "doi:10.1109/SYNASC.2014.78", size = "5 pages", abstract = "One of the main problems practitioners have to deal with is the identification of change and defect proneness of source code entities (e.g., classes). During the last years a lot of techniques have been employed for predicting change and defect proneness of classes. In this paper we study the capabilities of Genetic Programming for performing the addressed problem by measuring the precision and recall of the obtained predictions.", notes = "http://synasc.ro/2014", notes = " In the framework of SYNASC 2014 http://synasc.ro/2014/workshops/nca-2014/ Also known as \cite{7034728}", } @InProceedings{Maringer:2011:CIFEr, author = "Dietmar Maringer and Tikesh Ramtohul", title = "GP-based rebalancing triggers for the CPPI", booktitle = "IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr 2011)", year = "2011", month = "11-15 " # apr, address = "Paris", size = "8 pages", abstract = "The Constant Proportion Portfolio Insurance (CPPI) technique is a dynamic capital-protection strategy that aims at providing investors with a guaranteed minimum level of wealth at the end of a specified time horizon. A pertinent concern of issuers of CPPI products is when to perform portfolio readjustments. One way of achieving this is through the use of rebalancing triggers; this constitutes the main focus of this paper. We propose a genetic programming (GP) approach to evolve trigger-based rebalancing strategies that rely on some tolerance bounds around the CPPI multiplier, as well as on the time-dependent implied multiplier, to determine the timing sequence of the portfolio readjustments. We carry out experiments using GARCH datasets, and use two different types of fitness functions, namely variants of Tracking Error and Sortino ratio, for multiple scenarios involving different data and/or CPPI settings. We find that the GP-CPPI strategies yield better results than calendar-based rebalancing strategies in general, both in terms of expected returns and shortfall probability, despite the fitness measures having no special functionality that explicitly penalises floor violations. Since the results support the viability and feasibility of the proposed approach, potential extensions and ameliorations of the GP framework are also discussed.", keywords = "genetic algorithms, genetic programming, CPPI multiplier, GARCH datasets, GP-CPPI strategies, GP-based rebalancing triggers, Sortino ratio, constant proportion portfolio insurance technique, dynamic capital-protection strategy, expected returns, portfolio readjustments, shortfall probability, time-dependent implied multiplier, tracking error, trigger-based rebalancing strategies, autoregressive processes, insurance, investment, probability", DOI = "doi:10.1109/CIFER.2011.5953561", ISSN = "pending", notes = "Also known as \cite{5953561}", } @InProceedings{Marini:evobio12, author = "Simone Marini and Alessandra Conversi", title = "Understanding zooplankton long term variability through genetic programming", booktitle = "10th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, {EvoBIO 2012}", year = "2012", month = "11-13 " # apr, editor = "Mario Giacobini and Leonardo Vanneschi and William S. Bush", series = "LNCS", volume = "7246", publisher = "Springer Verlag", address = "Malaga, Spain", pages = "50--61", organisation = "EvoStar", isbn13 = "978-3-642-29065-7", DOI = "doi:10.1007/978-3-642-29066-4_5", keywords = "genetic algorithms, genetic programming, Ecological Modelling, Plankton Dynamics, Climate Change, Series", abstract = "Zooplankton are considered good indicators for understanding how oceans are affected by climate change. While climate influence on zooplankton abundance variability is currently accepted, its mechanisms are not understood, and prediction is not yet possible. We use Genetic Programming approach to identify which environmental variables, and at which extent, can be used to express zooplankton abundance dynamics. The zooplankton copepod long term (since 1988) time series from the L4 station in the Western English Channel, has been used as test case together with local environmental parameters and large scale climate indexes. The performed simulations identify a set of relevant ecological drivers and highlight the non linear dynamics of the Copepod variability. These results indicate GP to be a promising approach for understanding the long term variability of marine populations.", notes = "Plymouth, Devon. Part of \cite{Giacobini:2012:EvoBio} EvoBio'2012 held in conjunction with EuroGP2012, EvoCOP2012, EvoMusArt2012 and EvoApplications2012", } @InProceedings{Marini2016Martech, author = "Simone Marini and Ernesto Azzurro and Salvatore Coco and Joaquin {Del Rio} and Sergio Enguidanos and Emanuela Fanelli and Marc Nogueras and Valerio Sbragaglia and Daniel Toma and Jacopo Aguzzi", title = "Automatic fish counting from underwater video images: performance estimation and evaluation", booktitle = "7th International Workshop on Marine Technologies (MARTECH 2016)", year = "2016", editor = "Juan Jose Danobeitia", series = "ID. 23", address = "Instituto de Ciencias del Mar, Barcelona, Spain", month = "26-27 " # oct, organisation = "CSIC and UPC", keywords = "genetic algorithms, genetic programming", URL = "https://upcommons.upc.edu/handle/2117/99939", URL = "http://www.upc.edu/cdsarti/martech/usb_2016/papers/23.pdf", size = "4 pages", abstract = "Cabled observatories offer new opportunities to monitor species abundances at frequencies and durations never attained before. When nodes bear cameras, these may be transformed into the first sensor capable of quantifying biological activities at individual, population, species, and community levels, if automation image processing can be sufficiently implemented. Here, we developed a binary classifier for the fish automated recognition based on Genetic Programming tested on the images provided by OBSEA EMSO testing site platform located at 20 m of depth off Vilanova i la Gertru (Spain). The performance evaluation of the automatic classifier resulted in a 78percent of accuracy compared with the manual counting. Considering the huge dimension of data provided by cabled observatories and the difficulty of manual processing, we consider this result highly promising also in view of future implementation of the methodology to increase the accuracy.", notes = "Broken Feb 2022 http://www.upc.edu/cdsarti/martech/usb_2016/index.html broken Nov 2017 martech-workshop.org", } @Article{MARINI201872, author = "Simone Marini and Lorenzo Corgnati and Carlo Mantovani and Mauro Bastianini and Ennio Ottaviani and Emanuela Fanelli and Jacopo Aguzzi and Annalisa Griffa and Pierre-Marie Poulain", title = "Automated estimate of fish abundance through the autonomous imaging device {GUARD1}", journal = "Measurement", year = "2018", volume = "126", pages = "72--75", keywords = "genetic algorithms, genetic programming, Marine monitoring, Imaging device, Argo float, Content-Based Image Recognition, Pelagic fauna", ISSN = "0263-2241", DOI = "doi:10.1016/j.measurement.2018.05.035", URL = "http://www.sciencedirect.com/science/article/pii/S0263224118304226", abstract = "Many technologies have been developed for monitoring the ocean interior. Among them the monitoring approaches based on imaging devices are capable to disclose important data on species behaviour and spatio-temporal variations of richness and evenness. In this context, the Argo programme (http://doi.org/10.17882/42182) is a valuable instrument for monitoring the deep sea at global scale in space and time. Argo floats equipped with imaging devices are candidate to become a new monitoring tool for studying macro- and mega-fauna in large areas and for extended time periods, potentially providing monitoring results never attained before. This work summarises the results obtained on the automated fish recognition task performed on the images acquired by the GUARD1 imaging device", } @Article{Marini2018, author = "Simone Marini and Emanuela Fanelli and Valerio Sbragaglia and Ernesto Azzurro and Joaquin {Del Rio Fernandez} and Jacopo Aguzzi", title = "Tracking Fish Abundance by Underwater Image Recognition", journal = "Scientific Reports", year = "2018", volume = "8", pages = "Article number 13748", month = "13 " # sep, note = "Published on line", keywords = "genetic algorithms, genetic programming", ISSN = "2045-2322", DOI = "doi:10.1038/s41598-018-32089-8", size = "12 pages", abstract = "Marine cabled video-observatories allow the non-destructive sampling of species at frequencies and durations that have never been attained before. Nevertheless, the lack of appropriate methods to automatically process video imagery limits this technology for the purposes of ecosystem monitoring. Automation is a prerequisite to deal with the huge quantities of video footage captured by cameras, which can then transform these devices into true autonomous sensors. In this study, we have developed a novel methodology that is based on genetic programming for content-based image analysis. Our aim was to capture the temporal dynamics of fish abundance. We processed more than 20,000 images that were acquired in a challenging real-world coastal scenario at the OBSEA-EMSO testing-site. The images were collected at 30-min. frequency, continuously for two years, over day and night. The highly variable environmental conditions allowed us to test the effectiveness of our approach under changing light radiation, water turbidity, background confusion, and bio-fouling growth on the camera housing. The automated recognition results were highly correlated with the manual counts and they were highly reliable when used to track fish variations at different hourly, daily, and monthly time scales. In addition, our methodology could be easily transferred to other cabled video-observatories.", notes = "hosted on nasty web page...", } @InProceedings{Marino:2016:PPSN, author = "Francesco Marino and Giovanni Squillero and Alberto Tonda", title = "A General-Purpose Framework for Genetic Improvement", booktitle = "14th International Conference on Parallel Problem Solving from Nature", year = "2016", editor = "Julia Handl and Emma Hart and Peter R. Lewis and Manuel Lopez-Ibanez and Gabriela Ochoa and Ben Paechter", volume = "9921", series = "LNCS", pages = "345--352", address = "Edinburgh", month = "17-21 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Genetic Improvement, SBSE, Linear genetic programming Software engineering", isbn13 = "978-3-319-45823-6", DOI = "doi:10.1007/978-3-319-45823-6_32", size = "8 pages", abstract = "Genetic Improvement is an evolutionary-based technique. Despite its relatively recent introduction, several successful applications have been already reported in the scientific literature: it has been demonstrated able to modify the code complex programs without modifying their intended behaviour; to increase performance with regards to speed, energy consumption or memory use. Some results suggest that it could be also used to correct bugs, restoring the software's intended functionalities. Given the novelty of the technique, however, instances of Genetic Improvement so far rely upon ad-hoc, language-specific implementations. In this paper, we propose a general framework based on the software engineering's idea of mutation testing coupled with Genetic Programming, that can be easily adapted to different programming languages and objective. In a preliminary evaluation, the framework efficiently optimizes the code of the md5 hash function in C, Java, and Python.", notes = "XML, mutation testing, MD5 microGP http://ugp3.sourceforge.net/ PPSN2016", } @InProceedings{DBLP:conf/aiide/Marino19, author = "Julian R. H. Marino", title = "Learning Strategies for Real-Time Strategy Games with Genetic Programming", booktitle = "Proceedings of the Fifteenth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2019", editor = "Gillian Smith and Levi Lelis", year = "2019", pages = "219--220", publisher = "AAAI Press", month = oct # " 8-12", address = "Atlanta, Georgia, USA", keywords = "genetic algorithms, genetic programming", URL = "https://www.aaai.org/ojs/index.php/AIIDE/article/view/5249", URL = "https://ojs.aaai.org/index.php/AIIDE/article/view/5249/5105", timestamp = "Wed, 12 Aug 2020 18:56:30 +0200", biburl = "https://dblp.org/rec/conf/aiide/Marino19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", size = "2 pages", abstract = "Planning in real-time strategy (RTS) games is challenging due to their very large state and action spaces. Action abstractions have shown to be a promising approach for dealing with this challenge. Previous approaches induce action abstractions from a small set of hand-crafted strategies, which are used by algorithms to search only on the actions returned by the strategies. Previous works use a set of expert-designed strategies for inducing action abstractions. The main drawback of this approach is that it limits the agent behaviour to the knowledge encoded in the strategies. In this research, we focus on learning novel and effective strategies for RTS games, to induce action abstractions. In addition to being effective, we are interested in learning strategies that can be easily interpreted by humans, allowing a better understanding of the workings of the resulting agent.", notes = "Universidade de Sao Paulo", } @Article{MARINO:2022:asoc, author = "Julian R. H. Marino and Claudio F. M. Toledo", title = "Evolving interpretable strategies for zero-sum games", journal = "Applied Soft Computing", year = "2022", volume = "122", pages = "108860", keywords = "genetic algorithms, genetic programming, Evolutionary algorithm, RTS Games, Scripts, Intelligent agents, Decision-making", ISSN = "1568-4946", URL = "https://www.sciencedirect.com/science/article/pii/S1568494622002496", DOI = "doi:10.1016/j.asoc.2022.108860", size = "11 pages", abstract = "The present paper introduces Gesy, a genetic programming approach to script synthesis for zero-sum games. We will explore the sum-zero game context in Real-Time Strategy (RTS) games, where players must look for strategies (planning of actions) to maximize their gains or minimize their losses. The goal is to solve the script synthesis problem, which demands the synthesis of a computer program from a space of programs defined by a Domain-Specific Language (DSL). The synthesized program must encode a practical strategy for zero-sum games. Empirical results validate Gesy using the \mu RTS platform, an academic test bed game that presents the main features found in RTS commercial games. The results show that our method provides interpretable strategies that are competitive with state-of-the-art search-based approaches in terms of play strength. Moreover, once synthesised, scripts require only a tiny fraction of the time needed by search-based methods to decide on the agent next action", notes = "Departamento de Sistemas de Computacao, ICMC, Universidade de Sao Paulo, Brazil", } @InProceedings{Mariot:2017:GECCO, author = "Luca Mariot and Stjepan Picek and Domagoj Jakobovic and Alberto Leporati", title = "Evolutionary Algorithms for the Design of Orthogonal Latin Squares Based on Cellular Automata", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4920-8", address = "Berlin, Germany", pages = "306--313", size = "8 pages", URL = "http://doi.acm.org/10.1145/3071178.3071284", DOI = "doi:10.1145/3071178.3071284", acmid = "3071284", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, boolean functions, cellular automata, nonlinearity, orthogonal latin squares, pairwise balancedness, quaternary strings", month = "15-19 " # jul, abstract = "We investigate the design of Orthogonal Latin Squares (OLS) by means of Genetic Algorithms (GA) and Genetic Programming (GP). Since we focus on Latin squares generated by Cellular Automata (CA), the problem can be reduced to the search of pairs of Boolean functions that give rise to OLS when used as CA local rules. As it is already known how to design CA-based OLS with linear Boolean functions, we adopt the evolutionary approach to address the nonlinear case, experimenting with different encodings for the candidate solutions. In particular, for GA we consider single bitstring, double bitstring and quaternary string encodings, while for GP we adopt a double tree representation. We test the two metaheuristics on the spaces of local rules pairs with n = 7 and n = 8 variables, using two fitness functions. The results show that GP is always able to generate OLS, even if the optimal solutions found with the first fitness function are mostly linear. On the other hand, GA achieves a remarkably lower success rate than GP in evolving OLS, but the corresponding Boolean functions are always nonlinear.", notes = "Also known as \cite{Mariot:2017:EAD:3071178.3071284} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @Article{Mariot:2019:CandC, author = "Luca Mariot and Stjepan Picek and Alberto Leporati and Domagoj Jakobovic", title = "Cellular automata based {S}-boxes", journal = "Cryptography and communications", year = "2019", volume = "11", number = "1", pages = "41--62", month = jan, note = "Special Issue on Boolean Functions and Their Applications", keywords = "genetic algorithms, genetic programming, Cellular automata, S-box ,Cryptographic properties, Heuristics", ISSN = "1936-2447", DOI = "doi:10.1007/s12095-018-0311-8", abstract = "Cellular Automata (CA) represent an interesting approach to design Substitution Boxes (S-boxes) having good cryptographic properties and low implementation costs. From the cryptographic perspective, up to now there have been only ad-hoc studies about specific kinds of CA, the best known example being the ki nonlinear transformation used in Keccak. In this paper, we undertake a systematic investigation of the cryptographic properties of S-boxes defined by CA, proving some upper bounds on their nonlinearity and differential uniformity. Next, we extend some previous published results about the construction of CA-based S-boxes by means of a heuristic technique, namely Genetic Programming (GP). In particular, we propose a reverse engineering method based on De Bruijn graphs to determine whether a specific S-box is expressible through a single CA rule. Then, we use GP to assess if some CA-based S-box with optimal cryptographic properties can be described by a smaller CA. The results show that GP is able to find much smaller CA rules defining the same reference S-boxes up to the size 7by7, suggesting that our method could be used to find more efficient representations of CA-based S-boxes for hardware implementations. Finally, we classify up to affine equivalence all 3by3 and 4by4 CA-based S-boxes.", } @InProceedings{Mariot:2019:EuroGP, author = "Luca Mariot and Domagoj Jakobovic and Alberto Leporati and Stjepan Picek", title = "Hyper-bent {Boolean} Functions and Evolutionary Algorithms", booktitle = "EuroGP 2019: Proceedings of the 22nd European Conference on Genetic Programming", year = "2019", month = "24-26 " # apr, editor = "Lukas Sekanina and Ting Hu and Nuno Lourenco", series = "LNCS", volume = "11451", publisher = "Springer Verlag", address = "Leipzig, Germany", pages = "262--277", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Bent functions, Hyper-bent functions, Evolution strategies: Poster", isbn13 = "978-3-030-16669-4", URL = "https://www.springer.com/us/book/9783030166694", DOI = "doi:10.1007/978-3-030-16670-0_17", size = "16 pages", abstract = "Bent Boolean functions play an important role in the design of secure symmetric ciphers, since they achieve the maximum distance from affine functions allowed by Parsevals relation. Hyper-bent functions, in turn, are those bent functions which additionally reach maximum distance from all bijective monomial functions, and provide further security towards approximation attacks. Being characterized by a stricter definition, hyper-bent functions are rarer than bent functions, and much more difficult to construct. In this paper, we employ several evolutionary algorithms in order to evolve hyper-bent Boolean functions of various sizes. Our results show that hyper-bent functions are extremely difficult to evolve, since we manage to find such functions only for the smallest investigated size. Interestingly, we are able to identify this difficulty as not lying in the evolution of hyper-bent functions itself, but rather in evolving some of their components, i.e. bent functions. Finally, we present an additional parameter to evaluate the performance of evolutionary algorithms when evolving Boolean functions: the diversity of the obtained solutions.", notes = "http://www.evostar.org/2019/cfp_eurogp.php#abstracts Part of \cite{Sekanina:2019:GP} EuroGP'2019 held in conjunction with EvoCOP2019, EvoMusArt2019 and EvoApplications2019", } @InProceedings{Mariot:2020:EuroGP, author = "Luca Mariot and Stjepan Picek and Domagoj Jakobovic and Alberto Leporati", title = "An Evolutionary View on Reversible Shift-invariant Transformations", booktitle = "EuroGP 2020: Proceedings of the 23rd European Conference on Genetic Programming", year = "2020", month = "15-17 " # apr, editor = "Ting Hu and Nuno Lourenco and Eric Medvet", series = "LNCS", volume = "12101", publisher = "Springer Verlag", address = "Seville, Spain", pages = "118--134", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Shift-invariant transformations, Cellular automata, Reversibility", isbn13 = "978-3-030-44093-0", URL = "https://www.human-competitive.org/sites/default/files/mariot.txt", URL = "https://www.human-competitive.org/sites/default/files/an_evolutionary_view_on_reversible_shift-invariant_transformations.pdf", video_url = "https://www.youtube.com/watch?v=oABFvDN23vg", video_url = "http://www.human-competitive.org/sites/default/files/mariotvideo.mp4", DOI = "doi:10.1007/978-3-030-44094-7_8", size = "15 pages", abstract = "We consider the problem of evolving a particular kind of shift-invariant transformation: namely, Reversible Cellular Automata (RCA) defined by conserved landscape rules, using GA and GP. To this end, we employ three different optimization strategies: a single-objective approach carried out with GA and GP where only the reversibility constraint of marker CA is considered, a multi-objective approach based on GP where both reversibility and the Hamming weight are taken into account, and a lexicographic approach where GP first optimizes only the reversibility property until a conserved landscape rule is obtained, and then maximizes the Hamming weight while retaining reversibility. The results are discussed in the context of three different research questions stemming from exhaustive search experiments on conserved landscape CA, which concern (1) the difficulty of the associated optimization problem for GA and GP, (2) the utility of conserved landscape CA in the domain of cryptography and reversible computing, and (3) the relationship between the reversibility property and the Hamming weight.", notes = "Nominated for best paper. 2020 HUMIES finalist. http://www.evostar.org/2020/cfp_eurogp.php Part of \cite{Hu:2020:GP} EuroGP'2020 held in conjunction with EvoCOP2020, EvoMusArt2020 and EvoApplications2020", } @Article{Mariot:GPEM, author = "Luca Mariot and Stjepan Picek and Domagoj Jakobovic and Alberto Leporati", title = "Evolutionary algorithms for designing reversible cellular automata", journal = "Genetic Programming and Evolvable Machines", year = "2021", volume = "22", number = "4", pages = "429--461", month = dec, note = "Special Issue: Highlights of Genetic Programming 2020 Events", keywords = "genetic algorithms, genetic programming, Shift-invariant transformations, Cellular automata, Reversibility", ISSN = "1389-2576", URL = "https://rdcu.be/cyKGY", DOI = "doi:10.1007/s10710-021-09415-7", size = "33 pages", abstract = "Reversible Cellular Automata (RCA) are a particular kind of shift-invariant transformations characterized by dynamics composed only of disjoint cycles. They have many applications in the simulation of physical systems, cryptography, and reversible computing. we formulate the search of a specific class of RCA, namely, those whose local update rules are defined by conserved landscapes, as an optimization problem to be tackled with Genetic Algorithms (GA) and Genetic Programming (GP). In particular, our experimental investigation revolves around three different research questions, which we address through a single-objective, a multi-objective, and a lexicographic approach. In the single-objective approach, we observe that GP can already find an optimal solution in the initial population. This indicates that evolutionary algorithms are not needed when evolving only the reversibility of such CA, and a more efficient method is to generate at random syntactic trees that define the local update rule. On the other hand, GA and GP proved to be quite effective in the multi-objective and lexicographic approach to (1) discover a trade-off between the reversibility and the Hamming weight of conserved landscape rules, and (2) observe that conserved landscape CA cannot be used in symmetric cryptography because their Hamming weight (and thus their nonlinearity) is too low.", notes = "Cyber Security Research Group, Delft University of Technology, Mekelweg 2, Delft, The Netherlands", } @InProceedings{Mariot:2022:CEC, author = "Luca Mariot and Stjepan Picek and Domagoj Jakobovic and Marko Djurasevic and Alberto Leporati", booktitle = "2022 IEEE Congress on Evolutionary Computation (CEC)", title = "Evolutionary Construction of Perfectly Balanced Boolean Functions", year = "2022", editor = "Carlos A. Coello Coello and Sanaz Mostaghim", address = "Padua, Italy", month = "18-23 " # jul, isbn13 = "978-1-6654-6708-7", abstract = "Finding Boolean functions suitable for cryptographic primitives is a complex combinatorial optimization problem, since they must satisfy several properties to resist crypt-analytic attacks, and the space is very large, which grows super exponentially with the number of input variables. Recent research has focused on the study of Boolean functions that satisfy properties on restricted sets of inputs due to their importance in the development of the FLIP stream cipher. In this paper, we consider one such property, perfect balancedness, and investigate the use of Genetic Programming (GP) and Genetic Algorithms (GA) to construct Boolean functions that satisfy this property along with a good nonlinearity profile. We formulate the related optimization problem and define two encodings for the candidate solutions, namely the truth table and the weight wise balanced representations. Somewhat surprisingly, the results show that GA with the weightwise balanced representation outperforms GP with the classical truth table phenotype in finding highly nonlinear Weightwise Perfectly Balanced (WPB) functions. This is in stark contrast to previous findings on the evolution of balanced Boolean functions, where GP always performs best.", keywords = "genetic algorithms, genetic programming, Ciphers, Boolean functions, Input variables, Resists, Evolutionary computation, Boolean functions, balancedness, nonlinearity", DOI = "doi:10.1109/CEC55065.2022.9870427", notes = "Also known as \cite{9870427}", } @Article{Marjanovic:2016:JU, author = "Vladislav Marjanovic and Milos Milovancevic and Igor Mladenovic", title = "Prediction of {GDP} growth rate based on carbon dioxide (CO2) emissions", journal = "Journal of {CO2} Utilization", volume = "16", pages = "212--217", year = "2016", ISSN = "2212-9820", DOI = "doi:10.1016/j.jcou.2016.07.009", URL = "http://www.sciencedirect.com/science/article/pii/S2212982016301482", abstract = "The environment that governs the relationships between carbon dioxide (CO2) emissions and gross domestic product (GDP) changes over time due to variations in economic growth, regulatory policy and technology. The relationship between economic growth and carbon dioxide emissions is considered as one of the most important empirical relationships. However, rigorous economic causal analysis of the tradeoff between carbon dioxide (CO2) emissions and economic growth for credible climate change policies is still limited. The purpose of this research is to develop and apply the Extreme Learning Machine (ELM) to predict GDP based on CO2 emissions. The ELM results are compared with genetic programming (GP) and artificial neural network (ANN). The reliability of the computational models was accessed based on simulation results and using several statistical indicators. Coefficients of determination for ELM, ANN and GP methods were 0.9271, 0.8756 and 0.4475, respectively. Based upon simulation results, it is demonstrated that ELM can be used effectively in applications of GDP forecasting.", keywords = "genetic algorithms, genetic programming, Economic growth, Carbon dioxide, Prediction, Extreme learning machine", } @InProceedings{markose:2001:eafiof, author = "Sheri Markose and Edward Tsang and Hakan Er and Abdel Salhi", title = "Evolutionary Arbitrage For FTSE-100 Index Options and Futures", booktitle = "Proceedings of the 2001 Congress on Evolutionary Computation CEC2001", year = "2001", pages = "275--282", address = "COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "27-30 " # may, organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, FGP, Machine Discovery, Arbitrage, Options, Futures", ISBN = "0-7803-6658-1", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/TsangCEE2001.pdf", URL = "http://privatewww.essex.ac.uk/~scher/eddieProj/TsangCEE2001.doc", DOI = "doi:10.1109/CEC.2001.934401", abstract = "The objective in this paper is to develop and implement FGP-2 (Financial Genetic Programming) on intra daily tick data for stock index options and futures arbitrage in a manner that is suitable for online trading when windows of profitable arbitrage opportunities exist for short periods from one to ten minutes. Our benchmark for FGP-2 is the textbook rule for detecting arbitrage profits. This rule has the drawback that it awaits a contemporaneous profitable signal to implement an arbitrage in the same direction. A novel methodology of randomised sampling is used to train FGP-2 to pick up the fundamental arbitrage patterns. Care is taken to fine tune weights in the fitness function to enhance performance. As arbitrage opportunities are few, missed opportunities can be as costly as wrong recommendations to trade. Unlike conventional genetic programs, FGP-2 has a constraint satisfaction feature supplementing the fitness function that enables the user to train the FGP to specify a minimum and a maximum number of profitable arbitrage opportunities that are being sought. Historical sample data on arbitrage opportunities enables the user to set these minimum and maximum bounds. Good FGP rules for arbitrage are found to make a 3-fold improvement in profitability over the textbook rule. This application demonstrates the success of FGP-2 in its interactive capacity that allows experts to channel their knowledge into machine discovery", notes = "CEC-2001 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 01TH8546C, Library of Congress Number =", } @TechReport{markoso:2001:CFtr, author = "Sheri M. Markose", title = "The new evolutionary computational paradigm of complex adaptive systems. Challenges and prospects for economics and finance", institution = "Department of Economics, University of Essex", year = "2001", type = "Discussion paper series", number = "532", month = jul, note = "See Kluwer series on Computational Finance, Jan 2002", email = "scher@essex.ac.uk", keywords = "genetic algorithms, genetic programming", URL = "http://www.essex.ac.uk/economics/discussion-papers/papers-text/dp532.pdf", size = "53 pages", abstract = "The new evolutionary computational paradigm of market systems views these as complex adaptive systems. The major premise of 18th century classical political economy was that order in market systems is spontaneous or emergent, in that it is the result of 'human action but not of human design'. This early observation on the disjunction between system wide outcomes and capabilities of micro level rational calculation marks the provenance of modern evolutionary thought. However, it will take a powerful confluence of two 20th century epochal developments for the new evolutionary computational paradigm to rise to the challenge of providing long awaited explanations of what has remained anomalies or outside the ambit of traditional economic analysis. The first of these is the Godel-Turing-Post results on incompleteness and algorithmically unsolvable problems that delimit formalist calculation or deductive methods. The second is the Anderson-Holland-Arthur heterogeneous adaptive agent theory and models for inductive search, emergence and self-organised criticality which can crucially show and explicitly study the processes underpinning the emergence of ordered complexity. Multi-agent model simulation of asset price formation and the innovation based structure changing dynamics of capitalist growth are singled out for analysis of this disjunction between non-anticipating global outcomes and computational micro rationality.", notes = "See \cite{Markose:2002:gagpcf}", } @InCollection{MarkoseTsangEr:2002:gagpcf, author = "Sheri Markose and Edward Tsang and Hakan Er", title = "{EDDIE} for Stock Index Options and Futures Arbitrage", booktitle = "Genetic Algorithms and Genetic Programming in Computational Finance", publisher = "Kluwer Academic Press", year = "2002", editor = "Shu-Heng Chen", chapter = "14", pages = "281--308", keywords = "genetic algorithms, genetic programming, Machine Learning, Genetic Decision Trees, Arbitrage, Options Futures, Constraint Satisfaction", ISBN = "0-7923-7601-3", URL = "http://www.springer.com/economics/economic+theory/book/978-0-7923-7601-9", DOI = "doi:10.1007/978-1-4615-0835-9_14", abstract = "EDDIE-ARB (EDDIE stands for Evolutionary Dynamic Data Investment Evaluator) is a genetic program (GP) that implements a cross market arbitrage strategy in a manner that is suitable for online trading. Our benchmark for EDDIE-ARB is the Tucker (1991) put-call-futures (P-C-F) parity condition for detecting arbitrage profits in the index options and futures markets. The latter presents two main problems, (i) The windows for profitable arbitrage opportunities exist for short periods of one to ten minutes, (ii) Prom a large domain of search, annually, fewer than 3percent of these were found to be in the lucrative range of 500-800 profits per arbitrage. Standard ex ante analysis of arbitrage suffers from the drawback that the trader awaits a contemporaneous signal for a profitable price misalignment to implement an arbitrage in the same direction. Execution delays imply that this naive strategy may fail. A methodology of random sampling is used to train EDDIE-ARB to pick up the fundamental arbitrage patterns. The further novel aspect of EDDIE-ARB is a constraint satisfaction feature supplementing the fitness function that enables the user to train the GP how not to miss opportunities by learning to satisfy a minimum and maximum set on the number of arbitrage opportunities being sought. Good GP rules generated by EDDIE-ARB are found to make a 3-fold improvement in profitability over the naive ex ante rule.", notes = "part of \cite{chen:2002:gagpcf} Also known as: Evolutionary Decision Trees in FTSE-100 Index Options and Futures Arbitrage", } @InCollection{Markose:2002:gagpcf, author = "Sheri M. Markose", title = "The New Evolutionary Computational Paradigm of Complex Adaptive Systems: Challenges and Prospects for Economics and Finance", booktitle = "Genetic Algorithms and Genetic Programming in Computational Finance", publisher = "Kluwer Academic Press", year = "2002", editor = "Shu-Heng Chen", chapter = "21", pages = "443--484", keywords = "genetic algorithms, genetic programming, Complex Adaptive Systems, Emergence, Self-Organized Criticality, Algorithmic Unsolvability, Inductive Search, Innovation Market, Efficiency", ISBN = "0-7923-7601-3", URL = "http://www.essex.ac.uk/economics/discussion-papers/papers-text/dp532.pdf", URL = "http://www.springer.com/economics/economic+theory/book/978-0-7923-7601-9", DOI = "doi:10.1007/978-1-4615-0835-9_21", size = "53 pages", abstract = "The new evolutionary computational paradigm of market systems views these as complex adaptive systems. The major premise of 18th century classical political economy was that order in market systems is spontaneous or emergent, in that it is the result of human action but not of human design. This early observation on the disjunction between system wide outcomes and capabilities of micro level rational calculation marks the provenance of modern evolutionary thought. However, it will take a powerful confluence of two 20th century epochal developments for the new evolutionary computational paradigm to rise to the challenge of providing long awaited explanations of what has remained anomalies or outside the ambit of traditional economic analysis. The first of these is the Goedel-Turing-Post results on incompleteness and algorithmically unsolvable problems that delimit formalist calculation or deductive methods. The second is the Anderson-Holland-Arthur heterogeneous adaptive agent theory and models for inductive search, emergence and self-organised criticality which can crucially show and explicitly study the processes underpinning the emergence of ordered complexity. Multi agent model simulation of asset price formation and the innovation based structure changing dynamics of capitalist growth are singled out for analysis of this disjunction between non-anticipating global outcomes and computational micro rationality.", notes = "part of \cite{chen:2002:gagpcf}", } @Article{Markovic:2017:PASMA, author = "Dusan Markovic and Dalibor Petkovic and Vlastimir Nikolic and Milos Milovancevic and Biljana Petkovic", title = "Soft computing prediction of economic growth based in science and technology factors", journal = "Physica A: Statistical Mechanics and its Applications", volume = "465", pages = "217--220", year = "2017", ISSN = "0378-4371", DOI = "doi:10.1016/j.physa.2016.08.034", URL = "http://www.sciencedirect.com/science/article/pii/S0378437116305519", abstract = "The purpose of this research is to develop and apply the Extreme Learning Machine (ELM) to forecast the gross domestic product (GDP) growth rate. In this study the GDP growth was analyzed based on ten science and technology factors. These factors were: research and development (R&D) expenditure in GDP, scientific and technical journal articles, patent applications for nonresidents, patent applications for residents, trademark applications for nonresidents, trademark applications for residents, total trademark applications, researchers in R&D, technicians in R&D and high-technology exports. The ELM results were compared with genetic programming (GP), artificial neural network (ANN) and fuzzy logic results. Based upon simulation results, it is demonstrated that ELM has better forecasting capability for the GDP growth rate.", keywords = "genetic algorithms, genetic programming, Soft computing, GDP, Prediction, Science and technology factor", } @InProceedings{marks:1999:CGARDO, author = "Robert E. Marks and David F. Midgley and Lee G. Cooper and G. M. Shiraz", title = "Coevolution with the Genetic Algorithm: Repeated Differentiated Oligopolies", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1609--1615", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "real world applications", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-766.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-766.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Book{Marks:2017:iEI, author = "Robert J. {Marks, II} and William A. Dembski and Winston Ewert", title = "Introduction to Evolutionary Informatics", publisher = "World Scientific", year = "2017", keywords = "genetic algorithms, genetic programming, information theory", isbn13 = "9789813142145", URL = "https://www.amazon.co.uk/Introduction-Evolutionary-Informatics-Robert-Marks/dp/9813142146", DOI = "doi:10.1142/9974", size = "332 pages", notes = "Baylor University, USA", } @Article{Markus:2010:JH, author = "Momcilo Markus and Mohamad I. Hejazi and Peter Bajcsy and Orazio Giustolisi and Dragan A. Savic", title = "Prediction of weekly {nitrate-N} fluctuations in a small agricultural watershed in {Illinois}", journal = "Journal of Hydroinformatics", year = "2010", volume = "12", number = "3", pages = "251--261", month = jul, keywords = "genetic algorithms, genetic programming, artificial neural networks, drinking water, forecasting, naive Bayes model, nitrate-N", ISSN = "1464-7141", URL = "https://iwaponline.com/jh/article-pdf/12/3/251/386467/251.pdf", DOI = "doi:10.2166/hydro.2010.064", size = "11 pages", publisher = "IWA Publishing", abstract = "Agricultural nonpoint source pollution has been identified as one of the leading causes of surface water quality impairment in the United States. Such an impact is important, particularly in predominantly agricultural areas, where application of agricultural fertilisers often results in excessive nitrate levels in streams and rivers. When nitrate concentration in a public water supply reaches or exceeds drinking water standards, costly measures such as well closure or water treatment have to be considered. Thus, having accurate nitrate-N predictions is critical in making correct and timely management decisions. This study applied a set of data mining tools to predict weekly nitrate-N concentrations at a gauging station on the Sangamon River near Decatur, Illinois, USA. The data mining tools used in this study included artificial neural networks, evolutionary polynomial regression and the naive Bayes model. The results were compared using seven forecast measures. In general, all models performed reasonably well, but not all achieved best scores in each of the measures, suggesting that a multi-tool approach is needed. In addition to improving forecast accuracy compared with previous studies, the tools described in this study demonstrated potential for application in error analysis, input selection and ranking of explanatory variables, thereby designing cost-effective monitoring networks.", notes = "Institute of Natural Resource Sustainability, University of Illinois at Urbana-Champaign, 2204 Griffith Dr, Champaign, Illinois 61820, USA E-mail: mmarkus@illinois.edu Ven-Te Chow Hydrosystems Laboratory, Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, 205 North Mathews Ave, Urbana, IL 61801, USA National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, 1205 West Clark Street, Urbana, IL 61801, USA Department of Civil and Environmental Engineering, Technical University of Bari, II Engineering Faculty, Taranto via Turismo 8, 74100, Italy Centre for Water Systems, School of Engineering, Computing and Mathematics, University of Exeter, Harrison Building, North Park Road, Exeter EX4 4QF, UK", } @Article{Marlaithong:2023:ACC, author = "Tinnakorn Marlaithong and Vasco Chibante Barroso and Phond Phunchongharn", journal = "IEEE Access", title = "A Log Parsing Framework for {ALICE} O2 Facilities", year = "2023", volume = "11", pages = "69439--69457", abstract = "The ALICE (A Large Ion Collider Experiment) detector at the European Organization for Nuclear Research (CERN) generates a substantial volume of experimental data, demanding efficient online and offline processing. To enhance the stability and reliability of the ALICE computing system, this study introduces an Artificial Intelligence-based logging system designed to detect, identify, and resolve issues through the analysis of system runtime information contained in logs. Existing online log parsing methods, however, often lack full automation and generality, relying instead on manual parameter definition and regular expressions that are better suited for static logs. In this study, we propose a novel and fully automated online log parsing framework for ALICE O2 (Online-Offline). To overcome key challenges, we employ the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm to create ground truth, employ genetic programming to generate regular expressions, use the Artificial Bee Colony (ABC) algorithm for hyperparameter optimisation, and implement a log template reduction algorithm to reduce similarity among log templates. Our framework's effectiveness is validated through experiments on 5 benchmark log datasets and ALICE application logs, comparing its performance with the state-of-art online log parsing framework, Drain. The empirical results demonstrate the automated nature of our approach and its ability to achieve accurate parsing with high accuracy (i.e., 99.89percent on the ALICE application log).", keywords = "genetic algorithms, genetic programming, Real-time systems, Optimisation, Benchmark testing, Anomaly detection, Tuning, Task analysis, Systems architecture, Machine learning, ALICE experiment, FLP cluster, machine learning, online log parser, TF-IDF", DOI = "doi:10.1109/ACCESS.2023.3293406", ISSN = "2169-3536", notes = "Also known as \cite{10176271}", } @InProceedings{marmelstein:1998:pchGPpdta, author = "Robert E. Marmelstein and Gary B. Lamont", title = "Pattern Classification using a Hybrid Genetic Program Decision Tree Approach", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "223--231", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, data mining, decision trees, C4.5, GPCEE, XAI, DNF", ISBN = "1-55860-548-7", broken = "http://en.afit.af.mil/hpc/students/rmarmels/gp98.ps.gz", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1998/marmelstein_1998_pchGPpdta.pdf", size = "9 pages", notes = "GP-98. Pima Indians, Wisconsin Breast Cancer, SCUD missile FLIR", } @InProceedings{marmelstein:1998:GRaCCE, author = "Robert E. Marmelstein", title = "GRaCCE: A Genetic Environment for Data Mining", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "143", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "genetic algorithms, genetic programming", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/7621/http:zSzzSzwww.ai.mit.eduzSzpeoplezSzunamayzSzgecco-phdzSzmarmel-ann98.pdf/gracce-a-genetic-environment.pdf", URL = "http://citeseer.ist.psu.edu/103989.html", size = "1 page", long_abstract = "Data mining is the automated search for interesting and useful relationships between attributes in databases. In this regard, the rules used by classifiers are inherently interesting because they distinguish between similar looking data of differing class. In this paper, we introduce the Genetic Rule and Classifier Construction Environment (GRaCCE) as a means for extracting classification rules from data. GRaCCE uses a multi-stage, Genetic Algorithm (GA) based approach to first reduce the...", notes = "Nov 2012 http://citeseer.ist.psu.edu/103989.html appears to be a longer (6 page) version of one page that is actually in the GP-98 late breaking book. See also \cite{Marmelstein:1998:GPCEE} \cite{marmelstein:1998:ecdrs}. GP-98LB", } @InProceedings{Marmelstein:1998:GPCEE, author = "Robert E. Marmelstein and Gary B. Lamont", title = "GPCEE: A Genetic Programming Approach to Data Mining", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "260", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "genetic algorithms, genetic programming", size = "1 page", notes = "See also \cite{marmelstein:1998:GRaCCE} GP-98LB, GP-98PhD Student Workshop", } @InProceedings{marmelstein:1998:ecdrs, author = "Robert E. Marmelstein and Gary B. Lamont", title = "Evolving Compact Decision Rule Sets", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "144--150", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "genetic algorithms, genetic programming, GRaCCE", URL = "http://en.afit.af.mil/hpc/students/rmarmels/gp98pp.ps.gz", size = "7 pages", notes = "GP-98LB", } @PhdThesis{marmelstein:thesis, author = "Robert Evan Marmelstein", title = "Evolving Compact Decision Rule Sets", school = "Faculty of the Graduate School of Engineering of the Air Force Institute of Technology Air University", year = "1999", address = "USA", month = jun, keywords = "genetic algorithms, genetic programming, GRaCCE, Matlab", URL = "ftp://math.chtf.stuba.sk/pub/vlado/thesis_Marmelstein/thesis_Marmelstein.pdf", URL = "ftp://math.chtf.stuba.sk/pub/vlado/thesis_Marmelstein/thesis_Marmelstein.ps.gz", size = "271 pages", abstract = "With the increased proliferation of computing equipment, there has been a corresponding explosion in the number and size of databases. Although a great deal of time and effort is spent building and maintaining these databases, it is nonetheless rare that this valuable resource is exploited to its fullest. The principle reason for this paradox is that many organizations lack the insight and/or expertise to effectively translate this information into usable knowledge. While data mining technology holds the promise of automatically extracting useful patterns (such as decision rules) from data, this potential has yet to be realized. One of the major technical impediments is that the current generation of data mining tools produce decision rule sets that are very accurate, but extremely complex and difficult to interpret. As a result, there is a clear need for methods that yield decision rule sets that are both accurate and compact. The development of the Genetic Rule and Classifier Construction Environment (GRaCCE) is proposed as an alternative to existing decision rule induction (DRI) algorithms. GRaCCE is a multi-phase algorithm which harnesses the power of evolutionary search to mine classification rules from data. These rules are based on piece-wise linear estimates of the Bayes decision boundary within a winnowed subset of the data. Once a sufficient set of these hyper-planes are generated, a genetic algorithm (GA) based {"}0/1{"} search is performed to locate combinations of them that enclose class homogeneous regions of the data. It is shown that this approach enables GRaCCE to produce rule sets significantly more compact than those of other DRI methods while achieving a comparable level of accuracy. Since the principle of Occam's razor tells us to always prefer the simplest model that its the data, the rules found by GRaCCE are of greater utility than those identified by existing methods.", notes = "AFIT/DS/ENG/99-05 Approved for public release; distribution unlimited Appendix B. GRaCCE User's Guide", } @Book{Marmelstein:book, author = "Robert E. Marmelstein", title = "Evolving Compact Decision Rule Sets", publisher = "Storming Media", year = "1999", address = "USA", keywords = "genetic algorithms, genetic programming", ISBN = "1-4235-4475-7", isbn13 = "978-1288324286", URL = "https://www.amazon.co.uk/Evolving-Compact-Decision-Rule-Sets/dp/1288324286", URL = "http://www.brightsurf.com/brightsurf/books/1423544757/Evolving_Compact_Decision_Rule_Sets.html", URL = "https://www.abebooks.co.uk/servlet/SearchResults?bi=0&bx=off&ds=30&isbn=9781288324286&recentlyadded=all&sortby=17&sts=t", abstract = "This is a AIR FORCE INSTITUTE OF TECHNOLOGY WRIGHT-PATTERSON Air Force Base OH report procured by the Pentagon and made available for public release. It has been reproduced in the best form available to the Pentagon. It is not spiral-bound, but rather assembled with Velobinding in a soft, white linen cover. The Storming Media report number is A932463. The abstract provided by the Pentagon follows: While data mining technology holds the promise of automatically extracting useful patterns (such as decision rules) from data, this potential has yet to be realized. One of the major technical impediments is that the current generation of data mining tools produce decision rule sets that are very accurate, but extremely complex and difficult to interpret. As a result, there is a clear need for methods that yield decision rule sets that are both accurate and compact. The development of the Genetic Rule and Classifier Construction Environment (GRaCCE) is proposed as an alternative to existing decision rule induction (DRI) algorithms. GRaCCE is a multi-phase algorithm which harnesses the power of evolutionary search to mine classification rules from data. These rules are based on piece-wise linear estimates of the Bayes decision boundary within a winnowed subset of the data. Once a sufficient set of these hyper- planes are generated, a genetic algorithm (GA) based {"}0/1{"} search is performed to locate combinations of them that enclose class homogeneous regions of the data. It is shown that this approach enables GRaCCE to produce rule sets significantly more compact than those of other DRI methods while achieving a comparable level of accuracy. Since the principle of Occam's razor tells us to always prefer the simplest model that fits the data, the rules found by GRaCCE are of greater use than those identified by existing methods.", notes = "Spiral-bound ? Oct 2016 Appears to have been republished by BiblioScholar (21 Nov. 2012) ISBN-13: 978-1288324286", } @Article{marney:2000:jasss, author = "John Paul Marney and Heather F. E. Tarbert", title = "Why do simulation? Towards a working epistemology for practitioners of the dark arts", journal = "Journal of Artificial Societies and Social Simulation", year = "2000", volume = "3", number = "3", keywords = "genetic algorithms, genetic programming, reciprocal altruism, group living, segmentation", ISSN = "1460-7425", URL = "http://jasss.soc.surrey.ac.uk/3/4/4.html", abstract = "The purpose of this paper is to argue for clarity of methodology in social science simulation. Simulation is now at a stage in the social sciences where it is important to be clear why simulation should be used and what it is intended to achieve. The paper goes on to discuss a particularly important source of opposition to simulation in the social sciences which arises from perceived threats to the orthodox hard-core. This is illustrated by way of a couple of case studies. The paper then goes on to discuss defences to standard criticisms of simulation and the various positive reasons for using simulation in preference to other methods of theorising in particular situations.", notes = "GP mentioned as an example", } @InProceedings{marney:2000:CEF, author = "John Paul Marney and Heather F. E. Tarbert and Colin Fyfe", title = "Technical Trading versus Market Efficiency-A Genetic Programming Approach", booktitle = "Computing in Economics and Finance", year = "2000", address = "Universitat Pompeu Fabra, Barcelona, Spain", month = "6-8 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://ideas.repec.org/p/sce/scecf0/169.html", abstract = "In this paper genetic programming is used to investigate a number of long time series of price data for a stock exchange quoted share, in order to discern whether there are any patterns in the data which could be used for technical trading purposes. This extends the work done by the authors in a previous paper (Fyfe et al. 1999) which suggested that, although it was possible to find a rule which did outperform simple buy and hold, there were insufficient grounds for the rejection of the efficient market hypothesis. The purpose of the present paper is to investigate the robustness and generalisability of the conclusion reached by Fyfe et. al.", notes = "broken http://enginy.upf.es/SCE/index2.html RePEc:sce:scecf0:169", } @InProceedings{marney:2001:SCE, author = "John Paul Marney and D. Miller and Colin Fyfe and Heather F. E. Tarbert", title = "Risk Adjusted Returns to Technical Trading Rules: a Genetic Programming Approach", booktitle = "7th International Conference of Society of Computational Economics", year = "2001", address = "Yale", month = "28-29 " # jun, organisation = "Society for Computational Economics", keywords = "genetic algorithms, genetic programming", URL = "http://EconPapers.repec.org/RePEc:sce:scecf1:147", abstract = "This paper is a continuation of our investigation of the paradox of technical analysis in the stock market (Fyfe, Marney and Tarbert 1999), Marney et. al (2000). The Efficient Markets Hypothesis (hereafter the EMH) holds that there should be no discernible pattern in share price data or the prices of other frequently traded financial instruments, as financial markets are efficient. Prices therefore should follow an information-free random-walk. Nevertheless, technical analysis is a common and presumably profitable practice among investment professionals. Applications of Genetic Programming and Genetic Algorithms to the extraction of Technical Trading Patterns from financial data. The subset of technical trading research which is concerned with the application of GAs, GPs and neural networks is very new and underdeveloped and therefore of considerable potential. The most notable empirical work which has been done in this area is that of Neely, Dittmar and Weller (1996, 1997), Neely and Weller (2001) and Neely (2001). We have also done some work in this area ourselves (Fyfe et al. 1999, Marney et al. 2000). The theoretical underpinning for this kind of approach to finding technical trading patterns is provided by the work of Arthur et al. (1997).", abstract = "Using the main six trading currencies, Neely et al. (1996, 1997) find strong evidence of economically significant out-of-sample excess returns to technical trading rules identified by their genetic program. In Allen and Karjaleinen (1999) a genetic algorithm is used to find technical trading rules for the S&P index. Compared to a simple buy-and-hold strategy, these trading rules lead to positive excess returns which are statistically and economically significant. In Fyfe et. al. (1999), a GP is used to discover a successful buy rule. This discovery, as such, however, was not really a refutation of the EMH, as it was really a form of timing specific buy and hold, which was triggered only once. Nevertheless, the return is superior to buy and hold. Using the S&P 500 index, Neely (2001) finds no evidence that technical trading rules identified by a GP significantly outperform buy-and-hold on a risk-adjusted basis. For the case of intraday trading on the forex market, Neely and Weller (2001) find no evidence of excess returns to trading rules derived from a GP and an optimised linear forecasting model. Indeed Neely (2001) observes that a number of studies have generally evaluated raw excess returns rather than explicitly risk-adjusted returns, leaving unclear the implications of their work for the efficient markets hypothesis' (2001, p.1). On the other hand, Neely et al. (1996, 1997) did calculate betas associated with foreign currency portfolio holdings, and did not find evidence of excessive risk bearing. Brown, Geotzman and Kumar (1998) and Bessember and Chan (1998) can also be cited in favour of the hypothesis of superior risk-adjusted returns from technical trading signals. Marney et al. (2000) looked again at their 1999 findings by, amongst other things, adjusting for risk. It was found that although there were other rules which apparently performed well by being very active in the market, the impressive returns to these rules turn out on closer inspection to be illusory, as risk adjusted returns did not compare well with simple buy and hold. Nevertheless, paradoxically, we did find a useful role for technical trading. It is possible to substantially improve on buy and hold by timing it right. Hence our argument is that it is worth analysing the market to find a good intervention point. Purpose and method of the investigation Given that very little work has been done on generating technical trading rules which produce excess risk-adjusted profits, and given that the empirical evidence is somewhat ambiguous, there is clearly considerable scope for additional work in this area. What we propose to do then is to re-examine our previous findings, this time within a more rigorous framework which makes use of a wider data set, more extensive use of techniques of risk adjustment, and more demanding assessment of the robustness of the result with respect to GP representation. 1. Hypotheses Can the GP generate technical trading rules which will generate risk-adjusted excess returns out of sample? Secondly, the is there any further evidence for 'timing-specific' buy and hold. Thirdly, are there any technical trading rules which generalise across data sets or time-periods? 2. Data Set Our data set is drawn from long time series for 5 US shares from a disparate set of industrial sectors and also the S&P 500. 3. Risk adjustment In this study we look at a variety of risk measures including Betas, Sharpe ratios and the X* statistic. 4. The GP - As in Marney et al. (2000) we consider how robust our conclusion is with respect to the GP method used.", notes = "Broken Nov 2012 http://www.econ.yale.edu/sce01/confpage.html http://cowles.econ.yale.edu/conferences/2001/7intl.htm 22 aug 2004 http://ideas.repec.org/p/sce/scecf1/147.html CEF 2001", } @InProceedings{Marois:2021:CogSIMA, author = "Alexandre Marois and Loic Grossetete and Benedicte Chatelais and Daniel Lafond", title = "Evaluation of Evolutionary Algorithms Under Frugal Learning Constraints for Online Policy Capturing", booktitle = "2021 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)", year = "2021", pages = "73--79", abstract = "Decision making can be modelled in various ways for the design of decision-support systems. One strategy privileged for this purpose is policy capturing, i.e. using statistical techniques (and more recently machine learning) to model judgement policies. The Cognitive Shadow is a prototype tool suited for frugal learning that automatically learns a user's decision pattern in real time based on an ensemble of seven supervised learning algorithms. This tool can provide advisory warnings when the user decision is inconsistent with the predicted outcome. Evolutionary computation methods could reinforce the system's efficiency because of their ability to deal with computational complexity via evolution-inspired optimization mechanisms. The goal of this study was to assess the potential of evolutionary algorithms for frugal learning in an online policy capturing context. To do so, we tested three evolutionary algorithms on three different datasets (each split in three sizes), and compared both their prediction performance and training time with that of the other modeling techniques already implemented in the Cognitive Shadow system. Although all three evolutionary models were generally outperformed by non-evolutionary learning algorithms, one genetic programming method showed good prediction performance for the more complex use cases with the smaller datasets.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CogSIMA51574.2021.9475930", ISSN = "2379-1675", month = may, notes = "Also known as \cite{9475930}", } @Article{Maroufpoor:2019:AWM, author = "Saman Maroufpoor and Jalal Shiri and Eisa Maroufpoor", title = "Modeling the sprinkler water distribution uniformity by data-driven methods based on effective variables", journal = "Agricultural Water Management", year = "2019", volume = "215", pages = "63--73", month = "20 " # apr, keywords = "genetic algorithms, genetic programming, gene expression programming, coefficient of uniformity, k-fold testing, support vector machines, SVM, neural networks, ANN, neuro-fuzzy, sprinkler irrigation", ISSN = "0378-3774", identifier = "RePEc:eee:agiwat:v:215:y:2019:i:c:p:63-73", oai = "oai:RePEc:eee:agiwat:v:215:y:2019:i:c:p:63-73", URL = "https://www.sciencedirect.com/science/article/pii/S0378377418313258", DOI = "doi:10.1016/j.agwat.2019.01.008", abstract = "The coefficient of uniformity (CU), an important parameter in design of irrigation systems, affects the quality and return of investment in irrigation projects significantly, and is a good indicator of water losses. In this paper, a single model was proposed to obtain the CU values in four sprinkler types of ZK30, ZM22, AMBO, and LUXOR. Average wind speed, coarseness index (large and small nozzle diameters), and sprinkler/lateral spacing were used as input parameters to obtain the CU values through employing the artificial neural networks (ANN), neuro-fuzzy grid partitioning (NF-GP), neuro-fuzzy sub-clustering (NF-SC), least square support vector machine (LS-SVM) and gene expression programming (GEP) techniques. The available data set consisted of 294 samples that were used to evaluate the proposed methodology. The applied techniques were assessed through the robust k-fold testing data assignment mode. Based on the results, all the applied models presented good capability in estimating CU. The obtained results revealed that the coarseness index (large nozzle diameter) had the lowest impact on modelling CU is sprinkler irrigation systems.", } @InProceedings{Marques-Pita:2011:ieeeALife, author = "Manuel Marques-Pita and Luis M. Rocha", title = "Schema Redescription in Cellular Automata: Revisiting Emergence in Complex Systems", booktitle = "The 2011 IEEE Symposium on Artificial Life", year = "2011", editor = "Chrystopher Nehaniv and Terry Bossomaier and Hiroki Sayama", pages = "233--240", address = "Paris, France", month = apr # " 13-15", organisation = "IEEE Computational Intelligence Society", keywords = "genetic algorithms, genetic programming, nonlinear sciences, cellular automata and lattice Gases, Artificial Intelligence, Formal Languages and Automata, Neural and Evolutionary Computing, Quantitative Biology, Quantitative Methods", bibsource = "OAI-PMH server at export.arxiv.org", oai = "oai:arXiv.org:1102.1691", URL = "http://arxiv.org/abs/1102.1691", size = "8 pages", abstract = "We present a method to eliminate redundancy in the transition tables of Boolean automata: schema redescription with two symbols. One symbol is used to capture redundancy of individual input variables, and another to capture permutability in sets of input variables: fully characterising the canalisation present in Boolean functions. Two-symbol schemata explain aspects of the behaviour of automata networks that the characterization of their emergent patterns does not capture. We use our method to compare two well-known cellular automata for the density classification task: the human engineered CA GKL, and another obtained via genetic programming (GP). We show that despite having very different collective behaviour, these rules are very similar. Indeed, GKL is a special case of GP. Therefore, we demonstrate that it is more feasible to compare cellular automata via schema redescriptions of their rules, than by looking at their emergent behaviour, leading us to question the tendency in complexity research to pay much more attention to emergent patterns than to local interactions.", notes = "Not really on GP but does make use of GP result given by \cite{andre:1996:camc} http://coco.binghamton.edu/ieee-alife2011/", } @Article{marquez:2021:AS, author = "Jack Marquez and Oscar H. Mondragon and Juan D. Gonzalez", title = "An Intelligent Approach to Resource Allocation on Heterogeneous Cloud Infrastructures", journal = "Applied Sciences", year = "2021", volume = "11", number = "21", keywords = "genetic algorithms, genetic programming, cloud computing, resource allocation", ISSN = "2076-3417", URL = "https://www.mdpi.com/2076-3417/11/21/9940", DOI = "doi:10.3390/app11219940", abstract = "Cloud computing systems are rapidly evolving toward multicloud architectures supported on heterogeneous hardware. Cloud service providers are widely offering different types of storage infrastructures and multi-NUMA architecture servers. Existing cloud resource allocation solutions do not comprehensively consider this heterogeneous infrastructure. In this study, we present a novel approach comprised of a hierarchical framework based on genetic programming to solve problems related to data placement and virtual machine allocation for analytics applications running on heterogeneous hardware with a variety of storage types and nonuniform memory access. Our approach optimises data placement using the Hadoop File System on heterogeneous storage devices on multicloud systems. It guarantees the efficient allocation of virtual machines on physical machines with multiple NUMA (nonuniform memory access) domains by minimizing contention between workloads. We prove that our solutions for data placement and virtual machine allocation outperform other state-of-the-art approaches.", notes = "also known as \cite{app11219940}", } @Article{Marquez-Vera:2013:AI, author = "Carlos Marquez-Vera and Alberto Cano and Cristobal Romero and Sebastian Ventura", title = "Predicting student failure at school using genetic programming and different data mining approaches with high dimensional and imbalanced data", journal = "Applied Intelligence", year = "2013", volume = "38", number = "3", pages = "315--330", month = apr, keywords = "genetic algorithms, genetic programming, educational data mining, Predicting student performance, Classification, Educational data mining, Student failure, Grammar-based genetic programming", language = "English", publisher = "Springer", ISSN = "0924-669X", DOI = "doi:10.1007/s10489-012-0374-8", size = "16 pages", abstract = "Predicting student failure at school has become a difficult challenge due to both the high number of factors that can affect the low performance of students and the imbalanced nature of these types of datasets. In this paper, a genetic programming algorithm and different data mining approaches are proposed for solving these problems using real data about 670 high school students from Zacatecas, Mexico. Firstly, we select the best attributes in order to resolve the problem of high dimensionality. Then, rebalancing of data and cost sensitive classification have been applied in order to resolve the problem of classifying imbalanced data. We also propose to use a genetic programming model versus different white box techniques in order to obtain both more comprehensible and accuracy classification rules. The outcomes of each approach are shown and compared in order to select the best to improve classification accuracy, specifically with regard to which students might fail.", notes = "Also known as \cite{Marquez:2013:AI}", } @InProceedings{Marrega:2015:ieeeEMBC, author = "Luiz H. G. Marrega and Simone M. Silva and Elisangela F. Manffra and Julio C. Nievola", booktitle = "37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)", title = "Comparison between Decision Tree and Genetic Programming to distinguish healthy from stroke postural sway patterns", year = "2015", pages = "6820--6823", abstract = "Maintaining balance is a motor task of crucial importance for humans to perform their daily activities safely and independently. Studies in the field of Artificial Intelligence have considered different classification methods in order to distinguish healthy subjects from patients with certain motor disorders based on their postural strategies during the balance control. The main purpose of this paper is to compare the performance between Decision Tree (DT) and Genetic Programming (GP) - both classification methods of easy interpretation by health professionals - to distinguish postural sway patterns produced by healthy and stroke individuals based on 16 widely used posturographic variables. For this purpose, we used a posturographic dataset of time-series of centre-of-pressure displacements derived from 19 stroke patients and 19 healthy matched subjects in three quiet standing tasks of balance control. Then, DT and GP models were trained and tested under two different experiments where accuracy, sensitivity and specificity were adopted as performance metrics. The DT method has performed statistically significant (P <; 0.05) better in both cases, showing for example an accuracy of 72.8percent against 69.2percent from GP in the second experiment of this paper.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/EMBC.2015.7319960", ISSN = "1094-687X", month = aug, notes = "Also known as \cite{7319960}", } @Article{Marriboyina:2012:jcta, author = "Venkatadri Marriboyina and Hanumat G. Sastry and Lokanatha C. Reddy", title = "A Survey on Genetic Programming in Data Mining Tasks", journal = "Journal of Computer Technology \& Applications", year = "2012", volume = "3", number = "1", pages = "83--86", keywords = "genetic algorithms, genetic programming, Data mining, classification, clustering", ISSN = "0976-5697", URL = "http://stmjournals.com/index.php?journal=JoCTA&page=article&op=view&path[]=1701", abstract = "Genetic programming (GP) is a machine learning technique used to give the optimized solution for the user specified tasks from a population of computer programs based on a fitness function. Genetic programming provides automated and optimized solutions for searching of large, poorly defined search spaces and even with the complexities of high dimensionality, multi-modality and discontinuity with noise. Knowledge discovery is an extremely complex process in the real world databases. Various data mining techniques exist for knowledge discovery process, among them, genetic programming data mining techniques are more efficient and suitable. Hence, this paper discusses various GP-based techniques in the data mining field.", notes = "STM Journals", } @Article{Marrone:2016:PCS, author = "Stefano Marrone and Ugo Gentile", title = "Finding Resilient and Energy-saving Control Strategies in Smart Homes", journal = "Procedia Computer Science", volume = "83", pages = "976--981", year = "2016", note = "The 7th International Conference on Ambient Systems, Networks and Technologies (ANT 2016) / The 6th International Conference on Sustainable Energy Information Technology (SEIT-2016) / Affiliated Workshops", ISSN = "1877-0509", DOI = "doi:10.1016/j.procs.2016.04.195", URL = "http://www.sciencedirect.com/science/article/pii/S1877050916302289", abstract = "Evolutionary computing has demonstrated its effectiveness in supporting the development of robust and intelligent systems: when used in combination with formal and quantitative models, it becomes a primary tool in critical systems. Among the modern critical infrastructures, smart energy grids are getting a growing interest from many communities (academic, industrial and political) fostering the development of a robust energy distribution infrastructure. Energy grids are also an example of critical cyber physical social systems since their equilibrium can be perturbed not only by cyber and physical attacks but also by economical and social crises as well as changes in the consumption profiles. The paper illustrates a practical framework supporting the run-time evolution of the control logic inside the Smart Meter: the centre of modern Smart Homes. By combining the modelling and analysis capabilities of Fluid Stochastic Petri Nets and the flexibility of Genetic Programming, this approach can be used to adapt the control logic of the Smart Meters to the changes of the structure and functionalities of the Smart Home as well as of the operational environment. While the main objective of the evolution is to guarantee the energetic sustainability of the Smart Home, the fulfilment of the user's requirements about the energetic need of the home allows to preserve the identity of the Smart Meter during its evolution.", keywords = "genetic algorithms, genetic programming, Computer-based Critical Infrastructures, Smart Energy Grids, Self-Adaptive Systems, Fluid Stochastic Petri Nets", } @InProceedings{Marshall:2014:GECCOcomp, author = "Richard J. Marshall and Mark Johnston and Mengjie Zhang", title = "Hyper-heuristics, grammatical evolution and the capacitated vehicle routing problem", booktitle = "GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion", year = "2014", editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges", isbn13 = "978-1-4503-2881-4", keywords = "genetic algorithms, genetic programming, grammatical evolution, evolutionary combinatorial optimization and metaheuristics: Poster", pages = "71--72", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2598394.2598407", DOI = "doi:10.1145/2598394.2598407", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "A common problem when applying heuristics is that they often perform well on some problem instances, but poorly on others. We develop a hyper-heuristic approach, using Grammatical Evolution (GE), to generate heuristics for the Vehicle Routing Problem (VRP). Through a series of experiments we develop an approach that leads to solutions of acceptable quality to Vehicle Routing Problem instances with only limited prior knowledge of the problem to be solved.", notes = "Also known as \cite{2598407} Distributed at GECCO-2014.", } @InProceedings{Marshall:2014:SEAL, author = "Richard J. Marshall and Mark Johnston and Mengjie Zhang", title = "A Comparison between Two Evolutionary Hyper-Heuristics for Combinatorial Optimisation", booktitle = "Proceedings 10th International Conference on Simulated Evolution and Learning, SEAL 2014", year = "2014", editor = "Grant Dick and Will N. Browne and Peter Whigham and Mengjie Zhang and Lam Thu Bui and Hisao Ishibuchi and Yaochu Jin and Xiaodong Li and Yuhui Shi and Pramod Singh and Kay Chen Tan and Ke Tang", volume = "8886", series = "Lecture Notes in Computer Science", pages = "618--630", address = "Dunedin, New Zealand", month = dec # " 15-18", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-13562-5", DOI = "doi:10.1007/978-3-319-13563-2_52", size = "13 pages", abstract = "Developing and managing a general method of solving combinatorial optimisation problems reduces the need for expensive human experts when solving previously unseen variations to common optimisation problems. A hyper-heuristic provides such a method. Each hyper-heuristic has its own strengths and weaknesses and we research how these properties can be managed. We construct and compare simplified versions of two existing hyper-heuristics (adaptive and grammar-based), and analyse how each handles the trade-off between computation speed and quality of the solution. We test the two hyper-heuristics on seven different problem domains using the HyFlex framework. We conclude that both hyper-heuristics successfully identify and manipulate low-level heuristics to generate good solutions of comparable quality, but the adaptive hyper-heuristic consistently achieves this in a shorter computational time than the grammar based hyper-heuristic.", } @InProceedings{conf/seal/MarshallJZ14a, author = "Richard J. Marshall and Mark Johnston and Mengjie Zhang", title = "Developing a Hyper-Heuristic Using Grammatical Evolution and the Capacitated Vehicle Routing Problem", bibdate = "2014-11-12", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/seal/seal2014.html#MarshallJZ14a", booktitle = "Simulated Evolution and Learning - 10th International Conference, {SEAL} 2014, Dunedin, New Zealand, December 15-18, 2014. Proceedings", publisher = "Springer", year = "2014", volume = "8886", editor = "Grant Dick and Will N. Browne and Peter A. Whigham and Mengjie Zhang and Lam Thu Bui and Hisao Ishibuchi and Yaochu Jin and Xiaodong Li and Yuhui Shi and Pramod Singh and Kay Chen Tan and Ke Tang", isbn13 = "978-3-319-13562-5", pages = "668--679", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming, grammatical evolution", URL = "http://dx.doi.org/10.1007/978-3-319-13563-2", } @InProceedings{Marshall:2015:evoCOP, author = "Richard J. Marshall and Mark Johnston and Mengjie Zhang", title = "Hyper-heuristic Operator Selection and Acceptance Criteria", booktitle = "Proceedings of the 15th European Conference on Evolutionary Computation in Combinatorial Optimisation (EvoCOP2015)", year = "2015", editor = "Gabriela Ochoa and Francisco Chicano", volume = "9026", series = "Lecture Notes in Computer Science", pages = "99--113", address = "Copenhagen", month = "8-10 " # apr, organisation = "EvoStar", publisher = "Springer", isbn13 = "978-3-319-16467-0", DOI = "doi:10.1007/978-3-319-16468-7_9", abstract = "Earlier research has shown that an adaptive hyper-heuristic can be a successful approach to solving combinatorial optimisation problems. By using a pairing of an operator (low-level heuristic) selection vector and a solution acceptance criterion, an adaptive hyper-heuristic can manage development of a good solution within an unseen low-level problem domain in a commercially realistic computational time. However not all selection vectors and solution acceptance criteria pairings deliver competitive results when faced with differing problem instance features and computational time limits. We evaluate pairings of six different operator selection vectors and eight solution acceptance criteria, and monitor the performance of the adaptive hyper-heuristic when applying each pairing to a set of C Vehicle Routing Problem instances of the same size but with different features. The results show that a few pairings of operator selection vector and acceptance criterion perform consistently well, while others require a longer computational time to deliver competitive results. We also investigate some of the features of a problem instance that may influence the performance of the selection vector and acceptance criterion pairings.", notes = "No mention of genetic programming. EvoCOP2015 held in conjunction with EuroGP'2015, EvoMusArt2015 and EvoApplications2015", } @InCollection{marta:2003:PSGAADOP, author = "Andre C. Marta", title = "Parametric Study of a Genetic Algorithm using a Aircraft Design Optimization Problem", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2003", year = "2003", editor = "John R. Koza", pages = "133--142", address = "Stanford, California, 94305-3079 USA", month = "4 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms", URL = "http://www.genetic-programming.org/sp2003/Marta.pdf", notes = "part of \cite{koza:2003:gagp}", } @InProceedings{DBLP:conf/visigrapp/MartarelliRPAP24, author = "Rafael Junqueira Martarelli and Douglas Rodrigues and Clayton Reginaldo Pereira and Jurandy Almeida and Joao Paulo Papa", editor = "Petia Radeva and Antonino Furnari and Kadi Bouatouch and A. Augusto {de Sousa}", title = "Strategies for Classifier Selection Based on Genetic Programming for Multimedia Data Recognition", booktitle = "Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, {VISIGRAPP} 2024, Volume 2: VISAPP, Rome, Italy, February 27-29, 2024", pages = "788--795", publisher = "{SCITEPRESS}", year = "2024", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.5220/0012467600003660", DOI = "doi:10.5220/0012467600003660", timestamp = "Mon, 08 Apr 2024 14:14:40 +0200", biburl = "https://dblp.org/rec/conf/visigrapp/MartarelliRPAP24.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{Martens200910, author = "E. Martens and G. Gielen", title = "ANTIGONE: Top-down creation of analog-to-digital converter architectures", journal = "Integration, the VLSI Journal", volume = "42", number = "1", pages = "10--23", year = "2009", note = "AMF/RF CMOS Circuit design for wireless transceivers", ISSN = "0167-9260", DOI = "doi:10.1016/j.vlsi.2008.07.001", URL = "http://www.sciencedirect.com/science/article/B6V1M-4T0WJNT-1/2/78ea82c92c1ba53cc4f227f0d3e3067e", keywords = "genetic algorithms, genetic programming, EHW, Analog systems, Analog-to-digital converters, Synthesis, Evolutionary algorithms", abstract = "A new framework for high-level synthesis of analog and mixed-signal integrated systems is introduced. It focuses on the translation of a functional description into a behavioural model of a specific architecture with values for the parameters of its building blocks. An initial, simple, high-level solution is evolved into a more realistic low-level result by applying appropriate transformations of both architecture and parameters. This top-down heterogeneous optimisation algorithm deals readily with multifarious performance characteristics and diverse types of objectives, and integrates various sources of design knowledge and types of transformations. Furthermore, it creates the architecture rather than selecting it. As illustration of the methodology, a tool, ANTIGONE, has been written that allows to generate different types of A/D converters depending on the specifications like speed and accuracy.", } @Book{Martens:book, author = "Ewout S. J. Martens and Georges G. E. Gielen", title = "High-Level Modeling and Synthesis of Analog Integrated Systems", publisher = "Springer", year = "2008", series = "Analog Circuits and Signal Processing", keywords = "genetic algorithms, genetic programming, EHW", isbn13 = "978-1-4020-6801-0", DOI = "doi:10.1007/978-1-4020-6802-7", notes = "Is this GP? GP mentioned briefly? See also kuleuven.be PHD thesis http://hdl.handle.net/1979/840", size = "275 pages", } @InProceedings{Martens:2019:GECCOcomp, author = "Marcus Martens and Dario Izzo", title = "Neural network architecture search with differentiable cartesian genetic programming for regression", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", isbn13 = "978-1-4503-6748-6", pages = "181--182", address = "Prague, Czech Republic", DOI = "doi:10.1145/3319619.3322003", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", abstract = "While optimized neural network architectures are essential for effective training with gradient descent, their development remains a challenging and resource-intensive process full of trial-and-error iterations. We propose to encode neural networks with a differentiable variant of Cartesian Genetic Programming (dCGPANN) and present a memetic algorithm for architecture design: local searches with gradient descent learn the network parameters while evolutionary operators act on the dCGPANN genes shaping the network architecture towards faster learning. Studying a particular instance of such a learning scheme, we are able to improve the starting feed forward topology by learning how to rewire and prune links, adapt activation functions and introduce skip connections for chosen regression tasks. The evolved network architectures require less space for network parameters and reach, given the same amount of time, a significantly lower error on average.", notes = "Also known as \cite{3322003} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InCollection{martens:2000:ACXDCSGP, author = "Scott Martens", title = "Automatic Creation of XML Document Conversion Scripts by Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "269--278", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:2000:gagp}", } @Misc{DBLP:journals/corr/abs-2206-06213, author = "Marcus Martens and Dario Izzo", title = "Symbolic Regression for Space Applications: Differentiable Cartesian Genetic Programming Powered by Multi-objective Memetic Algorithms", howpublished = "arXiv", volume = "abs/2206.06213", year = "2022", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", URL = "https://doi.org/10.48550/arXiv.2206.06213", DOI = "doi:10.48550/arXiv.2206.06213", eprinttype = "arXiv", eprint = "2206.06213", timestamp = "Mon, 20 Jun 2022 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/corr/abs-2206-06213.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{Martin:2010:EvoGAMES, author = "Andrew Martin and Andrew Lim and Simon Colton and Cameron Browne", title = "Evolving 3D Buildings for the Prototype Video Game Subversion", booktitle = "EvoGAMES", year = "2010", editor = "Cecilia {Di Chio} and Stefano Cagnoni and Carlos Cotta and Marc Ebner and Aniko Ekart and Anna I. Esparcia-Alcazar and Chi-Keong Goh and Juan J. Merelo and Ferrante Neri and Mike Preuss and Julian Togelius and Georgios N. Yannakakis", volume = "6024", series = "LNCS", pages = "111--120", address = "Istanbul", month = "7-9 " # apr, organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-12238-5", DOI = "doi:10.1007/978-3-642-12239-2_12", abstract = "We investigate user-guided evolution for the development of virtual 3D building structures for the prototype (commercial) game Subversion, which is being developed by Introversion Software Ltd. Buildings are described in a custom plain-text markup language that can be parsed by Subversion's procedural generation engine, which renders the 3D models on-screen. The building descriptions are amenable to random generation, crossover and mutation, which enabled us to implement and test a user-driven evolutionary approach to building generation. We performed some fundamental experimentation with ten participants to determine how visually similar child buildings are to their parents, when generated in differing ways. We hope to demonstrate the potential of user-guided evolution for content generation in games in general, as such tools require very little training, time or effort to be employed effectively.", acmid = "2128664", notes = "Crossover based on GP's. EvoGAMES'2010 held in conjunction with EuroGP'2010 EvoCOP2010 EvoBIO2010", } @PhdThesis{tesis_carlos_martin_fernandez_2019, author = "Carlos {Martin Fernandez}", title = "Automatic generation of trading rules adjusted to the market state", title_sp = "Generacion automatica de reglas de inversion ajustadas al estado de mercado", school = "Departamento de Informatica, Universidad Carlos III de Madrid", year = "2019", address = "Spain", month = jun, keywords = "genetic algorithms, genetic programming, Grammatical Evolution, ECJ, ensembles, Trading rules, Evolutionary computation, Market state, Reglas de inversion, Estado de mercado, Generacion automatica", isbn13 = "978-84-09-14557-7", URL = "http://hdl.handle.net/10016/28771", URL = "https://e-archivo.uc3m.es/bitstream/handle/10016/28771/tesis_carlos_martin_fernandez_2019.pdf", size = "314 pages", abstract = "The search for profitable trading strategies has been driving research efforts for decades. Among the many different approaches that can be used to design trading rules (such as Particle Swarm Optimization, Genetic Algorithms, Artificial Neural Networks and Fuzzy Methods), there are some based on evolutionary computation that are especially interesting due to three key features: the process of rule generation is automatic, the resulting rules are interpretable, and their structure is flexible. Since Allen and Karjalainen published their seminal piece on evolution of trading rules using Genetic Programming (GP), many authors have made related contributions either based on the same technique, or Grammatical Evolution (GE). Most of these contributions generate investment rules based on a combination of raw market data and technical indicators and, unlike related approaches that use genetic algorithms or evolution strategies to optimize predefined rules, these have the advantage of creating flexible structures automatically. A common limitation is that it is often the case that the approaches are static and do not take into account the structural changes of the state of the market. Given that this phenomenon is very prevalent in financial time series, the decision rules are commonly derived from market environments that do not hold in test periods. The problem of adjusting to structural changes is that we must choose between two opposite extremes: keeping the same model over time, or updating it constantly. Even though the second might seem, at least in principle, more appropriate, there is a possibility that the constant change in the model will have undesirable consequences due to transaction costs. The evolutionary process of GP/GE considers commissions throughout the period as part of the fitness function, and that makes it select rules that generate a limited number of signals. However, it is possible that a constant model update interferes with that endogenous control mechanism of the number of purchase and sale orders. This thesis tackles with dynamic trading system solutions based on the use of ensembles and GE. The approach combines the possibility of changing the model as a reaction to changes in the price generation mechanism, with an inertia component that mitigates the consequences of overtrading. We also work with a different approach that is not based on ensembles but on a system that takes advantage of an internal hysteresis mechanisms that is part of the own models.", notes = "Supervisors: David Quintana Montero and Pedro Isasi", } @Article{MARTIN:2019:Neurocomputing, author = "Carlos Martin and David Quintana and Pedro Isasi", title = "Evolution of trading strategies with flexible structures: A configuration comparison", journal = "Neurocomputing", volume = "331", pages = "242--262", year = "2019", keywords = "genetic algorithms, genetic programming, Grammatical evolution, Evolutionary computation, Trading", ISSN = "0925-2312", URL = "https://e-archivo.uc3m.es/entities/publication/caa18a8b-1020-4f21-9b82-ed8eccc97fda", URL = "http://www.sciencedirect.com/science/article/pii/S0925231218314061", DOI = "doi:10.1016/j.neucom.2018.11.062", size = "39 pages", abstract = "Evolutionary Computation is often used in the domain of automated discovery of trading rules. Within this area, both Genetic Programming and Grammatical Evolution offer solutions with similar structures that have two key advantages in common: they are both interpretable and flexible in terms of their structure. The core algorithms can be extended to use automatically defined functions or mechanisms aimed to promote parsimony. The number of references on this topic is ample, but most of the studies focus on a specific setup. This means that it is not clear which is the best alternative. This work intends to fill that gap in the literature presenting a comprehensive set of experiments using both techniques with similar variations, and measuring their sensitivity to an increase in population size and composition of the terminal set. The experimental work, based on three S&P 500 data sets, suggest that Grammatical Evolution generates strategies that are more profitable, more robust and simpler, especially when a parsimony control technique was applied. As for the use of automatically defined function, it improved the performance in some experiments, but the results were inconclusive", } @Article{journals/asc/MartinQI19, title = "Grammatical Evolution-based ensembles for algorithmic trading", author = "Carlos Martin and David Quintana and Pedro Isasi", journal = "Applied Soft Computing", year = "2019", volume = "84", pages = "105713", month = nov, keywords = "genetic algorithms, genetic programming, grammatical evolution, Trading, Ensembles, Finance", ISSN = "1568-4946", URL = "https://www.sciencedirect.com/science/article/pii/S1568494619304946", bibdate = "2019-11-26", DOI = "doi:10.1016/j.asoc.2019.105713", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/asc/asc84.html#MartinQI19", abstract = "The literature on trading algorithms based on Grammatical Evolution commonly presents solutions that rely on static approaches. Given the prevalence of structural change in financial time series, that implies that the rules might have to be updated at predefined time intervals. We introduce an alternative solution based on an ensemble of models which are trained using a sliding window. The structure of the ensemble combines the flexibility required to adapt to structural changes with the need to control for the excessive transaction costs associated with over-trading. The performance of the algorithm is benchmarked against five different comparable strategies that include the traditional static approach, the generation of trading rules that are used for single time period and are subsequently discarded, and three alternatives based on ensembles with different voting schemes. The experimental results, based on market data, show that the suggested approach offers very competitive results against comparable solutions and highlight the importance of containing transaction costs.", notes = "Universidad Carlos III de Madrid, Department of Computer Science, Avda. Universidad 30, Leganes, Madrid, Spain", } @Article{journals/ijimai/MartinQI21, author = "Carlos Martin and David Quintana and Pedro Isasi", title = "Dynamic Generation of Investment Recommendations Using Grammatical Evolution", journal = "International Journal of Interactive Multimedia and Artificial Intelligence", year = "2021", volume = "6", number = "6", pages = "104--111", month = jun, keywords = "genetic algorithms, genetic programming, grammatical evolution, Dynamic Strategy, Evolutionary Computation, Finance, Structural Change, Trading", ISSN = "1989-1660", bibdate = "2021-06-01", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ijimai/ijimai6.html#MartinQI21", URL = "https://www.ijimai.org/journal/sites/default/files/2021-05/ijimai_6_6_11.pdf", DOI = "doi:10.9781/ijimai.2021.04.007", size = "8 pages", abstract = "The attainment of trading rules using Grammatical Evolution traditionally follows a static approach. A single rule is obtained and then used to generate investment recommendations over time. The main disadvantage of this approach is that it does not consider the need to adapt to the structural changes that are often associated with financial time series. We improve the canonical approach introducing an alternative that involves a dynamic selection mechanism that switches between an active rule and a candidate one optimised for the most recent market data available. The proposed solution seeks the flexibility required by structural changes while limiting the transaction costs commonly associated with constant model updates. The performance of the algorithm is compared with four alternatives: the standard static approach; a sliding window-based generation of trading rules that are used for a single time period, and two ensemble-based strategies. The experimental results, based on market data, show that the suggested approach beats the rest.", notes = "Department of Computer Science and Engineering, Universidad Carlos III de Madrid, Leganes (Spain)", } @InProceedings{Martin:1998:RDM, author = "Lionel Martin and Frederic Moal and Christel Vrain", title = "A Relational Data Mining Tool Based on Genetic Programming", booktitle = "Proceedings of the 2nd European Symposium on Principles of Data Mining and Knowledge Discovery ({PKDD}-98)", year = "1998", editor = "Jan M. {\.{Z}}ytkow and Mohamed Quafafou", volume = "1510", series = "Lecture Notes in Artificial Intelligence", pages = "130--138", address = "Nantes, France", publisher_address = "Berlin", month = "23--26 " # sep, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, data mining", isbn13 = "978-3-540-65068-3", DOI = "doi:10.1007/BFb0094813", size = "9 pages", abstract = "In this paper, we present a Data Mining tool based on Genetic Programming which enables to analyse complex databases, involving several relation schemes. In our approach, trees represent expressions of relational algebra and they are evaluated according to the way they discriminate positive and negative examples of the target concept. Nevertheless, relational algebra expressions are strongly typed and classical genetic operators, such as mutation and crossover, have been modified to prevent from building illegal expressions. The Genetic Programming approach that we have developed has been modelled in the framework of constraints.", notes = "p134 Nice discussion of keeping data mining tree queries valid under subtree crossover ", affiliation = "Universite du Orleans LIFO rue Leonard de Vinci BP 6759 45067 Orleans cedex 02 France rue Leonard de Vinci BP 6759 45067 Orleans cedex 02 France", } @InProceedings{martin:1999:DGP, author = "Lionel Martin and Frederic Moal and Christel Vrain", title = "Declarative expression of biases in Genetic Programming", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "401--408", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, classifier systems, context free grammars", ISBN = "1-55860-611-4", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco1999/martin_1999_dgp.pdf", abstract = "context free grammars, data mining application, SQL", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Misc{Martin_1998_3062, author = "Martin C. Martin", title = "Breaking Out of the Black Box: A New Approach to Robot Perception", school = "Robotics Institute, Carnegie Mellon University", month = jan, year = "1998", address = "Pittsburgh, PA, USA", note = "Thesis proposal", keywords = "genetic algorithms, genetic programming", URL = "http://www.ri.cmu.edu/pub_files/pub2/martin_martin_c_1998_1/martin_martin_c_1998_1.pdf", URL = "http://www.ri.cmu.edu/pub_files/pub2/martin_martin_c_1998_1/martin_martin_c_1998_1.ps.gz", size = "28 pages", abstract = "Surprisingly, the state of the art in avoiding obstacles using only vision--not sonar or laser rangefinders--is roughly half an hour between collisions (at 30 cm/s, in an office environment). After review ing the design and failure modes of several current systems, I compare psychology's understanding of perception to current computer/robot perception. There are fundamental differences--which lead to fundamental limitations with current computer perception. The key difference is that robot software is built out of {"}black boxes{"}, which have very restricted interactions with each other. In contrast, the human perceptual system is much more integrated. The claim is that a robot that performs any significant task, and does it as well as a person, can not be created out of {"}black boxes.{"} In fact, it would probably be too interconnected to be designed by hand--instead, tools will be needed to create such designs. To illustrate this idea, I propose to create a visual obstacle avoidence system on the Uranus mobile robot. The system uses a number of visual depth cues at each pixel, as well as depth cues from neighbouring pixels and previous depth estimates. Genetic Programming is used to combine these into a new depth estimate. The system learns by predicting both sonar readings and the next image. The design of the system is described, and design decisions are rationalized.", notes = "see also http://citeseer.ist.psu.edu/302181.html", } @InProceedings{martin2:2001:gecco, title = "Visual Obstacle Avoidance Using Genetic Programming: First Results", author = "Martin C. Martin", pages = "1107--1113", year = "2001", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)", editor = "Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon and Edmund Burke", address = "San Francisco, California, USA", publisher_address = "San Francisco, CA 94104, USA", month = "7-11 " # jul, keywords = "genetic algorithms, genetic programming, evolutionary robotics, Obstacle Avoidance, Computer Vision", ISBN = "1-55860-774-9", URL = "http://www.martincmartin.com/Dissertation/VisualObstacleAvoidanceGP.pdf", URL = "http://citeseer.ist.psu.edu/545038.html", URL = "http://gpbib.cs.ucl.ac.uk/gecco2001/d17.pdf", abstract = "Genetic Programming is used to create a reactive obstacle avoidance system for an autonomous mobile robot. The evolved programs take a black and white camera image as input and estimate the location of the lowest nonground pixel in a given column. Traditional computer vision operators such as Sobel gradient magnitude, median filters and the Moravec interest operator are combined arbitrarily. Five memory locations can also be read or written to. The first evolved program is now controlling the robot.", notes = "GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of \cite{spector:2001:GECCO}", } @PhdThesis{Martin_2001_3875, author = "Martin C. Martin", title = "The simulated evolution of robot perception", school = "Robotics Institute, Carnegie Mellon University", year = "2001", address = "Pittsburgh, PA, USA", month = dec, keywords = "genetic algorithms, genetic programming", oai = "oai:xtcat.oclc.org:OCLCNo/ocm48763718", URL = "http://www.ri.cmu.edu/pub_files/pub3/martin_martin_c_2001_1/martin_martin_c_2001_1.pdf", size = "167 pages", abstract = "This dissertation tackles the problem of using genetic programming to create the vision subsystem of a reactive obstacle avoidance algorithm for a mobile robot. To focus the search on computationally efficient algorithms while dealing images from a non-toy problem, the representation restricts computation to be over a window which moves vertically over the image. The evolved programs estimated the distance to the nearest object in various directions, given only a camera image as input. Using a typical supervised learning framework, images of the environment were collected from the robot{'}s camera and the correct distance in various directions determined by hand. Evolving programs were evaluated on this fixed training set and compared to the hand determined answers. Once the evolution was complete, obstacle avoidance programs were written to use the best evolved programs, and the combined system used to control a robot. The approach can be seen as automating the iterative design process. A researcher{'}s main contribution is typically at a high level -- techniques and frameworks -- yet most time is spent on an example problem, trying different instantiations until one works. When faced with such a problem, one can usually think of a half dozen very different approaches, and even write them out in pseudo code. The technique proposed here can be seen as searching the space spanned by that pseudo code. In a series of experiments, programs were evolved in three different ways for two different environments to both create working systems and push the limits of the approach. Even in this nascent form, the evolved programs work about as well as existing, hand written systems. They used a number of architectures, including a recurrent mathematical formula and a series of if statements similar to a decision tree but with non-linear relations between as many as five image statistics. They successfully coded around peculiarities of the imaging process and exploited regularities of the environment. Finally, when given a representation so general as to cause the genetic algorithm to fail, and hand constructed rough answer was used as a {'}seed,{'} which the genetic algorithm successively modified to cut its error rate by a factor of 5.8. This dissertation grew out of my conviction that critiques of Artificial Intelligence can be viewed constructively, as intellectual lighthouses to guide us closer to the fundamental nature of thought, to the real problems at the heart of intelligence. To not address them, to work on techniques with fundamental flaws, would be fooling oneself no matter how impressive the demonstrations. There seems to be something fundamental about AI that we are all missing, and I believe these critiques bring us closer to it. This dissertation describes the experiments and their results, discusses ways to develop them further, then presents critiques of AI and discusses the potential of this approach to overcome those critiques.", notes = "http://www.ri.cmu.edu/pubs/pub_3875.html#text_ref Martin Charles Martin", } @InProceedings{oai:CiteSeerPSU:547772, title = "Genetic Programming for Robot Vision", author = "Martin C. Martin", year = "2002", booktitle = "The Seventh International Conference on the Simulation of Adaptive Behavior (SAB'02)", editor = "Bridget Hallam and Dario Floreano", address = "Edinburgh, UK", month = "9-11 " # aug, keywords = "genetic algorithms, genetic programming", citeseer-isreferencedby = "oai:CiteSeerPSU:90959", citeseer-references = "oai:CiteSeerPSU:218933; oai:CiteSeerPSU:57892; oai:CiteSeerPSU:23206; oai:CiteSeerPSU:124886; oai:CiteSeerPSU:50260; oai:CiteSeerPSU:212034; oai:CiteSeerPSU:368283; oai:CiteSeerPSU:544929; oai:CiteSeerPSU:40597; oai:CiteSeerPSU:14506; oai:CiteSeerPSU:72759; oai:CiteSeerPSU:294737; oai:CiteSeerPSU:26627; oai:CiteSeerPSU:295170", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:547772", rights = "unrestricted", URL = "http://www.martincmartin.com/Dissertation/GeneticProgrammingForRobotVisionSAB2002Martin.pdf", URL = "http://citeseer.ist.psu.edu/547772.html", abstract = "Genetic Programming was used to create the vision subsystem of a reactive obstacle avoidance system for an autonomous mobile robot. The representation of algorithms was specifically chosen to capture the spirit of existing, hand written vision algorithms. Traditional computer vision operators such as Sobel gradient magnitude, median filters and the Moravec interest operator were combined arbitrarily. Images from an office hallway were used as training data. The evolved programs took a black and white camera image as input and estimated the location of the lowest non-ground pixel in a given column. The computed estimates were then given to a handwritten obstacle avoidance algorithm and used to control the robot in real time. Evolved programs successfully navigated in unstructured hallways, performing on par with hand-crafted systems.", notes = "http://www.isab.org.uk/sab02/program/", } @InProceedings{oai:CiteSeerPSU:544306, author = "Martin C. Martin", title = "Genetic programming for real world robot vision", booktitle = "IEEE/RSJ International Conference on Intelligent Robots and System", year = "2002", volume = "1", pages = "67--72", address = "EPFL, Lausanne, Switzerland", month = "30 " # sep # "-5 " # oct, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, collision avoidance, computerised navigation, graph grammars, mobile robots, robot vision, autonomous mobile robot, median filter, navigation, obstacle avoidance algorithm, parse trees, real world robot vision, vision algorithms", DOI = "doi:10.1109/IRDS.2002.1041364", URL = "http://www.martincmartin.com/Dissertation/GeneticProgrammingForRealWorldRobotVisionIROS2002Martin.pdf", URL = "http://citeseer.ist.psu.edu/544306.html", size = "6 pages", abstract = "The vision subsystem of an autonomous mobile robot was created using a form of evolutionary computation known as genetic programming. In this form, individuals are algorithms represented as parse trees. The primitives of the representation were specifically chosen to capture the spirit of existing vision algorithms. Thus, the evolutionary computation can be viewed as searching roughly the same space that researchers search when developing their system using trial and error. Traditional image operators such as the Sobel magnitude and a median filter were combined in arbitrary ways, and images from an unmodified office environment were used as training data. A hand written obstacle avoidance algorithm used the output of the best vision algorithm to avoid obstacles in real time. It performed as well as the existing hand written combined navigation and vision systems.", notes = "IROS Artificial Intelligence Lab., MIT, Cambridge, MA, USA ", } @Article{Martin:2006:PRL, author = "Martin C. Martin", title = "Evolving visual sonar: Depth from monocular images", journal = "Pattern Recognition Letters", year = "2006", volume = "27", number = "11", pages = "1174--1180", month = aug, note = "Evolutionary Computer Vision and Image Understanding", keywords = "genetic algorithms, genetic programming, Robotics, Visual navigation, Monocular vision", URL = "http://martincmartin.com/papers/EvolvingVisualSonarPatternRecognitionLetters2006.pdf", DOI = "doi:10.1016/j.patrec.2005.07.015", size = "7 pages", abstract = "To recover depth from images, the human visual system uses many monocular depth cues, which vision research has only begun to explore. Because a given image can have many possible interpretations, constraints are needed to eliminate ambiguity, and the most powerful constraints are domain specific. As an experiment in the automatic discovery and exploitation of constraints, genetic programming was used to find algorithms for obstacle detection. The algorithms are designed to be a replacement for sonar, returning the location of the nearest obstacle in a given direction. The evolved algorithms worked surprisingly well. Errors were largely transient. The algorithms generalised to both novel views of the office environment and to unseen obstacles. They were combined with a simple reactive wandering program originally written for sonar. The result exhibited good performance in an office environment, colliding only with obstacles outside the robot's field of view. Time to collision results and failure modes are presented. Code is available for download.", } @MastersThesis{PeterMartin:masters, author = "Peter Martin", title = "An Investigation into the use of Genetic Programming for Intelligent Network Service Creation", school = "Bournemouth University", year = "1998", keywords = "genetic algorithms, genetic programming", URL = "http://www.naiadhome.com/Peter_Martin_MSC_Dissertation.pdf", abstract = "Service creation is crucial to the success of Intelligent Networks (IN). However, the time required to develop complex services is increasing. By reducing the elapsed time needed to generate the service logic and by reducing the opportunity for implementation errors to appear in the service logic, a higher quality IN service can be delivered. This project explores an alternative method to the existing manual service creation, by exploiting the properties of Genetic Programming (GP). Genetic Programming is a powerful method for evolving computer programs via the process of natural selection. [Koz92]. The use of Genetic Programming to produce service logic programs for IN is analysed and a number of key features identified. Principally for GP to be of benefit to IN it must be able to reduce the time to create a service and reduce the number of implementation errors in the resultant program. Experimental evidence is presented that shows that using Genetic Programming is a viable method for service creation in Intelligent Networks, and can reduce the time to create a program by several orders of magnitude compared to a human. The case is also argued that since GP needs a fitness function to be developed, the initial specification should be of a higher quality than one produced for a human programmer, thereby reducing the number of errors in the final program. To implement the experimental prototype, existing methods of evolving complex systems using GP were researched. A new method of ensuring the property of closure is presented that does not constrain the development of novel service logic implementations, in contrast to existing methods commonly employed in GP. Further work is identified at the end to improve upon the performance and to explore more complex services.", notes = "See \cite{martin:2000:GPscin}, The project is based on the excellent Genetic Programming Kernel by Thomas Weinbrenner. GPSC.CC code in http://www.naiadhome.com/gpsc.tgz pix 'Finally I would like to thank Marconi Communications Limited, formerly GPT Limited for sponsoring me to do this MSc.'", } @InProceedings{martin:2000:GPscin, author = "Peter Martin", title = "Genetic Programming for Service Creation in Intelligent Networks", booktitle = "Genetic Programming, Proceedings of EuroGP'2000", year = "2000", editor = "Riccardo Poli and Wolfgang Banzhaf and William B. Langdon and Julian F. Miller and Peter Nordin and Terence C. Fogarty", volume = "1802", series = "LNCS", pages = "106--120", address = "Edinburgh", publisher_address = "Berlin", month = "15-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, polymorphic data, SBSE", ISBN = "3-540-67339-3", URL = "http://www.naiadhome.com/martin.pdf", URL = "http://www.naiadhome.com/martin.ps", URL = "http://citeseer.ist.psu.edu/567047.html", DOI = "doi:10.1007/978-3-540-46239-2_8", size = "15 pages", abstract = "Intelligent Networks are used by telephony systems to offer services to customers. The creation of these services has traditionally been performed by hand, and has required substantial effort, despite the advanced tools employed. An alternative to manual service creation using Genetic Programming is proposed that addresses some of the limitations of the manual process of service creation. The main benefit of using GP is that by focussing on what a service is required to do, as opposed to its implementation, it is more likely that the generated programs will be available on time and to budget, when compared to traditional software engineering techniques. The problem of closure is tackled by presenting a new technique for ensuring correct program syntax that maintains genetic diversity.", notes = "Autonomous Polymorphic Addressable Memory APAM. GPT GAIN INventor, INAP, SDF, Q.1211 Q.1214, complex telephone number translation. microsoft powerpoint slides http://www.naiadhome.com/eurogp2000_slides.ppt EuroGP'2000, part of \cite{poli:2000:GP}", } @InProceedings{martin:2001:gecco, title = "Building a Taxonomy of Genetic Programming", author = "Peter Martin", pages = "182", year = "2001", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)", editor = "Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon and Edmund Burke", address = "San Francisco, California, USA", publisher_address = "San Francisco, CA 94104, USA", month = "7-11 " # jul, keywords = "genetic algorithms, genetic programming: Poster, Taxonomy", ISBN = "1-55860-774-9", URL = "http://www.naiadhome.com/martin-taxonomy.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco2001/d02.pdf", notes = "GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of \cite{spector:2001:GECCO}", } @Article{martin:2001:GPEM, author = "Peter Martin", title = "A Hardware Implementation of a Genetic Programming System Using {FPGAs} and {Handel-C}", journal = "Genetic Programming and Evolvable Machines", year = "2001", volume = "2", number = "4", pages = "317--343", month = dec, keywords = "genetic algorithms, genetic programming, evolvable hardware, FPGA, Handel-C, parallel genetic algorithm", ISSN = "1389-2576", URL = "http://www.naiadhome.com/gpem-d.pdf", URL = "http://citeseer.ist.psu.edu/568511.html", URL = "https://rdcu.be/ddsvB", DOI = "doi:10.1023/A:1012942304464", size = "27 pages", abstract = "This paper presents an implementation of Genetic Programming using a Field Programmable Gate Array. This novel implementation uses a high level language to hardware compilation system, called Handel-C, to produce a Field Programmable Logic Array capable of performing all the functions required of a Genetic Programming System. Two simple test problems demonstrate that GP running on a Field Programmable Gate Array can outperform a software version of the same algorithm by exploiting the intrinsic parallelism available using hardware, and the geometric parallelisation of Genetic Programming.", notes = "Xilinx BG560 FPGA XCV2000e, Celoxica RC1000 FPGA board. See also \cite{martin:2002:EuroGP} Article ID: 386361", } @TechReport{oai:CiteSeerPSU:566603, title = "Analysis of the Behavior of a Hardware Implementation of {GP} using {FPGAs} and {Handel-C}", author = "Peter Martin and Riccardo Poli", year = "2002", institution = "Department of Computer Science, University of Essex", type = "Technical Report", number = "CSM-357", address = "Wivenhoe Park, Colchester, CO4 3SQ UK.", month = "24th " # jan, keywords = "genetic algorithms, genetic programming", citeseer-isreferencedby = "oai:CiteSeerPSU:96667", citeseer-references = "oai:CiteSeerPSU:233843; oai:CiteSeerPSU:36964; oai:CiteSeerPSU:186935; oai:CiteSeerPSU:178733; oai:CiteSeerPSU:336173; oai:CiteSeerPSU:503531; oai:CiteSeerPSU:502374", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:566603", rights = "unrestricted", URL = "http://www.naiadhome.com/csm-357.pdf", URL = "http://cswww.essex.ac.uk/technical-reports/2002/csm-357.ps", URL = "http://citeseer.ist.psu.edu/566603.html", abstract = "This paper analyses the behavior of a hardware implementation of Genetic Programming using Field Programmable Gate Arrays. Three crossover operators that limit the lengths of programs are analyzed. A truncating operator, a limiting operator that constrains the lengths of both offspring and a limiting operator that only constrains the length of one offspring. The latter has some interesting properties that suggest a new method of limiting code growth in the presence of fitness.", size = "15 pages", } @TechReport{oai:CiteSeerPSU:569263, title = "An Analysis of Random Number Generators for a Hardware Implementation of Genetic Programming using FPGAs and Handel-C", author = "Peter Martin", year = "2002", institution = "Department of Computer Science, University of Essex", type = "Technical Report", number = "CSM-358", address = "Wivenhoe Park, Colchester, CO4 3SQ UK.", month = "24th " # jan, keywords = "genetic algorithms, genetic programming", citeseer-isreferencedby = "oai:CiteSeerPSU:445301; oai:CiteSeerPSU:97408; oai:CiteSeerPSU:245036; oai:CiteSeerPSU:102011; oai:CiteSeerPSU:418692; oai:CiteSeerPSU:240174; oai:CiteSeerPSU:226120", citeseer-references = "oai:CiteSeerPSU:441838; oai:CiteSeerPSU:43964; oai:CiteSeerPSU:471279; oai:CiteSeerPSU:186935; oai:CiteSeerPSU:178733; oai:CiteSeerPSU:16085; oai:CiteSeerPSU:523041", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:569263", rights = "unrestricted", URL = "http://www.naiadhome.com/csm-358.pdf", URL = "http://cswww.essex.ac.uk/technical-reports/2002/csm-358.ps", URL = "http://citeseer.ist.psu.edu/569263.html", abstract = "This paper analyses the effect of using different random number generators (RNG) in a hardware implementation of Genetic Programming using Field Programmable Gate Arrays. Hardware systems have typically used RNGs based on Logical Feedback Shift Registers or Cellular Automata. Different configurations of these generators are evaluated as well as using a source of true random numbers and a standard multiply/add generator. We show that using a more sophisticated generator than a simple LFSR slightly improves the performance of the hardware GP system.", size = "13 pages", } @InProceedings{martin:2002:EuroGP, title = "A Pipelined Hardware Implementation of Genetic Programming using {FPGA}s and {Handel-C}", author = "Peter Martin", editor = "James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi", booktitle = "Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002", volume = "2278", series = "LNCS", pages = "1--12", publisher = "Springer-Verlag", address = "Kinsale, Ireland", publisher_address = "Berlin", month = "3-5 " # apr, year = "2002", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-43378-3", URL = "http://www.naiadhome.com/martin-04-b.pdf", DOI = "doi:10.1007/3-540-45984-7_1", abstract = "A complete Genetic Programming (GP) system implemented in a single FPGA is described in this paper. The GP system is capable of solving problems that require large populations and by using parallel fitness evaluations can solve problems in a much shorter time that a conventional GP system in software. A high level language to hardware compilation system called Handel-C is used for implementation.", notes = "EuroGP'2002, part of \cite{lutton:2002:GP}", } @InProceedings{martin2:2002:gecco, author = "Peter Martin and Riccardo Poli", title = "Crossover Operators For a Hardware Implementation Of {GP} Using {FPGAs} and {Handel-C}", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "845--852", address = "New York", publisher_address = "San Francisco, CA 94104, USA", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-878-8", URL = "http://www.naiadhome.com/gp063.df", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/gp284.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/gp284.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf", size = "8 pages", abstract = "This paper analyses the behaviour of the crossover operator in a hardware implementation of Genetic Programming using Field Programmable Gate Arrays. Three different crossover operators that limit the lengths of programs are analysed: A truncating operator, a limiting operator that constrains the lengths of both offspring and a limiting operator that only constrains the length of one offspring. The latter has some interesting properties that suggest a new method of limiting code growth in the presence of fitness.", notes = "GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)", } @InProceedings{martin:2002:gecco, author = "Peter Martin", title = "An Analysis Of Random Number Generators For a Hardware Implementation of Genetic Programming Using {FPGAs} And {Handel-C}", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "837--844", address = "New York", publisher_address = "San Francisco, CA 94104, USA", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-878-8", URL = "http://www.naiadhome.com/gp284.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/gp063.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/gp063.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf", size = "8 pages", abstract = "This paper analyses the effect of using different random number generators (RNG) in a hardware implementation of Genetic Programming using Field Programmable Gate Arrays. Hardware systems have typically used RNGs based on Logical Feedback Shift Registers or Cellular Automata. Different configurations of these generators are evaluated as well as using a source of true random numbers and a standard multiply/add generator. The results show that using a more sophisticated generator than a simple LFSR slightly improves the performance of GP.", notes = "GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)", } @PhdThesis{martin:thesis, author = "Peter N. Martin", title = "Genetic Programming in Hardware", school = "University of Essex", year = "2003", address = "University of Essex, Wivenhoe Park, Colchester, UK", month = mar, email = "Pete Martin ", keywords = "genetic algorithms, genetic programming", URL = "http://www.naiadhome.com/HardwareGeneticProgramming.pdf", URL = "http://ethos.bl.uk/OrderDetails.do?did=2&uin=uk.bl.ethos.272585", size = "214 pages", abstract = "Genetic Programming in Hardware This thesis describes a hardware implementation of a complete Genetic Programming (GP) system using a Field Programmable Gate Array, which is shown to speed-up GP by over 400 times when compared with a software implementation of the same algorithm. The hardware implements the creation of the initial population, breeding operators, parallel fitness evaluations and the output of the final result. The research was motivated by the observation that GP is usually implemented in software and run on general purpose computers. Although software implementations are flexible and easy to modify, they limit the performance of GP thus restricting the range of problems that GP can solve. The hypothesis is that implementing GP in hardware would speed up GP, allowing it to tackle problems which are currently too hard for software based GP. FPGAs are usually programmed using specialised hardware design languages. An alternative approach is used in this work that uses a high level language to hardware compilation system, called Handel-C. As part of this research, a number of general GP issues are also explored. The parameters of GP are described and arranged into a taxonomy of GP attributes. The taxonomy allows GP problems to be categorised with respect to their problem and GP specific attributes. The role that the GP algorithm plays in problem solving is shown to be part of a larger process called Meta-GP, which describes the overall process of developing a GP system and evolving a viable set of parameters to allow GP to solve a problem. Three crossover operators are investigated and a new operator, called single child limiting crossover, is presented. This operator appears to limit the tendency of GP to suffer from bloat. The economics of implementing GP in hardware are analysed and the costs and benefits are quantified. The thesis concludes by suggesting some applications for hardware GP.", notes = "uk.bl.ethos.272585", } @InProceedings{Martin:2013:GECCOcomp, author = "Matthew A. Martin and Daniel R. Tauritz", title = "Evolving black-box search algorithms employing genetic programming", booktitle = "GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion", year = "2013", editor = "Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A. N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks", isbn13 = "978-1-4503-1964-5", keywords = "genetic algorithms, genetic programming", pages = "1497--1504", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2464576.2482728", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Restricting the class of problems we want to perform well on allows Black Box Search Algorithms (BBSAs) specifically tailored to that class to significantly outperform more general purpose problem solvers. However, the fields that encompass BBSAs, including Evolutionary Computing, are mostly focused on improving algorithm performance over increasingly diversified problem classes. By definition, the payoff for designing a high quality general purpose solver is far larger in terms of the number of problems it can address, than a specialised BBSA. This paper introduces a novel approach to creating tailored BBSAs through automated design employing genetic programming. An experiment is reported which demonstrates its ability to create novel BBSAs which outperform established BBSAs including canonical evolutionary algorithms.", notes = "Also known as \cite{2482728} Distributed at GECCO-2013.", } @InProceedings{Martin:2014:GECCOcomp, author = "Matthew A. Martin and Daniel R. Tauritz", title = "Multi-sample evolution of robust black-box search algorithms", booktitle = "GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion", year = "2014", editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges", isbn13 = "978-1-4503-2881-4", keywords = "genetic algorithms, genetic programming, self-* search: Poster", pages = "195--196", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2598394.2598448", DOI = "doi:10.1145/2598394.2598448", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Black-Box Search Algorithms (BBSAs) tailored to a specific problem class may be expected to significantly outperform more general purpose problem solvers, including canonical evolutionary algorithms. Recent work has introduced a novel approach to evolving tailored BBSAs through a genetic programming hyper-heuristic. However, that first generation of hyper-heuristics suffered from over specialisation. This poster paper presents a second generation hyper-heuristic employing a multi-sample training approach to alleviate the overspecialization problem. A variety of experiments demonstrated the significant increase in the robustness of the generated algorithms due to the multi-sample approach, clearly showing its ability to outperform established BBSAs. The trade-off between a priori computational time and the generated algorithm robustness is investigated, demonstrating the performance gain possible given additional run-time.", notes = "Also known as \cite{2598448} Distributed at GECCO-2014.", } @InProceedings{Martin:2014:GECCOcompa, author = "Matthew A. Martin and Daniel R. Tauritz", title = "A problem configuration study of the robustness of a black-box search algorithm hyper-heuristic", booktitle = "GECCO 2014 4th workshop on evolutionary computation for the automated design of algorithms", year = "2014", editor = "John Woodward and Jerry Swan and Earl Barr", isbn13 = "978-1-4503-2881-4", keywords = "genetic algorithms, genetic programming", pages = "1389--1396", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2598394.2609872", DOI = "doi:10.1145/2598394.2609872", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Black-Box Search Algorithms (BBSAs) tailored to a specific problem class may be expected to significantly outperform more general purpose problem solvers, including canonical evolutionary algorithms. Recent work has introduced a novel approach to evolving tailored BBSAs through a genetic programming hyper-heuristic. However, that first generation of hyper-heuristics suffered from over-specialisation. This paper presents a study on the second generation hyper-heuristic which employs a multi-sample training approach to alleviate the over-specialisation problem. In particular, the study is focused on the affect that the multi-sample approach has on the problem configuration landscape. A variety of experiments are reported on which demonstrate the significant increase in the robustness of the generated algorithms to changes in problem configuration due to the multi-sample approach. The results clearly show the resulting BBSAs' ability to outperform established BBSAs, including canonical evolutionary algorithms. The trade-off between a priori computational time and the generated algorithm robustness is investigated, demonstrating the performance gain possible given additional run-time.", notes = "Also known as \cite{2609872} Distributed at GECCO-2014.", } @InProceedings{Martin:2015:GECCOcompa, author = "Matthew A. Martin and Alex R. Bertels and Daniel R. Tauritz", title = "Asynchronous Parallel Evolutionary Algorithms: Leveraging Heterogeneous Fitness Evaluation Times for Scalability and Elitist Parsimony Pressure", booktitle = "GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation", year = "2015", editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr", isbn13 = "978-1-4503-3488-4", keywords = "genetic algorithms, genetic programming: Poster", pages = "1429--1430", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739482.2764718", DOI = "doi:10.1145/2739482.2764718", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Many important problem classes lead to large variations in fitness evaluation times, such as is often the case in Genetic Programming where the time complexity of executing one individual may differ greatly from that of another. Asynchronous Parallel Evolutionary Algorithms (APEAs) omit the generational synchronization step of traditional EAs which work in well-defined cycles. This paper provides an empirical analysis of the scalability improvements obtained by applying APEAs to such problem classes, aside from the speed-up caused merely by the removal of the synchronization step. APEAs exhibit bias towards individuals with shorter fitness evaluation times, because they propagate faster. This paper demonstrates how this bias can be leveraged in order to provide a unique type of elitist parsimony pressure which rewards more efficient solutions with equal solution quality.", notes = "Also known as \cite{2764718} Distributed at GECCO-2015.", } @Article{MARTINALCANTARA:2023:seta, author = "Antonio Martin-Alcantara and Valentina Motta and Andrea Tarantino and Maria Grazia {De Giorgi}", title = "Design of a passive flow control solution for the mitigation of vortex induced vibrations on wind turbines blade sections as a response to extreme weather events", journal = "Sustainable Energy Technologies and Assessments", volume = "56", pages = "103053", year = "2023", ISSN = "2213-1388", DOI = "doi:10.1016/j.seta.2023.103053", URL = "https://www.sciencedirect.com/science/article/pii/S2213138823000450", keywords = "genetic algorithms, genetic programming, Load alleviation, Passive flow control, Deep stall, Wind turbine, Microcylinders", abstract = "The goal of the present work is to perform exploratory assessments on the suitability of microcylinder devices to mitigate wind turbine blade vortex induced vibration in small cross flow regimes, specially occurring under adverse weather events. For this purpose, the deep stall behavior of the NACA0021 has been evaluated for the first time on a wide range of high angles of attack (50degree-130degree) by means of CFD assessments, and the effects of microcylinders have been studied in terms of the reduction of aerodynamic coefficient magnitudes and fluctuations. In a first step, the passive flow control solution at 90degree has been analyzed for a total number of investigated cases equal to 15, keeping constant the diameter of the microcylinders and varying the relative positions of these devices with respect to the blade. Mitigations of the load standard deviations between 63percent and 97percent across all the tested angles of attack have been found. Additionally, genetic programming (GP) was used to obtain a more general viewpoint of the effect of the microcylinders on the aerodynamics forces. Assessments of the flow fields confirm that properly located microcylinders disrupt the coherent vortical structures in the wake, responsible for periodic loading on the blade section", } @InProceedings{Martin-Garcia:2014:CinC, author = "Juan Francisco Martin-Garcia and Inmaculada Mora-Jimenez and Arcadio Garcia-Alberola and Jose Luis Rojo-Alvarez", title = "Cardiac Arrhythmia Discrimination Using Evolutionary Computation", booktitle = "Computing in Cardiology Conference (CinC 2014)", year = "2014", month = sep, pages = "121--124", ISSN = "2325-8861", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=7042994", size = "4 pages", abstract = "The use of Implantable Cardioverter Defibrillators (ICD) for cardiac arrhythmia treatment implies a search for efficiency in terms of discrimination quality and computational complexity, given that improved efficiency will automatically turn into more effective therapy and longer battery lifetime. In this work, we applied evolutionary computation to create classifiers capable of discriminating between ventricular and supraventricular tachycardia (VT/SVT) in episodes registered by ICDs. Evolutionary computation comprises several paradigms emulating natural mechanisms for solving a problem, all of them characterised by a population of individuals (possible solutions) which evolve generation after generation to provide fitter solutions. Genetic programming was the paradigm chosen here because its solutions, coded as decision trees, can be both computationally simple and clinically interpretable. For the experiments, we considered electrograms (EGM) from episodes registered by ICDs in spontaneous/induced tachycardia, previously classified as VT/SVT by clinical experts from several Spanish healthcare centres. Training data were 38 real-valued samples, arranged as the concatenation of two beat segments: a sinus rhythm template immediately previous to the arrhythmic episode (basal reference), and the arrhythmic episode template. Several low complexity trees provided low error rates and allowed physiological interpretation. The best tree yielded an error rate of 1.8percent, with both sensitivity and specificity above 98percent. This solution compares two samples from the end of the arrhythmic pulse with another two samples from the sinus rhythm, pointing out to a relevant discrimination role of the lasting EGM.", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{7042994}", } @Article{martin-moreno:2022:IJERPH, author = "Jose M. Martin-Moreno and Antoni Alegre-Martinez and Victor Martin-Gorgojo and Jose Luis Alfonso-Sanchez and Ferran Torres and Vicente Pallares-Carratala", title = "Predictive Models for Forecasting Public Health Scenarios: Practical Experiences Applied during the First Wave of the {COVID-19} Pandemic", journal = "International Journal of Environmental Research and Public Health", year = "2022", volume = "19", number = "9", pages = "Article No. 5546", keywords = "genetic algorithms, genetic programming", ISSN = "1660-4601", URL = "https://www.mdpi.com/1660-4601/19/9/5546", DOI = "doi:10.3390/ijerph19095546", abstract = "Background: Forecasting the behaviour of epidemic outbreaks is vital in public health. This makes it possible to anticipate the planning and organization of the health system, as well as possible restrictive or preventive measures. During the COVID-19 pandemic, this need for prediction has been crucial. This paper attempts to characterise the alternative models that were applied in the first wave of this pandemic context, trying to shed light that could help to understand them for future practical applications. Methods: A systematic literature search was performed in standardized bibliographic repertoires, using keywords and Boolean operators to refine the findings, and selecting articles according to the main PRISMA 2020 statement recommendations. Results: After identifying models used throughout the first wave of this pandemic (between March and June 2020), we begin by examining standard data-driven epidemiological models, including studies applying models such as SIR (Susceptible-Infected-Recovered), SQUIDER, SEIR, time-dependent SIR, and other alternatives. For data-driven methods, we identify experiences using autoregressive integrated moving average (ARIMA), evolutionary genetic programming machine learning, short-term memory (LSTM), and global epidemic and mobility models. Conclusions: The COVID-19 pandemic has led to intensive and evolving use of alternative infectious disease prediction models. At this point it is not easy to decide which prediction method is the best in a generic way. Moreover, although models such as the LSTM emerge as remarkably versatile and useful, the practical applicability of the alternatives depends on the specific context of the underlying variable and on the information of the target to be prioritized. In addition, the robustness of the assessment is conditioned by heterogeneity in the quality of information sources and differences in the characteristics of disease control interventions. Further comprehensive comparison of the performance of models in comparable situations, assessing their predictive validity, is needed. This will help determine the most reliable and practical methods for application in future outbreaks and eventual pandemics.", notes = "also known as \cite{ijerph19095546}", } @Article{Martinez:2014:Medellin, author = "Carlos A Martinez and Juan D. Velasquez", title = "Predicci{\'o}n de los precios de contratos de electricidad usando programaci{\'o}n gen{\'e}tica con bloques funcionales", title_en = "Electricity contract price prediction using genetic programming with functional blocks", year = "2014", journal = "Revista de Ingenierias: Universidad de Medellin", number = "24", volume = "13", pages = "77--87", keywords = "genetic algorithms, genetic programming, electricity prices, prediction, time series, functional blocks, precios de la electricidad, programacion genetica, prediccion, series de tiempo, bloques funcionales", ISSN = "1692-3324", bibsource = "OAI-PMH server at dialnet.unirioja.es", identifier = "(Revista) ISSN 1692-3324", language = "spa", oai = "oai:dialnet.unirioja.es:ART0000748139", rights = "LICENCIA DE USO: Los documentos a texto completo incluidos en Dialnet son de acceso libre y propiedad de sus autores y/o editores. Por tanto, cualquier acto de reproducci{\'o}n, distribuci{\'o}n, comunicaci{\'o}n p{\'u}blica y/o transformaci{\'o}n total o parcial requiere el consentimiento expreso y escrito de aqu{\'e}llos. Cualquier enlace al texto completo de estos documentos deber{\'a} hacerse a trav{\'e}s de la URL oficial de {\'e}stos en Dialnet. M{\'a}s informaci{\'o}n: http://dialnet.unirioja.es/info/derechosOAI | INTELLECTUAL PROPERTY RIGHTS STATEMENT: Full text documents hosted by Dialnet are protected by copyright and/or related rights. This digital object is accessible without charge, but its use is subject to the licensing conditions set by its authors or editors. Unless expressly stated otherwise in the licensing conditions, you are free to linking, browsing, printing and making a copy for your own personal purposes. All other acts of reproduction and communication to the public are subject to the licensing conditions expressed by editors and authors and require consent from them. Any link to this document should be made using its official URL in Dialnet. More info: http://dialnet.unirioja.es/info/derechosOAI", URL = "http://dialnet.unirioja.es/servlet/oaiart?codigo=4993242", size = "12 pages", abstract = "The prediction of the prices of the contracts in non-regulated electricenergy markets is the key for the market agents to make strategic business~and operational decisions. The average prices of the contracts sold in the~Colombian electric energy market are predicted in this study by means~of a modified genetic programming algorithm. The developed model is~capable of capturing the intrinsic dynamics of the prices and the price~predictions for the upcoming months with a more accurate precision~than the ARIMA and DAN2 models for prediction horizons of 12 and 14~months, as they have been reported in the literature.", abstract = "La predicci{\'o}n de los precios de los contratos en los mercados energ{\'e}ticos~desregularizados es la clave para la toma de decisiones estrat{\'e}gicas~de negocio y operativas por los agentes del mercado. En este trabajo se~predicen los precios promedio de los contratos vendidos en el mercado~el{\'e}ctrico colombiano, utilizando un algoritmo de programaci{\'o}n gen{\'e}tica~modificado. El modelo desarrollado es capaz de capturar la din{\'a}mica~intr{\'i}nseca de los precios y las predicciones de precios para los pr{\'o}ximos~meses con mayor precisi{\'o}n que los modelos ARIMA y DAN2 para horizontes~de predicci{\'o}n de 12 y 24 meses, reportados en la literatura", notes = "in spanish", } @Article{Martinez:2015:ieeeLAT, author = "Carlos A. Martinez and Juan David Velasquez", journal = "IEEE Latin America Transactions", title = "Conceptual Developments in Genetic Programming for Time Series Forecasting", year = "2015", volume = "13", number = "8", pages = "2728--2733", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/TLA.2015.7332156", ISSN = "1548-0992", month = aug, abstract = "Objective: The aim of this paper is to analyse the main research areas in Genetic Programming (GP). Method: We used the systematic literature review method employing an automatic search with manual refining of papers published on GP between 1992 to 2012. Results: Just 63 studies meet all the requirements of the inclusion criteria. Conclusion: Although studies relating to the application of genetic programming in the forecast of time series were frequently presented, we find that the studies proposing changes in the original algorithm of GP with a theoretical support and a systematic procedure for the construction of model were scarce in the time 1992-2012.", notes = "Univ. Nac. de Colombia, Bogota, Colombia Also known as \cite{7332156}", } @Article{Martinez:2016:ieeeLatin, author = "Carlos Alberto Martinez and Juan D. Velasquez", journal = "IEEE Latin America Transactions", title = "An Efficient New Scheme of Fitness Evaluation in Genetic Programming using the R Language", year = "2016", volume = "14", number = "4", pages = "1866--1869", abstract = "The aim of this paper is to propose and analyse several fitness evaluation schemes for solving regression problems using Genetic Programming. The proposed schemes are designed considering the particularities and characteristics of the R Language, and particularly the capacities of the language for matrix manipulation and mathematical expressions evaluation. Experimental results show that some the proposed schemes are able to reduce until 99percent the time spend in fitness evaluation in comparison with the original genetic programming implementation using a traditional tree structure.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/TLA.2016.7483527", ISSN = "1548-0992", month = apr, notes = "Also known as \cite{7483527}", } @InProceedings{Martinez:2014:FIA:2591062.2591114, author = "Matias Martinez and Westley Weimer and Martin Monperrus", title = "Do the Fix Ingredients Already Exist? An Empirical Inquiry into the Redundancy Assumptions of Program Repair Approaches", booktitle = "Companion Proceedings of the 36th International Conference on Software Engineering, ICSE Companion 2014", year = "2014", pages = "492--495", address = "Hyderabad, India", publisher_address = "New York, NY, USA", publisher = "ACM", keywords = "genetic improvement, APR, SBSE, GenProg, automatic software repair, mining software repositories", acmid = "2591114", isbn13 = "978-1-4503-2768-8", URL = "http://arxiv.org/abs/1403.6322", DOI = "doi:10.1145/2591062.2591114", size = "4 pages", abstract = "Much initial research on automatic program repair has focused on experimental results to probe their potential to find patches and reduce development effort. Relatively less effort has been put into understanding the hows and whys of such approaches. For example, a critical assumption of the GenProg technique is that certain bugs can be fixed by copying and re-arranging existing code. In other words, GenProg assumes that the fix ingredients already exist elsewhere in the code. In this paper, we formalize these assumptions around the concept of temporal redundancy. A temporally redundant commit is only composed of what has already existed in previous commits. Our experiments show that a large proportion of commits that add existing code are temporally redundant. This validates the fundamental redundancy assumption of GenProg.", notes = "No mention of GP? Cited by \cite{Tichy:2015:Ubiquity}", } @TechReport{martinez:hal-01075976, type = "Technical Report", institution = "Inria", hal_id = "hal-01075976", hal_version = "v1", author = "Matias Martinez and Martin Monperrus", title = "{ASTOR:} Evolutionary Automatic Software Repair for Java", howpublished = "arXiv:1410.6651", year = "2014", month = "24 " # oct, keywords = "genetic algorithms, genetic programming, genetic improvement, APR, SBSE", biburl = "http://dblp.uni-trier.de/rec/bib/journals/corr/MartinezM14", bibsource = "dblp computer science bibliography, http://dblp.org", URL = "http://arxiv.org/abs/1410.6651", URL = "http://arxiv.org/pdf/1410.6651v1", URL = "https://hal.archives-ouvertes.fr/hal-01075976", size = "6 pages", abstract = "Context: During last years, many automatic software repair approaches have been presented by the software engineering research community. According to the corresponding papers, these approaches are able to repair real defects from open source projects. Problematic: Some previous publications in the automatic repair field do not provide the implementation of theirs approaches. Consequently, it is not possible for the research community to re-execute the original evaluation, to set up new evaluations (for example, to evaluate the performance against new defects) or to compare approaches against each others. Solution: We propose a publicly available automatic software repair tool called Astor. It implements three state-of-the-art automatic software repair approaches in the context of Java programs (including GenProg and a subset of PAR's templates). The source code of Astor is licensed under the GNU General Public Licence (GPL v2).", notes = "p2 'Astor uses genetic programming \cite{koza:book} as main evolutionary paradigm. In the automatic software repair field, previous works \cite{Weimer:2009:ICES},\cite{Arcuri09} have also used genetic programming for searching candidate patches.' https://github.com/SpoonLabs/astor Technical Report hal-01075976, Inria, 2014", } @Article{Martinez:2017:ESE, author = "Matias Martinez and Thomas Durieux and Romain Sommerard and Jifeng Xuan and Martin Monperrus", title = "Automatic repair of real bugs in java: a large-scale experiment on the defects4j dataset", journal = "Empirical Software Engineering", year = "2017", volume = "22", number = "4", pages = "1936--1964", month = aug, keywords = "genetic algorithms, genetic programming, genetic improvement, APR, Automatic bugfixing, Software repair, Bugs, Defects, Patches, Fixes, GenProg", ISSN = "1573-7616", URL = "https://hal.archives-ouvertes.fr/hal-01387556/", URL = "https://hal.archives-ouvertes.fr/hal-01387556/automatic-repair-defects4j.pdf", DOI = "doi:10.1007/s10664-016-9470-4", size = "29 pages", abstract = "Defects4J is a large, peer-reviewed, structured dataset of real-world Java bugs. Each bug in Defects4J comes with a test suite and at least one failing test case that triggers the bug. In this paper, we report on an experiment to explore the effectiveness of automatic test-suite based repair on Defects4J. The result of our experiment shows that the considered state-of-the-art repair methods can generate patches for 47 out of 224 bugs. However, those patches are only test-suite adequate, which means that they pass the test suite and may potentially be incorrect beyond the test-suite satisfaction correctness criterion. We have manually analysed 84 different patches to assess their real correctness. In total, 9 real Java bugs can be correctly repaired with test-suite based repair. This analysis shows that test-suite based repair suffers from under-specified bugs, for which trivial or incorrect patches still pass the test suite. With respect to practical applicability, it takes on average 14.8 minutes to find a patch. The experiment was done on a scientific grid, totalling 17.6 days of computation time. All the repair systems and experimental results are publicly available on Github in order to facilitate future research on automatic repair.", notes = "jgenprog and jkali https://github.com/SpoonLabs/astor defects4j-repair https://github.com/Spirals-Team/defects4j-repair/", } @InProceedings{Martinez:2013:CEC, article_id = "1619", author = "Yuliana Martinez and Enrique Naredo and Leonardo Trujillo and Edgar Galvan-Lopez", title = "Searching for Novel Regression Functions", booktitle = "2013 IEEE Conference on Evolutionary Computation", volume = "1", year = "2013", month = jun # " 20-23", editor = "Luis Gerardo {de la Fraga}", pages = "16--23", address = "Cancun, Mexico", keywords = "genetic algorithms, genetic programming, Novelty Search, Behaviour-based Search, Symbolic Regression", isbn13 = "978-1-4799-0453-2", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.365.5281", URL = "http://eplex.cs.ucf.edu/noveltysearch/userspage/CEC-2013.pdf", DOI = "doi:10.1109/CEC.2013.6557548", size = "8 pages", abstract = "The objective function is the core element in most search algorithms that are used to solve engineering and scientific problems, referred to as the fitness function in evolutionary computation. Some researchers have attempted to bridge this difference by reducing the need for an explicit fitness function. A noteworthy example is the novelty search (NS) algorithm, that substitutes fitness with a measure of uniqueness, or novelty, that each individual introduces into the search. NS employs the concept of behavioural space, where each individual is described by a domain-specific descriptor that captures the main features of an individuals performance. However, defining a behavioral descriptor is not trivial, and most works with NS have focused on robotics. This paper is an extension of recent attempts to expand the application domain of NS. In particular, it represents the first attempt to apply NS on symbolic regression with genetic programming (GP). The relationship between the proposed NS algorithm and recent semantics-based GP algorithms is explored. Results are encouraging and consistent with recent findings, where NS achieves below average performance on easy problems, and achieves very good performance on hard problems. In summary, this paper presents the first attempt to apply NS on symbolic regression, a continuation of recent research devoted at extending the domain of competence for behaviour-based search.", notes = "NS-GP-R coded in GPLAB MATLAB. Semantic Similarity-based Crossover \cite{Quang:2011:GPEM}. CEC 2013 - A joint meeting of the IEEE, the EPS and the IET.", } @InProceedings{Martinez:2014:EVOLVE, author = "Yuliana Martinez and Leonardo Trujillo and Enrique Naredo and Pierrick Legrand", title = "A Comparison of Fitness-Case Sampling Methods for Symbolic Regression with Genetic Programming", booktitle = "EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V", year = "2014", editor = "Alexandru-Adrian Tantar and Emilia Tantar and Jian-Qiao Sun and Wei Zhang and Qian Ding and Oliver Schuetze and Michael Emmerich and Pierrick Legrand and Pierre {Del Moral} and Carlos A. {Coello Coello}", volume = "288", series = "Advances in Intelligent Systems and Computing", pages = "201--212", address = "Peking", month = "1-4 " # jul, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Fitness-Case Sampling, Symbolic Regression, Performance Evaluation", isbn13 = "978-3-319-07493-1", DOI = "doi:10.1007/978-3-319-07494-8_14", abstract = "The canonical approach towards fitness evaluation in Genetic Programming (GP) is to use a static training set to determine fitness, based on a cost function averaged over all fitness-cases. However, motivated by different goals, researchers have recently proposed several techniques that focus selective pressure on a subset of fitness-cases at each generation. These approaches can be described as fitness-case sampling techniques, where the training set is sampled, in some way, to determine fitness. This paper shows a comprehensive evaluation of some of the most recent sampling methods, using benchmark and real-world problems for symbolic regression. The algorithms considered here are Interleaved Sampling, Random Interleaved Sampling, Lexicase Selection and a new sampling technique is proposed called Keep-Worst Interleaved Sampling (KW-IS). The algorithms are extensively evaluated based on test performance, over fitting and bloat. Results suggest that sampling techniques can improve performance compared with standard GP. While on synthetic benchmarks the difference is slight or none at all, on real-world problems the differences are substantial. Some of the best results were achieved by Lexicase Selection and Keep Worse-Interleaved Sampling. Results also show that on real-world problems overfitting correlates strongly with bloating. Furthermore, the sampling techniques provide efficiency, since they reduce the number of fitness-case evaluations required over an entire run.", } @Article{Martinez:2016:GPEM, author = "Yuliana Martinez and Leonardo Trujillo and Pierrick Legrand and Edgar Galvan-Lopez", title = "Prediction of expected performance for a genetic programming classifier", journal = "Genetic Programming and Evolvable Machines", year = "2016", volume = "17", number = "4", pages = "409--449", month = dec, keywords = "genetic algorithms, genetic programming, Problem difficulty, Supervised learning", ISSN = "1389-2576", URL = "https://mural.maynoothuniversity.ie/12329/1/Galvan_Prediction_2016.pdf", URL = "https://rdcu.be/d0k9b", DOI = "doi:10.1007/s10710-016-9265-9", size = "41 pages", abstract = "The estimation of problem difficulty is an open issue in genetic programming (GP). The goal of this work is to generate models that predict the expected performance of a GP-based classifier when it is applied to an unseen task. Classification problems are described using domain-specific features, some of which are proposed in this work, and these features are given as input to the predictive models. These models are referred to as predictors of expected performance. We extend this approach by using an ensemble of specialized predictors (SPEP), dividing classification problems into groups and choosing the corresponding SPEP. The proposed predictors are trained using 2D synthetic classification problems with balanced datasets. The models are then used to predict the performance of the GP classifier on unseen real-world datasets that are multidimensional and imbalanced. This work is the first to provide a performance prediction of a GP system on test data, while previous works focused on predicting training performance. Accurate predictive models are generated by posing a symbolic regression task and solving it with GP. These results are achieved by using highly descriptive features and including a dimensionality reduction stage that simplifies the learning and testing process. The proposed approach could be extended to other classification algorithms and used as the basis of an expert system for algorithm selection.", } @PhdThesis{tesis_Yuliana, author = "Yuliana Sarai {Martinez Ramos}", title = "Prediction Performance and Problem Difficulty in Genetic Programming", titletranslation = "Prediccion de rendimiento y dificultad de problemas en programacion genetica", school = "ITT, Instituto tecnologico de Tijuana", year = "2016", type = "Doctor in Engineering Sciences", address = "Tijuana, Baja California, Mexico", month = jun, keywords = "genetic algorithms, genetic programming, problem difficulty, prediction of expected performance, supervised learning", identifier = "tel-01668769", language = "en", oai = "oai:HAL:tel-01668769v1", URL = "https://hal.inria.fr/tel-01668769", URL = "https://hal.inria.fr/tel-01668769/document", URL = "https://hal.inria.fr/tel-01668769/file/tesis_Yuliana.pdf", URL = "https://tel.archives-ouvertes.fr/tel-01668769", size = "158 pages", abstract = "The estimation of problem difficulty is an open issue in Genetic Programming(GP). The goal of this work is to generate models that predict the expected performance of a GP-based classifier when it is applied to an unseen task. Classification problems are described using domain specific features, some of which are proposed in this work, and these features are given as input to the predictive models. These models are referred to as predictors of expected performance (PEPs). We extend this approach by using an ensemble of specialized predictors (SPEP), dividing classification problems into groups and choosing the corresponding SPEP. The proposed predictors are trained using 2D synthetic classification problems with balanced datasets. The models are then used to predict the performance of the GP classifier on unseen real world datasets that are multidimensional and imbalanced. This work is the first to provide a performance prediction of a GP system on testdata, while previous works focused on predicting training performance. Accurate predictive models are generated by posing a symbolic regression task and solving it with GP. These results are achieved by using highly descriptive features and including a dimensionality reduction stage that simplifies the learning and testing process. The proposed approach could be extended to other classification algorithms and used as the basis of an expert system for algorithm selection.", notes = "Supervisor: Leonardo Trujillo Reyes Also known as \cite{oai:HAL:tel-01668769v1} Also known as \cite{DBLP:phd/hal/Martinez16a}", } @Article{journals/jetai/MartinezNTLL17, title = "A comparison of fitness-case sampling methods for genetic programming", author = "Yuliana Martinez and Enrique Naredo and Leonardo Trujillo and Pierrick Legrand and Uriel Lopez", journal = "Journal of Experimental \& Theoretical Artificial Intelligence", year = "2017", number = "6", volume = "29", pages = "1203--1224", keywords = "genetic algorithms, genetic programming, fitness-case sampling, performance evaluation", bibdate = "2017-11-06", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/jetai/jetai29.html#MartinezNTLL17", DOI = "doi:10.1080/0952813X.2017.1328461", size = "22 pages", abstract = "Genetic programming (GP) is an evolutionary computation paradigm for automatic program induction. GP has produced impressive results but it still needs to overcome some practical limitations, particularly its high computational cost, overfitting and excessive code growth. Recently, many researchers have proposed fitness-case sampling methods to overcome some of these problems, with mixed results in several limited tests. This paper presents an extensive comparative study of four fitness-case sampling methods, namely: Interleaved Sampling, Random Interleaved Sampling, Lexicase Selection and Keep-Worst Interleaved Sampling. The algorithms are compared on 11 symbolic regression problems and 11 supervised classification problems, using 10 synthetic benchmarks and 12 real-world data-sets. They are evaluated based on test performance, overfitting and average program size, comparing them with a standard GP search. Comparisons are carried out using non-parametric multigroup tests and post hoc pairwise statistical tests. The experimental results suggest that fitness-case sampling methods are particularly useful for difficult real-world symbolic regression problems, improving performance, reducing overfitting and limiting code growth. On the other hand, it seems that fitness-case sampling cannot improve upon GP performance when considering supervised binary classification.", } @InProceedings{conf/sgai/Martinez-ArellanoNB12, author = "Giovanna Martinez-Arellano and Lars Nolle and John A. Bland", title = "Improving {WRF-ARW} Wind Speed Predictions using Genetic Programming", booktitle = "Research and Development in Intelligent Systems {XXIX}", year = "2012", editor = "Max Bramer and Miltos Petridis", pages = "347--360", address = "Cambridge, UK", month = dec # " 11-13", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4471-4739-8", URL = "http://dx.doi.org/10.1007/978-1-4471-4739-8", DOI = "doi:10.1007/978-1-4471-4739-8_27", language = "English", bibdate = "2013-01-22", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/sgai/sgai2012.html#Martinez-ArellanoNB12", abstract = "Numerical weather prediction models can produce wind speed forecasts at a very high space resolution. However, running these models with that amount of precision is time and resource consuming. In this paper, the integration of the Weather Research and Forecasting Advanced Research WRF (WRF-ARW) mesoscale model with four different downscaling approaches is presented. Three of the proposed methods are mathematical based approaches that need a predefined model to be applied. The fourth approach, based on genetic programming (GP), will implicitly find the optimal model to downscale WRF forecasts, so no previous assumptions about the model need to be made. WRFARW forecasts and observations at three different sites of the state of Illinois in the USA are analysed before and after applying the downscaling techniques. Results have shown that GP is able to successfully downscale the wind speed predictions, reducing significantly the inherent error of the numerical models.", notes = "SGAI Conf. Incorporating Applications and Innovations in Intelligent Systems XX Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence", } @InProceedings{Martinez-Arellano:2013:mendel, author = "Giovanna Martinez-Arellano and Lars Nolle", title = "Short-term wind power forecasting with {WRF-ARW} model and genetic programming", booktitle = "19th International Conference on Soft Computing, MENDEL 2013", year = "2013", editor = "Radomil Matousek", address = "Brno, Czech Republic", month = jun # " 26-28, Brno", organisation = "Brno University of Technology", keywords = "genetic algorithms, genetic programming", isbn13 = "978-80-214-4755-4", URL = "https://www.researchgate.net/publication/264397404_Short-term_wind_power_forecasting_with_WRF-ARW_model_and_genetic_programming", abstract = "Forecasting wind power in the short-term usually involves the use of numerical weather prediction models. These models need to run at very high resolutions to provide the best forecasts possible. Producing high resolution forecasts is resource and time consuming, which can be a problem when the forecasts need to be available for the grid operator on the day-ahead. This paper introduces a novel approach for short-term wind power prediction by combining the Weather Research and Forecasting - Advanced Research WRF model (WRF ARW) with genetic programming, using the latter one for final downscaling and prediction technique, estimating the total hourly power output on the day ahead at a wind farm located in Galicia, Spain", notes = "http://www.mendel-conference.org/", } @InProceedings{conf/sgai/Martinez-ArellanoN13, author = "Giovanna Martinez-Arellano and Lars Nolle", title = "Genetic Programming for Wind Power Forecasting and Ramp Detection", booktitle = "Proceedings of the Thirty-third SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence (AI 2013)", year = "2013", editor = "Max Bramer and Miltos Petridis", pages = "403--417", address = "Cambridge, UK", month = dec # " 10-12", organisation = "British Computer Society's Specialist Group on Artificial Intelligence", publisher = "Springer", keywords = "genetic algorithms, genetic programming", bibdate = "2013-12-08", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/sgai/sgai2013.html#Martinez-ArellanoN13", language = "English", isbn13 = "978-3-319-02620-6", URL = "http://dx.doi.org/10.1007/978-3-319-02621-3", URL = "http://dx.doi.org/10.1007/978-3-319-02621-3_30", DOI = "doi:10.1007/978-3-319-02621-3_30", abstract = "In order to incorporate large amounts of wind power into the electric grid, it is necessary to provide grid operators with wind power forecasts for the day ahead, especially when managing extreme situations: rapid changes in power output of a wind farm. These so-called ramp events are complex and difficult to forecast. Hence, they introduce a high risk of instability to the power grid. Therefore, the development of reliable ramp prediction methods is of great interest to grid operators. Forecasting ramps for the day ahead requires wind power forecasts, which usually involve numerical weather prediction models at very high resolutions. This is resource and time consuming. This paper introduces a novel approach for short-term wind power prediction by combining the Weather Research and Forecasting advanced Research WRF model (WRF-ARW) with genetic programming. The latter is used for the final downscaling step and as a prediction technique, estimating the total hourly power output for the day ahead at a wind farm located in Galicia, Spain. The accuracy of the predictions is above 85 percent of the total power capacity of the wind farm, which is comparable to computationally more expensive state-of-the-art methods. Finally, a ramp detection algorithm is applied to the power forecast to identify the time and magnitude of possible ramp events. The proposed method clearly outperformed existing ramp prediction approaches.", notes = "http://www.bcs-sgai.org/ai2013/ Research and Development in Intelligent Systems XXX, Incorporating Applications and Innovations in Intelligent Systems XXI. Proceedings of AI-2013, The Thirty-third SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence Nottingham Trent University, UK", } @InProceedings{Martinez-Arellano:2014:UKSim, author = "Giovanna {Martinez Arellano} and Richard Cant and Lars Nolle", booktitle = "16th AMSS International Conference on Computer Modelling and Simulation (UKSim 2014)", title = "Prediction of Jet Engine Parameters for Control Design Using Genetic Programming", year = "2014", month = "26-28 " # mar, pages = "45--50", address = "Cambridge", keywords = "genetic algorithms, genetic programming, jet engine, simulation", DOI = "doi:10.1109/UKSim.2014.64", size = "6 pages", abstract = "The simulation of a jet engine behaviour is widely used in many different aspects of the engine development and maintenance. Achieving high quality jet engine control systems requires the iterative use of these simulations to virtually test the performance of the engine avoiding any possible damage on the real engine. Jet engine simulations involve the use of mathematical models which are complex and may not always be available. This paper introduces an approach based on Genetic Programming (GP) to model different parameters of a small engine for control design such as the Exhaust Gas Temperature (EGT). The GP approach has no knowledge of the characteristics of the engine. Instead, the model is found by the evolution of models based on past measurements of parameters such as the pump voltage. Once the model is obtained, it is used to predict the behaviour of the jet engine one step ahead. The proposed approach is successfully applied for the simulation of a Behotec j66 jet engine and the results are presented.", notes = "Control of model aircraft jet engines. Constructing a model sufficient for control design. Fitness from difference between GP output and real measurement per training point plus tree size penalty. tournament size 20. Three models of different aspects of engine. Cited by \cite{EnriquezZarate:2017:ASC}. Also known as \cite{7046037}", } @PhdThesis{Martinez-Arellano:thesis, author = "Giovanna {Martinez Arellano}", title = "Forecasting Wind Power for the Day-Ahead Market using Numerical Weather Models and Computational Intelligence Techniques", school = "School of Science and Technology, Nottingham Trent University", year = "2015", address = "Nottingham, NG1 4BU, UK", month = apr, keywords = "genetic algorithms, genetic programming", URL = "http://irep.ntu.ac.uk/id/eprint/322/", URL = "http://irep.ntu.ac.uk/id/eprint/322/1/220627_Giovanna.Martinez-2015excl.3rdpartymaterial.pdf", size = "259 pages", abstract = "Wind power forecasting is essential for the integration of large amounts of wind power into the electric grid, especially during large rapid changes of wind generation. These changes, known as ramp events, may cause instability in the power grid. Therefore, detailed information of future ramp events could potentially improve the backup allocation process during the Day Ahead (DA) market (12 to 36 hours before the actual operation), allowing the reduction of resources needed, costs and environmental impact. It is well established in the literature that meteorological models are necessary when forecasting more than six hours into the future. Most state-of-the-art forecasting tools use a combination of Numerical Weather Prediction (NWP) forecasts and observations to estimate the power output of a single wind turbine or a whole wind farm. Although NWP systems can model meteorological processes that are related to large changes in wind power, these might be misplaced i.e. in the wrong physical position. A standard way to quantify such errors is by the use of NWP ensembles. However, these are computationally expensive. Here, an alternative is to use spatial fields, which are used to explore different numerical grid points to quantify variability. This strategy can achieve comparable results to typical numerical ensembles, which makes it a potential candidate for ramp characterisation.", notes = "'A Genetic Programming Approach for Wind Speed Downscaling' 'Wind Power Forecasting with Genetic Programming' 'Appendix F: The Wind Variability of Galicia'", } @Article{Martinez-Arellano:2017:ieeeTCIAIG, author = "Giovanna Martinez-Arellano and Richard Cant and David Woods", title = "Creating AI Characters for Fighting Games using Genetic Programming", journal = "IEEE Transactions on Computational Intelligence and AI in Games", year = "2017", volume = "9", number = "4", pages = "423--434", month = dec, keywords = "genetic algorithms, genetic programming, Adaptation models, Games, Learning (artificial intelligence), Real-time systems, AI, character, fighting games", ISSN = "1943-068X", DOI = "doi:10.1109/TCIAIG.2016.2642158", abstract = "This paper proposes a character generation approach for the M.U.G.E.N. fighting game that can create engaging AI characters using a computationally cheap process without the intervention of the expert developer. The approach uses a Genetic Programming algorithm that refines randomly generated character strategies into better ones using tournament selection. The generated AI characters were tested by twenty-seven human players and were rated according to results, perceived difficulty and how engaging the game play was. The main advantages of this procedure are that no prior knowledge of how to code the strategies of the AI character is needed and there is no need to interact with the internal code of the game. In addition, the procedure is capable of creating a wide diversity of players with different strategic skills, which could be potentially used as a starting point to a further adaptive process.", notes = "Also known as \cite{7792145}", } @Article{DBLP:journals/ijseke/Gil23, author = "Jorge Martinez-Gil", title = "A Comparative Study of Ensemble Techniques Based on Genetic Programming: A Case Study in Semantic Similarity Assessment", journal = "International Journal of Software Engineering and Knowledge Engineering", volume = "33", number = "2", pages = "289--312", year = "2023", month = feb, keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Ensemble learning, semantic similarity measurement", ISSN = "0218-1940", URL = "https://doi.org/10.1142/S0218194022500772", DOI = "doi:10.1142/S0218194022500772", timestamp = "Sat, 29 Apr 2023 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/ijseke/Gil23.bib", bibsource = "dblp computer science bibliography, https://dblp.org", size = "24 pages", abstract = "The challenge of assessing semantic similarity between pieces of text through computers has attracted considerable attention from industry and academia. New advances in neural computation have developed very sophisticated concepts, establishing a new state of the art in this respect. we go one step further by proposing new techniques built on the existing methods. To do so, we bring to the table the stacking concept that has given such good results and propose a new architecture for ensemble learning based on genetic programming. As there are several possible variants, we compare them all and try to establish which one is the most appropriate to achieve successful results in this context. Analysis of the experiments indicates that Cartesian Genetic Programming seems to give better average results.", notes = "Software Competence Center Hagenberg GmbH, Softwarepark 32a, 4232 Hagenberg, Austria", } @Misc{martinezgil2023automatic, author = "Jorge Martinez-Gil", title = "Automatic Design of Semantic Similarity Ensembles Using Grammatical Evolution", howpublished = "arXiv", year = "2023", month = "30 " # aug, note = "v5", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Ensemble Learning, Grammatical Evolution, Semantic Similarity Measurement", eprint = "2307.00925", archiveprefix = "arXiv", primaryclass = "cs.CL", URL = "https://arxiv.org/abs/2307.00925", code_url = "https://github.com/jorge-martinez-gil/sesige", size = "30 pages", abstract = "Semantic similarity measures are widely used in natural language processing to catalyze various computer-related tasks. However, no single semantic similarity measure is the most appropriate for all tasks, and researchers often use ensemble strategies to ensure performance. This research work proposes a method for automatically designing semantic similarity ensembles. In fact, our proposed method uses grammatical evolution, for the first time, to automatically select and aggregate measures from a pool of candidates to create an ensemble that maximises correlation to human judgment. The method is evaluated on several benchmark datasets and compared to state-of-the-art ensembles, showing that it can significantly improve similarity assessment accuracy and outperform existing methods in some cases. As a result, our research demonstrates the potential of using grammatical evolution to automatically compare text and prove the benefits of using ensembles for semantic similarity tasks. The source code that illustrates our approach can be downloaded from https://github.com/jorge-martinez-gil/sesige", notes = "'heavily based' on PonyGE2 \cite{DBLP:journals/corr/FentonMFFOH17}", } @InProceedings{Martinez-Julia:2021:IM, author = "Pedro Martinez-Julia and Ved P. Kafle and Hitoshi Asaeda", booktitle = "2021 IFIP/IEEE International Symposium on Integrated Network Management (IM)", title = "Automation and Multi-Objective Optimization of Virtual Network Embedding", year = "2021", pages = "63--71", abstract = "The need for automated management is continuously increasing, especially with the advent of network virtualization and slicing technologies. However, finding the optimum configuration for a virtual network before it is embedded onto the substrate network is a problem that cannot be resolved by exact and deterministic mathematical operations. In this paper we propose a novel heuristic for building an algorithm based on genetic programming for optimising the placement of virtual network function instances before they are deployed, so more instances can be deployed on the same substrate network without incurring in overloads and delays. Each solution given by our algorithm is based on a previous solution, following dynamic programming scheme to minimise processing and enforcement efforts. Therefore, the algorithm accomplishes with the time constraints set by current demands. We demonstrate this quality and compare our algorithm to previous solutions, also based on genetic programming and already providing quite fast responses for the embedding problem.", keywords = "genetic algorithms, genetic programming, Automation, Heuristic algorithms, Buildings, Dynamic programming, Delays, Time factors", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=9463978", ISSN = "1573-0077", month = may, notes = "Also known as \cite{9463978}", } @InProceedings{Martinez-Rodriguez:2018:IWISC, title = "Saliency improvement through genetic programming", author = "Diana E. {Martinez Rodriguez} and Marco A. Contreras-Cruz and Uriel Haile {Hernandez Belmonte} and Sergey Bereg and Victor Ayala-Ramirez", booktitle = "Proceedings of the 3rd International Workshop on Interactive and Spatial Computing, IWISC'18", year = "2018", editor = "Prabhakaran Balakrishnan and Ryan P. McMahan", pages = "29--38", address = "Richardson, Texas, USA", month = apr # " 12-13", publisher = "ACM", keywords = "genetic algorithms, genetic programming, evolutionary computation, saliency enhancement, salient object detection, visual saliency", isbn13 = "978-1-4503-5439-4", acmid = "3191809", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/iwisc/iwisc2018.html#RodriguezCBBA18", URL = "http://doi.acm.org/10.1145/3191801.3191809", URL = "http://dl.acm.org/citation.cfm?id=3191801", DOI = "doi:10.1145/3191801.3191809", abstract = "Visual saliency detection aims at finding regions of interest which contain relevant information in images. In the last years, several saliency methods have been proposed, however, it is still a challenging task in visualization, graphics and computer vision. Visual saliency has been useful in many tasks such as object segmentation, object detection, image retrieval, place recognition, human-computer interaction, among others. In this work, we present the design of a Genetic Programming Framework to improve the saliency maps generated from a determined saliency method. As output, we obtain a sequence of operators to improve a saliency map. We have tested this approach by using three saliency methods of the state-of-the-art. The validation of the generated solutions have been tested in three visual saliency image datasets. The results of the experiments show that the solution found by Genetic Programming outperforms the original input saliency model.", notes = "Also known as \cite{conf/iwisc/RodriguezCBBA18}, \cite{Martinez-Rodriguez:2018:SIT:3191801.3191809}", } @Article{MartinezCanillas:2009:CLEI, author = "Javier {Martinez Canillas} and Roberto Sanchez and Benjamin Baran", title = "Estimation Models Generation using Linear Genetic Programming", journal = "CLEI Electronic Journal", year = "2009", volume = "13", number = "3", pages = "paper 4", month = dec, note = "Regular Issue and Special Issue of Best Papers presented at CLEI 2008, Santa Fe, Argentina", keywords = "genetic algorithms, genetic programming, economic indicators, time series, forecasting", ISSN = "0717-5000", URL = "http://www.clei.cl/cleiej/papers/v12i3p4.pdf", URL = "http://www.clei.cl/cleiej/paper.php?id=172", size = "8 pages", abstract = "he use of decision rules and estimation techniques is increasingly common for decision making. In recent years studies were conducted which applies Genetic Programming (GP) to obtain rules to make predictions. A new branch in the area of Evolutionary Algorithms (EA) is Linear Genetic Programming (LGP). LGP evolves instructions sequences of an imperative programming language. This paper proposes estimation models generation for time series forecasting using LGP. The forecasting result for the Consumer Price Index (CPI) and the price of soybeans per ton shows the potential of this new proposal. Spanish Abstract: El uso de reglas de decision y tecnicas de estimacion es cada vez mas coman para la toma de decisiones. Recientemente se han hecho estudios usando programacion genetica para obtener reglas que hagan predicciones. Una area novedosa dentro de los algoritmos evolutivos es la programacion genetica lineal (LGP). LGP evoluciona secuencias de instrucciones de un lenguaje imperativo. Este trabajo propone generar modelos de estimacion para la prediccion de series de tiempo usando LGP. El resultado de la prediccion para el indice de precios al consumidor y el precio de la soja por tonelada muestra el potencial de esta propuesta.", notes = "CLEI (Latin-american Center for Informatics Studies) http://www.clei.cl/cleiej/index.html", } @PhdThesis{Martinez-Jaramillo:thesis, author = "Serafin Martinez-Jaramillo", title = "Artificial Financial Markets: An Agent Based Approach to Reproduce Stylized Facts and to study the Red Queen Effect", school = "Centre for Computational Finance and Economic Agents, University of Essex", year = "2007", address = "UK", month = jun, keywords = "genetic algorithms, genetic programming", URL = "http://cswww.essex.ac.uk/Research/CSP/finance/papers/Martinez-PhD2007.pdf", size = "198 pages", abstract = "Stock markets are very important in modern societies and their behaviour have serious implications in a wide spectrum of the world's population. Investors, governing bodies and the society as a whole could benefit from better understanding of the behaviour of stock markets. The traditional approach to analyze such systems is the use of analytical models. However, the complexity of financial markets represents a big challenge to the analytical approach. Most analytical models make simplifying assumptions, such as perfect rationality and homogeneous investors, which threaten the validity of analytical results. This motivates the use of alternative methods. For those reasons, the study of such markets is a fertile field to use the agent-based methodology. In this work, we developed an artificial financial market and used it to study the behaviour of stock markets. In this market, we model technical, fundamental and noise traders. The technical traders are non-simple genetic programming based agents that co-evolve (by means of their fitness function) by predicting investment opportunities in the market using technical analysis as the main tool. Such traders are equipped with an investment strategy that we consider to be realistic and we avoid any kind of strong assumptions about the agents' rationality, utility function or risk aversion.! Changes in some parameters and in the agents behaviour produce different properties of the stock price series that we analyze. In this paper we investigate the different conditions under which the statistical properties of an artificial stock market resemble those of the real financial markets. Additionally, we modeled the pressure to beat the market by a behavioural constraint imposed on the agents related to the Red Queen principle in evolution. The Red Queen principle is a metaphor of a co-evolutionary arms race between species. We investigate the effect of such constraint on the price dynamics and the wealth distribution of the agents after several periods of trading in the different simulation cases. We have demonstrated how evolutionary computation plays a key role in studying stock markets.", notes = "Supervisor: Edward P. K. Tsang Co-supervisor: Sheri Markose", } @Article{Martinez-Jaramillo:2009:ieeeTEC, author = "Serafin Martinez-Jaramillo and Edward P. K. Tsang", title = "An Heterogeneous, Endogenous and Coevolutionary {GP}-Based Financial Market", journal = "IEEE Transactions on Evolutionary Computation", year = "2009", month = feb, volume = "13", number = "1", pages = "33--55", keywords = "genetic algorithms, genetic programming, economics, multi-agent systems, pricing, series (mathematics), statistical analysis, stock markets, Red Queen principle, agent-based simulation, analytical models, behavioral constraint, coevolutionary GP-based financial market, economic learning, endogenous artificial market, evolutionary computation, fitness function, genetic programming based agents, homogeneous investors, investment opportunity, noise traders, perfect rationality, price generation, price series, real financial markets, statistical property, stock markets, technical traders", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2008.2011401", size = "23 pages", abstract = "Stock markets are very important in modern societies and their behavior has serious implications for a wide spectrum of the world's population. Investors, governing bodies, and society as a whole could benefit from better understanding of the behavior of stock markets. The traditional approach to analyzing such systems is the use of analytical models. However, the complexity of financial markets represents a big challenge to the analytical approach. Most analytical models make simplifying assumptions, such as perfect rationality and homogeneous investors, which threaten the validity of their results. This motivates alternative methods. In this paper, we report an artificial financial market and its use in studying the behavior of stock markets. This is an endogenous market, with which we model technical, fundamental, and noise traders. Nevertheless, our primary focus is on the technical traders, which are sophisticated genetic programming based agents that co- evolve (by learning based on their fitness function) by predicting investment opportunities in the market using technical analysis as the main tool. With this endogenous artificial market, we identify the conditions under which the statistical properties of price series in the artificial market resemble some of the properties of real financial markets. By performing a careful exploration of the most important aspects of our simulation model, we determine the way in which the factors of such a model affect the endogenously generated price. Additionally, we model the pressure to beat the market by a behavioral constraint imposed on the agents reflecting the Red Queen principle in evolution. We have demonstrated how evolutionary computation could play a key role in studying stock markets, mainly as a suitable model for economic learning on an agent- based simulation.", notes = "also known as \cite{4769014}", } @InProceedings{Martins:2018:LA-CCI, author = "Denis Mayr Lima Martins and Gottfried Vossen and Fernando Buarque {de Lima Neto}", booktitle = "2018 IEEE Latin American Conference on Computational Intelligence (LA-CCI)", title = "Discovering SQL Queries from Examples using Intelligent Algorithms", year = "2018", abstract = "Formulating database queries in terms of SQL is often a challenge for journalists, business administrators, and the growing number of non-database experts that are required to access and explore data. To alleviate this problem, we proposed a Query By Example (QBE) approach powered by intelligent algorithms that discovers database queries from a few tuple examples provided by the user. We investigated the effectiveness of three algorithms, namely, Greedy Search, Genetic Programming, and CART decision trees in discovering queries in two distinct databases. To the best of our knowledge, no other research has focused on the comparative analysis of such algorithms in the context of QBE. Our results show that CART decision trees were capable of discovering the most accurate queries. However, CART tends to produce long queries, which may hinder user interpretation. Finally, we suggest that the use of Interactive Evolutionary Computational Intelligence may improve the quality of queries discovered by Genetic Programming and may naturally incorporate diverse user preferences in the discovery process.", keywords = "genetic algorithms, genetic programming, Databases, Decision trees, Sensitivity, Sociology, Statistics,", DOI = "doi:10.1109/LA-CCI.2018.8625260", month = nov, notes = "Also known as \cite{8625260}", } @InProceedings{Martins:2018:GECCO, author = "Joao Francisco B. S. Martins and Luiz Otavio V. B. Oliveira and Luis F. Miranda and Felipe Casadei and Gisele L. Pappa", title = "Solving the exponential growth of symbolic regression trees in geometric semantic genetic programming", booktitle = "GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference", year = "2018", editor = "Hernan Aguirre and Keiki Takadama and Hisashi Handa and Arnaud Liefooghe and Tomohiro Yoshikawa and Andrew M. Sutton and Satoshi Ono and Francisco Chicano and Shinichi Shirakawa and Zdenek Vasicek and Roderich Gross and Andries Engelbrecht and Emma Hart and Sebastian Risi and Ekart Aniko and Julian Togelius and Sebastien Verel and Christian Blum and Will Browne and Yusuke Nojima and Tea Tusar and Qingfu Zhang and Nikolaus Hansen and Jose Antonio Lozano and Dirk Thierens and Tian-Li Yu and Juergen Branke and Yaochu Jin and Sara Silva and Hitoshi Iba and Anna I Esparcia-Alcazar and Thomas Bartz-Beielstein and Federica Sarro and Giuliano Antoniol and Anne Auger and Per Kristian Lehre", isbn13 = "978-1-4503-5618-3", pages = "1151--1158", address = "Kyoto, Japan", DOI = "doi:10.1145/3205455.3205593", publisher = "ACM", publisher_address = "New York, NY, USA", month = "15-19 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", abstract = "advances in Geometric Semantic Genetic Programming (GSGP) have shown that this variant of Genetic Programming (GP) reaches better results than its predecessor for supervised machine learning problems, particularly in the task of symbolic regression. However, by construction, the geometric semantic crossover operator generates individuals that grow exponentially with the number of generations, resulting in solutions with limited use. This paper presents a new method for individual simplification named GSGP with Reduced trees (GSGP-Red). GSGP-Red works by expanding the functions generated by the geometric semantic operators. The resulting expanded function is guaranteed to be a linear combination that, in a second step, has its repeated structures and respective coefficients aggregated. Experiments in 12 real-world datasets show that it is not only possible to create smaller and completely equivalent individuals in competitive computational time, but also to reduce the number of nodes composing them by 58 orders of magnitude, on average.", notes = "Also known as \cite{3205593} GECCO-2018 A Recombination of the 27th International Conference on Genetic Algorithms (ICGA-2018) and the 23rd Annual Genetic Programming Conference (GP-2018)", } @InProceedings{Martins-Lima:2023:EuroGP, author = "Bryan {Martins Lima} and Naiara Sachetti and Augusto Berndt and Cristina Meinhardt and Jonata {Tyska Carvalho}", title = "Adaptive Batch Size {CGP}: Improving accuracy and runtime for {CGP} Logic Optimization flow", booktitle = "EuroGP 2023: Proceedings of the 26th European Conference on Genetic Programming", year = "2023", month = "12-14 " # apr, editor = "Gisele Pappa and Mario Giacobini and Zdenek Vasicek", series = "LNCS", volume = "13986", publisher = "Springer Verlag", address = "Brno, Czech Republic", pages = "149--164", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, CGP, Logic synthesis, Evolutionary algorithms, Approximate Computing", isbn13 = "978-3-031-29572-0", URL = "https://rdcu.be/c8US8", DOI = "doi:10.1007/978-3-031-29573-7_10", size = "16 pages", abstract = "With the recent advances in the Machine Learning field, alongside digital circuits becoming more complex each day, machine learning based methods are being used in error-tolerant applications to solve the challenges imposed by large integrated circuits, where the designer can obtain a better overall circuit while relaxing its accuracy requirement. One of these methods is the Cartesian Genetic Programming (CGP), a subclass of Evolutionary Algorithms that uses concepts from biological evolution applied in electronic design automation. CGP-based approaches show advantages in the logic learning and logic optimization processes. However, the main challenge of CGP-based flows is the extensive runtime compared to other logic synthesis strategies. We propose a new strategy to tackle this challenge, called Adaptive Batch Size (ABS) CGP, in which the CGP algorithm incrementally improves the fitness estimation of the candidate solutions by using more terms of the truth table for eva", notes = "Part of \cite{Pappa:2023:GP} EuroGP'2023 held in conjunction with EvoCOP2023, EvoMusArt2023 and EvoApplications2023", } @InProceedings{conf/iconip/MarutaZNSK17, author = "Shunya Maruta and Yi Zuo and Masahiro Nagao and Hideyuki Sugiura and Eisuke Kita", title = "Grammatical Evolution Using Tree Representation Learning", booktitle = "Neural Information Processing - 24th International Conference, ICONIP 2017, Guangzhou, China, November 14-18, 2017, Proceedings, Part IV", editor = "Derong Liu and Shengli Xie and Yuanqing Li and Dongbin Zhao and El-Sayed M. El-Alfy", publisher = "Springer", year = "2017", pages = "346--355", series = "Lecture Notes in Computer Science", volume = "10637", keywords = "genetic algorithms, genetic programming, grammatical evolution, tree representation, multiple chromosomes, pointer allocation, genotype-phenotype map", bibdate = "2017-11-17", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/iconip/iconip2017-4.html#MarutaZNSK17", DOI = "doi:10.1007/978-3-319-70093-9_36", isbn13 = "978-3-319-70092-2", abstract = "Grammatical evolution (GE) is one of the evolutionary computations, which evolves genotype to map phenotype by using the Backus-Naur Form (BNF) syntax. GE has been widely employed to represent syntactic structure of a function or a program in order to satisfy the design objective. As the GE decoding process parses the genotype chromosome into array or list structures with left-order traversal, encoding process could change gene codons or orders after genetic operations. For improving this issue, this paper proposes a novel GE algorithm using tree representation learning (GETRL) and presents three contributions to the original GE, genetic algorithm (GA) and genetic programming (GP). Firstly, GETRL uses a tree-based structure to represent the functions and programs for practical problems. To be different from the traditional GA, GETRL adopts a genotype-to-phenotype encoding process, which transforms the genes structures for tree traversal. Secondly, a pointer allocation mechanism is introduced in this method, which allows the GETRL to pursue the genetic operations like typical GAs. To compare with the typical GP, however GETRL still generates a tree structure, our method adopts a phenotype-to-genotype decoding process, which allows the genetic operations be able to be apply into tree-based structure. Thirdly, due to each codon in GE has different expression meaning, genetic operations are quite different from GAs, in which all codons have the same meaning. In this study, we also suggest a multi-chromosome system and apply it into GETRL, which can prevent from overriding the codons for different objectives.", } @InProceedings{Marvel:2012:GECCOcomp, author = "Skylar Marvel and Alison Motsinger-Reif", title = "Grammatical evolution support vector machines for predicting human genetic disease association", booktitle = "GECCO 2012 Graduate Students Workshop", year = "2012", editor = "Alison Motsinger-Reif", isbn13 = "978-1-4503-1178-6", keywords = "genetic algorithms, genetic programming, Grammatical evolution, SVN", pages = "595--598", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330784.2330881", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Identifying genes that predict common, complex human diseases is a major goal of human genetics. This is made difficult by the effect of epistatic interactions and the need to analyze datasets with high-dimensional feature spaces. Many classification methods have been applied to this problem, one of the more recent being Support Vector Machines (SVM). Selection of which features to include in the SVM model and what parameters or kernels to use can often be a difficult task. This work uses Grammatical Evolution (GE) as a way to choose features and parameters. Initial results look promising and encourage further development and testing of this new approach.", notes = "Also known as \cite{2330881} Distributed at GECCO-2012. ACM Order Number 910122.", } @InProceedings{Marwala:2006:CEC, author = "Tshilidzi Marwala", title = "Bayesian Training of Neural Networks Using Genetic Programming", booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary Computation", year = "2006", editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas", pages = "7013--7017", address = "Vancouver", month = "16-21 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9487-9", DOI = "doi:10.1109/IJCNN.2006.247374", size = "5 pages", abstract = "Bayesian neural networks trained using Markov chain Monte Carlo (MCMC) and genetic programming in binary space within Metropolis framework is proposed. It is tested and compared to classical MCMC method and is observed to give better results than classical approach.", notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D", } @Article{Marwala:2007:PRL, author = "Tshilidzi Marwala", title = "Bayesian Training of Neural Networks Using Genetic Programming", journal = "Pattern Recognition Letters", year = "2007", volume = "28", number = "12", pages = "1452--1458", keywords = "genetic algorithms, genetic programming, Bayesian framework, Evolutionary programming, Neural networks", ISSN = "0167-8655", URL = "http://www.sciencedirect.com/science/article/B6V15-4NC38M7-5/2/dee1daa1b7f713474289040a57125fd4", DOI = "doi:10.1016/j.patrec.2007.03.004", abstract = "Bayesian neural network trained using Markov chain Monte Carlo (MCMC) and genetic programming in binary space within Metropolis framework is proposed. The algorithm proposed here has the ability to learn using samples obtained from previous steps merged using concepts of natural evolution which include mutation, crossover and reproduction. The reproduction function is the Metropolis framework and binary mutation as well as simple crossover, are also used. The proposed algorithm is tested on simulated function, an artificial taster using measured data as well as condition monitoring of structures and the results are compared to those of a classical MCMC method. Results confirm that Bayesian neural networks trained using genetic programming offers better performance and efficiency than the classical approach.", notes = "Also known as \cite{Marwala20071452}", } @Article{MARZBAN:2021:IJBM, author = "Nader Marzban and Ahmad Moheb and Svitlana Filonenko and Seyyed Hossein Hosseini and Mohammad Javad Nouri and Judy A. Libra and Gianluigi Farru", title = "Intelligent modeling and experimental study on methylene blue adsorption by sodium alginate-kaolin beads", year = "2021", journal = "International Journal of Biological Macromolecules", volume = "186", pages = "79--91", keywords = "genetic algorithms, genetic programming, Methylene blue adsorption, Sodium alginate-kaolin beads, Intelligent approaches", ISSN = "0141-8130", URL = "https://www.sciencedirect.com/science/article/pii/S0141813021014422", DOI = "doi:10.1016/j.ijbiomac.2021.07.006", size = "13 pages", abstract = "As tighter regulations on colour in discharges to water bodies are more widely implemented worldwide, the demand for reliable inexpensive technologies for dye removal grows. In this study, the removal of the basic dye, methylene blue, by adsorption onto low-cost sodium alginate-kaolin beads was investigated to determine the effect of operating parameters (initial dye concentration, contact time, pH, adsorbent dosage, temperature, agitation speed) on dye removal efficiency. The composite beads and individual components were characterized by a number of analytical techniques. Three models were developed to describe the adsorption as a function of the operating parameters using regression analysis, and two powerful intelligent modeling techniques, genetic programming and artificial neural network (ANN). The ANN model is best in predicting dye removal efficiency with R2 = 0.97 and RMSE = 3.59. The developed model can be used as a useful tool to optimize treatment processes using the promising adsorbent, to eliminate basic dyes from aqueous solutions. Adsorption followed a pseudo-second order kinetics and was best described by the Freundlich isotherm. Encapsulating the kaolin powder in sodium alginate resulted in removal efficiency of 99.56percent and a maximum adsorption capacity of 188.7 mg.g-1, a more than fourfold increase over kaolin alone", } @Article{MARZBAN:2022:jece, author = "Nader Marzban and Judy A. Libra and Seyyed Hossein Hosseini and Marcus G. Fischer and Vera Susanne Rotter", title = "Experimental evaluation and application of genetic programming to develop predictive correlations for hydrochar higher heating value and yield to optimize the energy content", journal = "Journal of Environmental Chemical Engineering", volume = "10", number = "6", pages = "108880", year = "2022", ISSN = "2213-3437", DOI = "doi:10.1016/j.jece.2022.108880", URL = "https://www.sciencedirect.com/science/article/pii/S2213343722017535", keywords = "genetic algorithms, genetic programming, Hydrothermal carbonization, HHV, Solid yield, Energy yield, Optimization", abstract = "The hydrothermal carbonization (HTC) process has been found to consistently improve biomass fuel characteristics by raising the higher heating value (HHV) of the hydrochar as process severity is increased. However, this is usually associated with a decrease in the solid yield (SY) of hydrochar, making it difficult to determine the optimal operating conditions to obtain the highest energy yield (EY), which combines the two parameters. In this study, a graph-based genetic programming (GP) method was used for developing correlations to predict HHV, SY, and EY for hydrochars based on published values from 42 biomasses and a broad range of HTC experimental systems and operating conditions, i.e., 5 lteq holding time (min) lteq 2208, 120 lteq temperature (degreeC) lteq 300, and 0096 lteq biomass to water ratio lteq 0.5. In addition, experiments were carried out with 5 pomaces at 4 temperatures and two reactor scales, 1 L and 18.75 L. The correlations were evaluated using this experimental data set in order to estimate prediction errors in similar experimental systems. The use of the correlations to predict HTC conditions to achieve the maximum EY is demonstrated for three common feedstocks, wheat straw, sewage sludge, and a fruit pomace. The prediction was confirmed experimentally with pomace at the optimized HTC conditions; we observed 6.9 percent error between the measured and predicted EY percent. The results show that the correlations can be used to predict the optimal operating conditions to produce hydrochar with the desired fuel characteristics with a minimum of actual HTC runs", } @Article{Marzukhi:2013:EI, author = "Syahaneim Marzukhi and Will N. Browne and Mengjie Zhang", title = "Adaptive artificial datasets through learning classifier systems for classification tasks", journal = "Evolutionary Intelligence", year = "2013", volume = "6", number = "2", pages = "93--107", keywords = "Learning classifier system, Pattern classification, Artificial dataset", publisher = "Springer", ISSN = "1864-5909", URL = "http://dx.doi.org/10.1007/s12065-013-0094-y", DOI = "doi:10.1007/s12065-013-0094-y", size = "15 pages", abstract = "n producing an artificial dataset, humans usually play a major role in creating and controlling the problem domain. In particular, humans set up and tune the problem's difficulty. If humans can set up the difficulty levels appropriately, then learning systems can solve classification tasks successfully. This paper introduces an autonomous classification problem generation approach. The problem's difficulty is adapted based on the classification agent's performance within the defined attributes. An automated problem generator has been created to evolve simulated datasets whilst the classification agent, in this case a learning classifier system (LCS), attempts to learn the evolving datasets. The idea here is to tune the problem's difficulty autonomously such that the problem's characteristics may be determined effectively. Furthermore, this framework can empirically test the learning bounds of the classification agent whilst lowering human involvement. Initially, tabu search was integrated in the problem generator to discover the best combination of domain features in order to adjust the problem's difficulty. In order to overcome stagnation in local optimum, a Pittsburgh-style LCSs, A-PLUS, was adapted for the first time to the problem generator. In this way, the effect of the problem's characteristics, e.g. noise, which alter the classification agent's performance, becomes human readable. Experiments confirm that the problem generator was able to tune the problem's difficulty either to make the problem 'harder' or 'easier' so that it can either 'increase' or decrease' the classification agent's performance", notes = "Not GP. Pittsburgh", } @InCollection{kinnear:masand, author = "Brij Masand", title = "Optimising Confidence of Text Classification by Evolution of Symbolic Expressions", booktitle = "Advances in Genetic Programming", publisher = "MIT Press", editor = "Kenneth E. {Kinnear, Jr.}", year = "1994", pages = "445--458", chapter = "21", keywords = "genetic algorithms, genetic programming, k-nn", institution = "Thinking Machines Corporation", URL = "http://www.amazon.co.uk/Advances-Genetic-Programming-Complex-Adaptive/dp/0262111888", URL = "http://cognet.mit.edu/sites/default/files/books/9780262277181/pdfs/9780262277181_chap21.pdf", DOI = "doi:10.7551/mitpress/1108.003.0027", size = "13 pages", abstract = "This paper reports some experiments in applying genetic algorithms for assessing the confidence of automatically assigned multiple keywords for news stories. Using Memory Based Reasoning (MBR) (a k-nearest neighbour method) to classify the stories, we would like to assign a confidence score per news story, that allows one to refer stories with low classification confidence to a human coder. Using Genetic Programming (GP) as used for program evolution by [Koza 1992], we discover and evolve symbolic expressions to compute confidence scores for news stories that allow a higher performance on subsets of the database while referring some stories to human editors. We have earlier reported recall and precision of 81percent and 72percent, if 100percent of the stories are coded automatically [Masand, Linoff and Waltz 1992]. Using the evolved confidence measures to refer some stories for manual coding, we can achieve about 80percent recall and 80percent precision for 92percent of the stories. This compares favourably with manually specified confidence functions that could classify 76percent of the database with an 80-8Opercent recall-precision requirement.", notes = "Presented at Genetic Programming Workshop of ICGA-93", notes = "Classification of New Stories, Very simple formulae evolved which do better than existing human attempts at automatic coding. Automatic results comparable to human success rates Part of \cite{kinnear:book}", } @InCollection{masand:1996:aigp2, author = "Brij Masand and Gregory Piatesky-Shapiro", title = "Discovering Time Oriented Abstractions in Historical Data to Optimize Decision Tree Classification", booktitle = "Advances in Genetic Programming 2", publisher = "MIT Press", year = "1996", editor = "Peter J. Angeline and K. E. {Kinnear, Jr.}", pages = "489--498", chapter = "24", address = "Cambridge, MA, USA", keywords = "genetic algorithms, genetic programming", ISBN = "0-262-01158-1", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6277523", DOI = "doi:10.7551/mitpress/1109.003.0031", size = "10 pages", abstract = "This paper explores the synergy between OP and decision tree-based classification. We are addressing the problem of identifying 'good' customers (e.g those who respond to special offers) by analysing historical customer billing data, using decision tree classifiers such as C4.5 [Quinlan 1993) and optimising that performance using OP [Koza 1992]. One difficult issue is how to transform and abstract raw historical data from several months for the purpose of analysis. We ad dress this by using OP to discover time oriented data abstractions of data. that enable improved prediction performance. than possible with the raw data alone. We also contrast the performance improvement obtained by generating random populations with comparable computational effort vs. OP evolution on smaller populations. Using C4.5 alone we are able to get a prediction error of about 38percent (on a 50-50percent test set of non-responders/responders) Using the additional derived fields from raw billing data, we are able to reduce the error to 35.9percent, a significant reduction for this domain. Each 1percent of improved performance (on real data) is worth about $1 million in potential increased revenues.", } @Article{Mascia:2014:COR, author = "Franco Mascia and Manuel Lopez-Ibanez and Jeremie Dubois-Lacoste and Thomas Stuetzle", title = "Grammar-based generation of stochastic local search heuristics through automatic algorithm configuration tools", journal = "Computer \& Operations Research", volume = "51", pages = "190--199", year = "2014", keywords = "genetic algorithms, genetic programming, Heuristics, Grammatical evolution, Automatic algorithm configuration, Bin packing, Flowshop scheduling", ISSN = "0305-0548", DOI = "doi:10.1016/j.cor.2014.05.020", URL = "http://www.sciencedirect.com/science/article/pii/S0305054814001555", abstract = "Several grammar-based genetic programming algorithms have been proposed in the literature to automatically generate heuristics for hard optimisation problems. These approaches specify the algorithmic building blocks and the way in which they can be combined in a grammar; the best heuristic for the problem being tackled is found by an evolutionary algorithm that searches in the algorithm design space defined by the grammar. In this work, we propose a novel representation of the grammar by a sequence of categorical, integer, and real-valued parameters. We then use a tool for automatic algorithm configuration to search for the best algorithm for the problem at hand. Our experimental evaluation on the one-dimensional bin packing problem and the permutation flowshop problem with weighted tardiness objective shows that the proposed approach produces better algorithms than grammatical evolution, a well-established variant of grammar-based genetic programming. The reasons behind such improvement lie both in the representation proposed and in the method used to search the algorithm design space.", } @InProceedings{Masek:2018:ASOR/DORS, author = "Martin Masek and Chiou Peng Lam and Luke Kelly and Lyndon Benke and Michael Papasimeon", title = "A Genetic Programming Framework for Novel Behaviour Discovery in Air Combat Scenarios", booktitle = "Data and Decision Sciences in Action 2", year = "2018", editor = "Andreas T. Ernst and Simon Dunstall and Rodolfo Garc{\'i}a-Flores and Marthie Grobler and David Marlow", series = "LNMIE", pages = "263--277", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-030-60135-5", DOI = "doi:10.1007/978-3-030-60135-5_19", abstract = "Behaviour trees offer a means to systematically decompose a behaviour into a set of steps within a tree structure. Genetic programming, which has at its core the evolution of tree-like structures, thus presents an ideal tool to identify novel behaviour patterns that emerge when the algorithm is guided by a set fitness function. In this paper, we present our framework for novel behaviour discovery using evolved behaviour trees, with some examples from the beyond-visual range air combat domain where distinct strategies emerge in response to modeling the effects of electronic warfare.", notes = "Published 2021 School of Science, Edith Cowan University, Perth, WA, Australia", } @InProceedings{Mashiane:2014:PRASA, author = "Thulani Mashiane and Nelishia Pillay", title = "Genetic Programming for Password Cracking, Phase One: Grammar Induction", booktitle = "Proceedings of the 2014 PRASA, RobMech and AfLaT International Joint Symposium", year = "2014", editor = "Martin Puttkammer and Roald Eiselen", pages = "176--182", address = "Cape Town, South Africa", month = "27-28 " # nov, publisher = "Pattern Recognition Association of South Africa (PRASA)", keywords = "genetic algorithms, genetic programming, grammar induction, password cracking, Java", isbn13 = "978-0-620-62617-0", URL = "http://hdl.handle.net/10204/10865", URL = "https://researchspace.csir.co.za/dspace/bitstream/handle/10204/10865/Mashiane_13997_2014.pdf", size = "7 pages", abstract = "Password cracking is the term commonly used to describe the illegal action of gaining access to clear text versions of user passwords. Hackers are notorious for stealing encrypted passwords and cracking them. The same action of password cracking can be used by system administrators to protect their systems from weak user passwords. By applying a password cracker to user passwords, weak or easy to crack passwords can be identified. Through the design of a password cracker system administrators can prevent weak passwords from being saved onto their systems. Users can also be made aware of the strength of the passwords they are currently employing. A manner in which password cracking can be made more effective is to produce a few guess words with a high probability of cracking a large number of passwords. Research has revealed the successful use of grammars to generate effective password guess words. In order to generate password grammars, genetic programming is applied to grammar induction for the purpose of inducing grammars that will be used as input to a password cracking tool. To achieve this goal the current paper looks at the performance of genetic programming in the induction of regular and context-free languages. The results of the experiments conducted are promising, with the genetic programming algorithm managing to induce twenty three of the twenty six context-free languages it was tested on. The value of this paper lies in the evaluation of the genetic programming technique for grammar induction. The output of the research will be used to build a genetic programming system which can evolve grammars to generate password guess words to crack user created passwords.", notes = "page 181 'For the context-free languages, the GP algorithm managed to evolve grammars for ten out of the eleven languages.' http://www.prasa.org/proceedings/2014/", } @InProceedings{Maslen:2022:CEC, author = "Jordan Maslen and Brian J. Ross", booktitle = "2022 IEEE Congress on Evolutionary Computation (CEC)", title = "Mixed Media in Evolutionary Art", year = "2022", editor = "Carlos A. Coello Coello and Sanaz Mostaghim", address = "Padua, Italy", month = "18-23 " # jul, isbn13 = "978-1-6654-6708-7", abstract = "Mixed media in the real world involves the creation of works of art that creatively combine a variety of media on the canvas, for example, watercolour, acrylic paint, and photographs. We present an evolutionary art system that implements a digital version of mixed media. A genetic programming system uses a language that renders different digital effects on a canvas. Each rendered effect takes the form of an art object, and the tree defines a set of art objects that together comprise a final rendered image. Available effects include procedural images (textures), image filters, and bitmaps. An art object is rendered onto the canvas via a predefined mask shape, which can range from simple geometric shapes such as circles or squares, to complex paintbrush strokes and paint splatters. Fitness evaluation measures the pixel by pixel colour distance between a rendered canvas and an input target image, which acts as a compositional guide for rendered images. Various runs of the system have produced an interesting variety of stylised, mixed-effect results, often appearing as abstract glitchy interpretations of target images.", keywords = "genetic algorithms, genetic programming, Image texture, Histograms, Art, Image color analysis, Shape, Graphics processing units, Media, evolutionary art, mixed media", DOI = "doi:10.1109/CEC55065.2022.9870271", notes = "Also known as \cite{9870271}", } @Article{Maslov:2005:IF, author = "Igor V. Maslov and Izidor Gertner", title = "Multi-sensor fusion: an Evolutionary algorithm approach", journal = "Information Fusion", year = "2006", volume = "7", pages = "304--330", number = "3", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6W76-4FBM1CY-2/2/e57f81dddd02342a16c54961518cedde", month = sep, keywords = "genetic algorithms, genetic programming, Information fusion, Global optimization, Heuristic methods, Evolutionary algorithms, Evolution strategies, Evolutionary programming", DOI = "doi:10.1016/j.inffus.2005.01.001", abstract = "Modern decision-making processes rely on data coming from different sources. Intelligent integration and fusion of information from distributed multi-source, multi-sensor network requires an optimisation-centred approach. Traditional optimization techniques often fail to meet the demands and challenges of highly dynamic and volatile information flow. New methods are required, which are capable of fully automated adjustment and self-adaptation to fluctuating inputs and tasks. One such method is Evolutionary algorithms (EA), a generic, flexible, and versatile framework for solving complex problems of global optimisation and search in real world applications. The evolutionary approach provides a valuable alternative to traditional methods used in information fusion, due to its inherent parallel nature and its ability to deal with difficult problems. However, the application of the algorithm to a particular problem is often more an art than science. Choosing the right model and parameters requires an in-depth understanding of the morphological development of the algorithm, as well as its recent advances and trends. This paper attempts to give a compact overview of both basic and advanced concepts, models, and variants of Evolutionary algorithms in various implementations and applications particularly those in information fusion. We have brought together material scattered throughout numerous books, journal papers, and conference proceedings. Strong emphasis is made on the practical aspects of the EA implementation, including specific and detailed recommendations drawn from these various sources. However, the practical aspects are discussed from the standpoint of concepts and models, rather than from applications in specific problem domains, which emphasise the generality of the provided recommendations across different applications including information fusion.", } @InProceedings{Maslyaev:2021:CEC, author = "Mikhail Maslyaev and Alexander Hvatov", booktitle = "2021 IEEE Congress on Evolutionary Computation (CEC)", title = "Multi-Objective Discovery of PDE Systems Using Evolutionary Approach", year = "2021", editor = "Yew-Soon Ong", pages = "596--603", address = "Krakow, Poland", month = "28 " # jun # "-1 " # jul, keywords = "genetic algorithms, genetic programming, equation discovery, system discovery, partial differential equation, PDE, data-driven methods, multi-objective optimization, MOEA/DD", isbn13 = "978-1-7281-8393-0", URL = "https://www.human-competitive.org/sites/default/files/entry_1.txt", URL = "https://www.human-competitive.org/sites/default/files/multi-objective_discovery_of_pde_systems_using_evolutionary_approach.pdf", DOI = "doi:10.1109/CEC45853.2021.9504712", size = "8 pages", abstract = "Usually, the data-driven methods of the systems of partial differential equations (PDEs) discovery are limited to the scenarios, when the result can be manifested as the single vector equation form. However, this approach restricts the application to the real cases, where, for example, the form of the external forcing is of interest for the researcher and can not be described by the component of the vector equation. In the paper, a multi-objective co-evolution algorithm is proposed. The single equations within the system and the system itself are evolved simultaneously to obtain the system. This approach allows discovering the systems with the form-independent equations. In contrast to the single vector equation, a component-wise system is more suitable for expert interpretation and, therefore, for applications. The example of the two-dimensional Navier-Stokes equation is considered.", notes = "Also known as \cite{9504712} Entered 2022 HUMIES", } @Article{Maslyaev:JCS, author = "Mikhail Maslyaev and Alexander Hvatov and Anna V. Kalyuzhnaya", title = "Partial differential equations discovery with {EPDE} framework: application for real and synthetic data", journal = "Journal of Computational Science", year = "2021", volume = "53", pages = "101345", month = jul, keywords = "genetic algorithms, genetic programming, Data-driven modeling, PDE discovery, Evolutionary algorithms, sparse regression, spatial fields, physical measurement data", ISSN = "1877-7503", timestamp = "Thu, 14 Oct 2021 09:07:15 +0200", biburl = "https://dblp.org/rec/journals/jocs/MaslyaevHK21.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "http://www.human-competitive.org/sites/default/files/entry_0.txt", URL = "http://www.human-competitive.org/sites/default/files/jocs_2020_0.pdf", URL = "https://www.human-competitive.org/sites/default/files/entry_1.txt", URL = "https://www.human-competitive.org/sites/default/files/partial_differential_equations_discovery_with_epde_framework_application_for_real_and_synthetic_data.pdf", URL = "https://www.sciencedirect.com/science/article/pii/S1877750321000429", DOI = "doi:10.1016/j.jocs.2021.101345", size = "11 pages", abstract = "Data-driven methods provide model creation tools for systems where the application of conventional analytical methods is restrained. The proposed method involves the data-driven derivation of a partial differential equation (PDE) for process dynamics, helping process simulation and study. The paper describes the methods that are used within the EPDE (Evolutionary Partial Differential Equations) partial differential equation discovery framework [1]. The framework involves a combination of evolutionary algorithms and sparse regression. Such an approach is versatile compared to other commonly used data-driven partial differential derivation methods by making fewer assumptions about the resulting equation. This paper highlights the algorithm features that allow data processing with noise, which is similar to the algorithm real-world applications. This paper is an extended version of the ICCS-2020 conference paper [2]", notes = "Entered 2021 HUMIES Entered 2022 HUMIES Also known as \cite{DBLP:journals/jocs/MaslyaevHK21} See also [2] M. Maslyaev, A. Hvatov, A. Kalyuzhnaya, Data-driven partial differential equations discovery approach for the noised multi-dimensional data, in: International Conference on Computational Science, Springer, 2020, pp. 86-100. ITMO University, 49 Kronverksky Pr. St. Petersburg, 197101, Russian Federation", } @InProceedings{Maslyaev:2022:CEC, author = "Mikhail Maslyaev and Alexander Hvatov", title = "Solver-Based Fitness Function for the Data-Driven Evolutionary Discovery of Partial Differential Equation", booktitle = "2022 IEEE Congress on Evolutionary Computation (CEC)", year = "2022", address = "Padua", month = "18-23 " # jul, keywords = "genetic algorithms, genetic programming, equation discovery, partial differential equation,fitness function selection, data-driven modeling", isbn13 = "978-1-6654-6709-4", URL = "https://www.human-competitive.org/sites/default/files/entry_1.txt", URL = "https://human-competitive.org/sites/default/files/entry_hvatov.txt", URL = "https://human-competitive.org/sites/default/files/solver-based_fitness_function_for_the_data-driven_evolutionary_discovery_of_partial_differential_equation_0.pdf", DOI = "doi:10.1109/CEC55065.2022.9870370", size = "8 pages", abstract = "Partial differential equations provide accurate models for many physical processes, although their derivation can be challenging, requiring a fundamental understanding of the modeled system. This challenge can be circumvented with the data-driven algorithms that obtain the governing equation only using observational data. One of the tools commonly used in search of the differential equation is the evolutionary optimization algorithm. we seek to improve the existing evolutionary approach to data-driven partial differential equation discovery by introducing a more reliable method of evaluating the quality of proposed structures, based on the inclusion of the automated algorithm of partial differential equations solving. In terms of evolutionary algorithms, we want to check whether the more computationally challenging fitness function represented by the equation solver gives the sufficient resulting solution quality increase with respect to the more simple one. The approach includes a computationally expensive equation solver compared with the baseline method, which used equation discrepancy to define the fitness function for a candidate structure in terms of algorithm convergence and required computational resources on the synthetic data obtained from the solution of the Korteweg-de Vries equation.", notes = "Entered 2022 HUMIES Entered 2023 HUMIES Also known as \cite{9870370}", } @InProceedings{Maslyaev:2023:GECCOcomp, author = "Mikhail Maslyaev and Alexander Hvatov", title = "Comparison of Single- and Multi- Objective Optimization Quality for Evolutionary Equation Discovery", booktitle = "Proceedings of the Companion Conference on Genetic and Evolutionary Computation", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "603--606", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, symbolic regression, dynamic system modeling, interpretable learning, differential equations, sparse regression: Poster", isbn13 = "979-8-4007-0120-7/23/07", URL = "https://human-competitive.org/sites/default/files/entry_hvatov.txt", URL = "https://human-competitive.org/sites/default/files/comparison_of_single-_and_multi-_objective_optimization_quality_for_evolutionary_equation_discovery.pdf", DOI = "doi:10.1145/3583133.3590601", size = "4 pages", abstract = "Evolutionary differential equation discovery proved to be a tool to obtain equations with less a priori assumptions than conventional approaches, such as sparse symbolic regression over the complete possible terms library. The equation discovery field contains two independent directions. The first one is purely mathematical and concerns differentiation, the object of optimization and its relation to the functional spaces and others. The second one is dedicated purely to the optimizatioal problem statement. Both topics are worth investigating to improve the algorithm's ability to handle experimental data a more artificial intelligence way, without significant pre-processing and a priori knowledge of their nature. we consider the prevalence of either single-objective optimization, which considers only the discrepancy between selected terms in the equation, or multi-objective optimization, which additionally takes into account the complexity of the obtained equation.", notes = "Entered 2023 HUMIES GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @Article{Maslyaev:2023:itmo, author = "Mikhail A. Maslyaev and Alexander A. Hvatov", title = "Multiobjective evolutionary discovery of equation-based analytical models for dynamical systems", journal = "Scientific and Technical Journal of Information Technologies, Mechanics and Optics", year = "2023", volume = "23", number = "1", pages = "97--104", keywords = "genetic algorithms, genetic programming, differential equation discovery, evolutionary optimization, multi-objective optimization, differential equations system, symbolic regression", URL = "https://human-competitive.org/sites/default/files/entry_hvatov.txt", URL = "https://human-competitive.org/sites/default/files/multiobjective_evolutionary_discovery_of_equation-based_analytical_models_for_dynamical_systems.pdf", URL = "https://ntv.ifmo.ru/file/article/21743.pdf", DOI = "doi:10.17586/2226-1494-2023-23-1-97-104", size = "8 pages", abstract = "an approach to modeling dynamical systems in case of unknown governing physical laws has been introduced. The systems of differential equations obtained by means of a data-driven algorithm are taken as the desired models. In this case, the problem of predicting the state of the process is solved by integrating the resulting differential equations. In contrast to classical data-driven approaches to dynamical systems representation, based on the general machine learning methods, the proposed approach is based on the principles, comparable to the analytical equation-based modeling. Models in forms of systems of differential equations, composed as combinations of elementary functions and operation with the structure, were determined by adapted multi-objective evolutionary optimization algorithm. Time-series describing the state of each element of the dynamic system are used as input data for the algorithm. To ensure the correct operation of the algorithm on data characterizing real-world processes, noise reduction mechanisms are introduced in the algorithm. The use of multicriteria optimization, held in the space of complexity and quality criteria for individual equations of the differential equation system, makes it possible to improve the diversity of proposed candidate solutions and, therefore, to improve the convergence of the algorithm to a model that best represents the dynamics of the process. The output of the algorithm is a set of Pareto-optimal solutions of the optimization problem where each individual of the set corresponds to one system of differential equations. In the course of the work, a library of data-driven modeling of dynamic systems based on differential equation systems was created. The behavior of the algorithm was studied on a synthetic validation dataset describing the state of the hunter-prey dynamic system given by the Lotka-Volterra equations. Finally, a toolset based on the solution of the generated equations was integrated into the algorithm for predicting future system states. The method is applicable to data-driven modeling of arbitrary dynamical systems (e.g. hydrometeorological systems) in cases where the processes can be described using differential equations. Models generated by the algorithm can be used as components of more complex composite models, or in an ensemble of methods as an interpretable component.", notes = "Entered 2023 HUMIES", } @InCollection{masonis:1999:VPCE, author = "J. Todd Masonis", title = "Valve Paradigm ``C'' Code Evolution", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1999", year = "1999", editor = "John R. Koza", pages = "140--146", address = "Stanford, California, 94305-3079 USA", month = "15 " # mar, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:1999:GAGPs}", } @InProceedings{Masood:2016:CEC, author = "Atiya Masood and Yi Mei and Gang Chen2 and Mengjie Zhang", title = "Many-Objective Genetic Programming for Job-Shop Scheduling", booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016)", year = "2016", editor = "Yew-Soon Ong", pages = "209--216", address = "Vancouver", month = "24-29 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-5090-0623-6", DOI = "doi:10.1109/CEC.2016.7743797", abstract = "In Job Shop Scheduling (JSS) problems, there are usually many conflicting objectives to consider, such as the makespan, mean flowtime, maximal tardiness, number of tardy jobs, etc. Most studies considered these objectives separately or aggregated them into a single objective (fitness function) and treat the problem as a single-objective optimization. Very few studies attempted to solve the multi-objective JSS with two or three objectives, not to mention the many-objective JSS with more than three objectives. In this paper, we investigate the many-objective JSS, which takes all the objectives into account. On the other hand, dispatching rules have been widely used in JSS due to its flexibility, scalability and quick response in dynamic environment. In this paper, we focus on evolving a set of trade-off dispatching rules for many-objective JSS, which can generate non-dominated schedules given any unseen instance. To this end, a new hybridized algorithm that combines Genetic Programming (GP) and NSGA-III is proposed. The experimental results demonstrates the efficacy of the newly proposed algorithm on the tested job-shop benchmark instances.", notes = "WCCI2016", } @InProceedings{conf/acalci/MasoodMCZ17, author = "Atiya Masood and Yi Mei and Gang Chen2 and Mengjie Zhang", title = "A {PSO}-Based Reference Point Adaption Method for Genetic Programming Hyper-Heuristic in Many-Objective Job Shop Scheduling", booktitle = "Artificial Life and Computational Intelligence - Third Australasian Conference, {ACALCI} 2017, Geelong, {VIC}, Australia, January 31 - February 2, 2017, Proceedings", editor = "Markus Wagner and Xiaodong Li and Tim Hendtlass", year = "2017", volume = "10142", isbn13 = "978-3-319-51690-5", pages = "326--338", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming, PSO", bibdate = "2017-05-26", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/acalci/acalci2017.html#MasoodMCZ17", DOI = "doi:10.1007/978-3-319-51691-2_28", } @InProceedings{Masood:2018:evocop, author = "Atiya Masood and Gang Chen2 and Yi Mei and Mengjie Zhang", title = "Reference Point Adaption Method for Genetic Programming Hyper-Heuristic in Many-Objective Job Shop Scheduling", booktitle = "The 18th European Conference on Evolutionary Computation in Combinatorial Optimisation, EvoCOP 2018", year = "2018", editor = "Arnaud Liefooghe and Manuel Lopez-Ibanez", series = "LNCS", volume = "10782", publisher = "Springer", pages = "116--131", address = "Parma, Italy", month = "4-6 " # apr, organisation = "Species", keywords = "genetic algorithms, genetic programming, Job Shop Scheduling, Many-objective optimization, Reference points", isbn13 = "978-3-319-77448-0", DOI = "doi:10.1007/978-3-319-77449-7_8", abstract = "Job Shop Scheduling (JSS) is considered to be one of the most significant combinatorial optimization problems in practice. It is widely evidenced in the literature that JSS usually contains many (four or more) potentially conflicting objectives. One of the promising and successful approaches to solve the JSS problem is Genetic Programming Hyper-Heuristic (GP-HH). This approach automatically evolves dispatching rules for solving JSS problems. This paper aims to evolve a set of effective dispatching rules for many-objective JSS with genetic programming and NSGA-III. NSGA-III originally defines uniformly distributed reference points in the objective space. Thus, there will be few reference points with no Pareto optimal solutions associated with them; especially, in the cases with discrete and non-uniform Pareto front, resulting in many useless reference points during evolution. In other words, these useless reference points adversely affect the performance of NSGAIII and genetic programming. To address the above issue, in this paper a new reference point adaptation mechanism is proposed based on the distribution of the candidate solutions.We evaluated the performance of the proposed mechanism on many-objective benchmark JSS instances. Our results clearly show that the proposed strategy is promising in adapting reference points and outperforms the existing state-of-the-art algorithms for many-objective JSS.", notes = "EvoCOP2018 held in conjunction with EuroGP'2018 EvoMusArt2018 and EvoApplications2018 http://www.evostar.org/2018/cfp_evocop.php", } @InProceedings{DBLP:conf/ausai/Masood0MAZ19, author = "Atiya Masood and Gang Chen2 and Yi Mei and Harith Al-Sahaf and Mengjie Zhang", editor = "Jixue Liu and James Bailey", title = "Genetic Programming with {Pareto} Local Search for Many-Objective Job Shop Scheduling", booktitle = "{AI} 2019: Advances in Artificial Intelligence - 32nd Australasian Joint Conference, Adelaide, SA, Australia, December 2-5, 2019, Proceedings", series = "Lecture Notes in Computer Science", volume = "11919", pages = "536--548", publisher = "Springer", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/978-3-030-35288-2_43", DOI = "doi:10.1007/978-3-030-35288-2_43", timestamp = "Mon, 15 Jun 2020 01:00:00 +0200", biburl = "https://dblp.org/rec/conf/ausai/Masood0MAZ19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{Masood:2020:CEC, author = "A. Masood and G. Chen and Y. Mei and H. Al-Sahaf and M. Zhang", title = "A Fitness-based Selection Method for {Pareto} Local Search for Many-Objective Job Shop Scheduling", booktitle = "2020 IEEE Congress on Evolutionary Computation (CEC)", year = "2020", editor = "Yaochu Jin", month = "19-24 " # jul, keywords = "genetic algorithms, genetic programming, Dispatching, Job shop scheduling, Optimisation, Search problems, Schedules, Sociology, Statistics", isbn13 = "978-1-7281-6929-3", DOI = "doi:10.1109/CEC48606.2020.9185881", abstract = "Genetic programming (GP) is considered the most popular method for automatically discovering and constructing dispatching rules for scheduling problems. Pareto Local Search (PLS) is a simple and effective local search method for tackling multi-objective combinatorial optimization problems. Researchers have studied the application of PLS to multiobjective evolutionary algorithms (MOEAs) with some success. In fact, by hybridizing global search with local search, the performance of many MOEAs can be noticeably improved. Despite its preliminary success, the practical use of PLS in GP is relatively limited. In this study, our aim is to enhance the quality of evolved dispatching rules for many-objective Job Shop Scheduling (JSS) through hybridizing GP with PLS techniques and designing an effective selection mechanism of initial solutions for PLS. In this paper, we propose a new GP-PLS algorithm that investigates whether the fitness-based selection mechanism for selecting initial solutions for PLS can increase the chance of discovering highly effective dispatching rules for many-objective JSS. To evaluate the effectiveness of our new algorithm, GPPLS is compared with the current state-of-the-art algorithms for many-objective JSS. The experimental results confirm that the proposed method can outperform the four recently proposed algorithms because of the proper use of local search techniques.", notes = "Also known as \cite{9185881}", } @PhdThesis{Masood:thesis, author = "Atiya Masood", title = "Many-Objective Genetic Programming for Job-Shop Scheduling", school = "School of Engineering and Computer Science, Victoria University of Wellington", year = "2020", address = "New Zealand", keywords = "genetic algorithms, genetic programming, Scheduling, Heuristic", URL = "http://researcharchive.vuw.ac.nz/handle/10063/9393", URL = "http://hdl.handle.net/10063/9393", URL = "http://researcharchive.vuw.ac.nz/bitstream/handle/10063/9393/thesis_access.pdf", size = "263 pages", abstract = "The Job Shop Scheduling (JSS) problem is considered to be a challenging one due to practical requirements such as multiple objectives and the complexity of production flows. JSS has received great attention because of its broad applicability in real-world situations. One of the prominent solutions approaches to handling JSS problems is to design effective dispatching rules. Dispatching rules are investigated broadly in both academic and industrial environments because they are easy to implement (by computers and shop floor operators) with a low computational cost. However, the manual development of dispatching rules is time-consuming and requires expert knowledge of the scheduling environment. The hyper-heuristic approach that uses genetic programming (GP) to solve JSS problems is known as GP-based hyper-heuristic (GP-HH). GP-HH is a very useful approach for discovering dispatching rules automatically. Although it is technically simple to consider only a single objective optimization for JSS, it is now widely evidenced in the literature that JSS by nature presents several potentially conflicting objectives, including the maximal flowtime, mean flowtime, and mean tardiness. A few studies in the literature attempt to solve many-objective JSS with more than three objectives, but existing studies have some major limitations. First, many-objective JSS problems have been solved by multi-objective evolutionary algorithms (MOEAs). However, recent studies have suggested that the performance of conventional MOEAs is prone to the scalability challenge and degrades dramatically with many-objective optimization problems (MaOPs). Many-objective JSS using MOEAs inherit the same challenge as MaOPs. Thus, using MOEAs for many-objective JSS problems often fails to select quality dispatching rules. Second, although the reference points method is one of the most prominent and efficient methods for diversity maintenance in many-objective problems, it uses a uniform distribution of reference points which is only appropriate for a regular Pareto-front. However, JSS problems often have irregular Pareto-front and uniformly distributed reference points do not match well with the irregular Pareto-front. It results in many useless points during evolution. These useless points can significantly affect the performance of the reference points-based algorithms. They cannot help to enhance the solution diversity of evolved Pareto-front in many-objective JSS problems. Third, Pareto Local Search (PLS) is a prominent and effective local search method for handling multi-objective JSS optimization problems but the literature does not discover any existing studies which use PLS in GP-HH. To address these limitations, this thesis's overall goal is to develop GP-HH approaches to evolving effective rules to handle many conflicting objectives simultaneously in JSS problems. To achieve the first goal, this thesis proposes the first many-objective GP-HH method for JSS problems to find the Pareto-fronts of nondominated dispatching rules. Decision-makers can use this GP-HH method for selecting appropriate rules based on their preference over multiple conflicting objectives. This study combines GP with the fitness evaluation scheme of a many-objective reference points-based approach. The experimental results show that the proposed algorithm significantly outperforms MOEAs such as NSGA-II and SPEA2. To achieve the second goal, this thesis proposes two adaptive reference point approaches (model-free and model-driven). In both approaches, the reference points are generated according to the distribution of the evolved dispatching rules. The model-free reference point adaptation approach is inspired by Particle Swarm Optimization (PSO). The model-driven approach constructs the density model and estimates the density of solutions from each defined sub-location in a whole objective space. Furthermore, the model-driven approach provides smoothness to the model by applying a Gaussian Process model and calculating the area under the mean function. The mean function area helps to find the required number of the reference points in each mean function. The experimental results demonstrate that both adaptive approaches are significantly better than several state-of-the-art MOEAs. To achieve the third goal, the thesis proposes the first algorithm that combines GP as a global search with PLS as a local search in many-objective JSS. The proposed algorithm introduces an effective fitness-based selection strategy for selecting initial individuals for neighborhood exploration. It defines the GP's proper neighborhood structure and a new selection mechanism for selecting the effective dispatching rules during the local search. The experimental results on the JSS benchmark problem show that the newly proposed algorithm can significantly outperform its baseline algorithm (GP-NSGA-III).", notes = "Summary in \cite{Masood:2022:sigevolution} Supervisors: Gang Chen, Yi Mei, Harith Al-Sahaf, Mengjie Zhang", } @InProceedings{Masood:2021:CEC, author = "Atiya Masood and Gang Chen2 and Mengjie Zhang", booktitle = "2021 IEEE Congress on Evolutionary Computation (CEC)", title = "Feature Selection for Evolving Many-Objective Job Shop Scheduling Dispatching Rules with Genetic Programming", year = "2021", editor = "Yew-Soon Ong", pages = "644--651", address = "Krakow, Poland", month = "28 " # jun # "-1 " # jul, isbn13 = "978-1-7281-8393-0", abstract = "JSS (Job Shop Scheduling) is a significant and challenging combinatorial optimization issue. Dispatching rules have been successfully used to determine scheduling decisions in the JSS challenges. Genetic programming (GP) has been widely used to discover and develop dispatching rules for various scheduling problems. However, there has been relatively little research into feature selection in GP-HH for many-objective JSS. In many conflicting objective contexts, it's also vital to quantify the contribution of features. This work presents a new two-stage GP-HH methodology for many-objective JSS with feature selection for changing rules. The quality of the solutions (dispatching rules) after incorporating the many-objective algorithm with feature selection is investigated in this paper. On a four-objective JSS problem, the suggested algorithm (FS-GP-NSGA-III) is compared to the standard GP-NSGA-III. The experimental results show that using GP to pick relevant features improves the algorithm's performance. Furthermore, the proposed technique generates rules that are minimal in size and easy to understand.", keywords = "genetic algorithms, genetic programming, Job shop scheduling, Processor scheduling, Evolutionary computation, Feature extraction, Dispatching, Standards, many-objective optimization, feature selection, hyper-heuristic, job shop scheduling", DOI = "doi:10.1109/CEC45853.2021.9504895", notes = "Also known as \cite{9504895}", } @InProceedings{Masood:2022:CEC, author = "Atiya Masood and Gang Chen2 and Yi Mei and Harith Al-Sahaf and Mengjie Zhang", booktitle = "2022 IEEE Congress on Evolutionary Computation (CEC)", title = "Genetic Programming Hyper-heuristic with Gaussian Process-based Reference Point Adaption for Many-Objective Job Shop Scheduling", year = "2022", editor = "Carlos A. Coello Coello and Sanaz Mostaghim", address = "Padua, Italy", month = "18-23 " # jul, isbn13 = "978-1-6654-6708-7", abstract = "Job Shop Scheduling (JSS) is an important real-world problem. However, the problem is challenging because of many conflicting objectives and the complexity of production flows. Genetic programming-based hyper-heuristic (GP-HH) is a useful approach for automatically evolving effective dispatching rules for many-objective JSS. However, the evolved Pareto-front is highly irregular, seriously affecting the effectiveness of GP-HH. Although the reference points method is one of the most prominent and efficient methods for diversity maintenance in many-objective problems, it usually uses a uniform distribution of reference points which is only appropriate for a regular Pareto-front. In fact, some reference points may never be linked to any Pareto-optimal solutions, rendering them useless. These useless reference points can significantly impact the performance of any reference-point-based many-objective optimization algorithms such as NSGA-III. This paper proposes a new reference point adaption process that explicitly constructs the distribution model using Gaussian process to effectively reduce the number of useless reference points to a low level, enabling a close match between reference points and the distribution of Pareto-optimal solutions. We incorporate this mechanism into NSGA-III to build a new algorithm called MARP-NSGA-III which is compared experimentally to several popular many-objective algorithms. Experiment results on a large collection of many-objective benchmark JSS instances clearly show that MARP-NSGA-III can significantly improve the performance by using our Gaussian Process-based reference point adaptation mechanism.", keywords = "genetic algorithms, genetic programming, Adaptation models, Job shop scheduling, Processor scheduling, Sociology, Gaussian processes, Production, Maintenance engineering, Many-objective Optimization, Evolutionary Computation, Gaussian Process, Adaptive reference points, Job Shop Scheduling", DOI = "doi:10.1109/CEC55065.2022.9870322", notes = "Also known as \cite{9870322}", } @Article{Masood:2022:sigevolution, author = "Atiya Masood", title = "Many-Objective Genetic Programming for Job-Shop Scheduling", journal = "SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation", year = "2022", volume = "15", number = "4", month = dec, keywords = "genetic algorithms, genetic programming, GPHH", ISSN = "1931-8499", URL = "https://evolution.sigevo.org/issues/HTML/sigevolution-15-4/index.htm#Many-Objective_Genetic_Programming_for_Job-Shop_Scheduling", DOI = "doi:10.1145/3584367.3584370", size = "4 pages", abstract = "Summary of PhD dissertation \cite{Masood:thesis} representing the first effort at many-objective optimization in job shop scheduling (JSS). It develops genetic programming hyperheuristic (GP-HH) approaches to evolve effective dispatching rules for many conflicting objectives in JSS problems. The aim is to develop GP-HH methods that alleviate issues related to many-objective optimisation in JSS problems and evolve new effective dispatching rules capable of enhancing job shops productivity.", notes = "https://evolution.sigevo.org/", } @InProceedings{Masood:2023:AJCAI, author = "Atiya Masood and Gang Chen2 and Yi Mei and Harith Al-Sahaf and Mengjie Zhang", title = "Genetic Programming with Adaptive Reference Points for Pareto Local Search in Many-Objective Job Shop Scheduling", booktitle = "36th Australasian Joint Conference on Artificial Intelligence, Part II", year = "2023", editor = "Tongliang Liu and Geoff Webb and Lin Yue and Dadong Wang", volume = "14472", series = "LNCS", pages = "466--478", address = "Brisbane, Australia", month = "28 " # nov # " - 1 " # dec, publisher = "Springer Nature", keywords = "genetic algorithms, genetic programming", isbn13 = "978-981-99-8391-9", DOI = "doi:10.1007/978-981-99-8391-9_37", } @Article{Masood:2025:ACCESS, author = "Atiya Masood and Mansoor Ebrahim and Fahad Najeeb and Syed {Muhammad Daniyal}", title = "Evolving Many-Objective Job Shop Scheduling Dispatching Rules via Genetic Programming With Adaptive Search Based on the Frequency of Features", journal = "IEEE Access", year = "2025", volume = "13", pages = "75020--75036", keywords = "genetic algorithms, genetic programming, Dispatching, Schedules, Job shop scheduling, Optimisation, Feature extraction, Search problems, Heuristic algorithms, Processor scheduling, Delays, Adaptive search, feature selection, hyper-heuristic, many-objective optimisation", ISSN = "2169-3536", DOI = "doi:10.1109/ACCESS.2025.3558521", abstract = "Job Shop Scheduling (JSS) is a critical application in diverse fields, such as cloud computing and manufacturing. Genetic Programming (GP) is acknowledged for its wide use in evolving dispatching rules for JSS, offering an automated approach to heuristic generation. A dispatching rule can use many machine-related, job-related, and system-related features to create scheduling heuristics in JSS. Proper feature selection is a critical factor for the success of heuristics. Moreover, there can be many features in JSS whose importance varies from one scenario to another. It has been shown that irrelevant and redundant features can adversely affect performance. Feature selection is a promising task to select relevant features and reduce genetic programming hyper-heuristics (GPHH) search space. However, more research is needed to quantify the contribution of features in the GPHH to many-objective JSS. The proposed algorithm introduces an adaptive search strategy that is implemented through re-initialization during the evolutionary process. In addition, relevant features are selected based on their frequency of occurrence in diverse sets of best individuals. The proposed algorithm, Adaptive Feature Selection-GP-NSGA-III (AFS-GP-NSGA-III), is compared with the FS-GP-NSGA-III and standard GP-NSGA-III on a four-objective JSS problem. The experimental results indicate that feature selection using GP and adaptive search can improve the performance of the algorithm. Furthermore, the findings suggest the practical applicability of the proposed algorithm for generating improved dispatching rules for training and unseen test instances using only the selected relevant feature subset.", notes = "Also known as \cite{10955196}", } @Article{MasoumiShahrBabak:2016:AOR, author = "Mojtaba Masoumi Shahr-Babak and Mohammad Javad Khanjani and Kourosh Qaderi", title = "Uplift capacity prediction of suction caisson in clay using a hybrid intelligence method (GMDH-HS)", journal = "Applied Ocean Research", volume = "59", pages = "408--416", year = "2016", ISSN = "0141-1187", DOI = "doi:10.1016/j.apor.2016.07.005", URL = "http://www.sciencedirect.com/science/article/pii/S0141118716302450", abstract = "Suction caissons are widely used for offshore facilities foundation or anchor system. They should be very stable and also to provide stability of main massive structures those are upon them. Suction caisson uplift capacity is the main issue to determine their stability. During recent years, many artificial intelligence (AI) methods such as artificial neural network (ANN), genetic programming (GP) and multivariate adaptive regression spline (MARS) have been used for suction caisson uplift capacity prediction. In this study, a novel hybrid intelligent method based on combination of group method of data handling (GMDH) and harmony search (HS) optimization method which is called GMDH-HS has been developed for suction caisson uplift capacity prediction. At first, the Mackey-Glass time series data were used for validation of developed method. The results of Mackey-Glass modeling were compared to conventional GMDH with two kinds of transfer function called GMDH1 and GMDH2. Five statistical indices such as coefficient of efficiency (CE), root mean square Error (RMSE), mean square relative error (MSRE), mean absolute percentage error (MAPE) and relative bias (RB) were used to evaluate performance of applied method. Then the GMDH-HS method has been used for suction caisson uplift capacity prediction. The 62 data set of laboratory measurements were collected from published literature that 51 sets used to train new developed method and the remaining data set used for testing. Not only the results of suction caisson uplift capacity prediction using GMDH-HS were evaluated with statistical indices, but also the results were compared to some artificial methods by previously works. The results indicated that performance of GMDH-HS was found more efficient when compared to other applied method in predicting the suction caisson uplift capacity.", keywords = "genetic algorithms, genetic programming, Uplift capacity, Suction caisson, GMDH, GMDH-HS, Prediction, Hybrid intelligent method", } @InProceedings{Masrom:2019:AiDAS, author = "S. Masrom and T. Mohd and N. S. Jamil and A. S. A. Rahman and N. Baharun", title = "Automated Machine Learning based on Genetic Programming: a case study on a real house pricing dataset", booktitle = "2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS)", year = "2019", pages = "48--52", month = sep, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/AiDAS47888.2019.8970916", abstract = "Designing an effective machine learning model for prediction or classification problem is a tedious endeavor. Significant time and expertise are needed to customize the model for a specific problem. A significant way to reduce the complicated design is by using Automated Machine Learning (AML) that can intelligently optimize the best pipeline suitable for a problem or dataset. demonstrates the use of an AML that has been developed with a meta-heuristic algorithm namely Genetic Programming (GP). Empirical experiment has been conducted to test the performances of AML on a real dataset of house prices in the area of Petaling Jaya, Selangor. The results show that the AML with GP able to produce the best pipeline of machine learning with high score of accuracy and minimal error.", notes = "Also known as \cite{8970916}", } @InProceedings{Masrom:2020:ICEIT, author = "Suraya Masrom and Rahayu Abdul Rahman and Norhayati Baharun and Abdullah Sani Abd Rahman", title = "Automated Machine Learning with Genetic Programming on Real Dataset of Tax Avoidance Classification Problem", booktitle = "Proceedings of the 2020 9th International Conference on Educational and Information Technology, ICEIT 2020", year = "2020", pages = "139--143", address = "Oxford, United Kingdom", month = "11-13 " # feb, publisher = "Association for Computing Machinery", keywords = "genetic algorithms, genetic programming, TPOT, AutoML, Tax Avoidance, Machine Learning, Classification", isbn13 = "9781450375085", URL = "https://doi.org/10.1145/3383923.3383942", DOI = "doi:10.1145/3383923.3383942", size = "5 pages", abstract = "Dealing with real application datasets often derive a stumbling block for machine learning algorithms to produce good results in solving either prediction or classification problems. Imbalance dataset is the major reason for this problem associated with missing values, small dimension of data size and very skewed data distribution. This paper demonstrates an empirical study that used Automated Machine Learning (AutoML) based on Genetic Programming (GP) named as AML TPOT. This is a very recent AML developed as an open source Python library and reported as a promising model by a few of researchers who have tested the algorithm. Nevertheless, most of the works on the AML TPOT were conducted on a set of common or benchmark datasets for machine learning testing. In this paper, the focus is on real and deviant dataset, which were collected according to the tax avoidance of the Government-Link Company in Malaysia. Comparison of the AML performances that tested on the dataset with different GP parameters setting is provided. Thus, this paper provides a fundamental knowledge on the experimental design and finding that will be useful for the AutoML based GP future improvement.", notes = "Universiti Teknologi MARA, Perak Branch Malaysia Also known as \cite{DBLP:conf/iceit/MasromRBR20} http://www.iceit.org/iceit2020.html", } @Article{Masrom:2020:IJ-AI, author = "Suraya Masrom and Masurah Mohamad and Shahirah Mohamed Hatim and Norhayati Baharun and Nasiroh Omar and Abdullah Sani Abd. Rahman", title = "Different mutation and crossover set of genetic programming in an automated machine learning", journal = "IAES International Journal of Artificial Intelligence", year = "2020", volume = "9", number = "3", pages = "402--408", month = sep, keywords = "genetic algorithms, genetic programming, TPOT, AutoML, AML, Automated machine learning, Classification, Crossover, Mutation", ISSN = "2089-4872", URL = "http://ijai.iaescore.com/index.php/IJAI/article/view/20499/pdf", DOI = "doi:10.11591/ijai.v9.i3.pp402-408", size = "7 pages", abstract = "Automated machine learning is a promising approach widely used to solve classification and prediction problems, which currently receives much attention for modification and improvement. One of the progressing works for automated machine learning improvement is the inclusion of evolutionary algorithm such as Genetic Programming. The function of Genetic Programming is to optimize the best combination of solutions from the possible pipelines of machine learning modeling, including selection of algorithms and parameters optimization of the selected algorithm. As a family of evolutionary based algorithm, the effectiveness of Genetic Programming in providing the best machine learning pipelines for a given problem or dataset is substantially depending on the algorithm parameterizations including the mutation and crossover rates. This paper presents the effect of different pairs of mutation and crossover rates on the automated machine learning performances that tested on different types of datasets. The finding can be used to support the theory that higher crossover rates used to improve the algorithm accuracy score while lower crossover rates may cause the algorithm to converge at earlier stage.", notes = "Yogyakarta Faculty of Computer and Mathematical Sciences,Universiti Teknologi MARA, Perak Branch, TapahCampus,Perak, Malaysia.", } @Misc{DBLP:journals/corr/abs-1910-10065, author = "Mohamed Massaoudi and Ines Chihi and Lilia Sidhom and Mohamed Trabelsi and Shady S. Refaat and Fakhreddine S. Oueslati", title = "Enhanced Evolutionary Symbolic Regression Via Genetic Programming for {PV} Power Forecasting", howpublished = "arXiv", volume = "abs/1910.10065", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "http://arxiv.org/abs/1910.10065", archiveprefix = "arXiv", eprint = "1910.10065", timestamp = "Wed, 24 Jun 2020 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/corr/abs-1910-10065.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{massey:eqc:gecco2004, author = "Paul Massey and John A. Clark and Susan Stepney", title = "Evolving Quantum Circuits and Programs Through Genetic Programming", booktitle = "Genetic and Evolutionary Computation -- GECCO-2004, Part II", year = "2004", editor = "Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell", series = "Lecture Notes in Computer Science", pages = "569--580", address = "Seattle, WA, USA", publisher_address = "Heidelberg", month = "26-30 " # jun, organisation = "ISGEC", publisher = "Springer-Verlag", volume = "3103", ISBN = "3-540-22343-6", ISSN = "0302-9743", URL = "http://www-users.cs.york.ac.uk/susan//bib/ss/nonstd/gecco04.pdf", DOI = "doi:10.1007/b98645", size = "12", keywords = "genetic algorithms, genetic programming, quantum computing", abstract = "Spector et al. have shown [1],[2],[3] that genetic programming can be used to evolve quantum circuits. In this paper, we present new results in this field, introducing probabilistic and deterministic quantum circuits that have not been previously published. We compare our techniques with those of Spector et al, and point out some differences in perspective between our two approaches. Finally, we show how, by using sets of functions rather than precise quantum states as fitness cases, our basic technique can be extended to evolve true quantum algorithms.", notes = "GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004) See \cite{massey:2006:EC}", } @InProceedings{1068288, author = "Paul Massey and John A. Clark and Susan Stepney", title = "Evolution of a human-competitive quantum fourier transform algorithm using genetic programming", booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation", year = "2005", editor = "Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and Edwin D. {de Jong} and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler", volume = "2", ISBN = "1-59593-010-8", pages = "1657--1663", address = "Washington DC, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2005/docs/p1657.pdf", DOI = "doi:10.1145/1068009.1068288", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "25-29 " # jun, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, evolutionary computing, experimentation, quantum computing, quantum Fourier transform", isbn13 = "978-1-59593-010-1", size = "7 pages", oai = "oai:CiteSeerXPSU:10.1.1.145.6550", abstract = "In this paper, we show how genetic programming (GP) can be used to evolve system-size-independent quantum algorithms, and present a human-competitive Quantum Fourier Transform (QFT) algorithm evolved by GP.", notes = "GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM Order Number 910052", } @Article{massey:2006:EC, author = "Paul Massey and John A. Clark and Susan Stepney", title = "Human-Competitive Evolution of Quantum Computing Artefacts by Genetic Programming", journal = "Evolutionary Computation", year = "2006", volume = "14", number = "1", pages = "21--40", month = "Spring", note = "Best of GECCO 2004 special issue", keywords = "genetic algorithms, genetic programming, quantum computing", URL = "http://www.mitpressjournals.org/doi/abs/10.1162/evco.2006.14.1.21", DOI = "doi:10.1162/106365606776022797", size = "20 pages", abstract = "We show how Genetic Programming (GP) can be used to evolve useful quantum computing artefacts of increasing sophistication and usefulness: firstly specific quantum circuits, then quantum programs, and finally system-independent quantum algorithms. We conclude the paper by presenting a human-competitive Quantum Fourier Transform (QFT) algorithm evolved by GP.", notes = "\cite{massey:eqc:gecco2004}", } @PhdThesis{Massey:thesis, author = "Paul S. Massey", title = "Searching for Quantum Software", school = "Department Of Computer Science, University of York", year = "2006", address = "UK", month = aug, keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.york.ac.uk/ftpdir/reports/2007/YCST/11/YCST-2007-11.pdf", size = "171 pages", abstract = "Quantum computing has the potential to bring a new class of previously intractable problems within the reach of computer science. Harnessing the quantum mechanical phenomena of superposition and entanglement, a quantum computer can manipulate vast amounts of information in a single computational step and perform certain operations exponentially faster than classical (i.e. non-quantum) computers. However, devising algorithms to harness the power of a quantum computer has proved extraordinarily difficult. Over twenty years after the publication of the first quantum algorithm in 1985, despite the efforts of a sizeable community of top-class researchers, only a handful of distinct algorithms have been discovered. This thesis makes the case that evolutionary search techniques can be used to discover quantum circuits, quantum programs and ultimately new quantum algorithms. It presents a number of original results, including an algorithm discovered by evolutionary search techniques which implements the Quantum Fourier Transform on n qubits, and an algorithm discovered by evolutionary search techniques which returns the maximum value for arbitrary permutation functions.", } @Article{Mastrocola:2025:GPEM, author = "Vicente Martin Mastrocola", title = "Julian Togelius: Artificial General Intelligence, The MIT Press Essential Knowledge series, 2024, paperback, 230 pages, ISBN:9780262549349", journal = "Genetic Programming and Evolvable Machines", year = "2025", volume = "26", pages = "Article no 17", note = "Book review", keywords = "genetic algorithms, genetic programming, AI", ISSN = "1389-2576", URL = "https://rdcu.be/eg9tH", DOI = "doi:10.1007/s10710-025-09515-8", size = "2 pages", } @InProceedings{Masui94, author = "Toshiyuki Masui", title = "Evolutionary Learning of Graph Layout Constraints from Examples", booktitle = "Proceedings of the ACM Symposium on User Interface Software and Technology", series = "Demonstrational User Interfaces", pages = "103--108", year = "1994", copyright = "(c) Copyright 1994 Association for Computing Machinery", publisher = "ACM", keywords = "genetic algorithms, genetic programming, Graphic object layout, Graph layout, Programming by example, Adaptive user interface", URL = "http://delivery.acm.org/10.1145/200000/192468/p103-masui.pdf?key1=192468&key2=4873616011&coll=GUIDE&dl=GUIDE&CFID=36810799&CFTOKEN=40084029", URL = "http://www.acm.org/pubs/articles/proceedings/uist/192426/p103-masui/p103-masui.pdf", DOI = "doi:10.1145/192426.192468", size = "6 pages", abstract = "We propose a new evolutionary method of extracting user preferences from examples shown to an automatic graph layout system. Using stochastic methods such as simulated annealing and genetic algorithms, automatic layout systems can find a good layout using an evaluation function which can calculate how good a given layout is. However, the evaluation function is usually not known beforehand, and it might vary from user to user. In our system, users show the system several pairs of good and bad layout examples, and the system infers the evaluation function from the examples using genetic programming technique. After the evaluation function evolves to reflect the preferences of the user, it is used as a general evaluation function for laying out graphs. The same technique can be used for a wide range of adaptive user interface systems.", notes = "MRnumber = C.UIST.94.103", } @TechReport{mataric:1995:cecprTR, author = "Maja Mataric and Dave Cliff", title = "Challenges in Evolving Controllers for Physical Robots", institution = "Computer Science Department, Brandeis University", year = "1995", number = "CS-95-184", keywords = "genetic algorithms, genetic programming, robots", URL = "http://robotics.usc.edu/~maja/publications/ras-fukuda.ps.gz", URL = "http://citeseer.ist.psu.edu/mataric96challenges.html", abstract = "Feasibility of applying evolutionary methods to automatically generating controllers for physical mobile robots. Overview state of the art, main approaches, key challenges, unanswered problems, promising directions", notes = "GP and other approaches surveyed", size = "34 pages", } @Article{mataric:1995:cecpr, author = "Maja J. Mataric and Dave Cliff", title = "Challenges in Evolving Controllers for Physical Robots", journal = "Journal of Robotics and Autonomous Systems", year = "1996", volume = "19", number = "1", pages = "67--83", month = oct, keywords = "genetic algorithms, genetic programming, Evolutionary computation, Robot control, Automated synthesis, Evolving controllers, Evolving hardware, Embodied systems, ffs, Morphology, Physical robots, Simulation", ISSN = "0921-8890", broken = "http://www.sciencedirect.com/science/article/B6V16-3SNN40Y-K/2/abf84cb35a7e2fd7cbc872e1621d4a5d", DOI = "doi:10.1016/S0921-8890(96)00034-6", abstract = "This paper discusses the feasibility of applying evolutionary methods to automatically generating controllers for physical mobile robots. We overview the state-of-the-art in the field, describe some of the main approaches, discuss the key challenges, unanswered problems, and some promising directions.", notes = "GP and other approaches surveyed", notes = "see also \cite{mataric:1995:cecprTR} ", } @InCollection{Mathieson2019, author = "Luke Mathieson and Natalie Jane {de Vries} and Pablo Moscato", title = "Using Network Alignment to Identify Conserved Consumer Behaviour Modelling Constructs", booktitle = "Business and Consumer Analytics: New Ideas", publisher = "Springer International Publishing", year = "2019", editor = "Pablo Moscato and Natalie Jane {de Vries}", chapter = "12", pages = "513--541", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-030-06222-4", DOI = "doi:10.1007/978-3-030-06222-4_12", abstract = "Extracting topological information from networks is a central problem in many fields including business analytics. With the increase in large-scale datasets, effectively comparing similarities and differences between networks is impossible without Automation. In some cases, computational search of simple subgraphs is used to understand the structure of a network. These approaches, however, miss the global picture of network similarity. Here we examine the Network Alignment problem, in which we look for a mapping between vertex sets of two networks preserving topological information. Elsewhere, we showed that data analytics problems are often of varied computational complexity. We prove that this problem is W[1]-completeW[1]- for several parameterizations. Since we expect large instances in the data analytics field, our result indicates that this problem is a prime candidate for metaheuristic approaches as it will be hard in practice to solve exact methods. We develop a memetic algorithm and demonstrate the effectiveness of the Network Alignment problem as a tool for discovering structural information through an application in the area of consumer behaviour modelling. We believe this to be the first demonstration of such an approach in the social sciences and in particular a consumer analytics application.", } @InProceedings{Matousek:2009:MENDEL, author = "Radomil Matousek and Jozef Bednar", title = "Grammatical Evolution: Epsilon Tube in Symbolic Regression Rask", booktitle = "15th International Conference on Soft Computing, MENDEL'09", year = "2009", editor = "R. Matousek and L. Nolle", address = "Brno, Czech Republic", month = jun # " 24-26", keywords = "genetic algorithms, genetic programming, grammatical evolution", isbn13 = "978-80-214-3884-2", notes = "http://www.mendel-conference.org/ ID0902", } @InProceedings{conf/wcecs_2009_II/193, title = "Grammatical Evolution: STE criterion in Symbolic Regression Task", author = "R. Matousek", booktitle = "Proceedings of the World Congress on Engineering and Computer Science, WCECS '09", year = "2009", editor = "S. I. Ao and Craig Douglas and W. S. Grundfest and Jon Burgstone", volume = "II", pages = "1050--1054", address = "San Francisco, USA", month = oct # " 20-22", publisher = "Newswood Limited", organization = "International Association of Engineers", keywords = "genetic algorithms, genetic programming, grammatical evolution, Grammatical Evolution, SSE, STE, Epsilon Tube, Laplace Distribution", isbn13 = "978-988-18210-2-7", URL = "http://www.iaeng.org/publication/WCECS2009/WCECS2009_pp1050-1054.pdf", size = "5 pages", abstract = "Grammatical evolution (GE) is one of the newest among computational methods (Ryan et al., 1998 \cite{Ryan:1998:mendle}), (O'Neill and Ryan, 2001 \cite{oneill:2001:TEC}). Basically, it is a tool used to automatically generate Backus-Naur-Form (BNF) computer programs. The method's evolution mechanism may be based on a standard genetic algorithm (GA). GE is very often used to solve the problem of a symbolic regression, determining a module's own parameters (as it is also the case of other optimization problems) as well as the module structure itself. A Sum Square Error (SSE) method is usually used as the testing criterion. In this paper, however, we will present the original method, which uses a Sum Epsilon Tube Error (STE) optimizing criterion. In addition, we will draw a possible parallel between the SSE and STE criteria describing the statistical properties of this new and promising minimizing method.", notes = "fitness like Koza's hits but minimum distance required for a near miss is changed during GP run. Suggests STE fitness follows Laplace (symmetric exponential) [Equation 6 says Binomial?] distribution whilst sum of errors squared follows Gaussian distribution. STE gives smoother fit (fig 3 and fig4). Minitab. 2 one dimensional problems. Lecture Notes in Engineering and Computer Science", } @InProceedings{Matousek:2019:CEC, author = "Radomil Matousek and Tomas Hulka", title = "Stabilization of Higher Periodic Orbits of the Chaotic Logistic and Henon Maps using Meta-evolutionary Approaches", booktitle = "2019 IEEE Congress on Evolutionary Computation (CEC)", year = "2019", pages = "1758--1765", month = jun, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2019.8790075", abstract = "This paper deals with an advanced adjustment of stabilization sequences for selected discrete chaotic systems by means of meta-evolutionary approaches. As the representative models of deterministic chaotic systems, one dimensional Logistic equation and two dimensional Henon map were used. The stability of the chaotic systems has been studied by computer simulations. The novelty of the approach is in an effective design of a new type of objective function, which is very important for the whole optimization process of higher periodic orbits. Furthermore, modern meta-heuristics were used for own design of proper stabilizing sequences. The used optimization methods are a grid-based Nelder-Mead Algorithm (NMA), Genetic Algorithm (GA) as well as Genetic Programming (GP). GP results show good capability of control law synthesis in case of higher periodic orbits. A connection of GP and second level optimization using GA or NMA displays better results than stand alone meta-heuristic techniques. Although the task of stabilizing the presented chaotic systems is known, its solution presented for periodic orbits two and four is not trivial.", notes = "Also known as \cite{8790075}", } @InProceedings{Matousek:2019:ICCAIRO, author = "Radomil Matousek and Tomas Hulka and Ladislav Dobrovsky and Jakub Kudela", title = "Sum Epsilon-Tube Error Fitness Function Design for {GP} Symbolic Regression: Preliminary Study", booktitle = "2019 International Conference on Control, Artificial Intelligence, Robotics Optimization (ICCAIRO)", year = "2019", pages = "78--83", month = dec, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICCAIRO47923.2019.00021", abstract = "Symbolic Regression (SR) is a well-studied method in Genetic Programming (GP) for discovering free-form mathematical models from observed data, which includes not only the model parameters but also its innate structure. Another level of the regression problem is the design of an appropriate fitness function, by which are individual solutions judged. This paper proposes a new fitness function design for symbolic regression problems called a Sum epsilon-Tube Error (STE). The function of this criterion can be visualized as a tube with a small radius that stretches along the entire domain of the approximated function. The middle of the tube is defined by points that match approximated valued (in the so-called control points). The evaluation function then compares, whether each approximated point does or does not belong to the area of the tube and counts the number of points outside of the epsilon-Tube. The proposed method is compared with the standard sum square error in several test cases, where the advantages and disadvantages of the design are discussed. The obtained results show great promise for the further development of the STE design and implementation.", notes = "Also known as \cite{9057172}", } @InProceedings{Matousek:2021:CEC, author = "Radomil Matousek and Rene Pierre Lozi and Tomas Hulka", title = "Stabilization of Higher Periodic Orbits of the {Lozi} and {Henon} Maps using Meta-evolutionary Approaches", booktitle = "2021 IEEE Congress on Evolutionary Computation (CEC)", year = "2021", editor = "Yew-Soon Ong", pages = "572--579", address = "Krakow, Poland", month = "28 " # jun # "-1 " # jul, keywords = "genetic algorithms, genetic programming, Chaos, Simulation, Optimization methods, Linear programming, Orbits, Trajectory, Chaos control, Evolutionary computation, Lozi map, Henon map, Optimization", isbn13 = "978-1-7281-8393-0", DOI = "doi:10.1109/CEC45853.2021.9504798", abstract = "This paper deals with an advanced adjustment of stabilization sequences for selected discrete chaotic systems by means of meta-evolutionary approaches. As the representative models of deterministic chaotic systems, a two dimensional Lozi map and two dimensional Henon map were used. The novelty of the approach is in an effective use of a new type of objective function, which is essential for the whole optimization process of higher periodic orbits as well as an effective use of advanced metaheuristic optimization methods. Although the task of stabilizing the Lozi and Henon chaotic systems is known, its solution presented for periodic orbit four is not trivial. The task of stabilizing the Lozi chaotic systems for period four is a new approach. Furthermore, modern meta-heuristics were used for own design of the external disturbance sequences. The used optimization methods are a naive grid-based algorithm (NG), a grid-based Nelder-Mead Algorithm (NM), a Genetic Algorithm (GA) as well as Genetic Programming (GP). A connection of GP and second level optimization using GA displays significantly better results than the given stand-alone meta-heuristic techniques.", notes = "p578 'It is obvious the progress of the GP opposite of NM and GA, and the rapid improvement two level optimization given by GP+, i.e. tuning GP by GA' Also known as \cite{9504798}", } @InProceedings{Matousek:2022:CEC, author = "Radomil Matousek and Tomas Hulka", title = "Stabilization of Higher Periodic Orbits of the Duffing Map using Meta-evolutionary Approaches: A Preliminary Study", booktitle = "2022 IEEE Congress on Evolutionary Computation (CEC)", year = "2022", editor = "Carlos A. Coello Coello and Sanaz Mostaghim", address = "Padua, Italy", month = "18-23 " # jul, keywords = "genetic algorithms, genetic programming, Chaos, Perturbation methods, Simulation, Metaheuristics, Time series analysis, Linear programming, Chaos control, Evolutionary computation, Nelder-Mead Algorithm, Duffing map, Optimization", isbn13 = "978-1-6654-6708-7", DOI = "doi:10.1109/CEC55065.2022.9870372", abstract = "This paper deals with an advanced adjustment of stabilization sequences for complex chaotic systems by means of meta-evolutionary approaches in the form of a preliminary study. In this study, a two dimensional discrete time dynamic system denoted as Duffing map, also called Holmes map, was used. In general, the Duffing oscillator model represents a real system in the field of nonlinear dynamics. For example, an excited model of a string choosing between two magnets. There are many articles on the stabilization of various chaotic maps, but attempts to stabilize the Duffing map, moreover, for higher orbits, are rather the exception. In the case of period four, this is a novelty. This paper presents several approaches to obtaining stabilizing perturbation sequences. The problem of stabilizing the Duffing map turns out to be difficult and is a good challenge for metaheuristic algorithms, and also as benchmark function. The first approach is the optimal parametrisation of the ETDAS model using multi-restart Nelder-Mead (NM) algorithm and Genetic Algorithm (GA). The second approach is to use the symbolic regression procedure. A perturbation model is obtained using Genetic Programming (GP). The third approach is two level optimization, where the best GP model is subsequently optimized using NM and GA algorithms. A novelty of the approach is also the effective use of the objective function, precisely in relation to the process of optimization of higher periodic paths.", notes = "Also known as \cite{9870372}", } @InProceedings{matousek:2024:CEC, author = "Radomil Matousek and Tomas Hulka and Rene Pierre Lozi and Jakub Kudela", title = "Semi-Stable Periodic Orbits of the Deterministic Chaotic Systems Designed by means of Genetic Programming", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Perturbation methods, Evolutionary computation, Predictive models, Linear programming, Orbits, Trajectory, Chaos control, Periodic orbit, Optimization, Semi-stable", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10611935", abstract = "The aim of this paper is to show the possibility of generating general semi-stable periodic orbits using genetic programming (GP). This concept is a GP design of a perturbation sequence that forces a defined dynamical system to behave periodically. Recall that periodic orbits in deterministic chaotic systems are trajectories along which the system moves at regular intervals. Despite the chaotic nature of these systems, periodic orbits represent the repetition of certain states of the system over time. In chaotic systems, these orbits are usually surrounded by complex, irregular trajectories, but are themselves defined by regularity and predictability. We should add that periodic orbits are important to chaos theory because they provide a basis for understanding the internal structure of chaotic systems. Although chaos is defined by unpredictability based on initial conditions and the complexity, these periodic orbits represent islands of predictability that can be analysed and modelled. GP and its symbolic regression capability is an ideal tool for finding both stable periodic orbits defined by stable points and general periodic orbits that rebuild attractive periodic states for the system. The objective function is also crucial for finding periodic orbits using GP. This function has been designed to achieve stable regions as well as the possibility of choosing the degree of the orbital. The test problem will consist of four systems of deterministic chaos, the so-called chaotic maps - the logistic map, the Henon map, the Lozi map and the Burgers map.", notes = "also known as \cite{10611935} WCCI 2024", } @Article{Mat-Radzi:2021:JPM, author = "Siti Fairuz {Mat Radzi} and Muhammad Khalis {Abdul Karim} and M Iqbal Saripan and Mohd Amiruddin {Abd Rahman} and Iza Nurzawani {Che Isa} and Mohammad Johari Ibahim", title = "Hyperparameter Tuning and Pipeline Optimization via Grid Search Method and {Tree-Based} {AutoML} in Breast Cancer Prediction", journal = "Journal of Personalized Medicine", year = "2021", volume = "11", number = "10", keywords = "genetic algorithms, genetic programming, TPOT", ISSN = "2075-4426", URL = "https://www.mdpi.com/2075-4426/11/10/978", DOI = "doi:10.3390/jpm11100978", code_url = "https://github.com/sitifairuz9609/TPOT-Automated-Machine-Learning-with-Radiomics-Features", abstract = "Automated machine learning (AutoML) has been recognised as a powerful tool to build a system that automates the design and optimises the model selection machine learning (ML) pipelines. In this study, we present a tree-based pipeline optimisation tool (TPOT) as a method for determining ML models with significant performance and less complex breast cancer diagnostic pipelines. Some features of pre-processors and ML models are defined as expression trees and optimal gene programming (GP) pipelines, a stochastic search system. Features of radiomics have been presented as a guide for the ML pipeline selection from the breast cancer data set based on TPOT. Breast cancer data were used in a comparative analysis of the TPOT-generated ML pipelines with the selected ML classifiers, optimised by a grid search approach. The principal component analysis (PCA) random forest (RF) classification was proven to be the most reliable pipeline with the lowest complexity. The TPOT model selection technique exceeded the performance of grid search (GS) optimisation. The RF classifier showed an outstanding outcome amongst the models in combination with only two pre-processors, with a precision of 0.83. The grid search optimised for support vector machine (SVM) classifiers generated a difference of 12percent in comparison, while the other two classifiers, naive Bayes (NB) and artificial neural network--multilayer perceptron (ANN-MLP), generated a difference of almost 39percent. The method's performance was based on sensitivity, specificity, accuracy, precision, and receiver operating curve (ROC) analysis.", notes = "also known as \cite{jpm11100978}", } @InProceedings{Matsumoto:2023:EuroGP, author = "Nicholas Matsumoto and Anil Kumar Saini and Pedro Ribeiro and Hyunjun Choi and Alena Orlenko and Leo-Pekka Lyytikainen and Jari O. Laurikka and Terho Lehtimaki and Sandra Batista and Jason H. Moore", title = "Faster Convergence with Lexicase Selection in Tree-based Automated Machine Learning", booktitle = "EuroGP 2023: Proceedings of the 26th European Conference on Genetic Programming", year = "2023", month = "12-14 " # apr, editor = "Gisele Pappa and Mario Giacobini and Zdenek Vasicek", series = "LNCS", volume = "13986", publisher = "Springer Verlag", address = "Brno, Czech Republic", pages = "165--181", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, TPOT, Lexicase, Parent Selection, NSGA-II, Convergence, Trie", isbn13 = "978-3-031-29572-0", URL = "https://rdcu.be/c8UUl", DOI = "doi:10.1007/978-3-031-29573-7_11", size = "17 pages", abstract = "In many evolutionary computation systems, parent selection methods can affect, among other things, convergence to a solution. In this paper, we present a study comparing the role of two commonly used parent selection methods in evolving machine learning pipelines in an automated machine learning system called Tree-based Pipeline Optimization Tool (TPOT). Specifically, we demonstrate, using experiments on multiple datasets, that lexicase selection leads to significantly faster convergence as compared to NSGA-II in TPOT. We also compare the exploration of parts of the search space by these selection methods using a trie data structure that contains information about the pipelines explored in a particular run.", notes = "See also https://arxiv.org/abs/2302.00731 Part of \cite{Pappa:2023:GP} EuroGP'2023 held in conjunction with EvoCOP2023, EvoMusArt2023 and EvoApplications2023", } @InProceedings{Matsumura:2016:SCIS, author = "Kohei Matsumura and Yoshiko Hanada and Keiko Ono", booktitle = "2016 Joint 8th International Conference on Soft Computing and Intelligent Systems (SCIS) and 17th International Symposium on Advanced Intelligent Systems (ISIS)", title = "Probabilistic Model-Based Multistep Crossover for Genetic Programming", year = "2016", pages = "154--159", abstract = "Deterministic Multistep crossover fusion (dMSXF) is one of promising crossover methods of a tree-based genetic programming. dMSXF performs a multistep local search from a parent in the direction approaching the other parent. In the local search, neighbourhood solutions are generated by operators based on a replacement, an insertion and a deletion of nodes to combine both parents' small trait step by step. Due to this mechanism, dMSXF can generate a wide variety of solution between parents. However, some random nodes are inserted or deleted in the solution at each step of the local search to satisfy constraints, which sometimes cause the generation of undesirable neighbourhood solutions. In this paper, we introduce a probabilistic model constructed by the search information to the generation of neighbourhood solutions in order to improve the search efficiency of dMSXF. The search performance of the proposed method is evaluated on symbolic regression problems and the Santa Fe Trail problem.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SCIS-ISIS.2016.0043", month = aug, notes = "Also known as \cite{7801630}", } @InProceedings{Matsushita:2008:ijcnn, author = "Haruna Matsushita and Yoshifumi Nishio", title = "Batch-Learning Self-Organizing Map with False-Neighbor Degree Between Neurons", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "2259--2266", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1821-3", file = "NN0660.pdf", DOI = "doi:10.1109/IJCNN.2008.4634110", abstract = "This study proposes a Batch-Learning Self- Organising Map with False-Neighbor degree between neurons (called BL-FNSOM). False-Neighbor degrees are allocated between adjacent rows and adjacent columns of BL-FNSOM. The initial values of all of the false-neighbor degrees are set to zero, however, they are increased with learning, and the false neighbour degrees act as a burden of the distance between map nodes when the weight vectors of neurons are updated. BLFNSOM changes the neighbourhood relationship more flexibly according to the situation and the shape of data although using batch learning. We apply BL-FNSOM to some input data and confirm that FN-SOM can obtain a more effective map reflecting the distribution state of input data than the conventional Batch-Learning SOM.", keywords = "genetic algorithms, genetic programming", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @InProceedings{matsuya:2002:SEAL, author = "Yuko Matsuya and Kotaro Hirasawa and Jinglu Hu and Junichi Murata", title = "Automatic Generation of {Boolean} Functions Using Genetic Network Programming", booktitle = "Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution And Learning (SEAL'02)", year = "2002", editor = "Lipo Wang and Kay Chen Tan and Takeshi Furuhashi and Jong-Hwan Kim and Xin Yao", address = "Orchid Country Club, Singapore", month = "18-22 " # nov, keywords = "genetic algorithms, genetic programming", ISBN = "981-04-7522-5", URL = "http://www.worldcat.org/title/seal02-proceedings-of-the-4th-asia-pacific-conference-on-simulated-evolution-and-learning-november-18-22-2002-orchid-country-club-singapore/oclc/51951214", abstract = "In this paper, a recently proposed Evolutionary Computation method called Genetic Network Programming (GNP) is applied to generate Boolean functions. GNP is based on Genetic Algorithm (GA) and Genetic Programming (GP). It has a network structure and can search for solutions effectively. GNP has been mainly applied to dynamic problems and has shown better performance compared to GP. However, its application to static problems has not yet been studied well. Thus in this paper, GNP is applied to generate Boolean functions as its extension to solving static problems. In the simulations, GNP succeeded in solving Even-n-Parity problem and Mirror Symmetry problem.", notes = "SEAL 2002 see http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.200.6410&rep=rep1&type=pdf", } @InProceedings{matsuzaki-miyao-tsujii:2005:ACL, author = "Takuya Matsuzaki and Yusuke Miyao and Jun'ichi Tsujii", title = "Probabilistic {CFG} with Latent Annotations", booktitle = "Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL'05)", month = jun, year = "2005", address = "Ann Arbor, Michigan, USA", publisher = "Association for Computational Linguistics", pages = "75--82", organisation = "Association for Computational Linguistics", URL = "http://www.aclweb.org/anthology-new/P/P05/P05-1010.pdf", URL = "http://www.aclweb.org/anthology/P05-1010", DOI = "doi:10.3115/1219840.1219850", size = "8 pages", abstract = "This paper defines a generative probabilistic model of parse trees, which we call PCFG-LA. This model is an extension of PCFG in which non-terminal symbols are augmented with latent variables. Fine grained CFG rules are automatically induced from a parsed corpus by training a PCFG-LA model using an EM-algorithm. Because exact parsing with a PCFG-LA is NP-hard, several approximations are described and empirically compared. In experiments using the Penn WSJ corpus, our automatically trained model gave a performance of 86.6percent (F1, sentences le 40 words), which is comparable to that of an unlexicalized PCFG parser created using extensive manual feature selection.", notes = "cited by \cite{Hasegawa:2009:ieeeTEC}", } @Article{journals/cea/MattarA17, author = "Mohamed A. Mattar and Ahmed I. Alamoud", title = "Gene expression programming approach for modeling the hydraulic performance of labyrinth-channel emitters", journal = "Computers and Electronics in Agriculture", year = "2017", volume = "142", pages = "450--460", keywords = "genetic algorithms, genetic programming, gene expression programming", bibdate = "2017-11-22", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/cea/cea142.html#MattarA17", DOI = "doi:10.1016/j.compag.2017.09.029", } @Article{mattar:NCaA, author = "Mohamed A. Mattar and A. A. Alazba", title = "{GEP} and {MLR} approaches for the prediction of reference evapotranspiration", journal = "Neural Computing and Applications", year = "2019", volume = "31", number = "10", pages = "5843--5855", month = oct, keywords = "genetic algorithms, genetic programming, gene expression programming, Evapotranspiration, Linear regression, Penman-Monteith", ISSN = "0941-0643", URL = "http://link.springer.com/article/10.1007/s00521-018-3410-8", DOI = "doi:10.1007/s00521-018-3410-8", size = "13 pages", abstract = "In this study, reference evapotranspiration (ETo) is modelled as one of the major items of hydrological applications from different combinations of climatic variables using two different techniques: gene expression programming (GEP) and multiple linear regression (MLR). The data used in modelling were collected from weather stations in Egypt through the CLIMWAT database. The Penman Monteith FAO-56 equation was considered as a reference target for ETo values depending on the entire climatic variables. The developed ETo models performances were compared and evaluated with regard to their predictive abilities using statistical criteria to identify the superiority of one modeling approach over the others and determine climatic variables which have a significant effect on ETo. The results indicated that GEP and MLR models contribution toward mean relative humidity and wind speed at 2 m height is greater compared to that of other variables. Meanwhile, when adding temperature data to models, solar radiation has a slight effect on increasing the accuracy of ETo estimate. Moreover, the lower statistical error criteria values of GEP models confirmed their better performance than MLR models and other empirical equations.", } @PhdThesis{Matteo:thesis, author = "Miraz Matteo", title = "Evolutionary Testing of Stateful Systems: a Holistic Approach", school = "Politecnico di Milano, Dipartimento di Elettronica e Informazione", year = "2010", type = "Dottorato di Ricerca in Ingegneria dell'Informazioneu", address = "Milan, Italy", keywords = "genetic algorithms, SBSE, testFul, JUnit test, mutation testing", URL = "http://dottoratoit.deib.polimi.it/?id=5005&y=2011", URL = "http://matteo.miraz.it/research/papers/evolutionarytestingofstatefulsystemsaholisticapproach/thesis.pdf", size = "128 pages", abstract = "Testing should be one of the key activities of every software development process. However it requires up to half of the software development effort when it is properly done. One of the main problems is the generation of smart tests to probe the system, which is both difficult and time-consuming. The research community has been proposing several ways to automate the generation of these tests; among them, the search-based techniques recently achieved significant results. This doctoral dissertation presents TestFul, our evolutionary testing approach for stateful systems; it is tailored to work on object-oriented systems. It uses a holistic approach to make the state of object evolve, to enable all the features the class provides, and to generate the shortest test with the utmost coverage for the class under test. We employ several complementary coverage criteria to drive the evolutionary search. We aim to generate tests with high fault detection effectiveness. To this end, we consider the system from complementary perspectives and we combine white-box analysis techniques with black-box ones. The evolutionary search is completed with a local one, and we establish a synergic cooperation between them. The evolutionary search concentrates on evolving the state of objects, while the local search detects the functionality not yet exercised, and directly targets them. All the proposal were subject to an extensive empirical validation. We devised a benchmark composed of independent benchmarks for tests, public libraries, and third party studies. As comparison, we consider both search-based, symbolic, and traditional (i.e., manually generated by human being) approaches. The achieved results were encouraging: TestFul efficiently generate tests for complex classes and outperforms the other approaches. The proposals presented in this dissertation open new interesting research directions. On one side, one can continue refining the search strategy, by considering more advanced search techniques and by leveraging more advanced coverage criteria. On the other side, one can adapt the approach to work either at a coarse-grained level and focus on the integration testing or on other kind of stateful systems (e.g., components or services).", notes = "The slides used during my defence are available http://www.slideshare.net/matteomiraz/dissertation-7030730 Is this GP? Entered for 2011 HUMIES GECCO 2011 supervisor: Luciano Baresi", } @InProceedings{mattfeld:1999:SSSSP, author = "Dirk C. Mattfeld", title = "Scalable Search Spaces for Scheduling Problems", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1616--1621", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "real world applications", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-760.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-760.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Article{Matthews:2006:AIEDAM, author = "Peter C. Matthews and David W. F. Standingford and Carren M. E. Holden and Ken M. Wallace", title = "Learning inexpensive parametric design models using an augmented genetic programming technique", journal = "Artificial Intelligence for Engineering Design, Analysis and Manufacturing", year = "2006", volume = "20", pages = "1--18", publisher = "Cambridge University Press", keywords = "genetic algorithms, genetic programming, Data Mining, Design Model Induction, Knowledge Elicitation, Metamodels SVM, GP-HEM, demes", DOI = "doi:10.10170S089006040606001X", size = "18 pages", abstract = "Previous applications of genetic programming (GP) have been restricted to searching for algebraic approximations mapping the design parameters e.g., geometrical parameters, to a single design objective e.g., weight. In addition, these algebraic expressions tend to be highly complex. By adding a simple extension to the GP technique, a powerful design data analysis tool is developed. This paper significantly extends the analysis capabilities of GP by searching for multiple simple models within a single population by splitting the population into multiple islands according to the design variables used by individual members. Where members from different islands 'cooperate', simple design models can be extracted from this cooperation. This relatively simple extension to GP is shown to have powerful implications to extracting design models that can be readily interpreted and exploited by human designers. The full analysis method, GP heuristics extraction method, is described and illustrated by means of a design case study.", notes = "School of Engineering, University of Durham, Durham, United Kingdom BAE Systems, Advanced Technology Centre, Filton, Bristol, United Kingdom Aerodynamic Methods and Tools, Airbus UK, Filton, Bristol, United Kingdom Engineering Design Centre, Engineering Department, University of Cambridge, Cambridge, United Kingdom Flat screen design.", } @Article{matthews:2001:idm, author = "Robert Matthews", title = "The Ideas Machine", journal = "New Scientist", year = "2001", number = "2274", pages = "26--29", month = "20 " # jan, keywords = "genetic algorithms, genetic programming", URL = "http://www.newscientist.com/article/mg16922744.000-the-ideas-machine.html", size = "4 pages", abstract = "STANDING outside the law courts in London, James Dyson triumphantly ....", notes = "{"}Computers that invent the future{"} cover. {"}Human inventiveness has reached the end of the road. Something far smarter is about to take over, says Robert Matthews{"}. page 26 Glossy overview of GA, GP. Concentrates upon John Koza's work on using GP to {"}invent{"} designs and patent infringement.", } @Article{Mattiussi2004-ID509, author = "Claudio Mattiussi and Markus Waibel and Dario Floreano", title = "Measures of Diversity for Populations and Distances Between Individuals with Highly Reorganizable Genomes", journal = "Evolutionary Computation", year = "2004", volume = "12", number = "4", pages = "495--515", month = "Winter", keywords = "genetic algorithms, genetic programming, Evolutionary computation, variable length genomes, population diversity, substring diversity, Tanimoto distance, Jaccard similarity, linguistic complexity, nucleotide diversity", ISSN = "1063-6560", URL = "http://asl.epfl.ch/aslInternalWeb/ASL/publications/uploadedFiles/MattiussiWaibelFloreano_MeasuresOfDiversity.pdfx", DOI = "doi:10.1162/1063656043138923", size = "21 pages", abstract = "we address the problem of defining a measure of diversity for a population of individuals whose genome can be subjected to major reorganisations during the evolutionary process. To this end, we introduce a measure of diversity for populations of strings of variable length defined on a finite alphabet, and from this measure we derive a semi-metric distance between pairs of strings. The definitions are based on counting the number of substrings of the strings, considered first separately and then collectively. This approach is related to the concept of linguistic complexity, whose definition we generalise from single strings to populations. Using the substring count approach we also define a new kind of Tanimoto distance between strings. We show how to extend the approach to representations that are not based on strings and, in particular, to the tree-based representations used in the field of genetic programming. We describe how suffix trees can allow these measures and distances to be implemented with a computational cost that is linear in both space and time relative to the length of the strings and the size of the population. The definitions were devised to assess the diversity of populations having genomes of variable length and variable structure during evolutionary computation runs, but applications in quantitative genomics, proteomics, and pattern recognition can be also envisaged.", notes = "Section 3.4 (10 lines) suggests using Tanimoto distance for tree GP p503. Page 507 says {"}Tanimoto ... O(lnn) complexity makes the direct implementation ... rapidly impractical for runtime diversity assessment when the population size (n) grows.{"} source code etc http://asl.epfl.ch/resources.php", } @Article{Mattiussi_2008_TEC, author = "Claudio Mattiussi and Dario Floreano", title = "Analog Genetic Encoding for the Evolution of Circuits and Networks", journal = "IEEE Transactions on Evolutionary Computation", year = "2007", volume = "11", number = "5", pages = "596--607", month = oct, keywords = "genetic algorithms, genetic programming, Analog circuit synthesis, analogue genetic encoding (AGE), analog network synthesis, evolutionary computation, genetic representation, neural network synthesis", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2006.886801", size = "12 pages", abstract = "This paper describes a new kind of genetic representation called analog genetic encoding (AGE). The representation is aimed at the evolutionary synthesis and reverse engineering of circuits and networks such as analogue electronic circuits, neural networks, and genetic regulatory networks. AGE permits the simultaneous evolution of the topology and sizing of the networks. The establishment of the links between the devices that form the network is based on an implicit definition of the interaction between different parts of the genome. This reduces the amount of information that must be carried by the genome, relatively to a direct encoding of the links. The application of AGE is illustrated with examples of analog electronic circuit and neural network synthesis. The performance of the representation and the quality of the results obtained with AGE are compared with those produced by genetic programming.", } @Article{Matveeva:2007:NAR, author = "Olga Matveeva and Yury Nechipurenko and Leo Rossi and Barry Moore and Pal Saetrom and Aleksey Y. Ogurtsov and John F. Atkins and Svetlana A. Shabalina", title = "Comparison of approaches for rational siRNA design leading to a new efficient and transparent method", journal = "Nucleic Acids Research", year = "2007", volume = "35", number = "8", pages = "e63", month = apr, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1093/nar/gkm088", abstract = "Current literature describes several methods for the design of efficient siRNAs with 19 perfectly matched base pairs and 2 nt overhangs. Using four independent databases totaling 3336 experimentally verified siRNAs, we compared how well several of these methods predict siRNA cleavage efficiency. According to receiver operating characteristics (ROC) and correlation analyses, the best programs were BioPredsi, ThermoComposition and DSIR. We also studied individual parameters that significantly and consistently correlated with siRNA efficacy in different databases. As a result of this work we developed a new method which uses linear regression fitting with local duplex stability, nucleotide position-dependent preferences and total G/C content of siRNA duplexes as input parameters. The new method's discrimination ability of efficient and inefficient siRNAs is comparable with that of the best methods identified, but its parameters are more obviously related to the mechanisms of siRNA action in comparison with BioPredsi. This permits insight to the underlying physical features and relative importance of the parameters. The new method of predicting siRNA efficiency is faster than that of ThermoComposition because it does not employ time-consuming RNA secondary structure calculations and has much less parameters than DSIR. It is available as a web tool called siRNA scales.", notes = "PMID: 4 methods, including \cite{Saetrom:2004:BI} compared", } @Article{Mauceri:GPEM, author = "Stefano Mauceri and James Sweeney and Miguel Nicolau and James McDermott", title = "Feature extraction by grammatical evolution for one‑class time series classification", journal = "Genetic Programming and Evolvable Machines", year = "2021", volume = "22", number = "3", pages = "267--295", month = sep, keywords = "genetic algorithms, genetic programming, grammatical evolution, Evolutionary computation, One-class classification, Time series", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-021-09403-x", size = "29 pages", abstract = "When dealing with a new time series classification problem, modelers do not know in advance which features could enable the best classification performance. We propose an evolutionary algorithm based on grammatical evolution to attain a data-driven feature-based representation of time series with minimal human intervention. The proposed algorithm can select both the features to extract and the sub-sequences from which to extract them. These choices not only impact classification performance but also allow understanding of the problem at hand. The algorithm is tested on 30 problems outperforming several benchmarks. Finally, in a case study related to subject authentication, we show how features learned for a given subject are able to generalise to subjects unseen during the extraction phase.", notes = "Natural Computing Research and Applications Group (NCRA), University College Dublin, Dublin, Ireland", } @InProceedings{mauch:2003:gecco, author = "Holger Mauch", title = "Evolving {Petri} Nets with a Genetic Algorithm", booktitle = "Genetic and Evolutionary Computation -- GECCO-2003", editor = "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller", year = "2003", pages = "1810--1811", address = "Chicago", publisher_address = "Berlin", month = "12-16 " # jul, volume = "2724", series = "LNCS", ISBN = "3-540-40603-4", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, poster", DOI = "doi:10.1007/3-540-45110-2_76", abstract = "In evolutionary computation many different representations ({"}genomes{"}) have been suggested as the underlying data structures, upon which the genetic operators act. Among the most prominent examples are the evolution of binary strings, real-valued vectors, permutations, finite automata, and parse trees. In this paper the use of place-transition nets, a low-level Petri net (PN) class [1,2], as the structures that undergo evolution is examined. We call this approach {"}Petri Net Evolution{"} (PNE). Structurally, Petri nets can be considered as specialized bipartite graphs. In their extended version (adding inhibitor arcs) PNs are as powerful as Turing machines. PNE is therefore a form of Genetic Programming (GP). Preliminary results obtained by evolving variable-size place-transition nets show the success of this approach when applied to the problem areas of boolean function learning and classification.", notes = "GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)", } @Article{maurya:2025:ExSy, author = "Pratibha Maurya and Arati Kushwaha and Om Prakash2", title = "Medical Data Classification Using Genetic Programming: A Systematic Literature Review", journal = "Expert Systems", year = "2025", volume = "42", number = "3", pages = "e70007", keywords = "genetic algorithms, genetic programming, classification, fitness function, medical datasets", URL = "https://onlinelibrary.wiley.com/doi/abs/10.1111/exsy.70007", eprint = "https://onlinelibrary.wiley.com/doi/pdf/10.1111/exsy.70007", DOI = "doi:10.1111/exsy.70007", size = "21 pages", abstract = "Background Medical data classification has always been a growing area of research. While machine learning techniques have been successfully applied in this field, the vast amount of data generated and the complexity of applications necessitate more robust and powerful methods, especially in the absence of domain expertise. Genetic programming (GP) being a flexible evolutionary approach can autonomously craft efficient classification programs merely from example data and has thus gained significant attention across various classification domains. Content This article presents a literature survey on the application of genetic programming to medical data classification. Reported studies are evaluated based on the examination of datasets, classifier architecture, and achieved classification accuracy. Additionally, we also discuss the strengths and weaknesses of genetic programming with other algorithms, covering aspects like classification accuracy, computational efficiency, interpretability, and resource consumption. The limitations of existing GP techniques and future directions are also presented in this study. Conclusion The study presented in this article indicates that GP-based classifiers perform better than other classifiers in the medical domain. To the best of our knowledge, this article is the first of its kind which discusses the application of GP explicitly in medical data classification. Through this article, we aim to enlighten the readers on key concepts of GP and encourage them to build new classifiers by exploring the potential and limitations of genetic programming.", } @Article{Mausa:2017:SC, author = "Goran Mausa and Tihana Galinac Grbac", title = "Co-evolutionary multi-population genetic programming for classification in software defect prediction: An empirical case study", journal = "Applied Soft Computing", year = "2017", volume = "55", pages = "331--351", month = jun, keywords = "genetic algorithms, genetic programming, SBSE, Classification, Coevolution, Software defect prediction", ISSN = "1568-4946", URL = "http://www.sciencedirect.com/science/article/pii/S1568494617300650", DOI = "doi:10.1016/j.asoc.2017.01.050", size = "21 pages", abstract = "Evolving diverse ensembles using genetic programming has recently been proposed for classification problems with unbalanced data. Population diversity is crucial for evolving effective algorithms. Multilevel selection strategies that involve additional colonization and migration operations have shown better performance in some applications. Therefore, in this paper, we are interested in analysing the performance of evolving diverse ensembles using genetic programming for software defect prediction with unbalanced data by using different selection strategies. We use colonization and migration operators along with three ensemble selection strategies for the multi-objective evolutionary algorithm. We compare the performance of the operators for software defect prediction datasets with varying levels of data imbalance. Moreover, to generalise the results, gain a broader view and understand the underlying effects, we replicated the same experiments on UCI datasets, which are often used in the evolutionary computing community. The use of multilevel selection strategies provides reliable results with relatively fast convergence speeds and outperforms the other evolutionary algorithms that are often used in this research area and investigated in this paper. This paper also presented a promising ensemble strategy based on a simple convex hull approach and at the same time it raised the question whether ensemble strategy based on the whole population should also be investigated.", } @InProceedings{mautner:1999:CMCSR, author = "Craig Mautner and Richard K. Belew", title = "Coupling Morphology and Control in a Simulated Robot", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1350--1357", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "artificial life, adaptive behavior and agents", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-027.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-027.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Misc{oai:CiteSeerPSU:451316, title = "Preventing Early Convergence in Genetic Programming by Replacing Similar Programs", author = "Dylan Mawhinney", year = "2000", month = oct # "~31", school = "RMIT", address = "Australia", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.rmit.edu.au/~vc/papers/mahwinney-hons.ps.gz", URL = "http://citeseer.ist.psu.edu/451316.html", citeseer-isreferencedby = "oai:CiteSeerPSU:86939; oai:CiteSeerPSU:207908; oai:CiteSeerPSU:211636; oai:CiteSeerPSU:66071; oai:CiteSeerPSU:82846", citeseer-references = "oai:CiteSeerPSU:188996; oai:CiteSeerPSU:322830; oai:CiteSeerPSU:18409; oai:CiteSeerPSU:9503; oai:CiteSeerPSU:500714; oai:CiteSeerPSU:189501; oai:CiteSeerPSU:32228; oai:CiteSeerPSU:189578; oai:CiteSeerPSU:17731; oai:CiteSeerPSU:189766; oai:CiteSeerPSU:269924; oai:CiteSeerPSU:125377; oai:CiteSeerPSU:300709; oai:CiteSeerPSU:194398; oai:CiteSeerPSU:229338; oai:CiteSeerPSU:48471; oai:CiteSeerPSU:142868; oai:CiteSeerPSU:144102; oai:CiteSeerPSU:28466; oai:CiteSeerPSU:308722; oai:CiteSeerPSU:62810; oai:CiteSeerPSU:191318", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:451316", rights = "unrestricted", size = "39 pages", abstract = "Genetic programming is a means of automatically evolving programs to perform a particular task or solve a particular problem using the Darwinian principle of survival of the fittest. Many genetic programming problems can suffer from early convergence, that is, the genetic programming run terminates before the optimal program has evolved. Early convergence is a hindrance to genetic programming especially for problems which need signi cant amounts of computing time. This project describes a method of preventing early convergence by replacing similar programs. A percentage of the most similar programs are replaced by randomly generated programs. This method uses the number of changes reported by the UNIX program diff to estimate how similar a program is to the rest of the population. We performed experiments using no replacement and replacement on the MAX problem, a problem known to suffer from early convergence, and the Robocup simulator league domain. Using a replacement rate of 10% in the MAX domain, increased the success rate from 16% (using no replacement) to 42%. Performing similarity replacement in the Robocup domain increased the number of runs which obtained successful players, from 2 out of the 5 runs using no replacement, to 4 out of the 5 runs using 10% replacement. The quality of the players in the successful runs was also improved. Performing replacement every 2nd, 5th, or 10th generation did not significantly reduce the number of successful runs in the MAX domain when using a replacement rate of 10%. Replacing a percentage of the most similar programs prevented early convergence more often than when no replacement was used. Our results suggest that performing similarity replacement is worthwhile in problems where the cost of computing the t...", notes = "Honours thesis? RMIT.EDU.AU down at present See also \cite{ciesielski:2002:poecigpbrosp}", } @InProceedings{icec94:maxwell, author = "Sidney R. {Maxwell III}", title = "Experiments with a coroutine execution model for genetic programming", booktitle = "Proceedings of the 1994 IEEE World Congress on Computational Intelligence", year = "1994", pages = "413--417a", volume = "1", address = "Orlando, Florida, USA", month = "27-29 " # jun, organisation = "IEEE", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, parallel programming, subroutines, iterative methods, coroutine execution model, synchronous parallel program execution, fitness comparison, execution time limits, iteration limits, infinite loops, infinite recursion, evolutionary progress, population tolerance", ISBN = "0-7803-1899-4", size = "6 pages", URL = "http://ieeexplore.ieee.org/iel2/1125/8059/00349915.pdf?isNumber=8059", DOI = "doi:10.1109/ICEC.1994.349915", abstract = "The genetic programming methodology is expanded with a coroutine model for the synchronous, parallel execution of the individual programs in the population. For certain classes of problem, namely those that support fitness comparison between individuals which are in a state of execution, this model allows the removal of execution time and iteration limits. Populations can then tolerate individuals with infinite loops (or in a suitable environment, infinite recursion), while still allowing evolutionary progress.", notes = "Earlier version 11 pages available electronically. See genetic-programming mailing list 14/12/93, 4/1/94 and 5/1/94 coroutine _model_ is described in terms of real program runtimes. Actually achieved by defining psuedo elapse time for each instruction (which is zero in some cases) and interrupting execution of the program after a certain number of these timesteps. Makes things controlable. Run on Artificial Ant Santa Fe Trail and claims better programs produced with less effort than Koza (GP1). Steady state pop of 1000, with 100 new individuals per cycle. Limit of 600 ticks (when comparing with \cite{koza:book}) Faster programs preferred. {"}The coroutine model found individuals which were more efficient (faster?) in solving the problem than the generational model{"} p417 Date: Mon, 24 Apr 2000 09:36:34 -0700 From: {"}Sidney R Maxwell III{"} > 1-How did Maxwell implement his method? Basically, I executed each individual a fixed number of steps (a 'configurable' number N, with a value of as little as1). Individuals added to the population were pre-executed an appropraite number of steps to ensure that all individuals in the population had executed the same number of steps. The problem that I was tackling was the Artificial Ant, for which evaluating fitness on partially executed individuals was meaningful. In early experiments, I executed all individuals in the population N steps. Later, as a run-time performance enhancement, I [simply] ensured that individuals being evaluated had executed the same number of steps before comparing their fitness. Cf. Levin search.", } @InProceedings{maxwell:1996:why, author = "S. R. Maxwell", title = "Why Might Some Problems Be Difficult for Genetic Programming to Find Solutions?", booktitle = "Late Breaking Papers at the Genetic Programming 1996 Conference Stanford University July 28-31, 1996", year = "1996", editor = "John R. Koza", pages = "125--128", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California 94305-3079, USA", month = "28--31 " # jul, publisher = "Stanford Bookstore", ISBN = "0-18-201031-7", keywords = "genetic algorithms, genetic programming", notes = "GP-96LB mutation operator swaps order of agruments of binary non-commutative functions The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670", } @InProceedings{maxwell:1999:THMMPL, author = "Bruce Maxwell and Sven Anderson", title = "Training Hidden Markov Models using Population-Based Learning", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "944", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "evolution strategies and evolutionary programming, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/Maxwell-Anderson-GECCO-1999.ps.gz", URL = "http://www.palantir.swarthmore.edu/maxwell/papers/pdfs/Maxwell-Anderson-GECCO-1999.ps.gz", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InCollection{may:1999:EECTHGP, author = "Damon May", title = "Evolution of Effective Communication Techniques for Hunting using Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1999", year = "1999", editor = "John R. Koza", pages = "147--154", address = "Stanford, California, 94305-3079 USA", month = "15 " # mar, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:1999:GAGPs}", } @Article{Mayer:2005:AS, author = "D. G. Mayer and B. P. Kinghorn and A. A. Archer", title = "Differential evolution - an easy and efficient evolutionary algorithm for model optimisation", journal = "Agricultural Systems", year = "2005", volume = "83", pages = "315--328", number = "3", owner = "wlangdon", broken = "http://www.sciencedirect.com/science/article/B6T3W-4CWSVR5-1/2/d9e644ff5e8d53cade196bda234702bf", month = mar, keywords = "genetic algorithms, genetic programming, Differential evolution, Optimisation, FORTRAN, Beef model", ISSN = "0308-521X", DOI = "doi:10.1016/j.agsy.2004.05.002", abstract = "Recently, evolutionary algorithms (encompassing genetic algorithms, evolution strategies, and genetic programming) have proven to be the best general method for the optimisation of large, difficult problems, including agricultural models. Differential evolution (DE) is one comparatively simple variant of an evolutionary algorithm. DE has only three or four operational parameters, and can be coded in about 20 lines of pseudo-code. Investigations of its performance in the optimisation of a challenging beef property model with 70 interacting management options (hence a 70-dimensional optimisation problem) indicate that DE performs better than Genial (a real-value genetic algorithm), which has been the preferred operational package thus far. Despite DE's apparent simplicity, the interacting key evolutionary operators of mutation and recombination are present and effective. In particular, DE has the advantage of incorporating a relatively simple and efficient form of self-adapting mutation. This is one of the main advantages found in evolution strategies, but these methods usually require the burdening overhead of doubling the dimensionality of the search-space to achieve this. DE's processes are illustrated, and model optimisations totalling over two years of Sun workstation computation are presented. These results show that the baseline DE parameters work effectively, but can be improved in two ways. Firstly, the population size does not need to be overly high, and smaller populations can be considerably more efficient; and second, the periodic application of extrapolative mutation may be effective in counteracting the contractive nature of DE's intermediate arithmetic recombination in the latter stages of the optimisations. This provides an escape mechanism to prevent sub-optimal convergence. With its ease of implementation and proven efficiency, DE is ideally suited to both novice and experienced users wishing to optimise their simulation models.", } @Unpublished{mayer:1997:ptga, author = "Helmut A. Mayer", title = "ptGAs - Genetics algorithms using promoter/teminator sequences", note = "Position paper at the Workshop on Exploring Non-coding Segments and Genetics-based Encodings at ICGA-97", month = "21 " # jul, year = "1997", address = "East Lansing, MI, USA", keywords = "genetic algorithms, introns, ptGA", URL = "http://www.cosy.sbg.ac.at/~helmut/Research/Papers/lansing97.ps.gz", notes = "http://garage.cse.msu.edu/icga97/workshops/workshopsIndex.html#encodings Oct 2016 link to icga97.ws gone", size = "3 pages", } @Article{mayer:1998:ptga, author = "Helmut A. Mayer", title = "ptGAs--Genetic Algorithms Evolving Noncoding Segments by Means of Promoter/Terminator Sequences", journal = "Evolutionary Computation", year = "1998", volume = "6", number = "4", pages = "361--386", month = "Winter", keywords = "genetic algorithms, chromosome structures, promoter/terminator sequences, noncoding segments, spontaneous crossover, combinatorial optimization.", URL = "http://www.mitpressjournals.org/doi/pdfplus/10.1162/evco.1998.6.4.361", DOI = "doi:10.1162/evco.1998.6.4.361", size = "26 pages", abstract = "In this article we present work on chromosome structures for genetic algorithms (GAs) based on biological principles. Mainly, the influence of noncoding segments on GA behavior and performance is investigated. We compare representations with noncoding sequences at predefined, fixed locations with {"}junk{"} code induced by the use of promoter/terminator sequences (ptGAs) that define start and end of a coding sequence, respectively. As one of the advantages of noncoding segments a few researchers have identified the reduction of the disruptive effects of crossover, and we solidify this argument by a formal analysis of crossover disruption probabilities for noncoding segments at fixed locations. The additional use of promoter/terminator sequences not only enables evolution of parameter values, but also allows for adaptation of number, size, and location of genes (problem parameters) on an artificial chromosome. Randomly generated chromosomes of fixed length carry different numbers of promoter/terminator sequences resulting in genes of varying size and location. Evolution of these ptGA chromosomes drives the number of parameters and their values to (sub)optimal solutions. Moreover, the formation of tightly linked building blocks is enhanced by self-organization of gene locations. We also introduce a new, nondisruptive crossover operator emerging from the ptGA gene structure with adaptive crossover rate, location, and number of crossover sites. For experimental comparisons of this genetic operator to conventional crossover in GAs, as well as properties of different ptGA chromosome structures, an artificial problem from the literature is used. Finally, the potential of ptGA is demonstrated on an NP-complete combinatorial optimization problem.", notes = "Evolutionary Computation (Journal) Special Issue: Variable-Length Representation and Noncoding Segments for Evolutionary Algorithms Edited by Annie S. Wu and Wolfgang Banzhaf", } @InCollection{mayer:1999:GCGAAEGC, author = "Marissa A. Mayer", title = "Graph Coloring using Genetic Algorithms: An Exploration of Genetic Clustering", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1999", year = "1999", editor = "John R. Koza", pages = "155--163", address = "Stanford, California, 94305-3079 USA", month = "15 " # mar, publisher = "Stanford Bookstore", keywords = "genetic algorithms", notes = "part of \cite{koza:1999:GAGPs}", } @InProceedings{Mayfield:2010:gecco, author = "Elijah Mayfield and Carolyn Penstein-Rose", title = "Using feature construction to avoid large feature spaces in text classification", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "1299--1306", keywords = "genetic algorithms, genetic programming, NLP, Natural Language Processing, Text analysis, SVM", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830483.1830714", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Feature space design is a critical part of machine learning. This is an especially difficult challenge in the field of text classification, where an arbitrary number of features of varying complexity can be extracted from documents as a preprocessing step. A challenge for researchers has consistently been to balance expressiveness of features with the size of the corresponding feature space, due to issues with data sparsity that arise as feature spaces grow larger. Drawing on past successes with genetic programming in similar problems outside of text classification, we propose and implement a technique for constructing complex features from simpler features, and adding these more complex features into a combined feature space which can then be used by more sophisticated machine learning classifiers. Applying this technique to a sentiment analysis problem, we show encouraging improvement in classification accuracy, with a small and constant increase in feature space size. We also show that the features we generate carry far more predictive power than any of the simple features they contain.", notes = "Also known as \cite{1830714} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @InProceedings{Maynard:2018:CEC, author = "Jeff A. Maynard and Alvaro Talavera and Leonardo Forero and Marco {Aurelio Pacheco}", title = "Estimating the Geological Properties in Oil Reservoirs through Multi-gene Genetic Programming", booktitle = "2018 IEEE Congress on Evolutionary Computation (CEC)", year = "2018", editor = "Marley Vellasco", address = "Rio de Janeiro, Brazil", month = "8-13 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2018.8477910", abstract = "Oil exploitation and production fields require allocating large investments to reduce low production-associated risks, which can be minimized by the successful characterization of oil reservoirs. The characterization process lies on geological property estimates generated during well-drilling procedures and on information extracted from 3D seismic data. Computational intelligence techniques proved to be efficient tools to estimate nonlinear relations, which can be applied to predict reservoir parameters. The aim of the current study is to address an approach based on the application of the Multi-Gene Genetic Programming (mgGP) algorithm to estimate porosity in an oil reservoir by using seismic data and well logs. The relation between seismic and porosity data about Namorado oil field was satisfactorily represented by means of mgGP.", notes = "WCCI2018", } @Article{MAYTZUC:2018:CILS, author = "O. {May Tzuc} and A. Bassam and M. Abatal and Youness {El Hamzaoui} and A. Tapia", title = "Multivariate optimization of {Pb(II)} removal for clinoptilolite-rich tuffs using genetic programming: A computational approach", journal = "Chemometrics and Intelligent Laboratory Systems", volume = "177", pages = "151--162", year = "2018", keywords = "genetic algorithms, genetic programming, Sensitivity analysis, Sorption process, Swarm particle optimization, Zeolite materials", ISSN = "0169-7439", DOI = "doi:10.1016/j.chemolab.2018.02.010", URL = "http://www.sciencedirect.com/science/article/pii/S0169743917306044", abstract = "In this study, a genetic programming (GP) model was developed to predict and optimize the Pb(II) removal capacity for natural, sodium, and acid-modified clinoptilolite-rich tuffs. Experimental process evaluated the sorption behavior of lead in aqueous solutions using unmodified and modified natural zeolite considering: the contact time, pH value, lead initial concentration, and sorbent dosage. The GP model was trained and tested with the experimental measurements and subsequently, compared with others multivariate analysis methods using three statistical criteria (coefficient of determination (R2), root mean square error (RMSE), and mean absolute percentage error (MAPE)). The results indicate that GP getting the better performance achieving a fitness of R2a =a 98.0percent, RMSEa =a 5.06a timesa 10-2, and MAPEa =a 17.58percent. Sensitivity analysis (SA) showed that the sorbent dosage was the most influential parameter with a sensitivity index of 0.219, following by the pH (0.059), and contact time (0.031). Based on GP model and SA, a multivariate optimization was conducted to compute the adequate conditions for a required sorption efficiency (98percent). Optimize values were obtained at 0.10a g of sorbent mass, pH 5.0, 300.0a mga L-1, and 5.1a min contact time for natural clinoptilolite-rich tuffs; 0.65a g of sorbent mass, pH 5.0, 400.0a mga L-1, and 3.6a min contact time for sodium modified clinoptilolite-rich tuffs; and 0.65a g of sorbent mass, pH 3.0, 400.0a mga L-1, and 71.6a min contact time for acid modified clinoptilolite-rich tuffs. The computational approach presented can perform an assessment with errors less than 6percent, indicating that it is a promising tool for the modeling and optimization of the sorption onto zeolite materials minimizing the time and operation cost. The proposed methodology can be used to take appropriate actions in the removing of this toxic heavy metal from the water. Besides, it can be implemented in studies corresponding to other sorption processes or similar", } @Article{MAYTZUC:2019:Measurement, author = "O. {May Tzuc} and I. Hernandez-Perez and E. V. Macias-Melo and A. Bassam and J. Xaman and B. Cruz", title = "Multi-gene genetic programming for predicting the heat gain of flat naturally ventilated roof using data from outdoor environmental monitoring", journal = "Measurement", volume = "138", pages = "106--117", year = "2019", keywords = "genetic algorithms, genetic programming, Heat gains, Building thermal measurement, Thermal comfort, Machine learning, Evolutionary programming, Sensitivity analysis", ISSN = "0263-2241", DOI = "doi:10.1016/j.measurement.2019.02.032", URL = "http://www.sciencedirect.com/science/article/pii/S0263224119301502", abstract = "In this work, a multi-gene genetic programming (MGGP) approach was implemented to predict the heat gain per square meter for flat naturally ventilated roof using experimental data set. Experiments were conducted using a test cell with an adjustable ventilated roof, designed and instrumented to measure the incoming heat flux under outdoor environmental conditions. An MGGP predictive model was trained and tested considering as input data: ambient air temperature, solar irradiation, wind speed, relative humidity, and different ventilated flat roof channel widths. The developed model was statistically compared with others multivariate analysis methods, achieving good statistical performance, high correlation fitness, and the best generalized performance capacity (RMSEa =a 3.74, R2a =a 94.52percent for training data and RMSEa =a 3.72, R2a =a 94.30percent for testing data). In addition, a sensitivity analysis was conducted to identify the relative importance of the input parameters in the predictive model. According to the results, the proposed methodology based on evolutionary programming is useful to model the complex nonlinear relationship between the ventilated roof heat gains and outdoor environment. Finally, the methodology based on MGGP can be applied to identify the adequate ventilated channel widths that ensure thermal comfort and energy saving", } @Article{mazinani:2016:Entropy, author = "Iman Mazinani and Zubaidah Binti Ismail and Shahaboddin Shamshirband and Ahmad Mustafa Hashim and Marjan Mansourvar and Erfan Zalnezhad", title = "Estimation of Tsunami Bore Forces on a Coastal Bridge Using an Extreme Learning Machine", journal = "Entropy", year = "2016", volume = "18", number = "5", keywords = "genetic algorithms, genetic programming", ISSN = "1099-4300", URL = "https://www.mdpi.com/1099-4300/18/5/167", DOI = "doi:10.3390/e18050167", abstract = "This paper proposes a procedure to estimate tsunami wave forces on coastal bridges through a novel method based on Extreme Learning Machine (ELM) and laboratory experiments. This research included three water depths, ten wave heights, and four bridge models with a variety of girders providing a total of 120 cases. The research was designed and adapted to estimate tsunami bore forces including horizontal force, vertical uplift and overturning moment on a coastal bridge. The experiments were carried out on 1:40 scaled concrete bridge models in a wave flume with dimensions of 24 m x 1.5 m x 2 m. Two six-axis load cells and four pressure sensors were installed to the base plate to measure forces. In the numerical procedure, estimation and prediction results of the ELM model were compared with Genetic Programming (GP) and Artificial Neural Networks (ANNs) models. The experimental results showed an improvement in predictive accuracy, and capability of generalisation could be achieved by the ELM approach in comparison with GP and ANN. Moreover, results indicated that the ELM models developed could be used with confidence for further work on formulating novel model predictive strategy for tsunami bore forces on a coastal bridge. The experimental results indicated that the new algorithm could produce good generalisation performance in most cases and could learn thousands of times faster than conventional popular learning algorithms. Therefore, it can be conclusively obtained that use of ELM is certainly developing as an alternative approach to estimate the tsunami bore forces on a coastal bridge.", notes = "also known as \cite{e18050167}", } @InProceedings{Mazouni:2014:IDC, author = "Romaissaa Mazouni and Abdellatif Rahmoun", title = "{AGGE}: A Novel Method to Automatically Generate Rule Induction Classifiers Using Grammatical Evolution", booktitle = "IDC 2014", year = "2014", editor = "David Camacho and Lars Braubach and Salvatore Venticinque and Costin Badica", volume = "570", series = "Studies in Computational Intelligence", pages = "279--288", publisher = "Springer", keywords = "genetic algorithms, genetic programming, grammatical evolution, agge: automatic generation of classifiers using grammatical evolution, context free grammar, rule induction algorithms, data mining, rule based classification", isbn13 = "978-3-319-10421-8", bibdate = "2014-10-09", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/idc/idc2014.html#MazouniR14", DOI = "doi:10.1007/978-3-319-10422-5_30", abstract = "One of the main and fundamental tasks of data mining is the automatic induction of classification rules from a set of examples and observations. A variety of methods performing this task have been proposed in the recent literature. Many comparative studies have been carried out in this field. However, the main common feature between these methods is that they are designed manually. In the meanwhile, there have been some successful attempts to automatically design such methods using Grammar-based Genetic Programming (GGP). In this paper, we propose a different system called Automatic Grammar Genetic Programming (AGGP) that can evolve complete java program codes. These codes represent a rule induction algorithm that uses a grammar evolution technique that governs a Backus Naur Form grammar definition mapping to a program. To perform this task, we will use binary strings as inputs to the mapper along with the Backus Naur Form grammar. Such binary strings represent possible potential solutions resulting from the initialised component and Weka building blocks, this would ease the induction process and makes induced programs short. Experimental results prove the efficiency of the proposed method. It is also shown that, compared to some recent and similar manual techniques (Prism, Ripper, Ridor, OneRule) the proposed method outperforms such techniques.A benchmark of well-known data sets is used for the sake of comparison.", } @InProceedings{Mazyad:2017:EA, author = "Ahmad Mazyad and Fabien Teytaud and Cyril Fonlupt", title = "Learning new Term Weighting Schemes with Genetic Programming", booktitle = "Artificial Evolution preprint", howpublished = "HAL", year = "2017", keywords = "genetic algorithms, genetic programming", identifier = "hal-01662138", language = "en", oai = "oai:HAL:hal-01662138v1", URL = "https://hal.inria.fr/hal-01662138", URL = "https://hal.inria.fr/hal-01662138/document", URL = "https://hal.inria.fr/hal-01662138/file/pg.pdf", size = "12 pages", abstract = "Text Classification (or Text Categorization) is a popular machine learning task which consists in assigning categories to documents. Feature weight methods are classic tools that are used in text categorization in order to assign a score to each term of a document based on a mathematical formula. In this paper, we are interested in automatically generating these formulas based on genetic programming. We experiment the generated formulas on three well-known benchmarks and state of the art classifiers.", notes = "Does not appear in Springer, LNCS 10764, EA 2017 https://link.springer.com/book/10.1007/978-3-319-78133-4 also known as \cite{oai:HAL:hal-01662138v1}", } @InProceedings{Mazyad:2018:GECCOcomp, author = "Ahmad Mazyad and Fabien Teytaud and Cyril Fonlupt", title = "Generating term weighting schemes through genetic programming", booktitle = "GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2018", editor = "Carlos Cotta and Tapabrata Ray and Hisao Ishibuchi and Shigeru Obayashi and Bogdan Filipic and Thomas Bartz-Beielstein and Grant Dick and Masaharu Munetomo and Silvino {Fernandez Alzueta} and Thomas Stuetzle and Pablo Valledor Pellicer and Manuel Lopez-Ibanez and Daniel R. Tauritz and Pietro S. Oliveto and Thomas Weise and Borys Wrobel and Ales Zamuda and Anne Auger and Julien Bect and Dimo Brockhoff and Nikolaus Hansen and Rodolphe {Le Riche} and Victor Picheny and Bilel Derbel and Ke Li and Hui Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Stephane Doncieux and Richard Duro and Joshua Auerbach and Harold {de Vladar} and Antonio J. Fernandez-Leiva and JJ Merelo and Pedro A. Castillo-Valdivieso and David Camacho-Fernandez and Francisco {Chavez de la O} and Ozgur Akman and Khulood Alyahya and Juergen Branke and Kevin Doherty and Jonathan Fieldsend and Giuseppe Carlo Marano and Nikos D. Lagaros and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Boris Naujoks and Vanessa Volz and Tea Tusar and Pascal Kerschke and Riyad Alshammari and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and John R. Woodward and Shin Yoo and John McCall and Nayat Sanchez-Pi and Luis Marti and Danilo Vasconcellos and Masaya Nakata and Anthony Stein and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Ivanoe {De Falco} and Antonio {Della Cioppa} and Ernesto Tarantino and Umberto Scafuri and P. G. M. Baltus and Giovanni Iacca and Ahmed Hallawa and Anil Yaman and Alma Rahat and Handing Wang and Yaochu Jin and David Walker and Richard Everson and Akira Oyama and Koji Shimoyama and Hemant Kumar and Kazuhisa Chiba and Pramudita Satria Palar", isbn13 = "978-1-4503-5764-7", pages = "268--269", address = "Kyoto, Japan", DOI = "doi:10.1145/3205651.3205799", publisher = "ACM", publisher_address = "New York, NY, USA", month = "15-19 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", abstract = "Term-Weighting Scheme (TWS) is an important step in text classification. It determines how documents are represented in Vector Space Model (VSM). Even though state-of-the-art TWSs exhibit good behaviours, a large number of new works propose new approaches and new TWSs that improve performances. Furthermore, it is still difficult to tell which TWS is well suited for a specific problem. In this paper, we are interested in automatically generating new TWSs with the help of evolutionary algorithms and especially genetic programming (GP). GP evolves and combines different statistical information and generates a new TWS based on the performance of the learning method. We experience the generated TWSs on three well-known benchmarks. Our study shows that even early generated formulas are quite competitive with the state-of-the-art TWSs and even in some cases outperform them.", notes = "Also known as \cite{3205799} GECCO-2018 A Recombination of the 27th International Conference on Genetic Algorithms (ICGA-2018) and the 23rd Annual Genetic Programming Conference (GP-2018)", } @PhdThesis{mazyad:tel-02010316, author = "Ahmad Mazyad", title = "Contribution to automatic text classification : metrics and evolutionary algorithms", school = "Universite du Littoral Cote d'Opale", year = "2018", address = "France", month = Nov, keywords = "genetic algorithms, genetic programming, NLP, Machine learning, Natural language processing, Text mining, FORMTEXT Classification of texts, Term Weighting Schemes, Optimization, Apprentissage automatique, Traitement du langage naturel, Exploration de texte, FORMTEXT Classification des textes, Sch{\'e}ma de Pond{\'e}ration des Termes, Optimisation, Programmation g{\'e}n{\'e}tique", number = "2018DUNK0487", hal_id = "tel-02010316", hal_version = "v1", URL = "https://tel.archives-ouvertes.fr/tel-02010316/file/These_Mazyad_Ahmad.pdf", URL = "https://tel.archives-ouvertes.fr/tel-02010316", size = "119 pages", abstract = "This thesis deals with natural language processing and text mining, at the intersection of machine learning and statistics. We are particularly interested in Term Weighting Schemes (TWS) in the context of supervised learning and specifically the Text Classification (TC) task. In TC, the multi-label classification task has gained a lot of interest in recent years. Multi-label classification from textual data may be found in many modern applications such as news classification where the task is to find the categories that a newswire story belongs to (e.g., politics, middle east, oil), based on its textual content, music genre classification (e.g., jazz, pop, oldies, traditional pop) based on customer reviews, film classification (e.g. action, crime, drama), product classification (e.g. Electronics, Computers, Accessories). Traditional classification algorithms are generally binary classifiers, and they are not suited for the multi-label classification. The multi-label classification task is, therefore, transformed into multiple single-label binary tasks. However, this transformation introduces several issues. First, terms distributions are only considered in relevance to the positive and the negative categories (i.e., information on the correlations between terms and categories is lost). Second, it fails to consider any label dependency (i.e., information on existing correlations between classes is lost). Finally, since all categories but one are grouped into one category (the negative category), the newly created tasks are imbalanced. This information is commonly used by supervised TWS to improve the effectiveness of the classification system. Hence, after presenting the process of multi-label text classification, and more particularly the TWS, we make an empirical comparison of these methods applied to the multi-label text classification task. We find that the superiority of the supervised methods over the unsupervised methods is still not clear. We show then that these methods are not fully adapted to the multi-label classification problem and they ignore much statistical information that could be used to improve the classification results. Thus, we propose a new TWS based on information gain. This new method takes into consideration the term distribution, not only regarding the positive and the negative categories but also in relevance to all classes. Finally, aiming at finding specialized TWS that also solve the issue of imbalanced tasks, we studied the benefits of using genetic programming for generating TWS for the text classification task. Unlike previous studies, we generate formulas by combining statistical information at a microscopic level (e.g., the number of documents that contain a specific term) instead of using complete TWS. Furthermore, we make use of categorical information such as (e.g., the number of categories where a term occurs). Experiments are made to measure the impact of these methods on the performance of the model. We show through these experiments that the results are positive.", notes = "Reuters-21578, Oshumed, Webkb. Porter stemming p87 'The GP-Based TWSs outperforms the best baseline schemes.' Supervisors: Prof. Cyril Fonlupt and MCF Fabien Teytaud", } @Article{110008152437, author = "Matthew McCawley and Seishi Takamura and Hirohisa Jozawa", title = "GPU-assisted evolutive image predictor generation", journal = "IEICE Technical Report. Image Engineering (IE)", year = "2010", volume = "110", number = "275", pages = "25--28", month = nov, keywords = "genetic algorithms, genetic programming, GPU, CUDA, lossless image coding", ISSN = "09135685", publisher = "IEICE", URL = "http://www.ieice.org/ken/paper/20101111b0co/eng/", URL = "http://ci.nii.ac.jp/naid/110008152437/", abstract = "Evolutive Image Coding has shown promising results in efficiency compared to other lossless coding methods, but until now, the processing power required for the fitness evaluation has limited its usefulness outside of large computer clusters. Using the CUDA programming language on comparatively inexpensive NVIDIA graphics cards, we have obtained speed increases of up to 150 times for the fitness evaluation. Some of the techniques we have used to improve performance include using the GPU's fast shared memory whenever possible as well as performing some calculations for which the GPU is not as well suited, such as a histogram-based calculation, on the CPU while the GPU simultaneously calculates the fitness evaluation in order to minimize idle time.", notes = "NTT Cyber Space Laboratories, NTT Corporation", } @InProceedings{McClintock:2008:cec, author = "James McClintock and Gary G. Yen", title = "A Two-Tiered, Agent Based Approach for Autonomous, Evolutionary Texture Generation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "3220--3227", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1823-7", file = "EC0707.pdf", DOI = "doi:10.1109/CEC.2008.4631234", abstract = "This paper proposes a two-tiered, evolutionary architecture for computer based synthesis of textures. In this architecture, a traditional tree based texture generation system is controlled by a set of evolutionary agents. The main contribution of this work is that the user is able to choose the degree of interaction and control they exert over the system. Evolutionary agents are designed to contain information about desirable image features, and they evolve based on user feedback. The agents in turn control the main evolutionary engine for generating textures. This system allows the computer to continue working when the designer leaves without limiting the designer's ability to control the texture generation process when they are available to interact with the system. An experimental implementation is developed to verify the utility of the proposed architecture for texture synthesis. Results show significant improvements in the average user ranking of the agents as the genetic algorithm progresses.", keywords = "genetic algorithms, genetic programming", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @InProceedings{McConaghy:1998:GPlsfpmpts, author = "Trent McConaghy and Henry Leung", title = "Genetic Programming with Least Squares for Fast, Precise Modeling of Polynomial Time Series", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "151--158", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "genetic algorithms, genetic programming", size = "8 pages", notes = "GP-98LB", } @InProceedings{mcconaghy:2000:IECON, author = "T. McConaghy and H. Leung and V. Varadan", title = "Functional reconstruction of dynamical systems from time series using genetic programming", booktitle = "26th Annual Conference of the IEEE Industrial Electronics Society, IECON 2000", year = "2000", volume = "3", pages = "2031--2034", address = "Nagoya", month = "22-28 " # oct, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IECON.2000.972588", abstract = "Reconstruction of a chaotic system from its measurement is a challenging problem. It requires the determination of an embedding dimension and a nonlinear mapping that approximates the underlying unknown dynamics. We propose the use of genetic programming (GP) to find the exact functional form and embedding dimension of an unknown dynamical system automatically. Using functional operators of addition, multiplication, and time-delay, with the least-squares estimation technique, we use GP to reconstruct the exact chaotic polynomial system and its embedding dimension from a time series. If the underlying dynamic does not come from a polynomial system, the proposed GP method will produce an optimal polynomial predictor for the time series. Simulations showed that the GP approach outperformed a radial basis function neural network in predicting both polynomial and nonpolynomial chaotic systems", } @InProceedings{mcconaghy:2005:DATE, author = "Trent McConaghy and Tom Eeckelaert and Georges Gielen", title = "{CAFFEINE}: Template-Free Symbolic Model Generation of Analog Circuits via Canonical Form Functions and Genetic Programming", booktitle = "Proceedings of the Design Automation and Test Europe (DATE) Conference", year = "2005", pages = "1082--1087", volume = "2", address = "Munich", month = "7-11 " # mar, organisation = "European Design and Automation Association, the EDA Consortium, the IEEE Computer Society - TTTC, ECSI, RAS and ACM SIGDA", keywords = "genetic algorithms, genetic programming", ISSN = "1530-1591", ISBN = "0-7695-2288-2", URL = "http://arxiv.org/abs/0710.4630", DOI = "doi:10.1109/DATE.2005.89", size = "6 pages", abstract = "automatically generate compact symbolic performance models of analog circuits with no prior specification of an equation template. The approach takes SPICE simulation data as input, which enables modeling of any nonlinear circuits and circuit characteristics. Genetic programming is applied as a means of traversing the space of possible symbolic expressions. A grammar is specially designed to constrain the search to a canonical form for functions. Novel evolutionary search operators are designed to exploit the structure of the grammar. The approach generates a set of symbolic models which collectively provide a tradeoff between error and model complexity. Experimental results show that the symbolic models generated are compact and easy to understand, making this an effective method for aiding understanding in analog design. The models also demonstrate better prediction quality than posynomials.", notes = "broken Oct 2024 http://www.date-conference.com/cgi-bin/prog05/show_conf_details.cgi?date=Thu paper id? 230, KU Leuven, BE Also known as DATE-2005-McConaghyEG https://bibtex.github.io/DATE-2005-McConaghyEG.html K.U. Leuven, ESAT-MICAS Kasteelpark Arenberg 10 B-3001 Leuven, Belgium", } @InCollection{mcconaghy:2005:GPTP, author = "Trent McConaghy and Georges Gielen", title = "Genetic Programming in Industrial Analog {CAD}: {Applications} and Challenges", booktitle = "Genetic Programming Theory and Practice {III}", year = "2005", editor = "Tina Yu and Rick L. Riolo and Bill Worzel", volume = "9", series = "Genetic Programming", chapter = "19", pages = "291--306", address = "Ann Arbor", month = "12-14 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Analogue, CAD, Synthesis, Industrial, Robust, Yield", ISBN = "0-387-28110-X", oai = "oai:CiteSeerX.psu:10.1.1.454.5785", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.454.5785", URL = "http://trent.st/content/2005-GPTP-synth_problem.pdf", DOI = "doi:10.1007/0-387-28111-8_19", size = "16 pages", abstract = "This paper investigates the application of genetic programming to problems in industrial analog computer-aided design (CAD). One CAD subdomain, analogue structural synthesis, is an often-cited success within the genetic programming (GP) literature, yet industrial use remains elusive. We examine why this is, by drawing upon our own experiences in bringing analogue CAD tools into industrial use. In sum, GP-synthesised designs need to be more robust in very specific ways. When robustness is considered, a GP methodology of today on a reasonable circuit problem would take 150 years on a 1,000-node 1-GHz cluster. Moore's Law cannot help either, because the problem itself is 'Anti-Mooreware' -- it becomes more difficult as Moore's Law progresses. However, we believe the problem is still approachable with GP; it will just take a significant amount of 'algorithm engineering'. We go on to describe the recent application of GP to two other analogue CAD subdomains: symbolic modelling and behavioural modeling. In contrast to structural synthesis, they are easier from a GP perspective, but are already at a level such that they can be exploited in industry. Not only is GP the only approach that gives interpretable SPICE-accurate nonlinear models, it turns out to outperform nine other popular blackbox approaches in a set of six circuit modeling problems.", notes = "part of \cite{yu:2005:GPTP} Published Jan 2006 after the workshop", } @InProceedings{McConaghy_2005_iscas, author = "Trent McConaghy and Georges Gielen", title = "IBMG: Interpretable Behavioral Model Generator for Nonlinear Analog Circuits via Canonical Form Functions and Genetic Programming", booktitle = "Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS)", year = "2005", pages = "5170--5173", month = "23-26 " # may, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", broken = "http://www.epapers.org/iscas2005/ESR/paper_details.php?paper_id=5387", DOI = "doi:10.1109/ISCAS.2005.1465799", URL = "http://trent.st/content/2005-ISCAS-ibmg.pdf", size = "4 pages", abstract = "This paper presents IBMG, an approach to generate behavioral models of nonlinear analog circuits, with the special distinction that it generates models that are compact, interpretable expressions, which are not restricted to any pre-defined functional templates. IBMG outputs a small set of interpretable nonlinear differential equations that approximate the time-domain behavior of the circuit being modeled. The approach uses genetic programming (GP), which evolves functions, but GP has been heavily modified so that the behavioral expressions follow a special canonical functional form grammar to remain interpretable. IBMG has explicit error control: it provides a set of models that trade off complexity and accuracy. Experimental results on a strongly nonlinear latch circuit demonstrate the usefulness of IBMG.", } @InProceedings{McConaghy_2005_iscas_2, author = "Trent McConaghy and Georges Gielen", title = "Analysis of Simulation-Driven Numerical Performance Modeling Techniques for Application to Analog Circuit Optimization", booktitle = "Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS)", year = "2005", volume = "2", pages = "1298--1301", month = "23-26 " # may, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, analog", DOI = "doi:10.1109/ISCAS.2005.1464833", URL = "http://trent.st/content/2005-ISCAS-blackbox.pdf", size = "4 pages", abstract = "There is promise of efficiency gains in simulator-in-the-loop analog circuit optimization if one uses numerical performance modeling on simulation data to relate design parameters to performance values. However, the choice of modeling approach can impact performance. We analyze and compare these approaches: polynomials, posynomials, genetic programming, feedforward neural networks, boosted feedforward neural networks, multivariate adaptive regression splines, support vector machines, and kriging. Experiments are conducted on a dataset used previously for posynomial modeling, showing the strengths and weaknesses of the different methods in the context of circuit optimization.", } @InProceedings{McConaghy:2006:DATE, author = "Trent McConaghy and Georges Gielen", title = "Double-Strength CAFFEINE: Fast Template-Free Symbolic Modeling of Analog Circuits via Implicit Canonical Form Functions and Explicit Introns", booktitle = "Proceedings of Design, Automation and Test in Europe, DATE '06", year = "2006", volume = "1", address = "Munich", month = "6-10 " # mar, keywords = "genetic algorithms, genetic programming, EHW, SPICE, analogue circuits, circuit simulation, evolutionary computation, optimisation, SPICE, analog circuit modelling, canonical functional form expressions in evolution, double-strength CAFFEINE method, explicit introns, Analog circuits, Circuit optimisation, Circuit simulation, Circuit testing, Circuit topology, Context modelling, Mathematical model, Nonlinear circuits, Predictive models", ISBN = "3-9810801-1-4", URL = "http://trent.st/content/2006-DATE-caffeine_double.pdf", DOI = "doi:10.1109/DATE.2006.244136", size = "6 pages", abstract = "CAFFEINE, introduced previously, automatically generates nonlinear, template-free symbolic performance models of analog circuits from SPICE data. Its key was a directly-interpretable functional form, found via evolutionary search. In application to automated sizing of analog circuits, CAFFEINE was shown to have the best predictive ability from among 10 regression techniques, but was too slow to be used practically in the optimisation loop. In this paper, we describe double-strength CAFFEINE, which is designed to be fast enough for automated sizing, yet retain good predictive abilities. We design smooth, uniform search operators which have been shown to greatly improve efficiency in other domains. Such operators are not straightforward to design; we achieve them in functions by simultaneously making the grammar-constrained functional form implicit, and embedding explicit 'introns' (subfunctions appearing in the candidate that are not expressed). Experimental results on six test problems show that double-strength CAFFEINE achieves an average speedup of 5times on the most challenging problems and 3times overall; thus making the technique fast enough for automated sizing", notes = "Also known as \cite{1656888}", } @InProceedings{1144147, author = "Trent McConaghy and Georges Gielen", title = "Canonical form functions as a simple means for genetic programming to evolve human-interpretable functions", booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation", year = "2006", editor = "Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos {Coello Coello} and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens", volume = "1", ISBN = "1-59593-186-4", pages = "855--862", address = "Seattle, Washington, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p855.pdf", DOI = "doi:10.1145/1143997.1144147", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "8-12 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, grammar, grammatical evolution", notes = "GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM Order Number 910060", } @InCollection{Mcconaghy:2007:GPTP, author = "Trent McConaghy and Pieter Palmers and Georges Gielen and Michiel Steyaert", title = "Genetic Programming with Reuse of Known Designs for Industrially Scalable, Novel Circuit Design", booktitle = "Genetic Programming Theory and Practice {V}", year = "2007", editor = "Rick L. Riolo and Terence Soule and Bill Worzel", series = "Genetic and Evolutionary Computation", chapter = "10", pages = "159--184", address = "Ann Arbor", month = "17-19" # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, synthesis, industrial, analog, integrated circuits, CAD", isbn13 = "978-0-387-76308-8", URL = "http://trent.st/content/2007-GPTP-novelty_mojito.pdf", DOI = "doi:10.1007/978-0-387-76308-8_10", size = "25 pages", abstract = "This paper shows how aggressive reuse of known designs brings orders-of-magnitude reduction in computational effort, and simultaneously resolves trust issues for synthesised designs, for genetic programming applied to automated structural design. Furthermore, it uses trustworthiness tradeoffs to handle addition of novelty in a trackable fashion. It uses a multi-objective algorithm with an age-layered population structure to avoid premature convergence. While the application here is analog circuit design, the methodology is general enough for many other problem domains.", notes = "part of \cite{Riolo:2007:GPTP} published 2008", affiliation = "Katholieke Universiteit Leuven Leuven Belgium", } @InProceedings{McConaghy:2007:DAC, author = "Trent McConaghy and Pieter Palmers and Georges Gielen and Michiel Steyaert", title = "Simultaneous Multi-Topology Multi-Objective Sizing Across Thousands of Analog Circuit Topologies", booktitle = "44th ACM/IEEE Conference on Design Automation, DAC '07", year = "2007", pages = "944--947", address = "San Diego, CA, USA", month = "4-8 " # jun, keywords = "genetic algorithms, genetic programming, EHW, analogue circuits, network topology, MOJITO, analog circuit topologies, two-stage operational amplifier topologies, Algorithm design and analysis, Analog circuits, Analog integrated circuits, Circuit topology, Evolutionary computation, Merging, Operational amplifiers, Permission, Switches, System-level design, Algorithms, Analog, Design, computer-aided design, integrated circuits, mixed-signal", isbn13 = "978-1-59593-627-1", URL = "http://trent.st/content/2007-DAC-mojito.pdf", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4261319", size = "4 pages", abstract = "This paper presents MOJITO, a system which optimises across thousands of analog circuit topologies simultaneously, and returns a set of sized topologies that collectively provide a performance tradeoff. MOJITO defines a space of possible topologies as a hierarchically organised combination of trusted analog building blocks. To minimise the setup burden: no topology selection rules or abstract behaviours need to be specified, and performance calculations are SPICE-based. The search algorithm is a novel multi-objective evolutionary algorithm that uses an age-layered population structure to balance exploration vs. exploitation. Results are shown for a space having 3528 one- and two-stage operational amplifier topologies.", notes = "50.4 Also known as \cite{4261319}", } @InCollection{Mcconaghy:2008:GPTP, author = "Trent McConaghy and Pieter Palmers and Georges Gielen and Michiel Steyaert", title = "Automated Extraction of Expert Domain Knowledge from Genetic Programming Synthesis Results", booktitle = "Genetic Programming Theory and Practice {VI}", year = "2008", editor = "Rick L. Riolo and Terence Soule and Bill Worzel", series = "Genetic and Evolutionary Computation", chapter = "8", pages = "111--125", address = "Ann Arbor", month = "15-17 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, synthesis, domain knowledge, multi-objective, data mining, analog, integrated circuits, age layered population structure", DOI = "doi:10.1007/978-0-387-87623-8_8", URL = "http://trent.st/content/2008-GPTP-synthesis_insight.pdf", size = "14 pages", isbn13 = "978-0-387-87622-1", abstract = "Recent work in genetic programming shows how expert domain knowledge can be input to a genetic programming (GP) synthesis system, to speed it up by orders of magnitude and give trustworthy results. On the flip side, this paper shows how expert domain knowledge can be output from the results of a synthesis run, in forms that are immediately recognisable and transferable for problem domain experts. Specifically, using the application of analog circuit design, this paper presents a methodology to automatically generate a decision tree for navigating from performance specifications to topology choice; a means to extract the relative importances of topology and parameters on performance; and to generate whitebox models that capture tradeoffs among performances. The extraction uses a combination of data-mining and genetic programming technologies. This paper also presents techniques to ensure that the GP-based synthesis system can indeed create a richly-populated, high-performance dataset, including: a parallel-computing, multi-objective age-layered population structure (ALPS) for fast and reliable convergence; average ranking on Pareto fronts (ARF) to handle many objectives; and generating good initial topology sizings via multigate constraint satisfaction. Results are shown on operational amplifier synthesis across thousands of topologies that generated a database containing thousands of Pareto-optimal designs across five objectives and dozens of constraints.", notes = "part of \cite{Riolo:2008:GPTP} published in 2009", } @InProceedings{McConaghy:2008:ICCAD, author = "Trent McConaghy and Pieter Palmers and Georges Gielen and Michiel Steyaert", title = "Automated extraction of expert knowledge in analog topology selection and sizing", booktitle = "IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2008", year = "2008", pages = "392--395", address = "San Jose, CA, USA", month = "10-13 " # nov, keywords = "genetic algorithms, genetic programming, EHW, Pareto analysis, analogue circuits, circuit optimisation, data mining, electronic engineering computing, expert systems, network topology, operational amplifiers, Pareto optimal set, analog circuit topology, analog topology selection, analog topology sizing, datamining perspective, expert knowledge automated extraction, operational amplifier design, performance specifications, sizing variables, specs-to-topology decision tree, topology choice, Analytical models, CMOS technology, Circuit simulation, Circuit topology, Databases, Decision trees, Design automation, Operational amplifiers, Performance analysis, Space technology", isbn13 = "978-1-4244-2819-9", URL = "http://trent.st/content/2008-ICCAD-cad_synthesis_insight.pdf", DOI = "doi:10.1109/ICCAD.2008.4681603", size = "4 pages", abstract = "This paper presents a methodology for analog designers to maintain their insights into the relationship among performance specifications, topology choice, and sizing variables, despite those insights being constantly challenged by changing process nodes and new specs. The methodology is to take a data-mining perspective on a Pareto Optimal Set of sized analog circuit topologies, then doing: extraction of a specs-to-topology decision tree; global nonlinear sensitivity analysis on topology and sizing variables; and determining analytical expressions of performance tradeoffs. These approaches are all complementary as they answer different designer questions. Once the knowledge is extracted, it can be readily distributed to help other designers, without needing further synthesis. Results are shown for operational amplifier design on a database containing thousands of Pareto Optimal designs across five objectives.", notes = "Also known as \cite{4681603}", } @PhdThesis{McConaghy:thesis, author = "Trent McConaghy", title = "Variation-aware structural synthesis and knowledge extraction of analog circuits", school = "Katholieke Universiteit Leuven", year = "2008", address = "Leuven, Belgium", month = nov, keywords = "genetic algorithms, genetic programming", notes = "Winner, 2009 EDAA Outstanding Dissertation Award http://www.edaa.com/dissertation_award.html See \cite{McConaghy:2009:VAASS}", } @InCollection{McConaghy:2009:GPTP, author = "Trent McConaghy", title = "Latent Variable Symbolic Regression for High-Dimensional Inputs", booktitle = "Genetic Programming Theory and Practice {VII}", year = "2009", editor = "Rick L. Riolo and Una-May O'Reilly and Trent McConaghy", series = "Genetic and Evolutionary Computation", address = "Ann Arbor", month = "14-16 " # may, publisher = "Springer", chapter = "7", pages = "103--118", keywords = "genetic algorithms, genetic programming, symbolic regression, latent variables, latent variable regression, LVR, analog, integrated circuits", isbn13 = "978-1-4419-1653-2", URL = "http://trent.st/content/2009-GPTP-caffeine_lvsr.pdf", DOI = "doi:10.1007/978-1-4419-1626-6_7", size = "17 pages", abstract = "This paper explores symbolic regression when there are hundreds of input variables, and the variables have similar influence which means that variable pruning (a priori, or on-the-fly) will be ineffective. For this problem, traditional genetic programming and many other regression approaches do poorly. We develop a technique based on latent variables, nonlinear sensitivity analysis, and genetic programming designed to manage the challenge. The technique handles 340-input variable problems in minutes, with promise to scale well to even higher dimensions. The technique is successfully verified on 24 real-world circuit modelling problems", notes = "part of \cite{Riolo:2009:GPTP}", } @Article{McConaghy:2009:ieeeCADICS, author = "Trent McConaghy and Georges G. E. Gielen", title = "Template-Free Symbolic Performance Modeling of Analog Circuits via Canonical-Form Functions and Genetic Programming", journal = "IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems", year = "2009", volume = "28", pages = "1162--1175", number = "8", month = aug, keywords = "genetic algorithms, genetic programming, SPICE, analogue circuits CAFFEINE, SPICE simulation data, analog circuits, arbitrary nonlinear circuits, canonical-form functions, compact interpretable symbolic performance models, kriging, neural networks, posynomials, product-of-sum layers, splines, sum-of-product layers, support vector machines, template-free symbolic performance modeling", ISSN = "0278-0070", URL = "http://trent.st/content/2009-TCAD-caffeine_scale.pdf", DOI = "doi:10.1109/TCAD.2009.2021034", size = "14 pages", abstract = "This paper presents CAFFEINE, a method to automatically generate compact interpretable symbolic performance models of analog circuits with no prior specification of an equation template. CAFFEINE uses SPICE simulation data to model arbitrary nonlinear circuits and circuit characteristics. CAFFEINE expressions are canonical-form functions: product-of-sum layers alternating with sum-of-product layers, as defined by a grammar. Multiobjective genetic programming trades off error with model complexity. On test problems, CAFFEINE models demonstrate lower prediction error than posynomials, splines, neural networks, kriging, and support vector machines. This paper also demonstrates techniques to scale CAFFEINE to larger problems.", notes = "Also known as \cite{5166638}", } @Article{McConaghy:2009:ieeeTCAD, author = "Trent McConaghy and Pieter Palmers and Michiel Steyaert and Georges G. E. Gielen", title = "Variation-Aware Structural Synthesis of Analog Circuits via Hierarchical Building Blocks and Structural Homotopy", journal = "IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems", year = "2009", volume = "28", number = "9", pages = "1281--1294", month = sep, keywords = "genetic algorithms, genetic programming, Pareto optimisation, analogue circuits, MOJITO-R, Pareto-optimal designs, analog circuits, decision tree, hierarchical building blocks, objective-function tightening levels, performance-topology relationship, structural homotopy, variation-aware structural synthesis, Analog, design automation, integrated circuit, multiobjective optimisation, process variation", ISSN = "0278-0070", URL = "http://trent.st/content/2009-TCAD-robust_mojito.pdf", DOI = "doi:10.1109/TCAD.2009.2023195", size = "14 pages", abstract = "This paper presents MOJITO-R, a tool that performs variation-aware structural synthesis of analog circuits. It returns trustworthy topologies by searching across a space of thousands of possible topologies defined by hierarchically organised analog structural building blocks. Structural homotopy conducts search at several objective-function tightening levels (numbers of process corners) simultaneously. Multiobjective evolutionary search returns sized topologies which trade off power, area, performances, and yield. An experimental validation run returned 78643 Pareto-optimal designs, having 982 sized topologies with various specification/yield combinations. A decision tree is extracted to visualise the performance-topology relationship.", notes = "Also known as \cite{5208483}", } @Book{McConaghy:2009:VAASS, author = "Trent McConaghy and Pieter Palmers and Peng Gao and Michiel Steyaert and Georges Gielen", title = "Variation-Aware Analog Structural Synthesis - A Computational Intelligence Approach", publisher = "Springer", year = "2009", series = "Analog Circuits and Signal Processing", address = "Netherlands", keywords = "genetic algorithms, genetic programming, EHW, Engineering, Circuits and Systems and Computing Methodologies", isbn13 = "978-90-481-2905-8", DOI = "doi:10.1007/978-90-481-2906-5", notes = "This book is based on the PhD research of Trent McConaghy \cite{McConaghy:thesis} and Pieter Palmers, in collaboration with Peng Gao and professors Georges Gielen and Michiel Steyaert [Mcc2008e]. Reviewed by \cite{Rieffel:2011:GPEM}", size = "326 pages", } @InCollection{McConaghy:2010:intro, author = "Trent McConaghy and Ekaterina Vladislavleva and Rick Riolo", title = "Genetic Programming Theory and Practice 2010: An Introduction", booktitle = "Genetic Programming Theory and Practice VIII", year = "2010", editor = "Rick Riolo and Trent McConaghy and Ekaterina Vladislavleva", series = "Genetic and Evolutionary Computation", volume = "8", address = "Ann Arbor, USA", month = "20-22 " # may, publisher = "Springer", pages = "xvii--xxviii", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4419-7746-5", URL = "http://www.springer.com/computer/ai/book/978-1-4419-7746-5", URL = "http://trent.st/content/2010-GPTP-introduction.pdf", size = "12 pages", abstract = "The toy problems are long gone, real applications are standard, and the systems have arrived. Genetic programming (GP) researchers have been designing and exploiting advances in theory, algorithm design, and computing power to the point where (traditionally) hard problems are the norm. As GP is being deployed in more real-world and hard problems, GP research goals are evolving to a higher level, to systems in which GP algorithms play a key role. The key goals in GP algorithm design are reasonable resource usage, high-quality results, and reliable convergence. To these GP algorithm goals, we add GP system goals: ease of system integration, end-user friendliness, and user control of the problem and interactivity. In this book, expert GP researchers demonstrate how they have been achieving and improving upon the key GP algorithm and system aims, to realize them on real-world / hard problems. This work was presented at the GP Theory and Practice (GPTP) 2010 workshop. This introductory chapter summarises how these experts' work is driving the frontiers of GP algorithms and GP systems in their application to ever-harder application domains.", notes = "part of \cite{Riolo:2010:GPTP}", } @InCollection{McConaghy:2010:GPTP, author = "Trent McConaghy", title = "Symbolic Density Models of One-in-a-Billion Statistical Tails via Importance Sampling and Genetic Programming", booktitle = "Genetic Programming Theory and Practice VIII", year = "2010", editor = "Rick Riolo and Trent McConaghy and Ekaterina Vladislavleva", series = "Genetic and Evolutionary Computation", volume = "8", address = "Ann Arbor, USA", month = "20-22 " # may, publisher = "Springer", chapter = "10", pages = "161--173", keywords = "genetic algorithms, genetic programming, symbolic regression, density estimation, importance sampling, Monte Carlo methods, memory, SRAM, integrated circuits, extreme-value statistics", isbn13 = "978-1-4419-7746-5", URL = "http://www.springer.com/computer/ai/book/978-1-4419-7746-5", URL = "http://trent.st/content/2010-GPTP-tails.pdf", DOI = "doi:10.1007/978-1-4419-7747-2_10", size = "14 pages", abstract = "This paper explores the application of symbolic regression for building models of probability distributions in which the accuracy at the distributions' tails is critical. The problem is of importance to cutting-edge industrial integrated circuit design, such as designing SRAM memory components (bitcells, sense amps) where each component has extremely low probability of failure. A naive approach is infeasible because it would require billions of Monte Carlo circuit simulations. This paper demonstrates a flow that efficiently generates samples at the tails using importance sampling, then builds genetic programming symbolic regression models in a space that captures the tails, the normal quantile space. These symbolic density models allow the circuit designers to analyse the tradeoff between high-sigma yields and circuit performance. The flow is validated on two modern industrial problems: a bitcell circuit on a 45nm TSMC process, and a sense amp circuit on a 28nm TSMC process.", notes = "part of \cite{Riolo:2010:GPTP}", } @InCollection{McConaghy:2011:GPTP, author = "Trent McConaghy", title = "{FFX}: Fast, Scalable, Deterministic Symbolic Regression Technology", booktitle = "Genetic Programming Theory and Practice IX", year = "2011", editor = "Rick Riolo and Ekaterina Vladislavleva and Jason H. Moore", series = "Genetic and Evolutionary Computation", address = "Ann Arbor, USA", month = "12-14 " # may, publisher = "Springer", chapter = "13", pages = "235--260", keywords = "genetic algorithms, genetic programming, technology, symbolic regression, pathwise, regularisation, real-world problems, machine learning, lasso, ridge regression, elastic net, integrated circuits", isbn13 = "978-1-4614-1769-9", URL = "http://trent.st/content/2011-GPTP-FFX-paper.pdf", DOI = "doi:10.1007/978-1-4614-1770-5_13", slides_url = "http://www.trent.st/content/2011-GPTP-FFX-slides.pdf", size = "27 pages", abstract = "Symbolic regression is a common application for genetic programming (GP). we present a new non-evolutionary technique for symbolic regression that, compared to competent GP approaches on real-world problems, is orders of magnitude faster (taking just seconds), returns simpler models, has comparable or better prediction on unseen data, and converges reliably and deterministically. I dub the approach FFX, for Fast Function Extraction. FFX uses a recently developed machine learning technique, pathwise regularised learning, to rapidly prune a huge set of candidate basis functions down to compact models. FFX is verified on a broad set of real-world problems having 13 to 1468 input variables, out performing GP as well as several state-of-the-art regression techniques.", notes = "part of \cite{Riolo:2011:GPTP}", affiliation = "Solido Design Automation Inc., Saskatoon, Canada", } @InProceedings{McConaghy:2011:CICC, author = "Trent McConaghy", title = "High-dimensional statistical modeling and analysis of custom integrated circuits", booktitle = "Proceedings of the IEEE Custom Integrated Circuits Conference (CICC 2011)", year = "2011", address = "San Jose, CA, USA", month = "19-21 " # sep, note = "invited paper", keywords = "genetic algorithms, genetic programming, integrated circuit design, integrated circuit modelling, statistical analysis, SPICE, compact equation extraction, custom circuit designers, custom integrated circuits, deterministic technique, high-dimensional statistical modelling, integrated circuit modelling problems, manual equation-based approach, Complexity theory, Equations, Integrated circuit modelling, Learning systems, Mathematical model, Niobium, Predictive models", isbn13 = "978-1-4577-0222-8", ISSN = "0886-5930", URL = "http://trent.st/content/2011-CICC-FFX-paper.pdf", DOI = "doi:10.1109/CICC.2011.6055329", slide_url = "http://trent.st/content/2011-CICC-FFX-slides.ppt", size = "8 pages", abstract = "Custom circuit designers have long favoured manual equation-based approaches in early design stages, because it gives excellent insight and control over the design. However, this flow is threatened: as modern process nodes advance, process variation affects circuit performance more strongly, hurting the accuracy of existing equations. Because designers are typically not statistical modeling experts, it is difficult to adapt the equations to incorporate statistical variations. This paper presents a fast, deterministic technique to help designers revise equations to account for statistical variation. Specifically, the technique extracts compact equations of performance as a function of process variables, even for cases when there are thousands of possible variables and the equations are highly nonlinear. In fact, it provides a whole set of equations that trade off simplicity versus accuracy compared to SPICE. The technique is validated on a broad range of custom integrated circuit modeling problems.", notes = "also known as \cite{6055329}", } @Article{McConaghy:2012:ieeetec, author = "Trent McConaghy and Pieter Palmers and Michiel Steyaert and Georges G. E. Gielen", title = "Trustworthy Genetic Programming-Based Synthesis of Analog Circuit Topologies Using Hierarchical Domain-Specific Building Blocks", journal = "IEEE Transactions on Evolutionary Computation", year = "2011", volume = "15", number = "4", pages = "557--570", month = aug, keywords = "genetic algorithms, genetic programming, Analog, Analog circuits, Design automation, Grammar, Integrated circuit modelling, Semiconductor process modeling, Solid modeling, Topology, design automation, evolutionary algorithm (EA), integrated circuit (IC), multiobjective optimisation", ISSN = "1089-778X", URL = "http://trent.st/content/2011-TEVC-mojito-ea.pdf", DOI = "doi:10.1109/TEVC.2010.2093581", size = "14 pages", abstract = "This paper presents MOJITO, a system that performs structural synthesis of analog circuits, returning designs that are trustworthy by construction. The search space is defined by a set of expert-specified, trusted, hierarchically-organised analog building blocks, which are organized as a parametrised context-free grammar. The search algorithm is a multiobjective evolutionary algorithm that uses an age-layered population structure to balance exploration versus exploitation. It is validated with experiments to search across more than 100000 different one-stage and two-stage opamp topologies, returning human-competitive results. The runtime is orders of magnitude faster than open-ended systems, and unlike the other evolutionary algorithm approaches, the resulting circuits are trustworthy by construction. The approach generalises to other problem domains which have accumulated structural domain knowledge, such as robotic structures, car assemblies, and modelling biological systems.", notes = "Also known as \cite{5699917}", } @InCollection{McConnell:1997:msGA, author = "K. John McConnell", title = "An Attempt to Determine Molecular Structure via Genetic Algorithms", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1997", publisher = "Stanford Bookstore", year = "1997", editor = "John R. Koza", pages = "138--146", address = "Stanford, California, 94305-3079 USA", month = "17 " # mar, keywords = "genetic algorithms", ISBN = "0-18-205981-2", abstract = "poor performance of this algorithm suggest it is not an effective technique for accurately estimating structure", notes = "part of \cite{koza:1997:GAGPs}", } @InProceedings{McCormack:evows05, author = "Jon McCormack", title = "Open Problems in Evolutionary Music and Art", booktitle = "Applications of Evolutionary Computing, EvoWorkshops2005: {EvoBIO}, {EvoCOMNET}, {EvoHOT}, {EvoIASP}, {EvoMUSART}, {EvoSTOC}", year = "2005", month = "30 " # mar # "-1 " # apr, editor = "Franz Rothlauf and Juergen Branke and Stefano Cagnoni and David W. Corne and Rolf Drechsler and Yaochu Jin and Penousal Machado and Elena Marchiori and Juan Romero and George D. Smith and Giovanni Squillero", series = "LNCS", volume = "3449", publisher = "Springer Verlag", address = "Lausanne, Switzerland", publisher_address = "Berlin", keywords = "genetic algorithms, genetic programming, evolutionary computation, ANN", ISBN = "3-540-25396-3", ISSN = "0302-9743", URL = "http://www.csse.monash.edu.au/~jonmc/research/Papers/OpenProblemsSV.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary;jsessionid=73ABB1E32E7A3495A97E50F90A14CB3B?doi=10.1.1.146.4684", DOI = "doi:10.1007/978-3-540-32003-6_43", size = "9 pages", abstract = "Applying evolutionary methods to the generation of music and art is a relatively new field of enquiry. While there have been some important developments, it might be argued that to date, successful results in this domain have been limited. Much of the present research can be characterized as finding adhoc methods that can produce subjectively interesting results. In this paper, it is argued that a stronger overall research plan is needed if the field is to develop in the longer term and attract more researchers. Five open problems are defined and explained as broad principle areas of investigation for evolutionary music and art. Each problem is explained and the impetus and background for it is described in the context of creative evolutionary systems.", notes = "Brief mention of evolutionary programming and tree and graph genomes. Cited by \cite{Loughran:GPEM20} EvoWorkshops2005", } @Article{mccormack:2006:sigevo, author = "Jon McCormack", title = "New Challenges for Evolutionary Music and Art", journal = "SIGEVOlution", year = "2006", volume = "1", number = "1", pages = "5--11", month = apr, keywords = "genetic algorithms, genetic programming, EMA, interactive evolution", URL = "http://www.sigevolution.org/2006/01/issue.pdf", DOI = "doi:10.1145/1138470.1138472", notes = "The search for an interesting phenotype genotype-phenotype mapping (tree) G B Birkhoff's aesthetic fitness = order/complexity. Role of the environment. The extended interface. p10: keyboard+mouse = 'the interface tools are mere intermediary inconveniences'. 'software instrument'", } @Article{McCormack:2022:GPEM, author = "Jon McCormack and Camilo {Cruz Gambardella}", title = "Complexity and aesthetics in generative and evolutionary art", journal = "Genetic Programming and Evolvable Machines", year = "2022", volume = "23", number = "4", pages = "535--556", month = dec, note = "Special Issue: Evolutionary Computation in Art, Music and Design", keywords = "genetic algorithms, genetic programming, Complexity, Aesthetics, Generative art, Evolutionary art, Fitness measure", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-022-09429-9", size = "22 pages", abstract = "we examine the concept of complexity as it applies to generative and evolutionary art and design. Complexity has many different, discipline specific definitions, such as complexity in physical systems (entropy), algorithmic measures of information complexity and the field of complex systems. We apply a series of different complexity measures to three different evolutionary art datasets and look at the correlations between complexity and individual aesthetic judgement by the artist (in the case of two datasets) or the physically measured complexity of generative 3D forms. Our results show that the degree of correlation is different for each set and measure, indicating that there is no overall better measure. However, specific measures do perform well on individual datasets, indicating that careful choice can increase the value of using such measures. We then assess the value of complexity measures for the audience by undertaking a large-scale survey on the perception of complexity and aesthetics. We conclude by discussing the value of direct measures in generative and evolutionary art, reinforcing recent findings from neuroimaging and psychology which suggest human aesthetic judgement is informed by many extrinsic factors beyond the measurable properties of the object being judged.", } @Misc{DBLP:journals/corr/abs-2109-12388, author = "Eric McCormick and Haoxiang Lang and Clarence W. {de Silva}", title = "Automated Multi-domain Engineering Design through Linear Graph and Genetic Programming", howpublished = "arXiv", volume = "abs/2109.12388", year = "2021", keywords = "genetic algorithms, genetic programming", URL = "https://arxiv.org/abs/2109.12388", eprinttype = "arXiv", eprint = "2109.12388", timestamp = "Mon, 04 Oct 2021 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/corr/abs-2109-12388.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{DBLP:journals/cis/McCormickLS22, author = "Eric McCormick and Haoxiang Lang and Clarence W. {de Silva}", title = "Automated Multi-Domain Engineering Design through linear graphs and Genetic Programming", journal = "Mechatron. Syst. Control.", volume = "50", number = "3", year = "2022", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.2316/J.2022.201-0295", DOI = "doi:10.2316/J.2022.201-0295", timestamp = "Mon, 10 Oct 2022 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/cis/McCormickLS22.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{mcdermott_oneill_brabazon:cec2010, author = "James McDermott and Michael O'Neill and Anthony Brabazon", title = "Interactive Interpolating Crossover in Grammatical Evolution", booktitle = "2010 IEEE World Congress on Computational Intelligence", pages = "3018--3025", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, grammatical evolution", isbn13 = "978-1-4244-6910-9", DOI = "doi:10.1109/CEC.2010.5585937", size = "8 pages", abstract = "Interactive interpolating crossover allows a user to quickly see a large number of individuals formed by interactively-controlled interpolation between two or more parents. We study it here for the first time in the context of grammatical evolution (GE). We define methods of quantifying the behaviour of interpolations and use them to compare two methods of performing interpolation and two encodings for GE, one standard and one new. We conclude that a Cartesian interpolation combined with a novel developmental-style GE encoding gives the most usable results. We make connections between our work and broader issues of genotype-phenotype mappings, landscapes, and operators.", notes = "currying, map, fmap, ADF, HO-ADF,W-gate turtle graphics, music WCCI 2010. Also known as \cite{5585937}", } @InProceedings{mcdermott_etal:cec2010, author = "James McDermott and Jonathan Byrne and John Mark Swafford and Michael O'Neill and Anthony Brabazon", title = "Higher-Order Functions in Aesthetic EC Encodings", booktitle = "2010 IEEE World Congress on Computational Intelligence", pages = "2816--2823", year = "2010", isbn13 = "978-1-4244-6910-9", address = "Barcelona, Spain", month = "18-23 " # jul, organization = "IEEE Computation Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, grammatical evolution", DOI = "doi:10.1109/CEC.2010.5586077", abstract = "The use of higher-order functions, as a method of abstraction and re-use in EC encodings, has been the subject of relatively little research. In this paper we introduce and give motivation for the ideas of higher-order functions, and describe their general advantages in EC encodings. We implement grammars using higher-order ideas for two problem domains, music and 3D architectural design, and use these grammars in the grammatical evolution paradigm. We demonstrate four advantages of higher-order functions (patterning of phenotypes, non-entropic mutations, compression of genotypes, and natural expression of artistic knowledge) which lead to beneficial results on our problems.", notes = "WCCI 2010. Also known as \cite{5586077}", } @InProceedings{mcdermott_etal:ppsn2010, author = "James McDermott and Edgar Galvan-Lopez and Michael O'Neill", title = "A Fine-Grained View of GP Locality with Binary Decision Diagrams as Ant Phenotypes", booktitle = "PPSN 2010 11th International Conference on Parallel Problem Solving From Nature", pages = "164--173", year = "2010", volume = "6238", editor = "Robert Schaefer and Carlos Cotta and Joanna Kolodziej and Guenter Rudolph", publisher = "Springer", series = "Lecture Notes in Computer Science", isbn13 = "978-3-642-15843-8", address = "Krakow, Poland", month = "11-15 " # sep, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1007/978-3-642-15844-5_17", abstract = "The property that neighbouring genotypes tend to map to neighbouring phenotypes, i.e. locality, is an important criterion in the study of problem difficulty. Locality is problematic in tree-based genetic programming (GP), since typically there is no explicit phenotype. Here, we define multiple phenotypes for the artificial ant problem, and use them to describe a novel fine-grained view of GP locality. This allows us to identify the mapping from an ant's behavioural phenotype to its concrete path as being inherently non-local, and show that therefore alternative genetic encodings and operators cannot make the problem easy. We relate this to the results of evolutionary runs.", notes = "Santa Fe trail Ant", } @InProceedings{mcdermott:2011:EuroGP, author = "James McDermott and Una-May O'Reilly and Leonardo Vanneschi and Kalyan Veeramachaneni", title = "How Far Is It From Here to There? A Distance that is Coherent with GP Operators", booktitle = "Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011", year = "2011", month = "27-29 " # apr, editor = "Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado", series = "LNCS", volume = "6621", publisher = "Springer Verlag", address = "Turin, Italy", pages = "190--202", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-20406-7", DOI = "doi:10.1007/978-3-642-20407-4_17", abstract = "The distance between pairs of individuals is a useful concept in the study of evolutionary algorithms. It is particularly useful to define a distance which is coherent with, i.e. related to, the action of a particular operator. We present the first formal, general definition of this operator-distance coherence. We also propose a new distance function, based on the multi-step transition probability (MSTP), that is coherent with any GP operator for which the one-step transition probability (1STP) between individuals can be defined. We give an algorithm for 1STP in the case of subtree mutation. Because MSTP is useful in GP investigations, but impractical to compute, we evaluate a variety of means to approximate it. We show that some syntactic distance measures give good approximations, and attempt to combine them to improve the approximation using a GP symbolic regression method. We conclude that 1STP itself is a sufficient indicator of MSTP for subtree mutation.", notes = " Hill climber. x*x+x*y on unit square. 15 syntactic distances. Cf \cite{blickle:thesis}. Part of \cite{Silva:2011:GP} EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011", } @InCollection{McDermott:2011:GPTP, author = "James McDermott and Edgar Galvan-Lopez and Michael O'Neill", title = "A Fine-Grained View of Phenotypes and Locality in Genetic Programming", booktitle = "Genetic Programming Theory and Practice IX", year = "2011", editor = "Rick Riolo and Ekaterina Vladislavleva and Jason H. Moore", series = "Genetic and Evolutionary Computation", address = "Ann Arbor, USA", month = "12-14 " # may, publisher = "Springer", chapter = "4", pages = "57--76", keywords = "genetic algorithms, genetic programming, Evolutionary computation, fitness landscape, problem difficulty, phenotype, locality, artificial ant, Boolean problems", isbn13 = "978-1-4614-1769-9", DOI = "doi:10.1007/978-1-4614-1770-5_4", abstract = "The locality of the mapping from genotype to phenotype is an important issue in the study of landscapes and problem difficulty in evolutionary computation. In tree-structured Genetic Programming (GP), the locality approach is not generally applied because no explicit genotype-phenotype mapping exists, in contrast to some other GP encodings. we define GP phenotypes in terms of semantics or behaviour. For a given problem, a model of one or more phenotypes and mappings between them may be appropriate e.g. g -> p_0, where g is the genotype, p_i are distinct types of phenotypes and f is fitness. Thus, the behaviour of each component mapping can be studied separately. The locality of the genotype-phenotype mapping can also be decomposed into the effects of the encoding and those of the operator's genotypic step-size. Two standard benchmark problem classes, Boolean and artificial ant, are studied in a principled way using this fine-grained view of locality. The method of studying locality with phenotypes seems useful in the case of the artificial ant, but Boolean problems provide a counter-example.", notes = "part of \cite{Riolo:2011:GPTP}", affiliation = "Evolutionary Design and Optimization, CSAIL, MIT, Cambridge, USA", } @InProceedings{McDermott:2011:GECCO, author = "James McDermott and Una-May O'Reilly", title = "An executable graph representation for evolutionary generative music", booktitle = "GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0557-0", pages = "403--410", keywords = "genetic algorithms, genetic programming, grammatical evolution, Digital entertainment technologies and arts", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001576.2001632", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "We focus on a representation for evolutionary music based on executable graphs in which nodes execute arithmetic functions. Input nodes supply time variables and abstract control variables, and multiple output nodes are mapped to MIDI data. The motivation is that multiple outputs from a single graph should tend to behave in related ways, a key characteristic of good music. While the graph itself determines the short-term behaviour of the music, the control variables can be used to specify large-scale musical structure. This separation of music into form and content enables novel compositional techniques well-suited to writing for games and film, as well as for standalone pieces. A mapping from integer-array genotypes to executable graph phenotypes means that evolution, both interactive and non-interactive, can be applied. Experiments with and without human listeners support several specific claims concerning the system's benefits.", notes = "Also known as \cite{2001632} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)", } @InProceedings{McDermott:2012:EvoMUSART, author = "James McDermott", title = "Graph Grammars as a Representation for Interactive Evolutionary {3D} Design", booktitle = "Proceedings of the 1st International Conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design, EvoMUSART 2012", year = "2012", month = "11-13 " # apr, editor = "Penousal Machado and Juan Romero and Adrian Carballal", series = "LNCS", volume = "7247", publisher = "Springer Verlag", address = "Malaga, Spain", pages = "199--210", organisation = "EvoStar", isbn13 = "978-3-642-29141-8", keywords = "genetic algorithms, genetic programming, grammatical evolution, graph grammars, 3D design, interactive evolutionary computation", DOI = "doi:10.1007/978-3-642-29142-5_18", abstract = "A new interactive evolutionary 3D design system is presented. The representation is based on graph grammars, a fascinating and powerful formalism in which sub-graphs, nodes and edges are iteratively rewritten by rules analogous to those of context-free grammars and shape grammars. The nodes of the resulting derived graph are labelled with Euclidean coordinates: therefore the graph fully represents a 3D beam design. Results from user-guided runs are reported, demonstrating the flexibility of the representation. Comparison with results using an alternative graph representation demonstrates that the graph grammar search space is rich in appealing, organised designs. A set of numerical graph features are defined in an attempt to computationally distinguish between good and bad areas of the search space, leading to the definition of a computational fitness function and non-interactive runs.", notes = "See also \cite{McDermott:2013:GPEM} Part of \cite{Machado:2012:EvoMusArt_proc} EvoMUSART'2012 held in conjunction with EuroGP2012, EvoCOP2012 EvoBIO2012 and EvoApplications2012", affiliation = "EvoDesignOpt,CSAIL, MIT, USA", } @InCollection{McDermott:2012:GPTP, author = "James McDermott and Kalyan Veeramachaneni and Una-May O'Reilly", booktitle = "Genetic Programming Theory and Practice X", year = "2012", series = "Genetic and Evolutionary Computation", editor = "Rick Riolo and Ekaterina Vladislavleva and Marylyn D. Ritchie and Jason H. Moore", title = "FlexGP.py: Prototyping Flexibly-Scaled, Flexibly-Factored Genetic Programming for the Cloud", publisher = "Springer", chapter = "14", pages = "205--221", address = "Ann Arbor, USA", month = "12-14 " # may, keywords = "genetic algorithms, genetic programming, C, loud, Island model, FlexGP, Distributed", isbn13 = "978-1-4614-6845-5", URL = "http://dx.doi.org/10.1007/978-1-4614-6846-2_14", DOI = "doi:10.1007/978-1-4614-6846-2_14", abstract = "Running genetic programming on the cloud presents researchers with great opportunities and challenges. We argue that standard island algorithms do not have the properties of elasticity and robustness required to run well on the cloud. We present a prototyped design for a decentralised, heterogeneous, robust, self-scaling, self-factoring, self-aggregating genetic programming algorithm. We investigate its properties using a software 'sandbox'.", notes = "part of \cite{Riolo:2012:GPTP} published after the workshop in 2013", } @InProceedings{McDermott:2012:GECCO, author = "James McDermott and David R. White and Sean Luke and Luca Manzoni and Mauro Castelli and Leonardo Vanneschi and Wojciech Jaskowski and Krzysztof Krawiec and Robin Harper and Kenneth {De Jong} and Una-May O'Reilly", title = "Genetic programming needs better benchmarks", booktitle = "GECCO '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference", year = "2012", editor = "Terry Soule and Anne Auger and Jason Moore and David Pelta and Christine Solnon and Mike Preuss and Alan Dorin and Yew-Soon Ong and Christian Blum and Dario Landa Silva and Frank Neumann and Tina Yu and Aniko Ekart and Will Browne and Tim Kovacs and Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and Giovanni Squillero and Nicolas Bredeche and Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and Martin Pelikan and Silja Meyer-Nienberg and Christian Igel and Greg Hornby and Rene Doursat and Steve Gustafson and Gustavo Olague and Shin Yoo and John Clark and Gabriela Ochoa and Gisele Pappa and Fernando Lobo and Daniel Tauritz and Jurgen Branke and Kalyanmoy Deb", isbn13 = "978-1-4503-1177-9", pages = "791--798", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", publisher = "ACM", publisher_address = "New York, NY, USA", note = "Winner GECCO 2022 ten year impact award", keywords = "genetic algorithms, genetic programming, Automatic programming, Program synthesis, Algorithms, Experimentation, Measurement, Benchmark", URL = "http://gpbenchmarks.org/wp-content/uploads/2019/08/paper1.pdf", DOI = "doi:10.1145/2330163.2330273", slides_url = "http://gpbenchmarks.org/wp-content/uploads/2019/08/benchmarks.pdf", size = "8 pages", abstract = "Genetic programming (GP) is not a field noted for the rigour of its benchmarking. Some of its benchmark problems are popular purely through historical contingency, and they can be criticized as too easy or as providing misleading information concerning real-world performance, but they persist largely because of inertia and the lack of good alternatives. Even where the problems themselves are impeccable, comparisons between studies are made more difficult by the lack of standardization. We argue that the definition of standard benchmarks is an essential step in the maturation of the field. We make several contributions towards this goal. We motivate the development of a benchmark suite and define its goals; we survey existing practice; we enumerate many candidate benchmarks; we report progress on reference implementations; and we set out a concrete plan for gathering feedback from the GP community that would, if adopted, lead to a standard set of benchmarks.", notes = "http://gpbenchmarks.org/ GP Benchmarks. See \cite{White:2013:GPEM} Also known as \cite{2330273} GECCO-2012 A joint meeting of the twenty first international conference on genetic algorithms (ICGA-2012) and the seventeenth annual genetic programming conference (GP-2012)", } @Article{McDermottSHBHFMSO:2012:EPBAAoSRGfEAD, author = "James McDermott and John Mark Swafford and Martin Hemberg and Jonathan Byrne and Erik Hemberg and Michael Fenton and Ciaran McNally and Elizabeth Shotton and Michael O'Neill", title = "An Assessment of String-Rewriting Grammars for Evolutionary Architectural Design", journal = "Environment and Planning B", year = "2012", volume = "39", number = "4", pages = "713--731", keywords = "genetic algorithms, genetic programming", URL = "http://www.envplan.com/abstract.cgi?id=b38037", DOI = "doi:10.1068/b38037", size = "19 pages", abstract = "Evolutionary methods afford a productive and creative alternative design workflow. Crucial to success is the choice of formal representation of the problem.String-rewriting context-free grammars (CFGs) are one common option in evolutionary computation, but their suitability for design is not obvious. Here, a CFG-based evolutionary algorithm for design is presented. The process of meta-design is described, in which the CFG is created and then refined to produce an improved design language. CFGs are contrasted with another grammatical formalism better known in architectural design: Stiny's shape grammars. The advantages and disadvantages of the two types of grammars for design tasks are discussed.", notes = "GEVA", } @InProceedings{mcdermott:2013:EuroGP, author = "James McDermott and Paula Carroll", title = "Program Optimisation with Dependency Injection", booktitle = "Proceedings of the 16th European Conference on Genetic Programming, EuroGP 2013", year = "2013", month = "3-5 " # apr, editor = "Krzysztof Krawiec and Alberto Moraglio and Ting Hu and A. Sima Uyar and Bin Hu", series = "LNCS", volume = "7831", publisher = "Springer Verlag", address = "Vienna, Austria", pages = "133--144", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, PODI", isbn13 = "978-3-642-37206-3", DOI = "doi:10.1007/978-3-642-37207-0_12", abstract = "For many real-world problems, there exist non-deterministic heuristics which generate valid but possibly sub-optimal solutions. The program optimisation with dependency injection method, introduced here, allows such a heuristic to be placed under evolutionary control, allowing search for the optimum. Essentially, the heuristic is 'fooled' into using a genome, supplied by a genetic algorithm, in place of the output of its random number generator. The method is demonstrated with generative heuristics in the domains of 3D design and communications network design. It is also used in novel approaches to genetic programming.", notes = "cited by \cite{DBLP:conf/ppsn/MoraglioM18} Part of \cite{Krawiec:2013:GP} EuroGP'2013 held in conjunction with EvoCOP2013, EvoBIO2013, EvoMusArt2013 and EvoApplications2013", } @Article{McDermott:2013:GPEM, author = "James McDermott", title = "Graph grammars for evolutionary {3D} design", journal = "Genetic Programming and Evolvable Machines", year = "2013", volume = "14", number = "3", pages = "369--393", month = sep, note = "Special issue on biologically inspired music, sound, art and design", keywords = "genetic algorithms, genetic programming, Grammatical evolution, Graph grammars, 3D design, Indirect representations", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-013-9190-0", size = "25 pages", abstract = "A new interactive evolutionary 3D design system is presented. The representation is based on graph grammars, a fascinating and powerful formalism in which nodes and edges are iteratively rewritten by rules analogous to those of context-free grammars and shape grammars. The nodes of the resulting derived graph are labelled with Euclidean coordinates: therefore the graph fully represents a 3D beam design. Results from user-guided runs are presented, demonstrating the flexibility of the representation. Comparison with results using an alternative graph representation demonstrates that the graph grammar search space is more rich in organised designs. A set of numerical features are defined over designs. They are shown to be effective in distinguishing between the designs produced by the two representations, and between designs labelled by users as good or bad. The features allow the definition of a non-interactive fitness function in terms of proximity to target feature vectors. In non-interactive experiments with this fitness function, the graph grammar representation out-performs the alternative graph representation, and evolution out-performs random search.", notes = "This paper is an expanded and improved version of Graph Grammars as a Representation for Interactive Evolutionary 3D Design, presented at EvoMUSART, Malaga, Spain, 2012, \cite{McDermott:2012:EvoMUSART}", } @InCollection{McDermott:2013:mHCI, author = "James McDermott and Dylan Sherry and Una-May O'Reilly", title = "Evolutionary and Generative Music Informs Music HCI--And Vice Versa", booktitle = "Music and Human-Computer Interaction", publisher = "Springer", year = "2013", editor = "Simon Holland and Katie Wilkie and Paul Mulholland and Allan Seago", series = "Springer Series on Cultural Computing", pages = "223--240", keywords = "genetic algorithms, genetic programming, ADF, high order function", isbn13 = "978-1-4471-2989-9", URL = "http://dx.doi.org/10.1007/978-1-4471-2990-5_13", DOI = "doi:10.1007/978-1-4471-2990-5_13", abstract = "This chapter suggests a two-way influence between the field of evolutionary and generative music and that of human-computer interaction and usability studies. The interfaces used in evolutionary and generative music can be made more effective and more satisfying to use with the influence of the ideas, methods, and findings of human computer interaction and usability studies. The musical representations which are a focus of evolutionary and generative music can enable new user-centric tools for mainstream music software. Some successful existing projects are described and some future work is proposed.", language = "English", } @InProceedings{mcdermott:2014:EuroGP, author = "James McDermott", title = "Measuring Mutation Operators' Exploration-Exploitation Behaviour and Long-Term Biases", booktitle = "17th European Conference on Genetic Programming", year = "2014", editor = "Miguel Nicolau and Krzysztof Krawiec and Malcolm I. Heywood and Mauro Castelli and Pablo Garcia-Sanchez and Juan J. Merelo and Victor M. {Rivas Santos} and Kevin Sim", series = "LNCS", volume = "8599", publisher = "Springer", pages = "100--111", address = "Granada, Spain", month = "23-25 " # apr, organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-662-44302-6", DOI = "doi:10.1007/978-3-662-44303-3_9", abstract = "We propose a simple method of directly measuring a mutation operator's short-term exploration-exploitation behaviour, based on its transition matrix. Higher values for this measure indicate a more exploitative operator. Since operators also differ in their degree of long-term bias towards particular areas of the search space, we propose a simple method of directly measuring this bias, based on the Markov chain stationary state. We use these measures to compare numerically the behaviours of two well-known mutation operators, the genetic algorithm per-gene bitflip mutation and the genetic programming subtree mutation.", notes = "Part of \cite{Nicolau:2014:GP} EuroGP'2014 held in conjunction with EvoCOP2014, EvoBIO2014, EvoMusArt2014 and EvoApplications2014", } @InProceedings{mcdermott:evoapps14, author = "James McDermott and Alexandros Agapitos and Anthony Brabazon and Michael O'Neill", title = "Geometric Semantic Genetic Programming for Financial Data", booktitle = "17th European Conference on the Applications of Evolutionary Computation", year = "2014", editor = "Anna Isabel Esparcia-Alcazar and Antonio Miguel Mora", series = "LNCS", volume = "8602", publisher = "Springer", pages = "215--226", address = "Granada", month = "23-25 " # apr, organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, Automated trading, Commodity, Exchange rate, Index, Semantics, Fitness landscape, Hill-climbing", isbn13 = "978-3-662-45522-7", DOI = "doi:10.1007/978-3-662-45523-4", size = "12 pages", abstract = "We cast financial trading as a symbolic regression problem on the lagged time series, and test a state of the art symbolic regression method on it. The system is geometric semantic genetic programming, which achieves good performance by converting the fitness landscape to a cone landscape which can be searched by hill-climbing. Two novel variants are introduced and tested also, as well as a standard hill-climbing genetic programming method. Baselines are provided by buy-and-hold and ARIMA. Results are promising for the novel methods, which produce smaller trees than the existing geometric semantic method. Results are also surprisingly good for standard genetic programming. New insights into the behaviour of geometric semantic genetic programming are also generated.", affiliation = "Financial Mathematics and Computation Research Cluster Natural Computing Research and Applications Group Complex and Adaptive Systems Laboratory, University College Dublin, Ireland", notes = "evoFIN EvoApplications2014 held in conjunction with EuroGP'2014, EvoCOP2014, EvoBIO2014, and EvoMusArt2014", } @Article{McDermott:2014:sigevolution, author = "James McDermott", title = "Visualising Evolutionary Search Spaces", journal = "SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation", year = "2014", volume = "7", number = "1", pages = "2--10", month = aug, keywords = "genetic algorithms, genetic programming", ISSN = "1931-8499", acmid = "2661736", publisher = "ACM", URL = "http://www.sigevolution.org/issues/SIGEVOlution0701.pdf", DOI = "doi:10.1145/2661735.2661736", code_url = "https://github.com/jmmcd/GPDistance", size = "9 pages", abstract = "Understanding the structure of search spaces can help us to design better search algorithms, and it is natural to try to understand search spaces by visualising them. For typical evolutionary search spaces, like the space of genetic programming trees, visualising them directly is impossible, because of their large dimensionality. However, we can use the idea of distances on search spaces to project them into two dimensions, expose their structure, and obtain useful and attractive visualisations.", } @Article{MCDERMOTT201641, author = "James McDermott and Richard S. Forsyth", title = "Diagnosing a disorder in a classification benchmark", journal = "Pattern Recognition Letters", year = "2016", volume = "73", pages = "41--43", keywords = "genetic algorithms, genetic programming, Machine learning, Classification, UCI, BUPA liver disorder, Benchmarks", ISSN = "0167-8655", URL = "https://www.sciencedirect.com/science/article/pii/S0167865516000088", DOI = "doi:10.1016/j.patrec.2016.01.004", abstract = "A large majority of the many hundreds of papers which use the UCI BUPA Liver Disorders data set as a benchmark for classification misunderstand the data and use an unsuitable dependent variable.", } @InProceedings{McDermott:2017:GECCO, author = "James McDermott and Miguel Nicolau", title = "Late-acceptance Hill-climbing with a Grammatical Program Representation", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4939-0", address = "Berlin, Germany", pages = "241--242", size = "2 pages", URL = "http://doi.acm.org/10.1145/3067695.3075984", DOI = "doi:10.1145/3067695.3075984", acmid = "3075984", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, grammar, hill-climbing", month = "15-19 " # jul, abstract = "The late-acceptance hill-climbing (LAHC) metaheuristic is a stochastic hill-climbing algorithm with a simple history mechanism, proposed by Burke and Bykov in 2008, which seems to give a remarkable and reliable performance improvement relative to hill-climbing itself. LAHC is here used for the first time for genetic programming problems, with a grammatical encoding. A novel variant of LAHC with an initial random sampling is also proposed. Performance of both is competitive with full population-based search.", notes = "Also known as \cite{McDermott:2017:LHG:3067695.3075984} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @InProceedings{McDermott:2019:EuroGP, author = "James McDermott", title = "Why is Auto-Encoding Difficult for Genetic Programming?", booktitle = "EuroGP 2019: Proceedings of the 22nd European Conference on Genetic Programming", year = "2019", month = "24-26 " # apr, editor = "Lukas Sekanina and Ting Hu and Nuno Lourenco", series = "LNCS", volume = "11451", publisher = "Springer Verlag", address = "Leipzig, Germany", pages = "131--145", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-030-16669-4", URL = "https://www.springer.com/us/book/9783030166694", DOI = "doi:10.1007/978-3-030-16670-0_9", size = "16 pages", abstract = "Unsupervised learning is an important component in many recent successes in machine learning. The autoencoder neural network is one of the most prominent approaches to unsupervised learning. Here, we use the genetic programming paradigm to create autoencoders and find that the task is difficult for genetic programming, even on small datasets which are easy for neural networks. We investigate which aspects of the autoencoding task are difficult for genetic programming.", notes = "EuroGP Best Paper Candidate http://www.evostar.org/2019/cfp_eurogp.php#abstracts Part of \cite{Sekanina:2019:GP} EuroGP'2019 held in conjunction with EvoCOP2019, EvoMusArt2019 and EvoApplications2019", } @Article{McDermott:2020:SNCS, author = "James McDermott", title = "When and Why Metaheuristics Researchers can Ignore ``No Free Lunch'' Theorems", journal = "SN Computer Science", year = "2020", volume = "1", pages = "60", keywords = "genetic algorithms, genetic programming, Metaheuristics, No free lunch, NFL, Evolutionary computation, Problem domain, Anthropic principle", ISSN = "2661-8907", URL = "http://arxiv.org/abs/1906.03280", DOI = "doi:https://doi.org/10.1007/s42979-020-0063-3", abstract = "The No Free Lunch (NFL) theorem for search and optimisation states that averaged across all possible objective functions on a fixed search space, all search algorithms perform equally well. Several refined versions of the theorem find a similar outcome when averaging across smaller sets of functions. We argue that NFL results continue to be misunderstood by many researchers, and addresses this issue in several ways. Existing arguments against real-world implications of NFL results are collected and re-stated for accessibility, and new ones are added. Specific misunderstandings extant in the literature are identified, with speculation as to how they may have arisen. This paper presents an argument against a common paraphrase of NFL findings. That algorithms must be specialised to problem domains in order to do well. After problematising the usually undefined term domain. It provides novel concrete counter-examples illustrating cases where NFL theorems do not apply. In conclusion it offers a novel view of the real meaning of NFL, incorporating the anthropic principle and justifying the position that in many common situations researchers can ignore NFL.", } @Article{McDermott:2022:sigevolution, author = "James McDermott and Gabriel Kronberger and Patryk Orzechowski and Leonardo Vanneschi and Luca Manzoni and Roman Kalkreuth and Mauro Castelli", title = "Genetic Programming Benchmarks: Looking Back and Looking Forward", journal = "SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation", year = "2022", volume = "15", number = "3", month = "Fall", keywords = "genetic algorithms, genetic programming, PSB2", ISSN = "1931-8499", URL = "https://evolution.sigevo.org/issues/HTML/sigevolution-15-3/index.htm#Genetic_Programming_Benchmarks_Looking_Back_and_Looking_Forward", DOI = "doi:10.1145/3578482.3578483", size = "19 pages", abstract = "The top image shows a set of scales, which are intended to bring to mind the ideas of balance and fair experimentation which are the focus of our article on genetic programming benchmarks", notes = "https://evolution.sigevo.org/", } @InProceedings{McDermott:2025:GPTP, author = "James McDermott", title = "Evolving Programs in the Lambda Calculus using Program Trace Optimisation", booktitle = "Genetic Programming Theory and Practice XXII", year = "2025", editor = "Wolfgang Banzhaf and Bogdan Burlacu and Stephen Kelly and Alexander Lalejini and Fabricio {Olivetti de Franca}", address = "Michigan State University, USA", month = jun # " 5-7", note = "lightning talk", keywords = "genetic algorithms, genetic programming, PTO", notes = "To be published after the workshop", } @Article{DBLP:journals/taas/DonnellDH23, author = "Nicola {Mc Donnell} and Jim Duggan and Enda Howley", title = "A Genetic Programming-based Framework for Semi-automated Multi-agent Systems Engineering", journal = "{ACM} Trans. Auton. Adapt. Syst.", volume = "18", number = "2", pages = "6:1--6:30", year = "2023", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1145/3584731", DOI = "doi:10.1145/3584731", timestamp = "Fri, 07 Jul 2023 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/taas/DonnellDH23.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{DBLP:journals/ijseke/McGaughranZ09, author = "Daniel McGaughran and Mengjie Zhang", title = "Evolving More Representative Programs with Genetic Programming", journal = "International Journal of Software Engineering and Knowledge Engineering (IJSEKE)", year = "2009", volume = "19", number = "1", pages = "1--22", publisher = "Imperial College Press", keywords = "genetic algorithms, genetic programming, Computer programs; artificial intelligence and knowledge engineering; automatic learning of programs; C++ code", ISSN = "0218-1940", DOI = "doi:10.1142/S021819400900409X", abstract = "This paper describes a new representation of tree-based genetic programs in Genetic Programming, an approach of artificial intelligence and knowledge engineering, in order to adopt a form more conducive to imperative functions as developed by human programmers. This representation incorporates the Abstract Syntax Tree form into a larger tree structure based on a Control Flow Graph, thereby causing statements to be chained together sequentially and allowing genetic programs to be output as (non-object-oriented) C++ code fragments. Maintaining or improving the evolutionary performance has been a key priority in this development. These prompt additional genetic operators to be defined to better preserve chains of statements than the traditional Mutation and Crossover operators, thereby encouraging a more efficient evolution of genetic programs. Experimental results suggest that adopting a chained approach can make a significant improvement in evolutionary performance over using ProgN functions that evaluate their children sequentially. The introduction of additional operators can improve the evolutionary performance even further. This approach can automatically generate computer programs for a particular problem using artificial intelligence and knowledge engineering approaches. In particular, the newly developed operators in the chained approach have great potential for generating human competitive programs in commonly used imperative programming languages such as C++.", notes = "Cited by \ref{Castle:2010:EuroGP} School of Engineering and Computing Sciences, Victoria University of Wellington, P.O. Box 600, Wellington, New Zealand", } @InProceedings{mcgee_etal:cec2010, author = "Richard McGee and Michael O'Neill and Anthony Brabazon", title = "The Syntax of Stock Selection: Grammatical Evolution of a Stock Picking Model", booktitle = "2010 IEEE World Congress on Computational Intelligence", pages = "4347--4354", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, grammatical evolution", isbn13 = "978-1-4244-6910-9", DOI = "doi:10.1109/CEC.2010.5586001", abstract = "A significant problem in the area of stock selection is that of identifying the factors that affect a security's return. While modern portfolio theory suggests a linear multi-factor model in the form of Arbitrage Pricing Theory it does not suggest the identity, or even the number, of risk factors in the model. Candidate factors for inclusion in a fundamental model can include hundreds of data points for each firm and with thousands of firms in the fund manager's selection universe the model specification problem encompasses a large, computationally intense search space. Grammatical Evolution (GE) is a form of evolutionary computing that has been used successfully in model induction problems involving large search spaces. GE is applied to evolve a stock selection model with a customised mapping process developed specifically to enhance the performance of evolutionary operators for this problem. Stock selection models are rated using fitness functions commonly employed in asset management; the information coefficient and the inter-quantile return spread. The findings of the paper indicate that evolutionary computing is an excellent tool for the development of stock picking models.", notes = "WCCI 2010. Also known as \cite{5586001}", } @InProceedings{McGhie:2020:SSCI, author = "Abigail McGhie and Bing Xue and Mengjie Zhang", title = "GPCNN: Evolving Convolutional Neural Networks using Genetic Programming", booktitle = "2020 IEEE Symposium Series on Computational Intelligence (SSCI)", year = "2020", pages = "2684--2691", abstract = "Image classification is an important task that has a wide range of applications. Convolutional neural networks (CNNs) are a common approach that can achieve promising performance in image classification. However, using CNNs to address a problem requires in-depth knowledge about CNN architectures and how it relates to the problem domain. Genetic programming (GP) as an evolutionary computation method can used to reduce the amount of knowledge required to design a CNN for a given problem domain by automatically searching for the optimal architecture. This paper proposes a new algorithm named, GPCNN, which encodes graph-based CNN architectures as trees and uses genetic operators, i.e. mutation, crossover and selection, to find better architectures. A more flexible crossover, partial subtree crossover, is also proposed to improve the search performance. As an preliminary work, GPCNN did not manage to achieve better performance than the state-of-the-art methods due to the limit on computational resource, but it is able to achieve better results than the baseline methods. More importantly, the proposed tree-based graph representation of CNN allows CNN architecture of various shapes, which has a great potential for future work in evolutionary automatic neural architecture search.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SSCI47803.2020.9308390", month = dec, notes = "Also known as \cite{9308390}", } @Article{McGovern:2002:BB, author = "Aoife C. McGovern and David Broadhurst and Janet Taylor and Naheed Kaderbhai and Michael K. Winson and David A. Small and Jem J. Rowland and Douglas B. Kell and Royston Goodacre", title = "Monitoring of complex industrial bioprocesses for metabolite concentrations using modern spectroscopies and machine learning: Application to gibberellic acid production", journal = "Biotechnology and Bioengineering", year = "2002", volume = "78", number = "5", pages = "527--538", month = "5 " # jun, keywords = "genetic algorithms, genetic programming, evolutionary computing, Fourier transform infrared spectroscopy, dispersive Raman spectroscopy, pyrolysis mass spectrometry", URL = "http://dbkgroup.org/Papers/biotechnol_bioeng_78_(527).pdf", URL = "http://www3.interscience.wiley.com/cgi-bin/fulltext/93514395/PDFSTART", DOI = "doi:10.1002/bit.10226", size = "12 pages", abstract = "Two rapid vibrational spectroscopic approaches (diffuse reflectance-absorbance Fourier transform infrared [FT-IR] and dispersive Raman spectroscopy), and one mass spectrometric method based on in vacuo Curie-point pyrolysis (PyMS), were investigated in this study. A diverse range of unprocessed, industrial fed-batch fermentation broths containing the fungus Gibberella fujikuroi producing the natural product gibberellic acid, were analyzed directly without a priori chromatographic separation. Partial least squares regression (PLSR) and artificial neural networks (ANNs) were applied to all of the information-rich spectra obtained by each of the methods to obtain quantitative information on the gibberellic acid titer. These estimates were of good precision, and the typical root-mean-square error for predictions of concentrations in an independent test set was <10% over a very wide titer range from 0 to 4925 ppm. However, although PLSR and ANNs are very powerful techniques they are often described as black box methods because the information they use to construct the calibration model is largely inaccessible. Therefore, a variety of novel evolutionary computation-based methods, including genetic algorithms and genetic programming, were used to produce models that allowed the determination of those input variables that contributed most to the models formed, and to observe that these models were predominantly based on the concentration of gibberellic acid itself. This is the first time that these three modern analytical spectroscopies, in combination with advanced chemometric data analysis, have been compared for their ability to analyze a real commercial bioprocess. The results demonstrate unequivocally that all methods provide very rapid and accurate estimates of the progress of industrial fermentations, and indicate that, of the three methods studied, Raman spectroscopy is the ideal bioprocess monitoring method because it can be adapted for on-line analysis. C 2002 Wiley Periodicals, Inc.", notes = "PMID: 12115122", } @InProceedings{Porter:2018:GI, author = "Christopher McGowan and Alexander Wild and Barry Porter", title = "Experiments in Genetic Divergence for Emergent Systems", booktitle = "GI-2018, ICSE workshops proceedings", year = "2018", editor = "Justyna Petke and Kathryn Stolee and William B. Langdon and Westley Weimer", pages = "9--16", address = "Gothenburg, Sweden", month = "2 " # jun, publisher = "ACM", publisher_address = "New York, NY, USA", note = "Best Paper", keywords = "genetic algorithms, genetic programming, genetic improvement", isbn13 = "978-1-4503-5753-1", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/icse2018/gi2018/papers/Porter_2018_GI.pdf", URL = "https://eprints.lancs.ac.uk/id/eprint/124101/1/main.pdf", DOI = "doi:10.1145/3194810.3194813", size = "8 pages", abstract = "Emergent software systems take a step towards tackling the ever-increasing complexity of modern software, by having systems self-assemble from a library of building blocks, and then continually re-assemble themselves from alternative building blocks to learn which compositions of behaviour work best in each deployment environment. One of the key challenges in emergent systems is populating the library of building blocks, and particularly a set of alternative implementations of particular building blocks, which form the runtime search space of optimal behaviour. We present initial work in using a fusion of genetic improvement and genetic synthesis to automatically populate a divergent set of implementations of the same functionality, allowing emergent systems to explore new behavioural alternatives without human input. Our early results indicate this approach is able to successfully yield useful divergent implementations of building blocks which are more suited than any existing alternative for particular operating conditions.", notes = "Note author order change. http://research.projectdana.com/gi2018mcgowan Slides: http://geneticimprovementofsoftware.com/wp-content/uploads/2018/06/presentation.pdf GI-2018 http://geneticimprovementofsoftware.com/events/papers#icse2018 part of \cite{Petke:2018:ICSEworkshop}", } @Article{McGregor:2005:GPEM, author = "S. McGregor and I. Harvey", title = "Embracing Plagiarism: Theoretical, Biological and Empirical Justification for Copy Operators in Genetic Optimisation", journal = "Genetic Programming and Evolvable Machines", year = "2005", volume = "6", number = "4", pages = "407--420", month = dec, keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Genetic operators, Boolean logic, evolutionary design, translocation, transposition", ISSN = "1389-2576", URL = "http://www.cogs.susx.ac.uk/users/inmanh/Plagiarism.pdf", DOI = "doi:10.1007/s10710-005-4804-9", size = "14 pages", abstract = "A novel genetic operator, the plagiarism operator, is introduced for evolutionary design and optimisation. This operator is analogous in some respects to crossover and to biological transposition. Plagiarism is shown to be theoretically superior to uniform mutation for generalised counting-ones problems, and also to outperform uniform mutation on certain classes of random fitness landscapes. Experimental results are presented showing that plagiarism speeds up the artificial evolution of certain digital logic circuits. The performance of this operator is interpreted in terms of the non-uniform distribution of genetic primitives in good solutions for certain problems.", notes = "p407 'the plariarism operator simply copies a genetic primitive from one locus to another'. p413 'uniform mutation is a biased operator'.", } @InProceedings{mcintyre:gecco03lbp, title = "A Grammatical Evolution Multi-Classifier through Crowding", pages = "219--226", author = "A. R. McIntyre and M. I. Heywood", year = "2003", address = "Chicago, USA", month = "12--16 " # jul, editor = "Bart Rylander", keywords = "genetic algorithms, genetic programming, grammatical evolution", booktitle = "Genetic and Evolutionary Computation Conference Late Breaking Papers", notes = "GECCO-2003LB", } @InProceedings{McIntyre:OMC:gecco2004, author = "A. R. McIntyre and M. I. Heywood", title = "On Multi-class Classification by Way of Niching", booktitle = "Genetic and Evolutionary Computation -- GECCO-2004, Part II", year = "2004", editor = "Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell", series = "Lecture Notes in Computer Science", pages = "581--592", address = "Seattle, WA, USA", publisher_address = "Heidelberg", month = "26-30 " # jun, organisation = "ISGEC", publisher = "Springer-Verlag", volume = "3103", ISBN = "3-540-22343-6", ISSN = "0302-9743", URL = "http://users.cs.dal.ca/~mheywood/X-files/Publications/andy-GECCO04.pdf", DOI = "doi:10.1007/b98645", size = "12", keywords = "genetic algorithms, genetic programming", abstract = "In recent literature, the niche enabling effects of crowding and the sharing algorithms have been systematically investigated in the context of Genetic Algorithms and are now established evolutionary methods for identifying optima in multi-modal problem domains. In this work, the niching metaphor is methodically explored in the context of a simultaneous multi-population GP classifier in order to investigate which (if any) properties of traditional sharing and crowding algorithms may be portable in arriving at a naturally motivated niching GP. For this study, the niching mechanisms are implemented in Grammatical Evolution to provide multi-category solutions from the same population in the same trial. Each member of the population belongs to a different niche in the GE search space corresponding to the data classes. The set of best individuals from each niche are combined hierarchically and used for multi-class classification on the familiar multi-class UCI data sets of Iris and Wine. A distinct preference for Sharing as opposed to Crowding is demonstrated with respect to population diversity during evolution and niche classification accuracy.", notes = "GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)", } @InProceedings{McIntyre:2005:CEC, author = "A. R. McIntyre and M. I. Heywood", title = "Toward Co-Evolutionary Training of a Multi-Class Classifier", booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary Computation", year = "2005", editor = "David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L. Gwenn Volkert and Dan Ashlock and Marc Schoenauer", volume = "3", pages = "2130--2137", address = "Edinburgh, UK", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "2-5 " # sep, organisation = "IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9363-5", DOI = "doi:10.1109/CEC.2005.1554958", size = "8 pages", abstract = "In this work the multi-class classification capabilities of genetic programming (GP) are explored in the context of a competitive co-evolutionary system, in which a population of GP classifiers is trained against an evolving population of trainers (exemplar selectors) with the goal of reducing GP training time for large multi-class classification problems. Moreover, the niche-enabling mechanisms established in the genetic algorithm (GA) literature, known as crowding and sharing, are implemented for the classifier population in order to provide multi-class solutions from a single population in the same trial. The results as presented in the paper indicate the appropriateness of the competitive co-evolutionary training approach under GP multi-class classification.", notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS.", } @InProceedings{1144148, author = "Andrew McIntyre and Malcolm Heywood", title = "MOGE: GP classification problem decomposition using multi-objective optimization", booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation", year = "2006", editor = "Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos {Coello Coello} and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens", volume = "1", ISBN = "1-59593-186-4", pages = "863--870", address = "Seattle, Washington, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p863.pdf", DOI = "doi:10.1145/1143997.1144148", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "8-12 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, multi-objective classification, program synthesis, synthesis", notes = "GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM Order Number 910060", } @InProceedings{McIntyre:2007:SMC, author = "A. R. McIntyre and M. I. Heywood", title = "Multi-Objective Competitive Coevolution for Efficient GP Classifier Problem Decomposition", booktitle = "Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics", year = "2007", pages = "1930--1937", address = "Montreal", month = "7-10 " # oct, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", ISBN = "1-4244-0991-8", URL = "http://users.cs.dal.ca/~mheywood/X-files/GradPubs.html#mcintyre", URL = "http://users.cs.dal.ca/~mheywood/X-files/Publications/armcnty_SMC07.pdf", DOI = "doi:10.1109/ICSMC.2007.4414009", size = "8 pages", abstract = "A novel approach to the classification of large and unbalanced multi-class data sets is presented where the widely acknowledged issues of scalability, solution transparency, and problem decomposition are addressed simultaneously within the context of the Genetic Programming (GP) paradigm. A cooperative coevolutionary training environment that employs multi-objective evaluation provides the basis for problem decomposition and reduced solution complexity, while scalability is achieved through a Pareto competitive coevolutionary framework, allowing the system to be applied to large data sets (tens or hundreds of thousands of exemplars) without recourse to hardware-specific speedups. Moreover, a key departure from the canonical GP approach to classification is used in which the output of GP is expressed in terms of a non-binary, local membership function (e.g. a Gaussian), where it is no longer necessary for an expression to represent an entire class. Decomposition is then achieved through reformulating the classification problem as one of cluster consistency, where an appropriate subset of the training patterns can be associated with each individual such that problems are solved by several specialist classifiers rather than by a single super individual.", notes = "http://www.smc2007.org/program.html NB armcnty_SMC07.pdf is 21 pages Also known as \cite{4414009}", } @PhdThesis{McIntyre:thesis, author = "Andrew R. McIntyre", title = "Novelty Detection + Coevolution = Automatic Problem Decomposition: A Framework for Scalable Genetic Programming Classifiers", school = "Dalhousie University", year = "2007", address = "Halifax, Nova Scotia, Canada", month = nov, keywords = "genetic algorithms, genetic programming", URL = "http://projectx25.cs.dal.ca/home/docs/armcintyre_phd_thesis_final.pdf", size = "442 pages", abstract = "A novel approach to the classification of large and unbalanced multi-class data sets is presented where the widely acknowledged issues of scalability, solution transparency, and problem decomposition are addressed simultaneously within the context of the Genetic Programming (GP) paradigm. A cooperative coevolutionary training environment that employs multi-objective evaluation provides the basis for problem decomposition and reduced solution complexity, while scalability is achieved through a Pareto competitive coevolutionary framework, allowing the system to be readily applied to large data sets (tens or hundreds of thousands of exemplars) without recourse to hardware-specific speedups. A key departure from the canonical GP approach to classification involves expressing the output of GP in terms of a non-binary, local membership function (e.g., a Gaussian). Decomposition is achieved by reformulating the classification problem as one of cluster consistency, where an appropriate subset of the training patterns can be associated with each individual such that problems are solved by several specialist classifiers as opposed to a singular `super' individual. Although multi-objective methods have previously been reported for GP classification domains, we explicitly formulate the objectives for cooperative behavior. Without this the user is left to choose a single individual as the overall solution from a front of solutions. This work is able to use the entire front of solutions without recourse to heuristics for selecting one individual over another or duplicating behaviors between different classifiers. Extensive benchmarking was performed against alternative frameworks for classification including Genetic Programming, Neural Networks, and deterministic methods. In contrast to classifiers evolved using competitive coevolution alone, we demonstrate the ability of the proposed coevolutionary model to provide a non-overlapping decomposition or association between learners and exemplars, while returning statistically significant improvements in classifier performance. In the case of the Neural Network methods, benchmarking is conducted against the more challenging second order neural learning algorithm of conjugate gradient optimization (previous comparisons limit Neural Networks to first order methods). The proposed evolutionary method was often significantly better than the non-linear Neural Network, whereas the linear model tended to work well or not at all. In effect, the evolutionary paradigm provided a more robust model for searching the space of non-linear models than provided under the neural gradient decent paradigm. With respect to deterministic methods, the problem of benchmarking stochastic versus deterministic algorithms is first addressed, with a new methodology established for making such comparisons. The ensuing comparison demonstrated that the evolutionary algorithms remain competitive with most data sets appearing to benefit from the proposed evolutionary methodology.", notes = "http://projectx25.cs.dal.ca/home/?pg=research Advisor: Dr. Malcolm I. Heywood (Dalhousie University, Faculty of Computer Science) External Examiner: Dr. Una-May O'Reilly (MIT Computer Science and Artificial Intelligence Lab) Graduate Committee: Dr. Evangelos E. Milios and Dr. Syed Sibte Raza Abidi (Dalhousie University, Faculty of Computer Science)", } @InProceedings{conf/eurogp/McIntyreH08, title = "Cooperative Problem Decomposition in Pareto Competitive Classifier Models of Coevolution", author = "Andrew R. McIntyre and Malcolm I. Heywood", bibdate = "2008-04-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/eurogp/eurogp2008.html#McIntyreH08", booktitle = "Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008", address = "Naples", month = "26-28 " # mar, publisher = "Springer", year = "2008", volume = "4971", editor = "Michael O'Neill and Leonardo Vanneschi and Steven Gustafson and Anna Isabel {Esparcia Alcazar} and Ivanoe {De Falco} and Antonio {Della Cioppa} and Ernesto Tarantino", isbn13 = "978-3-540-78670-2", pages = "289--300", series = "Lecture Notes in Computer Science", DOI = "doi:10.1007/978-3-540-78671-9_25", keywords = "genetic algorithms, genetic programming", notes = "Part of \cite{conf/eurogp/2008} EuroGP'2008 held in conjunction with EvoCOP2008, EvoBIO2008 and EvoWorkshops2008", } @InCollection{Mcintyre:2008:GPTP, author = "Andrew R. McIntyre and Malcolm I. Heywood", title = "Pareto Cooperative-Competitive Genetic Programming: A Classification Benchmarking Study", booktitle = "Genetic Programming Theory and Practice {VI}", year = "2008", editor = "Rick L. Riolo and Terence Soule and Bill Worzel", series = "Genetic and Evolutionary Computation", chapter = "4", pages = "43--61", address = "Ann Arbor", month = "15-17 " # may, publisher = "Springer", DOI = "doi:10.1007/978-0-387-87623-8_4", size = "18 pages", isbn13 = "978-0-387-87622-1", notes = "part of \cite{Riolo:2008:GPTP} published in 2009", keywords = "genetic algorithms, genetic programming", } @Article{McIntyre:2011:EC, author = "Andrew R. McIntyre and Malcolm I. Heywood", title = "Classification as Clustering: A Pareto Cooperative-Competitive GP Approach", journal = "Evolutionary Computation", year = "2011", volume = "19", number = "1", pages = "137--166", month = "Spring", keywords = "genetic algorithms, genetic programming, MOGA, Pareto", ISSN = "1063-6560", DOI = "doi:10.1162/EVCO_a_00016", size = "30 pages", abstract = "Intuitively population based algorithms such as Genetic Programming provide a natural environment for supporting solutions that learn to decompose the overall task between multiple individuals, or a team. This work presents a framework for evolving teams without recourse to pre-specifying the number of cooperating individuals. To do so, each individual evolves a mapping to a distribution of outcomes that, following clustering, establishes the parametrisation of a (Gaussian) local membership function. This gives individuals the opportunity to represent subsets of tasks, where the overall task is that of classification under the supervised learning domain. Thus, rather than each team member represent an entire class, individuals are free to identify unique subsets of the overall classification task. The framework is supported by techniques from Evolutionary Multi-objective Optimisation (EMO) and Pareto competitive coevolution. EMO establishes the basis for encouraging individuals to provide accurate yet non-overlaping behaviours; whereas competitive coevolution provides the mechanism for scaling to potentially large unbalanced data sets. Benchmarking is performed against recent examples of non-linear SVM classifiers over twelve UCI data sets with between 150 and 200,000 training instances. Solutions from the proposed Coevolutionary Multi-objective GP framework appear to provide a good balance between classification performance and model complexity, especially as the data set instance count increases.", } @InProceedings{mckay:1995:tsgasr, author = "Ben McKay and Mark J. Willis and Geoffrey W. Barton", title = "Using a Tree Structured Genetic Algorithm to Perform Symbolic Regression", booktitle = "First International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, GALESIA", year = "1995", editor = "A. M. S. Zalzala", volume = "414", pages = "487--492", address = "Sheffield, UK", publisher_address = "London, UK", month = "12-14 " # sep, publisher = "IEE", keywords = "genetic algorithms, genetic programming", ISBN = "0-85296-650-4", DOI = "doi:10.1049/cp:19951096", size = "6 pages", abstract = "In this contribution a tree structured genetic algorithm is described. The algorithm is used to generate non-linear models from process input-output data. Three examples are used to demonstrate the applicability of the technique within the domain of process engineering.", notes = "12--14 September 1995, Halifax Hall, University of Sheffield, UK see also http://www.iee.org.uk/LSboard/Conf/program/galprog.htm Uses correlation coefficient in fitness function as advocated by M. C. South Phd 1994 'The application of GAs to rule finding in data analysis', Newcastle upon Tyne, UK Final fixup? 'Mutation...replaces a node in the tree with another of the same degree'. Elitist. Pop size 20, G=100, Pcross=0.8 Pmut=0.5 'found to give good performance to date' 'non linear least-squares optimization to obtain 'best' value of the (new) constant(s) in the expression'. 'the fitness of a tree is weighted according to its size' (penalise bigger) Anti-bloat 2nd example 'Near Infra-red reflectance instrument for the inference of the protein contents of ground wheat' (old data, (1983, T.Fearn 'A misuse of ridge regression in the calibration of near infrared reflectance instrument', Appl Statistics, 32, 1, 73-79), various techniques already tried). GP 'provide simple non-linear model that provides far greater insight into the input-output model structure than other non-linear modelling techniques such as neural networks' RMS error also better than cited in literature (traditional stats and ANN). 3rd: recovery of contaminated transformer oil. GP solution robust to measurement error.", } @InProceedings{mckay:1995:cps, author = "Ben McKay and Mark J. Willis and Geoffrey W. Barton", title = "On the Application of Genetic Programming to Chemical Process Systems", booktitle = "1995 IEEE Conference on Evolutionary Computation", year = "1995", volume = "2", pages = "701--706", address = "Perth, Australia", publisher_address = "Piscataway, NJ, USA", month = "29 " # nov # " - 1 " # dec, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, chemical process systems, complex nonlinear relationships, irrelevant process inputs, mathematical models, nonintuitive process features, parsimonious model structures, relevant process inputs, chemical engineering computing, chemical industry", ISBN = "0-7803-2759-4", DOI = "doi:10.1109/ICEC.1995.487470", size = "6 pages", abstract = "In this contribution a genetic programming approach is used to develop mathematical models of chemical process systems. Having discussed genetic programming in general, two examples are used to reveal the utility of the technique. It is shown how the method can discriminate between relevant and irrelevant process inputs, evolving to yield parsimonious model structures that accurately represent process characteristics. This removes the need for restrictive assumptions about the form of the data and the structure of the required model. In addition, as the technique determines complex nonlinear relationships in the data, non-intuitive process features are revealed with comparative ease.", notes = "ICEC-95 Editors not given by IEEE, Organisers David Fogel and Chris deSilva. conference details at http://ciips.ee.uwa.edu.au/~dorota/icnn95.html", } @InProceedings{mckay:1996:GPidea, author = "Ben McKay and Mark Willis and Gary Montague and Geoffrey W. Barton", title = "Using Genetic Programming to Develop Inferential Estimation Algorithms", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "157--165", address = "Stanford University, CA, USA", publisher = "MIT Press", broken = "http://lorien.ncl.ac.uk/sorg/paper2.ps", size = "9 pages", abstract = "Genetic Programming (GP) is used to develop inferential estimation algorithms for two industrial chemical processes. Within this context, dynamic modelling procedures (as opposed to static or steady-state modelling) are often required if accurate inferential models are to be developed. Thus, a simple procedure is suggested so that the GP technique may be used for the development of dynamic process models. Using measurements from a vacuum distillation column and an industrial plasticating extrusion process, it is then demonstrated how the GP methodology can be used to develop reliable cost effective process models. A statistical analysis procedure is used to aid in the assessment of GP algorithm settings and to guide in the selection of the final model structure.", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap19.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96, MSWord postscript not cmpatible with Unix", } @TechReport{mckay:1996:ehmffp, author = "B. McKay and C. Sanderson and M. J. Willis and J. Barford and G. Barton", title = "Evolving a Hybrid Model of a Fed-batch Fermentation Process", institution = "Chemical Engineering, Newcastle University", year = "1996", address = "UK", keywords = "genetic algorithms, genetic programming", broken = "http://lorien.ncl.ac.uk/sorg/paper6.ps", size = "12 pages", abstract = "This paper presents a novel method for the system identification of the fed-batch fermentation process defined in the problem statement of the Biotechnological Control Forum Modelling and Control Competition. The identification methodology involves a hybrid of mechanistic modelling and Genetic Programming techniques. It provides an accurate model of the system which should be extremely useful in both the optimisation and control of this process. The performance of the model as a 25 time unit ahead predictor of product concentration (on unseen verification data) is such that the root mean square error between the actual and predicted output is less than 5percent over the range of interest.", notes = "MSword postscript not compatible with unix", } @TechReport{mckay:1996:cmc2p, author = "B. McKay and B. Lennox and M. J. Willis and G. W. Barton and G. A. Montague", title = "Extruder Modelling: A Comparison of two Paradigms", institution = "Chemical Engineering, Newcastle University", year = "1996", address = "UK", note = "Appears in Control '96", keywords = "genetic algorithms, genetic programming", broken = "http://lorien.ncl.ac.uk/sorg/paper5.ps", abstract = "In this contribution two data based modelling paradigms are compared. Using measurements from an industrial plasticating extrusion process, a locally recurrent neural network and a genetic programming algorithm are used to develop inferential models of the polymer viscosity. It is demonstrated that both techniques produce adequate non-linear dynamic inferential models. However, for this application the genetic programming technique adopted produces models that perform better than the locally recurrent neural network. Moreover, the final model produced by the algorithm has a simple transparent structure.", notes = "MSword postscript not compatible with unix, see also \cite{mckay:1996:exmc2p}", size = "6 pages", } @InProceedings{mckay:1996:iipGP, author = "B. McKay and M. J. Willis and H. G. Hiden and G. A. Montague and G. W. Barton", title = "Identification of Industrial Processes using Genetic Programming", booktitle = "Identification in Engineering Systems", year = "1996", editor = "M. I. Friswell and J. E. Mottershead", volume = "1", address = "Swansea, UK", month = mar, publisher = "The Cromwell Press Ltd", note = "Proceedings of the International Conference, ICIES", keywords = "genetic algorithms, genetic programming", ISBN = "0-86076-136-3", broken = "http://lorien.ncl.ac.uk/sorg/paper4.ps", URL = "http://michael.friswell.com/ies96.html", size = "10 pages", abstract = "Complex processes are often modelled using input-output data from experimental tests. Regression and neural network modelling techniques address this problem to some extent and are being increasingly used to develop optimisation or model-based control algorithms. Unfortunately, the latter methods provide no physical insight into the underlying structural relationships inherent within the data. Genetic Programming (GP) is currently finding application in the modelling of processes from experimental data. The nature of GP-based modelling is that solutions are `evolved' from a set of potential solutions in an environment which mimics Darwinian `survival of the fittest'. GP performs symbolic regression, determining both the structure and the complexity of the model during its evolution. In this contribution two examples are used to demonstrate the utility of the GP technique as a process modelling tool. It is concluded that GP techniques may have further applications in the modelling and identification of complex processes from experimental input-output data.", notes = "MSWord postscript not compatible with unix cited by \cite{yeun_2004_tec}", } @InProceedings{mckay:1996:eiocps, author = "Ben McKay and Justin Elsey and Mark J. Willis and Geoffrey W. Barton", title = "Evolving Input-Output Models of Chemical Process Systems Using Genetic Programming", booktitle = "IFAC '96", year = "1996", volume = "1", address = "San-Fransisco", keywords = "genetic algorithms, genetic programming", broken = "http://lorien.ncl.ac.uk/sorg/paper3.ps", size = "7 pages", abstract = "Complex processes are often modelled using input-output data from experimental tests. Regression and neural network modelling techniques are commonly used for this purpose. Unfortunately, these methods provide minimal structural insight into process characteristics. In this contribution, we propose the use of Genetic Programming (GP) as a method for developing input-output process models from experimental data. GP performs symbolic regression, determining both the structure and the complexity of the model during its evolution. This has the advantage that no a priori modelling assumptions have to be made. Moreover, the technique can discriminate between relevant and irrelevant process inputs, yielding parsimonious model structures that accurately represent process characteristics. Two examples are used to demonstrate the utility of the GP technique as a process modelling tool.", notes = "MSWord postscript not compatible with unix", } @Article{mckay:1996:ssmcps, author = "Ben McKay and Mark Willis and Geoffrey Barton", title = "Steady-state Modelling of Chemical Process System using Genetic Programming", journal = "Computers and Chemical Engineering", year = "1997", volume = "21", number = "9", pages = "981--996", keywords = "genetic algorithms, genetic programming, symbolic regression, process modelling", URL = "http://www.sciencedirect.com/science/article/B6TFT-3S9TDFC-5/2/339ca8a827eb95c025f2fe7bf8054f1c", ISSN = "0098-1354", DOI = "doi:10.1016/S0098-1354(96)00329-8", size = "16 pages", abstract = "Complex processes are often modelled using input-output data from experimental tests. Regression and neural network modelling techniques are commonly used for this purpose. Unfortunately, these methods provide minimal information about the model structure required to accurately represent process characteristics. In this contribution, we propose the use of Genetic Programming (GP) as a method for developing input-output process models from experimental data. GP performs symbolic regression, determining both the structure and the complexity of the model during its evolution. This has the advantage that no a priori modelling assumptions have to be made. Moreover, the technique can discriminate between relevant and irrelevant process inputs, yielding parsimonious model structures that accurately represent process characteristics. Following a tutorial example, the usefulness of the technique is demonstrated by the development of steady-state models for two typical processes, a vacuum distillation column and a chemical reactor system. A statistical analysis procedure is used to aid in the assessment of GP algorithm settings and to guide in the selection of the final model structure.", } @InProceedings{mckay:1996:exmc2p, author = "Ben McKay and Barry Lennox and Mark Willis and Geoffrey W. Barton and Gary Montague", title = "Extruder Modelling: A Comparison of two Paradigms", booktitle = "UKACC International Connference on Control'96", year = "1996", volume = "2", pages = "734--739", address = "Exeter, UK", publisher_address = "Savoy House, London, UK", month = "2-5 " # sep, publisher = "IEE", note = "Conference publication No. 427", keywords = "genetic algorithms, genetic programming", ISBN = "0-85296-668-7", URL = "http://scitation.aip.org/getpdf/servlet/GetPDFServlet?filetype=pdf&id=IEECPS0019960CP427000734000001&idtype=cvips&prog=normal", DOI = "doi:10.1049/cp:19960643", URL = "http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=656018", size = "6 pages", abstract = "two data based modelling paradigms are compared. Using measurements from an industrial plasticating extrusion process, a locally recurrent neural network and a genetic programming algorithm are used to develop inferential models of the polymer viscosity. It is demonstrated that both techniques produce adequate non-linear dynamic inferential models. However, for this application the genetic programming technique adopted produces models that perform better than the locally recurrent neural network. Moreover, the final model produced by the algorithm has a simple transparent structure.", notes = "see also tech report \cite{mckay:1996:cmc2p}", } @InProceedings{mckay:1999:NCRUGP, author = "Ben McKay and Mark Willis and Dominic Searson and Gary Montague", title = "Non-Linear Continuum Regression Using Genetic Programming", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1106--1111", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-443.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-443.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Article{Mckay:2000:TIMC, author = "Ben McKay and Mark Willis and Dominic Searson and Gary Montague", title = "Nonlinear continuum regression: an evolutionary approach", journal = "Transactions of the Institute of Measurement and Control", year = "2000", volume = "22", number = "2", pages = "125--140", email = "mark.willis@ncl.ac.uk", keywords = "genetic algorithms, genetic programming, continuum regression, process modelling, co-evolution", DOI = "doi:10.1177/014233120002200202", abstract = "genetic programming is combined with continuum regression to produce two novel non-linear continuum regression algorithms. The first is a sequential algorithm while the second adopts a team-based strategy. Having discussed continuum regression, the modifications required to extend the algorithm for non-linear modelling are outlined. The results of two case studies are then presented: the development of an inferential model of a food extrusion process and an input-output model of an industrial bioreactor. The superior performance of the sequential continuum regression algorithm, as compared to a similar sequential nonlinear partial least squares algorithm, is demonstrated. These applications clearly demonstrate that the team-based continuum regression strategy significantly outperforms both sequential approaches.", } @InProceedings{McKay:1994:mlwai, author = "R. I. (Bob) McKay and P. Whigham", title = "Genetic Programming and Inductive Logic", year = "1994", booktitle = "AI'94 Machine Learning Workshop", address = "Armidale, Australia", month = nov, keywords = "genetic algorithms, genetic programming", notes = "Refereed Regional and National Conference and Workshop Papers. See also \cite{whigham:1995:glrr},", } @InProceedings{McKay:2000:ECL, author = "R. I. (Bob) McKay", booktitle = "Second International Conference on Ecological Modelling and Learning", address = "Adelaide, Australia", notes = "Refereed International Conference Papers", title = "Spatial Learning using Fitness-Shared Genetic Programming", URL = "http://sc.snu.ac.kr/PAPERS/icamlem00.pdf", year = "2000", keywords = "genetic algorithms, genetic programming", } @InProceedings{McKay:2000:GECCO, author = "R I (Bob) McKay", title = "Fitness Sharing in Genetic Programming", pages = "435--442", year = "2000", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)", editor = "Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer", address = "Las Vegas, Nevada, USA", publisher_address = "San Francisco, CA 94104, USA", month = "10-12 " # jul, keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-708-0", URL = "http://gpbib.cs.ucl.ac.uk/gecco2000/GP256.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco2000/GP256.ps", notes = "cited by \cite{Helmuth:2015:GPTP} A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of \cite{whitley:2000:GECCO}", } @InProceedings{mckay:2000:pffgp, author = "Bob McKay", title = "Partial Functions in Fitness-Shared Genetic Programming", booktitle = "Proceedings of the 2000 Congress on Evolutionary Computation CEC00", year = "2000", pages = "349--356", volume = "1", address = "La Jolla Marriott Hotel La Jolla, California, USA", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "6-9 " # jul, organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, fitness, accurate solutions, fitness sharing, multiplexer definition learning, partial functions, performance, population parameters, recursive list membership function learning, total functions, functions, learning (artificial intelligence), list processing, multiplexing equipment, software performance evaluation", ISBN = "0-7803-6375-2", DOI = "doi:10.1109/CEC.2000.870316", abstract = "This paper investigates the use of partial functions and fitness sharing in genetic programming. Fitness sharing is applied to populations of either partial or total functions and the results compared. Applications to two classes of problem are investigated: learning multiplexer definitions, and learning (recursive) list membership functions. In both cases, fitness sharing approaches outperform the use of raw fitness, by generating more accurate solutions with the same population parameters. On the list membership problem, variants using fitness sharing on populations of partial functions outperform variants using total functions, whereas populations of total functions give better performance on some variants of multiplexer problems.", notes = "CEC-2000 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 00TH8512, Library of Congress Number = 00-018644", } @InProceedings{McKay:2000:APCSEL, publisher_address = "Piscataway, NJ, USA", author = "R. I. (Bob) McKay", booktitle = "Industrial Electronics Society, 2000. IECON 2000. 26th Annual Confjerence of the IEEE Third Asia-Pacific Conference on Simulated Evolution and Learning 2000", DOI = "doi:10.1109/IECON.2000.972452", ISBN = "0-7803-6456-2", address = "Nagoya, Japan", month = oct # " 22-28", notes = "Refereed International Conference Papers", pages = "2861--2866", publisher = "IEEE Press", title = "Committee Learning of Partial Functions in Fitness-Shared Genetic Programming", URL = "http://sc.snu.ac.kr/PAPERS/committee.pdf", url1 = "http://ieeexplore.ieee.org/xpls/absprintf.jsp?arnumber=972452", volume = "4", year = "2000", keywords = "genetic algorithms, genetic programming", abstract = "This paper investigates the application of committee learning to fitness-shared genetic programming. Committee learning is applied to populations of either partial and total functions, and using either fitness sharing or raw fitness, giving four treatments in all. The approaches are compared on three problems, the 6- and 11-multiplexer problems, and learning recursive list membership functions. As expected, fitness sharing gave better performance on all problems than raw fitness. The comparison between populations of partial and total functions with fitness sharing is more equivocal. The results are very similar, though slightly in favour of total functions. However there are strong indications that the average size of individuals in the partial function populations are smaller, and hence might be expected to generalise better, though this was not investigated in this paper", } @InProceedings{McKay:2000:jawies, author = "R. I. (Bob) McKay", booktitle = "Japan-Australia Workshop on Intelligent and Evolutionary Systems", address = "Yokosuka, Japan", month = nov, notes = "Refereed Regional and National Conference and Workshop Papers", pages = "89--96", title = "Partial Functions in Genetic Programming: Program Size and Evaluation Cost", URL = "http://sc.snu.ac.kr/PAPERS/AJ00.pdf", year = "2000", keywords = "genetic algorithms, genetic programming", } @Article{McKay:2001:EM, author = "R. I. (Bob) McKay", title = "Variants of genetic programming for species distribution modelling -- fitness sharing, partial functions, population evaluation", year = "2001", journal = "Ecological Modelling", volume = "146", pages = "231--241", number = "1-3", keywords = "genetic algorithms, genetic programming, Fitness sharing, Species distribution, Spatial learning", ISSN = "0304-3800", URL = "http://www.sciencedirect.com/science/article/B6VBS-44HYNCP-N/1/a4ef72e29b6f89efd2ddb1b22258ef06", DOI = "doi:10.1016/S0304-3800(01)00309-X", abstract = "We investigate the use of partial functions, fitness sharing and committee learning in genetic programming. The primary intended application of the work is in learning spatial relationships for ecological modelling. The approaches are evaluated using a well-studied ecological modelling problem, the greater glider population density problem. Combinations of the three treatments (partial functions, fitness sharing and committee learning) are compared on the dimensions of accuracy and computational cost. Fitness sharing significantly improves learning accuracy, and populations of partial functions substantially reduce computational cost. The results of committee learning are more equivocal, and require further investigation. The learned models are highly predictive, but also highly explanatory.", } @Unpublished{McKay:2001:awai, author = "R. I. (Bob) McKay", howpublished = "Australian Workshop on Artificial Life", address = "Adelaide, Australia", month = nov, note = "Keynote Address", title = "Complexity and Hierarchy in Natural and Artificial Systems", URL = "http://sc.snu.ac.kr/PAPERS/ALIFE1_Slides.pdf", year = "2001", keywords = "genetic algorithms, genetic programming", size = "16 slides", } @InProceedings{McKay:2001:awal, author = "R. I. (Bob) McKay and D. L. Essam", title = "Evolving Self-Reproducing Functional Programs", booktitle = "Proceedings of the 2001 Australian Workshop on Artificial Life", year = "2001", pages = "55--68", address = "Adelaide, Australia", keywords = "genetic algorithms, genetic programming, Self Reproduction, Grammar Guided Genetic Programming", URL = "http://sc.snu.ac.kr/PAPERS/selfreprod.pdf", size = "5 pages", abstract = "This paper investigates the problem of evolving a self-reproducing program. It is hoped that the analysis of this process might aid the understanding of the process by which the first self-reproducing molecules gave rise to life. We see this work as the meta-level of the well-known investigations into the biochemical precursors to life. This paper presents a sample self-reproducing program, defines the grammar for that program, and then presents the results when a grammar-guided genetic programming system attempts to find for itself a self-reproducing program.", notes = "Refereed Regional and National Conference and Workshop Papers", } @InProceedings{mckayabbass01:rtqrt, author = "Robert McKay and Hussein Abbass", title = "Anticorrelation Measures in Genetic Programming", booktitle = "Australasia-Japan Workshop on Intelligent and Evolutionary Systems", pages = "45--51", year = "2001", editor = "Nikola Kasabov and Peter Whigham", address = "University of Otago, Dunedin, New Zealand", month = "19-21st " # nov, keywords = "genetic algorithms, genetic programming, committee learning, fitness sharing, anti-correlation, population diversity", URL = "http://sc.snu.ac.kr/PAPERS/AJ01.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.80.9515", size = "7 pages", abstract = "We compare three diversity-preserving mechanisms, implicit fitness sharing, negative correlation learning, and a new form, root-quartic negative correlation learning, on a standard genetic programming problem, the 6multiplexer. On this problem, root-quartic negative correlation learning significantly outperforms standard negative correlation learning, and marginally outperforms implicit fitness sharing. We analyse the difference between standard and root-quartic negative correlation learning, and provide a partial explanation for the improved performance.", notes = "6-MUX DCTG-GP broken Nov 2012 http://divcom.otago.ac.nz/infosci/KEL/conferences/IESWorkshop/default.htm Perhaps also McKay, R I and Abbass H.A. 'Anti-correlation: A Diversity Promoting Mechanisms in Ensemble Learning'. The Australian Journal of Intelligent Information Processing Systems 7(3/4), 2001, Pp 139 - 149 \cite{McKay:2001:AJIIPS_2}.", } @Article{McKay:2001:AJIIPS_1, author = "R. I. (Bob) McKay", journal = "The Australian Journal of Intelligent Information Processing Systems", month = jul, number = "1/2", pages = "43--51", title = "An Investigation of Fitness Sharing in Genetic Programming", URL = "http://sc.snu.ac.kr/PAPERS/AJIIPSfitshr.pdf", volume = "7", year = "2001", keywords = "genetic algorithms, genetic programming", ISSN = "1321-2133", URL = "https://portal.issn.org/resource/ISSN/1321-2133", size = "8 pages", abstract = "This paper investigates fitness sharing in genetic programming. Implicit fitness sharing is applied to populations of programs. Three treatments are compared: raw fitness, pure fitness sharing, and a gradual change from fitness sharing to raw fitness. The 6- and 11-multiplexer problems are compared. Using the same population sizes, fitness sharing shows a large improvement in the error rate for both problems. Further experiments compare the treatments on learning recursive list membership functions; again, there are dramatic improvements in error rate. Conversely, fitness sharing runs achieve comparable results to raw fitness using populations two to three times smaller. Measures of population diversity suggest that the results are due to preservation of diversity and avoidance of premature convergence by the fitness sharing runs.", } @Article{McKay:2001:AJIIPS_2, author = "R. I. (Bob) McKay and Hussein A. Abbass", journal = "The Australian Journal of Intelligent Information Processing Systems", number = "3/4", pages = "139--149", title = "Anti-correlation: A Diversity Promoting Mechanisms in Ensemble Learning", URL = "http://sc.snu.ac.kr/PAPERS/AJIIPS_anticorr.pdf", volume = "7", year = "2001", keywords = "genetic algorithms, genetic programming, Anticorrelation, Artificial Neural Networks, committee learning, Ensemble learning, fitness sharing, diversity", abstract = "Anticorrelation has been used in training neural network ensembles. Negative correlation learning (NCL) is the state of the art anticorrelation measure. We present an alternative anticorrelation measure, RTQRTNCL, which shows significant improvements on our test examples for both artificial neural networks (ANN) and genetic programming (GP) learning machines. We analyse the behaviour of the negative correlation measure and derive a theoretical explanation of the improved performance of RTQRTNCL in larger ensembles.", notes = "ANN UCI Australian credit card. GP 6-mux. Ensemble of 4 single hidden layer backprop perceptrons. ", } @Unpublished{McKay:2002:icccs, author = "R. I. (Bob) McKay", howpublished = "International Conference on Communications, Circuits and Systems", address = "Chengdu, China", month = jul, note = "Keynote Address", title = "Inspirations from Nature: Evolution and Complexity", URL = "http://sc.snu.ac.kr/PAPERS/ICCCAS_Plenary.pdf", year = "2002", keywords = "genetic algorithms, genetic programming", size = "44 slides", } @Unpublished{McKay:2003:apsies, author = "R. I. (Bob) McKay", howpublished = "Asia-Pacific Symposium on Intelligent and Evolutionary Systems", address = "Kitakyushu, Japan", month = nov, note = "Keynote Address", title = "Machine Learning with Genetic Programming: Some Challenges", URL = "http://sc.snu.ac.kr/PAPERS/aj03.pdf", year = "2003", keywords = "genetic algorithms, genetic programming, Search Bias, Hierarchical, Developmental, Genotype-Phenotype mapping", size = "6 pages", abstract = "Genetic Programming has achieved a great deal in the twenty or so years since its inception. Nevertheless, there remain a wide range of challenges and open questions: what form do the building blocks of good programs take; how may they be identified and promoted? Particularly in the light of Daida's work on the structural restrictions of GP, there are critical questions on the appropriate representations for GP, and the possibility of non-traditional genetic operators. Finally, it is clear that the operation of GP systems still differs in important ways from that of biological systems, so there is opportunity for further inspiration from biological systems.", } @InProceedings{McKay:2004:ISEI, author = "R. I. McKay and Hoang Tuan Hao and Naoki Mori and Nguyen Xuan Hoai and Daryl Essam", title = "Model-Building with Interpolated Temporal Data", booktitle = "Proceedings of The Conference of the International Society for Ecological Informatics (ISEI04)", year = "2004", editor = "F. Recknagel", address = "Busan, Korea", month = oct # " 24-28", keywords = "genetic algorithms, genetic programming, Linear Interpolation, Modelling", URL = "http://seal.tst.adfa.edu.au/~z3106820/publications/isei4.pdf", size = "31 pages", abstract = "We compared models built on the original sample data, and on interpolated data, from the Lake Kasumigaura algal dataset, to evaluate the risk of mis-fitting based on the interpolated data.", notes = "http://www.isei4.org/", } @Unpublished{McKay:2005:isica_keynote, author = "R. I. (Bob) McKay", howpublished = "International Symposium on Intelligence, Computation and Applications", address = "Wuhan, China", month = apr, note = "Keynote Address", title = "Using Human Speech Structures to Model Reality: Grammars in Genetic Programming", URL = "http://sc.snu.ac.kr/PAPERS/GPGrammars.pdf", year = "2005", keywords = "genetic algorithms, genetic programming", size = "78 slides", } @InProceedings{McKay:2005:ISICA, publisher_address = "Wuhan, PRC", author = "R. I. (Bob) McKay and X. H. Nguyen and P. A. Whigham and Y. Shan", booktitle = "Progress in Intelligence Computation and Intelligence: Proceedings of the International Symposium on Intelligence, Computation and Applications", editor = "L. Kang and Z. Cai and Y. Yan", ISBN = "7-5625-1983-8", address = "Wuhan, PRC", month = apr, notes = "Refereed International Conference Papers", pages = "3--18", publisher = "China University of Geosciences Press", title = "Grammars in Genetic Programming: A Brief Review", URL = "http://sc.snu.ac.kr/PAPERS/isica05.pdf", year = "2005", keywords = "genetic algorithms, genetic programming", } @InProceedings{eurogpMcKayHoangEssamNguyen:, author = "Robert Ian McKay and Tuan Hao Hoang and Daryl Leslie Essam and Xuan Hoai Nguyen", title = "Developmental Evaluation in Genetic Programming: the Preliminary Results", editor = "Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art", booktitle = "Proceedings of the 9th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3905", year = "2006", address = "Budapest, Hungary", month = "10 - 12 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-33143-3", pages = "280--289", DOI = "doi:10.1007/11729976_25", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "This paper investigates developmental evaluation in Genetic Programming (GP). Extant GP systems, including developmental GP systems, typically exhibit modular and hierarchical structure only to the degree it is built-in by the designer; by contrast, biological systems exhibit a high degree of organisation in their genotypes. We hypothesise that even when GP systems are subject to changing environments, for which the adaptability arising from modular structure would be advantageous, the benefit is at the species rather than individual level, so that selection is very weak. By contrast, biological systems are selected repeatedly throughout their development process. We suggest that this difference is crucial; that if an individual is evaluated multiple times throughout its development, then modular structure can provide an adaptive advantage to that individual, and hence can be selected for by evolution. We investigate this hypothesis using Tree Adjoining Grammar Guided Genetic Programming (TAG3P), which has good properties for supporting evaluation during incremental development. Our preliminary results show that developmental TAG3P outperforms both original TAG3P and standard tree-based GP on an appropriate problem, in ways which suggest that modular solutions may have been developed.", notes = "Part of \cite{collet:2006:GP} EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006", } @Article{McKay:2006:EI, author = "R. I. (Bob) McKay and Hoang Tuan Hao and Naoki Mori and Nguyen Xuan Hoai and Daryl Essam", title = "Model-building with interpolated temporal data", journal = "Ecological Informics", year = "2006", volume = "1", number = "3", pages = "259--268", month = nov, note = "4th International Conference on Ecological Informatics", keywords = "genetic algorithms, genetic programming, Linear interpolation, Modelling", ISSN = "1574-9541", URL = "http://sc.snu.ac.kr/PAPERS/ISEI4-108_McKay.pdf", DOI = "doi:10.1016/j.ecoinf.2006.02.005", size = "31 pages", abstract = "Ecological data can be difficult to collect, and as a result, some important temporal ecological datasets contain irregularly sampled data. Since many temporal modelling techniques require regularly spaced data, one common approach is to linearly interpolate the data, and build a model from the interpolated data. However, this process introduces an unquantified risk that the data is over-fitted to the interpolated (and hence more typical) instances. Using one such irregularly-sampled dataset, the Lake Kasumigaura algal dataset, we compare models built on the original sample data, and on the interpolated data, to evaluate the risk of mis-fitting based on the interpolated data.", notes = "http://www.sciencedirect.com/science/journal/15749541", } @Unpublished{McKay:2006:apwgp, author = "R. I. (Bob) McKay", howpublished = "Third Asia-Pacific Workshop on Genetic Programming", address = "Hanoi, VietNam", month = oct, note = "Keynote Address", title = "A Research Introduction to Genetic Programming", URL = "http://sc.snu.ac.kr/PAPERS/aspgp06Presentation.pdf", year = "2006", keywords = "genetic algorithms, genetic programming", } @Unpublished{McKay:2007:isica, author = "R. I. (Bob) McKay", howpublished = "International Symposium on Intelligence, Computation and Applications", address = "Wuhan, China", month = sep, note = "Keynote Address", title = "Complexity in Genetic Programming: Using Entropy and Compression Metrics to Understand GP Behaviour", year = "2007", keywords = "genetic algorithms, genetic programming, bloat, size limit and introns, VC dimension", URL = "http://sc.snu.ac.kr/PAPERS/entcompress.pdf", notes = "'oppose convergence' eg 'Fitness sharing', 'Anticorrelation penalties', 'information based accuracy, parsimony', 'Disappointing results so far', 'Regular structure virtually never emerges in GP genotypes'. Generalisation see \cite{ieee-ec:Kushchu:2002}. 'Equivalent Decision Simplification'. 'Symbolic Regression' of cos(2x), '1000 runs, 500 population, 200 generations'. 'Information theory to understand genetic programming'. 'Entropy of (binary?) subtrees' up to 4 nodes. 'Compression and Regularity Regularity'. 'compressibility to measure complexity'. 'XMLPPM, an excellent tree compression algorithm'. 'Measure how much different runs discover the same building blocks'.", } @InProceedings{McKay:2007:CEC, author = "Robert Ian (Bob) McKay and Jungseok Shin and Tuan Hao Hoang and Xuan Hoai Nguyen and Naoki Mori", title = "Using Compression to Understand the Distribution of Building Blocks in Genetic Programming Populations", booktitle = "2007 IEEE Congress on Evolutionary Computation", year = "2007", editor = "Dipti Srinivasan and Lipo Wang", pages = "2501--2508", address = "Singapore", month = "25-28 " # sep, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "1-4244-1340-0", file = "1917.pdf", keywords = "genetic algorithms, genetic programming", URL = "http://sc.snu.ac.kr/PAPERS/cec07.pdf", DOI = "doi:10.1109/CEC.2007.4424785", abstract = "Compression algorithms generate a predictive model of data, using the model to reduce the number of bits required to transmit the data (in effect, transmitting only the differences from the model). As a consequence, the degree of compression achieved provides an estimate of the level of regularity in the data. Previous work has investigated the use of these estimates to understand the replication of building blocks within Genetic Programming (GP) individuals, and hence to understand how different GP algorithms promote the evolution of repeated common structure within individuals. Here, we extend this work to the population level, and use it to understand the extent of similarity between sub-structures within individuals in GP populations.", notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C", } @Article{McKay:2008:IJKBIES, author = "Bob McKay and Shu-Heng Chen and Xuan Hoai Nguyen", title = "Genetic Programming: An emerging engineering tool", journal = "International Journal of Knowledge-Based and Intelligent Engineering Systems", year = "2008", volume = "12", number = "1", pages = "1--2", note = "Guest Editorial", keywords = "genetic algorithms, genetic programming", ISSN = "1327-2314", publisher = "IOS Press", URL = "http://content.iospress.com/articles/international-journal-of-knowledge-based-and-intelligent-engineering-systems/kes00137", DOI = "doi:10.3233/KES-2008-12101", size = "2 pages", notes = "KES", } @InCollection{McKay20081464, author = "Bob McKay", title = "Evolutionary Algorithms", editor = "Sven Erik Jorgensen and Brian Fath", booktitle = "Encyclopedia of Ecology", publisher = "Academic Press", address = "Oxford", year = "2008", pages = "1464--1472", isbn13 = "978-0-08-045405-4", DOI = "doi:10.1016/B978-008045405-4.00157-9", URL = "http://www.sciencedirect.com/science/article/B9636-4SY6CH0-DJ/2/11adb71275768a6e15d7c1e63d29d946", keywords = "genetic algorithms, genetic programming, Ant colony optimization, Artificial immune system, Ecological modeling, Estimation of distribution algorithm, Evolution strategy, Evolutionary algorithm, Evolutionary computation, Machine learning, Messy genetic algorithm, Model fitting, Optimization, Particle swarm algorithm", abstract = "Evolutionary algorithms are widely used in ecological modeling. Darwin's theory of natural selection can be viewed as an algorithm for evolving fit organisms, with fit in this context meaning survivable. The insight of evolutionary computation is that the same algorithm can be applied to populations of problem solutions, with the fitness metric being defined so as to suit the requirements of the user. Subsequent advances in evolutionary theory can then be viewed as algorithm refinements, worthy of evaluation for possible inclusion in the method. Applications in ecological modeling range from tuning parameters of predefined models to fit the data, through generating predictive and/or explanatory models directly from the data, to modeling the coevolutionary processes in ecological systems. The original systems, evolution strategies and genetic algorithms (GAs), have since been joined by a family of related algorithms, notably classifier systems, genetic programming, messy GAs, estimation of distribution algorithms, ant colony and particle swarm algorithms, and artificial immune systems, all of which have something to offer the ecological modeler.", notes = "Author given as {"}B{"} McKay", } @InProceedings{DBLP:conf/gecco/McKayNCKMH09, author = "Robert I. McKay and Xuan Hoai Nguyen and James R. Cheney and MinHyeok Kim and Naoki Mori and Tuan Hao Hoang", title = "Estimating the distribution and propagation of genetic programming building blocks through tree compression", booktitle = "GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation", year = "2009", editor = "Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano {van Hemert} and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique Alba", pages = "1011--1018", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1569901.1570038", abstract = "Shin et al [19] and McKay et al [15] previously applied tree compression and semantics-based simplification to study the distribution of building blocks in evolving Genetic Programming populations. However their method could only give static estimates of the degree of repetition of building blocks in one generation at a time, supplying no information about the flow of building blocks between generations. Here, we use a state-of-the-art tree compression algorithm, xmlppm, to estimate the extent to which frequent building blocks from one generation are still in use in a later generation. While they compared the behaviour of different GP algorithms on one specific problem -- a simple symbolic regression problem -- we extend the analysis to a more complex problem, a symbolic regression problem to find a Fourier approximation to a sawtooth wave, and to a Boolean domain, odd parity.", notes = "GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009). ACM Order Number 910092.", } @Article{McKay:2010:GPEM, author = "Robert I. McKay and Nguyen Xuan Hoai and Peter Alexander Whigham and Yin Shan and Michael O'Neill", title = "Grammar-based Genetic Programming: a survey", journal = "Genetic Programming and Evolvable Machines", year = "2010", volume = "11", number = "3/4", pages = "365--396", month = sep, note = "Tenth Anniversary Issue: Progress in Genetic Programming and Evolvable Machines", keywords = "genetic algorithms, genetic programming, Evolutionary computation, Grammar, Context free, Regular, Tree adjoining", ISSN = "1389-2576", URL = "https://rdcu.be/dpy1j", DOI = "doi:10.1007/s10710-010-9109-y", size = "32 pages", abstract = "Grammar formalisms are one of the key representation structures in Computer Science. So it is not surprising that they have also become important as a method for formalizing constraints in Genetic Programming (GP). Practical grammar-based GP systems first appeared in the mid 1990s, and have subsequently become an important strand in GP research and applications. We trace their subsequent rise, surveying the various grammar-based formalisms that have been used in GP and discussing the contributions they have made to the progress of GP. We illustrate these contributions with a range of applications of grammar-based GP, showing how grammar formalisms contributed to the solutions of these problems. We briefly discuss the likely future development of grammar-based GP systems, and conclude with a brief summary of the field.", } @Article{McKee:2002:EJOR, author = "Thomas E. McKee and Terje Lensberg", title = "Genetic programming and rough sets: A hybrid approach to bankruptcy classification", journal = "European Journal of Operational Research", year = "2002", volume = "138", pages = "436--451", number = "2", keywords = "genetic algorithms, genetic programming, Rough sets, Bankruptcy, Hybrid models, Continuity theory", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6VCT-44X69C1-H/2/4757607399cd181dadad865b5a62c58f", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.619.594", broken = "http://sedok.narod.ru/s_files/poland/25.pdf", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.619.594", DOI = "doi:10.1016/S0377-2217(01)00130-8", abstract = "The high social costs associated with bankruptcy have spurred searches for better theoretical understanding and prediction capability. we investigate a hybrid approach to bankruptcy prediction, using a genetic programming algorithm to construct a bankruptcy prediction model with variables from a rough sets model derived in prior research. Both studies used data from 291 US public companies for the period 1991 to 1997. The second stage genetic programming model developed consists of a decision model that is 80% accurate on a validation sample as compared to the original rough sets model which was 67% accurate. Additionally, the genetic programming model reveals relationships between variables that are not apparent in either the rough sets model or prior research. These findings indicate that genetic programming coupled with rough sets theory can be an efficient and effective hybrid modelling approach both for developing a robust bankruptcy prediction model and for offering additional theoretical insights.", } @Article{McKenney:2012:SC, author = "Dave McKenney and Tony White", title = "Stock trading strategy creation using {GP} on {GPU}", journal = "Soft Computing", year = "2012", volume = "16", pages = "247--259", keywords = "genetic algorithms, genetic programming, GPU", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.1042.8614", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1042.8614", URL = "http://people.scs.carleton.ca/%7Edmckenne/5704/Paper/Final_Paper.pdf", abstract = "This paper investigates the speed improvements available when using a graphics processing unit (GPU) for evaluation of individuals in a genetic programming (GP) environment. An existing GP system is modified to enable parallel evaluation of individuals on a GPU device. Several issues related to implementing GP on GPU are discussed, including how to perform tree-based GP on a device without recursion support, as well as the effect that proper memory layout can have on speed increases when using CUDA-enabled nVidia GPU devices. The specific GP implementation is designed to evolve stock trading strategies using technical analysis indicators. The second goal of this research is to investigate the possible improvement in performance when training individuals on a larger number of stocks and training days. This increased training size (nearly 100,000 training points) is enabled due to the speedups realized by GPU evaluation. Several different scenarios were used to test various speed optimisations of GP evaluation on the GPU device, with a peak speedup factor of over 600 (when compared to sequential evaluation on a 2.4 GHz CPU). Also, it is found that increasing the number of stocks and the length of the training period can result in higher out-of-training testing profitability.", } @PhdThesis{McKenney_David_Greig:thesis, author = "Dave McKenney", title = "On Behaviour-Based Network Inference and Distribution-Based Network Control", school = "School of Computer Science, Carleton University", year = "2017", address = "Ottawa, Ontario, Canada", month = dec, URL = "https://repository.library.carleton.ca/concern/etds/nc580n61p?locale=en", URL = "https://repository.library.carleton.ca/concern/parent/nc580n61p/file_sets/dz010q99g", DOI = "doi:10.22215/etd/2017-12672", size = "169 pages", notes = "Not GP? mckenney-onbehaviourbasednetworkinferenceanddistributionbased.pdf Supervisor: Tony White", } @InCollection{mcmilin:2000:AIDSA, author = "Emily McMilin", title = "Adaptation of Internet Data Sending Algorithm", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "279--285", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:2000:gagp}", } @InProceedings{McMinn:2016:SBST, author = "Phil McMinn and Mark Harman and Gordon Fraser and Gregory M. Kapfhammer", booktitle = "2016 IEEE/ACM 9th International Workshop on Search-Based Software Testing (SBST)", title = "Automated Search for Good Coverage Criteria: Moving from Code Coverage to Fault Coverage through Search-Based Software Engineering", year = "2016", pages = "43--44", abstract = "We propose to use Search-Based Software Engineering to automatically evolve coverage criteria that are well correlated with fault revelation, through the use of existing fault databases. We explain how problems of bloat and overfitting can be ameliorated in our approach, and show how this new method will yield insight into faults - as well as better guidance for Search-Based Software Testing.", keywords = "genetic algorithms, genetic programming, SBSE", DOI = "doi:10.1109/SBST.2016.017", month = may, notes = "Also known as \cite{7810706}", } @InProceedings{McMullin:2010:gecco, author = "Michael McMullin and Terence Soule", title = "Constant versus variable arity operators in genetic programming", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "987--988", keywords = "genetic algorithms, genetic programming, Poster", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830483.1830663", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "In this paper we compare typical variable arity operators to constant arity operators in which extraneous branches are treated as no-ops. We suggest that the consistent arity implementation would perform poorer than the variable arity implementation, due to a large amount of non-productive changes experienced during the early life of a consistent arity individual. Contrary to the expected result, both algorithms performed nearly identically. The consistent arity population developed a better average population faster, indicating that it would be a better option for tasks requiring many options for success. The variable arity population developed much smaller individuals on average, taking up much less space. This may be partly due to the large proportion of arity one operators in the operator set. However, a comparison of the execution times produced surprisingly mixed results, with the variable arity approach sometime taking significantly more time despite producing significantly smaller trees.", notes = "truck backing up, symbolic regression, inter-twined spirals, Also known as \cite{1830663} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @Article{McNamara2009670, author = "John M. McNamara and Alasdair I. Houston", title = "Integrating function and mechanism", journal = "Trends in Ecology \& Evolution", volume = "24", number = "12", pages = "670--675", year = "2009", keywords = "genetic algorithms, genetic programming", ISSN = "0169-5347", DOI = "doi:10.1016/j.tree.2009.05.011", URL = "http://www.sciencedirect.com/science/article/B6VJ1-4X0RG2P-3/2/087336313b9c5cc3a38a773108632021", abstract = "Behavioural ecology often makes the assumption that animals can respond flexibly by adopting the optimal behaviour for each circumstance. However, as ethologists have long known, behaviour is determined by mechanisms that are not optimal in every circumstance. As we discuss here, we believe that it is necessary to integrate these separate traditions by considering the evolution of mechanisms, an approach referred to as [`]Evo-mecho'. This integration is timely because there is a growing awareness of the importance of environmental complexity in shaping behaviour; there are established and effective computational procedures for simulating evolution and there is rapidly increasing knowledge of the neuronal basis of decision-making. Although behavioural ecologists have built complex models of optimal behaviour in simple environments, we argue that they need to focus on simple mechanisms that perform well in complex environments.", notes = "GP only mentioned in {"}the way ahead{"}", } @InCollection{mcnames:1994:fnnGP, author = "James {McNames}", title = "Faster Neural Network Architectures from Genetic Algorithms", booktitle = "Artificial Life at Stanford 1994", year = "1994", editor = "John R. Koza", pages = "108--117", address = "Stanford, California, 94305-3079 USA", month = jun, organisation = "Stanford University", publisher = "Stanford Bookstore", keywords = "genetic algorithms", ISBN = "0-18-182105-2", notes = "This volume contains 22 papers written and submitted by students describing their term projects for the course in artificial life (Computer Science 425) at Stanford University offered during the spring quarter quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs425.html", } @InCollection{mcnutt:1997:crrc, author = "Greg McNutt", title = "Using Co-Evolution to Produce Robust Robot Control", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1997", publisher = "Stanford Bookstore", year = "1997", editor = "John R. Koza", pages = "159--167", address = "Stanford, California, 94305-3079 USA", month = "17 " # mar, keywords = "genetic algorithms, genetic programming", ISBN = "0-18-205981-2", abstract = "mobile robots navigate test course, coures evolve vased upon ability to cause robots to crash", notes = "part of \cite{koza:1997:GAGPs} simulation coevolution effective DGPC ", } @InProceedings{mcnutt:1997:crrcLB, author = "Greg McNutt", title = "Using Co-Evolution to Produce Robust Control", booktitle = "Late Breaking Papers at the 1997 Genetic Programming Conference", year = "1997", editor = "John R. Koza", pages = "141--149", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California, 94305-3079, USA", month = "13--16 " # jul, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-206995-8", notes = "GP-97LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670", } @InProceedings{McPhee:1995:acrep, author = "Nicholas Freitag McPhee and Justin Darwin Miller", title = "Accurate Replication in Genetic Programming", booktitle = "Genetic Algorithms: Proceedings of the Sixth International Conference (ICGA95)", year = "1995", editor = "Larry J. Eshelman", pages = "303--309", address = "Pittsburgh, PA, USA", publisher_address = "San Francisco, CA, USA", month = "15-19 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, bloat", ISBN = "1-55860-370-0", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.51.6390", URL = "http://www.mrs.umn.edu/~mcphee/Research/Accurate_replication.ps", URL = "http://citeseer.ist.psu.edu/mcphee95accurate.html", size = "7 pages", abstract = "One characteristic tendency of genetic programming is the production of considerably larger trees than expected. It has been suggested that this is related to the ability of individuals to replicate accurately. In this paper we present theoretical analysis which shows that, for certain specific cases, the pressure for accurate replication induces an increase in tree size. In particular, we show that among fit individuals, larger trees are more likely to yield semantically equivalent children via the crossover operator, leading to an overall increase in the average size of fit individuals. This is followed by experimental results consistent with our analysis. We also include the results of experiments where the expected growth in tree size was not observed, suggesting that this phenomenon,while common, is not universal.", notes = "Presents theoretical analysis that, in some cases, the preasure for acurate replication (ie for children to be as fit as their parents) induces and increase in size. INC-IGNORE, INC, (PLUS-IGNORE, PLUS, INC_DEC and INC-ID) problems. Claims presence of large semanticall inert subtrees inhits discovery of solution but once found they help population to converge to this solution. Suggests 'one should avoid function sets which can easily be manipulated to build semantically irrelevant subtrees'. Cited by \cite{mcphee:2001:EuroGP}", } @InProceedings{mcphee:1998:itetpGP, author = "Nicholas Freitag McPhee and Nicholas J. Hopper and Mitchell L. Reierson", title = "Impact of types on essentially typeless problems in {GP}", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "232--240", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, Hindley-Miller Typed Genetic Programming, HMGP", ISBN = "1-55860-548-7", URL = "http://www-users.cs.umn.edu/~hopper/typed_vs_untyped.ps", size = "9 pages", abstract = "Several researchers have shown type systems to be valuable in extending the range of problems (conveniently) addressed by Genetic Programming. There are other possible benefits of type systems, however, that derive from the new kinds of structural representation they make possible, and the effects that this has on the performance of recombination operators like the crossover operator. we compare the performance of Standard (untyped) Genetic Programming (SGP) and Hindley-Milner (typed) Genetic Programming (HMGP) on a suite of problems where an untyped representation (satisfying the closure property)is quite natural. We find that on several problems HMGP significantly out-performs SGP, while on other problems the performance of SGP and HMGP are essentially the same. We also suggest an intermediate representation that should provide many of the benefits of HMGP on these problems without requiring the complexity of a powerful type system", notes = "GP-98", } @InProceedings{mcphee:1998:sutherland, author = "Nicholas Freitag McPhee and Nicholas J. Hopper and Mitchell L. Reierson", title = "Sutherland: An extensible object-oriented software framework for evolutionary computation", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "241", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-548-7", URL = "http://facultypages.morris.umn.edu/~mcphee/Research/Sutherland/sutherland_gp98_announcement.ps.gz", URL = "http://citeseer.ist.psu.edu/89369.html", size = "1 page", notes = "GP-98 Object-oriented design techniques, Java. DAG??? Sutherland. Bit strings or parse trees", } @InProceedings{mcphee:1999:A, author = "Nicholas Freitag McPhee and Nicholas J. Hopper", title = "Analysis of genetic diversity through population history", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1112--1120", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-421.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-421.ps", URL = "https://dl.acm.org/doi/10.5555/2934046.2934071", size = "9 pages", abstract = "The idea that diversity in the population of a genetic algorithm affects the algorithm search efficiency is widely accepted. However, little is known about the amount of node level diversity present in Genetic Programming (GP) runs. In this paper, we introduce several techniques for measuring the diversity of a population based on the genetic history of the individuals. We then apply these measures to the genetic histories of several runs of four different problems. The results of this analysis show that a surprisingly small amount of diversity is present in the final population of a GP run. We conclude by suggesting a variety of other potential applications of these measures.", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{mcphee:1999:AAASRG, author = "Nicholas Freitag McPhee and Nicholas J. Hopper", title = "{AppGP}: An Alternative Structural Representation for {GP}", booktitle = "Proceedings of the Congress on Evolutionary Computation", year = "1999", editor = "Peter J. Angeline and Zbyszek Michalewicz and Marc Schoenauer and Xin Yao and Ali Zalzala", volume = "2", pages = "1377--1383", address = "Mayflower Hotel, Washington D.C., USA", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "6-9 " # jul, organisation = "Congress on Evolutionary Computation, IEEE / Neural Networks Council, Evolutionary Programming Society, Galesia, IEE", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, models of evolutionary computation, AppGP, local maxima, performance, standard genetic programming, standard subtree crossover, structural convergence", ISBN = "0-7803-5536-9 (softbound)", ISBN = "0-7803-5537-7 (Microfiche)", URL = "https://www-users.cs.umn.edu/~hoppernj/appgp.ps", DOI = "doi:10.1109/CEC.1999.782643", size = "7 pages", abstract = "It has been shown that standard genetic programming using standard subtree crossover is prone to a form of structural convergence which makes it extremely difficult to make changes near the root, occasionally causing runs to become trapped in local maxima. Based on these structural limitations we propose a different tree representation, AppGP, which we hope will avoid this problem in some cases. In this paper, we describe this representation, and compare its performance to the performance of standard GP on a suite of test problems. We find that on all of the test problems, AppGP does no worse than standard GP, and in several it does considerably better, suggesting that the representation warrants further study", notes = "CEC-99 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. Library of Congress Number = 99-61143", } @TechReport{McPhee00-22, author = "Nicholas Freitag McPhee and Riccardo Poli", title = "A schema theory analysis of the evolution of size in genetic programming with linear representations", institution = "University of Birmingham, School of Computer Science", number = "CSRP-00-22", month = nov, year = "2000", email = "N.F.McPhee@cs.bham.ac.uk, R.Poli@cs.bham.ac.uk", keywords = "genetic algorithms, genetic programming", file = "/2000/CSRP-00-22.ps.gz", URL = "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/2000/CSRP-00-22.ps.gz", abstract = "In this paper we use the schema theory presented in [Poli and McPhee, 2000] to better understand the changes in size distribution when using GP with standard crossover and linear structures. Applications of the theory to problems both with and without fitness suggest that standard crossover induces specific biases in the distributions of sizes, with a strong tendency to over sample small structures, and indicate the existence of strong redistribution effects that may be a major force in the early stages of a GP run. We also present two important theoretical results: An exact theory of bloat, and a general theory of how average size changes on flat landscapes with glitches. The latter implies the surprising result that a single program glitch in an otherwise flat fitness landscape is sufficient to drive the average program size of an infinite population, which may have important implications for the control of code growth.", notes = "published as \cite{mcphee:2001:EuroGP}", } @TechReport{McPhee00-24, author = "Nicholas Freitag McPhee and Riccardo Poli and Jon E Rowe", title = "A schema theory analysis of mutation size biases in genetic programming with linear representations", institution = "University of Birmingham, School of Computer Science", number = "CSRP-00-24", month = nov, year = "2000", email = "N.F.McPhee@cs.bham.ac.uk, R.Poli@cs.bham.ac.uk N.F.McPhee@cs.bham.ac.uk", keywords = "genetic algorithms, genetic programming", file = "/2000/CSRP-00-24.ps.gz", URL = "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/2000/CSRP-00-24.ps.gz", abstract = "In recent work we showed how developments in GP schema theory can be used to better understand the biases induced by the standard subtree crossover when genetic programming is applied to variable length linear structures. In this paper we use the schema theory to better understand the biases induced on linear structures by two common GP subtree mutation operators: FULL and GROW mutation. In both cases we find that the operators do have quite specific biases and typically strongly oversample shorter strings.", } @InProceedings{mcphee:2001:EuroGP, author = "Nicholas Freitag McPhee and Riccardo Poli", title = "A schema theory analysis of the evolution of size in genetic programming with linear representations", booktitle = "Genetic Programming, Proceedings of EuroGP'2001", year = "2001", editor = "Julian F. Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon", volume = "2038", series = "LNCS", pages = "108--125", address = "Lake Como, Italy", publisher_address = "Berlin", month = "18-20 " # apr, organisation = "EvoNET", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, Schema theory, Linear representations, Bloat, Length distributions, Fitness landscape glitches, One-then-zeros problem", ISBN = "3-540-41899-7", URL = "http://cswww.essex.ac.uk/staff/poli/papers/McPhee-EUROGP2001-ST-Linear-Bloat.pdf", URL = "http://citeseer.ist.psu.edu/502073.html", DOI = "doi:10.1007/3-540-45355-5_10", size = "18 pages", abstract = "In this paper we use the schema theory presented elsewhere in this volume to better understand the changes in size distribution when using GP with standard crossover and linear structures. Applications of the theory to problems both with and without fitness suggest that standard crossover induces specific biases in the distributions of sizes, with a strong tendency to over sample small structures, and indicate the existence of strong redistribution effects that may be a major force in the early stages of a GP run. We also present two important theoretical results: An exact theory of bloat, and a general theory of how average size changes on flat landscapes with glitches. The latter implies the surprising result that a single program glitch in an otherwise flat fitness landscape is sufficient to drive the average program size of an infinite population, which may have important implications for the control of code growth.", notes = "'sec 1.2 Why theory matters Six years ago McPhee and Miller \cite{McPhee:1995:acrep} examined bloat in the INC-IGNORE problem, an artificial problem ... only taken out 100 generations ... in a new run taken out to 3000 generations ... shows a much more complex picture ... it is unclear [what] the long term behavior [is].' EuroGP'2001, part of \cite{miller:2001:gp} Update of \cite{McPhee00-22}", } @InProceedings{mcphee:2001:astamsbgplr, author = "Nicholas Freitag McPhee and Riccardo Poli and Jonathan E. Rowe", title = "A Schema Theory Analysis of Mutation Size Biases in Genetic Programming with Linear Representations", booktitle = "Proceedings of the 2001 Congress on Evolutionary Computation CEC2001", year = "2001", pages = "1078--1085", address = "COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "27-30 " # may, organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, schema theory, mutation, linear representation, size bias", ISBN = "0-7803-6658-1", URL = "http://cswww.essex.ac.uk/staff/poli/papers/McPhee-CEC2001.pdf", URL = "http://cswww.essex.ac.uk/staff/poli/papers/postscript/McPhee-CEC2001.ps.gz", URL = "http://citeseer.ist.psu.edu/502355.html", URL = "http://citeseer.ist.psu.edu/501380.html", DOI = "doi:10.1109/CEC.2001.934311", abstract = "Understanding operator bias in evolutionary computation is important because it is possible for the operator's biases to work against the intended biases induced by the fitness function. In recent work we showed how developments in GP schema theory can be used to better understand the biases induced by the standard subtree crossover when genetic programming is applied to variable length linear structures. We use the schema theory to better understand the biases induced on linear structures by two common GP subtree mutation operators: FULL and GROW mutation. In both cases we find that the operators do have quite specific biases and typically strongly oversample shorter strings.", notes = "CEC-2001 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 01TH8546C, Library of Congress Number = . linear (unary) tree schemata. flat fitness landscape. biases of full mutation, grow mutation, No fitness. Full(unary) average length = 2*D-1. Limiting size distribution: 0 for size < D, flat region size < 2D, rapid falling size>=2D. Similar to subtree crossover. Grow(unary) discrete gamma distribution (cf. \cite{Rowe01} ) cf subtree crossover. {"}ones then zeros{"} unary problem. Subtree crossover bloat (at least to 75 generations). full no bloat, actually as with no fitness, {"}artifact of this particular problem{"}. Grow similar to no fitness.", } @TechReport{mcphee:2002:CSM, author = "Nicholas Freitag McPhee and Riccardo Poli", title = "Using schema theory to explore interactions of multiple operators", institution = "Department of Computer Science, University of Essex", year = "2002", number = "CSM-365", address = "Colchester, UK", month = feb, keywords = "genetic algorithms, genetic programming", URL = "http://cswww.essex.ac.uk/technical-reports/2002/csm-365.pdf", abstract = "In the last two years the schema theory for Genetic Programming (GP) has been applied to the problem of understanding the length biases of a variety of crossover and mutation operators on variable length linear structures. In these initial papers, operators were studied in isolation. In practice, however, they are typically used in various combinations, and in this paper we present the first schema theory analysis of the complex interactions of multiple operators. In particular we apply the schema theory to the use of standard subtree crossover, full mutation, and grow mutation (in varying proportions) to variable length linear structures in the one-then-zeros problem. We then show how the results can be used to guide choices about the relative proportion of these operators in order to achieve certain structural goals during a run.", size = "16 pages", } @InProceedings{mcphee:2002:gecco, author = "Nicholas Freitag {McPhee} and Riccardo Poli", title = "Using Schema Theory To Explore Interactions Of Multiple Operators", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "853--860", address = "New York", publisher_address = "San Francisco, CA 94104, USA", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "genetic algorithms, genetic programming, operator bias, operator interaction, operator proportion, schema theory, variable length linear structures", ISBN = "1-55860-878-8", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/GP139.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/GP139.ps", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf", abstract = "In the last two years the schema theory for Genetic Programming (GP) has been applied to the problem of understanding the length biases of a variety of crossover and mutation operators on variable length linear structures. In these initial papers, operators were studied in isolation. In practice, however, they are typically used in various combinations, and in this paper we present the first schema theory analysis of the complex interactions of multiple operators. In particular, we apply the schema theory to the use of standard subtree crossover, full mutation, and grow mutation (in varying proportions) to variable length linear structures in the one-then-zeros problem. We then show how the results can be used to guide choices about the relative proportion of these operators in order to achieve certain structural goals during a run.", notes = "GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)", } @InProceedings{mcphee:ots:gecco2004, author = "Nicholas Freitag McPhee and Alex Jarvis and Ellery Fussell Crane", title = "On the Strength of Size Limits in Linear Genetic Programming", booktitle = "Genetic and Evolutionary Computation -- GECCO-2004, Part II", year = "2004", editor = "Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell", series = "Lecture Notes in Computer Science", pages = "593--604", address = "Seattle, WA, USA", publisher_address = "Heidelberg", month = "26-30 " # jun, organisation = "ISGEC", publisher = "Springer-Verlag", volume = "3103", ISBN = "3-540-22343-6", ISSN = "0302-9743", DOI = "doi:10.1007/b98645", size = "12", keywords = "genetic algorithms, genetic programming", notes = "GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)", } @TechReport{mcphee:2007:wps32, title = "Semantic Building Blocks in Genetic Programming", author = "Nicholas Freitag McPhee and Brian Ohs and Tyler Hutchison", institution = "University of Minnesota Morris", year = "2007", type = "Working Paper Series", number = "Volume 3 Number 2", address = "600 East 4th Street, Morris, MN 56267, USA", month = "12 " # dec, keywords = "genetic algorithms, genetic programming", URL = "http://www.morris.umn.edu/academic/fclt/Working%20Papers/Morris_WP_3.2.pdf", notes = "Colour version of \cite{conf/eurogp/McPheeOH08}", size = "20 pages", } @InProceedings{conf/eurogp/McPheeOH08, title = "Semantic Building Blocks in Genetic Programming", author = "Nicholas Freitag McPhee and Brian Ohs and Tyler Hutchison", bibdate = "2008-04-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/eurogp/eurogp2008.html#McPheeOH08", booktitle = "Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008", address = "Naples", month = "26-28 " # mar, publisher = "Springer", year = "2008", volume = "4971", editor = "Michael O'Neill and Leonardo Vanneschi and Steven Gustafson and Anna Isabel {Esparcia Alcazar} and Ivanoe {De Falco} and Antonio {Della Cioppa} and Ernesto Tarantino", isbn13 = "978-3-540-78670-2", pages = "134--145", series = "Lecture Notes in Computer Science", DOI = "doi:10.1007/978-3-540-78671-9_12", size = "12 pages", abstract = "We present a new mechanism for studying the impact of subtree crossover in terms of semantic building blocks. This approach allows us to completely and compactly describe the semantic action of crossover, and provide insight into what does (or doesn't) make crossover effective. Our results make it clear that a very high proportion of crossover events (typically over 75percent in our experiments) are guaranteed to perform no immediately useful search in the semantic space. Our findings also indicate a strong correlation between lack of progress and high proportions of fixed contexts. These results then suggest several new, theoretically grounded, research areas.", keywords = "genetic algorithms, genetic programming", notes = "Table 3: Function set Binary AND, OR, NAND, and NOR. Terminal set x0,x1, . . . ,xn-1, where n is the number of variables. Initialisation PTC2 \cite{luke:2000:2ftcaGP}, with equal proportions of sizes 50, 70, and 100 nodes and maximum initial depth of 10 Number of generations 500. Tournament size 2. XO Probability 1. XO bias away from leaves None (all nodes are equally likely). Maximum size after XO 500 (If the resulting child is too large, then new parents are chosen independently and process begins again.). See also \cite{mcphee:2007:wps32}. Part of \cite{conf/eurogp/2008} EuroGP'2008 held in conjunction with EvoCOP2008, EvoBIO2008 and EvoWorkshops2008", } @InProceedings{McPhee:2008:gecco, author = "Nicholas F. McPhee and Riccardo Poli", title = "Memory with memory: Soft assignment in Genetic Programming", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "1235--1242", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1235.pdf", DOI = "doi:10.1145/1389095.1389336", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, linear GP, memory with memory, soft assignment, symbolic regression", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389336}", } @InProceedings{mcphee:2009:gecco, author = "Nicholas Freitag McPhee and Ellery Fussell Crane and Sara E. Lahr and Riccardo Poli", title = "Developmental plasticity in linear genetic programming", booktitle = "GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation", year = "2009", editor = "Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano {van Hemert} and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique Alba", pages = "1019--1026", address = "Montreal", month = "8-12 " # jul, organisation = "SigEvo", publisher = "ACM", publisher_address = "New York, NY, USA", note = "Nominated for best paper award in the GP track", keywords = "genetic algorithms, genetic programming, EDA", isbn13 = "978-1-60558-325-9", DOI = "doi:10.1145/1569901.1570039", size = "8 pages", abstract = "Biological organisms exhibit numerous types of plasticity, where they respond both developmentally and behaviorally to environmental factors. In some organisms, for example, environmental conditions can lead to the developmental expression of genes that would otherwise remain dormant, leading to significant phenotypic variation and allowing selection to act on these otherwise {"}invisible{"} genes. In contrast to biological plasticity, the vast majority of evolutionary computation systems, including genetic programming, are rigid and can only adapt to very limited external changes. In this paper we extend the N-gram GP system, a recently introduced estimation of distribution algorithm for program evolution, using Incremental Fitness-based Development (IFD), a novel technique which allows for developmental plasticity in the generation of linear-GP style programs. Tests with a large set of problems show that the new system outperforms the original N-gram GP system and is competitive with standard GP. Analysis of the evolved programs indicates that IFD allows for the generation of more complex programs than standard N-gram GP, with the generated programs often containing several separate sequences of instructions that are reused multiple times, often with variations.", notes = "n-gram GP, 3 gram linear GP. Removed 2nd and first order derived matrices by starting program with two null operations. Program increases size by greedy addition of blocks on instructions. Incremental blocks need not be three instruction long. Symbolic regression. Additional blocks added at random until fitness is improved or time out. One fixed (protected?) input register. ROUT different. memory with memory. {"}Does not scale well to large pools of constants{"} {"}IFD never hurts{"} IFD solutions both {"}more complex{"} and {"}mode modular{"} GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009). ACM Order Number 910092. Also known as \cite{DBLP:conf/gecco/McPheeCLP09}", } @InCollection{McPhee:2010:GPTP, author = "Nic McPhee", title = "Foreword", booktitle = "Genetic Programming Theory and Practice VIII", year = "2010", editor = "Rick Riolo and Trent McConaghy and Ekaterina Vladislavleva", series = "Genetic and Evolutionary Computation", volume = "8", address = "Ann Arbor, USA", month = "12-14 " # may, publisher = "Springer", pages = "xii--xvi", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4419-7746-5", URL = "http://www.springer.com/computer/ai/book/978-1-4419-7746-5", DOI = "doi:10.1007/978-1-4419-7747-2", size = "4 pages", notes = "part of \cite{Riolo:2010:GPTP}", } @InProceedings{McPhee:2015:GPTP, author = "Nicholas Freitag McPhee and David Donatucci and Thomas Helmuth", title = "Using Graph Databases to Explore Genetic Programming Run Dynamics", booktitle = "Genetic Programming Theory and Practice XIII", year = "2015", editor = "Rick Riolo and William P. Worzel and M. Kotanchek and A. Kordon", series = "Genetic and Evolutionary Computation", pages = "185--201", address = "Ann Arbor, USA", month = "14-16 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Graph database, Neo4j, Ancestry, Genealogy, Lexicase selection, Tournament selection", isbn13 = "978-3-319-34223-8", URL = "http://www.springer.com/us/book/9783319342214", DOI = "doi:10.1007/978-3-319-34223-8_11", abstract = "For both practical reasons and those of habit, most evolutionary computation research is presented in highly summary form. These summaries, however, often obscure or completely mask the profusion of specific selections, crossovers, and mutations that are ultimately responsible for the aggregate behaviours we are interested in. In this chapter we take a different approach and use the Neo4j graph database system to record and analyse the entire genealogical history of a set of genetic programming runs. We then explore a few of these runs in detail, discovering important properties of lexicase selection; these may in turn help us better understand the dynamics of lexicase selection, and the ways in which it differs from tournament selection. More broadly, we illustrate the value of recording and analysing this level of detail, both as a means of understanding the dynamics of particular runs, and as a way of generating questions and ideas for subsequent, broader study.", notes = " Part of \cite{Riolo:2015:GPTP} Published after the workshop in 2016", } @InProceedings{McPhee:2015:GECCO, author = "Nicholas Freitag McPhee and M. Kirbie Dramdahl and David Donatucci", title = "Impact of Crossover Bias in Genetic Programming", booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation", year = "2015", editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr", isbn13 = "978-1-4503-3472-3", pages = "1079--1086", keywords = "genetic algorithms, genetic programming", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739480.2754778", DOI = "doi:10.1145/2739480.2754778", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "In tree-based genetic programming (GP) with sub-tree crossover, the parent contributing the root portion of the tree (the root parent) often contributes more to the semantics of the resulting child than the non-root parent. Previous research demonstrated that when the root parent had greater fitness than the non-root parent, the fitness of the child tended to be better than if the reverse were true. Here we explore the significance of that asymmetry by introducing the notion of crossover bias, where we bias the system in favor of having the more fit parent as the root parent. In this paper we apply crossover bias to several problems. In most cases we found that crossover bias either improved performance or had no impact. We also found that the effectiveness of crossover bias is dependent on the problem, and significantly dependent on other parameter choices. While this work focuses specifically on sub-tree crossover in tree-based GP, artificial and biological evolutionary systems often have substantial asymmetries, many of which remain understudied. This work suggests that there is value in further exploration of the impacts of these asymmetries.", notes = "Also known as \cite{2754778} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)", } @InProceedings{McPhee:2016:GPTP, author = "Nicholas Freitag McPhee and Mitchell D. Finzel and Maggie M. Casale and Thomas Helmuth and Lee Spector", title = "A detailed analysis of a {PushGP} run", booktitle = "Genetic Programming Theory and Practice XIV", year = "2016", editor = "Rick Riolo and Bill Worzel and Brian Goldman and Bill Tozier", pages = "65--83", address = "Ann Arbor, USA", month = "19-21 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, PushGP, ancestry graph, lineage, inheritance", isbn13 = "978-3-319-97087-5", URL = "https://www.springer.com/us/book/9783319970875", DOI = "doi:10.1007/978-3-319-97088-2_5", abstract = "In evolutionary computation runs there is a great deal of data that could be saved and analysed. This data is often put aside, however, in favour of focusing on the final outcomes, typically captured and presented in the form of summary statistics and performance plots. Here we examine a genetic programming run in detail and trace back from the solution to determine how it was derived. To visualize this genetic programming run, the ancestry graph is extracted, running from the solution(s) in the final generation up to their ancestors in the initial random population. The key instructions in the solution are also identified, and a genetic ancestry graph is constructed, a subgraph of the ancestry graph containing only those individuals contributed genetic information (or instructions) to the solution. This visualization and our ability to trace these key instructions throughout the run allowed us to identify general inheritance patterns and key evolutionary moments in this run.", notes = " Part of \cite{Tozier:2016:GPTP} published after the workshop", } @InProceedings{McPhee:2016:GECCOcomp, author = "Nicholas Freitag McPhee and Maggie M. Casale and Mitchell Finzel and Thomas Helmuth and Lee Spector", title = "Visualizing Genetic Programming Ancestries", booktitle = "GECCO '16 Companion: Proceedings of the Companion Publication of the 2016 Annual Conference on Genetic and Evolutionary Computation", year = "2016", isbn13 = "978-1-4503-4323-7", pages = "1419--1426", keywords = "genetic algorithms, genetic programming, pushGP, liner genetic programming, Clojush, lexicase selection", month = "20-24 " # jul, organisation = "SIGEVO", address = "Denver, Colorado, USA", DOI = "doi:10.1145/2908961.2931741", publisher = "ACM", publisher_address = "New York, NY, USA", size = "8 pages", abstract = "Previous work has demonstrated the utility of graph databases as a tool for collecting, analysing, and visualizing ancestry in evolutionary computation runs. That work focused on sections of individual runs, whereas this paper illustrates the application of these ideas on the entirety of large runs (up to three hundred thousand individuals) and combinations of multiple runs. Here we use these tools to generate graphs showing all the ancestors of successful individuals from a variety of stack-based genetic programming runs on software synthesis problems. These graphs highlight important moments in the evolutionary process. They also allow us to compare the dynamics for successful and unsuccessful runs. As well as displaying these full ancestry graphs, we use a variety of standard techniques such as size, colour, pattern, labelling, and opacity to visualize other important information such as fitness, which genetic operators were used, and the distance between parent and child genomes. While this generates an extremely rich visualization, the amount of data can also be somewhat overwhelming, so we also explore techniques for filtering these graphs that allow us to better understand the key dynamics.", notes = "Titan graph database. Tinkerpop query tools. Graphviz dot. Mutation and crossover. Hyperselection events. Replace space with newline benchmark. p1420 'uses restricted Boltzmann machines (RBMs) to compress the 200 error values into 24-bit RGB color values' p1423 'presence of an individual could have had..impact..even if..never contributed genetic material' p1423 'unfilter' p1426 future dynamic tools. My pdf reader barfs Slides https://www.slideshare.net/NicMcPhee/visualizing-genetic-programming-ancestries Cites \cite{series/sci/BurlacuAWKK15}", } @InProceedings{McPhee:2017:GECCO, author = "Nicholas Freitag McPhee and Maggie M. Casale and Mitchell Finzel and Thomas Helmuth and Lee Spector", title = "Visualizing Genetic Programming Ancestries Using Graph Databases", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4939-0", address = "Berlin, Germany", pages = "245--246", size = "2 pages", URL = "http://doi.acm.org/10.1145/3067695.3075617", DOI = "doi:10.1145/3067695.3075617", acmid = "3075617", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, ancestry, graph database, visualization", month = "15-19 " # jul, abstract = "Previous work has demonstrated the utility of graph databases as a tool for collecting and analysing ancestry in evolutionary computation runs. That work focused on sections of individual runs, whereas this poster illustrates the application of these ideas on the entirety of large runs (up to one million individuals) and combinations of multiple runs. Here we use these tools to generate graphs showing all the ancestors of successful individuals from a variety of stack-based genetic programming runs on software synthesis problems. These graphs highlight important moments in the evolutionary process. They also allow us to compare the dynamics when using different evolutionary tools, such as different selection mechanisms or representations, as well as comparing the dynamics for successful and unsuccessful runs.", notes = "Also known as \cite{McPhee:2017:VGP:3067695.3075617} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @InProceedings{McPhee:2017:GECCOa, author = "Nicholas Freitag McPhee and Thomas Helmuth and Lee Spector", title = "Using Algorithm Configuration Tools to Optimize Genetic Programming Parameters: A Case Study", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4939-0", address = "Berlin, Germany", pages = "243--244", size = "2 pages", URL = "http://doi.acm.org/10.1145/3067695.3076097", DOI = "doi:10.1145/3067695.3076097", acmid = "3076097", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, SMAC, parameter optimization, pushGP, software synthesis", month = "15-19 " # jul, abstract = "We use Sequential Model-based Algorithm Configuration (SMAC) to optimize a group of parameters for PushGP, a stack-based genetic programming system, for several software synthesis problems. Applying SMAC to one particular problem leads to marked improvements in the success rate and the speed with which a solution was found for that problem. Applying these {"}tuned{"} parameters to four additional problems, however, only improved performance on one, and substantially reduced performance on another. This suggests that SMAC is overfitting, tuning the parameters in ways that are highly problem specific, and raises doubts about the value of using these {"}tuned{"} parameters on previously unsolved problems. Efforts to use SMAC to optimize PushGP parameters on other problems have been less successful due to a combination of long PushGP run times and low success rates, which make it hard for SMAC to acquire enough information in a reasonable amount of time.", notes = "Also known as \cite{McPhee:2017:UAC:3067695.3076097} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @Article{mcphee_2020_gpem, author = "Nicholas Freitag McPhee and William B. Langdon", title = "{GP+EM} 20 Anniversary Editorial", journal = "Genetic Programming and Evolvable Machines", year = "2020", volume = "21", number = "1-2", pages = "3--9", month = jun, note = "Guest Editorial", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/mcphee_2020_gpem.pdf", URL = "https://rdcu.be/dz2rI", DOI = "doi:10.1007/s10710-020-09386-1", size = "7 pages", abstract = "Special Issue on Twentieth Anniversary Issue", notes = "\cite{Araujo:GPEM20} \cite{Brabazon:GPEM20} \cite{Loughran:GPEM20} \cite{Pillay:GPEM20} \cite{Kovacic:GPEM20} \cite{Miller:GPEM20} \cite{Sipper:GPEM:gptp} \cite{DeLorenzo:GPEM20} \cite{langdon:2019:GPEM} \cite{OReilly:GPEM20} \cite{ONeill:GPEM20}", } @Misc{McPhee:2021:GPTP, author = "Nic McPhee and Erik Rauer", title = "Dynamically generating {GP} training cases using {QuickCheck}", booktitle = "Genetic Programming Theory and Practice XVIII", year = "2021", editor = "Wolfgang Banzhaf and Leonardo Trujillo and Stephan Winkler and Bill Worzel", address = "East Lansing, USA", month = "19-21 " # may, keywords = "genetic algorithms, genetic programming", notes = "Does not appear in published proceedings. See also https://digitalcommons.morris.umn.edu/cgi/viewcontent.cgi?article=1382&context=camp_assembly Erik Rauer Grad Year: Spring 2023", } @InProceedings{McPhee:2023:GPTP, author = "Nicholas Freitag McPhee and Richard Lussier", title = "The Impact of Step Limits on Generalization and Stability in Software Synthesis", booktitle = "Genetic Programming Theory and Practice XX", year = "2023", editor = "Stephan Winkler and Leonardo Trujillo and Charles Ofria and Ting Hu", series = "Genetic and Evolutionary Computation", pages = "87--104", address = "Michigan State University, USA", month = jun # " 1-3", publisher = "Springer", keywords = "genetic algorithms, genetic programming, PushGP, Step limits, Execution limits, Software synthesis, Generalisation, Stability", isbn13 = "978-981-99-8412-1", DOI = "doi:10.1007/978-981-99-8413-8_5", abstract = "Software synthesis research has historically relied on tools such as step limits to handle undesired behaviour like infinite loops. Here we explore the impact of different step limits on several benchmark problems, and see that these limits do affect the evolved behaviors both in terms of generalization and stability. To assess stability, we ran evolved programs with a range of step limits, and found several cases where programs failed to generalize with the step limit used during evolution, but generalized at other step limits. Two of our test problems evolved stable solutions in the sense that they correctly handled unseen test cases for all step limits above a certain point, i.e., correctly computed the answer. Our other two test problems, however, sometimes evolved unstable solutions which only generalised (i.e., correctly handled unseen test cases) for specific step limits. These programs relied on the step limit to terminate, and would no longer generalise if the step limit was modified slightly. This indicates that step limits can have a substantial impact on evolutionary performance, and suggests we need to revisit our notions of generalization in the context of evolutionary software synthesis.", notes = " Part of \cite{Hu:2023:GPTP} published after the workshop in 2024", } @InProceedings{McRee:2010:geccocomp, author = "Randall K. McRee", title = "Symbolic regression using nearest neighbor indexing", booktitle = "GECCO 2010 Symbolic regression workshop", year = "2010", editor = "Steven Gustafson and Mark Kotanchek", isbn13 = "978-1-4503-0073-5", keywords = "genetic algorithms, genetic programming", pages = "1983--1990", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830761.1830841", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "In this paper, we introduce a new nearest neighbour data structure and describe several ways that it may be used for symbolic regression. Compared to genetic programming alone an algorithm using nearest neighbor indexing can search a much larger space and even so, typically find smaller, more general models. In addition, we introduce permutation tests in order to discriminate between relevant and irrelevant features.", notes = "Kana Software - service experience software. Simple test formulea used by Veenhuis, Cerny. Also known as \cite{1830841} Distributed on CD-ROM at GECCO-2010. ACM Order Number 910102.", } @InProceedings{DBLP:conf/icse/MechtaevYR16, author = "Sergey Mechtaev and Jooyong Yi and Abhik Roychoudhury", title = "Angelix: Scalable Multiline Program Patch Synthesis via Symbolic Analysis", booktitle = "2016 IEEE/ACM 38th International Conference on Software Engineering (ICSE)", year = "2016", editor = "Laura K. Dillon and Willem Visser and Laurie Williams", pages = "691--701", address = "Austin, TX, USA", month = "14-22 " # may, keywords = "APR", ISSN = "1558-1225", timestamp = "Sat, 19 Oct 2019 20:20:11 +0200", biburl = "https://dblp.org/rec/conf/icse/MechtaevYR16.bib", URL = "http://mechtaev.com/publications/icse16.pdf", DOI = "doi:10.1145/2884781.2884807", size = "11 pages", abstract = "Since debugging is a time-consuming activity, automated program repair tools such as GenProg have garnered interest. A recent study revealed that the majority of GenProg repairs avoid bugs simply by deleting functionality. We found that SPR, a state-of-the-art repair tool proposed in 2015, still deletes functionality in their many plausible repairs. Unlike generate-and-validate systems such as GenProg and SPR, semantic analysis based repair techniques synthesise a repair based on semantic information of the program. While such semantics-based repair methods show promise in terms of quality of generated repairs, their scalability has been a concern so far. In this paper, we present Angelix, a novel semantics-based repair method that scales up to programs of similar size as are handled by search-based repair tools such as GenProg and SPR. This shows that Angelix is more scalable than previously proposed semantics based repair methods such as SemFix and DirectFix. Furthermore, our repair method can repair multiple buggy locations that are dependent on each other. Such repairs are hard to achieve using SPR and GenProg. In our experiments, Angelix generated repairs from large-scale real-world software such as wireshark and php, and these generated repairs include multi-location repairs. We also report our experience in automatically repairing the well-known Heartbleed vulnerability.", notes = "Not GP but does compare with GenProg (a genetic programming system) http://angelix.io https://github.com/mechtaev/angelix https://github.com/program-repair/program-repair.github.io", } @InProceedings{medeiros:2019:BCBE, author = "Romeu Medeiros and Ana Claudia S. Souza and Gustavo F. Rodrigues", title = "Mouse Control Interface Using Electrooculogram and Genetic Programming", booktitle = "XXVI Brazilian Congress on Biomedical Engineering", year = "2019", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-981-13-2517-5_51", DOI = "doi:10.1007/978-981-13-2517-5_51", } @InProceedings{Medernach:2013:GECCO, author = "David Medernach and Taras Kowaliw and Conor Ryan and Rene Doursat", title = "Long-term evolutionary dynamics in heterogeneous cellular automata", booktitle = "GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference", year = "2013", editor = "Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi", isbn13 = "978-1-4503-1963-8", pages = "231--238", keywords = "genetic algorithms, genetic programming", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2463372.2463395", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "In this work we study open-ended evolution through the analysis of a new model, HetCA, for 'heterogeneous cellular automata'. Striving for simplicity, HetCA is based on classical two-dimensional CA, but differs from them in several key ways: cells include properties of 'age', 'decay', and 'quiescence'; cells use a heterogeneous transition function, one inspired by genetic programming; and there exists a notion of genetic transfer between adjacent cells. The cumulative effect of these changes is the creation of an evolving ecosystem of competing cell colonies. To evaluate the results of our new model, we define a measure of phenotypic diversity on the space of cellular automata. Via this measure, we contrast HetCA to several controls known for their emergent behaviours---homogeneous CA and the Game of Life---and several variants of our model. This analysis demonstrates that HetCA has a capacity for long-term phenotypic dynamics not readily achieved in other models. Runs exceeding one million time steps do not exhibit stagnation or even cyclic behaviour. Further, we show that the design choices are well motivated, as the exclusion of any one of them disrupts the long-term dynamics.", notes = "Also known as \cite{2463395} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)", } @InProceedings{Medernach:2015:GECCOcomp, author = "David Medernach and Jeannie Fitzgerald and R. Muhammad Atif Azad and Conor Ryan", title = "Wave: Incremental Erosion of Residual Error", booktitle = "GECCO 2015 Semantic Methods in Genetic Programming (SMGP'15) Workshop", year = "2015", editor = "Colin Johnson and Krzysztof Krawiec and Alberto Moraglio and Michael O'Neill", isbn13 = "978-1-4503-3488-4", keywords = "genetic algorithms, genetic programming, Semantic Methods in (SMGP'15) Workshop", pages = "1285--1292", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739482.2768503", DOI = "doi:10.1145/2739482.2768503", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Typically, Genetic Programming (GP) attempts to solve a problem by evolving solutions over a large, and usually pre-determined number of generations. However, overwhelming evidence shows that not only does the rate of performance improvement drop considerably after a few early generations, but that further improvement also comes at a considerable cost (bloat). Furthermore, each simulation (a GP run), is typically independent yet homogeneous: it does not re-use solutions from a previous run and retains the same experimental settings. Some recent research on symbolic regression divides work across GP runs where the subsequent runs optimise the residuals from a previous run and thus produce a cumulative solution; however, all such subsequent runs (or iterations) still remain homogeneous thus using a pre-set, large number of generations (50 or more). This work introduces Wave, a divide and conquer approach to GP whereby a sequence of short but sharp, and dependent yet potentially heterogeneous GP runs provides a collective solution; the sequence is akin to a wave such that each member of the sequence (that is, a short GP run) is a period of the wave. Heterogeneity across periods results from varying settings of system parameters, such as population size or number of generations, and also by alternating use of the popular GP technique known as linear scaling. The results show that Wave trains faster and better than both standard GP and multiple linear regression, can prolong discovery through constant restarts (which as a side effect also reduces bloat), can innovatively leverage a learning aid, that is, linear scaling at various stages instead of using it constantly regardless of whether it helps and performs reasonably even with a tiny population size (25) which bodes well for real time or data intensive training.", notes = "Also known as \cite{2768503} Distributed at GECCO-2015.", } @InProceedings{Medernach:2015:GECCOcompa, author = "David Medernach and Jeannie Fitzgerald and R. Muhammad Atif Azad and Conor Ryan", title = "Wave: A Genetic Programming Approach to Divide and Conquer", booktitle = "GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation", year = "2015", editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr", isbn13 = "978-1-4503-3488-4", keywords = "genetic algorithms, genetic programming: Poster", pages = "1435--1436", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739482.2764659", DOI = "doi:10.1145/2739482.2764659", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "This work introduces Wave, a divide and conquer approach to GP whereby a sequence of short, and dependent but potentially heterogeneous GP runs provides a collective solution; the sequence akins wave such that each short GP run is a period of the wave. Heterogeneity across periods results from varying settings of system parameters, such as population size or number of generations, and also by alternating use of the popular GP technique known as linear scaling.", notes = "Also known as \cite{2764659} Distributed at GECCO-2015.", } @InProceedings{Medernach:2016:GECCO, author = "David Medernach and Jeannie Fitzgerald and R. Muhammad Atif Azad and Conor Ryan", title = "A New Wave: A Dynamic Approach to Genetic Programming", booktitle = "GECCO '16: Proceedings of the 2016 Annual Conference on Genetic and Evolutionary Computation", year = "2016", editor = "Tobias Friedrich", pages = "757--764", keywords = "genetic algorithms, genetic programming", month = "20-24 " # jul, organisation = "SIGEVO", address = "Denver, USA", publisher = "ACM", publisher_address = "New York, NY, USA", isbn13 = "978-1-4503-4206-3", DOI = "doi:10.1145/2908812.2908857", abstract = "Wave is a novel form of semantic genetic programming which operates by optimising the residual errors of a succession of short genetic programming runs, and then producing a cumulative solution. These short genetic programming runs are called periods, and they have heterogeneous parameters. In this paper we leverage the potential of Wave's heterogeneity to simulate a dynamic evolutionary environment by incorporating self adaptive parameters together with an innovative approach to population renewal. We conduct an empirical study comparing this new approach with multiple linear regression (MLR) as well as several evolutionary computation (EC) methods including the well known geometric semantic genetic programming (GSGP) together with several other optimised Wave techniques. The results of our investigation show that the dynamic Wave algorithm delivers consistently equal or better performance than Standard GP (both with or without linear scaling), achieves testing fitness equal or better than multiple linear regression, and performs significantly better than GSGP on five of the six problems studied.", notes = "BDS Group CSIS Department University of Limerick Ireland GECCO-2016 A Recombination of the 25th International Conference on Genetic Algorithms (ICGA-2016) and the 21st Annual Genetic Programming Conference (GP-2016)", } @PhdThesis{Medernach:thesis, author = "David Medernach", title = "Comparative study of effects of fitness landscape changes in open-ended evolutionary simulations and in Genetic Programming", school = "Department of Computer Science \& Information Systems, Faculty of Science and Engineering, University of Limerick", year = "2017", address = "Ireland", keywords = "genetic algorithms, genetic programming", URL = "https://ulir.ul.ie/bitstream/handle/10344/6103/Medernach_2017_comparative.pdf", URL = "http://hdl.handle.net/10344/6103", size = "265 pages", abstract = "The biological world where natural selection takes place is a world constantly affected by external physical phenomena, whether cyclical and regular, such as the rotation of the earth, or punctual such as when a meteorite strikes the earth. It is recognized that these phenomena affect the evolution of life, but their interaction with natural selection have not yet been fully explored. This thesis studies the effects of environmental fluctuations via evolutionary simulations. In particular, we propose to study them through simulations of Genetic Programming as well as Artificial Life evolving virtual ecosystems. We first present the history of natural selection as well as artificial life and genetic programming, focusing on the role of environmental fluctuations in these three fields of research. We then examine the effects of such fluctuations on virtual ecosystems. We create a new virtual ecosystem which we call HetCA, that is based on cellular automata with heterogeneous transition rules. In this simulation, we test effects of these fluctuations on the evolutionary progress as well as on the level of selection and show that the type of fluctuation determine the level of selection. Finally, we study the effects of such fluctuations in Genetic Programming, firstly through an extension of Random Interleaved Sampling and then by creating a new method of Genetic Programming which we call Wave. We note that, on the studied problems, Wave is a very competitive method compared to a selection of benchmarks including non-evolutionary computation based optimization methods.", notes = "Supervisor: Prof. Conor Ryan", } @Article{Mediero:2012:NHESS, author = "L. Mediero and L. Garrote and A. Chavez-Jimenez", title = "Improving probabilistic flood forecasting through a data assimilation scheme based on genetic programming", journal = "Natural Hazards and Earth System Sciences", year = "2012", volume = "12", number = "12", pages = "3719--3732", month = "19 " # dec, note = "Special Issue", publisher = "Copernicus GmbH", keywords = "genetic algorithms, genetic programming", ISSN = "1561-8633", bibsource = "OAI-PMH server at www.doaj.org", language = "eng", oai = "oai:doaj-articles:20b906057483acdeeac5ddd635567115", URL = "http://www.nat-hazards-earth-syst-sci.net/12/3719/2012/nhess-12-3719-2012.pdf", DOI = "doi:10.5194/nhess-12-3719-2012", size = "14 pages", abstract = "Opportunities offered by high performance computing provide a significant degree of promise in the enhancement of the performance of real-time flood forecasting systems. In this paper, a real-time framework for probabilistic flood forecasting through data assimilation is presented. The distributed rainfall-runoff real-time interactive basin simulator (RIBS) model is selected to simulate the hydrological process in the basin. Although the RIBS model is deterministic, it is run in a probabilistic way through the results of calibration developed in a previous work performed by the authors that identifies the probability distribution functions that best characterise the most relevant model parameters. Adaptive techniques improve the result of flood forecasts because the model can be adapted to observations in real time as new information is available. The new adaptive forecast model based on genetic programming as a data assimilation technique is compared with the previously developed flood forecast model based on the calibration results. Both models are probabilistic as they generate an ensemble of hydrographs, taking the different uncertainties inherent in any forecast process into account. The Manzanares River basin was selected as a case study, with the process being computationally intensive as it requires simulation of many replicas of the ensemble in real time.", notes = "http://www.natural-hazards-and-earth-system-sciences.net/", } @Article{Medjdoub:2011:GPEM, author = "Benachir Medjdoub", title = "{Paul Coates}: {Programming} architecture, Routlegde, 187 pp, ISBN: 978-0-415-45188-8", journal = "Genetic Programming and Evolvable Machines", year = "2012", volume = "12", number = "4", pages = "463--464", month = dec, note = "Book review", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dR8DQ", DOI = "doi:10.1007/s10710-011-9146-1", size = "2 pages", affiliation = "School of the Built Environment, University of Salford, Salford, UK", notes = "Review of \cite{Coates:2010:PA} published by Routledge 2010", } @InProceedings{Medland:2014:NaBIC, author = "Michael Medland and Kyle Harrison and Beatrice Ombuki-Berman", title = "Demonstrating the Power of Object-Oriented Genetic Programming via the Inference of Graph Models for Complex Networks", booktitle = "Sixth World Congress on Nature and Biologically Inspired Computing", year = "2014", editor = "Ana Maria Madureira and Ajith Abraham and Emilio Corchado and Leonilde Varela and Azah Kamilah Muda and Choo yun Huoy", pages = "305--311", address = "Porto, Portugal", month = "30 " # jul # " - 1 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-5937-2/14", DOI = "doi:10.1109/NaBIC.2014.6921896", abstract = "Traditionally, GP used a single tree-based representation which does not lend itself well to state-based programs or multiple behaviours. To alleviate this drawback, object-oriented GP (OOGP) introduced a means of evolving programs with multiple behaviours which could be easily extended to state-based programs. However, the production of programs which allowed embedded knowledge and produced readable code was still not easily addressed using the OOGP methodology. Exemplified through the evolution of graph models for complex networks, this paper demonstrates the benefits of a new approach to OOGP inspired by abstract classes and linear GP. Furthermore, the new approach to OOGP, named LinkableGP, facilitates the embedding of expert knowledge while also maintaining the benefits of OOGP.", notes = "NaBIC 2014 http://www.mirlabs.net/nabic14/", } @InProceedings{Medland:2014:GECCOcomp, author = "Michael Richard Medland and Kyle Robert Harrison and Beatrice Ombuki-Berman", title = "Incorporating expert knowledge in object-oriented genetic programming", booktitle = "GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion", year = "2014", editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges", isbn13 = "978-1-4503-2881-4", keywords = "genetic algorithms, genetic programming: Poster", pages = "145--146", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2598394.2598494", DOI = "doi:10.1145/2598394.2598494", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Genetic programming (GP) has proved to be successful at generating programs which solve a wide variety of problems. Object-oriented GP (OOGP) extends traditional GP by allowing the simultaneous evolution of multiple program trees, and thus multiple functions. OOGP has been shown to be capable of evolving more complex structures than traditional GP. However, OOGP does not facilitate the incorporation of expert knowledge within the resulting evolved type. This paper proposes an alternative OOGP methodology which does incorporate expert knowledge by the use of a user-supplied partially-implemented type definition, i.e. an abstract class.", notes = "Also known as \cite{2598494} Distributed at GECCO-2014.", } @InProceedings{Medland:2016:CEC, author = "Michael Richard Medland and Kyle Robert Harrison and Beatrice M. Ombuki-Berman", title = "Automatic Inference of Graph Models for Directed Complex Networks using Genetic Programming", booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016)", year = "2016", editor = "Yew-Soon Ong", pages = "2337--2344", address = "Vancouver", month = "24-29 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-5090-0623-6", DOI = "doi:10.1109/CEC.2016.7744077", abstract = "Complex networks are systems of entities that are interconnected through meaningful relationships, resulting in structures that have statistical complexities not formed by random chance. Many graph model algorithms have been proposed to model the observed behaviours of complex networks. However, constructing such graph models manually is both tedious and problematic. Moreover, many of the models proposed in the literature have been cited as having inaccuracies with respect to the complex networks they represent. Although recent studies have proposed using genetic programming to automate the construction of graph model algorithms, only one such study has considered directed networks. This paper proposes a GP-based inference system that automatically constructs graph models for directed complex networks. Furthermore, the system proposed in this paper facilitates the use of vertex attributes, e.g., age, to incorporate network semantics - something which previous works lack. The GP system was used to reproduce three well-known graph models. Results indicate that the networks generated by the (automatically) constructed models were structurally similar to networks generated by their respective target models.", notes = "WCCI2016", } @InProceedings{DBLP:conf/IEEEias/MedvetFB07, title = "Detection of Web Defacements by means of Genetic Programming", author = "Eric Medvet and Cyril Fillon and Alberto Bartoli", booktitle = "Third International Symposium on Information Assurance and Security, IAS 2007", year = "2007", editor = "Ning Zhang and Ajith Abraham", pages = "227--234", address = "Manchester", month = "29-31 " # aug, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Internet, Web sites, computer crime, Web detection, Web pages, Web site defacement, domain-specific knowledge, evolutionary computation", DOI = "doi:10.1109/IAS.2007.13", abstract = "Web site defacement, the process of introducing unauthorized modifications to a Web site, is a very common form of attack. Detecting such events automatically is very difficult because Web pages are highly dynamic and their degree of dynamism may vary widely across different pages. In this paper we propose a novel detection approach based on genetic programming (GP), an established evolutionary computation paradigm for automatic generation of algorithms. What makes GP particularly attractive in this context is that it does not rely on any domain-specific knowledge, whose description and synthesis is invariably a hard job. In a preliminary learning phase, GP builds an algorithm based on a sequence of readings of the remote page to be monitored and on a sample set of attacks. Then, we monitor the remote page at regular intervals and apply that algorithm, which raises an alert when a suspect modification is found. We developed a prototype based on a broader Web detection framework we proposed earlier and we tested our approach over a dataset of 15 dynamic Web pages, observed for about a month, and a collection of real Web defacements. We compared the results to those of a solution we developed earlier, whose design embedded a substantial amount of domain specific knowledge, and the results clearly show that GP may be an effective approach for this job.", notes = "http://www.ias07.org/ Also known as \cite{4299779}", } @InProceedings{Medvet:2017:EuroGP, author = "Eric Medvet", title = "A Comparative Analysis of Dynamic Locality and Redundancy in Grammatical Evolution", booktitle = "EuroGP 2017: Proceedings of the 20th European Conference on Genetic Programming", year = "2017", month = "19-21 " # apr, editor = "Mauro Castelli and James McDermott and Lukas Sekanina", series = "LNCS", volume = "10196", publisher = "Springer Verlag", address = "Amsterdam", pages = "326--342", organisation = "species", keywords = "genetic algorithms, genetic programming, Grammatical Evolution: Poster", isbn13 = "978-3-319-55695-6", DOI = "doi:10.1007/978-3-319-55696-3_21", abstract = "the most salient feature of Grammatical Evolution (GE) is a procedure which maps genotypes to phenotypes using the grammar production rules; however, the search effectiveness of GE may be affected by low locality and high redundancy, which can prevent GE to comply with the basic principle that offspring should inherit some traits from their parents. Indeed, many studies previously investigated the locality and redundancy of GE as originally proposed in [1]. In this paper, we extend those results by considering redundancy and locality during the evolution, rather than statically, hence trying to understand if and how they are influenced by the selective pressure determined by the fitness. Moreover, we consider not only the original GE formulation, but three other variants proposed later (BGE, piGE, and SGE). We experimentally find that there is an interaction between locality/redundancy and other evolution-related measures, namely diversity and growth of individual size. In particular, the combined action of the crossover operator and the genotype-phenotype mapper makes SGE less redundant at the beginning of the evolution, but with very high redundancy after some generations, due to the low phenotype diversity.", notes = "Part of \cite{Castelli:2017:GP} EuroGP'2017 held inconjunction with EvoCOP2017, EvoMusArt2017 and EvoApplications2017", } @InProceedings{Medvet:2017:evoApplications, author = "Eric Medvet and Alberto Bartoli and Jacopo Talamini", title = "Road Traffic Rules Synthesis Using Grammatical Evolution", booktitle = "20th European Conference on the Applications of Evolutionary Computation, Part {II}", year = "2017", editor = "Giovanni Squillero", series = "LNCS", volume = "10200", publisher = "Springer", pages = "173--188", address = "Amsterdam", month = "19-21 " # apr, organisation = "Species", keywords = "genetic algorithms, genetic programming, grammatical evolution", isbn13 = "978-3-319-55791-5; 978-3-319-55792-2", bibdate = "2017-05-21", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/evoW/evoappl2017-2.html#MedvetBT17", DOI = "doi:10.1007/978-3-319-55792-2_12", notes = "also known as \cite{conf/evoW/MedvetBT17} EvoApplications2017 held in conjunction with EuroGP'2017, EvoCOP2017 and EvoMusArt2017 http://www.evostar.org/2017/cfp_evoapps.php.", } @InProceedings{Medvet:2017:GECCO, author = "Eric Medvet", title = "Hierarchical Grammatical Evolution", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4939-0", address = "Berlin, Germany", pages = "249--250", size = "2 pages", URL = "http://doi.acm.org/10.1145/3067695.3075972", DOI = "doi:10.1145/3067695.3075972", acmid = "3075972", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, genotype-phenotype mapping, locality, redundancy, representation", month = "15-19 " # jul, abstract = "We present Hierarchical Grammatical Evolution (HGE) and its variant Weighted HGE (WHGE), two novel genotype-phenotype mapping procedures to be used in the Grammatical Evolution (GE) framework. HGE/WHGE are designed to exhibit better variational inheritance than standard GE without imposing any constraint on the structure of the genotype nor on the genetic operators. Our proposal considers the phenotype as a hierarchy of non-terminal expansions and is based on two key ideas: (i) the closer the non-terminal to be expanded to the root of the hierarchy the larger the genotype substring determining its expansion, and (ii) upon expansion, a non-terminal divides its genotype sub-string among the resulting non-terminals. We experimentally evaluate our proposals on a set of benchmark problems and show that for the majority of them WHGE outperforms GE (and its variant πGE).", notes = "Also known as \cite{Medvet:2017:HGE:3067695.3075972} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @InProceedings{Medvet:2017:GECCOa, author = "Eric Medvet and Fabio Daolio and Danny Tagliapietra", title = "Evolvability in Grammatical Evolution", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4920-8", address = "Berlin, Germany", pages = "977--984", size = "8 pages", URL = "http://doi.acm.org/10.1145/3071178.3071298", DOI = "doi:10.1145/3071178.3071298", acmid = "3071298", publisher = "ACM", publisher_address = "New York, NY, USA", month = "15-19 " # jul, keywords = "genetic algorithms, genetic programming, Grammatical Evolution, fitness-landscape, genotype-phenotype mapping, locality", abstract = "Evolvability is a measure of the ability of an Evolutionary Algorithm (EA) to improve the fitness of an individual when applying a genetic operator. Other than the specific problem, many aspects of the EA may impact on the evolvability most notably the genetic operators and, if present, the genotype-phenotype mapping function. Grammatical Evolution (GE) is an EA in which the mapping function plays a crucial role since it allows to map any binary genotype into a program expressed in any user-provided language, defined by a context-free grammar. While GE mapping favoured a successful application of GE to many different problems, it has also been criticized for scarcely adhering to the variational inheritance principle, which itself may hamper GE evolvability. In this paper, we experimentally study GE evolvability in different conditions, that is, problems, mapping functions, genotype sizes, and genetic operators. Results suggest that there is not a single factor determining GE evolvability: in particular, the mapping function alone does not deliver better evolvability regardless of the problem. Instead, GE redundancy, which itself is the result of the combined effect of several factors, has a strong impact on the evolvability.", notes = "Also known as \cite{Medvet:2017:EGE:3071178.3071298} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @InProceedings{Medvet:2017:GECCOb, author = "Eric Medvet and Alberto Bartoli and Giovanni Squillero", title = "An Effective Diversity Promotion Mechanism in Grammatical Evolution", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4939-0", address = "Berlin, Germany", pages = "247--248", size = "2 pages", URL = "http://doi.acm.org/10.1145/3067695.3076057", DOI = "doi:10.1145/3067695.3076057", acmid = "3076057", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, diversity, locality, performance, representation", month = "15-19 " # jul, abstract = "Grammatical Evolution is an Evolutionary Algorithm which can evolve programs in any language described by a context-free grammar. A sequence of bits (the genotype) is transformed into a string of the language (the phenotype) by means of a mapping function, and eventually into a fitness value. Unfortunately the flexibility brought by the mapping is also likely to introduce non-locality phenomena, reduce diversity, and consequently hamper the effectiveness of the algorithm. In this paper, we propose a novel technique for promoting diversity, able to operate on three different levels: genotype, phenotype, and fitness. The technique is quite general, independent both from the specific problem being tackled and from other components of the evolutionary algorithm, such as genotype-phenotype mapping, selection criteria, and genetic operators. We experimentally demonstrate its efficacy in a wide range of conditions and from different points of view. The results also confirm the preponderant importance of the phenotype-level analyses in diversity promotion.", notes = "Also known as \cite{Medvet:2017:EDP:3067695.3076057} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @InProceedings{Medvet:2017:GECCOc, author = "Eric Medvet and Tea Tusar", title = "The {DU} Map: A Visualization to Gain Insights into Genotype-phenotype Mapping and Diversity", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4939-0", address = "Berlin, Germany", pages = "1705--1712", size = "8 pages", URL = "http://doi.acm.org/10.1145/3067695.3082554", DOI = "doi:10.1145/3067695.3082554", acmid = "3082554", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, diversity, genotype-phenotype mapping, heat maps, redundancy, visualization", month = "15-19 " # jul, abstract = "The relation between diversity and genotype to phenotype mapping has been the focus of several studies. In those Evolutionary Algorithms (EAs) where the genotype is a sequence of symbols, the contribution of each of those symbols in determining the phenotype may vary greatly possibly being null. In the latter case, the unused portions of the genotype may host a large amount of the population diversity. However, reasoning on coarse-grained measures makes it hard to validate such a claim and, more in general, to gain insights into the interactions between genotype-phenotype mapping and diversity. In this paper, we propose a novel visualization which summarizes in a single, compact heat map (the DU map), three kinds of information: (a) how diverse are the genotypes in the population at the level of single symbols; (b) if and to what degree each individual symbol in the genotype contributes to the phenotype; (c) how the two previous measures vary during the evolution. We experimentally verify the usefulness of the DU map w.r.t. its primary goal and, more broadly, when used to analyse different EA design options. We apply it to Grammatical Evolution (GE) as it constitutes an ideal test bed for the DU map, due to the availability of different mapping functions.", notes = "Also known as \cite{Medvet:2017:DMV:3067695.3082554} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @InProceedings{Medvet:2018:EuroGP, author = "Eric Medvet and Alberto Bartoli", title = "On the Automatic Design of a Representation for Grammar-based Genetic Programming", booktitle = "EuroGP 2018: Proceedings of the 21st European Conference on Genetic Programming", year = "2018", month = "4-6 " # apr, editor = "Mauro Castelli and Lukas Sekanina and Mengjie Zhang and Stefano Cagnoni and Pablo Garcia-Sanchez", series = "LNCS", volume = "10781", publisher = "Springer Verlag", address = "Parma, Italy", pages = "101--117", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Meta-evolution", isbn13 = "978-3-319-77552-4", URL = "http://www.human-competitive.org/sites/default/files/medvet-paper.pdf", DOI = "doi:10.1007/978-3-319-77553-1_7", size = "16 pages", abstract = "A long-standing problem in Evolutionary Computation consists in how to choose an appropriate representation for the solutions. In this work we investigate the feasibility of synthesizing a representation automatically, for the large class of problems whose solution spaces can be defined by a context-free grammar. We propose a framework based on a form of meta-evolution in which individuals are candidate representations expressed with an ad hoc language that we have developed to this purpose. Individuals compete and evolve according to an evolutionary search aimed at optimizing such representation properties as redundancy, locality, uniformity of redundancy. We assessed experimentally three variants of our framework on established benchmark problems and compared the resulting representations to human-designed representations commonly used (e.g., classical Grammatical Evolution). The results are promising in the sense that the evolved representations indeed exhibit better properties than the human-designed ones. Furthermore, while those improved properties do not result in a systematic improvement of search effectiveness, some of the evolved representations do improve search effectiveness over the human-designed baseline.", notes = "2018 HUMIES finalist Part of \cite{Castelli:2018:GP} EuroGP'2018 held in conjunction with EvoCOP2018, EvoMusArt2018 and EvoApplications2018", } @InProceedings{Medvet:2018:GECCOcomp, author = "Eric Medvet and Alberto Bartoli and Andrea {De Lorenzo}", title = "Exploring the application of {GOMEA} to bit-string {GE}", booktitle = "GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2018", editor = "Carlos Cotta and Tapabrata Ray and Hisao Ishibuchi and Shigeru Obayashi and Bogdan Filipic and Thomas Bartz-Beielstein and Grant Dick and Masaharu Munetomo and Silvino {Fernandez Alzueta} and Thomas Stuetzle and Pablo Valledor Pellicer and Manuel Lopez-Ibanez and Daniel R. Tauritz and Pietro S. Oliveto and Thomas Weise and Borys Wrobel and Ales Zamuda and Anne Auger and Julien Bect and Dimo Brockhoff and Nikolaus Hansen and Rodolphe {Le Riche} and Victor Picheny and Bilel Derbel and Ke Li and Hui Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Stephane Doncieux and Richard Duro and Joshua Auerbach and Harold {de Vladar} and Antonio J. Fernandez-Leiva and JJ Merelo and Pedro A. Castillo-Valdivieso and David Camacho-Fernandez and Francisco {Chavez de la O} and Ozgur Akman and Khulood Alyahya and Juergen Branke and Kevin Doherty and Jonathan Fieldsend and Giuseppe Carlo Marano and Nikos D. Lagaros and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Boris Naujoks and Vanessa Volz and Tea Tusar and Pascal Kerschke and Riyad Alshammari and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and John R. Woodward and Shin Yoo and John McCall and Nayat Sanchez-Pi and Luis Marti and Danilo Vasconcellos and Masaya Nakata and Anthony Stein and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Ivanoe {De Falco} and Antonio {Della Cioppa} and Ernesto Tarantino and Umberto Scafuri and P. G. M. Baltus and Giovanni Iacca and Ahmed Hallawa and Anil Yaman and Alma Rahat and Handing Wang and Yaochu Jin and David Walker and Richard Everson and Akira Oyama and Koji Shimoyama and Hemant Kumar and Kazuhisa Chiba and Pramudita Satria Palar", isbn13 = "978-1-4503-5764-7", pages = "270--271", address = "Kyoto, Japan", DOI = "doi:10.1145/3205651.3205765", publisher = "ACM", publisher_address = "New York, NY, USA", month = "15-19 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, grammatical evolution", abstract = "We explore the application of GOMEA, a recent method for discovering and exploiting the model for a problem in the form of linkage, to Grammatical Evolution (GE). GE employs an indirect representation based on familiar bit-string genotypes and is applicable to any problem where the solutions may be described using a context-free grammar, which hence greatly favours its wide adoption. Being general purpose, the representation of GE raises the opportunity for benefiting from the potential of GOMEA to automatically discover and exploit the linkage. We analyse experimentally the application of GOMEA to two bit-string-based variants of GE representation (the original representation and the recent WHGE) and show that GOMEA is clearly beneficial when coupled to WHGE, whereas it delivers no significant advantages when coupled with GE.", notes = "Also known as \cite{3205765} GECCO-2018 A Recombination of the 27th International Conference on Genetic Algorithms (ICGA-2018) and the 23rd Annual Genetic Programming Conference (GP-2018)", } @InProceedings{Medvet:2018:PPSN, author = "Eric Medvet and Alberto Bartoli and Andrea {De Lorenzo} and Fabiano Tarlao", title = "{GOMGE}: Gene-pool Optimal Mixing on Grammatical Evolution", booktitle = "15th International Conference on Parallel Problem Solving from Nature", year = "2018", editor = "Anne Auger and Carlos M. Fonseca and Nuno Lourenco and Penousal Machado and Luis Paquete and Darrell Whitley", volume = "11101", series = "LNCS", pages = "223--235", address = "Coimbra, Portugal", month = "8-12 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Linkage, Family of Subsets, Representation", isbn13 = "978-3-319-99252-5", URL = "https://www.springer.com/gp/book/9783319992587", DOI = "doi:10.1007/978-3-319-99253-2_18", abstract = "Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) is a recent Evolutionary Algorithm (EA) in which the interactions among parts of the solution (i.e., the linkage) are learned and exploited in a novel variation operator. We present GOMGE, the extension of GOMEA to Grammatical Evolution (GE), a popular EA based on an indirect representation which may be applied to any problem whose solutions can be described using a context-free grammar (CFG). GE is a general approach that does not require the user to tune the internals of the EA to fit the problem at hand: there is hence the opportunity for benefiting from the potential of GOMEA to automatically learn and exploit the linkage. We apply the proposed approach to three variants of GE differing in the representation (original GE, SGE, and WHGE) and incorporate in GOMGE two specific improvements aimed at coping with the high degeneracy of those representations. We experimentally assess GOMGE and show that, when coupled with WHGE and SGE, it is clearly beneficial to both effectiveness and efficiency, whereas it delivers mixed results with the original GE.", notes = "PPSN2018 http://ppsn2018.dei.uc.pt This two-volume set LNCS 11101 and 11102 constitutes the refereed proceedings of the 15th International Conference on Parallel Problem Solving from Nature, PPSN 2018", } @Article{Medvet:2018:GPEM, author = "Eric Medvet and Marco Virgolin and Mauro Castelli and Peter A. N. Bosman and Ivo Goncalves and Tea Tusar", title = "Unveiling evolutionary algorithm representation with {DU} maps", journal = "Genetic Programming and Evolvable Machines", year = "2018", volume = "19", number = "3", pages = "351--389", month = sep, note = "Special issue on genetic programming, evolutionary computation and visualization", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, GE, WHGE, SGE, Geometric Semantic Genetic Programming, GSGP, Gene-pool Optimal Mixing Evolutionary Algorithm, GOMEA, Neuro-Evolution of Augmenting Topologies, NEAT, Representation, Diversity, Usage, Visualization, Heat maps", ISSN = "1389-2576", URL = "https://doi.org/10.1007/s10710-018-9332-5", DOI = "doi:10.1007/s10710-018-9332-5", size = "39 pages", abstract = "Evolutionary algorithms (EAs) have proven to be effective in tackling problems in many different domains. However, users are often required to spend a significant amount of effort in fine-tuning the EA parameters in order to make the algorithm work. In principle, visualization tools may be of great help in this laborious task, but current visualization tools are either EA-specific, and hence hardly available to all users, or too general to convey detailed information. In this work, we study the Diversity and Usage map (DU map), a compact visualization for analysing a key component of every EA, the representation of solutions. In a single heat map, the DU map visualizes for entire runs how diverse the genotype is across the population and to which degree each gene in the genotype contributes to the solution. We demonstrate the generality of the DU map concept by applying it to six EAs that use different representations (bit and integer strings, trees, ensembles of trees, and neural networks). We present the results of an online user study about the usability of the DU map which confirm the suitability of the proposed tool and provide important insights on our design choices. By providing a visualization tool that can be easily tailored by specifying the diversity (D) and usage (U) functions, the DU map aims at being a powerful analysis tool for EAs practitioners, making EAs more transparent and hence lowering the barrier for their use.", } @Article{Medvet:GPEM, author = "Eric Medvet and Alberto Bartoli and Andrea {De Lorenzo} and Fabiano Tarlao", title = "Designing automatically a representation for grammatical evolution", journal = "Genetic Programming and Evolvable Machines", year = "2019", volume = "20", number = "1", pages = "37--65", month = mar, keywords = "genetic algorithms, genetic programming, Grammatical evolution, Genotype-phenotype mapping, Meta-evolution", ISSN = "1389-2576", URL = "https://sites.google.com/site/machinelearningts/publications/international-journal-publications/designingautomaticallyarepresentationforgrammaticalevolution/2018-GENP-AutomaticRepresentationDesign.pdf", DOI = "doi:10.1007/s10710-018-9327-2", size = "29 pages", abstract = "A long-standing problem in evolutionary computation consists in how to choose an appropriate representation for the solutions. In this work we investigate the feasibility of synthesizing a representation automatically, for the large class of problems whose solution spaces can be defined by a context-free grammar. We propose a framework based on a form of meta-evolution in which individuals are candidate representations expressed with an ad hoc language that we have developed to this purpose. Individuals compete and evolve according to an evolutionary search aimed at optimizing such representation properties as redundancy, uniformity of redundancy, and locality. We assessed experimentally three variants of our framework on established benchmark problems and compared the resulting representations to human-designed representations commonly used (e.g., classical grammatical evolution). The results are promising as the evolved representations indeed exhibit better properties...", } @Proceedings{Medvet:2022:GP, title = "EuroGP 2022: Proceedings of the 25th European Conference on Genetic Programming", year = "2022", editor = "Eric Medvet and Gisele Pappa and Bing Xue", series = "LNCS", volume = "13223", address = "Madrid, Spain", month = "20-22 " # apr, organisation = "EvoStar, Species", publisher = "Springer Verlag", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-031-02055-1", DOI = "doi:10.1007/978-3-031-02056-8", size = "317 pages", abstract = "Contents: \cite{Alchirch:2022:EuroGP}, \cite{Carvalho:2022:EuroGP}, \cite{Fontbonne:2022:EuroGP}, \cite{Huang:2022:EuroGP}, \cite{Hurta:2022:EuroGP}, \cite{Indri:2022:EuroGP}, \cite{Nadizar:2022:EuroGP}, \cite{Nickerson:2022:EuroGP}, \cite{Orhand:2022:EuroGP}, \cite{Pietropolli:2022:EuroGP}, \cite{Raymond:2022:EuroGP}, \cite{Reuter:2022:EuroGP}, \cite{Rodrigues:2022:EuroGP}, \cite{Schweim:2022:EuroGP}, \cite{Sobania:2022:EuroGP}, \cite{Tseng:2022:EuroGP}, \cite{Videau:2022:EuroGP}, \cite{Wittenberg:2022:EuroGP}, \cite{Zhou:2022:EuroGP}", notes = "EuroGP'2022", } @InProceedings{Medvet:2023:GPTP, author = "Eric Medvet and Giorgia Nadizar", title = "{GP} for Continuous Control: Teacher or Learner? The Case of Simulated Modular Soft Robots", booktitle = "Genetic Programming Theory and Practice XX", year = "2023", editor = "Stephan Winkler and Leonardo Trujillo and Charles Ofria and Ting Hu", series = "Genetic and Evolutionary Computation", pages = "203--224", address = "Michigan State University, USA", month = jun # " 1-3", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-981-99-8412-1", DOI = "doi:10.1007/978-981-99-8413-8_11", abstract = "We consider the problem of optimizing a controller for agents whose observation and action spaces are continuous, i.e., where the controller is a multivariate real function. We use genetic programming (GP) for solving this optimization problem. Namely, we employ a multi-tree-based GP variant, where a candidate solution is an array of m trees, each encoding a univariate function of the agent observation. We compare this form of optimization against the more common one where the controller is a multi-layer perceptron, with a predefined topology, whose weights are optimized through (neuro)evolution (NE). Moreover, we consider an evolutionary algorithm, GraphEA, that directly evolves graphs, each having n input nodes and m output nodes. We apply these three approaches to the case of simulated modular soft robots, where a robot is an aggregation of identical soft modules, each employing a controller that processes the local observation and produces the local action. We find that, in our scenario, multi-tree-based GP is competitive with NE and tends to produce different behaviours. We then experimentally investigate the possibility of optimising a controller using another, pre-optimized one, as teacher, i.e., we realize a form of offline imitation learning. We consider all the teacher-learner pairs resulting from the three evolutionary algorithms and find that NE is a better learner than GP and GraphEA. However, controllers obtained through offline imitation learning are far less effective than those obtained through direct evolution. We hypothesize that this gap in effectiveness may be explained by the possibility, given by direct evolution, of exploring during the simulations a larger portion of the observation-action space.", notes = " Part of \cite{Hu:2023:GPTP} published after the workshop in 2024", } @Article{medvet:2025:GPEM, author = "Eric Medvet and Erica Salvato", title = "The science of soft robots, Koichi Suzumori, Kenjiro Fukuda, Ryuma Niiyama, and Kohei Nakajima: ISBN 978-9811951732, Springer 2023", journal = "Genetic Programming and Evolvable Machines", year = "2025", volume = "26", pages = "Article no 9", note = "Book Review", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/eiBJf", DOI = "doi:10.1007/s10710-025-09508-7", size = "3 pages", notes = "Department of Engineering and Architecture, University of Trieste, Via Valerio 6/1, 34127 Trieste, Italy", } @InProceedings{meeden:1998:bgrsrimsn, author = "Lisa Meeden", title = "Bridging the gap between robot simulations and reality with improved models of sensor noise", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "824--831", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "Evolutionary Robotics, Khepera, ANN, light following", ISBN = "1-55860-548-7", URL = "http://www.cs.swarthmore.edu/~meeden/papers/meeden.gp98.pdf", size = "8 pages", notes = "GP-98", } @Article{Meeden:2015:GPEM, author = "Lisa A. Meeden", title = "Angelo Cangelosi and Matthew Schlesinger: Developmental robotics MIT Press, 2015", journal = "Genetic Programming and Evolvable Machines", year = "2015", volume = "16", number = "3", pages = "397--398", month = sep, note = "Book Review", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dR8Fb", DOI = "doi:10.1007/s10710-015-9246-4", size = "2 pages", notes = "p397 'Cangelosi and Schlesinger's book provides a clear and accessible introduction to developmental robotics...'", } @InBook{Meetei:2022:AICDbio, author = "Potshangbam Angamba Meetei and Ruchi Jakhmola Mani and Vaishali Mehta and Deepshikha Pande Katare", title = "Intellection of biological life in current era", booktitle = "Artificial Intelligence and Computational Dynamics for Biomedical Research", pages = "53--66", publisher = "De Gruyter", year = "2022", keywords = "genetic algorithms, genetic programming, Bioinformatics, Biology, Drugs, Proteins, Artificial intelligence, Heuristic algorithms, Genomics, Biotechnology, Robots, Diseases", isbn13 = "9783110762082", URL = "https://ieeexplore.ieee.org/document/10790344", DOI = "doi:10.1515/9783110762044-004", abstract = "In the coming decades, the world will undergo a transformation. Hence, that period of time will be an era of advancing technologies such as big data analytics, bioinformatics analysis, artificial intelligence, robotics, quantum computing, deep learning, genetic algorithm, support vector machine, discriminant analysis, naive Bayes, nearest neighbours, neural networks, linear regression, hierarchical regression, ensembles, decision trees, neural network algorithm, artificial neural network, Kmeans clustering, Gaussian mixture, Python, machine learning, JAVA, hidden Markov and genetic programming. This chapter focuses on the rapidly evolving technologies that connect artificial intelligence in biology with bioinformatics and its techniques. Researchers faced a significant hurdle in describing the new topic 'Bioinformatics' in a systematic scientific manner while also drawing attention to its applications and services. This chapter examines evaluation, experimentation, predictive research, trial evaluation and biological science demonstration. Robotics with a wide range of tools, machines and computer science is used as technology to do complex tasks with human ability, which answers queries such as 'What is the evolutionary distance between two species?'; 'What does replicative origin's hidden message mean?'; 'What is the cause and pattern of mutation?'; 'In a certain gene, how and where does replication begin?'; 'Which of the cells is still attempting to replicate?'; 'What vaccine is the best to get in order to prepare for virus mutation?'; 'Where did my forefathers and mothers come from?' This chapter also describes the role of technology in the study of life and drug development, as well as whether bioinformatics is comparable to artificial intelligence in biology.", notes = "Also known as \cite{10790344} Department of Biotechnology, ManipurUniversity, Canchipur 795003, Imphal, India", } @Article{Meffert:2004:JM, author = "Klaus Meffert", title = "Auf Darwins Spuren - Genetische Algorithmen mit Java", journal = "JavaMagazin", year = "2004", keywords = "genetic algorithms, genetic programming", URL = "http://www.javamagazin.de/itr/ausgaben/psecom,id,206,nodeid,20.html", URL = "http://www.klaus-meffert.de/download/genetische_algorithmen_mit_java.pdf", size = "5 pages", notes = "deutsch", } @Article{Meffert:2005:JM, author = "Klaus Meffert", title = "Genetische Programmierung: leistungsfahiges Verfahren zur Problemlosung", journal = "JavaMagazin", year = "2005", month = aug, keywords = "genetic algorithms, genetic programming", URL = "http://www.javamagazin.de/itr/ausgaben/psecom,id,259,nodeid,20.html", URL = "http://www.klaus-meffert.de/download/genetische_programmierung_mit_java.pdf", abstract = "Das Konzept der Genetischen Programmierung hilft bei der evolutionaren Entwicklung ungewohnlicher Losungen. Selbst Organisationen wie die NASA greifen auf dieses Paradigma zuruck, um Probleme auf aussergewohnliche Weise zu losen. Aufbauend auf unserem Artikel zum Thema Genetische Algorithmen im Java Magazin 8.2004 \cite{Meffert:2004:JM} stellen wir in dieser Ausgabe die Genetische Programmierung vor.", notes = "deutsch. In german. Author of JGAP genetische_programmierung_mit_java.pdf Programmierte Evolution appears to be a fuller description (7 pages).", } @InProceedings{Megane:2021:EuroGP, author = "Jessica Megane and Nuno Lourenco and Penousal Machado", title = "Probabilistic Grammatical Evolution", booktitle = "EuroGP 2021: Proceedings of the 24th European Conference on Genetic Programming", year = "2021", editor = "Ting Hu and Nuno Lourenco and Eric Medvet", series = "LNCS", volume = "12691", publisher = "Springer Verlag", address = "Virtual Event", pages = "198--213", month = "7-9 " # apr, organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Probabilistic Context-Free Grammar, Probabilistic Grammatical Evolution, Genotype-to-Phenotype Mapping: Poster", isbn13 = "978-3-030-72811-3", DOI = "doi:10.1007/978-3-030-72812-0_13", abstract = "Grammatical Evolution (GE) is one of the most popular Genetic Programming (GP) variants, and it has been used with success in several problem domains. Since the original proposal, many enhancements have been proposed to GE in order to address some of its main issues and improve its performance. we propose Probabilistic Grammatical Evolution (PGE), which introduces a new genotypic representation and new mapping mechanism for GE. Specifically, we resort to a Probabilistic Context-Free Grammar (PCFG) where its probabilities are adapted during the evolutionary process, taking into account the productions chosen to construct the fittest individual. The genotype is a list of real values, where each value represents the likelihood of selecting a derivation rule. We evaluate the performance of PGE in two regression problems and compare it with GE and Structured Grammatical Evolution (SGE). The results show that PGE has a better performance than GE, with statistically significant differences, and achieved similar performance when comparing with SGE.", notes = "blueprint of grammar for GE. PCFG. Codons are continuous variable (not bytes). Codons updated for next generation. Pagie benchmark. UCI? Boston housing. Prominence of features == data mining?? Also Santa Fe Ant, 11-Mux? https://estagios.dei.uc.pt/cursos/mei/ano-lectivo-2020-2021/proposta-com-alunos-identificados/?idestagio=3902 http://www.evostar.org/2021/eurogp/ Part of \cite{Hu:2021:GP} EuroGP'2021 held in conjunction with EvoCOP2021, EvoMusArt2021 and EvoApplications2021", } @InProceedings{megane:2022:GECCO, author = "Jessica Megane and Nuno Lourenco and Penousal Machado", title = "Co-evolutionary Probabilistic Structured Grammatical Evolution", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference", year = "2022", editor = "Alma Rahat and Jonathan Fieldsend and Markus Wagner and Sara Tari and Nelishia Pillay and Irene Moser and Aldeida Aleti and Ales Zamuda and Ahmed Kheiri and Erik Hemberg and Christopher Cleghorn and Chao-li Sun and Georgios Yannakakis and Nicolas Bredeche and Gabriela Ochoa and Bilel Derbel and Gisele L. Pappa and Sebastian Risi and Laetitia Jourdan and Hiroyuki Sato and Petr Posik and Ofer Shir and Renato Tinos and John Woodward and Malcolm Heywood and Elizabeth Wanner and Leonardo Trujillo and Domagoj Jakobovic and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Inmaculada Medina-Bulo and Slim Bechikh and Andrew M. Sutton and Pietro Simone Oliveto", pages = "991--999", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, grammatical evolution, probabilistic algorithms, coevolution, grammar-based, gaussian mutation", isbn13 = "978-1-4503-9237-2", URL = "https://arxiv.org/abs/2204.08985", DOI = "doi:10.1145/3512290.3528833", video_url = "https://vimeo.com/723973418", code_url = "https://github.com/jessicamegane/co-psge", size = "9 pages", abstract = "This work proposes an extension to Structured Grammatical Evolution (SGE) called Co-evolutionary Probabilistic Structured Grammatical Evolution (Co-PSGE). In Co-PSGE each individual in the population is composed by a grammar and a genotype, which is a list of dynamic lists, each corresponding to a non-terminal of the grammar containing real numbers that correspond to the probability of choosing a derivation rule. Each individual uses its own grammar to map the genotype into a program. During the evolutionary process, both the grammar and the genotype are subject to variation operators.The performance of the proposed approach is compared to 3 different methods, namely, Grammatical Evolution (GE), Probabilistic Grammatical Evolution (PGE), and SGE on four different benchmark problems. The results show the effectiveness of the approach since Co-PSGE is able to outperform all the methods with statistically significant differences in the majority of the problems.", notes = "Pagie polynomial, Boston Housing, 5-bit Even Parity, 11-bit Boolean Multiplexer https://www.jessicamegane.pt/talk/co-evolutionary-probabilistic-structured-grammatical-evolution-at-gecco-2022/ GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @InProceedings{Megane:2022:CEC, author = "Jessica Megane and Nuno Lourenco and Penousal Machado", title = "Probabilistic Structured Grammatical Evolution", booktitle = "2022 IEEE Congress on Evolutionary Computation (CEC)", year = "2022", editor = "Carlos A. Coello Coello and Sanaz Mostaghim", address = "Padua, Italy", month = "18-23 " # jul, keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Sociology, Production, Benchmark testing, Syntactic, Germanium, Probabilistic logic, Search problems, Grammar-based Genetic Programming, Grammar Design, Probabilistic", isbn13 = "978-1-6654-6708-7", DOI = "doi:10.1109/CEC55065.2022.9870397", abstract = "The grammars used in grammar-based Genetic Programming (GP) methods have a significant impact on the quality of the solutions generated since they define the search space by restricting the solutions to its syntax. In this work, we propose Probabilistic Structured Grammatical Evolution (PSGE), a new approach that combines the Structured Grammatical Evolution (SGE) and Probabilistic Grammatical Evolution (PGE) representation variants and mapping mechanisms. The genotype is a set of dynamic lists, one for each non-terminal in the grammar, with each element of the list representing a probability used to select the next Probabilistic Context-Free Grammar (PCFG) derivation rule. PSGE statistically outperformed Grammatical Evolution (GE) on all six benchmark problems studied. In comparison to PGE, PSGE outperformed 4 of the 6 problems analysed.", notes = "Also known as \cite{9870397}", } @InProceedings{megane:2023:GECCOcomp, author = "Jessica Megane and Nuno Lourenco and Penousal Machado and Dirk Schweim", title = "The Influence of Probabilistic Grammars on Evolution", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "611--614", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, grammatical evolution, probabilistic: Poster", isbn13 = "9798400701191", DOI = "doi:10.1145/3583133.3590706", size = "4 pages", abstract = "Context-Free Grammars (CFGs) are used in Genetic Programming (GP) to encode the structure and syntax of programs, enabling efficient exploration of potential solutions and generation of well-formed and syntactically correct programs. Probabilistic Context-Free Grammars (PCFG) can be used to model the distribution of solutions to help guide the search process. Structured Grammatical Evolution (SGE) is a grammar-based GP algorithm that uses a list of dynamic lists as its genotype, where each list represents the ordered indexes of production rules to expand for each non-terminal in the grammar. Two recent variants incorporate PCFG into the SGE framework, where the probabilities of the production rule change during the evolutionary process, resulting in improved performance.This study examines the impact of these differences on the behavior of SGE and its variants, Probabilistic Structured Grammatical Evolution (PSGE) and Co-evolutionary Probabilistic Structured Grammatical Evolution (Co-PSGE), in terms of population tree depth, genotype size, new solutions generated, and runtime. The results indicate that the use of probabilistic alternatives can affect the growth of tree depth and size and increases the ability to generate new solutions.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @InProceedings{Megane:2024:EuroGP, author = "Jessica Megane and Eric Medvet and Nuno Lourenco and Penousal Machado", editor = "Mario Giacobini and Bing Xue and Luca Manzoni", title = "Grammar-Based Evolution of Polyominoes", booktitle = "EuroGP 2024: Proceedings of the 27th European Conference on Genetic Programming", year = "2024", volume = "14631", series = "LNCS", pages = "56--72", publisher = "Springer", address = "Aberystwyth", month = "3-5 " # apr, organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-031-56957-9", URL = "https://jessicamegane.pt/publication/2024-01-18-polyominoes", DOI = "doi:10.1007/978-3-031-56957-9_4", abstract = "Languages that describe two-dimensional (2-D) structures have emerged as powerful tools in various fields, encompassing pattern recognition and image processing, as well as modeling physical and chemical phenomena. One kind of two-dimensional structures is given by labeled polyominoes, i.e., geometric shapes composed of connected unit squares represented in a 2-D grid. In this paper, we present (a) a novel approach, based on grammars, for describing sets of labeled polyominoes that meet some predefined requirements and (b) an algorithm to develop labeled polyominoes using the grammar. We show that the two components can be used for solving optimization problems in the space of labeled polyominoes, similarly to what happens for strings in grammatical evolution (and its later variants). We characterise our algorithm for developing polyominoes in terms of representation-related metrics (namely, validity, redundancy, and locality), also by comparing different representations. We experimentally validate our proposal using a simple evolutionary algorithm on a few case studies where the goal is to obtain a target polyomino: we show that it is possible to enforce hard constraints in the search space of polyominoes, using a grammar, while performing the evolutionary search.", notes = "Nominated for best paper Part of \cite{Giacobini:2024:GP} EuroGP'2024 held in conjunction with EvoCOP2024, EvoMusArt2024 and EvoApplications2024", } @Article{Megane:2024:sigevolution, author = "Jessica Megane", title = "Summary of EvoStar 2024", journal = "SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation", year = "2024", volume = "17", number = "2", pages = "Article No: 2", month = jun, keywords = "genetic algorithms, genetic programming, EuroGP", ISSN = "1931-8499", URL = "https://evolution.sigevo.org/issues/HTML/sigevolution-17-2/index.html#Summary_of_EvoStar_2024", DOI = "doi:10.1145/3695933.3695935", abstract = "This year's EvoStar conference was held from April 3rd to 5th in the beautiful town of Aberystwyth, a place as delightful to visit as it is challenging to pronounce!", notes = "Aberystwyth, students, Christine Zarges, Penousal Machado, SPECIES, Jon Timmis, Julian Francis Miller Award: Hod Lipson and Dario Floreano Conor Ryan, Bing Xue, Colin Johnson, Emma Hart, and Ernesto Costa Computational Intelligence in Art and Design Bella Ciao, Amigos para Siempre, All my Loving, Grandola, Vila Morena Sabine Hauert National Library of Wales University of Coimbra", } @Article{Mehdizadeh:2018:WRM.Jan, title = "New Approaches for Estimation of Monthly Rainfall Based on GEP-ARCH and ANN-ARCH Hybrid Models", author = "Saeid Mehdizadeh and Javad Behmanesh and Keivan Khalili", journal = "Water Resources Management", year = "2018", volume = "32", number = "2", pages = "527--545", month = jan, keywords = "genetic algorithms, genetic programming, gene expression programming, ANN", bibsource = "OAI-PMH server at oai.repec.org", description = "Estimation, Rainfall, GEP-ARCH, ANN-ARCH", identifier = "RePEc:spr:waterr:v:32:y:2018:i:2:d:10.1007_s11269-017-1825-0", oai = "oai:RePEc:spr:waterr:v:32:y:2018:i:2:d:10.1007_s11269-017-1825-0", URL = "http://link.springer.com/10.1007/s11269-017-1825-0", DOI = "doi:10.1007/s11269-017-1825-0", publisher = "springer", abstract = "Accurate estimation of rainfall has an important role in the optimal water resources management, as well as hydrological and climatological studies. In the present study, two novel types of hybrid models, namely gene expression programming-autoregressive conditional heteroscedasticity (GEP-ARCH) and artificial neural networks-autoregressive conditional heteroscedasticity (ANN-ARCH) are introduced to estimate monthly rainfall time series. To fulfill this purpose, five stations with various climatic conditions were selected in Iran. The lagged monthly rainfall data was used to develop the different GEP and ANN scenarios. The performance of proposed hybrid models was compared to the GEP and ANN models using root mean square error (RMSE) and coefficient of determination (R2). The results show that the proposed GEP-ARCH and ANN-ARCH models give a much better performance than the GEP and ANN in all of the studied stations with various climates. Furthermore, the ANN-ARCH model generally presents better performance in comparison with the GEP-ARCH model.", } @Article{mehdizadeh:2018:WRM, author = "Saeid Mehdizadeh and Ali Kozekalani Sales", title = "A Comparative Study of Autoregressive, Autoregressive Moving Average, Gene Expression Programming and Bayesian Networks for Estimating Monthly Streamflow", journal = "Water Resources Management", year = "2018", volume = "32", number = "9", pages = "3001--3022", month = jul, keywords = "genetic algorithms, genetic programming, Gene Expression Programming", URL = "http://link.springer.com/article/10.1007/s11269-018-1970-0", DOI = "doi:10.1007/s11269-018-1970-0", abstract = "In the recent years, artificial intelligence techniques have attracted much attention in hydrological studies, while time series models are rarely used in this field. The present study evaluates the performance of artificial intelligence techniques including gene expression programming (GEP), Bayesian networks (BN), as well as time series models, namely autoregressive (AR) and autoregressive moving average (ARMA) for estimation of monthly streamflow. In addition, simple multiple linear regression (MLR) was also used. To fulfill this objective, the monthly streamflow data of Ponel and Toolelat stations located on Shafarood and Polrood Rivers, respectively in Northern Iran were used for the period of October 1964 to September 2014. In order to investigate the models accuracy, root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2) were employed as the error statistics. The obtained results demonstrated that the single AR and ARMA time series models had better performance in comparison with the single GEP, BN and MLR methods. Furthermore, in this study, six hybrid models known as GEP-AR, GEP-ARMA, BN-AR, BN-ARMA, MLR-AR and MLR-ARMA were developed to enhance the estimation accuracy of the monthly streamflow. It was concluded that the developed hybrid models were more accurate than the corresponding single artificial intelligence and time series models. The obtained results confirmed that the integration of time series models and artificial intelligence techniques could be of use to improve the accuracy of single models in modeling purposes related to the hydrological studies.", } @Article{Mehdizadeh:2025:atech, author = "Saman Abdanan Mehdizadeh and Mohammad Noshad and Mahsa Chaharlangi and Yiannis Ampatzidis", title = "{AI-driven} non-destructive detection of meat freshness using a multi-indicator sensor array and smartphone technology", journal = "Smart Agricultural Technology", year = "2025", volume = "10", pages = "100822", keywords = "genetic algorithms, genetic programming, Meat quality assessment, Sensor array, LDA-PCA Model", ISSN = "2772-3755", URL = "https://www.sciencedirect.com/science/article/pii/S2772375525000565", DOI = "doi:10.1016/j.atech.2025.100822", abstract = "This study presents the development of a sensor array for classifying meat samples (buffalo, lamb, and beef) based on their Total Volatile Basic Nitrogen (TVB-N) levels, a key indicator of freshness. The sensor array was created by depositing solutions of seven pH and redox indicators: Aniline blue, Nile blue, Alizarin red S, Cresol red, Methyl violet, Methyl orange, and Chlorophenol red. Classification was performed using Linear Discriminant Analysis-Principal Component Analysis (LDA-PCA) models and genetic programming (GP). The GP analysis identified Chlorophenol red, Cresol red, and Methyl violet as the most significant indicators, selected frequently over 1,000 iterations. The resulting mathematical models were implemented in a smartphone application, which achieved high classification accuracy, reporting strong performance metrics (aka., precision, accuracy, F1-score, specificity, sensitivity, error rate, and kappa coefficient) for all three meat types. The LDA-PCA model demonstrated discrimination accuracies of 96 percent for buffalo, 81 percent for lamb, and 88 percent for beef, with corresponding precision values of 90 percent, 84 percent, and 77 percent. These results highlight the potential of this method for real-time, reliable assessment of meat freshness", notes = "Department of Mechanics of Biosystems Engineering, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani, Iran", } @InProceedings{Mehne:2018:ICST, author = "Ben Mehne and Hiroaki Yoshida and Mukul R. Prasad and Koushik Sen and Divya Gopinath and Sarfraz Khurshid", title = "Accelerating Search-Based Program Repair", booktitle = "2018 IEEE 11th International Conference on Software Testing, Verification and Validation (ICST)", year = "2018", pages = "227--238", address = "Sweeden", month = "9-13 " # apr, keywords = "genetic algorithms, genetic programming, Genetic Improvement, APR, SBSE, RTS", isbn13 = "978-1-5386-5013-4", URL = "https://people.eecs.berkeley.edu/~ksen/papers/repair.pdf", DOI = "doi:10.1109/ICST.2018.00031", size = "12 pages", abstract = "Automatic program repair techniques offer the possibility of reducing, or even eliminating, the substantial manual effort that currently goes into the patching of software defects. However, current repair techniques take minutes or hours, to generate rather simple repairs, severely limiting their practical applicability. Search-based program repair represents a popular class of automatic repair techniques. Patch compilation and test case execution are the dominant contributors to runtime in this class of repair techniques. In this work we propose two complementary techniques, namely Location Selection and Test-Case Pruning, to improve the efficiency of search-based repair techniques. Location Selection reduces the number of repair candidates examined in arriving at a repair, thereby reducing the number of patch compilations as well as the overall number of test case evaluations during the repair process. Test-Case Pruning, on the other hand, optimizes the number of test cases executed per examined candidate. We implement the proposed techniques in the context of SPR, a state-of-the-art search-based repair tool, evaluate them on the GenProg benchmarks and observe that the proposed techniques provide a 3.9 fold speed-up, on average, without any degradation in repair quality.", notes = "Based on GenProg. University of California, Berkeley Also known as \cite{8367051},", } @InProceedings{mehnen03, author = "Jorn Mehnen and Thomas Michelitsch and Klaus Weinert", title = "Evolutionary Optimized Mold Temperature Control Strategies using a Multi-Polyline Approach", booktitle = "Genetic Programming, Proceedings of EuroGP'2003", year = "2003", editor = "Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa", volume = "2610", series = "LNCS", pages = "374--383", address = "Essex", publisher_address = "Berlin", month = "14-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-00971-X", DOI = "doi:10.1007/3-540-36599-0_35", abstract = "During the machining process the tools for pressure and injection molding have to keep an optimal working temperature. This temperature depends on the workpiece material and allows a safe, efficient and precise machining process. The compact and very expensive steel molds are penetrated with deep hole drilling bores that are combined to form mold temperature control circuits. Today the structure of these circuits are designed manually. Here, a new automatic layout system for mold temperature control strategies is introduced which uses a multiobjective fitness function. The circuits are encoded via a polyline approach. The complex optimization problem is solved using a variation of the evolution strategy. The evolutionary approach as well as first results of the system will be discussed.", notes = "EuroGP'2003 held in conjunction with EvoWorkshops 2003", } @InProceedings{mehr:2018:ICAFS, author = "Ali {Danandeh Mehr} and Farzaneh Bagheri and Rifat Resatoglu", title = "A Genetic Programming Approach to Forecast Daily Electricity Demand", booktitle = "13th International Conference on Theory and Application of Fuzzy Systems and Soft Computing -- ICAFS-2018", year = "2018", editor = "Rafik A. Aliev and Janusz Kacprzyk and Witold Pedrycz and Mo. Jamshidi and Fahreddin M. Sadikoglu", volume = "896", series = "Advances in Intelligent Systems and Computing", pages = "301--308", address = "Warsaw", month = "26-27 " # aug, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Electricity demand, Time series analysis", isbn13 = "978-3-030-04163-2", URL = "http://hdl.handle.net/20.500.12566/61", URL = "http://link.springer.com/chapter/10.1007/978-3-030-04164-9_41", DOI = "doi:10.1007/978-3-030-04164-9_41", abstract = "A number of recent researches have compared machine learning techniques to find more reliable approaches to solve variety of engineering problems. In the present study, capability of canonical genetic programming (GP) technique to model daily electrical energy consumption (ED) as an alternative for electrical demand prediction was investigated. For this aim, using the most recent ED data recorded at northern part of Nicosia, Cyprus, we put forward two daily prediction scenarios subjected to train and validate by GPdotNET, an open source GP software. Minimizing root mean square error between the modelled and observed data as the objective function, the best prediction model at each scenario has been presented for the city. The results indicated the promising role of GP for daily ED prediction in Nicosia, however it suffers from lagged prediction that must be considered in practical application", } @Article{mehr:2019:EMA, author = "Ali Danandeh Mehr and Mir Jafar Sadegh Safari", title = "Multiple genetic programming: a new approach to improve genetic-based month ahead rainfall forecasts", journal = "Environmental Monitoring and Assessment", year = "2019", volume = "192", number = "1", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s10661-019-7991-1", DOI = "doi:10.1007/s10661-019-7991-1", } @Article{mehr:2020:SNas, author = "Ali Danandeh Mehr", title = "An ensemble genetic programming model for seasonal precipitation forecasting", journal = "SN Applied Sciences", year = "2020", volume = "2", number = "11", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s42452-020-03625-x", DOI = "doi:10.1007/s42452-020-03625-x", } @Article{MEHR:2021:IS, author = "Ali Danandeh Mehr and Amir H. Gandomi", title = "{MSGP-LASSO:} An improved multi-stage genetic programming model for streamflow prediction", journal = "Information Sciences", year = "2021", volume = "561", pages = "181--195", month = jun, keywords = "genetic algorithms, genetic programming, LASSO, Multiple regression, Time series modeling, Streamflow, Sedre River", ISSN = "0020-0255", URL = "https://www.sciencedirect.com/science/article/pii/S0020025521001456", DOI = "doi:10.1016/j.ins.2021.02.011", abstract = "we present the development and verification of a new multi-stage genetic programming (MSGP) technique, called MSGP-LASSO, which was applied for univariate streamflow forecasting in the Sedre River, an intermittent river in Turkey. The MSGP-LASSO is a practical and cost-neutral improvement over classic genetic programming (GP) that increases modelling accuracy, while decreasing its complexity by coupling the MSGP and multiple regression LASSO methods. The new model uses average mutual information to identify the optimum lags, and root mean-square technique to minimize forecasting error. Based on Nash-Sutcliffe efficiency and bias-corrected Akaike information criterion, MSGP-LASSO is superior to GP, multigene GP, MSGP, and hybrid MSGP-least-square models. It is explicit and promising for real-life applications", } @Article{mehr:2021:TAC, author = "Ali Danandeh Mehr", title = "Seasonal rainfall hindcasting using ensemble multi-stage genetic programming", journal = "Theoretical and Applied Climatology", year = "2021", volume = "143", number = "1-2", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s00704-020-03438-3", DOI = "doi:10.1007/s00704-020-03438-3", } @Article{Danandeh-Mehr:2025:pce, author = "Ali {Danandeh Mehr} and Masood Jabarnejad and Mir Jafar Sadegh Safari", title = "{MOGGP:} A novel multi objective geometric genetic programming model for drought forecasting", journal = "Physics and Chemistry of the Earth, Parts A/B/C", year = "2025", volume = "138", pages = "103879", keywords = "genetic algorithms, genetic programming, Machine learning, Multi-objective optimization, Standardized precipitation evapotranspiration index (SPEI), Model complexity, Iraq, gene expression programming", ISSN = "1474-7065", URL = "https://www.sciencedirect.com/science/article/pii/S1474706525000294", DOI = "doi:10.1016/j.pce.2025.103879", abstract = "Drought is an environmental challenge, with devastating impacts across a wide range of sectors, including agriculture, economy, and ecosystems. Accurate drought forecasting models are necessary for sustainable water resources planning. Therefore, exploring the efficacy and parsimony of emerging machine learning (ML) techniques to enhance predictive drought forecasting models' accuracy while reducing their complexity is essential. This article introduces a novel hybrid evolutionary ML model, called MOGGP, and compares its efficiency with two evolutionary models, namely gene expression programming and multigene genetic programming as well as conventional Multilayer Perceptron. The new model integrates multi-objective geometric mean optimiser with a traditional symbolic genetic programming that allows parsimonious model selection through developing Pareto optimal solutions. Grided Standardized Precipitation Evapotranspiration Index (SPEI) datasets were employed for demonstrating MOGGP and verifying its efficiency. The results showed that annual cycle is not an effective input for the evolved evolutionary SPEI model. In addition, performance appraisal analysis revealed that the MOGGP consistently exhibits parsimonious models, superior to its counterparts, and excels in addressing multi-objective hydrological modelling problems", } @Article{Mehrafsa:2013:GPEM, author = "Amir Mehrafsa and Alireza Sokhandan and Ghader Karimian", title = "A high performance genetic algorithm using bacterial conjugation operator (HPGA)", journal = "Genetic Programming and Evolvable Machines", year = "2013", volume = "14", number = "4", pages = "395--427", month = dec, keywords = "genetic algorithms, Evolutionary algorithm, Bacterial conjugation, High performance, Real-time, Parameter less", ISSN = "1389-2576", language = "English", DOI = "doi:10.1007/s10710-013-9185-x", size = "33 pages", abstract = "In this paper an efficient evolutionary algorithm is proposed which could be applied to real-time problems such as robotics applications. The only parameter of the proposed algorithm is the Population Size which makes the proposed algorithm similar to parameter-less algorithms, and the only operator applied during the algorithm execution is the bacterial conjugation operator, which makes using and implementation of the proposed algorithm much easier. The procedure of the bacterial conjugation operator used in this algorithm is different from operators of the same name previously used in other evolutionary algorithms such as the pseudo bacterial genetic algorithm or the microbial genetic algorithm. For a collection of 23 benchmark functions and some other well-known optimisation problems, the experimental results show that the proposed algorithm has better performance when compared to particle swarm optimization and a simple genetic algorithm.", } @InProceedings{Mehrmand:2010:AISEW, author = "Arash Mehrmand and Robert Feldt", title = "A factorial experiment on scalability of search based software testing", booktitle = "AISEW 2010: Third Artificial Intelligence Techniques in Software Engineering Workshop", year = "2010", editor = "Ioannis Stamelos", address = "Larnaca, Cyprus", month = oct # " 7", keywords = "genetic algorithms, genetic programming, grammatical evolution, automated software testing, search-based software testing, random testing, java", URL = "http://sweng.csd.auth.gr/~aisew2010/Feldt.pdf", URL = "http://arxiv.org/abs/1101.2301", oai = "oai:arXiv.org:1101.2301", size = "11 pages", abstract = "Software testing is an expensive process, which is vital in the industry. Construction of the test-data in software testing requires the major cost and to decide which method to use in order to generate the test data is important. This paper discusses the efficiency of search-based algorithms (preferably genetic algorithm) versus random testing, in software test-data generation. This study differs from all previous studies due to sample programs (SUTs) which are used. Since we want to increase the complexity of SUTs gradually, and the program generation is automatic as well, Grammatical Evolution is used to guide the program generation. SUTs are generated according to the grammar we provide, with different levels of complexity. SUTs will first undergo genetic algorithm and then random testing. Based on the test results, this paper recommends one method to use for automation of software testing.", notes = "http://sweng.csd.auth.gr/~aisew2010/", } @InProceedings{Mehta:2023:HTC, author = "Jimil Mehta and M. T. Shah", booktitle = "2023 IEEE 11th Region 10 Humanitarian Technology Conference (R10-HTC)", title = "Optimization of Bioelectrochemical Systems with Power of Artificial Intelligence", year = "2023", pages = "494--499", abstract = "Bioelectrochemical systems (BESs) are sophisticated and advanced systems that use exoelectrogenic microbes to generate bioenergy. The integration of Artificial Intelligence (AI) plays a crucial role in comprehending, establishing connections, modelling, and predicting both microbial diversity and process parameters, ultimately enhancing the performance of BESs. This approach uses cutting-edge computational algorithms that are tailored to the specific architecture of BESs, saving time and improving efficiency compared to outdated manual methods. To achieve optimal outcomes, this study aims to examine and compare existing research endeavors while emphasizing the implementation of AI concepts in the field of bioelectrochemical systems. The AI techniques implemented to predict and optimise the behaviour of BES are Artificial Neural Network (ANN), Fuzzy Logic (FL), Multi Gene Genetic Programming (MGGP), and Support Vector Regression (SVR).", keywords = "genetic algorithms, genetic programming, Support vector machines, SVM, Fuzzy logic, Renewable energy sources, System performance, Supervised learning, Fuel cells, Artificial neural networks, ANN, Artificial Intelligence, Bioenergy, Bioelectrochemical System, Microbial fuel cell", DOI = "doi:10.1109/R10-HTC57504.2023.10461898", ISSN = "2572-7621", month = oct, notes = "Also known as \cite{10461898}", } @InProceedings{Mei:2015:CEC, author = "Yi Mei and Xiaodong Li and Flora Salim and Xin Yao", title = "Heuristic Evolution with Genetic Programming for Traveling Thief Problem", booktitle = "Proceedings of 2015 IEEE Congress on Evolutionary Computation (CEC 2015)", year = "2015", editor = "Yadahiko Murata", pages = "2753--2760", address = "Sendai, Japan", month = "25-28 " # may, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Traveling thief problem, memetic algorithm, interdependent optimization", isbn13 = "978-1-4799-7491-7", URL = "http://homepages.ecs.vuw.ac.nz/~yimei/Papers/CEC2015-MeiLiSalimYao.pdf", DOI = "doi:10.1109/CEC.2015.7257230", abstract = "In many real-world applications, one needs to deal with a large multi-silo problem with interdependent silos. In order to investigate the interdependency between silos (subproblems), the Traveling Thief Problem (TTP) was designed as a benchmark problem. TTP is a combination of two well-known sub-problems, Travelling Salesman Problem (TSP) and Knapsack Problem (KP). Although each sub-problem has been intensively investigated, the interdependent combination has been demonstrated to be challenging, and cannot be solved by simply solving the sub-problems separately. The Two-Stage Memetic Algorithm (TSMA) is an effective approach that has decent solution quality and scalability, which consists of a tour improvement stage and an item picking stage. Unlike the traditional TSP local search operators adopted in the former stage, the heuristic for the latter stage is rather intuitive. To further investigate the effect of item picking heuristic, Genetic Programming (GP) is employed to evolve a gain function and a picking function, respectively. The resultant two heuristics were tested on some representative TTP instances, and showed competitive performance, which indicates the potential of evolving more promising heuristics for solving TTP more systematically by GP.", notes = "1300 hrs 15200 CEC2015", } @InProceedings{Mei:2016:GECCO, author = "Yi Mei and Mengjie Zhang and Su Nguyen", title = "Feature Selection in Evolving Job Shop Dispatching Rules with Genetic Programming", booktitle = "GECCO '16: Proceedings of the 2016 Annual Conference on Genetic and Evolutionary Computation", year = "2016", editor = "Tobias Friedrich", pages = "365--372", keywords = "genetic algorithms, genetic programming, Evolutionary Combinatorial Optimization and Metaheuristics", month = "20-24 " # jul, organisation = "SIGEVO", address = "Denver, USA", publisher = "ACM", publisher_address = "New York, NY, USA", isbn13 = "978-1-4503-4206-3", DOI = "doi:10.1145/2908812.2908822", abstract = "Genetic Programming (GP) has been successfully used to automatically design dispatching rules in job shop scheduling. The goal of GP is to evolve a priority function that will be used to order the waiting jobs at each decision point, and decide the next job to be processed. To this end, the proper terminals (i.e. job shop features) have to be decided. When evolving the priority function, various job shop features can be included in the terminal set. However, not all the features are helpful, and some features are irrelevant to the rule. Including irrelevant features into the terminal set enlarges the search space, and makes it harder to achieve promising areas. Thus, it is important to identify the important features and remove the irrelevant ones to improve the GP-evolved rules. This paper proposes a domain-knowledge-free feature ranking and selection approach. As a result, the terminal set is significantly reduced and only the most important features are selected. The experimental results show that using only the selected features can lead to significantly better GP-evolved rules on both training and unseen test instances.", notes = "Victoria University of Wellington, Hoa Sen University GECCO-2016 A Recombination of the 25th International Conference on Genetic Algorithms (ICGA-2016) and the 21st Annual Genetic Programming Conference (GP-2016)", } @InProceedings{Mei:2016:CEC, author = "Yi Mei and Mengjie Zhang", title = "A Comprehensive Analysis on Reusability of GP-Evolved Job Shop Dispatching Rules", booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016)", year = "2016", editor = "Yew-Soon Ong", pages = "3590--3597", address = "Vancouver", month = "24-29 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-5090-0623-6", DOI = "doi:10.1109/CEC.2016.7744244", abstract = "Genetic Programming (GP) has been extensively used to automatically design dispatching rules for job shop scheduling problems. However, the previous studies only focus on the performance on the training instances. So far, there is no systematic investigation of the reusability of the GP-evolved rules on unseen instances. In practice, it is desirable to train the rules on smaller job shop instances, and apply them to larger instances with more jobs and machines to save training time. In this case, the reusability of the GP-evolved rules under different numbers of jobs and machines is an important issue. In this paper, a comprehensive investigation is conducted to analyse how the variation in the numbers of jobs and machines from the training set to the test set affects the reusability of the GP-evolved rules. It is found that in terms of minimizing makespan, the reusability of the GP-evolved rules highly depends on variation in the numbers of jobs and machines. A better reusability can be achieved by choosing training instances whose numbers of jobs and machines (or at least the ratio between the numbers of jobs and machines) are closer to that of the test instances. Furthermore, the ratio between the numbers of jobs and machines is demonstrated to be an important factor to reflect the complexity of an instance for dispatching rules. This study is the first systematic investigation on the reusability of GP-evolved dispatching rules.", notes = "WCCI2016", } @InProceedings{Mei:2017:EuroGP, author = "Yi Mei and Su Nguyen and Mengjie Zhang", title = "Evolving Time-Invariant Dispatching Rules in Job Shop Scheduling with Genetic Programming", booktitle = "EuroGP 2017: Proceedings of the 20th European Conference on Genetic Programming", year = "2017", month = "19-21 " # apr, editor = "Mauro Castelli and James McDermott and Lukas Sekanina", series = "LNCS", volume = "10196", publisher = "Springer Verlag", address = "Amsterdam", pages = "147--163", organisation = "species", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-55695-6", DOI = "doi:10.1007/978-3-319-55696-3_10", abstract = "Genetic Programming (GP) has achieved success in evolving dispatching rules for job shop scheduling problems, particularly in dynamic environments. However, there is still great potential to improve the performance of GP. One challenge that is yet to be addressed is the huge search space. In this paper, we propose a simple yet effective approach to improve the effectiveness and efficiency of GP. The new approach is based on a newly defined time-invariance property of dispatching rules, which is derived from the idea of translational invariance from machine learning. Then, we develop a new terminal selection scheme to guarantee the time-invariance throughout the GP process. The experimental studies show that by considering the time-invariance, GP can achieve much better rules in a much shorter time.", notes = "Part of \cite{Castelli:2017:GP} EuroGP'2017 held inconjunction with EvoCOP2017, EvoMusArt2017 and EvoApplications2017", } @Article{Mei:2017:ieeeETCI, author = "Yi Mei and Su Nguyen and Bing Xue and Mengjie Zhang", journal = "IEEE Transactions on Emerging Topics in Computational Intelligence", title = "An Efficient Feature Selection Algorithm for Evolving Job Shop Scheduling Rules With Genetic Programming", year = "2017", volume = "1", number = "5", pages = "339--353", month = oct, keywords = "genetic algorithms, genetic programming, Feature selection, hyper-heuristic, job shop scheduling", DOI = "doi:10.1109/TETCI.2017.2743758", abstract = "Automated design of job shop scheduling rules using genetic programming as a hyper-heuristic is an emerging topic that has become more and more popular in recent years. For evolving dispatching rules, feature selection is an important issue for deciding the terminal set of genetic programming. There can be a large number of features, whose importance/relevance varies from one to another. It has been shown that using a promising feature subset can lead to a significant improvement over using all the features. However, the existing feature selection algorithm for job shop scheduling is too slow and inapplicable in practice. In this paper, we propose the first practical feature selection algorithm for job shop scheduling. Our contributions are twofold. First, we develop a Niching-based search framework for extracting a diverse set of good rules. Second, we reduce the complexity of fitness evaluation by using a surrogate model. As a result, the proposed feature selection algorithm is very efficient. The experimental studies show that it takes less than 10percent of the training time of the standard genetic programming training process, and can obtain much better feature subsets than the entire feature set. Furthermore, it can find better feature subsets than the best-so-far feature subset.", notes = "Also known as \cite{8048081}", } @InProceedings{conf/seal/MeiNZ17, author = "Yi Mei and Su Nguyen and Mengjie Zhang", title = "Constrained Dimensionally Aware Genetic Programming for Evolving Interpretable Dispatching Rules in Dynamic Job Shop Scheduling", booktitle = "Proceedings of the 11th International Conference on Simulated Evolution and Learning, SEAL 2017", year = "2017", editor = "Yuhui Shi and Kay Chen Tan and Mengjie Zhang and Ke Tang and Xiaodong Li and Qingfu Zhang and Ying Tan and Martin Middendorf and Yaochu Jin", volume = "10593", series = "Lecture Notes in Computer Science", pages = "435--447", address = "Shenzhen, China", month = nov # " 10-13", publisher = "Springer", keywords = "genetic algorithms, genetic programming, C-DAGP", bibdate = "2017-11-03", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/seal/seal2017.html#MeiNZ17", isbn13 = "978-3-319-68758-2", URL = "https://homepages.ecs.vuw.ac.nz/~yimei/papers/SEAL17-MeiSuZhang.pdf", URL = "https://rdcu.be/d52e9", DOI = "doi:10.1007/978-3-319-68759-9_36", size = "13 pages", abstract = "This paper investigates the interpretability of the Genetic Programming (GP)-evolved dispatching rules for dynamic job shop scheduling problems. We incorporate the physical dimension of the features used in the terminal set of GP, and assume that the rules that aggregate the features with the same physical dimension are more interpretable. Based on this assumption, we define a new interpretability measure called dimension gap, and develop a Constrained Dimensionally Aware GP (C-DAGP) that optimises the effectiveness and interpretability simultaneously. In C-DAGP, the fitness is defined as a penalty function with a newly proposed penalty coefficient adaptation scheme. The experimental results show that the proposed C-DAGP can achieve better tradeoff between effectiveness and interpretability compared against the baseline GP and an existing DAGP.", } @InProceedings{Mei:2018:CEC, author = "Yi Mei and Mengjie Zhang", title = "Genetic Programming Hyper-heuristic for Stochastic Team Orienteering Problem with Time Windows", booktitle = "2018 IEEE Congress on Evolutionary Computation (CEC)", year = "2018", editor = "Marley Vellasco", address = "Rio de Janeiro, Brazil", month = "8-13 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2018.8477983", abstract = "This paper investigates the stochastic team orienteering problem with time windows, which is a well known problem to model personalised tourist trip design. Specifically, we consider the stochastic visit duration, which may make preplanned trip infeasible. Existing studies focus on optimising robust solutions in advance, which is not effective in adjusting the subsequent trip in real time. Decision making policies, on the other hand, are effective heuristics to this end. However, it is very challenging to manually design effective policies. In this paper, we investigate automatically evolving policies for the stochastic team orienteering problem with time windows by genetic programming hyper-heuristics. We designed novel problem-specific features for the terminal set, and a meta-algorithm for fitness evaluation. Furthermore, we developed two look-ahead features that can provide more fruitful information than the basic features for real-time decision making. The experimental studies showed that the proposed genetic programming hyper-heuristic can evolve policies that are much better than the manually designed policies. In addition, it seems that the look-ahead features are not so effective when directly included in the terminals. This suggests the requirement of more intelligent ways of incorporating lookahead information.", notes = "WCCI2018", } @InProceedings{Mei:2018:GECCOcomp, author = "Yi Mei and Mengjie Zhang", title = "Genetic programming hyper-heuristic for multi-vehicle uncertain capacitated arc routing problem", booktitle = "GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2018", editor = "Carlos Cotta and Tapabrata Ray and Hisao Ishibuchi and Shigeru Obayashi and Bogdan Filipic and Thomas Bartz-Beielstein and Grant Dick and Masaharu Munetomo and Silvino {Fernandez Alzueta} and Thomas Stuetzle and Pablo Valledor Pellicer and Manuel Lopez-Ibanez and Daniel R. Tauritz and Pietro S. Oliveto and Thomas Weise and Borys Wrobel and Ales Zamuda and Anne Auger and Julien Bect and Dimo Brockhoff and Nikolaus Hansen and Rodolphe {Le Riche} and Victor Picheny and Bilel Derbel and Ke Li and Hui Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Stephane Doncieux and Richard Duro and Joshua Auerbach and Harold {de Vladar} and Antonio J. Fernandez-Leiva and JJ Merelo and Pedro A. Castillo-Valdivieso and David Camacho-Fernandez and Francisco {Chavez de la O} and Ozgur Akman and Khulood Alyahya and Juergen Branke and Kevin Doherty and Jonathan Fieldsend and Giuseppe Carlo Marano and Nikos D. Lagaros and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Boris Naujoks and Vanessa Volz and Tea Tusar and Pascal Kerschke and Riyad Alshammari and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and John R. Woodward and Shin Yoo and John McCall and Nayat Sanchez-Pi and Luis Marti and Danilo Vasconcellos and Masaya Nakata and Anthony Stein and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Ivanoe {De Falco} and Antonio {Della Cioppa} and Ernesto Tarantino and Umberto Scafuri and P. G. M. Baltus and Giovanni Iacca and Ahmed Hallawa and Anil Yaman and Alma Rahat and Handing Wang and Yaochu Jin and David Walker and Richard Everson and Akira Oyama and Koji Shimoyama and Hemant Kumar and Kazuhisa Chiba and Pramudita Satria Palar", isbn13 = "978-1-4503-5764-7", pages = "141--142", address = "Kyoto, Japan", DOI = "doi:10.1145/3205651.3205661", publisher = "ACM", publisher_address = "New York, NY, USA", month = "15-19 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", abstract = "This paper investigates evolving routing policy for general Uncertain Capacitated Arc Routing Problems (UCARP) with any number of vehicles, and for the first time, designs a novel model for online decision making (i.e. meta-algorithm) for multiple vehicles in service simultaneously. Then, we develop a GPHH based on the meta-algorithm. The experimental studies show the GPHH can evolve much better policies than the state-of-the-art manually designed policy. In addition, the reusability of the evolved policies dramatically decreases when the number of vehicles changes, which suggests a retraining process when a new vehicle is brought or an existing vehicle breaks down.", notes = "Also known as \cite{3205661} GECCO-2018 A Recombination of the 27th International Conference on Genetic Algorithms (ICGA-2018) and the 23rd Annual Genetic Programming Conference (GP-2018)", } @InCollection{mei:2021:ADMLSA, author = "Yi Mei and Mazhar {Ansari Ardeh} and Mengjie Zhang", title = "Knowledge Transfer in Genetic Programming Hyper-heuristics", booktitle = "Automated Design of Machine Learning and Search Algorithms", publisher = "Springer", year = "2021", editor = "Nelishia Pillay and Rong Qu", series = "Natural Computing Series", pages = "149--169", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-030-72068-1", URL = "http://link.springer.com/chapter/10.1007/978-3-030-72069-8_9", DOI = "doi:10.1007/978-3-030-72069-8_9", abstract = "Genetic Programming Hyper-heuristics (GPHHs) have been successfully applied in various problem domains for automatically designing heuristics such as dispatching rules in scheduling and routing policies in vehicle routing. In the real world, it is normal to encounter related problem domains, such as the vehicle routing problem with different objectives, constraints, and/or graph topology. On one hand, different heuristics are required for different problem domains. On the other hand, the knowledge learned from solving previous related problem domains can be helpful for solving the current one. Most existing studies solve different problem domains in isolation, and train/evolve the heuristic for each of them from scratch. we investigate different mechanisms to improve the effectiveness and efficiency of the heuristic retraining by employing knowledge transfer. Specifically, in the context of GPHH, we explored the following two transfer strategies: (1) useful subtrees and (2) importance of terminals, and verified their effectiveness in a case study of the uncertain capacitated arc routing problem.", } @Article{Mei:TEVC, author = "Yi Mei and Qi Chen and Andrew Lensen and Bing Xue and Mengjie Zhang", title = "Explainable Artificial Intelligence by Genetic Programming: A Survey", journal = "IEEE Transactions on Evolutionary Computation", year = "2023", volume = "27", number = "3", pages = "621--641", month = jun, keywords = "genetic algorithms, genetic programming, XAI, Explainable artificial intelligence, Machine learning, Task analysis, Predictive models, Adaptation models, Training, Measurement", ISSN = "1941-0026", DOI = "doi:10.1109/TEVC.2022.3225509", size = "21 pages", abstract = "Explainable artificial intelligence has received great interest in the recent decade, due to its importance in critical application domains such as self-driving cars, law and healthcare. Genetic programming is a powerful evolutionary algorithm for machine learning. Compared with other standard machine learning models such as neural networks, the models evolved by GP tend to be more interpretable due to their model structure with symbolic components. However, interpretability has not been explicitly considered in genetic programming until recently, following the surge in popularity of explainable artificial intelligence. This paper provides a comprehensive review of the studies on genetic programming that can potentially improve the model interpretability, both explicitly and implicitly, as a byproduct. We group the existing studies related to explainable artificial intelligence by genetic programming into two categories. The first category considers the intrinsic interpretability, aiming to directly evolve more interpretable (and effective) models by genetic programming. The second category focuses on post-hoc interpretability, which uses genetic programming to explain other black-box machine learning models, or explain the models evolved by genetic programming by simpler models such as linear models. This comprehensive survey demonstrates the strong potential of genetic programming for improving the interpretability of machine learning models and balancing the complex trade-off between model accuracy and interpretability.", notes = "Also known as \cite{9965435}", } @InProceedings{Meier:2013:GECCO, author = "Andreas Meier and Mark Gonter and Rudolf Kruse", title = "Accelerating convergence in cartesian genetic programming by using a new genetic operator", booktitle = "GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference", year = "2013", editor = "Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi", pages = "981--988", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, optimization, genetic operator", isbn13 = "978-1-4503-1963-8", URL = "http://www.cmap.polytechnique.fr/~nikolaus.hansen/proceedings/2013/GECCO/proceedings/p981.pdf", DOI = "doi:10.1145/2463372.2463481", size = "8 pages", abstract = "Genetic programming algorithms seek to find interpretable and good solutions for problems which are difficult to solve analytically. For example, we plan to use this paradigm to develop a car accident severity prediction model for new occupant safety functions. This complex problem will suffer from the major disadvantage of genetic programming, which is its high demand for computational effort to find good solutions. A main reason for this demand is a low rate of convergence. we introduce a new genetic operator called forking to accelerate the rate of convergence. Our idea is to interpret individuals dynamically as centres of local Gaussian distributions and allow a sampling process in these distributions when populations get too homogeneous. We demonstrate this operator by extending the Cartesian Genetic Programming algorithm and show that on our examples convergence is accelerated by over 50percent on average. We finish this paper with giving hints about parametrisation of the forking operator for other problems.", notes = "Also known as \cite{2463481} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)", } @Article{mejia-zuluaga:2022:Remote_Sensing, author = "Paola Andrea Mejia-Zuluaga and Leon Dozal and Juan C. Valdiviezo-N.", title = "Genetic Programming Approach for the Detection of Mistletoe Based on {UAV} Multispectral Imagery in the Conservation Area of Mexico City", journal = "Remote Sensing", year = "2022", volume = "14", number = "3", keywords = "genetic algorithms, genetic programming", ISSN = "2072-4292", URL = "https://www.mdpi.com/2072-4292/14/3/801", DOI = "doi:10.3390/rs14030801", abstract = "The mistletoe Phoradendron velutinum (P. velutinum) is a pest that spreads rapidly and uncontrollably in Mexican forests, becoming a serious problem since it is a cause of the decline of 23.3 million hectares of conifers and broadleaves in the country. The lack of adequate phytosanitary control has negative social, economic, and environmental impacts. However, pest management is a challenging task due to the difficulty of early detection for proper control of mistletoe infestations. Automating the detection of this pest is important due to its rapid spread and the high costs of field identification tasks. This paper presents a Genetic Programming (GP) approach for the automatic design of an algorithm to detect mistletoe using multispectral aerial images. Our study area is located in a conservation area of Mexico City, in the San Bartolo Ameyalco community. Images of 148 hectares were acquired by means of an Unmanned Aerial Vehicle (UAV) carrying a sensor sensitive to the R, G, B, red edge, and near-infrared bands, and with an average spatial resolution of less than 10 cm per pixel. As a result, it was possible to obtain an algorithm capable of classifying mistletoe P. velutinum at its flowering stage for the specific case of the study area in conservation area with an Overall Accuracy (OA) of 96percent and a value of fitness function based on weighted Cohens Kappa (kw) equal to 0.45 in the test data set. Additionally, our methods performance was compared with two traditional image classification methods; in the first, a classical spectral index, named Intensive Pigment Index of Structure 2 (SIPI2), was considered for the detection of P. velutinum. The second method considers the well-known Support Vector Machine classification algorithm (SVM). We also compare the accuracy of the best GP individual with two additional indices obtained during the solution analysis. According to our experimental results, our GP-based algorithm outperforms the results obtained by the aforementioned methods for the identification of P. velutinum.", notes = "also known as \cite{rs14030801}", } @InProceedings{mejia-zuluaga:2022:AGDS, author = "Paola Andrea Mejia-Zuluaga and Leon Felipe Dozal-Garcia and Juan Carlos Valdiviezo-Navarro", title = "Detection of Phoradendron Velutinum Implementing Genetic Programming in Multispectral Aerial Images in Mexico City", booktitle = "Advances in Geospatial Data Science", year = "2022", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-030-98096-2_9", DOI = "doi:10.1007/978-3-030-98096-2_9", } @InProceedings{Meli:2016:CSOC, author = "Clyde Meli and Zuzana {Kominkova Oplatkova}", title = "{SPAM} Detection: Naive {Bayesian} Classification and {RPN} Expression-Based {LGP} Approaches Compared", booktitle = "Proceedings of the 5th Computer Science On-line Conference, CSOC 2016, Volume 2", year = "2016", editor = "Radek Silhavy and Roman Senkerik and Zuzana {Kominkova Oplatkova} and Petr Silhavy and Zdenka Prokopova", volume = "465", series = "Advances in Intelligent Systems and Computing", pages = "399--411", month = apr # " 27-30", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Reverse polish notation, Linear genetic programming", isbn13 = "978-3-319-33622-0", DOI = "doi:10.1007/978-3-319-33622-0_36", abstract = "An investigation is performed of a machine learning algorithm and the Bayesian classifier in the spam-filtering context. The paper shows the advantage of the use of Reverse Polish Notation (RPN) expressions with feature extraction compared to the traditional Naive Bayesian classifier used for spam detection assuming the same features. The performance of the two is investigated using a public corpus and a recent private spam collection, concluding that the system based on RPN LGP (Linear Genetic Programming) gave better results compared to two popularly used open source Bayesian spam filters.", notes = "Software Engineering Perspectives and Application in Intelligent Systems Preface says held in April 2015, but http://www.allconferences.com/c/5th-computer-science-on-line-conference-2016-csoc-2016-prague-2016-april-27 says Event Date/Time: Apr 27, 2016 End Date/Time: Apr 30, 2016", } @InProceedings{meli:2019:RASC, author = "Clyde Meli and Vitezslav Nezval and Zuzana Kominkova Oplatkova and Victor Buttigieg", title = "Spam Detection Using Linear Genetic Programming", booktitle = "Recent Advances in Soft Computing", year = "2019", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-319-97888-8_7", DOI = "doi:10.1007/978-3-319-97888-8_7", } @InProceedings{melin:2003:CINC, author = "Patricia Melin and Oscar Castillo", title = "Evolution of Modular Neural Networks Using a Hierarchical Genetic Algorithm Approach", booktitle = "Proceedings of the Sixth International Conference on Computational Intelligence and Natural Computing", year = "2003", address = "Embassy Suites Hotel and Conference Center, Cary, North Carolina USA", month = sep # " 26-30", keywords = "genetic algorithms, genetic programming", notes = "http://axon.cs.byu.edu/Dan/cinc03.html/index.html/ Broken Jan 2013 http://www.ee.duke.edu/JCIS/ Tijuana Institute of Technology, Mexico", } @InProceedings{Mellish:1998:INGL, author = "Chris Mellish and Alistair Knott and Jon Oberlander and Mick O'Donnell", title = "Experiments using stochastic search for text planning", booktitle = "Proceedings of the Ninth International Workshop on Natural Language Generation", year = "1998", editor = "Eduard Hovy", address = "Niagara-on-the-Lake, Ontario, Canada", month = "5-7 " # aug, keywords = "genetic algorithms", URL = "http://www.aclweb.org/anthology/W/W98/W98-1411.pdf", URL = "http://www.wagsoft.com/Papers/stochastic00.pdf", size = "10 pages", notes = "http://ai.uwaterloo.ca/~inlg98/ U of Edinburgh", } @InProceedings{Melo-Neto:2018:CEC, author = "Johnathan {Melo Neto} and Heder Bernardino and Helio Barbosa", title = "Hybridization of Cartesian Genetic Programming and Differential Evolution for Generating Classifiers based on Neural Networks", booktitle = "2018 IEEE Congress on Evolutionary Computation (CEC)", year = "2018", editor = "Marley Vellasco", address = "Rio de Janeiro, Brazil", month = "8-13 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", DOI = "doi:10.1109/CEC.2018.8477906", abstract = "Despite the significance of Artificial Neural Networks (ANNs) in practical situations and the several works available in the literature, to adjust its parameters remains as a current problem. Hence, the advent of methods to assist users during this modelling is relevant. Three hybrid techniques based on Cartesian Genetic Programming (CGP) and Differential Evolution (DE) are proposed here for the construction of ANNs. The developed methods carry out an uncoupled evolution of the topology (using CGP) and the weights (using DE). The ANNs are evolved for classification problems, and seven benchmark datasets are used in the computational experiments. Results show the superiority of the proposed methods when compared to other techniques from the literature", notes = "WCCI2018", } @InProceedings{Melo-Neto:2019:CEC, author = "Johnathan M. {Melo Neto} and Heder S. Bernardino and Helio J. C. Barbosa", title = "On the Impact of the Objective Function on Imbalanced Data using Cartesian Genetic Programming Neuroevolutionary Approaches", booktitle = "2019 IEEE Congress on Evolutionary Computation (CEC)", year = "2019", pages = "1860--1867", month = jun, keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, ANN", DOI = "doi:10.1109/CEC.2019.8789947", abstract = "The training of machine learning models for imbalanced data classification is a challenging task. Several metrics have been used to assess the performance of the classifiers. Each metric is appropriate for a class of problems, and some users often do not have a clear notion of which metric to use. In such cases, it is desirable that the chosen objective function provides good overall performance for most of the existing metrics. Here, three neuroevolutionary approaches based on Cartesian Genetic Programming are used in order to investigate the impact of optimizing accuracy, G-mean, Fbeta-score, and the area under the Receiver Operating Characteristic (ROC) curve when creating classifiers based on Artificial Neural Networks applied to imbalanced data classification problems. The results suggest that the optimization of G-mean and FB-score generate models that present a superior overall performance in all metrics.", notes = "Also known as \cite{8789947}", } @InProceedings{melvin:2004:otagp, author = "N. Melvin and R. Soricone and J. Waslo", title = "On the automaticity of genetic programming", booktitle = "14th International Conference on Electronics, Communications and Computers", year = "2004", month = feb, pages = "223--228", keywords = "genetic algorithms, genetic programming", URL = "http://csdl.computer.org/comp/proceedings/conielecomp/2004/2074/00/20740236abs.htm", DOI = "doi:10.1109/ICECC.2004.1269579", abstract = "Genetic/evolutionary algorithms, based upon an analogy to the mechanics of Mendelian genetics and Darwinian evolutionary theory, offer an automatic way to improve programs. The process cycles many times, selecting for reproduction among a population of program variants (represented by the chromosomes) to form the next generation, with mutation and crossover producing additional variation. This approach was tested in such classic problems as function evolution, function maximization, and the Traveling Salesman problem. While the basic approach proved powerful, its implementation required a non-automatic series of choices with respect to the parameters for the algorithm itself, the representation of chromosomes, the meanings of mutation and crossover, the possibility of other mechanisms such as inversion, and the evaluation of the fitness of the reproductive candidates. The most important message is that despite the automatic nature of the algorithm itself, knowledge of the problem domain is important to its implementation.", notes = "Authors Neville Melvin, Northern Arizona University Robert Soricone, Northern Arizona University James Waslo, Northern Arizona University", } @InProceedings{Menaka:2014:ICCCI, author = "K. Menaka and S. Karpagavalli", booktitle = "International Conference on Computer Communication and Informatics (ICCCI 2014)", title = "Mammogram classification using Extreme Learning Machine and Genetic Programming", year = "2014", month = jan, abstract = "Mammogram is an x-ray examination of breast. It is used to detect and diagnose breast disease in women who either have breast problems such as a lump, pain or nipple discharge as well as for women who have no breast complaints. Digitised mammographic image is analysed for masses, calcifications, or areas of abnormal density that may indicate the presence of cancer. Automated systems to analyse and classify the mammogram images as benign or malignant will drive the medical experts to take timely clinical decision. In this work, the mammogram classification task carried out using powerful supervised classification techniques namely Extreme Learning Machine with kernels like linear, polynomial, radial basis function and Genetic Programming. The various task involved in this work are image preprocessing, feature extraction, building models through training and testing the classifier. The two types of mammogram image, Benign and Malignant are considered in this work and 50 images for each type collected from Mini MIAS database. Selection of Region of Interest (ROI) from the original image and Adaptive Histogram Enhancement are applied on the mammogram image before extracting the intensity histogram and gray level co-occurrence matrix features. In the dataset, for training 80percent of the data are used and for testing 20percent of data are used. Models are built using Extreme Learning Machine and Genetic Programming. The performances of the models are tested with test dataset and the results are compared. The predictive accuracy and training time of the classifier Genetic Programming is substantially better than the classifier built using Extreme Learning Machine with kernels linear, polynomial and radial basis function.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICCCI.2014.6921724", notes = "Also known as \cite{6921724}", } @InProceedings{conf/seal/MendesSV10, title = "{PID} Step Response Using Genetic Programming", author = "Marcus Henrique Soares Mendes and Gustavo Luis Soares and Joao Antonio {de Vasconcelos}", booktitle = "Simulated Evolution and Learning - 8th International Conference, {SEAL} 2010, Kanpur, India, December 1-4, 2010. Proceedings", publisher = "Springer", year = "2010", volume = "6457", editor = "Kalyanmoy Deb and Arnab Bhattacharya and Nirupam Chakraborti and Partha Chakroborty and Swagatam Das and Joydeep Dutta and Santosh K. Gupta and Ashu Jain and Varun Aggarwal and J{\"u}rgen Branke and Sushil J. Louis and Kay Chen Tan", isbn13 = "978-3-642-17297-7", pages = "359--368", series = "Lecture Notes in Computer Science", URL = "http://dx.doi.org/10.1007/978-3-642-17298-4", DOI = "DOI:10.1007/978-3-642-17298-4_37", keywords = "genetic algorithms, genetic programming, control", abstract = "This paper describes an algorithm that generates analytic functions for PID step response characteristics (i.e. rise time, overshoot, settling time, peak time and integral of time weighted absolute error) in an application of a third-order plant. The algorithm uses genetic programming for symbolic regressions and provides formal expressions composed of variables, constants, elementary operators and mathematical functions. Results show a good fitting between the desired and obtained step response for DC motor positioning problem.", bibdate = "2010-12-01", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/seal/seal2010.html#MendesSV10", } @InProceedings{Mendes:2001:PKDD, author = "Roberto R. F. Mendes and Fabricio B. Voznika and Alex A. Freitas and Julio C. Nievola", title = "Discovering Fuzzy Classification Rules with Genetic Programming and Co-Evolution", booktitle = "5th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'01)", year = "2001", editor = "L. {de Raedt} and Arno Siebes", volume = "2168", series = "LNAI", pages = "314--325", address = "Freiburg, Germany", month = "3-7 " # sep, publisher = "Springer Verlag", keywords = "genetic algorithms, genetic programming, data mining, classification, co-evolution", URL = "https://www.cs.kent.ac.uk/people/staff/aaf/pubs.html", URL = "http://www.cs.kent.ac.uk/people/staff/aaf/pub_papers.dir/PKDD-2001.ps", URL = "http://citeseer.ist.psu.edu/521000.html", DOI = "doi:10.1007/3-540-44794-6_26", size = "12 pages", abstract = "In essence, data mining consists of extracting knowledge from data. This paper proposes a co-evolutionary system for discovering fuzzy classification rules. The system uses two evolutionary algorithms: a genetic programming (GP) algorithm evolving a population of fuzzy rule sets and a simple evolutionary algorithm evolving a population of of membership function definitions. The two populations co-evolve, so that the final result of the co-evolutionary process is a fuzzy rule set and a set of membership function definitions which are well adapted to each other. In addition, our system also has some innovative ideas with respect to the encoding of GP individuals representing rule sets. The basic idea is that our individual encoding scheme incorporates several syntactical restrictions that facilitate the handling of rule sets in disjunctive normal form. We have also adapted GP operators to better work with the proposed individual encoding scheme.", notes = "Broken Feb 2019 http://www.informatik.uni-freiburg.de/~ml/ecmlpkdd/index.html PKDD-2001 Broken Feb 2019 http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-42534-9 Comparison in \cite{yu:2004:ECDM}", } @InProceedings{mendes:2001:gecco, title = "Discovering Fuzzy Classification Rules with Genetic Programming and Co-Evolution", author = "Roberto R. F. Mendes and Fabricio de B. Voznika and Julio C. Nievola and Alex A. Freitas", pages = "183", year = "2001", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)", editor = "Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon and Edmund Burke", address = "San Francisco, California, USA", publisher_address = "San Francisco, CA 94104, USA", month = "7-11 " # jul, keywords = "genetic algorithms, genetic programming: Poster, evolution strategy, co-evolution, data mining, classification, fuzzy rules", ISBN = "1-55860-774-9", URL = "http://gpbib.cs.ucl.ac.uk/gecco2001/d02.pdf", notes = "GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of \cite{spector:2001:GECCO}", } @InProceedings{mendes:2001:dfcrgpc, author = "Roberto R. F. Mendes and Fabricio {de B. Voznika} and Julio C. Nievola and Alex A. Freitas", title = "Discovering Fuzzy Classification Rules with Genetic Programming and Co-Evolution", booktitle = "2001 Genetic and Evolutionary Computation Conference Late Breaking Papers", year = "2001", editor = "Erik D. Goodman", pages = "287--294", address = "San Francisco, California, USA", month = "9-11 " # jul, keywords = "genetic algorithms, genetic programming, CEFR-MINER", notes = "GECCO-2001LB Coevolution of two populations: GP population of fuzzy rule sets. Simple evolutionary algorithm evolving a population of membership function definitions. cf: \cite{delgado:1999:MHEDFS}", } @Article{Mendes:2013:IEEEMagnetics, author = "Marcus H. S. Mendes and Gustavo L. Soares and Jean-Louis Coulomb and Joao A. Vasconcelos", journal = "IEEE Transactions on Magnetics", title = "Appraisal of Surrogate Modeling Techniques: A Case Study of Electromagnetic Device", year = "2013", volume = "49", number = "5", pages = "1993--1996", size = "4 pages", keywords = "genetic algorithms, genetic programming, Interval robust optimisation, TEAM 22 problem, surrogate modelling", DOI = "doi:10.1109/TMAG.2013.2241401", ISSN = "0018-9464", abstract = "Simulations are successfully used to reproduce the behaviour of complex systems in many knowledge fields. The computational effort is a key factor when high-cost simulations are required in optimisation, principally, if the system to be optimised operates under uncertain conditions. In this context, surrogate modelling is useful to alleviate the CPU time. Hence, this paper presents a methodology to assess three surrogate techniques based on genetic programming (GP), a radial basis function neural network (RBF-NNs), and universal Kriging. These techniques are used in this paper to obtain analytical optimisation functions that are accurate, fast to evaluate and suitable for interval robust optimisation. The experiments were performed in a robust version of the TEAM 22 problem. The results show that the surrogate models obtained are reliable and appropriate for interval robust methods. The methodology presented is flexible and extensible to other problems in diverse fields of interest.", notes = "Also known as \cite{6514603}", } @Article{Mendes:2013:IEEEMagnetics2, author = "Marcus H. S. Mendes and Gustavo L. Soares and Jean-Louis Coulomb and Joao A. Vasconcelos", title = "A Surrogate Genetic Programming Based Model to Facilitate Robust Multi-Objective Optimization: A Case Study in Magnetostatics", journal = "IEEE Transactions on Magnetics", year = "2013", month = may, volume = "49", number = "5", pages = "2065--2068", keywords = "genetic algorithms, genetic programming, Finite element method, TEAM 22 problem, robust optimisation, surrogate model", DOI = "doi:10.1109/TMAG.2013.2238615", ISSN = "0018-9464", abstract = "A common drawback of robust optimisation methods is the effort expended to compute the influence of uncertainties, because the objective and constraint functions must be re-evaluated many times. This disadvantage can be aggravated if time-consuming methods, such as boundary or finite element methods are required to calculate the optimisation functions. To overcome this difficulty, we propose the use of genetic programming to obtain high-quality surrogate functions that are quickly evaluated. Such functions can be used to compute the values of the optimisation functions in place of the burdensome methods. The proposal has been tested on a version of the TEAM 22 benchmark problem with uncertainties in decision parameters. The performance of the methodology has been compared with results in the literature, ensuring its suitability, significant CPU time savings and substantial reduction in the number of computational simulations.", notes = "Also known as \cite{6514790}", } @InProceedings{Mendigoria:2021:HNICEM, author = "Christan Hail Mendigoria and Ronnie Concepcion and Ryan Rhay Vicerra and Andres Philip Mayol and Alvin Culaba and Elmer Dadios and Argel Bandala", booktitle = "2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)", title = "Optimization of Vacuum Drying Properties for Chlorococcum infusionum Microalgae Moisture Content Using Hybrid Genetic Programming and Genetic Algorithm", year = "2021", month = "28-30 " # nov, address = "Manila, Philippines", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-6654-0168-5", DOI = "doi:10.1109/HNICEM54116.2021.9732016", abstract = "Biofuel production serves as a viable alternative to conventional energy production systems which primarily relies on fossil fuels. Because of its increased protein and lipid accumulation properties, algal biomass has been deemed a feasible source for biofuel generation among the many types of biomass materials. Microalgal drying process, a preliminary process prior to biofuel production, is a crucial procedure which consumes a lot of energy. Thus, optimization of this process must be considered. As a response, this study aims to determine the optimal vacuum drying parameters such as the biomass thickness, drying temperature and vacuum pressure in reference to the moisture content of the microalgae, Chlorococcum infusionum, using hybrid evolutionary strategies of genetic programming (GP) and genetic algorithm (GA). GP was configured using the GPTIPSv2 tool to generate a symbolic function which is a fundamental element of GA optimization. GA was used to generate candidate solutions which were evaluated for goodness of fit through the developed function. Based on the results, this optimization generated parameter values of 5 mm, 69.4degreeC, and 178.3 mbar for biomass thickness, temperature, and pressure, respectively, which converges at the function value of121.344. This developed technique served as a non-invasive optimization model to computationally determine the optimal microalgal drying parameter values.", notes = "Also known as \cite{9732016}", } @InProceedings{Mendigoria:2021:TENCON, author = "Christan Hail Mendigoria and Ronnie Concepcion and Argel Bandala and Elmer Dadios and Oliver John Alajas and Heinrick Aquino and Ryan Rhay Vicerra and Joel Cuello", booktitle = "TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON)", title = "In Situ Indirect Measurement of Nitrate Concentration in Outdoor Tilapia Fishpond Based on Physico-limnological Sensors", year = "2021", pages = "498--503", abstract = "Excess nitrate concentration leads to excessive algal growth that reduces dissolved oxygen for aquatic animals. A significant strategy to preserve the water quality of aquatic systems is through nitrate level assessment. However, use of nitrate sensors and existing laboratory approach is costly and requires a huge effort. This study investigated the application of computational intelligence for measurement of nitrate concentration in a tilapia fishpond at Rizal province, Philippines, based on physico-limnological parameters such as temperature, electrical conductivity, and pH level. Artificial neural network (ANN) algorithms including feed-forward (FNN) and recurrent (RNN) neural networks were developed and optimized using genetic algorithm (GA) to improve their predicting performances. Genetic programming (GP), through GPTIPSv2 tool, was configured to generate a fitness function. This function is the principal component of GA optimization to produce optimal number of hidden neurons for ANN architecture that resulted in 2 neurons for GA-FNN and combination of 92, 31, and 11 neurons for each hidden layer using the GA-RNN model. Based on evaluation results, all models provided acceptable results with error and predictive accuracy values approaching 0 and 1, respectively. However, the GA-FNN model outperformed other models with 3.26 RMSE, 2.23 MAE, and 0.97 R2 values which proved to be the most effective and suitable model for the indirect measurement of nitrate concentration.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/TENCON54134.2021.9707207", ISSN = "2159-3450", month = dec, notes = "Also known as \cite{9707207}", } @InProceedings{Mendigoria:2022:HNICEM, author = "Christan Hail Mendigoria and Ronnie Concepcion and Maria Gemel Palconit and Heinrick Aquino and Oliver John Alajas and Elmer Dadios and Argel Bandala", booktitle = "2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)", title = "In Situ Indirect Detection of Phosphate Concentration from Aquaculture Water Using Physico-limnological Sensor-Based Feed-Forward Neural Network", year = "2022", abstract = "High phosphate levels in aquatic ecosystems induce eutrophication, which causes algae to overgrow and disrupt biodiversity. Determining the phosphate concentration is significant for maintaining optimal ecological function and water quality. In this study, five machine learning regression techniques were investigated for detecting the phosphate level of an aquaculture environment. The computational models employed physico-limnological data such as pH level, electrical conductivity, and water temperature as predictors, acquired from the tilapia fishpond in Rizal, Philippines. These models were evaluated based on the defined criteria such as the predictive performance and mean absolute error. All machine learning models produced acceptable results with R2 value greater than 0.8. Among these, the feed-forward neural network model is concluded to be the most effective phosphate prediction model with R2=0.93 and MAE=0.39. Furthermore, a hybrid approach of multigene genetic programming and genetic algorithm (MGGP-GA) was implemented for optimisation of phosphate level. GPTIPSv2, a MGGP tool, was used to create symbolic models. The model with the highest predictive accuracy and lower complexity was configured as the key component of GA architecture. This optimisation technique produced an optimum parameter value of 21.79degreeC for water temperature, 6.9 for pH, and 0.74 mS/cm for electrical conductivity with a fitness value evaluation of 7.521. With that, this approach serves as an effective technique for in situ managing the nutrient content, particularly the phosphate level of aquatic systems.", keywords = "genetic algorithms, genetic programming, Temperature sensors, Temperature measurement, Biological system modelling, Computational modelling, Machine learning, Water quality, Predictive models, aquaculture, machine learning, neural network, ANN, phosphate detection, physico-limnological parameters", DOI = "doi:10.1109/HNICEM57413.2022.10109612", ISSN = "2770-0682", month = dec, notes = "Also known as \cite{10109612}", } @InProceedings{mendonca:2024:CEC, author = "Israel Mendonca and Tirana Noor Fatyanosa and Masayoshi Aritsugi and Pedro Henrique {Gonzalez Silva}", title = "A Hybrid Approach with {BRKGA} and Data Mining for the Early/Tardy Scheduling Problem", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, Processor scheduling, Evolutionary computation, Scheduling, Data mining, Biological cells, random keys, BRKGA", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10611845", abstract = "This paper introduces a novel hybrid genetic algorithm combined with data mining to solve a version of the early/tardy scheduling problem in which no unforced idle time may be inserted in a sequence. The chromosome representation of the problem is based on random keys and we use the Biased Random-Key Genetic Algorithm (BRKGA) to establish an order in which jobs are scheduled. A data mining component gathers data from the evolutionary steps of BRKGA and suggests chromosomes based on past observations. In this way, we address a key challenge in BRKGA - the exploration of solutions near an individual gene - by leveraging data-driven insights to refine and enhance the search space. Comparative analysis reveals that our hybrid algorithm significantly benefits from the pattern recognition capabilities of data mining, leading to improved scheduled efficiency. The results of a series of computational experiments underscore the potential of this hybrid approach that, although requiring slightly longer computational times, is better than the previous baseline BRKGA algorithm in terms of solution quality.", notes = "also known as \cite{10611845} Is this GP? WCCI 2024", } @Article{Mendonca-de-Paiva:2020:Energies, author = "Gabriel {Mendonca de Paiva} and Sergio {Pires Pimentel} and Bernardo {Pinheiro Alvarenga} and Enes {Goncalves Marra} and Marco Mussetta and Sonia Leva", title = "Multiple Site Intraday Solar Irradiance Forecasting by Machine Learning Algorithms: {MGGP} and {MLP} Neural Networks", journal = "Energies", year = "2020", volume = "13", number = "11", keywords = "genetic algorithms, genetic programming", ISSN = "1996-1073", URL = "https://www.mdpi.com/1996-1073/13/11/3005", DOI = "doi:10.3390/en13113005", abstract = "The forecasting of solar irradiance in photovoltaic power generation is an important tool for the integration of intermittent renewable energy sources (RES) in electrical utility grids. This study evaluates two machine learning (ML) algorithms for intraday solar irradiance forecasting: multigene genetic programming (MGGP) and the multilayer perceptron (MLP) artificial neural network (ANN). MGGP is an evolutionary algorithm white-box method and is a novel approach in the field. Persistence, MGGP and MLP were compared to forecast irradiance at six locations, within horizons from 15 to 120 min, in order to compare these methods based on a wide range of reliable results. The assessment of exogenous inputs indicates that the use of additional weather variables improves irradiance forecastability, resulting in improvements of 5.68percent for mean absolute error (MAE) and 3.41percent for root mean square error (RMSE). It was also verified that iterative predictions improve MGGP accuracy. The obtained results show that location, forecast horizon and error metric definition affect model accuracy dominance. Both Haurwitz and Ineichen clear sky models have been implemented, and the results denoted a low influence of these models in the prediction accuracy of multivariate ML forecasting. In a broad perspective, MGGP presented more accurate and robust results in single prediction cases, providing faster solutions, while ANN presented more accurate results for ensemble forecasting, although it presented higher complexity and requires additional computational effort.", notes = "also known as \cite{en13113005}", } @Article{Mendyk:2015:CMMM, author = "Aleksander Mendyk and Sinan Gures and Renata Jachowicz and Jakub Szlek and Sebastian Polak and Barbara Wisniowska and Peter Kleinebudde", title = "From Heuristic to Mathematical Modeling of Drugs Dissolution Profiles: Application of Artificial Neural Networks and Genetic Programming", journal = "Computational and Mathematical Methods in Medicine", year = "2015", pages = "Article ID 863874", keywords = "genetic algorithms, genetic programming", bibsource = "OAI-PMH server at www.ncbi.nlm.nih.gov", language = "en", oai = "oai:pubmedcentral.nih.gov:4460208", rights = "Copyright 2015 Aleksander Mendyk et al.; This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.", publisher = "Hindawi Publishing Corporation", URL = "http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4460208/", URL = "http://www.ncbi.nlm.nih.gov/pubmed/26101544", URL = "http://dx.doi.org/10.1155/2015/863874", URL = "http://downloads.hindawi.com/journals/cmmm/2015/863874.pdf", size = "9 pages", abstract = "The purpose of this work was to develop a mathematical model of the drug dissolution (Q) from the solid lipid extrudates based on the empirical approach. Artificial neural networks (ANNs) and genetic programming (GP) tools were used. Sensitivity analysis of ANNs provided reduction of the original input vector. GP allowed creation of the mathematical equation in two major approaches: (1) direct modelling of Q versus extrudate diameter (d) and the time variable (t) and (2) indirect modelling through Weibull equation. ANNs provided also information about minimum achievable generalisation error and the way to enhance the original dataset used for adjustment of the equations' parameters. Two inputs were found important for the drug dissolution: d and t. The extrudates length (L) was found not important. Both GP modelling approaches allowed creation of relatively simple equations with their predictive performance comparable to the ANNs (root mean squared error (RMSE) from 2.19 to 2.33). The direct mode of GP modelling of Q versus d and t resulted in the most robust model. The idea of how to combine ANNs and GP in order to escape ANNs' black-box drawback without losing their superior predictive performance was demonstrated. Open Source software was used to deliver the state-of-the-art models and modelling strategies.", } @InProceedings{meneguzzi:2018:PRIMA, author = "Felipe Meneguzzi and Ramon Fraga Pereira and Nir Oren", title = "Sensor Placement for Plan Monitoring Using Genetic Programming", booktitle = "PRIMA 2018: Principles and Practice of Multi-Agent Systems", year = "2018", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-030-03098-8_40", DOI = "doi:10.1007/978-3-030-03098-8_40", } @InProceedings{Menezes:2008:ECCS, author = "Telmo Menezes and Ernesto Costa", title = "Artificial Brains as Networks of Computational Building Blocks", booktitle = "5th European Conference on Complex Systems", year = "2008", editor = "Sorin Solomon and Scott Kirkpatrick", address = "Jerusalem", month = sep # " 10-19", organisation = "GIACS and ONCE-CS", keywords = "genetic algorithms, genetic programming", URL = "http://www.jeruccs2008.org/files/tmenezes_eccs08.pdf", size = "10 pages", abstract = "The latest advances in the gridbrain agent brain model are presented. The gridbrain models brains as networks of computational components. The components are used as building blocks for computation, and provide base functionalities like: input/output, Boolean logic, arithmetic, clocks and memory. The multi-grid architecture as a way to process variable sized information from different sensory channels is addressed.We show how an evolutionary multi-agent simulation may use the gridbrain model to emerge behaviors. The Simulation Embedded Genetic Algorithm (SEGA), aimed at continuous multi-agent simulations with no generations is described. An experimental scenario is presented where agents must use information from two different sensory channels and cooperate to destroy moving targets in a continuous physical simulation. Results are analysed and synchronisation mechanism are shown to emerge.", notes = "http://www.jeruccs2008.org/", } @PhdThesis{Telmo_Menezes_PhD_Thesis, author = "Telmo {de Lucena Torres de Menezes}", title = "Evolutionary Computational Intelligence for Multi-Agent Simulations", school = "Universidade de Coimbra, Faculdade de Ciencias e Tecnologia, Departamento de Engenharia Informatica", year = "2008", address = "Portugal", month = dec, keywords = "genetic algorithms, genetic programming, Computational Intelligence, Multi-Agent Simulations, Evolutionary Computation, Complex Systems, Artificial Life, Emergence of Group-Behaviour, Bloat Control", URL = "http://telmomenezes.com/resources/Telmo_Menezes_PhD_Thesis.pdf", URL = "https://old.cisuc.uc.pt/publication/show/2458", URL = "http://hdl.handle.net/10316/9904", size = "294 pages", abstract = "The growing interest in multi-agent simulations, influenced by the advances in fields like the sciences of complexity and artificial life is related to a modern direction in computational intelligence research. Instead of building isolated artificial intelligence systems from the top-down, this new approach attempts to design systems where a population of agents and the environment interact and adaptation processes take place. We present a novel evolutionary platform to tackle the problem of evolving computational intelligence in multi-agent simulations. It consists of an artificial brain model, called the gridbrain, a simulation embedded evolutionary algorithm (SEEA) and a software tool, LabLOVE. The gridbrain model defines agent brains as heterogeneous networks of computational building blocks. A multi-layer approach allows gridbrains to process variable-sized information from several sensory channels. Computational building blocks allow for the use of base functionalities close to the underlying architecture of the digital computer. Evolutionary operators were devised to permit the adaptive complexification of gridbrains. The SEEA algorithm enables the embedding of evolutionary processes in a continuous multiagent simulation in a non-intrusive way. Co-evolution of multiple species is possible. Two bioinspired extensions to the base algorithm are proposed, with the goal of promoting the emergence of cooperative behaviours. The LabLOVE tool provides an object model where simulation scenarios are defined by way of local interactions. The representation of simulation object features as symbols mitigates the need for pre-defined agent sensory and action interfaces. This increases the freedom of evolutionary processes to generate diversified behaviors. Experimental results are presented, where our models are validated. The role of the several genetic operators and evolutionary parameters is analysed and discussed. Insights are gained, like the role of our recombination operator in bloat control or the importance of neutral search. In scenarios that require cooperation, we demonstrate the emergence of synchronisation behaviours that would be difficult to achieve under conventional approaches. Kin selection and group selection based approaches are compared. In a scenario where two species are in competition, we demonstrated the emergence of specialisation niches without the need for geographical isolation.", notes = "Thesis submitted to the University of Coimbra in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Informatics Engineering. Supervisor Doctor Ernesto Jorge Fernandes Costa Full Professor of the Department of Informatics Engineering of the Faculty of Sciences and Technology of the University of Coimbra)", } @InProceedings{Menezes:2011:EMoaBN, title = "Evolutionary Modeling of a Blog Network", author = "Telmo Menezes", pages = "908--915", booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary Computation", year = "2011", editor = "Alice E. Smith", month = "5-8 " # jun, address = "New Orleans, USA", organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Classification, clustering, data analysis and data mining, Coevolution and collective behaviour", DOI = "doi:10.1109/CEC.2011.5949715", abstract = "A common approach to produce theory to explain the genesis and dynamics of complex networks is to create multi-agent simulations that output networks with similar characteristics to the ones derived from real data. For example, a well know explanation for the power law degree distributions found in blog (and other) networks is the agent-level endogenous mechanism of preferential attachment. However, once simplifying assumptions are dropped, finding lower level behaviours that explain global network features can become difficult. One case, explored in this paper, is that of modelling a blog network generated by human agents with heterogeneous behaviours and a priori diversity. We propose an approach based on an hybrid strategy, combining a generic behavioural template created by a human designer with a set of programs evolved using genetic programming. We present experimental results that illustrate how this approach can be successfully used to discover a set of non-trivial agent-level behaviours that generate a network that fits observed data. We then use the model to make successful testable predictions about the real data. We analyse the diversity of behaviours found in the evolved model by clustering the agents according to the execution paths their programs take during the simulation. We show that these clusters map to different behaviours, giving credence to the need for exogenous, in addition to the more conventional endogenous explanations, for the dynamics of blog networks.", notes = "CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.", } @Article{Menezes:2014:SR, author = "Telmo Menezes and Camille Roth", title = "Symbolic regression of generative network models", journal = "Scientific Reports", year = "2014", volume = "4", number = "6284", month = "5 " # sep, keywords = "genetic algorithms, genetic programming, Machine learning. Applied mathematics, Scientific data, Software", ISSN = "2045-2322", URL = "http://www.telmomenezes.com/2014/09/using-evolutionary-computation-to-explain-network-growth/", URL = "http://www.nature.com/srep/2014/140905/srep06284/full/srep06284.html", DOI = "doi:10.1038/srep06284", size = "7 pages", abstract = "Networks are a powerful abstraction with applicability to a variety of scientific fields. Models explaining their morphology and growth processes permit a wide range of phenomena to be more systematically analysed and understood. At the same time, creating such models is often challenging and requires insights that may be counter-intuitive. Yet there currently exists no general method to arrive at better models. We have developed an approach to automatically detect realistic decentralised network growth models from empirical data, employing a machine learning technique inspired by natural selection and defining a unified formalism to describe such models as computer programs. As the proposed method is completely general and does not assume any pre-existing models, it can be applied out of the box to any given network. To validate our approach empirically, we systematically rediscover pre-defined growth laws underlying several canonical network generation models and credible laws for diverse real-world networks. We were able to find programs that are simple enough to lead to an actual understanding of the mechanisms proposed, namely for a simple brain and a social network.", notes = "Broken Oct 2021 https://groups.yahoo.com/neo/groups/genetic_programming/conversations/messages/6512 open source tool that implements the methodology describe in the paper: https://github.com/telmomenezes/synthetic", } @InProceedings{Menezes:2017:DOOCN, title = "Automatic Discovery of Families of Network Generative Processes", author = "Telmo Menezes and Camille Roth", booktitle = "Dynamics On and Of Complex Networks III", booktitle2 = "Machine Learning and Statistical Physics Approaches", year = "2017", editor = "Fakhteh Ghanbarnejad and Rishiraj Saha Roy and Fariba Karimiand Jean-Charles Delvenne and Bivas Mitra", series = "Springer Proceedings in Complexity", pages = "83--111", address = "Indianapolis, USA", publisher = "Springer", keywords = "genetic algorithms, genetic programming, computational social sciences, network science, evolutionary computations, machine learning, ML, social network analysis, SNA, artificial intelligence, complex networks, computer science, neural and evolutionary computing, social and information networks, humanities and social sciences, methods and statistics, sociology", ISSN = "2213-8684", isbn13 = "978-3-030-14682-5", oai = "oai:HAL:hal-02165035v1", URL = "https://arxiv.org/abs/1906.12332", URL = "https://hal.archives-ouvertes.fr/hal-02165035", URL = "https://hal.archives-ouvertes.fr/hal-02165035/document", URL = "https://hal.archives-ouvertes.fr/hal-02165035/file/Automatic_Discovery_of_Families_of_Network_Generative_Processes__HAL_.pdf", DOI = "doi:10.1007/978-3-030-14683-2_4", abstract = "Designing plausible network models typically requires scholars to form a priori intuitions on the key drivers of network formation. Oftentimes, these intuitions are supported by the statistical estimation of a selection of network evolution processes which will form the basis of the model to be developed. Machine learning techniques have lately been introduced to assist the automatic discovery of generative models. These approaches may more broadly be described as symbolic regression, where fundamental network dynamic functions, rather than just parameters, are evolved through genetic programming. This chapter first aims at reviewing the principles, efforts and the emerging literature in this direction, which is very much aligned with the idea of creating artificial scientists. Our contribution then aims more specifically at building upon an approach recently developed by us [Menezes and Roth, 2014] in order to demonstrate the existence of families of networks that may be described by similar generative processes. In other words, symbolic regression may be used to group networks according to their inferred genotype (in terms of generative processes) rather than their observed phenotype (in terms of statistical/topological features). Our empirical case is based on an original data set of 238 anonymised ego-centered networks of Facebook friends, further yielding insights on the formation of sociability networks.", notes = "http://doocn.org/ SPCOM", annote = "Centre Marc Bloch (CMB) ; Ministere de l'Europe et des Affaires etrangeres (MEAE)-Bundesministerium fur Bildung und Forschung-Ministere de l'Education nationale, de l{'}Enseignement superieur et de la Recherche (M.E.N.E.S.R.)-Centre National de la Recherche Scientifique (CNRS)", bibsource = "OAI-PMH server at api.archives-ouvertes.fr", contributor = "Centre Marc Bloch and This paper has been partially supported by the Algodiv grant (ANR-15-CE38-0001) funded by the ANR (French National Agency of Research). and Algopol ANR-12-CORD-0018,Politique des algorithmes(2012) and ALGODIV ANR-15-CE38-0001,Algodiv: Recommandation algorithmique et diversite des informations du web(2015)", description = "International audience", language = "en", relation = "info:eu-repo/semantics/altIdentifier/arxiv/1906.12332; info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-030-14683-2_4", rights = "info:eu-repo/semantics/OpenAccess", type = "info:eu-repo/semantics/bookPart", } @Article{Meng:2023:jBO, author = "Jia Meng and Guangxing Wang and Lingxi Zhou and Shenyi Jiang and Shuhao Qian and Lingmei Chen and Chuncheng Wang and Rushan Jiang and Chen Yang and Bo Niu and Yijie Liu and Zhihua Ding and Shuangmu Zhuo and Zhiyi Liu", title = "Mapping variation of extracellular matrix in human keloid scar by label-free multiphoton imaging and machine learning", journal = "Journal of Biomedical Optics", year = "2023", volume = "28", number = "4", pages = "045001", month = "8 " # apr, keywords = "genetic algorithms, genetic programming, TPOT, morphological feature, textural feature, machine learning, classification, keloid scar, multiphoton imaging", DOI = "doi:10.1117/1.JBO.28.4.045001", size = "14 pages", abstract = "Significance Rapid diagnosis and analysis of human keloid scar tissues in an automated manner are essential for understanding pathogenesis and formulating treatment solutions. Aim Our aim is to resolve the features of the extracellular matrix in human keloid scar tissues automatically for accurate diagnosis with the aid of machine learning. Approach Multiphoton microscopy was used to acquire images of collagen and elastin fibres. Morphological features, histogram, and grey-level co-occurrence matrix-based texture features were obtained to produce a total of 28 features. The minimum redundancy maximum relevancy feature selection approach was implemented to rank these features and establish feature subsets, each of which was employed to build a machine learning model through the tree-based pipeline optimization tool (TPOT). Results The feature importance ranking was obtained, and 28 feature subsets were acquired by incremental feature selection. The subset with the top 23 features was identified as the most accurate. Then stochastic gradient descent classifier optimized by the TPOT was generated with an accuracy of 96.15% in classifying normal, scar, and adjacent tissues. The area under curve of the classification results (scar versus normal and adjacent, normal versus scar and adjacent, and adjacent versus normal and scar) was 1.0, 1.0, and 0.99, respectively. Conclusions The proposed approach has great potential for future dermatological clinical diagnosis and analysis and holds promise for the development of computer-aided systems to assist dermatologists in diagnosis and treatment.", } @InProceedings{Meng:2010:ICCDA, author = "Lamei Meng and Yamin Li and Huanrong Zhu", title = "Method of plant texture image recognition based on genetic programming", booktitle = "2010 International Conference on Computer Design and Applications (ICCDA)", year = "2010", month = "25-27 " # jun, volume = "1", pages = "V1--370--V1--373", abstract = "The genetic programming classifier's evolution and the classified speed is quick, the real-time performance is good, the classified recognition needs the domain knowledge to be very few, it is advantageous for the promoted use. This article carries on the imagery processing to the gathering plant bark image. It uses the Gray Level Co-occurrence Matrix technique description image the texture feature and carries on the recognition and classification using the genetic programming algorithm to the plant image. Through it carries on the classified experiment to many kinds of plant bark image, it indicates that this method is feasible, has the good classified precision.", keywords = "genetic algorithms, genetic programming, classifier evolution, gray level co-occurrence matrix technique, imagery processing, plant bark image, plant texture image recognition, biology computing, botany, image classification, image colour analysis, image texture, matrix algebra", DOI = "doi:10.1109/ICCDA.2010.5540845", notes = "tree bark Coll. of Mech. & Electr. Eng., Agric. Univ. of Hebei, Baoding, China. Also known as \cite{5540845}", } @InProceedings{Meng:2010:cec, author = "QingBiao Meng and Shingo Mabu and Yu Wang and Kotaro Hirasawa", title = "Guiding the evolution of Genetic Network Programming with reinforcement learning", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Genetic Network Programming", isbn13 = "978-1-4244-6910-9", abstract = "Genetic Network Programming (GNP) is one of the evolutionary algorithms. It adopts a directed graph structure to represent a solution to a given problem. Agents judge situations and execute actions sequentially following the node transitions in the graph. On one hand, GNP possesses an advantage of node reusability, which makes it possible to realise a compact graph structure that represents a solution. On the other hand, the compact structure suggests that any connection might play a significant role in the solution, i.e., a slight change to the connections could tremendously influence the performance of the agents for the given task. The conventional GNP, however, lacks an effective way to evaluate and to take advantage of the connections. This paper thus proposes a reinforcement learning approach to learn GNP's subgraphs that contain a relatively small number of connections, and further proposes a partial reconstruction approach to modify the solution with the obtained subgraphs. These two approaches are combined together to form a new evolutionary learning model named GNP with Evolution-oriented Reinforcement Learning (GNP-ERL). Some experiments are conducted on the Tileworld testbed to verify the effectiveness of GNP-ERL, and the simulation results demonstrate that it outperforms the conventional GNP in both training and testing phases.", DOI = "doi:10.1109/CEC.2010.5586398", notes = "WCCI 2010. Also known as \cite{5586398}", } @Article{MENG:2024:asoc, author = "Wenyang Meng and Ying Li and Xiaoying Gao and Jianbin Ma", title = "Ensemble classifiers using multi-objective Genetic Programming for unbalanced data", journal = "Applied Soft Computing", volume = "158", pages = "111554", year = "2024", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2024.111554", URL = "https://www.sciencedirect.com/science/article/pii/S1568494624003284", keywords = "genetic algorithms, genetic programming, Multi-objective, Ensemble, Unbalanced", abstract = "Genetic Programming (GP) can be used to design effective classifiers due to its built-in feature selection and feature construction characteristics. Unbalanced data distributions affect the classification performance of GP classifiers. Some fitness functions have been proposed to solve the class imbalance problem of GP classifiers. However, with the evolution of GP, single-objective GP classifiers evaluated by a single fitness function have poor generalization ability. Moreover, using the best evolved GP classifier for decision-making can easily lead to the possibility of misclassification. In this paper, multi-objective GP is used to optimize multiple fitness functions including AUC approximation (Wmw), Distance (Dist), and Complexity to evolve ensemble classifiers, which jointly determines the class labels of unknown instances. Experiments on sixteen datasets show that our multi-objective GP can significantly improve classification performance compared with single-objective GP, and our proposed ensemble classifiers evolved by multi-objective GP can further improve the classification performance than the single best GP classifier. Comparisons with six GP-based and five traditional machine learning algorithms show that our proposed approaches can achieve significantly better classification performance on most cases", } @InProceedings{mengshoel:1998:dubn, author = "Ole J. Mengshoel and Daniel E. Goldberg and David C. Wilkins", title = "Deceptive and Other Functions of Unitation as {Bayesian} Networks", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "559--566", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms", notes = "SGA-98", } @InProceedings{mengshoel:1998:ecbn, author = "Ole J. Mengshoel", title = "Evolutionary Computation in {Bayesian} Networks", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "159 and 261", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "genetic algorithms, genetic programming", broken = "http://www-kbs.ai.uiuc.edu/kbs-publications/pub-173/gp98-abstract.ps", size = "1+1 page", abstract = "This abstract discusses issues in using genetic algorithms for computing the most probable explanations in Bayesian networks.", notes = "Also known as \cite{mengshoel98evolutionary} http://mlt.sv.cmu.edu/cis/publications/publication-bibtex.bib GP-98LB, GP-98PhD Student Workshop", } @InProceedings{mengshoel:1999:PCDCPR, author = "Ole J. Mengshoel and David E. Goldberg", title = "Probabilistic Crowding: Deterministic Crowding with Probabilisitic Replacement", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "409--416", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Menolascina:2007:CIBCB, author = "F. Menolascina and S. Tommasi and A. Paradiso and M. Cortellino and V. Bevilacqua and G. Mastronardi", title = "Novel Data Mining Techniques in {aCGH} based Breast Cancer Subtypes Profiling: the Biological Perspective", booktitle = "IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology, CIBCB '07", year = "2007", pages = "9--16", address = "Honolulu, USA", month = "1-5 " # apr, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, gene expression programming, ant miner, breast caner, decision trees, rule induction", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.628.4889", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.628.4889", URL = "http://s3.amazonaws.com/publicationslist.org/data/bevilacqua/ref-30/cibcib.pdf", DOI = "doi:10.1109/CIBCB.2007.4221198", size = "8 pages", abstract = "In this paper we present a comparative study among well established data mining algorithm (namely J48 and Naive Bayes Tree) and novel machine learning paradigms like Ant Miner and Gene Expression Programming. The aim of this study was to discover significant rules discriminating ER+ and ER-cases of breast cancer. We compared both statistical accuracy and biological validity of the results using common statistical methods and Gene Ontology. Some worth noting characteristics of these systems have been observed and analysed even giving some possible interpretations of findings. With this study we tried to show how intelligent systems can be employed in the design of experimental pipeline in disease processes investigation and how deriving high-throughput results can be validated using new computational tools. Results returned by this approach seem to encourage new efforts in this field.", } @Article{PNAS-2009-Menon-16829-34, author = "Vilas Menon and Nelson Spruston and William L. Kath", title = "A state-mutating genetic algorithm to design ion-channel models", journal = "Proceedings of the National Academy of Sciences", year = "2009", volume = "106", number = "39", pages = "16829--16834", month = sep # " 29", keywords = "genetic algorithms, genetic programming", URL = "http://www.pnas.org/content/106/39/16829.abstract", URL = "http://www.pnas.org/content/106/39/16829.full.pdf", DOI = "doi:10.1073/pnas.0903766106", URL = "http://www.pnas.org/cgi/content/full/0903766106/DCSupplemental", size = "6 pages", abstract = "Realistic computational models of single neurons require component ion channels that reproduce experimental findings. Here, a topology-mutating genetic algorithm that searches for the best state diagram and transition-rate parameters to model macroscopic ion-channel behaviour is described. Important features of the algorithm include a topology-altering strategy, automatic satisfaction of equilibrium constraints (microscopic reversibility), and multiple-protocol fitting using sequential goal programming rather than explicit weighting. Application of this genetic algorithm to design a sodium-channel model exhibiting both fast and prolonged inactivation yields a six-state model that produces realistic activity dependent attenuation of action-potential backpropagation in current-clamp simulations of a CA1 pyramidal neuron.", notes = "Chromosome is graph. Crossover only permitted between parents with the same structure. Mutation can add or delete edges.", } @Article{Meqdad:2022:IEEEAccess, author = "Maytham N. Meqdad and Fardin Abdali-Mohammadi and Seifedine Kadry", journal = "IEEE Access", title = "Meta Structural Learning Algorithm With Interpretable Convolutional Neural Networks for Arrhythmia Detection of Multisession {ECG}", year = "2022", volume = "10", pages = "61410--61425", abstract = "Detection of arrhythmia of electrocardiogram (ECG) signals recorded within several sessions for each person is a challenging issue, which has not been properly investigated in the past. This arrhythmia detection is challenging since a classification model that is constructed and tested using ECG signals maintains generalization when dealing with unseen samples. This article has proposed a new interpretable meta structural learning algorithm for this challenging detection. Therefore, a compound loss function was suggested including the structural feature extraction fault and space label fault with GUMBEL-SOFTMAX distribution in the convolutional neural network (CNN) models. The collaboration between models was carried out to create learning to learn features in models by transferring the knowledge among them when confronted by unseen samples. One of the deficiencies of a meta-learning algorithm is the non-interpretability of its models. Therefore, to create an interpretability feature for CNN models, they are encoded as the evolutionary trees of the genetic programming (GP) algorithms in this article. These trees learn the process of extracting deep structural features in the course of the evolution in the GP algorithm. The experimental results suggested that the proposed detection model enjoys an accuracy of 9percent regarding the classification of 7 types of arrhythmia in the samples of the Chapman ECG dataset recorded from 10646 patients in different sessions. Finally, the comparisons demonstrated the competitive performance of the proposed model concerning the other models based on the big deep models.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ACCESS.2022.3181727", ISSN = "2169-3536", notes = "Also known as \cite{9792273}", } @Article{meqdad:2022:Mathematics, author = "Maytham N. Meqdad and Fardin Abdali-Mohammadi and Seifedine Kadry", title = "A New 12-Lead {ECG} Signals Fusion Method Using Evolutionary {CNN} Trees for Arrhythmia Detection", journal = "Mathematics", year = "2022", volume = "10", number = "11", pages = "Article No. 1911", keywords = "genetic algorithms, genetic programming", ISSN = "2227-7390", URL = "https://www.mdpi.com/2227-7390/10/11/1911", DOI = "doi:10.3390/math10111911", abstract = "The 12 leads of electrocardiogram (ECG) signals show the heart activities from different angles of coronal and axial planes; hence, the signals of these 12 leads have functional dependence on each other. This paper proposes a novel method for fusing the data of 12-lead ECG signals to diagnose heart problems. In the first phase of the proposed method, the time-frequency transform is employed to fuse the functional data of leads and extract the frequency data of ECG signals in 12 leads. After that, their dependence is evaluated through the correlation analysis. In the second phase, a structural learning method is adopted to extract the structural data from these 12 leads. Moreover, deep convolutional neural network (CNN) models are coded in this phase through genetic programming. These trees are responsible for learning deep structural features from functional data extracted from 12 leads. These trees are upgraded through the execution of the genetic programming (GP) algorithm to extract the optimal features. These two phases are used together to fuse the leads of ECG signals to diagnose various heart problems. According to the test results on ChapmanECG, including the signals of 10,646 patients, the proposed method enjoys the mean accuracy of 97.60percent in the diagnosis of various types of arrhythmias in the Chapman dataset. It also outperformed the state-of-the-art methods.", notes = "also known as \cite{math10111911}", } @InProceedings{mercure:2001:AIChE, author = "Peter Kip Mercure and Guido F. Smits and Arthur Kordon", title = "Empirical Emulators for First Principle Models", booktitle = "AIChE Fall Annual Meeting", year = "2001", address = "Reno Hilton", month = "6 " # nov, organisation = "AIChe", keywords = "genetic algorithms, genetic programming", broken = "http://www.aiche.org/conferences/techprogram/paperdetail.asp?PaperID=2373&DSN=annual01", abstract = "Empirical emulators mimic the performance of first principle models by using various data-driven modeling techniques. The driving force for developing empirical emulators is the push for reducing the time and cost for new product development. Empirical emulators are especially effective when hard real-time optimization of a variety of complex fundamental models is needed. The increased robustness of the modern data-driven techniques (analytic neural networks, support vector machines, genetic programming, etc.) is a reliable basis for accurate representation of fundamental models and gives many opportunities for effective synergy between these two key modeling approaches. The main schemes for building empirical emulators are discussed in the paper. Several contemporary techniques for robust empirical emulator design are explored including analytic neural networks, recurrent neural networks and Genetic Programming (GP), and the capabilities of the proposed approach are illustrated with a case study for a simple first principle model. A key feature of empirical emulators is that the training data for empirical model building is generated by design of experiments from first principle models called simulators. This allows a high degree of freedom for development of reliable data-driven models. The most obvious scheme for implementation of empirical emulators is as accelerator of computational time for fundamental models (the gain is 103 to 105 times faster). Another possible scheme is to use the empirical emulator as an estimator of fundamental model performance. Of special importance to on-line optimization is a scheme using the empirical emulator to integrate different types of fundamental models (steady-state, dynamic, fluid, kinetic, thermal, etc). Most of the known empirical emulators are implemented as {"}classical{"} neural networks based on back-propagation learning algorithm. Their property of being universal approximators is a key theoretical result for successful emulation. At the same time {"}classical{"} neural networks suffer from a number of problems like: long computational time for training, convergence to local minima, sensitivity to weight generalization, too many tunable parameters, etc. These problems put serious limitations on the quality of the developed empirical model, increase development time, and require experienced model developers. An alternative empirical emulator based on analytic neural networks is described in the paper. A key advantage of analytic neural networks is that the function to be optimized is a quadratic function of the weights of the hidden-to-output layer error and has one global optimum. It is no longer possible to get stuck in local minima and the learning algorithm is not iterative. As a result, the data-driven modeling process is significantly reduced and the developed empirical models are parsimonious. Of special importance to empirical emulator's performance is the ability of analytic neural networks to deliver multiple-model solution with confidence limits. Empirical emulators with confidence limits are aware of their own performance which is essential for any data-driven model application, especially in real-time. In the case of emulating process dynamics a different type of recurrent neural networks are needed. Recurrent networks are neural networks with one or more local or global feedback loops. The application of feedback enables neural networks to acquire state representations, making them suitable for emulation of dynamic fundamental models. A proper structure of an empirical emulator to mimic dynamic behavior is based on a recurrent version of the analytic neural networks.", abstract = "Another approach to build a successful empirical emulator is genetic programming. By simulation of natural evolution and using genetic operators like crossover and mutation, genetic programming delivers empirical models in a form of explicit analytic functions between process inputs and outputs. The non-black-box form is a significant advantage of this type of empirical emulator. In principle, a functional relationship has better generalization capability and is a more reliable indicator of model performance outside the training range. This unique capability makes empirical emulators designed by genetic programming a promising modeling solution for process scale-up. The performance of empirical emulators based on analytic neural networks, analytic recurrent neural networks, and genetic programming is illustrated in a case study of emulating a phase change propagation in a solid.", notes = "American Institute of Chemical Engineers, 3 Park Ave, New York, N.Y., 10016-5991, U.S.A.", } @InCollection{meredith:2002:SMIRPGP, author = "Jeremy Meredith", title = "Solving the Material Interface Reconstruction Problem using Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2002", year = "2002", editor = "John R. Koza", pages = "139--147", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", URL = "http://www.genetic-programming.org/sp2002/Meredith.pdf", notes = "part of \cite{koza:2002:gagp}", } @InProceedings{merelo:1999:FTGM, author = "J. J. Merelo and J. Carpio and P. Castillo and V. M. Rivas and G. Romero", title = "Finding a needle in a haystack using hints and evolutionary computation: The case of Genetic Mastermind", booktitle = "Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference", year = "1999", editor = "Scott Brave and Annie S. Wu", pages = "184--192", address = "Orlando, Florida, USA", month = "13 " # jul, keywords = "Genetic Algorithms", notes = "GECCO-99LB", } @Article{Merelo:2006:ASC, author = "J. J. Merelo-Guervos and P. Castillo and V. M. Rivas", title = "Finding a needle in a haystack using hints and evolutionary computation: the case of evolutionary MasterMind", journal = "Applied Soft Computing", year = "2006", volume = "6", number = "2", pages = "170--179", month = jan, keywords = "Evolutionary algorithm, MasterMind", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2004.09.003", abstract = "In this paper we present a new version of an evolutionary algorithm that finds the hidden combination in the game of MasterMind by using hints on how close is a combination played to it. The evolutionary algorithm finds the hidden combination in an optimal number of guesses, is efficient in terms of memory and CPU, and examines only a minimal part of the search space. The algorithm is fast, and indeed previous versions can be played in real time on the world wide web. This new version of the algorithm is presented and compared with theoretical bounds and other algorithms. We also examine how the algorithm scales with search space size, and its performance for different values of the EA parameters.", notes = "cf GP mailing list about Thu, 08 Mar 2007 22:22:37 +0100 ", } @Misc{arXiv:cs/0701115v1, author = "J. J. Merelo and Antonio Mora-Garcia and J. L. J. Laredo and Juan Lupion and Fernando Tricas", title = "Browser-based distributed evolutionary computation: performance and scaling behavior", howpublished = "arXiv:cs/0701115v1", year = "2007", month = "18 " # jan, keywords = "genetic algorithms, Distributed, Parallel, Cluster Computing, Neural and Evolutionary Computing", URL = "http://arxiv.org/pdf/cs/0701115", size = "8 pages", abstract = "The challenge of ad-hoc computing is to find the way of taking advantage of spare cycles in an efficient way that takes into account all capabilities of the devices and interconnections available to them. In this paper we explore distributed evolutionary computation based on the Ruby on Rails framework, which overlays a Model-View-Controller on evolutionary computation. It allows anybody with a web browser (that is, mostly everybody connected to the Internet) to participate in an evolutionary computation experiment. Using a straightforward farming model, we consider different factors, such as the size of the population used. We are mostly interested in how they impact on performance, but also the scaling behaviour when a non-trivial number of computers is applied to the problem. Experiments show the impact of different packet sizes on performance, as well as a quite limited scaling behavior, due to the characteristics of the server. Several solutions for that problem are proposed.", notes = "not a GP cf genetic_programming@yahoogroups.com Fri, 25 May 2007 07:29:02 -0000", } @Article{Merezhnikov:2021:PCS, author = "Mark Merezhnikov and Alexander Hvatov", title = "Multi-objective closed-form algebraic expressions discovery approach application to the synthetic time-series generation", journal = "Procedia Computer Science", year = "2021", volume = "193", pages = "285--294", note = "10th International Young Scientists Conference in Computational Science, YSC2021, 28 June -- 2 July, 2021", keywords = "genetic algorithms, genetic programming, model interpretation, evolutionary algorithm, synthetic data, algebraic equation discovery", ISSN = "1877-0509", URL = "https://www.human-competitive.org/sites/default/files/entry_1.txt", URL = "https://www.human-competitive.org/sites/default/files/multi-objective_closed-form_algebraic_expressions_discovery_approach_application_to_the_synthetic_time-series_generation.pdf", URL = "https://www.sciencedirect.com/science/article/pii/S1877050921020706", DOI = "doi:10.1016/j.procs.2021.10.029", abstract = "Time-series modeling is a well-studied topic of classical analysis and machine learning. However, large datasets are required to obtain the model with a better prediction quality with the increasing model complexity. Therefore, some applications demand synthetic datasets that are preserving modeling-sensitive properties. Another application of synthetic data is data anonymization. The synthetic data generation algorithm may be split into two parts: the time-series modeling and the synthetic data generation parts. The model must be interpretable to obtain the synthetic data with good quality. The model parameter interpretation allows controlling generation by adding noise to different groups of parameters. In the paper, the evolutionary multi-objective closed-form algebraic expressions discovery approach that allows obtaining the model in the form that may be analyzed using the mathematics is proposed. The analysis allows the interpretation of the model parameters for the controllable generation of the synthetic data. The notion of synthetic data quality is discussed. The examples of the synthetic time-series generation based on two datasets with different properties are shown.", notes = "Entered 2022 HUMIES YSC2021 special issue edited by Alexandra Klimova, Angelos Bilas, Vangelis Harmandaris, Evangelia Kalligiannaki, Eleni Kanellou, Alexander Boukhanovsky Also known as \cite{MEREZHNIKOV2021285}", } @Article{merkle:2002:GPEM, author = "Daniel Merkle and Martin Middendorf", title = "Fast Ant Colony Optimization on Runtime Reconfigurable Processor Arrays", journal = "Genetic Programming and Evolvable Machines", year = "2002", volume = "3", number = "4", pages = "345--361", month = dec, keywords = "ACO, reconfigurable architectures, quadratic assignment", ISSN = "1389-2576", DOI = "doi:10.1023/A:1020936909085", abstract = "Ant Colony Optimization (ACO) is a metaheuristic used to solve combinatorial optimisation problems. As with other metaheuristics, like evolutionary methods, ACO algorithms often show good optimization behaviour but are slow when compared to classical heuristics. Hence, there is a need to find fast implementations for ACO algorithms. In order to allow a fast parallel implementation, we propose several changes to a standard form of ACO algorithms. The main new features are the non-generational approach and the use of a threshold based decision function for the ants. We show that the new algorithm has a good optimization behavior and also allows a fast implementation on reconfigurable processor arrays. This is the first implementation of the ACO approach on a reconfigurable architecture. The running time of the algorithm is quasi-linear in the problem size n and the number of ants on a reconfigurable mesh with n2 processors, each provided with only a constant number of memory words.", notes = "Article ID: 5103873", } @Article{MERKURYEVA:2019:procs, author = "Galina Merkuryeva and Aija Valberga and Alexander Smirnov", title = "Demand forecasting in pharmaceutical supply chains: A case study", journal = "Procedia Computer Science", year = "2019", volume = "149", pages = "3--10", note = "ICTE in Transportation and Logistics 2018 (ICTE 2018)", keywords = "genetic algorithms, genetic programming, Demand forecasting, Phamathetical supply chain, Logistics, Multiple linear regression, Symbolic regression", ISSN = "1877-0509", URL = "http://www.sciencedirect.com/science/article/pii/S1877050919301061", DOI = "doi:10.1016/j.procs.2019.01.100", size = "8 pages", abstract = "Demand forecasting plays a critical role in logistics and supply chain management. State-of-art methods and key challenges in demand forecasting for the pharmaceutical industry are discussed. An integrated procedure for in-market product demand forecasting and purchase order generation in the pharmaceutical supply chain is described. A case study for supply of pharmaceutical products from a wholesaler to a distribution company located in an emerging market is presented. Alternative forecasting scenarios for thebaseline demand calculations using the SMA model, multiple linear regressions and symbolic regression with genetic programming are experimentally investigated, and their practical implicationsare discussed", } @Article{Mermerdas:2013:CBM, author = "Kasim Mermerdas and Erhan Guneyisi and Mehmet Gesoglu and Turan Ozturan", title = "Experimental evaluation and modeling of drying shrinkage behavior of metakaolin and calcined kaolin blended concretes", journal = "Construction and Building Materials", volume = "43", month = jun, pages = "337--347", year = "2013", keywords = "genetic algorithms, genetic programming, Calcined kaolin, Computational modelling, Concrete, Drying shrinkage, Metakaolin, Statistical evaluation", ISSN = "0950-0618", DOI = "doi:10.1016/j.conbuildmat.2013.02.047", URL = "http://www.sciencedirect.com/science/article/pii/S0950061813001761", abstract = "In the first stage of the study presented herein, the findings of an experimental study on drying shrinkage behaviour of concretes incorporated with high reactivity commercial metakaolin (MK) and calcined kaolins (CKs) were reported. Free shrinkage strain measurements as well as corresponding weight loss were measured over 60 days of drying. Four different types of kaolins obtained from local sources were calcined and used as mineral admixture for concrete production. Moreover, commercial metakaolin of high purity was also used as reference material for comparison. In the second stage of the study, prediction models through gene expression programming (GEP) and multiple linear regression (MLR) were derived. The data set used for training and testing covers the experimental data presented in this study as well as additional ones collected from the literature. The parameters considered for developing the prediction model are related to the characteristic properties of mineral admixture, concrete composition, and drying period. As a result, CK incorporated concretes revealed comparable performance with MK incorporated ones in terms of drying shrinkage and weight loss. Furthermore, the prediction models yielded strong correlation with the experimental results. Statistical analyses also revealed that the proposed models can be handful tools in predicting the drying shrinkage strain of the concretes modified with MK.", } @Article{MERMERDAS:2023:conbuildmat, author = "Kasim Mermerdas and Erhan Guneyisi", title = "Effect of different types of calcined crude kaolins and high purity metakaolin on corrosion resistance of reinforcement in concretes: Experimental evaluation and analytical modeling", journal = "Construction and Building Materials", volume = "382", pages = "131288", year = "2023", ISSN = "0950-0618", DOI = "doi:10.1016/j.conbuildmat.2023.131288", URL = "https://www.sciencedirect.com/science/article/pii/S0950061823010012", keywords = "genetic algorithms, genetic programming, Corrosion, Electrical resistivity, Linear polarization resistance, Metakaolin, Modeling", abstract = "In this experimental study, the corrosion characteristics of steel reinforcement bars embedded in concretes, which were produced by incorporation of metakaolin obtained from high purity metakaolin or calcined raw kaolins, were investigated. The linear polarization resistance (LPR) method was benefited as the corrosion monitoring method. In concrete production, 0.40 water/binder ratio, 350 kg/m3 total binder amount, two different mineral admixture replacement levels of 5percent and 15percent were used. In addition, control samples were produced to evaluate the effectiveness of mineral additives. Reinforced concrete samples manufactured for accelerated corrosion monitoring were submerged into NaCl solutions of two different concentrations (2percent and 5percent solutions) to provide rapid anodic reactions in corrosion process. Additionally, the electrical resistance values of the concretes were tested to assess the corrosion behavior. After the experimental results were achieved, the obtained data were evaluated statistically and a new mathematical estimation model was developed using the gene-expression programming (GEP) method taking into account the effective parameters. This model was also compared with the results of the multiple linear regression, which is a statistical method. The analysis of the results based on LPR technique showed that the modified samples yielded higher corrosion resistance than the plain samples in both solution exposure conditions. Among the mineral additives used, it was observed that those with metakaolin addition had the highest resistance. While the coefficient of determination (R2) of the testing set produced for the GEP prediction model was around 0.9, the value obtained by multiple linear regression was found to be 0.433", } @Article{Mernik:2004:Informatica, author = "Marjan Mernik and Matej Crepinsek and Tomaz Kosar and Damijan Rebernak and Viljem Zumer", title = "Grammar-Based Systems: Definition and Examples", journal = "Informatica", year = "2004", volume = "28", number = "4", pages = "245--255", month = nov, keywords = "genetic algorithms, genetic programming, Context-free grammars, attribute grammars, grammar-based systems", ISSN = "0350-5596", broken = "http://www.informatica.si/vols/vol28_3_04abs.html#4", URL = "http://www.informatica.si/PDF/Informatica_2004_3.pdf", size = "11 pages", abstract = "Formal language theory is an important part of theoretical computer science and has also been applied in many practical applications. The importance of context-free grammars and attribute grammars for compiler construction and automatic generation for compilers/interpreters is already well known. However, grammars can be found in many other applications which are not as clearly related to their original application - language description and implementation. We call such systems grammar-based systems. No general comparison and classification has been done until now despite these systems having existed for a long time. The aim of this paper is to introduce and popularise grammar-based systems.", notes = "Section 'Evolutionary Computations' uses GP University of Maribor, Faculty of Electrical Engineering and Computer Science Smetanova ulica 17, 2000 Maribor, Slovenia", } @Article{Merrell:2024:engfracmech, author = "Jonas Merrell and John Emery and Robert M. Kirby and Jacob Hochhalter", title = "Stress intensity factor models using mechanics-guided decomposition and symbolic regression", journal = "Engineering Fracture Mechanics", year = "2024", volume = "310", pages = "110432", keywords = "genetic algorithms, genetic programming, Interpretable machine learning, GPSR, Semi-elliptical surface crack, Mechanics-based training, Stress intensity factor", ISSN = "0013-7944", URL = "https://www.sciencedirect.com/science/article/pii/S0013794424005952", DOI = "doi:10.1016/j.engfracmech.2024.110432", abstract = "The finite element method can be used to compute accurate stress intensity factors (SIFs) for cracks with complex geometries and boundary conditions. In contrast, handbook solutions act as surrogate SIF models that provide significantly faster evaluation times. However, the development of conventional surrogate SIF models relies on manual development based on low-order parameterizations. This limits surrogate model accuracy and generalizability. In this paper, we develop a framework for the automated development of mechanics-guided handbook SIF solutions by using interpretable machine learning via genetic programming for symbolic regression (GPSR). Formalizing the mechanics-based approach of Raju and Newman, SIF training data is decomposed into multiple subsets. This decomposition enables parallel GPSR model development of subfunctions, each of which accounts for specific geometrical corrections with respect to a known analytical model. Using this mechanics-based approach with GPSR allows for equations to be learnt with improved accuracy and reduced complexity relative to the Raju Newman equations while maintaining the inherent interpretability of mathematical expressions. In this paper, we present equations that match the complexity of the Raju Newman equations while having reduced error, as well as equations with similar errors and reduced complexity", } @InProceedings{merta:2019:CSMMMIS, author = "Jan Merta and Tomas Brandejsky", title = "Lifetime Adaptation in Genetic Programming for the Symbolic Regression", booktitle = "Computational Statistics and Mathematical Modeling Methods in Intelligent Systems", year = "2019", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-030-31362-3_33", DOI = "doi:10.1007/978-3-030-31362-3_33", } @Article{Merta:2022:NNW, author = "Jan Merta and Tomas Brandejsky", title = "Two-layer genetic programming", journal = "Neural Network World", year = "2022", volume = "32", number = "4", pages = "215--231", keywords = "genetic algorithms, genetic programming, two-layer genetic programming, ensemble learning, deep learning, ANN, boot-strapping, symbolic regression", ISSN = "1210-0552", URL = "http://nnw.cz/obsahy22.html#32.013", URL = "http://nnw.cz/doi/2022/NNW.2022.32.013.pdf", DOI = "doi:10.14311/nnw.2022.32.013", size = "17 pages", abstract = "a two-layer approach to genetic programming algorithm and the improvement of the training process using ensemble learning. Inspired by the performance leap of deep neural networks, the idea of a multilayered approach to genetic programming is proposed to start with two-layered genetic programming. The goal of the paper was to design and implement a two layer genetic programming algorithm, test its behaviour in the context of symbolic regression on several basic test cases, to reveal the potential to improve the learning process of genetic programming and increase the accuracy of the resulting models. The algorithm works in two layers. In the first layer, it searches for appropriate sub-models describing each segment of the data. In the second layer, it searches for the final model as a non-linear combination of these sub-models. Two-layer genetic programming coupled with ensemble learning techniques on the experiments performed showed the potential for improving the performance of genetic programming.", notes = "CTU FTS 2022 http://nnw.cz/ University of Pardubice, Faculty of Electrical Engineering and Informatics, Department of Software Technologies, Studentsk ́a 95, 53210 Pardubice, Czech Republic", } @InProceedings{Merta:2024:Informatics, author = "Jan Merta and Tomas Brandejsky", title = "Use of two-layer genetic programming for multidimensional symbolic regression", booktitle = "2024 IEEE 17th International Scientific Conference on Informatics (Informatics)", year = "2024", pages = "487--492", month = nov, keywords = "genetic algorithms, genetic programming, Benchmark testing, Informatics, Python, two-layer genetic programming, symbolic regression, multi-dimensional data, benchmarks", DOI = "doi:10.1109/Informatics62280.2024.10900766", abstract = "This paper focuses on exploring the potential benefits and advantages or disadvantages of a two-layer approach in genetic programming. The first section describes two-layer genetic programming itself and how it differs from its basic version. The Python programming language framework DEAP was used for the implementation. The focus of the paper is also to compare the results obtained by using this two-layer genetic programming with different configurations of the parameters with ordinary basic genetic programming on different multidimensional datasets and benchmarks.", notes = "Also known as \cite{10900766}", } @InProceedings{Mertan:2024:EuroGP, author = "Alican Mertan and Nick Cheney", editor = "Mario Giacobini and Bing Xue and Luca Manzoni", title = "Investigating Premature Convergence in Co-optimization of Morphology and Control in Evolved Virtual Soft Robots", booktitle = "EuroGP 2024: Proceedings of the 27th European Conference on Genetic Programming", year = "2024", volume = "14631", series = "LNCS", pages = "38--55", publisher = "Springer", address = "Aberystwyth", month = "3-5 " # apr, organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-031-56957-9", DOI = "doi:10.1007/978-3-031-56957-9_3", abstract = "Evolving virtual creatures is a field with a rich history and recently it has been getting more attention, especially in the soft robotics domain. The compliance of soft materials endows soft robots with complex behavior, but it also makes their design process unintuitive and in need of automated design. Despite the great interest, evolved virtual soft robots lack the complexity, and co-optimization of morphology and control remains a challenging problem. Prior work identifies and investigates a major issue with the co-optimization process, fragile co-adaptation of brain and body resulting in premature convergence of morphology. In this work, we expand the investigation of this phenomenon by comparing learnable controllers with proprioceptive observations and fixed controllers without any observations, whereas in the latter case, we only have the optimisation of the morphology. Our experiments in two morphology spaces and two environments that vary in complexity show, concrete examples of the existence of high-performing regions in the morphology space that are not able to be discovered during the co-optimization of the morphology and control, yet exist and are easily findable when optimizing morphologies alone. Thus this work clearly demonstrates and characterizes the challenges of optimizing morphology during co-optimization. Based on these results, we propose a new body-centric framework to think about the co-optimization problem which helps us understand the issue from a search perspective. We hope the insights we share with this work attract more attention to the problem and help us to enable efficient brain-body co-optimisation.", notes = "Part of \cite{Giacobini:2024:GP} EuroGP'2024 held in conjunction with EvoCOP2024, EvoMusArt2024 and EvoApplications2024", } @Article{Merugu:2012:IJARCET, title = "Effort estimation of software project", author = "R Raja Ramesh Merugu and Venkat Ravi Kumar Dammu", journal = "International Journal of Advanced Research in Computer Engineering \& Technology", publisher = "Shri Pannalal Research Institute of Technology", year = "2012", keywords = "genetic algorithms, genetic programming, SBSE, effort estimation, fuzzy logic, particle swarm optimisation, MMRE, neural networks", ISSN = "22781323", bibsource = "OAI-PMH server at www.doaj.org", oai = "oai:doaj-articles:65e54283cfc94cdfd7b789a43a65f1b0", URL = "http://ijarcet.org/wp-content/uploads/IJARCET-VOL-1-ISSUE-10-33-41.pdf", URL = "http://ijarcet.org/?p=1249", abstract = "The effort invested in a software project is probably one of the most important and most analysed variables in recent years in the process of project management. The limitation of algorithmic effort prediction models is their inability to cope with uncertainties and imprecision surrounding software projects at the early development stage. More recently attention has turned to a variety of machine learning methods, and soft computing in particular to predict software development effort. Soft computing is a consortium of methodologies centering in fuzzy logic, artificial neural networks and evolutionary computation. It is important to mention here that these methodologies are complementary and synergistic rather than competitive. They provide in one form or another flexible information processing capability for handling real life ambiguous situations. These methodologies are currently used for reliable and accurate estimate of software development effort which has always been a challenge for both the software industry and academia. The aim of this study is to analyse soft computing techniques in the existing models and to provide in depth review of software and project estimation techniques existing in industry and literature based on the different test datasets along with their strength and weaknesses.", } @InProceedings{merz:1999:GABQP, author = "Peter Merz and Bernd Freisleben", title = "Genetic Algorithms for Binary Quadratic Programming", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "417--424", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/merz_gecco99.ps.gz", URL = "http://agmerz.informatik.uni-kl.de/papers/gecco99.ps.gz", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99) Linear binary string representation", } @Misc{journals/corr/abs-1801-06030, author = "Naima Merzougui and Leila Djerou", title = "Multi-measures fusion based on multi-objective genetic programming for full-reference image quality assessment", howpublished = "arXiv", year = "2017", month = "4 " # dec, keywords = "genetic algorithms, genetic programming, image quality assessment, multi-objective optimisation, multigene", URL = "http://arxiv.org/abs/1801.06030", size = "6 pages", abstract = "In this paper, we exploit the flexibility of multi-objective fitness functions, and the efficiency of the model structure selection ability of a standard genetic programming (GP) with the parameter estimation power of classical regression via multi-gene genetic programming (MGGP), to propose a new fusion technique for image quality assessment (IQA) that is called Multi-measures Fusion based on Multi-Objective Genetic Programming (MFMOGP). This technique can automatically select the most significant suitable measures, from 16 full-reference IQA measures, used in aggregation and finds weights in a weighted sum of their outputs while simultaneously optimising for both accuracy and complexity. The obtained well-performing fusion of IQA measures are evaluated on four largest publicly available image databases and compared against state-of-the-art full-reference IQA approaches. Results of comparison reveal that the proposed approach outperforms other state-of-the-art recently developed fusion approaches.", notes = "note 2 authors", } @Article{merzougui:2021:JIST, author = "Naima Merzougui and Leila Djerou", title = "Multi-gene Genetic Programming based Predictive Models for Full-reference Image Quality Assessment", journal = "Journal of Imaging Science and Technology", year = "2021", volume = "65", number = "6", pages = "060409--1--060409--13", month = nov, keywords = "genetic algorithms, genetic programming", publisher = "Society for Imaging Science and Technology", DOI = "doi:10.2352/J.ImagingSci.Technol.2021.65.6.060409", abstract = "Many objective quality metrics for assessing the visual quality of images have been developed during the last decade. A simple way to fine tune the efficiency of assessment is through permutation and combination of these metrics. The goal of this fusion approach is to take advantage of the metrics used and minimize the influence of their drawbacks. In this paper, a symbolic regression technique using an evolutionary algorithm known as multi-gene genetic programming (MGGP) is applied for predicting subject scores of images in datasets using a combination of objective scores of a set of image quality metrics (IQM). By learning from image datasets, the MGGP algorithm can determine appropriate image quality metrics, from 21 metrics used, whose objective scores employed as predictors in the symbolic regression model, by optimizing simultaneously two competing objectives of model goodness of fit to data and model complexity. Six large image databases (namely LIVE, CSIQ, TID2008, TID2013, IVC and MDID) that are available in public domain are used for learning and testing the predictive models, according the k-fold-cross-validation and the cross dataset strategies. The proposed approach is compared against state-of-the-art objective image quality assessment approaches. Results of comparison reveal that the proposed approach outperforms other state-of-the-art recently developed fusion approaches.", notes = "Also \cite{DBLP:conf/iqsp/MerzouguiD22} See \cite{These_Finale-Merzougui} Article ID: jist1210 LESIA laboratory, University Mohamed Khider Biskra, Algeria Department of Computer Science and Information Technologies, University of Kasdi Merbah Ouargla, Algeria", } @InProceedings{DBLP:conf/iqsp/MerzouguiD22, author = "Naima Merzougui and Leila Djerou", editor = "Mohamed-Chaker Larabi and Mylene C. Q. Farias", title = "Multi-gene Genetic Programming based Predictive Models for Full-reference Image Quality Assessment", booktitle = "Image Quality and System Performance XIX, IQSP 2022", pages = "1--13", publisher = "Society for Imaging Science and Technology", year = "2022", address = "online", month = jan # " 15-26", keywords = "genetic algorithms, genetic programming", timestamp = "Wed, 19 Jul 2023 17:21:16 +0200", biburl = "https://dblp.org/rec/conf/iqsp/MerzouguiD22.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://doi.org/10.2352/J.ImagingSci.Technol.2021.65.6.060409", DOI = "doi:10.2352/J.IMAGINGSCI.TECHNOL.2021.65.6.060409", notes = "DOI gives journal jist1210 version \cite{merzougui:2021:JIST}", } @PhdThesis{These_Finale-Merzougui, author = "Naima Merzougui", title = "Metaheuristiques pour l'evaluation objective de la qualite d'images et de videos", school = "Departement d'informatique, Universite Mohamed Khider Biskra", year = "2022", address = "Algeria", month = "23 " # jun, keywords = "genetic algorithms, genetic programming, Metriques d'evaluation objective, Metaheuristiques d'optimisation, Optimisation multiobjective, Qualite visuelle, MGGP, MOGP, Objective evaluation metrics, Optimization metaheuristics, Multiobjective optimization, Visual quality.", URL = "http://thesis.univ-biskra.dz/5764/", URL = "http://thesis.univ-biskra.dz/5764/1/Th%C3%A8se%20Finale-Merzougui.pdf", size = "122 pages", abstract = "The work presented in this thesis, fits into the field of objective assessment of the quality of images and videos and more specifically on the assessment of quality with full reference. This is an active research area that has established itself with the rapid development of computer, communications and networking technologies. Despite the wealth of work in this field, and the abundance of objective evaluation methods (metrics), the problem of image quality evaluation remains. Indeed, the metrics presented are generally specific to a particular type of degradation, sensitive to data noisy by several types of degradation; hence the idea of moving towards new approaches calling on the merging of different metrics to quantify the quality image based on learning techniques and optimization meta-heuristics. In this thesis, we have developed new methods based on multigene genetic programming (MGGP) as an evolutionary learning technique inspired by natural evolution, allowing, from a learning set, to find good models combination of image quality metrics, simultaneously optimizing two competing objectives: the suitability of models for their correlation with the subjective scores of various types of distortions in the images, and the complexity of their structure. Due to the explosive growth of screen-oriented applications, we have exploited the advantages offered by MGGP to develop a generic method for the objective evaluation of images of screen content. The results of experiments carried out on the large benchmarks, showed the superior performance of the proposed methods compared to advanced measurements, including other recently published fusion approaches.", resume = "Le travail presente dans cette these s'inscrit dans le domaine d'evaluation objective de la qualite d'images et de videos et plus particulierement sur l'evaluation de qualite avec reference complete. Il s'agit d'un domaine de recherche actif qui s'est impose avec l'evolution rapide des technologies de l'informatique, des communications et des reseaux. Malgre la richesse des travaux dans ce domaine, et l'abondance des methodes objectives d'evaluation (les metriques), le probleme d'evaluation de qualite d'image reste toujours pose. En effet, les metriques presentees sont generalement specifiques a un type particulier de degradation, sensibles aux donnees bruitees par plusieurs types de degradations, d'ou l'idee de s'orienter vers de nouvelles approches recommandant la fusion de differentes metriques pour quantifier la qualite de l'image en se basant sur les techniques d'apprentissage et les metaheuristiques d'optimisation. Dans cette these, nous avons developpe de nouvelles methodes basees sur la programmation genetique multigene (MGGP) comme une technique d'apprentissage evolutive inspiree de l'evolution naturelle, permettant, a partir d'un ensemble d'apprentissage, de trouver de bons modeles de combinaison de metriques de qualite d'image, en optimisant simultanement deux objectifs concurrents : l'adequation de modeles par rapport a leur correlation avec les scores subjectifs de divers types de distorsions dans les images, et la complexite de leur structure. En raison de la croissance explosive des applications orientees ecran, nous avons exploite les avantages offerts par MGGP afin de developper une methode generique pour l'evaluation objective des images de contenu d'ecran. Les resultats d'experimentation menee sur les grands benchmarks, ont montre les performances superieures des methodes proposees par rapport aux mesures de pointe, y compris d'autres approches de fusion recemment publiees.", notes = "En francaise merzougui:2017:ISPA not found Feb 2025 \cite{merzougui:2021:JIST} Supervisor: Leila Djerou", } @InProceedings{merzougui:2022:ICAITA, author = "Naima Merzougui and Leila Djerou", title = "Genetic Programming for Screen Content Image Quality Assessment", booktitle = "International Conference on Artificial Intelligence: Theories and Applications", year = "2022", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-031-28540-0_5", DOI = "doi:10.1007/978-3-031-28540-0_5", notes = "Published 2023", } @InProceedings{Mesecan:2021:GI, author = "Ibrahim Mesecan and Michael C. Gerten and James I. Lathrop and Myra B. Cohen and Tomas {Haddad Caldas}", title = "{CRNRepair}: Automated Program Repair of Chemical Reaction Networks", booktitle = "GI @ ICSE 2021", year = "2021", month = "30 " # may, editor = "Justyna Petke and Bobby R. Bruce and Yu Huang and Aymeric Blot and Westley Weimer and W. B. Langdon", publisher = "IEEE", address = "internet", pages = "23--30", note = "best paper", keywords = "genetic algorithms, genetic programming, genetic improvement, program repair, APR, chemical reaction networks, PyGGI, XML, Matlab, Tabu search, Simulated Annealing, ChemTest, SBML", isbn13 = "978-1-6654-4466-8/21", code_url = "https://github.com/LavaOps/CRNRepair/", URL = "https://geneticimprovementofsoftware.com/paper_pdfs/gi2021icse/mesecan_gi-icse_2021.pdf", video_url = "https://www.youtube.com/watch?v=SjNsbERd6e0&list=PLI8fiFpB7BoKDaxvS7SQp0iA7fN7rrvDD&index=15", video_url = "https://www.youtube.com/watch?v=AtwAfPwdfJ4&list=PLI8fiFpB7BoKDaxvS7SQp0iA7fN7rrvDD&index=16", video_url = "https://www.youtube.com/watch?v=YD-JfwQOEiY&list=PLXTjhGKkSnI-se7uQneCX-pEDiDrQ7TIS&index=3", DOI = "doi:10.1109/GI52543.2021.00014", size = "8 pages", abstract = "Chemical reaction networks (CRNs) are abstractions of distributed networks that form the foundations of many natural phenomena such as biological processes. These can be encoded and/or compiled into DNA and have been shown to be Turing complete. Before CRNs are implemented in a physical environment, they are often simulated in programming environments. Like traditional programs, these CRN programs must be validated. Researchers have recently designed a software testing framework for CRNs, however, repairing CRN programs is still a manual task. While the programs are often small in size, finding and repairing the faults can be difficult without automated support. we present CRNRepair, a program repair framework for CRN programs. We built our framework on top of an existing APR framework. We use a testing infrastructure built in the Matlab SimBiology package and adapt it to use the SBML representation for its abstract syntax tree. In a case study on 19 mutant versions of 2 programs, we find plausible patches for 90 percent of one of the programs, and 50 percent of the other. We find several common types of repairs, which differ from the correct programs, but are functionally correct.", notes = "Video YD-JfwQOEiY Ibrahim Mesecan (Iowa). 15:15 Discussion chair: Aymeric Blot. 15:24 Q: W. B. Langdon, A: Ibrahim Mesecan, benchmark used in Michael C. Gerten et al 2020. 60percent patches like human, 38 percent not human like, eg speed up. A: Michael C. Gerten mutation set. 18:23 Q: Aymeric Blot testing without simulation? A: Ibrahim Mesecan yes (some common errors), Michael C. Gerten. 21:16 Westley Weimer, fitness, A: plateus a problem. 22:39 Justyna Petke state of the art + size of search state, A: 1st work using testing, some work with model checking but scales badly. Search spaces grows fast. 24:05 Q: open source, A: yes (eventually). part of \cite{Petke:2021:ICSEworkshop} http://geneticimprovementofsoftware.com/events/icse2021.html", } @InProceedings{Clark:2021:ASE-NIER, author = "Ibrahim Mesecan and Daniel Blackwell and David Clark and Myra B. Cohen and Justyna Petke", title = "{HyperGI}: Automated Detection and Repair of Information Flow Leakage", booktitle = "The 36th IEEE/ACM International Conference on Automated Software Engineering, New Ideas and Emerging Results track, ASE NIER 2021", year = "2021", editor = "Hourieh Khalajzadeh and Jean-Guy Schneider", pages = "1358--1362", address = "Melbourne", month = "15-19 " # nov, keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, APR, PyGGI 2, information flow leakage, Information Flow Control, IFC, Leak Localisation, noninterference security policy, Quantified Information Flow, QIF, Shannon Entropy", isbn13 = "978-1-6654-4784-3", eprint = "2108.12075", URL = "https://arxiv.org/abs/2108.12075", URL = "https://faculty.sites.iastate.edu/mcohen/files/inline-files/hyper-gi-2021.pdf", URL = "https://discovery.ucl.ac.uk/id/eprint/10136860/", DOI = "doi:10.1109/ASE51524.2021.9678758", size = "5 pages", abstract = "Maintaining confidential information control in software is a persistent security problem where failure means secrets can be revealed via program behaviors. Information flow control techniques traditionally have been based on static or symbolic analyses, limited in scalability and specialized to particular languages. When programs do leak secrets there are no approaches to automatically repair them unless the leak causes a functional test to fail. We present our vision for HyperGI, a genetic improvement framework that detects, localises and repairs information leakage. Key elements of HyperGI include (1) the use of two orthogonal test suites, (2) a dynamic leak detection approach which estimates and localises potential leaks, and (3) a repair component that produces a candidate patch using genetic improvement. We demonstrate the successful use of HyperGI on several programs which have no failing functional tests. We manually examine the resulting patches and identify trade-offs and future directions for fully realising our vision", notes = "See also \cite{Mesecan:2022:ASE} Secret Triangle program (100 percent repair impossible). Apple Talk (atalk) CVE-2009-3002. Underflow (underflow) CVE-2007-2875. CVE vulnerability database. Hypertesting is testing for hyperproperties. A hyperproperty can only be expressed as a property of more than one execution of a program. 'HyperGI is the use of two independent test suites, one used to test correct program semantics and a hypertest suite used to measure information leakage.' 'run fuzzers to gather functional tests'. 'generate hypertests;' 'run GP-based repair to try to decrease leakage' Compare with AFL and LibFuzzer. Multiple GP runs (pop=32 gens=50) 'binary search strategy was able to detect leaks for all three programs', fuzzing did not. in some 'cases our tests became flaky' (eg deleted return statement) 'HyperGI was able to find patches semantically-equivalent to developer fixes. It found patches reducing leakage in all three programs.' Also known as \cite{mesecan2021hypergi} Iowa State University, Ames, Iowa, USA https://conf.researchr.org/track/ase-2021/ase-2021-nier-track", } @InProceedings{Mesecan:2022:ASE, author = "Ibrahim Mesecan and Daniel Blackwell and David Clark and Myra B. Cohen and Justyna Petke", title = "Keeping Secrets: Multi-objective Genetic Improvement for Detecting and Reducing Information Leakage", booktitle = "37th IEEE/ACM International Conference on Automated Software Engineering", year = "2022", editor = "Julia Rubin and Shahar Maoz and Marouane Kessentini", pages = "Article no. 61", address = "Oakland Center, Michigan, USA", month = "10-14 " # oct, keywords = "genetic algorithms, genetic programming, Genetic Improvement, SBSE, PyGGI, JMetalPy, Software and its engineering, Search-based software engineering, Security and privacy, Software security engineering, Information Leakage, Automated Program Repair, APR, Quantified Information Flow, QIF", isbn13 = "9781450394758", URL = "https://discovery.ucl.ac.uk/id/eprint/10152572/1/main.pdf", DOI = "doi:10.1145/3551349.3556947", code_url = "https://github.com/anonymous183459/LeakReducer/", size = "12 pages", abstract = "Information leaks in software can unintentionally reveal private data, yet they are hard to detect and fix. Although several methods have been proposed to detect leakage, such as static verification-based approaches, they require specialist knowledge, and are time-consuming. Recently, HyperGI introduced a dynamic, hypertest-based approach that detects and produces potential fixes for information leakage. Its fitness function tries to balance information leakage and program correctness, but there may be a tradeoff between keeping program semantics and reducing information leakage. we ask if it is possible to automatically detect and repair information leakage in more realistic programs without requiring specialist knowledge. Our approach, called LeakReducer explicitly encodes the tradeoff between program correctness and information leakage as a multi-objective optimisation problem. We apply LeakReducer to a set of leaky programs including the well known Heartbleed bug. It is comparable with HyperGI on toy applications. In addition, we demonstrate it can find and reduce leakage in real applications and we see diverse solutions on our Pareto front. Upon investigation we find that having a Pareto front helps with some types of information leakage, but not all.", notes = "cites \cite{Clark:2021:ASE-NIER}. Triangle, Atalk, Underflow, Classify, Heartbleed, Bignum https://heartbleed.com/ p2 'This suggests repairing information flow leakage should be viewed as a multi-objective problem' (ie 2 objectives reduce leakage versus retain functionality). OpenSSL. Hypertests. p3 'program is deterministic' 'some flakiness in hypertests is tolerable' 'agnostic about the entropy in the high(secret) part of states' p4 AFL++ fuzz testing HashFuzz, manual seed (Test Augmentation). sec 3.2 Automated Hypertest Generation eh? p5 Leak Localization. NewIf NewFor tagged XML, Universal Ctags https://ctags.io/ Compare against HyperGI \cite{Clark:2021:ASE-NIER}. p6 https://cve.mitre.org/ AFL fuzzer address sanitizer, LLVM. p8 SPEA2, MOCell, NSGAII, NSGAIII, SPEA2 Iowa State University, Ames, Iowa, USA https://conf.researchr.org/home/ase-2022", } @InProceedings{Meshgi:2014:HIC, author = "Ali Meshgi and Petra Schmitter and Vladan Babovic and Ting Fong May Chui", title = "Predicting Baseflow Using Genetic Programing", booktitle = "11th International Conference on Hydroinformatics", year = "2014", address = "New York, USA", month = aug # " 17-21", organisation = "IAHR/IWA Joint Committee on Hydroinformatics", keywords = "genetic algorithms, genetic programming, Baseflow, Recursive Digital Filters, Numerical modeling", isbn13 = "978-0-692-28129-1", URL = "http://www.hic2014.org/proceedings/bitstream/handle/123456789/1589/1153.pdf", size = "8 pages", abstract = "Developing reliable methods to estimate baseflow has been a subject of research interest over the past decades due to its importance in catchment response and sustainable watershed management (e.g. ground water recharge vs. extraction). Limitations and complexities of existing methods have been addressed by a number of researchers. For instance, physically based numerical models are complex, requiring substantial computational time and data which may not be always available. Artificial Intelligence (AI) tools such as Genetic Programming (GP) have been used widely to reduce the challenges associated with complex hydrological systems without losing the physical meanings. However, up to date, in the absence of complex numerical models, baseflow is frequently estimated using statistically derived empirical equations without significant physical insights. This study investigates the capability of GP in estimating baseflow for a small intensively monitored semi-urban catchment (8.5 ha) located in Singapore. The validated GP model for Singapore is tested on a larger vegetation-dominated basin located in the USA (24 km2). For each study case, the baseflow predictions from the established GP model were compared with baseflow estimates obtained through the use of the Recursive Digital Filters (RDFs) method using the available discharge time series. The Nash-Sutcliffe efficiency of 0.94 and 0.91 are found with comparing the baseflow estimated by GP and RDFs in the first and second study sites, respectively. These results indicate that GP is an effective tool in determining baseflow. Overall, this study proposes a new approach which can predict the baseflow with only information on three parameters including minimum baseflow in dry period, area of the catchment and groundwater table.", notes = "Broken June 2021 http://www.hic2014.org/xmlui/", } @Article{Meshgi:2014:JH, author = "Ali Meshgi and Petra Schmitter and Vladan Babovic and Ting Fong May Chui", title = "An empirical method for approximating stream baseflow time series using groundwater table fluctuations", journal = "Journal of Hydrology", volume = "519, Part A", pages = "1031--1041", year = "2014", keywords = "genetic algorithms, genetic programming, Baseflow, Empirical equation, Numerical modelling", ISSN = "0022-1694", DOI = "doi:10.1016/j.jhydrol.2014.08.033", URL = "http://www.sciencedirect.com/science/article/pii/S002216941400643X", abstract = "Summary Developing reliable methods to estimate stream base flow has been a subject of interest due to its importance in catchment response and sustainable watershed management. However, to date, in the absence of complex numerical models, base-flow is most commonly estimated using statistically derived empirical approaches that do not directly incorporate physically-meaningful information. On the other hand, Artificial Intelligence (AI) tools such as Genetic Programming (GP) offer unique capabilities to reduce the complexities of hydrological systems without losing relevant physical information. This study presents a simple-to-use empirical equation to estimate baseflow time series using GP so that minimal data is required and physical information is preserved. A groundwater numerical model was first adopted to simulate baseflow for a small semi-urban catchment (0.043 km2) located in Singapore. GP was then used to derive an empirical equation relating baseflow time series to time series of groundwater table fluctuations, which are relatively easily measured and are physically related to baseflow generation. The equation was then generalised for approximating baseflow in other catchments and validated for a larger vegetation-dominated basin located in the US (24 km2). Overall, this study used GP to propose a simple-to-use equation to predict baseflow time series based on only three parameters: minimum daily baseflow of the entire period, area of the catchment and groundwater table fluctuations. It serves as an alternative approach for baseflow estimation in un-gauged systems when only groundwater table and soil information is available, and is thus complementary to other methods that require discharge measurements.", } @PhdThesis{Ali_Meshgi_PhD_Thesis, author = "Ali Meshgi", title = "Rainfall-Runoff Processes in Tropical Urban Environments", school = "National University of Singapore", year = "2015", address = "Singapore", month = "15 " # jan, keywords = "genetic algorithms, genetic programming, Modular approach, Baseflow, Quickflow, Land use contribution, Tropical urban environments", URL = "http://scholarbank.nus.edu.sg/handle/10635/119829", URL = "http://aunilo.uum.edu.my/Find/Record/sg-nus-scholar.10635-119829", size = "209 pages", abstract = "This study used Genetic Programming to establish a modular model consisting of two sub-models: (i) a baseflow module and (ii) a quickflow module to simulate the two hydrograph flow components. The relationship between the input variables in the model (i.e. meteorological data and catchment initial conditions) and its overall structure can be explained in terms of catchment hydrological processes. Therefore, the model is a partial greying of what is often a black-box approach in catchment modelling. Subsequently, this study used the modular model to predict both flow components of events as well as time series, and applied optimization techniques to estimate the contributions of various land uses (i.e. impervious, steep grassland, grassland on mild slope, mixed grasses and trees and relatively natural vegetation) towards baseflow and quickflow in tropical urban systems. This quantification facilitates the integration of water sensitive urban infrastructure for the sustainable development of water in tropical megacities.", notes = "Supervisors: Vladan Babovic and May Chui Also known as \cite{10635_119829}", } @Article{Meshgi:2015:JH, author = "Ali Meshgi and Petra Schmitter and Ting Fong May Chui and Vladan Babovic", title = "Development of a modular streamflow model to quantify runoff contributions from different land uses in tropical urban environments using Genetic Programming", journal = "Journal of Hydrology", volume = "525", pages = "711--723", year = "2015", ISSN = "0022-1694", DOI = "doi:10.1016/j.jhydrol.2015.04.032", URL = "http://www.sciencedirect.com/science/article/pii/S0022169415002917", abstract = "Summary The decrease of pervious areas during urbanization has severely altered the hydrological cycle, diminishing infiltration and therefore sub-surface flows during rainfall events, and further increasing peak discharges in urban drainage infrastructure. Designing appropriate waster sensitive infrastructure that reduces peak discharges requires a better understanding of land use specific contributions towards surface and sub-surface processes. However, to date, such understanding in tropical urban environments is still limited. On the other hand, the rainfall-runoff process in tropical urban systems experiences a high degree of non-linearity and heterogeneity. Therefore, this study used Genetic Programming to establish a physically interpretable modular model consisting of two sub-models: (i) a baseflow module and (ii) a quick flow module to simulate the two hydrograph flow components. The relationship between the input variables in the model (i.e. meteorological data and catchment initial conditions) and its overall structure can be explained in terms of catchment hydrological processes. Therefore, the model is a partial greying of what is often a black-box approach in catchment modelling. The model was further generalized to the sub-catchments of the main catchment, extending the potential for more widespread applications. Subsequently, this study used the modular model to predict both flow components of events as well as time series, and applied optimization techniques to estimate the contributions of various land uses (i.e. impervious, steep grassland, grassland on mild slope, mixed grasses and trees and relatively natural vegetation) towards baseflow and quickflow in tropical urban systems. The sub-catchment containing the highest portion of impervious surfaces (40percent of the area) contributed the least towards the baseflow (6.3percent) while the sub-catchment covered with 87percent of relatively natural vegetation contributed the most (34.9percent). The results from the quickflow module revealed average runoff coefficients between 0.12 and 0.80 for the various land uses and decreased from impervious (0.80), grass on steep slopes (0.56), grass on mild slopes (0.48), mixed grasses and trees (0.42) to relatively natural vegetation (0.12). The established modular model, reflecting the driving hydrological processes, enables the quantification of land use specific contributions towards the baseflow and quickflow components. This quantification facilitates the integration of water sensitive urban infrastructure for the sustainable development of water in tropical megacities.", keywords = "genetic algorithms, genetic programming, Modular approach, Baseflow, Quickflow, Land use contribution, Tropical urban environments", } @Article{Mesquita:2008:GPEM, author = "Antonio Mesquita", title = "Introduction to Evolvable Hardware: A Practical Guide for Designing Self-Adaptive Systems Garrison W. Greenwood and Andrew M. Tyrrell, Wiley-IEEE Press, October 2006, 208 pp, ISBN: 978-0-471-71977-9", journal = "Genetic Programming and Evolvable Machines", year = "2008", volume = "9", number = "3", pages = "275--277", month = sep, note = "book review", keywords = "genetic algorithms, evolvable hardware", ISSN = "1389-2576", URL = "https://rdcu.be/dR8Fh", DOI = "doi:10.1007/s10710-008-9058-x", size = "3 pages", } @InProceedings{DBLP:conf/icarcv/MessomW02, author = "Chris H. Messom and Matthew G. Walker", title = "Evolving cooperative robotic behaviour using distributed genetic programming", booktitle = "Seventh International Conference on Control, Automation, Robotics and Vision, ICARCV 2002", year = "2002", pages = "215--219", address = "Singapore", month = "2-5 " # dec, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", URL = "http://www.massey.ac.nz/~chmessom/MessomWalkerICARCV2002.pdf", DOI = "doi:10.1109/ICARCV.2002.1234823", abstract = "Cooperative robotic systems provide design and implementation challenges that are not easy to solve. This paper describes a parallel implementation for evolving cooperative robotic behaviour using an island model based genetic program on a cluster computer system. The application domain is robot soccer in which two robots must cooperate to avoid collisions with each other and score goals. The system has access to robot and ball positions and outputs velocity set points for the robot wheel motors. The evolved controllers are evaluated on a kinematic model that has been optimised to improve the time complexity of the genetic programming algorithm. The inter-process communication on the cluster is implemented using the message passing interface (MPI).", bibsource = "DBLP, http://dblp.uni-trier.de", } @Article{Metenidis:2004:EAAI, author = "Mihai Florin Metenidis and Marcin Witczak and Jozef Korbicz", title = "A novel genetic programming approach to nonlinear system modelling: application to the {DAMADICS} benchmark problem", journal = "Engineering Applications of Artificial Intelligence", year = "2004", volume = "17", pages = "363--370", number = "4", month = jun, note = "Selected Problems of Knowledge Representation", keywords = "genetic algorithms, genetic programming, system modelling, Parameter estimation", ISSN = "0952-1976", owner = "wlangdon", broken = "http://www.sciencedirect.com/science/article/B6V2M-4CKFH3B-1/2/49d6ac641b4455bbc65260281aa1ee55", DOI = "doi:10.1016/j.engappai.2004.04.009", abstract = "Nonlinear system modelling is a diverse research area where different kinds of methodologies can be employed. However, due to the large variety of this field, no approach imposes itself as the best one. The difficulty of system modelling consists in the necessity of approximating both the structure and the parameters of a system. That is why the choice of the approach to be used usually depends on a specific application. This paper presents a modified genetic programming approach for model structure selection combined with a classical technique for parameter estimation. In particular, various combinations of parameterised fixed length trees are proposed as candidate model structures. The algorithms that can be used to obtain a suitable form of these structures are proposed as well. The final part of the paper justifies the possibility of using this approach in practice, i.e. a comprehensive empirical study is performed with the data acquired from an industrial actuator.", notes = "Also known as \cite{FLORINMETENIDIS2004363}", } @InProceedings{metevier:2018:GPTP, author = "Blossom Metevier and Anil Kumar Saini and Lee Spector", title = "Lexicase Selection Beyond Genetic Programming", booktitle = "Genetic Programming Theory and Practice XVI", year = "2018", editor = "Wolfgang Banzhaf and Lee Spector and Leigh Sheneman", pages = "123--136", address = "Ann Arbor, USA", month = "17-20 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-030-04734-4", URL = "http://link.springer.com/chapter/10.1007/978-3-030-04735-1_7", DOI = "doi:10.1007/978-3-030-04735-1_7", abstract = "Lexicase selection is a selection method that was developed for parent selection in genetic programming. In this chapter, we present a study of lexicase selection in a non-genetic-programming context, conducted to investigate the broader applicability of the technique. Specifically, we present a framework for solving Boolean constraint satisfaction problems using a traditional genetic algorithm, with linear genomes of fixed length. We present results of experiments in this framework using three parent selection algorithms: lexicase selection, tournament selection (with several tournament sizes), and fitness-proportionate selection. The results show that when lexicase selection is used, more solutions are found, fewer generations are required to find those solutions, and more diverse populations are maintained. We discuss the implications of these results for the utility of lexicase selection more generally.", notes = "College of Information and Computer Sciences, University of Massachusetts, Amherst, USA", } @InProceedings{Mettler:2021:GECCOcomp, author = "Henrik D. Mettler and Maximilian Schmidt and Walter Senn and Mihai A. Petrovici and Jakob Jordan", title = "Evolving Neuronal Plasticity Rules using Cartesian Genetic Programming", booktitle = "Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion", year = "2021", editor = "Francisco Chicano and Alberto Tonda and Krzysztof Krawiec and Marde Helbig and Christopher W. Cleghorn and Dennis G. Wilson and Georgios Yannakakis and Luis Paquete and Gabriela Ochoa and Jaume Bacardit and Christian Gagne and Sanaz Mostaghim and Laetitia Jourdan and Oliver Schuetze and Petr Posik and Carlos Segura and Renato Tinos and Carlos Cotta and Malcolm Heywood and Mengjie Zhang and Leonardo Trujillo and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Fuyuki Ishikawa and Inmaculada Medina-Bulo and Frank Neumann and Andrew M. Sutton", pages = "285--286", address = "internet", series = "GECCO '21", month = jul # " 10-14", organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, Synaptic plasticity, metalearning: Poster", isbn13 = "978-1-4503-8351-6", DOI = "doi:10.1145/3449726.3459420", size = "2 pages", abstract = "We formulate the search for phenomenological models of synaptic plasticity as an optimization problem. We employ Cartesian genetic programming to evolve biologically plausible human-interpretable plasticity rules that allow a given network to successfully solve tasks from specific task families. While our evolving-to-learn approach can be applied to various learning paradigms, here we illustrate its power by evolving plasticity rules that allow a network to efficiently determine the first principal component of its input distribution.We demonstrate that the evolved rules perfom competitively with known hand-designed solutions. We explore how the statistical properties of the datasets used during the evolutionary search influences the form of the plasticity rules and discover new rules which are adapted to the structure of the corresponding datasets", notes = " GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)", } @InCollection{mettler:1999:ETMSNUGA, author = "Michael Mettler", title = "Evolution of a Time-Optimal Minimal Spanning Network Using Genetic Algorithms", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1999", year = "1999", editor = "John R. Koza", pages = "164--173", address = "Stanford, California, 94305-3079 USA", month = "15 " # mar, publisher = "Stanford Bookstore", keywords = "genetic algorithms", notes = "part of \cite{koza:1999:GAGPs}", } @MastersThesis{meulen:2001:msc, author = "P. G. M. {van der Meulen}", title = "{PM-DGP} A Distributed Genetic Programming Framework", school = "Laboratory for Signals \& Systems, Department of Electrical Engineering", year = "2001", type = "Master's thesis", address = "University of Twente, The Netherlands", month = oct, keywords = "genetic algorithms, genetic programming", URL = "http://heanet.dl.sourceforge.net/sourceforge/pmdgp/pmdgp-thesis.pdf", size = "89 pages", abstract = "In this report the design, implementation and usage of PM-DGP is described. PM-DGP is a fully object-oriented framework, written in C++, to aid in the implementation of genetic programming (GP) problems. It is the result of the search for a GP environment that allows a programmer to concentrate on writing the fitness function and the problem specific nodes he needs and let the system take care of the rest, including the distribution of the fitness evaluation. The system is freely available as it is released under the GNU public license. The framework supports important additions to GP like automatically defined functions and random constants. Once a problem has been implemented using PM-DGP the time consuming task of fitness evaluation can be distributed using the idle time of networked computers running Microsoft Windows or Linux using a GUI server and clients. The system is designed to be flexible and extensible. Many aspects of a GP run, the nodes, node sets, result type and genetic algorithm, are configurable at run time using a simple textual configuration file. The system offers a flexible yet efficient object-oriented genome interpreter that can work with different result types and uses a prefix coding to store its programs. When distributing the fitness evaluation the genome interpreter has to be sent to a client over the network only once after which the programs can be sent using only one to two bytes per node. Several example implementations of GP problems are included: symbolic regression, parity, artificial ant, royal tree and edge detector. An extensive tutorial showing how to implement the lawn mower problem is included in this report.", notes = "Graduation Committee: Ir. A.M. Bazen (EL-S&S) (supervisor) Dr. ir. S.H. Gerez (EL-S&S) (supervisor) H.J. Kip, Btw (Nedap) Ir. A. Kuip (Nedap) Dr. M.Poel (INF-TT) Prof. dr. ir. C.H. Slump (EL-S&S) Date: 11-Oct-2001 Report number: EL-S&S 019.01 http://sourceforge.net/projects/pmdgp/", } @InProceedings{Meuth:2010:geccocomp, author = "Ryan J. Meuth", title = "Meta-learning genetic programming", booktitle = "GECCO 2010 Late breaking abstracts", year = "2010", editor = "Daniel Tauritz", isbn13 = "978-1-4503-0073-5", keywords = "genetic algorithms, genetic programming", pages = "2101--2102", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830761.1830882", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "In computational intelligence, the term 'memetic algorithm' has come to be associated with the algorithmic pairing of a global search method with a local search method. In a sociological context, a 'meme' has been loosely defined as a unit of cultural information, the social analog of genes for individuals. Both of these definitions are inadequate, as 'memetic algorithm' is too specific, and ultimately a misnomer, as much as a 'meme' is defined too generally to be of scientific use. In this paper, we extend the notion of memes from a computational viewpoint and explore the purpose, definitions, design guidelines and architecture for effective memetic computing. Using two genetic programming test-beds (the even-parity problem and the Pac-Man video game), we demonstrate the power of high-order meme-based learning, known as meta-learning. With applications ranging from cognitive science to machine learning, meta-learning has the potential to provide much-needed stimulation to the field of computational intelligence by providing a framework for higher order learning.", notes = "Also known as \cite{1830882} Distributed on CD-ROM at GECCO-2010. ACM Order Number 910102.", } @PhdThesis{Meyer:thesis, author = "Georg Meyer", title = "Understanding the performance of decision strategies in dynamic environments", school = "University of Minnesota", year = "2012", address = "USA", month = aug, keywords = "genetic algorithms, genetic programming, Decision strategy, Dynamic decision making, Machine learning, Process control, Simulation", URL = "http://hdl.handle.net/11299/138310", URL = "http://purl.umn.edu/138310", URL = "http://conservancy.umn.edu/bitstream/handle/11299/138310/Meyer_umn_0130E_13096.pdf", size = "213 pages", abstract = "A decision strategy is systematic way of choosing among alternatives or eliminating options in order to arrive at a goal. Individuals apply decision strategies in dynamic environments that require repeated decision making where decisions are path-dependent, time-constrained, and the environment changes not only in response to the actions taken by the decision maker but also autonomously. In addition to being used by individual agents, decision strategies are found in organizations in the form of policies, guidelines, and algorithms. This research consists of three studies that apply a process control perspective to dynamic decision making. Study 1 investigates the features of decision strategies that affect performance. It finds that strategies perform well if they possess a strong mental model that accurately represents the decision problem or if they are well adapted to the problem environment. Based on these findings, Study 2 develops a machine learning approach to improve the mental model, and Study 3 develops an evolutionary approach to adapt decision strategies to a given environment. Both approaches are shown to be effective for constructing strategies with greater performance.", notes = "Supervisors: Paul E. Johnson and Gedas Adomavicius", } @Article{meyer:2001:pourlascience, author = "Jean-arcady Meyer and Agnes Guillot", title = "La robotique evolutionniste", journal = "Pour la Science", year = "2001", month = "juin", keywords = "genetic algorithms, genetic programming, robotique, robot, algorithmes evolutionnistes, Aibo, Elvis", broken = "http://www.pourlascience.com/numeros/pls-284/art-5.htm", URL = "http://www.pourlascience.fr/ewb_pages/a/article-la-robotique-evolutionniste-27347.php", abstract = "Des robots concus automatiquement par evolution et selection artificielles sont parfois plus performants que ceux concus par des etres humains.", notes = "Cute picture of Elvis \cite{nordin:1999:cimfa}", } @InProceedings{ML03, author = "Jennis Meyer-Spradow and J{\"o}rn Loviscach", title = "Evolutionary Design of {BRDFs}", booktitle = "Eurographics 2003 Short Paper Proceedings", pages = "301--306", year = "2003", editor = "M. Chover and H. Hagen and D. Tost", keywords = "genetic algorithms, genetic programming, GPU, Computer Graphics, Three-Dimensional Graphics and Realism", URL = "http://viscg.uni-muenster.de/publications/2003/ML03/evolutionary_web.pdf", size = "6 pages", abstract = "The look of a non-transparent material is determined by its bidirectional reflection distribution function (BRDF). To design 3-D objects for example for games or animation films thus includes to design BRDFs. However, as functions defined on a four-dimensional domain, these form a vast space that is very difficult to explore interactively. Typically, the infinite number of degrees of freedom is reduced to a tractable handful of parameters by introducing simplified physical models or heuristic approximations such as Phong's As the complexity of such approaches increases, they become difficult to master for a human operator. Even if many parameters are made accessible, an infinite variety of useful and/or interesting BRDFs remains hidden and inaccessible. We therefore propose a method of constructing BRDFs through genetic programming with a human operator making choices based on his or her preferences. With the pixel shader programmability of modern graphics cards this can be performed in real time.", notes = "broken Feb 2021 http://viscg.uni-muenster.de/publications/2003/ML03/ OpenGL nVidia GeForce FX 5800 fragment pixel shader assembler programs Instruction set: ADD add two vectors COS cosine of a scalar DP3 dot product using three components DP4 dot product using four components DST auxiliary operation for light attenuation EX2 the number 2 raised to a power given by a scalar LRP linear interpolation of two vectors MAD multiply and add vectors MOV copy vector to another register MUL multiply two vectors component-wise SIN sine of a scalar SUB subtract one vector from another X2D affine 2-D transform using two vectors. 25 frames per second. Up to 100 instructions", } @InProceedings{meysenburg:1999:RGPR, author = "Mark M. Meysenburg and James A. Foster", title = "Randomness and GA Performance, Revisited", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "425--432", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/prng-icga99.pdf", URL = "http://www.cs.uidaho.edu/~foster/pub/foster/papers/prng-icga99.pdf", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{meysenburg:1999:RGQGP, author = "Mark M. Meysenburg and James A. Foster", title = "Random Generator Quality and GP Performance", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1121--1126", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-611-4", URL = "http://citeseer.ist.psu.edu/471279.html", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-416.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-416.ps", abstract = "In previous studies, the authors found that pseudo-random number generator (PRNG) quality had little effect on the performance of a simple genetic algorithm (GA). This paper extends our work to the area of genetic programming (GP). We examine the effect of PRNG quality on the performance of GP techniques. We detail a set of PRNGs which generate random numbers through various techniques, and a method for evaluating the quality of these PRNGs. We explain the application of detailed statistical analysis to the results of many individual GP runs, over a set of four GP test problems. We found no evidence to support the notion that higher quality PRNGs caused improved GP performance.", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{meysenburg:2000:TG, author = "Mark M. Meysenburg", title = "The computational complexity of simple GA problems", booktitle = "Graduate Student Workshop", year = "2000", editor = "Conor Ryan and Una-May O'Reilly and William B. Langdon", pages = "293--296", address = "Las Vegas, Nevada, USA", month = "8 " # jul, keywords = "genetic algorithms, genetic programming", notes = "GECCO-2000WKS Part of \cite{wu:2000:GECCOWKS}", } @InProceedings{Meza-Sanchez:2018:COMRob, author = "M. Meza-Sanchez and E. Clemente and R. Villalvazo and G. Olague", title = "Bounding Velocity in Tracking Control of Unicycle Mobile Robots with Genetic Programming", booktitle = "2018 XX Congreso Mexicano de Robotica (COMRob)", year = "2018", abstract = "This paper introduces a methodology for the synthesis of nonlinear tracking controllers. This approach is applied to the navigation of unicycle mobile robots, where a constrained velocity is pursued. The proposed approach extends the notions of Behavior-based control by redefining the basis behaviors as analytic functions. The conception of natural behavior (which is composed by unforced, forced and learned behaviors) in order to characterize the properties, actions, and restrictions, of the mobile robot, is introduced. Within this approach, the Genetic Programming is dynamically introduced, as a learning process, in the structure of a Control-Theory-based tracking controller. Then, a search of the set of fittest learned behaviors, addressing the complete control problem, is carried out. A selected solution with high fitness value, from the discovered set of learned behaviors, is simulated to show the effectiveness of our proposed framework.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/COMROB.2018.8689409", month = sep, notes = "Also known as \cite{8689409}", } @Article{Meza-Sanchez:2019:IS, author = "Marlen Meza-Sanchez and Eddie Clemente and M. C. Rodriguez-Linan and Gustavo Olague", title = "Synthetic-analytic behavior-based control framework: Constraining velocity in tracking for nonholonomic wheeled mobile robots", journal = "Information Sciences", year = "2019", volume = "501", pages = "436--459", month = oct, keywords = "genetic algorithms, genetic programming, Synthetic-analytic behaviours, Bounded velocity, Evolutionary robotics, Nonholonomic mobile robots, Nonlinear tracking control", ISSN = "0020-0255", URL = "https://www.human-competitive.org/sites/default/files/meza-sanchez_0.txt", URL = "https://www.human-competitive.org/sites/default/files/synthetic-analytic_behavior-based_control_framework_constraining_velocity_in_tracking_for_nonholonomic_wheeled_mobile_robots_1.pdf", URL = "http://www.sciencedirect.com/science/article/pii/S0020025519305602", DOI = "doi:10.1016/j.ins.2019.06.025", video_url = "http://www.human-competitive.org/sites/default/files/meza-sanchezvideo.mp4", size = "24 pages", abstract = "This work presents a genetic programming control design methodology that extends the traditional behaviour-based control strategy towards a synthetic-analytic perspective. The proposed approach considers the internal and external dynamics of the system, providing solutions to a general structure, and including analytic functions, which can be studied within the Control Theory framework. The method is illustrated for the tracking control problem under bounded velocity restrictions of a nonholonomic wheeled mobile robot. A classic Control Theory (CT) based controller that solves the tracking problem (but not the velocity constraint requirement) is chosen from the literature; based on its stability properties, a modified structure where the search of suitable analytic basis behaviors, fulfilling both control objectives simultaneously, can be introduced. The proposed framework takes the form of a learning process based on Genetic Programming (GP) which generates a set of nonlinear tracking controllers satisfying pre-specified velocity bounds. A collection of 9113 suitable nonlinear solutions were obtained to augment the ground controller. Simulations and real-time experiments are performed to illustrate the effectiveness of the methodology through the testing of the models with the best performance, as well as those with lower structural complexity.", notes = "2020 HUMIES silver winner also known as \cite{MEZASANCHEZ2019436}", } @Article{Meza-Sanchez:IETcta, author = "Marlen Meza-Sanchez and Maria {del Carmen Rodriguez-Linan} and Eddie Clemente", title = "Family of controllers based on sector non-linear functions: an application for first-order dynamical systems", journal = "IET Control Theory \& Applications", year = "2020", volume = "14", number = "10", pages = "1387--1392", keywords = "genetic algorithms, genetic programming, position control, asymptotic stability, control system synthesis, Lyapunov methods, robust control, nonlinear control systems, feedback, first-order dynamical systems, tracking control problems, nominal controllers, saturation bounds, constant bound value, control variable, sector nonlinear functions, traditional proportional controller, Spatial variables control, Nonlinear control systems, Stability in control theory, Control system analysis and synthesis methods", publisher = "The Institution of Engineering and Technology", ISSN = "1751-8644", URL = "https://www.human-competitive.org/sites/default/files/meza-sanchez_0.txt", URL = "https://www.human-competitive.org/sites/default/files/family_of_controllers_based_on_sector_nonlinear_functions-an_application_for_first_order_dynamical_systems_1.pdf", URL = "https://digital-library.theiet.org/content/journals/10.1049/iet-cta.2019.0680", DOI = "doi:10.1049/iet-cta.2019.0680", size = "6 pages", abstract = "This study proposes the design of a family of controllers based on sector non-linear functions for first-order dynamical systems. Three new controllers that incorporate these types of functions are presented and analysed to validate the authors' premise. The proposed nominal controllers and an augmented version with integral action are presented. Asymptotic stability is proven under the Lyapunov theory and the controllers performance is compared against a traditional proportional controller. An empirically tuned relation depending on a constant bound value and an operation range is proposed; this is used to compute the gains of each controller. Simulation results with all of the controllers under saturation bounds are presented to illustrate the effectiveness of the method at solving the output regulation and the tracking control problems, under practical physical assumptions. The numerical comparison uses the L2 and Linfinity norms over the output error, and over the control variable, applying the same saturation bounds for each controller.", notes = "2020 HUMIES finalist. Departamento de Estudios de Posgrado e Investigacion, CONACYT-TecNM/I.T. Ensenada, Baja California, Mexico", } @Article{Meza-Sanchez:2023:GPEM, author = "Marlen Meza-Sanchez and M. C. Rodriguez-Linan and Eddie Clemente and Leonardo Herrera", title = "Evolutionary design of swing-up controllers for stabilization task of underactuated inverted pendulums", journal = "Genetic Programming and Evolvable Machines", year = "2023", volume = "24", pages = "Article number: 9", month = dec, note = "Online first", keywords = "genetic algorithms, genetic programming, Analytic behaviours, Swing-up control, Under-actuated systems, Stabiliaation", ISSN = "1389-2576", URL = "https://rdcu.be/dhxHk", DOI = "doi:10.1007/s10710-023-09457-z", size = "25 pages", abstract = "The development of control laws for underactuated mechanical systems with pendulum-like behaviors is of paramount importance due to their use in the modeling of more complex systems and other challenging tasks. The underactuated feature describes constraints in the maneuverability and capabilities of a mechanical system with the advantage of offering less energy consumption. In this work, a novel methodology for solving the automation of evolved nonlinear controllers for the swing-up phase of switching control laws for underactuated inverted pendulums is proposed. Automatic synthesis of linear controllers with optimal performance applied to linear systems modeled as transfer functions is a forward leap proposed by Koza in 2003. Our proposed approach introduces the nonlinear nature within the automated construction of a set of swing-up controllers integrating an evolutionary process based on Genetic Programming (GP). The presented framework is based on an analytic behaviorist setup th", notes = "Department of Industrial Engineering, Tecnologico Nacional de Mexico/Instituto Tecnologico de Tijuana, Calzada Del Tecnologico S/N, Fracc. Tomas Aquino, 22414, Tijuana, BC, Mexico", } @Article{Mezher:2011:IJCIE, author = "Mohd A. Mezher and Maysam F. Abbod", title = "Genetic Folding: Analyzing the {Mercer's} Kernels Effect in Support Vector Machine using Genetic Folding", journal = "International Journal of Computer and Information Engineering", year = "2011", volume = "5", number = "3", pages = "347--352", keywords = "genetic algorithms, genetic programming, Genetic Folding, GF, Evolutionary Algorithms, Support Vector Machine, Multi-Classification, Mercer Rules", publisher = "World Academy of Science, Engineering and Technology", index = "Open Science Index 51, 2011", ISSN = "1307-6892", URL = "https://publications.waset.org/vol/51", bibsource = "https://publications.waset.org/", URL = "https://publications.waset.org/pdf/5807", URL = "https://publications.waset.org/5807/pdf", size = "6 pages", abstract = "Genetic Folding (GF) a new class of EA named as is introduced for the first time. It is based on chromosomes composed of floating genes structurally organized in a parent form and separated by dots. Although, the genotype/phenotype system of GF generates a kernel expression, which is the objective function of superior classifier. In this work the question of the satisfying mapping-s rules in evolving populations is addressed by analyzing populations undergoing either Mercer-s or none Mercer-s rule. The results presented here show that populations undergoing Mercer rules improve practically models selection of Support Vector Machine (SVM). The experiment is trained multi-classification problem and tested on nonlinear Ionosphere dataset. The target of this paper is to answer the question of evolving Mercer-s rule in SVM addressed using either genetic folding satisfied kernel-s rules or not applied to complicated domains and problems.", notes = "Also known as \cite{(Open Science Index):https://publications.waset.org/pdf/5807}", } @Article{mhaya:2022:Materials, author = "Akram M. Mhaya and Hassan Amer Algaifi and Shahiron Shahidan and Sharifah Salwa Mohd Zuki and Mohamad Azim Mohammad Azmi and Mohd Haziman Wan Ibrahim and Ghasan Fahim Huseien", title = "Systematic Evaluation of Permeability of Concrete Incorporating Coconut Shell as Replacement of Fine Aggregate", journal = "Materials", year = "2022", volume = "15", number = "22", pages = "Article No. 7944", keywords = "genetic algorithms, genetic programming, gene expression programming", ISSN = "1996-1944", URL = "https://www.mdpi.com/1996-1944/15/22/7944", DOI = "doi:10.3390/ma15227944", abstract = "The concern about coconut shell disposal and natural fine aggregate depletion has prompted researchers to use coconut shell as aggregate in recent years. However, the majority of the present literature has focused on using coconut shell as a coarse aggregate replacement in concrete via the traditional method. In this study, concrete incorporating coconut shell as a fine aggregate replacement (10–100percent) was evaluated using permeability and water absorption tests in a systematic way. The response surface methodology (RSM) was first used to design the experimental works. In addition, an artificial neural network (ANN) and genetic expression programming (GEP) were also taken into account to mathematically predict the permeability and water absorption. Based on both experimental and theoretical modelling, three scenarios were observed. In the first scenario, high quality concrete was achieved when the replacement percentage of sand by coconut shell ranged from 0percent to 10percent. This is because both the permeability and water absorption were less than 1.5 × 10−11 m and 5percent, respectively. In the second scenario, an acceptable and reasonable low permeability (less than 2.7 × 10−11 m/s) and water absorption (less than 6.7percent) were also obtained when the replacement percentage increased up to 60percent. In contrast, the high content coconut shell, such as 90percent and 100percent, developed concrete with a high permeability and water absorption and was defined in the third scenario. It was also inferred that both the experimental and mathematical models (ANN, GEP, and RSM) have consistent and accurate results. The correlation statistics indicators (R2) were greater than 0.94 and the error was less than 0.3, indicating a strong correlation and minimum error. In conclusion, coconut shell could act as a good alternative material to produce cleaner concrete with an optimum value of 50percent as a fine aggregate replacement.", notes = "also known as \cite{ma15227944}", } @InProceedings{mi:2018:ICONIP, author = "Zeyu Mi and Lin Shang and Bing Xue", title = "{Multi-Dimensional} Optical Flow Embedded Genetic Programming for Anomaly Detection in Crowded Scenes", booktitle = "Proceedings of the 25th International Conference on Neural Information Processing, ICONIP 2018", year = "2018", editor = "Long Cheng and Andrew Chi Sing Leung and Seiichi Ozawa", volume = "11301", series = "LNCS", address = "Siem Reap, Cambodia", month = dec # " 13-16", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-030-04179-3_43", DOI = "doi:10.1007/978-3-030-04179-3_43", } @InProceedings{miagkikh:1999:AASCOPUPRLA, author = "Victor V. Miagkikh and William F. {Punch III}", title = "An Approach to Solving Combinatorial Optimization Problems Using a Population of Reinforcement Learning Agents", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1358--1365", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "artificial life, adaptive behavior and agents", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-032.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-032.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Miao:2024:CASE, author = "Zhiwen Miao and Chengran Lin", title = "Long Short Term Memory Autoencoder-aided Evolutionary Algorithm to Solve an Energy-Minimized Task Scheduling Problem", booktitle = "2024 IEEE 20th International Conference on Automation Science and Engineering (CASE)", year = "2024", pages = "3083--3088", month = aug, keywords = "genetic algorithms, genetic programming, Schedules, Sequential analysis, Processor scheduling, Evolutionary computation, Numerical simulation, Scheduling, Numerical models, Resource management, Long short term memory, Radio spectrum management", ISSN = "2161-8089", DOI = "doi:10.1109/CASE59546.2024.10711571", abstract = "This paper addresses a task scheduling problem with deadline constraints in a human-cyber-physical system, which contains three subproblems, i.e., allocating processer, and determining tasks' sequence and frequency. To efficiently find its energy-efficient solutions in a short time, an autoencoder-aided evolutionary algorithm is proposed. The main optimiser chosen for it is genetic programming. To extract the implicit relationship among three strongly-coupled subproblems, a novel long short term memory autoencoder is built. In it, a group of long short term memory units are used to learn major features of decision variables and generate a low-dimensional hidden representation of a solution. After that, some network-aided mutation operators are designed to generate offsprings in the resulting low-dimensional space with informative features. Numerical experiments comparing the proposed method with several competitive methods verify the effectiveness of the proposed method in finding high-quality schedules in a reasonable time.", notes = "Also known as \cite{10711571} College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China", } @TechReport{miccio:1995:pGPiBDD, author = "Christian Miccio and Eduardo Sanchez and Marco Tomassini", title = "Parallel Genetic Programming Induction of Binary Decision Diagrams", institution = "Ecole Polytechnique Federal de Lausanne, EPFL", year = "1995", number = "7", address = "Switzerland", month = nov, keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/miccio_1995_pGPiBDD.pdf", URL = "http://lslwww.epfl.ch/pages/publications/papers9195/home.html", size = "8 pages", abstract = "Genetic programming is a new technique for machine learning, program induction and optimization loosely based on an evolutionary paradigm. Genetic programming is easily amenable to parallel computing which help relieve the intrinsic slowness of the approach. We describe a parallel implementation of genetic programming on the T3D computer. We apply the system to a problem of induction of binary decision diagrams used in logical circuit design. It is shown that the results depend in a critical way on the representation of the decision diagrams and that the parallel implementation is able to find the correct solution with less computational effort than the sequential version.", notes = "miccio_1995_pGPiBDD.pdf appears to be copy of web page", } @InBook{michalewicz:1996:GADSEP.13, author = "Zbigniew Michalewicz", title = "Evolutionary Programming and Genetic Programming", booktitle = "Genetic Algorithms + Data Structures = Evolution Programs", year = "1996", chapter = "13", pages = "283--287", publisher = "Springer", keywords = "genetic algorithms, genetic programming, EP", URL = "http://link.springer.com/chapter/10.1007/978-3-662-03315-9_14", DOI = "doi:10.1007/978-3-662-03315-9_14", abstract = "In this chapter we review briefly two powerful evolutionary techniques; these are evolutionary programming (section 13.1) and genetic programming (section 13.2). These two techniques were developed a quarter of a century apart from each other; they aimed at different problems; they use different chromosomal representations for individuals in the population, and they put emphasis on different operators. Yet, they are very similar from our perspective of evolution programs: for particular tasks they aim at, they use specialized data structures (finite state machines and tree-structured computer programs) and specialised genetic operators. Also, both methods must control the complexity of the structure (some measure of the complexity of a finite state machine or a tree might be incorporated in the evaluation function). We discuss them in turn.", } @Article{MICHELL:2020:ASC, author = "Kevin Michell and Werner Kristjanpoller", title = "Strongly-typed genetic programming and fuzzy inference system: An embedded approach to model and generate trading rules", journal = "Applied Soft Computing", year = "2020", volume = "90", pages = "106169", month = may, keywords = "genetic algorithms, genetic programming, Strongly typed genetic programming, Stock market predictions, Recommendation system, Trading rules, Fuzzy inference system", ISSN = "1568-4946", URL = "http://www.sciencedirect.com/science/article/pii/S1568494620301095", DOI = "doi:10.1016/j.asoc.2020.106169", abstract = "Generating trading signals is an interesting topic and a hard problem to solve. This work uses fuzzy inference system (FIS) and strongly typed genetic programming (STGP) to generate trading rules for the US stock market, a framework that we call FISTGP. The two embedded models have not been widely evaluated in financial applications, and according to the literature, their combination could improve forecasting performance. The fitness function used to train the STGP model is based on accuracy, optimizing the buy and sell signals, taking a different approach to the classic optimization of return-risk ratio. The rules are generated in a FIS framework, and the final signal depends on the amount of information that the investor relies on. The model is suited to each investor as a recommendation of when to change portfolio composition according to his or her particular criteria. Ternary rules are generated based on an economic interpretation, considering the risk-free rate as a part of more demanding rules. The model is applied to 90 of the most traded and active stocks in the US stock market. This approach generates important recommendations and delivers useful information to investors. The results show that the proposed model outperforms the Buy and Hold (B&H) strategy by 28.62percent in the test period, considering excesses of return, with almost the same risk (1.28percent higher). The other base models underperform in comparison to the B&H, with the proposed model also outperforming them", notes = "Also known as \cite{MICHELL2020106169}", } @Article{DBLP:journals/soco/VK20, author = "Kevin Michell and Werner Kristjanpoller", title = "Generating trading rules on {US} Stock Market using strongly typed genetic programming", journal = "Soft Computing", volume = "24", number = "5", pages = "3257--3274", year = "2020", month = mar, keywords = "genetic algorithms, genetic programming, STGP, Strongly typed genetic programming, Rule generation, Stock market, Evolutionary computation, Portfolio composition", timestamp = "Thu, 13 Feb 2020 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/soco/VK20.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://doi.org/10.1007/s00500-019-04085-1", DOI = "doi:10.1007/s00500-019-04085-1", size = "18 pages", abstract = "Extracting rules from stock market data is an important and exciting problem, where investment decisions should be as clear and intuitive as possible in order for investors to choose the composition of their portfolios. Thus, it is important to guarantee that this process is done with a good framework and reliable techniques. In this context, portfolio composition is a puzzle with respect to selecting the appropriate assets and the optimal timing to invest. There are several models and algorithms to make these decisions, and in recent years, machine learning applications have been used to solve this puzzle with exceptional results. This technique allows a large amount of data to be processed, resulting in more informed recommendations on which asset to choose. Our study uses strongly typed genetic programming to generate rules to buy, hold and sell stocks in the US stock market, considering a rolling windows approach. We propose a different training approach, focusing the fitness function on a ternary decision based on the return prediction of each stock analyzed. The ternary rule matches perfectly with the three decisions: buy, hold and sell. Therefore, the rules are simple, intuitive, and easy for investors to understand. The results show that the proposed algorithm generates higher profits than the classical optimization approach. Moreover, the profits obtained are higher than the buy-and-hold strategy and the return of the indexes representative of the US stock market.", notes = "https://www.jpmorgan.com/global/research/machine-learning Universidad Tecnica Federico Santa Maria, Avenida Espana 1680, Valparaiso, Chile", } @InProceedings{Miconi:Avc:cec2005, author = "Thomas Miconi and Alastair Channon", title = "A virtual creatures model for studies in artificial evolution", booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary Computation", year = "2005", editor = "David Corne and Zbigniew Michalewicz and Bob McKay and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Gunther Raidl and Kay Chen Tan and Ali Zalzala", pages = "565--572", address = "Edinburgh, Scotland, UK", month = "2-5 " # sep, publisher = "IEEE Press", volume = "1", keywords = "genetic algorithms, genetic programming, ANN", ISBN = "0-7803-9363-5", URL = "http://www.channon.net/alastair/papers/cec2005.pdf", URL = "https://ieeexplore.ieee.org/document/1554733", URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=10417&isvol=1", URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=10417", DOI = "doi:10.1109/CEC.2005.1554733", size = "8 pages", abstract = "We present the results of our replication of Karl Sims' work on the evolution of artificial creatures in a physically realistic 3D environment. We used standard McCulloch-Pitts neurons instead of a more complex set of ad hoc neurons, which we believe makes our model a more general tool for future experiments in artificial (co-)evolution. We provide a detailed description of our model and freely accessible source code. We describe our results both qualitatively and quantitatively, including an analysis of some evolved neural controllers. To the best of our knowledge, our work is the first replication of Sims' efforts to achieve results comparable to Sims' in efficiency and complexity, with standard neurons and realistic Newtonian physics.", notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS.", } @InProceedings{DBLP:conf/ae/MiconiC05, author = "Thomas Miconi and Alastair Channon", title = "Analysing Co-evolution Among Artificial {3D} Creatures", booktitle = "7th International Conference and Evolution Artificielle, EA 2005", year = "2005", pages = "167--178", editor = "El-Ghazali Talbi and Pierre Liardet and Pierre Collet and Evelyne Lutton and Marc Schoenauer", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3871", ISBN = "3-540-33589-7", bibsource = "DBLP, http://dblp.uni-trier.de", address = "Lille, France", month = oct # " 26-28", note = "Revised Selected Papers", keywords = "genetic algorithms, genetic programming", URL = "http://www.channon.net/alastair/papers/ea05.pdf", DOI = "doi:10.1007/11740698_15", abstract = "This paper presents new accomplishments in the coevolution of neurally controlled agents, and introduces improved methods of coevolutionary analysis. The experiments reported, on the coevolution of physically simulated articulated creatures, are the first to demonstrate realistic co-adapted behaviours using general purpose neurons. The previous need for ad hoc (problem-specific) neurons was a barrier to the long-term evolution of new, emergent behaviours. Novel behaviours are identified using an improved coevolutionary analysis method that is both more informative and an order of magnitude cheaper than the original. Finally, individuals are cross-validated between evolutionary runs, in an improved procedure for evaluating global performance.", notes = "Published 2006", } @InProceedings{Miconi:2006:AL, author = "Thomas Miconi and Alastair Channon", title = "An Improved System for Artificial Creatures Evolution", booktitle = "Artificial Life X: Proceedings of the Tenth International Conference on the Simulation and Synthesis of Living Systems", year = "2006", editor = "Luis Mateus Rocha and Larry S. Yaeger and Mark A. Bedau and Dario Floreano and Robert L. Goldstone and Alessandro Vespignani", pages = "255--261", address = "Bloomington, IN, USA", publisher_address = "Cambridge, MA, USA", month = "3-7 " # jun, publisher = "MIT Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-262-68162-5", URL = "http://www.channon.net/alastair/papers/alife2006.pdf", size = "pages", notes = "http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=11018", } @InProceedings{Miconi:TNA:cec2006, author = "T. Miconi and A. Channon", title = "The N-Strikes-Out Algorithm: A Steady-State Algorithm for Coevolution", booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary Computation", year = "2006", editor = "Gary G. Yen and Simon M. Lucas and Gary Fogel and Graham Kendall and Ralf Salomon and Byoung-Tak Zhang and Carlos A. Coello Coello and Thomas Philip Runarsson", pages = "1639--1646", address = "Vancouver, BC, Canada", month = "16-21 " # jul, publisher = "IEEE Press", ISBN = "0-7803-9487-9", keywords = "genetic algorithms, genetic programming", URL = "http://www.channon.net/alastair/papers/cec2006.pdf", URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=11108", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1688505", DOI = "doi:10.1109/CEC.2006.1688505", size = "8 pages", abstract = "We introduce the N-strikes-out algorithm, a simple steady-state genetic algorithm for competitive coevolution. The algorithm can be summarised as follows: Run competitions between randomly chosen individuals, keep track of the number of defeats for each individual, and remove any individual which has been defeated N times. Naive application of the algorithm in 2-population problems leads to severe disengagement. We find that disengagement can be eliminated (for all tasks involving real-valued continuous scores) by determining victories and defeats between fellow members of the same species, using competitions against a single member of the opposing species as a point of comparison. We apply our algorithm to the box-grabbing problem for artificial 3D creatures introduced by Sims. We compare our algorithm with Sims' original Last Elite Opponent algorithm, and describe (and explain) different results obtained with two different implementations differing mainly by the harshness of their selection regimes", notes = "Also known as \cite{1688505}", } @InProceedings{conf/eurogp/Miconi08, title = "In Silicon No One Can Hear You Scream: Evolving Fighting Creatures", author = "Thomas Miconi", bibdate = "2008-04-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/eurogp/eurogp2008.html#Miconi08", booktitle = "Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008", address = "Naples", month = "26-28 " # mar, publisher = "Springer", year = "2008", volume = "4971", editor = "Michael O'Neill and Leonardo Vanneschi and Steven Gustafson and Anna Isabel {Esparcia Alcazar} and Ivanoe {De Falco} and Antonio {Della Cioppa} and Ernesto Tarantino", isbn13 = "978-3-540-78670-2", pages = "25--36", series = "Lecture Notes in Computer Science", DOI = "doi:10.1007/978-3-540-78671-9_3", keywords = "genetic algorithms, genetic programming", notes = "Part of \cite{conf/eurogp/2008} EuroGP'2008 held in conjunction with EvoCOP2008, EvoBIO2008 and EvoWorkshops2008", } @PhdThesis{Miconi08PhD, author = "Thomas Miconi", title = "The road to everywhere: Evolution, complexity and progress in natural and artificial systems", school = "School of Computer Science, University of Birmingham", year = "2008", address = "UK", month = aug, keywords = "genetic algorithms", URL = "http://etheses.bham.ac.uk/148/", URL = "http://etheses.bham.ac.uk/148/1/Miconi08PhD.pdf", size = "214 pages", abstract = "Evolution is notorious for its creative power, but also for giving rise to complex, unpredictable dynamics. As a result, practitioners of artificial evolution have encountered difficulties in predicting, analysing, or even understanding the outcome of their experiments. In particular, the concept of evolutionary {"}progress{"} (whether in the sense of performance increase or complexity growth) has given rise to much debate and confusion. After a careful description of the mechanisms of evolution and natural selection, we provide usable concepts of performance and progress in coevolution. In particular, we introduce a distinction between three types of progress: local, historical, and global, which we suggest underlies much of the confusion that surrounds coevolutionary dynamics. Similarly, we provide a comprehensive answer to the question of whether an 'arrow of complexity' exists in evolution. We introduce several methods to detect and analyse performance and progress in coevolutionary experiments. We propose a statistical measure (Fitness Transmission) to detect the presence of adaptive Darwinian evolution in a reproducing population, based solely on genealogic records; we also point out the limitations of a popular method (the Bedau-Packard statistics of evolutionary activity) for this purpose. To test and illustrate our results, we implement a rich experimental system, inspired by the seminal work of Karl Sims, in which virtual creatures can evolve and interact under various conditions in a physically realistic three-dimensional (3D) environment. To our knowledge, this is the first complete reimplementation and extension of Sims' results. We later extend this system with the introduction of physical combat between creatures, also a first. Finally, we introduce Evosphere, an open, planet-like environment in which 3D artificial creatures interact, reproduce and evolve freely. We conclude our discussion by using Fitness Transmission to detect the onset of adaptive evolution in this system.", notes = "Brief mention of GP Supervisor(s): Channon, Alastair D.", } @InProceedings{Miconi:2009:eurogp, author = "Thomas Miconi", title = "Why coevolution doesn't {"}work{"}: superiority and progress in coevolution", booktitle = "Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009", year = "2009", editor = "Leonardo Vanneschi and Steven Gustafson and Alberto Moraglio and Ivanoe {De Falco} and Marc Ebner", volume = "5481", series = "LNCS", pages = "49--60", address = "Tuebingen", month = apr # " 15-17", organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-01180-1", URL = "http://www.cs.bham.ac.uk/~txm/eurogp09.pdf", DOI = "doi:10.1007/978-3-642-01181-8_5", size = "12 pages", abstract = "Coevolution often gives rise to counter-intuitive dynamics that defy our expectations. Here we suggest that much of the confusion surrounding co-evolution results from imprecise notions of superiority and progress. In particular, we note that in the literature, three distinct notions of progress are implicitly lumped together: local progress (superior performance against current opponents), historical progress (superior performance against previous opponents) and global progress (superior performance against the entire opponent space). As a result, valid conditions for one type of progress are unduly assumed to lead to another. In particular, the confusion between historical and global progress is a case of a common error, namely using the training set as a test set. This error is prevalent among standard methods for coevolutionary analysis (CIAO, Master Tournament, Dominance Tournament, etc.) By clearly defining and distinguishing between different types of progress, we identify limitations with existing techniques and algorithms, address them, and generally facilitate discussion and understanding of co-evolution. We conclude that the concepts proposed in this paper correspond to important aspects of the coevolutionary process.", notes = "overfitting, hall of fame Part of \cite{conf/eurogp/2009} EuroGP'2009 held in conjunction with EvoCOP2009, EvoBIO2009 and EvoWorkshops2009", } @InCollection{mided:2002:MLPRCA, author = "Zachary Mided", title = "Machine Learning and Pattern Recognition using Cellular Automata", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2002", year = "2002", editor = "John R. Koza", pages = "148--157", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:2002:gagp}", } @InProceedings{Ingo_Mierswa:gecco05ws, author = "Ingo Mierswa and Katharina Morik", title = "Method Trees: Building Blocks for Self-Organizable Representations of Value Series", booktitle = "Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program", year = "2005", month = "25-29 " # jun, editor = "Franz Rothlauf and Misty Blowers and J{\"u}rgen Branke and Stefano Cagnoni and Ivan I. Garibay and Ozlem Garibay and J{\"o}rn Grahl and Gregory Hornby and Edwin D. {de Jong} and Tim Kovacs and Sanjeev Kumar and Claudio F. Lima and Xavier Llor{\`a} and Fernando Lobo and Laurence D. Merkle and Julian Miller and Jason H. Moore and Michael O'Neill and Martin Pelikan and Terry P. Riopka and Marylyn D. Ritchie and Kumara Sastry and Stephen L. Smith and Hal Stringer and Keiki Takadama and Marc Toussaint and Stephen C. Upton and Alden H. Wright", publisher = "ACM Press", address = "Washington, D.C., USA", keywords = "genetic algorithms, genetic programming", pages = "293--300", URL = "http://gpbib.cs.ucl.ac.uk/gecco2005wks/papers/0293.pdf", abstract = "We introduce a framework for automatic feature extraction from very large series. The extracted features build a new representation which is better suitable for a given learning task. The development of appropriate feature extraction methods is a tedious effort, particularly because every new classification task requires tailoring the feature set anew. Therefore, the simple building blocks defined in our framework can be combined to complex feature extraction methods. We employ a genetic programming approach guided by the performance of the learning classifier using the new representation. Our approach to evolve representations from series data requires a balance between the completeness of the methods on one side and the tractability of searching for appropriate methods on the other side. Some theoretical considerations illustrate the trade-off. After the feature extraction, a second process learns a classifier from the transformed data. The practical use of the methods is shown by two types of experiments in the domain of music data classification: classification of genres and classification according to user preferences.", notes = "Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006", } @PhdThesis{dissertation_mierswa, author = "Ingo Mierswa", title = "Non-convex and multi-objective optimization in data mining", title2 = "Non-Convex and Multi-Objective Optimization for Statistical Learning and Numerical Feature Engineering", school = "Fakultaet fuer Informatik LS 08 Kuenstliche Intelligenz, Technischen Universitaet Dortmund", year = "2009", address = "Germany", month = "27 " # apr, keywords = "genetic algorithms, genetic programming, Data mining, Multi-objective optimization, Non-convex optimization", URL = "https://eldorado.tu-dortmund.de/bitstream/2003/26104/1/dissertation_mierswa.pdf", URL = "https://eldorado.tu-dortmund.de/handle/2003/26104", URL = "http://hdl.handle.net/2003/26104", DOI = "doi:10.17877/DE290R-12761", size = "264 pages", notes = "Supervisor Prof. Dr. Katharina Morik In English", } @InProceedings{Miikkulainen:2019:GECCOcomp, author = "Risto Miikkulainen", title = "Evolution of neural networks", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", note = "Tutorial", isbn13 = "978-1-4503-6748-6", pages = "694--709", address = "Prague, Czech Republic", DOI = "doi:10.1145/3319619.3323380", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{3323380} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InProceedings{Miikkulainen:2020:GECCOcomp, author = "Risto Miikkulainen", title = "Evolution of Neural Networks", year = "2020", editor = "Richard Allmendinger and Hugo Terashima Marin and Efren Mezura Montes and Thomas Bartz-Beielstein and Bogdan Filipic and Ke Tang and David Howard and Emma Hart and Gusz Eiben and Tome Eftimov and William {La Cava} and Boris Naujoks and Pietro Oliveto and Vanessa Volz and Thomas Weise and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Rui Wang and Ran Cheng and Guohua Wu and Miqing Li and Hisao Ishibuchi and Jonathan Fieldsend and Ozgur Akman and Khulood Alyahya and Juergen Branke and John R. Woodward and Daniel R. Tauritz and Marco Baioletti and Josu Ceberio Uribe and John McCall and Alfredo Milani and Stefan Wagner and Michael Affenzeller and Bradley Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and Nayat Sanchez-Pi and Luis Marti and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and Matthew Johns and Nick Ross and Ed Keedwell and Herman Mahmoud and David Walker and Anthony Stein and Masaya Nakata and David Paetzel and Neil Vaughan and Stephen Smith and Stefano Cagnoni and Robert M. Patton and Ivanoe {De Falco} and Antonio {Della Cioppa} and Umberto Scafuri and Ernesto Tarantino and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Erik Hemberg and Riyad Alshammari and Tokunbo Makanju and Fuijimino-shi and Ivan Zelinka and Swagatam Das and Ponnuthurai Nagaratnam and Roman Senkerik", isbn13 = "9781450371278", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377929.3389858", DOI = "doi:10.1145/3377929.3389858", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion", pages = "495--525", size = "31 pages", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", note = "Tutorial", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{10.1145/3377929.3389858} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @Article{miikkulainen:2021:nmi, author = "Risto Miikkulainen and Stephanie Forrest", title = "A biological perspective on evolutionary computation", journal = "Nature machine intelligence", year = "2021", volume = "3", pages = "9--15", month = "18 " # jan, keywords = "genetic algorithms, genetic programming, Computational science, Evolutionary theory", ISSN = "2522-5839", URL = "https://rdcu.be/clFHY", URL = "https://www.nature.com/articles/s42256-020-00278-8.pdf", DOI = "doi:10.1038/s42256-020-00278-8", size = "7 pages", abstract = "Evolutionary computation is inspired by the mechanisms of biological evolution. With algorithmic improvements and increasing computing resources, evolutionary computation has discovered creative and innovative solutions to challenging practical problems. This paper evaluates how today's evolutionary computation compares to biological evolution and how it may fall short. A small number of well-accepted characteristics of biological evolution are considered: openendedness, major transitions in organisational structure, neutrality and genetic drift, multiobjectivity, complex genotype-to-phenotype mappings and co-evolution. Evolutionary computation exhibits many of these to some extent but more can be achieved by scaling up with available computing and by emulating biology more carefully. In particular, evolutionary computation diverges from biological evolution in three key respects: it is based on small populations and strong selection; it typically uses direct genotype to phenotype mappings; and it does not achieve major organizational transitions. These shortcomings suggest a roadmap for future evolutionary computation research, and point to gaps in our understanding of how biology discovers major transitions. Advances in these areas can lead to evolutionary computation that approaches the complexity and flexibility of biology, and can serve as an executable model of biological processes.", notes = "Perspectives Mentioned in Forrest GI @ ICSE 2021 keynote https://www.youtube.com/watch?v=ckM3PXs6hK8&list=PLI8fiFpB7BoKDaxvS7SQp0iA7fN7rrvDD broken Aug 2021 http://www.nature.com/natmachintel", } @InProceedings{1277304, author = "Mitsunori Miki and Masafumi Hashimoto and Yoshihisa Fujita", title = "Program search with simulated annealing", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "1754--1754", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1754.pdf", DOI = "doi:10.1145/1276958.1277304", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming: Poster, bloat, fixed temperatures, program search, simulated annealing, syntactic introns", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @Article{Mikus:2025:adhoc, author = "M. Mikus and Ja. Konecny and P. Kromer and K. Bancik and Ji. Konecny and J. Choutka and M. Prauzek", title = "Analysis of the computational costs of an evolutionary fuzzy rule-based internet-of-things energy management approach", journal = "Ad Hoc Networks", year = "2025", volume = "168", pages = "103715", keywords = "genetic algorithms, genetic programming, Evolutionary fuzzy rules, Energy management, Computational cost analysis, IoT wireless sensor node, Low-power hardware optimization, Machine learning integration", ISSN = "1570-8705", URL = "https://www.sciencedirect.com/science/article/pii/S1570870524003263", DOI = "doi:10.1016/j.adhoc.2024.103715", abstract = "This study presents an in-depth analysis of the computational costs associated with the application of an Evolutionary Fuzzy Rule-based (EFR) energy management system for Internet of Things (IoT) devices. In energy-harvesting IoT nodes, energy management is critical for sustaining long-term operation. The proposed EFR approach integrates fuzzy logic and genetic programming to autonomously control energy consumption based on available resources. The study evaluates the system's computational performance, particularly focusing on processing time, RAM and flash memory usage across various hardware configurations. Different compiler optimisation levels and floating-point unit (FPU) settings were also explored, comparing standard and pre-compiled algorithms. The results reveal computational times ranging from 2.43 to 5.23 ms, RAM usage peaking at 6.23 kB, and flash memory consumption between 19 kB and 32 kB. A significant reduction in computational overhead is achieved with optimised compiler settings and hardware FPU, highlighting the feasibility of deploying EFR-based energy management systems in low-power, resource-constrained IoT environments. The findings demonstrate the trade-offs between computational efficiency and energy management, with particular benefits observed in scenarios requiring real-time control in remote and energy-limited environments", } @InProceedings{Milano:2023:MetroAutomotive, author = "F. Milano and G. {Di Capua} and N. Oliva and F. Porpora and C. Bourelly and L. Ferrigno and M. Laracca", booktitle = "2023 IEEE International Workshop on Metrology for Automotive (MetroAutomotive)", title = "An Analytical Model for Lithium-Ion Batteries Based on Genetic Programming Approach", year = "2023", month = jun, pages = "35--40", keywords = "genetic algorithms, genetic programming, Analytical models, Voltage, Mathematical models, Batteries, Behavioural sciences, Titanium compounds, Batteries, Modelling, Multi-Objective Optimisation", DOI = "doi:10.1109/MetroAutomotive57488.2023.10219104", abstract = "a novel approach based on a Genetic Programming (GP) algorithm is proposed to develop behavioural models for Lithium batteries. In particular, this approach is herein adopted to analytically correlate the battery terminal voltage to its State of Charge (SoC) and Charge rate (C-rate) for discharging current profiles. The GP discovers the best possible analytical models, from which the optimal one is selected by weighing several criteria and enforcing a trade-off between the accuracy and the simplicity of the obtained mathematical function. The proposed models can be considered an extension of the behavioural models that are already in use, such as those based on equivalent electrical circuits. This GP approach can overcome some current limitations, such as the high time required to perform experimental tests to estimate the parameters of an equivalent electrical model (particularly effective since it must be repeated with the battery aging) and the need for some a-priory knowledge for the model estimation. In this paper, a Lithium Titanate Oxide battery has been considered as a case study, analysing its behaviour for SoC comprised between 5percent and 95percent and C-rate between 0.25C and 4.0C. a preliminary study on GP-based modelling, in which the best behavioural model is identified and tested, with performances that encourage further investigation of this kind of evolutionary approaches by testing them with experimental characterisation data.", notes = "Also known as \cite{10219104}", } @Misc{Milano:2018:arxiv, author = "Nicola Milano and Stefano Nolfi", title = "Scaling Up Cartesian Genetic Programming through Preferential Selection of Larger Solutions", howpublished = "arXiv", year = "2018", month = "22 " # oct, keywords = "genetic algorithms, genetic programming, cartesian genetic programming", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/corr/corr1810.html#abs-1810-09485", URL = "http://arxiv.org/abs/1810.09485", size = "14 pages", abstract = "We demonstrate how efficiency of Cartesian Genetic Programming method can be scaled up through the preferential selection of phenotypically larger solutions, i.e. through the preferential selection of larger solutions among equally good solutions. The advantage of the preferential selection of larger solutions is validated on the six, seven and eight-bit parity problems, on a dynamically varying problem involving the classification of binary patterns, and on the Paige regression problem. In all cases, the preferential selection of larger solutions provides an advantage in term of the performance of the evolved solutions and in term of speed, the number of evaluations required to evolve optimal or high-quality solutions. The advantage provided by the preferential selection of larger solutions can be further extended by self-adapting the mutation rate through the one-fifth success rule. Finally, for problems like the Paige regression in which neutrality plays a minor role, the advantage of the preferential selection of larger solutions can be extended by preferring larger solutions also among quasi-neutral alternative candidate solutions, i.e. solutions achieving slightly different performance.", notes = "p12 'Finally, for problems like the Paige regression in which neutrality plays a minor role, the advantage of the preferential selection of larger solutions can be further extended by preferring larger solutions also among quasi-neutral alternative candidate solutions, i.e. also among solutions achieving similar performance.'", } @Article{milano:2021:EI, author = "Nicola Milano and Stefano Nolfi", title = "Enhancing Cartesian genetic programming through preferential selection of larger solutions", journal = "Evolutionary Intelligence", year = "2021", volume = "14", number = "4", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", URL = "http://link.springer.com/article/10.1007/s12065-020-00421-9", DOI = "doi:10.1007/s12065-020-00421-9", } @PhdThesis{Milea:thesis, author = "Viorel Milea", title = "News Analytics for Financial Decision Support", school = "Erasmus University Rotterdam", year = "2013", address = "Holland", month = "7 " # feb, keywords = "genetic algorithms, genetic programming, decision support, finance, news analytics, semantic business information systems, semantic web", isbn13 = "978-90-5892-321-9", URL = "https://www.erim.eur.nl/doctoral-programme/phd-in-management/phd-projects/detail/469-news-analytics-for-financial-decision-support/", URL = "http://hdl.handle.net/1765/38673", URL = "https://repub.eur.nl/pub/38673", URL = "https://repub.eur.nl/pub/38673/EPS2013275LIS9789058923219.pdf", size = "204 pages", abstract = "This PhD thesis contributes to the newly emerged, growing body of scientific work on the use of News Analytics in Finance. Regarded as the next significant development in Automated Trading, News Analytics extends trading algorithms to incorporate information extracted from textual messages, by translating it into actionable, valuable knowledge. The thesis addresses one main theme: the incorporation of news into trading algorithms. This relates to three main tasks: i) the extraction of the information contained in news, ii) the representation of the information contained in news, and iii) the aggregation of this information into actionable knowledge. We validate our approach by designing and implementing three semantic systems: a system for the computational content analysis of European Central Bank statements, a system for incorporating news in stock trading strategies, and a time-aware system for trading based on analyst recommendations. The approach we choose for addressing these tasks is an interdisciplinary one. For the extraction of information from news we rely on approaches borrowed from Computer Science and Linguistics. The representation of the information contained in news is realized by using, and extending, the state-of-the-art in Semantic Web technology. We do this by bringing together insights from Logics, Metaphysics, and Computational Semantics. The aggregation of information is done by using techniques and results from Computational Intelligence and Finance", notes = "Kwantitatieve nieuwsanalyse voor financiele besluitvorming SIKS Dissertation Series No. 2013-01 https://siks.nl/ Supervisor Uzay Kaymak", } @PhdThesis{Miles:thesis, author = "Stanley Miles", title = "Adaptive efficiency of futures and stock markets : analysis and tests using a genetic programming", school = "York University", year = "2006", address = "TORONTO, ONTARIO, Canada", month = mar, keywords = "genetic algorithms, genetic programming", isbn13 = "978-0-494-19800-1", URL = "http://search.proquest.com/docview/304985250/F090C35B735040DEPQ/1?accountid=14511", URL = "https://www.library.yorku.ca/find/Search/Results?lookfor0[]=Miles&lookfor0[]=&type0[]=Author&join1=AND&lookfor1[]=Genetic+Programming&lookfor1[]=&type1[]=AllFields&mylang=en", size = "328 pages", abstract = "We propose a nonparametric method for finding approximate solutions to dynamic portfolio choice models: the use of genetic programming to directly estimate the optimal trading strategy. After we validate our methodology by conducting a simulation exercise to demonstrate that genetic programming can recover the true analytic solution to two models, we apply it to the path-dependent problem of a futures investor who is subject to initial and maintenance margin constraints, a problem that is difficult to solve using analytic methods. The resulting approximate solution in functional form can be used to complement the Monte Carlo numerical solution to this problem. We proceed to evaluate the performance of our nonparametric approach in the presence of estimation risk and model risk. We apply the algorithm to evolve trading strategies for 10 futures markets and 24 stock markets. We extend the results of recent studies that tested the efficient market hypothesis; these studies investigated whether market participants can find trading rules that use historical data as input that consistently produce abnormally high out-of-sample risk-adjusted returns (indicating that the markets are not efficient). Previous studies were limited to trading rules that returned simple buy/sell signals. Our approach is broader, allowing the study of trading strategies developed under a framework consistent with the standard financial economics model, with a trading strategy defined as the proportion of an investor's total wealth invested into the risky asset (that is, a strategy is a proportion rather than a simple buy/sell signal). The trading strategies evolved by our methodology demonstrate high out-of-sample risk-adjusted fitness for most futures markets, but strategies were produced for only a small fraction of periods because strategies were accepted only if they met criteria for in-sample fitness. Conversely, when our methodology was applied to the stock markets, it produced rules meeting the in-sample fitness criteria for most periods, but the rules were in general characterized by low out-of-sample risk-adjusted fitness. Because of the difficulty of evolving trading strategies that outperformed simple strategies, we conclude that the 10 futures markets and the 24 stock markets examined were adaptively efficient during the 1990's and the late 1980's.", notes = "Adaptive efficiency of futures and stock markets: Analysis and tests using a genetic programming approach NR19800", } @InCollection{Miles:2011:CMED, author = "Stan Miles and Barry Smith", title = "Can Investors Benefit from Using Trading Rules Evolved by Genetic Programming? A Test of the Adaptive Efficiency of U.S. Stock Markets with Margin Trading Allowed", booktitle = "Computational Methods in Economic Dynamics", publisher = "Springer", year = "2011", editor = "Herbert Dawid and Willi Semmler", volume = "13", series = "Dynamic Modeling and Econometrics in Economics and Finance", pages = "77--108", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-16942-7", DOI = "doi:10.1007/978-3-642-16943-4_5", abstract = "This paper employs genetic programming to develop trading rules, then uses these rules to test the efficient markets hypothesis. Unlike most similar research, the study both incorporates margin trading and returns trading rules that are more than simple buy-sell signals. Consistent with the standard portfolio model, a trading rule is defined here as the proportion of an investor's total wealth that is held in the form of stocks; because margin trading is allowed, the proportion can be greater than 1. The results show that the 24 individual stock markets studied were adaptively efficient between 1985 and 2005.", } @PhdThesis{Milev:thesis, author = "Jordan G. Milev", title = "Genetic Programming Use in Structural Modeling Applied to the Earnings-Returns Relation", school = "Yale", year = "2004", address = "USA", month = dec, keywords = "genetic algorithms, genetic programming", URL = "http://search.proquest.com/docview/305110188/AD4457348014431PQ/1?accountid=14511", size = "136 pages", abstract = "The selection of appropriate functional form for describing the relation between two economic variables has profound implications about the consistency and significance of estimated model parameters and about the predictions obtained from such a model. Until recently, nonparametric approaches have been the only solution to problems of model identification when the parametric form of the function is unknown. In the first part of the dissertation we develop an implementation of the algorithmic model selection technique of genetic programming (GP). We illustrate how it works and offer a brief comparison with nonparametric estimation methods. In the second part of the dissertation we specifically address a recent issue in the GP literature about overfitting and illustrate how it can be controlled. We also examine GP's ability to recognize a spurious regression and devise an illustrate a metric measuring the predictability of a data set using GP. In the third part of the dissertation we use GP to model how stock prices react to unanticipated accounting earnings. The result is a nonlinear parametric specification of the reaction of excess current-period stock price returns to the unexpected component of quarterly earnings. We confirm the existence of a nonlinear earnings response model that has superior in-sample and out-of-sample predictive power over the traditionally employed linear earnings regression. Our results have several implications: 1) it is important to incorporate forecast revisions in the earnings-returns specification; 2) when the earnings-returns relation is nonlinear, a nonsymmetric response to earnings announcements can be achieved even when the earnings response function itself is symmetric; 3) firms can affect the size of their earnings response coefficient by pre-announcing earnings; 4) appropriately accounting for the nonlinear form of the earnings-returns relation decreases the abnormal returns associated with earnings surprises. Our approach suggests an alternative to the linear earnings-returns relation which may provide suitable framework for future empirical work.", notes = "http://www.genealogy.ams.org/id.php?id=118746 Supervisor: Peter Charles Bonest Phillips http://korora.econ.yale.edu/phillips/info/pcbvita-0908.pdf UMI Microform 3152961", } @Article{Milfelner:2005:JMPT, author = "M. Milfelner and J. Kopac and F. Cus and U. Zuperl", title = "Genetic equation for the cutting force in ball-end milling", journal = "Journal of Materials Processing Technology", year = "2005", volume = "164-165", pages = "1554--1560", month = "15 " # may, note = "AMPT/AMME05 Part 2", keywords = "genetic algorithms, genetic programming, Cutting forces, Cutting parameters, Ball-end mill", DOI = "doi:10.1016/j.jmatprotec.2005.02.147", abstract = "The paper presents the development of the genetic equation for the cutting force for ball-end milling process. The development of the equation combines different methods and technologies like evolutionary methods, manufacturing technology, measuring and control technology and intelligent process technology with the adequate hardware and software support. Ball-end milling is a very common machining process in modern manufacturing processes. The cutting forces play the important role for the selection of the optimal cutting parameters in ball-end milling. In many cases the cutting forces in ball-end milling are calculated by equation from the analytical cutting force model. In the paper the genetic equation for the cutting forces in ball-end milling is developed with the use of the measured cutting forces and genetic programming. The experiments were made with the system for the cutting force monitoring in ball-end milling process. The obtained results show that the developed genetic equation fits very well with the experimental data. The developed genetic equation can be used for the cutting force estimation and optimisation of cutting parameters. The integration of the proposed method will lead to the reduction in production costs and production time, flexibility in machining parameter selection, and improvement of product quality.", } @InProceedings{Millan-Garcia:2021:evoapplications, author = "Laura {Millan Garcia} and Gabriel Kronberger and Jose Ignacio {Hidalgo Perez} and Ricardo {Fernandez Serrano} and Oscar Garnica and Gaspar {Gonzalez Doncel}", title = "Estimation of Grain-level Residual Stresses in a Quenched Cylindrical Sample of Aluminum Alloy {AA5083} using Genetic Programming", booktitle = "24th International Conference, EvoApplications 2021", year = "2021", month = "7-9 " # apr, editor = "Pedro Castillo and Juanlu Jimenez-Laredo", series = "LNCS", volume = "12694", publisher = "Springer Verlag", address = "virtual event", pages = "421--436", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Microscopic residual stress, Microstructure, Diffraction, Symbolic regression", isbn13 = "978-3-030-72698-0", DOI = "doi:10.1007/978-3-030-72699-7_27", abstract = "Residual stresses are originated during fabrication processes of metallic materials and their study is important to prevent catastrophic failure during service of components. There are two main types of residual stresses, depending on the length scale; macroscopic and microscopic. We present an approach using genetic programming to obtain the micro residual stresses in grains of a quenched cylindrical sample of aluminium alloy AA5083. This alloy has a micro-structure of formed grains with different orientation and stress state. To obtain the micro residual stresses of each grain we estimate the total residual stresses values for every crystallographic orientation using information from electron back-scattered and neutron diffraction experiments. This information includes orientation maps of the normal section to the cylinder axes and the particular orientation and dimensions of every grain. We assume that the micro residual stresses of each grain can be expressed as a function of these variables and use genetic programming to find this expression.", notes = "Department of Physical Metallurgy Centro Nacional de Investigaciones Metalurgicas (CENIM) C.S.I.C Madrid Spain http://www.evostar.org/2021/ EvoApplications2021 held in conjunction with EuroGP'2021, EvoCOP2021 and EvoMusArt2021", } @Article{MILLAN:2023:apples, author = "Laura Millan and Gabriel Kronberger and Ricardo Fernandez and Gizo Bokuchava and Patrice Halodova and Alberto Saez-Maderuelo and Gaspar Gonzalez-Doncel and J. Ignacio Hidalgo", title = "Prediction of microscopic residual stresses using genetic programming", journal = "Applications in Engineering Science", volume = "15", pages = "100141", year = "2023", ISSN = "2666-4968", DOI = "doi:10.1016/j.apples.2023.100141", URL = "https://www.sciencedirect.com/science/article/pii/S266649682300016X", keywords = "genetic algorithms, genetic programming, Material science, Machine learning, Symbolic regression, Residual stress, Neutron diffraction, Microstructure", abstract = "Metallurgical manufacturing processes commonly used in the industry (rolling, extrusion, shaping, machining, etc.) usually cause residual stress development which can remain after thermal heat treatments. These stresses can be detrimental for the in-service performance of structural components, which makes their study and understanding important. Residual stress variations are usually determined at a macroscopic scale (commonly, using diffraction methods). However, stress variations at the microscopic scale of the individual crystallites (grains), are also relevant. Contrary to the macroscopic residual stresses, microscopic residual stresses are difficult to quantify using conventional procedures. We propose to use machine learning to find equations that describe microscopic residual stresses. Concretely, we show that we are able to learn equations to reproduce the diffraction profiles from microstructural characteristics using genetic programming. We evaluate the learned equations using real neutron diffraction peaks as a reference, obtaining accurate results for the most frequent grain orientations with runtimes of a few minutes", } @Article{MILLANGARCIA:2023:jmrt, author = "L. Millan-Garcia and G. Bokuchava and P. Halodova and A. Saez-Maderuelo and G. Gonzalez-Doncel and J. I. Hidalgo and J. M. Velasco and R. Fernandez", title = "Using genetic programming and the stress equilibrium method to obtain the un-stressed lattice parameter for calculating residual stresses", journal = "Journal of Materials Research and Technology", volume = "23", pages = "1543--1558", year = "2023", ISSN = "2238-7854", DOI = "doi:10.1016/j.jmrt.2023.01.045", URL = "https://www.sciencedirect.com/science/article/pii/S2238785423000455", keywords = "genetic algorithms, genetic programming, Residual stress, Aluminum alloy, Rietveld analysis, Stress equilibrium, Lattice parameter", abstract = "While it is known that the crucial requirement for investigating residual stresses using diffraction is the use of a reliable unstressed lattice parameter, the robustness of genetic programming to accomplish this task will be shown here. The parameter obtained from genetic programming in the context of the stress equilibrium method is compared with values resulting from other approaches of this method. This gives support and strength to the use of genetic programming to investigate microscopic residual stresses from real, experimental information. This is, so far, absent in theoretical models recently proposed. Whereas residual stress fields determination by diffraction methods at a macroscopic scale (scale of the sample size) offers no serious difficulties, the stress determination at the microscopic scale (i.e., stresses varying among neighboring grains) is still a pending task. Understanding these microscopic stresses is, however, of a great technological importance, as they may be the cause of fatigue damage and/or stress corrosion cracking in many structural components. Despite that theoretical but solid alternatives, for example those based on phase field models, are being used to unveil the stresses developed at the grain scale after known thermo-mechanical treatments, the results obtained still need to be assessed by experimental results linked to real microstructures. On the contrary, recent works propose the use of genetic programming approaches to investigate these microscopic stresses on the basis of data recorded from real stressed samples; specifically, from neutron diffraction and detailed knowledge of the microstructure and its characteristics; e.g., the texture gradient developed", } @InProceedings{Millard:2012:SSBSE, author = "Alan G. Millard and David R. White and John A. Clark", title = "Searching for Pareto-optimal Randomised Algorithms", booktitle = "4th Symposium on Search Based Software Engineering", year = "2012", editor = "Gordon Fraser and Jerffeson {Teixeira de Souza} and Angelo Susi", volume = "7515", series = "Lecture Notes in Computer Science", pages = "183--197", address = "Riva del Garda, Italy", month = sep # " 28-30", publisher = "Springer", keywords = "genetic algorithms, genetic programming, SBSE, MOGA", isbn13 = "978-3-642-33118-3", DOI = "doi:10.1007/978-3-642-33119-0_14", size = "15 pages", abstract = "Randomised algorithms traditionally make stochastic decisions based on the result of sampling from a uniform probability distribution, such as the toss of a fair coin. In this paper, we relax this constraint, and investigate the potential benefits of allowing randomised algorithms to use non-uniform probability distributions. We show that the choice of probability distribution influences the non-functional properties of such algorithms, providing an avenue of optimisation to satisfy non-functional requirements. We use Multi-Objective Optimisation techniques in conjunction with Genetic Algorithms to investigate the possibility of trading-off non-functional properties, by searching the space of probability distributions. Using a randomised self-stabilising token circulation algorithm as a case study, we show that it is possible to find solutions that result in Pareto-optimal trade-offs between non-functional properties, such as self-stabilisation time, service time, and fairness.", } @InCollection{miller:1997:poenn, author = "Graham Miller", title = "Preventing Overfitting of Evolved Neural Networks", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1997", publisher = "Stanford Bookstore", year = "1997", editor = "John R. Koza", pages = "147--158", address = "Stanford, California, 94305-3079 USA", month = "17 " # mar, keywords = "genetic algorithms, genetic programming", ISBN = "0-18-205981-2", notes = "part of \cite{koza:1997:GAGPs}", } @InProceedings{750023, author = "Julian F. Miller and Peter Thomson", title = "Restricted Evaluation Genetic Algorithms with Tabu Search for Optimising {Boolean} Functions as Multi-Level {AND-EXOR} Networks", booktitle = "Selected Papers from AISB Workshop on Evolutionary Computing", year = "1996", ISBN = "3-540-61749-3", pages = "85--101", publisher = "Springer-Verlag", address = "Brighton, U.K.", month = "1-2 " # apr, series = "LNCS", volume = "1143", editor = "Terence C. Fogarty", keywords = "genetic algorithms, genetic programming", URL = "https://rdcu.be/dgo0t", DOI = "doi:10.1007/BFb0032775", abstract = "In GAs applied to engineering problems (in our case, the optimisation of logic circuits) the fitness function is usually complex and the fitness evaluation is time consuming. The run time is therefore a major consideration when designing a GA for optimisation, thus a look-up table for fitness evaluation is desirable. As a consequence, it is appropriate to limit the number of different chromosome fitness evaluations that any particular run of the GA will be allowed to examine. In this situation the user is uninterested in the number of generations required. It is necessary in this approach to guarantee the users that they will be able to find a good and reliable problem solution within the limited number of evaluations, and hence time available. We refer to this type of GA as a restricted evaluation GA. In this paper we suggest a number of hybrid algorithms which combine a GA with a neighbourhood search (TABU) technique to provide this performance and reliability. The effectiveness of each of these methods is compared and contrasted, and underlying principles are suggested as to why these techniques might prove to be useful in these types of problem.", notes = "305 pp., Softcover", } @InProceedings{749862, author = "Julian F. Miller and Peter Thomson and P. V. G. Bradbeer", title = "Ternary Decision Diagram Optimisation of {Reed-Muller} Logic Functions using a Genetic Algorithm for Variable and Simplification Rule Ordering", booktitle = "Selected Papers from AISB Workshop on Evolutionary Computing", year = "1995", ISBN = "3-540-60469-3", pages = "181--190", publisher = "Springer-Verlag", editor = "Terence C. Fogarty", number = "993", series = "Lecture Notes in Computer Science", address = "Sheffield, UK", month = "3-4 " # apr, keywords = "genetic algorithms", URL = "http://www.springer.com/computer/theoretical+computer+science/book/978-3-540-60469-3", DOI = "doi:10.1007/3-540-60469-3_34", isbn13 = "978-3-540-60469-3", size = "10 pages", abstract = "This paper details a method for reducing gate counts for logic functions in the Reed-Muller logic system, using a bottom up ternary decision diagram (TDD). The method employed uses a two chromosome genetic algorithm: to vary the ordering of both the function variables, and the TDD simplification rules, to achieve potentially substantial gate count savings in large expressions when compared with an earlier version which used a single chromosome representation for the variable ordering only. The results also compare very favourably with one of the best heuristic minimisers in the literature [EXMIN2].", } @InProceedings{miller:1998:edcrfgGA, author = "Julian F. Miller and Peter Thomson", title = "Evolving Digital Electronic Circuits for Real-Valued Function Generation using a Genetic Algorithm", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "863--868", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, Evolvable Hardware", ISBN = "1-55860-548-7", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.26.8117", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.26.8117.pdf", size = "6 pages", abstract = "In this paper we describe experiments which attempt to evolve digital electronic circuits whose purpose is to implement real signals. As a convenience we chose to evolve mathematical functions i.e. the square-root and sine. Real numbers in the range 0.00-0.99 are encoded in binary using four bits per decimal place. The chromosome used is exactly modelled on the resources available on the Xilinx 6216 re-configurable Field Programmable Gate Array (FPGA), so that evolved circuit designs may be simply implemented on this target device. We investigated a number of ways of presenting examples to the circuit so that the target function might be learnt, and also looked at two distinctly different fitness function definitions.", notes = "GP-98", } @InProceedings{656753, author = "Julian F. Miller and Peter Thomson", title = "Aspects of Digital Evolution: Geometry and Learning", booktitle = "Proceedings of the Second International Conference on Evolvable Systems", year = "1998", ISBN = "3-540-64954-9", pages = "25--35", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", URL = "http://www.elec.york.ac.uk/intsys/users/jfm7/ices98.pdf", DOI = "doi:10.1007/BFb0057604", abstract = "In this paper we present a new chromosome representation for evolving digital circuits. The representation is based very closely on the chip architecture of the Xilinx 6216 FPGA. We examine the effectiveness of evolving circuit functionality by using randomly chosen examples taken from the truth table. We consider the merits of a cell architecture in which functional cells alternate with routing cells and compare this with an architecture in which any cell can implement a function or be merely used for routing signals. It is noteworthy that the presence of elitism significantly improves the Genetic Algorithm performance.", } @InProceedings{668781, author = "Julian F. Miller and Peter Thomson", title = "Aspects of Digital Evolution: Evolvability and Architecture", booktitle = "Fifth International Conference on Parallel Problem Solving from Nature", year = "1998", editor = "Agoston E. Eiben and Thomas Back and Marc Schoenauer and Hans-Paul Schwefel", volume = "1498", series = "LNCS", pages = "927--936", address = "Amsterdam", publisher_address = "Berlin", month = "27-30 " # sep, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, Evolvable Hardware", ISBN = "3-540-65078-4", URL = "http://www.elec.york.ac.uk/intsys/users/jfm7/ppsn.ps", notes = "PPSN-V", } @Article{miller:1998:gp98, author = "Julian Miller", title = "GP98 Conference Report", journal = "EvoNews", year = "1998", volume = "1", number = "8", pages = "10", keywords = "genetic algorithms, genetic programming", URL = "http://evonet.lri.fr/evoweb/files/evonews/evonews8.pdf", notes = "EvoNews - The Newsletter of EvoNet", size = "1 page", notes = "review of \cite{koza:gp98}", } @InProceedings{651850, author = "Julian F. Miller", title = "Evolution of Digital Filters Using a Gate Array Model", booktitle = "Proceedings of the First European Workshops on Evolutionary Image Analysis, Signal Processing and Telecommunications", year = "1999", editor = "Riccardo Poli and Hans-Michael Voigt and Stefano Cagnoni and David Corne and George D. Smith and Terence C. Fogarty", volume = "1596", series = "LNCS", pages = "17--30", address = "Goteborg, Sweden", publisher_address = "Berlin", month = "26-27 " # may, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-65837-8", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/3662/http:zSzzSzwww.dcs.napier.ac.ukzSz~julianzSzevoiasp.pdf/miller98evolution.pdf", URL = "http://citeseer.ist.psu.edu/miller98evolution.html", abstract = "The traditional paradigm for digital filter design is based on the concept of a linear difference equation with the output response being a weighted sum of signal samples with usually floating point coefficients. Unfortunately such a model is necessarily expensive in terms of hardware as it requires many large bit additions and multiplications. In this paper it is shown how it is possible to evolve a small rectangular array of logic gates to perform low pass FIR filtering. The circuit is...", notes = "EvoWorkshops 1999: EvoIASP, EuroECTel", } @InProceedings{miller:1999:ACGP, author = "Julian F. Miller", title = "An empirical study of the efficiency of learning {Boolean} functions using a Cartesian Genetic Programming approach", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1135--1142", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, evolvable hardware", ISBN = "1-55860-611-4", URL = "http://citeseer.ist.psu.edu/153431.html", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-411.ps", URL = "https://dl.acm.org/doi/10.5555/2934046.2934074", abstract = "A new form of Genetic Programming (GP) called Cartesian Genetic Programming (CGP) is proposed in which programs are represented by linear integer chromosomes in the form of connections and functionalities of a rectangular array of primitive functions. The effectiveness of this approach is investigated for boolean even-parity functions (3,4,5), and the 2-bit multiplier. The minimum number of evaluations required to give a 0.99 probability of evolving a target function is used to measure the efficiency of the new approach. It is found that extremely low populations are most effective. A simple probabilistic hillclimber (PH) is devised which proves to be even more effective. For these boolean functions either method appears to be much more efficient than the GP and Evolutionary Programming (EP) methods reported. The efficacy of the PH suggests that boolean function learning may not be an appropriate problem for testing the effectiveness of GP and EP.", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{miller:1999:DFDGEA, author = "Julian F. Miller", title = "Digital Filter Design at Gate-level using Evolutionary Algorithms", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1127--1134", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, EHW, evolvable hardware", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-406.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Miller:1999:eh, author = "Julian F. Miller", title = "On the Filtering Properties of Evolved Gate Arrays", booktitle = "The First NASA/DoD Workshop on Evolvable Hardware", year = "1999", editor = "Adrian Stoica and Jason Lohn and Didier Keymeulen", pages = "2--11", address = "Pasadena, California", publisher_address = "1730 Massachusetts Avenue, N.W., Washington, DC 20036-1992, USA", month = "19-21 " # jul, organisation = "Jet Propulsion Laboratory, California Institute of Technology", publisher = "IEEE Computer Society", keywords = "genetic algorithms, genetic programming", ISBN = "0-7695-0256-3", URL = "http://www.elec.york.ac.uk/intsys/users/jfm7/eh99.pdf", abstract = "A small gate array is evolved extrinsically to carry out a low pass filtering task defined over fifteen different frequencies. The circuit is evolved by assessing its response digitised sine waves. Two different fitness functions are contrasted. One is based on computing the sum of the absolute differences between the actual response and that desired, the other is defined by examining characteristics of the Discrete Fourier Transform of the output. The gate arrays possess some linear properties, which means that they are capable of filtering composite signals which have not been encountered in training. This includes signals with noise added and with frequencies which are not in the training set.", notes = "EH1999 http://cism.jpl.nasa.gov/events/nasa_eh/", } @InProceedings{miller:2000:CGP, author = "Julian F. Miller and Peter Thomson", title = "Cartesian Genetic Programming", booktitle = "Genetic Programming, Proceedings of EuroGP'2000", year = "2000", editor = "Riccardo Poli and Wolfgang Banzhaf and William B. Langdon and Julian F. Miller and Peter Nordin and Terence C. Fogarty", volume = "1802", series = "LNCS", pages = "121--132", address = "Edinburgh", publisher_address = "Berlin", month = "15-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", ISBN = "3-540-67339-3", URL = "http://www.elec.york.ac.uk/intsys/users/jfm7/cgp-eurogp2000.pdf", URL = "http://citeseer.ist.psu.edu/424028.html", DOI = "doi:10.1007/978-3-540-46239-2_9", abstract = "This paper presents a new form of Genetic Programming called Cartesian Genetic Programming in which a program is represented as an indexed graph. The graph is encoded in the form of a linear string of integers. The inputs or terminal set and node outputs are numbered sequentially. The node functions are also separately numbered. The genotype is just a list of node connections and functions. The genotype is then mapped to an indexed graph that can be executed as a program. Evolutionary algorithms are used to evolve the genotype in a symbolic regression problem (sixth order polynomial) and the Santa Fe Ant Trail. The computational effort is calculated for both cases. It is suggested that hit effort is a more reliable measure of computational efficiency. A neutral search strategy that allows the fittest genotype to be replaced by another equally fit genotype (a neutral genotype) is examined and compared with non-neutral search for the Santa Fe ant problem. The neutral search proves to be much more effective.", notes = "EuroGP'2000, part of \cite{poli:2000:GP}", } @Proceedings{ICES2000edited, editor = "Julian F. Miller and Adrian Thompson and Peter Thomson and Terence C. Fogarty", title = "Proceedings of the Third International Conference on Evolvable Systems, ICES 2000", year = "2000", ISBN = "3-540-67338-5", publisher = "Springer-Verlag", address = "Edinburgh, Scotland, UK", month = "17-19 " # apr, series = "LNCS", volume = "1801", notes = "286 pp., Softcover Available online in SpringerLink", } @Article{miller:2000:, author = "J. F. Miller", title = "Review: First {NASA/DOD} Workshop on Evolvable Hardware 1999", journal = "Genetic Programming and Evolvable Machines", year = "2000", volume = "1", number = "1/2", pages = "171--174", month = apr, keywords = "genetic algorithms, evolvable hardware", ISSN = "1389-2576", URL = "https://rdcu.be/dR8Gi", DOI = "doi:10.1023/A:1017283000170", notes = "Article ID: 253709", } @Article{miller:2000:peddg1, author = "Julian F. Miller and Dominic Job and Vesselin K. Vassilev", title = "Principles in the Evolutionary Design of Digital Circuits-Part {I}", journal = "Genetic Programming and Evolvable Machines", year = "2000", volume = "1", number = "1/2", pages = "7--35", month = apr, keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, evolvable hardware, evolutionary computing, circuit design", ISSN = "1389-2576", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/19052/http:zSzzSzwww.sbu.ac.ukzSz~vassilvkzSzpaperszSzjgpem2000part1.pdf/miller00principles.pdf", URL = "http://citeseer.ist.psu.edu/miller00principles.html", URL = "https://rdcu.be/cT3VI", DOI = "doi:10.1023/A:1010016313373", size = "29 pages", abstract = "An evolutionary algorithm is used as an engine for discovering new designs of digital circuits, particularly arithmetic functions. These designs are often radically different from those produced by top-down, human, rule-based approaches. It is argued that by studying evolved designs of gradually increasing scale, one might be able to discern new, efficient, and generalizable principles of design. The ripple-carry adder principle is one such principle that can be inferred from evolved designs for one and two-bit adders. Novel evolved designs for three-bit binary multipliers are given that are 20 percent more efficient (in terms of number of two-input gates used) than the most efficient known conventional design.", notes = "Article ID: 253702", } @Article{miller:2000:peddg2, author = "Julian F. Miller and Dominic Job and Vesselin K. Vassilev", title = "Principles in the Evolutionary Design of Digital Circuits-Part {II}", journal = "Genetic Programming and Evolvable Machines", year = "2000", volume = "1", number = "3", pages = "259--288", month = jul, keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, evolvable hardware, evolutionary computing, circuit design, evolutionary algorithms, digital circuits, fitness landscapes, case based reasoning, principle extraction", ISSN = "1389-2576", URL = "https://rdcu.be/cT3VV", DOI = "doi:10.1023/A:1010066330916", size = "30 pages", abstract = "In a previous work it was argued that by studying evolved designs of gradually increasing scale, one might be able to discern new, efficient, and generalisable principles of design. These ideas are tested in the context of designing digital circuits, particularly arithmetic circuits. This process of discovery is seen as a principle extraction loop in which the evolved data is analysed both phenotypically and genotypically by processes of data mining and landscape analysis. The information extracted is then fed back into the evolutionary algorithm to enhance its search capabilities and hence increase the likelihood of identifying new principles which explain how to build systems which are too large to evolve.", notes = "Article ID: 264704", } @Proceedings{miller:2001:gp, title = "Genetic Programming, Proceedings of EuroGP'2001", year = "2001", editor = "Julian Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon", volume = "2038", series = "LNCS", address = "Lake Como, Italy", publisher_address = "Berlin", month = "18-20 " # apr, organisation = "EvoNET", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, Program Trees, BDD, VHDL, Linear representations, Parallel programming, Bloat, Image processing, Evolvability, Controller design, MAX Problem, modular robot, Pattern Recognition, Fixed points, Feature Extraction, VLSI CAD, Multipopulation structures, Cellular model, Boolean function landscape, Machine Learning, Robotic Arm, Iterated Function Systems, Evolution of size, DNA, Length distributions, Computational Complexity, Time Series prediction, Process modelling, Causality, Animat, Crossover bias, Multi-expression individuals, Symbolic Regression, One-then-zeros problem, Intrinsic Polymorphism, STGP, Knowledge Discovery, Dynamic Fitness, Grammatical Evolution, Genotype-Phenotype Mapping, Problem Generator, Turing machines, Genetic Reasoning, Artificial Retina, Block-oriented representation, distributed control, robust, Neutral mutation, Active Character Recognition, Inverse Kinematics, Evolvable Hardware, Layered Learning, Contour detection, Developmental Genetic Programming, Structure Optimisation, Strongly Typed GP, Linear tree structure, Distributed Genetic Programming, Polymorphism, Subtree-swapping Crossover, Grammatical evolution, Humanoid Robotics, Variable-length Genetic Algorithms, GP representation, PolyGP, Parallel evolutionary algorithms, Robot soccer, Multiple Sequence aligment, Quantum Computing, Robots, self-reconfigurable, ROC, Fitness landscape glitches, smart membrane, Multi-objective optimisation, Genetic operators, EASEA, Digit Recognition, Heuristic Learning, Hierarchical abstractions, Context Free Grammars, Subtree Encapsulation, Neutrality, Typed GP, Code Reuse, Crossover, Biotechnology, Graph-based Genetic Programming, Grammar, Multiagent systems, Discipulus, Digital Filters, Stereo Vision, Receiver Operating Characteristics, Handwritten digit classification, Evolution Strategies, Brain Building, Constraint handling, Bioinformatics, Adaption, Exploration vs. Exploitation, Image Processing, Linear Genome, Adaptation, scalable, Color Constancy, Electronic Design, Data Fusion, Binary Decision Diagrams, Adaptive Genotype to Phenotype Mappings, Combining Classifiers, Evolutionary Algorithms, Financial markets, Standard Crossover, Modularisation, Parallelism, Schema theory, Data Mining, Global Memory", ISBN = "3-540-41899-7", DOI = "doi:10.1007/3-540-45355-5", size = "391 pages approx", notes = "EuroGP'2001", } @InProceedings{ICES2001, author = "Julian F. Miller and Morten Hartmann", title = "Untidy Evolution: Evolving Messy Gates for Fault Tolerance", booktitle = "Evolvable Systems: From Biology to Hardware: 4th International Conference, ICES 2001", year = "2001", editor = "Yong Liu and Kiyoshi Tanaka and Masaya Iwata and Tetsuya Higuchi and Moritoshi Yasunaga", volume = "2210", series = "LNCS", pages = "14--25", address = "Tokyo, Japan", month = "3-5 " # oct, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-42671-X", ISSN = "0302-9743", URL = "http://www.elec.york.ac.uk/intsys/users/jfm7/ices2001.pdf", DOI = "doi:10.1007/3-540-45443-8_2", abstract = "The exploitation of the physical characteristics has already been demonstrated in the intrinsic evolution of electronic circuits. This paper is an initial attempt at creating a world in which {"}physics{"} can be exploited in simulation. As a starting point we investigate a model of gate-like components with added noise. We refer to this as a kind of messiness . The principal idea behind these messy gates is that artificial evolution makes a virtue of the untidiness. We are ultimately trying to study the question: What kind of components should we use in artificial evolution? Several experiments are described that show that the messy circuits have a natural robustness to noise, as well as an implicit fault-tolerance. In addition, it was relatively easy for evolution to generate novel circuits that were surprisingly efficient.", } @InProceedings{miller:2001:gecco, title = "Evolution of Program Size in Cartesian Genetic Programming", author = "Julian Miller", pages = "184", year = "2001", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)", editor = "Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon and Edmund Burke", address = "San Francisco, California, USA", publisher_address = "San Francisco, CA 94104, USA", month = "7-11 " # jul, keywords = "genetic algorithms, genetic programming, cartesian genetic programming: Poster", ISBN = "1-55860-774-9", URL = "http://gpbib.cs.ucl.ac.uk/gecco2001/d02.pdf", notes = "GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of \cite{spector:2001:GECCO}", } @InProceedings{miller:2001:eh, author = "Julian F. Miller and Morten Hartmann", title = "Evolving messy gates for fault tolerance: some preliminary findings", booktitle = "The Third NASA/DoD workshop on Evolvable Hardware", year = "2001", editor = "Didier Keymeulen and Adrian Stoica and Jason Lohn and Ricardo S. Zebulum", pages = "116--123", address = "Long Beach, California", publisher_address = "1730 Massachusetts Avenue, N.W., Washington, DC, 20036-1992, USA", month = "12-14 " # jul, organisation = "Jet Propulsion Laboratory, California Institute of Technology", publisher = "IEEE Computer Society", keywords = "genetic algorithms, genetic programming", ISBN = "0-7695-1180-5", URL = "http://www.elec.york.ac.uk/intsys/users/jfm7/eh01.pdf", abstract = "We investigate a preliminary model of gate-like components with added random noise. We refer to these types of components as messy. The principal idea behind messy gates is that evolving circuits using messy gates may confer some beneficial properties, one being fault tolerance. The exploitation of the physical characteristics has already been demonstrated in intrinsic evolution of electronic circuits. This provided some of the inspiration for the work reported in this paper. Here we are trying to create a simulateable world in which {"}physical characteristics{"} can be exploited. We are also trying to study the question: What kind of components are most useful in an evolutionary design scenario?", notes = "EH2001 http://cism.jpl.nasa.gov/ehw/events/nasaeh01/", } @InProceedings{miller:2001:wbcgpbp, author = "Julian Miller", title = "What Bloat? Cartesian Genetic Programming on {Boolean} Problems", booktitle = "2001 Genetic and Evolutionary Computation Conference Late Breaking Papers", year = "2001", editor = "Erik D. Goodman", pages = "295--302", address = "San Francisco, California, USA", month = "9-11 " # jul, keywords = "genetic algorithms, genetic programming, cartesian genetic programming, bloat, graph-based genetic programming, genotype-phenotype", URL = "http://www.elec.york.ac.uk/intsys/users/jfm7/gecco2001Late.pdf", abstract = "This paper presents an empirical study of the variation of program size over time, for a form of Genetic Programming called Cartesian Genetic Programming. Two main types of Cartesian genetic programming are examined: one uses a fully connected graph, with no redundant nodes, while the other allows partial connectedness and has redundant nodes. Studies are reported here for fitness based search and for a flat fitness landscape. The variation of program size with generation does not behave in a similar way to that reported in other studies on standard Genetic Programming. Depending on the form of Cartesian genetic programming, it is found that there is either very weak program bloat or zero bloat. It is argued that an important factor in the analysis of the change of program length is neutral drift, and that if genotype redundancy is present, the genetic neutral drift simultaneously improves search and compresses program code.", notes = "GECCO-2001LB", } @InCollection{510373, author = "Julian F. Miller and Tatiana Kalganova and Dominic Job and Natalia Lipnitskaya", title = "The genetic algorithm as a discovery engine: strange circuits and new principles", editor = "Peter J. Bentley and David Corne", booktitle = "Creative evolutionary systems", year = "2002", ISBN = "1-55860-673-4", pages = "443--466", chapter = "18", publisher = "Morgan Kaufmann Publishers Inc.", keywords = "genetic algorithms, genetic programming", URL = "https://www.sciencedirect.com/science/article/pii/B9781558606739500586", DOI = "doi:10.1016/B978-155860673-9/50058-6", abstract = "Publisher Summary This chapter puts forward the view that evolutionary algorithms together with the assemble-and-test methodology can be regarded as a discovery engine or creative machine for new designs. The chapter suggests that new principles may be discovered by examining a series of evolved designs, in this case, for arithmetic logic circuits. The chapter examines the concept of the space of all circuit representations but observes that similar ideas may well carry over to the general field of design. The human-designed algebras that form subsets of the space of all representations both for binary and multiple-valued systems are analogous to small “pools” of human principles, and by employing the blind evolutionary technique new principles may be discovered. The chapter looks at the difficult problem of principle extraction from evolved data. The chapter ends on a hopeful note that the process of learning new principles from a blind evolutionary process is just a matter of time.", notes = "Part of \cite{Bentley:2002:bookCES}", } @TechReport{miller:2002:sees, author = "J. F. Miller", title = "What is a Good Genotype-Phenotype Mapping for the Evolution of Computer Programs?", institution = "University of Hertfordshire, Computer Science", booktitle = "Software Evolution and Evolutionary Computation Symposium Abstracts", year = "2002", editor = "C. L. Nehaniv and M. Loomes and P. Marrow and P. Wernick", number = "364", type = "Technical Report", pages = "16", address = "University of Hertfordshire", month = "2 " # feb, keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/2002_sees/miller_2002_sees.pdf", broken = "http://homepages.feis.herts.ac.uk/~nehaniv/EN/seec.html", broken = "http://homepages.herts.ac.uk/~comqcln//EN/seec/abstracts/miller.html", abstract = "In this paper I describe the characteristics of a novel form of Genetic Programming called Cartesian Genetic Programming (CGP) which appears to benefit from a highly effective genotype-phenotype mapping. The mapping allows explicit genotype redundancy that a simple search algorithm can exploit via genetic drift. The continual process of genetic change leading to phenotypic change appears to lead to a highly effective search process. It is easy to demonstrate practically that the genetic drift is the primary source of the algorithm's power. Recently, I have investigated a developmental form of CGP in which one evolves the program for a single cell. The cell's program and context allows it to differentiate and divide and construct an 'organism' made of many cells. The organism however is just another larger Cartesian Genetic Program. It was anticipated that the new organism would be more evolvable because of its shorter genotype length. Early experiments appear not to show this however. I discuss the implications of this and attempt to make some progress on the question of what makes a good genotype-phenotype mapping for the evolution of computer programs.", size = "1 page", } @InProceedings{Miller:2002:eh, author = "Julian F. Miller and Keith Downing", editor = "Adrian Stoica and Jason Lohn and Rich Katz and Didier Keymeulen and Ricardo Salem Zebulum", month = "15-18 " # jul, year = "2002", title = "Evolution in Materio: Looking Beyond the Silicon Box", booktitle = "The 2002 {NASA/DoD} Conference on Evolvable Hardware", pages = "167--176", publisher = "IEEE Computer Society", address = "Alexandria, Virginia", organisation = "Jet Propulsion Laboratory, California Institute of Technology", keywords = "genetic algorithms, genetic programming, Evolvable Matter, Molecular Circuits, Evolvable Hardware,Intrinsic evolution", publisher_address = "10662 Los Vaqueros Circle, P.O. Box 3014, Los Alamitos, CA, 90720-1314, USA", email = "j.miller@cs.bham.ac.uk", ISBN = "0-7695-1718-8", URL = "http://www.elec.york.ac.uk/intsys/users/jfm7/eh2002.pdf", abstract = "It is argued that natural evolution is, par excellence, an algorithm that exploits the physical properties of materials. Such an exploitation of the physical characteristics has already been demonstrated in intrinsic evolution of electronic circuits. This paper is an attempt to point the way toward the exciting possibility of using artificial evolution to directly exploit the properties of materials, possibly at a molecular level. It is suggested that this may be best accomplished in materials not normally associated with electronic functions. Electronic components have been prefected by human designers to construct circuits using the traditional top-down methodology. Workers in artificial intrinsic hardware evolution have with the best of motives, been abusing such components. It is a tribute to the amazing resourcefulness of a blind evolutionary process that it has been possible to evolve new circuits in this way. Artificial evolution may be much more effective when the configurable medium has a rich and complicated physics. This idea is discussed and particular examples that look extremely promising are given. Ultimately it may be possible to evolve entirely new technologies and new sorts of computational systems may be devised that confer many advantages over conventional electronic technology.", notes = "EH2002 http://cism.jpl.nasa.gov/ehw/events/nasaeh02/", } @InProceedings{miller:2003:ICES, author = "Julian F. Miller and Peter Thomson", title = "A Developmental Method for Growing Graphs and Circuits", booktitle = "Evolvable Systems: From Biology to Hardware, Fifth International Conference, ICES 2003", year = "2003", editor = "Andy M. Tyrrell and Pauline C. Haddow and Jim Torresen", volume = "2606", series = "LNCS", pages = "93--104", address = "Trondheim, Norway", month = "17-20 " # mar, publisher = "Springer-Verlag", ISBN = "3-540-00730-X", keywords = "genetic algorithms, genetic programming", URL = "http://www.elec.york.ac.uk/intsys/users/jfm7/ices2003.pdf", DOI = "doi:10.1007/3-540-36553-2_9", abstract = "A review is given of approaches to growing neural networks and electronic circuits. A new method for growing graphs and circuits using a developmental process is discussed. The method is inspired by the view that the cell is the basic unit of biology. Programs that construct circuits are evolved to build a sequence of digital circuits at user specified iterations. The programs can be run for an arbitrary number of iterations so circuits of huge size could be created that could not be evolved. It is shown that the circuit building programs are capable of correctly predicting the next circuit in a sequence of larger even parity functions. The new method however finds building specific circuits more difficult than a non-developmental method.", notes = "ICES-2003", } @InProceedings{ecal2003, author = "Julian F. Miller", title = "Evolving Developmental Programs for Adaptation, Morphogenesis, and Self-Repair", editor = "Wolfgang Banzhaf and Thomas Christaller and Peter Dittrich and Jan T. Kim and Jens Ziegler", booktitle = "Advances in Artificial Life. 7th European Conference on Artificial Life", publisher = "Springer", series = "Lecture Notes in Artificial Intelligence", volume = "2801", year = "2003", month = "14-17 " # sep, address = "Dortmund, Germany", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-20057-6", URL = "http://www.elec.york.ac.uk/intsys/users/jfm7/ecal2003.pdf", DOI = "doi:10.1007/b12035", pages = "256--265", abstract = "A method for evolving a developmental program inside a cell to create multicellular organisms of arbitrary size and characteristics is described. The cell genotype is evolved so that the organism will organise itself into well defined patterns of differentiated cell types (e.g. the French Flag). In addition the cell genotypes are evolved to respond appropriately to environmental signals that cause metamorphosis of the whole organism. A number of experiments are described that show that the organisms exhibit emergent properties of self-repair and adaptation.", notes = "ECAL-2003 http://www.ecal2003.org/", } @InCollection{KumarBentley2003, author = "Julian F. Miller and Wolfgang Banzhaf", editor = "Sanjeev Kumar and Peter J. Bentley", title = "Evolving the program for a cell: from French flags to {Boolean} circuits", booktitle = "On Growth, Form and Computers", chapter = "15", pages = "278--301", publisher = "Academic Press", year = "2003", month = oct, keywords = "genetic algorithms, genetic programming, computational development, artificial life", ISBN = "0-12-428765-4", URL = "http://web.cs.mun.ca/~banzhaf/papers/chapter_finalrevision.pdf", abstract = "Introduction The development of an entire organism from a single cell is one of the most profound and awe inspiring phenomena in the whole of the natural world. The complexity of living systems itself dwarfs anything that man has produced. This is all the more the case for the processes that lead to these intricate systems. In each phase of the development of a multi-cellular being, this living system has to survive, whether stand-alone or supported by various structures and processes provided by other living systems. Organisms construct themselves, out of humble single-celled beginnings, riding waves of interaction between the information residing in their genomes inherited from the evolutionary past of their species via their progenitors and the resources of their environment.", notes = "part of \cite{kumar:gfc}", size = "33 pages", } @Article{MS:IEEETEC:06, title = "Redundancy and Computational Efficiency in Cartesian Genetic Programming", author = "Julian F. Miller and Stephen L. Smith", journal = "IEEE Transactions on Evolutionary Computation", year = "2006", volume = "10", number = "2", pages = "167--174", month = apr, keywords = "genetic algorithms, genetic programming, Cartesian genetic programming (CGP), code bloat, graph-based representations, introns", DOI = "doi:10.1109/TEVC.2006.871253", size = "8 pages", abstract = "The graph-based Cartesian genetic programming system has an unusual genotype representation with a number of advantageous properties. It has a form of redundancy whose role has received little attention in the published literature. The representation has genes that can be activated or deactivated by mutation operators during evolution. It has been demonstrated that this junk has a useful role and is very beneficial in evolutionary search. The results presented demonstrate the role of mutation and genotype length in the evolvability of the representation. It is found that the most evolvable representations occur when the genotype is extremely large and in which over 95 percent of the genes are inactive.", } @InProceedings{Miller:2008:geccocomp, author = "Julian Francis Miller and Simon L. Harding", title = "Cartesian genetic programming", year = "2008", editor = "Marc Ebner and Mike Cattolico and Jano {van Hemert} and Steven Gustafson and Laurence D. Merkle and Frank W. Moore and Clare Bates Congdon and Christopher D. Clack and Frank W. Moore and William Rand and Sevan G. Ficici and Rick Riolo and Jaume Bacardit and Ester Bernado-Mansilla and Martin V. Butz and Stephen L. Smith and Stefano Cagnoni and Mark Hauschild and Martin Pelikan and Kumara Sastry", isbn13 = "978-1-60558-131-6", booktitle = "GECCO-2008 tutorials", pages = "2701--2726", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p2701.pdf", DOI = "doi:10.1145/1388969.1389075", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, Cartesian genetic programming", notes = "Distributed on CD-ROM at GECCO-2008 ACM Order Number 910081. Also known as \cite{1389075}", } @Article{Miller:2010:GPEM, author = "Julian F. Miller and Riccardo Poli", title = "Editorial to tenth anniversary issue on progress in genetic programming and evolvable machines", journal = "Genetic Programming and Evolvable Machines", year = "2010", volume = "11", number = "3/4", pages = "247--250", month = sep, note = "Editorial: Tenth Anniversary Issue: Progress in Genetic Programming and Evolvable Machines", keywords = "genetic algorithms, genetic programming, evolvable hardware", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-010-9115-0", size = "4 pages", abstract = "The Genetic Programming and Evolvable Machines journal is 10 years old. This sizable special issue is intended to mark this special anniversary and take the opportunity to look back at what has been achieved in the last 10 years of Genetic Programming (GP) and to start charting the terrain for potential new developments in the next 10 years.", } @InProceedings{Miller:2010:geccocomp, author = "Julian F. Miller and Simon L. Harding", title = "Cartesian genetic programming", booktitle = "GECCO 2010 Specialized techniques and applications tutorials", year = "2010", editor = "Una-May O'Reilly", isbn13 = "978-1-4503-0073-5", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming", pages = "2927--2948", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830761.1830924", publisher = "ACM", publisher_address = "New York, NY, USA", notes = "Also known as \cite{1830924} Distributed on CD-ROM at GECCO-2010. ACM Order Number 910102.", } @InProceedings{Miller:2011:GECCOcomp, author = "Julian F. Miller and Simon L. Harding", title = "GECCO 2011 tutorial: cartesian genetic programming", booktitle = "GECCO 2011 Tutorials", year = "2011", editor = "Darrell Whitley", isbn13 = "978-1-4503-0690-4", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", pages = "1261--1284", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001858.2002136", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Cartesian Genetic Programming (CGP) is an increasingly popular and efficient form of Genetic Programming that was developed by Julian Miller in 1999 and 2000. In its classic form, it uses a very simple integer based genetic representation of a program in the form of a directed graph. Graphs are very useful program representations and can be applied to many domains (e.g. electronic circuits, neural networks). In a number of studies, CGP has been shown to be comparatively efficient to other GP techniques. It is also very simple to program. Since then, the classical form of CGP has been developed made more efficient in various ways. Notably by including automatically defined functions (modular CGP) and self-modification operators(self-modifying CGP). SMCGP was developed by Julian Miller, Simon Harding and Wolfgang Banzhaf. It uses functions that cause the evolved programs to change themselves as a function of time. Using this technique it is possible to find general solutions to classes of problems and mathematical algorithms (e.g. arbitrary parity, n-bit binary addition, sequences that provably compute pi and e to arbitrary precision, and so on). The tutorial will cover the basic technique, advanced developments and applications to a variety of problem domains.", notes = "Also known as \cite{2002136} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @Book{Miller:CGP, editor = "Julian F. Miller", title = "Cartesian Genetic Programming", publisher = "Springer", year = "2011", series = "Natural Computing Series", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, (CGP), Directed Graphs, Electronic Circuits, Evolutionary Art, Evolutionary Computing (EC), Evolvable Hardware (EHW), Image Processing, Modular (Embedded) CGP, Natural Computing, Self-modifying CGP", isbn13 = "978-3-642-17309-7", URL = "http://www.springer.com/computer/theoretical+computer+science/book/978-3-642-17309-7", DOI = "doi:10.1007/978-3-642-17310-3", size = "344 pages", abstract = "Cartesian Genetic Programming (CGP) is a highly effective and increasingly popular form of genetic programming. It represents programs in the form of directed graphs, and a particular characteristic is that it has a highly redundant genotype to phenotype mapping, in that genes can be noncoding. It has spawned a number of new forms, each improving on the efficiency, among them modular, or embedded, CGP, and self-modifying CGP. It has been applied to many problems in both computer science and applied sciences. This book contains chapters written by the leading figures in the development and application of CGP, and it will be essential reading for researchers in genetic programming and for engineers and scientists solving applications using these techniques. It will also be useful for advanced undergraduates and postgraduates seeking to understand and use a highly efficient form of genetic programming.", } @InCollection{Miller:2011:CGP.ch1, author = "Julian F. Miller", title = "Introduction to Evolutionary Computation and Genetic Programming", booktitle = "Cartesian Genetic Programming", publisher = "Springer", editor = "Julian F. Miller", year = "2011", series = "Natural Computing Series", chapter = "1", pages = "1--16", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", isbn13 = "978-3-642-17309-7", URL = "http://www.springer.com/computer/theoretical+computer+science/book/978-3-642-17309-7", DOI = "doi:10.1007/978-3-642-17310-3_1", notes = "part of \cite{Miller:CGP}", } @InCollection{Miller:2011:CGP.ch2, author = "Julian F. Miller", title = "Cartesian Genetic Programming", booktitle = "Cartesian Genetic Programming", publisher = "Springer", editor = "Julian F. Miller", year = "2011", series = "Natural Computing Series", chapter = "2", pages = "17--34", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, (CGP)", isbn13 = "978-3-642-17309-7", URL = "http://www.springer.com/computer/theoretical+computer+science/book/978-3-642-17309-7", DOI = "doi:10.1007/978-3-642-17310-3_2", abstract = "In this chapter, we describe the original and most widely known form of Cartesian genetic programming (CGP). CGP encodes computational structures, which we call programs in the form of directed acyclic graphs. We refer to this as classic CGP. However these program may be computer programs, circuits, rules, or other specialised computational entities.", notes = "part of \cite{Miller:CGP}", } @InCollection{Miller:2011:CGP.appA, key = "Julian F. Miller", title = "Resources for Cartesian Genetic Programming", booktitle = "Cartesian Genetic Programming", publisher = "Springer", editor = "Julian F. Miller", year = "2011", series = "Natural Computing Series", type = "Appendix", chapter = "A", pages = "337--339", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", isbn13 = "978-3-642-17309-7", URL = "http://www.springer.com/computer/theoretical+computer+science/book/978-3-642-17309-7", DOI = "doi:10.1007/978-3-642-17310-3", notes = "part of \cite{Miller:CGP}", size = "3 pages", } @InProceedings{Miller:2012:GECCOcomp, author = "Julian Francis Miller and Simon Harding", title = "GECCO 2012 tutorial: cartesian genetic programming", booktitle = "GECCO 2012 Specialized techniques and applications tutorials", year = "2012", editor = "Gabriela Ochoa", isbn13 = "978-1-4503-1178-6", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", pages = "1093--1116", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330784.2330932", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Cartesian Genetic Programming (CGP) is an increasingly popular and efficient form of Genetic Programming that was developed by Julian Miller in 1999 and 2000. In its classic form, it uses a very simple integer based genetic representation of a program in the form of a directed graph. Graphs are very useful program representations and can be applied to many domains (e.g. electronic circuits, neural networks). In a number of studies, CGP has been shown to be comparatively efficient to other GP techniques. It is also very simple to program. Since then, the classical form of CGP has been developed made more efficient in various ways. Notably, by including automatically defined functions (modular CGP) and self-modification operators (self-modifying CGP). SMCGP was developed by Julian Miller, Simon Harding and Wolfgang Banzhaf. It uses functions that cause the evolved programs to change themselves as a function of time. Using this technique it is possible to find general solutions to classes of problems and mathematical algorithms (e.g. arbitrary parity, n-bit binary addition, sequences that provably compute pi and e to arbitrary precision, and so on). The tutorial will cover the basic technique, advanced developments and applications to a variety of problem domains.", notes = "Also known as \cite{2330932} Distributed at GECCO-2012. ACM Order Number 910122.", } @InProceedings{Miller:2013:GECCOcomp, author = "Julian F. Miller and Maktuba Mohid", title = "Function optimization using cartesian genetic programming", booktitle = "GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion", year = "2013", editor = "Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A. N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks", isbn13 = "978-1-4503-1964-5", keywords = "genetic algorithms, genetic programming", pages = "147--148", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2464576.2464646", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "In function optimisation one tries to find a vector of real numbers that optimises a complex multi-modal fitness function. Although evolutionary algorithms have been used extensively to solve such problems, genetic programming has not. In this paper, we show how Cartesian Genetic Programming can be readily applied to such problems. The technique can successfully find many optima in a standard suite of benchmark functions. The work opens up new avenues of research in the application of genetic programming and also offers an extensive set of highly developed benchmarks that could be used to compare the effectiveness of different GP methodologies.", notes = "Also known as \cite{2464646} Distributed at GECCO-2013.", } @InProceedings{Miller:2013:GECCOcompa, author = "Julian F. Miller", title = "GECCO 2013 tutorial: cartesian genetic programming", booktitle = "GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion", year = "2013", editor = "Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A. N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks", isbn13 = "978-1-4503-1964-5", keywords = "genetic algorithms, genetic programming", pages = "715--740", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2464576.2464578", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Cartesian Genetic Programming (CGP) is an increasingly popular and efficient form of Genetic Programming. Cartesian Genetic Programming is a highly cited technique that was developed by Julian Miller in 1999 and 2000 from some earlier joint work of Julian Miller with Peter Thomson in 1997. In its classic form, it uses a very simple integer based genetic representation of a program in the form of a directed graph. Graphs are very useful program representations and can be applied to many domains (e.g. electronic circuits, neural networks). In a number of studies, CGP has been shown to be comparatively efficient to other GP techniques. It is also very simple to program. Since then, the classical form of CGP has been developed made more efficient in various ways. Notably by including automatically defined functions (modular CGP) and self-modification operators (self-modifying CGP). SMCGP was developed by Julian Miller, Simon Harding and Wolfgang Banzhaf. It uses functions that cause the evolved programs to change themselves as a function of time. Using this technique it is possible to find general solutions to classes of problems and mathematical algorithms (e.g. arbitrary parity, n-bit binary addition, sequences that provably compute pi and e to arbitrary precision, and so on). This tutorial is will cover the basic technique, advanced developments and applications to a variety of problem domains. The first edited book on CGP was published by Springer in September 2011. CGP has its own dedicated website https://www.cartesiangp.com (broken Jan 2023) ", notes = "Also known as \cite{2464578} Distributed at GECCO-2013.", } @InProceedings{Miller:2015:GECCOcomp, author = "Julian Miller and Andrew Turner", title = "Cartesian Genetic Programming", booktitle = "GECCO 2015 Introductory Tutorials", year = "2015", editor = "Anabela Simoes", isbn13 = "978-1-4503-3488-4", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", pages = "179--198", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739482.2756571", DOI = "doi:10.1145/2739482.2756571", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Cartesian Genetic Programming (CGP) is a well-known form of Genetic Programming developed by Julian Miller in 1999-2000. In its classic form, it uses a very simple integer address-based genetic representation of a program in the form of a directed graph. Graphs are very useful program representations and can be applied to many domains (e.g. electronic circuits, neural networks). It can handle cyclic or acyclic graphs. In a number of studies, CGP has been shown to be comparatively efficient to other GP techniques. It is also very simple to program. The classical form of CGP has undergone a number of developments which have made it more useful, efficient and flexible in various ways. These include self-modifying CGP (SMCGP), cyclic connections (recurrent-CGP), encoding artificial neural networks and automatically defined functions (modular CGP). SMCGP uses functions that cause the evolved programs to change themselves as a function of time. This makes it possible to find general solutions to classes of problems and mathematical algorithms (e.g. arbitrary parity, n-bit binary addition, sequences that provably compute pi and e to arbitrary precision, and so on). Recurrent-CGP allows evolution to create programs which contain cyclic, as well as acyclic, connections. This enables application to tasks which require internal states or memory. It also allows CGP to create recursive equations. CGP encoded artificial neural networks represent a powerful training method for neural networks. This is because CGP is able to simultaneously evolve the networks connections weights, topology and neuron transfer functions. It is also compatible with Recurrent-CGP enabling the evolution of recurrent neural networks. The tutorial will cover the basic technique, advanced developments and applications to a variety of problem domains. It will present a live demo of how the open source cgp library can be used.", notes = "Also known as \cite{2756571} Distributed at GECCO-2015.", } @InProceedings{Miller:2015:csdc, author = "Julian Francis Miller", title = "Cartesian Genetic Programming", booktitle = "Complex Systems Digital Campus E-conference, CS-DC'15", year = "2015", editor = "Paul Bourgine and Pierre Collet", pages = "Paper ID: 356", month = sep # " 30-" # oct # " 1", note = "Invited talk", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", URL = "http://cs-dc-15.org/", URL = "http://cs-dc-15.org/papers/multi-scale-dynamics/evol-comp-methods-2/cartesian-genetic-programming/", abstract = "Cartesian Genetic Programming (CGP) is a form of automatic program induction that uses an evolutionary algorithm to evolve graph-based representations of computational structures. It is a highly flexible and general technique that can find solutions in many problem domains (e.g. neural networks, mathematical equation induction, object recognition in images, digital and analogue circuit design, algorithm design...). Since its invention in 1999, it has been developed and made more efficient in various ways. It can automatically capture and evolve sub-functions (known as modules) and through the introduction of self-modification operators it is possible to find mathematically provable general solutions to classes of problems. This talk is given by the inventor of the technique.", notes = "1 October 2015 7:40 to 8:10 (UTC) Evolutionary Computing Methods session Does not appear in proceedings published by Springer 2017", } @InProceedings{Miller:2017:GECCO, author = "Julian F. Miller and Dennis G. Wilson", title = "A Developmental Artificial Neural Network Model for Solving Multiple Problems", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4939-0", address = "Berlin, Germany", pages = "69--70", month = "15-19 " # jul, publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, artificial neural networks, ANN, classification", URL = "http://doi.acm.org/10.1145/3067695.3075976", DOI = "doi:10.1145/3067695.3075976", acmid = "3075976", size = "2 pages", abstract = "A developmental model of an artificial neuron is presented. In this model, a pair of neural developmental programs develop an entire artificial neural network of arbitrary size. The pair of neural chromosomes are evolved using Cartesian Genetic Programming. During development, neurons and their connections can move, change, die or be created. We show that this two-chromosome genotype can be evolved to develop into a single neural network from which multiple conventional artificial neural networks can be extracted. The extracted conventional ANNs share some neurons across tasks. We have evaluated the performance of this method on three standard classification problems. The evolved pair of neuron programs can generate artificial neural networks that perform reasonably well on all three benchmark problems simultaneously. It appears to be the first attempt to solve multiple standard classification problems using a developmental approach.", notes = "Also known as \cite{Miller:2017:DAN:3067695.3075976} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @Article{journals/ijcia/MillerC14, author = "Nicholas C. Miller and Philip K. Chan", title = "Semantic Search Techniques for Learning Smaller {Boolean} Expression Trees in Genetic Programming", journal = "International Journal of Computational Intelligence and Applications", year = "2014", number = "3", volume = "13", month = sep, keywords = "genetic algorithms, genetic programming, semantic search, boolean", ISSN = "1469-0268", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.696.8161", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.696.8161", bibdate = "2014-10-17", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ijcia/ijcia13.html#MillerC14", URL = "http://cs.fit.edu/~pkc/papers/ijcia14.pdf", DOI = "doi:10.1142/S1469026814500187", size = "17 pages", abstract = "One sub-field of Genetic Programming (GP) which has gained recent interest is semantic GP, in which programs are evolved by manipulating program semantics instead of program syntax. This paper introduces a new semantic GP algorithm, called SGP+, which is an extension of an existing algorithm called SGP. New crossover and mutation operators are introduced which address two of the major limitations of SGP: large program trees and reduced accuracy on high-arity problems. Experimental results on deceptive Boolean problems show that programs created by the SGP+ are 3.8 times smaller while still maintaining accuracy as good as, or better than, SGP. Additionally, a statistically significant improvement in program accuracy is observed for several high-arity Boolean problems.", notes = "Department of Computer Sciences, Florida Institute of Technology, 150 W. University Blvd., Melbourne, FL 32901, USA", } @InProceedings{miller:2018:GPTP, author = "Julian F. Miller and Dennis G. Wilson and Sylvain Cussat-Blanc", title = "Evolving Developmental Programs That Build Neural Networks for Solving Multiple Problems", booktitle = "Genetic Programming Theory and Practice XVI", year = "2018", editor = "Wolfgang Banzhaf and Lee Spector and Leigh Sheneman", pages = "137--178", address = "Ann Arbor, USA", month = "17-20 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", isbn13 = "978-3-030-04734-4", URL = "http://link.springer.com/chapter/10.1007/978-3-030-04735-1_8", DOI = "doi:10.1007/978-3-030-04735-1_8", abstract = "A developmental model of an artificial neuron is presented. In this model, a pair of neural developmental programs develop an entire artificial neural network of arbitrary size. The pair of neural chromosomes are evolved using Cartesian Genetic Programming. During development, neurons and their connections can move, change, die or be created. We show that this two-chromosome genotype can be evolved to develop into a single neural network from which multiple conventional artificial neural networks can be extracted. The extracted conventional ANNs share some neurons across tasks. We have evaluated the performance of this method on three standard classification problems: cancer, diabetes and the glass datasets. The evolved pair of neuron programs can generate artificial neural networks that perform reasonably well on all three benchmark problems simultaneously. It appears to be the first attempt to solve multiple standard classification problems using a developmental approach.", } @Article{Miller:GPEM20, author = "Julian Francis Miller", title = "{Cartesian Genetic Programming}: its status and future", journal = "Genetic Programming and Evolvable Machines", year = "2020", volume = "21", number = "1-2", pages = "129--168", month = jun, note = "Twentieth Anniversary Issue", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, Evolutionary algorithms", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-019-09360-6", size = "40 pages", abstract = "Cartesian genetic programming, a well established method of genetic programming, is approximately 20 years old. It represents solutions to computational problems as graphs. Its genetic encoding includes explicitly redundant genes which are well-known to assist in effective evolutionary search. In this article, we review and compare many of the important aspects of the method and findings discussed since its inception. In the process, we make many suggestions for further work which could improve the efficiency of the CGP for solving computational problems.", } @InProceedings{Miller:2020:ALife, author = "Julian Francis Miller", title = "Evolving developmental neural networks to solve multiple problems", booktitle = "2020 Conference on Artificial Life", year = "2020", editor = "Josh Bongard and Juniper Lovato and Laurent Hebert-Dufresne and Radhakrishna Dasari and Lisa Soros", pages = "473--482", address = "online", month = "13-18 " # jul, organisation = "ISAL", publisher = "Massachusetts Institute of Technology", note = "Contributed talk", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, ANN", URL = "https://direct.mit.edu/isal/proceedings/isal2020/473/98437", DOI = "doi:10.1162/isal_a_00252", video_url = "https://www.youtube.com/watch?v=440BoGwtGWo", size = "10 pages", abstract = "We describe a neural model in which two evolved neural programs undergo development and form neural networks. The programs decide whether neurons and their dendrites move, change, die or replicate. We show that the programs can build a neural structure from which multiple conventional ANNs can be extracted each of which can solve a different computational", notes = "Montreal, Canada. See also \cite{Miller:2021:ALife} isal_a_00252 https://direct.mit.edu/isal/proceedings/isal2020/32/1/98387", } @InProceedings{Miller:2021:GPTP, author = "Julian Francis Miller", title = "Designing Multiple {ANNs} with Evolutionary Development: Activity Dependence", booktitle = "Genetic Programming Theory and Practice XVIII", year = "2021", editor = "Wolfgang Banzhaf and Leonardo Trujillo and Stephan Winkler and Bill Worzel", series = "Genetic and Evolutionary Computation", pages = "165--180", address = "East Lansing, USA", month = "19-21 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, ANN", isbn13 = "978-981-16-8112-7", DOI = "doi:10.1007/978-981-16-8113-4_9", abstract = "We use Cartesian genetic programming to evolve developmental programs that construct neural networks. One program represents the neuron soma and the other the dendrite. We show that the evolved programs can build a network from which multiple conventional ANNs can be extracted each of which can solve a different computational problem. We particularly investigate the utility of activity dependence (AD), where the signals passing through dendrites and neurons affect their properties.", notes = "Part of \cite{Banzhaf:2021:GPTP} published after the workshop in 2022", } @Article{Miller:2021:ALife, author = "Julian Francis Miller", title = "{IMPROBED}: Multiple Problem-Solving Brain via Evolved Developmental Programs", journal = "Artificial Life", year = "2021", volume = "27", number = "3-4", pages = "300--335", month = "Summer-Fall", note = "Special issue highlights from the 2020 Conference on Artificial Life", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, ANN, Computational neuro-inspired development, general artificial intelligence, evolutionary algorithms", ISSN = "1064-5462", URL = "https://doi.org/10.1162/artl_a_00346", eprint = "https://direct.mit.edu/artl/article-pdf/27/3%e2%80%934/300/2003291/artl_a_00346.pdf", DOI = "doi:10.1162/artl_a_00346", abstract = "Artificial neural networks (ANNs) were originally inspired by the brain; however, very few models use evolution and development, both of which are fundamental to the construction of the brain. We describe a simple neural model, called IMPROBED, in which two neural programs construct an artificial brain that can simultaneously solve multiple computational problems. One program represents the neuron soma and the other the dendrite. The soma program decides whether neurons move, change, die, or replicate. The dendrite program decides whether dendrites extend, change, die, or replicate. Since developmental programs build networks that change over time, it is necessary to define new problem classes that are suitable to evaluate such approaches. We show that the pair of evolved programs can build a single network from which multiple conventional ANNs can be extracted, each of which can solve a different computational problem. Our approach is quite general and it could be applied to a much wider variety of problems.", notes = "Published March 2022. See also \cite{Wilson:2022:sigevolution}", } @Article{Milone_2007_GPEM, author = "Diego H. Milone", title = "Adaptive learning of polynomial networks, genetic programming, backpropagation and {Bayesian} methods, series on genetic and evolutionary computation, Springer Science, New York, N. Nikolaev and H. Iba, 2006, Vol. XIV, 316 pp, ISBN 0:387-31239-0", journal = "Genetic Programming and Evolvable Machines", year = "2007", volume = "8", number = "3", pages = "289--291", month = sep, note = "Book review", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dR8GO", DOI = "doi:10.1007/s10710-007-9034-x", size = "3 pages", notes = "Review of \cite{nikolaev:2006:book}", } @InCollection{milstein:2000:CCCS, author = "Ido Milstein", title = "Co-Evolution of Communication in a Competitive Setting", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "286--295", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms", notes = "part of \cite{koza:2000:gagp}", } @Article{min:2003:WUJNS, author = "Liu Min and Hu Bao-qing", title = "Modeling dynamic systems by using the nonlinear difference equations based on genetic programming", journal = "Wuhan University Journal of Natural Sciences", year = "2003", volume = "8", number = "1", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/BF02899487", DOI = "doi:10.1007/BF02899487", } @InCollection{min:1994:constraint, author = "Sherman L. Min", title = "Feasibility of evolving self-learned pattern recognition applied towards the solution of a constrained system using genetic programming", booktitle = "Genetic Algorithms at Stanford 1994", year = "1994", editor = "John R. Koza", pages = "110--119", address = "Stanford, California, 94305-3079 USA", month = dec, organisation = "Stanford University", publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming, Rubik's Cube", ISBN = "0-18-187263-3", notes = "2 by 2 by 2 cube This volume contains 20 papers written and submitted by students describing their term projects for the course {"}Genetic Algorithms and Genetic Programming{"} (Computer Science 426) at Stanford University offered during the fall quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs426.html", } @InProceedings{conf/kes/MinarikS11, author = "Milos Minarik and Lukas Sekanina", title = "Evolution of Iterative Formulas Using Cartesian Genetic Programming", booktitle = "Proceedings of the 15th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (KES 2011) Part {I}", year = "2011", editor = "Andreas K{\"o}nig and Andreas Dengel and Knut Hinkelmann and Koichi Kise and Robert J. Howlett and Lakhmi C. Jain", volume = "6881", series = "Lecture Notes in Computer Science", pages = "11--20", address = "Kaiserslautern, Germany", month = sep # " 12-14", publisher = "Springer", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", isbn13 = "978-3-642-23850-5", DOI = "doi:10.1007/978-3-642-23851-2_2", size = "10 pages", abstract = "Many functions such as division or square root are implemented in hardware using iterative algorithms. We propose a genetic programming-based method to automatically design simple iterative algorithms from elementary functions. In particular, we demonstrated that Cartesian Genetic Programming can evolve various iterative formulae for tasks such as division or determining the greatest common divisor using a reasonable computational effort.", notes = "p19 'In our future work, we plan to evolve iterative formulas for other functions (square root, exponential etc.)' Goldschmidt Division, Euclidean Algorithm", affiliation = "Faculty of Information Technology, Brno University of Technology, Bozetechova 2, 612 66 Brno, Czech Republic", bibdate = "2011-09-13", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/kes/kes2011-1.html#MinarikS11", } @InProceedings{minarik:2014:EuroGP, author = "Milos Minarik and Lukas Sekanina", title = "Exploring the Search Space of Hardware / Software Embedded Systems by Means of GP", booktitle = "17th European Conference on Genetic Programming", year = "2014", editor = "Miguel Nicolau and Krzysztof Krawiec and Malcolm I. Heywood and Mauro Castelli and Pablo Garcia-Sanchez and Juan J. Merelo and Victor M. {Rivas Santos} and Kevin Sim", series = "LNCS", volume = "8599", publisher = "Springer", pages = "112--123", address = "Granada, Spain", month = "23-25 " # apr, organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-662-44302-6", DOI = "doi:10.1007/978-3-662-44303-3_10", abstract = "This paper presents a new platform for development of small application-specific digital embedded architectures based on a data path controlled by a microprogram. Linear genetic programming is extended to evolve a program for the controller together with suitable hardware architecture. Experimental results show that the platform can automatically design general solutions as well as highly optimised specialised solutions to benchmark problems such as maximum, parity or iterative division.", notes = "Part of \cite{Nicolau:2014:GP} EuroGP'2014 held in conjunction with EvoCOP2014, EvoBIO2014, EvoMusArt2014 and EvoApplications2014", } @InProceedings{Minarik:2017:EuroGP, author = "Milos Minarik and Lukas Sekanina", title = "On Evolutionary Approximation of Sigmoid Function for HW/SW Embedded Systems", booktitle = "EuroGP 2017: Proceedings of the 20th European Conference on Genetic Programming", year = "2017", month = "19-21 " # apr, editor = "Mauro Castelli and James McDermott and Lukas Sekanina", series = "LNCS", volume = "10196", publisher = "Springer Verlag", address = "Amsterdam", pages = "343--358", organisation = "species", keywords = "genetic algorithms, genetic programming: Poster", isbn13 = "978-3-319-55695-6", DOI = "doi:10.1007/978-3-319-55696-3_22", abstract = "Providing machine learning capabilities on low cost electronic devices is a challenging goal especially in the context of the Internet of Things paradigm. In order to deliver high performance machine intelligence on low power devices, suitable hardware accelerators have to be introduced. In this paper, we developed a method enabling to evolve a hardware implementation together with a corresponding software controller for key components of smart embedded systems. The proposed approach is based on a multi-objective design space exploration conducted by means of extended linear genetic programming. The approach was evaluated in the task of approximate sigmoid function design which is an important component of hardware implementations of neural networks. During these experiments, we automatically rediscovered some approximate sigmoid functions known from the literature. The method was implemented as an extension of an existing platform supporting concurrent evolution of hardware and software of embedded systems.", notes = "Part of \cite{Castelli:2017:GP} EuroGP'2017 held inconjunction with EvoCOP2017, EvoMusArt2017 and EvoApplications2017", } @PhdThesis{Minarik:thesis, author = "Milos Minarik", title = "Concurrent evolutionary design of hardware and software", school = "Brno University of Technology", year = "2018", address = "Czech Republic", keywords = "genetic algorithms, genetic programming, hardware software codesign, soubezna evoluce, evolucni navrh, evolucni optimalizace, aproximace sigmoidy, obrazove filtry, hardware software codesign, concurrent evolution, evolutionary design, evolutionary optimization, sigmoid approximation, image filters", URL = "https://hdl.handle.net/11012/187316", timestamp = "Wed, 04 May 2022 13:00:54 +0200", biburl = "https://dblp.org/rec/phd/basesearch/Minarik18.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://dspace.vutbr.cz/handle/11012/187316", URL = "https://dspace.vutbr.cz/bitstream/handle/11012/187316/thesis-1.pdf", size = "48 pages", abstract = "Genetic programming (GP) can, to some extent, automatically generate desired programs without asking the user to specify how to do it. It has been used to solve a wide range of practical problems and produce a number of human-competitive results in different fields. An interesting and practically untouched question is whether for a given problem, GP can generate a highly optimized programmable computational model (platform) together with a program running on the platform, solving the problem and satisfying all constrains such as on the area on a chip and speed. In a multi-objective scenario, the user would obtain a set of non-dominated solutions showing various tradeoffs between resources (the area, power consumption) and performance (the speed of execution). This problem can be seen as a concurrent development of hardware and software, simply, HW/SW codesign. This thesis explores the ways how to evolve hardware platforms together with programs in the case that the specification is given in terms of a set of input-output vectors. The initial model of the architecture was created and the evolutionary framework capable of maintaining and evolving the population of such architectures was implemented. Candidate micro-programmed architectures were evolved together with programs using extended linear genetic programming. Several simple experiments were carried out and the framework proved competitive with state-of-the-art methods. The framework was subsequently extended addressing the weak points identified during the initial experiments. The extended framework was validated by means of more complex experiments. One of them focused on an effective implementation of sigmoid function approximation. Various implementations of sigmoid approximation were evolved (sequentional as well as purely combinational). The proposed framework provided several well-known solutions and even optimized some of them for the particular input domain chosen for the experiment. The next set of experiments was supposed to evolve an image filter reducing salt-and-pepper impulse noise. The framework was able to evolve the concept of switching-based filter and even the variation of a switching-based median filter comparable to the filters commonly used. This thesis proved that small-size HW/SW systems can be designed and optimized by means of genetic programming. Moving to an automated evolutionary design of more complex HW/SW systems is an open research problem waiting for a future research.", notes = "2017 ? Also known as \cite{DBLP:phd/basesearch/Minarik18}", } @InProceedings{Mingo:2007:cec, author = "Jack Mario Mingo and Ricardo Aler", title = "Grammatical Evolution Guided by Reinforcement", booktitle = "2007 IEEE Congress on Evolutionary Computation", year = "2007", editor = "Dipti Srinivasan and Lipo Wang", pages = "1475--1482", address = "Singapore", month = "25-28 " # sep, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, grammatical evolution", isbn13 = "1-4244-1340-0", DOI = "doi:10.1109/CEC.2007.4424646", abstract = "Grammatical Evolution is an evolutionary algorithm able to develop, starting from a grammar, programs in any language. Starting from the point that individual learning can improve evolution, in this paper it is proposed an extension of Grammatical Evolution that looks at learning by reinforcement as a learning method for individuals. This way, it is possible to incorporate the Baldwinian mechanism to the evolutionary process. The effect is widened with the introduction of the Lamarck hypothesis. The system is tested in two different domains: a symbolic regression problem and an even parity Boolean function. Results show that for these domains, a system which includes learning obtains better results than a grammatical evolution basic system.", notes = "Q tree CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C", file = "1738.pdf", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/cec/cec2007.html#MingoA07", } @Article{Mingo:2008:Revista, author = "Jack M. Mingo and Ricardo Aler", title = "IWPAAMS2007-02: The Role Of The Lamarck Hypothesis In The Grammatical Evolution Guided By Reinforcement", journal = "Latin America Transactions, IEEE (Revista IEEE America Latina)", year = "2008", month = oct, volume = "6", number = "6", pages = "500--504", note = "In Spanish", keywords = "genetic algorithms, genetic programming, grammatical evolution, IWPAAMS2007-02, Kephera robot simulation, Lamarckian hypothesis, evolutionary algorithm, reinforcement learning, evolutionary computation, grammars, learning (artificial intelligence)", DOI = "doi:10.1109/TLA.2008.4908181", ISSN = "1548-0992", abstract = "Grammatical evolution is an evolutionary algorithm able to develop programs in any language, defined by a grammar. The evolutionary process may be improved if we let the individuals learn during their lifetime. with this aim, the grammatical evolution guided by reinforcement, an algorithm which merges evolution and learning, was created. Grammatical evolution guided by reinforcement uses a Lamarckian mechanism for replacing the original genotypes when a successful learning has occurred. This paper explores the role of the Lamarckian hypothesis. At the same time, grammatical evolution guided by reinforcement is tested in a new domain: autonomous navigation in a Kephera robot simulation.", notes = "Also known as \cite{4908181} Article also in proceedings?? 2007, editor = {J. Bajo, V. Alonso, L. Joyanes, J.M. Corchado}, isbn = {978-84-611-8858-1},", } @InProceedings{Mingo:2011:IWINAC, author = "Jack Mario Mingo and Ricardo Aler", title = "An Incremental Model of Lexicon Consensus in a Population of Agents by Means of Grammatical Evolution, Reinforcement Learning and Semantic Rules", year = "2011", booktitle = "4th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2011, Part I", editor = "Jose Ferrandez and Jose Alvarez Sanchez and Felix de la Paz and F. Toledo", volume = "6686", series = "Lecture Notes in Computer Science", pages = "40--49", address = "La Palma, Canary Islands, Spain", month = may # " 30 - " # jun # " 3", publisher = "Springer", keywords = "genetic algorithms, genetic programming, grammatical evolution, Swarm Intelligence, Language Acquisition and Language Development", DOI = "doi:10.1007/978-3-642-21344-1_5", size = "10 pages", abstract = "We present an incremental model of lexicon consensus in a population of simulated agents. The emergent lexicon is evolved with a hybrid algorithm which is based on grammatical evolution with semantic rules and reinforcement learning. The incremental model allows to add subsequently new agents and objects to the environment when a consensual language has emerged for a steady set of agents and objects. The main goal in the proposed system is to test whether the emergent lexicon can be maintained during the execution when new agents and object are added. The proposed system is completely based on grammars and the results achieved in the experiments show how building a language starting from a grammar can be a promising method in order to develop artificial languages.", affiliation = "Computer Science Department, Autonomous University of Madrid, Spain", } @InCollection{Mingo:2012:idarla, author = "Jack Mario Mingo and Ricardo Aler and Dario Maravall and Javier {de Lope}", title = "Static and Dynamic Multi-Robot Coverage with Grammatical Evolution Guided by Reinforcement and Semantic Rules", booktitle = "Intelligent Data Analysis for Real-Life Applications: Theory and Practice", publisher = "IGI Global", year = "2012", editor = "Rafael Magdalena-Benedito and Marcelino Martinez-Sober and Jose Maria Martinez-Martinez and Joan Vila-Frances and Pablo Escandell-Montero", chapter = "17", pages = "336--365", address = "Hershey", month = jun, keywords = "genetic algorithms, genetic programming, grammatical evolution", issn13 = "9781466618060", URL = "http://www.igi-global.com/book/intelligent-data-analysis-real-life/62622", DOI = "doi:10.4018/978-1-4666-1806-0.ch017", abstract = "In recent years there has been an increasing interest in the application of robot teams to solve some kind of problems. Although there are several environments and tasks where a team of robots can deliver better results than a single robot, one of the most active attention focus is concerned with solving coverage problems, either static or dynamic, mainly in unknown environments. The authors propose a method in this work to solve these problems in simulation by means of grammatical evolution of high-level controllers. Evolutionary algorithms have been successfully applied in many applications, but better results can be achieved when evolution and learning are combined in some way. This work uses one of this hybrid algorithms called Grammatical Evolution guided by Reinforcement but the authors enhance it by adding semantic rules in the grammatical production rules. This way, they can build automatic high-level controllers in fewer generations and the solutions found are more readable as well. Additionally, a study about the influence of the number of members implied in the evolutionary process is addressed.", } @Article{journals/cai/MingoAMA13, author = "Jack Mario Mingo and Ricardo Aler and Dario Maravall and Javier {de Lope Asiain}", title = "Investigations into Lamarckism, Baldwinism and Local Search in Grammatical Evolution Guided by Reinforcement", journal = "Computing and Informatics", year = "2013", number = "3", volume = "32", keywords = "genetic algorithms, genetic programming, grammatical evolution", bibdate = "2013-08-16", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/cai/cai32.html#MingoAMA13", pages = "595--627", URL = "http://www.cai.sk/ojs/index.php/cai/article/view/1735", size = "33 pages", abstract = "Grammatical Evolution Guided by Reinforcement is an extension of Grammatical Evolution that tries to improve the evolutionary process adding break a learning process for all the individuals in the population. With this aim, each individual is given a chance to learn through a reinforcement learning mechanism during its lifetime. The learning process is completed with a Lamarckian mechanism in which an original genotype is replaced by the best learnt genotype for the individual. In a way, Grammatical Evolution Guided by Reinforcement shares an important feature with other hybrid algorithms, i.e. global search in the evolutionary process combined with local search in the learning process. In this paper the role of the Lamarck Hypothesis is reviewed and a solution inspired only in the Baldwin effect is included as well. Besides, different techniques about the trade-off between exploitation and exploration in the reinforcement learning step followed by Grammatical Evolution Guided by Reinforcement are studied. In order to evaluate the results, the system is applied on two different domains: a simple autonomous navigation problem in a simulated Kephera robot and a typical Boolean function problem.", } @PhdThesis{Mingo:thesis, author = "Jack Mario {Mingo Postiglioni}", title = "Auto-Emergencia de Comunicacion Sintactica en Entornos Estaticos y Dinamicos para Grupos de Robots mediante Evolucion Gramatical y Aprendizaje por Refuerzo", school = "Universidad Politecnica de Madrid", year = "2014", address = "Spain", month = nov, keywords = "genetic algorithms, genetic programming", URL = "http://oa.upm.es/33171/", URL = "http://oa.upm.es/33171/1/JACK_MARIO_MINGO_POSTIGLIONI.pdf", size = "280 pages", abstract = "The idea of giving a language to a group of robots or artificial agents has been the subject of intense study in recent decades. The first attempts have focused on the development and emergence of a conventionally shared vocabulary. The advantages that can provide a common vocabulary are evident and therefore a more complex language that combines words would be even more beneficial. Thus some proposals are put forward towards the emergence of a consensual language with a syntactical structure in similar terms to the human language. This work follows this trend. Taking the human language as a model means taking some of the assumptions and theories that disciplines such as philosophy, psychology or linguistics among others have provided. According to these theoretical positions language has a double formal and functional dimension. Based on its formal dimension it seems clear that language follows rules, so that the use of a grammar has been considered essential for representation, but also because grammars are a very simple and powerful device that easily generates these symbolic structures. As for the functional dimension perhaps the most influential theory of recent times, the Theory of Speech Acts has been taken into account. This theory is based on the Wittgenstein's idea about that the meaning lies in the use of language, to the extent that it is understood as a way of acting and behaving. Having into account these issues this work implements some computational models in order to test if they allow a group of robots to reach in an autonomous way a shared language by means of individual interaction among them, that is by means of language games. Specifically, three different models of language for robots are proposed: 1. A reinforcement learning based model in which interactions and language use are key to its emergence. This model uses a static probabilistic generative grammar which is designed beforehand. The model is applied to two different groups: one formed exclusively by robots and other combining robots and a human. Therefore, in the second case the learning process is supervised by the human. 2. A model based on grammatical evolution that allows us to study not only the syntactic consensus, but also the very genesis of language. This model uses a universal grammar that allows robots to evolve for themselves the most appropriate grammar according to the current linguistic situation they deal with. 3. A model based on grammatical evolution and reinforcement learning that takes aspects of the previous models and increases their possibilities. This model allows robots to develop a language in order to adapt to dynamic language situations that can change over time and also allows the imposition of syntactical order restrictions which are very common in complex syntactic structures. All models involve a decentralised and self-organised approach so that none of the robots is the language's owner and everyone must cooperate and work together in a coordinated manner to achieve syntactic consensus. In each case experiments are presented in order to validate the proposed models, both in terms of success about the emergence of language and it relates to the study of important parallel issues, such as human-computer interaction or the very genesis of language.", abstract = "La idea de dotar a un grupo de robots o agentes artificiales de un lenguaje ha sido objeto de intenso estudio en las ultimas decadas. Como no podia ser de otra forma los primeros intentos se enfocaron hacia el estudio de la emergencia de vocabularios compartidos convencionalmente por el grupo de robots. Las ventajas que puede ofrecer un lexico comun son evidentes, como tambien lo es que un lenguaje con una estructura mas compleja, en la que se pudieran combinar palabras, seria todavia mas beneficioso. Surgen asi algunas propuestas enfocadas hacia la emergencia de un lenguaje consensuado que muestre una estructura sintactica similar al lenguaje humano, entre las que se encuentra este trabajo. Tomar el lenguaje humano como modelo supone adoptar algunas de las hipotesis y teorias que disciplinas como la filosofia, la psicologia o la linguistica entre otras se han encargado de proponer. Segun estas aproximaciones teoricas el lenguaje presenta una doble dimension formal y funcional. En base a su dimension formal parece claro que el lenguaje sigue unas reglas, por lo que el uso de una gramatica se ha considerado esencial para su representacion, pero tambien porque las gramaticas son un dispositivo muy sencillo y potente que permite generar facilmente estructuras simbolicas. En cuanto a la dimension funcional se ha tenido en cuenta la teoria quiza mas influyente de los ultimos tiempos, que no es otra que la Teoria de los Actos del Habla. Esta teoria se basa en la idea de Wittgenstein por la que el significado reside en el uso del lenguaje, hasta el punto de que este se entiende como una manera de actuar y de comportarse, en definitiva como una forma de vida. Teniendo presentes estas premisas en esta tesis se pretende experimentar con modelos computacionales que permitan a un grupo de robots alcanzar un lenguaje comun de manera autonoma, simplemente mediante interacciones individuales entre los robots, en forma de juegos de lenguaje. Para ello se proponen tres modelos distintos de lenguaje: Un modelo basado en gramaticas probabilisticas y aprendizaje por refuerzo en el que las interacciones y el uso del lenguaje son claves para su emergencia y que emplea una gramatica generativa estatica y disenada de antemano. Este modelo se aplica a dos grupos distintos: uno formado exclusivamente por robots y otro que combina robots y un humano, de manera que en este segundo caso se plantea un aprendizaje supervisado por humanos. Un modelo basado en evolucion gramatical que permite estudiar no solo el consenso sintactico, sino tambien cuestiones relativas a la genesis del lenguaje y que emplea una gramatica universal a partir de la cual los robots pueden evolucionar por si mismos la gramatica mas apropiada segun la situacion lingistica que traten en cada momento. Un modelo basado en evolucion gramatical y aprendizaje por refuerzo que toma aspectos de los anteriores y amplia las posibilidades de los robots al permitir desarrollar un lenguaje que se adapta a situaciones linguisticas dinamicas que pueden cambiar en el tiempo y tambien posibilita la imposicion de restricciones de orden muy frecuentes en las estructuras sintacticas complejas. Todos los modelos implican un planteamiento descentralizado y auto-organizado, de manera que ninguno de los robots es el dueno del lenguaje y todos deben cooperar y colaborar de forma coordinada para lograr el consenso sintactico. En cada caso se plantean experimentos que tienen como objetivo validar los modelos propuestos, tanto en lo relativo al exito en la emergencia del lenguaje como en lo relacionado con cuestiones paralelas de importancia, como la interaccion hombre-maquina o la propia genesis del lenguaje.", notes = "In Spanish Supervisors: Ricardo Aler and Dario Maravall Access (via http://oa.upm.es/33171/) allowed only for users at the campus until 18 June 2015", } @Article{Mingo:2016:IS, author = "Jack Mario Mingo and Ricardo Aler", title = "A competence-performance based model to develop a syntactic language for artificial agents", journal = "Information Sciences", year = "2016", volume = "373", pages = "79--94", month = "10 " # dec, keywords = "genetic algorithms, genetic programming, Grammatical evolution, Stochastic grammars, Reinforcement learning, Language games, Multi-agents systems", ISSN = "0020-0255", URL = "http://www.sciencedirect.com/science/article/pii/S0020025516306727", DOI = "doi:10.1016/j.ins.2016.08.088", size = "16 pages", abstract = "The hypothesis of language use is an attractive theory in order to explain how natural languages evolve and develop in social populations. In this paper we present a model partially based on the idea of language games, so that a group of artificial agents are able to produce and share a symbolic language with syntactic structure. Grammatical structure is induced by grammatical evolution of stochastic regular grammars with learning capabilities, while language development is refined by means of language games where the agents apply on-line probabilistic reinforcement learning. Within this framework, the model adapts the concepts of competence and performance in language, as they have been proposed in some linguistic theories. The first experiments in this article have been organized around the linguistic description of visual scenes with the possibility of changing the referential situations. A second and more complicated experimental setting is also analysed, where linguistic descriptions are enforced to keep word order constraints.", } @Article{Mingo:GPEM:shared_grammars, author = "Jack Mario Mingo and Ricardo Aler", title = "Evolution of shared grammars for describing simulated spatial scenes with grammatical evolution", journal = "Genetic Programming and Evolvable Machines", year = "2018", volume = "19", number = "1-2", pages = "235--270", month = jun, keywords = "genetic algorithms, genetic programming, Grammatical evolution, Dynamics of artificial languages, Language games, Multi-agent systems", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-017-9315-y", size = "36 pages", abstract = "We propose a model based on an evolutionary process combined with an adapted planning process to develop a limited spatial language with a syntactical structure in a team of artificial agents. Syntax is induced by means of a grammar and the grammar itself evolves in order to reach a syntactical agreement in the team. Evolution is implemented by adapting an evolutionary algorithm where each agent in the team manages a population of chromosomes that represent possible grammars. Grammars can be used by agents to generate utterances which are subsequently applied in language games to describe spatial relations. A planning process builds the sentences, but agents select the syntactical alternatives according to their current communicative intentions. Results in two different linguistic task show how a shared grammar can be developed in the group of agents.", } @InCollection{minns:1996:hmihc, author = "A. W. Minns and V. Babovic", title = "Hydrological Modelling in a Hydroinformatics Context", booktitle = "Distributed Hydrological Modelling", publisher = "Kluwer Academic Publishers", year = "1996", editor = "Michael B. Abbott and Jens Christian Refsgaard", pages = "297--312", keywords = "genetic algorithms, genetic programming", isbn13 = "978-0-7923-4042-3", URL = "http://www.springer.com/earth+sciences+and+geography/hydrogeology/book/978-0-7923-4042-3", } @PhdThesis{Minns:thesis, author = "Anthony William Minns", title = "Artificial Neural Networks as Subsymbolic Process Descriptors", school = "UNESCO-IHE Institute for Water Education, Technische Universiteit Delft", year = "1998", address = "The Netherlands", keywords = "genetic algorithms, genetic programming, Rainfall-runoff Modelling, ANN, Cellular automata, Neural networks (Computer science), Logic circuits, Hydraulic engineering, artificial intelligence, artificial neural networks, EAs, LCS, lattice gas dynamics, Storm, Extreme Events, Cantley Estate in Doncaster, Silk Stream Hendon Lane, Dollis Brook Colindeep Lane, Catchment, Brent Reservoir, London Clay, UK, RORB, IMP", publisher = "Balkema, Rotterdam", isbn13 = "9054104090", URL = "https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=24a4bb0e44d938f847cdcdd8667c82820b342bb9", URL = "https://ihedelftrepository.contentdm.oclc.org/digital/collection/phd1/id/22089/", URL = "https://catalogue.nla.gov.au/catalog/1915787.dc_xml", size = "140 pages", notes = "Section 6.2 brief description of GP performance in comparison with ANN. p65 'extreme compactness of the GP expressions' UK Flood Studies Report (Natural Environment Research Council, 1975)", } @Article{Minns:2000:JH, author = "Anthony W. Minns", title = "Subsymbolic methods for data mining in hydraulic engineering", journal = "Journal of Hydroinformatics", year = "2000", volume = "2", number = "1", pages = "3--13", month = jan, keywords = "genetic algorithms, genetic programming, artificial neural networks, ANN, data mining, subsymbolic methods", ISSN = "1464-7141", URL = "http://www.iwaponline.com/jh/002/0003/0020003.pdf", DOI = "doi:10.2166/hydro.2000.0002", size = "11 pages", abstract = "This paper describes the results of experiments with artificial neural networks (ANNs) and genetic programming (GP) applied to some problems of data mining. It is shown how these subsymbolic methods can discover usable relations in measured and experimental data with little or no a priori knowledge of the governing physical process characteristics. On the one hand, the ANN does not explicitly identify a form of model but this form is implicit in the ANN, being encoded within the distribution of weights. However, in cases where the exact form of the empirical relation is not considered as important as the ability of the formula to map the experimental data accurately, the ANN provides a very efficient approach. Furthermore, it is demonstrated how numerical schemes, and thus partial differential equations, may be derived directly from data by interpreting the weight distribution within a trained ANN. On the other hand, GP evolutionary force is directed towards the creation of models that take a symbolic form. The resulting symbolic expressions are generally less accurate than the ANN in mapping the experimental data, however, these expressions may sometimes be more easily examined to provide insight into the processes that created the data. An example is used to demonstrate how GP can generate a wide variety of formulae, of which some may provide genuine insight while others may be quite useless.", } @Article{Miorandi2009, author = "Daniele Miorandi and Lidia Yamamoto and Francesco {De Pellegrini}", title = "A survey of evolutionary and embryogenic approaches to autonomic networking", journal = "Computer Networks", year = "2010", volume = "54", number = "6", pages = "944--959", month = "29 " # apr, ISSN = "1389-1286", DOI = "doi:10.1016/j.comnet.2009.08.021", URL = "http://www.sciencedirect.com/science/article/B6VRG-4X6FNS9-2/2/9be45a6ee371c9df9bae76c468300991", keywords = "genetic algorithms, genetic programming, Autonomic networking, Evolutionary computation, Genetic Algorithm, Chemical computing, Artificial embryogenies", abstract = "The term 'autonomic networking' refers to network-level software systems capable of self-management, according to the principles outlined by the Autonomic Computing initiative. Autonomicity is widely recognized as a crucial property to harness the growing complexity of current networked systems. In this paper, we present a review of state-of-the-art techniques for the automated creation and evolution of software, with application to network-level functionalities. The main focus of the survey are biologically-inspired bottom-up approaches, in which complexity is grown from interactions among simpler units. First, we review evolutionary computation, highlighting aspects that apply to the automatic optimisation of computer programs in online, dynamic environments. Then, we review chemical computing, discussing its suitability as execution model for autonomic software undergoing self-optimization by code rewriting. Last, we survey approaches inspired by embryology, in which artificial entities undergo a developmental process. The overview is completed by an outlook into the major technical challenges for the application of the surveyed techniques to autonomic systems.", } @InProceedings{Miquilini:2016:CEC, author = "Patricia Miquilini and Rodrigo C. Barros and Vinicius V {de Melo} and Marcio P. Basgalupp", title = "Enhancing Discrimination Power with Genetic Feature Construction: A Grammatical Evolution Approach", booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016)", year = "2016", editor = "Yew-Soon Ong", pages = "3824--3831", address = "Vancouver", month = "24-29 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", isbn13 = "978-1-5090-0623-6", DOI = "doi:10.1109/CEC.2016.7744274", abstract = "Data set preprocessing is a critical step for the successful application of machine learning algorithms in classification tasks. Even though we rely on learning algorithms to pinpoint the optimal decision boundaries in the feature space by properly detecting latent relationships among the input features, their performance is often bounded by the discriminative power of the available features. Therefore, much effort has been devoted to developing preprocessing methods that are capable of transforming the input data with the final goal of aiding the machine learning algorithm in building high-quality classification models. One such a method is feature construction, which is a flexible preprocessing procedure that exploits linear and nonlinear transformations of the original feature space in an attempt to capture useful information that is not explicit in the original data. Since the task of feature construction can be modelled as a heuristic search in the space of novel latent features, this paper investigates an evolutionary approach for performing such a task, namely grammatical evolution (GE). In our proposed approach, GE is employed for building an extra novel feature from the available input data in order to maximize the predictive performance of the learning algorithm in training data. Results show that many interesting implicit relationships are indeed found by the evolutionary approach, improving the performance of two well known decision-tree induction algorithms.", notes = "WCCI2016", } @Article{Mirabdolazimi:2017:CBM, author = "S. M. Mirabdolazimi and Gh. Shafabakhsh", title = "Rutting depth prediction of hot mix asphalts modified with forta fiber using artificial neural networks and genetic programming technique", journal = "Construction and Building Materials", volume = "148", pages = "666--674", year = "2017", ISSN = "0950-0618", DOI = "doi:10.1016/j.conbuildmat.2017.05.088", URL = "http://www.sciencedirect.com/science/article/pii/S0950061817309753", abstract = "The most significant problems in the maintenance of highway networks are low strength against dynamic loads and short service life of pavements. In recent years using additive materials to improve the performance of asphalt mix under dynamic loading has been remarkably developed. Previous research show that adding appropriate polymer materials to hot mix asphalt improves the dynamic properties of these mixtures. A series of dynamic creep test were conducted under different temperatures and stress levels to evaluate rutting performance of asphalt samples. The proposed artificial neural networks (ANN) model for rutting depth has shown good agreement with experimental results. Beside, in this study a comparison is made between the Burgers model and genetic programming (GP) model in estimating the rutting depth of asphalt mix. Performance of the genetic programming model is quite satisfactory. The obtained results can be used to provide an appropriate approach to enhance the performance of asphalt pavements under dynamic loads.", keywords = "genetic algorithms, genetic programming, HMA, Rutting depth, Forta fiber, Artificial neural networks", } @InProceedings{Miragaia:2018:evoApplications, author = "Rolando Miragaia and Gustavo Reis and Francisco Fernandez and Tiago Inacio and Carlos Grilo", title = "{CGP4Matlab} A Cartesian Genetic Programming {MATLAB} Toolbox for Audio and Image Processing", booktitle = "21st International Conference on the Applications of Evolutionary Computation, EvoIASP 2018", year = "2018", editor = "Stefano Cagnoni and Mengjie Zhang", series = "LNCS", volume = "10784", publisher = "Springer", pages = "455--471", address = "Parma, Italy", month = "4-6 " # apr, organisation = "Species", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, MATLAB toolbox, Pitch estimation", isbn13 = "978-3-319-77537-1", DOI = "doi:10.1007/978-3-319-77538-8_31", code_url = "https://github.com/tiagoinacio/cgp4matlab", abstract = "This paper presents and describes CGP4Matlab, a powerful toolbox that allows to run Cartesian Genetic Programming within MATLAB. This toolbox is particularly suited for signal processing and image processing problems. The implementation of CGP4Matlab, which can be freely downloaded, is described. Some encouraging results on the problem of pitch estimation of musical piano notes achieved using this toolbox are also presented. Pitch estimation of audio signals is a very hard problem with still no generic and robust solution found. Due to the highly flexibility of CGP4Matlab, we managed to apply a new Cartesian genetic programming based approach to the problem of pitch estimation. The obtained results are comparable with the state of the art algorithms.", notes = "EvoApplications2018 held in conjunction with EuroGP'2018 EvoCOP2018 and EvoMusArt2018 http://www.evostar.org/2018/cfp_evoapps.php", } @InProceedings{Miragaia:2020:SSCI, author = "Rolando Miragaia and Gustavo Reis and Francisco Fernandez {de Vega} and Francisco Chavez", title = "Multi Pitch Estimation of Piano Music using Cartesian Genetic Programming with Spectral Harmonic Mask", booktitle = "2020 IEEE Symposium Series on Computational Intelligence (SSCI)", year = "2020", pages = "1800--1807", month = dec, keywords = "genetic algorithms, genetic programming, cartesian genetic programming", DOI = "doi:10.1109/SSCI47803.2020.9308178", abstract = "Piano notes recognition, or pitch estimation of piano notes has been a popular research topic for many years, and is still investigated nowadays. It is a fundamental task during the process of automatic music transcription (extracting the musical score from an acoustic signal). We take advantage of Cartesian Genetic Programming (CGP) to evolve mathematical functions that act as independent classifiers for piano notes. These classifiers are then used to identify the presence of piano notes in polyphonic audio signals. This paper describes our technique and the latest improvements made in our research. The main feature is the introduction of spectral harmonic masks in the binarization process for measuring the fitness values that has allowed to improve the classification rate: 1percent in the F-measure mean result. Our system architecture is also described to show the feasibility of its parallelization, which will reduce the computing time.", notes = "Also known as \cite{9308178}", } @InProceedings{DBLP:conf/sac/MiragaiaVR21, author = "Rolando Miragaia and Francisco {Fernandez de Vega} and Gustavo Reis", editor = "Chih-Cheng Hung and Jiman Hong and Alessio Bechini and Eunjee Song", title = "Evolving a multi-classifier system with cartesian genetic programming for multi-pitch estimation of polyphonic piano music", booktitle = "{SAC} '21: The 36th {ACM/SIGAPP} Symposium on Applied Computing, Virtual Event, Republic of Korea, March 22-26, 2021", pages = "472--480", publisher = "{ACM}", year = "2021", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", URL = "https://doi.org/10.1145/3412841.3441927", DOI = "doi:10.1145/3412841.3441927", timestamp = "Mon, 03 May 2021 01:00:00 +0200", biburl = "https://dblp.org/rec/conf/sac/MiragaiaVR21.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{miragaia:2021:AS, author = "Rolando Miragaia and Francisco Fernandez and Gustavo Reis and Tiago Inacio", title = "Evolving a {Multi-Classifier} System for {Multi-Pitch} Estimation of Piano Music and Beyond: An Application of Cartesian Genetic Programming", journal = "Applied Sciences", year = "2021", volume = "11", number = "7", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", ISSN = "2076-3417", URL = "https://www.mdpi.com/2076-3417/11/7/2902", DOI = "doi:10.3390/app11072902", abstract = "This paper presents a new method with a set of desirable properties for multi-pitch estimation of piano recordings. We propose a framework based on a set of classifiers to analyse audio input and to identify piano notes present in a given audio signal. Our system's classifiers are evolved using Cartesian genetic programming: we take advantage of Cartesian genetic programming to evolve a set of mathematical functions that act as independent classifiers for piano notes. Two significant improvements are described: the use of a harmonic mask for better fitness values and a data augmentation process for improving the training stage. The proposed approach achieves competitive results using F-measure metrics when compared to state-of-the-art algorithms. Then, we go beyond piano and show how it can be directly applied to other musical instruments, achieving even better results. Our system's architecture is also described to show the feasibility of its parallelization and its implementation as a real-time system. Our methodology is also a white-box optimisation approach that allows for clear analysis of the solutions found and for researchers to learn and test improvements based on the new findings.", notes = "also known as \cite{app11072902}", } @Misc{DBLP:journals/corr/abs-2202-04039, author = "Iliya Miralavy and Alexander Bricco and Assaf A. Gilad and Wolfgang Banzhaf", title = "Using Genetic Programming to Predict and Optimize Protein Function", howpublished = "arXiv", volume = "abs/2202.04039", year = "2022", month = "23 " # feb, keywords = "genetic algorithms, genetic programming", URL = "https://arxiv.org/abs/2202.04039", eprinttype = "arXiv", eprint = "2202.04039", timestamp = "Thu, 10 Feb 2022 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/corr/abs-2202-04039.bib", bibsource = "dblp computer science bibliography, https://dblp.org", size = "23 pages", abstract = "Protein engineers conventionally use tools such as Directed Evolution to find new proteins with better functionalities and traits. More recently, computational techniques and especially machine learning approaches have been recruited to assist Directed Evolution, showing promising results. we propose POET, a computational Genetic Programming tool based on evolutionary computation methods to enhance screening and mutagenesis in Directed Evolution and help protein engineers to find proteins that have better functionality. As a proof-of-concept we use peptides that generate MRI contrast detected by the Chemical Exchange Saturation Transfer contrast mechanism. The evolutionary methods used in POET are described, and the performance of POET in different epochs of our experiments with Chemical Exchange Saturation Transfer contrast are studied. Our results indicate that a computational modeling tool like POET can help to find peptides with 400 percent better functionality than used before.", notes = "see \cite{Miralavy:2022:PeerJ}", } @Article{Miralavy:2022:PeerJ, author = "Iliya Miralavy and Alexander R. Bricco and Assaf A. Gilad and Wolfgang Banzhaf", title = "Using genetic programming to predict and optimize protein function", journal = "PeerJ Physical Chemistry", year = "2022", month = sep # " 21", keywords = "genetic algorithms, genetic programming, POET, Theoretical and Computational Chemistry, Biophysical Chemistry, Protein optimization, Directed evolution, MRI contrast agents,Evolutionary computation", ISSN = "2689-7733", URL = "https://peerj.com/articles/pchem-24.pdf", DOI = "doi:10.7717/peerj-pchem.24", code_url = "https://dfzljdn9uc3pi.cloudfront.net/2022/pchem-24/1/POET_Python_Code.zip", data_url = "https://dfzljdn9uc3pi.cloudfront.net/2022/pchem-24/1/Dataset_with_sources_revised.csv", size = "29 pages", abstract = "Protein engineers conventionally use tools such as Directed Evolution to find new proteins with better functionalities and traits. More recently, computational techniques and especially machine learning approaches have been recruited to assist Directed Evolution, showing promising results. In this article, we propose POET, a computational Genetic Programming tool based on evolutionary computation methods to enhance screening and mutagenesis in Directed Evolution and help protein engineers to find proteins that have better functionality. As a proof-of-concept, we use peptides that generate MRI contrast detected by the Chemical Exchange Saturation Transfer contrast mechanism. The evolutionary methods used in POET are described, and the performance of POET in different epochs of our experiments with Chemical Exchange Saturation Transfer contrast are studied. Our results indicate that a computational modeling tool like POET can help to find peptides with 400% better functionality than used before.", notes = "AI-driven chemistry for drug design", } @InProceedings{Miralavy:2023:EuroGP, author = "Iliya Miralavy and Wolfgang Banzhaf", title = "Spatial Genetic Programming", booktitle = "EuroGP 2023: Proceedings of the 26th European Conference on Genetic Programming", year = "2023", month = "12-14 " # apr, editor = "Gisele Pappa and Mario Giacobini and Zdenek Vasicek", series = "LNCS", volume = "13986", publisher = "Springer Verlag", address = "Brno, Czech Republic", pages = "260--275", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Spatial Computing, Evolutionary Computation: Poster", isbn13 = "978-3-031-29572-0", URL = "https://rdcu.be/c8U1k", DOI = "doi:10.1007/978-3-031-29573-7_17", size = "16 pages", abstract = "An essential characteristic of brains in intelligent organisms is their spatial organization, in which different parts of the brain are responsible for solving different classes of problems. Inspired by this concept, we introduce Spatial Genetic Programming (SGP) - a new GP paradigm in which Linear Genetic Programming (LGP) programs, represented as graph nodes, are spread in a 2D space. Each individual model is represented as a graph and the execution order of these programs is determined by the network of interactions between them. SGP considers space as a first-order effect to optimize which aids with determining the suitable order of execution of LGP programs to solve given problems and causes spatial dynamics to appear in the system. RetCons are internal SGP operators which enhance the evolution of conditional pathways in SGP model structures. To demonstrate the effectiveness of SGP, we have compared its performance and internal dynamics with LGP and TreeGP for a diverse range of problems, most of which require decision making. Our results indicate that SGP, due to its unique spatial organization, outperforms the other methods and solves a wide range of problems. We also carry out an analysis of the spatial properties of SGP individuals.", notes = "Part of \cite{Pappa:2023:GP} EuroGP'2023 held in conjunction with EvoCOP2023, EvoMusArt2023 and EvoApplications2023", } @InProceedings{Miranda:2018:CEC, author = "Icaro Miranda and Marcelo Ladeira and Claus Aranha", title = "A Comparison Study Between Deep Learning and Genetic Programming Application in Cart Pole Balancing Problem", booktitle = "2018 IEEE Congress on Evolutionary Computation (CEC)", year = "2018", editor = "Marley Vellasco", address = "Rio de Janeiro, Brazil", month = "8-13 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-5090-6018-4", DOI = "doi:10.1109/CEC.2018.8477814", abstract = "This paper presents a comparative study between two data mining techniques: Genetic Programming (GP) and Deep Learning (DL). This comparison will be based on the cart pole balancing problem. We also compared the results with Q-Learning (QL), a classic algorithm that is also used in hybridizations with GP an DL for reinforcement learning problems. Our results presented that GP can rival DL for this kind of problem.", notes = "WCCI2018", } @InProceedings{Miranda:2019:gecco, author = "Icaro {Marcelino Miranda} and Claus Aranha and Marcelo Ladeira", title = "Classification of {EEG} Signals using Genetic Programming for Feature Construction", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and Anne Auger and Petr Posik and Leslie {Peprez Caceres} and Andrew M. Sutton and Nadarajen Veerapen and Christine Solnon and Andries Engelbrecht and Stephane Doncieux and Sebastian Risi and Penousal Machado and Vanessa Volz and Christian Blum and Francisco Chicano and Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and Jonathan Fieldsend and Jose Antonio Lozano and Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and Robin Purshouse and Thomas Baeck and Justyna Petke and Giuliano Antoniol and Johannes Lengler and Per Kristian Lehre", pages = "1275--1283", address = "Prague, Czech Republic", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", publisher = "ACM", keywords = "genetic algorithms, genetic programming, Classification, EEG, Dimensionality Reduction, Feature Construction, Feature Selection, K Complex, Sleep Spindles", isbn13 = "978-1-4503-6111-8", URL = "http://human-competitive.org/sites/default/files/gecco_paper_2019.pdf", DOI = "doi:10.1145/3321707.3321737", slides_url = "http://www.human-competitive.org/sites/default/files/marcelinoslides.pdf", size = "9 pages", abstract = "The analysis of electroencephalogram (EEG) waves is of critical importance for the diagnosis of sleep disorders, such as sleep apnea and insomnia, besides that, seizures, epilepsy, head injuries, dizziness, headaches and brain tumours. In this context, one important task is the identification of visible structures in the EEG signal, such as sleep spindles and K-complexes. The identification of these structures is usually performed by visual inspection from human experts, a process that can be error prone and susceptible to biases. Therefore there is interest in developing technologies for the automated analysis of EEG. In this paper, we propose a new Genetic Programming (GP) framework for feature construction and dimensionality reduction from EEG signals. We use these features to automatically identify spindles and K-complexes on data from the DREAMS project. Using 5 different classifiers, the set of attributes produced by GP obtained better AUC scores than those obtained from PCA or the full set of attributes. Also, the results obtained from the proposed framework obtained a better balance of Specificity and Recall than other models recently proposed in the literature. Analysis of the features most used by GP also suggested improvements for data acquisition protocols in future EEG examinations.", notes = "2019 Humies finalist. Slides: http://www.human-competitive.org/sites/default/files/marcelinoslides.pdf Also known as \cite{3321737} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA-2019) and the 24th Annual Genetic Programming Conference (GP-2019)", } @InProceedings{Miranda:2017:GECCOa, author = "Luis F. Miranda and Luiz Otavio V. B. Oliveira and Joao Francisco B. S. Martins and Gisele L. Pappa", title = "How Noisy Data Affects Geometric Semantic Genetic Programming", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4920-8", address = "Berlin, Germany", pages = "985--992", size = "8 pages", URL = "http://doi.acm.org/10.1145/3071178.3071300", DOI = "doi:10.1145/3071178.3071300", acmid = "3071300", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, geometric semantic genetic programming, noise impact, symbolic regression", month = "15-19 " # jul, abstract = "Noise is a consequence of acquiring and pre-processing data from the environment, and shows fluctuations from different sources, e.g., from sensors, signal processing technology or even human error. As a machine learning technique, Genetic Programming (GP) is not immune to this problem, which the field has frequently addressed. Recently, Geometric Semantic Genetic Programming (GSGP), a semantic-aware branch of GP, has shown robustness and high generalization capability. Researchers believe these characteristics may be associated with a lower sensibility to noisy data. However, there is no systematic study on this matter. This paper performs a deep analysis of the GSGP performance over the presence of noise. Using 15 synthetic datasets where noise can be controlled, we added different ratios of noise to the data and compared the results obtained with those of a canonical GP. The results show that, as we increase the percentage of noisy instances, the generalization performance degradation is more pronounced in GSGP than GP. However, in general, GSGP is more robust to noise than GP in the presence of up to 10percent of noise, and presents no statistical difference for values higher than that in the test bed.", notes = "Also known as \cite{Miranda:2017:NDA:3071178.3071300} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @InProceedings{Miranda:2015:GECCO, author = "Pericles Barbosa Miranda and Ricardo Bastos Prudencio", title = "GEFPSO: A Framework for PSO Optimization based on Grammatical Evolution", booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation", year = "2015", editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr", isbn13 = "978-1-4503-3472-3", pages = "1087--1094", keywords = "genetic algorithms, genetic programming, grammatical evolution", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739480.2754819", DOI = "doi:10.1145/2739480.2754819", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "In this work, we propose a framework to automatically generate effective PSO designs by adopting Grammatical Evolution (GE). In the proposed framework, GE searches for adequate structures and parameter values (e.g., acceleration constants, velocity equations and different particles' topology) in order to evolve the PSO design. For this, a high-level Backus--Naur Form (BNF) grammar was developed, representing the search space of possible PSO designs. In order to verify the performance of the proposed method, we performed experiments using 16 diverse continuous optimization problems, with different levels of difficulty. In the performed experiments, we identified the parameters and components that most affected the PSO performance, as well as identified designs that could be reused across different problems. We also demonstrated that the proposed method generates useful designs which achieved competitive solutions when compared to well succeeded algorithms from the literature.", notes = "Also known as \cite{2754819} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)", } @InProceedings{conf/bracis/MirandaP16, author = "Pericles B. C. Miranda and Ricardo B. C. Prudencio", title = "Tree-Based Grammar Genetic Programming to Evolve Particle Swarm Algorithms", booktitle = "2016 5th Brazilian Conference on Intelligent Systems (BRACIS)", year = "2016", pages = "25--30", address = "Recife, Brazil", month = "9-12", publisher = "IEEE Computer Society", keywords = "genetic algorithms, genetic programming, hyperheuristic, PSO, Particle Swarm Optimization, Algorithm Generation", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/bracis/bracis2016.html#MirandaP16", isbn13 = "978-1-5090-3566-3", DOI = "doi:10.1109/BRACIS.2016.016", size = "6 pages", abstract = "Particle Swarm Optimization (PSO) is largely used to solve optimization problems effectively. Nonetheless, the PSO performance depends on the fine tuning of different parameters. To make the algorithm design process more independent from human intervention, some researchers have treated this task as an optimization problem. Grammar-guided Genetic Programming algorithms (GGGP), in special, have been widely studied and applied in the context of algorithm optimization. GGGP algorithms produce customized designs based on a set of production rules defined in the grammar, differently from methods that simply select designs in a pre-defined limited search space. In this work, we proposed a tree-based GGGP technique for the generation of PSO algorithms. This paper intends to investigate whether this approach can improve the production of PSO algorithms when compared to other GGGP techniques already used to solve the current problem. In the experiments, a comparison between the tree-based and the commonly used linearized GGGP approach for the generation of PSO algorithms was performed. The results showed that the tree-based GGGP produced better algorithms than the counterparts. We also compared the algorithms generated by the tree-based technique to state-of-art optimization algorithms, and the results showed that the algorithms produced by the tree-based GGGP achieved competitive results.", notes = "See also \cite{Miranda:2016:BRACIS} Also known as \cite{7839557}", } @InProceedings{Miranda:2016:BRACIS, author = "Pericles B. C. Miranda and Ricardo B. C. Prudencio", booktitle = "2016 5th Brazilian Conference on Intelligent Systems (BRACIS)", title = "A Novel Context-Free Grammar to Guide the Construction of Particle Swarm Optimization Algorithms", year = "2016", pages = "295--300", abstract = "Particle Swarm Optimisation algorithm (PSO) has been largely studied over the years due to its flexibility and competitive results in different applications. Nevertheless, its performance depends on different aspects of design (e.g., inertia factor, velocity equation, topology). The task of deciding which is the best algorithm design to solve a particular problem is challenging due to the great number of possible variations and parameters to take into account. This work proposes a novel context-free grammar for Grammar-Guided Genetic Programming (GGGP) algorithms to guide the construction of Particle Swarm Optimizers. The proposed grammar addresses four aspects of the PSO algorithm that may strongly influence on its convergence: swarm initialization, neighbourhood topology, velocity update equation and mutation operator. To evaluate this approach, a GGGP algorithm was set with the proposed grammar and applied to optimise the PSO algorithm in 32 unconstrained continuous optimisation problems. In the experiments, we compared the designs generated considering the proposed grammar with the designs produced by other grammars proposed in the literature to automate PSO designs. The results obtained by the proposed grammar were better than the counterparts. Besides, we also compared the generated algorithms to state-of-art algorithms. The results have shown that the algorithms produced from the grammar achieved competitive results.", keywords = "genetic algorithms, genetic programming, PSO", DOI = "doi:10.1109/BRACIS.2016.061", month = oct, notes = "Also known as \cite{7839602}", } @Article{MIRANDA2017281, author = "Pericles B. C. Miranda and Ricardo B. C. Prudencio", title = "Generation of Particle Swarm Optimization algorithms: An experimental study using Grammar-Guided Genetic Programming", journal = "Applied Soft Computing", year = "2017", volume = "60", pages = "281--296", month = nov, keywords = "genetic algorithms, genetic programming, generation hyper-heuristics, Grammar-Guided Genetic Programming, Algorithm design, Particle Swarm Optimization, PSO", ISSN = "1568-4946", URL = "http://www.sciencedirect.com/science/article/pii/S1568494617303836", DOI = "doi:10.1016/j.asoc.2017.06.040", abstract = "Particle Swarm Optimization (PSO) is largely used to solve optimization problems effectively. Nonetheless, the PSO performance depends on the fine tuning of different parameters. To make the algorithm design process more independent from human intervention, some researchers have treated this task as an optimization problem. Grammar-Guided Genetic Programming (GGGP) algorithms, in particular, have been widely studied and applied in the context of algorithm optimization. GGGP algorithms produce customized designs based on a set of production rules defined in the grammar, differently from methods that simply select designs in a pre-defined limited search space. Although GGGP algorithms have been largely used in other contexts, they have not been deeply investigated in the generation of PSO algorithms. Thus, this work applies GGGP algorithms in the context of PSO algorithm design problem. Herein, we performed an experimental study comparing different GGGP approaches for the generation of PSO algorithms. The main goal is to perform a deep investigation aiming to identify pros and cons of each approach in the current task. In the experiments, a comparison between a tree-based GGGP approach and commonly used linear GGGP approaches for the generation of PSO algorithms was performed. The results showed that the tree-based GGGP produced better algorithms than the counterparts. We also compared the algorithms generated by the tree-based technique to state-of-the-art optimization algorithms, and it achieved competitive results.", } @InProceedings{Miranda:2020:CEC, author = "Luis Fernando Miranda and Luiz Otavio Oliveira and Joao Francisco Martins and Gisele Pappa", title = "Instance Selection for Geometric Semantic Genetic Programming", booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC 2020", year = "2020", editor = "Yaochu Jin", pages = "paper id24548", address = "internet", month = "19-24 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming: Poster", isbn13 = "978-1-7281-6929-3", DOI = "doi:10.1109/CEC48606.2020.9185867", abstract = "Geometric Semantic Genetic Programming (GSGP) is a method that exploits the geometric properties describing the spatial relationship between possible solutions to a problem in an n-dimensional semantic space. In symbolic regression problems, n is equal to the number of training instances. Although very effective, the GSGP semantic space can become excessively big in most real applications, where the value of n is high, having a negative impact on the effectiveness of the GSGP search process. This paper tackles this problem by reducing the dimensionality of GSGP semantic space in symbolic regression problems using instance selection methods. Our approach relies on weighting functions-to estimate the relative importance of each instance based on its position with respect to its nearest neighbours-and on dimensionality reduction techniques-to improve the notion of closeness between instances, generating datasets with simplified input spaces. Experiments were performed on a set of 15 datasets and our experimental analysis shows that using instance selection by instance weighting and dimensionality reduction does improve the effectiveness of the search with almost no impact on root mean square error results.", notes = "https://wcci2020.org/ Federal University of Minas Gerais, Brazil. Also known as \cite{9185867}", } @InProceedings{miranda:2022:GECCOcomp, author = "Thiago Miranda and Diorge Sardinha and Marcio Basgalupp and Ricardo Cerri", title = "A New Grammatical Evolution Method for Generating Deep Convolutional Neural Networks with Novel Topologies", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion", year = "2022", editor = "Heike Trautmann and Carola Doerr and Alberto Moraglio and Thomas Bartz-Beielstein and Bogdan Filipic and Marcus Gallagher and Yew-Soon Ong and Abhishek Gupta and Anna V Kononova and Hao Wang and Michael Emmerich and Peter A. N. Bosman and Daniela Zaharie and Fabio Caraffini and Johann Dreo and Anne Auger and Konstantin Dietric and Paul Dufosse and Tobias Glasmachers and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Tea Tusar and Dimo Brockhoff and Tome Eftimov and Pascal Kerschke and Boris Naujoks and Mike Preuss and Vanessa Volz and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Mark Coletti and Catherine (Katie) Schuman and Eric ``Siggy'' Scott and Robert Patton and Paul Wiegand and Jeffrey K. Bassett and Chathika Gunaratne and Tinkle Chugh and Richard Allmendinger and Jussi Hakanen and Daniel Tauritz and John Woodward and Manuel Lopez-Ibanez and John McCall and Jaume Bacardit and Alexander Brownlee and Stefano Cagnoni and Giovanni Iacca and David Walker and Jamal Toutouh and UnaMay O'Reilly and Penousal Machado and Joao Correia and Sergio Nesmachnow and Josu Ceberio and Rafael Villanueva and Ignacio Hidalgo and Francisco {Fernandez de Vega} and Giuseppe Paolo and Alex Coninx and Antoine Cully and Adam Gaier and Stefan Wagner and Michael Affenzeller and Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and William B. Langdon and Justyna Petke and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Paetzel and Alexander Wagner and Michael Heider and Nadarajen Veerapen and Katherine Malan and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Mohammad Nabi Omidvar and Yuan Sun and Ernesto Tarantino and De Falco Ivanoe and Antonio {Della Cioppa} and Scafuri Umberto and John Rieffel and Jean-Baptiste Mouret and Stephane Doncieux and Stefanos Nikolaidis and Julian Togelius and Matthew C. Fontaine and Serban Georgescu and Francisco Chicano and Darrell Whitley and Oleksandr Kyriienko and Denny Dahl and Ofer Shir and Lee Spector and Alma Rahat and Richard Everson and Jonathan Fieldsend and Handing Wang and Yaochu Jin and Erik Hemberg and Marwa A. Elsayed and Michael Kommenda and William {La Cava} and Gabriel Kronberger and Steven Gustafson", pages = "663--666", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, grammatical evolution, neural networks, image classification, neuroevolution", isbn13 = "978-1-4503-9268-6/22/07", DOI = "doi:10.1145/3520304.3529025", abstract = "Neuroevolution, a sub-field of AutoML, uses evolutionary algorithms to automate the process of creating Deep Neural Networks architectures. For problems with complex objects, such as neural networks, Grammar-based Evolutionary Algorithms (GEs) can be used to simplify the implementation and the experimentation by using grammar rules to describe what the components of the complex object are and how they can be connected, that is, they elegantly describe the search space of the problem. In this work, we propose a GE algorithm based on Structured Grammatical Evolution to generate deep convolutional neural networks. Our work has two major contributions: first, the neural networks may contain an arbitrary number and arrangement of skip connections; second, our skip connections may upscale lower-resolution inputs, allowing the generation of architectures such as U-Net. Our best model achieved 0.85 accuracy on CIFAR-10.", notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @InProceedings{conf/semeval/Miranda-Jimenez17, author = "Sabino Miranda-Jimenez and Mario Graff and Eric Sadit Tellez and Daniela Moctezuma", title = "{INGEOTEC} at {SemEval 2017} Task 4: A {B4MSA} Ensemble based on Genetic Programming for Twitter Sentiment Analysis", booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation, SemEval@ACL 2017", year = "2017", editor = "Steven Bethard and Marine Carpuat and Marianna Apidianaki and Saif M. Mohammad and Daniel M. Cer and David Jurgens", pages = "771--776", address = "Vancouver, Canada", month = aug # " 3-4", publisher = "Association for Computational Linguistics", keywords = "genetic algorithms, genetic programming", bibdate = "2017-08-16", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/semeval/semeval2017.html#Miranda-Jimenez17", URL = "http://aclanthology.info/volumes/proceedings-of-the-11th-international-workshop-on-semantic-evaluation-semeval-2017", URL = "http://aclweb.org/anthology/S17-2", DOI = "doi:10.18653/v1/S17-2130", size = "6 pages", abstract = "This paper describes the system used in SemEval-2017 Task 4 (Subtask A): Message Polarity Classification for both English and Arabic languages. Our proposed system is an ensemble of two layers, the first one uses our generic framework for multilingual polarity classification (B4MSA) and the second layer combines all the decision function values predicted by B4MSA systems using a non-linear function evolved using a Genetic Programming system, EvoDAG. With this approach, the best performances reached by our system were macro-recall 0.68 (English) and 0.477 (Arabic) which set us in sixth and fourth positions in the results table, respectively.", } @InProceedings{Miras:2018:evoApplications, author = "Karine Miras and Evert Haasdijk and Kyrre Glette and A. E. Eiben", title = "Search Space Analysis of Evolvable Robot Morphologies", booktitle = "21st International Conference on the Applications of Evolutionary Computation, EvoROBOT 2018", year = "2018", editor = "Kyrre Glette and Julien Hubert", series = "LNCS", volume = "10784", publisher = "Springer", pages = "703--718", address = "Parma, Italy", month = "4-6 " # apr, organisation = "Species", keywords = "genetic algorithms, genetic programming, Modular robots, Evolutionary Robotics, Morphology, Generative encoding, Novelty search", isbn13 = "978-3-319-77537-1", DOI = "doi:10.1007/978-3-319-77538-8_47", abstract = "We present a study on morphological traits of evolved modular robots. We note that the evolutionary search space, the set of obtainable morphologies, depends on the given representation and reproduction operators and we propose a framework to assess morphological traits in this search space regardless of a specific environment and/or task. To this end, we present eight quantifiable morphological descriptors and a generic novelty search algorithm to produce a diverse set of morphologies for any given representation. With this machinery, we perform a comparison between a direct encoding and a generative encoding. The results demonstrate that our framework permits to find a very diverse set of bodies, allowing a morphological diversity investigation. Furthermore, the analysis showed that despite the high levels of diversity, a bias to certain traits in the population was detected. Surprisingly, the two encoding methods showed no significant difference in the diversity levels of the evolved morphologies or their morphological traits.", notes = "EvoApplications2018 held in conjunction with EuroGP'2018 EvoCOP2018 and EvoMusArt2018 http://www.evostar.org/2018/cfp_evoapps.php", } @InProceedings{Mironovich:2017:ieeeINDIN, author = "Vladimir Mironovich and Maxim Buzdalov and Valeriy Vyatkin", booktitle = "2017 IEEE 15th International Conference on Industrial Informatics (INDIN)", title = "Automatic generation of function block applications using evolutionary algorithms: Initial explorations", year = "2017", pages = "700--705", abstract = "Automation of software development process has been a concern for a long time. Genetic programming is a well-known technique which uses evolutionary computation to generate or improve a computer program for a specific task without human participation. We consider the method which applies model checking and evolutionary computation towards the automatic generation of function block control applications for industrial automation systems. As a first step, we evaluate the effectiveness of a fitness function based on the number of satisfied computation tree logic formulas in UPPAAL query language for a manually created UPPAAL model. Results show that such fitness function and the (1+1) evolutionary algorithm can be successfully applied to generation of the required data connections in the IEC 61499 function block application.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/INDIN.2017.8104858", month = jul, notes = "Also known as \cite{8104858}", } @InProceedings{Mironovich:2018:GECCOcomp, author = "Vladimir Mironovich and Maxim Buzdalov and Valeriy Vyatkin", title = "From fitness landscape analysis to designing evolutionary algorithms: the case study in automatic generation of function block applications", booktitle = "GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2018", editor = "Carlos Cotta and Tapabrata Ray and Hisao Ishibuchi and Shigeru Obayashi and Bogdan Filipic and Thomas Bartz-Beielstein and Grant Dick and Masaharu Munetomo and Silvino {Fernandez Alzueta} and Thomas Stuetzle and Pablo Valledor Pellicer and Manuel Lopez-Ibanez and Daniel R. Tauritz and Pietro S. Oliveto and Thomas Weise and Borys Wrobel and Ales Zamuda and Anne Auger and Julien Bect and Dimo Brockhoff and Nikolaus Hansen and Rodolphe {Le Riche} and Victor Picheny and Bilel Derbel and Ke Li and Hui Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Stephane Doncieux and Richard Duro and Joshua Auerbach and Harold {de Vladar} and Antonio J. Fernandez-Leiva and JJ Merelo and Pedro A. Castillo-Valdivieso and David Camacho-Fernandez and Francisco {Chavez de la O} and Ozgur Akman and Khulood Alyahya and Juergen Branke and Kevin Doherty and Jonathan Fieldsend and Giuseppe Carlo Marano and Nikos D. Lagaros and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Boris Naujoks and Vanessa Volz and Tea Tusar and Pascal Kerschke and Riyad Alshammari and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and John R. Woodward and Shin Yoo and John McCall and Nayat Sanchez-Pi and Luis Marti and Danilo Vasconcellos and Masaya Nakata and Anthony Stein and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Ivanoe {De Falco} and Antonio {Della Cioppa} and Ernesto Tarantino and Umberto Scafuri and P. G. M. Baltus and Giovanni Iacca and Ahmed Hallawa and Anil Yaman and Alma Rahat and Handing Wang and Yaochu Jin and David Walker and Richard Everson and Akira Oyama and Koji Shimoyama and Hemant Kumar and Kazuhisa Chiba and Pramudita Satria Palar", isbn13 = "978-1-4503-5764-7", pages = "1902--1905", address = "Kyoto, Japan", DOI = "doi:10.1145/3205651.3208230", publisher = "ACM", publisher_address = "New York, NY, USA", month = "15-19 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, SBSE", abstract = "Search-based software engineering, a discipline that often requires finding optimal solutions, can be a viable source for problems that bridge theory and practice of evolutionary computation. In this research we consider one such problem: generation of data connections in a distributed control application designed according to the IEC 61499 industry standard. We perform the analysis of the fitness landscape of this problem and find why exactly the simplistic (1 + 1) evolutionary algorithm is slower than expected when finding an optimal solution to this problem. To counteract, we develop a population-based algorithm that explicitly maximises diversity among the individuals in the population. We show that this measure indeed helps to improve the running times.", notes = "Also known as \cite{3208230} \cite{Mironovich:2018:FLA:3205651.3208230} GECCO-2018 A Recombination of the 27th International Conference on Genetic Algorithms (ICGA-2018) and the 23rd Annual Genetic Programming Conference (GP-2018)", } @Article{Mirsabetnazar:2024:mtcomm, author = "A. Mirsabetnazar and R. Ansari and M. {Zargar Ershadi} and H. Rouhi", title = "Annular sector plates made of {FG} graphene origami-enabled auxetic metamaterial under bending and buckling loading: A {VDQ-based} approach", journal = "Materials Today Communications", year = "2024", volume = "41", pages = "110629", keywords = "genetic algorithms, genetic programming, Auxetic metamaterial, Graphene origami, Annular sector plate, Numerical modeling, Functionally graded material", ISSN = "2352-4928", URL = "https://www.sciencedirect.com/science/article/pii/S2352492824026102", DOI = "doi:10.1016/j.mtcomm.2024.110629", abstract = "The behaviours of annular sector plates made of functionally graded graphene origami-enabled auxetic metamaterials (FG-GOEAMs) under the action of buckling and bending loads are investigated herein using a numerical approach. Multiple GOEAM layers with graphene origami (GOri) content are considered for the plate which changes in layer-wise patterns. Moreover, the material properties of the plate are calculated using novel genetic programming-assisted micromechanical models. In order to derive the governing equations, the Mindlin plate theory in conjunction with Hamilton's principle is used within the framework of the variational differential quadrature (VDQ) method. The vector-matrix representation of the presented formulation can be beneficial from the viewpoint of implementing numerical approaches. The governing equations are then discretized and solved based on VDQ matrix differential and integral operators so as to obtain the critical buckling load and maximum lateral deflection of plates with different edge conditions. The effects of GOri content, folding degree and distribution pattern on the results are studied. It is revealed that increasing the GOri content leads to the negative Poisson's ratio (NPR) effect which respectively reduces and increases the lateral deflection and the critical buckling load", } @InProceedings{Asmatullah:2003:INMIC, author = "Asmatullah and Anwar M. Mirza and Asifullah Khan", title = "Blind Image Restoration Using Back Propagator", booktitle = "Proceedings of the 7th International Multi Topic Conference, INMIC 2003", year = "2003", pages = "55--58", address = "Islamabad", month = "8-9 " # dec, publisher = "IEEE", keywords = "AUROC", DOI = "doi:10.1109/INMIC.2003.1416615", abstract = "We describe the problem of restoring a blurred and noisy image without any prior knowledge of the blurring function and the statistics of additive noise. A multilayer feed-forward neural network based on backpropagation algorithm is used for image restoration. The neural network is trained by applying backpropagation with momentum for fast convergence. The results of the backpropagation neural network model are compared to that of Wiener filter for high, moderate and low signal to noise ratio (SNR) blur functions. Improvement in signal to noise ratio (ISNR) is taken as a performance measure. It is observed that backpropagation neural network learns well in each case and restores all the test images reasonably, while Wiener filter performs well for high and moderate SNR blur but performs poorly for the low SNR case. ISNR values of 5.58 db, 5.15 db and 5.13 db has been achieved with this scheme for the peppers image, in comparison to values of 4.17db, 2.71db and -0.93db using Wiener filter for high, moderate and low SNR blur respectively.", } @Article{MIRZAEE:2021:CERD, author = "Seyyed Abbas Mirzaee and Behruz Bayati and Mohammad Reza Valizadeh and Helder T. Gomes and Zahra Noorimotlagh", title = "Adsorption of diclofenac on mesoporous activated carbons: Physical and chemical activation, modeling with genetic programming and molecular dynamic simulation", journal = "Chemical Engineering Research and Design", volume = "167", pages = "116--128", year = "2021", ISSN = "0263-8762", DOI = "doi:10.1016/j.cherd.2020.12.025", URL = "https://www.sciencedirect.com/science/article/pii/S0263876220306213", keywords = "genetic algorithms, genetic programming, Activated carbon, Diclofenac, Chemical activation, Physical activation, Modeling with genetic programming, Molecular simulation", abstract = "This work aims at the preparation of AC from chemical activation (H3PO4, KOH, and HCl) and physical activation (thermal treatment under N2 atmosphere at 500 and 700 degreeC) of Astragalus Mongholicus (AM) (a low-cost bio-adsorbent and agro-industrial waste), used as carbon precursor. The obtained materials were further applied in the adsorption of diclofenac (DCF) from water/wastewater. The physicochemical properties of the as-prepared ACs and commercial activated carbons (CAC) were evaluated by SEM, XRD, FT-IR, and BET analyses, revealing the high surface area and mesoporous proportion of AC when compared to CAC . Adsorption results showed that the efficiency of AC-700 degreeC (774 m2 g-1) for DCF removal (92.29percent) was greater than that of AC-500 degreeC (648 m2 g-1, 83.5percent), AC-H3PO4 (596 m2 g-1, 80.8percent), AC-KOH (450 m2 g-1, 59.3percent), AC-HCl (156 m2 g-1, 29.8percent) and CAC (455 m2 g-1, 67.8percent). The optimization of effective parameters in adsorption was examined at a laboratory-scale using the selected AC-700 degreeC. The Langmuir isotherm and the pseudo-second-order model fitted well the experimental data. The regeneration efficiency was maintained at 96percent (DI-water) and 97percent (heating) after three cycles. Besides, genetic programming (GP) and molecular dynamics (MD) simulations were applied to predict the adsorption behavior of DCF from aqueous phase as well as in the ACs structure. It was found that the adsorption mechanisms involved were electrostatic interaction, cation-? interaction, and ?-? electron interaction", } @InProceedings{Mirzahosseini:2010:ISAP, author = "Mohammadreza R. Mirzahosseini and Amirhossein H. Alavi and Fereidoon {Moghadas Nejad} and Amirhossein H. Gandomi and Mahmoud Ameri", title = "Evaluation of Rutting Potential of Asphalt Mixtures Using Linear Genetic Programming", booktitle = "The 11th International Conference on Asphalt Pavements (ISAP 2010)", year = "2010", volume = "2", pages = "1527--1536?", address = "Nagoya, Japan", publisher_address = "57 Morehouse Lane Red Hook, NY 12571 USA Phone: 845-758-0400 Fax: 845-758-2634 Email: curran@proceedings.com", month = "1-6 " # aug, organisation = "International Society for Asphalt Pavements (ISAP) 6776 Lake Drive, Suite 215 Lino Lakes, MN 55014", publisher = "Curran Associates, Inc. (Sep 2011)", keywords = "genetic algorithms, genetic programming, Rutting, Flow number, Linear genetic programming, Regression Analysis, Marshall mix design", isbn13 = "978-1-61839-073-8", URL = "http://www.proceedings.com/12470.html", URL = "https://www.researchgate.net/publication/236619155_Evaluation_of_Rutting_Potential_of_Asphalt_Mixtures_Using_Linear_Genetic_Programming", size = "10 pages", abstract = "Rutting has been considered as the most serious distresses in flexible pavement for many years. Flow number obtained from uniaxial dynamic creep test is an explanatory index for the evaluation of rutting potential of asphalt mixtures. This is a pioneer study that presents a promising variant of genetic programming, namely linear genetic programming (LGP) to predict the flow number of dense asphalt-aggregate mixtures. Generalized LGP-based models were constructed to relate the flow number of Marshall specimens to the coarse and fine aggregate contents, percentage of air voids, percentage of voids in mineral aggregate, Marshall stability and flow. The comprehensive experimental database used for the development of the models was established upon a series of uniaxial dynamic creep tests conducted in this study. The contributions of the parameters affecting the flow number were determined through a sensitivity analysis. A multiple least squares regression (MLSR) analysis was performed using the same variables and same data sets to benchmark the LGP models. For more verification, a subsequent parametric study was conducted and the trends of the results were confirmed with the results of previous studies. The results indicate that the proposed LGP models are capable of effectively evaluating the flow number of asphalt mixtures. The LGP models are found to be significantly more accurate than the MLSR model.", notes = "Paper ID. 90316 http://www.gbv.de/dms/tib-ub-hannover/653947240.pdf gives pages as Volume II, 211--220?", } @Article{Mirzahosseini:2011:ESA, author = "Mohammad Reza Mirzahosseini and Alireza Aghaeifar and Amir Hossein Alavi and Amir Hossein Gandomi and Reza Seyednour", title = "Permanent deformation analysis of asphalt mixtures using soft computing techniques", journal = "Expert Systems with Applications", year = "2011", volume = "38", number = "5", pages = "6081--6100", keywords = "genetic algorithms, genetic programming, Multi expression programming, Asphalt pavements, Rutting, Artificial neural network, Marshall mix design, Formulation", ISSN = "0957-4174", URL = "http://www.sciencedirect.com/science/article/pii/S095741741001239X", DOI = "doi:10.1016/j.eswa.2010.11.002", size = "20 pages", abstract = "This study presents two branches of soft computing techniques, namely multi expression programming (MEP) and multilayer perceptron (MLP) of artificial neural networks for the evaluation of rutting potential of dense asphalt-aggregate mixtures. Constitutive MEP and MLP-based relationships were obtained correlating the flow number of Marshall specimens to the coarse and fine aggregate contents, percentage of bitumen, percentage of voids in mineral aggregate, Marshall stability, and Marshall flow. Different correlations were developed using different combinations of the influencing parameters. The comprehensive experimental database used for the development of the correlations was established upon a series of uniaxial dynamic creep tests conducted in this study. Relative importance values of various predictor variables of the models were calculated to determine the significance of each of the variables to the flow number. A multiple least squares regression (MLSR) analysis was performed to benchmark the MEP and MLP models. For more verification, a subsequent parametric study was also carried out and the trends of the results were confirmed with the experimental study results and those of previous studies. The observed agreement between the predicted and measured flow number values validates the efficiency of the proposed correlations for the assessment of the rutting potential of asphalt mixtures. The MEP-based straightforward formulae are much more practical for the engineering applications compared with the complicated equations provided by MLP.", } @InProceedings{Mirzahosseini:2013:TRB, author = "Mohammadreza Mirzahosseini and Yacoub M. Najjar and Amir Hossein Alavi and Amir Hossein Gandomi", title = "{ANN}-Based Prediction Model for Rutting Propensity of Asphalt Mixtures", booktitle = "The 92nd Transportation Research Board (TRB) Annual Meeting", year = "2013", pages = "Paper No. 13--2180", address = "Washington, D.C., USA", publisher_address = "USA", month = jan # " 13-17", organisation = "Transportation Research Board, National Research Council", keywords = "genetic algorithms, genetic programming, gene expression programming, multi expression programming simulated annealing, ANN", URL = "http://amonline.trb.org/2013-1.264263/13-2180-1-1.291788", URL = "http://assets.conferencespot.org/fileserver/file/45736/filename/2vd3kv.pdf", size = "18 pages", abstract = "This paper investigates the applicability of artificial neural network (ANN) for the prediction of the flow number of dense asphalt-aggregate mixtures. Percentages of coarse aggregate, filler, bitumen, air voids, voids in mineral aggregate, and Marshall Quotient were employed as the predictor variables. A comprehensive experimental database was used for the development of the model. The statistical measures of coefficient of determination, coefficient of efficiency, root mean squared error, and mean absolute error were used to evaluate the performance of the model. Sensitivity and parametric analyses were conducted and discussed. The ANN model accurately characterises the flow number of asphalt mixtures resulting in a very good prediction performance. The proposed model remarkably outperforms several existing prediction models for the flow number of asphalt mixtures.", notes = "'the entire database were compared with those provided by the gene expression programming (GEP), multi expression programming (MEP) (29), and hybrid GP and simulated annealing (GP/SA) models.' slides http://assets.conferencespot.org/fileserver/file/45735/filename/39e6f1.pdf 'The proposed ANN model significantly outperforms the existing models.' GEP MEP GP/SA 11 Mohammadreza Mirzahosseini* 12 Kansas State University 13 Department of Civil Engineering 14 Manhattan, KS 66506. 19 Yacoub M. Najjar 20 Department of Civil Engineering 21 University of Mississippi 22 University, MS 38677. 27 Amir HosseinAlavi 28 Iran University of Science and Technology 29 School of Civil Engineering 30 Tehran, Iran 34 Amir HosseinGandomi 35 The University of Akron 36 Department of Civil Engineering 37 Akron, OH 44325. ", } @Article{Mirzahosseini:2015:IJGM, author = "Mohammadreza Mirzahosseini and Yacoub M. Najjar and Amir H. Alavi and Amir H. Gandomi", title = "Next-Generation Models for Evaluation of the Flow Number of Asphalt Mixtures", journal = "International Journal of Geomechanics", year = "2015", volume = "15", number = "6", pages = "04015009", month = dec, keywords = "genetic algorithms, genetic programming, Asphalt pavements, Flow number, Machine learning, Marshall mix design, Prediction", publisher = "American Society of Civil Engineers", ISSN = "1532-3641", DOI = "doi:10.1061/(ASCE)GM.1943-5622.0000483", abstract = "This paper presents the development of next-generation prediction models for the flow number of dense asphalt-aggregate mixtures via an innovative machine learning approach. New nonlinear models were developed to predict the flow number using two robust machine learning techniques, called linear genetic programming (LGP) and artificial neural network (ANN). The flow number of Marshall specimens was formulated in terms of percentages of coarse aggregate, filler, bitumen, air voids, voids in mineral aggregate, and Marshall quotient. An experimental database containing 118 test results for Marshall specimens was used for the development of the models. Validity of the models was verified using parts of laboratory data that were not involved in the calibration process. The statistical measures of coefficient of determination, coefficient of efficiency, root-mean squared error, and mean absolute error were used to evaluate the performance of the models. Further, a multivariable least-squares regression (MLSR) analysis was carried out to benchmark the machine learning-based models against a classical approach. Sensitivity and parametric analyses were conducted and discussed. Given the results, the LGP and ANN models accurately characterize the flow number of asphalt mixtures. The LGP design equation reaches a comparable performance with the ANN model. The proposed models outperform the MLSR and other existing machine learning-based models for the flow number of asphalt mixtures.", notes = "1Dept. of Civil Engineering, Kansas State Univ., Manhattan, KS 66506 (corresponding author). 2Dept. of Civil Engineering, Univ. of Mississippi, University, MS 38677. 3Dept. of Civil and Environmental Engineering, Michigan State Univ., East Lansing, MI 48824. 4Dept. of Civil Engineering, Univ. of Akron, Akron, OH 44325.", } @Article{DusanMisevic02222006, author = "Dusan Misevic and Charles Ofria and Richard E Lenski", title = "Sexual reproduction reshapes the genetic architecture of digital organisms", journal = "Proceedings of the Royal Society B: Biological Sciences", volume = "273", number = "1585", pages = "457--464", year = "2006", month = feb # " 22", keywords = "genetic algorithms, Avida, epistasis, experimental evolution, modularity, recombination", URL = "http://rspb.royalsocietypublishing.org/content/273/1585/457.abstract", DOI = "doi:10.1098/rspb.2005.3338", size = "8 pages", abstract = "Modularity and epistasis, as well as other aspects of genetic architecture, have emerged as central themes in evolutionary biology. Theory suggests that modularity promotes evolvability, and that aggravating (synergistic) epistasis among deleterious mutations facilitates the evolution of sex. Here, by contrast, we investigate the evolution of different genetic architectures using digital organisms, which are computer programs that self-replicate, mutate, compete and evolve. Specifically, we investigate how genetic architecture is shaped by reproductive mode. We allowed 200 populations of digital organisms to evolve for over 10000 generations while reproducing either asexually or sexually. For 10 randomly chosen organisms from each population, we constructed and analysed all possible single mutants as well as one million mutants at each mutational distance from 2 to 10. The genomes of sexual organisms were more modular than asexual ones; sites encoding different functional traits had less overlap and sites encoding a particular trait were more tightly clustered. Net directional epistasis was alleviating (antagonistic) in both groups, although the overall strength of this epistasis was weaker in sexual than in asexual organisms. Our results show that sexual reproduction profoundly influences the evolution of the genetic architecture.", notes = "Although avida evolves programs there is no discussion about what they do. Modularity measured by how together genes which contribute to functionality are in the chromosome. Functionality determined by systematically replacing genes with NOPs and random sampling of replacing between 2 and 10 genes with null operations. Evolution either with two parent sex or with mutation. No population has both. Tries to control (in the statistical sense) for greater bloat in sexual populations. PCA. log transformed length. Supplementary Material http://www.pubmedcentral.nih.gov/picrender.fcgi?artid=1560214&blobname=rspb20053338s02.pdf PMCID: PMC1560214 ", } @Article{Mishra:2018:IJPQM, author = "Swayam Bikash Mishra and Siba Sankar Mahapatra", title = "An experimental investigation on strain controlled fatigue behaviour of FDM build parts", journal = "International Journal of Productivity and Quality Management", year = "2018", volume = "24", number = "3", pages = "323--345", month = jul # " 6", keywords = "genetic algorithms, genetic programming, analysis of variance, ANOVA, fatigue, fused deposition modelling, FDM, low cycle fatigue, LCF, rapid prototyping", bibsource = "OAI-PMH server at oai.repec.org", identifier = "RePEc:ids:ijpqma:v:24:y:2018:i:3:p:323-345", oai = "oai:RePEc:ids:ijpqma:v:24:y:2018:i:3:p:323-345", URL = "http://www.inderscience.com/link.php?id=92980", DOI = "doi:10.1504/IJPQM.2018.092980", abstract = "Fused deposition modelling (FDM) can build parts with complex geometry with relatively less material waste and time from computer aided design (CAD) file saved in stereolithography (.stl) format. Since FDM builds functional parts, it is not only subjected to static loading but also dynamic loading. The behaviour of build parts under repetitive cyclic loading resulting in fatigue needs to be established because it affects functionality as well as the durability. The present study aims at investigating the mechanism of fatigue and influence of FDM process parameters on fatigue life when the build parts are subjected to repetitive cyclic loads. Low cycle fatigue (LCF) test is carried out under strain-controlled mode for better characterisation of fatigue life of FDM build parts. Using response surface methodology, the relationship between FDM process parameters and fatigue life is developed. Genetic programming (GP) technique is adopted to predict the fatigue life of the build parts.", } @InCollection{Mitavskiy:FOGA2005, author = "Boris Mitavskiy and Jonathan E. Rowe", title = "A Schema-Based Version of {Geiringer's} Theorem for Nonlinear Genetic Programming with Homologous Crossover", year = "2005", series = "Lecture Notes in Computer Science", pages = "156--175", booktitle = "Foundations of Genetic Algorithms 8", editor = "Alden H. Wright and Michael D. Vose and Kenneth A. {De Jong} and Lothar M. Schmitt", address = "Berlin Heidelberg", publisher = "Springer-Verlag", volume = "3469", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-27237-2", DOI = "doi:10.1007/b138412", abstract = "Geiringer's theorem is a statement which tells us something about the limiting frequency of occurrence of a certain individual when a classical genetic algorithm is executed in the absence of selection and mutation. Recently Poli, Stephens, Wright and Rowe extended the original theorem of Geiringer to include the case of variable length genetic algorithms and linear genetic programming. Here a rather powerful version of Geiringer's theorem, which has been established recently by Mitavskiy, is used to derive a schema-based version of the theorem for nonlinear genetic programming with homologous crossover.", notes = "Workshop 5-9 January 2005 in Aizu-Wakamatsu City, Japan also known as \cite{conf/foga/MitavskiyR05}", } @Article{MR:EC:06, title = "An Extension of Geiringer's Theorem for a Wide Class of Evolutionary Search Algorithms", author = "Boris Mitavskiy and Jonathan Rowe", journal = "Evolutionary Computation", year = "2006", volume = "14", number = "1", pages = "87--118", month = "Spring", keywords = "genetic algorithms, genetic programming, crossover, schemata, Geiringer theorem, Markov process, stationary distribution, random walk on a group, mutation", URL = "http://www.mitpressjournals.org/doi/abs/10.1162/evco.2006.14.1.87", DOI = "doi:10.1162/evco.2006.14.1.87", size = "32 pages", abstract = "he frequency with which various elements of the search space of a given evolutionary algorithm are sampled is affected by the family of recombination (reproduction) operators. The original Geiringer theorem tells us the limiting frequency of occurrence of a given individual under repeated application of crossover alone for the classical genetic algorithm. Recently, Geiringer's theorem has been generalised to include the case of linear GP with homologous crossover (which can also be thought of as a variable length GA). In the current paper we prove a general theorem which tells us that under rather mild conditions on a given evolutionary algorithm, call it A, the stationary distribution of a certain Markov chain of populations in the absence of selection is unique and uniform. This theorem not only implies the already existing versions of Geiringer's theorem, but also provides a recipe of how to obtain similar facts for a rather wide class of evolutionary algorithms. The techniques which are used to prove this theorem involve a classical fact about random walks on a group and may allow us to compute and/or estimate the eigenvalues of the corresponding Markov transition matrix which is directly related to the rate of convergence towards the unique limiting distribution.", } @Article{Mitavskiy:2006:TCS, author = "Boris Mitavskiy and Jon Rowe", title = "Some Results about the Markov Chains Associated to GPs and to General EAs", journal = "Theoretical Computer Science", year = "2006", volume = "361", number = "1", pages = "72--110", month = "28 " # aug, keywords = "genetic algorithms, genetic programming, Evolutionary algorithms, Markov chain, Geiringer theorem, Stationary distribution, Mutation, Crossover, Fitness-proportional selection", DOI = "doi:10.1016/j.tcs.2006.04.006", abstract = "Geiringer's theorem is a statement which tells us something about the limiting frequency of occurrence of a certain individual when a classical genetic algorithm is executed in the absence of selection and mutation. Recently Poli, Stephens, Wright and Rowe extended the original theorem of Geiringer to include the case of variable-length genetic algorithms and linear genetic programming. In the current paper a rather powerful finite population version of Geiringer's theorem which has been established recently by previous Mitavskiy is used to derive a schema-based version of the theorem for nonlinear genetic programming with homologous crossover. The theorem also applies in the presence of 'node mutation'. The corresponding formula in case when 'node mutation' is present has been established. The limitation of the finite population Geiringer result is that it applies only in the absence of selection. In the current paper we also observe some general inequalities concerning the stationary distribution of the Markov chain associated to an evolutionary algorithm in which selection is the last (output) stage of a cycle. Moreover we prove an 'anti-communism' theorem which applies to a wide class of EAs and says that for small enough mutation rate, the stationary distribution of the Markov chain modelling the EA cannot be uniform.", notes = "Foundations of Genetic Algorithms", } @Article{Mitavskiy:2008:GPEM, author = "Boris Mitavskiy and Jonathan E. Rowe and Alden Wright and Lothar M. Schmitt", title = "Quotients of Markov chains and asymptotic properties of the stationary distribution of the Markov chain associated to an evolutionary algorithm", journal = "Genetic Programming and Evolvable Machines", year = "2008", volume = "9", number = "2", pages = "109--123", month = jun, note = "Special Issue on Theoretical foundations of evolutionary computation", keywords = "genetic algorithms, Markov chain, Stationary distribution, Quotient, Coarse graining, Evolutionary algorithm, Uniform population, Asymptotics, Mutation rate, Selection pressure", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-007-9038-6", size = "25 pages", abstract = "In this work, a method is presented for analysis of Markov chains modelling evolutionary algorithms through use of a suitable quotient construction. Such a notion of quotient of a Markov chain is frequently referred to as ``coarse graining'' in the evolutionary computation literature. We shall discuss the construction of a quotient of an irreducible Markov chain with respect to an arbitrary equivalence relation on the state space. The stationary distribution of the quotient chain is ``coherent'' with the stationary distribution of the original chain. Although the transition probabilities of the quotient chain depend on the stationary distribution of the original chain, we can still exploit the quotient construction to deduce some relevant properties of the stationary distribution of the original chain. As one application, we shall establish inequalities that describe how fast the stationary distribution of Markov chains modeling evolutionary algorithms concentrates on the uniform populations as the mutation rate converges to 0. Further applications are discussed. One of the results related to the quotient construction method is a significant improvement of the corresponding result of the authors' previous conference paper [Mitavskiy et al. (2006) In: Simulated Evolution and Learning, Proceedings of SEAL 2006, Lecture Notes in Computer Science v. 4247, Springer Verlag, pp 726-733]. This papers implications are all strengthened accordingly.", } @Article{Mitavskiy:2013:NC, author = "Boris S. Mitavskiy and Elio Tuci and Chris Cannings and Jonathan Rowe and Jun He", title = "Geiringer theorems: from population genetics to computational intelligence, memory evolutive systems and Hebbian learning", journal = "Natural Computing", year = "2013", volume = "12", number = "4", pages = "473--484", month = dec, keywords = "genetic algorithms, Geiringer theorems, Partially observable Markov decision processes, Monte-Carlo tree search, Reinforcement learning, Memory evolutive systems, Hebbian learning", publisher = "Springer", ISSN = "1567-7818", URL = "http://dx.doi.org/10.1007/s11047-013-9395-4", DOI = "doi:10.1007/s11047-013-9395-4", size = "12 pages", abstract = "The classical Geiringer theorem addresses the limiting frequency of occurrence of various alleles after repeated application of crossover. It has been adopted to the setting of evolutionary algorithms and, a lot more recently, reinforcement learning and Monte-Carlo tree search methodology to cope with a rather challenging question of action evaluation at the chance nodes. The theorem motivates novel dynamic parallel algorithms that are explicitly described in the current paper for the first time. The algorithms involve independent agents traversing a dynamically constructed directed graph that possibly has loops and multiple edges. A rather elegant and profound category-theoretic model of cognition in biological neural networks developed by a well-known French mathematician, professor Andree Ehresmann jointly with a neurosurgeon, Jan Paul Vanbremeersch over the last thirty years provides a hint at the connection between such algorithms and Hebbian learning.", notes = "Is this GP? Mention of Holland-Poli Schema theory Author Affiliations 1. Department of Computer Science, Aberystwyth University, Aberystwyth, SY23 3DB, UK 2. School of Mathematics and Statistics, University of Sheffield, Sheffield, S10 2RX, England, UK 3. School of Computer Science, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK", } @Book{mitchell:1996:iga, author = "Melanie Mitchell", title = "An Introduction to Genetic Algorithms", publisher = "MIT Press", year = "1996", keywords = "genetic algorithms", ISBN = "0-262-13316-4", URL = "http://www-mitpress.mit.edu/mitp/recent-books/cog/mitnh.html", URL = "http://www.santafe.edu/~mm/books.html", abstract = "Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics -- particularly in machine learning, scientific modeling, and artificial life -- and reviews a broad span of research, including the work of Mitchell and her colleagues. The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting {"}general purpose{"} nature of genetic algorithms as search methods that can be employed across disciplines. An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text. The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.", notes = "First 10 pages of Chapter 2 reviews genetic programming and page of Chapter 5 discusses tree encodings", size = "205 pages", } @Article{mitchell:1999:ARES, author = "Melanie Mitchell and Charles E. Taylor", title = "Evolutionary Computation: An Overview", journal = "Annual Review of Ecology and Systematics", year = "1999", volume = "30", pages = "593--616", copyright = "Copyright 1999 Annual Reviews", ISSN = "00664162", keywords = "genetic algorithms, genetic programming, Artificial Life, Computational Modeling", owner = "wlangdon", URL = "http://links.jstor.org/sici?sici=0066-4162%281999%2930%3C593%3AECAO%3E2.0.CO%3B2-P", size = "25 pages", } @Book{mitchell:1997:MLbook, author = "Tom M. Mitchell", title = "Machine Learning", publisher = "McGraw-Hill", year = "1997", keywords = "genetic algorithms, genetic programming", ISBN = "0-07-042807-7", notes = "Chapter 9 deals with genetic algorithms including a nice short survey of genetic programming (5 pages) but fails to intergrate GAs and GP into the main text on Machine learning", size = "414 pages", } @Article{oai:biomedcentral.com:1471-2407-6-159, title = "The use of genetic programming in the analysis of quantitative gene expression profiles for identification of nodal status in bladder cancer", author = "Anirban P Mitra and Arpit A Almal and Ben George and David W Fry and Peter F Lenehan and Vincenzo Pagliarulo and Richard J Cote and Ram H Datar and William P Worzel", journal = "BMC Cancer", year = "2006", volume = "6", number = "159", month = jun # "~16", publisher = "BioMed Central Ltd.", ISSN = "1471-2407", bibsource = "OAI-PMH server at www.biomedcentral.com", language = "en", oai = "oai:biomedcentral.com:1471-2407-6-159", rights = "Copyright 2006 Mitra et al; licensee BioMed Central Ltd.", keywords = "genetic algorithms, genetic programming, AUROC", URL = "http://www.biomedcentral.com/1471-2407/6/159", URL = "http://www.biomedcentral.com/content/pdf/1471-2407-6-159.pdf", DOI = "doi:10.1186/1471-2407-6-159", size = "16 pages", abstract = "Background Previous studies on bladder cancer have shown nodal involvement to be an independent indicator of prognosis and survival. This study aimed at developing an objective method for detection of nodal metastasis from molecular profiles of primary urothelial carcinoma tissues. Methods The study included primary bladder tumor tissues from 60 patients across different stages and 5 control tissues of normal urothelium. The entire cohort was divided into training and validation sets comprised of node positive and node negative subjects. Quantitative expression profiling was performed for a panel of 70 genes using standardized competitive RT-PCR and the expression values of the training set samples were run through an iterative machine learning process called genetic programming that employed an N-fold cross validation technique to generate classifier rules of limited complexity. These were then used in a voting algorithm to classify the validation set samples into those associated with or without nodal metastasis. Results The generated classifier rules using 70 genes demonstrated 81percent accuracy on the validation set when compared to the pathological nodal status. The rules showed a strong predilection for ICAM1, MAP2K6 and KDR resulting in gene expression motifs that cumulatively suggested a pattern ICAM1>MAP2K6>KDR for node positive cases. Additionally, the motifs showed CDK8 to be lower relative to ICAM1, and ANXA5 to be relatively high by itself in node positive tumors. Rules generated using only ICAM1, MAP2K6 and KDR were comparably robust, with a single representative rule producing an accuracy of 90percent when used by itself on the validation set, suggesting a crucial role for these genes in nodal metastasis. Conclusion Our study demonstrates the use of standardized quantitative gene expression values from primary bladder tumor tissues as inputs in a genetic programming system to generate classifier rules for determining the nodal status. Our method also suggests the involvement of ICAM1, MAP2K6, KDR, CDK8 and ANXA5 in unique mathematical combinations in the progression towards nodal positivity. Further studies are needed to identify more class-specific signatures and confirm the role of these genes in the evolution of nodal metastasis in bladder cancer.", notes = "p2 'Since scaling the gene expression levels to represent fold changes relative to a base value could have biased the significance of these gene' 65 samples. 11-fold cross validation. Max 7-genes per program. mixing of folds and majority voting scheme. 100 Generations. p6 Analysis of gene usage 'motifs' (requires GP, could not be done with other approaches. Indicate possible biochemical pathways. p7 'Gene transitivity'. p12 'hypothesis-generating nature of GP' p12 'A unique feature of GP is the final output, which consists of easily readable rules expressed as executable classifier programs that define tangible relationships between the most influential genes.' p12 'filtering can create an incomplete and biased dataset that may not be representative of many complex biological systems. The curse of dimensionality' p13.'hierarchical, KNN, K-means clustering and Neural Nets which do not scale easily to larger numbers of variables.' p13 GP can 'handle missing values in the data'.", } @PhdThesis{Mitra:thesis, author = "Anirban Pradip Mitra", title = "Predicting bladder cancer behavior by molecular expression profiling", school = "Keck School of Medicine, University of Southern California", year = "2009", type = "Pathobiology", address = "Los Angeles, California, USA", month = aug, keywords = "genetic algorithms, genetic programming, urothelial carcinoma, prognosis, quantitative expression profiling, microarray, immunohistochemistry", URL = "http://digitallibrary.usc.edu/cdm/ref/collection/p15799coll127/id/247267", size = "223 pages", abstract = "Urothelial carcinoma of the urinary bladder is the seventh most common type of cancer worldwide. In the western world, cigarette smoke is the most commonly implicated carcinogen for this disease. Bladder cancer presents itself as two prognostic variants -- the more common noninvasive Ta tumours that frequently recur but rarely invade the basement membrane, and the less common invasive tumors that tend to progress and metastasize. Traditional prognostic metrics, including tumor and nodal stage, are currently the best clinical predictors of subsequent behavior. While lymph node metastasis forebodes a poor prognosis, early detection can allow for radical lymphadenectomy with a curative intent. This manuscript begins by describing a study that used gene expression profiles generated from primary bladder tumours to construct signatures that could identify nodal metastasis. Genetic programming was used to identify classifiers that showed a strong predilection for ICAM1, MAP2K6 and KDR, and could detect nodal metastasis with reasonable sensitivity and specificity. Using similar pathway-based profiling approaches, this manuscript further describes studies that sought to determine if such molecular alterations could supplement traditional pathologic staging to better predict clinical outcome. The manuscript documents the identification and validation of a concise, biologically relevant gene panel comprising of JUN, MAP2K6, STAT3, and ICAM1 that could predict recurrence and survival in bladder cancer. Another study highlights attempts to identify genes profiled from primary noninvasive Ta tumours at first presentation that could predict local recurrence and tumour progression. The final study describes efforts to semi-quantitatively profile expressions of select proteins from primary bladder cancer tissues to analyse associations of their alterations with cigarette smoking, nonsteroidal anti-inflammatory drug use, and clinical outcome across all disease stages in a population-based cohort. These studies underscore the concept that a pathway-specific approach to profiling relevant biomolecules in bladder cancer can identify markers of prognostic significance, and patients who will recur and/or progress despite definitive surgery alone. Such identification of specific molecular alterations in individual tumours will allow for a more accurate and personalized prediction of prognosis, and also identify potential therapeutic targets.", notes = "Unrestricted", } @InCollection{mitra:2003:DTAUMGA, author = "Ashish Mitra", title = "Design of Transonic Airfoil Using Multiobjective Genetic Algorithms", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2003", year = "2003", editor = "John R. Koza", pages = "143--152", address = "Stanford, California, 94305-3079 USA", month = "4 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms", URL = "http://www.genetic-programming.org/sp2003/Mitra.pdf", notes = "part of \cite{koza:2003:gagp}", } @Article{Mittal:2013:IJARCET, author = "Mittu Mittal and Gagandeep Kaur", title = "{BBO} Comparison with other Nature Inspired Algorithms to Resolve Mixels", journal = "International Journal of Advanced Research in Computer Engineering \& Technology", year = "2013", volume = "2", number = "6", pages = "2114--2118", month = jun, keywords = "genetic algorithms, genetic programming, GP, ACO, BBO, DE, migration, mutation, PSO, remote sensing", ISSN = "2278-1323", bibsource = "OAI-PMH server at www.doaj.org", oai = "oai:doaj-articles:cab44e3afa090ffe3a62aec0c44566cd", URL = "http://ijarcet.org/wp-content/uploads/VOLUME-2-ISSUE-6-2114-2118.pdf", size = "5 pages", abstract = "Remote sensing is defined as a technique for acquiring the information about an object without making physical contact with that image via remote sensors. But the major problem of remotely sensed images is mixed pixel which always degrades the image quality. In this paper we attempted to present an approach for resolving the mixed pixels by using optimisation/ Evolutionary algorithm i.e. Bio-geography based optimisation. EAs are the most well known algorithms among nature inspired algorithms, which is based on the biological evolution in nature that is being responsible for the design of all living beings on earth. A family of successful EAs comprises genetic algorithm (GA), genetic programming (GP), Differential Evolution, evolutionary strategy (ES) , Artificial Bee Colony Algorithm (ABC), Particle swarm optimisation (PSO), Ant Colony Optimisation (ACO). This paper also deals with the comparison of BBO and others EAs so that we can proof BBO as best algorithm for resolving MIXELS problem.", notes = "Shri Pannalal Research Institute of Technology", } @Book{miu:2015:CHTMD, author = "Petre Miu", title = "Combine Harvesters: Theory, Modeling, and Design", publisher = "CRC Press", year = "2015", address = "Boca Raton, USA", keywords = "genetic algorithms, genetic programming, Matlab, Agriculture", ISBN = "9781-482-2823-7-5", URL = "https://www.taylorfrancis.com/books/mono/10.1201/b18852/combine-harvesters-petre-miu", DOI = "doi:10.1201/b18852", size = "482 pages", notes = "Section 2.5.2.3 (1 page?) on GP", } @InProceedings{Miyahara:2012:SCIS, author = "Tetsuhiro Miyahara and Tetsuji Kuboyama", booktitle = "Soft Computing and Intelligent Systems (SCIS) and 13th International Symposium on Advanced Intelligent Systems (ISIS), 2012 Joint 6th International Conference on", title = "Acquisition of glycan motifs using genetic programming and various fitness functions", year = "2012", pages = "1684--1689", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SCIS-ISIS.2012.6505277", size = "6 pages", abstract = "We apply a genetic programming approach to extraction of glycan motifs by using tag tree patterns and various fitness functions. Tag tree patterns obtained from some glycan data show characteristic tree structures. We consider the effects of using various fitness functions on obtained glycan motifs.", notes = "Also known as \cite{6505277}", } @Article{journals/jaciii/MiyaharaK14, author = "Tetsuhiro Miyahara and Tetsuji Kuboyama", title = "Learning of Glycan Motifs Using Genetic Programming and Various Fitness Functions", journal = "Journal of Advanced Computational Intelligence and Intelligent Informatics", year = "2014", number = "3", volume = "18", pages = "401--408", keywords = "genetic algorithms, genetic programming, tree patterns, glycan motifs", bibdate = "2014-09-17", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/jaciii/jaciii18.html#MiyaharaK14", URL = "http://www.fujipress.jp/finder/xslt.php?mode=present&inputfile=JACII001800030021.xml", DOI = "doi:10.20965/jaciii.2014.p0401", abstract = "We apply a genetic programming approach to learning of glycan motifs by using tag tree patterns and various fitness functions. Tag tree patterns obtained from some glycan data show characteristic tree structures. We examine the effects of using various fitness functions on GP processes and obtained glycan motifs. We also show that our method is applicable to tree structured data other than glycan data.", } @InProceedings{Miyashita:2000:GECCO, author = "Kazuo Miyashita", title = "Job-Shop Scheduling with Genetic Programming", pages = "505--512", year = "2000", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)", editor = "Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer", address = "Las Vegas, Nevada, USA", publisher_address = "San Francisco, CA 94104, USA", month = "10-12 " # jul, keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-708-0", URL = "http://gpbib.cs.ucl.ac.uk/gecco2000/GP041.pdf", broken = "doi:10.5555/2933718.2933809", URL = "https://dl.acm.org/doi/10.5555/2933718.2933809", size = "8 pages", abstract = "In order to solve a real-time scheduling problem, a computationally intensive search-based optimization method is not practical, but the efficient dispatching rule that is well-customized for the specific problem at hand can be an effective problem solving method. A dispatching rule is scheduling heuristics that decide the sequence of operations to be executed at each resource in the scheduling problem. However, developing a customized dispatching rule for specific scheduling problems is an arduous task even for domain experts or researchers in the scheduling problem. In this research, the author views scheduling problems as multi-agent problem solving and proposes an approach for synthesizing the dispatching rule by means of Genetic Programming (GP). In the preliminary experiments, the author got the results showing that GP-based multi-agent dispatching scheduler outperformed the well-known dispatching rules.", notes = "June 2024 DOI broken A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of \cite{whitley:2000:GECCO}", } @Article{Miyashita:2003:M, author = "Kazuo Miyashita and Sooyol Ok and Kazunori Hase", title = "Evolutionary generation of human-like bipedal locomotion", journal = "Mechatronics", year = "2003", volume = "13", number = "8-9", pages = "791--807", month = oct, keywords = "genetic algorithms, genetic programming, GP, Evolution, Bipedal locomotion, Neural oscillator, Central pattern generator (CPG)", ISSN = "0957-4158", URL = "http://elsevier.lib.sjtu.edu.cn/cgi-bin/sciserv.pl?collection=journals&journal=09574158&issue=v13i8-9&article=791_egohbl", DOI = "doi:10.1016/S0957-4158(03)00003-5", abstract = "We show how the computational model, which simulates the coordinated movements of human-like bipedal locomotion, can be evolutionarily generated without elaboration of manual coding. In the research of biomechanical engineering, robotics and neurophysiology, to clarify the mechanism of human bipedal walking is of their major interest. It can serve as a basis of developing several applications such as rehabilitation tools and humanoid robots. Nevertheless, because of complexity of human's neuronal system that interacts with the body dynamics system to make walking movements, much is left unknown about the control mechanism of locomotion, and researchers were looking for the optimal model of the neuronal system by extensive efforts of trials and errors. In this paper, we applied genetic programming to induce the model of the nervous system automatically and showed its effectiveness by simulating a human bipedal gait with the obtained model. Our experimental results show some promising evidences for evolutionary generation of the human-like bipedal locomotion.", notes = "Affiliations: a. AIST, Tsukuba East, Tsukuba, Ibaraki 305-8564, Japan b. CRL, 2-2-2, Hikaridai Seika-cho, Kyoto 619-0288, Japan c. AIST, Tsukuba Central 6, Tsukuba, Ibaraki 305-8566, Japan", } @InProceedings{MiyauchiMakoto:2012-02-28, author = "Makoto Miyauchi and Masaru Kudo and Yuya Ohta and Kimiyoshi Usami", title = "Power-Switch Drive-circuit generation for Ground-Bounce reduction using the Genetic-Programming", journal = "Technical report of IEICE. VLD", booktitle = "VLD2011-142", year = "2012", month = "28 " # feb, editor = "Makoto Ikeda", volume = "111", number = "450", pages = "133--138", address = "Oita", publisher = "The Institute of Electronics, Information and Communication Engineers", note = "in Japanese", keywords = "genetic algorithms, genetic programming, Ground Bounce, Power Switch Driver", URL = "http://ci.nii.ac.jp/naid/110009545710/en/", abstract = "Ground Bounce noise is a serious problem Power Gating technology. In this research, as compared with the Daisy Chain which is the conventional technique, the Ground Bounce has been reduced to 35percent and Wake-up time has been reduced 55percent by construction of an unbalanced Power-Switch-Driver-tree and buffer sizing that transmits a control signal to each Power Switch.", notes = "https://www.ieice.org/ken/index/ieice-techrep-111-450-e.html", } @InProceedings{Mizoguchi:1994:ICEC, author = "Jun'ichi Mizoguchi and Hitoshi Hemmi and Katsunori Shimohara", title = "Production genetic algorithms for automated hardware design through an evolutionary process", booktitle = "Proceedings of the First IEEE Conference on Evolutionary Computation", year = "1994", volume = "2", pages = "661--664", address = "Orlando, Florida, USA", month = "27-29 " # jun, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Evolvable hardware, grammar, HDL programs", DOI = "doi:10.1109/ICEC.1994.349980", size = "4 pages", abstract = "Production genetic algorithms is proposed to enable grammar structure as well as hardware description language (HDL) programs to evolve, toward an automated hardware design system through an evolutionary process. Evolutionary computation and methods make it possible to design hardware that works in unknown and non-stationary environments without explicit design knowledge. In the proposed system, hardware specifications, which produce circuit behaviours, are automatically generated as HDL programs according to the grammar defined as in a rewriting system and then evolve through production genetic algorithms (PGAs), also proposed here. The PGAs introduce new chromosome representation and genetic operators to create self-genesis mechanisms in hardware design similar to living systems. An experimental result shows that through an evolutionary process based on the PGAs, a hardware specification program expands its circuit scale and as a result increases its functionality", notes = "GA evolves population of grammar production rules which generate HDL program. Santa Fe Ant, cited by \cite{Orlov:2011:ieeeTEC} Also known as \cite{349980} ", } @InProceedings{Mizuno:2013:SICE, author = "Haruki Mizuno and Takashi Okamoto and Seiichi Koakutsu and Hironori Hirata", title = "A design method for the complex network growth model", booktitle = "Proceedings of SICE Annual Conference (SICE 2013)", year = "2013", month = "14-17 " # sep, pages = "571--576", keywords = "genetic algorithms, genetic programming, Complex Network, Network Growth Model, Network Design", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6736203", abstract = "Many systems that can be modelled using network structures appear in various fields such as informatics, social science, economics, ecology, biology, and engineering. If these systems can be modelled as complex network systems, the complex network design method that finds a desired network structure can become one of strong tools in large-scale system designs. Conventional complex network design methods can only generate a topology of desired network. They can not present the network growth rule. If a network growth model which contains a network growth rule is obtained, then the designer can obtain not only the topology of the desired network but also a guideline for designing desired network. In this study, we propose a complex network growth model design method. In the proposed method, the complex network growth model is obtained by two methods. One is the weighted function optimisation method with the PSO. The weighted function consists of feature quantities. The other is the direct growth model design method with the GP. The growth model is optimised with respect to feature quantities. We try to generate a network growth model which resembles the well-known BA model on the clustering coefficient. We confirm the effectiveness of the proposed method through numerical experiments.", notes = "Also known as \cite{6736203}", } @Article{Mladenovic:2016:AES, author = "Igor Mladenovic and Dusan Markovic and Milos Milovancevic and Miroljub Nikolic", title = "Extreme learning approach with wavelet transform function for forecasting wind turbine wake effect to improve wind farm efficiency", journal = "Advances in Engineering Software", volume = "96", pages = "91--95", year = "2016", ISSN = "0965-9978", DOI = "doi:10.1016/j.advengsoft.2016.02.011", URL = "http://www.sciencedirect.com/science/article/pii/S0965997816300588", abstract = "A wind turbine operating in the wake of another turbine and has a reduced power production because of a lower wind speed after rotor. The flow field in the wake behind the first row turbines is characterized by a significant deficit in wind velocity and increased levels of turbulence intensity. To maximize the wind farm net profit, the number of turbines installed in the wind farm should be different in depend on wind farm project investment parameters. Therefore modelling wake effect is necessary because it has a great influence on the actual energy output of a wind farm. In this paper, the extreme learning machine (ELM) coupled with wavelet transform (ELM-WAVELET) is used for the prediction of wind turbine wake effect in wind far. Estimation and prediction results of ELM-WAVELET model are compared with the ELM, genetic programming (GP), support vector machine (SVM) and artificial neural network (ANN) models. The following error and correlation functions are applied to evaluate the proposed models: Root Mean Square Error (RMSE), Coefficient of Determination (R2) and Pearson coefficient (r). The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by ELM-WAVELET approach (RMSE = 0.269) in comparison with the ELM (RMSE = 0.27), SVM (RMSE = 0.432), ANN (RMSE = 0.432) and GP model (RMSE = 0.433).", keywords = "genetic algorithms, genetic programming, Wind turbine, Wake model, Wind speed, Soft computing, Forecasting", notes = "University of Nis, Faculty of Economics, Trg kralja Aleksandra 11, Serbia", } @Article{Mladenovic:2016:RSER, author = "Igor Mladenovic and Svetlana Sokolov-Mladenovic and Milos Milovancevic and Dusan Markovic and Nenad Simeunovic", title = "Management and estimation of thermal comfort, carbon dioxide emission and economic growth by support vector machine", journal = "Renewable and Sustainable Energy Reviews", volume = "64", pages = "466--476", year = "2016", ISSN = "1364-0321", DOI = "doi:10.1016/j.rser.2016.06.034", URL = "http://www.sciencedirect.com/science/article/pii/S136403211630257X", abstract = "Urbanization and climate change are two defining environmental phenomena and these two processes are increasingly interconnected, as rapid urbanization is often accompanied by a change in lifestyle, increasing consumptions and energy uses, which contribute heavily towards climate change and thermal comfort. Success of public urban areas in attraction of residents depends on thermal comfort of the visitors. Thermal comfort of urban open spaces is variable, because it depends on climatic parameters and other influences, which are changeable throughout the year, as well as during the day. Therefore, the prediction of thermal comfort is significant in order to enable planning the time of usage of urban open spaces. This paper presents Support Vector Machine (SVM) to predict thermal comfort of visitors at an open urban area. Results from SVM-FFA were compared with two other soft computing method namely artificial neural network (ANN) and genetic programming (GP). The purpose of this research is also to predict carbon dioxide (CO2) emission based on the urban and rural population growth. Estimating carbon dioxide (CO2) emissions at an urban scale is the first step for adaptation and mitigation of climate change by local governments. The environment that governs the relationships between carbon dioxide (CO2) emissions and gross domestic product (GDP) changes over time due to variations in economic growth, regulatory policy and technology. The relationship between economic growth and carbon dioxide emissions is considered as one of the most important empirical relationships. GDP is also predicted based on CO2 emissions. The reliability of the computational models were accessed based on simulation results and using several statistical indicators.", keywords = "genetic algorithms, genetic programming, Thermal comfort, Economic growth, Carbon dioxide emission, Support vector machine", } @InProceedings{mladenovic:2024:SICAAI, author = "Tamara M. Mladenovic and Marko N. Zivanovic and Leo Benolic and Jelena N. Pavic and Nenad Filipovic", title = "Genetic Programming Approach in Better Understanding of the Relationship Between the Number of Viable Cells and Concentration of {O2-, NO2- and GSH} Produced in Cancer Cells Treated with {Pd(II)} Complexes", booktitle = "Applied Artificial Intelligence 2: Medicine, Biology, Chemistry, Financial, Games, Engineering", year = "2024", editor = "Nenad Filipovic", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "https://link.springer.com/chapter/10.1007/978-3-031-60840-7_20", DOI = "doi:10.1007/978-3-031-60840-7_20", notes = "The Second Serbian International Conference on Applied Artificial Intelligence (SICAAI)", } @Article{Mo:2021:ASC, author = "Hyunho Mo and Leonardo Lucio Custode and Giovanni Iacca", title = "Evolutionary neural architecture search for remaining useful life prediction", journal = "Applied Soft Computing", year = "2021", volume = "108", pages = "107474", month = sep, keywords = "genetic algorithms, Evolutionary algorithm, Convolutional neural network, Long short term memory, Remaining useful life, C-MAPSS", ISSN = "1568-4946", URL = "http://www.human-competitive.org/sites/default/files/humies-entry-iacca-2.txt", URL = "http://www.human-competitive.org/sites/default/files/iaccasecondentrypaper.pdf", URL = "https://www.sciencedirect.com/science/article/pii/S1568494621003975", DOI = "doi:10.1016/j.asoc.2021.107474", size = "47 pages", abstract = "With the advent of Industry 4.0, making accurate predictions of the remaining useful life (RUL) of industrial components has become a crucial aspect in predictive maintenance (PdM). To this aim, various Deep Neural Network (DNN) models have been proposed in the recent literature. However, while the architectures of these models have a large impact on their performance, they are usually determined empirically. To exclude the time-consuming process and the unnecessary computational cost of manually engineering these models, we present a Neural Architecture Search (NAS) technique based on an Evolutionary Algorithm (EA) applied to optimize the architecture of a DNN used to predict the RUL. The EA explores the combinatorial parameter space of a multi-head Convolutional Neural Network with Long Short Term Memory (CNN-LSTM) to search for the best architecture. In particular, our method requires minimum computational resources by making use of an early stopping policy and a history of the evaluated architectures. We dub the proposed method ENAS-PdM. To our knowledge, this is the first work where an EA-based NAS is used to optimize a CNN-LSTM architecture in the field of PdM. In our experiments, we use the well-established Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset from NASA. Compared to the current state-of-the-art, our method obtains better results in terms of two different metrics, RMSE and Score, when aggregating across all the C-MAPSS sub-datasets. Without aggregation, we achieve lower RMSE in 3 out of 4 sub-datasets. Our experimental results verify that the proposed method is a reliable tool for obtaining state-of-the-art RUL predictions and as such it can have a strong impact in several industrial applications, especially those with limited available computing power.", notes = "Entered 2021 HUMIES", } @Article{MO:2018:ASC, author = "Lili Mo and Ling Xie and Xiaoyi Jiang and Geer Teng and Lixiang Xu and Jin Xiao", title = "GMDH-based hybrid model for container throughput forecasting: Selective combination forecasting in nonlinear subseries", journal = "Applied Soft Computing", volume = "62", pages = "478--490", year = "2018", keywords = "genetic algorithms, genetic programming, Container throughput forecasting, Hybrid model, GMDH neural network, Selective combination forecasting", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2017.10.033", URL = "http://www.sciencedirect.com/science/article/pii/S1568494617306385", abstract = "The accurate forecasting of future container throughput is important for the construction, upgrade, and operation management of a port. This study introduces group method of data handling (GMDH) neural network and proposes a hybrid forecasting model based on GMDH (HFMG) to forecast container throughput. This model decomposes the original container throughput series into two parts: linear trend and nonlinear variation, and uses the seasonal autoregressive integrated moving average (SARIMA) approach to predict the linear trend. Considering the complexity of forecasting nonlinear subseries, the proposed model adopts three nonlinear single models, namely, support vector regression (SVR), back-propagation (BP) neural network, and genetic programming (GP), to predict the nonlinear subseries. Then, the model establishes selective combination forecasting by the GMDH neural network on the nonlinear subseries and obtains its combination forecasting results. Finally, the predictions of two parts are integrated to obtain the forecasting results of the original container throughput time series. The container throughput data of Xiamen and Shanghai Ports in China are used for empirical analysis, and the results show that the forecasting performance of the HFMG model is better than that of SARIMA model, as well as some hybrid forecasting models, such as SARIMA-SVR, SARIMA-GP, and SARIMA-BP. Finally, the monthly out-of-sample forecasts of container throughput for the two ports throughout 2016 are given", } @Article{DBLP:journals/ewc/MoayediMF21, author = "Hossein Moayedi and Mohammed Abdullahi Mu'azu and Loke Kok Foong", title = "Swarm-based analysis through social behavior of grey wolf optimization and genetic programming to predict friction capacity of driven piles", journal = "Eng. Comput.", volume = "37", number = "2", pages = "1277--1293", year = "2021", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/s00366-019-00885-z", DOI = "doi:10.1007/s00366-019-00885-z", timestamp = "Fri, 09 Apr 2021 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/ewc/MoayediMF21.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{moayedi:2021:ICISRM, author = "Hossein Moayedi", title = "Two Novel Predictive Networks for Slope Stability Analysis Using a Combination of Genetic Programming and Artificial Neural Network Techniques", booktitle = "Proceedings of the International Conference on Innovations for Sustainable and Responsible Mining", year = "2021", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-030-60839-2_6", DOI = "doi:10.1007/978-3-030-60839-2_6", } @Article{moayyedian:2023:IJAMT, author = "Mehdi Moayyedian and Mohammad Reza Chalak Qazani and Vahid Pourmostaghimi", title = "Optimized injection-molding process for thin-walled polypropylene part using genetic programming and interior point solver", journal = "The International Journal of Advanced Manufacturing Technology", year = "2023", volume = "124", number = "1 - 2", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s00170-022-10551-2", DOI = "doi:10.1007/s00170-022-10551-2", } @InProceedings{mock:1998:welp, author = "Kenrick J. Mock", title = "Wildwood: The Evolution of L-System Plants for Virtual Environments", booktitle = "Proceedings of the 1998 IEEE World Congress on Computational Intelligence", year = "1998", pages = "476--480", address = "Anchorage, Alaska, USA", month = "5-9 " # may, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Lindenmayer, Java, turtle-graphics, logo, interactive evolution, L-system representation, Wildwood project, artificial-life, plant evolution, simulation-style fitness function maximization, virtual environments, virtual gardener, virtual plant breeding, virtual worlds, biology computing, botany, digital simulation, functional analysis, virtual reality", ISBN = "0-7803-4869-9", file = "c082.pdf", URL = "http://www.math.uaa.alaska.edu/~afkjm/papers/Wildwood.doc", DOI = "doi:10.1109/ICEC.1998.699854", size = "5 pages", abstract = "a genetic algorithm was applied to a simplified L-system representation in order to generate artificial-life style plants for virtual work. Acting as a virtual gardener, a human selects which plants to breed, producing a unique new generation of plants. An experiment involving a simulation-style fitness function was also performed, and the virtual plants adapted to maximize the fitness function.", notes = "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE World Congress on Computational Intelligence. Java Intel Mult-user (MU) SDK virtual environment Moo. Cross pollination inside environment. Crossover and mutation. Hand bred mode, virtual gardeners, pop 5-10. 'It is possible to randomly generate interesting looking structures' p479. Simple fitness function W+30/H, pop 20 Demo http://www.math.uaa.alaska.edu/~afkjm/Lsys/test.html", } @InProceedings{Moctezuma:2017:TASS, author = "Daniela Moctezuma and Mario Graff and Sabino Miranda-Jimenez and Eric S. Tellez and Abel Coronado and Claudia N. Sanchez and Jose Ortiz-Bejar", title = "A Genetic Programming Approach to Sentiment Analysis for Twitter: TASS'17", booktitle = "TASS-2017: Workshop on Semantic Analysis at SEPLN", year = "2017", editor = "Julio Villena-Roman and Miguel Angel Garcia Cumbreras and Eugenio Martinez Camara and Manuel Carlos Diaz and Manuel Garcia Vega", pages = "23--28", address = "Murcia, Spain", month = sep # " 19", organisation = "La Sociedad Espanola para el Procesamiento del Lenguaje Natural", publisher = "CEUR", keywords = "genetic algorithms, genetic programming, NLP, Sentiment analysis, Opinion mining, Twitter", ISSN = "1613-0073", URL = "http://ceur-ws.org/Vol-1896/p1_ingeotec_tass2017.pdf", size = "6 pages", abstract = "we present the approach proposed by INGEOTEC team for global polarity classification at tweet level task of TASS-2017 contest. We use B4MSA algorithm, a proposed entropy-based term-weighting scheme and, EvoDAG as an ensemble", notes = "http://www.sepln.org/workshops/tass/2017/", } @Misc{oai:CiteSeerX.psu:10.1.1.604.610, author = "Marco Modesto and Moises G. {de Carvalho} and Walter {dos Santos}", title = "Record Deduplication By Evolutionary Means", howpublished = "CiteSeerX", year = "2002?", address = "Departamento de Ciencia da Computacao, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil", keywords = "genetic algorithms, genetic programming", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.604.610", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.604.610", URL = "http://homepages.dcc.ufmg.br/~nivio/cursos/pa06/seminarios/seminario16/seminario16.pdf", size = "5 pages", abstract = "Identifying record replicas in digital data repositories is a key step to improve the quality of content and services available, as well as to yield eventual sharing efforts. Several deduplication strategies are available, but most of them rely on manually chosen settings to combine evidence used to identify records as being replicas. In this work, we present the results of experiments we have carried out with a Machine Learning approach for the deduplication problem. Our approach is based on Genetic Programming (GP), that is able to automatically generate similarity functions to identify record replicas in a given repository. The generated similarity functions properly combine and weight the best evidence available among the record fields in order to tell when two distinct records represent the same real-world entity. On a previous work, fixed similarity functions were associated to each evidence. On the present work, the GP will be also used to choose the best evidence and similarity functions associations. The results of the experiments show that our approach outperforms the baseline method by Fellegi and Sunter. It also outperformed the previous GP results, using fixed evidence associations when identifying replicas in a data set containing researcher's personal data.", notes = "Oct 2017 reference by http://homepages.dcc.ufmg.br/~nivio/cursos/pa06/seminarios/", } @Article{moeeni:2017:JESS, author = "Hamid Moeeni and Hossein Bonakdari and Isa Ebtehaj", title = "Monthly reservoir inflow forecasting using a new hybrid {SARIMA} genetic programming approach", journal = "Journal of Earth System Science", year = "2017", volume = "126", number = "2", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s12040-017-0798-y", DOI = "doi:10.1007/s12040-017-0798-y", } @Article{DBLP:journals/soco/MoeiniN21, author = "Ramtin Moeini and Kamran Nasiri", title = "Hybridizing {ANN-NSGA-II} model with genetic programming method for reservoir operation rule curve determination (Case study {Zayandehroud} dam reservoir)", journal = "Soft Comput.", volume = "25", number = "22", pages = "14081--14108", year = "2021", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/s00500-021-06130-4", DOI = "doi:10.1007/s00500-021-06130-4", timestamp = "Wed, 03 Nov 2021 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/soco/MoeiniN21.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{DBLP:conf/bracis/MollerBGS20, author = "Frederico Jose Dias Moeller and Heder Soares Bernardino and Luciana Brugiolo Goncalves and Stenio Sa Rosario Furtado Soares", editor = "Ricardo Cerri and Ronaldo C. Prati", title = "A Reinforcement Learning Based Adaptive Mutation for Cartesian Genetic Programming Applied to the Design of Combinational Logic Circuits", booktitle = "Intelligent Systems - 9th Brazilian Conference, {BRACIS} 2020, Rio Grande, Brazil, October 20-23, 2020, Proceedings, Part {II}", series = "Lecture Notes in Computer Science", volume = "12320", pages = "18--32", publisher = "Springer", year = "2020", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", URL = "https://doi.org/10.1007/978-3-030-61380-8_2", DOI = "doi:10.1007/978-3-030-61380-8_2", timestamp = "Sat, 14 Nov 2020 00:00:00 +0100", biburl = "https://dblp.org/rec/conf/bracis/MollerBGS20.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{Mogavero:2016:SSCI, author = "A. Mogavero", booktitle = "2016 IEEE Symposium Series on Computational Intelligence (SSCI)", title = "Configurational optimizer of Combined Cycle Propulsion using Genetic Programming", year = "2016", abstract = "In most engineering applications the optimisation is employed after the conceptual design is already frozen with the only purpose of perfecting it. Although optimisation algorithms capable of optimising the configuration exist, their application is limited to electronics or control design, while for complex systems the conceptual design is often performed with manual trade off analyses among few options.", keywords = "genetic algorithms, genetic programming, combined cycle propulsion, configuration optimiser, Algorithm design and analysis, Engines, Integrated circuit modelling, Object oriented modelling, Optimisation, Rockets", DOI = "doi:10.1109/SSCI.2016.7850102", month = dec, notes = "Also known as \cite{7850102}", } @InCollection{mogensen:1994:macbeth, author = "Christian L. Mogensen", title = "{MacBeth} meets {A-Life}", booktitle = "Artificial Life at Stanford 1994", year = "1994", editor = "John R. Koza", pages = "118--128", address = "Stanford, California, 94305-3079 USA", month = jun, organisation = "Stanford University", publisher = "Stanford Bookstore", keywords = "genetic algorithms, game playing, hammurabi, {libGA}", ISBN = "0-18-182105-2", notes = "This volume contains 22 papers written and submitted by students describing their term projects for the course in artificial life (Computer Science 425) at Stanford University offered during the spring quarter quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs425.html", } @Article{MOGHADAM:2023:jhydrol, author = "Seyedeh Hadis Moghadam and Parisa-Sadat Ashofteh and Hugo A. Loaiciga", title = "Investigating the performance of data mining, lumped, and distributed models in runoff projected under climate change", journal = "Journal of Hydrology", volume = "617", pages = "128992", year = "2023", ISSN = "0022-1694", DOI = "doi:10.1016/j.jhydrol.2022.128992", URL = "https://www.sciencedirect.com/science/article/pii/S0022169422015621", keywords = "genetic algorithms, genetic programming, Climate change, River flow, Delta model, ClimGEN model, LARS-WG model, GP model, ANN model, IHACRES model, SWAT model", abstract = "This work evaluates the effects of climate change on the surface water resources (river flow) of the Sanjabi basin, Iran, by comparing data-mining, lumped, and distributed models, namely artificial neural networks (ANN), the identification of unit hydrographs and component flows from rainfall, evaporation, and streamflow (IHACRES) model, and the soil and water assessment tool (SWAT). Climate projections in terms of monthly temperature and rainfall made by 17 atmosphere-ocean general circulation models (AOGCMs) by the 5th Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC) under emission scenarios of Representative Concentration Pathways (RCPs) (RCP2.6, RCP4.5, and RCP8.5) during the baseline period 1971-2000 and future periods 2040-2069 and 2070-2099 are applied in the Sanjabi basin. The predictive skill of the AOGCMs is evaluated with performance criteria. The evaluation results indicate the CNRM-CM5 model features the best performance in terms of rainfall, average temperature, and minimum temperature projections, and the GFDL-CM3 provides the most accurate maximum temperature projections. Four downscaling methods (change factor (Delta), ClimGEN, LARS-WG, and Genetic Programming (GP)) are compared based on the R2, RMSE, MAE, and NSE. The predictive skill of the LARS-WG method was the highest. ANN, IHACRES, and SWAT are implemented to project future runoff following calibration and testing. The IHACRES model exhibits the best performance. The IHACRES model is applied to project future runoff under climate-change scenarios. The results indicate a reduction in runoff under all emission scenarios in the two future periods, with the RCP8.5 scenario featuring the largest reductions in runoff in 2040-2069 and 2070-2099 and being equal to 42.0 and 44.3percent, respectively", } @Article{Moghaddam:2016:Measurement, author = "Taher Baghaee Moghaddam and Mehrtash Soltani and Hamed Shahrokhi Shahraki and Shahaboddin Shamshirband and Noorzaily Bin Mohamed Noor and Mohamed Rehan Karim", title = "The use of SVM-FFA in estimating fatigue life of polyethylene terephthalate modified asphalt mixtures", journal = "Measurement", volume = "90", pages = "526--533", year = "2016", ISSN = "0263-2241", DOI = "doi:10.1016/j.measurement.2016.05.004", URL = "http://www.sciencedirect.com/science/article/pii/S0263224116301440", abstract = "To predict fatigue life of Polyethylene Terephthalate (PET) modified asphalt mixture, various soft computing methods such as Genetic Programming (GP), Artificial Neural Network (ANN), and Fuzzy Logic-based methods have been employed. In this study, an application of Support Vector Machine Firefly Algorithm (SVM-FFA) is implemented to predict fatigue life of PET modified asphalt mixture. The inputs are PET percentages, stress levels and environmental temperatures. The performance of proposed method is validated against observed experiment data. The results of the prediction using SVM-FFA are then compared to those of applying ANN and GP approach and it is concluded that SVM-FFA leads to more accurate results when compared to observed experiment data.", keywords = "genetic algorithms, genetic programming, Firefly algorithm, Support vector machine, PET modified asphalt mixtures, Environmental conditions, Fatigue life", } @Article{MohamadiBaghmolaei:2016:JML, author = "Mohamad Mohamadi-Baghmolaei and Reza Azin and Zahra Sakhaei and Rezvan Mohamadi-Baghmolaei and Shahriar Osfouri", title = "Novel Method for estimation of Gas/Oil relative Permeabilities", journal = "Journal of Molecular Liquids", volume = "224, Part B", pages = "1109--1116", year = "2016", ISSN = "0167-7322", DOI = "doi:10.1016/j.molliq.2016.08.055", URL = "http://www.sciencedirect.com/science/article/pii/S0167732216303166", abstract = "As the ages of most oil fields fall in the second half of their lives, many attempts have been made to enhance oil recovery in an efficient way. Gas injection into oil reservoirs for enhanced oil recovery (EOR) purposes requires relative permeability as a crucial issue in reservoir engineering. In this study, a new method is applied to predict relative permeabilities of gas/oil system related to various rock and fluid types. For this reason, a soft computing technique- Multi-gene genetic programming (MGGP) is employed to develop tools for prediction of relative permeability. The new methods are evaluated by experimental data extracted from open literature and are validated by extensive error analysis. The generated smart mathematical equations are able to predict relative permeabilities of gas/oil system with high accuracy and are applicable for various types of rock and fluid as well. In contrary to other reported correlations, the new novel equations require oil API and gas molecular weight as extra input variables to improve their estimating ability for every type of rock and fluid. The proposed technique is promising and encouraging for petroleum and reservoir engineers to be implemented for other gas/oil petro-physical properties.", keywords = "genetic algorithms, genetic programming, Reservoir engineering, Relative permeability, Gas injection, Empirical correlation", } @Article{MohamadiBaghmolaei:2016:JMLa, author = "Mohamad Mohamadi-Baghmolaei and Reza Azin and Zahra Sakhaei and Rezvan Mohamadi-Baghmolaei and Shahriar Osfouri", title = "Novel method for estimation of gas/oil relative permeabilities", journal = "Journal of Molecular Liquids", volume = "223", pages = "1185--1191", year = "2016", ISSN = "0167-7322", DOI = "doi:10.1016/j.molliq.2016.08.096", URL = "http://www.sciencedirect.com/science/article/pii/S016773221630318X", abstract = "As the ages of most oil fields fall in the second half of their lives, many attempts have been made to enhance oil recovery in an efficient way. Gas injection into oil reservoirs for enhanced oil recovery (EOR) purposes requires relative permeability as a crucial issue in reservoir engineering. In this study, a new method is applied to predict relative permeabilities of gas/oil system related to various rock and fluid types. For this reason, a soft computing technique - multi-gene genetic programming (MGGP) is employed to develop tools for prediction of relative permeability. The new methods are evaluated by experimental data extracted from open literature and are validated by extensive error analysis. The generated smart mathematical equations are able to predict relative permeabilities of gas/oil system with high accuracy and are applicable for various types of rock and fluid as well. In contrary to other reported correlations, the new novel equations require oil API and gas molecular weight as extra input variables to improve their estimating ability for every type of rock and fluid. The proposed technique is promising and encouraging for petroleum and reservoir engineers to be implemented for other gas/oil petro-physical properties.", keywords = "genetic algorithms, genetic programming, Reservoir engineering, Relative permeability, Gas injection, Empirical correlation", } @Article{Mohamed:2024:optik, author = "R. A. Mohamed and M. M. El-Nahass and M. Y. El-Bakry and El-Sayed A. El-Dahshan and E. H. Aamer and D. M. Habashy", title = "Computational intelligent techniques for predicting optical behavior of different materials", journal = "Optik", year = "2024", volume = "313", pages = "171986", month = oct, keywords = "genetic algorithms, genetic programming, Artificial neural network, ANN, Refractive index, Energy gap, Optical properties", ISSN = "0030-4026", URL = "https://www.sciencedirect.com/science/article/pii/S0030402624003851", DOI = "doi:10.1016/j.ijleo.2024.171986", abstract = "The current research introduces a comparison study of the use of artificial neural networks (ANN) and genetic programming (GP) for predicting the optical behaviour of different materials. The experimental data for a variety of materials, including semiconductors, insulators, oxides, and halides are extracted and used in ANN and GP as inputs. Simulation and prediction processes are carried out based on ANN and GP techniques. The most important aim presented in the current research is to obtain two equations of n as a function of Eg by the two based on ANN and GP models. The first numerical equation is obtained based on the ANN model exhibiting mean squared error (MSE) does not exceed 10-1. The second nonlinear equation is obtained based on the GP model with an acceptable fitness value. The estimated results based on the proposed approaches ANN and GP presented a great match with their targets. It demonstrated that the trained results including simulated and predicted results based on ANN and GP introduce excellent fitting compared with alike results obtained based on the conventional theoretical techniques. The mean absolute percentage error values prove that the ANN model is significantly more accurate than the GP model. Since lower error values suggest better prediction, hence it is clear that ANN performed better than GP. The equation derived by the ANN model is used to predict the refractive index for binary and ternary compounds. The values of the refractive index have been predicted for materials for which measurements have been made practically as a test step to ensure the accuracy of the results obtained through the deduced mathematical equations. Then, the application of these equations was generalised to materials that were not measured experimentally. A detailed discussion of the modelling results is introduced and proved that ANN and GP models are effective and successful tools for predicting the refractive index for different types of materials", } @Article{mohammad-azari:2020:EMaA, author = "Sahar Mohammad-Azari and Omid Bozorg-Haddad and Hugo A. Loaiciga", title = "State-of-art of genetic programming applications in water-resources systems analysis", journal = "Environmental Monitoring and Assessment", year = "2020", volume = "192", number = "2", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s10661-019-8040-9", DOI = "doi:10.1007/s10661-019-8040-9", } @Article{DBLP:journals/tinstmc/MohammadiNJ20, author = "Adel Mohammadi and Nader Nariman-Zadeh and Ali Jamali", title = "The archived-based genetic programming for optimal design of linear/non-linear controllers", journal = "Trans. Inst. Meas. Control", volume = "42", number = "8", pages = "1475--1491", year = "2020", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1177/0142331219891551", DOI = "doi:10.1177/0142331219891551", timestamp = "Fri, 09 Apr 2021 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/tinstmc/MohammadiNJ20.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{Mohammadi:2015:Energy, author = "Kasra Mohammadi and Shahaboddin Shamshirband and Por Lip Yee and Dalibor Petkovic and Mazdak Zamani and Sudheer Ch", title = "Predicting the wind power density based upon extreme learning machine", journal = "Energy", volume = "86", pages = "232--239", year = "2015", ISSN = "0360-5442", DOI = "doi:10.1016/j.energy.2015.03.111", URL = "http://www.sciencedirect.com/science/article/pii/S0360544215004600", abstract = "Precise predictions of wind power density play a substantial role in determining the viability of wind energy harnessing. In fact, reliable prediction is particularly useful for operators and investors to offer a secure situation with minimal economic risks. In this paper, a new model based upon ELM (extreme learning machine) is presented to estimate the wind power density. Generally, the two-parameter Weibull function has been normally used and recognized as a reliable method in wind energy estimations for most windy regions. Thus, the required data for training and testing were extracted from two accurate Weibull methods of standard deviation and power density. The validity of the ELM model is verified by comparing its predictions with SVM (Support Vector Machine), ANN (Artificial Neural Network) and GP (Genetic Programming) techniques. The wind powers predicted by all approaches are compared with those calculated using measured data. Based upon simulation results, it is demonstrated that ELM can be used effectively in applications of wind power predictions. In a nutshell, the survey results show that the proposed ELM model is suitable and precise to predict wind power density and has much higher performance than the other approaches examined in this study.", keywords = "genetic algorithms, genetic programming, Wind power density, ELM (extreme learning machine), Weibull method, Prediction", notes = "Faculty of Mechanical Engineering, University of Kashan, Kashan, Iran", } @Article{MOHAMMADI:2022:jtice, author = "Mohammad-Reza Mohammadi and Fahimeh Hadavimoghaddam and Saeid Atashrouz and Ali Abedi and Abdolhossein Hemmati-Sarapardeh and Ahmad Mohaddespour", title = "Toward predicting {SO2} solubility in ionic liquids utilizing soft computing approaches and equations of state", journal = "Journal of the Taiwan Institute of Chemical Engineers", volume = "133", pages = "104220", year = "2022", ISSN = "1876-1070", DOI = "doi:10.1016/j.jtice.2022.104220", URL = "https://www.sciencedirect.com/science/article/pii/S1876107022000190", keywords = "genetic algorithms, genetic programming, Sulfur dioxide solubility, Ionic liquids, Machine learning, Equation of state, Deep belief network, Group method of data handling", abstract = "Background: The use of novel and green solvents like ionic liquids (ILs) for the capture of air pollutant gases has gained extensive attention in recent years. However, getting reliable and fast predictions of gases solubility in ILs is complex. Methods Four soft computing methods including deep belief network (DBN), group method of data handling (GMDH), genetic programming (GP), and K-nearest neighbor (KNN) were used for estimating the solubility of sulfur dioxide (SO2) in ILs. A total of 374 experimental data points of SO2 solubility in 15 types of ILs were collected and used for model development. Moreover, Valderrama-Patel-Teja (VPT), Zudkevitch-Joffe (ZJ), Peng-Robinson (PR), Redlich-Kwong (RK), and Soave-Redlich-Kwong (SRK) equations of state (EOSs) were applied for the solubility predictions in the SO2 + ILs systems. Significant findings The results illustrated that DBN model is the most reliable predictive tool for the SO2 solubility in ILs by having an average absolute percent relative error (AAPRE) of 3.56percent. Furthermore, the proposed simple to use GMDH mathematical correlation also provides good estimations with an AAPRE of 8.05percent. Despite the weaker performance of the EOSs than the intelligent models, the PR EOS presented better estimations among other EOSs for the SO2 solubility in ILs.", } @Article{Mohammadnejad:2012:EES, author = "Ali Kafaei Mohammadnejad and Seyyed Mohammad Mousavi and Mohammad Torabi and Mehdi Mousavi and Amir Hossein Alavi", title = "Robust attenuation relations for peak time-domain parameters of strong ground motions", journal = "Environmental Earth Sciences", year = "2012", volume = "67", number = "1", pages = "53--70", month = sep, keywords = "genetic algorithms, genetic programming, Time-domain ground-motion parameters, Attenuation relationship, Simulated annealing, Nonlinear modelling", publisher = "Springer", language = "English", ISSN = "1866-6280", URL = "http://link.springer.com/article/10.1007%2Fs12665-011-1479-9", DOI = "doi:10.1007/s12665-011-1479-9", size = "18 pages", abstract = "This study presents new attenuation models for the estimation of peak ground acceleration (PGA), peak ground velocity (PGV), and peak ground displacement (PGD) using a hybrid method coupling genetic programming and simulated annealing, called GP/SA. The PGA, PGV, and PGD were formulated in terms of earthquake magnitude, earthquake source to site distance, average shear-wave velocity, and faulting mechanisms. A worldwide database of strong ground motions released by Pacific Earthquake Engineering Research Center (PEER) was employed to establish the models. A traditional genetic programming analysis was performed to benchmark the proposed models. For more validity verification, the GP/SA models were employed to predict the ground-motion parameters of the Iranian plateau earthquakes. Sensitivity and parametric analyses were carried out and discussed. The results show that the GP/SA attenuation models can offer precise and efficient solutions for the prediction of estimates of the peak time-domain characteristics of strong ground motions. The performance of the proposed models is better than or comparable with the attenuation relationships found in the literature.", } @Article{Mohammadnia:2021:JWE, author = "Sobhan Mohammadnia and Rasool Esmaeilyfard and Reza Akbari", journal = "Journal of Web Engineering", title = "An Efficient Method for Automatic Antipatterns Detection of {REST} Web Services", year = "2021", volume = "20", number = "6", pages = "1761--1780", abstract = "REST Web Services is a lightweight, maintainable, and scalable service accelerating client application development. The antipatterns of these services are inadequate and counter-productive design solutions. They have caused many qualitative problems in the maintenance and evolution of REST web services. This paper proposes an automated approach toward antipattern detection of the REST web services using Genetic Programming (GP). Three sets of generic, REST-specific and code-level metrics are considered. Twelve types of antipatterns are examined. The results are compared with the manual rule-based approach. The statistical analysis indicates that the proposed method has an average precision and recall scores of 98percent (95percent CI, 92.8percent to 100percent) and 82percent (95percent CI, 79.3percent to 84.7percent) and effectively detects REST antipatterns.", keywords = "genetic algorithms, genetic programming, Measurement, Statistical analysis, Manuals, Quality of service, Maintenance engineering, Service-oriented architecture, REST, web services, anti-patterns detection, service-oriented architecture (SOA), quality of service (QoS)", DOI = "doi:10.13052/jwe1540-9589.2063", ISSN = "1544-5976", month = sep, notes = "Also known as \cite{10247161}", } @Article{Mohammadzadeh:2016:EES, author = "Danial {Mohammadzadeh S} and Jafar Bolouri Bazaz and S. H. Vafaee Jani Yazd and Amir H. Alavi", title = "Deriving an intelligent model for soil compression index utilizing multi-gene genetic programming", journal = "Environmental Earth Sciences", year = "2016", volume = "75", number = "3", keywords = "genetic algorithms, genetic programming, Multi-gene genetic programming, Soil compression index, Soil engineering properties, Prediction", URL = "http://link.springer.com/article/10.1007/s12665-015-4889-2", DOI = "doi:10.1007/s12665-015-4889-2", } @Misc{journals/corr/abs-1907-04913, title = "Prediction of Compression Index of Fine-Grained Soils Using a Gene Expression Programming Model", author = "Danial {Mohammadzadeh S.} and Seyed-Farzan Kazemi and Amir Mosavi and Ehsan Nasseralshariati and Joseph H. M. Tah", howpublished = "arXiv", year = "2019", volume = "abs/1907.04913", keywords = "genetic algorithms, genetic programming, gene expression programming", URL = "http://arxiv.org/abs/1907.04913", bibdate = "2019-07-17", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/corr/corr1907.html#abs-1907-04913", abstract = "In construction projects, estimation of the settlement of fine-grained soils is of critical importance, and yet is a challenging task. The coefficient of consolidation for the compression index (Cc) is a key parameter in modeling the settlement of fine-grained soil layers. However, the estimation of this parameter is costly, time-consuming, and requires skilled technicians. To overcome these drawbacks, we aimed to predict Cc through other soil parameters, i.e., the liquid limit (LL), plastic limit (PL), and initial void ratio (e0). Using these parameters is more convenient and requires substantially less time and cost compared to the conventional tests to estimate Cc. This study presents a novel prediction model for the Cc of fine-grained soils using gene expression programming (GEP). A database consisting of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate Cc based on LL, PL, and e0. The performance of the developed GEP-based model was evaluated through the coefficient of determination (R2), the root mean squared error (RMSE), and the mean average error (MAE). The proposed model performed better in terms of R2, RMSE, and MAE compared to the other models.", } @Article{Mohanty:2013:ASC, author = "Ramakanta Mohanty and V. Ravi and M. R. Patra", title = "Hybrid intelligent systems for predicting software reliability", journal = "Applied Soft Computing", volume = "13", number = "1", month = jan, pages = "189--200", year = "2013", keywords = "genetic algorithms, genetic programming, SBSE, Software reliability, Multiple Linear Regression (MLR), Multivariate Adaptive Regression Splines (MARS), Back Propagation Neural Network (BPNN), Counter Propagation Neural Network (CPNN), Dynamic Evolving Neuro-Fuzzy Inference System (DENFIS), TreeNet, Group Method of Data Handling (GMDH), Recurrent architecture and ensemble model", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2012.08.015", URL = "http://www.sciencedirect.com/science/article/pii/S1568494612003626", abstract = "In this paper, we propose novel recurrent architectures for Genetic Programming (GP) and Group Method of Data Handling (GMDH) to predict software reliability. The effectiveness of the models is compared with that of well-known machine learning techniques viz. Multiple Linear Regression (MLR), Multivariate Adaptive Regression Splines (MARS), Backpropagation Neural Network (BPNN), Counter Propagation Neural Network (CPNN), Dynamic Evolving Neuro-Fuzzy Inference System (DENFIS), TreeNet, GMDH and GP on three datasets taken from literature. Further, we extended our research by developing GP and GMDH based ensemble models to predict software reliability. In the ensemble models, we considered GP and GMDH as constituent models and chose GP, GMDH, BPNN and Average as arbitrators. The results obtained from our experiments indicate that the new recurrent architecture for GP and the ensemble based on GP outperformed all other techniques.", } @Article{Mohanty:2015:ASC, author = "J. R. Mohanty and T. K. Mahanta and A. Mohanty and D. N. Thatoi", title = "Prediction of constant amplitude fatigue crack growth life of 2024 {T3 Al} alloy with R-ratio effect by {GP}", journal = "Applied Soft Computing", volume = "26", pages = "428--434", year = "2015", keywords = "genetic algorithms, genetic programming, Artificial neural network, Fatigue crack growth life, Fatigue crack growth rate, Load ratio", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2014.10.024", URL = "http://www.sciencedirect.com/science/article/pii/S1568494614005353", abstract = "The objective of this study is to develop a genetic programming (GP) based model to predict constant amplitude fatigue crack propagation life of 2024 T3 aluminium alloys under load ratio effect based on experimental data and to compare the results with earlier proposed ANN model. It is proved that genetic programming can effectively interpret fatigue crack growth rate data and can efficiently model fatigue life of the material system under investigation in comparison to ANN model.", } @Article{DBLP:journals/remotesensing/MohebzadehYL20, author = "Hamid Mohebzadeh and Junho Yeom and Taesam Lee", title = "Spatial Downscaling of {MODIS} Chlorophyll-a with Genetic Programming in South Korea", journal = "Remote. Sens.", volume = "12", number = "9", pages = "1412", year = "2020", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.3390/rs12091412", DOI = "doi:10.3390/rs12091412", timestamp = "Fri, 22 May 2020 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/remotesensing/MohebzadehYL20.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InCollection{Mohr:2013:evolve, author = "Esther Mohr and Guenter Schmidt and Sebastian Jansen", title = "A Comparative Study of Heuristic Conversion Algorithms, Genetic Programming and Return Predictability on the German Market", booktitle = "EVOLVE A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation", publisher = "Springer", year = "2013", editor = "Emilia Tantar and Alexandru-Adrian Tantar and Pascal Bouvry and Pierre {Del Moral} and Pierrick Legrand and Carlos A. {Coello Coello} and Oliver Schuetze", volume = "447", series = "Studies in Computational Intelligence", chapter = "12", pages = "393--414", keywords = "genetic algorithms, genetic programming, symbolic regression", isbn13 = "978-3-642-32725-4", DOI = "doi:10.1007/978-3-642-32726-1_12", abstract = "This paper evaluates the predictability of the heuristic conversion algorithms Moving Average Crossover and Trading Range Breakout in the German stock market. Hypothesis testing and a bootstrap procedure are used to test for predictive ability. Results show that the algorithms considered do not have predictive ability. Further, Genetic Programming is used to adapt the buying and selling rules of the investigated algorithms resulting in a new algorithm. Results show that a genetic programming approach does not lead to good new algorithms. We extend former works by using the Sortino Ratio as a measure of risk, and by applying competitive analysis.", notes = "Research papers that were presented at the international workshop EVOLVE 2011. EVOLVE 2011 was jointly organized by the University of Luxembourg, CINVESTAV, Mexico (Research and Advanced Studies Center of the National Polytechnic Institute of Mexico) and INRIA, France. 1. Saarland University, P.O. Box 151150, D-66041, Saarbruecken, Germany 2. Banking and Financial Services, University of Hohenheim, D-70593, Stuttgart, Germany doi:10.1007/978-3-642-32726-1_12", } @Article{Moilanen:1999:C, author = "Atte Moilanen", title = "Searching for Most Parsimonious Trees with Simulated Evolutionary Optimization", journal = "Cladistics", year = "1999", volume = "15", pages = "39--50", number = "1", owner = "wlangdon", keywords = "genetic algorithms, genetic programming, most parsimonious tree, evolutionary optimisation, parsimony computer program", URL = "http://www.helsinki.fi/~ihanski/Articles_by_others/Cladistics%201999%20Moilanen.pdf", URL = "http://www.sciencedirect.com/science/article/B6WCG-45K1GFT-C/2/26d8ad738876c47be070b90929205273", DOI = "doi:10.1006/clad.1998.0081", abstract = "This study describes novel algorithms for searching for most parsimonious trees. These algorithms are implemented as a parsimony computer program, PARSIGAL, which performs well even with difficult data sets. For high level search, PARSIGAL uses an evolutionary optimisation algorithm, which feeds good tree candidates to a branch-swapping local search procedure. This study also describes an extremely fast method of recomputing state sets for binary characters (additive or nonadditive characters with two states), based on packing 32 characters into a single memory word and recomputing the tree simultaneously for all 32 characters using fast bitwise logical operations. The operational principles of PARSIGAL are quite different from those previously published for other parsimony computer programs. Hence it is conceivable that PARSIGAL may be able to locate islands of trees that are different from those that are easily located with existing parsimony computer programs.", } @Article{ISI:000229393700005, author = "A Moilanen", title = "Methods for reserve selection: Interior point search", journal = "BIOLOGICAL CONSERVATION", year = "2005", volume = "124", number = "4", pages = "485--492", month = aug, DOI = "doi:10.1016/j.biocon.2005.02.012", ISSN = "0006-3207", keywords = "genetic algorithm, spatial reserve design, reserve selection, site selection algorithm, spatial optimisation, SITE-SELECTION, BIODIVERSITY CONSERVATION, HABITAT LOSS, NETWORKS, DESIGN, OPTIMISATION, ALGORITHMS, CONNECTIVITY, PROBABILITY, PERSISTENCE", unique-id = "ISI:000229393700005", } @Article{ISI:000229047500011, author = "A Moilanen", title = "Reserve selection using nonlinear species distribution models", journal = "AMERICAN NATURALIST", year = "2005", volume = "165", number = "6", pages = "695--706", month = jun, ISSN = "0003-0147", keywords = "genetic algorithm, spatial reserve design, reserve selection, site selection algorithm, habitat model, stochastic optimisation, SITE-SELECTION, BIODIVERSITY CONSERVATION, METAPOPULATION DYNAMICS, OPTIMISATION APPROACH, NATURAL AREAS, HABITAT LOSS, ALGORITHMS, NETWORKS, DESIGN, PERSISTENCE", unique-id = "ISI:000229047500011", } @Article{ISI:000175693800030, author = "A Moilanen and M Cabeza", title = "Single-species dynamic site selection", journal = "ECOLOGICAL APPLICATIONS", year = "2002", volume = "12", number = "3", pages = "913--926", month = jun, ISSN = "1051-0761", keywords = "economic constraint, genetic algorithm, incidence function model, local search, long-terns persistence, Melitaea diamina, metapopulation, objective function, reserve network design, site selection algorithm, METAPOPULATION DYNAMICS, RESERVE SELECTION, BUTTERFLY METAPOPULATION, VIABILITY ANALYSIS, EXTINCTION, NETWORKS, TURNOVER, MODEL, BIODIVERSITY, PERSISTENCE", unique-id = "ISI:000175693800030", } @Article{ISI:000168108300003, author = "A Moilanen", title = "Simulated evolutionary optimization and local search: Introduction and application to tree search", journal = "CLADISTICS-THE INTERNATIONAL JOURNAL OF THE WILLI HENNIG SOCIETY", year = "2001", volume = "17", number = "1, Part 2", pages = "S12--S25", month = mar, DOI = "doi:10.1006/clad.2000.0155", ISSN = "0748-3007", keywords = "GENETIC ALGORITHMS, tree search, genetic algorithm, evolutionary optimisation, local search, optimisation, minimum evolution tree, Parsigal, PARSIMONIOUS TREES, HARD", unique-id = "ISI:000168108300003", } @Article{Mojica200919, author = "Nelly Selem Mojica and Jorge Navarro and Pedro C. Marijuan and Rafael Lahoz-Beltra", title = "Cellular ``bauplans'': Evolving unicellular forms by means of Julia sets and Pickover biomorphs", journal = "Biosystems", year = "2009", volume = "98", number = "1", pages = "19--30", month = oct, keywords = "genetic algorithms, genetic programming, Cellular bauplans, Pickover biomorphs, Morphogenetic field, Julia set, Evolving fractal, Cytoskeletal mechanical forces, Organismic form", ISSN = "0303-2647", DOI = "doi:10.1016/j.biosystems.2009.07.002", broken = "http://www.sciencedirect.com/science/article/B6T2K-4WRD3P1-1/2/9d1dc224fa7f3b0696e38abe5aec4a63", size = "12 pages", abstract = "The universe of cellular forms has received scarce attention by mainstream neo-Darwinian views. The possibility that a fundamental trait of biological order may consist upon, or be guided by, developmental processes not completely amenable to natural selection was more akin to previous epochs of biological thought, i.e. the bauplan discussion. Thirty years ago, however, Lynn and Tucker studied the biological mechanisms responsible for defining organelles position inside cells. The fact that differentiated structures performing a specific function within the eukaryotic cell (i.e. mitochondrion, vacuole, or chloroplast) were occupying specific positions in the protoplasm was the observational and experimental support of the morphogenetic field notion at the cellular level. In the present paper we study the morphogenetic field evolution yielding from an initial population of undifferentiated cells to diversified unicellular organisms as well as specialised eukaryotic cell types. The cells are represented as Julia sets and Pickover biomorphs, simulating the effect of Darwinian natural selection with a simple genetic algorithm. The morphogenetic field defines the locations where cells are differentiated or sub-cellular components (or organelles) become organised. It may be realised by different possibilities, one of them by diffusing chemicals along the Turing model. We found that Pickover cells show a higher diversity of size and form than those populations evolved as Julia sets. Another novelty is the way that cellular organelles and cell nucleus fill in the cell, always in dependence on the previous cell definition as Julia set or Pickover biomorph. Our findings support the existence of specific attractors representing the functional and stable form of a differentiated cell--genuine cellular bauplans. The configuration of the morphogenetic field is attracted towards one or another attractor depending on the environmental influences as modelled by a particular fitness function. The model promotes the classical discussions of D'Arcy Thompson and the more recent views of Waddington, Goodwin and others that consider organisms as dynamical systems that evolve through a master plan of transformations, amenable to natural selection. Intriguingly, the model also connects with current developments on mechanobiology, highlighting the informational-developmental role that cytoskeletons may play.", notes = "GA uses complex mathematical functions. Department of Applied Mathematics, Faculty of Biological Sciences, Complutense University of Madrid, Madrid 28040, Spain", } @Article{Mojumder:2017:RSER, author = "Juwel Chandra Mojumder and Hwai Chyuan Ong and Wen Tong Chong and Nima Izadyar and Shahaboddin Shamshirband", title = "The intelligent forecasting of the performances in PV/T collectors based on soft computing method", journal = "Renewable and Sustainable Energy Reviews", volume = "72", pages = "1366--1378", year = "2017", ISSN = "1364-0321", DOI = "doi:10.1016/j.rser.2016.11.225", URL = "http://www.sciencedirect.com/science/article/pii/S1364032116309972", abstract = "Solar energy has been widely used in various aspects as the greatest promising and pollution free energy comparing with other available resources in nature. Photovoltaic-thermal (PV/T) is the most generative technology, which has been invented to use electrical energy and heat from the solar system. The article presents a novelty of using Extreme Learning Machine (ELM) into the air type PV/T technology. For this purposes, two air type PV/T designs were fabricated and practiced for a cooling fin design in the collector and finally, collected the experimental data, which was adapted to estimate electrical and thermal efficiency for the PV/T system. Then, the results of ELM prediction model were compared with Genetic Programming (GP) and Artificial Neural Networks (ANNs) models. The experimental result was accommodated to improving the predictive accuracy of the ELM approach in comparison. Further, outcome results indicate that developed ELM models can be used satisfactorily to formulate the predictive algorithm for PV/T performances. The ELM algorithm made a good generalization, which can learn very faster comparing with other conventional popular learning algorithms. The results revealed that the improved ELM model is a well fitted tool to predict the thermal and electrical efficiency with higher accuracy.", keywords = "genetic algorithms, genetic programming, Solar energy, Photovoltaic-thermal, Soft computing, Extreme learning machine (ELM), heat gain", } @InProceedings{Mokhtar:2024:ICDS, author = "N. F. M. Mokhtar and M. I. S. Amir", title = "Mathematical Modelling of {NHU} Cell Dynamics and Proliferation Using Interpretable Machine Learning", booktitle = "2024 Sixth International Conference on Intelligent Computing in Data Sciences (ICDS)", year = "2024", month = oct, keywords = "genetic algorithms, genetic programming, Computational modelling, Machine learning, Predictive models, Feature extraction, Mathematical models, Data models, Angular velocity, Dynamic programming, Videos, symbolic regression, NHU cells, evolutionary algorithms, mathematical modelling", DOI = "doi:10.1109/ICDS62089.2024.10756371", abstract = "This paper introduces a novel approach to predicting the number of cells in the Normal Human Urothelial (NHU) environment in untreated monolayer cultures over 48 hours. We employ symbolic regression to develop a mathematical model that delineates the relationship between cell growth over time, migration speed, and angular velocity. This data-driven technique scrutinises the cell growth curve, a concept well-known among biologists. Our methodology uses advanced computer vision techniques to monitor time-lapse videos, extracting detailed features such as cell growth, mean migration speed, and angular velocity. A significant innovation in our analysis is the application of Genetic Programming for symbolic regression, which enhances our ability to accurately predict cellular behaviours under control conditions. This approach allows the evolved network to precisely identify features crucial for understanding the responses of cells in untreated scenarios, providing unbiased, efficient insights into cell dynamics. This method supports the development of mathematical models for computational simulations, enhancing our understanding of cellular behaviours under normal conditions.", notes = "Also known as \cite{10756371} Institute for Mathematical Research, Universiti Putra Malaysia, Selangor, Malaysia", } @InProceedings{Molina:2021:ICSEcomp, author = "Facundo Molina and Pablo Ponzio and Nazareno Aguirre and Marcelo Frias", title = "{EvoSpex}: An Evolutionary Algorithm for Learning Postconditions (artifact)", booktitle = "IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)", year = "2021", editor = "Silvia Abrahao and Daniel Mendez", pages = "186--186", month = "25-28 " # may, keywords = "genetic algorithms, genetic programming, Genetic Improvement", isbn13 = "978-1-6654-1219-3/21/", code_url = "http://doi.org/10.5281/zenodo.4458256", code_url = "https://github.com/facumolina/evospex-ae", DOI = "doi:10.1109/ICSE-Companion52605.2021.00080", size = "2 pages", abstract = "Having the expected behaviour of software specified in a formal language can greatly improve the automation of software verification activities, since these need to contrast the intended behavior with the actual software implementation. Unfortunately, software many times lacks such specifications, and thus providing tools and techniques that can assist developers in the construction of software specifications are relevant in software engineering. As an aid in this context, we present EvoSpex, a tool that given a Java method, automatically produces a specification of the method current behaviour, in the form of post condition assertions. EvoSpex is based on generating software runs from the implementation (valid runs), making modifications to the runs to build divergent behaviors (invalid runs), and executing a genetic algorithm that tries to evolve a specification to satisfy the valid runs, and leave out the invalid ones. Our tool supports a rich JML-like assertion language, that can capture complex specifications, including sophisticated object structural properties.", notes = "is this GP? Department of Computer Science, FCEFQyN, University of Rio Cuarto, Argentina", } @InProceedings{Mollahasani:2011:CCE, author = "Ali Mollahasani and Amir Hossein Alavi and Amir Hossein Gandomi and Jafar {Boluori Bazaz}", title = "A New Prediction Model for Soil Deformation Modulus Based on PLT Results", booktitle = "Proceedings of the 9th International Symposium on Computational Civil Engineering, New Approaches in Numerical Analysis in Civil Engineering, 2011", year = "2011", editor = "Rodian Scinteie and Constantin Ionescu", pages = "53--61", address = "Iasi, Romania", month = "13 " # may, organisation = "Editura Societatii Academice Matei - Teiu Botez", keywords = "genetic algorithms, genetic programming, Discipulus, Linear genetic programming, Soil deformation moduli, Soil physical properties, Nonlinear modelling", isbn13 = "978-606-582-006-7", URL = "http://www.intersections.ro/Conferences/CCE2011.pdf", size = "9 pages", abstract = "In this study, a new empirical model was developed to predict the secant soil deformation modulus (Es) using linear genetic programming (LGP). The best LGP model was selected after developing and controlling several models with different combinations of the influencing parameters. The experimental database used for developing the model was established upon a series of plate load tests (PLT) conducted on different soil types. A sensitivity analysis was carried out to determine the contributions of the parameters affecting Es. The proposed model gives precise estimations of the soil deformation modulus.", notes = "Ali Mollahasani1, Amir Hossein Alavi2, Amir Hossein Gandomi2,3, Jafar Boluori Bazaz1 1Department of Civil Engineering, Ferdowsi University of Mashad, Mashad, Iran 2College of Civil Engineering, Iran University of Science and Technology, Tehran, Iran 3College of Civil Engineering, Tafresh University, Tafresh, Iran http://www.intersections.ro/Conferences/ cce2011@ce.cidi.ro", } @Article{Mollahasani:2011:CG, author = "Ali Mollahasani and Amir Hossein Alavi and Amir Hossein Gandomi", title = "Empirical modeling of plate load test moduli of soil via gene expression programming", journal = "Computers and Geotechnics", year = "2011", volume = "38", number = "2", pages = "281--286", month = mar, keywords = "genetic algorithms, genetic programming, Gene expression programming, Soil deformation moduli, Soil physical properties, Nonlinear modelling", ISSN = "0266-352X", URL = "http://www.sciencedirect.com/science/article/pii/S0266352X1000162X", DOI = "doi:10.1016/j.compgeo.2010.11.008", size = "6 pages", abstract = "New empirical models were developed to predict the soil deformation moduli using gene expression programming (GEP). The principal soil deformation parameters formulated were secant (Es) and reloading (Er) moduli. The proposed models relate Es and Er obtained from plate load-settlement curves to the basic soil physical properties. The best GEP models were selected after developing and controlling several models with different combinations of the influencing parameters. The experimental database used for developing the models was established upon a series of plate load tests conducted on different soil types at depths of 1-24m. To verify the applicability of the derived models, they were employed to estimate the soil moduli of a part of test results that were not included in the analysis. The external validation of the models was further verified using several statistical criteria recommended by researchers. A sensitivity analysis was carried out to determine the contributions of the parameters affecting Es and Er. The proposed models give precise estimates of the soil deformation moduli. The Es prediction model provides considerably better results in comparison with the model developed for Er. The simplified formulation for Es significantly outperforms the empirical equations found in the literature. The derived models can reliably be employed for pre-design purposes.", } @Article{moller:2023:AI, author = "Frederico Jose Dias Moller and Heder Soares Bernardino and Stenio Sa Rosario Furtado Soares and Lucas Augusto {Muller de Souza}", title = "An adaptive mutation for cartesian genetic programming using an epsilon-greedy strategy", journal = "Applied Intelligence", year = "2023", volume = "53", number = "22", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", URL = "http://link.springer.com/article/10.1007/s10489-023-04951-4", DOI = "doi:10.1007/s10489-023-04951-4", } @Article{molnar:1996:bits, author = "Darin Molnar", title = "Genetic Programming: Will Bill Gates become Billy Appleseed?", journal = "Computer Bits", year = "1996", volume = "6", number = "6", month = jun, keywords = "genetic algorithms, genetic programming", URL = "http://www.minetech.com/pdf/quotations/computerbits.pdf", broken = "http://www.computerbits.com/archive/9606/genetic.htm", URL = "http://www.genetic-programming.com/published/computerbits0696.pdf", URL = "http://www.genetic-programming.com/published/computerbits0696.html", size = "1 page", notes = "Newspaper style chat article computerbits.pdf is a fragment computerbits0696.pdf is from www.genetic-programming.com April 2019 Darin R. Molnar PhD 'Serving the world: A cross-cultural study of national ...' Capella University, June 2007. Supervisor: Kenneth K. Ames", } @InProceedings{Momm:2006:ASPRS, author = "Henrique Momm and Greg Easson and Dawn Wilkins", title = "Genetic Programming as a Preprocessing Tool to Aid Multi-Temporal Imagery Classification", booktitle = "Proceedings of the ASPRS 2006 Annual Conference", year = "2006", editor = "Alan Mikuni and George Hepner", address = "Reno, Nevada, USA", month = "15-" # may, organization = "American Society for Photogrammetry and Remote Sensing", keywords = "genetic algorithms, genetic programming, remote sensing", URL = "http://www.asprs.org/a/publications/proceedings/reno2006/0101.pdf", size = "11 pages", abstract = "Classification-based applications of remotely sensed data have increased significantly over the years. Very often, these data are gathered from different sources and in different formats causing the classification process to be scene specific. Alternatively, spectral band indices have been developed to emphasise some elements based on spectral characteristics and therefore improving the final classification accuracy. This research applies a multi-disciplinary approach in which genetic programming (GP) and standard unsupervised algorithms are integrated into a single iterative process to develop spectral indices for each element being investigated (such as water, impervious surfaces, dense vegetation, etc). A set of indices formed by mathematical and logical operations of the spectral bands are evolved using genetic operations. The application of non-linear indices enhances the relative spectral difference among the elements investigated improving the clustering capability of the data. The algorithm's ability to generalise provides an alternative to classify multi-temporal data with a single methodology. An example application is given for the water and impervious surface delineation using Landsat MSS, Landsat TM, and Landsat ETM+ imagery. Initial results are comparable to more labour intensive scene-specific supervised classification.", notes = "http://www.asprs.org/conference-archive/reno2006/final-prog.htm", } @InProceedings{Momm:2007:ASPRS, author = "Henrique G. Momm and Joel S. Kuszmaul and Greg Easson", title = "Integration of Logistic Regression and Genetic Programming to Model Coastal Louisiana Land Loss Using Remote Sensing", booktitle = "Proceedings of the ASPRS 2007 Annual Conference", year = "2007", editor = "Gary Florence", address = "Tampa, Florida, USA", month = "7-11 " # may, organization = "American Society for Photogrammetry and Remote Sensing", keywords = "genetic algorithms, genetic programming, remote sensing, logistic regression", URL = "http://www.asprs.org/a/publications/proceedings/tampa2007/0044.pdf", size = "8 pages", abstract = "The land loss along the Louisiana Coast has been recognised as a growing problem. Efforts have been concentrated in the creation of a Decision Support System (DSS) to better address the problem in which the correct water delineation from remotely sensed data is a critical part of this project. Two different approaches have been evaluated in previous studies: logistic regression and genetic programming. Herein a third approach is proposed by combining genetic programming with logistic regression. This hybrid approach merges the ability of logistic regression to deal with dichotomous data and to provide quantitative results with the optimisation characteristic of genetic programming to search the entire hypothesis space for the ``most fit'' hypothesis. Genetic programming modifies (using an iterative trial and error process) logistic regression models formed by vegetation indices built from basic function blocks defined in the function set (arithmetic operations) and in the terminal set (vegetation indices and spectral bands). Each candidate model is refined with a stepwise backward elimination using the level of significance associated with Chi-square test of each term and then evaluated based on the fitness function which is defined by: the model's, Kappa statistics and the number of terms in the model. The final output is a two-class (water and non-water) classified image of the most fit model.", notes = "http://www.asprs.org/conference-archive/tampa2007/", } @InProceedings{Momm:2008:SPIE, author = "Henrique G Momm and Greg Easson and Joel Kuszmaul", title = "Uncertainty analysis of an evolutionary algorithm to develop remote sensing spectral indices", booktitle = "Image Processing: Algorithms and Systems VI", year = "2008", editor = "Jaakko T. Astola and Karen O. Egiazarian and Edward R. Dougherty", volume = "6812", pages = "68120A.1--68120A.9", address = "San Jose, California, USA", month = "28 " # jan, publisher = "SPIE--The International Society for Optical Engineering", keywords = "genetic algorithms, genetic programming", DOI = "DOI:10.1117/12.766367", abstract = "The need for information extracted from remotely sensed data has increased in recent decades. To address this issue, research is being conducted to develop a complete multi-stage supervised object recognition system. The first stage of this system couples genetic programming with standard unsupervised clustering algorithms to search for the optimal preprocessing function. This manuscript addresses the quantification and the characterisation of the uncertainty involved in the random creation of the first set of candidate solutions from which the algorithm begins. We used a Monte Carlo type simulation involving 800 independent realisations and then analyzed the distribution of the final results. Two independent convergence approaches were investigated: [1] convergence based solely on genetic operations (standard) and [2] convergence based on genetic operations with subsequent insertion of new genetic material (restarting). Results indicate that the introduction of new genetic material should be incorporated into the preprocessing framework to enhance convergence and to reduce variability.", } @InProceedings{Momm:2008:MSA, author = "Henrique Momm and Greg Easson", title = "Assessment of a Non-linear Optimization Algorithm for Imagery Classification", booktitle = "Journal of the Mississippi Academy of Sciences", year = "2008", volume = "53", number = "1", pages = "96", note = "Seventy second annual meeting of the Mississippi Academy of Sciences, Whispering Woods, MS, USA, 20-22 February 2008", email = "msacad@bellsouth.net", keywords = "genetic algorithms, genetic programming, remote sensing", ISSN = "0076-9436", URL = "http://www.msstate.edu/org/MAS/jan08journal/jan08.pdf", size = "0.3 pages", abstract = "Results suggested that the overall variability increased with the introduction of additional variables despite the higher accuracy values. Conversely, the use of techniques such as population restarting significantly reduced the variability caused by the initial randomness process and therefore it is recommended to be incorporated into the framework.", } @PhdThesis{Momm:thesis, author = "Henrique Garcia Momm", title = "Evolutionary Computation for Information Extraction from Remotely Sensed Imagery", school = "Department of Geology and Geological Engineering, The University of Mississippi", year = "2008", month = may, address = "USA", keywords = "genetic algorithms, genetic programming, remote sensing, Applied science, Computer Science, Disaster management, Earth Science, Evolutionary Computation, Geotechnology, Image Processing, Information Extraction", URL = "http://search.proquest.com/docview/304514577", size = "196 pages", abstract = "Automated and semi-automated techniques have been researched as an alternative way to reduce human interaction and thus improve the information extraction process from imagery. This research developed an innovative methodology by integrating machine learning algorithms with image processing and remote sensing procedures to form the evolutionary framework . In this biologically-inspired methodology, non-linear solutions are developed by iteratively updating a set of candidate solutions through operations such as: reproduction, competition, and selection. Uncertainty analysis is conducted to quantitatively assess the system's variability due to the random generation of the initial set of candidate solutions, from which the algorithm begins. A new convergence approach is proposed and results indicate that it not only reduces the overall variability of the system but also the number of iterations needed to obtain the optimal solution. Additionally, the evolutionary framework is evaluated in solving different remote sensing problems, such as: non-linear inverse modelling, integration of image texture with spectral information, and multitemporal feature extraction. The investigations in this research revealed that the use of evolutionary computation to solve remote sensing problems is feasible. Results also indicate that, the evolutionary framework reduces the overall dimensionality of the data by removing redundant information while generating robust solutions regardless of the variations in the statistics and the distribution of the data. Thus, signifying that the proposed framework is capable of mathematically incorporating the non-linear relationship between features into the final solution.", notes = "UMI number 3361190", } @Article{Momm2009463, author = "H. G. Momm and Greg Easson and Joel Kuszmaul", title = "Evaluation of the use of spectral and textural information by an evolutionary algorithm for multi-spectral imagery classification", journal = "Computers, Environment and Urban Systems", year = "2009", volume = "33", number = "6", pages = "463--471", month = nov, note = "Spatial Data Mining-Methods and Applications", keywords = "genetic algorithms, genetic programming, Remote sensing, Image texture, Evolutionary computation, Optimization", ISSN = "0198-9715", DOI = "doi:10.1016/j.compenvurbsys.2009.07.007", size = "9 pages", abstract = "Considerable research has been conducted on automated and semi-automated techniques that incorporate image textural information into the decision process as an alternative to improve the information extraction from images while reducing time and cost. The challenge is the selection of the appropriate texture operators and the parameters to address a specific problem given the large set of available texture operators. In this study we evaluate the optimization characteristic of an evolutionary framework to evolve solutions combining spectral and textural information in non-linear mathematical equations to improve multi-spectral image classification. Twelve convolution-type texture operators were selected and divided into three groups. The application of these texture operators to a multi-spectral satellite image resulted into three new images (one for each of the texture operator groups considered). These images were used to evaluate the classification of features with similar spectral characteristics but with distinct textural pattern. Classification of these images using a standard image classification algorithm with and without the aid of the evolutionary framework have shown that the process aided by the evolutionary framework yield higher accuracy values in two out of three cases. The optimization characteristic of the evolutionary framework indicates its potential use as a data mining engine to reduce image dimensionality as the system improved accuracy values with reduced number of channels. In addition, the evolutionary framework reduces the time needed to develop custom solutions incorporating textural information, especially when the relation between the features being investigated and the image textural information is not fully understood.", notes = "Population Size 40 Candidate solutionsQuickBird", } @InProceedings{Momm:2010:gecco, author = "Henrique G. Momm and Greg Easson", title = "Population restarting: a study case of feature extraction from remotely sensed imagery using textural information", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "973--974", keywords = "genetic algorithms, genetic programming, Poster", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830483.1830656", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Image features with similar spectral information but with distinct textural characteristics can be extracted through the use of textural operators. The challenge resides in the selection of the appropriate texture operators and their parameters from a larger set of possible textural operators. This matter is especially difficult when addressing problems using remotely sensed imagery. The presence of additional spectral channels increases the search space.", notes = "Also known as \cite{1830656} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @Article{Momm:2011:JARS, author = "Henrique G. Momm and Greg Easson", title = "Evolving spectral transformations for multitemporal information extraction using evolutionary computation", journal = "Journal of Applied Remote Sensing", year = "2011", volume = "5", pages = "053564--1 to 053564--18", email = "henrique.momm@mtsu.edu", keywords = "genetic algorithms, genetic programming, multitemporal, evolutionary computation, remote sensing", ISSN = "1931-3195", publisher = "SPIE", URL = "http://remotesensing.spiedigitallibrary.org/article.aspx?articleid=1182443", DOI = "doi:10.1117/1.3662089", size = "18 pages", abstract = "Remote sensing plays an important role in assessing temporal changes in land features. The challenge often resides in the conversion of large quantities of raw data into actionable information in a timely and cost-effective fashion. To address this issue, research was undertaken to develop an innovative methodology integrating biologically-inspired algorithms with standard image classification algorithms to improve information extraction from multitemporal imagery. Genetic programming was used as the optimisation engine to evolve feature-specific candidate solutions in the form of nonlinear mathematical expressions of the image spectral channels (spectral indices). The temporal generalisation capability of the proposed system was evaluated by addressing the task of building rooftop identification from a set of images acquired at different dates in a cross-validation approach. The proposed system generates robust solutions (kappa values > 0.75 for stage 1 and > 0.4 for stage 2) despite the statistical differences between the scenes caused by land use and land cover changes coupled with variable environmental conditions, and the lack of radiometric calibration between images. Based on our results, the use of nonlinear spectral indices enhanced the spectral differences between features improving the clustering capability of standard classifiers and providing an alternative solution for multitemporal information extraction.", } @InProceedings{Monakhov:2018:APEIE, author = "Oleg G. Monakhov", booktitle = "2018 XIV International Scientific-Technical Conference on Actual Problems of Electronics Instrument Engineering (APEIE)", title = "Differential Evolution for Multi-Variant Evolutionary Synthesis of Nonlinear Models", year = "2018", pages = "487--491", abstract = "We propose a new multi-variant evolutionary algorithm based on differential evolution for solving the problem of construction of nonlinear models (mathematical expressions, functions, algorithms, and programs) using the given experimental data, sets of variables, basic functions and operations. We compare the proposed algorithm with the standard genetic programming algorithm (GP) and the Cartesian Genetic Programming (CGP) one. We show that the proposed algorithm exceeds the GP and CGP algorithms both in the time required for search for a solution (more than by an order of magnitude in the most cases) and in the probability of finding a given model.", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", DOI = "doi:10.1109/APEIE.2018.8545984", ISSN = "2473-8573", month = oct, notes = "Also known as \cite{8545984}", } @InCollection{Monakhov:2018:DSiA, author = "Oleg Monakhov and Emilia Monakhova", title = "An Algorithm of Multivariant Evolutionary Synthesis of Nonlinear Models with Real-Valued Chromosomes", booktitle = "Decision Science in Action", publisher = "Springer", year = "2018", editor = "Kusum Deep and Madhu Jain and Said Salhi", series = "Asset Analytics", chapter = "4", pages = "41--49", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, Multivariant evolutionary synthesis", isbn13 = "978-981-13-0859-8", URL = "https://link.springer.com/chapter/10.1007/978-981-13-0860-4_4#citeas", DOI = "doi:10.1007/978-981-13-0860-4_4", abstract = "We propose a new multivariant evolutionary algorithm for solving the problem of construction of nonlinear models (mathematical expressions, functions, algorithms, and programs) based on the given experimental data, sets of variables, basic functions, and operations. The proposed algorithm of multivariant evolutionary synthesis of nonlinear models includes a linear representation of a chromosome by real variables, simple operations in decoding of a genotype into a phenotype for interpreting a chromosome as a sequence of instructions, and also a multivariant method for presenting a set of models (expressions) using a single chromosome. We compare the proposed algorithm with the standard genetic programming algorithm (GP) and the Cartesian genetic programming (CGP) one. We show that the proposed algorithm exceeds the GP and CGP algorithms both in the time required for search for a solution (more than by an order of magnitude in the most cases) and in the probability of finding a given model.", notes = "Nov 2018 publisher gives year as 2019", } @Article{Mondal:2012:WRR, author = "Arpita Mondal and P. P. Mujumdar", title = "On the basin-scale detection and attribution of human-induced climate change in monsoon precipitation and streamflow", journal = "Water Resources Research", year = "2012", volume = "48", number = "10", pages = "W10520", month = oct, publisher = "American Geophysical Union", keywords = "genetic algorithms, genetic programming", ISSN = "1944-7973", bibsource = "OAI-PMH server at eprints.iisc.ernet.in", oai = "oai:eprints.iisc.ernet.in:45379", type = "Peer Reviewed", URL = "http://eprints.iisc.ernet.in/45379/", URL = "http://eprints.iisc.ernet.in/45379/1/wat_res_res_48_w10520_2012.pdf", DOI = "doi:10.1029/2011WR011468", size = "18 pages", abstract = "Detecting and quantifying the presence of human-induced climate change in regional hydrology is important for studying the impacts of such changes on the water resources systems as well as for reliable future projections and policy making for adaptation. In this article a formal fingerprint-based detection and attribution analysis has been attempted to study the changes in the observed monsoon precipitation and streamflow in the rain-fed Mahanadi River Basin in India, considering the variability across different climate models. This is achieved through the use of observations, several climate model runs, a principal component analysis and regression based statistical downscaling technique, and a Genetic Programming based rainfall-runoff model. It is found that the decreases in observed hydrological variables across the second half of the 20th century lie outside the range that is expected from natural internal variability of climate alone at 95percent statistical confidence level, for most of the climate models considered. For several climate models, such changes are consistent with those expected from anthropogenic emissions of greenhouse gases. However, unequivocal attribution to human-induced climate change cannot be claimed across all the climate models and uncertainties in our detection procedure, arising out of various sources including the use of models, cannot be ruled out. Changes in solar irradiance and volcanic activities are considered as other plausible natural external causes of climate change. Time evolution of the anthropogenic climate change ``signal'' in the hydrological observations, above the natural internal climate variability ``noise'' shows that the detection of the signal is achieved earlier in stream-flow as compared to precipitation for most of the climate models, suggesting larger impacts of human-induced climate change on streamflow than precipitation at the river basin scale.", notes = "GPTIPS", } @Article{Mondal:2013:IJSCE, author = "Swapan Kumar Mondal and Hitesh Tahbildar", title = "Automated Test Data Generation Using Fuzzy Logic-Genetic Algorithm Hybridization System for Class Testing Of Object Oriented Programming", journal = "International Journal of Soft Computing \& Engineering", year = "2013", volume = "3", number = "5", month = nov, pages = "40--49", keywords = "genetic algorithms, genetic programming, SBSE, UML, java, binary tree, fuzzy logic control (flc), mutation testing", bibsource = "OAI-PMH server at www.doaj.org", oai = "oai:doaj-articles:7869fb9f6f20f575cb8364fedf5f48b7", ISSN = "2231-2307", URL = "https://www.ijsce.org/portfolio-item/e1871113513/", URL = "http://www.ijsce.org/attachments/File/v3i5/E1871113513.pdf", size = "10 pages", abstract = "In this paper we have explained automatic test data generation particularly for class testing of object oriented programming. During test data generation we have implemented the Genetic program - Fuzzy logic control auxiliary hybridisation techniques. Some cases genetic algorithm has been used for optimised the desired results. As a future challenges we have made comments on the use of this new proposed technique. This proposed technique can be used for testing of industrial production oriented software.Production oriented software is use in Computer numerical control (C.N.C) machine.", } @Article{MONFARED:2020:MM, author = "Jalal Rostami Monfared and Abdolmajid Mousavi", title = "Design and simulation of nano-arbiters using quantum-dot cellular automata", journal = "Microprocessors and Microsystems", year = "2020", volume = "72", pages = "102926", month = feb, keywords = "genetic algorithms, genetic programming, cartesian Genetic Programming, Quantum-dot cellular automata, Round robin arbiter, Arbitration, Network-On-Chip", ISSN = "0141-9331", URL = "http://www.sciencedirect.com/science/article/pii/S0141933119301619", DOI = "doi:10.1016/j.micpro.2019.102926", abstract = "Arbiters are the essential components of the Network-On-Chip (NOC) systems and are used to resolve the contention problem where multiple requests must be handled for shared resources. On the other hand, with the ever-increasing downsizing trend in the fabrication technology, Quantum-dot Cellular Automata (QCA) with its nano scales and very low power consumption is a promising candidate for implementing future NOCs. In the current work, we design and simulate nano-arbiters using QCA with the following contributions: i) The 2-bit Basic Round Robin Arbiter (RRA) and the 2-bit Ping Pong Arbiter (PPA) are designed and simulated; ii) A solution for an erroneous condition found in the original circuit of RRA is reported and fixed; iii) We use Cartesian Genetic Programming (CGP) approach to simplify the RRA and PPA designs; iv) In order to leverage our QCA designs, we apply a more realistic clock distribution (2-DW clocking) and report the results. At the end, a one-to-one comparison of the two arbiters designed with QCA will be presented using such benchmarks as area, latency, etc. Our results show that in the 2-bit input mode, the PPA arbiter has the best overall performance", } @InProceedings{Moni:2019:ICIICT, author = "Vidya Moni", title = "Machine Learning to Predict Annual Stock Market Index - a Genetic Programming Approach", booktitle = "2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT)", year = "2019", address = "Chennai, India", month = "25-26 " # apr, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICIICT1.2019.8741439", isbn13 = "978-1-7281-1604-4", abstract = "The objective of this research was to generate an indicator of global political stability, by predicting the annual S&P 500 stock market index. This was done through machine learning, using a genetic programming approach, creating an algorithm with a template that takes into account the previous years' data of S&P 500 stock index, gold prices, the number of casualties in U.S. wars, crude oil prices, Dow Jones Industrial Average and rates of inflation in U.S. The prediction of this algorithm was highly accurate, within 1percent.", notes = "Also known as \cite{8741439}", } @InProceedings{Monier-Vinard:2018:ITherm, author = "Eric Monier-Vinard and Olivier Daniel and Valentin Bissuel and Brice Rogie and Minh-Nhat Nguyen and Najib Laraqi and Ismael Aliouat", booktitle = "2018 17th IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (ITherm)", title = "Hybridizing Nature-Inspired Algorithms to Derive Accurate Surrogate Thermal Model: Genetic and Particle Swarm Optimization", year = "2018", pages = "368--378", abstract = "The need for miniaturization leads IC packaging technologies toward more and more complex three dimensional geometries, which should be carefully addressed when dealing with thermal management. In order to model these devices in the necessary CFD simulations, Boundary Conditions Independent (BCI) Compact Thermal Models (CTM) were developed in the scope of the DELPHI consortium using Genetic Algorithm (GA) as the optimization technique. But at each level of packaging technology breakthrough, the ability to achieve the right balance between accuracy, reproducibility and speed of GA procedure shrank. The paper describes the first results when using a Particle Swarm Optimization (PSO) in place of genetic programming. So instead of reproduction, the swarm method updates the position and speed of each particle over time. This study presents different PSO variants found in the literature and implemented on 2 real test cases. They confirm that the PSO tends to fall easily in local optimums and even more as the component-model complexity grows. Trying to combine the benefits of both algorithms, a parallel GA-PSO hybridization is discussed in terms of methodology, accuracy, speed and robustness. Thus the promoted GA-PSO hybridization succeeds supplying the best solution 8 times more often, in half the computation time, for a multi-chip package. The study results confirm the feasibility to create blackbox models of various components having low-discrepancy in comparison with the authentic thermal behaviour.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ITHERM.2018.8419587", ISSN = "2577-0799", month = may, notes = "Also known as \cite{8419587}", } @InProceedings{Monperrus:2014:CRA:2568225.2568324, author = "Martin Monperrus", title = "A Critical Review of {"}Automatic Patch Generation Learned from Human-written Patches{"}: Essay on the Problem Statement and the Evaluation of Automatic Software Repair", booktitle = "Proceedings of the 36th International Conference on Software Engineering, ICSE 2014", year = "2014", pages = "234--242", address = "Hyderabad, India", publisher_address = "New York, NY, USA", publisher = "ACM", keywords = "GenProg, Bugs, automatic patch generation, automatic program fixing, automatic software repair, error recovery, faults", isbn13 = "978-1-4503-2756-5", acmid = "2568324", DOI = "doi:10.1145/2568225.2568324", size = "9 pages", abstract = "At ICSE'2013, there was the first session ever dedicated to automatic program repair. In this session, Kim et al. presented PAR, a novel template-based approach for fixing Java bugs. We strongly disagree with key points of this paper. Our critical review has two goals. First, we aim at explaining why we disagree with Kim and colleagues and why the reasons behind this disagreement are important for research on automatic software repair in general. Second, we aim at contributing to the field with a clarification of the essential ideas behind automatic software repair. In particular we discuss the main evaluation criteria of automatic software repair: understandability, correctness and completeness. We show that depending on how one sets up the repair scenario, the evaluation goals may be contradictory. Eventually, we discuss the nature of fix acceptability and its relation to the notion of software correctness.", notes = "'We strongly disagree with Kim et al.s \cite{Kim:2013:ICSE} paper on PAR.'", } @TechReport{monperrus:hal-01206501, author = "Martin Monperrus", title = "Automatic Software Repair: a Bibliography", institution = "Centre poor la Communication Scientifique Direct", year = "2015", number = "hal-01206501", address = "France", month = "21 " # nov, keywords = "genetic algorithms, genetic programming, genetic improvement, APR, Software Engineering, GenProg", hal_id = "hal-01206501", hal_version = "v1", URL = "https://hal.archives-ouvertes.fr/hal-01206501", URL = "http://www.monperrus.net/martin/survey-automatic-repair.pdf", size = "34 pages", abstract = "This article presents an annotated bibliography on automatic software repair. Automatic software repair consists of automatically finding a solution to software bugs, without human intervention. The uniqueness of this article is that it spans the research communities that contribute to this body of knowledge: software engineering, dependability, operating systems, programming languages and security. Furthermore, it provides a novel and structured overview of the diversity of bug oracles and repair operators used in the literature.", notes = "University of Lille and Inria. Little mention of GP except University of Virginia work. gismo automatic repair (program repair, self-repair) automatic fixing (bug fixing, program fixing) automatic patching healing (self-healing) automatic correction (self-correcting) automatic recovery (self-recovering) resilience automatic workaround survive (survival, survivability) rejuvenation biological metaphors: allergies, immunity, vaccination EXP, Jaff, Mutation, Metaprogram, Semfix, PAR, Nopol, Relifix, DirectFix, SPR, CodePhage, SearchRepair, Prophet, AutoFix-E, Alloy for repair, Pachika. .NET, Arithmetic overflow, Buffer overflow, Overflows, AutoPag, AFix, Android, SoupInt, malformed HTML, R2Fix, Proof, Reference implementation, architectural repair, test repair, failure-oblivious computing, missing checks, Carburizer, SQL injection, Findbugs, input filter generation, unhandled exceptions, contract, Software Rejuvenation, Microreboot, Checkpoint, recovery block, automatic workaround, data diversity, input rectification, Rx DieHard, Exterminator, ClearView, Assure, loop perforation, RCV, redundancy, assumption, patch, acceptability, overfitting, benchmark, crash-only, software, SafeDrive, Bristlecone, systematic edit, Coccinelle, HelpMeout, BugFix, MintHint, Excel. Latest version: http://www.monperrus.net/martin/survey-automatic-repair.pdf Updated in \cite{DBLP:journals/corr/abs-1807-00515} See \cite{Monperrus:2018:ACM_CSUR}", } @Misc{DBLP:journals/corr/abs-1807-00515, author = "Martin Monperrus", title = "Automatic Software Repair: a Bibliography", howpublished = "arXiv", year = "2018", month = "2 " # jul, keywords = "genetic algorithms, genetic programming, genetic improvement, APR, automatic bugfixing", eprint = "1807.00515", URL = "http://arxiv.org/abs/1807.00515", size = "26 pages", abstract = "This article presents a survey on automatic software repair. Automatic software repair consists of automatically finding a solution to software bugs without human intervention. This article considers all kinds of repairs. First, it discusses behavioural repair where test suites, contracts, models, and crashing inputs are taken as oracle. Second, it discusses state repair, also known as runtime repair or runtime recovery, with techniques such as checkpoint and restart, reconfiguration, and invariant restoration. The uniqueness of this article is that it spans the research communities that contribute to this body of knowledge: software engineering, dependability, operating systems, programming languages, and security. It provides a novel and structured overview of the diversity of bug oracles and repair operators used in the literature.", notes = "Replaces \cite{monperrus:hal-01206501} See \cite{Monperrus:2018:ACM_CSUR}", } @Article{Monperrus:2018:ACM_CSUR, author = "Martin Monperrus", title = "Automatic Software Repair: A Bibliography", journal = "ACM Computing Surveys", year = "2018", volume = "51", number = "1", pages = "article no 17", month = jan, keywords = "genetic algorithms, genetic programming, genetic improvement, APR, automatic bugfixing, Program repair, self-healing software", publisher = "Association for Computing Machinery", ISSN = "0360-0300", DOI = "doi:10.1145/3105906", size = "24 pages", abstract = "This article presents a survey on automatic software repair. Automatic software repair consists of automatically finding a solution to software bugs without human intervention. This article considers all kinds of repairs. First, it discusses behavioral repair where test suites, contracts, models, and crashing inputs are taken as oracle. Second, it discusses state repair, also known as runtime repair or runtime recovery, with techniques such as checkpoint and restart, reconfiguration, and invariant restoration. The uniqueness of this article is that it spans the research communities that contribute to this body of knowledge: software engineering, dependability, operating systems, programming languages, and security. It provides a novel and structured overview of the diversity of bug oracles and repair operators used in the literature.", notes = "Replaces \cite{DBLP:journals/corr/abs-1807-00515}", } @InProceedings{monsieurs:2001:gecco, title = "Increasing the diversity of a population in genetic programming", author = "Patrick Monsieurs and Eddy Flerackers", pages = "185", year = "2001", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)", editor = "Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon and Edmund Burke", address = "San Francisco, California, USA", publisher_address = "San Francisco, CA 94104, USA", month = "7-11 " # jul, keywords = "genetic algorithms, genetic programming: Poster, Diversity, Code reuse", ISBN = "1-55860-774-9", URL = "http://gpbib.cs.ucl.ac.uk/gecco2001/d02.pdf", size = "1 page", notes = "GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of \cite{spector:2001:GECCO}", } @InProceedings{monsieurs:2001:FD, author = "Patrick Monsieurs and Eddy Flerackers", title = "Reducing Bloat in Genetic Programming", booktitle = "Computational Intelligence : Theory and Applications", year = "2001", editor = "Bernd Reusch", volume = "2206", series = "LNCS", pages = "471--478", address = "Dortmund, Germany", month = "1-3 " # oct, organisation = "7th Fuzzy Days", publisher = "Springer-Verlag", email = "patrick.monsieurs@luc.ac.be", keywords = "genetic algorithms, genetic programming, bloat", ISBN = "3-540-42732-5", isbn13 = "978-3-540-42732-2", size = "8 pages", DOI = "doi:10.1007/3-540-45493-4_48", abstract = "In this paper, several techniques will be presented to constrain the growth of solutions that are constructed by genetic programming. The most successful technique imposes a maximum size on the created individuals of the population that depends solely on the size of the best individual of the population. This method will be compared with other methods to reduce bloat, demonstrating that this method reduces bloat significantly better than the other methods.", notes = "http://ls1-www.cs.uni-dortmund.de/fd7/", } @Misc{monsieurs:2001:dricGP, author = "Patrick Monsieurs and Eddy Flerackers", title = "Detecting and Removing Inactive Code in Genetic Programs", howpublished = "www", year = "2001", month = "7 " # nov, keywords = "genetic algorithms, genetic programming, intron, bloat", broken = "http://alpha.luc.ac.be/~lucp1089/DetectingAndRemovingInactiveCode.pdf", size = "10 pages", abstract = "This paper presents a technique to measure the influence a child node has on the result of its parent nodes in a program generated by genetic programming. Child nodes that have no influence are inactive, and can be removed from the individual without affecting the result of that individual, thus reducing its size. This technique is described for several types of non-terminal nodes, and the effect of the operation on the size of individuals and convergence speed of the population is tested experimentally.", } @PhdThesis{monsieurs:thesis, author = "Patrick Monsieurs", title = "Evolving Virtual Agents using Genetic Programming", school = "Limburg University", year = "2002", address = "Diepenbeek, Belgium", month = "5 " # dec, keywords = "genetic algorithms, genetic programming", URL = "http://hdl.handle.net/1942/8865", URL = "https://documentserver.uhasselt.be/bitstream/1942/8865/1/Patrick%20Monsieurs.pdf", broken = "http://alpha.luc.ac.be/~lucp1089/Doctoraatsthesis.pdf", broken = "http://www.edm.uhasselt.be/publications/show/148", broken = "http://tech.groups.yahoo.com/group/genetic_programming/message/1270", size = "179 pages", abstract = "Virtual environments are used in a diverse number of applications, ranging from medical applications, military simulations, modelling and engineering, to entertainment such as games or virtual communities. In these applications, virtual agents can be used to make the environment more realistic, perform tasks that are tedious and time consuming for humans, or even simulate the presence of other users in the environment. When constructing an agent for a virtual environment, several issues are encountered that must be resolved. First, a virtual agent must be able to explore and navigate in the virtual environment in a realistic way while avoiding collisions with obstacles. If the virtual agent does not have access to the internal representation of the environment, it will have to use its virtual sensors to observe the environment. In this thesis, an algorithm is presented to perform obstacle avoidance and map construction in a virtual environment using a synthetic vision sensor. The constructed map can then also be used to navigate in the environment. A second issue is communication between agents and users in the environment. Agents and users must be able to locate agents that can perform certain tasks, and agents may offer their services to users or other agents. These issues are discussed briefly in this thesis, and a prototype of a multi-agent virtual environment is presented. The most difficult issue of virtual agents is learning to solve problems in an environment, without knowing the constraints and rules of the environment in advance. This thesis will examine the use of genetic programming to train virtual agents. Two important problems are encountered when using genetic programming in this domain. First, programs constructed using genetic programming tend to grow rapidly before an acceptable solution is found. Several techniques will be presented to reduce the size of the evolved genetic programs, and a comparison will be made between these techniques. Secondly, evaluation of candidate solutions is usually very time consuming, making it impractical to maintain a large population of candidate solution. A large population is usually a requirement to evolve good solutions. Therefore, an algorithm to reduce the size of the population while maintaining the diversity of a larger population is presented. These optimisations will also be applied to the virtual multi-agent system of robotic soccer to examine the effects of these optimizations in a complex environment.", notes = "Summary in Flemish Nederlaans Robocup, parity, stgp, bloat, santafe ant, map construction, synthetic vision Advisors: Eddy Flerackers and F. {VAN REETH}", } @InProceedings{monsieurs03, author = "Patrick Monsieurs and Eddy Flerackers", title = "Reducing Population Size While Maintaining Diversity", booktitle = "Genetic Programming, Proceedings of EuroGP'2003", year = "2003", editor = "Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa", volume = "2610", series = "LNCS", pages = "142--152", address = "Essex", publisher_address = "Berlin", month = "14-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-00971-X", DOI = "doi:10.1007/3-540-36599-0_13", abstract = "This paper presents a technique to drastically reduce the size of a population, while still maintaining sufficient diversity for evolution. An advantage of a reduced population size is the reduced number of fitness evaluations necessary. In domains where calculation of fitness values is expensive, this results in a huge speedup of the search. Additionally, in the experiments performed, smaller populations also resulted in a faster convergence speed towards an optimal solution.", notes = "EuroGP'2003 held in conjunction with EvoWorkshops 2003", } @InProceedings{Montague:2024:evoapplications, author = "Kirsty Montague and Emma Hart and Ben Paechter", title = "A Hierarchical Approach to Evolving Behaviour-Trees for Swarm Control", booktitle = "27th International Conference, EvoApplications 2024", year = "2024", editor = "Stephen Smith and Joao Correia and Christian Cintrano", series = "LNCS", volume = "14634", publisher = "Springer", address = "Aberystwyth", month = "3-5 " # apr, pages = "178--193", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Swarm-robotics, Quality-Diversity, QD, MAP-Elites, Readability, XAI", isbn13 = "978-3-031-56851-0", URL = "https://napier-repository.worktribe.com/output/3504046/a-hierarchical-approach-to-evolving-behaviour-trees-for-swarm-control", URL = "https://rdcu.be/dDZSh", DOI = "doi:10.1007/978-3-031-56852-7_12", size = "16 pages", abstract = "Behaviour trees (BTs) are commonly used as controllers in robotic swarms due their modular composition and easily interpreted by humans. extra modules can easily be introduced and incorporated into new trees. Genetic Programming (GP) can evolving BTs to achieve a variety of sub-tasks (primitives) of a higher-level goal. we show that a hierarchical controller can be evolved that first uses GP to evolve a repertoire of primitives expressed as BTs, and then to evolve a high-level BT controller that leverages the evolved repertoire for a foraging task. We show that the hierarchical approach that uses BTs at two levels outperforms a baseline in which the BTs are evolved using only low-level nodes. we propose a method to improve the quality of the primitive repertoire, which in turn results in improved high-level BTs.", notes = "footbot robot ARGoS MTGP http://www.evostar.org/2024/ EvoApplications2024 held in conjunction with EuroGP'2024, EvoCOP2024 and EvoMusArt2024", } @TechReport{montana:stgp, author = "David J. Montana", title = "Strongly Typed Genetic Programming", institution = "Bolt Beranek and Newman, Inc.", year = "1993", type = "BBN Technical Report", number = "\#7866", address = "10 Moulton Street, Cambridge, MA 02138, USA", month = "7 " # may, notes = "Superceeded by \cite{montana:stgpEC} See also \cite{montana:stgp2}", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/ftp.io.com/papers/stgp.ps.Z", keywords = "genetic algorithms, genetic programming", } @TechReport{montana:stgp2, author = "David J. Montana", title = "Strongly Typed Genetic Programming", institution = "Bolt Beranek and Newman, Inc.", year = "1994", type = "BBN Technical Report", number = "\#7866", address = "10 Moulton Street, Cambridge, MA 02138, USA", month = mar, keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/ftp.io.com/papers/stgp2.ps.Z", notes = "Superceeded by \cite{montana:stgpEC} Replaces \cite{montana:stgp} add generic and void data types, local variables, run-time error trapping and reporting. Allows for non-protected operations, eg inversion of singular matrix is not protected.", size = "31 pages", } @Article{montana:stgpEC, author = "David J. Montana", title = "Strongly Typed Genetic Programming", journal = "Evolutionary Computation", year = "1995", volume = "3", number = "2", pages = "199--230", month = "Summer", keywords = "genetic algorithms, genetic programming, memory, automatic programming, strong typing, generic functions", ISSN = "1063-6560", URL = "http://vishnu.bbn.com/papers/stgp.pdf", DOI = "doi:10.1162/evco.1995.3.2.199", size = "32 pages", abstract = "Genetic programming is a powerful method for automatically generating computer programs via the process of natural selection (Koza, 1992). However, in its standard form, there is no way to restrict the programs it generates to those where the functions operate on appropriate data types. In the case when the programs manipulate multiple data types and contain functions designed to operate on particular data types, this can lead to unnecessarily large search times and/or unnecessarily poor generalisation performance. Strongly typed genetic programming (STGP) is an enhanced version of genetic programming that enforces data-type constraints and whose use of generic functions and generic data types makes it more powerful than other approaches to type-constraint enforcement. After describing its operation, we illustrate its use on problems in two domains, matrix/vector manipulation and list manipulation, which require its generality. The examples are (1) the multidimensional least-squares regression problem, (2) the multidimensional Kalman filter, (3) the list manipulation function NTH, and (4) the list manipulation function MAPCAR.", notes = "This supercedes \cite{montana:stgp} and \cite{montana:stgp2} Bolt Beranek and Newman, Inc.", } @InProceedings{montana:1996:ecl4nts, author = "David J. Montana and Steven Czerwinski", title = "Evolving Control Laws for a Network of Traffic Signals", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "333--338", address = "Stanford University, CA, USA", publisher = "MIT Press", URL = "http://vishnu.bbn.com/papers/gp96.pdf", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap44.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", size = "6 pages", abstract = "Optimally controlling the timings of traffic signals within a network of intersections is a difficult but important problem. Because the traffic signals need to coordinate their behaviour to achieve the common goal of optimising traffic ow through the network, this is a problem in collective intelligence. We apply a hybrid of a genetic algorithm and strongly typed genetic programming (STGP) to the problem of learning control laws which optimize aggregate performance. STGP learns the single basic decision tree to be executed by all the intersections when deciding whether to change the phase of the trafic signal. The genetic algorithm learns different constants to be used in these decision trees for different intersections, hence allowing specialisation based on dierences in geometry and traffic flow. Preliminary experimental work shows that our approach yields good performance on a variety of network configurations and that it can evolve control laws which induce cooperation, communication, and specialization among the traffic signals.", notes = "GP-96 Java demo at http://asd.bbn.com/papers/traffic/traffic.html", } @InProceedings{montana:1998:, author = "David Montana and Robert Popp and Suraj Iyer and Gordon Vidaver", title = "EvolvaWare: Genetic Programming for Optimal Design of Hardware-Based Algorithms", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "869--874", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, Evolvable Hardware", ISBN = "1-55860-548-7", URL = "http://vishnu.bbn.com/papers/gp98.pdf", notes = "GP-98", } @Article{Montana:2014:GPEM, author = "David Montana", title = "A response to ''Genetic programming and emergence''", journal = "Genetic Programming and Evolvable Machines", year = "2014", volume = "15", number = "1", pages = "95--97", month = mar, keywords = "genetic algorithms, genetic programming, Emergence, Top-down emergence, Multi-level complex systems, Evolution", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-013-9202-0", size = "3 pages", abstract = "Banzhaf (Genet Program Evol Mach, 2013) raises some interesting points about emergence in the context of genetic programming. However, his central tenet, that genetic programming is an example of top-down emergence, is invalidated by the fact that the evolutionary framework and the system being evolved are two separate structures. Rather, genetic programming is an instance of one emergent system designing a second one. Biological evolution provides a better example of what could be considered top-down emergence.", notes = "\cite{Banzhaf:2014:GPEM}", } @InProceedings{Montana:2009:eurogp, author = "Jose Luis Montana and Cesar Luis Alonso and Cruz Enrique Borges and Jose Luis Crespo", title = "New outcomes in Linear Genetic Programming: Adaptation, Performance and {Vapnik-Chervonenkis} Dimension of Straight Line Programs", booktitle = "Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009", year = "2009", editor = "Leonardo Vanneschi and Steven Gustafson and Alberto Moraglio and Ivanoe {De Falco} and Marc Ebner", volume = "5481", series = "LNCS", pages = "315--326", address = "Tuebingen", month = apr # " 15-17", organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Linear Genetic Programming, Vapnik-Chervonenkis dimension. poster", isbn13 = "978-3-642-01180-1", DOI = "doi:10.1007/978-3-642-01181-8_27", size = "12 pages", abstract = "We discuss here empirical comparison between model selection methods based on Linear Genetic Programming. Two statistical methods are compared: model selection based on Empirical Risk Minimisation (ERM) and model selection based on Structural Risk Minimization (SRM). For this purpose we have identified the main components which determine the capacity of some linear structures as classifiers showing an upper bound for the Vapnik-Chervonenkis (VC) dimension of classes of programs representing linear code defined by arithmetic computations and sign tests. This upper bound is used to define a fitness based on VC regularisation that performs significantly better than the fitness based on empirical risk.", notes = "Part of \cite{conf/eurogp/2009} EuroGP'2009 held in conjunction with EvoCOP2009, EvoBIO2009 and EvoWorkshops2009", } @InProceedings{conf/iccsa/MontanaABD11, author = "Jose L. Montana and Cesar Luis Alonso and Cruz Enrique Borges and Javier {de la Dehesa}", title = "Penalty Functions for Genetic Programming Algorithms", booktitle = "Proceedings of the International Conference on Computational Science and Its Applications (ICCSA 2011) Part {I}", year = "2011", editor = "Beniamino Murgante and Osvaldo Gervasi and Andres Iglesias and David Taniar and Bernady O. Apduhan", volume = "6782", pages = "550--562", series = "Lecture Notes in Computer Science", address = "Santander, Spain", month = jun # " 20-23", publisher = "Springer", keywords = "genetic algorithms, genetic programming, symbolic regression, inductive learning, regression model selection", isbn13 = "978-3-642-21927-6", DOI = "doi:10.1007/978-3-642-21928-3_40", size = "13 pages", abstract = "Very often symbolic regression, as addressed in Genetic Programming (GP), is equivalent to approximate interpolation. This means that, in general, GP algorithms try to fit the sample as better as possible but no notion of generalisation error is considered. As a consequence, overfitting, code-bloat and noisy data are problems which are not satisfactorily solved under this approach. Motivated by this situation we review the problem of Symbolic Regression under the perspective of Machine Learning, a well founded mathematical toolbox for predictive learning. We perform empirical comparisons between classical statistical methods (AIC and BIC) and methods based on Vapnik-Chrevonenkis (VC) theory for regression problems under genetic training. Empirical comparisons of the different methods suggest practical advantages of VC-based model selection. We conclude that VC theory provides methodological framework for complexity control in Genetic Programming even when its technical results seems not be directly applicable. As main practical advantage, precise penalty functions founded on the notion of generalisation error are proposed for evolving GP-trees.", affiliation = "Departamento de Matematicas, Estadistica y Computacion, Universidad de Cantabria, 39005 Santander, Spain", bibdate = "2011-06-20", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/iccsa/iccsa2011-1.html#MontanaABD11", } @Article{Montana:2016:ESA, author = "Jose L. Montana and Cesar L. Alonso and Cruz E. Borges and Cristina Tirnauca", title = "Model-driven regularization approach to straight line program genetic programming", journal = "Expert Systems with Applications", volume = "57", pages = "76--90", year = "2016", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2016.03.003", URL = "http://www.sciencedirect.com/science/article/pii/S095741741630094X", abstract = "This paper presents a regularization method for program complexity control of linear genetic programming tuned for transcendental elementary functions. Our goal is to improve the performance of evolutionary methods when solving symbolic regression tasks involving Pfaffian functions such as polynomials, analytic algebraic and transcendental operations like sigmoid, inverse trigonometric and radial basis functions. We propose the use of straight line programs as the underlying structure for representing symbolic expressions. Our main result is a sharp upper bound for the Vapnik Chervonenkis dimension of families of straight line programs containing transcendental elementary functions. This bound leads to a penalization criterion for the mean square error based fitness function often used in genetic programming for solving inductive learning problems. Our experiments show that the new fitness function gives very good results when compared with classical statistical regularization methods (such as Akaike and Bayesian Information Criteria) in almost all studied situations, including some benchmark real-world regression problems.", keywords = "genetic algorithms, genetic programming, Straight line program, Pfaffian operator, Symbolic regression", } @Article{Montealegre:2015:Tecnura, author = "Carvajal Montealegre and Carlos Javier", title = "Extracting classification rules from an informatic security incidents repository by genetic programming", journal = "Tecnura: Tecnolog{\'i}a y Cultura Afirmando el Conocimiento", year = "2015", number = "44", volume = "19", pages = "109--120", note = "in Spanish", keywords = "genetic algorithms, genetic programming", ISSN = "0123-921X", bibsource = "OAI-PMH server at dialnet.unirioja.es", language = "Spanish", oai = "oai:dialnet.unirioja.es:ART0000760905", URL = "http://dialnet.unirioja.es/servlet/oaiart?codigo=5024543", URL = "https://dialnet.unirioja.es/descarga/articulo/5024543.pdf", size = "12 pages", abstract = "En este art{\'i}culo se describe la obtenci{\'o}n de reglas de clasificaci{\'o}n sobre una colecci{\'o}n de datos de incidentes de seguridad informatica en un proceso de miner{\'i}a de datos, detallando el uso de la programaci{\'o}n gen{\'e}tica como un medio para modelar el comportamiento de los incidentes y representar las reglas en arboles de decisi{\'o}n. El proceso de extracci{\'o}n descrito incluye varios puntos, como la evaluaci{\'o}n del enfoque de programaci{\'o}n gen{\'e}tica, la forma de representar a los individuos y la afinaci{\'o}n de los parametros del algoritmo para elevar el rendimiento. Se concluye con un analisis de los resultados y la descripci{\'o}n de las reglas obtenidas, considerando las posibles soluciones para minimizar la ocurrencia de los ataques informaticos. El art{\'i}culo se basa en una parte de la tesis de grado Analisis de Incidentes de Seguridad Informatica Mediante Miner{\'i}a de Datos, para Modelado de Comportamiento y Reconocimiento de Patrones (Carvajal, 2012).", abstract = "This paper describes the data mining process to obtain classification rules over an information security incident data collection, explaining in detail the use of genetic programming as a mean to model the incidents behaviour and representing such rules as decision trees. The described mining process includes several tasks, such as the GP (Genetic Programming) approach evaluation, the individual's representation and the algorithm parameters tuning to upgrade the performance. The paper concludes with the result analysis and the description of the rules obtained, suggesting measures to avoid the occurrence of new informatics attacks. This paper is a part of the thesis work degree: Information Security Incident Analytics by Data Mining for Behavioral Modeling and Pattern Recognition (Carvajal, 2012).;", } @InProceedings{conf/epia/Monteiro0P21, author = "Mariana Monteiro and Nuno Lourenco and Francisco B. Pereira", title = "{FERMAT}: Feature Engineering with Grammatical Evolution", booktitle = "Progress in Artificial Intelligence - 20th EPIA Conference on Artificial Intelligence", year = "2021", editor = "Goreti Marreiros and Francisco S. Melo and Nuno Lau and Henrique Lopes Cardoso and Luis Paulo Reis", volume = "12981", series = "Lecture Notes in Computer Science", pages = "239--251", address = "Virtual Event", month = sep # " 7-9", publisher = "Springer", keywords = "genetic algorithms, genetic programming, grammatical evolution, feature engineering, drug development", isbn13 = "978-3-030-86229-9", bibdate = "2021-09-20", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/epia/epia2021.html#Monteiro0P21", DOI = "doi:10.1007/978-3-030-86230-5_19", abstract = "Feature engineering is a key step in a machine learning study. We propose FERMAT, a grammatical evolution framework for the automatic discovery of an optimal set of engineered features, with enhanced ability to characterise data. The framework contains a grammar specifying the original features and possible operations that can be applied to data. The optimisation process searches for a transformation strategy to apply to the original dataset, aiming at creating a novel characterisation composed by a combination of original and engineered attributes. FERMAT was applied to two real-world drug development datasets and results reveal that the framework is able to craft novel representations for data that foster the predictive ability of tree-based regression models.", } @InProceedings{Montes:2003:cancun, author = "H. A. Montes and J. L. Wyatt", title = "Cartesian Genetic Programming for Image Processing Tasks", booktitle = "Proceedings of Neural Networks and Computational Intelligence, NCI 2003", year = "2003", editor = "O. Castillo", address = "Cancun, Mexico", month = "19-21 " # may, publisher = "IASTED", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, Image Processing, Object Localisation", ISBN = "0-88986-347-4", URL = "http://www.actapress.com/Abstract.aspx?paperId=13619", URL = "http://www.cs.bham.ac.uk/~nah/bibtex/papers/montes03cartesian.pdf", size = "7 pages", abstract = "This paper presents experimental results on image analysis for a particular form of Genetic Programming called Cartesian Genetic Programming (CGP) in which programs use the structure of a graph represented as a linear sequence of integers. The efficency of this approach is investigated for the problem of Object Localization in a given image. This task is usually carried out by applying a series of well known image processing operators and commonly relies on the skills and expertise of the researchers. In this work, we present results from a number of runs on actual camera images, in which a set of fairly simple primitives were investigated.", notes = "ACTA press https://www.cs.bham.ac.uk/~jlw/publications/montes03cartesian.pdf", } @Article{DBLP:journals/rcs/MontesJO16, author = "Martin Montes Rivera and Marving Omar Aguilar Justo and Alberto Ochoa-Zezzatti", title = "Equations for Describing Behavior Tables in Thermodynamics Using Genetic Programming: Synthesizing the Saturated Water and Steam Table", journal = "Research in Computing Science", year = "2016", volume = "122", pages = "9--23", keywords = "genetic algorithms, genetic programming, Synthesising Tables, Saturated Water and Steam Tables", ISSN = "1870-4069", URL = "https://rcs.cic.ipn.mx/2016_122/Equations%20for%20Describing%20Behavior%20Tables%20in%20Thermodynamics%20Using%20Genetic%20Programming.pdf", URL = "https://rcs.cic.ipn.mx/2016_122/Equations%20for%20Describing%20Behavior%20Tables%20in%20Thermodynamics%20Using%20Genetic%20Programming.html", timestamp = "Thu, 12 Mar 2020 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/rcs/MontesJO16.bib", bibsource = "dblp computer science bibliography, https://dblp.org", size = "15 pages", abstract = "There are several tables with important data used in the calculus of different processes like machining tables, friction tables and thermodynamics processes tables, or as it is explored in this paper, the description of saturated water and steam table. We propose the generation of equations for describing the entire behavior of numerical values in a table using Genetic Programming (GP), when table data describes the variable behavior of a dependent function. This obtained equations simplify the calculus process without requiring several tables and allowing to work when tables are not available for a desired value of an independent variable, a common situation in thermodynamics. In this case it is tested the proposed algorithm for synthesising the saturated water and steam table.", notes = "Advances in Hybrid Intelligent Systems https://rcs.cic.ipn.mx/", } @InProceedings{conf/eurogp/Oca08, title = "Exposing a Bias Toward Short-Length Numbers in Grammatical Evolution", author = "Marco Antonio {Montes de Oca}", bibdate = "2008-04-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/eurogp/eurogp2008.html#Oca08", booktitle = "Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008", address = "Naples", month = "26-28 " # mar, publisher = "Springer", year = "2008", volume = "4971", editor = "Michael O'Neill and Leonardo Vanneschi and Steven Gustafson and Anna Isabel {Esparcia Alcazar} and Ivanoe {De Falco} and Antonio {Della Cioppa} and Ernesto Tarantino", isbn13 = "978-3-540-78670-2", pages = "278--288", series = "Lecture Notes in Computer Science", DOI = "doi:10.1007/978-3-540-78671-9_24", abstract = "Many automatically-synthesised programs have, like their hand-made counterparts, numerical parameters that need to be set properly before they can show an acceptable performance. Hence, any approach to the automatic synthesis of programs needs the ability to tune numerical parameters efficiently. Grammatical Evolution (GE) is a promising grammar-based genetic programming technique that synthesises numbers by concatenating digits. In this paper, we show that a naive application of this approach can lead to a serious number length bias that in turn affects efficiency. The root of the problem is the way the context-free grammar used by GE is defined. A simple, yet effective, solution to this problem is proposed.", URL = "http://iridia.ulb.ac.be/~mmontes/papers/eurogp2008.pdf", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", notes = "Part of \cite{conf/eurogp/2008} EuroGP'2008 held in conjunction with EvoCOP2008, EvoBIO2008 and EvoWorkshops2008", } @Article{MONTESDEOCAZAPIAIN:2023:commatsci, author = "David {Montes de Oca Zapiain} and J. Matthew D. Lane and Jay D. Carroll and Zachary Casias and Corbett C. Battaile and Saryu Fensin and Hojun Lim", title = "Establishing a data-driven strength model for beta-tin by performing symbolic regression using genetic programming", journal = "Computational Materials Science", volume = "218", pages = "111967", year = "2023", ISSN = "0927-0256", DOI = "doi:10.1016/j.commatsci.2022.111967", URL = "https://www.sciencedirect.com/science/article/pii/S0927025622006784", keywords = "genetic algorithms, genetic programming, Tin, Strength, Symbolic regression", abstract = "Tin (Sn) exhibits complex deformation behavior characterized by significant dependence of strength on temperature and strain rate. This work develops a strength model for tin by using genetic programming to perform symbolic regression on a set of compression tests at various strain rates and temperatures. The strength model developed in this work showed increased accuracy compared to traditional strength models. Furthermore, the developed strength model adequately predicted independent experimental data (i.e., data that was not used to train the model). Results demonstrate that genetic programming successfully established a valid analytical function that adequately characterizes the temperature and strain rate dependent strength behavior of tin. Therefore, demonstrating that the developed framework provides robust and accurate formulations of strength models", } @InCollection{Montiel:2014:HPC4SC, author = "Oscar Montiel and Juan J. Tapia and Francisco Javier Diaz Delgadillo and Nataly Medina Rodriguez", title = "Graphics Processing Unit programming and applications", booktitle = "High Performance Programming for Soft Computing", publisher = "CRC Press", year = "2014", editor = "Oscar Humberto Montiel Ross and Roberto Sepulveda", chapter = "4", pages = "94--109", keywords = "genetic algorithms, genetic programming, GPU, CUDA", isbn13 = "9781466586017", URL = "http://www.crcnetbase.com/doi/abs/10.1201/b16441-5", DOI = "doi:10.1201/b16441-5", notes = "https://www.crcpress.com/High-Performance-Programming-for-Soft-Computing/Ross-Sepulveda/9781466586024", } @InProceedings{Montoya:2023:ENC, author = "Nelson Montoya and Cynthia B. Perez and Luis A. Castro and Eddie Clemente", title = "Exploring Optimal Parameter Combinations for Genetic Programming in Robot Trajectory Generation", booktitle = "2023 Mexican International Conference on Computer Science (ENC)", year = "2023", month = sep, keywords = "genetic algorithms, genetic programming, Heuristic algorithms, Sociology, Search problems, Computational efficiency, Trajectory, Planning, Artificial Ant Problem, Robot Trajectory, Path Planning", ISSN = "2332-5712", DOI = "doi:10.1109/ENC60556.2023.10508697", abstract = "This work explores the relevance of appropriately selecting execution parameters in generating efficient routes for mobile robots in static environments using genetic programming (GP). The experiments conducted in this study focused on determining the optimal execution parameters and analysing the algorithm's behaviour in different scenarios. The results highlight the importance of adapting the execution parameters to the specific problem being addressed. The experiments show that larger populations and more generations are not always necessary for achieving satisfactory results. GP faces challenges finding optimal solutions in scenarios with dispersed food sources due to the increased search space dimensionality and multiple clusters. Moreover, a trade-off between computational effort and solution quality is observed, emphasizing the need to evaluate the parameter settings critically. This research highlights the importance of parameter adaptation in GP. It provides insights into reducing computational effort without compromising solution quality, thus enhancing the practical applicability of GP in real-world robotics and intelligent transportation systems.", notes = "Also known as \cite{10508697}", } @Article{Moons:2014:PLOSmed, author = "Karel G. M. Moons and Joris A. H. {de Groot} and Walter Bouwmeester and Yvonne Vergouwe and Susan Mallett and Douglas G. Altman and Johannes B. Reitsma and Gary S. Collins", title = "Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies: The CHARMS Checklist", journal = "PLOS Medicine", year = "2014", volume = "11", number = "10", pages = "e1001744", month = oct # " 14", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1371/journal.pmed.1001744", size = "12 pages", notes = "p1 'The checklist is designed to help form a review question for and appraisal of all types of primary prediction modelling studies, including, regressions, neural network, genetic programming, and vector machine learning models'", } @InProceedings{moore:1997:GPasox2pep, author = "Frank W. Moore and Oscar N. Garcia", title = "A Genetic Programming Approach to Strategy Optimization in the Extended Two-Dimensional Pursuer/Evader Problem", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming", pages = "249--254", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/moore/moore.gp97.ps.gz", abstract = "This paper describes a genetic programming system that evolves optimized solutions to the extended two-dimensional pursuer/evader problem. The pursuer/evader problem is a competitive zero-sum game in which an evader attempts to perform maneuvers to escape a faster, more agile pursuer. The extended problem is more realistic than previous formulations because the evader and pursuer are modeled as point masses that are capable of limited thrusting and turning forces, and are subject to drag forces and momentum. The pursuer initially aims at a predicted capture point, and uses proportional navigation to attempt to maintain a constant line-of-sight angle with the evader. The game ends favorably for the evader if it manages to stay outside the lethal radius of the pursuer for the duration of the encounter (limited by the effective range of the pursuer). To solve the extended two-dimensional pursuer/evader problem, a strategy must be identified by which an evader (such as an F-16C fighter aircraft) may maneuver to successfully evade pursuers (such as surface-to-air missiles) starting from a wide range of potentially lethal relative initial positions.", notes = "GP-97", } @InProceedings{Moore:1997:GPmsouu, author = "Frank W. Moore", title = "A Genetic Programming Methodology for Strategy Optimization Under Uncertainty", booktitle = "Late Breaking Papers at the 1997 Genetic Programming Conference", year = "1997", editor = "John R. Koza", pages = "294", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California, 94305-3079, USA", month = "13--16 " # jul, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-206995-8", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/moore/moore.gp97pp.ps.gz", notes = "GP-97LB PHD Students' workshop The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670 Poster at http://www.cs.wright.edu/people/faculty/agoshtas/gp97pp.html", } @InProceedings{moore:1997:mrbGP, author = "F. W. Moore and O. N. Garcia", title = "New Methodology for Reducing Brittleness in Genetic Programming", booktitle = "Proceedings of the National Aerospace and Electronics 1997 Conference (NAECON-97)", year = "1997", editor = "E. Pohl", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/moore/moore.naecon97.ps.gz", notes = " ", } @InProceedings{moore:1997:souux2dpe, author = "Frank W. Moore and Oscar N. Garcia", title = "A Methodology for Strategy Optimization Under Uncertainty in the Extended Two-Dimensional Pursuer/Evader Problem", booktitle = "Proceedings: Eighth Midwest Artificial Intelligence and Cognitive Science Conference (MAICS-97)", year = "1997", editor = "E. {Santos Jr.}", pages = "58--65", publisher = "AAAI Press", note = "AAAI Technical Report CF-97-01", keywords = "genetic algorithms, genetic programming, SAM, E2DPE, SA-6, SA-13, SA-15", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/moore/moore.maics97.ps.gz", size = "8 pages", abstract = "To solve the extended two-dimensional pursuer/evader problem, a strategy must be identified by which an evader such as an F-16C fighter aircraft may manoeuvre to successfully evade pursuers (such as surface-to-air missiles) launched from a wide range of potentially lethal relative initial positions. Uncertainty about the type of pursuer introduces a degree of complexity that is difficult to model using traditional analytic or control-theoretic approaches. This paper describes the implementation of a genetic programming system that uses training populations reflecting specific probability distributions to evolve optimized solutions to the extended two-dimensional pursuer/evader problem under conditions of uncertainty about the type of pursuer.", notes = "Some formatting difficulties with moore.maics97.ps Try moore.maics97.pdf ", } @InProceedings{moore:1997:noemuux2dpe, author = "F. W. Moore and O. N. Garcia", title = "A New Methodology for Optimizing Evasive Maneuvers Under Uncertainty in the Extended Two-Dimensional Pursuer/Evader Problem", booktitle = "Proceedings of the Ninth IEEE International Conference on Tools with Artificial Intelligence (ICTAI-97)", year = "1997", editor = "E. {Santos Jr.}", pages = "278--285", address = "Newport Beach, CA, USA", month = "3-8 " # nov, keywords = "genetic algorithms, genetic programming", ISBN = "0-8186-8203-5", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/moore/moore.ictai.ps.gz", DOI = "doi:10.1109/TAI.1997.632267", size = "8 pages", abstract = "Traditional analytic or control-theoretic solutions to the problem of optimising evasive manoeuvres in the extended two-dimensional pursuer/evader problem require the evader to execute specific sequences of maneuvers at precise pursuer/evader distances. These solutions depend upon several pursuer-specific characteristics, and fail to effectively account for uncertainty about the state of the pursuer. This paper describes the implementation of a genetic programming system that evolves optimised solutions to the extended two-dimensional pursuer/evader problem that do not depend upon knowledge of the pursuer's current state. Best-of-run programs execute strategies by which an evader may manoeuvre to successfully evade a pursuer starting from a wide range of relative initial positions, under conditions where the state of the pursuer is unknown or uncertain", notes = "moore.ictai.ps.gz some formating difficulties, fig 6 lost ", } @PhdThesis{moore:thesis, author = "Frank William Moore", title = "A methodology for Strategy Optimization Under Uncertainty", school = "Department of Computer Scienece and Engineering, Wright State University", year = "1997", address = "USA", month = "11 " # aug, keywords = "genetic algorithms, genetic programming", broken = "http://classify.oclc.org/classify2/ClassifyDemo?owi=1864647562", URL = "https://books.google.co.uk/books/about/A_Methodology_for_Strategy_Optimization.html?id=Wr9KHQAACAAJ&redir_esc=y", size = "173 pages", abstract = "The resulting genetic programming system evolves programs that combine maneuvers with electronic countermeasures to optimize aircraft survivability [from anti-aircraft missile]", notes = "160 Mbyte In my dissertation research and related work, I evolved strategies by which an aircraft could evade anti-aircraft missiles. The approach I took to fitness evaluation was to simulate an encounter between a missile (using proportional navigation) and an aircraft (controlled by stick and throttle commands issued by a control program). The simulation ran at 50 Hz (typical of aircraft flight control computers) Fitness was equated to aircraft survivability. The training population consisted of missiles launched from numerous potentially lethal positions. Aggregate program fitness reflected aircraft survivability against each missile in the training population (i.e., program X survived 25 out of 50 missiles in the training population; etc.). Best-of-run programs optimised survivability against the training population, and were subsequently tested against a large, representative test population of missiles to see how well the evolved solutions generalised. The problem with using simulation to evaluate fitness is that one has to execute each program from the evolved program population over N simulated time intervals, just to determine fitness against a single training case. (For my missile problem, typical simulated encounters lasted 20 seconds, thus entailing 1000 program executions PER FITNESS CASE.) So, we're talking about 2-3 orders of magnitude more computation than is typical for GP fitness evaluation. For the CPUs available to me, it was not uncommon for a run to take several days to complete. BUT the best-of-run program was an embedded real-time controller that executed specific aircraft manoeuvres (and, later on, deployed specific countermeasures) to optimize aircraft survivability. What makes that significant is the fact that, for the general missile countermeasures optimization problem under conditions of uncertainty about missile type and/or state, NO ANALYTICAL SOLUTION METHODOLOGY currently exists. I believe that by combining genetic programming with sophisticated simulators, we will be able to optimise programs that solve a wide range of control problems for which analytical solutions are difficult or impossible to identify. I'd like to see GP research move away from toy problems and onward to complex real-world applications, and I think this approach could help further that process. Regards to all. OCLC Work Id: 1864647562", } @InProceedings{moore:1998:imvbrpGP, author = "Frank W. Moore", title = "Improving Means and Variances of Best-of-Run Programs in Genetic Programming", booktitle = "Proceedings of the Ninth Midwest Artificial Intelligence and Cognitive Science Conference (MAICS-98)", year = "1998", editor = "M. W. Evens", pages = "95--101", address = "Russ Engineering Center, Wright State University, Dayton, Ohio, USA", month = "20-22 " # mar, publisher = "AAAI Press", keywords = "genetic algorithms, genetic programming", URL = "http://www.aaai.org/Library/MAICS/1998/maics98-013.php", URL = "http://www.aaai.org/Papers/MAICS/1998/MAICS98-013.pdf", size = "7 pages", abstract = "Genetic programming (GP) systems have traditionally used a fixed training population to evolve best-of-run programs according to problem-specific fitness criteria. The ideal GP training population would be sufficiently representative of each of the potentially difficult situations encountered during subsequent program use to allow the resulting best-of-run programs to handle each test situation in an optimized manner. Practical considerations limit the size of the training population, thus reducing the percentage of situations explicitly anticipated by that population. As a result, best-of-run programs may fail to exhibit sufficiently optimized performance during subsequent program testing. This paper summarizes an investigation into the effects of creating a new randomly generated training population prior to the fitness evaluation of each generation of programs. Test results suggest that this alternative approach to training can bolster generalization of evolved solutions, improving the mean program performance while significantly reducing variance in the fitness of best-of-run programs.", notes = "http://www.iue.indiana.edu/csci/maics98/", } @InProceedings{moore:1998:GPmmcouu, author = "F. W. Moore and O. N. Garcia", title = "A Genetic Programming Methodology for Missile Countermeasures Optimization Under Uncertainity", booktitle = "Evolutionary Programming VII: Proceedings of the Seventh Annual Conference on Evolutionary Programming", year = "1998", editor = "V. William Porto and N. Saravanan and D. Waagen and A. E. Eiben", volume = "1447", series = "LNCS", pages = "367--376", address = "Mission Valley Marriott, San Diego, California, USA", publisher_address = "Berlin", month = "25-27 " # mar, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-64891-7", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/moore/moore.ep98.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/moore/moore.ep98.doc.gz", DOI = "doi:10.1007/BFb0040753", URL = "http://link.springer.com/chapter/10.1007/BFb0040789", DOI = "doi:10.1007/BFb0040789", size = "10 pages", abstract = "This paper describes a new methodology for using genetic programming to solve the missile countermeasures optimisation problem. The resulting system evolves programs that combine maneuvers with additional countermeasures to optimise aircraft survivability under conditions of uncertainty.", notes = "EP-98. Wright State University. moore.ep98.doc.gz is a gzipped A Microsoft Word 8.0 of this paper", } @InProceedings{moore:1998:GPs3dmcopuu, author = "Frank W. Moore", title = "Genetic Programming Solves the Three-dimensional Missile Countermeasures Optimization Problem Under Uncertainty", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "242--245", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, MCO, 3DMCO, SAM, SA-6, SA-13, SA-15", ISBN = "1-55860-548-7", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1998/moore_1998_GPs3dmcopuu.pdf", size = "4 pages", abstract = "we describe a genetic programming solution to the three dimensional missile countermeasures optimisation problem. The resulting system builds upon previous research and improves upon state-of-the-art analytic solutions by evolving programs that combine maneuvers with additional counter-measures to optimize aircraft survival under conditions of uncertainty.", notes = "GP-98", } @Article{Moore:2002:EC, author = "Frank W. Moore", title = "A Methodology for Missile Countermeasures Optimization under Uncertainty", journal = "Evolutionary Computation", year = "2002", volume = "10", number = "2", pages = "129--149", month = "Summer", keywords = "genetic algorithms, genetic programming", ISSN = "1063-6560", DOI = "doi:10.1162/106365602320169820", abstract = "The missile countermeasures optimization problem is a complex strategy optimization problem that combines aircraft maneuvers with additional countermeasures in an attempt to survive attack from a single surface-launched, anti-aircraft missile. Classic solutions require the evading aircraft to execute specific sequences of maneuvers at precise distances from the pursuing missile and do not effectively account for uncertainty about the type and/or current state of the missile. This paper defines a new methodology for solving the missile countermeasures optimization problem under conditions of uncertainty. The resulting genetic programming system evolves programs that combine maneuvers with such countermeasures as chaff, flares, and jamming to optimize aircraft survivability. This methodology may be generalized to solve strategy optimization problems for intelligent, autonomous agents operating under conditions of uncertainty.", notes = "Department of Computer Science and Systems Analysis, Miami University, 230 Kreger Hall, Oxford, Ohio 45056, USA", } @Article{moore:1995:LabVIEW, author = "Jason H. Moore", title = "Artificial intelligence programming with {LabVIEW:} genetic algorithms for instrumentation control and optimization", journal = "Computer Methods and Programs in Biomedicine", year = "1995", volume = "47", number = "1", pages = "73--79", email = "jhm@superh.hg.med.umich.edu", keywords = "genetic algorithms, artificial intelligence, labview, graphical programming languages, instrumentation control, optimization", abstract = "A genetic algorithm for instrumentation control and optimization was developed using the LabVIEW graphical programming environment. The usefulness of this methodology for the optimization of a closed loop control instrument is demonstrated with minimal complexity and the programming is presented in detail to facilitate its adaptation to other LabVIEW applications. Closed loop control instruments have variety of applications in the biomedical sciences including the regulation of physiological processes such as blood pressure. The program presented here should provide a useful starting point for those wishing to incorporate genetic algorithm approaches to LabVIEW mediated optimization of closed loop control instruments.", notes = "NOT a GP. Fixed structure: 12 bit string. PMID: 7554864, UI: 96053901 Department of Human Genetics, University of Michigan Medical School, Ann Arbor 48109-0618, USA.", } @Misc{moore:2000:CAMDA, author = "Jason H. Moore and Joel S. Parker and Lance W. Hahn", title = "Symbolic Discriminant Analysis for Mining Gene Expression Patterns", booktitle = "Critical Assessment of Techniques for Microarray Data Analysis (CAMDA00)", year = "2000", address = "Levine Science Research Building, Duke University, Durham, N.C.", month = "18-19 " # dec, note = "submitted abstract", keywords = "genetic algorithms, genetic programming, SDA", URL = "http://www.camda.duke.edu/camda00/papers/days/papers/moore/paper.pdf", URL = "http://bioinformatics.duke.edu/CAMDA/CAMDA00/posters.asp#11", size = "1 page", abstract = "Linear discriminant analysis is a popular multivariate statistical approach for classification of observations into groups because the theory is well described and the method is easy to implement and interpret. However, an important limitation is that linear discriminant functions need to be pre-specified. That is, specific variables need to be selected and added linearly into the model. Only the coefficients are estimated from the data. To address this limitation, we developed symbolic discriminant analysis (SDA) for the automatic selection of gene expression variables and discriminant functions that can take any form. Our SDA approach is inspired by the symbolic regression approach of Koza (1992). We begin by defining the mathematical functions (e.g. +, -, /, *, log, sqrt, etc.) and the list of gene expression variables that could potentially be used as the building blocks for discriminant functions. Symbolic discriminant functions are evaluated by generating discriminant scores for each observation to be classified. The overlap in distributions of discriminant scores between groups is an estimate of the classification error. Class membership for new observations can be predicted from the discriminant score that separates the distributions. To identify optimal symbolic discriminant functions from the near infinite model space, we employed parallel genetic programming for machine learning on a 110 processor Beowulf-style parallel supercomputer. We applied the SDA approach to identifying subsets of gene expression variables and symbolic discriminant functions that can correctly classify and predict types of human acute leukemia. Using a leave-one-out cross-validation strategy, we identified no fewer than 15 different combinations of gene expression variables and symbolic discriminant functions that correctly classified 38/38 observations in the first dataset and correctly predicted 31/34 observations in the independent dataset. The most common gene identified across these models was the human synaptonemal complex protein 1 (SCP1) gene that is expressed in solid tumors and haematological malignancies. We conclude that the SDA approach provides a powerful alternative to traditional multivariate statistical methods for identifying gene expression patterns. The advantages of SDA include the ability to identify an important subset of gene expression variables from among thousands of candidates and the ability to identify the most appropriate mathematical functions relating the gene expression variables to a clinical endpoint. We anticipate this will be an important methodology to add to the repertoire of approaches for mining gene expression patterns.", notes = "Program in Human Genetics, Department of Molecular Physiology and Biophysics, Vanderbilt University Medical School, Nashville, TN 37232-0700", } @Article{moore:2002:SDA, author = "Jason H. Moore and Joel S. Parker and Nancy J. Olsen and Thomas M. Aune", title = "Symbolic Discriminant Analysis of Microarray Data in Automimmune Disease", journal = "Genetic Epidemiology", year = "2002", volume = "23", pages = "57--69", keywords = "genetic algorithms, genetic programming, DNA chip, rheumatoid arthritis, systemic lupus erythematosus, flu vaccine", DOI = "doi:10.1002/gepi.1117", abstract = "New laboratory technologies such as DNA microarrays have made it possible to measure the expression levels of thousands of genes simultaneously in a particular cell or tissue. The challenge for genetic epidemiologists will be to develop statistical and computational methods that are able to identify subsets of gene expression variables that classify and predict clinical endpoints. Linear discriminant analysis is a popular multivariate statistical approach for classification of observations into groups. This is because the theory is well described and the method is easy to implement and interpret. However, an important limitation is that linear discriminant functions need to be prespecified. To address this limitation and the limitation of linearity, we have developed symbolic discriminant analysis (SDA) for the automatic selection of gene expression variables and discriminant functions that can take any form. In the present study, we demonstrate that SDA is capable of identifying combinations of gene expression variables that are able to classify and predict autoimmune diseases", notes = "LilGP, PVM, LOOCV, 110 node beowulf, Linux", } @InProceedings{moore:2001:ECML, author = "Jason Moore and Joel Parker and Lance Hahn", title = "Symbolic Discriminant Analysis for Mining Gene Expression Patterns", booktitle = "12th European Conference on Machine Learning (ECML'01)", year = "2001", editor = "Luc {De Raedt} and Peter Flach", volume = "2167", series = "Lecture Notes in Computer Science", pages = "372--381", address = "Freiburg, Germany", month = "3-7 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-42536-5", DOI = "doi:10.1007/3-540-44795-4_32", size = "10 pages", abstract = "New laboratory technologies have made it possible to measure the expression levels of thousands of genes simultaneously in a particular cell or tissue. The challenge for computational biologists will be to develop methods that are able to identify subsets of gene expression variables that classify cells and tissues into meaningful clinical groups. Linear discriminant analysis is a popular multivariate statistical approach for classification of observations into groups. This is because the theory is well described and the method is easy to implement and interpret. However, an important limitation is that linear discriminant functions need to be pre-specified. To address this limitation and the limitation of linearity, we developed symbolic discriminant analysis (SDA) for the automatic selection of gene expression variables and discriminant functions that can take any form. We have implemented the genetic programming machine learning methodology for optimizing SDA in parallel on a Beowulf-style computer cluster.", notes = "http://www.informatik.uni-freiburg.de/~ml/ecmlpkdd/index.html", } @InCollection{moore:2002:ecmd, author = "J. H. Moore and J. S. Parker", title = "Evolutionary computation in microarray data analysis", booktitle = "Methods of Microarray Data Analysis", publisher = "Kluwer Academic Publishers", year = "2002", editor = "S. Lin and K. Johnson", pages = "23--35", address = "Boston", keywords = "genetic algorithms, genetic programming", isbn13 = "978-0-7923-7564-7", URL = "http://www.springer.com/life+sciences/biochemistry+%26+biophysics/book/978-0-7923-7564-7", notes = "Papers from CAMDA 2000, December 18-19, 2000, Duke University, Durham, NC, USA, Lin, Simon M.; Johnson, Kimberly F. (Eds.)", } @InProceedings{Moore:evowks03, author = "Jason Moore", title = "Cross Validation Consistency for the Assessment of Genetic Programming Results in Microarray Studies", booktitle = "Applications of Evolutionary Computing, EvoWorkshops2003: Evo{BIO}, Evo{COP}, Evo{IASP}, Evo{MUSART}, Evo{ROB}, Evo{STIM}", year = "2003", editor = "G{\"u}nther R. Raidl and Stefano Cagnoni and Juan Jes\'us Romero Cardalda and David W. Corne and Jens Gottlieb and Agn\`es Guillot and Emma Hart and Colin G. Johnson and Elena Marchiori and Jean-Arcady Meyer and Martin Middendorf", volume = "2611", series = "LNCS", pages = "99--106", address = "University of Essex, UK", publisher_address = "Berlin", month = "14-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, evolutionary computation, applications", isbn13 = "978-3-540-00976-4", DOI = "doi:10.1007/3-540-36605-9_10", abstract = "DNA microarray technology has made it possible to measure the expression levels of thousands of genes simultaneously in a particular cell or tissue. The challenge for computational biologists and bioinformaticists will be to develop methods that are able to identify subsets of gene expression variables and features that classify cells and tissues into meaningful biological and clinical groups. Genetic programming (GP) has emerged as a machine learning tool for variable and feature selection in microarray data analysis. However, a limitation of GP is a lack of cross validation strategies for the assessment of GP results. This is partly due to the inherent complexity of GP due to its stochastic properties. Here, we introduce and review cross validation consistency (CVC) as a new modeling strategy for use with GP. We review the application of CVC to symbolic discriminant analysis (SDA), a GP-based analytical strategy for mining gene expression patterns in DNA microarray data.", notes = "EvoWorkshops2003", } @InProceedings{moore:2003:gecco, author = "Jason H. Moore and Lance W. Hahn", title = "Grammatical Evolution for the Discovery of {Petri} Net Models of Complex Genetic Systems", booktitle = "Genetic and Evolutionary Computation -- GECCO-2003", editor = "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller", year = "2003", pages = "2412--2413", address = "Chicago", publisher_address = "Berlin", month = "12-16 " # jul, volume = "2724", series = "LNCS", ISBN = "3-540-40603-4", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, grammatical evolution, Real World Applications, Poster", DOI = "doi:10.1007/3-540-45110-2_139", size = "2 pages", abstract = "We propose a grammatical evolution approach for the automatic discovery of Petri net models of biochemical systems that are consistent with population level genetic models of disease susceptibility. We demonstrate the grammatical evolution approach routinely identifies interesting and useful Petri net models in a human-competitive manner. This study opens the door for hierarchical systems modeling of the relationship between genes, biochemistry, and measures of health.", notes = "GECCO-2003 A joint meeting of the twelvth international conference on genetic algorithms (ICGA-99) and the eigth annual genetic programming conference (GP-2003)", } @Article{moore:2003:BS, author = "Jason H. Moore and Lance W. Hahn", title = "Petri net modeling of high-order genetic systems using grammatical evolution", journal = "BioSystems", year = "2003", volume = "72", number = "1-2", pages = "177--186", month = nov, keywords = "genetic algorithms, genetic programming, grammatical evolution", DOI = "doi:10.1016/S0303-2647(03)00142-4", abstract = "Understanding how DNA sequence variations impact human health through a hierarchy of biochemical and physiological systems is expected to improve the diagnosis, prevention, and treatment of common, complex human diseases. We have previously developed a hierarchical dynamic systems approach based on Petri nets for generating biochemical network models that are consistent with genetic models of disease susceptibility. This modeling approach uses an evolutionary computation approach called grammatical evolution as a search strategy for optimal Petri net models. We have previously demonstrated that this approach routinely identifies biochemical network models that are consistent with a variety of genetic models in which disease susceptibility is determined by nonlinear interactions between two DNA sequence variations. In the present study, we evaluate whether the Petri net approach is capable of identifying biochemical networks that are consistent with disease susceptibility due to higher order nonlinear interactions between three DNA sequence variations. The results indicate that our model-building approach is capable of routinely identifying good, but not perfect, Petri net models. Ideas for improving the algorithm for this high-dimensional problem are presented.", notes = "PMID: 14642666 [PubMed - indexed for MEDLINE] http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Abstract&list_uids=14642666", } @Article{Moore:2004:DCDS, author = "Jason H. Moore and Lance W. Hahn", title = "Evaluation of a discrete dynamic systems approach for modeling the hierarchical relationship between genes, biochemistry, and disease susceptibility", journal = "Discrete and Continuous Dynamical Systems: Series B", year = "2004", volume = "4", number = "1", pages = "275--287", month = feb, keywords = "genetic algorithms, genetic programming, grammatical evolution, epistasis, gene-gene interactions, Petri nets", ISSN = "1531-3492", DOI = "doi:10.3934/dcdsb.2004.4.275", abstract = "A central goal of human genetics is the identification of combinations of DNA sequence variations that increase susceptibility to common, complex human diseases. Our ability to use genetic information to improve public health efforts to diagnose, prevent, and treat common human diseases will depend on our ability to understand the hierarchical relationship between complex biological systems at the genetic, cellular, biochemical, physiological, anatomical, and clinical endpoint levels. We have previously demonstrated that Petri nets are useful for building discrete dynamic systems models of biochemical networks that are consistent with nonlinear gene-gene interactions observed in epidemiological studies. Further, we have developed a machine learning approach that facilitates the automatic discovery of Petri net models thus eliminating the need for human-based trial and error approaches. In the present study, we evaluate this automated model discovery approach using four different nonlinear gene-gene interaction models. The results indicate that our model-building approach routinely identifies accurate Petri net models in a human-competitive manner. We anticipate that this general modeling strategy will be useful for generating hypotheses about the hierarchical relationship between genes, biochemistry, and measures of human health.", notes = "http://www.aimsciences.org/journals/home.jsp?journalID=2 2000 Mathematics Subject Classification. 92D30. Mathematical Models in Cancer A special issue based on the Cancer Workshop at Vanderbilt University 2002 Guest Editors: Mary Ann Horn and Glenn Webb This special issue can be ordered as a book", } @InProceedings{moore:evows04, author = "Jason Moore and Lance Hahn", title = "An Improved Grammatical Evolution Strategy for Hierarchical Petri Net Modeling of Complex Genetic Systems", booktitle = "Applications of Evolutionary Computing, EvoWorkshops2004: {EvoBIO}, {EvoCOMNET}, {EvoHOT}, {EvoIASP}, {EvoMUSART}, {EvoSTOC}", year = "2004", month = "5-7 " # apr, editor = "Guenther R. Raidl and Stefano Cagnoni and Jurgen Branke and David W. Corne and Rolf Drechsler and Yaochu Jin and Colin R. Johnson and Penousal Machado and Elena Marchiori and Franz Rothlauf and George D. Smith and Giovanni Squillero", series = "LNCS", volume = "3005", address = "Coimbra, Portugal", publisher = "Springer Verlag", publisher_address = "Berlin", pages = "63--72", keywords = "genetic algorithms, genetic programming, grammatical evolution, evolutionary computation", ISBN = "3-540-21378-3", DOI = "doi:10.1007/978-3-540-24653-4_7", abstract = "DNA sequence variations impact human health through a hierarchy of biochemical and physiological systems. Understanding the hierarchical relationships in the genotype-phenotype mapping is expected to improve the diagnosis, prevention, and treatment of common, complex human diseases. We previously developed a hierarchical dynamic systems approach based on Petri nets for generating biochemical network models that are consistent with genetic models of disease susceptibility. This strategy uses an evolutionary computation approach called grammatical evolution for symbolic manipulation and optimization of Petri net models. We previously demonstrated that this approach routinely identifies biochemical network models that are consistent with a variety of complex genetic models in which disease susceptibility is determined by nonlinear interactions between two DNA sequence variations. However, the modeling strategy was generally not successful when extended to modelling nonlinear interactions between three DNA sequence variations. In the present study, we evaluate a modified grammar for building Petri net models of Riochemical systems that are consistent with high order genetic models of disease susceptibility. The results indicate that our hierarchical model building approach is capable of identifying perfect Petri net models when an appropriate grammar is used.", notes = "EvoWorkshops2004", } @InProceedings{moore:sbm:gecco2004, author = "Jason H. Moore and Lance W. Hahn", title = "Systems Biology Modeling in Human Genetics Using Petri Nets and Grammatical Evolution", booktitle = "Genetic and Evolutionary Computation -- GECCO-2004, Part I", year = "2004", editor = "Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell", series = "Lecture Notes in Computer Science", pages = "392--401", address = "Seattle, WA, USA", publisher_address = "Heidelberg", month = "26-30 " # jun, organisation = "ISGEC", publisher = "Springer-Verlag", volume = "3102", ISBN = "3-540-22344-4", ISSN = "0302-9743", DOI = "doi:10.1007/b98643", size = "10", keywords = "genetic algorithms, genetic programming", notes = "GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)", } @InCollection{Moore:2006:GPTP, author = "Jason H. Moore and Bill C. White", title = "Genome-Wide Genetic Analysis Using Genetic Programming: The Critical Need for Expert Knowledge", booktitle = "Genetic Programming Theory and Practice {IV}", year = "2006", editor = "Rick L. Riolo and Terence Soule and Bill Worzel", volume = "5", series = "Genetic and Evolutionary Computation", pages = "11--28", address = "Ann Arbor", month = "11-13 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming", ISBN = "0-387-33375-4", DOI = "doi:10.1007/978-0-387-49650-4_2", size = "16 pages", abstract = "Human genetics is undergoing an information explosion. The availability of chip-based technology facilitates the measurement of thousands of DNA sequence variation from across the human genome. The challenge is to sift through these high-dimensional datasets to identify combinations of interacting DNA sequence variations that are predictive of common diseases. The goal of this study is to develop and evaluate a genetic programming (GP) approach to attribute selection and classification in this domain. We simulated genetic datasets of varying size in which the disease model consists of two interacting DNA sequence variations that exhibit no independent effects on class (i.e. epistasis). We show that GP is no better than a simple random search when classification accuracy is used as the fitness function. We then show that including pre-processed estimates of attribute quality using Tuned ReliefF (TuRF) in a multi-objective fitness function that also includes accuracy significantly improves the performance of GP over that of random search. This study demonstrates that GP may be a useful computational discovery tool in this domain. This study raises important questions about the general utility of GP for these types of problems, the importance of data pre-processing, the ideal functional form of the fitness function, and the importance of expert knowledge. We anticipate this study will provide an important baseline for future studies investigating the usefulness of GP as a general computational discovery tool for large-scale genetic studies.", notes = "part of \cite{Riolo:2006:GPTP} Published Jan 2007 after the workshop", } @InProceedings{Moore:PPSN:2006, author = "Jason H. Moore and Bill C. White", title = "Exploiting Expert Knowledge in Genetic Programming for Genome-Wide Genetic Analysis", booktitle = "Parallel Problem Solving from Nature - PPSN IX", year = "2006", editor = "Thomas Philip Runarsson and Hans-Georg Beyer and Edmund Burke and Juan J. Merelo-Guervos and L. Darrell Whitley and Xin Yao", volume = "4193", pages = "969--977", series = "LNCS", address = "Reykjavik, Iceland", publisher_address = "Berlin", month = "9-13 " # sep, publisher = "Springer-Verlag", ISBN = "3-540-38990-3", keywords = "genetic algorithms, genetic programming, SNP, MDR, GAlib", URL = "http://ppsn2006.raunvis.hi.is/proceedings/262.pdf", DOI = "doi:10.1007/11844297_98", size = "9 pages", abstract = "Human genetics is undergoing an information explosion. The availability of chip-based technology facilitates the measurement of thousands of DNA sequence variation from across the human genome. The challenge is to sift through these high-dimensional datasets to identify combinations of interacting DNA sequence variations that are predictive of common diseases. The goal of this paper was to develop and evaluate a genetic programming (GP) approach for attribute selection and modelling that uses expert knowledge such as Tuned ReliefF (TuRF) scores during selection to ensure trees with good building blocks are recombined and reproduced. We show here that using expert knowledge to select trees performs as well as a multiobjective fitness function but requires only a tenth of the population size. This study demonstrates that GP may be a useful computational discovery tool in this domain.", notes = "PPSN-IX NB human's are diploid", } @Article{Moore:2007:HH, author = "Jason H. Moore and Nate Barney and Chia-Ti Tsai and Fu-Tien Chiang and Jiang Gui and Bill C. White", title = "Symbolic Modeling of Epistasis", journal = "Human Heredity", year = "2007", volume = "63", number = "2", pages = "120--133", month = feb, keywords = "genetic algorithms, genetic programming, Data mining, Gene-gene interaction, Function mapping, Symbolic discriminant analysis", publisher = "www.karger.com/hhe", ISSN = "0001-5652", DOI = "doi:10.1159/000099184", size = "14 pages", abstract = "The workhorse of modern genetic analysis is the parametric linear model. The advantages of the linear modelling framework are many and include a mathematical understanding of the model fitting process and ease of interpretation. However, an important limitation is that linear models make assumptions about the nature of the data being modelled. This assumption may not be realistic for complex biological systems such as disease susceptibility where nonlinearities in the genotype to phenotype mapping relationship that result from epistasis, plastic reaction norms, locus heterogeneity, and phenocopy, for example, are the norm rather than the exception. We have previously developed a flexible modelling approach called symbolic discriminant analysis (SDA) that makes no assumptions about the patterns in the data. Rather, SDA lets the data dictate the size, shape, and complexity of a symbolic discriminant function that could include any set of mathematical functions from a list of candidates supplied by the user. Here, we outline a new five step process for symbolic model discovery that uses genetic programming (GP) for coarse-grained stochastic searching, experimental design for parameter optimisation, graphical modeling for generating expert knowledge, and estimation of distribution algorithms for fine-grained stochastic searching. Finally, we introduce function mapping as a new method for interpreting symbolic discriminant functions. We show that function mapping when combined with measures of interaction information facilitates statistical interpretation by providing a graphical approach to decomposing complex models to highlight synergistic, redundant, and independent effects of polymorphisms and their composite functions. We illustrate this five step SDA modeling process with a real case-control dataset.", notes = "PMID: 17283441", } @InProceedings{1277052, author = "Jason H. Moore and Nate Barney and Bill C. White", title = "Towards human-human-computer interaction for biologically-inspired problem-solving in human genetics", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "1", isbn13 = "978-1-59593-697-4", pages = "432--433", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p432.pdf", DOI = "doi:10.1145/1276958.1277052", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Biological Applications: Poster, genetic analysis, genetic epidemiology, human factors, Open Source Software, symbolic discriminant analysis, symbolic regression", abstract = "Genetic programming (GP) shows great promise for solving complex problems in human genetics. Unfortunately, many of these methods are not accessible to biologists. This is partly due to the complexity of the algorithms that limit their ready adoption and integration into an analysis or modelling paradigm that might otherwise only use univariate statistical methods. This is also partly due to the lack of user-friendly, open-source, platform independent, and freely-available software packages that are designed to be used by biologists for routine analysis. It is our objective to develop, distribute and support a comprehensive software package that puts powerful GP methods for genetic analysis in the hands of geneticists. It is our working hypothesis that the most effective use of such a software package would result from interactive analysis by both a biologist and a computer scientist (i.e. human-human-computer interaction). We summarise briefly here the design and implementation of an open-source software package called Symbolic Modeler (SyMod) that seeks to facilitate geneticist-bioinformaticist-computer interactions for problem solving in human genetics. More information can be found at www.epistasis.org or www.symbolicmodeler.org.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @InCollection{Moore:2007:GPTP, author = "Jason H. Moore and Nate Barney and Bill C. White", title = "Solving Complex Problems in Human Genetics Using Genetic Programming: The Importance of Theorist-Practitioner-computer Interaction", booktitle = "Genetic Programming Theory and Practice {V}", year = "2007", editor = "Rick L. Riolo and Terence Soule and Bill Worzel", series = "Genetic and Evolutionary Computation", chapter = "5", pages = "69--86", address = "Ann Arbor", month = "17-19" # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-0-387-76308-8", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.463.1917", URL = "http://vserver1.cscs.lsa.umich.edu/PmWiki/Farms/GPTP-07/uploads/Main/moorebecker.pdf", DOI = "doi:10.1007/978-0-387-76308-8_5", size = "18 pages", abstract = "Genetic programming (GP) shows great promise for solving complex problems in human genetics. Unfortunately, many of these methods are not accessible to biologists. This is partly due to the complexity of the algorithms that limit their ready adoption and integration into an analysis or modelling paradigm that might otherwise only use univariate statistical methods. This is also partly due to the lack of user-friendly, open-source, platform-independent, and freely-available software packages that are designed to be used by biologists for routine analysis. It is our objective to develop, distribute and support a comprehensive software package that puts powerful GP methods for genetic analysis in the hands of geneticists. It is our working hypothesis that the most effective use of such a software package would result from interactive analysis by both a biologist and a computer scientist (i.e. human-human-computer interaction). We present here the design and implementation of an open-source software package called Symbolic Modeler (SyMod) that seeks to facilitate geneticist -- bioinformaticist -- computer interactions for problem solving in human genetics. We present and discuss the results of an application of SyMod to real data and discuss the challenges associated with delivering a user-friendly GP-based software package to the genetics community.", notes = "part of \cite{Riolo:2007:GPTP} published 2008", affiliation = "Dartmouth College One Medical Center Drive HB7937 Lebanon NH 03756 USA", } @InProceedings{conf/evoW/MooreABW08, title = "Development and Evaluation of an Open-Ended Computational Evolution System for the Genetic Analysis of Susceptibility to Common Human Diseases", author = "Jason H. Moore and Peter C. Andrews and Nate Barney and Bill C. White", bibdate = "2008-04-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/evoW/evobio2008.html#MooreABW08", booktitle = "Proceedings of the 6th European Conference, on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, Evo{BIO} 2008", publisher = "Springer", year = "2008", volume = "4973", editor = "Elena Marchiori and Jason H. Moore", isbn13 = "978-3-540-78756-3", pages = "129--140", series = "Lecture Notes in Computer Science", DOI = "doi:10.1007/978-3-540-78757-0_12", address = "Naples, Italy", month = mar # " 26-28", keywords = "genetic algorithms, genetic programming, computational evolution", abstract = "An important goal of human genetics is to identify DNA sequence variations that are predictive of susceptibility to common human diseases. This is a classification problem with data consisting of discrete attributes and a binary outcome. A variety of different machine learning methods based on artificial evolution have been developed and applied to modelling the relationship between genotype and phenotype. While artificial evolution approaches show promise, they are far from perfect and are only loosely based on real biological and evolutionary processes. It has recently been suggested that a new paradigm is needed where artificial evolution is transformed to computational evolution (CE) by incorporating more biological and evolutionary complexity into existing algorithms. It has been proposed that CE systems will be more likely to solve problems of interest to biologists and biomedical researchers. The goal of the present study was to develop and evaluate a prototype CE system for the analysis of human genetics data. We describe here this new open-ended CE system and provide initial results from a simulation study that suggests more complex operators result in better solutions.", } @InCollection{Moore:2008:GPTP, author = "Jason H. Moore and Casey S. Greene and Peter C. Andrews and Bill C. White", title = "Does Complexity Matter? Artificial Evolution, Computational Evolution and the Genetic Analysis of Epistasis in Common Human Diseases", booktitle = "Genetic Programming Theory and Practice {VI}", year = "2008", editor = "Rick L. Riolo and Terence Soule and Bill Worzel", series = "Genetic and Evolutionary Computation", chapter = "9", pages = "125--145", address = "Ann Arbor", month = "15-17 " # may, publisher = "Springer", DOI = "doi:10.1007/978-0-387-87623-8_9", size = "20 pages", isbn13 = "978-0-387-87622-1", notes = "part of \cite{Riolo:2008:GPTP} published in 2009", keywords = "genetic algorithms, genetic programming", } @InCollection{Moore:2011:GPTP, author = "Jason H. Moore and Douglas P. Hill and Jonathan M. Fisher and Nicole Lavender and La Creis Kidd", title = "Human-Computer Interaction in a Computational Evolution System for the Genetic Analysis of Cancer", booktitle = "Genetic Programming Theory and Practice IX", year = "2011", editor = "Rick Riolo and Ekaterina Vladislavleva and Jason H. Moore", series = "Genetic and Evolutionary Computation", address = "Ann Arbor, USA", month = "12-14 " # may, publisher = "Springer", chapter = "9", pages = "153--171", keywords = "genetic algorithms, genetic programming, Computational Evolution, Genetic Epidemiology, epistasis, Prostate Cancer, Visualisation", isbn13 = "978-1-4614-1769-9", DOI = "doi:10.1007/978-1-4614-1770-5_9", abstract = "The paradigm of identifying genetic risk factors for common human diseases by analysing one DNA sequence variation at a time is quickly being replaced by research strategies that embrace the multivariate complexity of the genotype to phenotype mapping relationship that is likely due, in part, to nonlinear interactions among many genetic and environmental factors. Embracing the complexity of common diseases such as cancer requires powerful computational methods that are able to model nonlinear interactions in high-dimensional genetic data. Previously, we have addressed this challenge with the development of a computational evolution system (CES) that incorporates greater biological realism than traditional artificial evolution methods, such as genetic programming. Our results have demonstrated that CES is capable of efficiently navigating these large and rugged fitness landscapes toward the discovery of biologically meaningful genetic models of disease predisposition. Further, we have shown that the efficacy of CES is improved dramatically when the system is provided with statistical expert knowledge, derived from a family of machine learning techniques known as Relief, or biological expert knowledge, derived from sources such as protein-protein interaction databases. The goal of the present study was to apply CES to the genetic analysis of prostate cancer aggressiveness in a large sample of European Americans. We introduce here the use of 3D visualization methods to identify interesting patterns in CES results. Information extracted from the visualization through human-computer interaction are then provide as expert knowledge to new CES runs in a cascading framework. We present a CES-derived multivariate classifier and provide a statistical and biological interpretation in the context of prostate cancer prediction. The incorporation of human-computer interaction into CES provides a first step towards an interactive discovery system where the experts can be embedded in the computational discovery process. Our working hypothesis is that this type of human-computer interaction will provide more useful results for complex problem solving than the traditional black box machine learning approach.", notes = "part of \cite{Riolo:2011:GPTP}", affiliation = "Dartmouth Medical School, One Medical Center Drive, HB7937, Lebanon, NH 03756, USA", } @InCollection{Moore:2012:GPTP, author = "Jason H. Moore and Douglas P. Hill and Arvis Sulovari and LaCreis Kidd", title = "Genetic Analysis of Prostate Cancer Using Computational Evolution, Pareto-Optimization and Post-processing", booktitle = "Genetic Programming Theory and Practice X", year = "2012", series = "Genetic and Evolutionary Computation", editor = "Rick Riolo and Ekaterina Vladislavleva and Marylyn D. Ritchie and Jason H. Moore", publisher = "Springer", chapter = "7", pages = "87--101", address = "Ann Arbor, USA", month = "12-14 " # may, keywords = "genetic algorithms, genetic programming, Computational evolution, Genetic epidemiology, Epistasis, Gene-gene interactions", isbn13 = "978-1-4614-6845-5", URL = "http://dx.doi.org/10.1007/978-1-4614-6846-2_7", DOI = "doi:10.1007/978-1-4614-6846-2_7", abstract = "Given infinite time, humans would progress through modelling complex data in a manner that is dependent on prior expert knowledge. The goal of the present study is make extensions and enhancements to a computational evolution system (CES) that has the ultimate objective of tinkering with data as a human would. This is accomplished by providing flexibility in the model-building process and a meta-layer that learns how to generate better models. The key to the CES system is the ability to identify and exploit expert knowledge from biological databases or prior analytical results. Our prior results have demonstrated that CES is capable of efficiently navigating these large and rugged fitness landscapes toward the discovery of biologically meaningful genetic models of disease. Further, we have shown that the efficacy of CES is improved dramatically when the system is provided with statistical or biological expert knowledge. The goal of the present study was to apply CES to the genetic analysis of prostate cancer aggressiveness in a large sample of European Americans. We introduce here the use of Pareto-optimisation to help address overfitting in the learning system. We further introduce a post-processing step that uses hierarchical cluster analysis to generate expert knowledge from the landscape of best models and their predictions across patients. We find that the combination of Pareto-optimization and post-processing of results greatly improves the genetic analysis of prostate cancer.", notes = "part of \cite{Riolo:2012:GPTP} published after the workshop in 2013", } @InCollection{Moore:2013:GPTP, author = "Jason H. Moore and Douglas P. Hill and Andrew Saykin and Li Shen", title = "Exploring Interestingness in a Computational Evolution System for the Genome-Wide Genetic Analysis of Alzheimer's Disease", booktitle = "Genetic Programming Theory and Practice XI", year = "2013", series = "Genetic and Evolutionary Computation", editor = "Rick Riolo and Jason H. Moore and Mark Kotanchek", publisher = "Springer", chapter = "2", pages = "31--45", address = "Ann Arbor, USA", month = "9-11 " # may, keywords = "genetic algorithms, genetic programming, Computational evolution; Genetic epidemiology; Epistasis; Gene-gene interactions", isbn13 = "978-1-4939-0374-0", DOI = "doi:10.1007/978-1-4939-0375-7_2", abstract = "Susceptibility to Alzheimer's disease is likely due to complex interaction among many genetic and environmental factors. Identifying complex genetic effects in large data sets will require computational methods that extend beyond what parametric statistical methods such as logistic regression can provide. We have previously introduced a computational evolution system (CES) that uses genetic programming (GP) to represent genetic models of disease and to search for optimal models in a rugged fitness landscape that is effectively infinite in size. The CES approach differs from other GP approaches in that it is able to learn how to solve the problem by generating its own operators. A key feature is the ability for the operators to use expert knowledge to guide the stochastic search. We have previously shown that CES is able to discover nonlinear genetic models of disease susceptibility in both simulated and real data. The goal of the present study was to introduce a measure of interestingness into the modelling process. Here, we define interestingness as a measure of non-additive gene-gene interactions. That is, we are more interested in those CES models that include attributes that exhibit synergistic effects on disease risk. To implement this new feature we first pre-processed the data to measure all pairwise gene-gene interaction effects using entropy-based methods. We then provided these pre-computed measures to CES as expert knowledge and as one of three fitness criteria in three-dimensional Pareto optimisation. We applied this new CES algorithm to an Alzheimer's disease data set with approximately 520,000 genetic attributes. We show that this approach discovers more interesting models with the added benefit of improving classification accuracy. This study demonstrates the applicability of CES to genome-wide genetic analysis using expert knowledge derived from measures of interestingness.", notes = " Part of \cite{Riolo:2013:GPTP} published after the workshop in 2013", } @InProceedings{Moore:2014:GPTP, author = "Jason H. Moore and Casey S. Greene and Douglas P. Hill", title = "Identification of Novel Genetic Models of Glaucoma Using the {``EMERGENT''} Genetic Programming-Based Artificial Intelligence System", booktitle = "Genetic Programming Theory and Practice XII", year = "2014", editor = "Rick Riolo and William P. Worzel and Mark Kotanchek", series = "Genetic and Evolutionary Computation", pages = "17--35", address = "Ann Arbor, USA", month = "8-10 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Exploratory modelling for extracting relationships using genetic and evolutionary navigation techniques, Artificial intelligence, Glaucoma", isbn13 = "978-3-319-16029-0", DOI = "doi:10.1007/978-3-319-16030-6_2", abstract = "The genetic basis for primary open-angle glaucoma (POAG) is not yet understood but is likely the result of many interacting genetic variants that influence risk in the context of our local ecology. The complexity of the genotype to phenotype mapping relationship for common diseases like POAG necessitates analytical approaches that move beyond parametric statistical methods such as logistic regression that assume a particular mathematical model. This is particularly important in the era of big data where it is routine to collect and analyse data sets with hundreds of thousands of measured genetic variants in thousands of human subjects. We introduce here the Exploratory Modelling for Extracting Relationships using Genetic and Evolutionary Navigation Techniques (EMERGENT) algorithm as an artificial intelligence approach to the genetic analysis of common human diseases. EMERGENT builds models of genetic variation from lists of mathematical functions using a form of genetic programming called computational evolution. A key feature of the system is the ability to use pre-processed expert knowledge giving it the ability to explore model space much as a human would. We describe this system in detail and then apply it to the genetic analysis of POAG in the Glaucoma Gene Environment Initiative (GLAUGEN) study that included approximately 1,272 subjects with the disease and 1057 healthy controls. A total of 657,366 single-nucleotide polymorphisms (SNPs) from across the human genome were measured in these subjects and available for analysis. Analysis using the EMERGENT framework revealed a best model consisting of six SNPs that map to at least six different genes. Two of these genes have previously been associated with POAG in several studies. The others represent new hypotheses about the genetic basis of POAG. All of the SNPs are involved in non-additive gene-gene interactions. Further, the six genes are all directly or indirectly related through biological interactions to the vascular endothelial growth factor (VEGF) gene that is an actively investigated drug target for POAG. This study demonstrates the routine application of an artificial intelligence-based system for the genetic analysis of complex human diseases.", notes = " Part of \cite{Riolo:2014:GPTP} published after the workshop in 2015", } @InProceedings{Moore:2018:PSB, author = "Jason H. Moore and Maksim Shestov and Peter Schmitt and Randal S. Olson", title = "A heuristic method for simulating open-data of arbitrary complexity that can be used to compare and evaluate machine learning methods", booktitle = "Pacific Symposium on Biocomputing", year = "2018", editor = "Russ B. Altman and A. Keith Dunker and Lawrence Hunter and Marylyn D. Ritchie and Tiffany Murray and Teri E. Klein", pages = "259--267", address = "Hawaii, USA", month = "3-7 " # jan, organisation = "Institute for Computational Biology", keywords = "genetic algorithms, genetic programming, simulation, machine learning, open data", isbn13 = "978-981-3235-53-3", URL = "https://pubmed.ncbi.nlm.nih.gov/29218887/", URL = "http://psb.stanford.edu/psb-online/proceedings/psb18/moore.pdf", size = "9 pages", abstract = "A central challenge of developing and evaluating artificial intelligence and machine learning methods for regression and classification is access to data that illuminates the strengths and weaknesses of different methods. Open data plays an important role in this process by making it easy for computational researchers to easily access real data for this purpose. Genomics has in some examples taken a leading role in the open data effort starting with DNA microarrays. While real data from experimental and observational studies is necessary for developing computational methods it is not sufficient. This is because it is not possible to know what the ground truth is in real data. This must be accompanied by simulated data where that balance between signal and noise is known and can be directly evaluated. Unfortunately, there is a lack of methods and software for simulating data with the kind of complexity found in real biological and biomedical systems. We present here the Heuristic Identification of Biological Architectures for simulating Complex Hierarchical Interactions (HIBACHI) method and prototype software for simulating complex biological and biomedical data. Further, we introduce new methods for developing simulation models that generate data that specifically allows discrimination between different machine learning methods", notes = "A heuristic method for simulating open-data of arbitrary complexity that can be used to compare and evaluate machine learning methods*Jason H. Moore, Maksim Shestov, Peter Schmitt, Randal S. Olson Institute for Biomedical Informatics, University of Pennsylvania, D202 Richards Building, 3700 Hamilton Walk, Philadelphia, PA 19104 https://psb.stanford.edu/previous/psb18/", } @InCollection{Moore:2018:hbge, author = "Jason H. Moore and Moshe Sipper", title = "Grammatical Evolution Strategies for Bioinformatics and Systems Genomics", booktitle = "Handbook of Grammatical Evolution", publisher = "Springer", year = "2018", editor = "Conor Ryan and Michael O'Neill and J. J. Collins", chapter = "16", pages = "395--405", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", isbn13 = "978-3-319-78716-9", DOI = "doi:10.1007/978-3-319-78717-6_16", abstract = "Evolutionary computing methods are an attractive option for modelling complex biological and biomedical systems because they are inherently parallel, they conduct stochastic search through large solution spaces, they capitalize on the modularity of solutions, they have flexible solution representations, they can use expert knowledge, they can consider multiple fitness criteria, and they are inspired by how evolution optimizes fitness through natural selection. Grammatical evolution (GE) is a promising example of evolutionary computing because it generates solutions to a problem using a generative grammar. We review here several detailed examples of GE from the bioinformatics and systems genomics literature and end with some ideas about the challenges and opportunities for integrating GE into biological and biomedical discovery.", notes = "Part of \cite{Ryan:2018:hbge}", } @Article{Moore:2019:GPEM, author = "Jason H. Moore and Randal S. Olson and Yong Chen and Moshe Sipper", title = "Automated discovery of test statistics using genetic programming", journal = "Genetic Programming and Evolvable Machines", year = "2019", volume = "20", number = "1", pages = "127--137", month = mar, keywords = "genetic algorithms, genetic programming, Statistics, Optimization, Student's T test", ISSN = "1389-2576", URL = "http://human-competitive.org/sites/default/files/automated-discovery-of-test-statistics_0.pdf", URL = "https://rdcu.be/cTpQ0", DOI = "doi:10.1007/s10710-018-9338-z", size = "11 pages", abstract = "The process of developing new test statistics is laborious, requiring the manual development and evaluation of mathematical functions that satisfy several theoretical properties. Automating this process, hitherto not done, would greatly accelerate the discovery of much-needed, new test statistics. This automation is a challenging problem because it requires the discovery method to know something about the desirable properties of a good test statistic in addition to having an engine that can develop and explore candidate mathematical solutions with an intuitive representation. In this paper we describe a genetic programming-based system for the automated discovery of new test statistics. Specifically, our system was able to discover test statistics as powerful as the t test for comparing sample means from two distributions with equal variances.", notes = "2019 Humies finalist. Slides: http://www.human-competitive.org/sites/default/files/moore.pdf Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA", } @InProceedings{Moore:2019:GECCOcomp, author = "Jason H. Moore and Randal S. Olson and Yong Chen and Moshe Sipper", title = "Discovering test statistics using genetic programming", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", isbn13 = "978-1-4503-6748-6", pages = "29--30", address = "Prague, Czech Republic", DOI = "doi:10.1145/3319619.3326754", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{3326754} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @Article{Moore:2020:AlifeJ, author = "J. H. Moore and R. S. Olson and P. Schmitt and Y. Chen and E. Manduchi", title = "How Computational Experiments Can Improve Our Understanding of the Genetic Architecture of Common Human Diseases", journal = "Artificial Life", year = "2020", volume = "26", number = "1", pages = "23--37", month = apr, keywords = "genetic algorithms, genetic programming, Genetics, complexity, epistasis, simulation", DOI = "doi:10.1162/artl_a_00308", ISSN = "1064-5462", abstract = "Susceptibility to common human diseases such as cancer is influenced by many genetic and environmental factors that work together in a complex manner. The state of the art is to perform a genome-wide association study (GWAS) that measures millions of single-nucleotide polymorphisms (SNPs) throughout the genome followed by a one-SNP-at-a-time statistical analysis to detect univariate associations. This approach has identified thousands of genetic risk factors for hundreds of diseases. However, the genetic risk factors detected have very small effect sizes and collectively explain very little of the overall heritability of the disease. Nonetheless, it is assumed that the genetic component of risk is due to many independent risk factors that contribute additively. The fact that many genetic risk factors with small effects can be detected is taken as evidence to support this notion. It is our working hypothesis that the genetic architecture of common diseases is partly driven by non-additive interactions. To test this hypothesis, we developed a heuristic simulation-based method for conducting experiments about the complexity of genetic architecture. We show that a genetic architecture driven by complex interactions is highly consistent with the magnitude and distribution of univariate effects seen in real data. We compare our results with measures of univariate and interaction effects from two large-scale GWASs of sporadic breast cancer and find evidence to support our hypothesis that is consistent with the results of our computational experiment.", notes = "Also known as \cite{9082081}", } @Misc{Moore:2021:GPTP, author = "Jason Moore", title = "Automated machine learning using genetic programming", booktitle = "Genetic Programming Theory and Practice XVIII", year = "2021", editor = "Wolfgang Banzhaf and Leonardo Trujillo and Stephan Winkler and Bill Worzel", address = "East Lansing, USA", month = "19-21 " # may, keywords = "genetic algorithms, genetic programming", notes = "Not in published proceedings", } @InProceedings{Moore:2022:GPTP, author = "Jason Moore", title = "Genetic programming as an innovation engine for automated machine learning", booktitle = "Genetic Programming Theory and Practice XIX", year = "2022", editor = "Leonardo Trujillo and Stephan M. Winkler and Sara Silva and Wolfgang Banzhaf", address = "Ann Arbor, USA", month = jun # " 2-4", keywords = "genetic algorithms, genetic programming", notes = "Does not appear in published proceedings. See also \cite{Moore:2023:hbeml}", } @InCollection{Moore:2023:hbeml, author = "Jason H. Moore and Pedro H. Ribeiro and Nicholas Matsumoto and Anil K. Saini", title = "Genetic Programming as an Innovation Engine for Automated Machine Learning: The Tree-Based Pipeline Optimization Tool (TPOT)", booktitle = "Handbook of Evolutionary Machine Learning", publisher = "Springer Nature", year = "2023", editor = "Wolfgang Banzhaf and Penousal Machado and Mengjie Zhang", series = "Genetic and Evolutionary Computation (GEVO)", pages = "439--455", address = "Singapore", edition = "1", month = "2 " # nov, keywords = "genetic algorithms, genetic programming, TPOT", isbn13 = "978-981-99-3813-1", ISSN = "1932-0167", URL = "https://link.springer.com/book/10.1007/978-981-99-3814-8", DOI = "doi:10.1007/978-981-99-3814-8_14", abstract = "One of the central challenges of machine learning is the selection of methods for feature selection selection, feature engineering, and classification or regression algorithms for building an analytics pipeline. This is true for both novices and experts. Automated machine learning (AutoML) has emerged as a useful approach to generate machine learning pipelines without the need for manual construction and evaluation. We review here some challenges of building pipelines and present several of the first and most widely used AutoML methods and open-source software. We present in detail the Tree-based Pipeline Optimization Tool (TPOT) that represents pipelines as expression trees and uses genetic programming (GP) for discovery and optimisation. We present some of the extensions of TPOT and its application to real-world big data. We end with some thoughts about the future of AutoML and evolutionary machine learning.", } @Article{moore:2023:GPEM, author = "Jason H. Moore", title = "Is the evolution metaphor still necessary or even useful for genetic programming?", journal = "Genetic Programming and Evolvable Machines", year = "2023", volume = "24", number = "2", pages = "Article number: 21", month = dec, note = "Special Issue: Thirtieth Anniversary of Genetic Programming: On the Programming of Computers by Means of Natural Selection", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/drZcP", DOI = "doi:10.1007/s10710-023-09469-9", size = "3 pages", notes = "Response to \cite{langdon:jaws30} Peer commentary editors: Leonardo Vanneschi and Leonardo Trujillo \cite{Vanneschi:2023:GPEM} See also \cite{jaws30_reply}", } @InCollection{moore:2004:GPTP, author = "Scott A. Moore and Kurt DeMaagd", title = "Using a Genetic Program to Search for Supply Chain Reordering", booktitle = "Genetic Programming Theory and Practice {II}", year = "2004", editor = "Una-May O'Reilly and Tina Yu and Rick L. Riolo and Bill Worzel", chapter = "13", pages = "207--223", address = "Ann Arbor", month = "13-15 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, parameter tuning, supply chain, simulation, restocking policies, application", ISBN = "0-387-23253-2", DOI = "doi:10.1007/0-387-23254-0_13", abstract = "The authors investigate using genetic programming as a tool for finding good heuristics for supply chain restocking strategies. In this paper they outline their method that integrates a supply chain simulation with genetic programming. The simulation is used to score the population members for the evolutionary algorithm which is, in turn, used to search for members that might perform better on the simulation. The fitness of a population member reflects its relative performance in the simulation. This paper investigates both the effectiveness of this method and the parameter settings that make it more or less effective.", notes = "part of \cite{oreilly:2004:GPTP2}", } @InProceedings{Mora:2015:ReCoSoC, author = "Javier Mora and Andres Otero and Eduardo {de la Torre} and Teresa Riesgo", booktitle = "10th International Symposium on Reconfigurable Communication-centric Systems-on-Chip (ReCoSoC)", title = "Fast and compact evolvable systolic arrays on dynamically reconfigurable FPGAs", year = "2015", month = jun, keywords = "genetic algorithms, genetic programming, EHW", DOI = "doi:10.1109/ReCoSoC.2015.7238087", abstract = "Evolvable hardware may be considered as the result of a design methodology that employs an evolutionary algorithm to find an optimal solution to a given problem in the form of a digital circuit. Evolutionary algorithms typically require testing thousands of candidate solutions, taking long time to complete. It would be desirable to reduce this time to a few seconds for applications that require a fast adaptation to a problem. Also, it is important to consider architectures that may operate at high clock speeds in order to reach very speed-demanding situations. This paper presents an implementation on an FPGA of an evolvable hardware image filter based on a systolic array architecture that uses dynamic partial reconfiguration in order to change between different candidate solutions. The neighbour to neighbour connections of the array offer improved performance versus other approaches, like Cartesian Genetic Programming derived circuits. Time savings due to faster evaluation compensate the slower reconfiguration time compared with virtual reconfiguration approaches, but, at any rate, reconfiguration time has been improved also by reducing the elements to reconfigure to just the LUT contents of the configurable blocks. The techniques presented in this paper lead to circuits that may operate at up to 500 MHz (in a Virtex-5), filtering 500 megapixels per second, the processing element size of the array is reduced to 2 CLBs, and over 80000 evaluations per second in a multiple array structure in an FPGA permit to obtain good quality filters in around 3 seconds of evolution time.", notes = "Centro de Electron. Ind., Univ. Politec. de Madrid, Madrid, Spain Also known as \cite{7238087}", } @Article{mora:GPEM, author = "Javier Mora and Ruben Salvador and Eduardo {de la Torre}", title = "On the scalability of evolvable hardware architectures: comparison of systolic array and Cartesian genetic programming", journal = "Genetic Programming and Evolvable Machines", year = "2019", volume = "20", number = "2", pages = "155--186", month = jun, keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, FPGA, Evolvable hardware, EHW, Dynamic partial reconfiguration, Systolic array, Scalability", URL = "http://link.springer.com/article/10.1007/s10710-018-9340-5", DOI = "doi:10.1007/s10710-018-9340-5", abstract = "Evolvable hardware allows the generation of circuits that are adapted to specific problems by using an evolutionary algorithm (EA). Dynamic partial reconfiguration of FPGA LUTs allows making the processing elements (PEs) of these circuits small and compact, thus allowing large scale circuits to be implemented in a small FPGA area. This facilitates the use of these techniques in embedded systems with limited resources. The improvement on resource-efficient implementation techniques has allowed increasing the size of processing architectures from a few PEs to several hundreds. However, these large sizes pose new challenges for the EA and the architecture, which may not be able to take full advantage of the computing capabilities of its PEs. In this article, two different topologies, systolic array (SA) and Cartesian genetic programming (CGP), are scaled from small to large sizes and analysed, comparing their behaviour and efficiency at different sizes. Additionally, improvements on SA connectivity are studied. Experimental results show that, in general, SA is considerably more resource-efficient than CGP, needing up to 60percent fewer FPGA resources (LUTs) for a solution with similar performance, since the LUT usage per PE is 5 times smaller. Specifically, 10 by 10 SA has better performance than 5 by 10 CGP, but uses 50percent fewer resources", } @Article{Moradabadi:2014:GPEM, author = "Behnaz Moradabadi and Hamid Beigy", title = "A new real-coded {Bayesian} optimization algorithm based on a team of learning automata for continuous optimization", journal = "Genetic Programming and Evolvable Machines", year = "2014", volume = "15", number = "2", pages = "169--193", month = jun, keywords = "EDA, Estimation of distribution algorithms, Bayesian optimization algorithm, Learning automata", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-013-9206-9", size = "25 pages", abstract = "Estimation of distribution algorithms have evolved as a technique for estimating population distribution in evolutionary algorithms. They estimate the distribution of the candidate solutions and then sample the next generation from the estimated distribution. Bayesian optimisation algorithm is an estimation of distribution algorithm, which uses a Bayesian network to estimate the distribution of candidate solutions and then generates the next generation by sampling from the constructed network. The experimental results show that the Bayesian optimisation algorithms are capable of identifying correct linkage between the variables of optimisation problems. Since the problem of finding the optimal Bayesian network belongs to the class of NP-hard problems, typically Bayesian optimisation algorithms use greedy algorithms to build the Bayesian network. This paper proposes a new real-coded Bayesian optimisation algorithm for solving continuous optimisation problems that uses a team of learning automata to build the Bayesian network. This team of learning automata tries to learn the optimal Bayesian network structure during the execution of the algorithm. The use of learning automaton leads to an algorithm with lower computation time for building the Bayesian network. The experimental results reported here show the preference of the proposed algorithm on both uni-modal and multi-modal optimisation problems.", } @Article{Moradabadi:2016:GPEM, author = "Behnaz Moradabadi and Mohammad Mahdi Ebadzadeh and Mohammad Reza Meybodi", title = "A new real-coded stochastic {Bayesian} optimization algorithm for continuous global optimization", journal = "Genetic Programming and Evolvable Machines", year = "2016", volume = "17", number = "2", pages = "145--167", month = jun, keywords = "genetic algorithms, BOA", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-015-9255-3", size = "23 pages", abstract = "Estimation of distribution algorithms are considered to be a new class of evolutionary algorithms which are applied as an alternative to genetic algorithms. Such algorithms sample the new generation from a probabilistic model of promising solutions. The search space of the optimization problem is improved by such probabilistic models. In the Bayesian optimization algorithm (BOA), the set of promising solutions forms a Bayesian network and the new solutions are sampled from the built Bayesian network. This paper proposes a novel real-coded stochastic BOA for continuous global optimization by using a stochastic Bayesian network. In the proposed algorithm, the new Bayesian network takes advantage of using a stochastic structure (that there is a probability distribution function for each edge in the network) and the new generation is sampled from the stochastic structure. In order to generate a new solution, some new structure, and therefore a new Bayesian network is sampled from the current stochastic structure and the new solution will be produced from the sampled Bayesian network. Due to the stochastic structure used in the sampling phase, each sample can be generated based on a different structure. Therefore the different dependency structures can be preserved. Before the new generation is generated, the stochastic network's probability distributions are updated according to the fitness evaluation of the current generation. The proposed method is able to take advantage of using different dependency structures through the sampling phase just by using one stochastic structure. The experimental results reported in this paper show that the proposed algorithm increases the quality of the solutions on the general optimization benchmark problems.", notes = "Not GP", } @InProceedings{Moradi:2008:CCE, author = "M. Moradi and A. H. Alavi and M. A. Pashabavandpour and A. H. Gandomi and A. Askarinejad", title = "Soft Computing-Based approaches for the Prediction of Compressive Strength of Lime-Microsilica Stabilized Clayey Soils", booktitle = "Proceedings of the Sixth International Symposium on Computational Civil Engineering, Computational Models for Civil Engineering", year = "2008", editor = "Constantin Ionescu and Alex Horia Barbat and Rodian Scinteie and Alina Mihaela Nicuta", pages = "350--365", address = "Iasi, Romania", month = "30 " # may, organisation = "Editura Societatii Academice Matei - Teiu Botez", keywords = "genetic algorithms, genetic programming, Discipulus stabilised soil, ANN, multilayer perceptron, lime, microsilica, unconfined compressive strength", isbn13 = "ISBN 978-973-8955-41-7", URL = "http://www.intersections.ro/Conferences/CCE2008.pdf", size = "16 pages", abstract = "A range of stabilisers can be added to soil to stabilise it such as Lime, cement, microsilica, or a combination of these in order to improve the engineering properties of soil. Unconfined compressive strength (UCS) as a quality of stabilised soil is generally determined from laboratory tests. For appropriate selection of stabilizers and to avoid the need for the extensive and cumbersome experimental stabilisation tests on soils for every new construction situation, it is desirable to develop a mathematical model to be capable of predicting the strength from the properties of natural soil before compaction and stabilization and stabilizer quantities and types. Hence, in the present study, we investigated the application of soft computing techniques namely, multilayer perceptron (MLP) and genetic programming (GP) for the first time in the literature in order to develop the mathematical models to be able to predict the UCS of lime-microsilica stabilised clayey soils from the influencing parameters. Subsequently, a comparison between these methods was performed in terms of prediction performance. The data for developing the models were generated through an experimental study that was conducted to obtain the UCS of some inland clayey soils.", } @Article{MORADI:2020:CILS, author = "Peyman Moradi and Sajad Hayati and Tahereh Ghahrizadeh", title = "Modeling and optimization of lead and cobalt biosorption from water with Rafsanjan pistachio shell, using experiment based models of {ANN} and {GP}, and the grey wolf optimizer", journal = "Chemometrics and Intelligent Laboratory Systems", volume = "202", pages = "104041", year = "2020", ISSN = "0169-7439", DOI = "doi:10.1016/j.chemolab.2020.104041", URL = "http://www.sciencedirect.com/science/article/pii/S0169743919304435", keywords = "genetic algorithms, genetic programming, Biosorption, Heavy metal, Rafsanjan pistachio shell (RPS), Feed-forward neural network (FFNN), Genetic programming (GP), Grey wolf optimization (GWO)", abstract = "The biosorption of lead and cobalt from an aqueous solution is studied using Rafsanjan pistachio shell (RPS) as a biosorbent. The amount of removed metal depends on four factors including pH of the aqueous solution, initial concentration of metal (C0), biosorbent dosage (DB), and temperature (T). An efficient set of experiments is obtained in a lab-scale batch study. Feed-forward neural network (FFNN) and genetic programming (GP) methods are used for process modeling. The FFNN formula is further improved using the grey wolf optimization (GWO) algorithm and it converges to the test observations with regression index (R2) of 0.9932 and 0.9908 for Pb(II) and Co(II). The GP formula also gives an R2 value of 0.9657 and 0.9518 for Pb(II) and Co(II) adsorptions respectively. Using the grey wolf optimization (GWO) method proves t...", } @Article{MORADKHANI:2020:IJHMT, author = "M. A. Moradkhani and S. H. Hosseini and M. Valizadeh and Alireza Zendehboudi and G. Ahmadi", title = "A general correlation for the frictional pressure drop during condensation in mini/micro and macro channels", journal = "International Journal of Heat and Mass Transfer", volume = "163", pages = "120475", year = "2020", ISSN = "0017-9310", DOI = "doi:10.1016/j.ijheatmasstransfer.2020.120475", URL = "https://www.sciencedirect.com/science/article/pii/S0017931020334116", keywords = "genetic algorithms, genetic programming, Condensation, Mini/micro and macro channels, frictional pressure drop, two-phase flow", abstract = "A general nonlinear equation for estimating the frictional pressure drop during condensation of fluids in different mini/micro and macro channels was obtained using the genetic programming (GP). The developed model is similar to that of the Lockhart and Martinelli [1] model. For developing the new correlation, 7328 data points were collected from 35 sources, which cover a wide range of fluids, channel geometries, diameters, mass fluxes, and saturation temperatures for single-port and multi-port channels. The newly developed correlation for the two-phase flow predicted a wide range of conditions with an average absolute relative deviation (AARD) of 22.92percent. The same database was used for the evaluation of the available empirical correlations. Their deviations were significantly higher than that of the new correlation", } @Article{MORADKHANI:2021:PT, author = "M. A. Moradkhani and S. H. Hosseini and M. Olazar and H. Altzibar and M. Valizadeh", title = "Estimation of the minimum spouting velocity and pressure drop in open-sided draft tube spouted beds using genetic programming", journal = "Powder Technology", volume = "387", pages = "363--372", year = "2021", ISSN = "0032-5910", DOI = "doi:10.1016/j.powtec.2021.04.049", URL = "https://www.sciencedirect.com/science/article/pii/S0032591021003302", keywords = "genetic algorithms, genetic programming, Conical spouted bed, Open-sided draft tube, Minimum spouting velocity, Pressure drop", abstract = "Explicit dimensionless models are proposed for estimating the minimum spouting velocity, operating pressure drop and peak pressure drop in open-sided draft tubes spouted bed using the genetic programming (GP). The models are developed based on 664 experimental data for minimum spouting velocity, 660 for peak pressure drop and 652 for operating pressure drop. The parameters of significant influence are considered as GP input variables, and reliable semi-empirical correlations have been obtained. The models predict the hydrodynamic parameters analyzed with average absolute relative errors of 12.90percent, 15.99percent and 10.92percent for the minimum spouting velocity, peak pressure drop and operating pressure drop, respectively. A comparison of literature correlations with the present ones shows that the latter lead to considerably lower errors from the experimental data. The prediction capability of the new models was evaluated in a range of operating and geometric conditions and a close agreement with the experimental data was observed", } @Article{MORADKHANI:2021:IJR, author = "M. A. Moradkhani and S. H. Hosseini and P. Morshedi and M. Rahimi and Song Mengjie", title = "Saturated flow boiling inside conventional and mini/micro channels: A new general model for frictional pressure drop using genetic programming", journal = "International Journal of Refrigeration", volume = "132", pages = "197--212", year = "2021", ISSN = "0140-7007", DOI = "doi:10.1016/j.ijrefrig.2021.09.022", URL = "https://www.sciencedirect.com/science/article/pii/S0140700721003807", keywords = "genetic algorithms, genetic programming, Saturated flow boiling, Conventional and mini/micro channels, Frictional pressure drop, Two-phase flow, Programmation genetique, Ebullition en ecoulement sature, Canaux conventionnels et mini/micro-canaux, Chute de pression frictionnelle, Ecoulement diphasique", abstract = "This study presents a general explicit model for estimating the saturated flow boiling frictional pressure drop (FPD) in conventional (macro) and mini/micro channels heat exchangers. An extensive database including 6021 experimental data samples has been gathered from 42 published sources, covering a broad range of fluids, channel diameters and operating parameters. The new model is based on the separated model suggested by Lockhart and Martinelli (1949) for two-phase flow. Thus, the two-phase multiplier, ?lo2 has been estimated using the intelligent approach of genetic programming (GP). The presented model predicts the mentioned database with a reasonable value of average absolute relative deviation (AARD) of 21.34percent. Moreover, 74.85percent of predicted data have an error of lower than 30percent of the experimental values. The entire database is compared with ten well-known two-phase pressure drop correlations for the evaluation of previous models. But all of them showed a total AARD of more than 27percent. The GP model shows good accuracy for both conventional and mini/micro channels and different flow regimes, including low and high Reynolds numbers. In addition, it is applicable for estimating the boiling FPD in different operating conditions. Based on 752 additional data from 4 independent sources, the new model provides the best predictions for estimating the FPD in conventional and mini/micro channels", } @Article{MORADKHANI:2022:IJGGC, author = "M. A. Moradkhani and T. Kikhavani and S. H. Hosseini and B. {Van Der Bruggen} and B. Bayati", title = "Applying intelligent approaches to estimate the removal efficiency of heat stable salts from lean amine via electrodialysis", journal = "International Journal of Greenhouse Gas Control", volume = "113", pages = "103548", year = "2022", ISSN = "1750-5836", DOI = "doi:10.1016/j.ijggc.2021.103548", URL = "https://www.sciencedirect.com/science/article/pii/S1750583621002991", keywords = "genetic algorithms, genetic programming, Radial basis function, Electrodialysis, Heat stable salts, Lean amine", abstract = "Intelligent approaches based on radial basis function (RBF) neural networks and genetic programming (GP) were used to establish accurate models for estimating the removal efficiency of heat stable salts from lean amine via electrodialysis. The operating time, current intensity, membrane types, HSS concentration, and kind of concentrated solution were lumped into dimensionless groups. The groups with the most influence were selected based on the Pearson's correlation matrix for the models' inputs. The RBF model showed an excellent agreement with real data with average absolute relative error (AARE) of 1.90percent and R2 of 99.21percent. Then, an explicit empirical correlation was developed for the removal efficiency using the GP technique, which yielded AARE and R2 values of 5.74percent and 96.35percent, respectively. The performance of the GP and RBF models for estimating the removal efficiency of different ions for different types of membranes and operating conditions were assessed and reasonable results were achieved. Finally, to identify the most effective dimensionless groups to describe the removal efficiency, a sensitivity analysis based on the developed GP and RBF models was accomplished", } @Article{MORADKHANI:2022:applthermaleng, author = "M. A. Moradkhani and S. H. Hosseini and Lei Shangwen and Song Mengjie", title = "Intelligent computing approaches to forecast thickness and surface roughness of frost layer on horizontal plates under natural convection", journal = "Applied Thermal Engineering", volume = "217", pages = "119258", year = "2022", ISSN = "1359-4311", DOI = "doi:10.1016/j.applthermaleng.2022.119258", URL = "https://www.sciencedirect.com/science/article/pii/S1359431122011887", keywords = "genetic algorithms, genetic programming, Frost thickness, Frost surface roughness, Machine learning, Horizontal plates, Natural convection", abstract = "There is no existing predictive model for frost layer thickness and surface roughness on horizontal cold plates under the natural convection conditions. Accordingly, intelligent approaches were designed based upon 782 data for frost thickness and 191 data for frost surface roughness, which covered four stages of frosting process. Three machine learning methods of multilayer perceptron (MLP), Gaussian process regression (GPR), and radial basis function (RBF) were employed to design the predictive models for frost characteristics over horizontal cold plates in the natural convection environment. For the frost thickness, although almost all models provided excellent outputs, the RBF based model showed the highest accuracy with average absolute relative error (AARE) and coefficient of determination (R2) values of 1.23percent and 99.93percent, respectively, for the tested data. The RBF based model presented the superior results for frost surface roughness with an AARE of 1.21percent for all analyzed data. The proposed predictive methods were capable of predicting the impact of surface temperature on the frost characteristics at various stages of the process. A statistical analysis of earlier correlations revealed large deviations from the measured data caused by the differences in operating conditions. Thus, new explicit correlations were developed using the intelligent method of genetic programming, which showed the AAREs of 4.61percent and 16.72percent for frost thickness and frost surface roughness, respectively", } @Article{MORADKHANI:2022:ijrefrig, author = "M. A. Moradkhani and S. H. Hosseini and M. Karami", title = "Forecasting of saturated boiling heat transfer inside smooth helically coiled tubes using conventional and machine learning techniques", journal = "International Journal of Refrigeration", volume = "143", pages = "78--93", year = "2022", ISSN = "0140-7007", DOI = "doi:10.1016/j.ijrefrig.2022.06.036", URL = "https://www.sciencedirect.com/science/article/pii/S0140700722002195", keywords = "genetic algorithms, genetic programming, Boiling heat transfer, Helically coiled tubes, Two-phase flow, Modeling, Correlation, Smart approaches, Transfert de chaleur en ebullition, Tubes enroules en helice, Ecoulement diphasique, Modelisation, Correlation", abstract = "This study concerns to model the flow boiling heat transfer coefficient (HTC) in smooth helically coiled tubes. A dataset including 1035 samples was collected from 13 independent studies, enveloping a broad range of geometrical and operating conditions. The predictive capability of the earlier models was assessed for straight and coiled tubes by the analyzed database that they were not precise enough. Accordingly, a new empirical model based on the least square fitting method (LSFM) was constructed using seven input effective dimensionless factors. It was found that LSFM was not able to describe the complex and nonlinear nature of HTC in smooth helically coiled tubes. Furthermore, the intelligent method of genetic programming (GP) was used to obtain more accurate explicit correlation for HTC, which produced an acceptable average absolute relative error (AARE) of 17.35percent. Finally, the machine learning approaches of multilayer perceptron (MLP), Gaussian process regression (GPR), radial basis function (RBF) was also implemented to model HTC in smooth coiled tubes. Although all intelligent based models provided excellent results, the GPR model outperformed the others with an average absolute relative error (AARE) of 5.93percent for the tested dataset. In addition to the proposed models' performance, the most influential factors in controlling the boiling HTC in coiled tubes were also detected", } @InProceedings{Moradpour:2023:SGC, author = "Amir Mohammad Moradpour and Mohammad Hossein Alizadeh and Hamed Delkhosh", booktitle = "2023 13th Smart Grid Conference (SGC)", title = "A New Method Based on Symbolic Regression to Detect The Probability of False Data Injection Attacks on {PV} Generation", year = "2023", abstract = "The increasing penetration of renewable energy resources, such as photovoltaic (PV) systems, has caused significant concerns in power systems. As one of theses concerns, the escalating number of reported cyber-attacks worldwide raises major issues about the operation of PV systems, as it may potentially jeopardize the operation of their connected power systems, especially during contingencies. One attack that seriously threatens cyber-physical systems is False Data Injection (FDI). This paper examines and analyses the detection of data manipulation in a PV power plant using Genetic Programming (GP), where a data-driven symbolic regression-based power generation forecasting is used and a hybrid probability-based FDI attack detection method is proposed. This method effectively enhances the FDI attack detection speed without sacrificing the accuracy. The presented method has been implemented on the real data set of a PV power plant, and the results show the effectiveness of the proposed model.", keywords = "genetic algorithms, genetic programming, Photovoltaic systems, Training, Renewable energy sources, Prediction algorithms, Hybrid power systems, Photovoltaic, Cyber Attack, False Data Injection Attack", DOI = "doi:10.1109/SGC61621.2023.10459279", ISSN = "2572-6927", month = dec, notes = "Also known as \cite{10459279}", } @InProceedings{Mora-Garcia:2008:gecco, author = "Antonio M. {Mora Garcia} and Pedro A. Castillo Valdivieso and Juan J. Merelo Guervos and Eva Alfaro Cid and Anna I. Esparcia-Alcazar and Ken Sharman", title = "Discovering causes of financial distress by combining evolutionary algorithms and artificial neural networks", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "1243--1250", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1243.pdf", DOI = "doi:10.1145/1389095.1389337", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, artificial neural networks, financial distress prediction, self-organising maps", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389337}", } @InProceedings{Mora-Garcia:2010:EvoGAMES, author = "Antonio Mora and Juan Julian Merelo and Ramon Montoya and Pablo Garcia and Pedro Castillo and Juan Luis Jimenez and Anna Esparcia and Ana Martinez", title = "Evolving Bot's AI in Unreal", booktitle = "EvoGAMES", year = "2010", editor = "Cecilia {Di Chio} and Stefano Cagnoni and Carlos Cotta and Marc Ebner and Aniko Ekart and Anna I. Esparcia-Alcazar and Chi-Keong Goh and Juan J. Merelo and Ferrante Neri and Mike Preuss and Julian Togelius and Georgios N. Yannakakis", volume = "6024", series = "LNCS", pages = "171--180", address = "Istanbul", month = "7-9 " # apr, organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-12238-5", DOI = "doi:10.1007/978-3-642-12239-2_18", abstract = "This paper describes the design, implementation and results of an evolutionary bot inside the PC game Unreal, that is, an autonomous enemy which tries to beat the human player and/or some other bots. The default artificial intelligence (AI) of this bot has been improved using two different evolutionary methods: genetic algorithms (GAs) and genetic programming (GP). The first one has been applied for tuning the parameters of the hard-coded values inside the bot AI code. The second method has been used to change the default set of rules (or states) that defines its behaviour. Both techniques yield very good results, evolving bots which are capable to beat the default ones. The best results are yielded for the GA approach, since it just does a refinement following the default behaviour rules, while the GP method has to redefine the whole set of rules, so it is harder to get good results.", notes = "EvoGAMES'2010 held in conjunction with EuroGP'2010 EvoCOP2010 EvoBIO2010", } @InProceedings{Mora:2010:ijcnn, author = "A. M. Mora and L. J. Herrera and J. Urquiza and I. Rojas and J. J. Merelo", title = "Applying support vector machines and mutual information to book losses prediction", booktitle = "International Joint Conference on Neural Networks (IJCNN 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, SVM", isbn13 = "978-1-4244-6917-8", DOI = "doi:10.1109/IJCNN.2010.5596710", size = "7 pages", abstract = "This work presents a feasible solution to the problem of book losses prediction from financial and general data in companies. The specific problem tackled in this work corresponds to a real dataset of Spanish companies. A Mutual Information-based criterion has been applied in order to reduce the initial set of variables, and a Support Vector Machine classifier has been designed to perform the prediction. The results show that the proposed approach obtains an important reduction of the number of variables needed to perform the prediction, improving the generalisation capabilities of the model. The accuracy rates were above the 84percent in the test set, much better than those obtained by other soft-computing algorithms (such as Genetic Programming, Self-Organising Maps or Artificial Neural Networks) working with the same dataset and presented in previous works. The proposed approach shows to be promising and could be determinant in providing the experts with the right tools for the selection of the relevant factors and for the prediction in this difficult problem.", notes = "WCCI 2010. Also known as \cite{5596710}", } @InProceedings{moraglio:tio:gecco2004, author = "Alberto Moraglio and Riccardo Poli", title = "Topological Interpretation of Crossover", booktitle = "Genetic and Evolutionary Computation -- GECCO-2004, Part I", year = "2004", editor = "Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell", series = "Lecture Notes in Computer Science", pages = "1377--1388", address = "Seattle, WA, USA", publisher_address = "Heidelberg", month = "26-30 " # jun, organisation = "ISGEC", publisher = "Springer-Verlag", volume = "3102", ISBN = "3-540-22344-4", ISSN = "0302-9743", URL = "http://privatewww.essex.ac.uk/~amoragn/gecco2004fin.PDF", DOI = "doi:10.1007/b98643", DOI = "doi:10.1007/978-3-540-24854-5_131", size = "12", keywords = "genetic algorithms, genetic programming, geometric search", abstract = "In this paper we give a representation-independent topological definition of crossover that links it tightly to the notion of fitness landscape. Building around this definition, a geometric/topological framework for evolutionary algorithms is introduced that clarifies the connection between representation, genetic operators, neighbourhood structure and distance in the landscape. Traditional genetic operators for binary strings are shown to fit the framework. The advantages of this interpretation are discussed", notes = "GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)", } @TechReport{Moraglio:CSM430, author = "Alberto Moraglio and Riccardo Poli", title = "Geometric Landscape of Homologous Crossover for Syntactic Trees", institution = "Computer Science, University of Essex", year = "2005", type = "Geometric Landscape of Homologous Crossover for Syntactic Trees", number = "CSM 430", address = "Wivenhoe Park, Colchester, CO4 3SQ, UK", keywords = "genetic algorithms, genetic programming", URL = "http://cswww.essex.ac.uk/technical-reports/2005/csm-430.PDF", abstract = "Geometric crossover and geometric mutation are representation-independent operators that are well defined once a notion of distance over the solution space is defined. They were obtained as generalisations of genetic operators for binary strings and real vectors. Our geometric framework has been successfully applied to the permutation representation leading to a clarification and a natural unification of this domain. The relationship between search space, distances and genetic operators for syntactic trees is little understood. In this paper we apply the geometric framework to the syntactic tree representation and show how the well known structural distance is naturally associated with homologous crossover and subtree mutation.", notes = "Shorter version appears in CEC-2005 \cite{moraglio:2005:CEC}", size = "11 pages", } @InProceedings{moraglio:2005:CEC, author = "Alberto Moraglio and Riccardo Poli", title = "Geometric Landscape of Homologous Crossover for Syntactic Trees", booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary Computation (CEC-2005)", year = "2005", volume = "1", pages = "427--434", address = "Edinburgh", month = "2-4 " # sep, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9363-5", URL = "http://privatewww.essex.ac.uk/~amoragn/cec2005fin.PDF", DOI = "doi:10.1109/CEC.2005.1554715", abstract = "The relationship between search space, distances and genetic operators for syntactic trees is little understood. Geometric crossover and geometric mutation are representation-independent operators that are well-defined once a notion of distance over the solution space is defined. In this paper we apply this geometric framework to the syntactic tree representation and show how the well-known structural distance is naturally associated with homologous crossover and sub-tree mutation.", notes = "CEC 2005 - A joint meeting of the IEEE, the EPS, and the IEE. See also \cite{Moraglio:CSM430}", } @InProceedings{Moraglio:2005:evophd, author = "Alberto Moraglio", title = "Geometric Unification of Evolutionary Algorithms", booktitle = "European Graduate Student Workshop on Evolutionary Computation", year = "2006", editor = "Mario Giacobini and Jano {van Hemert}", pages = "45--58", address = "Budapest, Hungary", month = "10 " # apr, keywords = "genetic algorithms, genetic programming", URL = "http://www.vanhemert.co.uk/publications/EvoPhD2006.pdf", size = "14 pages", abstract = "Evolutionary algorithms are only superficially different and can be unified within an axiomatic geometric framework by abstraction of the solution representation. This framework describes the evolutionary search in a representation-independent way, purely in geometric terms, paving the road to a general theory of evolutionary algorithms. It also leads to a principled design methodology for the crossover operator for any solution representation.", notes = "broken Jan 2021 http://evonet.lri.fr/eurogp2006/?page=evophd", } @InProceedings{eurogp06:MoraglioPoliSeehuus, author = "Alberto Moraglio and Riccardo Poli and Rolv Seehuus", title = "Geometric Crossover for Biological Sequences", editor = "Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art", booktitle = "Proceedings of the 9th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3905", year = "2006", address = "Budapest, Hungary", month = "10 - 12 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-33143-3", pages = "121--132", URL = "http://cswww.essex.ac.uk/staff/rpoli/papers/eurogpalberto2006.pdf", DOI = "doi:10.1007/11729976_11", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "This paper extends a geometric framework for interpreting crossover and mutation to the case of sequences. This representation is important because it is the link between artificial evolution and biological evolution. We define and theoretically study geometric crossover for sequences under edit distance and show its intimate connection with the biological notion of sequence homology.", notes = "Part of \cite{collet:2006:GP} EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006", } @InProceedings{Moraglio:2007:FOGA, author = "Alberto Moraglio and Riccardo Poli", title = "Inbreeding Properties of Geometric Crossover and Non-geometric Recombinations", year = "2007", booktitle = "Foundations of Genetic Algorithms", editor = "Christopher R. Stephens and Marc Toussaint and Darrell Whitley and Peter F. Stadler", volume = "4436", series = "LNCS", pages = "1--14", address = "Mexico City", month = jan # " 8-11", organisation = "ACM SigEvo", publisher = "Springer", note = "Revised Selected Papers", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-73482-6", URL = "http://www.sigevo.org/foga-2007/program.html", DOI = "doi:10.1007/978-3-540-73482-6_1", abstract = "Geometric crossover is a representation-independent generalization of traditional crossover for binary strings. It is defined in a simple geometric way by using the distance associated with the search space. Many interesting recombination operators for the most frequently used representations are geometric crossovers under some suitable distance. Showing that a given recombination operator is a geometric crossover requires finding a distance for which offspring are in the metric segment between parents. However, proving that a recombination operator is not a geometric crossover requires excluding that one such distance exists. It is, therefore, very difficult to draw a clear-cut line between geometric crossovers and non-geometric crossovers. In this paper we develop some theoretical tools to solve this problem and we prove that some well-known operators are not geometric. Finally, we discuss the implications of these results.", notes = "Notes based on pre-publication slides http://www.sigevo.org/foga-2007/talks/Moraglio-FOGA07.ppt p21 No distance metric is possible for GP using only Koza style sub-tree crossover. Hence no fitness landscape for GP????? p7 Metric for Homologous GP crossover \cite{langdon:2000:fairxo}", } @InProceedings{eurogp07:moraglio, author = "Alberto Moraglio and Cecilia {Di Chio} and Riccardo Poli", title = "Geometric Particle Swarm Optimization", editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar", booktitle = "Proceedings of the 10th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "4445", year = "2007", address = "Valencia, Spain", month = "11-13 " # apr, pages = "125--136", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-71602-5", isbn13 = "978-3-540-71602-0", DOI = "doi:10.1007/978-3-540-71605-1_12", abstract = "Using a geometric framework for the interpretation of crossover of recent introduction, we show an intimate connection between particle swarm optimisation (PSO) and evolutionary algorithms. This connection enables us to generalise PSO to virtually any solution representation in a natural and straightforward way. We demonstrate this for the cases of Euclidean, Manhattan and Hamming spaces.", notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007", } @PhdThesis{Moraglio:thesis, author = "Alberto Moraglio", title = "Towards a Geometric Unification of Evolutionary Algorithms", school = "Department of Computer Science, University of Essex", year = "2007", address = "UK", month = nov, keywords = "genetic algorithms, genetic programming", URL = "http://eden.dei.uc.pt/~moraglio/Thesis_final.pdf", URL = "http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.446045", size = "392 pages", abstract = "Evolutionary algorithms are successful and widespread general problem solving methods that mimic in a simplified manner biological evolution. Whereas most of evolutionary algorithms share the same basic algorithmic structure, they differ in the solution representation - the genotype - and in the search operators employed - mutation and crossover - that are representation specific. In the research community there is a strong feeling that the evolutionary computation field needs unification and systematisation in a rational framework to survive its own exceptional growth of the last two decades. The lack of a common formal framework encompassing all solution representations have prevented EC researchers to build a truly general theory of evolutionary algorithms, as well as to develop a formal theory of search operators design for new representations and problems. In this thesis, we propose a general geometric framework that addresses these two important problems. The unification is made possible by surprisingly simple representation-independent geometric definitions of crossover and mutation using the notion of distance associated with the search space. This novel way of looking at genetic operators allows us to rethink various familiar aspects of evolutionary algorithms in a very general setting, simplifying and clarifying their relations. We show that many important genetic operators for the most frequently used representations fit this framework. This makes this framework highly relevant because it unifies pre-existing evolutionary algorithms. The abstract definitions of mutation and crossover can be used as formal recipes to build new mutations and crossovers for virtually any new solution representations and problems. We designed and tested new operators on a number of problems obtaining very good experimental results. The same abstract definitions of mutation and crossover can be used to build a truly general representation-independent theory of evolutionary algorithms. We started building such a theory and showed that all evolutionary algorithms with geometric crossover does the same type of search, convex search. This is a general and important result because it shows that a non-trivial representation-independent theory of evolutionary algorithms is possible.", notes = "Some chapters on GP trees. ISNI: 0000 0001 3418 9612 Supervisor: Riccardo Poli", } @InProceedings{Moraglio:2010:EuroGP, author = "Alberto Moraglio and Sara Silva", title = "Geometric Differential Evolution on the Space of Genetic Programs", booktitle = "Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010", year = "2010", editor = "Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar", volume = "6021", series = "LNCS", pages = "171--183", address = "Istanbul", month = "7-9 " # apr, organisation = "EvoStar", publisher = "Springer", note = "Best paper", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-12147-0", DOI = "doi:10.1007/978-3-642-12148-7_15", abstract = "Geometric Differential Evolution (GDE) is a very recently introduced formal generalization of traditional Differential Evolution (DE) that can be used to derive specific GDE for both continuous and combinatorial spaces retaining the same geometric interpretation of the dynamics of the DE search across representations. In this paper, we derive formally a specific GDE for the space of genetic programs. The result is a Differential Evolution algorithm searching the space of genetic programs by acting directly on their tree representation. We present experimental results for the new algorithm.", notes = "Part of \cite{Esparcia-Alcazar:2010:GP} EuroGP'2010 held in conjunction with EvoCOP2010 EvoBIO2010 and EvoApplications2010", } @InProceedings{Moraglio:2011:ThRaSH, author = "Alberto Moraglio and Krzysztof Krawiec and Colin Johnson", title = "Geometric Semantic Genetic Programming", booktitle = "The 5th workshop on Theory of Randomized Search Heuristics, ThRaSH'2011", year = "2011", editor = "Christian Igel and Per Kristian Lehre and Carsten Witt", address = "Copenhagen, Denmark", month = jul # " 8-9", keywords = "genetic algorithms, genetic programming", URL = "http://www.thrash-workshop.org/slides/moraglio.pdf", size = "56 slides", notes = "http://www.thrash-workshop.org/", } @InProceedings{Moraglio:2011:GECCO, author = "Alberto Moraglio and Sara Silva", title = "Geometric nelder-mead algorithm on the space of genetic programs", booktitle = "GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0557-0", pages = "1307--1314", keywords = "genetic algorithms, genetic programming", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001576.2001753", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "The Nelder-Mead Algorithm (NMA) is a close relative of Particle Swarm Optimization (PSO) and Differential Evolution (DE). In recent work, PSO, DE and NMA have been generalized using a formal geometric framework that treats solution representations in a uniform way. These formal algorithms can be used as templates to derive rigorously specific PSO, DE and NMA for both continuous and combinatorial spaces retaining the same geometric interpretation of the search dynamics of the original algorithms across representations. In previous work, a geometric NMA has been derived for the binary string representation and permutation representation. Furthermore, PSO and DE have already been derived for the space of genetic programs. In this paper, we continue this line of research and derive formally a specific NMA for the space of genetic programs. The result is a Nelder-Mead Algorithm searching the space of genetic programs by acting directly on their tree representation. We present initial experimental results for the new algorithm. The challenge tackled in the present work compared with earlier work is that the pair NMA and genetic programs is the most complex considered so far. This combination raises a number of issues and casts light on how algorithmic features can interact with representation features to give rise to a highly peculiar search behaviour.", notes = "Also known as \cite{2001753} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)", } @InProceedings{Moraglio:2011:GECCOcomp, author = "Alberto Moraglio", title = "Geometry of evolutionary algorithms", booktitle = "GECCO 2011 Tutorials", year = "2011", editor = "Darrell Whitley", isbn13 = "978-1-4503-0690-4", keywords = "genetic algorithms, genetic programming", pages = "1439--1468", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001858.2002144", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "The various flavors of Evolutionary Algorithms look very similar when cleared of algorithmically irrelevant differences such as domain of application and phenotype interpretation. Representation-independent algorithmic characteristics like the selection scheme can be freely exchanged between algorithms. Ultimately, the origin of the differences of the various flavors of Evolutionary Algorithms is rooted in the solution representation and relative genetic operators. Are these differences only superficial? Is there a deeper unity encompassing all Evolutionary Algorithms beyond the specific representation? Is a general mathematical framework unifying search operators for all solution representations at all possible? The aim of the tutorial is to introduce a formal, but intuitive, unified point of view on Evolutionary Algorithms across representations based on geometric ideas, which provides a possible answer to the above questions. It also presents the benefits for both theory and practice brought by this novel perspective. The key idea behind the geometric framework is that search operators have a dual nature. The same search operator can be defined (i) on the underlying solution representations and, equivalently, (ii) on the structure of the search space by means of simple geometric shapes, like balls and segments. These shapes are used to delimit the region of space that includes all possible offspring with respect to the location of their parents. The geometric definition of a search operator is of interest because it can be applied - unchanged - to different search spaces associated with different representations. This, in effect, allows us to define exactly the same search operator across representations in a rigorous way. The geometric view on search operators has a number of interesting consequences of which this tutorial will give a comprehensive overview. These include (i) a straightforward view on the fitness landscape seen by recombination operators, (ii) a formal unification of many pre-existing search operators across representations, (iii) a principled way of designing crossover operators for new representations, (iv) a principled way of generalising search algorithms from continuous to combinatorial spaces, and (v) the potential for a unified theory of evolutionary algorithms across representations.", notes = "Also known as \cite{2002144} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @Proceedings{Moraglio:2012:GP, title = "Proceedings of the 15th European Conference on Genetic Programming, EuroGP 2012", year = "2012", editor = "Alberto Moraglio and Sara Silva and Krzysztof Krawiec and Penousal Machado and Carlos Cotta", volume = "7244", series = "LNCS", address = "Malaga, Spain", month = "11-13 " # apr, organisation = "EvoStar", publisher = "Springer Verlag", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-29138-8", DOI = "doi:10.1007/978-3-642-29139-5", size = "~276 pages", } @InProceedings{Moraglio:2012:CEC, title = "Evolving Recursive Programs using Non-recursive Scaffolding", author = "Alberto Moraglio and Fernando Otero and Colin Johnson and Simon Thompson and Alex Freitas", pages = "2242--2249", booktitle = "Proceedings of the 2012 IEEE Congress on Evolutionary Computation", year = "2012", editor = "Xiaodong Li", month = "10-15 " # jun, URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.308.8964", URL = "http://www.cs.kent.ac.uk/people/staff/aaf/pub_papers.dir/CEC-2012-Moraglio-Proc.pdf", DOI = "doi:10.1109/CEC.2012.6256545", address = "Brisbane, Australia", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, grammar based GP, scaffolding, CFG-GP", size = "8 pages", abstract = "Genetic programming has proved capable of evolving solutions to a wide variety of problems. However, the successes have largely been with programs without iteration or recursion; evolving recursive programs has turned out to be particularly challenging. The main obstacle to evolving recursive programs seems to be that they are particularly fragile to the application of search operators: a small change in a correct recursive program generally produces a completely wrong program. In this paper, we present a simple and general method that allows us to pass back and forth from a recursive program to an equivalent non-recursive program. Finding a recursive program can then be reduced to evolving non-recursive programs followed by converting the optimum non-recursive program found to the equivalent recursive program. This avoids the fragility problem above, as evolution does not search the space of recursive programs. We present promising experimental results on a test-bed of recursive problems.", notes = "CFG-GP \cite{whigham:1995:GBGP} (STGP). Cited by \cite{Bladek:2016:GECCOcomp} WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the EPS and the IET.", } @InProceedings{conf/ppsn/MoraglioKJ12, author = "Alberto Moraglio and Krzysztof Krawiec and Colin G. Johnson", title = "Geometric Semantic Genetic Programming", booktitle = "Parallel Problem Solving from Nature, PPSN XII (part 1)", year = "2012", editor = "Carlos A. {Coello Coello} and Vincenzo Cutello and Kalyanmoy Deb and Stephanie Forrest and Giuseppe Nicosia and Mario Pavone", volume = "7491", series = "Lecture Notes in Computer Science", pages = "21--31", address = "Taormina, Italy", month = sep # " 1-5", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-32936-4", DOI = "doi:10.1007/978-3-642-32937-1_3", size = "11 pages", abstract = "Traditional Genetic Programming (GP) searches the space of functions/programs by using search operators that manipulate their syntactic representation, regardless of their actual semantics/behaviour. Recently, semantically aware search operators have been shown to outperform purely syntactic operators. In this work, using a formal geometric view on search operators and representations, we bring the semantic approach to its extreme consequences and introduce a novel form of GP, Geometric Semantic GP (GSGP), that searches directly the space of the underlying semantics of the programs. This perspective provides new insights on the relation between program syntax and semantics, search operators and fitness landscape, and allows for principled formal design of semantic search operators for different classes of problems. We derive specific forms of GSGP for a number of classic GP domains and experimentally demonstrate their superiority to conventional operators.", bibsource = "DBLP, http://dblp.uni-trier.de", affiliation = "School of Computer Science, University of Birmingham, UK", } @Article{Moraglio:2013:EC, author = "A. Moraglio and J. Togelius and S. Silva", title = "Geometric Differential Evolution for Combinatorial and Programs Spaces", journal = "Evolutionary Computation", year = "2013", volume = "21", number = "4", pages = "591--624", month = "Winter", keywords = "genetic algorithms, genetic programming, DE, NK, TSP, Sudoku, Differential evolution, representations, principled design of search operators, combinatorial spaces, theory", ISSN = "1063-6560", URL = "https://ore.exeter.ac.uk/repository/bitstream/handle/10871/13627/Geometric%20differential%20evolution%20for%20combinatorial%20and%20programs%20spaces.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.298.3320", DOI = "doi:10.1162/EVCO_a_00099", size = "34 pages", abstract = "Geometric differential evolution (GDE) is a recently introduced formal generalisation of traditional differential evolution (DE) that can be used to derive specific differential evolution algorithms for both continuous and combinatorial spaces retaining the same geometric interpretation of the dynamics of the DE search across representations. In this article, we first review the theory behind the GDE algorithm, then, we use this framework to formally derive specific GDE for search spaces associated with binary strings, permutations, vectors of permutations and genetic programs. The resulting algorithms are representation-specific differential evolution algorithms searching the target spaces by acting directly on their underlying representations. We present experimental results for each of the new algorithms on a number of well-known problems comprising NK-landscapes, TSP, and Sudoku, for binary strings, permutations and vectors of permutations. We also present results for the Regression, Artificial Ant, Parity and Multiplexer problems within the genetic programming domain. Experiments show that overall the new DE algorithms are competitive with well-tuned standard search algorithms.", notes = "Posted online on 27 Dec 2012. oai:CiteSeerX.psu:10.1.1.298.3320 is draft?", } @InProceedings{Moraglio:2013:GECCO, author = "Alberto Moraglio and Andrea Mambrini", title = "Runtime analysis of mutation-based geometric semantic genetic programming for basis functions regression", booktitle = "GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference", year = "2013", editor = "Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi", isbn13 = "978-1-4503-1963-8", pages = "989--996", keywords = "genetic algorithms, genetic programming", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2463372.2463492", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Geometric Semantic Genetic Programming (GSGP) is a recently introduced form of Genetic Programming (GP) that searches the semantic space of functions/programs. The fitness landscape seen by GSGP is always, for any domain and for any problem, unimodal with a linear slope by construction. This makes the search for the optimum much easier than for traditional GP, and it opens the way to analyse theoretically in a easy manner the optimisation time of GSGP in a general setting. Very recent work proposed a runtime analysis of mutation-based GSGP on the class of all Boolean functions. We present a runtime analysis of mutation-based GSGP on the class of all regression problems with generic basis functions (encompassing e.g., polynomial regression and trigonometric regression).", notes = "Also known as \cite{2463492} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)", } @InProceedings{Moraglio:2014:SMGP, author = "Alberto Moraglio and James McDermott and Michael O'Neill", title = "Geometric Semantic Grammatical Evolution", booktitle = "Semantic Methods in Genetic Programming", year = "2014", editor = "Colin Johnson and Krzysztof Krawiec and Alberto Moraglio and Michael O'Neill", address = "Ljubljana, Slovenia", month = "13 " # sep, note = "Workshop at Parallel Problem Solving from Nature 2014 conference", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", URL = "http://www.cs.put.poznan.pl/kkrawiec/smgp2014/uploads/Site/Moraglio.pdf", size = "2 pages", abstract = "Geometric Semantic Genetic Programming (GSGP) is a novel form of Genetic Programming (GP) based on a geometric theory of evolutionary algorithms that searches directly the semantic space of programs. We show how to extend this framework to Grammatical Evolution (GE). We refer to the new method as Geometric Semantic Grammatical Evolution (GSGE).", notes = "SMGP 2014 http://www.cs.put.poznan.pl/kkrawiec/smgp/?n=Site.SMGP2014", } @InProceedings{Moraglio:2014:SMGP2, author = "Alberto Moraglio", title = "An Efficient Implementation of GSGP using Higher-Order Functions and Memoization", booktitle = "Semantic Methods in Genetic Programming", year = "2014", editor = "Colin Johnson and Krzysztof Krawiec and Alberto Moraglio and Michael O'Neill", address = "Ljubljana, Slovenia", month = "13 " # sep, note = "Workshop at Parallel Problem Solving from Nature 2014 conference", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.put.poznan.pl/kkrawiec/smgp2014/uploads/Site/Moraglio2.pdf", code_url = "https://github.com/amoraglio/GSGP", size = "2 pages", abstract = "Geometric Semantic Genetic Programming (GSGP) [1] is a novel form of Genetic Programming (GP) that can be interpreted as searching directly the semantic space of programs. This new form of GP is very promising as it induces always a simple unimodal fitness landscape for any problem it is applied to, hence it converges to the optimum very quickly. A drawback of GSGP with crossover is the exponential growth of individuals due to the fact that the offspring tree contains both parent trees, hence individuals double their size at each generation. Vanneschi et al. [2] have proposed an implementation of GSGP with crossover using a complex pointer-based data structure that prevents the exponential growth by keeping trace of the ancestry of individuals rather than storing them directly. We propose a new implementation of GSGP also based on tracing the ancestry of individuals, that however does not explicitly build and maintain a data structure, but uses higher-order functions and memoization to achieve the same effect, leaving the burden of book-keeping to the compiler. The resulting implementation is fast, elegant and concise. A Python implementation (under 100 lines without comments) is on GitHub at https://github.com/amoraglio/GSGP.", notes = "SMGP 2014 http://www.cs.put.poznan.pl/kkrawiec/smgp/?n=Site.SMGP2014", } @InProceedings{Moraglio:2015:GECCOcomp, author = "Alberto Moraglio and Krzysztof Krawiec", title = "Semantic Genetic Programming", booktitle = "GECCO 2015 Advanced Tutorials", year = "2015", editor = "Anabela Simoes", isbn13 = "978-1-4503-3488-4", keywords = "genetic algorithms, genetic programming", pages = "603--627", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739482.2756587", DOI = "doi:10.1145/2739482.2756587", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Semantic genetic programming is a recent, rapidly growing trend in Genetic Programming (GP) that aims at opening the black box of the evaluation function and make explicit use of more information on program behaviour in the search. In the most common scenario of evaluating a GP program on a set of input-output examples (fitness cases), the semantic approach characterizes program with a vector of outputs rather than a single scalar value (fitness). The past research on semantic GP has demonstrated that the additional information obtained in this way facilitates designing more effective search operators. In particular, exploiting the geometric properties of the resulting semantic space leads to search operators with attractive properties, which have provably better theoretical characteristics than conventional GP operators. This in turn leads to dramatic improvements in experimental comparisons. The aim of the tutorial is to give a comprehensive overview of semantic methods in genetic programming, illustrate in an accessible way a formal geometric framework for program semantics to design provably good mutation and crossover operators for traditional GP problem domains, and to analyse rigorously their performance (runtime analysis). A number of real-world applications of this framework will be also presented. Other promising emerging approaches to semantics in GP will be reviewed. In particular, the recent developments in the behavioural programming, which aims at characterizing the entire program behaviour (and not only program outputs) will be covered as well. Current challenges and future trends in semantic GP will be identified and discussed. Selected methods and concepts will be accompanied with live software demonstrations. Also, efficient implementation of semantic search operators may be challenging. We will illustrate very efficient, concise and elegant implementations of these operators, which are available for download from the web.", notes = "Also known as \cite{2756587} Distributed at GECCO-2015.", } @InProceedings{Moraglio:2016:GECCOcomp, author = "Alberto Moraglio and Krzysztof Krawiec", title = "Semantic Genetic Programming", booktitle = "GECCO '16 Companion: Proceedings of the Companion Publication of the 2016 Annual Conference on Genetic and Evolutionary Computation", year = "2016", editor = "Tobias Friedrich and Frank Neumann and Andrew M. Sutton and Martin Middendorf and Xiaodong Li and Emma Hart and Mengjie Zhang and Youhei Akimoto and Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and Daniele Loiacono and Julian Togelius and Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and Faustino Gomez and Carlos M. Fonseca and Heike Trautmann and Alberto Moraglio and William F. Punch and Krzysztof Krawiec and Zdenek Vasicek and Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and Boris Naujoks and Enrique Alba and Gabriela Ochoa and Simon Poulding and Dirk Sudholt and Timo Koetzing", isbn13 = "978-1-4503-4323-7", pages = "639--662", month = "20-24 " # jul, organisation = "SIGEVO", address = "Denver, Colorado, USA", publisher = "ACM", note = "Tutorial", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1145/2908961.2926990", publisher_address = "New York, NY, USA", notes = "Distributed at GECCO-2016.", } @InProceedings{Moraglio:2017:GECCO, author = "Alberto Moraglio and Krzysztof Krawiec", title = "Geometric Semantic Genetic Programming for Recursive {Boolean} Programs", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4920-8", address = "Berlin, Germany", pages = "993--1000", size = "8 pages", URL = "http://doi.acm.org/10.1145/3071178.3071266", DOI = "doi:10.1145/3071178.3071266", acmid = "3071266", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, GSGP, boolean functions, geometric semantic genetic programming, principled design, recursive programs, semantics, Fibonacci function", month = "15-19 " # jul, URL = "https://ore.exeter.ac.uk/repository/bitstream/10871/27415/1/chris6.pdf", abstract = "Geometric Semantic Genetic Programming (GSGP) induces a unimodal fitness landscape for any problem that consists in finding a function fitting given input/output examples. Most of the work around GSGP to date has focused on real-world applications and on improving the originally proposed search operators, rather than on broadening its theoretical framework to new domains. We extend GSGP to recursive programs, a notoriously challenging domain with highly discontinuous fitness landscapes. We focus on programs that map variable-length Boolean lists to Boolean values, and design search operators that are provably efficient in the training phase and attain perfect generalization. Computational experiments complement the theory and demonstrate the superiority of the new operators to the conventional ones. This work provides new insights into the relations between program syntax and semantics, search operators and fitness landscapes, also for more general recursive domains.", notes = "Also known as \cite{Moraglio:2017:GSG:3071178.3071266} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @InProceedings{DBLP:conf/ppsn/MoraglioM18, author = "Alberto Moraglio and James McDermott", title = "Program Trace Optimization", booktitle = "Parallel Problem Solving from Nature, PPSN XV, Part II", year = "2018", editor = "Anne Auger and Carlos M. Fonseca and Nuno Louren{\c{c}}o and Penousal Machado and Lu{\'{\i}}s Paquete and L. Darrell Whitley", volume = "11102", series = "Lecture Notes in Computer Science", pages = "334--346", address = "Coimbra, Portugal", month = sep # " 8-12", publisher = "Springer", keywords = "genetic algorithms, genetic programming, grammatical evolution, Universal solution representation, Universal optimisation, operator design, genotype-phenotype mappings, TSP, Python", isbn13 = "978-3-319-99259-4", timestamp = "Sat, 09 Apr 2022 12:38:49 +0200", biburl = "https://dblp.org/rec/conf/ppsn/MoraglioM18.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "http://hdl.handle.net/10871/33150", URL = "https://ore.exeter.ac.uk/repository/bitstream/handle/10871/33150/ppsn2018.pdf", DOI = "doi:10.1007/978-3-319-99259-4_27", code_url = "https://github.com/program-trace-optimisation", size = "12 pages", abstract = "We introduce Program Trace Optimization (PTO), a system for universal heuristic optimization made easy. This is achieved by strictly separating the problem from the search algorithm. New problem definitions and new generic search algorithms can be added to PTO easily and independently, and any algorithm can be used on any problem. PTO automatically extracts knowledge from the problem specification and designs search operators for the problem. The operators designed by PTO for standard representations coincide with existing ones, but PTO automatically designs operators for arbitrary representations.", notes = "Sec 2.1 'The user supplies a solution generator, which implicitly defines the set of possible solutions. PTO runs it and traces its execution.'... 'The trace is a genotype.' 'execution context'. 'dictionary, i.e. a variable-length linear structure with entries identified by names rather than by indexes' cites \cite{mcdermott:2013:EuroGP} PTO is 'not a black-box method.' 'symbolic regression with grammatical evolution' Pagie-2D, Vladislavleva-4, Dow Chemical, Tower, Housing. http://www.github.com/ponyge/ponyge2 TSP: att48 berlin52 eil101 u159 a280 rat575 http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/", } @InCollection{Moraglio:2018:hbge, author = "Alberto Moraglio and James McDermott and Michael O'Neill", title = "Geometric Semantic Grammatical Evolution", booktitle = "Handbook of Grammatical Evolution", publisher = "Springer", year = "2018", editor = "Conor Ryan and Michael O'Neill and J. J. Collins", chapter = "7", pages = "163--188", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", isbn13 = "978-3-319-78716-9", DOI = "doi:10.1007/978-3-319-78717-6_7", abstract = "Geometric Semantic Genetic Programming (GSGP) is a novel form of Genetic Programming (GP), based on a geometric theory of evolutionary algorithms, which directly searches the semantic space of programs. In this chapter, we extend this framework to Grammatical Evolution (GE) and refer to the new method as Geometric Semantic Grammatical Evolution (GSGE). We formally derive new mutation and crossover operators for GE which are guaranteed to see a simple unimodal fitness landscape. This surprising result shows that the GE genotype-phenotype mapping does not necessarily imply low genotype-fitness locality. To complement the theory, we present extensive experimental results on three standard domains (Boolean, Arithmetic and Classifier).", notes = "Part of \cite{Ryan:2018:hbge}", } @InProceedings{Moraglio:2019:GECCOcomp, author = "Alberto Moraglio and Krzysztof Krawiec", title = "Semantic genetic programming", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", note = "Tutorial", isbn13 = "978-1-4503-6748-6", pages = "1032--1055", address = "Prague, Czech Republic", DOI = "doi:10.1145/3319619.3323378", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{3323378} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InProceedings{Moraglio:2020:GECCOcomp, author = "Alberto Moraglio and Krzysztof Krawiec", title = "Semantic Genetic Programming", year = "2020", editor = "Richard Allmendinger and Hugo Terashima Marin and Efren Mezura Montes and Thomas Bartz-Beielstein and Bogdan Filipic and Ke Tang and David Howard and Emma Hart and Gusz Eiben and Tome Eftimov and William {La Cava} and Boris Naujoks and Pietro Oliveto and Vanessa Volz and Thomas Weise and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Rui Wang and Ran Cheng and Guohua Wu and Miqing Li and Hisao Ishibuchi and Jonathan Fieldsend and Ozgur Akman and Khulood Alyahya and Juergen Branke and John R. Woodward and Daniel R. Tauritz and Marco Baioletti and Josu Ceberio Uribe and John McCall and Alfredo Milani and Stefan Wagner and Michael Affenzeller and Bradley Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and Nayat Sanchez-Pi and Luis Marti and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and Matthew Johns and Nick Ross and Ed Keedwell and Herman Mahmoud and David Walker and Anthony Stein and Masaya Nakata and David Paetzel and Neil Vaughan and Stephen Smith and Stefano Cagnoni and Robert M. Patton and Ivanoe {De Falco} and Antonio {Della Cioppa} and Umberto Scafuri and Ernesto Tarantino and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Erik Hemberg and Riyad Alshammari and Tokunbo Makanju and Fuijimino-shi and Ivan Zelinka and Swagatam Das and Ponnuthurai Nagaratnam and Roman Senkerik", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion", pages = "877--900", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", note = "Tutorial", keywords = "genetic algorithms, genetic programming", isbn13 = "9781450371278", URL = "https://doi.org/10.1145/3377929.3389884", DOI = "doi:10.1145/3377929.3389884", size = "24 pages", notes = "Also known as \cite{10.1145/3377929.3389884} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @Article{Moraglio:2025:GPEM, author = "Alberto Moraglio and Krzysztof Krawiec and Colin Johnson", title = "Editorial introduction to the special issue for the tenth anniversary of geometric semantic genetic programming", journal = "Genetic Programming and Evolvable Machines", year = "2025", volume = "26", pages = "Article no 16", note = "Online first", keywords = "genetic algorithms, genetic programming, GSGP", ISSN = "1389-2576", URL = "https://rdcu.be/eg3tN", DOI = "doi:10.1007/s10710-025-09514-9", size = "3 pages", notes = " p1 'genetic operators with specific semantic guarantees: crossover producing offspring with behaviour intermediate to their parents and mutation generating variants with behavior similar to the original program.' 'clear genotype–phenotype mapping between syntax and semantics' cites \cite{conf/ppsn/MoraglioKJ12} University of Exeter, Exeter, UK", } @InProceedings{Moraglio:2025:evoapplications, author = "Alberto Moraglio and James McDermott", title = "Geometric Particle Swarm Optimization in Program Trace Optimization", booktitle = "28th International Conference, EvoApplications 2025", year = "2025", editor = " Pablo Garcia-Sanchez and Emma Hart and Sarah L. Thomson", series = "LNCS", volume = "15613", pages = "145--159", publisher = "Springer", address = "Trieste, Italy", month = "3-5 " # apr, organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, grammatical evolution, Particle Swarm Optimization, PSO, Geometric PSO, GPSO, Program Trace Optimization, PTO, TSP, ANN", isbn13 = "978-3-031-90065-5", timestamp = "Wed, 07 May 2025 17:49:32 +0200", biburl = "https://dblp.org/rec/conf/evoapps/MoraglioM25.bib", bibsource = "dblp computer science bibliography, https://dblp.org", DOI = "doi:10.1007/978-3-031-90065-5_9", notes = "DBLP:conf/evoapps/MoraglioM25, http://www.evostar.org/2025/ EvoApplications2025 held in conjunction with EuroGP'2025, EvoCOP2025 and EvoMusArt2025", } @InCollection{moraleda:1999:CGOESTP, author = "Jorge Moraleda", title = "Custom Genetic Operators for the Euclidean Steiner Tree Problem", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1999", year = "1999", editor = "John R. Koza", pages = "174--183", address = "Stanford, California, 94305-3079 USA", month = "15 " # mar, publisher = "Stanford Bookstore", keywords = "genetic algorithms", notes = "part of \cite{koza:1999:GAGPs}", } @InProceedings{morales:2004:MICAI, author = "Carlos {Oliver Morales} and Katya {Rodriguez Vazquez}", title = "Symbolic Regression Problems by Genetic Programming with Multi-branches", booktitle = "MICAI 2004: Advances in Artificial Intelligence", year = "2004", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-540-24694-7_74", DOI = "doi:10.1007/978-3-540-24694-7_74", } @PhdThesis{Morales-Alvarado:thesis, author = "Rodrigo {Morales Alvarado}", title = "Automated Improvement of Software Design by Search-Based Refactoring", school = "Departement de genie informatique et genie logiciel, Ecole Polytechnique de Montreal", year = "2017", address = "Canada", month = dec, keywords = "genetic algorithms, genetic programming, Genetic Improvement, SBSE, Software maintenance, design quality, anti-patterns, refactoring, search-based software engineering, energy efficiency, task context, testing effort, NSGA-II, SPEA2, MOCell", URL = "https://publications.polymtl.ca/2878/", URL = "https://publications.polymtl.ca/2878/1/2017_RodrigoMoralesAlvarado.pdf", size = "175 pages", abstract = "Software maintenance cost is estimated to be more than 70percent of the total cost of system, because of many factors, including new user's requirements, the adoption of new technologies and the quality of software systems. From these factors, quality is the one that we can control and continually improved to prevent degradation of performance and reduction of effectiveness (a.k.a. design decay). Moreover, to stay competitive, the software industry has shortened its release cycles to deliver new products and features faster, which results in more pressure on developer teams and the acceleration of system's design evolution. One way to prevent design decay is the identification and correction of anti-patterns which are indicators of poor design quality. To improve design quality and remove anti-patterns, developers perform small behaviour-preserving transformations (a.k.a. refactoring). Manual refactoring is expensive, as it requires to (1) identify the code entities that need to be refactored; (2) generate refactoring operations for classes identified in the previous step; (3) find the correct order of application of the refactorings generated, to maximize the quality effect and to minimize conflicts. Hence, researchers and practitioners have formulated refactoring as an optimization problem and use search-based techniques to propose (semi)automated approaches to solve it. In this dissertation, we propose several approaches to tackle some of the major issues in existing refactoring tools, to assist developers in their maintenance and quality assurance activities. Our thesis is that it is possible to enhance automated refactoring by considering new dimensions: (1) developer's task context to prioritize the refactoring of relevant classes; (2) testing effort to improve testing cost after refactoring; (3) refactoring's conflict awareness to reduce refactoring effort; and (4) energy efficiency to improve energy consumption of mobile applications after refactoring. We propose four approaches: (1) ReCon, which leverages developer's task context to prioritize the refactoring of classes that are relevant to the developer's activity. Using ReCon, developers can remove a median of 50percent of anti-patterns during regular coding tasks, without disrupting their workflow. (2) RePOR, for an efficient refactoring scheduling, which results in a reduction of refactoring effort and execution time by 80percent. (3) TARF controls for the testing effort while refactoring. Results show that TARF can reduce a median of 48percent of the testing effort of a system after refactoring. (4) EARMO, is an automated approach for the refactoring of mobile applications, which is able to remove 84percent of anti-patterns and extend the battery life of devices by up to 29 minutes (for a multimedia app running continuously a typical scenario). We apply and validate our proposed approaches on several open-source systems to demonstrate their impact on design quality using well known quality models, and feedback from some authors of the systems studied.", notes = "Is this GP? Supervisors: Foutse Khomh and Giuliano Antoniol and Francisco Chicano", } @Article{MORAVEJ:2020:GSD, author = "Mojtaba Moravej and Pouria Amani and Seyed-Mohammad Hosseini-Moghari", title = "Groundwater level simulation and forecasting using interior search algorithm-least square support vector regression ({ISA-LSSVR})", journal = "Groundwater for Sustainable Development", volume = "11", pages = "100447", year = "2020", ISSN = "2352-801X", DOI = "doi:10.1016/j.gsd.2020.100447", URL = "http://www.sciencedirect.com/science/article/pii/S2352801X20302411", keywords = "genetic algorithms, genetic programming, Data-driven methods, Optimization, Karaj aquifer, Sensitivity analysis, Support vector machines", abstract = "Least square support vector regression (LSSVR) is a powerful data-driven method for simulation and forecasting, with two parameters to tune. In this study, these parameters were automatically tuned using the interior search algorithm (ISA) and genetic algorithm (GA). The main purpose is in situ simulation and forecast of monthly groundwater level in Karaj plain, Iran, using historical groundwater level, precipitation, and evaporation data. The results of the interior search algorithm-least support vector regression (ISA-LSSVR) and genetic algorithm-least support vector regression (GA-LSSVR) compared with genetic programming (GP) and adaptive neural fuzzy inference system (ANFIS). Based on average Nash-Sutcliffe criterion, the results revealed that the ISA-LSSVR improves the simulation and forecasting accuracy compared to other methods. Also, the results of the different model structure selection indicate that including precipitation and evaporation does not necessarily improve simulation and forecasting accuracy, but it would increase uncertainty. This increase suggests that groundwater level in the case study is affected by groundwater flow, recharge from leaky urban water infrastructure, and reduced recharge from precipitation due to impervious surfaces in urban areas rather than being solely governed by precipitation and evaporation. Finally, a sensitivity analysis was performed to assess the impacts of optimization algorithm parameters on the simulation and forecasting accuracy. The results indicate high and low sensitivity associated with GA and ISA, respectively. In conclusion, ISA-LSSVR was suggested as the best model due to computational efficiency, low sensitivity to its parameters, and high accuracy compared to other methods", } @Misc{DBLP:journals/corr/abs-2108-00382, author = "Matthew Andres Moreno and Santiago Rodriguez Papa and Alexander Lalejini and Charles Ofria", title = "{SignalGP-Lite}: Event Driven Genetic Programming Library for Large-Scale Artificial Life Applications", howpublished = "arXiv", volume = "abs/2108.00382", year = "2021", month = "1 " # aug, keywords = "genetic algorithms, genetic programming", URL = "https://arxiv.org/abs/2108.00382", eprinttype = "arXiv", eprint = "2108.00382", timestamp = "Thu, 05 Aug 2021 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/corr/abs-2108-00382.bib", bibsource = "dblp computer science bibliography, https://dblp.org", size = "28 pages", abstract = "Event-driven genetic programming representations have been shown to outperform traditional imperative representations on interaction-intensive problems. The event-driven approach organizes genome content into modules that are triggered in response to environmental signals, simplifying simulation design and implementation. Existing work developing event-driven genetic programming methodology has largely used the SignalGP library, which caters to traditional program synthesis applications. The SignalGP-Lite library enables larger-scale artificial life experiments with streamlined agents by reducing control flow overhead and trading run-time flexibility for better performance due to compile-time configuration. Here, we report benchmarking experiments that show an 8x to 30x speedup. We also report solution quality equivalent to SignalGP on two benchmark problems originally developed to test the ability of evolved programs to respond to a large number of signals and to modulate signal response based on context.", } @Article{Moreno:2023:GPEM, author = "Matthew Andres Moreno and Alexander Lalejini and Charles Ofria", title = "Matchmaker, matchmaker, make me a match: geometric, variational, and evolutionary implications of criteria for tag affinity", journal = "Genetic Programming and Evolvable Machines", year = "2023", volume = "24", pages = "Article number: 4", month = jun, note = "Online first", keywords = "genetic algorithms, genetic programming, Event-driven genetic programming, Tag-based referencing, Module-based genetic programming, Artificial gene regulatory networks", ISSN = "1389-2576", URL = "https://rdcu.be/c8q7u", DOI = "doi:10.1007/s10710-023-09448-0", sup_url = "https://static-content.springer.com/esm/art%3A10.1007%2Fs10710-023-09448-0/MediaObjects/10710_2023_9448_MOESM1_ESM.pdf", code_url = "https://github.com/devosoft/Empirical", code_url = "https://github.com/amlalejini/Exploring-tag-matching-metrics-in-SignalGP/tree/1.0", code_url = "https://github.com/mmore500/tag-olympics/tree/v1.1.1", size = "42 pages", abstract = "Genetic programming and artificial life systems commonly use tag matching to decide interactions between system components. However, the implications of criteria used to determine affinity between tags with respect evolutionary dynamics have not been directly studied. We investigate differences between tag-matching criteria with respect to geometric constraint and variation generated under mutation. In experiments, we find that tag-matching criteria can influence the rate of adaptive evolution and the quality of evolved solutions. Better understanding of the geometric, variational, and evolutionary properties of tag-matching criteria will facilitate more effective incorporation of tag matching into genetic programming and artificial life systems. By showing that tag-matching criteria influence connectivity patterns and evolutionary dynamics, our findings also raise fundamental questions about the properties of tag-matching systems in nature.", } @InProceedings{Moreno:2023:GPTP, author = "Matthew Andres Moreno", title = "Methods for Rich Phylogenetic Inference Over Distributed Sexual Populations", old_title = "Hereditary stratigraphy methods for phylogenetic inference over distributed EC populations", booktitle = "Genetic Programming Theory and Practice XX", year = "2023", editor = "Stephan Winkler and Leonardo Trujillo and Charles Ofria and Ting Hu", series = "Genetic and Evolutionary Computation", pages = "125--141", address = "Michigan State University, USA", month = jun # " 1-3", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-981-99-8412-1", DOI = "doi:10.1007/978-981-99-8413-8_7", abstract = "The structure of relatedness among members of an evolved population tells much of its evolutionary history. In application-oriented evolutionary computation (EC), such phylogenetic information can guide algorithm selection and tuning. Although traditional direct tracking approaches provide the perfect phylogenetic record, sexual recombination complicates management and analysis of this data. Taking inspiration from biological science, this work explores a reconstruction-based approach that uses end-state genetic information to estimate phylogenetic history after the fact. We apply recently developed hereditary stratigraphy genome annotations to lineages with sexual recombination to design devices germane to species phylogenies and gene trees. As shown through a series of validation experiments, the proposed instrumentation can discern genealogical history, population size changes, and selective sweeps. Fully decentralised by nature, these methods afford new observability at scale, in particular, for distributed EC systems. Such capabilities anticipate continued growth of computational resources available to EC. Accompanying open-source software aims to expedite the application of reconstruction-based phylogenetic analysis where pertinent.", notes = " Part of \cite{Hu:2023:GPTP} published after the workshop in 2024", } @InProceedings{moreno:2023:GECCOcomp, author = "Matthew Andres Moreno and Alexander Lalejini and Charles Ofria", title = "Tag Affinity Criteria Influence Adaptive Evolution", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Alberto Moraglio", pages = "35--36", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, event-driven genetic programming, artificial gene regulatory networks, tag-based referencing, module-based genetic programming", isbn13 = "9798400701191", DOI = "doi:10.1145/3583133.3595834", size = "2 pages", abstract = "This Hot-off-the-Press paper summarizes our recently published work, {"}Matchmaker, Matchmaker, Make Me a Match: Geometric, Variational, and Evolutionary Implications of Criteria for Tag Affinity{"} [8]. This work appeared in Genetic Programming and Evolvable Machines. Genetic programming systems commonly use tag matching to decide interactions between system components. However, the implications of criteria used to determine affinity between tags with respect evolutionary dynamics have not been directly studied. We investigate differences between tag-matching criteria with respect to geometric constraint and variation generated under mutation. In experiments, we find that tag-matching criteria can influence the rate of adaptive evolution and the quality of evolved solutions. Better understanding of the geometric, variational, and evolutionary properties of tag-matching criteria will facilitate more effective incorporation of tag matching into genetic programming systems. By showing that tag-matching criteria influence connectivity patterns and evolutionary dynamics, our findings also raise fundamental questions about the properties of tag-matching systems in nature.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @InProceedings{conf/iwcls/Moreno-TorresLGB09, title = "On the Homogenization of Data from Two Laboratories Using Genetic Programming", author = "Jose Garcia Moreno-Torres and Xavier Llora and David E. Goldberg and Rohit Bhargava", publisher = "Springer", year = "2009", volume = "6471", booktitle = "Learning Classifier Systems", series = "Lecture Notes in Computer Science", editor = "Jaume Bacardit and Will N. Browne and Jan Drugowitsch and Ester Bernad{\'o}-Mansilla and Martin V. Butz", isbn13 = "978-3-642-17507-7", pages = "185--197", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1007/978-3-642-17508-4_12", bibdate = "2010-11-30", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/iwcls/iwlcs2009.html#Moreno-TorresLGB09", abstract = "In experimental sciences, diversity tends to difficult predictive models' proper generalization across data provided by different laboratories. Thus, training on a data set produced by one lab and testing on data provided by another lab usually results in low classification accuracy. Despite the fact that the same protocols were followed, variability on measurements can introduce unforeseen variations that affect the quality of the model. This paper proposes a Genetic Programming based approach, where a transformation of the data from the second lab is evolved driven by classifier performance. A real-world problem, prostate cancer diagnosis, is presented as an example where the proposed approach was capable of repairing the fracture between the data of two different laboratories.", notes = "booktitle IWLCS. prostate cancer. Ninety three trees per individual!!! C4.5. Context free grammar but rules seem to be straight forward does it add anything above Lisp like tree? 'one point crossover' similar to Koza's sub tree crossover? Tournament size related to log(pop size). Population size proportional to number of trees. \cite{harris:thesis} and \cite{bot:2001:EuroGP}. Re-represent 93 attributes selected on first lab's data for use by same C4.5 classifier on second lab's data. (Includes attributes which are not used by the final C4.5 classifier). This works. Solves problem but p195 do not yet give 'any useful information'. Problem to complex? Too much redundant information in each of the 14Gigabytes of information? ", affiliation = "Department of Computer Science and Artificial Intelligence, Universidad de Granada, 18071 Granada, Spain", } @InProceedings{conf/isda/Moreno-TorresLG09, title = "Binary Representation in Gene Expression Programming: Towards a Better Scalability", author = "Jose Garcia Moreno-Torres and Xavier Llora and David E. Goldberg", booktitle = "Ninth International Conference on Intelligent Systems Design and Applications, ISDA '09", year = "2009", month = "30 " # nov # "-2 " # dec, pages = "1441--1444", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/isda/isda2009.html#Moreno-TorresLG09", keywords = "genetic algorithms, genetic programming, gene expression programming, machine learning, classifier systems", DOI = "doi:10.1109/ISDA.2009.33", publisher = "IEEE Computer Society", abstract = "One of the main problems that arises when using gene expression programming (GEP) conditions in learning classifier systems is the increasing number of symbols present as the problem size grows. When doing model-building LCS, this issue limits the scalability of such a technique, due to the cost required. This paper proposes a binary representation of GEP chromosomes to palliate the computation requirements needed. A theoretical reasoning behind the proposed representation is provided, along with empirical validation.", notes = "Also known as \cite{5363972}", } @Article{MorenoTorres2010, author = "Jose G. Moreno-Torres and Xavier Llora and David E. Goldberg and Rohit Bhargava", title = "Repairing fractures between data using genetic programming-based feature extraction: A case study in cancer diagnosis", journal = "Information Sciences", year = "2013", volume = "222", pages = "805--823", month = "10 " # feb, ISSN = "0020-0255", DOI = "doi:10.1016/j.ins.2010.09.018", URL = "http://www.sciencedirect.com/science/article/pii/S0020025510004585", URL = "http://www.sciencedirect.com/science/article/B6V0C-515SRJV-1/2/ba19f0969d5d756d2abd12ac6f843d9f", keywords = "genetic algorithms, genetic programming, Feature extraction, Fractures between data, Biological data, Cancer diagnosis, Different laboratories", size = "19 pages", abstract = "There is an underlying assumption on most model building processes: given a learnt classifier, it should be usable to explain unseen data from the same given problem. Despite this seemingly reasonable assumption, when dealing with biological data it tends to fail; where classifiers built out of data generated using the same protocols in two different laboratories can lead to two different, non-interchangeable, classifiers. There are usually too many uncontrollable variables in the process of generating data in the lab and biological variations, and small differences can lead to very different data distributions, with a fracture between data. This paper presents a genetics-based machine learning approach that performs feature extraction on data from a lab to help increase the classification performance of an existing classifier that was built using the data from a different laboratory which uses the same protocols, while learning about the shape of the fractures between data that motivated the bad behaviour. The experimental analysis over benchmark problems together with a real-world problem on prostate cancer diagnosis show the good behavior of the proposed algorithm.", notes = "Including Special Section on New Trends in Ambient Intelligence and Bio-inspired Systems", } @InProceedings{Moreno-Torres:2010:ISDA, author = "Jose G. Moreno-Torres and Francisco Herrera", title = "A preliminary study on overlapping and data fracture in imbalanced domains by means of Genetic Programming-based feature extraction", booktitle = "10th International Conference on Intelligent Systems Design and Applications (ISDA)", year = "2010", month = nov # " 29-" # dec # " 1", pages = "501--506", keywords = "genetic algorithms, genetic programming, bidimensional graph, data fracture, data mining, genetic programming-based feature extraction, imbalanced data classification, rough set theory, data mining, feature extraction, pattern classification, rough set theory", DOI = "doi:10.1109/ISDA.2010.5687214", size = "6 pages", abstract = "The classification of imbalanced data is a well-studied topic in data mining. However, there is still a lack of understanding of the factors that make the problem difficult. In this work, we study the two main reasons that make the classification of imbalanced datasets complex: overlapping and data fracture. We present a Genetic Programming-based feature extraction method driven by Rough Set Theory to help visualize the data in a bidimensional graph, to better understand how the presence of overlapping and data fractures affect classification performance.", notes = "Also known as \cite{5687214}", } @PhdThesis{Thesis_JGMorenoTorres_2013, author = "Jose Garcia Moreno-Torres", title = "Dataset Shift in Classification: Terminology, Benchmarks and Methods", school = "Departamento de Ciencias de la Computacin e Inteligencia Artificial, Universidad de Granada", year = "2013", address = "Spain", keywords = "genetic algorithms, genetic programming, GP-RFD, Informatica, Computacion, Bases de datos, Clasificacion, Terminologia, Metodos", URL = "http://sci2s.ugr.es/publications/ficheros/Thesis_JGMorenoTorres_2013.pdf", URL = "http://hdl.handle.net/10481/29456", size = "95 pages", notes = "in spanish and english. A proposal to solve Dataset Shift by means of Genetic Programming based Feature Extraction (GP-RFD) Supervisor Dr. Francisco Herrera Triguero Now Working at: Kreditech Holding SSL GmbH ? HeyJobs ?", } @Article{MorenoSalinas:2015:IFAC-PapersOnLine, author = "D. Moreno-Salinas and E. Besada-Portas and J. A. Lopez-Orozco and D. Chaos and J. M. {de la Cruz} and J. Aranda", title = "Symbolic Regression for Marine Vehicles Identification", journal = "IFAC-PapersOnLine", volume = "48", number = "16", pages = "210--216", year = "2015", note = "10th IFAC Conference on Manoeuvring and Control of Marine Craft MCMC 2015, Copenhagen, 24-26 August 2015", keywords = "genetic algorithms, genetic programming, Autonomous vehicles, marine systems, identification, symbolic regression", ISSN = "2405-8963", DOI = "doi:10.1016/j.ifacol.2015.10.282", URL = "http://www.sciencedirect.com/science/article/pii/S2405896315021734", abstract = "The mathematical models used in simulation must be reliable and trustworthy enough to describe the real systems with an appropriate accuracy. This simulation process is specially important in marine environment due to the changing environmental conditions, to the cost of the infrastructure needed to carry out tests, and to the need of calibration, deployment and recovery of the marine systems. If a reliable mathematical model of the vehicle is available, a part of the experimental tests can be avoided. In this paper we present a system identification technique based on genetic programming, the symbolic regression, to be applied on marine systems. In this sense, we show that it is possible to obtain a mathematical model of a ship for control purposes without the need of describing or knowing the model structure in advance, i.e., the identification itself provides the model structure that better describes the system. Thus, we can define a reliable black-box model that is computed in a simple way and where no many experimental data are needed. The model obtained is tested with additional data and manoeuvres to show its good performance and prediction ability.", } @InProceedings{naoki:1999:TEEMTSR, author = "Mori Naoki and Kita Hajime", title = "The Entropy Evaluation Method for the Thermodynamical Selection Rule", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "799", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-844.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-844.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Mori:2005:SMAPIP, author = "Naoki Mori", title = "A Novel Diversity Measure of Genetic Programming", booktitle = "Randomness and Computation: Joint Workshop ``New Horizons in Computing'' and ``Statistical Mechanical Approach to Probabilistic Information Processing''", year = "2005", address = "Sendai International Center, Sendai, Japan", month = "18-21 " # jul, note = "Extended Abstract", keywords = "genetic algorithms, genetic programming", URL = "http://www.smapip.is.tohoku.ac.jp/~smapip/2005/NHC+SMAPIP/ExtendedAbstracts/NaokiMori.pdf", size = "2 pages", abstract = "To solve this problem, I propose a novel diversity measure in genetic programming by means of subtree entropy. I also propose a tree simplification method which removes redundancy parts from an individual genotype. To show an advantage of our methods, the computational experiments are carried out taking a symbolic regression problem as an example.y", notes = "SMAPIP: Statistical-Mechanical Approach to Probabilistic Information Processing http://www.smapip.is.tohoku.ac.jp/~smapip/2005/NHC+SMAPIP/index-e.html", } @InProceedings{Mori:2007:IES, author = "Naoki Mori and R. I. (Bob) McKay and Xuan Hoai Nguyen and Daryl Essam", title = "How Different are Genetic Programs? Entropy Methods for Studying Diversity and Complexity in Genetic Programming", booktitle = "11th Asia-Pacific Workshop on Intelligent and Evolutionary Systems: IES 2007", year = "2007", pages = "8 pages", address = "Yokosuka, Kanagawa, Japan", month = "30 " # nov # "--2 " # dec, keywords = "genetic algorithms, genetic programming, theory, Equivalent Decision Simplification", URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.107.9011&rep=rep1&type=pdf", URL = "https://kaken.nii.ac.jp/ja/grant/KAKENHI-PROJECT-18700227/", URL = "https://cir.nii.ac.jp/crid/1574231875440322560", size = "8 pages", abstract = "We propose a new approach to the study of Genetic Programming dynamics, using genotypic entropy metrics. The aim of the approach is to provide a single framework for simultaneously studying diversity and individual complexity, both of the raw trees and their non-redundant sub-components. The entropy metrics are based on the entropy of small subtrees, and we show that the analysis is largely independent of the exact choice of subtree shape. To facilitate the study of redundancy, we apply powerful new methods for detecting and removing redundancy; while these methods are tailored to the particular function set used here, they are readily extensible to other function sets. We demonstrate the effectiveness of the approach by applying it to a well-known symbolic regression problem. Using the new methods, we measure quantitative relationships between solution complexity and diversity, explore relationships between population structure and problem success or failure, and analyse the relationships between the different solutions found by GP.", notes = "cos(2x), analysis small fixed templates", } @InProceedings{Mori:2009:dcaibscaal, title = "A New Method for Simplifying Algebraic Expressions in Genetic Programming Called Equivalent Decision Simplification", author = "Naoki Mori and Bob McKay and Nguyen Xuan Hoai and Daryl Essam and Saori Takeuchi", booktitle = "Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living", year = "2009", editor = "Sigeru Omatu and Miguel P. Rocha and Jose Bravo and Florentino Fernandez and Emilio Corchado and Andres Bustillo and Juan M. Corchado", volume = "5518", series = "Lecture Notes in Computer Science", pages = "171--178", address = "Salamanca, Spain", month = jun # " 10-12,", publisher = "Springer", note = "10th International Work-Conference on Artificial Neural Networks, IWANN 2009 Workshops", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-02480-1", DOI = "doi:10.1007/978-3-642-02481-8_24", abstract = "Symbolic Regression is one of the most important applications of Genetic Programming, but these applications suffer from one of the key issues in Genetic Programming, namely bloat - the uncontrolled growth of ineffective code segments, which do not contribute to the value of the function evolved, but complicate the evolutionary proces, and at minimum greatly increase the cost of evaluation. For a variety of reasons, reliable techniques to remove bloat are highly desirable - to simplify the solutions generated at the end of runs, so that there is some chance of understanding them, to permit systematic study of the evolution of the effective core of the genotype, or even to perform simplification of expressions during the course of a run. This paper introduces an alternative approach, Equivalent Decision Simplification, in which subtrees are evaluated over the set of regression points; if the subtrees evaluate to the same values as known simple subtrees, they are replaced. The effectiveness of the proposed method is confirmed by computer simulation taking simple Symbolic Regression problems as examples.", notes = "see also \cite{journals/jaciii/MoriMHET09} (23) Osaka Prefecture University, Osaka, Japan (24) Structural Complexity Laboratory, Seoul National University, Seoul, Korea (25) School of Information Technology and Elec. Eng., University of New South Wales ADFA, Canberra, Australia (26) Mitsubishi Electric Corporation, Tokyo, Japan", } @InProceedings{mori:2016:RSFM, author = "Naoki Mori", title = "Evolution of Day Trade Agent Strategy by Means of Genetic Programming with Machine Learning", booktitle = "Realistic Simulation of Financial Markets", year = "2016", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-4-431-55057-0_5", DOI = "doi:10.1007/978-4-431-55057-0_5", } @InProceedings{Moriarty:1998:henn, author = "David E. Moriarty and Risto Miikkulainen", title = "Hierarchical Evolution of Neural Networks", booktitle = "Proceedings of the 1998 IEEE World Congress on Computational Intelligence", year = "1998", pages = "428--433", address = "Anchorage, Alaska, USA", month = "5-9 " # may, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, SANE system, hierarchical approach, hierarchical evolution, network-level exploitive search, neural networks, neuro-evolution, neuron-level exploratory search, robot arm manipulation task, manipulator kinematics, neural nets", ISBN = "0-7803-4869-9", file = "c074.pdf", URL = "http://nn.cs.utexas.edu/downloads/papers/moriarty.icec98.ps.gz", DOI = "doi:10.1109/ICEC.1998.699793", size = "6 pages", abstract = "In most applications of neuro-evolution, each individual in the population represents a complete neural network. Retent work on the SANE system, however, has demonstrated that evolving individual neurons often produces a more efficient genetic search. This paper demonstrates that while SANE can solve easy tasks very quickly, it often stalls in larger problems. A hierarchical approach to neuro-evolution is presented that overcomes SANE' s difficul ties by integrating both a neuron-level exploratory search and a network-level exploitive search. In a robot arm manipulation task, the hierarchical approach outperforms both a neuron-based search and a network-based search.", notes = "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE World Congress on Computational Intelligence", } @Article{MORIKAWA:2019:PM, author = "Katsumi Morikawa and Keisuke Nagasawa and Katsuhiko Takahashi", title = "Job Shop Scheduling by Branch and Bound Using Genetic Programming", journal = "Procedia Manufacturing", volume = "39", pages = "1112--1118", year = "2019", note = "25th International Conference on Production Research Manufacturing Innovation: Cyber Physical Manufacturing August 9-14, 2019 | Chicago, Illinois (USA)", ISSN = "2351-9789", DOI = "doi:10.1016/j.promfg.2020.01.359", URL = "http://www.sciencedirect.com/science/article/pii/S2351978920304297", keywords = "genetic algorithms, genetic programming, scheduling, job shop, branch, bound, makespan", abstract = "A classical depth-first branch and bound (BB) method is adopted to minimize the makespan of job shops based on the disjunctive graph model. The engine of the BB is Giffler-Thompson's active schedule generation method. The performance of the BB method highly depends on the selection of child nodes in earlier branching stages. To support the selection decision, several features of nodes are stored under the BB method, and the correct selection at each branching stage is informed by the mixed-integer linear programming model. The stored data of a test problem instance is analyzed by genetic programming (GP) to generate rules for selecting the correct nodes. The depth-first BB method guided by the generated rules by GP is applied for 42 benchmark instances and exhibits competitive performance when compared with the baseline rule that always selects the child node with the smallest lower bound on makespan", } @InProceedings{Morin:2018:ESD:3239372.3239393, author = "Brice Morin and Jakob Hogenes and Hui Song and Nicolas Harrand and Benoit Baudry", title = "Engineering Software Diversity: A Model-Based Approach to Systematically Diversify Communications", booktitle = "Proceedings of the 21th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS'18", year = "2018", pages = "155--165", address = "Copenhagen, Denmark", month = "14-19 " # oct, publisher = "ACM", acmid = "3239393", isbn13 = "978-1-4503-4949-9", URL = "http://doi.acm.org/10.1145/3239372.3239393", DOI = "doi:10.1145/3239372.3239393", size = "11 pages", abstract = "Automated diversity is a promising mean of increasing the security of software systems. However, current automated diversity techniques operate at the bottom of the software stack (operating system and compiler), yielding a limited amount of diversity. We present a novel Model-Driven Engineering approach to the diversification of communicating systems, building on abstraction, model transformations and code generation. This approach generates significant amounts of diversity with a low overhead, and addresses a large number of communicating systems, including small communicating devices.", notes = "Not GP, might be interesting Section 4.2 'All the 60,000 diversified interactions were successful and allowed each client to communicate with their respective server-side protocol.'", } @InProceedings{conf/iscas/MoritzLH07, author = "Robin Moritz and Henry Leung and Xinping Huang", title = "Nonlinear Compensation for High Power Amplifiers using Genetic Programming", booktitle = "IEEE International Symposium on Circuits and Systems, ISCAS 2007", year = "2007", pages = "2323--2326", address = "New Orleans, USA", month = "27-30 " # may, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, autonomous system identification, communication system nonlinearities, communication systems, evolutionary algorithm, high power amplifiers, least-squares parameter estimator, linear amplification, nonlinear compensation, optimal rational model structure, predistorter, predistortion module, power amplifiers, telecommunication", DOI = "doi:10.1109/ISCAS.2007.378853", size = "4 pages", abstract = "Nonlinearities are inherent in a high power amplifier (HPA) resulting in undesirable distortion in communication systems. A predistortion module is usually cascaded in front of the HPA to compensate these nonlinearities. The structure of the predistorter is typically unknown and must be identified to attain a linear amplification. In order to achieve an autonomous system identification of the unknown predistorter structure, this paper presents an evolutionary algorithm based on genetic programming (GP). GP is used to search for an optimal rational model structure and is combined with a least-squares parameter estimator to estimate the parameters of the evolved models. The effectiveness of the proposed identification scheme has been verified through experiments and comparison with conventional predistortion techniques.", notes = "least-squares LS-GP design rational function predistorters", bibdate = "2007-07-16", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/iscas/iscas2007.html#MoritzLH07", } @Article{Morizumi:2012:IJECS, author = "Tetsuya Morizumi and Kazuhiro Suzuki and Masato Noto and Hirotsugu Kinoshita", title = "Multiagent system based on genetic access matrix analysis", journal = "International Journal of Electronic Commerce Studies", year = "2012", volume = "3", number = "2", pages = "305--324", note = "used first name--surname author ordering", keywords = "genetic algorithms, genetic programming, Access Control, Multiagent Systems, Swarm Intelligence, PSO, Ethical Aspect", publisher = "Academy of Taiwan Information Systems Research", ISSN = "20739729", bibsource = "OAI-PMH server at www.doaj.org", oai = "oai:doaj-articles:c348a91916058d8dac9becb5f60f55d1", URL = "http://www.academic-journals.org/ojs2/index.php/ijecs/article/view/1098", URL = "http://www.academic-journals.org/ojs2/index.php/ijecs/article/viewFile/1098/118", DOI = "doi:10.7903/ijecs.1098", size = "20 pages", abstract = "How should an individual contribute to the public good? Conversely, how does the public help the individual? We should analyse and alleviate conflicts in community clouds. Covert channels in the access matrix are caused by conflicts between public values and a private sense of values. We cannot control the information leaks from the covert channels by using only access control. We believe that the community cloud system should emphasise harmony between public values and a private sense of values. We interpret the access matrix as follows: The acts of the individual are generalised and symbolised by an access matrix that describes the access operations of the subject. We propose a multiagent system embodying the concept of swarm intelligence to analyse the covert channels that arise. Each agent has a group target and an individual target. The group target and an individual target include targets for generation of access and restriction of access. The system does not have any principle of universal control. Instead, an agent{'}s interactions are guided by metaheuristics for achieving targets. The social order of the whole society is made from the agents' interactions related to the group value target, group game target, an individual value target, and an individual game target. The conceptual framework and multiagent system presented here are intended to support people. If the covert channel problem can be solved, it will become possible for people to use community clouds safely.", } @InProceedings{moroni:2000:A, author = "Artemis Moroni and Fernando {Von Zuben} and Jonatas Manzolli", title = "ArTbitration", booktitle = "Genetic Algorithms in Visual Art and Music", year = "2000", editor = "Colin G. Johnson and Juan Jesus Romero Cardalda", pages = "143--145", address = "Las Vegas, Nevada, USA", month = "8 " # jul, keywords = "genetic algorithms, genetic programming", notes = "GECCO-2000WKS Part of \cite{wu:2000:GECCOWKS}", } @InProceedings{morris:2024:GECCO, author = "Clint Morris and Michael Jurado and Jason Zutty", title = "{LLM} Guided Evolution - The Automation of Models Advancing Models", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference", year = "2024", editor = "Jean-Baptiste Mouret and Kai Qin and Julia Handl and Xiaodong Li and Markus Wagner and Mario Garza-Fabre and Kate Smith-Miles and Richard Allmendinger and Ying Bi and Grant Dick and Amir H Gandomi and Marcella Scoczynski Ribeiro Martins and Hirad Assimi and Nadarajen Veerapen and Yuan Sun and Mario Andres Munyoz and Ahmed Kheiri and Nguyen Su and Dhananjay Thiruvady and Andy Song and Frank Neumann and Carla Silva", pages = "377--384", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, grammatical evolution, cartesian genetic programming, large language models, ANN, automated machine learning, evolutionary algorithms, Evolutionary Machine Learning", isbn13 = "979-8-4007-0494-9", DOI = "doi:10.1145/3638529.3654178", size = "8 pages", abstract = "In the realm of machine learning, traditional model development and automated approaches like AutoML typically rely on layers of abstraction, such as tree-based or Cartesian genetic programming. Our study introduces {"}Guided Evolution{"} (GE), a novel framework that diverges from these methods by using Large Language Models (LLMs) to directly modify code. GE leverages LLMs for a more intelligent, supervised evolutionary process, guiding mutations and crossovers. Our unique {"}Evolution of Thought{"} (EoT) technique further enhances GE by enabling LLMs to reflect on and learn from the outcomes of previous mutations. This results in a self-sustaining feedback loop that augments decision-making in model evolution. GE maintains genetic diversity, crucial for evolutionary algorithms, by leveraging LLMs' capability to generate diverse responses from expertly crafted prompts and modulate model temperature. This not only accelerates the evolution process but also injects expert like creativity and insight into the process. Our application of GE in evolving the ExquisiteNetV2 model demonstrates its efficacy: the LLM-driven GE autonomously produced variants with improved accuracy, increasing from 92.52\% to 93.34\%, without compromising model compactness. This underscores the potential of LLMs to accelerate the traditional model design pipeline, enabling models to autonomously evolve and enhance their own designs.", notes = "GECCO-2024 EML A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @MastersThesis{Morrison:1994:ibs, author = "Dan Morrison", title = "Development of a Prototype Intelligent Browsing System, utilising {Boolean} Query Generation using Genetic Programming", school = "University College, London", year = "1994", address = "Gower Street, London, WC1E 6BT, UK", month = sep, keywords = "genetic algorithms, genetic programming, Text Retrival, Automatic Query generation, iBrowse", size = "53 pages plus 40 pages of appendices", abstract = "The need was identified for a generic Information Retrival tool. Genetic Programming was selected as most suitable paradigm for providing the necessary adaptive intelligence. This was combined with conventional Bollean query search techniques. Each Query is treated as a genetic individual and a population of these is eveolved so as to move through the search space of all possible queries efficeiently. The criteria that guide this search is termed relevance feedback. This information is derived from the suer through tne evaluation of a document set and forms the basis of the fitness funtion. The best query produced in this way can then be used to scan other documents, ordering these according to relevance. These processes can be lined to produce an application that can learn by experience, requires no explicit instructions and can be apllied to a wide variety of IR situations. The development work was divided into three stages: design and implementation of an experimental software platform, research into viable configurations using this platform, and construction of working models. Stage one formed the focus of this project. The project specification was thus to produce a software system that can act as a testbed during experimentation in teh second stage and as an early prototype of future applications. This was achieved, the souce code being written in C++ to run on a PC.", notes = "Supervised by Chris Clack", } @InProceedings{Mosayebi:2020:GI9, author = "Mohsen Mosayebi and Manbir Sodhi", title = "Tuning Genetic Algorithm Parameters using Design of Experiments", booktitle = "9th edition of GI @ GECCO 2020", year = "2020", month = jul # " 8-12", editor = "Brad Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and John R. Woodward", publisher = "ACM", address = "Internet", pages = "1937--1944", organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, TSP, Tuning Parameters, Design of Experiments", isbn13 = "978-1-4503-7127-8", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/gecco2020/companion_files/wksp176s2-file1.pdf", DOI = "doi:10.1145/3377929.3398136", size = "8 pages", abstract = "Tuning evolutionary algorithms is a persistent challenge in thefield of evolutionary computing. The efficiency of an evolutionary algorithm relates to the coding of the algorithm, the design of the evolutionary operators and the parameter settings for evolution. we explore the effect of tuning the operators and parameters of a genetic algorithm for solving the Traveling Salesman Problem using Design of Experiments theory. Small scale problems are solved with specific settings of parameters including population size, crossover rate, mutation rate and the extent of elitism. Good values of the parameters suggested by the experiments are used to solve large scale problems. Computational tests show that the parameters selected by this process result in improved performance both in the quality of results obtained and the convergence rate when compared with untuned parameter settings.", notes = "TSPLIB gr17, eli101, pr1002 See also PhD 'the traveling salesman problem with job-times, multi-circuit, and variations' Mohsen Mosayebi, University of Rhode Island https://doi.org/10.23860/Mosayebi-Mohsen-2020 Broken Nov 2024 https://gi-gecco-20.gi-workshops.org/ Also known as \cite{Mosayebi:2020:GECCOcomp}. Also known as \cite{10.1145/3377929.3398136} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @InCollection{Moscato2019, author = "Pablo Moscato and Natalie Jane {de Vries}", title = "Marketing Meets Data Science: Bridging the Gap", booktitle = "Business and Consumer Analytics: New Ideas", publisher = "Springer International Publishing", year = "2019", editor = "Pablo Moscato and Natalie Jane {de Vries}", chapter = "1", pages = "3--117", keywords = "genetic algorithms, genetic programming, Analytics, Marketing and customer behaviour analytics, Data mining, Marketing", isbn13 = "978-3-030-06222-4", DOI = "doi:10.1007/978-3-030-06222-4_1", abstract = "It is certain that computer science is completely reformulating the way that business is being conducted around the world. We are witnessing the increasing availability of large volumes of data together with the advances in artificial intelligence, machine learning and optimization techniques. Breakthroughs in statistics, discrete applied mathematics and new algorithms are leading to the development of a new interdisciplinary field: data science. The purpose of this chapter is to provide a bridge, a short-cut to understand some of the questions that computer science deals with in a context of developing new techniques to get knowledge from data.", } @InProceedings{Moscato:2020:CEC, author = "Pablo Moscato and Haoyuan Sun and Mohammad Nazmul Haque", title = "Analytic Continued Fractions for Regression: Results on 352 datasets from the physical sciences", booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC 2020", year = "2020", editor = "Yaochu Jin", address = "internet", month = "19-24 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, memetic computing, regression, analytic continued fraction", isbn13 = "978-1-7281-6929-3", DOI = "doi:10.1109/CEC48606.2020.9185564", size = "8 pages", abstract = "We report on the results of a new memetic algorithm that employs analytic continued fractions as the basic representation of mathematical functions used for regression problems. We study the performance of our method in comparison with other ten machine learning approaches provided by the scikit-learn software collection. We used 352 datasets collected by Schaffer, which originated from real experiments in the physical sciences at the turn of the 20th century for which measurements were tabulated, and a governing functional relationship was postulated. Using leave-one-out cross-validation, in training our method ranks first in 350 out of the 352 datasets. Only six machine learning algorithms ranked first in at least one of the 352 datasets on testing; our approach ranked first 192 times, i.e. more all of the other algorithms combined. The results favourably speak about the robustness of our methodology. We conclude that the use of analytic continued fractions in regression deserves further study and we also advocate that Schaffer's data collection should also be included in the repertoire of datasets to test the performance of machine learning and regression algorithms.", notes = "School of Elect. Engg. and Computing, The University of Newcastle, Callaghan, Australia", } @Misc{Moscato:2020:superconductor, author = "Pablo Moscato and Mohammad Nazmul Haque and Kevin Huang and Julia Sloan and Jon C. {de Oliveira}", title = "Learning to extrapolate using continued fractions: Predicting the critical temperature of superconductor materials", howpublished = "arXiv", year = "2020", month = "8 " # nov, keywords = "genetic algorithms, genetic programming, Machine Learning (cs.LG), Superconductivity (cond-mat.supr-con), Artificial Intelligence (cs.AI), Neural and Evolutionary Computing (cs.NE), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Physical sciences, FOS: Physical sciences", URL = "https://arxiv.org/abs/2012.03774", DOI = "doi:10.48550/ARXIV.2012.03774", size = "17 pages", abstract = "n Artificial Intelligence we often seek to identify an unknown target function of many variables y=f(x) giving a limited set of instances S={(x(i),y(i))} with x(i) in D where D is a domain of interest. We refer to S as the training set and the final quest is to identify the mathematical model that approximates this target function for new x; with the set T={x(j)}x2 test, and obtains a sufficient number of important association rules in a short time. Experiments conducted on real world databases are also made to verify the performances of the proposed method.", keywords = "genetic algorithms, genetic programming", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @InProceedings{Taboada:2008:SICE, author = "Karla Taboada and Eloy Gonzales and Kaoru Shimada and Shingo Mabu and Kotaro Hirasawa", title = "Discovering fuzzy classification rules using Genetic Network Programming", booktitle = "SICE Annual Conference", year = "2008", month = "20-22 " # aug, pages = "1788--1793", address = "Japan", keywords = "genetic algorithms, genetic programming, association rule mining, classification rule mining, data mining, directed graph, evolutionary optimization, fuzzy classification rule, fuzzy set theory, genetic network programming, data mining, directed graphs, fuzzy set theory, pattern classification", DOI = "doi:10.1109/SICE.2008.4654954", abstract = "Classification rule mining is an active data mining research area. Most related studies have shown how binary valued datasets are handled. However, datasets in real-world applications, usually consist of fuzzy and quantitative values. As a result, the idea to combine the different approaches with fuzzy set theory has been applied more frequently in recent years. Fuzzy sets can help to overcome the so-called sharp boundary problem by allowing partial memberships to the different sets, not only 1 and 0. On the other hand, fuzzy sets theory has been shown to be a very useful tool because the mined rules are expressed in linguistic terms, which are more natural and understandable for human beings. This paper proposes the combination of fuzzy set theory and 'genetic network programming' (GNP) for discovering fuzzy classification rules from given quantitative data. GNP, as an extension of genetic algorithms (GA) and genetic programming (GP), is an evolutionary optimization technique that uses directed graph structures as genes instead of strings and trees; this feature contributes creating quite compact programs and implicitly memorizing past action sequences. At last, experimental results conducted on a real world database verify the performance of the proposed method.", notes = "Also known as \cite{4654954}", } @InProceedings{Taboada:2009:cec, author = "Karla Taboada and Shingo Mabu and Eloy Gonzales and Kaoru Shimada and Kotaro Hirasawa", title = "Genetic Network Programming for Fuzzy Association Rule-Based Classification", booktitle = "2009 IEEE Congress on Evolutionary Computation", year = "2009", editor = "Andy Tyrrell", pages = "2387--2394", address = "Trondheim, Norway", month = "18-21 " # may, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-2959-2", file = "P662.pdf", DOI = "doi:10.1109/CEC.2009.4983239", abstract = "This paper presents a novel classification approach that integrates fuzzy classification rules and Genetic Network Programming (GNP). A fuzzy discretization technique is applied to transform the dataset, particularly for dealing with quantitative attributes. GNP is an evolutionary optimization technique that uses directed graph structures as genes instead of strings and trees of Genetic Algorithms (GA) and Genetic Programming (GP), respectively. This feature contributes to creating quite compact programs and implicitly memorizing past action sequences. Therefore, in the proposed method, taking the GNP's structure into account 1) extraction of fuzzy classification rules is done without identifying frequent itemsets used in most Apriori-based data mining algorithms, 2) calculation of the support, confidence and Χ2 value is made in order to quantify the significance of the rules to be integrated into the classifier, 3) fuzzy membership values are used for fuzzy classification rules extraction, 4) fuzzy rules are mined through generations and stored in a general pool. On the other hand, parameters of the membership functions are evolved by non-uniform mutation in order to perform a more global search in the space of candidate membership functions. The performance of our algorithm has been compared with other relevant algorithms and the experimental results have shown the advantages and effectiveness of the proposed model.", keywords = "genetic algorithms, genetic programming, genetic network programming", notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR", } @InProceedings{Taboada:2009:ICCAS-SICE, author = "Karla S Taboada and Shingo Mabu and Eloy Gonzales and Kaoru Shimada and Kotaro Hirasawa", title = "Fuzzy Association Rule Mining and Classifier with Chi-squared Correlation Measure using Genetic Network Programming", booktitle = "ICRAS \& SICE International Joint Conference, ICCAS-SICE, 2009", year = "2009", month = "18-21 " # aug, address = "Fukuoka", pages = "3863--3869", publisher = "IEEE", isbn13 = "978-4-9077-6433-3", keywords = "genetic algorithms, genetic programming, chi-squared correlation measure, correlation analysis, directed graph structure, discovered rules evaluation, evolutionary optimization algorithm, fuzzy association rule mining, genetic network programming, statistical significance, support confidence framework, correlation methods, data mining, directed graphs, fuzzy set theory, pattern classification", URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=5332929", size = "7 pages", abstract = "One of the most important issues in any association rule mining is the interpretation and evaluation of discovered rules. Thus, most algorithms employ the support-confidence framework for evaluating association and classification rules. Unfortunately, recent studies show that the support and confidence measures are insufficient for filtering out uninteresting association rules, for instance, even strong association rules can be uninteresting and misleading. To deal with this limitation, the support-confidence framework can be supplemented with additional interestingness measures based on statistical significance and correlation analysis. In this paper, a novel fuzzy association rule-based classification approach is proposed, where chi2 is applied as a correlation measure. The algorithm is based on Genetic Network Programming (GNP) and discover comprehensible fuzzy association rules potentially useful for classification. GNP is an evolutionary optimization algorithm that uses directed graph structures as genes instead of strings and trees of Genetic Algorithms (GA) and Genetic Programming (GP), respectively. This feature contributes to creating quite compact programs and implicitly memorizing past action sequences. The proposed model consists of two major phases: 1 generating fuzzy class association rules by using GNP, 2 building a classifier based on the extracted fuzzy rules. In the first phase, chi2 is used for computing the correlation of the rules to be integrated into the classifier. In the second phase, the chi2 value is used as a weight of the rule when calculating the matching degree of the rule with new data. The performance of the proposed algorithm has been compared with other relevant algorithms and the experimental results have shown the advantages and effectiveness of the proposed model.", notes = "http://www.sice.or.jp/ICCAS-SICE2009/session_paperID_c.pdf Also known as \cite{5332929}", } @InProceedings{Taboada:2009:ieeeSMC, author = "Karla Taboada and Shingo Mabu and Eloy Gonzales and Kaoru Shimada and Kotaro Hirasawa", title = "Fuzzy classification rule mining based on Genetic Network Programming algorithm", booktitle = "IEEE International Conference on Systems, Man and Cybernetics, SMC 2009", year = "2009", month = oct, pages = "3860--3865", keywords = "genetic algorithms, genetic programming, association rule mining, association rule-based classification, data mining techniques, directed graph structures, evolutionary optimization algorithms, fuzzy classification rule mining, genetic network programming algorithm, data mining, directed graphs, fuzzy set theory", DOI = "doi:10.1109/ICSMC.2009.5346640", ISSN = "1062-922X", abstract = "Association rule-based classification is one of the most important data mining techniques applied to many scientific problems. In the last few years, extensive research has been carried out to develop enhanced methods and obtained higher classification accuracies than traditional classifiers. However, the current studies show that the association rule-based classifiers may also suffer some problems inherited from association rule mining such as handling of (1) continuous data and (2) the support/confidence framework. In this paper, a novel fuzzy classification model based on genetic network programming (GNP) that can deal with the above problems has been proposed. GNP is one of the evolutionary optimization algorithms that uses directed graph structures as solutions instead of strings (genetic algorithms) or trees (genetic programming). Therefore, GNP can deal with more complex problems by using the higher expression ability of graph structures. The performance of our algorithm has been compared with other relevant algorithms and the experimental results show the advantages and effectiveness of the proposed model.", notes = "Also known as \cite{5346640}", } @InProceedings{icga93:tackett, author = "Walter Alden Tackett", title = "Genetic Programming for Feature Discovery and Image Discrimination", year = "1993", booktitle = "Proceedings of the 5th International Conference on Genetic Algorithms, ICGA-93", editor = "Stephanie Forrest", publisher = "Morgan Kaufmann", pages = "303--309", size = "7 pages", address = "University of Illinois at Urbana-Champaign", month = "17-21 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/ftp.io.com/papers/GP.feature.discovery.ps.Z", notes = "Contrasts ANN, Binary Tree Classifier and GP on same test data. GP a bit better but 8 times faster in execution. GP(lisp) takes far more CPU to learn. Version in C (GP/C) 25 times faster than GP(lisp)", } @InProceedings{Tackett93, author = "Walter Alden Tackett", title = "Genetic Generation of {``}Dendritic{''} Trees for Image Classification", booktitle = "World Congress on Neural Networks, WCNN'93", publisher = "Lawrence Erlbaum Ass., Inc.", publisher_address = "Hillsdale, NJ, USA", pages = "IV 646--649", year = "1993", month = "11-15 " # jul, address = "Portland, Oregon, USA", keywords = "genetic algorithms, genetic programming, connectionism, cogann, ANN", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/ftp.io.com/papers/GP.feature.discovery.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/ftp.io.com/papers/GP.feature.discovery.ps.Z", size = "11 pages", abstract = "We apply Genetic Programming (GP) to the development of a processing tree for the classification of features extracted from images: measurements from a set of input nodes are weighted and combined through linear and nonlinear operations to form an output response. No constraints are placed upon size, shape, or order of processing within the network. This network is used to classify feature vectors extracted from IR imagery into target/nontarget categories using a database of 2000 training samples. Performance is tested against a separate database of 7000 samples. This represents a significant scaling up from the problems to which GP has been applied to date. Two experiments are performed: in the first set, we input classical statistical image features and minimize misclassification of target and non-target samples. In the second set of experiments, GP is allowed to form it's own feature set from primitive intensity measurements. For purposes of comparison, the same training and test sets are used to train two other adaptive classifier systems, the binary tree classifier and the Backpropagation neural network. The GP network achieves higher performance with reduced computational requirements. The contributions of GP schemata, or subtrees, to the performance of generated trees are examined.", abstract = "Genetic Programming (GP) is an adaptive method for generating executable programs from labeled training data. It differs from the conventional methods of Genetic Algorithms because it manipulates tree structures of arbitrary size and shape rather than fixed length binary strings. We apply GP to the development of a processing tree with a dendritic, or neuron-like structure: measurements from a set of input nodes are weighted and combined through linear and nonlinear operations to form an output response. Unlike conventional neural methods, no constraints are placed upon size, shape, or order of processing withing the network. This network is used to classify feature vectors extracted from IR imagery into target/nontarget catagories using a database of 2000 training samples. Performance is tested against a separate database of 7000 samples. For purposes of comparison, the same training and test sets are used to train two other adaptive classifier systems, the binary tree classifier and the Backpropagation neural network. The GP network acheives higher performance with reduced computational requirements.", } @Manual{sgpc_readme, title = "{SGPC}: Simple Genetic Programming in {C}", author = "Walter Alden Tackett and Aviram Carmi", year = "1993", keywords = "genetic algorithms, genetic programming", URL = "https://www.cs.cmu.edu/afs/cs/project/ai-repository/ai/areas/genetic/gp/systems/sgpc/0.html", URL = "https://www.cs.cmu.edu/afs/cs/project/ai-repository/ai/areas/genetic/gp/systems/sgpc/readme.txt", size = "337 lines", abstract = "Version 1.0 (c) 1993 by Walter Alden Tackett and Aviram Carmi This code and documentation is copyrighted and is not in the public domain. All rights reserved. Genetic Programming is a method of 'Adaptive Automatic Program Induction' originally created by John Koza and James Rice of Stanford University. SGPC is a C implementation of Genetic Programming: it is a C program which writes LISP programs. These programs are tailored by the system to solve a problem specified by the user. Koza and Rice have provided to the public a version of Genetic Programming which is written in LISP. SGPC offers greater portability and about 25-50 times improvement in execution speed due to a highly optimized C implementation.", notes = "1.1 (19-AUG-93) http://www-cgi.cs.cmu.edu/afs/cs/project/ai-repository/ai/areas/genetic/gp/systems/sgpc/sgpc110.tgz", } @InCollection{kinnear:tackett, author = "Walter Alden Tackett and Aviram Carmi", institution = "HMSC", title = "The Donut Problem: Scalability and Generalization in Genetic Programming", booktitle = "Advances in Genetic Programming", publisher = "MIT Press", editor = "Kenneth E. {Kinnear, Jr.}", year = "1994", pages = "143--176", chapter = "7", keywords = "genetic algorithms, genetic programming, Doughnut problem", URL = "http://www.amazon.co.uk/Advances-Genetic-Programming-Complex-Adaptive/dp/0262111888", URL = "http://cognet.mit.edu/sites/default/files/books/9780262277181/pdfs/9780262277181_chap7.pdf", DOI = "doi:10.7551/mitpress/1108.003.0012", size = "34 pages", abstract = "The Donut problem requires separating two toroidal distributions (classes) which are interlocked like links in a chain. The cross-section of each distribution is Gaussian distributed with standard deviation sigma. This problem possesses a variety of pathological traits: the mean of each distribution, for example, lies in the densest point of the other.", notes = "see also http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/ftp.io.com/papers/.message ICGA93.Donut.ps.Z - Preliminary version of Avi and Walter's ICGA93 paper Part of \cite{kinnear:book}", } @PhdThesis{Tackett:1994:thesis, author = "Walter Alden Tackett", title = "Recombination, Selection, and the Genetic Construction of Computer Programs", school = "University of Southern California, Department of Electrical Engineering Systems", year = "1994", address = "USA", month = apr, keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/ftp.io.com/papers/WAT_PHD_DissFull_USC94_Recombination_etc_Genetic_Construction_of_Computer_Programs.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/ftp.io.com/papers/watphd.tar.Z", broken = "http://digitallibrary.usc.edu/cdm/ref/collection/p15799coll20/id/187980", URL = "https://ceng.usc.edu/techreports/199x.html#1994", URL = "https://ceng.usc.edu/techreports/1994/Tackett%20CENG%2094-13.pdf", URL = "https://books.google.co.uk/books/about/Recombination_Selection_and_the_Genetic.html?id=bc6AnQEACAAJ", size = "167 pages", abstract = "Computational intelligence seeks as a basic goal to create artificial systems which mimic aspects of biological adaptation, behavior, perception, and reasoning. Toward that goal, genetic program induction - 'Genetic Programming' - has succeeded in automating an activity traditionally considered to be the realm of creative human endeavor. It has been applied successfully to the creation of computer programs which solve a diverse set of model problems. This naturally leads to questions such as: * Why does it work? * How does it fundamentally differ from existing methods? * What can it do that existing methods cannot? The research described here seeks to answer those questions through investigations on several fronts. Analysis is performed which shows that Genetic Programming has a great deal in common with heuristic search, long studied in the field of Artificial Intelligence. It introduces a novel aspect to that method in the form of the recombination operator which generates successors by combining parts of favorable strategies. On another track, we show that Genetic Programming is a powerful tool which is suitable for real-world problems. This done first by applying it to an extremely difficult induction problem and measuring performance against other state-of-the-art methods. We continue by formulating a model induction problem which not only captures the pathologies of the real world, but also parameterizes them so that variation in performance can be measured as a function of confounding factors. At the same time, we study how the properties of search can be varied through the effects of the selection operator. Combining the lessons of the search analysis with known properties of biological systems leads to the formulation of a new recombination operator which is shown to improve induction performance. In support of the analysis of selection and recombination, we define problems in which structure is precisely controlled. These allow fine discrimination of search performance which help to validate analytic predictions. Finally, we address a truly unique aspect of Genetic Programming, namely the exploitation of symbolic procedural knowledge in order to provide 'explanations' from genetic programs.", notes = "Also available as Available as Technical Report CENG 94-13, Dept. of Electrical Engineering Systems, University of Southern California, April 1994. Aug 2016 Use tar zxvf watphd.tar.Z to extract README and three .ps from compressed file", } @InProceedings{Tackett:1994:broodGP, author = "Walter Alden Tackett and Aviram Carmi", title = "The unique implications of brood selection for genetic programming", booktitle = "Proceedings of the 1994 IEEE World Congress on Computational Intelligence", year = "1994", volume = "1", pages = "160--165", address = "Orlando, Florida, USA", month = "27-29 " # jun, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, CPU investment, artificial genetic systems, biological genetic systems, brood selection, memory investment, soft selection, spontaneous abortion, optimisation", size = "6 pages", DOI = "doi:10.1109/ICEC.1994.350023", abstract = "In nature it is common for organisms, as quoted from (Kozlowski and Steams, 1989), to produce many offspring and then neglect, abort, reabsorb, or eat some of them, or allow them to eat each other. This phenomenon is known variously as soft selection, brood selection, spontaneous abortion, and a host of other terms depending upon both semantics and the stage of ontogeny and/or development at which the culling of offspring takes place. The bottom line of this behaviour in nature is the reduction of parental resource investment in offspring who are potentially less fit than others. The use of brood selection in genetic programming was first suggested in (Altenberg, 1993, 1994) as a method to select for representations of CTP with greater evolvability under recombination. We show that brood selection has benefits to artificial genetic systems analogous to those it confers upon biological genetic systems, specifically in terms of conservation of CPU investment and memory investment", } @InProceedings{Tackett:1995:grgsscp, author = "Walter Alden Tackett", title = "Greedy Recombination and Genetic Search on the Space of Computer Programs", booktitle = "Foundations of Genetic Algorithms 3", year = "1994", editor = "L. Darrell Whitley and Michael D. Vose", pages = "271--297", publisher_address = "San Francisco, CA, USA", address = "Estes Park, Colorado, USA", month = "31 " # jul # "--2 " # aug, organisation = "International Society for Genetic Algorithms", publisher = "Morgan Kaufmann", note = "Published 1995", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-356-5", URL = "http://www.amazon.co.uk/gp/search?index=books&linkCode=qs&keywords=1558603565", DOI = "doi:10.1016/B978-1-55860-356-1.50017-0", abstract = "Many natural organisms overproduce zygotes and subsequently decimate the ranks of offspring at some later stage of development. The basic purpose of this behavior is the reduction of parental resource investment in offspring which are less fit than others according to some metabolically cheap fitness measure. An important insight into this process is that for single-pair matings all offspring are products of the same parental genotypes: the selection taking place therefore seeks the most fit recombination of parental traits. This paper presents the Greedy Recombination operator RB(n) for genetic programming, which performs greedy selection among potential crossover sites of a mating pair. The properties of RB(n) are described both from a statistical standpoint and in terms of their effect upon search; comparisons are drawn to existing methods. We formulate the class of constructional problems, which allow precise control over the fitness structure in the space of expressions being searched. The constructional approach is used to create simple GP analogies of the Royal Road problems used to study classical GA. The effects of RB(n) upon search properties, fitness distributions, and genotypic variations are examined and contrasted with effects of selection and recombination methods.", notes = "FOGA-3 Royal Road, Deceptive Royal Road, Soft brood selection, comparision with evolution strategies (u,l)-ES, NOP_1, NOP_2, NOP_4 (intron like). {"}Performance (of SSGA) depends critically on how individuals are selected for replacement (ref De Jong FOGA-2){"} page 288. {"}greatest fitness variation is always achieved using the tournament selection method{"} p290. Claims to explain results in \cite{kinnear:kinnear} ", } @Article{Tackett:1995:mGP, author = "Walter Alden Tackett", title = "Mining the genetic program", journal = "IEEE Expert", year = "1995", volume = "10", number = "3", pages = "28--38", month = jun, keywords = "genetic algorithms, genetic programming", ISSN = "0885-9000", DOI = "doi:10.1109/64.393140", size = "11 pages", abstract = "A major challenge in applying genetic programming to expert-system development is that the ubiquitous presence of irrelevant code makes a genetically induced program difficult to understand. The trait-mining technique extracts the expressions that comprise the program's salient problem elements", notes = "IEEE Expert Special Track on Evolutionary Programming (P. J. Angeline editor) \cite{angeline:1995:er}", } @InCollection{Tackett:1997:HEC, author = "Walter Alden Tackett and K. Govinda Char", title = "Genetic programming applied to image discrimination", booktitle = "Handbook of Evolutionary Computation", publisher = "Oxford University Press", publisher_2 = "Institute of Physics Publishing", year = "1997", editor = "Thomas Baeck and David B. Fogel and Zbigniew Michalewicz", chapter = "section G8.2", keywords = "genetic algorithms, genetic programming", ISBN = "0-7503-0392-1", URL = "http://www.crcnetbase.com/isbn/9780750308953", URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.375.6494.pdf", DOI = "doi:10.1201/9780367802486", size = "10 pages", abstract = "Automatic target recognition (ATR) involves the determination of objects in natural scenes in different weather conditions and in the presence of various contaminants. This high degree of variability requires a flexible system control capable of adapting to the changing conditions. There is no single set of adaptive algorithms that would give consistent, reliable results when subject to the full variety of target conditions. Although genetic programming (GP) has been successfully applied to a wide variety of problems its performance in scaling up to real-world situations needs to be addressed. In this case study we present the simulation results of applying GP to ATR through the development of a processing tree for classification of features extracted from images: measurements from a set of input nodes are weighted and combined through linear and nonlinear operations to form an output response. No constraints are placed upon size, shape, or order of processing within the network. This network is used to classify feature vectors extracted from infra-red imagery into target/nontarget categories using a database of 2000 training samples. Performance is tested against a separate database of 7000 samples. This represents a significant scaling up from the problems to which GP has been applied to date. Two experiments are performed: in the first set, we input classical statistical image features and minimize misclassification of target and non-target samples. In the second set of experiments, GP is allowed to form its own feature set from primitive intensity measurements. For purposes of comparison, the same training and test sets are used to train two other adaptive classifier systems, the binary tree classifier and the multilayer perceptron/backpropagation neural network. The GP network achieves higher performance with reduced computational requirements. The contributions of GP building blocks, or subtrees, to the performance of generated trees are examined.", } @Article{TAGHIPOURANVARI:2023:engstruct, author = "Ataollah {Taghipour Anvari} and Saeed Babanajad and Amir H. Gandomi", title = "Data-Driven Prediction Models For Total Shear Strength of Reinforced Concrete Beams With Fiber Reinforced Polymers Using An Evolutionary Machine Learning Approach", journal = "Engineering Structures", volume = "276", pages = "115292", year = "2023", ISSN = "0141-0296", DOI = "doi:10.1016/j.engstruct.2022.115292", URL = "https://www.sciencedirect.com/science/article/pii/S0141029622013682", keywords = "genetic algorithms, genetic programming, Reinforced concrete, Material, Fiber reinforced polymer, FRP, Beam, Shear, Data-driven model, GEP", abstract = "The strength of Reinforced Concrete (RC) structural elements may need to be improved due to building usage changes or damages that occurred after exposure to extreme loads. Fiber Reinforced Polymer (FRP) is commonly being used to enhance the performance of reinforced concrete beams due to several advantages such as having high strength and being lightweight. To perform the analysis and design of the members, there is a need for accurate models to determine the total shear strength of the structural elements strengthened with FRP sheets. In this paper, genetic programming has been successfully used to develop models to predict the total shear strength of the reinforced concrete beams. A strategy is adopted here to find a simple yet accurate formula to estimate the shear strength. These models can correlate the total shear strength of the beams reinforced with FRP sheets to the geometric and material properties of RC beams and FRP sheets, without the need for expensive laboratory tests. A compressive database of the total shear strength of the RC beams with FRP sheets was created from the literature. External validation and sensitivity analysis, using various statistical criteria, were conducted to assess the precision and validity of the proposed models. Based on 785 RC beams strengthened by externally bonded FRP sheets, tested between 1992 and 2022, two data-driven models were developed to predict the total shear strength of RC beams strengthened with FRP. The calculated correlations for Models I and II are 0.883 and 0.940, respectively. Superior performance was obtained compared to other models from the literature in accuracy. The proposed models can be used for design purposes and the development of structural solutions for existing structures", } @Article{TaghizadehMehrjardi:2016:Geoderma, author = "R. Taghizadeh-Mehrjardi and K. Nabiollahi and R. Kerry", title = "Digital mapping of soil organic carbon at multiple depths using different data mining techniques in Baneh region, Iran", journal = "Geoderma", volume = "266", pages = "98--110", year = "2016", ISSN = "0016-7061", DOI = "doi:10.1016/j.geoderma.2015.12.003", URL = "http://www.sciencedirect.com/science/article/pii/S0016706115301543", abstract = "This study aimed to map SOC lateral, and vertical variations down to 1 m depth in a semi-arid region in Kurdistan Province, Iran. Six data mining techniques namely; artificial neural networks, support vector regression, k-nearest neighbour, random forests, regression tree models, and genetic programming were combined with equal-area smoothing splines to develop, evaluate and compare their effectiveness in achieving this aim. Using the conditioned Latin hypercube sampling method, 188 soil profiles in the study area were sampled and soil organic carbon content (SOC) measured. Eighteen ancillary data variables derived from a digital elevation model and Landsat 8 images were used to represent predictive soil forming factors in this study area. Findings showed that normalized difference vegetation index and wetness index were the most useful ancillary data for SOC mapping in the upper (0-15 cm) and bottom (60-100 cm) of soil profiles, respectively. According to 5-fold cross-validation, artificial neural networks (ANN) showed the highest performance for prediction of SOC in the four standard depths compared to all other data mining techniques. ANNs resulted in the lowest root mean square error and highest Lin's concordance coefficient which ranged from 0.07 to 0.20 log (kg/m3) and 0.68 to 0.41, respectively, with the first value in each range being for the top of the profile and second for the bottom. Furthermore, ANNs increased performance of spatial prediction compared to the other data mining algorithms by up to 36, 23, 21 and 13percent for each soil depth, respectively, starting from the top of the profile. Overall, results showed that prediction of subsurface SOC variation needs improvement and the challenge remains to find appropriate covariates that can explain it.", keywords = "genetic algorithms, genetic programming, Artificial neural network, Support vector regression, k-nearest neighbour, Random forest, Regression tree model", } @InProceedings{Tahan:2019:ICSPIS, author = "Marzieh Hajizadeh Tahan and Mohammad Ghasemzadeh", booktitle = "2019 5th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS)", title = "An Intelligent and Flexible Solution for the Balanced Spanning Tree Problem", year = "2019", abstract = "Construction of Balanced Spanning Tree is one of the problems in computer science with many applications such as in communications and social networks. A balanced spanning tree is obtained through a trade-off between finding the minimum spanning tree and the shortest path tree in a graph. Already some methods are presented for this problem; in this research work, we discuss the advantages and shortcomings of each one and then we present a new flexible intelligent method which is based on multiobjective genetic programming.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICSPIS48872.2019.9066030", month = dec, notes = "Also known as \cite{9066030}", } @Article{Tahir:2019:ACC, author = "Mirza Amaad Ul Haq Tahir and Sohail Asghar and Awais Manzoor and Muhammad Asim Noor", title = "A Classification Model For Class Imbalance Dataset Using Genetic Programming", journal = "IEEE Access", year = "2019", volume = "7", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ACCESS.2019.2915611", ISSN = "2169-3536", pages = "71013--71037", abstract = "Since the last few decades, a class imbalance has been one of the most challenging problems in various fields, such as data mining and machine learning. The particular state of an imbalanced dataset, where each class associated with a given dataset is distributed unevenly. This happens when the positive class is much smaller than the negative class. In this case, most standard classification algorithms do not identify examples related to the positive class. A positive class usually refers to the key interest of the classification task. In order to solve this problem, several solutions were proposed such as sampling-based over-sampling and under-sampling, changes at the classifier level or the combination of two or more classifiers. However the main problem is that most solutions are biased towards negative class, computationally expensive, have storage issues or taking long training time. An alternative approach to this problem is the genetic algorithm (GA), which has shown the promising results. The GA is an evolutionary learning algorithm that uses the principles of Darwinian evolution, it is a powerful global search algorithm. Moreover, the fitness function is a key parameter in GA. It determines how well a solution can solve the given problem. In this paper, we propose a solution which uses entropy and information gain as a fitness function in GA with an objective to improve the impurity and gives a more balanced result without changing the original dataset. The experiments conducted on different datasets demonstrate the effectiveness of the proposed solution in comparison with the several other state-of-the-art algorithms in term of Accuracy (Acc), geometric mean (GM), F-measure (FM), kappa, and Matthews correlation coefficient (MCC).", notes = "Also known as \cite{8709798}", } @InProceedings{tahmassebi:2017:CEC, author = "Amirhessam Tahmassebi and Amir H. Gandomi and Ian McCann and Mieke H. J. Schulte and Lianne Schmaal and Anna E. Goudriaan and Anke Meyer-Baese", booktitle = "2017 IEEE Congress on Evolutionary Computation (CEC)", title = "An evolutionary approach for {fMRI} big data classification", year = "2017", editor = "Jose A. Lozano", pages = "1029--1036", address = "Donostia, San Sebastian, Spain", publisher = "IEEE", isbn13 = "978-1-5090-4601-0", abstract = "Resting-state function magnetic resonance imaging (fMRI) images allow us to see the level of activity in a patient's brain. We consider fMRI of patients before and after they underwent a smoking cessation treatment. Two classes of patients have been studied here, that one took the drug N-acetylcysteine and the ones took a placebo. Our goal was to classify the relapse in nicotine-dependent patients as treatment or non-treatment based on their fMRI scans. The image slices of brain are used as the variable and as results here we deal with a big data problem with about 240,000 inputs. To handle this problem, the data had to be reduced and the first process in doing that was to create a mask to apply to all images. The mask was created by averaging the before images for all patients and selecting the top 40percent of voxels from that average. This mask was then applied to all fMRI images for all patients. The average of the difference in the before treatment and after fMRI images for each patient were found and these were flattened to one dimension. Then a matrix was made by stacking these 1D arrays on top of each other and a data reduction algorithm was applied on it. Lastly, this matrix was fed into some machine learning and Genetic Programming algorithms and leave-one-out cross-validation was used to test the accuracy. Out of all the data reduction machine learning algorithms used, the best accuracy was obtained using Principal Component Analysis along with Genetic Programming classifier. This gave an accuracy of 74percent, which we consider significant enough to suggest that there is a difference in the resting-state fMRI images of a smoker that undergoes this smoking cessation treatment compared to a smoker that receives a placebo.", keywords = "genetic algorithms, genetic programming, Big Data, biomedical MRI, brain, data reduction, drugs, image classification, learning (artificial intelligence), medical image processing, patient treatment, principal component analysis, N-acetylcysteine drug, brain image slices, data reduction algorithm, evolutionary approach, fMRI big data classification, fMRI images, function magnetic resonance imaging, genetic programming classifier, image masking, machine learning, nicotine-dependent patients, placebo drug, relapse classification, smoking cessation treatment, Blood, Correlation, Feature extraction, Machine learning algorithms, Magnetic resonance imaging", isbn13 = "978-1-5090-4601-0", DOI = "doi:10.1109/CEC.2017.7969421", month = "5-8 " # jun, notes = "IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969421}", } @InProceedings{Tahmassebi:2017:ds-o, author = "Amirhessam Tahmassebi and Amir H. Gandomi and Ian McCann and Mieke H. J. Schulte and Lianne Schmaal and Anna E. Goudriaan and Anke Meyer-Baese", title = "{fMRI} Smoking Cessation Classification Using Genetic Programming", booktitle = "Workshop on Data Science meets Optimization", year = "2017", address = "Spain", keywords = "genetic algorithms, genetic programming", URL = "http://ds-o.org/images/Workshop_papers/Gandomi.pdf", size = "8 pages", abstract = "Resting-state function magnetic resonance imaging (fMRI) images allow us to see the level of activity in a patient's brain. We consider fMRI of patients before and after they underwent a smoking cessation treatment. Two classes of patients have been studied here, that one took the drug N-acetylcysteine and the ones took a placebo. Our goal was to classify the relapse in nicotine-dependent patients as treatment or non-treatment based on their fMRI scans. The image slices of brain are used as the variable and as results here we deal with a big data problem with about 240,000 inputs. To handle this problem, the data had to be reduced and the first process in doing that was to create a mask to apply to all images. The mask was created by averaging the before images for all patients and selecting the top 40percent of voxels from that average. This mask was then applied to all fMRI images for all patients. The average of the difference in the before treatment and after fMRI images for each patient were found and these were flattened to one dimension. Then a matrix was made by stacking these 1D arrays on top of each other and a data reduction algorithm was applied on it. Lastly, this matrix was fed into some machine learning and Genetic Programming algorithms and leave-one-out cross-validation was used to test the accuracy. Out of all the data reduction machine learning algorithms used, the best accuracy was obtained using Principal Component Analysis along with Genetic Programming classifier. This gave an accuracy of 74percent, which we consider significant enough to suggest that there is a difference in the resting-state fMRI images of a smoker that undergoes this smoking cessation treatment compared to a smoker that receives a placebo.", notes = "'The impressive performance shown by GP compared with Machine Learning algorithms' ?CEC-2017 workshop, not in IEEE Xplor Oct 2017 ds-o.org badly broken by Oct 2017", } @InProceedings{Tahmassebi:2017:PEARC, author = "Amirhessam Tahmassebi and Amir H. Gandomi and Anke Meyer-Baese", title = "High Performance {GP}-Based Approach for {fMRI} Big Data Classification", booktitle = "Proceedings of the Practice and Experience in Advanced Research Computing 2017 on Sustainability, Success and Impact, PEARC17", year = "2017", pages = "57:1--57:4", address = "New Orleans, LA, USA", month = jul # " 9-13", publisher = "ACM", keywords = "genetic algorithms, genetic programming, Classification, High Performance Computing, fMRI Big Data", articleno = "57", isbn13 = "978-1-4503-5272-7", acmid = "3104145", DOI = "doi:10.1145/3093338.3104145", abstract = "We consider resting-state Functional Magnetic Resonance Imaging (fMRI) of two classes of patients: one that took the drug N-acetylcysteine (NAC) and the other one a placebo before and after a smoking cessation treatment. Our goal was to classify the relapse in nicotine-dependent patients as treatment or non-treatment based on their fMRI scans. 80percent accuracy was obtained using Independent Component Analysis (ICA) along with Genetic Programming (GP) classifier using High Performance Computing (HPC) which we consider significant enough to suggest that there is a difference in the resting-state fMRI images of a smoker that undergoes this smoking cessation treatment compared to a smoker that receives a placebo.", } @InProceedings{Tahmassebi:2017:GPTP, author = "Amirhessam Tahmassebi and Amir H. Gandomi", title = "Genetic Programming Based on Error Decomposition: A Big Data Approach", booktitle = "Genetic Programming Theory and Practice XV", editor = "Wolfgang Banzhaf and Randal S. Olson and William Tozier and Rick Riolo", year = "2017", series = "Genetic and Evolutionary Computation", pages = "135--147", address = "University of Michigan in Ann Arbor, USA", month = may # " 18--20", organisation = "the Center for the Study of Complex Systems", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-90511-2", URL = "https://link.springer.com/chapter/10.1007/978-3-319-90512-9_9", DOI = "doi:10.1007/978-3-319-90512-9_9", abstract = "An investigation of the deviations of error and correlation for different stages of the multi-stage genetic programming (MSGP) algorithm in multivariate nonlinear problems is presented. The MSGP algorithm consists of two main stages: (1) incorporating the individual effect of the predictor variables, (2) incorporating the interactions among the predictor variables. The MSGP algorithm formulates these two terms in an efficient procedure to optimize the error among the predicted and the actual values. In addition to this, the proposed pipeline of the MSGP algorithm is implemented with a combination of parallel processing algorithms to run multiple jobs at the same time. To demonstrate the capabilities of the MSGP, its performance is compared with standard GP in modelling a regression problem. The results illustrate that the MSGP algorithm outperforms standard GP in terms of accuracy, efficiency, and computational cost.", notes = "GPTP 2017, Part of \cite{Banzhaf:2017:GPTP} published after the workshop in 2018", } @Article{Tahmassebi:2018:Measurement, author = "Amirhessam Tahmassebi and Amir H. Gandomi", title = "Building energy consumption forecast using multi-objective genetic programming", journal = "Measurement", year = "2018", volume = "118", pages = "164--171", keywords = "genetic algorithms, genetic programming, Energy performance, Symbolic regression", ISSN = "0263-2241", URL = "https://www.sciencedirect.com/science/article/pii/S0263224118300447", DOI = "doi:10.1016/j.measurement.2018.01.032", abstract = "A multi-objective genetic programming (MOGP) technique with multiple genes is proposed to formulate the energy performance of residential buildings. Here, it is assumed that loads have linear relation in terms of genes. On this basis, an equation is developed by MOGP method to predict both heating and cooling loads. The proposed evolutionary approach optimizes the most significant predictor input variables in the model for both accuracy and complexity, while simultaneously solving the unknown parameters of the model. In the proposed energy performance model, relative compactness has the most and orientation the least contribution. The proposed MOGP model is simple and has a high degree of accuracy. The results show that MOGP is a suitable tool to generate solid models for complex nonlinear systems with capability of solving big data problems via parallel algorithms.", } @InProceedings{Tahmassebi:2018:CEC, author = "Amirhessam Tahmassebi and Amir H. Gandomi and Anke Meyer-Baese", title = "An Evolutionary Online Framework for {MOOC} Performance Using {EEG} Data", booktitle = "2018 IEEE Congress on Evolutionary Computation (CEC)", year = "2018", editor = "Marley Vellasco", address = "Rio de Janeiro, Brazil", month = "8-13 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", URL = "https://ieeexplore.ieee.org/abstract/document/8477862", DOI = "doi:10.1109/CEC.2018.8477862", abstract = "Massive Online Open Course (MOOC) is a scalable, free or affordable online course which emerged as one of the fastest growing distance education platforms in the past decade. One of the biggest challenges that threatens distance education is abnormality in the overall level of consciousness of students while they are taking the course. In this paper, an evolutionary online framework was proposed to improve the performance of MOOCs via noninvasive electro-physiological monitoring methods such as electroencephalography (EEG). Based on the proposed platform, EEG signals can be recorded from users while they are wearing any EEG headsets. EEG measures a brain's spontaneous voltage fluctuations resulting from ionic current within the neurons of the brain via multiple electrodes placed on the scalp. A total of eleven extracted features from EEG signals were employed as the inputs of the evolutionary classification algorithm to predict two classes of confused and not-confused for each individual. An accuracy of 89percent was considered significant enough to suggest that there is difference in the EEG signals of individuals with confusion versus not-confused individuals", notes = "WCCI2018", } @InProceedings{Tahmassebi:2018:CECa, author = "Amirhessam Tahmassebi and Amir H. Gandomi and Anke Meyer-Baese", title = "A Pareto Front Based Evolutionary Model for Airfoil Self-Noise Prediction", booktitle = "2018 IEEE Congress on Evolutionary Computation (CEC)", year = "2018", editor = "Marley Vellasco", address = "Rio de Janeiro, Brazil", month = "8-13 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Multi-Objective", URL = "https://ieeexplore.ieee.org/abstract/document/8477987", DOI = "doi:10.1109/CEC.2018.8477987", abstract = "According to NASA's report on the technologies that could reduce external aircraft noise by 10 dB, a challenge equally as important as finding approaches on airframe noise reduction is the demand to bring up strategies by which airframe noise can be predicted both accurately and rapidly. One of the components of the overall airframe noise is the self-noise of the airfoil itself. In this paper, an evolutionary symbolic implementation for airfoil self-noise prediction was proposed. Multi-objective genetic programming as a subset of evolutionary computation along with adaptive regression by mixing algorithm was used to create an executable fused model. The developed model was tested on the airfoil self-noise database and the performance of the developed model was compared to the previous works and benchmark machine learning algorithms. The reasonable results suggest that the proposed model can be applied to noise generation by low-Mach-number turbulent flows in aerospace, automotive, underwater, and wind turbine acoustic communities.", notes = "WCCI2018", } @InProceedings{conf/evoW/TahtaCS14, author = "Ugur Eray Tahta and Ahmet Burak Can and Sevil Sen", title = "Evolving a Trust Model for Peer-to-Peer Networks Using Genetic Programming", booktitle = "Applications of Evolutionary Computation - 17th European Conference, EvoApplications 2014, Granada, Spain, April 23-25, 2014, Revised Selected Papers", publisher = "Springer", year = "2014", volume = "8602", editor = "Anna Isabel Esparcia-Alcazar and Antonio Miguel Mora", pages = "3--14", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming, P2P", isbn13 = "978-3-662-45522-7", bibdate = "2014-12-02", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/evoW/evoappl2014.html#TahtaCS14", URL = "https://web.cs.hacettepe.edu.tr/~ssen/files/papers/EvoStar14.pdf", DOI = "doi:10.1007/978-3-662-45523-4", size = "12 pages", notes = "See \cite{Tahta:2015:ASC}", } @Article{Tahta:2015:ASC, author = "Ugur Eray Tahta and Sevil Sen and Ahmet Burak Can", title = "{GenTrust}: A genetic trust management model for peer-to-peer systems", journal = "Applied Soft Computing", year = "2015", volume = "34", pages = "693--704", keywords = "genetic algorithms, genetic programming, Evolutionary computation, Trust models, Reputation, Peer-to-peer systems, P2P", ISSN = "1568-4946", URL = "https://web.cs.hacettepe.edu.tr/~ssen/files/papers/GenTrust15.pdf", URL = "http://www.sciencedirect.com/science/article/pii/S156849461500280X", DOI = "doi:10.1016/j.asoc.2015.04.053", size = "33 pages", abstract = "In recent years, peer-to-peer systems have attracted significant interest by offering diverse and easily accessible sharing environments to users. However, this flexibility of P2P systems introduces security vulnerabilities. Peers often interact with unknown or unfamiliar peers and become vulnerable to a wide variety of attacks. Therefore, having a robust trust management model is critical for such open environments in order to exclude unreliable peers from the system. a new trust model for peer-to-peer networks called GenTrust is proposed. GenTrust has evolved by using genetic programming. In this model, a peer calculates the trustworthiness of another peer based on the features extracted from past interactions and the recommendations. Since the proposed model does not rely on any central authority or global trust values, it suits the decentralized nature of P2P networks. Moreover, the experimental results show that the model is very effective against various attackers, namely individual, collaborative, and pseudospoofing attackers. An analysis on features is also carried out in order to explore their effects on the results. This is the first study which investigates the use of genetic programming on trust management.", notes = "extension of \cite{conf/evoW/TahtaCS14}", } @PhdThesis{Tai:thesis, author = "Chung-Ching Tai", title = "Human Learning Behavior versus Software Trading: The Influences of Cognitive Capacity on Learning Behavior", school = "Department of Economics, National Chengchi University", year = "2008", address = "Taiwan", keywords = "genetic algorithms, genetic programming, Agent-based Computational Economic Models, Double Auction Markets, Trading Strategies, Learning, IQ, Heterogeneous Agents", URL = "http://thesis.lib.nccu.edu.tw/cgi-bin/gs32/gsweb.cgi/ccd=L.S.Df/record?r1=1&h1=0", URL = "http://nccur.lib.nccu.edu.tw/handle/140.119/35796", size = "157 pages", abstract = "The study of a series of human-agent interactions as well as computerised trading tournaments in double auction markets has exhibited a general superiority of computerized trading strategies over learning agents. The ineffectiveness of learning motivates the study of learning versus designed trading agents in this research. We therefore initiates a series of experiments to test the capability of learning GP agents and rationally-designed trading strategies. The results shows that with the cost of time, eventually learning agents can beat all other trading strategies. At the same time, the notion of intelligence is introduced into the model to investigate the influence of individual intelligence on learning ability. We use the population size of the GP trader as the proxy variable of IQ which is a measure of general intelligence. The results show that individuals with higher intelligence can perform better than those with lower intelligence, which manifests its importance discovered in Psychological research.", notes = "In Chinese. PDF online as several files. Advisor Shu Heng Chen", } @Article{Tajeri:2015:IJRMMS, author = "Shervin Tajeri and Ehsan Sadrossadat and Jafar Bolouri Bazaz", title = "Indirect estimation of the ultimate bearing capacity of shallow foundations resting on rock masses", journal = "International Journal of Rock Mechanics and Mining Sciences", volume = "80", pages = "107--117", year = "2015", ISSN = "1365-1609", DOI = "doi:10.1016/j.ijrmms.2015.09.015", URL = "http://www.sciencedirect.com/science/article/pii/S1365160915300423", abstract = "The success of a foundation design for structures is to precisely estimate the bearing capacity of underlying soils or rocks. To avoid the elaborate in-situ experimental methods, several approaches presented by various researchers for the estimation of the bearing capacity factor. Despite this fact, there still exists a serious need to develop more robust predictive models. The aim of this paper is to propose a novel formulation for the ultimate bearing capacity of shallow foundations resting on/in rock masses, using a powerful evolutionary computational technique, namely linear genetic programming. Thus, a comprehensive set of data is collected to develop the model. In order to evaluate the validity of the obtained model, several analyses are conducted and compared with those provided by other researchers. Consequently, the results clearly demonstrate the proposed model accurately characterize the bearing capacity factor and reach a notably better prediction performance than the traditional models.", keywords = "genetic algorithms, genetic programming, Rock mass properties, Ultimate bearing capacity, Shallow foundation, Evolutionary computation", notes = "Department of Civil Engineering, Ferdowsi University of Technology, Mashhad, Iran", } @MastersThesis{takac:masters, author = "Aleksandra Takac", title = "Genetic Programming in Data Mining - Cellular Approach", school = "Institute of Informatics Faculty of Mathematics, Physics and Informatics, Comenius University", year = "2003", address = "Bratislava, Slovakia", month = apr, keywords = "genetic algorithms, genetic programming", URL = "http://www.ii.fmph.uniba.sk/~takaca/thesis/thesis.pdf", size = "70 pages", notes = "First results will be here soon of cellular genetic programming on classification task on 3 different datasets (from the link above): German Credit, Australian Credit and Heart disease. Here is another very good link with dataset links : Datasets for Data Mining Also I recommend this very good website for datamining and knowledge discovery: http://www.kdnuggets.com/ The test I perform uses sql queries for evaluating individuals of population, genetic programing model is cellular, 2 types of attributes are considered continuous and qualitative. In all examples are 2 classes, the results can be compared to other algorithms that were in the project STATLOG. Method for testing the algorithm is 10 and 9-Fold Cross Validation. (01/04/2003) The experiment and results of classification task with cellular genetic programming. (05/04/2003) Third chapter - Data Mining", } @InProceedings{takac:2004:KN, author = "Aleksandra Takac", title = "Application of Cellular Genetic Programming in Data Mining", booktitle = "Znalosti", year = "2004", editor = "Vaclav Snasel and Michal Kratky", address = "Brno, Czech Republic", month = "25-27 " # feb, keywords = "genetic algorithms, genetic programming", URL = "http://www.ii.fmph.uniba.sk/~takaca/KN04.PDF", size = "12 pages", abstract = "Paper examines application of genetic programming framework in the problem of knowledge discovery in databases, more precisely in the task of classification. Genetic programming possesses certain advantages that make it suitable for application in data mining, such as robustness of algorithm or its convenient structure for rule generation to name a few. This study focuses on one type of parallel genetic algorithms ? cellular (diffusion) model. Emphasis is placed on the improvement of efficiency and scalability of data mining algorithm, which could be achieved by integration of algorithm with databases and by employing a cellular framework, as well as examining parallel approaches. Cellular model of genetic programming that exploits SQL queries is implemented and applied to classification task. Achieved results are compared with other machine learning algorithms.", notes = "http://www.fi.muni.cz/znalosti2004/profil_en.html.iso-8859-1", } @InProceedings{takadama:1999:HDGLAO, author = "Keiki Takadama and Takao Terano and Katsunori Shimohara", title = "How to Design Good Learning Agents in Organization", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1398--1405", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "artificial life, adaptive behavior and agents", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-045.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-045.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Article{takagi:2001:ieee, author = "Hideyuki Takagi", title = "Interactive Evolutionary Computation: Fusion of the Capabilities of {EC} Optimization and Human Evaluation", journal = "Proceedings of the IEEE", year = "2001", volume = "89", number = "9", pages = "1275--1296", month = sep, note = "Invited Paper", keywords = "genetic algorithms, genetic programming, computer graphics, data mining, evolutionary computation, human factors, interactive systems, robots, user interfaces, animation, computational intelligence, computer graphics, data mining, graphic arts, human factors, interactive evolutionary computation, IEC, robotics, user interface", ISSN = "0018-9219", DOI = "doi:10.1109/5.949485", size = "22 pages", abstract = "We survey the research on interactive evolutionary computation (IEC). The IEC is an EC that optimises systems based on subjective human evaluation. The definition and features of the IEC are first described and then followed by an overview of the IEC research. The overview primarily consists of application research and interface research. In this survey the IEC application fields include graphic arts and animation, 3D computer graphics lighting, music, editorial design, industrial design, facial image generation, speed processing and synthesis, hearing aid fitting, virtual reality, media database retrieval, data mining, image processing, control and robotics, food industry, geophysics, education, entertainment, social system, and so on. The interface research to reduce human fatigue is also included. Finally, we discuss the IEC from the point of the future research direction of computational intelligence. This paper features a survey of about 250 IEC research papers", notes = "CODEN: IEEPAD Inspec Accession Number: 7053972", } @InProceedings{Takagi:2002:SEAL, author = "Hideyuki Takagi and Norimasa Hayashida", booktitle = "Simulated Evolution And Learning (SEAL), 2002, the 4th Asia-Pacific Conference on", title = "Interactive {EC}-based Signal Processing", year = "2002", pages = "375--379", address = "Singapore", month = nov # " 18-22", keywords = "genetic algorithms, genetic programming", URL = "https://catalog.lib.kyushu-u.ac.jp/opac_download_md/1670073/SEAL2002_4.pdf", URL = "https://www.researchgate.net/publication/228945588_Interactive_ec-based_signal_processing", URL = "http://hdl.handle.net/2324/1670073", size = "5 pages", abstract = "We introduce new types of signal processing for which the characteristics of the signal processing filters are designed automatically by interactive evolutionary computation (IEC) based on human perception, such as hearing or vision. We first describe our existing works that use this approach, such as recovering distorted speech and hearing-aid fitting, as well as other related works in this field. Next, we evaluate the capabilities of visual-based image signal processing using IEC and compare it with conventional linear filters for the tasks of edge detection, high pass filtering, and horizontal / vertical component filtering. The experimental comparisons show that the performances of both methods are similar, which means that the new approach, without a priori knowledge on signal processing, is useful when signal processing users are not signal processing experts such as is the case in medical image processing or photo-retouch design.", notes = "Details from 1st author July 2019. SEAL-2002_all_pap.htm", } @InProceedings{takahashi:1999:AECTAGDS, author = "Eiichi Takahashi and Masahiro Murakawa and Kenji Toda and Tetsuya Higuchi", title = "An Evolvable-hardware-based Clock Timing Architecture towards {GigaHz} Digital Systems", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1204--1210", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, EHW, evolvable hardware", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-450.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-450.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{takahashi:1999:pfhGA, author = "Osamu Takahashi and Hajime Kita and Shigenobu Kobayashi", title = "Protein Folding by a Hierarchical Genetic Algorithm", booktitle = "fourth AROB", year = "1999", month = "19-22 " # jan, keywords = "genetic algorithms", notes = "Evolves branching chromosome using crossover ", } @Article{TAKAKI:2022:ailsci, author = "Katsushi Takaki and Tomoyuki Miyao", title = "Symbolic regression for the interpretation of quantitative structure-property relationships", journal = "Artificial Intelligence in the Life Sciences", volume = "2", pages = "100046", year = "2022", ISSN = "2667-3185", DOI = "doi:10.1016/j.ailsci.2022.100046", URL = "https://www.sciencedirect.com/science/article/pii/S2667318522000162", keywords = "genetic algorithms, genetic programming, Model interpretability, Quantitative structure-activity relationships, Quantitative structure-property relationships, Symbolic regression", abstract = "The interpretation of quantitative structure-activity or structure-property relationships is important in the field of chemoinformatics. Although multivariate linear regression models are typically interpretable, they do not generally have high predictive abilities. Symbolic regression (SR) combined with genetic programming (GP) is a well-established technique for generating the mathematical expressions that describe the relationships within a dataset. However, SR sometimes produces complicated expressions that are hard for humans to interpret. This paper proposes a method for generating simpler expressions by incorporating three filters into GP-based SR. The filters are further combined with nonlinear least-squares optimization to give filter-introduced GP (FIGP), which improves the predictive ability of SR models while retaining simple expressions. As a proof-of-concept, the quantitative estimate of drug-likeness and the synthetic accessibility score are predicted based on the chemical structures of compounds. Overall, FIGP generates less-complicated expressions than previous SR methods. In terms of predictive ability, FIGP is better than GP, but is outperformed by a support vector machine with a radial basis function kernel. Furthermore, quantitative structure-activity relationship models are constructed for three matching molecular series with biological targets. In the case of one target, the activity prediction models given by FIGP exhibit better predictive ability than multivariate linear regression and support vector regression with the radial basis function kernel, whereas for the remaining cases, FIGP is slightly less accurate than multivariate linear regression", } @InProceedings{TAKAMURA:2009:PCS, author = "Seishi Takamura and Masaaki Matsumura and Yoshiyuki Yashima", title = "Automatic pixel predictor construction using an evolutionary method", booktitle = "Picture Coding Symposium, 2009. PCS 2009", year = "2009", month = may, pages = "1--4", keywords = "genetic algorithms, genetic programming, automatic pixel predictor construction, evolutionary method, image predictor, image coding", DOI = "doi:10.1109/PCS.2009.5167448", abstract = "Evolutionary methods based on genetic programming (GP) enable dynamic algorithm generation, and have been successfully applied to many areas such as plant control, robot control, and stock market prediction. However, conventional image/video coding methods such as JPEG and H.264 all use fixed (non-dynamic) algorithms without exception. In this article, we introduce a GP-based image predictor that is specifically evolved for each input image. Preliminary results demonstrate 1.4percent and 1.7percent entropy reduction (overhead included) against the optimal linear predictor and CALIC's gradient adjusted predictor, respectively.", notes = "Also known as \cite{5167448}", } @InProceedings{TAKAMURA:2009:ICIP, author = "Seishi Takamura and Masaaki Matsumura and Yoshiyuki Yashima", title = "A study on an evolutionary pixel predictor and its properties", booktitle = "16th IEEE International Conference on Image Processing (ICIP), 2009", year = "2009", month = nov, pages = "1921--1924", keywords = "genetic algorithms, genetic programming, lossless image coding, prediction methods", abstract = "Evolutionary methods based on genetic programming (GP) enable dynamic algorithm generation, and have been successfully applied to many areas such as plant control, robot control, and stock market prediction. However, conventional image/video coding methods such as JPEG and H.264 all use fixed (non-dynamic) algorithms without exception. In this article, we introduce a GP-based image predictor that is specifically evolved for each input image. Experimental results demonstrate 2.9percent less entropy (overhead included) than CALIC's gradient adjusted predictor.", DOI = "doi:10.1109/ICIP.2009.5413714", ISSN = "1522-4880", notes = "Also known as \cite{5413714}", } @InProceedings{Takamura:2010:PCS, author = "Seishi Takamura and Masaaki Matsumura and Hirohisa Jozawa", title = "Accelerating pixel predictor evolution using edge-based class separation", booktitle = "Picture Coding Symposium (PCS 2010)", year = "2010", month = "8-10 " # dec, pages = "106--109", abstract = "Evolutionary methods based on genetic programming (GP) enable dynamic algorithm generation, and have been successfully applied to many areas such as plant control, robot control, and stock market prediction. However, one of the challenges of this approach is its high computational complexity. Conventional image/video coding methods such as JPEG and H.264 all use fixed (non-dynamic) algorithms without exception. However, one of the challenges of this approach is its high computational complexity. In this article, we introduce a GP-based image predictor that is specifically evolved for each input image, as well as local image properties such as edge direction. Via the simulation, proposed method demonstrated ~180 times faster evolution speed and 0.02-0.1 bit/pel lower bit rate than previous method.", keywords = "genetic algorithms, genetic programming, computational complexity, dynamic algorithm generation, edge-based class separation, evolutionary method, image coding, image predictor, pixel predictor evolution, video coding, image coding", DOI = "doi:10.1109/PCS.2010.5702434", notes = "NTT Cyber Space Laboratories, NTT Corporation. Also known as \cite{5702434}", } @InProceedings{Takamura:2013:DCC, author = "Seishi Takamura and Atsushi Shimizu", title = "Image Coding Using Nonlinear Evolutionary Transforms", booktitle = "Data Compression Conference (DCC 2013)", year = "2013", month = "20-22 " # mar, pages = "521--521", keywords = "genetic algorithms, genetic programming, transform coding, nonlinear transform", DOI = "doi:10.1109/DCC.2013.100", ISSN = "1068-0314", abstract = "Transform is one of the most important tools for image/video coding technology. In this paper, novel nonlinear transform generation based on genetic programming is proposed and implemented into H.264/AVC and HEVC reference software to enhance coding performance. The transform procedure itself is coded and transmitted. Despite this overhead, 0.590percent (vs. JM18.0) and 1.711percent (vs. HM5.0) coding gain was observed in our preliminary experiment.", notes = "Also known as \cite{6543131}", } @InProceedings{Takamura:2014:GECCOcomp, author = "Seishi Takamura and Atsushi Shimizu", title = "GPGPU-assisted denoising filter generation for video coding", booktitle = "GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion", year = "2014", editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges", isbn13 = "978-1-4503-2881-4", keywords = "genetic algorithms, genetic programming: Poster", pages = "151--152", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2598394.2598462", DOI = "doi:10.1145/2598394.2598462", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "State-of-the-art video coding technologies such as H.265/HEVC employ in-loop denoising filters such as deblocking filter and sample adaptive offset. This paper aims to develop a new type of in-loop denoising filter using an evolutionary method. To boost the evolution, GPGPU is used in filtering process. Generated filter is heavily nonlinear and content-specific. Simulation results demonstrate that proposed method generates better denoising filter in 100x shorter time. The bit rate reduction of 1.492 - 2.569percent was obtained against HM7.2 anchor, the reference software of H.265/HEVC.", notes = "Also known as \cite{2598462} Distributed at GECCO-2014.", } @InProceedings{7025832, author = "Seishi Takamura and Atsushi Shimizu", title = "Image/video coding based on evolutive unidirectional transforms", booktitle = "IEEE International Conference on Image Processing (ICIP 2014)", year = "2014", pages = "4097--4101", keywords = "genetic algorithms, genetic programming, H.265/HEVC, Symbolic regression, Transform coding", DOI = "doi:10.1109/ICIP.2014.7025832", size = "5 pages", abstract = "this paper proposes new type of coding scheme, which uses unidirectional transform, unlike conventional image/video coding schemes such as JPEG, MPEG-2, H.265/HEVC, whose encoder uses forward and inverse transforms and decoder uses inverse transform. In our proposal, even encoder (and decoder as well) uses unidirectional (in conventional manner, inverse) transform. Because of this preferable property, the encoder can arbitrarily design nonlinear, content-specific transforms, regardless of obtaining its inverse transform, to optimize coding performance. In our preliminary experiment, BD-Rate gain of 1.18 percent was observed against HEVC reference software HM12.", notes = "Proc. ICIP2014, BIO-L1.4", } @InProceedings{Takamura:2016:ICIP, author = "Seishi Takamura and Atsushi Shimizu", title = "Concurrent Evolution of Pixel Predictor and Context Modeling for Image Coding", booktitle = "2016 IEEE International Conference on image Processing, ICIP", year = "2016", editor = "Fernando Pereira and Gaurav Sharma", pages = "2147--2151", address = "Phoenix, Arizona, USA", month = "25-28 " # sep, organisation = "IEEE Signal Processing Society", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, image coding, pixel predictor, context modelling expression: Poster", URL = "http://2016.ieeeicip.org/Papers/viewpapers.asp?papernum=3228", DOI = "doi:10.1109/ICIP.2016.7532738", size = "5 pages", abstract = "Lossless image coding process predicts the value of current pixel from previously decoded pixel values. Then the prediction error is classified according to the context model. This classification splits the sources with different distributions and hence reduce the total entropy of the prediction error signals. In the literature, the predictor has been intensively studied. Some evolutionary approaches have been applied to generate a predictor to improve compression performance. However, the context modelling method has not relatively been well studied. We propose and investigate a novel method to automatically obtain evolved pair of pixel predictor and context modeling. Simulation results show 1.32-3.90percent bit-rate reduction against the pair of predictor and context modeler of one of the best conventional methods (CALIC). It is also demonstrated that the evolved algorithm's size is more compact than former results. We also found that context modeler is evolved in more complex form than the predictor.", notes = "ICIP 2016 Image and Video Coding. paper MPA-P3.3 NTT Corporation", } @InProceedings{Takeuchi:2021:ICT, author = "Haruto Takeuchi and Md. Kawsar Khan and Makoto Ohki", title = "Reference Points Generated on Unit Hypersurfaces for MaOEAs", booktitle = "2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)", year = "2021", pages = "614--619", abstract = "This paper proposes a method to uniformly generate reference points on a hypersurface for many-objective optimization evolutionary algorithms (MaOEAs). Recently, MaOEAs have been proposed to obtain selection pressure in a multidimensional objective space by using a reference point set, but there is no method for generating a reference point set that is supposed to incorporate user orientation. This paper proposes a method for generating uniform reference points on unit hyperspheres and unit hyperplanes in a multidimensional objective space. The proposed method is applied to the multi-objective genetic programming (GP) problem by non-dominated sorting genetic algorithm-III (NSGA-III) and to the multi-objective combinatorial optimization problem by multiobjective evolutionary algorithm based on decomposition (MOEA/D). As a result, we confirm that the proposed method gives non-inferior results compared to conventional methods. Since the proposed method can easily incorporate user orientation, this shows the effectiveness of the proposed method.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/3ICT53449.2021.9581958", month = sep, notes = "Also known as \cite{9581958}", } @InProceedings{Taktak:2006:EMBS, author = "A. F. G. Taktak and Christian Setzkorn and B. E. Damato", title = "Double-Blind Comparison of Survival Analysis Models Using a Bespoke Web System", booktitle = "Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE", year = "2006", pages = "2466--2469", address = "New York", month = aug, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IEMBS.2006.259797", abstract = "The aim of this study was to carry out a comparison of different linear and non-linear models from different centres on a common dataset in a double-blind manner to eliminate bias. The dataset was shared over the Internet using a secure bespoke environment called geoconda. Models evaluated included: (1) Cox model, (2) Log Normal model, (3) Partial Logistic Spline, (4) Partial Logistic Artificial Neural Network and (5) Radial Basis Function Networks. Graphical analysis of the various models with the Kaplan-Meier values were carried out in 3 survival groups in the test set classified according to the TNM staging system. The discrimination value for each model was determined using the area under the ROC curve. Results showed that the Cox model tended towards optimism whereas the partial logistic Neural Networks showed slight pessimism.", notes = "1557-170X", } @InProceedings{RefWorks:122, author = "A. F. G. Taktak and L. Antolini and M. Aung and P. Boracchi and I. Campbell and B. E. Damato and E. Ifeachor and N. Lama and P. Lisboa and G. Manikis and C. Setzkorn and V. Stalbovskaya and M. Zervakis and E. Biganzoli", year = "2006", title = "A Protocol for Double-Blind Evaluation of Prognostic Models", booktitle = "Proceedings of the 2nd European Workshop on the Assessment of Diagnostic and Prognostic Performance", pages = "40--49", keywords = "genetic algorithms, genetic programming", broken = "http://repository.liv.ac.uk/1194549/", notes = "EWADP Liverpool, 16-17 February 2006", } @Article{RefWorks:118, author = "A. Taktak and L. Antolini and M. Aung and P. Boracchi and I. Campbell and B. Damato and E. Ifeachor and N. Lama and P. Lisboa and C. Setzkorn and V. Stalbovskaya and E. Biganzoli", title = "Double-blind evaluation and benchmarking of survival models in a multi-centre study", journal = "Computers in Biology and Medicine", volume = "37", number = "8", pages = "1108--1120", year = "2007", month = aug, keywords = "genetic algorithms, genetic programming, Evaluation studies, Double-blind study, Multi-centre studies, Survival analysis, Uveal neoplasms", ISSN = "0010-4825", URL = "http://www.liv.ac.uk/~clfs12/NHS%20Dept%20Clinical%20Engineering%20Liverpool/Reseach_pages/Publications/660_taktak.pdf", URL = "http://www.sciencedirect.com/science/article/B6T5N-4MM8BMB-1/2/3497fbdd7ed395a4886e11a62f81314d", DOI = "doi:10.1016/j.compbiomed.2006.10.001", size = "13 pages", abstract = "Accurate modelling of time-to-event data is of particular importance for both exploratory and predictive analysis in cancer, and can have a direct impact on clinical care. This study presents a detailed double-blind evaluation of the accuracy in out-of-sample prediction of mortality from two generic non-linear models, using artificial neural networks benchmarked against a partial logistic spline, log-normal and COX regression models. A data set containing 2880 samples was shared over the Internet using a purpose-built secure environment called GEOCONDA (www.geoconda.com). The evaluation was carried out in three parts. The first was a comparison between the predicted survival estimates for each of the four survival groups defined by the TNM staging system, against the empirical estimates derived by the Kaplan-Meier method. The second approach focused on the accurate prediction of survival over time, quantified with the time dependent C index (Ctd). Finally, calibration plots were obtained over the range of follow-up and tested using a generalisation of the Hosmer-Lemeshow test. All models showed satisfactory performance, with values of Ctd of about 0.7. None of the models showed a systematic tendency towards over/under estimation of the observed survival at = 3 and 5 years. At = 10 years, all models underestimated the observed survival, except for COX regression which returned an overestimate. The study presents a robust and unbiased benchmarking methodology using a bespoke web facility. It was concluded that powerful, recent flexible modelling algorithms show a comparative predictive performance to that of more established methods from the medical and biological literature, for the reference data set.", notes = "Some confusion about 3rd author. PDF says _M_ Aung. Also known as \cite{Taktak20071108}", } @Article{Talbot:2010:IJSEA, author = "Vincent Talbot and Ilham Benyahia", title = "Complex Application Architecture Dynamic Reconfiguration Based on Multi-criteria Decision Making", journal = "International journal of Software Engineering \& Applications", year = "2010", volume = "1", number = "4", pages = "19--37", month = oct, keywords = "genetic algorithms, genetic programming, Complex applications, architecture performance optimization, architecture reconfiguration, multi-criterion", ISSN = "0976-2221", URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.174.7744&rep=rep1&type=pdf", URL = "http://www.airccse.org/journal/ijsea/", URL = "http://airccse.org/journal/ijsea/papers/1010ijsea2.pdf", DOI = "doi:10.5121/ijsea.2010.1402", size = "19 pages", abstract = "Intelligent Transportation Systems (ITS) are increasingly important since they aim to bring solutions to crucial problems related to transportation networks such as congestion and various road incidents. Management of ITS, as other complex and distributed applications, has to cope with unforeseeable events and incomplete data while guaranteeing a quality of service (QoS) defined by multiple criteria reflecting real-life needs. To enable applications to adapt to changing environments, we define a methodology of dynamic architecture reconfiguration based on multi-criteria decision making (MCDM) using evolutionary computing (EC) to find the best combination of architecture components. We use the Pareto Evolutionary Algorithm Adapting the Penalty (PEAP), a category of EC, selected in this paper to deal with time consuming online processing required by basic EC such as genetic algorithms. Our simulation results relating to road safety highlight the benefits of MCDM prior to such reconfiguration. We also address the problem of destabilization which can result from repeated reconfigurations in response to ongoing environment changes.", notes = "Very little mention of GP but does say follows \cite{conf/dms/BenyahiaT08} Universite du Quebec en Outaouais, Canada", } @InCollection{talbott:2003:ACTPUABGP, author = "Walter A. Talbott", title = "Automatic Creation of Team-Control Plans Using an Assignment Branch in Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2003", year = "2003", editor = "John R. Koza", pages = "206--212", address = "Stanford, California, 94305-3079 USA", month = "4 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", URL = "http://www.genetic-programming.org/sp2003/Talbott.pdf", notes = "part of \cite{koza:2003:gagp}", } @InProceedings{talbott:aco:gecco2004, author = "Walter A. Talbott", title = "Automatic Creation of Team-Control Plans Using an Assignment Branch in Genetic Programming", booktitle = "Genetic and Evolutionary Computation -- GECCO-2004, Part I", year = "2004", editor = "Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell", series = "Lecture Notes in Computer Science", pages = "201--212", address = "Seattle, WA, USA", publisher_address = "Heidelberg", month = "26-30 " # jun, organisation = "ISGEC", publisher = "Springer-Verlag", volume = "3102", ISBN = "3-540-22344-4", ISSN = "0302-9743", DOI = "doi:10.1007/b98643", size = "12", keywords = "genetic algorithms, genetic programming", notes = "GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)", } @Article{Taleby-Ahvanooey:2019:itiis, author = "Milad {Taleby Ahvanooey} and Qianmu Li and Ming Wu and Shuo Wang", title = "A Survey of Genetic Programming and Its Applications", journal = "KSII Transactions on Internet and Information Systems", year = "2019", volume = "13", number = "4", pages = "1765--1794", month = "29 " # apr, keywords = "genetic algorithms, genetic programming, Automatic Programming Genetic Operators", ISSN = "1976-7277", URL = "http://itiis.org/digital-library/manuscript/2311", DOI = "doi:10.3837/tiis.2019.04.002", size = "30 pages", abstract = "Genetic Programming (GP) is an intelligence technique whereby computer programs are encoded as a set of genes which are evolved using a Genetic Algorithm (GA). In other words, the GP employs novel optimization techniques to modify computer programs; imitating the way humans develop programs by progressively re-writing them for solving problems automatically. Trial programs are frequently altered in the search for obtaining superior solutions due to the base is GA. These are evolutionary search techniques inspired by biological evolution such as mutation, reproduction, natural selection, recombination, and survival of the fittest. The power of GAs is being represented by an advancing range of applications; vector processing, quantum computing, VLSI circuit layout, and so on. But one of the most significant uses of GAs is the automatic generation of programs. Technically, the GP solves problems automatically without having to tell the computer specifically how to process it. To meet this requirement, the GP uses GAs to a population of trial programs, traditionally encoded in memory as tree-structures. Trial programs are estimated using a fitness function and the suited solutions picked for re-evaluation and modification such that this sequence is replicated until a correct program is generated. GP has represented its power by modifying a simple program for categorizing news stories, executing optical character recognition, medical signal filters, and for target identification, etc. This paper reviews existing literature regarding the GPs and their applications in different scientific fields and aims to provide an easy understanding of various types of GPs for beginners.", notes = "http://itiis.org/about", } @MastersThesis{talko:mastersthesis, author = "Bret Talko", title = "A Rule-Based Approach for Constructing Neural Networks Using Genetic Programming", school = "University of Melbourne", year = "1999", month = mar, keywords = "genetic algorithms, genetic programming", broken = "msc_thesis.ps.gz", URL = "http://citeseer.ist.psu.edu/312140.html", size = "166 pages", abstract = "This thesis presents a novel use of Genetic Programming (GP) to evolve recurrent, weightless neural networks. The approach taken uses neural network construction rules as the data structures that undergo adaptation by the GP algorithm. These rules can be used to construct a neural network by adding neurons and connections to an initial basic network configuration. In addition to evolving the architectures of networks, the system evolves the formulae for the activation function of each neuron in the networks and the number of processing cycles for the networks. The system has been applied to a number of Boolean functions and it is shown that solution networks were able to be found for each. Some variations in the system design were investigated on the Boolean functions to identify possible improvements that could be made to the system which would result in better performance. One variation to the system design which resulted in a significantly large increase in the system performance was made by changing the construction rules that are used by the system. A number of characteristics of the produced networks were noted. Among them is the generation of network construction rules that are similar to each other. A system variation was made which succeeded in making the rules more diverse but does not generally result in better performance. Another characteristics of the networks is that their construction rules often contain unused and redundant rules. The construction rules were designed to allow efficient specification of networks which contain multiple instances of the same sub-network. The system uses this when discovering solution networks for Boolean functions which can be decomposed into two identical Boolean functions. Importantly, the system achieved significantly better results than a modified version of the system in which the features enabling efficient network specification were not present. This suggests that incorporating a modular construction process for building networks is useful for obtaining solution networks to decomposable problems.", notes = "p129 XOR {"}Therefore Gruau's result is significantly better than the GPNN result{"}", } @InProceedings{talko:1999:ai, author = "Bret Talko and Linda Stern and Les Kitchen", title = "Evolving Modular Neural Networks Using Rule-Based Genetic Programming", booktitle = "12th Australian Joint Conference on Artificial Intelligence", year = "1999", editor = "Norman Foo", volume = "1747", series = "LNCS", pages = "482--483", address = "Sydney, Australia", publisher_address = "Berlin", month = "6-10 " # dec, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-66822-5", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/12028/http:zSzzSzwww.cs.mu.oz.auzSz~talkozSzposterai99.pdf/evolving-modular-neural-networks.pdf", URL = "http://citeseer.ist.psu.edu/284438.html", broken = "http://fluid.mech.okayama-u.ac.jp/brett/evolving-modular-neural-networks.ps", URL = "http://www.springer.com/computer/ai/book/978-3-540-66822-0", size = "2 pages", abstract = "his paper describes a new approach for evolving recurrent neural networks using Genetic Programming. A system has been developed to train weightless neural networks using construction rules. The network construction rules are evolved by the Genetic Programming system which build the solution neural networks. The use of rules allows networks to be constructed modularly. Experimentation with decomposable Boolean functions has revealed that the performance of the system is superior to a...", notes = "http://www.cse.unsw.edu.au/~ai99/ masters thesis", } @InProceedings{talko:2000:PRICAI, author = "Bret Talko and Linda Stern and Les Kitchen", title = "Evolving Neural Networks for Decomposable Problems using Genetic Programming", booktitle = "PRICAI 2000 Topics in Artificial Intelligence: 6th Pacific Rim International Conference on Artificial Intelligence", pages = "446--456", year = "2000", editor = "Riichiro Mizoguchi and John K. Slaney", series = "Lecture Notes in Artifical Intelligence", volume = "1886", address = "Melbourne Convention Centre, Austrlia", month = "28 " # aug # "-1 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-67925-1", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1007/3-540-44533-1_46", abstract = "Many traditional methods for training neural networks using genetic algorithms and genetic programming do not have any special provisions for taking advantage of decomposable problems which can be solved by combining solutions to each subproblem. This paper describes a new approach to neural network construction using genetic programming which is designed to rapidly construct networks composed of similar subnetworks. A system has been developed to produce trained weightless neural networks by using construction rules to build the networks. The network construction rules are evolved by the genetic programming system. The system has been applied to decomposable Boolean problems and the results were compared with a modified version of the system in which networks cannot be constructed modularly. The modular version of the system obtains significantly better results than the non-modular version of the program.", notes = "PRICAI 2000 http://www3.cm.deakin.edu.au/pricai/ broken Mar 2021", } @Article{talpur:2024:Sensors, author = "Fauzia Talpur and Imtiaz Ali Korejo and Aftab Ahmed Chandio and Ali Ghulam and Mir. Sajjad Hussain Talpur", title = "{ML}-Based Detection of {DDoS} Attacks Using Evolutionary Algorithms Optimization", journal = "Sensors", year = "2024", volume = "24", number = "5", pages = "Article No. 1672", keywords = "genetic algorithms, genetic programming", ISSN = "1424-8220", URL = "https://www.mdpi.com/1424-8220/24/5/1672", DOI = "doi:10.3390/s24051672", abstract = "The escalating reliance of modern society on information and communication technology has rendered it vulnerable to an array of cyber-attacks, with distributed denial-of-service (DDoS) attacks emerging as one of the most prevalent threats. This paper delves into the intricacies of DDoS attacks, which exploit compromised machines numbering in the thousands to disrupt data services and online commercial platforms, resulting in significant downtime and financial losses. Recognizing the gravity of this issue, various detection techniques have been explored, yet the quantity and prior detection of DDoS attacks has seen a decline in recent methods. This research introduces an innovative approach by integrating evolutionary optimisation algorithms and machine learning techniques. Specifically, the study proposes XGB-GA Optimisation, RF-GA Optimisation, and SVM-GA Optimisation methods, employing Evolutionary Algorithms (EAs) Optimisation with Tree-based Pipelines Optimisation Tool (TPOT)-Genetic Programming. Datasets pertaining to DDoS attacks were used to train machine learning models based on XGB, RF, and SVM algorithms, and 10-fold cross-validation was employed. The models were further optimised using EAs, achieving remarkable accuracy scores: 99.99percent with the XGB-GA method, 99.50percent with RF-GA, and 99.99percent with SVM-GA. Furthermore, the study employed TPOT to identify the optimal algorithm for constructing a machine learning model, with the genetic algorithm pinpointing XGB-GA as the most effective choice. This research significantly advances the field of DDoS attack detection by presenting a robust and accurate methodology, thereby enhancing the cybersecurity landscape and fortifying digital infrastructures against these pervasive threats.", notes = "also known as \cite{s24051672}", } @InProceedings{tamaki:2002:IWES, author = "H. Tamaki and H. Murao and F. Furuta and S. Kitamura", title = "Emergent Design of Passive Filter Circuit - A Case of Using Multi-Objective Genetic Programming", booktitle = "Proceedings of the 4th International Workshop on Emergent Synthesis - IWES'02", year = "2002", pages = "23--28", address = "Kobe University, Japan", month = "9-10 " # may, keywords = "genetic algorithms, genetic programming", notes = "Broken Sep 2018 http://www.race.u-tokyo.ac.jp/uedalab/IWES/IWES02/", } @InProceedings{Tamboli:2011:CSNT, author = "Arifa S Tamboli and Medha A Shah", title = "A Generic Structure of Object Classification Using Genetic Programming", booktitle = "International Conference on Communication Systems and Network Technologies (CSNT 2011)", year = "2011", month = "3-5 " # jun, pages = "723--728", address = "Katra, Jammu", size = "6 pages", abstract = "In This paper a method for classification of two types of objects using genetic programming (GP) has been presented. These two objects are coins of different sizes, and different textures. The basic algorithm of genetic programming was presented and explained. The features used for training and testing are mean, standard deviation, skewness and kurtosis. Precision and recall were used as performance measures and they were the main building blocks in building the fitness function. They replaced the false alarm and detection rate that was used in previous works. The result figures as well as values of precision, recall, fitness values, time elapsed, and number of generations used in training was presented. The very basic structure of a GP system was implemented and proved that it can work well as a standalone computational algorithm.", keywords = "genetic algorithms, genetic programming, fitness function, kurtosis, object classification, object recognition, skewness, standard deviation, image classification, image texture", DOI = "doi:10.1109/CSNT.2011.154", notes = "pictures of money coins, textures. Also known as \cite{5966545}", } @InProceedings{Tamura:2011:SNPD, author = "Shinji Tamura and Teruhisa Hochin and Hiroki Nomiya", title = "Generation Method of Concurrency Control Program by Using Genetic Programming", booktitle = "12th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2011)", year = "2011", month = "6-8 " # jul, pages = "175--180", address = "Sydney, Australia", abstract = "This paper proposes a generation system of concurrency control program by using genetic programming (GP). This system generates concurrency control program according to the features of transactions, which are collections of database operations. Functions and terminals of trees representing program in GP, and the fitness measure function used in GP are proposed. The functions and the terminals include those changing and testing variables attached to data items and transactions as well as those checking the kind of operation etc. These will bring us general concurrency control program, which is beyond the combination of the parts of traditional concurrency control program. As the granularity of the functions and the terminals is small, the sub-trees, which are used for the popular concurrency control protocol, and are prepared in advance, are used. The fitness measure function considers the goodness of concurrency control program. The experiments show that a concurrency control program using locks could be generated under the concurrent environment, while a concurrency control program better than the two-phase locking protocol could be generated under the not-so-concurrent environment.", keywords = "genetic algorithms, genetic programming, concurrency control program, database operation, fitness measure function, two-phase locking protocol, concurrency control", DOI = "doi:10.1109/SNPD.2011.16", notes = "Also known as \cite{6063562}", } @InProceedings{Tamura:2012:ICIS, author = "Shinji Tamura and Teruhisa Hochin and Hiroki Nomiya", title = "Generation of Semantic Concurrency Control Program by Using Genetic Programming", booktitle = "11th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2012)", year = "2012", month = "30 " # may # " - 1 " # jun, pages = "289--294", size = "6 pages", address = "Shanghai", abstract = "This paper extends the concurrency control program generation system in order to support semantic concurrency control. The generation system is based on genetic programming (GP). Semantic concurrency control improves concurrency of transactions, which are collections of database operations, by considering the semantics of database operations. The generation system generates concurrency control program according to the compatibility of operations as well as the features of transactions. It is experimentally shown that the concurrency control program generation system could be adapted to the semantic concurrency control.", keywords = "genetic algorithms, genetic programming, database operations, semantic concurrency control program generation, transaction concurrency improvement, automatic programming, concurrency control, database management systems, programming language semantics, transaction processing", DOI = "doi:10.1109/ICIS.2012.55", notes = "Scheduling http://acis.cps.cmich.edu/ICIS2012/ Also known as \cite{6211111}", } @InProceedings{Tamura:2012:SMC, author = "Shinji Tamura and Teruhisa Hochin and Hiroki Nomiya", booktitle = "IEEE International Conference on Systems, Man, and Cybernetics (SMC 2012)", title = "Concurrency control program generation by decreasing nodes of program trees in genetic programming", year = "2012", pages = "1023--1028", address = "Seoul", keywords = "genetic algorithms, genetic programming, concurrency control, database management systems, program compilers, trees (mathematics), automatic defined function, concurrency control program generation, high-level symbol set, program tree node decrease, tree representation, Conferences, Cybernetics, Decision support systems, concurrency control, granularity, program generation", isbn13 = "978-1-4673-1713-9", DOI = "doi:10.1109/ICSMC.2012.6377863", abstract = "This paper tries to generate an appropriate concurrency control program by using genetic programming (GP). In GP, a program is represented with a tree. Nodes of a tree are selected from a symbol set. This paper tries two symbol sets: the high-level symbol set and the reduced one. The high-level one includes high-level symbols created by combining conventional ones. In the reduced symbol set, symbols are drastically decreased by changing the method of implementing the concurrency control program. Automatic defined functions (ADFs) are also used. Introducing high-level symbols caused the increase of the number of symbols. This made the program generation difficult. On the other hand, an appropriate program could be generated with the reduced symbol set. An ADF is also used in the program generated.", notes = "Also known as \cite{6377863}", } @Article{Tamura:2008:ECJ, author = "Kenji Tamura and Atsuko Mutoh and Tsuyoshi Nakamura and Hidenori Itoh", title = "Virus-evolutionary linear genetic programming", journal = "Electronics and Communications in Japan", year = "2008", volume = "91", number = "1", pages = "32--39", note = "Translated from Denki Gakkai Ronbunshi, Vol. 126-C, No. 7, July 2006, pp. 913--918", keywords = "genetic algorithms, genetic programming, Virus-Evolutionary Linear Genetic Programming, schema, linear representation, coevolution, virus theory of evolution", publisher = "Wiley Periodicals", URL = "https://zh.art1lib.com/book/1268426/ecd361", URL = "https://onlinelibrary.wiley.com/doi/abs/10.1002/ecj.10030", DOI = "doi:10.1002/ecj.10030", size = "8 pages", abstract = "Many kinds of evolutionary methods have been proposed. GA and GP in particular have demonstrated their effectiveness in various problems recently, and many systems have been proposed. One is Virus-Evolutionary Genetic Algorithm (VE-GA), and the other is Linear Genetic Programming in C (LGPC). The performance of each system has been reported. VE-GA is the coevolution system of host individuals and virus individuals. That can spread schema effectively among the host individuals by using virus infection and virus incorporation. LGPC implements the GP by representing the individuals to one dimension as if GA. LGPC can reduce a search cost of pointer and save machine memory, and can reduce the time to implement GP programs. We have proposed that a system introduce virus individuals in LGPC, and analyzed the performance of the system on two problems. Our system can spread schema among the population, and search solution effectively. The results of computer simulation show that this system can search for solution depending on LGPC applying problem character compared with LGPC.", notes = "Two populations (larger host tree+ virus variable length linear/subtree) co-evolve. p33 'strings of the virus population represent subsolutions' ... 'A virus copies its own gene string into a host by infection, while extracting a host substring by incorporation' (several virus individuals replaced by subtree [may not be tree] fragment of host tree) p34 'A virus searches a portion of host with a matching [whole of virus] structure' (like homologous crossover?) Two point or one point crossover in host(tree) population. Two point xo: 'Since the crossover points are chosen in portions with matching structural information, the tree structure is not destroyed.' Use 6-even parity as expect virus population to learn subsolutions. Ant expected to have no subsolutions. In English Chuo Gakuin University, Japan", } @Article{journals/jaciii/TamuraT13, author = "Kenji Tamura and Takashi Torii", title = "Development of Ghost Controller for {Ms Pac-Man} Versus Ghost Team with Grammatical Evolution", journal = "Journal of Advanced Computational Intelligence and Intelligent Informatics", year = "2013", volume = "17", number = "6", pages = "904--912", note = "Special Issue on Intelligent and Evolutionary Systems", keywords = "genetic algorithms, genetic programming, grammatical evolution,, evolutionary computation, ms. pac-man versus ghost team, multi-agent", bibdate = "2014-03-17", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/jaciii/jaciii17.html#TamuraT13", ISSN = "1343-0130", URL = "https://www.fujipress.jp/jaciii/jc/jacii001700060904/", DOI = "doi:10.20965/jaciii.2013.p0904", size = "9 pages", abstract = "These days, artificial intelligence (AI) has been used in game AI. Additionally, video game AI is studied actively in late years, for example, application of commercial game or competition etc. In many video games of recent years, real-time action and non-player characters have been required to attract players. This paper describes how to develop a ghost team controller using evolutionary system to play the video game, Ms Pac-Man. Ms Pac-Man has been used as a test bed of AI, especially multi-agent system. We propose a method to generate the ghost team controller with Grammatical Evolution. In case of developing Ms Pacman agent with Evolutionary Computation using fitness function, the criterion of the fitness is used its obtained high score in many cases. In contrast, ghost team has to prevent Ms Pac-man to get high score, namely hold score in check. However, if Ms Pacman is captured in low score by accident, its ghost strategy have a possibility to survive next generation, and if the ghosts pursue Ms Pac-man in a line, agent is not captured for all time. Therefore developing ghost team agent is required to avoid these issues, and we introduced a penalty to the fitness, grammar like instinct and to attack Ms Pac-Man on both sides. This paper introduces experimental data about the ghost team controller for Ms Pac-Man versus ghost team, we used ghost team agents and tested them Ms Pac-Man agents. The experimental results showed that proposed system could catch Ms Pac-Man agent compare with simple hand-coded ghost teams, and the evolved controller we made worked effectively. These results are concluded that proposed method works effectively for generating ghost controller.", } @InProceedings{tan:2018:AJCAI, author = "Boxiong Tan and Hui Ma and Yi Mei", title = "A Genetic Programming Hyper-heuristic Approach for Online Resource Allocation in {Container-Based} Clouds", booktitle = "Australasian Joint Conference on Artificial Intelligence", year = "2018", editor = "Tanja Mitrovic and Bing Xue and Xiaodong Li", volume = "11320", series = "LNCS", address = "Wellington, New Zealand", month = dec # " 11-14", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-030-03990-5", URL = "http://link.springer.com/chapter/10.1007/978-3-030-03991-2_15", DOI = "doi:10.1007/978-3-030-03991-2_15", } @InProceedings{Tan:2019:CEC, author = "Boxiong Tan and Hui Ma and Yi Mei", title = "A Hybrid Genetic Programming Hyper-Heuristic Approach for Online Two-level Resource Allocation in Container-based Clouds", booktitle = "2019 IEEE Congress on Evolutionary Computation, CEC 2019", year = "2019", editor = "Carlos A. Coello Coello", pages = "2681--2688", address = "Wellington, New Zealand", month = "10-13 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, container-based clouds, resource allocation, evolutionary computation, hyper-heuristic", isbn13 = "978-1-7281-2152-6", URL = "https://ecs.wgtn.ac.nz/Main/Here", DOI = "doi:10.1109/CEC.2019.8790220", size = "8 pages", abstract = "Container technology has become a new trend in both the software industry and cloud computing. Containers support the fast development of web applications and they have the potential to reduce energy consumption in data centres. Containers are usually first allocated to virtual machines (VMs) and VMs are allocated to physical machines. The container allocation is a challenging task which involves a two-level allocation problem. Current research overly simplifies the container allocation into a one-level allocation problem and uses simple rule-based approaches to solve the problem. As a result, the resource is not allocated efficiently which leads to high energy consumption. This paper provides a novel definition of the two-level container allocation problem. Then, we develop a hybrid approach using genetic programming hyper-heuristics combined with human-designed rules to solve the problem. The experiments show that our hybrid approach is able to significantly reduce energy consumption than solely using human-designed rules.", notes = "also known as \cite{8790220} Dec 2019 title tweak IEEE Catalog Number: CFP19ICE-ART", } @Article{Boxiong_Tan:Cloud, author = "Boxiong Tan and Hui Ma and Yi Mei and Mengjie Zhang", title = "A Cooperative Coevolution Genetic Programming Hyper-Heuristic Approach for On-line Resource Allocation in Container-based Clouds", journal = "IEEE Transactions on Cloud Computing", year = "2022", volume = "10", number = "3", pages = "1500--1514", keywords = "genetic algorithms, genetic programming", ISSN = "2168-7161", DOI = "doi:10.1109/TCC.2020.3026338", abstract = "Containers are lightweight and provide the potential to reduce more energy consumption of data centers than Virtual Machines (VMs) in container-based clouds. The on-line resource allocation is the most common operation in clouds. However, the on-line Resource Allocation in Container-based clouds (RAC) is new and challenging because of its two-level architecture, i.e. the allocations of containers to VMs and the allocation of VMs to physical machines. These two allocations interact with each other, and hence cannot be made separately. Since on-line container allocation requires a real-time response, most current allocation techniques rely on heuristics (e.g. First Fit and Best Fit), which do not consider the comprehensive information such as workload patterns and VM types. As a result, resources are not used efficiently and the energy consumption is not sufficiently optimized. We first propose a novel model of the on-line RAC problem with the consideration of VM overheads, VM types and an affinity constraint. Then, we design a Cooperative Coevolution Genetic Programming (CCGP) hyper-heuristic approach to solve the RAC problem. The CCGP can learn the workload patterns and VM types from historical workload traces and generate allocation rules. The experiments show significant improvement in energy consumption compared to the state-of-the-art algorithms.", notes = "Also known as \cite{9205601}", } @InProceedings{Tan:2006:ASPGP, title = "A logic program semantics-based framework for immune anomaly detection", author = "Chengyu Tan and Hongbin Dong and Yiwen Liang", booktitle = "Proceedings of the Third Asian-Pacific workshop on Genetic Programming", year = "2006", editor = "The Long Pham and Hai Khoi Le and Xuan Hoai Nguyen", pages = "76--85", ISSN = "18590209", address = "Military Technical Academy, Hanoi, VietNam", keywords = "genetic algorithms, genetic programming", URL = "http://gpbib.cs.ucl.ac.uk/aspgp06/Tan_2006_ASPGP.pdf", size = "10 pages", abstract = "Some algorithms of Artificial Immune System, such as negative selection algorithm, can not effectively capture semantic information in some complex problem spaces. In fact, many semantics exist in digit antigen space. If we can recognise based on the semantics of antigens, the accuracy of anomaly detection can be improved. In this paper, by the background of system call trails generated by process, we design a systematic framework of artificial immunity applied to process anomaly detection. This paper proposes to descript antigens and immune detectors with first order logic, and construct a time sequence model of immune detector based upon stable model theory in extended logic programming ELP. At last, we introduce a training strategy of new immune detector based on genetic inductive logic programming.", notes = "broken march 2020 http://www.aspgp.org", } @InProceedings{DBLP:conf/ijcai/TanC01, author = "Chew Lim Tan and Henry Wai Kit Chia", title = "Neural Logic Network Learning using Genetic Programming", year = "2001", editor = "Bernhard Nebel", ISBN = "1-55860-777-3", bibsource = "DBLP, http://dblp.uni-trier.de", booktitle = "Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, IJCAI 2001", pages = "803--808", address = "Seattle, USA", month = aug # " 4-10", publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", URL = "http://ijcai.org/Past_Proceedings/IJCAI-2001/PDF/.../IJCAI-2001-j.pdf", size = "6 pages", abstract = "Neural Logic Network or Neulonet is a hybrid of neural network expert systems. Its strength lies in its ability to learn and to represent human logic in decision making using component net rules. The technique originally employed in neulonet learning is backpropagation. However, the resulting weight adjustments will lead to a loss in the logic of the net rules. A new technique is now developed that allows the neulonet to learn by composing net rules using genetic programming. This paper presents experimental results to demonstrate this new and exciting capability in capturing human decision logic from examples. Comparisons will also be made between the use of net rules, and the use of standard Boolean logic of negation, disjunction and conjunction in evolutionary computation.", notes = "http://www.ijcai.org/past/ijcai-01/PrelimTechSchedule.htm", } @InProceedings{tan:2002:mmccrugp, author = "K. C. Tan and A. Tay and T. H. Lee and C. M. Heng", title = "Mining multiple comprehensible classification rules using genetic programming", booktitle = "Proceedings of the 2002 Congress on Evolutionary Computation CEC2002", editor = "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and Mark Shackleton", pages = "1302--1307", year = "2002", publisher = "IEEE Press", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", organisation = "IEEE Neural Network Council (NNC), Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", ISBN = "0-7803-7278-6", month = "12-17 " # may, notes = "CEC 2002 - A joint meeting of the IEEE, the Evolutionary Programming Society, and the IEE. Held in connection with the World Congress on Computational Intelligence (WCCI 2002)", keywords = "genetic algorithms, genetic programming, IF-THEN rule evolution, application domains, artificial immune system-like memory vector, benchmark data sets, concept mapping technique, covering algorithm, data mining, fitness evaluation, multiple comprehensible classification rules, redundant rule removal, simulation, tree representation, data mining, pattern classification, programming, redundancy", DOI = "doi:10.1109/CEC.2002.1004431", abstract = "Genetic Programming (GP) has been emerged as a promising approach to deal with classification task in data mining. This work extends the tree representation of GP to evolve multiple comprehensible IF-THEN classification rules. In the paper, we introduce a concept mapping technique for fitness evaluation of individuals. A covering algorithm that employs an artificial immune system-like memory vector is used to produce multiple rules as well as to remove redundant rules. The proposed GP classifier is validated upon nine benchmark datasets and the simulation results confirm the viability and effectiveness of the GP approach for solving data mining problems in a wide spectrum of application domains.", notes = "Michigan approach. GPc, groovy Java GP (gjprog), WEKA. problem specific population sizes 10-100 and w_1 and w_2. ", } @Article{Tan:2003:AIM, author = "K. C. Tan and Q. Yu and C. M. Heng and T. H. Lee", title = "Evolutionary computing for knowledge discovery in medical diagnosis", journal = "Artificial Intelligence in Medicine", year = "2003", volume = "27", pages = "129--154", number = "2", keywords = "genetic algorithms, genetic programming, Medical diagnosis, Knowledge discovery, Data mining, Evolutionary computing", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6T4K-47RRWS9-2/2/5c8dfaf6e49d194b0c8ed6e2fd1b5117", ISSN = "0933-3657", DOI = "doi:10.1016/S0933-3657(03)00002-2", abstract = "One of the major challenges in medical domain is the extraction of comprehensible knowledge from medical diagnosis data. a two-phase hybrid evolutionary classification technique is proposed to extract classification rules that can be used in clinical practice for better understanding and prevention of unwanted medical events. In the first phase, a hybrid evolutionary algorithm (EA) is used to confine the search space by evolving a pool of good candidate rules, e.g. genetic programming (GP) is applied to evolve nominal attributes for free structured rules and genetic algorithm (GA) is used to optimise the numeric attributes for concise classification rules without the need of discretisation. These candidate rules are then used in the second phase to optimize the order and number of rules in the evolution for forming accurate and comprehensible rule sets. The proposed evolutionary classifier (EvoC) is validated upon hepatitis and breast cancer datasets obtained from the UCI machine-learning repository. Simulation results show that the evolutionary classifier produces comprehensible rules and good classification accuracy for the medical datasets. Results obtained from t-tests further justify its robustness and invariance to random partition of datasets.", notes = "PMID: 12636976", } @Article{tan:2004:GPEM, author = "Kay Chen Tan", title = "Book Review: The Design of Innovation: Lessons from and for Competent Genetic Algorithms", journal = "Genetic Programming and Evolvable Machines", year = "2004", volume = "5", number = "1", pages = "107--110", month = mar, keywords = "genetic algorithms", ISSN = "1389-2576", URL = "https://rdcu.be/dR8W2", DOI = "doi:10.1023/B:GENP.0000017054.24706.3b", notes = "Review of book by David E. Goldberg. Published by: Kluwer Academic Publishers, Boston, hardbound, ISBN 1-4020-7098-5, 2002, 272 pp. list price: $79.95. Article ID: 5264737", } @Article{Tan:2016:PeerJ, author = "Mei Sze Tan and Jing Wei Tan and Siow-Wee Chang and Hwa Jen Yap and Sameem Abdul Kareem and Rosnah Binti Zain", title = "A genetic programming approach to oral cancer prognosis", journal = "PeerJ", year = "2016", pages = "e2482", month = "21 " # sep, keywords = "genetic algorithms, genetic programming, Computational Biology, Oncology, Computational Science, Oral cancer prognosis, Machine learning, Feature selection", URL = "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5036111/pdf/peerj-04-2482.pdf", DOI = "doi:10.7717/peerj.2482", abstract = "The potential of genetic programming (GP) on various fields has been attained in recent years. In bio-medical field, many researches in GP are focused on the recognition of cancerous cells and also on gene expression profiling data. the aim is to study the performance of GP on the survival prediction of a small sample size of oral cancer prognosis dataset, which is the first study in the field of oral cancer prognosis. Method. GP is applied on an oral cancer dataset that contains 31 cases collected from the Malaysia Oral Cancer Database and Tissue Bank System (MOCDTBS). The feature subsets that is automatically selected through GP were noted and the influences of this subset on the results of GP were recorded. In addition, a comparison between the GP performance and that of the Support Vector Machine (SVM) and logistic regression(LR) are also done in order to verify the predictive capabilities of the GP. Result. The result shows that GP performed the best (average accuracy of 83.87percent and average AUROC of 0.8341) when the features selected are smoking, drinking,chewing, histological differentiation of SCC, and oncogene p63. In addition, based on the comparison results, we found that the GP outperformed the SVM and LR in oral cancer prognosis. Discussion. Some of the features in the dataset are found to be statistically co-related.This is because the accuracy of the GP prediction drops when one of the feature in the best feature subset is excluded. Thus, GP provides an automatic feature selection function, which chooses features that are highly correlated to the prognosis of oral cancer. This makes GP an ideal prediction model for cancer clinical and genomic data that can be used to aid physicians in their decision making stage of diagnosis or prognosis.", notes = "Bioinformatics Program, Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia", } @InProceedings{Tan:2023:ICAC, author = "Muyao Tan and Liang Jin and Kunlun Li", booktitle = "2023 28th International Conference on Automation and Computing (ICAC)", title = "Expensive Multi-Objective of Pre-Oxidation Process Parameter Optimization Considering Heterogeneity", year = "2023", abstract = "The precursor pre-oxidation process, as a critical link in carbon fiber production, stabilizes the properties of carbon fiber precursors, lays the foundation for the carbonization process, and is also a step that can directly determine the structural properties of carbon fiber, so it has been widely concerned by academic circles. Evaluating the density and strength of pre-oxidized filaments is expensive, time-consuming, and has different cost consumption, leading to heterogeneous problems in optimising pre-oxidized process parameters. An optimisation algorithm of pre-oxidation process parameters based on collaborative migration agent genetic programming is proposed to solve this problem. This method establishes a single-objective multi-precision surrogate model of the pre-oxidation process and a collaborative model between the two targets. The cooperative model predicts the high-cost target to reduce the operating cost in the calculation process. Our experimental results show that the proposed algorithm performs well in optimising pre-oxidation process parameters.", keywords = "genetic algorithms, genetic programming, Costs, Automation, Collaboration, Production, Predictive models, Prediction algorithms, heterogeneity, pre-oxidation process, multi-objective, parameter optimisation", DOI = "doi:10.1109/ICAC57885.2023.10275289", month = aug, notes = "Also known as \cite{10275289}", } @InProceedings{Tan:ICSE:2015, author = "Shin Hwei Tan and Abhik Roychoudhury", title = "relifix: Automated Repair of Software Regressions", booktitle = "37th International Conference on Software Engineering", year = "2015", editor = "Gerardo Canfora and Sebastian Elbaum and Antonia Bertolino", pages = "471--482", address = "Florence Italy", month = may # " 16-24", organisation = "IEEE/ACM", publisher = "IEEE", keywords = "genetic improvement, APR, SBSE", isbn13 = "978-1-4799-1934-5", URL = "http://www.comp.nus.edu.sg/~shinhwei/relifix.pdf", DOI = "doi:10.1109/ICSE.2015.65", size = "12 pages", abstract = "Regression occurs when code changes introduce failures in previously passing test cases. As software evolves, regressions may be introduced. Fixing regression errors manually is time-consuming and error-prone. We propose an approach of automated repair of software regressions, called relifix, that considers the regression repair problem as a problem of reconciling problematic changes. Specifically, we derive a set of code transformations obtained from our manual inspection of 73 real software regressions; this set of code transformations uses syntactical information from changed statements. Regression repair is then accomplished via a search over the code transformation operators, which operator to apply, and where. Our evaluation compares the repairability of relifix with GenProg on 35 real regression errors. relifix repairs 23 bugs, while GenProg only fixes five bugs. We also measure the likelihood of both approaches in introducing new regressions given a reduced test suite. Our experimental results shows that our approach is less likely to introduce new regressions than GenProg.", notes = "Is this GP? Perhaps not but does seem to be systematically searching a list of possible code mutations, including tabu exclusions. Clang. Cites \cite{DBLP:journals/tse/GouesNFW12}. National University of Singapore http://2015.icse-conferences.org/ http://2015.icse-conferences.org/component/content/article?id=107", } @InProceedings{Tan:2016:ASP:2950290.2950295, author = "Shin Hwei Tan and Hiroaki Yoshida and Mukul R. Prasad and Abhik Roychoudhury", title = "Anti-patterns in Search-based Program Repair", booktitle = "Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering, FSE 2016", year = "2016", pages = "727--738", address = "Seattle, WA, USA", publisher = "ACM", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, Debugging, and repair, fault localization", isbn13 = "978-1-4503-4218-6", URL = "https://www.comp.nus.edu.sg/~abhik/pdf/FSE16.pdf", DOI = "doi:10.1145/2950290.2950295", acmid = "2950295", size = "12", abstract = "Search-based program repair automatically searches for a program fix within a given repair space. This may be accomplished by retrofitting a generic search algorithm for program repair as evidenced by the GenProg tool, or by building a customized search algorithm for program repair as in SPR. Unfortunately, automated program repair approaches may produce patches that may be rejected by programmers, because of which past works have suggested using human-written patches to produce templates to guide program repair. In this work, we take the position that we will not provide templates to guide the repair search because that may unduly restrict the repair space and attempt to overfit the repairs into one of the provided templates. Instead, we suggest the use of a set of anti-patterns --- a set of generic forbidden transformations that can be enforced on top of any search-based repair tool. We show that by enforcing our anti-patterns, we obtain repairs that localize the correct lines or functions, involve less deletion of program functionality, and are mostly obtained more efficiently. Since our set of anti-patterns are generic, we have integrated them into existing search based repair tools, including GenProg and SPR, thereby allowing us to obtain higher quality program patches with minimal effort.", } @PhdThesis{ShinHwei, author = "Shin Hwei Tan", title = "Design of repair operators for automated program repair", school = "National University of Singapore", year = "2017", address = "Singapore", month = "30 " # oct, keywords = "genetic algorithms, genetic programming, genetic improvement, APR", URL = "https://www.comp.nus.edu.sg/~abhik/Students/ShinHwei.pdf", size = "205 pages", notes = "Mentions of GenProg etc. CodeFlaws. Table 3.1 repair operator Supervisor: Abhik Roychoudhury", } @InProceedings{Tan:2018:ICSE, author = "Shin Hwei Tan and Zhen Dong and Xiang Gao and Abhik Roychoudhury", title = "Repairing Crashes in {Android} Apps", booktitle = "40th International Conference on Software Engineering", year = "2018", editor = "Marsha Chechik and Mark Harman", pages = "187--198", address = "Gothenburg, Sweden", month = "27 " # may # "-3 " # jun, publisher = "ACM", keywords = "genetic algorithms, genetic programming, genetic improvement, mobile computing, Software, Automatic programming, Software testing and debugging, Dynamic analysis, Automated repair, APR, Android apps, Crash, SBSE", isbn13 = "978-1-4503-5638", URL = "http://www.human-competitive.org/sites/default/files/tan-paper.pdf", URL = "https://www.icse2018.org/event/icse-2018-technical-papers-repairing-crashes-in-android-apps", URL = "http://www.shinhwei.com/droixicse_camera.pdf", DOI = "doi:10.1145/3180155.3180243", size = "12 pages", abstract = "Android apps are omnipresent, and frequently suffer from crashes leading to poor user experience and economic loss. Past work focused on automated test generation to detect crashes in Android apps. However, automated repair of crashes has not been studied. In this paper, we propose the first approach to automatically repair Android apps, specifically we propose a technique for fixing crashes in Android apps. Unlike most test-based repair approaches, we do not need a test-suite; instead a single failing test is meticulously analyzed for crash locations and reasons behind these crashes. Our approach hinges on a careful empirical study which seeks to establish common root-causes for crashes in Android apps, and then distills the remedy of these root-causes in the form of eight generic transformation operators. These operators are applied using a search-based repair framework embodied in our repair tool Droix. We also prepare a benchmark DroixBench capturing reproducible crashes in Android apps. Our evaluation of Droix on DroixBench reveals that the automatically produced patches are often syntactically identical to the human patch, and on some rare occasion even better than the human patch (in terms of avoiding regressions). These results confirm our intuition that our proposed transformations form a sufficient set of operators to patch crashes in Android.", notes = "p193 GenProg, Google Nexus 5x emulator 2018 HUMIES finalist https://www.icse2018.org/profile/xianggao", } @InProceedings{Tan:2025:GI, author = "Shin Hwei Tan", title = "Put on Your Tester Hat: Improving programs for Automated Program Generation", booktitle = "14th International Workshop on Genetic Improvement @ICSE 2025", year = "2025", editor = "Aymeric Blot and Vesna Nowack and Penn Faulkner Rainford and Oliver Krauss", pages = "v", address = "Ottawa", month = "27 " # apr, publisher = "IEEE", note = "Invited Keynote", keywords = "genetic algorithms, genetic programming, Genetic Improvement, APR", URL = "http://gpbib.cs.ucl.ac.uk/gi2025/blot_2025_GI.pdf", slide_url = "https://gpbib.cs.ucl.ac.uk/gi2025/gi_2025_slides/GIworkshop-keynote.pdf", size = "1 page", abstract = "Given either a specification written in natural language or an input program, automated program generation techniques produce a program according to the given specification or by modifying the input program. Automated program generation is a powerful technique that can be used for finding bugs in software systems that take programs as input or fixing bugs in the input programs. However, most existing techniques focus on automated program generation for automated program repair or program synthesis. This talk introduces a different perspective of automated program generation where we will discuss our latest results on automated program generation for testing static program analyzers by designing different types of program transformations. We will also explore and rethink about the automated program generation problem from the tester perspective. The new perspective could have huge potential for the design of new genetic improvement techniques to improve programs for automated program generation.", notes = "GI @ ICSE 2025, part of \cite{blot:2025:GI}", } @PhdThesis{Xuejun_Tan:thesis, author = "Xuejun Tan", title = "Computational Algorithms for Fingerprint Recognition", school = "Electrical Engineering, University of California, Riverside", year = "2003", address = "USA", month = jun, keywords = "genetic algorithms, genetic programming", URL = "http://search.proquest.com/docview/305355283", size = "200 pages", abstract = "Biometrics, which recognizes a person's identity using his/her physiological or behavioural characteristics, is inherently more reliable and capable than traditional methods. Biometric signs include fingerprint, face, gait, iris, voice, signature, etc. Among them, fingerprint is the one, which has been researched for a long time and shows the most promising future in real-world applications. However, because of the complex distortions among the different impressions of the same finger, fingerprint recognition is still a challenging problem. In this dissertation, our objective is to develop effective and efficient computational algorithms for an automatic fingerprint recognition system. The algorithms we address include: (1) Templates based minutiae extraction algorithm; (2) Triplets of minutiae based fingerprint indexing algorithm; (3) Genetic Algorithm based fingerprint matching algorithm; (4) Genetic Programming based feature learning algorithm for fingerprint classification; (5) Comparison of classification and indexing in identification; and (6) Fundamental performance analysis of fingerprint matching. All the experimental results are demonstrated on standard fingerprint database, NIST-4 fingerprint database. Although the algorithms we have developed can achieve a good performance in fingerprint recognition, we believe that there are still some problems need to be worked on to make automatic fingerprint recognition system more effective and efficient in real-world applications. We believe that it needs incorporation of researchers from different fields, such as Computer Science, Electrical Engineering, Physiology, Statistics, Social Sciences, etc. So that, it is possible to achieve a better fingerprint recognition performance, which is close to theoretical bound.", notes = "Supervisor: Bir Bhanu UMI Microform 3096780", } @Article{Tan:2006:tSMC, author = "Xuejun Tan and B. Bhanu and Yingqiang Lin", title = "Fingerprint classification based on learned features", journal = "IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews", year = "2005", volume = "35", number = "3", pages = "287--300", month = aug, keywords = "genetic algorithms, genetic programming, Bayes methods, feature extraction, fingerprint identification, image classification, learning (artificial intelligence), visual databases Bayesian classifier, NIST-4 database, composite operator discovery, feature extraction, feature-learning algorithm, fingerprint classification method, primitive image processing operations", DOI = "doi:10.1109/TSMCC.2005.848167", ISSN = "1094-6977", abstract = "In this paper, we present a fingerprint classification approach based on a novel feature-learning algorithm. Unlike current research for fingerprint classification that generally uses well defined meaningful features, our approach is based on Genetic Programming (GP), which learns to discover composite operators and features that are evolved from combinations of primitive image processing operations. Our experimental results show that our approach can find good composite operators to effectively extract useful features. Using a Bayesian classifier, without rejecting any fingerprints from the NIST-4 database, the correct rates for 4- and 5-class classification are 93.3percent and 91.6percent, respectively, which compare favourably with other published research and are one of the best results published to date.", } @Article{Tan:2013:ieeeCIM, author = "K. C. Tan", journal = "IEEE Computational Intelligence Magazine", title = "The {"}vision{"} of tomorrow! [Editor's Remarks]", year = "2013", month = feb, volume = "8", number = "1", pages = "2--2", DOI = "doi:10.1109/MCI.2012.2228575", ISSN = "1556-603X", abstract = "This special issue features a number of CI (computational intelligence) applications in computer vision and image processing as guest-edited by Mengjie Zhang, Mario Koeppen and Sergio Damas. With the research advancements and developments of computational intelligence in computer vision and image processing, it has been demonstrated to improve the accuracy of image segmentation, and assist medical professionals in their jobs with a shorter turn-around time as featured in our first article. Meanwhile the second feature article proposes a method to ensure that images obtained are not just sharp, but are also enhanced for ease of identifications. The third feature article showcases genetic programming in motion detection as used in vision systems like road cameras, where images may become distorted due to the high speed of a moving object, bad weather or other influences.", notes = "Also known as \cite{6410723}", } @InProceedings{Tanaka:2024:BigData, author = "Tsubasa Tanaka", title = "Integrating Machine Learning and Rule-Based Approaches in Symbolic Music Generation", booktitle = "2024 IEEE International Conference on Big Data (BigData)", year = "2024", pages = "3218--3223", month = dec, keywords = "genetic algorithms, genetic programming, Hands, Constraint handling, Computational modelling, Neural networks, Training data, Machine learning, Generative adversarial networks, Rhythm, Creativity, music composition, computer-assisted composition, rule-based approach, constraint satisfaction, generative adversarial network", ISSN = "2573-2978", DOI = "doi:10.1109/BigData62323.2024.10825900", abstract = "When composing a piece of artistic or experimental music, a composer often tries to create a new style of music that differs from existing styles. This contradicts the idea of conventional machine-learning-based generation systems because they learn from existing music pieces and imitate their styles. Therefore, generating music beyond the assumed range of styles would be difficult for these systems. For example, to generate atonal music by learning from tonal music would be difficult. On the other hand, in the context of computer-assisted composition, especially in classical contemporary music, rule-based approaches based on constraint programming or genetic programming have been relatively successful. With these approaches, composers can define original musical styles by designing rules in the form of constraints or fitness functions. However, the difficulty of these approaches is that composers must program every detail of the music, which is extremely burdensome. As an early step to creating a computer-assisted composition system that overcomes both difficulties, this paper proposes a new neural network model that integrates the style definition capability of rule-based approaches and the automation power of machine learning. The key idea is to generalise the GAN discriminator to repurpose it as a constraint solver beyond the GAN's usual function of imitating the original. In addition, as a proof of concept, this paper shows that the proposed model can actually output solutions satisfying some musical rules that are not necessarily relevant to the dataset.", notes = "Also known as \cite{10825900}", } @InProceedings{Tanemura:2023:GCCE, author = "Keito Tanemura and Yuji Sasaki and Shoei Takahashi and Yuki Tokuni and Hikaru Manabe and Ryohei Miyadera", booktitle = "2023 IEEE 12th Global Conference on Consumer Electronics (GCCE)", title = "Application of Genetic Programming to Unsolved Mathematical Problems {II}", year = "2023", pages = "608--609", abstract = "In this research, the authors developed a new Swift programming library of symbolic regression based on genetic programming. Symbolic regression is a field of artificial intelligence where AI looks for formulas that describe the given data. The data used for the research is winning positions of combinatorial games. Compared to the library presented at the last GCCE conference, this library gets two new features to select the fittest formulae.The first is to find a minimum number of formulae that describe the given data.The second is to separate the data into smaller subsets, and find formulae to describe each subset.With these two new features, this new Swift programming library of symbolic regression can be a powerful tool in the research of mathematics and science.", keywords = "genetic algorithms, genetic programming, Integer programming, Electric potential, Games, Search problems, Libraries, symbolic regression, combinatorial games, mixed integer programming", DOI = "doi:10.1109/GCCE59613.2023.10315661", ISSN = "2693-0854", month = oct, notes = "Also known as \cite{10315661}", } @Article{tanev:2000:pdiGP, author = "Ivan T. Tanev and Takashi Uozumi and Koichi Ono", title = "DCOM-based Parallel Distributed Implementation of GP", journal = "Parallel and Distributed Computing Practices", year = "2000", volume = "3", number = "1", month = mar, keywords = "genetic algorithms, genetic programming", ISSN = "1097-2803", URL = "http://www.scpe.org/index.php/scpe/article/view/181", abstract = "We present an approach for parallel distributed implementation of genetic programming, which is devoted to improve the computational performance of genetic programming by exploiting parallelism at the level of evaluation of the individuals. The approach is based on DCOM client-server model. Using the DCOM-paradigm offers the advantages of parallel distributed implementation of genetic programming, such as binary standardization, platform-, machine- and protocol-neutrality, and seamless integration with different Internet protocols. The developed implementation of genetic programming runs in LAN and/or Internet environments. The double-queued multi-threaded architecture of the DCOM-server, aimed to extend the functionality of the DCOM with features, such as asynchronous communications still implementing blocking-mode calls, and reduced communication overhead of the evaluation of simple GP-individuals, is developed. The implementation of batching, directed towards the alleviation of communication overhead during the evaluation of simple GP-individuals, is proposed. Analytically estimated and experimentally obtained performance evaluation results are discussed. The results show that clear super linear speedup can be achieved upon code growth in genetic programming.", notes = "parallel and distributed computing Journal renamed 'Scalable Computing: Practice and Experience'", } @InProceedings{tanev:2000:piGPc, author = "Ivan T. Tanev and Takashi Uozumi and Koichi Ono", title = "Parallel Implementation of Genetic Programming on Clusters", booktitle = "Late Breaking Papers at the GECCO'2000 Conference", year = "2000", editor = "Darrell Whitley", pages = "388--396", address = "Las Vegas, Nevada, USA", month = "8 " # jul, keywords = "genetic algorithms, genetic programming", abstract = "We present an approach for developing parallel distributed implementation of genetic programming (PDIGP) based on exploitation of the inherent parallelism among semi-isolated subpopulations. Proposed implementation runs on cost-efficient configurations of clusters in LAN and/or Internet environment. PDIGP features single global migration broker and centralized manager of the semi-isolated subpopulations, which contribute to achieving quick propagation of the globally fittest individuals among the subpopulations, reducing the performance demands to the communication network, and achieving flexibility of system configurations by introducing dynamically scaling up opportunities. PDIGP exploits distributed component object model (DCOM) as a communication paradigm, which offers generic support for the issues of naming, locating and protecting the distributed entities in PDIGP. Experimentally obtained results show that in some system configurations the computational effort is less than the computational effort in canonical panmictic GP. Analytically obtained and empirically proved results of the speedup of the computational performance indicate that PDIGP features linear, close to ideal characteristics, which, together with the observed reduction of the computational effort contribute to the acquaintance of hyper-linear overall speedup in developed PDIGP.", notes = "symbolic regression, many node distributed computing Muroran Institute of Technology Mizumoto 27-1, Muroran,JAPAN 050-8585", } @InProceedings{tanev:2000:PETTA, author = "Ivan Tanev and Takashi Uozumi and Koichi Ono", title = "Effect of Batching on Performance Characteristics of DCOM-based Distributed Implementation of Genetic Programming", booktitle = "Proceedings of International Conference on Performance Evaluation (PerETTA-2000)", year = "2000", pages = "32--37", keywords = "genetic algorithms, genetic programming", abstract = "performance characteristics of developed distributed implementation of genetic programming (DIGP). Proposed implementation exploits the inherent medium-grained parallelism among evaluation of the individuals in genetic programming. It runs on cost-efficient configurations of clusters in LAN and/or Internet environment. DIGP exploits distributed component object model (DCOM) as an underlying host to host communication paradigm, which offers such advantages as binary standardisation, platform-, machine- and protocol-neutrality and seamless integration with different Internet protocols. In addition, as a true system model, DCOM offers generic support for the issues of naming, locating and protecting the distributed entities in proposed implementation. However, as an application-level protocol, DCOM features significant software overheads, which, in some cases might be above the cost of distributed computations. We introduce the approach of batching for reducing the specific software overhead of DCOM, and present the analytically estimated and empirically proved effect on the performance characteristics of developed DIGP.", } @PhdThesis{tanev:thesis, author = "Ivan Tanev", title = "Metacomputer Implementation of Genetic Programming on Clusters", school = "Muroran Istitute of Technology", year = "2001", type = "D.Eng", address = "Muroran, Japan", keywords = "genetic algorithms, genetic programming", URL = "http://svopac.lib.muroran-it.ac.jp/webopac/ctlsrh.do", size = "87 pages", notes = "It seems to be sufficient to search for title and author with webopac/ctlsrh.do Feb 2014 no.136, 2001.3.23", } @InProceedings{tanev:2001:ICCS, author = "Ivan Tanev and Takashi Uozumi and Dauren Akhmetov", title = "Component Object Based Single System Image Middleware for Metacomputer Implementation of Genetic Programming on Clusters", booktitle = "Computational Science - ICCS 2001: International Conference", year = "2001", editor = "V. N. Alexandrov and J. J Dongarra and B. A. Juliano and R. S. Renner and C. J. K. Tan", volume = "2073", series = "LNCS", pages = "284--293", address = "San Francisco, CA, USA", publisher_address = "Heidelberg", month = may # " 28-30", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-42232-3", DOI = "doi:10.1007/3-540-45545-0_37", size = "10 pages", abstract = "We present a distributed component-object model (DCOM) based single system image middleware (SSIM) for metacomputer implementation of genetic programming (MIGP). MIGP is aimed to significantly improve the computational performance of genetic programming (GP) exploiting the inher-ent parallelism in GP among the evaluation of individuals. It runs on cost-effective clusters of commodity, non-dedicated, heterogeneous workstations. Developed SSIM represents these workstations as a unified virtual resource and addresses the issues of locating and allocating the physical resources, commu-nicating between the entities of MIGP, scheduling and load balance. Adopting DCOM as a communicating paradigm offers the benefits of software platform-and network protocol neutrality of proposed implementation; and the generic support for the issues of locating, allocating and security of the distributed enti-ties of MIGP. Presented results of experimentally obtained speedup character-istics show close to linear speedup of MIGP for solving the time series identifi-cation problem on cluster of 10 W2K workstations.", } @Article{tanev:2001:SA, author = "Ivan Tanev and Takashi Uozumi and Koichi Ono", title = "Scalable architecture for parallel distributed implementation of genetic programming on network of workstations", journal = "Journal of Systems Architecture", volume = "47", pages = "557--572", year = "2001", number = "7", month = jul, keywords = "genetic algorithms, genetic programming, Distributed component object model, Island model of parallelism, Network of workstations", ISSN = "1383-7621", DOI = "doi:10.1016/S1383-7621(01)00015-7", URL = "http://www.sciencedirect.com/science/article/B6V1F-43RV156-2/2/96f14334f4a466a6a7a6034c398ff8c4", size = "16 pages", abstract = "We present an approach for developing a scalable architecture for parallel distributed implementation of genetic programming (PDIGP). The approach is based on exploitation of the inherent parallelism among semi-isolated subpopulations in genetic programming (GP). Proposed implementation runs on cost-efficient configurations of networks on workstations in LAN and Internet environment. Developed architecture features single global migration broker and centralized manager of the semi-isolated subpopulations, which contribute to achieving quick propagation of the globally fittest individuals among the subpopulations, reducing the performance demands to the communication network, and achieving flexibility in system configurations by introducing dynamically scaling up opportunities. PDIGP exploits distributed component object model (DCOM) as a communication paradigm, which as a true system model offers generic support for the issues of naming, locating and protecting the distributed entities in proposed architecture of PDIGP. Experimentally obtained results of computational effort of proposed PDIGP are discussed. The results show that computational effort of PDIGP marginally differs from the computational effort in canonical panmictic GP evolving single large population. For PDIGP running on systems configurations with 16 workstations the computational effort is less than panmictic GP, while for smaller configurations it is insignificantly more. Analytically obtained and empirically proved results of the speedup of computational performance indicate that PDIGP features linear, close to ideal characteristics. Experimentally obtained results of PDIGP running on configurations with eight workstations show close to 8-fold overall speedup. These results are consistent with the anticipated cumulative effect of the insignificant increase of computational effort for the considered configuration and the close to linear speedup of computational performance.", } @InProceedings{Tanev:2001:ICOIN, author = "Ivan Tanev and Takashi Uozumi and Koichi Ono", title = "Parallel Genetic Programming: Component Object-based Distributed Collaborative Approach", booktitle = "Proceedings of the 15th International Conference on Information Networking (ICOIN-15)", year = "2001", pages = "129--136", keywords = "genetic algorithms, genetic programming, DCOM, DCPGP, Internet, autonomous subpopulations, centralized manager, coarse grained inherent parallelism, communicating entities, communication network, communication paradigm, component object based distributed collaborative approach, computational performance, cost-efficient clusters, distributed collaborative approach, distributed collaborative parallel GP, distributed component object model, dynamic scaling-up features, generic support, global migration broker, globally fittest individuals, linear speedup characteristics, parallel genetic programming, performance demands, semi-isolated subpopulations, true system model, Internet, distributed object management, groupware, parallel programming, workstation clusters", DOI = "doi:10.1109/ICOIN.2001.905345", size = "8 pages", abstract = "We discuss the feasibility of applying the distributed collaborative approach for improving the computational performance of genetic programming (GP), implemented on cost-efficient clusters or the Internet. Proposed approach exploits the coarse grained inherent parallelism in GP among relatively autonomous subpopulations. Developed architecture of distributed collaborative parallel GP (DCPGP) features single, global migration broker and centralised manager of the semi-isolated subpopulations, which contribute to quick propagation of the globally fittest individuals among the subpopulations, reducing the performance demands to the underlying communication network, and achieving dynamic scaling-up features. DCPGP exploits the distributed component object model (DCOM) as a communication paradigm, which as a true system model offers generic support for the issues of naming, locating and security of communicating entities of developed architecture. Experimentally obtained speedup results show that close to linear speedup characteristics of the prototype of DCPGP are achieved on network of 8 workstations.", } @InProceedings{Tanev:2002:gecco, author = "Ivan T. Tanev and Takashi Uozumi and Yoshiharu Morotome", title = "An Application Service Provider Approach For Hybrid Evolutionary Algorithm-based Real-world Flexible Job Shop Scheduling Problem", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "1219--1226", address = "New York", publisher_address = "San Francisco, CA 94104, USA", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "genetic algorithms, genetic programming, real world applications, application service, evolutionary algorithm, job shop scheduling, provider", ISBN = "1-55860-878-8", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/RWA307.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-20.pdf", abstract = "scheduling of customers' orders in factories of plastic injection machines (FPIM) as a case of real-world flexible job shop scheduling problem (FJSS). The objective of discussed work is to provide FPIM with high business speed which implies (a) providing a customers with convenient way for remote online access to the factory's database and (b) developing an efficient scheduling routine for planning the assignment of the submitted customers' orders to FPIM machines. Remote online access to FPIM database, approached via delivering the software as a Web-service in accordance with the application service provider (ASP) paradigm is proposed. As an approach addressing the issue of efficient scheduling routine a hybrid evolutionary algorithm (HEA) combining priority-dispatching rules (PDRs) with GA, is developed. An implementation of HEA as a database stored procedure is discussed. Performance evaluation results are presented. The results obtained for evolving a schedule of 400 customers' orders on experimental model of FPIM indicate that the business delays in order of half an hour can be achieved.", notes = "GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)", } @InProceedings{tanev:2002:GECCO:workshop, title = "Priority Dispatching Rules-based Genetic Representation For Hybrid Evolutionary Algorithm For Flexible Job Shop Scheduling Problem", author = "Ivan T. Tanev and Takashi Uozumi and Yoshiharu Morotome", pages = "187--192", booktitle = "{GECCO 2002}: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference", editor = "Alwyn M. Barry", year = "2002", month = "8 " # jul, publisher = "AAAI", address = "New York", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025", keywords = "genetic algorithms, genetic programming", abstract = "the genetic representation in the developed hybrid evolutionary algorithm, applied for real-world case of flexible job shop scheduling problem. The hybrid evolutionary algorithm, which combines priority-dispatching rules (PDRs) with genetic algorithms (GA), is discussed. PDRs offer the advantage of simplicity and low computational cost. GA incorporated into proposed algorithm addresses the myopic nature of PDRs and the necessity to empirically evolve the most suitable PDRs and their combination. In the developed indirect representation of schedules the chromosomes encode the strings of PDRs applied for assigning the orders to the specified machines. Empirically obtained performance evaluation results are discussed. The results show the proposed combination of GA with PDRs outperforms both the PDRs-only based random search and GA without use of PDRs. The solution to the problem of scheduling of 400 orders on the elaborated experimental system configuration is reached with probability of 90% within a runtime of half an hour.", notes = "Bird-of-a-feather Workshops, GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002) part of barry:2002:GECCO:workshop", } @Article{Tanev:2003:CCJ, author = "Ivan Tanev and Takashi Uozumi and Dauren Akhmetov", title = "Component Object Based Single System Image for Dependable Implementation of Genetic Programming on Clusters", journal = "Cluster Computing Journal", year = "2004", volume = "7", number = "4", pages = "347--356", month = oct, keywords = "genetic algorithms, genetic programming, dependable cluster computing, single system image, DCOM", ISSN = "1386-7857 (Paper) 1573-7543 (Online)", URL = "http://www.kluweronline.com/issn/1386-7857", DOI = "doi:10.1023/B:CLUS.0000039494.39217.c1", abstract = "We present a distributed component-object model (DCOM) based single system image (SSI) for dependable parallel implementation of genetic programming (DPIGP). DPIGP is aimed to significantly and reliably improve the computational performance of genetic programming (GP) exploiting the inherent parallelism in GP among the evaluation of individuals. It runs on cost-effective clusters of commodity, non-dedicated, heterogeneous workstations or PCs. Developed SSI represents the pool of heterogeneous workstations as a single, unified virtual resource a metacomputer, and addresses the issues of locating and allocating the physical resources, communicating between the entities of DPIGP, scheduling and load balancing. In addition, addressing the issue of fault tolerance, SSI allows for building a highly available metacomputer in which the cases of workstation failure result only in a corresponding partial degradation of the overall performance characteristics of DPIGP. Adopting DCOM as a communicating paradigm offers the benefits of software platform- and network protocol neutrality of proposed approach; and the generic support for the issues of locating, allocating and security of the distributed entities of DPIGP.", } @InProceedings{Tanev:2003:AROB, author = "Ivan Tanev", title = "DOM/XML-Based Portable Genetic Representation of Morphology, Behavior and Communication Abilities of Evolvable Agents", booktitle = "Proceedings of the 8th International Symposium on Artificial Life and Robotics (AROB-03)", year = "2003", pages = "185--188", keywords = "genetic algorithms, genetic programming, XPG", URL = "http://rnavi.ndl.go.jp/mokuji_html/000004184726.html", abstract = "the role of genetic representation in facilitating quick design of efficiently running offline learning via genetic programming (GP). An approach of using the widely adopted DOM/XML standard for representation of genetic programs and off-the-shelf DOM-parsers with build-in API for manipulating them is proposed. The approach features significant reduction of time consumption of usually slow software engineering of GP and offers a generic way to facilitate the reduction of computational effort by limitation of search space of genetic programming via handling of only semantically correct genetic programs. The concept is accomplished through strongly typed genetic programming (STGP), in which the use of W3C-recommended standard XML schema is proposed as a generic way to represent and impose the grammar rules in STGP. The ideas laid in the foundation of the proposed approach are verified on the implementation of GP for evolving social behaviour of agents in predator prey pursuit problem.", notes = "See also \cite{tanev:2004:ALR}", } @InProceedings{Tanev:2003:gecco, author = "Ivan Tanev and Katsunori Shimohara", title = "On Role of Implicit Interaction and Explicit Communications in Emergence of Social Behavior in Continuous Predators-Prey Pursuit Problem", booktitle = "Genetic and Evolutionary Computation -- GECCO-2003", editor = "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller", year = "2003", pages = "74--85", address = "Berlin", publisher = "Springer-Verlag", publisher_address = "Berlin", month = "12-16 " # jul, volume = "2723", series = "LNCS", isbn13 = "978-3-540-40602-0", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1007/3-540-45105-6_7", abstract = "We present the result of our work on use of genetic programming for evolving social behaviour of agents situated in inherently cooperative environment. We use predators-prey pursuit problem to verify our hypothesis that relatively complex social behavior may emerge from simple, implicit, locally defined, and therefore, robust and highly-scalable interactions between the predator agents. We propose a proximity perception model for the predator agents where only the relative bearings and the distances to the closest predator agent and to the prey are perceived. The instance of the problem we consider is more realistic than commonly discussed in that the world, the sensory and moving abilities of agents are continuous; and the sensors of agents feature limited range of 'visibility'. The results show that surrounding behaviour, evolved using proposed strongly typed genetic programming with exception handling (STGPE) emerges from local, implicit and proximity-defined interactions between the predator agents in both cases when multi-agents systems comprises (i) partially inferior predator agents (with inferior moving abilities and superior sensory abilities) and with (ii) completely inferior predator agents. In the latter case the introduction of short-term memory and explicit communication contributes to the improvement of performance of STGPE.", notes = "GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)", } @InProceedings{Tanev:2003:ECAL, author = "Ivan Tanev and Katsunori Shimohara", title = "Effects of Learning to Interact on the Evolution of Social Behavior of Agents in Continuous Predators-prey Pursuit Problem", booktitle = "Proceedings of the 7th European Conference on Artificial Life (ECAL 2003)", year = "2003", editor = "Wolfgang Banzhaf and Jens Ziegler and Thomas Christaller and Peter Dittrich and JanT Kim", volume = "2801", series = "Lecture Notes in Computer Science", pages = "138--145", publisher = "Springer", keywords = "genetic algorithms, genetic programming, multi-agent systems, emergence, learning", isbn13 = "978-3-540-20057-4", DOI = "doi:10.1007/978-3-540-39432-7_15", abstract = "the effect of learning to interact on the evolution of social behaviour of agents situated in inherently cooperative environment. Using continuous predators-prey pursuit problem we verified our hypothesis that relatively complex social behaviour may emerge from simple, implicit, locally defined, and therefore robust and highly-scalable interactions between the predator agents. We argue that the ability of agents to learn to perform simple, atomic acts of implicit interaction facilitates the performance of evolution of more complex, social behaviour. The empirical results show about two-fold decrease of computational effort of proposed strongly typed genetic programming (STGP), used as an algorithmic paradigm to evolve the social behavior of the agents, when STGP is combined with learning of agents to implicitly interact with each other.", } @InProceedings{Tanev:2003:CEC, author = "Ivan Tanev and Kikuo Yuta", title = "Epigenetic Programming: an Approach of Embedding Epigenetic Learning via Modification of Histones in Genetic Programming", booktitle = "Proceedings of the 2003 Congress on Evolutionary Computation CEC2003", year = "2003", editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon", pages = "2580--2587", publisher = "IEEE Press", address = "Canberra", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "8-12 " # dec, organisation = "IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", keywords = "genetic algorithms, genetic programming, epgenesis, histone code, Biological control systems, Biological system modelling, Cells (biology), DNA, Evolution (biology), Gene expression, Plastics, Robustness, Stability, DNA, biology computing, molecular biophysics, predator-prey systems, software agents, statistical analysis, DNA, chromatin structures, double cell representation, epigenetic learning, epigenetic programming, genotypic combinations, germ cell, histone modification, phylogenesis, predator-prey pursuit problem, somatic cell", ISBN = "0-7803-7804-0", DOI = "doi:10.1109/CEC.2003.1299413", abstract = "Extending the notion of inheritable genotype in genetic programming (GP) from the common model of DNA into chromatin (DNA and histones), we propose epigenetic programming as an approach, embedding an explicitly controlled gene expression via modification of histones in GP. We propose double cell representation of the simulated individuals, comprising somatic cell and germ cell, both represented by their respective chromatin structures. Following biologically plausible concepts, we regard the plastic phenotype of the somatic cell, achieved via controlled gene expression owing to modifications to histones (epigenetic learning, EL) as relevant for fitness evaluation, while the genotype of the germ cell to the phylogenesis of the individuals. The approach is verified on evolution of social behaviour of team of predator agents in predator-prey pursuit problem. The empirically obtained performance evaluation results indicate that EL contributes to more than 2-fold improvement of computational effort of the phylogenesis via GP. We view the cause for that in the cumulative effect of polyphenism and epigenetic stability. The former allows for phenotypic diversity of genotypically similar individuals, while the latter robustly preserves the individuals from the destructive effects of crossover by silencing of certain genotypic combinations and explicitly activating them only when they are most likely to be expressed in corresponding beneficial phenotypic traits.", notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.", } @InProceedings{Tanev:aspgp03, author = "Ivan Tanev and Michael Brozozowski", title = "The Effect of Explicit Communications on the Generality and Robustness of Evolved Team of Agents in Predator-Prey Problem", booktitle = "Proceedings of The First Asian-Pacific Workshop on Genetic Programming", year = "2003", editor = "Sung-Bae Cho and Nguyen Xuan Hoai and Yin Shan", pages = "31--37", address = "Rydges (lakeside) Hotel, Canberra, Australia", month = "8 " # dec, keywords = "genetic algorithms, genetic programming, STGP", ISBN = "0-9751724-0-9", abstract = "We present a comparative analysis of the effect of explicit communications among the predator agents on the generality and robustness of emerged social behaviour of agents in predator-prey pursuit problem. The social behavior is evolved employing strongly typed genetic programming with exception handling capabilities. We demonstrated that the relatively complex, social behavior emerges from simple, basic model of implicit, proximity defined interactions among the agents. Such model offers the benefits of simplicity and scalability. However, compared to the additionally proposed model of explicit communications among the agents, it features increased computational effort and inferior generality and robustness of the emergent behavior of the agents situated in noisy and uncertain environments. Explicit communications contribute to the anomalous reduction of performance degradation with increase of noise in agents perceptions. The reason for the anomaly is viewed in the favourable effect of adaptive, situationally dependent noise in the indirectly (trough communications) perceived environment.", notes = "aspgp03. XML", } @InProceedings{tanev:2004:arob2, author = "Ivan Tanev and Thomas Ray", title = "Evolution of Sidewinding Locomotion of Simulated Limbless, Weelless Robots", booktitle = "Proceedings of the 9th International Symposium on Artificial Life and Robotics (AROB-04)", year = "2004", editor = "M. Sugisaka and H. Tanaka", volume = "2", pages = "472--475", address = "Beppu, Japan", month = jan # " 28-30", email = "i.tanev@computer.org", keywords = "genetic algorithms, genetic programming, locomotion, open dynamics engine, side-winding, snake robot", abstract = "Inspired by the efficient method of locomotion of the rattlesnake Crotalus cerastes, the objective of this work is automatic design through genetic programming, of the fastest possible (sidewinding) locomotion of simulated limbless, wheelless artifacts. The realism of the simulation is ensured by employing the Open Dynamics Engine (ODE), which facilitates implementation of all the physical forces, resulting from the actuators (muscles), frictions, gravity, collisions and joints constrains. The empirically obtained results demonstrate that the complex side winding locomotion emerges from relatively simple motion patterns of morphological segments (vertebrae). The robustness of automatically evolved locomotion is verified by (i) minimal performance degradation when partial damage to the artifact is inflicted and (ii) the ability to tackle obstacles. Contributing to the better understanding of side-winding locomotion, this work could be considered as a step towards building real limbless, wheelless robots, which feature unique engineering characteristics and are able to perform robustly in difficult environments.", notes = "See also 2004 Artificial Life and Robotics Volume9(3) broken Nov 2015 http://isarob.org/index.php?main_page=journal_arob09 see also \cite{tanev:2005:ALR3}", } @InProceedings{tanev:2004:arob1, author = "Ivan Tanev and Katsunori Shimohara", title = "Implications of the Ability to Learn Simple Actions on the Efficiency of Evolution of Social Behavior of Agents", booktitle = "Proceedings of the 9th International Symposium on Artificial Life and Robotics (AROB-04)", year = "2004", editor = "Masanori Sugisaka and Hiroshi Tanaka", volume = "1", pages = "53--56", email = "i.tanev@computer.org", keywords = "genetic algorithms, genetic programming", abstract = "We investigate the effect of ability to learn simple actions on the performance characteristics of evolution of social behaviour of agents situated in inherently cooperative environment. Using continuous predators-prey pursuit problem we verified that relatively complex social behavior emerges from simple, implicit, locally defined, and thus robust and scalable interactions between the predator agents. Considering a distinct aspect of the phenomenon of emergence, we hypothesise that the ability of agents to learn how to perform simple, atomic acts of implicit interaction might facilitate the evolution of more complex behaviour. The empirical results indicate that incorporation of the proposed approach of learning in genetic programming (employed as an algorithmic paradigm to evolve the social behaviour of the agents) is associated with about two-fold decrease of computational effort of the evolution.", notes = "See \cite{tanev:2005:ALR1}", } @InProceedings{tanev:2004:iscie, author = "Ivan Tanev and Katsunori Shimohara", title = "On the Emergence of Social Behavior from Implicit, Proximity Defined Interactions in Predators-prey Pursuit Problem", booktitle = "Proceedings of the 48th Annual Conference of the Institute of Systems, Control and Information Engineers", year = "2004", pages = "639--640", month = may, keywords = "genetic algorithms, genetic programming", } @InProceedings{tanev:2004:edraaosloslwr, title = "Evolutionary Design, Robustness and Adaptation of Sidewinding Locomotion of Simulated Libmless Wheelless Robot", author = "Ivan Tanev and Thomas Ray and Andrzej Buller", volume = "2", pages = "2312--2319", email = "i_tanev@atr.jp", booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary Computation", year = "2004", publisher = "IEEE Press", month = "20-23 " # jun, address = "Portland, Oregon", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Evolutionary design \& evolvable hardware", DOI = "doi:10.1109/CEC.2004.1331186", abstract = "The objective of this work is automatic design through genetic programming, of the fastest possible locomotion of simulated snake-like robot (Snakebot). The realism of simulation is ensured by employing the Open Dynamics Engine software library. Empirical results demonstrate the emergence of sidewinding as fastest locomotion gait. Robustness of the sidewinding is illustrated by the ease with which Snakebot overcomes various types of obstacles. The ability of Snakebot to adapt to partial damage by gradually improving its velocity characteristics is shown. Discovering compensatory locomotion traits, Snakebot recovers completely from single damage and recovers a major extent of its original velocity when more significant damage is inflicted.", notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.", } @InProceedings{tanev:2004:gecco, author = "Ivan Tanev and Kikuo Yuta", title = "Implications of Epigenetic Learning via Modification of Histones on Performance of Genetic Programming", booktitle = "Genetic and Evolutionary Computation -- GECCO-2004, Part I", year = "2004", editor = "Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell", series = "Lecture Notes in Computer Science", pages = "213--224", address = "Seattle, WA, USA", publisher_address = "Heidelberg", month = "26-30 " # jun, organisation = "ISGEC", publisher = "Springer-Verlag", volume = "3102", email = "i_tanev@atr.jp", ISBN = "3-540-22344-4", ISSN = "0302-9743", DOI = "doi:10.1007/b98643", size = "12", keywords = "genetic algorithms, genetic programming", notes = "GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)", } @InProceedings{tanev:2004:gecco_snake, author = "Ivan Tanev and Thomas Ray and Andrzej Buller", title = "Evolution, Robustness, and Adaptation of Sidewinding Locomotion of Simulated Snake-Like Robot", booktitle = "Genetic and Evolutionary Computation -- GECCO-2004, Part I", year = "2004", editor = "Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell", series = "Lecture Notes in Computer Science", pages = "627--639", address = "Seattle, WA, USA", publisher_address = "Heidelberg", month = "26-30 " # jun, organisation = "ISGEC", publisher = "Springer-Verlag", volume = "3102", email = "i_tanev@atr.jp", ISBN = "3-540-22344-4", ISSN = "0302-9743", DOI = "doi:10.1007/b98643", size = "13", keywords = "genetic algorithms, genetic programming", notes = "GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)", } @InProceedings{tanev:2004:geccowks, author = "Ivan Tanev", title = "Implications of Incorporating Learning Probabilistic Context-sensitive Grammar in Genetic Programming on Evolvability of Adaptive Locomotion Gaits of Snakebot", editor = "R. Poli and S. Cagnoni and M. Keijzer and E. Costa and F. Pereira and G. Raidl and S. C. Upton and D. Goldberg and H. Lipson and E. {de Jong} and J. Koza and H. Suzuki and H. Sawai and I. Parmee and M. Pelikan and K. Sastry and D. Thierens and W. Stolzmann and P. L. Lanzi and S. W. Wilson and M. O'Neill and C. Ryan and T. Yu and J. F. Miller and I. Garibay and G. Holifield and A. S. Wu and T. Riopka and M. M. Meysenburg and A. W. Wright and N. Richter and J. H. Moore and M. D. Ritchie and L. Davis and R. Roy and M. Jakiela", booktitle = "GECCO 2004 Workshop Proceedings", year = "2004", month = "26-30 " # jun, address = "Seattle, Washington, USA", email = "i_tanev@atr.jp", keywords = "genetic algorithms, genetic programming", URL = "http://gpbib.cs.ucl.ac.uk/gecco2004/WEEC004.pdf", notes = "GECCO-2004WKS Distributed on CD-ROM at GECCO-2004", } @InProceedings{tanev:2004:SJSKAL, author = "Ivan Tanev", title = "Evolutionary Algorithms for Intelligent Software Design", booktitle = "Proceedings of the 2004 Sino-Japanese Symposium of Kansei and Artificial Life", year = "2004", pages = "60--66", email = "i_tanev@atr.jp", address = "Beijing, China", keywords = "genetic algorithms, genetic programming", } @InProceedings{Tanev:2004:CNUDU, author = "Ivan Tanev", title = "Steering the Mutation Operation in GP through Learning Probabilistic Context Sensitive Grammar", booktitle = "Proceedings of the 4th Joint Symposium between Chonnam National University and Doshisha University", year = "2004", pages = "31--32", month = "30 " # jul, keywords = "genetic algorithms, genetic programming", } @Article{tanev:2004:ALR, author = "Ivan Tanev", title = "DOM/XML-Based Portable Genetic Representation of Morphology, Behavior and Communication Abilities of Evolvable Agents", journal = "Artificial Life and Robotics", year = "2004", volume = "8", number = "1", pages = "52--56", email = "itanev@computer.org", keywords = "genetic algorithms, genetic programming", ISSN = "1433-5298", DOI = "doi:10.1007/s10015-004-0288-6", abstract = "This article presents the results of our work on the role of genetic representation in facilitating the quick design of efficiently running offline learning via genetic programming (GP). An approach using the widely adopted document object model/extensible mark-up language (DOM/XML) standard for the representation of genetic programs, and off-the-shelf DOM-parsers with built-in application programming interface (API) for manipulating them is proposed. This approach means a significant reduction in time in the usually slow software engineering of GP, and offers a generic way to facilitate the reduction of computational effort by limiting the search space of genetic programming by handling only semantically correct genetic programs. The concept is accomplished through strongly typed genetic programming (STGP), in which the use of W3C-recommended standard XML schema is proposed as a generic way to represent and impose the grammar rules in STGP. The ideas laid in the foundation of the proposed approach are verified by the implementation of GP in the evolving social behaviour of agents in predator-prey pursuit problem", } @InCollection{tanev:2004:EMTP, author = "Ivan Tanev and Thomas Ray and Andrzej Buller", title = "Evolution, Robustness and Adaptation of Sidewinding Locomotion of Simulated Snake-like Robot", booktitle = "Evolvable Machines: Theory \& Practice", publisher = "Springer", year = "2004", editor = "Nadia Nedjah and Luiza {de Macedo Mourelle}", volume = "161", series = "Studies in Fuzziness and Soft Computing", chapter = "2", pages = "21--41", address = "Berlin Hidelberg Germany", keywords = "genetic algorithms, genetic programming, reconfigurable hardware", ISBN = "3-540-22905-1", URL = "http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-175-22-33980449-0,00.html", notes = "Springer says published in 2005 but available Nov 2004", } @InProceedings{eurogp:Tanev05, author = "Ivan Tanev", editor = "Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. {van Hemert} and Marco Tomassini", title = "Incorporating Learning Probabilistic Context-Sensitive Grammar in Genetic Programming for Efficient Evolution and Adaptation of Snakebot", booktitle = "Proceedings of the 8th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3447", year = "2005", address = "Lausanne, Switzerland", month = "30 " # mar # " - 1 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-25436-6", pages = "155--166", DOI = "doi:10.1007/978-3-540-31989-4_14", DOI = "doi:10.1007/b107383", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "In this work we propose an approach of incorporating probabilistic learning context-sensitive grammar (PLCSG) in genetic programming (GP) employed for evolution and adaptation of locomotion gaits of simulated snake-like robot (Snakebot). In our approach PLCSG is derived from the originally defined context-free grammar, which usually expresses the syntax of genetic programs in canonical GP. During the especially introduced {"}steered{"} mutation the probabilities of applying each of particular production rules with multiple right-hand side alternatives in PLCSG depend on the context, and these probabilities are {"}learned{"} from the aggregated reward values obtained from the evolved best-of-generation Snakebots. Empirically obtained results verify that employing PLCSG contributes to the improvement of computational effort of both (i) the evolution of the fastest possible locomotion gaits for various fitness conditions and (ii) adaptation of these locomotion gaits to challenging environment and de-graded mechanical abilities of Snakebot. In all of the cases considered in this study, the locomotion gaits, evolved and adapted employing GP with PLCSG feature higher velocity and are obtained faster than with canonical GP.", notes = "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005", } @InProceedings{tanev_2005_WSTST, author = "Ivan Tanev", title = "Emergent Intelligent Properties of Evolving and Adapting Snake-like Robot's Locomotion", booktitle = "Soft Computing as Transdisciplinary Science and Technology: Proceedings of the fourth IEEE International Workshop WSTST'05", year = "2005", editor = "Ajith Abraham and Yasuhiko Dote and Takeshi Furuhashi and Mario Koppen and Azuma Ohuchi", volume = "29", series = "Advances in Soft Computing", pages = "641--652", address = "Muroran, Japan", month = may # " 25-27", publisher = "Springer", email = "itanev@mail.doshisha.ac.jp", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-25055-7", isbn13 = "978-3-540-25055-5", DOI = "doi:10.1007/3-540-32391-0_69", abstract = "Inspired by the efficient method of locomotion of the rattlesnake Crotalus cerastes, the objective of this work is to investigate the emergent properties of the automatically designed through genetic programming (GP) fastest possible (sidewinding), robust and adaptive locomotion gaits of simulated snake-like robot (Snakebot). Considering the notion of 'emergent intelligence' as the ability of Snakebot to achieve its goals (of moving fast) without the need to be explicitly taught about how to do so, we present the empirical results on the emergence of sidewinding locomotion as fastest locomotion of the Snakebot. The emergent properties of evolved robust sidewinding gaits featuring desired velocity characteristics of Snakebot in challenging environment are discussed. The ability of Snakebot to adapt to partial damage by gradually improving its velocity characteristics, and the emergent properties of obtained adaptive gaits are elaborated. Concluding on the practical implications of the analogy between the emergent properties of the robust and the adaptive locomotion gaits, this work could be viewed as a step towards building real Snakebots, which are able to perform robustly in difficult environment.", notes = "WSTST'05 http://www.springeronline.com/sgw/cda/frontpage/0,11855,4-175-72-46420067-0,00.html", } @InProceedings{1068125, author = "Ivan Tanev", title = "Learned mutation strategies in genetic programming for evolution and adaptation of simulated snakebot", booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation", year = "2005", editor = "Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and Edwin D. {de Jong} and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler", volume = "1", ISBN = "1-59593-010-8", pages = "687--694", address = "Washington DC, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2005/docs/p687.pdf", DOI = "doi:10.1145/1068009.1068125", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "25-29 " # jun, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Estimation of Distribution Algorithms, context-sensitive grammar, design, locomotion, mutation strategies, snakebot", notes = "GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM Order Number 910052", } @InProceedings{tanev:2005:GECCOLB, author = "Ivan Tanev and Michal Joachimczak and Hitoshi Hemmi and Katsunori Shimohara", title = "Evolving Driving Agent for Remote Control of Scaled Model of a Car", booktitle = "Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}", year = "2005", editor = "Franz Rothlauf", address = "Washington, D.C., USA", month = "25-29 " # jun, keywords = "genetic algorithms, genetic programming", URL = "http://gpbib.cs.ucl.ac.uk/gecco2005lbp/papers/50-tanev.pdf", abstract = "We present an approach for automatic design via genetic programming of the functionality of driving agent, able to remotely operate a scale model of a car running in a fastest possible way. The agent's actions are conveyed to the car via standard radio control transmitter. The agent perceives the environment from a live video feedback of an overhead camera. In order to cope with the inherent video feed latency we propose an approach of anticipatory modelling in which the agent considers its current actions based on anticipated intrinsic (rather than currently available, outdated) state of the car and its surrounding. The driving style of the agent is first evolved offline on a software simulator of the car and then adapted online to the real world. Experimental results demonstrate that on long runs the agent's-operated car is only marginally (about 5%) slower than a human-operated one, while the consistence of lap times posted by the evolved driving agent is better than that of a human. Presented work can be viewed as a step towards the development of a framework for automated design of the controllers of remotely operated vehicles capable to find an optimal solution to various tasks in different traffic situations and road conditions", notes = "Distributed on CD-ROM at GECCO-2005", } @InProceedings{Tanev:2005:JSES, author = "Ivan Tanev", title = "Learning Mutation Strategies in Genetic Programming and their Implications on Evolution, Adaptation and Robustness of Snakebot", booktitle = "Program and Abstracts of the 7th Annual Meeting of the Japanese Society of Evolutionary Studies", year = "2005", editor = "Masakado Kawata", pages = "41", address = "Japan", month = "26-29 " # aug, publisher = "The Society of Evolutionary Studies", note = "Abstract only", email = "itanev@mail.doshisha.ac.jp", keywords = "genetic algorithms, genetic programming", notes = "http://meme.biology.tohoku.ac.jp/evol2005/e/index.html Kawata mail.tains.tohoku.ac.uk http://meme.biology.tohoku.ac.jp/evol2005/sympo2_contents.html", } @Article{tanev:2005:TR, author = "Ivan Tanev and Thomas Ray and Andrzej Buller", title = "Automated Evolutionary Design, Robustness and Adaptation of Sidewinding Locomotion of Simulated Snake-like Robot", journal = "IEEE Transactions on Robotics", year = "2005", volume = "21", number = "4", pages = "632--645", month = aug, email = "i_tanev@atr.jp", keywords = "genetic algorithms, genetic programming, Adaptation, snake-like robot, robustness", DOI = "doi:10.1109/TRO.2005.851028", size = "14 pages", abstract = "Inspired by the efficient method of locomotion of the rattlesnake Crotalus cerastes, the objective of this work is automatic design through genetic programming (GP) of the fastest possible (sidewinding) locomotion of simulated limbless, wheel-less snake-like robot (Snakebot). The realism of simulation is ensured by employing the Open Dynamics Engine (ODE), which facilitates implementation of all physical forces, resulting from the actuators, joints constrains, frictions, gravity, and collisions. Reduction of the search space of the GP is achieved by representation of Snakebot as a system comprising identical morphological segments and by automatic definition of code fragments, shared among (and expressing the correlation between) the evolved dynamics of the vertical and horizontal turning angles of the actuators of Snakebot. Empirically obtained results demonstrate the emergence of sidewinding locomotion from relatively simple motion patterns of morphological segments. Robustness of the sidewinding Snakebot, which is considered to be the ability to retain its velocity when situated in an unanticipated environment, is illustrated by the ease with which Snakebot overcomes various types of obstacles such as a pile of or burial under boxes, rugged terrain, and small walls. The ability of Snakebot to adapt to partial damage by gradually improving its velocity characteristics is discussed. Discovering compensatory locomotion traits, Snakebot recovers completely from single damage and recovers a major extent of its original velocity when more significant damage is inflicted. Exploring the opportunity for automatic design and adaptation of a simulated artifact, this work could be considered as a step toward building real Snakebots, which are able to perform robustly in difficult environments.", } @InProceedings{tanev:2005:CEC, author = "Ivan Tanev and Michal Joachimczak and Hitoshi Hemmi and Katsunori Shimohara", title = "Evolution of the Driving Styles of Anticipatory Agent Remotely Operating a Scaled Model of Racing Car", booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary Computation (CEC-2005)", year = "2005", volume = "2", pages = "1891--1898", address = "Edinburgh", month = "2-4 " # sep, publisher = "IEEE", email = "itanev@mail.doshisha.ac.jp", keywords = "genetic algorithms, genetic programming, Automatic control, Cameras, Delay, Feedback, Feeds, Humans, Optimal control, Radio control, Radio transmitters, Remotely operated vehicles, evolutionary computation, mobile robots, remotely operated vehicles, software agents, agent actions, agent-operated car, anticipatory agent, anticipatory modelling, driving agent, driving style evolution, overhead camera, racing car, radio control transmitter, remote operation, remotely operated vehicle, software simulator, video feed latency, video feedback", ISBN = "0-7803-9363-5", DOI = "doi:10.1109/CEC.2005.1554918", size = "8 pages", abstract = "We present an approach for automated evolutionary design of driving agent, able to remotely operate a scale model of racing car running in a fastest possible way. The agent's actions are conveyed to the car via standard radio control transmitter. The agent perceives the environment from a live video feedback of an overhead camera. In order to cope with the inherent video feed latency, which renders even the straightforward tasks of following simple routes unsolvable, we implement an anticipatory modelling - the agent considers its current actions based on anticipated intrinsic (rather than currently available, outdated) state of the car and its surrounding. The driving style (i.e. the driving line combined with the speed at which the car travels along this line) is first evolved offline on a software simulator of the car and then adapted online to the real world. Experimental results demonstrate that on long runs the agent operated car is only marginally slower than a human operated one, while the consistence of lap times posted by the evolved driving style of the agent is better than that of a human. This work can be viewed as a step towards the development of a framework for automated design of the controllers of remotely operated vehicles capable to find an optimal solution to various tasks in different traffic situations and road conditions.", notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS.", } @Article{tanev:2005:GPEM, author = "Ivan Tanev and Michael Brzozowski and Katsunori Shimohara", title = "Evolution, Generality and Robustness of Emerged Surrounding Behavior in Continuous Predators-Prey Pursuit Problem", journal = "Genetic Programming and Evolvable Machines", year = "2005", volume = "6", number = "3", pages = "301--318", month = sep, note = "Published online: 25 August 2005", keywords = "genetic algorithms, genetic programming, emergence, multi agent systems, surrounding behaviour, strongly-typed genetic programming STGP", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-005-2989-6", size = "18 pages", abstract = "We present the result of our work on the use of strongly typed genetic programming with exception handling capabilities for the evolution of surrounding behaviour of agents situated in an inherently cooperative environment. The predators-prey pursuit problem is used to verify our hypothesis that relatively complex surrounding behavior may emerge from simple, implicit, locally defined, and therefore scalable interactions between the predator agents. Proposing two different communication mechanisms ((i) simple, basic mechanism of implicit interaction, and (ii) explicit communications among the predator agents) we present a comparative analysis of the implications of these communication mechanisms on evolution, generality and robustness of the emerged surrounding behaviour. We demonstrate that relatively complex-surrounding behaviour emerges even from implicit, proximity-defined interactions among the agents. Although the basic model offers the benefits of simplicity and scalability, compared to the enhanced model of explicit communications among the agents, it features increased computational effort and inferior generality and robustness of agents' emergent surrounding behaviour when the team of predator agents is evolved in noiseless environment and then tested in noisy and uncertain environment. Evolution in noisy environment virtually equalises the robustness and generality characteristics of both models. For both models however the increase of noise levels during the evolution is associated with evolving solutions, which are more robust to noise but less general to new, unknown initial situations.", notes = "DOM XML, explicit fitness parsimony preasure (anti bloat)", } @Article{tanev:2005:ALR1, author = "Ivan Tanev and Katsunori Shimohara", title = "Implications of the Ability to Learn Simple Actions on the Efficiency of Evolution of Social Behavior of Agents", journal = "Artificial Life and Robotics", year = "2005", volume = "9", number = "1", pages = "58--62", month = apr, keywords = "genetic algorithms, genetic programming, Multiagent system, Social behaviour", ISSN = "1433-5298", DOI = "doi:10.1007/s10015-004-0335-3", size = "5 pages", abstract = "We investigate the effect of the ability to learn simple actions on the performance characteristics of the evolution of social behaviour in agents situated in an inherently cooperative environment. Using a continuous predator-prey pursuit problem, we verified that relatively complex social behavior emerges from simple, implicit, locally defined, and thus robust and scalable interactions between the predator agents. Considering a distinct aspect of the phenomenon of emergence, we hypothesise that the ability of agents to learn how to perform simple, atomic acts of implicit interaction might facilitate the evolution of more complex behavior. The empirical results indicate that incorporation of the proposed approach to learning in genetic programming (employed as an algorithmic paradigm to evolve the social behavior of the agents) is associated with about a two-fold decrease in the computational effort of evolution.", notes = "ISAROB 2005 This work was presented, in part, at the 9th International Symposium on Artificial Life and Robotics, Oita, Japan, January 28-30, 2004 \cite{tanev:2004:arob1}", } @Article{tanev:2005:ALR3, author = "Ivan Tanev and Thomas Ray", title = "Evolution of Sidewinding Locomotion of Simulated Limbless, Wheelless Robots", journal = "Artificial Life and Robotics", year = "2005", volume = "9", number = "3", pages = "117--122", month = jul, keywords = "genetic algorithms, genetic programming, Locomotion, Open dynamics engine, Side-winding, Snake robot", ISSN = "1433-5298", DOI = "doi:10.1007/s10015-004-0332-6", size = "6 pages", abstract = "Inspired by the efficient method of locomotion of the rattlesnake Crotalus cerastes, the objective of this work was the automatic design through genetic programming of the fastest possible, side-winding locomotion of simulated limbless, wheel less artifacts. The realism of the simulation is ensured by employing open dynamics engine (ODE), which allows accounting for all the physical forces resulting from the actuators (muscles), friction, gravity, collisions, and joint constraints. The empirically obtained results demonstrate that the complex side-winding locomotion emerges from relatively simple motion patterns of morphological segments (vertebrae). The robustness of automatically evolved locomotion is verified by (i) the reasonable performance degradation when partial damage to the artifact is inflicted, and (ii) the ability to tackle obstacles. Contributing to the better understanding of the unique, side-winding locomotion, this work could be considered as a step toward building real limbless, wheelless robots, featuring unique engineering characteristics, which are able to perform robustly in difficult environments.", notes = "This work was presented, in part, at the 9th International Symposium on Artificial Life and Robotics, Oita, Japan, January 28-30, 2004 \cite{tanev:2004:arob2}", } @InProceedings{eurogp06:Tanev, author = "Ivan Tanev", title = "Emergent Generality of Adapted Locomotion Gaits of Simulated Snake-like Robot", editor = "Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art", booktitle = "Proceedings of the 9th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3905", year = "2006", address = "Budapest, Hungary", month = "10 - 12 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-33143-3", pages = "85--96", DOI = "doi:10.1007/11729976_8", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "In this work we consider the generality of locomotion gaits of simulated snake-like robot (Snakebot), adapted (via genetic programming, GP) to both (i) a challenging terrain and (ii) a partial mechanical damage. Discussing the emergence of common traits in these gaits, we elaborate on the strong correlation between their respective genotypes. We experimentally verify the generality of the adapted gaits in different unexpected environmental conditions and for various mechanical failures of the Snakebots. From an engineering standpoint, we suppose that in response to an eventual degradation of velocity, the Snakebot might activate a general locomotion gait, without the need to diagnose and treat the concrete underlying reason for such degradation. We view this work as a step towards building real Snakebots, which are able to perform robustly in difficult environment.", notes = "Part of \cite{collet:2006:GP} EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006 Not presented.", } @Article{tanev:2006:ISCIE, author = "Ivan Tanev and Thomas Ray and Katsunori Shimohara", title = "Exploring the Analogy in the Emergent Properties of Locomotion Gaits of Snakebot Adapted to Challenging Terrain and Partial Damage", journal = "Transactions of the Institute of Systems, Control and Information Engineers", year = "2006", volume = "19", number = "6", pages = "220--232", month = jun, address = "Japan", keywords = "genetic algorithms, genetic programming, emergence, snakebot, evolution, adaptation", URL = "http://www.iscie.or.jp/e/?Transactions%20of%20ISCIE%2C%20Vol.%2019", DOI = "doi:10.5687/iscie.19.220", size = "13 pages", abstract = "The objective of this work is to investigate the emergent properties of the gaits of the simulated snake-like robot, Snakebot. The gaits are automatically designed through Genetic Programming (GP) to be robust, general, adaptive, and the fastest possible sidewinding, locomotion. Considering the notion of emergent intelligence as the ability of Snakebot to achieve its goals (of moving fast) without the need to be explicitly taught how to do so, we present empirical results demonstrating the emergence of sidewinding locomotion from relatively simple motion patterns of morphological segments of Snakebot. We discuss the emergent properties of the evolved robust high velocity sidewinding locomotion gaits of Snakebot when situated in challenging environments. Then we elaborate on the ability of Snakebot to adapt to partial damage by gradually improving its velocity characteristics, and the emergent properties of obtained adaptive gaits. Verifying the practical implications of the analogy between the emergent properties of the robust and the adaptive sidewinding gaits, this work could be viewed as a step towards building real Snakebots, which are able to perform robustly in challenging environments.", } @InProceedings{tanev:2006:GECCO, author = "Ivan Tanev and Michal Joachimczak and Katsunori Shimohara", title = "Evolution of driving agent, remotely operating a scale model of a car with obstacle avoidance capabilities", booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation", year = "2006", editor = "Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos {Coello Coello} and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens", volume = "2", ISBN = "1-59593-186-4", pages = "1785--1792", address = "Seattle, Washington, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p1785.pdf", DOI = "doi:10.1145/1143997.1144286", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "8-12 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Real-World Applications, anticipatory modelling, driving agent, feedback latency", notes = "GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM Order Number 910060", } @InProceedings{tanev:2006:CNUDU, author = "Ivan Tanev", title = "Interactive Learning of Mutation Strategies in Genetic Programming", booktitle = "Proceedings of the 5th Joint Symposium between Doshisha University and Chonnam National University", year = "2006", pages = "83--87", month = aug, publisher = "Chonnam National University, Korea", keywords = "genetic algorithms, genetic programming, interactive evolution, snake-like robot, context-sensitive grammar", } @InProceedings{tanev:2006:WCSS, author = "Ivan Tanev and Sebastien Prudent and Katsunori Shimohara", title = "Hyperheuristic Genetic Representation of Evolvable Human Relations Networks", booktitle = "Proceedings of the First World Congress on Social Simulation (WCSS'06)", year = "2006", editor = "Takao Terano", volume = "2", address = "Kyoto, Japan", month = "21-25 " # aug, organisation = "Pacific Asian Association for Agent-based Approach in Social Systems Sciences (PAAA)", keywords = "genetic algorithms, genetic programming: Poster", notes = "http://www.paaa.econ.kyoto-u.ac.jp/wcss06/", } @Article{Tanev:2006:GPEM, author = "Ivan Tanev", title = "Genetic programming incorporating biased mutation for evolution and adaptation of Snakebot", journal = "Genetic Programming and Evolvable Machines", year = "2007", volume = "8", number = "1", pages = "39--59", month = mar, keywords = "genetic algorithms, genetic programming, Adaptation, Grammar, Locomotion, Snakebot", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-006-9008-4", size = "21 pages", abstract = "In this work we propose an approach for incorporating learning probabilistic context-sensitive grammar (LPCSG) in genetic programming (GP), employed for evolution and adaptation of locomotion gaits of a simulated snake-like robot (Snakebot). Our approach is derived from the original context-free grammar which usually expresses the syntax of genetic programs in canonical GP. Empirically obtained results verify that employing LPCSG contributes to the improvement of computational effort of both (i) the evolution of the fastest possible locomotion gaits for various fitness conditions and (ii) adaptation of these locomotion gaits to challenging environment and degraded mechanical abilities of the Snakebot.", } @InCollection{Tanev:2006:MRtna, author = "Ivan Tanev and Thomas Ray and Katsunori Shimohara", title = "On the Analogy of the Emergent Properties of Evolved Locomotion Gaits of Simulated Snakebot", chapter = "29", pages = "559--578", publisher = "Advanced Robotics Systems and pro literatur Verlag", year = "2006", booktitle = "Mobile Robotics: Towards New Applications", keywords = "genetic algorithms, genetic programming", ISBN = "3-86611-314-5", notes = "http://s.i-techonline.com/Book/Mobile-Robots-Towards-New-Applications/ISBN978-3-86611-314-5.html", } @InProceedings{Tanev:2007:SICE, author = "Ivan Tanev and Katsunori Shimohara", title = "{XGP}: {XML}-based Genetic Programming Framework", booktitle = "Proceedings of the 34th Symposium of the Society of Instrument and Control Engineers (SICE) on Intelligent Systems", year = "2007", publisher = "SICE", pages = "183--188", address = "Japan", publisher_address = "Tokyo, Japan", keywords = "genetic algorithms, genetic programming, XML", URL = "http://jglobal.jst.go.jp/en/public/200902245318134264", notes = "http://isd-si.doshisha.ac.jp/itanev/XGP_pic.htm", } @InProceedings{tanev:2007:SICEb, author = "Ivan Tanev and Katsunori Shimohara", title = "Evolution of Human Competitive Driving Agent Operating a Scale Model of a Car", booktitle = "Proceedings of SICE Annual Conference", year = "2007", pages = "1582--1587", month = "17-20 " # sep, organization = "Kagawa University, Japan", DOI = "doi:10.1109/SICE.2007.4421235", size = "6 pages", keywords = "genetic algorithms, genetic programming, artificial intelligence, Automatic control, Cameras, Delay, Driver circuits, Electronic mail, Humans, Information systems, Transmitters, remotely operated vehicles, road vehicles, telecontrol, adaptive racing games, car, human competitive driving agent, overhead video camera, standard RC transmitter, driving style, evolution, remote control", abstract = "We present an evolutionary design of the driving style of agent, remotely operating a scale model of a car in a human competitive way. The agent perceives the environment from an overhead video camera and conveys its actions via standard RC transmitter. In order to cope with the video feed latency we propose an anticipatory modelling in which the agent considers its current actions based on the anticipated intrinsic state of the car and surrounding. We formalised the driving style by defining the key parameters, which describe it, and applied genetic algorithms to evolve the optimal values of these parameters. The evolved agent is human competitive in that it yields both faster and more consistent lap times than those of a human around a predefined circuit. Presented work can be viewed as a step towards the development of a framework of adaptive racing games in which the human competes against a computerised opponent with matching capabilities.", } @InProceedings{Tanev:cec:2007a, author = "Ivan Tanev and Katsunori Shimohara", title = "On Human Competitiveness of the Evolved Agent Operating a Scale Model of a Car", booktitle = "2007 IEEE Congress on Evolutionary Computation", year = "2007", editor = "Dipti Srinivasan and Lipo Wang", pages = "3646--3653", month = "25-28 " # sep, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "1-4244-1340-0", file = "1800.pdf", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2007.4424945", abstract = "We present an approach for evolutionary design of the driving style of an agent, remotely operating a scale model of a car in a human competitive way. The agent perceives the environment from an overhead video camera and conveys its actions to the car via standard radio remote control transmitter. In order to cope with the video feed latency we propose an anticipatory modelling in which the agent considers its current actions based on the anticipated intrinsic (rather than currently available, outdated) state of the car and its surrounding. We formalised the notion of driving style by defining the key parameters, which describe it, and demonstrated the feasibility of applying genetic algorithms to evolve the optimal values of these parameters. The optimised driving style, employed by the agent, is human competitive in that it yields both faster and more consistent lap times than those of a human around a predefined circuit. Presented work can be viewed as a step towards the automated design of the control software of remotely operated vehicles capable to find an optimal solution to various tasks in a priori known environmental situations. Also, the results can be seen as a verification of the feasibility of developing a framework of adaptive racing games in which the human competes against a computerised opponent with matching capabilities, both operating physical, scale models of cars.", notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C", } @InProceedings{Tanev:cec:2007b, author = "Ivan Tanev and Katsunori Shimohara", title = "Interactive Learning of Consensus Sequences in Genetic Programming for Evolution of Snake-like Robot", booktitle = "2007 IEEE Congress on Evolutionary Computation", year = "2007", month = "25-28 " # sep, pages = "3662--3670", organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", ISBN = "1-4244-1340-0", keywords = "genetic algorithms, genetic programming", file = "1454.pdf", DOI = "doi:10.1109/CEC.2007.4424947", abstract = "We discuss an approach of incorporating interactively learned consensus sequences (ILCS) in genetic programming (GP) for efficient evolution of simulated Snakebot situated in a challenging environment. ILCS introduce a biased mutation in GP via probabilistic context sensitive grammar, in which the probabilities of applying the production rules with multiple right-hand side alternatives depend on the grammatical context. The distribution of these probabilities is learned interactively from the syntax of the Snakebots, exhibiting behavioural traits that according to the human observer are relevant for the emergence of ability to overcome obstacles. Because at the earlier stages of evolution these behavioral traits are not necessarily pertinent to the best performing (i.e. fastest) Snakebots, the user feedback provides the evolution with an additional insight about the promising areas in the fitness landscape. Empirical results verify that employing ILCS improves the efficiency of GP in that the evolved Snakebots are faster than those obtained via canonical GP.", notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C", } @InCollection{Tanev:2007:AASOS, author = "Ivan Tanev", title = "Learning Mutation Strategies for Evolution and Adaptation of a Simulated Snakebot", booktitle = "Advances in Applied Self-organizing Systems", publisher = "Springer", year = "2008", editor = "Mikhail Prokopenko", number = "25?", series = "Advanced Information and Knowledge Processing", chapter = "6", pages = "105--126", keywords = "genetic algorithms, genetic programming, snakebot, evolution, grammar", isbn_13 = "978-1-84628-981-1", ISSN = "1610-3947", broken = "http://www.springer.com/west/home/computer/communications?SGWID=4-148-22-173743913-0", DOI = "doi:10.1007/978-1-84628-982-8_6", notes = "also known as \cite{DBLP:series/aikp/Tanev08}", } @Article{Tanev:2007:sigevo, author = "Ivan Tanev and Katsunori Shimohara", title = "Towards Human Competitive Driving of Scale Model of a Car", journal = "SIGEVOlution", year = "2007", volume = "2", number = "4", pages = "14--26", month = "Winter", keywords = "genetic algorithms, genetic programming, XML/DOM", ISSN = "1931-8499", URL = "http://www.sigevolution.org/issues/pdf/SIGEVOlution200704.pdf", DOI = "doi:10.1145/1399962.1399964", size = "13 pages", abstract = "The success of the computer playing board games (e.g., chess [8], checkers [4], backgammon, tic-tac-toe, Noughts and Crosses) has long served as an indication of the progress in the field of artificial intelligence (AI). The expanding scope of applicability of AI, when the latter is employed to control the individual characters (agents) which are able to learn the environment, often including opponents, and to adopt an adaptive optimal (rather than a priori preprogrammed) playing tactics and strategy include soccer football [15], motocross and car racing [3, 21], etc. [4], [6]. we focus on the domain of car racing, and consider the problem of designing a driving agent, able to remotely control a scale model of a racing car, which runs in a human-competitive, fast and consistent way. Adhering to the commonly recognised criteria of the human competitiveness of automatically evolved solutions, we attempt to verify that the evolved driving agent hold its own or wins a regulated competition involving human contestants [11].", notes = "Published 25 June 2008", } @Article{Tanev:2008:JIRS, author = "Ivan Tanev and Katsunori Shimohara", title = "Evolution of Agent, Remotely Operating a Scale Model of a Car Through a Latent Video Feedback", journal = "Journal of Intelligent and Robotic Systems", year = "2008", volume = "52", number = "2", pages = "263--283", month = jun, keywords = "genetic algorithms, genetic programming, Anticipatory modelling , Driving agent, Feedback latency", DOI = "doi:10.1007/s10846-008-9212-y", abstract = "We present an evolution of an agent, remotely operating a fast running scale model of a car. The agent perceives the environment from overhead video camera and conveys its actions via radio control transmitter. In order to cope with the video feed latency we propose an anticipatory modelling in which the agent considers its actions based on the anticipated state of the car. The agent is first evolved offline on a software simulator and then adapted online to the real world. During the online evolution, the lap times improve to the values much close to the values obtained from the offline evolution. An online evolutionary optimisation of the avoidance of a small static obstacle with a priori known properties results in lap times that are virtually the same as the best lap times achieved on the same track without obstacles. This work can be viewed as a step towards the automated design of controllers of remotely operated vehicles capable to find an optimal solution to various tasks in a priori known environments.", } @InProceedings{Tanev:2008:gecco, author = "Ivan Tanev and Katsunori Shimohara", title = "Co-evolution of active sensing and locomotion gaits of simulated snake-like robot", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "257--264", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p257.pdf", DOI = "doi:10.1145/1389095.1389135", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, active sensing, locomotion, navigation, Snakebot, Artificial life, evolutionary robotics, adaptive behaviour, evolvable hardware", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389135}", } @InProceedings{DBLP:conf/sab/TanevYHS08, author = "Ivan Tanev and Hirotaka Yamazaki and Tomoyuki Hiroyasu and Katsunori Shimohara", title = "Evolution of General Driving Rules of a Driving Agent", booktitle = "From Animals to Animats 10, Proceedings of the 10th International Conference on Simulation of Adaptive Behavior, SAB 2008", year = "2008", editor = "Minoru Asada and John C. T. Hallam and Jean-Arcady Meyer and Jun Tani", series = "Lecture Notes in Computer Science", volume = "5040", pages = "488--0498", address = "Osaka, Japan", month = jul # " 7-12", publisher = "Springer", bibsource = "DBLP, http://dblp.uni-trier.de", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-69133-4", DOI = "doi:10.1007/978-3-540-69134-1_48", notes = "part of \cite{DBLP:conf/sab/2008}", } @InProceedings{Tanev:2008:SICE, author = "Ivan Tanev and Katsunori Shimohara", title = "Co-evolution of Sensing Morphology and Locomotion Control of Simulated Snakebot", booktitle = "Proceedings of the Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE)", year = "2008", pages = "1502--1505", month = "20-22 " # aug, organization = "The University of Electro-Communications, Chofu, Japan", keywords = "genetic algorithms, genetic programming, Automatic control, Legged locomotion, Medical robotics, Mobile robots, Morphology, Optimal control, Robot sensing systems, Robotics and automation, Robustness, Sensor phenomena and characterisation, evolutionary computation, legged locomotion, mathematical morphology, optimisation, path planning, sensors, steering systems, automated sensing morphology co-evolution, contact less wall-following navigation, differential steering, evolutionary optimised sensor, simulated snakebot locomotion gait control, snake-like robot, Snakebot, locomotion active sensing, simulated evolution", DOI = "doi:10.1109/SICE.2008.4654897", size = "4 pages", abstract = "We propose an approach of automated co-evolution of the optimal values of attributes of active sensing (orientation, range and timing of activation of sensors) and the control of locomotion gaits of simulated snake-like robot (Snakebot) that result in a fast speed of locomotion in a confined environment. The experimental results illustrate the emergence of a contactless wall-following navigation of fast sidewinding Snakebots. The wall-following is accomplished by means of differential steering, facilitated by the evolutionary defined control sequences incorporating the readings of evolutionary optimized sensors.", notes = "Also known as \cite{4654897}", } @Article{Tanev:2008:IS, author = "Ivan Tanev and Kikuo Yuta", title = "Epigenetic programming: Genetic programming incorporating epigenetic learning through modification of histones", journal = "Information Sciences", year = "2008", volume = "178", number = "23", pages = "4469--4481", month = "1 " # dec, note = "Special Section: Genetic and Evolutionary Computing", keywords = "genetic algorithms, genetic programming, epigenesis, learning histone code", ISSN = "0020-0255", DOI = "doi:10.1016/j.ins.2008.07.027", size = "13 pages", abstract = "We present the results of our work in simulating the recently discovered findings in molecular biology regarding the significant role which histones play in regulating the gene expression in eukaryotes. Extending the notion of inheritable genotype in evolutionary computation from the commonly considered model of DNA to chromatin (DNA and histones), we present epigenetic programming as an approach, incorporating an explicitly controlled gene expression through modification of histones in strongly-typed genetic programming (STGP). We propose a double cell representation of the simulated individuals, comprising somatic cell and germ cell, both represented by their respective chromatin structures. Following biologically plausible concepts, we regard the plastic phenotype of the somatic cell, achieved via controlled gene expression owing to modifications to histones (epigenetic learning, EL) as relevant for fitness evaluation, while the genotype of the germ cell corresponds to the phylogenesis of the individuals. The beneficial effect of EL on the performance characteristics of STGP is verified on evolution of social behaviour of a team of predator agents in the predator prey pursuit problem. Empirically obtained performance evaluation results indicate that EL contributes to about 2-fold improvement of computational effort of STGP. We trace the cause for that to the cumulative effect of polyphenism and epigenetic stability, both contributed by EL. The former allows for phenotypic diversity of genotypically similar individuals, while the latter robustly preserves the individuals from the destructive effects of crossover by silencing certain genotypic combinations and explicitly activating them only when they are most likely to be expressed in corresponding beneficial phenotypic traits.", } @Article{Tanev:2009:JCMSI, author = "Ivan Tanev and Katsunori Shimohara", title = "Evolution of Active Sensing for Wall-Following Navigation of Snake-Like Robot", journal = "SICE Journal of Control, Measurement, and System Integration (SICE JCMSI)", year = "2009", volume = "2", number = "4", pages = "222--228", month = jul, keywords = "genetic algorithms, genetic programming, locomotion, snakebot, active sensing, navigation", ISSN = "1882-4889", URL = "https://www.jstage.jst.go.jp/article/jcmsi/2/4/2_4_222/_article", URL = "https://www.jstage.jst.go.jp/article/jcmsi/2/4/2_4_222/_pdf", URL = "http://library.naist.jp/mylimedio/search/magazine.do?target=local&bibid=1317&lang=en", URL = "https://library.naist.jp/mylimedio/dl/page.do?issueid=89595&tocid=100929735&page=222-228", DOI = "doi:10.9746/jcmsi.2.222", size = "7 pages", abstract = "We propose an approach of automated co-evolution of the optimal values of attributes of active sensing (orientation, range and timing of activation of sensors) and the control of locomotion gaits of a simulated snake-like robot (Snakebot) that result in a fast speed of locomotion in a confined environment. The experimental results illustrate the emergence of a contactless wall-following navigation of fast sidewinding Snakebots. The wall-following is accomplished by means of differential steering, facilitated by the evolutionary defined control sequences incorporating the readings of evolutionary optimised sensors.", notes = "Department of Information Systems Design, Faculty of Engineering, Doshisha University", } @InProceedings{Tanev:2009:ICCAS-SICE, author = "Ivan Tanev and Katsunori Shimohara", title = "Interactively Learned Probabilistic Context-sensitive Grammar in Genetic Programming for the Evolution of Snake-like Robot", booktitle = "ICRAS \& SICE International Joint Conference, ICCAS-SICE, 2009", year = "2009", month = "18-21 " # aug, address = "Fukuoka", pages = "2732--2737", publisher = "IEEE", isbn13 = "978-4-9077-6433-3", keywords = "genetic algorithms, genetic programming, context-sensitive grammar, interactively learned consensus sequences, probabilistic context, snake-like robot evolution, snakebot, user feedback, context-sensitive grammars, learning (artificial intelligence), probability, robots", URL = "http://ieeexplore.ieee.org/search/srchabstract.jsp?tp=&arnumber=5333378", size = "6 pages", abstract = "We discuss an approach of incorporating interactively learned consensus sequences (ILCS) in genetic programming (GP) for efficient evolution of simulated Snakebot situated in a challenging environment. ILCS introduce a biased mutation in GP via probabilistic context sensitive grammar, in which the probabilities of applying the production rules with multiple right-hand side alternatives depend on the grammatical context. The distribution of these probabilities is learned interactively from the syntax of the Snakebots, exhibiting behavioral traits that according to the human observer are relevant for the emergence of ability to overcome obstacles. Because at the earlier stages of evolution these behavioral traits are not necessarily pertinent to the best performing (i.e. fastest) Snakebots, the user feedback provides the evolution with an additional insight about the promising areas in the fitness landscape. Empirical results verify that employing ILCS improves the efficiency of GP in that the evolved Snakebots are faster than those obtained via canonical GP.", notes = "http://www.sice.or.jp/ICCAS-SICE2009/ Also known as \cite{5333378}", } @Article{tanev:2010:ALR, author = "Ivan Tanev and Katsunori Shimohara", title = "{XML-based} genetic programming framework: design philosophy, implementation, and applications", journal = "Artificial Life and Robotics", year = "2010", volume = "15", number = "4", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s10015-010-0857-9", DOI = "doi:10.1007/s10015-010-0857-9", } @InProceedings{Tanev:2011:alife, author = "Tuze Kuyucu and Ivan Tanev and Katsunori Shimohara", title = "Genetic transposition inspired incremental genetic programming for efficient coevolution of locomotion and sensing of simulated snake-like robot", booktitle = "Advances in Artificial Life, ECAL 2011: Proceedings of the Eleventh European Conference on the Synthesis and Simulation of Living Systems", year = "2011", editor = "Tom Lenaerts and Mario Giacobini and Hugues Bersini and Paul Bourgine and Marco Dorigo and Rene Doursat", pages = "439--446", address = "Paris", month = "8-12 " # aug, organisation = "International Society of Artificial Life (ISAL)", publisher = "MIT Press", keywords = "genetic algorithms, genetic programming: Poster", isbn13 = "978-0-262-29714-1", URL = "http://mitpress.mit.edu/books/chapters/0262297140chap68.pdf", DOI = "doi:10.7551/978-0-262-29714-1-ch068", size = "8 pages", abstract = "Genetic transposition (GT) is a process of moving sequences of DNA to different positions within the genome of a single cell. It is recognised that the transposons (the jumping genes) facilitate the evolution of increasingly complex forms of life by providing the creative playground for the mutation where the latter could experiment with developing novel genetic structures without the risk of damaging the already existing, well-functioning genome. In this work we investigate the effect of a GT-inspired mechanism on the efficiency of genetic programming (GP) employed for coevolution of locomotion gaits and sensing of the simulated snake like robot (Snakebot). In the proposed approach, the task of coevolving the locomotion and the sensing morphology of Snakebot in a challenging environment is decomposed into two subtasks, implemented as two consecutive evolutionary stages. At first stage we employ GP to evolve a pool of simple, sensor less bots that are able to move fast in a smooth, open terrain. Then, during the second stage, we use these Snakebots to seed the initial population of the bots that are further subjected to coevolution of their locomotion control and sensing in a more challenging environment. For the second phase the seed is used as it is to create only part of a new individual, and the rest of the new individual's genetic makeup is created by a mutant copy of the seed. Experimental results suggest that the proposed two-staged GT inspired incremental evolution contributes to significant increase in the efficiency of the evolution of fast moving and sensing Snakebots.", notes = "Broken April 2025 http://www.ecal11.org/ Broken April 2025 Complete Proceedings e-Book Available at: http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=12760", } @InProceedings{Tanev:2011:GECCOcomp, author = "Ivan Tanev and Tuze Kuyucu and Katsunori Shimohara", title = "Incremental genetic programming via genetic transposition", booktitle = "GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0690-4", keywords = "genetic algorithms, genetic programming, Artificial life/robotics/evolvable hardware: Poster", pages = "19--20", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001858.2001870", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Genetic transposition is a process of moving sequences of DNA to different positions within the genome of a single cell. Inspired by the role of genetic transposons in biology, we introduce a genetic transposition inspired mechanism in genetic programming (GP). This mechanism, a simple variation from seeding in incremental evolution, provides a more effective approach to the evolution of systems with multiple features.", notes = "Also known as \cite{2001870} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @InProceedings{tanev:2012:EuroGP, author = "Ivan Tanev and Tuze Kuyucu and Katsunori Shimohara", title = "The Effect of Bloat on the Efficiency of Incremental Evolution of Simulated Snake-like Robot", booktitle = "Proceedings of the 15th European Conference on Genetic Programming, EuroGP 2012", year = "2012", month = "11-13 " # apr, editor = "Alberto Moraglio and Sara Silva and Krzysztof Krawiec and Penousal Machado and Carlos Cotta", series = "LNCS", volume = "7244", publisher = "Springer Verlag", address = "Malaga, Spain", pages = "242--253", organisation = "EvoStar", isbn13 = "978-3-642-29138-8", DOI = "doi:10.1007/978-3-642-29139-5_21", keywords = "genetic algorithms, genetic programming, Incremental genetic programming, Bloat, Neutrality", abstract = "We present the effect of bloat on the efficiency of incremental evolution of locomotion of simulated snake-like robot (Snakebot) situated in a challenging environment. In the proposed incremental genetic programming (IGP), the task of coevolving the locomotion gaits and sensing of the bot in a challenging environment is decomposed into two subtasks, implemented as two consecutive evolutionary stages. In the first stage we use genetic programming (GP) to evolve a pool of morphologically simple, sensor less Snakebots that move fast in a smooth, open terrain. Then, during the second stage, we use this pool to seed the initial population of Snakebots that are further subjected to coevolution of their locomotion control and sensing morphology in a challenging environment. The empirical results suggest that the bloat no immediate effect on the efficiency of the first stage of IGP. However, the bloated seed contributes to a much faster second stage of evolution. In average, the second stage with bloated seed reaches the best fitness values of the parsimony seeds about five times faster. We assume that this speedup is attributed to the neutral code that is used by IGP as an evolutionary playground to experiment with developing novel sensory abilities, without damaging the already evolved, fast locomotion of the bot.", notes = "Part of \cite{Moraglio:2012:GP} EuroGP'2012 held in conjunction with EvoCOP2012 EvoBIO2012, EvoMusArt2012 and EvoApplications2012", } @Article{Tanev:2014:GPEM, author = "Ivan Tanev and Tuze Kuyucu and Katsunori Shimohara", title = "{GP-induced} and explicit bloating of the seeds in incremental GP improves evolutionary success", journal = "Genetic Programming and Evolvable Machines", year = "2014", volume = "15", number = "1", pages = "37--60", month = mar, keywords = "genetic algorithms, genetic programming, Snakebot, Bloat, Genetic transposition, Incremental GP", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-013-9192-y", size = "24 pages", abstract = "The parsimony control in genetic programming (GP) is one of the limiting factors in the quick evolution of efficient solutions. A variety of parsimony pressure methods have been developed to address this issue. The effects of these methods on the efficiency of evolution are recognised to depend on the characteristics of the applied problem domain. On the other hand, the implications of using parsimony pressure in evolving the seeds for incremental genetic programming (IGP) are still poorly known and remain uninvestigated. In this work we present a study on the cumulative effect of the bloat and the seeding of the initial population on the efficiency of incremental evolution of simulated snake-like robot (Snakebot). In the proposed IGP, the task of coevolving the locomotion gaits and sensing of the bot in a challenging environment is decomposed into two sub-tasks, implemented as two consecutive evolutionary stages. First, to evolve the pools of sensor less Snakebots, we use GP featuring the following three bloat-control methods: (1) linear parametric parsimony pressure, (2) lexicographic parsimony pressure and (3) no bloat control. During the second stage of IGP, we use these pools to seed the initial population of Snakebots applying two methods of seeding: canonical seeding and seeding inspired by genetic transposition (GT).", notes = "Transponson, cf McClintock Maize. GT. ODE simulator. Genome 15*3 ??? or 3??? ADF 'Parsimony ...no ...implications ...on fitness' p51. Best with no parsimony on seeds p53. Cone shape used by sidewiding robot to turn. Apex of cone at either head or tail of robot, fig10. IGP. Delphi http://isd-si.doshisha.ac.jp/tkuyucu/TranspositionCode.htm", } @InProceedings{Tanev:2018:AIMSA, author = "Ivan Tanev and Milen Georgiev and Katsunori Shimohara and Thomas Ray", title = "Evolving a Team of Asymmetric Predator Agents That Do Not Compute in Predator-prey Pursuit Problem", booktitle = "18th International Conference on Artificial Intelligence: Methodology, Systems, Applications (AIMSA 2018)", editor = "Gennady Agre and Josef {van Genabith} and Thierry Declerck", year = "2018", volume = "11089", series = "Lecture Notes in Artificial Intelligence", pages = "240--251", address = "Varna, Bulgaria", month = "12-14 " # sep, publisher = "Springer", keywords = "genetic algorithms, multi-agent systems, Simple agents, Micro-robots, Asymmetric morphology, Predator-prey problem", isbn13 = "978-3-319-99343-0", DOI = "doi:10.1007/978-3-319-99344-7_22", abstract = "We herein revisit the predator-prey pursuit problem, using very simple predator agents. The latter, intended to model the emerging micro- and nano-robots, are morphologically simple. They feature a single line-of-sight sensor and a simple control of their two thrusters. The agents are behaviourally simple as well their decision-making involves no computing, but rather a direct mapping of the few perceived environmental states into the corresponding pairs of thrust values. We apply genetic algorithms to evolve such a mapping that results in the successful behaviour of the team of these predator agents. To enhance the generality of the evolved behavior, we propose an asymmetric morphology of the agents, an angular offset of their sensor. Our experimental results verify that the offset of both 20 degrees and 30 degrees yields efficient and consistent evolution of successful behaviors of the agents in all tested initial situations.", notes = "Not GP? See also \cite{DBLP:journals/information/GeorgievTSR19} Doshisha University, Kyotanabe, Japan", } @Article{Tang:2015:Neurocomputing, author = "Fei Tang and Sanfeng Chen and Xu Tan and Tao Hu and Guangming Lin and Zuo Kang", title = "Discovery scientific laws by hybrid evolutionary model", journal = "Neurocomputing", volume = "148", pages = "143--149", year = "2015", ISSN = "0925-2312", DOI = "doi:10.1016/j.neucom.2012.07.058", URL = "http://www.sciencedirect.com/science/article/pii/S0925231214009199", abstract = "Constructing a mathematical model is an important issue in engineering application and scientific research. Discovery high-level knowledge such as laws of natural science in the observed data automatically is a very important and difficult task in systematic research. The authors have got some significant results with respect to this problem. In this paper, high-level knowledge modelled by systems of ordinary differential equations (ODEs) is discovered in the observed data routinely by a hybrid evolutionary algorithm called HEA-GP. The application is used to demonstrate the potential of HEA-GP. The results show that the dynamic models discovered automatically in observed data by computer sometimes can compare with the models discovered by humanity. In addition, a prototype of KDD Automatic System has been developed which can be used to discover models in observed data automatically.", keywords = "genetic algorithms, genetic programming, Hybrid evolutionary algorithm, Discover scientific laws", } @Article{TANG:2023:ijheatmasstransfer, author = "Jiguo Tang and Shengzhi Yu and Hongtao Liu", title = "Development of correlations for steam condensation over a vertical tube in the presence of noncondensable gas using machine learning approach", journal = "International Journal of Heat and Mass Transfer", volume = "201", pages = "123609", year = "2023", ISSN = "0017-9310", DOI = "doi:10.1016/j.ijheatmasstransfer.2022.123609", URL = "https://www.sciencedirect.com/science/article/pii/S001793102201078X", keywords = "genetic algorithms, genetic programming, Condensation, Noncondensable gas, Machine learning, Multi-gene genetic programming", abstract = "Steam condensation is an important phenomenon encountered in nuclear reactor under severe accidents. Even though many correlations for predicting steam condensation heat transfer coefficient (HTC) in the presence of noncondensable gas (NCG) have been proposed over the past decade, a more reliable and accurate model is still required. Thus, in this study, multigene genetic programming (MGGP), a biologically inspired machine learning method, is applied to develop new correlations for condensation HTC of steam-NCG mixture over a vertical tube in turbulent free convection regime. To this end, a consolidated database with 1440 data points from 18 sources is compiled. Then, using the database, both a new empirical correlation and a MGGP model are developed for better comparison. The performance of the MGGP-based correlation selected using Pareto tournaments strategy is compared with the new developed empirical correlation and another 20 relevant correlations. The results reveal the superiority of the MGGP-based correlation. In addition, it is found that the tube length is excluded in the best-trained correlation, even though it is used as the input of MGGP, which agrees well with the results of previous theoretical and experimental studies. The present study demonstrates that MGGP is promising in developing explicit, accurate, and compact models for the complex heat transfer and multiphase flow phenomena such as steam condensation in the presence of NCG", } @Article{Tang:2002:CILS, author = "Kailin Tang and Tonghua Li", title = "Combining PLS with GA-GP for QSAR", journal = "Chemometrics and Intelligent Laboratory Systems", year = "2002", volume = "64", pages = "55--64", number = "1", keywords = "genetic algorithms, genetic programming, PLS, QSAR, Nonlinear modeling", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6TFP-46NXJ0Y-2/2/966def2759d210eea6e5312f9a0042c7", ISSN = "0169-7439", DOI = "doi:10.1016/S0169-7439(02)00050-3", abstract = "partial least squares (PLS) improved by genetic algorithm-genetic programming (GA-GP) is applied to deal with functions for inner relationship in quantitative structure-activity relationship (QSAR). PLS is used to build a linear or nonlinear model between the principal components and its activity, and GA-GP is applied to regressions and equations. It develops PLS models to increase the range of PLS modelling. Using the inner relationship of polynomial function, a set of 79 inhibitors of HIV-1 reverse transcriptase, derivatives of a recently reported HIV-1-specific lead: 1-[(2-hydroxyethoxy) methyl]-6-(phenylthio) thymine (HEPT) was studied. The obtained QSAR model shows high predictive ability, rcv=0.900. It demonstrates that this method is useful.", } @Article{Tang:2003:ACA, author = "Kailin Tang and Tonghua Li", title = "Comparison of different partial least-squares methods in quantitative structure-activity relationships", journal = "Analytica Chimica Acta", year = "2003", volume = "476", pages = "85--92", number = "1", abstract = "partial least-squares (PLS) is discussed. A new hybrid method combining PLS with GAGP, in which selection of variables, selection of functions and optimisation of parameters were carried at the same time without any foreknowledge, was studied. A number of PLS algorithms (linear PLS, QPLS, SPL-PLS, NPLSNGA) that have appeared were compared from a theoretical viewpoint. Eight practical results with all the compared methods indicated that nonlinear models are better than linear model. In nonlinear methods, GAGP-PLS is significant.", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6TF4-478B4KC-1/2/7ddb467dbe209747455621ebed20699f", keywords = "genetic algorithms, genetic programming, Partial least-squares, QSAR", DOI = "doi:10.1016/S0003-2670(02)01257-6", } @TechReport{ilpgp-ml-98, author = "Lappoon R. Tang and Mary Elaine Califf and Raymond J. Mooney", title = "An Experimental Comparison of Genetic Programming and Inductive Logic Programming on Learning Recursive List Functions", institution = "Artificial Intelligence Lab, University of Texas at Austin", year = "1998", number = "AI 98-271", address = "USA", month = may, keywords = "genetic algorithms, genetic programming, Inductive Logic Programming, Empirical Comparison", URL = "http://www.cs.utexas.edu/users/ml/papers/ilpgp-ml-98.pdf", URL = "http://www.cs.utexas.edu/users/ml/papers/ilpgp-ml-98.ps.gz", size = "14 pages", abstract = "This paper experimentally compares three approaches to program induction: inductive logic programming (ILP), genetic programming (GP), and genetic logic programming (GLP) (a variant of GP for inducing Prolog programs). Each of these methods was used to induce four simple, recursive, list-manipulation functions. The results indicate that ILP is the most likely to induce a correct program from small sets of random examples, while GP is generally less accurate. GLP performs the worst, and is rarely able to induce a correct program. Interpretations of these results in terms of differences in search methods and inductive biases are presented.", notes = "This paper will also be submitted to the 8th Int. Workshop on Inductive Logic Programming, 1998 Not in ILP 1998 proceedings https://doi.org/10.1007/BFb0027303 ILP'98 https://pages.cs.wisc.edu/~dpage/ilp98.html", } @Article{journals/nca/TangYL17, author = "Long Tang and Chunyan Yang and Weihua Li", title = "Adopting gene expression programming to generate extension strategies for incompatible problem", journal = "Neural Computing and Applications", year = "2017", number = "9", volume = "28", pages = "2649--2664", keywords = "genetic algorithms, genetic programming, gene expression programming", bibdate = "2017-08-03", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/nca/nca28.html#TangYL17", DOI = "doi:10.1007/s00521-016-2211-1", } @InProceedings{Tang:2011:PHM, author = "Peng Tang and Zhaohui Gan and Tommy W. S. Chow", title = "Clonal selection programming for rotational machine fault classification and diagnosis", booktitle = "Prognostics and System Health Management Conference (PHM-Shenzhen), 2011", year = "2011", month = "24-25 " # may, address = "Shenzhen", size = "6 pages", abstract = "The automatic control of technical systems requires increasingly advanced fault diagnosis to improve system reliability and safety. In this paper, a clonal selection programming (CSP)-based fault detection method is introduced. The CSP is inspired by genetic programming (GP) and immune programming (IP). The proposed method has been verified with electrical faults and mechanical faults operating at different rotating speeds. Machine vibration signals are translated into four feature vectors and encoded according to the structure of antibody. Then the extracted features are processed of a CSP-based classifier. Clone classifier uses a powerful search strategy that can get a near-optimal solution in a large search space. The experimental result indicates that the CSP based method can improve the performance significantly and very robust, which indicates that the method is extremely useful for practical industrial applications.", keywords = "genetic algorithms, genetic programming, clonal selection programming, electrical faults, fault detection method, fault diagnosis, immune programming, machine vibration signals, mechanical faults, rotational machine fault classification, electrical faults, failure analysis, fault diagnosis, machine testing", DOI = "doi:10.1109/PHM.2011.5939551", notes = "Also known as \cite{5939551}", } @InProceedings{Tang:2018:ICSI, title = "Optimal Shape Design of an Autonomous Underwater Vehicle Based on Gene Expression Programming", author = "Qirong Tang and Yinghao Li and Zhenqiang Deng and Di Chen and Ruiqin Guo and Hai Huang", pages = "132--141", series = "Lecture Notes in Computer Science", booktitle = "Advances in Swarm Intelligence - 9th International Conference, ICSI 2018, Shanghai, China, June 17-22, 2018, Proceedings, Part II", publisher = "Springer", year = "2018", volume = "10942", editor = "Ying Tan and Yuhui Shi and Qirong Tang", keywords = "genetic algorithms, genetic programming, gene expression programming, Autonomous underwater vehicle, Shape optimization, Multi-objective particle swarm optimization", isbn13 = "978-3-319-93817-2", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/swarm/icsi2018-2.html#TangLDCGH18", DOI = "doi:10.1007/978-3-319-93818-9_13", notes = "conf/swarm/TangLDCGH18", } @InProceedings{tang:2024:CEC, author = "Shanshan Tang and Qi Chen and Bing Xue and Min Huang and Mengjie Zhang", title = "Genetic Programming with Multi-Task Feature Selection for {Alzheimer's} Disease Diagnosis", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Accuracy, Parkinson's disease, Biomarkers, Multitasking, Feature extraction, Prediction algorithms, Space exploration, Symbolic regression, Alzheimer's disease prediction, Feature selection", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10611973", abstract = "Alzheimer's disease (AD) has been the most common cause of dementia making cognitive score prediction and important feature identification crucial for its diagnosis. Although sparse linear regression has been used for this purpose due to its simplicity, it often selects an excessive number of features to track the disease and assumes a linear relationship between input and output, which might not always hold. To address these limitations, genetic programming-based symbolic regression (GPSR) algorithms have been proposed. GPSR can select the important features by exploring the feature space and learning a regression model without any assumption of model structure. However, the generalisation ability of existing GPSR methods still needs to be improved. Considering the multiple related prediction tasks in AD studies, this work proposes a new method called linear scaled GPSR with multi-task feature selection (LSGPMTFS), to promote the prediction performance of each task by knowledge-sharing among multiple tasks. LSGPMTFS has two stages. The first stage learns a specific feature subset for each task. In the second stage, the model for each task is searched on the union of feature subsets selected from the first stage. The experimental results on authentic AD datasets demonstrate that the proposed algorithm can select a small set of important features with better learning and generalisation performance compared with other GPSR methods.", notes = "also known as \cite{10611973} WCCI 2024", } @Article{Tang:2020:CSIRP, author = "Yinqi Tang and Hongyang Jia and Naveen Verma", journal = "IEEE Transactions on Circuits and Systems I: Regular Papers", title = "Reducing Energy of Approximate Feature Extraction in Heterogeneous Architectures for Sensor Inference via Energy-Aware Genetic Programming", year = "2020", volume = "67", number = "5", pages = "1576--1587", abstract = "Hardware acceleration substantially enhances both energy efficiency and performance, but raises major challenges for programmability. This is especially true in the domain of approximate computing, where energy-approximation tradeoffs at the hardware level are extremely difficult to encapsulate in interfaces to the software level. The programmability challenges have motivated co-design of accelerators with program-synthesis frameworks, where the structured computations resulting from synthesis are exploited towards hardware specialization. This paper proposes energy-aware code synthesis targeting heterogeneous architectures for approximate computing. A heterogeneous architecture for embedded sensor inference is employed, demonstrated in custom silicon, where programmable feature extraction is mapped to an accelerator via genetic programming. The high level of accelerator specialization and structured mapping of computations to the accelerator enable robust energy models, which are then employed in a genetic-programming algorithm to improve the energy-approximation Pareto frontier. The proposed algorithm is demonstrated in an electroencephalogram-based seizure-detection application and an electrocardiogram-based arrhythmia-detection application. At the same level of baseline inference performance, the energy consumption of genetic-programming models executed on the accelerator is 57.percent and 21.percent lower, respectively, with the proposed algorithm, compared to a conventional algorithm without incorporating energy models for execution on the accelerator.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/TCSI.2019.2961643", ISSN = "1558-0806", month = may, notes = "Also known as \cite{8952908}", } @Article{Tangen:2014:GPEM, author = "Uwe Tangen", title = "On evolvability and robustness in the {matrix-GRT} model", journal = "Genetic Programming and Evolvable Machines", year = "2014", volume = "15", number = "3", pages = "343--374", month = sep, keywords = "genetic algorithms, artificial evolution, Evolvability, Emergence of replication, GRT model, RNA world, Protein world, Evolutionary robustness", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-014-9221-5", size = "32 pages", abstract = "Quantifying evolution and understanding robustness are best done with a system that is both rich enough to frustrate rigging of the answer and simple enough to permit comparison against either existing systems or absolute measures. Such a system is provided by the self-referential model matrix-genome, replication and translation, based on the concept of operators, which is introduced here. Ideas are also taken from the evolving micro-controller research. This new model replaces micro-controllers by simple matrix operations. These matrices, seen as abstract proteins, work on abstract genomes, peptides or other proteins. Studying the evolutionary properties shows that the protein-only hypothesis (proteins as active elements) shows poor evolvability and the RNA-before-protein hypothesis (genomes controlling) exhibits similar intricate evolutionary dynamics as in the micro-controller model. A simple possible explanation for this surprising difference in behaviour is presented. In addition to existing evolutionary models, dynamical and organisational changes or transitions occurring late in long-term experiments are demonstrated.", } @InProceedings{Tanigawa:2000:GECCO, author = "Toru Tanigawa and Qiangfu Zhao", title = "A Study on Efficient Generation of Decision Trees Using Genetic Programming", pages = "1047--1052", year = "2000", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)", editor = "Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer", address = "Las Vegas, Nevada, USA", publisher_address = "San Francisco, CA 94104, USA", month = "10-12 " # jul, keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-708-0", URL = "http://gpbib.cs.ucl.ac.uk/gecco2000/NN260.pdf", URL = "http://www.u-aizu.ac.jp/~qf-zhao/CONTRIBUTION/gecco2000.ps.Z", URL = "http://citeseer.ist.psu.edu/498621.html", size = "6 pages", abstract = "For pattern recognition, the decision trees (DTs) are more efficient than neural networks (NNs) for two reasons. First, the computations in making decisions are simpler. Second, important features can be selected automatically during the design process. On the other hand, NNs are adaptable, and thus have the ability to learn in changing environment.", notes = "A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of \cite{whitley:2000:GECCO}", } @InProceedings{taniguchi:2001:micscmagp, author = "Ken Taniguchi and Setsuya Kurahashi and Takao Terano", title = "Managing Information Complexity in a Supply Chain Model by Agent-Based Genetic Programming", booktitle = "2001 Genetic and Evolutionary Computation Conference Late Breaking Papers", year = "2001", editor = "Erik D. Goodman", pages = "413--420", address = "San Francisco, California, USA", month = "9-11 " # jul, keywords = "genetic algorithms, genetic programming", notes = "GECCO-2001LB. see also CEF2001 RePEc:sce:scecf1:238? \cite{Taniguchi:2001:CEF}", } @InProceedings{Taniguchi:2001:CEF, author = "Ken Taniguchi and Setsuya Kurahashi and Takao Terano", title = "Managing Information Complexity in a Supply Chain Model by Agent-Based Genetic Programming", booktitle = "7th International Conference of Society of Computational Economics", year = "2001", address = "Yale", month = "28-29 " # jun, organisation = "Society for Computational Economics", keywords = "genetic algorithms, genetic programming", URL = "http://ideas.repec.org/p/sce/scecf1/238.html", URL = "http://econpapers.repec.org/paper/scescecf1/238.htm", abstract = "We propose agent-based formulation of a Supply Chain Management (SCM) system for manufacturing firms. We model each firm as an intelligent agent, which communicates each other through the blackboard architecture in distributed artificial intelligence. To overcome the issues of conventional SCM systems, we employ the concept of information entropy, which represents the complexity of the purchase, sales, and inventory activities of each firm. Based on the idea, we implement an agent-based simulator to learn `good' decisions via genetic programming in a logic programming environment. From intensive experiments, our simulator have shown good performance against the dynamic environmental changes.", notes = "CEF 2001 number 238. See also \cite{taniguchi:2001:micscmagp}", } @InProceedings{Taniguchi:2002:MIC, author = "Ken Taniguchi and Setsuya Kurahashi and Takao Terano", title = "Managing Information Complexity of Supply Chains via Agent-Based Genetic Programming", volume = "2417", pages = "596", year = "2002", booktitle = "PRICAI 2002: Trends in Artificial Intelligence : 7th Pacific Rim International Conference on Artificial Intelligence", editor = "M. Ishizuka and A. Sattar", series = "LNAI", address = "Tokyo, Japan", publisher_address = "Heidelberg", month = "18-22 " # aug, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-44038-3", CODEN = "LNCSD9", ISSN = "0302-9743", bibdate = "Tue Sep 10 19:10:22 MDT 2002", DOI = "doi:10.1007/3-540-45683-X_67", acknowledgement = ack-nhfb, size = "1 page", abstract = "This paper proposes agent-based formulation of a Supply Chain Management (SCM) system for manufacturing firms. We model each firm as an intelligent agent, which communicates each other through the blackboard architecture in distributed artificial intelligence. To cope with the issues of conventional SCM systems, we employ the concept of information entropy, which represents the complexity of the purchase, sales, and inventory activities of each firm. Based on the idea, we implement an agent-based simulator to learn `good' decisions via genetic programming in a logic programming environment. From intensive experiments, our simulator have shown good performance against the dynamic environmental changes.", } @InProceedings{taniguchi:ktd:gecco2004, author = "Ken Taniguchi and Takao Terano", title = "Keeping the Diversity with Small Populations Using Logic-Based Genetic Programming", booktitle = "Genetic and Evolutionary Computation -- GECCO-2004, Part II", year = "2004", editor = "Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell", series = "Lecture Notes in Computer Science", pages = "724--725", address = "Seattle, WA, USA", publisher_address = "Heidelberg", month = "26-30 " # jun, organisation = "ISGEC", publisher = "Springer-Verlag", volume = "3103", ISBN = "3-540-22343-6", ISSN = "0302-9743", DOI = "doi:10.1007/b98645", size = "2", keywords = "genetic algorithms, genetic programming, Poster", notes = "GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)", } @InProceedings{taniguchi:2004:KBIIES, author = "Ken Taniguchi and Takao Terano", title = "Analyzing Dynamics of a Supply Chain Using Logic-Based Genetic Programming", booktitle = "Knowledge-Based Intelligent Information and Engineering Systems", year = "2004", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-540-30132-5_66", DOI = "doi:10.1007/978-3-540-30132-5_66", } @Article{oai:inderscience.com:7267, title = "Managing information complexity of supply chains via agent-based genetic programming", author = "Ken Taniguchi and Takao Terano", journal = "International Journal of Electronic Business", number = "3/4", publisher = "Inderscience Publishers", year = "2005", month = jun # "~30", volume = "3", ISSN = "1741-5063", bibsource = "OAI-PMH server at www.inderscience.com", language = "eng", oai = "oai:inderscience.com:7267", pages = "216--224", relation = "ISSN online: 1741-5063 ISSN print: 1470-6067 DOI: 10.1504/05.7267", rights = "Inderscience Copyright", source = "IJEB (2005), Vol 3 Issue 3/4, pp 216 - 224", keywords = "genetic algorithms, genetic programming, supply chain management, SCM, information entropy, information complexity, information management, manufacturing firms, decision making agents, blackboard architecture, distributed artificial intelligence, DAI, purchasing, sales, inventory, simulation, agent-based systems, multi-agent systems, e-business, electronic business", URL = "http://www.inderscience.com/link.php?id=7267", abstract = "This paper proposes agent-based formulation of a supply chain management (SCM) system for manufacturing firms. We model each firm as a decision-making agent, which communicates each other through the blackboard architecture in distributed artificial intelligence. To overcome the issues of conventional SCM systems, we employ the concept of information entropy, which represents the complexity of the purchase, sales, and inventory activities of each firm. Based on the idea, we implement an agent-based simulator to learn 'good' decisions via genetic programming in a logic-programming environment. From intensive experiments, our simulator has shown good performance against the dynamic environmental changes.", notes = "Address: Graduate School of Systems Management, University of Tsukuba, Otsuka 3-29-1, Bunkyo-ku, Tokyo 112 0012, Japan. ' Graduate School of Systems Management, University of Tsukuba, Otsuka 3-29-1, Bunkyo-ku, Tokyo 112 0012, Japan taniguti@gssm.otsuka.tsukuba.ac.jp, terano@gssm.otsuka.tsukuba.ac.jp", } @InProceedings{DBLP:conf/gecco/TanjiI09, author = "Makoto Tanji and Hitoshi Iba", title = "Program optimization by random tree sampling", booktitle = "GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation", year = "2009", editor = "Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano {van Hemert} and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique Alba", pages = "1131--1138", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1569901.1570053", abstract = "This paper describes a new program evolution method named PORTS (Program Optimization by Random Tree Sampling) which is motivated by the idea of preservation and control of tree fragments. We hypothesize that to reconstruct building blocks efficiently, tree fragments of any size should be preserved into the next generation, according to their differential fitnesses. PORTS creates a new individual by sampling from the promising trees by traversing and transition between trees instead of subtree crossover and mutation. Because the size of a fragment preserved during a generation update follows a geometric distribution, merits of the method are that it is relatively easy to predict the behavior of tree fragments over time and to control sampling size, by changing a single parameter. Our experimental results on three benchmark problems show that the performance of PORTS is competitive with SGP (Simple Genetic Programming). And we observed that there is a significant difference of fragment distribution between PORTS and simple GP.", notes = "GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009). ACM Order Number 910092.", } @InProceedings{Tanji:2010:gecco, author = "Makoto Tanji and Hitoshi Iba", title = "{ConBreO}: a music performance rendering system using hybrid approach of {IEC} and automated evolution", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "1275--1282", keywords = "genetic algorithms, genetic programming, Real world applications", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830483.1830711", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "This paper presents an IEC (Interactive Evolutionary Computation) system named ConBreO to render expressive music performance using Genetic Programming. The central problem of IEC is the limitation of number of fitness evaluations because of user fatigue. In the system, we introduce two support techniques for IEC. The first one is a hybrid approach of IEC and automated evolution which allows the system to evolve both of IEC and automated evolution. The second one is the selective presentation which selects a new individual to be evaluated by the user based on its expected improvement of fitness. Using the system, obtained expression rule won an award at a performance rendering contest which evaluates computer systems generating expressive musical performances. Our experiment shows that the selective presentation reduces the number of fitness evaluations required to construct the fitness prediction model and prevents the system evaluating unfruitful individuals.", notes = "Also known as \cite{1830711} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @PhdThesis{500000550739, author = "Makoto Tanji", title = "Program Evolution using Nonparametric Probabilistic Model", school = "The University of Tokyo", year = "2011", address = "Japan", keywords = "genetic algorithms, genetic programming", URL = "http://ci.nii.ac.jp/naid/500000550739", URL = "http://iss.ndl.go.jp/books/R100000002-I023389085-00", abstract = "This paper describes research results of Genetic Programming (GP) which uses nonparametric probabilistic model. Genetic Programming, a method of Evolutionary Computation which uses mechanism of biological evolution for optimization, can generate programs itself and functions by using tree structure data. It can be applied to complex problems which are difficult to solve by human such as the robot routing problem and the regression problem, etc. Conventional Evolutionary Computation methods, GP and GA (Genetic Algorithm), use crossover and mutation which have been inspired by sexual multiplication. In recent years, new approaches called EDA (Estimation of Distribution Algorithm) and PMBGA (Probabilistic Model Building Genetic Algorithm) which uses a probabilistic model from promising have shown promise. EDA and PMBGA can deal with the dependencies of variables and can solve complex problems in GA. Especially, it is widely accepted that EDA and PMBGA are well suited to problems which have dependencies between the variables. In the past decade, EDA-GP and PMBGP (Probabilistic Model Building GP) have been studied. EDA-GP uses PPT (Probabilistic Prototype Tree) or stochastic grammar as probabilistic model. However it is difficult to represent dependencies of solutions because of a variable length tree structure. Furthermore, the number of parameters and the number of promising solutions increase rapidly. As a result, the computational cost, CPU time and memory, will be high. In this research, a comparative experiment of EDA-GP using PPT model is reported. I propose two methods of GP to overcome the problems above: PORTS (Program Optimization by Random Tree Sampling) and PERCE (Program Evolution using Related Clique Extraction). These methods do not need to calculate and store parameters of a probabilistic model. Instead of storing them, PORTS and PERCE use current solutions directly to create new solutions. PORTS creates a new solution by sampling and concatenation of fragment of promising trees. The size of the fragments follows a geometric distribution to maintain the diversity of a population. Experimental result shows that PORTS solves GP-easy problems very fast than the conventional GP. PERCE uses a set of sub graphs, called ''related cliques'', that contains a number of PPT nodes relating each other. It does not require explicit calculation and storing of the parameters. Experimental result shows that PERCE solves GP-hard problems such as deceptive problem and multimodal problem. I summarize this research and discuss how to use GP for practical problems. Finally, further topics to be researched are presented and discussed.", } @InProceedings{Tanjil:2019:evomusart, author = "Fazle Tanjil and Brian J. Ross", title = "Deep Learning Concepts for Evolutionary Art", booktitle = "8th International Conference on Computational Intelligence in Music, Sound, Art and Design, EvoMusArt 2019", year = "2019", editor = "Aniko Ekart and Antonios Liapis and Luz Castro", series = "LNCS", volume = "11453", publisher = "Springer", pages = "1--17", address = "Leipzig, Germany", month = "24-26 " # apr, organisation = "Species", keywords = "genetic algorithms, genetic programming, Deep convolutional neural network, ANN, Evolutionary art", isbn13 = "978-3-030-16666-3", DOI = "doi:10.1007/978-3-030-16667-0_1", abstract = "Fig. 1. System Architecture. The CNN on left side is integrated with GP fitness. The same CNN is used (right side) to pre-evaluate a target image, to be used as a basis for determining feature map values to match with evolved images during fitness.", notes = "EvoMusArt2019 held in conjunction with EuroGP'2019 EvoCOP2019 and EvoApplications2019 http://www.evostar.org/2019/cfp_evomusart.php", } @InCollection{tannenbaum:2000:CPPGP, author = "David Tannenbaum", title = "Co-Evolution of Predator and Prey using Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "380--386", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:2000:gagp}", } @InProceedings{tanomaru:1996:tm, author = "Julio Tanomaru and Akio Azuma", title = "Automatic Generation of {Turing} Machines by a Genetic Approach", booktitle = "The First International Workshop on Machine Learning, Forecasting, and Optimization (MALFO96)", year = "1996", editor = "Daniel Borrajo and Pedro Isasi", pages = "173--184", address = "Gatafe, Spain", month = "10--12 " # jul, organisation = "Universidad Carlos III de Madrid", keywords = "genetic algorithms, genetic programming", ISBN = "84-89315-04-3", broken = "http://grial.uc3m.es/~dborrajo/malfo96.html", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/tanomaru_1996_tm.pdf", size = "12 pages", } @InProceedings{tanomaru:1998:etmx, author = "Julio Tanomaru", title = "Evolving {Turing} Machines from Examples", booktitle = "Artificial Evolution", year = "1993", editor = "J.-K. Hao and E. Lutton and E. Ronald and M. Schoenauer and D. Snyers", volume = "1363", series = "LNCS", pages = "167--180", address = "Nimes, France", month = oct, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1007/BFb0026599", size = "14 pages", abstract = "The aim of this paper is to investigate the application of evolutionary approaches to the automatic design of automata in general, and Turing machines, in particular. Here, each automaton is represented directly by its state transition table and the number of states is allowed to change dynamically as evolution takes place. This approach contrasts with less natural representation methods such as trees of genetic programming, and allows for easier visualization and hardware implementation of the obtained automata. Two methods are proposed, namely, a straightforward, genetic-algorithm-like one, and a more sophisticated approach involving several operators and the 1/5 rule of evolution strategy. Experiments were carried out for the automatic generation of Turing machines from examples of input and output tapes for problems of sorting, unary arithmetic, and language acceptance, and the results indicate the feasibility of the evolutionary approach. Since Turing machines can be viewed as general representations of computer programs, the proposed approach can be thought of as a step towards the generation of programs and algorithms by evolution.", notes = "AE'97", } @InProceedings{DBLP:conf/icinco/TantauPWO19, author = "Mathias Tantau and Lars Perner and Mark Wielitzka and Tobias Ortmaier", editor = "Oleg Gusikhin and Kurosh Madani and Janan Zaytoon", title = "Structure and Parameter Identification of Process Models with Hard Non-linearities for Industrial Drive Trains by Means of Degenerate Genetic Programming", booktitle = "Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2019", year = "2019", volume = "1", pages = "368--376", publisher = "SciTePress", month = jul # " 29-31", address = "Prague, Czech Republic", keywords = "genetic algorithms, genetic programming, Modelling, Simultaneous Identification of Structure and Parameters, Phenomenological Models, Backlash, Multiple-mass Resonators, Artificial Intelligence, Computational Intelligence, Evolutionary Computation and Control, Evolutionary Computing, Informatics in Control, Automation and Robotics, Intelligent Control Systems and Optimization, Optimization Algorithms, Soft Computing", isbn13 = "978-989-758-380-3", ISSN = "2184-2809", URL = "https://www.repo.uni-hannover.de/handle/123456789/10472", URL = "https://doi.org/10.5220/0007949003680376", DOI = "doi:10.5220/0007949003680376", timestamp = "Wed, 18 Sep 2019 16:30:21 +0200", biburl = "https://dblp.org/rec/conf/icinco/TantauPWO19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", size = "9 pages", abstract = "The derivation of bright-grey box models for electric drives with coupled mechanics, such as stacker cranes, robots and linear gantries is an important step in control design but often too time-consuming for the ordinary commissioning process. It requires structure and parameter identification in repeated trial and error loops. In this paper an automated genetic programming solution is proposed that can cope with various features, including highly non-linear mechanics (friction, backlash). The generated state space representation can readily be used for stability analysis, state control, Kalman filtering, etc. This, however, requires several special rules in the genetic programming procedure and an automated integration of features into the defining state space form. Simulations are carried out with industrial data to investigate the performance and robustness.", notes = "p372 'Each node is assigned a model type and the number of nodes with a certain model type defines the multiplicity of this submodel. Connections between nodes do not represent the flow of information but merrily the genetic connection as bases on a chromosome,' Crossover, cloning, Point mutation, insertion mutation, deletion mutation, chromosome mutation", } @Article{Tanyildizi20102612, author = "Harun Tanyildizi and Abdulkadir Cevik", title = "Modeling mechanical performance of lightweight concrete containing silica fume exposed to high temperature using genetic programming", journal = "Construction and Building Materials", volume = "24", number = "12", pages = "2612--2618", year = "2010", note = "Special Issue on Fracture, Acoustic Emission and NDE in Concrete (KIFA-5)", ISSN = "0950-0618", DOI = "doi:10.1016/j.conbuildmat.2010.05.001", URL = "http://www.sciencedirect.com/science/article/B6V2G-509SDKG-1/2/f514159db9cb4d6dce74ceab7beca37b", keywords = "genetic algorithms, genetic programming, Lightweight concrete, Silica fume, High temperature, Mechanical properties", abstract = "In this study, the mechanical performance of lightweight concrete exposed to high temperature has been modelled using genetic programming. The mixes incorporating 0%, 10%, 20% and 30% silica fumes were prepared. Two different cement contents (400 and 500 kg/m3) were used in this study. After being heated to temperatures of 20 degree Celsius, 200 degree C, 400 degree C and 800 degree C, respectively, the compressive and splitting tensile strength of lightweight concrete was tested. Empirical genetic programming based equations for compressive and splitting tensile strength were obtained in terms of temperature (T), cement content (C), silica fume content (SF), pumice aggregate content (A), water/cement ratio (W/C) and super plasticizer content (SP). Proposed genetic programming based equations are observed to be quite accurate as compared to experimental results.", } @Article{TANZIFI:2018:JCIS, author = "Marjan Tanzifi and Mohammad Tavakkoli Yaraki and Mojtaba Karami and Samira Karimi and Asieh Dehghani Kiadehi and Kianoush Karimipour and Shaobin Wang", title = "Modelling of dye adsorption from aqueous solution on polyaniline/carboxymethyl cellulose/TiO2 nanocomposites", journal = "Journal of Colloid and Interface Science", volume = "519", pages = "154--173", year = "2018", keywords = "genetic algorithms, genetic programming, Optimization, Polyaniline, Carboxymethyl cellulose sodium, TiO, Congo Red, Adsorption", ISSN = "0021-9797", DOI = "doi:10.1016/j.jcis.2018.02.059", URL = "http://www.sciencedirect.com/science/article/pii/S0021979718302078", abstract = "In the present study, a polyaniline/carboxymethyl cellulose/TiO2 nanocomposite (PAn/CMC/TiO2) was synthesized by a polymerization method, and was used for adsorption of Congo Red from aqueous solution. The effects of operational parameters of the adsorption process including pH, initial dye concentration, temperature, adsorbent dosage, and adsorption time on adsorption efficiency were investigated, and response surface methodology was used for their optimization. Optimal adsorption conditions were determined at pH of 2.6, initial concentration of 82mgL, temperature of 56 degreeC, adsorption time of 24a min, and adsorbent dose of 0.14a g. In addition, the system was also simulated using artificial neural network (ANN) and genetic programming (GP). It was found that the behavior of the system could be well predicted by ANN using 5, 1 and 8 neurons for input, middle and output layers, respectively. Kinetic and isothermal analyses showed that the maximum adsorption capacities were obtained at 94.28, 97.53 and 119.9 mgg by Langmuir model at temperatures of 25, 40 and 50 degreeC, respectively and that adsorption kinetics followed the pseudo-second-order model. The nano-adsorbent was also found to be reusable without a significant change in adsorption capacity for at least five adsorption-desorption cycles. Finally, the mechanism of dye adsorption on the nano-adsorbent was investigated and proposed", } @Article{TANZIFI:2020:Chemosphere, author = "Marjan Tanzifi and Mohammad {Tavakkoli Yaraki} and Zahra Beiramzadeh and Leily {Heidarpoor Saremi} and Mohammad Najafifard and Hojatollah Moradi and Mohsen Mansouri and Mojtaba Karami and Hossein Bazgir", title = "Carboxymethyl cellulose improved adsorption capacity of polypyrrole/{CMC} composite nanoparticles for removal of reactive dyes: Experimental optimization and {DFT} calculation", journal = "Chemosphere", volume = "255", pages = "127052", year = "2020", ISSN = "0045-6535", DOI = "doi:10.1016/j.chemosphere.2020.127052", URL = "http://www.sciencedirect.com/science/article/pii/S0045653520312455", keywords = "genetic algorithms, genetic programming, Polypyrrole, Carboxy methyl cellulose, Wastewater treatment, Optimization, Density functional theory", abstract = "In this study, polypyrrole/carboxymethyl cellulose nanocomposite particles (PPy/CMC NPs) were synthesized and applied for removal of reactive red 56 (RR56)and reactive blue 160 (RB160) as highly toxic dyes. The amount of CMC was found significantly effective on the surface adsorption efficiency. Different optimization methods including the genetic programming, response surface methodology, and artificial neural network (ANN) were used to optimize the effect of different parameters including pH, adsorption time, initial dye concentration and adsorbent dose. The maximum adsorption of RR56 and RB160 were found under the following optimum conditions: pH of 4 and 5, adsorption time of 55 min and 52 min for RR56 and RB160, respectively, initial dye concentration of 100 mg/L and adsorbent dose of 0.09 g for both dyes. were obtained for RR56 and RB160, respectively. Also, the results indicated that ANN method could predict the experimental adsorption data with higher accuracy than other methods. The analysis of ANN results indicated that the adsorbent dose is the main factor in RR56 removal, followed by time, pH and initial concentration, respectively. However, initial concentration mostly determines the RB160 removal process. The isotherm data for both dyes followed the Langmuir isotherm model with a maximum adsorption capacity of 104.9 mg/g and 120.7 mg/g for RR56 and RB160, respectively. In addition, thermodynamic studies indicated the endothermic adsorption process for both studied dyes. Moreover, DFT calculations were carried out to obtain more insight into the interactions between the dyes and adsorbent. The results showed that the hydrogen bondings and Van der Waals interactions are dominant forces between the two studied dyes and PPy/CMC composite. Furthermore, the interaction energies calculated by DFT confirmed the experimental adsorption data, where PPy/CMC resulted in higher removal of both dyes compared to PPy. The developed nanocomposite showed considerable reusability up to 3 cylces of the batch adsorption process", } @InProceedings{Tao:2020:SMC, author = "Jingjing Tao and Xiaomin Zhu and Li Ma and Meng Wu and Weidong Bao and Ji Wang", booktitle = "2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)", title = "Benign: An Automatic Optimization Framework for the Logic of Swarm Behaviors", year = "2020", pages = "2999--3005", abstract = "In the field of swarm intelligence, it is usually complicated to express the logic of swarm behaviours. Behaviour tree has drawn a lot of attention to be a practical approach to solving this problem in recent years. However, how to automatically design the logic of swarm behaviours according to the target of a task is the focus of swarm intelligence. Hence, we propose an automatic optimising framework named Benign which is capable of using gene expression programming (GEP) to optimise the logic of swarm behaviours. In Benign, the basic swarm behaviours and the relationships among those behaviours are mapped to nodes of behaviour tree by the method named Matt firstly. With these nodes, we design an artificial behaviour tree. After that, the artificial behaviour tree is transformed into an expression tree in GEP according to the method named Meet. Finally, GEP is used for optimisation to generate the expected logic of swarm behaviours. We conduct simulation experiments to validate the efficiency of Benign. The experimental results show the superiority of Benign. Compared with the logic of the artificial behaviour tree before optimisation, the conduction of the optimised logic of swarm behaviours increases efficiency by more than 50percent.", keywords = "genetic algorithms, genetic programming, gene expression programming, Transforms, Gene expression, Particle swarm optimisation, Task analysis, Optimisation, Cybernetics, swarm intelligence, swarm behaviour, behaviour tree", DOI = "doi:10.1109/SMC42975.2020.9283409", ISSN = "2577-1655", month = oct, notes = "Also known as \cite{9283409}", } @InProceedings{Tao:2021:BigDIA, author = "Jingjing Tao and Xiaomin Zhu and Li Ma and Meng Wu and Xiaoqing Li and Liyuan Niu", title = "A Logical Transformation Method of the Motion Rules for Swarms", booktitle = "2021 7th International Conference on Big Data and Information Analytics (BigDIA)", year = "2021", pages = "472--477", abstract = "Swarm intelligence involves designing appropriate rules which make swarms with the same limited ability organize themselves to accomplish desired tasks. Numerous studies have designed many different motion rules. According to a designed rule, a swarm can self-organize into a desired pattern, such as a gathering pattern or a flocking pattern. However, for swarms that need to complete multiple tasks, it tends to be not enough to devise motion rules of forming a single pattern. Therefore, this paper presents a logical transformation method of motion rules for swarms, which essentially matches different motion rules to corresponding conditional states. The method consists of two steps: the construction of the set of conditional states and the set of motion rules, and the matching optimization calculation using genetic programming. The feasibility of this method is verified by simulations.", keywords = "genetic algorithms, genetic programming, Design methodology, Big Data, Task analysis, Particle swarm optimization, Optimization, swarm intelligence, motion rules, gene programming, matching optimization", DOI = "doi:10.1109/BigDIA53151.2021.9619739", month = oct, notes = "Also known as \cite{9619739}", } @InProceedings{Tao:2022:CEC, author = "Ning Tao and Anthony Ventresque and Takfarinas Saber", title = "Multi-objective Grammar-guided Genetic Programming with Code Similarity Measurement for Program Synthesis", booktitle = "2022 IEEE Congress on Evolutionary Computation (CEC)", year = "2022", editor = "Carlos A. Coello Coello and Sanaz Mostaghim", address = "Padua, Italy", month = "18-23 " # jul, keywords = "genetic algorithms, genetic programming, Grammar-Guided Genetic Programming, G3P, Codes, Error analysis, Measurement uncertainty, Evolutionary computation, Benchmark testing, Software, Program Synthesis, Grammar-Guided Genetic Programming, Levenshtein Distance, Cosine, FuzzyWuzzy, TokenSortRatio, CCFinder, suffix-tree matching, SIM, Code Similarity, Multi-Objective Optimization, MOGP", isbn13 = "978-1-6654-6708-7", DOI = "doi:10.1109/CEC55065.2022.9870312", abstract = "Grammar-Guided Genetic Programming (G3P) is widely recognised as one of the most successful approaches for program synthesis, i.e., the task of automatically discovering an executable piece of code given user intent. G3P has been shown capable of successfully evolving programs in arbitrary languages that solve several program synthesis problems based only on a set of input/output examples. Despite its success, the restriction on the evolutionary system to only leverage input/output error rate during its assessment of the programs it derives limits its scalability to larger and more complex program synthesis problems. With the growing number and size of open software repositories and generative artificial intelligence approaches, there is a sizable and growing number of approaches for retrieving/generating source code (potentially several partial snippets) based on textual problem descriptions. Therefore, it is now, more than ever, time to introduce G3P to other means of user intent (particularly textual problem descriptions). In this paper, we would like to assess the potential for G3P to evolve programs based on their similarity to particular target codes of interest (obtained using some code retrieval/generative approach). Through our experimental evaluation on a well known program synthesis benchmark, we have shown that G3P successfully manages to evolve some of the desired programs with all four considered similarity measures. However, in its default configuration, G3P is not as successful with similarity measures as it is with the classical input/output error rate when solving program synthesis problems. Therefore, we propose a novel multi-objective G3P approach that combines the similarity to the target program and the traditional input/output error rate. Our experiments show that compared to the error-based G3P, the multiobjective G3P approach could improve the success rate of specific problems and has great potential to improve on the traditional G3P system.", notes = "Also known as \cite{9870312} 'no similarity measure that improves G3P success rate on all problems' 'Several small grammars are defined [one] each for a data type' 'two objectives (i.e., input/output error rate and the degree of similarity against a target code).' Number IO, Smallest, Median, String Lengths Backwards, Negative To Zero. 'similarity-based G3P system is not able to achieve a similar result as the error-based G3P system.'", } @InProceedings{tao:2022:OL, author = "Ning Tao and Anthony Ventresque and Takfarinas Saber", title = "Assessing {Similarity-Based} {Grammar-Guided} Genetic Programming Approaches for Program Synthesis", booktitle = "Optimization and Learning", year = "2022", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-031-22039-5_19", DOI = "doi:10.1007/978-3-031-22039-5_19", } @InProceedings{Tao:2023:LA-CCI, author = "Ning Tao and Anthony Ventresque and Takfarinas Saber", booktitle = "2023 IEEE Latin American Conference on Computational Intelligence (LA-CCI)", title = "Program Synthesis with Generative Pre-trained Transformers and Grammar-Guided Genetic Programming Grammar", year = "2023", abstract = "Grammar-Guided Genetic Programming (G3P) is widely recognised as one of the most successful approaches to program synthesis. Using a set of input/output tests, G3P evolves programs that fit a defined BNF grammar and that are capable of solving a wide range of program synthesis problems. However, G3P's inability to scale to more complex problems has limited its applicability. Recently, Generative Pre-trained Transformers (GPTs) have shown promise in revolutionizing program synthesis by generating code based on natural language prompts. However, challenges such as ensuring correctness and safety still need to be addressed as some GPT-generated programs might not work while others might include security vulnerabilities or blacklisted library calls. In this work, we proposed to combine GPT (in our case ChatGPT) with a G3P system, forcing any synthesised program to fit the BNF grammar-thus offering an opportunity to evolve/fix incorrect programs and reducing security threats. In our work, we leverage GPT-generated programs in G3P's initial population. However, since GPT-generated programs have an arbitrary structure, the initial work that we undertake is to devise a technique that maps such programs to a predefined BNF grammar before seeding the code into G3P's initial population. By seeding the grammar-mapped code into the population of our G3P system, we were able to successfully improve some of the desired programs using a well-known program synthesis benchmark. However, in its default configuration, G3P is not successful in fixing some incorrect GPT-generated programs-even when they are close to a correct program. We analysed the performance of our approach in depth and discussed its limitations and possible future improvements.", keywords = "genetic algorithms, genetic programming, ANN, Codes, Sociology, Transformers, Grammar, Security, Statistics, Program Synthesis, Grammar Guided Genetic Programming, Generative Pre-trained Transformers, Large Language Models, Grammar", DOI = "doi:10.1109/LA-CCI58595.2023.10409384", ISSN = "2769-7622", month = oct, notes = "Also known as \cite{10409384}", } @Article{Tao:2025:CSI, author = "Ning Tao and Anthony Ventresque and Vivek Nallur and Takfarinas Saber", title = "Grammar-obeying program synthesis: A novel approach using large language models and many-objective genetic programming", journal = "Computer Standards and Interfaces", year = "2025", volume = "92", pages = "103938", keywords = "genetic algorithms, genetic programming, Program synthesis, Grammar, LLMs, Grammar guided GP, Multi-objective", ISSN = "0920-5489", URL = "https://www.human-competitive.org/sites/default/files/saber_text_1.txt", URL = "https://www.human-competitive.org/sites/default/files/saber_paper_1.pdf", URL = "https://www.sciencedirect.com/science/article/pii/S0920548924001077", DOI = "doi:10.1016/j.csi.2024.103938", size = "10 pages", abstract = "Program synthesis is an important challenge that has attracted significant research interest, especially in recent years with advancements in Large Language Models (LLMs). Although LLMs have demonstrated success in program synthesis, there remains a lack of trust in the generated code due to documented risks (e.g., code with known and risky vulnerabilities). Therefore, it is important to restrict the search space and avoid bad programs. In this work, pre-defined restricted Backus-Naur Form (BNF) grammars are used, which are considered safe, and the focus is on identifying the most effective technique for grammar-obeying program synthesis, where the generated code must be correct and conform to the predefined grammar. It is shown that while LLMs perform well in generating correct programs, they often fail to produce code that adheres to the grammar. To address this, a novel Similarity-Based Many-Objective Grammar Guided Genetic Programming (SBMaOG3P) approach is proposed, leveraging the programs generated by LLMs in two ways: (i) as seeds following a grammar mapping process and (ii) as targets for similarity measure objectives. Experiments on a well-known and widely used program synthesis dataset indicate that the proposed approach successfully improves the rate of grammar-obeying program synthesis compared to various LLMs and the state-of-the-art Grammar-Guided Genetic Programming. Additionally, the proposed approach significantly improved the solution in terms of the best fitness value of each run for 21 out of 28 problems compared to G3P.", notes = "Entered 2025 HUMIES Also known as \cite{TAO2025103938} School of Computer Science, University College Dublin, Dublin, Ireland", } @PhdThesis{Ning_Tao_Thesis_15205926, author = "Ning Tao", title = "Multi-Objective Grammar-Guided Genetic Programming for Grammar-Obeying Program Synthesis", school = "School of Computer Science, University College Dublin", year = "2025", address = "Ireland", month = mar, keywords = "genetic algorithms, genetic programming, G3P, MOG3P, Large language models, LLM, ANN, IO, Natural language task, NLP, AI generated programs, Grammar compliance, BNF, Computer programming", URL = "http://hdl.handle.net/10197/28256", URL = "https://researchrepository.ucd.ie/entities/publication/b11d1a67-05ba-46eb-bf61-7f9b42245ea6", size = "163 pages", abstract = "Grammar-obeying program synthesis is a subfield of program synthesis (i.e., the task of automatically discovering an executable piece of code) that requires generated code to follow a Backus-Naur Form (BNF) grammar. Having code that obeys a grammar limits the structure of the code and the set of callable functions/methods/libraries, thus having an impact on (i) security: e.g., using malicious functionalities, blacklisted libraries, and questionable constructs, (ii) the computing environment: e.g., if the hardware is not able to run some functions or if some functions are too costly in terms of memory or energy, and (iii) code quality: improving code style and readability, reducing code smells, and enhancing consistency. Grammar-Guided Genetic Programming (G3P) is recognised as one of the most successful approaches for grammar-obeying program synthesis that evolves programs in arbitrary languages to solve program synthesis problems based on a set of input-output examples and predefined BNF grammars. Despite its success, the restriction on the evolutionary system to only leverage input-output error rate during its assessment of the programs it derives limits its scalability to larger and more complex program synthesis problems. Alternatively, while large language models (LLMs) are showing increasing success in program synthesis from a task description, they often struggle to generate accurate code due to ambiguity in task specifications, complex programming syntax, and lack of reliability in the generated code. Furthermore, their generative nature limits their ability to fix erroneous code with iterative LLM prompting. In this thesis, I focus on enhancing the performance of G3P for grammar-obeying program synthesis tasks. I begin by evaluating the state-of-the-art systems, including G3P and LLMs, in grammar-obeying program synthesis. Next, I explore how to map LLM-generated code into grammar-obeying forms, followed by seeding the grammar-mapped LLM-generated code into G3P initial population to improve the evolutionary process. Building on these foundations, I investigate the potential of using code similarity measures to guide the evolution of programs, leading to the development of Bi-Objective Grammar-Guided Genetic Programming (BOG3P) and Multi-Objective Grammar-Guided Genetic Programming (MOG3P), which combine input-output error rate with multiple similarity measures as objective functions. Finally, I integrate LLM generated code into the MOG3P framework in two ways: (i) using LLM-generated code as a seed for the evolutionary process through a grammar-mapping phase, and (ii) leveraging multiple similarity measures towards LLM-generated code to guide the search process throughout the evolution. Additionally, I further extend the MOG3P framework by incorporating code from multiple LLMs to enhance the diversity and effectiveness of the search process.", notes = "Student ID: 15205926 Supervisors: Takfarinas Saber, Anthony Ventresque, Vivek Nallur", } @InProceedings{Tao:2010:ICONIP, author = "Yanyun Tao and Minglu Li and Jian Cao", title = "Dynamic Population Variation Genetic Programming with Kalman Operator for Power System Load Modeling", booktitle = "17th International Conference Neural Information Processing (ICONIP 2010) - Theory and Algorithms, Part I", year = "2010", editor = "Kok Wai Wong and B. Sumudu U. Mendis and Abdesselam Bouzerdoum", volume = "6443", series = "Lecture Notes in Computer Science", pages = "520--531", address = "Sydney, Australia", month = nov # " 22-25", publisher = "Springer", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1007/978-3-642-17537-4_64", bibdate = "2010-11-22", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/iconip/iconip2010-1.html#TaoLC10", abstract = "According to the high accuracy of load model in power system, a novel dynamic population variation genetic programming with Kalman operator for load model in power system is proposed. First, an evolution load model called initial model in power system evolved by dynamic variation population genetic programming is obtained which has higher accuracy than traditional models. Second, parameters in initial model are optimised by Kalman operator for higher accuracy and an optimisation model is obtained. Experiments are used to illustrate that evolved model has higher accuracy 4.6-48percent than traditional models and It is also proved the performance of evolved model is prior to RBF network. Furthermore, the optimization model has higher accuracy 7.69-81.3percent than evolved model.", affiliation = "School of electronic information and electrical engineering, Shanghai JiaoTong University, Dongchuan road 800, 200240 Shanghai, China", notes = "10kV and 35kV transformers TanShi substation in China.", } @InProceedings{Tao:2010:CiSE, author = "Yanyun Tao and Wenhao Yuan and Jiajun Lin and Minglu Li", title = "Distribution-Estimation Gene Expression Programming", booktitle = "International Conference on Computational Intelligence and Software Engineering (CiSE), 2010", year = "2010", month = dec, abstract = "This paper presents a new form of gene expression programming based on distribution-estimation model with tree structure (TS) and graph structure (GS); The results of experiments indicates that the proposed approach achieves a good performance and EDGEP are effective in max problem and 6 multiplexer problem.", keywords = "genetic algorithms, genetic programming, Gene Expression Programming, GS, TS, distribution estimation gene expression programming, graph structure, max problem, multiplexer problem, tree structure, biocomputing, graph theory", DOI = "doi:10.1109/CISE.2010.5677035", notes = "Also known as \cite{5677035}", } @Article{Tao:2012:JSJU, author = "Yan-yun Tao and Jian Cao and Ming-lu Li", title = "Genetic programming using dynamic population variation for computational efforts reduction in system modeling", journal = "Journal of Shanghai Jiaotong University (Science)", year = "2012", volume = "17", number = "2", pages = "190--196", keywords = "genetic algorithms, genetic programming, dynamic population variation (DPV), stagnation phase, exponential pivot function, computationaleffort, average number of evaluation, diversity", URL = "http://link.springer.com/article/10.1007/s12204-012-1251-7", DOI = "doi:10.1007/s12204-012-1251-7", size = "7 pages", abstract = "we propose genetic programming (GP) using dynamic population variation (DPV)with four innovations for reducing computational efforts. A new stagnation phase definition and characteristicmeasure are defined for our DPV. The exponential pivot function is proposed to our DPV method in conjunctionwith the new stagnation phase definition. An appropriate population variation formula is suggested to accelerateconvergence. The efficacy of these innovations in our DPV is examined using six benchmark problems. Comparisonamong the different characteristic measures has been conducted for regression problems and the new proposedmeasure outperformed other measures. It is proved that our DPV has the capacity to provide solutions at a lowercomputational effort compared with previously proposed DPV methods and standard genetic programming inmost cases. Meanwhile, our DPV approach introduced in GP could also rapidly find an excellent solution as wellas standard GP in system modeling problems.", } @InProceedings{Tao:2012:CEC, title = "Using Module-level Evolvable hardware Approach in Design of Sequential Logic Circuits", author = "Yanyun Tao and Jian Cao and Yuzhen Zhang and Jiajun Lin and Minglu Li", pages = "2234--2241", booktitle = "Proceedings of the 2012 IEEE Congress on Evolutionary Computation", year = "2012", editor = "Xiaodong Li", month = "10-15 " # jun, DOI = "doi:10.1109/CEC.2012.6256546", size = "8 pages", address = "Brisbane, Australia", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, EHW, CGP, ECGP, ADF", abstract = "in this study, we propose a module-level Evolvable hardware (EHW) approach to design synchronous sequential circuits and minimise the circuit complexity (the number of logic gates and wires used). Firstly, we use evolutionary algorithm (EA) to implement states simplification and obtain near-optimal state assignment, which requires few logic gates and wires. Then, EHW evolves a set of high performing circuits and uses data mining method to find frequently evolved blocks (a component of logic gates) from these circuits in its pre-evolution stage. Frequently evolved blocks would be re-used in functional and terminals set for evolving better circuits. EHW has a faster convergence so that the circuit with small complexity could be evolved. Auto starting ability of circuits would also be test by the fitness function of EHW. Finally, two sequence detectors, two module counters, and ISCAS'89 circuit are used as the proof for our evolutionary design approach. Simulation results of experiments are given, and our evolutionary algorithm is shown to be better than other methods in terms of convergence time, success rate, and maximum fitness across generations.", notes = "GA, GP, CGP, ECGP, ADFs, increment evolution WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the EPS and the IET.", } @Article{journals/cai/TaoLC13, author = "Yanyun Tao and Minglu Li and Jian Cao", title = "A New Dynamic Population Variation in Genetic Programming", journal = "Computing and Informatics", year = "2013", number = "1", volume = "32", pages = "63--87", keywords = "genetic algorithms, genetic programming", ISSN = "1335-9150", bibdate = "2013-04-21", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/cai/cai32.html#TaoLC13", URL = "http://www.cai.sk/ojs/index.php/cai/article/view/1467", URL = "http://arxiv.org/abs/1304.3779", abstract = "A dynamic population variation (DPV) in genetic programming (GP) with four innovations is proposed for reducing computational effort and accelerating convergence during the run of GP. Firstly, we give a new stagnation phase definition and the characteristic measure for it. Secondly, we propose an exponential pivot function (EXP) in conjunction with the new stagnation phase definition. Thirdly, we propose an appropriate population variation formula for EXP. Finally, we introduce a scheme using an instruction matrix for producing new individuals to maintain diversity of the population. The efficacy of these innovations in our DPV is examined using four typical benchmark problems. Comparisons among the different characteristic measures have been conducted for regression problems and the proposed measure performed best in all characteristic measures. It is demonstrated that the proposed population variation scheme is superior to fixed and proportionate population variation schemes for sequence induction. It is proved that the new DPV has the capacity to provide solutions at a lower computational effort compared with previously proposed population variation methods and standard genetic programming in most problems.", } @Article{Tao:2013:GPEM, author = "Yanyun Tao and Yuzhen Zhang and Jian Cao and Yalong Huang", title = "A module-level three-stage approach to the evolutionary design of sequential logic circuits", journal = "Genetic Programming and Evolvable Machines", year = "2013", volume = "14", number = "2", pages = "191--219", month = jun, keywords = "genetic algorithms, genetic programming, evolvable hardware, cartesian genetic programming, Evolutionary approach, Module-level, Three-stage, Sequential circuits, Data mining, Frequently evolved blocks, Redundant states", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-012-9178-1", size = "29 pages", abstract = "In this study, we propose a module-level three-stage approach (TSA) to optimise the evolutionary design for synchronous sequential circuits. TSA has a three stages process, involving a genetic algorithm (GA), a pre-evolution, and a re-evolution. In the first stage, the GA simplifies the number of states and automatically searches the state assignment that can produce the circuit with small complexity. Then, the second stage evolves a set of high-performing circuits to acquire frequently evolved blocks, which will be re-used for more compact and simple solutions in the next stage. In this stage, a genetic programming (GP) is proposed for evolving the high-performing circuits and data mining is used as a finder of frequently evolved blocks in these circuits. In the final stage, the acquired blocks are encapsulated into the function and terminal set to produce a new population in the re-evolution. The blocks are expected to make the convergence faster and hence efficiently reduce the complexity of the evolved circuits. Seven problems of three types, sequence detectors, modulo-n counters and ISCAS89 circuits, are used to test our three-stage approach. The simulation results for these experiments are promising, and our approach is shown to be better than the other methods for sequential logic circuits design in terms of convergence time, success rate, and maximum fitness improvement across generations", } @InProceedings{Tao:2014:GECCOcomp, author = "Yanyun Tao and Yuzhen Zhang and Lijun Zhang and Chao Gu", title = "A projection-based decomposition in EHW method for design of relatively large circuits", booktitle = "GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion", year = "2014", editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges", isbn13 = "978-1-4503-2881-4", keywords = "genetic algorithms, genetic programming: Poster", pages = "153--154", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2598394.2598411", DOI = "doi:10.1145/2598394.2598411", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Scalability is the most important issue and not well-addressed in EHW field by far. To solve scalability, this paper proposes a novel EHW system called PD-ES, which integrates a Projection-based Decomposition (PD) and Evolutionary Strategy (ES). PD gradually decomposes a Boolean function by adaptively projecting it onto the property of variables, which makes the complexity and number of sub logic blocks minimised. The gate-level approach-CGP including ES searches complete solutions for these blocks. By employing PD into EHW system, the number of logic gates used for evolving and assembling the sub blocks decreases largely, and the scalability can be improved consequently. The MCNC circuits and n-parity circuits are used to prove the ability of PD-ES in solving scalability. The results illustrate that PD-ES is superior to 3SD-ES and fixed decomposition in evolving large circuits in terms of complexity reduction. Additionally, PD-ES makes success evolution in design of larger n-even-parity circuits as SDR has done.", notes = "Also known as \cite{2598411} Distributed at GECCO-2014.", } @InProceedings{Tao:2014:FSE, author = "Yida Tao and Jindae Kim and Sunghun Kim and Chang Xu", title = "Automatically Generated Patches As Debugging Aids: A Human Study", booktitle = "Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering, FSE 2014", year = "2014", pages = "64--74", address = "Hong Kong, China", publisher = "ACM", keywords = "genetic algorithms, genetic programming, GenProg, Debugging, automatic patch generation, human study", isbn13 = "978-1-4503-3056-5", URL = "http://doi.acm.org/10.1145/2635868.2635873", DOI = "doi:10.1145/2635868.2635873", acmid = "2635873", size = "11 pages", abstract = "Recent research has made significant progress in automatic patch generation, an approach to repair programs with less or no manual intervention. However, direct deployment of auto-generated patches remains difficult, for reasons such as patch quality variations and developers' intrinsic resistance. In this study, we take one step back and investigate a more feasible application scenario of automatic patch generation, that is, using generated patches as debugging aids. We recruited 95 participants for a controlled experiment, in which they performed debugging tasks with the aid of either buggy locations (i.e., the control group), or generated patches of varied qualities. We observe that: a) high-quality patches significantly improve debugging correctness; b) such improvements are more obvious for difficult bugs; c) when using low-quality patches, participants' debugging correctness drops to an even lower point than that of the control group; d) debugging time is significantly affected not by debugging aids, but by participant type and the specific bug to fix. These results highlight that the benefits of using generated patches as debugging aids are contingent upon the quality of the patches. Our qualitative analysis of participants' feedback further sheds light on how generated patches can be improved and better used as debugging aids.", notes = "Tao:2014:AGP:2635868.2635873,", } @InProceedings{Tao:2008:ieeeICCIS, author = "Yong-Qin Tao and Du-Wu Cui and Tai-Shan Yan", title = "Knowledge evolutionary algorithm based on granular computing", booktitle = "IEEE Conference on Cybernetics and Intelligent Systems, 2008", year = "2008", month = sep, pages = "341--346", keywords = "genetic algorithms, genetic programming, crossover operator, evolutionary characteristics, granular computing, knowledge evolutionary algorithm, knowledge granulation, mutation operator, evolutionary computation, knowledge engineering, mathematical operators", DOI = "doi:10.1109/ICCIS.2008.4670968", abstract = "Granular computing makes mainly use of the information of different granularities and hierarchies to solve problems of the uncertain, fuzzy, imprecise, part true and a number of information. This paper has analyzed the evolutionary characteristics of knowledge granulation and has proposed the evolution algorithm of knowledge granulation (EAKG). EAKG algorithm applies knowledge granulation to genetic programming and carries through the evaluation according to coverage degree and depends on degree to obtain some new rules. In addition, this paper has also given the recursive model of knowledge granulation evolution, crossover operator and mutation operator, etc. Through the experiments it has proved that it is the reasonable and effective to carry out solution of knowledge evolution with granule computing.", notes = "Also known as \cite{4670968}", } @Article{Tao:2018:ieeeJBHI, author = "Yanyun Tao and Yenming J. Chen and Xiangyu Fu and Bin Jiang and Yuzhen Zhang", journal = "IEEE Journal of Biomedical and Health Informatics", title = "Evolutionary ensemble learning algorithm to modeling of warfarin dose prediction for Chinese", year = "2018", keywords = "genetic algorithms, genetic programming, warfarin dose prediction, ensemble modeling, machine learning, regression model, genetic programming", ISSN = "2168-2194", DOI = "doi:10.1109/JBHI.2018.2812165", size = "12 pages", abstract = "An evolutionary ensemble modelling (EEM) method is developed to improve the accuracy of warfarin dose prediction. In EEM, genetic programming (GP) evolves diverse base models, and genetic algorithm optimises the parameters of the GP. The EEM model is assembled by using the prepared based models through a technique called bagging. In the experiment, a dataset of 289 Chinese patients, which is provided by The First Affiliated Hospital of Soochow University, is used for training, validation, and testing. The EEM model with selected feature groups is benchmarked with four machine-learning methods and three conventional regression models. Results show that the EEM model with M2+G group, namely, age, height, weight, gender, CYP2C9, VKORC1, and amiodarone, presents the largest coefficients of determination (R2), highest percentage of predicted dose within 20percent of the actual dose (20percent-p), smallest mean absolute error (mae), mean squared error (mse), root-mse on the test set, and the least decrease in R2 from the training set to the test set. In conclusion, the EEM method with M2+G delivers superior performance and can therefore be a suitable prediction model of warfarin dose for clinical application.", notes = "Soochow University, Suzhou, China. National Kaohsiung University of Science & Technology, Taiwan, R.O.C. The First Affiliated Hospital of Soochow University, Suzhou, China, Also known as \cite{8306883}", } @InProceedings{taou2016towards, title = "Towards Intelligent Biological Control: Controlling {Boolean} Networks with {Boolean} Networks", author = "Nadia S. Taou and David W. Corne and Michael A. Lones", booktitle = "19th European Conference on Applications of Evolutionary Computation, EvoApplications 2016, Part I", year = "2016", editor = "Giovanni Squillero and Paolo Burelli", volume = "9597", series = "Lecture Notes in Computer Science", pages = "351--362", address = "Porto, Portugal", month = mar # " 30 -- " # apr # " 1", organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Gene regulatory networks, Boolean networks, Control, Evolutionary algorithms", isbn13 = "978-3-319-31204-0", DOI = "doi:10.1007/978-3-319-31204-0_23", abstract = "Gene regulatory networks (GRNs) are the complex dynamical systems that orchestrate the activities of biological cells. In order to design effective therapeutic interventions for diseases such as cancer, there is a need to control GRNs in more sophisticated ways. Computational control methods offer the potential for discovering such interventions, but the difficulty of the control problem means that current methods can only be applied to GRNs that are either very small or that are topologically restricted. In this paper, we consider an alternative approach that uses evolutionary algorithms to design GRNs that can control other GRNs. This is motivated by previous work showing that computational models of GRNs can express complex control behaviours in a relatively compact fashion. As a first step towards this goal, we consider abstract Boolean network models of GRNs, demonstrating that Boolean networks can be evolved to control trajectories within other Boolean networks. The Boolean approach also has the advantage of a relatively easy mapping to synthetic biology implementations, offering a potential path to in vivo realisation of evolved controllers.", notes = "EvoApplications2016 held inconjunction with EuroGP'2016, EvoCOP2016 and EvoMUSART 2016", } @InProceedings{Taou:2018:EuroGP, author = "Nadia S. Taou and Michael A. Lones", title = "Towards In Vivo Genetic Programming: Evolving Boolean Networks to Determine Cell States", booktitle = "EuroGP 2018: Proceedings of the 21st European Conference on Genetic Programming", year = "2018", month = "4-6 " # apr, editor = "Mauro Castelli and Lukas Sekanina and Mengjie Zhang and Stefano Cagnoni and Pablo Garcia-Sanchez", series = "LNCS", volume = "10781", publisher = "Springer Verlag", address = "Parma, Italy", pages = "151--165", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-77552-4", DOI = "doi:10.1007/978-3-319-77553-1_10", abstract = "Within the genetic programming community, there has been growing interest in the use of computational representations motivated by gene regulatory networks (GRNs). It is thought that these representations capture useful biological properties, such as evolvability and robustness, and thereby support the evolution of complex computational behaviours. However, computational evolution of GRNs also opens up opportunities to go in the opposite direction: designing programs that could one day be implemented in biological cells. In this paper, we explore the ability of evolutionary algorithms to design Boolean networks, abstract models of GRNs suitable for refining into synthetic biology implementations, and show how they can be used to control cell states within a range of executable models of biological systems.", notes = "Part of \cite{Castelli:2018:GP} EuroGP'2018 held in conjunction with EvoCOP2018, EvoMusArt2018 and EvoApplications2018", } @Article{Tapiador:2009:PhysA, author = "J. E. Tapiador and J. C. Hernandez-Castro and J. A. Clark and S. Stepney", title = "Highly entangled multi-qubit states with simple algebraic structure", journal = "Journal of Physics A", year = "2009", volume = "42", number = "41", pages = "415301", keywords = "simulated annealing, Mathematical physics, Computational physics, Quantum information and quantum mechanics", URL = "http://stacks.iop.org/JPhysA/42/415301", DOI = "doi:10.1088/1751-8113/42/41/415301", size = "13 pages", abstract = "Recent works by Brown et al (2005 J. Phys. A: Math. Gen. 38 1119) and Borras et al (2007 J. Phys. A: Math. Theor. 40 13407) have explored numerical optimisation procedures to search for highly entangled multi-qubit states according to some computationally tractable entanglement measure. We present an alternative scheme based upon the idea of searching for states having not only high entanglement but also simple algebraic structure. We report results for 4, 5, 6, 7 and 8 qubits discovered by this approach, showing that many of such states do exist. In particular, we find a maximally entangled 6-qubit state with an algebraic structure simpler than the best results known so far. For the case of 7 qubits, we discover states with high, but not maximum, entanglement and simple structure, as well as other desirable properties. Some preliminary results are shown for the case of 8 qubits.", notes = "Journal of Physics A: Mathematical and Theoretical Preprint quant-ph/0904.3874", } @InProceedings{Tapiador:2010:CIT, author = "Juan E. Tapiador and John A. Clark", title = "Learning Autonomic Security Reconfiguration Policies", booktitle = "IEEE 10th International Conference on Computer and Information Technology (CIT)", year = "2010", pages = "902--909", month = jun, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CIT.2010.168", abstract = "We explore the idea of applying machine learning techniques to automatically infer risk-adaptive policies to reconfigure a network security architecture when the context in which it operates changes. To illustrate our approach, we consider the case of a MANET where nodes carrying sensitive services (e.g., web servers, key repositories, etc.) should consider relocating themselves into a different node to guarantee proper functioning. We use simulation to derive properties from a candidate policy, and then apply Genetic Programming and Multi-Objective Optimisation techniques to search for optimal candidates. The inferred policies take the form of risk-aware service relocation algorithms that autonomously dictate when and how to relocate services with the aim of keeping risk to a minimum. Since security policies often have implications in dimensions other than security, we force the learning process to consider also the consequences (performance, usability) of a given policy.", notes = "Also known as \cite{5578469}", } @Article{Tapkin:2013:MR, author = "Serkan Tapkin and Abdulkadir Cevik and Un Usar and Eren Gulsan", title = "Rutting prediction of asphalt mixtures modified by polypropylene fibers via repeated creep testing by utilising genetic programming", journal = "Materials Research", year = "2013", volume = "16", number = "2", pages = "277--292", keywords = "genetic algorithms, genetic programming, asphalt, polypropylene fibres, rutting potential, modelling, closed form solutions", ISSN = "1516-1439", bibsource = "OAI-PMH server at www.doaj.org", language = "eng", oai = "oai:doaj-articles:5c1352da861d9819875cd673c87da536", publisher = "ABM, ABC, ABPol", URL = "http://www.scielo.br/scielo.php?pid=S1516-14392013000200003&script=sci_arttext", DOI = "doi:10.1590/S1516-14392013005000012", size = "16 pages", abstract = "A novel application of genetic programming (GP) for modelling and presenting closed form solutions to the rutting prediction for polypropylene (PP) modified asphalt mixtures is investigated. Various PP fibres have been used for bitumen modification and repeated creep (RC) tests have been carried out. Marshall specimens, fabricated with multifilament 3 mm (M-03) type PP fibers at optimum bitumen content of 5percent have been tested under different load values and patterns at 50 degree C to investigate their rutting potential. It has been shown that the service lives of PP fibre-reinforced Marshall specimens are respectively longer than the control specimens under the same testing conditions (5 to 12 times). Input variables in the developed GP model use the physical properties of Marshall specimens such as PP type, specimen height, unit weight, voids in mineral aggregate, voids filled with asphalt, air voids, rest period and pulse counts. The performance of the accuracy of the proposed GP model is observed to be quite satisfactory. To obtain the main effects plot, detailed parametric studies have been performed. The presened closed form solution will also help further researchers willing to perform studies on the prediction of the rutting potential of asphalt without carrying out destructive tests for similar type of aggregate sources, bitumen, aggregate gradation, modification technique and laboratory conditions.", } @Misc{Tappler:2019:arxiv, author = "Martin Tappler and Bernhard K. Aichernig and Kim Guldstrand Larsen and Florian Lorber", title = "Learning Timed Automata via Genetic Programming", howpublished = "arXiv", year = "2019", month = "15 " # feb, edition = "v3", keywords = "genetic algorithms, genetic programming, software engineering, timed automata, automata learning, model learning, model inference", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/corr/corr1808.html#abs-1808-07744", URL = "http://arxiv.org/abs/1808.07744", size = "11 pages", abstract = "Model learning has gained increasing interest in recent years. It derives behavioural models from test data of black-box systems. The main advantage offered by such techniques is that they enable model-based analysis without access to the internals of a system. Applications range from fully automated testing over model checking to system understanding. Current work focuses on learning variations of finite state machines. However, most techniques consider discrete time. In this paper, we present a method for learning timed automata, finite state machines extended with real-valued clocks. The learning method generates a model consistent with a set of timed traces collected by testing. This generation is based on genetic programming, a search-based technique for automatic program creation. We evaluate our approach on 44 timed systems, comprising four systems from the literature and 40 randomly generated examples.", notes = "Fig. 6. Learned model of the car alarm system (CAS).", } @InProceedings{Tarasevich:2020:TSP, author = "M. Tarasevich and A. Tepljakov and E. Petlenkov and V. Vansovits", title = "Modeling and Identification of an Industrial Hot Water Boiler", booktitle = "2020 43rd International Conference on Telecommunications and Signal Processing (TSP)", year = "2020", pages = "285--290", abstract = "One of the most important open issues in industrial automation is a nonlinear dynamical system identification. In the following paper, the grey and black box approaches are implemented for hot water boiler modeling and identification. First-principles approach is applied to the hot water boiler modeling, and symbolic regression is performed for identification of the same industrial process. Then, the implementation of both methods is described. The resulting models are validated and compared on the basis of performance metrics. Finally, the encountered problems in the realization of the methods are reviewed.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/TSP49548.2020.9163503", month = jul, notes = "Also known as \cite{9163503}", } @InProceedings{Tarasevich:2021:TSP, author = "Maksimilian Tarasevich and Aleksei Tepljakov and Eduard Petlenkov and Vitali Vansovits", title = "Genetic Programming based Identification of an Industrial Process", booktitle = "2021 44th International Conference on Telecommunications and Signal Processing (TSP)", year = "2021", pages = "134--140", abstract = "In the field of industrial automation, it is essential to develop and improve mathematical methods that assist in obtaining more accurate models of real-world systems. In the following paper, a machine learning tool is applied to the problem of identifying a model of an industrial process. Symbolic regression and genetic programming are a successful combination of methods using which one can identify a nonlinear model in analytical form based on data collected from a process during routine operation. In this paper, a detailed description of the method implementation as well as necessary data preprocessing steps are presented. Then, the resulting models are validated on an industrial data set and compared on the basis of performance metrics with more classical methods and previous results achieved by the authors. Finally, the encountered problems in the realization of the methods are reflected upon.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/TSP52935.2021.9522588", month = jul, notes = "Also known as \cite{9522588}", } @InProceedings{Tarekegn:2020:EuroGP, author = "Adane Tarekegn and Fulvio Ricceri and Giuseppe Costa and Elisa Ferracin and Mario Giacobini", title = "Detection of Frailty using Genetic Programming : The Case of Older People in {Piedmont, Italy}", booktitle = "EuroGP 2020: Proceedings of the 23rd European Conference on Genetic Programming", year = "2020", month = "15-17 " # apr, editor = "Ting Hu and Nuno Lourenco and Eric Medvet", series = "LNCS", volume = "12101", publisher = "Springer Verlag", address = "Seville, Spain", pages = "228--243", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, healthinformatics, old age, OAP, human Frailty, Prediction, class Imbalanced data", isbn13 = "978-3-030-44093-0", video_url = "https://www.youtube.com/watch?v=uUPibE2s5_0", DOI = "doi:10.1007/978-3-030-44094-7_15", abstract = "Frailty appears to be the most problematic expression of elderly people. Frail older adults have a high risk of mortality, hospitalization, disability and other adverse outcomes, resulting in burden to individuals, their families, health care services and society. Early detection and screening would help to deliver preventive interventions and reduce the burden of frailty. For this purpose, several studies have been conducted to detect frailty that demonstrates its association with mortality and other health outcomes. Most of these studies have concentrated on the possible risk factors associated with frailty in the elderly population; however, efforts to identify and predict groups of elderly people who are at increased risk of frailty is still challenging in clinical settings. In this paper, Genetic Programming (GP) is exploited to detect and define frailty based on the whole elderly population of the Piedmont, Italy, using administrative databases of clinical characteristics and so...", notes = "Also known as \cite{DBLP:conf/eurogp/TarekegnRCFG20}. http://www.evostar.org/2020/cfp_eurogp.php Part of \cite{Hu:2020:GP} EuroGP'2020 held in conjunction with EvoCOP2020, EvoMusArt2020 and EvoApplications2020", } @InProceedings{tarekegn:2023:ICAISC, author = "Adane Nega Tarekegn and Faouzi Alaya Cheikh and Muhammad Sajjad and Mohib Ullah", title = "Towards Detecting Freezing of Gait Events Using Wearable Sensors and Genetic Programming", booktitle = "International Conference on Artificial Intelligence and Soft Computing", year = "2023", address = "Zakopane, Poland", month = "18-22 " # jun, publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-031-42505-9_24", DOI = "doi:10.1007/978-3-031-42505-9_24", } @Article{Tarekegn:2020:JMIRmi, author = "Adane Tarekegn and Fulvio Ricceri and Giuseppe Costa and Elisa Ferracin and Mario Giacobini", title = "Predictive Modeling for Frailty Conditions in Elderly People: Machine Learning Approaches", journal = "JMIR Medical Informatics", year = "2020", volume = "8", number = "6", keywords = "genetic algorithms, genetic programming, predictive modeling, frailty, machine learning, imbalanced dataset, elderly people, classification", ISSN = "2291-9694", URL = "https://www.sciencedirect.com/science/article/pii/S2291969420001878", DOI = "doi:10.2196/16678", abstract = "Backgrou", } @PhdThesis{Tarlao:thesis, author = "Fabiano Tarlao", title = "Genetic Programming Techniques for Regular Expression inference from Examples", school = "Universita degli Studi di Trieste", year = "2017", address = "Italy", month = "26 " # mar, keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Machine-Learning, Text-Extraction, Prediction, Pattern-Matching", URL = "http://hdl.handle.net/11368/2908187", URL = "https://arts.units.it/retrieve/handle/11368/2908187/187501/Thesis%20PHD%20Fabiano%20Tarlao.pdf", size = "154 pages", abstract = "In the recent years, Machine Learning techniques have emerged as a new way to obtain solutions for a given problem, the novelty of the Machine Learning approach lies in the ability to automatically learn solutions by only looking at the observations of phenomena or examples of the expected behaviour.Machine learning methods are, in other words, able to generate models, rules or programs starting from a descriptive set of data for the given problem. Besides, Machine Learning techniques may be adopted when the problem exceeds the human ability to find out a solution and are, at the present time, a viable solution also in fields that were previously dominated by the human intelligence: language translation, image recognition, car driving, sentiment analysis, computer programming and also arts and creativity. At present time the Machine Learning tools are often a cost-effective alternative to employing human experts. In this thesis we will describe the work developed at the Machine Learning Lab1 at University of Trieste, consisting in novel Machine Learning techniques aimed at the solution of real world problems of practical interest: automatic synthesis of regular expressions for text extraction and text classification tasks; an approach for the continuous reauthentication of web users; design of algorithms for author verification for text documents; author profiling for text messages; automatic generation of fake textual reviews. Among them the main contribution of this thesis is the design and implementation of new algorithms for the automatic generation of regular expressions for text extraction, based solely on examples of the desired behavior [21, 23, 31]. This is a long-standing problem where we aim at generating a regular expression that generalises the extraction behavior represented by some examples, i.e., strings annotated by a user with the desired portions to be extracted. The proposed algorithms are based on an evolutionary approach called Genetic Programming (GP), that is an evolutionary computing paradigm which implements an heuristic search, in a space of candidate solution, in a way that mimic the natural evolution process. The results demonstrate that our new algorithms have higher effectiveness than previous proposals and demonstrate that our algorithms are able to generate regular expressions in a way that is competitive with human experts both in terms of effectiveness and generation time [24, 33]. Thanks to these achievements,the proposed method has been awarded with the Silver Medal at the 13th Annual Humies Award 2, an international competition that establishes the state of the art in genetic and evolutionary computation and is open to human-competitive results that are equal to or better than the most recent human-created solution to a long-standing problem. The result of our research has been also released as an opensource framework 3 and as a web application demo 4 where users are free to provide text extraction examples to the application and obtain the corresponding regular expression. Later in this thesis we will extend our work on automatic generation of regular expressions for text extraction from examples in order to operate in an Active learning scenario. In this scenario the user is not required to annotate all the examples at once but the Active learning tool interacts with the user in order to assist him during the annotation of the extractions in examples. We will propose our Active learning method [22, 26] that is based on our previous GP algorithms and the results will demonstrate that our active learning tool reduces the user annotation effort while providing comparable effectiveness for the generated regular expressions. Moreover, in this thesis we will consider two applications of the proposed regular expressions generator, adapted in order to cope with text categorization problems that are different from text extraction:(i) the Regex Golf game and (ii) the identification of Genic Interactions in sentences. The Regex Golf is a game where the player should write he shortest regular expression that accepts the strings in a positive set and does not accept strings in a negative set. We will show that our GP algorithm is able to play this game effectively and we will demonstrate that our algorithm is competitive with human players [20]. In the second case, we will consider the problem of automatically identifying sentences that contain interactions between genes and proteins inside a text document [30]. Our proposal requires solely a dictionary of genes and proteins and a small set of sample sentences in natural language. The proposed method generates a model in form of regular expressions that represents the relevant syntax patterns in terms of standard part-of-speech annotations. We will assess our approach on realistic datasets and show an accuracy that is sufficiently high to be of practical interest and that is in line with significant baseline methods. The following contributions leave the field of the Genetic Programming algorithms and will propose solutions based on other Machine Learning methodologies, ranging from Grammatical Evolution to Support Vector Machines and Random Forests to Recurrent Neural Networks. We will propose a methodology for predicting the accuracy of the text extractor [25] that may be inferred with the proposed GP method. We will employ several prediction techniques and the results suggest that reliable predictions for tasks of practical complexity may indeed be obtained quickly and without actually generating the entity extractor. Later, we will approach the problem of the automatic text extraction from another perspective and we will propose a novel learning algorithm that is able to generate a string similarity function tailored to problems of syntax-based entity extraction from unstructured text streams [27]. The proposed algorithm, based on an evolutionary paradigm named Grammatical Evolution, takes in input pairs of strings along with an indication of whether they adhere or not adhere to the same syntactic pattern. The results suggest that the proposed approach is indeed feasible and that the learned similarity function is more effective than the Levenshtein distance and the Jaccard similarity index. Hence, we will propose a system for continuous reauthentication of web users based on the observed mouse dynamics [144]; the key feature of our proposal is that no specific software needs to be installed on client machines. We obtain accuracy in the order of 97percent, which is aligned with earlier proposals. Then, we will approach the user authentication problem [14], this task consists in determining if an unknown document was authored by the same author of a set of documents with the same author. Our methods has been submitted to the 2015 PAN competition and achieved the first position in the final rank for the Spanish language. Hence, we will approach the user profiling problem [19], this task consists in predicting some attributes of an author, i.e gender, age, analysing a set of his/her Twitter tweets. We consider several sets of stylometric and content features, and different decision algorithms. Finally, we will investigate the feasibility of two tools capable of generating (i) fake reviews for a given scientific paper automatically [28] and (ii) fake consumer reviews for a restaurant automatically [29]. We experimentally assessed our methods on human subjects and the results highlight the ability of our methods to produce reviews that often look credible and may subvert the human decision.", notes = "Supervisor Alberto Bartoli", } @InCollection{tarnikova:2000:DSSNPGP, author = "Yuliya Tarnikova", title = "Discovering Strategies for Solving a Number Puzzle using Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "387--396", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:2000:gagp}", } @Article{Tashakori-Abkenar:2017:ieeeTenergyC, author = "Alireza {Tashakori Abkenar} and Ali Nazari and Shantha D. Gamini Jayasinghe and Ajay Kapoor and Michael Negnevitsky", journal = "IEEE Transactions on Energy Conversion", title = "Fuel Cell Power Management Using Genetic Expression Programming in All-Electric Ships", year = "2017", volume = "32", number = "2", pages = "779--787", abstract = "All-electric ships (AES) are considered as an effective solution for reducing greenhouse gas emissions as they provide a better platform to use alternative clean energy sources such as fuel cells (FC) in place of fossil fuel. Even though FCs are promising alternative, their response is not fast enough to meet load transients that can occur in ships at sea. Therefore, high-density rechargeable battery storage systems are required to achieve stable operation under such transients. Generally, in such hybrid systems, dc/dc converters are used to interface the FC and battery into the dc link. This paper presents an intelligent FC power management strategy to improve FC performance at various operating points without employing dc/dc interfacing converters. A hybrid AES drive line model using genetic programming is used using Simulink and GeneXProTools4 to formulate operating FC voltage based on the load current, FC air, and fuel flow rates. Genetic algorithm is used to adjust air and fuel flow rates to keep the FC within the safe operating range at different power demands. The proposed method maintains FC performance as well as reduces fuel consumption, and, thereby, ensures the optimal power sharing between the FC and the lithium-ion battery in AES application.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/TEC.2017.2693275", ISSN = "0885-8969", month = jun, notes = "Also known as \cite{7898826}", } @InProceedings{conf/emo/TatsukawaNOF13, author = "Tomoaki Tatsukawa and Taku Nonomura and Akira Oyama and Kozo Fujii", title = "A New Multiobjective Genetic Programming for Extraction of Design Information from Non-dominated Solutions", bibdate = "2013-03-13", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/emo/emo2013.html#TatsukawaNOF13", booktitle = "Evolutionary Multi-Criterion Optimization - 7th International Conference, {EMO} 2013, Sheffield, {UK}, March 19-22, 2013. Proceedings", publisher = "Springer", year = "2013", volume = "7811", editor = "Robin C. Purshouse and Peter J. Fleming and Carlos M. Fonseca and Salvatore Greco and Jane Shaw", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-37139-4", pages = "528--542", series = "Lecture Notes in Computer Science", URL = "http://dx.doi.org/10.1007/978-3-642-37140-0", DOI = "doi:10.1007/978-3-642-37140-0_40", abstract = "We propose a new type of multi-objective genetic programming (MOGP) for multi-objective design exploration (MODE). The characteristic of the new MOGP is the simultaneous symbolic regression to multiple objective functions using correlation coefficients. This methodology is applied to non-dominated solutions of the multi-objective design optimisation problem to extract information between objective functions and design parameters. The result of MOGP is symbolic equations that are highly correlated to each objective function through a single GP run. These equations are also highly correlated to several objective functions. The results indicate that the proposed MOGP is capable of finding new design parameters more closely related to the objective functions than the original design parameters. The proposed MOGP is applied to the test problem and the practical design problem to evaluate the capability.", } @InProceedings{Tauritz:2016:GECCOcomp, author = "Daniel R. Tauritz and John Woodward", title = "Hyper-Heuristics", booktitle = "GECCO '16 Companion: Proceedings of the Companion Publication of the 2016 Annual Conference on Genetic and Evolutionary Computation", year = "2016", editor = "Tobias Friedrich and Frank Neumann and Andrew M. Sutton and Martin Middendorf and Xiaodong Li and Emma Hart and Mengjie Zhang and Youhei Akimoto and Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and Daniele Loiacono and Julian Togelius and Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and Faustino Gomez and Carlos M. Fonseca and Heike Trautmann and Alberto Moraglio and William F. Punch and Krzysztof Krawiec and Zdenek Vasicek and Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and Boris Naujoks and Enrique Alba and Gabriela Ochoa and Simon Poulding and Dirk Sudholt and Timo Koetzing", isbn13 = "978-1-4503-4323-7", pages = "273--304", note = "tutorial", address = "Denver, Colorado, USA", month = "20-24 " # jul, keywords = "genetic algorithms, genetic programming", organisation = "SIGEVO", DOI = "doi:10.1145/2908961.2926978", publisher = "ACM", publisher_address = "New York, NY, USA", notes = "Distributed at GECCO-2016.", } @InProceedings{Tauritz:2019:GECCOcomp, author = "Daniel R. Tauritz and John Woodward", title = "Hyper-heuristics tutorial", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", isbn13 = "978-1-4503-6748-6", pages = "770--805", address = "Prague, Czech Republic", DOI = "doi:10.1145/3319619.3323382", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{3323382} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InProceedings{Tauritz:2020:GECCOcomp, author = "Daniel R. Tauritz and John Woodward", title = "Hyper-Heuristics Tutorial", year = "2020", editor = "Richard Allmendinger and Hugo Terashima Marin and Efren Mezura Montes and Thomas Bartz-Beielstein and Bogdan Filipic and Ke Tang and David Howard and Emma Hart and Gusz Eiben and Tome Eftimov and William {La Cava} and Boris Naujoks and Pietro Oliveto and Vanessa Volz and Thomas Weise and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Rui Wang and Ran Cheng and Guohua Wu and Miqing Li and Hisao Ishibuchi and Jonathan Fieldsend and Ozgur Akman and Khulood Alyahya and Juergen Branke and John R. Woodward and Daniel R. Tauritz and Marco Baioletti and Josu Ceberio Uribe and John McCall and Alfredo Milani and Stefan Wagner and Michael Affenzeller and Bradley Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and Nayat Sanchez-Pi and Luis Marti and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and Matthew Johns and Nick Ross and Ed Keedwell and Herman Mahmoud and David Walker and Anthony Stein and Masaya Nakata and David Paetzel and Neil Vaughan and Stephen Smith and Stefano Cagnoni and Robert M. Patton and Ivanoe {De Falco} and Antonio {Della Cioppa} and Umberto Scafuri and Ernesto Tarantino and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Erik Hemberg and Riyad Alshammari and Tokunbo Makanju and Fuijimino-shi and Ivan Zelinka and Swagatam Das and Ponnuthurai Nagaratnam and Roman Senkerik", isbn13 = "9781450371278", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377929.3389855", DOI = "doi:10.1145/3377929.3389855", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion", pages = "652--681", size = "30 pages", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", note = "Tutorial", keywords = "genetic algorithms, genetic programming, grammatical evolution, genetic improvement", notes = "Also known as \cite{10.1145/3377929.3389855} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @InProceedings{Tavafi:2017:GECCO, author = "Amir Tavafi and Wolfgang Banzhaf", title = "A Hybrid Genetic Programming Decision Making System for {RoboCup} Soccer Simulation", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4920-8", address = "Berlin, Germany", pages = "1025--1032", size = "8 pages", URL = "http://doi.acm.org/10.1145/3071178.3071194", DOI = "doi:10.1145/3071178.3071194", acmid = "3071194", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, RoboCup, decision making, multi-agent systems, soccer simulation", month = "15-19 " # jul, abstract = "In this contribution we propose a hybrid genetic programming approach for evolving a decision making system in the domain of RoboCup Soccer (Simulation League). Genetic programming has been rarely used in this domain in the past, due to the difficulties and restrictions of the soccer simulation. The real-time requirements of robot soccer and the lengthy evaluation time even for simulated games provide a formidable obstacle to the application of evolutionary approaches. Our new method uses two evolutionary phases, each of which compensating for restrictions and limitations of the other. The first phase produces some evolved GP individuals applying an off-game evaluation system which can be trained on snapshots of game situations as they actually happened in earlier games, and corresponding decisions tagged as correct or wrong. The second phase uses the best individuals of the first phase as input to run another GP system to evolve players in a real game environment where the quality of decisions is evaluated through winning or losing during real-time runs of the simulator. We benchmark the new system against a baseline system used by most simulation league teams, as well as against winning systems of the 2016 tournament.", notes = "Also known as \cite{Tavafi:2017:HGP:3071178.3071194} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @Article{TAVAKOLI:2023:istruc, author = "Pouya Tavakoli and Hossein Rahami", title = "Generating synthetic ground motions reaching target spectrum with the optimization approach", journal = "Structures", year = "2023", volume = "58", pages = "105480", month = dec, keywords = "genetic algorithms, genetic programming, Synthetic ground motion, Artificial earthquake, Artificial ground motion, Artificial accelerogram, Target spectrum, Covariance matrix adaptation evolution strategy(CMA-ES) method, Optimization algorithms, Envelope function", ISSN = "2352-0124", URL = "https://www.sciencedirect.com/science/article/pii/S2352012423015680", DOI = "doi:10.1016/j.istruc.2023.105480", abstract = "Due to the scarcity of earthquake records and the necessity of the earthquakes matched with the predefined design response spectrum (DRS) for time history analysis, designers need to generate artificial earthquakes. The smoothness of the DRS prevents conformity in the majority of methods, and deviation happens, especially in the constant region. To overcome this condition, the present study intends to incorporate the optimization algorithm with the synthetic earthquake method to reach the ASCE DRS. Indeed, the statistical algorithm is used to explore the global optimum with more accuracy and faster convergence in comparison with other algorithms. The results demonstrate that the proposed method is ideally compatible with the DRS with trivial errors. Furthermore, the novel approach has been applied to Kobe and Tabas earthquakes in order to extract the envelope function to improve compatibility in the time domain. In essence, the proposed method is not only compatible with the DRS simply but also appropriate for simulating real earthquakes. Eventually, with regard to the recommendation of seismic regulations for applying several records in dynamic analysis, one of the robust advantages of this method is to generate various earthquakes compatible with the specific target spectrum", } @InProceedings{tavares:2004:eurogp, author = "Jorge Tavares and Penousal Machado and Amilcar Cardoso and Francisco B. Pereira and Ernesto Costa", title = "On the Evolution of Evolutionary Algorithms", booktitle = "Genetic Programming 7th European Conference, EuroGP 2004, Proceedings", year = "2004", editor = "Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule", volume = "3003", series = "LNCS", pages = "389--398", address = "Coimbra, Portugal", publisher_address = "Berlin", month = "5-7 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-21346-5", URL = "http://www.cisuc.uc.pt/acg/dlfile.php?fn=583_pub_paper-eurogp-crc.pdf", DOI = "doi:10.1007/978-3-540-24650-3_37", abstract = "We discuss the evolution of several components of a traditional Evolutionary Algorithm, such as genotype to phenotype mappings and genetic operators, presenting a formalised description of how this can be attained. We then focus on the evolution of mapping functions, for which we present experimental results achieved with a meta-evolutionary scheme.", notes = "Hyper-heuristic Part of \cite{keijzer:2004:GP} EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004", } @InProceedings{Tavares:2010:PPSN, author = "Jorge Tavares and Francisco Baptista Pereira", title = "Evolving Strategies for Updating Pheromone Trails: A Case Study with the TSP", booktitle = "PPSN 2010 11th International Conference on Parallel Problem Solving From Nature", year = "2010", editor = "Robert Schaefer and Carlos Cotta and Joanna Kolodziej and Guenter Rudolph", publisher = "Springer", pages = "523--532", series = "Lecture Notes in Computer Science", address = "Krakow, Poland", month = "11-15 " # sep, volume = "6238", keywords = "genetic algorithms, genetic programming, genetic programming, ant colony optimisation", isbn13 = "978-3-642-15870-4", DOI = "doi:10.1007/978-3-642-15871-1_53", abstract = "Ant Colony Optimization is a bio-inspired technique that can be applied to solve hard optimisation problems. A key issue is how to design the communication mechanism between ants that allows them to effectively solve a problem. We propose a novel approach to this issue by evolving the current pheromone trail update methods. Results obtained with the TSP show that the evolved strategies perform well and exhibit a good generalisation capability when applied to larger instances.", affiliation = "CISUC, Department of Informatics Engineering, University of Coimbra, Polo II - Pinhal de Marrocos, 3030 Coimbra, Portugal", } @InProceedings{tavares:2011:EuroGP, author = "Jorge Tavares and Francisco B. Pereira", title = "Designing Pheromone Update Strategies with Strongly Typed Genetic Programming", booktitle = "Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011", year = "2011", month = "27-29 " # apr, editor = "Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado", series = "LNCS", volume = "6621", publisher = "Springer Verlag", address = "Turin, Italy", pages = "85--96", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-20406-7", DOI = "doi:10.1007/978-3-642-20407-4_8", abstract = "Ant Colony algorithms are population-based methods widely used in combinatorial optimisation problems. We propose a strongly typed genetic programming approach to automatically evolve the communication mechanism that allows ants to cooperatively solve a given problem. Results obtained with several TSP instances show that the evolved pheromone update strategies are effective, exhibit a good generalisation capability and are competitive with human designed variants.", notes = "cf hyper heuristics. 51 city travelling salesman problem. Part of \cite{Silva:2011:GP} EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011", } @InProceedings{Tavares:2011:GECCO, author = "Jorge Tavares and Francisco B. Pereira", title = "Towards the development of self-ant systems", booktitle = "GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0557-0", pages = "1947--1954", keywords = "genetic algorithms, genetic programming, Self-* search", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001576.2001838", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "We propose a computational framework for the self-generation of components used by an Ant Colony Optimization algorithm. The approach relies on Strongly Typed Genetic Programming to automatically seek for effective update pheromone strategies. Best evolved strategies are then inserted in an Ant Colony Algorithm used to find good quality solutions for the Quadratic Assignment Problem. Results reveal that evolved update rules are competitive with human designed variants and can be effectively reused on different instances of the same problem. Moreover, we investigate the possibility of evolving general strategies that can be used across different optimization problems.", notes = "Also known as \cite{2001838} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)", } @InProceedings{tavares:2012:EuroGP, author = "Jorge Tavares and Francisco B. Pereira", title = "Automatic Design of Ant Algorithms with Grammatical Evolution", booktitle = "Proceedings of the 15th European Conference on Genetic Programming, EuroGP 2012", year = "2012", month = "11-13 " # apr, editor = "Alberto Moraglio and Sara Silva and Krzysztof Krawiec and Penousal Machado and Carlos Cotta", series = "LNCS", volume = "7244", publisher = "Springer Verlag", address = "Malaga, Spain", pages = "206--217", organisation = "EvoStar", isbn13 = "978-3-642-29138-8", DOI = "doi:10.1007/978-3-642-29139-5_18", keywords = "genetic algorithms, genetic programming, grammatical evolution, Ant colony optimization", abstract = "We propose a Grammatical Evolution approach to the automatic design of Ant Colony Optimisation algorithms. The grammar adopted by this framework has the ability to guide the learning of novel architectures, by rearranging components regularly found on human designed variants. Results obtained with several TSP instances show that the evolved algorithmic strategies are effective, exhibit a good generalisation capability and are competitive with human designed variants.", notes = "Part of \cite{Moraglio:2012:GP} EuroGP'2012 held in conjunction with EvoCOP2012 EvoBIO2012, EvoMusArt2012 and EvoApplications2012", } @Article{Tay:2008:GPEM, author = "Joc Cing Tay and Cheun Hou Tng and Chee Siong Chan", title = "Environmental effects on the coevolution of pursuit and evasion strategies", journal = "Genetic Programming and Evolvable Machines", year = "2008", volume = "9", number = "1", pages = "5--37", month = mar, keywords = "genetic algorithms, genetic programming, Pursuit and evasion, Chemical Genetic Programming, Competitive coevolution, Game of tag", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-007-9049-3", size = "33 pages", abstract = "The game of tag is frequently used in the study of pursuit and evasion strategies that are discovered through competitive coevolution. The aim of coevolution is to create an arms race where opposing populations cyclically evolve in incremental improvements, driving the system towards better strategies. A coevolutionary simulation of the game of tag involving two populations of agents; pursuers and evaders, is developed to investigate the effects of a boundary and two obstacles. The evolution of strategies through Chemical Genetic Programming optimises the mapping of genotypic strings to phenotypic trees. Four experiments were conducted, distinguished by speed differentials and environmental conditions. Designing experiments to evaluate the efficacy of emergent strategies often reveal necessary steps needed for coevolutionary progress. The experiments that excluded obstacles and boundaries provided design pointers to ensure coevolutionary progress as well as a deeper understanding of strategies that emerged when obstacles and boundaries were added. In the latter, we found that an awareness of the environment and the pursuer was not critical in an evader's strategy to survive, instead heading to the edge of the boundary or behind an obstacle in a bid to throw-off or hide from the pursuer or simply turn in circles was often sufficient, thereby revealing possible suboptimal strategies that were environment specific. We also observed that a condition for coevolutionary progress was that the problem complexity must be surmountable by at least one population; that is, some pursuer must be able to tag an opponent. Due to the use of amino-acid building blocks in our Chemical Genetic Program, our simulations were able to achieve significant complexity in a short period of time.", } @Article{Tay2008453, title = "Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems", author = "Joc Cing Tay and Nhu Binh Ho", journal = "Computers \& Industrial Engineering", volume = "54", number = "3", pages = "453--473", year = "2008", ISSN = "0360-8352", DOI = "doi:10.1016/j.cie.2007.08.008", URL = "http://www.sciencedirect.com/science/article/B6V27-4PKXBN1-1/2/5821882f2443c0fb1fff7c462c34e793", keywords = "genetic algorithms, genetic programming, Flexible job shop, Production scheduling, Dispatching rules", abstract = "We solve the multi-objective flexible job-shop problems by using dispatching rules discovered through genetic programming. While Simple Priority Rules have been widely applied in practice, their efficacy remains poor due to lack of a global view. Composite dispatching rules have been shown to be more effective as they are constructed through human experience. In this paper, we evaluate and employ suitable parameter and operator spaces for evolving composite dispatching rules using genetic programming, with an aim towards greater scalability and flexibility. Experimental results show that composite dispatching rules generated by our genetic programming framework outperforms the single dispatching rules and composite dispatching rules selected from literature over five large validation sets with respect to minimum makespan, mean tardiness, and mean flow time objectives. Further results on sensitivity to changes (in coefficient values and terminals among the evolved rules) indicate that their designs are robust.", } @PhdThesis{TaySHX, author = "Serene Hui Xin Tay", title = "Comprehensive framework for hydrodynamic modelling and prediction of Singapore regional water", school = "National University of Singapore", year = "2015", address = "Singapore", month = "11 " # may, keywords = "genetic algorithms, genetic programming, hydrodynamic, model, Singapore, Malacca Strait, tide", URL = "http://www.scholarbank.nus.edu.sg/handle/10635/119622", URL = "http://www.scholarbank.nus.edu.sg/bitstream/handle/10635/119622/TaySHX.pdf", size = "276 pages", abstract = "he main objective of the research presented in this thesis is to understand the hydrodynamics in Singapore regional waters and develop a comprehensive framework that involves both modelling and data assimilation approaches to effectively and efficiently deliver accurate hydrodynamic hindcast and forecast. In conclusion, separate modelling approaches are developed and implemented to cater for accurate hindcast and forecast computations, through better understanding of (i) tidal behaviour in the region through multiple domains modelling, (ii) non-tidal phenomenon, such as the seasonal water level variation in the Malacca Strait, (iii) uncertainty characterization of model prediction, and (iv) use of genetic programming as a model residual correction tool to improve model prediction in the forecasting mode.", notes = "Singapore-Delft Water Alliance (SDWA) Supervisor: Vladan Babovic", } @Article{Tay:2016:PE, author = "Serene Hui Xin Tay and Vladan Babovic", title = "Understanding Water Level Residuals in Malacca Strait Using Genetic Programming", journal = "Procedia Engineering", volume = "154", pages = "1267--1274", year = "2016", note = "12th International Conference on Hydroinformatics (HIC 2016) - Smart Water for the Future", ISSN = "1877-7058", DOI = "doi:10.1016/j.proeng.2016.07.458", URL = "http://www.sciencedirect.com/science/article/pii/S1877705816318471", abstract = "Hydrodynamics are highly complex in Malacca Strait as it is where tides from Indian Ocean and South China Sea interact. Highly varying topography and geometry, river discharges from land and seasonal monsoon climate contribute further complication to the local flow dynamics and usually requires numerical model to resolve. However, no matter how well the numerical model is calibrated, residual will exist due to imperfect description of underlying physics and lack of high quality input data. Numerous studies have applied data-driven methods to correct numerical model prediction by forecasting the residuals, and shown that these methods are undeniably effective and efficient and being great value to more traditional modelling approaches. However, in complex hydrodynamic system of Malacca Strait, instead of simply treating numerical model residual as a numerical mismatch and addressing it as a time series problem by local correction, in this paper a more interesting and meaningful effort to uncover the underlying dynamics is attempted. This paper explores the ability of genetic programming to unearth the embedded components or dependencies of the numerical model residual in Malacca Strait.", keywords = "genetic algorithms, genetic programming, water level, residuals, Malacca Strait", } @Article{Tayarani:2014:ieeeTEC, author = "Mohammad-H. Tayarani-N. and Xin Yao and Hongming Xu", journal = "IEEE Transactions on Evolutionary Computation", title = "Meta-heuristic Algorithms in Car Engine Design: a Literature Survey", year = "2015", volume = "19", number = "5", month = oct, pages = "609--629", note = "Accepted", keywords = "genetic algorithms, genetic programming, DE, ES, EDA, AIS, Calibration, Control systems, Engines, Fuels, Optimisation, Timing", DOI = "doi:10.1109/TEVC.2014.2355174", ISSN = "1089-778X", size = "21 pages", abstract = "Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviours in biology, flock behaviours of some birds, annealing in metallurgy, etc. Due to their great potential in solving hard optimisation problems, metaheuristic algorithms have found their ways into automobile engine design. There are different optimisation problems arising in different areas of car engine management including calibration, control system, fault diagnosis and modelling. In this paper we review the state-of-the-art applications of different metaheuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimising engine control systems, engine fault diagnosis, optimising different parts of engines and modelling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimisation, particle swarm optimisation, memetic algorithms, and artificial immune system.", notes = "Also known as \cite{6893031}", } @InCollection{taylor:1995:DPTCA, author = "Caz Taylor", title = "Discovering Patterns in Two-Dimensional Cellular Automata", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1995", year = "1995", editor = "John R. Koza", pages = "269--278", address = "Stanford, California, 94305-3079 USA", month = "11 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms", ISBN = "0-18-195720-5", notes = "part of \cite{koza:1995:gagp}", } @InProceedings{taylor:1998:gadiirsab, author = "Janet Taylor and Jem J Rowland and Richard J Gilbert and Alun Jones and Michael K Winson and Douglas B Kell", title = "Genetic Algorithm Decoding for the Interpretation of Infra-red Spectra in Analytical Biotechnology", booktitle = "Late Breaking Papers at EuroGP'98: the First European Workshop on Genetic Programming", year = "1998", editor = "Riccardo Poli and W. B. Langdon and Marc Schoenauer and Terry Fogarty and Wolfgang Banzhaf", pages = "21--25", address = "Paris, France", publisher_address = "School of Computer Science", month = "14-15 " # apr, publisher = "CSRP-98-10, The University of Birmingham, UK", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/csrp-98-10.pdf", size = "5 pages", abstract = "This paper presents an initial study into the use of a modified Genetic Algorithm (GA) in the analysis of multivariate spectroscopic data that relates to rapid screening for metabolite overproduction in the context of process improvement in the pharmaceutical industry. The development of a GA based method for both wavelength selection and relationship modelling is compared with that of a partial Least Squares Regression (PLS) in the formation of a predictive model of metabolite concentration. Initial results indicate that the combination of a GA structure and alternative mutation strategies is capable of producing a model that is accurate in terms of predictive ability and concise in the output expression produced.", notes = "EuroGP'98LB part of \cite{Poli:1998:egplb}", } @InProceedings{taylor:1998:GPiftis, author = "Janet Taylor and Jem J. Rowland and Royston Goodacre and Richard J. Gilbert and Michael K. Winson and Douglas B. Kell", title = "Genetic Programming in the Interpretation of Fourier Transform Infrared Spectra: Quantification of Metabolites of Pharmaceutical Importance", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "377--380", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-548-7", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1998/taylor_1998_GPiftis.pdf", notes = "GP-98", } @InProceedings{taylor:1998:GParsabs, author = "Janet Taylor and Jem Rowland and Douglas Kell", title = "Genetic Programming Applied to the Rapid Spectroscopic Analysis of Biological Samples", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "212 and 266", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "genetic algorithms, genetic programming", size = "1+1 page", notes = "GP-98LB, GP-98PhD Student Workshop", } @Article{taylor:1998:dpmsGP:aises, author = "Janet Taylor and Royston Goodacre and William G. Wade and Jem J. Rowland and Douglas B. Kell", title = "The deconvolution of pyrolysis mass spectra using genetic programming: application to the identification of some Eubacterium species", journal = "FEMS Microbiology Letters", year = "1998", volume = "160", pages = "237--246", organisation = "Federation of European Microbiological Societies", publisher = "Elsevier Science", keywords = "genetic algorithms, genetic programming, Chemometrics, Eubacterium, pyrolysis mass spectrometry", size = "10 pages", DOI = "doi:10.1016/S0378-1097(98)00038-X", abstract = "Pyrolysis mass spectrometry was used to produce complex biochemical fingerprints of Eubacterium exiguum, E. infirmum, E. tardum and E. timidum. To examine the relationship between these organisms the spectra were clustered by canonical variates analysis, and four clusters, one for each species, were observed. In an earlier study we trained artificial neural networks to identify these clinical isolates successfully; however, the information used by the neural network was not accessible from this so-called 'black box' technique. To allow the deconvolution of such complex spectra (in terms of which masses were important for discrimination) it was necessary to develop a system that itself produces 'rules' that are readily comprehensible. We here exploit the evolutionary computational technique of genetic programming; this rapidly and automatically produced simple mathematical functions that were also able to classify organisms to each of the four bacterial groups correctly and unambiguously. Since the rules used only a very limited set of masses, from a search space some 50 orders of magnitude greater than the dimensionality actually necessary, visual discrimination of the organisms on the basis of these spectral masses alone was also then possible.", notes = " PMID: 9532743", } @PhdThesis{JanetTaylor:2000:thesis, author = "Janet Taylor", title = "Genetic Programming and Genetic Algorithms in the Spectroscopic Analysis of Biological Samples", school = "University of Wales, Aberystwyth", year = "2000", address = "UK", keywords = "genetic algorithms, genetic programming", URL = "http://tinyurl.com/cshwk97", notes = "Aberystwyth University Available at Physical Sciences Library Physical Sciences Library (AC1.W1.T2) MMS ID 994368313402418", } @InProceedings{taylor:2001:sasgsam, author = "Janet Taylor and Jem J. Rowland and Douglas B. Kell", title = "Spectral Analysis via Supervised Genetic Search with Application-specific Mutations", booktitle = "Proceedings of the 2001 Congress on Evolutionary Computation CEC2001", year = "2001", pages = "481--486", address = "COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "27-30 " # may, organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, biotechnology, supervised, spectroscopy, calibration, IR analytical spectra interpretation, adaptive averaging, application-specific mutations, chemical constituent concentration, complex biological sample, explanatory, expression optimisation, optical spectra, output expression, quasi-continuous properties, selective optimisation, signal-to-noise ratio, spectral analysis, spectral regions, spectral resolution, supervised genetic search, biology computing, infrared spectra, learning (artificial intelligence), spectrochemical analysis, spectroscopy computing", ISBN = "0-7803-6658-1", DOI = "doi:10.1109/CEC.2001.934430", abstract = "We present a method in which a genetic algorithm is used to optimise an expression in order to provide a supervised method for interpretation of the infrared analytical spectra of complex biological samples. The aim is to produce a model that can predict the value of a measurand of interest, such as the concentration of a particular chemical constituent, from a complex infrared spectrum of biological material. The method we describe is in some ways analogous to genetic programming but it more readily allows the output expression to be constrained in complexity and permits its general form to be specified by the user, thereby enhancing its explanatory ability. The quasi-continuous properties of optical spectra are exploited by mutations that explore spectral regions adjacent to selected variables, and provide adaptive averaging of spectral regions so as to provide selective optimisation of the tradeoff between spectral resolution and signal-to-noise ratio", notes = "CEC-2001 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 01TH8546C, Library of Congress Number = Hybrid GA-GP? High throughput screening. Fourier transform. E. Coli. Hill climbing. Exponent not useful? Discusses comparison with authors' previous GP approach.", } @InCollection{taylor:1994:lamarckian, author = "Stewart Taylor", title = "Using {Lamarckian} Evolution to Increase the Effectiveness of Neural Network Training with a Genetic Algorithm and Backpropagation", booktitle = "Artificial Life at Stanford 1994", year = "1994", editor = "John R. Koza", pages = "181--186", address = "Stanford, California, 94305-3079 USA", month = jun, organisation = "Stanford University", publisher = "Stanford Bookstore", keywords = "genetic algorithms", ISBN = "0-18-182105-2", notes = "This volume contains 22 papers written and submitted by students describing their term projects for the course in artificial life (Computer Science 425) at Stanford University offered during the spring quarter quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs425.html", } @InCollection{taylor:1994:juggling, author = "Stewart N. Taylor", title = "Evolution by Genetic Programming of a Spatial Robot Juggling Algorithm", booktitle = "Genetic Algorithms at Stanford 1994", year = "1994", editor = "John R. Koza", pages = "160--169", address = "Stanford, California, 94305-3079 USA", month = dec, organisation = "Stanford University", publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-187263-3", notes = "See \cite{stewart:1995:juggling} 3 result producing branches + 3 ADFs This volume contains 20 papers written and submitted by students describing their term projects for the course {"}Genetic Algorithms and Genetic Programming{"} (Computer Science 426) at Stanford University offered during the fall quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs426.html", } @InProceedings{stewart:1995:juggling, author = "Stewart N. Taylor", title = "Evolution by Genetic Programming of a Spatial Robot Juggling Control Algorithm", booktitle = "Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications", year = "1995", editor = "Justinian P. Rosca", pages = "104--110", address = "Tahoe City, California, USA", month = "9 " # jul, keywords = "genetic algorithms, genetic programming, multi-tree", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/stewart_1995_juggling.pdf", size = "7 pages", abstract = "Keeps a ball aloft by hitting it with a hard paddle and a particular {"}read-life{"} robot model.", notes = "3 result producing branches plus 2+ ADFs. {"}Fitness measure is subject to a fair ammount of deception{"} part of \cite{rosca:1995:ml} See \cite{taylor:1994:juggling}", } @PhdThesis{TJTaylor:thesis, author = "Timothy John Taylor", title = "From Artificial Evolution to Artificial Life", school = "Division of Informatics, University of Edinburgh", year = "1999", address = "UK", keywords = "genetic algorithms, genetic programming", URL = "http://homepages.inf.ed.ac.uk/timt/papers/thesis/", URL = "http://homepages.inf.ed.ac.uk/timt/papers/thesis/thesis.ps.gz", URL = "http://homepages.inf.ed.ac.uk/timt/papers/thesis/thesis.pdf", URL = "http://www.tim-taylor.com/papers/thesis/", size = "317 pages", abstract = "This work addresses the question: What are the basic design considerations for creating a synthetic model of the evolution of living systems (i.e. an `artificial life' system)? It can also be viewed as an attempt to elucidate the logical structure (in a very general sense) of biological evolution. However, with no adequate definition of life, the experimental portion of the work concentrates on more specific issues, and primarily on the issue of open-ended evolution. An artificial evolutionary system called Cosmos, which provides a virtual operating system capable of simulating the parallel processing and evolution of a population of several thousand self-reproducing computer programs, is introduced. Cosmos is related to Ray's established Tierra system, but there are a number of significant differences. A wide variety of experiments with Cosmos, which were designed to investigate its evolutionary dynamics, are reported. An analysis of the results is presented, with particular attention given to the role of contingency in determining the outcome of the runs. The results of this work, and consideration of the existing literature on artificial evolutionary systems, leads to the conclusion that artificial life models such as this are lacking on a number of theoretical and methodological grounds. It is emphasised that explicit theoretical considerations should guide the design of such models, if they are to be of scientific value. An analysis of various issues relating to self-reproduction, especially in the context of evolution, is presented, including some extensions to von Neumann's analysis of self-reproduction. This suggests ways in which the evolutionary potential of such models might be improved. In particular, a shift of focus is recommended towards a more careful consideration of the phenotypic capabilities of the reproducing individuals. Phenotypic capabilities fundamentally involve interactions with the environment (both abiotic and biotic), and it is further argued that the theoretical grounding upon which these models should be based must include consideration of the kind of environments and the kind of interactions required for open-ended evolution. A number of useful future research directions are identified. Finally, the relevance of such work to the original goal of modelling the evolution of living systems (as opposed to the more general goal of modelling open-ended evolution) is discussed. It is suggested that the study of open-ended evolution can lead us to a better understanding of the essential properties of life, but only if the questions being asked in these studies are phrased appropriately.", } @InProceedings{tchernev:1998:fxNm, author = "Elko Tchernev", title = "Forth Crossover Is Not a Macromutation?", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "381--386", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-548-7", broken = "http://userpages.umbc.edu/~etcher1/gppaper/forcro.htm", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1998/tchernev_1998_fxNm.pdf", size = "6 pages", abstract = "Recent results in Genetic Programming suggest that subtree crossover, as traditionally used, does not usefully combine genetic information from both parents, but plays the role of macromutation instead. The following paper proposes and investigates the effects of a different type of crossover, Forth crossover, inspired by the eponymous programming language. Using the mechanism of Headless Chicken Crossover in a series of experiments, it is argued that Forth crossover is strictly different from, and in the experiments performed, better than both subtree crossover and macromutation.", notes = "GP-98", } @InProceedings{tchernev:2002:gecco:lbp, title = "Stack-Correct Crossover Methods in Genetic Programming", author = "Elko Tchernev", booktitle = "Late Breaking Papers at the Genetic and Evolutionary Computation Conference ({GECCO-2002})", editor = "Erick Cant{\'u}-Paz", year = "2002", month = jul, pages = "443--449", address = "New York, NY", publisher = "AAAI", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025", keywords = "genetic algorithms, genetic programming, stack-based GP", notes = "Late Breaking Papers, {GECCO-2002}. A joint meeting of the eleventh International Conference on Genetic Algorithms ({ICGA-2002}) and the seventh Annual Genetic Programming Conference ({GP-2002}) part of cantu-paz:2002:GECCO:lbp many types of crossover, two boxes, sextic polynomial problems", } @InProceedings{tchernev:2004:lbp, author = "Elko B. Tchernev and Dhananjay S. Phatak", title = "Control structures in linear and stack-based Genetic Programming", booktitle = "Late Breaking Papers at the 2004 Genetic and Evolutionary Computation Conference", year = "2004", editor = "Maarten Keijzer", address = "Seattle, Washington, USA", month = "26 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://gpbib.cs.ucl.ac.uk/gecco2004/LBP041.pdf", abstract = "Genetic Programming, or GP, has traditionally used prefix trees for representation and reproduction, with implicit flow control. The different clauses (the evaluation condition, the if and else sections, etc.) are all subtrees of the flow-control node. Linear and stack-based representations, however, require explicit nodes to define the extent of the control structures. This paper introduces a stack-based technique for correct control structure creation and crossover, and discusses its implementation issues in linear and stack-based GP. A set of flow-control nodes is presented, and examples given for evolving an artificial ant on the Santa Fe and Los Altos Trails, with and without looping constructs.", notes = "Part of \cite{keijzer:2004:GECCO:lbp}", } @InProceedings{Tchernev:gecco05lbp, author = "Elko Tchernev and Dhananjay Phatak", title = "Queue-based Genetic Programming", booktitle = "Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}", year = "2005", month = "25-29 " # jun, editor = "Franz Rothlauf", address = "Washington, D.C., USA", keywords = "genetic algorithms, genetic programming, Tree, Stack, Queue, Data Structures, graphs and networks, lists, stacks and queues, trees. Theory of Computation, Models of Computation, push-down automata, self-modifying machines, probabilistic computation, Artificial Intelligence, Learning, connectionism and neural nets, ANN", URL = "http://gpbib.cs.ucl.ac.uk/gecco2005lbp/papers/65-tchernev.pdf", size = "4 pages", abstract = "We describe the use of a queue instead of a stack or a parse tree for the internal representation and genetic operations of a Genetic Programming system. implementation issues and application areas are discussed", notes = "Distributed on CD-ROM at GECCO-2005 'Crossover in queue-based GP is particularly disruptive'. Failed to solve easy two box problem. Hopes for more sucess when evolving artificial neural networks.", } @InCollection{tea:2000:GAATSP, author = "Hakara Tea", title = "Genetic Algorithms Applied to the Traveling Salesman Problem", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "397--406", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms", notes = "part of \cite{koza:2000:gagp}", } @Unpublished{hampo:toolkit, author = "C. Tebbe and R. J. Hampo and B. D. Bryant and K. A. Marko", title = "Genetic Programming Toolkit", year = "1995?", note = "Ford Proprietary", keywords = "genetic algorithms, genetic programming", notes = "Ford Motor Company Research program (C++) for solving automotive problems with genetic programming. Software and manual.", } @InProceedings{Tedin:2013:CIVEMSA, author = "Rafael Tedin and J. A. Becerra and Richard J. Duro and Fernando {Lopez Pena}", title = "Computational Intelligence based construction of a Body Condition Assessment system for cattle", booktitle = "IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA 2013)", year = "2013", month = "15-17 " # jul, pages = "185--190", address = "Milan", keywords = "genetic algorithms, genetic programming, biology computing, regression analysis, Cows, Image segmentation", DOI = "doi:10.1109/CIVEMSA.2013.6617418", size = "6 pages", abstract = "The objective of this paper is to describe a Computational Intelligence based Automatic Body Conditioning System for cattle we have called Automatic Body Condition Assessment (ABiCA). It is an automatic body condition scoring system for dairy cattle that aims to overcome the flaws of the subjective and time consuming scoring task that is usually carried out by experts. No special set-ups are needed since the system uses pictures taken using normal hand-held cameras. ABiCA is split into two components. A first component for the segmentation of the rear-end shape of a cow from its picture through Active Shape Models Active Shape Models (ASMs) that are evolved using an evolutionary algorithm. The second component is in charge of estimating the Body Condition Score (BCS) of a cow from the shape provided by the ASM. Several classifiers and a symbolic regression function evolved by means of genetic programming techniques are tested for this task. The whole system is tested over a set of images coming from different cattle farms and its goodness provided in terms of the classifications obtained by a set of experts.", notes = "Also known as \cite{6617418}", } @Article{Teegavarapu2009106, author = "Ramesh S. V. Teegavarapu and Mohammad Tufail and Lindell Ormsbee", title = "Optimal functional forms for estimation of missing precipitation data", journal = "Journal of Hydrology", volume = "374", number = "1-2", pages = "106--115", year = "2009", ISSN = "0022-1694", DOI = "doi:10.1016/j.jhydrol.2009.06.014", URL = "http://www.sciencedirect.com/science/article/B6V6C-4WH8CFX-4/2/2dc8195d13308dffa2798503d7038279", keywords = "genetic algorithms, genetic programming, Missing precipitation data, Spatial interpolation, Distance weighting methods, Fixed function set genetic algorithm method, Optimal functional forms", abstract = "A fixed functional set genetic algorithm method (FFSGAM) is proposed and is investigated in the current study to obtain optimal functional forms for estimating missing precipitation data. The FFSGAM provides functional forms with optimal combination of parameters of surrogate and actual measures of strength of correlation among observations for estimating missing data. The method uses genetic algorithms and a nonlinear optimisation formulation to obtain optimal functional forms and coefficients, respectively. Historical daily precipitation data available from 15 rain gauge stations from the state of Kentucky, USA, are used to test the functional forms and derive conclusions about the efficacy of the proposed method for estimating missing precipitation data. The tests of FFSGAM at two rainfall gaging stations in Kentucky, using multiple error and performance indices, indicate that better estimates of precipitation can be obtained compared to those from a traditional inverse distance weighting technique. Also, results from the use of the method confirm its robustness when only six rain gaging stations out of 14 were used for estimating missing data.", notes = "GA used to evolve mathematical formulae", } @PhdThesis{teich:thesis, author = "Tobias Teich", title = "Optimierung von Maschinenbelegungspl{\"{a}}nen unter Benutzung heuristischer Verfahren", school = "Department of of Economics, Technical University of Chemnitz", year = "1998", address = "Germany", month = "30 " # jun, keywords = "genetic algorithms, genetic programming", URL = "http://www.tu-chemnitz.de/wirtschaft/bwl7/bibtex.php?lit_id=307", URL = "https://www.tu-chemnitz.de/forschung/promotionen.php?jahr=1998&fakult=Wirtschaftswissenschaften", URL = "http://www.amazon.de/Optimierung-Maschinenbelegungspl%C3%A4nen-Benutzung-heuristischer-Verfahren/dp/3890126146", size = "364 pages", publisher = "Josef Eul Verlag", publisher_address = "Lohmar, Cologne, Germany", ISBN = "3-89012-614-6", notes = " ", } @Article{Tejera:2021:Molecules, author = "Eduardo Tejera and Yunierkis Perez-Castillo and Andrea Chamorro and Alejandro Cabrera-Andrade and Maria Eugenia Sanchez", title = "A Multi-Objective Approach for Drug Repurposing in Preeclampsia", journal = "Molecules", year = "2021", volume = "26", number = "4", pages = "777", month = feb # " 3", keywords = "genetic algorithms, genetic programming, TPOT, preeclampsia, multi-objective models, drugs repurposing, machine learning, python", publisher = "MDPI", ISSN = "1420-3049", DOI = "doi:10.3390/molecules26040777", size = "19 pages", abstract = "Preeclampsia is a hypertensive disorder that occurs during pregnancy. It is a complex disease with unknown pathogenesis and the leading cause of fetal and maternal mortality during pregnancy. Using all drugs currently under clinical trial for preeclampsia, we extracted all their possible targets from the DrugBank and ChEMBL databases and labeled them as targets. The proteins labeled as off-targets were extracted in the same way but while taking all antihypertensive drugs which are inhibitors of ACE and/or angiotensin receptor antagonist as query molecules. Classification models were obtained for each of the 55 total proteins (45 targets and 10 off-targets) using the TPOT pipeline optimization tool. The average accuracy of the models in predicting the external dataset for targets and off-targets was 0.830 and 0.850, respectively. The combinations of models maximizing their virtual screening performance were explored by combining the desirability function and genetic algorithms. The virtual screening performance metrics for the best model were: the Boltzmann-Enhanced Discrimination of ROC (BEDROC)alpha=160.9 = 0.258, the Enrichment Factor (EF)1percent = 31.55 and the Area Under the Accumulation Curve (AUAC) = 0.831. The most relevant targets for preeclampsia were: AR, VDR, SLC6A2, NOS3 and CHRM4, while ABCG2, ERBB2, CES1 and REN led to the most relevant off-targets. A virtual screening of the DrugBank database identified estradiol, estriol, vitamins E and D, lynestrenol, mifrepristone, simvastatin, ambroxol, and some antibiotics and antiparasitics as drugs with potential application in the treatment of preeclampsia", notes = "Grupo de Bio-Quimioinformatica, Universidad de Las Americas, Quito 170513, Ecuador PMID: 33546161; PMCID: PMC7913128", } @Article{TELIKANI2020, author = "Akbar Telikani and Amir H. Gandomi and Asadollah Shahbahrami", title = "A survey of evolutionary computation for association rule mining", journal = "Information Sciences", year = "2020", volume = "524", pages = "318--352", month = jul, keywords = "genetic algorithms, genetic programming, Data mining, Association rule mining, Evolutionary computation, Swarm intelligent", ISSN = "0020-0255", URL = "http://www.sciencedirect.com/science/article/pii/S002002552030164X", DOI = "doi:10.1016/j.ins.2020.02.073", abstract = "Association Rule Mining (ARM) is a significant task for discovering frequent patterns in data mining. It has achieved great success in a plethora of applications such as market basket, computer networks, recommendation systems, and healthcare. In the past few years, evolutionary computation-based ARM has emerged as one of the most popular research areas for addressing the high computation time of traditional ARM. Although numerous papers have been published, there is no comprehensive analysis of existing evolutionary ARM methodologies. we review emerging research of evolutionary computation for ARM. We discuss the applications on evolutionary computations for different types of ARM approaches including numerical rules, fuzzy rules, high-utility itemsets, class association rules, and rare association rules. Evolutionary ARM algorithms were classified into four main groups in terms of the evolutionary approach, including evolution-based, swarm intelligence-based, physics-inspired, and hybrid approaches. Furthermore, we discuss the remaining challenges of evolutionary ARM and discuss its applications and future topics.", notes = "Also known as \cite{TELIKANI2020318} Highlights We present a review of trends and directions in EC-based association rule mining. 221 algorithms were collected between 2000 and 2019 using a research methodology. We review algorithms according to meta-heuristic approaches in nine groups. Applications and the current problems and opportunities are described.", } @Article{Telikani:2021:ACMComputSurv, author = "Akbar Telikani and Amirhessam Tahmassebi and Wolfgang Banzhaf and Amir H. Gandomi", title = "Evolutionary Machine Learning: A Survey", journal = "ACM Computing Surveys", year = "2021", volume = "54", number = "8", pages = "Article 161", month = oct, keywords = "genetic algorithms, genetic programming, Machine learning, Artificial intelligence, AI, Evolutionary computation, learning optimization, swarm intelligence", URL = "https://ro.uow.edu.au/test2021/3261/", URL = "https://dl.acm.org/doi/fullHtml/10.1145/3467477", DOI = "doi:10.1145/3467477", size = "35 pages", abstract = "Evolutionary Computation (EC) approaches are inspired by nature and solve optimization problems in a stochastic manner. They can offer a reliable and effective approach to address complex problems in real-world applications. EC algorithms have recently been used to improve the performance of Machine Learning (ML) models and the quality of their results. Evolutionary approaches can be used in all three parts of ML: preprocessing (e.g., feature selection and resampling), learning (e.g., parameter setting, membership functions, and neural network topology), and postprocessing (e.g., rule optimization, decision tree/support vectors pruning, and ensemble learning). This article investigates the role of EC algorithms in solving different ML challenges. We do not provide a comprehensive review of evolutionary ML approaches here; instead, we discuss how EC algorithms can contribute to ML by addressing conventional challenges of the artificial intelligence and ML communities. We look at the contributions of EC to ML in nine sub-fields: feature selection, resampling, classifiers, neural networks, reinforcement learning, clustering, association rule mining, and ensemble methods. For each category, we discuss evolutionary machine learning in terms of three aspects: problem formulation, search mechanisms, and fitness value computation. We also consider open issues and challenges that should be addressed in future work.", } @InProceedings{Teller:1993:lmm, author = "A. Teller", title = "Learning mental models", booktitle = "Proceedings of the Fifth Workshop on Neural Networks: An International Conference on Computational Intelligence: Neural Networks, Fuzzy Systems, Evolutionary Programming, and Virtual Reality", year = "1993", organisation = "The Society for Computer Simulation", URL = "http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/LearnModels.ps", keywords = "genetic algorithms, genetic programming, memory", abstract = "The process of learning is not always as simple as mapping inputs to the best outputs. Often internal state is needed to distinguish between observably identical states of the world. Genetic programming has concentrated on solving problems in the functional/reactive arena, in part because of the absence of a natural way to incorporate memory into the paradigm. This paper presents a simple addition to the genetic programming paradigm that seamlessly incorporates the evolution of the effective gathering, storage, and retrieval of arbitrarily complicated state information. Experimental results show that the effective production and use of complex state structures can be evolved and that agents evolving the use of memory quickly and permanently displace purely reactive and non-deterministic functions. These results may not only aid future research into the causes and constituents of mental models but may expand the types of problems that can be practically tackled by genetic programming.", notes = "You can get these papers by anonymous ftp to any CMU machine. (e.g. GS61.SP.CS.CMU.EDU (128.2.203.143) or J.GP.CS.CMU.EDU (128.2.250.198)) then cd to /afs/cs/usr/astro/public/papers/ Since several come from the Mac, they won't work in GhostView, but they should print fine. Tartarus", } @InCollection{kinnear:teller, author = "Astro Teller", title = "The Evolution of Mental Models", booktitle = "Advances in Genetic Programming", publisher = "MIT Press", editor = "Kenneth E. {Kinnear, Jr.}", year = "1994", chapter = "9", pages = "199--219", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/MentalModels.ps", URL = "http://www.amazon.co.uk/Advances-Genetic-Programming-Complex-Adaptive/dp/0262111888", URL = "http://cognet.mit.edu/sites/default/files/books/9780262277181/pdfs/9780262277181_chap9.pdf", DOI = "doi:10.7551/mitpress/1108.003.0014", size = "21 pages", abstract = "Most interesting problems do not have solutions that are simple mappings from the inputs to the correct outputs; some kind of internal state or memory is needed to operate well or optimally in these domains. Traditionally, genetic programming has concentrated on solving problems in the functional/reactive arena. This may be due in part to the absence of a natural way to incorporate memory into the paradigm. This chapter proposes a simple, Turing-complete addition to the genetic programming paradigm that seamlessly incorporates the evolution of the effective gathering, storage, and retrieval of arbitrarily complicated state information. A new environment is presented and used to evaluate this addition to the paradigm. Experimental results show that the effective production and use of complex memory structures can be evolved and that functions evolving the intelligent use of state quickly and permanently displace purely reactive and non-deterministic functions. These results may aid future research into the causes and constituents of mental models and are shown to open the field of genetic programming to include all learning strategies that are Turing-possible.", notes = "Addition of 20 memory elements via READ and WRITE to box pushing inside a matrix of 6*6 cells You can get these papers by anonymous ftp to any CMU machine. (broken Mar 2019 e.g. GS61.SP.CS.CMU.EDU (128.2.203.143) or J.GP.CS.CMU.EDU (128.2.250.198)) then cd to /afs/cs/usr/astro/public/papers/ Since several come from the Mac, they won't work in GhostView, but they should print fine. Part of \cite{kinnear:book}", } @InProceedings{fairs94:teller, author = "Astro Teller", title = "Genetic Programming, Indexed memory, the Halting problem, and other curiosities", booktitle = "Proceedings of the 7th annual Florida Artificial Intelligence Research Symposium", year = "1994", pages = "270--274", address = "Pensacola, Florida, USA", month = may, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/Curiosities.ps", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/ftp.io.com/papers/Curiosities.ps", abstract = "The genetic programming (GP) paradigm was designed to evolve functions that are progressively better approximations to some target function. The introduction of memory into GP has opened the Pandora's box which is algorithms. It has been shown that the combination of GP and Indexed Memory can be used to evolve any target algorithm. What has not been shown is the practicality of doing so. This paper addresses some of the fundamental issues in the process of evolving algorithms and proposes a variety of partial solutions, in general and for GP in particular.", notes = "You can get these papers by anonymous ftp to any CMU machine. (e.g. GS61.SP.CS.CMU.EDU (128.2.203.143) or J.GP.CS.CMU.EDU (128.2.250.198)) then cd to /afs/cs/usr/astro/public/papers/ Since several come from the Mac, they won't work in GhostView, but they should print fine. Discuses anytime algorithm for extracting {"}answer{"} from evolved program via its use of indexed memory.", size = "5 pages", } @InProceedings{wcci94:teller, author = "Astro Teller", title = "Turing Completeness in the Language of Genetic Programming with Indexed Memory", booktitle = "Proceedings of the 1994 IEEE World Congress on Computational Intelligence", year = "1994", volume = "1", pages = "136--141", address = "Orlando, Florida, USA", month = "27-29 " # jun, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/Turing.ps", DOI = "doi:10.1109/ICEC.1994.350027", size = "6 pages", abstract = "Genetic Programming is a method for evolving functions that find approximate or exact solutions to problems. There are many problems that traditional Genetic Programming (GP) cannot solve, due to the theoretical limitations of its paradigm. A Turing machine (TM) is a theoretical abstraction that express the extent of the computational power of algorithms. Any system that is Turing complete is sufficiently powerful to recognize all possible algorithms. GP is not Turing complete. This paper will prove that when GP is combined with the technique of indexed memory, the resulting system is Turing complete. This means that, in theory, GP with indexed memory can be used to evolve any algorithm.", notes = "Proof that Language of GP+Indexed Memory is Turing Complete, Nb does NOT show GP+IM itself will solve anything. You can get these papers by anonymous ftp to any CMU machine. (e.g. GS61.SP.CS.CMU.EDU (128.2.203.143) or J.GP.CS.CMU.EDU (128.2.250.198)) then cd to /afs/cs/usr/astro/public/papers/ Since several come from the Mac, they won't work in GhostView, but they should print fine.", } @TechReport{TechTeller, author = "Astro Teller and Manuela Veloso", title = "{PADO}: Learning Tree Structured Algorithms for Orchestration into an Object Recognition System", year = "1995", institution = "Department of Computer Science, Carnegie Mellon University", address = "Pittsburgh, PA, USA", number = "CMU-CS-95-101", URL = "http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/PADO-Tech-Report.ps", URL = "https://www.cs.cmu.edu/~coral/old/publications/b2hd-TechTeller.html", keywords = "genetic algorithms, genetic programming, memory, Machine Learning, Signal Understanding, Data Mining, Algorithm Evolution", size = "34 pages", size = "2.54 Mbytes", abstract = "Around the world there are innumerable databases of information. The quantity of information available has created a high demand for automatic methods for searching these databases and extracting specific kinds of information. Unfortunately, the information in these databases increasingly contains signals that have no corresponding classification symbols. Examples include databases of images, sounds, etc. A few systems have been written to help solve these search and retrieve issues. But we can not write a new system for every kind of signal we want to recognize and extract. Some work has been done on automating (i.e. learning) the task of identifying desired signal elements. It would be useful to automate (learn) not just a part of the classification function, but the entire signal identification program. It would be helpful if we could use the same learning architecture to automatically create these programs for distinguishing many different classes of the same signal type. It would be better still if we could use the same learning architecture to create these programs even for signal types as different as images and sound waves. We introduce PADO (Parallel Architecture Discovery and Orchestration), a learning architecture designed to deliver this. PADO has at its core a variant of genetic programming (GP) that extends the paradigm to explore the space of algorithms. PADO learns the entire classification algorithm for an arbitrary signal type with arbitrary signal class distinctions. This architecture has been designed specifically for signal understanding and classification. The architecture of PADO and its achievements on the recovery of visual and acoustic signal classes from test databases are the subjects of this article.", notes = "You can get these papers by anonymous ftp to any CMU machine. (e.g. GS61.SP.CS.CMU.EDU (128.2.203.143) or J.GP.CS.CMU.EDU (128.2.250.198)) then cd to /afs/cs/usr/astro/public/papers/ Since several come from the Mac, they won't work in GhostView, but they should print fine. Our printer barfed at page 10! This appears to be very close to \cite{teller:1995:PADO}", } @Article{Teller-ESJ, author = "Astro Teller and Manuela Veloso", title = "Program Evolution for Data Mining", editor = "Sushil Louis", publisher = "JAI Press", journal = "The International Journal of Expert Systems", year = "1995", volume = "8", number = "3", pages = "216--236", keywords = "genetic algorithms, genetic programming, memory", URL = "http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/Astro-ESJ.ps", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Astro-ESJ.ps.Z", size = "21 pages", abstract = "Around the world there are innumerable databases of information. The quantity of information available has created a high demand for automatic methods for searching these databases and extracting specific kinds of information. Unfortunately, the information in these databases increasingly contains signals that have no corresponding classification symbols. Examples include databases of images, sounds, etc. A few systems have been written to help solve these search and retrieve issues. But we can not write a new system for every kind of signal we want to recognize and extract. Some work has been done on automating (i.e. learning) the task of identifying desired signal elements. It would be useful to automate (learn) not just a part of the classification function, but the entire signal identification program. It would be helpful if we could use the same learning architecture to automatically create these programs for distinguishing many different classes of the same signal type. It would be better still if we could use the same learning architecture to create these programs even for signal types as different as images and sound waves. We introduce PADO (Parallel Architecture Discovery and Orchestration), a learning architecture designed to deliver this. PADO has at its core a variant of genetic programming (GP) that extends the paradigm to explore the space of algorithms. PADO learns the entire classification algorithm for an arbitrary signal type with arbitrary signal class distinctions. This architecture has been designed specifically for signal understanding and classification. The architecture of PADO and its achievements on the recovery of visual and acoustic signal classes from test databases are the subjects of this article. ", notes = "Third Quarter. Special Issue on Genetic Algorithms and Knowledge Bases.", } @InCollection{teller:1995:PADO, author = "Astro Teller and Manuela Veloso", title = "PADO: A New Learning Architecture for Object Recognition", booktitle = "Symbolic Visual Learning", publisher = "Oxford University Press", year = "1996", editor = "Katsushi Ikeuchi and Manuela Veloso", pages = "81--116", keywords = "genetic algorithms, genetic programming, memory", URL = "http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/PADO.ps", abstract = "Most artificial intelligence systems today work on simple problems and artificial domains because they rely on the accurate sensing of the task world. Object recognition is a crucial part of the sensing challenge and machine learning stands in a position to catapult object recognition into real world domains. Given that, to date, machine learning has not delivered general object recognition, we propose a different point of attack: the learning architectures themselves. We have developed a method for directly learning and combining algorithms in a new way that imposes little burden on or bias from the humans involved. This learning architecture, PADO, and the new results it brings to the problem of natural image object recognition is the focus of this chapter.", notes = "This is NOT the same as \cite{TechTeller}. The overlap is about 20 of the 34 pages but it is different enough", size = "34 pages", } @InProceedings{Teller-EPIA, author = "Astro Teller and Manuela Veloso", title = "A Controlled Experiment: Evolution for Learning Difficult Image Classification", booktitle = "Seventh Portuguese Conference On Artificial Intelligence", year = "1995", publisher = "Springer-Verlag", series = "Lecture Notes in Computer Science", volume = "990", pages = "165--176", address = "Funchal, Madeira Island, Portugal", month = oct # " 3-6", keywords = "genetic algorithms, genetic programming, memory", URL = "http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/TellerVelosoEPIA.ps", abstract = "The signal-to-symbol problem is the task of converting raw sensor data into a set of symbols that Artificial Intelligence systems can reason about. We have developed a method for directly learning and combining algorithms that map signals into symbols. This new method is based on evolutionary computation and imposes little burden on or bias from the humans involved. Previous papers of ours have focused on PADO, our learning architecture. We showed how it applies to the general signal-to-symbol task and in particular the impressive results it brings to natural image object recognition. The most exciting challenge this work has received is the idea that PADO's success in natural image object recognition may be due to the underlying simplicity of the problems we posed it. This implicitly assumes that our approach may suffer from many of same afflictions that traditional computer vision approaches suffer in natural image object recognition. This paper responds to this challenge by designing and executing a controlled experiment specifically designed to solidify PADO's claim to success.", notes = "EPIA'95 ", } @InProceedings{teller:1995:db, author = "Astro Teller", title = "The Discovery of Algorithms for Automatic Database Retrieval", booktitle = "Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications", year = "1995", editor = "Justinian P. Rosca", pages = "76--88", address = "Tahoe City, California, USA", month = "9 " # jul, keywords = "genetic algorithms, genetic programming, memory", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/teller_1995_db.pdf", size = "13 pages", abstract = "PADO", notes = "part of \cite{rosca:1995:ml}", } @InProceedings{teller-FSS-GP, author = "Astro Teller", title = "Language Representation Progression in Genetic Programming", booktitle = "Working Notes for the AAAI Symposium on Genetic Programming", year = "1995", editor = "E. V. Siegel and J. R. Koza", pages = "106--113", address = "MIT, Cambridge, MA, USA", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025, USA", month = "10--12 " # nov, publisher = "AAAI", keywords = "genetic algorithms, genetic programming, memory", URL = "http://www.aaai.org/Papers/Symposia/Fall/1995/FS-95-01/FS95-01-015.pdf", URL = "http://www.aaai.org/Library/Symposia/Fall/fs95-01.php", size = "8 pages", abstract = "The signal-to-symbol problem is the task of converting raw sensor data into a set of symbols that Artificial Intelligence systems can reason about. We have developed a method for directly learning and combining algorithms that map signals into symbols. This new method is based on Genetic Programming (GP). Previous papers have focused on PADO, our learning architecture. We showed how PADO applies to the general signal-to-symbol task and in particular the positive results it brings to natural image object recognition. Originally, PADO's programs were written in a Lisp-like language formulated in~\cite{teller2}. PADO's programs are now written in a very different language. Using this new language, PADO's performance has increased substantially on several domains including two vision domains this paper will mention. This paper will discuss these two language representations, the results they produced, and some analysis of the performance improvement. The higher level goals of this paper are to give some justification for PADO's specific language progression, some explanation for the improved performance this progression generated, and to offer PADO's new language representation as an advancement in GP.", notes = "AAAI-95f GP. Part of \cite{siegel:1995:aaai-fgp} {\em Telephone:} 415-328-3123 {\em Fax:} 415-321-4457 {\em email} info@aaai.org {\em URL:} http://www.aaai.org/", } @InProceedings{Teller-ICEC-95, author = "Astro Teller and Manuela Veloso", title = "Algorithm Evolution for Face Recognition: What Makes a Picture Difficult", booktitle = "International Conference on Evolutionary Computation", year = "1995", volume = "2", pages = "608--613", address = "Perth, Australia", publisher_address = "Piscataway, NJ, USA", month = "1--3 " # dec, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, memory, computer vision, Face recognition, Machine learning, Computer vision, Image databases, Neural networks, ANN, Machine learning algorithms, Computer science, Evolutionary computation, Humans", ISBN = "0-7803-2759-4", URL = "http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/icecFinal.ps", URL = "http://citeseer.ist.psu.edu/558916.html", DOI = "doi:10.1109/ICEC.1995.487453", size = "6 pages", abstract = "One of the classic problems in computer vision is the face recognition problem. In general this problem can take on a wide variety of forms, but the most common face recognition problem is Who is this a picture of? Evolution computation has, in the past, been applied indirectly to this problem through techniques like learning Neural Networks. This paper introduces a Genetic Programming style approach to learning algorithms that directly investigate face images and are coordinated into a face recognition system. Through a series of experiments, we will show that evolved algorithms can accomplish the face recognition task. We will also highlight several pitfalls and misconceptions surrounding face recognition as a learning problem.", notes = "ICEC-95 PADO also known as \cite{487453} conference details at broken Aug 2024 http://ciips.ee.uwa.edu.au/~dorota/icnn95.html", } @InCollection{teller:1996:aigp2, author = "Astro Teller", title = "Evolving Programmers: The Co-evolution of Intelligent Recombination Operators", booktitle = "Advances in Genetic Programming 2", publisher = "MIT Press", year = "1996", editor = "Peter J. Angeline and K. E. {Kinnear, Jr.}", pages = "45--68", chapter = "3", address = "Cambridge, MA, USA", keywords = "genetic algorithms, genetic programming, memory", ISBN = "0-262-01158-1", URL = "http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/AiGPII.ps", URL = "http://www.cs.cmu.edu/afs/cs/usr/astro/mosaic/chapterII/chapterII.html", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6277500", DOI = "doi:10.7551/mitpress/1109.003.0007", size = "24 pages", abstract = "The genetic programming process searches over a fitness landscape. The shape of this landscape is determined by the task to be solved and the representation in which the population members are expressed. The movement through this space is determined by the operators that act to recombine the population members. These factors make it imperative that our search for increased power and understanding in genetic programming include the study and improvement of representations and operators. This chapter describes a process for learning SMART recombination programs in a co-evolutionary process and a new representation for the evolution of algorithms. How these SMART operator programs are created, how they act, how they co-evolve with a main population of programs, and experimental results on their use are the subjects of this chapter. ", notes = "PADO + SMART recombination html version available from http://www.cs.cmu.edu/~astro/", } @InProceedings{teller:1996:npirp, author = "Astro Teller and Manuela Veloso", title = "Neural Programming and an Internal Reinforcement Policy", booktitle = "Late Breaking Papers at the Genetic Programming 1996 Conference Stanford University July 28-31, 1996", year = "1996", editor = "John R. Koza", pages = "186--192", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California 94305-3079, USA", month = "28--31 " # jul, publisher = "Stanford Bookstore", ISBN = "0-18-201031-7", keywords = "genetic algorithms, genetic programming, ANN", notes = "GP-96LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670", } @InProceedings{teller:1996:npirpSV, author = "Astro Teller and Manuela Veloso", title = "Neural Programming and an Internal Reinforcement Policy", booktitle = "International Conference Simulated Evolution and Learning", year = "1996", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, ANN", URL = "http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/AS.ps", URL = "http://www.cs.cmu.edu/afs/cs/usr/astro/mosaic/astroseal/astro/seal.html", size = "8 pages", abstract = "An important reason for the continued popularity of Artificial Neural Networks (ANNs) in the machine learning community is that the gradient-descent backpropagation procedure gives ANNs a locally optimal change procedure and, in addition, a framework for understanding the ANN learning performance. Genetic programming (GP) is also a successful evolutionary learning technique that provides powerful parameterized primitive constructs. Unlike ANNs, though, GP does not have such a principled procedure for changing parts of the learned system based on its current performance. This paper introduces Neural Programming, a connectionist representation for evolving programs that maintains the benefits of GP. The connectionist model of Neural Programming allows for a regression credit-blame procedure in an evolutionary learning system. We describe a general method for an informed feedback mechanism for Neural Programming, Internal Reinforcement. We introduce an Internal Reinforcement procedure and demonstrate its use through an illustrative experiment.", notes = "html version available from http://www.cs.cmu.edu/~astro/ SEAL, PADO bucket-brigade IRNP reach given level of performace in 30% of generations taken by NP", } @InProceedings{Teller:1997:acnfc, author = "Astro Teller and David Andre", title = "Automatically Choosing the Number of Fitness Cases: The Rational Allocation of Trials", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming", pages = "321--328", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", URL = "http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/GR.ps", size = "8 pages", abstract = "For many problems to which genetic programming has been applied, choosing the number of fitness cases with which to evaluate the individuals is a crucial decision. If too few fitness cases are used, overfitting may occur, and the measured fitness of an individual may not be representative of its true fitness. On the other hand, if too many fitness cases are used, a great deal of computer time can be wasted. This paper presents a method for the Rational Allocation of Trials (RAT) that dynamically allocates a boundedly optimal number of fitness cases for each individual. RAT allocates individuals to tournaments prior to their evaluation, and then, borrowing from previous work in model selection, allocates trials (fitness cases) only to those individuals for whom the cost of evaluating another fitness case is outweighed by the expected utility that the new information will provide. For most evolutionary computation approaches, including genetic programming, and for most problems, the RAT algorithm will provide significant time savings at minimal additional system complexity.", notes = "GP-97", } @InProceedings{teller:1997:aesu, author = "Astro Teller", title = "Algorithm Evolution for Signal Understanding", booktitle = "Late Breaking Papers at the 1997 Genetic Programming Conference", year = "1997", editor = "John R. Koza", pages = "299", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California, 94305-3079, USA", month = "13--16 " # jul, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-206995-8", URL = "http://ijcai.org/Past%20Proceedings/IJCAI-97-VOL2/PDF/111.pdf", notes = "GP-97LB PHD Students' workshop The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670 See also http://ijcai.org/Past%20Proceedings/IJCAI-97-VOL2/PDF/111.pdf IJCAI-97 page 1544, DOCTORAL CONSORTIUM ABSTRACTS", } @PhdThesis{AstroTeller:thesis, author = "Astro Teller", title = "Algorithm Evolution with Internal Reinforcement for Signal Understanding", school = "School of Computer Science, Carnegie Mellon University", year = "1998", address = "Pittsburgh, USA", month = "5 " # dec, keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/thesis.ps", size = "5.9 Mbytes, 166 pages", abstract = "Automated program evolution has existed in some form for almost forty years. Signal understanding (e.g., signal classification) has been a scientific concern for longer than that. Generating a general machine learning signal understanding system has more recently attracted considerable research interest. First, this thesis defines and creates a general machine learning approach for signal understanding independent of the signal's type and size. This is accomplished through an evolutionary strategy of signal understanding programs that is an extension of genetic programming. Second, this thesis introduces a suite of sub-mechanisms that increase the power of genetic programming and contribute to the understanding of the learning technique developed. The central algorithmic innovation of this thesis is the process by which a novel principled credit-blame assignment is introduced and incorporated into the evolution of algorithms, thus improving the evolutionary process. This principled credit-blame assignment is done through a new program representation called neural programming and applied through a set of principled processes collectively called internal reinforcement in neural programming. This thesis concentrates on these algorithmic innovations in real world signal domains where the signals are typically large and/or poorly understood. This evolutionary learning of algorithms takes place in PADO, a system developed in this thesis for ``parallel algorithm discovery and orchestration'' and as a demonstrably effective strategy for divide-and-conquer in signal classification domains. This thesis includes an extensive empirical evaluation of the techniques developed in a rich variety of real-world signals. The results obtained demonstrate, among other things, the effectiveness of principled credit-blame assignment in algorithm evolution. This work is unique in three aspects. No other currently existing system can learn to classify or otherwise ``symbolize'' signals with no space or size penalties for the signal's size or type. No other system based on genetic programming currently exists that purposefully generates and orchestrates a variety of experts along problem specific lines. And, most centrally, the thesis introduces the first analytically sound mechanism for explaining and reinforcing specific parts of an evolving program. The goal of this thesis is to argue, explain, and demonstrate how representation and search are intimately connected in evolutionary computation and to address these dual concerns in the context of the evolution of Turing complete programs. Ideally, this thesis will inspire future research in this same area and along similar lines.", notes = "Publication Number: CMU-CS-98-132", } @InCollection{teller:1999:aigp3, author = "Astro Teller", title = "The Internal Reinforcement of Evolving Algorithms", booktitle = "Advances in Genetic Programming 3", publisher = "MIT Press", year = "1999", editor = "Lee Spector and William B. Langdon and Una-May O'Reilly and Peter J. Angeline", chapter = "14", pages = "325--354", address = "Cambridge, MA, USA", month = jun, keywords = "genetic algorithms, genetic programming", ISBN = "0-262-19423-6", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/aigp3/ch14.pdf", language = "en", oai = "oai:CiteSeerXPSU:10.1.1.143.371", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.143.371", DOI = "doi:10.7551/mitpress/1110.003.0019", size = "30 pages", abstract = "There is a fundamental problem with genetic programming as it is currently practised, the genetic recombination operators that drive the learning process act at random, without regard to how the internal components of the programs to be recombined behaved during training. This research introduces a method of program transformations that is principled, based on the program's internal behaviour, and significantly more likely than random local sampling to improve the transformed programs' fitness values. The contribution of our research is a detailed approach by which principled credit-blame assignment can be brought to GP and that credit-blame assignment can be focused to improve that same evolutionary process. This principled credit-blame assignment is done through a new program representation called neural programming and applied through a set of principled processes called, collectively, internal reinforcement in neural programming. This internal reinforcement of evolving programs is presented here as a first step toward the desired gradient descent in program space.", notes = "AiGP3 See http://cognet.mit.edu", } @Article{Teller:2000:AI, author = "Astro Teller and Manuela Veloso", title = "Internal reinforcement in a connectionist genetic programming approach", journal = "Artificial Intelligence", volume = "120", pages = "165--198", year = "2000", number = "2", month = jul, keywords = "genetic algorithms, genetic programming, Machine learning, Evolutionary computation, Signal understanding, Internal reinforcement, Neural programming, Bucket brigade", URL = "http://www.cs.cmu.edu/~coral/publinks/mmv/AIJ-Astro.pdf", broken = "http://www.cs.cmu.edu/~coral/publications/b2hd-AIJ-Astro.html", URL = "http://citeseer.ist.psu.edu/41715.html", broken = "http://www.sciencedirect.com/science/article/B6TYF-40TY77M-1/1/c54fc0ab842b831a76c9e61e1c1c6b85", DOI = "doi:10.1016/S0004-3702(00)00023-0", size = "34 pages", abstract = "Genetic programming (GP) can learn complex concepts by searching for the target concept through evolution of a population of candidate hypothesis programs. However, unlike some learning techniques, such as Artificial Neural Networks (ANNs), GP does not have a principled procedure for changing parts of a learned structure based on that structure's performance on the training data. GP is missing a clear, locally optimal update procedure, the equivalent of gradient-descent backpropagation for ANNs. This article introduces a new algorithm, internal reinforcement, for defining and using performance feedback on program evolution. This internal reinforcement principled mechanism is developed within a new connectionist representation for evolving parameterised programs, namely neural programming. We present the algorithms for the generation of credit and blame assignment in the process of learning programs using neural programming and internal reinforcement. The article includes a comprehensive overview of genetic programming and empirical experiments that demonstrate the increased learning rate obtained by using our principled program evolution approach.", notes = "oai:CiteSeerPSU:558697 broken Oct 2022 http://citeseer.ist.psu.edu/558697.html gives a slightly different version", } @InProceedings{icml00-astro, author = "Astro Teller and Manuela Veloso", title = "Efficient Learning through Evolution: Neural Programming and Internal Reinforcement", booktitle = "Proceedings of the Seventeenth International Conference on Machine Learning", month = jun # " 29 - " # jul # " 2", year = "2000", bib2html_pubtype = "Refereed Conference", bib2html_rescat = "Other", editor = "Pat Langley", pages = "959--966", address = "Stanford University, Standord, CA, USA", publisher = "Morgan Kaufmann Publishers Inc.", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-707-2", citeseer-isreferencedby = "oai:CiteSeerPSU:94197", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:558985", rights = "unrestricted", URL = "http://www.cs.cmu.edu/~coral/publinks/mmv/icml00-astro.pdf", URL = "http://citeseer.ist.psu.edu/558985.html", URL = "http://citeseer.ist.psu.edu/330400.html", URL = "http://dl.acm.org/citation.cfm?id=645529.657961", acmid = "657961", size = "8 pages", abstract = "Genetic programming (GP) can learn complex concepts by searching for the target concept through evolution of population of candidate hypothesis programs. However, unlike some learning techniques, such as Artificial neural networks (ANNs), GP does not have a principled procedure for changing parts of a learned structure based on that structure's performance on the training data. GP is missing a clear, locally optimal update procedure, an equivalent of gradient-descent backpropagation for ANNs. This article introduces a new mechanism, {"}internal reinforcement, {"} for defining and using performance feedback on program evolution. A new connectionist representation for evolving parameterised programs, {"}neural programming{"} is also introduced. We present the algorithms for the generation of credit and blame assignment in the process of learning programs using neural programming and internal reinforcement. The article includes some of our extensive experiments that demonstrate the increased learning rate obtained by using our principled program evolution approach.", notes = "Also nown as \cite{Teller:2000:ELT:645529.657961} ICML 2000", } @Article{Temperton:2015:wired, author = "James Temperton", title = "Code 'transplant' could revolutionise programming", journal = "Wired.co.uk", year = "2015", month = "30 " # jul, note = "Online", keywords = "genetic algorithms, genetic programming, genetic improvement", URL = "http://www.wired.co.uk/news/archive/2015-07/30/code-organ-transplant-software-myscalpel", size = "approx 2 pages", abstract = "Code has been automatically transplanted from one piece of software to another for the first time, with researchers claiming the breakthrough could radically change how computer programs are created....", notes = "Review of \cite{Barr:2015:ISSTA} \cite{Marginean:2015:SSBSE}", } @InProceedings{DBLP:conf/ahs/TempestiMZ06, author = "Gianluca Tempesti and Pierre-Andre Mudry and Guillaume Zufferey", title = "Hardware/Software Coevolution of Genome Programs and Cellular Processors", year = "2006", booktitle = "First NASA/ESA Conference on Adaptive Hardware and Systems (AHS 2006)", editor = "Adrian Stoica and Tughrul Arslan and Martin Suess and Senay Yal\c{c}in and Didier Keymeulen and Tetsuya Higuchi and Ricardo Salem Zebulum and Nizamettin Aydin", pages = "129--136", address = "Istanbul, Turkey", month = "15-18 " # jun, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, evolvable hardware, evolutionary computation, hardware-software codesign, logic partitioning, microprocessor chips, reconfigurable architectures, biological organism, cellular processor, configurable processor architecture, evolutionary technique, genome program, hardware design, hardware/software coevolution", ISBN = "0-7695-2614-4", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1109/AHS.2006.51", abstract = "The application of evolutionary techniques to the design of custom processing elements bears a strong relation to the natural process that led to the co-evolution of cells and genomes in biological organisms. As such, it is an interesting avenue for an effective application of evolutionary approaches in the domain of hardware design. The architecture of conventional non-configurable processors, however, is ill-adapted to this kind of approach, as evolution can operate exclusively on the software (the genome) and not on the hardware that executes it, leading to scalability issues that seem very difficult to overcome. Building on a family of configurable processors we developed in the past years, in this article we introduce a design methodology that allows the architecture of the processor to co-evolve together with the code to be executed", } @Article{TENG:2021:PCS, author = "Bin Teng and Yufeng Shi and Xin Wang and Yunchuan Sun", title = "Generating and Optimizing Human-Readable Quantitative Program Trading Strategies through a Genetic Programming Framework", journal = "Procedia Computer Science", volume = "187", pages = "613--617", year = "2021", note = "2020 International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI2020", keywords = "genetic algorithms, genetic programming, Quantitative program trading, Expression tree, Regularization", ISSN = "1877-0509", URL = "https://www.sciencedirect.com/science/article/pii/S1877050921009200", DOI = "doi:10.1016/j.procs.2021.04.112", abstract = "In this paper, we provide a highly flexible genetic programming framework for automatic generation and optimization of program trading strategies. We propose the input/output modules and their implementation methods, decoupled from the GP kernel, making it a priori-posteriori framework for trading practitioners. For human-readable purposes, we also give various empirical regularization methods, including NSGA-II multi-objective selection, as well as experimentally effective performance measures", } @InProceedings{conf/case/TengDHWTL19, author = "Yifei Teng and Shaofeng Du and Zhenjun Hong and Xuhui Wu and Yunna Tian and Dongni Li", title = "A Novel Grammatical Evolution Algorithm for Automatic Design of Scheduling Heuristics$^*$", publisher = "IEEE", year = "2019", booktitle = "15th IEEE International Conference on Automation Science and Engineering, CASE 2019", address = "Vancouver, BC, Canada", month = aug # " 22-26", pages = "579--584", keywords = "genetic algorithms, genetic programming, grammatical evolution", isbn13 = "978-1-7281-0356-3", bibdate = "2019-09-24", URL = "https://ieeexplore.ieee.org/xpl/conhome/8827189/proceeding", DOI = "doi:10.1109/COASE.2019.8842909", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/case/case2019.html#TengDHWTL19", } @InProceedings{Tengg:2007:PDCAT, author = "Allan Tengg and Andreas Klausner and Bernhard Rinner", title = "Task Allocation in Distributed Embedded Systems by Genetic Programming", booktitle = "Eighth International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT '07", year = "2007", month = dec, pages = "26--30", keywords = "genetic algorithms, SBSE, NP-complete combinatorial optimization problem, dataflow graphs, distributed embedded system, task allocation method, combinatorial mathematics, computational complexity, embedded systems", DOI = "doi:10.1109/PDCAT.2007.41", notes = "Not a GP. Also known as \cite{4420137}", } @InProceedings{Tenkanen:2009:ICIS, author = "Atte Tenkanen", title = "Searching for better measures: Generating similarity functions for abstract musical objects", booktitle = "IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009", year = "2009", month = nov, volume = "4", pages = "472--476", keywords = "genetic algorithms, genetic programming, abstract musical objects, distance measures, music information retrieval, pitch-class set theory, similarity functions, information retrieval, music, set theory", DOI = "doi:10.1109/ICICISYS.2009.5357625", abstract = "Several similarity and distance measures have been developed for different purposes and applications in various research fields. For example, scholars have used them to evaluate similarities between tonalities, melodies and rhythms for music information retrieval. In this study, similarity functions are generated automatically. We focus on similarities between the so-called pitch-class sets that belong to the field of pitch-class set theory. Pitch-class set theory offers a well-defined mathematical framework for categorising musical objects and describing their relationships. An output, consisting of similarity values between the abstract pitch-class sets, is produced by means of a generated function. We then compare these values with empirical results by means of statistical methods. We also compare the performance of a generated function with that of REL (David Lewin 1980), perhaps the most successful similarity function in the field. The achieved results are encouraging: some of the generated functions are able to produce stronger correlations with empirical data than REL. As a satisfying by-product, the results hint at the fact that there may be a connection between the perceived closeness of pitch-class sets and Shepard's universal cognitive models. While the present application context is musical set theory, we stress that similar procedures can be applied to other areas of research as well.", notes = "Also known as \cite{5357625}", } @InProceedings{Teodorescu:2005:IEEnsscr, author = "Liliana Teodorescu", title = "High energy physics data analysis with gene expression programming", booktitle = "IEEE Nuclear Science Symposium Conference Record", year = "2005", volume = "1", pages = "143--147", month = "23-29 " # oct, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Gene Expression Programming, data analysis, high energy physics instrumentation computing, evolutionary algorithm, gene expression programming, high energy physics data analysis", DOI = "doi:10.1109/NSSMIC.2005.1596225", abstract = "Gene expression programming is a new evolutionary algorithm that overcomes many limitations of the more established genetic algorithms and genetic programming. Its first application to high energy physics data analysis is presented. The algorithm was successfully used for event selection on samples with both low and high background level. The signal/background classification accuracy was over 90percent in all cases.", notes = "ISSN: 1082-3654 INSPEC Accession Number:8976991", } @Article{Teodorescu:2006:IEEETNS, author = "Liliana Teodorescu", title = "Gene Expression Programming Approach to Event Selection in High Energy Physics", journal = "IEEE Transactions on Nuclear Science", year = "2006", volume = "53", number = "4 (part2)", pages = "2221--2227", month = aug, keywords = "genetic algorithms, genetic programming, Gene Expression Programming, Event selection, evolutionary algorithms", ISSN = "0018-9499", DOI = "doi:10.1109/TNS.2006.878571", size = "7 pages", abstract = "Gene Expression Programming is a new evolutionary algorithm that overcomes many limitations of the more established Genetic Algorithms and Genetic Programming. Its first application to high energy physics data analysis is presented. The algorithm was successfully used for event selection on samples with both low and high background level. It allowed automatic identification of selection rules that can be interpreted as cuts applied on the input variables. The signal/background classification accuracy was over 90percent in all cases.", } @Article{Teodorescu2008409, author = "Liliana Teodorescu and Daniel Sherwood", title = "High Energy Physics event selection with Gene Expression Programming", journal = "Computer Physics Communications", volume = "178", number = "6", pages = "409--419", year = "2008", ISSN = "0010-4655", DOI = "doi:10.1016/j.cpc.2007.10.003", URL = "http://www.sciencedirect.com/science/article/B6TJ5-4R29FNK-1/2/4e3abbd674450ca48d43711fdb1b4f95", keywords = "genetic algorithms, genetic programming, Gene Expression Programming, Evolutionary algorithms, Event selection, Classification, High Energy Physics", abstract = "Gene Expression Programming is a new evolutionary algorithm that overcomes many limitations of the more established Genetic Algorithms and Genetic Programming. Its application to event selection in high energy physics data analysis is presented using as an example application the selection of KS particles produced in e+e- interactions at 10 GeV and reconstructed in the decay mode KS-->[pi]+[pi]-. The algorithm was used for automatic identification of classification criteria for signal/background separation. For the problem studied and for data samples with signal to background ratios between 0.25 and 5, the classification accuracy obtained with the criteria developed by the GEP algorithm was in the range of 92-95%.", } @InProceedings{terashima-marin:1999:ECSSET, author = "Hugo Terashima-Marin and Peter Ross and Manuel Valenzuela-Rendon", title = "Evolution of Constraint Satisfaction Strategies in Examination Timetabling", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "635--642", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-825.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-825.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{teredesai:2001:EuroGP, author = "Ankur Teredesai and J. Park and Venugopal Govindaraju", title = "Active Handwritten Character Recognition using Genetic Programming", booktitle = "Genetic Programming, Proceedings of EuroGP'2001", year = "2001", editor = "Julian F. Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon", volume = "2038", series = "LNCS", pages = "371--379", address = "Lake Como, Italy", publisher_address = "Berlin", month = "18-20 " # apr, organisation = "EvoNET", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, Pattern Recognition, Active Character Recognition, Digit Recognition, Handwritten digit classification: Poster", ISBN = "3-540-41899-7", DOI = "doi:10.1007/3-540-45355-5_30", size = "10 pages", abstract = "This paper is intended to demonstrate the effective use of genetic programming in handwritten character recognition. When the resources used by the classifier increase incrementally and depend on the complexity of classification task, we term such a classifier as active. The design and implementation of active classifiers based on genetic programming principles becomes very simple and efficient. Genetic Programming has helped optimize handwritten character recognition problem in terms of feature set selection. We propose an implementation with dynamism in pre-processing and classification of handwritten digit images. This paradigm will supplement existing methods by providing better performance in terms of accuracy and processing time per image for classification. Different levels of informative detail can be present in image data and our proposed paradigm helps highlight these information rich zones. We compare our performance with passive and active handwritten digit classification schemes that are based on other pattern recognition techniques.", notes = "EuroGP'2001, part of \cite{miller:2001:gp}", } @InProceedings{oai:CiteSeerPSU:553870, title = "On-Line Digit Recognition using Off-Line Features", author = "A. Teredesai and E. Ratzlaff and J. Subrahmonia and V. Govindaraju", year = "2002", booktitle = "Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP'02)", address = "SAC, Ahmedabad, India", keywords = "genetic algorithms, genetic programming", citeseer-isreferencedby = "oai:CiteSeerPSU:92773; oai:CiteSeerPSU:281360; oai:CiteSeerPSU:428408", citeseer-references = "oai:CiteSeerPSU:125873", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:553870", rights = "unrestricted", URL = "http://www.ee.iitb.ac.in/~icvgip/PAPERS/321.pdf", URL = "http://citeseer.ist.psu.edu/553870.html", size = "6 pages", abstract = "This paper describes a classification method for on-line handwritten digits based on off-line image representations. The goal is to use image-based features to improve classifier accuracy for on-line handwritten input. In this paper we describe an initial framework that can be used to achieve this goal. This framework for handwritten digit classification is based on genetic programming (GP). Several issues in preprocessing, transformation of data from on-line to off-line domains and feature extraction are described. Results are reported on the UNIPEN digit dataset.", notes = "http://www.ee.iitb.ac.in/~icvgip/schedule.htm", } @PhdThesis{Teredesai:thesis, author = "Ankur Mukund Teredesai", title = "Active Pattern Recognition Using Genetic Programming", school = "State University of New York at Buffalo", year = "2002", address = "Buffalo, New York, USA", month = sep, keywords = "genetic algorithms, genetic programming", URL = "http://search.proquest.com/docview/305243136", size = "156 pages", abstract = "The need for faster and robust methods for pattern recognition and data mining is ever increasing. Classical machine learning algorithms have always been used in a variety of domains like optical character recognition (OCR), speech recognition and information extraction. Different levels of informative detail can be present in different regions of a pattern image. Classifiers which selectively use features corresponding to discriminating regions in making decisions for particular classes are called active classifiers. Design of active classifiers requires the pattern recognition technique to blend feature discovery within the classifier training phase. This dual task of feature discovery and classifier training can be combined to make the learning algorithm adaptive. This dissertation titled Active Pattern Recognition using Genetic Programming highlights the need for applications to be adaptive. Traditional machine learning algorithms for classification can be made dynamic in terms of feature selection, computational resource and scalability. This dissertation describes how to make one such algorithm (Genetic Programming) active, scalable and recurrent. The proposed extensions are used to develop classifiers for handwritten digit recognition. Genetic programming (GP) is a biologically motivated machine learning technique like genetic algorithms (GA). The essential idea is to represent states (classification models in our case) as chromosomes (encoded as expression trees) and to evolve a population of new offspring trees by selectively pairing parent trees. We first illustrate how GP based active classifiers are developed for handwritten digit recognition. A two-stage classification method motivated by pair-wise confusion between digits is then explored. Inspired by the performance for off-line hand written digit classification, a strategy to classify on-line handwritten digits based on off-line features and GP is developed. We then present a recurrent-GP framework which extends the proposed active pattern recognition paradigm for applications where the length of the feature vector is dynamic. One of the key deterrents in using evolutionary computation techniques for complex real-world applications in pattern recognition and data mining is their non-scalable nature in terms of computational requirements. We have designed a new Efficient-GP technique to address these issues. The dissertation concludes by discussing the role of this paradigm in computational machine learning theory.", notes = "http://www.cedar.buffalo.edu/papers/dissertations.html Doctoral Dissertations supervisor: Venu Govindaraju http://genealogy.math.ndsu.nodak.edu/id.php?id=104577 UMI Microform 3076535", } @InProceedings{teredesai:2004:iiegbcfaprt, title = "Issues in Evolving GP based Classifiers for a Pattern Recognition Task", author = "Ankur Teredesai and Venu Govindaraju", pages = "509--515", booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary Computation", year = "2004", publisher = "IEEE Press", month = "20-23 " # jun, address = "Portland, Oregon", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Real-world applications", URL = "http://www.cs.rit.edu/~amt/pubs/AMTcec04final.pdf", DOI = "doi:10.1109/CEC.2004.1330899", abstract = "This paper discusses issues when evolving Genetic Programming (GP) classifiers for a pattern recognition task such as handwritten digit recognition. Developing elegant solutions for handwritten digit classification is a challenging task. Similarly, design and training of classifiers using genetic programming is a relatively new approach in pattern recognition as compared to other traditional techniques. Several strategies for GP training are outlined and the empirical observations are reported. The issues we faced such as training time, a variety of fitness landscapes and accuracy of results are discussed. Care has been taken to test GP using a variety of parameters and on several handwritten digits datasets.", notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.", } @Article{teredesai_2005_PR, author = "Ankur Teredesai and Venu Govindaraju", title = "GP-based secondary classifiers", journal = "Pattern Recognition", year = "2005", volume = "38", number = "4", pages = "505--512", month = apr, keywords = "genetic algorithms, genetic programming, Handwritten digit recognition, Feature selection, Classification, Secondary classifiers", DOI = "doi:10.1016/j.patcog.2004.06.010", abstract = "Genetic programmingnext term (GP) is used to evolve secondary classifiers for disambiguating between pairs of handwritten digit images. The inherent property of feature selection accorded by GP is exploited to make sharper decision between conflicting classes. Classification can be done in several steps with an available feature set and a mixture of strategies. A two-step classification strategy is presented in this paper. After the first step of the classification using the full feature set, the high confidence recognition result will lead to an end of the recognition process. Otherwise a secondary classifier designed using a sub-set of the original feature set and the information available from the earlier classification step will help classify the input further. The feature selection mechanism employed by GP selects important features that provide maximum separability between classes under consideration. In this way, a sharper decision on fewer classes is obtained at the secondary classification stage. The full feature set is still available in both stages of classification to retain complete information. An intuitive motivation and detailed analysis using confusion matrices between digit classes is presented to describe how this strategy leads to improved recognition performance. In comparison with the existing methods, our method is aimed for increasing recognition accuracy and reliability. Results are reported for the BHA test-set and the NIST test-set of handwritten digits.", } @InProceedings{terragni-fse-2020, author = "Valerio Terragni and Gunel Jahangirova and Paolo Tonella and Mauro Pezze", title = "Evolutionary Improvement of Assertion Oracles", booktitle = "Proceedings of the 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2020", year = "2020", editor = "Myra Cohen and Thomas Zimmermann", pages = "1178--1189", address = "Sacramento, California, USA", month = "8--13 " # nov, keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, multi-objective genetic programming, co-evolution, Daikon", isbn13 = "9781450370431", URL = "https://www.pre-crime.eu/techreps/TR-Precrime-2020-02.pdf", URL = "https://2020.esec-fse.org/details/fse-2020-papers/184/Evolutionary-Improvement-of-Assertion-Oracles", DOI = "doi:10.1145/3368089.3409758", video_url = "https://www.youtube.com/watch?v=-fZVT3FoFcE", code_url = "https://doi.org/10.5281/zenodo.3877079", size = "12 pages", abstract = "Assertion oracles are executable Boolean expressions placed inside the program that should pass (return true) for all correct executions and fail (return false) for all incorrect executions. Because designing perfect assertion oracles is difficult, assertions often fail to distinguish between correct and incorrect executions. In other words, they are prone to false positives and false negatives. We propose GAssert (Genetic ASSERTion improvement), the first technique to automatically improve assertion oracles. Given an assertion oracle and evidence of false positives and false negatives, GAssert implements a novel co-evolutionary algorithm that explores the space of possible assertions to identify one with fewer false positives and false negatives. Our empirical evaluation on 34 Java methods from 7 different Java code bases shows that GAssert effectively improves assertion oracles. GAssert outperforms two baselines (random and invariant-based oracle improvement), and is comparable with and in some cases even outperformed human-improved assertions.", notes = "Java, EvoSuite, Major, Oasis ISSTA 2016. Rooted Boolean tree <= 50 nodes. Fitness FP ~~ FN << size (minimise). coevolution of 2 populations (FP dominant, FN dominant), exchange between them. Binary tournament selection, Best-Match Selection (not covered in 1st parent). Crossover and 2 types of mutation. Seed populations with Daikon or assert(true)?? Randoop test generation, PIT mutation testing. Comparison with random, human amazon mechanical turk. Fri 13 Nov 2020 08:35 - 08:36 at Virtual room 1 - Testing 4. TR-Precrime-2020-02.pdf(25 pages) not identical to FSE 2020 paper USI Lugano, Switzerland", } @InProceedings{terragni-fse-2020:HOP, author = "Valerio Terragni and Gunel Jahangirova and Mauro Pezze and Paolo Tonella", title = "Improving Assertion Oracles with Evolutionary Computation", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion", series = "GECCO '21", year = "2021", editor = "Carola Doerr", month = "10-14 " # jul, pages = "45--46", organisation = "SIGEVO", address = "Internet", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, Evolutionary Computation, Program Assertions, Test Generation, Mutation Analysis, Co-evolutionary Algorithms, Daikon, coevolution, Randoop", isbn13 = "978-1-4503-8351-6", DOI = "doi:10.1145/3449726.3462722", size = "2 pages", abstract = "Assertion oracles are executable Boolean expressions placed inside a software program that verify the correctness of test executions. A perfect assertion oracle passes (returns true) for all correct executions and fails (returns false) for all incorrect executions. Because designing perfect assertion oracles is difficult, assertions often fail to distinguish between correct and incorrect executions. In other words, they are prone to false positives and false negatives. GAssert is the first technique to automatically improve assertion oracles by reducing false positives and false negatives. Given an assertion oracle and a set of correct and incorrect program states, GAssert employs a novel co-evolutionary algorithm that explores the space of possible assertions to identify one with fewer false positives and false negatives. Our evaluation on 34 Java methods shows that GAssert effectively improves assertion oracles.", notes = "Short version of \cite{terragni-fse-2020} University of Auckland", } @InProceedings{EPTCS26.13, author = "German Terrazas and Dario Landa-Silva and Natalio Krasnogor", title = "Towards the Design of Heuristics by Means of Self-Assembly", booktitle = "Proceedings Sixth Workshop on Developments in Computational Models: Causality, Computation, and Physics, DCM 2010", year = "2010", editor = "S. Barry Cooper and Prakash Panangaden and Elham Kashefi", volume = "26", series = "EPTCS 26", pages = "135--146", address = "Edinburgh, UK", month = "9-10 " # jul, publisher = "Open Publishing Association", keywords = "genetic algorithms", ISSN = "2075-2180", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.607.3330", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.607.3330", URL = "http://www.cs.nott.ac.uk/~jds/research/files/dls_eptcs2010.pdf", URL = "http://eptcs.web.cse.unsw.edu.au/content.cgi?DCM2010", URL = "http://eptcs.web.cse.unsw.edu.au/paper.cgi?DCM2010.13", URL = "https://arxiv.org/abs/1006.1681v1", DOI = "doi:10.4204/EPTCS.26", URL = "http://eptcs.web.cse.unsw.edu.au/paper.cgi?DCM2010.13.pdf", size = "12 pages", abstract = "The current investigations on hyper-heuristics design have sprung up in two different flavours: heuristics that choose heuristics and heuristics that generate heuristics. In the latter, the goal is to develop a problem-domain independent strategy to automatically generate a good performing heuristic for the problem at hand. This can be done, for example, by automatically selecting and combining different low-level heuristics into a problem specific and effective strategy. Hyper-heuristics raise the level of generality on automated problem solving by attempting to select and/or generate tailored heuristics for the problem at hand. Some approaches like genetic programming have been proposed for this. In this paper, we explore an elegant nature-inspired alternative based on self-assembly construction processes, in which structures emerge out of local interactions between autonomous components. This idea arises from previous works in which computational models of self-assembly were subject to evolutionary design in order to perform the automatic construction of user-defined structures. Then, the aim of this paper is to present a novel methodology for the automated design of heuristics by means of self-assembly.", notes = "Not GP? TSP Electronic Proceedings in Theoretical Computer Science", } @InProceedings{terrio:dcfr, author = "M. Terrio and M. I. Heywood", title = "Directing Crossover for Reduction of Bloat in GP", booktitle = "IEEE CCECE 2003: IEEE Canadian Conference on Electrical and Computer Engineering", year = "2002", editor = "W. Kinsner and A. Seback and K. Ferens", pages = "1111--1115", month = "12-15 " # may, organisation = "IEEE Canada", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Code Bloat", ISBN = "0-7803-7515-7", URL = "http://users.cs.dal.ca/~mheywood/X-files/Publications/CCECE-272.pdf", URL = "http://citeseer.ist.psu.edu/758918.html", abstract = "A method is proposed to reduce the amount of inviable code (or bloat) produced in individuals while searching for a parsimonious solution under tree structured genetic programming. Known as directed crossover, this process involves the identification of highly fit nodes to use as crossover points during operator application. Three test problems, including medical data classification, are used to assess the performance of directed crossover when applied at various thresholds. Results, collected over 1260 independent runs, identify conditions under which directed crossover reduces code bloat.", } @InProceedings{Terrio:ONC:gecco2004, author = "M. David Terrio and Malcolm I. Heywood", title = "On Naive Crossover Biases with Reproduction for Simple Solutions to Classification Problems", booktitle = "Genetic and Evolutionary Computation -- GECCO-2004, Part II", year = "2004", editor = "Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell", series = "Lecture Notes in Computer Science", pages = "678--689", address = "Seattle, WA, USA", publisher_address = "Heidelberg", month = "26-30 " # jun, organisation = "ISGEC", publisher = "Springer-Verlag", volume = "3103", ISBN = "3-540-22343-6", ISSN = "0302-9743", DOI = "doi:10.1007/b98645", URL = "http://users.cs.dal.ca/~mheywood/X-files/Publications/dave-GECCO04.pdf", size = "12", keywords = "genetic algorithms, genetic programming", abstract = "A series of simple biases to the selection of crossover points in tree structured genetic programming are investigated with respect to the provision of parsimonious solutions. Such a set of biases has a minimal computational overhead as they are based on information already used to estimate the fitness of individuals. Reductions to code bloat are demonstrated for the real world classification problems investigated. Moreover, bloated solutions provided by a uniform crossover operator often appear to defeat the application of MAPLE simplification heuristics.", notes = "GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)", } @InProceedings{terry:evows06, author = "Michael A. Terry and Jonathan Marcus and Matthew Farrell and Varun Aggarwal and Una-May O'Reilly", title = "{GRACE:} Generative Robust Analog Circuit Exploration", booktitle = "Applications of Evolutionary Computing, EvoWorkshops2006: {EvoBIO}, {EvoCOMNET}, {EvoHOT}, {EvoIASP}, {EvoInteraction}, {EvoMUSART}, {EvoSTOC}", year = "2006", month = "10-12 " # apr, editor = "Franz Rothlauf and Jurgen Branke and Stefano Cagnoni and Ernesto Costa and Carlos Cotta and Rolf Drechsler and Evelyne Lutton and Penousal Machado and Jason H. Moore and Juan Romero and George D. Smith and Giovanni Squillero and Hideyuki Takagi", series = "LNCS", volume = "3907", publisher = "Springer Verlag", address = "Budapest", publisher_address = "Berlin", pages = "332--343", keywords = "genetic algorithms, genetic programming, ehw", ISBN = "3-540-33237-5", URL = "http://people.csail.mit.edu/unamay/publications-dir/grace-evohot.pdf", DOI = "doi:10.1007/11732242_30", abstract = "We motivate and describe an analog evolvable hardware design platform named GRACE (i.e. Generative Robust Analogue Circuit Exploration). GRACE combines coarse-grained, topological circuit search with intrinsic testing on a Commercial Off-The-Shelf (COTS) field programmable device, the AN221E04. It is suited for adaptive, fault tolerant system design as well as CAD flow applications.", notes = "part of \cite{evows06}", } @Article{OzlemTerzi:2005:JAS, author = "Ozlem Terzi and M. Erol Keskin", title = "Evaporation Estimation using Gene Expression Programming", journal = "Journal of Applied Sciences", year = "2005", volume = "5", number = "3", pages = "508--512", keywords = "genetic algorithms, genetic programming, gene expression programming, Penmann Method, Lake Egirdir", ISSN = "1812-5654", URL = "http://www.ansinet.org/fulltext/jas/jas53508-512.pdf", notes = "Asian Network for Scientific Information Faculty of Technical Education, Suleyman Demirel University, Isparta 32260, Turkey", } @Article{journals/nca/Terzi13, author = "Ozlem Terzi", title = "Daily pan evaporation estimation using gene expression programming and adaptive neural-based fuzzy inference system", journal = "Neural Computing and Applications", year = "2013", number = "3-4", volume = "23", pages = "1035--1044", keywords = "genetic algorithms, genetic programming, gene expression programming, GEP", bibdate = "2013-09-24", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/nca/nca23.html#Terzi13", URL = "http://dx.doi.org/10.1007/s00521-012-1027-x", } @Article{journals/jifs/Terzi14, author = "Ozlem Terzi", title = "A genetic programming approach to river flow modeling", journal = "Journal of Intelligent and Fuzzy Systems", year = "2014", number = "5", volume = "27", pages = "2211--2219", keywords = "genetic algorithms, genetic programming", bibdate = "2014-11-03", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/jifs/jifs27.html#Terzi14", URL = "http://dx.doi.org/10.3233/IFS-141185", } @Article{Terzi:2005:JAS, author = "Serdal Terzi", title = "Modeling the Deflection Basin of Flexible Highway Pavements by Gene Expression Programming", journal = "Journal of Applied Sciences", year = "2005", volume = "5", number = "2", pages = "309--314", keywords = "genetic algorithms, genetic programming, gene expression programming, Flexible highway pavements, nondestructive testing", ISSN = "1812-5654", URL = "http://www.ansinet.org/fulltext/jas/jas52309-314.pdf", abstract = "Gene Expression Programming (GEP) is used in modelling the deflection basins measured on the surface of the flexible pavements. Back calculation of the pavement layer moduli are well-accepted procedures for the evaluation of the structural capacity of pavements. The ultimate aim of the backcalculation process from Nondestructive Testing (NDT) results is to estimate the pavement material properties. Using back calculation analysis, in situ material properties can be back calculated from the measured field data through appropriate analysis techniques. In order to back calculate reliable moduli, deflection basin must be realistically modelled. In this study, GEP was used to model the deflection basin characteristics. Experimental deflection data groups from NDT are used to show the capability of the GEP approach in modelling the deflection bowl. This approach can be easily and realistically performed to solve the problems which do not have a formulation or function about the solution.", notes = "Asian Network for Scientific Information Faculty of Technical Education, Suleyman Demirel University, Isparta 32260, Turkey", } @Article{TESHNEHDEL:2020:SETA, author = "Saeid Teshnehdel and Seyedasghar Mirnezami and Aniseh Saber and Ali Pourzangbar and Abdul Ghani Olabi", title = "Data-driven and numerical approaches to predict thermal comfort in traditional courtyards", journal = "Sustainable Energy Technologies and Assessments", volume = "37", pages = "100569", year = "2020", ISSN = "2213-1388", DOI = "doi:10.1016/j.seta.2019.100569", URL = "http://www.sciencedirect.com/science/article/pii/S2213138819305697", keywords = "genetic algorithms, genetic programming, Thermal comfort, PET, PMV, Traditional courtyards", abstract = "This paper studies the climactic performance of the 10 traditional courtyards located in warm-dry climates of Kashan and cold climates of Ardabil based on shading and sunlit coverage. The modelling process comprises two sections: first, a number of numerical simulations are run using Envi-met software to detail the shading and sunlit percentage, PET and PMV in the samples of interest. These numerical models are validated on the basis of the results made available by field observations. Such validation revealed an excellent agreement between the numerical solution and the benchmarking data. Afterwards, GP is used to evolve some equations for predicting PET and PMV using the data points derived from the numerical simulations. The results suggest that regarding the thermal indices (PET and PMV), there is a high correlation between the shadow and sunlit effects and thermal comfort in Kashan's houses in comparison with Ardabil houses. However, in tropical regions (Kashan), summer shading and winter sunlit have a greater effect on thermal comfort and temperature adjustment than cold regions. Moreover, the statistical criterion, as well as reliability analysis and contour plots show that the GP developed formulas can be exploited in predicting the PET and PMV based on the shading percentage", } @InProceedings{tettamanzi:1996:GP-f, author = "Andrea G. B. Tettamanzi", title = "Genetic Programming without Fitness", booktitle = "Late Breaking Papers at the Genetic Programming 1996 Conference Stanford University July 28-31, 1996", year = "1996", editor = "John R. Koza", pages = "193--195", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California 94305-3079, USA", month = "28--31 " # jul, publisher = "Stanford Bookstore", ISBN = "0-18-201031-7", keywords = "genetic algorithms, genetic programming", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/14001/http:zSzzSzmago.crema.unimi.itzSzGP96.pdf/tettamanzi96genetic.pdf", URL = "http://citeseer.ist.psu.edu/296420.html", abstract = "This paper provides a short, informal illustration of a selection scheme based on the key idea of competition, particularly suited for genetic programming, which provides a way to do without the explicit definition of a fitness function. In many tasks, competition between two individuals on one problem instance chosen according to some probability can be a valid alternative to defining an appropriate fitness function that includes a priori knowledge of the problem, which requires...", notes = "GP-96LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670", } @InProceedings{Tetteh:2021:EuroGP, author = "Michael Tetteh and Douglas Mota Dias and Conor Ryan", title = "Evolution of Complex Combinational Logic Circuits Using Grammatical Evolution with {SystemVerilog}", booktitle = "EuroGP 2021: Proceedings of the 24th European Conference on Genetic Programming", year = "2021", editor = "Ting Hu and Nuno Lourenco and Eric Medvet", series = "LNCS", volume = "12691", publisher = "Springer Verlag", address = "Virtual Event", pages = "146--161", month = "7-9 " # apr, organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Digital circuit design, Evolvable Hardware, EHW, Hardware Description Languages, HDLs, Verilog, System Verilog, Yosys", isbn13 = "978-3-030-72811-3", DOI = "doi:10.1007/978-3-030-72812-0_10", abstract = "Scalability problems have hindered the progress of Evolvable Hardware in tackling complex circuits. The two key issues are the amount of testing (for example, a 64-bit x 64-bit add-shift multiplier problem has 264+64 test cases) and low level that hardware works at: a circuit to implement 64-bit by 64-bit add-shift multiplier would require approximately 33234 gates when synthesized using the powerful Yosys Open SYnthesis Suite tool. We use Grammatical Evolution and SystemVerilog, a Hardware Description Language (HDL), to evolve fully functional parameterised adder, multiplier and selective parity circuits with default input bit-width sizes of 64-bit + 64-bit, 64-bit by 64-bit and 128-bit respectively...", notes = "Lero BDS. Corner cases. Lexicase selection. Parameterised for loop in BNF grammar. Always block in BNF grammar. http://www.evostar.org/2021/eurogp/ Part of \cite{Hu:2021:GP} EuroGP'2021 held in conjunction with EvoCOP2021, EvoMusArt2021 and EvoApplications2021", } @Article{tetteh:2023:Algorithms, author = "Michael Tetteh and Allan {de Lima} and Jack McEllin and Aidan Murphy and Douglas Mota Dias and Conor Ryan", title = "Evolving {Multi-Output} Digital Circuits Using {Multi-Genome} Grammatical Evolution", journal = "Algorithms", year = "2023", volume = "16", number = "8", pages = "Article No. 365", keywords = "genetic algorithms, genetic programming, grammatical evolution", ISSN = "1999-4893", URL = "https://www.mdpi.com/1999-4893/16/8/365", DOI = "doi:10.3390/a16080365", abstract = "Grammatical Evolution is a Genetic Programming variant which evolves problems in any arbitrary language that is BNF compliant. Since its inception, Grammatical Evolution has been used to solve real-world problems in different domains such as bio-informatics, architecture design, financial modelling, music, software testing, game artificial intelligence and parallel programming. Multi-output problems deal with predicting numerous output variables simultaneously, a notoriously difficult problem. We present a Multi-Genome Grammatical Evolution better suited for tackling multi-output problems, specifically digital circuits. The Multi-Genome consists of multiple genomes, each evolving a solution to a single unique output variable. Each genome is mapped to create its executable object. The mapping mechanism, genetic, selection, and replacement operators have been adapted to make them well-suited for the Multi-Genome representation and the implementation of a new wrapping operator. Additionally, custom grammar syntax rules and a cyclic dependency-checking algorithm have been presented to facilitate the evolution of inter-output dependencies which may exist in multi-output problems. Multi-Genome Grammatical Evolution is tested on combinational digital circuit benchmark problems. Results show Multi-Genome Grammatical Evolution performs significantly better than standard Grammatical Evolution on these benchmark problems.", notes = "also known as \cite{a16080365}", } @InCollection{Teunen:1997:apgoGP, author = "Remco Teunen", title = "Automatic Pronunciation Generation from Orthography using Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1997", publisher = "Stanford Bookstore", year = "1997", editor = "John R. Koza", pages = "207--215", address = "Stanford, California, 94305-3079 USA", month = "17 " # mar, keywords = "genetic algorithms, genetic programming", ISBN = "0-18-205981-2", abstract = "phonetic translation of a given word", notes = "part of \cite{koza:1997:GAGPs}", } @Misc{teuscher:1999:RPSFATRE, author = "Christof Teuscher", title = "Romero's Pilgrimage to Santa Fe: A Tale of Robot Evolution", booktitle = "GECCO-99 Student Workshop", year = "1999", editor = "Una-May O'Reilly", pages = "409--410", address = "Orlando, Florida, USA", month = "13 " # jul, keywords = "genetic algorithms, evolutionary programming, robotics", URL = "http://www.teuschers.ch/christof/romero.html", notes = "GECCO-99WKS Part of wu:1999:GECCOWKS", } @Misc{oai:eprints.pascal-network.org:1586, author = "Olivier Teytaud and Marc Schoenauer and Sylvain Gelly and Nicolas Bredeche", title = "A statistical learning approach to bloat and universal consistency in genetic programming", year = "2005", abstract = "Universal Consistency, the convergence to the minimum possible error rate in learning through genetic programming (GP), and Code bloat, the excessive increase of code size, are important issues in GP. This paper proposes a theoretical analysis of universal consistency and code bloat in the framework of symbolic regression in GP, from the viewpoint of Statistical Learning Theory, a well grounded mathematical toolbox for Machine Learning. Two kinds of bloat must be distinguished in that context, depending whether the target function has finite description length or not. Then, the Vapnik-Chervonenkis dimension of programs is computed, and we prove that a parsimonious fitness ensures Universal Consistency (i.e. the fact that the solution minimising the empirical error does converge to the best possible error when the number of examples goes to infinity). However, it is proved that the standard method consisting in choosing a maximal program size depending on the number of examples might still result in programs of infinitely increasing size with their accuracy; a fitness biased by parsimony pressure is proposed. This fitness avoids unnecessary bloat while nevertheless preserving the Universal Consistency.", bibsource = "OAI-PMH server at eprints.pascal-network.org", oai = "oai:eprints.pascal-network.org:1586", URL = "http://hal.archives-ouvertes.fr/docs/00/04/19/72/PDF/antibloatGecco2005_long_version.pdf", URL = "http://eprints.pascal-network.org/archive/00001586/", URL = "http://eprints.pascal-network.org/archive/00001586/01/eabloat.pdf", keywords = "genetic algorithms, genetic programming, VC, Learning/Statistics \& Optimisation", size = "8 pages", notes = "pascal-network.org URLs appear broken June 2015. See also GECCO 2005 \cite{1068309}. Also known as \cite{oai:hal.ccsd.cnrs.fr:inria-00000549_v1}", } @InProceedings{Teytaud:2006:CEC, author = "Olivier Teytaud", title = "Why Simulation-Based Approaches with Combined Fitness are a Good Approach for Mining Spaces of {Turing}-equivalent Functions", booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary Computation", year = "2006", editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas", pages = "987--994", address = "Vancouver", month = "16-21 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9487-9", DOI = "doi:10.1109/CEC.2006.1688320", size = "8 pages", abstract = "We show negative results about the automatic generation of programs within bounded-time. Combining recursion theory and statistics, we contrast these negative results with positive computability results for iterative approaches like genetic programming, provided that the fitness combines e.g. fastness and size. We then show that simulation-based approaches (approaches evaluating only by simulation the quality of programs) like GP are not too far from the minimal time required for evaluating these combined fitnesses.", notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D", } @InProceedings{conf/mcu/Teytaud07, author = "Olivier Teytaud", title = "Slightly Beyond {Turing}'s Computability for Studying Genetic Programming", booktitle = "Proceedings of the 5th International Conference on Machines, Computations, and Universality, MCU 2007", year = "2007", editor = "J{\'e}r{\^o}me Olivier Durand-Lose and Maurice Margenstern", volume = "4664", series = "Lecture Notes in Computer Science", pages = "279--290", address = "Orl{\'e}ans, France", month = sep # " 10-13", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-74592-1", DOI = "doi:10.1007/978-3-540-74593-8_24", size = "12 pages", abstract = "Inspired by genetic programming (GP), we study iterative algorithms for non-computable tasks and compare them to naive models. This framework justifies many practical standard tricks from GP and also provides complexity lower-bounds which justify the computational cost of GP thanks to the use of Kolmogorov's complexity in bounded time.", notes = "Symbolic regression", bibdate = "2007-08-28", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/mcu/mcu2007.html#Teytaud07", } @InProceedings{tezuka:1999:A, author = "Masaru Tezuka and Masahiro Hiji", title = "A genetic algorithm approach to improve production schedule", booktitle = "Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference", year = "1999", editor = "Scott Brave and Annie S. Wu", pages = "254--259", address = "Orlando, Florida, USA", month = "13 " # jul, notes = "GECCO-99LB", } @InProceedings{Thadani:2006:ADCOM, title = "Evolutionary Selection of Kernels in Support Vector Machines", author = "Kanchan Thadani and Ashutosh and V. K. Jayaraman and V. Sundararajan", booktitle = "International Conference on Advanced Computing and Communications, ADCOM 2006", year = "2006", month = dec, pages = "19--24", keywords = "genetic algorithms, genetic programming, gene expression programming, bank transaction data set, cancer data, evolutionary algorithm, kernel function, machine learning algorithm, pattern classification, support vector machine, bank data processing, cancer, evolutionary computation, genetics, learning (artificial intelligence), medical computing, pattern classification, support vector machines", URL = "http://ieeexplore.ieee.org/iel5/4289832/4289833/04289849.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.138.5378", URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4289849&userType=&tag=1", DOI = "doi:10.1109/ADCOM.2006.4289849", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", contributor = "CiteSeerX", language = "en", oai = "oai:CiteSeerXPSU:10.1.1.138.5378", abstract = "A machine learning algorithm using evolutionary algorithms and Support Vector Machines is presented. The kernel function of support vector machines are evolved using recently introduced Gene Expression Programming algorithms. This technique trains a support vector machine with the kernel function most suitable for the training data set rather than pre-specifying the kernel function. The fitness of the kernel is measured by calculating cross validation accuracy. SVM trained with the fittest kernels is then used to classify previously unseen data. The algorithm is elucidated using preliminary case studies for classification of cancer data and bank transaction data set. It is shown that the Evolutionary Support Vector Machine has good generalization properties when compared with Support Vector Machines using standard (polynomial and radial basis) kernel functions.", notes = "Kanchan Thadani is with the Scientific and Engineering Computing Group, Centre For Development of Advanced Computing, Pune University campus, Pune-411007, India Ashutosh is with the Persistent System Pvt.Ltd., Persistent Towers, Erandwane, Pune, India V.K. Jayaraman is with the Chemical Engineering Division, National Chemical Laboratory, Pashan, Pune-411008, India V. Sundararajan is with the Scientific and Engineering Computing Group, Centre For Development of Advanced Computing, Pune University campus, Pune-411007, India", } @InProceedings{Thawonmas:2011:CIG, author = "Ruck Thawonmas and Yoshinori Tani", title = "Frame Selection Using Iterative Grammatical Evolution for Automatic Comic Generation from Game Logs", booktitle = "Proceedings of the 2011 IEEE Conference on Computational Intelligence and Games", year = "2011", pages = "31--38", address = "Seoul, South Korea", month = "31 " # aug # " - 3 " # sep, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", URL = "http://cilab.sejong.ac.kr/cig2011/proceedings/CIG2011/papers/paper6.pdf", size = "8 pages", notes = "like newspaper comic strips", } @InCollection{thedens:1994:ddbd, author = "Daniel R. Thedens", title = "Detector Design by Genetic Programming for Automated Border Definition in Cardiac Magnetic Resonance Images", booktitle = "Genetic Algorithms at Stanford 1994", year = "1994", editor = "John R. Koza", pages = "170--179", address = "Stanford, California, 94305-3079 USA", month = dec, organisation = "Stanford University", publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-187263-3", notes = "This volume contains 20 papers written and submitted by students describing their term projects for the course {"}Genetic Algorithms and Genetic Programming{"} (Computer Science 426) at Stanford University offered during the fall quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs426.html", } @InProceedings{theiler99evolving, author = "James P. Theiler and Neal R. Harvey and Steven P. Brumby and John J. Szymanski and Steve Alferink and Simon J. Perkins and Reid B. Porter and Jeffrey J. Bloch", title = "Evolving Retrieval Algorithms with a Genetic Programming Scheme", booktitle = "Proceedings of SPIE 3753 Imaging Spectrometry V", year = "1999", editor = "Michael R. Descour and Sylvia S. Shen", pages = "416--425", organisation = "SPIE--The International Society for Optical Engineering", keywords = "genetic algorithms, genetic programming", URL = "http://public.lanl.gov/jt/Papers/ga-spie.ps", URL = "http://citeseer.ist.psu.edu/theiler99evolving.html", DOI = "doi:10.1117/12.366303", size = "10 pages", abstract = "The retrieval of scene properties (surface temperature, material type, vegetation health, etc.) from remotely sensed data is the ultimate goal of many earth observing satellites. The algorithms that have been developed for these retrievals are informed by physical models of how the raw data were generated. This includes models of radiation as emitted and/or rejected by the scene, propagated through the atmosphere, collected by the optics, detected by the sensor, and digitised by the electronics. To some extent, the retrieval is the inverse of this ''forward'' modelling problem. But in contrast to this forward modeling, the practical task of making inferences about the original scene usually requires some ad hoc assumptions, good physical intuition, and a healthy dose of trial and error. The standard MTI data processing pipeline will employ algorithms developed with this traditional approach. But we will discuss some preliminary research on the use of a genetic programming scheme to ''evolve'' retrieval algorithms. Such a scheme cannot compete with the physical intuition of a remote sensing scientist, but it may be able to automate some of the trial and error. In this scenario, a training set is used, which consists of multispectral image data and the associated ''ground truth;'' that is, a registered map of the desired retrieval quantity. The genetic programming scheme attempts to combine a core set of image processing primitives to produce an IDL (Interactive Data Language) program which estimates this retrieval quantity from the raw data.", bibsource = "OAI-PMH server at prodweb.osti.gov", oai = "oai:osti.gov:772989", subject = "59 BASIC BIOLOGICAL SCIENCES; ALGORITHMS; DATA PROCESSING; GENETICS; IMAGE PROCESSING; OPTICS; PLANTS; PROGRAMMING; RADIATIONS; REMOTE SENSING; SATELLITES", URL = "http://www.osti.gov/servlets/purl/772989-pvDHZz/native/", notes = "3753-47 Published: 10/1999 Order this volume http://bookstore.spie.org/cgi-bin/order.acgi?t=a&v=3753&of=1 also known as \cite{oai:osti.gov:772989}", } @InProceedings{Theodoridis:2008:icra, author = "Theodoros Theodoridis and Alexandros Agapitos and Huosheng Hu and Simon M. Lucas", title = "Ubiquitous Robotics in Physical Human Action Recognition: A Comparison Between Dynamic ANNs and GP", booktitle = "2008 IEEE International Conference on Robotics and Automation", year = "2008", pages = "3064--3069", month = may, keywords = "genetic algorithms, genetic programming, dynamic artificial neural network, perception-to-action architecture, physical human action recognition, ubiquitous 3D sensory tracker system, ubiquitous mobile robot, mobile robots, neural nets, object recognition, time series, ubiquitous computing", ISSN = "1050-4729", DOI = "doi:10.1109/ROBOT.2008.4543676", abstract = "Two different classifier representations based on dynamic Artificial Neural Networks (ANNs) and Genetic Programming (GP) are being compared on a human action recognition task by an ubiquitous mobile robot. The classification methodologies used, process time series generated by an indoor ubiquitous 3D tracker which generates spatial points based on 23 reflectable markers attached on a human body. This investigation focuses mainly on class discrimination of normal and aggressive action recognition performed by an architecture which implements an interconnection between an ubiquitous 3D sensory tracker system and a mobile robot to perceive, process, and classify physical human actions. The 3D tracker and the robot are used as a perception-to-action architecture to process physical activities generated by human subjects. Both classifiers process the activity time series to eventually generate surveillance assessment reports by generating evaluation statistics indicating the classification accuracy of the actions recognised.", notes = "http://www.icra2008.org/ Also known as \cite{4543676}", } @InProceedings{Theodoridis:2009:ICIA, author = "Theodoros Theodoridis and Alexandros Agapitos and Huosheng Hu and Simon M. Lucas", title = "Mechanical Attributes for Modeling and Classification of Physical Activities", booktitle = "2009 IEEE International Conference on Information and Automation (ICIA-2009)", year = "2009", pages = "528--533", address = "Zhuhai, Macau, China", month = jun # " 22-24", publisher = "IEEE", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-3608-8", DOI = "doi:10.1109/ICINFA.2009.5204980", abstract = "A rigorous investigation on the synergy of mechanical attributes to engineer tactics for measuring human activity in terms of forces, as well as to provide independency and discrimination clarity of action recognition using linear and non-linear classification methodologies from data mining and evolutionary computation, are the main objectives where this paper focuses on. Mechanical analysis is employed to mathematically describe and model human movement by using a number of mechanical features inspired mainly from Kinematics Dynamics. Such features employ a twofold role on the descriptive analysis of an activity, initially to provide statistics regarding inertial expressions, probable hazard levels, body-status of energy loss, and finally to exploit these attributes by decomposing the 3D time series data for pattern recognition in terms of actions and behaviours. The performance statistics are being used by a mobile robot for remote surveillance within a smart environment.", notes = "INSPEC Accession Number: 10837574", } @InProceedings{Theodoridis:2010:ICIA, author = "Theodoros Theodoridis and Alexandros Agapitos and Huosheng Hu", title = "A QA-TSK fuzzy model vs evolutionary decision trees towards nonlinear action pattern recognition", booktitle = "Proceedings of the 2010 IEEE International Conference on Information and Automation", year = "2010", pages = "1813--1818", address = "Harbin, China", month = jun # " 20-23", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, QA-TSK fuzzy model, activity recognition statistics, dimensionality reduction preprocessing, evolutionary decision trees, fuzzy quadruple TSK model, nonlinear action pattern recognition, statistical features, ubiquitous 3D marker based tracker, decision trees, fuzzy set theory, pattern recognition, statistical analysis", DOI = "doi:10.1109/ICINFA.2010.5512225", abstract = "A comparison among three linear methodologies, a novel auto-adjusted fuzzy quadruple TSK model (QA-TSK) and two evolutionary decision tree representations, is presented. The three architectures make use of a vast number of primitives to reconfigure and evolve their internal structures of the classifier models so that to discriminate among spatial physical activities. Such primitives like statistical features employ a twofold role, initially to model the data set in a dimensionality reduction preprocessing and finally to exploit these attributes to recognise pattern actions. The performance statistics are used for remote surveillance within a smart environment incorporating an ubiquitous 3D marker based tracker which acquires the time series data streams, whereas activity recognition statistics are being generated through an off-line process.", notes = "Also known as \cite{5512225}", } @InProceedings{Theodoridis:2010:IROS, author = "Theodoros Theodoridis and Panos Theodorakopoulos and Huosheng Hu", title = "Evolving aggressive biomechanical models with genetic programming", booktitle = "2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)", year = "2010", month = "18-22 " # oct, address = "Taipei", pages = "2495--2500", abstract = "A repertory of nine biomechanical aggressive activities is investigated in this paper, in our effort to instigate a new paradigm at aggregating descriptive mathematical models with evolutionary, symbolic program representations. Such representations are based on shared biomechanical primitives inspired from kinematics, dynamics, and energetics. Our intention is twofold, initially to study the nature of aggressive biomechanical models and then to classify their physical activities by evolving expression-trees with biomechanical synthesis. The methodology targets on evolving expression programs using the Gaussian Ground-plan Projection Area model, to discriminate among three aggressive behaviours and recognise the individual actions involved. For the n-class problem, three programs have been evolved, each for an aggressive behaviour such as the arm-Launch, the legLaunch, and the bodyLaunch behaviour, so that to be able to examine separately the evolvable characteristics induced. The proposed approach has evidently shown strong classification and discrimination performances.", keywords = "genetic algorithms, genetic programming, Gaussian ground-plan projection area, aggressive biomechanical models, dynamics, energetics, expression-trees, individual actions, kinematics, n-class problem, physical activities, symbolic program representations, biomechanics, pattern classification", DOI = "doi:10.1109/IROS.2010.5650485", ISSN = "2153-0858", notes = "Also known as \cite{5650485}", } @InProceedings{Theodoridis:2011:GECCO, author = "Theodoros Theodoridis and Alexandros Agapitos and Huosheng Hu", title = "A gaussian groundplan projection area model for evolving probabilistic classifiers", booktitle = "GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0557-0", pages = "1339--1346", keywords = "genetic algorithms, genetic programming", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001576.2001757", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "In this paper, an investigation of evolvable probabilistic classifiers is conducted, along with a thorough comparison between a classical Gaussian distance model, and the induction of Gaussian-to-circle projection model. The newly introduced model refers to a distance fitness measure, based on the projection of Gaussian distributions with geometric circles. The projection architecture aims to model and classify physical aggressive behaviours, by using biomechanical primitives. The primitives are being used to model the dynamics of the aggressive activities, by evolving biomechanical classifiers, which can discriminate between three behaviours and six actions. Both evolutionary models have shown strong discrimination performances on recognising the individual actions of each behaviour. From the comparison, the proposed model outperformed the classical one with three ensemble programs.", notes = "Also known as \cite{2001757} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)", } @Article{Theodoridis:2013:ieeeHMS, author = "Theodoros Theodoridis and Huosheng Hu", title = "Modeling Aggressive Behaviors With Evolutionary Taxonomers", journal = "IEEE Transactions on Human-Machine Systems", year = "2013", volume = "43", number = "3", pages = "302--313", month = may, keywords = "genetic algorithms, genetic programming, Action recognition, Gaussian fitness models, biomechanical primitives, time-series classification", ISSN = "2168-2291", DOI = "doi:10.1109/TSMC.2013.2252337", size = "12 pages", abstract = "The pivotal idea of recognising human aggressive behaviours underlines how a taxonomer models such actions to perform recognition. In this paper, we investigate both the recognition and modelling of aggressive behaviors using kinematic (3-D) and electromyographic performance data. For this purpose, the Gaussian ground-plan projection area model has been assessed as an excellent evolutionary paradigm for the multiclass action and behaviour recognition problem. In fact, it has shown superior classification accuracy with and without the use of ensemble models compared with the standard Gaussian (distance and area) models and other metrics of divergence, when dedicated groups of actions (behaviors) are being modelled. Genetic Programming is being employed to construct behavior-based taxonomers with a biomechanical primitive language. The modeling process revealed a representative subset of parameters (limbs, body segments, and marker coordinates) that are selected through the evolutionary process.", notes = "Also known as \cite{6502260}", } @InProceedings{conf/eann/TheofilatosDAGPLM12, title = "Efficient Computational Prediction and Scoring of Human Protein-Protein Interactions Using a Novel Gene Expression Programming Methodology", author = "Konstantinos A. Theofilatos and Christos M. Dimitrakopoulos and Maria A. Antoniou and Efstratios F. Georgopoulos and Stergios Papadimitriou and Spiros Likothanassis and Seferina Mavroudi", booktitle = "Engineering Applications of Neural Networks, 13th International Conference, {EANN} 2012, London, {UK}, September 20-23, 2012. Proceedings", pages = "472--481", series = "Communications in Computer and Information Science", publisher = "Springer", year = "2012", volume = "311", editor = "Chrisina Jayne and Shigang Yue and Lazaros S. Iliadis", keywords = "genetic algorithms, genetic programming, gene expression programming", isbn13 = "978-3-642-32908-1; 978-3-642-32909-8", URL = "http://dx.doi.org/10.1007/978-3-642-32909-8", bibdate = "2014-01-25", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/eann/eann2012.html#TheofilatosDAGPLM12", } @InProceedings{DBLP:conf/iciit/ThiPT23, author = "Hien Nguyen Thi and Thi-Thu-Hong Phan and Cao Truong Tran", title = "Genetic Programming for Bee Audio Classification", booktitle = "Proceedings of the 8th International Conference on Intelligent Information Technology, ICIIT 2023", pages = "246--250", publisher = "ACM", year = "2023", month = feb # " 24-26", address = "Da Nang, Vietnam", keywords = "genetic algorithms, genetic programming, Honey bees, Apis mellifera, Parameter setting, Machine learning, Convolutional neural networks, ANN, Audio classification", isbn13 = "9781450399616", URL = "https://doi.org/10.1145/3591569.3591612", DOI = "doi:10.1145/3591569.3591612", timestamp = "Fri, 21 Jul 2023 01:00:00 +0200", biburl = "https://dblp.org/rec/conf/iciit/ThiPT23.bib", bibsource = "dblp computer science bibliography, https://dblp.org", size = "5 pages", abstract = "Applying machine learning (ML) methods based on audio data recording inside the hive is a promising solution to detect changes in the beehive. We also compare our proposal with the results from a previous study. The experiment results show that with the right configuration of parameters, GP can achieve better results than well-known methods for the task of classifying bee sound samples.", notes = "BUZZ2 dataset. RawConvNet LogisticRegression (LR), Random Forest (RF), k-Nearest Neighbour (KNN k=5) SVM with a linear kernel one SVM (OVR), Multilayer Perceptron Classifier (MLP) deep learning (DL)", } @InProceedings{thi:2017:AICT, author = "Thuong Pham Thi and Xuan Hoai Nguyen and Tri Thanh Nguyen", title = "A Study on Fitness Representation in Genetic Programming", booktitle = "Advances in Information and Communication Technology", year = "2017", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-319-49073-1_13", DOI = "doi:10.1007/978-3-319-49073-1_13", } @InProceedings{thi:2022:AIC, author = "Thuong Pham Thi", title = "Cartesian Genetic Programming: Some New Detections", booktitle = "Advances in Information and Communication", year = "2022", editor = "Kohei Arai", volume = "439", series = "Lecture Notes in Networks and Systems", pages = "294--313", address = "San Francisco, USA", month = "3-4 " # mar, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", isbn13 = "978-3-030-98015-3", URL = "http://link.springer.com/chapter/10.1007/978-3-030-98015-3_20", DOI = "doi:10.1007/978-3-030-98015-3_20", abstract = "we propose a variant of the fitness function for Cartesian Genetic Programming, called CGP-nMean. Besides, we also analyze systematically the dependence of this method on its experimental parameters to find the relationship between them and to define what is the best configuration. In order to evaluate the effectiveness of the proposed approach, we run experiments on eighteen symbolic regression problems. The experimental results show that (1) the generalizability of the learned model by CGP-nMean is significantly better than that of standard CGP on the most of tested problems; (2) the performance of CGP is significantly affected by experimental parameters, and controlling these parameters will give us the best configuration of CGP-nMean (CGP-nMean(Best)).", notes = "FICC 2022 University of Information and Communication Technology - Thai Nguyen University, Thai Nguyen City, Vietnam", } @Article{10.1109/TKDE.2005.199, author = "Claire J. Thie and Christophe Giraud-Carrier", title = "Learning Concept Descriptions with Typed Evolutionary Programming", journal = "IEEE Transactions on Knowledge and Data Engineering", volume = "17", number = "12", year = "2005", ISSN = "1041-4347", pages = "1664--1677", publisher = "IEEE Computer Society", address = "Los Alamitos, CA, USA", keywords = "genetic algorithms, genetic programming, STGP, Concept learning, typed evolutionary programming", DOI = "doi:10.1109/TKDE.2005.199", abstract = "Examples and concepts in traditional concept learning tasks are represented with the attribute-value language. While enabling efficient implementations, we argue that such propositional representation is inadequate when data is rich in structure. This paper describes STEPS, a strongly-typed evolutionary programming system designed to induce concepts from structured data. STEPS' higher-order logic representation language enhances expressiveness, while the use of evolutionary computation dampens the effects of the corresponding explosion of the search space. Results on the PTE2 challenge, a major real-world knowledge discovery application from the molecular biology domain, demonstrate promise.", notes = "Claire Julia Kennedy", } @InProceedings{thierens:1999:ESNGP, author = "Dirk Thierens", title = "Estimating the Significant Non-Linearities in the Genome Problem-Coding", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "643--648", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-810.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-810.pdf", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Proceedings{gecco:2007, title = "GECCO 2007: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", address = "London, UK", publisher_address = "New York, NY, USA", month = "7-11 " # jul, publisher = "ACM Press", keywords = "genetic algorithms, genetic programming, Ant Colony Optimisation, Swarm Intelligence, Artificial Immune Systems, Artificial Life, Evolutionary Robotics, Adaptive Behaviour, Evolvable Hardware, Biological Applications, Coevolution, Estimation of Distribution Algorithms, Evolution Strategies, Evolutionary Programming, Evolutionary Multiobjective Optimisation, Formal Theory, Generative and Developmental Systems, Genetics-Based Machine Learning, Real-World Applications, Search-Based Software Engineering", isbn13 = "978-1-59593-697-4", URL = "http://dl.acm.org/citation.cfm?id=1276958", size = "2269 pages", abstract = "These proceedings contain the papers presented at the 9th Annual Genetic and Evolutionary Computation COnference (GECCO-2007), held in London, UK, July 7-11, 2007.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071 Also known as \cite{Lipson:2007:1276958}", } @InProceedings{Thierens:2016:GECCOcomp, author = "Dirk Thierens and Peter A. N. Bosman", title = "Model-Based Evolutionary Algorithms", booktitle = "GECCO '16 Companion: Proceedings of the Companion Publication of the 2016 Annual Conference on Genetic and Evolutionary Computation", year = "2016", editor = "Tobias Friedrich and Frank Neumann and Andrew M. Sutton and Martin Middendorf and Xiaodong Li and Emma Hart and Mengjie Zhang and Youhei Akimoto and Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and Daniele Loiacono and Julian Togelius and Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and Faustino Gomez and Carlos M. Fonseca and Heike Trautmann and Alberto Moraglio and William F. Punch and Krzysztof Krawiec and Zdenek Vasicek and Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and Boris Naujoks and Enrique Alba and Gabriela Ochoa and Simon Poulding and Dirk Sudholt and Timo Koetzing", isbn13 = "978-1-4503-4323-7", pages = "385--412", address = "Denver, Colorado, USA", note = "tutorial", month = "20-24 " # jul, keywords = "genetic algorithms, genetic programming", organisation = "SIGEVO", DOI = "doi:10.1145/2908961.2926975", publisher = "ACM", publisher_address = "New York, NY, USA", notes = "Distributed at GECCO-2016.", } @InProceedings{Thierens:2019:GECCOcomp, author = "Dirk Thierens and Peter A. N. Bosman", title = "Model-based evolutionary algorithms", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", isbn13 = "978-1-4503-6748-6", pages = "806--836", address = "Prague, Czech Republic", DOI = "doi:10.1145/3319619.3323386", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{3323386} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InProceedings{Thierens:2020:GECCOcomp, author = "Dirk Thierens and Peter A. N. Bosman", title = "Model-Based Evolutionary Algorithms: GECCO 2020 Tutorial", year = "2020", editor = "Richard Allmendinger and Hugo Terashima Marin and Efren Mezura Montes and Thomas Bartz-Beielstein and Bogdan Filipic and Ke Tang and David Howard and Emma Hart and Gusz Eiben and Tome Eftimov and William {La Cava} and Boris Naujoks and Pietro Oliveto and Vanessa Volz and Thomas Weise and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Rui Wang and Ran Cheng and Guohua Wu and Miqing Li and Hisao Ishibuchi and Jonathan Fieldsend and Ozgur Akman and Khulood Alyahya and Juergen Branke and John R. Woodward and Daniel R. Tauritz and Marco Baioletti and Josu Ceberio Uribe and John McCall and Alfredo Milani and Stefan Wagner and Michael Affenzeller and Bradley Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and Nayat Sanchez-Pi and Luis Marti and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and Matthew Johns and Nick Ross and Ed Keedwell and Herman Mahmoud and David Walker and Anthony Stein and Masaya Nakata and David Paetzel and Neil Vaughan and Stephen Smith and Stefano Cagnoni and Robert M. Patton and Ivanoe {De Falco} and Antonio {Della Cioppa} and Umberto Scafuri and Ernesto Tarantino and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Erik Hemberg and Riyad Alshammari and Tokunbo Makanju and Fuijimino-shi and Ivan Zelinka and Swagatam Das and Ponnuthurai Nagaratnam and Roman Senkerik", isbn13 = "9781450371278", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377929.3389868", DOI = "doi:10.1145/3377929.3389868", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion", pages = "590--619", size = "30 pages", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", note = "Tutorial", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{10.1145/3377929.3389868} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @InProceedings{Thoelke:2022:MODELS, author = "Henri Thoelke and Jens Kosiol", title = "A Multiplicity-Preserving Crossover Operator on Graphs", booktitle = "Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings", year = "2022", pages = "588--597", address = "Montreal, Quebec, Canada", publisher = "Association for Computing Machinery", keywords = "genetic algorithms, genetic programming, search-based software engineering, SBSE, evolutionary algorithms, crossover, model-driven optimization, MDO, consistency-preservation", isbn13 = "9781450394673", URL = "https://doi.org/10.1145/3550356.3561587", DOI = "doi:10.1145/3550356.3561587", size = "10 pages", abstract = "Evolutionary algorithms usually explore a search space of solutions by means of crossover and mutation. While a mutation consists of a small, local modification of a solution, crossover mixes the genetic information of two solutions to compute a new one. For model-driven optimization (MDO), where models directly serve as possible solutions (instead of first transforming them into another representation), only recently a generic crossover operator has been developed. Using graphs as a formal foundation for models, we further refine this operator in such a way that additional well-formedness constraints are preserved: We prove that, given two models that satisfy a given set of multiplicity constraints as input, our refined crossover operator computes two new models as output that also satisfy the set of constraints.", notes = "is this GP? See https://arxiv.org/pdf/2208.10881.pdf for Extended version Philipps-Universitaet Marburg Marburg, Germany", } @InProceedings{thomas:1999:isvGPdmfd, author = "James D Thomas and Katia Sycara", title = "The importance of simplicity and validation in genetic programming for data mining in financial data", booktitle = "Data Mining with Evolutionary Algorithms: Research Directions", year = "1999", editor = "Alex Alves Freitas", pages = "7--11", address = "Orlando, Florida", publisher_address = "445 Burgess Drive, Menlo Park, California 94025, USA", month = "18 " # jul, publisher = "AAAI Press", note = "Technical Report WS-99-06", keywords = "genetic algorithms, genetic programming, data mining", ISBN = "1-57735-090-1", URL = "http://www.cs.cmu.edu/afs/cs/user/jthomas/Web/Papers/gecco99.ps", URL = "http://www.ri.cmu.edu/pub_files/pub2/thomas_james_1999_2/thomas_james_1999_2.pdf", URL = "http://citeseer.ist.psu.edu/323257.html", size = "5 pages", abstract = "A genetic programming system for data mining trading rules out of past foreign exchange data is described. The system is tested on real data from the dollar/yen and dollar/DM markets, and shown to produce considerable excess returns in the dollar/yen market. Design issues relating to potential rule complexity and validation regimes are explored empirically. Keeping potential rules as simple as possible is shown to be the most important component of success. Validation issues are more complicated. Inspection of fitness on a validation set is used to cut-off search in hopes of avoiding overfitting. Additional attempts to use the validation set to improve performance are shown to be ineffective in the standard framework. An examination of correlations between performance on the validation set and on the test set leads to an understanding of how such measures can be marginally benificial; unfortunately, this suggests that further attemps to improve performance through validation will prove difficult", notes = "Joint AAAI-99 & GECCO-99 Workshop. Workshop information at http://www.ppgia.pucpr.br/~dmea/", } @InCollection{Thomas:2002:gagpcf, author = "James D. Thomas and Katia Sycara", title = "{GP} and the Predictive Power of Internet Message Traffic", booktitle = "Genetic Algorithms and Genetic Programming in Computational Finance", publisher = "Kluwer Academic Press", year = "2002", editor = "Shu-Heng Chen", chapter = "4", pages = "81--102", keywords = "genetic algorithms, genetic programming, Computational Finance, Internet Message Boards", ISBN = "0-7923-7601-3", URL = "http://www.springer.com/economics/economic+theory/book/978-0-7923-7601-9", DOI = "doi:10.1007/978-1-4615-0835-9_4", abstract = "This paper investigates the predictive power of the volume of messages produced on internet stock-related measure boards. We introduce a specialized GP learner and demonstrate that it produces trading rules that outperform appropriate buy and hold strategy benchmarks in measures of risk adjusted returns. We compare the results to those attained by using other relevant variables, lags of price and volume, and find that the the message board volume produces clearly superior results. We experiment with alternative representations for the GP trading rule learner. Finally, we find a potential regime shift in the market reaction to the message volume data, and speculate about future trends.", notes = "part of \cite{chen:2002:gagpcf}", } @InProceedings{1144169, author = "Russell Thomason and Terence Soule", title = "Redundant genes and the evolution of robustness", booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation", year = "2006", editor = "Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos {Coello Coello} and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens", volume = "1", ISBN = "1-59593-186-4", pages = "959--960", address = "Seattle, Washington, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p959.pdf", DOI = "doi:10.1145/1143997.1144169", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "8-12 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming: Poster, bloat, code bloat, genetic robustness, program synthesis, redundant genes, robustness, synthesis", notes = "GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM Order Number 910060", } @InProceedings{1277293, author = "Russell Thomason and Terence Soule", title = "Novel ways of improving cooperation and performance in ensemble classifiers", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "1708--1715", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1708.pdf", DOI = "doi:10.1145/1276958.1277293", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, performance analysis", abstract = "There are two common methods of evolving teams of genetic programs. Research suggests Island approaches produce teams of strong individuals that cooperate poorly and Team approaches produce teams of weak individuals that cooperate strongly. Ideally, teams should be composed of strong individuals that cooperate well. In this paper we present a new class of algorithms called Orthogonal Evolution of Teams (OET) that overcomes the weaknesses of current Island and Team approaches by applying evolutionary pressure at both the level of teams and individuals during selection and replacement. We present four novel algorithms in this new class and compare their performance to Island and Team approaches as well as multi-class Adaboost on a number of classification problems.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @InProceedings{conf/eurogp/ThomasonHS08, title = "Training Time and Team Composition Robustness in Evolved Multi-agent Systems", author = "Russell Thomason and Robert B. Heckendorn and Terence Soule", bibdate = "2008-04-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/eurogp/eurogp2008.html#ThomasonHS08", booktitle = "Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008", address = "Naples", month = "26-28 " # mar, publisher = "Springer", year = "2008", volume = "4971", editor = "Michael O'Neill and Leonardo Vanneschi and Steven Gustafson and Anna Isabel {Esparcia Alcazar} and Ivanoe {De Falco} and Antonio {Della Cioppa} and Ernesto Tarantino", isbn13 = "978-3-540-78670-2", pages = "1--12", series = "Lecture Notes in Computer Science", DOI = "doi:10.1007/978-3-540-78671-9_1", keywords = "genetic algorithms, genetic programming", notes = "Part of \cite{conf/eurogp/2008} EuroGP'2008 held in conjunction with EvoCOP2008, EvoBIO2008 and EvoWorkshops2008", } @MastersThesis{Thomemann:1992:masters, author = "U. W. Thonemann", title = "Verbesserung des Simulated Annealing unter Anwendung Genetischer Programmierung am Beispiel des Diskreten Quadratischen Layoutproblems", school = "University of Paderborn, Germany", year = "1992", keywords = "genetic algorithms, genetic programming", size = "pages", } @InProceedings{thompson:1996:SiE, author = "Adrian Thompson", title = "Silicon Evolution", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "Genetic Algorithms", pages = "444--452", address = "Stanford University, CA, USA", publisher = "MIT Press", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap74.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", broken = "http://www.cogs.susx.ac.uk/users/adrianth/gp96/paper.ps.Z", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.29.7790", size = "9 pages", abstract = "The advent of new families of reconfigurable integrated circuits makes it possible for artificial evolution to manipulate a real physical substrate to produce electronic circuits evaluated in the real world. This raises new issues about the potential nature of electronic circuits, because evolution uses no modelling, abstraction or analysis; only physical behaviour. The simplifying constraints of conventional design methodologies can be dropped, allowing evolution to exploit the full range of physical dynamics available from the silicon medium. This claim is investigated theoretically and in simulation, before presenting the first reported direct evolution of the configuration of a Field Programmable Gate Array (FPGA). Evolution is seen to harness its natural dynamics and exploit them in achieving a real-world task.", notes = "GP-96 GA paper", } @InCollection{thompson:1995:AYRSFGPRCFT, author = "Howard Thompson", title = "Are Your Ready for Some Football? Genetically Produced Ratings for College Football Teams", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1995", year = "1995", editor = "John R. Koza", pages = "279--290", address = "Stanford, California, 94305-3079 USA", month = "11 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-195720-5", notes = "part of \cite{koza:1995:gagp}", } @Article{Thomson:2019:sigevolution, author = "Sarah Thomson", title = "{EvoStar 2019}", journal = "SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation", year = "2019", volume = "12", number = "2", pages = "7--9", month = aug, keywords = "genetic algorithms, genetic programming", ISSN = "1931-8499", URL = "https://evolution.sigevo.org/issues/SIGEVOlution1202.pdf", DOI = "doi:10.1145/3357514.3357516", acmid = "3357516", size = "3 pages", abstract = "Describes 2019 EuroGP \cite{Sekanina:2019:GP}, EvoApplications, EvoCOP, EvoMusArt", } @Article{THON:2022:cej, author = "Christoph Thon and Ann-Christin Boettcher and Felix Moehlen and Minghui Yu and Arno Kwade and Carsten Schilde", title = "Multi-modal framework to model wet milling through numerical simulations and artificial intelligence (part 2)", journal = "Chemical Engineering Journal", year = "2022", volume = "450", pages = "137947", month = "15 " # dec, keywords = "genetic algorithms, genetic programming, Wet stirred media mills, Genetic reinforcement learning, CFD-DEM simulation, Predictive mill models", ISSN = "1385-8947", URL = "https://www.sciencedirect.com/science/article/pii/S1385894722034337", DOI = "doi:10.1016/j.cej.2022.137947", size = "12 pages", abstract = "Modelling of stirred media mills is crucial because of their broad use in various industries, ranging from mechanochemistry and mining to the production of batteries and pharmaceuticals. Stirred media mills are responsible for a considerable portion of the global energy demand. However, requirements exist regarding highly specific or uniform particle sizes, process conditions, and reduced wear or abrasion. Multi-modal modelling, which is the intelligent integration of different approaches, such as experiments, simulations, and AI, benefits from respective advantages of each approach. In the first study, results of an experiment conducted via magnetic tracking of a tracer bead was compared with those of simulations, and the inner mill mechanisms were investigated. The two-way coupled computer fluid dynamics discrete element method (CFD-DEM) simulations allowed the investigation of subsequent modelling through AI methods [1]. A novel AI training technique called genetic reinforcement learning (hereinafter, GRL), which combines neural nets with genetic algorithms, was demonstrated for cases with limited data. Furthermore, genetic programming was applied to derive transparent mathematical equations based on the generated data. Using these methods and experimentally validated simulation data, predictive models were trained, and mathematical equations were derived. Relative velocity distributions in the entire simulation domain as well as spatial distributions via heatmaps were predicted and evaluated for independent cases. Systematic predictions for the characteristic relative velocity values were generated instantaneously for varying tip speeds and bead diameters in a parameter space, which would have required 1-10 years through simulations. Finally, a transparent equation was generated via genetic programming", notes = "Institute for Particle Technology (iPAT), Technische Universitaet Braunschweig, Volkmaroder Str. 5, D-38104 Braunschweig, Germany", } @InProceedings{Thonemann:1994:SAGP, author = "Ulrich Wilhelm Thonemann", title = "Finding improved simulated annealing schedules with genetic programming", booktitle = "Proceedings of the 1994 IEEE World Congress on Computational Intelligence", year = "1994", volume = "1", pages = "391--395", address = "Orlando, Florida, USA", month = "27-29 " # jun, publisher = "IEEE Press", DOI = "doi:10.1109/ICEC.1994.349919", keywords = "genetic algorithms, genetic programming, simulated annealing, quadratic assignment problem QAP, combinatorial optimisation problem, heuristics, optimal annealing schedule, performance, quadratic assignment problem, combinatorial mathematics, optimisation, scheduling simulated annealing", size = "5 pages", abstract = "Many combinatorial problems are too difficult to be solved optimally, and hence heuristics are used to obtain good solutions in reasonable time. A heuristic that has been successfully applied to a variety of problems is simulated annealing. However, the performance of simulated annealing strongly depends on the appropriate choice of a key parameter, the annealing schedule. Usually, researchers experiment with a number of manually created annealing schedules and then choose the one that performs best for their algorithms. This work applies genetic programming to replace this manual search. For a given problem, we search for an optimal annealing schedule. We demonstrate the potential of this new approach by optimising the annealing schedule for one of the hardest combinatorial optimisation problem, the quadratic assignment problem. We introduce a new algorithm for solving the quadratic assignment problem that performs extremely well, and we outline properties of good annealing schedules", notes = "Uses GP to generate cooling schedule for simulated annealing. Demonstrates this on a series of QAP and compares very favourably with published QAP results. GP fitness found by running simulated annealing, so end up doing loads of work. Best cooling schedules found are problem dependant but several are highly oscillatory and most don't drop to zero!", } @Article{bolte:1996:oSAsGP, author = "Andreas Bolte and Ulrich Wilhelm Thonemann", title = "Optimizing Simulated Annealing Schedules with Genetic Programming", journal = "European Journal of Operational Research", year = "1996", volume = "92", number = "2", pages = "402--416", month = "19 " # jul, keywords = "genetic algorithms, genetic programming, Optimization, Simulated annealing, Quadratic assignment problem", URL = "http://www.sciencedirect.com/science/article/B6VCT-3VW8NPR-14/2/d6032805608b3a86412054ccde16f0e6", ISSN = "0377-2217", DOI = "doi:10.1016/0377-2217(94)00350-5", abstract = "Combinatorial optimisation problems are encountered in many areas of science and engineering. Most of these problems are too difficult to be solved optimally, and hence heuristics are used to obtain {"}good{"} solutions in reasonable time. One heuristic that has been successfully applied to a variety of problems is Simulated Annealing. The performance of Simulated Annealing depends on the appropriate choice of a key parameter, the annealing schedule. Researchers usually experiment with some manually created annealing schedules and then use the one that performs best in their algorithms. This work replaces this manual search by Genetic Programming, a method based on natural evolution. We demonstrate the potential of this new approach by optimizing the annealing schedule for a well-known combinatorial optimisation problem, the Quadratic Assignment Problem. We introduce two new algorithms for solving the Quadratic Assignment Problem that perform extremely well and outperform existing Simulated Annealing algorithms.", notes = " ", } @InProceedings{Thong-on:2017:ICITEE, author = "Purit Thong-on and Ukrit Watchareeruetai", booktitle = "2017 9th International Conference on Information Technology and Electrical Engineering (ICITEE)", title = "Detection of fibrosis in liver biopsy images using multi-objective genetic programming", year = "2017", abstract = "This paper proposes an automatic construction of feature extractor for liver fibrosis detection using a multiobjective genetic programming approach in which a constructed feature extractor was measured in different aspects in which becomes the objectives of the evolutionary run. The result of the evolutionary run is a set of solutions with different strengths and weaknesses. A solution from each experiment is selected and compared with a benchmark hand craft method in by each experiment and top-five manners. One of the best result obtained has 2.09 fibrosis estimation error which is less than the benchmark method with 2.63 fibrosis estimation error.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICITEED.2017.8250486", month = oct, notes = "Also known as \cite{8250486}", } @Article{THOPPIL:2017:MTP, author = "Nikhil M. Thoppil and Kishore Kumar Kandi and N. Selvaraj and C. S. P. Rao", title = "An evolutionary approach for modeling and optimization of gelcasting of ceramics", journal = "Materials Today: Proceedings", volume = "4", number = "8", pages = "8296--8306", year = "2017", note = "International Conference on Advancements in Aeromechanical Materials for Manufacturing (ICAAMM-2016): Organized by MLR Institute of Technology, Hyderabad, Telangana, India", keywords = "genetic algorithms, genetic programming, Gelcasting, colloidal processing, Ceramics, MGGP, NSGA-II, Evolutionary algorithm, Modelling, Multiobjective optimization, Pareto optimal solution", ISSN = "2214-7853", DOI = "doi:10.1016/j.matpr.2017.07.172", URL = "http://www.sciencedirect.com/science/article/pii/S2214785317314694", abstract = "An integrated evolutionary based approach is presented for the modeling and optimization of gelcasting of ceramics. Gelcasting is a well-established colloidal processing method with a short forming time, high yields, high green capacity and low-cost machining, and has been used to prepare high-quality and complex-shaped dense/porous ceramic parts. The gelcasting constituents are reactive chemicals, which directly influences the characteristic properties of the product. Fused Silica (SiO2) ceramics has been prepared at different mix-proportions of solid loading, monomer content and monomer to cross linker ratio. Accurate prediction models to estimate flexural strength, and porosity were evolved from the experimental data using a new potential evolutionary algorithm called multigene genetic programming (MGGP). Subsequently, the developed model has been used for optimization of the mix-proportion of gelcasting constituents. The problem was formulated as a multiobjective optimization problem and a popular evolutionary algorithm, non-dominated sorting genetic algorithm-II (NSGA-II), was used and thereby retrieves the Pareto-optimal solutions set", } @InProceedings{Thorhauer:2013:GECCO, author = "Ann Thorhauer and Franz Rothlauf", title = "Structural difficulty in grammatical evolution versus genetic programming", booktitle = "GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference", year = "2013", editor = "Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi", isbn13 = "978-1-4503-1963-8", pages = "997--1004", keywords = "genetic algorithms, genetic programming, grammatical evolution", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2463372.2463491", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Genetic programming (GP) has problems with structural difficulty as it is unable to search effectively for solutions requiring very full or very narrow trees. As a result of structural difficulty, GP has a bias towards narrow trees which means it searches effectively for solutions requiring narrow trees. This paper focuses on the structural difficulty of grammatical evolution (GE). In contrast to GP, GE works on variable-length binary strings and uses a grammar in Backus-Naur Form (BNF) to map linear genotypes to phenotype trees. The paper studies whether and how GE is affected by structural difficulty. For the analysis, we perform random walks through the search space and compare the structure of the visited solutions. In addition, we compare the performance of GE and GP for the Lid problem. Results show that GE representation is biased, this means it has problems with structural difficulty. For binary trees, GE has a bias towards narrow and deep structures; thus GE outperforms standard GP if optimal solutions are composed of very narrow and deep structures. In contrast, problems where optimal solutions require more dense trees are easier to solve for GP than for GE.", notes = "Also known as \cite{2463491} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)", } @InProceedings{Thorhauer:2014:PPSN, author = "Ann Thorhauer and Franz Rothlauf", title = "On the Locality of Standard Search Operators in Grammatical Evolution", booktitle = "13th International Conference on Parallel Problem Solving from Nature", year = "2014", editor = "Thomas Bartz-Beielstein and Juergen Branke and Bogdan Filipic and Jim Smith", publisher = "Springer", isbn13 = "978-3-319-10761-5", pages = "465--475", series = "Lecture Notes in Computer Science", address = "Ljubljana, Slovenia", month = "13-17 " # sep, volume = "8672", keywords = "genetic algorithms, genetic programming, grammatical evolution", DOI = "doi:10.1007/978-3-319-10762-2_46", abstract = "Offspring should be similar to their parents and inherit their relevant properties. This general design principle of search operators in evolutionary algorithms is either known as locality or geometry of search operators, respectively. It takes a geometric perspective on search operators and suggests that the distance between an offspring and its parents should be less than or equal to the distance between both parents. This paper examines the locality of standard search operators used in grammatical evolution (GE) and genetic programming (GP) for binary tree problems. Both standard GE and GP search operators suffer from low locality since a substantial number of search steps result in an offspring whose distance to one of its parents is greater than the distance between both of its parents. Furthermore, the locality of standard GE search operators is higher than that of standard GP search operators, which allows more focused search in GE.", notes = "PPSN-XIII", } @InProceedings{Thorhauer:2015:GECCO, author = "Ann Thorhauer and Franz Rothlauf", title = "On the Bias of Syntactic Geometric Recombination in Genetic Programming and Grammatical Evolution", booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation", year = "2015", editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr", isbn13 = "978-1-4503-3472-3", pages = "1103--1110", keywords = "genetic algorithms, genetic programming, grammatical evolution", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739480.2754726", DOI = "doi:10.1145/2739480.2754726", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "For fixed-length binary representations as used in genetic algorithms, standard recombination operators (e.g.,~one-point crossover) are unbiased. Thus, the application of recombination only reshuffles the alleles and does not change the statistical properties in the population. Using a geometric view on recombination operators, most search operators for fixed-length strings are geometric, which means that the distances between offspring and their parents are less than, or equal to, the distance between their parents. In genetic programming (GP) and grammatical evolution (GE), the situation is different since the recombination operators are applied to variable-length structures. Thus, most recombination operators for GE and GP are not geometric. This paper focuses on the bias of recombination in GE and GP and studies whether the application of recombination alone produces specific types of solutions with a higher probability. We consider two different types of recombination operators: standard recombination and syntactic geometric recombination. In our experiments, we performed random walks through the binary tree search space and found that syntactic geometric recombination operators are biased and strongly reduce population diversity. In a performance comparison, we found that syntactic geometric recombination leads to large fitness improvements in the first generations, but that fitness converges after several generations and no further search is possible.", notes = "Also known as \cite{2754726} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)", } @InProceedings{Thorhauer:2016:PPSN, author = "Ann Thorhauer", title = "On the Non-uniform Redundancy in Grammatical Evolution", booktitle = "14th International Conference on Parallel Problem Solving from Nature", year = "2016", editor = "Julia Handl and Emma Hart and Peter R. Lewis and Manuel Lopez-Ibanez and Gabriela Ochoa and Ben Paechter", volume = "9921", series = "LNCS", pages = "292--302", address = "Edinburgh", month = "17-21 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Redundant representation, Binary trees, Bias", isbn13 = "978-3-319-45823-6", DOI = "doi:10.1007/978-3-319-45823-6_27", abstract = "This paper investigates the redundancy of representation in grammatical evolution (GE) for binary trees. We analyze the entire GE solution space by creating all binary genotypes of predefined length and map them to phenotype trees, which are then characterized by their size, depth and shape. We find that the GE representation is strongly non-uniformly redundant. There are huge differences in the number of genotypes that encode one particular phenotype. Thus, it is difficult for GE to solve problems where the optimal tree solutions are underrepresented. In general, the GE mapping process is biased towards short tree structures, which implies high GE performance if the optimal solution requires small programs.", notes = "PPSN2016", } @PhdThesis{phd/dnb/Thorhauer17, author = "Ann Thorhauer", title = "Bias and locality in grammatical evolution", school = "Johannes Gutenberg-Universitaet Mainz", year = "2017", address = "Germany", keywords = "genetic algorithms, genetic programming, grammatical evolution", bibdate = "2018-11-26", biburl = "https://dblp.org/rec/phd/dnb/Thorhauer17.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "http://d-nb.info/1170996558", broken = "https://publications.ub.uni-mainz.de/theses/ergebnis.php?suchart=teil&Lines_Displayed=0&sort=o.date_year+DESC%2C+o.title&suchfeld1=freitext&suchwert1=&opt1=AND&opt2=AND&suchfeld3=date_year&suchwert3=&startindex=0&page=0&dir=2&suche=&suchfeld2=oa.person&suchwert2=Thorhauer%2C%20Ann", idn = "1170996558", size = "92 pages", notes = "openscience.ub.uni-mainz.de broken Dec 2021", } @PhdThesis{Anu_Thubagere_BBE, author = "Anupama J. Thubagere", title = "Programming Complex Behavior in {DNA}-based Molecular Circuits and Robots", school = "Biology and Biological Engineering, California Institute of Technology", year = "2017", month = "23 " # may, keywords = "genetic algorithms, genetic programming, Bioengineering", URL = "https://thesis.library.caltech.edu/10323/", URL = "https://resolver.caltech.edu/CaltechTHESIS:06082017-194534497", URL = "http://thesis.library.caltech.edu/10323/1/Anu_Thubagere_BBE.pdf", DOI = "doi:10.7907/Z9WD3XMS", size = "137 pages", abstract = "Integrated electronic circuits, like those found in cellphones and computers, are ubiquitous in our information-driven society. The success of electronics has, in part, been due its modular architecture that enables individual components to be independently improved while the overall device functionality remains unchanged. Over the last two decades the emerging field of dynamic DNA nanotechnology has been trying to apply the underlying philosophy of electronics to biochemical circuits. DNA nanotechnology employs rationally designed DNA molecules as building blocks of biochemical circuits that can, in principle, enable powerful applications like diagnostics and therapeutics. Researchers in the field of DNA nanotechnology have developed simple elements to construct biomolecular systems with desired functions. They have also developed molecular compilers for defining design principles. The cost of DNA synthesis has decreased by over three orders of magnitude in the past decade. This has lead to a non-trivial number of small scale circuits, like DNA-based logic gates and chemical oscillators, being implemented. However, the scalability of this approach has yet to be clearly demonstrated. In this thesis, we will discuss our main contributions to facilitating the advancement of DNA nanotechnology by developing systematic approaches for constructing modular DNA building blocks. These modules can be used to construct bio-chemical circuits and molecular robotic systems. The performance of the modules can be individually tuned and integrated into large-scale systems. Using automated circuit-design software and cheap unpurified DNA, we demonstrated the design and construction of a complex synthetic biochemical circuit consisting of 78 distinct DNA species. The circuit is capable of computing the transition rules of a cell updating its state based on its neighbouring cells, defined in a classic computational model called cellular automata. Using a bottom-up approach, we first characterized the component necessary for basic Boolean logic computation. We then systematically integrated more circuit elements and eventually constructed the full circuit. By developing a systematic procedure for building DNA-based circuits using unpurified components, we significantly simplified the experimental procedure. By using unpurified DNA components, we reduced the cost and technical barrier for circuit construction, thus making the design and synthesis of complex. Next we demonstrated a cargo sorting DNA nano-robot, using a simple algorithm and modular building blocks. The DNA robot has a leg and two foot domains for exploring a two-dimensional DNA origami surface, and an arm and hand domain for picking up randomly located cargos and dropping them off at their designated locations. It is completely autonomous and is programmed to perform a random walk without requiring an external energy source. Further, we demonstrated sorting multiple copies of two distinct cargo species on the same origami. Additionally, by compartmentalising each sorting task on a single origami, we showed that two distinct sorting tasks can be implemented on different origami simultaneously in the same test tube. The recognition of a cargo is embedded in its destination, therefore it is possible to scale up the system simply by having multiple types of cargos. The same robot design can be used for performing multiple instances of distinct tasks in parallel. The different modules can be integrated to perform diverse functions, including applications in time-release targeted therapeutics.", notes = "Caltech Anupama Thubagere Jagadeesh Biology and Biological Engineering Advisor: Lulu Qian Richard M. Murray (co-advisor)", } @InProceedings{Thuong:2017:GECCO, author = "Pham Thi Thuong and Nguyen Xuan Hoai and Xin Yao", title = "Combining Conformal Prediction and Genetic Programming for Symbolic Interval Regression", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4920-8", address = "Berlin, Germany", pages = "1001--1008", month = "15-19 " # jul, publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, conformal prediction, interval prediction, linear quantile regression, quantile regression, quantile regression forests, symbolic regression", URL = "http://www.cmap.polytechnique.fr/~nikolaus.hansen/proceedings/2017/GECCO/proceedings/proceedings_files/pap447s3-file1.pdf", URL = "http://doi.acm.org/10.1145/3071178.3071280", DOI = "doi:10.1145/3071178.3071280", acmid = "3071280", size = "8 pages", abstract = "Symbolic regression has been one of the main learning domains for Genetic Programming. However, most work so far on using genetic programming for symbolic regression only focus on point prediction. The problem of symbolic interval regression is for each input to find a prediction interval containing the output with a given statistical confidence. This problem is important for many risk-sensitive domains (such as in medical and financial applications). In this paper, we propose the combination of conformal prediction and genetic programming for solving the problem of symbolic interval regression. We study two approaches called black-box conformal prediction genetic programming (black-box CPGP) and white-box conformal prediction genetic programming (white-box CPGP) on a number of benchmarks and previously used problems. We compare the performance of these approaches with two popular interval regressors in statistic and machine learning domains, namely, the linear quantile regression and quantile random forest. The experimental results show that, on the two performance metrics, black-box CPGP is comparable to the linear quantile regression and not much worse than the quantile random forest on validity and much better than them on efficiency.", notes = "Also known as \cite{Thuong:2017:CCP:3071178.3071280} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @InProceedings{thuong:2023:ICTA, author = "Pham Thi Thuong and Hoang Thi Canh and Nguyen Thi Dung and Nguyen Thu Phuong and Nguyen Lan Oanh", title = "Genetic Programming A Preliminary Study of Knowledge Transfer in Mutation", booktitle = "International Conference on Advances in Information and Communication Technology", year = "2023", volume = "847", series = "LNNS", pages = "261--268", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-031-49529-8_28", DOI = "doi:10.1007/978-3-031-49529-8_28", } @InProceedings{tian:1999:ASSABFSEV, author = "Yajie Tian and Nobuo Sannomiya and Toru Yokokura", title = "A Simulation Study on Adaptive Behavior of Fish Schools under Environmental Variation", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1451", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "artificial life, adaptive behavior and agents, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-007.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-007.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{tian:2024:CEC, author = "Yuan Tian and Yi Mei and Mengjie Zhang", title = "Learning Heuristics via Genetic Programming for Multi-Mode Resource-Constrained Project Scheduling", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Schedules, Sequential analysis, Operations research, Processor scheduling, Buildings, Project management, Project Scheduling, Multiple Modes, Hyper-heuristics", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10612172", abstract = "The multi-mode resource-constrained project scheduling problem (MRCPSP) is a challenging problem for researchers and practitioners in operations research and project management. MRCPSP involves both selecting the execution mode for each activity and sequencing the activities in the schedule. Thus, activity prioritisation and mode selection are the two main decisions in building a project schedule. A rule-based heuristic approach is commonly used for solving this problem in practical complex scenarios. However, designing effective project scheduling rules highly relies on the expertise of professionals and domain knowledge. To address the above issue, this paper proposes a genetic programming-based hyper-heuristic (GPHH) to design heuristic rules automatically. Various decision strategies based on decision orders are proposed and their impact on the capacity of GPHH to learn effective scheduling rules is investigated. The experiment results demonstrate the evolved rules generated by GPHH outperform the existing manual heuristic rules and making two decisions simultaneously is identified as the most effective strategy.", notes = "also known as \cite{10612172} WCCI 2024", } @InProceedings{Tian:2025:EvoCOP, author = "Yuan Tian and Yi Mei and Mengjie Zhang", title = "Generating (Semi-)active Schedules for Dynamic Multi-mode Project Scheduling Using Genetic Programming Hyper-heuristics", booktitle = "25th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2025", year = "2025", editor = "Martin S. Krejca and Markus Wagner", series = "LNCS", volume = "15610", pages = "232--248", publisher = "Springer", address = "Trieste, Italy", month = "3-5 " # apr, organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Dynamic project scheduling, Multiple modes, Uncertain duration, Decision-making procedure, GPHH, DMRCPSP", isbn13 = "978-3-031-86848-1", timestamp = "Thu, 01 May 2025 20:21:42 +0200", biburl = "https://dblp.org/rec/conf/evocop/TianMZ25.bib", bibsource = "dblp computer science bibliography, https://dblp.org", DOI = "doi:10.1007/978-3-031-86849-8_15", notes = "DBLP:conf/evocop/TianMZ25, http://www.evostar.org/2025/ EvoCOP2025 held in conjunction with EuroGP'2025, EvoApplications2025 and EvoMusArt2025", } @Article{Tichy:2015:Ubiquity, author = "Walter Tichy", title = "Automated Bug Fixing: An Interview with {Westley Weimer}, Department of Computer Science, {University of Virginia} and {Martin Monperrus}, {University of Lille and INRIA, Lille, France}", journal = "Ubiquity", issue_date = "March 2015", year = "2015", volume = "2015", number = "March", month = mar, pages = "1--11", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, GenProg", ISSN = "1530-2180", acmid = "2746519", publisher = "ACM", address = "New York, NY, USA", URL = "http://doi.acm.org/10.1145/2746519", DOI = "doi:10.1145/2746519", size = "11 pages", abstract = "Fixing bugs manually is expensive, time-consuming, and unpleasant. How about getting the computer to fix the bugs, automatically? Automatically repairing them might save us from misunderstandings, lack of time, carelessness, or plain old laziness. But this brings into question some fundamental limitations. Yet in the past 10 years, a number of young scientists have taken on automatic bug fixing. This interview discusses the approximations currently in use and how far they can take us.", notes = "who invented automatic bugfixing? Sigrid Eldh at Ericsson Cites \cite{LeGoues:2012:ICSE} \cite{legouesWFSQJO2013} \cite{Weimer:2009:ICES} \cite{Monperrus:2014:CRA:2568225.2568324} \cite{Martinez:2014:FIA:2591062.2591114} Karlsruhe Institute of Technology, Germany Also known as \cite{Tichy:2015:ABF:2752510.2746519}", } @InProceedings{conf/semweb/TiddidM16, title = "Learning to Assess Linked Data Relationships Using Genetic Programming", author = "Ilaria Tiddi and Mathieu d'Aquin and Enrico Motta", editor = "Paul T. Groth and Elena Simperl and Alasdair J. G. Gray and Marta Sabou and Markus Kroetzsch and Freddy Lecue and Fabian Floeck and Yolanda Gil", bibdate = "2017-05-22", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/semweb/iswc2016-1.html#TiddidM16", booktitle = "The Semantic Web - {ISWC} 2016 - 15th International Semantic Web Conference, Kobe, Japan, October 17-21, 2016, Proceedings, Part {I}", year = "2016", volume = "9981", isbn13 = "978-3-319-46522-7", pages = "581--597", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1007/978-3-319-46523-4_35", } @InProceedings{tikhe:2019:AiWM, author = "Kshitija S. Tikhe and Basavraj S. Balapgol and Sandip T. Mali", title = "Estimation of Landfill Gas Using Genetic Programming", booktitle = "Advances in Waste Management", year = "2019", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-981-13-0215-2_12", DOI = "doi:10.1007/978-981-13-0215-2_12", } @Book{Timmis:2002:ICARIS, title = "1st International Conference on Artificial Immune Systems: ICARIS 2002", year = "2002", editor = "Jon Timmis and P. J Bentley", address = "University of Kent at Canterbury", publisher = "University of Kent at Canterbury Printing Unit", ISBN = "1-902671-32-5", isbn13 = "9781902671321", URL = "http://kar.kent.ac.uk/13740/", size = "240 pages", abstract = "Maybe the strangest thing about immune systems is the fact that we need them at all. The natural world has evolved such wonderfully intertwined ecosystems, with every living thing supporting or dependent on other forms of life. With such harmony, why should we need an internal security system as massively complex as our immune systems? The trouble is caused not by cooperative symbiosis, but by antagonistic symbiosis. Life tries to find every niche available in order to exist, and unfortunately there is plenty of space within us for parasites, bacteria, fungi and viruses to stretch their metaphorical legs. So, throughout our evolutionary history, in order to stay alive, we've had to fight. Somehow our bodies had to learn to detect and repair damage, remove invaders, and remember those invaders to make us immune next time. They had to learn to detect foreign forms of life within us that they had never experienced before, and indeed that had never existed before. And yet they must never be confused by the helpful bacteria in our guts, the food we eat, or the growing foetus within a mother's womb. Along the way, our bodies also learned how to detect malfunctions within themselves: cancerous cells proliferating wildly or immune cells attacking ourselves instead of our attackers. To achieve these marvels, evolution created a whole collection of chemicals, organs and cells distributed throughout us. We don't really understand how it all works any more than we understand how our brains work. But, like our brains, we do know enough to be very impressed at the capabilities of natural immune systems. We also know enough to create a diverse range of computer algorithms based on the workings of different aspects of our immune systems. So computer science meets immunobiology. The result is the young, but vigorous field known as Artificial Immune Systems. Inspired by the natural immune system, computer scientists now create evolving, learning and adapting computer systems that (amongst many applications) can recognise patterns, detect faults, keep computer networks secure, and optimise solutions. ICARIS 2002 is the first ever international conference dedicated entirely to the field of Artificial Immune Systems (AIS). In these proceedings you will find 26 papers written by the leading scientists in the field, from 11 different countries, describing an impressive array of ideas, technologies and applications for AIS. We couldn't have organised this conference without these researchers, so we thank them all for coming. We also couldn't have organised ICARIS without the excellent work of all of the programme committee, our publicity chair, Simon Garrett, and our conference secretary, Jenny Oatley. Finally, we wish to thank the following for their contribution to the success of this conference: European Office of Aerospace Research and Development, Air Force Office of Scientific Research, United States Air Force Research Laboratory and the International Society of Genetic and Evolutionary Computation (ISGEC). Whether you are new to the field, or are one of its established researchers, we welcome you to Canterbury, and hope you enjoy ICARIS 2002! Peter J. Bentley Jon Timmis Conference chairs", } @Proceedings{DBLP:conf/icaris/2003, editor = "Jon Timmis and Peter J. Bentley and Emma Hart", title = "Second International Conference on Artificial Immune Systems: ICARIS 2003", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "2787", year = "2003", month = sep # " 1-3", address = "Edinburgh, UK", ISBN = "3-540-40766-9", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "This book constitutes the refereed proceedings of the Second International Conference on Artificial Immune Systems, ICARIS 2003, held in Edinburgh, UK in ...", keywords = "Computers", } @Misc{oai:arXiv.org:cs/0512071, title = "``Going back to our roots'': second generation biocomputing", note = "Submitted to the International Journal of Unconventional Computing", author = "Jon Timmis and Martyn Amos and Wolfgang Banzhaf and Andy Tyrrell", year = "2005", month = dec # "~16", howpublished = "arXiv", bibsource = "OAI-PMH server at arXiv.org", oai = "oai:arXiv.org:cs/0512071", keywords = "genetic algorithms, genetic programming, EHW, AIS, Artificial Intelligence, Neural and Evolutionary Computing", URL = "http://arxiv.org/abs/cs/0512071", size = "36 pages", abstract = "Researchers in the field of biocomputing have, for many years, successfully 'harvested and exploited' the natural world for inspiration in developing systems that are robust, adaptable and capable of generating novel and even 'creative' solutions to human-defined problems. However, in this position paper we argue that the time has now come for a reassessment of how we exploit biology to generate new computational systems. Previous solutions (the 'first generation' of biocomputing techniques), whilst reasonably effective, are crude analogues of actual biological systems. We believe that a new, inherently inter-disciplinary approach is needed for the development of the emerging 'second generation' of bio-inspired methods. This new modus operandi will require much closer interaction between the engineering and life sciences communities, as well as a bidirectional flow of concepts, applications and expertise. We support our argument by examining, in this new light, three existing areas of biocomputing (genetic programming, artificial immune systems and evolvable hardware), as well as an emerging area (natural genetic engineering) which may provide useful pointers as to the way forward.", notes = "See \cite{Timmis:2006:IJUC}", } @Article{Timmis:2006:IJUC, author = "Jon Timmis and Martyn Amos and Wolfgang Banzhaf and Andy Tyrrell", title = "Going Back to our Roots: Second Generation Biocomputing", journal = "International Journal of Unconventional Computing", year = "2006", volume = "2", number = "4", pages = "349--378", note = "Special Issue: Grand Challenge in Non-Classical Computation", keywords = "genetic algorithms, genetic programming", ISSN = "1548-7199", broken = "http://www.oldcitypublishing.com/pdf/596", URL = "http://www.oldcitypublishing.com/IJUC/IJUCabstracts/IJUC2.4abstracts/IJUCv2n4p349-378Timmis.html", URL = "http://www.cs.mun.ca/~banzhaf/papers/JUC2006.pdf", size = "33 pages", abstract = "Researchers in the field of biocomputing have, for many years, successfully used the natural world as inspiration for developing systems that are robust, adaptable and capable of generating novel and even 'creative' solutions to human-defined problems. However, in this position paper we argue that the time has now come for a reassessment of how we exploit biology to generate new computational systems. Previous solutions (the 'first generation' of biocomputing techniques), whilst reasonably effective, are crude analogues of actual biological systems. We believe that a new, inherently inter-disciplinary approach is needed for the development of the emerging 'second generation' of bio-inspired methods. This new modus operandi will require much closer interaction between the engineering and life sciences communities, as well as a bidirectional flow of concepts, applications and expertise.We support our argument by examining, in this new light, three existing areas of biocomputing (genetic programming, artificial immune systems and evolvable hardware), as well as an emerging area (natural genetic engineering) which may provide useful pointers as to the way forward.", notes = "See also \cite{oai:arXiv.org:cs/0512071}. http://www.oldcitypublishing.com/IJUC/IJUC.html", } @InProceedings{Timperley:2014:ALIFE, author = "Christopher Timperley and Susan Stepney", title = "Reflective Grammatical Evolution", booktitle = "Proceedings of the Fourteenth International Conference of the Synthesis and Simulation of Living Systems, ALIFE 14", year = "2014", editor = "Hiroki Sayama and John Rieffel and Sebastian Risi and Rene Doursat and Hod Lipson", series = "Complex Adaptive Systems", pages = "71--78", address = "New York", month = "30 " # jul # "-2 " # aug, organisation = "International Society for Artificial Life", publisher = "MIT Press", keywords = "genetic algorithms, genetic programming, grammatical evolution, reflection, open-ended evolution, robustness", isbn13 = "978-0-262-32621-6", URL = "https://www.mitpressjournals.org/toc/isal/26", URL = "https://www.mitpressjournals.org/doi/pdf/10.1162/978-0-262-32621-6-ch013", DOI = "doi:10.7551/978-0-262-32621-6-ch013", size = "8 pages", abstract = "Our long term goal is to develop an open-ended reflective software architecture to support open-ended evolution. Here we describe a preliminary experiment using reflection to make simple programs evolved via Grammatical Evolution robust to mutations that result in coding errors. We use reflection in the domain of grammatical evolution (GE) to achieve a novel means of robustness by autonomously repairing damaged programs, improving continuity in the search and allowing programs to be evolved effectively using soft grammars. In most implementations of GE, individuals whose programs encounter errors are assigned the worst possible fitness; using the techniques described here, these individuals may be allowed to continue evolving. We describe two different approaches to achieving robustness through reflection, and evaluate their effectiveness through a series of experiments carried out on benchmark regression problems. Results demonstrate a statistically significant improvement on the fitness of the best individual found during evolution.", notes = "REVAC Also known as \cite{SS-ALife14b} ALIFE 14 broken Nov 2020 http://mitpress.mit.edu/books/artificial-life-14 ALIFE14NYC@gmail.com", } @InProceedings{Timperley:2017:SSBSE, author = "Christopher Steven Timperley and Susan Stepney and Claire {Le Goues}", title = "An Investigation into the Use of Mutation Analysis for Automated Program Repair", booktitle = "Proceedings of the 9th International Symposium on Search Based Software Engineering, SSBSE 2017", year = "2017", editor = "Tim Menzies and Justyna Petke", volume = "10452", series = "LNCS", pages = "99--114", address = "Paderborn, Germany", month = sep # " 9-11", publisher = "Springer", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, GenProg", isbn13 = "978-3-319-66299-2", URL = "https://www.cs.cmu.edu/~clegoues/docs/legoues-ssbse17.pdf", DOI = "doi:10.1007/978-3-319-66299-2_7", size = "16 pages", abstract = "Research in Search-Based Automated Program Repair has demonstrated promising results, but has nevertheless been largely confined to small, single-edit patches using a limited set of mutation operators. Tackling a broader spectrum of bugs will require multiple edits and a larger set of operators, leading to a combinatorial explosion of the search space. This motivates the need for more efficient search techniques. We propose to use the test case results of candidate patches to localise suitable fix locations. We analysed the test suite results of single-edit patches, generated from a random walk across 28 bugs in 6 programs. Based on the findings of this analysis, we propose a number of mutation-based fault localisation techniques, which we subsequently evaluate by measuring how accurately they locate the statements at which the search was able to generate a solution. After demonstrating that these techniques fail to result in a significant improvement, we discuss why this may be the case, despite the successes of mutation-based fault localisation in previous studies.", notes = "Is this GP? Uses GenProg http://ssbse17.github.io/ Co-located with FSE/ESEC 2017", } @Article{Tinos:2007:GPEM, author = "Renato Tinos and Shengxiang Yang", title = "A self-organizing random immigrants genetic algorithm for dynamic optimization problems", journal = "Genetic Programming and Evolvable Machines", year = "2007", volume = "8", number = "3", pages = "255--286", month = "Septembe", keywords = "genetic algorithms, Self-organised criticality, Dynamic optimisation problems, Random immigrants", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-007-9024-z", size = "32 pages", abstract = "In this paper a genetic algorithm is proposed where the worst individual and individuals with indices close to its index are replaced in every generation by randomly generated individuals for dynamic optimisation problems. In the proposed genetic algorithm, the replacement of an individual can affect other individuals in a chain reaction. The new individuals are preserved in a subpopulation which is defined by the number of individuals created in the current chain reaction. If the values of fitness are similar, as is the case with small diversity, one single replacement can affect a large number of individuals in the population. This simple approach can take the system to a self-organising behaviour, which can be useful to control the diversity level of the population and hence allows the genetic algorithm to escape from local optima once the problem changes due to the dynamics.", } @InProceedings{Tisdale:2020:GECCOcomp, author = "Braden N. Tisdale and Aaron Scott Pope and Daniel R. Tauritz", title = "Dynamic Primitive Granularity Control: An Exploration of Unique Design Considerations", year = "2020", editor = "Richard Allmendinger and Hugo Terashima Marin and Efren Mezura Montes and Thomas Bartz-Beielstein and Bogdan Filipic and Ke Tang and David Howard and Emma Hart and Gusz Eiben and Tome Eftimov and William {La Cava} and Boris Naujoks and Pietro Oliveto and Vanessa Volz and Thomas Weise and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Rui Wang and Ran Cheng and Guohua Wu and Miqing Li and Hisao Ishibuchi and Jonathan Fieldsend and Ozgur Akman and Khulood Alyahya and Juergen Branke and John R. Woodward and Daniel R. Tauritz and Marco Baioletti and Josu Ceberio Uribe and John McCall and Alfredo Milani and Stefan Wagner and Michael Affenzeller and Bradley Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and Nayat Sanchez-Pi and Luis Marti and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and Matthew Johns and Nick Ross and Ed Keedwell and Herman Mahmoud and David Walker and Anthony Stein and Masaya Nakata and David Paetzel and Neil Vaughan and Stephen Smith and Stefano Cagnoni and Robert M. Patton and Ivanoe {De Falco} and Antonio {Della Cioppa} and Umberto Scafuri and Ernesto Tarantino and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Erik Hemberg and Riyad Alshammari and Tokunbo Makanju and Fuijimino-shi and Ivan Zelinka and Swagatam Das and Ponnuthurai Nagaratnam and Roman Senkerik", isbn13 = "9781450371278", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377929.3398131", DOI = "doi:10.1145/3377929.3398131", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion", pages = "1906--1914", size = "9 pages", keywords = "genetic algorithms, genetic programming, evolutionary computation, primitive granularity control, generative hyper-heuristic", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", abstract = "Dynamic primitive granularity control (DPGC) is a promising avenue for improving the performance of genetic programming (GP). However, it remains almost entirely unexplored. Further, it may pose many unique challenges in its design and implementation that traditional GP implementations do not. This paper presents an implementation of DPGC in order to determine what aspects of conventional GP design and implementation require special consideration. There are some common techniques used in GP that have been found here to negatively impact DPGC's ability to improve performance. Parsimony pressure appears to disproportionately penalize low-level primitives, and a mixed-granularity population suffers from heavy biases towards particular granularity levels, seemingly to the detriment of evolution. This paper provides hypotheses as to why these conventional techniques harm DPGC implementations, as well as several potential alternatives for use in the future that may remedy these detrimental effects.", notes = "Also known as \cite{10.1145/3377929.3398131} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @InProceedings{tiso:2023:GECCO, author = "Stefano Tiso and Pedro Carvalho and Nuno Lourenco and Penousal Machado", title = "Biological Insights on {Grammar-Structured} Mutations Improve Fitness and Diversity", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "558--567", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, mutation, optimizers, grammar-guided genetic programming", isbn13 = "9798400701191", DOI = "doi:10.1145/3583131.3590472", size = "10 pages", abstract = "Grammar-Guided Genetic Programming (GGGP) employs a variety of concepts from evolutionary theory to autonomously design solutions for a given task. Recent insights from evolutionary biology can lead to further improvements in GGGP algorithms. In this paper, we propose a new mutation approach called Facilitated Mutation (FM) that is based on the theory of Facilitated Variation. We evaluate the performance of FM on the evolution of neural network optimizers for image classification, a relevant task in Evolutionary Computation, with important implications for the field of Machine Learning. We compare FM and FM combined with crossover (FMX) against a typical mutation approach to assess the benefits of the approach. We find that FMX provides statistical improvements in key metrics, creating a superior optimizer overall (+0.5\% average test accuracy), improving the average quality of solutions (+53\% average population fitness), and discovering more diverse high-quality behaviors (+523 high-quality solutions discovered on average). Additionally, FM and FMX reduce the number of fitness evaluations in an evolutionary run, reducing computational costs. FM's implementation cost is minimal and the approach is theoretically applicable to any algorithm where genes are associated witha grammar non-terminal, making this approach applicable in many existing GGGP systems.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @MastersThesis{Tng:mastersthesis, author = "John Cheun Hou Tng", title = "An interactive wildlife development framework for {3D} racing game", school = "School of Computer Engineering, Nanyang Technological University", year = "2009", address = "Singapore", URL = "http://hdl.handle.net/10356/15461", abstract = "This thesis describes the development and framework of an interactive wildlife system for the next generation of highly interactive computer games. Current games have racing themes that include animated animals which are non-interactive. This means that animals in these games do not react to a player's action. For example, players in such games cannot pursue an animal. This wildlife system creates interactive animals for racing games where players can observe animals' behaviour through high resolution animations, hear sounds they produced or chase them around. This is a new kind of game play experience that has not been implemented before. Interactivity with the animals lets players feel that these animals are part of the game and not merely decorations. This enhances the realism of the game and makes the game more interesting. This wildlife system shows that with the use of graphics hardware and optimisation techniques, it is possible to implement a cost and resource efficient interactive and realistic wildlife system for 3D racing games.", notes = "Supervisor: Narendra Shivaji Chaudhari (SCE) Tay Joc Cing (SCE) MASTER OF ENGINEERING http://www.researchgate.net/publication/27640377_An_interactive_wildlife_development_framework_for_3D_racing_game", } @InProceedings{to:2005:CEC, author = "Cuong To and Jiri Vohradsky", title = "Classification of Proteomic Kinetic Patterns using Supervised Genetic Programming", booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary Computation", year = "2005", editor = "David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L. Gwenn Volkert and Dan Ashlock and Marc Schoenauer", volume = "2", pages = "1823--1830", address = "Edinburgh, UK", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "2-5 " # sep, organisation = "IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, SWICZ, s. coelicolor", ISBN = "0-7803-9363-5", DOI = "doi:10.1109/CEC.2005.1554909", abstract = "The rapidly emerging field of quantitative proteomics has established itself as a credible approach for understanding of the biology of whole organisms. Classification of proteins according to the level of their expression during a particular process allows discovering causal relationships among genes and proteins involved in the process. we present a supervised method of classification of proteomic kinetic patterns based on genetic programming allowing for extraction of user defined patterns from a database of kinetic profiles. The method combines robustness of genetic programming algorithm with the flexibility given by user interaction.", notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS.", } @InProceedings{To:2006:ASPGP, title = "A combination of Genetic Programming and Cluster Analysis for Single Class", author = "Cuong To and Jiri Vohradsky", booktitle = "Proceedings of the Third Asian-Pacific workshop on Genetic Programming", year = "2006", editor = "The Long Pham and Hai Khoi Le and Xuan Hoai Nguyen", pages = "13--23", ISSN = "18590209", address = "Military Technical Academy, Hanoi, VietNam", keywords = "genetic algorithms, genetic programming", URL = "http://gpbib.cs.ucl.ac.uk/aspgp06/To_Vohradsky_ASPGP06_finalcopy.pdf", size = "11 pages", abstract = "algorithm for single class based on genetic programming (GP) is introduced. Single class problem is converted into symbolic regression, and then genetic programming is used to search a polynomial function which approximates symbolic regression problem. Four real databases (one transcriptomics, one proteomics, and two breast cancers) were used to test the algorithm and a comparison with six well-known algorithms was done. The results prove that the algorithm is a rather good one.", notes = "broken march 2020 http://www.aspgp.org", } @InProceedings{conf/rivf/ToV06, author = "Cuong To and Jiri Vohradsky", title = "A combination of kernel methods and genetic programming for gene expression pattern classification", booktitle = "Research, Innovation and Vision for the Future, 2006 International Conference on", year = "2006", editor = "Patrick Bellot and Vu Duong and Marc Bui", pages = "214--221", address = "Ho Chi Minh City, Vietnam", month = feb # " 12-16", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Database searching, Pattern classification, Pattern recognition", ISBN = "1-4244-0316-2", DOI = "doi:10.1109/RIVF.2006.1696440", size = "8 pages", abstract = "The rapidly emerging field of quantitative proteomics has established itself as a credible approach for understanding of the biology of whole organisms. Classification of proteins according to the level of their expression during a particular process allows discovering causal relationships among genes and proteins involved in the process. In this paper, we would like to propose a new algorithm for pattern classification, allowing for extraction of user defined patterns from a database of kinetic gene expression profiles. This algorithm is a combination of kernel methods and genetic programming. The algorithm was tested on publicly available transcriptomic and proteomic time series datasets and the results showed that the algorithm could find all similar patterns in the database with very low misclassification rate.", notes = "The database is available at http://proteom.biomed.cas.cz The database is available at http://genomewww.stanford.edu/serum/clusters.html", bibdate = "2009-02-26", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/rivf/rivf2006.html#ToV06", } @PhdThesis{To:thesis, author = "Cuong Chieu To", title = "Data Mining in Transcriptomics and Proteomics", school = "University of West Bohemia in Pilsen", year = "2006", address = "30614 Pilsen, Czech Republic", month = jun, keywords = "genetic algorithms, genetic programming, Software", language = "English", URL = "https://vufind.techlib.cz/Record/000644036", URL = "http://sun2.biomed.cas.cz/mbu/proteom/download/cuongthesis.pdf", size = "122 pages", abstract = "This work presents algorithms based on evolutionary computation for pattern classification and identification of protein networks and their application for transcriptomics and proteomics databases. There are three parts in this work. The first part (chapters 1-3) brings overview of pattern recognition methods applied in biology, pattern classification algorithms using evolutionary computation, and methods for inference of protein networks. Chapter 2 and 3 are overview of background methods such as genetic algorithm, genetic programming, kernel methods and cluster analysis. The second part consists of chapters 4 to 8. Two algorithms for binary classification and three for single class classification are shown. Their applications for transcriptomics and proteomics database and comparisons with six popular pattern classification methods are tested. The last part which contains chapter 9 and 10 shows two algorithms for inference of protein interaction networks. Parallel evolutionary computing using the island model was applied in the second part and the third part to increase the performance and the quality of results. Chapter 11 presents the programs using the above mentioned algorithms. In appendix B, two breast cancer databases are used to test the pattern classification algorithms that are mentioned in the second part.", notes = "Copies of this report are available on (broken Oct 2023) http://www.kiv.zcu.cz/publications/ K 63381 z Supervisor: Jiri Vohradsky", } @Article{To:2008:BMCb, author = "Cuong C To and Jiri Vohradsky", title = "Supervised inference of gene-regulatory networks", journal = "BMC Bioinformatics", year = "2008", volume = "9", number = "2", month = jan # " 4", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1186/1471-2105-9-2", abstract = "Background Inference of protein interaction networks from various sources of data has become an important topic of both systems and computational biology. Here we present a supervised approach to identification of gene expression regulatory networks. Results The method is based on a kernel approach accompanied with genetic programming. As a data source, the method uses gene expression time series for prediction of interactions among regulatory proteins and their target genes. The performance of the method was verified using Saccharomyces cerevisiae cell cycle and DNA/RNA/protein biosynthesis gene expression data. The results were compared with independent data sources. Finally, a prediction of novel interactions within yeast gene expression circuits has been performed. Conclusion Results show that our algorithm gives, in most cases, results identical with the independent experiments, when compared with the YEASTRACT database. In several cases our algorithm gives predictions of novel interactions which have not been reported.", notes = "PMID:", } @InProceedings{To:2009:ISPA, author = "Cuong To and Tuan D. Pham", title = "Analysis of Cardiac Imaging Data using Decision Tree based Parallel Genetic Programming", booktitle = "Proceedings of 6th International Symposium on Image and Signal Processing and Analysis, ISPA 2009", year = "2009", month = sep, pages = "317--320", keywords = "genetic algorithms, genetic programming, LogitBoost, cardiac diagnosis, cardiac imaging, decision tree, least square methods, linear discriminant analysis, linear regression, logistic regression, parallel genetic programming, single proton emission computed tomography, support vector machine, biology computing, cardiology, decision trees, least squares approximations, medical image processing, parallel programming, regression analysis, single photon emission computed tomography, support vector machines", URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5297730", ISSN = "1845-5921", size = "4 pages", abstract = "We propose an algorithm for generating diagnostic rules for cardiac diagnoses. Diagnostic rules are presented in decision tree forms that are created by genetic programming. The algorithm was tested by using cardiac single proton emission computed tomography images. In comparisons with other six well-known methods including support vector machine, LogitBoost, logistic regression, linear discriminant analysis, linear regression and least square methods; the proposed algorithm is superior. We also show that parallel genetic programming can be used to improve the performance of the proposed algorithm.", notes = "http://www.isispa.org/ispa09/ Also known as \cite{5297730}", } @InProceedings{To:2013:GECCOcomp, author = "Cuong To and Mohamed Elati", title = "A parallel genetic programming for single class classification", booktitle = "GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion", year = "2013", editor = "Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A. N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks", isbn13 = "978-1-4503-1964-5", keywords = "genetic algorithms, genetic programming", pages = "1579--1586", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2464576.2466811", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "In this paper, we present an algorithm based on genetic programming for single (one) class classification that uses one set containing similar patterns in training process. This type of problem is called single (one) class classification, a novel detection. The proposed algorithm was tested and compared to seven other traditional methods based on two publicly available transcriptomic and proteomic time series datasets and two public breast cancer datasets. The results show that the algorithm could find most similar patterns in the databases with rather low misclassification rates. We also applied parallel genetic programming for this algorithm and it proves that the island model can give better solutions than sequential genetic programming.", notes = "Also known as \cite{2466811} Distributed at GECCO-2013.", } @InProceedings{todd:1999:DMOSDSUGANN, author = "David S. Todd and Pratyush Sen", title = "Directed Multiple Objective Search of Design Spaces Using Genetic Algorithms and Neural Networks", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1738--1743", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "real world applications", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-718.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-718.pdf", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InCollection{99evmus, author = "Peter M. Todd and Gregory M. Werner", title = "Frankensteinian approaches to evolutionary music composition", booktitle = "Musical Networks: Parallel Distributed Perception and Performance", publisher = "MIT Press", year = "1999", editor = "Niall Griffith and Peter M. Todd", pages = "313--340", keywords = "genetic algorithms, genetic programming, music, coevolution, algorithmic composition, evolutionary algorithms, learning, musical rules", ISBN = "0-262-07181-9", URL = "http://www-abc.mpib-berlin.mpg.de/users/ptodd/publications/99evmus/99evmus.pdf", DOI = "doi:10.7551/mitpress/4812.003.0015", size = "25 pages", abstract = "Victor Frankenstein sought to create an intelligent being imbued with the rules of civilized human conduct, who could further learn how to behave and possibly even evolve through successive generations into a more perfect form. Modern human composers similarly strive to create intelligent algorithmic music composition systems that can follow prespecified rules, learn appropriate patterns from a collection of melodies, or evolve to produce output more perfectly matched to some aesthetic criteria. Here we review recent efforts aimed at each of these three types of algorithmic composition. We focus particularly on evolutionary methods, and indicate how monstrous many of the results have been. We present a new method that uses coevolution to create linked artificial music critics and music composers, and describe how this method can attach the separate parts of rules, learning, and evolution together into one coherent body.", notes = "Survey. Mentions \cite{Spector:1994:ccaga} and \cite{Spector:1995:irdms} cf Lee Spector's email to GP list Wed, 27 Apr 2005 21:21:24 EDT", } @Article{Tofiq:2014:JH, author = "F. A. Tofiq and A. Guven", title = "Prediction of design flood discharge by statistical downscaling and General Circulation Models", journal = "Journal of Hydrology", year = "2014", volume = "517", month = "19 " # sep, pages = "1145--1153", keywords = "genetic algorithms, genetic programming, Linear genetic programming, Design flood, Peak monthly discharge, Statistical downscaling", ISSN = "0022-1694", DOI = "doi:10.1016/j.jhydrol.2014.06.028", URL = "http://www.sciencedirect.com/science/article/pii/S0022169414004880", size = "9 pages", abstract = "The global warming and the climate change have caused an observed change in the hydrological data; therefore, forecasters need re-calculated scenarios in many situations. Downscaling, which is reduction of time and space dimensions in climate models, will most probably be the future of climate change research. However, it may not be possible to redesign an existing dam but at least precaution parameters can be taken for the worse scenarios of flood in the downstream of the dam location. The purpose of this study is to develop a new approach for predicting the peak monthly discharges from statistical downscaling using linear genetic programming (LGP). Attempts were made to evaluate the impacts of the global warming and climate change on determining of the flood discharge by considering different scenarios of General Circulation Models. Reasonable results were achieved in downscaling the peak monthly discharges directly from daily surface weather variables (NCEP and CGCM3) without involving any rainfall-runoff models.", notes = "Civil Engineering Department, Gaziantep University, Gaziantep, Turkey", } @PhdThesis{Togelius:thesis, author = "Julian Togelius", title = "Optimization, Imitation and Innovation: Computational Intelligence and Games", school = "Department of Computer Science, University of Essex", year = "2007", address = "UK", month = sep, keywords = "genetic algorithms, genetic programming", URL = "http://julian.togelius.com/thesis.pdf", size = "202 pages", abstract = "This thesis concerns the application of computational intelligence techniques, mainly neural networks and evolutionary computation, to computer games. This research has three parallel and non-exclusive goals: to develop ways of testing machine learning algorithms, to augment the entertainment value of computer games, and to study the conditions under which complex general intelligence can evolve. Each of these goals is discussed at some length, and the research described is also discussed in the light of current open questions in computational intelligence in general and evolutionary robotics in particular. A number of experiments are presented, divided into three chapters: optimisation, imitation and innovation. The experiments in the optimization chapter deals with optimising certain aspects of computer games using unambiguous fitness measures and evolutionary algorithms or other reinforcement learning algorithms. In the imitation chapter, supervised learning techniques are used to imitate aspects of behaviour or dynamics. Finally, the innovation chapter provides examples of using evolutionary algorithms not as pure optimisers, but rather as innovating new behaviour or structures using complex, nontrivial fitness measures. Most of the experiments in this thesis are performed in one of two games based on a simple car racing simulator, and one of the experiments extends this simulator to the control of a real world radio-controlled model car. The other games that are used as experimental environments are a helicopter simulation game and the multi-agent foraging game Cellz. Among the main achievements of the thesis are a method for personalised content creation based on modelling player behaviour and evolving new game content (such as racing tracks), a method for evolving control for non-recoverable robots (such as racing cars) using multiple models, and a method for multi-population competitive co-evolution.", } @InProceedings{Togelius:2007:cec, author = "Julian Togelius and Peter Burrow and Simon M. Lucas", title = "Multi-Population Competitive Co-Evolution of Car Racing Controllers", booktitle = "2007 IEEE Congress on Evolutionary Computation", year = "2007", editor = "Dipti Srinivasan and Lipo Wang", pages = "4043--4050", address = "Singapore", month = "25-28 " # sep, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "1-4244-1340-0", file = "1549.pdf", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2007.4424998", abstract = "Multi-population competitive co-evolution is explored as a way of developing controllers for a simple (but definitely not trivial) car racing game. The three main uses we see for this method are to evolve more complex general intelligence than would be possible with other methods, to compare different evolvable architectures for controllers, and to develop behaviourally diverse populations of agents for computer games. Nine-population co-evolution is compared with single-population co-evolution and standard evolution strategies, steady-state and generational versions of the algorithm are compared, and a number of different controller architectures are compared with each other.", notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C", } @InProceedings{Togelius:2008:cec, author = "Julian Togelius and Renzo {De Nardi} and Alberto Moraglio", title = "Geometric PSO + GP = Particle Swarm Programming", booktitle = "Proceedings of the IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "3594--3600", address = "Hong Kong", month = "1-6 " # jun, organisation = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1823-7", file = "EC0771.pdf", keywords = "genetic algorithms, genetic programming, PSO", URL = "http://julian.togelius.com/Togelius2008Geometric.pdf", DOI = "doi:10.1109/CEC.2008.4631284", size = "7 pages", abstract = "Geometric particle swarm optimisation (GPSO) is a recently introduced formal generalisation of traditional particle swarm optimization (PSO) that applies naturally to both continuous and combinatorial spaces. In this paper we apply GPSO to the space of genetic programs represented as expression trees, uniting the paradigms of genetic programming and particle swarm optimisation. The result is a particle swarm flying through the space of genetic programs. We present initial experimental results for our new algorithm.", notes = "Santa Fe ant, sextic polynomial CEC 2008 - A joint meeting of the IEEE, the EPS, and the IET. WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET. INSPEC Accession Number: 10250984", } @InProceedings{Togelius2:2008:cec, author = "Julian Togelius and Renzo {De Nardi} and Alberto Moraglio", title = "Geometric PSO + GP = Particle Swarm Programming", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "3594--3600", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", notes = "Duplicate of \cite{Togelius:2008:cec} Keywords etc removed May 2016", } @Article{Togelius:2008:GPEM, author = "Julian Togelius and Simon Lucas and Ho Duc Thang and Jonathan M. Garibaldi and Tomoharu Nakashima and Chin Hiong Tan and Itamar Elhanany and Shay Berant and Philip Hingston and Robert M. MacCallum and Thomas Haferlach and Aravind Gowrisankar and Pete Burrow", title = "The 2007 IEEE CEC simulated car racing competition", journal = "Genetic Programming and Evolvable Machines", year = "2008", volume = "9", number = "4", pages = "295--329", month = dec, keywords = "genetic algorithms, genetic programming, ANN, NEAT, CoSyNE, grammar-based PerlGP, Java, fuzzy, RL", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-008-9063-0", abstract = "This paper describes the simulated car racing competition that was arranged as part of the 2007 IEEE Congress on Evolutionary Computation. Both the game that was used as the domain for the competition, the controllers submitted as entries to the competition and its results are presented. With this paper, we hope to provide some insight into the efficacy of various computational intelligence methods on a well-defined game task, as well as an example of one way of running a competition. In the process, we provide a set of reference results for those who wish to use the simple race game to benchmark their own algorithms. The paper is co-authored by the organisers and participants of the competition.", notes = "http://julian.togelius.com/cec2007competition/", } @InProceedings{Togelius:2010:cec, author = "Julian Togelius and Sergey Karakovskiy and Robin Baumgarten", title = "The 2009 Mario AI Competition", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-6910-9", abstract = "This paper describes the 2009 Mario AI Competition, which was run in association with the IEEE Games Innovation Conference and the IEEE Symposium on Computational Intelligence and Games. The focus of the competition was on developing controllers that could play a version of Super Mario Bros as well as possible. We describe the motivations for holding this competition, the challenges associated with developing artificial intelligence for platform games, the software and API developed for the competition, the competition rules and organization, the submitted controllers and the results. We conclude the paper by discussing what the outcomes of the competition can teach us both about developing platform game AI and about organising game AI competitions. The first two authors are the organizers of the competition, while the third author is the winner of the competition.", DOI = "doi:10.1109/CEC.2010.5586133", notes = "WCCI 2010. Also known as \cite{5586133}", } @Article{Togelius:2011:TCIAIG, author = "Julian Togelius and Georgios N. Yannakakis and Kenneth O. Stanley and Cameron Browne", title = "Search-based Procedural Content Generation: A Taxonomy and Survey", journal = "IEEE Transactions on Computational Intelligence and AI in Games", year = "2011", volume = "3", number = "3", pages = "172--186", month = sep, keywords = "genetic algorithms, genetic programming, Algorithm design and analysis, Buildings, Encoding, Evolutionary computation, Games, Optimisation, Weapons, computer graphics, search problems, SBSE, evolutionary algorithm, metaheuristic search algorithm, search-based procedural content generation, Computer graphics, design automation", ISSN = "1943-068X", URL = "http://julian.togelius.com/Togelius2011Searchbased.pdf", DOI = "doi:10.1109/TCIAIG.2011.2148116", size = "15 pages", abstract = "The focus of this survey is on research in applying evolutionary and other metaheuristic search algorithms to automatically generating content for games, both digital and nondigital (such as board games). The term search-based procedural content generation is proposed as the name for this emerging field, which at present is growing quickly. A taxonomy for procedural content generation is devised, centring on what kind of content is generated, how the content is represented and how the quality/fitness of the content is evaluated; search-based procedural content generation in particular is situated within this taxonomy. This article also contains a survey of all published papers known to the authors in which game content is generated through search or optimisation, and ends with an overview of important open research problems.", } @Article{Togelius:2013:GPEM, author = "Julian Togelius and Mike Preuss and Nicola Beume and Simon Wessing and Johan Hagelback and Georgios N. Yannakakis and Corrado Grappiolo", title = "Controllable procedural map generation via multiobjective evolution", journal = "Genetic Programming and Evolvable Machines", year = "2013", volume = "14", number = "2", pages = "245--277", month = jun, keywords = "genetic algorithms, Real-time strategy games, RTS, Procedural content generation, Evolutionary computation, Pareto, Multiobjective optimisation, StarCraft", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-012-9174-5", size = "33 pages", abstract = "This paper shows how multiobjective evolutionary algorithms can be used to procedurally generate complete and playable maps for real-time strategy (RTS) games. We devise heuristic objective functions that measure properties of maps that impact important aspects of gameplay experience. To show the generality of our approach, we design two different evolvable map representations, one for an imaginary generic strategy game based on Hartman's, and one for the classic RTS game StarCraft. The effect of combining tuples or triples of the objective functions are investigated in systematic experiments, in particular which of the objectives are partially conflicting. A selection of generated maps are visually evaluated by a population of skilled StarCraft players, confirming that most of our objectives correspond to perceived gameplay qualities. Our method could be used to completely automate in-game controlled map generation, enabling player-adaptive games, or as a design support tool for human designers.", } @InProceedings{Togelius:2020:GECCOcomp, author = "Julian Togelius and Sebastian Risi and Georgios N. Yannakakis", title = "Evolutionary Computation and Games", year = "2020", editor = "Richard Allmendinger and Hugo Terashima Marin and Efren Mezura Montes and Thomas Bartz-Beielstein and Bogdan Filipic and Ke Tang and David Howard and Emma Hart and Gusz Eiben and Tome Eftimov and William {La Cava} and Boris Naujoks and Pietro Oliveto and Vanessa Volz and Thomas Weise and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Rui Wang and Ran Cheng and Guohua Wu and Miqing Li and Hisao Ishibuchi and Jonathan Fieldsend and Ozgur Akman and Khulood Alyahya and Juergen Branke and John R. Woodward and Daniel R. Tauritz and Marco Baioletti and Josu Ceberio Uribe and John McCall and Alfredo Milani and Stefan Wagner and Michael Affenzeller and Bradley Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and Nayat Sanchez-Pi and Luis Marti and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and Matthew Johns and Nick Ross and Ed Keedwell and Herman Mahmoud and David Walker and Anthony Stein and Masaya Nakata and David Paetzel and Neil Vaughan and Stephen Smith and Stefano Cagnoni and Robert M. Patton and Ivanoe {De Falco} and Antonio {Della Cioppa} and Umberto Scafuri and Ernesto Tarantino and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Erik Hemberg and Riyad Alshammari and Tokunbo Makanju and Fuijimino-shi and Ivan Zelinka and Swagatam Das and Ponnuthurai Nagaratnam and Roman Senkerik", isbn13 = "9781450371278", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion", pages = "620--651", size = "32 pages", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", note = "Tutorial", keywords = "genetic algorithms, genetic programming", URL = "https://www.um.edu.mt/library/oar/bitstream/123456789/80906/1/Evolutionary_computation_and_games_2020.pdf", URL = "https://doi.org/10.1145/3377929.3389854", DOI = "doi:10.1145/3377929.3389854", video_url = "https://www.youtube.com/watch?v=TCcJiwYbQto", notes = "Also known as \cite{10.1145/3377929.3389854} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @Article{Togun20103401, author = "Necla Togun and Sedat Baysec", title = "Genetic programming approach to predict torque and brake specific fuel consumption of a gasoline engine", journal = "Applied Energy", volume = "87", number = "11", pages = "3401--3408", year = "2010", ISSN = "0306-2619", DOI = "doi:10.1016/j.apenergy.2010.04.027", URL = "http://www.sciencedirect.com/science/article/B6V1T-506P5PR-2/2/3ce08e476cfb1819b6e03a4571cad2cd", keywords = "genetic algorithms, genetic programming, Gasoline engine, Torque, Brake specific fuel consumption, Explicit solution, Modelling engine", abstract = "This study presents genetic programming (GP) based model to predict the torque and brake specific fuel consumption a gasoline engine in terms of spark advance, throttle position and engine speed. The objective of this study is to develop an alternative robust formulations based on experimental data and to verify the use of GP for generating the formulations for gasoline engine torque and brake specific fuel consumption. Experimental studies were completed to obtain training and testing data. Of all 81 data sets, the training and testing sets consisted of randomly selected 63 and 18 sets, respectively. Considerable good performance was achieved in predicting gasoline engine torque and brake specific fuel consumption by using GP. The performance of accuracies of proposed GP models are quite satisfactory (R2 = 0.9878 for gasoline engine torque and R2 = 0.9744 for gasoline engine brake specific fuel consumption). The prediction of proposed GP models were compared to those of the neural network modeling, and strictly good agreement was observed between the two predictions. The proposed GP formulation is quite accurate, fast and practical.", } @Article{Tohumoglu:2007:CMPB, author = "Gulay Tohumoglu and Ayse G. Canseven and Abdulkadir Cevik and Nesrin Seyhan", title = "Formulation of {ELF} magnetic fields' effects on malondialdehyde level and myeloperoxidase activity in kidney using genetic programming", journal = "Computer Methods and Programs in Biomedicine", year = "2007", volume = "86", number = "1", pages = "1--9", month = apr, keywords = "genetic algorithms, genetic programming, MDA level, MPO activity, ELF magnetic fields", DOI = "doi:10.1016/j.cmpb.2006.12.006", abstract = "In vivo exposure effects of electromagnetic fields (EMFs) on various tissues of experiment animals have been investigated. In this sense, modelling and formulation of these biological effects have been of significant importance. In this study extremely low frequency (ELF) EMFs effects on malondialdehyde (MDA) level and myeloperoxidase (MPO) activity in kidney of guinea pigs exposed to 50 Hz magnetic fields of 1 mT, 2 mT and 3 mT have been presented. It has been planned to determine whether genetic programming (GP) is appropriate to analyse and formulate these biological effects. Consequently, it has been observed that GP can be effectively used to model MDA level and MPO activity. The performances of prediction of the proposed GP formulation versus actual experimental values are found to be quite satisfactory in terms of standard deviation and correlation coefficient. It is concluded that the GP application serves to form a database for the researchers in this field, without exposing tissues to EMF and without using too many guinea pigs.", } @InProceedings{Tokuhara:2016:IWCIA, author = "Fumiya Tokuhara and Tetsuhiro Miyahara and Yusuke Suzuki and Tomoyuki Uchida and Tetsuji Kuboyama", booktitle = "2016 IEEE 9th International Workshop on Computational Intelligence and Applications (IWCIA)", title = "Using canonical representations of block tree patterns in acquisition of characteristic block preserving outerplanar graph patterns", year = "2016", pages = "93--99", abstract = "We consider evolutionary learning, based on Genetic Programming, for acquiring characteristic graph structures from positive and negative outer planar graph data. We use block preserving outer planar graph patterns as representations of graph structures. Block tree patterns are tree representations of block preserving outer planar patterns, and have the structure of unrooted trees some of whose vertices have ordered adjacent vertices. In this paper we propose canonical representations, which are representations having the structure of rooted and ordered trees, of block tree patterns in acquiring characteristic block preserving outerplanar graph patterns. Then we give an algorithm for calculating canonical representations of block tree patterns. Preliminary experimental results show the algorithm is effective in reducing the run time of our evolutionary learning method.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IWCIA.2016.7805755", month = nov, notes = "Also known as \cite{7805755}", } @InProceedings{Tokuhara:2016:IIAI-AAI, author = "Fumiya Tokuhara and Tetsuhiro Miyahara and Yusuke Suzuki and Tomoyuki Uchida and Tetsuji Kuboyama", booktitle = "2016 5th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI)", title = "Acquisition of Characteristic Block Preserving Outerplanar Graph Patterns by Genetic Programming Using Label Information", year = "2016", pages = "203--210", abstract = "Many chemical compounds can be expressed by a class of graphs called outer-planar graphs. By taking advantage of this tractable class of graphs, we use block preserving outerplanar graph patterns having structured variables for expressing structural features of outerplanar graphs. We propose a method for acquiring characteristic block preserving outerplanar graph patterns from positive and negative outerplanar graph data by Genetic Programming using vertex and edge label information of positive examples. We report experimental results on real chemical compound data.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IIAI-AAI.2016.212", month = jul, notes = "Also known as \cite{7557603}", } @InProceedings{Tokuhara:2017:IWCIA, author = "Fumiya Tokuhara and Tetsuhiro Miyahara and Tetsuji Kuboyama and Yusuke Suzuki and Tomoyuki Uchida", booktitle = "2017 IEEE 10th International Workshop on Computational Intelligence and Applications (IWCIA)", title = "Acquisition of multiple block preserving outerplanar graph patterns by an evolutionary method for graph pattern sets", year = "2017", pages = "191--197", abstract = "Knowledge acquisition from graph structured data is an important task in machine learning and data mining. Block preserving outer planar graph patterns are graph structured patterns having structured variables and are suited to represent characteristic graph structures of graph data modelled as outerplanar graphs. We propose a learning method for acquiring characteristic multiple block preserving outerplanar graph patterns by evolutionary computation using graph pattern sets as individuals, from positive and negative outerplanar graph data, in order to represent characteristic graph structures more precisely.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IWCIA.2017.8203583", month = nov, notes = "Also known as \cite{8203583}", } @InProceedings{Tokuhara:2019:IWCIA, author = "Fumiya Tokuhara and Shiho Okinaga and Tetsuhiro Miyahara and Yusuke Suzuki and Tetsuji Kuboyama and Tomoyuki Uchida", title = "Using Label Information in a Genetic Programming Based Method for Acquiring Block Preserving Outerplanar Graph Patterns with Wildcards", booktitle = "2019 IEEE 11th International Workshop on Computational Intelligence and Applications (IWCIA)", year = "2019", pages = "95--100", month = nov, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IWCIA47330.2019.8955031", ISSN = "1883-3977", abstract = "Machine learning and data mining from graph structured data have gained much attention. Many chemical compounds can be expressed by outerplanar graphs. We propose a method for acquiring characteristic block preserving outerplanar graph patterns with wildcards for vertex and edge labels, from positive and negative outerplanar graph data, by Genetic Programming using label connecting information of positive examples. We report experimental results on real chemical compound data and synthetic data.", notes = "Faculty and Graduate School of Information Sciences, Hiroshima City University, Hiroshima, Japan Also known as \cite{8955031}", } @InProceedings{DBLP:conf/gecco/TolayK09, author = "Paresh Tolay and Rajeev Kumar", title = "Evolution of hyperheuristics for the biobjective graph coloring problem using multiobjective genetic programming", booktitle = "GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation", year = "2009", editor = "Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano {van Hemert} and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique Alba", pages = "1939--1940", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming, Poster", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1569901.1570247", abstract = "We consider a formulation of the biobjective soft graph coloring problem so as to simultaneously minimize the number of colors used as well as the number of edges that connect vertices of the same color. We aim to evolve hyperheuristics for this class of problem using multiobjective genetic programming (MOGP). The major advantage being that these hyperheuristics can then be applied to any instance of this problem. We test the hyperheuristics on benchmark graph coloring problems, and in the absence of an actual Pareto-front, we compare the solutions obtained with existing heuristics. We then further improve the quality of hyperheuristics evolved, and try to make them closer to human-designed heuristics.", notes = "GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009). ACM Order Number 910092.", } @InProceedings{tomassini:ppsn2002:pp641, author = "Marco Tomassini and Leonardo Vanneschi and Francisco Fernandez and German Galeano", title = "Experimental Investigation Of Three Distributed Genetic Programming Models", booktitle = "Parallel Problem Solving from Nature - PPSN VII", address = "Granada, Spain", month = "7-11 " # sep, pages = "641--650", year = "2002", editor = "Juan J. Merelo-Guervos and Panagiotis Adamidis and Hans-Georg Beyer and Jose-Luis Fernandez-Villacanas and Hans-Paul Schwefel", number = "2439", series = "Lecture Notes in Computer Science, LNCS", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, Parallel EAs", ISBN = "3-540-44139-5", DOI = "doi:10.1007/3-540-45712-7_62", abstract = "Three models of distributed Genetic Programming are presented comprising synchronous and asynchronous communication. These three models are compared with each other and with the standard panmictic model on three well known Genetic Programming benchmarks. The measures used are the computational effort, the phenotypic entropy of the populations, and the execution time. We find that all the distributed models are better than the sequential one in terms of effort and time. The differences among the distributed models themselves are rather small in terms of effort but one of the asynchronous models turns out to be significantly faster. The entropy confirms that migration helps in conserving some phenotypic diversity in the populations.", } @InProceedings{tomassini:2003:gecco, author = "Marco Tomassini and Leonardo Vanneschi and Francisco Fern{\'a}ndez and Germ{\'a}n Galeano", title = "Diversity in Multipopulation Genetic Programming", booktitle = "Genetic and Evolutionary Computation -- GECCO-2003", editor = "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller", year = "2003", pages = "1812--1813", address = "Chicago", publisher_address = "Berlin", month = "12-16 " # jul, volume = "2724", series = "LNCS", ISBN = "3-540-40603-4", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, poster", URL = "http://personal.disco.unimib.it/Vanneschi/GECCO_2003_Diversity.pdf", DOI = "doi:10.1007/3-540-45110-2_77", abstract = "In the past few years, we have done a systematic experimental investigation of the behavior of multipopulation GP [2] and we have empirically observed that distributing the individuals among several loosely connected islands allows not only to save computation time, due to the fact that the system runs on multiple machines, but also to find better solution quality. These results have often been attributed to better diversity maintenance due to the periodic migration of groups of {"}good{"} individuals among the subpopulations. We also believe that this might be the case and we study the evolution of diversity in multi-island GP. All the diversity measures that we use in this paper are based on the concept of entropy of a population , defined as . If we are considering phenotypic diversity, we define Fj as the fraction of individuals in having a certain fitness , where is the total number of fitness values in . In this case, the entropy measure will be indicated as or simply Hp. To define genotypic diversity, we use two different techniques. The first one consists in partitioning individuals in such a way that only identical individuals belong to the same group. In this case, we have considered Fj as the fraction of trees in the population having a certain genotype , where is the total number of genotypes in and the entropy measure will be indicated as or simply HG. The second technique consists in defining a distance measure, able to quantify the genotypic diversity between two trees. In this case, Fj is the fraction of individuals having a given distance from a fixed tree (called origin), where is the total number of distance values from the origin appearing in and the entropy measure will be indicated as or simply Hg. The tree distance used is Ekart's and Nemeth's definition [1].", notes = "GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)", } @InProceedings{tomassini:2003:EA, author = "Marco Tomassini and Leonardo Vanneschi and Francisco Fernandez and German Galeano", title = "A Study of Diversity in Multipopulation Genetic Programming", booktitle = "Evolution Artificielle, 6th International Conference", year = "2003", editor = "Pierre Liardet and Pierre Collet and Cyril Fonlupt and Evelyne Lutton and Marc Schoenauer", volume = "2936", series = "Lecture Notes in Computer Science", pages = "243--255", address = "Marseilles, France", month = "27-30 " # oct, publisher = "Springer", note = "Revised Selected Papers", keywords = "genetic algorithms, genetic programming, Artificial Evolution", ISBN = "3-540-21523-9", URL = "http://personal.disco.unimib.it/Vanneschi/EA_2003_Diversity.pdf", DOI = "doi:10.1007/b96080", DOI = "doi:10.1007/978-3-540-24621-3_20", size = "13 pages", abstract = "using multiple communicating populations instead of a single panmictic one may help in maintaining diversity during GP runs. After defining suitable genotypic and phenotypic diversity measures, we apply them to three standard test problems. The experimental results indicate that using multiple populations helps in maintaining phenotypic diversity. We hypothesise that this could be one of the reasons for the better performance observed for distributed GP with respect to panmictic GP. Finally, we trace a sort of history of the optimum individual for a set of distributed GP runs, trying to understand the dynamics that help in maintaining diversity in distributed GP.", bibsource = "DBLP, http://dblp.uni-trier.de", notes = "EA'03", } @InProceedings{tomassini:2004:antfdspigp, title = "A New Technique for Dynamic Size Populations in Genetic Programming", author = "Marco Tomassini and Leonardo Vanneschi and Jerome Cuendet and Francisco Fernandez", pages = "486--493", booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary Computation", year = "2004", publisher = "IEEE Press", month = "20-23 " # jun, address = "Portland, Oregon", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Theory of evolutionary algorithms, Multiobjective evolutionary algorithms", DOI = "doi:10.1109/CEC.2004.1330896", abstract = "New techniques for dynamically changing the size of populations during the execution of genetic programming systems are proposed. Two models are presented, allowing to add and suppress individuals on the basis of some particular events occurring during the evolution. These models allow to find solutions of better quality, to save considerable amounts of computational effort and to find optimal solutions more quickly, at least for the set of problems studied here, namely the artificial ant on the Santa Fe trail, the even parity 5 problem and one instance of the symbolic regression problem. Furthermore, these models have a positive effect on the well known problem of bloat and act without introducing additional computational cost.", notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.", } @Book{Tomassini:book, author = "Marco Tomassini", title = "Spatially Structured Evolutionary Algorithms: Artificial Evolution in Space and Time", publisher = "Springer", year = "2005", volume = "17?", series = "Natural Computing Series", keywords = "genetic algorithms, genetic programming, Island EAs, Asynchronous Islands, Lattice Cellular EAs, MPI, MIMD, MPICH, GPC++, PRNG", isbn13 = "978-3-642-06339-8", URL = "https://link.springer.com/book/10.1007/3-540-29938-6", DOI = "doi:10.1007/3-540-29938-6", size = "199 pages", abstract = "Uses graph concepts as a unifying theme to express the state of the art in spatially structured evolutionary algorithms Includes new material on non-standard networked population structures such as small-world networks This is the first book devoted to spatial and temporal aspects of genetic algorithms. Useful to a broad readership of students and researchers in fields involving the topological structures of populations and their evolution Includes supplementary material: sn.pub/extras Setting the Stage for Structured Populations Island Models Island Models: Empirical Properties Lattice Cellular Models Lattice Cellular Models: Empirical Properties Random and Irregular Cellular Populations Coevolutionary Structured Models Some Nonconventional Models", } @Article{tomassini:2005:EC, author = "Marco Tomassini and Leonardo Vanneschi and Philippe Collard and Manuel Clergue", title = "A Study of Fitness Distance Correlation as a Difficulty Measure in Genetic Programming", journal = "Evolutionary Computation", year = "2005", volume = "13", number = "2", pages = "213--239", month = "Summer", keywords = "genetic algorithms, genetic programming, problem difficulty, program landscapes, fitness distance correlation", ISSN = "1063-6560", DOI = "doi:10.1162/1063656054088549", size = "27 pages", abstract = "We present an approach to genetic programming difficulty based on a statistical study of program fitness landscapes. The fitness distance correlation is used as an indicator of problem hardness and we empirically show that such a statistic is adequate in nearly all cases studied here. However, fitness distance correlation has some known problems and these are investigated by constructing an artificial landscape for which the correlation gives contradictory indications. Although our results confirm the usefulness of fitness distance correlation, we point out its shortcomings and give some hints for improvement in assessing problem hardness in genetic programming.", publisher = "MIT Press", notes = "http://mitpress.mit.edu/catalog/item/default.asp?ttype=4&tid=25", } @Article{Tomassini:2007:GPEM, author = "Marco Tomassini and L. Luthi and M. Giacobini and W. B. Langdon", title = "The Structure of the Genetic Programming Collaboration Network", journal = "Genetic Programming and Evolvable Machines", year = "2007", volume = "8", number = "1", pages = "97--103", month = mar, keywords = "genetic algorithms, genetic programming, scientific collaboration, social networks, communities", ISSN = "1389-2576", URL = "https://rdcu.be/c39D3", URL = "http://arxiv.org/abs/0704.0551", DOI = "doi:10.1007/s10710-006-9018-2", data_url = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/gp-code/Tomassini_2007_GPEM/16-jan-2007.tar.gz", size = "7 pages", abstract = "The genetic programming bibliography aims to be the most complete reference of papers on genetic programming. In addition to locating publications, it contains coauthor and coeditor relationships which have not previously been studied. These reveal some similarities and differences between our field and collaborative social networks in other scientific fields.", notes = "letter Data describing evolution of GP bibliography social network coauthor cooperation small world graph available via ftp and http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/gp-code/Tomassini_2007_GPEM/16-jan-2007.tar.gz", } @Article{evol-gp-PhysA-final, author = "Marco Tomassini and Leslie Luthi", title = "Empirical analysis of the evolution of a scientific collaboration network", journal = "Physica A", year = "2007", volume = "385", pages = "750--764", note = "Available online 25 July 2007", keywords = "genetic algorithms, genetic programming, Network evolution, Preferential attachment, Scientific collaboration, Social networks", ISSN = "0378-4371", URL = "http://www.sciencedirect.com/science/article/B6TVG-4P8GWXG-7/2/5836255114267d1a22b1d1fa47215fc9", DOI = "doi:10.1016/j.physa.2007.07.028", size = "15 pages", abstract = "We present an analysis of the temporal evolution of a scientific coauthorship network, the genetic programming network. We find evidence that the network grows according to preferential attachment, with a slightly sublinear rate. We empirically find how a giant component forms and develops, and we characterise the network by several other time-varying quantities: the mean degree, the clustering coefficient, the average path length, and the degree distribution. We find that the first three statistics increase over time in the growing network; the degree distribution tends to stabilise toward an exponentially truncated power-law. We finally suggest an effective network interpretation that takes into account the aging of collaboration relationships.", notes = " ", } @Article{Tomassini:2009:GPEM, author = "Marco Tomassini and Leonardo Vanneschi", title = "Introduction: special issue on parallel and distributed evolutionary algorithms, part I", journal = "Genetic Programming and Evolvable Machines", year = "2009", volume = "10", number = "4", pages = "339--341", month = dec, ISSN = "1389-2576", DOI = "doi:10.1007/s10710-009-9094-1", size = "3 pages", notes = "Editorial Special issue on parallel and distributed evolutionary algorithms, part I See \cite{Tomassini:2010:GPEM} for part 2", } @Article{Tomassini:2010:GPEM, author = "Marco Tomassini and Leonardo Vanneschi", title = "Guest editorial: special issue on parallel and distributed evolutionary algorithms, part two", journal = "Genetic Programming and Evolvable Machines", year = "2010", volume = "11", number = "2", pages = "129--130", month = jun, note = "Editorial special issue on parallel and distributed evolutionary algorithms, part two", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-010-9106-1", size = "2 pages", notes = "See \cite{Tomassini:2009:GPEM} for part 1", } @InProceedings{tominaga:2003:gecco, author = "Kazuto Tominaga and Tomoya Suzuki and Kazuhiro Oka", title = "An Encoding Scheme for Generating lambda-Expressions in Genetic Programming", booktitle = "Genetic and Evolutionary Computation -- GECCO-2003", editor = "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller", year = "2003", pages = "1814--1815", address = "Chicago", publisher_address = "Berlin", month = "12-16 " # jul, volume = "2724", series = "LNCS", ISBN = "3-540-40603-4", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, poster", URL = "https://rdcu.be/dqNGr", DOI = "doi:10.1007/3-540-45110-2_78", size = "2 pages", abstract = "To apply genetic programming (GP) to evolve -expressions, we devised an encoding scheme that encodes -expressions into trees. This encoding has closure property, i.e., any combination of terminal and non-terminal symbols forms a valid -expression. We applied this encoding to a simple symbolic regression problem over Church numerals and the objective function was successfully obtained. This encoding scheme will provide a good foothold for exploring fundamental properties of GP by making use of lambda-calculus.", notes = "GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)", } @Article{Yasuyuki_Tomita2008154, title = "A Motif Detection and Classification Method for Peptide Sequences Using Genetic Programming", author = "Yasuyuki Tomita and Ryuji Kato and Mina Okochi and Hiroyuki Honda", journal = "Journal of Bioscience and Bioengineering", volume = "106", number = "2", pages = "154--161", year = "2008", publisher = "The Society for Biotechnology, Japan", keywords = "genetic algorithms, genetic programming, property motif, peptide, alignment, fuzzy neural network", DOI = "doi:10.1263/jbb.106.154", abstract = "An exploration of common rules (property motifs) in amino acid sequences has been required for the design of novel sequences and elucidation of the interactions between molecules controlled by the structural or physical environment. In the present study, we developed a new method to search property motifs that are common in peptide sequence data. Our method comprises the following two characteristics: (i) the automatic determination of the position and length of common property motifs by calculating the physicochemical similarity of amino acids, and (ii) the quick and effective exploration of motif candidates that discriminates the positives and negatives by the introduction of genetic programming (GP). Our method was evaluated by two types of model data sets. First, the intentionally buried property motifs were searched in the artificially derived peptide data containing intentionally buried property motifs. As a result, the expected property motifs were correctly extracted by our algorithm. Second, the peptide data that interact with MHC class II molecules were analysed as one of the models of biologically active peptides with buried motifs in various lengths. Twofold MHC class II binding peptides were identified with the rule using our method, compared to the existing scoring matrix method. In conclusion, our GP based motif searching approach enabled to obtain knowledge of functional aspects of the peptides without any prior knowledge.", notes = "Department of Biotechnology, School of Engineering, Nagoya University, Nagoya, Japan. PMID: 18804058 [PubMed - in process]", } @InProceedings{tomlinson:1999:OCCSIBRL, author = "Andy Tomlinson and Larry Bull", title = "On Corporate Classifier Systems: Increasing the Benefits of Rule Linkage", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "649--656", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Tomoyuki:2015:CEC, author = "Hiroyasu Tomoyuki and Shiraishi Toshihide and Yoshida Tomoya and Yamamoto Utako", title = "A Feature Transformation Method using Multiobjective Genetic Programming for Two-Class Classification", booktitle = "Proceedings of 2015 IEEE Congress on Evolutionary Computation (CEC 2015)", year = "2015", editor = "Yadahiko Murata", pages = "2989--2995", address = "Sendai, Japan", month = "25-28 " # may, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-7491-7", DOI = "doi:10.1109/CEC.2015.7257261", abstract = "In this paper, we investigate a method of performing feature transformation on input data in a 1-dimensional space in order to increase the accuracy of classifiers. Through optimized feature transformation, it is possible to create data which generate the models with high accuracy than the original data. We use Genetic Programming (GP) to find a feature transformation function. We proposed evaluation functions using GP and have been successful in finding transformation functions with a high degree of accuracy. On the other hand, where there is a deviation in the number of data items belonging to multiple classes, classes with a large number of data items are more accurate than those that do not. In order to resolve this, referring to existing research, we examined a method of handling the problem of improving accuracy and correcting class imbalanced accuracy from the generated models based on multi-purpose optimization. We then investigated the method of multi-purpose optimization and how to determine the threshold for classification. The results of the investigation were that we could obtain a transformation function that was more accurate and could consider the accuracy of multiple classes simultaneously.", notes = "1320 hrs 15569 CEC2015", } @InProceedings{tonda:2012:EuroGP, author = "Alberto Paolo Tonda and Evelyne Lutton and Romain Reuillon and Giovanni Squillero and Pierre-Henri Wuillemin", title = "Bayesian Network Structure Learning from Limited Datasets through Graph Evolution", booktitle = "Proceedings of the 15th European Conference on Genetic Programming, EuroGP 2012", year = "2012", month = "11-13 " # apr, editor = "Alberto Moraglio and Sara Silva and Krzysztof Krawiec and Penousal Machado and Carlos Cotta", series = "LNCS", volume = "7244", publisher = "Springer Verlag", address = "Malaga, Spain", pages = "254--265", organisation = "EvoStar", isbn13 = "978-3-642-29138-8", DOI = "doi:10.1007/978-3-642-29139-5_22", keywords = "genetic algorithms, genetic programming, Evolutionary computation, Bayesian network structure learning, Bayesian networks, Graph representation", abstract = "Bayesian networks are stochastic models, widely adopted to encode knowledge in several fields. One of the most interesting features of a Bayesian network is the possibility of learning its structure from a set of data, and subsequently use the resulting model to perform new predictions. Structure learning for such models is a NP-hard problem, for which the scientific community developed two main approaches: score-and-search metaheuristics, often evolutionary-based, and dependency-analysis deterministic algorithms, based on stochastic tests. State-of-the-art solutions have been presented in both domains, but all methodologies start from the assumption of having access to large sets of learning data available, often numbering thousands of samples. This is not the case for many real-world applications, especially in the food processing and research industry. This paper proposes an evolutionary approach to the Bayesian structure learning problem, specifically tailored for learning sets of limited size. Falling in the category of score-and-search techniques, the methodology exploits an evolutionary algorithm able to work directly on graph structures, previously used for assembly language generation, and a scoring function based on the Akaike Information Criterion, a well-studied metric of stochastic model performance. Experimental results show that the approach is able to outperform a state-of-the-art dependency-analysis algorithm, providing better models for small datasets.", notes = "Part of \cite{Moraglio:2012:GP} EuroGP'2012 held in conjunction with EvoCOP2012 EvoBIO2012, EvoMusArt2012 and EvoApplications2012", } @Article{Tonda:GPEM:Inspyred, author = "Alberto Tonda", title = "{Inspyred}: Bio-inspired algorithms in {Python}", journal = "Genetic Programming and Evolvable Machines", year = "2020", volume = "21", number = "1-2", pages = "269--272", month = jun, note = "Software Review", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dR8XA", DOI = "doi:10.1007/s10710-019-09367-z", size = "4 pages", notes = "https​://aaron​garre​tt.githu​b.io/woffo​rd-webs/)", } @InProceedings{tonda:2023:GECCO, author = "Alberto Tonda and Isabelle Alvarez and Sophie Martin and Giovanni Squillero and Evelyne Lutton", title = "Towards Evolutionary Control Laws for Viability Problems", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "1464--1472", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, viable feedback, machine learning, viability theory", isbn13 = "9798400701191", DOI = "doi:10.1145/3583131.3590415", size = "9 pages", abstract = "The mathematical theory of viability, developed to formalize problems related to natural and social phenomena, investigates the evolution of dynamical systems under constraints. A main objective of this theory is to design control laws to keep systems inside viable domains. Control laws are traditionally defined as rules, based on the current position in the state space with respect to the boundaries of the viability kernel. However, finding these boundaries is a computationally expensive procedure, feasible only for trivial systems. We propose an approach based on Genetic Programming (GP) to discover control laws for viability problems in analytic form. Such laws could keep a system viable without the need of computing its viability kernel, facilitate communication with stakeholders, and improve explainability. A candidate set of control rules is encoded as GP trees describing equations. Evaluation is noisy, due to stochastic sampling: initial conditions are randomly drawn from the state space of the problem, and for each, a system of differential equations describing the system is solved, creating a trajectory. Candidate control laws are rewarded for keeping viable as many trajectories as possible, for as long as possible. The proposed approach is evaluated on established benchmarks for viability and delivers promising results.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @Article{Tonda:GPEM:PySR, author = "Alberto Tonda", title = "Review of {PySR}: high-performance symbolic regression in Python and Julia", journal = "Genetic Programming and Evolvable Machines", year = "2025", volume = "26", pages = "Article no 7", note = "Software Review", note = "Online first", keywords = "genetic algorithms, genetic programming, Julia", ISSN = "1389-2576", URL = "https://rdcu.be/d44sw", DOI = "doi:10.1007/s10710-024-09503-4", size = "4 pages", notes = "Applied Mathematics and Computer Science, Paris Saclay Joint Research Unit (UMR 518 MIA-PS), INRAE, Universite Paris-Saclay, Palaiseau, France", } @InProceedings{Tonda:2025:GPTP, author = "Alberto Tonda", title = "Language Model-Driven Program Synthesis with Program Trace Optimization on the Abstraction and Reasoning Corpus", booktitle = "Genetic Programming Theory and Practice XXII", year = "2025", editor = "Wolfgang Banzhaf and Bogdan Burlacu and Stephen Kelly and Alexander Lalejini and Fabricio {Olivetti de Franca}", address = "Michigan State University, USA", month = jun # " 5-7", note = "lightning talk", keywords = "genetic algorithms, genetic programming, ANN, PTO", notes = "To be published after the workshop", } @InProceedings{Tonella:2004:ISSTA, author = "Paolo Tonella", title = "Evolutionary Testing of Classes", booktitle = "Proceedings of the ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA)", year = "2004", editor = "Gregg Rothermel", pages = "119--128", address = "Boston, MA, USA", publisher_address = "New York, NY, USA", month = "11-14 " # jul, publisher = "ACM", keywords = "genetic algorithms, genetic programming, SBSE, Verification, eToc, Java, Junit, object-Oriented testing, automated testcase generation, Testing and Debugging", ISBN = "1-58113-820-2", URL = "http://selab.fbk.eu/tonella/papers/issta2004.ps.gz", DOI = "doi:10.1145/1007512.1007528", size = "10 pages", abstract = "Object oriented programming promotes reuse of classes in multiple contexts. Thus, a class is designed and implemented with several usage scenarios in mind, some of which possibly open and generic. Correspondingly, the unit testing of classes cannot make too strict assumptions on the actual method invocation sequences, since these vary from application to application. In this paper, a genetic algorithm is exploited to automatically produce test cases for the unit testing of classes in a generic usage scenario. Test cases are described by chromosomes, which include information on which objects to create, which methods to invoke and which values to use as inputs. The proposed algorithm mutates them with the aim of maximizing a given coverage measure. The implementation of the algorithm and its application to classes from the Java standard library are described.", notes = "eTOC. Assertion (added by hand) based fitness. (AST visitor) Javac pretty printer customised to print instrumented code. java reflection. CUT: StringTokenizer, BitSet, HashMap, LinkedList, Stack. TreeSet. Also available as SEN article doi:10.1145/1013886.1007528 Also known as \cite{1007528}", } @InProceedings{tonelli:2024:GECCOcomp, author = "Teresa Tonelli and Gloria Pietropolli and Gabriele Sbaiz and Luca Manzoni", title = "Genetic Programming for the Reconstruction of Delay Differential Equations in Economics", booktitle = "GECCO Student Workshop", year = "2024", editor = "Amir H Gandomi", pages = "2119--2122", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, symbolic regression, differential equations, delay differential equations, kalecki's business cycle model", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3664164", size = "4 pages", abstract = "In this paper we explore the reconstruction of a delay differential equation (DDE) model from economics, the Kalecki's business cycle model, with a variant of genetic programming (GP) encoding the structure of DDEs. The results of this preliminary work show that GP can correctly reconstruct the model starting only from approximated data, showing that the investigation of GP for interpretable reconstruction of DDEs can be a worthwile research direction.", notes = "GECCO-2024 Student Workshop A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{ICARCV2000Tongchim, author = "Shisanu Tongchim and Prabhas Chongstitvatana", title = "Nearest Neighbor Migration in Parallel Genetic Programming for Automatic Robot Programming", booktitle = "Proceedings of the Sixth International Conference on Control, Automation, Robotics and Vision", year = "2000", month = dec, address = "Singapore", keywords = "genetic algorithms, genetic programming, Parallel Genetic Programming, Mobile Robot Navigation", URL = "http://www.cp.eng.chula.ac.th/faculty/pjw/paper/tongchim-226.pdf", abstract = "This work presents a study of parallelization of genetic programming for automatically creating a robot control program in a mobile robot navigation problem. A nearest neighbor migration topology is proposed to reduce the communication time. This study compares the performance both in terms of the solution quality and the gain in execution time. The timing analysis is investigated to give insight into the behavior of parallel implementations. The results show that the parallel algorithm with asynchronous migration using 10 processors is 32 times faster than the serial algorithm.", notes = "Citation from author", } @InProceedings{AROB2000, author = "Shisanu Tongchim and Prabhas Chongstitvatana", title = "Comparison between Synchronous and Asynchronous Implementation of Parallel Genetic Programming", booktitle = "Proceedings of the Fifth International Symposium on Artificial Life and Robotics (AROB)", year = "2000", volume = "1", editor = "Masanori Sugisaka and Hirochi Tanaka", pages = "251--254", month = "26-28 " # jan, address = "Oita, Japan", organisation = "International Society for Artificial Life and Robotics (ISAROB)", keywords = "genetic algorithms, genetic programming", broken = "http://isarob.org/index.php?main_page=journal_arob05", URL = "http://www.cp.eng.chula.ac.th/faculty/pjw/paper/2000/arob5.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.26.5542", size = "4 pages", abstract = "An evolutionary method such as Genetic Programming (GP) can be used to solve a large number of complex problems in various application domains. However, one obvious shortcoming of GP is that it usually uses a substantial amount of processing time to arrive at a solution. In this paper, we present the parallel implementations that can reduce the processing time by using a coarse-grained model for parallelisation and an asynchronous migration. The problem chosen to examine the parallel GP is a mobile robot navigation problem. The experimental results show that superlinear speedup of GP can be achieved.", notes = "http://isarob.org/", } @InProceedings{Tongchim:ASIAN99, author = "Shisanu Tongchim and Prabhas Chongstitvatana", title = "Asynchronous Migration in Parallel Genetic Programming", booktitle = "Proceedings of Asian Computing Science Conference", year = "1999", editor = "P. S. Thiagarajan and Roland Yap", volume = "1742", series = "LNCS", pages = "388--389", address = "Phuket, Thailand", publisher_address = "Berlin", month = "10-12 " # dec, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, robot simulation", ISBN = "3-540-66856-X", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/20674/http:zSzzSzwww.cp.eng.chula.ac.thzSzfacultyzSzpjwzSzpaperzSzasian99.pdf/asynchronous-migration-in-parallel.pdf", URL = "http://citeseer.ist.psu.edu/421063.html", size = "2 pages", abstract = "this paper, the quality of the solution is definedintermsoftherobustness. The robustness of the generated programs from the parallel algorithm was demonstrated to be better than the serial algorithm. Consequently, the amount of work from the parallel algorithm in this experiment was not less than the serial algorithm.", } @Article{tongchim:2001:ALR, author = "Shisanu Tongchim and Prabhas Chongstitvatana", title = "Parallel genetic programming: Synchronous and asynchronous migration", journal = "Artificial Life and Robotics", year = "2001", volume = "5", number = "4", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/BF02481500", DOI = "doi:10.1007/BF02481500", } @PhdThesis{Tongchim:thesis, author = "Shisanu Tongchim", title = "Adaptive parameter control in genetic algorithms", school = "Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University", year = "2004", address = "Thailand", keywords = "genetic algorithms, Adaptive control systems", URL = "http://orange.cp.eng.chula.ac.th/ISLLAB/publication.html", URL = "http://orange.cp.eng.chula.ac.th/ISLLAB/Shisanu-thesis.pdf", URL = "http://cuir.car.chula.ac.th/handle/123456789/1470", URL = "http://en.scientificcommons.org/48547612", size = "107 pages", abstract = "This thesis proposes a method to overcome the parameter setting problem of genetic algorithms. This method is denoted as 'Adaptive Parameter Control Algorithm' (APCA). The concept of APCA is based on two levels of genetic algorithms. The task level genetic algorithm (lower level genetic algorithm) solves the original problem, while the meta-level genetic algorithm (upper level genetic algorithm) optimises the parameters of the task level. Both levels operate concurrently. Each individual in the population of the meta-level genetic algorithm is a parameter set for the task level genetic algorithm. The evaluation of each individual in the meta-level population is carried out by assigning it as the parameter set of the task level genetic algorithm, the performance of the task level genetic algorithm is then used as the fitness. The task level genetic algorithm with multiple subpopulations is used to parallelize the evaluation of the meta-level population.This fits well with a coarse-grained model parallel genetic algorithm. The empirical results indicate that APCA is not only faster than other algorithms, but APCA also more reliably finds optimal solutions.", language = "English", ISBN = "974-17-5899-5", notes = "Advisor: Prabhas Chongstitvatana http://www.cp.eng.chula.ac.th/~piak/grad-students.htm", } @InProceedings{topchy:2001:gecco, title = "Faster Genetic Programming based on Local Gradient Search of Numeric Leaf Values", author = "Alexander Topchy and William F. Punch", pages = "155--162", year = "2001", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)", editor = "Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon and Edmund Burke", address = "San Francisco, California, USA", publisher_address = "San Francisco, CA 94104, USA", month = "7-11 " # jul, keywords = "genetic algorithms, genetic programming, gradient optimization, algorithmic, differentiation, Baldwin effect, Lamarckian learning, symbolic regression", ISBN = "1-55860-774-9", URL = "http://garage.cse.msu.edu/papers/GARAGe01-07-01.pdf", URL = "http://citeseer.ist.psu.edu/523881.html", URL = "http://gpbib.cs.ucl.ac.uk/gecco2001/d01.pdf", abstract = "We examine the effectiveness of gradient search optimization of numeric leaf values for Genetic Programming. Genetic search for tree-like programs at the population level is complemented by the optimization of terminal values at the individual level. Local adaptation of individuals is made easier by algorithmic differentiation. We show how conventional random constants are tuned by gradient descent with minimal overhead. Several experiments with symbolic regression problems are performed to demonstrate the approach's effectiveness. Effects of local learning are clearly manifest in both improved approximation accuracy and selection changes when periods of local and global search are interleaved. Special attention is paid to the low overhead of the local gradient descent. Finally, the inductive bias of local learning is quantified.", notes = "GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of \cite{spector:2001:GECCO}", } @InProceedings{Topcu:2014:WSC, author = "O. Topcu and L. Yilmaz", booktitle = "Winter Simulation Conference (WSC 2014)", title = "Agent-supported simulation for coherence-driven workflow discovery and evaluation", year = "2014", month = dec, pages = "419--428", abstract = "This article proposes a generic agent-supported symbiotic simulation architecture that generates and evaluates competing coherence-driven workflows using genetic programming. Workflows are examined by an agent-supported multi-simulation environment that allows inducing variation and assessing the outcome of the candidate workflows under a variety of environmental scenarios. The evaluation strategy builds on a coherence-driven selection mechanism that views assessment as a constraint satisfaction problem. The proposed system is also based on an introspective architecture that facilitates monitoring the activities of competing agent simulations as well as the status of the environment to determine the success of the candidate workflow with respect to given or emergent goals.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/WSC.2014.7019908", notes = "Also known as \cite{7019908}", } @Article{torabi:NPL, author = "Ali Torabi and Arash Sharifi and Mohammad Teshnehlab", title = "Using Cartesian Genetic Programming Approach with New Crossover Technique to Design Convolutional Neural Networks", journal = "Neural Processing Letters", year = "2023", volume = "55", pages = "5451--5471", month = oct, keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, ANN, Convolutional neural network, Neural architecture search, Crossover, Multiple sequence alignment", ISSN = "1370-4621", URL = "https://rdcu.be/ddL4Z", URL = "http://link.springer.com/article/10.1007/s11063-022-11093-0", DOI = "doi:10.1007/s11063-022-11093-0", size = "21 pages", abstract = "In image classification problems, Convolutional Neural Networks (CNNs) are deep neural networks that include a variety of different layers aimed at classifying images. Until today, the most promising and state-of-the-art method in image recognition tasks is CNN. Tuning the deep network with a large number of hyperparameters to maximize performance would be an excruciating task that requires lots of time and engineering efforts. To construct that high-performance architecture, experts should go through a lot of trial and error. Neural Architecture Search is a way to automatically fabricate an accurate network architecture. An evolutionary algorithm called Cartesian Genetic Programming (CGP) with a new crossover operation based on the multiple Sequence Alignment algorithm is proposed in this paper to construct an appropriate neural network without the burden of building manually. This new method has a remarkable improvement over a standard CGP only by adding a crossover operator. The datasets for training on the proposed method were CIFAR-10 and CIFAR-100. The results show that it achieves a good balance between accuracy and the number of trainable parameters compared to the other state-of-the-art methods", notes = "Department of Control Engineering, K.N. Toosi University of Technology, Tehran, Iran", } @InProceedings{Torben-Nielsen:2006:CEC, author = "Ben Torben-Nielsen and Karl Tuyls and Eric O. Postma", title = "Shaping Realistic Neuronal Morphologies: An Evolutionary Computation Method", booktitle = "Proceedings of IJCNN '06 the 2006 International Joint Conference on Neural Networks", year = "2006", editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas", pages = "1300--1307", address = "Vancouver", month = "16-21 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Lindenmayer-System", ISBN = "0-7803-9487-9", URL = "http://www.cs.unimaas.nl/b.torben-nielsen/ijcnn_draft.pdf", URL = "http://ieeexplore.ieee.org/xpl/RecentCon.jsp?punumber=11216", DOI = "doi:10.1109/IJCNN.2006.246733", size = "8 pages", abstract = "Neuronal morphology plays a crucial role in the information processing capabilities of neurons. Despite the importance of morphology for neural functionality, biological data is scarce and hard to obtain. Therefore, virtual neurons are devised to allow extensive modelling and experimenting. The main problem with current virtual-neuron generation methods is that they impose severe a priori constraints on the virtual morphologies. These constraints are based on widespread assumptions and beliefs about the morphology of real neurons. To overcome this problem, we present EvOL-Neuron, a new method based on L-Systems and Evolutionary Computation that imposes a posteriori constraints on candidate virtual neuron morphologies. As a proof of principle, our experiments show the power of the new method. Moreover, our method revealed a limitation in the description of neural morphology in the literature. We empirically show that Hillman's fundamental parameters of neuron morphology are satisfactory but not sufficient to describe neuronal morphology. The results are discussed and an outline for future research is given. We conclude that we succeeded in devising a new method for virtual-neuron generation that does not impose a priori limitations on the virtual-neuron morphology.", notes = " IEEExplore says pages 573--580 but pages are 1300-1307 in proceedings CDROM WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D", } @InProceedings{Torben-Nielsen:2007:ECAL, author = "Benjamin Torben-Nielsen", title = "Evolving Virtual Neuronal Morphologies: a case study in Genetic L-Systems Programming", booktitle = "9th European Conference on Artificial Life, ECAL", year = "2007", editor = "Fernando {Almeida e Costa} and LuisMateus Rocha and Ernesto Costa and Inman Harvey and Antonio Coutinho", volume = "4648", series = "LNCS", pages = "1089--1099", address = "Lisbon", month = sep # " 10-14", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-74912-7", DOI = "doi:10.1007/978-3-540-74913-4_109", abstract = "Virtual neurons are digitised representations of biological neurons, with an emphasis on their morphology. In previous research we presented a proof of principle of reconstructing virtual neuronal morphologies by means of Genetic L-Systems Programming (GLP) [13]. However, the results were limited due to a hard evolutionary search process and a minimalistic fitness function. In this work we analysed the search process and optimised the GLP configuration to enhance the search process. In addition, we designed a neuron type-specific fitness function which provides an incremental assessment of the evolved structures. The results are significantly better and relevant issues are discussed.", notes = "ECAL-2007", } @PhdThesis{Thesis_B_Torbennielsen, author = "Benjamin Torben-Nielsen", title = "Dendritic Morphology: Function shapes structure", school = "Tilberg University", year = "2008", address = "The Netherlands", month = "3 " # dec, keywords = "genetic algorithms, genetic programming, ANN, L-System", URL = "https://pure.uvt.nl/portal/en/publications/dendritic-morphology%286bcfb474-2b5f-4032-833d-389e94d4de9c%29.html", URL = "https://pure.uvt.nl/portal/files/1047785/Thesis_B_Torbennielsen.pdf", isbn13 = "9789071382734", size = "209 pages", notes = "p93 GP used in chapter 4 Tuyls, K.P., Co-promotor, External person van den Herik, H.J., Promotor Postma, E.O., Promotor Dissertation Series No. 2008-35. Printed by Gildeprint.", } @InProceedings{Toropov:1998:, author = "Vassili V. Toropov and Luis F. Alvarez", title = "Application of Genetic Programming to the Choice of a Structure of Multipoint Approximations", booktitle = "1st ISSMO/NASA Internet Conf. on Approximations and Fast Reanalysis in Engineering Optimization", year = "1998", month = jun # " 14-27", organisation = "ISSMO/NASA/AIAA", note = "Published on a CD ROM", keywords = "genetic algorithms, genetic programming", URL = "http://www.brad.ac.uk/staff/vtoropov/luis/paper.htm", size = "9 pages", notes = "broken Sep 2018 ISSMO at http://www.aero.ufl.edu/~issmo/program.htm Nice www page. {"}The simplified model is characterized not only by its structure (to be found by the GP) but also by a set of tuning parameters a to be found by model tuning, i.e. the least squares fitting of the model into the set of values of the original response function:{"} {"}The allocation of tuning parameters a to an individual tree follows the basic algebraic rules. To identify the parameters of the expression by the nonlinear least-squares fitting, i.e. to solve the optimization problem in (1), a combination of a GA and a nonlinear mathematical programming method [9] is used. The output of the GA is the initial guess for the subsequent derivative-based optimization method which amounts to a variation of the Newton's method in which the Hessian matrix is approximated by the secant (quasi-Newton) updating method. Once the technique comes sufficiently close to a local solution, it normally converges quite rapidly. To promote convergence from poor starting guesses the algorithm uses the adaptive update of the Hessian and, consequently, the algorithm is reduced to either a Gauss-Newton or Levenberg-Marquardt method. {"} {"}Three-bar truss optimization problem{"} {"}The output of the algorithm still needs some manual post-processing in order to get rid of those terms in the expression that give a null or tiny contribution, for example when the same value is added and subtracted. It is then suggested to run the problem several times in order to identify, by comparison, the most likely components.{"}", } @InProceedings{toropov:1998:GPcsga, author = "Vassili V. Toropov and Luis F. Alvarez", title = "Application of Genetic Programming to the Choice of a Structure of Global Approximations", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "387--390", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-548-7", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1998/toropov_1998_GPcsga.pdf", notes = "GP-98", } @InCollection{Toropov1998551, author = "V. V. Toropov and L. F. Alvarez", title = "Application of genetic programming and response surface methodology to optimization and inverse problems", editor = "M. Tanaka and G. S. Dulikravich", booktitle = "Inverse Problems in Engineering Mechanics", publisher = "Elsevier Science Ltd", address = "Oxford", year = "1998", pages = "551--560", isbn13 = "978-0-08-043319-6", DOI = "doi:10.1016/B978-008043319-6/50062-5", URL = "http://www.sciencedirect.com/science/article/B8558-4P9W00Y-28/2/7eacdd774e680f545fccdea2ddc0a1fb", keywords = "genetic algorithms, genetic programming", abstract = "Genetic Programming methodology is used for the creation of approximation functions in the solution of optimization and inverse problems. Genetic Programming is a relatively new form of Artificial Intelligence, and is based on the ideas of Darwinian evolution and genetics. Two important aspects of the problem are addressed: the choice of the plan of experiments and the model tuning using the least-squares response surface fitting. A test example is presented where the technique is applied to a simple optimization problem.", } @InCollection{Torres:2006:WSC, author = "Juan Torres and Ashraf Saad and Elliot Moore", title = "Evaluation of Objective Features for Classification of Clinical Depression in Speech by Genetic Programming", booktitle = "Soft Computing in Industrial Applications", publisher = "Springer", year = "2006", editor = "Ashraf Saad and Erel Avineri and Keshav Dahal and Muhammad Sarfraz and Rajkumar Roy", volume = "39", series = "Advances in Soft Computing", pages = "132--143", month = "18 " # sep # " - 6 " # oct, keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.armstrong.edu/wsc11/pdf/pap119s2-file1.pdf", DOI = "doi:10.1007/978-3-540-70706-6", abstract = "This paper presents the results of applying a Genetic Programming (GP) based feature selection algorithm to find a small set of highly discriminating features for the detection of clinical depression from a patient's speech. While the performance of the GP-based classifiers was not as good as hoped for, several Bayesian classifiers were trained using the features found via GP and it was determined that these features do hold good discriminating power. The similarity of the feature sets found using GP for different observational groupings suggests that these features are likely to generalize well and thus provide good results with other clinical depression speech databases.", notes = "slides http://www.cs.armstrong.edu/wsc11/slides/119.pdf WSC11 2006 published 2007", size = "12 pages", } @Article{Torres2009283, author = "Ricardo {da S. Torres} and Alexandre X. Falcao and Marcos A. Goncalves and Joao P. Papa and Baoping Zhang and Weiguo Fan and Edward A. Fox", title = "A genetic programming framework for content-based image retrieval", journal = "Pattern Recognition", volume = "42", number = "2", pages = "283--292", year = "2009", note = "Learning Semantics from Multimedia Content", ISSN = "0031-3203", DOI = "DOI:10.1016/j.patcog.2008.04.010", URL = "http://www.sciencedirect.com/science/article/B6V14-4SD29JV-1/2/e923d1aa7f24919e6066b9b28215c356", keywords = "genetic algorithms, genetic programming, Content-based image retrieval, Shape descriptors, Image analysis", abstract = "The effectiveness of content-based image retrieval (CBIR) systems can be improved by combining image features or by weighting image similarities, as computed from multiple feature vectors. However, feature combination do not make sense always and the combined similarity function can be more complex than weight-based functions to better satisfy the users' expectations. We address this problem by presenting a Genetic Programming framework to the design of combined similarity functions. Our method allows nonlinear combination of image similarities and is validated through several experiments, where the images are retrieved based on the shape of their objects. Experimental results demonstrate that the GP framework is suitable for the design of effective combinations functions.", } @InProceedings{WSEAS_286_Torres, author = "Socrates Torres and Monica Larre and Josi Torres", title = "A String Representation Methodology to Generate Syntactically Valid Genetic Programs", year = "2002", month = "12-16 " # may, booktitle = "WSEAS IMCCAS-ISA-SOSM and MEM-MCP", pages = "2861--2866", address = "Cancun, Mexico", organisation = "The World Scientific and Engineering Academy and Society (WSEAS)", keywords = "genetic algorithms, genetic programming, String Representation, Symbolic Regression", URL = "http://www.wseas.us/e-library/conferences/mexico2002/papers/286.pdf", size = "6 pages", abstract = "The need to generate not only syntactically valid Genetic Programs but also programs which remain syntactically valid, even after the applying of the crossover and the mutation operators is crucial for the good evolving of the genetic programs. In this sense, programs shall be represented such way that constants, variables and operators inherent to the problem be correctly represented during all the evolving process long. Actually, a string representation does not get the above requirements due to the syntactically wrong programs produced by genetic operators. This paper propose a String Representation Methodology to generate syntactically valid Genetic Programs. The Symbolic Regression is the area which is showed our representation.", notes = "???Also published in WSEAS TRANSACTIONS on SYSTEMS Issue 2, Volume 1, April 2002 ISSN 1109-2777,pages 290--295???", } @InProceedings{Torres-Vazquez:2006:SIS, author = "Anna Torres-Vazquez and Julio C. Hernandez-Castro and Juan M. Estevez-Tapiador and Arturo Ribagorda", title = "On the use of Genetic Programming to develop cryptographic hashes", booktitle = "First International Workshop on Secure Information Systems (SIS'06)", year = "2006", editor = "Konrad Wrona", address = "Wisla, Poland", month = nov # " 6-10", keywords = "genetic algorithms, genetic programming", URL = "http://www.seg.inf.uc3m.es/papers/2008recsi2.pdf", size = "9 pages", abstract = "Nowadays, hash functions are suffering a crisis due to the development of new attacks. Therefore, it is necessary to think about new construction schemes for hash functions, as well as new compression algorithms. In this paper, Genetic Programming is used to generate a compression function which will be applied to a scheme that also takes advantage of the recently proposed T-functions", notes = "Co-located with the XXII Autumn Meeting of Polish Information Processing Society http://www.FIMCSIT.pti.katowice.pl 79. SIS 2006 http://www.econferences.org/multiconference/01/papers_accepted.php", } @Article{torresen:2002:GPEM, author = "Jim Torresen", title = "A Scalable Approach to Evolvable Hardware", journal = "Genetic Programming and Evolvable Machines", year = "2002", volume = "3", number = "3", pages = "259--282", month = sep, keywords = "genetic algorithms, evolvable hardware, classifier systems, digital logic, evolvable hardware, FPGA", ISSN = "1389-2576", DOI = "doi:10.1023/A:1020163325179", abstract = "Evolvable Hardware (EHW) has been proposed as a new method for designing systems for complex real-world applications. However, so far, only relatively simple systems have been shown to be evolvable. In this paper, it is proposed that concepts from biology should be applied to EHW techniques to make EHW more applicable to solving complex problems. One such concept has led to the increased complexity scheme presented, where a system is evolved by evolving smaller sub-systems. Experiments with two different tasks illustrate that inclusion of this scheme substantially reduces the number of generations required for evolution. Further, for the prosthesis control task, the best performance is obtained by the novel approach. The best circuit evolved performs better than the best trained neural network.", notes = "Article ID: 5091791", } @InProceedings{Torresen:2020:GECCOcomp, author = "Jim Torresen", title = "Addressing Ethical Challenges within Evolutionary Computation Applications: GECCO 2020 Tutorial", year = "2020", editor = "Richard Allmendinger and Hugo Terashima Marin and Efren Mezura Montes and Thomas Bartz-Beielstein and Bogdan Filipic and Ke Tang and David Howard and Emma Hart and Gusz Eiben and Tome Eftimov and William {La Cava} and Boris Naujoks and Pietro Oliveto and Vanessa Volz and Thomas Weise and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Rui Wang and Ran Cheng and Guohua Wu and Miqing Li and Hisao Ishibuchi and Jonathan Fieldsend and Ozgur Akman and Khulood Alyahya and Juergen Branke and John R. Woodward and Daniel R. Tauritz and Marco Baioletti and Josu Ceberio Uribe and John McCall and Alfredo Milani and Stefan Wagner and Michael Affenzeller and Bradley Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and Nayat Sanchez-Pi and Luis Marti and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and Matthew Johns and Nick Ross and Ed Keedwell and Herman Mahmoud and David Walker and Anthony Stein and Masaya Nakata and David Paetzel and Neil Vaughan and Stephen Smith and Stefano Cagnoni and Robert M. Patton and Ivanoe {De Falco} and Antonio {Della Cioppa} and Umberto Scafuri and Ernesto Tarantino and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Erik Hemberg and Riyad Alshammari and Tokunbo Makanju and Fuijimino-shi and Ivan Zelinka and Swagatam Das and Ponnuthurai Nagaratnam and Roman Senkerik", isbn13 = "9781450371278", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377929.3389894", DOI = "doi:10.1145/3377929.3389894", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion", pages = "1206--1223", size = "18 pages", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", note = "Tutorial", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{10.1145/3377929.3389894} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @Article{TorresTrevino:2013:ESA, author = "Luis M. Torres-Trevino and Indira G. Escamilla-Salazar and Bernardo Gonzalez-Ortiz and Rolando Praga-Alejo", title = "An expert system for setting parameters in machining processes", journal = "Expert Systems with Applications", volume = "40", number = "17", pages = "6877--6884", year = "2013", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2013.06.051", URL = "http://www.sciencedirect.com/science/article/pii/S0957417413004466", keywords = "genetic algorithms, genetic programming, Expert systems, Symbolic regression, Processes modelling, Machining processes", } @InProceedings{Tosch:2012:GECCOcomp, author = "Emma Tosch and Lee Spector", title = "Achieving COSMOS: a metric for determining when to give up and when to reach for the stars", booktitle = "1st workshop on Understanding Problems (GECCO-UP)", year = "2012", editor = "Kent McClymont and Ed Keedwell", isbn13 = "978-1-4503-1178-6", keywords = "genetic algorithms, genetic programming", pages = "417--424", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330784.2330848", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "The utility of current metrics used in genetic programming (GP) systems, such as computational effort and mean-best-fitness, varies depending upon the problem and the resource that needs to be optimized. Inferences about the underlying system can only be made when a sufficient number of runs are performed to estimate the relevant metric within some confidence interval. This paper proposes a new algorithm for determining the minimum number of independent runs needed to make inferences about a GP system. As such, we view our algorithm as a meta-metric that should be satisfied before any inferences about a system are made. We call this metric COSMOS, as it estimates the number of independent runs needed to achieve the Convergence Of Sample Means Of the Order Statistics. It is agnostic to the underlying GP system and can be used to evaluate extant performance metrics, as well as problem difficulty. We suggest ways for which COSMOS may be used to identify problems for which GP may be uniquely qualified to solve.", notes = "Also known as \cite{2330848} Distributed at GECCO-2012. ACM Order Number 910122.", } @Article{Toth:2003:ActaCybernetica, author = "Zoltan Toth", title = "A Graphical User Interface for Evolutionary Algorithms", journal = "Acta Cybernetica", year = "2003", volume = "16", number = "2", pages = "337--365", keywords = "genetic algorithms, genetic programming", URL = "https://www.inf.u-szeged.hu/actacybernetica/edb/vol16n2/Toth_2003_ActaCybernetica.xml", URL = "http://www.inf.u-szeged.hu/actacybernetica/edb/vol16n2/pdf/Toth_2003_ActaCybernetica.pdf", size = "29 pages", abstract = "The purpose of {\em Generic Evolutionary Algorithms Programming Library (GEA\footnote{The project's home page can be found at \tt http://gea.ztoth.net})} system is to provide researchers with an easy-to-use, widely applicable and extendible programming library which solves real-world optimization problems by means of evolutionary algorithms. It contains algorithms for various evolutionary methods, implemented genetic operators for the most common representation forms for individuals, various selection methods, and examples on how to use and expand the library. All these functions assure that {\em GEA} can be effectively applied on many problems. {\em GraphGEA} is a graphical user interface to {\em GEA} written with the GTK API. The numerous parameters of the evolutionary algorithm can be set in appropriate dialogue boxes. The program also checks the correctness of the parameters and saving/restoring of parameter sets is also possible. The selected evolutionary algorithm can be executed interactively on the specified optimization problem through the graphical user interface of {\em GraphGEA}, and the results and behaviour of the EA can be observed on several selected graphs and drawings. While the main purpose of {\em GEA} is solving optimization problems, that of {\em GraphGEA} is education and analysis. It can be of great help for students understanding the characteristics of evolutionary algorithms and researchers of the area can use it to analyse an EA's behavior on particular problems.", notes = "https://www.inf.u-szeged.hu/en/kutatas/acta-cybernetica", } @Article{toulouse:2006:GPEM, author = "Michel Toulouse", title = "Book-Review: Automatic Quantum Computer Programming: A Genetic Programming Approach", journal = "Genetic Programming and Evolvable Machines", year = "2006", volume = "7", number = "1", pages = "125--126", month = mar, keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dR8X2", DOI = "doi:10.1007/s10710-006-4866-3", size = "2 pages", notes = "Review of \cite{spector:book} Series: Genetic Programming, Vol. 7, Spector, Lee, 2004, 168 p., Hardcover, ISBN: 1-4020-7894-3", } @InCollection{Toussaint:FOGA2005, author = "Marc Toussaint", title = "Compact Genetic Codes as a Search Strategy of Evolutionary Processes", year = "2005", series = "Lecture Notes in Computer Science", pages = "75--94", booktitle = "Foundations of Genetic Algorithms 8", editor = "Alden H. Wright and Michael D. Vose and Kenneth A. {De Jong} and Lothar M. Schmitt", address = "Berlin Heidelberg", publisher = "Springer-Verlag", volume = "3469", keywords = "genetic algorithms", ISBN = "3-540-27237-2", DOI = "doi:10.1007/11513575_5", abstract = "The choice of genetic representation crucially determines the capability of evolutionary processes to find complex solutions in which many variables interact. The question is how good genetic representations can be found and how they can be adapted online to account for what can be learned about the structure of the problem from previous samples. We address these questions in a scenario that we term indirect Estimation-of-Distribution: We consider a decorrelated search distribution (mutational variability) on a variable length genotype space. A one-to-one encoding onto the phenotype space then needs to induce an adapted phenotypic variability incorporating the dependencies between phenotypic variables that have been observed successful previously. Formalizing this in the framework of Estimation-of-Distribution Algorithms, an adapted phenotypic variability can be characterized as minimizing the Kullback-Leibler divergence to a population of previously selected individuals (parents). Our core result is a relation between the Kullback-Leibler divergence and the description length of the encoding in the specific scenario, stating that compact codes provide a way to minimize this divergence. A proposed class of Compression Evolutionary Algorithms and preliminary experiments with an L-system compression scheme illustrate the approach. We also discuss the implications for the self-adaptive evolution of genetic representations on the basis of neutrality (s-evolution) towards compact codes.", notes = "Workshop 5-9 January 2005 in Aizu-Wakamatsu City, Japan", } @InProceedings{conf/dmin/SunST06, title = "Parallel Hybrid Clustering using Genetic Programming and Multi-Objective Fitness with Density ({PYRAMID})", author = "Samir Tout and William Sverdlik and Junping Sun", booktitle = "Proceedings of the 2006 International Conference on Data Mining, {DMIN} 2006", publisher = "CSREA Press", year = "2006", editor = "Sven F. Crone and Stefan Lessmann and Robert Stahlbock", ISBN = "1-60132-004-3", pages = "197--203", address = "Las Vegas, Nevada, {USA}", month = jun # " 26-29", bibdate = "2006-12-19", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/dmin/dmin2006.html#SunST06", keywords = "genetic algorithms, genetic programming, Data Mining, Clustering, Parallelism, Density", URL = "http://ww1.ucmss.com/books/LFS/CSREA2006/DMI3100.pdf", size = "7 pages", abstract = "Clustering is the process of locating patterns in large data sets. It is an active research area that provides value to scientific as well as business applications. Practical clustering faces several challenges including: identifying clusters of arbitrary shapes, sensitivity to the order of input, dynamic determination of the number of clusters, outlier handling, processing speed of massive data sets, handling higher dimensions, and dependence on user-supplied parameters. Many studies have addressed one or more of these challenges. This study proposes an algorithm called parallel hybrid clustering using genetic programming and multi-objective fitness with density (PYRAMID). While still leaving significant challenges unresolved, such as handling higher dimensions and dependence on user-supplied parameters, PYRAMID employs a combination of data parallelism, a form of genetic programming, and a multiobjective density-based fitness function in the context of clustering to resolve most of the above challenges. Preliminary experiments have yielded promising results.", notes = "Samir Tout*, William Sverdlik**, and Junping Sun* *Nova Southeastern University, Fort Lauderdale, Florida, USA **Eastern Michigan University, Ypsilanti, Michigan, USA", } @PhdThesis{Tout:thesis, author = "Samir R. Tout", title = "Parallel Hybrid Clustering using Genetic Programming and Multi-Objective Fitness with Density {(PYRAMID)}", school = "Computer and Information Sciences, Nova Southeastern University", year = "2006", address = "USA", month = may # " 3", keywords = "genetic algorithms, genetic programming", ISBN = "0-542-56083-6", URL = "https://gscisweb.scis.nova.edu/dlist/webview.cfm", URL = "http://search.proquest.com/docview/304910230", size = "295 pages", abstract = "Clustering is the art of locating patterns in large data sets. It is an active research area that provides value to scientific as well as business applications. There are some challenges that face practical clustering including: identifying clusters of arbitrary shapes, sensitivity to the order of input, dynamic determination of the number of clusters, outlier handling, high dependency on user-defined parameters, processing speed of massive data sets, and the potential to fall into sub-optimal solutions. Many studies that were conducted in the realm of clustering have addressed some of these challenges. This study proposes a new approach, called parallel hybrid clustering using genetic programming and multi-objective fitness with density (PYRAMID), that tackles several of these challenges from a different perspective. PYRAMID employs genetic programming to represent arbitrary cluster shapes and circumvent falling in local optima. It accommodates large data sets and avoids dependency on the order of input by quantizing the data space, i.e., the space on which the data set resides, thus abstracting it into hyper-rectangular cells and creating genetic programming individuals as concatenations of these cells. Thus the cells become the subject of clustering, rather than the data points themselves. PYRAMID also uses a density-based multi-objective fitness function to handle outliers. It gathers statistics in a pre-processing step and uses them so not to rely on user-defined parameters. Finally, PYRAMID employs data parallelism in a master-slave model in an attempt to cure the inherent slow performance of evolutionary algorithms and provide speedup. A master processor distributes the clustering data evenly onto multiple slave processors. The slave processors conduct the clustering on their local data sets and report their clustering results back to the master, which consolidates them by merging the partial results into a final clustering solution. This last step also involves determining the number of clusters dynamically and labeling them accordingly. Experiments have demonstrated that, using these features, PYRAMID offers an advantage over some of the existing approaches by tackling the clustering challenges from a different angle.", notes = "Supervisor: Junping Sun UMI Microform 3208008", } @Article{Tout:2007:IAENG, title = "A Hybrid Approach to Cluster Detection", author = "Samir Tout and Junping Sun and William Sverdlik", journal = "IAENG International Journal of Computer Science", year = "2007", volume = "34", number = "1", month = "15 " # aug, keywords = "genetic algorithms, genetic programming, Data Mining, Clustering, Density, Parallelism", ISSN = "1819-9224", URL = "http://www.iaeng.org/IJCS/issues_v34/issue_1/IJCS_34_1_14.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.148.7725", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", contributor = "CiteSeerX", language = "en", oai = "oai:CiteSeerXPSU:10.1.1.148.7725", abstract = "Recent technological advances require computer algorithms that can effectively analyze and classify data on a large scale that was unachievable just a few years ago. For instance, in response to a query, commercial search engines routinely consider web pages amounting into billions while genomic searches may deal with a search space of a similar or even higher magnitude. Clustering algorithms are an ideal choice to quickly categorize data; they are conceptually simple and require little background knowledge. Many clustering algorithms have been introduced in recent decades; but each approach brought along new challenges to consider, such as outlier handling, detection of arbitrary shaped clusters, processing speed, and dependence on user supplied parameters. PYRAMID, or parallel hybrid clustering using genetic programming and multiobjective fitness with density, is a clustering algorithm that we introduced in a previous research. It addresses several of the above challenges by using a combination of data parallelism, a form of genetic programming, and a multi-objective density-based fitness function. This paper summarizes some of the characteristics of PYRAMID along with experiments that were performed on multiple challenging datasets. Empirical results derived from these experiments are presented and future directions are proposed.", notes = "Samir Tout is a consultant with Keane, Inc., 24901 Northwestern Hwy, Southfield, MI 48075 and an adjunct professor at the Department of Computer Science, Eastern Michigan University, Ypsilanti, MI, 48197 Junping Sun is a professor at the Graduate School of Computer and Information Sciences, Nova Southeastern University, 3301 College Avenue, Fort Lauderdale, Florida 33314, USA William Sverdlik is an associate professor at the Department of Computer Science, Eastern Michigan University, Ypsilanti, MI, 48197", } @InProceedings{Toutouh:2020:GECCO, author = "Jamal Toutouh and Erik Hemberg and Una-May O'Reilly", title = "Re-Purposing Heterogeneous Generative Ensembles with Evolutionary Computation", year = "2020", editor = "Carlos Artemio {Coello Coello} and Arturo Hernandez Aguirre and Josu Ceberio Uribe and Mario Garza Fabre and Gregorio {Toscano Pulido} and Katya Rodriguez-Vazquez and Elizabeth Wanner and Nadarajen Veerapen and Efren Mezura Montes and Richard Allmendinger and Hugo Terashima Marin and Markus Wagner and Thomas Bartz-Beielstein and Bogdan Filipic and Heike Trautmann and Ke Tang and John Koza and Erik Goodman and William B. Langdon and Miguel Nicolau and Christine Zarges and Vanessa Volz and Tea Tusar and Boris Naujoks and Peter A. N. Bosman and Darrell Whitley and Christine Solnon and Marde Helbig and Stephane Doncieux and Dennis G. Wilson and Francisco {Fernandez de Vega} and Luis Paquete and Francisco Chicano and Bing Xue and Jaume Bacardit and Sanaz Mostaghim and Jonathan Fieldsend and Oliver Schuetze and Dirk Arnold and Gabriela Ochoa and Carlos Segura and Carlos Cotta and Michael Emmerich and Mengjie Zhang and Robin Purshouse and Tapabrata Ray and Justyna Petke and Fuyuki Ishikawa and Johannes Lengler and Frank Neumann", isbn13 = "9781450371285", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377930.3390229", DOI = "doi:10.1145/3377930.3390229", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference", pages = "425--434", size = "10 pages", keywords = "genetic algorithms, ANN, ensembles, generative adversarial networks, diversity", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", abstract = "Generative Adversarial Networks (GANs) are popular tools for generative modeling. The dynamics of their adversarial learning give rise to convergence pathologies during training such as mode and discriminator collapse. In machine learning, ensembles of predictors demonstrate better results than a single predictor for many tasks. In this study, we apply two evolutionary algorithms (EAs) to create ensembles to re-purpose generative models, i.e., given a set of heterogeneous generators that were optimized for one objective (e.g., minimize Frechet Inception Distance), create ensembles of them for optimizing a different objective (e.g., maximize the diversity of the generated samples). The first method is restricted by the exact size of the ensemble and the second method only restricts the upper bound of the ensemble size. Experimental analysis on the MNIST image benchmark demonstrates that both EA ensembles creation methods can re-purpose the models, without reducing their original functionality. The EA-based demonstrate significantly better performance compared to other heuristic-based methods. When comparing both evolutionary, the one with only an upper size bound on the ensemble size is the best.", notes = "Also known as \cite{10.1145/3377930.3390229} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @InCollection{townsend:2000:SGWGPGA, author = "Jason Townsend", title = "Search in Grid World using Genetic Programming and Genetic Algorithms", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "407--414", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:2000:gagp}", } @InProceedings{Tozier:2015:GPTP, author = "Bill Tozier", title = "{GP} As If You Meant It: Real and Imaginary User Experience", booktitle = "Genetic Programming Theory and Practice XIII", year = "2015", editor = "Rick Riolo and William P. Worzel and M. Kotanchek and A. Kordon", series = "Genetic and Evolutionary Computation", pages = "59--78", address = "Ann Arbor, USA", month = "14-16 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Mindful practice, Design process, Coding kata, Praxis, Mangle of Practice", isbn13 = "978-3-319-34223-8", URL = "http://www.springer.com/us/book/9783319342214", DOI = "doi:10.1007/978-3-319-34223-8_4", abstract = "I present a kata called GP As If You Meant It, aimed at advanced users of genetic programming. Inspired by code katas that are popular among software developers, it's an exercise designed to help participants hone their skills through mindful practice. Its intent is to surface certain unquestioned habits common in our field: to make the participants painfully aware of the tacit justification for certain GP algorithm design decisions they may otherwise take for granted. In the exercise, the human players are charged with trying to rescue an ineffectual but unstoppable GP system (which is the other player), which has been set up to only use random guessing but they must do so by incrementally modifying the search process without interrupting it. The exercise is a game for two players, plus a Facilitator who acts as a referee. The human User player examines the state of the GP run in order to make amendments to its rules, using a very limited toolkit. The other player is the automated GP System itself, which adds to a growing population of solutions by applying the search operators and evaluation functions specified by the User player. The User's goal is to convince the System to produce good enough answers to a target supervised learning problem chosen by the Facilitator. To further complicate the task, the User must also provide the Facilitator with convincing justifications, or warrants, which explain each move she makes. The Facilitator chooses the initial search problem, provides training data, and most importantly is empowered to disqualify any of the User's moves if unconvinced by the accompanying warrants. As a result, the User is forced to work around our field's most insidious habit: that of stopping it and starting over again with different parameters. In the process of working within these constraints, the participants (Facilitator and User) are made mindful of the habits they have already developed, tacitly or explicitly, for coping with pathologies and symptoms encountered in their more typical work with GP.", notes = " Part of \cite{Riolo:2015:GPTP} Published after the workshop in 2016", } @Proceedings{Tozier:2016:GPTP, title = "Genetic Programming Theory and Practice XIV", year = "2016", editor = "William Tozier and Brian W. Goldman and Bill Worzel and Rick Riolo", address = "Ann Arbor, USA", month = "19-21 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-97087-5", URL = "https://www.springer.com/us/book/9783319970875", size = "252 pages", } @Article{trabelsi:2022:Energies, author = "Mohamed Trabelsi and Mohamed Massaoudi and Ines Chihi and Lilia Sidhom and Shady S. Refaat and Tingwen Huang and Fakhreddine S. Oueslati", title = "An Effective Hybrid Symbolic Regression-Deep Multilayer Perceptron Technique for {PV} Power Forecasting", journal = "Energies", year = "2022", volume = "15", number = "23", pages = "Article No. 9008", keywords = "genetic algorithms, genetic programming", ISSN = "1996-1073", URL = "https://www.mdpi.com/1996-1073/15/23/9008", DOI = "doi:10.3390/en15239008", abstract = "The integration of Photovoltaic (PV) systems requires the implementation of potential PV power forecasting techniques to deal with the high intermittency of weather parameters. In the PV power prediction process, Genetic Programming (GP) based on the Symbolic Regression (SR) model has a widespread deployment since it provides an effective solution for nonlinear problems. However, during the training process, SR models might miss optimal solutions due to the large search space for the leaf generations. This paper proposes a novel hybrid model that combines SR and Deep Multi-Layer Perceptron (MLP) for one-month-ahead PV power forecasting. A case study analysis using a real Australian weather dataset was conducted, where the employed input features were the solar irradiation and the historical PV power data. The main contribution of the proposed hybrid SR-MLP algorithm are as follows: (1) The training speed was significantly improved by eliminating unimportant inputs during the feature selection process performed by the Extreme Boosting and Elastic Net techniques; (2) The hyperparameters were preserved throughout the training and testing phases; (3) The proposed hybrid model made use of a reduced number of layers and neurons while guaranteeing a high forecasting accuracy; (4) The number of iterations due to the use of SR was reduced. The presented simulation results demonstrate the higher forecasting accuracy (reductions of more than 20percent for Root Mean Square Error (RMSE) and 30 percent for Mean Absolute Error (MAE) in addition to an improvement in the R2 evaluation metric) and robustness (preventing the SR from converging to local minima with the help of the ANN branch) of the proposed SR-MLP model as compared to individual SR and MLP models.", notes = "also known as \cite{en15239008}", } @InProceedings{Trajkovski:2011:IIT, author = "Igor Trajkovski and Zharko Aleksovski", title = "Learning to sort by using evolution", booktitle = "International Conference on Innovations in Information Technology (IIT 2011)", year = "2011", month = "25-27 " # apr, pages = "250--254", address = "Abu Dhabi", size = "5 pages", abstract = "This paper present a work where Genetic Programming (GP) was used to the task of evolving imperative sort programs. A variety of interesting lessons were learnt. With proper selection of the primitives, sorting programs were evolved that are both general and non-trivial. Unique aspect of our approach is that we represent the individual programs with simple assembler code, rather than usual tree like structure. We also report the effect of different parameters on quality of the programs and time needed for finding the solution.", keywords = "genetic algorithms, genetic programming, assembler code, imperative sort programs, tree like structure, sorting", DOI = "doi:10.1109/INNOVATIONS.2011.5893827", notes = "Also known as \cite{5893827}", } @Article{journals/memetic/TranXZ16, author = "Binh Tran and Bing Xue and Mengjie Zhang", title = "Genetic programming for feature construction and selection in classification on high-dimensional data", journal = "Memetic Computing", year = "2016", volume = "8", number = "1", pages = "3--15", month = mar, keywords = "genetic algorithms, genetic programming, Feature construction, Feature selection, Classification, High-dimensional data", ISSN = "1865-9284", bibdate = "2016-02-17", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/memetic/memetic8.html#TranXZ16", URL = "http://dx.doi.org/10.1007/s12293-015-0173-y", URL = "https://openaccess.wgtn.ac.nz/articles/journal_contribution/Genetic_programming_for_feature_construction_and_selection_in_classification_on_high-dimensional_data/14312465", DOI = "doi:10.1007/s12293-015-0173-y", size = "13 pages", abstract = "Classification on high-dimensional data with thousands to tens of thousands of dimensions is a challenging task due to the high dimensionality and the quality of the feature set. The problem can be addressed by using feature selection to choose only informative features or feature construction to create new high-level features. Genetic programming (GP) using a tree-based representation can be used for both feature construction and implicit feature selection. This work presents a comprehensive study to investigate the use of GP for feature construction and selection on high-dimensional classification problems. Different combinations of the constructed and/or selected features are tested and compared on seven high-dimensional gene expression problems, and different classification algorithms are used to evaluate their performance. The results show that the constructed and/or selected feature sets can significantly reduce the dimensionality and maintain or even increase the classification accuracy in most cases. The cases with overfitting occurred are analysed via the distribution of features. Further analysis is also performed to show why the constructed feature can achieve promising classification performance.", } @InProceedings{Tran:2017:EuroGP, author = "Binh Tran and Bing Xue and Mengjie Zhang", title = "Using Feature Clustering for {GP}-Based Feature Construction on High-Dimensional Data", booktitle = "EuroGP 2017: Proceedings of the 20th European Conference on Genetic Programming", year = "2017", month = "19-21 " # apr, editor = "Mauro Castelli and James McDermott and Lukas Sekanina", series = "LNCS", volume = "10196", publisher = "Springer Verlag", address = "Amsterdam", pages = "210--226", organisation = "species", keywords = "genetic algorithms, genetic programming, Feature construction, Feature clustering, Classification, High-dimensional data", isbn13 = "978-3-319-55695-6", DOI = "doi:10.1007/978-3-319-55696-3_14", size = "17 pages", abstract = "Feature construction is a pre-processing technique to create new features with better discriminating ability from the original features. Genetic programming (GP) has been shown to be a prominent technique for this task. However, applying GP to high-dimensional data is still challenging due to the large search space. Feature clustering groups similar features into clusters, which can be used for dimensionality reduction by choosing representative features from each cluster to form the feature subset. Feature clustering has been shown promising in feature selection; but has not been investigated in feature construction for classification. This paper presents the first work of using feature clustering in this area. We propose a cluster-based GP feature construction method called CGPFC which uses feature clustering to improve the performance of GP for feature construction on high-dimensional data. Results on eight high-dimensional datasets with varying difficulties show that the CGPFC constructed features perform better than the original full feature set and features constructed by the standard GP constructor based on the whole feature set.", notes = "Part of \cite{Castelli:2017:GP} EuroGP'2017 held inconjunction with EvoCOP2017, EvoMusArt2017 and EvoApplications2017", } @InProceedings{Tran:2015:CEC, author = "Cao Truong Tran and Peter Andreae and Mengjie Zhang", title = "Impact of Imputation of Missing Values on Genetic Programming based Multiple Feature Construction for Classification", booktitle = "Proceedings of 2015 IEEE Congress on Evolutionary Computation (CEC 2015)", year = "2015", editor = "Yadahiko Murata", pages = "2398--2405", address = "Sendai, Japan", month = "25-28 " # may, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-7491-7", DOI = "doi:10.1109/CEC.2015.7257182", abstract = "Missing values are a common problem in many real world databases. A common way to cope with this problem is to use imputation methods to fill missing values with plausible values. Genetic programming-based multiple feature construction (GPMFC) is a filter approach to multiple feature construction for classifiers using Genetic programming. The GPMFC algorithm has been demonstrated to improve classification performance in decision tree and rule-based classifiers for complete data, but it has not been tested on imputed data. This paper studies the effect of GPMFC on classification accuracy with imputed data and how the choice of different imputation methods (mean imputation, hot deck imputation, Knn imputation, EM imputation and MICE imputation) affects classifiers using constructed features. Results show that GPMFC improves classification performance for datasets with a small amount of missing values. The combination of GPMFC and MICE imputation, in most cases, enhances classification performance for datasets with varying amounts of missing values and obtains the best classification accuracy.", notes = "0950 hrs 15225 CEC2015", } @InProceedings{Tran:2015:GECCO, author = "Cao Truong Tran and Mengjie Zhang and Peter Andreae", title = "Multiple Imputation for Missing Data Using Genetic Programming", booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation", year = "2015", editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr", isbn13 = "978-1-4503-3472-3", pages = "583--590", keywords = "genetic algorithms, genetic programming, Evolutionary Machine Learning", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739480.2754665", DOI = "doi:10.1145/2739480.2754665", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Missing values are a common problem in many real world databases. Inadequate handing of missing data can lead to serious problems in data analysis. A common way to cope with this problem is to use imputation methods to fill missing values with plausible values. This paper proposes GPMI, a multiple imputation method that uses genetic programming as a regression method to estimate missing values. Experiments on eight datasets with six levels of missing values compare GPMI with seven other popular and advanced imputation methods on two measures: the prediction accuracy and the classification accuracy. The results show that, in most cases, GPMI not only achieves better prediction accuracy, but also better classification accuracy than the other imputation methods.", notes = "Also known as \cite{2754665} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)", } @InProceedings{Tran:2016:EuroGP, author = "Cao Truong Tran and Mengjie Zhang and Peter Andreae", title = "A Genetic Programming-based Imputation Method for Classification with Missing Data", booktitle = "EuroGP 2016: Proceedings of the 19th European Conference on Genetic Programming", year = "2016", month = "30 " # mar # "--1 " # apr, editor = "Malcolm I. Heywood and James McDermott and Mauro Castelli and Ernesto Costa and Kevin Sim", series = "LNCS", volume = "9594", publisher = "Springer Verlag", address = "Porto, Portugal", pages = "149--163", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-30668-1", DOI = "doi:10.1007/978-3-319-30668-1_10", abstract = "Many industrial and real-world datasets suffer from an unavoidable problem of missing values. The ability to deal with missing values is an essential requirement for classification because inadequate treatment of missing values may lead to large errors on classification. The problem of missing data has been addressed extensively in the statistics literature, and also, but to a lesser extent in the classification literature. One of the most popular approaches to deal with missing data is to use imputation methods to fill missing values with plausible values. Some powerful imputation methods such as regression-based imputations in MICE \cite{van1999flexible} are often suitable for batch imputation tasks. However, they are often expensive to impute missing values for every single incomplete instance in the unseen set for classification. This paper proposes a genetic programming-based imputation (GPI) method for classification with missing data that uses genetic programming as a regression method to impute missing values. The experiments on six benchmark datasets and five popular classifiers compare GPI with five other popular and advanced regression-based imputation methods in MICE on two measures: classification accuracy and computation time. The results showed that, in most cases, GPI achieves classification accuracy at least as good as the other imputation methods, and sometimes significantly better. However, using GPI to impute missing values for every single incomplete instance is dramatically faster than the other imputation methods.", notes = "Part of \cite{Heywood:2016:GP} EuroGP'2016 held in conjunction with EvoCOP2016, EvoMusArt2016 and EvoApplications2016", } @InProceedings{Tran:2016:GECCOcomp, author = "Cao Truong Tran and Mengjie Zhang and Peter Andreae and Bing Xue", title = "Directly Constructing Multiple Features for Classification with Missing Data using Genetic Programming with Interval Functions", booktitle = "GECCO '16 Companion: Proceedings of the Companion Publication of the 2016 Annual Conference on Genetic and Evolutionary Computation", year = "2016", editor = "Tobias Friedrich and Frank Neumann and Andrew M. Sutton and Martin Middendorf and Xiaodong Li and Emma Hart and Mengjie Zhang and Youhei Akimoto and Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and Daniele Loiacono and Julian Togelius and Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and Faustino Gomez and Carlos M. Fonseca and Heike Trautmann and Alberto Moraglio and William F. Punch and Krzysztof Krawiec and Zdenek Vasicek and Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and Boris Naujoks and Enrique Alba and Gabriela Ochoa and Simon Poulding and Dirk Sudholt and Timo Koetzing", pages = "69--70", keywords = "genetic algorithms, genetic programming: Poster", month = "20-24 " # jul, organisation = "SIGEVO", address = "Denver, USA", publisher = "ACM", publisher_address = "New York, NY, USA", isbn13 = "978-1-4503-4323-7", DOI = "doi:10.1145/2908961.2909002", abstract = "Missing values are a common issue in many industrial and real-world datasets. Genetic programming-based multiple feature construction (GPMFC) is a recent promising filter approach to constructing multiple features for classification using genetic programming (GP). GPMFC has been demonstrated to improve classification performance and reduce the complexity of many decision trees and rule-based classifiers, but it cannot work with missing data. To deal with missing data, this paper propose IGPMFC, an extension of GPMFC that use interval functions as the GP function set to directly construct multiple features for classification with missing data. Empirical results on five datasets and four classifiers show that IGPMFC can substantially improve the performance and reduce the complexity of the classifiers when faced with missing data.", notes = "Distributed at GECCO-2016.", } @InProceedings{Tran:2016:CEC, author = "Cao Truong Tran and Mengjie Zhang and Peter Andreae", title = "Directly Evolving Classifiers for Missing Data using Genetic Programming", booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016)", year = "2016", editor = "Yew-Soon Ong", pages = "5278--5285", address = "Vancouver", month = "24-29 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-5090-0623-6", DOI = "doi:10.1109/CEC.2016.7748361", abstract = "Missing values are a common issue in many industrial and real-world datasets. Coping with datasets containing missing values is an important requirement for classification because inadequate treatment of missing values may result in large errors on classification. Genetic programming (GP) has been successfully used to evolve classifiers, but it has been applied mainly to complete data. This paper proposes IGP, a GP method for directly evolving classifiers for missing data. In order to directly evolve classifiers for missing data, IGP uses interval functions as the GP function set and builds a set of classifiers for each classification problem. Experiments on 10 benchmark datasets compared IGP with five other classifiers on classification performance. Experimental results showed that, in most cases, IGP achieves significantly better classification accuracy than the other methods.", notes = "WCCI2016", } @Article{Tran:2015:EMS, author = "H. D. Tran and N. Muttil and B. J. C. Perera", title = "Selection of significant input variables for time series forecasting", journal = "Environmental Modelling \& Software", volume = "64", pages = "156--163", year = "2015", ISSN = "1364-8152", DOI = "doi:10.1016/j.envsoft.2014.11.018", URL = "http://www.sciencedirect.com/science/article/pii/S1364815214003442", abstract = "Appropriate selection of inputs for time series forecasting models is important because it not only has the potential to improve performance of forecasting models, but also helps reducing cost in data collection. This paper presents an investigation of selection performance of three input selection techniques, which include two model-free techniques, partial linear correlation (PLC) and partial mutual information (PMI) and a model-based technique based on genetic programming (GP). Four hypothetical datasets and two real datasets were used to demonstrate the performance of the three techniques. The results suggested that the model-free PLC technique due to its computational simplicity and the model-based GP technique due to its ability to detect non-linear relationships (demonstrated by its relatively good performance on a hypothetical complex non-linear dataset) are recommended for the input selection task. Candidate inputs which are selected by both these recommended techniques should be considered as significant inputs.", keywords = "genetic algorithms, genetic programming, Time series forecasting, Input variable selection, Partial mutual information, Correlation", } @InProceedings{Tran:2016:FTC, author = "Khiem Tran and Thanh Duong and Quyen Ho", title = "Credit scoring model: A combination of genetic programming and deep learning", booktitle = "2016 Future Technologies Conference (FTC)", year = "2016", pages = "145--149", month = "6-7 " # dec, address = "San Francisco, USA", keywords = "genetic algorithms, genetic programming, ANN, Credit scoring, deep learning, neural network, machine learning", isbn13 = "978-1-5090-4172-5", DOI = "doi:10.1109/FTC.2016.7821603", size = "5 pages", abstract = "In recent years, the market of customer lending grows rapidly, that is a reason why credit scoring becomes a core task of financial institutes. Many models based on machine learning have been widely using and providing robust performance. Because most machine learning based models are black-box, it is hard to see the relations between input data and scoring results. Therefore, this paper focuses on improving both the accuracy and the reliability of machine learning based model. Thus, we propose a hybrid idea to combine the power of deep learning network and the comprehensive genetic programming which is extracted rules to build a robust credit model. Our empirical experiment on Australian/German customer credit data sets shows that our model provides the best accuracy, highly reduce credit risk, and reliable IF-THEN rules.", notes = "Also known as \cite{7821603}", } @InProceedings{Tran:2016:SSCI, author = "Binh Tran and Mengjie Zhang and Bing Xue", title = "Multiple feature construction in classification on high-dimensional data using {GP}", booktitle = "2016 IEEE Symposium Series on Computational Intelligence (SSCI)", year = "2016", month = "6-9 " # dec, address = "Athens, Greece", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-5090-4241-8", DOI = "doi:10.1109/SSCI.2016.7850130", abstract = "Feature construction and feature selection are common pre-processing techniques to obtain smaller but better discriminating feature sets than the original ones. These two techniques are essential in high-dimensional data with thousands or tens of thousands of features where there may exist many irrelevant and redundant features. Genetic programming (GP) is a powerful technique that has shown promising results in feature construction and feature selection. However, constructing multiple features for high-dimensional data is still challenging due to its large search space. In this paper, we propose a GP-based method that simultaneously performs multiple feature construction and feature selection to automatically transform high-dimensional datasets into much smaller ones. Experiment results on six datasets show that the size of the generated feature set is less than 4percent of the original feature set size and it significantly improves the performance of K-Nearest Neighbour, Naive Bayes and Decision Tree algorithms on 15 out of 18 comparisons. Compared with the single feature construction method using GP, the proposed method has better performance on half cases and similar on the other half. Comparisons between the constructed features, the selected features and the combination of both constructed and selected features by the propose method reveal different preferences of the three learning algorithms on these feature sets.", notes = "Also known as \cite{7850130}", } @InProceedings{conf/ausai/TranXZ17, author = "Binh Tran and Bing Xue and Mengjie Zhang", title = "Class Dependent Multiple Feature Construction Using Genetic Programming for High-Dimensional Data", booktitle = "AI 2017: Advances in Artificial Intelligence, 30th Australasian Joint Conference", year = "2017", editor = "Wei Peng and Damminda Alahakoon and Xiaodong Li", volume = "10400", series = "Lecture Notes in Computer Science", pages = "182--194", address = "Melbourne, VIC, Australia", month = aug # " 19-20", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Class-dependent, Feature construction, Feature selection, Classification, High-dimensional data", isbn13 = "978-3-319-63003-8; 978-3-319-63004-5", bibdate = "2017-07-14", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ausai/ausai2017.html#TranXZ17", DOI = "doi:10.1007/978-3-319-63004-5_15", size = "13 pages", abstract = "Genetic Programming (GP) has shown promise in feature construction where high-level features are formed by combining original features using predefined functions or operators. Multiple feature construction methods have been proposed for high-dimensional data with thousands of features. Results of these methods show that several constructed features can maintain or even improve the discriminating ability of the original feature set. However, some particular features may have better ability than other features to distinguish instances of one class from other classes. Therefore, it may be more difficult to construct a better discriminating feature when combing features that are relevant to different classes. In this study, we propose a new GP-based feature construction method called CDFC that constructs multiple features, each of which focuses on distinguishing one class from other classes. We propose a new representation for class-dependent feature construction and a new fitness function to better evaluate the constructed feature set. Results on eight datasets with varying difficulties showed that the features constructed by CDFC can improve the discriminating ability of thousands of original features in most cases. Results also showed that CFDC is more effective and efficient than the hybrid MGPFC method which was shown to have better performance than standard GP to feature construction.", } @PhdThesis{Binh_Ngan_Tran:thesis, author = "Binh Ngan Tran", title = "Evolutionary Computation for Feature Manipulation in Classification on High-dimensional Data", school = "Computer Science, Victoria University of Wellington", year = "2018", address = "New Zealand", keywords = "genetic algorithms, genetic programming", URL = "https://researcharchive.vuw.ac.nz/xmlui/handle/10063/7078", URL = "http://hdl.handle.net/10063/7078", URL = "https://researcharchive.vuw.ac.nz/xmlui/bitstream/handle/10063/7078/thesis_access.pdf", size = "275 pages", abstract = "More and more high-dimensional data appears in machine learning, especially in classification tasks. With thousands of features, these datasets bring challenges to learning algorithms not only because of the curse of dimensionality but also the existence of many irrelevant and redundant features. Therefore, feature selection and feature construction (or feature manipulation in short) are essential techniques in preprocessing these datasets. While feature selection aims to select relevant features, feature construction constructs high-level features from the original ones to better represent the target concept. Both methods can decrease the dimensionality and improve the performance of learning algorithms in terms of classification accuracy and computation time. Although feature manipulation has been studied for decades, the task on high-dimensional data is still challenging due to the huge search space. Existing methods usually face the problem of stagnation in local optima and/or require high computation time. Evolutionary computation techniques are well-known for their global search. Particle swarm optimisation (PSO) and genetic programming (GP) have shown promise in feature selection and feature construction, respectively. However, the use of these techniques to high-dimensional data usually requires high memory and computation time. The overall goal of this thesis is to investigate new approaches to using PSO for feature selection and GP for feature construction on high-dimensional classification problems. This thesis focuses on incorporating a variety of strategies into the evolutionary process and developing new PSO and GP representations to improve the effectiveness and efficiency of PSO and GP for feature manipulation on high-dimensional data. This thesis proposes a new PSO based feature selection approach to high-dimensional data by incorporating a new local search to balance global and local search of PSO. A hybrid of wrapper and filter evaluation method which can be sped up in the local search is proposed to help PSO achieve better performance, scalability and robustness on high-dimensional data. The results show that the proposed method significantly outperforms the compared methods in 80percent of the cases with an increase up to 16percent average accuracy while reduces the number of features from one to two orders of magnitude. This thesis develops the first PSO based feature selection via discretisation method that performs both multivariate discretization and feature selection in a single stage to achieve better solutions than applying these techniques separately in two stages. Two new PSO representations are proposed to evolve cut-points for multiple features simultaneously. The results show that the proposed method selects less than 4.6percent of the features in all cases to improve the classification performance from 5percent to 23percent in most cases. This thesis proposes the first clustering-based feature construction method to improve the performance of single-tree GP on high-dimensional data. A new feature clustering method is proposed to automatically group similar features into the same group based on a given redundancy level. The results show that compared with standard GP, the new method can select less than half of the features to construct a new high-level feature that achieves significantly better accuracy in most cases. The combination of the single constructed feature and the selected ones achieves the best performance among different feature sets created from a single tree. This thesis develops the first class-dependent multiple feature construction method using multi-tree GP for high-dimensional data. A new GP representation and a new filter fitness function that combines two filter measures are proposed to evaluate the whole set of constructed features more effectively and efficiently. The results show that in 83percent of the cases, with less than 10 constructed features, the class-dependent method increases up to 32percent average accuracy on using all the original thousands of features and 10percent on using those constructed by the class-independent method.", notes = "Supervisors: Mengjie Zhang, Bing Xue", } @Article{TRAN:2019:patcog, author = "Binh Tran and Bing Xue and Mengjie Zhang", title = "Genetic programming for multiple-feature construction on high-dimensional classification", journal = "Pattern Recognition", year = "2019", volume = "93", pages = "404--417", month = sep, keywords = "genetic algorithms, genetic programming, Feature construction, Classification, Class dependence, High-dimensional data", ISSN = "0031-3203", URL = "http://www.sciencedirect.com/science/article/pii/S0031320319301815", DOI = "doi:10.1016/j.patcog.2019.05.006", abstract = "Data representation is an important factor in deciding the performance of machine learning algorithms including classification. Feature construction (FC) can combine original features to form high-level ones that can help classification algorithms achieve better performance. Genetic programming (GP) has shown promise in FC due to its flexible representation. Most GP methods construct a single feature, which may not scale well to high-dimensional data. This paper aims at investigating different approaches to constructing multiple features and analysing their effectiveness, efficiency, and underlying behaviours to reveal the insight of multiple-feature construction using GP on high-dimensional data. The results show that multiple-feature construction achieves significantly better performance than single-feature construction. In multiple-feature construction, using multi-tree GP representation is shown to be more effective than using the single-tree GP thanks to the ability to consider the interaction of the newly constructed features during the construction process. Class-dependent constructed features achieve better performance than the class-independent ones. A visualisation of the constructed features also demonstrates the interpretability of the GP-based FC approach, which is important to many real-world applications", } @InProceedings{Tran:2017:GECCO, author = "Cao Truong Tran and Mengjie Zhang and Peter Andreae and Bing Xue", title = "Multiple Imputation and Genetic Programming for Classification with Incomplete Data", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4920-8", address = "Berlin, Germany", pages = "521--528", size = "8 pages", URL = "http://doi.acm.org/10.1145/3071178.3071181", DOI = "doi:10.1145/3071178.3071181", acmid = "3071181", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, classification, incomplete data, missing data, multiple imputation", month = "15-19 " # jul, abstract = "Many industrial and research datasets suffer from an unavoidable issue of missing values. One of the most common approaches to solving classification with incomplete data is to use an imputation method to fill missing values with plausible values before applying classification algorithms. Multiple imputation is a powerful approach to estimating missing values, but it is very expensive to use multiple imputation to estimate missing values for a single instance that needs to be classified. Genetic programming (GP) has been widely used to construct classifiers for complete data, but it seldom has been used for incomplete data. This paper proposes an approach to combining multiple imputation and GP to evolve classifiers for incomplete data. The proposed method uses multiple imputation to provide a high quality training data. It also searches for common patterns of missing values, and uses GP to build a classifier for each pattern of missing values. Therefore, the proposed method generates a set of classifiers that can be used to directly classify any new incomplete instance without requiring imputation. Experimental results show that the proposed method not only can be faster than other common methods for classification with incomplete data but also can achieve better classification accuracy.", notes = "Also known as \cite{Tran:2017:MIG:3071178.3071181} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @InProceedings{Tran:2017:GECCOa, author = "Cao Truong Tran and Mengjie Zhang and Peter Andreae and Bing Xue", title = "Genetic Programming Based Feature Construction for Classification with Incomplete Data", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4920-8", address = "Berlin, Germany", pages = "1033--1040", size = "8 pages", URL = "http://doi.acm.org/10.1145/3071178.3071183", DOI = "doi:10.1145/3071178.3071183", acmid = "3071183", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, classification, feature construction, incomplete data", month = "15-19 " # jul, abstract = "Missing values are an unavoidable problem in many real-world datasets. Dealing with incomplete data is an crucial requirement for classification because inadequate treatment of missing values often causes large classification error. Feature construction has been successfully applied to improve classification with complete data, but it has been seldom applied to incomplete data. Genetic programming-based multiple feature construction (GPMFC) is a current encouraging feature construction method which uses genetic programming to evolve new multiple features from original features for classification tasks. GPMFC can improve the accuracy and reduce the complexity of many decision trees and rule-based classifiers; however, it cannot directly work with incomplete data. This paper proposes IGPMFC which is extended from GPMFC to tackle with incomplete data. IGPMFC uses genetic programming with interval functions to directly evolve multiple features for classification with incomplete data. Experimental results reveal that not only IGPMFC can substantially improve the accuracy, but also can reduce the complexity of learnt classifiers facing with incomplete data.", notes = "Also known as \cite{Tran:2017:GPB:3071178.3071183} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @InProceedings{Tran:2018:AJCAI, author = "Cao Truong Tran and Mengjie Zhang and Bing Xue and Peter Andreae", title = "Genetic Programming with Interval Functions and Ensemble Learning for Classification with Incomplete Data", booktitle = "Australasian Joint Conference on Artificial Intelligence", year = "2018", editor = "Tanja Mitrovic and Bing Xue and Xiaodong Li", volume = "11320", series = "LNCS", pages = "577--589", address = "Wellington, New Zealand", month = dec # " 11-14", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-030-03990-5", URL = "http://link.springer.com/chapter/10.1007/978-3-030-03991-2_53", DOI = "doi:10.1007/978-3-030-03991-2_53", notes = "conf/ausai/TranZXA18", } @Article{tran:2020:fncir, author = "Lina M. Tran and Andrew J. Mocle and Adam I. Ramsaran and Alexander D. Jacob and Paul W. Frankland and Sheena A. Josselyn", title = "Automated Curation of {CNMF-E-Extracted ROI} Spatial Footprints and Calcium Traces Using Open-Source {AutoML} Tools", journal = "Frontiers in Neural Circuits", year = "2020", volume = "14", number = "42", month = jul # " 15", keywords = "genetic algorithms, genetic programming, TPOT, AutoSklearn, Python, calcium imaging, open-source, machine learning, microendoscopy, image processing, ROI, region of interest", ISSN = "1662-5110", code_url = "https://github.com/jf-lab/cnmfe-reviewer", URL = "https://www.biorxiv.org/content/10.1101/2020.03.13.991216v1", DOI = "doi:10.3389/fncir.2020.00042", abstract = "In vivo 1-photon (1p) calcium imaging is an increasingly prevalent method in behavioral neuroscience. Numerous analysis pipelines have been developed to improve the reliability and scalability of pre-processing and ROI extraction for these large calcium imaging datasets. Despite these advancements in pre-processing methods, manual curation of the extracted spatial footprints and calcium traces of neurons remains important for quality control. Here, we propose an additional semi-automated curation step for sorting spatial footprints and calcium traces from putative neurons extracted using the popular constrained non-negative matrix factorization for microendoscopic data (CNMF-E) algorithm. We used the automated machine learning (AutoML) tools TPOT and AutoSklearn to generate classifiers to curate the extracted ROIs trained on a subset of human-labeled data. AutoSklearn produced the best performing classifier, achieving an F1 score greater than 92 percent on the ground truth test dataset. This automated approach is a useful strategy for filtering ROIs with relatively few labeled data points and can be easily added to pre-existing pipelines currently using CNMF-E for ROI extraction.", notes = "Hospital for Sick Children, Neurosciences and Mental Health, Toronto, ON, Canada The datasets and code generated for this study can be found in the cnmfe-reviewer GitHub repository (https://github.com/jf-lab/cnmfe-reviewer). PMID: 32792911; PMCID: PMC7384547", } @InProceedings{Tran:2018:EuroGP, author = "Minh Duc Tran and Claudia d'Amato and Binh Thanh Nguyen and Andrea G. B. Tettamanzi", title = "Comparing Rule Evaluation Metrics for the Evolutionary Discovery of Multi-Relational Association Rules in the Semantic Web", booktitle = "EuroGP 2018: Proceedings of the 21st European Conference on Genetic Programming", year = "2018", month = "4-6 " # apr, editor = "Mauro Castelli and Lukas Sekanina and Mengjie Zhang and Stefano Cagnoni and Pablo Garcia-Sanchez", series = "LNCS", volume = "10781", publisher = "Springer Verlag", address = "Parma, Italy", pages = "289--305", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming: Poster", isbn13 = "978-3-319-77552-4", DOI = "doi:10.1007/978-3-319-77553-1_18", abstract = "We carry out a comparison of popular asymmetric metrics, originally proposed for scoring association rules, as building blocks for a fitness function for evolutionary inductive programming. In particular, we use them to score candidate multi-relational association rules in an evolutionary approach to the enrichment of populated knowledge bases in the context of the Semantic Web. The evolutionary algorithm searches for hidden knowledge patterns, in the form of SWRL rules, in assertional data, while exploiting the deductive capabilities of ontologies. Our methodology is to compare the number of generated rules and total predictions when the metrics are used to compute the fitness function of the evolutionary algorithm. This comparison, which has been carried out on three publicly available ontologies, is a crucial step towards the selection of suitable metrics to score multi-relational association rules that are generated from ontologies.", notes = "Part of \cite{Castelli:2018:GP} EuroGP'2018 held in conjunction with EvoCOP2018, EvoMusArt2018 and EvoApplications2018", } @Article{Traore20131, author = "Seydou Traore and Aytac Guven", title = "New algebraic formulations of evapotranspiration extracted from gene-expression programming in the tropical seasonally dry regions of West Africa", journal = "Irrigation Science", year = "2013", volume = "31", number = "1", pages = "1--10", month = jan, keywords = "genetic algorithms, genetic programming, Gene Expression Programming, Algebraic formulae, Burkina Faso, Complex functions, Conventional methods, Critical variables, Dry region, Meteorological data, Modelling process, Predictive abilities, Reference evapotranspiration, Sub-Saharan Africa, Weather data, West Africa, Algebra,Meteorology, Water supply, accuracy assessment, agrometeorology, climate modelling, data set, seasonality, Burkina Faso, Joturus pichardi", ISSN = "0342-7188", URL = "http://link.springer.com/article/10.1007%2Fs00271-011-0288-y", DOI = "doi:10.1007/s00271-011-0288-y", size = "10 pages", abstract = "Within hydrological nonlinear complex functions, taking only few parameters into the modelling process is still a challenging task. The present paper has for objective to investigate for the first time the predictive ability of the Gene-expression Programming (GEP) for modeling reference evapotranspiration (ETo) using routing weather data from the tropical seasonally dry regions of West Africa in Burkina Faso. The regions under study are located in three agro-climatic zones, Bobo Dioulasso in the Guinea Savanna zone, and Dedougou and Fada N'Gourma in the Sudan zone, and Ouagadougou in the Sudano-Sahelian Savanna zone. Several meteorological data combinations are used as inputs to the GEP to estimate ETo, and their performances are evaluated using R-squared and RMSE. Statistically, it was found that GEP can be an alternative to the conventional methods, and its accuracy improves significantly up to R-squared (0.979) and RMSE(0.108) when critical variables are taking into account in the model. The results revealed that GEP model is fairly a promising approach with the advantage to provide successfully simple algebraic formulas ease to use without recourse to the full set of meteorological data requirement for accurately estimate ETo in Sub-Saharan Africa regions.", affiliation = "Department of Irrigation Development and Planning of the Ministry of Agricultural, Hydraulic and Fisheries Resources, 03 BP 7053 Ouagadougou 03, Burkina Faso; Department of Civil Engineering, University of Gaziantep, 27310 Gaziantep, Turkey", correspondence_address1 = "Guven, A.; Department of Civil Engineering, University of Gaziantep, 27310 Gaziantep, Turkey; email: aguven@gantep.edu.tr", language = "English", document_type = "Article", } @InProceedings{trautwein:2025:EuroGP, author = "Julian Trautwein and Michael Heider and Henning Cui and Joerg Haehner", title = "Ant-based Metaheuristics Struggle to Solve the Cartesian Genetic Programming Learning Task", booktitle = "European Conference on Genetic Programming, EuroGP 2025", year = "2025", editor = "Bing Xue and Luca Manzoni and Illya Bakurov", volume = "15609", series = "LNCS", pages = "139--155", address = "Trieste", month = "23-25 " # apr, publisher = "Springer Nature", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, Evolution Strategies, Evolutionary Algorithm, Ant Colony Optimization, ACO, Ant-based Meta-heuristic", isbn13 = "978-3-031-89990-4", URL = "https://rdcu.be/ejgEt", DOI = "doi:10.1007/978-3-031-89991-1_9", abstract = "Ant-based metaheuristics have successfully been applied to a variety of different graph-based problems. However, for Cartesian Genetic Programming (CGP) only the impact of Max-Min Ant Systems has been tested. In this work, we try to fill this gap by applying four different popular ant-based metaheuristics as the optimiser (and therefore training algorithm) of CGP. The idea of combining CGP with ant-based metaheuristics is not novel but older works' experimental design may not meet today's standard. To compare these metaheuristics to the Evolution Strategies (ESs) commonly used in CGP, we benchmark against a standard CGP variant that uses a simplistic (1+4)-ES, mutation, and no crossover. Additionally, we include ($$\mu + \lambda $$)-ES and (($$\mu,\ \lambda $$)-ES in our experiments. We analyse the performance on the datasets from the symbolic regression, regression and classification domains. By tuning and evaluating various configurations, we cannot affirm a significant improvement by using ant-based methods with CGP as we encounter premature convergence – even with those ant-based metaheuristics that were originally proposed to overcome such problems. Despite our results being of negative nature, this work still gives important and interesting insights into the training of CGP models. The key contributions of our work are this more thorough benchmarking of these optimisers that has been done before. This should clear up doubts about the capabilities of ant-based metaheuristics in CGP. Furthermore, we include a roadmap on how they can be addressed to solve this complex optimisation problem from the model building domain of machine learning.", notes = "also known as \cite{paper_64_cartesian_genetic_programming_learning_task} Part of \cite{Xue:2025:GP} EuroGP'2025 held in conjunction with EvoCOP2025, EvoMusArt2025 and EvoApplications2025", } @Article{traxler:2022:Polymers, author = "Ines Traxler and Christian Marschik and Manuel Farthofer and Stephan Laske and Joerg Fischer", title = "Application of Mixing Rules for Adjusting the Flowability of Virgin and Post-Consumer Polypropylene as an Approach for Design from Recycling", journal = "Polymers", year = "2022", volume = "14", number = "13", pages = "Article No. 2699", keywords = "genetic algorithms, genetic programming", ISSN = "2073-4360", URL = "https://www.mdpi.com/2073-4360/14/13/2699", DOI = "doi:10.3390/polym14132699", abstract = "To enable the use of recyclates in thermoformed polypropylene products with acceptable optical appearance and good mechanical stability, a multilayer structure of virgin and recycled material can be used. When producing multilayer films with more than two layers, the used materials should have similar melt flow properties to prevent processing instabilities. In the case of a three-layer film, post-consumer recyclates are often hidden in the core layer. Due to the inconsistent melt flow properties of post-consumer recyclates, the adjustment of the melt flow properties of the core layer to those of the outer layers has to be realized by blending with virgin materials. In order to understand the effect of mixing with a virgin material with a certain pre-defined melt flow rate (MFR), material mixtures with different mixing partners from various sources were realized in this study. Hence, the pre-defined virgin material was mixed with (i) virgin materials, (ii) artificial recyclates out of a mixture of different virgin materials, and (iii) commercially available recyclates. These blends with mixing partner contents ranging from 0–100percent in 10percent increments were prepared by compounding and the MFR of each mixture was determined. For a mathematical description of the mixing behaviour and furthermore for a proper MFR prediction of the material mix, existing mixing rules were tested on the three pre-defined sample groups. Therefore, this paper shows the applicability of different mixing rules for the prediction of the MFR of material blends. Furthermore, a new mixing rule was developed using symbolic regression based on genetic programming, which proved to be the most accurate predictive model.", notes = "also known as \cite{polym14132699}", } @Article{Trefzer:2011:GPEM, author = "Martin A. Trefzer", title = "Justin Lee: Morphogenetic Evolvable Hardware VDM, 2008, ISBN 978-3-639-05716-4", journal = "Genetic Programming and Evolvable Machines", year = "2011", volume = "12", number = "1", pages = "79--80", month = mar, note = "Book Review", keywords = "genetic algorithms, genetic programming, evolvable hardware", ISSN = "1389-2576", URL = "https://rdcu.be/dR8Yx", DOI = "doi:10.1007/s10710-010-9116-z", size = "2 pages", affiliation = "University of York York UK", } @Article{Trefzer:2012:ieeeTEC, author = "Martin A. Trefzer and Tuze Kuyucu and Julian F. Miller and Andy M. Tyrrell", title = "On the Advantages of Variable Length GRNs for the Evolution of Multicellular Developmental Systems", journal = "IEEE Transactions on Evolutionary Computation", year = "2013", volume = "17", number = "1", pages = "100--121", month = feb, keywords = "genetic algorithms, genetic programming, GRN, gene regulatory network", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2012.2185848", size = "22 pages", abstract = "Biological genomes have evolved over a period of millions of years and comprise thousands of genes, even for the simplest organisms. However, in nature, only 12percent of the genes play an active role in creating and maintaining the organism, while the majority are evolutionary fossils. This raises the question whether a considerably larger number of (partly redundant) genes are required in order to effectively build a functional developmental system, of which, in the final system only a fraction is required for the latter to function. This paper investigates different approaches to creating artificial developmental systems (ADSs) based on variable length gene regulatory networks (GRNs). The GRNs are optimised using an evolutionary algorithm (EA). A comparison is made between the different variable length representations and fixed length representations. It is shown that variable length GRNs can achieve both reducing computational effort during optimisation and increasing speed and compactness of the resulting ADS, despite the higher complexity of the encoding required. The results may also improve the understanding of how to effectively model GRN based developmental systems. Taking results of all experiments into account makes it possible to create an overall ranking of the different patterns used as a testbench in terms of their complexity. This ranking may aid to compare related work against. In addition this allows a detailed assessment of the ADS used and enables the identification of missing mechanisms.", notes = "also known as \cite{6151118}", } @InProceedings{Trenaman:1997:agentslb, author = "Adrian Trenaman", title = "A Framework for the Evolution of Autonomous Agents", booktitle = "Late Breaking Papers at the 1997 Genetic Programming Conference", year = "1997", editor = "John R. Koza", pages = "246--254", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California, 94305-3079, USA", month = "13--16 " # jul, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-206995-8", notes = "GP-97LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670", } @InProceedings{trenaman:1999:FIEACGP, author = "Adrian Trenaman", title = "Further Investigations into the Evolution of Agents with Concurrent Genetic Programming", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1452", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, artificial life, adaptive behavior and agents, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-044.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-044.ps", abstract = "java, Tartarus, Dozer", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{trenaman-ai97, author = "Adrian Trenaman", title = "Towards the Evolution of Stateful Autonomous Agents", pages = "56--63", editor = "Paul Mc Kevitt", booktitle = "Eight Irish Conference on Artificial Intelligence (AI-97)", year = "1997", volume = "2", address = "University of Ulster, Magee College, Derry, Northern Ireland", publisher = "University of Ulster at Coleraine", month = "10-13 " # sep, keywords = "genetic algorithms, genetic programming", notes = "Sep 2018 See also http://www.paulmckevitt.com/imvipai97/prog.txt", } @InProceedings{trenaman:1998:cGPueseapke, author = "Adrian Trenaman", title = "Concurrent Genetic Programming and the Use of Explicit State to Evolve Agents in Partially-Known Environments", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "391--398", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-548-7", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1998/trenaman_1998_cGPueseapke.pdf", notes = "GP-98", } @InProceedings{trenaman:1998:cGPtpr, author = "Adrian Trenaman", title = "Concurrent Genetic Programming and the Tartarus Problem Revisited", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "213--220", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "genetic algorithms, genetic programming", size = "8 pages", notes = "GP-98LB", } @InProceedings{Trenaman:1998:cGProbMP, author = "Adrian Trenaman", title = "Concurrent Genetic Programming and Robot Motion Planning", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "267", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "genetic algorithms, genetic programming", size = "1 page", notes = "GP-98LB, GP-98PhD Student Workshop", } @InProceedings{trenaman:1999:cGPtda, author = "Adrian Trenaman", title = "Concurrent Genetic Programming, Tartarus and Dancing Agents", booktitle = "Genetic Programming, Proceedings of EuroGP'99", year = "1999", editor = "Riccardo Poli and Peter Nordin and William B. Langdon and Terence C. Fogarty", volume = "1598", series = "LNCS", pages = "270--282", address = "Goteborg, Sweden", publisher_address = "Berlin", month = "26-27 " # may, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-65899-8", DOI = "doi:10.1007/3-540-48885-5_23", abstract = "Evolutionary approaches such as genetic programming have often been applied to the automatic design of controllers for autonomous agents in virtual worlds. This paper applies a multi-tree genetic programming representation to the Tartarus world. Agent-controllers are evolved whose behaviour is the emergent effect of the interleaved evaluation of the program trees. Agents with good fitness and of very low complexity are evolved, and it is found that this technique evolves agents that exploit the characteristics of the runtime scheduler to provide an implicit rather than explicit form of state in the form of a fixed dance.", notes = "EuroGP'99, part of \cite{poli:1999:GP}", } @PhdThesis{trenaman:thesis, author = "Adrian Trenaman", title = "The Evolution of Autonomous Agents Using Concurrent Genetic Programming", school = "Department of Computer Science, National University of Ireland, Maynooth", year = "1999", address = "Ireland", month = oct, keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/trenaman/at_thesis1.ps.gz", size = "136 pages", abstract = "This thesis addresses the issue of how computational agents interact with and represent their environment in order to effect goal-achieving behaviour. It argues that the internal representations used by the agent to describe objects in the world should be based on how the agent perceives these objects and not necessarily on the representations a human designer might impose. A bottom-up methodology is proposed for the automatic design of distributed algorithms and internal representations to control autonomous agents. In particular, this thesis proposes and evaluates a new mechanism for the evolution of agents: {"}concurrent genetic programming''. In this encoding scheme an agent is controlled by a set of evolved programs that are executed concurrently to yield an emergent control algorithm for the agent. This encoding forms a natural interpretation of the emergent principles of the discipline of artificial life in an evolutionary context, and so elucidates the ability of evolutionary computation to create such emergent systems. The performance of the approach is investigated as a function of several parameters. These are: using different numbers of programs in the agents, explicit memory, distributed memory architectures, deterministic and non-deterministic scheduling strategies, different levels of granularity of concurrency, and the evolution of scheduling strategy. These issues are investigated through the application of concurrent genetic programming to the standard Tartarus and Dozer virtual-robotics benchmarks. It is shown that concurrent genetic programming produces better agents for these environments than a conventional genetic programming approach. It does this by employing an implicit form of state that supports the development of cyclical behaviour strategies. Implicit representations of the environment are acquired at an evolutionary level rather than at the level of the agent's experience. Although this form of internal representation leads to fit agents, it does not exhibit the formation of explicit models of the agent's environment. Instead, it allows the development of a form of internal state appropriate to achieving good fitness.", notes = " ", } @InProceedings{Trendelenburg:2008:gecco, author = "Stanis Trendelenburg and Joachim Becker and Fabian Henrici and Yiannos Manoli", title = "A {GP} algorithm for efficient synthesis of {GM-C} filters on a hexagonal {FPAA} structure", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "295--296", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p295.pdf", DOI = "doi:10.1145/1389095.1389146", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, evolvable hardware, filter synthesis, microelectronics, Artificial life, evolutionary robotics, adaptive behaviour, Computer Applications, Electronics, Artificial Intelligence, Heuristic Methods, Algorithms: Poster", abstract = "This work presents an approach based on Genetic Programming for the synthesis of continuous-time analogue filters on a field-programmable analog array. A multi-objective algorithm is used to synthesize both the topology and parameter values of G m -C filter structures to be instantiated on the FPAA based on a given filter specification. The presented algorithm is highly adapted to the underlying hardware platform, with the goal of making an efficient crossover of high-quality building blocks possible without biasing certain types of schemata. By manipulating of the program tree in the instantiation phase, it is assured that the resulting synthesized structure fits within the physical constraints of the underlying hardware platform.", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389146}", } @Misc{Trif:2013:IJERA, author = "Silvia Trif and Adrian Visoiu", title = "Gene Expression Programming Based Dataset Decoration for Improved Churn Prediction", keywords = "genetic algorithms, genetic programming, gene expression programming, business intelligence, churn prediction, classification, data mining", abstract = "Mobile network operators rely on business intelligence tools to derive valuable information regarding their subscribers. A key objective is to reduce churn rate among subscribers. The mobile operator needs to know in advance which subscribers are at risk of becoming churners. This problem is solved with classification algorithms having as input data derived from the large volumes of usage details recorded. For certain categories of subscribers, available data is limited to call details records. Using this primary data, a dataset is created to be conveniently used by a classification algorithm. Classification quality using this initial dataset is improved by a proposed method for dataset decoration. Additional attributes are derived from the initial dataset through generation, based on gene expression programming. Classification results obtained using the decorated dataset show that the derived attributes are relevant for the studied problem.", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.418.6870", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.418.6870", URL = "http://www.ijera.com/papers/Vol3_issue3/GC3310851089.pdf", } @Article{Tripathi:2018:IJCA, author = "Ankita Tripathi and Ravi Datta Sharma and Shrawan Kumar Trivedi", title = "Identification of Plant Species using Supervised Machine Learning", journal = "International Journal of Computer Applications", year = "2018", volume = "182", number = "13", pages = "6--12", month = sep, keywords = "genetic algorithms, genetic programming, Plant Species Identification, Machine Learning Classifiers, Pattern Recognition, Plant Species, Leaf image, Machine learning, F-Value, FP rate, Training time", publisher = "Foundation of Computer Science (FCS), NY, USA", address = "New York, USA", ISSN = "0975-8887", URL = "http://www.ijcaonline.org/archives/volume182/number13/29920-2018917755", URL = "https://www.ijcaonline.org/archives/volume182/number13/tripathi-2018-ijca-917755.pdf", DOI = "doi:10.5120/ijca2018917755", size = "7 pages", abstract = "This research emphasizes on the plant species recognition which is considered as an important area of research in plant biotechnology. Artificial intelligence and machine learning have a prominent place in such research. In this study, a boosted evolutionary plant species classifier has been developed that works on ensemble of classifier methods. This classifier identifies different species of plants with the help of different texture and shape features of leaf image. A publicly available plant image dataset has been incorporated where features are extracted with the help of image processing tools. The proposed classifier is trained and tested with the help of these features. Further, proposed classifier is compared with other popular machine learning classifier viz. Bayesian, Naive Bayes, SVM, J48, Random forest, Genetic Programming. Proposed evolutionary classifier was found to be good in terms of F-Value, FP rate and TP rate whereas SVM was found to be under performing predictor in this study. However, the training time of the proposed classifier was high.", notes = "Also known as \cite{10.5120/ijca2018917755} www.ijcaonline.org Amity institute of biotechnology, Amity University, Gurgaon, India", } @Article{journals/ijiit/TripathiGTK11, author = "Arpit Tripathi and Pulkit Gupta and Aditya Trivedi and Rahul Kala", title = "Wireless Sensor Node Placement Using Hybrid Genetic Programming and Genetic Algorithms", journal = "International Journal of Intelligent Information Technologies", year = "2011", number = "2", volume = "7", pages = "63--83", keywords = "genetic algorithms, genetic programming", ISSN = "1548-3657", URL = "http://www.igi-global.com/article/wireless-sensor-node-placement-using/54067", DOI = "doi:10.4018/jiit.2011040104", abstract = "The ease of use and re-configuration in a wireless network has played a key role in their widespread growth. The node deployment problem deals with an optimal placement strategy of the wireless nodes. This paper models a wireless sensor network, consisting of a number of nodes, and a unique sink to which all the information is transmitted using the shortest connecting path. Traditionally the systems have used Genetic Algorithms for optimal placement of the nodes that usually fail to give results in problems employing large numbers of nodes or higher areas to be covered. This paper proposes a hybrid Genetic Programming (GP) and Genetic Algorithm (GA) for solving the problem. While the GP optimises the deployment structure, the GA is used for actual node placement as per the GP optimised structure. The GA serves as a slave and GP serves as master in this hierarchical implementation. The algorithm optimises total coverage area, energy , lifetime of the network, and the number of nodes deployed. Experimental results show that the algorithm could place the sensor nodes in a variety of scenarios. The placement was found to be better than random placement strategy as well as the Genetic Algorithm placement strategy.", notes = "Indian Institute of Information Technology and Management Gwalior, India", bibdate = "2011-05-27", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ijiit/ijiit7.html#TripathiGTK11", } @Article{Tripathi:2016:CMS, author = "Manwendra K. Tripathi and Subhas Ganguly and Partha Dey and P. P. Chattopadhyay", title = "Evolution of glass forming ability indicator by genetic programming", journal = "Computational Materials Science", volume = "118", pages = "56--65", year = "2016", ISSN = "0927-0256", DOI = "doi:10.1016/j.commatsci.2016.02.037", URL = "http://www.sciencedirect.com/science/article/pii/S0927025616300763", abstract = "A symbolic regression technique has been employed to evolve the functional relationship among the characteristic transformation temperatures, viz. glass transition temperature (Tg), onset crystallization temperature (Tx) and offset temperature of melting (Tl) concerning glass forming ability (GFA) of bulk metallic glasses (BMGs). The critical diameters (Dmax) of 410 reported BMGs, along with their Tg, Tx and Tl values, forms the training data, for a genetic programming based computer code which attempts to evolve an expression leading to high correlation with Dmax as the target variable. Another set of recently reported 184 BMGs data, is used to assess the performance of the evolved expression. The evolved expression shows significantly improved correlations with critical diameter Dmax, for training data, test data and training and test data considered together. The same also compares well with the high correlation GFA indicators reported earlier in the literature.", keywords = "genetic algorithms, genetic programming, Glass forming ability, GFA criteria, Bulk metallic glass", notes = "National Institute of Technology Raipur, Raipur 492010, India", } @Article{TRIPATHI:2017:Intermetallics, author = "Manwendra K. Tripathi and P. P. Chattopadhyay and Subhas Ganguly", title = "A predictable glass forming ability expression by statistical learning and evolutionary intelligence", journal = "Intermetallics", volume = "90", pages = "9--15", year = "2017", keywords = "genetic algorithms, genetic programming, Meta-modeling, Glass forming ability (GFA), Bulk metallic glass (BMG), Principal component analysis (PCA), Genetic programming (GP), Combinatorial analysis", ISSN = "0966-9795", DOI = "doi:10.1016/j.intermet.2017.06.008", URL = "http://www.sciencedirect.com/science/article/pii/S0966979517302650", abstract = "This paper demonstrates how principal component analysis of multivariate BMG alloy data and the genetic programming of the extracted features in the form of principal components can be used to develop a meta-modeling scheme for GFA expression. The proposed GFA model can estimate the glass forming potential of an alloy from its composition data, unlike the characteristic temperature based glass forming ability expressions, consisting of Tg, Txand Tl. The BMG alloys have been described by means of generic attributes of the constituent elements and corresponding composition of the alloy yielding a multi-dimensional descriptor space for a 594 BMGs compiled from literature. The PCA model of the data base plausibly reduced the dimensionality into a two dimension in terms of two extracted features by first two principle components capturing the 82percent of the data knowledge. Successively, these principle components are used to develop a constitutive model for glass forming ability using genetic programming. The combinatorial analysis of the meta-model for GFA expression is applied to the prediction of potential compositional zone in five different experimentally explored ternary systems. The predicted composition zones are discussed in the context of available experimental data in literature and the energy of formation of the stable phases in respective alloy systems", } @Article{Trist:2015:IJAT, author = "Karen Trist and Vic Ciesielski and Perry Barile", title = "An artist's experience in using an evolutionary algorithm to produce an animated artwork", journal = "International Journal of Arts and Technology", publisher = "Inderscience Publishers", year = "2015", month = jan # "~11", volume = "4", number = "2", pages = "155--167", keywords = "genetic algorithms, genetic programming, evolutionary programming, evolved art, new media art, software art, animation, algorithmic art, genetic art, NPR, non-photorealistic rendering, eucalyptus trees, aesthetics, tempo, energy", ISSN = "1754-8861", bibsource = "OAI-PMH server at www.inderscience.com", URL = "http://www.inderscience.com/link.php?id=39842", DOI = "doi:10.1504/IJART.2011.039842", abstract = "We describe an artist's journey of working with an evolutionary algorithm to create an artwork suitable for exhibition in a gallery. Software based on the evolutionary algorithm produces animations which engage the viewer with a target image slowly emerging from a random collection of greyscale lines. The artwork consists of a grid of movies of eucalyptus tree targets. Each movie resolves with different aesthetic qualities, tempo and energy. The artist exercises creative control by choice of target and values for evolutionary and drawing parameters.", } @InProceedings{Trivedi:2013:CSE, author = "Shrawan Kumar Trivedi and Shubhamoy Dey", title = "An Enhanced Genetic Programming Approach for Detecting Unsolicited Emails", booktitle = "16th IEEE International Conference on Computational Science and Engineering (CSE 2013)", year = "2013", month = "3-5 " # dec, pages = "1153--1160", address = "Sydney", keywords = "genetic algorithms, genetic programming, spam, Enhanced Genetic Programming, SVM, J48, Random Forest, Probabilistic classifiers, Unsolicited Emails, Machine Learning Classifiers, Ensemble, Performance Accuracy, F-Value, False Positive Rate, Sensitivity", DOI = "doi:10.1109/CSE.2013.171", abstract = "Identification of unsolicited emails (spams) is now a well-recognised research area within text classification. A good email classifier is not only evaluated by performance accuracy but also by the false positive rate. This research presents an Enhanced Genetic Programming (EGP) approach which works by building an ensemble of classifiers for detecting spams. The proposed classifier is tested on the most informative features of two public ally available corpi (Enron and Spam assassin) found using Greedy stepwise search method. Thereafter, the proposed ensemble of classifiers is compared with various Machine Learning Classifiers: Genetic Programming (GP), Bayesian, Naive Bayes (NB), J48, Random forest (RF), and SVM. Results of this study indicate that the proposed classifier (EGP) is the best classifier among those compared in terms of performance accuracy as well as false positive rate.", notes = "Also known as \cite{6755352}", } @Article{trivino:2012:IJBAS-IJENS, author = "Jorge Eduardo {Ortiz Trivino} and Mauro {Florez Calderon}", title = "General Method Of Multivariate Non-Linear Regression Based On Genetic Programming", journal = "International Journal of Basic and Applied Sciences", year = "2012", volume = "12", number = "3", month = "10 " # jun, ISSN = "2077-1223", keywords = "genetic algorithms, genetic programming, Model, non linear function, structure", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.418.6751", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.418.6751", URL = "https://www.ijens.org/IJBASVol12Issue03.html", URL = "http://www.ijens.org/Vol_12_I_03/121803-6464-IJBAS-IJENS.pdf", size = "12 pages", abstract = "In this paper we show the method phi to estimate the structure and parameters of a non-linear function of real value phi from a data set {(x1, x2,..., xj,..., xn,; y1)}i=m i=n taken from that function. The technique phi is based in the Holland's genetic algorithm, this employ the genetic operations of selection, crossover and mutation. But unlike this, the individuals are dynamic structures called trees, allowing its size can grow without restrictions and these ones can become a better representation of the desired response. The experimentation shows that the method is efficient for both linear and nonlinear functions as well as for multivaried cases", notes = "Universidad Nacional de Colombia", } @Article{journals/jaihc/TroianoBA16, author = "Luigi Troiano and Cosimo Birtolo and Roberto Armenise", title = "Searching optimal menu layouts by linear genetic programming", journal = "Journal of Ambient Intelligence and Humanized Computing", year = "2016", number = "2", volume = "7", keywords = "genetic algorithms, genetic programming", bibdate = "2016-03-29", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/jaihc/jaihc7.html#TroianoBA16", pages = "239--256", URL = "http://dx.doi.org/10.1007/s12652-015-0322-7", abstract = "Designing effective menu systems is a key ingredient to usable graphical user interfaces. This task generally relies only on human ability in building hierarchical structures. However, trading off different and partially opposite guidelines, standards and practices is time consuming and can exceed human skills in problem solving. Recent advances are showing that this task can be addressed by generative approaches which exploit evolutionary algorithms as means for evolving different and unexpected solutions. The search of optimal solutions is made not trivial due to different alternatives which lead to local optima and constraints which can invalidate large sectors of the search space and make valid solutions sparse. This problem can be addressed by choosing an appropriate algorithm. In this paper we face the problem of searching optimal solutions by Linear Genetic Programming in particular, and we compare the solution to more conventional approaches based on simple genetic algorithms and genetic programming. Experimental results are discussed and compared to human-made solutions.", } @InProceedings{Troise:2017:GPTP, author = "Sarah Anne Troise and Thomas Helmuth", title = "Lexicase Selection with Weighted Shuffle", booktitle = "Genetic Programming Theory and Practice XV", editor = "Wolfgang Banzhaf and Randal S. Olson and William Tozier and Rick Riolo", year = "2017", series = "Genetic and Evolutionary Computation", pages = "89--104", address = "University of Michigan in Ann Arbor, USA", month = may # " 18--20", organisation = "the Center for the Study of Complex Systems", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-90511-2", URL = "https://link.springer.com/chapter/10.1007/978-3-319-90512-9_6", DOI = "doi:10.1007/978-3-319-90512-9_6", abstract = "Semantic-aware methods in genetic programming take into account information about programs performances across a set of test cases. Lexicase parent selection, a semantic-aware selection, randomly shuffles the list of test cases and places more emphasis on those test cases that randomly appear earlier in the ordering than those that appear later in the ordering. In this work, we explore methods for weighting this shuffling of test cases to give some test cases more influence over selection than others. We design and test a variety of weighted shuffle algorithms and methods for weighting test cases. In experiments on two program synthesis benchmark problems, we find that none of these methods significantly outperform regular lexicase selection. We analyse these results by examining how each method affects population diversity, and find that those methods that perform much worse also have significantly lower diversity.", notes = "GPTP 2017, Part of \cite{Banzhaf:2017:GPTP} published after the workshop in 2018", } @Article{Trotman2005_Article_LearningToRank, author = "Andrew Trotman", title = "Learning to Rank", journal = "Information Retrieval", year = "2005", volume = "8", pages = "359--381", month = jan, keywords = "genetic algorithms, genetic programming, searching, document ranking, machine learning", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.1042.5037", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1042.5037", URL = "http://ccc.inaoep.mx/%7Evillasen/bib/Trotman-lerningRank07.pdf", URL = "https://rdcu.be/dR76m", DOI = "doi:10.1007/s10791-005-6991-7", size = "23 pages", abstract = "New general purpose ranking functions are discovered using genetic programming. The TREC WSJ collection was chosen as a training set. A baseline comparison function was chosen as the best of inner product, probability, cosine, and Okapi BM25. An elitist genetic algorithm with a population size 100 was run 13 times for 100 generations and the best performing algorithms chosen from these. The best learnt functions, when evaluated against the best baseline function (BM25), demonstrate some significant performance differences, with improvements in mean average precision as high as 32percent observed on one TREC collection not used in training. In no test is BM25 shown to significantly outperform the best learnt function.", } @Article{TRUDA:2021:JBI, author = "Gianluca Truda and Patrick Marais", title = "Evaluating warfarin dosing models on multiple datasets with a novel software framework and evolutionary optimisation", journal = "Journal of Biomedical Informatics", volume = "113", pages = "103634", year = "2021", ISSN = "1532-0464", DOI = "doi:10.1016/j.jbi.2020.103634", URL = "https://www.sciencedirect.com/science/article/pii/S1532046420302628", keywords = "genetic algorithms, genetic programming, Warfarin, Machine learning, Python, Supervised learning, Anticoagulant, Pharmacogenetics, Software", abstract = "Warfarin is an effective preventative treatment for arterial and venous thromboembolism, but requires individualised dosing due to its narrow therapeutic range and high individual variation. Many machine learning techniques have been demonstrated in this domain. This study evaluated the accuracy of the most promising algorithms on the International Warfarin Pharmacogenetics Consortium dataset and a novel clinical dataset of South African patients. Support vectors and linear regression were amongst the top performers in both datasets and performed comparably to recent stacked ensemble approaches, whilst neural networks were one of the worst performers in both datasets. We also introduced genetic programming to automatically optimise model architectures and hyperparameters without human guidance. Remarkably, the generated models were found to match the performance of the best models hand-crafted by human experts. Finally, we present a novel software framework (Warfit-learn) for warfarin dosing research. It leverages the most successful techniques in preprocessing, imputation, and parallel evaluation, with the goal of accelerating research and making results in this domain more reproducible", } @Misc{DBLP:journals/corr/abs-1912-09503, author = "Alexandre Trudeau and Christopher M. Clark", title = "Multi-Robot Path Planning Via Genetic Programming", howpublished = "arXiv", volume = "abs/1912.09503", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "http://arxiv.org/abs/1912.09503", archiveprefix = "arXiv", eprint = "1912.09503", timestamp = "Fri, 03 Jan 2020 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/corr/abs-1912-09503.bib", bibsource = "dblp computer science bibliography, https://dblp.org", size = "18 pages", abstract = "This paper presents a Genetic Programming (GP) approach to solving multi-robot path planning (MRPP) problems in single-lane workspaces, specifically those easily mapped to graph representations. GP's versatility enables this approach to produce programs optimizing for multiple attributes rather than a single attribute such as path length or completeness. When optimizing for the number of time steps needed to solve individual MRPP problems, the GP constructed programs outperformed complete MRPP algorithms, i.e. Push-Swap-Wait (PSW), by 54.1percent. The GP constructed programs also consistently outperformed PSW in solving problems that did not meet PSW's completeness conditions. Furthermore, the GP constructed programs exhibited a greater capacity for scaling than PSW as the number of robots navigating within an MRPP environment increased. This research illustrates the benefits of using Genetic Programming for solving individual MRPP problems, including instances in which the number of robots exceeds the number of leaves in the tree-modeled work space.", notes = "ARMS 2019 Workshop (AAMAS)", } @InProceedings{1144151, author = "Leonardo Trujillo and Gustavo Olague", title = "Synthesis of interest point detectors through genetic programming", booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation", year = "2006", editor = "Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos {Coello Coello} and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens", volume = "1", ISBN = "1-59593-186-4", pages = "887--894", address = "Seattle, Washington, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p887.pdf", DOI = "doi:10.1145/1143997.1144151", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "8-12 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, feature representation, invariants, program synthesis, synthesis, theory", size = "8 pages", abstract = "This contribution presents a novel approach for the automatic generation of a low-level feature extractor that is useful in higher-level computer vision tasks. Specifically, our work centers on the well-known computer vision problem of interest point detection. We pose interest point detection as an optimization problem, and are able to apply Genetic Programming to generate operators that exhibit human-competitive performance when compared with state-of-the-art designs. This work uses the repeatability rate that is applied as a benchmark metric in computer vision literature as part of the GP fitness function, together with a measure of the entropy related with the point distribution across the image. This two measures promote geometric stability and global separability under several types of image transformations. This paper introduces a Genetic Programming implementation that was able to discover a modified version of the DET operator [Beaudet, 1978], that shows a surprisingly high-level of performance. In this work emphasis was given to the balance between genetic programming and domain knowledge expertise to obtain results that are equal or better than human created solutions.", notes = "Bronze HUMIES winner GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM Order Number 910060 see also \cite{Olague:2006:sigevo}.", } @InProceedings{Trujillo:2006:ICPR, author = "L. Trujillo and G. Olague", title = "Using Evolution to Learn How to Perform Interest Point Detection", booktitle = "ICPR 2006 18th International Conference on Pattern Recognition", year = "2006", editor = "X. Y Tang et al.", volume = "1", pages = "211--214", month = "20-24 " # aug, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", URL = "http://www.genetic-programming.org/hc2006/Olague-Paper-2-ICPR-2006.pdf", DOI = "doi:10.1109/ICPR.2006.1153", abstract = "The performance of high-level computer vision applications is tightly coupled with the low-level vision operations that are commonly required. Thus, it is advantageous to have low-level feature extractors that are optimal with respect to a desired performance criteria. This paper presents a novel approach that uses Genetic Programming as a learning framework that generates a specific type of low-level feature extractor: Interest Point Detector. The learning process is posed as an optimization problem. The optimization criterion is designed to promote the emergence of the detectors' geometric stability under different types of image transformations and global separability between detected points. This concept is represented by the operators repeatability rate [11]. Results prove that our approach is effective at automatically generating low-level feature extractors. This paper presents two different evolved operators: IPGP1 and IPGP2. Their performance is comparable with the Harris [5] operator given their excellent repeatability rate. Furthermore, the learning process was able to rediscover the DET corner detector proposed by Beaudet.", } @InProceedings{Trujillo2:2008:gecco, author = "Leonardo Trujillo and Gustavo Olague and Evelyne Lutton and Francisco {Fernandez de Vega}", title = "Multiobjective design of operators that detect points of interest in images", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "1299--1306", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1299.pdf", DOI = "doi:10.1145/1389095.1389344", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, interest point detection, multiobjective optimisation", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389344}", } @Article{Trujillo:2008:EC, author = "Leonardo Trujillo and Gustavo Olague", title = "Automated Design of Image Operators that Detect Interest Points", journal = "Evolutionary Computation", year = "2008", volume = "16", number = "4", pages = "483--507", month = "Winter", keywords = "genetic algorithms, genetic programming", ISSN = "1063-6560", DOI = "doi:10.1162/evco.2008.16.4.483", abstract = "This work describes how evolutionary computation can be used to synthesize low-level image operators that detect interesting points on digital images. Interest point detection is an essential part of many modern computer vision systems that solve tasks such as object recognition, stereo correspondence, and image indexing, to name but a few. The design of the specialized operators is posed as an optimization/search problem that is solved with genetic programming (GP), a strategy still mostly unexplored by the computer vision community. The proposed approach automatically synthesizes operators that are competitive with state-of-the-art designs, taking into account an operator's geometric stability and the global separability of detected points during fitness evaluation. The GP search space is defined using simple primitive operations that are commonly found in point detectors proposed by the vision community. The experiments described in this paper extend previous results (Trujillo and Olague, 2006a,b) by presenting 15 new operators that were synthesized through the GP-based search. Some of the synthesized operators can be regarded as improved man made designs because they employ well-known image processing techniques and achieve highly competitive performance. On the other hand, since the GP search also generates what can be considered as unconventional operators for point detection, these results provide a new perspective to feature extraction research.", notes = "Part of special issue on Evolutionary Computer Vision \cite{Cagnoni:2008:EC}", } @InCollection{Trujillo:2009:EIASP, author = "Leonardo Trujillo and Gustavo Olague", title = "Detecting Scale-Invariant Regions Using Evolved Image Operators", booktitle = "Evolutionary Image Analysis and Signal Processing", publisher = "Springer", year = "2009", editor = "Stefano Cagnoni", volume = "213", series = "Studies in Computational Intelligence", pages = "21--40", address = "Berlin / Heidelberg", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-01635-6", ISSN = "1860-949X", DOI = "doi:10.1007/978-3-642-01636-3_2", abstract = "This chapter describes scale-invariant region detectors that are based on image operators synthesised through Genetic Programming (GP). Interesting or salient regions on an image are of considerable usefulness within a broad range of vision problems, including, but not limited to, stereo vision, object detection and recognition, image registration and content-based image retrieval. A GP-based framework is described where candidate image operators are synthesized by employing a fitness measure that promotes the detection of stable and dispersed image features, both of which are highly desirable properties. After a significant number of experimental runs, a plateau of maxima was identified within the search space that contained operators that are similar, in structure and/or functionality, to basic LoG or DoG filters. Two such operators with the simplest structure were selected and embedded within a linear scale space, thereby making scale-invariant feature detection a straightforward task. The proposed scale-invariant detectors exhibit a high performance on standard tests when compared with state-of-the-art techniques. The experimental results exhibit the ability of GP to construct highly reusable code for a well known and hard task when an appropriate optimisation problem is framed.", notes = "EvoISAP, EvoNET, EvoStar", } @InProceedings{Trujillo:2010:gecco, author = "Leonardo Trujillo and Pierrick Legrand and Jacques Levy-Vehel", title = "The estimation of h{\"{o}}lderian regularity using genetic programming", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "861--868", keywords = "genetic algorithms, genetic programming, Signal regularity, Holder exponent", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.220.3708", URL = "http://hal.inria.fr/docs/00/53/89/43/PDF/t10fp182-trujillo.pdf", DOI = "doi:10.1145/1830483.1830641", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "This paper presents a Genetic Programming (GP) approach to synthesise estimators for the pointwise Holder exponent in 2D signals. It is known that irregularities and singularities are the most salient and informative parts of a signal. Hence, explicitly measuring these variations can be important in various domains of signal processing. The point wise Holder exponent provides a characterisation of these types of features. However, current methods for estimation cannot be considered to be optimal in any sense. Therefore, the goal of this work is to automatically synthesise operators that provide an estimation for the Holderian regularity in a 2D signal. This goal is posed as an optimisation problem in which we attempt to minimize the error between a prescribed regularity and the estimated regularity given by an image operator. The search for optimal estimators is then carried out using a GP algorithm. Experiments confirm that the GP-operators produce a good estimation of the Holder exponent in images of multifractional Brownian motions. In fact, the evolved estimators significantly outperform a traditional method by as much as one order of magnitude. These results provide further empirical evidence that GP can solve difficult problems of applied mathematics.", notes = "Also known as \cite{1830641} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @InProceedings{trujillo:2011:EuroGP, author = "Leonardo Trujillo and Sara Silva and Pierrick Legrand and Leonardo Vanneschi", title = "An empirical study of functional complexity as an indicator of overfitting in Genetic Programming", booktitle = "Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011", year = "2011", month = "27-29 " # apr, editor = "Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado", series = "LNCS", volume = "6621", publisher = "Springer Verlag", address = "Turin, Italy", pages = "262--273", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming: poster", isbn13 = "978-3-642-20406-7", DOI = "doi:10.1007/978-3-642-20407-4_23", abstract = "Recently, it has been stated that the complexity of a solution is a good indicator of the amount of overfitting it incurs. However, measuring the complexity of a program, in Genetic Programming, is not a trivial task. In this paper, we study the functional complexity and how it relates with overfitting on symbolic regression problems. We consider two measures of complexity, Slope-based Functional Complexity, inspired by the concept of curvature, and Regularity-based Functional Complexity based on the concept of Holderian regularity. In general, both complexity measures appear to be poor indicators of program overfitting. However, results suggest that Regularity-based Functional Complexity could provide a good indication of overfitting in extreme cases.", notes = "Part of \cite{Silva:2011:GP} EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011", } @InProceedings{trujillo2:2011:EuroGP, author = "Leonardo Trujillo and Yuliana Mart\'inez and Patricia Melin", title = "Estimating classifier performance with Genetic Programming", booktitle = "Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011", year = "2011", month = "27-29 " # apr, editor = "Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado", series = "LNCS", volume = "6621", publisher = "Springer Verlag", address = "Turin, Italy", pages = "274--285", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming: poster", isbn13 = "978-3-642-20406-7", DOI = "doi:10.1007/978-3-642-20407-4_24", abstract = "A fundamental task that must be addressed before classifying a set of data, is that of choosing the proper classification method. In other words, a researcher must infer which classifier will achieve the best performance on the classification problem in order to make a reasoned choice. This task is not trivial, and it is mostly resolved based on personal experience and individual preferences. This paper presents a methodological approach to produce estimators of classifier performance, based on descriptive measures of the problem data. The proposal is to use Genetic Programming (GP) to evolve mathematical operators that take as input descriptors of the problem data, and output the expected error that a particular classifier might achieve if it is used to classify the data. Experimental tests show that GP can produce accurate estimators of classifier performance, by evaluating our approach on a large set of 500 two-class problems of multimodal data, using a neural network for classification. The results suggest that the GP approach could provide a tool that helps researchers make a reasoned decision regarding the applicability of a classifier to a particular problem.", notes = "Part of \cite{Silva:2011:GP} EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011", } @InProceedings{Trujillo:2011:GECCO, author = "Leonardo Trujillo and Yuliana Martinez and Edgar Galvan-Lopez and Pierrick Legrand", title = "Predicting problem difficulty for genetic programming applied to data classification", booktitle = "GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0557-0", pages = "1355--1362", keywords = "genetic algorithms, genetic programming", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001576.2001759", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "During the development of applied systems, an important problem that must be addressed is that of choosing the correct tools for a given domain or scenario. This general task has been addressed by the genetic programming (GP) community by attempting to determine the intrinsic difficulty that a problem poses for a GP search. This paper presents an approach to predict the performance of GP applied to data classification, one of the most common problems in computer science. The novelty of the proposal is to extract statistical descriptors and complexity descriptors of the problem data, and from these estimate the expected performance of a GP classifier. We derive two types of predictive models: linear regression models and symbolic regression models evolved with GP. The experimental results show that both approaches provide good estimates of classifier performance, using synthetic and real-world problems for validation. In conclusion, this paper shows that it is possible to accurately predict the expected performance of a GP classifier using a set of descriptors that characterize the problem data.", notes = "Also known as \cite{2001759} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)", } @InProceedings{Trujillo:2011:GECCOcomp, author = "Leonardo Trujillo and Yuliana Martinez and Patricia Melin", title = "How many neurons?: a genetic programming answer", booktitle = "GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0690-4", keywords = "genetic algorithms, genetic programming, Genetics based machine learning: Poster", pages = "175--176", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001858.2001956", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "The goal of this paper is to derive predictive models that take as input a description of a problem and produce as output an estimate of the optimal number of hidden nodes in an Artificial Neural Network (ANN). We call such computational tools Direct Estimators of Neural Network Topology (DENNT), an use Genetic Programming (GP) to evolve them. The evolved DENNTs take as input statistical and complexity descriptors of the problem data, and output an estimate of the optimal number of hidden neurons.", notes = "Also known as \cite{2001956} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @Article{Trujillo:2011:SC, author = "Leonardo Trujillo", title = "Genetic programming with one-point crossover and subtree mutation for effective problem solving and bloat control", journal = "Soft Computing - A Fusion of Foundations, Methodologies and Applications", year = "2011", volume = "15", number = "8", pages = "1551--1567", month = aug, keywords = "genetic algorithms, genetic programming", publisher = "Springer Berlin / Heidelberg", ISSN = "1432-7643", DOI = "doi:10.1007/s00500-010-0687-7", abstract = "Genetic programming (GP) is one of the most widely used paradigms of evolutionary computation due to its ability to automatically synthesise computer programs and mathematical expressions. However, because GP uses a variable length representation, the individuals within the evolving population tend to grow rapidly without a corresponding return in fitness improvement, a phenomenon known as bloat. In this paper, we present a simple bloat control strategy for standard tree-based GP that achieves a one order of magnitude reduction in bloat when compared with standard GP on benchmark tests, and practically eliminates bloat on two real-world problems. Our proposal is to substitute standard subtree crossover with the one-point crossover (OPX) developed by Poli and Langdon (Second online world conference on soft computing in engineering design and manufacturing, Springer, Berlin ( 1997 )) \cite{poli:1997:1pxoWSC2c}, while maintaining all other GP aspects standard, particularly subtree mutation. OPX was proposed for theoretical purposes related to GP schema theorems, however since it curtails exploration during the search it has never achieved widespread use. In our results, on the other hand, we are able to show that OPX can indeed perform an effective search if it is coupled with subtree mutation, thus combining the bloat control capabilities of OPX with the exploration provided by standard mutation.", affiliation = "Instituto Tecnologico de Tijuana, Av. Tecnologico S/N, Fracc. Tomas Aquino, Tijuana, BC, Mexico", } @InProceedings{Trujillo:2012:GECCOcomp, author = "Leonardo Trujillo and Yuliana Martinez and Edgar Galvan Lopez and Pierrick Legrand", title = "A comparative study of an evolvability indicator and a predictor of expected performance for genetic programming", booktitle = "GECCO Companion '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion", year = "2012", editor = "Terry Soule and Anne Auger and Jason Moore and David Pelta and Christine Solnon and Mike Preuss and Alan Dorin and Yew-Soon Ong and Christian Blum and Dario Landa Silva and Frank Neumann and Tina Yu and Aniko Ekart and Will Browne and Tim Kovacs and Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and Giovanni Squillero and Nicolas Bredeche and Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and Martin Pelikan and Silja Meyer-Nienberg and Christian Igel and Greg Hornby and Rene Doursat and Steve Gustafson and Gustavo Olague and Shin Yoo and John Clark and Gabriela Ochoa and Gisele Pappa and Fernando Lobo and Daniel Tauritz and Jurgen Branke and Kalyanmoy Deb", isbn13 = "978-1-4503-1178-6", keywords = "genetic algorithms, Genetic programming: Poster", pages = "1489--1490", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330784.2331006", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "An open question within Genetic Programming (GP) is how to characterize problemdifficulty. The goal is to develop predictive tools that estimate how difficult a problemis for GP to solve. Here we consider two groups of methods. We call the first group Evolvability Indicators (EI), measures that capture how amendable the fitness landscape is to a GP search. Examples of EIs are Fitness Distance Correlation (FDC) and Negative Slope Coefficient (NSC). The second group are Predictors of Expected Performance (PEP), models that take as input a set of descriptive attributes of a problem and predict the expected performance of GP. This paper compares an EI, the NSC, and a PEP model for a GP classifier. Results suggest that the EI does not correlate with the performance of the GP classifiers. Conversely, the PEP models show a high correlation with GP performance.", notes = "Also known as \cite{2331006} Distributed at GECCO-2012. ACM Order Number 910122.", } @Article{Trujillo201261, author = "Leonardo Trujillo and Pierrick Legrand and Gustavo Olague and Jacques Levy-Vehel", title = "Evolving estimators of the pointwise Hoelder exponent with Genetic Programming", title2 = "Evolving estimators of the pointwise Holder exponent with Genetic Programming", journal = "Information Sciences", volume = "209", pages = "61--79", year = "2012", ISSN = "0020-0255", DOI = "doi:10.1016/j.ins.2012.04.043", URL = "http://www.sciencedirect.com/science/article/pii/S0020025512003386", URL = "http://hal.inria.fr/hal-00643387", URL = "http://hal.inria.fr/docs/00/64/33/87/PDF/INS-S-11-01794-extrait.pdf", language = "ENG", oai = "oai:hal.inria.fr:hal-00643387", keywords = "genetic algorithms, genetic programming, Hoelder regularity, Local image description", abstract = "The regularity of a signal can be numerically expressed using Hoelder exponents, which characterise the singular structures a signal contains. In particular, within the domains of image processing and image understanding, regularity-based analysis can be used to describe local image shape and appearance. However, estimating the Hoelder exponent is not a trivial task, and current methods tend to be computationally slow and complex. This work presents an approach to automatically synthesise estimators of the pointwise Hoelder exponent for digital images. This task is formulated as an optimisation problem and Genetic Programming (GP) is used to search for operators that can approximate a traditional estimator, the oscillations method. Experimental results show that GP can generate estimators that achieve a low error and a high correlation with the ground truth estimation. Furthermore, most of the GP estimators are faster than traditional approaches, in some cases their run time is orders of magnitude smaller. This result allowed us to implement a real-time estimation of the Hoelder exponent on a live video signal, the first such implementation in current literature. Moreover, the evolved estimators are used to generate local descriptors of salient image regions, a task for which a stable and robust matching is achieved, comparable with state-of-the-art methods. In conclusion, the evolved estimators produced by GP could help expand the application domain of Hoelder regularity within the fields of image analysis and signal processing.", notes = "Entered for 2013 HUMIES GECCO 2013", } @InProceedings{ERA_2012_NSC, author = "Leonardo Trujillo and Yuliana Martinez and Edgar Galvan-Lopez and Pierrick Legrand", title = "A comparison of predictive measures of problem difficulty for classification with Genetic Programming", booktitle = "ERA 2012", year = "2012", address = "Tijuana, Mexico", month = nov # " 14-16", organisation = "El Centro de Investigacion y Desarrollo de Tecnologia Digital, CITEDI, Research and Development Center Digital Technology", keywords = "genetic algorithms, genetic programming, Performance prediction, Classification", URL = "http://hal.inria.fr/hal-00757363", URL = "http://hal.inria.fr/docs/00/75/73/63/PDF/ERA_2012_NSC.pdf", bibsource = "OAI-PMH server at hal.archives-ouvertes.fr", language = "ENG", oai = "oai:hal.inria.fr:hal-00757363", type = "conference proceeding", size = "12 pages", abstract = "In the field of Genetic Programming (GP) a question exists that is difficult to solve; how can problem difficulty be determined? In this paper the overall goal is to develop predictive tools that estimate how difficult a problem is for GP to solve. Here we analyse two groups of methods. We call the first group Evolvability Indicators (EI), measures that capture how amendable the fitness landscape is to a GP search. The second are Predictors of Expected Performance (PEP), models that take as input a set of descriptive attributes of a problem and predict the expected performance of a GP system. These predictive variables are domain specific thus problems are described in the context of the problem domain. This paper compares an EI, the Negative Slope Coefficient, and a PEP model for a GP classifier. Results suggest that the EI does not correlate with the performance of GP classifiers. Conversely, the PEP models show a high correlation with GP performance. It appears that while an EI estimates the difficulty of a search, it does not necessarily capture the difficulty of the underlying problem. However, while PEP models treat GP as a computational black-box, they can produce accurate performance predictions.", notes = "http://era.citedi.mx/site/", } @InProceedings{Trujillo:2013:EVOLVE, author = "Leonardo Trujillo and Enrique Naredo and Yuliana Martinez", title = "Preliminary Study of Bloat in Genetic Programming with Behavior-Based Search", booktitle = "EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV", year = "2013", editor = "Michael Emmerich and Andre Deutz and Oliver Schuetze and Thomas Baeck and Emilia Tantar and Alexandru-Adrian and Pierre {Del Moral} and Pierrick Legrand and Pascal Bouvry and Carlos A. Coello", volume = "227", series = "Advances in Intelligent Systems and Computing", pages = "293--305", address = "Leiden, Holland", month = jul # " 10-13", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Bloat, Novelty Search", isbn13 = "978-3-319-01127-1", DOI = "doi:10.1007/978-3-319-01128-8_19", abstract = "Bloat is one of the most interesting theoretical problems in genetic programming (GP), and one of the most important pragmatic limitations in the development of real-world GP solutions. Over the years, many theories regarding the causes of bloat have been proposed and a variety of bloat control methods have been developed. It seems that one of the underlying causes of bloat is the search for fitness; as the fitness-causes-bloat theory states, selective bias towards fitness seems to unavoidably lead the search towards programs with a large size. Intuitively, however, abandoning fitness does not appear to be an option. This paper, studies a GP system that does not require an explicit fitness function, instead it relies on behavior-based search, where programs are described by the behavior they exhibit and selective pressure is biased towards unique behaviours using the novelty search algorithm. Initial results are encouraging, the average program size of the evolving population does not increase with novelty search; i.e., bloat is avoided by focusing on novelty instead of quality.", } @InProceedings{Trujillo:2013:CEC, article_id = "1616", author = "Leonardo Trujillo and Mario Garcia-Valdez and Francisco Fernandez-de-Vega and Juan-J. Merelo", title = "Fireworks: Evolutionary art project based on EvoSpace-Interactive", booktitle = "2013 IEEE Conference on Evolutionary Computation", volume = "1", year = "2013", month = jun # " 20-23", editor = "Luis Gerardo {de la Fraga}", pages = "2871--2878", address = "Cancun, Mexico", keywords = "genetic algorithms, Genetic programming, Animation, Evolutionary Distributed algorithms, cloud computing, interactive evolutionary algorithm, linear genetic programming", isbn13 = "978-1-4799-0453-2", DOI = "doi:10.1109/CEC.2013.6557918", size = "8 pages", abstract = "This paper presents a collaborative-interactive evolutionary algorithm (C-IEA) that evolves artistic animations and is executed on the web. The application is called Fireworks, since the animations that are produced are similar to an elaborate fireworks display. The system is built using the EvoSpace platform for distributed and asynchronous evolutionary algorithms. EvoSpace provides a central repository for the evolving population and remote clients, called EvoWorkers, that interact with the system to perform fitness evaluation using an interactive approach. The artistic animations are coded using the Processing programming language that facilitates rapid development of computer graphics applications for artists and graphic designers. The system promotes user collaboration and interaction by allowing many users to participate in population evaluation and because the system incorporates social networking. Initial results show that the proposed C-IEA can allow users to produce interesting artistic artifacts that incorporate preferences from several users, evolving dynamic animations that are unique within evolutionary art.", notes = "CEC 2013 - A joint meeting of the IEEE, the EPS and the IET.", } @InProceedings{Trujillo:2013:GECCOcomp, author = "Leonardo Trujillo and Lee Spector and Enrique Naredo and Yuliana Martinez", title = "A behavior-based analysis of modal problems", booktitle = "GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion", year = "2013", editor = "Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A. N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks", isbn13 = "978-1-4503-1964-5", keywords = "genetic algorithms, genetic programming", pages = "1047--1054", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2464576.2482682", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Genetic programming (GP) has proved to be a powerful tool for (semi)automated problem solving in various domains. However, while the algorithmic aspects of GP have been a primary object of study, there is a need to enhance the understanding of the problems where GP is applied. One particular goal is to categorise problems in a meaningful way, in order to select the best tools that can possibly be used to solve them. This paper studies modal problems, a conceptual class of problems recently proposed by Spector at GECCO 2012. Modal problems are those for which a solution program requires different modes of operation for different contexts. The thesis of this paper is that modality, in this sense, is better understood by analysing program performance in behavioural space. The behaviour-based perspective is seen as part of a scale of different forms of analysing performance; with a coarse view given by a global fitness value and a highly detailed view provided by the semantics approach. On the other hand, behavioral analysis is seen as a flexible approach where the context of a program's performance is considered at in a domain-specific manner. The experimental evidence presented here suggests that behaviour-based search could allow a GP to find programs with disjoint behavioural structures, that can satisfy the requirements of each mode of operation of a modal problem.", notes = "Also known as \cite{2482682} Distributed at GECCO-2013.", } @InProceedings{trujillo:2014:EuroGP, author = "Leonardo Trujillo and Luis Munoz and Enrique Naredo and Yuliana Martinez", title = "NEAT, There's No Bloat", booktitle = "17th European Conference on Genetic Programming", year = "2014", editor = "Miguel Nicolau and Krzysztof Krawiec and Malcolm I. Heywood and Mauro Castelli and Pablo Garcia-Sanchez and Juan J. Merelo and Victor M. {Rivas Santos} and Kevin Sim", series = "LNCS", volume = "8599", publisher = "Springer", pages = "174--185", address = "Granada, Spain", month = "23-25 " # apr, organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-662-44302-6", DOI = "doi:10.1007/978-3-662-44303-3_15", abstract = "The Operator Equalisation (OE) family of bloat control methods have achieved promising results in many domains. In particular, the Flat-OE method, that promotes a flat distribution of program sizes, is one of the simplest OE methods and achieves some of the best results. However, Flat-OE, like all OE variants, can be computationally expensive. This work proposes a simplified strategy for bloat control based on Flat-OE. In particular, bloat is studied in the NeuroEvolution of Augmenting Topologies (NEAT) algorithm. NEAT includes a very simple diversity preservation technique based on speciation and fitness sharing, and it is suggested that with some minor tuning, speciation in NEAT can promote a flat distribution of program size. Results indicate that this is the case in two benchmark problems, in accordance with results for Flat-OE. In conclusion, NEAT provides a worthwhile strategy that could be extrapolated to other GP systems, for effective and simple bloat control.", notes = "Part of \cite{Nicolau:2014:GP} EuroGP'2014 held in conjunction with EvoCOP2014, EvoBIO2014, EvoMusArt2014 and EvoApplications2014", } @Article{Trujillo:2015:GPEM, author = "Leonardo Trujillo", title = "{Kenneth O. Stanley} and {Joel Lehman}: Why greatness cannot be planned: the myth of the objective", journal = "Genetic Programming and Evolvable Machines", year = "2015", volume = "16", number = "4", pages = "559--561", month = dec, note = "Book review", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dR8YS", DOI = "doi:10.1007/s10710-015-9250-8", size = "3 pages", notes = "Springer, 2015, Softcover, ISBN: 978-3-319-15523-4", } @Article{Trujillo:2016:IS, author = "Leonardo Trujillo and Luis Munoz and Edgar Galvan-Lopez and Sara Silva", title = "neat Genetic Programming: Controlling Bloat Naturally", journal = "Information Sciences", year = "2016", volume = "333", pages = "21--43", month = "10 " # mar, keywords = "genetic algorithms, genetic programming, Bloat, NeuroEvolution of augmenting topologies, Flat operator equalization;", ISSN = "0020-0255", URL = "http://mural.maynoothuniversity.ie/12330/", URL = "http://mural.maynoothuniversity.ie/12330/1/Galvan_neat_2016.pdf", URL = "http://www.sciencedirect.com/science/article/pii/S0020025515008038", DOI = "doi:10.1016/j.ins.2015.11.010", size = "23", abstract = "Bloat is one of the most widely studied phenomena in Genetic Programming (GP), it is normally defined as the increase in mean program size without a corresponding improvement in fitness. Several theories have been proposed in the specialized GP literature that explain why bloat occurs. In particular, the Crossover-Bias Theory states that the cause of bloat is that the distribution of program sizes during evolution is skewed in a way that encourages bloat to appear, by punishing small individuals and favouring larger ones. Therefore, several bloat control methods have been proposed that attempt to explicitly control the size distribution of programs within the evolving population. This work proposes a new bloat control method called neat-GP, that implicitly shapes the program size distribution during a GP run. neat-GP is based on two key elements: (a) the NeuroEvolution of Augmenting Topologies algorithm (NEAT), a robust heuristic that was originally developed to evolve neural networks; and (b) the Flat Operator Equalization bloat control method, that explicitly shapes the program size distributions toward a uniform or flat shape. Experimental results are encouraging in two domains, symbolic regression and classification of real-world data. neat-GP can curtail the effects of bloat without sacrificing performance, outperforming both standard GP and the Flat-OE method, without incurring in the computational overhead reported by some state-of-the-art bloat control methods", notes = "Presentation \cite{Trujillo:2016:GECCOcomp} Also known as \cite{nuimeprn12330}", } @InProceedings{Trujillo:2016:GPTP, title = "Local Search is Underused in Genetic Programming", author = "Leonardo Trujillo and Emigdio Z-Flores and Perla S. {Juarez Smith} and Pierrick Legrand and Sara Silva and Mauro Castelli and Leonardo Vanneschi and Oliver Schuetze and Luiz Munoz", booktitle = "Genetic Programming Theory and Practice XIV", year = "2016", editor = "William Tozier and Brian W. Goldman and Bill Worzel and Rick Riolo", series = "Genetic and Evolutionary Computation", pages = "119--137", address = "Ann Arbor, USA", month = "19-21 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Local Search, Bloat, NEAT", hal_id = "hal-01388426", hal_version = "v1", isbn13 = "978-3-319-97087-5", URL = "https://hal.inria.fr/hal-01388426", URL = "https://www.researchgate.net/publication/312016495_Local_Search_is_Underused_in_Genetic_Programming", URL = "https://www.springer.com/us/book/9783319970875", size = "18 pages", abstract = "There are two important limitations of standard tree-based genetic programming (GP). First, GP tends to evolve unnecessarily large programs, what is referred to as bloat. Second, GP uses inefficient search operators that focus on modifying program syntax. The first problem has been studied in many works, with many bloat control proposals. Regarding the second problem, one approach is to use alternative search operators, for instance geometric semantic operators, to improve convergence. In this work, our goal is to experimentally show that both problems can be effectively addressed by incorporating a local search optimizer as an additional search operator. Using real-world problems, we show that this rather simple strategy can improve the convergence and performance of tree-based GP, while reducing program size. Given these results, a question arises: why are local search strategies so uncommon in GP? A small survey of popular GP libraries suggests to us that local search is underused in GP systems. We conclude by outlining plausible answers for this question and highlighting future work.", notes = "also known as \cite{leonardo:hal-01388426} Instituto Tecnologico de Tijuana, Mexico Part of \cite{Tozier:2016:GPTP} published after the workshop", } @InProceedings{Trujillo:2016:GECCOcomp, author = "Leonardo Trujillo and Luis Munoz and Edgar Galvan-Lopez and Sara Silva", title = "neat Genetic Programming: Controlling Bloat Naturally", booktitle = "GECCO 2016 Hot of the Press", year = "2016", editor = "Benjamin Doerr and Tobias Friedrich and Frank Neumann and Andrew M. Sutton and Martin Middendorf and Xiaodong Li and Emma Hart and Mengjie Zhang and Youhei Akimoto and Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and Daniele Loiacono and Julian Togelius and Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and Faustino Gomez and Carlos M. Fonseca and Heike Trautmann and Alberto Moraglio and William F. Punch and Krzysztof Krawiec and Zdenek Vasicek and Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and Boris Naujoks and Enrique Alba and Gabriela Ochoa and Simon Poulding and Dirk Sudholt and Timo Koetzing", pages = "hop104", keywords = "genetic algorithms, genetic programming", month = "20-24 " # jul, organisation = "SIGEVO", address = "Denver, USA", publisher = "ACM", publisher_address = "New York, NY, USA", notes = "see \cite{Trujillo:2016:IS} In GECCO-2016 but not in ACM digital library.", } @InProceedings{trujillo:2018:GPTP, author = "Leonardo Trujillo and Luis Munoz and Uriel Lopez and Daniel E. Hernandez", title = "Untapped Potential of Genetic Programming: Transfer Learning and Outlier Removal", booktitle = "Genetic Programming Theory and Practice XVI", year = "2018", editor = "Wolfgang Banzhaf and Lee Spector and Leigh Sheneman", pages = "193--207", address = "Ann Arbor, USA", month = "17-20 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-030-04734-4", URL = "http://link.springer.com/chapter/10.1007/978-3-030-04735-1_10", DOI = "doi:10.1007/978-3-030-04735-1_10", abstract = "In the era of Deep Learning and Big Data, the place of Genetic Programming (GP) within the Machine Learning area seems difficult to define. Whether it is due to technical constraints or conceptual barriers, GP is currently not a paradigm of choice for the development of state-of-the-art machine learning systems. Nonetheless, there are important features of the GP approach that make it unique and should continue to be actively explored and studied. In this work we focus on two aspects of GP that have previously received little or no attention, particularly in tree-based GP for symbolic regression. First, on the potential of GP to perform transfer learning, where solutions evolved for one problem are transferred to another. Second, on the potential of GP individuals to detect the true underlying structure of an input dataset and detect anomalies in the input data, what are known as outliers. This work presents initial results on both issues, with the goal of fostering discussion and showing that there is still untapped potential in the GP paradigm.", } @Article{trujillo:2018:sigevolution, author = "Leonardo Trujillo and W. B. Langdon", title = "{EuroGP 2018} Panel Debate: {Genetic Programming} in the Era of {Deep Neural Networks}", journal = "SIGEVOlution", year = "2018", volume = "11", number = "2", pages = "3--6", month = "12 " # jul, keywords = "genetic algorithms, genetic programming, ANN, LLM", ISSN = "1931-8499", URL = "http://www.sigevolution.org/issues/SIGEVOlution1102.pdf", DOI = "doi:10.1145/3264700.3264701", size = "4 pages", abstract = "In April this year the programme chairs of EuroGP, the largest conference dedicated to genetic programming, Mauro Castelli and Lukas Sekanina, organised a panel discussion and debate about genetic programming with respect to the undoubted recent successes of other Artificial Intelligence techniques, particularly Deep Learning. The discussion was moderated by Prof. Wolfgang Banzhaf who holds the John R. Koza chair in genetic programming at Michigan State University in the USA. He started by inviting each member of the panel to give their position.", } @Article{TRUJILLO:2020:IS, author = "Leonardo Trujillo and Uriel Lopez and Pierrick Legrand", title = "{SOAP:} Semantic outliers automatic preprocessing", journal = "Information Sciences", volume = "526", pages = "86--101", year = "2020", ISSN = "0020-0255", DOI = "doi:10.1016/j.ins.2020.03.071", URL = "http://www.sciencedirect.com/science/article/pii/S0020025520302516", keywords = "genetic algorithms, genetic programming, Outliers, Semantics, Robust regression", abstract = "Genetic Programming (GP) is an evolutionary algorithm for the automatic generation of symbolic models expressed as syntax trees. GP has been successfully applied in many domain, but most research in this area has not considered the presence of outliers in the training set. Outliers make supervised learning problems difficult, and sometimes impossible, to solve. For instance, robust regression methods cannot handle more than 50percent of outlier contamination, referred to as their breakdown point. This paper studies problems where outlier contamination is high, reaching up to 90percent contamination levels, extreme cases that can appear in some domains. This work shows, for the first time, that a random population of GP individuals can detect outliers in the output variable. From this property, a new filtering algorithm is proposed called Semantic Outlier Automatic Preprocessing (SOAP), which can be used with any learning algorithm to differentiate between inliers and outliers. Since the method uses a GP population, the algorithm can be carried out for free in a GP symbolic regression system. The approach is the only method that can perform such an automatic cleaning of a dataset without incurring an exponential cost as the percentage of outliers in the dataset increases", } @Article{DBLP:journals/soco/TrujilloGGTP20, author = "Leonardo Trujillo and Ernesto {Alvarez Gonzalez} and Edgar Galvan and Juan J. Tapia and Antonin Ponsich", title = "On the analysis of hyper-parameter space for a genetic programming system with iterated {F-Race}", journal = "Soft Computing", volume = "24", number = "19", pages = "14757--14770", year = "2020", month = oct, keywords = "genetic algorithms, genetic programming, Hyper-parameter optimisation, Iterated F-Race", URL = "https://doi.org/10.1007/s00500-020-04829-4", DOI = "doi:10.1007/s00500-020-04829-4", timestamp = "Sat, 19 Sep 2020 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/soco/TrujilloGGTP20.bib", bibsource = "dblp computer science bibliography, https://dblp.org", size = "14 pages", abstract = "Evolutionary algorithms (EAs) have been with us for several decades and are highly popular given that they have proved competitive in the face of challenging problems features such as deceptiveness, multiple local optima, among other characteristics. However, it is necessary to define multiple hyper-parameter values to have a working EA, which is a drawback for many practitioners. In the case of genetic programming (GP), an EA for the evolution of models and programs, hyper-parameter optimization has been extensively studied only recently. This work builds on recent findings and explores the hyper-parameter space of a specific GP system called neat-GP that controls model size. This is conducted using two large sets of symbolic regression benchmark problems to evaluate system performance, while hyper-parameter optimization is carried out using three variants of the iterated F-Race algorithm, for the first time applied to GP. From all the automatic parametrisations produced by optimization process, several findings are drawn. Automatic parametrizations do not outperform the manual configuration in many cases, and overall, the differences are not substantial in terms of testing error. Moreover, finding parametrisations that produce highly accurate models that are also compact is not trivially done, at least if the hyper-parameter optimization process (F-Race) is only guided by predictive error. This work is intended to foster more research and scrutiny of hyper-parameters in EAs, in general, and GP, in particular.", notes = "Tecnologico Nacional de Mexico/IT de Tijuana, Tijuana, BC, Mexico", } @Article{Trujillo:2021:IEEESoftware, author = "Leonardo Trujillo and Omar M. Villanueva and Daniel E. Hernandez", title = "A Novel Approach For Search-Based Program Repair", journal = "IEEE Software", year = "2021", volume = "38", number = "4", pages = "36--42", month = jul # "-" # aug, keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, automatic program repair, APR, GenProg, computer bugs, search problems, maintenance engineering, standards, linear programming, statistics, space exploration, Novelty Search", ISSN = "1937-4194", DOI = "doi:10.1109/MS.2021.3070552", size = "7 pages", abstract = "In search-based software engineering, automatic bug repair methods use use an objective function that measures a patch quality to guide the search. This work presents results of the first study that focuses on solution novelty instead. Novelty search works under the assumption that most real-world problems are intrinsically difficult. By shifting how program patches are evaluated, away from quality and towards novelty, this technique increases a bug repair system's ability to explore the solution space, produce more viable patches and repair more bugs.", notes = "'By shifting how program patches are evaluated, away from quality and toward novelty, the novelty search technique increases a bug-repair system ability to explore the solution space, produce more viable patches, and repair more bugs.' p38 'to apply NS to an evolutionary algorithm, only the fitness function has to be modified.' p41 'NS-GenProg out performs standard GenProg in terms of total bug repairs' Tried on: ManyBugs (gmp gzip libtiff lighttpd python php). 3 mutations: delete, add, swap. Fig 2 population diversity Tecnlogico Nacional de Mexico/IT de Tijuana, Tijuana, Mexico", } @Misc{DBLP:journals/corr/abs-2106-04034, author = "Leonardo Trujillo and Jose Manuel {Munoz Contreras} and Daniel E. Hernandez and Mauro Castelli and Juan J. Tapia", title = "{GSGP-CUDA} - a {CUDA} framework for Geometric Semantic Genetic Programming", howpublished = "arXiv", volume = "abs/2106.04034", year = "2021", keywords = "genetic algorithms, genetic programming, Geometric Semantic Genetic Programming, GPU", URL = "https://arxiv.org/abs/2106.04034", eprinttype = "arXiv", eprint = "2106.04034", timestamp = "Tue, 29 Jun 2021 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/corr/abs-2106-04034.bib", bibsource = "dblp computer science bibliography, https://dblp.org", notes = "See \cite{Trujillo:2022:SoftwareX}", } @Article{Trujillo:2022:SoftwareX, author = "Leonardo Trujillo and Jose Manuel {Munoz Contreras} and Daniel E. Hernandez and Mauro Castelli and Juan J. Tapia", title = "{GSGP-CUDA} - A {CUDA} framework for Geometric Semantic Genetic Programming", journal = "SoftwareX", year = "2022", volume = "18", pages = "101085", month = jun, keywords = "genetic algorithms, genetic programming, Geometric Semantic Genetic Programming, separate generations, elitism, stack-based, GSGP-C++, parallel computing, SIMD, CUDA, GPU, GPGPU, C/C++/CUDA, CUBLAS", ISSN = "2352-7110", URL = "https://www.softxjournal.com/article/S2352-7110(22)00060-7/pdf", URL = "https://www.sciencedirect.com/science/article/pii/S2352711022000607", DOI = "doi:10.1016/j.softx.2022.101085", code_url = "https://github.com/ElsevierSoftwareX/SOFTX_2020_38", size = "7 pages", abstract = "Geometric Semantic Genetic Programming (GSGP) is a state-of-the-art machine learning method based on evolutionary computation. GSGP performs search operations directly at the level of program semantics, which can be done more efficiently than operating at the syntax level like most GP systems. Efficient implementations of GSGP in C++ exploit this fact, but not to its full potential. This paper presents GSGP-CUDA, the first CUDA implementation of GSGP and the most efficient, which exploits the intrinsic parallelism of GSGP using GPUs. Results show speedups greater than 1000 fold relative to the state-of-the-art sequential implementation, during the model training process. Additionally, our implementation allows the user to seamlessly make inferences over new data through the best evolved model, opening the possibility of using GSGP on Big Data problems.", notes = "See \cite{DBLP:journals/corr/abs-2106-04034}. Also known as \cite{TRUJILLO2022101085} p2 'a linear representation is used'. 'real-valued symbolic regression.' 'mutation is usually sufficient'. 'compute the semantics and fitness of an offspring using only the semantic vectors.' using 'building and updating [pop_size by number_fitness_cases] semantic matrices' 'maximum program size k.' 'fitness given by the Root Mean Squared Error (RMSE).' cudaOccupancyMaxPotentialBlockSize p3 '+, −, * and protected division. ... probabilities: 0.8 for a function, 0.14 for a problem variable or feature, and 0.04 for an ephemeral random constant.' (ERC) p4 Quadro P4000 (1792 cores), Tesla P100 (3584 cores). 'virtual machine' 64-bit Ubuntu Linux. popsize = 10240, 50 generations. ", } @Article{Trujillo:2022:GPEM, author = "Leonardo Trujillo and Ting Hu and Nuno Lourenco and Mengjie Zhang", title = "Editorial Introduction", journal = "Genetic Programming and Evolvable Machines", year = "2022", volume = "23", number = "3", pages = "305--307", month = sep, keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/cUpQh", DOI = "doi:10.1007/s10710-022-09437-9", size = "3 pages", abstract = "EuroGP 2021 and the GECCO 2021 GP Track. highlights of GP in 2021", notes = "Tecnologico Nacional de Mexico/IT de Tijuana, Tijuana, Mexico", } @Article{DBLP:journals/aicom/TrujilloNMG23, author = "Leonardo Trujillo and Joel Nation and Luis Munoz and Edgar Galvan", title = "Predicting the success of transfer learning for genetic programming using {DeepInsight} feature space alignment", journal = "{AI} Commun.", volume = "36", number = "3", pages = "159--173", year = "2023", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.3233/AIC-230104", DOI = "doi:10.3233/AIC-230104", timestamp = "Mon, 05 Feb 2024 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/aicom/TrujilloNMG23.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{Trujillo:2025:GPTP, author = "Leonardo Trujillo", title = "Heeding good advise for research in genetic programming", booktitle = "Genetic Programming Theory and Practice XXII", year = "2025", editor = "Wolfgang Banzhaf and Bogdan Burlacu and Stephen Kelly and Alexander Lalejini and Fabricio {Olivetti de Franca}", address = "Michigan State University, USA", month = jun # " 5-7", keywords = "genetic algorithms, genetic programming", notes = "To be published after the workshop", } @Article{Trujillo-Romero:2013:IJBIC, author = "Felipe Trujillo-Romero", title = "Generation of neural networks using a genetic algorithm approach", journal = "International Journal of Bio-Inspired Computation", year = "2013", month = oct # "~17", volume = "5", number = "5", pages = "289--302", keywords = "genetic algorithms, genetic programming, GP, neural networks, evolutionary algorithms, evolutionary entity, alpha-numeric character recognition, classification.", ISSN = "1758-0374", bibsource = "OAI-PMH server at www.inderscience.com", language = "eng", publisher = "Inderscience Publishers", URL = "http://www.inderscience.com/link.php?id=57183", DOI = "DOI:10.1504/IJBIC.2013.057183", abstract = "This paper discusses the generation of neural networks that are obtained from the evolution of individual's population in a genetic algorithm. For achieving this, the population of individuals for the genetic algorithm is formed of structural elements which constitute the neural networks. These elements include the number of layers, neurons per layer, transfer functions and the connections between neurons in the network, among others. These individuals as can be seen a structure which has the ability to evolve rather than a standard genotype. Furthermore, the size of the individuals is not defined and depends mainly on the neural network which in turn depends on the problem to be solved. This structure considered as an evolutionary entity, is able to evolve until convergence towards a suitable structure is achieved. The fitness function is specified with the features of the problem to be solved by the neural network. This algorithm has been tested successfully in solving classification problems, as in the case of alpha-numerical character recognition, and has been compared against a neural network obtained by conventional means. Better results were obtained with the neural network generated by using genetic programming of this type of evolutionary entities.", } @InCollection{Truscott:2011:GPTP, author = "Philip D. Truscott and Michael F. Korns", title = "Detecting Shadow Economy Sizes with Symbolic Regression", booktitle = "Genetic Programming Theory and Practice IX", year = "2011", editor = "Rick Riolo and Ekaterina Vladislavleva and Jason H. Moore", series = "Genetic and Evolutionary Computation", address = "Ann Arbor, USA", month = "12-14 " # may, publisher = "Springer", chapter = "11", pages = "195--210", keywords = "genetic algorithms, genetic programming, abstract expression grammars, customised scoring, grammar template genetic programming, universal form goal search", isbn13 = "978-1-4614-1769-9", DOI = "doi:10.1007/978-1-4614-1770-5_11", abstract = "we examine the use of symbolic regression to tackle a real world problem taken from economics: the estimation of the size a country's 'shadow' economy. this is a country's total monetary economic activity after subtracting the official Gross Domestic Product. A wide variety of methodologies are now used to estimate this. Some have been criticised for an excessive reliance on subjective predictive variables. Others use predictive data that are not available for many developing countries. we explore the feasibility of developing a general-purpose regression formula using objective development indicators. The dependent variables were 260 shadow economy measurements for various countries from the period 1990-2006. Using 16 independent variables, seven basis functions, and a depth of one grammar level a search space of 1013 was created. we focus on the power conferred by an abstract expression grammar allowing the specification of a universal goal formula with grammar depth control, and the customisation of the scoring process that defines the champion formula that 'survives' the evolutionary process. Initial searching based purely on R-Squared failed to produce plausible shadow economy estimates. Later searches employed a customized scoring methodology. This produced a good fit based on four variables: GDP, energy consumption squared, this size of the urban population, and the square of this figure. The same formula produced plausible estimates for an out of sample set of 510 countries for the years 2003-2005 and 2007. Though shadow economy prediction will be controversial for some time to come, this methodology may be the most powerful estimation formula currently available for purposes that require verifiable data and a single global formula.", notes = "part of \cite{Riolo:2011:GPTP}", affiliation = "Department of Information Systems and Computer Science, Ateneo de Manila University, Loyola Hts, Quezon City, Philippines", } @InCollection{Truscott:2013:GPTP, author = "Philip Truscott and Michael F. Korns", title = "Explaining Unemployment Rates with Symbolic Regression", booktitle = "Genetic Programming Theory and Practice XI", year = "2013", series = "Genetic and Evolutionary Computation", editor = "Rick Riolo and Jason H. Moore and Mark Kotanchek", publisher = "Springer", chapter = "7", pages = "119--135", address = "Ann Arbor, USA", month = "9-11 " # may, keywords = "genetic algorithms, genetic programming, Abstract expression grammars, Symbolic regression, Non-linear regression", isbn13 = "978-1-4939-0374-0", DOI = "doi:10.1007/978-1-4939-0375-7_7", abstract = "Much of the research on the accuracy of symbolic regression (SR) has focused on artificially constructed search problems where there is zero noise in the data. Such problems admit of exact solutions but cannot tell us how accurate the search process is in a noisy real world domain. To explore this question symbolic regression is applied here to an area of research which has been well-travelled by regression modellers: the prediction of unemployment rates. A respected dataset was selected, the CEP-OECD Labor Market Institutions Database, to provide a testing environment for a variety of searches. Metrics of success for this paper went beyond the normal yardsticks of statistical significance to demand plausibility. Here it is assumed that a plausible model must be able to predict unemployment rates out of the sample period for six future years: this metric is referred to as the out of sample R-squared. We conclude that the two packages tested, Eureqa and ARC, can produce models that go beyond the power of traditional stepwise regression. ARC, in particular, is able to replicate the format of published economic research because ARC contains a high level Regression Query Language (RQL). This research produced a number of models that are consistent with published economic research, have in sample R-squared values over 0.80, no negative unemployment rates, and out of sample R-squared values above 0.45. It is argued that SR offers significant new advantages to social science researchers.", notes = " Part of \cite{Riolo:2013:GPTP} published after the workshop in 2013", } @InProceedings{Truscott:2015:GPTP, author = "Phil Truscott and Michael F. Korns", title = "Predicting Product Choice with Symbolic Regression and Classification", booktitle = "Genetic Programming Theory and Practice XIII", year = "2015", editor = "Rick Riolo and William P. Worzel and M. Kotanchek and A. Kordon", series = "Genetic and Evolutionary Computation", pages = "203--217", address = "Ann Arbor, USA", month = "14-16 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Abstract regression grammars Genetic algorithms Symbolic regression Classification Non-linear regression", isbn13 = "978-3-319-34223-8", URL = "http://www.springer.com/us/book/9783319342214", DOI = "doi:10.1007/978-3-319-34223-8_12", abstract = "Market researchers often conduct surveys to measure how much value consumers place on the various features of a product. The resulting data should enable managers to combine these utility values in different ways to predict the market share of a product with a new configuration of features. Researchers assess the accuracy of these choice models by measuring the extent to which the summed utilities can predict actual market shares when respondents choose from sets of complete products. The current paper includes data from 201 consumers who gave ratings to 18 cell phone features and then ranked eight complete cell phones. A simple summing of the utility values predicted the correct product on the ranking task for 22.8 percent of respondents. Another accuracy measurement is to compare the market shares for each product using the ranking task and the estimated market shares based on summed utilities. This produced a mean absolute difference between ranked and estimated market shares of 7.8 percent. The current paper applied two broad strategies to improve these prediction methods. Various evolutionary search methods were used to classify the data for each respondent to predict one of eight discrete choices. The fitness measure of the classification approach seeks to reduce the Classification Error Percent (CEP) which minimizes the percent of incorrect classifications. This produced a significantly better fit with the hit rate rising from 22.8 to 35.8 percent. The mean absolute deviation between actual and estimated market shares declined from 7.8 to 6.1 percent (p. <0.01). A simple language specification will be illustrated to define symbolic regression and classification searches.", notes = " Part of \cite{Riolo:2015:GPTP} Published after the workshop in 2016", } @Article{Tsai200911994, author = "Chih-Fong Tsai and Yu-Feng Hsu and Chia-Ying Lin and Wei-Yang Lin", title = "Intrusion detection by machine learning: A review", journal = "Expert Systems with Applications", volume = "36", number = "10", pages = "11994--12000", year = "2009", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2009.05.029", URL = "http://www.sciencedirect.com/science/article/B6V03-4WBC1NH-C/2/3e0271b6e5009bc945abd584bdb46c71", keywords = "genetic algorithms, genetic programming, Intrusion detection, Machine learning, Hybrid classifiers, Ensemble classifiers", abstract = "The popularity of using Internet contains some risks of network attacks. Intrusion detection is one major research problem in network security, whose aim is to identify unusual access or attacks to secure internal networks. In literature, intrusion detection systems have been approached by various machine learning techniques. However, there is no a review paper to examine and understand the current status of using machine learning techniques to solve the intrusion detection problems. This chapter reviews 55 related studies in the period between 2000 and 2007 focusing on developing single, hybrid, and ensemble classifiers. Related studies are compared by their classifier design, datasets used, and other experimental setups. Current achievements and limitations in developing intrusion detection systems by machine learning are present and discussed. A number of future research directions are also provided.", notes = "survey", } @Article{Tsai201111032, author = "Hsing-Chih Tsai and Yong-Huang Lin", title = "Modular neural network programming with genetic optimization", journal = "Expert Systems with Applications", volume = "38", number = "9", pages = "11032--11039", year = "2011", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2011.02.147", URL = "http://www.sciencedirect.com/science/article/B6V03-52BGCPB-2/2/707c22583fca77726a94edea04a48c8d", keywords = "genetic algorithms, genetic programming, Artificial intelligence, High order neural network, ANN, Concrete", abstract = "This study proposes a modular neural network (MNN) that is designed to accomplish both artificial intelligent prediction and programming. Each modular element adopts a high-order neural network to create a formula that considers both weights and exponents. MNN represents practical problems in mathematical terms using modular functions, weight coefficients and exponents. This paper employed genetic algorithms to optimise MNN parameters and designed a target function to avoid over-fitting. Input parameters were identified and modular function influences were addressed in manner that significantly improved previous practices. In order to compare the effectiveness of results, a reference study on high-strength concrete was adopted, which had been previously studied using a genetic programming (GP) approach. In comparison with GP, MNN calculations were more accurate, used more concise programmed formulae, and allowed the potential to conduct parameter studies. The proposed MNN is a valid alternative approach to prediction and programming using artificial neural networks.", } @Article{Tsai2011161, author = "Hsing-Chih Tsai", title = "Weighted operation structures to program strengths of concrete-typed specimens using genetic algorithm", journal = "Expert Systems with Applications", volume = "38", number = "1", pages = "161--168", year = "2011", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2010.06.034", URL = "http://www.sciencedirect.com/science/article/pii/S0957417410005385", keywords = "genetic algorithms, genetic programming, Weighted formula, Prediction, Concrete strength", abstract = "This study introduces weighted operation structures (WOS) to program engineering problems, in which each WOS adopts a fixed binary tree topology. The first WOS layer serves as the parameter input entrance. The target is produced at the eventual layer using both values and a mathematical formula. Each WOS element is operated by two front nodal inputs, an undetermined function, and two undetermined weights to produce one nodal output. This study proposes the novel concept of introducing weights into a WOS. Doing so provides two unique advantages: (1) achieving a balance between the influences of two front inputs and (2) incorporating weights throughout the generated formulae. Such a formula is composed of a certain quantity of optimised functions and weights. To determine function selections and proper weights, genetic algorithm is employed for optimisation. Case studies herein focused on three kinds of concrete-typed specimen strengths: (1) concrete compressive strength, (2) deep beam shear strength, and (3) squat wall shear strength. Results showed that the proposed WOS can provide accurate results that nearly equal the results obtainable using the familiar neural network. The weighted formula, however, offers a distinct advantage in that it can be programmed for practical cases.", } @Article{Tsai2011526, author = "Hsing-Chih Tsai", title = "Using weighted genetic programming to program squat wall strengths and tune associated formulas", journal = "Engineering Applications of Artificial Intelligence", volume = "24", number = "3", pages = "526--533", year = "2011", ISSN = "0952-1976", DOI = "doi:10.1016/j.engappai.2010.08.010", URL = "http://www.sciencedirect.com/science/article/B6V2M-512KGGT-1/2/19ea4426ab2d8ed33e75c91b78297d2f", keywords = "genetic algorithms, genetic programming, Weighted formulae, Prediction, Squat wall strength", abstract = "This study developed a weighted genetic programming (WGP) approach to study the squat wall strength. The proposed WGP evolves on genetic programming (GP), an evolutionary algorithm-based methodology that employs a binary tree topology and optimised functional operators. Weight coefficients were introduced to each GP linkage in the tree in order to create a new weighted genetic programming (WGP) approach. The proposed WGP offers two distinct advantages, including: (1) a balance of influences is struck between the two front input branches and (2) weights are incorporated throughout generated formulae. Resulting formulae contain a certain quantity of optimised functions and weights. Genetic algorithms are employed to accomplish WGP optimisation of function selection and proper weighting tasks. Case studies herein focused on a reference study of squat wall strength. Results demonstrated that the proposed WGP provides accurate results and formula outputs. This paper further used WGP to tune referenced formulas, which yielded a final formula that combined the positive attributes of both WGP and analytical models.", } @Article{journals/ewc/TsaiL11, author = "Hsing-Chih Tsai and Yong-Huang Lin", title = "Predicting high-strength concrete parameters using weighted genetic programming", journal = "Engineering with Computers", year = "2011", volume = "27", number = "4", pages = "347--355", publisher = "Springer", keywords = "genetic algorithms, genetic programming, weighted formula, prediction, high-strength concrete, weighted genetic programming", ISSN = "0177-0667", DOI = "doi:10.1007/s00366-011-0208-z", size = "9 pages", abstract = "Genetic programming (GP) is an evolutionary algorithm-based methodology that employs a binary tree topology with optimised functional operators. This study introduced weight coefficients to each GP linkage in a tree in order to create a new weighted genetic programming (WGP) approach. Two distinct advantages of the proposed WGP include (1) balancing the influences of the two front input branches and (2) incorporating weights throughout generated formulae. Resulting formulae contain a certain quantity of optimised functions and weights. Genetic algorithms are employed to accomplish WGP optimisation of function selection and proper weighting tasks. Case studies presented herein highlight a high-strength concrete reference study. Results showed that the proposed WGP not only improves GP in terms of introduced weight coefficients, but also provides both accurate results and formula outputs.", affiliation = "Department of Construction Engineering, National Taiwan University of Science and Technology, #43, Sec. 4, Keelung Rd, Taipei, 106 Taiwan, ROC", bibdate = "2011-09-23", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ewc/ewc27.html#TsaiL11", } @Article{Tsai:2013:GPEM, author = "Hsing-Chih Tsai and Chan-Ping Pan", title = "Improving analytical models of circular concrete columns with genetic programming polynomials", journal = "Genetic Programming and Evolvable Machines", year = "2013", volume = "14", number = "2", pages = "221--243", month = jun, keywords = "genetic algorithms, genetic programming, Models, Compressive strength, Strain, Concrete columns, Polynomials", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-012-9176-3", size = "23 pages", abstract = "This study improves weighted genetic programming and uses proposed novel genetic programming polynomials (GPP) for accurate prediction and visible formulae/polynomials. Representing confined compressive strength and strain of circular concrete columns in meaningful representations makes parameter studies, sensitivity analysis, and application of pruning techniques easy. Furthermore, the proposed GPP is used to improve existing analytical models of circular concrete columns. Analytical results demonstrate that the GPP performs well in prediction accuracy and provides simple polynomials as well. Three identified parameters improve the analytical models the lateral steel ratio improves both compressive strength and strain of the target models of circular concrete columns; compressive strength of unconfined concrete specimen improves the strength equation; and tie spacing improves the strain equation.", } @Article{tsai:2013:NCA, author = "Hsing-Chih Tsai and Yaw-Yauan Tyan and Yun-Wu Wu and Yong-Huang Lin", title = "Determining ultimate bearing capacity of shallow foundations using a genetic programming system", journal = "Neural Computing and Applications", year = "2013", volume = "23", number = "7-8", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s00521-012-1150-8", DOI = "doi:10.1007/s00521-012-1150-8", } @Article{tsai:2019:KSCEjce, author = "Hsing-Chih Tsai and Min-Chih Liao", title = "Modeling Torsional Strength of Reinforced Concrete Beams using Genetic Programming Polynomials with Building Codes", journal = "KSCE Journal of Civil Engineering", year = "2019", volume = "23", number = "8", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s12205-019-1292-7", DOI = "doi:10.1007/s12205-019-1292-7", } @InProceedings{tsakonas_role_2000, author = "A. Tsakonas and G. Dounias and A. Merikas", title = "The {Role} of {Genetic} {Algorithms} and {Wavelets} in {Computational} {Intelligence}-based {Decision} {Support} for {Stock} {Exchange} {Daily} {Trading}", booktitle = "7th {Congress} of {SIGEF}", publisher = "SIGEF Association", year = "2000", pages = "195--208", address = "Chania, Greece", keywords = "genetic algorithms, genetic programming, financial decision support, neural networks, neuro-fuzzy systems, wavelets", URL = "http://www.sigef.net/congress/previous-congress/item/465-finance-and-economics#Tsakonas", URL = "https://www.researchgate.net/publication/225285068_The_role_of_genetic_algorithms_and_wavelets_in_computational_intelligence_based_decision_support_for_stock_exchange_daily_trading", abstract = "In this paper is explored the suitability of genetic algorithms for constructing rule bases, as part of a hybrid decision support architecture, involving neural networks for wavelet-filtered daily stock rates of change. Specifically, the main structure of the suggested methodology combines a wavelet-based noise removal system, a 'multilayer perceptron feedforward neural network' and finally a fuzzy system, which provide the trader with both, linguistic and numerical output, representing a buy/hold/sell strategy. The use of wavelet filtering in data pre-processing, improves the predictability of neural networks, however, it involves the selection of proper wavelet bases. Therefore, by applying genetic algorithms in fuzzy rule bases for optimizing the decision policy, the paper aims at offering a decision support, independent of the selection of the wavelet basis. It is also demonstrated how, based on the test results, the overall system is able to make successful trend prediction, which is then used to create an output similar to the policy that traders would apply if foreword price movement was considered to be known.", } @InProceedings{tsakonas_throughput_2001, author = "A. Tsakonas and C. Papadopoulos and G. Dounias", title = "Calculation of throughput for production lines with buffers using computational intelligence", booktitle = "The Sixth International Conference on Measurement and Control in Complex Systems, MCCS 2001", year = "2001", editor = "V. M. Dubova", pages = "11--15", address = "Vinnitsa State Technical University, Ukraine", publisher_address = "Ukraine", month = oct # " 8-12", publisher = "UNIVERSUM-Vinnitsa, BG", keywords = "genetic algorithms, genetic programming, computational intelligence, decomposition techniques, symbolic regression, throughput", ISBN = "966-641-039-7", URL = "http://mde-lab.aegean.gr/images/stories/docs/CC21.pdf", size = "5 pages", abstract = "The domain of serial production lines lacks the existence of general formulas for acquiring useful measurements and line characteristics such as throughput. Throughput is called the average number of jobs per hour that can flow through a production line. The obvious complexity of the domain due to combinatorial explosion depends on the number of workstations involved in the examined line the capacity of buffers existing within the workstations the variability in processing times etc. The authors attempt to approximate this problem by applying modern genetic programming techniques [Koza 1992] [Koza 1994] [Angeline et. al 1996] in other words creative programming techniques that belong to the area of computational intelligence and learning. Genetic programming is an automated method for creating a working computer program from a high-level problem statement o f the problem. The evolutionary search adopted uses the Darwinian principle of survival of the fittest and is patterned after naturally occurring operations including crossover (i.e. sexual recombination) mutation gene duplication gene deletion etc. The objective of this work is to obtain an analytical formula for throughput x in terms of the above mentioned production line parameters (i.e. of the number of stations size of buffers mean processing time) assuming there are sufficient jobs at the beginning of the line to ensure that the first station is never starved of jobs and that the last station is never blocked. Through this paper different formulas are given for each size of short production lines with respect to their line length and then an additional attempt is described and analysed for unifying all the throughput formulas obtained during the initial approach. The formulas obtained are quite long but easily programmable in a single line of source code and thus very useful for immediate use in real world applications.", notes = "The throughput rate of short exponential production lines with finite intermediate buffers using genetic programming approximation techniques broken 2018 http://www.vstu.vinnica.ua/mccs2001 Published 2002? KYCC-2001 http://catalog.odnb.odessa.ua/opac/index.php?url=/notices/index/IdNotice:12348/Source:default http://mde-lab.aegean.gr/research-material", } @InProceedings{Tsakonas:2001:EUNITEa, title = "Hybrid Computational Intelligence for Handling Diagnosis of Aphasia", author = "Athanasios Tsakonas and Georgios Dounias and Diedrich {Graf Von Keyserlingk} and Hubertus Axer", booktitle = "Proceedings of the CD-rom EUNITE-01, European Symposium on Intelligent Technologies, Hybrid Systems and their Implementation on Smart Adaptive Systems Verlag-Mainz", year = "2001", address = "Tenerife, Spain", keywords = "genetic algorithms, genetic programming, hybrid computational intelligence, medical diagnosis, aphasia, Aachen Aphasia Test", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.148.5734", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", contributor = "CiteSeerX", language = "en", oai = "oai:CiteSeerXPSU:10.1.1.148.5734", abstract = "This paper presents two models based on hybrid computational intelligence, for the classification between different types of aphasia. Aphasia is a human syndrome, often due to brain damage. Its effects usually are the patient's disability in the usage or comprehension of words, as well as difficulties in reading, or writing, or articulation. The proposed methodology is mainly related to genetic programming, an extension to the well-known genetic algorithms approach. As a search methodology, genetic programming is used in various domains, where symbolic regression is needed. The hybrid methodology consists of the genetic programming approach and a heuristic rule-based scheme. The data used are nominal and ordinal, corresponding to patient scorings valued in free interviews by physicians. In most cases, the results are competitive to the average human diagnosis. Moreover, they are comprehensive by human experts, enabling them to draw conclusions on the significance of the patient scorings. The subjective nature of the application domain, focuses the interest of the paper into the acquired results, their interpretation and their practical importance for the medical experts.", notes = "broken http://www.elite-foundation.org/ELITE/programme%202001.htm", } @InProceedings{Tsakonas:2001:EUNITEpap, title = "A Hybrid Computational Intelligence Approach Combining Genetic Programming and Heuristic Classification for Pap-Smear Diagnosis", author = "Athanasios Tsakonas and Georgios D. Dounias and Jan Jantzen and Beth Bjerregaard", booktitle = "Proceedings of the CD-rom EUNITE-01, European Symposium on Intelligent Technologies, Hybrid Systems and their Implementation on Smart Adaptive Systems Verlag-Mainz", year = "2001", pages = "516--515", address = "Tenerife, Spain", keywords = "genetic algorithms, genetic programming, hybrid computational intelligence, medical diagnosis, pap-smear test, heuristic classification, evolutionary computation, intelligent systems", URL = "http://www.eunite.org/eunite/events/eunite2001/Papers/13354_P_Dounias.pdf", size = "10 pages", language = "en", abstract = "The paper suggests the combined use of different computational intelligence (CI) techniques in a hybrid scheme, as an effective approach to medical diagnosis. Getting to know the advantages and disadvantages of each computational intelligence technique in the recent years, the time has come for proposing successful combinations of CI tools and techniques for the improvement of decision making, diagnosis and classification in complex domains of application. In the current approach genetic programming is embedded within a heuristic scheme for classification of medical records into different diagnoses. The final result is a short but robust rule based classification scheme, achieving high degree of classification accuracy (exceeding 90percent of accuracy for most classes) in a meaningful and user-friendly representation form for the medical expert. The domain of application analysed through the paper is the well-known Pap-Test problem, corresponding to a numerical database, which consists of 450 medical records, 25 diagnostic attributes and 5 different diagnostic classes. Experimental data are divided in two equal parts for the training and testing phase, and 8 mutually dependent rules for diagnosis are generated. Medical experts comment on the nature, the meaning and the usability of the acquired results.", notes = "broken http://www.elite-foundation.org/ELITE/programme%202001.htm", } @InProceedings{tsakonas_data_2001, author = "Athanasios Tsakonas and George Dounias and Hubertus Axer and Diedrich {Graf von Keyserlingk}", title = "Data classification using fuzzy rule-based systems represented as genetic programming type-constrained trees", booktitle = "UK Workshop on Computational Intelligence", year = "2001", editor = "Donna Bolland", volume = "1", pages = "162--168", address = "Edinburgh, UK", month = "10-12 " # sep, keywords = "genetic algorithms, genetic programming, aphasia, data classification, fuzzy rule based systems", URL = "http://www.dai.ed.ac.uk/homes/ukci-01/programme", URL = "https://pdfs.semanticscholar.org/76e1/f94fd064287f38e044523b1d1e40e0164a53.pdf", size = "9 pages", abstract = "This paper presents a genetic programming implementation of fuzzy rule based systems for data classification. The genetic programming is used not only as a variable-length search mechanism but the fuzzy rule-based functionality is incorporated inside the genetic programs. The model is tested on real world data from the medical domain and is compared to other computational intelligent approaches. Results denote the operability of our model and seem promising in domains where a large parameter space or incomplete domain knowledge restrains the user from applying well-defined fuzzy classifiers.", notes = "http://www.dai.ed.ac.uk/homes/ukci-01/", } @InCollection{tsakonas_how_2001, author = "A Tsakonas and G. Dounias and A Merikas", title = "How wavelets and genetic algorithms can assist {Intelligent} {Hybrid} {Methodologies} in handling data driven {Stock} {Exchange} daily trading", booktitle = "Fuzzy {Sets} in {Management}, {Economics} and {Marketing}", editor = "C. Zopounidis and P. M. Pardalos and G. Baourakis", year = "2001", publisher = "World Scientific Publishers", month = oct, pages = "195--210", keywords = "genetic algorithms, genetic programming, financial decision support, neural networks, neuro-fuzzy systems, wavelets", isbn13 = "978-981-02-4753-9", URL = "https://doi.org/10.1142/9789812810892_0013", DOI = "doi:10.1142/9789812810892_0013", abstract = "In this paper is explored the suitability of genetic algorithms for constructing rule bases, as part of a hybrid decision support architecture, involving neural networks for wavelet-filtered daily stock rates of change. Specifically, the main structure of the suggested methodology combines a wavelet-based noise removal system, a 'multilayer perceptron feedforward neural network' and finally a fuzzy system, which provide the trader with both, linguistic and numerical output, representing a buy/hold/sell strategy. The use of wavelet filtering in data pre-processing, improves the predictability of neural networks, however, it involves the selection of proper wavelet bases. Therefore, by applying genetic algorithms in fuzzy rule bases for optimizing the decision policy, the paper aims at offering a decision support, independent of the selection of the wavelet basis. It is also demonstrated how, based on the test results, the overall system is able to make successful trend prediction, which is then used to create an output similar to the policy that traders would apply if foreword price movement was considered to be known.", } @Article{tsakonas_generalized_2001, author = "Athanasios Tsakonas and Helen Kitrinou and Georgios Dounias", title = "Generalized Short-stage Multichannel Queuing Models Using Genetic Algorithms: A Real-World Application to Seaports", journal = "Journal of Management Sciences and Regional Development", year = "2001", volume = "3", pages = "215--231", month = jul, keywords = "genetic algorithms, genetic programming, computational intelligence, queuing systems, seaport operating cost optimization, transportation problems", ISSN = "1107-9819", publisher = "Constantine Porphyrogenetus International Association", URL = "http://www.stt.aegean.gr/geopolab/_private/MANAGEMENT%20SCIENCES%20AND%20REGIONAL%20DEVELOPMENT%203%20-%20Tsakonas%20Kitrinou%20Dounias%20-%20Shortstage%20multichannel%20queuing%20models%20genetic%20algorithms.%20Seaports.pdf", URL = "https://pdfs.semanticscholar.org/7f32/d6de7a91d93e352489a7c19652f1e196f9bf.pdf", size = "17 pages", abstract = "This paper introduces genetic algorithms for inducing high-level knowledge from available domain data, succeeding to obtain generalized solutions for a short-stage multi-channel queuing model. The domain of application, refers to the transportation problem of transit storage and reload in seaports. Specifically, when a ship approaches the port, can be served by more than one service channel, in other words the seaport represents a queuing system. The seaport system forwards ships and lorries into the port, moves vehicles and cranes between two positions i.e. warehouses and berths, and finally loads and unloads cargoes from ships and lorries. Between the two load/unload processes taking place in both, ships and lorries the transit storage process is embedded, thus forming in fact a three stage multi-channel queuing system. The standard process of working with such a queuing problem supposes Poisson distribution in all the service stages, definition of the service and waiting costs and the construction of an objective function for finding the best-cost solution. The solution produced above is generalized by applying a genetic algorithm approach for finding the best seaport configuration (i.e. optimal number of cranes, warehouses and lorries needed) among a possible set of them, which will offer the minimum seaport operating cost. The paper demonstrates that, when a set of possible configurations is effectively coded into a genetic population, the best solution might be achieved in a reasonably short time and well approximated.", notes = "MSRD http://www.stt.aegean.gr/geopolab/Journal%20of%20Management%20Sciences%20and%20Regional%20Development.htm", } @InProceedings{oai:CiteSeerPSU:573568, title = "A Scheme for the Evolution of Feedforward Neural Networks using {BNF}-Grammar Driven Genetic Programming", author = "Athanasios D. Tsakonas and Georgios Dounias", year = "2002", citeseer-isreferencedby = "oai:CiteSeerPSU:98726", citeseer-references = "oai:CiteSeerPSU:46964; oai:CiteSeerPSU:63750; oai:CiteSeerPSU:17850; oai:CiteSeerPSU:267599; oai:CiteSeerPSU:271953; oai:CiteSeerPSU:186821; oai:CiteSeerPSU:30755", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:573568", rights = "unrestricted", booktitle = "European Symposium on Intelligent Technologies, Hybrid Systems and their implementation on Smart Adaptive Systems", address = "Algarve, Portugal", month = "19-21 " # sep, organisation = "European Network of Excellence EUNITE", keywords = "genetic algorithms, genetic programming, pima indians", URL = "http://www2.ba.aegean.gr/members/tsakonas/Algarve2002.pdf", URL = "http://citeseer.ist.psu.edu/573568.html", size = "7 pages", abstract = "This paper presents our attempt to automatically define feedforward neural networks using genetic programming. Neural networks have been recognized as powerful approximation and classification tools. On the other hand, the genetic programming has been used effectively for the production of intelligent systems, such as the neural networks. In order to reduce the search space and guide the search process we employ grammar restrictions to the genetic programming population individuals. To implement these restrictions, we selected to apply a context-free grammar, such as a BNF grammar. The proposed grammar extends developments of cellular encoding, inherits present advances and manages to express arbitrarily large and connected neural networks. Our implementation uses parameter passing by reference in order to emulate the parallel processing of neural networks into the genetic programming tree individuals. The system is tested in two real-world domains denoting its potential future use.", } @InProceedings{oai:CiteSeerPSU:563995, title = "Soft Computing-Based Result Prediction of Football Games", author = "A. Tsakonas and G. Dounias and S. Shtovba and V. Vivdyuk", booktitle = "The Ist International Conference on Inductive Modelling (ICIM'2002)", year = "2002", editor = "V. Hrytsyk", pages = "15--23", address = "Lviv, Ukraine", month = "20-25 " # may, organisation = "National Academy of Sciences of Ukraine (NASU)", keywords = "genetic algorithms, genetic programming", ISSN = "0135-5465?", citeseer-isreferencedby = "oai:CiteSeerPSU:95800", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:563995", rights = "unrestricted", URL = "http://www2.ba.aegean.gr/members/tsakonas/Lvov2002.pdf", URL = "http://citeseer.ist.psu.edu/563995.html", size = "8 pages", abstract = "Soft computing methods for result prediction of football games based on fuzzy rules, neural networks and genetic programming techniques, are proposed in the article. The models are taking into account the following features of football teams: difference of infirmity factors; difference of dynamics profile; difference of ranks; host factor; personal score of the teams. Testing shows that the proposed models achieve a satisfactory estimation of the actual game results. The current work concludes with the recommendation of soft-computing techniques as a powerful approach, either for the creation of result prediction models of diverse sport championships, or as effective data extrapolation mechanisms in case of limited available statistics.", notes = "Broken Dec 2019 http://www.gmdh.net/ICIM/ not verified", } @InProceedings{tsakonas_comparing_2002, author = "A. Tsakonas and G. Dounias and S. Shtovba and V. Vivdyuk", title = "Comparing the effectiveness of support vector machines with fuzzy, neuro-fuzzy and genetic programming approaches in result prediction of football games", booktitle = "ICAIS-2002", year = "2002", address = "Crimea, Ucraine", month = sep, keywords = "genetic algorithms, genetic programming, fuzzy techniques, genetic programming, neuro-fuzzy approaches, result prediction of football games, soft computing, support vector machines", URL = "http://mde-lab.aegean.gr/images/stories/docs/CC32.pdf", size = "3 pages", abstract = "A soft computing method for result prediction of football games based on machine learning techniques such as support vector machines is proposed in this article. The method is taking into account the following features of football terms: difference of infirmity factors; difference of dynamics profile; difference of ranks; host factor; personal score of the teams. Testing shows that the proposed model achieves a satisfactory estimation of the actual game outcomes. The current work concludes with the recommendation of support vector machines technique as a powerful approach, for the creation of result prediction models of diverse sport championships.", notes = "http://mde-lab.aegean.gr/research-material", } @Article{Tsakonas:2004:JAL, author = "Athanasios Tsakonas and Vasilios Aggelis and Ioannis Karkazis and Georgios Dounias", title = "An evolutionary system for neural logic networks using genetic programming and indirect encoding", journal = "Journal of Applied Logic", year = "2004", volume = "2", pages = "349--379", number = "3", abstract = "Nowadays, intelligent connectionist systems such as artificial neural networks have been proved very powerful in a wide area of applications. Consequently, the ability to interpret their structure was always a desirable feature for experts. In this field, the neural logic networks (NLN) by their definition are able to represent complex human logic and provide knowledge discovery. However, under contemporary methodologies, the training of these networks may often result in non-comprehensible or poorly designed structures. we propose an evolutionary system that uses current advances in genetic programming that overcome these drawbacks and produces neural logic networks that can be arbitrarily connected and are easily interpretable into expert rules. To accomplish this task, we guide the genetic programming process using a context-free grammar and we encode indirectly the neural logic networks into the genetic programming individuals. We test the proposed system in two problems of medical diagnosis. Our results are examined both in terms of the solution interpretability that can lead in knowledge discovery, and in terms of the achieved accuracy. We draw conclusions about the effectiveness of the system and we propose further research directions.", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B758H-4C8P84V-1/2/e66a004270eeee4e1c50fa3e09ddd003", keywords = "genetic algorithms, genetic programming, Symbolic connectionist systems, Neural logic networks, Grammar-guided genetic programming, Cellular encoding, Coronary artery disease diagnosis, Cardiac SPECT diagnosis", DOI = "doi:10.1016/j.jal.2004.03.005", } @InProceedings{Tsakonas:2004:SIGEF, author = "A. Tsakonas and G. Dounias", title = "Grammar-guided genetic programming for fuzzy rule-based classification in credit management", booktitle = "11th meeting of the International Association for Fuzzy-Set Management and Economy", year = "2004", address = "Reggio Calabria \& Messina, Italy", month = "8-9 " # nov, keywords = "genetic algorithms, genetic programming", isbn13 = "9788882961466", URL = "https://eprints.bournemouth.ac.uk/17874/", URL = "https://eprints.bournemouth.ac.uk/17874/1/tsakonas-dounias-sigef-2004.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.5637", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", contributor = "CiteSeerX", language = "en", oai = "oai:CiteSeerXPSU:10.1.1.149.5637", size = "15 pages", abstract = "The study presents a computational intelligent methodology for fuzzy rule-based classification of enterprises into different categories of credit risk. The presented methodology correspond to an approach to the problem of classifying credit applicants, according to the need for reduction of complexity, higher classification accuracy, and comprehensibility of the acquired decision rules. The data used are both of numerical and linguistic nature and they represent a real world problem, that of deciding whether a loan should be granted or not, in respect to financial details of customers applying for that loan, to a private bank of a southern province of the European Union. The techniques involved in the rule-based categorization task are the inductive machine learning and the type-constrained genetic programming. We examine a two-step model, with a sample of 124 enterprises that applied for a loan, each of which is described by 76 (mainly financial) decision variables, and classified to one of the seven predetermined classes. Special attention is given to the comprehensibility and the ease of use for the acquired decision rules. The application of the proposed methods can make the classification task easier and may minimize significantly the amount of required credit data. We consider that the methodology may also give the chance for the extraction of a comprehensible credit management model or even the incorporation of a related decision support system in banking. The overall architecture of the model can be continuously retrained and reformed, by adding every new credit-risk case, becoming more and more accurate and robust classification models over time", notes = "Aristotle University of Thessaloniki, Dept. of Informatics, Artificial Intelligence and Information Analysis Lab broken Nov 2023 http://gandalf.fcee.urv.es/sigef/", } @Article{Tsakonas:2004:AIM, author = "Athanasios Tsakonas and Georgios Dounias and Jan Jantzen and Hubertus Axer and Beth Bjerregaard and Diedrich {Graf von Keyserlingk}", title = "Evolving rule-based systems in two medical domains using genetic programming", journal = "Artificial Intelligence in Medicine", year = "2004", volume = "32", pages = "195--216", number = "3", abstract = "Summary Objective: To demonstrate and compare the application of different genetic programming (GP) based intelligent methodologies for the construction of rule-based systems in two medical domains: the diagnosis of aphasia's subtypes and the classification of pap-smear examinations. Material: Past data representing (a) successful diagnosis of aphasia's subtypes from collaborating medical experts through a free interview per patient, and (b) correctly classified smears (images of cells) by cyto-technologists, previously stained using the Papanicolaou method. Methods: Initially a hybrid approach is proposed, which combines standard genetic programming and heuristic hierarchical crisp rule-base construction. Then, genetic programming for the production of crisp rule based systems is attempted. Finally, another hybrid intelligent model is composed by a grammar driven genetic programming system for the generation of fuzzy rule-based systems. Results: Results denote the effectiveness of the proposed systems, while they are also compared for their efficiency, accuracy and comprehensibility, to those of an inductive machine learning approach as well as to those of a standard genetic programming symbolic expression approach. Conclusion: The proposed GP-based intelligent methodologies are able to produce accurate and comprehensible results for medical experts performing competitive to other intelligent approaches. The aim of the authors was the production of accurate but also sensible decision rules that could potentially help medical doctors to extract conclusions, even at the expense of a higher classification score achievement.", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6T4K-4DPSHH7-1/2/621e877a6e662298c25372811ae23041", month = nov, keywords = "genetic algorithms, genetic programming, Hybrid intelligence, Grammar driven GP, Genetic-fuzzy systems, Inductive machine learning, Medical decision making, Aphasia, Pap-smear test", DOI = "doi:10.1016/j.artmed.2004.02.007", notes = "cites \cite{Tsakonas:2001:EUNITEa} and \cite{Tsakonas:2001:EUNITEpap} PMID: 15531151", } @InProceedings{Tsakonas:2004:EUNITE, author = "Athanasios D. Tsakonas and Georgios Dounias", title = "Neural Logic Networks in Two Medical Decision Tasks", booktitle = "Fourth European Symposium on Intelligent Technologies and their implementation on Smart Adaptive Systems, EUNITE 2004", year = "2004", address = "Aachen, Germany", month = "10-12 " # jun, keywords = "genetic algorithms, genetic programming, Neural logic networks, Postoperative treatment, Breast cancer", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.5908", URL = "http://athanasiostsakonas.jimdo.com/app/download/2923955351/4af470df/d1cdc4a049a736b7ed25c1ff7b47df0eb770fe20/tsakonas-dounias-eunite-04.pdf", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", contributor = "CiteSeerX", language = "en", oai = "oai:CiteSeerXPSU:10.1.1.149.5908", abstract = "Two real-world problems of the medical domain are addressed in this work using a novel approach belonging to the area of neural-symbolic systems. Specifically, we apply evolutionary techniques for the development of neural logic networks of arbitrary length and topology. The evolutionary algorithm is consisted of grammar guided genetic programming using cellular encoding for the representation of neural logic networks into population individuals. The application area is consisted of the diagnosis of patient postoperative treatment and the diagnosis of the Breast cancer. The extracted solutions maintain their interpretability into simple and comprehensible logical rules. The overall system is shown capable to generate arbitrarily connected and interpretable evolved solutions leading to potential knowledge extraction.", notes = "Winner Special Medical Award http://www.eunite.org/eunite/ http://www.eunite.org/eunite/events/eunite2004/look_back/look_back.htm", } @InProceedings{Tsakonas:2004:ieeeIS, author = "Athanasios Tsakonas", title = "Towards neural-symbolic integration: the evolutionary neural logic networks", booktitle = "2nd International IEEE Conference on Intelligent Systems, 2004", year = "2004", month = jun, volume = "1", pages = "156--161", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, cellular encoding, computational intelligence, diabetes diagnosis, evolutionary computation, grammar guided genetic programming, hepatitis diagnosis, hepatitis patients, knowledge extraction, neural logic networks, neural-symbolic integration, diseases, feature extraction, medical diagnostic computing, neural nets", URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1344655", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.7608", language = "en", oai = "oai:CiteSeerXPSU:10.1.1.149.7608", abstract = "This work presents the application of a new methodology for the production of neural logic networks into two real-world problems from the medical domain. Namely, we apply grammar guided genetic programming using cellular encoding for the representation of neural logic networks into population individuals. The application area is consisted of the diagnosis of diabetes and the diagnosis of the course of hepatitis patients. The system is proved able to generate arbitrarily connected and interpretable evolved solutions leading to potential knowledge extraction.", notes = "Also know as \cite{1344655}", } @InProceedings{tsakonas_automated_2004, author = "Athanasios Tsakonas and Georgios Dounias", title = "Automated {Expert} {Knowledge} {Base} {Generation} {Using} {Genetic} {Programming}", booktitle = "{SETN} 04: {Companion} {Volume} {Proceedings}", editor = "G. A. Vouros and T. Panagiotopoulos", year = "2004", pages = "109--118", address = "Samos, Greece", ISBN = "960-431-910-8", URL = "http://eprints.bournemouth.ac.uk/17868/1/tsakonas-dounias-SETN-2004.pdf", keywords = "genetic algorithms, genetic programming, expert systems, neural logic networks, grammar guided genetic programming, cellular encoding", abstract = "Nowadays a large number of intelligent systems for decision-making have yielded encouraging results. However, it is commonly acknowledged that expert system technology has some drawbacks. In particular, the knowledge bases of expert systems do not evolve. To solve this problem, a solution that integrated neural networks and expert systems has recently been proposed, namely the application of neural logic networks. This integrated system combines the strength of rule-based semantic structure and the learning capability of connectionist architecture. Nevertheless, the early approaches of this model carried the disadvantage of producing poor results or solutions that could not be interpreted straightforward. In this work, we overcome the these problems and we propose a system that is capable of producing arbitrary large and connected neural logic networks that can easily be interpreted into sets of expert rules. To accomplish this task we adopt a genetic programming approach, guided through grammars and we encode indirectly the architecture into genetic programming individuals. We test and make conclusions on the effectiveness of the proposed system into two real-world decision making domains.", } @InProceedings{conf/anns/TsakonasD05, author = "Athanasios Tsakonas and Georgios Dounias", title = "An Architecture-Altering and Training Methodology for Neural Logic Networks: Application in the Banking Sector", editor = "Kurosh Madani", booktitle = "Proceedings of the 1st International Workshop on Artificial Neural Networks and Intelligent Information Processing, ANNIIP 2005,", year = "2005", pages = "82--93", address = "Barcelona, Spain", month = sep, publisher = "INSTICC Press", note = "In conjunction with ICINCO 2005", keywords = "genetic algorithms, genetic programming", isbn13 = "972-8865-36-8", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.8601", bibsource = "DBLP, http://dblp.uni-trier.de", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", contributor = "CiteSeerX", language = "en", oai = "oai:CiteSeerXPSU:10.1.1.149.8601", abstract = "Neural logic networks, Grammar-guided genetic programming, Credit scoring Artificial neural networks have been universally acknowledged for their ability on constructing forecasting and classifying systems. Among their desirable features, it has always been the interpretation of their structure, aiming to provide further knowledge for the domain experts. A number of methodologies have been developed for this reason. One such paradigm is the neural logic networks concept. Neural logic networks have been especially designed in order to enable the interpretation of their structure into a number of simple logical rules and they can be seen as a network representation of a logical rule base. Although powerful by their definition in this context, neural logic networks have performed poorly when used in approaches that required training from data. Standard training methods, such as the back-propagation, require the network's synapse weight altering, which destroys the network's interpretability. The methodology in this paper overcomes these problems and proposes an architecture-altering technique, which enables the production of highly antagonistic solutions while preserving any weight-related information. The implementation involves genetic programming using a grammar-guided training approach, in order to provide arbitrarily large and connected neural logic networks. The methodology is tested in a problem from the banking sector with encouraging results.", } @Article{Tsakonas:2006:IS, author = "Athanasios Tsakonas", title = "A comparison of classification accuracy of four genetic programming-evolved intelligent structures", journal = "Information Sciences", year = "2006", volume = "176", number = "6", pages = "691--724", month = "22 " # mar, keywords = "genetic algorithms, genetic programming, Context-free grammars, Decision trees, Artificial neural networks, Fuzzy rule-based systems, Fuzzy Petri-nets", DOI = "doi:10.1016/j.ins.2005.03.012", abstract = "We investigate the effectiveness of GP-generated intelligent structures in classification tasks. Specifically, we present and use four context-free grammars to describe (1) decision trees, (2) fuzzy rule-based systems, (3) feedforward neural networks and (4) fuzzy Petri-nets with genetic programming. We apply cellular encoding in order to express feedforward neural networks and fuzzy Petri-nets with arbitrary size and topology. The models then are examined thoroughly in six well-known real world data sets. Results are presented in detail and the competitive advantages and drawbacks of the selected methodologies are discussed, in respect to the nature of each application domain. Conclusions are drawn on the effectiveness and efficiency of the presented approach.", notes = "http://www.elsevier.com/wps/find/journaldescription.cws_home/505730/description#description", } @Article{Tsakonas:Bpw:06, author = "Athanasios Tsakonas and George Dounias and Michael Doumpos and Constantin Zopounidis", title = "Bankruptcy prediction with neural logic networks by means of grammar-guided genetic programming", journal = "Expert Systems With Applications", year = "2006", volume = "30", number = "3", pages = "449--461", month = apr, note = "Intelligent Information Systems for Financial Engineering", keywords = "genetic algorithms, genetic programming, Bankruptcy, Neural logic networks, Grammar-Guided genetic programming, Cellular encoding", DOI = "doi:10.1016/j.eswa.2005.10.009", abstract = "The paper demonstrates the efficient use of hybrid intelligent systems for solving the classification problem of bankruptcy. The aim of the study is to obtain classification schemes able to predict business failure. Previous attempts to form efficient classifiers for the same problem using intelligent or statistical techniques are discussed throughout the paper. The application of neural logic networks by means of genetic programming is proposed. This is an advantageous approach enabling the interpretation of the network structure through set of expert rules, which is a desirable feature for field experts. These evolutionary neural logic networks are consisted of an innovative hybrid intelligent methodology, by which evolutionary programming techniques are used for obtaining the best possible topology of a neural logic network. The genetic programming process is guided using a context-free grammar and indirect encoding of the neural logic networks into the genetic programming individuals. Indicative classification results are presented and discussed in detail in terms of both, classification accuracy and solution interpretability.", } @InProceedings{Tsakonas:2006:EFS, author = "A. Tsakonas and N. Ampazis and G. Dounias", title = "Towards a Comprehensible and Accurate Credit Management Model: Application of Four Computational Intelligence Methodologies", booktitle = "2006 International Symposium on Evolving Fuzzy Systems", year = "2006", pages = "295--299", address = "Ambleside", month = sep, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9719-3", DOI = "doi:10.1109/ISEFS.2006.251142", abstract = "The paper presents methods for classification of applicants into different categories of credit risk using four different computational intelligence techniques. The selected methodologies involved in the rule-based categorisation task are (1) feedforward neural networks trained with second order methods (2) inductive machine learning, (3) hierarchical decision trees produced by grammar-guided genetic programming and (4) fuzzy rule based systems produced by grammar-guided genetic programming. The data used are both numerical and linguistic in nature and they represent a real-world problem, that of deciding whether a loan should be granted or not, in respect to financial details of customers applying for that loan, to a specific private EU bank. We examine the proposed classification models with a sample of enterprises that applied for a loan, each of which is described by financial decision variables (ratios), and classified to one of the four predetermined classes. Attention is given to the comprehensibility and the ease of use for the acquired decision models. Results show that the application of the proposed methods can make the classification task easier and - in some cases - may minimise significantly the amount of required credit data. We consider that these methodologies may also give the chance for the extraction of a comprehensible credit management model or even the incorporation of a related decision support system in banking", notes = "Dept. of Financial & Manage. Eng., Aegean Univ., Chios", } @InProceedings{Tsakonas:2006:ISEFS, author = "Athanasios Tsakonas and Nikolaos Ampazis and Georgios Dounias", title = "Towards a Comprehensible and Accurate Credit Management Model: Application of Four Computational Intelligence Methodologies", booktitle = "2006 International Symposium on Evolving Fuzzy Systems", year = "2006", month = sep, pages = "295--299", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, applicant classification, banking, computational intelligence, credit management model, credit risk, feedforward neural networks, fuzzy rule based systems, grammar-guided genetic programming, hierarchical decision trees, inductive machine learning, rule-based categorization, second order methods, bank data processing, decision trees, feed forward neural nets, fuzzy systems, grammars, learning by example, risk management", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.7374", DOI = "doi:10.1109/ISEFS.2006.251142", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", contributor = "CiteSeerX", language = "en", oai = "oai:CiteSeerXPSU:10.1.1.149.7374", abstract = "The paper presents methods for classification of applicants into different categories of credit risk using four different computational intelligence techniques. The selected methodologies involved in the rule-based categorization task are (1) feed forward neural networks trained with second order methods (2) inductive machine learning, (3) hierarchical decision trees produced by grammar-guided genetic programming and (4) fuzzy rule based systems produced by grammar-guided genetic programming. The data used are both numerical and linguistic in nature and they represent a real-world problem, that of deciding whether a loan should be granted or not, in respect to financial details of customers applying for that loan, to a specific private EU bank. We examine the proposed classification models with a sample of enterprises that applied for a loan, each of which is described by financial decision variables (ratios), and classified to one of the four predetermined classes. Attention is given to the comprehensibility and the ease of use for the acquired decision models. Results show that the application of the proposed methods can make the classification task easier and - in some cases - may minimize significantly the amount of required credit data. We consider that these methodologies may also give the chance for the extraction of a comprehensible credit management model or even the incorporation of a related decision support system in banking", notes = "also known as \cite{4016706}", } @Article{journals/aai/TsakonasD07, author = "Athanasios Tsakonas and Georgios Dounias", title = "Evolving Neural-Symbolic Systems Guided by Adaptive Training Schemes: Applications in Finance", journal = "Applied Artificial Intelligence", volume = "21", number = "7", year = "2007", pages = "681--706", month = aug, keywords = "genetic algorithms, genetic programming, adaptive training, symbolic connectionist systems, neural logic networks, grammar-guided genetic", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.2917", DOI = "doi:10.1080/08839510701492603", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", bibsource = "DBLP, http://dblp.uni-trier.de", contributor = "CiteSeerX", language = "en", oai = "oai:CiteSeerXPSU:10.1.1.149.2917", abstract = "The paper presents a hybrid and adaptive intelligent methodology, based on neural logic networks and grammar-guided genetic programming. The aim of the study is to demonstrate how to generate efficient neural logic networks with the aid of genetic programming methods trained adaptively through an innovative scheme. The proposed adaptive training scheme of the genetic programming mechanism, leads to the generation of high diversity solutions and small sized individuals. The overall methodology is advantageous due to the adaptive training scheme proposed, for offering both, accurate and interpretable results in the form of expert rules. Moreover, a sensitivity analysis study is provided within the paper, comparing the performance of the proposed evolutionary neural logic networks methodology, with well-known competitive inductive machine learning approaches. Two financial domains of application have been selected to demonstrate the capabilities of the proposed methodology, (a) classification of credit applicants for consumer loans of a German bank and (b) the credit-scoring decision-making process in an Australian bank. Results seem encouraging since the proposed methodology outperforms a number of competitive existing statistical and intelligent methodologies, while it also produces handy decision rules, short in length and transparent in meaning and use.", } @InProceedings{conf/icsoft/TsakonasD08, author = "Athanasios Tsakonas and Georgios Dounias", title = "Application of Genetic Programming in Software Engineering Empirical Data Modelling", booktitle = "Proceedings of the Third International Conference on Software and Data Technologies ICSOFT (PL/DPS/KE) 2008", year = "2008", editor = "Jos{\'e} Cordeiro and Boris Shishkov and Alpesh Ranchordas and Markus Helfert", pages = "295--300", address = "Porto, Portugal", month = jul # " 5-8", publisher = "INSTICC Press", keywords = "genetic algorithms, genetic programming, SBSE", isbn13 = "978-989-8111-51-7", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.148.5003", bibsource = "DBLP, http://dblp.uni-trier.de", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", contributor = "CiteSeerX", language = "en", oai = "oai:CiteSeerXPSU:10.1.1.148.5003", abstract = "Research in software engineering data analysis has only recently incorporated computational intelligence methodologies. Among these approaches, genetic programming retains a remarkable position, facilitating symbolic regression tasks. In this paper, we demonstrate the effectiveness of the genetic programming paradigm, in two major software engineering duties, effort estimation and defect prediction. We examine data domains from both the commercial and the scientific sector, for each task. The proposed model is proved superior to past literature works.", } @InProceedings{conf/setn/TsakonasD08, author = "Athanasios Tsakonas and Georgios Dounias", title = "Predicting Defects in Software Using Grammar-Guided Genetic Programming", booktitle = "Proceedings 5th Hellenic Conference on AI, SETN 2008", year = "2008", editor = "John Darzentas and George A. Vouros and Spyros Vosinakis and Argyris Arnellos", series = "Lecture Notes in Computer Science", volume = "5138", pages = "413--418", address = "Syros, Greece", month = oct # " 2-4", publisher = "Springer", keywords = "genetic algorithms, genetic programming, SBSE, Software engineering, defect prediction", isbn13 = "978-3-540-87880-3", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.3005", DOI = "doi:10.1007/978-3-540-87881-0_42", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", contributor = "CiteSeerX", bibsource = "DBLP, http://dblp.uni-trier.de", language = "en", oai = "oai:CiteSeerXPSU:10.1.1.149.3005", abstract = "The knowledge of the software quality can allow an organization to allocate the needed resources for the code maintenance. Maintaining the software is considered as a high cost factor for most organizations. Consequently, there is need to assess software modules in respect of defects that will arise. Addressing the prediction of software defects by means of computational intelligence has only recently become evident. In this paper, we investigate the capability of the genetic programming approach for producing solution composed of decision rules. We applied the model into four software engineering databases of NASA. The overall performance of this system denotes its competitiveness as compared with past methodologies, and is shown capable of producing simple, highly accurate, tangible rules.", } @InProceedings{conf/ic3k/TsakonasD09, title = "Deriving Models for Software Project Effort Estimation by Means of Genetic Programming", author = "Athanasios Tsakonas and Georgios Dounias", booktitle = "Proceedings of the International Conference on Knowledge Discovery and Information Retrieval, {KDIR} 2009", year = "2009", editor = "Ana L. N. Fred", pages = "34--42", address = "Funchal, Madeira, Portugal", month = oct # " 6-8", publisher = "INSTICC Press", keywords = "genetic algorithms, genetic programming, SBSE", isbn13 = "978-989-674-011-5", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.2051", bibdate = "2010-03-03", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ic3k/kdir2009.html#TsakonasD09", size = "9 pages", abstract = "This paper presents the application of a computational intelligence methodology in effort estimation for software projects. Namely, we apply a genetic programming model for symbolic regression; aiming to produce mathematical expressions that (1) are highly accurate and (2) can be used for estimating the development effort by revealing relationships between the project's features and the required work. We selected to investigate the effectiveness of this methodology into two software engineering domains. The system was proved able to generate models in the form of handy mathematical expressions that are more accurate than those found in literature.", notes = "http://kdir.ic3k.org/Abstracts/2009/KDIR_2009_Abstracts.htm", } @InProceedings{conf/ijcci/TsakonasG11, author = "Athanasios Tsakonas and Bogdan Gabrys", title = "Evolving Takagi-Sugeno-Kang Fuzzy Systems using Multi Population Grammar-Guided Genetic Programming", booktitle = "Proceedings of the International Conference on Evolutionary Computation Theory and Applications and the Proceedings of the International Conference on Fuzzy Computation Theory and Applications [parts of the International Joint Conference on Computational Intelligence (IJCCI (ECTA-FCTA) 2011)", year = "2011", editor = "Agostinho C. Rosa and Janusz Kacprzyk and Joaquim Filipe and Antonio Dourado Correia", pages = "278--281", address = "Paris, France", month = "24-26 " # oct, publisher = "SciTePress", keywords = "genetic algorithms, genetic programming, fuzzy rule based systems, evolutionary computation:poster", isbn13 = "978-989-8425-83-6", URL = "http://eprints.bournemouth.ac.uk/18460/1/Tsak%2DGabr%2DECTA%2D2011%2DCamRdy.pdf", URL = "http://eprints.bournemouth.ac.uk/18460/", URL = "http://dblp.l3s.de/d2r/page/publications/conf/ijcci/TsakonasG11", size = "4 pages", abstract = "This work proposes a novel approach for the automatic generation and tuning of complete Takagi-Sugeno-Kang fuzzy rule based systems. The examined system aims to explore the effects of a reduced search space for a genetic programming framework by means of grammar guidance that describes candidate structures of fuzzy rule based systems. The presented approach applies context-free grammars to generate individuals and evolve solutions through the search process of the algorithm. A multi-population approach is adopted for the genetic programming system, in order to increase the depth of the search process. Two candidate grammars are examined in one regression problem and one system identification task. Preliminary results are included and discussion proposes further research directions.", notes = "ECTA http://www.ecta.ijcci.org/ http://www.ijcci.org/IJCCI2011/", bibdate = "2012-05-03", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ijcci/ijcci2011-2.html#TsakonasG11", } @Article{Tsakonas2012, author = "Athanasios Tsakonas and Bogdan Gabrys", title = "{GRADIENT}: Grammar-driven genetic programming framework for building multi-component, hierarchical predictive systems", journal = "Expert Systems with Applications", volume = "39", number = "18", pages = "13253--13266", year = "2012", month = "15 " # dec, keywords = "genetic algorithms, genetic programming, Multi-level prediction systems, Ensemble systems, Function approximation, Non-linear regression", ISSN = "0957-4174", URL = "http://www.sciencedirect.com/science/article/pii/S0957417412007920", DOI = "doi:10.1016/j.eswa.2012.05.076", size = "14 pages", abstract = "This work presents the GRADIENT (GRAmmar-DrIven ENsemble sysTem) framework for the generation of hybrid multi-level predictors for function approximation and regression analysis tasks. The proposed model uses a context-free grammar guided genetic programming for the automatic building of multi-component prediction systems with hierarchical structures. A multi-population evolutionary algorithm together with resampling and cross-validatory approaches are used to increase component models' diversity and facilitate more robust and efficient search for accurate solutions. The system has been tested on a number of synthetic and publicly available real-world regression and time series problems for a range of configurations in order to identify and subsequently illustrate and discuss its characteristics and performance. GRADIENT has been shown to be very competitive and versatile when compared to a number of state-of-the-art prediction methods.", } @Article{Tsakonas:2013:ESA, author = "Athanasios Tsakonas", title = "Local and global optimization for Takagi-Sugeno fuzzy system by memetic genetic programming", journal = "Expert Systems with Applications", volume = "40", number = "8", pages = "3282--3298", year = "2013", keywords = "genetic algorithms, genetic programming, Neuro-fuzzy systems, Context-free grammars, Evolutionary computation, Recursive least squares", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2012.12.099", URL = "http://www.sciencedirect.com/science/article/pii/S0957417412013413", abstract = "This work presents a method to incorporate standard neuro-fuzzy learning for Takagi-Sugeno fuzzy systems that evolve under a grammar driven genetic programming (GP) framework. This is made possible by introducing heteroglossia in the functional GP nodes, enabling them to switch behaviour according to the selected learning stage. A context-free grammar supports the expression of arbitrarily sized and composed fuzzy systems and guides the evolution. Recursive least squares and backpropagation gradient descent algorithms are used as local search methods. A second generation memetic approach combines the genetic programming with the local search procedures. Based on our experimental results, a discussion is included regarding the competitiveness of the proposed methodology and its properties. The contributions of the paper are: (i) introduction of an approach which enables the application of local search learning for intelligent systems evolved by genetic programming, (ii) presentation of a model for memetic learning of Takagi-Sugeno fuzzy systems, (iii) experimental results evaluating model variants and comparison with state-of-the-art models in benchmarking and real-world problems, (iv) application of the proposed model in control.", } @Article{Tsakonas:2013:ASC, author = "Athanasios Tsakonas and Bogdan Gabrys", title = "A fuzzy evolutionary framework for combining ensembles", journal = "Applied Soft Computing", volume = "13", number = "4", pages = "1800--1812", year = "2013", keywords = "genetic algorithms, genetic programming, Ensemble systems, Function approximation, Fuzzy rule based systems", ISSN = "1568-4946", URL = "http://www.sciencedirect.com/science/article/pii/S1568494612005716", DOI = "doi:10.1016/j.asoc.2012.12.027", abstract = "We propose an evolutionary framework for the production of fuzzy rule bases where each rule executes an ensemble of predictors. The architecture, the rule base and the composition of the ensembles are evolved over time. To achieve this, we employ a context-free grammar within a hybrid genetic programming system using a multi-population model. As base predictors, multilayer perceptron neural networks and support vector machines are available. We apply the system to several function approximation and regression tasks and compare the results with recent research and state-of-the-art models. We conclude that the proposed architecture is competitive and has a number of very desirable features supporting automation of predictive model building and their adaptation over time. Finally, we suggest further potential research directions.", } @Article{tsang:1998:eddie, author = "Edward P. K. Tsang and Jin Li and James M. Butler", title = "{EDDIE} beats the bookies", journal = "Software: Practice and Experience", year = "1998", volume = "28", number = "10", pages = "1033--1043", keywords = "genetic algorithms, genetic programming, finance, forecasting, horse racing, investment", ISSN = "0038-0644", URL = "http://cswww.essex.ac.uk/CSP/finance/papers/TsBuLi-Eddie-Software98.pdf", URL = "https://onlinelibrary.wiley.com/doi/abs/10.1002/(SICI)1097-024X(199808)28:10%3C1033::AID-SPE198%3E3.0.CO%3B2-1", DOI = "doi:10.1002/(SICI)1097-024X(199808)28:10%3C1033::AID-SPE198%3E3.0.CO%3B2-1", size = "15 pages", abstract = "Investment involves the maximisation of return on ones investment whilst minimising risk. Good forecasting, which often requires expert knowledge, can help to reduce risk. In this paper, we propose a genetic programming-based system, EDDIE (Evolutionary Dynamic Data Investment Evaluator), as a forecasting tool. Genetic programming is inspired by evolution theory, and has been demonstrated to be successful in other areas. EDDIE interacts with the users and generates decision trees, which can also be seen as rule sets. We argue that EDDIE is suitable for forecasting because apart from using the power of genetic programming to efficiently search the space of decision trees, it allows expert knowledge to be channelled into forecasting, and it generates rules which can easily be understood and verified. EDDIE has been applied to horse racing and achieved outstanding results. When experimented on 180 handicap races (real data) in the UK, it out-performed other common strategies used in horse race betting by great margins. The idea was then extended to financial forecasting. When tested on historical S&P-500 data EDDIE achieved a respectable annual rate of return over a three and a half year period. While luck may play a part in the success of EDDIE, our experimental results do indicate that EDDIE is a tool which deserves more research. c 1998 John Wiley & Sons, Ltd.", notes = "See also \cite{butler:1995:eddie}", } @Article{Tsang:2000:JME, author = "Edward P. K. Tsang and Jin Li and Sheri Markose and Hakan Er and Abdel Salhi and Giulia Iori", title = "{EDDIE} In Financial Decision Making", journal = "Journal of Management and Economics", year = "2000", volume = "4", number = "4", month = nov, keywords = "genetic algorithms, genetic programming, financial forecasting", URL = "http://cswww.essex.ac.uk/CSP/finance/papers/Tsang-Eddie-JMgtEcon2000.ps", broken = "http://cswww.essex.ac.uk/CSP/finance/papers/EDDIE2000.htm", broken = "http://www.econ.uba.ar/servicios/publicaciones/journal4/contents/EDDIE%20in%20Financial%20Decision%20Making%20%28Tsang%29.htm", abstract = "This paper gives an overview of the EDDIE project. It describes the principles and applications of EDDIE in making financial decisions, including applications to share prices and indices forecasting and arbitrage. EDDIE is designed as an interactive decision tool, not a replacement of expert knowledge. Experts channel their knowledge into the system through (a) selection and preparation of data and (b) providing feedback to EDDIE. EDDIE's main role is to explore interactions between variables and to find thresholds for the variables. Performance of EDDIE depends on both the quality of the users' input and the efficiency of its genetic programming based search engine.", notes = "Sep 2019 someone has trashed econ.uba.ar", } @InProceedings{Tsang:2000:COF, author = "Edward P. K. Tsang and Jin Li", title = "Combining Ordinal Financial Predictions with Genetic Programming", volume = "1983", year = "2000", booktitle = "Intelligent Data Engineering and Automated Learning - IDEAL 2000: Data Mining, Financial Engineering, and Intelligent Agents", series = "Lecture Notes in Computer Science", editor = "Kwong Sak Leung and Lai-Wan Chan and Helen Meng", pages = "532--537", address = "Shatin, N.T., Hong Kong, China", month = "13-15 " # dec, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-41450-9", ISSN = "0302-9743", CODEN = "LNCSD9", bibdate = "Tue Sep 10 19:08:58 MDT 2002", acknowledgement = ack-nhfb, URL = "http://www.cs.bham.ac.uk/~jxl/cercialink/web/publication/TsangLi-Ideal2000.pdf", DOI = "doi:10.1007/3-540-44491-2_77", size = "6 pages", abstract = "Ordinal data play an important part in financial forecasting. For example, advice from expert sources may take the form of bullish, bearish or sluggish, or buy or do not buy. This paper describes an application of using Genetic Programming (GP) to combine investment opinions. The aim is to combine ordinal forecast from different opinion sources in order to make better predictions. We tested our implementation, FGP (Financial Genetic Program-ming), on two data sets. In both cases, FGP generated more accurate rules than the individual input rules.", } @InCollection{Tsang:2002:gagpcf, author = "Edward P. K. Tsang and Jin Li", title = "{EDDIE} for financial forecasting", booktitle = "Genetic Algorithms and Genetic Programming in Computational Finance", publisher = "Kluwer Academic Press", year = "2002", editor = "Shu-Heng Chen", chapter = "7", pages = "161--174", keywords = "genetic algorithms, genetic programming, Financial forecasting, precision, genetic decision", ISBN = "0-7923-7601-3", URL = "http://cswww.essex.ac.uk/CSP/finance/papers/TsangLi-FGP-Chen_CompFinance.pdf", URL = "http://www.springer.com/economics/economic+theory/book/978-0-7923-7601-9", DOI = "doi:10.1007/978-1-4615-0835-9_7", size = "14 pages", abstract = "EDDIE is a genetic-programming based system for channelling expert knowledge into forecasting. FGP-2 is an implementation of EDDIE for Financial forecasting. The novelty of FGP-2 is that, as a forecasting tool, it provides the user with a handle for tuning the precision against the rate of missing opportunities. This allows the user to pick investment opportunities with greater confidence.", notes = "part of \cite{chen:2002:gagpcf}", } @Article{Tsang:2004:DSS, author = "Edward Tsang and Paul Yung and Jin Li", title = "{EDDIE}-Automation, a decision support tool for financial forecasting", journal = "Decision Support Systems", year = "2004", volume = "37", pages = "559--565", number = "4", month = sep, note = "Data mining for financial decision making", keywords = "genetic algorithms, genetic programming, Financial forecasting tools", ISSN = "0167-9236", owner = "wlangdon", URL = "http://cswww.essex.ac.uk/CSP/finance/papers/TsYuLi-Eddie-Dss2004.pdf", broken = "http://www.sciencedirect.com/science/article/B6V8S-4903GV9-1/2/d6ba531a46ce45526ff9015e4447409a", DOI = "doi:10.1016/S0167-9236(03)00087-3", abstract = "Evolutionary Dynamic Data Investment Evaluator (EDDIE) is a genetic programming (GP)-based decision support tool for financial forecasting. EDDIE itself does not replace forecasting experts. It serves to improve the productivity of experts in searching the space of decision trees, with the aim to improve the odds in its user's favour. The efficacy of EDDIE has been reported in the literature. However, discovering patterns in historical data is only the first step towards building a practical financial forecasting tool. Data preparation, rules organisation and application are all important issues. This paper describes an architecture that embeds EDDIE for learning from and monitoring the stock market.", notes = "Special Issue on Data Mining for Financial Decision Making Also known as \cite{TSANG2004559}", } @Article{tsang05:_chanc, author = "Edward P. K. Tsang and Sheri Markose and Hakan Er", title = "Chance discovery in stock index option and future arbitrage", journal = "New Mathematics and Natural Computation", year = "2005", volume = "1", number = "3", pages = "435--447", month = nov, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1142/S1793005705000251", abstract = "The prices of the option and futures of a stock both reflect the market's expectation of futures changes of the stock's price. Their prices normally align with each other within a limited window. When they do not, arbitrage opportunities arise: an investor who spots the misalignment will be able to buy (sell) options on the one hand, and sell (buy) futures on the other and make risk-free profits. Historical data suggest that option and futures prices on the LIFFE Market do not align occasionally. Arbitrage chances are rare. Besides, they last for seconds only before the market adjusts itself. The challenge is not only to discover such chances, but to discover them ahead of other arbitragers. In the past, we have introduced EDDIE as a genetic programming tool for forecasting. This paper describes EDDIE-ARB, a specialisation of EDDIE, for forecasting arbitrage opportunities. As a tool, EDDIE-ARB was designed to enable economists and computer scientists to work together to identify relevant independent variables. Trained on historical data, EDDIE-ARB was capable of discovering rules with high precision. Tested on out-of-sample data, EDDIE-ARB out-performed a naive ex ante rule, which reacted only when misalignments were detected. This establishes EDDIE-ARB as a promising tool for arbitrage chances discovery. It also demonstrates how EDDIE brings domain experts and computer scientists together.", } @InProceedings{eurogp06:TsangJin, author = "Edward Tsang and Nanlin Jin", title = "Incentive Method to Handle Constraints in Evolutionary", editor = "Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art", booktitle = "Proceedings of the 9th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3905", year = "2006", address = "Budapest, Hungary", month = "10 - 12 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-33143-3", pages = "133--144", DOI = "doi:10.1007/11729976_12", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "This paper introduces Incentive Method to handle both hard and soft constraints in an evolutionary algorithm for solving some multi-constraint optimisation problems. The Incentive Method uses hard and soft constraints to help allocating heuristic search effort more effectively. The main idea is to modify the objective fitness function by awarding differential incentives according to the defined qualitative preferences, to solution sets which are divided by their satisfaction to constraints. It does not exclude the right to access search spaces that violate some or even all constraints. We test this technique through its application on generating solutions for a classic infinite-horizon extensive-form game. It is solved by an Evolutionary Algorithm incorporated by Incentive method. Experimental results are compared with results from a penalty method and from a non-constraint setting. Statistic analysis suggests that Incentive Method is more effective than the other two techniques for this specific problem.", notes = "Part of \cite{collet:2006:GP} EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006 Two Co-evolving populations. Dividing the cake.", } @InProceedings{Tsarev:2011:GECCOcomp, author = "Fedor Tsarev and Kirill Egorov", title = "Finite state machine induction using genetic algorithm based on testing and model checking", booktitle = "GECCO 2011 Graduate students workshop", year = "2011", editor = "Miguel Nicolau", isbn13 = "978-1-4503-0690-4", keywords = "genetic algorithms, genetic programming", pages = "759--762", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001858.2002085", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "In this paper, we describe the method of finite state machine (FSM) induction using genetic algorithm with fitness function, cross-over and mutation based on testing and model checking. Input data for the genetic algorithm is a set of tests and a set of properties described using linear time logic. Each test consists of an input sequence of events and the corresponding output action sequence. In previous works testing and model checking were used separately in genetic algorithms. Usage of such an approach is limited because the behaviour of system usually cannot be described by tests only. So, additional validation or verification is needed. Calculation of fitness function based only on verification do not perform well because there are very few possible values of fitness function (verification gives only yes or no answer). The approach described is tested on the problem of finite state machine induction for elevator doors controlling. Using tests only the genetic algorithm constructs the finite machine working improperly in some cases. Usage of verification allows to induct the correct finite state machine.", notes = "Also known as \cite{2002085} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @InProceedings{tseng:1999:M, author = "Chris Tseng and Arkady Epshteyn", title = "Modular learning with genetic aggregation (MOLGA) in data prediction", booktitle = "Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference", year = "1999", editor = "Scott Brave and Annie S. Wu", pages = "260--267", address = "Orlando, Florida, USA", month = "13 " # jul, notes = "GECCO-99LB", } @InProceedings{Tseng:2022:EuroGP, author = "Sabrina Tseng and Erik Hemberg and Una-May O'Reilly", title = "Synthesizing Programs from Program Pieces using Genetic Programming and Refinement Type Checking", booktitle = "EuroGP 2022: Proceedings of the 25th European Conference on Genetic Programming", year = "2022", editor = "Eric Medvet and Gisele Pappa and Bing Xue", series = "LNCS", volume = "13223", publisher = "Springer Verlag", address = "Madrid, Spain", pages = "197--211", month = "20-22 " # apr, organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, LiquidHaskell, Z3: Poster", isbn13 = "978-3-031-02055-1", URL = "https://www.evostar.org/2022/poster-session-in-gather/", DOI = "doi:10.1007/978-3-031-02056-8_13", code_url = "https://github.com/sabrinatseng/GAble", slide_url = "https://easychair.org/smart-slide/author/qQ9v", size = "15 pages", abstract = "Program synthesis automates the process of writing code, which can be a very useful tool in allowing people to better leverage computational resources. However, a limiting factor in the scalability of current program synthesis techniques is the large size of the search space, especially for complex programs. We present a new model for synthesising programs which reduces the search space by composing programs from program pieces, which are component functions provided by the user. Our method uses genetic programming search with a fitness function based on refinement type checking, which is a formal verification method that checks function behavior expressed through types. We evaluate our implementation of this method on a set of 3 benchmark problems, observing that our fitness function is able to find solutions in fewer generations than a fitness function that uses example test cases. These results indicate that using refinement types and other formal methods within genetic programming can improve the performance and practicality of program synthesis.", notes = "http://www.evostar.org/2022/eurogp/ Part of \cite{Medvet:2022:GP} EuroGP'2022 held inconjunction with EvoApplications2022 EvoCOP2022 EvoMusArt2022", } @Article{Tseng:2005:CSI, author = "Tzu-Laing (Bill) Tseng and Wen-Yau Liang and Chun-Che Huang and Tsung-Yu Chian", title = "Applying genetic algorithm for the development of the components-based embedded system", journal = "Computer Standards \& Interfaces", year = "2005", volume = "27", pages = "621--635", number = "6", abstract = "The embedded system is primarily designed for a particular piece of equipment and it varies on a case-by-case basis. The functionality is required to be specific to the equipment and consequently the application domain is limited. The software embedded in the system also faces problem due to the limitation of the hardware capacity. It is necessary for the designers to consider the hardware capacity and software specification simultaneously while an embedded system is developed. If hardware and software are taken into account concurrently, the design applicability and efficiency are decreased. The evolutionary computing (EC), which comprises techniques of evolutionary programming, evolution strategies, genetic algorithms, and genetic programming has been widely used to solve optimisation problems for large scale and complex systems. It is capable to escape not only from local optima due to population based approach, but also from unbiased nature, which enables it to perform well in a situation with little domain knowledge. Therefore, this study proposes an evolutionary approach that applies the characteristics of software reuse, the metrics for the object-oriented concept, and the genetic algorithm to effectively manage and optimize the embedded system. This approach is implemented in the World Wide Web environment. Numerous results associated with performance enhancements of the algorithm are presented", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6TYV-4F5S6C2-1/2/47d01c7228b5c5fa4f02db5227be3171", month = jun, keywords = "genetic algorithms, genetic programming, Embedded system, Software components", DOI = "doi:10.1016/j.csi.2004.12.001", } @PhdThesis{Yao-Ting.Tseng:thesis, title = "Recognition and assessment of seafloor vegetation using a single beam echosounder", author = "Yao-Ting Tseng", year = "2009", school = "Department of Imaging and Applied Physics, Centre for Marine Science and Technology, Curtin University of Technology", address = "Perth, Australia", month = feb, keywords = "genetic algorithms, genetic programming, GPLAB, single beam echo sounder, sea floor vegetation, recognition and assessment, plant benthos, ecological balance, environmental managers", bibsource = "OAI-PMH server at espace.library.curtin.edu.au", language = "en", oai = "oai:espace.library.curtin.edu.au:128517", rights = "unrestricted", URL = "http://espace.library.curtin.edu.au:80/R?func=dbin-jump-full&local_base=gen01-era02&object_id=128517", size = "246 pages", abstract = "This study focuses on the potential of using a single beam echosounder as a tool for recognition and assessment of seafloor vegetation. Sea floor vegetation is plant benthos and occupies a large portion of the shallow coastal bottoms. It plays a key role in maintaining the ecological balance by influencing the marine and terrestrial worlds through interactions with its surrounding environment. Understanding of its existence on the sea floor is essential for environmental managers. Due to the important role of sea floor vegetation to the environment, a detailed investigation of acoustic methods that can provide effective recognition and assessment of the sea floor vegetation by using available sonar systems is necessary. One of the frequently adopted approaches to the understanding of ocean environment is through the mapping of the sea floor. Available acoustic techniques vary in kinds and are used for different purposes. Because of the wide scope of available techniques and methods which can be employed in the field, this study has limited itself to sonar techniques of normal incidence configuration relative to sea-floors in selected regions and for particular marine habitats. For this study, a single beam echo-sounder operating at two frequencies was employed. Integrated with the echo sounder was a synchronized optical system. The synchronization mechanism between the acoustic and optical systems provided capabilities to have very accurate ground truth recordings for the acoustic data, which were then used as a supervised training data set for the recognition of seaflood vegetation. In this study, results acquired and conclusions made were all based on the comparison against the photographic recordings. The conclusion drawn from this investigation is only as accurate as within the selected habitat types and within very shallow water regions. In order to complete this study, detailed studies of literature and deliberately designed field experiments were carried out. Acoustic data classified with the help of the synchronized optical system were investigated by several methods. Conventional methods such as statistics and multivariate analyses were examined. Conventional methods for the recognition of the collected data gave some useful results but were found to have limited capabilities. When seeking for more robust methods, an alternative approach, Genetic Programming (GP), was tested on the same data set for comparison. Ultimately, the investigation aims to understand potential methods which can be effective in differentiating the acoustic backscatter signals of the habitats observed and subsequently distinguishing between the habitats involved in this study.", notes = "pdf scattered Supervisor Prof. Alexander Gavrilov, Alec Duncan", } @InProceedings{Tserenchimed:2011:GECCOcomp, author = "Badarch Tserenchimed and Shu Liu and Hitoshi Iba", title = "A trading method in {FX} using evolutionary algorithms: extensions based on reverse trend and settlement timing", booktitle = "GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0690-4", keywords = "genetic algorithms, genetic programming: Poster", pages = "139--140", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001858.2001937", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "In foreign exchange (FX) markets, the key issues to achieve profitable trading rules are the combination of the indicators, selection of their parameters, and decision of the trade timing for orders and settlements. In this paper, we present a trading system using a combination of genetic algorithm (GA) and genetic programming (GP). Unlike related researches on this problem, our work focuses on two aspects. First, a calculation of appropriate settlement timing is proposed, to make more profits and less losses. Second, reverse trend data are generated using in-sample data, to overcome the over fitting problem and suppress the risk of loss. To examine the effectiveness of the method, we employed simulations using real-world trading intraday data. It is verified the enhanced capability of our method to make consistent gain out-of-sample and avoid large draw-downs.", notes = "Also known as \cite{2001937} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @Article{TSIONAS:2020:IJHM, author = "Mike G. Tsionas and A. George Assaf", title = "Symbolic regression for better specification", journal = "International Journal of Hospitality Management", volume = "91", pages = "102638", year = "2020", ISSN = "0278-4319", DOI = "doi:10.1016/j.ijhm.2020.102638", URL = "http://www.sciencedirect.com/science/article/pii/S0278431920301900", keywords = "genetic algorithms, genetic programming, Symbolic Regression, Specification", abstract = "This note introduces the concept of symbolic regression (SR) to tourism and hospitality research. SR uses genetic programming to find the model that best fits the data without a need to pre-specify a functional form or to impose a certain model as a starting point. In other words, SR helps to uncover the intrinsic characteristics of the data at hand. Our view is that SR can serve as an improved method of testing for misspecification. In this note, we propose to derive the true functional form of the residual using SR. We then use this information to improve the forecasts of the linear regression model and, to perform hypothesis tests if needed", } @Article{Tsoulos:Gcr:06, author = "Ioannis G. Tsoulos and Isaac E. Lagaris", title = "Genetically controlled random search: a global optimization method for continuous multidimensional functions", journal = "Computer Physics Communications", year = "2006", volume = "174", number = "2", pages = "152--159", month = "15 " # jan, keywords = "genetic algorithms, genetic programming, Global optimisation, Stochastic methods, Grammatical evolution", URL = "http://www.cs.uoi.gr/~lagaris/papers/GCRS.pdf", DOI = "doi:10.1016/j.cpc.2005.09.007", abstract = "A new stochastic method for locating the global minimum of a multidimensional function inside a rectangular hyperbox is presented. A sampling technique is employed that makes use of the procedure known as grammatical evolution. The method can be considered as a {"}genetic{"} modification of the Controlled Random Search procedure due to Price. The user may code the objective function either in C++ or in Fortran 77. We offer a comparison of the new method with others of similar structure, by presenting results of computational experiments on a set of test functions. Program summary Title of program: GenPrice Catalogue identifier:ADWP Program summary URL: http://cpc.cs.qub.ac.uk/summaries/ADWP Program available from: CPC Program Library, Queen's University of Belfast, N. Ireland Computer for which the program is designed and others on which it has been tested: the tool is designed to be portable in all systems running the GNU C++ compiler Installation: University of Ioannina, Greece Programming language used: GNU-C++, GNU-C, GNU Fortran-77 Memory required to execute with typical data: 200 KB No. of bits in a word: 32 No. of processors used: 1 Has the code been vectorised or parallelised?: no No. of lines in distributed program, including test data, etc.:13 135 No. of bytes in distributed program, including test data, etc.: 78 512 Distribution format: tar.gz Nature of physical problem: A multitude of problems in science and engineering are often reduced to minimising a function of many variables. There are instances that a local optimum does not correspond to the desired physical solution and hence the search for a better solution is required. Local optimisation techniques are frequently trapped in local minima. Global optimization is hence the appropriate tool. For example, solving a nonlinear system of equations via optimisation, employing a {"}least squares{"} type of objective, one may encounter many local minima that do not correspond to solutions, i.e. minima with values far from zero. Method of solution: Grammatical Evolution is used to accelerate the process of finding the global minimum of a multidimensional, multimodal function, in the framework of the original {"}Controlled Random Search{"} algorithm. Typical running time: Depending on the objective function.", notes = "PACS: 02.60.-x; 02.60.Pn; 07.05.Kf; 02.70.Lq; 07.05.Mh", } @Article{Tsoulos:GAt:06, author = "Ioannis G. Tsoulos and Dimitris Gavrilis and Evangelos Dermatas", title = "{GDF:} A tool for function estimation through grammatical evolution", journal = "Computer Physics Communications", year = "2006", volume = "174", number = "7", pages = "555--559", month = "1 " # apr, keywords = "genetic algorithms, genetic programming, Function approximation, Stochastic methods, Grammatical evolution", DOI = "doi:10.1016/j.cpc.2005.11.003", abstract = "This article introduces a tool for data fitting that is based on genetic programming and especially on the grammatical evolution technique. The user needs to input a series of points and the accompanied dimensionality n and the tool will produce via the genetic programming paradigm a function Click to view the MathML source which is an approximate solution to the symbolic regression problem. The tool is entirely written in ANSI C++ and it can be installed in any UNIX system. Program summary Title of program: GDF Catalogue identifier:ADXC Program obtainable from: CPC Program Library, Queen's University of Belfast, N. Ireland Program summary URL: http://cpc.cs.qub.ac.uk/summaries/ADXC Computer for which the program is designed and others on which is has been tested:The tool is designed to be portable in all systems running the GNU C++ compiler Installation: University of Ioannina and University of Patras, Greece Programming language used:GNU-C++ Memory required to execute with typical data:200 KB No. of bits in a word: 32 No. of processors used: 1 Has the code been vectorised or parallelised?: No No. of bytes in distributed program, including test data, etc.: 33 469 No. of lines in distributed program, including test data, etc.: 5704 Distribution format: tar.gz Solution method: Functional forms are being created by genetic programming which are approximations for the symbolic regression problem.", notes = "PACS: 02.30.Mv; 02.60.-x; 02.60.Ed; 07.05.Mh", } @Article{Tsoulos:2006:GPEM, author = "I. G. Tsoulos and I. E. Lagaris", title = "Solving differential equations with genetic programming", journal = "Genetic Programming and Evolvable Machines", year = "2006", volume = "7", number = "1", pages = "33--54", month = mar, keywords = "genetic algorithms, genetic programming, Grammatical evolution, Differential equations, Evolutionary modelling", ISSN = "1389-2576", URL = "http://www.cs.uoi.gr/~lagaris/papers/PREPRINTS/DEwGE.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.307.7287", DOI = "doi:10.1007/s10710-006-7009-y", size = "22 pages", abstract = "A novel method for solving ordinary and partial differential equations, based on grammatical evolution is presented. The method forms generations of trial solutions expressed in an analytical closed form. Several examples are worked out and in most cases the exact solution is recovered. When the solution cannot be expressed in a closed analytical form then our method produces an approximation with a controlled level of accuracy. We report results on several problems to illustrate the potential of this approach.", notes = "ODE, PDE", } @Article{Tsoulos:2006:CPC, author = "Ioannis G. Tsoulos and Isaac E. Lagaris", title = "{GenAnneal:} Genetically modified Simulated Annealing", journal = "Computer Physics Communications", year = "2006", volume = "174", number = "10", pages = "846--851", month = "15 " # may, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1016/j.cpc.2005.12.011", abstract = "A modification of the standard Simulated Annealing (SA) algorithm is presented for finding the global minimum of a continuous multidimensional, multimodal function. We report results of computational experiments with a set of test functions and we compare to methods of similar structure. The accompanying software accepts objective functions coded both in Fortran 77 and C++.", notes = "b Physics Department, University of South Africa (UNISA), Pretoria, South Africa Title of program:GenAnneal Catalogue identifier:ADXI_v1_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/ADXI_v1_0.html", } @Article{Tsoulos2007976, author = "Ioannis G. Tsoulos and Dimitris Gavrilis and Evangelos Dermatas", title = "{GDF} v2.0, an enhanced version of {GDF}", journal = "Computer Physics Communications", year = "2007", volume = "177", number = "12", pages = "976--977", month = "15 " # dec, keywords = "genetic algorithms, genetic programming, grammatical evolution, Function approximation, Stochastic methods, Grammatical evolution", ISSN = "0010-4655", oai = "oai:CiteSeerX.psu:10.1.1.541.2453", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.541.2453", URL = "http://users.cs.uoi.gr/~itsoulos/publications/gdf2.pdf", DOI = "DOI:10.1016/j.cpc.2007.08.008", broken = "http://www.sciencedirect.com/science/article/B6TJ5-4PJM9P8-4/2/ee8eb3dbd6401fb5375a6f4034f76feb", size = "15 pages", abstract = "An improved version of the function estimation program GDF is presented. The main enhancements of the new version include: multi-output function estimation, capability of defining custom functions in the grammar and selection of the error function. The new version has been evaluated on a series of classification and regression datasets, that are widely used for the evaluation of such methods. It is compared to two known neural networks and outperforms them in 5 (out of 10) datasets. Program summary Title of program: GDF v2.0 Catalogue identifier: ADXC_v2_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/ADXC_v2_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC license, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 98[thin space]147 No. of bytes in distributed program, including test data, etc.: 2[thin space]040[thin space]684 Distribution format: tar.gz Programming language: GNU C++ Computer:The program is designed to be portable in all systems running the GNU C++ compiler Operating system: Linux, Solaris, FreeBSD RAM: 200000 bytes Classification: 4.9 Does the new version supersede the previous version?: Yes Nature of problem: The technique of function estimation tries to discover from a series of input data a functional form that best describes them. This can be performed with the use of parametric models, whose parameters can adapt according to the input data. Solution method: Functional forms are being created by genetic programming which are approximations for the symbolic regression problem. Reasons for new version: The GDF package was extended in order to be more flexible and user customizable than the old package. The user can extend the package by defining his own error functions and he can extend the grammar of the package by adding new functions to the function repertoire. Also, the new version can perform function estimation of multi-output functions and it can be used for classification problems. Summary of revisions: The following features have been added to the package GDF: - Multi-output function approximation. The package can now approximate any function . This feature gives also to the package the capability of performing classification and not only regression. - User defined function can be added to the repertoire of the grammar, extending the regression capabilities of the package. This feature is limited to 3 functions, but easily this number can be increased. - Capability of selecting the error function. The package offers now to the user apart from the mean square error other error functions such as: mean absolute square error, maximum square error. Also, user defined error functions can be added to the set of error functions. - More verbose output. The main program displays more information to the user as well as the default values for the parameters. Also, the package gives to the user the capability to define an output file, where the output of the gdf program for the testing set will be stored after the termination of the process. Additional comments: A technical report describing the revisions, experiments and test runs is packaged with the source code. Running time: Depending on the train data.", } @Article{Tsoulos2008269, author = "Ioannis Tsoulos and Dimitris Gavrilis and Euripidis Glavas", title = "Neural network construction and training using grammatical evolution", journal = "Neurocomputing", volume = "72", number = "1-3", pages = "269--277", year = "2008", note = "Machine Learning for Signal Processing (MLSP 2006) / Life System Modelling, Simulation, and Bio-inspired Computing (LSMS 2007)", ISSN = "0925-2312", DOI = "doi:10.1016/j.neucom.2008.01.017", URL = "http://www.sciencedirect.com/science/article/B6V10-4S1C894-3/2/9beaf5f426239399e63b31456dcbc52a", keywords = "genetic algorithms, genetic programming, Grammatical evolution, Neural network, Context-free grammar", abstract = "The term neural network evolution usually refers to network topology evolution leaving the network's parameters to be trained using conventional algorithms. In this paper we present a new method for neural network evolution that evolves the network topology along with the network parameters. The proposed method uses grammatical evolution to encode both the network and the parameters space. This allows for a better description of the network using a formal grammar allowing the network architect to shape the resulting search space in order to meet each problem requirement. The proposed method is compared with other three methods for neural network training and is evaluated using 9 known classification problems and 9 known regression problems. In all 18 datasets, the proposed method outperforms its competitors.", } @Article{Tsoulos2008843, author = "Ioannis G. Tsoulos and I. E. Lagaris", title = "{GenMin:} An enhanced genetic algorithm for global optimization", journal = "Computer Physics Communications", volume = "178", number = "11", pages = "843--851", year = "2008", ISSN = "0010-4655", DOI = "doi:10.1016/j.cpc.2008.01.040", URL = "http://www.sciencedirect.com/science/article/B6TJ5-4RR8YW1-2/2/9b76a8b289abccf9ec864e11a54573a7", keywords = "genetic algorithms, genetic programming, Grammatical evolution, Global optimization, Stochastic methods, Stopping rule", abstract = "A new method that employs grammatical evolution and a stopping rule for finding the global minimum of a continuous multidimensional, multimodal function is considered. The genetic algorithm used is a hybrid genetic algorithm in conjunction with a local search procedure. We list results from numerical experiments with a series of test functions and we compare with other established global optimization methods. The accompanying software accepts objective functions coded either in Fortran 77 or in C++. Program summary Program title: GenMin Catalogue identifier: AEAR_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEAR_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 35[thin space]810 No. of bytes in distributed program, including test data, etc.: 436[thin space]613 Distribution format: tar.gz Programming language: GNU-C++, GNU-C, GNU Fortran 77 Computer: The tool is designed to be portable in all systems running the GNU C++ compiler Operating system: The tool is designed to be portable in all systems running the GNU C++ compiler RAM: 200 KB Word size: 32 bits Classification: 4.9 Nature of problem: A multitude of problems in science and engineering are often reduced to minimizing a function of many variables. There are instances that a local optimum does not correspond to the desired physical solution and hence the search for a better solution is required. Local optimization techniques are frequently trapped in local minima. Global optimization is hence the appropriate tool. For example, solving a nonlinear system of equations via optimization, employing a least squares type of objective, one may encounter many local minima that do not correspond to solutions (i.e. they are far from zero). Solution method: Grammatical evolution and a stopping rule. Running time: Depending on the objective function. The test example given takes only a few seconds to run.", } @Article{Tsoulos20092385, author = "Ioannis G. Tsoulos and Dimitris Gavrilis and Euripidis Glavas", title = "Solving differential equations with constructed neural networks", journal = "Neurocomputing", volume = "72", number = "10-12", pages = "2385--2391", year = "2009", note = "Lattice Computing and Natural Computing (JCIS 2007) / Neural Networks in Intelligent Systems Designn (ISDA 2007)", ISSN = "0925-2312", DOI = "doi:10.1016/j.neucom.2008.12.004", URL = "http://www.sciencedirect.com/science/article/B6V10-4V9S3C3-3/2/73dafb81780ffc228f23d8bbaf911aa3", keywords = "genetic algorithms, genetic programming, Differential equations, Neural networks, Grammatical evolution", abstract = "A novel hybrid method for the solution of ordinary and partial differential equations is presented here. The method creates trial solutions in neural network form using a scheme based on grammatical evolution. The trial solutions are enhanced periodically using a local optimisation procedure. The proposed method is tested on a series of ordinary differential equations, systems of ordinary differential equations as well as on partial differential equations with Dirichlet boundary conditions and the results are reported.", } @Article{Tsoulos:2019:EJERS, author = "Ioannis G. Tsoulos and Alexandros Tzallas and Dimitrios Tsalikakis", title = "Genetic Feature Construction Genetic Feature Construction: a parallel implementation of a genetic programming tool for feature construction", journal = "European Journal of Engineering and Technology Research", year = "2019", volume = "4", number = "5", month = may, keywords = "genetic algorithms, genetic programming, neural networks, ANN, feature construction, MPI, RBF, PCA, MIpack", ISSN = "2736-576X", URL = "https://www.ejers.org/index.php/ejers/article/view/1272", DOI = "doi:10.24018/ejers.2019.4.5.1272", code_url = "https://github.com/itsoulos/FeatureConstruction", size = "7 pages", abstract = "a parallel implementation of a recently introduced method for feature construction is described. This technique uses parallel genetic algorithms along with RBF neural networks to create new features from the original ones by discovering the hidden relations between patterns. The method is tested on series of classification problems from a variety of areas and the results are reported. The accompanied software is written entirely in ANSI C++ using the well established MPI library for parallelisation.", notes = "University of Ioannina, Greece Royal Library of Belgium", } @Article{TSOULOS:2019:SoftwareX, author = "Ioannis G. Tsoulos and Alexandros Tzallas and Dimitris Tsalikakis", title = "{NNC:} A tool based on Grammatical Evolution for data classification and differential equation solving", journal = "SoftwareX", volume = "10", pages = "100297", year = "2019", ISSN = "2352-7110", DOI = "doi:10.1016/j.softx.2019.100297", URL = "http://www.sciencedirect.com/science/article/pii/S2352711019301128", keywords = "genetic algorithms, genetic programming, Neural networks, Grammatical evolution, Stochastic methods", abstract = "A genetic programming tool is demonstrated for data classification and differential equation solving. The fundamental element of the method is the well-known technique of Grammatical Evolution. The method constructs artificial neural networks in a C - like programming language in order to learn the input data. The paper presents the programming tool NNC (Neural Network Constructor) as long as a series of experiments", } @Article{Tsoulos:IJCIS, author = "Ioannis G. Tsoulos", title = "Creating classification rules using Grammatical Evolution", journal = "International Journal of Computational Intelligence Studies", year = "2020", volume = "9", number = "1/2", pages = "161--171", keywords = "genetic algorithms, genetic programming, Grammatical evolution, Data classification, Stochastic methods", ISSN = "1755-4977", URL = "https://www.inderscience.com/info/inarticle.php?artid=106477", DOI = "doi:10.1504/IJCISTUDIES.2020.106477", abstract = "A genetic programming based method is introduced for data classification. The fundamental element of the method is the well - known technique of Grammatical Evolution. The method constructs classification programs in a C like programming language in order to classify the input data, producing simple if else rules. The paper introduces the method as well as the conducted experiments on a series of datasets against other well known classification methods.", notes = "Department of Computer Engineering, School of Applied Techhnology, Technological Educational Institute of Epirus, 47100 Arta, Greece IJCIStudies http://www.inderscience.com/jhome.php?jcode=IJCISTUDIES ", } @Article{Tsoulos:2022:Algorithms, author = "Ioannis G. Tsoulos", title = "{QFC}: A Parallel Software Tool for Feature Construction, Based on Grammatical Evolution", journal = "Algorithms", year = "2022", volume = "15", number = "8", pages = "article number 295", keywords = "genetic algorithms, genetic programming, grammatical evolution, , ANN, feature construction", ISSN = "1999-4893", URL = "https://www.mdpi.com/1999-4893/15/8/295", DOI = "doi:10.3390/a15080295", size = "19 pages", abstract = "This paper presents and analyzes a programming tool that implements a method for classification and function regression problems. This method builds new features from existing ones with the assistance of a hybrid algorithm that makes use of artificial neural networks and grammatical evolution. The implemented software exploits modern multi-core computing units for faster execution. The method has been applied to a variety of classification and function regression problems, and an extensive comparison with other methods of computational intelligence is made.", notes = "Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece https://www.mdpi.com/journal/algorithms", } @Article{tsoulos:2023:AI, author = "Ioannis G. Tsoulos and Alexandros Tzallas and Evangelos Karvounis", title = "Adapting the Parameters of {RBF} Networks Using Grammatical Evolution", journal = "AI", year = "2023", volume = "4", number = "4", pages = "1059--1078", keywords = "genetic algorithms, genetic programming, grammatical evolution", ISSN = "2673-2688", URL = "https://www.mdpi.com/2673-2688/4/4/54", DOI = "doi:10.3390/ai4040054", abstract = "Radial basis function networks are widely used in a multitude of applications in various scientific areas in both classification and data fitting problems. These networks deal with the above problems by adjusting their parameters through various optimisation techniques. However, an important issue to address is the need to locate a satisfactory interval for the parameters of a network before adjusting these parameters. This paper proposes a two-stage method. In the first stage, via the incorporation of grammatical evolution, rules are generated to create the optimal value interval of the network parameters. During the second stage of the technique, the mentioned parameters are fine-tuned with a genetic algorithm. The current work was tested on a number of datasets from the recent literature and found to reduce the classification or data fitting error by over 40percent on most datasets. In addition, the proposed method appears in the experiments to be robust, as the fluctuation of the number of network parameters does not significantly affect its performance.", notes = "also known as \cite{ai4040054}", } @InProceedings{Tsujimura:2012:FedCSIS, author = "Takeshi Tsujimura and Hiroki Fukushima and Yoshihiro Minato and Kiyotaka Izumi", booktitle = "Federated Conference on Computer Science and Information Systems (FedCSIS 2012)", title = "Laser trail shape identification technique for robot navigation based on genetic programming", year = "2012", month = "9-12 " # sep, address = "Poland", pages = "161--166", keywords = "genetic algorithms, genetic programming, edge detection", isbn13 = "978-1-4673-0708-6", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6354313", size = "6 pages", abstract = "This paper proposes a meta-heuristic image processing application for mobile robot navigation. It classifies figures that are drawn on a wall by hand with a laser pointer. Image processing technique extracts optical flow of the laser beam trail, which represents vectors along edges of shapes. Genetic programming learns geometric characteristics of laser trail shapes and creates classification algorithm. Three typical figures, such as a circle, a triangle, and a square, are evaluated and identified in high accuracy. We have investigated the effects of genetic programming parameters on the performance of shape identification. As a result, proposal system makes it possible to command robots by easy and intuitive action of drawing a figure only with a laser pointer.", notes = "\cite{Fukushima:2012:SICE}. Also known as \cite{6354313}", } @InProceedings{Tsujimura:2014:AE, author = "Takeshi Tsujimura and Takahiro Hashimoto and Kiyotaka Izumi", booktitle = "International Conference on Applied Electronics (AE 2014)", title = "Genetic reasoning for finger sign identification based on forearm electromyogram", year = "2014", month = sep, pages = "297--302", abstract = "This paper proposes a meta-heuristic data-clustering application to identify finger signs only by measuring surface electromyogram (EMG) of a forearm. It classifies EMG signal patterns peculiar to finger signs. Genetic programming learns intensity characteristics of EMG signals, and creates classification algorithm. Three typical finger signs are evaluated in terms of generated EMG. Experiments are conducted to reveal the successful identification of finger signs in real time.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/AE.2014.7011724", ISSN = "1803-7232", notes = "Department of Mechanical Engineering, Saga University, 840-8502 Japan Also known as \cite{7011724}", } @InProceedings{Tsujimura:2015:ICIEV, author = "Takeshi Tsujimura and Kosuke Urata and Kiyotaka Izumi", booktitle = "2015 International Conference on Informatics, Electronics Vision (ICIEV)", title = "Hand sign classification techniques based on forearm electromyogram signals", year = "2015", abstract = "This paper describes classification techniques to distinguish hand signs based only on electro-myogram signals of a forearm. Relationship between finger gesture and forearm electro myogram is investigated by two signal processing approaches; an empirical thresholding method and meta heuristic method. The former method judges muscle activity according to the criteria experimentally determined in advance, and evaluates activity pattern of muscles. The latter learns the electromyogram characteristics and automatically creates classification algorithm applying genetic programming. Discrimination experiments of typical hand signs are carried out to evaluate the effectiveness of the proposed methods.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICIEV.2015.7334031", month = jun, notes = "Dept. of Mech. Eng., Saga Univ., Saga, Japan Also known as \cite{7334031}", } @InProceedings{Tsukada:2010:ijcnn, author = "Masahiro Tsukada and Hirokazu Madokoro and Kazuhito Sato", title = "Unsupervised and adaptive category classification for a vision-based mobile robot", booktitle = "International Joint Conference on Neural Networks (IJCNN 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-6917-8", abstract = "This paper presents an unsupervised category classification method for time-series images that combines incremental learning of Adaptive Resonance Theory-2 (ART-2) and self-mapping characteristic of Counter Propagation Networks (CPNs). Our method comprises the following procedures: 1) generating visual words using Self-Organising Maps (SOM) from 128-dimensional descriptors in each feature point of a Scale-Invariant Feature Transform (SIFT), 2) forming labels using unsupervised learning of ART-2, and 3) creating and classifying categories on a category map of CPNs for visualising spatial relations between categories. We use a vision system on a mobile robot for taking time-series images. Experimental results show that our method can classify objects into categories according to their change of appearance during the movement of a robot.", DOI = "doi:10.1109/IJCNN.2010.5596323", notes = "WCCI 2010. Also known as \cite{5596323}", } @InProceedings{Tsunoda:2009:ISDA, author = "Denise F. Tsunoda and Alex A. Freitas and Heitor S. Lopes", title = "MAHATMA: A Genetic Programming-Based Tool for Protein Classification", booktitle = "Ninth International Conference on Intelligent Systems Design and Applications, ISDA '09", year = "2009", month = "30 " # nov # "-2 " # dec, pages = "1136--1142", keywords = "genetic algorithms, genetic programming, MAHATMA, amino acids, biological functions, enzymes, evolutionary computation method, genetic programming-based tool, heuristic method, motifs, protein classification, protein data bank, biology computing, pattern classification, proteins", DOI = "doi:10.1109/ISDA.2009.14", abstract = "Proteins can be grouped into families according to some features such as hydrophobicity, composition or structure, aiming to establish common biological functions. This paper presents a system that was conceived to discover features (particular sequences of amino acids, or motifs) that occur very often in proteins of a given family but rarely occur in proteins of other families. These features can be used for the classification of unknown proteins, that is, to predict their function by analyzing their primary structure. Experiments were done with a set of enzymes extracted from the protein data bank. The heuristic method used was based on genetic programming using operators specially tailored for the target problem. The final performance was measured using sensitivity (Se) and specificity (Sp). The best results obtained for the enzyme dataset suggest that the proposed evolutionary computation method is very effective to find predictive features (motifs) for protein classification.", notes = "Also known as \cite{5364152}", } @Article{journals/soco/TsunodaFL11, author = "Denise Fukumi Tsunoda and Alex Alves Freitas and Heitor Silverio Lopes", title = "A genetic programming method for protein motif discovery and protein classification", journal = "Soft Computing - A Fusion of Foundations, Methodologies and Applications", year = "2011", volume = "15", number = "10", pages = "1897--1908", publisher = "Springer", keywords = "genetic algorithms, genetic programming, evolutionary algorithms, data mining, proteins patterns discovery", ISSN = "1432-7643", DOI = "doi:10.1007/s00500-010-0624-9", size = "12 pages", abstract = "Proteins can be grouped into families according to some features such as hydrophobicity, composition or structure, aiming to establish common biological functions. This paper presents MAHATMA memetic algorithm based highly adapted tool for motif ascertainment-a system that was conceived to discover features (particular sequences of amino acids, or motifs) that occur very often in proteins of a given family but rarely occur in proteins of other families. These features can be used for the classification of unknown proteins, that is, to predict their function by their primary structure. Experiments were done with a set of enzymes extracted from the Protein Data Bank. The heuristic method used was based on genetic programming using operators specially tailored for the target problem. The final performance was measured using sensitivity, specificity and hit rate. The best results obtained for the enzyme dataset suggest that the proposed evolutionary computation method is effective in finding predictive features (motifs) for protein classification.", notes = "PDB, MAHATMA From the issue entitled Special Issue on Intelligent Systems, Design and Applications (ISDA 2009)", affiliation = "Federal University of Parana, Av. Prefeito Lothario Meissner, 632, Room 38, Curitiba, PR, Brazil", bibdate = "2011-09-14", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/soco/soco15.html#TsunodaFL11", } @InProceedings{Tsutiya:2016:SICE, author = "Daisuke Tsutiya and Takeshi Tsujimura and Kiyotaka Izumi", booktitle = "2016 55th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE)", title = "Fingerspelling translation based on forearm electoromyogram", year = "2016", pages = "944--949", abstract = "In this paper, We designed a finger-spelling identification system based on surface electromyogram and genetic programming. Identification algorithm is generated by learning AIEMG signals of finger spellings using genetic programming. Some experiments are carried out to classify five Japanese characters to verify usefulness of the identification functions. A result of the identification, the identification rate for learning in all of the finger characters is 100percent. Identification rate of the test is totally 84percent.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SICE.2016.7749228", month = sep, notes = "Also known as \cite{7749228}", } @InProceedings{tsutsui:1999:MRSCRCGA, author = "Shigeyoshi Tsutsui and Masayuki Yamamura and Takahide Higuchi", title = "Multi-parent Recombination with Simplex Crossover in Real Coded Genetic Algorithms", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "657--664", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/Tsutsui_icga99.pdf", URL = "http://www.hannan-u.ac.jp/~tsutsui/ps/icga99.pdf", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Tsuzuki:2006:ieeeCDC, author = "T. Tsuzuki and K. Kuwada and Y. Yamashita", title = "Searching for control {Lyapunov-Morse} functions using genetic programming for global asymptotic stabilization of nonlinear systems", booktitle = "45th IEEE Conference on Decision and Control", year = "2006", pages = "5114--5119", address = "San Diego, USA", month = "13-15 " # dec, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", ISBN = "1-4244-0171-2", DOI = "doi:10.1109/CDC.2006.377681", abstract = "The purpose of this paper is searching for control Lyapunov-Morse functions (CLMF) using genetic programming (GP) for one-input-affine systems on general manifolds. The CLMF is an extended control Lyapunov function (CLF) to have multiple critical points. It is shown that a C 1 global stabiliser is derived from a CLF for the system. As in the case of CLF, a discontinuous global asymptotic stabiliser is obtained from a CLMF. However, there is no method constructing CLMF systematically. Then, we propose a method of searching for CLMF via GP to design the global asymptotic stabilizer", notes = "Graduate Sch. of Inf. Sci. & Technol., Hokkaido Univ., Sapporo", } @InProceedings{Tu:2020:CEC, author = "Chaofan Tu and Menglin Cui", booktitle = "2020 IEEE Congress on Evolutionary Computation (CEC)", title = "Learning Regular Expressions for Interpretable Medical Text Classification Using a Pool-based Simulated Annealing Approach", year = "2020", editor = "Yaochu Jin", month = "19-24 " # jul, keywords = "genetic algorithms, genetic programming, Medical diagnostic imaging, Simulated annealing, Task analysis, Neural networks, Machine learning, Medical services, Knowledge engineering, simulated annealing, regular expression, medical text classification", isbn13 = "978-1-7281-6929-3", DOI = "doi:10.1109/CEC48606.2020.9185650", abstract = "In this paper, we propose a rule-based engine composed of high-quality and interpretable regular expressions for medical text classification. The regular expressions are autogenerated by a constructive heuristic method and optimized using a Pool-based Simulated Annealing (PSA) approach. Although existing Deep Neural Network (DNN) methods present high quality performance in most Natural Language Processing (NLP) applications, the solutions are regarded as uninterpretable {"}black boxes{"} to humans. Therefore, rule-based methods are often introduced when interpretable solutions are needed, especially in the medical field. However, the construction of regular expressions can be extremely labour-intensive for large data sets. This research aims to reduce the manual efforts while maintaining high-quality solutions. The Pool-based Simulated Annealing method is proposed to automatically optimize the performance of machine-generated regular expressions without human interference. The proposed method is tested on real-life data provided by one of China's largest online medical platforms. Experimental results show that the proposed PSA method further improves the performance of initial machine-generated regular expressions compared with other meta-heuristics such as Genetic Programming. We also believe that the proposed method can serve as a vital complementary tool for the existing machine learning approaches in text classification applications when high levels of interpretability of the solutions are required.", notes = "Also known as \cite{9185650}", } @InProceedings{tucker:1999:EEPMLDBN, author = "Allan Tucker and Xiaohui Liu", title = "Extending Evolutionary Programming Methods to the Learning of Dynamic {Bayesian} Networks", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "923--929", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "evolution strategies and evolutionary programming", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/Tucker_GECCO99.pdf", URL = "http://www.brunel.ac.uk/~cssrajt/Papers/GECCO99.doc", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @PhdThesis{Tucker:thesis, author = "Allan Brice James Tucker", title = "The automatic explanation of Multivariate Time Series with large time lags", school = "Birkbeck College, University of London", year = "2001", address = "UK", keywords = "genetic algorithms, genetic programming, Bayesian networks", URL = "https://www.dcs.bbk.ac.uk/site/assets/files/1025/tucker.pdf", URL = "http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.246924", size = "223 pages", abstract = "Due to the advances in data capture and storage techniques over the last decade, the size of Multivariate Time Series (MTS) data being recorded has grown massively. Many of these MTS are characterised by a large number of interdependent variables with large possible time lags. If new and useful knowledge is to be automatically learnt from this type of data in order to aid the understanding of the underlying processes, a paradigm must be identified that is capable of modelling data with these characteristics but at the same time exhibiting transparency in how it models the data. A key challenge is that the number of possible models is very large since it does not only depend on the number of time series variables, but also on the size of possible time lags between causes and effects. In this thesis a general framework is described for automatically learning probabilistic models from MTS with large time lags and high dimensionality in order to explain the underlying processes involved. Specifically, a novel method to learn dynamic Bayesian networks for explanation from these series is developed. This involves an efficient pre-processing stage, which effectively groups MTS variables in order to reduce the dimensionality of the problem. After pre-processing, a combination of Evolutionary Programming, Genetic Algorithms and heuristics is used to speed up convergence when learning models. In addition, an approach is looked at for the off-line learning of dynamic Bayesian networks with changing dependency structures. All experiments have been carried out on a mixture of synthetic and real data taken from an oil refinery repository. The resultant models are used to generate explanations that are evaluated in several ways, including reviewing the feedback from chemical process engineers. These results have demonstrated that the proposed framework is very promising in terms of both efficiency and accuracy", notes = "Lilliefors Test. PDF lacks title page etc. Supervisor Xiaohui Liu", } @InProceedings{Tufail:2005:WWERC, author = "Mohammad Tufail and Lindell Ormsbee", title = "Optimal Load Allocations by Linkage of Evolutionary Optimization Algorithms with Inductive Models of Watershed Response", booktitle = "World Water and Environmental Resources Congress 2005", year = "2005", editor = "Raymond Walton", address = "Anchorage, Alaska, USA", month = may # " 15-19", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1061/40792(173)349", abstract = "Two separate optimisation strategies (genetic algorithm and box complex method) are compared in the development of optimal nutrient load allocations for the water quality-impaired Beargrass Creek Watershed in Louisville, Jefferson County, Kentucky. The optimal load allocations are determined by linking the optimization algorithms with receiving water inductive models developed for the lower reaches of the watershed. The inductive models are developed from (1) a synthesis of both input and output response variables as derived from a continuous simulation of the watershed using a calibrated HSPF model, and (2) a synthesis of the continuous and discrete water quality data sampled over the last 2 years in the watershed. Inductive model construction is performed by use of artificial neural networks and the use of functional fixed-set genetic programming. The use of inductive models provides a more computational efficient framework for linkage with an optimisation model for use in developing an optimal loading strategy.", notes = "c2005 ASCE", } @Article{Tufail:2006:JH, author = "Mohammad Tufail and Lindell E. Ormsbee", title = "A fixed functional set genetic algorithm (FFSGA) approach for function approximation", journal = "Journal of Hydroinformatics", year = "2006", volume = "8", number = "3", pages = "193--206", month = jul, keywords = "genetic algorithms, genetic programming, artificial neural networks, friction factor, functional approximation, turbulent pipe flow", ISSN = "1464-7141", URL = "http://www.iwaponline.com/jh/008/0193/0080193.pdf", DOI = "doi:10.2166/hydro.2006.021", size = "14 pages", abstract = "This paper describes a simple mathematical technique that uses a genetic algorithm and least squares optimisation to obtain a functional approximation (or computer program) for a given data set. Such an optimal functional form is derived from a pre-defined general functional formulation by selecting optimal coefficients, decision variable functions, and mathematical operators. In the past, functional approximations have routinely been obtained through the use of linear and non-linear regression analysis. More recent methods include the use of genetic algorithms and genetic programming. An example application based on a data set extracted from the commonly used Moody diagram has been used to demonstrate the utility of the proposed method. The purpose of the application was to determine an explicit expression for friction factor and to compare its performance to other available techniques. The example application results in the development of closed form expressions that can be used for evaluating the friction factor for turbulent pipe flow. These expressions compete well in accuracy with other known methods, validating the promise of the proposed method in identifying useful functions for physical processes in a very effective manner. The proposed method is simple to implement and has the ability to generate simple and compact explicit expressions for a given response function.", } @PhdThesis{Tufail:thesis, author = "Mohammad Tufail", title = "Optimal Water Quality Management Strategies for urban Watersheds using Macro-level Simulation models linked with Evolutionary algorithms", school = "Civil Engineering, Engineering Department, University of Kentucky", year = "2006", address = "Lexington, Kentucky, USA", keywords = "genetic algorithms, genetic programming", URL = "http://uknowledge.uky.edu/cgi/viewcontent.cgi?article=1316&context=gradschool_diss", URL = "http://uknowledge.uky.edu/gradschool_diss/313", size = "317 pages", abstract = "Urban watershed management poses a very challenging problem due to the various sources of pollution and there is a need to develop optimal management models that can facilitate the process of identifying optimal water quality management strategies. A screening level, comprehensive, and integrated computational methodology is developed for the management of point and non-point sources of pollution in urban watersheds. The methodology is based on linking macro-level water quality simulation models with efficient nonlinear constrained optimisation methods for urban watershed management.The use of macro-level simulation models in lieu of the traditional and complex deductive simulation models is investigated in the optimal management framework for urban watersheds. Two different types of macro-level simulation models are investigated for application to watershed pollution problems namely explicit inductive models and simplified deductive models. Three different types of inductive modelling techniques are used to develop macro-level simulation models ranging from simple regression methods to more complex and nonlinear methods such as artificial neural networks and genetic functions. A new genetic algorithm (GA) based technique of inductive model construction called Fixed Functional Set Genetic Algorithm (FFSGA) is developed and used in the development of macro-level simulation models. A novel simplified deductive model approach is developed for modelling the response of dissolved oxygen in urban streams impaired by point and non-point sources of pollution. The utility of this inverse loading model in an optimal management framework for urban watersheds is investigated. In the context of the optimization methods, the research investigated the use of parallel methods of optimisation for use in the optimal management formulation. These included an evolutionary computing method called genetic optimisation and a modified version of the direct search method of optimisation called the Shuffled Box Complex method of constrained optimisation. The resulting optimal management model obtained by linking macro-level simulation models with efficient optimisation models is capable of identifying optimal management strategies for an urban watershed to satisfy water quality and economic related objectives. Finally, the optimal management model is applied to a real world urban watershed to evaluate management strategies for water quality management leading to the selection of near-optimal strategies.", notes = "Paper 313. First Advisor: Lindell E. Ormsbee", } @InCollection{tufts93, author = "Patrick Tufts", title = "Parallel Case Evaluation for Genetic Programming", booktitle = "1993 Lectures in Complex Systems", publisher = "Addison-Wesley", year = "1995", editor = "Lynn Nadel and Daniel L. Stein", volume = "VI", series = "Santa Fe Institute Studies in the Science of Complexity", pages = "591--596", keywords = "genetic algorithms, genetic programming", URL = "http://www.barnesandnoble.com/w/1993-lectures-in-complex-systems-lynn-nadel/1000235256", isbn13 = "9780201483680", notes = " From GP-list Wed, 21 Jun 95 18:00:35 EDT deals primarily with data-parallel GP, but mentions some work I did on time-series prediction. I applied GP to the credit card attrition problem -- predicting when a cardholder is going to drop one card in favor of another. The book should be on shelves by July 1995. From GP-list Thu, 18 Jun 1998 20:29:58 EDT I wrote what is probably the first massively parallel version of GP. It is in *Lisp for the CM-5. It uses SIMD parallelism in the eval step, and is probably only useful for functions where you have a large number of test cases that you can distribute across the processors (for example: data mining and time-series prediction) ", } @InProceedings{tufts:1995:dcGPcs, author = "Patrick Tufts", title = "Dynamic Classifiers: Genetic Programming and Classifier Systems", booktitle = "Working Notes for the AAAI Symposium on Genetic Programming", year = "1995", editor = "E. V. Siegel and J. R. Koza", pages = "114--119", address = "MIT, Cambridge, MA, USA", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025, USA", month = "10--12 " # nov, publisher = "AAAI", keywords = "genetic algorithms, genetic programming", URL = "http://www.aaai.org/Papers/Symposia/Fall/1995/FS-95-01/FS95-01-016.pdf", URL = "http://www.aaai.org/Library/Symposia/Fall/fs95-01.php", size = "6 pages", abstract = "The Dynamic Classifier System extends the traditional classifier system by replacing its fixed-width ternary representation with Lisp expressions. Genetic programming applied to the classifiers allows the system to discover building blocks in a flexible, fitness directed manner. In this paper, I describe the prior art of problem decomposition using genetic programming and classifier systems. I then show how the proposed system builds oil work in these two areas, extending them in a way that provides for flexible representation and fitness directed discovery of useful building blocks.", notes = "AAAI-95f GP. Part of \cite{siegel:1995:aaai-fgp} The abstract for my paper on the Dynamic Classifier System (DCS) and the slides from my talk may be found at: http://www.cs.brandeis.edu/~zippy/papers.html {\em Telephone:} 415-328-3123 {\em Fax:} 415-321-4457 {\em email} info@aaai.org {\em URL:} http://www.aaai.org/", } @InCollection{tufts:1996:aigp2, author = "Patrick Tufts", title = "Genetic Programming Resources on the World-Wide Web", booktitle = "Advances in Genetic Programming 2", publisher = "MIT Press", year = "1996", editor = "Peter J. Angeline and K. E. {Kinnear, Jr.}", pages = "499--505", chapter = "A", address = "Cambridge, MA, USA", keywords = "genetic algorithms, genetic programming", ISBN = "0-262-01158-1", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6277541", DOI = "doi:10.7551/mitpress/1109.003.0032", size = "7 pages", abstract = "This appendix describes some of the resources related to Genetic Programming (GP) that are available on-line. For each resource, there is a brief description and a URL.", } @InProceedings{tuite:evoapps11, author = "Cliodhna Tuite and Alexandros Agapitos and Michael O'Neill and Anthony Brabazon", title = "A Preliminary Investigation of Overfitting in Evolutionary Driven Model Induction: Implications for Financial Modelling", booktitle = "Applications of Evolutionary Computing, EvoApplications 2011: {EvoCOMNET}, {EvoFIN}, {EvoHOT}, {EvoMUSART}, {EvoSTIM}, {EvoTRANSLOG}", year = "2011", month = "27-29 " # apr, editor = "Cecilia {Di Chio} and Anthony Brabazon and Gianni {Di Caro} and Rolf Drechsler and Marc Ebner and Muddassar Farooq and Joern Grahl and Gary Greenfield and Christian Prins and Juan Romero and Giovanni Squillero and Ernesto Tarantino and Andrea G. B. Tettamanzi and Neil Urquhart and A. Sima Uyar", series = "LNCS", volume = "6625", publisher = "Springer Verlag", address = "Turin, Italy", publisher_address = "Berlin", pages = "120--130", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, grammatical evolution", isbn13 = "978-3-642-20519-4", DOI = "doi:10.1007/978-3-642-20520-0_13", size = "11 pages", abstract = "This paper investigates the effects of early stopping as a method to counteract overfitting in evolutionary data modelling using Genetic Programming. Early stopping has been proposed as a method to avoid model over training, which has been shown to lead to a significant degradation of out-of-sample performance. If we assume some sort of performance metric maximisation, the most widely used early training stopping criterion is the moment within the learning process that an unbiased estimate of the performance of the model begins to decrease after a strictly monotonic increase through the earlier learning iterations. We are conducting an initial investigation on the effects of early stopping in the performance of Genetic Programming in symbolic regression and financial modelling. Empirical results suggest that early stopping using the above criterion increases the extrapolation abilities of symbolic regression models, but is by no means the optimal training-stopping criterion in the case of a real-world financial dataset.", notes = "Part of \cite{DiChio:2011:evo_b} EvoApplications2011 held inconjunction with EuroGP'2011, EvoCOP2011 and EvoBIO2011", } @InProceedings{Tuite:2011:GECCOcomp, author = "Cliodhna Tuite and Alexandros Agapitos and Michael O'Neill and Anthony Brabazon", title = "Early stopping criteria to counteract overfitting in genetic programming", booktitle = "GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", pages = "203--204", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", publisher = "ACM", keywords = "genetic algorithms, genetic programming, grammatical evolution, symbolic regression, overfitting: Poster", isbn13 = "978-1-4503-0690-4", DOI = "doi:10.1145/2001858.2001971", publisher_address = "New York, NY, USA", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.1023.6399", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1023.6399", URL = "http://researchrepository.ucd.ie/bitstream/handle/10197/3538/Early_Stopping_Criteria_to_Counteract_Overfitting_in_Genetic_Programming.pdf", size = "2 pages", abstract = "Early stopping typically stops training the first time validation fitness disimproves. This may not be the best strategy given that validation fitness can subsequently increase or decrease. We examine the effects of stopping subsequent to the first disimprovement in validation fitness, on symbolic regression problems. Stopping points are determined using criteria which measure generalisation loss and training progress. Results suggest that these criteria can improve the generalistion ability of symbolic regression functions evolved using Grammar-based GP.", notes = "Also known as \cite{2001971} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @InCollection{Tuite:NCFE:2011, author = "Cliodhna Tuite and Alexandros Agapitos and Michael O'Neill and Anthony Brabazon", title = "Tackling Overfitting in Evolutionary-driven Financial Model Induction", booktitle = "Natural Computing in Computational Finance (Volume 4)", publisher = "Springer", year = "2012", editor = "Anthony Brabazon and Michael O'Neill and Dietmar Maringer", volume = "380", series = "Studies in Computational Intelligence", chapter = "8", pages = "141--161", keywords = "genetic algorithms, genetic programming, US Corporate Payout Policy, Symbolic Regression", isbn13 = "978-3-642-23335-7", URL = "http://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-3-642-23335-7", } @InProceedings{Tuite:2013:GECCOcomp, author = "Cliodhna Tuite and Michael O'Neill and Anthony Brabazon", title = "Towards a dynamic benchmark for genetic programming", booktitle = "GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion", year = "2013", editor = "Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A. N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks", isbn13 = "978-1-4503-1964-5", keywords = "genetic algorithms, genetic programming", pages = "151--152", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2464576.2464649", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Following a recent call for a suite of benchmarks for genetic programming, we investigate the criteria for a meaningful dynamic benchmark for GP. We explore the design of a dynamic benchmark for symbolic regression, based on semantic distance between evaluated functions, where larger distances serve as a proxy for greater environmental change. We do not find convincing evidence that lower semantic distance is a good proxy for greater ease in adapting to a change. We conclude that due to fundamental characteristics of GP, it is difficult to come up with a single dynamic benchmark problem which is generally applicable.", notes = "Also known as \cite{2464649} Distributed at GECCO-2013.", } @InProceedings{tuite:mendel:2014, author = "Cliodhna Tuite and Michael O'Neill and Anthony Brabazon", title = "The Relationship between Semantic Distance and Performance in Dynamic Symbolic Regression Problems", booktitle = "Mendel 2014 The 20th International Conference on Soft Computing", year = "2014", address = "Brno, Czech Republic", keywords = "genetic algorithms, genetic programming, dynamic environments, symbolic regression, semantic distance", URL = "http://ncra.ucd.ie/papers/mendel2014_tuite.pdf", abstract = "Several methods which apply genetic programming (GP) in dynamic optimisation environments implicitly assume that the smaller the semantic change in the optimization goal, the better the adaptation of the GP population to the new target. However, GP searches over genetic operator-based fitness landscapes. As such, relative distances between solution points in genetic operator-based landscapes may not be related to the semantic distances between points. Our experiments examine whether decreasing the semantic distance between first and second-period target functions in symbolic regression problems result in improved performance in the second period. As a control, we also investigate how re-initialising the GP population in the second period performs in comparison with using a continuous GP population across the two periods. We find that decreasing the semantic distance does result in better performance in the second period, and that re-initializing the GP population under performs a continuous population at low semantic distances.", notes = "http://www.mendel-conference.org/tmp/ScheduleMendel2014e.pdf", } @InCollection{Tuite:2018:ohCEF, author = "Cliodhna Tuite and Michael O'Neill and Anthony Brabazon", title = "Economic and Financial Modeling with Genetic Programming - A Review", booktitle = "The Oxford Handbook of Computational Economics and Finance", publisher = "Oxford University Press", year = "2018", editor = "Shu-Heng Chen and Mak Kaboudan and Ye-Rong Du", series = "Oxford Handbooks", chapter = "8", pages = "266--293", keywords = "genetic algorithms, genetic programming, evolutionary computation, trading rules, price forecasting, stock selection, derivatives pricing, agent-based modelling", isbn13 = "9780199844371", URL = "https://global.oup.com/academic/product/the-oxford-handbook-of-computational-economics-and-finance-9780199844371", DOI = "doi:10.1093/oxfordhb/9780199844371.013.10", size = "29 pages", abstract = "This chapter focuses on genetic programming (GP), a stochastic optimization and model induction technique. An advantage of GP is that the modeller need not select the exact parameters to be used in the model beforehand. Rather, GP can effectively search a complex model space defined by a set of building blocks specified by the modeler. This flexibility has allowed GP to be used for many applications. The chapter reviews some of the most significant developments using GP: forecasting, stock selection, derivative pricing and trading, bankruptcy and credit risk assessment, and agent-based and economic modelling. Conclusions reached by studies investigating similar problems do not always agree; however, GP has proved useful across a wide range of problem areas. Recent and future work is increasingly concerned with adapting genetic programming to more dynamic environments and ensuring that solutions generalize robustly to out-of-sample data, to further improve model performance", } @PhdThesis{tuite:thesis, author = "Cliodhna Tuite", title = "Genetic Programming in Uncertain Environments", school = "School of Computer Science, University College Dublin", year = "2018", address = "Ireland", month = "7 " # sep, keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Financial Modeling, Efficient Market Hypothesis, UK Gross Domestic Product, GDP, Irish unemployment, semantic distance", URL = "http://ncra.ucd.ie/index.php/publications/", URL = "http://ncra.ucd.ie/?s=Tuite", URL = "https://sites.google.com/view/cliodhnatuite", size = "247 pages", abstract = "As an optimisation technique, Genetic Programming (GP) often has to operate in the presence of environmental uncertainty. This uncertainty can be due to noise in the training data, or due to variables which change after optimization has taken place, motivating an increased requirement for generalization. Uncertainty can also arise in the presence of a time-varying fitness function which motivates the need for dynamic optimization techniques. The investigations in this thesis are motivated by this requirement for generalization, and need for dynamic optimisation techniques. We obtain a number of significant results which contribute to the body of knowledge concerning generalization and dynamic optimization using genetic programming, as summarised below. We first illustrate for a small number of symbolic regression target functions, that overfitting is an issue even in the absence of noise in the training data, when GP is expected to extrapolate. We implement sophisticated early stopping criteria and our results show that these criteria can identify the optimal point at which to stop training. We investigate the implementation of a landscape-based dynamic benchmark for GP, but discover that it is not possible to both consistently set fitness values for trees which are identical upon evaluation, while simultaneously tuning the shape of our fitness landscape. We investigate the establishment of a symbolic regression-based dynamic benchmark for GP: using continuous populations, our experimental results show that it is possible to create a benchmark by varying the semantic distance between successive target functions in order to tune the magnitude of change. Finally, we look at two real-world economic datasets of interest, and produce a GP system which statistically significantly out performs a naive benchmark. Our results show that our system performance is not particularly sensitive to the length of historical training data provided. Over multiple time windows, when we compare using continuous populations against using re-initialized populations, our experimental results show that both approaches perform approximately equivalently on these dynamic problems.", notes = "CliodhnaTuite_PhDThesis.pdf Supervisors: Michael O'Neill and Anthony Brabazon", } @Article{Tung20091062, author = "Ching-Pin Tung and Tsung-Yu Lee and Yi-Chen E. Yang and Yun-Ju Chen", title = "Application of genetic programming to project climate change impacts on the population of Formosan Landlocked Salmon", journal = "Environmental Modelling \& Software", volume = "24", number = "9", pages = "1062--1072", year = "2009", ISSN = "1364-8152", DOI = "doi:10.1016/j.envsoft.2009.02.012", broken = "http://www.sciencedirect.com/science/article/B6VHC-4VX9WM8-2/2/cf40c0b7ba64a44a3228c61420514a7e", keywords = "genetic algorithms, genetic programming, Fish population, Climate change, Global warming", abstract = "This work presents a novel methodology, genetic programming (GP), for developing environmental response functions for Formosan Landlocked Salmon (Oncorhynchus masou formosanus); these functions are then applied to evaluate the impacts of climate changes. Average daily temperature and maximal flows between two sampling periods were adopted as principal factors for categorizing environmental conditions. The GP successfully identified the response functions for various environmental categories. The response functions were further applied to assess the impact of climate change. Fourteen future possible climate scenarios were derived based on the equilibrium and transition experiments by GCMs. Impact assessment results indicated that climate change may significantly influence populations of Formosan Landlocked Salmon due to more frequent higher temperatures. Adaptation strategies are required to mitigate the impact of global climate change as current conservation measures for Formosan Landlocked Salmon habitat only reduce local human-induced effects. In the situation of complicated relationships between fish population and environmental conditions, GP provides a useful tool to obtain some information from the limited data.", notes = "Bioenvironmental Systems Engineering, National Taiwan University, No. 1 Roosevelt Road, Section 4, Taipei 106, Taiwan", } @Article{ETasoft96, author = "Edward Tunstel and Mo Jamshidi", title = "On Genetic Programming of Fuzzy Rule-Based Systems for Intelligent Control", journal = "International Journal of Intelligent Automation and Soft Computing", year = "1996", volume = "2", number = "3", pages = "273--284", keywords = "genetic algorithms, genetic programming", URL = "http://www-robotics.jpl.nasa.gov/people/Edward_Tunstel/GP_Fuzzy96.ps", URL = "http://citeseer.ist.psu.edu/77465.html", size = "15 pages", abstract = "Intelligent robot navigation can be achieved using a control system comprised of a collection of special-purpose motion routines, or behaviors. An approach to behavior coordination in multi-behavior systems is described with emphasis on evolution of fuzzy coordination rules using the genetic programming (GP) paradigm. Both conventional GP and steady-state GP are applied to evolve a fuzzy-behavior for sensor-based goal-seeking to be used in a hierarchical fuzzy navigation controller. The usefulness of GP is demonstrated by simulating performance of evolved coordination rules for autonomous navigation.", notes = " ", } @InProceedings{ETissci96, author = "Edward Tunstel and Tanya Lippincott", title = "Genetic Programming of Fuzzy Coordination Behaviors for Mobile Robots", booktitle = "International Symposium on Soft Computing for Industry, 2nd World Automation Congress", year = "1996", pages = "647--652", address = "Montpellier, France", month = may, keywords = "genetic algorithms, genetic programming", URL = "http://www-robotics.jpl.nasa.gov/people/Edward_Tunstel/issci96.ps", URL = "http://citeseer.ist.psu.edu/tunstel96genetic.html", size = "6 pages", abstract = "Intelligent robot navigation can be achieved using a control system comprised of a collection of special-purpose motion routines, or behaviors. An approach to behavior coordination in multi-behavior systems is described with emphasis on evolution of fuzzy coordination rules using the genetic programming (GP) paradigm. Both conventional GP and steady-state GP are applied to evolve a fuzzy-behavior for sensor-based goal-seeking to be used in a hierarchical fuzzy navigation controller. The usefulness of GP is demonstrated by simulating performance of evolved coordination rules for autonomous navigation.", notes = " ", } @Article{ETasoft97, author = "Edward Tunstel and Tanya Lippincott and Mo Jamshidi", title = "Behavior Hierarchy for Autonomous Mobile Robots: Fuzzy-behavior modulation and evolution", journal = "International Journal of Intelligent Automation and Soft Computing, Special Issue: Autonomous Control Engineering at NASA ACE Center", year = "1997", volume = "3", number = "1", pages = "37--49", keywords = "genetic algorithms, genetic programming", URL = "http://www-robotics.jpl.nasa.gov/people/Edward_Tunstel/acesi.ps", size = "14 pages", abstract = "Realization of autonomous behavior in mobile robots, using fuzzy logic control, requires formulation of rules which are collectively responsible for necessary levels of intelligence. Such a collection of rules can be conveniently decomposed and efficiently implemented as a hierarchy of fuzzy-behaviors. This article describes how this can be done using a behavior-based architecture. A behavior hierarchy and mechanisms of control decision-making are described. In addition, an approach to behavior coordination is described with emphasis on evolution of fuzzy coordination rules using the genetic programming (GP) paradigm. Both conventional GP and steady-state GP are applied to evolve a fuzzy-behavior for sensor-based goal-seeking. The usefulness of the behavior hierarchy, and partial design by GP, is evident in performance results of simulated autonomous navigation.", notes = " ", } @PhdThesis{tunstel:thesis, author = "Edward W. Tunstel", title = "Adaptive Hierarchy of Distributed Fuzzy Control: Application to Behavior Control of Rovers", school = "Electrical and Computer Engineering, University of New Mexico", year = "1996", address = "Albuquerque, New Mexico, NM 87131, USA", month = dec, keywords = "genetic algorithms, genetic programming, robot", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/TunstelPhD.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/TunstelPhD.ps.gz", size = "134+17 pages", abstract = "This dissertation addresses the synthesis of knowledge-based controllers for complex autonomous systems that interact with the real world. A fuzzy logic rule-based architecture is developed for intelligent control of dynamic systems possessing a significant degree of autonomy. It represents a novel approach to controller synthesis which incorporates fuzzy control theory into the framework of behavior-based control. The controller intelligence is distributed amongst a number of individual fuzzy logic controllers and systems arranged in a hierarchical structure such that system behaviour at any given level is a function of behaviour at the level(s) below. This structure addresses the combinatorial problem associated with large rule-base cardinality, as the totality of rules in the system are not processed during any control cycle. A method of computationally evolving fuzzy rule-bases is also introduced. It is based on the genetic programming paradigm of evolutionary computation and directly manipulates linguistic terminology of the system. This provides a systematic rule-base design method which is more direct than current approaches that mandate numerical encoding/decoding of rule representations. Finally, a mechanism for multi-rule base coordination is devised by generalisation of fuzzy logic theoretic concepts. It is incorporated to endow the system with the capability to dynamically adapt its control policy in response to goals, internal system state, and perception of the environment. The validity and practical utility of the approach is verified by application to autonomous navigation control of wheeled mobile robots, or rovers. Simulated and experimental navigation results produced by the adaptive hierarchy of distributed fuzzy control are reported. Results show that the proposed ideas can be useful for realisation of autonomous rovers that are meant to be deployed in dynamic and possibly unstructured environments. This class of computer-controlled, wheeled mobile vehicles includes industrial mobile robots, automated guided vehicles, office or hospital robots, and in some cases natural terrain vehicles such as planetary rovers. The proposed intelligent control architecture is generally applicable to autonomous systems whose overall behaviour can be decomposed into a bottom-up hierarchy of increased behavioural complexity, or a decentralised structure of multiple rule-bases.", notes = "OCLC Number: 37306598. ProQuest Dissertations Publishing,  1996. 9709808 Author: Edward W. {Tunstel, Jr.} Adviser Mohammad Jamshidi", } @InProceedings{Tunstel:1999:isairas, author = "E. Tunstel", title = "Evolution of Autonomous Self-Righting Behaviors for Articulated Nanorovers", booktitle = "5th International Symposium on Artificial Intelligence, Robotics and Automation in Space", year = "1999", editor = "G. Hirzinger and M. Montemerlo and K. Tsuchiya", pages = "341--346", address = "ESTEC, Noordwijk, The Netherlands", month = "1-3 " # jun, publisher = "ESA", keywords = "genetic algorithms, genetic programming", URL = "http://trs-new.jpl.nasa.gov/dspace/bitstream/2014/17585/1/99-1007.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.138.4187", URL = "http://articles.adsabs.harvard.edu//full/1999ESASP.440..341T/0000341.000.html", URL = "http://citeseer.ist.psu.edu/378858.html", language = "en", oai = "oai:CiteSeerXPSU:10.1.1.138.4187", abstract = "Miniature rovers with articulated mobility mechanisms are being developed for planetary surface exploration on Mars and small solar system bodies. These vehicles are designed to be capable of autonomous recovery from overturning during surface operations. This paper describes a proposed computational means of developing motion behaviours that achieve the autonomous recovery function. Its aim is to reduce the effort involved in developing self-righting control behaviors. The approach is based on the integration of evolutionary computing with a dynamics simulation environment for evolving and evaluating motion behaviours. The automated behavior design approach is outlined and its underlying genetic programming infrastructure is described.", notes = "WEA-22-4 http://conferences.esa.int/99a02/index.html", } @InCollection{tunstel:2001:fsceabh, author = "E. W. Tunstel", title = "Fuzzy-Behavior Synthesis, Coordination, and Evolution in an Adaptive Behavior Hierarchy", booktitle = "Fuzzy Logic Techniques for Autonomous Vehicle Navigation", publisher = "Physica-Verlag", year = "2001", editor = "Dimiter Driankov and Alessandro Saffiotti", volume = "61", series = "Studies in Fuzziness and Soft Computing Series", chapter = "9", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-7908-1341-8", URL = "http://www.springer.com/engineering/production+engineering/book/978-3-7908-1341-8", abstract = "Autonomous controllers with adaptive behavioral capabilities must accommodate uncertainty. Fuzzy logic is particularly useful for this purpose. A behaviour-based fuzzy control approach to autonomous local navigation is presented here. A hierarchical architecture is described which consists of multiple sets of fuzzy rules. Each rule set represents a decision or motion behaviour that contributes to a dynamic map from stimuli to goal-oriented actions. Synthesis of a behaviour hierarchy which combines primitive motion behaviors to produce navigation behaviours is discussed. This is coupled with a technique for multi-behaviour coordination for which fuzzy rule learning by genetic programming is applied. The overall approach is verified by experimental results in an indoor environment without use of explicit maps. Performance is demonstrated on a mobile robot with significant mechanical imperfections. Descriptions of the experiments, apparatus, and setting are included.", } @Unpublished{turing48:_intel, author = "A. M. Turing", title = "Intelligent machinery", year = "1948", note = "Report for National Physical Laboratory. Reprinted in Ince, D. C. (editor). 1992. Mechanical Intelligence: Collected Works of A. M. Turing. Amsterdam: North Holland. Pages 107127. Also reprinted in Meltzer, B. and Michie, D. (editors). 1969. Machine Intelligence 5. Edinburgh: Edinburgh University Press \cite{TuringAM:maci69}.", keywords = "genetic algorithms, genetic programming", URL = "https://www.npl.co.uk/getattachment/about-us/History/Famous-faces/Alan-Turing/80916595-Intelligent-Machinery.pdf", size = "22 pages", abstract = "Summary The possible ways in which machinery might be made to show intelligent behaviour are discussed. The analogy with the human brain is used as a guiding principle. It is pointed out that the potentialities of the human intelligence can only be realised if suitable education is provided. The investigation mainly centres round an analogous teaching process applied to machines. The idea of an unorganised machine is defined, and it is suggested that the infant human cortex is of this nature. Simple examples of such machines are given, and their education by means of rewards and punishments is discussed. In one case the education process is carried through until the organisation is similar to that of an ACE.", notes = "67/228 p19 Intellectual, Genetical and Cultural searches. https://i.reddit.com/r/MachineLearning/comments/c6gs1q/d_alan_turings_intelligent_machinery_1948/", } @Article{oai:cogprints.soton.ac.uk:499, title = "Computing Machinery and Intelligence", author = "A. M. Turing", journal = "Mind", year = "1950", volume = "49", number = "236", month = oct, pages = "433--460", bibsource = "OAI-PMH server at cogprints.ecs.soton.ac.uk", identifier = "Turing, A. M. (1950) Computing Machinery and Intelligence. Mind 59:pp.~433-460.", oai = "oai:cogprints.soton.ac.uk:499", keywords = "genetic algorithms, genetic programming, Language, Machine Learning, Cognitive Psychology, Philosophy of Mind, Artificial Intelligence, Robotics", ISSN = "0026-4423", URL = "http://www.cs.umbc.edu/471/papers/turing.pdf", URL = "http://cogprints.org/499/", broken = "http://cogprints.ecs.soton.ac.uk/archive/00000499/", broken = "http://cogprints.ecs.soton.ac.uk/archive/00000499/00/turing.htm", eprint = "https://academic.oup.com/mind/article-pdf/LIX/236/433/30123314/lix-236-433.pdf", DOI = "doi:10.1093/mind/LIX.236.433", size = "28 pages", abstract = "I propose to consider the question, 'Can machines think?' This should begin with definitions of the meaning of the terms 'machine' and 'think.' The definitions might be framed so as to reflect so far as possible the normal use of the words, but this attitude is dangerous, If the meaning of the words 'machine' and 'think' are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to the question, 'Can machines think?' is to be sought in a statistical survey such as a Gallup poll. But this is absurd. Instead of attempting such a definition I shall replace the question by another, which is closely related to it and is expressed in relatively unambiguous words. The new form of the problem can be described in terms of a game which we call the 'imitation game.' It is played with three people, a man (A), a woman (B), and an interrogator (C) who may be of either sex. The interrogator stays in a room apart front the other two. The object of the game for the interrogator is to determine which of the other two is the man and which is the woman. He knows them by labels X and Y, and at the end of the game he says either 'X is A and Y is B' or 'X is B and Y is A.' The interrogator is allowed to put questions to A and B. We now ask the question, 'What will happen when a machine takes the part of A in this game?' Will the interrogator decide wrongly as often when the game is played like this as he does when the game is played between a man and a woman? These questions replace our original, 'Can machines think?'", notes = "cogprints.ecs.soton.ac.uk/archive/00000499 contains OCR errors and one table has been deleted. Human cloning. 'Random element'. Suggests digital computers are not chaotic. Prediction for performance in 2000. 'Obvious connection between this process and evolution', 'hereditary material', 'mutation', 'Natural selection' (interactive evolution, cf \cite{unemi:1998:AFSS} amongst many). Experiments not 'considered successful'. Mechanical 'scientific induction'. Chess, speaking English. p460 'We can only see a short distance ahead, but we can see plenty there that needs to be done.'", } @InCollection{TuringAM:maci69, author = "Alan M. Turing", title = "Intelligent Machinery", booktitle = "Machine Intelligence", year = "1969", editor = "Bernard Meltzer and Donald Michie", publisher = "Edinburgh University Press", volume = "5", annote = "Hodges page 377 note 6.53.", chapter = "1", pages = "3--23", address = "Edinburgh, UK", keywords = "genetic algorithms, genetic programming, robot, Manchester Machine, ENIAC, ACE,", URL = "https://hashingit.com/elements/research-resources/1948-intelligent-machinery.pdf", size = "21 pages", abstract = "The possible ways in which machinery might be made to show intelligent behaviour are discussed...", notes = "paper machines for playing chess. Boltzmann temperature and condenser voltage as limit to computational accuracy and hence run time. [computing] machine changing its own instructions [autonomous] machine [simulating man] allowed to roam the countryside... instead we propose ... a brain ... without ... a body. chess, chequers, bridge, poker, learning [huma] lnguages, translation [human] languages (eg english/french), crptography, math. mimicking education [of the computer]. [Teacher/Education of computer by] pleasure-pain systems Intellectual, genetical and cultural searches finding a program [continue search] until the machine proved a theorem there is the genetical or evolutionary search by which a combination of genes is looked for, the criterion being survival value... intelellectual activitity consists mainly of various kinds of search. Published after the author's death. First version \cite{turing48:_intel}", size = "20 pages", } @InProceedings{Turk:2008:SHMD, author = "Radomir Turk and Goran Kugler and Milan Tercelj and Iztok Perus and Miha Kovacic", title = "Genetic Programming and {CAE} Neural Networks approach for Prediction of the Bending Capability of {ZnTiCu} Sheets", booktitle = "SHMD 2008, SUMMARIES OF LECTURES", year = "2008", keywords = "genetic algorithms, genetic programming", broken_abstract_url = "http://public.carnet.hr/metalurg/Metalurgija/2008_vol_47/No_3/MET_47_3.pdf", abstract = "59. R. Turk, G. Kugler, M. Ter~elj, I. Peru{*, M. Kova~i}**, Faculty of Natural Sciences, Ljubljana, Slovenia, *Faculty of Civil and Geodetic Engineering, Ljubljana, Slovenia, **Store steel, Ltd, Store, Slovenia Genetic programming and cae neural networks approach for prediction of the bending capability of ZnTiCu sheets. Genetic programming (GP) and CAE NN analysis have been applied for the prediction of bending capability of rolled ZnTiCu alloy sheet. Investigation revealed that an analysis with CAE NN is faster than GP but less accurate for lower amount of data. Both methods enable good assessment of separate influencing parameters in the complex system.", notes = "Abstract of meeting given in Metallurgy, Vol. 47 No. 3, July 2008, page 272 ISSN 0543-5846, http://hrcak.srce.hr/index.php?show=toc&id_broj=2061 See also \cite{Turk:2008:M}", } @Article{Turk:2008:M, author = "Radomir Turk and Iztok Perus and Miha Kovacic and Goran Kugler and Milan Tercelj", title = "Genetic Programming and CAE Neural Networks approach for Prediction of the Bending Capability of {ZnTiCu} Sheets", journal = "Metallurgy", journal2 = "Metalurgija (Sisak)", year = "2008", volume = "47", number = "4", pages = "301--305", keywords = "genetic algorithms, genetic programming, rolling, ZnTiCu alloy, bending, CAE neural networks, Klju~ne rije~i: valjanje, ZnTiCu legure, savijanje, genetsko programiranje, CAE neuralne mre`e", ISSN = "0543-5846", URL = "http://hrcak.srce.hr/file/41131", URL = "http://public.carnet.hr/metalurg/Metalurgija/2008_vol_47/No_4/MET_47_4_301_305_Turk.pdf", size = "5 pages", abstract = "Genetic programming (GP) and CAE NN analysis have been applied for the prediction of bending capability of rolled ZnTiCu alloy sheet. Investigation revealed that an analysis with CAE NN is faster than GP but less accurate for lower amount of data. Both methods enable good assessment of separate influencing parameters in the complex system.", abstract = "Primjena genetskog programiranja u CAE neuronskih mre`a za prognozu izdr`ljivosti kod savijanja ZnTiCu traka. Metode genetskog programiranja (GP) i CAE NN bile su upotrebljene za studij utjecaja izdr`ljivosti savijanja tankih plo~a slitine ZnTiCu. Istra`ivanje je pokazalo da je CAE NN metoda br`a od GP metode a istovremeno je manje precizna pri manjoj bazi podataka. Obje primenjene metode dobro vrednuju utjecaj parcijalnih komponenata kompleksnog sistema.", notes = "Faculty of Natural Sciences, Ljubljana, Slovenia. In english. Portal of scientific journals of Croatia http://hrcak.srce.hr/index.php", } @Article{Turky:GPEM:Cooperative, author = "Ayad Turky and Nasser R. Sabar and Andy Song", title = "Cooperative evolutionary heterogeneous simulated annealing algorithm for google machine reassignment problem", journal = "Genetic Programming and Evolvable Machines", year = "2018", volume = "19", number = "1-2", pages = "183--210", month = jun, note = "Special Issue on Automated Design and Adaptation of Heuristics for Scheduling and Combinatorial Optimisation", keywords = "genetic algorithms", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-017-9305-0", size = "28 pages", abstract = "This paper investigates the Google machine reassignment problem (GMRP). GMRP is a real world optimisation problem which is to maximise the usage of cloud machines. Since GMRP is computationally challenging problem and exact methods are only advisable for small instances, meta-heuristic algorithms have been used to address medium and large instances. This paper proposes a cooperative evolutionary heterogeneous simulated annealing (CHSA) algorithm for GMRP. The proposed algorithm consists of several components devised to generate high quality solutions. Firstly, a population of solutions is used to effectively explore the solution space. Secondly, CHSA uses a pool of heterogeneous simulated annealing algorithms in which each one starts from a different initial solution and has its own configuration. Thirdly, a cooperative mechanism is designed to allow parallel searches to share their best solutions. Finally, a restart strategy based on mutation operators is proposed to improve the search performance and diversification. The evaluation on 30 diverse real-world instances shows that the proposed CHSA performs better compared to cooperative homogeneous SA and heterogeneous SA with no cooperation. In addition, CHSA outperformed the current state-of-the-art algorithms, providing new best solutions for eleven instances. The analysis on algorithm behaviour clearly shows the benefits of the cooperative heterogeneous approach on search performance.", notes = "Not GP?", } @PhdThesis{TurkyAyad2019Bhaf, author = "Ayad Mashaan Turky", title = "Bi-level hyper-heuristic approaches for combinatorial optimisation problems", school = "College of Science, Engineering, and Health, RMIT University", year = "2019", address = "Melbourne, Victoria, Australia", month = jul, keywords = "genetic algorithms, genetic programming, hyper-heuristic, combinatorial optimisation problems, multi-capacity bin packing problem, google machine reassignment problem, bi-level, bin packing problem", language = "eng", URL = "https://research-repository.rmit.edu.au/articles/thesis/Bi-level_hyper-heuristic_approaches_for_combinatorial_optimisation_problems/27580371.pdf", URL = "https://research-repository.rmit.edu.au/articles/thesis/Bi-level_hyper-heuristic_approaches_for_combinatorial_optimisation_problems/27580371?file=50749800", URL = "https://academics.rmit.edu.au/andy-song/teaching#29-Feb-2016", size = "159 pages", abstract = "Many real-world combinatorial optimisation problems (COPs) are too complex to be handled in polynomial time using exact methods. One way to solve such problems is using approximation or heuristic algorithms which produce good quality solutions within a reasonable amount of time. Local searches are a class of approximation algorithms for dealing with COPs and have been shown to be very effective in solving large-scale COPs. Several local search algorithms have been proposed in literature where different ones use different rules or mechanisms to approach the search space of a given COP. However, due to the complexity and variability of characteristics in different COPs, it is very different to decide which local search algorithm should be used. Indeed, it is very different to design a single local search algorithm that can perform well across a diverse set of COP instances. Furthermore, even for a given local search algorithm, its performance critically hinges on the setting of its internal components, such as the operators/parameters that should be included or adjusted, and this may vary from one instance to another. To deal with these issues, this thesis proposes bi-level hyper-heuristic approaches that use various local and diverse sets of operators for solving COPs. The proposed frameworks control the selection of the local search algorithm and operators that should be used at each decision point. The appropriate mix of the local search algorithm with the operators are determined adaptively during the search process. In this thesis, the search spaces of the local search algorithm and operators are formulated as bi-level heuristic search spaces that interact with each other during the selection process. This thesis introduces two new hyper-heuristic approaches. One approach comprises a two-stage hyper-heuristic local search to adaptively select local search algorithm and its components. The local search algorithms are selected in the first stage and the operators for the selected local search are chosen in the second stage. The two stages interact with each other by exchanging information in order to arrive at a better decision. The second approach is based on two interleaved ant colonies that integrates the strengths of several local search algorithms and their components. This new framework is an improvement on its predecessor in several respects, but the key contributions are related to the designing of dual ant colonies and the use of multiple evaluation criteria. We formulate the search spaces of local search algorithms and their components as a graph to be searched by the proposed framework. The dual ant colonies work an interleaved manner as an adaptive selection mechanism where the first one controls the selection of which local search algorithm should be applied, while the second one chooses the local search algorithm components for the selected local search algorithm. These design colonies exploit and exchange information in a co-operative manner to effectively guide the search and the selection process. To test the generality, consistency and performance of the proposed frameworks, two COPs are considered: a multi-capacity bin packing problem (MCBPP) and a google machine reassignment problem (GMRP). Results demonstrate that the proposed frameworks obtain competitive results (if not best results for some instances), on all problem domains when compared to the best-known methods in the literature.", notes = "RMIT University = Royal Melbourne Institute of Technology supervisors: Andy Song and Nasser R. Sabar and Simon Dunstall", } @Article{turner2013incorporation, author = "Alexander P. Turner and Michael A. Lones and Luis A. Fuente and Susan Stepney and Leo S. D. Caves and Andy M. Tyrrell", title = "The incorporation of epigenetics in artificial gene regulatory networks", journal = "Biosystems", volume = "112", number = "2", pages = "56--62", year = "2013", month = may, note = "Selected papers from the 9th International Conference on Information Processing in Cells and Tissues", keywords = "genetic algorithms, genetic programming, Artificial gene regulation, Epigenetics, Dynamical systems, Chaos control, Evolutionary algorithms", publisher = "Elsevier", ISSN = "0303-2647", URL = "http://www.sciencedirect.com/science/article/pii/S0303264713000579", DOI = "doi:10.1016/j.biosystems.2013.03.013", abstract = "Artificial gene regulatory networks are computational models that draw inspiration from biological networks of gene regulation. Since their inception they have been used to infer knowledge about gene regulation and as methods of computation. These computational models have been shown to possess properties typically found in the biological world, such as robustness and self organisation. Recently, it has become apparent that epigenetic mechanisms play an important role in gene regulation. This paper describes a new model, the Artificial Epigenetic Regulatory Network (AERN) which builds upon existing models by adding an epigenetic control layer. Our results demonstrate that AERNs are more adept at controlling multiple opposing trajectories when applied to a chaos control task within a conservative dynamical system, suggesting that AERNs are an interesting area for further investigation.", notes = "PMID: 23499812", } @InProceedings{turner2015evolving, author = "Alexander P. Turner and Martin A. Trefzer and Michael A. Lones and Andy M. Tyrrell", title = "Evolving Efficient Solutions to Complex Problems Using the Artificial Epigenetic Network", booktitle = "10th International Conference on Information Processing in Cells and Tissues, IPCAT 2015", year = "2015", editor = "Michael Lones and Andy Tyrrell and Stephen Smith and Gary Fogel", volume = "9303", series = "LNCS", address = "San Diego, CA, USA", month = sep # " 14-16", publisher = "Springer International Publishing", pages = "153--165", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1007/978-3-319-23108-2_13", abstract = "The artificial epigenetic network (AEN) is a computational model which is able to topologically modify its structure according to environmental stimulus. This approach is inspired by the functionality of epigenetics in nature, specifically, processes such as chromatin modifications which are able to dynamically modify the topology of gene regulatory networks. The AEN has previously been shown to perform well when applied to tasks which require a range of dynamical behaviours to be solved optimally. In addition, it has been shown that pruning of the AEN to remove non-functional elements can result in highly compact solutions to complex dynamical tasks. In this work, a method has been developed which provides the AEN with the ability to self prune throughout the optimisation process, whilst maintaining functionality. To test this hypothesis, the AEN is applied to a range of dynamical tasks and the most optimal solutions are analysed in terms of function and structure.", notes = "Affiliated with Department of Electronics, University of York", } @InProceedings{Turner:2013:GECCO, author = "Andrew James Turner and Julian Francis Miller", title = "Cartesian genetic programming encoded artificial neural networks: a comparison using three benchmarks", booktitle = "GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference", year = "2013", editor = "Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi", isbn13 = "978-1-4503-1963-8", pages = "1005--1012", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2463372.2463484", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Neuroevolution, the application of evolutionary algorithms to artificial neural networks (ANNs), is well-established in machine learning. Cartesian Genetic Programming (CGP) is a graph-based form of Genetic Programming which can easily represent ANNs. Cartesian Genetic Programming encoded ANNs (CGPANNs) can evolve every aspect of an ANN: weights, topology, arity and node transfer functions. This makes CGPANNs very suited to situations where appropriate configurations are not known in advance. The effectiveness of CGPANNs is compared with a large number of previous methods on three benchmark problems. The results show that CGPANNs perform as well as or better than many other approaches. We also discuss the strength and weaknesses of each of the three benchmarks.", notes = "Also known as \cite{2463484} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)", } @InProceedings{Turner2013a, author = "Andrew James Turner and Julian Francis Miller", title = "The Importance of Topology Evolution in NeuroEvolution: A Case Study Using Cartesian Genetic Programming of Artificial Neural Networks", booktitle = "Research and Development in Intelligent Systems XXX", year = "2013", editor = "Max Bramer and Miltos Petridis", pages = "213--226", address = "Cambridge", month = "10-12 " # dec, organisation = "British Computer Society's Specialist Group on Artificial Intelligence", publisher = "Springer International Publishing", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-02620-6", URL = "http://dx.doi.org/10.1007/978-3-319-02621-3_15", DOI = "doi:10.1007/978-3-319-02621-3_15", abstract = "NeuroEvolution (NE) is the application of evolutionary algorithms to Artificial Neural Networks (ANN). This paper reports on an investigation into the relative importance of weight evolution and topology evolution when training ANN using NE. This investigation used the NE technique Cartesian Genetic Programming of Artificial Neural Networks (CGPANN). The results presented show that the choice of topology has a dramatic impact on the effectiveness of NE when only evolving weights; an issue not faced when manipulating both weights and topology. This paper also presents the surprising result that topology evolution alone is far more effective when training ANN than weight evolution alone. This is a significant result as many methods which train ANN manipulate only weights.", notes = "http://www.bcs-sgai.org/ai2013/ Incorporating Applications and Innovations in Intelligent Systems XXI Proceedings of AI-2013, The Thirty-third SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence", } @InProceedings{turner:2014:EuroGP, author = "Andrew Turner and Julian Miller", title = "Cartesian Genetic Programming: Why No Bloat?", booktitle = "17th European Conference on Genetic Programming", year = "2014", editor = "Miguel Nicolau and Krzysztof Krawiec and Malcolm I. Heywood and Mauro Castelli and Pablo Garcia-Sanchez and Juan J. Merelo and Victor M. {Rivas Santos} and Kevin Sim", series = "LNCS", volume = "8599", publisher = "Springer", pages = "222--233", address = "Granada, Spain", month = "23-25 " # apr, organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming: poster", isbn13 = "978-3-662-44302-6", URL = "http://www.andrewjamesturner.co.uk/files/EuroGP2014.pdf", DOI = "doi:10.1007/978-3-662-44303-3_19", size = "12 pages", abstract = "For many years now it has been known that Cartesian Genetic Programming (CGP) does not exhibit program bloat. Two possible explanations have been proposed in the literature: neutral genetic drift and length bias. We empirically disprove both of these and thus, reopens the question as to why CGP does not suffer from bloat. It has also been shown for CGP that using a very large number of nodes considerably increases the effectiveness of the search. we propose a new explanation as to why this may be the case.", notes = "Poster: http://andrewjamesturner.co.uk/files/EuroGPposter.pdf Part of \cite{Nicolau:2014:GP} EuroGP'2014 held in conjunction with EvoCOP2014, EvoBIO2014, EvoMusArt2014 and EvoApplications2014", } @InProceedings{Turner:2014:PPSN, author = "Andrew Turner and Julian Miller", title = "Recurrent Cartesian Genetic Programming", booktitle = "13th International Conference on Parallel Problem Solving from Nature", year = "2014", editor = "Thomas Bartz-Beielstein and Juergen Branke and Bogdan Filipic and Jim Smith", publisher = "Springer", isbn13 = "978-3-319-10761-5", pages = "476--486", series = "Lecture Notes in Computer Science", address = "Ljubljana, Slovenia", month = "13-17 " # sep, volume = "8672", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1007/978-3-319-10762-2_47", abstract = "This paper formally introduces Recurrent Cartesian Genetic Programming (RCGP), an extension to Cartesian Genetic Programming (CGP) which allows recurrent connections. The presence of recurrent connections enables RCGP to be successfully applied to partially observable tasks. It is found that RCGP significantly outperforms CGP on two partially observable tasks: artificial ant and sunspot prediction. The paper also introduces a new parameter, recurrent connection probability, which biases the number of recurrent connections created via mutation. Suitable choices of this parameter significantly improve the effectiveness of RCGP.", notes = "PPSN-XIII", } @Article{Turner2014f, author = "Andrew James Turner and Julian Francis Miller", title = "NeuroEvolution: Evolving Heterogeneous Artificial Neural Networks", journal = "Evolutionary Intelligence", year = "2014", volume = "7", number = "3", pages = "135--154", month = nov, note = "Special Issue: Evolution in UK 20", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, ANN, Heterogeneous Artificial Neural Networks, NeuroEvolution, Evolutionary Algorithms, Artificial Neural Networks, Computational intelligence", publisher = "Springer", ISSN = "1864-5909", URL = "http://dx.doi.org/10.1007/s12065-014-0115-5", DOI = "doi:10.1007/s12065-014-0115-5", size = "20 pages", abstract = "NeuroEvolution is the application of Evolutionary Algorithms to the training of Artificial Neural Networks. Currently the vast majority of NeuroEvolutionary methods create homogeneous networks of user defined transfer functions. This is despite NeuroEvolution being capable of creating heterogeneous networks where each neuron's transfer function is not chosen by the user, but selected or optimised during evolution. This paper demonstrates how NeuroEvolution can be used to select or optimise each neuron's transfer function and empirically shows that doing so significantly aids training. This result is important as the majority of NeuroEvolutionary methods are capable of creating heterogeneous networks using the methods described.", } @Article{Turner:2015:GPEM, author = "Andrew James Turner and Julian Francis Miller", title = "Introducing a cross platform open source Cartesian Genetic Programming library", journal = "Genetic Programming and Evolvable Machines", year = "2015", volume = "16", number = "1", pages = "83--91", month = mar, keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Software library, NeuroEvolution", ISSN = "1389-2576", URL = "http://dx.doi.org/10.1007/s10710-014-9233-1", DOI = "doi:10.1007/s10710-014-9233-1", size = "9 pages", abstract = "Cartesian Genetic Programming (CGP) is a form of Genetic Programming which encodes computational structures as generic cyclic/acyclic graphs. This letter introduces a new cross platform CGP library intended for use in teaching, academic research and real world applications. This new CGP library is currently capable of evolving symbolic expressions, Boolean logic circuits and Artificial Neural Networks but can easily be extended to other domains. The CGP library, documentation and tutorials are all available at www.cgplibrary.co.uk.", notes = "CGP-Library", } @InProceedings{Turner:2015:GECCOcomp, author = "Andrew James Turner and Julian Francis Miller", title = "Recurrent Cartesian Genetic Programming Applied to Series Forecasting", booktitle = "GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation", year = "2015", editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr", isbn13 = "978-1-4503-3488-4", keywords = "genetic algorithms, genetic programming, cartesian genetic programming: Poster", pages = "1499--1500", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739482.2764647", DOI = "doi:10.1145/2739482.2764647", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Recurrent Cartesian Genetic Programming is a recently proposed extension to Cartesian Genetic Programming which allows cyclic program structures to be evolved. We apply both standard and Recurrent Cartesian Genetic Programming to the domain of series forecasting. Their performance is then compared to a number of well-known classical forecasting approaches. Our results show that not only does Recurrent Cartesian Genetic Programming outperform standard Cartesian Genetic Programming, but it also outperforms many standard forecasting techniques.", notes = "Also known as \cite{2764647} Distributed at GECCO-2015.", } @PhdThesis{Turner:thesis, author = "Andrew James Turner", title = "Evolving Artificial Neural Networks using Cartesian Genetic Programming", school = "Electronics, University of York", year = "2015", address = "UK", month = sep, keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, ANN, Fibonacci sequence", URL = "http://etheses.whiterose.ac.uk/12035/1/thesis.pdf", URL = "http://etheses.whiterose.ac.uk/12035/", size = "336 pages", abstract = "NeuroEvolution is the application of Evolutionary Algorithms to the training of Artificial Neural Networks. NeuroEvolution is thought to possess many benefits over traditional training methods including: the ability to train recurrent network structures, the capability to adapt network topology, being able to create heterogeneous networks of arbitrary transfer functions, and allowing application to reinforcement as well as supervised learning tasks. This thesis presents a series of rigorous empirical investigations into many of these perceived advantages of NeuroEvolution. In this work it is demonstrated that the ability to simultaneously adapt network topology along with connection weights represents a significant advantage of many NeuroEvolutionary methods. It is also demonstrated that the ability to create heterogeneous networks comprising a range of transfer functions represents a further significant advantage. This thesis also investigates many potential benefits and drawbacks of NeuroEvolution which have been largely overlooked in the literature. This includes the presence and role of genetic redundancy in NeuroEvolution's search and whether program bloat is a limitation. The investigations presented focus on the use of a recently developed NeuroEvolution method based on Cartesian Genetic Programming. This thesis extends Cartesian Genetic Programming such that it can represent recurrent program structures allowing for the creation of recurrent Artificial Neural Networks. Using this newly developed extension, Recurrent Cartesian Genetic Programming, and its application to Artificial Neural Networks, are demonstrated to be extremely competitive in the domain of series forecasting", } @Article{Turner:2015:GPEMa, author = "Andrew James Turner and Julian Francis Miller", title = "Neutral genetic drift: an investigation using Cartesian Genetic Programming", journal = "Genetic Programming and Evolvable Machines", year = "2015", volume = "16", number = "4", pages = "531--558", month = dec, keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Neutral genetic drift, Genetic redundancy", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-015-9244-6", size = "28 pages", abstract = "Neutral genetic drift is an evolutionary mechanism which can strongly aid the escape from local optima. This makes neutral genetic drift an increasingly important property of Evolutionary Computational methods as more challenging applications are approached. Cartesian Genetic Programming (CGP) is a Genetic Programming technique which contains explicit, as well as the more common implicit, genetic redundancy. As explicit genetic redundancy is easily identified and manipulated it represents a useful tool for investigating neutral genetic drift. The contributions of this paper are as follows. Firstly the paper presents a substantial evaluation of the role and benefits of neutral genetic drift in CGP. Here it is shown that the benefits of explicit genetic redundancy are additive to the benefits of implicit genetic redundancy. This is significant as it indicates that that levels of implicit genetic redundancy present in other Evolutionary Computational methods may be insufficient to fully use neutral genetic drift. It is also shown than the identification and manipulation of explicit genetic redundancy is far easier than for implicit genetic redundancy. This is significant as it makes the investigations here possible and leads to new possibilities for allowing more effective use of neutral genetic drift. This is the case not only for CGP, but many other Evolutionary Computational methods which contain explicit genetic redundancy. Finally, it is also shown that neutral genetic drift has additional benefits other than aiding the escape from local optima", notes = "Electronics Department, Intelligent Systems Group, The University of York, York, UK", } @Article{Turner:2016:GPEM, author = "Andrew James Turner and Julian Francis Miller", title = "Recurrent Cartesian Genetic Programming of Artificial Neural Networks", journal = "Genetic Programming and Evolvable Machines", year = "2017", volume = "18", number = "2", pages = "185--212", month = jun, keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, ANN, NeuroEvolution, Forecasting", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-016-9276-6", size = "28 pages", abstract = "Cartesian Genetic Programming of Artificial Neural Networks is a NeuroEvolutionary method based on Cartesian Genetic Programming. Cartesian Genetic Programming has recently been extended to allow recurrent connections. This work investigates applying the same recurrent extension to Cartesian Genetic Programming of Artificial Neural Networks in order to allow the evolution of recurrent neural networks. The new Recurrent Cartesian Genetic Programming of Artificial Neural Networks method is applied to the domain of series forecasting where it is shown to significantly outperform all standard forecasting techniques used for comparison including autoregressive integrated moving average and multilayer perceptrons. An ablation study is also performed isolating which specific aspects of Recurrent Cartesian Genetic Programming of Artificial Neural Networks contribute to it's effectiveness for series forecasting.", } @InProceedings{Turner:2008:gecco, author = "Chris J. Turner and Ashutosh Tiwari and Jorn Mehnen", title = "A genetic programming approach to business process mining", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "1307--1314", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1307.pdf", DOI = "doi:10.1145/1389095.1389345", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, business process mining, graph based representation", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389345}", } @PhdThesis{Christopher_Turner_thesis_2009, title = "A genetic programming based business process mining approach", author = "Christopher James Turner", year = "2009", school = "School of applied Sciences, Cranfield University", address = "UK", month = may, keywords = "genetic algorithms, genetic programming", URL = "https://dspace.lib.cranfield.ac.uk/bitstream/1826/4471/1/Christopher_Turner_thesis_2009.pdf", URL = "http://hdl.handle.net/1826/4471", URL = "http://ethos.bl.uk/OrderDetails.do?did=27&uin=uk.bl.ethos.515106", size = "303 pages", bibsource = "OAI-PMH server at dspace.lib.cranfield.ac.uk", contributor = "Ashutosh (supervisor) Tiwari", language = "en", oai = "oai:dspace.lib.cranfield.ac.uk:1826/4471", abstract = "As business processes become ever more complex there is a need for companies to understand the processes they already have in place. To undertake this manually would be time consuming. The practice of process mining attempts to automatically construct the correct representation of a process based on a set of process execution logs. The aim of this research is to develop a genetic programming based approach for business process mining. The focus of this research is on automated/semi automated business processes within the service industry (by semi automated it is meant that part of the process is manual and likely to be paper based). This is the first time a GP approach has been used in the practice of process mining. The graph based representation and fitness parsing used are also unique to the GP approach. A literature review and an industry survey have been undertaken as part of this research to establish the state-of-the-art in the research and practice of business process modelling and mining. It is observed that process execution logs exist in most service sector companies are not used for process mining. The development of a new GP approach is documented along with a set of modifications required to enable accuracy in the mining of complex process constructs, semantics and noisy process execution logs. In the context of process mining accuracy refers to the ability of the mined model to reflect the contents of the event log on which it is based; neither over describing, including features that are not recorded in the log, or under describing, just including the most common features leaving out low frequency task edges, the contents of the event log. The complexity of processes, in terms of this thesis, involves the mining of parallel constructs, processes containing complex semantic constructs (And/XOR split and join points) and processes containing 20 or more tasks. The level of noise mined by the business process mining approach includes event logs which have a small number of randomly selected tasks missing from a third of their structure. A novel graph representation for use with GP in the mining of business processes is presented along with a new way of parsing graph based individuals against process execution logs. The GP process mining approach has been validated with a range of tests drawn from literature and two case studies, provided by the industrial sponsor, using live process data. These tests and case studies provide a range of process constructs to fully test and stretch the GP process mining approach. An outlook is given into the future development of the GP process mining approach and process mining as a practice.", notes = "uk.bl.ethos.515106 Supervisor: Ashutosh Tiwari", } @Article{Turner:2024:CACM, author = "Jack Turner and Elliot J. Crowley and Michael F. P. O'Boyle", title = "Neural Architecture Search as Program Transformation Exploration", journal = "Communications of the ACM", year = "2024", volume = "67", number = "10", pages = "92--100", month = oct, keywords = "genetic algorithms, genetic programming, genetic improvement, Artificial Intelligence and Machine Learning, deep neural networks (DNNs), ANN, NAS, LLM, TVM, CIFAR-10, Fisher Potential, parallel computing, GPU, BlockSwap", publisher = "ACM", address = "New York, NY, USA", ISSN = "0001-0782", DOI = "doi:10.1145/3624775", size = "9 pages", abstract = "Improving the performance of deep neural networks (DNNs) is important to both the compiler and neural architecture search (NAS) communities. Compilers apply program transformations in order to exploit hardware parallelism and memory hierarchy. However, legality concerns mean they fail to exploit the natural robustness of neural networks. In contrast, NAS techniques mutate networks by operations such as the grouping or bottlenecking of convolutions, exploiting the resilience of DNNs. In this work, we express such neural architecture operations as program transformations whose legality depends on a notion of representational capacity. This allows them to be combined with existing transformations into a unified optimization framework. This unification allows us to express existing NAS operations as combinations of simpler transformations. Crucially, it allows us to generate and explore new tensor convolutions. We prototyped the combined framework in TVM and were able to significantly reduce inference time and NAS search time. representational capacity, new tensor convolutions, reduce inference time three fold", notes = "Not GP but apply automatic search to get better program. Loop interchange, interchange, bottleneck 'bottleneck(B). This is shown in row 4 of Figure 1. From a NAS point of view, this is a standard operation reducing complexity with a minimal effect on representational capacity. From a program transformation point of view, this is illegal as the computed values are changed.' search 'cell design space' polyhedral Program Transformations (Strip-Mining. Tiling. interchange) Legality 'lexicographical ordering of the iteration instances is preserved' eh? must be? Is guaranteed? How? By whome? Does it matter in ANN where data flows are layer to layer? 'Fisher Potential as a Legality Check' (Turner, J. et al. BlockSwap: Fisher-guided block substitution for network compression on a budget. ICLR 2020) https://www.pure.ed.ac.uk/ws/portalfiles/portal/132577775/Blockswap_Fisher_Guided_TURNER_DOA01012020_AFV.pdf 'Fisher Potential of a proposed network architecture is the sum of the Fisher Information for each layer ...' 'Many good networks are also discarded' 'the space is large and densely populated' ARM A57, Nvidia 128-Core Maxwell Jetson Nano, Intel Core i7, Nvidia 1080Ti. ResNet-34, ResNext-29-2x64d, DenseNet-161 Only 1000 points tested (in five minutes)? How is search guided? FBNet ImageNet Pareto optimal 'The original version of this paper was published in ASPLOS 2021'", } @InProceedings{turner:2009:evobio, author = "Stephen D. Turner and Marylyn D. Ritchie and William S. Bush", title = "Conquering the Needle-in-a-Haystack: How Correlated Input Variables Beneficially Alter the Fitness Landscape for Neural Networks", booktitle = "EvoBIO 2009, Proceedings of the 7th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics", year = "2009", editor = "Clara Pizzuti and Marylyn Ritchie", volume = "5483", series = "Lecture Notes in Computer Science", pages = "80--91", address = "Tuebingen, Germany", publisher_address = "Berlin Heidelberg New York", month = apr # " 15-17", publisher = "Springer", keywords = "genetic algorithms, genetic programming, NiH", isbn13 = "978-3-642-01183-2", DOI = "doi:10.1007/978-3-642-01184-9_8", size = "12 pages", abstract = "Evolutionary algorithms such as genetic programming and grammatical evolution have been used for simultaneously optimizing network architecture, variable selection, and weights for artificial neural networks. Using an evolutionary algorithm to perform variable selection while searching for non-linear interactions is akin to searching for a needle in a haystack. There is, however, a considerable amount of correlation among variables in biological datasets, such as in microarray or genetic studies. Using the XOR problem, we show that correlation between non-functional and functional variables alters the variable selection fitness landscape by broadening the fitness peak over a wider range of potential input variables. Furthermore, when suboptimal weights are used, local optima in the variable selection fitness landscape appear centered on each of the two functional variables. These attributes of the fitness landscape may supply building blocks for evolutionary search procedures, and may provide a rationale for conducting a local search for variable selection.", notes = "EvoBIO2009", } @InProceedings{conf/evoW/TurnerDR10, title = "Grammatical Evolution of Neural Networks for Discovering Epistasis among Quantitative Trait Loci", author = "Stephen D. Turner and Scott M. Dudek and Marylyn D. Ritchie", booktitle = "8th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EvoBIO 2010)", publisher = "Springer", year = "2010", editor = "Clara Pizzuti and Marylyn D. Ritchie and Mario Giacobini", volume = "6023", pages = "86--97", series = "Lecture Notes in Computer Science", address = "Istanbul, Turkey", month = apr # " 7-9", keywords = "genetic algorithms, genetic programming, grammatical evolution", isbn13 = "978-3-642-12210-1", DOI = "doi:10.1007/978-3-642-12211-8", abstract = "A fundamental goal of human genetics is the discovery of polymorphisms that predict common, complex diseases. It is hypothesized that complex diseases are due to a myriad of factors including environmental exposures and complex genetic risk models, including gene-gene interactions. Such interactive models present an important analytical challenge, requiring that methods perform both variable selection and statistical modeling to generate testable genetic model hypotheses. Decision trees are a highly successful, easily interpretable data-mining method that are typically optimized with a hierarchical model building approach, which limits their potential to identify interactive effects. To overcome this limitation, we use evolutionary computation, specifically grammatical evolution, to build decision trees to detect and model gene-gene interactions. Currently, we introduce the Grammatical Evolution Decision Trees (GEDT) method, and demonstrate that GEDT has power to detect interactive models in a range of simulated data, revealing GEDT to be a promising new approach for human genetics.", bibdate = "2010-04-13", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/evoW/evobio2010.html#TurnerDR10", affiliation = "North Carolina State University Department of Computer Science Raleigh NC USA 27695", } @InProceedings{Turner-Baggs:evoapps13, author = "Jazz Alyxzander Turner-Baggs and Malcolm I. Heywood", title = "On GPU Based Fitness Evaluation with Decoupled Training Partition Cardinality", booktitle = "Applications of Evolutionary Computing, EvoApplications 2013: EvoCOMNET, EvoCOMPLEX, EvoENERGY, EvoFIN, EvoGAMES, EvoIASP, EvoINDUSTRY, EvoNUM, EvoPAR, EvoRISK, EvoROBOT, EvoSTOC", year = "2013", month = "3-5 " # apr, editor = "Anna I. Esparcia-Alcazar and Antonio Della Cioppa and Ivanoe {De Falco} and Ernesto Tarantino and Carlos Cotta and Robert Schaefer and Konrad Diwold and Kyrre Glette and Andrea Tettamanzi and Alexandros Agapitos and Paolo Burrelli and J. J. Merelo and Stefano Cagnoni and Mengjie Zhang and Neil Urquhart and Kevin Sim and Aniko Ekart and Francisco {Fernandez de Vega} and Sara Silva and Evert Haasdijk and Gusz Eiben and Anabela Simoes and Philipp Rohlfshagen", series = "LNCS", volume = "7835", publisher = "Springer Verlag", address = "Vienna", publisher_address = "Berlin", pages = "489--498", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, GPGPU, SBB", isbn13 = "978-3-642-37191-2", DOI = "doi:10.1007/978-3-642-37192-9_49", size = "10 pages", abstract = "GPU acceleration of increasingly complex variants of evolutionary frameworks typically assume that all the training data used during evolution resides on the GPU. Such an assumption places limits on the style of application to which evolutionary computation can be applied. Conversely, several coevolutionary frameworks explicitly decouple fitness evaluation from the size of the training partition. Thus, a subset of training exemplars is coevolved with the population of evolved individuals. In this work we articulate the design decisions necessary to support Pareto archiving for Genetic Programming under a commodity GPU platform. Benchmarking of corresponding CPU and GPU implementations demonstrates that the GPU platform is still capable of providing a times ten reduction in computation time.", notes = "nVidia GTX 660Ti. KDD 1999, Shuttle. Does not give speed in terms of GP operations per second GPops \cite{langdon:2008:eurogp}. EvoApplications2013 held in conjunction with EuroGP2013, EvoCOP2013, EvoBio'2013 and EvoMusArt2013", } @InProceedings{turton:1996:geog, author = "I. Turton and S. Openshaw and G. Diplock", title = "Some Geographic Applications of Genetic Programming on the {Cray} T3D Supercomputer", booktitle = "UK Parallel'96", year = "1996", editor = "Chris R. Jesshope and Alex V. Shafarenko", pages = "135--150", address = "University of Surrey", month = "3-5 " # jul, organisation = "BCS Parallel Processing Specialist Group", publisher = "Springer", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-76068-7", isbn13 = "978-3-540-76068-9", URL = "http://www.geog.leeds.ac.uk/papers/96-5/96-5.pdf", URL = "http://citeseer.ist.psu.edu/turton96some.html", DOI = "doi:10.1007/978-1-4471-1504-5_10", size = "16 pages", abstract = "The paper describes some geographical applications of a parallel GP code which is run on a Cray T3D 512 processor supercomputer to create new types of well performing mathematical models. A series of results are described which allude to the potential power of the method for which there are many practical applications in spatial data rich environments where there are no suitable existing models and no soundly based theoretical framework on which to base them", notes = "'Replacement of ephemeral constant by a parameter, the value of which is optimised using an embedded non-linar paramter estimation proceedure.' Spatial Interaction Modelling, Seattle, USA Subglacial Water System Case Study. Trapridge Glacier, Yukon. From messages to genetic-programming@cs.stanford.edu Tue, 03 Sep 1996 08:52:15 BST Thu, 05 Sep 1996 08:38:51 BST Basically we use an asyncronous steady state system which implicitly gives preferential treatment to faster evaluating models. Its written in F77 and MPI at the moment but RSN I will be reworking it as F90 with some more functionallity such as user configurable islands and such like. genetic-programming@cs.stanford.edu Tue, 10 Sep 1996 08:32:17 BST I have produced a program that converts s-experessions to infix equations that can be pasted into maple (or free equivalents, I'd guess) which has a simplify command. My code is at ftp://gam.leeds.ac.uk/pub/ian/pretoin.f", } @Misc{turton:1998:summary, author = "Ian Turton and Stan Openshaw", title = "High Performance Computing and Geography: developments, issues and case studies", howpublished = "www", year = "1998", month = "16 " # nov, keywords = "genetic algorithms, genetic programming", broken = "http://www.ccg.leeds.ac.uk/ian/hpciwp/hpciwp.html", abstract = "The Centre for Computational Geography and the School of Computer Studies at the University of Leeds formed a consortium under the EPSRC's High Performance Computing Initiative in 1994. This paper outlines some of the results that were obtained during the first two years of the three year project.", notes = "Fortran MPI 512 node Cray T3D. Working Paper 97/2, School of Geography, University of Leeds? Published as \cite{Turton:1998:EPA}?", } @Article{Turton:1998:EPA, author = "I. Turton and S. Openshaw", title = "High-performance computing and geography: developments, issues, and case studies", journal = "Environment and Planning A", year = "1998", volume = "30", number = "10", pages = "1839--1856", publisher = "Pion Ltd", keywords = "genetic algorithms, genetic programming, Fortran", URL = "http://www.envplan.com/abstract.cgi?id=a301839", DOI = "doi:10.1068/a301839", size = "18 pages", abstract = "In this paper we outline some of the results that were obtained by the application of a Cray T3D parallel supercomputer to human geography problems. We emphasise the fundamental importance of high-performance computing (HPC) as a future relevant paradigm for doing geography. We offer an introduction to recent developments and illustrate how new computational intelligence technologies can start to be used to make use of opportunities created by data riches from geographic information systems, artificial intelligence tools, and HPC in geography.", } @MastersThesis{GILP1997Tveit, author = "Amund Tveit", title = "Genetic Inductive Logic Programming", school = "Norwegian University of Science and Technology", year = "1997", type = "MSc Thesis", address = "IDI/NTNU, N-7491 Trondheim, Norway", email = "ae@amundtveit.info", keywords = "genetic algorithms, genetic programming, inductive logic programming, ILP", broken = "http://amundtveit.info/publications/1997/MScThesisAbstract.php", size = "179KB", abstract = "The most used method of finding logical rules from data, inductive logic programming (ILP), has shown successful, but unfortunately not very scalable with increasing problem size. In this report a model for doing induction of logical rules, using the concepts of the potentially more scalable method of genetic algorithm, is suggested. Five strategies of reducing the search space in the representation are suggested: pruning by logical entailment, pruning by integrity constraints, pruning by logic factorisation, pruning by range restriction, and pruning using a heuristic fitness function on the cohesion of literals. The genetic operators suggested are applying these pruning search strategies. The model has yet to be implemented and tried out in an experimental setting.", notes = "Try amundtveit.com? Related work: broken 2018 http://amundtveit.info/publications/1997/MScThesisAbstract.php#CitedBy", } @InProceedings{Tyler:2009:cec, author = "Anna L. Tyler and Bill C. White and Casey S. Greene and Peter C. Andrews and Richard Cowper-Sal-lari and Jason H. Moore", title = "Development and Evaluation of an Open-Ended Computational Evolution System for the Creation of Digital Organisms with Complex Genetic Architecture", booktitle = "2009 IEEE Congress on Evolutionary Computation", year = "2009", editor = "Andy Tyrrell", pages = "2907--2912", address = "Trondheim, Norway", month = "18-21 " # may, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-2959-2", file = "P105.pdf", DOI = "doi:10.1109/CEC.2009.4983308", abstract = "Epistasis, or gene-gene interaction, is a ubiquitous phenomenon that is inadequately addressed in human genetic studies. There are few tools that can accurately identify high order epistatic interactions, and there is a lack of general understanding as to how epistatic interactions fit into genetic architecture. Here we approach both problems through the lens of genetic programming (GP). It has recently been proposed that increasing open-endedness of GP will result in more complex solutions that better acknowledge the complexity of human genetic datasets. Moreover, the solutions evolved in open-ended GP can serve as model organisms in which to study general effects of epistasis on phenotype. Here we introduce a prototype computational evolution system that implements an open-ended GP and generates organisms that display epistatic interactions. These interactions are significantly more prevalent and have a greater effect on fitness than epistatic interactions in organisms generated in the absence of selection.", keywords = "genetic algorithms, genetic programming", notes = "epistasis in open-ended GP by replacing two instructions (genes) with NOP. Fig 2. shows first instruction liable to have epistasis with many other instructions in (linear) organism. Nested toroidal grids (8 neighbours) of populations: mutation probability (1x1), mutation operators (3x3), solution operations (9x9), Avida like code (18x18). Regression 2x+4. C++. Fitness of each operator given by averaging over 3x3 organisms it has evolved. 100 gens (5 secs) bloats average 8-> 20 instructions per organism. P2911 {"}Alife and GP poised to help us take great strides in our understanding of human genetic and the genetic of human common disease. cites \cite{DusanMisevic02222006}. CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR", } @InCollection{Tyrrell:2015:EHPA, author = "Andy M. Tyrrell and Martin A. Trefzer", title = "Representations and Algorithms", booktitle = "Evolvable Hardware From Practice to Application", publisher = "Springer", year = "2015", editor = "Martin A. Trefzer and Andy M. Tyrrell", series = "Natural Computing Series", chapter = "3", pages = "89--118", keywords = "genetic algorithms, genetic programming, EHW, Cartesian Genetic Programming (CGP", isbn13 = "978-3-662-44615-7", DOI = "doi:10.1007/978-3-662-44616-4_3", abstract = "Since the early days of Evolvable Hardware the field has expanded beyond the use of simple Evolutionary Algorithms on simple electronic devices to encompass many different combinations of Evolutionary and Biologically Inspired Algorithms with various physical devices (or simulations of physical devices). The field of Evolvable Hardware can be split into the two related areas of Evolvable Hardware Design (including optimisation) and Adaptive Hardware (Haddow and Tyrrell, 2011; Yao and Higuchi, 1999). Evolvable Hardware Design is the use of Evolvable and Biologically Inspired Algorithms for creating physical devices and designs (or their optimisation); examples of fields where Evolvable Hardware Design has had some success include analogue and digital electronics, antennas, MEMS chips and optical systems as well as quantum circuits (Greenwood and Tyrrell, 2006).", } @InProceedings{Tzallas:2016:TELFOR, author = "Alexandros T. Tzallas and Ioannis Tsoulos and Markos G. Tsipouras and Nikolaos Giannakeas and Iosif Androulidakis and Elena Zaitseva", title = "Classification of {EEG} signals using feature creation produced by grammatical evolution", booktitle = "2016 24th Telecommunications Forum (TELFOR)", year = "2016", address = "Belgrade, Serbia", month = "22-23 " # nov, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Feature Extraction, Feature Construction, Classification, EEG, Epilepsy", DOI = "doi:10.1109/TELFOR.2016.7818809", size = "4 pages", abstract = "A state-of-the-art method based on a grammatical evolution approach is used in this study to classify EEG signals. The method is able to construct nonlinear mappings of the original features in order to improve their effectiveness when used as input into artificial intelligence techniques. Several features are initially extracted from the EEG signals which are subsequently used to create the non-linear mappings. Then, a classification stage is applied, using multi-layer perceptron (MLP) and radial basis functions (RBF), to categorize the EEG signals. The proposed method is evaluated using a benchmark epileptic EEG dataset and promising results are reported.", notes = "Also known as \cite{7818809}", } @Article{Tzimourta:2018:I, author = "Katerina D. Tzimourta and Ioannis Tsoulos and Thanasis Bilero and Alexandros T. Tzallas and Markos G. Tsipouras and Nikolaos Giannakeas", title = "Direct Assessment of Alcohol Consumption in Mental State Using Brain Computer Interfaces and Grammatical Evolution", journal = "Inventions", year = "2018", volume = "3", number = "3", article_number = "51", keywords = "genetic algorithms, genetic programming", ISSN = "2411-5134", URL = "http://www.mdpi.com/2411-5134/3/3/51", DOI = "doi:10.3390/inventions3030051", abstract = "Alcohol consumption affects the function of the brain and long-term excessive alcohol intake can lead to severe brain disorders. Wearable electroencephalogram (EEG) recording devices combined with Brain Computer Interface (BCI) software may serve as a tool for alcohol-related brain wave assessment. In this paper, a method for mental state assessment from alcohol-related EEG recordings is proposed. EEG recordings are acquired with the Emotiv EPOC+, after consumption of three separate doses of alcohol. Data from the four stages (alcohol-free and three levels of doses) are processed using the OpenViBE platform. Spectral and statistical features are calculated, and Grammatical Evolution is employed for discrimination across four classes. Obtained results in terms of accuracy reached high levels (89.95percent), which renders the proposed approach suitable for direct assessment of the driver's mental state for road safety and accident avoidance in a potential in-vehicle smart system.", notes = "also known as \cite{inventions3030051}", } @InProceedings{Uchibe98f, author = "Eiji Uchibe and Masateru Nakamura and Minoru Asada", title = "Cooperative Behavior Acquisition in a Multiple Mobile Robot Environment by Co-evolution", booktitle = "RoboCup-98: Robot Soccer World Cup II", year = "1998", editor = "Minoru Asada and Hiroaki Kitano", volume = "1604", series = "Lecture Notes in Computer Science", pages = "273--285", address = "Paris", month = "2-3 " # jul, publisher = "Springer", keywords = "genetic algorithms, genetic programming", ISSN = "0302-9743", ISBN = "3-540-66320-7", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1007/3-540-48422-1_22", URL = "http://www.er.ams.eng.osaka-u.ac.jp/Paper/1998/Uchibe98f.pdf", size = "14 pages", abstract = "Coevolution has recently been receiving increased attention as a method for multi agent simultaneous learning. This paper discusses how multiple robots can emerge cooperative behaviours through co-evolutionary processes. As an example task, a simplified soccer game with three learning robots is selected and a GP (genetic programming) method is applied to individual population corresponding to each robot so as to obtain cooperative and competitive behaviors through evolutionary processes. The complexity of the problem can be explained twofold: co-evolution for cooperative behaviours needs exact synchronisation of mutual evolutions, and three robot coevolution requires well-complicated environment setups that may gradually change from simpler to more complicated situations so that they can obtain cooperative and competitive behaviors simultaneously in a wide range of search area in various kinds of aspects. Simulation results are shown, and a discussion is given.", notes = "http://www.robocup.org/games/98paris/31232.html", } @PhdThesis{Uchibe:thesis, author = "Eiji Uchibe", title = "Cooperative Behavior Acquisition by Learning and Evolution in a Multi-Agent Environment for Mobile Robots", school = "Department of Mechanical Engineering for Computer-Controlled Machinery, Osaka University", year = "1999", type = "Doctor of Engineering", address = "Japan", month = jan, keywords = "genetic algorithms, genetic programming", URL = "http://www.irp.oist.jp/nc/uchibe/paper/phd.pdf", size = "134 pages", abstract = "The objective of my research described in this dissertation is to realize learning and evolutionary methods for multiagent systems. This dissertation mainly consists of four parts. We propose a method that acquires the purposive behaviours based on the estimation of the state vectors in Chapter 3. In order to acquire the cooperative behaviors in multiagent environments, each learning robot estimates the Local Prediction Model (hereafter LPM) between the learner and the other objects separately. The LPM estimate the local interaction while reinforcement learning copes with the global interaction between multiple LPMs and the given tasks. Based on the LPMs which satisfies the Markovian environment assumption as possible, robots learn the desired behaviours using reinforcement learning. We also propose a learning schedule in order to make learning stable especially in the early stage of multiagent systems. Chapter 4 discusses how an agent can develop its behaviour according to the complexity of the interactions with its environment. A method for controlling the complexity is proposed for a vision-based mobile robot. The agent estimates the full set of state vectors with the order of the major vector components based on the LPM. The environmental complexity is defined in terms of the speed of the agent while the complexity of the state vector is the number of the dimensions of the state vector. According to the increase of the speed of its own or others, the dimension of the state vector is increased by taking a trade-off between the size of the state space and the learning time. The vector-valued reward function is discussed in order to cope with the multiple tasks in Chapter 5. Unlike the traditional weighted sum of several reward functions, we introduce a discounted matrix to integrate them in order to estimate the value function, which evaluates the current action strategy. Owing to the extension of the value function, the learning agent can estimate the future multiple reward from the environment appropriately. Chapter 6 discusses how multiple robots can emerge cooperative behaviours through co-evolutionary processes. A genetic programming method is applied to individual population corresponding to each robot so as to obtain cooperative and competitive behaviors. The complexity of the problem can be explained twofold: co-evolution for cooperative behaviours needs exact synchronisation of mutual evolutions, and three robot co-evolution requires well-complicated environment setups that may gradually change from simpler to more complicated situations. As an example task, several simplified soccer games are selected to show the validity of the proposed methods. Finally, discussion and concluding remarks on our work are given.", notes = "Thesis Supervisor : Minoru Asada Title : Professor of Graduate School of Engineering, Department of Adaptive Machine Systems, Osaka University Thesis Committee : Minoru Asada, Chair Yoshiaki Shirai Masao Ikeda Copyright 1999 Eiji Uchibe", } @InProceedings{uchibe:1999:CCBAMRC, author = "Eiji Uchibe and Masateru Nakamura and Minoru Asada", title = "Cooperative and Competitive Behavior Acquisition for Mobile Robots through Co-evolution", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1406--1413", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, artificial life, adaptive behavior and agents", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-033.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-033.ps", abstract = "REVOLVER", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Uchibe:2000:IAS, author = "Eiji Uchibe and Masakazu Yanase and Minoru Asada", title = "Behavior Generation for a Mobile Robot Based on the Adaptive Fitness Function", booktitle = "Intelligent Autonomous Systems 6, IAS 6", year = "2000", editor = "E. Pagello and F. Groen and T. Arai and R. Dillman and A. Stentz", pages = "3--10", address = "Venice, Italy", month = jul # " 25-27", publisher = "IOS Press", keywords = "genetic algorithms, genetic programming", ISBN = "1-58603-078-7", URL = "http://www.er.ams.eng.osaka-u.ac.jp/Paper/2000/Uchibe00a.pdf", size = "8 pages", abstract = "We have to prepare the evaluation (fitness) function to evaluate the performance of the robot when we apply the machine learning techniques to the robot application. In many cases, the fitness function is composed of several aspects. Simple implementation to cope with the multiple fitness function is a weighted summation. This paper presents an adaptive fitness function for the evolutionary computation to obtain the purposive behaviours through changing the weights for the fitness function. As an example task, a shooting behaviour in a simplified soccer game is selected to show the validity of the proposed method. Simulation results and real experiments are shown, and a discussion is given.", notes = "http://www.dei.unipd.it/ricerca/ias6/ http://www.iospress.nl/loadtop/load.php?isbn=1586030787 see also \cite{Uchibe:2002:RAS}", } @Article{Uchibe:2002:RAS, author = "Eiji Uchibe and Masakazu Yanase and Minoru Asada", title = "Behavior generation for a mobile robot based on the adaptive fitness function", journal = "Robotics and Autonomous Systems", year = "2002", volume = "40", pages = "69--77", number = "2-3", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6V16-460M7KP-1/2/e4f9df58e7ab6e0403221cb825b818f4", keywords = "genetic algorithms, genetic programming, Behaviour acquisition, Adaptive fitness function, Multiple optimization problem, RoboCup", DOI = "doi:10.1016/S0921-8890(02)00232-4", abstract = "We have to prepare the evaluation (fitness) function to evaluate the performance of the robot when we apply the machine learning techniques to the robot application. In many cases, the fitness function is composed of several aspects. Simple implementation to cope with the multiple fitness functions is a weighted summation. This paper presents an adaptive fitness function for the evolutionary computation to obtain the purposive behaviours through changing the weights for the fitness function. As an example task, a basic behavior in a simplified soccer game (shooting a ball into the opponent goal) is selected to show the validity of the adaptive fitness function. Simulation results and real experiments are shown, and a discussion is given.", notes = "See also \cite{Uchibe:2000:IAS}", } @Article{Udrescu:2020:SciAdv, author = "Silviu-Marian Udrescu and Max Tegmark", title = "{AI Feynman}: A physics-inspired method for symbolic regression", journal = "Science Advances", year = "2020", volume = "6", number = "16", month = "15 " # apr, keywords = "genetic algorithms, genetic programming, research article, computer science", ISSN = "2375-2548", publisher = "American Association for the Advancement of Science", URL = "https://advances.sciencemag.org/content/advances/6/16/eaay2631.full.pdf", URL = "https://pubmed.ncbi.nlm.nih.gov/32426452/", DOI = "doi:10.1126/sciadv.aay2631", code_url = "https://github.com/SJ001/AI-Feynman", size = "17 pages", abstract = "A core challenge for both physics and artificial intelligence (AI) is symbolic regression: finding a symbolic expression that matches data from an unknown function. Although this problem is likely to be NP-hard in principle, functions of practical interest often exhibit symmetries, separability, compositionality, and other simplifying properties. In this spirit, we develop a recursive multidimensional symbolic regression algorithm that combines neural network fitting with a suite of physics-inspired techniques. We apply it to 100 equations from the Feynman Lectures on Physics, and it discovers all of them, while previous publicly available software cracks only 71; for a more difficult physics-based test set, we improve the state-of-the-art success rate from 15 to 90percent.", notes = "Is this GP? First tries enumeration of GP like functions rather than search before other approaches, eg 6 layer ANN. 'our neural network enables eliminating variables by discovering symmetries and separability.' https://space.mit.edu/home/tegmark/aifeynman.html Python SymPy package. (Test set) 20 equations, extracted from other seminal physics books. Comparison with Eurequa \cite{Dubcakova:2011:GPEM} \cite{Science09:Schmidt}. '... in reverse Polish notation (RPN), which would require about 100 times the age of our universe for the brute-force method'. Replacing (1/4 pi eta) by Coulomb constant. Introducing new operators into brute force search representation based on RPN to speed search. ANN that can 'repeatedly reduce a problem to simpler ones, eliminating dependent variables by discovering properties such as symmetries and separability in the unknown function'. Additional noise. See also \cite{LaCava:2021:NeurIPS} Department of Physics and Center for Brains, Minds and Machines, Massachusetts Institute of Technology", } @InProceedings{Uesaka:1999:ISCAS, author = "Kazuyoshi Uesaka and Masayuki Kawamata", title = "Synthesis of low coefficient sensitivity digital filters using genetic programming", booktitle = "Proceedings of the 1999 IEEE International Symposium on Circuits and Systems, ISCAS '99", year = "1999", volume = "3", pages = "307--310", address = "Orlando, FL, USA", month = "30 " # may # "-2 " # jun, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", URL = "http://www.mk.ecei.tohoku.ac.jp/papers/data/Uesaka-ITC-CSCC-1998.pdf", URL = "http://citeseer.ist.psu.edu/263691.html", size = "4 pages", abstract = "This paper proposes a new approach to the synthesis of low coefficient sensitivity digital filters using Genetic Programming (GP). GP is applied to the synthesis problem by establishing a mapping between the S-expressions and the filter structures. Genetic operators are then applied to the S-expressions in order to change the connections between the elements in the filter structures. The fitness measure that includes the coefficient sensitivity enables the selection operation to choose low sensitivity filter structures. In this paper, two coefficient sensitivity measures are used-the magnitude sensitivity and the relative sensitivity. A numerical example is presented to demonstrate that the sensitivity of the filter structure synthesized by GP is lower than that of other low coefficient sensitivity filter structures proposed so far", } @InProceedings{Uesaka:2000:ISCAS, author = "Kazuyoshi Uesaka and Masayuki Kawamata", title = "Synthesis of low-sensitivity second-order digital filter using genetic programming with automatically defined functions", booktitle = "Proceedings of the IEEE International Symposium on Circuits and Systems, ISCAS 2000", year = "2000", volume = "1", pages = "359--362", address = "Geneva", month = "28-31 " # may, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, IIR digital filter structures, S-expressions, automatically defined functions, fitness measure, genetic programming, global optimisation technique, low coefficient sensitivity, low-sensitivity digital filter design, magnitude sensitivity, second-order digital filter, subroutines, synthesis method, IIR filters, digital filters, filtering theory, iterative methods, optimisation, sensitivity", DOI = "doi:10.1109/ISCAS.2000.857104", size = "5 pages", abstract = "This paper proposes a synthesis method for low coefficient sensitivity second-order IIR digital filter structures using Genetic Programming with Automatically Defined Functions (GP-ADF). In this paper, digital filter structures are represented as S-expressions with subroutines. It is easy to generate syntactically valid S-expressions and perform the genetic operations because the representation is suitable for GP. In a numerical example, we use the fitness measure including the magnitude sensitivity, and demonstrate that the proposed method can synthesize efficiently very low coefficient sensitivity filter structures", } @Article{uesaka:2000:sl2df, author = "Kazuyoshi Uesaka and Masayuki Kawamata", title = "Synthesis of low-sensitivity second-order digital filters using genetic programming with automatically defined functions", journal = "IEEE Signal Processing Letters", year = "2000", volume = "7", number = "4", pages = "83--85", month = apr, keywords = "genetic algorithms, genetic programming, IIR filters, second-order digital filters, automatically defined functions, IIR digital filter, filter synthesis method, S-expressions, fitness measure, magnitude sensitivity, low coefficient sensitivity filters, ADF", ISSN = "1070-9908", URL = "http://ieeexplore.ieee.org/iel5/97/18028/00833004.pdf", abstract = "This letter proposes a synthesis method for low coefficient sensitivity second-order IIR digital filter structures using genetic programming with automatically defined functions (GP-ADF). In this letter, digital filter structures are represented as S-expressions with subroutines. It is easy to generate syntactically valid S-expressions and perform the genetic operations, because the representation is suitable for GP. A numerical example uses the fitness measure, including the magnitude sensitivity, and demonstrates that the proposed method can synthesize efficiently very low coefficient sensitivity filter structures.", } @Article{Uesaka:2001:CDS, author = "K. Uesaka and M. Kawamata", title = "Heuristic synthesis of low coefficient sensitivity second-order digital filters using genetic programming", journal = "IEE Proceedings. Circuits, Devices and Systems", year = "2001", volume = "148", number = "3", pages = "121--125", month = jun, keywords = "genetic algorithms, genetic programming, coefficient sensitivity, computer program, heuristic synthesis, second-order digital filter, circuit CAD, circuit optimisation, digital filters, sensitivity", ISSN = "1350-2409", DOI = "doi:10.1049/ip-cds:20010342", size = "5 pages", abstract = "The authors propose a new approach to the synthesis of low coefficient sensitivity second-order digital filter sections using genetic programming (GP). GP is applied to the synthesis problem by establishing a mapping between the filter structures and computer programs. Genetic operators change the computer programs in order to change the connections between the elements in the filter structures, and consequently change the coefficient sensitivities of those filter structures. The fitness measure that includes the coefficient sensitivities enables the selection operator to choose low sensitivity filter structures. In the paper, two coefficient sensitivity measures are used: the magnitude sensitivity and the relative sensitivity. A numerical example is presented to demonstrate that the sensitivity of the filter synthesised by GP is lower than that of other low coefficient sensitivity filter structures proposed so far", notes = "CODEN: ICDSE7", } @Article{Uesaka:2003:CS, author = "Kazuyoshi Uesaka and Masayuki Kawamata", title = "Evolutionary synthesis of digital filter structures using genetic programming", journal = "IEEE Transactions on Circuits and Systems {II}: Analog and Digital Signal Processing", year = "2003", volume = "50", number = "12", pages = "977--983", month = dec, keywords = "genetic algorithms, genetic programming, IIR filters, computability, digital filters, roundoff errors, transfer function matrices, S-expressions, automatically defined functions, computability, difference equations, digital filter structures, digital filter wordlength effects, evolutionary synthesis method, fitness measure, fourth-order filter, genetic programming, global optimization, infinite-impulse response filters, low-coefficient sensitivity, low-output roundoff noise, matrix representation, subroutines, transfer function", ISSN = "1057-7130", DOI = "doi:10.1109/TCSII.2003.820240", abstract = "This paper presents a synthesis method for infinite-impulse response (IIR) digital filter structures using genetic programming with automatically defined functions (GP-ADF). In the proposed method, digital filter structures are represented as S-expressions with subroutines, which are written directly from the set of difference equations. This paper also shows the condition for the constructing the S-expressions that represent the filter structures without delay-free loops. Numerical examples synthesize two-filter structures: the low-coefficient sensitivity fourth-order filter structure and the low-output roundoff noise second-order filter structure.", notes = "Inspec Accession Number: 7830391", } @Article{Uhde:2023:GPEM, author = "Florian Uhde", title = "Framework for unsupervised incremental evolution of stylized images", journal = "Genetic Programming and Evolvable Machines", year = "2023", volume = "24", number = "1", pages = "Article no. 2", month = jun, note = "Special Issue: Evolutionary Computation in Art, Music and Design", keywords = "genetic algorithms, Artistic rendering, Computational art, Unity3d, GPU", ISSN = "1389-2576", URL = "https://rdcu.be/c6DOS", DOI = "doi:10.1007/s10710-023-09449-z", size = "21 pages", abstract = "examines and showcases a framework to generate artworks using evolutionary algorithms. Based on the idea of an incremental abstract artistic process stylized images are generated from different input images without human supervision. After explaining the underlying concept, the solution space of different styles is explored and its properties for style consistency and style variety are discussed. A first step towards better control of the outcome is implemented through masking, followed by a discussion about potential improvements and further research.", notes = "Is this GP? An incremental artistic process. C# HLSL Unity3d GeneticSharp https://github.com/giacomelli/GeneticSharp/ Faculty of Computer Science, Otto-von-Guericke-University, Magdeburg, Germany", } @InProceedings{Ujjin:2002:gecco, author = "Supiya Ujjin and Peter J. Bentley", title = "Evolving Good Recommendations", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "1271", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/rwa102.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-21.pdf", address = "New York", publisher_address = "San Francisco, CA 94104, USA", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "real world applications, poster paper, genetic algorithms, recommender systems", ISBN = "1-55860-878-8", notes = "MovieLens GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)", } @InProceedings{Ukita:2015:ICEMS, author = "K. Ukita and K. Ishikawa and W. Kitagawa and T. Takeshita", booktitle = "18th International Conference on Electrical Machines and Systems (ICEMS)", title = "Investigation of shape filter for electromagnetic device by structural optimization with GP", year = "2015", pages = "1272--1277", month = oct, keywords = "genetic algorithms, genetic programming", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7385235", DOI = "doi:10.1109/ICEMS.2015.7385235", abstract = "Recently, one of the most important problems is energy shortage. Therefore, it is required high efficiency motors and high accuracy analysis tools. Shape optimisation and topology optimisation are often used for structural optimisation of motors. Authors simplify optimal model by filters because it is often complicated. However, filters cause deterioration of electromagnetic characteristics of motors. In this paper, shape complexity of analysis model is added in fitness value in genetic programming (GP). Authors investigate usefulness of filters which can search optimal model while taking shape complexity into account.", notes = "Also known as \cite{7385235}", } @InProceedings{ain:2017:CEC, author = "Qurrat {Ul Ain} and Bing Xue and Harith Al-Sahaf and Mengjie Zhang", booktitle = "2017 IEEE Congress on Evolutionary Computation (CEC)", title = "Genetic programming for skin cancer detection in dermoscopic images", year = "2017", editor = "Jose A. Lozano", pages = "2420--2427", address = "Donostia, San Sebastian, Spain", publisher = "IEEE", isbn13 = "978-1-5090-4601-0", abstract = "Development of an effective skin cancer detection system can greatly assist the dermatologist while significantly increasing the survival rate of the patient. To deal with melanoma detection, knowledge of dermatology can be combined with computer vision techniques to evolve better solutions. Image classification can significantly help in diagnosing the disease by accurately identifying the morphological structures of skin lesions responsible for developing cancer. Genetic Programming (GP), an emerging Evolutionary Computation technique, has the potential to evolve better solutions for image classification problems compared to many existing methods. In this paper, GP has been used to automatically evolve a classifier for skin cancer detection and also analysed GP as a feature selection method. For combining knowledge of dermatology and computer vision techniques, GP has been given domain specific features provided by the dermatologists as well as Local Binary Pattern features extracted from the dermoscopic images. The results have shown that GP has significantly outperformed or achieved comparable performance compared to the existing methods for skin cancer detection.", keywords = "genetic algorithms, genetic programming, cancer, computer vision, feature selection, image classification, medical image processing, patient diagnosis, GP, computer vision techniques, dermoscopic images, disease diagnosis, domain specific features, evolutionary computation technique, feature selection method, local binary pattern features, melanoma detection, patient survival rate, skin cancer detection, Feature extraction, Image color analysis, Malignant tumors, Mutual information, Sensitivity, Skin, Skin cancer", isbn13 = "978-1-5090-4601-0", DOI = "doi:10.1109/CEC.2017.7969598", month = "5-8 " # jun, notes = "IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969598}", } @InProceedings{Ain:2018:PRICAI, author = "Qurrat {Ul Ain} and Bing Xue and Harith Al-Sahaf and Mengjie Zhang", title = "Genetic Programming for Feature Selection and Feature Construction in Skin Cancer Image Classification", booktitle = "PRICAI 2018: Trends in Artificial Intelligence - 15th Pacific Rim International Conference on Artificial Intelligence, Proceedings, Part I", year = "2018", editor = "Xin Geng and Byeong-Ho Kang", volume = "11012", series = "Lecture Notes in Computer Science", pages = "732--745", address = "Nanjing, China", month = aug # " 28-31", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-97303-6", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/pricai/pricai2018a.html#AinXAZ18", DOI = "doi:10.1007/978-3-319-97304-3_56", abstract = "The incidence of skin cancer, particularly, malignant melanoma, continues to increase worldwide. If such a cancer is not treated at an early stage, it can be fatal. A computer system based on image processing and computer vision techniques, having good diagnostic ability, can provide a quantitative evaluation of these skin cancer cites called skin lesions. The size of a medical image is usually large and therefore requires reduction in dimensionality before being processed by a classification algorithm. Feature selection and construction are effective techniques in reducing the dimensionality while improving classification performance. This work develops a novel genetic programming (GP) based two-stage approach to feature selection and feature construction for skin cancer image classification. Local binary pattern is used to extract gray and colour features from the dermoscopy images. The results of our proposed method have shown that the GP selected and constructed features have promising ability to improve the performance of commonly used classification algorithms. In comparison with using the full set of available features, the GP selected and constructed features have shown significantly better or comparable performance in most cases. Furthermore, the analysis of the evolved feature sets demonstrates the insights of skin cancer properties and validates the feature selection ability of GP to distinguish between benign and malignant cancer images.", notes = "conf/pricai/AinXAZ18", } @InProceedings{Ain:2018:AJCAI, author = "Qurrat {Ul Ain} and Harith Al-Sahaf and Bing Xue and Mengjie Zhang", title = "A Multi-tree Genetic Programming Representation for Melanoma Detection Using Local and Global Features", booktitle = "Australasian Joint Conference on Artificial Intelligence", year = "2018", editor = "Tanja Mitrovic and Bing Xue and Xiaodong Li", volume = "11320", series = "LNCS", pages = "111--123", address = "Wellington, New Zealand", month = dec # " 11-14", publisher = "Springer", keywords = "genetic algorithms, genetic programming, image classification, feature extraction, feature selection, melanoma detection", isbn13 = "978-3-030-03990-5", URL = "https://link.springer.com/chapter/10.1007%2F978-3-030-03991-2_12", DOI = "doi:10.1007/978-3-030-03991-2_12", size = "13 pages", abstract = "Melanoma is the deadliest type of skin cancer that accounts for nearly 75percent of deaths associated with it. However, survival rate is high, if diagnosed at an early stage. This study develops a novel classification approach to melanoma detection using a multi-tree genetic programming (GP) method. Existing approaches have employed various feature extraction methods to extract features from skin cancer images, where these different types of features are used individually for skin cancer image classification. However they remain unable to use all these features together in a meaningful way to achieve performance gains. In this work, Local Binary Pattern is used to extract local information from gray and colour images. Moreover, to capture the global information, colour variation among the lesion and skin regions, and geometrical border shape features are extracted. Genetic operators such as crossover and mutation are designed accordingly to fit the objectives of our proposed method. The performance of the proposed method is assessed using two skin image datasets and compared with six commonly used classification algorithms as well as the single tree GP method. The results show that the proposed method significantly outperformed all these classification methods. Being interpretable, this method may help dermatologist identify prominent skin image features, specific to a type of skin cancer.", notes = "conf/ausai/AinAXZ18", } @InProceedings{ulain:2019:CEC, author = "Qurrat {Ul Ain} and Bing Xue and Harith Al-Sahaf and Mengjie Zhang", title = "Multi-tree Genetic Programming with A New Fitness Function for Melanoma Detection", booktitle = "2019 IEEE Congress on Evolutionary Computation, CEC 2019", year = "2019", editor = "Carlos A. Coello Coello", pages = "880--887", address = "Wellington, New Zealand", month = "10-13 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-7281-2152-6", DOI = "doi:10.1109/CEC.2019.8790282", size = "8 pages", abstract = "The occurrence of malignant melanoma had enormously increased since past decades. For accurate detection and classification, not only discriminative features are required but a properly designed model to combine these features effectively is also needed. In this study, the multi-tree representation of genetic programming (GP) has been used to effectively combine different types of features and evolve a classification model for the task of melanoma detection. Local binary patterns have been used to extract pixel-level informative features. For incorporating the properties of ABCD (asymmetrical property, border shape, colour variation and geometrical characteristics) rule of dermoscopy, various features have been used to include local and global information of the skin lesions. To meet the requirements of the proposed multi-tree GP representation, genetic operators such as crossover and mutation are designed accordingly. Moreover, a new weighted fitness function is designed to evolve", notes = "Also known as \cite{8790282}, IEEE Catalog Number: CFP19ICE-ART", } @InProceedings{UlAin:2019:IVCNZ, author = "Qurrat {Ul Ain} and Bing Xue and Harith Al-Sahaf and Mengjie Zhang", booktitle = "2019 International Conference on Image and Vision Computing New Zealand (IVCNZ)", title = "Genetic Programming for Multiple Feature Construction in Skin Cancer Image Classification", year = "2019", month = dec, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IVCNZ48456.2019.8961001", ISSN = "2151-2205", abstract = "Skin cancer is a common cancer worldwide, with melanoma being the most deadly form which is treatable when diagnosed at an early stage. This study develops a novel classification approach using multi-tree genetic programming (GP), which not only targets melanoma detection but is also capable of distinguishing between ten different classes of skin cancer effectively from lesion images. Selecting a suitable feature extraction method and the way different types of features are combined are important aspects to achieve performance gains. Existing approaches remain unable to effectively design a way to combine various features. Moreover, they have not used multi-channel multi-resolution spatial/frequency information for effective feature construction. In this work, wavelet-based texture features from multiple color channels are employed which preserve all the local, global, color and texture information concurrently. Local Binary Pattern, lesion color variation, and geometrical border shape features are also extracted from various color channels. The performance of the proposed method is evaluated using two skin image datasets and compared with an existing multi-tree GP method, ten single-tree GP methods, and six commonly used classification algorithms. The results reveal the goodness of the proposed method which significantly outperformed all these classification methods and demonstrate the potential to help dermatologist in making a diagnosis in real-time situations.", notes = "Also known as \cite{8961001}", } @InProceedings{Ain:2020:GECCO, author = "Qurrat {Ul Ain} and Harith Al-Sahaf and Bing Xue and Mengjie Zhang", title = "A Genetic Programming Approach to Feature Construction for Ensemble Learning in Skin Cancer Detection", year = "2020", editor = "Carlos Artemio {Coello Coello} and Arturo Hernandez Aguirre and Josu Ceberio Uribe and Mario Garza Fabre and Gregorio {Toscano Pulido} and Katya Rodriguez-Vazquez and Elizabeth Wanner and Nadarajen Veerapen and Efren Mezura Montes and Richard Allmendinger and Hugo Terashima Marin and Markus Wagner and Thomas Bartz-Beielstein and Bogdan Filipic and Heike Trautmann and Ke Tang and John Koza and Erik Goodman and William B. Langdon and Miguel Nicolau and Christine Zarges and Vanessa Volz and Tea Tusar and Boris Naujoks and Peter A. N. Bosman and Darrell Whitley and Christine Solnon and Marde Helbig and Stephane Doncieux and Dennis G. Wilson and Francisco {Fernandez de Vega} and Luis Paquete and Francisco Chicano and Bing Xue and Jaume Bacardit and Sanaz Mostaghim and Jonathan Fieldsend and Oliver Schuetze and Dirk Arnold and Gabriela Ochoa and Carlos Segura and Carlos Cotta and Michael Emmerich and Mengjie Zhang and Robin Purshouse and Tapabrata Ray and Justyna Petke and Fuyuki Ishikawa and Johannes Lengler and Frank Neumann", isbn13 = "9781450371285", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377930.3390228", DOI = "doi:10.1145/3377930.3390228", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference", pages = "1186--1194", size = "9 pages", keywords = "genetic algorithms, genetic programming, ensemble classifiers, multi-class classification, melanoma detection, feature construction", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", abstract = "Ensembles of classifiers have proved to be more effective than a single classification algorithm in skin image classification problems. Generally, the ensembles are created using the whole set of original features. However, some original features can be redundant and may not provide useful information in building good ensemble classifiers. To deal with this, existing feature construction methods that usually generate new features for only a single classifier have been developed but they fit the training data too well, resulting in poor test performance. This study develops a new classification method that combines feature construction and ensemble learning using genetic programming (GP) to address the above limitations. The proposed method is evaluated on two benchmark real-world skin image datasets. The experimental results reveal that the proposed algorithm has significantly outperformed two existing GP approaches, two state-of-the-art convolutional neural network methods, and ten commonly used machine learning algorithms. The evolved individual that is considered as a set of constructed features helps identify prominent original features which can assist dermatologists in making a diagnosis.", notes = "Also known as \cite{10.1145/3377930.3390228} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @Article{UlAin:ieeeETCI, author = "Qurrat {Ul Ain} and Harith Al-Sahaf and Bing Xue and Mengjie Zhang", title = "Generating Knowledge-Guided Discriminative Features Using Genetic Programming for Melanoma Detection", journal = "IEEE Transactions on Emerging Topics in Computational Intelligence", year = "2021", volume = "5", number = "4", pages = "554--569", month = aug, keywords = "genetic algorithms, genetic programming, feature selection, feature construction, image classification, melanoma detection", ISSN = "2471-285X", DOI = "doi:10.1109/TETCI.2020.2983426", size = "16 pages", abstract = "Melanoma is the deadliest form of skin cancer that causes around 7percent of deaths worldwide. However, most of the skin cancers can be cured, especially if detected and treated early. Existing approaches have employed various feature extraction methods, where different types of features are used individually for skin image classification which may not provide sufficient information to the classification algorithm necessary to discriminate between classes, leading to sub-optimal performance. This study develops a novel skin image classification method using multi-tree genetic programming (GP). To capture local information from gray and color skin images, Local Binary Pattern is used in this work. In addition, for capturing global information, variation in color within the lesion and the skin regions, and domain-specific lesion border shape features are extracted. GP with a multi-tree representation is employed to use multiple types of features. Genetic operators such as crossover and mutation are designed accordingly in order to select a single type of features at terminals in one tree of the GP individual. The performance of the proposed method is assessed using two skin image datasets having images captured from multiple modalities, and compared with six most commonly used classification algorithms as well as the standard (single-tree) wrapper and embedded GP methods. The results show that the proposed method has significantly outperformed all these classification methods. Being interpretable and fast in terms of the computation time, this method can help dermatologist identify prominent skin image features, specific to a type of skin cancer in real-time situations.", notes = "Also known as \cite{9072194}", } @PhdThesis{UlAin:thesis, author = "Qurrat Ul Ain", title = "Genetic Programming based Feature Manipulation for Skin Cancer Image Classification", school = "Victoria University of Wellington", year = "2020", address = "New Zealand", keywords = "genetic algorithms, genetic programming, Image classification, Melanoma detection, Feature selection, Feature Construction, Skin cancer detection, ANN", URL = "http://hdl.handle.net/10063/9392", URL = "http://researcharchive.vuw.ac.nz/handle/10063/9392", URL = "http://researcharchive.vuw.ac.nz/bitstream/handle/10063/9392/thesis_access.pdf", size = "258 pages", abstract = "Skin image classification involves the development of computational methods for solving problems such as cancer detection in lesion images,and their use for biomedical research and clinical care. Such methods aim at extracting relevant information or knowledge from skin images that can significantly assist in the early detection of disease. Skin images are enormous, and come with various artifacts that hinder effective feature extraction leading to inaccurate classification. Feature selection and feature construction can significantly reduce the amount of data while improving classification performance by selecting prominent features and constructing high-level features. Existing approaches mostly rely on expert intervention and follow multiple stages for pre-processing, feature extraction, and classification, which decreases the reliability, and increases the computational complexity. Since good generalization accuracy is not always the primary objective, clinicians are also interested in analysing specific features such as pigment network, streaks, and blobs responsible for developing the disease; interpretable methods are favoured. In Evolutionary Computation, Genetic Programming (GP) can automatically evolve an interpretable model and address the curse of dimensionality (through feature selection and construction). GP has been successfully applied to many areas, but its potential for feature selection, feature construction, and classification in skin images has not been thoroughly investigated. The overall goal of this thesis is to develop a new GP approach to skin image classification by using GP to evolve programs that are capable of automatically selecting prominent image features, constructing new high-level features, interpreting useful image features which can help dermatologist to diagnose a type of cancer, and are robust to processing skin images captured from specialized instruments and standard cameras. This thesis focuses on using a wide range of texture, color, frequency-based, local,and global image properties at the terminal nodes of GP to classify skin cancer images from multiple modalities effectively. This thesis develops new two-stage GP methods using embedded and wrapper feature selection and construction approaches to automatically generating a feature vector of selected and constructed features for classification. The results show that wrapper approach outperforms the embedded approach, the existing baseline GP and other machine learning methods, but the embedded approach is faster than the wrapper approach. This thesis develops a multitree GP based embedded feature selection approach for melanoma detection using domain specific and domain independent features. It explores suitable crossover and mutation operators to evolve GP classifiers effectively and further extends this approach using a weighted fitness function. The results show that these multi-tree approaches outperformed single tree GP and other classification methods. They identify that a specific feature extraction method extracts most suitable features for particular images taken from a specific optical instrument. This thesis develops the first GP method using frequency-based wavelet features, where the wrapper based feature selection and construction methods automatically evolve useful constructed features to improve the classification performance. The results show the evidence of successful feature construction by significantly outperforming existing GP approaches, state-of-the-art CNN, and other classification methods. This thesis develops a GP approach to multiple feature construction for ensemble learning in classification. The results show that the ensemble method outperformed existing GP approaches, state-of-the-art skin image classification, and commonly used ensemble methods. Further analysis of the evolved constructed features identified important image features that can potentially help the dermatologist identify further medical procedures in real-world situations.", notes = "supervisors Bing Xue and Mengjie Zhang and Harith Al-Sahaf", } @InProceedings{ul-ain:2024:GECCO, author = "Qurrat {Ul Ain} and Harith Al-Sahaf and Bing Xue and Mengjie Zhang", title = "Feature Extraction with Automated Scale Selection in Skin Cancer Image Classification: A Genetic Programming Approach", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference", year = "2024", editor = "Ruhul Sarker and Patrick Siarry and Julia Handl and Xiaodong Li and Markus Wagner and Mario Garza-Fabre and Kate Smith-Miles and Richard Allmendinger and Ying Bi and Grant Dick and Amir H Gandomi and Marcella Scoczynski Ribeiro Martins and Hirad Assimi and Nadarajen Veerapen and Yuan Sun and Mario Andres Munyoz and Ahmed Kheiri and Nguyen Su and Dhananjay Thiruvady and Andy Song and Frank Neumann and Carla Silva", pages = "1363--1372", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, feature extraction, cancer detection, image classification, Real World Applications", isbn13 = "979-8-4007-0494-9", DOI = "doi:10.1145/3638529.3654071", size = "10 pages", abstract = "Early detection of cancer is vital for reducing mortality rates, but medical images come in various resolutions, often captured from diverse devices, and pose challenges due to high inter-class and intra-class variability. Integrating various feature descriptors enhances high-level feature extraction for improved classification. Having varied structure sizes of tumor characteristics in these medical images, extracting features from a single scale might not provide meaningful or discriminative features. Genetic Programming (GP) proves effective in this context due to its flexible representation and global search capabilities. Unlike existing GP methods relying on extracting features from a single scale of the input image, this paper introduces a novel GP-based feature learning approach that automatically selects scales and combines image descriptors for skin cancer detection. The method learns global features from diverse scales, leading to improved classification performance on dermoscopic and standard camera image datasets. The evolved solutions not only enhance classification but also pinpoint the most effective scales and feature descriptors for different skin cancer image datasets. The proposed method generates interpretable models, aiding medical practitioners in diagnoses by identifying cancer characteristics captured through automatically selected feature descriptors in the evolutionary process.", notes = "GECCO-2024 RWA A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{Ullah:2011:ICARA, author = "Fahad Ullah", title = "American Sign Language recognition system for hearing impaired people using Cartesian Genetic Programming", booktitle = "5th International Conference on Automation, Robotics and Applications (ICARA 2011)", year = "2011", month = "6-8 " # dec, pages = "96--99", address = "Wellington, New Zealand", size = "4 pages", abstract = "American Sign Language (ASL) is a well developed and standard way of communication for hearing impaired people living in English speaking communities. Since the advent of modern technology, different intelligent computer-aided applications have been developed that can recognise hand gestures and hence translate gestures into understandable forms. In this paper, ASL based hand gesture recognition system is presented that uses evolutionary programming technique called Cartesian Genetic Programming (CGP). Hand gesture images representing different English alphabets are used to train the CGP based system and then it is tested for a different set of images. The sign recognition accuracy obtained is around 90percent. Also, a chat application is proposed with a possible solution to boost the accuracy of the recognition up to 100percent.", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, American sign language recognition system, evolutionary programming, hand gesture images, hand gesture recognition system, hearing impaired people, intelligent computer-aided applications, gesture recognition, handicapped aids", DOI = "doi:10.1109/ICARA.2011.6144863", notes = "Also known as \cite{6144863}", } @InProceedings{Ullah:2012:ISCI, author = "Fahad Ullah and Gul Muhammad Khan and Sahibzada A. Mahmud", title = "Exploiting developmental plasticity in Cartesian Genetic Programming", booktitle = "IEEE Symposium on Computers Informatics (ISCI 2012)", year = "2012", month = "18-20 " # mar, pages = "180--184", size = "5 pages", address = "Penang, Malaysia", abstract = "In this paper, the effect of developmental plasticity is investigated in Cartesian Genetic Programming (CGP); an evolutionary algorithm that uses a directed graph to represent its genetic architecture. Developmental Plasticity is the adaptability of an organism to change in its surrounding environment. A Developmental Output is used to computationally develop the phenotype that has already been passed through a genetic evolution. To manifest the idea of developmental plasticity in the form of digital circuits, binary multiplexing functions are used in the CGP implementation. Two experiments-prime number test and image recognition test-are conducted so that to analyse the effect of Developmental Plasticity in CGP. Simulation results demonstrate that the plasticity based CGP achieves better performance when compared to conventional CGP in terms of its adaptability and learning in general.", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, binary multiplexing functions, developmental plasticity, digital circuits, directed graph, evolutionary algorithm, genetic architecture, genetic evolution, image recognition test, phenotype, prime number test, directed graphs", DOI = "doi:10.1109/ISCI.2012.6222690", notes = "Also known as \cite{6222690}", } @InProceedings{Ullah:2012:HPCC-ICESS, author = "Fahad Ullah and Gul M. Khan and Sahibzada Ali Mahmud", booktitle = "High Performance Computing and Communication 2012 IEEE 9th International Conference on Embedded Software and Systems (HPCC-ICESS), 2012 IEEE 14th International Conference on", title = "Intelligent Bandwidth Management Using Fast Learning Neural Networks", year = "2012", month = "25-27 " # jun, address = "Liverpool", pages = "867--872", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming bandwidth allocation, computer network management, learning (artificial intelligence), neural nets, scheduling, telecommunication traffic, video streaming, CGPANN, MPEG-4 video stream traffic, bandwidth efficiency, fast learning neural network algorithm, fast learning neural networks, frame drop rate, frame size prediction error, historical data, intelligent bandwidth management, multiuser MPEG-4 traffic, scheduling system, single user MPEG-4 traffic, Artificial neural networks, Bandwidth, Estimation, Multimedia communication, Prediction algorithms, Streaming media, Transform coding, MPEG-4, bandwidth management, evolutionary algorithm, scheduling, traffic estimation", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6332261", DOI = "doi:10.1109/HPCC.2012.123", isbn13 = "978-1-4673-2164-8", abstract = "A fast learning neural network based scheduling system is presented to predict the frames on a single and multi-user MPEG-4 traffic and to distribute the bandwidth accordingly. MPEG-4 video stream traffic from various sources is used to evaluate the capability of this algorithm. A Fast learning Neural network algorithm also termed as Cartesian Genetic Programming Evolved Artificial Neural Network (CGPANN) is used as a forecaster to predict the size of the next frame based on the historical data consisting of previous 10 frames in the buffer for each individual user. A range of scenarios are exploited and analysed for the frame size prediction error, bandwidth efficiency and the frame drop rate for the whole system as well as every user involved obtaining outstanding results. For the best case, the system - with 50 users using the streaming service - has 35percent of bandwidth efficiency with very low frame drop frequency.", notes = "Also known as \cite{6332261}", } @Article{Ullah:2020:ACC, author = "Qazi Zia Ullah and Gul Muhammad Khan and Shahzad Hassan", journal = "IEEE Access", title = "Cloud Infrastructure Estimation and Auto-Scaling Using Recurrent Cartesian Genetic Programming-Based ANN", year = "2020", volume = "8", pages = "17965--17985", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, ANN", DOI = "doi:10.1109/ACCESS.2020.2966678", ISSN = "2169-3536", abstract = "Use of cloud resources has increased with the increasing trend of organizations and governments towards cloud adaptation. This increase in cloud resource usage, leads to enormous amounts of energy consumption by cloud data center servers. Energy can be conserved in a cloud server by demand-based scaling of resources. But reactive scaling may lead to excessive scaling. That, in turn, results in enormous energy consumption by useless scale up and scale down. The scaling granularity can also result in excessive scaling of the resource. Without a proper mechanism for estimating cloud resource usage may lead to significant scaling overheads. To overcome, such inefficiencies, we present Cartesian genetic programming based neural network for resource estimation and a rule-based scaling system for IaaS cloud server. Our system consists of a resource monitor, a resource estimator and a scaling mechanism. The resource monitor takes resource usage and feeds to the estimator for efficient estimation of resources. The scaling system uses the resource estimator's output for scaling the resource with the granularity of a CPU core. The proposed method has been trained and tested with real traces of Bitbrains data center, producing promising results in real-time. It has shown better prediction accuracy and energy efficiency than predictive scaling systems from literature.", notes = "Also known as \cite{8960529}", } @Article{ullah:2021:Electronics, author = "Qazi Zia Ullah and Gul Muhammad Khan and Shahzad Hassan and Asif Iqbal and Farman Ullah and Kyung Sup Kwak", title = "A Cartesian Genetic Programming Based Parallel Neuroevolutionary Model for Cloud Server's {CPU} Usage Prediction", journal = "Electronics", year = "2021", volume = "10", number = "1", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", ISSN = "2079-9292", URL = "https://www.mdpi.com/2079-9292/10/1/67", DOI = "doi:10.3390/electronics10010067", abstract = "Cloud computing use is exponentially increasing with the advent of industrial revolution 4.0 technologies such as the Internet of Things, artificial intelligence, and digital transformations. These technologies require cloud data centers to process massive volumes of workloads. As a result, the data centers consume gigantic amounts of electrical energy, and a large portion of data center electrical energy comes from fossil fuels. It causes greenhouse gas emissions and thus ensuing in global warming. An adaptive resource mechanism of cloud data center resources is vital to get by with this huge problem. The adaptive system will estimate the resource use and then adjust the resources accordingly. Cloud resource use estimation is a two-fold challenging task. First, the cloud workloads are sundry, and second, clients requests are uneven. In the literature, several machine learning models have estimated cloud resources, of which artificial neural networks (ANNs) have shown better performance. Conventional ANNs have a fixed topology and allow only to train their weights either by back-propagation or neuroevolution such as a genetic algorithm. In this paper, we propose Cartesian genetic programming (CGP) neural network (CGPNN). The CGPNN enhances the performance of conventional ANN by allowing training of both its parameters and topology, and it uses a built-in sliding window. We have trained CGPNN with parallel neuroevolution that searches for global optimum through numerous directions. The resource use traces of the Bitbrains data center is used for validation of the proposed CGPNN and compared results with machine learning models from the literature on the same data set. The proposed method has outstripped the machine learning models from the literature and resulted in 97percent prediction accuracy.", notes = "also known as \cite{electronics10010067}", } @Article{Ulloa-Cazarez:2018:AAI, author = "Rosa Leonor Ulloa-Cazarez and Cuauhtemoc Lopez-Martin and Alain Abran and Cornelio Yanez-Marquez", title = "Prediction of Online Students Performance by Means of Genetic Programming", journal = "Applied Artificial Intelligence", year = "2018", volume = "32", number = "9-10", pages = "858--881", keywords = "genetic algorithms, genetic programming", ISSN = "0883-9514", publisher = "Taylor and Francis", DOI = "doi:10.1080/08839514.2018.1508839", size = "24 pages", abstract = "Problem: Online higher education (OHE) failure rates reach 40percent worldwide. Prediction of student performance at early stages of the course calendar has been proposed as strategy to prevent student failure. Objective: To investigate the application of genetic programming (GP) to predict the final grades (FGs) of on-line students using grades from an early stage of the course as the independent variable. Method: Data were obtained from the learning management system; we performed statistical analyses over FGs as dependent variable and 11 independent variables; two statistical and one GP models were generated; the prediction accuracies of the models were compared by means of a statistical test. Results: GP model was better than statistical models with confidence levels of 90percent and 99 percent for the training testing data sets respectively. These results suggest that GP could be implemented for supporting decision making process in OHE for early student failure prediction.", notes = "Department of Information Systems, Universidad de Guadalajara, Zapopan, Mexico Also known as \cite{journals/aai/Ulloa-CazarezMA18},", } @Article{Ulloa-Cazarez:GPEM, author = "Rosa Leonor Ulloa-Cazarez", title = "{Joseph E. Aoun}: Robot-proof: higher education at the age of artificial intelligence", journal = "Genetic Programming and Evolvable Machines", year = "2020", volume = "21", number = "1-2", pages = "265--267", month = jun, note = "Book review", keywords = "genetic algorithms, genetic programming, AI, STEM, Data literacy, white collar workers, Co-Op", ISSN = "1389-2576", URL = "https://link.springer.com/article/10.1007/s10710-019-09365-1", URL = "https://rdcu.be/dR8Zy", DOI = "doi:10.1007/s10710-019-09365-1", size = "3 pages", notes = "MIT Press, 2018, pp 216, ISBN: 978-0-262-53597-7 'evolvable machines will acquire by themselves creative skills and provide technical support to other electronic systems'", } @Article{journals/jam/UmarSSP20, title = "Predicting the Viscosity of Petroleum Emulsions Using Gene Expression Programming (GEP) and Response Surface Methodology (RSM)", author = "Abubakar A. Umar and Ismail M. Saaid and Aliyu A. Sulaimon and Rashidah M. Pilus", journal = "J. Appl. Math", year = "2020", volume = "2020", keywords = "genetic algorithms, genetic programming, gene expression programming", bibdate = "2020-07-16", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/jam/jam2020.html#UmarSSP20", pages = "6215352:1--6215352:9", DOI = "doi:10.1155/2020/6215352", } @InProceedings{Umeda:2018:GCCE, author = "Taichi Umeda and Kazuya Shibagaki and Yusuke Nozaki and Masaya Yoshikawa", booktitle = "2018 IEEE 7th Global Conference on Consumer Electronics (GCCE)", title = "Lethal Genes Aware Genetic Programming Analysis for RO PUF", year = "2018", pages = "758--759", abstract = "Security of edge computing devices has become the most important issue in internet of things IoT era. Regarding the security, physical unclonable function (PUF) as forgery prevention technology has attracted attention. PUF uses the random physical feature of semiconductor as unique ID. From a viewpoint of feasibility of PUF, evaluation of tamper resistance is important. The evaluation accuracy depends on modelling accuracy of PUF. The modeling requires information of the detailed architecture of PUF. On the other hand, genetic programming (GP) based analysis which can analysis without PUF models has been proposed recently. However, the GP based analysis generates many lethal genes, which reduce evaluation accuracy, during analysis. This study proposes a new GP based analysis, which not only generates no lethal gene, but also requires no PUF models.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/GCCE.2018.8574699", ISSN = "2378-8143", month = oct, notes = "Also known as \cite{8574699}", } @InProceedings{Umeda:2018:ICCIA, author = "Taichi Umeda and Yusuke Nozaki and Masaya Yoshikawa", title = "Dynamic Adaptive Mutation Based Genetic Programming for Ring Oscillator {PUF}", booktitle = "2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)", year = "2018", pages = "210--213", month = jul, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICCIA.2018.00047", abstract = "Recently, the damage caused by semiconductor counterfeit has become a serious problem in our lives. Physical Unclonable Function (PUF) has attracted attention as a countermeasure method. In the countermeasures, a ring oscillator (RO) PUF is one of the most popular PUFs. Regarding tamper resistance of RO PUFs, Genetic Programming (GP) based attacks have been proposed. However, the GP based attacks only apply the basic genetic strategy. To evaluate tamper resistance of RO PUF accurately, improvement of GP based attack for RO PUF is important. Therefore, this study proposes an accurate attack which is based on GP using dynamic adaptive mutation.", notes = "Also known as \cite{8711500}", } @Article{umer:2022:Materials, author = "Usama Umer and Syed Hammad Mian and Muneer Khan Mohammed and Mustufa Haider Abidi and Khaja Moiduddin and Hossam Kishawy", title = "Tool Wear Prediction When Machining with Self-Propelled Rotary Tools", journal = "Materials", year = "2022", volume = "15", number = "12", pages = "Article No. 4059", keywords = "genetic algorithms, genetic programming", ISSN = "1996-1944", URL = "https://www.mdpi.com/1996-1944/15/12/4059", DOI = "doi:10.3390/ma15124059", abstract = "The performance of a self-propelled rotary carbide tool when cutting hardened steel is evaluated in this study. Although various models for evaluating tool wear in traditional (fixed) tools have been introduced and deployed, there have been no efforts in the existing literature to predict the progression of tool wear while employing self-propelled rotary tools. The work-tool geometric relationship and the empirical function are used to build a flank wear model for self-propelled rotary cutting tools. Cutting experiments are conducted on AISI 4340 steel, which has a hardness of 54–56 HRC, at various cutting speeds and feeds. The rate of tool wear is measured at various intervals of time. The constant in the proposed model is obtained using genetic programming. When experimental and predicted flank wear are examined, the established model is found to be competent in estimating the rate of rotary tool flank wear progression.", notes = "also known as \cite{ma15124059}", } @InProceedings{Unachak:2021:JCSSE, author = "Prakarn Unachak and Prayat Puangjaktha", title = "Evolving Compact Prediction Model for PM2.5 level of Chiang Mai Using Multiobjective Multigene Symbolic Regression", booktitle = "2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE)", year = "2021", abstract = "In recent years, fine particulate matter (PM2.5) has caused economic and health-related adversities to people of Northern Thailand. An accurate predictive model would allow residents to take precautions for their safeties. Also, a human-readable predictive model can lead to better understandings of the issues. In this paper, we use multigene symbolic regression, a genetic programming (GP) approach, to create predictive models for PM2.5 levels in the next 3 hours. This approach creates mathematical models consists of multiple simpler trees for equivalent expressiveness to conventional GP. We also used Non-dominated Sorting Genetic Algorithm-II (NSGA-II), a multiobjective optimization technique, to ensure accurate yet compact models. Using pollutants and meteorological data from Yupparaj Wittayalai monitoring station, combined with satellite-based fire hotspots data from Fire Information of Resource Management System (FIRMS), our approach has created compact human-readable models with better or comparable accuracies to benchmark approaches, as well as identifies possible nonlinear relationships in the dataset.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/JCSSE53117.2021.9493833", ISSN = "2642-6579", month = jun, notes = "Also known as \cite{9493833}", } @PhdThesis{Unal:thesis, author = "Hamit Taner Unal", title = "A novel architecture design for multi-layer neural networks by using genetic algorithms: neural logic networks circuits", school = "Selcuk Universitesi Fen Bilimleri Enstitusu", year = "2023", month = "9 " # mar, keywords = "genetic algorithms, genetic programming, ANN, Artificial Intelligence, Deep Learning, Machine Learning, Artificial Neural Networks, Optimization, Yapay Zeka, Derin Ogrenme, Makine Ogrenmesi, Yapay Sinir Aglari, Optimizasyon, Genetik Algoritma", URL = "https://hdl.handle.net/20.500.12395/50746", URL = "https://acikerisim.selcuk.edu.tr/bitstreams/395b70b2-a99f-454c-b960-61f130fc9c7c/download", size = "170 pages", abstract = "We introduce Neural Logic Circuits (NLC), a novel artificial intelligence paradigm inspired by all-or-none character of biological neurons and neuroplasticity of human brain. Developed as an alternative to multilayer artificial neural networks and deep learning models, the proposed paradigm is constructed by artificial neurons arranged in three-dimensional space and synaptic connections between those neurons. The artificial neurons are represented by logic Gates and Genetic Algorithms are used for the design and continuous optimization of neural connections in the network architecture. Unlike classical ANNs, our proposed structure has no layers and weights. All operations are performed in binary and there exists no complex operations such as activation functions or backpropagation. With the help of training data, the optimization algorithm builds the network from minimal scale and augments gradually. The proposed NLC paradigm is evaluated through a serious of experiments by using advanced accuracy metrics and the results are compared with modern and competitive machine learning algorithms. The experimental results proved the efficiency of our proposed paradigm by outperforming all other machine learning models on all test instances. Our NLC model achieved 23.83percent better performance on F1-Score and 11.47percent better performance on Balanced Accuracy compared to average of best results by other evaluated models. This is a clear indicator of our models superior ability to classify imbalanced datasets. The resulting NLC networks turned out to be very sparse structures, indicating strong plausibility of biological facts. The sparsity of those networks also proved that NLC can be used as a competitive feature selector. The transparent structure of the circuits obtained with NLC highlights the parameters that affect the result and makes important contributions to explainable artificial intelligence.", notes = "In turkish", } @Article{UNCUOGLU:2022:asoc, author = "Erdal Uncuoglu and Hatice Citakoglu and Levent Latifoglu and Savas Bayram and Mustafa Laman and Mucella Ilkentapar and A. Alper Oner", title = "Comparison of neural network, Gaussian regression, support vector machine, long short-term memory, multi-gene genetic programming, and M5 Trees methods for solving civil engineering problems", journal = "Applied Soft Computing", volume = "129", pages = "109623", year = "2022", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2022.109623", URL = "https://www.sciencedirect.com/science/article/pii/S156849462200672X", keywords = "genetic algorithms, genetic programming, Machine learning methods, Construction cost, Laterally loaded pile, Evaporation, Artificial neural network, Multi-gene genetic programming, M5Tree", abstract = "In this study, it was investigated that how machine learning (ML) methods show performance in different problems having different characteristics. Six ML approaches including Artificial neural networks (ANN), gaussian process regression (GPR), support vector machine regression (SVMR), long short-term memory (LSTM), multi-gene genetic programming (MGGP) and M5 model tree (M5Tree) were used to analyze three independent civil engineering problems belonging to construction management, geotechnical engineering, and hydrological engineering sub-disciplines. Mean absolute percentage error (MAPE), root mean square error (RMSE), coefficient of determination (R2), relative root means square error (RRMSE), Nash-Sutcliffe efficiency (NSE), Kling-Gupta efficiency (KGE), and overall index of model performance (OI) criteria were used to evaluate the performances of the models. Besides performance criteria, the relative performances of the six ML models were assessed using Taylor diagram, Violin diagram and One-Tailed Wilcoxon Signed-Rank Test. For each of the problem considered in this study, the effectiveness of the input parameters on the output parameter has been defined using the Relief Method and Correlation Coefficient. The results show that ANN and MGGP models yielded the most successful estimations for three different problems considered. The best prediction was achieved by MGGP model for hydrological engineering problem. For the construction management, geotechnical engineering problems, the best results were obtained using the ANN model. All models were reliable to solve the geotechnical engineering and hydrological engineering problems while LSTM and SVMR models are not reliable to solve the construction management problem. The most and least effective input parameters on output parameter were contract cost (CC) and work definition number (WDN) for the managerial data set. On the other hand, the most and least effective input parameters on the output parameters for the experimental and natural data sets have been obtained as width of the pile (B), rotation degree (R) and minimum temperature (Tmin), streamflow (Q) data, respectively. The number of data and data selection have a significant effect on the homogeneity of the data set and its representativeness of the problem. The error values obtained in test stage are affected from this condition. The equations to calculate the outputs of each of the problem considered were obtained using MGGP and M5Tree models", } @InProceedings{unemi:1998:AFSS, author = "Tatsuo Unemi", title = "A Design of Multi-Field User Interface for Simulated Breeding", booktitle = "The Third Asian Fuzzy System Symposium", year = "1998", pages = "489--494", address = "Kyungnam University, Masan, Korea", month = "18-21 " # jun, organisation = "Korea Fuzzy Logic and Intelligent Systems Society (KFIS)", keywords = "genetic algorithms, genetic programming, simulated breeding, interactive evolutionary computing, graphical user interface, genetic art", URL = "https://www.koreascience.or.kr/article/CFKO199811920543230.page", URL = "ftp://ftp.t.soka.ac.jp/users/unemi/papers/AFSS98.pdf", size = "6 pages", abstract = "a design of graphical user interface for a simulated breeding tool with multifield. The term field is used here as a population of visualized individuals that are candidates of selection. Multi-field interface enables the user to breed his/her favorite phenotypes by selection independently in each field, and he/she can copy arbitrary individual into another field. As known on genetic algorithms, a small population likely leads to premature convergence trapped by a local optimum, and migration among plural populations is useful to escape from local optimum. The multi-field user interface provides easy implementation of migration and wider diversity. We show the usefulness of multi-field user interface through an example of a breeding system of 2D CG images.", notes = "AFSS'98 cited by \cite{takagi:2001:ieee} Department of Information Systems Science, Soka University, Hachioji, Japan See also https://doi.org/10.1007/978-1-84882-285-6_12", } @InProceedings{unemi:1999:KES, author = "Tatsuo Unemi", title = "SBART 2.4: breeding 2D CG images and movies and creating a type of collage", booktitle = "Third International Conference on Knowledge-Based Intelligent Information Engineering Systems, KES 1999", year = "1999", pages = "288--291", address = "Adelaide, Australia", month = "31 " # aug # "-1 " # sep, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Interactive Evolutionary Computing, Simulated Breeding, CG Art", URL = "ftp://ftp.t.soka.ac.jp/users/unemi/papers/KES99.pdf", size = "4 pages", abstract = "Proposes a method to embed a function to create a type of collage into an application of interactive evolutionary computing for artistic computer graphics (CG) design named SBART (Simulated Breeding for ART). It is an application of the simulated breeding method, which is an extended popular version of K. Sims' (Computer Graphics, vol. 25, no. 4, pp. 319-28, 1991) system that enables the user to create an abstract drawing by selecting his/her favourite images displayed on a computer screen. The latest version (v. 2.4) includes a facility to create a type of collage from external image data and files. By combining it with other, previously implemented functions, such as a multi-field user interface, direct editing of genotypes, and so on, the variety of production is greatly extended", notes = "cited by \cite{takagi:2001:ieee}", } @InProceedings{unemi:2000:SAI, author = "Tatsuo Unemi", title = "SBART 2.4: An IEC tool for creating 2D images, movies, and collage", booktitle = "Genetic Algorithms in Visual Art and Music", year = "2000", editor = "Colin G. Johnson and Juan Jesus Romero Cardalda", pages = "153--157", address = "Las Vegas, Nevada, USA", month = "8 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://www.intlab.soka.ac.jp/~unemi/1/unizh/paper/GAVAM2k.pdf", URL = "http://citeseer.ist.psu.edu/369752.html", abstract = "An overview of the inside of SBART 2.4, an interactive tool to create an abstract 2D images, collage, and movies. It is one of the successors of Karl Sims system running on a small size computer, which uses a function to calculate the color value for each pixel as a genotype. All of the ranges and domains are three dimensional vectors. It has multi-field user interface to enhance the diversity of production. It also has optional facilities to create collage of external images, and to make a short movie.", notes = "GECCO-2000WKS Part of \cite{wu:2000:GECCOWKS}", } @InProceedings{Unemi:2010:cec, author = "Tatsuo Unemi", title = "{SBArt4} -- {Breeding} abstract animations in realtime", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, GPU", isbn13 = "978-1-4244-6910-9", abstract = "SBART was developed in early 1990's as one of the derivatives from Artificial Evolution by Karl Sims. It has a functionality to create a movie from a bred image through post-processing. The innovation of graphics processing unit (GPU) in these years improved the calculation performance to be fast enough to realize breeding animations in realtime on the personal computer. SBArt4 uses the advantage of GPU by compiling each expression in genotype into a type of OpenGL shading language. Even when it renders each frame of the animation in realtime, it achieves enough speed for users to evaluate the product of an abstract animation immediately. Though there were a number of problems because of architectural difference between CPU and GPU, almost compatible functionalities with the previous version have been implemented including reference to an external image and integer-based bitwise operation. Through experimental executions on some different hardware configurations, it was certified that it runs fast enough on recent consumer machines though some older machines are not powerful enough.", DOI = "doi:10.1109/CEC.2010.5586293", notes = "WCCI 2010. Also known as \cite{5586293}", } @InProceedings{uno:1999:EBESIL, author = "Kimitaka Uno and Akira Namatame", title = "Evolutionary Behaviors Emerged through Strategic Interactions in the Large", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1414--1421", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "artificial life, adaptive behavior and agents", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-050.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-050.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{WSEAS_458-183_Unold, author = "Olgierd Unold", title = "Context--free grammar induction using evolutionary methods", booktitle = "WSEAS AIC-ISTASC-ISCGAV", year = "2003", month = "15-17 " # nov, editor = "Nikos Mastorakis", address = "Rhodes, Greece", organisation = "The World Scientific and Engineering Academy and Society (WSEAS)", keywords = "genetic algorithms, genetic programming, Grammatical inference, context--free grammars, natural language processing, evolutionary computation", URL = "http://www.wseas.us/e-library/conferences/digest2003/papers/digest.htm", URL = "http://www.wseas.us/e-library/conferences/digest2003/papers/458-183.pdf", size = "6 pages", abstract = "The research into the ability of building self-learning natural language parser based on context--free grammar (CFG ) was presented. The paper investigates the use of evolutionary methods: a genetic algorithm, a genetic programming and learning classifier systems for inferring CFG based parser. The experiments were conducted on the real set of natural language sentences. The gained results confirm the feasibility of applying evolutionary algorithms for context-free grammatical inference.", } @InProceedings{upadhyay:2024:CEC, author = "Parth C. Upadhyay and G. N. DeSouza and John A. Lory", title = "Sequential Transfer with Multi-Objective Genetic Algorithm for Feature Selection of Small, High-Dimensional Datasets", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, Accuracy, Evolutionary computation, Feature extraction, Genetics, Linear programming, Robustness, Optimization, Genetic algorithm, feature selection, sequential transfer learning, multi-objective optimization", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10612068", abstract = "Performing feature selection on a small number of instances with high-dimensional datasets poses a needed challenge in preventing over-fitting. To address this issue, this paper proposes a sequential transfer-learning approach combined with a multi-objective genetic algorithm (STMO-GA) for feature selection. Firstly, for the multi-objective component of our method, we employ a Non-dominated Sorting Genetic Algorithm (NSGA-II) to generate a Pareto front. Then, features are ranked based on their number of appearances in the same Pareto front. Next, during the sequential knowledge transfer process, the ranked features are iteratively selected until a predetermined n number of features remains. This feature subspace is further refined by a k-fold cross-validation operation, starting from the rank-one feature, to determine the cut-off of the n features that will remain. Comparative evaluations against both GA-based as well as traditional feature selection methods demonstrate that the proposed method achieves superior classification accuracy, while retaining the smallest number or a comparable number of features.", notes = "also known as \cite{10612068} Is this GP? WCCI 2024", } @InProceedings{Urbano:2013:ECAL, author = "Paulo Urbano and Loukas Georgiou", title = "Improving Grammatical Evolution in {Santa Fe} Trail using Novelty Search", booktitle = "Advances in Artificial Life, ECAL 2013", year = "2013", editor = "Pietro Lio and Orazio Miglino and Giuseppe Nicosia and Stefano Nolfi and Mario Pavone", series = "Complex Adaptive Systems", pages = "917--924", address = "Taormina, Italy", month = sep # " 2-6", publisher = "MIT Press", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", isbn13 = "978-0-262-31709-2", DOI = "doi:10.7551/978-0-262-31709-2-ch137", size = "8 pages", abstract = "Grammatical Evolution is an evolutionary algorithm that can evolve complete programs using a Backus Naur form grammar as a plug-in component to describe the output language. An important issue of Grammatical Evolution, and evolutionary computation in general, is the difficulty in dealing with deceptive problems and avoid premature convergence to local optima. Novelty search is a recent technique, which does not use the standard fitness function of evolutionary algorithms but follows the gradient of behavioural diversity. It has been successfully used for solving deceptive problems mainly in neuro-evolutionary robotics where it was originated. This work presents the first application of Novelty Search in Grammatical Evolution (as the search component of the later) and benchmarks this novel approach in a well known deceptive problem, the Santa Fe Trail. For the experiments, two grammars are used: one that defines a search space semantically equivalent to the original Santa Fe Trail problem as defined by Koza and a second one which were widely used in the Grammatical Evolution literature, but which defines a biased search space. The application of novelty search requires to characterise behaviour, using behaviour descriptors and compare descriptions using behaviour similarity metrics. The conducted experiments compare the performance of standard Grammatical Evolution and its Novelty Search variation using four intuitive behaviour descriptors. The experimental results demonstrate that Grammatical Evolution with Novelty Search outperforms the traditional fitness based Grammatical Evolution algorithm in the Santa Fe Trail problem demonstrating a higher success rates and better solutions in terms of the required steps.", notes = "jGE Netlogo. http://www.dmi.unict.it/ecal2013/ http://mitpress.mit.edu/books/advances-artificial-life-ecal-2013 ECAL-2013", } @InProceedings{conf/tpnc/UrbanoNT14, author = "Paulo Urbano and Enrique Naredo and Leonardo Trujillo", title = "Generalization in Maze Navigation Using Grammatical Evolution and Novelty Search", booktitle = "Third International Conference on Theory and Practice of Natural Computing, TPNC 2014", year = "2014", editor = "Adrian Horia Dediu and Manuel Lozano and Carlos Martin-Vide", volume = "8890", series = "Lecture Notes in Computer Science", pages = "35--46", address = "Granada, Spain", month = dec # " 9-11", publisher = "Springer", keywords = "genetic algorithms, genetic programming, grammatical evolution", bibdate = "2014-12-05", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/tpnc/tpnc2014.html#UrbanoNT14", isbn13 = "978-3-319-13748-3", URL = "https://rdcu.be/dHi36", URL = "http://dx.doi.org/10.1007/978-3-319-13749-0", DOI = "doi:10.1007/978-3-319-13749-0_4", size = "12 pages", abstract = "Recent research on evolutionary algorithms has begun to focus on the issue of generalization. While most works emphasize the evolution of high quality solutions for particular problem instances, others are addressing the issue of evolving solutions that can generalize in different scenarios, which is also the focus of the present paper. we compare fitness-based search, Novelty Search (NS), and random search in a set of generalization oriented experiments in a maze navigation problem using Grammatical Evolution (GE), a variant of Genetic Programming. Experimental results suggest that NS outperforms the other search methods in terms of evolving general navigation behaviours that are able to cope with different initial conditions within a static deceptive maze.", notes = "LabMAg, Universidade de Lisboa, 1749-016, Lisbon, Portugal", } @InProceedings{Urbanowicz:2008:gecco, author = "Ryan J. Urbanowicz and Nate Barney and Bill C. White and Jason H. Moore", title = "Mask functions for the symbolic modeling of epistasis using genetic programming", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "339--346", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p339.pdf", DOI = "doi:10.1145/1389095.1389154", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, function set, genetic analysis, genetic epidemiology, genetic mask, symbolic discriminant analysis, symbolic regression, Two-Locus model, Bioinformatics, computational biology", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389154}", } @InProceedings{Urbanowicz:2017:GPTP, author = "Ryan J. Urbanowicz and Ben Yang and Jason H. Moore", title = "Problem Driven Machine Learning by Co-evolving Genetic Programming Trees and Rules in a Learning Classifier System", booktitle = "Genetic Programming Theory and Practice XV", editor = "Wolfgang Banzhaf and Randal S. Olson and William Tozier and Rick Riolo", year = "2017", series = "Genetic and Evolutionary Computation", pages = "55--71", address = "University of Michigan in Ann Arbor, USA", month = may # " 18--20", organisation = "the Center for the Study of Complex Systems", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-90511-2", URL = "https://link.springer.com/chapter/10.1007/978-3-319-90512-9_4", DOI = "doi:10.1007/978-3-319-90512-9_4", abstract = "A persistent challenge in data mining involves matching an applicable as well as effective machine learner to a target problem. One approach to facilitate this process is to develop algorithms that avoid modelling assumptions and seek to adapt to the problem at hand. Learning classifier systems (LCSs) have proven themselves to be a flexible, interpretable, and powerful approach to classification problems. They are particularly advantageous with respect to multivariate, complex, or heterogeneous patterns of association. While LCSs have been successfully adapted to handle continuous-valued endpoint (i.e. regression) problems, there are still some key performance deficits with respect to model prediction accuracy and simplicity when compared to other machine learners. In the present study we propose a strategy towards improving LCS performance on supervised learning continuous-valued endpoint problems. Specifically, we hypothesize that if an LCS population includes and co-evolves two disparate representations (i.e. LCS rules, and genetic programming trees) than the system can adapt the appropriate representation to best capture meaningful patterns of association, regardless of the complexity of that association, or the nature of the endpoint (i.e. discrete vs. continuous). To successfully integrate these modelling representations, we rely on multi-objective fitness (i.e. accuracy, and instance coverage) and an information exchange mechanism between the two representation species. This paper lays out the reasoning for this approach, introduces the proposed methodology, and presents basic preliminary results supporting the potential of this approach as an area for further evaluation and development.", notes = "GPTP 2017, Part of \cite{Banzhaf:2017:GPTP} published after the workshop in 2018", } @InProceedings{Urbanowicz:2022:GPTP, author = "Ryan Urbanowicz and Robert Zhang and Yuhan Cui and Pranshu Suri", title = "STREAMLINE: A Simple, Transparent, End-To-End Automated Machine Learning Pipeline Facilitating Data Analysis and Algorithm Comparison", booktitle = "Genetic Programming Theory and Practice XIX", year = "2022", editor = "Leonardo Trujillo and Stephan M. Winkler and Sara Silva and Wolfgang Banzhaf", series = "Genetic and Evolutionary Computation", pages = "201--231", address = "Ann Arbor, USA", month = jun # " 2-4", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-981-19-8459-4", DOI = "doi:10.1007/978-981-19-8460-0_9", abstract = "Machine learning (ML) offers powerful methods for detecting and modeling associations often in data with large feature spaces and complex associations. Many useful tools/packages (e.g. scikit-learn) have been developed to make the various elements of data handling, processing, modeling, and interpretation accessible. However, it is not trivial for most investigators to assemble these elements into a rigorous, replicatable, unbiased, and effective data analysis pipeline. Automated machine learning (AutoML) seeks to address these issues by simplifying the process of ML analysis for all. Here, we introduce STREAMLINE, a simple, transparent, end-to-end AutoML pipeline designed as a framework to easily conduct rigorous ML modeling and analysis (limited initially to binary classification). STREAMLINE is specifically designed to compare performance between datasets, ML algorithms, and other AutoML tools. It is unique among other autoML tools by offering a fully transparent and consistent baseline of comparison using a carefully designed series of pipeline elements including (1) exploratory analysis, (2) basic data cleaning, (3) cross validation partitioning, (4) data scaling and imputation, (5) filter-based feature importance estimation, (6) collective feature selection, (7) ML modeling with Optuna hyperparameter optimization across 15 established algorithms (including less well-known Genetic Programming and rule-based ML), (8) evaluation across 16 classification metrics, (9) model feature importance estimation, (10) statistical significance comparisons, and (11) automatically exporting all results, plots, a PDF summary report, and models that can be easily applied to replication data.", notes = "Part of \cite{Banzhaf:2022:GPTP} published after the workshop in 2023", } @Misc{DBLP:journals/corr/abs-2203-00528, author = "Thomas Uriot and Marco Virgolin and Tanja Alderliesten and Peter A. N. Bosman", title = "On genetic programming representations and fitness functions for interpretable dimensionality reduction", howpublished = "arXiv", volume = "abs/2203.00528", year = "2022", keywords = "genetic algorithms, genetic programming", URL = "https://arxiv.org/abs/2203.00528", eprinttype = "arXiv", eprint = "2203.00528", timestamp = "Mon, 07 Mar 2022 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/corr/abs-2203-00528.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{uriot:2022:GECCO, author = "Thomas Uriot and Marco Virgolin and Tanja Alderliesten and Peter Bosman", title = "On genetic programming representations and fitness functions for interpretable dimensionality reduction", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference", year = "2022", editor = "Alma Rahat and Jonathan Fieldsend and Markus Wagner and Sara Tari and Nelishia Pillay and Irene Moser and Aldeida Aleti and Ales Zamuda and Ahmed Kheiri and Erik Hemberg and Christopher Cleghorn and Chao-li Sun and Georgios Yannakakis and Nicolas Bredeche and Gabriela Ochoa and Bilel Derbel and Gisele L. Pappa and Sebastian Risi and Laetitia Jourdan and Hiroyuki Sato and Petr Posik and Ofer Shir and Renato Tinos and John Woodward and Malcolm Heywood and Elizabeth Wanner and Leonardo Trujillo and Domagoj Jakobovic and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Inmaculada Medina-Bulo and Slim Bechikh and Andrew M. Sutton and Pietro Simone Oliveto", pages = "458--466", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, Evolutionary Machine Learning, dimensionality reduction, interpretability unsupervised learning", isbn13 = "978-1-4503-9237-2", DOI = "doi:10.1145/3512290.3528849", abstract = "Dimensionality reduction (DR) is an important technique for data exploration and knowledge discovery. However, most of the main DR methods are either linear (e.g., PCA), do not provide an explicit mapping between the original data and its lower-dimensional representation (e.g., MDS, t-SNE, isomap), or produce mappings that cannot be easily interpreted (e.g., kernel PCA, neural-based autoencoder). Recently genetic programming (GP) has been used to evolve interpretable DR mappings in the form of symbolic expressions. There exists a number of ways in which GP can be used to this end and no study exists that performs a comparison. In this paper, we fill this gap by comparing existing GP methods as well as devising new ones. We evaluate our methods on several benchmark datasets based on predictive accuracy and on how well the original features can be reconstructed using the lower-dimensional representation only. Finally we qualitatively assess the resulting expressions and their complexity. We find that various GP methods can be competitive with state-of-the-art DR algorithms and that they have the potential to produce interpretable DR mappings.", notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @InProceedings{conf/ppsn/UrliWN12, author = "Tommaso Urli and Markus Wagner and Frank Neumann", title = "Experimental Supplements to the Computational Complexity Analysis of Genetic Programming for Problems Modelling Isolated Program Semantics", booktitle = "Parallel Problem Solving from Nature, PPSN XII (part 1)", year = "2012", editor = "Carlos A. {Coello Coello} and Vincenzo Cutello and Kalyanmoy Deb and Stephanie Forrest and Giuseppe Nicosia and Mario Pavone", volume = "7491", series = "Lecture Notes in Computer Science", pages = "102--112", address = "Taormina, Italy", month = sep # " 1-5", publisher = "Springer", keywords = "genetic algorithms, genetic programming, problem complexity, multiple objective optimisation, experimental evaluation", isbn13 = "978-3-642-32936-4", DOI = "doi:10.1007/978-3-642-32937-1_11", size = "11 pages", abstract = "In this paper, we carry out experimental investigations that complement recent theoretical investigations on the runtime of simple genetic programming algorithms [3, 7]. Crucial measures in these theoretical analyses are the maximum tree size that is attained during the run of the algorithms as well as the population size when dealing with multi-objective models. We study those measures in detail by experimental investigations and analyse the runtime of the different algorithms in an experimental way.", notes = "Cited by \cite{Nguyen:2013:foga}", bibsource = "DBLP, http://dblp.uni-trier.de", affiliation = "DIEGM, Universita degli Studi di Udine, 33100 Udine, Italy", } @InProceedings{Urquhart:2021:evoapplications, author = "Neil Urquhart and Emma Hart and Silke Hoehl", title = "Automated, Explainable Rule Extraction from {MAP-Elites} archives", booktitle = "24th International Conference, EvoApplications 2021", year = "2021", month = "7-9 " # apr, editor = "Pedro Castillo and Juanlu Jimenez-Laredo", series = "LNCS", volume = "12694", publisher = "Springer Verlag", address = "virtual event", pages = "258--272", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-030-72698-0", DOI = "doi:10.1007/978-3-030-72699-7_17", abstract = "Quality-diversity (QD) algorithms that return a large archive of elite solutions to a problem provide insights into how high-performing solutions are distributed throughout a feature-space defined by a user. they are often described as illuminating the feature-space, providing a qualitative illustration of relationships between features and objective quality. However, if there are 1000s of solutions in an archive, extracting a succinct set of rules that capture these relationships in a quantitative manner (i.e. as a set of rules) is challenging. We propose two methods for the automated generation of rules from data contained in an archive; the first uses Genetic Programming and the second, a rule-induction method known as CN2. Rules are generated from large archives of data produced by running MAP-Elites on an urban logistics problem. A quantitative and qualitative evaluation that includes the end-user demonstrate that the rules are capable of fitting the data, but also highlights some mismatches between the model used by the optimiser and that assumed by the user.", notes = "http://www.evostar.org/2021/ EvoApplications2021 held in conjunction with EuroGP'2021, EvoCOP2021 and EvoMusArt2021", } @InProceedings{ursem:2002:gpwsofaedas, author = "Rasmus K. Ursem and Thiemo Krink", title = "Genetic Programming with Smooth Operators for Arithmetic Expressions: Diviplication and Subdition", booktitle = "Proceedings of the 2002 Congress on Evolutionary Computation CEC2002", editor = "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and Mark Shackleton", pages = "1372--1377", year = "2002", publisher = "IEEE Press", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", organisation = "IEEE Neural Network Council (NNC), Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", ISBN = "0-7803-7278-6", month = "12-17 " # may, notes = "CEC 2002 - A joint meeting of the IEEE, the Evolutionary Programming Society, and the IEE. Held in connection with the World Congress on Computational Intelligence (WCCI 2002)", keywords = "genetic algorithms, genetic programming, arithmetic expressions, search space, smooth operators, system identification problem, search problems", URL = "http://www.evalife.dk/publications/RKU_CEC2002_smooth_operators.ps.gz", URL = "http://citeseer.ist.psu.edu/527466.html", DOI = "doi:10.1109/CEC.2002.1004443", abstract = "This paper introduces the smooth operators for arithmetic expressions as an approach to smoothing the search space in Genetic Programming (GP). Smooth operator GP interpolates between arithmetic operators such as times and divide, thereby allowing a gradual adaptation to the problem. The suggested approach is compared to traditional GP on a system identification problem", } @InProceedings{urzelai:1998:irs, author = "Joseba Urzelai and Dario Floreano and Marco Dorigo and Marco Colombetti", title = "Incremental Robot Shaping", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "832--842", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "Evolutionary Robotics", ISBN = "1-55860-548-7", notes = "GP-98", } @InProceedings{Ushakov:2010:TCSET, author = "Eduard Ushakov", title = "Evolutionary algorithms in control systems engineering", booktitle = "2010 International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science (TCSET)", year = "2010", month = feb, pages = "204", address = "Lviv-Slavske, Ukraine", keywords = "genetic algorithms, genetic programming, control systems engineering, conventional optimisation, evolutionary algorithms, control system synthesis", isbn13 = "978-966-553-875-2", URL = "http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5446117", size = "1 page", abstract = "Summary form only given. This paper introduces the session describing the state-of-the-art in the application of evolutionary computation in control system engineering. Evolutionary methods such as genetic algorithms (GAs) and genetic programming (GP) are particularly suitable in problems for which conventional optimisers are inefficient or inappropriate, rather than simply as an alternative to conventional optimisation. This session will be of interest to the control engineering community as a whole, and will provide an educational background to those not familiar with these methods as well as presenting new results and applications.", notes = "Also known as \cite{5446117}", } @PhdThesis{Ushie:thesis, author = "Ogri James Ushie", title = "Intelligent optimisation of analogue circuits using particle swarm optimisation, genetic programming and genetic folding", school = "Brunel University", year = "2016", address = "UK", month = apr, keywords = "genetic algorithms, genetic programming, Genetic programming/folding, Modified symbolic circuit analysis in matlab (mscam), Artificial intelligence, Evolutionary computing, Swarm intelligence", URL = "http://bura.brunel.ac.uk/handle/2438/13643", URL = "https://bura.brunel.ac.uk/bitstream/2438/13643/1/FulltextThesis.pdf", size = "184 pages", abstract = "This research presents various intelligent optimisation methods which are: genetic algorithm (GA), particle swarm optimisation (PSO), artificial bee colony algorithm (ABCA), firefly algorithm (FA) and bacterial foraging optimisation (BFO). It attempts to minimise analogue electronic filter and amplifier circuits, taking a cascode amplifier design as a case study, and using the above-mentioned intelligent optimisation algorithms with the aim of determining the best among them to be used. Small signal analysis (SSA) conversion of the cascode circuit is performed while mesh analysis is applied to transform the circuit to matrices form. Computer programmes are developed in Matlab using the above mentioned intelligent optimisation algorithms to minimise the cascode amplifier circuit. The objective function is based on input resistance, output resistance, power consumption, gain, upper frequency band and lower frequency band. The cascode circuit result presented, applied the above-mentioned existing intelligent optimisation algorithms to optimise the same circuit and compared the techniques with the one using Nelder-Mead and the original circuit simulated in PSpice. Four circuit element types (resistors, capacitors, transistors and operational amplifier (op-amp)) are targeted using the optimisation techniques and subsequently compared to the initial circuit. The PSO based optimised result has proven to be best followed by that of GA optimised technique regarding power consumption reduction and frequency response. This work modifies symbolic circuit analysis in Matlab (MSCAM) tool which uses Netlist from PSpice or from simulation to generate matrices. These matrices are used for optimisation or to compute circuit parameters. The tool is modified to handle both active and passive elements such as inductors, resistors, capacitors, transistors and op-amps. The transistors are transformed into SSA and opamp use the SSA that is easy to implement in programming. Results are presented to illustrate the potential of the algorithm. Results are compared to PSpice simulation and the approach handled larger matrices dimensions compared to that of existing symbolic circuit analysis in Matlab tool (SCAM). The SCAM formed matrices by adding additional rows and columns due to how the algorithm was developed which takes more computer resources and limit its performance. Next to this, this work attempts to reduce component count in high-pass, low-pass, and all-pass active filters. Also, it uses a lower order filter to realise same results as higher order filter regarding frequency response curve. The optimisers applied are GA, PSO (the best two methods among them) and Nelder-Mead (the worst method) are used subsequently for the filters optimisation. The filters are converted into their SSA while nodal analysis is applied to transform the circuit to matrices form. Highpass, lowpass, and allpass active filters results are presented to demonstrate the effectiveness of the technique. Results presented have shown that with a computer code, a lower order opamp filter can be applied to realise the same results as that of a higher order one. Furthermore, PSO can realise the best results regarding frequency response for the three results, followed by GA whereas Nelder-Mead has the worst results. Furthermore, this research introduced genetic folding (GF), MSCAM, and automatically simulated Netlist into existing genetic programming (GP), which is a new contribution in this work, which enhances the development of independent Matlab toolbox for the evolution of passive and active filter circuits. The active filter circuit evolution especially when operational amplifier is involved as a component is of it first kind in circuit evolution. In the work, only one software package is used instead of combining PSpice and Matlab in electronic circuit simulation. This saves the elapsed time for moving the simulation between the two platforms and reduces the cost of subscription. The evolving circuit from GP using Matlab simulation is automatically transformed into a symbolic Netlist also by Matlab simulation. The Netlist is fed into MSCAM; where MSCAM uses it to generate matrices for the simulation. The matrices enhance frequency response analysis of low-pass, high-pass, band-pass, band-stop of active and passive filter circuits. After the circuit evolution using the developed GP, PSO is then applied to optimise some of the circuits. The algorithm is tested with twelve different circuits (five examples of the active filter, four examples of passive filter circuits and three examples of transistor amplifier circuits) and the results presented have shown that the algorithm is efficient regarding design.", notes = "Supervsior: M. Abbod supervisor: T. Kalganova", } @Article{Ushie:2017:electroscope, author = "Ogri J. Ushie and Maysam F. Abbod and Evans C. Ashigwuike", title = "Evolution of Active Filter Circuits Design Using Genetic Programming", journal = "International Journal of Electrical and Telecommunication System Research", year = "2017", volume = "9", number = "9", pages = "19--28", month = sep, keywords = "genetic algorithms, genetic programming, modified symbolic circuit analysis in Matlab, genetic folding, genetic programming and automatically simulated netlist", ISSN = "0795-2260", URL = "http://www.electroscopejournal.org.ng/index.php/electros/article/view/116/108", URL = "http://www.electroscopejournal.org.ng/index.php/electros/article/view/116/108.pdf", size = "10 pages", abstract = "This research seeks to introduce genetic folding (GF), modified symbolic circuit analysis in Matlab (MSCAM), and automatically simulated Netlist into existing genetic programming (GP) which is a new contribution of this paper. It enhances the development of independent Matlab toolbox for the evolution of active filter circuits. The active filter circuit evolution, especially when operational amplifiers are involved as components, is of the first kind in circuit evolution. The research uses only one software package instead of combining PSpice and Matlab in electronic circuit simulation as in existing GP. This saves the elapsed time for moving the simulation between the two platforms and reduces the cost of subscription. The evolving circuit from GP/F using Matlab simulation is automatically transformed into a symbolic Netlist. The Netlist is fed into MSCAM; where MSCAM uses it to generate matrices for the simulation. The matrices enhance frequency response analysis of four different active filter circuits (low-pass, high-pass, band-pass, band-stop of active filter circuits). Results presented proved the algorithm's efficiency regarding design wise. The research also provided an alternative method of using GP/F for the evolution of active filter circuit, especially when operational amplifier is involved as a component.", notes = "http://www.electroscopejournal.org.ng/index.php/electros/issue/view/10 electroscope.editor@gmail.com Department of Electronic and Computer Engineering, Nnamdi Azikiwe University, PMB 5052 Awka, Nigeria.", } @Article{usman:2007:IJAMCS, author = "Imran Usman and Asifullah Khan and Rafiullah Chamlawi and Abdul Majid", title = "Image Authenticity and Perceptual Optimization via Genetic Algorithm and a Dependence Neighborhood", journal = "International Journal of Applied Mathematics and Computer Sciences", year = "2007", volume = "4", number = "1", pages = "615--620", keywords = "genetic algorithms, genetic programming, Digital watermarking, fragile watermarking, Image authentication", ISSN = "1305-5313", URL = "http://www.waset.org/ijamcs/v4/v4-1-7.pdf", size = "6 pages", abstract = "Information hiding for authenticating and verifying the content integrity of the multimedia has been exploited extensively in the last decade. We propose the idea of using genetic algorithm and non-deterministic dependence by involving the unwatermarkable coefficients for digital image authentication. Genetic algorithm is used to intelligently select coefficients for watermarking in a DCT based image authentication scheme, which implicitly watermark all the un-watermarkable coefficients also, in order to thwart different attacks. Experimental results show that such intelligent selection results in improvement of imperceptibility of the watermarked image, and implicit watermarking of all the coefficients improves security against attacks such as cover-up, vector quantisation and transplantation.", notes = "Broken Dec 2022 http://www.waset.org/ijamcs/", } @InProceedings{Usman:2008:wsc, author = "Imran Usman and Asifullah Khan and Rafiullah Chamlawi and Tae-Sun Choi", old_title = "A Generalized Approach for Embedding Watermark in Digital Images using LDPC Codes and Genetic Programming", title = "Perceptual Shaping in Digital Image Watermarking Using LDPC Codes and Genetic Programming", old_booktitle = "WSC 2008 Online World Conference on Soft Computing in Industrial Applications", booktitle = "Applications of Soft Computing", year = "2008", editor = "J. Mehnen and M. Koppen A. Saad and A. Tiwari", series = "Advances in Soft Computing", volume = "58", pages = "509--518", address = "http://wsc-2008.softcomputing.org", month = "10--21 " # nov, organisation = "World Federation on Soft Computing", publisher = "Springer", note = "Published 2009", keywords = "genetic algorithms, genetic programming, Digital Watermarking, Low-Density Parity Check codes, Discrete Cosine Transform, Human Visual System", isbn13 = "978-3-540-89618-0", DOI = "doi:10.1007/978-3-540-89619-7_50", abstract = "In this work, we present a generalized scheme for embedding watermark in digital images used for commercial aims. Genetic Programming is used to develop appropriate visual tuning functions, in accordance with Human Visual System, which cater for watermark imperceptibility-robustness trade off in the presence of a series of probable attacks. The use of low-density parity check codes for information encoding further enhances watermark robustness. Experimental results on a dataset of test images show marked improvement in robustness, when compared to the conventional approaches with the same level of visual quality. The proposed scheme is easy to implement and ensures significant robustness for watermarking a large number of small digital images.", notes = "WSC2008 Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Nilore, Islamabad Pakistan", } @Article{Usman:2009:IJICIC, author = "Imran Usman and Asifullah Khan and Asad Ali and Tae-Sun Choi", title = "Reversible Watermarking Based on Intelligent Coefficient Selection and Integer Wavelet Transform", journal = "International Journal of Innovative Computing, Information and Control", year = "2009", volume = "5", number = "12(A)", pages = "4675--4682", month = dec, keywords = "genetic algorithms, genetic programming, Reversible watermarking, Integer wavelet transform, Payload, Imperceptibility, Coefficient selection", ISSN = "1349-418X", URL = "http://www.ijicic.org/vol-5(12)a-2.htm", abstract = "This work presents a loss less data hiding method using integer wavelet transform and Genetic Programming (GP) based intelligent coefficient selection scheme. By exploiting information about the amplitude of the wavelet coefficient and the type of the sub band, GP is used to evolve a mathematical function in view of the payload size and imperceptibility of the marked image. The evolved mathematical function acts like a compact but robust coefficient map for the reversible watermarking approach. Information is embedded into the least significant bit-plane of those high frequency wavelet coefficients that are intelligently selected by the Genetic Programming module. The proposed approach does not only extract the hidden information, but also recovers the original image content. Experimental results demonstrate the effectiveness of this scheme in terms of payload and imperceptibility.", } @Article{Usman2010332, author = "Imran Usman and Asifullah Khan", title = "BCH coding and intelligent watermark embedding: Employing both frequency and strength selection", journal = "Applied Soft Computing", volume = "10", number = "1", pages = "332--343", year = "2010", keywords = "genetic algorithms, genetic programming, Digital watermarking, Genetic Programming, Discrete Cosine Transform, BCH coding, Watermark embedding", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2009.08.004", URL = "http://www.sciencedirect.com/science/article/B6W86-4WYDMVY-3/2/1a633a12967fa19ed0cd96f8e0fea4f4", abstract = "This paper presents a novel approach of adaptive visual tuning of a watermark in Discrete Cosine Transform (DCT) domain. The proposed approach intelligently selects appropriate frequency bands as well as optimal strength of alteration. Genetic Programming (GP) is applied to structure the watermark by exploiting both the characteristics of human visual system and information pertaining to a cascade of conceivable attacks. The developed visual tuning expressions are dependent on frequency and luminance sensitivities, and contrast masking. To further enhance robustness, spread spectrum based watermarking and Bose-Chadhuri-Hocquenghem (BCH) coding is employed. The combination of spread spectrum sequence, BCH coding and GP based non-linear structuring makes it extremely difficult for an attacker to gain information about the secret knowledge of the watermarking system. Experimental results show the superiority of the proposed approach against the existing approaches. Especially, the margin of improvement in robustness will be of high importance in medical and context aware related applications of watermarking.", } @Article{Usman2011582, author = "Imran Usman and Asifullah Khan and Rafiullah Chamlawi", title = "Employing intelligence in the embedding and decoding stages of a robust watermarking system", journal = "AEU - International Journal of Electronics and Communications", volume = "65", number = "6", pages = "582--588", year = "2011", ISSN = "1434-8411", DOI = "doi:10.1016/j.aeue.2010.08.009", URL = "http://www.sciencedirect.com/science/article/B7GWW-51R51HH-1/2/bf42ab85616fd89b6afca28f8079e934", keywords = "genetic algorithms, genetic programming, Artificial Neural Networks, Perceptual shaping, Support Vector Machines", abstract = "This letter presents a novel approach of incorporating intelligence in the encoding and decoding structures of a watermarking system. The employment of computational intelligence makes the watermarking system resistant against a series of attacks, which may occur during the storage or communication of the watermarked work. Keeping in view the Human Visual System, Genetic Programming is used to generate functions which select optimum strength and location of transform domain coefficients for watermark embedding. Support Vector Machines and Artificial Neural Networks are employed at the decoding side to learn about the distortions due to attacks and counteract them. Especially, the proposed system is quite effective for robust watermarking applications of small size images such as those displayed on portable devices and on line catalogues.", } @InProceedings{Usman:2019:WCCS, author = "Imran Usman", title = "Anomalous Crowd Behavior Detection in Time Varying Motion Sequences", booktitle = "2019 4th World Conference on Complex Systems (WCCS)", year = "2019", month = apr, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICoCS.2019.8930795", abstract = "Automated crowd behaviour detection has become a prime research area in recent years. Due to inherent complexities in video sequences and foreground motion patterns, crowd motion analysis faces many challenges. This work uses a statistical model for representation and extraction of local motion patterns in order to generate the feature set. It then uses a Genetic Programming (GP) based classifier to classify normal and abnormal behavior patterns through a supervised learning mechanism. The developed classifier is generic in nature and can be easily implemented in hardware. Experimental results on public datasets validate that the proposed scheme outperforms contemporary techniques in terms of classification accuracy and effectiveness.", notes = "Also known as \cite{8930795}", } @Article{Usman:2020:ACC, author = "Imran Usman and Khaled A. Almejalli", title = "Intelligent Automated Detection of Microaneurysms in Fundus Images Using Feature-Set Tuning", journal = "IEEE Access", year = "2020", volume = "8", pages = "65187--65196", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ACCESS.2020.2985543", ISSN = "2169-3536", abstract = "Due to the widespread of diabetes mellitus and its associated complications, a need for early detection of the leading symptoms in the masses is felt like never before. One of the earliest signs is the presence of microaneurysms (MAs) in the fundus images. This work presents a new technique for automatic detection of MAs in color fundus images. The proposed technique uses Genetic Programming (GP) and a set of 28 selected features from the preprocessed fundus images in order to evolve a mathematical expression. Through the binearisation of the fitness scores, the optimal expression is evolved generation by generation through a stepwise enhancement process. The best expression is then used as a classifier for real world applications. Experimental results using three publically available datasets validate the usefulness of the proposed technique and its ability to outperform the state of the art contemporary approaches.", notes = "Also known as \cite{9056469}", } @Article{Ustoorikar2008177, author = "Ketaki Ustoorikar and M. C. Deo", title = "Filling up gaps in wave data with genetic programming", journal = "Marine Structures", volume = "21", number = "2-3", pages = "177--195", year = "2008", ISSN = "0951-8339", DOI = "doi:10.1016/j.marstruc.2007.12.001", URL = "http://www.sciencedirect.com/science/article/B6V41-4RR20Y8-1/2/76cfad2398264322e376b67c08880225", keywords = "genetic algorithms, genetic programming, Data gaps, Neural networks, Wave heights", abstract = "A given time series of significant wave heights invariably contains smaller or larger gaps or missing values due to a variety of reasons ranging from instrument failures to loss of recorders following human interference. In-filling of missing information is widely reported and well documented for variables like rainfall and river flow, but not for the wave height observations made by rider buoys. This paper attempts to tackle this problem through one of the latest soft computing tools, namely, genetic programming (GP). The missing information in hourly significant wave height observations at one of the data buoy stations maintained by the US National Data Buoy Center is filled up by developing GP models through spatial correlations. The gap lengths of different orders are artificially created and filled up by appropriate GP programs. The results are also compared with those derived using artificial neural networks (ANN). In general, it is found that the in-filling done by GP rivals that by ANN and many times becomes more satisfactory, especially when the gap lengths are smaller. Although the accuracy involved reduces as the amount of gap increases, the missing values for a long duration of a month or so can be filled up with a maximum average error up to 0.21m in the high seas.", } @InProceedings{Uto:2009:WHISPERS, author = "Kuniaki Uto and Yukio Kosugi and Toshinari Ogatay", title = "Evaluation of oak wilt index based on genetic programming", booktitle = "First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS '09", year = "2009", month = aug, pages = "1--4", keywords = "genetic algorithms, genetic programming, binary operations, extraction performance, normalized oak wilt index, physically interpretable index, plant physiology, remotely sensed hyperspectral image, tree construction, image processing, vegetation mapping", DOI = "doi:10.1109/WHISPERS.2009.5289107", abstract = "We proposed a normalized oak wilt index (NWI) to extract oak wilt area from remotely sensed hyperspectral image of forest in our previous work. The NWI, which is designed based on factitious characterization of spectral profiles of oak wilt, realised satisfactory extraction performance. In this paper, we propose a genetic-programming-based search method for physically interpretable index. The search procedure consists of two stages, i.e. extraction for significant binary operations and tree construction, in expectation of dealing with more subtle problem, e.g. estimation of quantities of ingredients of vegetation. The selected binary operations are consistent with plant physiology. The extraction performance of proposed method based on fewer binary operations stands comparison with NWI's performance.", notes = "Also known as \cite{5289107}", } @Article{Uusitalo:2024:GPEM, author = "Severi Uusitalo and Anna Kantosalo and Antti Salovaara and Tapio Takala and Christian Guckelsberger", title = "Creative collaboration with interactive evolutionary algorithms: a reflective exploratory design study", journal = "Genetic Programming and Evolvable Machines", year = "2024", volume = "25", pages = "Article number: 4", note = "Online first", keywords = "genetic algorithms, genetic programming, Human-computer co-creativity, Interactive evolutionary algorithm, Introspection, Autoethnography, Longitudinal study, Design", ISSN = "1389-2576", URL = "https://rdcu.be/duGFW", DOI = "doi:10.1007/s10710-023-09477-9", size = "23 pages", abstract = "Progress in AI has brought new approaches for designing products via co-creative human-computer interaction. In architecture, interior design, and industrial design, computational methods such as evolutionary algorithms support the designer's creative process by revealing populations of computer-generated design solutions in a parametric design space. Because the benefits and shortcomings of such algorithms use in design processes are not yet fully understood, the authors studied the intricate interactions of an industrial designer employing an interactive evolutionary algorithm for a non-trivial creative product design task. In an in-depth report on the in-situ longitudinal experiences arising between the algorithm, human designer, and environment, from ideation to fabrication, they reflect on the algorithm role in inspiring design, its relationship to fixation, and the stages of the creative process in which it yielded perceived value. The paper concludes with proposals for future research into co-creative AI in design exploration and creative practice.", notes = "Department of Design, Aalto University, Espoo, Finland", } @Article{Uyumaz:2014:JHI, author = "Ali Uyumaz and Ali {Danandeh Mehr} and Ercan Kahya and Hilal Erdem", title = "Rectangular side weirs discharge coefficient estimation in circular channels using linear genetic programming approach", journal = "Journal of Hydroinformatics", year = "2014", volume = "16", number = "6", pages = "1318--1330", month = nov, keywords = "genetic algorithms, genetic programming, linear genetic programming, circular channels, discharge measurement, side weirs", URL = "https://iwaponline.com/jh/article-abstract/16/6/1318/3338/Rectangular-side-weirs-discharge-coefficient?redirectedFrom=fulltext", DOI = "doi:10.2166/hydro.2014.112", size = "13 pages", abstract = "Side weirs are diversion structures extensively used in irrigation, flood protection and combined sewer systems. Accurate estimation of the discharge coefficient (Cd) of side weirs is essential to compute the water surface profile over the weirs and to determine the lateral outflow rate from the system. In this paper, we have used a linear genetic programming (LGP) technique to develop new empirical formulas for the estimation of Cd of sharp-edged rectangular side weirs located in circular channels. For this aim, we have employed a total of 1,686 laboratory experimental observations in both sub- and supercritical flow regimes in order to train and validate the proposed models. The performance of the LGP-based models was also compared with those of different multilinear and nonlinear regression models in terms of root mean squared errors, mean absolute errors, and determination coefficient. The results indicated that an explicit LGP-based model using only mathematical functions could be employed successfully in Cd estimation in both sub- and supercritical flow conditions. Genetic-based sensitivity analysis among the input parameters demonstrated that Froude number at upstream of the weir has the most impact on the Cd estimation.", notes = "IWA Publishing. International Association for Hydro-Environment Engineering and Research. International Association of Hydrological Sciences", } @InProceedings{Vacher:2025:GPTP, author = "Quentin Vacher", title = "Advancing Machine Learning with Tangled Program Graphs: The {Gegelati} Framework for Efficient and Reproducible Solutions", booktitle = "Genetic Programming Theory and Practice XXII", year = "2025", editor = "Wolfgang Banzhaf and Bogdan Burlacu and Stephen Kelly and Alexander Lalejini and Fabricio {Olivetti de Franca}", address = "Michigan State University, USA", month = jun # " 5-7", note = "lightning talk", keywords = "genetic algorithms, genetic programming", notes = "To be published after the workshop", } @InProceedings{Vafaee:2008:ICMLA, title = "Adaptively Evolving Probabilities of Genetic Operators", author = "Fatemeh Vafaee and Weimin Xiao and Peter C. Nelson and Chi Zhou", booktitle = "Seventh International Conference on Machine Learning and Applications, ICMLA '08", year = "2008", month = "11-13 " # dec, pages = "292--299", address = "La Jolla, San Diego, USA", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, gene expression programming, mathematical operators, probability adaptive method, differential evolution, evolved evolutionary algorithm, genetic operator probability, numerical optimization model, supplementary mutation operator", DOI = "doi:10.1109/ICMLA.2008.45", abstract = "This work is concerned with proposing an adaptive method to dynamically adjust genetic operator probabilities throughout the evolutionary process. The proposed method relies on the individual preferences of each chromosome, rather than the global behavior of the whole population. Hence, each individual carries its own set of parameters, including the probabilities of the genetic operators. The carried parameters undergo the same evolutionary process as the carriers--the chromosomes - do. We call this method Evolved Evolutionary Algorithm (E2A) as it has an additional evolutionary process to evolve control parameters. Furthermore, E2A employs a supplementary mutation operator (DE-mutation) which uses the previously overlooked numerical optimization model known as the Differential Evolution to expedite the optimization rate of the genetic parameters. To leverage our previous work, we used Gene Expression Programming (GEP) as a benchmark to determine the performance of our proposed method. Nevertheless, E2A can be easily extended to other genetic programming variants. As the experimental results on a wide array of regression problems demonstrate, the E2A method reveals a faster rate of convergence and provides fitter ultimate solutions. However, to further expose the power of the E2A method, we compared it to related methods using self-adaptation previously applied to Genetic Algorithms. Our benchmarking on the same set of regression problems proves the supremacy of our proposed method both in the accuracy and simplicity of the final solutions.", notes = "also known as \cite{4724989}", } @PhdThesis{Vafaee:thesis, author = "Fatemeh Vafaee", title = "Controlling Genetic Operator Rates in Evolutionary Algorithms", school = "University of Illinois at Chicago", year = "2011", address = "USA", keywords = "genetic algorithms, genetic programming, Markov chain model", broken = "http://indigo.uic.edu/handle/10027/8765/browse?value=Vafaee%2C+Fatemeh.&type=author", URL = "http://hdl.handle.net/10027/12777", URL = "https://www.proquest.com/openview/d831bc94fd5da97622b2b8af893cf4b6/1?pq-origsite=gscholar&cbl=18750", size = "472 pages", notes = "UMI 3472443 University of Illinois at Chicago ProQuest Dissertations & Theses,  2011. 3472443. GEP, AGEP, DE-GEP, HKY-GA, MCGA, SSRGA-II, MAXSAT, HAEA Supervisor: Peter C. Nelson", } @PhdThesis{Vahdat:thesis, author = "Ali R. Vahdat", title = "Symbiotic evolutionary subspace clustering (S-ESC)", school = "Dalhousie University", year = "2012", address = "Halifax, Nova Scotia, Canada", month = nov, keywords = "genetic algorithms, genetic programming", URL = "https://web.cs.dal.ca/~mheywood/Thesis/PhD.html", URL = "http://hdl.handle.net/10222/40629", URL = "https://dalspace.library.dal.ca/bitstream/handle/10222/40629/Vahdat-Ali-PhD-CS-Nov2013.pdf", size = "174 pages", abstract = "Application domains with large attribute spaces, such as genomics and text analysis,necessitate clustering algorithms with more sophistication than traditional clustering algorithms. More sophisticated approaches are required to cope with the large dimensionality and cardinality of these data sets. Subspace clustering, a generalisation of traditional clustering, identifies the attribute support for each cluster as well as the location and number of clusters. In the most general case, attributes associated with each cluster could be unique. The proposed algorithm, Symbiotic Evolutionary Sub-space Clustering (S-ESC) borrows from symbiosis in the sense that each clustering solution is defined in terms of a host (a single member of the host population) and a number of coevolved cluster centroids (or symbionts in an independent symbiont population). Symbionts define clusters and therefore attribute subspaces, whereas hosts define sets of clusters to constitute a non-degenerate solution. The symbiotic representation of S-ESC is the key to making it scalable to high-dimensional data sets, while an integrated subsampling process makes it scalable to tasks with a large number of data items. A bi-objective evolutionary method is proposed to identify the unique attribute support of each cluster while detecting its data instances. Benchmarking is performed against a well known test suite of subspace clustering data sets with four well-known comparator algorithms from both the full dimensional and subspace clustering literature: EM, MINECLUS, PROCLUS, STATPC and a generic genetic algorithm-based subspace clustering. Performance of the S-ESC algorithm was found to be robust across a wide cross-section of properties with a common parameterisation used throughout. This was not the case for the comparator algorithms. Specifically, performance could be sensitive to a particular data distribution or parametersweeps might be necessary to provide comparable performance. A comparison is also made relative to a non-symbiotic genetic algorithm. In this case each individual represents the set of clusters comprising a subspace cluster solution. Benchmarking indicates that the proposed symbiotic framework can be demonstrated to be superior once again. The S-ESC code and data sets are publicly available.", notes = "Is this GP? Supervisor: Malcolm I. Heywood", } @InProceedings{Vahdat:2014:GECCOcomp, author = "Ali Vahdat and Aaron Atwater and Andrew R. McIntyre and Malcolm I. Heywood", title = "On the application of GP to streaming data classification tasks with label budgets", booktitle = "GECCO 2014 Workshop on Evolutionary Computation for Big Data and Big Learning", year = "2014", editor = "Jaume Bacardit and Ignacio Arnaldo and Kalyan Veeramachaneni and Una-May O'Reilly", isbn13 = "978-1-4503-2881-4", keywords = "genetic algorithms, genetic programming", pages = "1287--1294", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2598394.2611385", DOI = "doi:10.1145/2598394.2611385", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "A framework is introduced for applying GP to streaming data classification tasks under label budgets. This is a fundamental requirement if GP is going to adapt to the challenge of streaming data environments. The framework proposes three elements: a sampling policy, a data subset and a data archiving policy. The sampling policy establishes on what basis data is sampled from the stream, and therefore when label information is requested. The data subset is used to define what GP individuals evolve against. The composition of such a subset is a mixture of data forwarded under the sampling policy and historical data identified through the data archiving policy. The combination of sampling policy and the data subset achieve a decoupling between the rate at which the stream passes and the rate at which evolution commences. Benchmarking is performed on two artificial data sets with specific forms of sudden shift and gradual drift as well as a well known real-world data set.", notes = "Also known as \cite{2611385} Distributed at GECCO-2014.", } @InProceedings{Vahdat:2015:EuroGP, author = "Ali Vahdat and Jillian Morgan and Andrew R. McIntyre and Malcolm I. Heywood and A. Nur Zincir-Heywood", title = "Tapped Delay Lines for {GP} Streaming Data Classification with Label Budgets", booktitle = "18th European Conference on Genetic Programming", year = "2015", editor = "Penousal Machado and Malcolm I. Heywood and James McDermott and Mauro Castelli and Pablo Garcia-Sanchez and Paolo Burelli and Sebastian Risi and Kevin Sim", series = "LNCS", volume = "9025", publisher = "Springer", pages = "126--138", address = "Copenhagen", month = "8-10 " # apr, organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, Streaming data classification, Non-stationary, Class imbalance, Benchmarking", isbn13 = "978-3-319-16500-4", DOI = "doi:10.1007/978-3-319-16501-1_11", abstract = "Streaming data classification requires that a model be available for classifying stream content while simultaneously detecting and reacting to changes to the underlying process generating the data. Given that only a fraction of the stream is visible at any point in time (i.e. some form of window interface) then it is difficult to place any guarantee on a classifier encountering a well mixed distribution of classes across the stream. Moreover, streaming data classifiers are also required to operate under a limited label budget (labelling all the data is too expensive). We take these requirements to motivate the use of an active learning strategy for decoupling genetic programming training epochs from stream throughput. The content of a data subset is controlled by a combination of Pareto archiving and stochastic sampling. In addition, a significant benefit is attributed to support for a tapped delay line (TDL) interface to the stream, but this also increases the dimensionality of the task. We demonstrate that the benefits of assuming the TDL can be maintained through the use of oversampling without recourse to additional label information. Benchmarking on 4 dataset demonstrates that the approach is particularly effective when reacting to shifts in the underlying properties of the stream. Moreover, an online formulation for class-wise detection rate is assumed, where this is able to robustly characterise classifier performance throughout the stream.", notes = "Part of \cite{Machado:2015:GP} EuroGP'2015 held in conjunction with EvoCOP2015, EvoMusArt2015 and EvoApplications2015", } @InCollection{Vahdat:2015:hbgpa, author = "Ali Vahdat and Jillian Morgan and Andrew R. McIntyre and Malcolm I. Heywood and Nur Zincir-Heywood", title = "Evolving GP classifiers for streaming data tasks with concept change and label budgets: A benchmark study", booktitle = "Handbook of Genetic Programming Applications", publisher = "Springer", year = "2015", editor = "Amir H. Gandomi and Amir H. Alavi and Conor Ryan", chapter = "18", pages = "451--480", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-20882-4", DOI = "doi:10.1007/978-3-319-20883-1_18", abstract = "Streaming data classification requires that several additional challenges are addressed that are not typically encountered in off-line supervised learning formulations. Specifically, access to data at any training generation is limited to a small subset of the data, and the data itself is potentially generated by a non-stationary process. Moreover, there is a cost to requesting labels, thus a label budget is enforced. Finally, an any-time classification requirement implies that it must be possible to identify a champion classifier for predicting labels as the stream progresses. In this work, we propose a general framework for deploying genetic programming (GP) to streaming data classification under these constraints. The framework consists of a sampling policy and an archiving policy that enforce criteria for selecting data to appear in a data subset. Only the exemplars of the data subset are labeled, and it is the content of the data subset that training epochs are performed against. Specific recommendations include support for GP task decomposition/modularity and making additional training epochs per data subset. Both recommendations make significant improvements to the baseline performance of GP under streaming data with label budgets. Benchmarking issues addressed include the identification of datasets and performance measures.", } @InProceedings{Vaidya:2022:ICSBT, author = "Gauri Vaidya and Luise Ilg and Meghana Kshirsagar and Enrique Naredo and Conor Ryan", title = "HyperEstimator: Evolving Computationally Efficient {CNN} Models with Grammatical Evolution", booktitle = "Proceedings of the 19th International Conference on Smart Business Technologies (ICSBT 2022)", year = "2022", editor = "Fons Wijnhoven and Slimane Hammoudi and Marten {van Sinderen}", pages = "57--68", address = "Lisbon, Portugal", organization = "INSTICC", publisher = "SciTePress", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, ANN, deep learning, Convolutional Neural Networks, Grammatical Evolution, Machine Learning, GPU, Business Modelling, Hyperparameters, Smart City", isbn13 = "978-989-758-587-6", ISSN = "2184-772X", URL = "https://pure.ul.ie/en/publications/hyperestimator-evolving-computationally-efficient-cnn-models-with", DOI = "doi:10.5220/0011324800003280", size = "12 pages", abstract = "... dramatically leading to massive project costs. Some of the factors for vast expenses for DL models can be attributed to the computational costs incurred during training, massive storage requirements, along with specialized hardware such as Graphical Processing Unit (GPUs). This research seeks to address some of the challenges mentioned above. Our approach, HyperEstimator, estimates the optimal values of hyperparameters for a given Convolutional Neural Networks (CNN) model and dataset using a suite of Machine Learning algorithms. Our approach consists of three stages: (i) obtaining c andidate values for hyperparameters with Grammatical Evolution; (ii) prediction of optimal values of hyperparameters with supervised ML techniques; (iii) training CNN model for object detection. As a case study, the CNN models are validated by using a real-time video dataset representing road traffic captured in some Indian cities. The results are also compared against CIFAR10 and CIFAR100 benchmark datasets.", notes = "also known as \cite{icsbt22}", } @Article{Vaidya:2023:Algorithms, author = "Gauri Vaidya and Meghana Kshirsagar and Conor Ryan", title = "Grammatical Evolution-Driven Algorithm for Efficient and Automatic Hyperparameter Optimisation of Neural Networks", journal = "Algorithms", year = "2023", volume = "16", number = "7", pages = "article number 319", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, ANN, search space pruning, machine learning, combinatorial optimisation, computer vision, metaheuristics, hyperparameter tuning, deep learning, neural networks", ISSN = "1999-4893", URL = "https://www.mdpi.com/1999-4893/16/7/319", DOI = "doi:10.3390/a16070319", size = "16 pages", abstract = "... Neural networks have revolutionised the way we approach problem solving across multiple domains; however, their effective design and efficient use of computational resources is still a challenging task. One of the most important factors influencing this process is model hyperparameters which vary significantly with models and datasets. Recently, there has been an increased focus on automatically tuning these hyperparameters to reduce complexity and to optimise resources. From traditional human-intuitive tuning methods to random search, grid search, Bayesian optimisation, and evolutionary algorithms, significant advancements have been made in this direction that promise improved performance while using fewer resources. we propose HyperGE, a two-stage model for automatically tuning hyperparameters driven by grammatical evolution (GE), a bioinspired population-based machine learning algorithm. GE provides an advantage in that it allows users to define their own grammar for generating solutions, making it ideal for defining search spaces across datasets and models. We test HyperGE to fine-tune VGG-19 and ResNet-50 pre-trained networks using three benchmark datasets. We demonstrate that the search space is significantly reduced by a factor of about ninty percent Stage 2 with fewer number of trials. HyperGE could become an invaluable tool within the deep learning community, allowing practitioners greater freedom when exploring complex problem domains for hyperparameter fine-tuning.", notes = "also known as \cite{a16070319}", } @Article{vaidyanathan:2003:AC, author = "Seetharaman Vaidyanathan and David I. Broadhurst and Douglas B. Kell and Royston Goodacre", title = "Explanatory Optimization of Protein Mass Spectrometry via Genetic Search", journal = "Analytical Chemistry", year = "2003", volume = "75", number = "23", pages = "6679--6686", keywords = "genetic algorithms, genetic programming", URL = "http://dbkgroup.org/Papers/AnalChem75(6679-6686).pdf", DOI = "doi:10.1021/ac034669a", size = "8 pages", abstract = "Optimizing experimental conditions for the effective analysis of intact proteins by mass spectrometry is challenging, as many analytical factors influence the spectral quality, often in very different ways for different proteins and especially with complex protein mixtures. We show that genetic search methods are highly effective in this kind of optimization and that it was possible in 6 generations with a total of <500 experiments out of some 1014 to find good combinations of experimental variables (electrospray ionization mass spectral settings) that would not have been detected by optimizing each variable alone (i.e., the search space is epistatic). Moreover, by inspecting the evolution of the variables to be optimized using genetic programming, we discovered an important relationship between two of the mass spectrometer settings that accounts for much of this success. Specifically, the conditions that were evolved included very low values of skimmer 1 voltage (the sample cone) and a skimmer 2 voltage (extraction cone) above a threshold that would nevertheless minimize the potential difference between the sample and extraction skimmers. The discovery of this relationship demonstrates the hypothesis-generating ability of genetic search in optimization processes where the size of the search space means that little or no a priori knowledge of the optimal conditions is available.", } @Article{VALARMATHI:2021:BSPC, author = "R. Valarmathi and T. Sheela", title = "Heart disease prediction using hyper parameter optimization ({HPO)} tuning", journal = "Biomedical Signal Processing and Control", volume = "70", pages = "103033", year = "2021", ISSN = "1746-8094", DOI = "doi:10.1016/j.bspc.2021.103033", URL = "https://www.sciencedirect.com/science/article/pii/S1746809421006303", keywords = "genetic algorithms, genetic programming, Hyper parameter tuning, Heart disease, Grid search, Randomized search, TPOT classifier", abstract = "Coronary artery disease prediction is considered to be one of the most challenging tasks in the health care industry. In our research, we propose a prediction system to detect the heart disease. Three Hyper Parameter Optimization (HPO) techniques Grid Search, Randomized Search and Genetic programming (TPOT Classifier) were proposed to optimize the performance of Random forest classifier and XG Boost classifier model. The performance of the two models Random Forest and XG Boost were compared with the existing studies. The performance of the models is evaluated with the publicly available datasets Cleveland Heart disease Dataset (CHD) and Z-Alizadeh Sani dataset. Random Forest along with TPOT Classifier achieved the highest accuracy of 97.52percentfor CHD Dataset. Random Forest with Randomized Search achieved the highest accuracy of 80.2percent, 73.6percent and 76.9percent for the diagnosis of the stenos is of three vessels LAD, LCX and RCA respectively with Z-Alizadeh Sani Dataset. The results were compared with the existing studies focusing on prediction of heart disease that were found to outperform their results significantly", } @InProceedings{valdes:2006:IJCNN, author = "Julio J. Valdes and Alan J. Barton", title = "Virtual Reality Visual Data Mining via Neural Networks obtained from Multi-objective Evolutionary Optimization: Application to Geophysical Prospecting", booktitle = "International Joint Conference on Neural Networks, IJCNN'06", year = "2006", pages = "4862--4869", address = "Sheraton Vancouver Wall Centre Hotel, Vancouver, BC, Canada", month = "16-21 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Gene Expression Programming", DOI = "doi:10.1109/IJCNN.2006.247165", abstract = "A method for the construction of Virtual Reality spaces for visual data mining using multi-objective optimisation with genetic algorithms on non-linear discriminant (NDA) neural networks is presented. Two neural network layers (output and last hidden) are used for the construction of simultaneous solutions for: a supervised classification of data patterns and an unsupervised similarity structure preservation between the original data matrix and its image in the new space. A set of spaces are constructed from selected solutions along the Pareto front. This strategy represents a conceptual improvement over spaces computed by single-objective optimisation. In addition, genetic programming (in particular gene expression programming) is used for finding analytic representations of the complex mappings generating the spaces (a composition of NDA and orthogonal principal components). The presented approach is domain independent and is illustrated via application to the geophysical prospecting of caves.", } @InCollection{DBLP:journals/trs/ValdesB07, author = "Julio J. Valdes and Alan J. Barton", title = "Finding Relevant Attributes in High Dimensional Data: A Distributed Computing Hybrid Data Mining Strategy", year = "2007", booktitle = "Transactions on Rough Sets VI", publisher = "Springer", volume = "4374", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming", pages = "366--396", DOI = "doi:10.1007/978-3-540-71200-8_20", bibsource = "DBLP, http://dblp.uni-trier.de", isbn13 = "978-3-540-71198-8", abstract = "In many domains the data objects are described in terms of a large number of features (e.g. microarray experiments, or spectral characterizations of organic and inorganic samples). A pipelined approach using two clustering algorithms in combination with Rough Sets is investigated for the purpose of discovering important combinations of attributes in high dimensional data. The Leader and several k-means algorithms are used as fast procedures for attribute set simplification of the information systems presented to the rough sets algorithms. The data described in terms of these fewer features are then discretized with respect to the decision attribute according to different rough set based schemes. From them, the reducts and their derived rules are extracted, which are applied to test data in order to evaluate the resulting classification accuracy in crossvalidation experiments. The data mining process is implemented within a high throughput distributed computing environment. Nonlinear transformation of attribute subsets preserving the similarity structure of the data were also investigated. Their classification ability, and that of subsets of attributes obtained after the mining process were described in terms of analytic functions obtained by genetic programming (gene expression programming), and simplified using computer algebra systems. Visual data mining techniques using virtual reality were used for inspecting results. An exploration of this approach (using Leukemia, Colon cancer and Breast cancer gene expression data) was conducted in a series of experiments. They led to small subsets of genes with high discrimination power.", } @InProceedings{1274028, author = "Julio J. Valdes and Alan J. Barton", title = "Computational intelligence techniques: a study of scleroderma skin disease", booktitle = "Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2007)}", year = "2007", month = "7-11 " # jul, editor = "Peter A. N. Bosman", isbn13 = "978-1-59593-698-1", pages = "2580--2587", address = "London, United Kingdom", keywords = "genetic algorithms, genetic programming, differential evolution, genomics, grid computing, hybrid evolutionary-classical optimisation, Particle Swarm Optimisation, rough sets, scleroderma disease, similarity structure preservation, virtual reality, visual data mining", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p2580.pdf", DOI = "doi:10.1145/1274000.1274028", publisher = "ACM Press", publisher_address = "New York, NY, USA", abstract = "This paper presents an analysis of microarray gene expression data from patients with and without scleroderma skin disease using computational intelligence and visual data mining techniques. Virtual reality spaces are used for providing unsupervised insight about the information content of the original set of genes describing the objects. These spaces are constructed by hybrid optimization algorithms based on a combination of Differential Evolution (DE) and Particle Swarm Optimization respectively, with deterministic Fletcher-Reeves optimisation. A distributed-pipelined data mining algorithm composed of clustering and cross-validated rough sets analysis is applied in order to find subsets of relevant attributes with high classification capabilities. Finally, genetic programming (GP) is applied in order to find explicit analytic expressions for the characteristic functions of the scleroderma and the normal classes. The virtual reality spaces associated with the set of function arguments (genes) are also computed. Several small subsets of genes are discovered which are capable of classifying the data with complete accuracy. They represent genes potentially relevant to the understanding of the scleroderma disease.", notes = "Distributed on CD-ROM at GECCO-2007 ACM Order No. 910071", } @Article{Valdes2007498, author = "Julio J. Valdes and Alan J. Barton", title = "Multi-objective evolutionary optimization for constructing neural networks for virtual reality visual data mining: Application to geophysical prospecting", journal = "Neural Networks", volume = "20", number = "4", pages = "498--508", year = "2007", note = "Computational Intelligence in Earth and Environmental Sciences", ISSN = "0893-6080", DOI = "DOI:10.1016/j.neunet.2007.04.009", URL = "http://www.sciencedirect.com/science/article/B6T08-4NMWR88-4/2/e4bffc079293e68dbe63509d3cfa17cc", keywords = "genetic algorithms, genetic programming, gene expression programming, Visual data mining, Virtual reality, Multi-objective optimization, Neural networks, Geophysical prospecting", abstract = "A method for the construction of virtual reality spaces for visual data mining using multi-objective optimization with genetic algorithms on nonlinear discriminant (NDA) neural networks is presented. Two neural network layers (the output and the last hidden) are used for the construction of simultaneous solutions for: (i) a supervised classification of data patterns and (ii) an unsupervised similarity structure preservation between the original data matrix and its image in the new space. A set of spaces are constructed from selected solutions along the Pareto front. This strategy represents a conceptual improvement over spaces computed by single-objective optimization. In addition, genetic programming (in particular gene expression programming) is used for finding analytic representations of the complex mappings generating the spaces (a composition of NDA and orthogonal principal components). The presented approach is domain independent and is illustrated via application to the geophysical prospecting of caves.", } @InProceedings{Valdes:2007:cecVR, author = "J. J. Valdes and A. J. Barton", title = "Visualizing High Dimensional Objective Spaces for Multi-objective Optimization: A Virtual Reality Approach", booktitle = "2007 IEEE Congress on Evolutionary Computation", year = "2007", editor = "Dipti Srinivasan and Lipo Wang", pages = "4199--4206", address = "Singapore", month = "25-28 " # sep, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "1-4244-1340-0", file = "1535.pdf", keywords = "genetic algorithms, genetic programming, Pareto optimisation, data mining, data visualisation, knapsack problems, virtual reality4 dimensional knapsack problem, Pareto fronts, high dimensional objective spaces, multi-objective evolutionary algorithms, multi-objective optimization, virtual reality, visual representations", DOI = "doi:10.1109/CEC.2007.4425019", abstract = "This paper presents an approach for constructing visual representations of high dimensional objective spaces using virtual reality. These spaces arise from the solution of multi-objective optimization problems with more than 3 objective functions which lead to high dimensional Pareto fronts which are difficult to use. This approach is preliminarily investigated using both theoretically derived high dimensional Pareto fronts for a test problem (DTLZ2) and practically obtained objective spaces for the 4 dimensional knapsack problem via multi-objective evolutionary algorithms like HLGA, NSGA, and VEGA. The expected characteristics of the high dimensional fronts in terms of relative sizes, sequencing, embedding and asymmetry were systematically observed in the constructed virtual reality spaces.", notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C Also known as \cite{4425019}", } @InProceedings{Valdes:2007:cec, author = "Julio J. Valdes and Alan J. Barton and Robert Orchard", title = "Virtual Reality High Dimensional Objective Spaces for Multi-Objective Optimization: An Improved Representation", booktitle = "2007 IEEE Congress on Evolutionary Computation", year = "2007", editor = "Dipti Srinivasan and Lipo Wang", pages = "4191--4198", address = "Singapore", month = "25-28 " # sep, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "1-4244-1340-0", file = "1796.pdf", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2007.4425018", abstract = "This paper presents an approach for constructing improved visual representations of high dimensional objective spaces using virtual reality. These spaces arise from the solution of multi-objective optimisation problems with more than 3 objective functions which lead to high dimensional Pareto fronts. The 3-D representations of m-dimensional Pareto fronts, or their approximations, are constructed via similarity structure mappings between the original objective spaces and the 3-D space. Alpha shapes are introduced for the representation and compared with previous approaches based on convex hulls. In addition, the mappings minimising a measure of the amount of dissimilarity loss are obtained via genetic programming. This approach is preliminarily investigated using both theoretically derived high dimensional Pareto fronts for a test problem (DTLZ2) and practically obtained objective spaces for the 4 dimensional knapsack problem via multi-objective evolutionary algorithms like HLGA, NSGA, and VEGA. The improved representation captures more accurately the real nature of the m-dimensional objective spaces and the quality of the mappings obtained with genetic programming is equivalent to those computed with classical optimization algorithms.", notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C", } @InProceedings{Valdes:2008:ijcnn, author = "Julio J. Valdes and Antonio Pou and Robert Orchard", title = "Characterization of Climatic Variations in Spain at the Regional Scale: A Computational Intelligence Approach", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "470--476", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1821-3", file = "NN0185.pdf", DOI = "doi:10.1109/IJCNN.2008.4633834", ISSN = "1098-7576", keywords = "genetic algorithms, genetic programming, Fletcher-Reeves optimization, Kolmogorov-Smirnov dissimilarity analysis, Spain, cluster analysis, computational intelligence approach, data mining, differential evolution, hybrid optimization, principal component analysis, regional climatic variation, similarity-preservation feature generation, time-varying climatic variation characterization, climatology, data mining, geophysical techniques, geophysics computing, pattern clustering, principal component analysis", abstract = "Computational intelligence and other data mining techniques are used for characterising regional and time varying climatic variations in Spain in the period 1901-2005. Daily maximum temperature data from 10 climatic stations are analysed (with and without missing values) using principal components (PC), similarity-preservation feature generation, clustering, Kolmogorov-Smirnov dissimilarity analysis and genetic programming (GP). The new features were computed using hybrid optimisation (differential evolution and Fletcher- Reeves) and GP. From them, a scalar regional climatic index was obtained which identifies time landmarks and changes in the climate rhythm. The equations obtained with GP are simpler than those obtained with PC and they highlight the most important sites characterising the regional climate. Whereas the general consensus is that there has been a clear and smooth trend towards warming during the last decades, the results suggest that the picture may probably be much more complicated than what is usually assumed.", notes = "Also known as \cite{4633834} WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @Article{Valdes:2008:GPEM, author = "Julio J. Valdes and Alan J. Barton and Arsalan S. Haqqani", title = "Analysis of mass spectrometry data of cerebral stroke samples: an evolutionary computation approach to resolve and quantify peptide peaks", journal = "Genetic Programming and Evolvable Machines", year = "2008", volume = "9", number = "3", pages = "257--274", month = sep, keywords = "genetic algorithms, Mass spectroscopy, Proteomics, Medicine, Differential evolution, Evolutionary computation, Model fitting", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-008-9057-y", abstract = "A preliminary investigation of cerebral stroke samples injected into a mass spectrometer is performed from an evolutionary computation perspective. The detection and resolution of peptide peaks is pursued for the purpose of automatically and accurately determining unlabelled peptide quantities. A theoretical peptide peak model is proposed and a series of experiments are then pursued (most within a distributed computing environment) along with a data preprocessing strategy that includes (i) a deisotoping step followed by (ii) a peak picking procedure, followed by (iii) a series of evolutionary computation experiments oriented towards the investigation of their capability for achieving the aforementioned goal. Results from four different genetic algorithms (GA) and one differential evolution (DE) algorithm are reported with respect to their ability to find solutions that fit within the framework of the presented theoretical peptide peak model. Both unconstrained and constrained (as determined by a course grained preprocessing stage) solution space experiments are performed for both types of evolutionary algorithms. Good preliminary results are obtained.", } @InProceedings{Valdes:2009:IJCNN, author = "Julio J. Valdes and Antonio Pou", title = "Climatic variation of the structure of maximum daily temperatures in Spain: A combined statistical and computational intelligence approach", booktitle = "International Joint Conference on Neural Networks, IJCNN 2009", year = "2009", month = "14-19 " # jun, address = "Atlanta, Georgia, US", pages = "3172--3179", keywords = "genetic algorithms, genetic programming, Iberian Peninsula, Mediterranean climates, Spanish meteorological stations, classical optimization, climatic variation, computational intelligence approach, geographical distribution, maximum daily temperatures, multimodal empirical distribution function, statistical approach, artificial intelligence, climatology, geophysics computing, meteorology, statistical distributions", DOI = "doi:10.1109/IJCNN.2009.5178649", ISSN = "1098-7576", abstract = "Two blocks (1904-1921 and 1990-2007) of daily maximum temperature data from seventeen Spanish meteorological stations exhibit a multimodal empirical distribution function (EDF). Most of the stations show important differences in their EDF for each one of the considered periods of time, a fact that reveals the complexity of climatic changes within the accepted general warming trend of the Iberian Peninsula. As a tentative approach to understand the underlying structure of data, each EDF has been decomposed on two normal distributed functions. The parameters describing these functions for each station and for each time period have been space-optimized and visualized using classical optimization and genetic programming. The changes in the geographical distribution of the classes derived from the analysis point towards a recent greater role of Mediterranean climates, spreading its influence to the interior of the Peninsula. The general picture, however, is much more complex than a linear warming and a number of stations even show negative trends. This study is considered to be a preliminary methodological exploration of future procedures destined to close the gap between data driven analysis and what models based upon first principles may tell.", notes = "Also known as \cite{5178649}", } @InProceedings{Valdes:2010:cec, author = "Julio J. Valdes", title = "Evolutionary computation based nonlinear transformations to low dimensional spaces for sensor data fusion and Visual Data Mining", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, DE", isbn13 = "978-1-4244-6910-9", abstract = "Data fusion approaches are nowadays needed and also a challenge in many areas, like sensor systems monitoring complex processes. This paper explores evolutionary computation approaches to sensor fusion based on unsupervised nonlinear transformations between the original sensor space (possibly highly-dimensional) and lower dimensional spaces. Domain-independent implicit and explicit transformations for Visual Data Mining using Differential Evolution and Genetic Programming aiming at preserving the similarity structure of the observed multivariate data are applied and compared with classical deterministic methods. These approaches are illustrated with a real world complex problem: Failure conditions in Auxiliary Power Units in aircraft. The results indicate that the evolutionary approaches used were useful and effective at reducing dimensionality while preserving the similarity structure of the original data. Moreover the explicit models obtained with Genetic Programming simultaneously covered both feature selection and generation. The evolutionary techniques used compared very well with their classical counterparts, having additional advantages. The transformed spaces also help in visualising and understanding the properties of the sensor data.", DOI = "doi:10.1109/CEC.2010.5585951", notes = "WCCI 2010. Also known as \cite{5585951}", } @InProceedings{Valdes:2010:ijcnn, author = "Julio J. Valdes and Antonio Pou", title = "Central {England} temperatures and solar activity: A Computational Intelligence approach", booktitle = "International Joint Conference on Neural Networks (IJCNN 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-6917-8", abstract = "Two Computational Intelligence techniques, neural networks-based Multivariate Time Series Model Mining (MVTSMM) and Genetic Programming (GP), have been used to explore the possible relationship between solar activity and temperatures in Central England for the 1721 to 1967 period. Data driven analysis of multivariate, heterogeneous and incomplete time series are used in order to understand the extreme complexity of the climate machinery and to detect the possible relative contribution of influencing processes, like the Sun, whose decadal and centennial role in the climate is still debated. Experiments were carried out using each one of these techniques and their combination. Time-lag spectra obtained by means of MVTSMM seems to indicate time stamps of some of the relevant Earth-climate and solar variations on the temperature record. The equations provided by GP approximated analytically the relative contribution of particular solar activity time-lags. These preliminary results, even if they still are insufficient to support or discredit possible physical mechanisms, are interesting and encouraging to explore more in that direction.", DOI = "doi:10.1109/IJCNN.2010.5596455", notes = "WCCI 2010. Also known as \cite{5596455}", } @InCollection{Valdes:2010:GECma, title = "Genetic Programming for Exploring Medical Data Using Visual Spaces", author = "Julio J. Valdes and Alan J. Barton and Robert Orchard", booktitle = "Genetic and Evolutionary Computation: Medical Applications", publisher = "John Wiley and Sons, Ltd", year = "2010", editor = "Stephen L. Smith and Stefano Cagnoni", chapter = "5.3", pages = "149--172", keywords = "genetic algorithms, genetic programming, genetic programming - for exploring medical data using visual spaces, visual space, for use within data mining and other exploratory endeavours, visual space taxonomy, property(ies) of objects within constructed visual space - satisfying paradigms, visual space interpretation taxonomy, visual space characteristics examination, function space constructed - within unsupervised paradigm, rough sets - for general visual space characterisation, visual space mapping computation - using genetic programming, virtual reality visual spaces - purposes of visual data mining and meta-mining", isbn13 = "9780470748138", DOI = "doi:10.1002/9780470973134.ch9", abstract = "This chapter contains sections titled: * Introduction * Visual Spaces * Experimental Settings * Medical Examples * Future Directions", } @InProceedings{Valdes:2020:CEC, author = "Julio J. Valdes and Zachary Baird and Sreeraman Rajan and Miodrag Bolic", title = "Radar-based Noncontact Human Activity Classification Using Genetic Programming", booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC 2020", year = "2020", editor = "Yaochu Jin", pages = "paper id24140", address = "internet", month = "19-24 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-7281-6929-3", DOI = "doi:10.1109/CEC48606.2020.9185814", abstract = "This paper analyses the structure of the feature space of radar data collected from real subjects either still or in motion and provides dimensionality reduction and modeling through genetic programming. Three movement classes: sedentary and still, sedentary with movements, and walking contained in the returns obtained from a single channel continuous wave phase-modulated radar are considered. Unsupervised methods are used for finding the intrinsic dimensionality of the space of the original features and nonlinear mappings are used to obtain lower dimensional representations. The classification results for the original and the reduced dimension data are similar, thus, indicating the redundancy of the eliminated features. The white-box models obtained through genetic programming is then compared with the conventional black-box models obtained through supervised classification using random trees, extreme learning machines and multilayer perceptron. For this problem, the explicit white-box models obtained with genetic programming produced equal or better classification accuracies than those obtained with black-box approaches. In addition to explainability, the genetic programming models found have the additional advantage of involving only a few relevant predictors, exhibiting good feature selection capabilities.", notes = "https://wcci2020.org/ Digital Technologies Research Centre, National Research Council Canada, Ottawa, Canada; Department of Systems and Computer Engineering, Carleton University, Ottawa, Canada; School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada. Also known as \cite{9185814}", } @InProceedings{Valek:2022:SMC, author = "Matej Valek and Lukas Sekanina", booktitle = "2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)", title = "Evolutionary Approximation in Non-Local Means Image Filters", year = "2022", pages = "2762--2769", abstract = "The non-local means image filter is a non-trivial denoising algorithm for color images using floating-point arithmetic operations in its reference software implementation. In order to simplify this algorithm for an on-chip implementation, we investigate the impact of various number representations and approximate arithmetic operators on the quality of image filtering. We employ Cartesian Genetic Programming (CGP) to evolve approximate implementations of a 20-bit signed multiplier which is then applied in the image filter instead of the conventional 32-bit floating-point multiplier. In addition to using several techniques that reduce the huge design cost, we propose a new mutation operator for CGP to improve the search quality and obtain better approximate multipliers than with CGP using the standard mutation operator. Image filters using evolved approximate multipliers can save 3percent in power consumption of multiplication operations for a negligible drop in the image filtering quality.", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", DOI = "doi:10.1109/SMC53654.2022.9945091", ISSN = "2577-1655", month = oct, notes = "Also known as \cite{9945091}", } @InProceedings{Valencia:2007:SenSys, author = "Philip Valencia", title = "In situ Genetic Programming for Wireless Sensor Networks", booktitle = "SenSys 2007 Doctoral Colloquium", year = "2007", editor = "Mike Hazas and Athanassios Boulis and Lewis Girod", pages = "37--41", address = "Sydney", keywords = "genetic algorithms, genetic programming, Artificial Intelligence, Automatic Programming, Wireless Sensor Network", URL = "http://www.comp.lancs.ac.uk/~hazas/sensys07dc/summaries/valencia.pdf", size = "5 pages", abstract = "This research proposes in situ distributed genetic programming (IDGP) as a method for achieving continual post-deployment system optimisation in wireless sensor networks (WSN). A number of significant challenges are identified that would need to be addressed in order to achieve the desired outcome. These include: distributed evaluation of global fitness and non-detrimental distributed genetic programming on resource constrained hardware. Preliminary tests indicate that such an approach is plausible and even suitable for specific deployments. Achieving networks that can adapt and evolve their behaviour throughout the life of the deployment, would reduce, or even remove completely the need for post-deployment reprogramming.", notes = "crossover only. GP on motes. Cited by \cite{Schulte:2013:ARB:2451116.2451151}. SenSys 2007 http://sensys.acm.org/2007/Home.html Doctoral Colloquium http://www.comp.lancs.ac.uk/~hazas/sensys07dc/", } @InProceedings{Valencia:2010:IPSN, author = "Philip Valencia and Peter Lindsay and Raja Jurdak", title = "Distributed genetic evolution in WSN", booktitle = "Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN'10", year = "2010", editor = "Tarek Abdelzaher and Thiemo Voigt and Adam Wolisz", pages = "13--23", address = "Stockholm, Sweden", publisher_address = "New York, NY, USA", month = apr # " 12-16", publisher = "ACM", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-60558-988-6", URL = "http://www2.ee.kth.se/conferences/cpsweek2010/PDF/IPSN/p013-valencia.pdf", DOI = "doi:10.1145/1791212.1791215", size = "11 pages", abstract = "Wireless Sensor Actuator Networks (WSANs) extend wireless sensor networks through actuation capability. Designing robust logic for WSANs however is challenging since nodes can affect their environment which is already inherently complex and dynamic. Fixed (offline) logic does not have the ability to adapt to significant environmental changes and can fail under changed conditions. To address this challenge, we present In situ Distributed Genetic Programming (IDGP) as a framework for evolving logic post-deployment (online) and implement this framework on a physically deployed WSAN. To demonstrate the features of the framework including individual, cooperative and heterogeneous evolution, we apply it to two simple optimisation problems requiring sensing, communications and actuation. The experiments confirm that IDGP can evolve code to achieve a system wide objective function and is resilient to unexpected environmental changes.", notes = "Also known as \cite{1791215}", } @InProceedings{Valencia:2010:geccocomp, author = "Philip Valencia and Raja Jurdak and Peter Lindsay", title = "Fitness importance for online evolution", booktitle = "GECCO 2010 Late breaking abstracts", year = "2010", editor = "Daniel Tauritz", isbn13 = "978-1-4503-0073-5", keywords = "genetic algorithms, genetic programming", pages = "2117--2118", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830761.1830890", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "To complement standard fitness functions, we propose {"}Fitness Importance{"} (FI) as a novel meta-heuristic for online learning systems. We define FI and show how it can be used to dynamically bias the population composition in order to vary the instantaneous system performance at a tradeoff to learning capability. The effect of FI is demonstrated on a simple light-sensing and light-actuating optimisation problem running on multiple wireless sensor network devices. We also describe how FI can be used with the In situ Distributed Genetic Programming (IDGP) framework to balance learning and performing for resource-constrained computing devices which evolve their logic continuously.", notes = "Also known as \cite{1830890} Distributed on CD-ROM at GECCO-2010. ACM Order Number 910102.", } @Misc{Valencia:2014:arXiv, author = "Philip Valencia and Aiden Haak and Alban Cotillon and Raja Jurdak", title = "Genetic Programming for Smart Phone Personalisation", howpublished = "arXiv:1408.2288", year = "2014", month = "11 " # aug, keywords = "genetic algorithms, genetic programming, genetic improvement", URL = "http://arxiv.org/abs/1408.2288", URL = "http://arxiv.org/pdf/1408.2288", URL = "http://jurdak.com/ASC-GP.pdf", size = "43 pages", abstract = "Personalisation in smart phones requires adaptability to dynamic context based on user mobility, application usage and sensor inputs. Current personalisation approaches, which rely on static logic that is developed a priori, do not provide sufficient adaptability to dynamic and unexpected context. This paper proposes genetic programming (GP), which can evolve program logic in realtime, as an online learning method to deal with the highly dynamic context in smart phone personalisation. We introduce the concept of collaborative smart phone personalisation through the GP Island Model, in order to exploit shared context among co-located phone users and reduce convergence time. We implement these concepts on real smartphones to demonstrate the capability of personalisation through GP and to explore the benefits of the Island Model. Our empirical evaluations on two example applications confirm that the Island Model can reduce convergence time by up to two-thirds over standalone GP personalisation.", notes = "Preprint submitted to Applied Soft Computing, see \cite{Valencia:2014:ASC}.", } @Article{Valencia:2014:ASC, author = "Philip Valencia and Aiden Haak and Alban Cotillon and Raja Jurdak", title = "Genetic programming for smart phone personalisation", journal = "Applied Soft Computing", year = "2014", volume = "25", pages = "86--96", month = dec, keywords = "genetic algorithms, genetic programming, genetic improvement, Island Model, Personalization, Smart phone, Online evolutionary", ISSN = "1568-4946", URL = "http://www.sciencedirect.com/science/article/pii/S1568494614004232", DOI = "doi:10.1016/j.asoc.2014.08.058", size = "11 pages", abstract = "Personalisation in smart phones requires adaptability to dynamic context based on user mobility, application usage and sensor inputs. Current personalisation approaches, which rely on static logic that is developed a priori, do not provide sufficient adaptability to dynamic and unexpected context. This paper proposes genetic programming (GP), which can evolve program logic in realtime, as an on line learning method to deal with the highly dynamic context in smart phone personalisation. We introduce the concept of collaborative smart phone personalisation through the GP Island Model, in order to exploit shared context among co-located phone users and reduce convergence time. We implement these concepts on real smart phones to demonstrate the capability of personalisation through GP and to explore the benefits of the Island Model. Our empirical evaluations on two example applications confirm that the Island Model can reduce convergence time by up to two-thirds over standalone GP personalisation.", notes = "p87 'injecting random programs ... can be beneficial ... energy-efficient...' AGP http://sourceforge.net/projects/agpframework/ google RSS reader, pop=5, max depth=3. Wifi energy, pop=12. Collaboration two Samsung pop=10 Also \cite{Valencia:2014:arXiv}", } @PhdThesis{Valencia:thesis, author = "Philip Juan Valencia", title = "In situ Distributed Genetic Programming: An Online Learning Framework for Resource Constrained Networked Devices", school = "School of Information Technology and Electrical Engineering, The University of Queensland", year = "2016", address = "Australia", keywords = "genetic algorithms, genetic programming, IoT, Android Genetic Programming, AGP, IDGP, Ulfsark, Mote, Fleck3b Mote, 0801 Artificial Intelligence and Image Processing, Embedded Systems, Internet of Things, Machine learning, Exploration and exploitation, Metaheuristic, WSN, Online Learning, Robotics", URL = "https://espace.library.uq.edu.au/view/UQ:386470/s4109212_final_thesis.pdf", URL = "https://espace.library.uq.edu.au/view/UQ:386470", DOI = "doi:10.14264/uql.2016.295", size = "270 pages", abstract = "This research presents In situ Distributed Genetic Programming (IDGP) as a framework for distributively evolving logic while attempting to maintain acceptable average performance on highly resource-constrained embedded networked devices. The framework is motivated by the proliferation of devices employing microcontrollers with communications capability and the absence of online learning approaches that can evolve programs for them. Swarm robotics, Internet of Things (IoT) devices including smart phones, and arguably the most constrained of the embedded systems, Wireless Sensor Networks (WSN) motes, all possess the capabilities necessary for the distributed evolution of logic - specifically the abilities of sensing, computing, actuation and communications. Genetic programming (GP) is a mechanism that can evolve logic for these devices using their native logic representation (i.e. programs) and so technically GP could evolve any behaviour that can be coded on the device. IDGP is designed, implemented, demonstrated and analysed as a framework for evolving logic via genetic programming on highly resource-constrained networked devices in real-world environments while achieving acceptable average performance. Designed with highly resource-constrained devices in mind, IDGP provides a guide for those wishing to implement genetic programming on such systems. Furthermore, an implementation on mote class devices is demonstrated to evolve logic for a time-varying sense-compute-act problem and another problem requiring the evolution of primitive communications. Distributed evolution of logic is also achieved by employing the Island Model architecture, and a comparison of individual and distributed evolution (with the same and slightly different goals) presented. This demonstrates the advantage of leveraging the fact that such devices often reside within networks of devices experiencing similar conditions. Since GP is a population-based metaheuristic which relies on the diversity of the population to achieve learning, many, if not most, programs within the population exhibit poor performance. As such, the average observed performance (pool fitness) of the population using the standard GP learning mechanism is unlikely to be acceptable for online learning scenarios. This is suspected to be the reason why no previous attempts have been made to deploy standard GP as an online learning approach. Nonetheless, the benefits of GP for evolving logic on such devices are compelling and motivated the design of a novel satisficing heuristic called Fitness Importance (FI). FI is population-based heuristic used to bias the evaluation of candidate solutions such that an acceptable average fitness (AAF) is achieved while also achieving ongoing, though diminished, learning capacity. This trade off motivated further investigation into whether dynamically adjusting the average performance in response to AAF would be superior to a constant, balanced, performing-learning approach. Dynamic and constant strategies were compared on a simple problem where the AAF target was changed during evolution, revealing that dynamically tracking the AAF target can yield a higher success rate in meeting the AAF. The combination of IDGP and FI offers a novel approach for achieving online learning with GP on highly resource-constrained embedded systems. Furthermore, it simultaneously considers the acceptable average performance of the system which may change during the operational lifetime. This approach could be applied to swarm and cooperative robot systems, WSN motes or IoT devices allowing them to cooperatively learn and adapt their logic locally to meet dynamic performance requirements.", notes = "CSIRO. Adaptive Agents and Intelligent Robotics. Neural, Evolutionary and Fuzzy Computation. Ubiquitous Computing. Supervisors: Peter Lindsay and Raja Jurdak", } @Article{Valencia-Hernandez:ACCESS, author = "I. Valencia-Hernandez and C. A. Reyes-Garcia and Alicia Morales-Reyes and G. C. Lopez-Armas and J. A. Fuentes-Tomas and E Mezura-Montes", title = "Integrating a Fuzzy Fitness Function in Genetic Programming to Generate Breast Tissue Segmentation Models.", journal = "IEEE Access", note = "Early Access", keywords = "genetic algorithms, genetic programming, Image segmentation, Breast, Mammography, Breast tissue, Fats, Medical diagnostic imaging, Computational modelling, Feature extraction, Accuracy, Fuzzy fitness function, Medical Image Segmentation, Breast density", ISSN = "2169-3536", DOI = "doi:10.1109/ACCESS.2025.3565462", abstract = "Genetic programming (GP) and fuzzy logic to automatically segment mammography images. GP allows the evolution of optimised segmentation models, guided by a fuzzy logic-based fitness function that incorporates medical criteria to improve the consistency and accuracy of the segmentation process. Unlike conventional approaches, this function optimises the segmentation and provides a descriptive representation of the breast tissue, allowing a closer evaluation to that performed by specialists. The proposed method was evaluated in the INbreast and BCDR databases, obtaining a Jaccard index of 0.82 and 0.78, respectively, and a comparative analysis was performed using ROC curves, reaching an AUC of 0.91 in INbreast and 0.89 in BCDR, demonstrating the model's ability to discriminate between fibroglandular and fat tissue. Its performance was compared with state-of-the-art methods, such as LIBRA, hybrid segmentation with Fuzzy C-Means, and NASGP-Net, showing that integrating fuzzy logic in genetic programming to lead the search allows competitive results with a lower computational burden. These results demonstrate the impact of fuzzy fitness functions in the evolution of segmentation models, highlighting the effectiveness of this approach in improving the segmentation and classification of medical images, in addition to the descriptive capabilities inherent to the fuzzy fitness function.", notes = "Also known as \cite{10979870}", } @InProceedings{Valencia-Ramirez:2014:ROPEC, author = "J. M. Valencia-Ramirez and J. A. Raya and J. R. Cedeno and R. R. Suarez and H. J. Escalante and M. Graff", booktitle = "IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC 2014)", title = "Comparison between Genetic Programming and full model selection on classification problems", year = "2014", month = nov, abstract = "Genetic Programming (GP) has been shown to be a competitive classification technique. GP is generally enhanced with a novel crossover, mutation, or selection mechanism, in order to compare the performance of this improvement with the performance of a standard GP. Although these comparisons show the capabilities of GP, it also makes harder, for a new comer, to figure out whether a traditional GP would have a competitive classification performance, when compared to state-of-the-art techniques. In this work, we try to fill this gap by comparing a standard GP, a GP with minor modifications and a ensemble of GP with two competitive techniques, namely support vector machines and a procedure that performs full model selection (Particle Swarm Model Selection). The results show that GP has better performance on problems with high dimensionality and large training sets and it is competitive on the rest of the problems tested. The former result is interesting because while Particle Swarm Model Selection is tailored to perform a data preprocessing and feature selection, GP is automatically performing these tasks and producing better classifiers.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ROPEC.2014.7036349", notes = "Also known as \cite{7036349}", } @Article{Valencia-Ramirez:2016:GPEM, author = "Jose Maria Valencia-Ramirez and Mario Graff and Hugo Jair Escalante and Jaime Cerda-Jacobo", title = "An iterative genetic programming approach to prototype generation", journal = "Genetic Programming and Evolvable Machines", year = "2017", volume = "18", number = "2", pages = "123--147", month = jun, keywords = "genetic algorithms, genetic programming, K-nearest neighbours, Prototype generation, Pattern classification", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-016-9279-3", size = "25 pages", abstract = "In this paper, we propose a genetic programming (GP) approach to the problem of prototype generation for nearest-neighbour (NN) based classification. The problem consists of learning a set of artificial instances that effectively represents the training set of a classification problem, with the goal of reducing the storage requirements and the computational cost inherent in KNN classifiers. This work introduces an iterative GP technique to learn such artificial instances based on a non-linear combination of instances available in the training set. Experiments are reported in a benchmark for prototype generation. Experimental results show our approach is very competitive with the state of the art, in terms of accuracy and in its ability to reduce the training set size.", } @InProceedings{valente:2001:WSC, author = "Solivan A. Valente and Heitor S. Lopes and Lucia V. R. Arruda", title = "Genetic algorithms for the assembly line balancing problem: a real-world automotive application", booktitle = "Soft Computing in Industry - Recent Applications", publisher = "Springer-Verlag", year = "2002", editor = "R. Roy and M. Koppen and S. Ovaska and T. Fukuhashi and F. Hoffman", pages = "319--328", keywords = "genetic algorithms, assembly line, automotive", ISBN = "1-85233-539-4", URL = "https://link.springer.com/book/10.1007/978-1-4471-0123-9", URL = "http://www.cpgei.cefetpr.br/~hslopes/publicacoes/2001/wsc2001.zip", notes = "WSC6", } @Article{Valenti:2008:GPEM, author = "Cesare Valenti", title = "A genetic algorithm for discrete tomography reconstruction", journal = "Genetic Programming and Evolvable Machines", year = "2008", volume = "9", number = "1", pages = "85--96", month = mar, keywords = "genetic algorithms, Discrete tomography, Stability problem", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-007-9051-9", size = "12 pages", abstract = "The aim of this paper is the description of an experiment carried out to verify the robustness of two different approaches for the reconstruction of convex polyominoes in discrete tomography. This is a new field of research, because it differs from classic computerised tomography, and several problems are still open. In particular, the stability problem is tackled by using both a modified version of a known algorithm and a new genetic approach. The effect of both, instrumental and quantisation noises has been considered too.", } @InProceedings{valenzuela:1999:EDCT, author = "Christine L. Valenzuela", title = "Evolutionary Divide and Conquer (II) for the TSP", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1744--1749", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "real world applications", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-716.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-716.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{conf/biocomp/ValenzuelaRRCR07, author = "Olga Valenzuela and Fernando Rojas Ruiz and Ignacio Rojas Ruiz and Maria {del Mar Cepero} and Francisco Javier Rojas", title = "A Genetic Programming Approach for Classification of the Spontaneous Termination of Atrial Fibrillation", booktitle = "International Conference on Bioinformatics \& Computational Biology, BIOCOMP 2007", year = "2007", editor = "Hamid R. Arabnia and Mary Qu Yang and Jack Y. Yang", volume = "I", pages = "296--300", address = "Las Vegas, Nevada, USA", month = jun # " 25-28", publisher = "CSREA Press", keywords = "genetic algorithms, genetic programming, classification, biomedical applications, feature selection", isbn13 = "1-60132-040-X", URL = "http://atc.ugr.es/I+D+i/SICA/2007/congresos/BIOCOMP_2007_1_0296.pdf", size = "3 pages", notes = "Feb 2013 BIOCOMP_2007_1_0296.pdf contains only 3 pages http://www.world-academy-of-science.org/worldcomp07/ws/program/bic28 See also \cite{journals/aai/ValenzuelaRRPBHG09}", bibdate = "2007-12-14", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/biocomp/biocomp2007-1.html#ValenzuelaRRCR07", } @Article{journals/aai/ValenzuelaRRPBHG09, title = "Intelligent System Based on Genetic Programming for Atrial Fibrillation Classification", author = "Olga Valenzuela and Ignacio Rojas and Francisco Javier Rojas and Hector Pomares and Jose Luis Bernier and Luis Javier Herrera and Alberto Guillen", journal = "Applied Artificial Intelligence", year = "2009", number = "10", volume = "23", pages = "895--909", publisher = "Taylor \& Francis Group", keywords = "genetic algorithms, genetic programming", ISSN = "0883-9514", DOI = "doi:10.1080/08839510903363420", bibdate = "2009-11-17", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/aai/aai23.html#ValenzuelaRRPBHG09", abstract = "This article focuses on the development of intelligent classifiers in the area of biomedicine, focusing on the problem of diagnosing cardiac diseases based on the electrocardiogram (ECG), or more precisely, on the differentiation of the types of atrial fibrillations. First of all, we will study the ECG, and the treatment of the ECG in order to work with it with this specific pathology. In order to achieve this we will study different ways of elimination, in the best possible way, of any activity that is not caused by the auriculars. We will study and imitate the ECG treatment methodologies and the characteristics extracted from the electrocardiograms that were used by the researchers who obtained the best results in the Physionet Challenge, where the classification of ECG recordings according to the type of atrial fibrillation (AF) that they showed, was realized. We will extract a great amount of characteristics, partly those used by these researchers and additional characteristics that we consider to be important for the distinction previously mentioned. A new method based on evolutionary algorithms will be used to realize a selection of the most relevant characteristics and to obtain a classifier that will be capable of distinguishing the different types of this pathology.", } @InCollection{DBLP:reference/ai/ValenzuelaRRGHRC09, author = "Olga Valenzuela and Ignacio Rojas and Fernando Rojas and Alberto Guillen and Luis Javier Herrera and Fernando J. Rojas and Maria {del Mar Cepero}", title = "Intelligent Classifier for Atrial Fibrillation {(ECG)}", booktitle = "Encyclopedia of Artificial Intelligence", publisher = "IGI Global", year = "2009", editor = "Juan R. Rabunal and Julian Dorado and Alejandro Pazos", chapter = "134", pages = "910--916", address = "Hershey, PA, USA", keywords = "genetic algorithms, genetic programming", timestamp = "Sun, 25 Jul 2021 11:43:38 +0200", biburl = "https://dblp.org/rec/reference/ai/ValenzuelaRRGHRC09.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "http://www.igi-global.com/Bookstore/Chapter.aspx?TitleId=10351", DOI = "doi:10.4018/978-1-59904-849-9.ch134", abstract = "This chapter is focused on the analysis and classification of arrhythmias. An arrhythmia is any cardiac pace that is not the typical sinusoidal one due to alterations in the formation and/or transportation of the impulses. In pathological conditions, the depolarization process can be initiated outside the sinoatrial (SA) node and several kinds of extra-systolic or ectopic beatings can appear. Besides, electrical impulses can be blocked, accelerated, deviated by alternate trajectories and can change its origin from one heart beat to the other, thus originating several types of blockings and anomalous connections. In both situations, changes in the signal morphology or in the duration of its waves and intervals can be produced on the ECG, as well as a lack of one of the waves. This work is focused on the development of intelligent classifiers in the area of biomedicine, focusing on the problem of diagnosing cardiac diseases based on the electrocardiogram (ECG), or more precisely on the differentiation of the types of atrial fibrillations. First of all we will study the ECG, and the treatment of the ECG in order to work with it, with this specific pathology. In order to achieve this we will study different ways of elimination, in the best possible way, of any activity that is not caused by the auriculars. We will study and imitate the ECG treatment methodologies and the characteristics extracted from the electrocardiograms that were used by the researchers that obtained the best results in the Physionet Challenge, where the classification of ECG recordings according to the type of Atrial Fibrillation (AF) that they showed, was realised. We will extract a great amount of characteristics, partly those used by these researchers and additional characteristics that we consider to be important for the distinction mentioned before. A new method based on evolutionary algorithms will be used to realise a selection of the most relevant characteristics and to obtain a classifier that will be capable of distinguishing the different types of this pathology.", } @Article{valeri:2023:cels, author = "Jacqueline A. Valeri and Luis R. Soenksen and Katherine M. Collins and Pradeep Ramesh and George Cai and Rani Powers and Nicolaas M. Angenent-Mari and Diogo M. Camacho and Felix Wong and Timothy K. Lu and James J. Collins", title = "{BioAutoMATED}: An end-to-end automated machine learning tool for explanation and design of biological sequences", journal = "Cell Systems", year = "2023", volume = "14", number = "6", pages = "525--542.e9", keywords = "genetic algorithms, genetic programming, TPOT, automated machine learning, architecture search, hyperparameter optimization, biological sequences", ISSN = "2405-4712", URL = "https://www.sciencedirect.com/science/article/pii/S2405471223001515", DOI = "doi:10.1016/j.cels.2023.05.007", abstract = "The design choices underlying machine-learning (ML) models present important barriers to entry for many biologists who aim to incorporate ML in their research. Automated machine-learning (AutoML) algorithms can address many challenges that come with applying ML to the life sciences. However, these algorithms are rarely used in systems and synthetic biology studies because they typically do not explicitly handle biological sequences (e.g., nucleotide, amino acid, or glycan sequences) and cannot be easily compared with other AutoML algorithms. Here, we present BioAutoMATED, an AutoML platform for biological sequence analysis that integrates multiple AutoML methods into a unified framework. Users are automatically provided with relevant techniques for analyzing, interpreting, and designing biological sequences. BioAutoMATED predicts gene regulation, peptide-drug interactions, and glycan annotation, and designs optimized synthetic biology components, revealing salient sequence characteristics. By automating sequence modeling, BioAutoMATED allows life scientists to incorporate ML more readily into their work.", notes = "also known as \cite{VALERI2023525} PMID: 37348466 Valeri PhD thesis, Computationally-Guided Discovery and Design of Biological Sequences and Small Molecules, MIT, August 2023", } @InProceedings{valigiani:2004:eurogp, author = "Gregory Valigiani and Cyril Fonlupt and Pierre Collet", title = "Analysis of GP Improvement Techniques over the Real-World Inverse Problem of Ocean Color", booktitle = "Genetic Programming 7th European Conference, EuroGP 2004, Proceedings", year = "2004", editor = "Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule", volume = "3003", series = "LNCS", pages = "174--186", address = "Coimbra, Portugal", publisher_address = "Berlin", month = "5-7 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-21346-5", DOI = "doi:10.1007/978-3-540-24650-3_16", abstract = "This paper is a follow-up of Maarten Keijzer's award-winning EUROGP'03 paper [\cite{keijzer03}], that suggests using Interval Arithmetic (IA) and Linear Scaling (LS) in Genetic Programming algorithms. The ideas exposed in this paper were so nice that it was decided to experiment with them on a real-world problem on which the LIL research team had some experience and results with: the Ocean Colour Inverse Problem. After extensive testing of IA, LS as well as a progressive learning method using thresholds (T), results seem to show that functions evolved with GP algorithms that do not implement IA may output erroneous values outside the learning set, while LS and T methods produce solutions with a greater generalisation error. A simple and apparently harmless improvement over standard GP is also proposed, that consists in weighting operands of + and - operators.", notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004", } @InProceedings{DBLP:conf/isaim/ValipourGSR20, author = "Mohammad Valipour and Sandra M. Guzman and Mohammad Ali Gholami Sefidkouhi and Mahmoud Raeini-Sarjaz", title = "Using Genetic Algorithms and Gene Expression Programming to Estimate Evapotranspiration with Limited Meteorological Data", booktitle = "International Symposium on Artificial Intelligence and Mathematics, ISAIM 2020", year = "2020", month = jan # " 6-8", address = "Fort Lauderdale, Florida, USA", publisher = "AAAI", keywords = "genetic algorithms, genetic programming, gene expression programming", URL = "http://isaim2020.cs.ou.edu/papers/ISAIM2020_Agriculture_Valipour_etal.pdf", timestamp = "Wed, 30 Sep 2020 17:26:07 +0200", biburl = "https://dblp.org/rec/conf/isaim/ValipourGSR20.bib", bibsource = "dblp computer science bibliography, https://dblp.org", size = "6 pages", abstract = "genetic algorithm (GA) was employed to detect the most important variables for estimating ETo among mean temperature (Tmean), maximum temperature (Tmax), minimum temperature (Tmin), sunshine hours (n), relative humidity (RH), and wind speed (WS). The results show that Tmean and WS are the most important meteorological variables to model evapotranspiration in Iran. Then, we selected gene expression programming (GEP) to model ETo based on Tmean and WS historical data. The results indicate that the GEP has good performance for semi-arid and Mediterranean climates compared to very humid and some arid regions. In addition, GEP is an effective solution when there is insufficient meteorological data available.", notes = "Department of Agricultural and Biological Engineering, Indian River Research and Education Center, University of Florida, Fort Pierce, USA https://isaim2020.cs.ou.edu/program.html", } @InProceedings{vallejo:1999:RAFCGP, author = "Edgar E. Vallejo and Fernando Ramos", title = "Result-Sharing: A Framework for Cooperation in Genetic Programming", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1238", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-435.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-435.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{vallejo:2000:MICAI, author = "Edgar E. Vallejo and Fernando Ramos", title = "Evolving Insect Locomotion Using Cooperative Genetic Programming", booktitle = "MICAI 2000: Advances in Artificial Intelligence", year = "2000", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/10720076_16", DOI = "doi:10.1007/10720076_16", } @InProceedings{vallejo:2001:EuroGP, author = "Edgar E. Vallejo and Fernando Ramos", title = "Evolving {Turing} Machines for Biosequence Recognition and Analysis", booktitle = "Genetic Programming, Proceedings of EuroGP'2001", year = "2001", editor = "Julian F. Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon", volume = "2038", series = "LNCS", pages = "192--203", address = "Lake Como, Italy", publisher_address = "Berlin", month = "18-20 " # apr, organisation = "EvoNET", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, Bioinformatics, DNA, Turing machines, Multiple Sequence aligment", ISBN = "3-540-41899-7", DOI = "doi:10.1007/3-540-45355-5_15", size = "12 pages", abstract = "This article presents a genetic programming system for biosequence recognition and analysis. In our model, a population of Turing machines evolves the capability of biosequence recognition using genetic algorithms. We use HIV sequences as the working example. Experimental results indicate that evolved Turing machines are capable of recognizing HIV sequences in a collection of training sets. In addition, we demonstrate that the evolved Turing machines can be used to approximate the multiple sequence alignment problem.", notes = "EuroGP'2001, part of \cite{miller:2001:gp}", } @PhdThesis{Valsalam:thesis, author = "Vinod K. Valsalam", title = "Utilizing Symmetry in Evolutionary Design", school = "Department of Computer Sciences, The University of Texas at Austin", year = "2010", address = "Austin, TX, USA", month = aug, note = "Available as Technical Report AI-10-04", keywords = "genetic algorithms, genetic programming", URL = "http://www.genetic-programming.org/hc2011/03-Valsalam/Valsalam-Text.txt", URL = "http://nn.cs.utexas.edu/downloads/papers/valsalam.phdtr10.pdf", size = "120 pages", abstract = "Can symmetry be used as a design principle to constrain evolutionary search, making it more effective? This dissertation aims to show that this is indeed the case, in two ways. First, an approach called ENSO is developed to evolve modular neural network controllers for simulated multilegged robots. Inspired by how symmetric organisms have evolved in nature, ENSO uses group theory to break symmetry systematically, constraining evolution to explore promising regions of the search space. As a result, it evolves effective controllers even when the appropriate symmetry constraints are difficult to design by hand. The controllers perform equally well when transferred from simulation to a physical robot. Second, the same principle is used to evolve minimal-size sorting networks. In this different domain, a different instantiation of the same principle is effective: building the desired symmetry step-by-step. This approach is more scalable than previous methods and finds smaller networks, thereby demonstrating that the principle is general. Thus, evolutionary search that uses symmetry constraints is shown to be effective in a range of challenging applications.", notes = "Is this GP? Entered for 2011 HUMIES GECCO 2011 dissertation supervisor: Risto Miikkulainen", } @InProceedings{Valtchanov:2012:C3S2E, author = "Valtchan Valtchanov and Joseph Alexander Brown", title = "Evolving Dungeon Crawler Levels With Relative Placement", booktitle = "C3S2E'12 Fifth International C* Conference on Computer Science \& Software Engineering", year = "2012", editor = "B. C. Desai and S. Mudur and E. Vassev", pages = "27--35", address = "Montreal", month = "27-29 " # jun, publisher = "ACM", keywords = "genetic algorithms, genetic programming, games, Performance, procedural content generation, evolutionary computation, level generation", isbn13 = "978-1-4503-1084-0/12/06", URL = "http://www.uoguelph.ca/~jbrown16/Vvaltchanov_c3s2e12.pdf", DOI = "doi:10.1145/2347583.2347587", size = "9 pages", abstract = "Procedural Content Generation (PCG) is the process of automating the construction of media types for use in game development, the movie industry, and other creative fields. By approaching the process of media creation as a search for content which is evaluated to express desirable features in a well-defined manner, we are able to apply evolutionary techniques such as genetic programming. This can greatly decrease the effort required to bring a project to completion by allowing artists and developers to focus on guiding the creation process. The specific generation process addressed is that of map creation for dungeon crawler video games. The search method proposed allows artists and developers to guide the generation process by specifying a set of tiles that define the composition of each map, and a fitness function that defines its structure.", notes = "broken Aug 2022 http://confsys.encs.concordia.ca/c3s2e/c3s2e-12/c3s2e12.php", } @Article{Vamsi:2015:IJCA, author = "M. N. {Vamsi Thalatam} and Allam {Appa Rao}", title = "A Novel Genetic Programming Approach for Inferring Gene Regulatory Network", journal = "International Journal of Computer Applications", year = "2015", volume = "119", number = "15", pages = "43--46", month = jun, keywords = "genetic algorithms, genetic programming, Computational intelligence, Gene Regulatory Networks", ISSN = "0975-8887", URL = "https://www.ijcaonline.org/archives/volume119/number15/21147-4213/", URL = "https://research.ijcaonline.org/volume119/number15/pxc3904213.pdf", DOI = "doi:10.5120/21147-4213", size = "4 pages", abstract = "Computational intelligence (CI) techniques are well suited to many of the problems arising in biology as they have flexible information processing capabilities for handling huge volume of real life data with noise, ambiguity, missing values, and so on. To process the executed knowledge through CI approaches needs techniques from computer science & engineering, mathematics and statistics. It involves in-depth study with in the areas of genomic signals, gene regulation and homeostatic regulation. The evolutionary computing techniques like genetic programming plays vital role to effectively resolve several crucial problems related to genomics. The core objective of this work is to study and model the different regulation mechanisms involved in the living organisms and propose accurate evolutionary algorithms for inferring Gene Regulatory Networks", notes = "www.ijcaonline.org", } @Book{Vamsi:GPGRN, author = "Mohana Naga Vamsi and Allam Apparao", title = "Genetic Programming for Gene Regulatory Networks: {IRMA} Data Analysis", title2 = "Decoding the Secrets of Gene Regulation: A Cutting-Edge Genetic Programming Approach", year = "2023", address = "Moldova", month = "4 " # aug, publisher = "LAP Lambert Academic Publishing", keywords = "genetic algorithms, genetic programming, RPN", isbn13 = "978-6206766223", URL = "https://www.amazon.com/Genetic-Programming-Gene-Regulatory-Networks/dp/B0CGT6GJ34", URL = "https://my.lap-publishing.com/catalog/details//store/gb/book/978-620-6-76622-3/genetic-programming-for-gene-regulatory-networks:-irma-data-analysis", size = "136 pages", abstract = "the application of Computational Intelligence techniques in biology, particularly in understanding gene regulatory networks and homeostatic regulation. These techniques, such as genetic algorithms and genetic programming, play a crucial role in analyzing gene expression data and inferring regulatory networks. Polish notation to fit ordinary differential equation models to gene regulatory systems based on time series gene expression data. The algorithm shows promising results in inferring gene regulatory networks in both Switch ON and Switch OFF conditions. It outperforms existing state-of-the-art methods like BANJO, NIR+TSNI, and GRNGen in terms of precision, sensitivity, and accuracy metrics (F-score). also applied to infer the gene regulatory network of the GABA signalling family in the Rat Central Nervous System.", notes = " See also \cite{Vamsi:2015:IJCA}", } @Article{journals/tim/HaeverbekeSB21, author = "Maxime {Van Haeverbeke} and Michiel Stock and Bernard {De Baets}", title = "Practical Equivalent Electrical Circuit Identification for Electrochemical Impedance Spectroscopy Analysis With Gene Expression Programming", journal = "IEEE Transactions on Instrumentation and Measurement", year = "2021", volume = "70", keywords = "genetic algorithms, genetic programming, gene expression programming, GEP, electrochemical impedance spectroscopy (eis), equivalent electrical circuit, measurement noise", ISSN = "0018-9456", bibdate = "2021-11-03", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/tim/tim70.html#HaeverbekeSB21", URL = "https://doi.org/10.1109/TIM.2021.3113116", DOI = "doi:10.1109/TIM.2021.3113116", size = "12 pages", abstract = "Researchers relying on electrochemical impedance spectroscopy need to decide which equivalent electrical circuit to use to analyse their measurements. Here, we present an identification algorithm based on gene expression programming to support this decision. It is accompanied by some measures to enhance the interpretability of the resulting circuits, such as the removal of redundant components to avoid overly complex circuits. We also provide the option to depart from an initial population of widely applied circuits, allowing for quick identification of known circuits that are capable of modelling the measurement data. As the number of measurements per experiment is typically rather limited in real-life experiments, we examine the number needed to find an adequate circuit topology for two example circuits. Next, the algorithm is tested on impedance simulations for a variety of circuits. Noise robustness is evaluated by subjecting the impedance measurements to increasing amounts of Gaussian noise, demonstrating that the algorithm still works well even for noise levels that are significantly higher than what is typically encountered in practice. Finally, we validate the algorithm by identifying the appropriate circuit for impedance measurements from a biological application.", } @InProceedings{vanbelle:2002:EuroGP, title = "Uniform Subtree Mutation", author = "Terry {Van Belle} and David H. Ackley", editor = "James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi", booktitle = "Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002", volume = "2278", series = "LNCS", pages = "152--161", publisher = "Springer-Verlag", address = "Kinsale, Ireland", publisher_address = "Berlin", month = "3-5 " # apr, year = "2002", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-43378-3", URL = "http://www.cs.unm.edu/~treport/tr/02-02/uniform_mutation.ps.gz", DOI = "doi:10.1007/3-540-45984-7_15", abstract = "Genetic programming methods often suffer from `code bloat,' in which evolving solution trees rapidly become unmanageably large. To provide a measure of sensitivity to tree size in a natural way, we introduce a simple uniform subtree mutation (USM) operator that provides an approximately constant probability of mutation per tree node, rather than per tree. To help model circumstances where tree size cannot be ignored, we introduce a new notion of computational effort called size effort. Initial empirical tests show that genetic programming using only uniform subtree mutation reduces evolved tree sizes dramatically, compared to crossover, but does impact solution quality somewhat. In some cases, however, using using a combination of USM and crossover yielded both smaller trees and superior performance, as measured both by size effort and traditional metrics.", notes = "EuroGP'2002, part of \cite{lutton:2002:GP}", } @InProceedings{vanbelle:2002:gecco, author = "Terry {Van Belle} and David H. Ackley", title = "Code Factoring And The Evolution Of Evolvability", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "1383--1390", address = "New York", publisher_address = "San Francisco, CA 94104, USA", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "genetic algorithms, genetic programming, search-based software engineering, automatically defined functions, code factoring, dynamic environment, evolution of evolvability, software engineering", ISBN = "1-55860-878-8", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/SBSE170.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/SBSE170.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-25.pdf", notes = "GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002) Wed, 28 Jan 2004 17:06:27 MST genetic_programming@yahoogroups.com Actually, the experiments in our GECCO 2002 paper _did_ use the standard tree depth < 17 cutoff on both branches, which is a form of parsimony pressure. This should have been reported, but unfortunately wasn't :-( Even though the typical RPBs generated were much shallower than the cutoff, it could have affected the RPB evolution, and probably helped keep down the ADF sizes somewhat. Lilgp", } @InProceedings{belle:2003:ICES, author = "Werner {Van Belle} and Tom Mens and Theo D'Hondt", title = "Using Genetic Programming to Generate Protocol Adaptors for Interprocess Communication", booktitle = "Evolvable Systems: From Biology to Hardware, Fifth International Conference, ICES 2003", year = "2003", editor = "Andy M. Tyrrell and Pauline C. Haddow and Jim Torresen", volume = "2606", series = "LNCS", pages = "422--433", address = "Trondheim, Norway", month = "17-20 " # mar, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-00730-X", URL = "http://prog.vub.ac.be/Publications/2003/vub-prog-tr-03-33.pdf", DOI = "doi:10.1007/3-540-36553-2_38", size = "12 pages", abstract = "As mobile devices become more powerful, interprocess communication becomes increasingly more important. Unfortunately, this larger freedom of mobility gives rise to unknown environments. In these environments, processes that want to communicate with each other will be unable to do so because of protocol conflicts. Although conflicting protocols can be remedied by using adaptors, the number of possible combinations of different protocols increases dramatically. Therefore we propose a technique to generate protocol adaptors automatically. This is realised by means of genetically engineered classifier systems that use Petri nets as a specification for the underlying protocols. This paper reports on an experiment that validates this approach.", notes = "ICES-2003", } @PhdThesis{VanBelle:thesis, author = "Werner {Van Belle}", title = "Creation of an Intelligent Concurrency Adaptor in order to mediate the Differences between Conflicting Concurrency Interfaces", school = "Computer Science Department, Vrije Universiteit Brussel", year = "2003", address = "Pleinlaan 2, 1050 Brussels, Belgium", month = May, keywords = "genetic algorithms, genetic programming, distributed systems, mobile multi-agent systems, Petri-nets, concurrency strategies, reinforcement learning, component based development", URL = "http://soft.vub.ac.be/soft/dissertations", URL = "http://prog.vub.ac.be/Publications/2003/vub-prog-phd-03-01.pdf", size = "300 pages", abstract = "The dissertation you are about to read, tries to solve one of the more prominent problems within open distributed systems namely: concurrency management between components written by different manufacturers. All too often, the concurrency strategy provided or required by a component is badly documented and rarely matches the concurrency strategy provided or required by another component. Whenever this happens there is a concurrency interface conflict. Solving these conflicts requires a substantial amount of resources with respect to software engineering: the time necessary to understand the problem, the time necessary to solve the problem, and above all the resources towards maintaining a working concurrency strategy that mediates between the different components. Indeed, in open distributed systems, components can be updated without prior notification and without guarantees that the new interface is backward compatible. Such updates can range from syntactic modifications over slight semantic differences to completely new concurrency strategies. For example, changing a nested locking strategy to a non-nested locking strategy or changing a non-blocking server to work synchronously. In order to solve the problem of conflicting concurrency interfaces we will create a concurrency adaptor that resolves incompatibilities between incompatible concurrency strategies. We do this in two steps: first we require a certain amount of extra information to be present: every provided and required interface should be documented by means of coloured Petri-nets and certain checkpoints are to be placed in the code to check the liveness. Second, we construct a concurrency adaptor that can be placed between the different communicating components. This is done by means of a hybrid approach: first the adaptor will try to gain freedom by bypassing all the existing concurrency strategies. For a client a stub concurrency interface is generated that will keep the client alive. For a server a stub concurrency interface is generated that will allow anything to happen; in essence bypassing the concurrency strategy entirely. The concurrency adaptor is finished by plugging in an existing, formally guaranteed to work concurrency strategy between the two stub concurrency interfaces. Bypassing a server's behaviour is achieved by means of a runtime formal deduction. Given the current state of the Petri-net and the required state a prolog program deduces what should happen. Bypassing a clients behavior is achieved with a reinforcement learning algorithm that maximises the reward it receives from the component itself. The rewards are based on check-points as specified by the component itself. When placing a guaranteed to work concurrency strategy between the different stub concurrency-interfaces, we need a meta-protocol that is understood by this central concurrency strategy. This meta-protocol specifies which resources are present and which locking/unlocking operations can work upon them. The meta-protocol is deduced entirely from the Petri-nets involved. The approach presented in this dissertation provides a substantial added value to the programmer of components in open distributed systems. He now only needs to specify what he requires or provides as a concurrency strategy within his component. He no longer needs to take into account the concurrency strategy offered by other components. This might reduce development and maintenance time drastically. A second advantage of using Petri-nets is that interfaces are not only documented, but that this information can be verified automatically: whenever necessary the formal specification can be tested against the actual working of a component.", notes = "Programming Technology Lab (PROG), DINF, VUB, Pleinlaan 2, 1050 Brussels FKFO, IWT Adviser: Prof. Dr. Theo D'Hondt co-Adviser: Dr. Tom Mens", } @InProceedings{Belle:2003:gecco, author = "Terry {Van Belle} and David H. Ackley", title = "Adaptation and Ruggedness in an Evolvability Landscape", booktitle = "Genetic and Evolutionary Computation -- GECCO-2003", editor = "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller", year = "2003", pages = "150--151", address = "Chicago", publisher_address = "Berlin", month = "12-16 " # jul, volume = "2723", series = "LNCS", ISBN = "3-540-40602-6", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, A-Life, Adaptive Behavior, Agents, and Ant Colony Optimization, Poster", DOI = "doi:10.1007/3-540-45105-6_18", size = "2 pages", notes = "GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eights Annual Genetic Programming Conference (GP-2003)", } @InProceedings{vanBerkel:2012:GECCOcomp, author = "Sjors {van Berkel} and Daniel Turi and Andrei Pruteanu and Stefan Dulman", title = "Automatic discovery of algorithms for multi-agent systems", booktitle = "GECCO 2012 Evolutionary computation and multi-agent systems and simulation (ECoMASS)", year = "2012", editor = "Forrest Stonedahl and Rick Riolo", isbn13 = "978-1-4503-1178-6", keywords = "genetic algorithms, genetic programming", pages = "337--344", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330784.2330833", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Automatic algorithm generation for large-scale distributed systems is one of the holy grails of artificial intelligence and agent-based modeling. It has direct applicability in future engineered (embedded) systems, such as mesh networks of sensors and actuators where there is a high need to harness their capabilities via algorithms that have good scalability characteristics. NetLogo has been extensively used as a teaching and research tool by computer scientists, for example for exploring distributed algorithms. Inventing such an algorithm usually involves a tedious reasoning process for each individual idea. In this paper, we report preliminary results in our effort to push the boundary of the discovery process even further, by replacing the classical approach with a guided search strategy that makes use of genetic programming targeting the NetLogo simulator. The effort moves from a manual model implementation to an automated discovery process. The only activity that is required is the implementation of primitives and the configuration of the tool-chain. In this paper, we explore the capabilities of our framework by re-inventing five well-known distributed algorithms.", notes = "Also known as \cite{2330833} Distributed at GECCO-2012. ACM Order Number 910122.", } @InProceedings{Vandana:2023:SEFET, author = "Vandana and Bibaswan Bose and Akhil Garg", booktitle = "2023 IEEE 3rd International Conference on Sustainable Energy and Future Electric Transportation (SEFET)", title = "Sensitivity Analysis of Battery Digital Twin Design Variables Using Genetic Programming", year = "2023", abstract = "The advancement of digital twin (DT) technology improves battery performance and lifespan. Although precise forecasting, selection of design variables, and risk reduction are challenging. Therefore, it is critical in implementation of practical DT to investigate the sensitivity of feature implications on state estimation thoroughly. Hence in this paper, an analysis of features has been piloted using voltage and current characteristics. First, features have been extracted from performance values. Secondly, genetic programming (GP) has been set up to reflect the impact on state estimations. Structural risk minimization is used as a fitness function to maximize the DT's objective function, while GP-battery state estimation is implemented. An illustrative example is presented to evaluate the state of experimental data generated in the lab under controlled environmental conditions. Based on the analysis, the state of charge shows precision incorporation of all features, while the change in current over voltage shows the improvement in state of energy estimation. State of power is more sensitive towards changes in voltage concerning changes in current, and state of health offers better accuracy to the present voltage over the current applied. A sensitivity rating has been compared to design the role of the feature variable.", keywords = "genetic algorithms, genetic programming, Transportation, Voltage, Feature extraction, Linear programming, Batteries, Digital twins, Structural Risk Minimization, feature extraction, State Estimation, Sensitivity Analysis", DOI = "doi:10.1109/SeFeT57834.2023.10244776", month = aug, notes = "Also known as \cite{10244776} Centre for Automotive Research and Tribology, Indian Institute of Technology,Delhi, India", } @InProceedings{vandering:2004:CHEP, author = "Erik Vandering", title = "Genetic Programming and its application to {HEP}", booktitle = "Computing in High Energy Physics, CHEP'04", year = "2004", editor = "J. Harvey", address = "Interlaken, Switzerland", month = "27 " # sep # "-1 " # oct, keywords = "genetic algorithms, genetic programming", URL = "http://uscms-docdb.fnal.gov/cgi-bin/ShowDocument?docid=1848", URL = "http://indico.cern.ch/event/0/session/4/contribution/49/material/slides/0.pdf", abstract = "Genetic programming is a machine learning technique, popularized by Koza in 1992, in which computer programs which solve user-posed problems are automatically discovered. Populations of programs are evaluated for their fitness of solving a particular problem. New populations of ever increasing fitness are generated by mimicking the biological processes underlying evolution. These processes are principally genetic recombination, mutation, and survival of the fittest. Genetic programming has potential advantages over other machine learning techniques such as neural networks and genetic algorithms in that the form of the solution is not specified in advance and the program can grow as large as necessary to adequately solve the posed problem. This talk will give an overview and demonstration of the genetic programming technique and show a successful application in high energy physics: the automatic construction of an event filter for FOCUS which is more powerful than the experiment's usual methods of event selection. We have applied this method to the study of doubly Cabibbo suppressed decays of charmed hadrons ($D^+$, $D_s^+$, and $\Lambda_c^+$).", notes = "Tutorial 0.pdf are presentation slides. http://chep2004.web.cern.ch/chep2004/ VANDERBILT UNIVERSITY", } @InCollection{vanhoucke:2000:SDACGP, author = "Vincent Vanhoucke", title = "Speech Detection in Adverse Conditions using Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "415--424", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", URL = "http://vincent.vanhoucke.com/publications/vanhoucke-koza00.pdf", notes = "part of \cite{koza:2000:gagp}", } @InProceedings{Sankararajan:2016:ICTCSDM, author = "S. Vanitha and C. Sivapragasam and N. V. N. Nampoothiri", title = "Modeling of Dissolved Oxygen Using Genetic Programming Approach", booktitle = "International Conference on Theoretical Computer Science and Discrete Mathematics", year = "2016", editor = "S. Arumugam and Jay Bagga and Lowell W. Beineke and B. S. Panda", volume = "10398", series = "Lecture Notes in Computer Science", pages = "445--452", address = "Krishnankoil, India", month = "19-21 " # dec, publisher = "Springer", keywords = "genetic algorithms, genetic programming, dissolved oxygen, mathematical modelling", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ictcsdm/ictcsdm2016.html#VanithaSN16", isbn13 = "978-3-319-64419-6", DOI = "doi:10.1007/978-3-319-64419-6_56", size = "8 pages", abstract = "Genetic Programming (GP) based modelling is suggested for modelling the variation of Dissolved Oxygen (DO) under controlled conditions in the presence and absence of toxicant. The results indicated that GP is able to evolve robust physically meaningful models even with small dataset by selecting the most relevant functions from the set of functions given for the modelling. It is interesting to note that the evolved models clearly reflect the underlying non-linearity of the process distinctly for both the case studies.", notes = "Vanitha Sankararajan Also known as \cite{conf/ictcsdm/VanithaSN16}", } @Article{Vanitha:2017:EMS, author = "Sankararajan Vanitha and Neelakandhan Nampoothiri and Chandrasekaran Sivapragasam", title = "Modeling of constructed wetland performance in BOD5 removal for domestic wastewater under changes in relative humidity using genetic programming", journal = "Environmental Monitoring and Assessment", year = "2017", volume = "189", number = "4", pages = "164", month = "15 " # mar, keywords = "genetic algorithms, genetic programming", ISSN = "1573-2959", URL = "https://doi.org/10.1007/s10661-017-5857-y", DOI = "doi:10.1007/s10661-017-5857-y", abstract = "Despite the extensive use of constructed wetland (CW) as an effective method for domestic wastewater treatment, there is lack of clarity in arriving at well-defined design guidelines. This is particularly due to the fact that the design of CW is dependent on many inter-connected parameters which interact in a complex manner. Consequently, different researchers in the past have tried to address different aspects of this complexity. In this study, an attempt is made to model the influence of relative humidity (RH) in the effectiveness of BOD5 removal. Since it is an accepted fact that plants respond to change in humidity, it is necessary to take this parameter into consideration particularly when the CW is to be designed involving changes in relative humidity over a shorter time horizon (say a couple of months). This study reveals that BOD5out depends on the ratio of BOD5in and relative humidity. An attempt is also made to model the outlet BOD5 using genetic programming with inlet BOD5 and relative humidity as input parameters.", notes = "See also \cite{Sivapragasam:2017:EMA}", } @MastersThesis{vanLaar:mastersthesis, author = "Daan {van Laar}", title = "Fitness Landscape Analysis applied to functional Genetic Improvement", school = "Utrecht University", year = "2021", address = "The Netherlands", month = "15 " # nov, keywords = "genetic algorithms, genetic programming, genetic improvement, PyGGI, Fitness Landscape Analysis, Evolutionary Computing, Program Transformation, Computing Science", URL = "https://studenttheses.uu.nl/handle/20.500.12932/224", URL = "https://studenttheses.uu.nl/bitstream/handle/20.500.12932/224/Thesis_vFinal.pdf", size = "43 pages", abstract = "Genetic Improvement is the concept of a computer improving human-written code. This improves either the functional or the non-functional properties of the program. Genetic Improvement uses mutations to find improved versions of the original program. This makes the search space for Genetic Improvement very large. Furthermore for functional improvement, the fitness landscape forms large plateaus. In this masters thesis, we will attempt to analyse the search space of Genetic Improvement using Fitness Landscape Analysis techniques to achieve a better understanding of the search space. To achieve this, we have edited the PyGGI framework to perform a random walk, and to analyse how large plateaus are. The PyGGI framework has been edited in such a way that it suits our needs and has such a performance that the experiments can be concluded in a reasonable amount of time. We perform the Genetic Improvement process on programs selected from the Bears benchmark, which contains many programs with bugs and test suites. The results of this masters thesis conclude that while the plateaus are near-infinitely big, a random walk over the plateau often finds the global optimum. The only cases where the global optimum could not be found are the experiments which could not be improved with the used set of mutations. These results are in line with similar results in researches in this area.", notes = "Fernanda Madeiral, Simon Urli, Marcelo Maia, and Martin Monperrus. BEARS: An Extensible Java Bug Benchmark for Automatic Program Repair Studies. First supervisor: Dirk Thierens", } @InProceedings{vanLon:2012:GECCOcomp, author = "Rinde R. S. {van Lon} and Tom Holvoet and Greet {Vanden Berghe} and Tom Wenseleers and Juergen Branke", title = "Evolutionary synthesis of multi-agent systems for dynamic dial-a-ride problems", booktitle = "GECCO 2012 Evolutionary computation and multi-agent systems and simulation (ECoMASS)", year = "2012", editor = "Forrest Stonedahl and Rick Riolo", isbn13 = "978-1-4503-1178-6", keywords = "genetic algorithms, genetic programming", pages = "331--336", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330784.2330832", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "In dynamic dial-a-ride problems a fleet of vehicles need to handle transportation requests within time. We research how to create a decentralized multi-agent system that can solve the dynamic dial-a-ride problem. Normally multi-agent systems are hand designed for each specific application. In this paper we research the applicability of genetic programming to automatically program a multi-agent system that solves dial-a-ride problems. We evaluated the evolved system by running a number of simulations and compared it's performance to a selection hyper-heuristic. The results shows that genetic programming can be a viable alternative to hand constructing multi-agent systems.", notes = "Also known as \cite{2330832} Distributed at GECCO-2012. ACM Order Number 910122.", } @Article{vanLon:2017:GPEM, author = "Rinde R. S. {van Lon} and Juergen Branke and Tom Holvoet", title = "Optimizing agents with genetic programming: an evaluation of hyper-heuristics in dynamic real-time logistics", journal = "Genetic Programming and Evolvable Machines", year = "2018", volume = "19", number = "1-2", pages = "93--120", month = jun, note = "Special Issue on Automated Design and Adaptation of Heuristics for Scheduling and Combinatorial Optimisation", keywords = "genetic algorithms, genetic programming, Hyper-heuristics, Multi-agent systems Logistics, Decentralized, Centralized, Operational research, Optimization, Real-time", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-017-9300-5", size = "28 pages", abstract = "Dynamic pickup and delivery problems (PDPs) require online algorithms for managing a fleet of vehicles. Generally, vehicles can be managed either centrally or decentrally. A common way to coordinate agents de-centrally is to use the contract-net protocol (CNET) that uses auctions to allocate tasks among agents. To participate in an auction, agents require a method that estimates the value of a task. Typically, this method involves an optimization algorithm, e.g. to calculate the cost to insert a customer. Recently, hyper-heuristics have been proposed for automated design of heuristics. Two properties of automatically designed heuristics are particularly promising: (1) a generated heuristic computes quickly, it is expected therefore that hyper-heuristics perform especially well for urgent problems, and (2) by using simulation-based evaluation, hyper-heuristics can create a rule of thumb that anticipates situations in the future. In the present paper we empirically evaluate whether hyper-heuristics, more specifically genetic programming (GP), can be used to improve agents decentrally coordinated via CNET. We compare several GP settings and compare the resulting heuristic with existing centralized and decentralized algorithms based on the OptaPlanner optimization library. The tests are conducted in real-time on a dynamic PDP dataset with varying levels of dynamism, urgency, and scale. The results indicate that the evolved heuristic always outperforms the optimization algorithm in the decentralized multi-agent system (MAS) and often outperforms the centralized optimization algorithm. Our paper demonstrates that designing MASs using genetic programming is an effective way to obtain competitive performance compared to traditional operational research approaches. These results strengthen the relevance of decentralized agent based approaches in dynamic logistics.", } @InProceedings{vanneschi:2002:gecco:workshop, title = "A Study on Fitness Distance Correlation and Problem Difficulty for Genetic Programming", author = "Leonardo Vanneschi and Marco Tomassini", pages = "307--310", booktitle = "Graduate Student Workshop", editor = "Sean Luke and Conor Ryan and Una-May O'Reilly", year = "2002", month = "8 " # jul, publisher = "AAAI", address = "New York", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025", keywords = "genetic algorithms, genetic programming", URL = "http://personal.disco.unimib.it/Vanneschi/GECCO_2002_PHD_WORKSHOP.pdf", notes = "Bird-of-a-feather Workshops, GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002) part of barry:2002:GECCO:workshop", } @InProceedings{vanneschi03, author = "Leonardo Vanneschi and Marco Tomassini and Philippe Collard and Manuel Clergue", title = "Fitness Distance Correlation in Structural Mutation Genetic Programming", booktitle = "Genetic Programming, Proceedings of EuroGP'2003", year = "2003", editor = "Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa", volume = "2610", series = "LNCS", pages = "455--464", address = "Essex", publisher_address = "Berlin", month = "14-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-00971-X", DOI = "doi:10.1007/3-540-36599-0_43", abstract = "A new kind of mutation for genetic programming based on the structural distance operators for trees is presented in this paper. We firstly describe a new genetic programming process based on these operators (we call it structural mutation genetic programming). Then we use structural distance to calculate the fitness distance correlation coefficient and we show that this coefficient is a reasonable measure to express problem difficulty for structural mutation genetic programming for the considered set of problems, i.e. unimodal trap functions, royal trees and MAX problem.", notes = "EuroGP'2003 held in conjunction with EvoWorkshops 2003", } @InProceedings{vanneschi+tomassini:2003:gecco:workshop, title = "Pros and Cons of Fitness Distance Correlation in Genetic Programming", author = "Leonardo Vanneschi and Marco Tomassini", pages = "284--287", booktitle = "{GECCO 2003}: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference", editor = "Alwyn M. Barry", year = "2003", month = "11 " # jul, publisher = "AAAI", address = "Chigaco", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025", URL = "http://personal.disco.unimib.it/Vanneschi/GECCO_2003_PHD_WORKSHOP_Hardness.pdf", notes = "Bird-of-a-feather Workshops, GECCO-2003. A joint meeting of the twelth International Conference on Genetic Algorithms (ICGA-2003) and the eigth Annual Genetic Programming Conference (GP-2003) part of barry:2003:GECCO:workshop", keywords = "genetic algorithms, genetic programming", } @InProceedings{vanneschi:2003:gecco, author = "Leonardo Vanneschi and Marco Tomassini and Manuel Clergue and Philippe Collard", title = "Difficulty of Unimodal and Multimodal Landscapes in Genetic Programming", booktitle = "Genetic and Evolutionary Computation -- GECCO-2003", editor = "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller", year = "2003", pages = "1788--1799", address = "Chicago", publisher_address = "Berlin", month = "12-16 " # jul, volume = "2724", series = "LNCS", ISBN = "3-540-40603-4", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1007/3-540-45110-2_70", abstract = "Fitness distance correlation as a measure of problem difficulty in genetic programming. A new definition of distance, called structural distance, is used and suitable mutation operators for the program space are defined. The difficulty is studied for a number of problems, including, for the first time in GP, multimodal ones, both for the new hand-tailored mutation operators and standard crossover. Results are in agreement with empirical observations, thus confirming that fitness distance correlation can be considered a reasonable index of difficulty for genetic programming, at least for the set of problems studied here.", notes = "GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)", } @InProceedings{vanneschi:2003:fdcigpacc, author = "L. Vanneschi and M. Tomassini and P. Collard and M. Clergue", title = "Fitness distance correlation in genetic programming: A constructive counterexample", booktitle = "Proceedings of the 2003 Congress on Evolutionary Computation CEC2003", editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon", pages = "289--296", year = "2003", publisher = "IEEE Press", address = "Canberra", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "8-12 " # dec, organisation = "IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", keywords = "genetic algorithms, genetic programming, Algorithm design and analysis, Genetic mutations, Hamming distance, Laboratories, Sampling methods, Statistics, Stochastic processes, Tree data structures, statistical analysis, constructive counterexample, fitness distance correlation coefficient, hand-tailored function, infallible measure, problem difficulty", ISBN = "0-7803-7804-0", DOI = "doi:10.1109/CEC.2003.1299587", abstract = "The fitness distance correlation coefficient has been shown to be a reasonable measure to quantify problem difficulty in genetic algorithms and genetic programming for a wide set of problems. In this paper we present an hand-tailored function for which fitness distance correlation fails to correctly predict problem difficulty in genetic programming. This counterexample proves that fitness distance correlation, although reliable, is not an infallible measure to quantify problem difficulty.", notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.", } @InProceedings{vanneschi:fca:gecco2004, author = "Leonardo Vanneschi and Manuel Clergue and Philippe Collard and Marco Tomassini and S\'ebastien V\'erel", title = "Fitness Clouds and Problem Hardness in Genetic Programming", booktitle = "Genetic and Evolutionary Computation -- GECCO-2004, Part II", year = "2004", editor = "Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell", series = "Lecture Notes in Computer Science", pages = "690--701", address = "Seattle, WA, USA", publisher_address = "Heidelberg", month = "26-30 " # jun, organisation = "ISGEC", publisher = "Springer-Verlag", volume = "3103", ISBN = "3-540-22343-6", ISSN = "0302-9743", URL = "http://www.i3s.unice.fr/~verel/publi/gecco04-FCandPbHardnessGP.pdf", URL = "https://rdcu.be/dnXdl", DOI = "doi:10.1007/978-3-540-24855-2_76", DOI = "doi:10.1007/b98645", size = "12 pages", keywords = "genetic algorithms, genetic programming", abstract = "Abstract. This paper presents an investigation of genetic programming fitness landscapes. We propose a new indicator of problem hardness for tree-based genetic programming, called negative slope coefficient, based on the concept of fitness cloud. The negative slope coefficient is a predictive measure, i.e. it can be calculated without prior knowledge of the global optima.The fitness cloud is generated via a sampling of individuals obtained with the Metropolis-Hastings method. The reliability of the negative slope coefficient is tested on a set of well known and representative genetic programming benchmarks, comprising the binomial-3 problem, the even parity problem and the artificial ant on the Santa Fe trail.", notes = "GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)", } @PhdThesis{Vanneschi:thesis, author = "Leonardo Vanneschi", title = "Theory and Practice for Efficient Genetic Programming", school = "Faculty of Sciences, University of Lausanne", year = "2004", address = "Switzerland", email = "lvanneschi@yahoo.it", keywords = "genetic algorithms, genetic programming", URL = "http://old.disco.unimib.it/Vanneschi/thesis_vanneschi.pdf", URL = "https://serval.unil.ch/en/notice/serval:BIB_41817", size = "342 pages", abstract = "Genetic programming is a machine learning technique to automatically create computer programs from high-level specifications of a problem. It achieves this goal by genetically breeding a population of computer programs using the principles of Darwinian natural selection and biologically inspired operations. Selection is obtained by attaching to each program a fitness value, which quantifies how well it solves the problem. In spite of the numerous practical successes of genetic programming and the good quality of theory that has been developed until nowadays, some unsolved problems persist. In synthesis: methodologies to predict, or to measure, the difficulty of problems have not been developed yet (i.e. no technique to measure the capability of genetic programming to find good solutions for a given problem exists) and genetic programming is, in general, a slow and resource consuming process. Proposing solutions to these problems is the guiding thread of this thesis. Two measures of problem difficulty are presented:fitness distance correlation (based on the idea that what makes a problem difficult or easy for genetic programming is the relationship between fitness and distance to the goal) and negative slope coefficient (which quantifies some aspects of evolvability, i.e. the ability of genetic operators to produce offsprings that are fitter than their parents). These measures are based on statistical sampling of the search space. Both of them succeed in correctly measuring the difficulty of a wide range of different problems. Advantages and drawbacks of these measures are discussed in depth. Furthermore, a discussion of the concept of fitness landscape, on which these two measures are inspired, is proposed. Among the main causes of inefficiency of genetic programming, one may mention: premature convergence (or the tendency to produce populations in which all the individuals have similar characteristics), bloat (or progressive individuals' code growth) and the fact that fitness evaluation often requires the execution of programs on many different input data (known as fitness cases). This thesis shows how distributing individuals into separate communicating subpopulations naturally counteracts premature convergence and bloat, allowing genetic programming to find solutions of better quality, more quickly. The advantage of parallelising genetic programming is twofold: on the one hand it enables to achieve time savings by distributing the computational effort on a set of calculating agents, on the other hand, the parallel setting offers benefits from the algorithmic point of view, in analogy with the natural parallel evolution of spatially distributed populations. Furthermore, techniques to dynamically tune the size of populations, and to limit the number of fitness cases to be tested, in order to save computational effort, are proposed.", } @InProceedings{conf/aiia/VanneschiTCC05, title = "A Survey of Problem Difficulty in Genetic Programming", author = "Leonardo Vanneschi and Marco Tomassini and Philippe Collard and Manuel Clergue", year = "2005", pages = "66--77", editor = "Stefania Bandini and Sara Manzoni", series = "Lecture Notes in Computer Science", volume = "3673", booktitle = "AI*IA 2005: Advances in Artificial Intelligence, 9th Congress of the Italian Association for Artificial Intelligence, Proceedings", address = "Milan, Italy", month = sep # " 21-23", organisation = "Italian Association for Artificial Intelligence", publisher = "Springer", bibdate = "2005-10-26", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/aiia/aiia2005.html#VanneschiTCC05", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-29041-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1007/11558590_7", size = "12 pages", abstract = "a study of fitness distance correlation and negative slope coefficient as measures of problem hardness for genetic programming. Advantages and drawbacks of both these measures are presented both from a theoretical and empirical point of view. Experiments have been performed on a set of well-known hand-tailored problems and 'real-life-like' GP benchmarks.", } @InProceedings{eurogp06:VanneschiTomassiniCollardVerel, author = "Leonardo Vanneschi and Marco Tomassini and Philippe Collard and S\'ebastien V\'erel", title = "Negative Slope Coefficient. A Measure to Characterize Genetic Programming", editor = "Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art", booktitle = "Proceedings of the 9th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3905", year = "2006", address = "Budapest, Hungary", month = "10 - 12 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-33143-3", pages = "178--189", DOI = "doi:10.1007/11729976_16", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "Negative slope coefficient has been recently introduced and empirically proven a suitable hardness indicator for some well known genetic programming benchmarks, such as the even parity problem, the binomial-3 and the artificial ant on the Santa Fe trail. Nevertheless, the original definition of this measure contains several limitations. This paper points out some of those limitations, presents a new and more relevant definition of the negative slope coefficient and empirically shows the suitability of this new definition as a hardness measure for some genetic programming benchmarks, including the multiplexer, the intertwined spirals problem and the royal trees.", notes = "Part of \cite{collet:2006:GP} EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006", } @InProceedings{eurogp06:VanneschiGustafsonMauri, author = "Leonardo Vanneschi and Steven Gustafson and Giancarlo Mauri", title = "Using Subtree Crossover Distance to Investigate Genetic Programming Dynamics", editor = "Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art", booktitle = "Proceedings of the 9th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3905", year = "2006", address = "Budapest, Hungary", month = "10 - 12 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-33143-3", pages = "238--249", URL = "http://www.gustafsonresearch.com/research/publications/eurogp-2006.pdf", DOI = "doi:10.1007/11729976_21", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "To analyse various properties of the search process of genetic programming it is useful to quantify the distance between two individuals. Using operator-based distance measures can make this analysis more accurate and reliable than using distance measures which have no relationship with the genetic operators. This paper extends a recent definition of a distance measure based on subtree crossover for genetic programming. Empirical studies are presented that show the suitability of this measure to dynamically calculate the fitness distance correlation coefficient during the evolution, to construct a fitness sharing system for genetic programming and to measure genotypic diversity in the population. These experiments confirm the accuracy of the new measure and its consistency with the subtree crossover genetic operator.", notes = "Part of \cite{collet:2006:GP} EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006 Not a distance metric. Fitness sharing", } @InProceedings{1144062, author = "Leonardo Vanneschi and Giancarlo Mauri and Andrea Valsecchi and Stefano Cagnoni", title = "Heterogeneous cooperative coevolution: strategies of integration between GP and GA", booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation", year = "2006", editor = "Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos {Coello Coello} and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens", volume = "1", ISBN = "1-59593-186-4", pages = "361--368", address = "Seattle, Washington, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p361.pdf", DOI = "doi:10.1145/1143997.1144062", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "8-12 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Coevolution", notes = "GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM Order Number 910060", } @InProceedings{1144152, author = "Leonardo Vanneschi and Yuri Pirola and Philippe Collard", title = "A Quantitative Study of Neutrality in {GP Boolean} Landscapes", booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation", year = "2006", editor = "Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos {Coello Coello} and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens", volume = "1", ISBN = "1-59593-186-4", pages = "895--902", address = "Seattle, Washington, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p895.pdf", URL = "https://hal.archives-ouvertes.fr/hal-00164691", DOI = "doi:10.1145/1143997.1144152", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "8-12 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, complexity measures, even parity, fitness landscapes, neutrality, performance measures, Even Parity", size = "8 pages", abstract = "Neutrality of some Boolean parity fitness landscapes is investigated in this paper. Compared with some well known contributions on the same issue, we define some new measures that help characterizing neutral landscapes, we use a new sampling methodology, which captures some features that are disregarded by uniform random sampling, and we introduce new genetic operators to define the neighborhood of tree structures. We compare the fitness landscape induced by two different sets of functional operators (Nand) and (Xor Not). The different characteristics of the neutral networks seem to justify the different difficulties of these landscapes for genetic programming.", notes = "Also known as \cite{collard:hal-00164691} GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM Order Number 910060", } @InProceedings{eurogp07:vanneschi, author = "Leonardo Vanneschi and Marco Tomassini and Philippe Collard and S\'ebastien Verel and Yuri Pirola and Giancarlo Mauri", title = "A Comprehensive View of Fitness Landscapes with Neutrality and Fitness Clouds", editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar", booktitle = "Proceedings of the 10th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "4445", year = "2007", address = "Valencia, Spain", month = "11-13 " # apr, pages = "241--250", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-71602-5", isbn13 = "978-3-540-71602-0", DOI = "doi:10.1007/978-3-540-71605-1_22", abstract = "We define a set of measures that capture some different aspects of neutrality in evolutionary algorithms fitness landscapes from a qualitative point of view. If considered all together, these measures offer a rather complete picture of the characteristics of fitness landscapes bound to neutrality and may be used as broad indicators of problem hardness. We compare the results returned by these measures with the ones of negative slope coefficient, a quantitative measure of problem hardness that has been recently defined and with success rate statistics on a well known genetic programming benchmark: the multiplexer problem. In order to efficaciously study the search space, we use a sampling technique that has recently been introduced and we show its suitability on this problem.", notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007", } @InCollection{Vanneschi:2007:GPTP, author = "Leonardo Vanneschi", title = "Investigating Problem Hardness of Real Life Applications", booktitle = "Genetic Programming Theory and Practice {V}", year = "2007", editor = "Rick L. Riolo and Terence Soule and Bill Worzel", series = "Genetic and Evolutionary Computation", chapter = "7", pages = "107--125", address = "Ann Arbor", month = "17-19" # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-0-387-76308-8", DOI = "doi:10.1007/978-0-387-76308-8_7", size = "18 pages", abstract = "This chapter represents a first attempt to characterise the fitness landscapes of real-life Genetic Programming applications by means of a predictive algebraic difficulty indicator. The indicator used is the Negative Slope Coefficient, whose efficacy has been recently empirically demonstrated on a large set of hand-tailored theoretical test functions and well known GP benchmarks. The real-life problems studied belong to the field of Biomedical applications and consist of automatically assessing a mathematical relationship between a set of molecular descriptors from a given dataset of drugs and some important pharmacokinetic parameters. The parameters considered here are Human Oral Bioavailability, Median Oral Lethal Dose, and Plasma Protein Binding levels. The availability of good prediction tools for pharmacokinetics parameters like these is critical for optimising the efficiency of therapies, maximising medical success rate and minimizing toxic effects. The experimental results presented in this chapter show that the Negative Slope Coefficient seems to be a reasonable tool to characterise the difficulty of these problems, and can be used to choose the most effective Genetic Programming configuration (fitness function, representation, parameters' values) from a set of given ones.", notes = "part of \cite{Riolo:2007:GPTP} published 2008", affiliation = "Dipartimento di Informatica, Sistemistica e Comunicazione (D.I.S.Co.), University of Milano, Bicocca Milan, Italy", } @InProceedings{1277309, author = "Leonardo Vanneschi and Denis Rochat and Marco Tomassini", title = "Multi-optimization improves genetic programming generalization ability", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "1759--1759", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1759.pdf", DOI = "doi:10.1145/1276958.1277309", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming: Poster, generalisation, multiobjective optimisation, symbolic regression, verification", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @InProceedings{1274130, author = "Leonardo Vanneschi and Sebastien Verel", title = "Fitness landscapes and problem hardness in evolutionary computation", booktitle = "Genetic and Evolutionary Computation Conference {(GECCO2007)} tutorial presentations", year = "2007", month = "7-11 " # jul, editor = "Aniko Ekart", isbn13 = "978-1-59593-698-1", pages = "3690--3733", address = "London, United Kingdom", keywords = "genetic algorithms, genetic programming, evolutionary algorithm, fitness landscape, problem Hardness", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p3690.pdf", DOI = "doi:10.1145/1274000.1274130", URL = "http://www-lisic.univ-littoral.fr/~verel/talks/Intro-PART2-Vanneschi.pdf", publisher = "ACM Press", publisher_address = "New York, NY, USA", notes = "Intro-PART2-Vanneschi.pdf are GP theory slides: Fitness Distance Correlation (fdc). Structural Distance \cite{ekart:2002:EuroGP}: Structural Mutation Genetic Programming (SMGP). Negative Slope Coefficient (nsc). Fitness cloud. nsc: Even Parity Problem and Artificial Ant on the Santa Fe Trail, \cite{1277209} GECCO-2007, page 1341 'negative slope coefficient is an empirical measure of problem hardness based the analysis of offspring-fitness vs. parent-fitness scatterplots'. Crossover \cite{eurogp:GustafsonV05} SCD pseudo-distance Distributed on CD-ROM at GECCO-2007 ACM Order No. 910071", } @Proceedings{conf/eurogp/2009, title = "Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009", year = "2009", editor = "Leonardo Vanneschi and Steven Gustafson and Alberto Moraglio and Ivanoe {De Falco} and Marc Ebner", volume = "5481", series = "LNCS", address = "Tuebingen", month = apr # " 15-17", organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://evostar.na.icar.cnr.it/EuroGP/page24/page24.html", DOI = "doi:10.1007/978-3-642-01181-8", size = "~360 pages", notes = "EuroGP'2009 held in conjunction with EvoCOP2009, EvoBIO2009 and EvoWorkshops2009", } @InProceedings{DBLP:conf/gecco/VanneschiC09, author = "Leonardo Vanneschi and Giuseppe Cuccu", title = "Variable size population for dynamic optimization with genetic programming", booktitle = "GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation", year = "2009", editor = "Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano {van Hemert} and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique Alba", pages = "1895--1896", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming, Poster", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1569901.1570222", abstract = "A new model of Genetic Programming with variable size population is presented in this paper and applied to the reconstruction of target functions in dynamic environments (i.e. problems where target functions change with time). The suitability of this model is tested on a set of benchmarks based on some well known symbolic regression problems. Experimental results confirm that our variable size population model finds solutions of the same quality as the ones found by standard Genetic Programming, but with a smaller amount of computational effort.", notes = "GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009). ACM Order Number 910092.", } @InProceedings{DBLP:conf/gecco/VanneschiG09, author = "Leonardo Vanneschi and Steven Gustafson", title = "Using crossover based similarity measure to improve genetic programming generalization ability", booktitle = "GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation", year = "2009", editor = "Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano {van Hemert} and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique Alba", pages = "1139--1146", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1569901.1570054", abstract = "Generalization is a very important issue in Machine Learning. In this paper, we present a new idea for improving Genetic Programming generalization ability. The idea is based on a dynamic two-layered selection algorithm and it is tested on a real-life drug discovery regression application. The algorithm begins using root mean squared error as fitness and the usual tournament selection. A list of individuals called ``repulsors'' is also kept in memory and initialized as empty. As an individual is found to overfit the training set, it is inserted into the list of repulsors. When the list of repulsors is not empty, selection becomes a two-layer algorithm: individuals participating to the tournament are not randomly chosen from the population but are themselves selected, using the average dissimilarity to the repulsors as a criterion to be maximized. Two kinds of similarity/dissimilarity measures are tested for this aim: the well known structural (or edit) distance and the recently defined subtree crossover based similarity measure. Although simple, this idea seems to improve Genetic Programming generalization ability and the presented experimental results show that Genetic Programming generalizes better when subtree crossover based similarity measure is used, at least for the test problems studied in this paper.", notes = "GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009). ACM Order Number 910092.", } @InProceedings{DBLP:conf/epia/VanneschiS09, author = "Leonardo Vanneschi and Sara Silva", title = "Using Operator Equalisation for Prediction of Drug Toxicity with Genetic Programming", booktitle = "Progress in Artificial Intelligence, 14th Portuguese Conference on Artificial Intelligence, EPIA 2009", year = "2009", editor = "Luis Seabra Lopes and Nuno Lau and Pedro Mariano and Luis Mateus Rocha", volume = "5816", series = "LNAI", pages = "65--76", address = "Aveiro, Portugal", month = oct # " 12-15", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-04685-8", DOI = "doi:10.1007/978-3-642-04686-5_6", abstract = "Predicting the toxicity of new potential drugs is a fundamental step in the drug design process. Recent contributions have shown that, even though Genetic Programming is a promising method for this task, the problem of predicting the toxicity of molecular compounds is complex and difficult to solve. In particular, when executed for predicting drug toxicity, Genetic Programming undergoes the well-known phenomenon of bloat, i.e. the growth in code size during the evolutionary process without a corresponding improvement in fitness. We hypothesize that this might cause overfitting and thus prevent the method from discovering simpler and potentially more general solutions. For this reason, in this paper we investigate two recently defined variants of the operator equalization bloat control method for Genetic Programming. We show that these two methods are bloat free also when executed on this complex problem. Nevertheless, overfitting still remains an issue. Thus, contradicting the generalized idea that bloat and overfitting are strongly related, we argue that the two phenomena are independent from each other and that eliminating bloat does not necessarily eliminate overfitting.", notes = "EPIA http://dx.doi.org/10.1007/978-3-642-04686-5", bibsource = "DBLP, http://dblp.uni-trier.de", } @InProceedings{conf/ijcci/VanneschiC09, author = "Leonardo Vanneschi and Giuseppe Cuccu", title = "A Study of Genetic Programming Variable Population Size for Dynamic Optimization Problems", booktitle = "International Conference on Evolutionary Computation (ICEC 2009)", editor = "Antonio Dourado and Agostinho Rosa and Kurosh Madani", year = "2009", pages = "119--126", address = "Madeira, Portugal", month = "5-7 " # oct, organization = "INSTICC", publisher = "SciTePress", keywords = "genetic algorithms, genetic programming, Variable Size Population, Dynamic Optimization", isbn13 = "978-989-674-014-6", URL = "http://www.idsia.ch/~giuse/papers/van09icec.pdf", URL = "https://www.scitepress.org/PublicationsDetail.aspx?ID=p73OqwRRp1I=&t=1", DOI = "doi:10.5220/0002314701190126", bibdate = "2010-03-03", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ijcci/ijcci2009.html#VanneschiC09", size = "8 pages", abstract = "A new model of Genetic Programming with variable size population is presented in this paper and applied to the reconstruction of target functions in dynamic environments (i.e. problems where target functions change with time). The suitability of this model is tested on a set of benchmarks based on some well known symbolic regression problems. Experimental results confirm that our variable size population model finds solutions of similar quality to the ones found by standard Genetic Programming, but with a smaller amount of computational effort.", notes = "https://www.scitepress.org/ProceedingsDetails.aspx?ID=BKrT7ZO14Ew=&t=1 broken http://www.icec.ijcci.org/Abstracts/2009/ICEC_2009_Abstracts.htm", } @Article{Vanneschi:2009:JAEA, title = "Classification of Oncologic Data with Genetic Programming", author = "Leonardo Vanneschi and Francesco Archetti and Mauro Castelli and Ilaria Giordani", journal = "Journal of Artificial Evolution and Applications", year = "2009", volume = "2009", publisher = "Hindawi Publishing Corporation", keywords = "genetic algorithms, genetic programming", URL = "http://downloads.hindawi.com/journals/jaea/2009/848532.pdf", DOI = "doi:10.1155/2009/848532", ISSN = "16876229", bibsource = "OAI-PMH server at www.doaj.org", language = "eng", oai = "oai:doaj-articles:809187cab9ca01fd2c12625e6010851b", abstract = "Discovering the models explaining the hidden relationship between genetic material and tumor pathologies is one of the most important open challenges in biology and medicine. Given the large amount of data made available by the DNA Microarray technique, Machine Learning is becoming a popular tool for this kind of investigations. In the last few years, we have been particularly involved in the study of Genetic Programming for mining large sets of biomedical data. In this paper, we present a comparison between four variants of Genetic Programming for the classification of two different oncologic datasets: the first one contains data from healthy colon tissues and colon tissues affected by cancer; the second one contains data from patients affected by two kinds of leukemia (acute myeloid leukemia and acute lymphoblastic leukemia). We report experimental results obtained using two different fitness criteria: the receiver operating characteristic and the percentage of correctly classified instances. These results, and their comparison with the ones obtained by three nonevolutionary Machine Learning methods (Support Vector Machines, MultiBoosting, and Random Forests) on the same data, seem to hint that Genetic Programming is a promising technique for this kind of classification.", notes = "Article ID 848532", } @InProceedings{Vanneschi:2010:EvoBIO, author = "Leonardo Vanneschi and Antonella Farinaccio and Mario Giacobini and Marco Antoniotti and Giancarlo Mauri and Paolo Provero", title = "Identification of Individualized Feature Combinations for Survival Prediction in Breast Cancer: A Comparison of Machine Learning Techniques", booktitle = "8th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2010", year = "2010", editor = "Clara Pizzuti and Marylyn D. Ritchie and Mario Giacobini", volume = "6023", series = "LNCS", pages = "110--121", address = "Istanbul", month = "7-9 " # apr, organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-12210-1", DOI = "doi:10.1007/978-3-642-12211-8_10", abstract = "The ability to accurately classify cancer patients into risk classes, i.e. to predict the outcome of the pathology on an individual basis, is a key ingredient in making therapeutic decisions. In recent years gene expression data have been successfully used to complement the clinical and histological criteria traditionally used in such prediction. Many gene expression signatures have been developed, i.e. sets of genes whose expression values in a tumor can be used to predict the outcome of the pathology. Here we investigate the use of several machine learning techniques to classify breast cancer patients using one of such signatures, the well established 70-gene signature. We show that Genetic Programming performs significantly better than Support Vector Machines, Multilayered Perceptron and Random Forest in classifying patients from the NKI breast cancer dataset, and slightly better than the scoring-based method originally proposed by the authors of the seventy-gene signature. Furthermore, Genetic Programming is able to perform an automatic feature selection. Since the performance of Genetic Programming is likely to be improvable compared to the out-of-the-box approach used here, and given the biological insight potentially provided by the Genetic Programming solutions, we conclude that Genetic Programming methods are worth further investigation as a tool for cancer patient classification based on gene expression data.", notes = "EvoBIO'2010 held in conjunction with EuroGP'2010 EvoCOP2010 and EvoApplications2010", } @InProceedings{Vanneschi:2010:gecco, author = "Leonardo Vanneschi and Mauro Castelli and Sara Silva", title = "Measuring bloat, overfitting and functional complexity in genetic programming", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "877--884", keywords = "genetic algorithms, genetic programming", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830483.1830643", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Recent contributions clearly show that eliminating bloat in a genetic programming system does not necessarily eliminate overfitting and vice-versa. This fact seems to contradict a common agreement of many researchers known as the minimum description length principle, which states that the best model is the one that minimises the amount of information needed to encode it. Another common agreement is that over fitting should be, in some sense, related to the functional complexity of the model. The goal of this paper is to define three measures to respectively quantify bloat, overfitting and functional complexity of solutions and show their suitability on a set of test problems including a simple bidimensional symbolic regression test function and two real-life multidimensional regression problems. The experimental results are encouraging and should pave the way to further investigation. Advantages and drawbacks of the proposed measures are discussed, and ways to improve them are suggested. In the future, these measures should be useful to study and better understand the relationship between bloat, overfitting and functional complexity of solutions.", notes = "Also known as \cite{1830643} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @InProceedings{Vanneschi:2010:geccocomp, author = "Leonardo Vanneschi", title = "Fitness landscapes and problem hardness in genetic programming", booktitle = "GECCO 2010 Specialized techniques and applications tutorials", year = "2010", editor = "Una-May O'Reilly", isbn13 = "978-1-4503-0073-5", keywords = "genetic algorithms, genetic programming", pages = "2711--2738", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830761.1830916", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "The performance of searching agents, or metaheuristics, like evolutionary algorithms (genetics algorithms, genetic programming, etc.) or local search algorithms (simulated annealing, tabu search, etc.) depend on some properties of the search space structure. One concept that allows us to analyse the search space is the fitness landscape. In the case of Genetic Programming, defining and handling fitness landscapes is a particularly hard task, given the complexity of the structures being evolved of the genetic operators used. This tutorial presents some general definitions of fitness landscape. Subsequently, we will try to instantiate the concept of fitness landscape to Genetic Programming, discussing problems. The concept of landscape geometry will be introduced and some of the most common landscape geometries and the dynamics of Genetic Programming on those landscapes will be discussed. After that, the binding between fitness landscapes and problem difficulty will be discussed and a set of measures that characterise the difficulty of a metaheuristic in searching solutions in a fitness landscape are analysed. Among those measures, particular relevance will be given to Fitness Distance Correlation (FDC), Negative Slope Coefficient (NSC), a set of measures bound to the concept of Neutrality and some distance metrics and/or similarity measures that are consistent with the most commonly used genetic operators (in particular the recently defined subtree crossover based distance). Finally, some open questions about fitness landscapes are discussed.", notes = "Also known as \cite{1830916} Distributed on CD-ROM at GECCO-2010. ACM Order Number 910102.", } @Article{Vanneschi:2011:IA, author = "Leonardo Vanneschi and Luca Mussi and Stefano Cagnoni", title = "Hot topics in Evolutionary Computation", journal = "Intelligenza Artificiale", year = "2011", volume = "5", number = "1", pages = "5--17", keywords = "genetic algorithms, genetic programming, Evolutionary Computation, evolutionary algorithms, evolutionary computation theory, neuro-evolutionary systems, complex systems, Swarm Intelligence, GPU-based parallel processing", ISSN = "1724-8035", broken = "http://iospress.metapress.com/content/J71T7406790K0QW1", DOI = "doi:10.3233/IA-2011-0001", size = "13 pages", abstract = "We introduce the special issue on Evolutionary Computation (EC) reporting a non-exhaustive list of topics which have recently attracted much interest from the EC community, with particular regard to the ones dealt with by the papers included in this issue: EC research, hybrid neuro-evolutionary systems and synergies between EC and complex systems. In addition, we introduce a more technological emerging topic: the parallel implementation of evolutionary and Swarm Intelligence algorithms on graphics processor units (GPUs), by which new applications of evolutionary algorithms have been made possible, even in real-time environments.", } @Article{Vanneschi:2011:bmcBDM, author = "Leonardo Vanneschi and Antonella Farinaccio and Giancarlo Mauri and Mauro Antoniotti and Paolo Provero and Mario Giacobini", title = "A comparison of machine learning techniques for survival prediction in breast cancer", journal = "BioData Mining", year = "2011", volume = "4", pages = "Article no: 12", keywords = "genetic algorithms, genetic programming, Machine Learning, NKI breast cancer", ISSN = "1756-0381", URL = "https://rdcu.be/efete", DOI = "doi:10.1186/1756-0381-4-12", size = "13 pages", abstract = "Background The ability to accurately classify cancer patients into risk classes, i.e. to predict the outcome of the pathology on an individual basis, is a key ingredient in making therapeutic decisions. In recent years gene expression data have been successfully used to complement the clinical and histological criteria traditionally used in such prediction. Many 'gene expression signatures' have been developed, i.e. sets of genes whose expression values in a tumour can be used to predict the outcome of the pathology. Here we investigate the use of several machine learning techniques to classify breast cancer patients using one of such signatures, the well established 70-gene signature. Results We show that Genetic Programming performs significantly better than Support Vector Machines, Multilayered Perceptrons and Random Forests in classifying patients from the NKI breast cancer data set, and comparably to the scoring-based method originally proposed by the authors of the 70-gene signature. Furthermore, Genetic Programming is able to perform an automatic feature selection. Conclusions Since the performance of Genetic Programming is likely to be improvable compared to the out-of-the-box approach used here, and given the biological insight potentially provided by the Genetic Programming solutions, we conclude that Genetic Programming methods are worth further investigation as a tool for cancer patient classification based on gene expression data.", } @InProceedings{Vanneschi:2011:GECCO, author = "Leonardo Vanneschi and Mauro Castelli and Luca Manzoni", title = "The K landscapes: a tunably difficult benchmark for genetic programming", booktitle = "GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0557-0", pages = "1467--1474", keywords = "genetic algorithms, genetic programming", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001576.2001773", publisher = "ACM", publisher_address = "New York, NY, USA", size = "8 pages", abstract = "The NK landscapes are a well known benchmark for genetic algorithms (GAs) in which it is possible to tune the ruggedness of the fitness landscape by simply modifying the value of a parameter K. They have successfully been used in many theoretical studies, allowing researchers to discover interesting properties of the GAs dynamics in presence of rugged landscapes. A similar benchmark does not exist for genetic programming (GP) yet. Nevertheless, during the EuroGP conference debates of the last few years, the necessity of defining new benchmark problems for GP has repeatedly been expressed by a large part of the attendees. This paper is intended to fill this gap, by introducing an extension of the NK landscapes to tree based GP, that we call K landscapes. In this benchmark, epistasis are expressed as growing mutual interactions between the substructures of a tree as the parameter K increases. The fact that the problem becomes more and more difficult as the value of K increases is experimentally demonstrated. Interestingly, we also show that GP bloats more and more as K increases.", notes = "Also known as \cite{2001773} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)", } @Article{Vanneschi2011, author = "Leonardo Vanneschi and Yuri Pirola and Giancarlo Mauri and Marco Tomassini and Philippe Collard and Sebastien Verel", title = "A study of the neutrality of {Boolean} function landscapes in genetic programming", journal = "Theoretical Computer Science", year = "2012", volume = "425", pages = "34--57", month = "30 " # mar, keywords = "genetic algorithms, genetic programming, Neutrality, Fitness landscapes, Boolean functions, Problem difficulty, Negative slope coefficient", publisher = "Elsevier", ISSN = "0304-3975", hal_id = "hal-00563462", URL = "https://hal.archives-ouvertes.fr/hal-00563462/file/vpm_neutr.pdf", URL = "http://www.sciencedirect.com/science/article/B6V1G-52HS632-1/2/f5ec50d27bab9c8e20d74cab43c83de8", DOI = "doi:10.1016/j.tcs.2011.03.011", size = "24 pages", abstract = "The neutrality of genetic programming Boolean function landscapes is investigated. Compared with some well-known contributions on the same issue, (i) we first define new measures which help in characterising neutral landscapes; (ii) we use a new sampling methodology, which captures features that are disregarded by uniform random sampling; (iii) we introduce new genetic operators to define the neighbourhood of tree structures; and (iv) we compare the fitness landscape induced by different sets of functional operators. This study indicates the existence of a relationship between our neutrality measures and the performance of genetic programming for the problems studied.", notes = "Theoretical Foundations of Evolutionary Computation", } @InCollection{VanneschiPoliHNC2011, author = "Leonardo Vanneschi and Riccardo Poli", title = "Genetic Programming: Introduction, Applications, Theory and Open Issues", booktitle = "Handbook of Natural Computing", publisher = "Springer", year = "2012", editor = "Grzegorz Rozenberg and Thomas Baeck and Joost N. Kok", volume = "2", chapter = "24", pages = "709--739", month = "19 " # aug, keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-92909-3", URL = "http://cswww.essex.ac.uk/staff/poli/papers/VanneschiPoliHNC2011.pdf", URL = "http://www.springer.com/computer/theoretical+computer+science/book/978-3-540-92911-6", DOI = "doi:10.1007/978-3-540-92910-9_24", size = "31 pages", abstract = "Genetic programming (GP) is an evolutionary approach that extends genetic algorithms to allow the exploration of the space of computer programs. Like other evolutionary algorithms, GP works by defining a goal in the form of a quality criterion (or fitness) and then using this criterion to evolve a set (or population) of candidate solutions (individuals) by mimicking the basic principles of Darwinian evolution. GP breeds the solutions to problems using an iterative process involving the probabilistic selection of the fittest solutions and their variation by means of a set of genetic operators, usually crossover and mutation. GP has been successfully applied to a number of challenging real-world problem domains. Its operations and behaviour are now reasonably well understood thanks to a variety of powerful theoretical results. In this chapter, the main definitions and features of GP are introduced and its typical operations are described. Some of its applications are then surveyed. Some important theoretical results in this field, including some very recent ones, are reviewed and some of the most challenging open issues and directions for future research are discussed.", notes = "The Mechanics of Tree-Based GP, Examples of Real-World Applications of GP, GP Theory, Open Issues", size = "31 pages", } @InProceedings{vanneschi:evobio12, author = "Leonardo Vanneschi and Matteo Mondini and Martino Bertoni and Alberto Ronchi and Mattia Stefano", title = "{GeNet}: A Graph-Based Genetic Programming Framework for the Reverse Engineering of Gene Regulatory Networks", booktitle = "10th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, {EvoBIO 2012}", year = "2012", month = "11-13 " # apr, editor = "Mario Giacobini and Leonardo Vanneschi and William S. Bush", series = "LNCS", volume = "7246", publisher = "Springer Verlag", address = "Malaga, Spain", pages = "97--109", organisation = "EvoStar", isbn13 = "978-3-642-29065-7", DOI = "doi:10.1007/978-3-642-29066-4_9", keywords = "genetic algorithms, genetic programming", abstract = "A standard tree-based genetic programming system, called GRNGen, for the reverse engineering of gene regulatory networks starting from time series datasets, was proposed in EvoBIO 2011. Despite the interesting results obtained on the simple IRMA network, GRNGen has some important limitations. For instance, in order to reconstruct a network with GRNGen, one single regression problem has to be solved by GP for each gene. This entails a clear limitation on the size of the networks that it can reconstruct, and this limitation is crucial, given that real genetic networks generally contain large numbers of genes. In this paper we present a new system, called GeNet, which aims at overcoming the main limitations of GRNGen, by directly evolving entire networks using graph-based genetic programming. We show that GeNet finds results that are comparable, and in some cases even better, than GRNGen on the small IRMA network, but, even more importantly (and contrarily to GRNGen), it can be applied also to larger networks. Last but not least, we show that the time series datasets found in literature do not contain a sufficient amount of information to describe the IRMA network in detail.", notes = "Part of \cite{Giacobini:2012:EvoBio} EvoBio'2012 held in conjunction with EuroGP2012, EvoCOP2012, EvoMusArt2012 and EvoApplications2012", } @InProceedings{vanneschi:2013:EuroGP, author = "Leonardo Vanneschi and Mauro Castelli and Luca Manzoni and Sara Silva", title = "A New Implementation of Geometric Semantic GP and its Application to Problems in Pharmacokinetics", booktitle = "Proceedings of the 16th European Conference on Genetic Programming, EuroGP 2013", year = "2013", month = "3-5 " # apr, editor = "Krzysztof Krawiec and Alberto Moraglio and Ting Hu and A. Sima Uyar and Bin Hu", series = "LNCS", volume = "7831", publisher = "Springer Verlag", address = "Vienna, Austria", pages = "205--216", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-37206-3", DOI = "doi:10.1007/978-3-642-37207-0_18", abstract = "Moraglio et al. have recently introduced new genetic operators for genetic programming, called geometric semantic operators. These operators induce a unimodal fitness landscape for all the problems consisting in matching input data with known target outputs (like regression and classification). This feature facilitates genetic programming evolvability, which makes these operators extremely promising. Nevertheless, Moraglio et al. leave open problems, the most important one being the fact that these operators, by construction, always produce offspring that are larger than their parents, causing an exponential growth in the size of the individuals, which actually renders them useless in practice. In this paper we overcome this limitation by presenting a new efficient implementation of the geometric semantic operators. This allows us, for the first time, to use them on complex real-life applications, like the two problems in pharmacokinetics that we address here. Our results confirm the excellent evolvability of geometric semantic operators, demonstrated by the good results obtained on training data. Furthermore, we have also achieved a surprisingly good generalisation ability, a fact that can be explained considering some properties of geometric semantic operators, which makes them even more appealing than before.", notes = "Part of \cite{Krawiec:2013:GP} EuroGP'2013 held in conjunction with EvoCOP2013, EvoBIO2013, EvoMusArt2013 and EvoApplications2013", } @InCollection{Vanneschi:2013:GPTP, author = "Leonardo Vanneschi and Sara Silva and Mauro Castelli and Luca Manzoni", title = "Geometric Semantic Genetic Programming for Real Life Applications", booktitle = "Genetic Programming Theory and Practice XI", year = "2013", series = "Genetic and Evolutionary Computation", editor = "Rick Riolo and Jason H. Moore and Mark Kotanchek", publisher = "Springer", chapter = "11", pages = "191--209", address = "Ann Arbor, USA", month = "9-11 " # may, keywords = "genetic algorithms, genetic programming, Geometric semantic operators, Fitness landscapes, Overfitting, Parameter tuning", isbn13 = "978-1-4939-0374-0", DOI = "doi:10.1007/978-1-4939-0375-7_11", abstract = "In a recent contribution we have introduced a new implementation of geometric semantic operators for Genetic Programming. Thanks to this implementation, we are now able to deeply investigate their usefulness and study their properties on complex real-life applications. Our experiments confirm that these operators are more effective than traditional ones in optimising training data, due to the fact that they induce a unimodal fitness landscape. Furthermore, they automatically limit over fitting, something we had already noticed in our recent contribution, and that is further discussed here. Finally, we investigate the influence of some parameters on the effectiveness of these operators, and we show that tuning their values and setting them a-priori may be wasted effort. Instead, if we randomly modify the values of those parameters several times during the evolution, we obtain a performance that is comparable with the one obtained with the best setting, both on training and test data for all the studied problems.", notes = " Part of \cite{Riolo:2013:GPTP} published after the workshop in 2013", } @Article{Vanneschi:2013:GPEM, author = "Leonardo Vanneschi and Matteo Mondini and Martino Bertoni and Alberto Ronchi and Mattia Stefano", title = "Gene regulatory networks reconstruction from time series datasets using genetic programming: a comparison between tree-based and graph-based approaches", journal = "Genetic Programming and Evolvable Machines", year = "2013", volume = "14", number = "4", pages = "431--455", month = dec, keywords = "genetic algorithms, genetic programming, Gene regulatory networks, Tree-based GP, Graph-based GP", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-013-9183-z", size = "25 pages", abstract = "Genetic programming researchers have shown a growing interest in the study of gene regulatory networks in the last few years. Our team has also contributed to the field, by defining two systems for the automatic reverse engineering of gene regulatory networks called GRNGen and GeNet. In this paper, we revise this work by describing in detail the two approaches and empirically comparing them. The results we report, and in particular the fact that GeNet can be used on large networks while GRNGen cannot, encourage us to pursue the study of GeNet in the future. We conclude the paper by discussing the main research directions that we are planning to investigate to improve GeNet.", } @InProceedings{Vanneschi:2013:NICSO, author = "Leonardo Vanneschi", title = "Applications of Genetic Programming in Drug Discovery and Pharmacokinetics", booktitle = "VI International Workshop on Nature Inspired Cooperative Strategies for Optimization (NICSO 2013)", year = "2013", editor = "German Terrazas and Fernando Esteban Barril Otero and Antonio D. Masegosa", volume = "512", series = "Studies in Computational Intelligence", pages = "x", address = "Canterbury, United Kingdom", month = sep # " 2-4", publisher = "Springer", note = "Plenary Talk", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-01691-7", DOI = "doi:10.1007/978-3-319-01692-4", size = "0.5 pages", abstract = "The success of a drug treatment is strongly correlated with the ability of a molecule to reach its target in the patient's organism without inducing toxic effects. Moreover the reduction of cost and time associated with drug discovery and development is becoming a crucial requirement for pharmaceutical industry. Therefore computational methods allowing reliable predictions of newly synthesised compounds properties are of utmost relevance. In this talk, I discuss the role of Genetic Programming (GP) in predictive pharmacokinetics, considering the estimation of adsorption, distribution, metabolism, excretion and toxicity processes (ADMET) that a drug undergoes into the patient's organism. In particular, I discuss the ability of GP to predict oral bioavailability (F), median oral lethal dose (LD50) and plasma-protein binding levels (PPB). Since these parameters respectively characterise the percentage of initial drug dose that effectively reaches the systemic blood circulation, the harmful effects and the distribution into the organism of a drug, they are essential for the selection of potentially effective molecules. In the last part of the talk, I show and discuss how recently defined geometric semantic genetic operators can dramatically affect the performances of GP for this kind of application, in particular on out-of-sample test data.", notes = "Only abstract in proceedings. See also \cite{Freitas:2013:NICSO} http://www.nicso2013.org/programme.html http://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-3-319-01691-7", } @Article{journals/memetic/Vanneschi14, title = "Improving genetic programming for the prediction of pharmacokinetic parameters", author = "Leonardo Vanneschi", journal = "Memetic Computing", year = "2014", number = "4", volume = "6", pages = "255--262", keywords = "genetic algorithms, genetic programming", bibdate = "2014-11-24", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/memetic/memetic6.html#Vanneschi14", URL = "http://dx.doi.org/10.1007/s12293-014-0143-9", } @Article{Vanneschi:2014:GPEM, author = "Leonardo Vanneschi and Mauro Castelli and Sara Silva", title = "A survey of semantic methods in genetic programming", journal = "Genetic Programming and Evolvable Machines", year = "2014", volume = "15", number = "2", pages = "195--214", month = jun, keywords = "genetic algorithms, genetic programming, Semantics, Genotype/phenotype", ISSN = "1389-2576", URL = "http://link.springer.com/article/10.1007/s10710-013-9210-0", DOI = "doi:10.1007/s10710-013-9210-0", size = "20 pages", abstract = "Several methods to incorporate semantic awareness in genetic programming have been proposed in the last few years. These methods cover fundamental parts of the evolutionary process: from the population initialisation, through different ways of modifying or extending the existing genetic operators, to formal methods, until the definition of completely new genetic operators. The objectives are also distinct: from the maintenance of semantic diversity to the study of semantic locality; from the use of semantics for constructing solutions which obey certain constraints to the exploitation of the geometry of the semantic topological space aimed at defining easy-to-search fitness landscapes. All these approaches have shown, in different ways and amounts, that incorporating semantic awareness may help improving the power of genetic programming. This survey analyses and discusses the state of the art in the field, organising the existing methods into different categories. It restricts itself to studies where semantics is intended as the set of output values of a program on the training data, a definition that is common to a rather large set of recent contributions. It does not discuss methods for incorporating semantic information into grammar-based genetic programming or approaches based on formal methods. The objective is keeping the community updated on this interesting research track, hoping to motivate new and stimulating contributions.", } @InProceedings{Vanneschi:2015:evoApplications, author = "Leonardo Vanneschi and Mauro Castelli and Ernesto Costa and Alessandro Re and Henrique Vaz and Victor Lobo and Paulo Urbano", title = "Improving Maritime Awareness with Semantic Genetic Programming and Linear Scaling: Prediction of Vessels Position Based on {AIS} Data", booktitle = "18th European Conference on the Applications of Evolutionary Computation", year = "2015", editor = "Antonio M. Mora and Giovanni Squillero", series = "LNCS", volume = "9028", publisher = "Springer", pages = "732--744", address = "Copenhagen", month = "8-10 " # apr, organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-16548-6", DOI = "doi:10.1007/978-3-319-16549-3_59", abstract = "Maritime domain awareness deals with the situational understanding of maritime activities that could impact the security, safety, economy or environment. It enables quick threat identification, informed decision making, effective action support, knowledge sharing and more accurate situational awareness. In this paper, we propose a novel computational intelligence framework, based on genetic programming, to predict the position of vessels, based on information related to the vessels past positions in a specific time interval. Given the complexity of the task, two well known improvements of genetic programming, namely geometric semantic operators and linear scaling, are integrated in a new and sophisticated genetic programming system. The work has many objectives, for instance assisting more quickly and effectively a vessel when an emergency arises or being able to chase more efficiently a vessel that is accomplishing illegal actions. The proposed system has been compared to two different versions of genetic programming and three non-evolutionary machine learning methods, outperforming all of them on all the studied test cases.", notes = "evoCOMNET+evoRISK EvoApplications2015 held in conjunction with EuroGP'2015, EvoCOP2015 and EvoMusArt2015 http://www.evostar.org/2015/cfp_evoapps.php", } @InProceedings{Vanneschi:2015:NEO, author = "Leonardo Vanneschi", title = "An Introduction to Geometric Semantic Genetic Programming", booktitle = "NEO 2015: Results of the Numerical and Evolutionary Optimization Workshop NEO 2015 held at September 23-25 2015 in Tijuana, Mexico", year = "2015", editor = "Oliver Schuetze and Leonardo Trujillo and Pierrick Legrand and Yazmin Maldonado", volume = "663", series = "Studies in Computational Intelligence", pages = "3--42", publisher = "Springer", keywords = "genetic algorithms, genetic programming, semantic genetic programming", isbn13 = "978-3-319-44003-3", DOI = "doi:10.1007/978-3-319-44003-3_1", abstract = "For all supervised learning problems, where the quality of solutions is measured by a distance between target and output values (error), geometric semantic operators of genetic programming induce an error surface characterized by the absence of locally suboptimal solutions (unimodal error surface). So, genetic programming that uses geometric semantic operators, called geometric semantic genetic programming, has a potential advantage in terms of evolvability compared to many existing computational methods. This fosters geometric semantic genetic programming as a possible new state-of-the-art machine learning methodology. Nevertheless, research in geometric semantic genetic programming is still much in demand. This chapter is oriented to researchers and students that are not familiar with geometric semantic genetic programming, and are willing to contribute to this exciting and promising field. The main objective of this chapter is explaining why the error surface induced by geometric semantic operators is unimodal, and why this fact is important. Furthermore, the chapter stimulates the reader by showing some promising applicative results that have been obtained so far. The reader will also discover that some properties of geometric semantic operators may help limiting overfitting, bestowing on genetic programming a very interesting generalization ability. Finally, the chapter suggests further reading and discusses open issues of geometric semantic genetic programming.", notes = "Published 2017", } @InProceedings{vanneschi:2017:CEC, author = "Leonardo Vanneschi and Illya Bakurov and Mauro Castelli", booktitle = "2017 IEEE Congress on Evolutionary Computation (CEC)", title = "An initialization technique for geometric semantic GP based on demes evolution and despeciation", year = "2017", editor = "Jose A. Lozano", pages = "113--120", address = "Donostia, San Sebastian, Spain", publisher = "IEEE", isbn13 = "978-1-5090-4601-0", abstract = "Initializing the population is a crucial step for genetic programming, and several strategies have been proposed so far. The issue is particularly important for geometric semantic genetic programming, where initialization is known to play a very important role. In this paper, we propose an initialization technique inspired by the biological phenomenon of demes despeciation, i.e. the combination of demes of previously distinct species into a new population. In synthesis, the initial population of geometric semantic genetic programming is created using the best individuals of a set of separate subpopulations, or demes, some of which run standard genetic programming and the others geometric semantic genetic programming for few generations. Geometric semantic genetic programming with this novel initialization technique is shown to outperform geometric semantic genetic programming using the traditional ramped half-and-half algorithm on six complex symbolic regression applications. More specifically, on the studied problems, the proposed initialization technique allows us to generate solutions with comparable or even better generalization ability, and of significantly smaller size than the ramped half-and-half algorithm.", keywords = "genetic algorithms, genetic programming, regression analysis, biological phenomenon, complex symbolic regression applications, demes despeciation, demes evolution, geometric semantic GP, initialization technique, Evolution (biology), Semantics, Sociology, Standards, Statistics", isbn13 = "978-1-5090-4601-0", DOI = "doi:10.1109/CEC.2017.7969303", month = "5-8 " # jun, notes = "IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969303}", } @InProceedings{vanneschi:2017:CECa, author = "Leonardo Vanneschi and Bernardo Galvao", booktitle = "2017 IEEE Congress on Evolutionary Computation (CEC)", title = "A parallel and distributed semantic Genetic Programming system", year = "2017", editor = "Jose A. Lozano", pages = "121--128", address = "Donostia, San Sebastian, Spain", publisher = "IEEE", isbn13 = "978-1-5090-4601-0", abstract = "In the last few years, geometric semantic genetic programming has incremented its popularity, obtaining interesting results on several real life applications. Nevertheless, the large size of the solutions generated by geometric semantic genetic programming is still an issue, in particular for those applications in which reading and interpreting the final solution is desirable. In this paper, we introduce a new parallel and distributed genetic programming system, with the objective of mitigating this drawback. The proposed system (called MPHGP, which stands for Multi-Population Hybrid Genetic Programming) is composed by two subpopulations, one of which runs geometric semantic genetic programming, while the other runs a standard multi-objective genetic programming algorithm that optimizes, at the same time, training error and the size of the solutions. The two subpopulations evolve independently and in parallel, exchanging individuals at prefixed synchronization instants. The presented experimental results, obtained on five real-life symbolic regression applications, suggest that MPHGP is able to find solutions that are comparable, or even better, than the ones found by geometric semantic genetic programming, both on training and on unseen testing data. At the same time, MPHGP is also able to find solutions that are significantly smaller than the ones found by geometric semantic genetic programming.", keywords = "genetic algorithms, genetic programming, algorithm theory, geometry, MPHGP, distributed semantic genetic programming system, geometric semantic genetic programming, multiobjective genetic programming algorithm, multipopulation hybrid genetic programming, prefixed synchronization instants, symbolic regression applications, Optimization, Semantics, Sociology, Standards, Statistics, Training", isbn13 = "978-1-5090-4601-0", DOI = "doi:10.1109/CEC.2017.7969304", month = "5-8 " # jun, notes = "IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969304}", } @InProceedings{vanneschi:2017:CECb, author = "Leonardo Vanneschi and Mauro Castelli and Ivo Goncalves and Luca Manzoni and Sara Silva", booktitle = "2017 IEEE Congress on Evolutionary Computation (CEC)", title = "Geometric semantic genetic programming for biomedical applications: A state of the art upgrade", year = "2017", editor = "Jose A. Lozano", pages = "177--184", address = "Donostia, San Sebastian, Spain", publisher = "IEEE", isbn13 = "978-1-5090-4601-0", abstract = "Geometric semantic genetic programming is a hot topic in evolutionary computation and recently it has been used with success on several problems from Biology and Medicine. Given the young age of geometric semantic genetic programming, in the last few years theoretical research, aimed at improving the method, and applicative research proceeded rapidly and in parallel. As a result, the current state of the art is confused and presents some 'holes'. For instance, some recent improvements of geometric semantic genetic programming have never been applied to some popular biomedical applications. The objective of this paper is to fill this gap. We consider the biomedical applications that have more frequently been used by genetic programming researchers in the last few years and we systematically test, in a consistent way, using the same parameter settings and configurations, all the most popular existing variants of geometric semantic genetic programming on all those applications. Analysing all these results, we obtain a much more homogeneous and clearer picture of the state of the art, that allows us to draw stronger conclusions.", keywords = "genetic algorithms, genetic programming, medical computing, biomedical applications, evolutionary computation, geometric semantic genetic programming, parameter settings, Drugs, Electronic mail, GSM, Proteins, Semantics", isbn13 = "978-1-5090-4601-0", DOI = "doi:10.1109/CEC.2017.7969311", month = "5-8 " # jun, notes = "IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969311}", } @InProceedings{Vanneschi:2017:GECCO, author = "Leonardo Vanneschi and Mauro Castelli and Luca Manzoni and Krzysztof Krawiec and Alberto Moraglio and Sara Silva and Ivo Goncalves", title = "{PSXO}: Population-wide Semantic Crossover", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4939-0", address = "Berlin, Germany", pages = "257--258", size = "2 pages", URL = "http://doi.acm.org/10.1145/3067695.3076003", DOI = "doi:10.1145/3067695.3076003", acmid = "3076003", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, inverse matrix, population-wide crossover, real-life problems, semantics", month = "15-19 " # jul, abstract = "Since its introduction, Geometric Semantic Genetic Programming (GSGP) has been the inspiration to ideas on how to reach optimal solutions efficiently. Among these, in 2016 Pawlak has shown how to analytically construct optimal programs by means of a linear combination of a set of random programs. Given the simplicity and excellent results of this method (LC) when compared to GSGP, the author concluded that GSGP is overkill. However, LC has limitations, and it was tested only on simple benchmarks. In this paper, we introduce a new method, Population-Wide Semantic Crossover (PSXO), also based on linear combinations of random programs, that overcomes these limitations. We test the first variant (Inv) on a diverse set of complex real-life problems, comparing it to LC, GSGP and standard GP. We realize that, on the studied problems, both LC and Inv are outperformed by GSGP, and sometimes also by standard GP. This leads us to the conclusion that GSGP is not overkill. We also introduce a second variant (GPinv) that integrates evolution with the approximation of optimal programs by means of linear combinations. GPinv outperforms both LC and Inv on unseen test data for the studied problems.", notes = "Also known as \cite{Vanneschi:2017:PPS:3067695.3076003}, \cite{vanneschi2017psxo} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @InProceedings{Vanneschi:2018:EuroGP, author = "Leonardo Vanneschi and Kristen Scott and Mauro Castelli", title = "A Multiple Expression Alignment Framework for Genetic Programming", booktitle = "EuroGP 2018: Proceedings of the 21st European Conference on Genetic Programming", year = "2018", month = "4-6 " # apr, editor = "Mauro Castelli and Lukas Sekanina and Mengjie Zhang and Stefano Cagnoni and Pablo Garcia-Sanchez", series = "LNCS", volume = "10781", publisher = "Springer Verlag", address = "Parma, Italy", pages = "166--183", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-77552-4", DOI = "doi:10.1007/978-3-319-77553-1_11", abstract = "Alignment in the error space is a recent idea to exploit semantic awareness in genetic programming. In a previous contribution, the concepts of optimally aligned and optimally coplanar individuals were introduced, and it was shown that given optimally aligned, or optimally coplanar, individuals, it is possible to construct a globally optimal solution analytically. As a consequence, genetic programming methods, aimed at searching for optimally aligned, or optimally coplanar, individuals were introduced. In this paper, we critically discuss those methods, analysing their major limitations and we propose new genetic programming systems aimed at overcoming those limitations. The presented experimental results, conducted on five real-life symbolic regression problems, show that the proposed algorithms outperform not only the existing methods based on the concept of alignment in the error space, but also geometric semantic genetic programming and standard genetic programming.", notes = "Part of \cite{Castelli:2018:GP} EuroGP'2018 held in conjunction with EvoCOP2018, EvoMusArt2018 and EvoApplications2018", } @Article{VANNESCHI:2018:ESA, author = "Leonardo Vanneschi and David Micha Horn and Mauro Castelli and Ales Popovic", title = "An artificial intelligence system for predicting customer default in e-commerce", journal = "Expert Systems with Applications", volume = "104", pages = "1--21", year = "2018", keywords = "genetic algorithms, genetic programming, Risk management, Credit scoring, Machine learning, Optimization", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2018.03.025", URL = "http://www.sciencedirect.com/science/article/pii/S0957417418301702", abstract = "The growing number of e-commerce orders is leading to increased risk management to prevent default in payment. Default in payment is the failure of a customer to settle a bill within 90 days upon receipt. Frequently, credit scoring (CS) is employed to identify customers' default probability. CS has been widely studied, and many computational methods have been proposed. The primary aim of this work is to develop a CS model to replace the pre-risk check of the e-commerce risk management system Risk Solution Services (RSS), which is currently one of the most used systems to estimate customers' default probability. The pre-risk check uses data from the order process and includes exclusion rules and a generic CS model. The new model is supposed to replace the whole pre-risk check and has to work both in isolation and in integration with the RSS main risk check. An application of genetic programming (GP) to CS is presented in this paper. The model was developed on a real-world dataset provided by a well-known German financial solutions company. The dataset contains order requests processed by RSS. The results show that GP outperforms the generic CS model of the pre-risk check in both classification accuracy and profit. GP achieved competitive classificatory accuracy with several state-of-the-art machine learning methods, such as logistic regression, support vector machines and boosted trees. Furthermore, the GP model can be used in combination with the RSS main risk check to create a model with even higher discriminatory power", } @Article{vanneschi:2018:IJCSM, author = "Leonardo Vanneschi and Mauro Castelli and Kristen Scott and Ales Popovic", title = "Accurate High Performance Concrete Prediction with an {Alignment-Based} Genetic Programming System", journal = "International Journal of Concrete Structures and Materials", year = "2018", volume = "12", number = "1", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1186/s40069-018-0300-5", DOI = "doi:10.1186/s40069-018-0300-5", } @Article{Vanneschi:2019:swarmEC, author = "Leonardo Vanneschi and Mauro Castelli and Kristen Scott and Leonardo Trujillo", title = "Alignment-based genetic programming for real life applications", journal = "Swarm and Evolutionary Computation", year = "2019", volume = "44", pages = "840--851", month = feb, keywords = "genetic algorithms, genetic programming, Geometric semantic operators, Alignment, Error space, Real-life applications", ISSN = "2210-6502", URL = "http://www.sciencedirect.com/science/article/pii/S2210650218300208", DOI = "doi:10.1016/j.swevo.2018.09.006", size = "12 pages", abstract = "A recent discovery has attracted the attention of many researchers in the field of genetic programming: given individuals with particular characteristics of alignment in the error space, called optimally aligned, it is possible to reconstruct a globally optimal solution. Furthermore, recent preliminary experiments have shown that an indirect search consisting of looking for optimally aligned individuals can have benefits in terms of generalization ability compared to a direct search for optimal solutions. For this reason, defining genetic programming systems that look for optimally aligned individuals is becoming an ambitious and important objective. Nevertheless, the systems that have been introduced so far present important limitations that make them unusable in practice, particularly for complex real-life applications. In this paper, we overcome those limitations, and we present the first usable alignment-based genetic programming system, called nested alignment genetic programming (NAGP). The presented...", notes = "also known as \cite{VANNESCHI2018}", } @InProceedings{Vanneschi:2020:EuroGP, author = "Leonardo Vanneschi and Mauro Castelli and Luca Manzoni and Sara Silva and Leonardo Trujillo", title = "Is k Nearest Neighbours Regression Better than {GP}?", booktitle = "EuroGP 2020: Proceedings of the 23rd European Conference on Genetic Programming", year = "2020", month = "15-17 " # apr, editor = "Ting Hu and Nuno Lourenco and Eric Medvet", series = "LNCS", volume = "12101", publisher = "Springer Verlag", address = "Seville, Spain", pages = "244--261", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-030-44093-0", video_url = "https://youtu.be/gavQB2XAoac", video_url = "https://youtu.be/rbUN4JRv02M", DOI = "doi:10.1007/978-3-030-44094-7_16", abstract = "This work starts from the empirical observation that k nearest neighbours (KNN) consistently outperforms state-of-the-art techniques for regression, including geometric semantic genetic programming (GSGP). However, KNN is a memorization, and not a learning, method, i.e. it evaluates unseen data on the basis of training observations, and not by running a learned model. This paper takes a first step towards the objective of defining a learning method able to equal KNN, by defining a new semantic mutation, called random vectors-based mutation (RVM). GP using RVM, called RVMGP, obtains results that are comparable to KNN, but still needs training data to evaluate unseen instances. A comparative analysis sheds some light on the reason why RVMGP outperforms GSGP, revealing that RVMGP is able to explore the semantic space more uniformly. This finding opens a question for the future: is it possible to define a new genetic operator, that explores the semantic space as uniformly as RVM does, but that still allows us to evaluate unseen instances without using training data?", notes = "https://youtu.be/gavQB2XAoac http://www.evostar.org/2020/cfp_eurogp.php Part of \cite{Hu:2020:GP} EuroGP'2020 held in conjunction with EvoCOP2020, EvoMusArt2020 and EvoApplications2020", } @Article{VANNESCHI:2021:ESA, author = "Leonardo Vanneschi and Mauro Castelli", title = "Soft target and functional complexity reduction: A hybrid regularization method for genetic programming", journal = "Expert Systems with Applications", volume = "177", pages = "114929", year = "2021", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2021.114929", URL = "https://www.sciencedirect.com/science/article/pii/S0957417421003705", keywords = "genetic algorithms, genetic programming, Regularisation, Soft target, Functional complexity, Hybrid system", abstract = "Regularization is frequently used in supervised machine learning to prevent models from overfitting. This paper tackles the problem of regularization in genetic programming. We apply, for the first time, soft target regularization, a method recently defined for artificial neural networks, to genetic programming. Also, we introduce a novel measure of functional complexity of the genetic programming individuals, aimed at quantifying their degree of curvature. We experimentally demonstrate that both the use of soft target regularization, and the minimization of the complexity during learning, are often able to reduce overfitting, but they are never able to eliminate it. On the other hand, we demonstrate that the integration of these two strategies into a novel hybrid genetic programming system can completely eliminate overfitting, for all the studied test cases. Last but not least, consistently with what found in the literature, we offer experimental evidence of the fact that the size of the genetic programming models has no correlation with their generalization ability", } @Book{Vanneschi:book, author = "Leonardo Vanneschi and Sara Silva", title = "Lectures on Intelligent Systems", publisher = "Springer", year = "2023", series = "Genetic and Evolutionary Computation", keywords = "genetic algorithms, genetic programming, Artificial Intelligence, text book", isbn13 = "978-3-031-17921-1", ISSN = "1932-0175", ISSN = "1619-7127", URL = "https://link.springer.com/book/10.1007/978-3-031-17922-8", DOI = "doi:10.1007/978-3-031-17922-8", size = "XIV+349 pages", abstract = "Table of contents (12 chapters) Computational Intelligence for Optimization Optimization Problems and Local Search Genetic Algorithms Particle Swarm Optimization Machine Learning Introduction to Machine Learning Decision Tree Learning Artificial Neural Networks Genetic Programming Bayesian Learning Support Vector Machines Ensemble Methods Unsupervised Learning: Clustering Algorithms", notes = "Reviewed by \cite{Ombuki-Berman:2024:GPEM}", } @Article{Vanneschi:2023:GPEM, author = "Leonardo Vanneschi and Leonardo Trujillo", title = "Introduction to the peer commentary special section on {``Jaws 30'' by W. B. Langdon}", journal = "Genetic Programming and Evolvable Machines", year = "2023", volume = "24", number = "2", pages = "Article number: 18", month = dec, note = "Special Issue: Thirtieth Anniversary of Genetic Programming: On the Programming of Computers by Means of Natural Selection \cite{koza:book}", keywords = "genetic algorithms, genetic programming, genetic improvement, GPU, design of ensembles, XAI, APR", ISSN = "1389-2576", URL = "https://rdcu.be/drY40", DOI = "doi:10.1007/s10710-023-09466-y", size = "2 pages", notes = "See \cite{langdon:jaws30} \cite{squillero:2023:GPEM}, \cite{castelli:2023:GPEM}, \cite{heywood:2023:GPEM}, \cite{bartoli:2023:GPEM}, \cite{moore:2023:GPEM}, \cite{johnson:2023:GPEM}, \cite{jaws30_reply}", } @InProceedings{Vanneschi:2024:EuroGP, author = "Leonardo Vanneschi", editor = "Mario Giacobini and Bing Xue and Luca Manzoni", title = "{SLIM-GSGP}: The Non-bloating Geometric Semantic Genetic Programming", booktitle = "EuroGP 2024: Proceedings of the 27th European Conference on Genetic Programming", year = "2024", volume = "14631", series = "LNCS", pages = "125--141", publisher = "Springer", address = "Aberystwyth", month = "3-5 " # apr, organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Geometric Semantic Genetic Programming, Inflate and Deflate Mutations, Model Interpretability", isbn13 = "978-3-031-56957-9", URL = "https://rdcu.be/dOkvz", DOI = "doi:10.1007/978-3-031-56957-9_8", size = "17 pages", abstract = "Geometric semantic genetic programming (GSGP) is a successful variant of genetic programming (GP), able to induce a unimodal error surface for all supervised learning problems. However, a limitation of GSGP is its tendency to generate offspring larger than their parents, resulting in continually growing program sizes. This leads to the creation of models that are often too complex for human comprehension. This paper presents a novel GSGP variant, the Semantic Learning algorithm with Inflate and deflate Mutations (SLIM-GSGP). SLIM_GSGP retains the essential theoretical characteristics of traditional GSGP, including the induction of a unimodal error surface and introduces a novel geometric semantic mutation, the deflate mutation, which generates smaller offspring than its parents. The study introduces four SLIMGSGP variants and presents experimental results demonstrating that, across six symbolic regression test problems, SLIM GSGP consistently evolves models with equal or superior performance on unseen data compared to traditional GSGP and standard GP. These SLIM-GSGP models are significantly smaller than those produced by traditional GSGP and are either smaller or of comparable size to standard GP models. Notably, the compactness of SLIM_GSGP models allows for human interpretation.", notes = "Part of \cite{Giacobini:2024:GP} EuroGP'2024 held in conjunction with EvoCOP2024, EvoMusArt2024 and EvoApplications2024", } @InProceedings{Vanneschi:2024:GPTP, author = "Leonardo Vanneschi and Davide Farinati and Diogo Rasteiro and Liah Rosenfeld and Gloria Pietropolli and Sara Silva", title = "Exploring Non-Bloating Geometric Semantic Genetic Programming", booktitle = "Genetic Programming Theory and Practice XXI", year = "2024", editor = "Stephan M. Winkler and Wolfgang Banzhaf and Ting Hu and Alexander Lalejini", series = "Genetic and Evolutionary Computation", pages = "237--258", address = "University of Michigan, USA", month = jun # " 6-8", publisher = "Springer", keywords = "genetic algorithms, genetic programming, GSGP", isbn13 = "978-981-96-0076-2", DOI = "doi:10.1007/978-981-96-0077-9_12", notes = "Published in 2025 after the workshop", } @InProceedings{VanVeldhuizen:1998:eccpf, author = "David A. {Van Veldhuizen} and Gary B. Lamont", title = "Evolutionary Computation and Convergence to a {Pareto} Front", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "221--228", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "genetic algorithms, genetic programming, MOP, GA, ES, GP, EP", URL = "http://www.lania.mx/~ccoello/EMOO/vanvel2.ps.gz", URL = "https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=f329eb18a4549daa83fae28043d19b83fe8356fa", size = "7.1 pages", abstract = "Research into solving multiobjective optimisation problems (MOP) has as one of its an overall goals that of developing and defining foundations of an Evolutionary Computation (EC)-based MOP theory. In this paper, we introduce relevant MOP concepts, and the notion of Pareto optimality, in particular. Specific notation is defined and theorems are presented ensuring Pareto based Evolutionary Algorithm (EA) implementations are clearly understood. Then, a specific experiment investigating the convergence of an arbitrary EA to a Pareto front is presented. This experiment gives a basis for a theorem showing a specific multiobjective EA statistically converges to the Pareto front. We conclude by using this work to justify further exploration into the theoretical foundations of EC-based MOP solution methods.", notes = "Matlab, GEATbx GP-98LB", } @PhdThesis{VanVeldhuizen:thesis, author = "David A. {Van Veldhuizen}", title = "Multiobjective Evolutionary Algorithms: Classification, Analysis, and New Innovations", school = "Graduate School of Engineering, Air Force Institute of Technology", year = "1999", address = "Wright-Patterson Air Force Base, Ohio, USA", month = jun, keywords = "genetic algorithms, genetic programming, EMO, MOP, EA, MOEA, building blocks, MOMGA", URL = "https://apps.dtic.mil/dtic/tr/fulltext/u2/a364478.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.42.1823", size = "272 pages", notes = "Not GP? AFIT/DS/ENG/99-01 19990616 031 Also known as \cite{Veldhuizen99multiobjectiveevolutionary} Supervisor: Gary B. Lamont", } @InProceedings{vanyi:2000:grden, author = "Robert Vanyi and Gabriella Kokai and Zoltan Toth and T-unde Peto", title = "Grammatical Retina Description with Enhanced Methods", booktitle = "Genetic Programming, Proceedings of EuroGP'2000", year = "2000", editor = "Riccardo Poli and Wolfgang Banzhaf and William B. Langdon and Julian F. Miller and Peter Nordin and Terence C. Fogarty", volume = "1802", series = "LNCS", pages = "193--208", address = "Edinburgh", publisher_address = "Berlin", month = "15-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-67339-3", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/18942/ftp:zSzzSzftp.informatik.uni-erlangen.dezSzlocalzSzinf2zSzPaperszSzeurogp.pdf/grammatical-retina-description-with.pdf", URL = "http://citeseer.ist.psu.edu/392580.html", DOI = "doi:10.1007/978-3-540-46239-2_14", abstract = "In this paper the enhanced version of the GREDEA system is presented. The main idea behind the system is that with the help of evolutionary algorithms a grammatical description of the blood circulation of the human retina can be inferred. The system uses parametric Lindenmayer systems as description language. It can be applied on patients with diabetes who need to be monitored over long periods of time. Since the first version some improvements were made, e.g. new fitness function and new genetic operators. In this paper these changes are described.", notes = "EuroGP'2000, part of \cite{poli:2000:GP}", } @InProceedings{vanyi2:2001:gecco, title = "Giving Structural Descriptions of Tree-like Objects from Binary Images Using Genetic Programming", author = "Robert Vanyi and Gabriella Kokai", pages = "163--172", year = "2001", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)", editor = "Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon and Edmund Burke", address = "San Francisco, California, USA", publisher_address = "San Francisco, CA 94104, USA", month = "7-11 " # jul, keywords = "genetic algorithms, genetic programming, binary images, Lindenmayer systems, branching, structures, structural descriptions, image reconstruction", ISBN = "1-55860-774-9", URL = "http://gpbib.cs.ucl.ac.uk/gecco2001/GP174.ps", size = "8 pages", abstract = "The aim of this paper is to present a general method for structurally describing binary images. Such a method has to be reasonably fast and should give a correct and usable result. The convergence speed highly depends on the fitness calculation, therefore several possible fitness functions and their computations are discussed. To achieve a correct result, the evolutionary operators and the evolution process have to be designed carefully. They are also discussed in this paper.", notes = "GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of \cite{spector:2001:GECCO}", } @InProceedings{Vanyi:2003:Asiivpoaodt, author = "Robert Vanyi and Szilvia Zvada", title = "Avoiding syntactically incorrect individuals via parameterized operators applied on derivation trees", booktitle = "Proceedings of the 2003 Congress on Evolutionary Computation CEC2003", year = "2003", editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon", volume = "4", pages = "2791--2798", publisher = "IEEE Press", address = "Canberra", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "8-12 " # dec, organisation = "IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", keywords = "genetic algorithms, genetic programming, context-free grammar, context-free language, derivation trees, evolutionary algorithm, parametrised operators, syntactically incorrect individuals, context-free languages, context-sensitive grammars, evolutionary computation, trees mathematics", ISBN = "0-7803-7804-0", URL = "http://www2.informatik.uni-erlangen.de/publication/download/vanyi_zvada_cec2003.ps.gz", DOI = "doi:10.1109/CEC.2003.1299442", size = "8 pages", abstract = "Evolutionary algorithms can be efficiently used to solve many different problems, without knowing much about the nature of the solution. One of the most appreciated property of these algorithms is simplicity. However, this simplicity causes an unguided nature; superfluous or even invalid individuals may be produced taking valuable time from the algorithm. In this paper a preliminary method is described for avoiding such individuals during the evolution of strings from a context-free language. Our method operates on the derivation trees of the underlying context-free grammar and keeps nevertheless the simplicity and randomness of the evolutionary algorithms. Though the system is designed for context-free languages, the method can be extended to higher level languages, too.", notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE. Also known as \cite{1299442}", } @InProceedings{vanyi:ood:gecco2004, author = "R\'obert V\'anyi", title = "Object Oriented Design and Implementation of a General Evolutionary Algorithm", booktitle = "Genetic and Evolutionary Computation -- GECCO-2004, Part II", year = "2004", editor = "Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell", series = "Lecture Notes in Computer Science", pages = "1275--1286", address = "Seattle, WA, USA", publisher_address = "Heidelberg", month = "26-30 " # jun, organisation = "ISGEC", publisher = "Springer-Verlag", volume = "3103", ISBN = "3-540-22343-6", ISSN = "0302-9743", DOI = "doi:10.1007/b98645", size = "12", keywords = "genetic algorithms, genetic programming", notes = "GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)", } @InProceedings{vanyi:2004:gew:rvan, author = "Robert Vanyi and Szilvia Zvada", title = "Syntactically Correct Genetic Programming", editor = "R. Poli and S. Cagnoni and M. Keijzer and E. Costa and F. Pereira and G. Raidl and S. C. Upton and D. Goldberg and H. Lipson and E. {de Jong} and J. Koza and H. Suzuki and H. Sawai and I. Parmee and M. Pelikan and K. Sastry and D. Thierens and W. Stolzmann and P. L. Lanzi and S. W. Wilson and M. O'Neill and C. Ryan and T. Yu and J. F. Miller and I. Garibay and G. Holifield and A. S. Wu and T. Riopka and M. M. Meysenburg and A. W. Wright and N. Richter and J. H. Moore and M. D. Ritchie and L. Davis and R. Roy and M. Jakiela", booktitle = "GECCO 2004 Workshop Proceedings", year = "2004", month = "26-30 " # jun, address = "Seattle, Washington, USA", keywords = "genetic algorithms, genetic programming, grammatical evolution", URL = "http://gpbib.cs.ucl.ac.uk/gecco2004/WGEW004.pdf", notes = "GECCO-2004WKS Distributed on CD-ROM at GECCO-2004", } @InProceedings{vanyi:evows05, author = "Robert Vanyi", title = "Practical Evaluation of Efficient Fitness Functions for Binary Images", booktitle = "Applications of Evolutionary Computing, EvoWorkshops2005: {EvoBIO}, {EvoCOMNET}, {EvoHOT}, {EvoIASP}, {EvoMUSART}, {EvoSTOC}", year = "2005", month = "30 " # mar # "-1 " # apr, editor = "Franz Rothlauf and Juergen Branke and Stefano Cagnoni and David W. Corne and Rolf Drechsler and Yaochu Jin and Penousal Machado and Elena Marchiori and Juan Romero and George D. Smith and Giovanni Squillero", series = "LNCS", volume = "3449", publisher = "Springer Verlag", address = "Lausanne, Switzerland", publisher_address = "Berlin", pages = "314--324", keywords = "genetic algorithms, genetic programming, evolutionary computation", ISBN = "3-540-25396-3", ISSN = "0302-9743", DOI = "doi:10.1007/b106856", abstract = "Genetic Programming can be used to evolve complex objects. One field, where GP may be used is image analysis. There are several works using evolutionary methods to process, analyse or classify images. All these procedures need an appropriate fitness function, that is a similarity measure. However, computing such measures usually needs a lot of computational time. To solve this problem, the notion of efficiently computable fitness functions was introduced, and their theory was already examined in detail. the practical aspects of these fitness functions are discussed.", notes = "EvoWorkshops2005", } @Article{Vanyi:2008:GPEM, author = "Robert Vanyi", title = "Compositional evolution: the impact of sex, symbiosis and modularity on the gradualist framework of evolution, MIT Press, Vienna Series in Theoretical Biology, Vol. 6, Richard A. Watson, 2006, 300 p., Hardcover, ISBN 0-262-23243-X", journal = "Genetic Programming and Evolvable Machines", year = "2008", volume = "9", number = "1", pages = "97--99", month = mar, note = "Book Review", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dR8Z9", DOI = "doi:10.1007/s10710-007-9033-y", size = "3 pages", } @PhdThesis{Vanyi:thesis, author = "Robert Vanyi", title = "Derivation Tree Based Genetic Programming", school = "Faculty of Science and Informatics, University of Szeged", year = "2013", address = "Hungary", month = "16 " # oct, keywords = "genetic algorithms, genetic programming, DTGP", URL = "http://doktori.bibl.u-szeged.hu/id/eprint/1659", URL = "http://doktori.bibl.u-szeged.hu/1659/1/vanyi_thesis.pdf", URL = "http://doktori.bibl.u-szeged.hu/1659/2/vanyi_booklet_en.pdf", URL = "http://doktori.bibl.u-szeged.hu/1659/3/vanyi_booklet_hu.pdf", DOI = "doi:10.14232/phd.1659", size = "118 pages", abstract = "describes Derivation Tree Based Genetic Programming. after introducing the problem domain in this section, the relevant subjects from the field of evolutionary algorithms and formal grammars are summarised. Section 4 describes the DTGP method, whereas Section 5 provides a summary of some extensions of the base method, like introducing semantic constraints. Section 6 gives an overview of the results regarding several applications of DTGP, and Section 7 lists the major findings of the thesis.", notes = "In English. vanyi_booklet_en.pdf 28 page summary. vanyi_booklet_hu.pdf Hungarian summary. Supervisor: Prof. Dr.-Ing. Gabriella Kokai", } @Article{varadan:2001:TIE, author = "Vinay Varadan and Henry Leung", title = "Reconstruction of polynomial systems from noisy time-series measurements using genetic programming", journal = "IEEE Transactions on Industrial Electronics", year = "2001", volume = "48", number = "4", pages = "742--748", month = aug, keywords = "genetic algorithms, genetic programming, noise, polynomials, signal reconstruction, accurate parameter estimate, addition, embedding dimension, functional operators, improved least-squares method, multiplication, noisy time-series measurements, orthogonal Euclidean distance, polynomial systems reconstruction, time delay, unknown polynomial structure", ISSN = "0278-0046", DOI = "doi:10.1109/41.937405", size = "7 pages", abstract = "The problem of functional reconstruction of a polynomial system from its noisy time-series measurement is addressed in this paper. The reconstruction requires the determination of the embedding dimension and the unknown polynomial structure. The authors propose the use of genetic programming (GP) to find the exact functional form and embedding dimension of an unknown polynomial system from its time-series measurement. Using functional operators of addition, multiplication and time delay, they use GP to reconstruct the exact polynomial system and its embedding dimension. The proposed GP approach uses an improved least-squares (ILS) method to determine the parameters of a polynomial system. The ILS method is based on the orthogonal Euclidean distance to obtain an accurate parameter estimate when the series is corrupted by measurement noise. Simulations show that the proposed ILS-GP method can successfully reconstruct a polynomial system from its noisy time-series measurements", notes = "CODEN: ITIED6 INSPEC Accession Number:7007126", } @Article{VLB06, title = "Dynamical model reconstruction and accurate prediction of power-pool time series", author = "Vinay Varadan and Henry Leung and Eloi Bosse", journal = "IEEE Transactions on Instrumentation and Measurement", volume = "55", number = "1", month = feb, year = "2006", pages = "327--336", keywords = "genetic algorithms, genetic programming, Lyapunov methods, chaos, delay estimation, fractals, least squares approximations, nonlinear dynamical systems, power markets, prediction theory, time series, Lyapunov-dimension calculation, Lyapunov-spectrum, attractor-dimension, chaos, correlation-dimension calculation, delay embedding, delay estimation, dynamical model reconstruction, embedding dimension, fractal dimension, fractal-dimension estimates, least squares genetic programming, local state-space predictor, low-dimensional chaotic dynamical system, nonlinear dynamics, nonlinear time-series analysis, nonlinearity tests, power price, power-pool demand, power-pool time series prediction, prediction analysis, radial basis function neural network, stationarity tests, Chaos, Lyapunov exponents, fractal dimension, GP, local prediction, nonlinear time-series analysis, power price and demand prediction, power-pool time series, radial basis function (RBF) neural net", ISSN = "0018-9456", DOI = "doi:10.1109/TIM.2005.861492", size = "10 pages", abstract = "The emergence of the power pool as a popular institution for trading of power in different countries has led to increased interest in the prediction of power demand and price. We investigate whether the time series of power-pool demand and price can be modelled as the output of a low-dimensional chaotic dynamical system by using delay embedding and estimation of the embedding dimension, attractor-dimension or correlation-dimension calculation, Lyapunov-spectrum and Lyapunov-dimension calculation, stationarity and nonlinearity tests, as well as prediction analysis. Different dimension estimates are consistent and show close similarity, thus increasing the credibility of the fractal-dimension estimates. The Lyapunov spectrum consistently shows one positive Lyapunov exponent and one zero exponent with the rest being negative, pointing to the existence of chaos. The authors then propose a least squares genetic programming (LS-GP) to reconstruct the nonlinear dynamics from the power-pool time series. Compared to some standard predictors including the radial basis function (RBF) neural network and the local state-space predictor, the proposed method does not only achieve good prediction of the power-pool time series but also accurately predicts the peaks in the power price and demand based on the data sets used in the present study.", notes = "INSPEC Accession Number:8768025 Dept. of Electr. Eng., Columbia Univ., New York, NY, USA", } @Article{Vardhan:2016:Measurement, author = "Harsha Vardhan and Ankit Garg and Jinhui Li and Akhil Garg", title = "Measurement of Stress Dependent Permeability of Unsaturated Clay", journal = "Measurement", year = "2016", volume = "91", pages = "371--376", month = sep, keywords = "genetic algorithms, genetic programming, Permeability prediction, Firouzkouh clay, Permeability modelling, Stress", ISSN = "0263-2241", DOI = "doi:10.1016/j.measurement.2016.05.062", URL = "http://www.sciencedirect.com/science/article/pii/S0263224116302299", abstract = "Unsaturated permeability in soil is useful in assessment of water consumption by roots and suction distribution in slopes, which in turn, helps in design for the stability of slopes/covers. Stress factor is important in understanding the behaviour of the unsaturated soils because it has a significant effect on its permeability as it brings micropores and minipores changes in the soil structure. Past studies have often neglected to measure its effect on the permeability property of the soil. The present study will introduce an optimization framework of genetic programming (GP) in developing the explicit relation of the permeability and the stress of the unsaturated clay. Experimental validation of the GP model will be done using the metrics such as the coefficient of determination, the root mean square error and the mean absolute percentage error. 2-D analysis of the model will be useful for experts to monitor the permeability property unsaturated clay.", notes = "Department of Civil and Environmental Engineering, Indian Institute of Technology, Guwahati", } @Article{Vardy:2008:GPEM, author = "Andrew Vardy", title = "Maja J. Mataric: The Robotics Primer", journal = "Genetic Programming and Evolvable Machines", year = "2008", volume = "9", number = "1", pages = "101--103", month = mar, note = "Book Review", ISSN = "1389-2576", URL = "https://rdcu.be/dR80m", DOI = "doi:10.1007/s10710-007-9053-7", size = "3 pages", notes = "MIT Press, Cambridge, 2007, 300 pp, $30.00, ISBN 0-262-63354-X", } @Article{Vardy:2014:SwarmIntl, author = "Andrew Vardy and Gregory Vorobyev and Wolfgang Banzhaf", title = "Cache Consensus: Rapid Object Sorting by a Robotic Swarm", journal = "Swarm Intelligence", year = "2014", volume = "8", number = "1", pages = "61--87", month = mar, keywords = "genetic algorithms, genetic programming, swarm intelligence, Swarm robotics, Patch sorting, Clustering, Localisation", DOI = "doi:10.1007/s11721-014-0091-5", abstract = "We present a new method which allows a swarm of robots to sort arbitrarily arranged objects into homogeneous clusters. In the ideal case, a distributed robotic sorting method should establish a single homogeneous cluster for each object type. This can be achieved with existing methods, but the rate of convergence is considered too slow for real-world application. Previous research on distributed robotic sorting is typified by randomised movement with a pick-up/deposit behaviour that is a probabilistic function of local object density. We investigate whether the ability of each robot to localise and return to remembered places can improve distributed sorting performance. In our method, each robot maintains a cache point for each object type. Upon collecting an object, it returns to add this object to the cluster surrounding the cache point. Similar to previous biologically inspired work on distributed sorting, no explicit communication between robots is implemented. However, the robots can still come to a consensus on the best cache for each object type by observing clusters and comparing their sizes with remembered cache sizes. We refer to this method as cache consensus. Our results indicate that incorporating this localisation capability enables a significant improvement in the rate of convergence. We present experimental results using a realistic simulation of our targeted robotic platform. A subset of these experiments is also validated on physical robots.", } @Article{Varela:2022:GPEM, author = "Daniel Varela and Jose {Santos Reyes}", title = "Evolving cellular automata schemes for protein folding modeling using the {Rosetta} atomic representation", journal = "Genetic Programming and Evolvable Machines", year = "2022", volume = "23", number = "2", pages = "225--252", month = jun, keywords = "genetic algorithms, Protein folding, Neural cellular automata, ANN, Differential evolution, DE", ISSN = "1389-2576", URL = "https://rdcu.be/cM9nb", DOI = "doi:10.1007/s10710-022-09427-x", size = "28 pages", abstract = "Protein folding is the dynamic process by which a protein folds into its final native structure. This is different to the traditional problem of the prediction of the final protein structure, since it requires a modeling of how protein components interact over time to obtain the final folded structure. In this study we test whether a model of the folding process can be obtained exclusively through machine learning. To this end, protein folding is considered as an emergent process and the cellular automata tool is used to model the folding process. A neural cellular automaton is defined, using a connectionist model that acts as a cellular automaton through the protein chain to define the dynamic folding. Differential evolution is used to automatically obtain the optimized neural cellular automata that provide protein folding. We tested the methods with the Rosetta coarse-grained atomic model of protein representation, using different proteins to analyze the modeling of folding and the structure refinement that the modeling can provide, showing the potential advantages that such methods offer, but also difficulties that arise.", notes = "no mention of GP? Department of Biochemistry and Structural Biology, University of Lund, Lund, Sweden", } @InProceedings{Munoz2015CIKM, author = "Javier Alvaro Vargas Munoz and Ricardo {da Silva Torres} and Marcos Andre Goncalves", title = "A Soft Computing Approach for Learning to Aggregate Rankings", booktitle = "Proceedings of the 24th {ACM} International on Conference on Information and Knowledge Management, {CIKM}", year = "2015", pages = "83--92", address = "Melbourne, Australia", month = oct # " 19 - 23", keywords = "genetic algorithms, genetic programming", bibsource = "dblp computer science bibliography, http://dblp.org", biburl = "http://dblp.uni-trier.de/rec/bib/conf/cikm/MunozTG15", timestamp = "Thu, 12 Nov 2015 16:33:35 +0100", URL = "http://doi.acm.org/10.1145/2806416.2806478", DOI = "doi:10.1145/2806416.2806478", abstract = "This paper presents an approach to combine rank aggregation techniques using a soft computing technique -- Genetic Programming -- in order to improve the results in Information Retrieval tasks. Previous work shows that by combining rank aggregation techniques in an agglomerative way, it is possible to get better results than with individual methods. However, these works either combine only a small set of lists or are performed in a completely ad-hoc way. Therefore, given a set of ranked lists and a set of rank aggregation techniques, we propose to use a supervised genetic programming approach to search combinations of them that maximize effectiveness in large search spaces. Experimental results conducted using four datasets with different properties show that our proposed approach reaches top performance in most datasets. Moreover, this cross-dataset performance is not matched by any other baseline among the many we experiment with, some being the state-of-the-art in learning-to-rank and in the supervised rank aggregation tasks. We also show that our proposed framework is very efficient, flexible, and scalable.", } @InProceedings{DBLP:conf/racs/VarniabHK19, author = "Mahsa Shokri Varniab and Chih-Cheng Hung and Vahid Khalilzad-Sharghi", editor = "Chih-Cheng Hung and Qianbin Chen and Xianzhong Xie and Christian Esposito and Jun Huang and Juw Won Park and Qinghua Zhang", title = "Classification of multiclass datasets using genetic programming", booktitle = "Proceedings of the Conference on Research in Adaptive and Convergent Systems, {RACS} 2019, Chongqing, China, September 24-27, 2019", pages = "76--82", publisher = "{ACM}", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1145/3338840.3355656", DOI = "doi:10.1145/3338840.3355656", timestamp = "Thu, 14 Nov 2019 12:38:38 +0100", biburl = "https://dblp.org/rec/conf/racs/VarniabHK19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{journals/umcs/VarretteMB12, author = "Sebastien Varrette and Jakub Muszynski and Pascal Bouvry", title = "Hash function generation by means of Gene Expression Programming", journal = "Annales UMCS, Informatica", year = "2012", number = "3", volume = "12", pages = "37--53", month = dec, keywords = "genetic algorithms, genetic programming, gene expression programming", bibdate = "2013-12-16", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/umcs/umcs12.html#VarretteMB12", ISSN = "1732-1360", URL = "http://dx.doi.org/10.2478/v10065-012-0027-x", DOI = "doi:10.2478/v10065-012-0027-x", size = "17 pages", abstract = "Cryptographic hash functions are fundamental primitives in modern cryptography and have many security applications (data integrity checking, cryptographic protocols, digital signatures, pseudo random number generators etc.). At the same time novel hash functions are designed (for instance in the framework of the SHA-3 contest organized by the National Institute of Standards and Technology (NIST)), the cryptanalysts exhibit a set of statistical metrics (propagation criterion, frequency analysis etc.) able to assert the quality of new proposals. Also, rules to design {"}good{"} hash functions are now known and are followed in every reasonable proposal of a new hash scheme. This article investigates the ways to build on this experiment and those metrics to generate automatically compression functions by means of Evolutionary Algorithms (EAs). Such functions are at the heart of the construction of iterative hash schemes and it is therefore crucial for them to hold good properties. Actually, the idea to use nature-inspired heuristics for the design of such cryptographic primitives is not new: this approach has been successfully applied in several previous works, typically using the Genetic Programming (GP) heuristic [1]. Here, we exploit a hybrid meta-heuristic for the evolutionary process called Gene Expression Programming (GEP) [2] that appeared far more efficient computationally speaking compared to the GP paradigm used in the previous papers. In this context, the GEPHashSearch framework is presented. As it is still a work in progress, this article focuses on the design aspects of this framework (individuals definitions, fitness objectives etc.) rather than on complete implementation details and validation results. Note that we propose to tackle the generation of compression functions as a multi-objective optimization problem in order to identify the Pareto front i.e. the set of non-dominated functions over the four fitness criteria considered. If this goal is not yet reached, the first experimental results in a mono-objective context are promising and open the perspective of fruitful contributions to the cryptographic community", } @InProceedings{Vaseux:2013:GECCOcomp, author = "Loic Vaseux and Fernando E. B. Otero and Tom Castle and Colin G. Johnson", title = "Event-based graphical monitoring in the {EpochX} genetic programming framework", booktitle = "GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion", year = "2013", editor = "Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A. N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks", isbn13 = "978-1-4503-1964-5", keywords = "genetic algorithms, genetic programming", pages = "1309--1316", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2464576.2482710", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "EpochX is a genetic programming framework with provision for event management - similar to the Java event model - allowing the notification of particular actions during the life cycle of the evolutionary algorithm. It also provides a flexible Stats system to gather statistics measures. This paper introduces a graphical interface to the EpochX genetic programming framework, taking full advantage of EpochX's event management. A set of representation-independent and tree-dependent GUI components are presented, showing how statistic information can be presented in a rich format using the information provided by EpochX's Stats system.", notes = "Also known as \cite{2482710} Distributed at GECCO-2013.", } @InProceedings{conf/eurogp/VasicekS08, title = "Hardware Accelerators for Cartesian Genetic Programming", author = "Zdenek Vasicek and Lukas Sekanina", booktitle = "Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008", year = "2008", editor = "Michael O'Neill and Leonardo Vanneschi and Steven Gustafson and Anna Isabel {Esparcia Alcazar} and Ivanoe {De Falco} and Antonio {Della Cioppa} and Ernesto Tarantino", address = "Naples", month = "26-28 " # mar, publisher = "Springer", volume = "4971", series = "Lecture Notes in Computer Science", pages = "230--241", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", isbn13 = "978-3-540-78670-2", bibdate = "2008-04-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/eurogp/eurogp2008.html#VasicekS08", URL = "https://www.fit.vut.cz/research/publication/8590/", DOI = "doi:10.1007/978-3-540-78671-9_20", size = "12 pages", abstract = "A new class of FPGA-based accelerators is presented for Cartesian Genetic Programming (CGP). The accelerators contain a genetic engine which is reused in all applications. Candidate programs (circuits) are evaluated using application-specific virtual reconfigurable circuit (VRC) and fitness unit. Two types of VRCs are proposed. The first one is devoted for symbolic regression problems over the fixed point representation. The second one is designed for evolution of logic circuits. In both cases a significant speedup of evolution (30 to 40 times) was obtained in comparison with a highly optimised software implementation of CGP. This speedup can be increased by creating multiple fitness units.", notes = "Faculty of Information Technology, Brno University of Technology, Czech Republic Part of \cite{conf/eurogp/2008} EuroGP'2008 held in conjunction with EvoCOP2008, EvoBIO2008 and EvoWorkshops2008", } @InProceedings{Vasicek:2008:AHS, author = "Zdenek Vasicek and Ladislav Capka and Lukas Sekanina", title = "Analysis of Reconfiguration Options for a Reconfigurable Polymorphic Circuit", booktitle = "NASA/ESA Conference on Adaptive Hardware and Systems, AHS '08", year = "2008", month = jun, pages = "3--10", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, REPOMO, evolutionary circuit design, evolvable hardware, polymorphic hardware, reconfigurable chip, reconfigurable polymorphic circuit, reconfigurable polymorphic module, reconfiguration options, reconfiguration subsystem, circuit optimisation, integrated circuit design", DOI = "doi:10.1109/AHS.2008.25", abstract = "Reconfigurable POlymorphic MOdule (REPOMO) will be a new reconfigurable chip intended for experimental applications of evolvable and polymorphic hardware. In this paper, we analyze various reconfiguration options for this platform with the aim of finding such a reconfiguration subsystem which maximizes the success rate of evolutionary circuit design conducted using REPOMO. An interesting outcome of this analysis is that a relatively high success rate of evolutionary design can be achieved using relatively simple reconfiguration options which have to be implemented in hardware. These results are also relevant for evolutionary circuit design which is performed using Cartesian Genetic Programming.", notes = "Also known as \cite{4584248}", } @InProceedings{Vasicek:2009:AHS, author = "Zdenek Vasicek and Michal Bidlo and Lukas Sekanina and Jim Torresen and Kyrre Glette and Marcus Furuholmen", title = "Evolution of Impulse Bursts Noise Filters", booktitle = "NASA/ESA Conference on Adaptive Hardware and Systems, AHS 2009", year = "2009", month = "29 2009-" # aug # " 1", pages = "27--34", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, evolutionary algorithm, evolutionary circuit design, impulse burst noise filter, median filter, burst noise, image denoising, impulse noise, median filters", DOI = "doi:10.1109/AHS.2009.33", abstract = "The paper deals with evolutionary design of impulse burst noise filters. As proposed filters use the filtering window of 5times5 pixels, the design method has to be able to manage 25 eight-bit inputs. The large number of inputs results in an evolutionary algorithm not able to produce reasonably working filters because of the so-called scalability problem of evolutionary circuit design. However, the filters are designed using an extended version of Cartesian Genetic Programming which enables to reduce the number of inputs by selecting the most important of them. Experimental evaluation of the method has shown that evolved filters exhibit better results than conventional solutions based on various median filters.", notes = "Also known as \cite{5325476}", } @InProceedings{Vasicek:2011:DATE, author = "Zdenek Vasicek and Lukas Sekanina", title = "A global postsynthesis optimization method for combinational circuits", booktitle = "Design, Automation Test in Europe Conference Exhibition (DATE), 2011", year = "2011", editor = "Lothar Thiele", pages = "1525--1528", address = "Grenoble, France", month = "14-18 " # mar, publisher = "IEEE", isbn13 = "978-1-61284-208-0", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, Analog circuits, Benchmark testing, Circuit synthesis, Indexes, Logic gates, Runtime", ISSN = "1530-1591", URL = "http://www.fit.vutbr.cz/research/view_pub.php?id=9521", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5763326", DOI = "doi:10.1109/DATE.2011.5763326", size = "4 pages", abstract = "A genetic programming-based circuit synthesis method is proposed that enables to globally optimise the number of gates in circuits that have already been synthesised using common methods such as ABC and SIS. The main contribution is a proposal for a new fitness function that enables to significantly reduce the fitness evaluation time in comparison to the state of the art. The fitness function performs optimised equivalence checking using a SAT solver. It is shown that the equivalence checking time can significantly be reduced when knowledge of the parent circuit and its mutated offspring is taken into account. For a cost of a random, results of conventional synthesis conducted using SIS and ABC were improved by 20--40percent for the LGSynth93 benchmarks.", notes = "Silver winner 2011 HUMIES GECCO 2011 Faculty of Information Technology, Bmo University of Technology, Brno, Czech Republic Also known as \cite{5763326}", } @InProceedings{Vasicek:2011:EDoRNIF, title = "Evolutionary Design of Robust Noise-Specific Image Filters", author = "Zdenek Vasicek and Michal Bidlo", pages = "269--276", booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary Computation", year = "2011", editor = "Alice E. Smith", month = "5-8 " # jun, address = "New Orleans, USA", organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, 2D signal processing, cartesian genetic programming representation, digital image processing, evolutionary algorithm, evolutionary design, impulse noise, iterative filtering algorithm, noise intensity, noise median filter, nonlinear noise detection, nonnoise pixels, robust noise specific image filter design, image representation, impulse noise, iterative methods, median filters", DOI = "doi:10.1109/CEC.2011.5949628", ISSN = "Pending", abstract = "Evolutionary design has shown as a powerful technique in solving various engineering problems. One of the areas in which this approach succeeds is digital image processing. Impulse noise represents a basic type of non-linear noise typically affecting a single pixel in different regions of the image. In order to eliminate this type noise median filters have usually been applied. However, for higher noise intensity or wide range of the noise values this approach leads to corrupting non-noise pixels as well which results in images that are smudged or lose some details after the filtering process. Therefore, advanced filtering techniques have been developed including a concept of noise detection or iterative filtering algorithms. In case of the high noise intensity, a single filtering step is insufficient to eliminate the noise and obtain a reasonable quality of the filtered image. Therefore, iterative filters have been introduced. In this paper we apply an evolutionary algorithm combined with Cartesian Genetic Programing representation to design image filters for the impulse noise that are able to compete with some of the best conventionally used iterative filters. We consider the concept of noise detection to be designed together with the filter itself by means of the evolutionary algorithm. Finally, it will be shown that if the evolved filter is applied iteratively on the filtered image, a high-quality results can be obtained using lower computational effort of the filtering process in comparison with the conventional iterative filters.", notes = "CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET. Also known as \cite{5949628}", } @InProceedings{Vasicek:2011:AHS, author = "Zdenek Vasicek and Michal Bidlo and Lukas Sekanina and Kyrre Glette", title = "Evolutionary design of efficient and robust switching image filters", booktitle = "2011 NASA/ESA Conference on Adaptive Hardware and Systems (AHS)", year = "2011", month = "6-9 " # jun, pages = "192--199", address = "San Diego, USA", size = "8 pages", abstract = "This paper proposes an evolutionary approach based on Cartesian Genetic Programming to the design of image filters for impulse burst noise. The impulse burst noise belongs to more serious image distortions that cause a loss of information in a series of pixels together. The results introduced herein represent a continuation of our research in the design of high-quality image filters. Whilst the previous experiments considered only basic impulse burst noise in which a burst corrupting a series of pixels could take a single value, this paper is devoted to the filtering of more realistic noise of this type where the pixels in a burst can take different values. In order to increase the probability of removing the noise pixels while retaining other pixels unchanged, the concept of switching filter will be applied. In our case it means that the filter system is designed by evolution of both a filter circuit and a noise detector. We show that the proposed method is able to design an efficient and robust impulse burst noise filter that exhibits better filtering properties in comparison with several conventional approaches and, moreover, it is also suitable for a high-speed image processing.", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, evolutionary design, filter circuit, high-speed image processing, image distortions, image filters, impulse burst noise, noise detector, probability, burst noise, distortion, filtering theory, image processing, probability", DOI = "doi:10.1109/AHS.2011.5963935", notes = "Also known as \cite{5963935}", } @Article{Vasicek:2011:GPEM, author = "Zdenek Vasicek and Lukas Sekanina", title = "Formal verification of candidate solutions for post-synthesis evolutionary optimization in evolvable hardware", journal = "Genetic Programming and Evolvable Machines", year = "2011", volume = "12", number = "3", pages = "305--327", month = sep, note = "Special Issue Title: Evolvable Hardware Challenges", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-011-9132-7", size = "23 pages", abstract = "We use a formal verification algorithm to reduce the fitness evaluation time for evolutionary post-synthesis optimisation in evolvable hardware. The proposed method assumes that a fully functional digital circuit is available. A post-synthesis optimisation is then conducted using Cartesian Genetic Programming (CGP) which uses a satisfiability problem solver to decide whether a candidate solution is functionally correct or not. It is demonstrated that the method can optimise digital circuits of tens of inputs and thousands of gates. Furthermore, the number of gates was reduced for the LGSynth93 benchmark circuits by 37.8percent on average with respect to results of the conventional SIS tool.", notes = "Silver winner 2011 HUMIES GECCO 2011 ", affiliation = "Faculty of Information Technology, Brno University of Technology, Brno, Czech Republic", } @InProceedings{vasicek:2012:EuroGP, author = "Zdenek Vasicek and Karel Slany", title = "Efficient Phenotype Evaluation in Cartesian Genetic Programming", booktitle = "Proceedings of the 15th European Conference on Genetic Programming, EuroGP 2012", year = "2012", month = "11-13 " # apr, editor = "Alberto Moraglio and Sara Silva and Krzysztof Krawiec and Penousal Machado and Carlos Cotta", series = "LNCS", volume = "7244", publisher = "Springer Verlag", address = "Malaga, Spain", pages = "266--278", organisation = "EvoStar", isbn13 = "978-3-642-29138-8", URL = "https://www.fit.vut.cz/research/publication/10045", DOI = "doi:10.1007/978-3-642-29139-5_23", code_url = "http://www.fit.vutbr.cz/~vasicek/cgp/", code_url = "http://www.fit.vutbr.cz/~vasicek/cgp/?pg=accel", size = "13 pages", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, Fitness evaluation, Acceleration, Symbolic regression, machine code, evalfunc, AMD64 CPU, mprotect, Machine Code Vectorization, SIMD, SSE", abstract = "This paper describes an efficient acceleration technique designed to speedup the evaluation of candidate solutions in Cartesian Genetic Programming (CGP). The method is based on translation of the CGP phenotype to a binary machine code that is consequently executed. The key feature of the presented approach is that the introduction of the translation mechanism into common fitness evaluation procedure requires only marginal knowledge of target CPU instruction set. The proposed acceleration technique is evaluated using a symbolic regression problem in floating point domain. It is shown that for a cost of small changes in a common CGP implementation, a significant speedup can be obtained even on a common desktop CPU. The accelerated version of CGP implementation accompanied with performance analysis is available for free download from http://www.fit.vutbr.cz/~vasicek/cgp", notes = "SSE 128 bit, ie not AVX Also known as \cite{FITPUB10045} Part of \cite{Moraglio:2012:GP} EuroGP'2012 held in conjunction with EvoCOP2012 EvoBIO2012, EvoMusArt2012 and EvoApplications2012", } @InProceedings{Vasicek:2012:CEC, title = "On Area Minimization of Complex Combinational Circuits Using Cartesian Genetic Programming", author = "Zdenek Vasicek and Lukas Sekanina", pages = "825--832", booktitle = "Proceedings of the 2012 IEEE Congress on Evolutionary Computation", year = "2012", editor = "Xiaodong Li", month = "10-15 " # jun, DOI = "doi:10.1109/CEC.2012.6256649", address = "Brisbane, Australia", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, Evolvable hardware and software", abstract = "The paper deals with the evolutionary post synthesis optimization of complex combinational circuits with the aim of reducing the area on a chip as much as possible. In order to optimise complex circuits, Cartesian Genetic Programming (CGP) is employed where the fitness function is based on a formal equivalence checking algorithm rather than evaluating all possible input assignments. The standard selection strategy of CGP is modified to be more explorative and so agile in very rugged fitness landscapes. It was shown on the LGSynth93 benchmark circuits that the modified selection strategy leads to more compact circuits in roughly 50percent cases. The average area improvement is 24percent with respect to the results of conventional synthesis. Delay of optimised circuits was also analysed.", notes = "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the EPS and the IET.", } @InProceedings{vasicek:2014:EuroGP, author = "Zdenek Vasicek and Michal Bidlo", title = "On Evolution of Multi-Category Pattern Classifiers Suitable for Embedded Systems", booktitle = "17th European Conference on Genetic Programming", year = "2014", editor = "Miguel Nicolau and Krzysztof Krawiec and Malcolm I. Heywood and Mauro Castelli and Pablo Garcia-Sanchez and Juan J. Merelo and Victor M. {Rivas Santos} and Kevin Sim", series = "LNCS", volume = "8599", publisher = "Springer", pages = "234--245", address = "Granada, Spain", month = "23-25 " # apr, organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming :poster", isbn13 = "978-3-662-44302-6", DOI = "doi:10.1007/978-3-662-44303-3_20", abstract = "This paper addresses the problem of evolutionary design of classifiers for the recognition of handwritten digit symbols by means of Cartesian Genetic Programming. Two different design scenarios are investigated: the design of multiple-output classifier, and design of multiple binary classifiers. The goal is to evolve classification algorithms that employ substantially smaller amount of operations in contrast with conventional approaches such as Support Vector Machines. Even if the evolved classifiers do not reach the accuracy of the tuned SVM classifier, it will be shown that the accuracy is higher than 93percent and the number of required operations is a magnitude lower.", notes = "Part of \cite{Nicolau:2014:GP} EuroGP'2014 held in conjunction with EvoCOP2014, EvoBIO2014, EvoMusArt2014 and EvoApplications2014", } @InProceedings{Vasicek:2014:isddecs, author = "Zdenek Vasicek and Lukas Sekanina", booktitle = "17th International Symposium on Design and Diagnostics of Electronic Circuits Systems", title = "Evolutionary design of approximate multipliers under different error metrics", year = "2014", month = apr, pages = "135--140", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", abstract = "Approximate circuits are digital circuits which are intentionally designed in such a way that the specification is not met in terms of functionality in order to obtain some improvements in power consumption, performance or area, in comparison with fully functional circuits. In this paper, we propose to design approximate circuits using evolutionary design techniques. In particular, different error metrics are used to assess the circuit functionality. The proposed method begins with a fully functional circuit which is then intentionally degraded by Cartesian genetic programming (CGP) to obtain a circuit with a predefined error. In the second phase, CGP is used to minimise the number of gates or another error criterion. The effect of various error metrics on the search performance, area and power consumption is evaluated in the task of multiplier design.", DOI = "doi:10.1109/DDECS.2014.6868777", notes = "Also known as \cite{6868777}", } @Article{Vasicek:2014:ieeeTEC, author = "Zdenek Vasicek and Lukas Sekanina", title = "Evolutionary Approach to Approximate Digital Circuits Design", journal = "IEEE Transactions on Evolutionary Computation", year = "2015", volume = "19", number = "3", pages = "432--444", month = jun, keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Approximate Computing, Digital circuits, Population Seeding", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2014.2336175", size = "13 pages", abstract = "In approximate computing, the requirement of perfect functional behaved can be relaxed because some applications are inherently error resilient. Approximate circuits, which fall into the approximate computing paradigm, are designed in such a way that they do not fully implement the logic behavior given by the specification and hence their accuracy can be exchanged for lower area, delay or power consumption. In order to automate the design process, we propose to evolve approximate digital circuits which show a minimal error for a supplied amount of resources. The design process which is based on Cartesian Genetic Programming (CGP) can be repeated many times in order to obtain various tradeoffs between the accuracy and area. A heuristic seeding mechanism is introduced to CGP which allows for improving not only the quality of evolved circuits, but also reducing the time of evolution. The efficiency of the proposed method is evaluated for the gate as well as the functional level evolution. In particular, approximate multipliers and median circuits which show very good parameters in comparison with other available implementations were constructed by means of the proposed method.", notes = "also known as \cite{6848841}", } @InProceedings{Vasicek:2014:ICES, author = "Vojtech Mrazek and Zdenek Vasicek", title = "Acceleration of transistor-level evolution using Xilinx Zynq Platform", booktitle = "2014 IEEE International Conference on Evolvable Systems", year = "2014", pages = "9--16", address = "Orlando, FL, USA", month = "9-12 " # dec, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-4479-8", DOI = "doi:10.1109/ICES.2014.7008716", size = "8 pages", abstract = "the aim of this paper is to introduce a new accelerator developed to address the problem of evolutionary synthesis of digital circuits at transistor level. The proposed accelerator, based on recently introduced Xilinx Zynq platform, consists of a discrete simulator implemented in programmable logic and an evolutionary algorithm running on a tightly coupled embedded ARM processor. The discrete simulator was introduced in order to achieve a good trade-off between the precision and performance of the simulation of transistor-level circuits. The simulator is implemented using the concept of virtual reconfigurable circuit and operates on multiple logic levels which enables to evaluate the behavior of candidate transistor-level circuits at a reasonable level of detail. In this work, the concept of virtual reconfigurable circuit was extended to enable bidirectional data flow which represents the basic feature of transistor level circuits. According to the experimental evaluation, the proposed architecture speeds up the evolution in one order of magnitude compared to an optimized software implementation. The developed accelerator is used in the evolution of basic logic circuits having up to 5 inputs. It is shown that solutions competitive to the circuits obtained by conventional design methods can be discovered.", notes = "Also known as \cie{t7008716}", } @InProceedings{Vasicek:2014:ICESa, author = "Zdenek Vasicek and Lukas Sekanina", title = "How to evolve complex combinational circuits from scratch?", booktitle = "2014 IEEE International Conference on Evolvable Systems", year = "2014", pages = "133--140", address = "Orlando, FL, USA", month = "9-12 " # dec, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-4479-8", DOI = "doi:10.1109/ICES.2014.7008732", size = "8 pages", abstract = "One of the serious criticisms of the evolutionary circuit design method is that it is not suitable for the design of complex large circuits. This problem is especially visible in the evolutionary design of combinational circuits, such as arithmetic circuits, in which a perfect response is requested for every possible combination of inputs. This paper deals with a new method which enables us to evolve complex circuits from a randomly seeded initial population and without providing any information about the circuit structure to the evolutionary algorithm. The proposed solution is based on an advanced approach to the evaluation of candidate circuits. Every candidate circuit is transformed to a corresponding binary decision diagram (BDD) and its functional similarity is determined against the specification given as another BDD. The fitness value is the Hamming distance between the output vectors of functions represented by the two BDDs. It is shown in the paper that the BDD-based evaluation procedure can be performed much faster than evaluating all possible assignments to the inputs. It also significantly increases the success rate of the evolutionary design process. The method is evaluated using selected benchmark circuits from the LGSynth91 set. For example, a correct implementation was evolved for a 28-input frg1 circuit. The evolved circuit contains less gates (a 57percent reduction was obtained) than the result of a conventional optimization conducted by ABC.", notes = "Also known as \cite{7008732}", } @InProceedings{Vasicek:2015:EuroGPa, author = "Zdenek Vasicek", title = "{Cartesian GP} in Optimization of Combinational Circuits with Hundreds of Inputs and Thousands of Gates", booktitle = "18th European Conference on Genetic Programming", year = "2015", editor = "Penousal Machado and Malcolm I. Heywood and James McDermott and Mauro Castelli and Pablo Garcia-Sanchez and Paolo Burelli and Sebastian Risi and Kevin Sim", series = "LNCS", volume = "9025", publisher = "Springer", pages = "139--150", address = "Copenhagen", month = "8-10 " # apr, organisation = "EvoStar", note = "best paper award at EuroGP 2015", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, Evolutionary optimization, Combinational circuits, Formal verification", isbn13 = "978-3-319-16500-4", DOI = "doi:10.1007/978-3-319-16501-1_12", abstract = "A new approach to the evolutionary optimization of large digital circuits is introduced in this paper. In contrast with evolutionary circuit design, the goal of the evolutionary circuit optimization is to minimize the number of gates (or other non-functional parameters) of already functional circuit. The method combines a circuit simulation with a formal verification in order to detect the functional inequivalence of the parent and its offspring. An extensive set of 100 benchmarks circuits is used to evaluate the performance of the method as well as the evolutionary approach. Moreover, the role of neutral mutations in the context of evolutionary optimization is investigated. In average, the method enabled a 34percent reduction in gate count even if the optimizer was executed only for 15 minutes", notes = "Part of \cite{Machado:2015:GP} EuroGP'2015 held in conjunction with EvoCOP2015, EvoMusArt2015 and EvoApplications2015", } @InProceedings{Vasicek:2015:EuroGP, author = "Zdenek Vasicek and Lukas Sekanina", title = "Circuit Approximation Using Single and Multi-Objective {Cartesian GP}", booktitle = "18th European Conference on Genetic Programming", year = "2015", editor = "Penousal Machado and Malcolm I. Heywood and James McDermott and Mauro Castelli and Pablo Garcia-Sanchez and Paolo Burelli and Sebastian Risi and Kevin Sim", series = "LNCS", volume = "9025", publisher = "Springer", pages = "217--229", address = "Copenhagen", month = "8-10 " # apr, organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, Evolutionary design, Approximate computing, Approximate circuits, Multi-objective approach: Poster", isbn13 = "978-3-319-16500-4", DOI = "doi:10.1007/978-3-319-16501-1_18", abstract = "In this paper, the approximate circuit design problem is formulated as a multi-objective optimisation problem in which the circuit error and power consumption are conflicting design objectives. We compare multi-objective and single-objective Cartesian genetic programming in the task of parallel adder and multiplier approximation. It is analysed how the setting of the methods, formulating the problem as multi-objective or single-objective, and constraining the execution time can influence the quality of results. One of the conclusions is that the multi-objective approach is useful if the number of allowed evaluations is low. When more time is available, the single-objective approach becomes more efficient.", notes = "Part of \cite{Machado:2015:GP} EuroGP'2015 held in conjunction with EvoCOP2015, EvoMusArt2015 and EvoApplications2015", } @InProceedings{Vasicek:2015:GECCOcomp, author = "Zdenek Vasicek and Lukas Sekanina", title = "Evolutionary Approximation of Complex Digital Circuits", booktitle = "GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation", year = "2015", editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr", isbn13 = "978-1-4503-3488-4", keywords = "genetic algorithms, genetic programming, cartesian genetic programming: Poster", pages = "1505--1506", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739482.2764657", DOI = "doi:10.1145/2739482.2764657", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Circuit approximation has been developed in recent years as a viable method for constructing energy efficient electronic systems. An open problem is how to effectively obtain approximate circuits showing good compromises between key circuit parameters -- the error, power consumption, area and delay. The use of evolutionary algorithms in the task of circuit approximation has led to promising results; however, only relative simple circuit instances have been tackled because of the scalability problems of the evolutionary design method. We propose to replace the most time consuming part of the evolutionary design algorithm, i.e. the fitness calculation exponentially depending on the number of circuit inputs, by an equivalence checking algorithm operating over Binary Decision Diagrams (BDDs). Approximate circuits are evolved using Cartesian genetic programming which calls a BDD solver to calculate the fitness value of candidate circuits. The method enables to obtain approximate circuits consisting of tens of inputs and hundreds of gates and showing desired trade-off between key circuit parameters.", notes = "Also known as \cite{2764657} Distributed at GECCO-2015.", } @Article{Vasicek:2016:GPEM, author = "Zdenek Vasicek and Lukas Sekanina", title = "Evolutionary design of complex approximate combinational circuits", journal = "Genetic Programming and Evolvable Machines", year = "2016", volume = "17", number = "2", pages = "169--192", month = jun, keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, approximate circuit, Binary decision diagram, Fitness function", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-015-9257-1", size = "24 pages", abstract = "Functional approximation is one of the methods allowing designers to approximate circuits at the level of logic behaviour. By introducing a suitable functional approximation, power consumption, area or delay of a circuit can be reduced if some errors are acceptable in a particular application. As the error quantification is usually based on an arithmetic error metric in existing approximation methods, these methods are primarily suitable for the approximation of arithmetic and signal processing circuits. This paper deals with the approximation of general logic (such as pattern matching circuits and complex encoders) in which no additional information is usually available to establish a suitable error metric and hence the error of approximation is expressed in terms of Hamming distance between the output values produced by a candidate approximate circuit and the accurate circuit. We propose a circuit approximation method based on Cartesian genetic programming in which gate-level circuits are internally represented using directed acyclic graphs. In order to eliminate the well-known scalability problems of evolutionary circuit design, the error of approximation is determined by binary decision diagrams. The method is analysed in terms of computational time and quality of approximation. It is able to deliver detailed Pareto fronts showing various compromises between the area, delay and error. Results are presented for 16 circuits (with 27-50 inputs) that are too complex to be approximated by means of existing evolutionary circuit design methods.", } @InProceedings{Vasicek:2016:FPL, author = "Zdenek Vasicek and Lukas Sekanina", title = "Search-Based Synthesis of Approximate Circuits Implemented into FPGAs", booktitle = "26th International Conference on Field-Programmable Logic and Applications", year = "2016", editor = "Jason Anderson and Philip Brisk", address = "Lausanne, Switzerland", month = "29 " # aug # "-2 " # sep, organisation = "EPFL", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", URL = "http://www.fit.vutbr.cz/~sekanina/pubs.php.cs?id=11127", DOI = "doi:10.1109/FPL.2016.7577305", size = "4 pages", abstract = "Approximate computing is capable of exploiting the error resilience of various applications with the aim of improving their parameters such as performance, energy consumption and area on a chip. In this paper, a new systematic approach for the approximation and optimization of circuits intended for LUT-based field programmable gate arrays (FPGAs) is proposed. In order to deliver a good trade-off between the quality of processing and implementation cost, the method employs a genetic programming-based optimization engine. The circuits are internally represented and optimized at the gate level. The resulting LUT-based netlists are obtained using a commercial FPGA tool. In the experimental part, four commonly available commercial FPGA design tools (Xilinx ISE, Xilinx Vivado, Precision, and Quartus) and state-of-the-art academia circuit synthesis and optimization tool ABC are compared. The quality of approximated circuits is evaluated using relaxed equivalence checking by means of Binary decision diagrams. An important conclusion is that the improvements (i.e. area reductions) at the gate level are preserved by the FPGA design tools and thus the number of LUTs is also adequately reduced. It was shown that the current state-of-the-art synthesis tools provide (for some instances) the results that are far from an optimum. For example, a 40percent reduction (68 LUTs) was achieved for clmb benchmark circuit (Bus Interface) without introducing any error. Additional 43percent reduction can be obtained by introducing only a 0.1percent error.", notes = "slides http://fpl2016.org/slides/S1a_7.pptx http://fpl2016.org/ Also known as \cite{7577305}", } @InProceedings{Vasicek:2016:SSCI, author = "Z. Vasicek and V. Mrazek and L. Sekanina", booktitle = "2016 IEEE Symposium Series on Computational Intelligence (SSCI)", title = "Evolutionary functional approximation of circuits implemented into FPGAs", year = "2016", abstract = "In many applications it is acceptable to allow a small error in the result if significant improvements are obtained in terms of performance, area or energy efficiency. Exploiting this principle is particularly important for FPGA-based solutions that are inherently subject to many resources-oriented constraints. This paper devises an automated method that enables to approximate circuit components which are often implemented in multiple instances in FPGA-based accelerators. The approximation process starts with a fully functional gate-level circuit, which is approximated by means of Cartesian Genetic Programming reflecting the error metric and constraints formulated by the user. The evolved circuits are then implemented for a particular FPGA by common FPGA synthesis and optimisation tools. It is shown using five different FPGA tools, that the approximations obtained by CGP working at the gate level are preserved at the level look-up tables of FPGAs. The proposed method is evaluated in the task of 8-bit adder, 8-bit multiplier, 9-input median and 25-input median approximation.", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, EHW", DOI = "doi:10.1109/SSCI.2016.7850173", month = dec, notes = "Also known as \cite{7850173}", } @Article{Vasicek:2016:GPEMa, author = "Zdenek Vasicek and Vojtech Mrazek", title = "Trading between quality and non-functional properties of median filter in embedded systems", journal = "Genetic Programming and Evolvable Machines", year = "2017", volume = "18", number = "1", month = mar, pages = "45--82", keywords = "genetic algorithms, genetic programming, Genetic improvement, Cartesian genetic programming, Median function, Comparison network, Permutation principle, Median filter", ISSN = "1389-2576", language = "english", URL = "http://www.fit.vutbr.cz/research/view_pub.php.cs?id=11077", URL = "https://rdcu.be/dfOly", DOI = "doi:10.1007/s10710-016-9275-7", size = "38 pages", abstract = "Genetic improvement has been used to improve functional and non-functional properties of software. In this paper, we propose a new approach that applies a genetic programming (GP)-based genetic improvement to trade between functional and non-functional properties of existing software. The paper investigates possibilities and opportunities for improving non-functional parameters such as execution time, code size, or power consumption of median functions implemented using comparator networks. In general, it is impossible to improve non-functional parameters of the median function without accepting occasional errors in results because optimal implementations are available. In order to address this issue, we proposed a method providing suitable compromises between accuracy, execution time and power consumption. Traditionally, a randomly generated set of test vectors is employed so as to assess the quality of GP individuals. We demonstrated that such an approach may produce biased solutions if the test vectors are generated inappropriately. In order to measure the accuracy of determining a median value and avoid such a bias, we propose and formally analyse new quality metrics which are based on the positional error calculated using the permutation principle introduced in this paper. It is shown that the proposed method enables the discovery of solutions which show a significant improvement in execution time, power consumption, or size with respect to the accurate median function while keeping errors at a moderate level. Non-functional properties of the discovered solutions are estimated using data sets and validated by physical measurements on physical microcontrollers. The benefits of the evolved implementations are demonstrated on two real-world problems---sensor data processing and image processing. It is concluded that data processing software modules offer a great opportunity for genetic improvement. The results revealed that it is not even necessary to determine the median value exactly in many cases which helps to reduce power consumption or increase performance. The discovered implementations of accurate, as well as approximate median functions, are available as C functions for download and can be employed in a custom application http://www.fit.vutbr.cz/research/groups/ehw/median", } @InCollection{Vasicek:2017:miller, author = "Zdenek Vasicek", title = "Bridging the Gap Between Evolvable Hardware and Industry Using Cartesian Genetic Programming", booktitle = "Inspired by Nature: Essays Presented to Julian F. Miller on the Occasion of his 60th Birthday", publisher = "Springer", year = "2017", editor = "Susan Stepney and Andrew Adamatzky", volume = "28", series = "Emergence, Complexity and Computation", chapter = "2", pages = "39--55", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, EHW", isbn13 = "978-3-319-67996-9", DOI = "doi:10.1007/978-3-319-67997-6_2", abstract = "Advancements in technology developed in the early nineties have enabled researchers to successfully apply techniques of evolutionary computation in various problem domains. As a consequence, a new research direction referred to as evolvable hardware (EHW) focusing on the use of evolutionary algorithms to create specialized electronics has emerged. One of the goals of the early pioneers of EHW was to evolve complex circuits and overcome the limits of traditional design. Unfortunately, evolvable hardware found itself in a critical stage around 2010 and a very pessimistic future for EHW-based digital circuit synthesis was predicted. The problems solved by the community were of the size and complexity of that achievable in fifteens years ago and seldom compete with traditional designs. The scalability problem has been identified as one of the most difficult problems that researchers are faced with and it was not clear whether there existed a path forward that would allow the field to progress. Despite that, researchers have continued to investigate how to overcome the scalability issues and significant progress has been made in the area of evolutionary synthesis of digital circuits in recent years. The goal of this chapter is to summarize the progress in the evolutionary synthesis of gate-level digital circuits, and to identify the challenges that need to be addressed to enable evolutionary methods to penetrate into industrial practice.", notes = "part of \cite{miller60book} https://link.springer.com/bookseries/10624", } @InProceedings{7927241, author = "Zdenek Vasicek and Vojtech Mrazek and Lukas Sekanina", title = "Towards low power approximate {DCT} architecture for {HEVC} standard", booktitle = "Design, Automation Test in Europe Conference Exhibition (DATE), 2017", year = "2017", pages = "1576--1581", address = "Lausanne, Switzerland", month = "27-31 " # mar, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Discrete cosine transforms", ISSN = "1558-1101", DOI = "doi:10.23919/DATE.2017.7927241", size = "6 pages", abstract = "Video processing performed directly on IoT nodes is one of the most performance as well as energy demanding applications for current IoT technology. In order to support real-time high-definition video, energy-reduction optimizations have to be introduced at all levels of the video processing chain. This paper deals with an efficient implementation of Discrete Cosine Transform (DCT) blocks employed in video compression based on the High Efficiency Video Coding (HEVC) standard. The proposed multiplier less 4-input DCT implementations contain approximate adders and subtractors that were obtained using genetic programming. In order to manage the complexity of evolutionary approximation and provide formal guarantees in terms of errors of key circuit components, the worst and average errors were determined exactly by means of Binary decision diagrams. Under conditions of our experiments, approximate 4-input DCTs show better quality/power trade-offs than relevant implementations available in the literature. For example, 25percent power reduction for the same error was obtained in comparison with a recent highly optimized implementation.", } @InProceedings{Vasicek:2019:DATE, author = "Zdenek Vasicek and Vojtech Mrazek and Lukas Sekanina", title = "Automated Circuit Approximation Method Driven by Data Distribution", booktitle = "2019 Design, Automation Test in Europe Conference Exhibition (DATE)", year = "2019", editor = "Juergen Teich and Franco Fummi", pages = "96--101", address = "Florence", month = "25-29 " # mar, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-9819263-2-3", ISSN = "1530-1591", DOI = "doi:10.23919/DATE.2019.8714977", size = "6 pages", abstract = "We propose an application-tailored data-driven fully automated method for functional approximation of combinational circuits. We demonstrate how an application-level error metric such as the classification accuracy can be translated to a component-level error metric needed for an efficient and fast search in the space of approximate low-level components that are used in the application. This is possible by employing a weighted mean error distance (WMED) metric for steering the circuit approximation process which is conducted by means of genetic programming. WMED introduces a set of weights (calculated from the data distribution measured on a selected signal in a given application) determining the importance of each input vector for the approximation process. The method is evaluated using synthetic benchmarks and application-specific approximate MAC (multiply-and-accumulate) units that are designed to provide the best tradeoff between the classification accuracy and power consumption of two image classifiers based on neural networks.", notes = "also known as \cite{8714977}", } @InProceedings{vasicek:2024:CEC, author = "Zdenek Vasicek", title = "Automated Synthesis of Commutative Approximate Arithmetic Operators", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, Power demand, Neural networks, Evolutionary computation, Libraries, Logic, Adders, approximate circuit design, approximate arithmetic circuits", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10612202", abstract = "Approximate computing, leveraging the inherent resilience to errors, emerges as a promising strategy for reducing power consumption in digital systems. The primary objective of this paper is to introduce an efficient method based on Cartesian Genetic Programming for designing approximate arithmetic circuits with commutative property. Specifically, this work focuses on the design of 8-bit approximate multipliers and 32-bit approximate adders, both serving as foundational components for hardware accelerators in neural networks. We have identified that while the design of commutative approximate adders poses no issues for evolution, the design of commutative approximate multipliers represents a challenging problem causing the commonly used CGP stuck at highly sub-optimal solutions. In response to this challenge, we propose a novel application-specific mutation operator. This operator significantly enhances the efficiency of the search process, enabling the discovery of solutions that were previously unreachable. The achieved results revealed that imposing the requirement for a commutative property does not substantially compromise the quality-error trade-offs of the obtained approximate circuits, making the resulting Pareto front comparable to that of unconstrained designs.", notes = "also known as \cite{10612202} WCCI 2024", } @Article{Vasilakis:2013:CE, author = "Georgios A. Vasilakis and Konstantinos A. Theofilatos and Efstratios F. Georgopoulos and Andreas Karathanasopoulos and Spiros D. Likothanassis", title = "A Genetic Programming Approach for EUR/USD Exchange Rate Forecasting and Trading", journal = "Computational Economics", year = "2013", volume = "42", number = "4", pages = "415--431", keywords = "genetic algorithms, genetic programming, Evolutionary algorithms, Tournament selection, Exchange forecasting, EUR/USD exchange rates, Financial trading strategies", ISSN = "0927-7099", publisher = "Springer", DOI = "doi:10.1007/s10614-012-9345-8", URL = "http://results.ref.ac.uk/Submissions/Output/1762292", size = "17 pages", abstract = "The purpose of this article is to present a novel genetic programming trading technique in the task of forecasting the next day returns when trading the EUR/USD exchange rate based on the exchange rates of historical data. Aiming at testing its effectiveness, we benchmark the forecasting performance of our genetic programming implementation with three traditional strategies (naive strategy, MACD, and a buy & hold strategy) plus a hybrid evolutionary artificial neural network approach. The proposed genetic programming technique was found to demonstrate the highest trading performance in terms of annualised return and information ratio when compared to all other strategies which have been used. When more elaborate trading techniques, such as leverage, were combined with the examined models, the genetic programming approach still presented the highest trading performance. To the best of our knowledge, this is the first time that genetic programming is applied in the problem of effectively modelling and trading with the EUR/USD exchange rate. Our application now offers practitioners with an effective and extremely promising set of results when forecasting in the foreign exchange market. The developed genetic programming environment is implemented using the C++ programming language and includes a variation of the genetic programming algorithm with tournament selection.", uk_research_excellence_2014 = "D - Journal article", } @InProceedings{vassilev:1999:dce, author = "V. K. Vassilev and J. F. Miller and T. C. Fogarty", title = "Digital circuit evolution: the ruggedness and neutrality of two-bitmultiplier landscapes", booktitle = "IEE Half-day Colloquium on Evolutionary Hardware Systems", year = "1999", editor = "D. M. Harvest", pages = "6/1--6/4", address = "London, UK", publisher_address = "London", publisher = "IEE", note = "Ref. No. 1999/033", keywords = "genetic algorithms, EHW", URL = "http://citeseer.ist.psu.edu/196181.html", size = "4 pages", abstract = "The two-bit multiplier is a simple electronic circuit, small enough to be feasible for evolutionary design, and practically useful as a fundamental building block used in the synthesis of many digital systems. To attain understanding of the evolvability of this digital circuit, we consider its evolutionary design as a search on a fitness landscape. We study the structure of two-bit multiplier landscapes in terms of their ruggedness and neutrality. The motivation behind this research is to attain better understanding of how these characteristics are related to the feasibility of evolving digital circuits", notes = "INSPEC Accession Number: 6314637 Cited by \cite{Leier:2003:Etssoqp}", } @InProceedings{648391, author = "Vesselin K. Vassilev and Julian F. Miller and Terence C. Fogarty", title = "The Evolution of Computation in Co-evolving Demes of Non-uniform Cellular Automata for Global Synchronisation", booktitle = "Proceedings of the 5th European Conference on Advances in Artificial Life", year = "1999", ISBN = "3-540-66452-1", pages = "159--169", publisher = "Springer-Verlag", } @InProceedings{Vassilev:1999:eh, author = "Vesselin K. Vassilev and Julian F. Miller and Terence C. Fogarty", title = "On the Nature of Two-Bit Multiplier Landscapes", booktitle = "The First NASA/DoD Workshop on Evolvable Hardware", year = "1999", editor = "Adrian Stoica and Jason Lohn and Didier Keymeulen", pages = "36--45", address = "Pasadena, California", publisher_address = "1730 Massachusetts Avenue, N.W., Washington, DC 20036-1992, USA", month = "19-21 " # jul, organisation = "Jet Propulsion Laboratory, California Institute of Technology", publisher = "IEEE Computer Society", keywords = "genetic algorithms, genetic programming", ISBN = "0-7695-0256-3", URL = "http://citeseer.ist.psu.edu/196181.html", DOI = "doi:10.1109/EH.1999.785433", abstract = "The two-bit multiplier is a simple electronic circuit, small enough to be evolvable, and practically useful for the implementation of many digital systems. In this paper, we study the structure of the two-bit multiplier fitness landscapes generated by circuit evolution on an idealised model of a field-programmable gate array. The two-bit multiplier landscapes are challenging. The difficulty in studying these landscapes stems from the genotype representation which allows us to evolve the functionality and connectivity of an array of logic cells. Here, the genotypes are simply strings defined over two completely different alphabets. This makes the study of the corresponding landscapes much more involved. We outline a model for studying the two-bit multiplier landscapes and estimate the amplitudes derived from the Fourier transform of these landscapes. We show that the two-bit multiplier landscapes can be characterised in terms of subspaces, determined by the interactions between the genotype partitions.", notes = "EH1999 http://cism.jpl.nasa.gov/events/nasa_eh/ also known as \cite{785433}", } @InProceedings{vassilev99digital, author = "Vesselin K. Vassilev and Julian F. Miller and Terence C. Fogarty", title = "Digital Circuit Evolution and Fitness Landscapes", booktitle = "Proceedings of the Congress on Evolutionary Computation", year = "1999", editor = "Peter J. Angeline and Zbyszek Michalewicz and Marc Schoenauer and Xin Yao and Ali Zalzala", volume = "2", pages = "1299--1306", address = "Mayflower Hotel, Washington D.C., USA", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "6-9 " # jul, organisation = "Congress on Evolutionary Computation, IEEE / Neural Networks Council, Evolutionary Programming Society, Galesia, IEE", publisher = "IEEE Press", keywords = "circuit optimisation, digital circuits, evolutionary computation, field programmable gate arrays, sequences, alphabets, combinatorial problems, connectivity, correlation characteristics, digital circuit evolution, engineering problem, evolutionary search, field-programmable gate array, fitness landscapes, functionality, genotype representation, idealised model, logic cell array, optimisation problems, sequences", ISBN = "0-7803-5536-9 (softbound)", ISBN = "0-7803-5537-7 (Microfiche)", DOI = "doi:10.1109/CEC.1999.782595", abstract = "We study the fitness landscapes generated by evolving digital circuits using an idealised model of a field-programmable gate array. It appears that the fitness landscapes of this engineering problem are quite different from many recently studied landscapes, often defined over simplified combinatorial and optimisation problems. The difference stems from the genotype representation which allows us to evolve the functionality and connectivity of an array of logic cells. Here, the genotypes are sequences which are defined over two completely different alphabets. We propose a model for studying the structure of these landscapes and measure correlation characteristics of the landscapes. It is furthermore shown that the evolutionary search can be improved when the results of the analysis are taken into account", notes = "CEC-99 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. Library of Congress Number = 99-61143", } @InProceedings{Vassilev:1999:eh2, author = "Vesselin K. Vassilev and Julian F. Miller and Terence C. Fogarty", title = "Co-evolving Demes of Non-uniform Cellular Automata for Synchronisation", booktitle = "The First NASA/DoD Workshop on Evolvable Hardware", year = "1999", editor = "Adrian Stoica and Jason Lohn and Didier Keymeulen", pages = "111--119", address = "Pasadena, California", publisher_address = "1730 Massachusetts Avenue, N.W., Washington, DC 20036-1992, USA", month = "19-21 " # jul, organisation = "Jet Propulsion Laboratory, California Institute of Technology", publisher = "IEEE Computer Society", keywords = "genetic algorithms, genetic programming", ISBN = "0-7695-0256-3", DOI = "doi:10.1109/EH.1999.785442", abstract = "Emergent computation refers to systems in which global information processing appears as a result of the interactions among many components, each of which may be a system that exhibits an ability for emergent computation at a different level of self-organisation. In this paper, we employ a modification of cellular programming to evolve cellular machines for synchronisation. This allows global computation to occur by many local interactions among computational demes of interacting cells. The computational machine, derived from the non-uniform cellular automata model, consists of a grid of cells which are co-evolved in isolated demes. We describe experiments which show that demes can be co-evolved to perform non-trivial computation. We also analyse the mechanisms of computation within the different synchronising demes. Our results not only show that the co-evolution of demes is possible, but that they can attain high computational performance through co-operative action.", notes = "EH1999 http://cism.jpl.nasa.gov/events/nasa_eh/", } @Article{EC, author = "Vesselin K. Vassilev and Terence C. Fogarty and Julian F. Miller", title = "Information Characteristics and the Structure of Landscapes", journal = "Evolutionary Computation", volume = "8", number = "1", year = "2000", pages = "31--60", publisher = "MIT Press", month = "Spring", keywords = "genetic algorithms, genetic programming", ISSN = "1063-6560", DOI = "doi:10.1162/106365600568095", size = "30 pages", abstract = "Various techniques for statistical analysis of the structure of fitness landscapes have been proposed. An important feature of these techniques is that they study the ruggedness of landscapes by measuring their correlation characteristics. This paper proposes a new information analysis of fitness landscapes. The underlying idea is to consider a fitness landscape as an ensemble of objects that are related to the fitness of neighbouring points. Three information characteristics of the ensemble are defined and studied. They are termed: information content, partial information content, and information stability. The information characteristics of a range of landscapes with known correlation features are analysed in an attempt to reveal the advantages of the information analysis. We show that the proposed analysis is an appropriate tool for investigating the structure of fitness landscapes.", notes = "Two-Bit Multiplier. NK landscapes PMID: 10753230 Chaitin, 1978. Autocorrelation for two types of Nk. Clustering of local optima. incidence matrix, information contents as entropy S, modality", } @InProceedings{Vassilev:2000:GECCO, author = "Vesselin K. Vassilev and Julian F. Miller", title = "Embedding Landscape Neutrality to Build a Bridge from the Conventional to a More Efficient Three-bit Multiplier Circuit", pages = "539", year = "2000", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)", editor = "Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer", address = "Las Vegas, Nevada, USA", publisher_address = "San Francisco, CA 94104, USA", month = "10-12 " # jul, keywords = "genetic algorithms, genetic programming, poster", ISBN = "1-55860-708-0", URL = "http://gpbib.cs.ucl.ac.uk/gecco2000/EH184.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco2000/EH184.ps", size = "1 page", notes = "School of Computing, Napier University, Edinburgh, EH14 1DJ, UK A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of \cite{whitley:2000:GECCO}", } @InProceedings{ICES2000, author = "Vesselin K. Vassilev and Julian F. Miller", title = "The Advantages of Landscape Neutrality in Digital Circuit Evolution", booktitle = "Proceedings of the Third International Conference on Evolvable Systems", year = "2000", ISBN = "3-540-67338-5", pages = "252--263", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, Fitness Landscape, Digital Circuit, Neutral Network, ANN, Evolutionary Design, Neutral Mutation", URL = "https://rdcu.be/dgo9d", DOI = "doi:10.1007/3-540-46406-9_25", size = "12 pages", abstract = "The paper studies the role of neutrality in the fitness landscapes associated with the evolutionary design of digital circuits and particularly the three-bit binary multiplier. For the purpose of the study, digital circuits are evolved extrinsically on an array of logic cells. To evolve on an array of cells, a genotype-phenotype mapping has been devised by which neutrality can be embedded in the resulting fitness landscape. It is argued that landscape neutrality is beneficial for digital circuit evolution.", } @InProceedings{Vassilev:2000:eh1, author = "Vesselin K. Vassilev and Julian F. Miller", title = "Scalability Problems of Digital Circuit Evolution: Evolvability and Efficient Designs", booktitle = "The Second NASA/DoD workshop on Evolvable Hardware", year = "2000", editor = "Jason Lohn and Adrian Stoica and Didier Keymeulen", pages = "55--64", address = "Palo Alto, California", publisher_address = "1730 Massachusetts Avenue, N.W., Washington, DC, 20036-1992, USA", month = "13-15 " # jul, organisation = "Jet Propulsion Laboratory, California Institute of Technology", publisher = "IEEE Computer Society", keywords = "genetic algorithms, genetic programming", ISBN = "0-7695-0762-X", DOI = "doi:10.1109/EH.2000.869342", abstract = "A major problem in the evolutionary design of combinational circuits is the problem of scale. This refers to the design of electronic circuits in which the number of gates required to implement the optimal circuit is too high to search the space of all designs in reasonable time, even by evolution. The reason is twofold: firstly, the size of the search space becomes enormous as the number of gates required to implement the circuit is increased, and secondly, the time required to calculate the fitness of a circuit grows as the size of the truth table of the circuit. We study the evolutionary design of combinational circuits, particularly the three-bit multiplier circuit, in which the basic building blocks are small sub-circuits, modules inferred from other evolved designs. The structure of the resulting fitness landscapes is studied and it is shown that in general the principles of evolving digital circuits are scalable. Thus to evolve digital circuits using modules is faster, since the building blocks of the circuit are sub-circuits rather than two-input gates. This can also be a disadvantage, since the number of gates of the evolved designs grows as the size of the modules used.", notes = "EH2000 http://ic-www.arc.nasa.gov/ic/eh2000/index.html", } @InProceedings{Vassilev:2000:eh2, author = "Vesselin K. Vassilev and Dominic Job and Julian F. Miller", title = "Towards the Automatic Design of More Efficient Digital Circuits", booktitle = "The Second NASA/DoD workshop on Evolvable Hardware", year = "2000", editor = "Jason Lohn and Adrian Stoica and Didier Keymeulen", pages = "151--160", address = "Palo Alto, California", publisher_address = "1730 Massachusetts Avenue, N.W., Washington, DC, 20036-1992, USA", month = "13-15 " # jul, organisation = "Jet Propulsion Laboratory, California Institute of Technology", publisher = "IEEE Computer Society", keywords = "genetic algorithms, genetic programming", ISBN = "0-7695-0762-X", DOI = "doi:10.1109/EH.2000.869353", abstract = "We introduce a new methodology of evolving electronic circuits by which the process of evolutionary design is guaranteed to produce a functionally correct solution. The method employs a mapping to represent an electronic circuit on an array of logic cells that is further encoded within a genotype. The mapping is many-to-one and thus there are many genotypes that have equal fitness values. Genotypes with equal fitness values define subgraphs in the resulting fitness landscapes referred to as neutral networks. This is further used in the design of a neutral network that connects the conventional with other more efficient designs. To explore such a network a navigation strategy is defined by which the space of all functionally correct circuits can be explored. We show that very efficient digital circuits can be obtained by evolving from the conventional designs. Results for several binary multiplier circuits such as the three and four-bit multipliers are reported. The evolved solution for the three-bit multiplier consists of 23 two-input logic gates that in terms of number of two-input gates used is 23: 3 percentages more efficient than the most efficient known conventional design. The logic operators required to implement this circuit are 14 ANDs, 9 XORs, and 2 inversions (NOT). The evolved four-bit multiplier consists of 57 two-input logic gates that are 10: 9 percentages more efficient (in terms of number of two-input gates used) than the most efficient known conventional design. The optimal size of the target circuits is also studied by measuring the length of the neutral walks from the obtained designs.", notes = "EH2000 http://ic-www.arc.nasa.gov/ic/eh2000/index.html", } @PhdThesis{Vassilev:thesis, author = "Vesselin K. Vassilev", title = "Fitness landscapes and search in the evolutionary design of digital circuits", school = "Napier University", year = "2000", address = "Edinburgh, UK", keywords = "genetic algorithms, genetic programming, EHW", broken = "https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.322508", notes = "Also known as \cite{DBLP:phd/ethos/Vassilev00} Oct 2024 ethos.bl.uk 'We are continuing to experience a major technology outage as a result of a cyber-attack' uk.bl.ethos.322508 ISNI: 0000 0001 3543 2039", } @InCollection{vassilev2003, author = "Vesselin K. Vassilev and Terence C. Fogarty and Julian F. Miller", title = "Smoothness, ruggedness and neutrality of fitness landscapes: from theory to application", booktitle = "Advances in evolutionary computing: theory and applications", editor = "Ashish Ghosh and Shigeyoshi Tsutsui", year = "2003", pages = "3--44", publisher = "Springer-Verlag New York, Inc.", ISBN = "3-540-43330-9", DOI = "doi:10.1007/978-3-642-18965-4_1", abstract = "The theory of fitness landscapes has been developed to provide a suitable mathematical framework for studying the evolvability of a variety of complex systems. In evolutionary computation the notion of evolvability refers to the efficiency of evolutionary search. It has been shown that the structure of a fitness landscape affects the ability of evolutionary algorithms to search. Three characteristics specify the structure of landscapes. These are the landscape smoothness, ruggedness and neutrality. The interplay of these characteristics plays a vital role in evolutionary search. This has motivated the appearance of a variety of techniques for studying the structure of fitness landscapes. An important feature of these techniques is that they characterize the landscapes by their smoothness and ruggedness, ignoring the existence of neutrality. Perhaps, the reason for this is that the role of neutrality in evolutionary search is still poorly understood.", } @InProceedings{vassiliadis_performance_2011, author = "Vassilios Vassiliadis and Nikolaos Thomaidis and George Dounias", title = "On the {Performance} and {Convergence} {Properties} of {Hybrid} {Intelligent} {Schemes}: {Application} on {Portfolio} {Optimization} {Domain}", year = "2011", booktitle = "Applications of {Evolutionary} {Computation} {EVOFIN}-2011, Part 2", editor = "Cecilia Di Chio and Anthony Brabazon and Gianni A. Di Caro and Rolf Drechsler and Muddassar Farooq and Joern Grahl and Gary Greenfield and Christian Prins and Juan Romero and Giovanni Squillero and Ernesto Tarantino and Andrea G. B. Tettamanzi and Neil Urquhart and A. Sima Uyar", volume = "6625", series = "Lecture Notes in Computer Science", pages = "131--140", address = "Turin, Italy", month = apr, publisher = "Springer", keywords = "genetic algorithms, genetic programming, continuous ACO, portfolio optimization", isbn13 = "978-3-642-20520-0", URL = "https://link.springer.com/chapter/10.1007/978-3-642-20520-0_14", DOI = "doi:10.1007/978-3-642-20520-0_14", abstract = "Hybrid intelligent algorithms, especially those who combine nature-inspired techniques, are well known for their searching abilities in complex problem domains and their performance. One of their main characteristic is that they manage to escape getting trapped in local optima. In this study, two hybrid intelligent schemes are compared both in terms of performance and convergence ability in a complex financial problem. Particularly, both algorithms use a type of genetic algorithm for asset selection and they differ on the technique applied for weight optimization: the first hybrid uses a numerical function optimization method, while the second one uses a continuous ant colony optimization algorithm. Results indicate that there is great potential in combining characteristics of nature-inspired algorithms in order to solve NP-hard optimization problems.", } @InProceedings{vassiliadis_performance_2011-1, author = "Vassilios Vassiliadis and Vassiliki Bafa and George Dounias", title = "On the performance of a hybrid genetic algorithm: application on the portfolio management problem", booktitle = "AFE-11", year = "2011", pages = "70--78", address = "Samos, Greece", keywords = "genetic algorithms, evolutionary mechanisms, financial heuristics, hybrid algorithm, portfolio optimization", URL = "http://mde-lab.aegean.gr/images/stories/docs/CC77.pdf", size = "8 pages", abstract = "In this study, a hybrid intelligent scheme which combines a genetic algorithm with a numerical optimization technique is applied to a cardinality-constrained portfolio management problem. Specifically, the objective function aims at maximizing the Sortino Ratio with a constraint on tracking error volatility. What is more, results from the proposed algorithm are compared with other financial and intelligent heuristics, such as financial rule-of-thumbs and simulated annealing. In order to obtain a better insight on the hybrid's behaviour, out-of-sample results are shown. The contribution of this work is twofold. Firstly, some useful conclusions regarding the performance of the proposed hybrid algorithm are drawn, based on experimental simulations. Secondly, some basic points, based on the comparison between the proposed algorithm and the benchmark heuristics, are highlighted. Finally, concerning the cardinality-constrained optimization problem, financial implications are discussed in some extent.", notes = "Not GP. http://mde-lab.aegean.gr/research-material", } @InProceedings{vassilopoulos:2007:EANEMS, author = "Anastasios P. Vassilopoulos and Efstratios F. Georgopoulos and Thomas Keller", title = "Genetic Programming in Modelling of Fatigue Life of Composite Materials", booktitle = "Experimental Analysis of Nano and Engineering Materials and Structures", year = "2007", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-1-4020-6239-1_99", DOI = "doi:10.1007/978-1-4020-6239-1_99", } @Article{Vassilopoulos20081634, author = "Anastasios P. Vassilopoulos and Efstratios F. Georgopoulos and Thomas Keller", title = "Comparison of genetic programming with conventional methods for fatigue life modeling of {FRP} composite materials", journal = "International Journal of Fatigue", volume = "30", number = "9", pages = "1634--1645", year = "2008", ISSN = "0142-1123", DOI = "doi:10.1016/j.ijfatigue.2007.11.007", URL = "http://www.sciencedirect.com/science/article/B6V35-4R6B2M8-1/2/1d030f0598bc4659ba08b55c82930264", keywords = "genetic algorithms, genetic programming, Fatigue, Life prediction, S-N curve", abstract = "Genetic programming is used in this paper for modeling the fatigue life of several fiber-reinforced composite material systems. It is shown that if the genetic programming tool is adequately trained, it can produce theoretical predictions that compare favorably with corresponding predictions by other, conventional methods for the interpretation of fatigue data. For the comparison of results, curves produced by the genetic programming tool are plotted together with curves produced by three other commonly used methods for the analysis of composite material fatigue data: linear regression, Whitney's Weibull statistics and Sendeckyj's wear-out model. The modeling accuracy of this computational technique, whose application for this purpose is novel, is very high. The proposed modeling technique presents certain advantages compared to conventional methods. The new technique is a stochastic process that leads straight to a multi-slope S-N curve that follows the trend of the experimental data, without the need for any assumptions.", } @InProceedings{Vassilopoulos:2009:ICCM, author = "Anastasios P. Vassilopoulos and Thomas Keller", title = "Modeling of the fatigue life of adhesively-bonded {FRP} joints with genetic programming", booktitle = "17th International Conference on Composite Materials, ICCM-17", year = "2009", pages = "F13:13", address = "Edinburgh, UK", month = "27-31 " # jul, organisation = "IOM Communications Ltd on behalf of The British Composites Society, a division of The Institute of Materials, Minerals and Mining", note = "CD-ROM", keywords = "genetic algorithms, genetic programming, temperature, Fatigue, life prediction, adhesive joints, Fibre-reinforced plastic, Glass Fiber-reinforced plastic", bibsource = "OAI-PMH server at infoscience.epfl.ch", language = "en", oai = "oai:infoscience.epfl.ch:146571", URL = "http://infoscience.epfl.ch/record/146571", URL = "http://www.iccm-central.org/Proceedings/ICCM17proceedings/Themes/Behaviour/FATIGUE%20OF%20COMPOSITES/F13%2013%20Vassilopoulos.pdf", size = "9 pages", abstract = "A novel computational technique, called genetic programming, is used in this work to model the fatigue life of adhesively-bonded FRP joints subjected to tensile fatigue loading under different environmental conditions. It is proved that genetic programming can effectively interpret fatigue data, without the need for the adoption of any assumptions, and can accurately model fatigue life of the material system under investigation.", notes = "Since recently, artificial neural network was the only method that was used for the fatigue life modeling of composite materials and structures. A novel, in this field, computational technique, called genetic programming, is used in this work to model the fatigue life of adhesively-bonded FRP joints subjected to tensile fatigue loading under different environmental conditions.", notes = "Jan 2021 broken www.iccm17.org", } @InCollection{VASSILOPOULOS:2020:FLPCCSE, author = "Anastasios P. Vassilopoulos and Efstratios F. Georgopoulos", title = "10 - Computational intelligence methods for the fatigue life modeling of composite materials", editor = "Anastasios P. Vassilopoulos", booktitle = "Fatigue Life Prediction of Composites and Composite Structures (Second Edition)", publisher = "Woodhead Publishing", edition = "Second Edition", pages = "349--383", year = "2020", series = "Woodhead Publishing Series in Composites Science and Engineering", isbn13 = "978-0-08-102575-8", DOI = "doi:10.1016/B978-0-08-102575-8.00010-3", URL = "http://www.sciencedirect.com/science/article/pii/B9780081025758000103", keywords = "genetic algorithms, genetic programming, Fatigue, Composites, Artificial neural network, ANFIS, S-N curves", abstract = "Novel computational methods such as artificial neural networks, adaptive neuro-fuzzy inference systems and genetic programming are used in this chapter for the modeling of the nonlinear behavior of composite laminates subjected to constant amplitude loading. The examined computational methods are stochastic nonlinear regression tools, and can therefore be used to model the fatigue behavior of any material, provided that sufficient data are available for training. They are material independent methods that simply follow the trend of the available data, in each case giving the best estimate of their behavior. Application on a wide range of experimental data gathered after fatigue testing glass/epoxy and glass/polyester laminates proved that their modeling ability compares favorably with, and is to some extent superior to, other modeling techniques", } @Article{Vastl:2024:ACC, author = "Martin Vastl and Jonas Kulhanek and Jiri Kubalik and Erik Derner and Robert Babuska", journal = "IEEE Access", title = "{SymFormer:} End-to-End Symbolic Regression Using Transformer-Based Architecture", year = "2024", volume = "12", pages = "37840--37849", abstract = "Many real-world systems can be naturally described by mathematical formulas. The task of automatically constructing formulas to fit observed data is called symbolic regression. Evolutionary methods such as genetic programming have been commonly used to solve symbolic regression tasks, but they have significant drawbacks, such as high computational complexity. Recently, neural networks have been applied to symbolic regression, among which the transformer-based methods seem to be most promising. After training a transformer on a large number of formulas, the actual inference, i.e., finding a formula for new, unseen data, is very fast (in the order of seconds). This is considerably faster than state-of-the-art evolutionary methods. The main drawback of transformers is that they generate formulas without numerical constants, which have to be optimised separately, yielding suboptimal results. We propose a transformer-based approach called SymFormer, which predicts the formula by outputting the symbols and the constants simultaneously. This helps to generate formulas that fit the data more accurately. In addition, the constants provided by SymFormer serve as a good starting point for subsequent tuning via gradient descent to further improve the model accuracy. We show on several benchmarks that SymFormer outperforms state-of-the-art methods while having faster inference.", keywords = "genetic algorithms, genetic programming, Transformers, Mathematical models, Vectors, Symbols, Decoding, Optimisation, Predictive models, Neural networks, ANN, Computational complexity, Benchmark testing, Regression analysis, Symbolic regression", DOI = "doi:10.1109/ACCESS.2024.3374649", ISSN = "2169-3536", notes = "Also known as \cite{10462113}", } @Article{Vasu:2011:IJDMMM, title = "A hybrid under-sampling approach for mining unbalanced datasets: applications to banking and insurance", author = "Madireddi Vasu and Vadlamani Ravi", publisher = "Inderscience Publishers", year = "2011", month = mar # "~03", volume = "3", keywords = "genetic algorithms, genetic programming, insurance fraud detection, credit card churn prediction, data mining; unbalanced datasets, machine learning, banking, classifiers, classifier performance, k-means clustering, support vector machines, SVM, logistic regression, multilayer perceptron, radial basis function networks, RBF neural networks, GMDH, decision trees", ISSN = "1759-1171", bibsource = "OAI-PMH server at www.inderscience.com", journal = "Int. J. of Data Mining and Modelling and Management", issue = "1", language = "eng", pages = "75--105", relation = "ISSN online: 1759-1171 ISSN print: 1759-1163", rights = "Inderscience Copyright", source = "IJDMMM (2011), Vol 3 Issue 1, pp 75 - 105", URL = "http://www.inderscience.com/link.php?id=38812", DOI = "doi:10.1504/IJDMMM.2011.038812", abstract = "In solving unbalanced classification problems, machine learning algorithms are overwhelmed by the majority class and consequently misclassify the minority class observations. Here, we propose a hybrid under-sampling approach to improve the performance of classifiers. The proposed approach first employs k-reverse nearest neighbour (kRNN) method to detect the outliers from majority class. After removing the outliers, using K-means clustering, K-clusters are selected to further reduce the influence of the majority class. Then, we employed support vector machine (SVM), logistic regression (LR), multi layer perceptron (MLP), radial basis function network (RBF), group method of data handling (GMDH), genetic programming (GP) and decision tree (J48) for classification purpose. The effectiveness of the proposed approach was demonstrated on datasets taken from insurance fraud detection and credit card churn in banking domain. Ten-fold cross validation method was used in the study. It is observed that the proposed approach improved the performance of the classifiers.", } @InProceedings{Vatamanu:2015:SYNASC, author = "Cristina Vatamanu and Dragos Gavrilut and Razvan Benchea and Henri Luchian", booktitle = "17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)", title = "Feature Extraction Using Genetic Programming with Applications in Malware Detection", year = "2015", pages = "224--231", abstract = "This paper extends the authors' previous research on a malware detection method, focusing on improving the accuracy of the perceptron based - One Side Class Perceptron algorithm via the use of Genetic Programming. We are concerned with finding a proper balance between the three basic requirements for malware detection algorithms: (a) that their training time on large datasets falls below acceptable upper limits; (b) that their false positive rate (clean/legitimate files/software wrongly classified as malware) is as close as possible to 0 and (c) that their detection rate is as close as possible to 1. When the first two requirements are set as objectives for the design of detection algorithms, it often happens that the third objective is missed: the detection rate is low. This study focuses on improving the detection rate while preserving the small training time and the low rate of false positives. Another concern is to use the perceptron-based algorithm's good performance on linearly separable data, by extracting features from existing ones. In order to keep the overall training time low, the huge search space of possible extracted features is efficiently explored in terms of time and memory foot-print using Genetic Programming; better separability is sought for. For experiments we used a dataset consisting of 350,000 executable files with an initial set of 300 Boolean features describing each of them. The feature-extraction algorithm is implemented in a parallel manner in order to cope with the size of the data set. We also tested different ways of controlling the growth in size of the variable-length chromosomes. The experimental results show that the features produced by this method are better than the best ones obtained through mapping allowing for an increase in detection rate.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SYNASC.2015.43", month = sep, notes = "Romania Bitdefender Anti-virus Res. Lab., Al. I. Cuza Univ. of Iasi, Iasi, Romania Also known as \cite{7426087}", } @Article{Vatankhah:2014:JPSE, author = "Ali R. Vatankhah", title = "Comment on 'Gene expression programming analysis of implicit Colebrook-White equation in turbulent flow friction factor calculation'", journal = "Journal of Petroleum Science and Engineering", year = "2014", volume = "124", month = dec, pages = "402--405", ISSN = "0920-4105", DOI = "doi:10.1016/j.petrol.2013.12.001", URL = "http://www.sciencedirect.com/science/article/pii/S0920410513003495", keywords = "genetic algorithms, genetic programming, explicit approximations, Colebrook friction factor, Darcy-Weisbach equation, turbulent flow, fixed point method", abstract = "Recently, it is investigated \cite{Samadianfard2012} the potential of genetic programming based technique in estimating flow friction factor in comparison with the most currently available explicit alternatives to the Colebrook's equation. Using iterative solution of the Colebrook's equation which is accurate to six significant digits, this discussion showed that the proposed approximation by the author for friction factor is not very accurate (the errors increase up to 7.374percent), thus it is proposed two new accurate approximations (with maximum error less than 0.022percent and 0.008percent) for estimating flow friction factor.", } @InProceedings{Vaughan:2015:SAI, author = "Neil Vaughan", booktitle = "Science and Information Conference (SAI 2015)", title = "Swapping algorithm and meta-heuristic solutions for combinatorial optimization n-queens problem", year = "2015", pages = "102--104", month = jul, DOI = "doi:10.1109/SAI.2015.7237132", abstract = "This research proposes the swapping algorithm a new algorithm for solving the n-queens problem, and provides data from experimental performance results of this new algorithm. A summary is also provided of various meta-heuristic approaches which have been used to solve the n-queens problem including neural networks, evolutionary algorithms, genetic programming, and recently Imperialist Competitive Algorithm (ICA). Currently the Cooperative PSO algorithm is the best algorithm in the literature for finding the first valid solution. Also the research looks into the effect of the number of hidden nodes and layers within neural networks and the effect on the time taken to find a solution. This paper proposes a new swapping algorithm which swaps the position of queens.", notes = "Not GP Fac. of Sci. & Technol., Bournemouth Univ. (BU) Bournemouth, Bournemouth, UK Also known as \cite{7237132}", } @Article{Vaupotic:2006:JAMME, author = "Bostjan Vaupotic and Miha Kovacic and Mirko Ficko and Joze Balic", title = "Concept of automatic programming of NC machine for metal plate cutting by genetic algorithm method", journal = "Journal of Achievements in Materials and Manufacturing Engineering", year = "2006", volume = "14", number = "1-2", pages = "131--139", month = jan # "-" # feb, keywords = "genetic algorithms", URL = "http://www.journalamme.org/papers_cams05/199.pdf", size = "9 pages", abstract = "This paper presents the concept of automatic programs of the NC machine for metal plate cutting by the genetic algorithm method. The paper is limited to automatic creation of NC programs for two-dimensional cutting of material by means of adaptive heuristic search algorithms. Automatic creation of NC programs in laser cutting of materials combines the CAD concepts, the recognition of features and creation and optimisation of NC programs. The proposed intelligent system is capable of recognising automatically the nesting of products in the layout, to determine the incisions and sequences of cuts forming the laid out products. The position of incision is determined at the relevant places on the cut. The system is capable of finding the shortest path between individual cuts and to record the NC program. It would be appropriate to orient future researches towards conceiving an improved system for three-dimensional cutting with optional determination of positions of incisions, with the capability to sense collisions and with optimization of the speed and acceleration during cutting. The proposed system assures automatic preparation of NC program without NC programmer. The proposed concept shows a higher degree of universality, efficiently and reliability. It can be simply adapted to other NC-machines. [COBISS.SI-ID 10525718]", notes = "not GP? Permutation GA, stock cutting JAMME info@journalamme.org", } @InProceedings{vavak:1998:pGAvlssrrfeg, author = "F. Vavak and K. A. Jukes and T. C. Fogarty", title = "Performance of a Genetic Algorithm with Variable Local Search Range Relative to Frequency of the Environmental Changes", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "602--608", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms", notes = "SGA-98", } @InProceedings{Vaverka:2016:SSCI, author = "Filip Vaverka and Radek Hrbacek and Lukas Sekanina", booktitle = "2016 IEEE Symposium Series on Computational Intelligence (SSCI)", title = "Evolving component library for approximate high level synthesis", year = "2016", address = "Athens", month = "6-9 " # dec, keywords = "genetic algorithms, genetic programming, cartesian genetic programming", DOI = "doi:10.1109/SSCI.2016.7850168", abstract = "An approximate computing approach has recently been introduced for high level circuit synthesis (HLS) in order to make good use of approximate circuits at system and block level. It is assumed in HLS algorithms that a component library containing various implementations of elementary circuit components is available. An open problem is how to construct such a component library in the context of approximate computing, where the component's error is a new design variable and hence many compromise implementations exist for a given component. In this paper, we first introduce a multi-objective Cartesian genetic programming method to create a comprehensive component library containing hundreds of Pareto optimal implementations of approximate 8-bit adders and multipliers, where the error, area and delay are simultaneously optimised. Another multi-objective evolutionary algorithm is employed to solve the so called binding problem of HLS, in which suitable approximate components are assigned to nodes of the data flow graph describing a complex digital circuit. Two approaches are then proposed and compared in order to reduce the size of the library of approximate components. It is shown that a random sub-sampling of the component library provides satisfactory results in the context of our study. The proposed methods are evaluated using two benchmark circuits - the reduce (sum) and DCT circuits.", notes = "Also known as \cite{7850168}", } @Article{VAZINIMODABBER:2021:RE, author = "Hossein {Vazini Modabber} and Mohammad Hasan {Khoshgoftar Manesh}", title = "Optimal exergetic, exergoeconomic and exergoenvironmental design of polygeneration system based on gas Turbine-Absorption Chiller-Solar parabolic trough collector units integrated with multi-effect desalination-thermal vapor compressor- reverse osmosis desalination systems", journal = "Renewable Energy", volume = "165", pages = "533--552", year = "2021", ISSN = "0960-1481", DOI = "doi:10.1016/j.renene.2020.11.001", URL = "https://www.sciencedirect.com/science/article/pii/S0960148120317262", keywords = "genetic algorithms, genetic programming, Polygeneration, Desalination, Multi objective optimization, Economic, Environmental", abstract = "In the recent years, considering aridity problem of the country and high potential of desalinating the seawater in the southern and northern coasts, focusing on the poly-generation cycles of power and distillate with the lowest possible cost and emission of the pollutants has been increased. In this research, the study of the trigeneration system of power, heat and desal water located in the Qeshm island has been conducted. The potentials of the existing unit have been evaluated and the different scenarios have been proposed to improve the performance of the system. Setting the inlet air cooling system up to the gas cycle is one of the schemes proposed to diminish the undesirable effects of the ambient conditions. Also integrating the existing MED desalination unit with RO system and using solar thermal collector field in order to improve the performance of the system and to propose the optimal scheme for the operating unit has been investigated. The conventional and the advanced exergy, exergo-economic and exergo-environmental analyzes based on life cycle assessment have been used to evaluate the existing and the proposed systems. The multi objective optimization process has been performed to maximize the exergetic efficiency and to minimize the cost and environmental impact of the product of the system. Considering the complexity of the problem, using the genetic programming to generate the objective functions has been conducted. In order to apply the optimization process on the existing and the proposed system, multi objective genetic algorithm (MOGA) and multi objective water cycle algorithm (MOWCA) have been used. Multi objective water cycle algorithm has been performed for the first time at the energy problems in this research. The results shows that using the inlet air cooling system has decreased the fuel consumption, total costs and environmental impacts of the system by 1019 tons/year, 914 k$/year and 197 kpts/year, respectively. Also integrating the existing unit with the solar thermal collector field to achieve an increase of 4.77percent in efficiency of the system has been investigated. Five different types of STC at two configurations have been evaluated and the thermodynamic, economic and environmental optimal solution has led to calculate 9081 m2 area of required collectors. Using RO desalination unit in the downstream of MED has prevented the energy leakage and increased the distillate production rate by 255.12 tons/h. The optimization processes using two methods shows the capability of the MOWCA and lead to an increase of 12.66percent in exergetic efficiency and decreased the total cost and environmental impact rate of the system by 47.4$/h and 49.2 pts/h, respectively", } @Article{Vazquez-Rodriguez:2006:AAI, author = "Jose Antonio Vazquez {Rodriguez} and Abdellah Salhi", title = "Hybrid Evolutionary Methods for the solution of Complex Scheduling Problems", journal = "Research in Computing Sciences", year = "2006", volume = "20 Advances in Artificial Intelligence", pages = "17--28", keywords = "genetic algorithms, genetic programming", editors = "A. Gelbukh and S. Torres and I. Lopez", URL = "https://pdfs.semanticscholar.org/60d4/b3eadbbd8281a600a88ff12b35b2d5cc3838.pdf", size = "12 pages", notes = "http://www.rcs.cic.ipn.mx/ May be Research on Computing Science, ISSN 1665-9899", } @InCollection{Vazquez-Rodriguez:2007:EvoSchd, author = "Jose Antonio {Vazquez Rodriguez} and Abdellah Salhi", title = "A Robust Meta-Hyper-Heuristic Approach to Hybrid Flow-Shop Scheduling", booktitle = "Evolutionary Scheduling", publisher = "Springer", year = "2007", editor = "Keshav P. Dahal and Kay Chen Tan and Peter I. Cowling", volume = "49", series = "Studies in Computational Intelligence", pages = "125--142", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-48584-1", DOI = "doi:10.1007/978-3-540-48584-1_5", abstract = "Combining meta-heuristics and specialised methods is a common strategy to generate effective heuristics. The inconvenience of this practice, however, is that, often, the resulting hybrids are ineffective on related problems. Moreover, frequently, a high cost must be paid to develop such methods. To overcome these limitations, the idea of using a hyper-heuristic to generate information to assist a meta-heuristic, is explored. The devised approach is tested on the Hybrid Flow Shop (HFS) scheduling problem in 8 different forms, each with a different objective function. Computational results suggest that this approach is effective on all 8 problems considered. Its performance is also comparable to that of specialised methods for HFS with a particular objective function.", } @Article{Vazquez-Rodriguez:2011:JORS, author = "Jose Antonio Vazquez Rodriguez and Gabriela Ochoa", title = "On the automatic discovery of variants of the {NEH} procedure for flow shop scheduling using genetic programming", journal = "Journal of the Operational Research Society", year = "2011", number = "2", volume = "62", pages = "381--396", keywords = "genetic algorithms, genetic programming, heuristics, production, hyper-heuristics", ISSN = "0160-5682", URL = "http://www.cs.stir.ac.uk/~goc/papers/NEHGP_JORS.pdf", URL = "http://www.palgrave-journals.com/jors/journal/v62/n2/full/jors2010132a.html", DOI = "doi:10.1057/jors.2010.132", URL = "http://results.ref.ac.uk/Submissions/Output/944105", size = "16 pages", abstract = "We use genetic programming to find variants of the well-known Nawaz, En-score and Ham (NEH) heuristic for the permutation flow shop problem. Each variant uses a different ranking function to prioritise operations during schedule construction. We have tested our ideas on problems where jobs have release times, due dates, and weights and have considered five objective functions: makespan, sum of tardiness, sum of weighted tardiness, sum of completion times and sum of weighted completion times. The implemented genetic programming system has been carefully tuned and used to generate one variant of NEH for each objective function. The new NEHs, obtained with genetic programming, have been compared with the original NEH and randomised NEH versions on a large set of benchmark problems. Our results indicate that the NEH variants discovered by genetic programming are superior to the original NEH and its stochastic version on most of the problems investigated.", bibdate = "2011-01-25", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/jors/jors62.html#RodriguezO11", uk_research_excellence_2014 = "This paper presents an automatic genetic programming approach to design specialised variants of the most successful constructive heuristic for the well studied flow shop scheduling problem. The proposed methodology significantly outperforms the original heuristic on the benchmarks studied. Once a variant of the heuristic, targeted to a class of instances, is discovered, it can be applied to quickly solve a new instance. The exploration of genetic programming to generate new heuristics plays a key role in a new major EPSRC programme grant (EP/J017515/1) of pounds6.8M between UCL, Stirling, York and Birmingham, which started in 2012.", } @Article{DBLP:journals/cys/VazquezLG19, author = "Eder {Vazquez Vazquez} and Yulia Ledeneva and Rene Arnulfo {Garcia Hernandez}", title = "Learning Relevant Models using Symbolic Regression for Automatic Text Summarization", journal = "Computacion y Sistemas", year = "2019", volume = "23", number = "1", pages = "127", keywords = "genetic algorithms, genetic programming, NLP, Natural language processing, gold standard, topline, symbolic regression, data modeling, automatic text summarisation task, ATS", ISSN = "1405-5546", timestamp = "Thu, 11 Feb 2021 23:28:05 +0100", biburl = "https://dblp.org/rec/journals/cys/VazquezLG19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "http://www.cys.cic.ipn.mx/ojs/index.php/CyS/article/view/2921", URL = "https://www.cys.cic.ipn.mx/ojs/index.php/CyS/article/view/2921", URL = "https://www.cys.cic.ipn.mx/ojs/index.php/CyS/article/view/2921/2604", DOI = "doi:10.13053/CyS-23-1-2921", size = "15 pages", abstract = "Natural Language Processing (NLP) methods allow us to understand and manipulate natural language text or speech to do useful things. There are several specific techniques in this area, and although new approaches to solving the problems arise, its evaluation remains similar. NLP methods are regularly evaluated by a gold standard, which contains the correct results which must be obtained by a method. In this situation, it is desirable that NLP methods can close as possible to the results of the gold standard being evaluated. One of the most outstanding NLP task is the Automatic Text Summarization (ATS). ATS task consists in reducing the size of a text while preserving their information content. In this paper, a method for describing the ideal behavior (gold standard) of an ATS system, is proposed. The proposed method can obtain models that describe the ideal behavior which is described by the topline. In this work, eight models for ATS are obtained. These models generate better results than other models used in the state-of-the-art on ATS task.", notes = "in English", } @InProceedings{veach:1996:rrvgGA, author = "Marshall S. Veach", title = "Recognition and Reconstruction of Visibility Graphs Using a Genetic Algorithm", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "Genetic Algorithms", pages = "491--498", address = "Stanford University, CA, USA", publisher = "MIT Press", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap81.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96 GA paper", } @InCollection{veach:1995:RRVGUGA, author = "Marshall S. Veach", title = "Recognition and Reconstruction of Visibility Graphs Using a Genetic Algorithm", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1995", year = "1995", editor = "John R. Koza", pages = "291--300", address = "Stanford, California, 94305-3079 USA", month = "11 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms", ISBN = "0-18-195720-5", notes = "part of \cite{koza:1995:gagp}", } @InProceedings{Vedarajan:1997:ipoGA, author = "Ganesh Vedarajan and Louis Chi Chan and David Goldberg", title = "Investment Portfolio Optimization using Genetic Algorithms", booktitle = "Late Breaking Papers at the 1997 Genetic Programming Conference", year = "1997", editor = "John R. Koza", pages = "255--263", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California, 94305-3079, USA", month = "13--16 " # jul, publisher = "Stanford Bookstore", keywords = "genetic algorithms", ISBN = "0-18-206995-8", notes = "GP-97LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670", } @InProceedings{veenhuis:2005:CEC, author = "Christian Veenhuis and Mario K{\"o}ppen and J{\"o}rg Kr{\"u}ger and Bertram Nickolay", title = "Tree Swarm Optimization: An Approach to PSO-based Tree Discovery", booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary Computation", year = "2005", editor = "David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L. Gwenn Volkert and Dan Ashlock and Marc Schoenauer", volume = "2", pages = "1238--1245", address = "Edinburgh, UK", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "2-5 " # sep, organisation = "IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, PSO, TSO", ISBN = "0-7803-9363-5", DOI = "doi:10.1109/CEC.2005.1554832", abstract = "In recent years a swarm-based optimisation methodology called Particle Swarm Optimisation (PSO) has developed. PSO is highly explorative and primarily used in function optimisation. proposes a swarm-based learning algorithm based on PSO which is able to discover trees in tree spaces. Particles are flying through a tree space forming flocks around peaks of a fitness function. Because it inherits the explorative property of PSO, it needs only few evaluations to find suitable trees.", notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS. symbolic regression, artificial ant, TSO. 'Tree swarm optimisation uses full trees for the particles.' All members of the swarm have the same structure and length. Number of dimensions in continious PSO space = size of terminal set + size of function set. No attempt at placing similar symbols near each other. 'position trees' and 'velocity trees'. Section 3.5 'Distances between full symbol trees' Four symbolic regression. 'But as in PSO the exact global optimum cannot be found very often for difficult problems.' Santa Fe artificial ant. 'In 64 percent of the runs, solutions collecting all food items are found.' UCI iris", } @InProceedings{Veenhuis:2009:eurogp, author = "Christian Veenhuis", title = "Tree based Differential Evolution", booktitle = "Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009", year = "2009", editor = "Leonardo Vanneschi and Steven Gustafson and Alberto Moraglio and Ivanoe {De Falco} and Marc Ebner", volume = "5481", series = "LNCS", pages = "208--219", address = "Tuebingen", month = apr # " 15-17", organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-01180-1", DOI = "doi:10.1007/978-3-642-01181-8_18", notes = "Not presented. Part of \cite{conf/eurogp/2009} EuroGP'2009 held in conjunction with EvoCOP2009, EvoBIO2009 and EvoWorkshops2009", } @InProceedings{Veenhuis:2013:EPIA, author = "Christian B. Veenhuis", title = "Structure-Based Constants in Genetic Programming", booktitle = "Proceedings of the 16th Portuguese Conference on Artificial Intelligence, EPIA 2013", year = "2013", editor = "Luis Correia and Luis Paulo Reis and Jose Cascalho", volume = "8154", series = "Lecture Notes in Computer Science", pages = "126--137", address = "Angra do Heroismo, Azores, Portugal", month = sep # " 9-12", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Constant, Structure-based Constant, Constant Function, Subtree Relationship, Full Tree Normalisation, Generic Benchmark, Polynomial Benchmark, Sum-of-Gaussians Benchmark", isbn13 = "978-3-642-40668-3", URL = "http://link.springer.com/chapter/10.1007%2F978-3-642-40669-0_12", DOI = "doi:10.1007/978-3-642-40669-0_12", size = "12", abstract = "Evolving constants in Genetic Programming is still an open issue. As real values they cannot be integrated in GP trees in a direct manner, because the nodes represent discrete symbols. Present solutions are the concept of ephemeral random constants or hybrid approaches, which have additional computational costs. Furthermore, one has to change the GP algorithm for them. This paper proposes a concept, which does not change the GP algorithm or its components. Instead, it introduces structure-based constants realised as functions, which can be simply added to each function set while keeping the original GP approach. These constant functions derive their constant values from the tree structures of their child-trees (subtrees). That is, a constant is represented by a tree structure being this way under the influence of the typical genetic operators like subtree crossover or mutation. These structure-based constants were applied to symbolic regression problems. They outperformed the standard approach of ephemeral random constants. Their results together with their better properties make the structure-based constant concept a possible candidate for the replacement of the ephemeral random constants.", } @MastersThesis{veennan:mastersthesis, author = "C. J. Veenman", title = "Positional Genetic Programming", school = "Dept. of Mathematics and Computer Science", year = "1996", address = "Leiden University, The Netherlands", month = nov, keywords = "genetic algorithms, genetic programming", broken = "http://www-ict.its.tudelft.nl/~cor/thesis96.ps.gz", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/2905/http:zSzzSzwww-ict.its.tudelft.nlzSz~corzSzthesis96.pdf/veenman96positional.pdf", URL = "http://citeseer.ist.psu.edu/veenman96positional.html", size = "66 pages", abstract = "In this report a number of new reproduction operators for genetic programming (GP) is introduced. These operators have in common that they link the genetic information to a position in the expression or program tree. The GP engine, that implements the operators, limits both the operator arity and tree depth. In this way the expression trees become restricted structures. The information linking together with the fixed structures, transform the GP engine into a genetic algorithm with even better performance than common GP. Without loss of effectivity, these adjustments to ordinary GP have the schema theory apply well to GP.", notes = " Positional Genetic Programming (genetic algorithms with encoded tree structures), 1996. This report describes a genetic programming engine implemented as a genetic algorithm, i.e. it uses fixed sized chromosomes and allows for ordinary crossover operators. Positional Genetic Programming turns out to perform better than the usual genetic programming scheme, that uses variable sized structures and random subtree exchange. See also \cite{eiben:email:10-Nov-1997} thesis96.ps.gz doesnt work with ghostview 6-apr-98", } @InProceedings{Veer-Samara-Sihman:2024:ICIICS, author = "Rana {Veer Samara Sihman} and Muntader M. Almusawi and Mohammed {Hussein Fallah} and T. Saravanan and N Rajesh", title = "Green Roof Optimization Using Multi Objective Optimization with Genetic Programming Based Artificial Neural Network", booktitle = "2024 International Conference on Integrated Intelligence and Communication Systems (ICIICS)", year = "2024", month = nov, keywords = "genetic algorithms, genetic programming, Heating systems, Uncertainty, Urban areas, Artificial neural networks, Feature extraction, Linear programming, Optimisation, Meteorology, Load modelling, artificial neural network, fuzzy if-then rule, green roof system, multi objective optimisation", DOI = "doi:10.1109/ICIICS63763.2024.10859609", abstract = "In recent years, the greenhouse gases are rising increasingly and the extreme usage of vestige energy resources helped in the green spaces development including green roof systems, which is an important focus in urban areas. In existing, green roof designs are optimised with different methods such as Fuzzy framework-based optimisation. However, the framework is a single object based optimisation so, the framework struggle to handle multi conflict objects. To overcome this issue, Multi Objective Optimisation with Genetic Programming based Artificial Neural Network (MOO-GPANN) is proposed to optimise the green roof design. Initially, the input data is taken from NYC Green Roof Footprints (NYC-GRF) dataset and further processed into feature extraction with fuzzy if-then rule. Then, the optimal features are selected by using Multi Objective Optimisation (MOO) based on ranking diversity. Finally, the prediction done by using Genetic Programming based Artificial Neural Network (GPANN). From the results, the proposed MOO-GPANN model gave better results than existing Adaptive Neuro-Fuzzy Inference System (ANFIS) by providing results for the objective functions including energy consumption (PMV), Heating Load (HL), Cooling Load (CL) and comfort levels (CFL) respectively.", notes = "Also known as \cite{10859609}", } @InProceedings{Veeramachaneni:2010:gecco, author = "Kalyan Veeramachaneni and Katya Vladislavleva and Matt Burland and Jason Parcon and Una-May O'Reilly", title = "Evolutionary optimization of flavors", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "1291--1298", keywords = "genetic algorithms, genetic programming, Real world applications", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830483.1830713", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "We have acquired panelist data that provides hedonic (liking) ratings for a set of 40 flavors each composed of the same 7 ingredients at different concentration levels. Our goal is to use this data and predict other flavors, composed of the same ingredients in new combinations, which the panelist will like. We describe how we first employ Pareto-Genetic Programming (GP) to generate a surrogate for the human panelist from the 40 observations. This surrogate, in fact an ensemble of GP symbolic regression models, can predict liking scores for flavors outside the observations and provide a confidence in the prediction. We then employ a multi-objective particle swarm optimisation (MOPSO) to design a well and consistently liked flavor suite for a panelist. The MOPSO identifies flavors that are well liked, i.e., high liking score, and consistently-liked, i.e., of maximum confidence. Further, we generate flavors that are well and consistently liked by a cluster of panelists, by giving the MOPSO slightly different objectives.", notes = "Also known as \cite{1830713} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @Article{Veeramachaneni:2011:GPEM, author = "Kalyan Veeramachaneni", title = "Hitoshi Iba, Topon Kumar Paul, Yoshohiko Hasegawa: Applied genetic programming and machine learning CRC Press, 327 pp, ISBN: 978-1-4398-0369-1", journal = "Genetic Programming and Evolvable Machines", year = "2011", volume = "12", number = "2", pages = "179--180", month = jun, note = "Book Review", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dR80W", DOI = "doi:10.1007/s10710-010-9126-x", size = "2 pages", notes = "review of \cite{Iba:2009:AGPML}", } @Article{Veeramachaneni:2012:GPEM, author = "Kalyan Veeramachaneni and Ekaterina Vladislavleva and Una-May O'Reilly", title = "Knowledge mining sensory evaluation data: genetic programming, statistical techniques, and swarm optimization", journal = "Genetic Programming and Evolvable Machines", year = "2012", volume = "13", number = "1", pages = "103--133", month = mar, note = "Special Section on Evolutionary Algorithms for Data Mining", keywords = "genetic algorithms, genetic programming, food, scent, flavor, Symbolic regression, Sensory science, Ensembles, Non-linear optimisation, Variable selection, Pareto, Hedonic evaluation, Complexity control, PSO, bloat", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-011-9153-2", size = "31 pages", abstract = "Knowledge mining sensory evaluation data is a challenging process due to extreme sparsity of the data, and a large variation in responses from different members (called assessors) of the panel. The main goals of knowledge mining in sensory sciences are understanding the dependency of the perceived liking score on the concentration levels of flavours' ingredients, identifying ingredients that drive liking, segmenting the panel into groups with similar liking preferences and optimising flavors to maximise liking per group. Our approach employs (1) Genetic programming (symbolic regression) and ensemble methods to generate multiple diverse explanations of assessor liking preferences with confidence information; (2) statistical techniques to extrapolate using the produced ensembles to unobserved regions of the flavor space, and segment the assessors into groups which either have the same propensity to like flavors, or are driven by the same ingredients; and (3) two-objective swarm optimization to identify flavors which are well and consistently liked by a selected segment of assessors.", affiliation = "CSAIL, MIT, 32 Vassar Street, D-540, Cambridge, MA, USA", } @InProceedings{Veeramachaneni:2013:GECCO, author = "Kalyan Veeramachaneni and Owen Derby and Dylan Sherry and Una-May O'Reilly", title = "Learning regression ensembles with genetic programming at scale", booktitle = "GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference", year = "2013", editor = "Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi", isbn13 = "978-1-4503-1963-8", pages = "1117--1124", keywords = "genetic algorithms, genetic programming", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2463372.2463506", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "In this paper we examine the challenge of producing ensembles of regression models for large datasets. We generate numerous regression models by concurrently executing multiple independent instances of a genetic programming learner. Each instance may be configured with different parameters and a different subset of the training data. Several strategies for fusing predictions from multiple regression models are compared. To overcome the small memory size of each instance, we challenge our framework to learn from small subsets of training data and yet produce a prediction of competitive quality after fusion. This decreases the running time of learning which produces models of good quality in a timely fashion. Finally, we examine the quality of fused predictions over the progress of the computation.", notes = "Also known as \cite{2463506} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)", } @Article{journals/grid/VeeramachaneniA15, author = "Kalyan Veeramachaneni and Ignacio Arnaldo and Owen Derby and Una-May O'Reilly", title = "{FlexGP} - Cloud-Based Ensemble Learning with Genetic Programming for Large Regression Problems", journal = "Journal of Grid Computing", year = "2015", number = "3", volume = "13", bibdate = "2015-12-10", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/grid/grid13.html#VeeramachaneniA15", pages = "391--407", keywords = "genetic algorithms, genetic programming, MRGP, ARM, gossip protocol, Matlab, NSGA-II, ANN, seeding, Cloud computing, Ensemble learning, Symbolic regression", URL = "https://dspace.mit.edu/bitstream/handle/1721.1/103516/10723_2014_9320_ReferencePDF.pdf", URL = "http://dx.doi.org/10.1007/s10723-014-9320-9", DOI = "doi:10.1007/s10723-014-9320-9", size = "17 pages", abstract = "We describe FlexGP, the first Genetic Programming system to perform symbolic regression on large-scale datasets on the cloud via massive data-parallel ensemble learning. Flex-GP provides a decentralized, fault tolerant parallelization framework that runs many copies of Multiple Regression Genetic Programming, a sophisticated symbolic regression algorithm, on the cloud. Each copy executes with a different sample of the data and different parameters. The framework can create a fused model or ensemble on demand as the individual GP learners are evolving. We demonstrate our framework by deploying 100 independent GP instances in a massive data-parallel manner to learn from a dataset composed of 515K exemplars and 90 features, and by generating a competitive fused model in less than 10 minutes.", notes = "DataModeler \cite{Friese:2012:dortmund} and Eureqa \cite{Science09:Schmidt} Data Modeller, Eurequa, embarrassingly parallel, LASSO, factor subsets, NSGA-II,regularized linear regression, Vopal Wabbit, million song dataset DynEq GP, producer effect https://flexgp.github.io/flexgp/", } @InProceedings{Veerapen:2017:GI, author = "Nadarajen Veerapen and Fabio Daolio and Gabriela Ochoa", title = "Modelling Genetic Improvement Landscapes with Local Optima Networks", booktitle = "GI-2017", year = "2017", editor = "Justyna Petke and David R. White and W. B. Langdon and Westley Weimer", pages = "1543--1548", address = "Berlin", month = "15-19 " # jul, publisher = "ACM", note = "best presentation prize", keywords = "genetic algorithms, genetic programming, genetic improvement, fitness landscape, Local Optima Network, iterated local search, ILS, multiple hill climber", isbn13 = "978-1-4503-4939-0", URL = "http://geneticimprovementofsoftware.com/wp-content/uploads/2017/05/veerapen2017_local_optima_networks.pdf", DOI = "doi:10.1145/3067695.3082518", size = "6 pages", abstract = "Local optima networks are a compact representation of the global structure of a search space. They can be used for analysis and visualisation. This paper provides one of the first analyses of program search spaces using local optima networks. These are generated by sampling the search space by recording the progress of an Iterated Local Search algorithm. Source code mutations in comparison and Boolean operators are considered. The search spaces of two small benchmark programs, the triangle and TCAS programs, are analysed and visualised. Results show a high level of neutrality, i.e. connected test-equivalent mutants. It is also generally relatively easy to find a path from a random mutant to a mutant that passes all test case", notes = "triangle_comparison_ops.zip etc in http://hdl.handle.net/11667/89 Mutation of comparisons and Boolean operators super mutant, libtooling Clang-LLVM, escape edges, igraph", } @Article{Veerapen:2018:GPEM, author = "Nadarajen Veerapen and Gabriela Ochoa", title = "Visualising the global structure of search landscapes: genetic improvement as a case study", journal = "Genetic Programming and Evolvable Machines", year = "2018", volume = "19", number = "3", pages = "317--349", month = sep, note = "Special issue on genetic programming, evolutionary computation and visualization", keywords = "genetic algorithms, genetic programming, genetic improvement", ISSN = "1389-2576", URL = "http://hdl.handle.net/1893/27485", URL = "http://hdl.handle.net/11667/120", URL = "https://doi.org/10.1007/s10710-018-9328-1", DOI = "doi:10.1007/s10710-018-9328-1", size = "33 pages", abstract = "The search landscape is a common metaphor to describe the structure of computational search spaces. Different landscape metrics can be computed and used to predict search difficulty. Yet, the metaphor falls short in visualisation terms because it is hard to represent complex landscapes, both in terms of size and dimensionality. This paper combines local optima networks, as a compact representation of the global structure of a search space, and dimensionality reduction, using the t-distributed stochastic neighbour embedding algorithm, in order to both bring the metaphor to life and convey new insight into the search process. As a case study, two benchmark programs, under a genetic improvement bug-fixing scenario, are analysed and visualised using the proposed method. Local optima networks for both iterated local search and a hybrid genetic algorithm, across different neighbourhoods, are compared, highlighting the differences in how the landscape is explored.", notes = "Triangle Program, TCAS, LON, t-SNE Research Data http://hdl.handle.net/11667/120 'LibTooling library of Clang-LLVM to parse the programs, build the abstract syntax trees, and rewrite'", } @InProceedings{conf/esorics/VeggalamRHB16, title = "{IFuzzer}: An Evolutionary Interpreter Fuzzer Using Genetic Programming", author = "Spandan Veggalam and Sanjay Rawat and Istvan Haller and Herbert Bos", bibdate = "2017-05-23", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/esorics/esorics2016-1.html#VeggalamRHB16", booktitle = "Proceedings of the 21st European Symposium on Research in Computer Security, ESORICS 2016, Part I", publisher = "Springer", year = "2016", volume = "9878", editor = "Ioannis G. Askoxylakis and Sotiris Ioannidis and Sokratis K. Katsikas and Catherine A. Meadows", isbn13 = "978-3-319-45743-7", pages = "581--601", month = sep # " 26-30", address = "Heraklion, Greece", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming, SBSE, fuzzing system, security vulnerability, evolutionary computing", URL = "https://link.springer.com/chapter/10.1007%2F978-3-319-45744-4_29", DOI = "doi:10.1007/978-3-319-45744-4_29", abstract = "We present an automated evolutionary fuzzing technique to find bugs in JavaScript interpreters. Fuzzing is an automated black box testing technique used for finding security vulnerabilities in the software by providing random data as input. However, in the case of an interpreter, fuzzing is challenging because the inputs are piece of codes that should be syntactically/semantically valid to pass the interpreter's elementary checks. On the other hand, the fuzzed input should also be uncommon enough to trigger exceptional behaviour in the interpreter, such as crashes, memory leaks and failing assertions. In our approach, we use evolutionary computing techniques, specifically genetic programming, to guide the fuzzer in generating uncommon input code fragments that may trigger exceptional behaviour in the interpreter. We implement a prototype named IFuzzer to evaluate our technique on real-world examples. IFuzzer uses the language grammar to generate valid inputs. We applied IFuzzer first on an older version of the JavaScript interpreter of Mozilla (to allow for a fair comparison to existing work) and found 40 bugs, of which 12 were exploitable. On subsequently targeting the latest builds of the interpreter, IFuzzer found 17 bugs, of which four were security bugs.", } @Article{Vehi:2020:jhi, author = "Josep Vehi and Ivan Contreras and Silvia Oviedo and Lyvia Biagi and Arthur Bertachi", title = "Prediction and prevention of hypoglycaemic events in type-1 diabetic patients using machine learning", journal = "Health Informatics Journal", year = "2020", volume = "26", number = "1", pages = "703--718", month = mar, keywords = "genetic algorithms, genetic programming, grammatical evolution, blood glucose prediction, decision support systems, diabetes management, hypoglycaemia, machine learning, type-1 diabetes", ISSN = "1460-4582", URL = "https://pubmed.ncbi.nlm.nih.gov/31195880/", URL = "https://journals.sagepub.com/doi/full/10.1177/1460458219850682", DOI = "doi:10.1177/1460458219850682", size = "16 pages", abstract = "Tight blood glucose control reduces the risk of microvascular and macrovascular complications in patients with type 1 diabetes. However, this is very difficult due to the large intra-individual variability and other factors that affect glycaemic control. The main limiting factor to achieve strict control of glucose levels in patients on intensive insulin therapy is the risk of severe hypoglycaemia. Therefore, hypoglycaemia is the main safety problem in the treatment of type 1 diabetes, negatively affecting the quality of life of patients suffering from this disease. Decision support tools based on machine learning methods have become a viable way to enhance patient safety by anticipating adverse glycaemic events. This study proposes the application of four machine learning algorithms to tackle the problem of safety in diabetes management: (1) grammatical evolution for the mid-term continuous prediction of blood glucose levels, (2) support vector machines to predict hypoglycaemic events during postprandial periods, (3) artificial neural networks to predict hypoglycaemic episodes overnight, and (4) data mining to profile diabetes management scenarios. The proposal consists of the combination of prediction and classification capabilities of the implemented approaches. The resulting system significantly reduces the number of episodes of hypoglycaemia, improving safety and providing patients with greater confidence in decision-making", notes = "Universitat de Girona, Spain; Centro de Investigacion Biomedica en Red de Diabetes y Enfermedades Metabolicas Asociadas (CIBERDEM), Spain journals.sagepub.com/home/jhi", } @InProceedings{Veiga:2015:CEC, author = "Rafael Veiga and Joao Marcos {de Freitas} and Heder Bernardino and Helio Barbosa and Neuza Alcatara-Neves", title = "Using Grammar-based Genetic Programming to Determine Characteristics of Multiple Infections and Environmental Factors in the Development of Allergies and Asthma", booktitle = "Proceedings of 2015 IEEE Congress on Evolutionary Computation (CEC 2015)", year = "2015", editor = "Yadahiko Murata", pages = "1604--1611", address = "Sendai, Japan", month = "25-28 " # may, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-7491-7", DOI = "doi:10.1109/CEC.2015.7257079", abstract = "In recent decades asthma and allergies had great increase worldwide, being currently a serious global health problem. The causes of these disorders are unknown, but the most accepted hypothesis is that improving hygiene and reducing infections may be the main cause of this increase. Both asthma and allergies are complex diseases with strong environmental influence, so the use of versatile tools such as genetic programming can be important in the understanding of those conditions. We applied genetic programming to data obtained from 1296 children. Data related to chronic viral infections and environmental factors were used to classify in asthmatic and non-asthmatic, IgE and SPT in order to assess allergy. For asthma, viral infections were not relevant while for IgE and SPT they were. The use of genetic programming is shown to be a powerful tool to help understand those conditions.", notes = "1650 hrs 15521 CEC2015", } @Article{Veiga:2018:BMCbi, author = "Rafael V. Veiga and Helio J. C. Barbosa and Heder S. Bernardino and Joao M. Freitas and Caroline A. Feitosa and Sheila M. A. Matos and Neuza M. Alcantara-Neves and Mauricio L. Barreto", title = "Multiobjective grammar-based genetic programming applied to the study of asthma and allergy epidemiology", journal = "BMC Bioinformatics", year = "2018", volume = "19", number = "245", keywords = "genetic algorithms, genetic programming, Asthma, Allergy, Classifier", DOI = "doi:10.1186/s12859-018-2233-z", size = "16 pages", abstract = "Background: Asthma and allergies prevalence increased in recent decades, being a serious global health problem. They are complex diseases with strong contextual influence, so that the use of advanced machine learning tools such as genetic programming could be important for the understanding the causal mechanisms explaining those conditions. Here, we applied a multiobjective grammar-based genetic programming (MGGP) to a dataset composed by 1047 subjects. The dataset contains information on the environmental, psychosocial, socioeconomics, nutritional and infectious factors collected from participating children. The objective of this work is to generate models that explain the occurrence of asthma, and two markers of allergy: presence of IgE antibody against common allergens, and skin prick test positivity for common allergens (SPT). Results: The average of the accuracies of the models for asthma higher in MGGP than C4.5. IgE were higher in MGGP than in both, logistic regression and C4.5. MGGP had levels of accuracy similar to RF, but unlike RF, MGGP was able to generate models that were easy to interpret. Conclusions: MGGP has shown that infections, psychosocial, nutritional, hygiene, and socioeconomic factors may be related in such an intricate way, that could be hardly detected using traditional regression based epidemiological techniques. The algorithm MGGP was implemented in c++ and is available", notes = "http://bitbucket.org/ciml-ufjf/ciml-lib Methodology article Open Access", } @TechReport{Vekaria:1997:GPgdTR, author = "K. Vekaria and C. Clack", title = "Haploid Genetic Programming with Dominance", institution = "University College London", year = "1997", type = "Research Note", number = "RN/97/121", keywords = "genetic algorithms, genetic programming", URL = "http://website.lineone.net/~kanta/publications/haploidGP.ps", URL = "http://citeseer.ist.psu.edu/148827.html", abstract = "This paper presents a new crossover operator for genetic programming -- dominance crossover. Dominance crossover is similar to the use of dominance in nature. In nature, dominance is used as a genotype to phenotype mapping when an organism carries pairs (or more than one) chromosome, but here we use dominance on a haploid structure. The haploid form contains all the information relevant to the problem, and is the structure that is widely used in evolutionary algorithms. Dominance crossover is used as a way of retaining and promoting successful genes (those which increased the individual's fitness in the current generation) into the next generation. Current crossover operators fail to exploit knowledge acquired in previous generations and rely highly on selection pressures. Dominance crossover in theory allows this exploitation to occur during crossover but we highlight a problem with the application of dominance crossover with genetic programming.", notes = " See also \cite{Vekaria:1997:GPgd} ", size = "6 pages", } @InProceedings{Vekaria:1997:GPgd, author = "Kanta Vekaria", title = "Genetic Programming With Gene Dominance", booktitle = "Late Breaking Papers at the 1997 Genetic Programming Conference", year = "1997", editor = "John R. Koza", pages = "300", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California, 94305-3079, USA", month = "13--16 " # jul, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-206995-8", URL = "http://website.lineone.net/~kanta/publications/gp97.ps", size = "1 page", notes = "GP-97LB PHD Students' workshop The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670 /gp97.ps doesnt work with ghostview See also \cite{Vekaria:1997:GPgdTR}", } @InProceedings{vekaria:1998:sxGA, author = "Kanta Vekaria and Chris Clack", title = "Selective Crossover in Genetic Algorithms", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "609", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms", URL = "ftp://bells.cs.ucl.ac.uk/functional/papers/Published/sga98.ps", notes = "SGA-98", } @InProceedings{vekaria:1999:BIARO, author = "Kanta Vekaria and Chris Clack", title = "Biases Introduced by Adaptive Recombination Operators", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "670--677", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/Vekaria_gecco99.ps", URL = "http://website.lineone.net/~kanta/publications/gecco99.ps", URL = "ftp://bells.cs.ucl.ac.uk/functional/papers/Published/gecco99.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{vekaria:1999:S, author = "Kanta Vekaria and Chris Clack", title = "Schema propagation in selective crossover", booktitle = "Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference", year = "1999", editor = "Scott Brave and Annie S. Wu", pages = "268--275", address = "Orlando, Florida, USA", month = "13 " # jul, notes = "GECCO-99LB 4 August 2005 from http://www.cs.ucl.ac.uk/staff/C.Clack/research/publications.html says (Equation 5 in this paper has an error; a correction is provided in the two papers {"}Hitchhikers Get Around{"}, and {"}Royal Road Encodings and Schema Propagation{"}). Gzipped PDF(6.12M) ftp://bells.cs.ucl.ac.uk/functional/papers/Published/gecco99schemaprop.pdf.gz", } @InProceedings{Velasco:2015:GECCOcomp, author = "J. Manuel Velasco and Stephan Winkler and J. Ignacio Hidalgo and Oscar Garnica and Juan Lanchares and J. Manuel Colmenar and Esther Maqueda and Marta Botella and Jose-Antonio Rubio", title = "Data-Based Identification of Prediction Models for Glucose", booktitle = "GECCO 2015 Medical Applications of Genetic and Evolutionary Computation (MedGEC'15) Workshop", year = "2015", editor = "Stephen L. Smith and Stefano Cagnoni and Robert M. Patton", isbn13 = "978-1-4503-3488-4", keywords = "genetic algorithms, genetic programming", pages = "1327--1334", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739482.2768508", DOI = "doi:10.1145/2739482.2768508", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Diabetes mellitus is a disease that affects to hundreds of million of people worldwide. Maintaining a good control of the disease is critical to avoid severe long-term complications. One of the main problems that arise in the (semi) automatic control of diabetes, is to get a model explaining how glucose levels in blood vary with insulin, food intakes and other factors, fitting the characteristics of each individual or patient. In this paper we compare genetic programming techniques with a set of classical identification techniques: classical simple exponential smoothing, Holt's smoothing (linear, exponential and damped), classical Holt and Winters methods and auto regressive integrated moving average modelling. We consider predictions horizons of 30, 60, 90 and 120 minutes. Experimental results shows the difficulty of predicting glucose values for more than 60 minutes and the necessity of adapt GP techniques for those dynamic environments.", notes = "Also known as \cite{2768508} Distributed at GECCO-2015.", } @InProceedings{Velasco:2017:evoApplications, author = "Jose Manuel Velasco and Oscar Garnica and Sergio Contador and Jose Manuel Colmenar and Esther Maqueda and Marta Botella and Juan Lanchares and Jose Ignacio Hidalgo", title = "Enhancing Grammatical Evolution Through Data Augmentation: Application to Blood Glucose Forecasting", booktitle = "20th European Conference on the Applications of Evolutionary Computation", year = "2017", editor = "Giovanni Squillero", series = "LNCS", volume = "10199", publisher = "Springer", pages = "142--157", address = "Amsterdam", month = "19-21 " # apr, organisation = "Species", keywords = "genetic algorithms, genetic programming, grammatical evolution", isbn13 = "978-3-319-55848-6; 978-3-319-55849-3", bibdate = "2017-05-21", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/evoW/evoappl2017-1.html#VelascoGCCMBLH17", DOI = "doi:10.1007/978-3-319-55849-3_10", notes = "also known as \cite{conf/evoW/VelascoGCCMBLH17} EvoApplications2017 held in conjunction with EuroGP'2017, EvoCOP2017 and EvoMusArt2017 http://www.evostar.org/2017/cfp_evoapps.php.", } @InProceedings{velasco:2017:CEC, author = "Jose Manuel Velasco and Oscar Garnica and Sergio Contador and Juan Lanchares and Esther Maqueda and Marta Botella and J. Ignacio Hidalgo", booktitle = "2017 IEEE Congress on Evolutionary Computation (CEC)", title = "Data augmentation and evolutionary algorithms to improve the prediction of blood glucose levels in scarcity of training data", year = "2017", editor = "Jose A. Lozano", pages = "2193--2200", address = "Donostia, San Sebastian, Spain", publisher = "IEEE", month = "5-8 " # jun, keywords = "genetic algorithms, genetic programming, grammatical evolution, biochemistry, blood, diseases, evolutionary computation, learning (artificial intelligence), medical computing, pattern classification, sugar, artificial pancreas systems, blood glucose control, blood glucose level prediction, classification system, data augmentation, data collection, diabetes mellitus type 1 patients, evolutionary algorithms, grammatical evolution model, insulin bolus sizes, patient response, personal factors, scenario selection, training data scarcity, Data models, Grammar, Insulin, Predictive models, Time series analysis", isbn13 = "978-1-5090-4601-0", DOI = "doi:10.1109/CEC.2017.7969570", abstract = "Diabetes Mellitus Type 1 patients are waiting for the arrival of the Artificial Pancreas. Artificial Pancreas systems will control the blood glucose of patients, improving their quality of life and reducing the risks they face daily. At the core of the Artificial Pancreas, an algorithm will forecast future glucose levels and estimate insulin bolus sizes. Grammatical Evolution has been proved as a suitable algorithm for predicting glucose levels. Nevertheless, one of the main obstacles that researches have found for training the Grammatical Evolution models is the lack of significant amounts of data. As in many other fields in medicine, the collection of data from real patients is very complex along with the fact that the patient's response can vary in a high degree due to a lot of personal factors which can be seen as different scenarios. In this paper, we propose both a classification system for scenario selection and a data augmentation algorithm that generates synthetic glucose time series from real data. Our experimental results show that, in a scarce data context, Grammatical Evolution models can get more accurate and robust predictions using scenario selection and data augmentation.", notes = "IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969570}", } @Article{velasco:2018:MC, author = "Jose Manuel Velasco and Oscar Garnica and Juan Lanchares and Marta Botella and J. Ignacio Hidalgo", title = "Combining data augmentation, {EDAs} and grammatical evolution for blood glucose forecasting", journal = "Memetic Computing", year = "2018", volume = "10", number = "3", pages = "267--277", keywords = "genetic algorithms, genetic programming, grammatical evolution, Diabetes, Time series forecasting, Data augmentation", URL = "https://rdcu.be/cz6rt", URL = "http://link.springer.com/article/10.1007/s12293-018-0265-6", DOI = "doi:10.1007/s12293-018-0265-6", size = "11 pages", abstract = "The ideal solution for diabetes mellitus type 1 patients is the generalization of artificial pancreas systems. Artificial pancreas will control blood glucose levels of diabetics, improving their quality of live. At the core of the system, an algorithm will forecast future glucose levels as a function of food ingestion and insulin bolus sizes. In previous works several evolutionary computation techniques has been proposed as modeling or identification techniques in this area. One of the main obstacles that researchers have found for training the models is the lack of significant amounts of data. As in many other fields in medicine, the collection of data from real patients is not an easy task, since it is necessary to control the environmental and patient conditions. In this paper, we propose three evolutionary algorithms that generate synthetic glucose time series using real data from a patient. This way, the models can be trained with an augmented data set. The synthetic time series are used to train grammatical evolution models that work together in an ensemble. Experimental results show that, in a scarce data context, grammatical evolution models can get more accurate and robust predictions using data augmentation. In particular we reduce the number of potentially dangerous predictions to 0 for a 30 min horizon, 2.5percent for 60 min, 3.6percent on 90 min and 5.5percent for 2 h. The Ensemble approach presented in this paper showed excellent performance when compared to not only a classical approach such as ARIMA, but also with other grammatical evolution approaches. We tested our techniques with data from real patients.", notes = "Universidad Complutense, Madrid, Spain", } @Article{Velasquez-Mora:2024:REINCISOL, author = "Jonathan Geovanny {Velasquez Mora} and Carlos Patricio {Arroyo Vilela}", title = "Modern Control Systems for industrial processes: State of the art for cases of traffic congestion, through resolution of genetic and control algorithms", title_es = "Sistemas de Control Moderno para procesos industriales: Estado del arte para casos de congestion vehicular, mediante resolucion d algoritmos geneticos y de control", journal = "La realidad tecnologica en tiempos de inteligencia artificial en Institutos Tecnologicos de Ecuador", year = "2024", volume = "3", number = "5", pages = "1296--1321", keywords = "genetic algorithms, genetic programming, Road Traffic, neural networks, ANN, Control, trafico, redes neuronales, controlador", ISSN = "2953-6421", URL = "https://www.reincisol.com/ojs/index.php/reincisol/article/view/151/276", DOI = "doi:10.59282/reincisol.V3(5)1296-1321", size = "26 pages", abstract = "The steady increase of vehicles on the roads has recently made road congestion a crucial challenge. To cope with current traffic conditions and meet the growing demand for transportation, effective solutions are needed in urban transportation systems. However, introducing changes in urban infrastructures often involves significant constraints in time and feasibility. Therefore, optimizing traffic signal timing (TST) emerges as one of the fastest and most cost-effective methods to reduce congestion at intersections and improve traffic flow in urban networks. Researchers have been exploring various approaches, along with technological advances, to improve TST. This article aims to review recent literature from January 2015 to January 2020, focusing on computational intelligence (CI)-based simulation approaches and CI-based strategies for optimizing TST and traffic signal control (TSC) systems. Through this analysis, we seek to provide insight into existing research, identify gaps, and suggest possible directions for future research in this field. Given the complex dynamics of traffic flows, simulation tools play a crucial role in the analysis of their behavior. Currently, microsimulation tools are widely used in TST-related research. Therefore, this article provides a critical review of some of the most widely used microsimulation packages to illuminate their effectiveness and applicability. Our review highlights that approximately 77% of the articles reviewed employ some form of microsimulation tool. Therefore, it is clearly beneficial to conduct a review, categorization, and comparison of the predominant microsimulation tools to guide future research. In conclusion, we provide insight into the future trajectory of research in these areas, paving the way for further advances and innovations.", notes = "In Spanish Corporacion Electrica del Ecuador", } @Article{VelayLizancos:2017:CBM, author = "Mirian Velay-Lizancos and Juan Luis Perez-Ordonez and Isabel Martinez-Lage and Pablo Vazquez-Burgo", title = "Analytical and genetic programming model of compressive strength of eco concretes by {NDT} according to curing temperature", journal = "Construction and Building Materials", volume = "144", pages = "195--206", year = "2017", ISSN = "0950-0618", DOI = "doi:10.1016/j.conbuildmat.2017.03.123", URL = "http://www.sciencedirect.com/science/article/pii/S0950061817305068", abstract = "The use of non-destructive testing for estimating the compressive strength of concrete has great advantages both short term and long term. In the case of eco-concrete with recycled materials, it is of particular interest, since its use in many regulations is conditional on further studies. In this research we analyze the applicability of the most common models for estimating compressive strength by combining nondestructive testing. Specifically it was applied to 11 concrete with cement CEM-I, 3 self-compacting concrete and 8 vibrated concrete. There are two reference concretes (one of each) and the rest of concretes either have changed water/cement ratio or they contain different percentages of recycled materials (recycled aggregate fine and coarse together, or biomass ashes). Destructive tests have been made (compressive strength) and non-destructive (ultrasonic pulse velocity and compressive strength) in all concretes, at different ages and different curing temperatures, obtaining a total of 181 data sets. New estimation models were proposed for compressive strength with factors such as the curing temperature, the temperature history, the density of the concrete and the quantity of additive. These models substantially improve the results obtained with the usual methods. Finally, using genetic programming, it has managed to obtain an equation that allows, safely, estimating compressive strength with the information of non-destructive testing. The equation obtained improves current predictions with the peculiarity that minimizes uncertain results.", keywords = "genetic algorithms, genetic programming, Ultrasonic pulse velocity, Concrete, Estimation of compressive strength, NDT, Recycled aggregate, Biomass ash, Compressive strength, Eco-concrete, Maturity", } @PhdThesis{Velikonja:thesis, author = "Peter Velikonja", title = "Autonomous Music Via Artificial Evolution", school = "Department of Music, Princeton University", year = "2004", address = "USA", month = jan, keywords = "genetic algorithms, genetic programming", URL = "http://www.worldcat.org/title/autonomous-music-via-artificial-evolution/oclc/54361714", URL = "http://search.proquest.com/docview/908176681", size = "70 pages", abstract = "The potential of a computer to compose original music using a quasi-autonomous method is explored. Using a genetic-programming technique, an artificial life environment controls an additive-synthesis audio engine. Emphasis is placed on the musical quality of results rather than on a particular research goal. The problem of human versus evolutionary time is discussed, as is the degree of control a composer may exercise over a nominally autonomous process. We know from experience that computers need careful guidance to create even the simplest musical sounds; but the potential of computers has inspired many composers to use them as tools or even as active partners. Computers excel at repetition and numeric calculation--which is not surprising, as computer programs consist fundamentally of variable assignments and loops. It is not a simple matter to construct music from these building blocks. The formal constructs of programming languages do not translate naturally into musical syntax, and obtaining aural complexity from a computer is always a challenge. Yet composers persist with this compelling notion: we know computers can work tirelessly on intricate problems, so we can imagine them creating a new kind of music--perhaps one not fully ruled by human logic. We may not understand the results; nonetheless we are curious. The paper is in four parts, which are progressively less technical. Part 1 explains why artificial evolution should be a useful technique for automatic music composition. It explains why frames of digital audio are generated rather than musical notes or phrases; an overview of genetic programming is followed by test examples. Part 2 applies the technique to create musical sounds. Part 3 introduces methods a composer might follow to obtain musical variety. Part 4 describes two musical compositions created by the author, Suite for Proteins and Passacaglia Polymer C3 . An Appendix describes the C++ implementation. Audio and code examples are included on two CDs.", notes = "Passacaglia Polymer C3, Suite for Proteins 'The complete code is in hypotenuse.tar, which compiles and runs on Windows (MSVC 6.0), Mac OS X and linux.' https://www.princeton.edu/.../List-of-Doctorates-and-Dissertation-Titles-most-recent.doc Perry R. Cook OCLC Number: 54361714 UMI Microform 3110252 Oct 2016 Proquest URL already broken but it is still on the web pages, use search", } @InProceedings{vella-zarb:2024:GECCOcomp, author = "David {Vella Zarb} and Geoff Parks and Timoleon Kipouros", title = "Synergistic Utilization of {LLMs} for Program Synthesis", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion", year = "2024", editor = "Ting Hu and Aniko Ekart", pages = "539--542", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, program synthesis, large language models, ANN, evolutionary algorithms: Poster", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3654426", size = "4 pages", abstract = "Advances in Large Language Models (LLMs) have led them to be used as black boxes in several evolutionary algorithms for program synthesis. While these methods tend to be agnostic about which model is used, they only allow for using one. This paper suggests that using a combination of LLMs to seed population-based algorithms introduces more variation and leads to a wider variety of problems that can be solved, due to leveraging the strengths of component LLMs. We test this on the PSB2 suite, using the Search, Execute, Instruct, Debug and Rank (SEIDR) algorithm. In all cases examined, we find that using a combination of LLMs leads to more problems solved and better test pass rates compared to using the best individual model. We also find that the computational cost, as measured in terms of excess programs generated, is lowered.", notes = "GECCO-2024 GP A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @Article{Veltri:2015:ieeeacmCBBI, author = "Daniel Veltri and Uday Kamath and Amarda Shehu", journal = "IEEE/ACM Transactions on Computational Biology and Bioinformatics", title = "Improving Recognition of Antimicrobial Peptides and Target Selectivity through Machine Learning and Genetic Programming", year = "2015", abstract = "Growing bacterial resistance to antibiotics is spurring research on using naturally-occurring antimicrobial peptides (AMPs) as templates for novel drug design. While experimentalists mainly focus on systematic point mutations to measure the effect on antibacterial activity, the computational community seeks to understand what determines such activity in a machine learning setting. The latter seeks to identify the biological signals or features that govern activity. In this paper, we advance research in this direction through a novel method that constructs and selects complex sequence-based features which capture information about distal patterns within a peptide. Comparative analysis with state-of-the-art methods in AMP recognition reveals our method is not only among the top performers, but it also provides transparent summaries of antibacterial activity at the sequence level. Moreover, this paper demonstrates for the first time the capability not only to recognise that a peptide is an AMP or not but also to predict its target selectivity based on models of activity against only Gram-positive, only Gram-negative, or both types of bacteria. The work described in this paper is a step forward in computational research seeking to facilitate AMP design or modification in the wet laboratory.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/TCBB.2015.2462364", ISSN = "1545-5963", notes = "Daniel Veltri is with the School of Systems Biology, George Mason University Fairfax, VA 22030 Also known as \cite{7172462}", } @InProceedings{vemuri:1995:epSISAL, author = "V. Rao Vemuri and Patrick Miller", title = "Evolving Parallel SISAL Programs Using GP", booktitle = "Working Notes for the AAAI Symposium on Genetic Programming", year = "1995", editor = "E. V. Siegel and J. R. Koza", pages = "120--121", address = "MIT, Cambridge, MA, USA", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025, USA", month = "10--12 " # nov, publisher = "AAAI", keywords = "genetic algorithms, genetic programming", URL = "http://www.aaai.org/Papers/Symposia/Fall/1995/FS-95-01/FS95-01-017.pdf", URL = "http://www.aaai.org/Library/Symposia/Fall/fs95-01.php", size = "2 pages", abstract = "The genetic programming (GP) paradigm an off shoot of genetic algorithms (GA). GP envisaged as an automatic method for generating computer programs. In GP we use populations of data structures (or, programs) that are evaluated some problem specific criterion. More fit structures are propagated to future generations of populations through genetic operations that are similar to those used in GAs.", notes = "The case against C. Parallel programming. AAAI-95f GP. Part of \cite{siegel:1995:aaai-fgp} {\em Telephone:} 415-328-3123 {\em Fax:} 415-321-4457 {\em email} info@aaai.org {\em URL:} http://www.aaai.org/", } @InProceedings{venegas:2022:BDiF, author = "Percy Venegas and Isabel Britez and Fernand Gobet", title = "Ensemble Models Using Symbolic Regression and Genetic Programming for Uncertainty Estimation in {ESG} and Alternative Investments", booktitle = "Big Data in Finance", year = "2022", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-031-12240-8_5", DOI = "doi:10.1007/978-3-031-12240-8_5", } @Misc{Venema:2004:bloat, author = "Victor Venema", title = "An idea to combat bloat in genetic programming", howpublished = "Blog", year = "2004", month = "30 " # dec, keywords = "genetic algorithms, genetic programming, evolution, evolutionary search, science, Proglets, proglet-GP, Random Parallel Genetic Programming, RPGP, speciation scheme", URL = "https://uni-bonn.viven.org//essays/2004/genetic_programming_and_bloat.html", URL = "http://variable-variability.blogspot.com/2011/04/", size = "1 pages", notes = "'closely mimic computation in nature' This post was first published on my homepage on 30 December 2004. Last update: 18 January 2005 (added two references and links on bloat reduction methods)", } @Misc{Venkat:2010:IJHIT, title = "Genetic Algorithms and Programming-An Evolutionary Methodology", author = "T. Venkat Narayana Rao and Srikanth Madiraju", journal = "International Journal of Hybrid Information Technology", year = "2010", volume = "3", number = "4", month = oct, keywords = "genetic algorithms, genetic programming, subtree, chromosomes, mutation", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.303.8499", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.303.8499", URL = "http://www.sersc.org/journals/IJHIT/vol3_no4_2010/1.pdf", abstract = "Genetic programming (GP) is an automated method for creating a working computer program from a high-level problem statement of a problem. Genetic programming starts from a high-level statement of what needs to be done and automatically creates a computer program to solve the problem. In artificial intelligence, genetic programming (GP) is an evolutionary algorithm-based methodology inspired by biological evolution to find computer programs that perform a user defined task. It is a specialisation of genetic algorithms (GA) where each individual is a computer program. It is a machine learning technique used to optimise a population of computer programs according to a fitness span determined by a program{'}s ability to perform a given computational task. This paper presents a idea of the various principles of genetic programming which includes, relative effectiveness of mutation, crossover, breeding computer programs and fitness test in genetic programming. The literature of traditional genetic algorithms contains related studies, but through GP, it saves time by freeing the human from having to design complex algorithms. Not only designing the algorithms but creating ones that give optimal solutions than traditional counterparts in noteworthy ways.", } @Article{venkata-vara-prasad:2021:Er, author = "D {Venkata Vara Prasad} and P {Senthil Kumar} and Lokeswari Y Venkataramana and G Prasannamedha and S Harshana and S {Jahnavi Srividya} and K Harrinei and Sravya Indraganti", title = "Automating water quality analysis using {ML} and auto {ML} techniques", journal = "Environmental research", year = "2021", volume = "202", pages = "111720", month = nov, keywords = "genetic algorithms, genetic programming, TPOT, Algorithms, Artificial Intelligence, Food Analysis, Humans, Machine Learning, Water Quality, AutoML, SMOTE, Water quality index", ISSN = "1096-0953", DOI = "doi:10.1016/j.envres.2021.111720", abstract = "Generation of unprocessed effluents, municipal refuse, factory wastes, junking of compostable and non-compostable effluents has hugely contaminated nature-provided water bodies like rivers, lakes and ponds. Therefore, there is a necessity to look into the water standards before the usage. This is a problem that can greatly benefit from Artificial Intelligence (AI). Traditional methods require human inspection and is time consuming. Automatic Machine Learning (AutoML) facilities supply machine learning with push of a button, or, on a minimum level, ensure to retain algorithm execution, data pipelines, and code, generally, are kept from sight and are anticipated to be the stepping stone for normalising AI. However, it is still a field under research. This work aims to recognize the areas where an AutoML system falls short or outperforms a traditional expert system built by data scientists. Keeping this as the motive, this work dives into the Machine Learning (ML) algorithms for comparing AutoML and an expert architecture built by the authors for Water Quality Assessment to evaluate the Water Quality Index, which gives the general water quality, and the Water Quality Class, a term classified on the basis of the Water Quality Index. The results prove that the accuracy of AutoML and TPOT was 1.4 percent higher than conventional ML techniques for binary class water data. For Multi class water data, AutoML was 0.5 percent higher and TPOT was 0.6percent higher than conventional ML techniques.", notes = "PMID: 34297938", } @Article{Venkatesan:2010:IJCA, author = "S. Venkatesan and S. Srinivasa Rao Madane", title = "Face Detection by Hybrid Genetic and Ant Colony Optimization Algorithm", journal = "International Journal of Computer Applications", year = "2010", volume = "9", number = "4", pages = "8--13", month = nov, publisher = "Foundation of Computer Science", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, feature extraction, ACOG algorithm, ant colony optimisation", ISSN = "0975-8887", bibsource = "OAI-PMH server at www.doaj.org", oai = "oai:doaj-articles:5731535eddfcad8df84d2275b68417d8", source = "International Journal of Computer Applications", URL = "http://www.ijcaonline.org/volume9/number4/pxc3871854.pdf", size = "6 pages", abstract = "Over the last Twenty years, several different techniques have been proposed for computer recognition of human faces. The localisation of human faces in digital images is a fundamental step in the process of face recognition. In this paper, a Hybrid algorithm is proposed to detect faces using Ant Colony Optimization and Genetic programming algorithms. Evolutionary process of Ant Colony Optimisation algorithm adapts genetic operations to enhance ant movement towards solution state. The algorithm converges to the optimal final solution, by accumulating the most effective sub-solutions.", } @Article{venkateswarlu:2021:IJGGE, author = "Hasthi Venkateswarlu and Shivpreet Sharma and A. Hegde", title = "Performance of Genetic Programming and Multivariate Adaptive Regression Spline Models to Predict Vibration Response of Geocell Reinforced Soil Bed: A Comparative Study", journal = "International Journal of Geosynthetics and Ground Engineering", year = "2021", volume = "7", number = "3", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s40891-021-00306-6", DOI = "doi:10.1007/s40891-021-00306-6", } @Article{venkatraman:2004:CIM, author = "Vishwesh Venkatraman and Andrew Rowland Dalby and Zheng Rong Yang", title = "Evaluation of Mutual Information and Genetic Programming for Feature Selection in {QSAR}", journal = "Journal of Chemical Information and Modeling", year = "2004", volume = "44", number = "5", pages = "1686--1692", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1021/ci049933v", abstract = "Feature selection is a key step in Quantitative Structure Activity Relationship (QSAR) analysis. Chance correlations and multicollinearity are two major problems often encountered when attempting to find generalised QSAR models for use in drug design. Optimal QSAR models require an objective variable relevance analysis step for producing robust classifiers with low complexity and good predictive accuracy. Genetic algorithms coupled with information theoretic approaches such as mutual information have been used to find near-optimal solutions to such multicriteria optimisation problems. In this paper, we describe a novel approach for analyzing QSAR data based on these methods. Our experiments with the Thrombin dataset, previously studied as part of the KDD (Knowledge Discovery and Data Mining) Cup 2001 demonstrate the feasibility of this approach. It has been found that it is important to take into account the data distribution, the rule {"}interestingness{"}, and the need to look at more invariant and monotonic measures of feature selection.", notes = "http://pubs.acs.org/journals/jcisd8/index.html American Chemical Society S0095-2338(04)09933-0 School of Biological Sciences, University of Exeter, Exeter EX4 4QF, Great Britain and School of Engineering and Computer Science, University of Exeter, Exeter EX4 4QF, Great Britain PMID: 15446827", } @InProceedings{conf/afrigraph/VenterH07, author = "Johannes Venter and Alexandre Hardy", title = "Generating plants with gene expression programming", booktitle = "Proceedings of the 5th International Conference on Computer Graphics, Virtual Reality, Visualisation and Interaction in Africa, Afrigraph 2007", year = "2007", editor = "Hannah Slay and Stephen N. Spencer and Shaun Bangay", pages = "159--167", address = "Grahamstown, South Africa", month = oct # " 29-31", publisher = "ACM", keywords = "genetic algorithms, genetic programming, gene expression programming, L-systems, Plant modelling, aesthetic selection", isbn13 = "978-1-59593-906-7", DOI = "doi:10.1145/1294685.1294712", size = "10 pages", abstract = "The simulated evolution of botanical trees and other plants is explored in this paper. We present a model to simplify the creation of plants by following the genotype/phenotype approach of Gene Expression Programming to generate L-Systems. Our model describes a specific organism (a plant) with a genotype that can be expressed as an L-System. This L-System, and subsequently it's interpreted graphical image, forms the phenotype, and is used to assess the fitness of the organism. The human eye is used as a fitness function, and a user assigns fitness ratings to the organisms in a population based on the aesthetic value of the images. The user is able to create a plant without knowledge of the underlying algorithms or specific botanical knowledge.", notes = "Transposition http://www.ahardy.za.net/gepplants interactive evolution", bibdate = "2008-01-09", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/afrigraph/afrigraph2007.html#VenterH07", } @Article{Ventura:2009:SC, author = "Sebastian Ventura and Cristobal Romero and Amelia Zafra and Jose A. Delgado and Cesar Hervas", title = "JCLEC: a Java framework for evolutionary computation", journal = "Soft Computing - A Fusion of Foundations, Methodologies and Applications", year = "2008", volume = "12", number = "4", pages = "381--392", keywords = "genetic algorithms, genetic programming", ISSN = "1432-7643", DOI = "doi:10.1007/s00500-007-0172-0", abstract = "In this paper we describe JCLEC, a Java software system for the development of evolutionary computation applications. This system has been designed as a framework, applying design patterns to maximise its reusability and adaptability to new paradigms with a minimum of programming effort. JCLEC architecture comprises three main modules: the core contains all abstract type definitions and their implementation; experiments runner is a scripting environment to run algorithms in batch mode; finally, GenLab is a graphical user interface that allows users to configure an algorithm, to execute it interactively and to visualise the results obtained. The use of JCLEC system is illustrated though the analysis of one case study: the resolution of the 0/1 knapsack problem by means of evolutionary algorithms.", affiliation = "University of Cordoba, Campus Universitario de Rabanales Department of Computer Sciences and Numerical Analysis edificio Albert Einstein. 14071 Cordoba Spain", notes = "open source. Used later in GP experiments", } @Book{Ventura:2012:GPnew, editor = "Sebastian Ventura", title = "Genetic Programming - New Approaches and Successful Applications", publisher = "InTech", year = "2012", keywords = "genetic algorithms, genetic programming", isbn13 = "978-953-51-0809-2", URL = "http://www.intechopen.com/books/genetic-programming-new-approaches-and-successful-applications", DOI = "doi:10.5772/3102", size = "284 pages", abstract = "Genetic programming (GP) is a branch of Evolutionary Computing that aims the automatic discovery of programs to solve a given problem. Since its appearance, in the earliest nineties, GP has become one of the most promising paradigms for solving problems in the artificial intelligence field, producing a number of human-competitive results and even patentable new inventions. And, as other areas in Computer Science, GP continues evolving quickly, with new ideas, techniques and applications being constantly proposed. The purpose of this book is to show recent advances in the field of GP, both the development of new theoretical approaches and the emergence of applications that have successfully solved different real world problems. The volume is primarily aimed at postgraduates, researchers and academics, although it is hoped that it may be useful to undergraduates who wish to learn about the leading techniques in GP.", notes = "Open access book CC BY 3.0 license", } @Book{Ventura:2016:pmea, author = "Sebastian Ventura and Jose Maria Luna", title = "Pattern Mining with Evolutionary Algorithms", publisher = "Springer", year = "2016", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-33857-6", URL = "http://www.springer.com/us/book/9783319338576", DOI = "doi:10.1007/978-3-319-33858-3", size = "XIII, 190 pages", notes = "Reviewed by \cite{Xue:GPEM:bookreview} Quality Measures in Pattern Mining Pages 27-44 Introduction to Evolutionary Computation Pages 45-61 Pattern Mining with Genetic Algorithms Pages 63-85 Genetic Programming in Pattern Mining Pages 87-117 \cite{Ventura:2016:GPPM} Multiobjective Approaches in Pattern Mining Pages 119-139 Supervised Local Pattern Mining Pages 141-161 Mining Exceptional Relationships Between Patterns Pages 163-176 Scalability in Pattern Mining Pages 177-190 University of Cordoba, Spain", } @InCollection{Ventura:2016:GPPM, author = "Sebastian Ventura and Jose Maria Luna", title = "Genetic Programming in Pattern Mining", booktitle = "Pattern Mining with Evolutionary Algorithms", publisher = "Springer", year = "2016", chapter = "5", pages = "87--117", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-33858-3", DOI = "doi:10.1007/978-3-319-33858-3_5", abstract = "genetic programming for the mining of patterns of interest and the extraction of accurate relationships between patterns. The current chapter first describes the canonical representation of genetic programming and the use of grammars to restrict the search space. Then, it describes different approaches based on genetic programming for mining association rules of interest, paying special attention to the grammars used to restrict the search space, the genetic operators applied and the fitness functions considered by different approaches. Finally, this chapter deals with a series of application domains in which the use of genetic programming for mining association rules has been a successfully applied.", notes = "Part of \cite{Ventura:2016:pmea}", } @InProceedings{venturini:1997:igakdd, author = "G. Venturini and M. Slimane and F. Morin and J.-P. {Asselin de Beauville}", title = "On Using Interactive Genetic Algorithms for Knowledge Discovery in Databases", booktitle = "Genetic Algorithms: Proceedings of the Seventh International Conference", year = "1997", editor = "Thomas Back", pages = "696--703", address = "Michigan State University, East Lansing, MI, USA", publisher_address = "San Francisco, CA, USA", month = "19-23 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, Data Mining", ISBN = "1-55860-487-1", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/icga1997/venturini_1997_igakdd.pdf", size = "8 pages", abstract = "This paper presents an new interactive algorithm for exploring numerical databases and discovering regularities, called Genetic Interactive Data Explorer (GIDE). GIDE, which is based on the specifications of a previous prototype (Venturini et al. 1996), uses the principles of interactive genetic algorithms to evolve, in cooperation with the domain expert, 2D graphical representations of the data. These 2D representations are encoded by two new variables represented as Lisp functions using the genetic programming paradigm. The domain expert selects interesting representations which can be further improved by the genetic algorithm. The interaction between the expert and the knowledge discovery tool has been greatly improved. Results are presented on several standard databases.", notes = "ICGA-97 Iris, Wine database, glass database, Pima indians diabetes database", } @Article{Vera-Licona2014, author = "Paola Vera-Licona and Abdul Jarrah and Luis David Garcia-Puente and John McGee and Reinhard Laubenbacher", title = "An algebra-based method for inferring gene regulatory networks", journal = "BMC Systems Biology", year = "2014", volume = "8", number = "37", month = mar, keywords = "genetic algorithms, genetic programming", ISSN = "1752-0509", URL = "https://doi.org/10.1186/1752-0509-8-37", DOI = "doi:10.1186/1752-0509-8-37", size = "16 pages", abstract = "The inference of gene regulatory networks (GRNs) from experimental observations is at the heart of systems biology. This includes the inference of both the network topology and its dynamics. While there are many algorithms available to infer the network topology from experimental data, less emphasis has been placed on methods that infer network dynamics. Furthermore, since the network inference problem is typically underdetermined, it is essential to have the option of incorporating into the inference process, prior knowledge about the network, along with an effective description of the search space of dynamic models. Finally, it is also important to have an understanding of how a given inference method is affected by experimental and other noise in the data used.", notes = "EA applied to graph? Is it GP? see also http://www.shsu.edu/~ldg005/data/recomb.pdf", } @Article{Vera-Rivera:2021:A, author = "Fredy H. Vera-Rivera and Eduard Puerto and Hernan Astudillo and Carlos Mauricio Gaona", title = "Microservices Backlog-A Genetic Programming Technique for Identification and Evaluation of Microservices From User Stories", journal = "IEEE Access", year = "2021", volume = "9", pages = "117178--117203", abstract = "The microservice granularity directly affects the quality attributes and usage of computational resources of the system, determining optimal microservice granularity is an open research topic. Microservices granularity is defined by the number of operations exposed by the microservice, the number of microservices that compose the whole application, and its complexity and dependencies. This paper describes {"}Microservice Backlog (MB){"}, a semiautomatic model for defining and evaluating the granularity of microservice-based applications; MB uses genetic programming technique to calculate at design time the granularity of each microservice from the user stories in the {"}product backlog{"} or release planning; the genetic algorithm combined coupling, cohesion, granularity, semantic similarity, and complexity metrics to define the number of microservices, and the user stories associated with each microservice. MB decomposes the candidate microservices, allowing to analyze graphically the size of each microservice, as well as its complexity, dependencies, coupling, cohesion metrics, and the number of calls or requests between microservices. The resulting decomposition (number of microservices and their granularity) performed by MB shows less coupling, higher cohesion, less complexity, fewer user stories associated with each microservice, and fewer calls among microservices. MB was validated against three existing methods, using two state-of-the-art applications (Cargo Tracking and JPet-Store), and one real-life application (Foristom Conferences). The development team and/or architect can use metrics to identify the critical points of the system and determine at design time how the microservice-based application will be implemented.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ACCESS.2021.3106342", ISSN = "2169-3536", notes = "Also known as \cite{9519691}", } @InProceedings{Verdaguer-Gonzalez:2025:SIMPAR, author = "Aaron Verdaguer-Gonzalez and Magi Dalmau-Moreno and Luis Merino and Nestor Garcia", title = "Boosting Behavior Tree Generation for Robots with Large Language Models and Genetic Programming", booktitle = "2025 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR)", year = "2025", month = apr, keywords = "genetic algorithms, genetic programming, Uncertainty, Service robots, Scalability, Large language models, Pipelines, Robot sensing systems, Behavioural sciences, Usability, Robots, Behaviour Trees, Human-Robot Interaction, Simulation", DOI = "doi:10.1109/SIMPAR62925.2025.10979061", abstract = "Mobile robots are becoming increasingly ubiquitous in modern society, requiring more human-like interaction capabilities, such as following operator instructions and collaborating with humans. Conventional robot programming methods often fall short in achieving these complex behaviours. Behaviour Trees (BTs) offer a promising alternative due to their modularity, scalability and reactivity. We propose using general purpose, system-prompted Large Language Model (LLM) assistants to decompose task descriptions into executable BTs, which are subsequently refined using Genetic Programming (GP) and a state machine like low-resource BT execution simulator, where they will be tested for task completion. This approach eliminates the need for fine-tuning LLMs, thereby reducing computational costs and saving time and energy. Our method successfully solves all proposed scenarios, enhances applicability across diverse environments, and democratizes behaviour generation for non-experts, outperforming baseline methods in efficiency.", notes = "Also known as \cite{10979061}", } @InProceedings{Verdier:2018:CDC, author = "Cees F. Verdier and Manuel Mazo", booktitle = "2018 IEEE Conference on Decision and Control (CDC)", title = "Formal Synthesis of Analytic Controllers for Sampled-Data Systems via Genetic Programming", year = "2018", pages = "4896--4901", abstract = "This paper presents an automatic formal controller synthesis method for nonlinear sampled-data systems with safety and reachability specifications. Fundamentally, the presented method is not restricted to polynomial systems and controllers. We consider a periodically switched controllers based on a Control Lyapunov Barrier-like function. The proposed method uses genetic programming to synthesize these function in analytic form, as well as the controller modes. Correctness of the controller are subsequently verified by means of a Satisfiability Modulo Theories solver. Effectiveness of the proposed methodology is demonstrated on multiple systems.", keywords = "genetic algorithms, genetic programming, Switches, Safety, Grammar, State feedback, Production", DOI = "doi:10.1109/CDC.2018.8619121", ISSN = "2576-2370", month = dec, notes = "Also known as \cite{8619121}", } @Article{VERDIER:2017:IFAC-PapersOnLine, author = "C. F. Verdier and Manuel {Mazo, Jr.}", title = "Formal Controller Synthesis via Genetic Programming", journal = "IFAC-PapersOnLine", volume = "50", number = "1", pages = "7205--7210", year = "2017", note = "20th IFAC World Congress", keywords = "genetic algorithms, genetic programming, Formal methods, Lyapunov methods", ISSN = "2405-8963", DOI = "doi:10.1016/j.ifacol.2017.08.1362", URL = "http://www.sciencedirect.com/science/article/pii/S2405896317318979", abstract = "This paper presents an automatic controller synthesis method for nonlinear systems with reachability and safety specifications. The proposed method consists of genetic programming in combination with an SMT solver, which are used to synthesize both a control Lyapunov function and the modes of a switched state feedback controller. The resulting controller consists of a set of analytic expressions and a switching law based on the control Lyapunov function, which together guarantee the imposed specifications. The effectiveness of the proposed approach is shown on a 2D pendulum", } @Misc{DBLP:journals/corr/abs-2003-14322, author = "Cees F. Verdier and Manuel {Mazo Jr.}", title = "Formal controller synthesis for hybrid systems using genetic programming", howpublished = "arXiv", volume = "abs/2003.14322", year = "2020", keywords = "genetic algorithms, genetic programming", URL = "https://arxiv.org/abs/2003.14322", archiveprefix = "arXiv", eprint = "2003.14322", timestamp = "Thu, 02 Apr 2020 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/corr/abs-2003-14322.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @PhdThesis{Verdier:thesis, author = "Cees Ferdinand Verdier", title = "Formal synthesis of analytic controllers: An evolutionary approach", school = "Delft University of Technology", year = "2020", address = "The Netherlands", month = "21 " # oct # " 2020", keywords = "genetic algorithms, genetic programming, Formal controller synthesis, Hybrid systems control, Temporal logic, Lyapunov methods, reachability analysis", URL = "https://research.tudelft.nl/en/publications/formal-synthesis-of-analytic-controllers-an-evolutionary-approach", URL = "https://research.tudelft.nl/files/83671846/Dissertation_Cees_F_Verdier.pdf", DOI = "doi:10.4233/uuid:70f6704f-30e4-4e1a-8c74-9fe2b699a80d", size = "147 pages", abstract = "Control design for modern safety-critical cyber-physical systems still requires significant expert-knowledge, since for general hybrid systems with temporal logic specifications there are no constructive methods. Nevertheless, in recent years multiple approaches have been proposed to automatically synthesize correct-by-construction controllers. However, typically these methods either result in enormous look-up tables, require online optimization, or are highly dependent on expert-knowledge. The goal of this thesis is to propose a novel approach that overcomes these limitations, i.e. to propose a framework for automatic controller synthesis, capable of synthesizing closed-form controllers for hybrid systems with temporal logic specifications, without a heavy reliance on expert-knowledge. To this end, we draw inspiration from the human design process and use two methods that show great similarities to it, namely evolutionary algorithms and counterexample-guided inductive synthesis (CEGIS). Specifically, we use genetic programming (GP), an evolutionary algorithm capable of evolving entire programs. This makes it possible to automatically discover the structure of a solution. Moreover, it enables the synthesis of compact closed-form controllers, circumventing the need for look-up tables or online optimization. In combination with GP, we use the concept of CEGIS to refine candidate solutions based on counterexamples, until the controller is guaranteed to satisfy the desired specification. we propose two CEGIS-based synthesis frameworks, which differ in the employed verification paradigms, namely using either (co-synthesized) Lyapunov-like functions or reachability analysis. Both frameworks result in correct-by-construction compact closed-form controllers, where the use of expert-knowledge is optional. Both frameworks are capable of synthesising sampled-data controllers, enabling implementation in embedded hardware with limited memory and computation power, forming a stepping stone towards faster automation.", notes = "An electronic version of this dissertation is available at http://repository.tudelft.nl/ Supervisors: M. {Mazo Espinosa} and R. Babuska", } @Article{VERDIER:2022:automatica, author = "Cees Ferdinand Verdier and Niklas Kochdumper and Matthias Althoff and Manuel Mazo", title = "Formal synthesis of closed-form sampled-data controllers for nonlinear continuous-time systems under {STL} specifications", journal = "Automatica", volume = "139", pages = "110184", year = "2022", ISSN = "0005-1098", DOI = "doi:10.1016/j.automatica.2022.110184", URL = "https://www.sciencedirect.com/science/article/pii/S0005109822000292", keywords = "genetic algorithms, genetic programming, Achievable controller performance, Optimal controller synthesis for systems with uncertainties, Formal controller synthesis, Temporal logic, Reachability analysis", abstract = "We propose a counterexample-guided inductive synthesis framework for the formal synthesis of closed-form sampled-data controllers for nonlinear systems to meet STL specifications over finite-time trajectories. Rather than stating the STL specification for a single initial condition, we consider an (infinite and bounded) set of initial conditions. Candidate solutions are proposed using genetic programming, which evolves controllers based on a finite number of simulations. Subsequently, the best candidate is verified using reachability analysis; if the candidate solution does not satisfy the specification, an initial condition violating the specification is extracted as a counterexample. Based on this counterexample, candidate solutions are refined until eventually a solution is found (or a user-specified number of iterations is met). The resulting sampled-data controller is expressed as a closed-form expression, enabling both interpretability and the implementation in embedded hardware with limited memory and computation power. The effectiveness of our approach is demonstrated for multiple systems", } @Article{journals/ijseke/VergilioP06, title = "A Grammar-guided Genetic Programming Framework Configured for Data Mining and Software Testing", author = "Silvia Regina Vergilio and Aurora Trinidad Ramirez Pozo", journal = "International Journal of Software Engineering and Knowledge Engineering", year = "2006", number = "2", volume = "16", pages = "245--268", bibdate = "2006-05-22", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ijseke/ijseke16.html#VergilioP06", keywords = "genetic algorithms, genetic programming, Evolutionary computation, data mining, software testing, grammars", DOI = "doi:10.1142/S0218194006002781", abstract = "Genetic Programming (GP) is a powerful software induction technique that can be applied to solve a wide variety of problems. However, most researchers develop tailor-made GP tools for solving specific problems. These tools generally require significant modifications in their kernel to be adapted to other domains. In this paper, we explore the Grammar-Guided Genetic Programming (GGGP) approach as an alternative to overcome such limitation. We describe a GGGP based framework, named Chameleon, that can be easily configured to solve different problems. We explore the use of Chameleon in two domains, not usually addressed by works in the literature: in the task of mining relational databases and in the software testing activity. The presented results point out that the use of the grammar-guided approach helps us to obtain more generic GP frameworks and that they can contribute in the explored domains.", } @Misc{physics/0612221, author = "Amit Verma and Prasanta K. Panigrahi and Jitendra C. Parikh", title = "{Characterizing and modeling cyclic behavior in non-stationary time series through multi-resolution analysis}", eprint = "physics/0612221", year = "2006", howpublished = "ArXiv Physics e-prints", month = "22 " # dec, adsurl = "http://adsabs.harvard.edu/cgi-bin/nph-bib_query?bibcode=2006physics..12221A&db_key=PRE", adsnote = "Provided by the Smithsonian/NASA Astrophysics Data System", keywords = "genetic algorithms, genetic programming", URL = "http://arxiv.org/PS_cache/physics/pdf/0612/0612221.pdf", size = "12 pages", abstract = "A method based on wavelet transform and genetic programming is proposed for characterising and Modeling variations at multiple scales in non-stationary time series. The cyclic variations, extracted by wavelets and smoothened by cubic splines, are well captured by genetic programming in the form of dynamical equations. For the purpose of illustration, we analyse two different non-stationary financial time series, S&P CNX Nifty closing index of the National Stock Exchange (India) and Dow Jones industrial average closing values through Haar, Daubechies-4 and continuous Morlet wavelets for studying the character of fluctuations at different scales, before modelling the cyclic behaviour through GP. Cyclic variations emerge at intermediate time scales and the corresponding dynamical equations reveal characteristic behavior at different scales.", } @InProceedings{Verma:2016:ICC, author = "Devendra Verma and Purva Goel and Veena Patil-Shinde and Sanjeev S. Tambe", booktitle = "2016 Indian Control Conference (ICC)", title = "Use genetic programming for selecting predictor variables and modeling in process identification", year = "2016", pages = "230--237", abstract = "Availability of an accurate and robust dynamic model is essential for implementing the model dependent process control. When first principles based modelling becomes difficult, tedious and/or costly, a dynamic model in the black-box form is obtained (process identification) by using the measured input-output process data. Such a dynamic model frequently contains a number of time delayed inputs and outputs as predictor variables. The determination of the specific predictor variables is usually done via a trial and error approach that requires an extensive computational effort. The computational intelligence (CI) based data-driven modelling technique, namely, genetic programming (GP) can search and optimise both the structure and parameters of a linear/nonlinear dynamic process model. It is also capable of choosing those predictor variables that significantly influence the model output. Thus usage of GP for process identification helps in avoiding the extensive time and efforts involved in the selection of the time delayed input-output variables. This advantageous GP feature has been illustrated in this study by conducting process identification of two chemical engineering systems. The results of the GP-based identification when compared with those obtained using the transfer function based identification clearly indicates the out performance by the former method.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/INDIANCC.2016.7441133", month = jan, notes = "Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Pune, India Also known as \cite{7441133}", } @InProceedings{Verma:2020:GECCOcomp, author = "Siddharth Verma and Piyush Borole and Ryan Urbanowicz", title = "Evolving Genetic Programming Trees in a Rule-Based Learning Framework", year = "2020", editor = "Richard Allmendinger and Hugo Terashima Marin and Efren Mezura Montes and Thomas Bartz-Beielstein and Bogdan Filipic and Ke Tang and David Howard and Emma Hart and Gusz Eiben and Tome Eftimov and William {La Cava} and Boris Naujoks and Pietro Oliveto and Vanessa Volz and Thomas Weise and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Rui Wang and Ran Cheng and Guohua Wu and Miqing Li and Hisao Ishibuchi and Jonathan Fieldsend and Ozgur Akman and Khulood Alyahya and Juergen Branke and John R. Woodward and Daniel R. Tauritz and Marco Baioletti and Josu Ceberio Uribe and John McCall and Alfredo Milani and Stefan Wagner and Michael Affenzeller and Bradley Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and Nayat Sanchez-Pi and Luis Marti and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and Matthew Johns and Nick Ross and Ed Keedwell and Herman Mahmoud and David Walker and Anthony Stein and Masaya Nakata and David Paetzel and Neil Vaughan and Stephen Smith and Stefano Cagnoni and Robert M. Patton and Ivanoe {De Falco} and Antonio {Della Cioppa} and Umberto Scafuri and Ernesto Tarantino and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Erik Hemberg and Riyad Alshammari and Tokunbo Makanju and Fuijimino-shi and Ivan Zelinka and Swagatam Das and Ponnuthurai Nagaratnam and Roman Senkerik", isbn13 = "9781450371278", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377929.3390071", DOI = "doi:10.1145/3377929.3390071", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion", pages = "233--234", size = "2 pages", keywords = "genetic algorithms, genetic programming, co-evolution, symbolic regression, learning classifier systems, rule-based machine learning", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", abstract = "Rule-based machine learning (RBML) algorithms such as learning classifier systems (LCS) are well suited to classification problems with complex interactions and heterogeneous associations. Alternatively, genetic programming (GP) has a complementary set of strengths and weaknesses best suited to regression problems and homogeneous associations. Both approaches yield largely interpretable solutions. An ideal ML algorithm would have the capacity to adapt and blend representation to best suit the problem at hand. In order to combine the strengths of these respective algorithm representations, a framework allowing coexistence and co-evolution of trees and rules is needed. In this work, we lay the empirical groundwork for such a framework by demonstrating the capability of GP trees to be evolved within an LCS-algorithm framework with comparable performance to a set of standard GP frameworks. We discuss how these results support the feasibility of a GP-LCS framework and next-step challenges to be addressed.", notes = "Also known as \cite{10.1145/3377929.3390071} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @Article{DBLP:journals/ese/VescanPLE21, author = "Andreea Vescan and Adrian Pintea and Lukas Linsbauer and Alexander Egyed", title = "Genetic programming for feature model synthesis: a replication study", journal = "Empir. Softw. Eng.", volume = "26", number = "4", pages = "58", year = "2021", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/s10664-021-09947-7", DOI = "doi:10.1007/s10664-021-09947-7", timestamp = "Fri, 14 May 2021 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/ese/VescanPLE21.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @MastersThesis{oai:CiteSeerPSU:196426, title = "Genetic algorithms in {Haskell} with polytypic programming", author = "Mans Vestin", year = "1999", school = "Goteborg University", address = "Sweden", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.chalmers.se/~patrikj/poly/others/geneticalgorithmsinhaskellwithpolytypicprogramming.ps.gz", URL = "http://citeseer.ist.psu.edu/196426.html", size = "42 pages", citeseer-isreferencedby = "oai:CiteSeerPSU:19650", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:196426", abstract = "Evolution is the main optimising tool of the nature. You just have to look at all the animals, plants and human beings to be amazed by their complex and well suited genes. This is not the work of a random search through all possible genotypes, it is the result of a general and efficient optimising algorithm. Genetic algorithms and genetic programming are methods based on the concept of evolution and Darwinian natural selection. They are powerful methods of finding solutions to a vide variety of problems. This text describes a program written in the functional language Haskell, where evolution is used to solve three example problems. Two examples concern picture generation, and the third is a planning problem. Due to the use of polytypism, the program is easily extended to solve new problems using the same algorithm.", } @InProceedings{vezhentseva:2012:ISGTe, author = "O. V. Svezhentseva and N. I. Voropai", booktitle = "3rd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies (ISGT Europe, 2012)", title = "Optimization of supply source allocation in the problem of rational configuration of electricity supply system", year = "2012", address = "Berlin", size = "7 pages", abstract = "The paper considers the problem of expansion planning of complex distributed electricity supply systems as a hierarchy of problems. At the first stage the problems of choosing a rational configuration of the electricity supply system are solved. A method is suggested to optimise placement of supply sources and assignment of consumers to them, which is one of the main problems to be solved at the first stage. The method is based on the genetic programming algorithms. A case study applied to test the developed algorithms is presented. The obtained results are discussed and the problems of further research are formulated.", keywords = "genetic algorithms, genetic programming, power distribution planning, complex distributed electricity supply system, consumer assignment, electricity supply system, expansion planning problem, genetic programming algorithm, rational configuration, supply source allocation optimisation, supply source placement optimisation, Biological cells, Electricity, Linear programming, Resource management, Sociology, Statistics, Electricity supply systems, configuration, optimisation, placement of supply sources", DOI = "doi:10.1109/ISGTEurope.2012.6465714", ISSN = "2165-4816", notes = "Also known as \cite{6465714}", } @InProceedings{DBLP:conf/icaisc/VianaCJ21, author = "Monique Simplicio Viana and Rodrigo Colnago Contreras and Orides Morandin Junior", editor = "Leszek Rutkowski and Rafal Scherer and Marcin Korytkowski and Witold Pedrycz and Ryszard Tadeusiewicz and Jacek M. Zurada", title = "A New Genetic Improvement Operator Based on Frequency Analysis for Genetic Algorithms Applied to Job Shop Scheduling Problem", booktitle = "Artificial Intelligence and Soft Computing - 20th International Conference, {ICAISC} 2021, Virtual Event, June 21-23, 2021, Proceedings, Part {I}", series = "Lecture Notes in Computer Science", volume = "12854", pages = "434--450", publisher = "Springer", year = "2021", keywords = "genetic algorithms", URL = "https://doi.org/10.1007/978-3-030-87986-0_39", DOI = "doi:10.1007/978-3-030-87986-0_39", timestamp = "Thu, 23 Jun 2022 19:57:15 +0200", biburl = "https://dblp.org/rec/conf/icaisc/VianaCJ21.bib", bibsource = "dblp computer science bibliography, https://dblp.org", notes = "not GP?", } @InProceedings{vianna:1998:ehmsvphf, author = "Luiz S. Ochi and Dalessandro S. Vianna and Lucia M. A. Drummond and Andre O. Victor", title = "An Evolutionary Hybrid Metaheuristic for Solving the Vehicle Routing Problem with Heterogeneous Fleet", booktitle = "Proceedings of the First European Workshop on Genetic Programming", year = "1998", editor = "Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer and Terence C. Fogarty", volume = "1391", series = "LNCS", pages = "187--195", address = "Paris", publisher_address = "Berlin", month = "14-15 " # apr, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-64360-5", DOI = "doi:10.1007/BFb0055938", abstract = "We present a new hybrid metaheuristic which combines Genetic Algorithms and Scatter Search coupled with a decomposition-into-petals procedure for solving a class of Vehicle Routing and Scheduling Problems. Its performance is evaluated for a heterogeneous fleet model, which is considered a problem much harder to solve than the homogeneous vehicle routing problem.", notes = "EuroGP'98", affiliation = "UFF R. Sao Paulo Pos-Grad. Ciencia da Computacao Niteroi 24210-130 Rio de Janeiro Brazil", } @InProceedings{DBLP:conf/acalci/VickersSB17, author = "Darwin Vickers and Jacob Soderlund and Alan Blair", title = "Co-Evolving Line Drawings with Hierarchical Evolution", booktitle = "Third Australasian Conference on Artificial Life and Computational Intelligence, ACALCI 2017", year = "2017", editor = "Markus Wagner and Xiaodong Li and Tim Hendtlass", series = "Lecture Notes in Computer Science", volume = "10142", pages = "39--49", address = "Geelong, Victoria, Australia", month = jan # " 31 - " # feb # " 2", publisher = "Springer", keywords = "genetic algorithms, genetic programming, HERCL, Artist-critic coevolution, Artificial creativity, Adversarial training", timestamp = "Thu, 14 Jan 2021 17:33:16 +0100", biburl = "https://dblp.org/rec/conf/acalci/VickersSB17.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://www.cse.unsw.edu.au/~blair/pubs/2017VickersSoderlundBlairACALCI.pdf", URL = "https://doi.org/10.1007/978-3-319-51691-2_4", DOI = "doi:10.1007/978-3-319-51691-2_4", size = "11 pages", abstract = "We use an adversarial approach inspired by biological coevolution to generate complex line drawings without human guidance. Artificial artists and critics work against each other in an iterative competitive framework, forcing each to become increasingly sophisticated to outplay the other. Both the artists and critics are implemented in hercl, a framework combining linear and stack-based Genetic Programming, which is well suited to coevolution because the number of competing agents is kept small while still preserving diversity. The aesthetic quality of the resulting images arises from the ability of the evolved hercl programs, making judicious use of register adjustments and loops, to produce repeated substructures with subtle variations, in the spirit of low-complexity art.", } @InProceedings{Vidal:2009:EA, author = "Franck P. Vidal and Delphine Lazaro-Ponthus and Samuel Legoupil and Jean Louchet and Evelyne Lutton and Jean-Marie Rocchisani", title = "Artificial Evolution for {3D PET} Reconstruction", booktitle = "Artificial Evolution, EA 2009", year = "2009", editor = "Pierre Collet and Nicolas Monmarche and Pierrick Legrand and Marc Schoenauer and Evelyne Lutton", volume = "5975", series = "Lecture Notes in Computer Science,", pages = "37--48", address = "Strasbourg, France", month = "26-28 " # oct, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Positron Emission Tomography, Positron Emission Tomography Imaging, Compton Scattering, Bright Area, Tomographic Reconstruction", isbn13 = "978-3-642-14155-3", DOI = "doi:10.1007/978-3-642-14156-0_4", abstract = "This paper presents a method to take advantage of artificial evolution in positron emission tomography reconstruction. This imaging technique produces datasets that correspond to the concentration of positron emitters through the patient. Fully 3D tomographic reconstruction requires high computing power and leads to many challenges. Our aim is to reduce the computing cost and produce datasets while retaining the required quality. Our method is based on a coevolution strategy (also called Parisian evolution) named fly algorithm. Each fly represents a point of the space and acts as a positron emitter. The final population of flies corresponds to the reconstructed data. Using marginal evaluation, the fly's fitness is the positive or negative contribution of this fly to the performance of the population. This is also used to skip the relatively costly step of selection and simplify the evolutionary algorithm.", } @InProceedings{Vidal:2009:ieeeMIC, author = "Franck P. Vidal and Delphine Lazaro-Ponthus and Samuel Legoupil and Jean Louchet and Evelyne Lutton and Jean-Marie Rocchisani", title = "{PET} reconstruction using a cooperative coevolution strategy", booktitle = "Proceedings of the IEEE Medical Imaging Conference 2009", year = "2009", address = "Orlando, Florida, USA", month = oct, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", } @InProceedings{Vidal:2009:ieeeNSS, author = "Franck P. Vidal and Jean Louchet and Evelyne Lutton and Jean-Marie Rocchisani", title = "{PET} reconstruction using a cooperative coevolution strategy in {LOR} space", booktitle = "IEEE Nuclear Science Symposium Conference Record", year = "2009", pages = "3363--3366", address = "Orlando, Florida", month = "24 " # oct # "-1 " # nov, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/NSSMIC.2009.5401758", } @InProceedings{Vidal:2010:evows, author = "Franck P. Vidal and Jean Louchet and Jean-Marie Rocchisani and Evelyne Lutton", title = "New genetic operators in the fly algorithm: application to medical {PET} image reconstruction", booktitle = "Evolutionary Computation in Image Analysis and Signal Processing, EvoApplications 2010, Part I", year = "2010", editor = "Cecilia {Di Chio} and Stefano Cagnoni and Carlos Cotta and Marc Ebner and Aniko Ekart and Anna I. Esparcia-Alcazar and Chi-Keong Goh and Juan J. Merelo and Ferrante Neri and Mike Preuss and Julian Togelius and Georgios N. Yannakakis", volume = "6024", series = "LNCS", address = "Istanbul Technical University, Turkey", month = "7-9 " # apr, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-12238-5", DOI = "doi:10.1007/978-3-642-12239-2_30", } @InProceedings{Vidal:2010:AAPM, author = "F. P. Vidal and J. Louchet and J.-M. Rocchisani and E. Lutton", title = "Flies for {PET}: an Artificial Evolution Strategy for Image Reconstruction in Nuclear Medicine", booktitle = "Fifty-second annual meeting of the american association of physicists in medicine", year = "2010", address = "Philadelphia, PA, USA", month = jul, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1118/1.3468200", notes = "SU-GG-I-164", } @InProceedings{Vidal:2010:PPSN, author = "Franck P. Vidal and Evelyne Lutton and Jean Louchet and Jean-Marie Rocchisani", title = "Threshold selection, mitosis and dual mutation in cooperative co-evolution: application to medical {3D} tomography", booktitle = "11th International Conference on Parallel Problem Solving From Nature, PPSN 2010", year = "2010", volume = "6238", series = "LNCS", pages = "414--423", address = "Krakow, Poland", month = sep, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-15843-8", DOI = "doi:10.1007/978-3-642-15844-5_42", } @Article{Vidal:2023:ACC, author = "Juan Ferreira Vidal and Adriana Rosa Garcez Castro", journal = "IEEE Access", title = "Diagnosing Faults in Power Transformers With Variational Autoencoder, Genetic Programming, and Neural Network", year = "2023", volume = "11", pages = "30529--30545", abstract = "This work presents a new approach for the diagnosis of incipient faults in power transformers by considering dissolved gas analysis (DGA). A multilayer perceptron (MLP) neural network was trained to diagnose the type of transformer fault. For training and testing of the classifier, data were used from in-service transformers obtained from the IEC TC 10 database and other data obtained from the literature. To address the imbalance of the data from the database adopted and thus improve the generalisation power of the classifier, a data augmentation technique based on a variational autoencoder neural network was used. For the selection and extraction of characteristics from the inputs to the classifier, a technique based on genetic programming (GP) is proposed, which allows the creation of a new n-dimensional space of characteristics, providing a greater ability to increase interclass distances and intraclass compaction. For the performance analysis of the proposed classifier, comparisons were made using the classification results obtained through the IEC 60599 conventional fault diagnosis method and other trained MLPs without the use of data augmentation and the proposed characteristics extractor. The results obtained demonstrate the applicability of the proposed methodology for fault diagnosis, with the proposed system obtaining an accuracy of 95.18percent in the test basis, which is higher than the results achieved by the other methods used to perform a comparison and analysis of results.", keywords = "genetic algorithms, genetic programming, Databases, Evolutionary computation, Dissolved gas analysis, Neural networks, ANN, Data mining, Oil insulation, Dissolved gas analysis, IEC 60599, optimisation, evolutionary computation", DOI = "doi:10.1109/ACCESS.2023.3258544", ISSN = "2169-3536", notes = "Also known as \cite{10075442}", } @InProceedings{Videau:2022:EuroGP, author = "Mathurin Videau and Alessandro Leite and Olivier Teytaud and Marc Schoenauer", title = "Multi-Objective Genetic Programming for Explainable Reinforcement Learning", booktitle = "EuroGP 2022: Proceedings of the 25th European Conference on Genetic Programming", year = "2022", editor = "Eric Medvet and Gisele Pappa and Bing Xue", series = "LNCS", volume = "13223", publisher = "Springer Verlag", address = "Madrid, Spain", pages = "278--293", month = "20-22 " # apr, organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Reinforcement Learning, Explainable Reinforcement Learning (XRL), Genetic Programming Reinforcement Learning (GPRL): Poster", isbn13 = "978-3-031-02055-1", URL = "https://universite-paris-saclay.hal.science/hal-03886307/", DOI = "doi:10.1007/978-3-031-02056-8_18", abstract = "Deep reinforcement learning has met noticeable successes recently for a wide range of control problems. However, this is typically based on thousands of weights and non-linearities, making solutions complex, not easily reproducible, uninterpretable and heavy. The present paper presents genetic programming approaches for building symbolic controllers. Results are competitive, in particular in the case of delayed rewards, and the solutions are lighter by orders of magnitude and much more understandable.", notes = "M. Videau: Now at Meta AI Research. http://www.evostar.org/2022/eurogp/ Part of \cite{Medvet:2022:GP} EuroGP'2022 held inconjunction with EvoApplications2022 EvoCOP2022 EvoMusArt2022", } @Article{Vidnerova:GPEM, author = "Petra Vidnerova", title = "{Hitoshi Iba}: Evolutionary approach to machine learning and deep neural networks: neuro-evolution and gene regulatory networks", subtitle = "Springer, 2018, Hardcover, 245 pp, ISBN: 978-981-13-0199-5", journal = "Genetic Programming and Evolvable Machines", year = "2019", volume = "20", number = "2", pages = "151--153", month = jun, note = "Book review", keywords = "genetic algorithms, genetic programming, ANN", ISSN = "1389-2576", URL = "https://rdcu.be/dR81l", DOI = "doi:10.1007/s10710-019-09350-8", size = "3 pages", notes = "Book review of \cite{Iba:2018:book}", } @Article{journals/ijon/ViegasRGMSSAS18, author = "Felipe Viegas and Leonardo C. {da Rocha} and Marcos Andre Goncalves and Fernando Mourao and Giovanni Sa and Thiago Salles and Guilherme Andrade and Isac Sandin", title = "A Genetic Programming approach for feature selection in highly dimensional skewed data", journal = "Neurocomputing", year = "2018", volume = "273", pages = "554--569", keywords = "genetic algorithms, genetic programming", bibdate = "2017-11-22", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ijon/ijon273.html#ViegasRGMSSAS18", DOI = "doi:10.1016/j.neucom.2017.08.050", } @Article{VIEIRA:2023:eswa, author = "Antonio A. C. Vieira and Jose Rui Figueira and Rui Fragoso", title = "A multi-objective simulation-based decision support tool for wine supply chain design and risk management under sustainability goals", journal = "Expert Systems with Applications", volume = "232", pages = "120757", year = "2023", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2023.120757", URL = "https://www.sciencedirect.com/science/article/pii/S0957417423012599", keywords = "genetic algorithms, genetic programming, Supply chain design, Sustainability, Risk management, Multi-objective simulation-optimization, Simulation-based heuristic", abstract = "Sustainable Supply Chain Network Design (SCND) poses a complex challenge, as it often requires trade-offs in decision-making. The problem becomes even more challenging when there is a need to integrate risk management into the design process. It is steered by this challenge that the purpose of the paper is twofold. First, it proposes a Simulation-Based Decision Support System (SBDSS) for a real case study of a Portuguese wine SCND problem. Second, it analyzes different wine Supply Chain (SC) configurations under risk situations and sustainability objectives. The main contributions of this research are as follows. First, it concerns the method that was employed, which, contrarily to traditional approaches in literature (e.g., stochastic or genetic programming), consists of a simulation approach, in which agents and their interactions in an environment with uncertainty are modeled. Second, for the generation of non-dominated solutions, a simheuristic was employed, which combines multi-objective optimization with simulation. Finally, the type of industry that was considered, due to the lack of cases focusing on it in literature. The results suggest that the proposed approach determines a set of Pareto-front solutions that consider the sustainability dimensions of SCND, as well as risk situations. Furthermore, the implications that several parameters have on the sustainability of the designed SCs were assessed, as well as the impacts that the considered risks have on the performance of SCs. In particular, it was found that transportation risks impacted considerably more the performance of the SC than the risks on the prices of energy and raw materials", } @InProceedings{Vijaya-Chandran:2022:ISSE, author = "Vinayak {Vijaya Chandran} and Roopa Adepu", booktitle = "2022 IEEE International Symposium on Systems Engineering (ISSE)", title = "Reduced Order Modeling of a Heat Exchanger with a Stacking Ensemble to reduce Computational Inefficiencies", year = "2022", abstract = "Reduced Order Modeling is a technique for reducing the computational complexity of a model while preserving the expected fidelity within a controlled error. One of the techniques used to create a Reduced Order Model (ROM) is Artificial Neural Networks (ANN). A successful approach to reducing the variance of ANN model prediction is to train multiple models instead of a single model and to combine the predictions from these models, which is commonly called Ensemble learning. When the predictions from the multiple models are combined using another regression model, it is called Stacking ensemble. This paper studies the effectiveness of using Genetic programming algorithm in taking the outputs of each model as input and attempting to learn how to best combine the input predictions to make a better output prediction.The above-mentioned approach is used to create a ROM for a crossflow heat exchanger steady-state component. There are 6 inputs parameters namely Cold & Hot inlet temperature, Cold & Hot outlet pressure and Cold & Hot inlet flow. There are four outputs namely Hot & Cold outlet temperature and Hot & Cold inlet pressure. A multi-input single output (MISO) ROM is created for each of the outputs. There are 3 different configurations of ANNs used to cover a good range of the Hyperparameter values. The output from each of the ANNs is then combined using Genetic Programming Algorithm. The Overall model has an R2 value of above 9percent for each of the outputs. The ROM thus created can run simulations at a much faster rate. The ROM of the HX component is a black box and can be shared with third party without any concerns over propriety information loss.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ISSE54508.2022.10005464", ISSN = "2687-8828", month = oct, notes = "Also known as \cite{10005464}", } @Article{Vijayaraghavan:2015:Measurement, author = "R. Vijayaraghavan and A. Garg and V. Vijayaraghavan and Liang Gao", title = "Development of energy consumption model of abrasive machining process by a combined evolutionary computing approach", journal = "Measurement", volume = "75", pages = "171--179", year = "2015", ISSN = "0263-2241", DOI = "doi:10.1016/j.measurement.2015.07.055", URL = "http://www.sciencedirect.com/science/article/pii/S0263224115004066", abstract = "Abrasive machining is employed for improving surface characteristics of components used in oil and gas applications. Optimization of power consumed in abrasive machining process is vital from environmental standpoint that requires the formulation of the generalized and an explicit mathematical model. In the present work, we propose to study the power consumption in abrasive machining process using a combined evolutionary computing approach based on Multi-Adaptive Regression Splines (MARS) and Genetic Programming (GP) techniques. Sensitivity and parametric analysis have also been conducted to capture the dynamics of process by unveiling dominant input variables and hidden non-linear relationships. It is concluded that selection of optimal machining time and abrasive is necessary for achieving better environmental performance of abrasive machining process.", keywords = "genetic algorithms, genetic programming, Abrasive machining, MARS, Energy consumption, Modelling", } @Article{Vijayaraghavan:2014:Measurement, author = "V. Vijayaraghavan and A. Garg and C. H. Wong and K. Tai and S. S. Mahapatra", title = "Measurement of properties of graphene sheets subjected to drilling operation using computer simulation", journal = "Measurement", volume = "50", pages = "50--62", year = "2014", ISSN = "0263-2241", DOI = "doi:10.1016/j.measurement.2013.12.028", URL = "http://www.sciencedirect.com/science/article/pii/S0263224113006490", keywords = "genetic algorithms, genetic programming, Graphene modelling, GPTIPS, Nanomaterial modeling", } @Article{Vijayaraghavan:2014:TA, author = "V. Vijayaraghavan and A. Garg and C. H. Wong and K. Tai and Pravin M. Singru and Liang Gao and K. S. Sangwan", title = "A molecular dynamics based artificial intelligence approach for characterizing thermal transport in nanoscale material", journal = "Thermochimica Acta", volume = "594", pages = "39--49", year = "2014", ISSN = "0040-6031", DOI = "doi:10.1016/j.tca.2014.08.029", URL = "http://www.sciencedirect.com/science/article/pii/S0040603114003992", abstract = "A molecular dynamics (MD)-based-artificial intelligence (AI) simulation approach is proposed to investigate thermal transport of carbon nanotubes (CNTs). In this approach, the effect of size, chirality and vacancy defects on the thermal conductivity of CNTs is first analysed using MD simulation. The data obtained using the MD simulation is then fed into the paradigm of an AI cluster comprising multi-gene genetic programming, which was specifically designed to formulate the explicit relationship of thermal transport of CNT with respect to system size, chirality and vacancy defect concentration. Performance of the proposed model is evaluated against the actual results. We find that our proposed MD-based-AI model is able to model the phenomenon of thermal conductivity of CNTs very well, which can be then used to complement the analytical solution developed by MD simulation. Based on sensitivity and parametric analysis, it was found that length has most dominating influence on thermal conductivity of CNTs.", keywords = "genetic algorithms, genetic programming, Thermal conductivity, Transport properties, Nanostructures, Ab initio calculations, Defects", } @Article{Vijayaraghavan:2016:JCP, author = "V. Vijayaraghavan and A. Garg and Liang Gao and R. Vijayaraghavan and Guoxing Lu", title = "A finite element based data analytics approach for modeling turning process of Inconel 718 alloys", journal = "Journal of Cleaner Production", year = "2016", volume = "137", pages = "1619--1627", month = "20 " # nov, keywords = "genetic algorithms, genetic programming, Finite element analysis, Machining, Turning, Inconel 718", ISSN = "0959-6526", URL = "http://www.sciencedirect.com/science/article/pii/S0959652616302694", DOI = "doi:10.1016/j.jclepro.2016.04.010", size = "9 pages", abstract = "Turning is a primary metal cutting process deployed extensively for producing components to required shape and dimensions. A commonly used material is Inconel 718, which exhibits an inferior economic feasibility in terms of turning due to its poor machinability characteristics. A combined finite element based data analytics model is introduced in this work. Finite element modelling was used to predict the cutting force while Genetic Programming was used to obtain the mathematical relation between the process variables and the cutting force. The weighted parameter analysis was conducted on the mathematical model which revealed that depth of cut and cutting angle exerts significant influence on the cutting force. As turning process is generally specified by a given depth of cut which dictates the material removal rate, optimization of tool cutting angle can result in enhanced power savings. It is anticipated that the findings obtained from this study can result in greater power savings in turning process of hard-to-machine materials which can lead to a sustainable manufacturing process.", notes = "School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore", } @Article{Vijayaraghavan:2017:Measurement, author = "V. Vijayaraghavan and A. Garg and K. Tai and Liang Gao", title = "Thermo-mechanical modeling of metallic alloys for nuclear engineering applications", journal = "Measurement", volume = "97", pages = "242--250", year = "2017", ISSN = "0263-2241", DOI = "doi:10.1016/j.measurement.2016.11.003", URL = "http://www.sciencedirect.com/science/article/pii/S0263224116306406", abstract = "Austenitic stainless steel 304 (SS304) alloy has been used exclusively in nuclear power systems due to its excellent mechanical properties at elevated temperature environments. Despite its wide popularity, the effect of various factors such as temperature, applied strain, and strain rate on the mechanical strength of the alloy needs to be investigated. In light of this, this research article focuses on development of a finite element based analytical modeling approach for modeling the mechanical strength of SS304 with respect to considered input factors. The proposed analytical approach combines the interface of finite element modeling and the heuristic optimization algorithm of genetic programming. The developed analytical model shows good conformance of the mechanical strength with the experimental observations. Sensitivity and parametric analysis of the derived model was also able to accurately predict the elastic and plastic regime of the alloy and shows that temperature remains the major factor in influencing the mechanical strength of the alloy. The proposed approach is anticipated to be useful for nuclear engineers for optimizing the design criteria for nuclear pressure vessels which can lead to increased material savings and hence lead to more sustainable design of nuclear power generation facilities.", keywords = "genetic algorithms, genetic programming, Thermomechanical analysis, Finite element modeling, Mechanical strength, SS304 alloy", } @Article{VIJAYARAGHAVAN:2018:Measurement, author = "V. Vijayaraghavan and Akhil Garg and Liang Gao", title = "Fracture mechanics modelling of lithium-ion batteries under pinch torsion test", journal = "Measurement", volume = "114", pages = "382--389", year = "2018", keywords = "genetic algorithms, genetic programming, Short-circuit, Lithium-ion battery failure, Finite element analysis, Energy storage system", ISSN = "0263-2241", DOI = "doi:10.1016/j.measurement.2017.10.008", URL = "http://www.sciencedirect.com/science/article/pii/S0263224117306334", abstract = "For the design of batteries to sustain the crash tests, the mechanical strength (force generated) on the battery can be evaluated to understand its fundamental effect on possible failure (such as breaking of separator and short-circuit) of batteries. In this perspective, this study proposed a holistic approach to evaluate the maximum force generated on the battery when subjected to the pinch-torsion test. The fundamentals of the test are understood by formation of Finite element analysis (FEA) model and validated based on experiments. The inputs in FEA such as the temperature, the displacement and the strain rate are varied and the maximum generated force is observed on the battery. The quantification of the finite element data is further performed by an optimization approach of GP. It was found that the GP model for an evaluation of mechanical force on the battery is accurate. The robustness in the model is validated by design of its simulation for 10,000 runs. 2-D and 3-D surface analysis suggests that the displacement due to indentation is the most dominant followed by the temperature and the strain rate. The findings from the analysis can pave the way for design of new battery that comprises of higher strength when subjected to the crash tests", } @InProceedings{Vikhar:2016:ICGTSPICC, author = "Pradnya A. Vikhar", booktitle = "2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC)", title = "Evolutionary algorithms: A critical review and its future prospects", year = "2016", pages = "261--265", abstract = "Evolutionary algorithm (EA) emerges as an important optimisation and search technique in the last decade. EA is a subset of Evolutionary Computations (EC) and belongs to set of modern heuristics based search method. Due to flexible nature and robust behaviour inherited from Evolutionary Computation, it becomes efficient means of problem solving method for widely used global optimisation problems. It can be used successfully in many applications of high complexity. This paper presents a critical overview of Evolutionary algorithms and its generic procedure for implementation. It further discusses the various practical advantages using evolutionary algorithms over classical methods of optimisation. It also includes unusual study of various invariants of EA like Genetic Programming (GP), Genetic Algorithm (GA), Evolutionary Programming (EP) and Evolution Strategies (ES). Extensions of EAs in the form of Memetic algorithms (MA) and distributed EA are also discussed. Further the paper focuses on various refinements done in area of EA to solve real life problems.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICGTSPICC.2016.7955308", month = dec, notes = "Also known as \cite{7955308}", } @PhdThesis{Vilbig:thesis, author = "Alexander Georg Vilbig", title = "Komponentenbasiertes Rapid Prototyping am Beispiel der Biomolekularen Sequenzanalyse", school = "Institut fuer Informatik, Technische Universitaet Muenchen", year = "2001", address = "Germany", month = "25 " # jun, keywords = "genetic algorithms, genetic programming, componentware, rapid prototyping, ontology, biomolecular sequence analysis, komponentbasierte Softwareentwicklung, Rapid Prototyping, Ontologie, Genetischer Algorithmus, biomolekulare Sequenzanalyse", URL = "http://mediatum.ub.tum.de/?id=601704", URL = "https://mediatum.ub.tum.de/doc/601704/file.pdf", size = "306 pages", abstract = "The development of large and complex software systems is a very difficult, long, and costly task. Therefore, it is highly desirable to gather requirements for a future system as completely and precisely as possible in order to meet the user's actual needs and discover potential problems. Functional prototypes of the system in question facilitate this subtask of system development because they represent a solid basis for the dialogue between user and developer. Software components are particularly well suited for the construction of functional prototypes as they comprise existing functionality which is offered to their environment through well-defined interfaces. By composition, it is thus possible to construct larger systems rather quickly. However, search, selection, and combination of suitable components is still a largely manual task which gets even more complicated by insufficient descriptions of the offered functionality. This work therefore develops an advanced approach to component-based rapid prototyping which allows a widely automated construction of functional prototypes from given requirements and provided components. It proposes a conceptual framework with a clear, task-oriented structure which facilitates the use of particularly suitable models and procedures. Thereby, functionality on the application level may be understood as a manipulation of well-defined concepts within an ontology, while its realization on the technical level is described as typical interactions between participating interfaces. The tolerant matching between required and provided functionality leads to differently composed prototype variants which are iteratively optimized by an evolutionary heuristics. Thus, it is possible to consider numerous alternative solutions with justifiable effort. Moreover, potential components for design and implementation of the future system may be evaluated very early in the development process. A reference implementation as well as an exemplary application domain, biomolecular sequence analysis, ensure effectiveness and practical relevance of the presented approach. Additionally, various extensions demonstrate flexibility and future potential of the conceptual framework itself.", kurzfassung = "Die Entwicklung umfangreicher und komplexer Software-Systeme ist eineueberausanspruchsvolle Aufgabe, deren Durchfuehrung mit einem hohen Aufwand an Zeitund Kosten verbunden ist. Daher ist es wuenschenswert, die Anforderungen an einzukuenftiges System moeglichst genau und vollstaendig zu erfassen, um den Beduerfnissen des Anwenders tatsaechlich gerecht zu werden und auftretende Maengel fruehzeitig zu entdecken. Funktionale Prototypen des spaeteren Systems erleichtern dieseTeilaufgabe der Systementwicklung erheblich, da somit eine tragfaehige Grundlagefuer den Dialog zwischen Anwender und Entwickler geschaffen wird. Software-Komponenten bieten sich in besonderer Weise zur Konstruktion funktionaler Prototypen an, weil diese bereits implementierte Funktionalitaet zusammenfassen undueber ausgezeichnete Schnittstellen ihrer Umgebung zur Verfuegungstellen. Durch Komposition entstehen so verhaeltnismaessig raschuebergeordnete Systeme mit umfangreicher Funktionalitaet. Dennoch verbleiben Suche, Auswahl undVerknuepfung geeigneter Komponenten alsueberwiegend manuelle Schritte der Konstruktion, die zudem durch ungenuegende Beschreibung der angebotenen Funktionalitaet erheblich erschwert werden. In dieser Arbeit wird daher ein fortgeschrittener Ansatz fuer komponentenbasiertes Rapid Prototyping entwickelt, der eine weitgehend automatisierte Erstellung funktionaler Prototypen an Hand vorgegebener Anforderungen und bereitgestellter Komponenten ermoeglicht. Hierfuer wird ein konzeptueller Rahmen vorgeschlagen, dessen klare, aufgabenbezogene Strukturierung der Problemstellungden Einsatz jeweils besonders geeigneter Modelle und Verfahren erleichtert. Sokann Funktionalitaet auf anwendungsbezogener Ebene als Manipulation definierter Konzepte einer Ontologie verstanden werden, waehrend deren Realisierung auftechnischer Ebene durch typische Interaktionen beteiligter Schnittstellen beschrieben wird. Der tolerante Abgleich zwischen erwuenschter und angebotener Funktionalitaet fuehrt zu unterschiedlich zusammengesetzten Prototyp-Varianten, welchedurch eine evolutionaere Heuristik schrittweise optimiert werden. Auf diese Weise koennen auch zahlreiche alternative Loesungen mit vertretbaremAufwand betrachtet werden. Darueber hinaus lassen sich potentielle Komponentenfuer Entwurf und Implementierung des spaeteren Systems fruehzeitig hinsichtlich ihrer Eignung beurteilen. Die Effektivitaet und praktische Relevanz des vorgestelltenAnsatzes wird durch eine Referenz-Implementierung sowie einen exemplarisch untersuchten Anwendungsbereich, die biomolekulare Sequenzanalyse, sichergestellt. Zudem belegen vielfaeltige Erweiterungen die Flexibilitaet und das zukuenftige Potential der erarbeiteten Konzeption.", notes = "In German Supervisor: Manfred Broy", } @InProceedings{Villanueva:2020:GECCO, author = "Omar M. Villanueva and Leonardo Trujillo and Daniel E Hernandez", title = "Novelty Search for Automatic Bug Repair", year = "2020", editor = "Carlos Artemio {Coello Coello} and Arturo Hernandez Aguirre and Josu Ceberio Uribe and Mario Garza Fabre and Gregorio {Toscano Pulido} and Katya Rodriguez-Vazquez and Elizabeth Wanner and Nadarajen Veerapen and Efren Mezura Montes and Richard Allmendinger and Hugo Terashima Marin and Markus Wagner and Thomas Bartz-Beielstein and Bogdan Filipic and Heike Trautmann and Ke Tang and John Koza and Erik Goodman and William B. Langdon and Miguel Nicolau and Christine Zarges and Vanessa Volz and Tea Tusar and Boris Naujoks and Peter A. N. Bosman and Darrell Whitley and Christine Solnon and Marde Helbig and Stephane Doncieux and Dennis G. Wilson and Francisco {Fernandez de Vega} and Luis Paquete and Francisco Chicano and Bing Xue and Jaume Bacardit and Sanaz Mostaghim and Jonathan Fieldsend and Oliver Schuetze and Dirk Arnold and Gabriela Ochoa and Carlos Segura and Carlos Cotta and Michael Emmerich and Mengjie Zhang and Robin Purshouse and Tapabrata Ray and Justyna Petke and Fuyuki Ishikawa and Johannes Lengler and Frank Neumann", isbn13 = "9781450371285", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377930.3389845", DOI = "doi:10.1145/3377930.3389845", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference", pages = "1021--1028", size = "8 pages", keywords = "genetic algorithms, genetic programming, grammatical evolution, genetic improvement, APR, GenProg, novelty search", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", abstract = "Genetic Improvement (GI) focuses on the development of evolutionary methods to automate software engineering tasks, such as performance improvement or software bugs removal. Concerning the latter, one of the earliest and most well-known methods in this area is the Genetic Program Repair (GenProg), a variant of Genetic Programming (GP). However, most GI systems encounter problems that are derived from the fact that they operate directly at source code level. These problems include highly neutral fitness landscapes and loss of diversity during the search, which are always undesirable in search and optimization tasks. This paper explores the use of Novelty Search (NS) with GenProg, since it can allow a search process to overcome these type of issues. While NS has been combined with GP before, and recently used with other GI systems, in the area of automatic bug repair NS has not been used until this work. Results show that GenProg with NS outperforms the original algorithm in some cases, based on an extensive experimental evaluation.", notes = "See also \cite{Trujillo:2021:IEEESoftware} manybugs benchmark v GenProg Also known as \cite{10.1145/3377930.3389845} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @Article{Villar2012219, author = "Jose R. Villar and Alba Berzosa and Enrique {de la Cal} and Javier Sedano and Marco Garcia-Tamargo", title = "Multi-objective learning of white box models with low quality data", journal = "Neurocomputing", year = "2012", volume = "75", number = "1", pages = "219--225", month = jan, note = "Brazilian Symposium on Neural Networks (SBRN 2010) International Conference on Hybrid Artificial Intelligence Systems (HAIS 2010)", keywords = "genetic algorithms, genetic programming, Low quality data, Multi-objective simulated annealing, Energy efficiency", ISSN = "0925-2312", DOI = "doi:10.1016/j.neucom.2011.02.025", URL = "http://www.sciencedirect.com/science/article/pii/S0925231211004115", size = "7 pages", abstract = "Improving energy efficiency in buildings represents one of the main challenges faced by engineers. In fields like lighting control systems, the effect of low quality sensors compromises the control strategy and the emergence of new technologies also degrades the data quality introducing linguistic values. This research analyses the aforementioned problem and shows that, in the field of lighting control systems, the uncertainty in the measurements gathered from sensors should be considered in the design of control loops. To cope with this kind of problems Hybrid Intelligent methods will be used. Moreover, a method for learning equation-based white box models with this low quality data is proposed. The equation-based models include a representation of the uncertainty inherited in the data. Two different evolutionary algorithms are use for learning the models: the well-known NSGA-II genetic algorithm and a multi-objective simulated annealing algorithm hybridised with genetic operators. The performance of both algorithms is found valid to evolve this learning process. This novel approach is evaluated with synthetic problems.", } @Article{Villarreal:2016:Neurocomputing, author = "B. Lorena Villarreal and Gustavo Olague and J. L. Gordillo", title = "Synthesis of odor tracking algorithms with genetic programming", journal = "Neurocomputing", volume = "175, Part B", pages = "1019--1032", year = "2016", ISSN = "0925-2312", DOI = "doi:10.1016/j.neucom.2015.09.108", URL = "http://www.sciencedirect.com/science/article/pii/S0925231215015970", abstract = "At the moment, smell sensors for odour source localization in mobile robotics represent a topic of interest for researchers around the world. In particular, we introduce in this paper the idea of developing biologically inspired sniffing robots in combination with bioinspired techniques such as evolutionary computing. The aim is to approach the problem of creating an artificial nose that can be incorporated into a real working system, while considering the environmental model and odour behaviour, the perception system, and algorithm for tracking the odour plume. Current algorithms try to emulate animal behaviour in an attempt to replicate their capability to follow odours. Nevertheless, odour perception systems are still in their infancy and far from their biological counterpart. This paper presents a proposal in which a real-working artificial nose is tested as a perception system within a mobile robot. Genetic programming is used as the learning technique platform to develop odour source localization algorithms. Experiments in simulation and with an actual working robot are presented and the results compared with two algorithms. The quality of results demonstrates that genetic programming is able to recreate chemotaxis behaviour by considering mathematical models for odour propagation and perception system.", keywords = "genetic algorithms, genetic programming, Odour tracking algorithm, Bio-inspired nose, Chemical sensors, Sniffing robot", notes = "Center for Robotics and Intelligent Systems, Tecnologico de Monterrey, Monterrey, N.L., Mexico", } @InProceedings{Villegas-Cortez:2010:EvoIASP, author = "Juan Villegas-Cortez and Gustavo Olague and Juan Humberto {Sossa Azuela} and Carlos Aviles-Cruz and Andres Ferreyra", title = "Automatic Synthesis of Associative Memories through Genetic Programming: A First Co-evolutionary Approach", booktitle = "EvoIASP", year = "2010", editor = "Cecilia {Di Chio} and Stefano Cagnoni and Carlos Cotta and Marc Ebner and Aniko Ekart and Anna I. Esparcia-Alcazar and Chi-Keong Goh and Juan J. Merelo and Ferrante Neri and Mike Preuss and Julian Togelius and Georgios N. Yannakakis", volume = "6024", series = "LNCS", pages = "344--351", address = "Istanbul", month = "7-9 " # apr, organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-12238-5", URL = "http://dx.doi.org/10.1007/978-3-642-12239-2_36", DOI = "doi:10.1007/978-3-642-12239-2_36", size = "8 pages", abstract = "Associative Memories (AMs) are mathematical structures specially designed to associate input patterns with output patterns within a single stage. Since the last fifty years all reported AMs have been manually designed. The paper describes a Genetic Programming based methodology able to create a process for the automatic synthesis of AMs. It paves a new area of research that permits for the first time to propose new AMs for solving specific problems. In order to test our methodology we study the application of AMs for real value patterns. The results illustrate that it is possible to automatically generate AMs that achieve good recall performance for problems commonly used in pattern recognition research.", notes = "EvoIASP'2010 held in conjunction with EuroGP'2010 EvoCOP2010 EvoBIO2010", bibdate = "2010-04-12", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/evoW/evoappl2010-1.html#Villegas-CortezOASF10", affiliation = "Departamento de Electronica, Universidad Autonoma Metropolitana - Azcapotzalco, Av. San Pablo 180 Col. Reynosa, 02200 Mexico, D.F., Mexico", } @Article{Villegas-Cortez:2011:RMdF, title = "Evolutionary Associative Memories Through Genetic Programming", author = "J. Villegas-Cortez and J. H. Sossa and C. Aviles-Cruz and G. Olague", year = "2011", journal = "Revista Mexicana de Fisica", volume = "57", number = "2", pages = "110--116", month = apr, email = "rmf@smf2.fciencias.unam.mx", publisher = "Sociedad Mexicana de Fisica A.C.", keywords = "genetic algorithms, genetic programming, computer science and technology, neural engineering, image quality, contrast, resolution, noise, image analysis", ISSN = "0035001X", URL = "http://www.redalyc.org/src/inicio/ArtPdfRed.jsp?iCve=57019378003", broken = "http://www.doaj.org/doaj?func=openurl&genre=article&issn=0035001X&date=2011&volume=57&issue=2&spage=110", URL = "http://www.redalyc.org/articulo.oa?id=57019378003", URL = "http://www.redalyc.org/pdf/570/57019378003.pdf", oai = "oai:doaj-articles:7e43d5cf78112ddf1a27e6ef60afb3ac", bibsource = "OAI-PMH server at www.doaj.org", size = "7 pages", abstract = "Associative Memories (AMs) are useful devices designed to recall output patterns from input patterns. Each input-output pair forms an association. Thus, AMs store associations among pairs of patterns. An important feature is that since its origins AMs have been manually designed. This way, during the last 50 years about 26 different models and variations have been reported. In this paper, we illustrate how new models of AMs can be automatically generated through Genetic Programming (GP) based methodology. In particular, GP provides a way to successfully facilitate the search for an AM in the form of a computer program. The efficiency of the proposal was conducted by means of two tests based on binary and real-valued patterns. The experimental results show that it is possible to automatically generate AMs that achieve good results for the selected pattern recognition problems. This opens a new research area that allows, for the first time, synthesising new AMs to solve specific problems.", abstract = "Las memorias asociativas (AMs) son estructuras matematicas especificamente disenadas para recuperar patrones de entrada con patrones de salida. Cada par asociado (entrada-salida) forma una asociacion, es asi que la AM almacena las asociaciones entre los pares. Desde sus origenes las AMs han sido disenadas manualmente, y durante los ultimos 50 anos se han reportado un aproximado de 26 modelos de AMs con sus variantes. En este trabajo mostramos un nuevo modelo de AMs que es generado de forma automatica por medio de Programacion Genetica. Este trabajo abre una nueva area de investigacion que permite por primera vez sintetizar nuevas AMs para resolver problemas especificos. Para probar la eficiencia de nuestra propuesta la hemos aplicado para los casos de patrones en valores binarios y reales. Los experimentos muestran que es posible la generacion automatica de AMs para alcanzar buenos resultados para algunos problemas comunes del area de reconocimiento de patrones.", notes = "In english", } @InCollection{Villegas-Cortez:2012:PABA, author = "Juan Villegas-Cortez and Gustavo Olague and Humberto Sossa and Carlos Aviles", title = "Evolutionary Associative Memories through Genetic Programming", booktitle = "Parallel Architectures and Bioinspired Algorithms", publisher = "Springer", year = "2012", editor = "Francisco {Fernandez de Vega} and Jose Ignacio {Hidalgo Perez} and Juan Lanchares", volume = "415", series = "Studies in Computational Intelligence", chapter = "7", pages = "171--188", keywords = "genetic algorithms, genetic programming, coevolution", isbn13 = "978-3-642-28788-6", URL = "http://www.amazon.com/Architectures-Bioinspired-Algorithms-Computational-Intelligence/dp/3642287883", DOI = "doi:10.1007/978-3-642-28789-3_8", abstract = "Natural systems apply learning during the process of adaptation, as a way of developing strategies that help to succeed them in highly complex scenarios. In particular, it is said that the plans developed by natural systems are seen as a fundamental aspect in survival. Today, there is a huge interest in attempting to replicate some of their characteristics by imitating the processes of evolution and genetics in artificial systems using the very well-known ideas of evolutionary computing. For example, some models for learning adaptive process are based on the emulation of neural networks that are further evolved by the application of an evolutionary algorithm. In this work, we present the evolution of a kind of neural network that is collectible known as associative memories (AMs) and which are considered as a practical tool for reaching learning tasks in pattern recognition problems. AMs are complex operators, based on simple arithmetical functions, which are used to recall patterns in terms of some input data. AMs are considered as part of artificial neural networks (ANN), mainly due to its primary conception; nevertheless, the idea inherent to their mathematical formulation provides a powerful description that helps to reach a specific goal despite the numerous changes that can happen during its operation. In this chapter, we describe the idea of building new AMs through genetic programming (GP) based on the coevolutionary paradigm. The methodology that is proposed consists in splitting the problem in two populations that are used to evolve simultaneously both processes of association and recall that are commonly used in AM's. Experimental results on binary and real value patterns are provided in order to illustrate the benefits of applying the paradigm of evolutionary computing to the synthesis of associative memories.", affiliation = "Departamento de Electronica, Universidad Autonoma Metropolitana - Azcapotzalco, Av. San Pablo 180 Col. Reynosa, 02200 Mexico D.F., Mexico", } @InProceedings{Villegas-Cortez:2018:MICAI, author = "J. Villegas-Cortez and C. Aviles-Cruz and A. Zuniga-Lopez and S. Cordero-Sanchez and F. {Fernandez De Vega} and F. {Chavez de la O}", booktitle = "2018 Seventeenth Mexican International Conference on Artificial Intelligence (MICAI)", title = "Evolution of Statistical Descriptors for the Image Recognition of Natural Sceneries by Means of Genetic Programming for CBIR Improvement", year = "2018", pages = "45--50", abstract = "The rise of the Internet involves the simultaneous growth of the number of images in it. This amount of images comprises roughly more than half of the Internet content. This situation poses an open problem: how to recognize images from their own analysis without the use of labels describing their content or the analysis of documents or pages where the images being analyzed are appearing. In this work we present a novel approach for improving both the analysis technique and the classification of images of natural sceneries by using the content based image retrieval (CBIR) methodology which is applied for visual search. This improvement consists of the multigene evolution by means of genetic programming of new statistical texture descriptors in accordance with the type of scenery under analysis, the amount of the descriptors being used and the number of images. The percentage of recognition reaches up to 8percent for natural scenery images considering 5 classes, showing a satisfactory improvement with new evolved solutions.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/MICAI46078.2018.00015", month = oct, notes = "Also known as \cite{9046512}", } @Article{VILORIA:2020:procs, author = "Amelec Viloria and Nelson Alberto {Lizardo Zelaya} and Noel Varela", title = "Design and simulation of vehicle controllers through genetic algorithms", journal = "Procedia Computer Science", volume = "175", pages = "453--458", year = "2020", note = "The 17th International Conference on Mobile Systems and Pervasive Computing (MobiSPC),The 15th International Conference on Future Networks and Communications (FNC),The 10th International Conference on Sustainable Energy Information Technology", ISSN = "1877-0509", DOI = "doi:10.1016/j.procs.2020.07.064", URL = "http://www.sciencedirect.com/science/article/pii/S1877050920317452", keywords = "genetic algorithms, genetic programming, Design, simulation, Vehicle controllers", abstract = "Genetic Programming (GP) is a population-based evolutionary technique, which, unlike a Genetic Algorithm (GA) does not work on a fixed-length data structure, but on a variable-length structure and aims to evolve functions, models or programs, rather than finding a set of parameters. There are different histories of driver development, so different proposals of the use of PG to evolve driver structures are presented. In the case of an autonomous vehicle, the development of a steering controller is complex in the sense that it is a non-linear system, and the control actions are very limited by the maximum angle allowed by the steering wheels. This paper presents the development of an autonomous vehicle controller with Ackermann steering evolved by means of Genetic Programming", } @InProceedings{viloria:2020:ICBDCCC, author = "Amelec Viloria and Mercedes Gaitan Angulo and Sadhana J. Kamatkar and Juan {de la Hoz - Hernandez} and Jesus Garcia Guiliany and Osman Redondo Bilbao and Hugo {Hernandez-P}", title = "Prediction Rules in {E}-Learning Systems Using Genetic Programming", booktitle = "Proceedings of 6th International Conference on Big Data and Cloud Computing Challenges", year = "2020", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-981-32-9889-7_5", DOI = "doi:10.1007/978-981-32-9889-7_5", } @InProceedings{Virgolin:2017:GECCO, author = "Marco Virgolin and Tanja Alderliesten and Cees Witteveen and Peter A. N. Bosman", title = "Scalable Genetic Programming by Gene-pool Optimal Mixing and Input-space Entropy-based Building-block Learning", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4920-8", address = "Berlin, Germany", pages = "1041--1048", publisher = "ACM", publisher_address = "New York, NY, USA", month = "15-19 " # jul, keywords = "genetic algorithms, genetic programming, building blocks, linkage learning, optimal mixing, program synthesis", URL = "https://homepages.cwi.nl/~bosman/publications/2017_scalablegeneticprogramming.pdf", URL = "http://doi.acm.org/10.1145/3071178.3071287", DOI = "doi:10.1145/3071178.3071287", code_url = "https://github.com/marcovirgolin/GP-GOMEA", acmid = "3071287", size = "8 pages", abstract = "The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) is a recently introduced model-based EA that has been shown to be capable of outperforming state-of-the-art alternative EAs in terms of scalability when solving discrete optimization problems. One of the key aspects of GOMEA's success is a variation operator that is designed to extensively exploit linkage models by effectively combining partial solutions. Here, we bring the strengths of GOMEA to Genetic Programming (GP), introducing GP-GOMEA. Under the hypothesis of having little problem-specific knowledge, and in an effort to design easy-to-use EAs, GP-GOMEA requires no parameter specification. On a set of well-known benchmark problems we find that GP-GOMEA outperforms standard GP while being on par with more recently introduced, state-of-the-art EAs. We furthermore introduce Input-space Entropy-based Building-block Learning (IEBL), a novel approach to identifying and encapsulating relevant building blocks (subroutines) into new terminals and functions. On problems with an inherent degree of modularity, IEBL can contribute to compact solution representations, providing a large potential for knock-on effects in performance. On the difficult, but highly modular Even Parity problem, GP-GOMEA+IEBL obtains excellent scalability, solving the 14-bit instance in less than 1 hour.", notes = "Also known as \cite{Virgolin:2017:SGP:3071178.3071287} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @InProceedings{Virgolin:2018:GECCO, author = "Marco Virgolin and Tanja Alderliesten and Arjan Bel and Cees Witteveen and Peter A. N. Bosman", title = "Symbolic regression and feature construction with {GP-GOMEA} applied to radiotherapy dose reconstruction of childhood cancer survivors", booktitle = "GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference", year = "2018", editor = "Hernan Aguirre and Keiki Takadama and Hisashi Handa and Arnaud Liefooghe and Tomohiro Yoshikawa and Andrew M. Sutton and Satoshi Ono and Francisco Chicano and Shinichi Shirakawa and Zdenek Vasicek and Roderich Gross and Andries Engelbrecht and Emma Hart and Sebastian Risi and Ekart Aniko and Julian Togelius and Sebastien Verel and Christian Blum and Will Browne and Yusuke Nojima and Tea Tusar and Qingfu Zhang and Nikolaus Hansen and Jose Antonio Lozano and Dirk Thierens and Tian-Li Yu and Juergen Branke and Yaochu Jin and Sara Silva and Hitoshi Iba and Anna I Esparcia-Alcazar and Thomas Bartz-Beielstein and Federica Sarro and Giuliano Antoniol and Anne Auger and Per Kristian Lehre", isbn13 = "978-1-4503-5618-3", pages = "1395--1402", address = "Kyoto, Japan", DOI = "doi:10.1145/3205455.3205604", publisher = "ACM", publisher_address = "New York, NY, USA", month = "15-19 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", abstract = "the recently introduced Gene-pool Optimal Mixing Evolutionary Algorithm for Genetic Programming (GP-GOMEA) has been shown to find much smaller solutions of equally high quality compared to other state-of-the-art GP approaches. This is an interesting aspect as small solutions better enable human interpretation. In this paper, an adaptation of GP-GOMEA to tackle real-world symbolic regression is proposed, in order to find small yet accurate mathematical expressions, and with an application to a problem of clinical interest. For radiotherapy dose reconstruction, a model is sought that captures anatomical patient similarity. This problem is particularly interesting because while features are patient-specific, the variable to regress is a distance, and is defined over patient pairs. We show that on benchmark problems as well as on the application, GP-GOMEA outperforms variants of standard GP. To find even more accurate models, we further consider an evolutionary meta learning approach, where GP-GOMEA is used to construct small, yet effective features for a different machine learning algorithm. Experimental results show how this approach significantly improves the performance of linear regression, support vector machines, and random forest, while providing meaningful and interpretable features.", notes = "Also known as \cite{3205604} GECCO-2018 A Recombination of the 27th International Conference on Genetic Algorithms (ICGA-2018) and the 23rd Annual Genetic Programming Conference (GP-2018)", } @InProceedings{Virgolin:2019:GECCO, author = "Marco Virgolin and Tanja Alderliesten and Peter A. N. Bosman", title = "Linear scaling with and within semantic backpropagation-based genetic programming for symbolic regression", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference", year = "2019", editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and Anne Auger and Petr Posik and Leslie {Peprez Caceres} and Andrew M. Sutton and Nadarajen Veerapen and Christine Solnon and Andries Engelbrecht and Stephane Doncieux and Sebastian Risi and Penousal Machado and Vanessa Volz and Christian Blum and Francisco Chicano and Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and Jonathan Fieldsend and Jose Antonio Lozano and Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and Robin Purshouse and Thomas Baeck and Justyna Petke and Giuliano Antoniol and Johannes Lengler and Per Kristian Lehre", isbn13 = "978-1-4503-6111-8", pages = "1084--1092", address = "Prague, Czech Republic", DOI = "doi:10.1145/3321707.3321758", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, Machine learning, semantic backpropagation, linear scaling", size = "9 pages", abstract = "Semantic Backpropagation (SB) is a recent technique that promotes effective variation in tree-based genetic programming. The basic idea of SB is to provide information on what output is desirable for a specified tree node, by propagating the desired root-node output back to the specified node using inversions of functions encountered along the way. Variation operators then replace the subtree located at the specified node with a tree for which the output is closest to the desired output, by searching in a pre-computed library. In this paper, we propose two contributions to enhance SB specifically for symbolic regression, by incorporating the principles of Keijzer Linear Scaling (LS). In particular, we show how SB can be used in synergy with the scaled mean squared error, and we show how LS can be adopted within library search. We test our adaptations using the well-known variation operator Random Desired Operator (RDO), comparing to its baseline implementation, and to traditional crossover and mutation. Our experimental results on real-world datasets show that SB enhanced with LS substantially improves the performance of RDO, resulting in overall the best performance among all tested GP algorithms.", notes = "\cite{keijzer03} \cite{keijzer:2004:GPEM} Also known as \cite{3321758} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @Misc{DBLP:journals/corr/abs-1904-02050, author = "Marco Virgolin and Tanja Alderliesten and Cees Witteveen and Peter A. N. Bosman", title = "A Model-based Genetic Programming Approach for Symbolic Regression of Small Expressions", howpublished = "arXiv", volume = "abs/1904.02050", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "http://arxiv.org/abs/1904.02050", archiveprefix = "arXiv", eprint = "1904.02050", timestamp = "Wed, 24 Apr 2019 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/corr/abs-1904-02050.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{Virgolin:EC, author = "M. Virgolin and T. Alderliesten and C. Witteveen and P. A. N. Bosman", title = "Improving Model-based Genetic Programming for Symbolic Regression of Small Expressions", journal = "Evolutionary Computation", year = "2021", volume = "29", number = "2", pages = "211--237", month = "Summer", keywords = "genetic algorithms, genetic programming, symbolic regression, linkage, GOMEA, machine learning, interpretability", ISSN = "1063-6560", URL = "https://doi.org/10.1162/evco_a_00278", DOI = "doi:10.1162/evco_a_00278", size = "27 pages", abstract = "The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) is a model-based EA framework that has been shown to perform well in several domains, including Genetic Programming (GP). Differently from traditional EAs where variation acts blindly, GOMEA learns a model of interdependencies within the genotype, i.e., the linkage, to estimate what patterns to propagate. In this article, we study the role of Linkage Learning (LL) performed by GOMEA in Symbolic Regression (SR). We show that the non-uniformity in the distribution of the genotype in GP populations negatively biases LL, and propose a method to correct for this. We also propose approaches to improve LL when ephemeral random constants are used. Furthermore, we adapt a scheme of interleaving runs to alleviate the burden of tuning the population size, a crucial parameter for LL, to SR.We run experiments on 10 real-world datasets, enforcing a strict limitation on solution size, to enable interpretability. We find that the new LL method outperforms the standard one, and that GOMEA outperforms both traditional and semantic GP. We also find that the small solutions evolved by GOMEA are competitive with tuned decision trees, making GOMEA a promising new approach to SR.", notes = "Life Science and Health group, CWI, Centrum Wiskunde and Informatica, Amsterdam,1098 XG, the Netherlands PMID: 32574084 Also known as \cite{doi:10.1162/evco\_a\_00278}", } @InProceedings{Virgolin:2020:PPSN, author = "Marco Virgolin and Andrea {De Lorenzo} and Eric Medvet and Francesca Randone", title = "Learning a Formula of Interpretability to Learn Interpretable Formulas", booktitle = "16th International Conference on Parallel Problem Solving from Nature, Part II", year = "2020", editor = "Thomas Baeck and Mike Preuss and Andre Deutz and Hao Wang2 and Carola Doerr and Michael Emmerich and Heike Trautmann", volume = "12270", series = "LNCS", pages = "79--93", address = "Leiden, Holland", month = "7-9 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Explainable artificial intelligence, XAI, Interpretable machine learning, Symbolic regression, Multi-objective", isbn13 = "978-3-030-58114-5", URL = "https://arxiv.org/abs/2004.11170", DOI = "doi:10.1007/978-3-030-58115-2_6", code_url = "https://github.com/MaLeLabTs/GPFormulasInterpretability?tab=readme-ov-file", size = "15 pages", abstract = "Many risk-sensitive applications require Machine Learning (ML) models to be interpretable. Attempts to obtain interpretable models typically rely on tuning, by trial-and-error, hyper-parameters of model complexity that are only loosely related to interpretability. We show that it is instead possible to take a meta-learning approach: an ML model of non-trivial Proxies of Human Interpretability (PHIs) can be learned from human feedback, then this model can be incorporated within an ML training process to directly optimize for interpretability. We show this for evolutionary symbolic regression. We first design and distribute a survey finalized at finding a link between features of mathematical formulas and two established PHIs, simulatability and decomposability. Next, we use the resulting dataset to learn an ML model of interpretability. Lastly, we query this model to estimate the interpretability of evolving solutions within bi-objective genetic programming. We perform experiments on five synthetic and eight real-world symbolic regression problems, comparing to the traditional use of solution size minimization. The results show that the use of our model leads to formulas that are, for a same level of accuracy-interpretability trade-off, either significantly more or equally accurate. Moreover, the formulas are also arguably more interpretable. Given the very positive results, we believe that our approach represents an important stepping stone for the design of next-generation interpretable (evolutionary) ML algorithms.", notes = "v2 28 May 2020 https://arxiv.org/abs/2004.11170 says 'Accepted at PPSN2020' PPSN2020", } @Article{VIRGOLIN:2020:swarm, author = "Marco Virgolin and Tanja Alderliesten and Peter A. N. Bosman", title = "On explaining machine learning models by evolving crucial and compact features", journal = "Swarm and Evolutionary Computation", volume = "53", pages = "100640", year = "2020", ISSN = "2210-6502", DOI = "doi:10.1016/j.swevo.2019.100640", URL = "http://www.sciencedirect.com/science/article/pii/S2210650219305036", keywords = "genetic algorithms, genetic programming, Feature construction, Interpretable machine learning, GOMEA", abstract = "Feature construction can substantially improve the accuracy of Machine Learning (ML) algorithms. Genetic Programming (GP) has been proven to be effective at this task by evolving non-linear combinations of input features. GP additionally has the potential to improve ML explainability since explicit expressions are evolved. Yet, in most GP works the complexity of evolved features is not explicitly bound or minimized though this is arguably key for explainability. In this article, we assess to what extent GP still performs favorably at feature construction when constructing features that are (1) Of small-enough number, to enable visualization of the behavior of the ML model; (2) Of small-enough size, to enable interpretability of the features themselves; (3) Of sufficient informative power, to retain or even improve the performance of the ML algorithm. We consider a simple feature construction scheme using three different GP algorithms, as well as random search, to evolve features for five ML algorithms, including support vector machines and random forest. Our results on 21 datasets pertaining to classification and regression problems show that constructing only two compact features can be sufficient to rival the use of the entire original feature set. We further find that a modern GP algorithm, GP-GOMEA, performs best overall. These results, combined with examples that we provide of readable constructed features and of 2D visualizations of ML behavior, lead us to positively conclude that GP-based feature construction still works well when explicitly searching for compact features, making it extremely helpful to explain ML models", } @PhdThesis{DBLP:phd/basesearch/Virgolin20, author = "Marco Virgolin", title = "Design and Application of Gene-pool Optimal Mixing Evolutionary Algorithms for Genetic Programming", school = "Delft University of Technology, Netherlands", year = "2020", month = "8 " # jun, note = "Winner Best PhD thesis GECCO 2021", keywords = "genetic algorithms, genetic programming, evolutionary algorithms, machine learning, pediatric cancer, radiotherapy, GP-GOMEA", isbn13 = "978-94-6384-138-2", URL = "http://resolver.tudelft.nl/uuid:03641b5f-f8f6-4ff9-be7f-11948f6d3cc7", URL = "https://repository.tudelft.nl/islandora/object/uuid:03641b5f-f8f6-4ff9-be7f-11948f6d3cc7/datastream/OBJ/download", DOI = "doi:10.4233/uuid:03641b5f-f8f6-4ff9-be7f-11948f6d3cc7", timestamp = "Tue, 09 Jun 2020 01:00:00 +0200", biburl = "https://dblp.org/rec/phd/basesearch/Virgolin20.bib", bibsource = "dblp computer science bibliography, https://dblp.org", size = "263 pages", abstract = "Machine learning is impacting modern society at large, thanks to its increasing potential to efficiently and effectively model complex and heterogeneous phenomena. While machine learning models can achieve very accurate predictions in many applications, they are not infallible. In some cases, machine learning models can deliver unreasonable outcomes. For example, deep neural networks for self-driving cars have been found to provide wrong steering directions based on the lighting conditions of street lanes (e.g., due to cloudy weather). In other cases, models can capture and reflect unwanted biases that were concealed in the training data. For example, deep neural networks used to predict likely jobs and social status of people based on their pictures, were found to consistently discriminate based on gender and ethnicity. This was later attributed to human bias in the labels of the training data.", notes = "Winner 2021 SIGEVO Dissertation Award https://sig.sigevo.org/index.html/tiki-index.php?page=SIGEVO+Dissertation+Award supervisors: P.A.N. Bosman C. Witteveen, T. Alderliesten", } @Article{Virgolin:2020:JMI, author = "Marco Virgolin and Ziyuan Wang and Tanja Alderliesten and Peter A. N. Bosman", title = "Machine learning for the prediction of pseudorealistic pediatric abdominal phantoms for radiation dose reconstruction", journal = "Journal of Medical Imaging", year = "2020", volume = "7", number = "4", pages = "046501", month = "30 " # jul, note = "Winner Silver HUMIES", keywords = "genetic algorithms, genetic programming, machine learning, pediatric cancer, radiation treatment, dose reconstruction, phantom, Spleen, Liver, Lawrencium, Databases, Machine learning, Cancer, Image segmentation, Computed tomography, Radiography", publisher = "SPIE", ISSN = "2329-4302", URL = "http://www.human-competitive.org/sites/default/files/humies_entry_virgolin.txt", URL = "http://www.human-competitive.org/sites/default/files/virgolinpaperapreprint_0.pdf", DOI = "doi:10.1117/1.JMI.7.4.046501", video_url = "http://www.human-competitive.org/sites/default/files/humies2021_virgolin_1.mp4", video_url = "https://youtu.be/jowH-xIvthU", size = "25 pages", abstract = "Purpose: Current phantoms used for the dose reconstruction of long-term childhood cancer survivors lack individualization. We design a method to predict highly individualised abdominal three-dimensional (3-D) phantoms automatically. Approach: We train machine learning (ML) models to map (2-D) patient features to 3-D organ-at-risk (OAR) metrics upon a database of 60 pediatric abdominal computed tomographies with liver and spleen segmentations. Next, we use the models in an automatic pipeline that outputs a personalized phantom given the patient's features, by assembling 3-D imaging from the database. A step to improve phantom realism (i.e., avoid OAR overlap) is included. We compare five ML algorithms, in terms of predicting OAR left-right (LR), anterior-posterior (AP), inferior-superior (IS) positions, and surface Dice-Sorensen coefficient (sDSC). Furthermore, two existing human-designed phantom construction criteria and two additional control methods are investigated for comparison. Results: Different ML algorithms result in similar test mean absolute errors: approx 8mm for liver LR, IS, and spleen AP, IS; approx 5mm for liver AP and spleen LR; approx 80 percent for abdomen sDSC; and approx 60 percent to 65 percent for liver and spleen sDSC. One ML algorithm (GP-GOMEA) significantly performs the best for 6/9 metrics. The control methods and the human-designed criteria in particular perform generally worse, sometimes substantially (+5-mm error for spleen IS, -10 percent sDSC for liver). The automatic step to improve realism generally results in limited metric accuracy loss, but fails in one case (out of 60). Conclusion: Our ML-based pipeline leads to phantoms that are significantly and substantially more individualized than currently used human-designed criteria.", notes = "Modern genetic programming reconstructs radiation dose for cancer patients better than humans. Most children treated for cancer suffer adverse effects from their treatment decades later. Today: 3D libraries of humans organ layout used by manual expert, replace by genetic programming personalised 3D surrogate (4:42) GP replace whole manual pipeline (4:59) See also \cite{Virgolin:2020:PMB} Human-interpretable. Cutting-edge GP (8:27) https://github.com/marcovirgolin/GP-GOMEA \cite{Virgolin:2017:GECCO} Model size v. accuracy R^2 CWI, Amsterdam UMC, TUDelft, Institute of Oncology Ljubljana, Princess maxima center pediatric oncology, LUMC, UMC Utrecht, Princess Margret Cancer Centre UHN, the university of Manchester. 2021 HUMIES prize giving video https://www.youtube.com/watch?v=jrT0sfq6WjM 43:10 -- 47:08 Childhood radiation treatment 2D images, years later 3D radiation dose outside target human tissue", } @Misc{DBLP:journals/corr/abs-2009-06037, author = "Marco Virgolin", title = "Simple Simultaneous Ensemble Learning in Genetic Programming", howpublished = "arXiv", volume = "abs/2009.06037", year = "2020", keywords = "genetic algorithms, genetic programming", URL = "https://arxiv.org/abs/2009.06037", archiveprefix = "arXiv", eprint = "2009.06037", timestamp = "Thu, 17 Sep 2020 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/corr/abs-2009-06037.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{Virgolin:2020:MI, author = "Marco Virgolin and Ziyuan Wang and Tanja Alderliesten and Peter A. N. Bosman", title = "Machine learning for automatic construction of pediatric abdominal phantoms for radiation dose reconstruction", booktitle = "Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications", year = "2020", month = mar # "~02", editor = "P-H. Chen and T. M. Deserno", volume = "11318", series = "SPIE", keywords = "genetic algorithms, genetic programming, dose reconstruction, machine learning, pediatric cancer, phantom, radiation treatment", bibsource = "OAI-PMH server at ir.cwi.nl", language = "en", oai = "oai:cwi.nl:29558", URL = "https://ir.cwi.nl/pub/29558", URL = "https://ir.cwi.nl/pub/29558/29558.pdf", URL = "https://www.spiedigitallibrary.org/conference-proceedings-of-spie", DOI = "doi:10.1117/12.2548969", size = "9 pages", abstract = "The advent of Machine Learning (ML) is proving extremely beneficial in many healthcare applications. In pediatric oncology, retrospective studies that investigate the relationship between treatment and late adverse effects still rely on simple heuristics. To capture the effects of radiation treatment, treatment plans are typically simulated on virtual surrogates of patient anatomy called phantoms. Currently, phantoms are built to represent categories of patients based on reasonable yet simple criteria. This often results in phantoms that are too generic to accurately represent individual anatomies. We present a novel approach that combines imaging data and ML to build individualized phantoms automatically. We design a pipeline that, given features of patients treated in the pre-3D planning era when only 2D radiographs were available, as well as a database of 3D Computed Tomography (CT) imaging with organ segmentations, uses ML to predict how to assemble a patient-specific phantom. Using 60 abdominal CTs of pediatric patients between 2 to 6 years of age, we find that our approach delivers significantly more representative phantoms compared to using current phantom building criteria, in terms of shape and location of two considered organs (liver and spleen), and shape of the abdomen. Furthermore, as interpretability is often central to trust ML models in medical contexts, among other ML algorithms we consider the Gene-pool Optimal Mixing Evolutionary Algorithm for Genetic Programming (GP-GOMEA), that learns readable mathematical expression models. We find that the readability of its output does not compromise prediction performance as GP-GOMEA delivered the best performing models.", } @Article{Virgolin:2020:PMB, author = "M Virgolin and Ziyuan Wang and B V Balgobind and I W E M {van Dijk} and J Wiersma and P S Kroon and G O Janssens and M {van Herk} and D C Hodgson and L {Zadravec Zaletel} and C R N Rasch and A Bel and P A N Bosman and T Alderliesten", title = "Surrogate-free machine learning-based organ dose reconstruction for pediatric abdominal radiotherapy", journal = "Physics in Medicine \& Biology", year = "2020", volume = "65", number = "24", pages = "245021", month = dec, keywords = "genetic algorithms, genetic programming, cancer, CT, dose reconstruction, radiotherapy dosimetry, machine learning, planemulation, childhood cancer, late adverse effects", publisher = "{IOP} Publishing", URL = "http://www.human-competitive.org/sites/default/files/humies_entry_virgolin.txt", URL = "http://www.human-competitive.org/sites/default/files/virgolinpaperbpreprint.pdf", URL = "https://doi.org/10.1088/1361-6560/ab9fcc", DOI = "doi:10.1088/1361-6560/ab9fcc", size = "16 pages", abstract = "To study radiotherapy-related adverse effects, detailed dose information (3D distribution) is needed for accurate dose-effect modeling. For childhood cancer survivors who underwent radiotherapy in the pre-CT era, only 2D radiographs were acquired, thus 3D dose distributions must be reconstructed from limited information. State-of-the-art methods achieve this by using 3D surrogate anatomies. These can however lack personalisation and lead to coarse reconstructions. We present and validate a surrogate-free dose reconstruction method based on Machine Learning (ML). Abdominal planning CTs (n = 142) of recently-treated childhood cancer patients were gathered, their organs at risk were segmented, and 300 artificial Wilms tumour plans were sampled automatically. Each artificial plan was automatically emulated on the 142 CTs, resulting in 42600 3D dose distributions from which dose-volume metrics were derived. Anatomical features were extracted from digitally reconstructed radiographs simulated from the CTs to resemble historical radiographs. Further, patient and radiotherapy plan features typically available from historical treatment records were collected. An evolutionary ML algorithm was then used to link features to dose-volume metrics. Besides 5-fold cross validation, a further evaluation was done on an independent dataset of five CTs each associated with two clinical plans. Cross-validation resulted in mean absolute errors less than or equal 0.6 Gy for organs completely inside or outside the field. For organs positioned at the edge of the field, mean absolute errors less than or equal 1.7 Gy for, less than or equal 2.9 Gy for, and less than or equal 13 percent for and, were obtained, without systematic bias. Similar results were found for the independent dataset. To conclude, we proposed a novel organ dose reconstruction method that uses ML models to predict dose-volume metric values given patient and plan features. Our approach is not only accurate, but also efficient, as the setup of a surrogate is no longer needed.", notes = "Entered 2021 HUMIES with \cite{Virgolin:2020:JMI}", } @InProceedings{Virgolin:2021:GECCO, author = "Marco Virgolin", title = "Genetic Programming is Naturally Suited to Evolve Bagging Ensembles", booktitle = "Proceedings of the 2021 Genetic and Evolutionary Computation Conference", year = "2021", editor = "Francisco Chicano and Alberto Tonda and Krzysztof Krawiec and Marde Helbig and Christopher W. Cleghorn and Dennis G. Wilson and Georgios Yannakakis and Luis Paquete and Gabriela Ochoa and Jaume Bacardit and Christian Gagne and Sanaz Mostaghim and Laetitia Jourdan and Oliver Schuetze and Petr Posik and Carlos Segura and Renato Tinos and Carlos Cotta and Malcolm Heywood and Mengjie Zhang and Leonardo Trujillo and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Fuyuki Ishikawa and Inmaculada Medina-Bulo and Frank Neumann and Andrew M. Sutton", pages = "830--839", address = "internet", series = "GECCO '21", month = jul # " 10-14", organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, Bagging, ensemble learning, machine learning, evolutionary algorithms", isbn13 = "9781450383509", code_url = "https://github.com/marcovirgolin/2SEGP", DOI = "doi:10.1145/3449639.3459278", size = "10 page", abstract = "Learning ensembles by bagging can substantially improve the generalisation performance of low-bias, high-variance estimators, including those evolved by Genetic Programming (GP). To be efficient, modern GP algorithms for evolving (bagging) ensembles typically rely on several (often inter-connected) mechanisms and respective hyper-parameters, ultimately compromising ease of use. we provide experimentalevidence that such complexity might not be warranted. Weshow that minor changes to fitness evaluation and selectionare sufficient to make a simple and otherwise-traditional GP algorithm evolve ensembles efficiently. The key to our proposal is to exploit the way bagging works to compute, for eachindividual in the population, multiple fitness values (insteadof one) at a cost that is only marginally higher than the oneof a normal fitness evaluation. Experimental comparisons onclassification and regression tasks taken and reproduced fromprior studies show that our algorithm fares very well against state-of-the-art ensemble and non-ensemble GP algorithms.We further provide insights into the proposed approach by (i)scaling the ensemble size, (ii) ablating the changes to selection,(iii) observing the evolvability induced by traditional subtreevariation. Code: https://github.com/marcovirgolin/2SEGP.", notes = "Chalmers University of Technology GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)", } @Misc{DBLP:journals/corr/abs-2204-02046, author = "Marco Virgolin and Eric Medvet and Tanja Alderliesten and Peter A. N. Bosman", title = "Less is More: {A} Call to Focus on Simpler Models in Genetic Programming for Interpretable Machine Learning", howpublished = "arXiv", volume = "abs/2204.02046", year = "2022", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.48550/arXiv.2204.02046", DOI = "doi:10.48550/arXiv.2204.02046", eprinttype = "arXiv", eprint = "2204.02046", timestamp = "Wed, 06 Apr 2022 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/corr/abs-2204-02046.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Manual{virirakis::genetica, title = "{GENETICA}: Genetic Evolution of Novel Entities Through the Interpretation of Composite Abstracts. Documentation of the Prototype version", author = "Lefteris Virirakis", year = "2003?", keywords = "genetic algorithms, genetic programming", URL = "http://www.genetica-informatics.org/GENETICA_Documentation.pdf", } @Article{virirakis:2003:TEC, author = "Lefteris Virirakis", title = "{GENETICA}: {A} Computer Language That Supports General Formal Expression With Evolving Data Structures", journal = "IEEE Transactions on Evolutionary Computation", year = "2003", volume = "7", number = "5", pages = "456--481", month = oct, keywords = "genetic algorithms, genetic programming, GP, problem solving, architectural CAD, data structures, evolutionary computation, formal logic, high level languages, programming environments, G-CAD, GENETICA, architectural design, artificial intelligence, computer language, data abstractions, data generation scenarios, data structure, domain specific languages, evolutionary computation, evolving data structures, experimental results, formal logic, general formal expression, genetic programming, genotypes, optimization, problem-solving, problem-solving method, programming environment", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2003.816581", size = "26 pages", abstract = "We presents a general problem-solving method combining the principles of artificial intelligence and evolutionary computation. The problem-solving method is based on the computer language GENETICA, which stands for Genetic Evolution of Novel Entities Through the Interpretation of Composite Abstractions. GENETICAs programming environment includes a computational system that evolves data abstractions, viewed as genotypes of data generation scenarios for a GENETICA program, with respect to either confirmation or optimisation goals. A problem can be formulated as a GENETICA program, while the solution is represented as a data structure resulting from an evolved data generation scenario. This approach to problem solving offers: 1) generality, since it concerns virtually any problem stated in formal logic; 2) effectiveness, since formally expressed problem-solving knowledge can be incorporated in the problem statement; and 3) creativity, since unpredictable solutions can be obtained by evolved data structures. It is shown that domain specific languages, including genetic programming ones, that inherit GENETICAs features can be developed in GENETICA. The language G-CAD, specialised to problem solving in the domain of architectural design, is presented as a case study followed by experimental results.", notes = "Inspec Accession Number: 7757988", } @Article{Visco:2024:compbiomed, author = "Valeria Visco and Antonio Robustelli and Francesco Loria and Antonella Rispoli and Francesca Palmieri and Alessia Bramanti and Albino Carrizzo and Carmine Vecchione and Francesco Palmieri and Michele Ciccarelli and Gianni D'Angelo", title = "An explainable model for predicting Worsening Heart Failure based on genetic programming", journal = "Computers in Biology and Medicine", year = "2024", volume = "182", pages = "109110", keywords = "genetic algorithms, genetic programming, Heart failure, Worsening Heart Failure, Explainable diagnosis, Artificial Intelligence, Explainable classification", ISSN = "0010-4825", URL = "https://www.sciencedirect.com/science/article/pii/S0010482524011958", DOI = "doi:10.1016/j.compbiomed.2024.109110", abstract = "Heart Failure (HF) poses a challenge for our health systems, and early detection of Worsening HF (WHF), defined as a deterioration in symptoms and clinical and instrumental signs of HF, is vital to improving prognosis. Predicting WHF in a phase that is currently undiagnosable by physicians would enable prompt treatment of such events in patients at a higher risk of WHF. Although the role of Artificial Intelligence in cardiovascular diseases is becoming part of clinical practice, especially for diagnostic and prognostic purposes, its usage is often considered not completely reliable due to the incapacity of these models to provide a valid explanation about their output results. Physicians are often reluctant to make decisions based on unjustified results and see these models as black boxes. This study aims to develop a novel diagnostic model capable of predicting WHF while also providing an easy interpretation of the outcomes. We propose a threshold-based binary classifier built on a mathematical model derived from the Genetic Programming approach. This model clearly indicates that WHF is closely linked to creatinine, sPAP, and CAD, even though the relationship of these variables and WHF is almost complex. However, the proposed mathematical model allows for providing a 3D graphical representation, which medical staff can use to better understand the clinical situation of patients. Experiments conducted using retrospectively collected data from 519 patients treated at the HF Clinic of the University Hospital of Salerno have demonstrated the effectiveness of our model, surpassing the most commonly used machine learning algorithms. Indeed, the proposed GP-based classifier achieved a 96percent average score for all considered evaluation metrics and fully supported the controls of medical staff. Our solution has the potential to impact clinical practice for HF by identifying patients at high risk of WHF and facilitating more rapid diagnosis, targeted treatment, and a reduction in hospitalizations", } @Article{Visoiu:2009:IEJ, title = "Structure Refinement for Vulnerability Estimation Models using Genetic Algorithm Based Model Generators", author = "Adrian Visoiu", journal = "Informatica Economica Journal", year = "2009", volume = "13", number = "1", pages = "64--74", keywords = "genetic algorithms, genetic programming, gene expression programming, model structure refinement, model generators, software vulnerabilities, performance criteria, software metrics", ISSN = "14531305", bibsource = "OAI-PMH server at www.doaj.org", publisher = "Inforec Association", language = "eng", oai = "oai:doaj-articles:6c9a9e252b96627dc31205813bf03004", URL = "http://revistaie.ase.ro/content/49/007%20-%20Visoiu.pdf", URL = "http://revistaie.ase.ro/49.html", broken = "http://www.doaj.org/doaj?func=openurl\&genre=article\&issn=14531305\&date=2009\&volume=13\&issue=1\&spage=64", size = "11 pages", abstract = "In this paper, a method for model structure refinement is proposed and applied in estimation of cumulative number of vulnerabilities according to time. Security as a quality characteristic is presented and defined. Vulnerabilities are defined and their importance is assessed. Existing models used for number of vulnerabilities estimation are enumerated, inspecting their structure. The principles of genetic model generators are inspected. Model structure refinement is defined in comparison with model refinement and a method for model structure refinement is proposed. A case study shows how the method is applied and the obtained results.", } @Misc{DBLP:journals/corr/abs-2211-17234, author = "Max Vistrup", title = "Genetic Programming with Local Scoring", howpublished = "arXiv", volume = "abs/2211.17234", year = "2022", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.48550/arXiv.2211.17234", DOI = "doi:10.48550/arXiv.2211.17234", eprinttype = "arXiv", eprint = "2211.17234", timestamp = "Fri, 02 Dec 2022 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/corr/abs-2211-17234.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @TechReport{vitanyi:1997:gfourmmc, author = "Paul Vitanyi", title = "Genetic Fitness Optimization Using Rapidly Mixing Markov Chains", institution = "NeuroCOLT", year = "1997", type = "technical report", number = "NC-TR-005", address = "Computer Science, Royal Holloway, Egham, Surrey, England", month = feb, email = "neurocolt@dcs.rhbnc.ac.uk", keywords = "evolutionary computation", URL = "http://www.neurocolt.com/tech_reps/1997/nc-tr-97-005.ps.gz", URL = "http://www.neurocolt.com/abs/1997/abs97005.html", abstract = "A notion of highly probable fitness optimization through evolutionary computing runs on small size populations in a very general setting is proposed. This has applications to evolutionary learning. Based on rapidly mixing Markov chains, the approach pertains to most types of evolutionary genetic algorithms, genetic programming and the like. For systems having associated rapidly mixing Markov chains and appropriate stationary distributions the new method finds optimal programs (individuals) with probability almost 1. Algorithmically, the novel approach prescribes a strategy of executing many short computation runs, rather than one long computation run. Given an arbitrary evolutionary program it may be infeasible to determine whether its associated matrix is rapidly mixing. In our proposed structured evolutionary program discipline, the development of the program and the guaranty of the rapidly mixing property go hand in hand. We conclude with a tentative toy example.", notes = "See also \cite{alt96*67} and \cite{Vitanyi:2000:DEP} Although refers several time to GP, approach is evolutionary as a whole, ie not just GP", size = "17 pages", } @InProceedings{alt96*67, author = "Paul Vitanyi", title = "Genetic fitness optimization using rapidly mixing {Markov} chains", pages = "67--82", ISBN = "3-540-61863-5", editor = "Setsuo Arikawa and Arun K. Sharma", booktitle = "Proceedings of the 7th International Workshop on Algorithmic Learning Theory", month = oct # "~23--25", series = "LNAI", volume = "1160", publisher = "Springer-Verlag", address = "Berlin", year = "1996", notes = "see also \cite{vitanyi:1997:gfourmmc} and \cite{Vitanyi:2000:DEP}", } @Article{Vitanyi:2000:DEP, author = "Paul Vitanyi", title = "A discipline of evolutionary programming", journal = "Theoretical Computer Science", year = "2000", volume = "241", number = "1--2", pages = "3--23", month = "28 " # jun, keywords = "genetic algorithms, genetic programming, Neural and Evolutionary Computing, Artificial Intelligence, Computational Complexity, Data Structures and Algorithms, Learning, Multiagent Systems", ISSN = "0304-3975", CODEN = "TCSCDI", bibdate = "Tue Oct 31 11:38:29 MST 2000", URL = "http://xxx.lanl.gov/abs/cs.NE/9902006", URL = "http://homepages.cwi.nl/~paulv/papers/genetic.ps", URL = "http://www.elsevier.nl/gej-ng/10/41/16/175/21/22/article.pdf", size = "21 pages", abstract = "Genetic fitness optimization using small populations or small population updates across generations generally suffers from randomly diverging evolutions. We propose a notion of highly probable fitness optimization through feasible evolutionary computing runs on small size populations. Based on rapidly mixing Markov chains, the approach pertains to most types of evolutionary genetic algorithms, genetic programming and the like. We establish that for systems having associated rapidly mixing Markov chains and appropriate stationary distributions the new method finds optimal programs (individuals) with probability almost 1. To make the method useful would require a structured design methodology where the development of the program and the guarantee of the rapidly mixing property go hand in hand. We analyze a simple example to show that the method is implementable. More significant examples require theoretical advances, for example with respect to the Metropolis filter.", notes = "Update of \cite{alt96*67} Presented at Dagstuhl Feb 2004. Generic to evolutionary computation, rather than specifically on GP.", } @Article{Vitanyi:2006:Surikagaku, author = "P. Vitanyi", title = "Asshuku ni Motozuita Hanyou na Ruijido Sokuteihou", journal = "Surikagaku", year = "2006", number = "519", pages = "54--59", month = sep, note = "Japanese, translated by O. Watanabe, English title: Universal similarity based on compression", keywords = "genetic algorithms, genetic programming", } @Article{Vitanyi:2013:philtrans, author = "Paul M. B. Vitanyi", title = "Similarity and denoising", journal = "Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences", year = "2013", volume = "371", number = "1984", keywords = "genetic algorithms, genetic programming, similarity, denoising, individual data, Kolmogorov complexity, information distance, lossy compression", ISSN = "1364-503X", URL = "http://rsta.royalsocietypublishing.org/content/371/1984/20120091", eprint = "http://rsta.royalsocietypublishing.org/content/371/1984/20120091.full.pdf", DOI = "doi:10.1098/rsta.2012.0091", publisher = "The Royal Society", size = "16 pages", abstract = "We can discover the effective similarity among pairs of finite objects and denoise a finite object using the Kolmogorov complexity of these objects. The drawback is that the Kolmogorov complexity is not computable. If we approximate it, using a good real-world compressor, then it turns out that on natural data the processes give adequate results in practice. The methodology is parameter-free, alignment-free and works on individual data. We illustrate both methods with examples.", } @InProceedings{DBLP:conf/flairs/VitelGW21, author = "Dmytro Vitel and Alessio Gaspar and R. Paul Wiegand", editor = "Eric Bell and Fazel Keshtkar", title = "A Neutral Rewrite Mutation Operator for Genetic Programming applied to Boolean Domain Problems", booktitle = "Proceedings of the Thirty-Fourth International Florida Artificial Intelligence Research Society Conference", year = "2021", month = may # " 17-19", address = "North Miami Beach, Florida, USA", keywords = "genetic algorithms, genetic programming, rewrite, neutrality", timestamp = "Tue, 07 Sep 2021 16:27:28 +0200", biburl = "https://dblp.org/rec/conf/flairs/VitelGW21.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://journals.flvc.org/FLAIRS/article/view/128527", URL = "https://journals.flvc.org/FLAIRS/article/view/128527/130051.pdf", URL = "https://doi.org/10.32473/flairs.v34i1.128527", DOI = "doi:10.32473/flairs.v34i1.128527", size = "6 pages", abstract = "The effect of semantically neutral tree rewrites is analysed in the context of genetic programming applied to Boolean domain problems. Different setups of the proposed Neutral Rewrite Operator are studied from the perspective of improving performance.", notes = "https://sites.google.com/view/flairs-34/home University of South Florida", } @InProceedings{Vitola:2012:LASCAS, author = "Jaime Vitola and Adriana Sanabria and Cesar Pedraza and Johanna Sepulveda", title = "Parallel algorithm for evolvable-based Boolean synthesis on GPUs", booktitle = "Third IEEE Latin American Symposium on Circuits and Systems (LASCAS 2012)", year = "2012", month = "29 " # feb # "-2 " # mar, DOI = "doi:10.1109/LASCAS.2012.6180339", size = "4 pages", abstract = "The use of evolutionary algorithms in the Boolean synthesis is an attractive alternative to generate interesting and efficient hardware structures, with a high computational load. This paper presents the implementation of a parallel genetic programming (PGP) for boolean synthesis on a GPU-CPU based platform. Our implementation uses the island model, that allows the parallel and independent evolution of the PGP through the multiple processing units of the GPU and the multiple cores of a new generation desktop processors. We tested multiple mapping alternatives of the PGP on the platform in order to optimise the PGP response time. As a result we show that our approach achieves a speedup up to 33.", keywords = "genetic algorithms, genetic programming, GPU, GPU-CPU based platform, PGP response time, evolutionary algorithms, evolvable-based Boolean synthesis, graphics processing units, independent evolution, island model, multiple cores, multiple mapping alternatives, multiple processing units, new generation desktop processors, parallel algorithm, parallel genetic programming, graphics processing units, parallel algorithms", notes = "CUDA, Mersenne-twister, each subpopulation in shared memory, selection, crossover, mutation, migration via global memory and PC? Twin nVidia 240GT. Comparitor=Even Parity?? No absolute speed measure given (cf. \cite{langdon:2008:eurogp}). 2-D tree, fitness function see \cite{Pedraza:2011:JSC} Also known as \cite{6180339}", } @InProceedings{Vitola:2014:CWCAS, author = "Jaime Vitola and Cesar Pedraza and Jose I. Martinez and Johanna Sepulveda", booktitle = "5th IEEE Colombian Workshop on Circuits and Systems (CWCAS 2014)", title = "Cartesian genetic algorithm for Boolean synthesis with power consumption restriction", year = "2014", address = "Bogota, Colombia", month = "16-17 " # oct, keywords = "genetic algorithms, genetic programming, cartesian genetic programming", DOI = "doi:10.1109/CWCAS.2014.6994608", size = "4 pages", abstract = "The use of evolutionary algorithms in the Boolean synthesis is an interesting technique to generate hardware structures with multiple restrictions. However, one characteristic of these algorithms is their high computational load. This paper presents the implementation of a parallel cartesian genetic programming (CGP) for Boolean synthesis on a FPGA-CPU based platform. Power consumption and critical path restrictions were included into the algorithm in order to generate structures to solve any problem. As results a 2-bit comparator is presented, as well as response time and data transitions probability.", notes = "Also known as \cite{6994608}", } @InCollection{Vladislavleva:2007:GPTP, author = "Ekaterina Vladislavleva and Guido Smits and Mark Kotanchek", title = "Better Solutions Faster: Soft Evolution of Robust Regression Models In Pareto genetic programming", booktitle = "Genetic Programming Theory and Practice {V}", year = "2007", editor = "Rick L. Riolo and Terence Soule and Bill Worzel", series = "Genetic and Evolutionary Computation", chapter = "2", pages = "13--32", address = "Ann Arbor", month = "17-19" # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-0-387-76308-8", DOI = "doi:10.1007/978-0-387-76308-8_2", size = "19 pages", abstract = "Better solutions faster is the reality of the industrial modelling world, now more than ever. Efficiency requirements, market pressures, and ever changing data force us to use symbolic regression via genetic programming (GP) in a highly automated fashion. This is why we want our GP system to produce simple solutions of the highest possible quality with the lowest computational effort, and a high consistency in the results of independent GP runs. In this chapter, we show that genetic programming with a focus on ranking in combination with goal softening is a very powerful way to improve the efficiency and effectiveness of the evolutionary search. Our strategy consists of partial fitness evaluations of individuals on random subsets of the original data set, with a gradual increase in the subset size in consecutive generations. From a series of experiments performed on three test problems, we observed that those evolutions that started from the smallest subset sizes (10percent) consistently led to results that are superior in terms of the goodness of fit, consistency between independent runs, and computational effort. Our experience indicates that solutions obtained using this approach are also less complex and more robust against over-fitting. We find that the near-optimal strategy of allocating computational budget over a GP run is to evenly distribute it over all generations. This implies that initially, more individuals can be evaluated using small subset sizes, promoting better exploration. Exploitation becomes more important towards the end of the run, when all individuals are evaluated using the full data set with correspondingly smaller population sizes.", notes = "part of \cite{Riolo:2007:GPTP} published 2008", affiliation = "Tilburg University Tilburg The Netherlands", } @PhdThesis{vladislavleva:2008:thesis, author = "Ekaterina Vladislavleva", title = "Model-based Problem Solving through Symbolic Regression via Pareto Genetic Programming", school = "Tilburg University", year = "2008", address = "Tilburg, the Netherlands", month = aug, isbn13 = "978 90 5668 217 0", keywords = "genetic algorithms, genetic programming, symbolic regression", publisher = "CentER, Center for Economic Research", URL = "https://research.tilburguniversity.edu/en/publications/model-based-problem-solving-through-symbolic-regression-via-paret", broken = "http://arno.uvt.nl/show.cgi?fid=80764", broken = "http://center.uvt.nl/gs/thesis/vladislavleva.html", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/vladislavleva_2008_thesis.pdf", size = "288 pages", abstract = "The main focus of this dissertation is identification of relationships from given input-output data by means of symbolic regression. The challenging task of symbolic regression is to identify and express a real or simulated system or a process, based on a limited number of observations of the system's behaviour. The system under study is being characterised by some important control parameters which need to be available for an observer, but usually are difficult to monitor, e.g. they need to be measured in a lab, simulated or observed in real time only, or at high time and computational expenses. Empirical modelling attempts to express these critical control variables via other controllable variables that are easier to monitor, can be measured more accurately or timely, are cheaper to simulate, etc. Symbolic regression provides such expressions of crucial process characteristics, or, response variables, defined (symbolically) as mathematical functions of some of the easy-to-measure input variables, and calls these expressions empirical input-response models. Examples of these are (i) structure-activity relationships in pharmaceutical research, which define the activity of a drug through the physical structure of molecules of drug components, (ii) structure-property relationships in material science, which define product qualities, such as shininess, opacity, smell, or stiffness through physical properties of composites and processing conditions, or (iii) economic models, e.g. expressing return on investment through daily closes of S&P 500 quotes and in ation rates.", abstract = "Industrial modelling problems that are tractable for symbolic regression have two main characteristics: (1) No or little information is known about the underlying system producing the data, and therefore no assumptions on model structure can be made; (2) The available data is high-dimensional, and often not balanced, with either abundant or insufficient number of samples. To discover plausible models with realistic time and computational efforts, symbolic regression exploits a stochastic iterative search technique, based on artificial evolution of model expressions. This method, called genetic programming looks for appropriate expressions of the response variable in the space of all valid formulae containing a minimal set of input variables and a proposed set of basic operators and constants. At each step, the genetic programming system considers a sufficiently large quantity of various formulae, selects the subset of the best formulae according to certain user-defined criteria of goodness, and (re)combines the best formulae to create a rich set of potential solutions for the next step. This approach is inspired by principles of natural selection, where the offspring that inherits good features from both parents increases the chances to be successful in survival, adaptation, and further propagation. The challenge and the rationale of performing evolutionary search is to balance the exploitation of the good solutions discovered so far, with exploration of the new areas of the search space, where even better solutions may be found.", abstract = "The fact that symbolic regression via genetic programming (GP) does not impose any assumptions on the structure of the input-output models means that the model structure is to a large extent determined by data and also by selection objectives used in the evolutionary search. On one hand, it is an advantage and the unique capability compared with other global approximation techniques, since it potentially allows to develop inherently simpler models than, for example, by interpolation with polynomials or spatial correlation analysis. On the other hand, the absence of constraints on model structure is the greatest challenge for symbolic regression since it vastly increases the search space of possible solutions which is already inherently large. A special multi-objective flavour of a genetic programming search is considered, called Pareto GP. Pareto GP used for symbolic regression has strong advantages in creating diverse sets of regression models, satisfying competing criteria of model structural simplicity and model prediction accuracy.", abstract = "This thesis extends the Pareto genetic programming methodology by additional generic model selection and generation strategies that (1) drive the modelling engine to creation of models of reduced non-linearity and increased generalisation capabilities, and (2) improve the effectiveness of the search for robust models by goal softening, adaptive fitness evaluations, and enhanced training strategies. In addition to the new strategies for model development and model selection, this dissertation presents a new approach for analysis, ranking, and compression of given multi-dimensional input-output data for the purpose of balancing the information content in undesigned data sets. To present contributions of this research in the context of real-life problem solving, the dissertation exploits a generic framework of adaptive model-based problem solving used in many industrial modelling applications. This framework consists of an iterative feed-back loop over: (Part I) data generation, analysis and adaptation, (Part II) model development, and (Part III) problem analysis and reduction.", abstract = "Part I of the thesis consists of Chapter 2 and is devoted to data analysis. It studies the ways to balance multi-dimensional input-output data for making further modelling more successful. Chapter 2 proposes several novel methods for interpretation and manipulation of given high-dimensional input-output data such as relative weighting the data, ranking the data records in the order of increasing importance, and accessing the compressibility and information content of a multi-dimensional data set. All methods exploit the geometrical structure of the data and relative distances to nearest-in-the-input space neighbours. All methods treat response values differently, assuming that the data belongs to a response surface, which needs to be identified. Part II of the thesis consist of Chapters 3-7 and addresses the model induction method - Pareto genetic programming. Since time to solution, or, more accurately, time-to-convincing-solution is a major practical challenge of evolutionary search algorithms, and Pareto GP in particular, Part II focuses on algorithmic enhancements of Pareto GP that lead it to the discovery of better solutions faster (i.e. solutions of sufficient quality at a smaller computational effort, or of considerably better quality at the same computational effort).", abstract = " In Chapter 3 a general description of the Pareto GP methodology is presented in a framework of evolutionary search, as an iterative loop over the stages of model generation, model evaluation, and model selection. In Chapter 5 a novel strategy for model selection through explicit non-linearity control is presented. A new complexity measure called the order of non-linearity of symbolic models is introduced and used successfully either as an independent optimisation criterion or alternated with expressional complexity, which is both cases leads to the development of models with improved extrapolative capabilities. In Chapter 5 a new way of fitness evaluation of models is introduced that exploits the data balancing methods of Chapter 2 with a modified definition of prediction accuracy for imbalanced data. Two different methods of data balancing for GP are presented. One uses the weights reflecting the relative information content of input-output data records directly for performing regression with the weighted prediction error. The second method compresses the input-output data set to a smaller subset of similar information content and performs standard regression on a subset of data with enhanced exploration.", abstract = "In Chapter 6 an alternative strategy for model evaluation in Pareto GP is introduced. It is based on the principles of goal softening and ordinal optimisation, and is designed to improve the balance of exploitation and exploration in the evolutionary search. In Chapter 7 a new method for incremental evolutions on essential data records is presented. It combines ideas of data balancing (from Chapter 2) and goal softening (from Chapter 6) into one new framework. In this framework, the data records ordered according to decreasing importance are added incrementally to the modelling system, starting from a very small subset with very large population size. The population size decreases in the course of evolution as the training subset size increases, to keep the computational budget constant per iteration. Statistically significant performance improvements of these methods as compared with the standard Pareto GP are observed on a number of various regression case studies. Part III of the thesis is devoted to Problem Analysis and Reduction. It assumes that the models developed in Part II are carefully scrutinised, interpreted, and validated for drawing preliminary conclusions on the difficulty of the modelling problem. Part III consists of Chapter 8 and presents some practical aspects of using symbolic regression results for estimating problem difficulty, in particular for variable selection, convergence identification, trustworthiness evaluation, and adaptive data collection.", } @Article{Vladislavleva:2009:TEC, author = "Ekaterina J. Vladislavleva and Guido F. Smits and Dick {den Hertog}", title = "Order of Nonlinearity as a Complexity Measure for Models Generated by Symbolic Regression via Pareto Genetic Programming", journal = "IEEE Transactions on Evolutionary Computation", year = "2009", volume = "13", number = "2", pages = "333--349", month = apr, keywords = "genetic algorithms, genetic programming, computational complexity, regression analysis, Pareto genetic programming, best-fit polynomial, data-driven regression models, nonlinearity order, symbolic regression, Complexity theory, Polynomials, Computational modeling, Chebyshev approximation, Data models, Approximation methods, Least squares approximation, model selection, Complexity, evolutionary multiobjective optimization, extrapolation, GP, industrial data analysis", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2008.926486", abstract = "his paper presents a novel approach to generate data-driven regression models that not only give reliable prediction of the observed data but also have smoother response surfaces and extra generalization capabilities with respect to extrapolation. These models are obtained as solutions of a genetic programming (GP) process, where selection is guided by a tradeoff between two competing objectives numerical accuracy and the order of nonlinearity. The latter is a novel complexity measure that adopts the notion of the minimal degree of the best-fit polynomial, approximating an analytical function with a certain precision. Using nine regression problems, this paper presents and illustrates two different strategies for the use of the order of nonlinearity in symbolic regression via GP. The combination of optimization of the order of nonlinearity together with the numerical accuracy strongly outperforms conventional optimisation of a size-related expressional complexity and the accuracy with respect to extrapolative capabilities of solutions on all nine test problems. In addition to exploiting the new complexity measure, this paper also introduces a novel heuristic of alternating several optimization objectives in a 2-D optimization framework. Alternating the objectives at each generation in such a way allows us to exploit the effectiveness of 2-D optimization when more than two objectives are of interest (in this paper, these are accuracy, expressional complexity, and the order of nonlinearity). Results of the experiments on all test problems suggest that alternating the order of nonlinearity of GP individuals with their structural complexity produces solutions that are both compact and have smoother response surfaces, and, hence, contributes to better interpretability and understanding.", notes = "also known as \cite{4632147}", } @Article{Vladislavleva:2010:ieeeTEC, author = "Ekaterina Vladislavleva and Guido Smits and Dick {den Hertog}", title = "On the Importance of Data Balancing for Symbolic Regression", journal = "IEEE Transactions on Evolutionary Computation", year = "2010", volume = "14", number = "2", pages = "252--277", month = apr, keywords = "genetic algorithms, genetic programming, Compression, data balancing, data scoring, data weighting, fitting, information content, modeling, subset selection, symbolic regression", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2009.2029697", size = "26 pages", abstract = "Symbolic regression of input-output data conventionally treats data records equally. We suggest a framework for automatic assignment of weights to data samples, which takes into account the sample's relative importance. In this paper, we study the possibilities of improving symbolic regression on real-life data by incorporating weights into the fitness function. We introduce four weighting schemes defining the importance of a point relative to proximity, surrounding, remoteness, and nonlinear deviation from k nearest-in-the-input-space neighbors. For enhanced analysis and modeling of large imbalanced data sets we introduce a simple multidimensional iterative technique for subsampling. This technique allows a sensible partitioning (and compression) of data to nested subsets of an arbitrary size in such a way that the subsets are balanced with respect to either of the presented weighting schemes. For cases where a given input output data set contains some redundancy, we suggest an approach to considerably improve the effectiveness of regression by applying more modeling effort to a smaller subset of the data set that has a similar information content. Such improvement is achieved due to better exploration of the search space of potential solutions at the same number of function evaluations. We compare different approaches to regression on five benchmark problems with a fixed budget allocation. We demonstrate that the significant improvement in the quality of the regression models can be obtained either with the weighted regression, exploratory regression using a compressed subset with a similar information content, or exploratory weighted regression on the compressed subset, which is weighted with one of the proposed weighting schemes.", notes = "also known as \cite{5325864}", } @InProceedings{Vladislavleva:2010:EuroGP, author = "Katya Vladislavleva and Kalyan Veeramachaneni and Una-May O'Reilly", title = "Learning a Lot from Only a Little: Genetic Programming for Panel Segmentation on Sparse Sensory Evaluation Data", booktitle = "Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010", year = "2010", editor = "Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar", volume = "6021", series = "LNCS", pages = "244--255", address = "Istanbul", month = "7-9 " # apr, organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Symbolic regression, Pareto GP, panel segmentation, survey modeling, hedonic, sensory evaluation, GP, ensembles", isbn13 = "978-3-642-12147-0", DOI = "doi:10.1007/978-3-642-12148-7_21", abstract = "We describe a data mining framework that derives panelist information from sparse flavour survey data. One component of the framework executes genetic programming ensemble based symbolic regression. Its evolved models for each panelist provide a second component with all plausible and uncorrelated explanations of how a panelist rates flavours. The second component bootstraps the data using an ensemble selected from the evolved models, forms a probability density function for each panelist and clusters the panelists into segments that are easy to please, neutral, and hard to please.", notes = "Part of \cite{Esparcia-Alcazar:2010:GP} EuroGP'2010 held in conjunction with EvoCOP2010 EvoBIO2010 and EvoApplications2010", } @InProceedings{Vladislavleva:2010:gecco, author = "Katya Vladislavleva and Kalyan Veeramachaneni and Matt Burland and Jason Parcon and Una-May O'Reilly", title = "Knowledge mining with genetic programming methods for variable selection in flavor design", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "941--948", keywords = "genetic algorithms, genetic programming", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830483.1830651", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "This paper presents a novel approach for knowledge mining from a sparse and repeated measures dataset. Genetic programming based symbolic regression is employed to generate multiple models that provide alternate explanations of the data. This set of models, called an ensemble, is generated for each of the repeated measures separately. These multiple ensembles are then used to generate information about, (a) which variables are important in each ensemble, (b) cluster the ensembles into different groups that have similar variables that drive their response variable, and (c) measure sensitivity of response with respect to the important variables. We apply our methodology to a sensory science dataset. The data contains hedonic evaluations (liking scores), assigned by a diverse set of human testers, for a small set of flavors composed from seven ingredients. Our approach: (1) identifies the important ingredients that drive the liking score of a panelist and (2) segments the panelists into groups that are driven by the same ingredient, and (3) enables flavour scientists to perform the sensitivity analysis of liking scores relative to changes in the levels of important ingredients.", notes = "Also known as \cite{1830651} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @Article{Vladislavleva:2013:RE, author = "Ekaterina Vladislavleva and Tobias Friedrich and Frank Neumann and Markus Wagner", title = "Predicting the energy output of wind farms based on weather data: Important variables and their correlation", journal = "Renewable Energy", volume = "50", year = "2013", pages = "236--243", keywords = "genetic algorithms, genetic programming, Wind energy, Prediction, Data Modeller", ISSN = "0960-1481", URL = "http://www.sciencedirect.com/science/article/pii/S0960148112003874", DOI = "doi:10.1016/j.renene.2012.06.036", size = "8 pages", abstract = "Wind energy plays an increasing role in the supply of energy world wide. The energy output of a wind farm is highly dependent on the weather conditions present at its site. If the output can be predicted more accurately, energy suppliers can coordinate the collaborative production of different energy sources more efficiently to avoid costly overproduction. In this paper, we take a computer science perspective on energy prediction based on weather data and analyse the important parameters as well as their correlation on the energy output. To deal with the interaction of the different parameters, we use symbolic regression based on the genetic programming tool DataModeler. Our studies are carried out on publicly available weather and energy data for a wind farm in Australia. We report on the correlation of the different variables for the energy output. The model obtained for energy prediction gives a very reliable prediction of the energy output for newly supplied weather data.", notes = "See oai:arXiv.org:1109.1922 http://arxiv.org/abs/1109.1922", } @Article{VLASIC:2019:CIE, author = "Ivan Vlasic and Marko Durasevic and Domagoj Jakobovic", title = "Improving genetic algorithm performance by population initialisation with dispatching rules", journal = "Computer \& Industrial Engineering", year = "2019", volume = "137", pages = "106030", month = nov, keywords = "genetic algorithms, genetic programming, Scheduling, Unrelated machines environment, Dispatching rules, Population initialisation", ISSN = "0360-8352", URL = "http://www.sciencedirect.com/science/article/pii/S0360835219304899", DOI = "doi:10.1016/j.cie.2019.106030", abstract = "Scheduling is an important process that is present in many real world scenarios where it is essential to obtain the best possible results. The performance and execution time of algorithms that are used for solving scheduling problems are constantly improved. Although metaheuristic methods by themselves already obtain good results, many studies focus on improving their performance. One way of improvement is to generate an initial population consisting of individuals with better quality. For that purpose a variety of methods can be designed. The benefit of scheduling problems is that dispatching rules (DRs), which are simple heuristics that provide good solutions for scheduling problems in a small amount of time, can be used for that purpose. The goal of this paper is to analyse whether the performance of genetic algorithms can be improved by using such simple heuristics for initialising the starting population of the algorithm. For that purpose both manual and different kinds of automatically designed DRs were used to initialise the starting population of a genetic algorithm. In case of the manually designed DRs, all existing DRs for the unrelated machines environment were used, whereas the automatically designed DRs were generated by using genetic programming. The obtained results clearly demonstrate that using populations initialised by DRs leads to a significantly better performance of the genetic algorithm, especially when using automatically designed DRs. Furthermore, it is also evident that such a population initialisation strategy also improves the convergence speed of the algorithm, since it allows it to obtain significantly better results in the same amount of time. Additionally, the DRs have almost no influence on the execution speed of the genetic algorithm since they construct the schedule in time which is negligible when compared to the execution of the genetic algorithm. Based on the obtained results it can be concluded that initialising individuals by using DRs significantly improves both the convergence and performance of genetic algorithm, without the need of having to manually design new complicated initialisation procedures and without increasing the execution time of the genetic algorithm", } @InProceedings{voicu:1999:TA, author = "Anca M. Voicu and Richard C. Barrett and Liviu I. Voicu and Harley R. Myler", title = "Trade models generated by evolutionary programming: A comparison with the gravity trade model", booktitle = "Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference", year = "1999", editor = "Scott Brave and Annie S. Wu", pages = "276--283", address = "Orlando, Florida, USA", month = "13 " # jul, keywords = "genetic algorithms, genetic programming", notes = "GECCO-99LB", } @Article{Vojodi:2013:IVC, author = "Hakime Vojodi and Ali Fakhari and Amir Masoud Eftekhari Moghadam", title = "A new evaluation measure for color image segmentation based on genetic programming approach", journal = "Image and Vision Computing", volume = "31", number = "11", pages = "877--886", year = "2013", ISSN = "0262-8856", DOI = "doi:10.1016/j.imavis.2013.08.002", URL = "http://www.sciencedirect.com/science/article/pii/S0262885613001285", keywords = "genetic algorithms, genetic programming, Image segmentation, Evaluation measure, Intra-region, Inter-region, Over-segmentation", } @InProceedings{Volk:evows09, author = "Katharina Volk and Julian Miller and Stephen L. Smith", title = "Multiple Network CGP for the Classification of Mammograms", booktitle = "Applications of Evolutionary Computing, EvoWorkshops2009: {EvoCOMNET}, {EvoENVIRONMENT}, {EvoFIN}, {EvoGAMES}, {EvoHOT}, {EvoIASP}, {EvoINTERACTION}, {EvoMUSART}, {EvoNUM}, {EvoPhD}, {EvoSTOC}, {EvoTRANSLOG}", year = "2009", month = "15-17 " # apr, editor = "Mario Giacobini and Ivanoe {De Falco} and Marc Ebner", series = "LNCS", volume = "5484", publisher = "Springer Verlag", address = "Tubingen, Germany", pages = "405--413", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming", isbn13 = "978-3-642-01128-3", DOI = "doi:10.1007/978-3-642-01129-0_45", abstract = "This paper presents a novel representation of Cartesian genetic programming (CGP) in which multiple networks are used in the classification of high resolution X-rays of the breast, known as mammograms. CGP networks are used in a number of different recombination strategies and results are presented for mammograms taken from the Lawrence Livermore National Laboratory database.", notes = "EvoWorkshops2009", } @Misc{Volkstorf:2002:png, author = "Charlie Volkstorf", title = "Axiomatic Synthesis of a Prime Number Generator and other Number Theoretic Programs using a Program Calculus", howpublished = "Mathematics Preprints", year = "2002", month = "8 " # oct, URL = "http://www.mathpreprints.com/math/Preprint/CharlieVolkstorf/20021008.1/1", size = "10 pages", abstract = "We examine the problem of Automatic Programming, in which a computer program is generated from a nonprocedural definition of the desired functionality. We propose a system in which programs are represented by 1st order Predicate Calculus wffs. A set of 8 universal rules of inference is introduced, which transform a fixed set of known primitive programs into new programs, in the same manner as classic axiomatic theorem-proving. It is shown that existing Automatic Programming systems either require the user to write a program in a high level language, or generate only a few specific trivial programs. Furthermore, to synthesise programs with loops requires a proof by induction using quantifiers, a task that many researchers consider to be unsolved and not amenable to automation. We avoid this problem by treating programs as primitive objects, with the details of generic loops hidden within axiomized primitive programs. We formally synthesize several programs from number theory, including prime number testing and generation. We note that prior research in the generation of programs of this complexity has been limited to informal discussion of a prime number generator. We show that our approach is also applicable to programs that retrieve from databases. An extension of this system, using 2nd order predicate calculus to represent properties of formal systems, has been used to synthesise theorems from Recursion Theory, Incompleteness in Logic, and the Semantic Paradoxes.", notes = "http://www.resumesafari.com/resumes/203_Volkstorf.htm", } @InProceedings{Vonk:1995:etd, author = "E. Vonk and L. C. Jain and L. P. J. Veelenturf and R. Hibbs", title = "Integrating evolutionary computation with neural networks", booktitle = "Proceedings Electronic Technology Directions to the Year 2000", year = "1995", pages = "137--143", month = "23-25 " # may, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, evolutionary algorithms, evolutionary strategies, neural networks", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.540.6770", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.540.6770", URL = "https://research.utwente.nl/files/5433148/00403480.pdf", DOI = "doi:10.1109/ETD.1995.403480", size = "7 pages", abstract = "There is a tremendous interest in the development of the evolutionary computation techniques as they are well suited to deal with optimisation of functions containing a large number of variables. This paper presents a brief review of evolutionary computing techniques. It also discusses briefly the hybridization of evolutionary computation and neural networks and presents a solution of a classical problem using neural computing and evolutionary computing techniques", notes = "many techniques, brief mention of GP. Published by IEEE 2000. Also known as \cite{403480}", } @InProceedings{Mammen:2007:cec, author = "Sebastian {von Mammen} and Christian Jacob", title = "Genetic Swarm Grammar Programming: Ecological Breeding Like a Gardener", booktitle = "2007 IEEE Congress on Evolutionary Computation", year = "2007", editor = "Dipti Srinivasan and Lipo Wang", pages = "851--858", address = "Singapore", month = "25-28 " # sep, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "1-4244-1340-0", file = "1181.pdf", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2007.4424559", abstract = "We recently introduced swarm grammars as an extension of Lindenmayer systems to model dynamic growth processes in 3D space through a large number of interacting (swarm) agents. Grammatical rewrite rules define different types of agents and their evolution over time. Sets of parameters determine specific interaction behaviours among the generated swarms.", notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C", } @Article{vonMammen:2009:cim, author = "Sebastian Von Mammen and Christian Jacob", journal = "IEEE Computational Intelligence Magazine", title = "The evolution of swarm grammars, growing trees, crafting art, and bottom-up design", year = "2009", month = aug, volume = "4", number = "3", pages = "10--19", keywords = "genetic algorithms, genetic programming", ISSN = "1556-603X", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.384.9486", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.384.9486", broken = "http://www.vonmammen.org/science/CIS_SGs.pdf", DOI = "doi:10.1109/MCI.2009.933096", size = "10 pages", abstract = "We presented swarm grammars as an extension of Lindenmayer systems. Instead of applying a single turtle agent to convert linear strings into 3D structures, we use a swarm of agents which navigate in 3D space and-as a side effect-place structural building blocks into their environment. The swarm grammars are used to specify how the setup of agent types changes over time. Additional agent parameters determine the agents' behaviours and their interaction dynamics. Both the grammar rules and the agent parameters are evolvable and can change over time-either automatically at replication and collision events among the agents, or triggered by external tinkering from a supervising breeder. When swarm grammars are applied to concrete problems, constraints on the developmental processes as well as on the emerging structures may provide the basis for an automatic evolutionary algorithm.", } @InProceedings{voss:1999:EAFSO, author = "Mark S. Voss and Christopher M. Foley", title = "Evolutionary Algorithm For Structural Optimization", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "678--685", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/Voss_genetic.pdf", URL = "http://www.evolutionarystructures.com/papers/genetic.pdf", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{voss:1999:TA, author = "Mark S. Voss and Christopher M. Foley", title = "The (mu, lambda, alpha, beta) distribution: A selection scheme for ranked populations", booktitle = "Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference", year = "1999", editor = "Scott Brave and Annie S. Wu", pages = "284--291", address = "Orlando, Florida, USA", month = "13 " # jul, notes = "GECCO-99LB", } @InProceedings{2001SPIE.4512..193V, author = "Mark S. Voss and Xin Feng", title = "Emergent system identification using particle swarm optimization", booktitle = "Procceedings of SPIE: Complex Adaptive Structures", year = "2001", month = oct, adsnote = "Provided by the NASA Astrophysics Data System", editor = "William B. Spillman", volume = "4512", pages = "193--202", organisation = "SPIE--The International Society for Optical Engineering", keywords = "genetic algorithms, genetic programming, PSO, GMDH", URL = "http://www.evolutionarystructures.com/papers/88338032.pdf", URL = "http://adsabs.harvard.edu/cgi-bin/nph-bib_query?2001SPIE.4512..193V", URL = "http://citeseer.ist.psu.edu/560788.html", DOI = "doi:10.1117/12.446767", size = "10 pages", abstract = "Complex Adaptive Structures can be viewed as a combination of Complex Adaptive Systems and fully integrated autonomous Smart Structures. Traditionally when designing a structure, one combines rules of thumb with theoretical results to develop an acceptable solution. This methodology will have to be extended for Complex Adaptive Structures, since they, by definition, will participate in their own design. In this paper we introduce a new methodology for Emergent System Identification that is concerned with combining the methodologies of self-organising functional networks (GMDH - Alexy G. Ivakhnenko), Particle Swarm Optimization (PSO - James Kennedy and Russell C. Eberhart) and Genetic Programming (GP - John Koza). This paper will concentrate on the use of Particle Swarm Optimisation in this effort and discuss how Particle Swarm Optimization relates to our ultimate goal of emergent self-organizing functional networks that can be used to identify overlapping internal structural models. The ability for Complex Adaptive Structures to identify emerging internal models will be a key component for their success.", } @PhdThesis{Voss:thesis, author = "Mark Seth Voss", title = "The Group Method for Cartesian Programming: A New Methodology for Complex Adaptive Functional Networks", school = "Electrical and Computer Engineering, Marquette University", year = "2002", address = "Milwaukee, Wisconsin, USA", month = "Spring", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, GMDH, PSO", URL = "https://epublications.marquette.edu/engin_diss/index.6.html", URL = "https://epublications.marquette.edu/dissertations_mu/1784/", abstract = "This dissertation proposes a new methodology for modelling and identifying non-linear systems called the Group Method of Cartesian Programming. This new methodology combines the ideas of nonlinear functional networks and statistical optimization via the Group Method of Data Handling and Particle Swarm Optimization, respectively. The utility of Particle Swarm Optimization is demonstrated by applying it to the System Identification problem. In particular, Particle Swarm Optimization is used to determine the constants for several autoregressive moving average (ARMA) models. The ARMA models discovered using Particle Swarm Optimization were found to be competitive with traditional gradient based optimization techniques. Particle Swarm Optimization was next integrated into the Group Method of Data Handling methodology. It was demonstrated that it is practical to use statistical optimization (Particle Swarm Optimization) within a complex adaptive functional network (The Group Method of Data Handling). A methodology of Gaussian Regularization was developed that has the potential to further improve the adaptive modeling capabilities of a Complex Adaptive Functional Network. Several applications were used to illustrate the use of Particle Swarm Optimization and the Group Method of Data Handling. In particular the new Group Method of Cartesian Programming was used for optimal sensor design. The positive results of the sensor study lend support for further research that would implement all of the ideas set forth in this dissertation.", notes = "First Advisor: Xin Feng Second Advisor: James Hieinen Third Advisor: Stephen Heinrich", } @InProceedings{voss:2002:gecco, author = "Mark S. Voss and Xin Feng", title = "A New Methodology For Emergent System Identification Using Particle Swarm Optimization ({PSO}) And The Group Mehtod Data Handling ({GMDH})", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "1227--1232", address = "New York", publisher_address = "San Francisco, CA 94104, USA", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "genetic algorithms, genetic programming, real world applications, genetic programming, GMDH, group method for data handling, particle swarm optimization, PSO, system identification", ISBN = "1-55860-878-8", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/RWA289_Fixed.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-20.pdf", URL = "http://dl.acm.org/citation.cfm?id=2955491.2955702", acmid = "2955702", abstract = "A new methodology for Emergent System Identification is proposed in this paper. The new method applies the self-organizing Group Method of Data Handling (GMDH) functional networks, Particle Swarm Optimization (PSO), and Genetic Programming (GP) that is effective in identifying complex dynamic systems. The focus of the paper will be on how Particle Swarm Optimization (PSO) is applied within Group Method of Data Handling (GMDH) which is used as the modelling framework.", notes = "Also known as \cite{Voss:2002:NME:2955491.2955702} GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)", } @InProceedings{voss:gecco03lbp, author = "Mark S. Voss", title = "Social Programming on {MARS}: A Benchmark Study", booktitle = "Genetic and Evolutionary Computation Conference Late Breaking Papers", year = "2003", editor = "Bart Rylander", pages = "307--314", address = "Chicago, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, PSO, Cartesian Genetic Programming, CGP, GMDH", URL = "http://www.evolutionarystructures.com/papers/voss_gecco2003.pdf", size = "8 pages", abstract = "The Social Programming methodology is based on combining the Particle Swarm methodology with The Group Method of Data Handling and Cartesian Programming. In this paper the performance of the Social Programming methodology is compared with results published in the literature for a well know benchmark function. The results from Social Programming were competitive with methods recently proposed in the literature.", notes = "STOGANOFF GECCO-2003LB", } @Article{Voth:2002:ieeeIS, author = "Danna Voth", title = "Nature's guide to robot design", journal = "IEEE Intelligent Systems", year = "2002", month = nov # "/" # dec, volume = "17", number = "6", pages = "4--6", keywords = "genetic algorithms, genetic programming, autonomous robotics, PSO", ISSN = "1541-1672", DOI = "doi:10.1109/MIS.2002.1134354", size = "2.3 pages", notes = "Pop science. Chalmers university: Peter Nordin, Krister Wolff. Drones, Icosystems, Martinoli, moorebots Also known as \cite{1134354}", } @InProceedings{vowk:2004:ALwks, author = "Barkley Vowk and Alexander (Sasha) Wait and Christian Schmidt", title = "An Evolutionary Approach Generates Human Competitive Coreware Programs", booktitle = "Workshop and Tutorial Proceedings Ninth International Conference on the Simulation and Synthesis of Living Systems(Alife {XI})", year = "2004", editor = "Mark Bedau and Phil Husbands and Tim Hutton and Sanjeev Kumar and Hideaki Sizuki", pages = "33--36", address = "Boston, Massachusetts", month = "12 " # sep, note = "Artificial Chemistry and its applications workshop", keywords = "genetic algorithms, genetic programming, pMARS, redcode", notes = "ALIFE9 rec.games.corewar. Some similarity with estimation of distribution (EDA) algorithms for starting evolution? Cf Markov chains. p47 'On average there are four cross-overs between parents in each child.' Internet process farms work out to 'commodity PCs'? Variable mutation rate. 'Kryptonites, (cryptonides) inhibit further evolution' 'two imps and an imp-gate' p49-50 lists evolved and improved code.", } @InProceedings{vrajitoru:1999:GPOAGA, author = "Dana Vrajitoru", title = "Genetic Programming Operators Applied to Genetic Algorithms", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "686--693", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, classifier systems", ISBN = "1-55860-611-4", URL = "http://www.cs.iusb.edu/~danav/papers/GA-312.pdf", URL = "http://citeseer.ist.psu.edu/453750.html", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-312.ps.gz", abstract = "Like other learning paradigms, the performance of the genetic algorithms (GAs) is dependent on the parameter choice, on the problem representation, and on the fitness landscape. Accordingly, a GA can show good or weak results even when applied on the same problem. Following this idea, the crossover operator plays an important role, and its study is the object of the present paper. A mathematical analysis has led us to construct a new form of crossover operator inspired from genetic programming (GP) that we have already applied in field of information retrieval. In this paper we extend the previous results and compare the new operator with several known crossover operators under various experimental conditions", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Article{Vrettaros:2011:IJTEL, author = "John Vrettaros and John Pavlopoulos and Athanasios S. Drigas and Kostas Hrissagis", title = "{GPNN} techniques in learning assessment systems", journal = "International Journal of Technology Enhanced Learning", year = "2011", volume = "3", number = "4", pages = "415--429", month = jan, keywords = "genetic algorithms, genetic programming, neural network, assessment system, expert system, learners, e-learning, ANN", publisher = "Inderscience Publishers", ISSN = "1753-5263", bibsource = "OAI-PMH server at www.inderscience.com", language = "eng", URL = "http://www.inderscience.com/link.php?id=41284", size = "16 pages", abstract = "The goal of this study is the development of an assessment system with the support of a neural network approach optimised with the use of genetic programming. The data used as training data are real data derived from an educational project. The developed system is able to assess learners' answers through various criteria and has been proved capable of assessing data from both single select and multiple choice questions in an e-learning environment.", notes = "E-tutor", } @InProceedings{vriend:1999:TDIPGA, author = "Nicolaas J. Vriend", title = "The Difference between Individual and Population Genetic Algorithms", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "812", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems, poster papers", ISBN = "1-55860-611-4", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Article{Vroomans:2025:GPEM, author = "Renske Vroomans", title = "Review: ``Computational evolution of neural and morphological development'', Yaochu Jin, ISBN 978-981-99-1853-9, Springer, 2023", journal = "Genetic Programming and Evolvable Machines", year = "2025", volume = "26", pages = "Article no 1", note = "Book Review", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/ehxbL", DOI = "doi:10.1007/s10710-024-09499-x", size = "2 pages", notes = "Published: 28 November 2024 University of Cambridge, Cambridge, UK", } @InProceedings{Vu:2006:ASPGP, title = "A memetic evolutionary algorithms", author = "Manh Xuan Vu and Thanh Thuy Nguyen", booktitle = "Proceedings of the Third Asian-Pacific workshop on Genetic Programming", year = "2006", editor = "The Long Pham and Hai Khoi Le and Xuan Hoai Nguyen", pages = "59--75", ISSN = "18590209", address = "Military Technical Academy, Hanoi, VietNam", keywords = "genetic algorithms, genetic programming", URL = "http://gpbib.cs.ucl.ac.uk/aspgp06/Aspgp_Xuan.pdf", size = "7 pages", abstract = "One of the important properties of evolutionary algorithms is to keep the diversity of the population. This paper presents an algorithm which can be regarded as the integration between Genetic Algorithm (GA) and Evolutionary Strategy (ES). This algorithm has many good properties, especially it satisfies the above important property.", notes = "broken march 2020 http://www.aspgp.org", } @InProceedings{Vu:2019:gecco, author = "Tuong Manh Vu and Charlotte Probst and Joshua M. Epstein and Alan Brennan and Mark Strong and Robin C. Purshouse", title = "Toward inverse generative social science using multi-objective genetic programming", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and Anne Auger and Petr Posik and Leslie {Peprez Caceres} and Andrew M. Sutton and Nadarajen Veerapen and Christine Solnon and Andries Engelbrecht and Stephane Doncieux and Sebastian Risi and Penousal Machado and Vanessa Volz and Christian Blum and Francisco Chicano and Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and Jonathan Fieldsend and Jose Antonio Lozano and Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and Robin Purshouse and Thomas Baeck and Justyna Petke and Giuliano Antoniol and Johannes Lengler and Per Kristian Lehre", pages = "1356--1363", address = "Prague, Czech Republic", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", publisher = "ACM", keywords = "genetic algorithms, genetic programming, generative social science, multi-objective optimization", isbn13 = "978-1-4503-6111-8", URL = "http://human-competitive.org/sites/default/files/vu-paper.pdf", DOI = "doi:10.1145/3321707.3321840", size = "8 pages", abstract = "Generative mechanism-based models of social systems, such as those represented by agent-based simulations, require that intra-agent equations (or rules) be specified. However there are often many different choices available for specifying these equations, which can still be interpreted as falling within a particular class of mechanisms. Whilst it is important for a generative model to reproduce historically observed dynamics, it is also important for the model to be theoretically enlightening. Genetic programs (our own included) often produce concatenations that are highly predictive but are complex and hard to interpret theoretically. Here, we develop a new method,based on multi-objective genetic programming, for automating the exploration of both objectives simultaneously. We demonstrate the method by evolving the equations for an existing agent-based simulation of alcohol use behaviours based on social norms theory, the initial model structure for which was developed by a team of human modellers. We discover a trade-off between empirical fit and theoretical interpretability that offers insight into the social norms processes that influence the change and stasis in alcohol use behaviours over time.", notes = "Entered 2019 Humies. Also known as \cite{3321840} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA-2019) and the 24th Annual Genetic Programming Conference (GP-2019)", } @Article{Vu:2020:Complexity, author = "Tuong M. Vu and Charlotte Buckley and Hao Bai and Alexandra Nielsen and Charlotte Probst and Alan Brennan and Paul Shuper and Mark Strong and Robin C. Purshouse", title = "Multiobjective Genetic Programming Can Improve the Explanatory Capabilities of Mechanism-Based Models of Social Systems", journal = "Complexity", year = "2020", volume = "2020", pages = "Article ID 8923197", month = "05 " # jun, note = "Special issue: Frontiers in Data-Driven Methods for Understanding, Prediction, and Control of Complex Systems", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", ISSN = "1076-2787", DOI = "doi:10.1155/2020/8923197", size = "20 pages", abstract = "The generative approach to social science, in which agent-based simulations (or other complex systems models) are executed to reproduce a known social phenomenon, is an important tool for realist explanation. However, a generative model, when suitably calibrated and validated using empirical data, represents just one viable candidate set of entities and mechanisms. The model only partially addresses the needs of an abductive reasoning process: specifically it does not provide insight into other viable sets of entities or mechanisms nor suggests which of these are fundamentally constitutive for the phenomenon to exist. In this paper, we propose a new model discovery framework that more fully captures the needs of realist explanation. The framework exploits the implicit ontology of an existing human-built generative model to propose and test a plurality of new candidate model structures. Genetic programming is used to automate this search process. A multiobjective approach is used, which enables multiple perspectives on the value of any particular generative model, such as goodness of fit, parsimony, and interpretability, to be represented simultaneously. We demonstrate this new framework using a complex systems modeling case study of change and stasis in societal alcohol use patterns in the US over the period 1980 to 2010. The framework is successful in identifying three competing explanations of these alcohol use patterns, using novel integrations of social role theory not previously considered by the human modeler. Practitioners in complex systems modeling should use model discovery to improve the explanatory utility of the generative approach to realist social science.", notes = "PonyGE2 Academic Editor: Murari Andrea Wiley Hindawi partnership.", } @InProceedings{DBLP:conf/emo/VuDBBP21, author = "Tuong Manh Vu and Eli Davies and Charlotte Buckley and Alan Brennan and Robin C. Purshouse", title = "Using Multi-objective Grammar-Based Genetic Programming to Integrate Multiple Social Theories in Agent-Based Modeling", booktitle = "11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021", year = "2021", editor = "Hisao Ishibuchi and Qingfu Zhang and Ran Cheng and Ke Li and Hui Li and Handing Wang and Aimin Zhou", volume = "12654", series = "Lecture Notes in Computer Science", pages = "721--733", address = "Shenzhen, China", month = mar # " 28-31", publisher = "Springer", keywords = "genetic algorithms, genetic programming, grammatical evolution", timestamp = "Wed, 07 Apr 2021 16:01:51 +0200", biburl = "https://dblp.org/rec/conf/emo/VuDBBP21.bib", bibsource = "dblp computer science bibliography, https://dblp.org", DOI = "doi:10.1007/978-3-030-72062-9_57", abstract = "Different theoretical mechanisms have been proposed for explaining complex social phenomena. For example, explanations for observed trends in population alcohol use have been postulated based on norm theory, role theory, and others. Many mechanism-based models of phenomena attempt to translate a single theory into a simulation model. However, single theories often only represent a partial explanation for the phenomenon. The potential of integrating theories together, computationally, represents a promising way of improving the explanatory capability of generative social science. This paper presents a framework for such integrative model discovery, based on multi-objective grammar-based genetic programming (MOGGP). The framework is demonstrated using two separate theory-driven models of alcohol use dynamics based on norm theory and role theory. The proposed integration considers how the sequence of decisions to consume the next drink in a drinking occasion may be influenced by factors from the different theories. A new grammar is constructed based on this integration. Results of the MOGGP model discovery process find new hybrid models that outperform the existing single-theory models and the baseline hybrid model. Future work should consider and further refine the role of domain experts in defining the meaningfulness of models identified by MOGGP", } @InProceedings{Vu:2021:IGSS, author = "Tuong Manh Vu and Charlotte Buckley and Alan Brennan and Robin Purshouse", title = "Challenges of three-objective bi-level model discovery: Application to alcohol use in New York State, 1985-2015", booktitle = "Inverse Generative Social Science Workshop 2021", year = "2021", address = "online", month = jun # " 8-10", keywords = "genetic algorithms, genetic programming, EMO, NY, USA", URL = "https://www.igss-workshop.org/abstracts", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/igss-workshop_2021.pdf", abstract = "grammar-based genetic programming with three optimization objectives: male alcohol use, female alcohol use, complexity.", notes = "See also \cite{Greig:2021:ALife} April 2023 igss-workshop_2021.pdf from https://www.igss-workshop.org/abstracts University of Sheffield, United Kingdom", } @Article{Vu2021_Article_SoftwareReviewPonyGE2, author = "Tuong Manh Vu", title = "Software review: {Pony GE2}", journal = "Genetic Programming and Evolvable Machines", year = "2021", volume = "22", number = "3", pages = "383--385", month = sep, keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Python 3.5", ISSN = "1389-2576", URL = "https://rdcu.be/dR81Q", DOI = "doi:10.1007/s10710-021-09409-5", size = "3 pages", notes = " https://github.com/PonyGE/PonyGE2 https://r01cascade.bitbucket.io/ University of Sheffield, Sheffield, UK", } @InProceedings{Vukusic:2006:ISMB, author = "Ivana Vukusic and Sushma-Nagaraja Grellscheid and Thomas Wiehe", title = "Features of sequence composition and population genetical measures of selection to analyse alternatively spliced exons and introns", booktitle = "14th Annual International Conference on Intelligent Systems For Molecular Biology", year = "2006", editor = "Goran Neshich", pages = "L-30", address = "Fortaleza, Brazil", month = "6-10 " # aug, organisation = "International Society for Computational Biology", keywords = "genetic algorithms, genetic programming, Discipulus, Poster", URL = "http://www.iscb.org/cms_addon/conferences/ismb2006/archive/ismb2006.cbi.cnptia.embrapa.br/posters_list.php", abstract = "Short Abstract: We have developed a binary classifier based on Genetic Programming (GP) to predict whether a given gene sequence is spliced constitutively or alternatively. The prediction accuracies are greater than 85percent on the dataset of retained introns. Furthermore we showed that skipped exons show traces of positive selection.", abstract = "Long Abstract: Features of sequence composition and population genetical measures of selection to analyse alternatively spliced exons and introns Alternative pre-mRNA splicing is a major source of mammalian transcriptome and proteome diversity. Aberrant splicing is an important cause for genetic diseases and cancer. Until a few years ago it was believed that almost 95percent of all genes undergo constitutive splicing, which always proceeds in a removal of introns which is followed by a merge of exons. It is now widely believed that alternative splicing is the rule rather than the exception and that up to 74 percent of all human genes are alternatively spliced. Whether an exon or an intron will be included or excluded in the transcripts of a gene of a certain cell type is influenced by the information contained in the sequence of the exon and the flanking intronic region. It is commonly accepted that no single factor dictates whether or not an exon will be spliced into a transcript. Instead it is probably a combinatorial effect of various factors that include cis-acting sequences and trans-acting splicing factors. To predict whether a given gene sequence is spliced constitutively or alternatively we used the technique of Genetic Programming (GP). GP is a sub-discipline of Machine Learning. Basic ideas of GP are inspired by the paradigm of Darwinian evolution. New programs are 'bred' from a population of existing programs and subject to selection, mutation and recombination. We used the GP system 'Discipulus', a supervised learning system, which generates programs on data that describe a certain problem. The features provided to this system are in form of a 'feature-matrix', containing e.g. nucleotide composition, length, motifs etc. After each GP run Discipulus collects the information, of how often each feature was used in the thirty best programs, in a so-called 'input-impact'-table. This table can be used to reveal the 'best features' for a certain classification problem. The system has been tested on extended version of the AltSplice data base. Here, we concentrated on cassette exons (SCE) and retained introns (SIR) and analysed 27,519 constitutively spliced exons and 9641 cassette exons including their upstream and downstream introns; in addition we focused on the analysis of the difference of 33,316 constitutively spliced introns compared to 2712 retained introns. The classifier shows very high prediction accuracy on the SIR data: sensitivity is 91.4percent and specificity is 81.9percent. In contrast, on the SCE data the prediction accuracy is lower: sensitivity is 48.2percent and specificity is 70.3percent. This suggests that sequence properties, such as those collected in the GP feature matrix, are better suited to detect alternative splicing of introns than that of exons. A possible biological reason is that the constraints imposed by the genetic code affect (coding) exons but not introns.", abstract = "During cross-validation we have collected and analysed the five input-impact-tables resulting from each GP run. A frequency value of 5 of a certain feature means that in all 5 GP runs the 30 best programs contained this feature. The most frequently used features of the SCE data are: Number of A residues (frequency value: 5), GGG sequences (frequency value: 3,6) and the number of C residues (frequency value: 1). Although every single run starts with a new population of randomly generated programs, a similar pattern occurred in all other runs performed during cross-validation. The best feature on the SIR data set is the number of A residues (frequency value: 4,1), followed by GC divided by length (frequency value: 1.8) and the number of T residues (frequency value: 1.4) in accordance with the fact that exonic splicing enhancer tend to be purine rich sequences. To see whether selection, positive or negative, acts differently in alternatively than constitutively spliced exons we extracted for our lists of exons all annotated sequence polymorphisms from the latest release of the HapMap database. A common measure to test for the presence of positive selection is Tajima's D. We find that Tajima's D is smaller in the European population compared to Africa. Also, Tajima's D is smaller in the skipped compared to the constitutive exon dataset in both populations, indicating an elevated level of positive directional selection in alternatively spliced genes. Linkage disequilibrium is higher in derived populations and in alternatively spliced genes in all populations. We also find a slightly elevated level of genetic diversity close to the splice boundaries in alternative exons. However, while these features indicate a general trend, the sequence polymorphism data are too sparse in order to be used as a predictor of alternative versus constitutively spliced exons in particular cases.", notes = "http://ismb2006.cbi.cnptia.embrapa.br/ http://www.iscb.org/cms_addon/conferences/ismb2006/archive/ismb2006.cbi.cnptia.embrapa.br/posters_list.php", } @Article{Vukusica:2007:G, author = "Ivana Vukusic and Sushma Nagaraja Grellscheid and Thomas Wiehe", title = "Applying genetic programming to the prediction of alternative mRNA splice variants", journal = "Genomics", year = "2007", volume = "89", number = "4", pages = "471--479", month = apr, keywords = "genetic algorithms, genetic programming, Alternative splicing, Cassette exon, Intron retention, Feature matrix, Splice signals", DOI = "doi:10.1016/j.ygeno.2007.01.001", abstract = "Genetic programming (GP) can be used to classify a given gene sequence as either constitutively or alternatively spliced. We describe the principles of GP and apply it to a well-defined data set of alternatively spliced genes. A feature matrix of sequence properties, such as nucleotide composition or exon length, was passed to the GP system Discipulus To test its performance we concentrated on cassette exons (SCE) and retained introns (SIR). We analysed 27,519 constitutively spliced and 9641 cassette exons including their neighbouring introns; in addition we analysed 33316 constitutively spliced introns compared to 2712 retained introns. We find that the classifier yields highly accurate predictions on the SIR data with a sensitivity of 92.1percent and a specificity of 79.2percent. Prediction accuracies on the SCE data are lower, 47.3percent (sensitivity) and 70.9percent (specificity), indicating that alternative splicing of introns can be better captured by sequence properties than that of exons.", notes = "PMID: 17276654 [PubMed - indexed for MEDLINE]", } @PhdThesis{Vukusic:thesis, author = "Ivana Vukusic", title = "Alternative {pre-mRNA} Splicing: Signals and Evolution", school = "der Mathematisch-Naturwissenschaftlichen Fakultat, der Universitat zu Koeln", year = "2008", address = "Cologne, Germany", month = "17 " # nov, keywords = "genetic algorithms, genetic programming", URL = "http://kups.ub.uni-koeln.de/2606/", URL = "http://kups.ub.uni-koeln.de/2606/1/dissertation_IV_ivana.pdf", size = "142 pages", abstract = "Alternative pre-mRNA splicing is a major source of transcriptome and proteome diversity. In humans, aberrant splicing is a cause for genetic disease and cancer. Until recently it was believed that almost 95percent of all genes undergo constitutive splicing, where introns are always excised and exons are always included into the mature mRNA transcript. It is now widely accepted that alternative splicing is the rule rather than the exception and that perhaps more than 75percent of all human genes are alternatively spliced. Despite its importance and its potential role in causing disease, the molecular basis of alternative splicing is still not fully understood. The incompleteness of our knowledge about the human transcriptome makes ab initio predictions of alternative splicing a recent, but important research area. This thesis investigates different aspects of alternative splicing in humans, based upon computational large-scale analyses. We introduce a genetic programming approach to predict alternative splicing events without using expressed sequence tags (ESTs). In contrast to existing methods, our approach relies on sequence information only, and is therefore independent of the existence of orthologous sequences. We analysed 27,519 constitutively spliced and 9,641 cassette exons (SCE) together with their neighbouring introns; in addition we analyzed 33,316 constitutively spliced introns and 2,712 retained introns (SIR). We find that our tool for classifying yields highly accurate predictions on the SIR data, with a sensitivity of 92.1percent and a specificity of 79.2percent. Prediction accuracies on the SCE data are lower: 47.3percent (sensitivity) and 70.9percent (specificity), indicating that alternative splicing of introns can be better captured by sequence properties than that of exons. We critically question these findings and in particular discuss the huge impact of the feature 'length' on predictions in retained introns. We find that the number of adenosines in an exon, called 'feature A' is a highly prominent feature for classification of exons. Adenosines are especially overrepresented in the most abundant exonic splicing enhancers, found in constitutive exons. Furthermore we comment on inconsistencies of the nomenclature and on problems of handling the splicing data. We make suggestions to improve the terminology. For further in silico exploration of sequence properties of exons, we generated a dataset of synthetic exons. We describe a general rule for creating sequences with similar exonic splicing enhancer and -silencer densities to real exons, as well as similar exonic splicing enhancer networks. We find that exonic splicing enhancer densities are well suited for differentiating real and randomised exons, whereas the densities of SR protein binding sites are largely uninformative. Generally, we find that features described on small scale experimental data are not transferable to computational large-scale analyses, which makes creation of rules for alternative splicing prediction based only upon DNA/RNA sequence, an extraordinarily difficult task. According to our findings, we suggest that in case of the SCE, only 20percent, and in case of SIR, only 30percent of the whole splicing information is encoded on sequence level. In the last chapter we investigated the question whether alternative splicing may be connected to adaptive evolutionary processes in a species or population. Unfortunately, the currently available population genetic tools are not sensitive enough to identify traces of positive or balancing selection on the scale of a few 100bp. Additional problems are the incomplete SNP databases and SNP ascertainment bias. The evolutionary role of alternative splicing remains, at least for the moment, speculative.", notes = "In English, Koeln", } @InCollection{Vyas:2015:hbgpa, author = "Renu Vyas and Purva Goel and Sanjeev S. Tambe", title = "Genetic Programming Applications in Chemical Sciences and Engineering", booktitle = "Handbook of Genetic Programming Applications", publisher = "Springer", year = "2015", editor = "Amir H. Gandomi and Amir H. Alavi and Conor Ryan", chapter = "5", pages = "99--140", keywords = "genetic algorithms, genetic programming, Symbolic regression, Classification, Chemical sciences and engineering, Computational intelligence", isbn13 = "978-3-319-20882-4", DOI = "doi:10.1007/978-3-319-20883-1_5", abstract = "Genetic programming (GP) (Koza, Genetic programming: a paradigm for genetically breeding populations of computer programs to solve problems, Stanford University, Stanford, 1990) was originally proposed for automatically generating computer programs that would perform pre-defined tasks. There exist two other important GP applications, namely classification and symbolic regression that are being used widely in pattern recognition and data-driven modelling, respectively. As compared to the classification, GP has found more applications for its capability to effectively perform symbolic regression (SR). Given an input-output data set SR can search and optimize an appropriate linear/non-linear data-fitting function and all its parameters. The GP-based symbolic regression (GPSR) offers an attractive avenue to extract correlations, explore candidate models and provide optimal solutions to the data-driven modeling problems. Despite its novelty and effectiveness, GP, unlike artificial neural networks and support vector regression, has not seen an explosive growth in its applications. Owing to the availability of feature-rich and user-friendly software packages as also faster computers (including parallel computing devices), there has been a spate of research publications in recent years exploiting the significant potential of GP for diverse classification and modelling applications in chemistry and related sciences and engineering. Accordingly, this chapter provides a bird's eye-view of the ever increasing applications of GP in the chemical sciences and engineering with the objective of bringing out its immense potential in solving diverse problems. The present chapter not only focuses on the important GP-applications but also offers guidelines to develop optimal GP models. Additionally, a non-exclusive list of GP software packages is provided.", } @Article{Vyas:2016:TCBB, author = "Renu Vyas and Sanket Bapat and Purva Goel and M. Karthikeyan and S. S. Tambe and B. D. Kulkarni", journal = "IEEE/ACM Transactions on Computational Biology and Bioinformatics", title = "Application of Genetic Programming (GP) Formalism for Building Disease Predictive Models from Protein-Protein Interactions (PPI) Data", year = "2016", abstract = "Protein-protein interactions (PPIs) play a vital role in the biological processes involved in the cell functions and disease pathways. The experimental methods known to predict PPIs require tremendous efforts and the results are often hindered by the presence of a large number of false positives. Herein, we demonstrate the use of a new Genetic Programming (GP) based Symbolic Regression (SR) approach for predicting PPIs related to a disease. In a case study, a dataset consisting of one hundred and thirty five PPI complexes related to cancer was used to construct a generic PPI predicting model with good PPI prediction accuracy and generalisation ability. A high correlation coefficient(CC) of 0.893, low root mean square error (RMSE) and mean absolute percentage error (MAPE) values of 478.221 and 0.239, respectively were achieved for both the training and test set outputs. To validate the discriminatory nature of the model, it was applied on a dataset of diabetes complexes where it yielded significantly low CC values. Thus, the GP model developed here serves a dual purpose: (a)a predictor of the binding energy of cancer related PPI complexes, and (b)a classifier for discriminating PPI complexes related to cancer from those of other diseases.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/TCBB.2016.2621042", ISSN = "1545-5963", notes = "Also known as \cite{7707365}", } @InProceedings{wada:1994:essf, author = "{Ken-nosuke} Wada and Yoshiko Wada and Hirofumi Doi and {Shin-ichi} Tanaka and Mitsuri Furusawa", title = "Evolutionary Systems: Structures and Functions", booktitle = "Proceedings of IEEE International Conference on Evolutionary Computation (ICEC-94), World Congress on Computational Intelligence", publisher = "IEEE Computer Society Press, New York", year = "1994", pages = "796--801", address = "Orlando, Florida, USA", month = "27-29 " # jun, keywords = "genetic algorithms, cellular automata", notes = "Not really a GP but a wide ranging article, includes evolution of cellular automata. On a 0-1 knapsack problem GA does better than Simulated annealing and Hopfield neural net but branch and bound does better than it. models gene duplication, different mutation rates on the two strands of DNA (observed in E-coli). ERATO. Argues for mutations building up where they make no difference until environment changes, when some may be beneficial. ", } @InProceedings{wadheEtAl:2005a, author = "M. Wahde and J. Pettersson and H. Sandholt and K. Wolff", title = "Behavioral Selection Using the Utility Function Method: A Case Study Involving a Simple Guard Robot", booktitle = "Proceedings of the 3rd International Symposium on Autonomous Minirobots for Research and Edutainment (AMiRE'05)", year = "2005", editor = "K. Murase and K. Sekiyama and N. Kubota and T. Naniwa and J. Sitte", pages = "261--266", address = "Fukui, Japan", month = "20-22 " # sep, organization = "IEEE-RAS", publisher = "Springer", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-28496-6", bibsource = "DBLP, http://dblp.uni-trier.de", URL = "http://www.me.chalmers.se/~mwahde/AdaptiveSystems/Publications/WahdeEtAl_AMiRE2005.pdf", abstract = "The performance of the utility function method for behavioural organisation is investigated in the framework of a simple guard robot. In order to achieve the best possible results, it was found that high-order polynomials should be used for the utility functions, even though the use such polynomials, involving many terms, increases the running time needed for the evolutionary algorithm to find good solutions.", notes = "Also known as \cite{DBLP:conf/amire/WahdePSW05}", } @InCollection{wagman:2003:SPEAAGTA, author = "Liad Wagman", title = "Stock Portfolio Evaluation: An Application of Genetic-Programming-Based Technical Analysis", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2003", year = "2003", editor = "John R. Koza", pages = "213--220", address = "Stanford, California, 94305-3079 USA", month = "4 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", URL = "http://www.genetic-programming.org/sp2003/Wagman.pdf", size = "8 pages", abstract = "Recent studies in financial economics suggest that technical analysis may have merit to predictability of stock. When attempting to create an efficient portfolio of stocks, there are numerous factors to consider. The problem is that the evaluation involves many qualitative factors, which causes most approximations to go off track. This paper presents a genetic programming approach to portfolio evaluation. By using a set of fitness heuristics over a population of stock portfolios, the goal is to find a portfolio that has a high expected return over investment.", notes = "part of \cite{koza:2003:gagp}", } @Article{Altenberg:1994EEGP, author = "Gunter P. Wagner and Lee Altenberg", year = "1996", title = "Complex Adaptations and the Evolution of Evolvability", journal = "Evolution", pages = "967--976", volume = "50", number = "3", keywords = "genetic algorithms, theoretical biology, modular genotype-phenotype map", URL = "http://dynamics.org/Altenberg/FILES/GunterLeeCAEE.pdf", URL = "http://dynamics.org/Altenberg/FILES/GunterLeeCAEE.ps.gz", URL = "http://dynamics.org/Altenberg/PAPERS/CAEE/", size = "24 pages", abstract = "The problem of complex adaptations is studied in two largely disconnected research traditions: evolutionary biology and evolutionary computer science. This paper summarizes the results from both areas and compares their implications. In evolutionary computer science it was found that the Darwinian process of mutation, recombination and selection is not universally effective in improving complex systems like computer programs or chip designs. For adaptation to occur, these systems must possess {"}evolvability{"}, i.e. the ability of random variations to sometimes produce improvement. It was found that evolvability critically depends on the way genetic variation maps onto phenotypic variation, an issue known as the representation problem. The genotype-phenotype map determines the variability of characters, which is the propensity to vary. Variability needs to be distinguished from variation, which are the actually realized differences between individuals. The genotype-phenotype map is the common theme underlying such varied biological phenomena as genetic canalization, developmental constraints, biological versatility, developmental dissociability, morphological integration, and many more. For evolutionary biology the representation problem has important implications: how is it that extant species acquired a genotype-phenotype map which allows improvement by mutation and selection? Is the genotype-phenotype map able to change in evolution? What are the selective forces, if any, that shape the genotype-phenotype map? We propose that the genotype-phenotype map can evolve by two main routes: epistatic mutations, or the creation of new genes. A common result for organismic design is modularity. By modularity we mean a genotype-phenotype map in which there are few pleiotropic effects among characters serving different functions, with pleiotropic effects falling mainly among characters that are part of a single functional complex. Such a design is expected to improve evolvability by limiting the interference between the adaptation of different functions. Several population genetic models are reviewed that are intended to explain the evolutionary origin of a modular design. While our current knowledge is insufficient to assess the plausibility of these models, they form the beginning of a framework for understanding the evolution of the genotype-phenotype map. Copyright 1996 Gunter Wagner and Lee Altenberg", notes = "survey connecting real genetics with evolutionary computation", } @InProceedings{wagner:1999:HCCS, author = "Kyle Wagner", title = "Habitat, Communication and Cooperative Strategies", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "694--701", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-842.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-842.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Misc{journals/corr/abs-1103-5797, title = "Computational Complexity Results for Genetic Programming and the Sorting Problem", author = "Markus Wagner and Frank Neumann", journal = "CoRR", year = "2011", volume = "abs/1103.5797", note = "informal publication", bibdate = "2011-04-01", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/corr/corr1103.html#abs-1103-5797", URL = "http://arxiv.org/abs/1103.5797", keywords = "genetic algorithms, genetic programming", abstract = "Genetic Programming (GP) has found various applications. Understanding this type of algorithm from a theoretical point of view is a challenging task. The first results on the computational complexity of GP have been obtained for problems with isolated program semantics. With this paper, we push forward the computational complexity analysis of GP on a problem with dependent program semantics. We study the well-known sorting problem in this context and analyse rigorously how GP can deal with different measures of sortedness.", } @InProceedings{Wagner:2011:ThRaSH, author = "Markus Wagner and Frank Neumann", title = "Computational Complexity of GP and Sorting", booktitle = "The 5th workshop on Theory of Randomized Search Heuristics, ThRaSH'2011", year = "2011", editor = "Christian Igel and Per Kristian Lehre and Carsten Witt", address = "Copenhagen, Denmark", month = jul # " 8-9", keywords = "genetic algorithms, genetic programming", URL = "http://www.thrash-workshop.org/slides/wagner.pdf", size = "20 slides", notes = "http://www.thrash-workshop.org/", } @InProceedings{Wagner:2014:CEC, title = "Single- and Multi-Objective Genetic Programming: New Runtime Results for {SORTING}", author = "Markus Wagner and Frank Neumann", pages = "125--132", booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary Computation", year = "2014", month = "6-11 " # jul, editor = "Carlos A. {Coello Coello}", address = "Beijing, China", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Theoretical Foundations of Bio-inspired Computation", URL = "http://cs.adelaide.edu.au/~markus/pub/2014cec-sorting.pdf", DOI = "doi:10.1109/CEC.2014.6900310", size = "8 pages", abstract = "In genetic programming, the size of a solution is typically not specified in advance and solutions of larger size may have a larger benefit. The flexibility often comes at the cost of the so-called bloat problem: individuals grow without providing additional benefit to the quality of solutions, and the additional elements can block the optimisation process. Consequently, problems that are relatively easy to optimise cannot be handled by variable-length evolutionary algorithms. In this article, we present several new bounds for different single and multi-objective algorithms on the sorting problem, a problem that typically lacks independent and additive fitness structures.", notes = "WCCI2014", } @Article{Wagner:2015:EC, author = "Markus Wagner and Frank Neumann and Tommaso Urli", title = "On the Performance of Different Genetic Programming Approaches for the SORTING Problem", journal = "Evolutionary Computation", year = "2015", volume = "23", number = "4", pages = "583--609", month = "Winter", keywords = "genetic algorithms, genetic programming, Computational complexity, genetic programming, variable-length representation, sortedness, single-objective optimisation, multi-objective optimization", ISSN = "1063-6560", DOI = "doi:10.1162/EVCO_a_00149", size = "27 pages", abstract = "In genetic programming, the size of a solution is typically not specified in advance and solutions of larger size may have a larger benefit. The flexibility often comes at the cost of the so-called bloat problem: individuals grow without providing additional benefit to the quality of solutions, and the additional elements can block the optimisation process. Consequently, problems that are relatively easy to optimise cannot be handled by variable-length evolutionary algorithms. In this article, we analyse different single- and multi-objective algorithms on the sorting problem, a problem that typically lacks independent and additive fitness structures. We complement the theoretical results with comprehensive experiments to indicate the tightness of existing bounds, and to indicate bounds where theoretical results are missing.", notes = "The University of Adelaide, Australia. DIEGM, Universita degli Studi di Udine, Udine, Italy", } @InProceedings{Wagner:2016:GI, author = "Markus Wagner", title = "Speeding up the proof strategy in formal software verification", booktitle = "Genetic Improvement 2016 Workshop", year = "2016", editor = "Justyna Petke and David R. White and Westley Weimer", pages = "1137--1138", address = "Denver", publisher_address = "New York, NY, USA", month = jul # " 20-24", organisation = "SIGEvo", publisher = "ACM", keywords = "genetic algorithms, genetic programming, Genetic Improvement, SBSE, Formal software verification, runtime improvement", URL = "http://cs.adelaide.edu.au/~markus/pub/2016-gecco-gi-verification.pdf", URL = "http://geneticimprovementofsoftware.com/wp-content/uploads/2016/06/Speeding-up-the-Proof_Strategy_in_Formal_Software_Verification.pdf", DOI = "doi:10.1145/2908961.2931690", size = "2 pages", abstract = "The functional correctness of safety- and security-critical software is of utmost importance. Nowadays, this can be achieved through computer assisted verification. While formal verification itself typically poses a steep learning-curve for anyone who wants to apply it, its applicability is further hindered by its (typically) low runtime performance. With the increasing popularity of algorithm parameter tuning and genetic improvement, we see a great opportunity for assisting verification engineers in their daily tasks.", notes = "KeY, JML Java Modelling Language, JavaDL. GIT history magic numbers. GECCO-2016 Workshop http://geneticimprovementofsoftware.com/", } @Proceedings{Wagner:2017:GECCOcomp, title = "GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2017", address = "Berlin, Germany", publisher = "ACM", publisher_address = "New York, NY, USA", editor = "Markus Wagner and Julia Handl and Ryan Urbanowicz and Kuber Karthik and Danilo Vasconcellos Vargas and Silvino Fernandez Alzueta and Pablo Valledor Pellicer and Thomas Stuetzle and John R. Woodward and Daniel R. Tauritz and Manuel Lopez-Ibanez and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Dejan Tusar and Stephane Doncieux and Joshua Auerbach and Richard Duro and Harold de Vladar and Jose Santos and Amarda Shehu and Mostafa Ellabaan and Stefan Wagner and Michael Affenzeller and Frank Neumann and Markus Wagner and Paul Kaufmann and Oliver Kramer and P. G. M. Baltus and Giovanni Iacca and M. N. Andraud and Vanessa Volz and Boris Naujoks and Frank Moore and Gunes Kayacik and Nur Zincir-Heywood and Anna I Esparcia-Alcazar and Westley Weimer and Justyna Petke and David R. White and William B. Langdon and Nadarajen Veerapen and Fabio Daolio and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Giovanni Squillero and Alberto Tonda and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and John McCall and Dirk Thierens and William {La Cava} and Randal Olson and Patryk Orzechowski and Ernesto Tarantino and Ivanoe De Falco and Antonio Della Cioppa and Umberto Scafuri and Nicolas Bredeche and Evert Haasdijk and Abraham Prieto and Heiko Hamann and Jared Moore and Anthony Clark and David Walker and Richard Everson and Jonathan Fieldsend and Bogdan Filipic and Alma Rahat and Handing Wang and Yaochu Jin", month = "15-19 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, Evolutionary Rule-Based Machine Learning, Industrial Applications of Metaheuristics (IAM), Evolutionary Computation for the Automated Design of Algorithms (ECADA), Black Box Optimization Benchmarking 2017 (BBOB 2017), Evolution in Cognition, Evolutionary Computation in Computational Biology, Evolutionary Computation Software Systems (EvoSoft), Evolutionary Methods for Smart Grid Applications, Exploration of Inaccessible Environments through Hardware/Software Co-evolution, Funding Sources (focus on Europe), GECCO Student Workshop, Genetic and Evolutionary Computation in Defense, Security and Risk Management, Genetic Improvement, Landscape-Aware Heuristic Search, Measuring and Promoting Diversity in Evolutionary Algorithms, Medical Applications of Genetic and Evolutionary Computation (MedGEC), Model-Based Evolutionary Algorithms (MBEA), New Standards for Benchmarking in Evolutionary Computation Research, Parallel and Distributed Evolutionary Inspired Methods, Evolving Collective Behaviors in Robotics (ECBR), Simulation in Evolutionary Robotics, Visualisation Methods in Genetic and Evolutionary Computation (VizGEC 2017), Women@GECCO, Surrogate-Assisted Evolutionary Optimisation (SAEOpt 2017)", isbn13 = "978-1-4503-4939-0", URL = "https://dl.acm.org/citation.cfm?id=3067695", DOI = "doi:10.1145/3067695", notes = "Distributed at GECCO-2017. Also known as \cite{2017:3067695}", } @InProceedings{wagner:2001:gpepcftsp, author = "Neal Wagner and Zbigniew Michalewicz", title = "Genetic Programming with Efficient Population Control for Financial Time Series Prediction", booktitle = "2001 Genetic and Evolutionary Computation Conference Late Breaking Papers", year = "2001", editor = "Erik D. Goodman", pages = "458--462", address = "San Francisco, California, USA", month = "9-11 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://www.coe.uncc.edu/~nwagner/gecco/GeccoPresentation_files/v3_document.htm", notes = "GECCO-2001LB, bloat control by dynamic size/depth limits", } @Article{wagner:2007:tec, author = "Neal Wagner and Zbigniew Michalewicz and Moutaz Khouja and Rob Roy McGregor", title = "Time Series Forecasting for Dynamic Environments: The {DyFor} Genetic Program Model", journal = "IEEE Transactions on Evolutionary Computation", year = "2007", volume = "11", number = "4", pages = "433--452", month = aug, keywords = "genetic algorithms, genetic programming, Dynamic, forecasting, parameter adaptation, time series", ISSN = "1389-2576", DOI = "doi:10.1109/TEVC.2006.882430", size = "20 pages", abstract = "Several studies have applied genetic programming (GP) to the task of forecasting with favourable results. However, these studies, like those applying other techniques, have assumed a static environment, making them unsuitable for many real-world time series which are generated by varying processes. This study investigates the development of a new dynamic GP model that is specifically tailored for forecasting in nonstatic environments. This Dynamic Forecasting Genetic Program (DyFor GP) model incorporates features that allow it to adapt to changing environments automatically as well as retain knowledge learned from previously encountered environments. The DyFor GP model is tested for forecasting efficacy on both simulated and actual time series including the U.S. Gross Domestic Product and Consumer Price Index Inflation. Results show that the performance of the DyFor GP model improves upon that of benchmark models for all experiments. These findings highlight the DyFor GP's potential as an adaptive, nonlinear model for real-world forecasting applications and suggest further investigations.", } @InProceedings{Wagner:2008:gecco, author = "Neal Wagner and Zbigniew Michalewicz", title = "An analysis of adaptive windowing for time series forecasting in dynamic environments: further tests of the {DyFor} GP model", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "1657--1664", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1657.pdf", DOI = "doi:10.1145/1389095.1389406", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, dynamic, forecasting, time series, uncertain environments, Real-World application", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389406}", } @Article{Wagner:2009:JBVELA, title = "Forecasting the Periodic Net Discount Rate with Genetic Programming", author = "Neal F Wagner and Mark A Thompson", journal = "Journal of Business Valuation and Economic Loss Analysis", year = "2009", volume = "4", number = "1", pages = "Art. 4", publisher = "The Berkeley Electronic Press", month = oct # "~23", keywords = "genetic algorithms, genetic programming, periodic net discount rate, forecasts, Empirical and Conceptual", URL = "http://www.bepress.com/jbvela/vol4/iss1/art4", DOI = "doi:10.2202/1932-9156.1072", bibsource = "OAI-PMH server at www.bepress.com", oai = "oai:bepress.com:jbvela-1072", size = "13 pages", abstract = "This paper examines the periodic net discount rate using genetic programming (GP) techniques to build better short-term forecasts. Standard GP techniques require human judgment as to which data window to use, which may be problematic due to structural breaks and persistence (or long memory) in the net discount rate. We use a recently developed extension of GP to overcome this problem. While our results show no significant out-of-sample forecast improvement relative to the linear alternative or random walk model over the full sample, they do provide evidence as to the stochastic nature of the net discount rate considering the AR(3) model yielded lower forecasting errors in the post-1982 sample.", notes = "Neal F. Wagner, SolveIT Software Pty Ltd Mark A. Thompson, Texas Tech University", } @TechReport{wagner:2004:tr, author = "Stefan Wagner and Michael Affenzeller", title = "The HeuristicLab Optimization Environment", institution = "Johannes Kepler University Linz", year = "2004", address = "Austria", keywords = "genetic algorithms, genetic programming", URL = "http://www.heuristiclab.com/publications/papers/wagner04c.pdf", abstract = "a new generic, flexible and extensible optimization environment named HeuristicLab developed by Michael Affenzeller and the author since 2002", notes = "C# microsoft .net p12 plugin for GP available version 1.0.1 http://www.heuristiclab.com", size = "15 pages", } @PhdThesis{Wagner2009, author = "Stefan Wagner", title = "Heuristic Optimization Software Systems - Modeling of Heuristic Optimization Algorithms in the {HeuristicLab} Software Environment", school = "Johannes Kepler University", year = "2009", type = "Doctor Technicae", address = "Linz, Austria", month = may # " 7", keywords = "genetic algorithms, genetic programming", } @InProceedings{Wagner:2011:GECCOcomp, author = "Stefan Wagner and Gabriel Kronberger", title = "Algorithm and experiment design with heuristiclab: an open source optimization environment for research and education", booktitle = "GECCO 2011 Tutorials", year = "2011", editor = "Darrell Whitley", isbn13 = "978-1-4503-0690-4", keywords = "genetic algorithms, genetic programming", pages = "1411--1438", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001858.2002143", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "The tutorial demonstrates how to apply and analyse metaheuristics using HeuristicLab, an open source optimisation environment. It will be shown how to parametrise and execute evolutionary algorithms to solve combinatorial optimisation problems (travelling salesman, vehicle routing) as well as data analysis problems (regression, classification). The attendees will learn how to assemble different algorithms and parameter settings to a large scale optimization experiment and how to execute such experiments on multi-core or cluster systems. Furthermore, the experiment results will be compared using HeuristicLab's interactive charts for visual and statistical analysis to gain knowledge from the executed test runs. To complete the tutorial, it will be sketched briefly how HeuristicLab can be extended with further optimization problems and how custom optimization algorithms can be modelled using the graphical algorithm designer. Additional details on HeuristicLab can be found at http://dev.heuristiclab.com.", notes = "Also known as \cite{2002143} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @InProceedings{Wagner2014, author = "S. Wagner and G. Kronberger and A. Beham and M. Kommenda and A. Scheibenpflug and E. Pitzer and S. Vonolfen and M. Kofler and S. Winkler and V. Dorfer and M. Affenzeller", title = "Architecture and Design of the {HeuristicLab} Optimization Environment", booktitle = "First Australian Conference on the Applications of Systems Engineering, ACASE", year = "2012", editor = "Robin Braun and Zenon Chaczko and Franz Pichler", volume = "6", series = "Topics in Intelligent Engineering and Informatics", pages = "197--261", address = "Sydney, Australia", month = feb # " 6-8", publisher = "Springer International Publishing", note = "Selected and updated papers", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-01435-7", URL = "http://dx.doi.org/10.1007/978-3-319-01436-4_10", DOI = "doi:10.1007/978-3-319-01436-4_10", abstract = "Many optimisation problems cannot be solved by classical mathematical optimization techniques due to their complexity and the size of the solution space. In order to achieve solutions of high quality though, heuristic optimization algorithms are frequently used. These algorithms do not claim to find global optimal solutions, but offer a reasonable tradeoff between runtime and solution quality and are therefore especially suitable for practical applications. In the last decades the success of heuristic optimization techniques in many different problem domains encouraged the development of a broad variety of optimization paradigms which often use natural processes as a source of inspiration (as for example evolutionary algorithms, simulated annealing, or ant colony optimization). For the development and application of heuristic optimization algorithms in science and industry, mature, flexible and usable software systems are required. These systems have to support scientists in the development of new algorithms and should also enable users to apply different optimization methods on specific problems easily. The architecture and design of such heuristic optimization software systems impose many challenges on developers due to the diversity of algorithms and problems as well as the heterogeneous requirements of the different user groups. In this chapter the authors describe the architecture and design of their optimization environment HeuristicLab which aims to provide a comprehensive system for algorithm development, testing, analysis and generally the application of heuristic optimization methods on complex problems.", notes = "Published by Springer 2014. as Advanced Methods and Applications in Computational Intelligence. Series editors Klempous, Ryszard and Nikodem, Jan and Jacak, Witold and Chaczko, Zenon", } @InProceedings{4705, author = "Stefan Wagner and Michael Affenzeller and Andreas Scheibenpflug", title = "Automatic Adaption of Operator Probabilities in Genetic Algorithms with Offspring Selection", booktitle = "Computer Aided Systems Theory, EUROCAST 2015", year = "2015", editor = "Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia", volume = "9520", series = "Lecture Notes in Computer Science", pages = "433--438", address = "Las Palmas, Gran Canaria, Spain", month = feb, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-27340-2", URL = "http://link.springer.com/chapter/10.1007/978-3-319-27340-2_54", DOI = "doi:10.1007/978-3-319-27340-2_54", abstract = "When offspring selection is applied in genetic algorithms, multiple crossover and mutation operators can be easily used together as crossover and mutation results of insufficient quality are discarded in the additional selection step after creating new solutions. Therefore, the a priori choice of appropriate crossover and mutation operators becomes less critical and it even turned out that multiple operators reduce the bias, broaden the search, and thus lead to higher solution quality in the end. However, using crossover and mutation operators which often produce solutions not passing the offspring selection criterion also increases the selection pressure and consequently the number of evaluated solutions.", } @InProceedings{6348, author = "Stefan Wagner and Andreas Beham and Michael Affenzeller", title = "Analysis and Visualization of the Impact of Different Parameter Configurations on the Behavior of Evolutionary Algorithms", booktitle = "Computer Aided Systems Theory, EUROCAST 2017", year = "2017", editor = "Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia", volume = "10671", series = "Lecture Notes in Computer Science", pages = "439--446", address = "Las Palmas de Gran Canaria, Spain", month = feb, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-74717-0", URL = "https://link.springer.com/chapter/10.1007%2F978-3-319-74718-7_53", DOI = "doi:10.1007/978-3-319-74718-7_53", abstract = "Evolutionary algorithms are generic and flexible optimization algorithms which can be applied to many optimization problems in different domains. Depending on the specific type of evolutionary algorithm, they offer several parameters such as population size, mutation probability, crossover and mutation operators, or number of elite solutions. How these parameters are set has a crucial impact on the algorithm's search behaviour and thus affects its performance. Therefore, parameter tuning is an important and challenging task in each application of evolutionary algorithms in order to retrieve satisfying results.", notes = "Published 2018?", } @PhdThesis{Wagy:thesis, author = "Mark David Wagy", title = "Enabling Machine Science through Distributed Human Computing", school = "Computer Science, University of Vermont", year = "2016", address = "USA", month = oct, keywords = "genetic algorithms, genetic programming", URL = "https://scholarworks.uvm.edu/cgi/viewcontent.cgi?article=1617&context=graddis", URL = "https://scholarworks.uvm.edu/graddis/618", size = "178 pages", abstract = "Distributed human computing techniques have been shown to be effective ways of accessing the problem-solving capabilities of a large group of anonymous individuals over the World Wide Web. They have been successfully applied to such diverse domains as computer security, biology and astronomy. The success of distributed human computing in various domains suggests that it can be used for complex collaborative problem solving. Thus it could be used for machine science: using machines to facilitate the vetting of disparate human hypotheses for solving scientific and engineering problems. In this thesis, we show that machine science is possible through distributed human computing methods for some tasks. By enabling anonymous individuals to collaborate in a way that parallels the scientific method -- suggesting hypotheses, testing and then communicating them for vetting by other participants -- we demonstrate that a crowd can together define robot control strategies, design robot morphologies capable of fast-forward locomotion and contribute features to machine learning models for residential electric energy usage. We also introduce a new methodology for empowering a fully automated robot design system by seeding it with intuitions distilled from the crowd. Our findings suggest that increasingly large, diverse and complex collaborations that combine people and machines in the right way may enable problem solving in a wide range of fields.", notes = "Supervisor: Josh Bongard", } @InProceedings{wakaki:2003:gecco, author = "Hiromi Wakaki and Hitoshi Iba", title = "{AVICE}: Evolving Avatar's Movernent", booktitle = "Genetic and Evolutionary Computation -- GECCO-2003", editor = "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller", year = "2003", pages = "1816--1817", address = "Chicago", publisher_address = "Berlin", month = "12-16 " # jul, volume = "2724", series = "LNCS", ISBN = "3-540-40603-4", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, poster", DOI = "doi:10.1007/3-540-45110-2_79", abstract = "In recent years, the widespread penetration of the Internet has led to the use of diverse expressions over the Web. Among them, many appear to have strong video elements. However, few expressions are based on human beings, with whom we are most familiar. This is deemed to be attributable to the fact that it is not easy to generate human movements. This is attributable to the fact that the creation of movements by hand requires intuition and technology. With this in mind, this study proposes a system that supports the creation of 3D avatars' movements based on interactive evolutionary computation, as a system that facilitates creativity and enables ordinary Users with no special skills to generate dynamic animations easily, and as a method of movement description.", notes = "GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)", } @Article{Wak01, author = "Julie Wakefield", title = "Complexity's Buisness Model", journal = "Scientific American", year = "2001", pages = "24--25", month = jan, keywords = "genetic algorithms", URL = "http://www.sciam.com/2001/0101issue/0101techbus1.html", size = "2 pages", notes = "general. A few examples of USA commercially succesful applications of GAs.", } @Article{Walker:2018:GPEM, author = "David J. Walker", title = "Visualisation with treemaps and sunbursts in many-objective optimisation", journal = "Genetic Programming and Evolvable Machines", year = "2018", volume = "19", number = "3", pages = "421--452", month = sep, note = "Special issue on genetic programming, evolutionary computation and visualization", keywords = "genetic algorithms, Many-objective optimisation, Visualisation , Evolutionary computation", ISSN = "1389-2576", URL = "https://doi.org/10.1007/s10710-018-9329-0", DOI = "doi:10.1007/s10710-018-9329-0", size = "32 pages", abstract = "Visualisation is an important aspect of evolutionary computation, enabling practitioners to explore the operation of their algorithms in an intuitive way and providing a better means for displaying their results to problem owners. The presentation of the complex data arising in many-objective evolutionary algorithms remains a challenge, and this work examines the use of treemaps and sunbursts for visualising such data. We present a novel algorithm for arranging a tree-map so that it explicitly displays the dominance relations that characterise many-objective populations, as well as considering approaches for creating trees with which to represent multi- and many-objective solutions. We show that treemaps and sunbursts can be used to display important aspects of evolutionary computation, such as the diversity and convergence of a search population, and demonstrate the approaches on a range of test problems and a real-world problem from the literature.", } @InProceedings{walker:2004:eurogp, author = "James Alfred Walker and Julian Francis Miller", title = "Evolution and Acquisition of Modules in Cartesian Genetic Programming", booktitle = "Genetic Programming 7th European Conference, EuroGP 2004, Proceedings", year = "2004", editor = "Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule", volume = "3003", series = "LNCS", pages = "187--197", address = "Coimbra, Portugal", publisher_address = "Berlin", month = "5-7 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", ISBN = "3-540-21346-5", URL = "http://www.elec.york.ac.uk/intsys/users/jfm7/eurogp2004.pdf", DOI = "doi:10.1007/978-3-540-24650-3_17", abstract = "We present automatic module acquisition and evolution within the graph based Cartesian Genetic Programming method. The method has been tested on a set of even parity problems and compared with Cartesian Genetic Programming without modules. Results are given that show that the new modular method evolves solutions up to 20 times quicker than the original non-modular method and that the speedup is more pronounced on larger problems. Analysis of some of the evolved modules shows that often they are lower order parity functions. Prospects for further improvement of the method are discussed.", notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004", } @InProceedings{1068287, author = "James Alfred Walker and Julian Francis Miller", title = "Investigating the performance of module acquisition in cartesian genetic programming", booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation", year = "2005", editor = "Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and Edwin D. {de Jong} and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler", volume = "2", ISBN = "1-59593-010-8", pages = "1649--1656", address = "Washington DC, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2005/docs/p1649.pdf", DOI = "doi:10.1145/1068009.1068287", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "25-29 " # jun, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, computational effort, design, digital adders, digital comparators, digital multipliers, modularity, module acquisition, performance", notes = "GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM Order Number 910052", } @InProceedings{conf/ices/WalkerM05, title = "Improving the Evolvability of Digital Multipliers Using Embedded Cartesian Genetic Programming and Product Reduction", author = "James Alfred Walker and Julian Francis Miller", year = "2005", pages = "131--142", editor = "Juan Manuel Moreno and Jordi Madrenas and Jordi Cosp", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3637", booktitle = "Evolvable Systems: From Biology to Hardware, 6th International Conference, ICES 2005, Proceedings", address = "Sitges, Spain", month = sep # " 12-14", bibdate = "2005-10-26", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ices/ices2005.html#WalkerM05", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", ISBN = "3-540-28736-1", DOI = "doi:10.1007/11549703_13", size = "12 pages", abstract = "Embedded Cartesian Genetic Programming (ECGP) is a form of Genetic Programming based on an acyclic directed graph representation. We investigate the use of ECGP together with a technique called Product Reduction (PR) to reduce the time required to evolve a digital multiplier. The results are compared with Cartesian Genetic Programming (CGP) with and without PR and show that ECGP improves evolvability and also that PR improves the performance of both techniques by up to eight times on the digital multiplier problems tested.", } @InProceedings{1144153, author = "James Alfred Walker and Julian Francis Miller and Rachel Cavill", title = "A multi-chromosome approach to standard and embedded cartesian genetic programming", booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation", year = "2006", editor = "Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos {Coello Coello} and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens", volume = "1", ISBN = "1-59593-186-4", pages = "903--910", address = "Seattle, Washington, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p903.pdf", DOI = "doi:10.1145/1143997.1144153", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "8-12 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, automatically defined functions, Cartesian genetic programming, digital circuits, embedded Cartesian genetic programming, evolution, module acquisition, multi-chromosome, multi-chromosome evolutionary strategy, ES, program synthesis, synthesis", notes = "GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM Order Number 910060", } @InProceedings{1144154, author = "James Alfred Walker and Julian Francis Miller", title = "Embedded cartesian genetic programming and the lawnmower and hierarchical-if-and-only-if problems", booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation", year = "2006", editor = "Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos {Coello Coello} and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens", volume = "1", ISBN = "1-59593-186-4", pages = "911--918", address = "Seattle, Washington, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p911.pdf", DOI = "doi:10.1145/1143997.1144154", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "8-12 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, automatically defined functions, Cartesian genetic programming, embedded Cartesian genetic programming, evolution, hierarchical-if-and-only-if, lawnmower problem, module acquisition, program synthesis, synthesis", notes = "GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM Order Number 910060", } @InProceedings{eurogp07:jwalker1, author = "James Alfred Walker and Julian Francis Miller", title = "Predicting Prime Numbers Using Cartesian Genetic Programming", editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar", booktitle = "Proceedings of the 10th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "4445", year = "2007", address = "Valencia, Spain", month = "11-13 " # apr, pages = "205--216", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", ISBN = "3-540-71602-5", isbn13 = "978-3-540-71602-0", DOI = "doi:10.1007/978-3-540-71605-1_19", abstract = "Prime generating polynomial functions are known that can produce sequences of prime numbers (e.g. Euler polynomials). However, polynomials which produce consecutive prime numbers are much more difficult to obtain. In this paper, we propose approaches for both these problems. The first uses Cartesian Genetic Programming (CGP) to directly evolve integer based prime-prediction mathematical formulae. The second uses multi-chromosome CGP to evolve a digital circuit, which represents a polynomial. We evolved polynomials that can generate 43 primes in a row. We also found functions capable of producing the first 40 consecutive prime numbers, and a number of digital circuits capable of predicting up to 208 consecutive prime numbers, given consecutive input values. Many of the formulae have been previously unknown.", notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007", } @InProceedings{eurogp07:jwalker2, author = "James Alfred Walker and Julian Francis Miller", title = "Changing the Genospace: Solving GA Problems with Cartesian Genetic Programming", editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar", booktitle = "Proceedings of the 10th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "4445", year = "2007", address = "Valencia, Spain", month = "11-13 " # apr, pages = "261--270", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", ISBN = "3-540-71602-5", isbn13 = "978-3-540-71602-0", DOI = "doi:10.1007/978-3-540-71605-1_24", abstract = "Embedded Cartesian Genetic Programming (ECGP) is an extension of Cartesian Genetic Programming (CGP) capable of acquiring, evolving and re-using partial solutions. In this paper, we apply for the first time CGP and ECGP to the ones-max and order-3 deceptive problems, which are normally associated with Genetic Algorithms. Our approach uses CGP and ECGP to evolve a sequence of commands for a tape-head, which produces an arbitrary length binary string on a piece of tape. Computational effort figures are calculated for CGP and ECGP and our results compare favourably with those of Genetic Algorithms.", notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007", } @InProceedings{1277295, author = "James Alfred Walker and Julian Francis Miller", title = "Solving real-valued optimisation problems using cartesian genetic programming", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "1724--1730", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1724.pdf", DOI = "doi:10.1145/1276958.1277295", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, embedded Cartesian Genetic programming, evolutionary programming, modules, real valued function optimisation", abstract = "Classical Evolutionary Programming (CEP) and Fast Evolutionary Programming (FEP) have been applied to realvalued function optimisation. Both of these techniques directly evolve the real-values that are the arguments of the real-valued function. In this paper we have applied a form of genetic programming called Cartesian Genetic Programming (CGP) to a number of real-valued optimisation benchmark problems. The approach we have taken is to evolve a computer program that controls a writing-head, which moves along and interacts with a finite set of symbols that are interpreted as real numbers, instead of manipulating the real numbers directly. In other studies, CGP has already been shown to benefit from a high degree of neutrality. We hope to exploit this for real-valued function optimisation problems to avoid being trapped on local optima. We have also used an extended form of CGP called Embedded CGP (ECGP) which allows the acquisition, evolution and re-use of modules. The effectiveness of CGP and ECGP are compared and contrasted with CEP and FEP on the benchmark problems. Results show that the new techniques are very effective.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @PhdThesis{James.Alfred.Walker:thesis, author = "James Alfred Walker", title = "The automatic acquisition, evolution and re-use of modules in cartesian genetic programming", school = "University of York", year = "2007", address = "UK", keywords = "genetic algorithms, genetic programming", URL = "http://ethos.bl.uk/OrderDetails.do?did=1&uin=uk.bl.ethos.444766", notes = "uk.bl.ethos.444766", } @Article{Walker_2008_TEC, author = "James Alfred Walker and Julian Francis Miller", title = "The Automatic Acquisition, Evolution and Reuse of Modules in Cartesian Genetic Programming", journal = "IEEE Transactions on Evolutionary Computation", year = "2008", volume = "12", number = "4", pages = "397--417", month = aug, keywords = "genetic algorithms, genetic programming, Automatically defined functions (ADFs), Cartesian genetic programming (CGP), embedded Cartesian genetic programming (ECGP), genetic programming (GP), graph-based representations, modularity, module acquisition, ECGP", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2007.903549", URL = "http://results.ref.ac.uk/Submissions/Output/3354578", size = "21 pages", abstract = "This paper presents a generalisation of the graph-based genetic programming (GP) technique known as Cartesian genetic programming (CGP). We have extended CGP by using automatic module acquisition, evolution, and reuse. To benchmark the new technique, we have tested it on: various digital circuit problems, two symbolic regression problems, the lawnmower problem, and the hierarchical if-and-only-if problem. The results show the new modular method evolves solutions quicker than the original nonmodular method, and the speedup is more pronounced on larger problems. Also, the new modular method performs favourably when compared with other GP methods. Analysis of the evolved modules shows they often produce recognisable functions. Prospects for further improvements to the method are discussed.", notes = "8-even parity, 3-bit adder, 3-multiplier, 3-comparison, HIFF, ADF. Refers to PDGP, pushGP, ADM. pop=5 (1+4)-ES, 1000 generations. suggestion (p401) that ECGP may suffer bloat, stack overflow and out of memory errors. Combined with duplicate entry \cite{DBLP:journals/tec/WalkerM08} October 2010. INSPEC Accession Number: 10118371", uk_research_excellence_2014 = "The paper advances evolutionary computing. It is the definitive journal article on automatically evolved sub-functions in Cartesian Genetic Programming (CGP). CGP, invented by Miller, has become a highly cited technique used in evolutionary computation. The paper includes detailed statistically rigorous comparisons with other GP methods and shows that CGP is one of the most efficient forms of Genetic Programming. The results contributed to work undertaken in an EPSRC funded project (EP/F062192/1).", } @InProceedings{Walker:2009:cec, author = "James Alfred Walker and James A. Hilder and Andy M. Tyrrell", title = "Towards Evolving Industry-Feasible Intrinsic Variability Tolerant CMOS Designs", booktitle = "2009 IEEE Congress on Evolutionary Computation", year = "2009", editor = "Andy Tyrrell", pages = "1591--1598", address = "Trondheim, Norway", month = "18-21 " # may, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-2959-2", file = "P444.pdf", DOI = "doi:10.1109/CEC.2009.4983132", abstract = "As the size of CMOS devices is approaching the atomic level, the increasing intrinsic device variability is leading to higher failure rates in conventional CMOS designs. This paper introduces a design tool capable of evolving CMOS topologies using a modified form of Cartesian Genetic Programming and a multi-objective strategy. The effect of intrinsic variability within the design is then analysed using statistically enhanced SPICE models based on 3D-atomistic simulations. The goal is to produce industry-feasible topology designs which are more tolerant to the random fluctuations that will be prevalent in future technology nodes. The results show evolved XOR and XNOR CMOS topologies and compare the impact of threshold voltage variation on the evolved designs with those from a standard cell library.", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR", } @Article{Walker:2009:GPEM, author = "James Alfred Walker and Katharina Volk and Stephen L. Smith and Julian Francis Miller", title = "Parallel evolution using multi-chromosome cartesian genetic programming", journal = "Genetic Programming and Evolvable Machines", year = "2009", volume = "10", number = "4", pages = "417--445", month = dec, note = "Special issue on parallel and distributed evolutionary algorithms, part I", keywords = "genetic algorithms, genetic programming, Multiple chromosomes, Cartesian genetic programming, Digital circuits, Mammography, Parallelisation", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-009-9093-2", size = "29 pages", abstract = "Parallel and distributed methods for evolutionary algorithms have concentrated on maintaining multiple populations of genotypes, where each genotype in a population encodes a potential solution to the problem. In this paper, we investigate the parallelisation of the genotype itself into a collection of independent chromosomes which can be evaluated in parallel. We call this multi-chromosomal evolution (MCE). We test this approach using Cartesian Genetic Programming and apply MCE to a series of digital circuit design problems to compare the efficacy of MCE with a conventional single chromosome approach (SCE). MCE can be readily used for many digital circuits because they have multiple outputs. In MCE, an independent chromosome is assigned to each output. When we compare MCE with SCE we find that MCE allows us to evolve solutions much faster. In addition, in some cases we were able to evolve solutions with MCE that we unable to with SCE. In a case-study, we investigate how MCE can be applied to to a single objective problem in the domain of image classification, namely, the classification of breast X-rays for cancer. To apply MCE to this problem, we identify regions of interest (RoI) from the mammograms, divide the RoI into a collection of sub-images and use a chromosome to classify each sub-image. This problem allows us to evaluate various evolutionary mutation operators which can pairwise swap chromosomes either randomly or topographically or reuse chromosomes in place of other chromosomes.", notes = "computational effort. p443 'multi-chromosomes as population members, where chromosomes can be evaluated independently'", } @InProceedings{Walker:2010:ICES, author = "James Alfred Walker and James A Hilder and Andy M. Tyrrell", title = "Measuring the Performance and Intrinsic Variability of Evolved Circuits", booktitle = "Proceedings of the 9th International Conference Evolvable Systems: From Biology to Hardware, ICES 2010", year = "2010", editor = "Gianluca Tempesti and Andy M. Tyrrell and Julian F. Miller", series = "Lecture Notes in Computer Science", volume = "6274", pages = "1--12", address = "York", month = sep # " 6-8", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", isbn13 = "978-3-642-15322-8", DOI = "doi:10.1007/978-3-642-15323-5_1", abstract = "This paper presents a comparison between conventional and multi-objective Cartesian Genetic Programming evolved designs for a 2-bit adder and a 2-bit multiplier. Each design is converted from a gate-level schematic to a transistor level implementation, through the use of an open-source standard cell library, and simulated in NGSPICE in order to generate industry standard metrics, such as propagation delay and dynamic power. Additionally, a statistical intrinsic variability analysis is performed, in order to see how each design is affected by intrinsic variability when fabricated at a cutting-edge technology node. The results show that the evolved design for the 2-bit adder is slower and consumes more power than the conventional design. The evolved design for the 2-bit multiplier was found to be faster but consumed more power than the conventional design, and that it was also more tolerant to the effects of intrinsic variability in both timing and power. This provides evidence that in the future, evolutionary-based approaches could be a feasible alternative for optimising designs at cutting-edge technology nodes, where traditional design methodologies are no longer appropriate, providing speed and power information about the standard cell library is used.", affiliation = "Intelligent Systems Group, Department of Electronics, University of York, Heslington, York, YO10 5DD UK", } @InProceedings{Walker:2010:ICESb, author = "James Alfred Walker and Yang Liu and Gianluca Tempesti and Andy M. Tyrrell", title = "Automatic Code Generation on a MOVE Processor Using Cartesian Genetic Programming", booktitle = "Proceedings of the 9th International Conference Evolvable Systems: From Biology to Hardware, ICES 2010", year = "2010", editor = "Gianluca Tempesti and Andy M. Tyrrell and Julian F. Miller", series = "Lecture Notes in Computer Science", volume = "6274", pages = "238--249", address = "York", month = sep # " 6-8", publisher = "Springer", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", isbn13 = "978-3-642-15322-8", DOI = "doi:10.1007/978-3-642-15323-5_21", abstract = "This paper presents for the first time the application of Cartesian Genetic Programming to the evolution of machine code for a simple implementation of a MOVE processor. The effectiveness of the algorithm is demonstrated by evolving machine code for a 4-bit multiplier with three different levels of parallelism. The results show that 100percent successful solutions were found by CGP and by further optimising the size of the solutions, it is possible to find efficient implementations of the 4-bit multiplier that have the potential to be human competitive . Further analysis of the results revealed that the structure of some solutions followed a known general design methodology.", affiliation = "Intelligent Systems Group, Department of Electronics, University of York, Heslington, York, YO10 5DD UK", } @Article{Walker:2011:GPEM, author = "James Alfred Walker and James A. Hilder and Dave Reid and Asen Asenov and Scott Roy and Campbell Millar and Andy M. Tyrrell", title = "The evolution of standard cell libraries for future technology nodes", journal = "Genetic Programming and Evolvable Machines", year = "2011", volume = "12", number = "3", pages = "234--256", month = sep, note = "Special Issue Title: Evolvable Hardware Challenges", keywords = "genetic algorithms, evolvable hardware, Intrinsic variability, Optimisation, Multi-objective, Standard cell library", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-011-9131-8", size = "22 pages", abstract = "Evolvable Hardware has been a discipline for over 15 years. Its application has ranged from simple circuit design to antenna design. However, research in the field has often been criticised for not addressing real world problems. Intrinsic variability has been recognised as one of the major challenges facing the semiconductor industry. This paper describes an approach that optimises designs within a standard cell library by altering the transistor dimensions. The proposed approach uses a Multi-objective Genetic Algorithm to optimise the device widths within a standard cell. The designs are analysed using statistically enhanced transistor models (based on 3D-atomistic simulations) and statistical Spice simulations. The goal is to extract high-speed and low-power designs, which are more tolerant to the random fluctuations present in current and future technology nodes. The results show improvements in both the speed and power of the optimised standard cells and that the impact of threshold voltage variation is reduced.", } @InCollection{Walker:2011:CGP, author = "James Alfred Walker and Julian F. Miller and Paul Kaufmann and Marco Platzner", title = "Problem Decomposition in Cartesian Genetic Programming", booktitle = "Cartesian Genetic Programming", publisher = "Springer", editor = "Julian F. Miller", year = "2011", series = "Natural Computing Series", chapter = "3", pages = "35--99", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", isbn13 = "978-3-642-17309-7", URL = "http://www.springer.com/computer/theoretical+computer+science/book/978-3-642-17309-7", DOI = "doi:10.1007/978-3-642-17310-3_3", abstract = "Scalability has become a major issue and a hot topic of research for the GP community, as researchers are moving on to investigate more complex problems. Throughout nature and conventional human design principles, modular structures are extensively used to tackle complex problems by decomposing them into smaller, simpler subproblems, which can be independently solved. Modularity is defined as the degree to which an entity can be represented in terms of smaller functional blocks. These smaller functional blocks are known as modules. In this chapter, a new approach called Embedded CGP (ECGP), is described that is capable of dynamically acquiring, evolving, and reusing modules to exploit modularity. Alternative approaches for acquiring modules within ECGP are also discussed before describing Modular CGP (MCGP), an enhancement to ECGP that allows the use of nested modules to see if further performance improvements are possible. Finally, an approach that uses the concept of multiple chromosomes in order to allow CGP and ECGP to exploit modularity through compartmentalisation is described.", notes = "part of \cite{Miller:CGP}", } @Article{journals/ijaras/WalkerLTTT12, author = "James Alfred Walker and Yang Liu and Gianluca Tempesti and Jon Timmis and Andy M. Tyrrell", title = "Automatic Machine Code Generation for a Transport Triggered Architecture using Cartesian Genetic Programming", journal = "International Journal of Adaptive, Resilient and Autonomic Systems", year = "2012", volume = "3", number = "4", pages = "32--50", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", ISSN = "1947-9220", DOI = "doi:10.4018/jaras.2012100103", bibdate = "2013-03-06", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ijaras/ijaras3.html#WalkerLTTT12", abstract = "Transport triggered architectures are used for implementing bio-inspired systems due to their simplicity, modularity and fault-tolerance. However, producing efficient, optimised machine code for such architectures is extremely difficult, since computational complexity has moved from the hardware-level to the software-level. Presented is the application of Cartesian Genetic Programming (CGP) to the evolution of machine code for a simple implementation of transport triggered architecture. The effectiveness of the algorithm is demonstrated by evolving machine code for a 4-bit multiplier with three different levels of parallelism. The results show that 100percent successful solutions were found by CGP and by further optimising the size of the solutions, it is possible to find efficient implementations of the 4-bit multiplier. Further analysis of the solutions showed that use of loops within the CGP function set could be beneficial and was demonstrated by repeating the earlier 4-bit multiplier experiment with the addition of a loop function.", } @InProceedings{Walker:2013:SSCI, author = "James Alfred Walker and Martin A. Trefzer and Andy M. Tyrrell", title = "Designing Function Configuration Decoders for the {PAnDA} architecture using Multi-objective Cartesian Genetic Programming", booktitle = "IEEE International Conference on Evolvable Systems, ICES 2013", year = "2013", editor_ssci-2013 = "P. N. Suganthan", editor = "Andy M. Tyrrell and Pauline C. Haddow", pages = "96--103", address = "Singapore", month = "16-19 " # apr, keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", DOI = "doi:10.1109/ICES.2013.6613288", size = "8 pages", abstract = "The Programmable Analogue and Digital Array (PAnDA) is a novel reconfigurable architecture, which allows variability aware design and rapid prototyping of digital systems. Exploiting the configuration options of the architecture allows the post-fabrication correction and optimisation of circuits directly in hardware using bio-inspired techniques. In order to reduce the overhead of extra configuration memory and area consumption, a portion of the configuration memory required to configure the logic functionality of the Configurable Analogue Blocks (CABs) in the PAnDA architecture is replaced by Function Configuration Decoders (FCDs). In the past, bio-inspired approaches based on Cartesian Genetic Programming have been demonstrated as a suitable method for designing such circuit topologies. As the area of the FCDs is a primary concern, in addition to performance, a form of CGP which uses a multi-objective strategy (MOCGP) is used to evolve FCD designs for the two types of CAB present in the PAnDA architecture. The results show that MOCGP is capable of evolving and optimising FCDs that are optimal for area and performance for both CABs. A PAnDA prototype chip containing FCDs is currently being fabricated. Also, when compared with designs produced by a commercial synthesis tool, the MO-CGP designs are smaller, faster, and more power efficient.", notes = "ICES 2013 http://www.ntu.edu.sg/home/epnsugan/index_files/SSCI2013/ICES2013.htm also known as \cite{6613288}", } @Article{walker:2003:AB, author = "Joanne Walker and Simon Garrett and Myra Wilson", title = "Evolving Controllers for Real Robots: A Survey of the Literature", journal = "Adaptive Behavior", year = "2003", volume = "11", number = "3", pages = "179--203", month = sep, keywords = "genetic algorithms, genetic programming, evolutionary robotics, physical robots, simulation, training, lifelong adaptation by evolution, GA, EP", URL = "http://users.aber.ac.uk/jnw/pubs/Walkeretal.pdf", DOI = "doi:10.1177/1059712303113003", abstract = "For many years, researchers in the field of mobile robotics have been investigating the use of genetic and evolutionary computation (GEC) to aid the development of mobile robot controllers. Alongside the fundamental choices of the GEC mechanism and its operators, which apply to both simulated and physical evolutionary robotics, other issues have emerged which are specific to the application of GEC to physical mobile robotics. This article presents a survey of recent methods in GEC-developed mobile robot controllers, focusing on those methods that include a physical robot at some point in the learning loop. It simultaneously relates each of these methods to a framework of two orthogonal issues: the use of a simulated and/or a physical robot, and the use of finite, training phase evolution prior to a task and/or lifelong adaptation by evolution during a task. A list of evaluation criteria are presented and each of the surveyed methods are compared to them. Analyses of the framework and evaluation criteria suggest several possibilities; however, there appear to be particular advantages in combining simulated, training phase evolution (TPE) with lifelong adaptation by evolution (LAE) on a physical robot.", } @InProceedings{Walker:1997:mdcva, author = "John Walker", title = "Methodologies to the Design and Control of Virtual Agents", booktitle = "Late Breaking Papers at the 1997 Genetic Programming Conference", year = "1997", editor = "John R. Koza", pages = "301", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California, 94305-3079, USA", month = "13--16 " # jul, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-206995-8", notes = "GP-97LB PHD Students' workshop The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670 Air combat simulation, 2 agents", } @Article{oai:CiteSeerPSU:530668, author = "Matthew Walker", title = "Evolution of a Robotic Soccer Player", journal = "Research Letters in the Information and Mathematical Sciences", year = "2002", volume = "3", number = "1", pages = "15--23", month = apr, keywords = "genetic algorithms, genetic programming, RoboCup, MiroSot", ISSN = "1175-2777", citeseer-isreferencedby = "oai:CiteSeerPSU:86374; oai:CiteSeerPSU:110667", citeseer-references = "oai:CiteSeerPSU:18409; \cite{oai:CiteSeerPSU:454905}; oai:CiteSeerPSU:107311", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:530668", URL = "http://www.massey.ac.nz/~wwiims/research/letters/volume3number1/02_walker.pdf", URL = "http://hdl.handle.net/10179/4362", URL = "http://mro.massey.ac.nz/bitstream/handle/10179/4362/Evolution_of_a_Robotic_Soccer_Player.pdf", URL = "http://citeseer.ist.psu.edu/530668.html", size = "9 pages", abstract = "Robotic soccer is a complex domain where, rather than hand-coding computer programs to control the players, it is possible to create them through evolutionary methods. This has been successfully done before by using genetic programming with high-level genes. Such an approach is, however, limiting. This work attempts to reduce that limit by evolving control programs using genetic programming with low-level nodes.", notes = "http://www.massey.ac.nz/~wwiims/research/letters/ Research Letters welcomes papers from staff and graduate students at Massey University in the areas of: Computer Science, Information Science, Mathematics, Statistics and the Physical and Engineering Sciences. Massey University Albany Campus, Auckland, New Zealand", } @InProceedings{eurogp07:walker, author = "Matthew Walker and Howard Edwards and Chris Messom", title = "Confidence Intervals for Computational Effort Comparisons", editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar", booktitle = "Proceedings of the 10th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "4445", year = "2007", address = "Valencia, Spain", month = "11-13 " # apr, pages = "23--32", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-71602-5", isbn13 = "978-3-540-71602-0", DOI = "doi:10.1007/978-3-540-71605-1_3", abstract = "When researchers make alterations to the genetic programming algorithm they almost invariably wish to measure the change in performance of the evolutionary system. No one specific measure is standard, but Koza's computational effort statistic is frequently used. In this paper the use of Koza's statistic is discussed and a study is made of three methods that produce confidence intervals for the statistic. It is found that an approximate 95percent confidence interval can be easily produced.", notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007", } @InProceedings{1277294, author = "Matthew Walker and Howard Edwards and Chris Messom", title = "The reliability of confidence intervals for computational effort comparisons", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "1716--1723", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1716.pdf", DOI = "doi:10.1145/1276958.1277294", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, confidence intervals, measurement, Wilson's method", abstract = "This paper analyses the reliability of confidence intervals for Koza's computational effort statistic. First, we conclude that dependence between the observed minimum generation and the observed cumulative probability of success leads to the production of more reliable confidence intervals for our preferred method. Second, we show that confidence intervals from 80% to 95% have appropriate levels of performance. Third, simulated data is used to consider the effect of large minimum generations and the confidence intervals are again found to be reliable. Finally, results from four large datasets collected from real genetic programming experiments are used to provide even more empirical evidence that the method for producing confidence intervals is reliable.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @InProceedings{1277310, author = "Matthew Walker and Howard Edwards and Chris Messom", title = "{"}Success effort{"} for performance comparisons", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "1760--1760", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1760.pdf", DOI = "doi:10.1145/1276958.1277310", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming: Poster, confidence intervals, hit effort, measurement, performance Statistics, success effort", abstract = "This paper looks at the production of a confidence interval for a statistic we rename success effort.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @InProceedings{Walker:2007:cec, author = "Matthew Walker and Howard Edwards and Chris Messom", title = "Success Effort and Other Statistics for Performance Comparisons in Genetic Programming", booktitle = "2007 IEEE Congress on Evolutionary Computation", year = "2007", editor = "Dipti Srinivasan and Lipo Wang", pages = "4631--4638", address = "Singapore", month = "25-28 " # sep, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "1-4244-1340-0", file = "1658.pdf", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2007.4425079", abstract = "This paper looks at the statistics used to compare variations to the genetic programming method. Previous work in this area has been dominated by the use of mean best-of-run fitness and Koza's minimum computational effort. This article re-introduces a statistic we name success effort and analyses two methods to produce confidence intervals for the statistic. We then compare success effort and four other performance measures and conclude that success effort is a sometimes more powerful statistic than computational effort and a more desirable measure than the other statistics.", notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C", } @PhdThesis{Walker:thesis, author = "Matthew Garry William Walker", title = "Developing and evaluating incremental evolution using high quality performance measures for genetic programming", school = "Computer Science, Massey University", year = "2007", type = "Doctor of Philosophy", address = "Albany, Auckland, New Zealand", keywords = "genetic algorithms, genetic programming, Incremental evolution, Performance measures", URL = "http://hdl.handle.net/10179/738", URL = "http://muir.massey.ac.nz/bitstream/10179/738/1/02whole.pdf", size = "267 pages", abstract = "This thesis is divided into two parts. The first part considers and develops some of the statistics used in genetic programming (GP) while the second uses those statistics to study and develop a form of incremental evolution and an early termination heuristic for GP. The first part looks in detail at success proportion, Koza's minimum computational effort, and a measure we rename {"}success effort{"}. We describe and develop methods to produce confidence intervals for these measures as well as confidence intervals for the difference and ratio of these measures. The second part studies Jackson's fitness-based incremental evolution. If the number of fitness evaluations are considered (rather than the number of generations) then we find some potential benefit through reduction in the effort required to find a solution. We then automate the incremental evolution method and show a statistically significant improvement compared to GP with automatically defined functions (ADFs). The success effort measure is shown to have the critical advantage over Koza's measure as it has the ability to include a decreasing cost of failure. We capitalise on this advantage by demonstrating an early termination heuristic that again offers a statistically significant advantage.", notes = "Open BEAGLE Supervisors: Chris Messom and Martin Johnson", } @InCollection{walker:1995:ceuga, author = "R. F. Walker and E. W. Haasdijk and M. C. Gerrets", title = "Credit Evaluation Using a Genetic Algorithm", booktitle = "Intelligent Systems for Finance and Business", publisher = "Wiley", year = "1995", editor = "Suran Goonatilake and Philip Treleaven", chapter = "3", pages = "39--59", keywords = "genetic algorithms, genetic programming", ISBN = "0-471-94404-1", URL = "http://www.amazon.com/Intelligent-Systems-Finance-Business-Goonatilake/dp/0471944041", notes = "http://www.cs.ucl.ac.uk/staff/S.Goonatilake/busbook.html contains info on book. Authors from Cap Volmac, Daltonlaan 400, PO BOX 2575, 3500 GN Utrecht, The Netherlands. OMEGA Credit scoring for loans based on characteristics of the applicant (eg salary, age, marital status) {"}In all trials conducted to date it (GAAF, a GP like GA) outperformed any known method for credit scoring.{"} Mon, 17 Mar 1997 22:25:28 Company now called {"}Cap Gemini the Netherlands{"}", } @InProceedings{walker:1999:AWGP, author = "Reginald L. Walker", title = "Assessment of the Web using Genetic Programming", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1750--1755", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, real world applications, information retrieval, internet, world wide web, search engines", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-727.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-727.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Article{Walker:2001:PC, author = "Reginald L. Walker", title = "Search engine case study: searching the web using genetic programming and {MPI}", journal = "Parallel Computing", volume = "27", pages = "71--89", year = "2001", number = "1-2", month = jan, keywords = "genetic algorithms, genetic programming, Distributed computing, Information retrieval, World Wide Web, Search engines", URL = "http://www.sciencedirect.com/science/article/B6V12-42K5HNX-4/1/57eb870c72fb7768bb7d824557444b72", ISSN = "0167-8191", DOI = "doi:10.1016/S0167-8191(00)00089-2", abstract = "The generation of a Web page follows distinct sources for the incorporation of information. The earliest format of these sources was an organized display of known information determined by the page designers' interest and/or design parameters. The sources may have been published in books or other printed literature, or disseminated as general information about the page designer. Due to a growth in Web pages, several new search engines have been developed in addition to the refinement of the already existing ones. The use of the refined search engines, however, still produces an array of diverse information when the same set of keywords are used in a Web search. Some degree of consistency in the search results can be achieved over a period of time when the same search engine is used, yet, most initial Web searches on a given topic are treated as final after some form of refinement/adjustment of the keywords used in the search process. To determine the applicability of a genetic programming (GP) model for the diverse set of Web documents, search strategies behind the current search engines for the World Wide Web were studied. The development of a GP model resulted in a parallel implementation of a pseudo-search engine indexer simulator. The training sets used in this study provided a small snapshot of the computational effort required to index Web documents accurately and efficiently. Future results will be used to develop and implement Web crawler mechanisms that are capable of assessing the scope of this research effort. The GP model results were generated on a network of SUN workstations and an IBM SP2.", } @InProceedings{walker:2001:pcsumec, author = "Reginald L. Walker", title = "Parallel Clustering System Using the Methodologies of Evolutionary Computations", booktitle = "Proceedings of the 2001 Congress on Evolutionary Computation CEC2001", year = "2001", pages = "831--938", address = "COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "27-30 " # may, organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Bioinformatics Evolutionary computations, Biological modeling, Cluster analysis, Distributive/parallel computing, MPI, WWW, SUN workstations, Tocorime Apicu project, Web pages, World Wide Web, adaptive probe sets, Bioinformatics approach, evolutionary computations, fitness measures, fitness-enhancing mechanisms, message passing interface, parallel clustering system, performance, search engine, Internet, evolutionary computation, information resources, message passing", ISBN = "0-7803-6658-1", DOI = "doi:10.1109/CEC.2001.934276", abstract = "Several versions of the parallel clustering system were studied to improve performance of its initial implementation. The current versions were restricted to 1024 Web pages which, in turn, were used to create adaptive probe sets that were distributed to each indexer node. The probe sets were used to compute fitness measures associated with each indexer node used to create sub-species for the purpose of applying the new and traditional GA/GP operators. Speedup resulted from fitness-enhancing mechanisms that provided information results from previous fitness measurements of previous generations, such as the non-genetic transmission of cultural information. The clustering results are being used in the Tocorime Apicu project to develop a bioinformatic approach to the design and validation of an integrated, experimental search engine. This model provides a foundation for an evolutionary expansion of this computational model as World Wide Web (WWW) documents continue to grow. The clustering results were generated using message passing interface (MPI) on a network of SUN workstations", notes = "CEC-2001 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 01TH8546C, Library of Congress Number = . {"}only females of equal status compete in order to ...mate{"} p831 {"}even-numbered node ID{"}. Tocorime Apicu. bloat. cf pygmies and civil servants? experimental www search engine", } @InProceedings{Walker:2001:DLB, author = "Reginald L. Walker", title = "Dynamic Load Balancing Model: Preliminary Results for Parallel Pseudo-search Engine Indexers Crawler Mechanisms Using {MPI} and Genetic Programming", year = "2001", booktitle = "4th International Conference on Vector and Parallel Processing - VECPAR 2000. Selected Papers and Invited Talks", editor = "J. M. L. M. Palma and J. Dongarra and V. Hernandez", volume = "1981", series = "Lecture Notes in Computer Science", pages = "61--74", address = "Porto, Portugal", month = "21-23 " # jun, publisher = "Springer", keywords = "genetic algorithms, genetic programming", CODEN = "LNCSD9", ISSN = "0302-9743", bibdate = "Sat Feb 2 13:03:02 MST 2002", DOI = "doi:10.1007/3-540-44942-6_5", acknowledgement = ack-nhfb, isbn13 = "978-3-540-41999-0", abstract = "Methodologies derived from Genetic Programming (GP) and Knowledge Discovery in Databases (KDD) were used in the parallel implementation of the indexer simulator to emulate the current World Wide Web (WWW) search engine indexers. This indexer followed the indexing strategies that were employed by AltaVista and Inktomi that index each word in each Web document. The insights gained from the initial implementation of this simulator have resulted in the initial phase of the adaption of a biological model. The biological model will offer a basis for future developments associated with an integrated Pseudo-Search Engine. The basic characteristics exhibited by the model will be translated so as to develop a model of an integrated search engine using GP. The evolutionary processes exhibited by this biological model will not only provide mechanisms for the storage, processing, and retrieval of valuable information but also for Web crawlers, as well as for an advanced communication system. The current Pseudo-Search Engine Indexer, capable of organizing limited subsets of Web documents, provides a foundation for the first simulator of this model. Adaptation of the model for the refinement of the Pseudo-Search Engine establishes order in the inherent interactions between the indexer, crawler and browser mechanisms by including the social (hierarchical) structure and simulated behavior of this complex system. The simulation of behavior will engender mechanisms that are controlled and coordinated in their various levels of complexity. This unique model will also provide a foundation for an evolutionary expansion of the search engine as WWW documents continue to grow. The simulator results were generated using Message Passing Interface (MPI) on a network of SUN workstations and an IBM SP2 computer system.", } @InProceedings{walker:1999:SNN, author = "Richard Walker and Orazio Miglino", title = "Simulating exploratory behavior in evolving Neural Networks", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1422--1428", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "artificial life, adaptive behavior and agents", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-026.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-026.pdf", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Walkinshaw:2016:ICSME, author = "Neil Walkinshaw and Mathew Hall", booktitle = "2016 IEEE International Conference on Software Maintenance and Evolution (ICSME)", title = "Inferring Computational State Machine Models from Program Executions", year = "2016", pages = "122--132", month = oct, keywords = "genetic algorithms, genetic programming", URL = "http://bibtex.github.io/ICSME-2016-WalkinshawH.html", URL = "https://eprints.whiterose.ac.uk/127869/1/ICSME2016FinalSubmission.pdf", DOI = "doi:10.1109/ICSME.2016.74", size = "11 pages", abstract = "The challenge of inferring state machines from log data or execution traces is well-established, and has led to the development of several powerful techniques. Current approaches tend to focus on the inference of conventional finite state machines or, in few cases, state machines with guards. However, these machines are ultimately only partial, because they fail to model how any underlying variables are computed during the course of an execution, they are not computational. In this paper we introduce a technique based upon Genetic Programming to infer these data transformation functions, which in turn render inferred automata fully computational. Instead of merely determining whether or not a sequence is possible, they can be simulated, and be used to compute the variable values throughout the course of an execution. We demonstrate the approach by using a Cross-Validation study to reverse-engineer complete (computational) EFSMs from traces of established implementations.", notes = "Also known as \cite{7816460}", } @Misc{oai:arXiv.org:1608.03181, title = "Uncertainty-Driven Black-Box Test Data Generation", author = "Neil Walkinshaw and Gordon Fraser", year = "2016", month = aug # "~10", abstract = "We can never be certain that a software system is correct simply by testing it, but with every additional successful test we become less uncertain about its correctness. In absence of source code or elaborate specifications and models, tests are usually generated or chosen randomly. However, rather than randomly choosing tests, it would be preferable to choose those tests that decrease our uncertainty about correctness the most. In order to guide test generation, we apply what is referred to in Machine Learning as Query Strategy Framework: We infer a behavioural model of the system under test and select those tests which the inferred model is least certain about. Running these tests on the system under test thus directly targets those parts about which tests so far have failed to inform the model. We provide an implementation that uses a genetic programming engine for model inference in order to enable an uncertainty sampling technique known as query by committee, and evaluate it on eight subject systems from the Apache Commons Math framework and JodaTime. The results indicate that test generation using uncertainty sampling outperforms conventional and Adaptive Random Testing.", bibsource = "OAI-PMH server at export.arxiv.org", oai = "oai:arXiv.org:1608.03181", keywords = "genetic algorithms, genetic programming, SBSE, software engineering", URL = "http://arxiv.org/abs/1608.03181", notes = "See \cite{Walkinshaw:2017:ICST}", } @InProceedings{Walkinshaw:2017:ICST, author = "Neil Walkinshaw and Gordon Fraser", booktitle = "2017 IEEE International Conference on Software Testing, Verification and Validation (ICST)", title = "Uncertainty-Driven Black-Box Test Data Generation", year = "2017", pages = "253--263", month = mar, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICST.2017.30", abstract = "We can never be certain that a software system is correct simply by testing it, but with every additional successful test we become less uncertain about its correctness. In absence of source code or elaborate specifications and models, tests are usually generated or chosen randomly. However, rather than randomly choosing tests, it would be preferable to choose those tests that decrease our uncertainty about correctness the most. In order to guide test generation, we apply what is referred to in Machine Learning as Query Strategy Framework: We infer a behavioural model of the system under test and select those tests which the inferred model is least certain about. Running these tests on the system under test thus directly targets those parts about which tests so far have failed to inform the model. We provide an implementation that uses a genetic programming engine for model inference in order to enable an uncertainty sampling technique known as query by committee, and evaluate it on eight subject systems from the Apache Commons Math framework and JodaTime. The results indicate that test generation using uncertainty sampling outperforms conventional and Adaptive Random Testing.", notes = "See also \cite{oai:arXiv.org:1608.03181} Also known as \cite{7927980}", } @InProceedings{Wallin:Sc:cec2005, author = "David Wallin and Conor Ryan and R. Muhammad Atif Azad", title = "Symbiogenetic coevolution", booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary Computation", year = "2005", editor = "David Corne and Zbigniew Michalewicz and Bob McKay and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Gunther Raidl and Kay Chen Tan and Ali Zalzala", pages = "1613--1620", address = "Edinburgh, Scotland, UK", month = "2-5 " # sep, publisher = "IEEE Press", volume = "2", ISBN = "0-7803-9363-5", URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=10417&isvol=2", URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=10417", DOI = "doi:10.1109/CEC.2005.1554882", URL = "http://ieeexplore.ieee.org/iel5/10417/33080/01554882.pdf?tp=&isnumber=&arnumber=1554882", keywords = "genetic algorithms, symbiogenetic, parasite mutation", abstract = "In this paper we introduce a cooperative revolutionary algorithm based on the ideas of endosymbiosis. We compare it to a generational GA on two deceptive and decomposable problems and show that it has better scaling properties as the problem size increases. We then analyse what effect crossover and parasite mutation has on its performance and conclude that a high parasite mutation rate is preferred over a lower rate and that crossover has no, or a very small, effect on its performance.", notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS", } @InProceedings{Wallis:2018:ReaLX, author = "Tom Wallis and Tim Storer", title = "Process Fuzzing as an Approach to Genetic Programming", booktitle = "Proceedings of the SICSA Workshop on Reasoning, Learning and Explainability", year = "2018", editor = "Kyle Martin and Nirmalie Wiratunga and Leslie S. Smith", volume = "2151", series = "CEUR Workshop Proceedings", address = "Aberdeen, UK", month = jun # " 27", publisher = "CEUR-WS.org", keywords = "genetic algorithms, genetic programming, SBSE, Py-DySoFu, aspect orientation framework", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/sicsa/realx2018.html#WallisS18", URL = "http://nbn-resolving.de/urn:nbn:de:0074-2151-7", URL = "http://ceur-ws.org/Vol-2151/Paper_S3.pdf", code_url = "https://github.com/twsswt/pydysofu", size = "5 pages", abstract = "Genetic Programming (GP) has recently seen a growing application in the area of writing and improving computer programs. Generally, for experiments in this area, bespoke tools are constructed to perform research. In this paper, it is demonstrated that GP behaviour can be achieved via process fuzzing, and an implementation of the adaptation of ASTs for GP behaviour in the process fuzzing tool PyDySoFu is described.", notes = "SICSA ReaLX http://ceur-ws.org/Vol-2151/ Python Dynamic Source Fuzzer https://github.com/twsswt/pydysofu Also known as \cite{conf/sicsa/WallisS18} School of Computing Science, University of Glasgow", } @InProceedings{Wallner:2025:ICSTW, author = "Felix Wallner and Bernhard K. Aichernig and Florian Lorber and Martin Tappler", title = "Mutating Skeletons: Learning Timed Automata via Domain Knowledge", booktitle = "2025 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)", year = "2025", pages = "67--77", month = mar, keywords = "genetic algorithms, genetic programming, Software testing, Runtime, Learning automata, Model checking, Skeleton, Reflection, Real-time systems, Timing, Formal verification, model-learning, timed automata, domain knowledge", ISSN = "2159-4848", DOI = "doi:10.1109/ICSTW64639.2025.10962513", abstract = "Formal verification techniques, such as model checking, can provide valuable insights and guarantees for (safety-critical) devices and their possible behaviour. However, these guarantees only hold true as long as the model correctly reflects the system. Automata learning provides a huge advantage there as it enables not only the automatic creation of the needed models but also ensures their correct reflection of the system behaviour. However, and this holds especially true for real-time systems, model learning techniques can become very time consuming. To combat this, we show how to integrate given domain knowledge into an existing approach based on genetic programming to speed up the learning process. In particular, we show how the genetic programming approach can take a (possibly abstracted, incomplete or incorrect) untimed skeleton of an automaton, which can often be obtained very cheaply, and augment it with timing behaviour to form timed automata in a fast and efficient manner. We demonstrate the approach on several examples of varying sizes.", notes = "Also known as \cite{10962513}", } @InProceedings{walsh:1996:Paragen, author = "Paul Walsh and Conor Ryan", title = "Paragen: A Novel Technique for the Autoparallelisation of Sequential Programs using Genetic Programming", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE", pages = "406--409", address = "Stanford University, CA, USA", publisher = "MIT Press", isbn13 = "9780262315876", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap56.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", size = "6 pages", abstract = "The Paragen system is a new technique for the automatic conversion of sequential programs into functionally equivalent parallel programs. This technique uses GP to generate a highly parallel program from an original sequential program, while preserving functionality. We show that a genetic search avoids the complexities introduced by standard data dependency techniques and can also introduce further efficiencies by automatically reordering program statements. This paper gives a brief introduction to the problem of autoparallelisation followed by a discussion of the design and implementation issues of the system. Results demonstrate the effectiveness of the Paragen system by the automatic conversion of a number of complex program segments.", notes = "GP-96", } @InProceedings{walsh:1998:epfp, author = "Paul Walsh", title = "Evolving Pure Functional Programs", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "399--402", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-548-7", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1998/walsh_1998_epfp.pdf", notes = "GP-98", } @InProceedings{walsh:1998:gii, author = "Robert W. Walsh and Bryant A. Julstrom", title = "Generalized Instant Insanity: A GA-Difficult Problem", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "229--232", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "genetic algorithms", size = "4 pages", notes = "GP-98LB", } @InProceedings{walsh:1999:AFSFESIHLP, author = "Paul Walsh", title = "A Functional Style and Fitness Evaluation Scheme for Inducting High Level Programs", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1211--1216", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-455.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-455.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{walsh:1999:APEAHPI, author = "Paul J. Walsh", title = "A Parallel Evolutionary Algorithm for High-Level Program Induction", booktitle = "Proceedings of the Congress on Evolutionary Computation", year = "1999", editor = "Peter J. Angeline and Zbyszek Michalewicz and Marc Schoenauer and Xin Yao and Ali Zalzala", volume = "2", pages = "1027--1034", address = "Mayflower Hotel, Washington D.C., USA", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "6-9 " # jul, organisation = "Congress on Evolutionary Computation, IEEE / Neural Networks Council, Evolutionary Programming Society, Galesia, IEE", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, parallel and distributed processing, FGP system, FP, distributed computing environment, functional genetic programming, high level solutions, high-level program induction, higher order functions, iteration, near linear speed-up, parallel evolutionary algorithm, parallel implementation, programming problems, pure functional language, recursion, functional languages, functional programming, parallel algorithms, parallel programming", ISBN = "0-7803-5536-9 (softbound)", ISBN = "0-7803-5537-7 (Microfiche)", DOI = "doi:10.1109/CEC.1999.782536", abstract = "The paper discusses issues relating to the implementation of a new technique for the induction of high level programs, Functional genetic programming (FGP). The FGP system is based on the pure functional language FP, which uses higher order functions and avoids the use of explicitly named objects, While the FGP system can produce high level solutions to a number of common programming problems involving iteration and recursion, this technique is computationally expensive. A parallel implementation that addresses this limitation is presented and it is shown that near linear speed-up can be achieved across a distributed computing environment", notes = "CEC-99 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. Library of Congress Number = 99-61143", } @Article{Walters:1994:GERM, author = "D. Eric Walters and R. Michael Hinds", title = "Genetically Evolved Receptor Models (GERM): A Computational Approach to Construction of Receptor Models", journal = "Journal of Medicinal Chemistry", year = "1994", volume = "37", number = "16", pages = "2527--2536", month = aug, keywords = "genetic algorithms, genetic programming", ISSN = "0022-2623", DOI = "doi:10.1021/jm00042a006", size = "10 pages", abstract = "Given the three-dimensional structure of a receptor site, there are several methods available for designing ligands to occupy the site; frequently, the three-dimensional structure of interesting receptors is not known, however. The GERM program uses a genetic algorithm to produce atomic-level models of receptor sites, based on a small set of known structure-activity relationships. The evolved models show a high correlation between calculated intermolecular energies and bioactivities; they also give reasonable predictions of bioactivity for compounds which were not included in model generation. Such models may serve as starting points for computational or human ligand design efforts.", notes = " reprints available on request--send a mailing address D. Eric Walters, Ph.D., Associate Professor, Biological Chemistry Finch University of Health Sciences/The Chicago Medical School 3333 Green Bay Road, North Chicago, IL 60064 ph 708-578-3000, x-498;fax 708-578-3240; email: walterse@mis.fuhscms.edu", } @Article{Waltz:2006:IS, title = "AI's 10 to Watch", author = "David L. Waltz", journal = "Intelligent Systems", year = "2006", volume = "21", number = "3", pages = "5--14", month = jan # "-" # feb, keywords = "genetic algorithms, genetic programming, null algorithmic biology, artificial intelligence, description logics, human cognition, human-level AI, linguistics, multi-agent coordination, social networks, the Semantic Web", ISSN = "1541-1672", DOI = "doi:10.1109/MIS.2006.40", size = "10 pages", abstract = "The recipients of the IEEE Intelligent Systems 10 to Watch award--Eyal Amir, Regina Barzilay, Jennifer Golbeck, Tom Griffiths, Steve Gustafson, Carsten Lutz, Pragnesh Jay Modi, Marta Sabou, and Richard A. Watson--discuss their current research and their visions of AI for the future. This article is part of a special issue on the Future of AI.", abstract = "Creative Problem Solving with Genetic Programming Steven Gustafson is a computer scientist at the General Electric Global Research Center in Niskayuna, New York. As a member of the Computational Intelligence Lab, he develops and applies advanced AI and machine learning algorithms for complex problem solving. He received his PhD in computer science from the University of Nottingham, UK, where he was a research fellow in the Automated Scheduling, Optimisation and Planning Research Group. He received his BS and MS in computer science from Kansas State University, where he was a research assistant in the Knowledge Discovery in Databases Laboratory. His PhD dissertation, an analysis of a biologically inspired search algorithm in the space of computer programs, was nominated for the British Computer Society and the Conference of Professors and Heads of Computing Distinguished Dissertation award, which recognises the top PhD thesis in the UK computer science community. In 2005 and 2006, he coauthored papers that won the Best Paper Award at the European Conference on Genetic Programming. Outside of work, Gustafson enjoys literature and travelling with his wife and infant son.", abstract = "AI and Algorithmic Biology Richard A. Watson University of Southampton Richard A. Watson is a senior lecturer in the natural systems research group at the University of Southampton's School of Electronics and Computer Science. He received his BA in AI from the University of Sussex in 1990 and then worked in industry for about five years. Returning to academia, he chose Sussex again for an MSc in knowledge-based systems, where he was introduced to evolutionary modelling. His PhD in computer science at Brandeis University (2002) resulted in 22 publications and a dissertation addressing the algorithmic concepts underlying the major transitions in evolution. A postdoctoral position at Harvard University's Department of Organismic and Evolutionary Biology provided training to complement his computer science background. He now has over 35 journal and conference publications on topics spanning artificial life, robotics, evolutionary computation, and computational biology. At Southampton, he's establishing a new research group and leading preparation of a new MSc in complexity science. He is the author of Compositional Evolution: The Impact of Sex, Symbiosis, and Modularity on the Gradualist Framework of Evolution (MIT Press, 2006).", } @Article{Waltz:2009:science, author = "David Waltz and Bruce G. Buchanan", title = "Automating Science", journal = "Science", year = "2009", volume = "324", number = "5923", pages = "43--44", month = "3 " # apr, note = "Perspective", keywords = "genetic algorithms, genetic programming", ISSN = "0036-8075", DOI = "doi:10.1126/science.1172781", size = "2 pages", abstract = "The idea of automating aspects of scientific activity dates back to the roots of computer science, if not to Francis Bacon.", notes = "\cite{King:2009:Science} \cite{Science09:Schmidt} Center for Computational Learning Systems, Columbia University, New York, NY 10115, USA. Computer Science Department, University of Pittsburgh, Pittsburgh, PA 15260, USA.", } @Article{DBLP:journals/asc/WanWZCBWZLG24, author = "Han-Wen Wan and Meng-Han Wu and Wen-Shan Zhao and Han Cheng and Ying Bi and Xian-Fang Wang and Xiang-Rui Zhang and Yan Li and Wei-Feng Guo", title = "Label reusing based graph neural network for unbalanced classification of personalized driver genes in cancer", journal = "Applied Soft Computing", year = "2024", volume = "159", number = "C", pages = "111658", month = jul, keywords = "genetic algorithms, genetic programming, Personalized driver genes, Graph attention neural network, Label reuse, Semi-supervised learning, Tumor heterogeneity", publisher = "Elsevier Science Publishers B. V.", ISSN = "1568-4946", timestamp = "Fri, 31 May 2024 21:05:36 +0200", biburl = "https://dblp.org/rec/journals/asc/WanWZCBWZLG24.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://doi.org/10.1016/j.asoc.2024.111658", DOI = "doi:10.1016/J.ASOC.2024.111658", size = "15 pages", abstract = "It is a big challenge to identify personalized driver genes (PDGs) for understanding tumor heterogeneity of cancer individual patients. From the perspective of machine learning, identifying PDGs is an inherent class imbalance issue due to the fewer known driver genes than most passenger ones. However, existing machine learning based methods including unsupervised and supervised learning based methods ignore the importance of limited well-established cancer tissue specific driver genes(CSDGs) for this class imbalance issue. Here we converted the PDG prediction issue to a semi-supervised classification task and a novel method (namely PersonalizedGNN) was developed to identify PDGs by using graph attention neural network and label reuse strategy in personalized gene interaction network (PGIN). PersonalizedGNN effectively uses the structure information of PGIN and the limited well-established CSDG information for achieving promising performance. Using the breast cancer and lung cancer datasets from The Cancer Genome Atlas, we validated our method and compared it with other advanced methods. PersonalizedGNN showed outstanding potential in identifying cancer driver genes in terms of prediction precision. Furthermore, we could discover subtype-specific de novo cancer driver genes and in vitro cell-based assays for a novel driver gene FZD7 in lung squamous cell carcinoma cells further validated the PersonalizedGNN. In summary, PersonalizedGNN offers a new effective perspective for discovering PDGs by considering information of prior known CSDGs in PGIN which help researchers understand tumor heterogeneity of cancer individual patients.", } @Article{Wan:2023:SJ, author = "Jianfen Wan and Hongrun Yin and Kai Liu and Chengjun Zhu and Xiaoxiang Guan and Jiafeng Yao", journal = "IEEE Sensors Journal", title = "A Hybrid Genetic Expression Programming and Genetic Algorithm ({GEP-GA)} of Auto-Modeling Electrical Equivalent Circuit for Particle Structure Measurement With Electrochemical Impedance Spectroscopy ({EIS)}", year = "2023", volume = "23", number = "5", pages = "4344--4351", abstract = "A hybrid Genetic Expression Program and Genetic Algorithm (GEP-GA) is proposed for auto-modelling the Electrical Equivalent Circuit (EEC) of particle structure measurement with Electrochemical Impedance Spectroscopy (EIS). It combines the GEP with the ability of searching mathematical models and the GA with the ability of electrical parameters global searching. The GEP is used to modelling EEC of EIS, the GA is used to calculate the electrical parameters and fitness of the EEC. The hybrid method has the advantage of no need manually set. Firstly, the method is used to reconstruct EEC of simulation model and simultaneously calculate its electrical parameters. The results show that the auto-modelling EEC is same as the simulation's, and the accuracy of calculating electrical parameters over 99.5percent. Secondly, a portable and detachable sensor is proposed to measure the EIS of particle solution. Finally, the GEP-GA is verified by auto-modelling the EEC of $10 ~\mu \text{m}$ Poly Methyl Methacrylate (10 PMMA), the linear correlation coefficient of EIS between experiment and auto-modelling EEC ${R}^{{2}}={0.99874}$ . Meanwhile, the EIS of $10 ~\mu \text{m}$ Polystyrene Magnetic particles (10 PSM), and $10 ~\mu \text{m}$ , $20 ~\mu \text{m}$ , $30 ~\mu \text{m}$ Polystyrene particles (10 PS, 20 PS, 30 PS) are also fitted with the EEC model of 10 PMMA. The results show that their linear correlation coefficient ${R}^{{2}}>{0.998}$ . It indicates that the hybrid of GEP-GA can auto-modelling the EEC and simultaneously calculate the electrical parameters. Furthermore, the electrical characters of the particle suspension and the particle structure can be measured with these electrical parameters.", keywords = "genetic algorithms, genetic programming, Decoding, Statistics, Sociology, Encoding, Equivalent circuits, Sensors, Electrochemical impedance spectroscopy, auto-modelling electrical equivalent circuit, particle structure, genetic expression program", DOI = "doi:10.1109/JSEN.2021.3106160", ISSN = "1558-1748", month = mar, notes = "Also known as \cite{9517272}", } @InProceedings{wan:2011:EuroGP, author = "Mingxu Wan and Thomas Weise and Ke Tang", title = "Novel Loop Structures and the Evolution of Mathematical Algorithms", booktitle = "Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011", year = "2011", month = "27-29 " # apr, editor = "Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado", series = "LNCS", volume = "6621", publisher = "Springer Verlag", address = "Turin, Italy", pages = "49--60", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-20406-7", DOI = "doi:10.1007/978-3-642-20407-4_5", abstract = "In this paper, we analyse the capability of GP to synthesise non-trivial, non-approximative, and deterministic mathematical algorithms with integer-valued results. Such algorithms usually involve loop structures. We raise the question which representation for loops would be most efficient. We define five tree-based program representations which realise the concept of loops in different ways, including two novel methods which use the convergence of variable values as implicit stopping criteria. Based on experiments on four problems under three fitness functions (error sum, hit rate, constant 1) we find that GP can statistically significantly outperform random walks. Still, evolving said algorithms seems to be hard for GP and the success rates are not high. Furthermore, we found that none of the program representations could consistently outperform the others, but the two novel methods with indirect stopping criteria are used to a much higher degree than the other three loop instructions.", notes = "Part of \cite{Silva:2011:GP} EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011", } @InProceedings{Wang:2013:DEXA, author = "Anqi Wang and Hui Ma and Mengjie Zhang", title = "Genetic Programming with Greedy Search for Web Service Composition", booktitle = "24th International Conference on Database and Expert, DEXA 2013", year = "2013", editor = "Hendrik Decker and Lenka Lhotska and Sebastian Link and Josef Basl and A Min Tjoa", volume = "8056", series = "Lecture Notes in Computer Science", pages = "9--17", address = "Prague, Czech Republic", month = aug # " 26-29", organisation = "University of Economics", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-40172-5", DOI = "doi:10.1007/978-3-642-40173-2_2", size = "9 pages", abstract = "Service compositions build new web services by orchestrating sets of existing web services provided in service repositories. Due to the increasing number of available web services, the search space for finding best service compositions is growing exponentially. In this paper, a combination of genetic programming and random greedy search is proposed for service composition. The greedy algorithm is used to generate valid and locally optimised individuals to populate the initial generation for genetic programming, and to perform mutation operations during genetic programming. A full experimental evaluation has been carried out using public benchmark test cases with repositories of up to 15,000 web services and 31,000 properties. The results show good performance in searching for best service compositions, where the number of atomic web services used and the tree depth are used as objectives for minimisation.", notes = "http://www.dexa.org/accepted_papers?cid=301 paper 43. Database and Expert Systems Applications", } @InProceedings{Wang:2019:SMILE, author = "Bailin Wang and Yuzhuo Liu and Liying Hao", title = "Autometic Generation of Dispatching Rule for Hybrid Flow Shop Scheduling based on Genetic Programming", booktitle = "2019 IEEE International Conference on Smart Manufacturing, Industrial Logistics Engineering (SMILE)", year = "2019", pages = "169--173", month = apr, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SMILE45626.2019.8965274", abstract = "Genetic programming-based automatic heuristic generation is a hyper heuristic that can produce the key functions in a meta-heuristic for a problem. This paper studies the automatic heuristic generation method to automated generate dispatching rules for the hybrid flow shop scheduling problem, a classic scheduling problem with strong industrial background. First, a meta-heuristic based on list scheduling is given. Then a genetic programming is proposed to generate a dispatching rule which is to produce a job list in the metaheuristic. Experimental results indicate that the generated dispatching rules are superior to the classic rules.", notes = "Also known as \cite{8965274}", } @InProceedings{Wang:2015:CEC, author = "Bing Wang and Hemant Singh and Tapabrata Ray", title = "A Multi-objective Genetic Programming Approach to Uncover Explicit and Implicit Equations from Data", booktitle = "Proceedings of 2015 IEEE Congress on Evolutionary Computation (CEC 2015)", year = "2015", editor = "Yadahiko Murata", pages = "1129--1136", address = "Sendai, Japan", month = "25-28 " # may, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-7491-7", DOI = "doi:10.1109/CEC.2015.7257016", abstract = "Identification of implicit and explicit relationships in a data is a generic problem commonly encountered in many fields of science and engineering. In the case of explicit relations, one is interested in identifying a compact and an accurate predictor function i.e. y = f(x), while in the implicit case, one is interested in identifying an equation of the form f(x) = 0. In both these classes of problems, one would need to search through a space of mathematical expressions, while minimizing some form of error metric. Such expressions are commonly identified using genetic programming (GP). While methods to uncover explicit equations have been studied extensively in the literature, there have been limited attempts to solve implicit cases. Since there are infinite trivial implicit forms that can be generated from a given set of data, the choice of an appropriate error metric is critical in the context of implicit equation mining. In this paper, we introduce a multiobjective genetic programming approach (MOGPA) for the solution of both classes of problems. The maximum depth of a GP-tree is used as the first objective reflecting the complexity/compactness of the expressions, while the mean error, either in the predictor variable or the implicit derivatives is used as the second objective during the course of search. The performance of the approach is illustrated using four examples. The approach delivers expressions of various complexities spanning a range of accuracy levels in a single run, unlike single objective GP formulations. It was able to identify more compact and accurate explicit forms than those from previously reported studies, and the correct, most compact expressions for implicit cases.", notes = "1025 hrs 15335 CEC2015", } @Article{WANG:2022:jmst, author = "Changxin Wang and Yan Zhang and Cheng Wen and Mingli Yang and Turab Lookman and Yanjing Su and Tong-Yi Zhang", title = "Symbolic regression in materials science via dimension-synchronous-computation", journal = "Journal of Materials Science \& Technology", volume = "122", pages = "77--83", year = "2022", ISSN = "1005-0302", DOI = "doi:10.1016/j.jmst.2021.12.052", URL = "https://www.sciencedirect.com/science/article/pii/S1005030222002055", keywords = "genetic algorithms, genetic programming, Symbolic regression, Band gap, Dimensional calculation", abstract = "There is growing interest in applying machine learning techniques in the field of materials science. However, the interpretation and knowledge extracted from machine learning models is a major concern, particularly as formulating an explicit model that provides insight into physics is the goal of learning. In the present study, we propose a framework that uses the filtering ability of feature engineering, in conjunction with symbolic regression to extract explicit, quantitative expressions for the band gap energy from materials data. We propose enhancements to genetic programming with dimensional consistency and artificial constraints to improve the search efficiency of symbolic regression. We show how two descriptors attributed to volumetric and electronic factors, from 32 possible candidates, explicitly express the band gap energy of NaCl-type compounds. Our approach provides a basis to capture underlying physical relationships between materials descriptors and target properties", } @InProceedings{conf/icsi/WangMZZZ14, author = "Chaoxue Wang and Chunsen Ma and Xing Zhang and Kai Zhang and Wumei Zhu", title = "Co-evolutionary Gene Expression Programming and Its Application in Wheat Aphid Population Forecast Modelling", bibdate = "2015-03-19", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/swarm/icsi2014-1.html#WangMZZZ14", booktitle = "Advances in Swarm Intelligence - 5th International Conference, {ICSI} 2014, Hefei, China, October 17-20, 2014, Proceedings, Part {I}", publisher = "Springer", year = "2014", volume = "8794", pages = "275--283", editor = "Ying Tan and Yuhui Shi and Carlos A. Coello Coello", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-11856-7", series = "Lecture Notes in Computer Science", URL = "http://dx.doi.org/10.1007/978-3-319-11857-4", } @InProceedings{conf/bmei/WangZWM15, author = "Chao-xue Wang and Jing-jing Zhang and Shu-ling Wu and Chun-sen Ma", title = "An improved gene expression programming algorithm based on hybrid strategy", bibdate = "2016-02-18", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/bmei/bmei2015.html#WangZWM15", booktitle = "8th International Conference on Biomedical Engineering and Informatics, {BMEI} 2015, Shenyang, China, October 14-16, 2015", publisher = "IEEE", year = "2015", editor = "Li Bai and Lipo Wang and Liangshan Shao and Jinguang Sun and Zhiyong Tao and Sen Lin", isbn13 = "978-1-5090-0022-7", pages = "639--643", keywords = "genetic algorithms, genetic programming, gene expression programming", URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7394813", DOI = "doi:10.1109/BMEI.2015.7401582", } @Article{journals/informaticaSI/WangZWZT17, author = "Chao-xue Wang and Jing-jing Zhang and Shu-ling Wu and Fan Zhang and Jolanda G. Tromp", title = "An Improved Gene Expression Programming Based on Niche Technology of Outbreeding Fusion", journal = "Informatica (Slovenia)", year = "2017", volume = "41", number = "1", pages = "25--30", month = mar, note = "Special issue on End-user, Privacy, Security and Copyright issues", keywords = "genetic algorithms, genetic programming, gene expression programming", ISSN = "0350-5596", bibdate = "2018-01-23", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/informaticaSI/informaticaSI41.html#WangZWZT17", URL = "http://www.informatica.si/index.php/informatica/article/view/1593", URL = "http://www.informatica.si/index.php/informatica/article/view/1593/960", size = "6 pages", abstract = "An improved Gene Expression Programming (GEP) based on niche technology of outbreeding fusion (OFN-GEP) is proposed to overcome the insufficiency of traditional GEP in this paper. The main improvements of OFN-GEP are as follows: (1) using the population initialization strategy of gene equilibrium to ensure that all genes are evenly distributed in the coding space as far as possible; (2) introducing the outbreeding fusion mechanism into the niche technology, to eliminate the kin individuals, fuse the distantly related individuals, and promote the gene exchange between the excellent individuals from niches. To validate the superiority of the OFN-GEP, several improved GEP proposed in the related literatures and OFN-GEP are compared about function finding problems. The experimental results show that OFN-GEP can effectively restrain the premature convergence phenomenon, and promises competitive performance not only in the convergence speed but also in the quality of solution.", notes = "Povzetek: V prispevku je predstavljena izboljsava genetskih algoritmov na osnovi nis in genetskega zapisa. In English", } @InProceedings{Wang:2017:SEAL, author = "Chen Wang and Hui Ma and Aaron Chen and Sven Hartmann", title = "{GP}-Based Approach to Comprehensive Quality-Aware Automated Semantic Web Service Composition", booktitle = "Proceedings of the 11th International Conference on Simulated Evolution and Learning, SEAL-2017", year = "2017", editor = "Yuhui Shi and Kay Chen Tan and Mengjie Zhang and Ke Tang and Xiaodong Li and Qingfu Zhang and Ying Tan and Martin Middendorf and Yaochu Jin", volume = "10593", series = "Lecture Notes in Computer Science", pages = "170--183", address = "Shenzhen, China", month = nov # " 10-13", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-68759-9", URL = "https://doi.org/10.1007/978-3-319-68759-9_15", DOI = "doi:10.1007/978-3-319-68759-9_15", size = "14 pages", abstract = "Comprehensive quality-aware semantic web service composition aims to optimise semantic matchmaking quality and Quality of service (QoS) simultaneously. It is an NP-hard problem due to its huge search space. Therefore, heuristics have to be employed to generate near-optimal solutions. Existing works employ Evolutionary Computation (EC) techniques to solve combinatorial optimisation problems in web service composition. In particular, Genetic Programming (GP) has shown its promise. The tree-based representation used in GP is flexible to represent different composition constructs as inner nodes, but the semantic matchmaking information can not be directly obtained from the representation. To overcome this disadvantage, we propose a tree-like representation to directly cope with semantic matchmaking information. Meanwhile, a GP-based approach to comprehensive quality-aware semantic web service composition is proposed with explicit support for our representation. We also design specific genetic operation that effectively maintain the correctness of solutions during the evolutionary process. We conduct experiments to explore the effectiveness and efficiency of our GP-based approach using a benchmark dataset with real-world composition tasks.", } @InProceedings{Wang:2023:evoapplications, author = "Chen Wang and Hui Ma and Gang Chen2 and Victoria Huang and Yongbo Yu and Kameron Christopher", title = "Energy-Aware Dynamic Resource Allocation in Container-Based Clouds via Cooperative Coevolution Genetic Programming", booktitle = "26th International Conference, EvoApplications 2023", year = "2023", month = apr # " 12-14", editor = "Joao Correia and Stephen Smith and Raneem Qaddoura", series = "LNCS", volume = "13989", publisher = "Springer Verlag", address = "Brno, Czech Republic", pages = "539--555", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, container-based clouds, container allocation, energy efficiency, hyper-heuristic", isbn13 = "978-3-031-30229-9", DOI = "doi:10.1007/978-3-031-30229-9_35", size = "17 pages", notes = "http://www.evostar.org/2023/ EvoApplications2023 held in conjunction with EuroGP'2023, EvoCOP2023 and EvoMusArt2023", } @InProceedings{wang:2023:AusDM, author = "Chunyu Wang and Qi Chen and Bing Xue and Mengjie Zhang", title = "Shapley Value Based Feature Selection to Improve Generalization of Genetic Programming for High-Dimensional Symbolic Regression", booktitle = "Australasian Conference on Data Science and Machine Learning, AusDM 2023", year = "2023", pages = "163--176", address = "Auckland, New Zealand", month = "11-13 " # dec, publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-981-99-8696-5_12", DOI = "doi:10.1007/978-981-99-8696-5_12", notes = "Published 2024", } @InProceedings{wang:2024:GECCOcomp5, author = "Chunyu Wang and Qi Chen and Bing Xue and Mengjie Zhang", title = "Multi-task Genetic Programming with Semantic based Crossover for Multi-output Regression", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion", year = "2024", editor = "Ting Hu and Aniko Ekart", pages = "543--546", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, multi-output regression, evolutionary multitask optimization: Poster", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3654282", size = "4 pages", abstract = "Multi-output regression involves predicting two or more target variables simultaneously. In contrast to its single-output counterpart, multi-output regression poses additional challenges primarily because the target variables are frequently interdependent. Achieving accurate predictions for one variable may necessitate a thorough consideration of its relationships with other variables. In this paper, multi-output regression problems are regarded as multi-task optimization problems where predicting one output variable is considered as one task. A new multi-task multi-population genetic programming method is proposed to solve the problem. The method uses the semantic based crossover operator to transfer positive knowledge and accelerate convergence. Additionally, it adopts an offspring reservation strategy to keep the quality of the individuals for the corresponding tasks. The empirical results demonstrate that our proposed method significantly enhances the training and the test performances of multi-task multi-population GP and also outperforms standard GP on five real-world multi-output regression datasets.", notes = "GECCO-2024 GP A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @Article{wang:2024:DSE, author = "Chunyu Wang and Qi Chen and Bing Xue and Mengjie Zhang", title = "Improving Generalization of Genetic Programming for High-Dimensional Symbolic Regression with Shapley Value Based Feature Selection", journal = "Data Science and Engineering", year = "2024", keywords = "genetic algorithms, genetic programming", URL = "https://link.springer.com/article/10.1007/s41019-024-00270-x", DOI = "doi:10.1007/s41019-024-00270-x", } @Article{Wang:2025:patcog, author = "Chunyu Wang and Qi Chen and Bing Xue and Mengjie Zhang", title = "Semantics-guided multi-task genetic programming for multi-output regression", journal = "Pattern Recognition", year = "2025", volume = "161", pages = "111289", keywords = "genetic algorithms, genetic programming, Multi-output regression, Evolutionary multi-task optimization", ISSN = "0031-3203", URL = "https://www.sciencedirect.com/science/article/pii/S0031320324010409", DOI = "doi:10.1016/j.patcog.2024.111289", abstract = "Multi-output regression entails the simultaneous prediction of two or more output variables, presenting greater complexities than single-output regression due to the frequent interdependent relationships of these variables. Such dependencies mean that accurately predicting one variable typically requires careful analysis of its relationships with others. In this paper, multi-output regression problems are treated as multi-task problems, with a prediction of one output variable as a distinct task. A new multi-task multi-population genetic programming method is proposed to solve the problem. The method incorporates a semantics based crossover operator to identify the most informative subtree from a similar task that facilitates positive knowledge transfer. Empirical results indicate that our method significantly improves the training and testing performances of other multi-task GP methods, surpassing standard GP and GP with regressor chain on most examined regression datasets. Further analysis reveals that our proposed method can generate high-quality solutions by knowledge transfer and efficiently evolves similar GP models for analogous output variables, significantly enhancing positive knowledge transfer", } @InProceedings{Wang:2009:ICIII, author = "Dongping Wang and Jianqiang Peng and Zhen Yan", title = "Application of Genetic Programming to Prediction of Rural Labor Migration", booktitle = "International Conference on Information Management, Innovation Management and Industrial Engineering, 2009", year = "2009", month = dec, volume = "3", pages = "181--184", keywords = "genetic algorithms, genetic programming, labor migration account, migration decision making, rural labor migration prediction, rural labor orderly transformation, rural labour, decision making, forecasting theory, grey systems, labour resources", DOI = "doi:10.1109/ICIII.2009.353", abstract = "Accurate prediction of labor migration account is important basis of correct migration decision-making and reasonable and orderly transformation of rural labor. Because labor migration account is influenced by various uncertain factors of society and economy, but random constant in terminal sets of GP can balance the influence of related factor of measure and, therefore GP is applied to predict rural labor migration. After the program trained by training samples, prediction model of rural labor migration is affected by time variable is established and is tested by test samples. The results showed that the function which adopt GP has good fitting and forecasting effect, and model with simple structure can effectively avoid artificial error which is formed by various uncertain factors, so it is feasible to predict of rural labor migration.", notes = "Also known as \cite{5369810}", } @InProceedings{Wang:2011:FASE, author = "Farn Wang and Jung-Hsuan Wu and Chung-Hao Huang and Kai-Hsiang Chang", title = "Evolving a Test Oracle in Black-Box Testing", booktitle = "Fundamental Approaches to Software Engineering, FASE 2011", year = "2011", editor = "Dimitra Giannakopoulou and Fernando Orejas", volume = "6603", series = "Lecture Notes in Computer Science", pages = "310--325", address = "Saarbruecken, Germany", month = mar # " 26 - " # apr # " 3", publisher = "Springer", keywords = "genetic algorithms, genetic programming, SBSE, BNF grammar, test oracle, input/output list relation language, support vector machine, SVM, ANN, black-box testing", isbn13 = "978-3-642-19810-6", DOI = "doi:10.1007/978-3-642-19811-3_22", abstract = "Software testing is an important and expensive activity to the software industry, with testing accounting for over half of the cost of software. To ease this problem, test automation is very critical to the process of software testing. One important issue in this automation is to automatically determine whether a program under test (PUT) responds the correct(expected) output for an arbitrary input. In this paper, we model PUTs in black-box way, i.e. processing and responding a list of numbers, and design input/output list relation language (IOLRL) to formally describe the relations between the input and output lists. Given several labelled test cases(test verdicts are set), we use genetic programming to evolve the most distinguishing relations of these test cases in IOLRL and encode the test cases into bit patterns to build a classifier with support vector machine as the constructed test oracle. This classifier can be used to automatically verify if a program output list is the expected one in processing a program input list. The main contribution of this work are the designed IOLRL and the approach to construct test oracle with evolve relations in IOLRL. The experiments show the constructed test oracle has good performance even when few labelled test cases are supplied.", notes = "Weinbrenner's GPC++ \cite{weinbrenner:1997:diploma} libsvm, http://www.csie.ntu.edu.tw/~cjlin/libsvm binary search, quick sort, set intersection Held as Part of the Joint European Conferences on Theory and Practice of Software, ETAPS 2011 1.Dept. of Electrical Engineering, National Taiwan University 2.Graduate Institute of Electronic Engineering, National Taiwan University", } @InProceedings{Wang:2005:ICNNB, author = "Feng Wang and Yuanxiang Li", title = "Multi-objective Adaptive Scheme for Analog Circuit Design Based on Two-layer Genetic Programming", booktitle = "ICNN\&B'05. International Conference on Neural Networks and Brain", year = "2005", volume = "1", pages = "274--278", month = "13-15 " # oct, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICNNB.2005.1614614", abstract = "Analog circuits are very important in many high-speed applications. Since the size of analog circuit is becoming larger and more complex, the design is becoming more and more difficult. In this paper, a novel design approach which uses a divide-and-conquer method to evolve the analog circuits is proposed, and a multi-objective adaptive scheme is presented at the same time. The fitness function can be integrated into a vector and the circuit can be evolved more practically than before by this multi-objective adaptive scheme. The experimental results on the designs of the voltage amplifier and the low-pass filter show that this algorithm is efficient.", notes = "CD-ROM Department of Computer Science and Technology, Wuhan University, Wuhan, Hubei, China. E-mail: wangfengwhu@hotmail.com", } @Article{FengWang:2002:ZRB, author = "Feng Wang and Liangsheng Qu", title = "Extracting and Optimizing Sound Features in Mechanical Fault Diagnosis Using Genetic Programming", journal = "Journal of Xi'an Jiaotong University", year = "2002", volume = "36", number = "12", pages = "1307--1310", month = dec, keywords = "genetic algorithms, genetic programming, fault diagnosis, information fusion, pattern recognition", broken = "http://direct.bl.uk/bld/PlaceOrder.do?UIN=123943703&ETOC=RN", broken = "http://www.wanfangdata.com.cn/qikan/periodical.Articles/xajtdxxb/xajt2002/0212pdf/021224.pdf?login needed?", broken = "http://unit.xjtu.edu.cn/unit/xb/zrb/02/0212/xbe21224.html", URL = "http://en.cnki.com.cn/Article_en/CJFDTotal-XAJT200212024.htm", abstract = "A fault detection method is introduced, which uses compound features optimised by genetic programming based on single features. Some single features can be combined to form a compound feature. A compound feature obtained from sound signals is used to diagnose faults of rolling bearings, and its effectiveness is verified in practice. Based on the method, a new method is presented, which is improved by the information fusion technique. The features from sound signals and vibration signals are combined, and a new compound feature can be obtained by genetic programming. This feature can be used to detect faults of rolling bearings with higher efficiency and reliability.", notes = "Broken Dec 2012 http://unit.xjtu.edu.cn/unit/xb/zrb/ School of Mechanical Engineering,Xi'an Jiaotong University,Xi'an 710049,China", } @InProceedings{Wang:2006:MLC, author = "Feng Wang and Yuan-Xiang Li", title = "Analog Circuit Design Automation Using Neural Network-Based Two-Level Genetic Programming", booktitle = "2006 International Conference on Machine Learning and Cybernetics", year = "2006", pages = "2087--2092", address = "Dalian", month = aug, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", ISBN = "1-4244-0061-9", DOI = "doi:10.1109/ICMLC.2006.258348", abstract = "The design of analog circuits starts with a high-level statement of the circuit's desired behaviour and requires creating a circuit that satisfies the specified design goals. The difficulty of the problem of analog circuit design is well known, and there is no previously known general automated technique to design an analog circuit from a high-level statement of the circuit's desired behaviour. This paper proposes a two-layer evolutionary scheme based on Genetic Programming (GP) and Neural Network (NN), which uses a divide-and-conquer approach to design the analog circuits. Corresponding to the NN-TLGP, a new representation of circuit has been proposed here and it is more helpful to generate expectant circuit graphs. This algorithm can perform the circuits with dynamical size, circuit topology, and component values. The experimental results on the two design work show that this algorithm is efficient.", notes = "Department of Computer Science, Wuhan University, Wuhan, 430072, China; State Key Lab of Software Engineering, Wuhan University, Wuhan, 430072, China.", } @Article{Wang:2007:AMC, author = "Feng Wang and Yuanxiang Li and Li Li and Kangshun Li", title = "Automated analog circuit design using two-layer genetic programming", journal = "Applied Mathematics and Computation", year = "2007", volume = "185", number = "2", pages = "1087--1097", month = "15 " # feb, note = "Special Issue on Intelligent Computing Theory and Methodology", keywords = "genetic algorithms, genetic programming, Evolutionary computation, Evolvable hardware, Analogue circuit design, Electrical circuits", DOI = "doi:10.1016/j.amc.2006.07.029", abstract = "Analog circuits are very important in many high-speed applications such as communications. Since the size of analog circuit is becoming larger and more complex, the design is becoming more and more difficult. This paper proposes a two-layer evolutionary scheme based on genetic programming (GP), which uses a divide-and-conquer approach to evolve the analog circuits. Corresponding to the two-layer GP, a new representation of circuit has been proposed here and it is more helpful to generate expectant circuit graphs. This algorithm can evolve the circuits with dynamical size, circuit topology, and component values. The experimental results on the designs of the voltage amplifier and the low-pass filter show that this algorithm is efficient.", notes = "a School of Computer Science, Wuhan University, Wuhan 430072, PR China b State Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, PR China c International Software School, Wuhan University, Wuhan 430072, PR China d School of Information Engineering, Jiangxi University of Science and Technology, Jiangxi 341000, PR China", } @InProceedings{Wang14:2008:cec, author = "Feng Wang and Yuanxiang Li and Kangshun Li and Zhiyi Lin", title = "A New Circuit Representation Method for Analog Circuit Design Automation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "1976--1980", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1823-7", file = "EC0475.pdf", DOI = "doi:10.1109/CEC.2008.4631059", abstract = "The Analog circuits are very important in many high-speed applications such as communications. Since the size of analog circuit is becoming larger and more complex, the design is becoming more and more difficult. This paper proposes a new circuit representation method based on a two layer evolutionary scheme with Genetic Programming (TLGP), which uses a divide-and-conquer approach to evolve the analog circuits. This representation has the desirable property which is more helpful to generate expectant circuit graphs. And it is capable of generating various kinds of circuits by evolving the circuits with dynamical size, circuit topology, and component values. The experimental results on the designs of the voltage amplifier and the low-pass filter show that this method is efficient.", keywords = "genetic algorithms, genetic programming", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @InProceedings{Wang:2010:YC-ICT, author = "Feng Wang2 and Xinshun Xu", title = "{AdaGP-Rank}: Applying boosting technique to genetic programming for learning to rank", booktitle = "IEEE Youth Conference on Information Computing and Telecommunications (YC-ICT)", year = "2010", month = nov, pages = "259--262", abstract = "One crucial task of learning to rank in the field of information retrieval (IR) is to determine an ordering of documents according to their degree of relevance to the user given query. In this paper, a learning method is proposed named AdaGP-Rank by applying boosting techniques to genetic programming. This approach uses genetic programming to evolve ranking functions while a process inspired from AdaBoost technique helps the evolved ranking functions concentrate on the ranking of those documents associating those `hard' queries. Based on the confidence coefficients, the ranking functions obtained at each boosting round are then combined into a final strong ranker. Experiments conform that AdaGP-Rank has general better performance than several state-of-the-art ranking algorithms on the benchmark data sets.", keywords = "genetic algorithms, genetic programming, AdaBoost technique, AdaGP-Rank, boosting technique, confidence coefficients, document ordering, information retrieval, learning, user given query, document handling, learning (artificial intelligence), query processing", DOI = "doi:10.1109/YCICT.2010.5713094", notes = "Also known as \cite{5713094}", } @PhdThesis{Gongtao_Wang:thesis, author = "Gongtao Wang", title = "Application of genetic programming and artificial neural networks to improve engineering optimization", school = "Lamar University", year = "1998", type = "Doctor of Engineering", address = "Texas, USA", month = dec, keywords = "genetic algorithms, genetic programming", URL = "http://search.proquest.com/docview/304558089", size = "105 pages", abstract = "The mathematical models of many engineering problems are very complex and computationally intensive. These complex models are repeatedly used to solve problems. Each optimization process is almost equally burdensome. One solution is to use a response surface model (RSM) to simulate the computationally burdensome model. Several researchers have tried to use conventional regression to simplify computationally intensive optimization models. In most reported efforts of this kind, a quadratic RSM is created from the data collected from previous operations of the computationally intensive model. Optimization is then performed on this simplified model rather than on the complex one. The original model is then consulted at the proposed optimum to verify that all constraints are satisfied. There are two fundamental problems with this approach. The first is that these methods will be inherently inaccurate whenever the underlying function is not quadratic. The second is that it can not recall what was learned about the shape of the design space after an optimization is completed. This research will combine Genetic Programming with Neural Networks to create an RSM. A new way to perform regression is described. This method can discover the underlying simple functional form of a computationally intensive optimization model over the entire design space. A more accurate regression model will be built by using this proper functional form. This RSM can then be saved from one optimization run to the next to serve as a memory of the global and local shape of the design space. This field study develops a new method, which merges Genetic Programming and Neural Networks into an integrated system to perform regression. Experiments are then carried out to compare the existing methods with the developed method when used for optimization. The experimental data shows that the new method is effective if the optimization model is computationally intensive and a set of historical data is available. If the two conditions are satisfied and the same optimum is reached, the computational efficiency improved 94.5percent by using the RSM obtained with the developed method, as opposed to optimizing the original model. Compared to using a quadratic RSM, the efficiency is improved 76percent, as the same optimum is reached.", notes = "Supervisor: Victor Zaloom UMI Microform 9938783", } @InProceedings{Wang:2022:IMCEC, author = "Guan Wang and Shaoliang Hu and Bowen Feng and Huimin Liu and Zhibing Zhu", booktitle = "2022 IEEE 5th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC)", title = "A Method for Predicting the Remaining Useful Life of Bearings Based on Genetic Programming", year = "2022", volume = "5", pages = "1592--1596", abstract = "In the remaining life prediction of bearings, feature extraction is crucial because it directly determines the prediction accuracy of the model. In response to this problem, this paper proposes a feature extraction method based on genetic programming. First, the multi-dimensional features are combined into an independent feature combination in the form of a feature tree, and then an improved fitness function is designed. After many iterations, The feature combination with the highest fitness is finally output, which is called the optimization feature. Finally, the least square method is used to predict the optimization characteristic curve, and the life prediction can be carried out by combining with the failure model. Finally, the public data set of bearing full life is used to predict the remaining service life of the bearing with the optimization feature as the model, which verifies the accuracy of the algorithm prediction.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IMCEC55388.2022.10019961", ISSN = "2693-2776", month = dec, notes = "Also known as \cite{10019961}", } @Article{Wang:2020:Biophotonics, author = "Guangxing Wang and Yang Sun and Youting Chen and Qiqi Gao and Dongqing Peng and Hongxin Lin and Zhenlin Zhan and Zhiyi Liu and Shuangmu Zhuo", title = "Rapid identification of human ovarian cancer in second harmonic generation images using radiomics feature analyses and tree-based pipeline optimization tool", journal = "Journal of Biophotonics", year = "2020", volume = "13", number = "9", pages = "e202000050", month = sep, keywords = "genetic algorithms, genetic programming, TPOT", DOI = "doi:10.1002/jbio.202000050", abstract = "Ovarian cancer is currently one of the most common cancers of the female reproductive organs, and its mortality rate is the highest among all types of gynecologic cancers. Rapid and accurate classification of ovarian cancer plays an important role in the determination of treatment plans and prognoses. Nevertheless, the most commonly used classification method is based on histopathological specimen examination, which is time‐consuming and labor‐intensive. Thus, in this study, we use radiomics feature extraction methods and the automated machine learning tree‐based pipeline optimization tool (TPOT) for analysis of 3D, second harmonic generation images of benign, malignant and normal human ovarian tissues, to develop a high‐efficiency computer‐aided diagnostic model. Area under the receiver operating characteristic curve values of 0.98, 0.96 and 0.94 were obtained, respectively, for the classification of the three tissue types. Furthermore, this approach can be readily applied to other related tissues and diseases, and has great potential for improving the efficiency of medical diagnostic processes.", notes = "National Natural Science Foundation of China PMID: 32500634", } @Article{WANG:2020:enggeo, author = "Han-Lin Wang and Zhen-Yu Yin", title = "High performance prediction of soil compaction parameters using multi expression programming", journal = "Engineering Geology", volume = "276", pages = "105758", year = "2020", ISSN = "0013-7952", DOI = "doi:10.1016/j.enggeo.2020.105758", URL = "http://www.sciencedirect.com/science/article/pii/S0013795220305810", keywords = "genetic algorithms, genetic programming, Soil compaction, Optimum water content, Maximum dry density, Atterberg limits, Grain size distribution", abstract = "Previous prediction models for soil compaction parameters were developed using limited data of specific soils and their accuracy also needs to be improved. This study presents the development of a new prediction model for the soil compaction parameters (i.e. optimum water content and maximum dry density) using the multi expression programming (MEP). Numerous soil compaction tests with a wide range of soil classifications and compaction energies are first collected to form a large database. Then, the optimal setting of the MEP code parameters is investigated and determined. The explicit formulations for the two key compaction parameters are finally proposed. The validity and the sensitivity analysis of the model are conducted. The results show that the proposed model enables to predict the soil compaction parameters for all kinds of soils in the database with high accuracy. The monotonicity analysis of the predicted compaction parameters with each input property (four physical properties of soil and one compaction energy) verifies the correctness and the validity of proposed model, showing consistency with the monotonicity concerning the actual data in the database. From the sensitivity analysis about the relevance of each input property on the predicted compaction parameters, it is indicated that the plastic limit and the fines content have more significant influences on the prediction results, while the effect of the liquid limit is the least pronounced", } @InProceedings{conf/iconip/WangLL17a, author = "HanRui Wang and KeSen Li and KunHong Liu", title = "A Genetic Programming Based {ECOC} Algorithm for Microarray Data Classification", booktitle = "Neural Information Processing - 24th International Conference, ICONIP 2017, Guangzhou, China, November 14-18, 2017, Proceedings, Part VI", editor = "Derong Liu and Shengli Xie and Yuanqing Li and Dongbin Zhao and El-Sayed M. El-Alfy", publisher = "Springer", year = "2017", volume = "10639", series = "Lecture Notes in Computer Science", pages = "683--691", keywords = "genetic algorithms, genetic programming", bibdate = "2017-11-19", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/iconip/iconip2017-6.html#WangLL17a", isbn13 = "978-3-319-70135-6", DOI = "doi:10.1007/978-3-319-70136-3_72", abstract = "Microarray technology aims to discover the relationship between genes and cancers. But the analysis of multiclass microarray datasets is a difficult problem in considering the small sample size along with the class imbalance problem. In this paper, we propose a Genetic Programing (GP) based Error Correcting Output Codes (ECOC) algorithm to tackle this problem. In our GP framework, each individual represents a codematrix, and a legality checking mechanism is applied to avoid the production of illegal codematrices. So the algorithm evolves towards optimum ECOC codematrices. In experiments, our algorithm is compared with other methods based on four famous microarray datasets. Experimental results prove that our algorithm can achieve better results in most cases.", } @InProceedings{Wang:2007:CIBCB, author = "Haixin Wang and Lijun Qian and Edward Dougherty", title = "Inference of Gene Regulatory Networks using S-System: A Unified Approach", booktitle = "IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology, CIBCB '07", year = "2007", editor = "Gwenn Volkert", pages = "82--89", address = "Honolulu", month = "1-5 " # apr, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", ISBN = "1-4244-0710-9", URL = "http://old.pvamu.edu/edir/lijun/files/papers/CIBCB2007.pdf", size = "8 pages", abstract = "In this paper, a unified approach to infer gene regulatory networks using the S-system model is proposed. In order to discover the structure of large-scale gene regulatory networks, a simplified S-system model is proposed that enables fast parameter estimation to determine the major gene interactions. If a detailed S-system model is desirable for a subset of genes, a two-step method is proposed where the range of the parameters will be determined first using genetic programming and recursive least square estimation. Then the exact values of the parameters will be calculated using a multi-dimensional optimisation algorithm. Both downhill simplex algorithm and modified Powell algorithm are tested for multi-dimensional optimization. Simulation results using both synthetic data and real microarray measurements demonstrate the effectiveness of the proposed methods", notes = "http://www.cs.kent.edu/~volkert/CIBCB07/sessions.html 2 and 5 genes, microarray, time series gene expression, z-score fitness INSPEC Accession Number: 9507409 ", } @InProceedings{Wang:2006:GENSIPS, author = "Haixin Wang and Lijun Qian and Edward Dougherty", title = "Inference of gene regulatory networks using genetic programming and Kalman filter", booktitle = "IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS '06", year = "2006", pages = "27--28", address = "College Station, TX, USA", month = may, keywords = "genetic algorithms, genetic programming", ISBN = "1-4244-0385-5", URL = "http://old.pvamu.edu/edir/lijun/files/papers/Qian-Gensips2006.pdf", DOI = "doi:10.1109/GENSIPS.2006.353139", size = "2 pages", abstract = "In this paper, gene regulatory networks are inferred through evolutionary modelling and time-series microarray measurements. A nonlinear differential equation model is adopted and an iterative algorithm is proposed to identify the model, where genetic programming is applied to identify the structure of the model and Kalman filtering is employed to estimate the parameters in each iteration. Simulation results using synthetic data and microarray measurements show the effectiveness of the proposed scheme.", notes = " ", } @InProceedings{Wang:2007:ICNC, author = "Haixin Wang and Lijun Qian and E. Dougherty", title = "Modeling Genetic Regulatory Networks by Sigmoidal Functions: A Joint Genetic Algorithm and Kalman Filtering Approach", booktitle = "Third International Conference on Natural Computation, ICNC 2007", year = "2007", volume = "2", pages = "324--328", address = "Haikou", month = "24-27 " # aug, keywords = "genetic algorithms, genetic programming", isbn13 = "978-0-7695-2875-5", URL = "http://old.pvamu.edu/edir/lijun/files/papers/ICNC2007.pdf", DOI = "doi:10.1109/ICNC.2007.478", size = "5 pages", abstract = "In this paper, the problem of genetic regulatory network inference from time series microarray experiment data is considered. A noisy sigmoidal model is proposed to include both system noise and measurement noise. In order to solve this nonlinear identification problem (with noise), a joint genetic algorithm and Kalman filtering approach is proposed. Genetic algorithm is applied to minimise the fitness function and Kalman filter is employed to estimate the weight parameters in each iteration. The effectiveness of the proposed method is demonstrated by using both synthetic data and microarray measurements.", notes = " INSPEC Accession Number: 9873877 Prairie View A&M Univ., Prairie View;", } @Article{Wang:2010:ietSB, author = "H. Wang and L. Qian and E. Dougherty", title = "Inference of gene regulatory networks using S-system: a unified approach", journal = "IET Systems Biology", year = "2010", month = mar, volume = "4", number = "2", pages = "145--156", URL = "http://wanghaixin.com/papers/ssystem.pdf", DOI = "doi:10.1049/iet-syb.2008.0175", ISSN = "1751-8849", abstract = "With the increased availability of DNA microarray time-series data, it is possible to discover dynamic gene regulatory networks (GRNs). S-system is a promising model to capture the rich dynamics of GRNs. However, owing to the complexity of the inference problem and limited number of available data comparing to the number of unknown kinetic parameters, S-system can only be applied to a very small GRN with few parameters. This significantly limits its applications. A unified approach to infer GRNs using the S-system model is proposed. In order to discover the structure of large-scale GRNs, a simplified S-system model is proposed that enables fast parameter estimation to determine the major gene interactions. If a detailed S-system model is desirable for a subset of genes, a two-step method is proposed where the range of the parameters will be determined first using genetic programming and recursive least square estimation. Then the mean values of the parameters will be estimated using a multi-dimensional optimisation algorithm. Both the downhill simplex algorithm and modified Powell algorithm are tested for multi-dimensional optimisation. A 50-dimensional synthetic model with 51 parameters for each gene is tested for the applicability of the simplified S-system model. In addition, real measurement data pertaining to yeast protein synthesis are used to demonstrate the effectiveness of the proposed two-step method to identify the detailed interactions among genes in small GRNs.", keywords = "genetic algorithms, genetic programming, 50-dimensional synthetic model, DNA microarray time-series, downhill simplex algorithm, dynamic gene regulatory networks, gene interactions, kinetic parameters, modified Powell algorithm, multi-dimensional optimisation algorithm, parameter estimation, recursive least square estimation, simplified S-system model, two-step method, yeast protein synthesis, genetics, lab-on-a-chip, least squares approximations, molecular biophysics, proteins, recursive estimation", notes = "Also known as \cite{5430862}", } @InProceedings{Wang:2019:CEC, author = "Hao Wang2 and Yitan Lou and Thomas Baeck", title = "Hyper-Parameter Optimization for Improving the Performance of Grammatical Evolution", booktitle = "2019 IEEE Congress on Evolutionary Computation, CEC 2019", year = "2019", editor = "Carlos A. Coello Coello", pages = "2649--2656", address = "Wellington, New Zealand", month = "10-13 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, grammatical evolution", isbn13 = "978-1-7281-2152-6", DOI = "doi:10.1109/CEC.2019.8790026", size = "8 pages", abstract = "State-of-the-art Grammatical Evolution systems such as PonyGE2 have a number of hyper-parameters that control the behaviour of the internal evolutionary algorithm for evolving the representations of programs. In this paper, a variant of the efficient global optimization (EGO) algorithm is applied for optimizing these hyper-parameters of the PonyGE2-system. This approach is tested on four test problems used in the Grammatical Evolution community: StringMatch, symbolic regression (the `Vladislavleva-4' problem), bank note classification and the so-called Pymax task. The experimental results show that the average performance of the GE system is improved significantly (between 25percent and 168percent) on all of the test problems. In addition, the resulting overall best hyper-parameter settings are substantially different from the defaults used in PonyGE2.", notes = "also known as \cite{8790026}, IEEE Catalog Number: CFP19ICE-ART", } @Article{Wang:ieeeACC, author = "Hao Wang3 and Guangming Dong and Jin Chen", title = "Application of Improved Genetic Programming for Feature Extraction in the Evaluation of Bearing Performance Degradation", journal = "IEEE Access", year = "2020", volume = "8", pages = "167721--167730", month = sep # " 24", keywords = "genetic algorithms, genetic programming, Bearing performance degradation, feature extraction, health indicator, prediction, remaining useful life", ISSN = "2169-3536", DOI = "doi:10.1109/ACCESS.2020.3019439", abstract = "In the evaluation of bearing performance degradation, discovering a good HI (Health Indicator) is one of the most crucial parts, because it determines whether a precise result can be obtained in the prediction of remaining useful life. In this paper, GP (Genetic Programming), which is a heuristic iterative search algorithm inspired by the theory of biological evolution, is improved in genetic operation and fitness function, and a feature weighted matrix is used in GP innovatively. The improved GP is applied to discover a HI by fusing multiple features, which is very close to linearity. Furthermore, by optimizing the discovered HIs, an optimization HI is obtained, which has a higher fitness and can get a more precise result in the prediction of RUL. The proposed approach is verified in the experimental data for the entire life of the bearing provided by 2012 IEEE PHM challenge, and a total of three bearings are used in the verification.", notes = "State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200000, China Also known as \cite{9177003}", } @InProceedings{Wang:2016:ICWS, author = "Hanzhang Wang and Ali Ouni and Marouane Kessentini and Bruce Maxim and William I. Grosky", booktitle = "2016 IEEE International Conference on Web Services (ICWS)", title = "Identification of Web Service Refactoring Opportunities as a Multi-objective Problem", year = "2016", pages = "586--593", abstract = "We propose, in this paper, to consider the problem of Web service anti-patterns detection as a multi-objective problem where examples of Web service anti patterns and well-designed code are used to generate detection rules. To this end, we use multi-objective genetic programming (MOGP) to find the best combination of metrics that maximizes the detection of Web service antipattern examples and minimises the detection of well-designed Web service design examples. We report the results of an empirical study using 8 different types of common Web service antipatterns. We compared our multi-objective formulation with random search, one existing mono-objective approach, and one state-of-the-art detection technique not based on heuristic search. Statistical analysis of the obtained results demonstrates that our approach is efficient in antipattern detection, on average, with a precision score of 94percent and a recall score of 92percent.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICWS.2016.81", month = jun, notes = "Also known as \cite{7558051}", } @InProceedings{Wang:2012:WI-IAT, author = "Hongbing Wang and Jiancai Zhou and Xuan Zhou", booktitle = "IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology (WI-IAT 2012)", title = "Causal Graph Based Dynamic Optimization of Hierarchies for Factored MDPs", year = "2012", volume = "1", pages = "579--582", month = "4-7 " # dec, address = "Macau, China", isbn13 = "978-1-4673-6057-9", DOI = "doi:10.1109/WI-IAT.2012.59", size = "4 pages", abstract = "This paper presents an approach based on casual graph to optimise the task hierarchies for Hierarchical Reinforcement Learning (HRL). We conducted experiments to show that the resulting task hierarchies can improve effectiveness of reinforcement leaning.", keywords = "genetic algorithms, genetic programming, Complex systems, casual graph, hierarchical reinforcement learning", notes = "Also known as \cite{6511944}", } @Article{DBLP:journals/jsjkx/WangC16a, author = "Hongxia Wang and Bo Cao", title = "Chinese Stock Market Efficiency Testing Based on Genetic Programming", title2 = "{\unicode{22522}}{\unicode{20110}}{\unicode{36951}}{\unicode{20256}}{\unicode{32534}}{\unicode{31243}}{\unicode{30340}}{\unicode{20013}}{\unicode{22269}}{\unicode{32929}}{\unicode{31080}}{\unicode{24066}}{\unicode{22330}}{\unicode{26377}}{\unicode{25928}}{\unicode{24615}}{\unicode{26816}}{\unicode{39564}}", journal = "Computer Science", journal2 = "{\unicode{35745}}{\unicode{31639}}{\unicode{26426}}{\unicode{31185}}{\unicode{23398}}", volume = "43", number = "{Z6}", pages = "538--541", year = "2016", keywords = "genetic algorithms, genetic programming, Market Efficiency, Chinese stock market", ISSN = "1002-137X", URL = "https://doi.org/10.11896/j.issn.1002-137X.2016.6A.128", DOI = "doi:10.11896/j.issn.1002-137X.2016.6A.128", timestamp = "Fri, 27 Mar 2020 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/jsjkx/WangC16a.bib", bibsource = "dblp computer science bibliography, https://dblp.org", size = "4 pages", abstract = "There is a contradiction between the modern capital market theory and the financial investment practice.And the contradiction is about the effective market hypothesis and the technical analysis.The use of the popular technology trading rules to examine the effectiveness of the stock market may lead to two types of conclusion deviation.The tree structure is used to represent the candidate solutions in genetic programming which can well describe the technical trading rules.The genetic programming algorithm is used to generate technical trading strategy in this paper.The strategy is used to test Shanghai indexes and five stocks in the Shanghai and Shenzhen stock markets. The back test results show that genetic programming generates the best technical trading strategy with significant excess profit compared with buy-and-hold strategy and the usual popular technical indicator.Therefore,the conclusion can be made that Chinese stock market has not achieved weak-form efficiency.", notes = "In Chinese", } @Article{journals/tsmc/WangFTG05, title = "Knowledge interaction with genetic programming in mechatronic systems design using bond graphs", author = "Jiachuan Wang and Zhun Fan and Janis P. Terpenny and Erik D. Goodman", journal = "IEEE Transactions on Systems, Man, and Cybernetics, Part C", year = "2005", number = "2", volume = "35", pages = "172--182", month = may, bibdate = "2006-01-23", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/tsmc/tsmcc35.html#WangFTG05", keywords = "genetic algorithms, genetic programming, band-pass filters, bond graphs, intelligent design assistants, knowledge acquisition, mechanical engineering computing, mechatronics, micromechanical devices, MEMS bandpass filter design application, bond graphs, evolutionary computation, knowledge discovery, knowledge interaction, mechatronic systems design, quarter-car suspension control system synthesis, unified network synthesis approach, Bond graphs, MEMS filter design, controller synthesis, knowledge interaction, mechatronics", DOI = "doi:10.1109/TSMCC.2004.841915", size = "11 pages", abstract = "This paper describes a unified network synthesis approach for the conceptual stage of mechatronic systems design using bond graphs. It facilitates knowledge interaction with evolutionary computation significantly by encoding the structure of a bond graph in a genetic programming tree representation. On the one hand, since bond graphs provide a succinct set of basic design primitives for mechatronic systems modelling, it is possible to extract useful modular design knowledge discovered during the evolutionary process for design creativity and reusability. On the other hand, design knowledge gained from experience can be incorporated into the evolutionary process to improve the topologically open-ended search capability of genetic programming for enhanced search efficiency and design feasibility. This integrated knowledge-based design approach is demonstrated in a quarter-car suspension control system synthesis and a MEMS bandpass filter design application.", notes = "openbeagle", } @InProceedings{Wang:2017:ISCID, author = "JiaJun Wang and KunHong Liu and MengXin Sun and QingQi Hong", booktitle = "2017 10th International Symposium on Computational Intelligence and Design (ISCID)", title = "Genetic Programming Based {ECOC} for Multiclass Microarray Data Classification", year = "2017", volume = "1", pages = "280--283", abstract = "A genetic programming (GP) based algorithm is proposed to improve the performance of ECOC algorithms for multiclass microarray datasets. The individual of our GP is revised to solve a set of binary class problems decomposed by an ECOC algorithm directly, which picking up genes with biological significance simultaneously. Experimental results prove the effectiveness of our algorithm in five data sets.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ISCID.2017.75", month = dec, notes = "Also known as \cite{8275770}", } @InCollection{wang:2003:IFSGP, author = "Jen-Shiang Wang", title = "Influences of Function Sets in Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2003", year = "2003", editor = "John R. Koza", pages = "221--229", address = "Stanford, California, 94305-3079 USA", month = "4 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:2003:gagp}", } @InProceedings{wang:gecco03lbp, title = "Integrated Active and Passive Mechatronic System Design Using Bond Graphs and Genetic Programming", pages = "322--329", author = "Jiachuan Wang and Janis Terpenny", year = "2003", address = "Chicago, USA", month = "12--16 " # jul, editor = "Bart Rylander", keywords = "genetic algorithms, genetic programming", booktitle = "Genetic and Evolutionary Computation Conference Late Breaking Papers", notes = "GECCO-2003LB", } @PhdThesis{Jiachuan_Wang:thesis, author = "Jiachuan Wang", title = "Integrated coevolutionary synthesis of mechatronic systems using bond graphs", school = "University of Massachusetts - Amherst", year = "2004", address = "USA", month = jan # " 1", keywords = "genetic algorithms, genetic programming. Industrial engineering, Mechanical engineering, Computer science", URL = "http://scholarworks.umass.edu/dissertations/AAI3152758/", URL = "http://search.proquest.com/docview/305175569", size = "178 pages", abstract = "Mechatronics is a natural stage in the evolution of modern products, many containing components from different engineering domains, such as mechanical, electrical, and software control systems. As part of concurrent engineering practise, mechatronics is a synergistic system design philosophy to optimise the system as a whole simultaneously. Yet there is still lack of support of this design principle in practice. To date, conventional design tools have been limited to single domain problems and require a trial-and-error synthesis process. In order to support the concurrent synthesis process of mechatronic products, theoretical modeling of multi-domain engineering systems, with a formal unified representation and a well-defined algorithmic and flexible synthesis procedure, is needed. These are essential to accommodate the complexity of such systems and support the design automation process. In this work, multi-domain mechatronic system design is treated as a network synthesis problem, extending from single domain electrical network synthesis. Desired design performance is specified in an impedance matrix that captures the dynamic relations of effort and flow variables at input-output interaction ports. An extended multi-port bond graph representation is developed to unify power and signal flows at a high-level abstraction across engineering domains, which also integrates active control system design. The unified representation of both physical systems and their control systems in bond graphs is achieved by applying {"}controller design in the physical domain{"} philosophy, to design and synthesise the whole system simultaneously at the conceptual design stage. The graphical structure of bond graphs being close to reality also gives intuitive physical insight of the interactions among physical components for detailed level design realisation and simulation in different domains to verify the entire system. This approach makes full use of computational power to automatically explore the design space for both design configuration and parametrisation using biology-inspired optimisation techniques: genetic algorithms, genetic programming, and coevolution. Bond graph elements are encoded as genetic programming functional and terminal primitives, to evolve low-level building blocks to high-level functionality by applying genetic operations based on population-based natural evolution. It aids design exploration of a wider range of possible creative design options and achieves synergy in coevolving different subsystems, including both active control strategies and physical system design configurations, for overall system optimality. Two mechatronic design case studies are provided: a one-axis robotic manipulator system and a quarter-car suspension system. The computational results are compared with the design solutions obtained by human designers from trial-and-error synthesis and theoretical analysis. The coevolutionary synthesis approach is capable of discovering design options with better performance, more creativity and flexibility than those perceived by human designers. It is our belief that the establishment of such mechanism will enhance the capability of computers to automatically generate and evaluate innovative and alternative solutions to multi-domain dynamic systems and enable intelligent assistance to engineering designers at the early stages of system modelling and development for concurrent engineering practices.", notes = "supervisor Janis P. Terpenny UMI Microform 3152758", } @Article{DBLP:journals/aiedam/WangFTG08, author = "Jiachuan Wang and Zhun Fan and Janis P. Terpenny and Erik D. Goodman", title = "Cooperative body-brain coevolutionary synthesis of mechatronic systems", journal = "Artificial Intelligence for Engineering Design, Analysis and Manufacturing", volume = "22", number = "3", year = "2008", pages = "219--234", DOI = "doi:10.1017/S0890060408000152", bibsource = "DBLP, http://dblp.uni-trier.de", keywords = "genetic algorithms, genetic programming, Bond Graphs, Cooperative Coevolutionary Synthesis, Mechatronics", size = "16 pages", abstract = "To support the concurrent design processes of mechatronic subsystems, unified mechatronics modeling and cooperative body-brain coevolutionary synthesis are developed. In this paper, both body-passive physical systems and brain-active control systems can be represented using the bond graph paradigm. Bond graphs are combined with genetic programming to evolve low-level building blocks into systems with high-level functionalities including both topological configurations and parameter settings. Design spaces of coadapted mechatronic subsystems are automatically explored in parallel for overall design optimality. A quarter-car suspension system case study is provided. Compared with conventional design methods, semiactive suspension designs with more creativity and flexibility are achieved through this approach.", notes = "coevolution, Open Beagle", } @InProceedings{Wang:2015:icfcst, author = "Jie Wang and Jiwei Liu and Bin Feng and Gang Hou", booktitle = "2015 Ninth International Conference on Frontier of Computer Science and Technology", title = "The Dynamic Evaluation Strategy for Evolvable Hardware", year = "2015", pages = "91--95", abstract = "Evolvable hardware (EHW) has recently become a highly attractive topic for the Fault-tolerant System design because it offers a way of adapting hardware to different environments. However, it is time-consuming when circuits become complex. According to our research, the most time consuming period in genetic algorithm (GA) is the fitness evaluation. To reduce the time, a new method based on fitness evaluation expansion GA is proposed. The fitness evaluation is divided into two stages by a threshold. When the generation is lower than the threshold, a fitness estimate strategy is introduced to estimate the offspring's fitness. When really evolving the fitness, a self-adaptive random sampling model is applied to select the output node from the Cartesian Genetic Programming (CGP) array. During the evolution process, the random sampling probability can be adjusted dynamically with the concentration degree of individuals, which can short the evaluation time and accelerate the convergence. Experiments show that this method can obtain about 5 times speedup while getting an ideal circuit.", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, EHW", DOI = "doi:10.1109/FCST.2015.35", ISSN = "2159-6301", month = aug, notes = "Sch. of Software Technol., Dalian Univ. of Technol., Dalian, China Also known as \cite{7314656}", } @InProceedings{Wang:2020:DSA, author = "Jie Wang and Shuangmin Deng and Junjie Kang and Gang Hou and Kuanjiu Zhou and Chi Lin", title = "A Real-Time Fault Location Mechanism Combining {CGP} Code and Deep Learning", booktitle = "2019 6th International Conference on Dependable Systems and Their Applications (DSA)", year = "2020", pages = "311--316", month = jan, keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", DOI = "doi:10.1109/DSA.2019.00047", abstract = "The rapid increase in the scale and complexity of the circuit system has led to serious problems in safety and reliability. Therefore, fault tolerance was proposed. Fault location as part of fault tolerance is indispensable. However, fault location methods are mostly limited to small data volume and high system complexity. How to achieve the fault location of the circuit system has always been a focus question. This paper proposes a Hierarchical Multi-module Fault Location Mechanism (HMFLM). Cartesian Genetic Programming (CGP) is exploited to generate circuits and random injects faults into it. The model matching library is used to store the training model of the layering module circuit and detect circuit faults in real time. The recovery priority of the fault circuits use Fault Analysis Tree (FAT) to determine, therefore, we can effectively facilitate fault recovery. The results show HMFLM can effectively locate multiple faults and improves the real-time and reliability of fault diagnosis.", notes = "Also known as \cite{9045826}", } @Article{Wang:2008:IETcdt, author = "Jin Wang and Qiao Song Chen and Chong Ho Lee", title = "Design and implementation of a virtual reconfigurable architecture for different applications of intrinsic evolvable hardware", journal = "IET Computers Digital Techniques", year = "2008", month = sep, volume = "2", number = "5", pages = "386--400", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, Celoxica RC1000 peripheral component interconnect, Xilinx Virtex xcv2000E FPGA, character recogniser, field programmable gate arrays, fitness value calculation unit, function element network, function level evolution, gate level evolution, intrinsic evolvable hardware, phenotype representation, virtual reconfigurable architecture, field programmable gate arrays, peripheral interfaces, reconfigurable architectures", DOI = "doi:10.1049/iet-cdt:20070124", ISSN = "1751-8601", abstract = "The authors present a novel virtual reconfigurable architecture (VRA) for realising real-world applications of intrinsic evolvable hardware (EHW) on field programmable gate arrays (FPGAs). The phenotype representation of the proposed evolvable system is based on a two-dimensional function element (FE) network. Compared with the traditional Cartesian genetic programming, the proposed approach includes more connection restrictions in the FE network to reduce genotype length. Another innovative feature of the VRA is that the whole evolvable system, which consists of an evolutionary algorithm unit, a fitness value calculation unit and an FE array unit, can be realised on a single FPGA. On this work, a custom Xilinx Virtex xcv2000E FPGA, which is fitted in the Celoxica RC1000 peripheral component interconnect (PCI) board is used as the hardware platform. The main motive of the research is to design a general, flexible evolvable system with powerful computation ability to achieve intrinsic evolution. As examples, the proposed evolvable system is devoted to evolve two real-world applications: a character recogniser and an image operator by using gate level evolution and function level evolution, respectively. The experimental results show that the VRA can bring higher computational ability and more flexibility than traditional approach to intrinsic EHW.", notes = "Also known as \cite{4609375}", } @InProceedings{conf/ictai/WangLL06, title = "Condition Matrix Based Genetic Programming for Rule Learning", author = "Jin Feng Wang and Kin-Hong Lee and Kwong-Sak Leung", year = "2006", booktitle = "18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)", pages = "315--322", address = "Arlington, VA, USA", month = nov # " 13-15", publisher = "IEEE Computer Society", bibdate = "2007-01-04", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ictai/ictai2006.html#WangLL06", keywords = "genetic algorithms, genetic programming", ISBN = "0-7695-2728-0", DOI = "doi:10.1109/ICTAI.2006.45", abstract = "Most genetic programming paradigms are population-based and require huge amount of memory. In this paper, we review the Instruction Matrix based Genetic Programming which maintains all program components in a instruction matrix (IM) instead of manipulating a population of programs. A genetic program is extracted from the matrix just before it is being evaluated. After each evaluation, the fitness of the genetic program is propagated to its corresponding cells in the matrix. Then, we extend the instruction matrix to the condition matrix (CM) for generating rule base from datasets. CM keeps some of characteristics of IM and incorporates the information about rule learning. In the evolving process, we adopt an elitist idea to keep the better rules alive to the end. We consider that genetic selection maybe lead to the huge size of rule set, so the reduct theory borrowed from Rough Sets is used to cut the volume of rules and keep the same fitness as the original rule set. In experiments, we compare the performance of Condition Matrix for Rule Learning (CMRL) with other traditional algorithms. Results are presented in detail and the competitive advantage and drawbacks of CMRL are discussed.", notes = "http://www.nvc.cs.vt.edu/ictai06/", } @InProceedings{wang:2025:GI, author = "Jingyuan Wang and Carol Hanna and Justyna Petke", title = "Large Language Model based Code Completion is an Effective Genetic Improvement Mutation", booktitle = "14th International Workshop on Genetic Improvement @ICSE 2025", year = "2025", editor = "Aymeric Blot and Vesna Nowack and Penn {Faulkner Rainford} and Oliver Krauss", address = "Ottawa", month = "27 " # apr, note = "Best paper", keywords = "genetic algorithms, genetic programming, Genetic Improvement", URL = "https://gpbib.cs.ucl.ac.uk/gi2025/wang_2025_GI.pdf", URL = "https://geneticimprovementofsoftware.com/events/icse2025#accepted-papers", URL = "https://discovery.ucl.ac.uk/id/eprint/10204242/", video_url = "https://gpbib.cs.ucl.ac.uk/gi2025/gi_2025_slides/hanna_icse2025_a.mp4", slide_url = "https://gpbib.cs.ucl.ac.uk/gi2025/gi_2025_slides/hanna_icse2025_a.pdf", size = "8 pages", abstract = "we introduce a novel large language model (LLM)-based masking mutation operator for Genetic Improvement (GI), which leverages code completion capabilities of large language models to replace masked code segments with contextually relevant modifications. Our approach was tested on five open-source Java projects, where we compared its effectiveness against both traditional GI mutations and an existing LLM-based replacement mutation operator using random sampling and local search algorithms. Results show that the masking mutation operator creates a search space with more compiling and test-passing patches, reducing model response time by up to 60.7% compared to the replacement mutation. Additionally, it outperforms the replacement mutation in achieving the highest runtime improvement on four out of five projects and discovers more runtime-improving patches across all projects. However, combining the masking mutation with traditional GI mutations yielded inconsistent results, suggesting further investigation is needed. This study highlights the promise of LLM-based code completion to boost the efficiency and effectiveness of GI for automated software optimisation.", notes = "GI @ ICSE 2025, part of \cite{blot:2025:GI} ", } @InCollection{wang:2000:SMCGA, author = "Jinlin Wang", title = "Scheduling of a Machining Cell using Genetic Algorithm", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "425--434", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms", notes = "part of \cite{koza:2000:gagp}", } @InProceedings{Wang:2010:gecco, author = "Jun Wang2 and Ying Tan", title = "A novel genetic programming based morphological image analysis algorithm", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "979--980", keywords = "genetic algorithms, genetic programming, Poster", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830483.1830659", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "This paper gives an applicable genetic programming(GP) approach to solve the binary image analysis and gray scale image enhancement problems. By showing a section of binary image and the corresponding goal image, this algorithm automatically produces a mathematical morphological operation sequence to transform the target into the goal. While the operation sequence is applied to the whole image, the objective of image analysis is achieved. With well-defined chromosome structure and evolution strategy, the effectiveness of evolution is promoted and more complex morphological operations can be composed in a short sequence. In addition, this algorithm is also applied to infrared finger vein grey scale images to enhance the region of interest. Whose effect is examined by an application of identity authentication, and the accuracy of authentication is promoted.", notes = "Also known as \cite{1830659} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @InProceedings{conf/swarm/WangT11, author = "Jun Wang2 and Ying Tan", title = "A Novel Genetic Programming Algorithm for Designing Morphological Image Analysis Method", booktitle = "Proceedings of the Second International Conference on Advances in Swarm Intelligence (ICSI 2011) Part {I}", editor = "Ying Tan and Yuhui Shi and Yi Chai and Guoyin Wang", year = "2011", volume = "6728", series = "Lecture Notes in Computer Science", pages = "549--558", address = "Chongqing, China", month = jun # " 12-15", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-21514-8", DOI = "doi:10.1007/978-3-642-21515-5_65", size = "10 pages", abstract = "In this paper, we propose an applicable genetic programming approach to solve the problems of binary image analysis and gray scale image enhancement. Given a section of original image and the corresponding goal image, the proposed algorithm evolves for generations and produces a mathematic morphological operation sequence, and the result performed by which is close to the goal. When the operation sequence is applied to the whole image, the objective of image analysis is achieved. In this sequence, only basic morphological operations- erosion and dilation, and logical operations are used. The well-defined chromosome structure leads brings about more complex morphological operations can be composed in a short sequence. Because of a reasonable evolution strategy, the evolution effectiveness of this algorithm is guaranteed. Tested by the binary image features analysis, this algorithm runs faster and is more accurate and intelligible than previous works. In addition, when this algorithm is applied to infrared finger vein grey scale images to enhance the region of interest, more accurate features are extracted and the accuracy of discrimination is promoted.", affiliation = "Key Laboratory of Machine Perception (Ministry of Education), Peking University, P.R. China", bibdate = "2011-06-06", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/swarm/icsi2011-1.html#WangT11", } @InProceedings{Wang:2011:GECCO, author = "Jun Wang2 and Ying Tan", title = "Morphological image enhancement procedure design by using genetic programming", booktitle = "GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0557-0", pages = "1435--1442", keywords = "genetic algorithms, genetic programming", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001576.2001769", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Not presented", notes = "Also known as \cite{2001769} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)", } @InProceedings{conf/seal/WangBA10, title = "Evolving Stories: Tree Adjoining Grammar Guided Genetic Programming for Complex Plot Generation", author = "Kun Wang and Vinh Bui and Hussein A. Abbass", booktitle = "Simulated Evolution and Learning - 8th International Conference, {SEAL} 2010, Kanpur, India, December 1-4, 2010. Proceedings", publisher = "Springer", year = "2010", volume = "6457", editor = "Kalyanmoy Deb and Arnab Bhattacharya and Nirupam Chakraborti and Partha Chakroborty and Swagatam Das and Joydeep Dutta and Santosh K. Gupta and Ashu Jain and Varun Aggarwal and J{\"u}rgen Branke and Sushil J. Louis and Kay Chen Tan", isbn13 = "978-3-642-17297-7", pages = "135--145", series = "Lecture Notes in Computer Science", URL = "http://dx.doi.org/10.1007/978-3-642-17298-4", DOI = "doi:10.1007/978-3-642-17298-4_14", keywords = "genetic algorithms, genetic programming", abstract = "in this paper, we develop a tree adjoining grammar (TAG) to capture semantics of a story with long-distance causal dependency, and present a computational framework for story plot generation. Under this framework, TAG is derived and a story plot is represented by a derivation tree of TAG. The generated plots are then evolved using grammar guided genetic programming (GGGP) to generate creative, interesting and complex story plots. To evaluate these newly generated plots, a human-in-the-loop approach is used. An experimental study was carried out, in which this framework was used to produce creative, interesting and complex plots from a predesigned fabula based on a story known as 'The magpie and the water bottle'. The experimental study demonstrated that TAG and GGGP can potentially contribute significantly to complex automatic story plot generation.", bibdate = "2010-12-01", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/seal/seal2010.html#WangBA10", } @Article{Wang:2025:jobe, author = "Kun Wang2 and Tianhao Shen and Jingyu Wei and Jinlong Liu and Weicheng Hu", title = "An intelligent framework for deriving formulas of aerodynamic forces between high-rise buildings under interference effects using symbolic regression algorithms", journal = "Journal of Building Engineering", year = "2025", volume = "99", pages = "111614", keywords = "genetic algorithms, genetic programming, High-rise buildings, Interference effects, Symbolic regression, Machine learning, Sensitivity analysis", ISSN = "2352-7102", URL = "https://www.sciencedirect.com/science/article/pii/S2352710224031826", DOI = "doi:10.1016/j.jobe.2024.111614", abstract = "Numerous high-rise buildings in megacities create complex interference effects, significantly impacting aerodynamic forces and leading to severe wind-induced disasters. While current machine learning applications predominantly use black-box models with SHapley Additive exPlanations (SHAP) to study these effects, they fall short in providing explicit formulas for practical engineering use. This study pioneers an intelligent framework designed to derive explicit formulas for evaluating interference effects on high-rise buildings. The framework uses multiple symbolic regression algorithms based on genetic programming to generate these formulas, which are then evaluated for accuracy and complexity. Sensitivity and physical trend analyses were performed on the most accurate expressions. In addition, a combination of Extreme Gradient Boosting (XGBoost) and SHAP was used to verify the consistency of the results. The study found that the Offspring Selection Genetic Programming (OS-GP) symbolic regression model excelled in both accuracy and complexity. Sensitivity analysis confirmed the influence of contributing factors on aerodynamic forces, consistent with the results from XGBoost and SHAP, thus further validating the accuracy and interpretability of the OS-GP model. Physical trend analysis revealed that formulas derived from OS-GP align more closely with wind tunnel results compared to those obtained from XGBoost. Overall, the proposed symbolic regression expressions offer significant advantages for engineering applications due to their simplicity and high accuracy, providing valuable guidance for wind-resistant design and urban planning", } @Article{Wang:2023:TVT, author = "Lei Wang and Guanzhang Liu and Jiang Xue and Kat-Kit Wong", journal = "IEEE Transactions on Vehicular Technology", title = "Channel Prediction Using Ordinary Differential Equations for {MIMO} systems", year = "2023", volume = "72", number = "2", pages = "2111--2119", month = feb, keywords = "genetic algorithms, genetic programming", ISSN = "1939-9359", DOI = "doi:10.1109/TVT.2022.3211661", abstract = "Channel state information (CSI) estimation is part of the most fundamental problems in 5G wireless communication systems. In mobile scenarios, outdated CSI will have a serious negative impact on various adaptive transmission systems, resulting in system performance degradation. To obtain accurate CSI, it is crucial to predict CSI at future moments. In this paper, we propose an efficient channel prediction method in multiple-input multiple-output (MIMO) systems, which combines genetic programming (GP) with higher-order differential equation (HODE) modeling for prediction, named GPODE. In the first place, the variation of one-dimensional data is depicted by using higher-order differential, and the higher-order differential data is modeled by GP to obtain an explicit model. Then, a definite order condition is given for the modeling of HODE, and an effective prediction interval is given. In order to accommodate to the rapidly changing channel, the proposed method is improved by taking the rough prediction results of Autoregression (AR) model as a priori, i.e., Im-GPODE channel prediction method. Given the effective interval, an online framework is proposed for the prediction. To verify the validity of the proposed methods, We use the data generated by the Cluster Delay Line (CDL) channel model for validation. The results show that the proposed methods has higher accuracy than other traditional prediction methods.", notes = "Also known as \cite{9910958}", } @Article{Wang:2017:ieeeTIM, author = "Lijuan Wang and Jinyu Liu and Yong Yan and Xue Wang and Tao Wang", journal = "IEEE Transactions on Instrumentation and Measurement", title = "Gas-Liquid Two-Phase Flow Measurement Using Coriolis Flowmeters Incorporating Artificial Neural Network, Support Vector Machine, and Genetic Programming Algorithms", year = "2017", volume = "66", number = "5", pages = "852--868", month = may, keywords = "genetic algorithms, genetic programming, ANN, SVM", DOI = "doi:10.1109/TIM.2016.2634630", ISSN = "0018-9456", abstract = "Coriolis flowmeters are well established for the mass flow measurement of single-phase flow with high accuracy. In recent years, attempts have been made to apply Coriolis flowmeters to measure two-phase flow. This paper presents data driven models that are incorporated into Coriolis flowmeters to measure both the liquid mass flowrate and the gas volume fraction of a two-phase flow mixture. Experimental work was conducted on a purpose-built two-phase flow test rig on both horizontal and vertical pipelines for a liquid mass flowrate ranging from 700 to 14500 kg/h and a gas volume fraction between 0percent and 30percent. Artificial neural network (ANN), support vector machine (SVM), and genetic programming (GP) models are established through training with the experimental data. The performance of backpropagation-ANN (BP-ANN), radial basis function-ANN (RBF-ANN), SVM, and GP models is assessed and compared. Experimental results suggest that the SVM models are superior to the BP-ANN, RBF-ANN, and GP models for two-phase flow measurement in terms of robustness and accuracy. For liquid mass flowrate measurement with the SVM models, 93.49percent of the experimental data yield a relative error less than +-1percent on the horizontal pipeline, while 96.17percent of the results are within +-1percent on the vertical installation. The SVM models predict the gas volume fraction with a relative error less than +-10percent for 93.10percent and 94.25percent of the test conditions on the horizontal and vertical installations, respectively.", notes = "Also known as \cite{7790803}", } @InProceedings{LimanWang:1998:sDNA:DR, author = "Liman Wang and Qinghua Liu and Anthony G. Frutos and Susan D. Gillmor and Andrew J. Thiel and Todd C. Strother and Anne E. Condon and Robert M. Corn and Max G. Lagally and Lloyd M. Smith", title = "Surface-Based DNA Computing Operations: DESTROY and READOUT", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "233 and 268--269", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "DNA Computing", size = "1+2 page", notes = "GP-98LB, GP-98PhD Student Workshop", } @Article{Wang:1999:BS, author = "Liman Wang and Qinghua Liu and Anthony G. Frutos and Susan D. Gillmor and Andrew J. Thiel and Todd C. Strother and Anne E. Condon and Robert M. Corn and Max G. Lagally and Lloyd M. Smith", title = "Surface-based DNA computing operations: DESTROY and READOUT", journal = "Biosystems", year = "1999", volume = "52", number = "1-3", pages = "189--191", month = oct, keywords = "genetic algorithms, genetic programming, Restriction enzyme, Cycle sequencing, PCR, Addressed array", ISSN = "0303-2647", DOI = "doi:10.1016/S0303-2647(99)00046-5", abstract = "DNA computing on surfaces is where complex combinatorial mixtures of DNA molecules are immobilised on a substrate and subsets are tagged and enzymatically modified (DESTROY) in repeated cycles of the DNA computation. A restriction enzyme has been chosen for the surface DESTROY operation. For the READOUT operation, both cycle sequencing and PCR amplification followed by addressed array hybridisation were studied to determine the DNA sequences after the computations.", notes = " PMID: 10636044", } @Article{Wang:2015:ieeeTEC, author = "Lin Wang and Bo Yang and Shoude Wang and Zhifeng Liang", title = "Building Image Feature Kinetics for Cement Hydration using Gene Expression Programming with Similarity Weight Tournament Selection", journal = "IEEE Transactions on Evolutionary Computation", year = "2015", volume = "19", number = "5", pages = "679--693", month = oct, keywords = "genetic algorithms, genetic programming, gene expression programming, Evolutionary Computation, Similarity Weight Tournament, Reverse Modeling, Cement Hydration Kinetics, image processing, Portland cement", ISSN = "1089-778X", URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6948258", DOI = "doi:10.1109/TEVC.2014.2367111", size = "15 pages", abstract = "The physical properties of cement are strongly influenced by the development of microstructure and cement hydration. Therefore, the investigation of microstructure for cement paste enables us to understand the hydration process and to predict the physical properties. However, the unreliability of phase classification and segmentation in image affect the description of microstructure, as well as the prediction of properties and the simulation of hydration. This paper studies the dynamic relationship between microstructure and physical properties from the image itself. The relationship between compressive strength and microstructure image features is built as the form of image feature kinetics using gene expression programming from observed microtomography images. A similarity weight tournament selection is also proposed to increase the diversity of population and improve the performance of gene expression programming. Experimental results manifest that the evolved image feature kinetics not only perform well in fitting training data but also exhibit superior generalisation ability.", notes = "Lin Wang is with Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan, 250022, China. Also known as \cite{6948258}", } @InProceedings{Wang:2016:CEC, author = "Lin Wang and Bo Yang and Jeff Orchard", title = "Discovering Grid-Cell Models Through Evolutionary Computation", booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016)", year = "2016", editor = "Yew-Soon Ong", pages = "4683--4690", address = "Vancouver", month = "24-29 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-5090-0623-6", DOI = "doi:10.1109/CEC.2016.7744388", abstract = "One of the main tasks in neuroscience research is to interpret the activity of neurons. Given some neuroscientific data, such as spike trains, one tries to decipher how the activity of the neurons relate to the outside world and/or the behaviour of the animal. The discovery of place cells and grid cells are great examples - discoveries that garnered a Nobel Prize in 2014. However, the spatial patterns exhibited by such cells are only the beginning of our understanding of spatial representation in the brain. In this paper, we apply an evolutionary algorithm to discover spatial patterns exhibited in cells from the entorhinal cortex to see (1) if we can automatically deduce an accurate model for the hexagonal-grid pattern, and (2) if we can discover a more general model that also incorporates grid-cell-like variants that have been observed, but not understood.", notes = "WCCI2016", } @InProceedings{conf/smc/WangOYA16, author = "Lin Wang and Jeff Orchard and Bo Yang and Ajith Abraham", title = "Improving gene expression programming using diversity preservation tournament and its application in grid cell modeling", booktitle = "IEEE International Conference on Systems, Man, and Cybernetics (SMC 2016)", year = "2016", publisher = "IEEE", pages = "424--429", month = "9-12 " # oct, address = "Budapest, Hungary", keywords = "genetic algorithms, genetic programming, gene expression programming", bibdate = "2017-05-17", bibsource = "DBLP, http://dblp.uni-trier.de/https://doi.org/10.1109/SMC.2016.7844278; DBLP, http://dblp.uni-trier.de/db/conf/smc/smc2016.html#WangOYA16", isbn13 = "978-1-5090-1897-0", DOI = "doi:10.1109/SMC.2016.7844278", URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7830913", } @Article{Wang:2025:engstruct, author = "Le-Rong Wang and Qing-Feng Liu and Bo Wu and Xin-Yu Zhao", title = "Explicit backbone curve prediction of corroded reinforced concrete columns using simulated annealing-optimized genetic programming", journal = "Engineering Structures", year = "2025", volume = "335", pages = "120316", keywords = "genetic algorithms, genetic programming, Corroded reinforced concrete column, Backbone curve, Machine learning, Symbolic regression", ISSN = "0141-0296", URL = "https://www.sciencedirect.com/science/article/pii/S0141029625007072", DOI = "doi:10.1016/j.engstruct.2025.120316", abstract = "black boxes, the GP-SR method delivers transparent, interpretable equations, making it highly accessible for researchers and practitioners alike. In particular, the GP-SR framework is optimised in this study by a simulated annealing (SA) algorithm, which enhances the framework's ability to escape local optima and greatly accelerates the search for optimal symbolic regression models. To ground our model, a comprehensive experimental database, comprising 130 corroded RC columns with flexural failure under cyclic loading, is compiled in this study. This dataset underpins feature selection, model training, and validation. The results demonstrate that the SA-optimised GP-SR method not only outperforms conventional approaches in predicting the post-corrosion strength of RC columns but also does so with high precision for lateral deformation predictions across a spectrum of levels of corrosion. Moreover, despite the constraints posed by the limited collected data, the SA-optimised GP-SR model demonstrates potential in extending its predictive scope to columns exhibiting flexural-shear failure. This broader generalizability enhances the model's usefulness, offering up an expedient approach for modelling and evaluating the residual seismic capacity of corroded RC columns", } @InProceedings{Wang:2009:cec, author = "Lutao Wang and Shingo Mabu and Fengming Ye and Kotaro Hirasawa", title = "Genetic Network Programming with Rule Accumulation Considering Judgment Order", booktitle = "2009 IEEE Congress on Evolutionary Computation", year = "2009", editor = "Andy Tyrrell", pages = "3176--3182", address = "Trondheim, Norway", month = "18-21 " # may, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-2959-2", file = "P044.pdf", DOI = "doi:10.1109/CEC.2009.4983346", abstract = "Genetic Network Programming (GNP) is an evolutionary algorithm derived form GA and GP. It can deal with complex problems in dynamic environments efficiently and effectively because of its directed graph structure, reusability of nodes, and implicit memory function. This paper proposed a new method to optimize GNP algorithm by strengthening its exploitation ability through extracting and using rules. In the former research, the order of judgment node chain is ignored. The basic idea of GNP with Rule Accumulation Considering Judgment Order (GNP with RA) is to extract rules with order having high fitness values from each individual and store them in the pool every generation. A rule is defined as a sequence of successive judgment results and a processing node, which represents the good experiences of the past behaviors. As a result, the rule pool serves as an experience set of GNP obtained in the evolution process. By extracting the rules during the evolution period and then matching them with the situations of the environment, we could guide agents' behavior properly and get better performance of the agents. In this paper, GNP with RA is applied to the problem of determining agents' behaviors and Tile-world was used as the simulation environment in order to evaluate its effectiveness. The simulation results demonstrate that GNP with RA could have better performances than the conventional GNP method both in the average fitness value and stability.", keywords = "genetic algorithms, genetic programming, genetic network programming", notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR. Also known as \cite{4983346}", } @InProceedings{Wang:2010:cec, author = "Lutao Wang and Shingo Mabu and Qingbiao Meng and Kotaro Hirasawa", title = "Genetic Network Programming with generalized rule accumulation", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Genetic Network Programming", isbn13 = "978-1-4244-6910-9", abstract = "Genetic Network Programming(GNP) is a newly developed evolutionary computation method using a directed graph as its gene structure, which is its unique feature. It is competent for dealing with complex problems in dynamic environments and is now being well studied and applied to many real-world problems such as: elevator supervisory control, stock price prediction, traffic volume forecast and data mining, etc. This paper proposes a new method to accumulate evolutionary experiences and guide agent's actions by extracting and using generalised rules. Each generalized rule is a state-action chain which contains the past information and the current information. These generalised rules are accumulated and updated in the evolutionary period and stored in the rule pool which serves as an experience set for guiding new agent's actions. We designed a two-stage architecture for the proposed method and applied it to the Tile-world problem, which is an excellent benchmark for multi-agent systems. The simulation results demonstrated the efficiency and effectiveness of the proposed method in terms of both generalisation ability and average fitness values and showed that the generalised rule accumulation method is especially remarkable when dealing with non-Markov problems.", DOI = "doi:10.1109/CEC.2010.5586284", notes = "WCCI 2010. Also known as \cite{5586284}", } @InProceedings{Wang:2009:ICCAS-SICE, author = "Lutao Wang and Shingo Mabu and Fengming Ye and Kotaro Hirasawa", title = "Rule Accumulation Method with Modified Fitness Function based on Genetic Network Programming", booktitle = "ICCAS-SICE, 2009", year = "2009", month = "18-21 " # aug, address = "Fukuoka", pages = "1000--1005", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, genetic network programming, GNP-RA, Agent, directed graph structure, fitness function, implicit memory function, node reusability, rule accumulation method, tile-world simulation environment, directed graphs, logic programming", isbn13 = "978-4-9077-6433-3", URL = "http://ieeexplore.ieee.org/search/srchabstract.jsp?tp=&arnumber=5334897", size = "6 pages", abstract = "Genetic Network Programming (GNP) extended from GA and GP is competent for the complex problems in dynamic environments because of its directed graph structure, reusability of nodes and implicit memory function. In this paper, a new method to extract and accumulate rules from GNP is proposed. The general idea is to update the fitness values of the rules accumulatively, rather than just replacing them in the former research. That is, the rules which appear frequently in different generations are given higher fitness values because they represent good universal experiences from the past behaviors. By extracting the rules during the evolutionary period and then matching them with agents' environments, we could guide the agents properly and get better rewards. In order to test the efficiency and effectiveness of the proposed method, we applied the proposed method to the problem of Tile-world as the simulation environment. Simulation results demonstrate the effectiveness of the proposed method.", notes = "Also known as \cite{5334897}", } @InProceedings{Wang:2011:GNPwURA, title = "Genetic Network Programming with Updating Rule Accumulation", author = "Lutao Wang and Shingo Mabu and Kotaro Hirasawa", pages = "2259--2266", booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary Computation", year = "2011", editor = "Alice E. Smith", month = "5-8 " # jun, address = "New Orleans, USA", organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, genetic network programming, Evolutionary computation theory, Evolutionary simulation-based optimisation, Evolutionary games and multi-agent systems", DOI = "doi:10.1109/CEC.2011.5949895", abstract = "Conventional evolutionary computation methods aim to find elite individuals as the optimal solutions. The rule accumulation method tries to find good experiences from individuals throughout the generations and store them as decision rules, which is regarded as solutions. Genetic Network Programming (GNP) is competent for dynamic environments because of its directed graph structure, reusability of nodes and partially observable processes. A GNP based rule accumulation method has been studied and applied to the stock trading problem. However, with the changing of dynamic environments, the old rules in the rule pool are incompetent for guiding new agent's actions, thus updating these rules becomes necessary. This paper proposes a new method to update the accumulated rules in accordance with the environment changes. Sarsa- learning which is a good on-line learning policy is combined with off-line evolution to generate better individuals and update the rules in the rule pool. Tileworld problem which is an excellent benchmark for multi-agent systems is used as the simulation environment. Simulation results demonstrate the efficiency and effectiveness of the proposed method in dealing with the changing environments.", notes = "CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.", } @InProceedings{Wang:2012:CECd, title = "Rule Accumulation Method Based on Credit Genetic Network Programming", author = "Lutao Wang and Wei Xu and Shingo Mabu and Kotaro Hirasawa", pages = "3651--3658", booktitle = "Proceedings of the 2012 IEEE Congress on Evolutionary Computation", year = "2012", editor = "Xiaodong Li", month = "10-15 " # jun, DOI = "doi:10.1109/CEC.2012.6253004", address = "Brisbane, Australia", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Evolutionary games and multi-agent systems, Evolutionary simulation-based optimization, Intelligent systems applications, Genetic Network Programming", abstract = "As a new promising evolutionary computation method, Genetic Network Programming (GNP) is good at generating action rules for multi-agent control in dynamic environments. However, some unimportant nodes exist in the program of GNP. These nodes serve as some redundant information which decreases the performance of GNP and the quality of the generated rules. In order to prune these nodes, this paper proposes a novel method named Credit GNP, where a credit branch is added to each node. When the credit branch is visited, the node is neglected and its function is not executed, so that the unimportant nodes could be jumped. The probability of visiting this credit branch and to which node it is jumped is determined by both evolution and Sarsa-learning, therefore, the unimportant nodes could be pruned automatically. Simulation results on the Tile-world problem show that the proposed method could get better programs and generate better and more general rules.", notes = "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the EPS and the IET.", } @PhdThesis{LutaoWang:thesis, author = "Lutao Wang", title = "Genetic Network Programming Based Rule Accumulation for Agent Control", school = "Waseda University", year = "2013", address = "Japan", month = jan, keywords = "genetic algorithms, genetic programming, Genetic Network Programming", URL = "http://hdl.handle.net/2065/40059", URL = "https://irdb.nii.ac.jp/en/00835/0002067653", URL = "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/40059/1/Honbun-6141.pdf", URL = "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/40059/2/Shinsa-6141.pdf", URL = "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/40059/3/Gaiyo-6141.pdf", size = "118 pages", abstract = "Multi-agent control is a hot but challenging research topic which covers many research fields, such as evolutionary computation, machine learning, neural networks, etc.. Various approaches have been proposed to guide agents' actions in Different environments. Evolutionary Computation (EC) is often chosen to control agents' behaviours since it can generate the best control policy through evolution. As a powerful machine learning approach, Reinforcement Learning (RL) is competent for agent control since it enables agents to directly interact with environments and get rewards through trial and errors. It is fast and efficient in dealing with some simple problems. However, its state-action pairs may become exponentially large in complex environments, which is computationally intractable. Neural Networks (NNs) could also be used to guide agents' actions since it can map between the input of the environment information and the output of control signals. However, in some high dynamic environments which are uncertain and changing all the time, NN could not work. Genetic Network Programming is a newly developed EC method which chooses the directed graph structure as its chromosome. High expression ability of the graph structure, reusability of nodes and realisation of partially observable processes enable GNP to deal with many complex problems in dynamic environments. One of the disadvantages of GNP is that its gene structure may become too complicated after generations of the training. In the testing phase, it might not be able to adapt to the new environment easily and its generalisation ability is not good. This is because the implicit memory function of GNP can not memorise enough information of the environment, which is incompetent in dealing with the agent control problems in high dynamic environments. Therefore, explicit memory should be associated with GNP in order to explore its full potential. Various research has revealed that memory schemes could enhance EC in dynamic environments. This is because storing the useful historical information into the memory could improve the search ability of EC. Inspired by this idea, a GNP-based explicit memory scheme named Genetic Network Programming with Rule Accumulation is proposed in this thesis. Focusing on this issue, it is studied in the following chapters of this thesis how to create action rules and use them for agent control, how to improve the performance in Non-Markov environments, how to prune the bad rules to improve the quality of the rule pool, how to build a rule pool adapting to the environment changes and how to create more general rules for agent control in dynamic environments. The organisation of this thesis is as follows. Chapter 1 describes the research background, problems to be solved and outline of the thesis. Some classical methods in the domain of evolutionary computation and reinforcement learning are reviewed. Chapter 2 designs the general framework of GNP-RA, which contains two stages, the training stage and the testing stage. In the training stage, the node transitions of GNP are recorded as rules and stored into the rule pool generation by generation. In the testing stage, all the rules in the rule pool are used to determine agents' actions through a unique matching degree calculation. The very different point of GNP-RA from the basic GNP is that GNP-RA uses a great number of rules to determine agents' actions. However, GNP could use only one rule corresponding to its node transition. Therefore, the generalisation ability of GNP-RA is better than that of GNP. Moreover, GNP-RA could make use of the previous experiences in the form of rules to determine agents' current action, which means that GNP-RA could learn from agents' past behaviours. This also helps the current agent to take correct actions and improve its performance. Simulations on the tile-world demonstrate that GNP-RA could achieve higher fitness values and better generalisation ability.", abstract = "Chapter 3 aims to solve the perceptual aliasing problem and improve the performance for multi-agent control in non-Markov environments. The perceptual aliasing problem refers to that different situations seem identical to agents, but different optimal actions are required. In order to solve this problem, a new rule-based model, GNP with multi-order rule accumulation (GNP-MRA) is proposed in this chapter. Each multi-order rule records not only the current environment information and agent's actions, but also the previous environment information and agent's actions, which helps agents to distinguish the aliasing situations and take proper actions. Simulation results prove the effectiveness of GNP-MRA, and reveal that the higher the rule order is, the more information it can record, and the more easily agents can distinguish different aliasing situations. Therefore, multi-order rules are more efficient for agent control in non-Markov environments. Chapter 4 focuses on how to improve the quality of the rule pool. Two improvements are made in order to realise this. One of them is that during the rule generation, reinforcement learning is combined with evolution in order to create more efficient rules. The obtained knowledge during the running of the program could be used to select the proper processing for judgements, i.e., better rules. The second approach is that after the rule generation, a unique rule pruning method using bad individuals is proposed. The basic idea is that good individuals are used as rule generators and bad individuals are used as monitors to filter the generated bad rules. Four pruning methods are proposed and their performances are discussed and compared. After pruning the bad rules, the good rules could stand out and contribute to better performances. Simulation results demonstrate the efficiency and effectiveness of the enhanced rule-based model. Chapter 5 is devoted to improve the adaptability of GNP-RA depending on the environment changes. GNP-RA has poor adaptability to the dynamic environments since it always uses the old rules in the rule pool for agent control. Generally speaking, the environment keeps changing all the time, while the rules in the rule pool remain the same. Therefore, the old rules in the rule pool become incompetent to guide agents' actions in the new environments. In order to solve this problem, Sarsa-learning is used as a tool to update the old rules to cope with the inexperienced situations in the new environments. The basic idea is that when evolution ends, the elite individual of GNP-RA still execute Sarsa-learning to update the Q table. With the changes of the Q table, the node transitions could be changed in accordance with the environment, bringing some new rules. These rules are used to update the rule pool, so that the rule pool could adapt to the changing environments. Chapter 6 tries to improve the generalisation ability of GNP-RA by pruning the harmful nodes. In order to realise this, Credit GNP is proposed in this chapter. Firstly, Credit GNP has a unique structure, where each node has an additional credit branch which can be used to skip the harmful nodes. This gene structure has more exploration ability than the conventional GNP-RA. Secondly, Credit GNP combines evolution and reinforcement learning, i.e., off-line evolution and on-line learning to prune the harmful nodes. Which node to prune and how many nodes to prune are determined automatically considering different environments. Thirdly, Credit GNP could select the really useful nodes and prune the harmful ones dynamically and flexibly considering different situations. Therefore, Credit GNP could determine the optimal size of the program along with the changing environments. Simulation results demonstrated that Credit GNP could generate not only more compact programs, but also more general rules. The generalisation ability of GNP-RA was improved by Credit GNP. Chapter 7 makes conclusions of this thesis by describing the achievements of the proposed methods based on the simulation", } @Article{WANG:2021:ATE, author = "Ningbo Wang and Congbo Li and Wei Li and Mingli Huang and Dongfeng Qi", title = "Effect analysis on performance enhancement of a novel air cooling battery thermal management system with spoilers", journal = "Applied Thermal Engineering", volume = "192", pages = "116932", year = "2021", ISSN = "1359-4311", DOI = "doi:10.1016/j.applthermaleng.2021.116932", URL = "https://www.sciencedirect.com/science/article/pii/S1359431121003793", keywords = "genetic algorithms, genetic programming, Battery thermal management system, Air cooling, Spoiler, Genetic programming model", abstract = "To solve a series of thermal runaway problems caused by temperature and the cost problem caused by the excessive volume of the battery thermal management system (BTMS), this paper presents a novel air cooling BTMS which reduces the temperature and volume. In this study, we install the spoilers in the battery gap spacing, which can effectively improve the heat dissipation performance of the battery. Firstly, this paper discusses the influence of the shape, number and length of the spoilers on the maximum temperature (MaxT) and temperature uniformity of the battery module. After computational fluid dynamics (CFD) simulation, this paper takes a BTMS with 16 long straight spoilers as plan 1. Compared with the initial plan without spoilers, the MaxT of plan 1 is reduced by 3.52 K. Secondly, Latin hypercube sampling (LHS) is used to sample and then establish the genetic programming (GP) model for the MaxT and the volume of plan 1. Finally, this paper combines CFD simulation with the multi-objective genetic algorithm (MOGA) to drive the optimization process. The optimization results show that the MaxT of the battery module is 307.58 K, and the volume of BTMS is 12644460 mm3. Compared with plan 1, the MaxT is reduced by 2.24 K, and the volume is reduced by 4.87percent. This result has guiding significance for improving the heat dissipation of Z-shaped air cooling BTMS and saving the cost in the industry", } @InProceedings{conf/icnc/Wang0WMD13, author = "Peng Wang and Xiaoping Hu and Meiping Wu and Hua Mu and Lei Deng", title = "Gene expression programming based matching suitability analysis in geomagnetic aided navigation", booktitle = "ICNC", year = "2013", publisher = "IEEE", pages = "718--722", address = "Shenyang, China", month = "23-25 " # jul, keywords = "genetic algorithms, genetic programming, gene expression programming, matching suitability, geomagnetic aided navigation, feature synthesis", isbn13 = "978-1-4673-4714-3", bibdate = "2014-05-21", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icnc/icnc2013.html#Wang0WMD13", URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6811386", DOI = "doi:10.1109/ICNC.2013.6818069", size = "5 pages", abstract = "In geomagnetic aided navigation (GAN), matching suitability denotes the navigability of candidate matching areas (CMAs) and can be characterized by the suitable-matching features extracted from geomagnetic map. However, the consistency between the single suitable-matching feature and matching probability is not satisfactory. Therefore the suitable-matching features are considered to be synthesised in order to analyze the matching suitability more effectively. gene expression programming (GEP) is used for feature synthesis, and correlation coefficient is treated as the fitness function. Experimental results show that the evolutionary synthetical feature is effective and owns more excellent performance than the single suitable-matching feature. The conclusions of this article can be used for selecting suitable-matching areas and further afford guidance for trajectory planning.", notes = "College of Mechatronic Engineering and Automation, National University of Defense Technology, Changsha, China", } @InProceedings{wang:2022:AI, author = "Peng Wang2 and Bing Xue and Jing Liang and Mengjie Zhang", title = "Niching-Assisted Genetic Programming for Finding Multiple High-Quality Classifiers", booktitle = "AI 2022: Advances in Artificial Intelligence", year = "2022", publisher = "Springer", keywords = "genetic algorithms, genetic programming, XAI, Multiple optimal programs, Classification", URL = "http://link.springer.com/chapter/10.1007/978-3-031-22695-3_20", DOI = "doi:10.1007/978-3-031-22695-3_20", notes = "School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand", } @InProceedings{wang:2024:CEC, author = "Peng Wang2 and Bing Xue and Jing Liang and Mengjie Zhang", title = "Dimensionality Reduction for Classification Using Divide-and-Conquer Based Genetic Programming", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Dimensionality reduction, Limiting, Accuracy, Clustering algorithms, Evolutionary computation, Feature extraction", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10612123", abstract = "Dimensionality reduction (DR) is to obtain meaningful low-dimensional representation concealed within high-dimensional data. Genetic programming (GP) has been used to achieve DR for classification because of the flexible representations and promising performance. However, one drawback of many current GP-based DR methods is the high computational demands, potentially limiting their utility in high-dimensional scenarios. To tackle this issue, this work develops a new GP algorithm to achieve DR for classification. A divide-and-conquer strategy based on feature clustering is proposed to split the original feature set into multiple small feature groups. Each tree in the multi-tree GP is used to learn one high-level feature from each feature group. The performance of the proposed method is examined on 10 classification datasets with varying difficulty. The results indicate that the new GP method achieves significantly better classification performance than commonly used feature construction and feature selection methods. More importantly, the proposed GP method uses fewer nodes but achieves better or comparable classification accuracy than the baseline multi-tree GP method.", notes = "also known as \cite{10612123} WCCI 2024", } @InProceedings{wang:2010:ICCI, author = "Pu Wang and Edward P. K. Tsang and Thomas Weise and Ke Tang and Xin Yao", title = "Using GP to Evolve Decision Rules for Classification in Financial Data Sets", booktitle = "9th IEEE International Conference on Cognitive Informatics (ICCI 2010)", year = "2010", editor = "Fuchun Sun and Yingxu Wang and Jianhua Lu and Bo Zhang and Witold Kinsner and Lotfi A. Zadeh", pages = "722--727", address = "Tsinghua University, Beijing, China", month = "7-9 " # jul, publisher = "IEEE", note = "Special Session on Evolutionary Computing", email = "tweise@gmx.de", keywords = "genetic algorithms, genetic programming, data mining, financial data sets, genetic decision trees, Decision rules, Classification, Forecasting, Finance, EDDIE, FGP, AUC, Entropy, financial forecasting, genetic programming approach, investment, machine learning, financial data processing, investment, learning (artificial intelligence), pattern classification", isbn13 = "978-1-4244-8040-1", URL = "http://www.it-weise.de/documents/files/WTWTY2010UGPTEDRFCIFDS.pdf", URL = "http://home.ustc.edu.cn/~wuyou308/publications/paper1.pdf", DOI = "doi:10.1109/COGINF.2010.5599820", size = "9 pages", abstract = "Financial forecasting is a lucrative and complicated application of machine learning. In this paper, we focus on the finding investment opportunities. We therefore explore four different Genetic Programming approaches and compare their performances on real-world data. We find that the novelties we introduced in some of these approaches indeed improve the results. However, we also show that the Genetic Programming process itself is still very inefficient and that further improvements are necessary if we want this application of GP to become successful.", notes = "http://www.icci2010.edu.cn/ Also known as \cite{5599820}", } @InProceedings{Wang:2011:AMGPwDTLSfCP, title = "A Memetic Genetic Programming with Decision Tree-based Local Search for Classification Problems", author = "Pu Wang and Ke Tang and Edward Tsang and Xin Yao", pages = "916--923", booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary Computation", year = "2011", editor = "Alice E. Smith", month = "5-8 " # jun, address = "New Orleans, USA", organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, area under ROC curve, classification problems, classifier, decision tree-based local search, fitness function, memetic computing, memetic genetic programming, statistical genetic decision tree, training algorithms, decision trees, learning (artificial intelligence), pattern classification, search problems", DOI = "doi:10.1109/CEC.2011.5949716", abstract = "In this work, we propose a new genetic programming algorithm with local search strategies, named Memetic Genetic Programming (MGP), for classification problems. MGP aims to acquire a classifier with large Area Under the ROC Curve (AUC), which has been proved to be a better performance metric for traditionally used metrics (e.g., classification accuracy). Three new ideas are presented in our new algorithm. First, a new representation called statistical genetic decision tree (SGDT) for GP is proposed on the basis of Genetic Decision Tree (GDT). Second, a new fitness function is designed by using statistic information from SGDT. Third, the concept of memetic computing is introduced into SGDT. As a result, the MGP is equipped with a local search method based on the training algorithms for decision trees. The efficacy of the MGP is empirically justified against a number of relevant approaches.", notes = "CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.", } @Article{Wang:2013:Neurocomputing, author = "Pu Wang and Ke Tang and Thomas Weise and E. P. K. Tsang and Xin Yao", title = "Multiobjective genetic programming for maximizing {ROC} performance", journal = "Neurocomputing", year = "2014", volume = "125", pages = "102--118", keywords = "genetic algorithms, genetic programming, Classification, ROC analysis, AUC, ROCCH, Evolutionary multiobjective algorithm, Memetic algorithm, Decision tree", ISSN = "0925-2312", URL = "http://home.ustc.edu.cn/~wuyou308/doc/mogp.pdf", DOI = "doi:10.1016/j.neucom.2012.06.054", URL = "http://www.sciencedirect.com/science/article/pii/S0925231213001938", size = "17 pages", abstract = "In binary classification problems, receiver operating characteristic (ROC) graphs are commonly used for visualising, organising and selecting classifiers based on their performances. An important issue in the ROC literature is to obtain the ROC convex hull (ROCCH) that covers potentially optima for a given set of classifiers [1]. Maximising the ROCCH means to maximise the true positive rate (tpr) and minimise the false positive rate (fpr) for every classifier in ROC space, while tpr and fpr are conflicting with each other. In this paper, we propose multiobjective genetic programming (MOGP) to obtain a group of nondominated classifiers, with which the maximum ROCCH can be achieved. Four different multiobjective frameworks, including Nondominated Sorting Genetic Algorithm II (NSGA-II), Multiobjective Evolutionary Algorithms Based on Decomposition (MOEA/D), Multiobjective selection based on dominated hypervolume (SMS-EMOA), and Approximation-Guided Evolutionary Multi-Objective (AG-EMOA) are adopted into GP, because all of them are successfully applied into many problems and have their own characters. To improve the performance of each individual in GP, we further propose a memetic approach into GP by defining two local search strategies specifically designed for classification problems. Experimental results based on 27 well-known UCI data sets show that MOGP performs significantly better than single objective algorithms such as FGP, GGP, EGP, and MGP, and other traditional machine learning algorithms such as C4.5, Naive Bayes, and PRIE. The experiments also demonstrate the efficacy of the local search operator in the MOGP framework.", notes = "Selected papers from the 9th International Symposium of Neural Networks, July 2012. Advances in Neural Network Research and Applications. Advances in Bio-Inspired Computing: Techniques and Applications", } @Misc{DBLP:journals/corr/abs-1303-3145, author = "Pu Wang and Michael Emmerich and Rui Li and Ke Tang and Thomas Baeck and Xin Yao", title = "Convex Hull-Based Multi-objective Genetic Programming for Maximizing ROC Performance", year = "2013", volume = "abs/1303.3145", month = "15 " # mar, note = "v2", keywords = "genetic algorithms, genetic programming", bibsource = "DBLP, http://dblp.uni-trier.de", URL = "http://arxiv.org/abs/1303.3145", size = "23 pages", abstract = "ROC is usually used to analyse the performance of classifiers in data mining. ROC convex hull (ROCCH) is the least convex major-ant (LCM) of the empirical ROC curve, and covers potential optima for the given set of classifiers. Generally, ROC performance maximisation could be considered to maximise the ROCCH, which also means to maximize the true positive rate (tpr) and minimise the false positive rate (fpr) for each classifier in the ROC space. However, tpr and fpr are conflicting with each other in the ROCCH optimisation process. Though ROCCH maximisation problem seems like a multi-objective optimisation problem (MOP), the special characters make it different from traditional MOP. In this work, we will discuss the difference between them and propose convex hull-based multi-objective genetic programming (CH-MOGP) to solve ROCCH maximization problems. Convex hull-based sort is an indicator based selection scheme that aims to maximize the area under convex hull, which serves as a unary indicator for the performance of a set of points. A selection procedure is described that can be efficiently implemented and follows similar design principles than classical hyper-volume based optimization algorithms. It is suggested that by using a tailored indicator-based selection scheme CH-MOGP gets more efficient for ROC convex hull approximation than algorithms which compute all Pareto optimal points. To test our hypothesis we compare the new CH-MOGP to MOGP with classical selection schemes, including NSGA-II, MOEA/D) and SMS-EMOA. Meanwhile, CH-MOGP is also compared with traditional machine learning algorithms such as C4.5, Naive Bayes and PRIE. Experimental results based on 22 well-known UCI data sets show that CH-MOGP outperforms significantly traditional EMOAs.", notes = "See \cite{Wang:2014:ieeeEC}", } @PhdThesis{PuWang:thesis, author = "Pu Wang", school = "The School of Computer Science and Technology of the University of Science and Technology of China", year = "2013", address = "China", keywords = "genetic algorithms, genetic programming", broken = "http://blog.it-weise.de/teaching/students/#wuyou308", notes = "Main Supervisors: Ke Tang and Xin Yao. In Chinese", } @Article{Wang:2014:ieeeEC, author = "Pu Wang and Michael Emmerich and Rui Li and Ke Tang and Thomas Baeck and Xin Yao", title = "Convex Hull-Based Multi-objective Genetic Programming for Maximizing Receiver Operating Characteristic Performance", journal = "IEEE Transactions on Evolutionary Computation", year = "2015", volume = "19", number = "2", pages = "188--200", month = apr, keywords = "genetic algorithms, genetic programming, Classification, ROC Convex Hull, Evolutionary Multi-objective Algorithm, Memetic Algorithm", DOI = "doi:10.1109/TEVC.2014.2305671", ISSN = "1089-778X", size = "13 pages", abstract = "The receiver operating characteristic (ROC) is commonly used to analyse the performance of classifiers in data mining. An important topic in ROC analysis is the ROC convex hull (ROCCH), which is the least convex majorant (LCM) of the empirical ROC curve and covers potential optima for a given set of classifiers. ROCCH maximisation problems have been taken as multi-objective optimisation problem (MOPs) in some previous work. However, the special characteristics of ROCCH maximisation problem makes it different from traditional MOPs. In this work, the difference will be discussed in detail and a new convex hull-based multi-objective genetic programming (CH-MOGP) is proposed to solve ROCCH maximisation problems. Specifically, convex hull-based without redundancy sorting (CWR-sorting) is introduced, which is an indicator based selection scheme that aims to maximise the area under the convex hull. A novel selection procedure is also proposed based on the proposed sorting scheme. It is suggested that by using a tailored indicator-based selection, CH-MOGP becomes more efficient for ROC convex hull approximation than algorithms which compute all Pareto optimal points. Empirical studies are conducted to compare CH-MOGP to both existing machine learning approaches and multi-objective genetic programming (MOGP) methods with classical selection schemes. Experimental results show that CH-MOGP outperforms the other approaches significantly.", notes = "See \cite{DBLP:journals/corr/abs-1303-3145} Also known as \cite{6762993}", } @InProceedings{Wang:2011:ICICIS, author = "Qingchun Wang and Aishu Wang", title = "Evolving Computing and Automatic Programming", booktitle = "International Conference on Internet Computing Information Services (ICICIS 2011)", year = "2011", month = "17-18 " # sep, pages = "213--214", address = "Hong Kong", size = "2 pages", abstract = "Automatic programming is not only one of the central goals of computer science, but also the goal computer science workers are striving for. Some science workers have been exploring and researching in this field for a long time. Significant advances have been made on evolving computer theory, especially on Genetic Programming (for short G.P.) theory and method. Those bring hopes to automatic programming.", keywords = "genetic algorithms, genetic programming, automatic programming, computer science, computer theory, automatic programming", DOI = "doi:10.1109/ICICIS.2011.61", notes = "Also known as \cite{6063233}", } @InProceedings{Wang:2020:CASE, author = "Runsen Wang and Yilan Shen and Weihao Wang and Leyuan Shi", title = "Formulation and Methods for a Class of Two-stage Flow-shop Scheduling Problem with the Batch Processor", booktitle = "2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)", year = "2020", pages = "728--733", abstract = "Motivated by the heat-treating process in a launch vehicles manufacturing plant, we study a two-stage scheduling problem with limited waiting time where the first stage is a batch processor and the second stage is a discrete machine. A mixed-integer programming model is developed and two lower bounds are derived to measure the performance of proposed algorithms. An efficient heuristic together with worst-case analysis is also proposed. Genetic Programming approaches are applied to the flow-shop scheduling problem. Numerical results demonstrate that the proposed algorithms perform better than other meta-heuristics in different production scenarios.", keywords = "genetic algorithms, genetic programming, Batch production systems, Job shop scheduling, Processor scheduling, Heating systems, Numerical models, Heuristic algorithms", DOI = "doi:10.1109/CASE48305.2020.9216748", ISSN = "2161-8089", month = aug, notes = "Also known as \cite{9216748}", } @InProceedings{Wang:2019:GECCOb, author = "Shaolin Wang and Yi Mei and Mengjie Zhang", title = "Novel Ensemble Genetic Programming Hyper-Heuristics for Uncertain Capacitated Arc Routing Problem", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference", year = "2019", editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and Anne Auger and Petr Posik and Leslie {Peprez Caceres} and Andrew M. Sutton and Nadarajen Veerapen and Christine Solnon and Andries Engelbrecht and Stephane Doncieux and Sebastian Risi and Penousal Machado and Vanessa Volz and Christian Blum and Francisco Chicano and Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and Jonathan Fieldsend and Jose Antonio Lozano and Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and Robin Purshouse and Thomas Baeck and Justyna Petke and Giuliano Antoniol and Johannes Lengler and Per Kristian Lehre", pages = "1093--1101", address = "Prague, Czech Republic", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, routing and network design problems, hyper-heuristic, ensemble learning, uncertain capacity arc routing problem", isbn13 = "978-1-4503-6111-8", DOI = "doi:10.1145/3321707.3321797", size = "9 pages", abstract = "The Uncertain Capacitated Arc Routing Problem (UCARP) is an important problem with many real-world applications. A major challenge in UCARP is to handle the uncertain environment effectively and reduce the recourse cost upon route failures. Genetic Programming Hyper-heuristic (GPHH) has been successfully applied to automatically evolve effective routing policies to make real-time decisions in the routing process. However, most existing studies obtain a single complex routing policy which is hard to interpret. In this paper, we aim to evolve an ensemble of simpler and more interpretable routing policies than a single complex policy. By considering the two critical properties of ensemble learning, i.e., the effectiveness of each ensemble element and the diversity between them, we propose two novel ensemble GP approaches namely DivBaggingGP and DivNichGP. DivBaggingGP evolves the ensemble elements sequentially, while DivNichGP evolves them simultaneously. The experimental results showed that both DivBaggingGP and DivNichGP could obtain more interpretable routing policies than the single complex routing policy. DivNichGP can achieve better test performance than DivBaggingGP as well as the single routing policy evolved by the current state-of-the-art GPHH. This demonstrates the effectiveness of evolving both effective and interpretable routing policies using ensemble learning.", notes = "Also known as \cite{3321797} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InProceedings{Wang:2019:SSCI, author = "Shaolin Wang and Yi Mei and John Park and Mengjie Zhang", booktitle = "2019 IEEE Symposium Series on Computational Intelligence (SSCI)", title = "A Two-Stage Genetic Programming Hyper-Heuristic for Uncertain Capacitated Arc Routing Problem", year = "2019", pages = "1606--1613", month = dec, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SSCI44817.2019.9002912", abstract = "Genetic Programming Hyper-heuristic (GPHH) has been successfully applied to automatically evolve effective routing policies to solve the complex Uncertain Capacitated Arc Routing Problem (UCARP). However, GPHH typically ignores the interpretability of the evolved routing policies. As a result, GP-evolved routing policies are often very complex and hard to be understood and trusted by human users. In this paper, we aim to improve the interpretability of the GP-evolved routing policies. To this end, we propose a new Multi-Objective GP (MOGP) to optimise the performance and size simultaneously. A major issue here is that the size is much easier to be optimised than the performance, and the search tends to be biased to the small but poor routing policies. To address this issue, we propose a simple yet effective Two-Stage GPHH (TS-GPHH). In the first stage, only the performance is to be optimised. Then, in the second stage, both objectives are considered (using our new MOGP). The experimental results showed that TS-GPHH could obtain much smaller and more interpretable routing policies than the state-of-the-art single-objective GPHH, without deteriorating the performance. Compared with traditional MOGP, TS-GPHH can obtain a much better and more widespread Pareto front.", notes = "Also known as \cite{9002912}", } @InProceedings{Wang:2020:SSCI, author = "Shaolin Wang and Yi Mei and Mengjie Zhang", title = "Towards Interpretable Routing Policy: A Two Stage Multi-Objective Genetic Programming Approach with Feature Selection for Uncertain Capacitated Arc Routing Problem", booktitle = "2020 IEEE Symposium Series on Computational Intelligence (SSCI)", year = "2020", pages = "2399--2406", abstract = "The Uncertain Capacitated Arc Routing Problem (UCARP) is the dynamic and stochastic counterpart of the well-known Capacitated Arc Routing Problem (CARP). UCARP has a wide range of real-world applications. One of the main challenge in UCARP is to handle the uncertain environment effectively. Routing policy-based approaches are promising technique for solving UCARP as they can respond to the uncertain environment in the real time. However, manually designing effective routing policies is time consuming and heavily replies on domain knowledge. Genetic Programming Hyper-heuristic (GPHH) has been successfully applied to UCARP to automatically evolve effective routing policies. However, the evolved routing policies are usually hard to interpret. In this paper, we aim to improve the potential interpretability of the GP-evolved routing policies by considering both program size and number of distinguished features. To this end, we propose a Two Stage Multi-Objective Genetic Programming Hyper Heuristic approach with Feature Selection (TSFSMOGP). We compared TSFSMOGP with the state-of-the-art single-objective GPHH, a two-stage GPHH with feature selection and a two-stage Multi-Objective GP. The experimental results showed that TSFSMOGP can evolve effective, compact, and thus potentially interpretable routing policies.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SSCI47803.2020.9308588", month = dec, notes = "Also known as \cite{9308588}", } @InProceedings{Wang:2020:CEC, author = "Shaolin Wang and Yi Mei and Mengjie Zhang", title = "A Multi-Objective Genetic Programming Hyper-Heuristic Approach to Uncertain Capacitated Arc Routing Problems", booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC 2020", year = "2020", editor = "Yaochu Jin", pages = "paper id24334", address = "internet", month = "19-24 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-7281-6929-3", DOI = "doi:10.1109/CEC48606.2020.9185890", abstract = "The Uncertain Capacitated Arc Routing Problem (UCARP) is a very important problem which has many real world applications. Genetic Programming Hyper-heuristic (GPHH), which can automatically evolve effective routing policies, is considered as a promising technique that can handle UCARP effectively. However, GP-evolved routing policies are often very complex and hard to be understood and trusted by human users. In this paper, we aim to improve the interpretability of the GP-evolved routing policies by reducing the size of the GP-evolved routing policies since smaller routing policies tend to be easier to understand. We propose a new Multi-Objective GP (MOGP) to optimise the performance (total cost) and size simultaneously. One main challenge is that the size is much easier to be optimised than the performance. Thus, the population tends to be biased to the small but poor routing policies and quickly lose the ability of exploration. To address this issue, we propose a MOGP approach with $\alpha$ dominance strategy ($\alpha$-MOGP) which can balance the tradeoff between performance and individual size. The experimental results showed that $\alpha$-MOGP could obtain much smaller routing policies than the state-of-the-art single-objective GPHH, without deteriorating the performance. Compared with traditional MOGP, $\alpha$-MOGP can obtain a much better and more widespread Pareto front.", notes = "https://wcci2020.org/ Victoria University of Wellington, New Zealand. Also known as \cite{9185890}", } @InProceedings{Wang:2021:GECCO, author = "Shaolin Wang and Yi Mei and Mengjie Zhang", title = "Two-Stage Multi-Objective Genetic Programming with Archive for Uncertain Capacitated Arc Routing Problem", booktitle = "Proceedings of the 2021 Genetic and Evolutionary Computation Conference", year = "2021", editor = "Francisco Chicano and Alberto Tonda and Krzysztof Krawiec and Marde Helbig and Christopher W. Cleghorn and Dennis G. Wilson and Georgios Yannakakis and Luis Paquete and Gabriela Ochoa and Jaume Bacardit and Christian Gagne and Sanaz Mostaghim and Laetitia Jourdan and Oliver Schuetze and Petr Posik and Carlos Segura and Renato Tinos and Carlos Cotta and Malcolm Heywood and Mengjie Zhang and Leonardo Trujillo and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Fuyuki Ishikawa and Inmaculada Medina-Bulo and Frank Neumann and Andrew M. Sutton", pages = "287--295", address = "internet", series = "GECCO '21", month = jul # " 10-14", organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, Hyper-Heuristic, Uncertain Capacitated ArcRouting Problem, Evolutionary Multi-Objective Optimisation", isbn13 = "9781450383509", DOI = "doi:10.1145/3449639.3459298", size = "9 pages", abstract = "Genetic Programming Hyper-Heuristic (GPHH) is a promising technique to automatically evolve effective routing policies to handle the uncertain environment in the Uncertain Capacitated Arc Routing Problem (UCARP). Previous studies mainly focus on the effectiveness of the evolved routing policies, but the size is ignored. This paper aims to develop new GPHH methods to optimise the effectiveness and the size simultaneously. There are two challenges. First, it is much easier for GP to generate small but ineffective individuals than effective ones, thus the search can be easily stuck with small but ineffective individuals. Second, the effectiveness evaluation in GPHH is stochastic, making it challenging to identify and retain effective individuals. To address these issues, we develop a Two-Stage Multi-Objective GP algorithm with Archive (TSNSGPII-a). The two-stage framework addresses the bias towards the size. The external archive stores potentially effective individuals that may be lost during the evolution, and reuses them to generate offspring. The experimental results show that TSNSGPII-a can obtain significantly better routing policies than the existing state-of-the-art approaches in terms of both effectiveness and size. If selecting the most effective routing policy from the Pareto front, TSNSGPII-a can obtain significantly smaller routing policies with statistically comparable or significantly better effectiveness.", notes = "Victoria University of Wellington GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)", } @Article{Genetic_Programming_with_Niching_for_Uncertain_Capacitated_Arc_Routing_Problem, author = "Shaolin Wang and Yi Mei and Mengjie Zhang and Xin Yao", title = "Genetic Programming with Niching for Uncertain Capacitated Arc Routing Problem", journal = "IEEE Transactions on Evolutionary Computation", year = "2022", volume = "26", number = "1", pages = "73--87", month = feb, keywords = "genetic algorithms, genetic programming, capacitated Arc Routing, Hyper-Heuristic, Stochastic Optimisation, Program Simplification", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2021.3095261", size = "15 pages", abstract = "The uncertain capacitated arc routing problem is an important optimisation problem with many real-world applications. Genetic programming is considered a promising hyper-heuristic technique to automatically evolve routing policies that can make effective real-time decisions in an uncertain environment. Most existing research on genetic programming hyper-heuristic for the uncertain capacitated arc routing problem only focused on the test performance aspect. As a result, the routing policies evolved by genetic programming are usually too large and complex, and hard to comprehend. To evolve effective, smaller and simpler routing policies, this paper proposes a novel genetic programming approach, which simplifies the routing policies during the evolutionary process using a niching technique. The simplified routing policies are stored in an external archive. We also developed new elitism, parent selection and breeding schemes for generating offspring from the original population and the archive. The experimental results show that the newly proposed approach can achieve significantly better test performance than the current state-of-the-art genetic programming algorithms for uncertain capacitated arc routing problem. The evolved routing policies are smaller, and thus potentially more interpretable.", notes = "also known as \cite{9475970} Evolutionary Computation Research Group, School of Engineering and Computer Science, Victoria University of Wellington, Wellington 6140, New Zealand.", } @InProceedings{Wang:2021:CEC, author = "Shaolin Wang and Yi Mei and Mengjie Zhang", title = "A Multi-Objective Genetic Programming Approach with Self-Adaptive alpha Dominance to Uncertain Capacitated Arc Routing Problem", booktitle = "2021 IEEE Congress on Evolutionary Computation (CEC)", year = "2021", editor = "Yew-Soon Ong", pages = "636--643", address = "Krakow, Poland", month = "28 " # jun # "-1 " # jul, keywords = "genetic algorithms, genetic programming, MOGP, Evolutionary computation, Routing, Tuning, Optimization, Convergence", isbn13 = "978-1-7281-8393-0", DOI = "doi:10.1109/CEC45853.2021.9504956", abstract = "The Uncertain Capacitated Arc Routing Problem (UCARP) has a variety of real-world applications. Genetic Programming Hyper-heuristic (GPHH) is considered a promising technique to handle UCARP. Many scholars have shown the power of GPHH of evolving effective routing policies. However, the size of the evolved routing policies is ignored. Typically, smaller routing policies can have better interpretability and generalisation. Thus, it is necessary to optimise the size along with the effectiveness. The objective selection bias issue arises as the size is much easier to be optimised than effectiveness. The Pareto front is biased to the size gradually during the evolutionary process. To address this issue, we develop an alpha dominance criteria based Multi-Objective GP with a self-adaptive a scheme (aMOGP-sa). The basic idea of the a-dominance criteria is to set tradeoff rates between objectives. For different instances, the search space can be very different. In this case, the self-adaptive a scheme is employed to automatically tuning the a value during the evolutionary process so that we can identify a valid alpha value for different instances. This paper examines the proposed algorithm in eight different problem instances. The experimental results showed that ?MOGP-sa could effectively handle the objective selection bias issue, and evolve much better Pareto front on Hyper-Volume and Inverted Generational Distance than the current state-of-the-art MOGP approach for UCARP in terms of effectiveness and size on all instances. Also, aMOGP-sa can evolve much smaller routing policies than the state-of-art single-objective GPHH without sacrificing effectiveness.", notes = "Also known as \cite{9504956}", } @InProceedings{DBLP:conf/ssci/WangMZ21, author = "Shaolin Wang and Yi Mei and Mengjie Zhang", title = "An Improved Multi-Objective Genetic Programming Hyper-Heuristic with Archive for Uncertain Capacitated Arc Routing Problem", booktitle = "{IEEE} Symposium Series on Computational Intelligence, {SSCI} 2021, Orlando, FL, USA, December 5-7, 2021", pages = "1--8", publisher = "{IEEE}", year = "2021", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1109/SSCI50451.2021.9660154", DOI = "doi:10.1109/SSCI50451.2021.9660154", timestamp = "Thu, 03 Feb 2022 00:00:00 +0100", biburl = "https://dblp.org/rec/conf/ssci/WangMZ21.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{wang:2022:GECCO2, author = "Shaolin Wang and Yi Mei and Mengjie Zhang", title = "Local Ranking Explanation for Genetic Programming Evolved Routing Policies for Uncertain Capacitated Arc Routing Problems", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference", year = "2022", editor = "Alma Rahat and Jonathan Fieldsend and Markus Wagner and Sara Tari and Nelishia Pillay and Irene Moser and Aldeida Aleti and Ales Zamuda and Ahmed Kheiri and Erik Hemberg and Christopher Cleghorn and Chao-li Sun and Georgios Yannakakis and Nicolas Bredeche and Gabriela Ochoa and Bilel Derbel and Gisele L. Pappa and Sebastian Risi and Laetitia Jourdan and Hiroyuki Sato and Petr Posik and Ofer Shir and Renato Tinos and John Woodward and Malcolm Heywood and Elizabeth Wanner and Leonardo Trujillo and Domagoj Jakobovic and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Inmaculada Medina-Bulo and Slim Bechikh and Andrew M. Sutton and Pietro Simone Oliveto", pages = "314--322", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", note = "Best Paper Award ECOM Track", keywords = "genetic algorithms, genetic programming, Evolutionary Combinatorial Optimization and Metaheuristics, uncertain capacitated Arc routing problem, local explanation, hyper-heuristic", isbn13 = "978-1-4503-9237-2", DOI = "doi:10.1145/3512290.3528723", abstract = "The Uncertain Capacitated Arc Routing Problem (UCARP) is a well-known combinatorial optimisation problem that has many real-world applications. Genetic Programming is usually used to handle UCARP by evolving effective routing policies, which can respond to the uncertain environment in real-time. Previous studies mainly focus on the effectiveness of the routing policies but ignore the interpretability. we focus on post-hoc interpretability, which explains a pre-trained complex routing policy. Unlike the existing explanation methods for classification/regression models, the behaviour of a routing policy is characterised as a ranking process rather than predicting a single output. To address this issue, this paper proposes a Local Ranking Explanation (LRE) method for GP-evolved routing policies for UCARP. Given a UCARP decision situation, LRE trains a linear model that gives the same ranks of the candidate tasks as those of the explained routing policy. The experimental results demonstrate that LRE can obtain more interpretable linear models that have highly correlated and consistent behaviours with the original routing policy in most decision situations. By analysing coefficients and attribute importance of the linear model, we managed to provide a local explanation of the original routing policy in a decision situation.", notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @Article{Shaolin_Wang:ieeeTEC, author = "Shaolin Wang and Yi Mei and Mengjie Zhang", title = "A Multi-Objective Genetic Programming Algorithm with alpha-dominance and Archive for Uncertain Capacitated Arc Routing Problem", journal = "IEEE Transactions on Evolutionary Computation", year = "2023", volume = "27", number = "6", pages = "1633--1647", month = dec, keywords = "genetic algorithms, genetic programming", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2022.3195165", abstract = "The uncertain capacitated arc routing problem is an important combinatorial optimization problem with many applications in the real world. Genetic programming hyper heuristic has been successfully used to automatically evolve routing policies, which can make real-time routing decisions for uncertain capacitated arc routing problems. It is desired to evolve routing policies that are both effective and small/simple to be easily understood. The effectiveness and size are two potentially conflicting objectives. A further challenge is the objective selection bias issue, i.e., it is much more likely to obtain small but ineffective routing policies than the effective ones that are typically large. In this paper, we propose a new multi-objective genetic programming algorithm to evolve effective and small routing policies. The new algorithm employs the a dominance strategy with a newly proposed α adaptation scheme to address the objective selection bias issue. In addition, it cont", notes = "also known as \cite{9845191}", } @InProceedings{wang:2024:GECCOcomp6, author = "Shaolin Wang and Yi Mei and Mengjie Zhang", title = "A Preliminary Counterfactual Explanation Method for Genetic {Programming-Evolved} Rules: A Case Study on Uncertain Capacitated Arc Routing Problem", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion", year = "2024", editor = "Ting Hu and Aniko Ekart", pages = "547--550", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, hyper-heuristic, uncertain capacitated arc routing problem, counterfactual explanation: Poster", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3654192", size = "4 pages", abstract = "In this study, we propose a novel method to enhance the interpretability of Genetic Programming Hyper-Heuristics (GPHH) by employing counterfactual explanations for Genetic Programming (GP) evolved rules in dynamic stochastic combinatorial optimisation problems. Focusing on GP-evolved rules, such as dispatching and routing policies, our research tackles the challenge of their complexity and improves user understanding. We illustrate our methodology using the Uncertain Capacitated Arc Routing Problem (UCARP) as a case study. The approach involves analyzing potential candidates in a scenario to understand why some are not selected. We introduce metrics to evaluate the quality of counterfactual explanations and adapt optimization methods to produce them. Additionally, we present a new attribute importance method based on these explanations. This research contributes to enhancing the transparency of GPHH in UCARP and may provide a reference point for future investigations into evolutionary computation and decision-making systems.", notes = "GECCO-2024 GP A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @Article{Shaolin_Wang:ieeeTEC2, author = "Shaolin Wang and Yi Mei and Mengjie Zhang", title = "Explaining Genetic Programming-Evolved Routing Policies for Uncertain Capacitated Arc Routing Problems", journal = "IEEE Transactions on Evolutionary Computation", year = "2024", volume = "28", number = "4", pages = "918--932", month = aug, keywords = "genetic algorithms, genetic programming, Task analysis, Routing, Costs, Computational modeling, Vehicle dynamics, Training, Random variables, Capacitated arc routing, hyper-heuristic, interpretability", ISSN = "1941-0026", DOI = "doi:10.1109/TEVC.2023.3238741", size = "15 pages", abstract = "Genetic programming has been successfully used to evolve routing policies that can make real-time routing decisions for uncertain arc routing problems. Although the evolved routing policies are highly effective, they are typically very large and complex, and hard to be understood and trusted by real users. Existing studies have attempted to improve the interpretability by developing new genetic programming approaches to evolve both effective and interpretable (e.g., with smaller program size) routing policies. However, they still have limitations due to the trade-off between effectiveness and interpretability. To address this issue, we propose a new post-hoc explanation approach to explaining the effective but complex routing policies evolved by genetic programming. The new approach includes a local ranking explanation and a global explanation module. The local ranking explanation uses particle swarm optimisation to learn an interpretable linear model that accurately explains the local behaviour of the routing policy for each decision situation. Then, the global explanation module uses a clustering technique to summarise the local explanations into a global explanation. The experimental results and case studies on the benchmark datasets show that the proposed method can obtain accurate and understandable explanations of the routing policies evolved for uncertain arc routing problems. Our explanation approach is not restricted to uncertain arc routing, but has a great potential to be generalised to other optimisation and machine learning problems such as learning classifier systems and reinforcement learning.", notes = "Also known as \cite{10024365}", } @Article{Wang:2019:AWM, author = "Sheng Wang and Jinjiao Lian and Yuzhong Peng and Baoqing Hu and Hongsong Chen", title = "Generalized reference evapotranspiration models with limited climatic data based on random forest and gene expression programming in {Guangxi, China}", journal = "Agricultural Water Management", year = "2019", volume = "221", pages = "220--230", month = "20 " # jul, keywords = "genetic algorithms, genetic programming, gene expression programming, water resources, climate change impact, variable importance, karst region", identifier = "RePEc:eee:agiwat:v:221:y:2019:i:c:p:220-230", oai = "oai:RePEc:eee:agiwat:v:221:y:2019:i:c:p:220-230", URL = "https://www.sciencedirect.com/science/article/pii/S0378377419305499", DOI = "doi:10.1016/j.agwat.2019.03.027", abstract = "Accurate estimation of reference evapotranspiration (ET0) is very important in hydrological cycle research, and is essential in agricultural water management and allocation. The application of the standard model (FAO-56 Penman-Monteith) to estimate ET0 is restricted due to the absence of required meteorological data. Although many machine learning algorithms have been applied in modelling ET0 with fewer meteorological variables, most of the models are trained and tested using data from the same station, their performances outside the training station are not evaluated. This study aims to investigate generalisation ability of the random forest (RF) algorithm in modelling ET0 with different input combinations (refer to different circumstances in missing data), and compares this algorithm with the gene-expression programming (GEP) method using the data from 24 weather stations in a karst region of southwest China. The ET0 estimated by the FAO-56 Penman-Monteith model was used as a reference to evaluate the derived RF-based and GEP-based models, and the coefficient of determination (R2), Nash-Sutcliffe coefficiency of efficiency (NSCE), root of mean squared error (RMSE), and percent bias (PBIAS) were used as evaluation criteria. The results revealed that the derived RF-based generalisation ET0 models are successfully applied in modelling ET0 with complete and incomplete meteorological variables (R2, NSCE, RMSE and PBIAS ranged from 0.637 to 0.987, 0.626 to 0.986, 0.107 to 0.563 mm day{$-$}1, and {$-$}2.916 percent to 1.571 percent, respectively), and seven RF-based models corresponding to different incomplete data circumstances are proposed. The GEP-based generalisation ET0 models are also proposed, and they produced promising results (R2, NSCE, RMSE and PBIAS ranged from 0.639 to 0.944, 0.636 to 0.942, 0.222 to 0.555 mm day{$-$}1, and {$-$}1.98 percent to 0.248 percent, respectively). Although the RF-based ET0 models performed slightly better than the GEP-based models, the GEP approach has the ability to give explicit expressions between the dependent and independent variables, which is more convenient for irrigators with minimal computer skills. Therefore, we recommend applying the RF-based models in water balance research, and the GEP-based models in agricultural irrigation practice. Moreover, the models performance decreased with periods due to climate change impact on ET0. At last, both of the two methods have the ability to assess the importance of predictors, the order of the importance of meteorological variables on ET0 in Guangxi is: sunshine duration, air temperature, relative humidity, and wind speed.", } @InProceedings{conf/icic/WangCW09, title = "Function Sequence Genetic Programming", author = "Shixian Wang and Yuehui Chen and Peng Wu", booktitle = "5th International Conference on Intelligent Computing, ICIC 2009", year = "2009", volume = "5755", editor = "De-Shuang Huang and Kang-Hyun Jo and Hong-Hee Lee and Hee-Jun Kang and Vitoantonio Bevilacqua", series = "Lecture Notes in Computer Science", pages = "984--992", address = "Ulsan, South Korea", month = sep # " 16-19", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Function Sequence Genetic Programming, factorial problem, stock index prediction", isbn13 = "978-3-642-04019-1", DOI = "doi:10.1007/978-3-642-04020-7_106", bibdate = "2009-09-25", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icic/icic2009-2.html#WangCW09", abstract = "Genetic Programming(GP) can obtain a program structure to solve complex problem. This paper presents a new form of Genetic Programming, Function Sequence Genetic Programming (FSGP). We adopt function set like Genetic Programming, and define data set corresponding to its terminal set. Besides of input data and constants, data set include medium variables which are used not only as arguments of functions, but also as temporary variables to store function return value. The program individual is given as a function sequence instead of tree and graph. All functions run orderly. The result of executed program is the return value of the last function in the function sequences. This presentation is closer to real handwriting program. Moreover it has an advantage that the genetic operations are easy implemented since the function sequence is linear. We apply FSGP to factorial problem and stock index prediction. The initial simulation results indicate that the FSGP is more powerful than the conventional genetic programming both in implementation time and solution accuracy.", } @InProceedings{Wang:2011:ICNC, author = "Shixian Wang and Qingjie Zhao and Yuehui Chen and Peng Wu", title = "Function Sequence Genetic Programming for pattern classification", booktitle = "Seventh International Conference on Natural Computation (ICNC 2011)", year = "2011", month = "26-28 " # jul, volume = "2", pages = "1092--1096", address = "Shanghai", size = "5 pages", abstract = "Pattern classification is one of the most researched problems in Artificial Intelligence. Genetic Programming (GP) has been used to construct classifiers by many researchers. Function Sequence Genetic Programming (FSGP) is a new variant of GP, base on which constructing classifier has not been investigated now. This paper explores the application of FSGP to pattern classification. Base on FSGP, binary classifier and multi-classifier are constructed. Experiments on four well-known data sets are made to demonstrate the classification performance of FSGP.", keywords = "genetic algorithms, genetic programming, FSGP, GP, artificial intelligence, classifier construction, function sequence genetic programming, pattern classification, artificial intelligence, pattern classification", DOI = "doi:10.1109/ICNC.2011.6022170", ISSN = "2157-9555", notes = "WBCD, Pima, Iris, Wine (UCI) Also known as \cite{6022170}", } @Article{WANG:2023:asoc, author = "Shuai Wang and Shichen Huang and Shuai Liu and Ying Bi", title = "Not just select samples, but exploration: Genetic programming aided remote sensing target detection under deep learning", journal = "Applied Soft Computing", volume = "145", pages = "110570", year = "2023", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2023.110570", URL = "https://www.sciencedirect.com/science/article/pii/S1568494623005884", keywords = "genetic algorithms, genetic programming, Auxiliary feature detection, Evolutionary computation, Remote sensing images, Sample selection, Target detection, ANN", abstract = "The data of target detection in remote sensing images are diverse, and the detection results of some categories with a small number of samples are poor. In order to solve this problem, most of the existing methods focus on the category with a small number of samples through data augmentation, but this will bring huge loss of original information, resulting in the decline of the effectiveness of some categories when improving the effectiveness. Additionally, since remote sensing image targets are small, numerous and densely distributed, the mixing degree of target and background is high, making them hardly distinguished. Therefore, a loss-based sample selection mechanism is proposed to enhance the category samples with low proportion. In the training process, we select between the original samples and enhanced samples through loss feedback, so as to retain the original sample information as much as possible and improve the detection performance. On this basis, an auxiliary feature detection module is proposed. First, the module detects the highly mixed area between the object to be detected and the background, and uses a series of image enhancement operations to build a genetic programming (GP) tree to separate the object from the background as much as possible, so that the detector can better extract and detect target features. Compared with other latest related algorithms, the loss-based sample selection mechanism and evolutionary auxiliary feature detection method proposed in this paper can improve the detection performance of low proportion categories through the sample selection mechanism, and improve the robustness to background clutter interference through evolutionary auxiliary feature detection. The proposed approach effectively improves the detection performance and performs well in remote sensing target detection", } @InProceedings{conf/cikm/WangML09, title = "Learning to rank using evolutionary computation: immune programming or genetic programming?", author = "Shuaiqiang Wang and Jun Ma and Jiming Liu", booktitle = "Proceedings of the 18th ACM Conference on Information and Knowledge Management, CIKM 2009", year = "2009", editor = "David Wai-Lok Cheung and Il-Yeol Song and Wesley W. Chu and Xiaohua Hu and Jimmy J. Lin", pages = "1879--1882", address = "Hong Kong", month = nov # " 2-6", publisher = "ACM", keywords = "genetic algorithms, genetic programming, Poster session 6", isbn13 = "978-1-60558-512-3", DOI = "doi:10.1145/1645953.1646254", bibdate = "2009-11-17", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/cikm/cikm2009.html#WangML09", abstract = "Nowadays ranking function discovery approaches using Evolutionary Computation (EC), especially Genetic Programming (GP), have become an important branch in the Learning to Rank for Information Retrieval (LR4IR) field. Inspired by the GP based learning to rank approaches, we provide a series of generalized definitions and a common framework for the application of EC in learning to rank research. Besides, according to the introduced framework, we propose RankIP, a ranking function discovery approach using Immune Programming (IP). Experimental results demonstrate that RankIP evidently outperforms the baselines. In addition, we study the differences between IP and GP in theory and experiments. Results show that IP is more suitable for LR4IR due to its high diversity.", } @Article{Wang:2010:esa, author = "Shuaiqiang Wang and Jun Ma and Qiang He", title = "An immune programming-based ranking function discovery approach for effective information retrieval", journal = "Expert Systems with Applications", year = "2010", volume = "37", number = "8", pages = "5863--5871", keywords = "genetic algorithms, genetic programming, Information retrievalInformation retrieval, Learning to rank, Immune programming, Evolutionary computation, Machine learning", ISSN = "0957-4174", URL = "http://www.sciencedirect.com/science/article/B6V03-4YDT3VY-1/2/5d51ce9997fad3e696db420a8662f16f", DOI = "doi:10.1016/j.eswa.2010.02.019", abstract = "In this paper, we propose RankIP, the first immune programming (IP) based ranking function discovery approach. IP is a novel evolution based machine learning algorithm with the principles of immune systems, which is verified to be superior to Genetic Programming (GP) on the convergence of algorithm according to their experimental results in Musilek et al. (2006). However, such superiority of IP is mainly demonstrated for optimization problems. RankIP adapts IP to the learning to rank problem, a typical classification problem. In doing this, the solution representation, affinity function, and high-affinity antibody selection require completely different treatments. Besides, two formulae focusing on selecting best antibody for test are designed for learning to rank. Experimental results demonstrate that the proposed RankIP outperforms the state-of-the-art learning-based ranking methods significantly in terms of P@n,MAP and NDCG@n.", notes = "Also known as \cite{Wang20105863}", } @Article{Wang:2010:IPL, author = "Shuaiqiang Wang and Jun Ma and Jiming Liu and Xiaofei Niu", title = "Evolving choice structures for genetic programming", journal = "Information Processing Letters", year = "2010", volume = "110", number = "20", pages = "871--876", month = "30 " # sep, keywords = "genetic algorithms, genetic programming, Program derivation, Evolutionary computation, Choice structure", ISSN = "0020-0190", URL = "http://www.sciencedirect.com/science/article/B6V0F-50K5T29-1/2/9ef3031afe03a6d15f2f0a468fca26ec", DOI = "doi:10.1016/j.ipl.2010.07.014", abstract = "It is quite difficult but essential for Genetic Programming (GP) to evolve the choice structures. Traditional approaches usually ignore this issue. They define some if-structures functions according to their problems by combining if-else statement, conditional criteria and elemental functions together. Obviously, these if-structure functions depend on the specific problems and thus have much low reusability. Based on this limitation of GP, in this paper we propose a kind of termination criterion in the GP process named Combination Termination Criterion (CTC). By testing CTC, the choice structures composed of some basic functions independent to the problems can be evolved successfully. Theoretical analysis and experiment results show that our method can evolve the programs with choice structures effectively within an acceptable additional time.", notes = "Also known as \cite{Wang2010871}", } @InProceedings{Wang:2011:SIGIR, author = "Shuaiqiang Wang and Byron J. Gao and Ke Wang and Hady W. Lauw", title = "Parallel learning to rank for information retrieval", booktitle = "Proceedings of the 34th international ACM SIGIR conference on Research and development in Information", series = "SIGIR '11", year = "2011", isbn13 = "978-1-4503-0757-4", address = "Beijing, China", pages = "1083--1084", numpages = "2", URL = "http://doi.acm.org/10.1145/2009916.2010060", DOI = "doi:10.1145/2009916.2010060", acmid = "2010060", publisher = "ACM", keywords = "genetic algorithms, genetic programming: Poster, cooperative coevolution, information retrieval, learning to rank, mapreduce, parallel algorithms", abstract = "Learning to rank represents a category of effective ranking methods for information retrieval. While the primary concern of existing research has been accuracy, learning efficiency is becoming an important issue due to the unprecedented availability of large-scale training data and the need for continuous update of ranking functions. In this paper, we investigate parallel learning to rank, targeting simultaneous improvement in accuracy and efficiency.", } @InProceedings{Wang.Shuaiqiang:2011:AAAI, author = "Shuaiqiang Wang and Byron Gao and Ke Wang and Hady Lauw", title = "{CCRank:} Parallel Learning to Rank with Cooperative Coevolution", booktitle = "Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence", year = "2011", editor = "Wolfram Burgard and Dan Roth", address = "San Francisco, California USA", publisher_address = "Menlo Park, California, USA", month = aug # " 7-11", organisation = "Association for the Advancement of Artificial Intelligence", publisher = "AAAI Press", keywords = "genetic algorithms, genetic programming", URL = "http://www.aaai.org/ocs/index.php/AAAI/AAAI11/paper/view/3563", URL = "http://www.aaai.org/ocs/index.php/AAAI/AAAI11/paper/view/3563/4060.pdf", size = "6 pages", abstract = "We propose CCRank, the first parallel algorithm for learning to rank, targeting simultaneous improvement in learning accuracy and efficiency. CCRank is based on cooperative coevolution (CC), a divide-and-conquer framework that has demonstrated high promise in function optimisation for problems with large search space and complex structures. Moreover, CC naturally allows parallelisation of sub-solutions to the decomposed subproblems, which can substantially boost learning efficiency. With CCRank, we investigate parallel CC in the context of learning to rank. Extensive experiments on benchmarks in comparison with the state-of-the-art algorithms show that CCRank gains in both accuracy and efficiency.", notes = "http://www.aaai.org/Conferences/AAAI/aaai11.php", } @InProceedings{DBLP:conf/cikm/WangGWCY12, author = "Shuaiqiang Wang and Byron J. Gao and Shuangling Wang and Guibao Cao and Yilong Yin", title = "Polygene-based evolution: a novel framework for evolutionary algorithms", booktitle = "21st ACM International Conference on Information and Knowledge Management, CIKM'12", year = "2012", editor = "Xue-wen Chen and Guy Lebanon and Haixun Wang and Mohammed J. Zaki", pages = "2263--2266", address = "Maui, HI, USA", month = oct # " 29 - " # nov # " 2", publisher = "ACM", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4503-1156-4", DOI = "doi:10.1145/2396761.2398616", bibsource = "DBLP, http://dblp.uni-trier.de", size = "4 pages", abstract = "In this paper, we introduce polygene-based evolution, a novel framework for evolutionary algorithms (EAs) that features distinctive operations in the evolution process. In traditional EAs, the primitive evolution unit is gene, where genes are independent components during evolution. In polygene-based evolutionary algorithms (PGEAs), the evolution unit is polygene, i.e., a set of co-regulated genes. Discovering and maintaining quality polygenes can play an effective role in evolving quality individuals. Polygenes generalise genes, and PGEAs generalize EAs. Implementing the PGEA framework involves three phases: polygene discovery, polygene planting, and polygene-compatible evolution. Extensive experiments on function optimisation benchmarks in comparison with the conventional and state-of-the-art EAs demonstrate the potential of the approach in accuracy and efficiency improvement.", notes = "No mention of GP http://www.cikm2012.org/accepted_papers.php", } @Article{Wang:2015:ieeeKDE, author = "Shuaiqiang Wang and Yun Wu and Byron J. Gao and Ke Wang and Hady W. Lauw and Jun Ma", title = "A Cooperative Coevolution Framework for Parallel Learning to Rank", journal = "IEEE Transactions on Knowledge and Data Engineering", year = "2015", volume = "27", number = "12", pages = "3152--3165", month = dec, keywords = "genetic algorithms, genetic programming, Cooperative coevolution, learning to rank, information retrieval, immune programming", ISSN = "1041-4347", DOI = "doi:10.1109/TKDE.2015.2453952", size = "14 pages", abstract = "We propose CCRank, the first parallel framework for learning to rank based on evolutionary algorithms (EA), aiming to significantly improve learning efficiency while maintaining accuracy. CCRank is based on cooperative coevolution (CC), a divide-and-conquer framework that has demonstrated high promise in function optimization for problems with large search space and complex structures. Moreover, CC naturally allows parallelization of sub-solutions to the decomposed sub-problems, which can substantially boost learning efficiency. With CCRank, we investigate parallel CC in the context of learning to rank. We implement CCRank with three EA-based learning to rank algorithms for demonstration. Extensive experiments on benchmark datasets in comparison with the state-of-the-art algorithms show the performance gains of CCRank in efficiency and accuracy.", notes = "Also known as \cite{7152946}", } @Article{Wang:2018:FCS, author = "Shuaiqiang Wang and Yilong Yin", title = "Polygene-based evolutionary algorithms with frequent pattern mining", journal = "Frontiers of Computer Science", year = "2018", volume = "12", number = "5", pages = "950--965", month = oct, keywords = "genetic algorithms, genetic programming, PGEA, polygenes, evolutionary algorithms, function optimization, associative classification data mining", ISSN = "2095-2228", URL = "https://link.springer.com/article/10.1007/s11704-016-6104-3", DOI = "doi:10.1007/s11704-016-6104-3", size = "16 pages", abstract = "we introduce polygene-based evolution, a novel framework for evolutionary algorithms (EAs) that features distinctive operations in the evolutionary process. In traditional EAs, the primitive evolution unit is a gene, wherein genes are independent components during evolution. In polygene-based evolutionary algorithms (PGEAs), the evolution unit is a polygene, i.e., a set of co-regulated genes. Discovering and maintaining quality polygenes can play an effective role in evolving quality individuals. Polygenes generalize genes, and PGEAs generalize EAs. Implementing the PGEA framework involves three phases: (I) polygene discovery, (II) polygene planting, and (III) polygene-compatible evolution. For Phase I, we adopt an associative classification-based approach to discover quality polygenes. For Phase II, we perform probabilistic planting to maintain the diversity of individuals. For Phase III, we incorporate polygene-compatible crossover and mutation in producing the next generation of individuals. Extensive experiments on function optimization benchmarks in comparison with the conventional and state-of-the-art EAs demonstrate the potential of the approach in terms of accuracy and efficiency improvement.", notes = "Is this GP? Research Center of Big Data Applications, Qilu University of Technology, Jinan 250100, China Supplementary material is available for this article at https://doi.org/10.1007/s11704-016-6104-3 and is accessible for authorized users.", } @Article{SichunWang:2005:JCRD, author = "Sichun Wang and Taishan Zhang and Zhiyun Yin and Chuwen Zhang", title = "Stability Analysis of Multiobjective Decision Functions Based on {GP}", journal = "Journal of Computer Research and Development", year = "2005", volume = "42", number = "8", pages = "1318--1323", keywords = "genetic algorithms, genetic programming, stability analysis, decision functions, decision support system", URL = "http://219.238.6.200/article?code=crad20050806&jccode=58", DOI = "doi:10.1360/crad20050806", abstract = "In multi-objective decision problems many values must be assigned, such as the importance of the different criteria and the values of the alternatives with respect to subjective criteria. Since these assignments are approximate, it is very important to analyse the sensitivity of results when small modifications of the assignments are made. When solving a multicriteria decision problem, it is desirable to choose a decision function that leads to a solution as stable as possible. Proposed here is a method based on genetic programming that produces better decision functions than the commonly used ones. The theoretical expectations are validated by case studies.", notes = "Code:crad20050806 http://219.238.6.200/journal?code=58 College of Information Science and Engineering, Central South University, Changsha 410083, China Institute of Information Science, College of Hunan Business, Changsha 410205, China Copyright 2005 Science in China Press ?ICP?05023005?", } @InProceedings{Wang:2009:BIFE, author = "Sichun Wang", title = "A Constructive Method of Multicriteria Decision Functions Based on GP Algorithm", booktitle = "International Conference on Business Intelligence and Financial Engineering, BIFE '09", year = "2009", month = jul, pages = "118--121", keywords = "genetic algorithms, genetic programming, AHP arithmetic mean, constructive method, genetic programming algorithm, global stability, multicriteria decision functions, multicriteria decision-making functions, decision making, decision theory, stability", DOI = "doi:10.1109/BIFE.2009.36", abstract = "In order to construct a decision-making function of the certain problem, a constructive method of multicriteria decision-making functions based on genetic programming algorithm is proposed in this paper. By introducing the indicator of global stability and the value of integrated utility to the decision-making function, the stability of its solution is improved. Empirical results show that the decision-making function constructed by the genetic program is more stable than the decision-making function constructed by AHP arithmetic mean.", notes = "Also known as \cite{5208923}", } @InProceedings{Wang:2009:ICICTA, author = "Sichun Wang", title = "Solving the Optimal Solution of Weight Vectors on GP-Decision Tree", booktitle = "Second International Conference on Intelligent Computation Technology and Automation, ICICTA '09", address = "Changsha, Hunan, China", year = "2009", month = "10-11 " # oct, volume = "4", pages = "329--332", keywords = "genetic algorithms, genetic programming, GP-decision tree, GPA, decision-making problem, genetic programming algorithm, partitioned node error rate reduction, tree nodes error rate, trend forecasting model, weight vector, decision making, decision trees, vectors", DOI = "doi:10.1109/ICICTA.2009.795", isbn13 = "978-0-7695-3804-4", abstract = "In this paper, a novel approach based on genetic programming algorithm (GPA) is proposed to solve the optimal solution of weight vectors on GP-decision tree. In this GP-decision tree algorithm, the GP-decision tree is constructed according to the error rate of tree nodes and the error rate reduction of partitioned nodes. By using this algorithm, not only the weight vectors of tree nodes can be solved, but also the structure of GP-decision tree can be determined. Experimental results show this algorithm is efficient and the right trend forecasting model can be selected by using this GP-decision tree algorithm.", notes = "Also known as \cite{5288288}", } @InProceedings{Wang:2010:IEEC, author = "Sichun Wang and Yanhui Wu", title = "A Large-Scale Data Classifying Approach Based on GP", booktitle = "2nd International Symposium on Information Engineering and Electronic Commerce (IEEC 2010)", year = "2010", month = "23-25 " # jul, abstract = "The method that the utility of genetic programming (GP) is used to create and use ensembles in data mining is demonstrated in the paper . Given its representational power in the model of complex non-linearities in the data, GP is seen to be effective at learning diverse patterns in the data. With different models capturing varied data relationships, GP models are ideally suited for combination in ensembles. Experimental results show that different GP models are dissimilar both in terms of the functional form as well as with respect to the variables defining the models.", keywords = "genetic algorithms, genetic programming, data mining, large scale data classifying approach, data mining, pattern classification", DOI = "doi:10.1109/IEEC.2010.5533265", notes = "Eng. Manage. Inst., Hunan Univ. of Commerce, Changsha, China Also known as \cite{5533265}", } @InProceedings{wang:2005:CEC, author = "Singer X. J. Wang and Peter Lichodzijewski", title = "Boolean Genetic Programming for Promoter Recognition in Eukaryotes", booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary Computation", year = "2005", editor = "David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L. Gwenn Volkert and Dan Ashlock and Marc Schoenauer", volume = "1", pages = "683--690", address = "Edinburgh, UK", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "2-5 " # sep, organisation = "IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9363-5", URL = "http://flame.cs.dal.ca/~piotr/01554749.pdf", DOI = "doi:10.1109/CEC.2005.1554749", abstract = "Fixed-length Genetic Programming is applied to the problem of promoter identification in eukaryotes. The goal is to generate solutions that can be easily interpreted and compared with known promoter characteristics. Using a boolean function set applied to boolean registers, inputs, and constant values, the approach builds a logical expression whose value gives the classification decision. Evaluated on a dataset of human promoters and non-promoters from coding regions, the approach is found to generate concise solutions that yield good specificity but poor sensitivity. Analysis of the programs that are generated indicates that a well-known, biologically significant, characteristic of promoter regions is successfully identified. Suggested future work involves implementing the system using fuzzy logic.", notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS.", } @Article{Wang:2025:engfailanal, author = "Songbo Wang and Yanchen Fu and Sifan Ban and Zhuo Duan and Jun Su", title = "Genetic evolutionary deep learning for fire resistance analysis in fibre-reinforced polymers strengthened reinforced concrete beams", journal = "Engineering Failure Analysis", year = "2025", volume = "169", pages = "109149", keywords = "genetic algorithms, genetic programming, Fire resistance, Strengthened reinforced concrete beams, Light Gradient-Boosting Machine, SHapley Additive exPlanations, ANN", ISSN = "1350-6307", URL = "https://www.sciencedirect.com/science/article/pii/S1350630724011956", DOI = "doi:10.1016/j.engfailanal.2024.109149", abstract = "Fibre Reinforced Polymers (FRP) have become increasingly popular for strengthening reinforced concrete (RC) structures due to their structural benefits. However, a major concern with FRP-strengthened concrete members is their poor fire resistance. This study introduces a genetic evolutionary deep learning (DL) approach that uses the Light Gradient-Boosting Machine (LightGBM) algorithm, enhanced with a Genetic Algorithm for hyperparameter optimisation, alongside Genetic Programming (GP) to assess the fire resistance performance of strengthened RC beams. A substantial dataset comprising 20,000 data points, derived from numerically modelled results validated through experimental studies, underpins the data-driven DL analyses. The LightGBM model demonstrates high predictive accuracy for fire resistance time and deflection at failure of the FRP-strengthened RC beams, with coefficient of determination (R2) values of 0.923 and 0.789, respectively. Although the GP model shows lower accuracy (R2 values of 0.642 and 0.643), it provides explicit equations that facilitate a deeper understanding of the DL model and ease of application. A graphical user interface software, incorporating these two DL models, has been developed to enable engineers to apply these insights in practice without requiring coding skills. Furthermore, an assessment of feature influences was conducted, visually depicting their impact on the output results, thus enhancing interpretability for engineering applications", } @Article{WANG:2024:swevo, author = "Tonghao Wang and Xingguang Peng and Tao Wang and Tong Liu and Demin Xu", title = "Automated design of action advising trigger conditions for multiagent reinforcement learning: A genetic programming-based approach", journal = "Swarm and Evolutionary Computation", volume = "85", pages = "101475", year = "2024", ISSN = "2210-6502", DOI = "doi:10.1016/j.swevo.2024.101475", URL = "https://www.sciencedirect.com/science/article/pii/S2210650224000087", keywords = "genetic algorithms, genetic programming, Multiagent reinforcement learning, Action advising, Multiagent systems", abstract = "Action advising is a popular and effective approach to accelerating independent multiagent reinforcement learning (MARL), especially for the learning systems that all the agents learn from scratch and the roles of them (advisors or advisees) cannot be predefined. The key component of action advising is the trigger condition, which answers the question of when to advise. Previous works mainly focus on the design of novel trigger conditions manually; however, since those conditions are often designed heuristically, the performance may be affected by the preference of the designers. To this end, this paper tries to solve the action advising problem automatically using genetic programming (GP), an evolutionary computation technique. A framework incorporating GP to action advising is provided, together with a novel population initialization method to enhance the performance. Empirical studies are provided to demonstrate the effectiveness of the proposed framework. More importantly, thanks to the high transparency of GP, comprehensive analysis is also conducted based on the results. Interesting and inspiring insights to the action advising problem are condensed from the discussions, which may provide guidance to future works", } @InProceedings{Wang:2009:CVMP, author = "Tinghuai Wang and Andrew Mansfield and Rui Hu and John P. Collomosse", title = "An Evolutionary Approach to Automatic Video Editing", booktitle = "Conference for Visual Media Production, CVMP '09", year = "2009", month = "12-13 " # nov, pages = "127--134", abstract = "Digital video has become affordable and attractive to home users, but skill and manual labour are still required to transform amateur footage into aesthetically pleasing movies. We present a novel algorithm for transforming raw home video footage into concise, temporally salient clips. We interpret the sequence of editing operations applied to footage as a `program' comprising cutting, panning and zooming constructs. We develop a Genetic Programming (GP) framework for representing and evolving such programs. Under this framework, the search for an aesthetically pleasing video edit becomes a search for the optimal genetic program. Our aesthetic criterion promotes the inclusion of people in shots, whilst penalising rapid shot changes or shot changes in the presence of camera motion. We present results on some representative home videos.", keywords = "genetic algorithms, genetic programming, automatic video editing, camera motion, digital video, genetic programming framework, home video footage, optimal genetic program, video cameras, video signal processing", DOI = "doi:10.1109/CVMP.2009.8", notes = "Also known as \cite{5430070}", } @InProceedings{Wang:2008:ICNC, author = "Wen-chuan Wang and Chun-tian Cheng and Lin Qiu", title = "Genetic Programming with Rough Sets Theory for Modeling Short-term Load Forecasting", booktitle = "Fourth International Conference on Natural Computation, ICNC '08", year = "2008", month = oct, volume = "6", pages = "306--310", keywords = "genetic algorithms, genetic programming, China, GuiZhou power grid, electric power operation, evolutional algorithm, load demand, rough sets theory, short-term load forecasting, load forecasting, power grids, rough set theory", DOI = "doi:10.1109/ICNC.2008.141", abstract = "The accurate and robust short-term load forecasting (STLF) plays a significant role in electric power operation. The accuracy of STLF is greatly related to the selected the main relevant influential factors. However, how to select appropriate influential factor is a difficult task because of the randomness and uncertainties of the load demand and its influential factors. In this paper, a novel method of genetic programming (GP) with rough sets (RS) theory is developed to model STLF to improve the accuracy and enhance the robustness of load forecasting results. RS theory is employed to process large data and eliminate redundant information in order to find relevant factors to the short-term load, which are used as sample sets to establish forecasting model by means of GP evolutional algorithm. The presented model is applied to forecast short-term load using the actual data from GuiZhou power grid in China. The forecasted results are compared with BP artificial neural Network with RS theory, and it is shown that the presented forecasting method is more accurate and efficient.", notes = "Also known as \cite{4667850}", } @Article{Wang2009294, author = "Wen-Chuan Wang and Kwok-Wing Chau and Chun-Tian Cheng and Lin Qiu", title = "A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series", journal = "Journal of Hydrology", volume = "374", number = "3-4", pages = "294--306", year = "2009", ISSN = "0022-1694", DOI = "doi:10.1016/j.jhydrol.2009.06.019", URL = "http://www.sciencedirect.com/science/article/B6V6C-4WK48G6-1/2/7cf0d9cf0adb10d24201878b9773ca27", keywords = "genetic algorithms, genetic programming, Monthly discharge time series forecasting, ARMA, ANN, ANFIS, GP, SVM", abstract = "Developing a hydrological forecasting model based on past records is crucial to effective hydropower reservoir management and scheduling. Traditionally, time series analysis and modeling is used for building mathematical models to generate hydrologic records in hydrology and water resources. Artificial intelligence (AI), as a branch of computer science, is capable of analyzing long-series and large-scale hydrological data. In recent years, it is one of front issues to apply AI technology to the hydrological forecasting modeling. In this paper, autoregressive moving-average (ARMA) models, artificial neural networks (ANNs) approaches, adaptive neural-based fuzzy inference system (ANFIS) techniques, genetic programming (GP) models and support vector machine (SVM) method are examined using the long-term observations of monthly river flow discharges. The four quantitative standard statistical performance evaluation measures, the coefficient of correlation (R), Nash-Sutcliffe efficiency coefficient (E), root mean squared error (RMSE), mean absolute percentage error (MAPE), are employed to evaluate the performances of various models developed. Two case study river sites are also provided to illustrate their respective performances. The results indicate that the best performance can be obtained by ANFIS, GP and SVM, in terms of different evaluation criteria during the training and validation phases.", } @InProceedings{conf/icicic/WangLHL06, title = "A Preliminary Study on Constructing Decision Tree with Gene Expression Programming", author = "Weihong Wang and Qu Li and Shanshan Han and Hai Lin", booktitle = "First International Conference on Innovative Computing, Information and Control (ICICIC 2006)", year = "2006", pages = "222--225", address = "Beijing, China", month = "30 " # aug # " - 1 " # sep, publisher = "IEEE Computer Society", bibdate = "2007-01-05", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icicic/icicic2006-1.html#WangLHL06", keywords = "genetic algorithms, genetic programming, Gene Expression Programming", ISBN = "0-7695-2616-0", DOI = "doi:10.1109/ICICIC.2006.22", abstract = "Gene expression programming (GEP) is a kind of genotype/phenotype based genetic algorithm. Its successful application in classification rules mining has gained wide interest in data mining and evolutionary computation fields. However, current GEP based classifiers represent classification rules in the form of expression tree, which is less meaningful and expressive than decision tree. Whats more, these systems adopt one-against-all learning strategy, i.e. to solve a n-class with n runs, each run solving a binary classification task. In this paper, a GEP decision tree(GEPDT) system is presented, the system can construct a decision tree for classification without priori knowledge about the distribution of data, at the same time, GEPDT can solve a n-class problem in a single run, preliminary results show that the performance of GEP based decision tree is comparable to ID3.", } @InProceedings{Wang:2016:SSBSE, author = "Weiwei Wang and Ruilian Zhao and Ying Shang and Yong Liu2", title = "Test Data Generation Efficiency Prediction Model for EFSM based on MGGP", booktitle = "Proceedings of the 8th International Symposium on Search Based Software Engineering, SSBSE 2016", year = "2016", editor = "Federica Sarro and Kalyanmoy Deb", volume = "9962", series = "LNCS", pages = "176--191", address = "Raleigh, North Carolina, USA", month = "8-10 " # oct, publisher = "Springer", keywords = "genetic algorithms, genetic programming, SBSE, Multi-gene genetic programming, Extended finite state machine, Test data generation efficiency predictive model", isbn13 = "978-3-319-47106-8", DOI = "doi:10.1007/978-3-319-47106-8_12", abstract = "Most software testing researches on Extended Finite State Machine (EFSM) have focused on automatic test sequence and data generation. The analysis of test generation efficiency is still inadequate. In order to investigate the relationship between EFSM test data generation efficiency and its influence factors, according to the feasible transition paths of EFSMs, we build a multi-gene genetic programming (MGGP) predictive model to forecast EFSM test data generation efficiency. Besides, considering standard genetic programming (GP) and neural network are commonly employed in predictive models, we conduct experiments to compare MGGP model with GP model and back propagation (BP) neural network model on their predictive ability. The results show that, MGGP model is able to effectively predict EFSM test data generation efficiency, and compared with GP model and BP model, MGGP model's predictive ability is stronger. Moreover, the correlation among the influence factors will not affect its predictive performance.", notes = "GP? co-located with ICSME-2016", } @InProceedings{conf/icnc/WangLL13b, author = "Weihong Wang and Wenrou Lin and Qu Li", title = "Image retrieval based on Multi Expression Programming algorithms", publisher = "IEEE", year = "2013", keywords = "genetic algorithms, genetic programming", bibdate = "2014-05-21", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icnc/icnc2013.html#WangLL13b", booktitle = "ICNC", pages = "1359--1364", URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6811386", DOI = "DOI:10.1109/ICNC.2013.6818191", } @InProceedings{10.1109/SKG.2006.55, author = "Weiwu Wang and Yuanxiang Li and Shengwu Xiong and Bojin Zheng", title = "Identify Discontinuous Parameter of Parabolic System via Point-Tree Structured Genetic Programming", booktitle = "Proceedings of The 2nd International Conference on Semantics, Knowledge and Grid (SKG2006)", year = "2006", series = "International Conference on Semantics, Knowledge and Grid", pages = "43", address = "Guilin, Guangxi, China", editor = "Hai Zhuge and Toru Ishida", publisher_address = "Los Alamitos, CA, USA", month = "1-3 " # nov, publisher = "IEEE Computer Society", keywords = "genetic algorithms, genetic programming", ISBN = "0-7695-2673-X", DOI = "doi:10.1109/SKG.2006.55", abstract = "In this paper, we apply point-tree structured genetic programming (PTGP) to identify a discontinuous parameter of parabolic system. The discontinuous parameter can be decomposed into several subcomponents. Each subcomponent can be represented as a simple function. We call these simple functions as sub-functions. We use the PTGP individual to represent the discontinuous parameter, and evolve these individuals to learn it. During the evolution, the subfunctions and their domains co-evolve and co-adapt automatically. The numerical experiments show PTGP can identify the discontinuous parameter successfully. Compare to the stand genetic programming approach, the performance is improved significantly.", notes = "http://www.culturegrid.net/SKG2006/Final.htm Weiwu Wang, Wuhan University, China Yuanxiang Li, Wuhan University, China Shengwu Xiong, Wuhan University of Technology, China Bojin Zheng, South-Central University For Nationalities, China IEEE Computer Society Order Number E2673 reprints@computer.org Also known as \cite{conf/skg/WangLXZ06}", } @InProceedings{Wang:2009:ICNC, author = "Wenchuan Wang and Dongmei Xu and Lin Qiu and Jianqin Ma", title = "Genetic Programming for Modelling Long-Term Hydrological Time Series", booktitle = "Fifth International Conference on Natural Computation, ICNC '09", year = "2009", month = aug, volume = "4", pages = "265--269", keywords = "genetic algorithms, genetic programming, artificial neural network, autocorrelation function, evolutionary computing method, flow prediction method, hydrological time series forecasting, lagged input variable, monthly river flow discharge, reservoir inflow sequence data, root mean square error, transparent-structured system identification, channel flow, correlation methods, forecasting theory, identification, mean square error methods, neural nets, prediction theory, time series", DOI = "doi:10.1109/ICNC.2009.210", abstract = "In recent years, artificial neural networks (ANN) have emerged as a novel identification technique for the forecasting of hydrological time series. However, they represent their knowledge in terms of a weight matrix that is not accessible to human understanding at present. The purpose of this study is to develop a flow prediction method, based on the genetic programming (GP), which is an evolutionary computing method that provides `transparent' and structured system identification. In terms of statistical characteristic of reservoir inflow sequence data, the autocorrelation function is employed to make certain amount of lagged input variables and the root mean square error is adopted as fitness of evaluation. The GP model is examined using the long-term observations of monthly river flow discharges. Through the comparison of its performance with those of the ANN, it is demonstrated that the GP model is an effective algorithm to forecast the long-term discharges.", notes = "Also known as \cite{5366249}", } @Article{wang:2008:KJCE, author = "Xiao-Hong Wang and Yang-Dong Hu and Yu-Gang Li", title = "Synthesis of nonsharp distillation sequences via genetic programming", journal = "Korean Journal of Chemical Engineering", year = "2008", volume = "25", number = "3", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s11814-008-0068-4", DOI = "doi:10.1007/s11814-008-0068-4", } @Article{Wang20081908, author = "Xiao-Hong Wang and Yu-Gang Li and Yang-Dong Hu and Ying-Long Wang", title = "Synthesis of heat-integrated complex distillation systems via Genetic Programming", journal = "Computers \& Chemical Engineering", volume = "32", number = "8", pages = "1908--1917", year = "2008", ISSN = "0098-1354", DOI = "doi:10.1016/j.compchemeng.2007.10.009", URL = "http://www.sciencedirect.com/science/article/B6TFT-4R063YH-1/2/ea77b51431d11bd24f1d8983fd44b279", keywords = "genetic algorithms, genetic programming, Complex distillation system, Synthesis", abstract = "This paper addresses the application of Genetic Programming (GP) to the synthesis of heat-integrated complex distillation system and the flowsheet of complex separation can be expressed directly using GP's special hierarchical structure. A series of unique encoding method and solution strategy is proposed and some evolutionary factor is improved based on the domain knowledge of chemical engineering. A shortcut method is applied to calculate all required design parameters. Conventional and complex columns, thermally coupled (linked) side strippers and side rectifiers, fully thermally coupled columns as well as heat integration between any different columns are simultaneously considered. Two illustrating examples are presented to demonstrate the effective computational strategies.", } @Article{Wang201045, author = "Xiao-Hong Wang and Yu-Gang Li", title = "Stochastic {GP} synthesis of heat integrated nonsharp distillation sequences", journal = "Chemical Engineering Research and Design", volume = "88", number = "1", pages = "45--54", year = "2010", ISSN = "0263-8762", DOI = "doi:10.1016/j.cherd.2009.06.012", URL = "http://www.sciencedirect.com/science/article/B8JGF-4WT39Y6-1/2/d634c77788b840af602017d1a6ccb5f2", keywords = "genetic algorithms, genetic programming, Nonsharp distillation sequences, Multicomponent products, Heat integration, Synthesis, Optimization", abstract = "Genetic programming (GP) is used to solve the synthesis problem of heat integration nonsharp distillation sequences and the optimization objective of it is to seek the optimal heat integration nonsharp flow based on minimizing the annually total cost. Meanwhile, the major technological parameters for these important equipments are given. Combining with the domain knowledge of chemical engineering, some evolutionary factors are improved, and a set of special encoding method and heat integration strategy is proposed to deal with this kind of problem. The system structural variable is optimized by GP and the continuous variable is optimized by the complex algorithm simultaneously. Because GP has the automatically searching function, the optimal heat integration solution can be found automatically without any superstructures of nonsharp distillation sequences. Three illustrating examples are presented to demonstrate the effective computational strategies.", } @Article{WANG:2020:CEPPI, author = "Xiao-hong Wang and Ming-gao Li and Yuan-peng Zhang and Juan Hong and Wen-kui Li and Xin Ding and Yu-gang Li", title = "Research on the integration process of energy saving distillation-membrane separation based on genetic programming to achieve clean production", journal = "Chemical Engineering and Processing - Process Intensification", volume = "151", pages = "107885", year = "2020", ISSN = "0255-2701", DOI = "doi:10.1016/j.cep.2020.107885", URL = "http://www.sciencedirect.com/science/article/pii/S0255270119314059", keywords = "genetic algorithms, genetic programming, Separation optimization of azeotrope, Distillation-membrane separation integration, Carbon dioxide emissions", abstract = "The separation of industrial azeotropes by a new integrated process of distillation-membrane separation has the prominent advantages for energy saving and environmental protection. In view of the variety of industrial azeotrope types, this paper proposes a comprehensive solution strategy based on intelligent genetic programming (GP) for the distillation-membrane separation integration process. Based on Visual Studio platform, GP is written by C++ language. This algorithm can quickly calculate the economic optimal distillation-membrane separation process for any azeotrope separation. In this paper, the tert-Butanol-water azeotrope, the 2-Propanol-water azeotrope and the Pervap 2510 membrane are taken as calculation examples to verify the feasibility and extensibility of the algorithm. The cost of carbon dioxide emission is taken into account.The results show that the algorithm can quickly and accurately search the optimal integrative process of various feed conditions and carbon dioxide emission conditions. The analysis of multiple optimal data shows that the membrane area has great influenced by the change of feed composition, which further influences the membrane separation cost. The cost models of various kinds of membranes can be modified at any time, thus it can be helpful for the future industrial application and further modification of membrane which is still in the experimental stage", } @InProceedings{XiuqinWang:2008:ICNC, author = "Xiuqin Wang and Hao Wang and Guangsheng Ma", title = "Variable Topology Cartesian Genetic Programming for Combinational Circuit", booktitle = "Fourth International Conference on Natural Computation, ICNC '08", year = "2008", month = oct, volume = "7", pages = "306--310", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, auto-design, combinational circuit, evolvable hardware, genetic coding, self-adapt, self-organization, self-repair, variable topology Cartesian genetic programming, combinational circuits", DOI = "doi:10.1109/ICNC.2008.522", abstract = "Evolvable hardware is consisted of hardware and evolvable algorithm. It has the ability of self-organization, self-adapt, self-repair and auto-design. Because of its important application values and the shortcomings of traditional methods, generating evolvable circuit with quick speed and obtaining the optimal evolvable circuit are the hotspot in the field of evolvable circuit. In this paper a two stage evolution algorithm is proposed, which use variable topology genetic coding to improve the flexibility of Cartesian Genetic Programming (CGP). Simulation results demonstrate the validity and performance of the proposed method.", notes = "Also known as \cite{4667990}", } @Article{WANG:2021:OE, author = "Yanxu Wang and Zegao Yin and Yong Liu", title = "Predicting the bulk drag coefficient of flexible vegetation in wave flows based on a genetic programming algorithm", journal = "Ocean Engineering", volume = "223", pages = "108694", year = "2021", ISSN = "0029-8018", DOI = "doi:10.1016/j.oceaneng.2021.108694", URL = "https://www.sciencedirect.com/science/article/pii/S0029801821001293", keywords = "genetic algorithms, genetic programming, Aquatic vegetation, Wave flows, Bulk drag coefficient, Predictor", abstract = "The prediction of the bulk drag coefficient (CD) for aquatic vegetation is of great significance for evaluating the influence of vegetation on the hydrodynamic processes in wave environments. Different CD empirical formulas have been mostly proposed as functions of either Reynolds (Re) number or Keulegan-Carpenter (KC) number in the literature, and the influences of other wave and vegetation parameters on CD were often ignored. The difference in formulas is largely attributable to inconsistent uses of characteristic velocity and length scales in the definitions of Re and KC. By considering the vegetation and hydrodynamic characteristics in this study, new Re and KC numbers were redefined using the mean pore velocity and vegetation-related hydraulic radius. Besides, a genetic programming algorithm was adopted to develop a robust relationship between CD and possible dimensionless variables based on extensive experimental data. Ultimately, a new CD predictor that has a similar form to that of the classical expression was obtained without any prespecified forms before searching. It turns out that the new predictor depends on not only the new KC number but also the submergence ratio and Ursell number. Compared with the existing predictors, the proposed CD predictor exhibits a considerable improvement in predictive ability for a wider parameter space", } @Article{Wang:2006:GPEM, author = "Yao Wang and Mark Wineberg", title = "Estimation of evolvability genetic algorithm and dynamic environments", journal = "Genetic Programming and Evolvable Machines", year = "2006", volume = "7", number = "4", pages = "355--382", month = dec, keywords = "genetic algorithms, Evolvability, Dynamic environment, Price's equation", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-006-9015-5", size = "28 pages", abstract = "This article investigates the of applicability of adding evolvability promoting mechanisms to a genetic algorithm to enhance its ability to handle perpetually novel dynamic environments, especially one that has stationary periods allowing the Genetic Algorithm (GA) to converge on a temporary global optimum.We use both biological and evolutionary computation (EC) definitions of evolvability to create two measures: one based on the improvements in fitness; the other based on the amount of genotypic change. These two evolvability measures are used alongside fitness to modify how selection proceeds in the GA. We call this modified GA the Estimation of Evolvability Genetic Algorithm (EEGA). When tested against a regular GA (with random immigrants), the EEGA is able to track the global optimum more closely than the GA dug the dynamic period. Unlike most GA extensions, the EEGA works effectively at a lower level of diversity than does the GA, showing that it is the quality of the diverse members in the population and not just the quantity that helps the GA evolve.", notes = "p357 'evolution tends to retain solutions that have a more evolvable genetic system' VEGA like. 3 dynamic selection pressures. Efficient diversity measures. F8F2. Binary graycode.", } @Article{WANG:2024:swevoa, author = "Yao Wang and Xianpeng Wang and Lixin Tang", title = "Evolutionary modeling approach based on multiobjective genetic programming for strip quality prediction", journal = "Swarm and Evolutionary Computation", volume = "86", pages = "101519", year = "2024", ISSN = "2210-6502", DOI = "doi:10.1016/j.swevo.2024.101519", URL = "https://www.sciencedirect.com/science/article/pii/S221065022400052X", keywords = "genetic algorithms, genetic programming, Interpretable modeling, Product quality prediction, Data-mechanism fusion, Multiobjective genetic programming, Continuous annealing", abstract = "In the iron and steel industry, hardness is one of the key indicators of strip quality in the continuous annealing production line (CAPL). However, the complex production process and the strong coupled nonlinearity between process parameters make it difficult to develop accurate mechanism models and pose a challenge for data-driven modeling approaches. More importantly, most of the data-driven learning methods lack interpretability and cannot characterize the mathematical relationship between process parameters and product quality, which in turn makes it extremely hard to understand the process mechanism. Therefore, this paper proposes an interpretable modeling approach (IMA) based on feature decomposition and ensemble to construct interpretable analytical models between process parameters and strip quality. In the IMA, a data-mechanism fusion-based feature decomposition (DM_FD) method is first applied to cope with high-dimensional input feature problems. Then, an improved multiobjective genetic programming algorithm (iMOGP) is developed to construct interpretability sub-models. Finally, a sparse optimization ensemble method (SOE) is used to integrate the sub-models to achieve interpretability and good generalization. Experimental results based on practical strip data demonstrate that the proposed IMA can cope well with high-dimensional input features and achieve model interpretability compared with commonly used machine learning methods and genetic programming (GP)-based modeling methods while ensuring better accuracy and generalization", } @Article{Wang:2017:JH, author = "Yu-Fei Wang and Wen-Xin Huai and Wei-Jie Wang", title = "Physically sound formula for longitudinal dispersion coefficients of natural rivers", journal = "Journal of Hydrology", volume = "544", pages = "511--523", year = "2017", keywords = "genetic algorithms, genetic programming, Canonical form, Contaminant transport, Longitudinal dispersion coefficients, Natural rivers", ISSN = "0022-1694", DOI = "doi:10.1016/j.jhydrol.2016.11.058", URL = "http://www.sciencedirect.com/science/article/pii/S0022169416307740", abstract = "The longitudinal dispersion coefficient (k) is necessary for a plethora of mass transport applications in fluids, but a general formulation for k remains lacking. In this study, we propose a canonical form for k that reflects the physics of dispersion and suits complex flow conditions encountered in natural streams. This general form is much more concise than previous predictors. A predictor for k of natural streams is also obtained using a genetic programming(GP) without pre-specified correlations among field data or a pre-specified form of the predictor. This predictor is physically sound (i.e. exhibits the aforementioned canonical form) and appears to be commensurate to or better than previous estimates of k. A grey model, which measures the proximity of data to a target shape (i.e. the proposed physically sound form), is also used to verify that the canonical form is appropriate. A formulation for k in natural rivers is obtained by using a GP. Its form is consistent with the canonical form.", } @Article{WANG:2021:ECSS, author = "Yunwei Wang and Jun Chen and Hui Cai and Qian Yu and Zeng Zhou", title = "Predicting water turbidity in a macro-tidal coastal bay using machine learning approaches", journal = "Estuarine, Coastal and Shelf Science", volume = "252", pages = "107276", year = "2021", ISSN = "0272-7714", DOI = "doi:10.1016/j.ecss.2021.107276", URL = "https://www.sciencedirect.com/science/article/pii/S0272771421001293", keywords = "genetic algorithms, genetic programming, Water turbidity, Artificial neural network, Support vector machine, Macro-tidal coastal bay", abstract = "Water turbidity is of particular importance for diffusion and migration of nutrients and contaminants, biological production, and ecosystem health in coastal turbid areas. The estimation of water turbidity is therefore significant for studies of coastal dynamics. Many factors influence turbidity in complex and nonlinear ways, making accurate estimations of turbidity a challenging task. In this study, three machine learning models, Artificial Neural Networks (ANN), Genetic Programming (GP), and Support Vector Machine (SVM) are developed for better estimation and prediction of the tidally-averaged sea surface turbidity. The observational data of tides and waves at a macro-tidal coastal bay, Jiangsu coast, China are used as model inputs. Through data reduction, it is found that tidal average sea surface turbidity is most determined by the average tidal range of the two preceding tidal cycles (2 and 3 tidal periods before the present one, respectively) and the tidal average significant wave height of the present tidal cycle of turbidity. These three machine learning models all show successful estimations of turbidity, and comparisons of the optimized models indicate that ANN shows the best performance and GP helps to provide physically meaningful predictors. This study provides an example of developing a predictive machine learning algorithm with a limited dataset (94 tidal cycles). The generality of the present predictors can be reinforced with much more data from a variety of coastal environments", } @InProceedings{5231228, author = "Yunwu Wang", title = "Using Fuzzy Expert System Based on Genetic Algorithms for Intrusion Detection System", booktitle = "International Forum on Information Technology and Applications, IFITA '09", year = "2009", month = may, volume = "2", pages = "221--224", keywords = "genetic algorithms, artificial intelligence, association rules, fuzzy expert system, fuzzy logic, intrusion detection system, membership function, data mining, expert systems, fuzzy logic, security of data", DOI = "doi:10.1109/IFITA.2009.107", notes = "Not on GP. Also known as \cite{5231228}", } @InProceedings{Wang:2009:WGEC, author = "Xiao-Ning Wang and Xiao-Wei Han and Zhong-Hu Yuan and Hong-Ying Zhao", title = "A Fast Motion Vector Estimation Method Based on Color Multidimension Projection", booktitle = "3rd International Conference on Genetic and Evolutionary Computing, WGEC '09", year = "2009", month = "14-17 " # oct, pages = "311--314", abstract = "An approach of fast motion estimation (ME), aiming at the stabilisation problem of video surveillance system on driving vehicle, is proposed in the paper. The information of colour space based on multidimension projection and brightness based on grayscale projection (GP) are jointly considered. Compared with the traditional approaches of GP, the presented approach can be applied to achieve higher precision and remain real-time performance. Furthermore, it is more robust than GP, especially when brightness distribution of video image is nearly even. Meanwhile, scaling factor estimation is also applied, which is helpful to estimate motion vector(MV) from scaling motion mode.", keywords = "genetic algorithms, genetic programming, colour multidimension projection, color space information, driving vehicle, fast motion estimation, fast motion vector estimation, grayscale projection, scaling motion mode, stabilisation problem, video image, video surveillance system, image colour analysis, motion estimation, video surveillance", DOI = "doi:10.1109/WGEC.2009.136", notes = "Also known as \cite{5402887}", } @Article{XinWang:2025:engstruct, author = "Xin Wang and Zhao-Dong Xu and Jun Dai and Haoyan Peng and Zhenghan Chen and Zhuangzhuang Wang and Zhiheng Xia and Fengmin Su", title = "Flutter behavior of functionally graded graphene origami-reinforced auxetic metamaterial composite laminated plates in supersonic flow", journal = "Engineering Structures", year = "2025", volume = "336", pages = "120318", keywords = "genetic algorithms, genetic programming, Graphene origami, Flutter analysis, Eigenvalue, FSDT, Modified Ritz method", ISSN = "0141-0296", URL = "https://www.sciencedirect.com/science/article/pii/S0141029625007096", DOI = "doi:10.1016/j.engstruct.2025.120318", abstract = "This paper presents an aeroelastic characteristic of functionally graded graphene origami-reinforced auxetic metamaterial (FG-GORAM) composite laminated plates. The equivalent elastic parameters of each layer, including elastic modulus, Poisson's ratio, and density, are determined using the Halpin-Tsai model, modified through genetic programming (GP) and molecular dynamics (MD). The artificial boundary springs were introduced to simulate various boundary constraints, and the laminated plate's mathematical model was constructed using the first-order shear deformation theory (FSDT) coupled with first-order piston theory. The energy function of the FG-GORAM plate was derived based on Hamilton's principle, and the solution was obtained using the modified Ritz method. The accuracy and validity of the present method were confirmed through detailed convergence analysis and comparisons with the published literature data. Furthermore, extensive parametric analyses were conducted to examine the effects of boundary conditions, structural parameters of the plates, and different configurations of graphene origami. The results indicate that the flutter performance of FG-GORAM composite laminated plates is highly dependent on the boundary conditions of the plate and the different graphene origami configurations, including their distribution type, content, and folding degree. It is found that the FG-GORAM composite plate had better flutter performance than its plate without the addition of origami graphene. These findings provide a crucial theoretical foundation and technological guidance for the design of such advanced materials", } @InProceedings{wang:2005:UKCI, author = "Xue Zhong Wang and Frances V. Buontempo and Mulaisho Mwense and Anita Young and Daniel Osborn", title = "Induction of Decision Trees Using Genetic Programming for the Development of SAR Toxicity Models", booktitle = "The 5th annual UK Workshop on Computational Intelligence", year = "2005", editor = "Boris Mirkin and George Magoulas", pages = "169--175", address = "London", month = "5-7 " # sep, organisation = "Birkbeck College, London Knowledge Lab", keywords = "genetic algorithms, genetic programming, QSAR, EPTree", URL = "http://www.dcs.bbk.ac.uk/ukci05/ukci05proceedings.pdf", size = "7 pages", abstract = "Automatic induction of decision tress and production rules from data to develop structure-activity relationship (SAR) models for toxicity prediction of chemicals has recently received much attention and the majority of methodologies reported in the literature are based upon recursive partitioning employing greedy searches to choose the best splitting attribute and value at each node. These approaches can be successful however the greedy search will necessarily miss regions of the search space. Recent literature has demonstrated the applicability of genetic programming to decision tree induction to overcome this problem. This paper presents a variant of this novel approach, using fewer mutation options and a simpler fitness function, demonstrating its utility in inducing decision trees for ecotoxicity data, via a case study of two datasets giving improved accuracy and generalisation ability over a popular decision tree inducer.", notes = "UKCI 2005 http://www.dcs.bbk.ac.uk/ukci05/ vibrio fischeri -- 75 compounds LC50 1093 descriptors cholorella vulgaris -- EC50 80 organic compounds University of leeds, AstraZeneca Brixham. Monk's Wood Huntingdon", } @InProceedings{Wang:2018:ISCID, author = "Yang Wang and Tieke Li and Bailin Wang", title = "Generation of Dispatching Rules for Hot Rolling Batch Scheduling of Seamless Steel Tube Based on Genetic Programming", booktitle = "2018 11th International Symposium on Computational Intelligence and Design (ISCID)", year = "2018", volume = "02", pages = "362--365", month = dec, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ISCID.2018.10183", ISSN = "2473-3547", abstract = "The single machine scheduling problem with specification sequence-dependent setup time and tardy is studied, with the objective to minimize total tardiness. The setup time depends on the specification difference between adjacent batches. A genetic programming(GP) is designed to generate dispatching rules autonomously to solve the problem. Computational experiment is carried out with benchmark rules, and the results illustrate that the dispatching rules generated by GP have the best effectiveness in solving this kind of problem.", notes = "Also known as \cite{8695624}", } @Article{WANG:2020:AMC, author = "Yufang Wang and Haiyan Wang and Shuhua Zhang", title = "Prediction of daily {PM2.5} concentration in China using data-driven ordinary differential equations", journal = "Applied Mathematics and Computation", volume = "375", pages = "125088", year = "2020", ISSN = "0096-3003", DOI = "doi:10.1016/j.amc.2020.125088", URL = "http://www.sciencedirect.com/science/article/pii/S0096300320300576", keywords = "genetic algorithms, genetic programming, Concentration data, Least square method, ODE models, prediction", abstract = "Accurate reporting and forecasting of PM2.5 concentration are important for improving public health. In this paper, we propose a daily prediction method of PM2.5 concentration by using data-driven ordinary differential equation (ODE) models. Specifically, based on the historical PM2.5 concentration, this method combines genetic programming and orthogonal least square method to evolve the ODE models, which describe the transport of PM2.5 and then uses the data-driven ODEs to predict the air quality in the future. Experiment results show that the ODE models obtain similar prediction results as the typical statistical model, and the prediction results from this method are relatively good. To our knowledge, this is the first attempt to evolve data-driven ODE models to study PM2.5 prediction", } @Article{WANG:2017:AOR, author = "Zhe Wang and Zhihua Chen and Hongbo Liu and Zechao Zhang", title = "Numerical study on lateral buckling of pipelines with imperfection and sleeper", journal = "Applied Ocean Research", volume = "68", pages = "103--113", year = "2017", keywords = "genetic algorithms, genetic programming, Subsea pipelines, Lateral buckling, Sleeper, Finite element", ISSN = "0141-1187", DOI = "doi:10.1016/j.apor.2017.08.010", URL = "http://www.sciencedirect.com/science/article/pii/S0141118717304741", abstract = "Lateral buckling is an important issue in unburied high-temperature and high-pressure (HT/HP) subsea pipelines systems. The imperfection-sleeper method is one of the most well-known methods used to control lateral buckling of HT/HP pipelines. Pipelines-sleeper-seabed numerical models are established and verified to analyze the buckling behavior of pipelines using the imperfection-sleeper method. The influence of six main factors on lateral buckling behavior is investigated in details based on the numerical results. Equations of buckling displacement (buckling displacement is defined by the final displacement of the middle point of the pipelines), critical buckling force, and buckling stress (Mises stress) are proposed using the gene expression programming technique. These equations show good accuracy and can be used to assist in the design of sleepers and assess the compressive and stress levels of pipelines", } @Article{DBLP:journals/pieee/WangO18, author = "Zheng Wang2 and Michael F. P. O'Boyle", title = "Machine Learning in Compiler Optimization", journal = "Proceedings of the IEEE", year = "2018", volume = "106", number = "11", pages = "1879--1901", month = nov, keywords = "genetic algorithms, genetic programming, SBSE, Compiler, Machine Learning, Code Optimisation, Program Tuning, GPU, OpenCL", URL = "https://eprints.lancs.ac.uk/id/eprint/89859/1/main.pdf", URL = "https://doi.org/10.1109/JPROC.2018.2817118", DOI = "doi:10.1109/JPROC.2018.2817118", timestamp = "Fri, 02 Oct 2020 14:42:27 +0200", biburl = "https://dblp.org/rec/journals/pieee/WangO18.bib", bibsource = "dblp computer science bibliography, https://dblp.org", size = "21 pages", abstract = "In the last decade, machine-learning-based compilation has moved from an obscure research niche to a mainstream activity. In this paper, we describe the relationship between machine learning and compiler optimization and introduce the main concepts of features, models, training, and deployment. We then provide a comprehensive survey and provide a road map for the wide variety of different research areas. We conclude with a discussion on open issues in the area and potential research directions. This paper provides both an accessible introduction to the fast moving area of machine-learning-based compilation and a detailed bibliography of its main achievements.", notes = "3) Energy Consumption, especially GPU, OpenMP and NUMA. predicting the best compiler flags, eg GPUs CUDA, OpenMP, OpenCL. loop unroll factor. Petabricks. fast Fourier transformation (FFT) FFTW3. avoid overfitting.Feature Engineering. complied using different compiler passes with different orders. Hardware performance counter values, such as executed instruction counts and cache miss rate. task co-location. The core issue is that systems are so complex that it is impossible to know for sure when to use such an optimization. opens up the possibility of much greater creativity and new research areas.", } @InProceedings{Wang:2010:CINC, author = "Zhen-chao Wang and Pei Du", title = "A practical technology of combining genetic programming with artificial intelligence", booktitle = "Second International Conference on Computational Intelligence and Natural Computing Proceedings (CINC), 2010", year = "2010", month = "13-14 " # sep, volume = "2", pages = "300--303", abstract = "In applying genetic programming (GP) to a problem, it is difficult to select the set of terminals, the fitness measure, and the set of primitive functions. Correspondingly, GP often cannot converge to a physical significant solution efficiently. A practical technology of combining artificial intelligence with GP (AIGP) is proposed to increase the speed of converging. In AIGP some rules are used to optimise the individuals created by genetic operations of reproduction, crossover, and mutation based on the principal of artificial intelligence. Two experiments are provided to testify the effectiveness of AIGP.", keywords = "genetic algorithms, genetic programming, AIGP, artificial intelligence, artificial intelligence, convergence", DOI = "doi:10.1109/CINC.2010.5643728", notes = "Coll. of Electron. & Inf. Eng., Hebei Univ., Baoding, China Also known as \cite{5643728}", } @InProceedings{Wang:2019:CEC3, author = "Tao Wang3 and Xingguang Peng and Yapei Wu and Jian Gao", title = "A {GP} Based Two-Layer Framework for Data-Driven Modeling of Swarm Self-Organizing Rules", booktitle = "2019 IEEE Congress on Evolutionary Computation, CEC 2019", year = "2019", editor = "Carlos A. Coello Coello", pages = "174--181", address = "Wellington, New Zealand", month = "10-13 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, swarm intelligence, selforganizing rules", isbn13 = "978-1-7281-2152-6", DOI = "doi:10.1109/CEC.2019.8790126", size = "8 pages", abstract = "There are many swarms of creatures in nature, which lead to a lot of highly ordered and beautiful emergence behaviours. For the modelling of self-organising rules, most of the existing literature focuses on modeling according to special knowledge of physics or biology. Some self-organizing models are proposed in the literature which has been validated by the reproduction of certain emergence motion pattern, such as torus, or flocking. However, there are few studies about data driven modelling of self-organizing rules of swarms. In this paper, we propose a prior knowledge free (i.e., data-driven) approach to learn the self-organizing rules of moving swarms. We use a Genetic Programming (GP) based two-layer framework to optimise the self-organizing model which is consist of neighbour selection rules and corresponding reaction rules. The proposed data-driven modeling method is validated by modeling of three typical collective behaviours (highly parallel group, dynamic parallel group and torus swarm behavior) only according to the simulation data generated from Vicsek and Couzin models. An analysis is conducted with expression tree simplification, swarm behaviour reproduction and global metric evaluation. Results show that the proposed method can learn classic self-organising rules effectively.", notes = "Also known as \cite{8790126} IEEE Catalog Number: CFP19ICE-ART", } @InProceedings{Wang:2019:ICC, author = "Zhihong Wang and Yi Guo and Zhen Li and Minwei Tang and Tianmei Qi and Jixiang Wang", title = "Research on Microblog Rumor Events Detection via Dynamic Time Series Based {GRU} Model", booktitle = "ICC 2019 - 2019 IEEE International Conference on Communications (ICC)", year = "2019", month = may, DOI = "doi:10.1109/ICC.2019.8761457", ISSN = "1938-1883", abstract = "The convenience of online social media in communication and information dissemination has made it an ideal place for spreading rumor events and automatically debunking rumor events is a crucial problem. However, it is a challenging task to employ traditional classification approaches to rumor events detection since they rely on hand-crafted features which require daunting manual efforts. Besides, the various posts on a rumor event will debate its realness over time, and the distribution of the posts is special in time dimension. Thus, this paper presents a novel method for rumor event detection based on a dynamic time series (DTS) algorithm and a two layer Gated Recurrent Unit (GRU) model, named 2-GRU-DTS. The proposed model uses the DTS algorithm to retain the distribution information of social events over time and uses the two layers GRU model to learn the hidden event representations. Experimental results on real datasets from Sina Weibo demonstrate that our proposed 2-GRU-DTS model outperforms latest rumor event detection algorithms.", notes = "is this GP? East China University of Science and Technology, Shanghai, China Also known as \cite{8761457}", } @InProceedings{DBLP:conf/misnc/WangJLYC17, author = "Chen-Shu Wang and Chun-Jung Juan and Tung-Yao Lin and Chun-Chang Yeh and Shang-Yu Chiang", title = "Prediction Model of Cervical Spine Disease Established by Genetic Programming", booktitle = "Proceedings of the 4th Multidisciplinary International Social Networks Conference, {MISNC} '17, Bangkok, Thailand, July 17-19, 2017", pages = "38:1--38:6", publisher = "{ACM}", year = "2017", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1145/3092090.3092097", DOI = "doi:10.1145/3092090.3092097", timestamp = "Mon, 21 Mar 2022 12:00:07 +0100", biburl = "https://dblp.org/rec/conf/misnc/WangJLYC17.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{Wang:TEVC, author = "Qinyu Wang and Ying Bi and Bing Xue and Mengjie Zhang", journal = "IEEE Transactions on Evolutionary Computation", title = "Genetic Programming With Flexible Region Detection for Fine-Grained Image Classification", note = "Early access", abstract = "Fine-grained image classification (FGIC) is an important computer vision task with many real-world applications. However, FGIC is challenging due to intra-class variations and inter-class similarities, especially when there is limited training data. To address these challenges, a new genetic programming approach with flexible region detection, GP-RD, is proposed for different FGIC tasks, i.e., flower and fish classification tasks. The proposed GP-RD approach can automatically highlight the object, detect regions of interest, extract effective features, and combine global, local, and/or colour features for classification. The performance of GP-RD is evaluated on flower and fish classification tasks within the FGIC domain, using datasets with varying classes. In comparison with seven benchmark methods, GP-RD achieves significantly better performance in most comparisons. Further analysis demonstrates the interpretability, effectiveness, and efficiency of the proposed approach.", keywords = "genetic algorithms, genetic programming, Feature extraction, Task analysis, Image classification, Training, Flowering plants, Fish, Training data, Region Detection, Fine-Grained Image Classification, Feature Extraction", DOI = "doi:10.1109/TEVC.2024.3379257", ISSN = "1941-0026", notes = "Also known as \cite{10475668}", } @InProceedings{Wang:2024:evoapplications, author = "Qinyu Wang and Ying Bi and Bing Xue and Mengjie Zhang", title = "Genetic Programming with Aggregate Channel Features for Flower Localization Using Limited Training Data", booktitle = "27th International Conference, EvoApplications 2024", year = "2024", editor = "Stephen Smith and Joao Correia and Christian Cintrano", series = "LNCS", volume = "14635", publisher = "Springer", address = "Aberystwyth", month = "3-5 " # apr, pages = "196--211", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Aggregate channel features, Flower localisation", isbn13 = "978-3-031-56854-1", URL = "https://rdcu.be/dD0js", DOI = "doi:10.1007/978-3-031-56855-8_12", size = "16 pages", abstract = "Flower localisation is a crucial image pre-processing step for subsequent classification/recognition that confronts challenges with diverse flower species, varying imaging conditions, and limited data. Existing flower localisation methods face limitations, including reliance on colour information, low model interpretability, and a large demand for training data. This paper proposes a new genetic programming (GP) approach called ACFGP with a novel representation to automated flower localisation with limited training data. The novel GP representation enables ACFGP to evolve effective programs for generating aggregate channel features and achieving flower localization in diverse scenarios. Comparative evaluations against the baseline benchmark algorithm and YOLOv8 demonstrate ACFGP superior performance. Further analysis highlights the effectiveness of the aggregate channel features generated by ACFGP programs, demonstrating the superiority of ACFGP in addressing challenging flower localisation tasks.", notes = "http://www.evostar.org/2024/ EvoApplications2024 held in conjunction with EuroGP'2024, EvoCOP2024 and EvoMusArt2024", } @InProceedings{Wang:2022:ICCT, author = "Lei Wang and Guanzhang Liu and Jiang Xue", booktitle = "2022 IEEE 22nd International Conference on Communication Technology (ICCT)", title = "Channel Prediction Using a System of Ordinary Differential Equation", year = "2022", pages = "1009--1014", abstract = "For massive Multiple-input Multiple-output (MIMO) systems, it is crucial to predict channel state information (CSI) at future moments. Outdated CSI in mobile scenarios will have a serious negative impact on the transmission system, resulting in system performance degradation. Timely and accurate channel prediction can compensate for the loss of system performance caused by mobility. We propose a hybrid evolutionary method to identify ordinary differential equation (ODE) systems to predict CSI, called HEODE. First of all, the evolution algorithm based on tree structure is used to evolve the system architecture, and the explicit ODE model is obtained. Then, the parameters of ODEs are optimised by optimisation algorithm. Finally, the optimal ODE model for CSI prediction is obtained. Besides, the effective of interval prediction is given. Compared to the autoregressive (AR) and genetic programming (GP) based prediction methods, simulation results show that the proposed method is robust and effective.", keywords = "genetic algorithms, genetic programming, System performance, Simulation, Systems architecture, Massive MIMO, Predictive models, Ordinary differential equations, Prediction algorithms, Massive MIMO, Channel prediction, Hybrid evolutionary method, Optimisation, Ordinary differential equation", DOI = "doi:10.1109/ICCT56141.2022.10073053", ISSN = "2576-7828", month = nov, notes = "Also known as \cite{10073053}", } @Article{Peng_Wang:ECJ, author = "Peng Wang2 and Bing Xue and Jing Liang and Mengjie Zhang", title = "Genetic Programming for Automatically Evolving Multiple Features to Classification", journal = "Evolutionary Computation", note = "Just accepted", keywords = "genetic algorithms, genetic programming, Classification, Dimensionality reduction", ISSN = "1063-6560", URL = "https://doi.org/10.1162/evco_a_00359", eprint = "https://direct.mit.edu/evco/article-pdf/doi/10.1162/evco_a_00359/2470769/evco_a_00359.pdf", DOI = "doi:10.1162/evco_a_00359", size = "27 pages", abstract = "Performing classification on high-dimensional data poses a significant challenge due to the huge search space. Moreover, complex feature interactions introduce an additional obstacle. The problems can be addressed by using feature selection to select relevant features or feature construction to construct a small set of high-level features. However, performing feature selection or feature construction only might make the feature set suboptimal. To remedy this problem, this study investigates the use of genetic programming for simultaneous feature selection and feature construction in addressing different classification tasks. The proposed approach is tested on 16 datasets and compared with seven methods including both feature selection and feature constructions techniques. The results show that the obtained feature sets with the constructed and/or selected features can significantly increase the classification accuracy and reduce the dimensionality of the datasets. Further analysis reveals the complementarity of the obtained features leading to the promising classification performance of the proposed method.", notes = "School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China", } @Article{Wang:2024:TETCI, author = "Pengda Wang and Mingjie Lu and Weiqing Yan and Dong Yang and Zhaowei Liu", title = "Graph Structure Learning With Automatic Search of Hyperparameters Based on Genetic Programming", journal = "IEEE Transactions on Emerging Topics in Computational Intelligence", year = "2024", volume = "8", number = "6", pages = "4155--4164", month = dec, keywords = "genetic algorithms, genetic programming, Graph neural networks, Statistics, Sociology, Noise measurement, Optimisation, Hyperparameter optimisation, Graphical models, hyperparameters optimisation, graph structure learning, genetic model", ISSN = "2471-285X", DOI = "doi:10.1109/TETCI.2024.3386833", abstract = "Graph neural networks (GNNs) rely heavily on graph structures and artificial hyperparameters, which may increase computation and affect performance. Most GNNs use original graphs, but the original graph data has problems with noise and incomplete information, which easily leads to poor GNN performance. For this kind of problem, recent graph structure learning methods consider how to generate graph structures containing label information. The settings of some hyperparameters will also affect the expression of the GNN model. This paper proposes a genetic graph structure learning method (Genetic-GSL). Different from the existing graph structure learning methods, this paper not only optimises the graph structure but also the hyperparameters. Specifically, different graph structures and different hyperparameters are used as parents; the offspring are cross-mutated through the parents; and then excellent offspring are selected through evaluation to achieve dynamic fitting of the graph structure and hyperparameters. Experiments show that, compared with other methods, Genetic-GSL basically improves the performance of node classification tasks by 1.2percent. With the increase in evolution algebra, Genetic-GSL has good performance on node classification tasks and resistance to adversarial attacks.", notes = "Also known as \cite{10504544} University of Science and Technology of China, Hefei, Anhui, China", } @Article{Wang:2025:TEVC, author = "Yuquan Wang and Naiming Xie and Nanlei Chen and Hui Ma and Gang Chen2", title = "Genetic Programming Hyper Heuristic With Elitist Mutation for Integrated Order Batching and Picker Routing Problem", journal = "IEEE Transactions on Evolutionary Computation", year = "2025", volume = "29", number = "2", pages = "346--359", month = apr, keywords = "genetic algorithms, genetic programming, Routing, Metaheuristics, Evolutionary computation, Search problems, Layout, Processor scheduling, Job shop scheduling, Heuristic algorithms, Elitist mutation (EM), genetic programming hyper heuristic (GPHH), integrated order batching and picker routing (IOBPR) problem", ISSN = "1941-0026", DOI = "doi:10.1109/TEVC.2025.3532022", abstract = "Integrated order batching and picker routing (IOBPR) is a complex combinatorial optimisation problem in real-world intelligent manufacturing systems. Heuristics are often used for solving such complex scheduling problems. Manually designing scheduling heuristics suffer from two limitations: 1) few problem features can be taken into account and 2) the design process is time consuming. Genetic programming hyper heuristic (GPHH) approaches have been proposed on many scheduling problems to automatically evolve effective heuristics. However, existing GPHH approaches are often problem specific and requires careful design of problem specific terminal sets and evolution operators. The aim of this work is to develop a GPHH approach to evolve heuristics for the IOBPR problem. In particular, we propose a novel terminal set (NT) with three types of terminals, and a GPHH with elitist mutation (GPHH-EM) algorithm. Extensive experiments demonstrate that the heuristics evolved by GPHH-EM can significantly outperform other state-of-the-art competing algorithms designed by human experts. Further analysis indicates that the three types of terminals effectively complement to improve evolved heuristics for decision making. Furthermore, the newly developed elitist mutation operator expedites the evolutionary process for GPHH to find high-quality heuristics.", notes = "Also known as \cite{10847907}", } @InProceedings{1144317, author = "Stefan Wappler and Joachim Wegener", title = "Evolutionary unit testing of object-oriented software using strongly-typed genetic programming", booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation", year = "2006", editor = "Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos {Coello Coello} and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens", volume = "2", ISBN = "1-59593-186-4", pages = "1925--1932", address = "Seattle, Washington, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p1925.pdf", DOI = "doi:10.1145/1143997.1144317", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "8-12 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Search-Based Software Engineering, automated test case generation, evolutionary testing, object-orientation, strongly-typed genetic programming, test coverage of code, testing tools, verification, Java, OO unit testing, STGP", size = "8 pages", abstract = "Evolutionary algorithms have successfully been applied to software testing. Not only approaches that search for numeric test data for procedural test objects have been investigated, but also techniques for automatically generating test programs that represent object-oriented unit test cases. Compared to numeric test data, test programs optimized for object-oriented unit testing are more complex. Method call sequences that realize interesting test scenarios must be evolved. An arbitrary method call sequence is not necessarily feasible due to call dependences which exist among the methods that potentially appear in a method call sequence. The approach presented in this paper relies on a tree-based representation of method call sequences by which sequence feasibility is preserved throughout the entire search process. In contrast to other approaches in this area, neither repair of individuals nor penalty mechanisms are required. Strongly typed genetic programming is employed to generate method call trees. In order to deal with runtime exceptions, we use an extended distance-based fitness function. We performed experiments with four test objects. The initial results are promising: high code coverages were achieved completely automatically for all of the test objects.", notes = "GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM Order Number 910060. Two stage EA: GP (ECJ) run first then every tree's {"}numerical parameters{"} optimised by a separate EA (GEATbx). Polymorphic set-based typing. Aim is to cover a particular program branch in class under test (CUT). Fitness is linear combination of three (software engineering) metrics. Static analysis done using Tatsubori's OpenJava. GP pop=10. GEATbx 4 pops, each of ten. Problems stack, BitSet, full test coverage of each obtained.", } @InProceedings{Wappler:2006:CEC, author = "Stefan Wappler and Joachim Wegener", title = "Evolutionary Unit Testing Of Object-Oriented Software Using A Hybrid Evolutionary Algorithm", booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary Computation", year = "2006", editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas", pages = "3193--3200", address = "Vancouver", month = "16-21 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, SBSE", ISBN = "0-7803-9487-9", URL = "http://www.systematic-testing.com/documents/WCCI2006_0462.pdf", DOI = "doi:10.1109/CEC.2006.1688400", size = "8 pages", abstract = "Evolutionary algorithms have been successfully applied in the area of software testing. However, previous approaches in the area of object-oriented testing are limited in terms of test case feasibility due to call dependences and runtime exceptions. In this paper, we present a search-based approach to automatically generating test cases for object oriented software. It relies on a tree-based representation of method call sequences. Strongly-typed genetic programming is employed to generate method call trees which respect the call dependences among the methods. We apply a new kind of distance-based fitness function that accounts for runtime exceptions. In a case study, the approach outperformed random testing in terms of achieved coverage and it produced test cases achieving full branch coverage for a test object that makes ample use of explicit runtime exceptions.", notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D", } @InProceedings{Wappler:2007:ASE, author = "Stefan Wappler and Ina Schieferdecker", title = "Improving evolutionary class testing in the presence of non-public methods", booktitle = "ASE '07: Proceedings of the twenty-second IEEE/ACM international conference on Automated Software Engineering", year = "2007", pages = "381--384", address = "Atlanta, Georgia, USA", publisher_address = "New York, NY, USA", publisher = "ACM", keywords = "genetic algorithms, genetic programming, SBSE, unit testing, object-orientation, encapsulation, evolutionary testing, POSTER SESSION", isbn13 = "978-1-59593-882-4", URL = "http://www.systematic-testing.com/documents/ASE07_wappler_Paper.pdf", DOI = "doi:10.1145/1321631.1321689", size = "4 pages", abstract = "Automating the generation of object-oriented unit tests is a challenging task. This is mainly due to the complexity and peculiarities that the principles of object-orientation imply. One of these principles is the encapsulation of class members which prevents non-public methods and attributes of the class under test from being freely accessed. This paper suggests an improvement of our automated search-based test generation approach which particularly addresses the test of non-public methods. We extend our objective functions by an additional component that accounts for encapsulation. Additionally, we propose a modification of the search space which increases the efficiency of the approach. The value of the improvement in terms of achieved code coverage is demonstrated by a case study with 7 real-world test objects. In contrast to other approaches which break encapsulation in order to test non-public methods, the tests generated by our approach inherently guarantee that class invariants are not violated. At the same time, refactorings of the encapsulated class members will not break the generated tests", notes = "Also known as \cite{1321689}", } @PhdThesis{Wappler:thesis, author = "Stefan Wappler", title = "Automatic Generation Of Object-Oriented Unit Tests Using Genetic Programming", title_de = "Automatische Generierung objektorientierter Unit-Tests mittels Genetischer Programmierung", school = "Institut fur Softwaretechnik und Theoretische Informatik, Elektrotechnik und Informatik, Technische Universitat Berlin", year = "2007", address = "Germany", month = "19 " # dec, keywords = "genetic algorithms, genetic programming, SBSE, Test case generation, automation, object orientation,", URL = "http://opus.kobv.de/tuberlin/volltexte/2008/1733/", URL = "http://opus.kobv.de/tuberlin/volltexte/2008/1733/pdf/wappler_stefan.pdf", URN = "urn:nbn:de:kobv:83-opus-17330", size = "172 pages", abstract = "Automating the generation of object-oriented unit tests for structural testing techniques has been challenging many researchers due to the benefits it promises in terms of cost saving and test quality improvement. It requires test sequences to be generated, each of which models a particular scenario in which the class under test is examined. The generation process aims at obtaining a preferably compact set of test sequences which attains a high degree of structural coverage. The degree of achieved structural coverage indicates the adequacy of the tests and hence the test quality in general. Existing approaches to automatic test generation for object-oriented software mainly rely either on symbolic execution and constraint solving, or on a particular search technique. However, these approaches suffer from various limitations which negatively affect both their applicability in terms of classes for which they are feasible, and their effectiveness in terms of achievable structural coverage. The approaches based on symbolic execution and constraint solving inherit the limitations of these techniques, which are, for instance, issues with scalability and problems with loops, arrays, and complex predicates. The search-based approaches encounter problems in the presence of complex predicates and complex method call dependences. In addition, existing work addresses neither testing non-public methods without breaking data encapsulation, nor the occurrence of runtime exceptions during test generation. Yet, data encapsulation, non-public methods, and exception handling are fundamental concepts of object-oriented software and require also particular consideration for testing. This thesis proposes a new approach to automating the generation of object-oriented unit tests. It employs genetic programming, a recent meta-heuristic optimisation technique, which allows formulating the task of test sequence generation as a search problem more suitably than the search techniques applied by the existing approaches. The approach enables testing non-public methods and accounts for run time exceptions by appropriately designing the objective functions that are used to guide the genetic programming search. The value of the approach is shown by a case study with real-world classes that involve non-public methods and runtime exceptions. The structural coverage achieved by the approach is contrasted with that achieved by a random approach and two commercial test sequence generators. In most of the cases, the approach of this thesis outperformed the other methods.", abstract = "Die Automatisierung der Testfallermittlung fur den struktur-orientierten Unit-Test objektorientierter Software verspricht enorme Kostenreduktion und Qualitatssteigerung fur ein Softwareentwicklungsprojekt. Die Herausforderung besteht darin, automatisch Testsequenzen zu generieren, die eine hohe Uberdeckung des Quellcodes der zu testenden Klasse erreichen. Diese Testsequenzen modellieren bestimmte Szenarien, in denen die zu testende Klasse gepruft wird. Der Grad an erzielter Code-Uberdeckung ist ein Mass fur die Testabdeckung und damit der Testqualitat generell. Die existierenden Automatisierungsansatze beruhen hauptsachlich auf entweder symbolischer Ausfuhrung und Constraint-Losung oder auf einem Suchverfahren. Sie haben jedoch verschiedene Begrenzungen, die sowohl ihre Anwendbarkeit fur unterschiedliche zu testende Klassen als auch ihre Effektivitat im Hinblick auf die erreichbare Code-Uberdeckung einschranken. Die Ansatze basierend auf symbolischer Ausfuhrung und Constraint-Losung weisen die Beschrankungen dieser Techniken auf. Dies sind beispielsweise Einschrankungen hinsichtlich der Skalierbarkeit und bei der Verwendung bestimmter Programmierkonstrukte wie Schleifen, Felder und komplexer Pradikate. Die suchbasierten Ansatze haben Schwierigkeiten bei komplexen Pradikaten und komplexen Methodenaufrufabhangigkeiten. Die Ansatze adressieren weder den Test nicht-offentlicher Methoden, ohne die Objektkapselung zu verletzen, noch die Behandlung von Laufzeitausnahmen wahrend der Testgenerierung. Objektkapselung, nicht-offentliche Methoden und Laufzeitausnahmen sind jedoch grundlegende Konzepte objektorientierter Software, die besonderes Augenmerk wahrend des Tests erfordern. Die vorliegende Dissertation schlagt einen neuen Ansatz zur automatischen Generierung objektorientierter Unit-Tests vor. Dieser Ansatz verwendet Genetische Programmierung, ein neuartiges meta-heuristisches Optimierungsverfahren. Dadurch kann die Testsequenz-Generierung geeigneter als Suchproblem formuliert werden als es die existierenden Ansatze gestatten. Effektivere Suchen nach Testsequenzen zur Erreichung von hoher Code-Uberdeckung werden so ermoglicht. Der Ansatz umfasst ausserdem den Test nicht-offentlicher Methoden ohne Kapselungsbruch und berucksichtigt Laufzeitausnahmen, indem er die fur die Suche verwendeten Zielfunktionen adequat definiert. Eine umfangreiche Fallstudie demonstriert die Effektivitat des Ansatzes. Die dabei verwendeten Klassen besitzen nicht-offentliche Methoden und fuhren in zahlreichen Fallen zu Laufzeitausnahmen wahrend der Testgenerierung. Die erreichten Code-Uberdeckungen werden den Ergebnissen eines Zufallsgenerators sowie zweier kommerzieller Testsequenz-Generatoren gegenubergestellt. In der Mehrheit der Falle ubertraf der hier vorgeschlagene Ansatz die alternativen Generatoren.", } @InCollection{ward:1997:morse, author = "David Ward", title = "A Program to Decode Morse Code Developed with a Genetic Programming Technique", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1997", publisher = "Stanford Bookstore", year = "1997", editor = "John R. Koza", pages = "216--225", address = "Stanford, California, 94305-3079 USA", month = "17 " # mar, keywords = "genetic algorithms, genetic programming", ISBN = "0-18-205981-2", abstract = "accuracy typically greater than 95 percent", notes = "part of \cite{koza:1997:GAGPs}", } @InCollection{warren:1994:stockpp, author = "Mark A. Warren", title = "Stock Price Prediction Using Genetic Programming", booktitle = "Genetic Algorithms at Stanford 1994", year = "1994", editor = "John R. Koza", pages = "180--184", address = "Stanford, California, 94305-3079 USA", month = dec, organisation = "Stanford University", publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-187263-3", notes = "{"}While these experiments failed to find THE prediction model, they did demonstrate that ... recent price is a very good indicator of a stocks' performance.{"} p182 This volume contains 20 papers written and submitted by students describing their term projects for the course {"}Genetic Algorithms and Genetic Programming{"} (Computer Science 426) at Stanford University offered during the fall quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs426.html", } @InCollection{warren:1999:ACSDMHDC, author = "James Warren", title = "A Co-Evolutionary Scheme for Discovering Maximal Hamming Distance Codes", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1999", year = "1999", editor = "John R. Koza", pages = "236--244", address = "Stanford, California, 94305-3079 USA", month = "15 " # mar, publisher = "Stanford Bookstore", keywords = "genetic algorithms", notes = "part of \cite{koza:1999:GAGPs}", } @InProceedings{wasiewicz:1997:CITA, author = "Piotr Wasiewicz and Jan Mulawka", title = "Genetic programming in optimization of Algorithms", booktitle = "Computational Intelligence Theory and Applications", year = "1997", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/3-540-62868-1_171", DOI = "doi:10.1007/3-540-62868-1_171", } @Article{WM01, author = "P. Wasiewicz and J. J. Mulawka", title = "Molecular Genetic Programming", journal = "Soft Computing - A Fusion of Foundations, Methodologies and Applications", publisher = "Springer Verlag", number = "5", volume = "2", year = "2001", pages = "106--113", month = apr, keywords = "genetic algorithms, genetic programming, DNA computing, Evolutionary programming, Data flow computer", ISSN = "1432-7643", DOI = "doi:10.1007/s005000000077", size = "8 pages", abstract = "The paper addresses a new implementation of genetic (or evolutionary) programming by using molecular approach. Our method is based on dataflow techniques in {DNA} computing. After description of fundamental operations on {DNA} molecules and construction of logical functions the genetic programming method is introduced. We propose a way to handle these graph encoding molecules and which can be considered a genetic programming algorithm; a short discussion about experiments in implementing parts of this procedure is added.", } @InProceedings{watabe:1999:SCOPMGA, author = "Hirokazu Watabe and Tsukasa Kawaoka", title = "Solving Combinatorial Optimization Problems with Multi-Step Genetic Algorithms", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "813", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems, poster papers", ISBN = "1-55860-611-4", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Watanabe:2009:IJCNN, author = "Naoki Watanabe and Kazuhiko Fukami and Hitoki Imamura and Katsuki Sonoda and Soichiro Yamane", title = "Flood forecasting technology with radar-derived rainfall data using Genetic Programming", booktitle = "International Joint Conference on Neural Networks, IJCNN 2009", year = "2009", pages = "3311--3318", address = "Atlanta, Georgia, USA", month = jun # " 14-19", keywords = "genetic algorithms, genetic programming, floods, hydrological techniques, radarGMDH, flood disasters, flood forecasting technology, radar-derived rainfall, radar-derived rainfall data, water level forecasting model, water-level prediction", DOI = "doi:10.1109/IJCNN.2009.5178691", abstract = "Implementation of flood forecasting system is crucial for reducing flood disasters urgently and effectively. The authors propose a new method of flood forecasting using genetic programming (GP) and GMDH. Traditional method based on physical model takes time to analyze the hydrologic and hydraulic characteristics of a river, but the new method has potential to make a water level forecasting model from ground-based or radar-derived rainfall automatically by learning the past data of river water level or dam inflow and rainfall, which will be useful in particular for medium-to-small scale rivers. Case studies were conducted for the water-level prediction at the Saba and the Onga Rivers in Japan. The results from both the case studies were encouraging to promote the new method, because the water-level predictions with 6-hour lead time were relatively good. Furthermore, comparative analysis about the incorporation of spatial distribution of rainfall in the upstream brought out the necessity of the combined incorporation of both direct and averaging area for better accuracy.", notes = "also known as \cite{5178691}", } @InProceedings{Watchareeruetai:2008:cec, author = "Ukrit Watchareeruetai and Yoshinori Takeuchi and Tetsuya Matsumoto and Noboru Ohnishi", title = "Transformation of Redundant Representations of Linear Genetic Programming into Canonical Forms for Efficient Extraction of Image Features", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "1996--2003", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1823-7", file = "EC0478.pdf", DOI = "doi:10.1109/CEC.2008.4631062", abstract = "Recently, evolutionary computation (EC) has been adopted to search for effective feature extraction programs for given image recognition problems. For this approach, feature extraction programs are constructed from a set of primitive operations (POs), which are usually general image processing and pattern recognition operations. In this paper, we focus on an approach based on a variation of linear genetic programming (LGP). We describe the causes of redundancies in LGP based representation, and propose a transformation that converts the redundant LGP representation into a canonical form, in which all redundancies are removed. Based on this transformation, we present a way to reduce computation time, i.e., the evolutionary search that avoids executions of redundant individuals. Experimental results demonstrate a success in computation time reduction; around 7-62percent of total compuation time can be reduced. Also, we have experimented with an evolutionary search that prohibits existence of redundant individuals. When selection pressure is high enough, its search performance is better than that of conventional evolutionary search.", keywords = "genetic algorithms, genetic programming", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @InProceedings{Watchareeruetai:2008:ieeeSMCIA, author = "Ukrit Watchareeruetai and Yoshinori Takeuchi and Tetsuya Matsumoto and Hiroaki Kudo and Noboru Ohnishi", title = "Improving search performance of linear genetic programming based image recognition program synthesis by redundancy-removed recombination", booktitle = "IEEE Conference on Soft Computing in Industrial Applications, 2008. SMCia '08", year = "2008", pages = "393--398", address = "Muroran, Japan", month = "25-27 " # jun, keywords = "genetic algorithms, genetic programming, image recognition, linear programming, search problems evolutionary search, image recognition program synthesis, linear genetic programming, nonredundant offspring, redundancy-removed recombination", DOI = "doi:10.1109/SMCIA.2008.5045996", abstract = "This paper propose a new recombination method, named redundancy-removed recombination, for linear genetic programming based image recognition program synthesis. The redundancy-removed recombination produces an offspring (by conventional crossover or mutation), and then adopts a canonical transformation to convert the offspring into its canonical form, in which it can be verified whether it has been evolved before (redundant). If the offspring is redundant, it is prohibited and recombination is repeated until non-redundant offspring, which has never be born in the evolutionary search, is produced. Experimental results show that the use of the redundancy-removed recombination improved the performance of evolutionary search; it converged to the global optimum faster than the use of conventional recombinations. Also we found that the redundancy-removed recombination can construct longer programs and concentrate on those areas, whereas the conventional ones cannot.", notes = "Also known as \cite{5045996}", } @InCollection{series/sci/WatchareeruetaiMTKO09, author = "Ukrit Watchareeruetai and Tetsuya Matsumoto and Yoshinori Takeuchi and Hiroaki Kudo and Noboru Ohnishi", title = "Efficient Construction of Image Feature Extraction Programs by Using Linear Genetic Programming with Fitness Retrieval and Intermediate-Result Caching", booktitle = "Foundations of Computational Intelligence - Volume 4: Bio-Inspired Data Mining", publisher = "Springer", year = "2009", volume = "204", editor = "Ajith Abraham and Aboul Ella Hassanien and Andr{\'e} Carlos Ponce Leon Ferreira {de Carvalho}", series = "Studies in Computational Intelligence", pages = "355--375", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-01087-3", URL = "http://dx.doi.org/10.1007/978-3-642-01088-0", DOI = "doi:10.1007/978-3-642-01088-0_15", abstract = "This chapter describes a bio-inspired approach for automatic construction of feature extraction programs (FEPs) for a given object recognition problem. The goal of the automatic construction of FEPs is to cope with the difficulties in FEP design. Linear genetic programming (LGP) [4], a variation of evolutionary algorithms, is adopted. A population of FEPs is constructed from a set of basic image processing operations-which are used as primitive operators (POs), and their performances are optimised in the evolutionary process. Here we describe two techniques that improve the efficiency of the LGP-based program construction. One is to use fitness retrieval to avoid wasteful evaluations of the programs discovered before. The other one is to use intermediate-result caching, to avoid evaluation of the program-parts which were recently executed. The experimental results show that much computation time of the LGP-based FEP construction can be reduced by using these two techniques.", bibdate = "2010-04-20", bibsource = "DBLP, http://dblp.uni-trier.de/db/series/sci/sci204.html#WatchareeruetaiMTKO09", } @InProceedings{Watchareeruetai:2009:SMC, author = "Ukrit Watchareeruetai and Tetsuya Matsumoto and Yoshinori Takeuchi and Hiroaki Kudo and Noboru Ohnishi", title = "Construction of image feature extractors based on multi-objective genetic programming with redundancy regulations", booktitle = "IEEE International Conference on Systems, Man and Cybernetics, 2009. SMC 2009", year = "2009", pages = "1328--1333", address = "Texas, USA", month = oct # " 11-14", keywords = "genetic algorithms, genetic programming, feature extraction, linear programming, sorting, MOGP-based FEPs construction system, NSGA-II, feature extraction programs, image feature extractors, linear genetic programming, multiobjective genetic programming, nondominated sorting evolutionary algorithm, population diversity, program representation Multi-objective optimization, image feature extraction, non-dominated sorting, redundancy regulation", isbn13 = "978-1-4244-2793-2", DOI = "doi:10.1109/ICSMC.2009.5346242", abstract = "This paper proposes a multi-objective genetic programming (MOGP) for automatic construction of feature extraction programs (FEPs). The proposed method is modified from a well known non-dominated sorting evolutionary algorithm, i.e., NSGA-II. The key differences of the method are related with redundancies in program representation. We apply redundancy regulations in three main processes of the MOGP, i.e., population truncation, sampling, and offspring generation, to improve population diversity. Experimental results exhibit that the proposed MOGP-based FEPs construction system provides obviously better performance than the original non-dominated sorting approach.", notes = "Dept. of Media Sci., Nagoya Univ., Nagoya, Japan; Also known as \cite{5346242}", } @Article{Watchareeruetai:2009:IS, author = "Ukrit Watchareeruetai and Tetsuya Matsumoto and Noboru Ohnishi and Hiroaki Kudo and Yoshinori Takeuchi", title = "Acceleration of Genetic Programming by Hierarchical Structure Learning: A Case Study on Image Recognition Program Synthesis", journal = "IEICE Transactions on Information and Systems", year = "2009", volume = "E92-D", number = "10", pages = "2094--2102", month = oct, email = "ukrit@ieee.org", publisher = "IEICE", keywords = "genetic algorithms, genetic programming, hierarchical structure acceleration, learning node, training subsets, population integration", ISSN = "0916-8532", URL = "http://search.ieice.org/bin/summary.php?id=e92-d_10_2094&category=D&year=2009&lang=E&abst=", abstract = "We propose a learning strategy for acceleration in learning speed of genetic programming (GP), named hierarchical structure GP (HSGP). The HSGP exploits multiple learning nodes (LNs) which are connected in a hierarchical structure, e.g., a binary tree. Each LN runs conventional evolutionary process to evolve its own population, and sends the evolved population into the connected higher-level LN. The lower-level LN evolves the population with a smaller subset of training data. The higher-level LN then integrates the evolved population from the connected lower-level LNs together, and evolves the integrated population further by using a larger subset of training data. In HSGP, evolutionary processes are sequentially executed from the bottom-level LNs to the top-level LN which evolves with the entire training data. In the experiments, we adopt conventional GPs and the HSGPs to evolve image recognition programs for given training images. The results show that the use of hierarchical structure learning can significantly improve learning speed of GPs. To achieve the same performance, the HSGPs need only 30-40percent of the computation cost needed by conventional GPs.", } @Article{Watchareeruetai:2010:ieiceTIS, author = "Ukrit Watchareeruetai and Tetsuya Matsumoto and Yoshinori Takeuchi and Hiroaki Kudo and Noboru Ohnishi", title = "Multi-Objective Genetic Programming with Redundancy-Regulations for Automatic Construction of Image Feature Extractors", journal = "IEICE Transactions on Information and Systems", year = "2010", number = "9", volume = "93-D", pages = "2614--2625", keywords = "genetic algorithms, genetic programming, multi-objective optimization, redundancy regulation, image feature extraction, non-dominated sorting", ISSN = "1745-1361", URL = "http://search.ieice.org/bin/summary.php?id=e93-d_9_2614", bibdate = "2010-12-04", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ieicet/ieicet93d.html#WatchareeruetaiMTKO10", abstract = "We propose a new multi-objective genetic programming (MOGP) for automatic construction of image feature extraction programs (FEPs). The proposed method was originated from a well known multi-objective evolutionary algorithm (MOEA), i.e., NSGA-II. The key differences are that redundancy-regulation mechanisms are applied in three main processes of the MOGP, i.e., population truncation, sampling, and offspring generation, to improve population diversity as well as convergence rate. Experimental results indicate that the proposed MOGP-based FEP construction system outperforms the two conventional MOEAs (i.e., NSGA-II and SPEA2) for a test problem. Moreover, we compared the programs constructed by the proposed MOGP with four human-designed object recognition programs. The results show that the constructed programs are better than two human-designed methods and are comparable with the other two human-designed methods for the test problem.", } @Article{journals/jip/WatchareeruetaiTMKO10, author = "Ukrit Watchareeruetai and Yoshinori Takeuchi and Tetsuya Matsumoto and Hiroaki Kudo and Noboru Ohnishi", title = "Evaluations of Feature Extraction Programs Synthesized by Redundancy-removed Linear Genetic Programming: A Case Study on the Lawn Weed Detection Problem", journal = "Journal of Information Processing", year = "2010", volume = "18", pages = "164--174", month = apr, keywords = "genetic algorithms, genetic programming", ISSN = "1882-6652", URL = "https://www.jstage.jst.go.jp/article/ipsjjip/18/0/18_0_164/_pdf/-char/en", DOI = "doi:10.2197/ipsjjip.18.164", size = "11 pages", abstract = "This paper presents an evolutionary synthesis of feature extraction programs for object recognition. The evolutionary synthesis method employed is based on linear genetic programming which is combined with redundancy-removed recombination. The evolutionary synthesis can automatically construct feature extraction programs for a given object recognition problem, without any domain-specific knowledge. Experiments were done on a lawn weed detection problem with both a low-level performance measure, i.e., segmentation accuracy, and an application-level performance measure, i.e., simulated weed control performance. Compared with four human-designed lawn weed detection methods, the results show that the performance of synthesised feature extraction programs is significantly better than three human-designed methods when evaluated with the low-level measure, and is better than two human-designed methods according to the application-level measure.", notes = "Department of Media Science, Graduate School of Information Science, Nagoya University", bibdate = "2011-09-14", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/jip/jip18.html#WatchareeruetaiTMKO10", } @Article{Watchareeruetai:2011:GPEM, author = "Ukrit Watchareeruetai and Yoshinori Takeuchi and Tetsuya Matsumoto and Hiroaki Kudo and Noboru Ohnishi", title = "Redundancies in linear GP, canonical transformation, and its exploitation: a demonstration on image feature synthesis", journal = "Genetic Programming and Evolvable Machines", year = "2011", volume = "12", number = "1", pages = "49--77", month = mar, keywords = "genetic algorithms, genetic programming, Linear genetic programming, Redundant representation, Canonical form, Canonical transformation, Feature extraction", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-010-9118-x", abstract = "This paper concerns redundancies in representation of linear genetic programming (GP). We identify the causes of redundancies in linear GP and propose a canonical transformation that converts original linear representations into a canonical form in which structural redundancies are removed. In canonical form, we can easily verify whether two representations represent an identical program. We then discuss exploitation of the proposed canonical transformation, and demonstrate a way to improve search performance of linear GP by avoiding redundant individuals. Experiments were conducted with an image feature synthesis problem. Firstly, we have verified that there are really a lot of redundancies in conventional linear GP. We then investigate the effect of avoiding redundant individuals. The results yield that linear GP with avoidance of redundant individuals obviously outperforms conventional linear GP.", affiliation = "Nagoya University Department of Media Science, Graduate School of Information Science Furo-cho, Chikusa-ku Nagoya 464-8603 Japan", notes = "replaces \cite{Watchareeruetai:2008:cec}", } @MastersThesis{WATCHAREERUETAI:thesis, author = "Ukrit WATCHAREERUETAI", title = "Lawn weed detection methods using image processing techniques for automatic weed control systems", school = "Nagoya University", year = "2006", address = "Japan", URL = "http://www.ohnishi.m.is.nagoya-u.ac.jp/research/2006/M_Ukrit.pdf", abstract = "This thesis aims to study about lawn weed detection methods using image processing techniques for automatic weed control systems. Three gray-scale based methods, i.e., Bayesian classifier based method (BC), support vector machine based method (SVM), and morphological operations based method (MO), are proposed for detecting weeds in all seasons. Also, a fast and simple colour information based weed detection method is proposed. It is designed for detecting weeds when the colour of weeds and lawns are clearly different, especially in winter. Moreover, this thesis proposes a winter image discrimination method for deciding from an input image whether the colour information based method should be employed. This enables to make a hybrid method, i.e., a combination between the gray-scale based detection method and the color based method, can be realized. Performances of the proposed detection methods are evaluated by using two types of simulated automatic weeding systems, i.e., chemical based and electrical spark discharge based systems, and are compared with the gray-scale uniformity analysis method which was proposed by Ahmad et al. The weed image database used in the experiments consists of four datasets taken from four different seasons in Japan. To compare all gray-scale based methods, the experiments are done in two ways, i.e., testing all four datasets as one big dataset and testing each dataset separately. In the case of testing one big dataset, the MO method gives the best performance for the chemical based system while the SVM method can be considered as the most appropriate methods for the electrical spark discharge based system. In the case of separately testing each dataset, the results are different from the previous case. The BC method seems to be more appropriate method than the others. It is better than the other methods for two datasets in the case of chemical based system and for three datasets in the case of electrical spark discharge based system. In the case of testing by using only winter image dataset, the proposed colour information based method gives better results than the other gray-scale based methods for both chemical based and electrical spark discharge based systems. Its computational complexity is also less than those of the other methods. To test the proposed winter image discrimination method, all images from four datasets are used. The result shows that the method can completely discriminate the images of winter dataset from the images of the other seasons.", notes = "350503079 Not on GP? https://sites.google.com/site/wukrit/thai", } @InProceedings{waters:1999:GPCEFALE, author = "Michael Waters and John Sheppard", title = "Genetic Programming and Co-Evolution with Exogenous Fitness in an Artificial Life Environment", booktitle = "Proceedings of the Congress on Evolutionary Computation", year = "1999", editor = "Peter J. Angeline and Zbyszek Michalewicz and Marc Schoenauer and Xin Yao and Ali Zalzala", volume = "3", pages = "1641--1648", address = "Mayflower Hotel, Washington D.C., USA", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "6-9 " # jul, organisation = "Congress on Evolutionary Computation, IEEE / Neural Networks Council, Evolutionary Programming Society, Galesia, IEE", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, coevolution, AI Wars, artificial life environment, artificial organisms, co-evolution, commercially available environment, decision processes, endogenous fitness, evolutionary computation, evolutionary performance, exogenous fitness, fitness factors, fitness function, fitness landscape, hostile environment, artificial life, competitive algorithms, decision theory", ISBN = "0-7803-5536-9 (softbound)", ISBN = "0-7803-5537-7 (Microfiche)", URL = "http://citeseer.ist.psu.edu/waters99genetic.html", DOI = "doi:10.1109/CEC.1999.785471", abstract = "The study of artificial life involves simulating biological or sociological processes with a computer. Combining artificial life with techniques from evolutionary computation frequently involves modelling the behaviour or decision processes of artificial organisms within a society in such a way that genetic algorithms can be applied to modify these models and enhance behavior over time. Typically, endogenous fitness is used with co-evolution. We explore the use of an exogenous fitness function with genetic programming and co-evolution to develop individuals and species capable of competing in a hostile environment. To facilitate the study, we use a commercially available environment-AI Wars-to host the organisms and run the experiments. Results from our experiments, though preliminary, indicate the ability of coevolution, genetic programming, and exogenous fitness to evolve fit individuals. The results also suggest the ability to assess the nature of the fitness landscape and the impact of various fitness factors on evolutionary performance", notes = "CEC-99 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. Library of Congress Number = 99-61143", } @Article{watkins:2004:GPEM, author = "Andrew Watkins and Jon Timmis and Lois Boggess", title = "Artificial Immune Recognition System ({AIRS}): {An} Immune-Inspired Supervised Learning Algorithm", journal = "Genetic Programming and Evolvable Machines", year = "2004", volume = "5", number = "3", pages = "291--317", month = sep, keywords = "AIS, supervised learning, artificial immune systems, classification, neural networks", ISSN = "1389-2576", DOI = "doi:10.1023/B:GENP.0000030197.83685.94", abstract = "inception and subsequent revisions of an immune-inspired supervised learning algorithm, Artificial Immune Recognition System (AIRS). It presents the immunological components that inspired the algorithm and describes the initial algorithm in detail. The discussion then moves to revisions of the basic algorithm that remove certain unnecessary complications of the original version. Experimental results for both versions of the algorithm are discussed and these results indicate that the revisions to the algorithm do not sacrifice accuracy while increasing the data reduction capabilities of AIRS.", notes = "Article ID: 5272976", } @InProceedings{Watkinson:2023:GI, author = "Myles Watkinson and Alexander Brownlee", title = "Updating {Gin}'s profiler for current Java", booktitle = "12th International Workshop on Genetic Improvement @ICSE 2023", year = "2023", editor = "Vesna Nowack and Markus Wagner and Gabin An and Aymeric Blot and Justyna Petke", pages = "23--28", address = "Melbourne, Australia", month = "20 " # may, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Genetic Improvement, JFR, JUnit", isbn13 = "979-8-3503-1232-4", URL = "https://storre.stir.ac.uk/handle/1893/34912", URL = "http://gpbib.cs.ucl.ac.uk/gi2023/Watkinson_2023_GI.pdf", DOI = "doi:10.1109/GI59320.2023.00015", slides_url = "http://gpbib.cs.ucl.ac.uk/gi2023/upgrading_gin.pdf", video_url = "http://gpbib.cs.ucl.ac.uk/gi2023/upgrading_gin_2023-05-17_10-36-43.mp4", video_url = "https://www.youtube.com/watch?v=nFBVxAs70wU&list=PLI8fiFpB7BoJLh6cUpGBjyeB1hM9DET1V&index=8", size = "6 pages", abstract = "Genetic improvement is a young and growing field. With much research still to be done, a number of tools to support the research community have emerged, with Gin being one such tool targeted at GI for Java. One core component of Gin is the profiler, which is used to identify hot methods in target applications: methods where the CPU spends most time and so may offer the most fertile sections of code for improvements to run time. Gin's profiler is HPROF, which was included with JDKs up to version 8. HPROF is no longer supported and so needs replaced if Gin is to support later versions of Java. Furthermore, little investigation has been made within the GI community comparing different profiling approaches. With this paper and its associated accepted pull request, we replace Gin's CPU profiler with Java Flight Recorder (JFR) to allow Gin to be applied to current Java code, allowing researchers working in GI with more recent JVMs to easily integrate profiling in their pipeline. We also contribute an experimental comparison of the HPROF and JFR profilers for the JVM.", notes = "GI @ ICSE 2023, part of \cite{Nowack:2023:GI}", } @InProceedings{watson:1996:ifs, author = "A. H. Watson and I. C. Parmee", title = "Identification Of Fluid Systems Using Genetic Programming", booktitle = "Proceedings of the Second Online Workshop on Evolutionary Computation (WEC2)", year = "1996", number = "2", pages = "45--48", broken = "http://www.bioele.nuee.nagoya-u.ac.jp/wec2/", month = "4--22 " # mar, organisation = "Research Group on ECOmp of the Society of Fuzzy Theory and Systems (SOFT)", publisher = "Nagoya University, Japan", keywords = "genetic algorithms, genetic programming, Fluid Systems, Evolutionary Computing", broken = "http://www.bioele.nuee.nagoya-u.ac.jp/wec2/papers/files/watson.ps", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/4894/http:zSzzSzwww.bioele.nuee.nagoya-u.ac.jpzSzwec2zSzpaperszSzfileszSzwatson.pdf/watson96identification.pdf", URL = "http://citeseer.ist.psu.edu/watson96identification.html", size = "4 pages", abstract = "In recent years, applied researchers have become increasingly interested in Adaptive Search (AS), techniques such as the Genetic Algorithm (GA), and Genetic Programming GP, for engineering design. This paper illustrates the effectiveness of the genetic programming paradigm for simple fluid systems identification problems. The objective of the paper is to establish methods for systems identification using GP and sets of empirical data. The manipulation and optimisation of these approximate functions that describe the physical process is achieved using the GP approach and by the development of complementary AS techniques. Two new GP operators are introduced, the first searches through possible values of terminals for a particular functional tree structure, and the second uses functional induction to improve the performance of the technique.", } @InProceedings{Watson:1997:ssGPccc, author = "Andrew H. Watson and Ian C. Parmee", title = "Steady State Genetic Programming With Constrained Complexity Crossover", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming", pages = "329", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1997/Watson_1997_ssGPccc.pdf", size = "1 page", notes = "GP-97", } @InProceedings{Watson:1997:cgtdsGPast, author = "Andrew H. Watson", title = "Calibrating Gas Turbine Design Software using Genetic Programming and Adaptive Search Techniques", booktitle = "Late Breaking Papers at the 1997 Genetic Programming Conference", year = "1997", editor = "John R. Koza", pages = "302", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California, 94305-3079, USA", month = "13--16 " # jul, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-206995-8", notes = "GP-97LB PHD Students' workshop The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670", } @InProceedings{watson:1997:ssGPcccssp, author = "Andrew H. Watson and Ian C. Parmee", title = "Steady State Genetic Programming with Constrained Complexity Crossover Using Species Sub Population", booktitle = "Genetic Algorithms: Proceedings of the Seventh International Conference", year = "1997", editor = "Thomas Back", pages = "315--321", address = "Michigan State University, East Lansing, MI, USA", publisher_address = "San Francisco, CA, USA", month = "19-23 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-487-1", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/icga1997/Watson_1997_ssGPccc.pdf", size = "7 pages", abstract = "We introduce an alternative approach to Genetic Programming (GP), which is based upon a steady state population and a novel constrained complexity crossover operator. This technique, called {"}DRAM-GP{"} (i.e. Distributed, Rapid, Attenuated Memory Genetic Programming), uses node complexity weightings as a basis for speciation. The population is decomposed into smaller sub-populations which communicate with each other through the action of crossover. The effectiveness of this method is demonstrated by successful application to Boolean concept formation and to symbolic regression problems. The results show that improved performance is possible with a dramatic reduction in population size and associated memory requirements.", notes = "ICGA-97", } @InProceedings{watson:1998:ACDM, author = "Andrew H. Watson and Ian C. Parmee", title = "Improving Engineering Design Models Using An Alternative Genetic Programming Approach", booktitle = "Adaptive Computing in Design and Manufacture", year = "1998", editor = "Ian C. Parmee", pages = "193--206", address = "Plymouth, England", month = "21-23 " # apr, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4471-1589-2", URL = "http://link.springer.com/chapter/10.1007/978-1-4471-1589-2_15", DOI = "doi:10.1007/978-1-4471-1589-2_15", abstract = "This paper describes an alternative approach to Genetic Programming (GP) for engineering design model development. The algorithm is initially developed to solve Boolean induction and simple symbolic regression problems within a discrete search space. This technique, called DRAM-GP (i.e. Distributed, Rapid, Attenuated Memory GP), is based upon a steady state population using a novel constrained complexity crossover operator. Node complexity weightings are introduced to provide a basis for speciation. Separate species of solutions, classified by complexity can be established which act as discrete GP sub-populations communicating with each other via crossover. The technique is extended to incorporate both continuous and discrete search spaces (HDRAM-GP i.e. Hybrid DRAM-GP). HDRAM-GP includes a real numbered Genetic Algorithm (GA) to aid search in the continuous space. Its application is demonstrated on engineering fluid dynamics systems.", } @PhdThesis{Watson:thesis, author = "Andrew Harry Watson", title = "An investigation of evolutionary computing in systems identification for preliminary design", school = "School of Computing, University of Plymouth", year = "1999", address = "UK", month = mar, keywords = "genetic algorithms, genetic programming", isni = "0000 0001 3564 1180", URL = "http://hdl.handle.net/10026.1/1669", URL = "https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.481293", URL = "https://pearl.plymouth.ac.uk/bitstream/handle/10026.1/1669/A.H.Watson.PDF", size = "193 pages", abstract = "This research investigates the integration of evolutionary techniques for symbolic regression. In particular the genetic programming paradigm is used together with other evolutionary computational techniques to develop novel approaches to the improvement of areas of simple preliminary design software using empirical data sets. It is shown that within this problem domain, conventional genetic programming suffers from several limitations, which are overcome by the introduction of an improved genetic programming strategy based on node complexity values, and using a steady state algorithm with subpopulations. A further extension to the new technique is introduced which incorporates a genetic algorithm to aid the search within continuous problem spaces, increasing the robustness of the new method. The work presented here represents an advance in the field of genetic programming for symbolic regression with significant improvements over the conventional genetic programming approach. Such improvement is illustrated by extensive experimentation using both simple test functions and real-world design examples.", notes = "In collaboration with Rolls Royce. Supervisor: Ian Parmee, Graham Purchase Item number 9004013663 design software: friction factor turbulent pipe flow, laminar 2D sudden expansion flow, thermal paint jet turbine blade", } @InProceedings{watson:1998:mbbi, author = "Richard A. Watson and Gregory S. Hornby and Jordan B. Pollack", title = "Modeling Building-Block Interdependency", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "234--240", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "genetic algorithms", size = "7 pages", abstract = "The Building-Block Hypothesis appeals to the notion of problem decomposition and the assembly of solutions from sub-solutions. Accordingly, there have been many varieties of GA test problems with a structure based on building-blocks. Many of these problems use deceptive fitness functions to model interdependency between the bits within a block. However, very few have any model of interdependency between building-blocks; those that do are not consistent in the type of interaction used intra-block and inter-block. This paper discusses the inadequacies of the various test problems in the literature and clarifies the concept of building-block interdependency. We formulate a principled model of hierarchical interdependency that can be applied through many levels in a consistent manner and introduce Hierarchical If-and-only-if (H-IFF) as a canonical example. We present some empirical results of GAs on H-IFF showing that if population diversity is maintained and linkage is tight then the GA is able to identify and manipulate building-blocks over many levels of assembly, as the Building-Block Hypothesis suggests.", notes = "GP-98LB see PPSN 1998 paper http://eprints.ecs.soton.ac.uk/12013/", } @InProceedings{watson:1999:APAMNMPOP, author = "Jean-Paul Watson", title = "A Performance Assessment of Modern Niching Methods for Parameter Optimization Problems", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "702--709", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{watson:1999:ICGA, author = "Richard A. Watson and Jordan B. Pollack", title = "Incremental Commitment in Genetic Algorithms", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "710--717", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems, Messy Genetic Algorithms, Hierarchical-if-and-only-if (H-IFF)", ISBN = "1-55860-611-4", URL = "http://www.demo.cs.brandeis.edu/papers/gecco_icga.pdf", URL = "http://www.demo.cs.brandeis.edu/papers/gecco_icga.ps.gz", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-887.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-887.ps", abstract = "Successful recombination in the simple GA requires that interdependent genes be close to each other on the genome. Several methods have been proposed to reorder genes on the genome when the given ordering is unfavorable. The Messy GA (MGA) is one such 'moving-locus' scheme. However, gene reordering is only part of the Messy picture. The MGA uses another mechanism that is influential in enabling successful recombination. Specifically, the use of partial specification (or variable length genomes) allows the individuals themselves, rather than the ordering of genes within an individual, to represent which genes 'go together' during recombination. This paper examines this critical feature of the MGA and illustrates the impact that partial specification has on recombination. We formulate an Incremental Commitment GA that uses partially specified representations and recombination inspired by the MGA but separates these features from the moving-locus aspects and many of the other features of the existing algorithm.", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{watson:1999:HS, author = "Richard A. Watson and Jordan B. Pollack", title = "Hierarchically consistent test problems for genetic algorithms: Summary and additional results", booktitle = "Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference", year = "1999", editor = "Scott Brave and Annie S. Wu", pages = "292--297", address = "Orlando, Florida, USA", month = "13 " # jul, keywords = "Genetic Algorithms", notes = "GECCO-99LB", } @InProceedings{Watson:2025:GPTP, author = "Richard Watson", title = "Cognition-First Evolution", booktitle = "Genetic Programming Theory and Practice XXII", year = "2025", editor = "Wolfgang Banzhaf and Bogdan Burlacu and Stephen Kelly and Alexander Lalejini and Fabricio {Olivetti de Franca}", address = "Michigan State University, USA", month = jun # " 5-7", note = "Keynote", keywords = "genetic algorithms, genetic programming", notes = "To be published after the workshop", } @InProceedings{watson:2014:cig, author = "Scott Watson and Wolfgang Banzhaf and Andrew Vardy", title = "Automated Design for Playability in Computer Game Agents", booktitle = "IEEE Conference on Computational Intelligence and Games, CIG 2014", year = "2014", address = "Dortmund", month = "26-29 " # aug, publisher = "IEEE", keywords = "genetic algorithms, FSM, role playing games, NPC, agents", DOI = "doi:10.1109/CIG.2014.6932860", size = "8 pages", abstract = "This paper explores whether a novel approach to the creation of agent controllers has potential to overcome some of the drawbacks that have prevented novel controller architectures from being widely implemented. This is done by using an evolutionary algorithm to generate finite state machine controllers for agents in a simple role playing game. The concept of minimally playable games is introduced to serve as the basis of a method of evaluating the fitness of a game's agent controllers.", notes = "Not GP? Fitness function, Plantagenet See also \cite{watson15machiavellian1} doi:10.1109/CGames.2015.7272956", } @Article{watts:1998:jas, author = "Jon M. Watts", title = "Animats: computer-simulated animals in behavioral research", journal = "Journal of Animal Science", year = "1998", volume = "76", number = "10", pages = "2596--2604", keywords = "genetic algorithms, genetic programming", URL = "http://jas.fass.org/cgi/reprint/76/10/2596.pdf", URL = "http://jas.fass.org/cgi/reprint/76/10/2596", size = "9 pages", abstract = "The term animat refers to a class of simulated animals. This article is intended as a nontechnical introduction to animat research. Animats can be robots interacting with the real world or computer simulations. In this article, the use of computer-generated animats is emphasised. The scientific use of animats has been pioneered by artificial intelligence and artificial life researchers. Behaviour-based artificial intelligence uses animats capable of autonomous and adaptive activity as conceptual tools in the design of usefully intelligent systems. Artificial life proponents view some human artifacts, including informational structures that show adaptive behavior and self-replication, as animats may do, as analogous to biological organisms. Animat simulations may be used for rapid and inexpensive evaluation of new livestock environments or management techniques. The animat approach is a powerful heuristic for understanding the mechanisms that underlie behavior. The simple rules and capabilities of animat models generate emergent and sometimes unpredictable behavior. Adaptive variability in animat behavior may be exploited using artificial neural networks. These have computational properties similar to natural neurons and are capable of learning. Artificial neural networks can control behavior at all levels of an animat's functional organization. Improving the performance of animats often requires genetic programming. Genetic algorithms are computer programs that are capable of self-replication, simulating biological reproduction. Animats may thus evolve over generations. Selective forces may be provided by a human overseer or be part of the simulated environment. Animat techniques allow researchers to culture behavior outside the organism that usually produces it. This approach could contribute new insights in theoretical ethology on questions including the origins of social behavior and cooperation, adaptation, and the emergent nature of complex behavior. Animat studies applied to domestic animals have been few so far, and have involved simulations of space use by swine. I suggest other applications, including modeling animal movement during human handling and the effects of environmental enrichment on the satisfaction of behavioral needs. Appropriate use of animat models in a research program could result in savings of time and numbers of animals required. This approach may therefore come to be viewed as both ethically and economically advantageous.", notes = "Department of Herd Medicine and Theriogenology, Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, Canada. PMID: 9814899 [PubMed - indexed for MEDLINE]", } @InCollection{waugh:1994:priceoi, author = "Lawrence Waugh", title = "Complexity and Survivability: The Price of Intelligence under Genetic Pressure", booktitle = "Artificial Life at Stanford 1994", year = "1994", editor = "John R. Koza", pages = "187--195", address = "Stanford, California, 94305-3079 USA", month = jun, organisation = "Stanford University", publisher = "Stanford Bookstore", keywords = "genetic algorithms, machine learning, complexity, natural selection", ISBN = "0-18-182105-2", notes = "Alife toroidal world simulation. Creature's brain is neural network. This volume contains 22 papers written and submitted by students describing their term projects for the course in artificial life (Computer Science 425) at Stanford University offered during the spring quarter quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs425.html", } @InCollection{wayland:2000:SPAEGP, author = "John Wayland", title = "Solving the 5-Tile Puzzle: An Exercise in Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "435--441", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:2000:gagp}", } @Misc{wayner:2002:db, author = "Peter Wayner", title = "Digital Biology", howpublished = "http://slashdot.org/article.pl?sid=02/03/04/195222", year = "2002", month = "11 " # mar, note = "Book review", keywords = "genetic algorithms", abstract = "Does a good job of bridging the analogical gap between the worlds of computers and biology; may not be deep but will probably enlighten readers with an interest in either or both of these fields. Peter J. Bentley's book {"}Digital Biology{"}, ISBN 0-7432-0447-6", } @PhdThesis{Weatheritt:thesis, author = "Jack Weatheritt", title = "The Development of Data Driven Approaches to Further Turbulence Closures", school = "Faculty of Engineering and the Environment, Institute for Complex Systems Simulation, University of Southampton", year = "2015", address = "UK", month = nov, keywords = "genetic algorithms, genetic programming, Gene Expression Programming", URL = "https://eprints.soton.ac.uk/388092/", URL = "https://eprints.soton.ac.uk/388092/1/Final%2520Thesis.pdf", size = "261 pages", abstract = "The closure of turbulence models at all levels of fidelity is addressed, using unconventional methods that rely on data. The purpose of the thesis is not to present new models of turbulence per se, but rather the main focus is to develop the methodologies that created them. The main tool, Gene Expression Programming, is a versatile evolutionary algorithm. Implementations of the algorithm allow for symbolic regression of scalar and tensor fields and the clustering of data sets. The last two applications are novel algorithms. Scalar field regression is used to construct length scale damping functions for Hybrid RANS/LES. Direct Numerical Simulation snapshots are filtered to mimic Hybrid RANS/LES flow fields and from this new damping functions are created. Two closures are constructed, one from data in a turbulent pipe and another from slices along the classic backward facing step geometry. The new closures are tested for a range of separated flow applications. Tests alongside existing closures of the same class show that both new methods adapt to the local mesh resolution and turbulence level at least as well as other hybrid closures. Tensor field regression is used to construct non-linear stress-strain relationships in a Reynolds-Averaged Navier-Stokes framework. A common two-equation model is modified by including a further term that accounts for extra anisotropy with respect to the Boussinesq approximation. This model term, regressed from time averaged Direct Numerical Simulation data, turns the linear closure into an Explicit Algebraic Stress Model. The training data is taken from the reverse flow region behind a backward facing step. When applied to the classic periodic hills case, the subclass of models generated are found to greatly improve the prediction with respect to the linear model. A subclass of models is created in order to test the ability of the evolutionary algorithm. The deviation from the periodic hills reference data is quantified and used as a metric for model performance. The key finding is that improved performance of the Gene Expression Programming framework corresponded to improved prediction of the periodic hills. The final application of Gene Expression Programming, the clustering of datasets, is used to group Reynolds stress structures into distinct types. Firstly, reference Direct Numerical Simulation data obtained in a turbulent channel is categorised into six distinct groups. These groups are then compared to structures from Hybrid RANS/LES. These groups help to show that Hybrid RANS/LES structures do not correctly capture the near-wall cycle of turbulence. Instead there is an artificial cycle that is characterised by an incorrect buffer layer, defined by tall, long and thin structures. Further, streaky structures lie on the interface between Reynolds-Averaged Navier-Stokes and Large Eddy Simulation. These structures are free to move in the vertical direction and seriously contribute to discrepancies in the second order statistics.", notes = "Also known as \cite{soton388092} Aerodynamics & Flight Mechanics Group Supervisor: Richard Sandberg", } @Article{Weaver:2004:COCB, author = "Daniel C. Weaver", title = "Applying data mining techniques to library design, lead generation and lead optimization", journal = "Current Opinion in Chemical Biology", year = "2004", volume = "8", pages = "264--270", number = "3", abstract = "Many data mining techniques have been applied to activity and ADMET datasets and the resulting models are being used to understand quantitative structure-activity relationships and design new libraries. This review summarises data mining concepts and discuss their application to library design, lead generation (particularly for sequential screening) and lead optimisation (specifically for generating and interpreting QSAR models). Also, this review discusses recent comparative studies between data mining techniques and draws some conclusions about the patterns emerging in the drug discovery data mining field.", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6VRX-4CB69R1-2/2/84a354cec9064ed07baab6a07998c942", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1016/j.cbpa.2004.04.005", notes = "Array Biopharma, Inc., 3200 Walnut Street, Boulder, Colorado 80303, USA PMID: 15183324 [PubMed - indexed for MEDLINE]", } @Article{Weber:2009:GPEM, author = "Matthieu Weber and Ferrante Neri and Ville Tirronen", title = "Distributed differential evolution with explorative-exploitative population families", journal = "Genetic Programming and Evolvable Machines", year = "2009", volume = "10", number = "4", pages = "343--371", month = dec, keywords = "genetic algorithms, Differential evolution, Distributed systems, Population size reduction, Multi-family distributed algorithms", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-009-9089-y", size = "29 pages", abstract = "This paper proposes a novel distributed differential evolution algorithm, namely Distributed Differential Evolution with Explorative Exploitative Population Families (DDE-EEPF). In DDE-EEPF the sub-populations are grouped into two families. Sub-populations belonging to the first family have constant population size, are arranged according to a ring topology and employ a migration mechanism acting on the individuals with the best performance. This first family of sub-populations has the role of exploring the decision space and constituting an external evolutionary framework. The second family is composed of sub-populations with a dynamic population size: the size is progressively reduced. The sub-populations belonging to the second family are highly exploitative and are supposed to quickly detect solutions with a high performance. The solutions generated by the second family then migrate to the first family. In order to verify its viability and effectiveness, the DDE-EEPF has been run on a set of various test problems and compared to four distributed differential evolution algorithms. Numerical results show that the proposed algorithm is efficient for most of the analyzed problems, and outperforms, on average, all the other algorithms considered in this study.", } @InCollection{weck:1999:hmugp, author = "Barry Weck and Charles L. Karr", title = "Hydrocyclone Model Using Genetic Programming", year = "1999", pages = "285--297", booktitle = "Industrial Applications of Genetic Algorithms", editor = "Charles L. Karr and L. Michael Freeman", address = "Boca Raton, FL", publisher = "CRC Press", series = "Computational Intelligence", keywords = "genetic algorithms, genetic programming", ISBN = "0-8493-9801-0", URL = "http://www.crcpress.com/product/isbn/9780849398018", } @Article{DBLP:journals/eswa/WedashwaraMOK16, author = "Wirarama Wedashwara and Shingo Mabu and Masanao Obayashi and Takashi Kuremoto", title = "Combination of genetic network programming and knapsack problem to support record clustering on distributed databases", journal = "Expert Systems with Applications", volume = "46", pages = "15--23", year = "2016", month = "15 " # mar, keywords = "genetic algorithms, genetic programming, Genetic network programming, Database clustering, Knapsack problem, Record clustering", ISSN = "0957-4174", URL = "https://doi.org/10.1016/j.eswa.2015.10.006", DOI = "doi:10.1016/j.eswa.2015.10.006", timestamp = "Fri, 26 May 2017 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/eswa/WedashwaraMOK16.bib", bibsource = "dblp computer science bibliography, https://dblp.org", size = "15 pages", abstract = "This research involves implementation of genetic network programming (GNP) and standard dynamic programming to solve the knapsack problem (KP) as a decision support system for record clustering in distributed databases. Fragment allocation with storage capacity limitation problem is a background of the proposed method. The problem of storage capacity is to distribute sets of fragments into several sites (clusters). Total amount of fragments in each site must not exceed the capacity of site, while the distribution process must keep the relation (similarity) between fragments within each site. The objective is to distribute big data to certain sites with the limited amount of capacities by considering the similarity of distributed data in each site. To solve this problem, GNP is used to extract rules from big data by considering characteristics (value ranges) of each attribute in a dataset. The proposed method also provides partial random rule extraction method in GNP to discover frequent patterns in a database for improving the clustering algorithm, especially for large data problems. The concept of KP is applied to the storage capacity problem and standard dynamic programming is used to distribute rules to each site by considering similarity (value) and data amount (weight) related to each rule to match the site capacities. From the simulation results, it is clarified that the proposed method shows some advantages over the conventional clustering algorithms, therefore, the proposed method provides a new clustering method with an additional storage capacity problem.", notes = "Graduate School of Science and Engineering, Yamaguchi University, Tokiwadai 2-16-1, Ube, Yamaguchi 755-8611, Japan", } @InProceedings{Wedashwara:2019:SIET, author = "Wirarama Wedashwara and Candra Ahmadi and I. Wayan Agus Arimbawa and I. Gede Eka Wiantara Putra", booktitle = "2019 International Conference on Sustainable Information Engineering and Technology (SIET)", title = "Internet of Things based Smart Energy Audit using Evolutionary Fuzzy Association Rule Mining", year = "2019", pages = "142--147", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SIET48054.2019.8986148", month = sep, abstract = "Energy audits are investigations, studies and examination of power vitality use, with the point of proficiency without lessening the exhibition of the system. The paper presented Internet of Things (IoT) based Smart Energy Audit by implementing electrical data collection using Wireless Sensor Network (WSN) and Decision Support System (DSS) using Evolutionary Fuzzy Association Rule Mining (EFARM). The developed system aims to collects data using IoT nodes and summarise data pattern using EFARM in interpretation of Fuzzy Rules and Tree Based Evolutionary Computation (EC). The evaluation performed in five rooms within a week and shown EFARM capable to: Interpreted patterns of electricity consumption in load samples by achieving a high average, confident and score; Compare consumption patterns between measurement areas and the type of measured load; Summarise the rules similarity between areas as general rules and dissimilarities that only exist in some areas as a specific rule, using singletree interpretation; Interpretation of comparison between power consumption patterns with measurable performance, namely temperature and light intensity.", notes = "Mataram University, Mataram, Indonesia Also known as \cite{8986148}", } @InProceedings{Wedashwara:2020:ICADEIS, author = "Wirarama Wedashwara and I {Wayan Agus Arimbawa} and Andy {Hidayat Jatmika} and Ariyan Zubaidi and Tatang Mulyana", title = "{IoT} based Smart Small Scale Solar Energy Planning using Evolutionary Fuzzy Association Rule Mining", booktitle = "2020 International Conference on Advancement in Data Science, E-learning and Information Systems (ICADEIS)", year = "2020", month = "20-21 " # oct, address = "Lombok, Indonesia", keywords = "genetic algorithms, genetic programming, EFARM, Solar Energy", isbn13 = "978-1-7281-8272-8", DOI = "doi:10.1109/ICADEIS49811.2020.9276905", size = "6 pages", abstract = "Along-Track Stereo Sun Glitter (ATSSG) shows Indonesia especially Lombok has high solar energy potential, not only on large scale but also small scale such as for hybrid-based electricity savings. The amount of energy that can be saved through solar power is difficult to predict without measurement and planning. The paper proposed the Smart Small Scale Solar Energy Planning using Internet of Things (IoT) by collaborating Wireless Sensor Network (WSN) as data collector and Evolutionary Fuzzy Association Rule Mining (EFARM) as Decision Support System (DSS). WSN collects data generated solar energy by the solar panel and direct current (DC) energy usage by electrical devices. Then both collected data are processed by EFARM through an interpretation of tree-based fuzzy rule extractor to conclude the potential of energy efficiency. The Evaluation is carried out for two weeks using two solar panels with light intensity, temperature, and humidity sensors as a comparison for environment condition and generated energy. Through evaluation EFARM has shown the capability to Interpreted patterns of generated energy and energy consumption by achieving a high average of supports(0.247,0.236), confidence(0.393,0.219) and scores(0.335,0.127) for full-length rules; describe the rules correlation between generated energy and energy consumption to conclude the potential of energy efficiency, and make decision support for the number of panels and batteries to be added with relatively low mean square error (6.094).", notes = "Also known as \cite{9276905}", } @InProceedings{1277382, author = "David C. Wedge and Simon J. Gaskell and Simon J. Hubbard and Douglas B. Kell and King Wai Lau and Claire Eyers", title = "Peptide detectability following ESI mass spectrometry: prediction using genetic programming", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "2219--2225", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p2219.pdf", DOI = "doi:10.1145/1276958.1277382", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Real-World Applications, AUROC, input selection, mass spectrometry, proteomics", abstract = "The accurate quantification of proteins is important in several areas of cell biology, biotechnology and medicine. Both relative and absolute quantification of proteins is often determined following mass spectrometric analysis of one or more of their constituent peptides. However, in order for quantification to be successful, it is important that the experimenter knows which peptides are readily detectable under the mass spectrometric conditions used for analysis. In this paper, genetic programming is used to develop a function which predicts the detectability of peptides from their calculated physico-chemical properties. Classification is carried out in two stages: the selection of a good classifier using the AUROC objective function and the setting of an appropriate threshold. This allows the user to select the balance point between conflicting priorities in an intuitive way. The success of this method is found to be highly dependent on the initial selection of input parameters. The use of brood recombination and a modified version of the multi-objective FOCUS method are also investigated. While neither has a significant effect on predictive accuracy, the use of the FOCUS method leads to considerably more compact solutions.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071 Data lineraliy transformed to -1+1 range. Binary classification, AUROC. feature selection 393, 34 or 6 inputs. PPV. QconCat.", } @InProceedings{Wedge:2008:gecco, author = "David C. Wedge and Douglas B. Kell", title = "Rapid prediction of optimum population size in genetic programming using a novel genotype - Fitness Correlation", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "1315--1322", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1315.pdf", DOI = "doi:10.1145/1389095.1389346", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, control parameters, genotype-fitness correlation, landscape, real-world", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389346}", } @Article{Wedge2009365, author = "David C. Wedge and Arindam Das and Rene Dost and Jeff Kettle and Marie-Beatrice Madec and John J. Morrison and Martin Grell and Douglas B. Kell and Tim H. Richardson and Stephen Yeates and Michael L. Turner", title = "Real-time vapour sensing using an OFET-based electronic nose and genetic programming", journal = "Sensors and Actuators B: Chemical", volume = "143", number = "1", pages = "365--372", year = "2009", keywords = "genetic algorithms, genetic programming, OFET, Electronic nose, Pattern recognition, Real-time, Multiparametric", ISSN = "0925-4005", DOI = "doi:10.1016/j.snb.2009.09.030", URL = "http://www.sciencedirect.com/science/article/B6THH-4X97CWW-3/2/14db15f5516874fbcbbecdd36c5b9987", URL = "http://results.ref.ac.uk/Submissions/Output/2859886", abstract = "Electronic noses (e-noses) are increasingly being used as vapour sensors in a range of application areas. E-noses made up of arrays of organic field-effect transistors (OFETs) are particularly valuable due the range and diversity of the information which they provide concerning analyte binding. This study demonstrates that arrays of OFETs, when combined with a data analysis technique using Genetic Programming (GP), can selectively detect airborne analytes in real time. The use of multiple parameters - on resistance, off current and mobility - collected from multiple transistors coated with different semiconducting polymers gives dramatic improvements in the sensitivity (true positive rate), specificity (true negative rate) and speed of sensing. Computer-controlled data collection allows the identification of analytes in real-time, with a time-lag between exposure and detection of the order of 4s.", uk_research_excellence_2014 = "D - Journal article", } @InProceedings{Weerasinghe:2020:ICAC, author = "P. S. R. Weerasinghe and R. A. M. D. K. Ranasinghe and M. M. V. R. B. Mahanthe and P. G. C. B. Samarakoon and W. H. Rankothge and D. Kasthurirathna", title = "Real-Time Decision Optimization Platform for Airline Operations", booktitle = "2020 2nd International Conference on Advancements in Computing (ICAC)", year = "2020", volume = "1", pages = "281--286", month = dec, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICAC51239.2020.9357157", abstract = "With close to 4 billion origin-destination passenger journeys worldwide, airline operations have become a crucial factor in the global economy. With the increasing number of journeys and passengers, managing the daily operations of airlines have become a complicated task. We have proposed a real-time decision optimization platform for airline operations with the following subsystems: (1) determine the optimum path for a flight, (2) optimum fleet assignment, (3) optimum gate allocation, (4) optimum crew allocation. We have used an approximation (heuristics) based optimization approach: Genetic Programming (GP) to implement the modules. The results of our proposed platform illustrate that, the decision-making process of Airline Operations Control Center (AOCC) can be optimized, and dynamic change requirements can be accommodated.", notes = "Also known as \cite{9357157}", } @Article{Wei:2014:EPA, author = "Chenghao Wei and Wenhu Tang and Qinghua Wu", journal = "IET Electric Power Applications", title = "Dissolved gas analysis method based on novel feature prioritisation and support vector machine", year = "2014", month = sep, volume = "8", number = "8", pages = "320--328", keywords = "genetic algorithms, genetic programming, SVM", DOI = "doi:10.1049/iet-epa.2014.0085", ISSN = "1751-8660", abstract = "Dissolved gas analysis (DGA) has been widely used for the detection of incipient faults in oil-filled transformers. This research presents a novel approach to DGA feature prioritisation and classification, which considers not only the relations between a fault type and specific gas ratios but also their statistical characteristics based on data derived from on site inspections. Firstly, new gas features are acquired based on the analysis of current international gas interpretation standards. Combined with conventional gas ratios, all features are then prioritised by using the Kolmogorov-Smirnov test. The rankings are obtained by using their values of maximum statistic distance. The first three features in ranking are employed as input vectors to a multi-layer support vector machine, whose tuning parameters are acquired by particle swarm optimisation. In the experiment, a bootstrap technique is implemented to approximately equalise sample numbers of different fault cases. A common 10-fold cross-validation technique is employed for performance assessment. Typical artificial intelligence classifiers with gas features extracted from genetic programming are evaluated for comparison purposes.", notes = "Dept. of Electr. Eng. & Electron., Univ. of Liverpool, Liverpool, UK Also known as \cite{6894472}", } @Article{WEI:2024:swevo, author = "Luona Wei and Ming Chen and Lining Xing and Qian Wan and Yanjie Song and Yuning Chen and Yingwu Chen", title = "Knowledge-transfer based genetic programming algorithm for multi-objective dynamic agile earth observation satellite scheduling problem", journal = "Swarm and Evolutionary Computation", volume = "85", pages = "101460", year = "2024", ISSN = "2210-6502", DOI = "doi:10.1016/j.swevo.2023.101460", URL = "https://www.sciencedirect.com/science/article/pii/S2210650223002328", keywords = "genetic algorithms, genetic programming, Agile satellite scheduling, Multi-objective, Knowledge transfer", abstract = "The multi-objective dynamic agile earth observation satellite scheduling problem (MO-DAEOSSP) aims to schedule a set of real-time arrival requests and form a reasonable observation plan to satisfy various criteria. According to the requirements in practical applications, the total profit and the average image quality of scheduled requests are taken as optimization goals in this study. Compared to manually designed heuristics and iterative-based methods used in previous research, genetic programming based hyper heuristics (GPHH) can automatically evolve high-quality heuristic rules (HRs) for real-time scheduling without being highly dependent on expert knowledge. In this paper, a knowledge-transfer based multi-objective GPHH framework (KT-MOGP) is proposed, equipped with a heuristic-based simulation considering the idle monitoring, to evolve non-dominated HRs for solving MO-DAEOSSP. The heuristic-based simulation generates feasible schedules and returns fitness values for given HRs, which are the individuals evolved by KT-MOGP. KT-MOGP applies a knowledge transfer mechanism to accelerate convergence. Once a source problem is trained, its non-dominated solutions are extracted and their feature importance is transferred to guide the initialization of another target problem, by which the knowledge generated during the training process can be fully used. Experimental results on three sets of instances show that KT-MOGP outperforms the existing GPHH-based method and that the evolved HRs are competitive compared to several classical constructive heuristics and multi-objective evolutionary algorithms. The results also show the effectiveness of the proposed knowledge transfer-based initialization. To the best of our knowledge, this study is the first attempt to consider both multi-objective scenarios and real-time arrival requests", } @Article{journals/jifs/WeiJW16, author = "Shuang Wei and Defu Jiang and Feng Wang", title = "A new multiobjective genetic programming approach using compromise distance ranking for automated design of nonlinear system design", journal = "Journal of Intelligent and Fuzzy Systems", year = "2016", number = "1", volume = "31", keywords = "genetic algorithms, genetic programming, Multiobjective genetic programming (GP), compromise distance, nonlinear system design, evolutionary multiobjective optimization (EMO)", bibdate = "2017-05-28", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/jifs/jifs31.html#WeiJW16", pages = "601--611", DOI = "doi:10.3233/IFS-162174", } @Article{WEI:2023:powtec, author = "Tao Wei and Shuo Yang and Lianze Wang", title = "Modeling of pertinent parameters influence on the time dependent mass transfer coefficient of particulate matter under the sink effect", journal = "Powder Technology", volume = "425", pages = "118536", year = "2023", ISSN = "0032-5910", DOI = "doi:10.1016/j.powtec.2023.118536", URL = "https://www.sciencedirect.com/science/article/pii/S0032591023003200", keywords = "genetic algorithms, genetic programming, Particulate matter, Particle sink effect, Mass transfer coefficient, Multi-gene genetic programming, Non-linear multiple regression", abstract = "One of the most crucial evaluation metrics for the performance of particle sink purification technology is the time-dependent mass transfer coefficient (TDMTC). Therefore, it is very helpful for designers and developers to accurately describe the functional relationship between different influence parameters and the TDMTC. In this paper, four influence parameters (the applied voltage (V), interelectrode distance (dc), porosity (P), and shape (n) of the collecting electrode) were considered, and then non-linear multiple regression (NLMR) and multi-gene genetic programming (MGGP) methods were used to establish prediction models of the TDMTC. Results showed that V and n were the most significant factors, followed by dc and P. Both multi-factor models could accurately predict the TDMTC under the sink effect with a maximum prediction error of 20percent and 15percent, respectively. Moreover, for particulate matter (PM) with different size fractions, MGGP models could improve the prediction accuracy by 5-10percent compared to NLMR models", } @Article{Tingyang_Wei:ETC, author = "Tingyang Wei and Wei-Li Liu and Jinghui Zhong and Yue-Jiao Gong", title = "Multiclass Classification on High Dimension and Low Sample Size Data using Genetic Programming", journal = "IEEE Transactions on Emerging Topics in Computing", year = "2022", volume = "10", number = "2", pages = "704--718", month = apr # "-" # jun, keywords = "genetic algorithms, genetic programming, Gene Expression Programming", ISSN = "2168-6750", DOI = "doi:10.1109/TETC.2020.3034495", abstract = "Multiclass classification is one of the most fundamental tasks in data mining. However, traditional data mining methods rely on the model assumption, they generally can suffer from the overfitting problem on high dimension and low sample size (HDLSS) data. Trying to address multiclass classification problems on HDLSS data from another perspective, we use Genetic Programming (GP), an intrinsic evolutionary classification algorithm that can implement feature construction automatically without model assumption. This paper develops an ensemble-based genetic programming classification framework, the Sigmoid-based Ensemble Gene Expression Programming (SEGEP). To relieve the problem of output conflict in GP-based multiclass classifiers, the proposed method employs a flexible probability representation with continuous relaxation to better integrate the output of all the binary classifiers, an effective data division strategy to further enhance the ensemble performance, and a novel sampling strategy to refine the existing GP-based binary classifier. The experiment results indicate that SE-GEP can attain better classification accuracy compared to other GP methods. Moreover, the comparison with other representative machine learning methods indicates that SE-GEP is a competitive method for multiclass classification in HDLSS data.", notes = "Also known as \cite{9242277}", } @InProceedings{Wei:2008:cec, author = "Wei Wei and Huiyu Zhou and Kaoru Shimada and Shingo Mabu and Kotaro Hirasawa", title = "Comparative Association Rules Mining Using Genetic Network Programming (GNP) with Attributes Accumulation Mechanism and its Application to Traffic Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "292--298", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1823-7", file = "EC0090.pdf", DOI = "doi:10.1109/CEC.2008.4630813", abstract = "In this paper, we present a method of comparative association rules mining using Genetic Network Programming (GNP) with attributes accumulation mechanism in order to uncover association rules between different datasets. GNP is an evolutionary approach which can evolve itself and find the optimal solutions. The motivation of the comparative association rules mining method is to use the data mining approach to check two or more databases instead of one, so as to find the hidden relations among them. The proposed method measures the importance of association rules by using the absolute difference of confidences among different databases and can get a number of interesting rules. Association rules obtained by comparison can help us to find and analyse the explicit and implicit patterns among a large amount of data. For the large attributes case, the calculation is very time-consuming, when the conventional GNP based data mining is used. So, we have proposed an attribute accumulation mechanism to improve the performance. Then, the comparative association rules mining using GNP has been applied to a complicated traffic system. By mining and analysing the rules under different traffic situations, it was found that we can get interesting information of the traffic system.", keywords = "genetic algorithms, genetic programming", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @InProceedings{Wei:2006:WCICA, author = "Xunkai Wei and Yinghong Li", title = "Aero-Engine Dynamic Start Model Based on Parsimonious Genetic Programming", booktitle = "The Sixth World Congress on Intelligent Control and Automation, WCICA 2006", year = "2006", volume = "1", pages = "1478--1482", address = "Dalian", publisher = "IEEE", keywords = "genetic algorithms, genetic programming", ISBN = "1-4244-0332-4", DOI = "doi:10.1109/WCICA.2006.1712595", abstract = "A novel parsimonious genetic programming (PGP) algorithm together with a novel aero-engine optimum data-driven dynamic start process model based on PGP was proposed. The method uses traditional GP to generate nonlinear input-output models that are represented in a binary tree structure. It introduces error reduction ratio (Err) to estimate the contribution of each branch of the tree, which refers to basic function term that cannot be decomposed any more according to special given rule. It applies orthogonal least squares algorithm (OLS) to eliminate complex redundant subtrees and then enhance convergence speed of GP. It is expected to obtain simple, reliable and exact linear-in-parameters nonlinear model via GP evolution algorithm. Application to real aero-engine start process test data validates that the proposed method can generate more robust and interpretable models. It is a rather promising method for complex nonlinear systems modelling with rather little prior system knowledge", notes = "Dept. of Aircraft & Power Eng., Air Force Eng. Univ., Xi'an", } @Article{Wei:2010:NCA, title = "Parsimonious genetic programming for complex process intelligent modeling: algorithm and applications", author = "Xunkai Wei and Yinghong Li and Yue Feng", journal = "Neural Computing and Applications", year = "2010", number = "2", volume = "19", pages = "329--335", keywords = "genetic algorithms, genetic programming", ISSN = "0941-0643", publisher = "Springer London", DOI = "doi:10.1007/s00521-009-0308-5", size = "7 pages", abstract = "A novel genetic programming (GP) algorithm called parsimonious genetic programming (PGP) for complex process intelligent modeling was proposed. First, the method uses traditional GP to generate nonlinear input-output model sets that are represented in a binary tree structure according to special decomposition method. Then, it applies orthogonal least squares algorithm (OLS) to estimate the contribution of the branches, which refers to basic function term that cannot be decomposed anymore, to the accuracy of the model, so as to eliminate complex redundant subtrees and enhance convergence speed. Finally, it obtains simple, reliable and exact linear in parameters nonlinear model via GP evolution. Simulations validate that the proposed method can generate more robust and interpretable models, which is obvious and easy for realization in real applications. For the proposed algorithm, the whole modeling process is fully automatic, which is a rather promising method for complex process intelligent modeling.", bibdate = "2011-02-22", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/nca/nca19.html#WeiLF10", affiliation = "Air Force Engineering University Engineering Institute Department of Aircraft and Power Engineering Shaanxi, Xi'an 710038 China", } @Article{Wei:2020:FUZZ, author = "Zi-Xiao Wei and Faiyaz Doctor and Yan-Xin Liu and Shou-Zen Fan and Jiann-Shing Shieh", journal = "IEEE Transactions on Fuzzy Systems", title = "An Optimized Type-2 Self-Organizing Fuzzy Logic Controller Applied in Anesthesia for Propofol Dosing to Regulate BIS", year = "2020", volume = "28", number = "6", pages = "1062--1072", abstract = "During general anesthesia, anesthesiologists who provide anesthetic dosage traditionally play a fundamental role to regulate bispectral index (BIS). However, in this article, an optimized type-2 self-organizing fuzzy logic controller (SOFLC) is designed for a target controlled infusion pump related to propofol dosing guided by BIS, to realize automatic control of general anesthesia. The type-2 SOFLC combines a type-2 fuzzy logic controller with a self-organizing mechanism to facilitate online training while able to contend with operational uncertainties. A novel data-driven surrogate model and genetic programming-based strategy is introduced for optimizing the type-2 SOFLC parameters offline to handle interpatient variability. A pharmacological model is built for simulation in which different optimization strategies are tested and compared. Simulation results are presented to demonstrate the applicability of our approach and show that the proposed optimization strategy can achieve better control performance in terms of steady-state error and robustness.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/TFUZZ.2020.2969384", ISSN = "1941-0034", month = jun, notes = "Also known as \cite{8970336}", } @InProceedings{Weimer:2009:ICES, author = "Westley Weimer and ThanhVu Nguyen and Claire {Le Goues} and Stephanie Forrest", title = "Automatically Finding Patches Using Genetic Programming", booktitle = "International Conference on Software Engineering (ICSE) 2009", year = "2009", editor = "Stephen Fickas", pages = "364--374", address = "Vancouver", month = may # " 16-24", note = "Winner ACM SIGSOFT Distinguished Paper Award. Gold medal at 2009 HUMIES. Ten-Year Most Influential Paper \cite{Weimer:2019:ICSE}", keywords = "genetic algorithms, genetic programming, genetic improvement, APR", isbn13 = "978-1-4244-3453-4", URL = "http://www.cs.unm.edu/~tnguyen/papers/genprog.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.147.8995", URL = "http://www.cs.virginia.edu/~csl9q/docs/legoues-icse2009-genprog-preprint.pdf", DOI = "doi:10.1109/ICSE.2009.5070536", size = "11 pages", language = "en", oai = "oai:CiteSeerXPSU:10.1.1.147.8995", abstract = "Automatic repair of programs has been a longstanding goal in software engineering, yet debugging remains a largely manual process. We introduce a fully automated method for locating and repairing bugs in software. The approach works on off-the-shelf legacy applications and does not require formal specifications, program annotations or special coding practices. Once a program fault is discovered, an extended form of genetic programming is used to evolve program variants until one is found that both retains required functionality and also avoids the defect in question. Standard test cases are used to exercise the fault and to encode program requirements. After a successful repair has been discovered, it is minimized using structural differencing algorithms and delta debugging. We describe the proposed method and report results from an initial set of experiments demonstrating that it can successfully repair ten different C programs totaling 63,000 lines in under 200 seconds, on average.", notes = " slides http://www.cs.uoregon.edu/events/icse2009/images/postPosters/Automatically%20Finding%20Patches%20Using%20Genetic%20Programming.pdf ICSE best paper http://www.cs.uoregon.edu/events/icse2009/home/ Winner The IFIP TC2 Manfred Paul Award for Excellence in Software: Theory and Practice http://www.cs.uoregon.edu/events/icse2009/awards/#ifip Winner ACM SIGSOFT Distinguished Papers Award https://www.sigsoft.org/awards/distinguishedPaperAward.html Cf. \cite{DBLP:conf/gecco/ForrestNWG09} best paper GECCO 2009. Gold medal at 2009 HUMIES, GECCO. http://www.genetic-programming.org/hc2009/cfe2009.html http://www.genetic-programming.org/hc2009/1-Forrest/Forrest-Presentation.pdf Most Influential Paper at ICSE 2019 slideshare: https://www.slideshare.net/ClaireLeGoues/it-does-what-you-say-not-what-you-mean-lessons-from-a-decade-of-program-repair Listed on https://program-repair.org/bibliography.html", } @Article{Weimer:2010:ACM, author = "Westley Weimer and Stephanie Forrest and Claire {Le Goues} and ThanhVu Nguyen", title = "Automatic program repair with evolutionary computation", journal = "Communications of the ACM", volume = "53", number = "5", year = "2010", pages = "109--116", month = jun, publisher = "ACM", address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, SBSE", ISSN = "0001-0782", URL = "http://www.cs.virginia.edu/~weimer/p/p109-weimer.pdf", DOI = "doi:10.1145/1735223.1735249", size = "8 pages", abstract = "There are many methods for detecting and mitigating software errors but few generic methods for automatically repairing errors once they are discovered. This paper highlights recent work combining program analysis methods with evolutionary computation to automatically repair bugs in off-the-shelf legacy C programs. The method takes as input the buggy C source code, a failed test case that demonstrates the bug, and a small number of other test cases that encode the required functionality of the program. The repair procedure does not rely on formal specifications, making it applicable to a wide range of extant software for which formal specifications rarely exist.", notes = "Research highlights. The material in this paper is taken from two original publications, \cite{DBLP:conf/gecco/ForrestNWG09} and \cite{Weimer:2009:ICES}. Also known as \cite{1735249}", } @InProceedings{Weimer:2013:SSBSE, author = "Westley Weimer", title = "Advances in Automated Program Repair and a Call to Arms", booktitle = "Symposium on Search-Based Software Engineering", year = "2013", editor = "Guenther Ruhe and Yuanyuan Zhang", volume = "8084", series = "Lecture Notes in Computer Science", pages = "1--3", address = "Leningrad, Russia", month = aug # " 24-26", publisher = "Springer", note = "Invited keynote", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, SBSE, GenProg", isbn13 = "978-3-642-39741-7", URL = "http://www.cs.ucl.ac.uk/staff/s.yoo/papers/Xie2013kx.pdf", DOI = "doi:10.1007/978-3-642-39742-4_1", size = "3 pages", abstract = "In this keynote address I survey recent success and momentum in the subfield of automated program repair. I also encourage the search-based software engineering community to rise to various challenges and opportunities associated with test oracle generation, large-scale human studies, and reproducible research through benchmarks. I discuss recent advances in automated program repair, focusing on the search-based GenProg technique but also presenting a broad overview of the subfield. I argue that while many automated repair techniques are correct by construction or otherwise produce only a single repair (e.g., AFix [13], Axis [17], Coker and Hafiz [4], Demsky and Rinard [7], Gopinath et al. [12], Jolt [2], Juzi [8], etc.), the majority can be categorised as generate and validate approaches that enumerate and test elements of a space of candidate repairs and are thus directly amenable to search-based software engineering and mutation testing insights (e.g., ARC [1], AutoFix-E [23], ARMOR [3], CASC [24], ClearView [21], Debroy and Wong [6], FINCH [20], PACHIKA [5], PAR [14], SemFix [18], Sidiroglou and Keromytis [22], etc.). I discuss challenges and advances such as scalability, test suite quality, and repair quality while attempting to convey the excitement surrounding a subfield that has grown so quickly in the last few years that it merited its own session at the 2013 International Conference on Software Engineering [3,4,14,18]. Time permitting, I provide a frank discussion of mistakes made and lessons learnt with GenProg [15]. In the second part of the talk, I pose three challenges to the SBSE community. I argue for the importance of human studies in automated software engineering. I present and describe multiple how to examples of using crowd sourcing (e.g., Amazon's Mechanical Turk) and massive on-line education (MOOCs) to enable SBSE-related human studies [10,11]. I argue that we should leverage our great strength in testing to tackle the increasingly-critical problem of test oracle generation (e.g., [9]) - not just test data generation - and draw supportive analogies with the subfields of specification mining and invariant detection [16,19]. Finally, I challenge the SBSE community to facilitate reproducible research and scientific advancement through benchmark creation, and support the need for such efforts with statistics from previous accepted papers.", } @InProceedings{Weimer-ase2013, author = "Westley Weimer and Zachary P. Fry and Stephanie Forrest", title = "Leveraging Program Equivalence for Adaptive Program Repair: Models and First Results", booktitle = "28th IEEE/ACM International Conference on Automated Software Engineering", year = "2013", editor = "Ewen Denney and Tevfik Bultan and Andreas Zeller", pages = "356--366", address = "Palo Alto, USA", month = nov # " 11-15", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, SBSE, Automated program repair, GenProg, mutation testing, program equivalence, search-based software engineering", URL = "http://www.cs.virginia.edu/~weimer/p/weimer-ase2013-preprint.pdf", DOI = "doi:10.1109/ASE.2013.6693094", size = "11 pages", abstract = "Software bugs remain a compelling problem. Automated program repair is a promising approach for reducing cost, and many methods have recently demonstrated positive results. However, success on any particular bug is variable, as is the cost to find a repair. This paper focuses on generate-and-validate repair methods that enumerate candidate repairs and use test cases to define correct behaviour. We formalise repair cost in terms of test executions, which dominate most test-based repair algorithms. Insights from this model lead to a novel deterministic repair algorithm that computes a patch quotient space with respect to an approximate semantic equivalence relation. This allows syntactic and dataflow analysis techniques to dramatically reduce the repair search space. Generate-and-validate program repair is shown to be a dual of mutation testing, suggesting several possible cross-fertilisations. Evaluating on 105 real-world bugs in programs totalling 5 million lines of code LOC and involving 10000 tests, our new algorithm requires an order-of-magnitude fewer test evaluations than the previous state-of-the-art and is over three times more efficient monetarily.", notes = "ase2013.org/ See also \cite{Qi:2015:APP:2771783.2771791} INSPEC Accession Number: 1402267 Also known as \cite{6693094}", } @InProceedings{DBLP:conf/dsn/WeimerFKGH16, author = "Westley Weimer and Stephanie Forrest and Miryung Kim and Claire {Le Goues} and Patrick Hurley", title = "Trusted Software Repair for System Resiliency", booktitle = "46th Annual {IEEE/IFIP} International Conference on Dependable Systems and Networks Workshops, {DSN} Workshops 2016", year = "2016", pages = "238--241", address = "Toulouse, France", month = jun # " 28 - " # jul # " 1", publisher = "{IEEE} Computer Society", keywords = "genetic algorithms, genetic programming, SBSE, APR, GenProg, DIG, k-induction, partial correctness oracle, theorem prover, Z3, Grafter, buggy GCD", isbn13 = "978-1-5090-3688-2", timestamp = "Wed, 16 Oct 2019 14:14:55 +0200", biburl = "https://dblp.org/rec/conf/dsn/WeimerFKGH16.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://doi.org/10.1109/DSN-W.2016.64", DOI = "doi:10.1109/DSN-W.2016.64", size = "4 pages", abstract = "We describe ongoing work to increase trust in resilient software systems. Automated software repair techniques promise to increase system resiliency, allowing missions to continue in the face of software defects. While a number of program repair approaches have been proposed, the most scalable and applicable of those techniques can be the most difficult to trust. Using approximate solutions to the oracle problem, we consider three approaches by which trust can be re-established in a post-repair system. Each approach learns or infers a different form of partial model of correct behaviour from pre-repair observations; post-repair systems are evaluated with respect to those models. We focus on partial oracles modelled from external execution signals, derived from similar code fragment behavior, and inferred from invariant relations over local variables. We believe these three approaches can provide an expanded assessment of trust in a repaired, resilient system.", notes = "Measure at runtime number of instructions, number of branches, supervised learning of bug fix quality. Zune bug. Differential testing. Test transplantation (from existing code clones). Approximations to the test Oracle Problem. Slides https://web.eecs.umich.edu/~weimerw/p/weimer-dsn2016-trusted-pres.pdf", } @InProceedings{Weimer:2016:GECCOcomp, author = "Westley Weimer and Justyna Petke and David R. White", title = "Genetic Improvement 2016 Chairs' Welcome and Organization", booktitle = "Genetic Improvement 2016 Workshop", year = "2016", editor = "Westley Weimer and Justyna Petke and David R. White", pages = "1129--1130", keywords = "genetic algorithms, genetic programming, genetic improvement", month = "20-24 " # jul, organisation = "SIGEVO", address = "Denver, USA", publisher = "ACM", publisher_address = "New York, NY, USA", isbn13 = "978-1-4503-4323-7", DOI = "doi:10.1145/2908961.2931686", abstract = "It is our great pleasure to welcome you to the 2nd Workshop on Genetic Improvement...", notes = "Distributed at GECCO-2016.", } @Misc{Weimer:2019:ICSE, author = "Westley Weimer and ThanhVu Nguyen and Claire {Le Goues} and Stephanie Forrest", title = "It Does What You Say, Not What You Mean: Lessons From A Decade of Program Repair", howpublished = "ICSE 2019 Plenary Most Inflential Paper", year = "2019", month = "30 " # may, keywords = "genetic algorithms, genetic programming, genetic improvement, APR", URL = "https://conf.researchr.org/profile/icpc-2019/westleyweimer", URL = "https://2019.icse-conferences.org/details/icse-2019-Plenary-Sessions/19/It-Does-What-You-Say-Not-What-You-Mean-Lessons-From-A-Decade-of-Program-Repair", video_url = "https://www.youtube.com/watch?v=dluaKBhXdtY", abstract = "we present lessons learned, good ideas, and thoughts on the future, with an eye toward informing junior researchers about the realities and opportunities of a long-running project. We highlight some notions from the original paper that stood the test of time, some that were not as prescient, and some that became more relevant as industrial practice advanced. We place the work in context, highlighting perceptions from software engineering and evolutionary computing, then and now, of how program repair could possibly work. We discuss the importance of measurable benchmarks and reproducible research in bringing scientists together and advancing the area. We give our thoughts on the role of quality requirements and properties in program repair. From testing to metrics to scalability to human factors to technology transfer, software repair touches many aspects of software engineering, and we hope a behind-the-scenes exploration of some of our struggles and successes may benefit researchers pursuing new projects.", notes = "\cite{Weimer:2009:ICES}", } @Misc{Weimer:2022:GI, author = "Westley Weimer", title = "From Deep Learning to Human Judgments: Lessons for Genetic Improvement", howpublished = "GI @ GECCO 2022", year = "2022", editor = "Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and W. B. Langdon and Justyna Petke", address = "Boston, USA", month = "9 " # jul, note = "{Invited keynote}", keywords = "genetic algorithms, genetic programming, genetic improvement", slides_url = "http://geneticimprovementofsoftware.com/slides/gi2022gecco/weimer-keynote-gi-gecco-22.pdf", video_url = "https://www.youtube.com/watch?v=90z0k3jwhU8&list=PLI8fiFpB7BoIHgl5CsdtjfWvHlE5N6pje", size = "30 slides, 52 minutes 25 seconds", notes = "Slides only GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @Misc{weinbrenner:1997:diploma, author = "Thomas Weinbrenner", title = "Genetic Programming Techniques Applied to Measurement Data", howpublished = "Diploma Thesis", year = "1997", school = "Institute for Mechatronics, Technical University of Darmstadt", type = "Diplomabeit 1362", address = "Germany", month = feb, keywords = "genetic algorithms, genetic programming, System identification, Genetic Programming C++ class library, helicopter engine", broken = "http://www.emk.e-technik.tu-darmstadt.de/~thomasw/da1362.ps.gz", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/21598/http:zSzzSzwww.emk.e-technik.tu-darmstadt.dezSz~thomaswzSzda1362.pdf/weinbrenner97genetic.pdf", URL = "http://citeseer.ist.psu.edu/weinbrenner97genetic.html", size = "79 pages", abstract = "In this project, an unknown system structure was identified using the Genetic Programming technique. A program was developed that, instead of combining linear systems, evolves nonlinear ordinary differential equations to describe a system. The solution space was increased by using this approach. Automatically defined functions were used to represent the ordinary differential equations. A new way was introduced to speed up the evolution of genetic trees. The genetic trees representing the equations were written in C notation to a file to be compiled by a C compiler and evaluated by the computer. This required modifications to the Genetic Programming kernel formerly used. The modifications facilitated evaluation of a complete generation at one time to minimise compiler overhead. In order to build up a C++ class hierarchy, the kernel was completely restructured. A lot of features like shrink mutation, variable tournament size, improved deme handling etc. were also added. A parameter study was carried out to investigate the influence of important control parameters. The Genetic Programming system was applied to a known, nonlinear system to verify its ability. After this proved to be successful, the input/output response data of a helicopter engine was used to identify that system. Candidate models were derived from that analysis.", notes = "Documentation on final year project. New version of Adam Fraser's GPc++ http://www.emk.e-technik.tu-darmstadt.de/~thomasw/gpc++0.5.2.tar.gz", } @TechReport{oai:eldorado:0x00041622, title = "Reconstruction of Physical Correlations Using Symbolic Regression", author = "Klaus Weinert and Marc Stautner", year = "2001", institution = "Dept. of Machining Technology, University of Dortmund", address = "D-44227 Dortmund, Germany", month = jul # " 24", keywords = "genetic algorithms, genetic programming", language = "ENG", oai = "oai:eldorado:0x00041622", rights = "These documents can be used freely according to copyright laws. They can be printed freely. It is not allowed to distribute them further on.", URL = "http://hdl.handle.net/2003/5415", URL = "https://eldorado.tu-dortmund.de/bitstream/2003/5415/1/116.pdf", abstract = "Modeling the particle flow mechanisms in orthogonal cutting of turning processes is a vital task in mechanical engineering. This paper presents a new approach that differs from techniques like finite element analyzes (FEA) or molecular dynamics (MD). Using symbolic regression, a genetic programming system evolves mathematical formulae that describe the trajectories of single particles of steel recorded during the turning process by a high-speed camera.", notes = "WINanalyze", size = "9 pages", } @InProceedings{weinert2:2001:gecco, title = "Reconstruction of Particle Flow Mechanisms with Symbolic Regression via Genetic Programming", author = "Klaus Weinert and Marc Stautner", pages = "1439--1443", year = "2001", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)", editor = "Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon and Edmund Burke", address = "San Francisco, California, USA", publisher_address = "San Francisco, CA 94104, USA", month = "7-11 " # jul, keywords = "genetic algorithms, genetic programming, real world applications, Symbolic regression, Flow mechanisms, Mechanical engineering", ISBN = "1-55860-774-9", URL = "http://gpbib.cs.ucl.ac.uk/gecco2001/d24.pdf", notes = "GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of \cite{spector:2001:GECCO}", } @InProceedings{weinert:2001:gecco, title = "Evolutionary Surface Reconstruction Using CSG-NURBS-Hybrids", author = "Klaus Weinert and Tobias Surmann and Jorn Mehnen", pages = "1456", year = "2001", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)", editor = "Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon and Edmund Burke", address = "San Francisco, California, USA", publisher_address = "San Francisco, CA 94104, USA", month = "7-11 " # jul, keywords = "genetic algorithms, genetic programming, real world applications: Poster, surface reconstruction, NURBS, CSG", ISBN = "1-55860-774-9", URL = "http://gpbib.cs.ucl.ac.uk/gecco2001/d25.pdf", notes = "GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of \cite{spector:2001:GECCO}", } @InProceedings{weinert:2002:EuroGP, title = "Parallel Surface Reconstruction", author = "Klaus Weinert and Tobias Surmann and J{\"o}rn Mehnen", editor = "James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi", booktitle = "Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002", volume = "2278", series = "LNCS", pages = "93--102", publisher = "Springer-Verlag", address = "Kinsale, Ireland", publisher_address = "Berlin", month = "3-5 " # apr, year = "2002", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-43378-3", DOI = "doi:10.1007/3-540-45984-7_9", abstract = "The task of surface reconstruction is to find a mathematical representation of a surface which is given only by a set of discrete sampling points. The mathematical description in the computer allows to save or transfer the geometric data via internet, to manipulate (e.g. for aerodynamic or design specific reasons) or to optimize the machining of the work pieces. The reconstruction of the shape of an object is a difficult mathematical and computer scientific problem. For this reason a GP/ES-hybrid algorithm has been used. Due to the high complexity of the problem and in order to speed up the reconstruction process, the algorithm has been enhanced to work as a multipopulation GP/ES that runs in parallel on a network of standard PCs.", notes = "EuroGP'2002, part of \cite{lutton:2002:GP}", } @InProceedings{weinert:2002:EuroGPa, title = "A New View on Symbolic Regression", author = "Klaus Weinert and Marc Stautner", editor = "James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi", booktitle = "Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002", volume = "2278", series = "LNCS", pages = "113--122", publisher = "Springer-Verlag", address = "Kinsale, Ireland", publisher_address = "Berlin", month = "3-5 " # apr, year = "2002", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-43378-3", DOI = "doi:10.1007/3-540-45984-7_11", abstract = "Symbolic regression is a widely used method to reconstruct mathematical correlations. This paper presents a new graphical representation of the individuals reconstructed in this process. This new three dimensional representation allows the user to recognize certain possibilities to improve his setup of the process parameters. Furthermore this new representation allows a wider usage of the generated three dimensional objects with nearly every CAD program for further use. To show the practical usage of this new representation it was used to reconstruct mathematical descriptions of the dynamics in a machining process namely in orthogonal cutting.", notes = "EuroGP'2002, part of \cite{lutton:2002:GP}", } @InProceedings{conf/adma/WeinertL06, title = "{GEPCLASS}: {A} Classification Rule Discovery Tool Using Gene Expression Programming", author = "Wagner R. Weinert and Heitor S. Lopes", booktitle = "Advanced Data Mining and Applications, Proceedings of the Second International Conference, {ADMA}", year = "2006", editor = "Xue Li and Osmar R. Za{\"i}ane and Zhanhuai Li", volume = "4093", series = "Lecture Notes in Computer Science", pages = "871--880", address = "Xi'an, China", month = aug # " 14-16", publisher = "Springer", bibdate = "2006-08-21", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/adma/adma2006.html#WeinertL06", keywords = "genetic algorithms, genetic programming, Gene Expression Programming", ISBN = "3-540-37025-0", DOI = "doi:10.1007/11811305_95", abstract = "This work describes the use of a recently proposed technique gene expression programming for knowledge discovery in the data mining task of data classification. We propose a new method for rule encoding and genetic operators that preserve rule integrity, and implemented a system, named GEPCLASS. Due to its encoding scheme, the system allows the automatic discovery of flexible rules, better fitted to data. The performance of GEPCLASS was compared with two genetic programming systems and with C4.5, over four data sets in a five-fold cross-validation procedure. The predictive accuracy for the methods compared were similar, but the computational effort needed by GEPCLASS was significantly smaller than the other. GEPCLASS was able to find simple and accurate rules as it can handle continuous and categorical attributes.", } @Article{Weinert20106, author = "Wagner Rodrigo Weinert and Heitor Silverio Lopes", title = "Evaluation of dynamic behavior forecasting parameters in the process of transition rule induction of unidimensional cellular automata", journal = "Biosystems", volume = "99", number = "1", pages = "6--16", year = "2010", ISSN = "0303-2647", DOI = "doi:10.1016/j.biosystems.2009.08.002", URL = "http://www.sciencedirect.com/science/article/B6T2K-4X0XFDD-1/2/0604807ff3e25dde5b1b6902b792e157", keywords = "genetic algorithms, genetic programming, Cellular automata, Dynamic behavior forecasting parameters, Dynamic systems, Evolutionary computation", abstract = "The simulation of the dynamics of a cellular systems based on cellular automata (CA) can be computationally expensive. This is particularly true when such simulation is part of a procedure of rule induction to find suitable transition rules for the CA. Several efforts have been described in the literature to make this problem more treatable. This work presents a study about the efficiency of dynamic behaviour forecasting parameters (DBFPs) used for the induction of transition rules of CA for a specific problem: the classification by the majority rule. A total of 8 DBFPs were analysed for the 31 best-performing rules found in the literature. Some of these DBFPs were highly correlated each other, meaning they yield the same information. Also, most rules presented values of the DBFPs very close each other. An evolutionary algorithm, based on gene expression programming, was developed for finding transition rules according a given preestablished behavior. The simulation of the dynamic behavior of the CA is not used to evaluate candidate transition rules. Instead, the average values for the DBFPs were used as reference. Experiments were done using the DBFPs separately and together. In both cases, the best induced transition rules were not acceptable solutions for the desired behavior of the CA. We conclude that, although the DBFPs represent interesting aspects of the dynamic behavior of CAs, the transition rule induction process still requires the simulation of the dynamics and cannot rely only on the DBFPs.", } @Article{Weinert:2015:IJICA, author = "Wagner Rodrigo Weinert and Heitor Silverio Lopes", title = "Data mining with a parallel rule induction system based on gene expression programming", journal = "International Journal of Innovative Computing and Applications", publisher = "Inderscience Publishers", year = "2015", month = mar # "~21", volume = "3", number = "3", pages = "136--143", keywords = "genetic algorithms, genetic programming, evolutionary computation, gene expression programming, GEP, data mining, parallel rule induction, data classification, bioinformatics", bibsource = "OAI-PMH server at www.inderscience.com", ISSN = "1751-6498", URL = "http://www.inderscience.com/link.php?id=41914", DOI = "doi:10.1504/IJICA.2011.041914", abstract = "A parallel rule induction system based on gene expression programming (GEP) is reported in this paper. The system was developed for data classification. The parallel processing environment was implemented on a cluster using a message-passing interface. A master-slave GEP was implemented according to the Michigan approach for representing a solution for a classification problem. A multiple master-slave system (islands) was implemented in order to observe the co-evolution effect. Experiments were done with ten datasets, and algorithms were systematically compared with C4.5. Results were analysed from the point of view of a multi-objective problem, taking into account both predictive accuracy and comprehensibility of induced rules. Overall results indicate that the proposed system achieves better predictive accuracy with shorter rules, when compared with C4.5.", } @TechReport{W2006DGPFa, author = "Thomas Weise", title = "Genetic Programming for Sensor Networks", year = "2006", month = jan, affiliation = "University of Kassel", address = "University of Kassel", institution = "University of Kassel", organization = "University of Kassel", school = "University of Kassel", notes = "DGPF Home: http://dgpf.sourceforge.net/ The work is online available at http://www.it-weise.de/documents/index.html#W2006DGPFa The report can be downloaded at http://www.it-weise.de/documents/files/W2006DGPFa.pdf Contact Thomas Weise at tweise@gmx.de or http://www.it-weise.de/", email = "tweise@gmx.de", affiliation = "University of Kassel, Distributed Systems Group", language = "en", URL = "http://www.it-weise.de/documents/index.html#W2006DGPFa", URL = "http://dgpf.sourceforge.net/documents/001-2006-01-20-paper.pdf", URL = "http://www.it-weise.de/documents/files/W2006DGPFa.pdf", copyright = "unrestricted", keywords = "genetic algorithms, genetic programming, DGPF, Sensor Network, Sensor Node", rights = "unrestricted", abstract = "DGPF Home: http://dgpf.sourceforge.net/ In this paper we present an approach to automated program code generation for sensor nodes and other small devices using Genetic Programming. We give a short introduction to Genetic Algorithms. Our new Distributed Genetic Programming Framework facilitates the development of sensor network applications. Genetic evolution of programs requires program testing. Therefore we use a simulation environment for distributed systems of sensor nodes. The simulation model takes into account characteristic features of sensor nodes, such as unreliable communication and resource constraints. Two application examples are presented that demonstrate the feasibility of our approach and its potential to create robust and adaptive code for sensor network applications.", language = "en", size = "16 pages", } @InProceedings{DGPF06b, author = "Thomas Weise and Kurt Geihs", title = "Genetic Programming Techniques for Sensor Networks", year = "2006", booktitle = "Proceedings of 5. GI/ITG KuVS Fachgesprach Drahtlose Sensornetze", editor = "Pedro Jose Marron", pages = "21--25", address = "University of Stuttgart, Stuttgart, Germany", month = jul, organisation = "Faculty of Computer Science, Electrical Engineering, and Information Technology, University of Stuttgart", note = "Technical Report No. 2006/07", affiliation = "University of Kassel, Distributed Systems Group, Wilhelmsh{\"o}her Allee 73, 34121 Kassel, Germany", language = "en", rights = "unrestricted", keywords = "genetic algorithms, genetic programming, DGPF, Sensor Network, Sensor Node", URL = "http://dgpf.sourceforge.net/documents/006-2006-07-17-kuvs_paper.pdf", URL = "ftp://ftp.informatik.uni-stuttgart.de/pub/library/ncstrl.ustuttgart_fi/TR-2006-07/TR-2006-07.pdf", URL = "http://www.it-weise.de/documents/files/W2006DGPFb.pdf", URL = "http://elib.uni-stuttgart.de/opus/volltexte/2006/2838/", abstract = "In this paper we present an approach to automated program code generation for sensor nodes and other small devices. Using Genetic Programming, we are able to discover algorithms that solve certain problems. Furthermore, non-functional properties like code size, memory usage, and communication frequency can be optimised using multiobjective search techniques. The evolution of algorithms requires program testing, which we perform using a customised simulation environment for sensor networks. The simulation model takes into account characteristic features of sensor nodes, such as unreliable communication and resource constraints. An application example is presented that demonstrates the feasibility of our approach and its potential to create robust and adaptive code for sensor network applications.", notes = "Proceedings published a Technical Report No. 2006/07 These are the proceedings of the 5th GI/ITG KuVS Fachgesprach Drahtlose Sensornetze (expert talk on wireless sensor networks) held at the University Stuttgart in July, 2006. The program included among others papers on sensor network hardware, routing, middleware, localisation, programming abstractions and modelling. While the presentations were given in German most of the papers are in English. Also known as \cite{WG2006DGPFb}", } @InProceedings{WG2006DGPFc, author = "Thomas Weise and Kurt Geihs", title = "DGPF -- An Adaptable Framework for Distributed Multi-Objective Search Algorithms Applied to the Genetic Programming of Sensor Networks", pages = "157--166", year = "2006", type = "Research Talk Paper", affiliation = "University of Kassel, FB-16, Distributed Systems Group, Wilhelmsh{\"o}her Allee 73, 34121 Kassel, Germany", copyright = "unrestricted", abstract = "We present DGPF, a framework providing multi-objective, auto-adaptive search algorithms with a focus on Genetic Programming. We first introduce a Common Search API, suitable to explore arbitrary problem spaces with different search algorithms. Using our implementation of Genetic Algorithms as an example, we elaborate on the distribution utilities of the framework which enable local, Master/Slave, Peer-To-Peer, and P2P/MS hybrid distributed search execution. We also discuss how heterogeneous searches consisting of multiple, cooperative search algorithms can be constructed. Sensor networks are distributed systems of nodes with scarce resources. We demonstrate how Genetic Programming based on our framework can be applied to create algorithms for sensor nodes that use these resources very efficiently.", keywords = "genetic algorithms, genetic programming, DGPF, Sensor Network, Sensor Node", language = "en", booktitle = "Proceedings of the Second International Conference on Bioinspired Optimization Methods and their Application, BIOMA 2006", editor = "Bogdan Filipi{\v{c}} and Jurij {\v{S}}ilc", isbn13 = "978-961-6303-81-1", publisher = "Jo{\v{z}}ef Stefan Institute", month = "9-10 " # oct, address = "Jo{\v{z}}ef Stefan International Postgraduate School, Ljubljana, Slovenia", language = "en", URL = "http://www.it-weise.de/documents/files/WG2006DGPFc.pdf", } @InProceedings{WGB2007DGPFb, author = "Thomas Weise and Kurt Geihs and Philipp Andreas Baer", title = "Genetic Programming for Proactive Aggregation Protocols", pages = "167--173", year = "2007", type = "Conference Paper", affiliation = "University of Kassel, FB-16, Distributed Systems Group, Wilhelmsh{\"o}her Allee 73, 34121 Kassel, Germany", copyright = "restricted", abstract = "We present an approach for automated generation of proactive aggregation protocols using Genetic Programming. First a short introduction into aggregation and proactive protocols is given. We then show how proactive aggregation protocols can be specified abstractly. To be able to use Genetic Programming to derive such protocol specifications, we describe a simulation based fitness assignment method. We have applied our approach successfully to the derivation of aggregation protocols. Experimental results are presented that were obtained using our own Distributed Genetic Programming Framework. The results are very encouraging and demonstrate clearly the utility of our approach.", contents = "We present an approach for automated generation of proactive aggregation protocols using Genetic Programming. First a short introduction into aggregation and proactive protocols is given. We then show how proactive aggregation protocols can be specified abstractly. To be able to use Genetic Programming to derive such protocol specifications, we describe a simulation based fitness assignment method. We have applied our approach successfully to the derivation of aggregation protocols. Experimental results are presented that were obtained using our own Distributed Genetic Programming Framework. The results are very encouraging and demonstrate clearly the utility of our approach.", keywords = "genetic algorithms, genetic programming, Proactive Aggregation Protocols, Aggregation, Sensor Networks, DGPF, Symbolic Regression", language = "en", booktitle = "Proceedings of Adaptive and Natural Computing Algorithms, 8th International Conference, ICANNGA 2007, Part I", address = "Warsaw University of Technology, Warsaw, Poland", month = apr # "~11-14,", editor = "Bart{\l}omiej Beliczy{\'n}ski and Andrzej Dzieli{\'n}ski and Marcin Iwanowski and Bernardete Ribeiro", publisher = "Springer Berlin Heidelberg New York", series = "Lecture Notes in Computer Science", volume = "4431", DOI = "doi:10.1007/978-3-540-71618-1", ISSN = "0302-9743", isbn13 = "978-3-540-71589-4", URL = "http://www.it-weise.de/documents/files/WGB2007DGPFb.pdf", DOI = "doi:10.1007/978-3-540-71618-1_19", notes = "ICANNGA 2007 see http://icannga07.ee.pw.edu.pl/", } @TechReport{WZKG2007DGPFd, author = "Thomas Weise and Michael Zapf and Mohammad Ullah Khan and Kurt Geihs", title = "Genetic Programming meets Model-Driven Development", type = "Kasseler Informatikschriften (KIS)", number = "2007, 2", pages = "1--8", year = "2007", month = jul # "~2,", address = "University of Kassel, FB-16, Distributed Systems Group, Wilhelmsh{\"o}her Allee 73, 34121 Kassel, Germany", institution = "University of Kassel", organization = "University of Kassel", notes = "Persistent Identifier: urn:nbn:de:hebis:34-2007070218786", URL = "http://www.it-weise.de/documents/files/WZKG2007DGPFd.pdf", URL = "http://kobra.bibliothek.uni-kassel.de/handle/urn:nbn:de:hebis:34-2007070218786", copyright = "unrestricted", abstract = "Genetic programming is known to provide good solutions for many problems like the evolution of network protocols and distributed algorithms. In such cases it is most likely a hardwired module of a design framework that assists the engineer to optimise specific aspects of the system to be developed. It provides its results in a fixed format through an internal interface. In this paper we show how the usefulness of genetic programming can be increased remarkably by isolating it as a component and integrating it into the model-driven software development process. Our genetic programming framework produces XMI-encoded UML models that can easily be loaded into widely available modelling tools which in turn posses code generation as well as additional analysis and test capabilities. We use the evolution of a distributed election algorithm as an example to illustrate how genetic programming can be combined with model-driven development. This example clearly illustrates the advantages of our approach - the generation of source code in different programming languages.", keywords = "genetic algorithms, genetic programming, GP, Model Driven Development, MDD, Model Driven Architecture, MDA, XMI, MOF-Skript, UML, Distributed Algorithms", language = "en", notes = "cited by \cite{Weise:2007:HIS}", } @InProceedings{Weise:2007:HIS, author = "Thomas Weise and Michael Zapf and Mohammad Ullah Khan and Kurt Geihs", title = "Genetic Programming meets Model-Driven Development", booktitle = "7th International Conference on Hybrid Intelligent Systems, HIS 2007", year = "2007", language = "en", editor = "Andreas K{\"o}nig and Mario K{\"o}ppen and Ajith Abraham and Christian Igel and Nikola Kasabov", pages = "332--335", address = "Kaiserslautern, Germany", month = "17-19 " # sep, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, SBSE, GP, Model Driven Development, MDD, Model Driven Architecture, MDA, XMI, MOF-Skript, UML, Distributed Algorithms, SBSE", isbn13 = "978-0-7695-2946-2", URL = "http://www.it-weise.de/documents/files/WZKG2007DGPFg.pdf", DOI = "doi:10.1109/ICHIS.2007.4344073", DOI = "doi:10.1109/HIS.2007.11", abstract = "Genetic programming is known to provide good solutions for many problems like the evolution of network protocols and distributed algorithms. In such cases it is most likely a hardwired module of a design framework that assists the engineer to optimise specific aspects of the system to be developed. It provides its results in a fixed format through an internal interface. In this paper we show how the usefulness of genetic programming can be increased remarkably by isolating it as a component and integrating it into the model-driven software development process. Our genetic programming framework produces XMI-encoded UML models that can easily be loaded into widely available modelling tools which in turn posses code generation as well as additional analysis and test capabilities. We use the evolution of a distributed election algorithm as an example to illustrate how genetic programming can be combined with model-driven development. This example clearly illustrates the advantages of our approach - the generation of source code in different programming languages.", contents = "* Introduction\\* Genetic Programming and MDD\\- Model-Driven Development\\- Combining MDD and GP\\* Evolving Distributed Algorithms\\- Evolving an Election Algorithm\\* Creating a PIM\\- Control Flow Model\\- Data Model\\- Modeling the Primitive Operations\\* Transforming the UML Models\\* Conclusion", notes = "also known as \cite{WZKG2007DGPFg} \cite{4344073} Library of Congress Number 2007936727, Product Number E2946. see http://his07.hybridsystem.com/ Longer version of \cite{WZKG2007DGPFd}. AST, PIM PSM, multi-objective GP MOGP, MOFScript modelware, eclipse modeling framework, C, Java.", } @TechReport{WAGVZ2007DMC, author = "Thomas Weise and Stefan Achler and Martin G{\"{o}}b and Christian Voigtmann and Michael Zapf", title = "Evolving Classifiers -- Evolutionary Algorithms in Data Mining", volume = "2007", number = "2007, 4", pages = "1--20", publisher = "University of Kassel", year = "2007", month = sep # "~28,", type = "Kasseler Informatikschriften (KIS)", affiliation = "University of Kassel, FB-16, Distributed Systems Group, Wilhelmsh{\"o}her Allee 73, 34121 Kassel, Germany", location = "University of Kassel", address = "University of Kassel", institution = "University of Kassel", organization = "University of Kassel", school = "University of Kassel", howpublished = "online", notes = "Persistent Identifier: urn:nbn:de:hebis:34-2007092819260", URL = "http://www.it-weise.de/documents/files/WAGVZ2007DMC.pdf", URL = "http://kobra.bibliothek.uni-kassel.de/handle/urn:nbn:de:hebis:34-2007092819260", abstract = "Data mining means to summarise information from large amounts of raw data. It is one of the key technologies in many areas of economy, science, administration and the Internet. In this report we introduce an approach for using evolutionary algorithms to breed fuzzy classifier systems. This approach was exercised as part of a structured procedure by the students Achler, G{\"o}b, and Voigtmann as contribution to the 2006 Data-Mining-Cup contest, yielding encouragingly positive results.", contents = "* Introduction\\* Data Mining\\* Related Work\\- Evolutionary Algorithms\\- Genetic Algorithms\\- Learning Classifier Systems\\* A Structured Approach to Data Mining\\* Applying the Structured Approach\\- The Problem Definition\\- Initial Analysis\\- Analysis of the Evolutionary Process\\- Contest Results and Placement\\* Conclusion and Future Work\\* References", keywords = "Evolutionary Computation, Data Mining, Data-Mining-Cup, DMC 2007, Evolutionary Algorithm, Genetic Algorithm, Learning Classifier System, Classifier System", language = "en", } @InProceedings{WZG2007DGPFi, author = "Thomas Weise and Michael Zapf and Kurt Geihs", title = "Rule-based Genetic Programming", booktitle = "Proceedings of BIONETICS 2007, 2nd International Conference on Bio-Inspired Models of Network, Information, and Computing Systems", publisher = "Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering (ICST), IEEE, ACM", year = "2007", pages = "8--15", month = "10-12 " # dec, affiliation = "University of Kassel", address = "Radisson SAS Beke Hotel, 43. Terez krt., Budapest H-1067, Hungary", keywords = "genetic algorithms, genetic programming, Rule-based Genetic Programming, GP, Distributed Systems, Critical Section, Epistasis, Neutrality, Learning Classifier Systems", isbn13 = "978-963-9799-05-9", language = "en", URL = "http://www.it-weise.de/documents/files/WZG2007RBGP.pdf", DOI = "doi:10.1109/BIMNICS.2007.4610073", abstract = "In this paper we introduce a new approach for Genetic Programming, called rule-based Genetic Programming, or RBGP in short. A program evolved in the RBGP syntax is a list of rules. Each rule consists of two conditions, combined with a logical operator, and an action part. Such rules are independent from each other in terms of position (mostly) and cardinality (always). This reduces the epistasis drastically and hence, the genetic reproduction operations are much more likely to produce good results than in other Genetic Programming methodologies. we apply RBGP to a hard problem in distributed systems. With it, we are able to obtain emergent algorithms for mutual exclusion at a distributed critical section.", notes = "Also known as \cite{4610073}", } @Book{W2007GOEB, author = "Thomas Weise", title = "Global Optimization Algorithms -- Theory Application", howpublished = "Online as e-book", edition = "second", publisher = "Thomas Weise", institution = "University of Kassel, Distributed Systems Group", organization = "University of Kassel, Distributed Systems Group", language = "en", broken = "http://www.it-weise.de/", year = "2008", keywords = "genetic algorithms, genetic programming, global optimization, evolutionary algorithms, Sigoa, DGPF, Java", URL = "http://www.it-weise.de/projects/book.pdf", URL = "https://scholar.google.com/citations?user=rT9StgcAAAAJ", abstract = "This e-book is devoted to global optimization algorithms, which are methods to find optimal solutions for given problems. It especially focuses on Evolutionary Computation by discussing evolutionary algorithms, genetic algorithms, Genetic Programming, Learning Classifier Systems, Evolution Strategy, Differential Evolution, Particle Swarm Optimization, and Ant Colony Optimization. It also elaborates on other metaheuristics like Simulated Annealing, Extremal Optimization, Tabu Search, and Random Optimization. The book is no book in the conventional sense: Because of frequent updates and changes, it is not really intended for sequential reading but more as some sort of material collection, encyclopedia, or reference work where you can look up stuff, find the correct context, and are provided with fundamentals.", notes = "Second edition, Self-Published", size = "864 pages. See also broken Sep 2024 {"}Optimization Algorithms{"} https://thomasweise.github.io/oa/en/optimization_algorithms_en.pdf 2023-08-02", } @InProceedings{conf/eurogp/WeiseZG08, title = "Evolving Proactive Aggregation Protocols", author = "Thomas Weise and Michael Zapf and Kurt Geihs", bibdate = "2008-04-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/eurogp/eurogp2008.html#WeiseZG08", booktitle = "Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008", address = "Naples", month = "26-28 " # mar, publisher = "Springer", year = "2008", volume = "4971", editor = "Michael O'Neill and Leonardo Vanneschi and Steven Gustafson and Anna Isabel {Esparcia Alcazar} and Ivanoe {De Falco} and Antonio {Della Cioppa} and Ernesto Tarantino", isbn13 = "978-3-540-78670-2", pages = "254--265", series = "Lecture Notes in Computer Science", DOI = "doi:10.1007/978-3-540-78671-9_22", keywords = "genetic algorithms, genetic programming", notes = "Part of \cite{conf/eurogp/2008} EuroGP'2008 held in conjunction with EvoCOP2008, EvoBIO2008 and EvoWorkshops2008", } @InProceedings{Weise:2008:ICIW, author = "Thomas Weise and Steffen Bleul and Diana Comes and Kurt Geihs", title = "Different Approaches to Semantic Web Service Composition", booktitle = "Proceedings of The Third International Conference on Internet and Web Applications and Services, ICIW 2008", year = "2008", address = "Athens, Greece", month = jun, organization = "IEEE", publisher = "IEEE Computer Society Press", keywords = "Web Service Composition, Semantic Compositions, Semantic Web, IDDFS, Greedy Search, Genetic Algorithm", URL = "http://www.it-weise.de/documents/files/WBCG2008ICIW.pdf", abstract = "Semantic web service composition is about finding services from a repository that are able to accomplish a specified task if executed. The task is defined in a form of a composition request which contains a set of available input parameters and a set of wanted output parameters. Instead of the parameter values, concepts from an ontology describing their semantics are passed to the composition engine. The parameters of the services in the repository the composer works on are semantically annotated in the same way as the parameters in the request. The composer then finds a sequence of services, called a composition. If the input parameters given in the request are provided, the services of this sequence can subsequently be executed and will finally produce the wanted output parameters. In this paper, three different approaches to semantic web service composition are formally defined and compared with each other: an uninformed search in form of an IDDFS algorithm, a greedy informed search based on heuristic functions, and a multi-objective genetic algorithm.", } @TechReport{Weise:2008:KIS3, author = "Thomas Weise and Hendrik Skubch and Michael Zapf and Kurt Geihs", title = "Global Optimization Algorithms and their Application to Distributed Systems", institution = "University Kassel, Fachbereich 16: Elektrotechnik/Informatik", year = "2009", type = "Kasseler Informatikschriften (KIS)", number = "2008, 3", address = "Universitat Kassel, Fachbereich 16: Elektrotechnik/Informatik, Wilhelmshoher Allee 73, 34121 Kassel, Germany", month = oct, keywords = "genetic algorithms, genetic programming, Global Optimization, Evolutionary Computation, Evolutionary Algorithms, Simulated Annealing, Tabu Search, Distributed Systems, Routing, Protocol Synthesis, Topology, Terminal Assignment, Security, Intrusion Detection, Broadcast, Multicast, Configuration", URL = "http://www.it-weise.de/documents/files/WSZG2008GOAATATDS.pdf", abstract = "In this report, we discuss the application of Global Optimisation and Evolutionary Computation to distributed systems. We therefore selected and classified many publications, giving an insight into the wide variety of optimisation problems which arise in distributed systems. Some interesting approaches from different areas will be discussed in greater detail with the use of illustrative examples.", size = "69 pages", } @InProceedings{Weise:2008:gecco, author = "Thomas Weise and Stefan Niemczyk and Hendrik Skubch and Roland Reichle and Kurt Geihs", title = "A tunable model for multi-objective, epistatic, rugged, and neutral fitness landscapes", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "795--802", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p795.pdf", DOI = "doi:10.1145/1389095.1389252", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "Genetic Algorithm, Fitness Landscape, Multi-Objective, Epistasis, Ruggedness, Neutrality, Model, Benchmark, deceptiveness, overfitting, oversimplification, ruggedness, Formal theory", URL = "http://www.it-weise.de/documents/files/WNSRG2008GECCO.pdf", abstract = "The fitness landscape of a problem is the relation between the solution candidates and their reproduction probability. In order to understand optimization problems, it is essential to also understand the features of fitness landscapes and their interaction. In this paper we introduce a model problem that allows us to investigate many characteristics of fitness landscapes. Specifically noise, affinity for overfitting, neutrality, epistasis, multi-objectivity, and ruggedness can be independently added, removed, and fine-tuned. With this model, we contribute a useful tool for assessing optimization algorithms and parameter settings.", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389252}", } @Article{Weise:2009:IJCIA, author = "Thomas Weise and Michael Zapf and Mohammad Ullah Khan and Kurt Geihs", title = "Combining Genetic Programming and Model-Driven Development", journal = "International Journal of Computational Intelligence and Applications (IJCIA)", year = "2009", volume = "8", number = "1", month = mar, pages = "37--52", keywords = "genetic algorithms, genetic programming, evolutionary algorithms, model-driven development, model-driven architecture, UML, XMI, MOFScript, distributed systems, distributed algorithms, sensor networks", URL = "http://www.it-weise.de/documents/files/WZKG2009DGPFz.pdf", DOI = "doi:10.1142/S1469026809002436", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.699.1181", abstract = "Genetic Programming is known to provide good solutions for many problems like the evolution of network protocols and distributed algorithms. In most cases it is a hardwired module of a design framework assisting the engineer in optimising specific aspects in system development. In this article we show how the utility of Genetic Programming can be increased remarkably by isolating it as a component and integrating it into the model-driven software development process. Our Genetic Programming framework produces XMI-encoded UML models that can easily be loaded into widely available modeling tools, which in turn offer code generation as well as additional analysis and test capabilities. We use the evolution of a distributed election algorithm as an example to illustrate how Genetic Programming can be combined with model-driven development.", } @PhdThesis{Weise:thesis, author = "Thomas Weise", title = "Evolving Distributed Algorithms with Genetic Programming", school = "Department of Electrical Engineering and Computer Science, University of Kassel", year = "2009", address = "Germany", month = may, keywords = "genetic algorithms, genetic programming", URL = "https://kobra.bibliothek.uni-kassel.de/bitstream/urn:nbn:de:hebis:34-2009051127217/3/DissertationThomasWeise.pdf", size = "264 pages", abstract = "Distributed systems are one of the most vital components of the economy. The most prominent example is probably the internet, a constituent element of our knowledge society. During the recent years, the number of novel network types has steadily increased. Amongst others, sensor networks, distributed systems composed of tiny computational devices with scarce resources, have emerged. The further development and heterogeneous connection of such systems imposes new requirements on the software development process. Mobile and wireless networks, for instance, have to organize themselves autonomously and must be able to react to changes in the environment and to failing nodes alike. Researching new approaches for the design of distributed algorithms may lead to methods with which these requirements can be met efficiently. In this thesis, one such method is developed, tested, and discussed in respect of its practical utility. Our new design approach for distributed algorithms is based on Genetic Programming, a member of the family of evolutionary algorithms. Evolutionary algorithms are metaheuristic optimization methods which copy principles from natural evolution. They use a population of solution candidates which they try to refine step by step in order to attain optimal values for predefined objective functions. The synthesis of an algorithm with our approach starts with an analysis step in which the wanted global behavior of the distributed system is specified. From this specification, objective functions are derived which steer a Genetic Programming process where the solution candidates are distributed programs. The objective functions rate how close these programs approximate the goal behavior in multiple randomized network simulations. The evolutionary process step by step selects the most promising solution candidates and modifies and combines them with mutation and crossover operators. This way, a description of the global behavior of a distributed system is translated automatically to programs which, if executed locally on the nodes of the system, exhibit this behavior. In our work, we test six different ways for representing distributed programs, comprising adaptations and extensions of well-known Genetic Programming methods (SGP, eSGP, and LGP), one bio-inspired approach (Fraglets), and two new program representations called Rule-based Genetic Programming (RBGP, eRBGP) designed by us. We breed programs in these representations for three well-known example problems in distributed systems: election algorithms, the distributed mutual exclusion at a critical section, and the distributed computation of the greatest common divisor of a set of numbers. Synthesizing distributed programs the evolutionary way does not necessarily lead to the envisaged results. In a detailed analysis, we discuss the problematic features which make this form of Genetic Programming particularly hard. The two Rule-based Genetic Programming approaches have been developed especially in order to mitigate these difficulties. In our experiments, at least one of them (eRBGP) turned out to be a very efficient approach and in most cases, was superior to the other representations.", zusammenfassung = "Verteilte Systeme stellen eine der wichtigsten Komponenten der Wirtschaft dar. Das wohl bekannteste solche System, das Internet, ist zentraler Bestandteil unserer Wissensgesellschaft. In den letzten Jahren hat die Anzahl neuartiger Netzwerke stetig zugenommen. So entstanden unter Anderem mit Sensornetzen auch verteilte Systeme, die aus kleinen, in ihren Ressourcen stark begrenzten, Knoten bestehen. Die stetige Weiterentwicklung und heterogene Vermaschung solcher Systeme stellt neue Anforderungen an die Softwarearchitekten. Mobile und kabellose Netze mussen sich beispielsweise selbststuandig organisieren und autonom auf ausfallende Knoten oder sich uandernde Umgebungsvariablen reagieren. Um diesen Anforderungen entgegenzutreten ist es daher notwendig, neue Methoden fuur den Entwurf verteilter Systeme verstuarkt zu erforschen. Ziel dieser Arbeit ist es, einen solchen neuen Ansatz zu entwickeln, zu erproben und im Hinblick auf seine praktische Anwendbarkeit zu diskutieren. Unser neuer Ansatz zur Synthese verteilter Algorithmen basiert auf der Genetischen Programmierung, einem Mitglied der Familie der evolutionuaren Algorithmen. Evolutionuare Algorithmen sind der natuurlichen Evolution nachempfundene, metaheuristische Optimierungsverfahren. Sie verwenden eine Population von vielen Luosungskandidaten und versuchen, diese schrittweise immer weiter anzupassen, um optimale Werte fuur vorher definierte Zielfunktionen zu erreichen. Die Erzeugung eines verteilten Algorithmus mit unserem Ansatz beginnt mit einer Analysephase, in der das gewuunschte globale Verhalten eines Systems spezifiziert wird. Aus dieser Spezifikation werden Zielfunktionen abgeleitet, die den Prozess der Genetischen Programmierung steuern, bei dem die Luosungskandidaten verteilte Programme sind. Die Zielfunktionen bewerten, wie weit diese Programme das spezifizierte Verhalten in mehreren zufallsbasierten Netzwerksimulation annuahern. Schritt fuur Schritt wuahlt der evolutionuare Prozess die vielversprechendsten Luosungskandidaten aus und veruandert und kombiniert sie mit Hilfe von Mutations- und Crossover-Operatoren. Auf diese Weise wird die Beschreibung des globalen Verhaltens eines verteilten Systems vollautomatisch in Programme umgerechnet, die dieses Verhalten erreichen, wenn sie lokal auf seinen Knoten ausgefuuhrt werden. In unserer Arbeit testen wir sechs verschiedene Darstellungsformen verteilter Programme auf ihre Tauglichkeit zu diesem Zweck. Drei davon sind Anpassungen und Erweiterungen bekannter Ansuatze (SGP, eSGP, LGP), eine stammt aus der biologisch-inspirierten Forschung (Fraglets) und zwei neue Methoden, genannt Regelbasierte Genetische Programmierung, wurden von uns selbst entwickelt (RBGP, eRBGP). In unseren Experimenten zuuchten wir mit diesen Darstellungsformen Programme fuur drei bekannte Beispielprobleme in verteilten Systemen: Wahlalgorithmen, den verteilte wechselseitige Ausschluss am kritischen Abschnitt und die verteilte Berechnung des gruossten gemeinsamen Teilers einer Menge von natuurlichen Zahlen. Die evolutionuare Synthese von verteilten Programmen fuuhrt nicht immer zum gewuunschten Ziel. In einer ausfuuhrlichen Analyse legen wir die Eigenschaften dar, welche diese Form der Genetischen Programmierung besonders schwer machen. Die beiden regelbasierten Ansuatze wurden speziell auf Basis dieser Analyse entworfen. In den Experimenten stellte sich einer der beiden (die erweiterte regelbasierte Genetische Programmierung eRBGP) als besonders effizient heraus. Eine ausfuuhrliche deutsche Zusammenfassung dieser Dissertation ist in Chapter E auf Seite 207 enthalten.", notes = "1st Reviewer: Prof. Dr. Kurt Geihs (University of Kassel) 2nd Reviewer: Prof. Dr. Christian Tschudin (University of Basel) Date of Defense: 2009-05-04", } @InProceedings{WeiseZ:2009:GEC, author = "Thomas Weise and Michael Zapf", title = "Evolving distributed algorithms with genetic programming: election", booktitle = "GEC '09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation", year = "2009", editor = "Lihong Xu and Erik D. Goodman and Guoliang Chen and Darrell Whitley and Yongsheng Ding", bibsource = "DBLP, http://dblp.uni-trier.de", pages = "577--584", address = "Shanghai, China", organisation = "SigEvo", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.188.4970", URL = "http://www.it-weise.de/documents/files/WZ2009EDAWGPE.pdf", DOI = "doi:10.1145/1543834.1543913", publisher = "ACM", publisher_address = "New York, NY, USA", month = jun # " 12-14", isbn13 = "978-1-60558-326-6", keywords = "genetic algorithms, genetic programming", abstract = "In this paper, we present a detailed analysis of the application of Genetic Programming to the evolution of distributed algorithms. This research field has many facets which make it especially difficult. These aspects are discussed and countermeasures are provided. Six different Genetic Programming approaches (SGP, eSGP, LGP, RBGP, eRBGP, and Fraglets) are applied to the election problem as case study using these countermeasures. The results of the experiments are analysed statistically and discussed thoroughly.", notes = "Also known as \cite{DBLP:conf/gecco/WeiseZ09} part of \cite{DBLP:conf/gec/2009}", } @InProceedings{Weise:2010:ICCI, author = "Thomas Weise and Raymond Chiong", title = "Evolutionary Data Mining Approaches for Rule-based and Tree-based Classifiers", booktitle = "9th IEEE International Conference on Cognitive Informatics (ICCI 2010)", year = "2010", editor = "Fuchun Sun and Yingxu Wang and Jianhua Lu and Bo Zhang and Witold Kinsner and Lotfi A. Zadeh", pages = "696--703", address = "Tsinghua University, Beijing, China", month = "7-9 " # jul, publisher = "IEEE", note = "Special Session on Evolutionary Computing", email = "tweise@gmx.de", keywords = "genetic algorithms, genetic programming, data mining, decision trees, rule-based classifiers, C4.5 approach, decision trees, evolutionary algorithms, evolutionary data mining approach, random-forest approach, rule set encoding, rule-based classifier, supervised data mining approach, tree-based classifiers, data mining, knowledge based systems, pattern classification", isbn13 = "978-1-4244-8040-1", URL = "http://www.it-weise.de/documents/files/WC2010EDMAFRBATBC.pdf", DOI = "doi:10.1109/COGINF.2010.5599821", abstract = "Data mining is an important process, with applications found in many business, science and industrial problems. While a wide variety of algorithms have already been proposed in the literature for classification tasks in large data sets, and the majority of them have been proven to be very effective, not all of them are flexible and easily extensible. In this paper, we introduce two new approaches for synthesising classifiers with Evolutionary Algorithms (EAs) in supervised data mining scenarios. The first method is based on encoding rule sets with bit string genomes and the second one uses Genetic Programming to create decision trees with arbitrary expressions attached to the nodes. Comparisons with some sophisticated standard approaches, such as C4.5 and Random-Forest, show that the performance of the evolved classifiers can be very competitive. We further demonstrate that both proposed approaches work well across different configurations of the EAs.", notes = "http://www.icci2010.edu.cn/ Also known as \cite{5599821}", } @InCollection{Weise:2010:ISALA, author = "Thomas Weise and Raymond Chiong", title = "Evolutionary Approaches and their Applications to Distributed Systems", booktitle = "Intelligent Systems for Automated Learning and Adaptation: Emerging Trends and Applications", publisher = "IGI Global", year = "2010", editor = "Raymond Chiong", chapter = "6", pages = "114--149", keywords = "genetic algorithms, genetic programming, Evolutionary Computation, Distributed Systems, Networks, Ant Colony Optimisation, Topology, Routing, Protocols, Security, Configuration", isbn13 = "978-1-60566-798-0", URL = "http://www.igi-global.com/bookstore/Chapter.aspx?TitleId=38453", DOI = "doi:10.4018/978-1-60566-798-0.ch006", abstract = "The ubiquitous presence of distributed systems has drastically changed the way the world interacts, and impacted not only the economics and governance but also the society at large. It is therefore important for the architecture and infrastructure within the distributed environment to be continuously renewed in order to cope with the rapid changes driven by the innovative technologies. However, many problems in distributed computing are either of dynamic nature, large scale, NP complete, or a combination of any of these. In most cases, exact solutions are hardly found. As a result, a number of intelligent nature-inspired algorithms have been used recently, as these algorithms are capable of achieving good quality solutions in reasonable computational time. Among all the nature-inspired algorithms, evolutionary algorithms are considerably the most extensively applied ones. This chapter presents a systematic review of evolutionary algorithms employed to solve various problems related to distributed systems. The review is aimed at providing an insight of evolutionary approaches, in particular genetic algorithms and genetic programming, in solving problems in five different areas of network optimization: network topology, routing, protocol synthesis, network security, and parameter settings and configuration. Some interesting applications from these areas will be discussed in detail with the use of illustrative examples.", notes = "http://www.igi-global.com/bookstore/TitleDetails.aspx?TitleId=635 Thomas Weise (University of Kassel, Germany); Raymond Chiong (Swinburne University of Technology (Sarawak Campus)", } @Article{Weise:2011:ieeeTEC, author = "Thomas Weise and Ke Tang", title = "Evolving Distributed Algorithms with Genetic Programming", journal = "IEEE Transactions on Evolutionary Computation", year = "2012", volume = "16", number = "2", pages = "242--265", month = apr, keywords = "genetic algorithms, genetic programming, Algorithm design and analysis, Approximation algorithms, Computational modelling, Distributed algorithms, Optimisation, Protocols, Critical section, GCD, LGP, SGP, distributed algorithms, election, distributed greatest common divisor DGCD, fraglets, mutual exclusion, rule-based genetic programming, SBSE", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2011.2112666", size = "24 pages", abstract = "In this paper, we evaluate the applicability of genetic programming (GP) for the evolution of distributed algorithms. We carry out a large-scale experimental study in which we tackle three well-known problems from distributed computing with six different program representations. For this purpose, we first define a simulation environment in which phenomena such as asynchronous computation at changing speed and messages taking over each other, i.e., out-of-order message delivery, occur with high probability. Second, we define extensions and adaptations of established GP approaches (such as tree-based and linear GP) in order to make them suitable for representing distributed algorithms. Third, we introduce novel rule-based GP methods designed especially with the characteristic difficulties of evolving algorithms (such as epistasis) in mind. Based on our extensive experimental study of these approaches, we conclude that GP is indeed a viable method for evolving non-trivial, deterministic, non-approximative distributed algorithms. Furthermore, one of the two rule-based approaches is shown to exhibit superior performance in most of the tasks and thus can be considered as an interesting idea also for other problem domains.", notes = "Indexed memory, scope, MOGP, Pareto (size v fitness not just for anti bloat reasons), GCD, Election (distributed smallest), mutual exclusion-distributed locking, LCS, rule-based genetic programming RBGP, eRBGP, Turing complete, tree GP, linear GP LGP, ADF, for loop, while loop, jmp, call, fraglets \cite{conf/wac/YamamotoT05}, interrupt service routine, various network connections. Statements like evolved 'algorithm is not correct' section VI-B which seem at odds with rest of text. Population 512, 700+ generation, homologous multipoint crossover, subtree crossover, 4 types of mutation. comparison with random walk. GP run 8 minutes. marginal fairness CSMA. Also known as \cite{6026925}", } @InProceedings{Weise:2012:GECCO, author = "Thomas Weise and Alexandre Devert and Ke Tang", title = "A developmental solution to (dynamic) capacitated arc routing problems using genetic programming", booktitle = "GECCO '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference", year = "2012", editor = "Terry Soule and Anne Auger and Jason Moore and David Pelta and Christine Solnon and Mike Preuss and Alan Dorin and Yew-Soon Ong and Christian Blum and Dario Landa Silva and Frank Neumann and Tina Yu and Aniko Ekart and Will Browne and Tim Kovacs and Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and Giovanni Squillero and Nicolas Bredeche and Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and Martin Pelikan and Silja Meyer-Nienberg and Christian Igel and Greg Hornby and Rene Doursat and Steve Gustafson and Gustavo Olague and Shin Yoo and John Clark and Gabriela Ochoa and Gisele Pappa and Fernando Lobo and Daniel Tauritz and Jurgen Branke and Kalyanmoy Deb", isbn13 = "978-1-4503-1177-9", pages = "831--838", keywords = "genetic algorithms, genetic programming", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330163.2330278", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "A developmental, ontogenic approach to Capacitated Arc Routing Problems (CARPs) is introduced. The genotypes of this method are constructive heuristics specified as trees of mathematical functions which are evolved with Genetic Programming (GP). In a genotype-phenotype mapping, they guide a virtual vehicle which starts at the depot. The genotype is used to compute a heuristic value for each edge with unsatisfied demands. Local information such as the visiting costs from the current position, the remaining load of the vehicle, and the edge demands are available to the heuristic. The virtual vehicle then serves the edge with the lowest heuristic value and is located at its end. This process is repeated until all requirements have been satisfied. The resulting phenotypes are sets of tours which, in turn, are sequences of edges. We show that our method has three advantages: 1) The genotypes can be reused to seed the population in new GP runs. 2) The size of the genotypes is independent from the problem scale. 3) The evolved heuristics even work well in modified or dynamic scenarios and are robust in the presence of noise.", notes = "Also known as \cite{2330278} GECCO-2012 A joint meeting of the twenty first international conference on genetic algorithms (ICGA-2012) and the seventeenth annual genetic programming conference (GP-2012)", } @InProceedings{Weise:2014:CEC, title = "Evolving Exact Integer Algorithms with Genetic Programming", author = "Thomas Weise and Mingxu Wan and Ke Tang and Xin Yao", pages = "1816--1823", booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary Computation", year = "2014", month = "6-11 " # jul, editor = "Carlos A. {Coello Coello}", address = "Beijing, China", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, Genetic programming, Representation and operators", DOI = "doi:10.1109/CEC.2014.6900292", abstract = "The synthesis of exact integer algorithms is a hard task for Genetic Programming (GP), as it exhibits epistasis and deceptiveness. Most existing studies in this domain only target few and simple problems or test a small set of different representations. In this paper, we present the (to the best of our knowledge) largest study on this domain to date. We first propose a novel benchmark suite of 20 non-trivial problems with a variety of different features. We then test two approaches to reduce the impact of the negative features: (a) a new nested form of Transactional Memory (TM) to reduce epistatic effects by allowing instructions in the program code to be permutated with less impact on the program behaviour and (b) our recently published Frequency Fitness Assignment method (FFA) to reduce the chance of premature convergence on deceptive problems. In a full-factorial experiment with six different loop instructions, TM, and FFA, we find that GP is able to solve all benchmark problems, although not all of them with a high success rate. Several interesting algorithms are discovered. FFA has a tremendous positive impact while TM turns out not to be useful.", notes = "WCCI2014", } @Article{Weise:2013:ieeeTEC, author = "Thomas Weise and Mingxu Wan and Pu Wang and Ke Tang and Alexandre Devert and Xin Yao", journal = "IEEE Transactions on Evolutionary Computation", title = "Frequency Fitness Assignment", year = "2014", volume = "18", number = "2", month = apr, pages = "226--243", keywords = "genetic algorithms, genetic programming, Combinatorial Optimisation, Diversity, Fitness Assignment, Frequency, Numerical Optimisation", DOI = "doi:10.1109/TEVC.2013.2251885", ISSN = "1089-778X", size = "18 pages", abstract = "Metaheuristic optimisation procedures such as Evolutionary Algorithms are usually driven by an objective function which rates the quality of a candidate solution. However, it is not clear in practice whether an objective function adequately rewards intermediate solutions on the path to the global optimum and it may exhibit deceptiveness, epistasis, neutrality, ruggedness, and a lack of causality. In this paper, we introduce the Frequency Fitness H, subject to minimisation, that rates how often solutions with the same objective value have been discovered so far. The ideas behind this method are that good solutions are hard to find and that if an algorithm gets stuck at a local optimum, the frequency of the objective values of the surrounding solutions will increase over time, which will eventually allow it to leave that region again. We substitute a Frequency Fitness Assignment process (FFA) for the objective function into several different optimisation algorithms. We conduct a comprehensive set of experiments: the synthesis of algorithms with Genetic Programming (GP), the solution of MAX-3SAT problems with Genetic Algorithms, classification with Memetic Genetic Programming, and numerical optimisation with a (1+1) Evolution Strategy, in order to verify the utility of FFA. Given that they have no access to the original objective function at all, it is surprising that for some problems (e.g., the algorithm synthesis task) the FFA-based algorithm variants perform significantly better. However, this cannot be guaranteed for all tested problems. We thus also analyse scenarios where algorithms using FFA do not perform better or even worse than with the original objective functions.", notes = "Also known as \cite{6476662}", } @InProceedings{Weiss:2017:ASE, author = "Aaron Weiss and Arjun Guha and Yuriy Brun", title = "Tortoise: Interactive System Configuration Repair", booktitle = "Proceedings of the 32nd IEEE/ACM International Conference on Automated Software Engineering, ASE 2017", year = "2017", pages = "625--636", address = "Urbana, IL, USA", month = "30 " # oct # " - 3 " # nov, keywords = "genetic algorithms, genetic programming, genetic improvement, APR, Puppet", isbn13 = "978-1-5386-3976-4", URL = "https://people.cs.umass.edu/~brun/pubs/pubs/Weiss17ase.pdf", URL = "https://doi.org/10.1109/ASE.2017.8115673", DOI = "doi:10.1109/ASE.2017.8115673", abstract = "System configuration languages provide powerful abstractions that simplify managing large-scale, networked systems. Thousands of organizations now use configuration languages, such as Puppet. However, specifications written in configuration languages can have bugs and the shell remains the simplest way to debug a misconfigured system. Unfortunately, it is unsafe to use the shell to fix problems when a system configuration language is in use: a fix applied from the shell may cause the system to drift from the state specified by the configuration language. Thus, despite their advantages, configuration languages force system administrators to give up the simplicity and familiarity of the shell. we present a synthesis-based technique that allows administrators to use configuration languages and the shell in harmony. Administrators can fix errors using the shell and the technique automatically repairs the higher-level specification written in the configuration language. The approach (1) produces repairs that are consistent with the fix made using the shell; (2) produces repairs that are maintainable by minimizing edits made to the original specification; (3) ranks and presents multiple repairs when relevant; and (4) supports all shells the administrator may wish to use. We implement our technique for Puppet, a widely used system configuration language, and evaluate it on a suite of benchmarks under 42 repair scenarios. The top-ranked repair is selected by humans 76percent of the time and the human-equivalent repair is ranked 1.31 on average.", notes = "also known as \cite{8115673}", } @InProceedings{weiss:1999:TGAIPPSE, author = "Gary M. Weiss", title = "Timeweaver: a Genetic Algorithm for Identifying Predictive Patterns in Sequences of Events", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "718--725", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/Weiss_gecco99.pdf", URL = "http://www.research.rutgers.edu/~gweiss/papers/gecco99.pdf", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InCollection{Weisser:2010:ntt, author = "Roman Weisser and Pavel Osmera and Jan Roupec and Radomil Matousek", title = "Transplant Evolution for Optimization of General Controllers", booktitle = "New Trends in Technologies: Control, Management, Computational Intelligence and Network Systems", publisher = "InTech", year = "2010", editor = "Er Meng Joo", chapter = "5", keywords = "genetic algorithms, genetic programming, grammatical evolution, differential evolution, transplant evolution", isbn13 = "978-953-307-213-5", DOI = "doi:10.5772/10419", size = "18 pages", abstract = "The aim of this paper is to describe a new optimisation method that can create control equations of general regulators. For this type of optimization a new method was created and we call it Two-Level Transplant Evolution (TLTE). This method allowed us to apply advanced methods of optimisation, for example direct tree reducing of tree structure of control equation. The reduction method was named Arithmetic Tree Reducing (ART). For the optimisation of control equations of general controllers it is suitable to combine two evolutionary algorithms. The main goal in the first level of TLTE is the optimisation of the structure of general controllers. In the second level of TLTE the concrete parameters are optimised and the unknown abstract parameters in the structure of equations are set. The method TLTE was created by the combination of the Transplant Evolution method (TE) and the Differential Evolution method (DE). The Transplant Evolution (TE) optimises the structure of the solution with...", notes = "'grammatical rules are chosen randomly'. shortening structural mutation, algebraic reducing tree ART VUT in Bruno", } @InCollection{weller:2002:GVGEBI, author = "Chris Weller", title = "Generation of Vector-Based Graphics from Existing Bitmap Images by Means of the Genetic Algorithm", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2002", year = "2002", editor = "John R. Koza", pages = "243--252", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms", URL = "http://www.genetic-programming.org/sp2002/Weller.pdf", notes = "part of \cite{koza:2002:gagp} fitness based on pixel wise comparison", } @InProceedings{Wells:2024:EGLSF, author = "Dylan Wells and Julie Rolla and Jacob Weiler and Amy L Connolly and Bryan Reynolds", title = "Evolving Antennas From Building Blocks Using Evolutionary Algorithms", booktitle = "2024 Fall Meeting of the APS Eastern Great Lakes Section", year = "2024", pages = "E01.00039", address = "Marietta College, Marietta, Ohio, USA", publisher_address = "1 Physics Ellipse, College Park, MD 20740-3844, USA", month = oct # " 18-19", publisher = "American Physical Society", keywords = "genetic algorithms, genetic programming, GENETIS: Poster", URL = "https://meetings.aps.org/Meeting/EGLSF24/Session/E01.39", abstract = "Evolutionary algorithms use concepts of biological evolution in order to find solutions to difficult optimization problems. This work presents a genetic algorithm to evolve arbitrary antenna designs towards the goal of either matching a desired radiation pattern or maximizing sensitivity in user-specified directions. The algorithm constructs antennas by combining geometric shapes like building blocks. We explore the results of evolutions towards both fitness metrics in single-frequency (300 MHz) and broadband (200 MHz to 800 MHz) applications. This work was initially inspired from astrophysics experiments that are reliant on sensitive detectors, and future work will include metrics from astrophysical simulation software as the measure of fitness.", notes = "The research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration(80NM0018D0004) 4:15 PM, Friday, October 18, 2024 https://meetings.aps.org/Meeting/EGLSF24/Session/E01", } @InProceedings{Welsch:2020:GECCO, author = "Thomas Welsch and Vitaliy Kurlin", title = "Synthesis through Unification Genetic Programming", year = "2020", editor = "Carlos Artemio {Coello Coello} and Arturo Hernandez Aguirre and Josu Ceberio Uribe and Mario Garza Fabre and Gregorio {Toscano Pulido} and Katya Rodriguez-Vazquez and Elizabeth Wanner and Nadarajen Veerapen and Efren Mezura Montes and Richard Allmendinger and Hugo Terashima Marin and Markus Wagner and Thomas Bartz-Beielstein and Bogdan Filipic and Heike Trautmann and Ke Tang and John Koza and Erik Goodman and William B. Langdon and Miguel Nicolau and Christine Zarges and Vanessa Volz and Tea Tusar and Boris Naujoks and Peter A. N. Bosman and Darrell Whitley and Christine Solnon and Marde Helbig and Stephane Doncieux and Dennis G. Wilson and Francisco {Fernandez de Vega} and Luis Paquete and Francisco Chicano and Bing Xue and Jaume Bacardit and Sanaz Mostaghim and Jonathan Fieldsend and Oliver Schuetze and Dirk Arnold and Gabriela Ochoa and Carlos Segura and Carlos Cotta and Michael Emmerich and Mengjie Zhang and Robin Purshouse and Tapabrata Ray and Justyna Petke and Fuyuki Ishikawa and Johannes Lengler and Frank Neumann", isbn13 = "9781450371285", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377930.3390208", DOI = "doi:10.1145/3377930.3390208", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference", pages = "1029--1036", size = "8 pages", keywords = "genetic algorithms, genetic programming, STUN GP, CDGP, grammatical evolution, divide and conquer, search based program synthesis", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", abstract = "We present a new method, Synthesis through Unification Genetic Programming (STUN GP), which synthesizes provably correct programs using a Divide and Conquer approach. This method first splits the input space by undergoing a discovery phase that uses Counterexample-Driven Genetic Programming (CDGP) to identify a set of programs that are provably correct under unknown unification constraints. The STUN GP method then computes these restraints by synthesizing predicates with CDGP that strictly map inputs to programs where the output will be correct. This method builds on previous work towards applying Genetic Programming (GP) to Syntax Guided Synthesis (SyGus) problems that seek to synthesize programs adhering to a formal specification rather than a fixed set of input-output examples. We show that our method is more scalable than previous CDGP variants, solving several benchmarks from the SyGus Competition that cannot be solved by CDGP. STUN GP significantly cuts into the gap between GP and state-of-the-art SyGus solvers and further demonstrates Genetic Programming's potential for Program Synthesis.", notes = "program synthesis, proof tool, SMTlib, microsoft Z3, generates counter-examples. Grammar. max4( x y x w)...max12() check-synth. CLIA. STUN GP Also known as \cite{10.1145/3377930.3390208} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @Article{wen:2021:SichuanUniversity, author = "Da-Guang Wen and Si-Xian Hu and Zhen-Lin Li and Xiang-Bing Deng and Chuan Tian and Xin Li and Xin-Rong Wang and Qi Leng and Chun-Chao Xia", title = "Application of Automated Machine Learning Based on Radiomics Features of {T2WI} and {RS-EPI} {DWI} to Predict Preoperative {T} Staging of Rectal Cancer", journal = "Journal of Sichuan University. Medical science edition", year = "2021", volume = "52", number = "4", pages = "698--705", month = jul, keywords = "genetic algorithms, genetic programming, TPOT, China, Diffusion Magnetic Resonance Imaging, Echo-Planar Imaging, Humans, Machine Learning, Rectal Neoplasms/diagnostic imaging/surgery, Retrospective Studies, Automated machine learning, Radiomics, Rectal cancer, T stage", ISSN = "1672-173X", DOI = "doi:10.12182/20210460201", abstract = "OBJECTIVE: To explore the radiomics features of T2 weighted image (T2WI) and readout-segmented echo-planar imaging (RS-EPI) plus diffusion-weighted imaging (DWI), to develop an automated machine-learning model based on the said radiomics features, and to test the value of this model in predicting preoperative T staging of rectal cancer. METHODS: The study retrospectively reviewed 131 patients who were diagnosed with rectal cancer confirmed by the pathology results of their surgical specimens at West China Hospital of Sichuan University between October, 2017 and December, 2018. In addition, these patients had preoperative rectal MRI. Tumor regions from preoperative MRI were manually segmented by radiologists with the ITK-SNAP software from T2WI and RS-EPI DWI images. PyRadiomics was used to extract 200 features-100 from T2WI and 100 from the apparent diffusion coefficient (ADC) calculated from the RS-EPI DWI. MWMOTE and NEATER were used to resample and balance the dataset, and 13 cases of T (1-2) stage simulation cases were added. The overall dataset was divided into a training set (111 cases) and a test set (37 cases) by a ratio of 3∶1. Tree-based Pipeline Optimization Tool (TPOT) was applied on the training set to optimize model parameters and to select the most important radiomics features for modeling. Five independent T stage models were developed accordingly. Accuracy and the area under the curve ( AUC) of receiver operating characteristic (ROC) were used to pick out the optimal model, which was then applied on the training set and the original dataset to predict the T stage of rectal cancer. RESULTS: The performance of the the five T staging models recommended by automated machine learning were as follows: The accuracy for the training set ranged from 0.802 to 0.838, sensitivity, from 0.762 to 0.825, specificity, from 0.833 to 0.896, AUC, from 0.841 to 0.893, and average precision (AP) from 0.870 to 0.901. After comparison, an optimal model was picked out, with sensitivity, specificity and AUC for the training set reaching 0.810, 0.875, and 0.893, respectively. The sensitivity, specificity and AUC for the test set were 0.810, 0.813, and 0.810, respectively. The sensitivity, specificity and AUC for the original dataset were 0.810, 0.830, and 0.860, respectively. CONCLUSION: Based on the radiomics data of T2WI and RS-EPI DWI, the model established by automated machine learning showed a fairly high accuracy in predicting rectal cancer T stage.", notes = "PMID: 34323052", } @Article{Wen:2022:RemoteSensing, author = "Caiyun Wen and Miao Lu and Ying Bi and Shengnan Zhang and Bing Xue and Mengjie Zhang and Qingbo Zhou and Wenbin Wu", title = "An Object-Based Genetic Programming Approach for Cropland Field Extraction", journal = "Remote Sensing", year = "2022", volume = "14", number = "5", article-number = "1275", month = "5 " # mar, note = "Special Issue Progresses in Agro-Geoinformatics", keywords = "genetic algorithms, genetic programming, object-based, cropland fields", ISSN = "2072-4292", URL = "https://www.mdpi.com/2072-4292/14/5/1275", DOI = "doi:10.3390/rs14051275", size = "17 pages", abstract = "Cropland fields are the basic spatial units for agricultural management, and information about their distribution is critical for analyzing agricultural investments and management. However, the extraction of cropland fields of smallholder farms is a challenging task because of their irregular shapes and diverse spectrum. we proposed a new object-based Genetic Programming (GP) approach to extract cropland fields. The proposed approach used the multiresolution segmentation (MRS) method to acquire objects from a very high resolution (VHR) image, and extracted spectral, shape and texture features as inputs for GP. Then GP was used to automatically evolve the optimal classifier to extract cropland fields. The results show that the proposed approach has obtained high accuracy in two areas with different landscape complexities. Further analysis show that the GP approach significantly outperforms five commonly used classifiers, including K-Nearest Neighbor (KNN), Decision Tree (DT), Naive Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF). By using different numbers of training samples, GP can maintain high accuracy with any volume of samples compared to other classifiers.", notes = "Also known as \cite{rs14051275} p14 'Compared to ... KNN, DT, NB, SVM and RF; GP performed significantly better' Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China", } @Article{DBLP:journals/cea/WenLBXSSWW25, author = "Caiyun Wen and Miao Lu and Ying Bi and Lang Xia and Jing Sun and Yun Shi and Yanbing Wei and Wenbin Wu", title = "Customized crop feature construction using genetic programming for early- and in-season crop mapping", journal = "Computers and Electronics in Agriculture", year = "2025", volume = "231", pages = "109949", month = apr, keywords = "genetic algorithms, genetic programming, Remote Sensing, Crop mapping, Feature Construction, Customized feature", ISSN = "0168-1699", timestamp = "Fri, 07 Mar 2025 18:31:09 +0100", biburl = "https://dblp.org/rec/journals/cea/WenLBXSSWW25.bib", bibsource = "dblp computer science bibliography, https://dblp.org", DOI = "doi:10.1016/J.COMPAG.2025.109949", size = "12 pages", abstract = "Early- and in-season crop mapping provides vital information for precision agriculture. It is still a challenge for early- and in-season crop mapping because of the limited available images and similar spectral information. This study aims to enhance early- and in-season crop mapping by developing a Genetic Programming (GP) method to construct customized crop features. GP automatically generated candidate features for the target-crop using early- or in-season images, selected programs with substantial value disparities between target and non-target crops through the fitness function, and finally outputted the customized feature after the evolutionary process. These customized features were then compared with commonly used spectral bands and vegetation indices to evaluate their effectiveness for early- and in-season crop mapping. The results proved that the customized crop features had significant advantages in both early- and in-season crop mapping. The early-season accuracy in April after crop planting was 3.97 percent to 9.53 percent higher than spectral features and vegetation indices. Based on the incremental classification for the in-season crop mapping, the customized crop features maintained the best performance. Advantages of customized crop features include the ability to automatically select effective bands of useful months without requiring expert knowledge, the ability to catch and enlarge the subtle spectral differences with the early- and in-season images, and the little information redundancy compared with spectral features and vegetation indices. It can be concluded that the customized crop features are outstanding for early- and in-season crop mapping.", notes = "State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China", } @InProceedings{Wen:2009:IITA-JCAI, author = "Chih-Hung Wen and Wen-Tsao Pan", title = "Construct for Investment Strategy Model through Genetic Programming Planning", booktitle = "First IITA International Joint Conference on Artificial Intelligence, 2009. JCAI '09", year = "2009", month = "25-26 " # apr, address = "Hainan Island, China", pages = "252--255", keywords = "genetic algorithms, genetic programming, China Steel stocks, Dow Jones average, decision tree modelling, genetic programming planning, investment model, investment strategy classification capability, investment strategy model, stock price, decision trees, investment, pricing, stock markets", DOI = "doi:10.1109/JCAI.2009.121", abstract = "This thesis takes three approaches in strategic sense respectively: Call, put and hold. First of all, it collects daily information and relevant factors which could influence the stock price one day ahead of the actual trading for China Steel stocks. These factors include aspects in the stock's fundamental, share volume, technical performance in addition to Dow Jones average plus processing these information and subsequent normalization. Lastly, genetic programming planning is applied to construct investment model accordingly, in addition to conducting comparison analyses regarding the investment strategy classification capabilities for the decision tree modelling. From the end results of validity in classification accuracy for these two models, the findings of this research indicate that genetic programming planning is the better and preferred model in the sense of classification capability when comparing to that of decision tree model.", notes = "Also known as \cite{5158987}", } @InProceedings{wen:2024:GECCO, author = "Jiahao Wen and Junlan Dong and Jinghui Zhong", title = "Sign Change Detection based Fitness Evaluation for Automatic Implicit Equation Discovery", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference", year = "2024", editor = "Ting Hu and Aniko Ekart and Julia Handl and Xiaodong Li and Markus Wagner and Mario Garza-Fabre and Kate Smith-Miles and Richard Allmendinger and Ying Bi and Grant Dick and Amir H Gandomi and Marcella Scoczynski Ribeiro Martins and Hirad Assimi and Nadarajen Veerapen and Yuan Sun and Mario Andres Munyoz and Ahmed Kheiri and Nguyen Su and Dhananjay Thiruvady and Andy Song and Frank Neumann and Carla Silva", pages = "980--987", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, fitness evaluation, implicit symbolic regression, sign change detection", isbn13 = "979-8-4007-0494-9", DOI = "doi:10.1145/3638529.3654015", size = "8 pages", abstract = "Automatic implicit equation discovery is a meaningful and challenging problem in symbolic regression. The current common methods for the automatic discovery of implicit equations include derivative calculations and comprehensive learning. However, both methods come with their own set of challenges. Derivative calculations pose difficulties in handling sparse data. The comprehensive learning method may encounter problems associated with multiple multiplications, making it difficult to find the optimal equation. Inspired by Bolzano's theorem,we propose a new evaluation mechanism known as the {"}Sign Change Detection (SCD) based Fitness Evaluation{"}. The main idea of our proposed mechanism is to approximate the solution of an equation using Bolzano's theorem. This mechanism can overcome the limitations associated with derivative calculations and comprehensive learning methods. Furthermore, we integrate this mechanism with self-learning gene expression programming (SL-GEP) to develop a new SCD-GEP method. Experimental results have shown that the proposed method surpasses the compared approaches in discovering implicit equations, achieving a higher success rate in finding optimal solutions.", notes = "GECCO-2024 GP A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @Article{wen:2018:pjoes, author = "Lei Wen and Qiao Li and Yue Li and Zeyang Ma", title = "Carbon Emission and Economic Growth Model of Beijing Based on Symbolic Regression", journal = "Polish Journal of Environmental Studies", year = "2018", volume = "27", number = "1", pages = "365--372", keywords = "genetic algorithms, genetic programming, carbon emissions, symbolic regression, EKC curve, M-curve model, L-curve model", ISSN = "1230-1485", URL = "https://www.pjoes.com/abstracts/2018/Vol27/No01/39.html", URL = "https://www.pjoes.com/pdf/27.1/Pol.J.Environ.Stud.Vol.27.No.1.365-372.pdf", DOI = "doi:10.15244/pjoes/74155", size = "8 pages", abstract = "With the continuous improvement of the economy, more and more attention has been paid to environmental problems. Beijing is China's economic, political, and cultural centre, and its low-carbon development by external concerns. In this paper, the relationship between economic development and environmental pollution is analysed by using the symbolic regression method, which is based on the data of per capita Carbon Dioxide emissions, total energy consumption, energy intensity, and per capita GDP in Beijing city during 1980-2015. The study found that the presence of the M-curve model between per capita CO2 emissions and per capita GDP, total energy consumption, and per capita GDP are in line with the traditional model of the EKC curve, and that the L-curve model exists between the energy intensity and per capita GDP, respectively, with promising performance. Based on our analysis, we present policy suggestions for reducing carbon emissions and developing a low-carbon economy in Beijing.", notes = "Department of Economics and Management, North China Electric Power University, Baoding, Hebei 071003, China 39 https://www.pjoes.com/ Pol. J. Environ. Stud. The articles published in Polish Journal of Environmental Studies can be downloaded free of charge only for personal scientific research.", } @InProceedings{WenLZ:2009:GEC, author = "Lingyun Wen and Guiquan Liu and Yinghai Zhao", title = "HS-Model: a hierarchical statistical subtree-generating model for genetic programming", booktitle = "GEC '09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation", year = "2009", editor = "Lihong Xu and Erik D. Goodman and Guoliang Chen and Darrell Whitley and Yongsheng Ding", bibsource = "DBLP, http://dblp.uni-trier.de", pages = "1005--1008", address = "Shanghai, China", organisation = "SigEvo", DOI = "doi:10.1145/1543834.1543994", publisher = "ACM", publisher_address = "New York, NY, USA", month = jun # " 12-14", isbn13 = "978-1-60558-326-6", keywords = "genetic algorithms, genetic programming, Poster", abstract = "In genetic programming with subtrees, two issues are crucial: how to acquire promising subtrees efficiently and how to keep these subtrees to be used repeatedly in the evolutional process. In this paper, we propose a hierarchical statistical model for program trees, named HS-Model, to deal with both the above issues. The HS-Model conducts statistic analysis of the current population and generates superior subtrees automatically with efficiency. The HS-Model leaves out the tedious operations to keep the promising subtrees for reusing and also omits updating the subtree library according to certain criterion. Experimental results on solving the classical artificial ant problem proved the effectiveness and the efficiency of our proposed method.", notes = "Also known as \cite{DBLP:conf/gecco/WenLZ09} part of \cite{DBLP:conf/gec/2009}", } @InProceedings{Wen:2012:CEC, title = "Application of Free Pattern Search on the Surface Roughness Prediction in End Milling", author = "Long Wen and Liang Gao and Xinyu Li and Guohui Zhang and Yang Yang", pages = "765--770", booktitle = "Proceedings of the 2012 IEEE Congress on Evolutionary Computation", year = "2012", editor = "Xiaodong Li", month = "10-15 " # jun, DOI = "doi:10.1109/CEC.2012.6256605", address = "Brisbane, Australia", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Data mining, Classification, clustering, data analysis and data mining", abstract = "Surface roughness has a great influence on the product properties. Predicting the surface roughness is an important work for modern manufacturing industry. In this paper, a novel prediction method called Free Pattern Search (FPS) is proposed to explicitly construct the surface roughness prediction model. FPS takes the advantage of the expression tree in gene expression programming (GEP) to encode the solution and to expresses a non-determinative tree using a fixed length individual. FPS is inspired by Pattern Search (PS) and hybrid a scatter manipulator to keep the diversity of the population. Three machining parameters, the spindle speed, feed rate and the depth of cut are used as the independent input variables when prediction the surface roughness in end milling. Experiments are conducted to verify the performance of FPS and FPS obtains good results compared with other algorithm. The predictive model found by FPS agrees with the experimental result. The variable relations are also showed in the predictive model, and the results shows that they are fit to the experiments well.", notes = "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the EPS and the IET.", } @Article{WEN:2020:RESS, author = "Pengfei Wen and Shuai Zhao and Shaowei Chen and Yong Li", title = "A Generalized Remaining Useful Life Prediction Method for Complex Systems Based on Composite Health Indicator", journal = "Reliability Engineering \& System Safety", pages = "107241", year = "2020", ISSN = "0951-8320", DOI = "doi:10.1016/j.ress.2020.107241", URL = "http://www.sciencedirect.com/science/article/pii/S0951832020307419", keywords = "genetic algorithms, genetic programming, Multiple sensors, Data fusion, Degradation modeling, Remaining useful life, Prognostics", abstract = "As one of the key techniques in Prognostics and Health Management (PHM), accurate Remaining Useful Life (RUL) prediction can effectively reduce the number of downtime maintenance and significantly improve economic benefits. In this paper, a generalized RUL prediction method is proposed for complex systems with multiple Condition Monitoring (CM) signals. A stochastic degradation model is proposed to characterize the system degradation behavior, based on which the respective reliability characteristics such as the RUL and its Confidence Interval (CI) are explicitly derived. Considering the degradation model, two desirable properties of the Health Indicator (HI) are put forward and their respective quantitative evaluation methods are developed. With the desirable properties, a nonlinear data fusion method based on Genetic Programming (GP) is proposed to construct a superior composite HI. In this way, the multiple CM signals are fused to provide a better prediction capability. Finally, the proposed integrated methodology is validated on the C-MAPSS data set of aircraft turbine engines", } @Article{Wen:2021:IoT, author = "Pengfei Wen and Yong Li and Shaowei Chen and Shuai Zhao", title = "Remaining Useful Life Prediction of IIoT-Enabled Complex Industrial Systems With Hybrid Fusion of Multiple Information Sources", journal = "IEEE Internet of Things Journal", year = "2021", volume = "8", number = "11", pages = "9045--9058", abstract = "Industrial Internet of Things has significantly boosted predictive maintenance for complex industrial systems, where the accurate prediction of remaining useful life (RUL) with high-level confidence is challenging. By aggregating multiple informative sources of system degradation, information fusion can be applied to improve the prediction accuracy and reduce the uncertainty. It can be performed on the data-level, feature-level, and decision-level. To fully exploit the available degradation information, this article proposes a hybrid fusion method on both the data level and decision level to predict the RUL. On the data level, genetic programming (GP) is adopted to integrate physical sensor sources into a composite health indicator (HI), resulting in an explicit nonlinear data-level fusion model. Subsequently, the predictions of the RUL based on each physical sensor and the developed composite HI are synthesized in the framework of belief functions theory, as the decision-level fusion method. Moreover, the decision-level method is flexible for incorporating other statistical data-driven methods with explicit estimations of the RUL. The proposed method is verified via a case study on NASA's C-MAPSS data set. Compared to the single-level fusion methods, the results confirm the superiority of the proposed method for higher accuracy and certainty of predicting the RUL.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/JIOT.2021.3055977", ISSN = "2327-4662", month = jun, notes = "Also known as \cite{9343303}", } @InProceedings{Wen:2016:CEC, author = "Yu-Wei Wen and Chuan-Kang Ting", title = "Learning Ensemble of Decision Trees through Multifactorial Genetic Programming", booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016)", year = "2016", editor = "Yew-Soon Ong", pages = "5293--5300", address = "Vancouver", month = "24-29 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Multifactorial evolution, ensemble learning, decision tree", isbn13 = "978-1-5090-0623-6", DOI = "doi:10.1109/CEC.2016.7748363", size = "8 pages", abstract = "Genetic programming (GP) has received considerable successes in machine learning tasks such as prediction and classification. Ensemble learning enables the collaboration of multiple classifiers and effectively improves the classification accuracy. Learning an ensemble of classifiers with GP can simply be achieved by repeated runs of GP; however, the computational cost will be multiplied as well. Recently, multifactorial evolution was proposed to concurrently solve multiple problems with a single population. This study uses the multifactorial evolution and designs a multifactorial genetic programming (MFGP) for efficiently learning an ensemble of decision trees. In the MFGP, each task is associated with one run of GP. The multifactorial evolution enables MFGP to evolve multiple GP classifiers for an ensemble in a single run, which saves a substantial amount of computational cost at repeated runs of GP. The experimental results show that MFGP can learn an ensemble with comparable accuracy, precision, and recall to conventional ensemble learning methods, whereas MFGP requires much less computational resource. The satisfactory outcomes validate the advantages of MFGP in ensemble learning.", notes = "WCCI2016", } @InProceedings{Wen:2017:ieeeCISRAM, author = "Zhiwei Wen and Biao Chen", booktitle = "2017 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM)", title = "Load dispatch optimization of {AGC} system based on improved genetic algorithm", year = "2017", pages = "278--281", month = nov, keywords = "genetic algorithms, genetic programming, automatic generation control, coal consumption, load Dispatch, thermal power plant", DOI = "doi:10.1109/ICCIS.2017.8274787", abstract = "The characteristics of coal consumption of thermal power plants are typical nonlinear and time-varying. It's difficult to obtain an accurate model by traditional method. A multi-objective optimisation method based on improved genetic algorithm for load Dispatch of generator units has been proposed. The influence factors of load critical point and response rate has been studied. The genetic programming algorithm is used to automatically fit the coal consumption characteristic curve of the generator units. By adjusting the weights of factors, dynamic dispatch model based on rapidity and economy has been constructed. Simulation result shows the effectiveness in meeting the dispatch requirements and reducing the coal consumption.", notes = "Is the GP? Also known as \cite{8274787}", } @Article{wen:2023:RS, author = "Zitong Wen and Lu Zhuo and Qin Wang and Jiao Wang and Ying (Emily) Liu and Sichan Du and Ahmed Abdelhalim and Dawei Han", title = "Data Fusion for Estimating High-Resolution Urban Heatwave Air Temperature", journal = "Remote Sensing", year = "2023", volume = "15", number = "16", pages = "Article No. 3921", month = "8 " # aug, note = "Special Issue Geo-Information and Integration for Smart and Friendly Cities", keywords = "genetic algorithms, genetic programming, air temperature, data fusion, downscaling, ERA5-Land, MODIS", ISSN = "2072-4292", URL = "https://www.mdpi.com/2072-4292/15/16/3921", DOI = "doi:10.3390/rs15163921", size = "22 pages", abstract = "High-resolution air temperature data is indispensable for analysing heatwave-related non-accidental mortality. However, the limited number of weather stations in urban areas makes obtaining such data challenging. Multi-source data fusion has been proposed as a countermeasure to tackle such challenges. Satellite products often offered high spatial resolution but suffered from being temporally discontinuous due to weather conditions. The characteristics of the data from reanalysis models were the opposite. However, few studies have explored the fusion of these datasets. This study is the first attempt to integrate satellite and reanalysis datasets by developing a two-step downscaling model to generate hourly air temperature data during heatwaves in London at 1 km resolution. Specifically, MODIS land surface temperature (LST) and other satellite-based local variables, including normalised difference vegetation index (NDVI), normalised difference water index (NDWI), modified normalised difference water index (MNDWI), elevation, surface emissivity, and ERA5-Land hourly air temperature were used. The model employed genetic programming (GP) algorithm to fuse multi-source data and generate statistical models and evaluated using ground measurements from six weather stations. The results showed that our model achieved promising performance with the RMSE of 0.335 degree C, R-squared of 0.949, MAE of 1.115 degree Celsius, and NSE of 0.924. Elevation was indicated to be the most effective explanatory variable. The developed model provided continuous, hourly 1 km estimations and accurately described the temporal and spatial patterns of air temperature in London. Furthermore, it effectively captured the temporal variation of air temperature in urban areas during heatwaves, providing valuable insights for assessing the impact on human health.", notes = "also known as \cite{rs15163921}", } @Article{Wen:2025:jag, author = "Zitong Wen and Lu Zhuo and Meiling Gao and Dawei Han", title = "How can we improve data integration to enhance urban air temperature estimations?", journal = "International Journal of Applied Earth Observation and Geoinformation", year = "2025", volume = "140", pages = "104599", keywords = "genetic algorithms, genetic programming, Air temperature, High resolution, Data integration, Weather radar, Crowdsourced weather station", ISSN = "1569-8432", URL = "https://www.sciencedirect.com/science/article/pii/S1569843225002468", DOI = "doi:10.1016/j.jag.2025.104599", abstract = "High-resolution urban air temperatures are indispensable for analysing excess mortality during heatwaves. As a crucial method for obtaining high-resolution data, multi-source data integration has been widely used in urban temperature estimations. However, current research predominantly focuses solely on integrating official weather station observations, satellite products, and reanalysis datasets. Despite the significant cooling effect of rainfall on air temperatures, no studies have explored the contribution of rainfall-related variables to high-resolution air temperature estimations. Additionally, due to the scarcity of official weather stations, quantifying the impact of station density remains an underexplored research direction. To tackle these challenges, we innovatively integrated satellite products, reanalysis datasets, and weather radar data with air temperature observations from crowdsourced weather stations. Using genetic programming, we developed statistical downscaling models to estimate high spatiotemporal resolution (1-km, hourly) air temperatures in London during the summers of 2019 and 2022. The models achieved RMSEs of 1.694 degreeC (2019) and 1.785 degreeC (2022), R-squared values of 0.867 and 0.862, and MAEs of 1.276 degreeC and 1.278 degreeC, respectively. Notably, the accuracy of the models was found to improve with increased weather station density, particularly when the density was below 0.5 stations per 100 km2. Moreover, high-resolution rainfall observations significantly impacted the accuracy of air temperature estimations, second only to elevation, highlighting the potential of integrating radar data. These findings can provide valuable insights for scholars aiming to improve data integration for enhancing urban air temperature estimations", } @InProceedings{Wendlinger:2021:EuroGP, author = "Lorenz Wendlinger and Julian Stier and Michael Granitzer", title = "Evofficient: Reproducing a Cartesian Genetic Programming Method", booktitle = "EuroGP 2021: Proceedings of the 24th European Conference on Genetic Programming", year = "2021", editor = "Ting Hu and Nuno Lourenco and Eric Medvet", series = "LNCS", volume = "12691", publisher = "Springer Verlag", address = "Virtual Event", pages = "162--178", month = "7-9 " # apr, organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, GPU, ANN, Neural Architecture Search, Reproduction, Object recognition, Convolutional Neural Network", isbn13 = "978-3-030-72811-3", DOI = "doi:10.1007/978-3-030-72812-0_11", abstract = "Designing Neural Network Architectures requires expert knowledge and extensive parameter searches. Neural Architecture Search (NAS) aims to change that by automating the design process. It is important that these approaches are reproducible so they can be used in real-life scenarios. In our work, we reproduce a genetic programming approach to designing convolutional neural networks called CGP-CNN. We show that this is difficult and requires many changes to the training scheme, reducing real-life applicability. We achieve a final accuracy of 90.6percent pm0.005, substantially lower than the reported 93.7percent pm0.005. This negates some of the benefits of using CGP-CNN for NAS. We establish a random search as a consensus baseline and show that it produces similar results to the evolutionary method of CGP-CNN. To assess the adaptability and generality of the presented algorithm, it is applied to CIFAR-100 and SVHN with a final accuracy of 63.1percent and 95.6percent, respectively. We extend the investigated NAS by two methods for predicting candidate fitnesses from partial learning curves. This improves CGP-CNN runtime efficiency by a factor of 1.69", notes = "CGP-CNN \cite{Suganuma:2017:GECCO} 1+2 ES Noise due to GPU implementation??? Random search (only) 2 percent worse! How to make training CNN faster by 1.7 fold. NAS=Neural architecture search. http://www.evostar.org/2021/eurogp/ Part of \cite{Hu:2021:GP} EuroGP'2021 held in conjunction with EvoCOP2021, EvoMusArt2021 and EvoApplications2021", } @InProceedings{DBLP:conf/gecco/WeningerHXA09, author = "Tim Weninger and William H. Hsu and Jing Xia and Waleed Aljandal", title = "An evolutionary approach to constructive induction for link discovery", booktitle = "GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation", year = "2009", editor = "Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano {van Hemert} and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique Alba", pages = "1941--1942", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming, Poster", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1569901.1570248", abstract = "This paper presents a genetic programming-based symbolic regression approach to the construction of relational features in link analysis applications. Specifically, we consider the problems of predicting, classifying and annotating friends relations in friends networks, based upon features constructed from network structure and user profile data. We explain how the problem of classifying a user pair in a social network, as directly connected or not, poses the problem of selecting and constructing relevant features. We use genetic programming to construct features, represented by multiple symbol trees with base features as their leaves. In this manner, the genetic program selects and constructs features that may not have been originally considered, but possess better predictive properties than the base features. Finally, we present classification results and compare these results with those of the control and similar approaches.", notes = "GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009). ACM Order Number 910092.", } @InProceedings{DBLP:conf/gecco/WeningerHXA09a, author = "Tim Weninger and William H. Hsu and Jing Xia and Waleed Aljandal", title = "An evolutionary approach to constructive induction for link discovery", booktitle = "GECCO-2009 Late-Breaking Papers", year = "2009", editor = "Anna I. Esparcia and Ying-ping Chen and Gabriela Ochoa and Ender Ozcan and Marc Schoenauer and Anne Auger and Hans-Georg Beyer and Nikolaus Hansen and Steffen Finck and Raymond Ros and Darrell Whitley and Garnett Wilson and Simon Harding and W. B. Langdon and Man Leung Wong and Laurence D. Merkle and Frank W. Moore and Sevan G. Ficici and William Rand and Rick Riolo and Nawwaf Kharma and William R. Buckley and Julian Miller and Kenneth Stanley and Jaume Bacardit and Will Browne and Jan Drugowitsch and Nicola Beume and Mike Preuss and Stephen L. Smith and Stefano Cagnoni and Jim DeLeo and Alexandru Floares and Aaron Baughman and Steven Gustafson and Maarten Keijzer and Arthur Kordon and Clare Bates Congdon and Laurence D. Merkle and Frank W. Moore", pages = "2167--2172", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1570256.1570295", abstract = "This paper presents a genetic programming-based symbolic regression approach to the construction of relational features in link analysis applications. Specifically, we consider the problems of predicting, classifying and annotating friends relations in friends networks, based upon features constructed from network structure and user profile data. We first document a data model for the blog service LiveJournal, and define a set of machine learning problems such as predicting existing links and estimating inter-pair distance. Next, we explain how the problem of classifying a user pair in a social network, as directly connected or not, poses the problem of selecting and constructing relevant features. We use genetic programming to construct features, represented by multiple symbol trees with base features as their leaves. In this manner, the genetic program selects and constructs features that may not have been originally considered, but possess better predictive properties than the base features. Finally, we present classification results and compare these results with those of the control and similar approaches.", notes = "PhD University of Illinois 22 Aug 2013 http://hdl.handle.net/2142/45402 Discovering roles and types from hierarchical information networks (not GP) Distributed on CD-ROM at GECCO-2009. ACM Order Number 910092.", } @Article{Weraduwage:2015:fps, author = "Sarathi M. Weraduwage and Jin Chen and Fransisca C. Anozie and Alejandro Morales and Sean E. Weise and Thomas D. Sharkey", title = "The relationship between leaf area growth and biomass accumulation in {Arabidopsis thaliana}", year = "2015", journal = "Frontiers in Plant Science", volume = "6", number = "167", month = "09 " # apr, keywords = "genetic algorithms, genetic programming, plant science, carbon partitioning, photosynthesis, leaf area, leaf thickening, growth, specific leaf area", publisher = "Frontiers Media S.A.", ISSN = "1664-462X", bibsource = "OAI-PMH server at www.ncbi.nlm.nih.gov", language = "en", oai = "oai:pubmedcentral.nih.gov:4391269", URL = "http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4391269/", URL = "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4391269/pdf/fpls-06-00167.pdf", DOI = "doi:10.3389/fpls.2015.00167", size = "21 pages", abstract = "Leaf area growth determines the light interception capacity of a crop and is often used as a surrogate for plant growth in high-throughput phenotyping systems. The relationship between leaf area growth and growth in terms of mass will depend on how carbon is partitioned among new leaf area, leaf mass, root mass, reproduction, and respiration. A model of leaf area growth in terms of photosynthetic rate and carbon partitioning to different plant organs was developed and tested with Arabidopsis thaliana L. Heynh. ecotype Columbia (Col-0) and a mutant line, gigantea-2 (gi-2), which develops very large rosettes. Data obtained from growth analysis and gas exchange measurements was used to train a genetic programming algorithm to parametrise and test the above model. The relationship between leaf area and plant biomass was found to be non-linear and variable depending on carbon partitioning. The model output was sensitive to the rate of photosynthesis but more sensitive to the amount of carbon partitioned to growing thicker leaves. The large rosette size of gi-2 relative to that of Col-0 resulted from relatively small differences in partitioning to new leaf area vs. leaf thickness.", notes = "PMID: 25914696 https://www.frontiersin.org/journals/plant-science#", } @Misc{Werner:1997, author = "J. C. Werner and J. {Sotelo Jr}", title = "Active Control of noise in ducts using genetic algorithms", journal = "II Symposium on Research Polytechnic School of USP-1997.", year = "1997", keywords = "genetic algorithms", } @Misc{Werner:1998, author = "J. C. Werner and J. {Sotelo Jr}", title = "Active control of noise in ducts", journal = "I Iberian-American Congress of Acoustic SOBRAC - Brazil 1998.", year = "1998", keywords = "genetic algorithms", } @TechReport{Werner:1999:PACT, author = "J. C. Werner", title = "A graphic methodology to develop parallel application applying genetic algorithms to acoustic noise cancellation", institution = "Sao Paulo University", year = "1999", keywords = "genetic algorithms, genetic programming", URL = "http://www.geocities.com/jamwer2002/pact98.pdf", abstract = "Conclusions. The transaction diagram used to develop genetic algorithms to run into a parallel real-time architecture can help the designer to better manage the software resources avoiding critical points. A diagnosis program will be developed to check the performance of the software in realtime processing. The graphical interface allow an easy optimisation and common routines visualisation of the software. The documentation is quickly updated and very condensed: Fig. 2 and 3 software was coded into 3.000 lines in C language. The authors acknowledge FAPESP for sponsoring the research (*) and CNPq for granting a doctoral scholarship (**).", } @PhdThesis{werner:thesis, author = "James Cunha Werner", title = "Active Noise Control in Ducts Using Genetic Algorithms", school = "Mechanical Engineering Department, Sao Paulo University", year = "1999", address = "Brazil", month = sep # " 24", email = "jamwer@usp.br", keywords = "genetic algorithms, genetic programming", broken = "http://puck.mcca.ep.usp.br/~jamwer/tese.mpg huge movie", URL = "http://www.geocities.ws/jamwer2002/tese.zip", abstract = "Genetic Programming + Genetic algorithm = Genetic Control This thesis addresses the problem of actively control acoustic noise in ducts through the application of genetic algorithm - GA and genetic programming - GP (called genetic control - GC). Genetic programming obtain a self structured autonomous control model and genetic algorithms adapt model's parameters under real time. Three different strategies were adopted with GA. In the Simple Genetic Algorithm (SGA) each individual of a generation represents a specific frequency, phase and amplitude used in cancellation of noise and the fitness function is the average energy of the signal. The Successive Approach Genetic Algorithm (SAGA) is a modification of SGA, where a first level procedure searches for candidate frequencies and a second level improves them between fixed limits, with phase and amplitude. To run in real time, a gain/delay model was coded into the chromosome. A simulation model was developed to test the software and to analyses the behaviour of the genetic algorithm parameters. The software was designed to work in a parallel DSP TMS320C44 architecture managing processors communication and shared memory with high performance. A mono processor version was developed to control the duct system under real time with noise reduction. The acoustic feedback was removed through the microphone confinement, special sound boxes and through adaptive model approach. Genetic programming applied to the system converges to the genetic algorithms gain/delay model as foreseen by the theory and experiment", abstract = "Esta tese estuda o problema de controlar o ruido ac\'{u}stico em dutos mediante o fornecimento de energia ac\'{u}stica, atraves da associa\c{c}\~{a}o do algoritmo genetico - GA e da programa\c{c}\~{a}o genetica - GP (constituindo o controle genetico - GC).A programa\c{c}\~{a}o genetica e utilizada para obter um modelo de controle auto estruturado e aut\^{o}nomo, e o algoritmo genetico e utilizado para adaptar os par\^{a}metros do modelo em tempo real. Foram adotadas tr\^{e}s estrategias de adapta\c{c}\~{a}o usando o GA. Uma, com o algoritmo genetico simples (SGA): cada individuo de uma gera\c{c}\~{a}o representa uma freq{\"{u}}\^{e}ncia, fase e amplitude especificas, usadas no cancelamento do ruido, sendo a fun\c{c}\~{a}o de desempenho obtida pela media da energia do sinal. Segunda, a de refinamento sucessivo (SAGA) foi utilizada em dois niveis: um nivel codificando a freq{\"{u}}\^{e}ncia e depois um nivel refinando-a junto com a fase e a amplitude. Finalmente, a terceira abordagem utiliza em tempo real um modelo de atraso e ganho codificado no cromossomo. Um simulador foi desenvolvido com um modelo simplificado para testar o software e para analisar o comportamento dos par\^{a}metros do algoritmo genetico. O software foi migrado para trabalhar em arquitetura paralela de DSPs TMS320C44, gerenciando a comunica\c{c}\~{a}o entre os processadores e a memoria compartilhada com alto desempenho. Uma vers\~{a}o com um processador TMS320C32 foi desenvolvida para controlar o sistema do duto em tempo real, reduzindo o ruido em todas as faixas de freq{\"{u}}\^{e}ncia. O tratamento da realimenta\c{c}\~{a}o ac\'{u}stica foi feito atraves de: confinamento do microfone, confec\c{c}\~{a}o de caixas ac\'{u}sticas especiais e mediante a remo\c{c}\~{a}o atraves de um modelo baseado na tecnica adaptativa. A programa\c{c}\~{a}o genetica aplicada ao sistema, convergiu para o modelo de atraso e ganho, utilizado pelo GA e previsto pela teoria.", notes = "in Portuguese and microsoft word", } @TechReport{werner:2000:rep1, author = "James C. Werner and Mehmet E. Aydin and Terence C. Fogarty", title = "Evolving genetic algorithm for Job Shop Scheduling problems", institution = "London South Bank University", year = "2001", address = "School of Computing, Information Systems and Mathematics, South Bank University, 103 Borough Road, London SE1 0AA, UK", keywords = "genetic algorithms, genetic programming", URL = "http://www.geocities.com/jamwer2002/rep1.pdf", size = "5 pages", abstract = "an attempt to evolve genetic algorithms by a particular genetic programming method to make it able to solve the classical Job Shop Scheduling problem (JSSP), which is a type of very well known hard combinatorial optimisation problems. The aim is to look for a better GA such that solves JSSP with preferable scores. This looking up procedure is done by evolving GA with GP. First we solve a set of job shop scheduling benchmarks by using a conventional GA and then an association of GP to evolve a GA. The instance of JSSP tackled are available in OR literature.", } @TechReport{werner:2001:rep2, author = "James Cunha Werner and Terence C. Fogarty", title = "Genetic Algorithm applied in Clustering Datasets", institution = "London South Bank University", year = "2001", address = "SCISM, South Bank University, 103 Borough Road, London SE1 0AA", keywords = "genetic algorithms, genetic programming", URL = "http://www.geocities.com/jamwer2002/rep2.pdf", abstract = "compares the clustering technique k-means and two different approaches of Genetic Algorithms to a sample dataset, and in EachMovie dataset. The comparison between both techniques shows a better result of GA.", } @TechReport{werner:2001:rep3, author = "James Cunha Werner and Terence C. Fogarty", title = "Map algorithm in routing problems using genetic algorithm", institution = "London South Bank University", year = "2001", address = "SCISM, South Bank University, 103 Borough Road, London SE1 0AA", keywords = "genetic algorithms, genetic programming", URL = "http://www.geocities.com/jamwer2002/rep3.pdf", abstract = "a new routing problem generation algorithm for travelling salesman problem, delivery or vehicle scheduling attending to their constraints, where usually incompatible solutions are discarded or a punishment factor is applied, with waste computer processing.", notes = "IFORS Triennial Conference Edinburgh, Scotland July 8-12,2002", } @TechReport{werner:2001:rep4, author = "James Cunha Werner and Terence C. Fogarty", title = "Crop Planning System for tilling optimisation", institution = "London South Bank University", year = "2001", address = "SCISM, South Bank University, 103 Borough Road, London SE1 0AA", keywords = "genetic algorithms, genetic programming", URL = "http://www.geocities.com/jamwer2002/rep4.pdf", abstract = "the optimisation project developed in Brazil through the implantation of Sun Sparc Stations centralising the crop planning system to support decisions about the date to start harvest, areas to be sampled and cropped, simulation of farming activities during crop and between harvest.", notes = "OR for development prize competition 2002 IFORS Triennial Conference Edinburgh/ Scotland July 8-12,2002", } @TechReport{werner:2002:gpphys, author = "James Cunha Werner", title = "The Physics behind genetic programming", institution = "London South Bank University", year = "2001", address = "SCISM, South Bank University, 103 Borough Road, London SE1 0AA", keywords = "genetic algorithms, genetic programming, Calculus of Variations, Euler-Lagrange Equation", URL = "http://www.geocities.com/jamwer2002/gpphys.pdf", size = "8 pages", abstract = "the historic scenery of calculus of variations (CV), one of the central tools of theoretical physics, and its relationship with genetic programming (GP) algorithms, a search method with would be considered a numerical solution for the method of variations. Conclusion. This paper establishes a relationship between the CV and GP as its numerical methods. The central goal of CV is determining the functional that attend to some constraints solving fundamentals differential equations by analytical methods while GP try to obtain the solution applying genetic operators in tree coded chromosomes. The differential displacement in analytical solution assumes the format of a change into the functional structure through the application of crossover and mutation operators. The action integral has its similar in the fitness function, with in the same way is obtained during all solution interval time. The initial condition appears in both approaches defining a realisation of an intrinsic solution (we termed Cognitive Structure) that is holistic, i.e., complete and self-contained. It?s a solution not for one single problem, but for a large class of similar problems. Under this point of view, we would divide any problem solution as two different levels: one for the CS search, and the other to its adaptation to one realization. A general overview is: the information available of the problem feeds GP software, with after some generations obtain the cognitive structure of the problem, or the best available at this moment with minimise the fitness function, i.e., the action for the system. This structure needs to be adapted to the real conditions of the system.", } @TechReport{werner:2001:rep6, author = "James Cunha Werner and Terence C. Fogarty", title = "Genetic programming applied to strategies learning", institution = "London South Bank University", year = "2001", address = "SCISM, South Bank University, 103 Borough Road, London SE1 0AA", keywords = "genetic algorithms, genetic programming", URL = "http://www.geocities.com/jamwer2002/rep6.pdf", abstract = "This study addresses the problem of knowledge acquisition to decision taken, applied to Tic-tac-toe game: a fix structure and rules, with a reasonable number of solutions, each one carrying to a different result (win, lost or tie). The comparative study focus the case where all possible states are modelled by genetic programming, and a set of rules are applied against the opponent. The other approach is the acquisition of knowledge on fly, applying each solution for a number of trials, and using genetic programming to obtain the better solution.", notes = "EuroGP2002 5th European Conference on Genetic Programming 3-5 April, Kinsale, Ireland", } @InProceedings{werner:2001:PKDD, author = "J. C. Werner and T. C. Fogarty", title = "Genetic programming applied to Collagen disease \& thrombosis", booktitle = "PKDD 2001 Challenge on Thrombosis data", year = "2001", address = "Freiburg, Germany", month = sep # " 3-7", keywords = "genetic algorithms, genetic programming", URL = "http://www.geocities.com/jamwer2002/pkdd01.pdf", URL = "http://lisp.vse.cz/challenge/pkdd2001/werner.pdf", URL = "http://citeseer.ist.psu.edu/457939.html", size = "6 pages", abstract = "how to obtain a mathematical discriminate function to quantify the severity of a disease with genetic programming (GP). It was applied to thrombosis testing because it is important to develop a fast, reliable and accurate test to identify the mechanism of thrombosis occurrence", } @InProceedings{werner:2001:idamap, author = "James Cunha Werner and Terence C. Fogarty", title = "Genetic programming applied to severe diseases diagnosis", booktitle = "Proceedings Intelligent Data Analysis in Medicine and Pharmacology (IDAMAP-2001)", year = "2001", note = "a workshop at MedInfo-2001", keywords = "genetic algorithms, genetic programming, data mining, classification", URL = "http://www.geocities.com/jamwer2002/idamap01.pdf", broken = "http://www.ailab.si/idamap/idamap2001/papers/werner.pdf", abstract = "This paper addresses the problem of how to obtain a mathematical discriminate function to quantify the severity of a disease with genetic programming (GP). It was applied to breast-cancer testing because it is important to develop a reliable but inexpensive test to identify women with high risk for a more expensive and accurate clinical procedure.", notes = "IDAMAP workshop http://www.ailab.si/idamap/idamap2001/ https://file.biolab.si/biolab/idamap/idamap2001/scientific.html https://file.biolab.si/biolab/idamap/idamap2001/papers/werner.pdf", } @Misc{werner:2001:SIGKDD1, author = "J. C. Werner", title = "Genetic programming applied to pharmaceutical drugs design", booktitle = "Data mining Cup 2001 of The Seventh ACM SIGKDD International Conference on Knowledge discovery and data mining", howpublished = "www", year = "2001", editor = "Christos Hatzis and David Page", address = "San Francisco", month = aug # " 26-29", keywords = "genetic algorithms, genetic programming", URL = "http://www.geocities.com/jamwer2002/tromb.pdf", size = "4 pages", notes = "http://www.acm.org/sigkdd/kdd2001/ http://www.cs.wisc.edu/~dpage/kddcup2001/", } @Misc{werner:2001:SIGKDD2, author = "J. C. Werner", title = "Genetic programming applied to gene function identification", booktitle = "Data mining Cup 2001 of The Seventh ACM SIGKDD International Conference on Knowledge discovery and data mining", howpublished = "www", year = "2001", editor = "Christos Hatzis and David Page", address = "San Francisco", month = aug # " 26-29", URL = "http://www.geocities.com/jamwer2002/genfunc.pdf", keywords = "genetic algorithms, genetic programming", notes = "http://www.acm.org/sigkdd/kdd2001/ http://www.cs.wisc.edu/~dpage/kddcup2001/", } @Misc{werner:2001:SIGKDD3, author = "J. C. Werner", title = "Genetic programming applied to gene location identification", booktitle = "Data mining Cup 2001 of The Seventh ACM SIGKDD International Conference on Knowledge discovery and data mining", howpublished = "www", year = "2001", editor = "Christos Hatzis and David Page", address = "San Francisco", month = aug # " 26-29", URL = "http://www.geocities.com/jamwer2002/genloc.pdf", keywords = "genetic algorithms, genetic programming", notes = "http://www.acm.org/sigkdd/kdd2001/ http://www.cs.wisc.edu/~dpage/kddcup2001/", } @InProceedings{werner:2002:EuroGP, title = "Genetic control applied to asset managements", author = "James Cunha Werner and Terence C. Fogarty", editor = "James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi", booktitle = "Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002", volume = "2278", series = "LNCS", pages = "192--201", publisher = "Springer-Verlag", address = "Kinsale, Ireland", publisher_address = "Berlin", month = "3-5 " # apr, year = "2002", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-43378-3", URL = "http://www.geocities.com/jamwer2002/markop.pdf", DOI = "doi:10.1007/3-540-45984-7_19", abstract = "This paper address the problem of investment optimisation, with deals with obtain stock time series from data extracted of graphics available in internet, predict assets price by adaptive algorithms, optimise the portfolio with genetic algorithms and obtain a recursive model of portfolio composition on-fly using genetic programming, all steps integrated to obtain an automatic management. The final result is a real-time update portfolio composition for each asset.", notes = "EuroGP'2002, part of \cite{lutton:2002:GP} FTSE 100 http://www.geocities.com/jamwer2002/markop.pdf is slightly(?) different from publishd version", } @InProceedings{werner:2002:OR, author = "J. C. Werner and T. C. Fogarty", title = "Artificial intelligence applied to rescheduling and optimisation", booktitle = "2002 Simulation Study Group Two-day Workshop Proceedings", year = "2002", editor = "T. Eldabi and S Robinson and S. J. E Taylor and P. A. Wilcox", address = "University of Birmingham, UK", month = "20-21 " # mar, publisher = "The OR Society", keywords = "genetic algorithms, genetic programming", URL = "http://www.geocities.com/jamwer2002/simtrans.pdf", notes = "http://www.orsoc.org.uk/shop/ors/bob21.htm The contents of this publication is not available online. If you would like further information, please contact: Gill Townsend tel: +44 (0)121 233 9300 email: townsend@orsoc.org.uk", } @InProceedings{werner03, author = "James Cunha Werner and Tatiana Kalganova", title = "Disease modeling using Evolved Discriminate Function", booktitle = "Genetic Programming, Proceedings of EuroGP'2003", year = "2003", editor = "Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa", volume = "2610", series = "LNCS", pages = "465--474", address = "Essex", publisher_address = "Berlin", month = "14-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-00971-X", URL = "http://www.geocities.com/jamwer2002/eurogp2003.pdf", DOI = "doi:10.1007/3-540-36599-0_44", abstract = "Precocious diagnosis increases the survival time and patient quality of life. It is a binary classification, exhaustively studied in the literature. This paper innovates proposing the application of genetic programming to obtain a discriminate function. This function contains the disease dynamics used to classify the patients with as little false negative diagnosis as possible. If its value is greater than zero then it means that the patient is ill, otherwise healthy. A graphical representation is proposed to show the influence of each dataset attribute in the discriminate function. The experiment deals with Breast Cancer and Thrombosis and Collagen diseases diagnosis. The main conclusion is that the discriminate function is able to classify the patient using numerical clinical data, and the graphical representation displays patterns that allow understanding of the model.", notes = "EuroGP'2003 held in conjunction with EvoWorkshops 2003", } @InProceedings{werner:2003:PKDD, author = "James Cunha Werner and Tatiana Kalganova", title = "Risk Evaluation using Evolvable Discriminate Function", booktitle = "The {ECML/PKDD-2003} Discovery Challenge Workshop", year = "2003", editor = "Petr Berka", pages = "120--134", address = "Cavtat-Dubrovnik, Croatia", month = sep # " 23", keywords = "genetic algorithms, genetic programming, medical diagnostic", URL = "http://www.geocities.com/jamwer2002/arte1.pdf", size = "12 pages", abstract = "This essay proposes a new approach to risk evaluation using disease mathematical modelling. The mathematical model is an algebraic equation of the available database attributes and is used to evaluate the patient condition. If its value is greater than zero it means that the patient is ill (or in risk condition), otherwise healthy. In practice risk evaluation has been a very difficult problem mainly due its sporadic behaviour (suddenly, the patient has a stroke, etc as a condition aggravation) and its database representation. The database contains, under the label of risk patient data, information of the patient condition that sometimes is in risk condition and sometimes is not, introducing errors in the algorithm training. The study was applied to Atherosclerosis database from Discovery Challenge 2003 - ECML/PKDD 2003 workshop.", notes = "http://lisp.vse.cz/challenge/ecmlpkdd2003/chall2003.htm 14th European Conference on Machine Learning and 7th European Conference on Principles and Practice of Knowledge Discovery in Databases ECML/PKDD-2003", } @InProceedings{6349, author = "Bernhard Werth and Erik Pitzer and Michael Affenzeller", title = "A Fair Performance Comparison of Different Surrogate Optimization Strategies", booktitle = "Computer Aided Systems Theory, EUROCAST 2017", year = "2017", editor = "Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia", volume = "10671", series = "Lecture Notes in Computer Science", pages = "408--415", address = "Las Palmas de Gran Canaria, Spain", month = feb, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Surrogate models, Evolutionary algorithms, Black-box optimization", isbn13 = "978-3-319-74718-7", URL = "https://link.springer.com/chapter/10.1007/978-3-319-74718-7_49", DOI = "doi:10.1007/978-3-319-74718-7_49", size = "8", abstract = "Much of the literature found on surrogate models presents new approaches or algorithms trying to solve black-box optimization problems with as few evaluations as possible. The comparisons of these new ideas with other algorithms are often very limited and constrained to non-surrogate algorithms or algorithms following very similar ideas as the presented ones. This work aims to provide both an overview over the most important general trends in surrogate assisted optimization and a more wide-spanning comparison in a fair environment by reimplementation within the same software framework.", notes = "Published 2018?", } @Article{westbury:2003:brmic, author = "Chris Westbury and Lori Buchanan and Michael Sanderson and Mijke Rhemtulla and Leah Phillips", title = "Using genetic programming to discover nonlinear variable interactions", journal = "Behavior Research Methods, Instruments, \& Computers", year = "2003", volume = "35", number = "2", pages = "202--216", month = may, keywords = "genetic algorithms, genetic programming", ISSN = "0743-3808", publisher = "Springer-Verlag", URL = "http://link.springer.com/article/10.3758/BF03202543", DOI = "doi:10.3758/BF03202543", size = "15 pages", abstract = "Psychology has to deal with many interacting variables. The analyses usually used to uncover such relationships have many constraints that limit their utility. We briefly discuss these and describe recent work that uses genetic programming to evolve equations to combine variables in nonlinear ways in a number of different domains. We focus on four studies of interactions from lexical access experiments and psychometric problems. In all cases, genetic programming described nonlinear combinations of items in a manner that was subsequently independently verified. We discuss the general implications of genetic programming and related computational methods for multivariate problems in psychology", notes = "PMID: 12834075", } @TechReport{oai:CiteSeerPSU:506281, title = "``GenPlan'': Combining Genetic Programming and Planning", author = "C Henrik Westerberg and John Levine", institution = "School of Informatics, University of Edingburgh", year = "2000", citeseer-isreferencedby = "oai:CiteSeerPSU:200269; oai:CiteSeerPSU:79841; oai:CiteSeerPSU:445142; oai:CiteSeerPSU:165982", citeseer-references = "oai:CiteSeerPSU:87325; oai:CiteSeerPSU:554819", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:506281", rights = "unrestricted", number = "EDI-INF-RR-0104", address = "Edingburgh, UK", month = dec, keywords = "genetic algorithms, genetic programming", URL = "http://www.inf.ed.ac.uk/publications/online/0104.pdf", URL = "http://www.aiai.ed.ac.uk/~johnl/papers/westerberg-plansig00.ps", URL = "http://citeseer.ist.psu.edu/506281.html", abstract = "Planning is a difficult and fundamental problem of AI. An alternative solution to planning may lie in applying Genetic Programming to the planning problem. As such a Genetic Planner was constructed to assess the feasibility of this idea. This paper introduces the topics of Genetic Programming and Genetic Planning and introduces the algorithm used to implement the Genetic Planner. The Genetic Planner was applied to three classical planning domains: STRIPS Blocks Domain, Briefcase Domain, and the Logistics Domain. The Genetic Planner produced good results for both the STRIPS Blocks Domain and the Briefcase Domain. However further work is required before it can solve any problem from the Logistics Domain besides the trivial ones. There is also some comparison of GenPlan with both BlackBox and SINERGY. The first implementation provided many avenues for further research: quick partial plan formation for seeding the Genetic Planner's initial population, more intelligent fitness functions, and an intelligent form of crossover and mutation. Further research into the feasibility of the Genetic Planner to plan in alternative domains besides classical planning is also important.", notes = "appears in Procs PLANSIG 2000 see \cite{westerberg:2000:PLANSIG}", size = "pages", } @InProceedings{westerberg:2000:PLANSIG, author = "C Henrik Westerberg and John Levine", title = "``GenPlan'': Combining Genetic Programming and Planning", booktitle = "19th Workshop of the UK Planning and Scheduling Special Interest Group (PLANSIG 2000)", year = "2000", editor = "Max Garagnani", address = "The Open University, Milton Keynes, UK", month = "14-15 " # dec, keywords = "genetic algorithms, genetic programming", ISSN = "1368-5708", URL = "http://www.cis.strath.ac.uk/~henrik/publications/genplan1.ps", URL = "http://mcs.open.ac.uk/plansig2000/Papers/westerberg.pdf", size = "11 pages", abstract = "Planning is a difficult and fundamental problem of AI. An alternative solution to planning may lie in applying Genetic Programming to the planning problem. As such a Genetic Planner was constructed to assess the feasibility of this idea. This paper introduces the topics of Genetic Programming and Genetic Planning and introduces the algorithm used to implement the Genetic Planner. The Genetic Planner was applied to three classical planning domains: STRIPS Blocks Domain, Briefcase Domain, and the Logistics Domain. The Genetic Planner produced good results for both the STRIPS Blocks Domain and the Briefcase Domain. However further work is required before it can solve any problem from the Logistics Domain besides the trivial ones. There is also some comparison of GenPlan with both Black-Box and SINERGY. The first implementation provided many avenues for further research: quick partial plan formation for seeding the Genetic Planner's initial population, more intelligent Fitness functions, and an intelligent form of crossover and mutation. Further research into the feasibility of the Genetic Planner to plan in alternative domains besides classical planning is also important.", notes = "http://mcs.open.ac.uk/plansig2000/ See also \cite{oai:CiteSeerPSU:506281}", } @InProceedings{westerberg:2001:EvoWorks, author = "C. Henrik Westerberg and John Levine", title = "Investigations of Different Seeding Strategies in a Genetic Planner", booktitle = "Applications of Evolutionary Computing", year = "2001", editor = "Egbert J. W. Boers and Stefano Cagnoni and Jens Gottlieb and Emma Hart and Pier Luca Lanzi and Gunther R. Raidl and Robert E. Smith and Harald Tijink", volume = "2037", series = "LNCS", pages = "505--514", address = "Lake Como, Italy", publisher_address = "Berlin", month = "18 " # apr, organisation = "EvoNET", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, population seeding, classical planning, STRIPS, one point crossover, linear representation, plan, artificial intelligence, blocks world", ISBN = "3-540-41920-9", URL = "http://www.cis.strath.ac.uk/~henrik/publications/EvoSTIM_HenrikWesterberg.ps", URL = "http://www.aiai.ed.ac.uk/~johnl/papers/westerberg-evostim01.ps", URL = "http://citeseer.ist.psu.edu/505122.html", abstract = "Planning is a difficult and fundamental problem of AI. An alternative solution to traditional planning techniques is to apply Genetic Programming. As a program is similar to a plan a Genetic Planner can be constructed that evolves plans to the plan solution. One of the stages of the Genetic Programming algorithm is the initial population seeding stage. We present five alternatives to simple random selection based on simple search. We found that some of these strategies did improve the initial population, and the efficiency of the Genetic Planner over simple random selection of actions.", notes = "EvoWorkshops2001. Fitness by simulation", } @InProceedings{oai:CiteSeerPSU:507073, title = "Optimising Plans using Genetic Programming", author = "C. Henrik Westerberg and John Levine", booktitle = "6th European Conference on Planning (ECP-01)", year = "2001", editor = "Amedeo Cesta", address = "Toledo, Spain", month = sep # " 12-14", keywords = "genetic algorithms, genetic programming", citeseer-isreferencedby = "oai:CiteSeerPSU:80030; oai:CiteSeerPSU:106014", citeseer-references = "oai:CiteSeerPSU:88066; oai:CiteSeerPSU:212034; oai:CiteSeerPSU:87325; oai:CiteSeerPSU:345046", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:507073", rights = "unrestricted", URL = "http://www.cis.strath.ac.uk/~henrik/publications/ukci01.ps", URL = "http://www.aiai.ed.ac.uk/~johnl/papers/westerberg-ecp01.ps", URL = "http://citeseer.ist.psu.edu/507073.html", size = "6 pages", abstract = "Finding the shortest plan for a given planning problem is extremely hard. We present a domain independent approach for plan optimisation based on Genetic Programming. The algorithm is seeded with correct plans created by hand-encoded heuristic policy sets. The plans are very unlikely to be optimal but are created quickly. The suboptimal plans are then evolved using a generational algorithm towards the optimal plan. We present initial results from Blocks World and found that GP method almost always improved sub-optimal plans, often drastically.", notes = "http://scalab.uc3m.es/~ecp01/", } @InProceedings{westerberg:2002:gecco:workshop, title = "Elite Crossover in Genetic Planning", author = "C. Henrik Westerberg", pages = "311--314", booktitle = "Graduate Student Workshop", editor = "Sean Luke and Conor Ryan and Una-May O'Reilly", year = "2002", month = "8 " # jul, publisher = "AAAI", address = "New York", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025", keywords = "genetic algorithms, genetic programming", URL = "http://www.cis.strath.ac.uk/~henrik/publications/gec.ps", notes = "Bird-of-a-feather Workshops, GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002) part of barry:2002:GECCO:workshop", } @PhdThesis{Westerberg:thesis, author = "Carl Henrik Westerberg", title = "An Investigation into the use of Evolutionary Algorithms for Fully Automated Planning", school = "Artificial Intelligence Applications Institute, School of Informatics, University of Edinburgh", year = "2006", address = "UK", keywords = "genetic algorithms, genetic programming, Artifcial Intelligence", URL = "http://www.cis.strath.ac.uk/~henrik/publications/thesis.pdf", URL = "http://hdl.handle.net/1842/11824", URL = "https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.663665", size = "329 pages", abstract = "This thesis presents a new approach to the Artificial Intelligence (AI) problem of fully automated planning. Planning is the act of deliberation before acting that guides rational behaviour and is a core area of AI. Many practical real-world problems can be classed as planning problems, therefore practical and theoretical developments in AI planning are well motivated. Unfortunately, planning for even toy domains is hard, many different search algorithms have been proposed, and new approaches are actively encouraged. The approach taken in this thesis is to adopt ideas from Evolutionary Algorithms (EAs) and apply the techniques to fully automated plan synthesis. EA methods have enjoyed great success in many problem areas of AI. They are a new kind of search technique that have their foundation in evolution. Previous attempts to apply EAs to plan synthesis have promised encouraging results, but have been ad-hoc and piecemeal. This thesis thoroughly investigates the approach of applying evolutionary search to the fully automated planning problem. This is achieved by developing and modifying a proof of concept planner called GENPLAN. Before EA-based systems can be used, a thorough examination of various parameter settings must be explored. Once this was completed, the performance of GENPLAN was evaluated using a selection of benchmark domains and other competition style planners. The difficulties raised by the benchmark domains and the extent to which they cause problems for the approach are highlighted along with problems associated with EA search. Modifications are proposed and experimented with in an attempt to alleviate some of the identified problems. EAs offer a flexible framework for fully automated planning, but demonstrate a clear weakness across a range of currently used benchmark domains for plan synthesis.", notes = "Supervisor: John Levine", } @InProceedings{Westerdale:1997:nowork, author = "T. H. Westerdale", title = "Classifier Systems--No Wonder They Don't Work", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, classifier systems", pages = "529--537", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", notes = "GP-97", } @InProceedings{chaiyaratana:evows06, author = "Wannasak Wetcharaporn and Nachol Chaiyaratana and Sanpachai Huvanandana", title = "Enhancement of an Automatic Fingerprint Identification System Using a Genetic Algorithm and Genetic Programming", booktitle = "Applications of Evolutionary Computing, EvoWorkshops2006: {EvoBIO}, {EvoCOMNET}, {EvoHOT}, {EvoIASP}, {EvoInteraction}, {EvoMUSART}, {EvoSTOC}", year = "2006", month = "10-12 " # apr, editor = "Franz Rothlauf and Jurgen Branke and Stefano Cagnoni and Ernesto Costa and Carlos Cotta and Rolf Drechsler and Evelyne Lutton and Penousal Machado and Jason H. Moore and Juan Romero and George D. Smith and Giovanni Squillero and Hideyuki Takagi", series = "LNCS", volume = "3907", publisher = "Springer Verlag", address = "Budapest", publisher_address = "Berlin", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-33237-5", pages = "368--379", DOI = "doi:10.1007/11732242_33", abstract = "the use of a genetic algorithm and genetic programming for the enhancement of an automatic fingerprint identification system (AFIS). The recognition engine within the original system functions by transforming the input fingerprint into a feature vector or finger code using a Gabor filter bank and attempting to create the best match between the input fingercode and the database fingercodes. A decision to either accept or reject the input fingerprint is then carried out based upon whether the norm of the difference between the input fingercode and the best-matching database fingercode is within the threshold or not. The efficacy of the system is in general determined from the combined true acceptance and true rejection rates. In this investigation, a genetic algorithm is applied during the pruning of the fingercode while the search by genetic programming is executed for the purpose of creating a mathematical function that can be used as an alternative to the norm operator. The results indicate that with the use of both genetic algorithm and genetic programming the system performance has improved significantly.", notes = "part of \cite{evows06}", } @Article{Wexler1984211, author = "Bernard C. Wexler", title = "Hyperlipidemia, hyperglycemia and hypertension in repeatedly bred parents of the obese spontaneously hypertensive rat (Obese/SHR) unaccompanied by arteriosclerosis", journal = "Atherosclerosis", volume = "51", number = "2-3", pages = "211--222", year = "1984", ISSN = "0021-9150", DOI = "doi:10.1016/0021-9150(84)90169-2", URL = "http://www.sciencedirect.com/science/article/B6T12-4FY51X4-21H/2/31d35eb7856574df55bc0f41c98efd13", notes = "not on GP", } @InCollection{Whigham:1992:STRCS, publisher_address = "Berlin, Germany", author = "P. A. Whigham and R. I. (Bob) McKay and J. R. Davis", booktitle = "Theories and Methods of Spatio-Temporal Reasoning in Geographic Space", editor = "A. U. Frank and I. Campari and U. Formentini", isbn13 = "978-3-540-55966-5", ISSN = "0302-9743", address = "Pisa, Italy", month = sep, notes = "Book Chapter", pages = "402--417", publisher = "Springer-Verlag", series = "Springer Lecture Notes in Computer Science", title = "Machine Induction of Geospatial Knowledge", URL = "http://sc.snu.ac.kr/PAPERS/Pisa.pdf", url1 = "http://www.springer.com/west/home?SGWID=4-102-22-1387865-0&changeHeader=true&referer=www.springeronline.com&SHORTCUT=www.springer.com/3-540-55966-3", volume = "639", year = "1992", keywords = "genetic algorithms, genetic programming", size = "16 pages", abstract = "Machine learning techniques such as tree induction have become accepted tools for developing generalisations of large data sets, typically for use with production rule systems in prediction and classification. The advent of computer based cartography and the field of geographic information systems (GIS) has seen a wealth of spatial data generated and used for decision making and modelling. We examine the implications of inductive techniques applied to geospatial data in a logical framework. It is argued that spatial induction systems will benefit from the ability to extend their initial representation language, through feature and relation construction. The enormous search spaces involved imply a need for strong biasing techniques to control the generation of possible representations of the data for all but the most trivial of cases. A heavily constrained geospatial domain, topographic representation, is described as one simplified example of induction across a vector description of space.", } @InProceedings{whigham:1994:GPsi, author = "P. A. Whigham", title = "Genetic programming and spatial information", booktitle = "Proceedings of the 7th Australian Joint Conference on Artificial Intelligence (AI'94)", year = "1994", editor = "Chengqi Zhang and John K Debenham and Dickson Lukose", pages = "124--131", publisher_address = "Singapore", publisher = "World Scientific Publishing Company", keywords = "genetic algorithms, genetic programming", ISBN = "981-02-1920-2", URL = "http://www.amazon.com/Artificial-Intelligence-Proceedings-Australian-Conference/dp/9810219202", notes = " OCLC Number: 31958009", } @InProceedings{whigham:1995:GBGP, author = "P. A. Whigham", title = "Grammatically-based Genetic Programming", booktitle = "Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications", year = "1995", editor = "Justinian P. Rosca", pages = "33--41", address = "Tahoe City, California, USA", month = "9 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://divcom.otago.ac.nz/sirc/Peterw/Publications/ml95.zip", URL = "https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=1f419439e870490840ab379d2de368ace1b9777b", size = "9 pages", abstract = "The genetic programming (GP) paradigm is a functional approach to inductively forming programs. The use of natural selection based on a fitness function for reproduction of the program population has allowed many problems to be solved that require a non-fixed representation. Attempts to extend GP have focussed on typing the language to restrict crossover and to ensure legal programs are always created. We describe the use of a context free grammar to define the structure of the initial language and to direct crossover and mutation operators. The use of a grammar to...", notes = "context free grammar to define the structure of the initial population and to direct crossover and mutation operators part of \cite{rosca:1995:ml}", } @TechReport{whigham:1995:ggls, author = "Peter A. Whigham", title = "Grammatical Genetic Learning and Schemata: Restated", institution = "Department of Computer Science, University College, University of New South Wales, Australia", year = "1995", type = "Technical Report", number = "CS13/95", keywords = "genetic algorithms, genetic programming", } @InProceedings{whigham:1995:ingp, author = "P. A. Whigham", title = "Inductive Bias and Genetic Programming", booktitle = "First International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, GALESIA", year = "1995", editor = "A. M. S. Zalzala", volume = "414", pages = "461--466", address = "Sheffield, UK", publisher_address = "London, UK", month = "12-14 " # sep, publisher = "IEE", keywords = "genetic algorithms, genetic programming, context free grammar", ISBN = "0-85296-650-4", URL = "http://divcom.otago.ac.nz/sirc/Peterw/Publications/galesia.zip", URL = "http://citeseer.ist.psu.edu/whigham95inductive.html", URL = "http://ieeexplore.ieee.org/iel3/3532/10616/00501939.pdf?tp=&arnumber=501939&isnumber=10616", abstract = "Many engineering problems may be described as a search for one near optimal description amongst many possibilities, given certain constraints. Search techniques such as genetic programming, seem appropriate to represent many problems. The paper describes a grammatically based learning technique based upon the genetic programming paradigm, that allows declarative biasing and modifies the bias as the evolution proceeds. The use of bias allows complex problems to be represented and searched efficiently", notes = "12--14 September 1995, Halifax Hall, University of Sheffield, UK see also http://www.iee.org.uk/LSboard/Conf/program/galprog.htm Using 6-multiplexor problem shows using a syntax (of the correct sort, specified using a context free grammar) to constrain the form of the program trees helps GP solve the problem. More restrictions, easier it is. Then presents a method based on the syntax of the fitest member of the population to modify the grammar whilst the GP runs. Shows improvement on 6-multiplexor. Still greater improvements obtained by introducing a fitness for rules within the grammar. This weakly biases the grammar, ie all legal program are still legal, but now some are more likley to be produced than they where before the fitness of the grammar rules where changed. 10\% of population each generation regenerated using his {"}replacement{"} operator.", } @InCollection{whigham:1995:glrr, author = "P. A. Whigham and R. I. McKay", title = "Genetic approaches to learning recursive relations", booktitle = "Progress in Evolutionary Computation", publisher = "Springer-Verlag", year = "1995", editor = "Xin Yao", volume = "956", series = "Lecture Notes in Artificial Intelligence", pages = "17--27", publisher_address = "Heidelberg, Germany", keywords = "genetic algorithms, genetic programming, Machine Learning, Inductive Logic Programming", isbn13 = "978-3-540-60154-8", URL = "https://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.74.8561", DOI = "doi:10.1007/3-540-60154-6_44", size = "11 pages", abstract = "The genetic programming (GP) paradigm is a new approach to inductively forming programs that describe a particular problem. The use of natural selection based on a fitness function for reproduction of the program population has allowed many problems to be solved that require a non-fixed representation. Issues of typing and language forms within the genetic programming paradigm are discussed. The recursive nature of many geospatial problems leads to a study of learning recursive definitions in a subset of a functional language. The inadequacy of GP to create recursive definitions is argued, and a class of problems hypothesised that are difficult for genetic approaches. Operations from the field of Inductive Logic Programming, such as the V and W operators, are shown to have analogies with GP crossover but are able to handle some recursive definitions. Applying a genetic approach to ILP operators is proposed as one approach to learning recursive relations.", notes = " p18 'the negative results...suggest that GP is _not_ suitable for discovering recursive definitions'. Tries ILP+GP. Tries to learn LISP member function with CAR, CDR, EQ, ATOM, MEMBER.Stack limit of 40 calls was imposed. No solutions found (without ILP), due to fitness function and halting problem? RLGG.", affiliation = "University College, University of New South Wales Australian Defence Force Academy Department of Computer Science 2600 Canberra ACT Australia 2600 Canberra ACT Australia", } @InProceedings{whigham:1995:stcfg, author = "P. A. Whigham", title = "A Schema Theorem for Context-Free Grammars", booktitle = "1995 IEEE Conference on Evolutionary Computation", year = "1995", volume = "1", pages = "178--181", address = "Perth, Australia", publisher_address = "Piscataway, NJ, USA", month = "29 " # nov # " - 1 " # dec, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", URL = "http://divcom.otago.ac.nz/sirc/Peterw/Publications/schema.zip", URL = "http://citeseer.ist.psu.edu/whigham95schema.html", DOI = "doi:10.1109/ICEC.1995.489140", abstract = "The basic Schema Theorem for genetic algorithms is modified for a grammatically-based learning system. A context-free grammar is used to define a language in which each sentence is mapped to a fitness value. The derivation trees associated with these sentences are used to define the structure of schemata. The effect of crossover and mutation on schemata is described. A schema theorem is developed which describes how sentences of a language are propagated during evolution.", notes = "ICEC-95 Held December 1995, at University of Western Australia, Perth, Australia. Editors not given by IEEE, Organisers David Fogel and Chris deSilva. conference details at http://ciips.ee.uwa.edu.au/~dorota/icnn95.html", } @InProceedings{whigham:1996:sblbGP, author = "P. A. Whigham", title = "Search Bias, Language Bias, and Genetic Programming", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "230--237", address = "Stanford University, CA, USA", publisher = "MIT Press", URL = "ftp://www.cs.adfa.edu.au/pub/xin/whigham_gp96.ps.gz", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap28.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", size = "9 pages", abstract = "The use of bias with automated learning systems has become an important area of research. The use of bias with evolutionary techniques of learning has been shown to have advantages when complex structures are evolved. This is especially true when the semantics of the evolving population of structures is not explicitly represented or analysed during the evolution. This paper describes a framework which brings together two types of bias, namely search bias (the way new structures are created) and language bias (the form of possible structures that may be created). The resulting system extends genetic programming by allowing declarative bias with both the form of possible solutions that are created and the method by which they are transformed.", notes = "GP-96 ", } @PhdThesis{whigham:1996:phd, author = "Peter Alexander Whigham", title = "Grammatical Bias for Evolutionary Learning", school = "School of Computer Science, University College, University of New South Wales, Australian Defence Force Academy", year = "1996", address = "Canberra, Australia", month = "14 " # oct, keywords = "genetic algorithms, genetic programming", URL = "http://divcom.otago.ac.nz/sirc/Peterw/Publications/thesis.zip", URL = "https://dl.acm.org/citation.cfm?id=923829", URL = "https://search.proquest.com/docview/304319299", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/whigham_1996_phd.pdf", size = "172 pages", abstract = "A framework for declarative bias, based on the genetic programming paradigm (GP), is presented. The system, CFG-GP, encapsulates background knowledge, inductive bias and program structure. A context-free grammar is used to create a population of programs, represented by their corresponding derivation trees. These computer programs evolve using the principle of Darwinian selection. The grammar biases the form of language that is expressible and the inductive hypotheses that are generated. Using a formal grammar to define the space of legal statements allows a declarative language bias to be stated. The defined language may express knowledge in the form of program structure and incorporate explicit beliefs about the structure of possible solutions. Additionally, the form of the initial population of programs may be explicitly biased using a merit selection operation. This probabilistically biases particular statements generated from the grammar. The program induction system, CFG-GP, represents search bias with three operators, namely selective crossover, selective mutation and directed mutation. Each of these operators allows a bias to be explicitly defined in terms of how programs are modified and how the search for a solution proceeds. Hence, both a search and language bias are declaratively represented in an evolutionary framework. The use of a grammar to define language bias explicitly separates this bias from the learning system. Hence, the opportunity exists for the learning system to modify this bias as an additional strategy for learning. A general technique is described to modify the initial grammar while the evolution for a solution proceeds. Feedback between the evolving grammar and the population of programs is shown to improve the convergence of the learning system. The generalising properties of the learnt grammar are demonstrated by incrementally adapting a grammar for a class of problems. A theoretical framework, based on the schema theorem for Genetic Algorithms (GA), is presented for CFG-GP. The formal structure of a grammar allows a clear and concise definition of a building block for a general program. The result is shown to be a generalisation of both fixed-length (GA) and variable-length (GP) representations within the one framework.", } @Unpublished{whigham:1997:mrr, author = "P. A. Whigham and P. F. Crapper", title = "Applying Genetic Programming to Model Rainfall-Runoff", note = "CSIRO Land and Water, Canberra, Australia", month = oct, year = "1997", keywords = "genetic algorithms, genetic programming", notes = "Draft. Welsh and austrialian riverbasins modelled. IHACRES unsatisfactory on oz catchment area but ok in wales. CFG-GP ok on both. Published as \cite{whigham:1997:mrrP} ?", size = "6 pages", } @InProceedings{whigham:1997:mrrP, author = "P. A. Whigham and P. F. Crapper", title = "Applying Genetic Programming to Model Rainfall-Runoff", booktitle = "International Congress on Modelling and Simulation: Proceedings", year = "1997", editor = "A. David McDonald and Michael McAleer", pages = "1701--1706", address = "University of Tasmania, Hobart", month = "8-11 " # dec, organisation = "MSSA", publisher = "The Modelling and Simulation Society of Australia Inc.", keywords = "genetic algorithms, genetic programming", ISBN = "0-86422-826-0", URL = "http://www.mssanz.org.au/MODSIM97/Vol%204/Whigham.pdf", size = "6 pages", notes = "MODSIM 97 http://www.mssanz.org.au/MODSIM97/MODSIM97.htm OCLC Number: 154645163 see also \cite{whigham:1997:mrr} ", } @InProceedings{whigham:1997:epdfg, author = "P. A. Whigham", title = "Evolving a Program defined by a Formal Grammar", booktitle = "Fourth International Conference on Neural Information Processing -- The Annual Conference of the Asian Pacific Neural Network Assembly (ICONIP'97)", year = "1997", address = "Dunedin, New Zealand", keywords = "genetic algorithms, genetic programming", URL = "http://jglobal.jst.go.jp/en/public/20090422/200902183737494003", notes = " ", } @InCollection{whigham:1999:aigp3, author = "Peter A. Whigham and Peter F. Crapper", title = "Time series Modelling Using Genetic Programming: An Application to Rainfall-Runoff Models", booktitle = "Advances in Genetic Programming 3", publisher = "MIT Press", year = "1999", editor = "Lee Spector and William B. Langdon and Una-May O'Reilly and Peter J. Angeline", chapter = "5", pages = "89--104", address = "Cambridge, MA, USA", month = jun, keywords = "genetic algorithms, genetic programming", ISBN = "0-262-19423-6", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/aigp3/ch05.pdf", language = "en", oai = "oai:CiteSeerXPSU:10.1.1.136.2581", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.136.2581", DOI = "doi:10.7551/mitpress/1110.003.0008", abstract = "We describes the application of a grammatically-based Genetic Programming system to discover rainfall-runoff relationships for two vastly different catchments. A context-free grammar is used to define the search space for the mathematical language used to express the evolving programs. A daily time series of rainfall-runoff is used to train the evolving population. A deterministic lumped parameter model, based on the unit hydrograph, is compared with the results of the evolved models on an independent data set. The favourable results of the Genetic Programming approach show that machine learning techniques are potentially a useful tool for developing hydrological models, especially when the relationship between rainfall and runoff is poor.", notes = "AiGP3 See http://cognet.mit.edu", } @Article{whigham:2001:edc, author = "Peter A. Whigham", title = "Book Review: {Evolutionary} Design by Computers", journal = "Genetic Programming and Evolvable Machines", year = "2001", volume = "2", number = "1", pages = "79--84", month = mar, keywords = "genetic algorithms, genetic programming, evolvable hardware", ISSN = "1389-2576", URL = "https://rdcu.be/dR82h", DOI = "doi:10.1023/A:1010074717057", notes = "{"}Evolutionary Design by Computers{"} was edited by Peter J Bentley and published by Morgan Kaufmann ISBN 1-55860-605-X Article ID: 319815", } @InProceedings{whigham:2001:esc, author = "P. A. Whigham and J. Keukelaar", title = "Evolving Structure-Optimising Content", booktitle = "Proceedings of the 2001 Congress on Evolutionary Computation CEC2001", year = "2001", pages = "1228--1235", address = "COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "27-30 " # may, organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, grammar", ISBN = "0-7803-6658-1", URL = "http://divcom.otago.ac.nz/sirc/Peterw/Publications/cec2001.pdf", DOI = "doi:10.1109/CEC.2001.934331", abstract = "This paper describes the initial results of a new form of evolutionary system specifically designed for time series modelling. The system combines a grammatically based Genetic Programming system with various optimisation techniques. The system uses the evolutionary system to construct the structure of equations and optimisation techniques to essentially fill in the details. Three forms of optimisation are described: optimisation of constants in an equation; the optimisation of both the constants and variables in an equation; and the use of a hill climbing mutation to further tune the evolved and optimised equations. Preliminary results indicate that this combination of techniques produces significant improvements in convergence based on the training data, and produces equivalent generalisation on unseen data, for a given number of population member evaluations.", notes = "CEC-2001 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 01TH8546C, Library of Congress Number = . TSOGP hybrid context-free grammar GP with... Powell, simplex. variable arity tree nodes. Evaluation tree speedup via terminal links. Lake Kasumigaura (Japan) water quality.", } @Article{Whigham:2000:EM, author = "P. A. Whigham", title = "Induction of a marsupial density model using genetic programming and spatial relationships", journal = "Ecological Modelling", volume = "131", pages = "299--317", year = "2000", number = "2-3", keywords = "genetic algorithms, genetic programming, Machine learning, Spatial patterns, Habitat prediction", URL = "http://www.sciencedirect.com/science/article/B6VBS-40V4BS0-F/2/e4af01a33144a2b89762925cc1c0722c", DOI = "doi:10.1016/S0304-3800(00)00248-9", abstract = "Machine learning techniques have been developed that allow the induction of spatial models for the prediction of habitat types and population distribution. However, most learning approaches are based on a propositional language for the development of models and therefore cannot express a wide range of possible spatial relationships that exist in the data. This paper compares the application of a functional evolutionary machine learning technique for prediction of marsupial density to some standard machine learning techniques. The ability of the learning system to express spatial relationships allows an improved predictive model to be developed, which is both parsimonious and understandable. Additionally, the maps produced from this approach have a generalised appearance of the measured glider density, suggesting that the underlying preferred habitat properties of the greater glider have been identified.", } @Article{Whigham:2001:EM2, author = "Peter A. Whigham and Friedrich Recknagel", title = "An inductive approach to ecological time series modelling by evolutionary computation", year = "2001", journal = "Ecological Modelling", volume = "146", pages = "275--287", number = "1-3", keywords = "genetic algorithms, genetic programming", URL = "http://www.sciencedirect.com/science/article/B6VBS-44HYNCP-T/1/d33b3386f4d8934ac004f4d985e411ba", abstract = "Building time series models for ecological systems that can be physically interpreted is important both for understanding the dynamics of these natural systems and the development of decision support systems. This work describes the application of an evolutionary computation framework for the discovery of predictive equations and rules for phytoplankton abundance in freshwater lakes from time series data. The suggested framework evolves several different equations and rules, based on limnological and climate variables. The results demonstrate that non-linear processes in natural systems may be successfully modelled through the use of evolutionary computation techniques. Further, it shows that a grammar based genetic programming system may be used as a tool for exploring the driving processes underlying freshwater system dynamics.", } @Article{Whigham:2001:MCM, author = "P. A. Whigham and P. F. Crapper", title = "Modelling rainfall-runoff using genetic programming", journal = "Mathematical and Computer Modelling", volume = "33", pages = "707--721", year = "2001", number = "6-7", month = mar # "-" # apr, keywords = "genetic algorithms, genetic programming, Rainfall runoff", ISSN = "0895-7177", DOI = "doi:10.1016/S0895-7177(00)00274-0", URL = "http://www.sciencedirect.com/science/article/B6V0V-42R1KRY-G/1/226d0ab4c2f13472b01ada47c8473fbf", abstract = "Genetic programming is an inductive form of machine learning that evolves a computer program to perform a task defined by a set of presented (training) examples and has been successfully applied to problems that are complex, nonlinear and where the size, shape, and overall form of the solution are not explicitly known in advance. We describe the application of a grammatically-based genetic programming system to discover rainfall-runoff relationships for two vastly different catchments. A context-free grammar is used to define the search space for the mathematical language used to express the evolving programs. A daily time series of rainfall-runoff is used to train the evolving population. A deterministic lumped parameter model, based on the unit hydrograph, is compared with the results of the evolved models on an independent data set. The favourable results of the genetic programming approach show that machine learning techniques are potentially a useful tool for developing hydrological models, especially when surface water movement and water losses are poorly understood.", } @InProceedings{Whigham:2005:SIRC, author = "Peter A. Whigham and Grant Dick", title = "Fixation of Neutral Alleles in Spatially Structured Populations via Genetic Drift: Describing the spatial structure of faster-than-panmictic configurations", booktitle = "The 17th Annual Colloquium of the Spatial Information Research Centre SIRC 2005", year = "2005", editor = "P. A. Whigham", pages = "81--90", address = "University of Otago, Dunedin, New Zealand", month = nov # " 24th-25th", organisation = "Spatial Information Research Centre", keywords = "genetic algorithms, genetic programming, genetic drift, networks, neutral allele fixation, panmictic populations, hyperfixation", ISBN = "1-877139-90-4", URL = "http://www.business.otago.ac.nz/SIRC05/conferences/2005/10_whigham.pdf", size = "10 pages", abstract = "spatially-structured populations described as a network, and examines the properties of these networks in terms of their affect on fixation of neutral alleles due solely to genetic drift. Individuals are modelled as two allele, one locus haploid, diploid and tetraploid structures. The time to fixation for a variety of network configurations is discovered through simulation. The concept of hyperfixation is introduced, which refers to when time to fixation for a network of n nodes occurs more rapidly than the corresponding panmictic n node structure. A hyperfixation index, h, is developed that attempts to characterise a spatial arrangement such that when h < 1 hyperfixation will occur. Issues regarding fixation with ploidy independence, and possible improvements to the described hyperfixation index.", notes = "http://www.business.otago.ac.nz/SIRC05/conferences/index2005.html", } @InProceedings{Whigham:2006:ASPGP, title = "GP and Bloat: Absorbing boundaries and spatial structures", author = "Peter A. Whigham and G. Dick", booktitle = "Proceedings of the Third Asian-Pacific workshop on Genetic Programming", year = "2006", editor = "The Long Pham and Hai Khoi Le and Xuan Hoai Nguyen", pages = "1--12", ISSN = "18590209", address = "Military Technical Academy, Hanoi, VietNam", keywords = "genetic algorithms, genetic programming", URL = "http://gpbib.cs.ucl.ac.uk/aspgp06/Whigham_bloat.pdf", size = "12 pages", abstract = "examines the behaviour of bloat for GP tree structures using three different topologies: a panmictic, ring and star structure. Initially genetic drift is examined and the results showing the influence of a lower absorbing boundary are examined for each space. A simple selection model is then applied and analysed for bloat. A conjecture regarding the influence of inbreeding, due to spatial structure, is presented as one mechanism for bloat reduction. The paper shows that spatially-structured GP results in a tradeoff between convergence, diversity and the size of individuals.", notes = "broken march 2020 http://www.aspgp.org", } @Article{Whigham:2008:GPEM, author = "P. A. Whigham and Grant Dick", title = "Evolutionary dynamics for the spatial Moran process", journal = "Genetic Programming and Evolvable Machines", year = "2008", volume = "9", number = "2", pages = "157--170", month = jun, note = "Special Issue on Theoretical foundations of evolutionary computation", keywords = "genetic algorithms, Moran process, Spatial evolutionary algorithm, Graph-based model, Fixation, Genetic drift, Local selection", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-007-9046-6", size = "14 pages", abstract = "Evolutionary dynamics for the Moran process have been previously examined within the context of fixation behaviour for introduced mutants, where it was demonstrated that certain spatial structures act as amplifiers of selection. This article will revisit the assumptions for this spatial Moran process and show that proportional global fitness, introduced as part of the Moran process, is necessary for the amplification of selection to occur. Here it is shown that under the condition of local proportional fitness selection the amplification property no longer holds. In addition, regular structures are also shown to have a modified fixation probability from a panmictic population when local selection is applied. Theoretical results from population genetics, which suggest fixation probabilities are independent of geography, are discussed in relation to these local graph-based models and shown to have different assumptions and therefore not to be in conflict with the presented results. This paper examines the issue of fixation probability of an introduced advantageous allele in terms of spatial structure and various spatial parent selection models. The results describe the relationship between structured populations and individual selective advantage in a problem independent manner. This is of significant interest to the theory of fine-grained spatially-structured evolutionary algorithms since the interaction of selection and space for diversity maintenance, selection strength and convergence underlies resulting evolutionary trajectories.", } @Article{Whigham:2010:ieeeTEC, author = "Peter A. Whigham and Grant Dick", title = "Implicitly Controlling Bloat in Genetic Programming", journal = "IEEE Transactions on Evolutionary Computation", year = "2010", volume = "14", number = "2", pages = "173--190", month = apr, keywords = "genetic algorithms, genetic programming, Bloat, Book reviews, Convergence, Data mining, Mathematical model, Pediatrics, elitism, inbreeding, spatially-structured evolutionary algorithm", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2009.2027314", size = "18 pages", abstract = "During the evolution of solutions using genetic programming (GP) there is generally an increase in average tree size without a corresponding increase in fitness---a phenomenon commonly referred to as bloat. Although previously studied from theoretical and practical viewpoints there has been little progress in deriving controls for bloat which do not explicitly refer to tree size. Here, the use of spatial population structure in combination with local elitist replacement is shown to reduce bloat without a subsequent loss of performance. Theoretical concepts regarding inbreeding and the role of elitism are used to support the described approach. The proposed system behavior is confirmed via extensive computer simulations on benchmark problems. The main practical result is that by placing a population on a torus, with selection defined by a Moore neighborhood and local elitist replacement, bloat can be substantially reduced without compromising performance.", notes = "also known as \cite{5352336}", } @InProceedings{Whigham:2010:cec, author = "Peter A. Whigham and Rasika Withanawasam and Timothy Crack and I. M. Premachandra", title = "Evolving trading strategies for a limit-order book generator", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-6910-9", abstract = "A grammatical evolutionary model (GE) is used to evolve trading strategies for a limit-order book model. A modified version of a limit-order book generator, based on the original work of Maslov [1], is used to produce limit-order book tick data. The evolved trading strategies demonstrate profit-making ability even though the Maslov model is fundamentally based on random behaviour.", DOI = "doi:10.1109/CEC.2010.5586114", notes = "WCCI 2010. Also known as \cite{5586114}", } @InProceedings{Whigham:2011:GECCO, author = "Peter A. Whigham and Rasika Withanawasam", title = "Evolving a robust trader in a cyclic double auction market", booktitle = "GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0557-0", pages = "1451--1458", keywords = "genetic algorithms, genetic programming, grammatical evolution", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001576.2001771", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "A computational model of a double auction market is introduced and extended to allow a controlled cyclic behaviour in the price signal to be developed. Traders are evolved to maximise profit in this market using Grammatical Evolution, and their properties studied for a range of periods and amplitude of the trend in the price signal. The trader grammar allows decision making based on simple trading rules incorporating the concepts of moving-average oscillators and trading range break-out. The results of this investigation demonstrate that traders evolve a short waiting period between decisions, and that there underlying decision logic reflects the scale of the market price frequency. Evidence is presented that suggests evolving a robust profit-making trader, for a range of price frequency changes, requires the training data to have high frequency variation. More generally, to evolve robust solutions for any complex GP problem, a set of local models or an ensemble and state-based approach, is implied by the results.", notes = "Also known as \cite{2001771} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)", } @InProceedings{Whigham:2015:GECCO, author = "Peter A. Whigham and Grant Dick and James Maclaurin and Caitlin A. Owen", title = "Examining the {"}Best of Both Worlds{"} of Grammatical Evolution", booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation", year = "2015", editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr", isbn13 = "978-1-4503-3472-3", pages = "1111--1118", keywords = "genetic algorithms, genetic programming, grammatical evolution", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739480.2754784", DOI = "doi:10.1145/2739480.2754784", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Grammatical Evolution (GE) has a long history in evolutionary computation. Central to the behaviour of GE is the use of a linear representation and grammar to map individuals from search spaces into problem spaces. This genotype to phenotype mapping is often argued as a distinguishing property of GE relative to other techniques, such as context-free grammar genetic programming (CFG-GP). Since its initial description, GE research has attempted to incorporate information from the grammar into crossover, mutation, and individual initialisation, blurring the distinction between genotype and phenotype and creating GE variants closer to CFG-GP. This is argued to provide GE with the best of both worlds, allowing degrees of grammatical bias to be introduced into operators to best suit the given problem. This paper examines the behaviour of three grammar-based search methods on several problems from previous GE research. It is shown that, unlike CFG-GP, the performance of pure GE on the examined problems closely resembles that of random search. The results suggest that further work is required to determine the cases where the best of both worlds of GE are required over a straight CFG-GP approach.", notes = "Also known as \cite{2754784} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)", } @Article{Whigham:2014:GPEM, author = "Peter A. Whigham", title = "Wolfgang Banzhaf: Genetic programming and emergence", journal = "Genetic Programming and Evolvable Machines", year = "2014", volume = "15", number = "1", pages = "99--101", month = mar, keywords = "genetic algorithms, genetic programming, Repeated motifs, Drift, Recombination, Emergence", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-013-9204-y", size = "3 pages", abstract = "This commentary demonstrates that for genetic programming with recombination and drift repeated motif patterns emerge within individuals more often than chance. This demonstrates that such patterns emerge without the need for selection. In addition, this effect is amplified when the effective population size is reduced.", notes = "\cite{Banzhaf:2014:GPEM}", } @Article{Whigham:2017:GPEM, author = "Peter A. Whigham and Grant Dick and James Maclaurin", title = "On the mapping of genotype to phenotype in evolutionary algorithms", journal = "Genetic Programming and Evolvable Machines", year = "2017", volume = "18", number = "3", pages = "353--361", month = sep, note = "Special Peer Commentary on Mapping of Genotype to Phenotype in Evolutionary Algorithms", keywords = "genetic algorithms, genetic programming, Grammatical evolution, Biological analogy, Representation", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-017-9288-x", size = "9 pages", abstract = "Analogies with molecular biology are frequently used to guide the development of artificial evolutionary search. A number of assumptions are made in using such reasoning, chief among these is that evolution in natural systems is an optimal, or at least best available, search mechanism, and that a decoupling of search space from behaviour encourages effective search. In this paper, we explore these assumptions as they relate to evolutionary algorithms, and discuss philosophical foundations from which an effective evolutionary search can be constructed. This framework is used to examine grammatical evolution (GE), a popular search method that draws heavily upon concepts from molecular biology. We identify several properties in GE that are in direct conflict with those that promote effective evolutionary search. The paper concludes with some recommendations for designing representations for effective evolutionary search.", notes = "See \cite{Spector:2017:GPEM} and \cite{Whigham:2017:GPEM2} A comment to this article is available at http://dx.doi.org/10.1007/s10710-017-9289-9 \cite{Whigham:2017:GPEM2} A comment to this article is available at http://dx.doi.org/10.1007/s10710-017-9296-x \cite{Foster:2017:GPEM} A comment to this article is available at http://dx.doi.org/10.1007/s10710-017-9295-y \cite{Squillero:2017:GPEM} A comment to this article is available at http://dx.doi.org/10.1007/s10710-017-9294-z \cite{Ryan:2017:GPEM} A comment to this article is available at http://dx.doi.org/10.1007/s10710-017-9293-0 \cite{O'Neill:2017:GPEM} A comment to this article is available at http://dx.doi.org/10.1007/s10710-017-9292-1 (Link broken March 2017) A comment to this article is available at http://dx.doi.org/10.1007/s10710-017-9291-2 \cite{Ekart:2017:GPEM} A comment to this article is available at http://dx.doi.org/10.1007/s10710-017-9290-3 \cite{Altenberg:2017:GPEM} A comment to this article is available at http://dx.doi.org/10.1007/s10710-017-9287-y \cite{Spector:2017:GPEM}", } @Article{Whigham:2017:GPEM2, author = "Peter A. Whigham and Grant Dick and James Maclaurin", title = "Just because it works: a response to comments on ``On the Mapping of Genotype to Phenotype in Evolutionary Algorithms''", journal = "Genetic Programming and Evolvable Machines", year = "2017", volume = "18", number = "3", pages = "399--405", month = sep, note = "Special Peer Commentary on Mapping of Genotype to Phenotype in Evolutionary Algorithms", keywords = "genetic algorithms, genetic programming, Grammatical evolution, Biological analogy, Representation", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-017-9289-9", size = "7 pages", abstract = "This response examines the context and implications of the comments to ``On the Mapping of Genotype to Phenotype in Evolutionary Algorithms'' that appears in this journal. The notion of metaphor is first considered and then the general themes of the commentaries addressed. The response subsequently focuses on representation and operators, noting that many of the comments support our basic premise. The main conclusion is that Sterelny's conditions do form a suitable basis for representation and operator design and that the collection of responses form an excellent basis for further discussion and research in evolutionary computation.", notes = "Introduction in \cite{Spector:2017:GPEM}, \cite{Whigham:2017:GPEM}", } @InProceedings{white:2004:AL, author = "Bill C. White and Jason H. Moore", title = "Systems biology thought experiments in human genetics using artificial life and grammatical evolution", booktitle = "Artificial Life {XI} Ninth International Conference on the Simulation and Synthesis of Living Systems", year = "2004", editor = "Jordan Pollack and Mark Bedau and Phil Husbands and Takashi Ikegami and Richard A. Watson", pages = "581--586", address = "Boston, Massachusetts", month = "12-15 " # sep, publisher = "The MIT Press", keywords = "genetic algorithms, genetic programming, grammatical evolution", ISBN = "0-262-66183-7", URL = "http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=6278776", DOI = "doi:10.7551/mitpress/1429.003.0098", size = "6 pages", abstract = "A goal of systems biology and human genetics is to understand how DNA sequence variations impact human health through a hierarchy of biochemical, metabolic, and physiological systems. We present here a proof-of-principle study that demonstrates how artificial life in the form of agent-based simulation can be used to generate hypothetical systems biology models that are consistent with pre-defined genetic models of disease susceptibility. Here, an evolutionary computing strategy called grammatical evolution is used to discover artificial life models. The goal of these studies is to perform thought experiments about the nature of complex biological systems that are consistent with genetic models of disease susceptibility. It is anticipated that the utility of this approach will be the generation of biological hypotheses that can then be tested using experimental systems.", notes = "ALIFE9", } @InProceedings{white:2005:CEC, author = "Bill C. White and Joshua C. Gilbert and David M. Reif and Jason H. Moore", title = "A Statistical Comparison of Grammatical Evolution Strategies in the Domain of Human Genetics", booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary Computation", year = "2005", editor = "David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L. Gwenn Volkert and Dan Ashlock and Marc Schoenauer", volume = "1", pages = "491--497", address = "Edinburgh, UK", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "2-5 " # sep, organisation = "IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, PSO, grammatical evolution", ISBN = "0-7803-9363-5", DOI = "doi:10.1109/CEC.2005.1554723", abstract = "Detecting and characterising genetic predictors of human disease susceptibility is an important goal in human genetics. New chip-based technologies are available that facilitate the measurement of thousands of DNA sequence variations across the human genome. Biologically-inspired stochastic search algorithms are expected to play an important role in the analysis of these high-dimensional datasets. We simulated datasets with up to 6000 attributes using two different genetic models and statistically compared the performance of grammatical evolution, grammatical swarm, and random search for building symbolic discriminant functions. We found no statistical difference among search algorithms within this specific domain.", notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS. also appears at pages 676-682", } @InProceedings{White2:2008:gecco, author = "David R. White and John Clark and Jeremy Jacob and Simon M. Poulding", title = "Searching for resource-efficient programs: low-power pseudorandom number generators", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "1775--1782", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1775.pdf", URL = "http://www.cs.york.ac.uk/~drw/rnggecco.pdf", DOI = "doi:10.1145/1389095.1389437", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, genetic improvement, automatic programming, multi-dynective optimisation, non-functional Requirements, search based software engineering, Search-based software engineering", abstract = "Non-functional properties of software, such as power consumption and memory usage, are important factors in designing software for resource-constrained platforms. This is an area where Search-Based Software Engineering has yet to be applied, and this paper investigates the potential of using Genetic Programming and Multi-Objective Optimisation as key tools in satisfying non-functional requirements. We outline the benefits of such an approach and give an example application of evolving pseudorandom number generators and performing power-functionality trade-offs.", notes = "See also presentation COW 39 http://crest.cs.ucl.ac.uk/cow/the_39th_cow_23_and_24_february_2015/ GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389437}", } @InProceedings{White:2009:eurogp, author = "David R. White and Simon Poulding", title = "A Rigorous Evaluation of Crossover and Mutation in Genetic Programming", booktitle = "Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009", year = "2009", editor = "Leonardo Vanneschi and Steven Gustafson and Alberto Moraglio and Ivanoe {De Falco} and Marc Ebner", volume = "5481", series = "LNCS", pages = "220--231", address = "Tuebingen", month = apr # " 15-17", organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-01180-1", DOI = "doi:10.1007/978-3-642-01181-8_19", URL = "http://results.ref.ac.uk/Submissions/Output/1867870", size = "12 pages", abstract = "The role of crossover and mutation in Genetic Programming (GP) has been the subject of much debate since the emergence of the field. In this paper, we contribute new empirical evidence to this argument using a rigorous and principled experimental method applied to six problems common in the GP literature. The approach tunes the algorithm parameters to enable a fair and objective comparison of two different GP algorithms, the first using a combination of crossover and reproduction, and secondly using a combination of mutation and reproduction. We find that crossover does not significantly outperform mutation on most of the problems examined. In addition, we demonstrate that the use of a straightforward Design of Experiments methodology is effective at tuning GP algorithm parameters.", notes = "DOE, {"}millions of runs{"}, ECJ, factorial design. Vargha-Delaney A statistic. DoE better than response surface methodology and central composite design. Sebase. Part of \cite{conf/eurogp/2009} EuroGP'2009 held in conjunction with EvoCOP2009, EvoBIO2009 and EvoWorkshops2009", uk_research_excellence_2014 = "This was one of the first papers in GP to employ highly rigorous empirical method, as well as directly addressing a controversial topic (see Spector and Luke's earlier works) using this approach. It was the first to consider response surface modelling, scientific significance, and apply the Vargha-Delaney measure to GP performance. The paper was short-listed for a best paper award at EuroGP, the premier annual conference on Genetic Programming.", } @PhdThesis{White:thesis, author = "David R. White", title = "Genetic Programming for Low-Resource Systems", school = "Department of Computer Science, University of York", year = "2009", address = "UK", month = dec, keywords = "genetic algorithms, genetic programming, SBSE, Low-Resource Systems, Embedded Systems, Evolutionary Computation, Fibonacci", URL = "http://etheses.whiterose.ac.uk/757/", URL = "http://www-users.cs.york.ac.uk/~drw/papers/thesis/drwthesis.pdf", URL = "http://ethos.bl.uk/OrderDetails.do?did=15&uin=uk.bl.ethos.516366", size = "185 pages", abstract = "Embedded systems dominate the computing landscape. This dominance is increasing with the advent of ubiquitous computing whereby lightweight, low-resource systems are being deployed on a vast scale. These systems present new engineering challenges: high-volume production places a stronger emphasis on absolute cost, resources available to executing software are highly constrained, and physical manufacturing capabilities approach hard limits. Add to this the sensitive nature of many of these systems, such as smart cards used for financial transactions, and the development of these systems becomes a formidable engineering challenge. For the software engineer, the incentive to produce efficient and resource-aware software for these platforms is great, yet existing tools do not support them well in this task. It is difficult to assess the impact of decisions made at the source code level in terms of how they change a system's resource consumption. Existing tool chains, together with the very complex interactions of software and their host processors, can produce unforeseen implications at run-time of even small changes. We could describe such a situation as an instance of programming the unprogrammable, and Genetic Programming is one solution method used for such problems. Genetic Programming, inspired by nature's ability to solve problems involving complex interactions and strong pressures on resource consumption, is a clear candidate for attacking the challenges presented in these systems. Genetic Programming facilitates the creation and manipulation of source code in a way that grants us fine control over its measurable characteristics. In this thesis, I investigate the potential of Genetic Programming as a tool in controlling the non-functional properties of software, as a new method of designing code for low-resource systems. I demonstrate the feasibility of this approach, and investigate some of the ways Genetic Programming could be used by a practitioner. In doing so, I also identify key components that any application of Genetic Programming to such a domain will require. I review current low-resource system optimisation, Genetic Programming and methods for simultaneously handling multiple requirements. I present a series of empirical investigations designed to provide evidence for and against a set of hypotheses regarding the success of Genetic Programming in solving problems within the low-resource systems domain. These experiments include the creation of new software, the improvement of existing software and the fine-grained control of resource usage in general. To conclude, I review the progress made, reassess my hypotheses, and outline how these new methods can be carried forward to a wide range of applications.", notes = "uk.bl.ethos.516366", } @InProceedings{White:2010:EuroGP, author = "David R. White and Juan M. E. Tapiador and Julio Cesar Hernandez-Castro and John A. Clark", title = "Fine-Grained Timing using Genetic Programming", booktitle = "Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010", year = "2010", editor = "Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar", volume = "6021", series = "LNCS", pages = "325--336", address = "Istanbul", month = "7-9 " # apr, organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming, SBSE", isbn13 = "978-3-642-12147-0", DOI = "doi:10.1007/978-3-642-12148-7_28", abstract = "In previous work, we have demonstrated that it is possible to use Genetic Programming to minimise the resource consumption of software, such as its power consumption or execution time. In this paper, we investigate the extent to which Genetic Programming can be used to gain fine-grained control over software timing. We introduce the ideas behind our work, and carry out experimentation to find that Genetic Programming is indeed able to produce software with unusual and desirable timing properties, where it is not obvious how a manual approach could replicate such results. In general, we discover that Genetic Programming is most effective in controlling statistical properties of software rather than precise control over its timing for individual inputs. This control may find useful application in cryptography and embedded systems.", notes = "ECJ, C code, M5 Simulator. Evolved linear and quadratic floating point codes have better timing characteristics than hand written C code. Timing to encode OR. Timing to conceal randomised PRNG output. TAC Part of \cite{Esparcia-Alcazar:2010:GP} EuroGP'2010 held in conjunction with EvoCOP2010 EvoBIO2010 and EvoApplications2010", } @Article{White:2011:ieeeTEC, author = "David R. White and Andrea Arcuri and John A. Clark", title = "Evolutionary Improvement of Programs", journal = "IEEE Transactions on Evolutionary Computation", year = "2011", volume = "15", number = "4", pages = "515--538", month = aug, keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, Coevolution, embedded systems, execution time, genetic programming, multiobjective optimisation, nonfunctional criteria, search based software engineering STGP, SPEA2", ISSN = "1089-778X", URL = "http://crest.cs.ucl.ac.uk/fileadmin/crest/sebasepaper/WhiteAC.pdf", video_url = "http://crest.cs.ucl.ac.uk/cow/50/videos/arcuri_cow50_480p.mp4", DOI = "doi:10.1109/TEVC.2010.2083669", size = "24 pages", abstract = "Most applications of genetic programming (GP) involve the creation of an entirely new function, program or expression to solve a specific problem. In this paper, we propose a new approach that applies GP to improve existing software by optimising its non-functional properties such as execution time, memory usage, or power consumption. In general, satisfying non-functional requirements is a difficult task and often achieved in part by optimizing compilers. However, modern compilers are in general not always able to produce semantically equivalent alternatives that optimise non-functional properties, even if such alternatives are known to exist: this is usually due to the limited local nature of such optimisations. In this paper, we discuss how best to combine and extend the existing evolutionary methods of GP, multiobjective optimization, and coevolution in order to improve existing software. Given as input the implementation of a function, we attempt to evolve a semantically equivalent version, in this case optimised to reduce execution time subject to a given probability distribution of inputs. We demonstrate that our framework is able to produce non-obvious optimizations that compilers are not yet able to generate on eight example functions. We employ a coevolved population of test cases to encourage the preservation of the function's semantics. We exploit the original program both through seeding of the population in order to focus the search, and as an oracle for testing purposes. As well as discussing the issues that arise when attempting to improve software, we employ rigorous experimental method to provide interesting and practical insights to suggest how to address these issues.", notes = "also known as \cite{5688317} ECJ, C function, java, gnu -o2 gcc, hall of fame. M5 simulator (alpha microprocessor). Functionality 128 times more important than efficiency. Compilation and run time errors give fitness death penalty. Branch coverage. Run time limit. Java overflow. R nnls. Presented in part at COW50: The 50th CREST Open Workshop - Genetic Improvement http://crest.cs.ucl.ac.uk/cow/50/ http://crest.cs.ucl.ac.uk/cow/50/slides/cow50_Arcuri.pdf http://crest.cs.ucl.ac.uk/cow/50/videos/arcuri_cow50_480p.mp4", } @Article{White:2011:GPEM, author = "David R. White", title = "Software review: the {ECJ} toolkit", journal = "Genetic Programming and Evolvable Machines", year = "2012", volume = "13", number = "1", pages = "65--67", month = mar, keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dR82C", DOI = "doi:10.1007/s10710-011-9148-z", code_url = "https://cs.gmu.edu/~eclab/projects/ecj/", size = "3 pages", notes = "School of Computing Science, University of Glasgow, Lilybank Gardens, Glasgow, G12 8QQ, UK", } @Article{White:2013:GPEM, author = "David R. White and James McDermott and Mauro Castelli and Luca Manzoni and Brian W. Goldman and Gabriel Kronberger and Wojciech Jaskowski and Una-May O'Reilly and Sean Luke", title = "Better {GP} benchmarks: community survey results and proposals", journal = "Genetic Programming and Evolvable Machines", year = "2013", volume = "14", number = "1", pages = "3--29", month = mar, keywords = "genetic algorithms, genetic programming, benchmarks, community survey", ISSN = "1389-2576", URL = "http://gpbenchmarks.org/wp-content/uploads/2014/09/GP-Benchmarks-GPEM-2013-preprint-correction-v2.pdf", URL = "https://dspace.mit.edu/handle/1721.1/104909", URL = "https://rdcu.be/c1FfZ", DOI = "doi:10.1007/s10710-012-9177-2", code_url = "http://gpbenchmarks.org/", size = "28 pages", abstract = "We present the results of a community survey regarding genetic programming benchmark practices. Analysis shows broad consensus that improvement is needed in problem selection and experimental rigour. While views expressed in the survey dissuade us from proposing a large-scale benchmark suite, we find community support for creating a blacklist of problems which are in common use but have important flaws, and whose use should therefore be discouraged. We propose a set of possible replacement problems.", notes = "GP Benchmarks. Extension of \cite{McDermott:2012:GECCO}", } @Misc{DBLP:journals/corr/White14, author = "David White", title = "An Overview of Schema Theory", howpublished = "arXiv", year = "2014", month = "12 " # jan, keywords = "genetic algorithms, genetic programming", volume = "abs/1401.2651", eprint = "1401.2651", URL = "http://arxiv.org/abs/1401.2651", timestamp = "Wed, 07 Jun 2017 14:41:41 +0200", biburl = "https://dblp.org/rec/bib/journals/corr/White14", bibsource = "dblp computer science bibliography, https://dblp.org", size = "27 pages", abstract = "The purpose of this paper is to give an introduction to the field of Schema Theory written by a mathematician and for mathematicians. In particular, we endeavor to to highlight areas of the field which might be of interest to a mathematician, to point out some related open problems, and to suggest some large-scale projects. Schema theory seeks to give a theoretical justification for the efficacy of the field of genetic algorithms, so readers who have studied genetic algorithms stand to gain the most from this paper. However, nothing beyond basic probability theory is assumed of the reader, and for this reason we write in a fairly style. Because the mathematics behind the theorems in schema theory is relatively elementary, we focus more on the motivation and philosophy. Many of these results have been proven elsewhere, so this paper is designed to serve a primarily expository role. We attempt to cast known results in a new light, which makes the suggested future directions natural. This involves devoting a substantial amount of time to the history of the field. We hope that this exposition will entice some mathematicians to do research in this area, that it will serve as a road map for researchers new to the field, and that it will help explain how schema theory developed. Furthermore, we hope that the results collected in this document will serve as a useful reference. Finally, as far as the author knows, the questions raised in the final section are new.", notes = "Some GP theory", } @InProceedings{White:2015:gi, author = "David R. White and Jeremy Singer", title = "Rethinking Genetic Improvement Programming", booktitle = "Genetic Improvement 2015 Workshop", year = "2015", editor = "William B. Langdon and Justyna Petke and David R. White", pages = "845--846", address = "Madrid", publisher_address = "New York, NY, USA", month = "11-15 " # jul, organisation = "SIGEvo", publisher = "ACM", keywords = "genetic algorithms, genetic programming, Genetic Improvement", isbn13 = "978-1-4503-3488-4", URL = "http://gpbib.cs.ucl.ac.uk/gi2015/rethinking_gi.pdf", URL = "http://doi.acm.org/10.1145/2739482.2768426", DOI = "doi:10.1145/2739482.2768426", size = "2 pages", abstract = "We re-examine the central motivation behind Genetic Improvement Programming (GIP), and argue that the most important insight is the concept of applying Genetic Programming to existing software. This viewpoint allows us to make several observations about potential directions for GIP research", notes = "position paper, slides: http://gpbib.cs.ucl.ac.uk/gi2015/gi_2015_slides/white/rethinking_gi.pdf Also known as \cite{2768426} Distributed at GECCO-2015.", } @InProceedings{White:2015:GECCOcompa, author = "David R. White and Shin Yoo and Jeremy Singer", title = "The Programming Game: Evaluating MCTS as an Alternative to GP for Symbolic Regression", booktitle = "GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation", year = "2015", editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr", isbn13 = "978-1-4503-3488-4", keywords = "genetic algorithms, genetic programming, ECJ, Monte Carlo Tree Search, UCT, Nested: Poster", pages = "1521--1522", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739482.2764655", DOI = "doi:10.1145/2739482.2764655", publisher = "ACM", publisher_address = "New York, NY, USA", size = "2 pages", abstract = "We develop previous work by Tristan Cazenave applying Monte Carlo Tree Search (MCTS) to programming. We compare MCTS to Genetic Programming (GP) and find that MCTS is competitive with GP for standard benchmarks.", notes = "Keijzer6, Vladislavleva4, Pagie1, Nguyen7 Also known as \cite{2764655} Distributed at GECCO-2015.", } @InProceedings{White:2016:GI, author = "David White", title = "Guiding Unconstrained Genetic Improvement", booktitle = "Genetic Improvement 2016 Workshop", year = "2016", editor = "Justyna Petke and Westley Weimer and David R. White", pages = "1133--1134", address = "Denver", publisher_address = "New York, NY, USA", month = jul # " 20-24", organisation = "SIGEvo", publisher = "ACM", keywords = "genetic algorithms, genetic programming, Genetic Improvement, SBSE", URL = "http://geneticimprovementofsoftware.com/wp-content/uploads/2016/06/Guiding_Unconstrained_Genetic-Improvement.pdf", DOI = "doi:10.1145/2908961.2931688", size = "2 pages", abstract = "This paper argues that the potential for arbitrary transformation is what differentiates GI from other program transformation work. With great expressive power comes great responsibility, and GI has had mixed success finding effective program repairs and optimisations. The search must be better guided in order to improve solution quality.", notes = "GECCO-2016 Workshop http://geneticimprovementofsoftware.com/", } @InProceedings{White:2017:DPO, author = "David R. White and Leonid Joffe and Edward Bowles and Jerry Swan", booktitle = "20th European Conference on the Applications of Evolutionary Computation", title = "Deep Parameter Tuning of Concurrent Divide and Conquer Algorithms in {Akka}", year = "2017", editor = "Giovanni Squillero and Kevin Sim", volume = "10200", series = "Lecture Notes in Computer Science", pages = "35--48", address = "Amsterdam", month = "19-21 " # apr, organisation = "Species", publisher = "Springer", keywords = "genetic algorithms, Genetic Improvement, Deep Parameter Optimisation, SBSE, Concurrency, Scala, JVM, Akka, Deep Parameter Tuning, Divide and Conquer, FFT, Matrix Multiplication, Quicksort", isbn13 = "978-3-319-55792-2", URL = "http://eprints.whiterose.ac.uk/117963/1/", URL = "http://eprints.whiterose.ac.uk/117963/1/optimizing_akka_concurrency.pdf", DOI = "doi:10.1007/978-3-319-55792-2_3", size = "15 pages", abstract = "Akka is a widely-used high-performance and distributed computing toolkit for fine-grained concurrency, written in Scala for the Java Virtual Machine. Although Akka elegantly simplifies the process of building complex parallel software, many crucial decisions that affect system performance are deferred to the user. Employing the method of Deep Parameter Tuning to extract embedded magic numbers from source code, we use the CMA-ES evolutionary computation algorithm to optimise the concurrent implementation of three widely-used divide-and-conquer algorithms within the Akka toolkit: Quicksort, Strassen's matrix multiplication and the Fast Fourier Transform FFT.", notes = "'4.1 Tuned Parameters', eg 'Quicksort: a hard-coded Threshold parameter determines the point below which the sequential algorithm should be used' 'Halving the execution time of FFT and producing an order of magnitude speed-up of Strassen's algorithm.' EvoApplications2017 held in conjunction with EuroGP'2017, EvoCOP2017 and EvoMusArt2017 http://www.evostar.org/2017/cfp_evoapps.php", } @InProceedings{White:2017:GI, author = "David R. White", title = "{GI} in No Time", booktitle = "GI-2017", year = "2017", editor = "Justyna Petke and David R. White and W. B. Langdon and Westley Weimer", pages = "1549--1550", address = "Berlin", month = "15-19 " # jul, publisher = "ACM", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, GIN, Optimisation, Automated Programming", isbn13 = "978-1-4503-4939-0", URL = "http://dx.doi.org/doi:10.1145/3067695.3082515", URL = "http://geneticimprovementofsoftware.com/wp-content/uploads/2017/05/white2017_gin.pdf", URL = "https://github.com/gintool/gin/blob/master/doc/gin.pdf", DOI = "doi:10.1145/3067695.3082515", code_url = "https://github.com/gintool/gin", acmid = "3082515", size = "2 pages", abstract = "We describe a small, simple, and lightweight micro-framework for the Genetic Improvement of Java code. We call the framework GI in no time, or Gin. Gin is designed to be a straightforward, hackable, GI tool for Java. It currently lacks large features found in comparable program repair tools, but nonetheless it is capable of performing optimisation of a Java class via local search. We hope that providing this contribution will encourage researchers to collaborate on GI tool development, whilst lowering the barrier to entry for those interested in experimenting with GI. It is intended to serve both as a tool kit to be extended, and also an example of how GI can be implemented. We discuss some of the design principles behind Gin, and outline observations made during its development.", notes = "GIN works at the statement level. https://github.com/drdrwhite/gin", } @InProceedings{White:2019:GPTP, author = "David R. White and Benjamin Fowler and Wolfgang Banzhaf and Earl T. Barr", title = "Modelling Genetic Programming as a Simple Sampling Algorithm", booktitle = "Genetic Programming Theory and Practice XVII", year = "2019", editor = "Wolfgang Banzhaf and Erik Goodman and Leigh Sheneman and Leonardo Trujillo and Bill Worzel", pages = "367--381", address = "East Lansing, MI, USA", month = "16-19 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Schema theory, Convergence, Covariance, Price's theorem", isbn13 = "978-3-030-39957-3", URL = "http://www.cs.mun.ca/~banzhaf/papers/GPTP_2019_White_2020.pdf", DOI = "doi:10.1007/978-3-030-39958-0_18", size = "15 pages", abstract = "This chapter proposes a new model of tree-based Genetic Programming (GP) as a simple sampling algorithm that samples minimal schemata (subsets of the solution space) described by a single concrete node at a single position in the expression tree. We show that GP explores these schemata in the same way across three benchmarks, rapidly converging the population to a specific function at each position throughout the upper layers of the expression tree. This convergence is driven by covariance between membership of a simple schema and rank fitness. We model this process using Prices theorem \cite{price:nature} and provide empirical evidence to support our model. The chapter closes with an outline of a modification of the standard GP algorithm that reinforces this bias by converging populations to fit schemata in an accelerated way.", notes = "Keijzer-6 sqrt. Korns-12, Vladislavleva-4 Part of \cite{Banzhaf:2019:GPTP}, published after the workshop", } @PhdThesis{Jason_Robert_White:thesis, author = "Jason Robert White", title = "Integration of Reaction Kinetics Theory and Gene Expression Programming to Infer Reaction Mechanism", school = "Chemical Engineering, University of Connecticut", year = "2014", address = "USA", keywords = "genetic algorithms, genetic programming, gene expression programming, network inference, reaction kinetics, viral dynamics, human immunodeficiency virus, systems biology, AIDS", URL = "http://opencommons.uconn.edu/dissertations/371/", URL = "http://opencommons.uconn.edu/cgi/viewcontent.cgi?article=6579&context=dissertations", size = "199 pages", abstract = "Mechanistic mathematical models of biological systems have been used to describe biological phenomena, including human disease, in the hope that one day these models may be used to better understand diseases, as well as to develop and optimize therapeutic strategies. Evolutionary algorithms, such as genetic programming, may be used to symbolically regress mathematical models describing chemical and biochemical species for which kinetic data are available. However, current evolutionary algorithms are restricted to the formulation of simple or approximate models due to the computational cost of evolving mechanistic models for more complex systems. It was hypothesized that chemical reaction kinetic theory could be used to sufficiently reduce the model search space for an evolutionary algorithm such that it would be possible to infer mechanistic mathematical models of complex biological interactions. An evolutionary algorithm capable of formulating mass action kinetic models of biological systems from time series data sets was developed for a system of n-species using heuristics from chemical reaction kinetic theory and a gene expression programming (GEP) based approach. The resulting algorithm was then successfully validated on a general model of viral dynamics that accounted for six pathways relating the change in viral template, viral genome, and viral structural protein concentrations over time. The algorithm was applied to generate cohort-specific models of HIV dynamics from a clinical data set. HIV-1 infection models were defined as sets of two ordinary differential equations describing the change in CD4+ T-cell and HIV-1 concentrations over time. The evolved models were used to generate hypotheses regarding treatment effectiveness and the potential for viral rebound in three cohorts of HIV-1 positive individuals receiving different Highly Active Antiretroviral Therapy (HAART) regimens. It was hypothesized by the algorithm that HAART was effective in stopping HIV-1 propagation in two of the three cohorts studied. In the other cohort, it was hypothesized that HIV-1 continued to propagate and that there was the potential for viral rebound. The result of this work was the development of an algorithm that can be used for the generation of complex mechanistic biological models based upon kinetic data with potential uses in fields ranging from biomedical to biotechnological.", notes = "Supervisor Ranjan Srivastava", } @InProceedings{White:2017:evoApplications, author = "Jason R. White and Ranjan Srivastava", title = "Integration of Reaction Kinetics Theory and Gene Expression Programming to Infer Reaction Mechanism", booktitle = "20th European Conference on the Applications of Evolutionary Computation", year = "2017", editor = "Giovanni Squillero", series = "LNCS", volume = "10199", publisher = "Springer", pages = "53--66", address = "Amsterdam", month = "19-21 " # apr, organisation = "Species", keywords = "genetic algorithms, genetic programming, gene expression programming, Evolutionary algorithm, Biochemical kinetics, Mechanistic modeling", isbn13 = "978-3-319-55848-6; 978-3-319-55849-3", bibdate = "2017-05-21", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/evoW/evoappl2017-1.html#WhiteS17", DOI = "doi:10.1007/978-3-319-55849-3_4", abstract = "Mechanistic mathematical models of biomolecular systems have been used to describe biological phenomena in the hope that one day these models may be used to enhance our fundamental understanding of these phenomena, as well as to optimize and engineer biological systems. An evolutionary algorithm capable of formulating mass action kinetic models of biological systems from time series data sets was developed for a system of n-species. The strategy involved using a gene expression programming (GEP) based approach and heuristics based on chemical kinetic theory. The resulting algorithm was successfully validated by recapitulating a nonlinear model of viral dynamics using only a 'noisy' set of time series data. While the system analyzed for this proof-of-principle study was relatively small, the approach presented here is easily parallelizable making it amenable for use with larger systems. Additionally, greater efficiencies may potentially be realized by further taking advantage of the problem domain along with future breakthroughs in computing power and algorithmic advances", notes = "also known as \cite{conf/evoW/WhiteS17} EvoApplications2017 held in conjunction with EuroGP'2017, EvoCOP2017 and EvoMusArt2017 http://www.evostar.org/2017/cfp_evoapps.php.", } @InProceedings{White:2010:cec, author = "Spencer K. White and Tony Martinez and George Rudolph", title = "Generating a novel sort algorithm using Reinforcement Programming", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-6910-9", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.419.9164", URL = "http://axon.cs.byu.edu/papers/Spencer.CEC2010Proc.pdf", DOI = "doi:10.1109/CEC.2010.5586457", size = "8 pages", abstract = "Reinforcement Programming (RP) is a new approach to automatically generating algorithms, that uses reinforcement learning techniques. This paper describes the RP approach and gives results of experiments using RP to generate a generalised, in-place, iterative sort algorithm. The RP approach improves on earlier results that that use genetic programming (GP). The resulting algorithm is a novel algorithm that is more efficient than comparable sorting routines. RP learns the sort in fewer iterations than GP and with fewer resources. Results establish interesting empirical bounds on learning the sort algorithm: A list of size 4 is sufficient to learn the generalized sort algorithm. The training set only requires one element and learning took less than 200,000 iterations. RP has also been used to generate three binary addition algorithms: a full adder, a binary incrementer, and a binary adder.", notes = "WCCI 2010. Also known as \cite{5586457}", } @Article{White:2012:CI, author = "Spencer K. White and Tony R. Martinez and George L. Rudolph", title = "Automatic Algorithm Development Using New Reinforcement Programming Techniques", journal = "Computational Intelligence", year = "2012", volume = "28", number = "2", pages = "176--208", keywords = "genetic algorithms, genetic programming", timestamp = "Wed, 06 Jun 2012 17:36:13 +0200", biburl = "http://dblp.uni-trier.de/rec/bib/journals/ci/WhiteMR12", bibsource = "dblp computer science bibliography, http://dblp.org", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.303.5628", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.303.5628", URL = "http://axon.cs.byu.edu/papers/RP-CI.pdf", DOI = "doi:10.1111/j.1467-8640.2012.00413.x", abstract = "Reinforcement Programming (RP) is a new approach to automatically generating algorithms that uses reinforcement learning techniques. This paper introduces the RP approach and demonstrates its use to generate a generalised, in-place, iterative sort algorithm. The RP approach improves on earlier results that use genetic programming (GP). The resulting algorithm is a novel algorithm that is more efficient than comparable sorting routines. RP learns the sort in fewer iterations than GP and with fewer resources. Experiments establish interesting empirical bounds on learning the sort algorithm: A list of size 4 is sufficient to learn the generalised sort algorithm. The training set only requires one element and learning took less than 200,000 iterations. Additionally RP was used to generate three binary addition algorithms: a full adder, a binary incrementer, and a binary adder.", } @InProceedings{white:1998:ASGA, author = "Tony White and Bernard Pagurek and Franz Oppacher", title = "ASGA: Improving the Ant System by Integration with Genetic Algorithms", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "610--617", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms", notes = "SGA-98", } @InProceedings{white:1999:AORBA, author = "Tony White and Bernard Pagurek", title = "Application Oriented Routing with Biologically-inspired Agents", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1453--1454", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "artificial life, adaptive behavior and agents, poster papers", ISBN = "1-55860-611-4", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{White:2008:gecco, author = "Tony White and Amirali Salehi-Abari", title = "A swarm-based crossover operator for genetic programming", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "1345--1346", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1345.pdf", DOI = "doi:10.1145/1389095.1389356", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, ant colony optimisation, automatic programming, evolutionary computation, swarm-based algorithms: Poster", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389356}", } @InProceedings{conf/ieaaie/WhiteFO11, author = "Tony White and Jinfei Fan and Franz Oppacher", title = "Basic Object Oriented Genetic Programming", booktitle = "Proceedings of the 24th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE 2011) Part {I}", year = "2011", editor = "Kishan G. Mehrotra and Chilukuri K. Mohan and Jae C. Oh and Pramod K. Varshney and Moonis Ali", volume = "6703", series = "Lecture Notes in Computer Science", pages = "59--68", address = "Syracuse, NY, USA", month = jun # " 28-" # jul # " 1", publisher = "Springer", note = "Modern Approaches in Applied Intelligence", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-21821-7", DOI = "doi:10.1007/978-3-642-21822-4_7", size = "10 pages", abstract = "This paper applies object-oriented concepts to genetic programming (GP) in order to improve the ability of GP to scale to larger problems. A technique called Basic Object-Oriented GP (Basic OOGP) is proposed that manipulates object instances incorporated in a computer program being represented as a linear array. Basic OOGP is applied to the even-parity problem and compared to GP, Liquid State GP and Traceless GP. The results indicate that OOGP can solve certain problems with smaller populations and fewer generations.", affiliation = "School of Computer Science, Carleton University, 1125 Colonel By Drive, Ottawa, Ontario K1S 5B6, Canada", bibdate = "2011-07-04", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ieaaie/ieaaie2011-1.html#WhiteFO11", } @InProceedings{whitley:1995:pole, author = "Darrell Whitley and Frederic Gruau and Larry Pyeatt", title = "Cellular Encoding Applied to Neurocontrol", booktitle = "Genetic Algorithms: Proceedings of the Sixth International Conference (ICGA95)", year = "1995", editor = "Larry J. Eshelman", pages = "460--467", address = "Pittsburgh, PA, USA", publisher_address = "San Francisco, CA, USA", month = "15-19 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-370-0", URL = "http://www.cs.colostate.edu/~genitor/1995/poles.pdf", size = "8 pages", abstract = "Neural networks are trained for balancing 1 and 2 poles attached to a cart on a fixed track. For one variant of the single pole system, only pole angle and cart position variables are supplied as inputs; the network must learn to compute velocities. All of the problems are solved using a fixed architecture and using a new version of cellular encoding that evolves an application specific architecture with real-valued weights. The learning times and generalization capabilities are compared for neural networks developed using both methods. After a post processing simplification, topologies produced by cellular encoding were very simple and could be analysed. Architectures with no hidden units were produced for the single pole and the two pole problem when velocity information is supplied as an input. Moreover, these linear solutions display good generalization. For all the control problems, cellular encoding can automatically generate architectures whose complexity and structure reflect the features of the problem to solve.", } @InProceedings{whitley:1999:AFLPGBE, author = "D. Whitley", title = "A Free Lunch Proof for Gray versus Binary Encodings", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "726--733", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/Whitley_gecco99a.pdf", URL = "http://www.cs.colostate.edu/~genitor/1999/gecco99a.pdf", abstract = "{"}Gray codes [better than binary] over a clear and pragmatically defined subset of all possible functions{"} p733", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Proceedings{whitley:2000:GECCO, title = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)", year = "2000", publisher = "Morgan Kaufmann", editor = "Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer", address = "Las Vegas, Nevada, USA", publisher_address = "San Francisco, CA 94104, USA", month = "10-12 " # jul, keywords = "genetic algorithms, genetic programming, evolvable hardware, classifier systems, evolution strategies, evolutionary programming, artificial life, adaptive agents, ant colony optimization, DNA computing, molecular computing, neural networks, data mining, evolutionary robotics, genetic scheduling", ISBN = "1-55860-708-0", URL = "http://www.cs.colostate.edu/~genitor/GECCO-2000/gecco2000mainpage.htm", URL = "http://gpbib.cs.ucl.ac.uk/gecco2000/gecco00.bib", size = "1088 pages", notes = "A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000)", } @Proceedings{whitley:2000:GECCOlb, title = "Late Breaking Papers at the 2000 Genetic and Evolutionary Computation Conference", year = "2000", editor = "Darrell Whitley", address = "Las Vegas, Nevada, USA", month = "8 " # jul, URL = "http://www.cs.colostate.edu/~genitor/GECCO-2000/late-breaking-schedule.htm", size = "444 pages", } @Article{Whitley:2001:IST, author = "Darrell Whitley", title = "An overview of evolutionary algorithms: practical issues and common pitfalls", journal = "Information and Software Technology", year = "2001", volume = "43", pages = "817--831", number = "14", month = "15 " # dec, keywords = "genetic algorithms, genetic programming, Evolution strategies, Evolutionary programming, Search, Automated programming, Parallel algorithms", URL = "http://www.cs.colostate.edu/~genitor/2001/overview.pdf", broken = "http://www.sciencedirect.com/science/article/B6V0B-44D4196-3/1/f92596f9bf285c4ec4553d4cc40da4a0", DOI = "doi:10.1016/S0950-5849(01)00188-4", abstract = "An overview of evolutionary algorithms is presented covering genetic algorithms, evolution strategies, genetic programming and evolutionary programming. The schema theorem is reviewed and critiqued. Gray codes, bit representations and real-valued representations are discussed for parameter optimization problems. Parallel Island models are also reviewed, and the evaluation of evolutionary algorithms is discussed.", } @InProceedings{1144155, author = "Darrell Whitley and Marc Richards and Ross Beveridge and Andre' {da Motta Salles Barreto}", title = "Alternative evolutionary algorithms for evolving programs: evolution strategies and steady state {GP}", booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation", year = "2006", editor = "Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos {Coello Coello} and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens", volume = "1", ISBN = "1-59593-186-4", pages = "919--926", address = "Seattle, Washington, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p919.pdf", DOI = "doi:10.1145/1143997.1144155", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "8-12 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, evolution strategies, ES, steady-state genetic algorithms, Automatic Programming, Program Synthesis", size = "8 pages", abstract = "In contrast with the diverse array of genetic algorithms, the Genetic Programming (GP) paradigm is usually applied in a relatively uniform manner. Heuristics have developed over time as to which replacement strategies and selection methods are best. The question addressed in this paper is relatively simple: since there are so many variants of evolutionary algorithm, how well do some of the other well known forms of evolutionary algorithm perform when used to evolve programs trees using s-expressions as the representation? Our results suggest a wide range of evolutionary algorithms are all equally good at evolving programs, including the simplest evolution strategies", notes = "GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM Order Number 910060 Winner best paper.", } @Misc{whitley:2007:FOGA, author = "Darrell Whitley", title = "FOGA and THEORY: Past, Present, Future", howpublished = "www", year = "2007", month = jan, note = "Slides for invited tutorial at FOGA 9", keywords = "genetic algorithms, genetic programming, hill climbing", URL = "http://www.sigevo.org/foga-2007/talks/whitley-FOGA07.pdf", size = "64 pages", notes = "p43--49 ECJ, Santa Fe Ant, Symbolic regression, cart-pole balancing, eating food 2D, UAF drones (simulation) p45 The (1,10)-ES dominates the two GP methods. The (1,10)-ES is a stochastic hill-climber that accepts non-improving moves. Does _not_ stay still. Not elitist.", } @InProceedings{Wick:2021:EuroGP, author = "Jordan Wick and Erik Hemberg and Una-May O'Reilly", title = "Getting a Head Start on Program Synthesis with Genetic Programming", booktitle = "EuroGP 2021: Proceedings of the 24th European Conference on Genetic Programming", year = "2021", editor = "Ting Hu and Nuno Lourenco and Eric Medvet", series = "LNCS", volume = "12691", publisher = "Springer Verlag", address = "Virtual Event", pages = "263--279", month = "7-9 " # apr, organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, grammatical evolution, grammar, program synthesis, multi task: Poster", isbn13 = "978-3-030-72811-3", URL = "https://alfagroup.csail.mit.edu/sites/default/files/documents/2021.%20Getting%20a%20Head%20Start%20on%20Program%20Synthesis%20with%20Genetic%20Programming.pdf", DOI = "doi:10.1007/978-3-030-72812-0_17", size = "16 pages", abstract = "We explore how to give Genetic Programming (GP) a headstart to synthesise a programming problem. Our method uses a related problem and introduces a schedule that directs GP to solve the related problem first either fully or to some extent first, or at the same time. In addition, if the related problem solutions are written by students or evolved by GP, we explore the extent to which initialising the GP population with some of these solutions provides a head start. We find that having a population solve one programming problem before working to solve a related programming problem helps to a greater extent as the targeted problems and the intermediate problems themselves are selected to be more challenging.", notes = "ALFA, PonyGE2. MIT python student courses. Count vowels, count bob, count both (COMBO). PI grow, pop=800. Novelty selection. http://www.evostar.org/2021/eurogp/ Part of \cite{Hu:2021:GP} EuroGP'2021 held in conjunction with EvoCOP2021, EvoMusArt2021 and EvoApplications2021", } @Article{DBLP:journals/cacm/WidderG24, author = "David Gray Widder and Claire {Le Goues}", title = "What Is a `Bug'?", journal = "Communications of the ACM", year = "2024", volume = "67", number = "11", pages = "32--34", month = nov, keywords = "genetic algorithms, genetic programming, APR, GenderMag, Ethics Bugs", timestamp = "Wed, 06 Nov 2024 22:18:16 +0100", biburl = "https://dblp.org/rec/journals/cacm/WidderG24.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://cacm.acm.org/opinion/what-is-a-bug/", DOI = "doi:10.1145/3662730", size = "3 pages", abstract = "On subjectivity, epistemic power, and implications for computing research", } @InProceedings{Widera:2009:cec, author = "Pawel Widera and Jonathan M. Garibaldi and Natalio Krasnogor", title = "Evolutionary Design of the Energy Function for Protein Structure Prediction", booktitle = "2009 IEEE Congress on Evolutionary Computation", year = "2009", editor = "Andy Tyrrell", pages = "1305--1312", address = "Trondheim, Norway", month = "18-21 " # may, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-2959-2", file = "P284.pdf", DOI = "doi:10.1109/CEC.2009.4983095", abstract = "Automatic protein structure predictors use the notion of energy to guide the search towards good candidate structures. The energy functions used by the state-of-the-art predictors are defined as a linear combination of several energy terms designed by human experts. We hypothesised that the energy based guidance could be more accurate if the terms were combined more freely. To test this hypothesis, we designed a genetic programming algorithm to evolve the protein energy function. Using several different fitness functions we examined the potential of the evolutionary approach on a set of candidate structures generated during the protein structure prediction process. Although our algorithms were able to improve over the random walk, the fitness of the best individuals was far from the optimum. We discuss the shortcomings of our initial algorithm design and the possible directions for further research.", keywords = "genetic algorithms, genetic programming", notes = "Winner 2010 HUMIES GECCO 2010 CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR", } @Article{Widera:2009:GPEM, author = "Pawel Widera and Jonathan M. Garibaldi and Natalio Krasnogor", title = "GP challenge: evolving energy function for protein structure prediction", journal = "Genetic Programming and Evolvable Machines", year = "2010", volume = "11", number = "1", pages = "61--88", month = mar, keywords = "genetic algorithms, genetic programming, Protein structure prediction, Protein energy function", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-009-9087-0", abstract = "One of the key elements in protein structure prediction is the ability to distinguish between good and bad candidate structures. This distinction is made by estimation of the structure energy. The energy function used in the best state-of-the-art automatic predictors competing in the most recent CASP (Critical Assessment of Techniques for Protein Structure Prediction) experiment is defined as a weighted sum of a set of energy terms designed by experts. We hypothesised that combining these terms more freely will improve the prediction quality. To test this hypothesis, we designed a genetic programming algorithm to evolve the protein energy function. We compared the predictive power of the best evolved function and a linear combination of energy terms featuring weights optimised by the Nelder-Mead algorithm. The GP based optimisation outperformed the optimised linear function. We have made the data used in our experiments publicly available in order to encourage others to further investigate this challenging problem by using GP and other methods, and to attempt to improve on the results presented here.", notes = "Winner 2010 HUMIES GECCO 2010 ", } @PhdThesis{Widera:thesis, author = "Pawel Widera", title = "Automated design of energy functions for protein structure prediction by means of genetic programming and improved structure similarity assessment", school = "University of Nottingham", year = "2010", address = "UK", month = mar, keywords = "genetic algorithms, genetic programming", bibsource = "OAI-PMH server at etheses.nottingham.ac.uk", oai = "oai:etheses.nottingham.ac.uk:1394", URL = "http://etheses.nottingham.ac.uk/1394/1/thesis.pdf", URL = "http://etheses.nottingham.ac.uk/1394/", URL = "http://ethos.bl.uk/OrderDetails.do?did=56&uin=uk.bl.ethos.519682", size = "144 pages", abstract = "The process of protein structure prediction is a crucial part of understanding the function of the building blocks of life. It is based on the approximation of a protein free energy that is used to guide the search through the space of protein structures towards the thermodynamic equilibrium of the native state. A function that gives a good approximation of the protein free energy should be able to estimate the structural distance of the evaluated candidate structure to the protein native state. This correlation between the energy and the similarity to the native is the key to high quality predictions. State-of-the-art protein structure prediction methods use very simple techniques to design such energy functions. The individual components of the energy functions are created by human experts with the use of statistical analysis of common structural patterns that occurs in the known native structures. The energy function itself is then defined as a simple weighted sum of these components. Exact values of the weights are set in the process of maximisation of the correlation between the energy and the similarity to the native measured by a root mean square deviation between coordinates of the protein backbone. In this dissertation I argue that this process is oversimplified and could be improved on at least two levels. Firstly, a more complex functional combination of the energy components might be able to reflect the similarity more accurately and thus improve the prediction quality. Secondly, a more robust similarity measure that combines different notions of the protein structural similarity might provide a much more realistic baseline for the energy function optimisation. To test these two hypotheses I have proposed a novel approach to the design of energy functions for protein structure prediction using a genetic programming algorithm to evolve the energy functions and a structural similarity consensus to provide a reference similarity measure. The best evolved energy functions were found to reflect the similarity to the native better than the optimised weighted sum of terms, and therefore opening a new interesting area of research for the machine learning techniques.", notes = "Winner 2010 HUMIES GECCO 2010 uk.bl.ethos.519682", } @InProceedings{Widera:2010:geccocomp, author = "Pawel Widera and Jaume Bacardit and Natalio Krasnogor and Carlos Garcia-Martinez and Manuel Lozano", title = "Evolutionary symbolic discovery for bioinformatics, systems and synthetic biology", booktitle = "GECCO 2010 Symbolic regression workshop", year = "2010", editor = "Steven Gustafson and Mark Kotanchek", isbn13 = "978-1-4503-0073-5", keywords = "genetic algorithms, genetic programming", pages = "1991--1998", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830761.1830842", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Symbolic regression and modelling are tightly linked in many Bioinformatics, Systems and Synthetic Biology problems. In this paper we briefly overview two problems, and the approaches we have use to tackle them, that can be deemed to represent this entwining of regression and modeling, namely, the evolutionary discovery of (1) effective energy functions for protein structure prediction and (2) models that capture biological behaviour at the gene, signalling and metabolic networks level. These problems are not, strictly speaking, {"}regression problems{"} but they do share several characteristics with the latter, namely, a symbolic representation of a solution is sought, this symbolic representation must be human understandable and the results obtained by the symbolic and human interpretable solution must fit the available data without over-learning.", notes = "Also known as \cite{1830842} Distributed on CD-ROM at GECCO-2010. ACM Order Number 910102.", } @Unpublished{widland:1999:ehiGP, author = "Tom Widland and Kevin Oishi and Alex Feuchter and Ryan Duryea and Ryan Davies", title = "Evolution of Hive Intelligence Using Genetic Programming", note = "WWW pages", year = "1999", email = "Tom Widland ", keywords = "genetic algorithms, genetic programming", URL = "http://www.supercomputingchallenge.org/98-99/finalreports/006/", broken = "http://www.challenge.nm.org/archive/98-99/finalreports/006/", abstract = "Executive Summary: Our project deals with the evolution of hive intelligence using genetic programming with the classic video game Pacman as our model environment. Pacman is an arcade game where a group of {"}ghosts{"} try to catch a Pacman as he attempts to eat all the dots in a maze in order to progress to the next level. Hive intelligence is the concept that a group of individual organisms working together as a cohesive unit can efficiently accomplish a defined task. In our model of Pacman, the ghosts are the individual organisms that are assigned the task of catching Pacman in a maze as quickly as possible. They work together as a team, communicating with each other to catch the Pacmen. At the end of each simulation our program rates them on a fitness scale to determine their prowess as a team. The ghost team that catches the most Pacmen in a specified amount of time gets the highest fitness score. We take the fittest teams and mix their programs (genes) together using a crossover algorithm. We then run another series of simulations and our program tests the fitness of the new generation of ghost teams. Our results show that genetic programming is a powerful means of evolving a routine to be more effective then any human created algorithm. The applications of such a process are staggering. In almost any situation in which computer programs are used to perform a single, definable task in varying situations, genetic programming can be used to increase the efficiency of the program. From simulating the function of organs in the human body to the exploration of planets, genetic programming is a useful tool in creating the best routines for the job.", notes = "Code pacman.cpp etc in allfiles.tgz", } @InProceedings{wieczorek:2019:CIMBB, author = "Wojciech Wieczorek and Olgierd Unold", title = "{GP-Based} Grammatical Inference for Classification of Amyloidogenic Sequences", booktitle = "Computational Intelligence Methods for Bioinformatics and Biostatistics", year = "2019", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-030-14160-8_9", DOI = "doi:10.1007/978-3-030-14160-8_9", } @Article{wieczorek:2020:AS, author = "Wojciech Wieczorek and Olgierd Unold and Lukasz Strak", title = "Parsing Expression Grammars and Their Induction Algorithm", journal = "Applied Sciences", year = "2020", volume = "10", number = "23", keywords = "genetic algorithms, genetic programming, genetic improvement", ISSN = "2076-3417", URL = "https://www.mdpi.com/2076-3417/10/23/8747", DOI = "doi:10.3390/app10238747", abstract = "Grammatical inference (GI), i.e., the task of finding a rule that lies behind given words, can be used in the analyses of amyloidogenic sequence fragments, which are essential in studies of neurodegenerative diseases. In this paper, we developed a new method that generates non-circular parsing expression grammars (PEGs) and compares it with other GI algorithms on the sequences from a real dataset. The main contribution of this paper is a genetic programming-based algorithm for the induction of parsing expression grammars from a finite sample. The induction method has been tested on a real bioinformatics dataset and its classification performance has been compared to the achievements of existing grammatical inference methods. The evaluation of the generated PEG on an amyloidogenic dataset revealed its accuracy when predicting amyloid segments. We show that the new grammatical inference algorithm achieves the best ACC (Accuracy), AUC (Area under ROC curve), and MCC (Mathew's correlation coefficient) scores in comparison to five other automata or grammar learning methods.", notes = "also known as \cite{app10238747}", } @PhdThesis{wieloch2013semantic, author = "Bartosz Wieloch", title = "Semantic Extensions for Genetic Programming", school = "Institute of Computing Science, Poznan University of Technology", year = "2013", address = "Poznan, Poland", month = jun, keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.put.poznan.pl/bwieloch/papers/Wieloch_PhD.pdf", size = "159 pages", abstract = "Genetic Programming (GP) is a most popular approach to automatic generation of computer programs. Standard methods applied in GP use raw fragments of evolved programs to construct new, hopefully better ones. These methods, except the selection phase, passover the behaviour of the modified programs and operate mainly on their syntax. In this dissertation we follow alternative, semantic-oriented approach that concentrates on the actual behavior of programs in population to determine how to construct the new ones. This research trend grew up as an attempt to overcome weaknesses of methods that rely only on syntax analysis. Recent contributions suggest that semantic extensions to GP can be a remedy to poor performance of classical, syntactic methods. Therefore, in this dissertation we firstly present the advantages and disadvantages of several possible descriptions of program's behaviour. Then we introduce the concept of semantics used in all semantic extensions presented throughout this thesis. The first semantic extension presented in this thesis is a method population initialisation which forces the individuals in population to be semantically unique. We also show selected semantic-aware variants of crossover and mutation operators. In particular, we test how they perform with and without our initialization method. Next, we introduce and formalise our novel concept of desired semantics that describes the desired behaviour for given part of a program. Then we propose several methods that employ desired semantics to create new programs by combining matching parts. We show that some of these methods significantly outperform other methods, semantic as well as syntactic ones. The second important proposition of this thesis is the concept of functional modularity. Functional modularity involves defining modules based on their semantic properties instead of syntactical ones, like, e.g., the frequency of occurring some code fragments. Functional modularity can be used to decompose a problem into potentially easier parts (subproblems), and then to solve the subproblems in isolation or together. All the described methods are illustrated with extensive experimental verification of their performance on a carefully prepared benchmark suite that contains problems from various domains. On this suite, we show the overall advantage of semantic-aware extensions, especially for methods that rely on desired semantics.", notes = "Supervisor: Krzysztof Krawiec", } @InProceedings{Wieloch:2013:GECCO, author = "Bartosz Wieloch and Krzysztof Krawiec", title = "Running programs backwards: instruction inversion for effective search in semantic spaces", booktitle = "GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference", year = "2013", editor = "Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi", isbn13 = "978-1-4503-1963-8", pages = "1013--1020", keywords = "genetic algorithms, genetic programming", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2463372.2463493", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "The instructions used for solving typical genetic programming tasks have strong mathematical properties. In this study, we leverage one of such properties: invertibility. A search operator is proposed that performs an approximate reverse execution of program fragments, trying to determine in this way the desired semantics (partial outcome) at intermediate stages of program execution. The desired semantics determined in this way guides the choice of a subprogram that replaces the old program fragment. An extensive computational experiment on 20 symbolic regression and Boolean domain problems leads to statistically significant evidence that the proposed Random Desired Operator outperforms all typical combinations of conventional mutation and crossover operators.", notes = "Also known as \cite{2463493} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)", } @InProceedings{Wiens:2000:GECCOlb, author = "Andrea L. Wiens and Brian J. Ross", title = "Gentropy: Evolutionary 2D Texture Generation", pages = "418--424", booktitle = "Late Breaking Papers at the 2000 Genetic and Evolutionary Computation Conference", year = "2000", editor = "Darrell Whitley", address = "Las Vegas, Nevada, USA", month = "8 " # jul, keywords = "genetic algorithms, genetic programming", notes = "Part of \cite{whitley:2000:GECCOlb}", } @Article{wiens:2002:cg, author = "Andrea L. Wiens and Brian J. Ross", title = "Gentropy: evolving {2D} textures", journal = "Computers and Graphics", year = "2002", volume = "26", number = "1", pages = "75--88", month = feb, keywords = "genetic algorithms, genetic programming, Procedural textures, Evolution, graphics", broken = "http://www.sciencedirect.com/science?_ob=MImg&_imagekey=B6TYG-4549VJN-2-15&_cdi=5618&_orig=browse&_coverDate=02%2F28%2F2002&_sk=999739998&wchp=dGLSlV-lSzBS&_acct=C000010182&_version=1&_userid=125795&md5=15555074969fef108d1b1b0fcbecf47e&ie=f.pdf", URL = "http://www.cosc.brocku.ca/~bross/research/Gentropy_evolving_2D_textures.pdf", URL = "http://citeseer.ist.psu.edu/503669.html", ISSN = "0097-8493", DOI = "doi:10.1016/S0097-8493(01)00159-5", broken = "http://www.sciencedirect.com/science/article/B6TYG-4549VJN-2/2/24dcbc61a6223c0a688098e8317256b8", abstract = "Gentropy is a genetic programming system that evolves two-dimensional procedural textures. It synthesizes textures by combining mathematical and image manipulation functions into formulas. A formula can be re-evaluated with arbitrary texture-space coordinates, to generate a new portion of the texture in texture space. Most evolutionary art programs are interactive, and require the user to repeatedly choose the best images from a displayed generation. Gentropy uses an unsupervised approach, where one or more target texture image are supplied to the system, and represent the desired texture features, such as colour, shape and smoothness (contrast). Then, Gentropy evolves textures independent of any further user involvement. The evolved texture will not be identical to the target texture, but rather, will exhibit characteristics similar to it. When more than one texture is supplied as a target, multiobjective feature analysis is performed. These feature tests may be combined and given different priorities during evaluation. It is therefore possible to use several target images, each with its own fitness function measuring particular visual characteristics. Gentropy also permits the use of multiple subpopulations, each of which may use its own texture evaluation criteria and target texture.", notes = "http://www.cosc.brocku.ca/~bross/gentropy/", } @InProceedings{Wiglasz:2016:EuroGP, author = "Michal Wiglasz and Michaela Drahosova", title = "Plastic Fitness Predictors Coevolved with Cartesian Programs", booktitle = "EuroGP 2016: Proceedings of the 19th European Conference on Genetic Programming", year = "2016", month = "30 " # mar # "--1 " # apr, editor = "Malcolm I. Heywood and James McDermott and Mauro Castelli and Ernesto Costa and Kevin Sim", series = "LNCS", volume = "9594", publisher = "Springer Verlag", address = "Porto, Portugal", pages = "164--179", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", isbn13 = "978-3-319-30668-1", DOI = "doi:10.1007/978-3-319-30668-1_11", abstract = "Coevolution of fitness predictors, which are a small sample of all training data for a particular task, was successfully used to reduce the computational cost of the design performed by cartesian genetic programming. However, it is necessary to specify the most advantageous number of fitness cases in predictors, which differs from task to task. This paper introduces a new type of directly encoded fitness predictors inspired by the principles of phenotypic plasticity. The size of the coevolved fitness predictor is adapted in response to the learning phase that the program evolution goes through. It is shown in 5 symbolic regression tasks that the proposed algorithm is able to adapt the number of fitness cases in predictors in response to the solved task and the program evolution flow.", notes = "Part of \cite{Heywood:2016:GP} EuroGP'2016 held in conjunction with EvoCOP2016, EvoMusArt2016 and EvoApplications2016", } @InProceedings{Wiglasz:2017:GlobalSIP, author = "Michal Wiglasz and Lukas Sekanina", title = "Evolutionary Approximation of Gradient Orientation Module in {HOG-based} Human Detection System", booktitle = "2017 IEEE Global Conference on Signal and Information Processing GlobalSIP 2017", year = "2017", editor = "Z. Jane Wang and Costas Kotropoulos and Qionghai Dai", pages = "1300--1304", address = "Montreal", month = nov # " 14-16", publisher = "IEEE Signal Processing Society", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, functional approximation, Histogram of oriented gradients: Poster", isbn13 = "978-1-5090-5989-8", language = "english", URL = "http://www.fit.vutbr.cz/research/view_pub.php?id=11441", URL = "http://www.fit.vutbr.cz/~iwiglasz/pubs.php.en?id=11441&yfile=%2Fpub%2F11441%2F0001300.pdf", DOI = "doi:10.1109/GlobalSIP.2017.8309171", size = "5 page", abstract = "The histogram of oriented gradients (HOG) feature extraction is a computer vision method widely used in embedded systems for detection of objects such as pedestrians. We used Cartesian genetic programming (CGP) to exploit the error resilience in the HOG algorithm. We evolved new approximate implementations of the arctangent function, which is typically employed to compute the gradient orientations. When the best evolved approximations are integrated into the SW implementation of the HOG algorithm, not only the execution time, but also the classification accuracy was improved in comparison with the accurate implementation and the state-of-the art approximate implementations.", notes = "Replaced by \cite{Wiglasz:2018:ieeeCompIntl} MIT and INRIA pedestrian datasets. Lena image. SVM. Intel Xeon (with hardware support for arctan) Approximate 8-bit arctan evolved by CGP \cite{Sekanina:2011:CGP.ch6} (might replace CORDIC iterative algorithm). Fitness based on hits. https://2017.ieeeglobalsip.org/Papers/PublicSessionIndex3.asp?Sessionid=1063 Also known as \cite{8309171}", } @InProceedings{Wiglasz:2018:ieeeCompIntl, author = "Michal Wiglasz and Lukas Sekanina", booktitle = "2018 IEEE Symposium Series on Computational Intelligence (SSCI)", title = "Cooperative Coevolutionary Approximation in {HOG-based} Human Detection Embedded System", year = "2018", pages = "1313--1320", month = "18-21 " # nov, address = "Bangalore, India", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, Approximate computing, Cooperative coevolution", DOI = "doi:10.1109/SSCI.2018.8628910", size = "8 pages", abstract = "The histogram of oriented gradients (HOG) feature extraction is a computer vision method widely used in embedded systems for detection of objects such as pedestrians. We used cooperative coevolutionary Cartesian genetic programming (CGP) to exploit the error resilience in the HOG algorithm. We evolved new approximate implementations of the arctan and square root functions, which are typically employed to compute the gradient orientations and magnitudes. When the best evolved approximations are integrated into the software implementation of the HOG algorithm, not only the execution time, but also the classification accuracy was improved in comparison with approximations evolved separately using CGP and also compared to the state-of-the art approximate implementations. As the evolved code does not contain any loops and branches, it is suitable for the follow-up low-power hardware implementation.", notes = "Replaces \cite{Wiglasz:2017:GlobalSIP} SVM LIBLINEAR p1319 'CGP to evolve new approximate implementations of the arctan and square root functions' Also known as \cite{8628910}", } @InProceedings{Wijayaweera:2012:SLAAI, author = "W. J. L. N. Wijayaweera and A. S. Karunananda", title = "Framework for Discovery of Data Models Using Genetic Programming", booktitle = "Proceeding of the ninth Annual Sessions Sri Lanka Association for Artificial Intelligence (SLAAI 2012)", year = "2012", editor = "Nalin Wickramarachchi and H. U. W. Ratnayake", pages = "48--56", address = "Sri Lanka", publisher_address = "Nawala, Nugegoda, Sri Lanka", month = "18 " # dec, publisher = "The Open University", keywords = "genetic algorithms, genetic programming", URL = "http://slaai.lk/proc/2012/p2012.pdf", URL = "http://dl.lib.mrt.ac.lk/handle/123/14527", size = "9 pages", abstract = "The field of Genetic Programming in Artificial Intelligence strives to get computers to solve a problem without explicitly coding a solution by a programmer. Genetic Programming is a relatively new technology, which comes under automatic programming. After the initial work by John R. Koza in genetic programming, much research work have been done to discover data models in various datasets. These work have been rather domain specific and little attention has been given to develop generic framework for modelling and experimenting with genetic programming solutions for real world problems. This paper discusses a project to develop a visual environment, named as GPVLab, to design and experiment with genetic programming solutions for real world problems. GPVLab has successfully discovered data models for various data sets and according to the main evaluation it is evident that GPVLab can generate solutions which provide better results in 56percent of the time. It is concluded that GPVLab can be used to model genetic programming application very conveniently. GPVLab can be used not only for discovering data models but also doing various experiments in genetic programming.", notes = "World Bank Data Catalog Faculty of Information Technology, University of Moratuwa, Sri Lanka", } @InProceedings{Wijesinghe:2007:cec, author = "Gayan Wijesinghe and Vic Ciesielski", title = "Using Restricted Loops in Genetic Programming for Image Classification", booktitle = "2007 IEEE Congress on Evolutionary Computation", year = "2007", editor = "Dipti Srinivasan and Lipo Wang", pages = "4569--4576", address = "Singapore", month = "25-28 " # sep, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, STGP, greyscale image classification, infinite loops, invalid programs, no-loop method accuracy, restricted loops, image classification", ISBN = "1-4244-1340-0", file = "1664.pdf", DOI = "doi:10.1109/CEC.2007.4425070", abstract = "Loops are rarely used in genetic programming due to issues such as detecting infinite loops and invalid programs. In this paper we present a restricted form of loops that is specifically designed to be evolved in image classifiers. Particularly, we evolve classifiers that use these loops to perform calculations on image regions chosen by the loops. We have compared this method to another classification method that only uses individual pixels in its calculations. These two methods are tested on two synthesised and one non-synthesised grey scale image classification problems of varying difficulty. The most difficult problem requires determining heads or tails of 320 by 320 pixel images of a US one cent coin at any angle. On this problem, the accuracy of the loops approach was 97.80% in contrast to the no-loop method accuracy of 79.46%. Use of loops also reduces over fitting of training data. We also found that loop methods overfit less when only a few training examples are available.", notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C", } @InProceedings{Wijesinghe:2008:cec, author = "Gayan Wijesinghe and Shahrul Badariah Mat Sah and Vic Ciesielski", title = "Grid vs. Arbitrary Placement of Tiles for Generating Animated Photomosaics", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "2734--2740", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1823-7", file = "EC0617.pdf", DOI = "doi:10.1109/CEC.2008.4631165", abstract = "A traditional photo-mosaic is a still image where a larger picture is created by selectively arranging small picture tiles on a blank, gridded canvas. We show interesting and engaging animations can be generated from an evolutionary search for the final photomosaic image. We then investigate two different tile placement strategies for generating the animations. In the first strategy tiles can only be placed in fixed cells in a 2 dimensional grid and it is not possible for tiles to overlap. This strategy is implemented with a genetic algorithm. In the second strategy, which is implemented using genetic programming, the tiles can be placed in any position and at an arbitrary rotation. It is possible for one tile to be placed on top of another so a method for dealing with overlap is needed. We have investigated three methods for dealing with overlap. The second strategy generates more engaging animations but at considerably increased computational cost. We conclude that evolutionary search can be used to produce very engaging animations in which a target image gradually emerges from an initial random collection of tiles.", keywords = "genetic algorithms, genetic programming", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @InProceedings{Wijesinghe:2008:gecco, author = "Gayan Wijesinghe and Vic Ciesielski", title = "Experiments with indexed {FOR-loops} in genetic programming", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", pages = "1347--1348", address = "Atlanta, GA, USA", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, machine learning, philosophical aspects of evolutionary computing, representations, theory: Poster", isbn13 = "978-1-60558-130-9", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1347.pdf", DOI = "doi:10.1145/1389095.1389357", size = "2 pages", abstract = "We investigated how indexed FOR-loops, such as the ones found in procedural programming languages, can be implemented in genetic programming. We use them to train programs that learn the repeating unit string of a given regular binary pattern string and can reproduce the learnt pattern to an arbitrary size, specified by a parameter N. We discovered that this particular problem, where the solution needs to scale with multiple size-instances of the problem, is very hard to solve without the help of domain knowledge.", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389357}", } @InProceedings{DBLP:conf/seal/WijesingheC08, author = "Gayan Wijesinghe and Victor Ciesielski", title = "Parameterised Indexed FOR-Loops in Genetic Programming and Regular Binary Pattern Strings", booktitle = "Proceedings of the 7th International Conference on Simulated Evolution And Learning (SEAL '08)", year = "2008", editor = "Xiaodong Li and Michael Kirley and Mengjie Zhang and David G. Green and Victor Ciesielski and Hussein A. Abbass and Zbigniew Michalewicz and Tim Hendtlass and Kalyanmoy Deb and Kay Chen Tan and J{\"u}rgen Branke and Yuhui Shi", volume = "5361", series = "Lecture Notes in Computer Science", pages = "524--533", address = "Melbourne, Australia", month = dec # " 7-10", publisher = "Springer", keywords = "genetic algorithms, genetic programming, STGP", isbn13 = "978-3-540-89693-7", DOI = "doi:10.1007/978-3-540-89694-4_53", abstract = "We present two methods to represent and use parameterised indexed FOR-loops in genetic programming. They are tested on learning the repetitive unit of regular binary pattern strings to reproduce these patterns to user specified arbitrary lengths. Particularly, we investigate the effectiveness of low-level and high-level functions inside these loops for the accuracy and the semantic efficiency of solutions. We used 5 test cases at increasing difficulty levels and our results show the high-level approach producing solutions in at least 19percent of the runs when the low-level approach struggled to produce any in most cases.", bibsource = "DBLP, http://dblp.uni-trier.de", notes = "LOOP, PATCH, INDEX. Pattern (eg 1001)", } @InProceedings{Wijesinghe:2010:cec, author = "Gayan Wijesinghe and Vic Ciesielski", title = "Evolving programs with parameters and loops", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-6910-9", abstract = "At the current state of the art, genetic programs do not contain two constructs that commonly occur in programs written by humans, that is, loops and functions with parameters. In this paper we describe an investigation into the evolution of programs for a problem that can only be solved by evolving a parameterised program with one or more loops. We provide training examples of the desired program behaviour for a number of problem sizes and require the evolution of a program P(n) that will give the correct output for any value of n. We have chosen a problem, that of reproducing a binary string to a given number of bits, that can be made harder or easier by adjusting various aspects of the formulation. We are interested seeing in which formulations lead to success and which do not. We conclude that programs with parameters and loops can be successfully evolved if the search space is appropriately restricted by (1) grammars which restrict the possible program structures, (2) limits on program depth and (3) limits on the range of random constants.", DOI = "doi:10.1109/CEC.2010.5586018", notes = "WCCI 2010. Also known as \cite{5586018}", } @InProceedings{Wild:2019:SSBSE, author = "Alexander Wild and Barry Porter", title = "General Program Synthesis using Guided Corpus Generation and Automatic Refactoring", booktitle = "SSBSE 2019", year = "2019", editor = "Shiva Nejati and Gregory Gay", volume = "11664", series = "LNCS", pages = "89--104", address = "Tallinn, Estonia", month = "31 " # aug # " - 1 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, genetic improvement, ANN, SBSE", isbn13 = "978-3-030-27454-2", URL = "http://barryfp.com/swc/pdfs/2019_ssbse_wild.pdf", DOI = "doi:10.1007/978-3-030-27455-9_7", size = "15 pages", abstract = "Program synthesis aims to produce source code based on a user specification, raising the abstraction level of building systems and opening the potential for non-programmers to synthesise their own bespoke services. Both genetic programming (GP) and neural code synthesis have proposed a wide range of approaches to solving this problem, but both have limitations in generality and scope. We propose a hybrid search-based approach which combines (i) a genetic algorithm to autonomously generate a training corpus of programs centred around a set of highly abstracted hints describing interesting features; and (ii) a neural network which trains on this data and automatically refactors it towards a form which makes a more ideal use of the neural network's representational capacity. When given an unseen program represented as a small set of input and output examples, our neural network is used to generate a rank-ordered search space of what it sees as the most promising programs; we then iterate through this list up to", notes = "Is this GI? https://bitbucket.org/AlexanderWildLancaster/automaticrefactoringsynthesis.git Python, Tensorflow, JVM openjdk. C like programs are output of multi-layer ANN (8 intermediate residual layers?) line by line. Input 1700 neurons connected to 10 program I/O examples concatinated together. Protect loop control variables? Penalise poor multiple uses of array elements? Examples: Absoulte..Sum vales. http://ssbse19.mines-albi.fr/", } @InProceedings{Wild:2021:GECCOcomp, author = "Alexander Wild and Barry Porter", title = "Neurally Guided Transfer Learning for Genetic Programming", booktitle = "Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion", year = "2021", editor = "Francisco Chicano and Alberto Tonda and Krzysztof Krawiec and Marde Helbig and Christopher W. Cleghorn and Dennis G. Wilson and Georgios Yannakakis and Luis Paquete and Gabriela Ochoa and Jaume Bacardit and Christian Gagne and Sanaz Mostaghim and Laetitia Jourdan and Oliver Schuetze and Petr Posik and Carlos Segura and Renato Tinos and Carlos Cotta and Malcolm Heywood and Mengjie Zhang and Leonardo Trujillo and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Fuyuki Ishikawa and Inmaculada Medina-Bulo and Frank Neumann and Andrew M. Sutton", pages = "267--268", address = "internet", series = "GECCO '21", month = jul # " 10-14", organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, Transfer Learning, Neural Networks, ANN: Poster", isbn13 = "978-1-4503-8351-6", DOI = "doi:10.1145/3449726.3459511", size = "2 pages", abstract = "A key challenge in GP is how to learn from the past, so that the successful synthesis of simple programs can feed in to more challenging unsolved programs. we present a transfer learning (TL) mechanism for GP which extracts fragments from programs it synthesises, then employs deep neural networks to identify new problems to deploy them into, to boost performance. This end-to-end system requires no human identification of which programs to use as donors for TL, the system only needs IO examples.", notes = " GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)", } @Article{Wild:2022:TELO, author = "Alexander Wild and Barry Porter", title = "Multi-donor Neural Transfer Learning for Genetic Programming", journal = "ACM Transactions on Evolutionary Learning and Optimization", year = "2022", volume = "2", number = "4", articleno = "12", month = dec, keywords = "genetic algorithms, genetic programming, neural networks, ANN, transfer learning", ISSN = "2688-299X", DOI = "doi:10.1145/3563043", size = "40 pages", abstract = "Genetic programming (GP), for the synthesis of brand new programs, continues to demonstrate increasingly capable results towards increasingly complex problems. A key challenge in GP is how to learn from the past so that the successful synthesis of simple programs can feed into more challenging unsolved problems. Transfer Learning (TL) in the literature has yet to demonstrate an automated mechanism to identify existing donor programs with high-utility genetic material for new problems, instead relying on human guidance. In this article we present a transfer learning mechanism for GP which fills this gap: we use a Turing-complete language for synthesis, and demonstrate how a neural network (NN) can be used to guide automated code fragment extraction from previously solved problems for injection into future problems. Using a framework which synthesises code from just 10 input-output examples, we first study NN ability to recognise the presence of code fragments in a larger program, then present an end-to-end system which takes only input-output examples and generates code fragments as it solves easier problems, then deploys selected high-utility fragments to solve harder ones. The use of NN-guided genetic material selection shows significant performance increases, on average doubling the percentage of programs that can be successfully synthesised when tested on two different problem corpora, compared with a non-transfer-learning GP baseline.", notes = "https://dlnext.acm.org/journal/telo", } @PhdThesis{Wild:thesis, author = "Alexander Newton Wild", title = "Neural Network Guided Transfer Learning for Genetic Programming", school = "Lancaster University", year = "2023", address = "UK", month = "17 " # aug, keywords = "genetic algorithms, genetic programming, ANN", language = "English", URL = "https://www.research.lancs.ac.uk/portal/services/downloadRegister/394985031/2023wildphd.pdf", DOI = "doi:10.17635/lancaster/thesis/2090", size = "186 pages", abstract = "Programming-by-Example, and code synthesis in general, is a field with many different sub-fields, involving many forms of machine learning and computational logic. With advantages and disadvantages to each, attempts to build effective hybrid solutions would seem to be a promising direction. Transfer Learning (TL) provides a good framework for this, as it allows one of the classic code synthesis techniques, Genetic Programming, to be augmented by past success, to target a particular code synthesis system to the problem domain it is facing. TL allows one type of machine learning algorithm, in this thesis a neural network, to support the core GP process, and combine the strengths of both. This thesis explores the concept of hybrid code synthesis approaches, and then brings the identified strongest elements of each approach together into a single neural network driven Transfer Learning system for Genetic Programming. The TL system operates autonomously, without any human intervention required after the problem set (in example only format) is presented to the system. The thesis first studies how to structure a training corpus for a neural network, across two different experiments, exploring how the constraints placed on a corpus can result in superior training. After this, it studies how GP processes can be guided, to ensure that a hypothetical NN guide would be useful if it could be created and how it can best assist the GP. Finally, it combines the previous studies together into the full end-to-end TL system and tests its performance across two separate problem domains", notes = "Supervisor: Barry Porter", } @InProceedings{Wildman:2006:apsis, author = "R. A. Wildman and D. S. Weile", title = "Genetic programming-based geometry reconstruction of conducting cylinders", booktitle = "IEEE Antennas and Propagation Society International Symposium", year = "2006", pages = "2083--2086", month = "9-14 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", ISBN = "1-4244-0123-2", DOI = "doi:10.1109/APS.2006.1710992", abstract = "A general method for optimising 2D geometries was presented and applied to an inverse scattering problem. This method uses a tree-structured chromosome that represents the geometric combination of several convex polygons. The convex polygons used as inputs are represented by the convex hull of variable-length lists of 2D points. Genetic operators were developed to modify the tree and the point lists. In the two examples given, the method successfully reconstructed the metallic scattering targets resulting in low error in the scattered fields. This method is flexible enough that it could be used in any 2D electromagnetic design problem as presented, and it can also be extended to three dimensions.", notes = "CD-ROM Dept. of Electr. & Comput. Eng., Delaware Univ., Newark, DE, USA", } @InProceedings{Wildman:2006:sap, author = "R. A. Wildman and D. S. Weile", title = "Genetic algorithm-based geometry reconstruction of convex conducting cylinders", booktitle = "IEEE Society International Symposium on Antennas and Propagation", year = "2006", pages = "2079--2082", month = "9-14 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", ISBN = "1-4244-0123-2", DOI = "doi:10.1109/APS.2006.1710991", abstract = "In this paper a GA-based method for geometry reconstruction of 2D convex shapes was presented and tested successfully on several scattering targets. While the case of convex scattering targets seems limited, this work is the basis of a more general method that uses genetic programming techniques. Tree-structured chromosomes can be used to represent the geometric combination (union, subtraction, etc.) of convex polygons, which provides a very flexible representation of arbitrary 2D geometries. Moreover, the representation can be extended quite easily to an arbitrary number of dimensions.", notes = "CD-ROM Dept. of Electr. & Comput. Eng., Delaware Univ., Newark, DE, USA", } @Article{Wildman:2007:ieeeTAP, author = "Raymond A. Wildman and Daniel S. Weile", title = "Geometry Reconstruction of Conducting Cylinders Using Genetic Programming", journal = "IEEE Transactions on Antennas and Propagation", year = "2007", volume = "55", number = "3", pages = "629--636", month = mar, keywords = "genetic algorithms, genetic programming, Boolean functions, computational electromagnetics, computational geometry, conducting bodies, electromagnetic wave scattering, Boolean combination, conducting cylinder, encoding, geometry reconstruction, inverse scattering problem, tree-shaped chromosome, two-dimensional conductors", DOI = "doi:10.1109/TAP.2007.891565", ISSN = "0018-926X", abstract = "A genetic programming-based method for the imaging of two-dimensional conductors is presented. Geometry is encoded in this scheme using a tree-shaped chromosome to represent the Boolean combination of convex polygons into an arbitrary two-dimensional geometry. The polygons themselves are encoded as the convex hull of variable-length lists of points that reside in the terminal nodes of the tree. A set of genetic operators is defined for efficiently solving the inverse scattering problem. Specifically, the encoding scheme allows for a standard genetic programming crossover operator, and several mutation operators are designed in consideration of the encoding scheme. Several results are presented that demonstrate the method on a number of different shapes", } @PhdThesis{Wildman:thesis, author = "Raymond A. Wildman", title = "Geometry optimization and computational electromagnetics: Methods and applications", school = "Electrical Engineering, University of Delaware", year = "2007", address = "USA", month = "Fall", keywords = "genetic algorithms, genetic programming, Applied sciences, Electromagnetics, Integral equations, Inverse scattering, Optimization, Time domain", isbn13 = "9780549388876", URL = "https://search.proquest.com/docview/304870571", size = "191 pages", abstract = "A new geometry optimization scheme, based on computational geometry methods, is developed and applied to electromagnetic problems. Geometry optimization is an important problem and has applications in inverse scattering and electromagnetic device design. The basic method uses a novel geometric representation that can represent any topology and is also amenable to stochastic optimization methods. Though only developed here for two-dimensional problems, the method can be extended to three dimensions without altering any of its useful properties. As motivation, a phononic band gap design problem is first developed and attempted using a pixel filling approach. Though decent results are achieved, the possible solutions are inherently limited by the geometric representation. The new method is then introduced and applied to the inverse scattering of conducting cylinders. Subsequently, homogeneous and inhomogeneous dielectric inverse scattering problems are solved and the efficiency of the method is addressed using local search methods. Finally, several advances in electromagnetic solvers, specifically time domain Nystroem methods, are reported. These methods offer advantages over other competing methods and could be used with different geometry design problems.", notes = "Supervisor: Daniel S. Weile ProQuest Dissertations Publishing, 2007. 3291727", } @InProceedings{Wilfert:2021:ETFA, author = "Jonas Wilfert and Simon Stieber and Frederik Wilhelm and Wolfgang Reif", title = "Genetic Programming for Fiber-Threading for Fiber-Reinforced Plastics", booktitle = "2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)", year = "2021", abstract = "Setting up fiber-threading for a pultrusion line is tedious, error prone and takes a long time. Between 100 and 1000 fibers have to be arranged into a two-dimensional shape, which have to be threaded between several support plates without causing crossovers. When manually planning this process based on intuition, it is hard to keep track of the complexity. This slows the process down to where it can take several hours or several days, and shortening this duration reduces the cost considerably. As planning the setup takes up a large chunk of time, we are proposing a simulation and an algorithm to automatically calculate how the fiber bundles need to be threaded from the creels through the support plates to result in the desired shape. Using a three-dimensional simulation for collision detection in conjunction with a genetic algorithm, we are able to shorten the planning of the fibers to around 10 minutes on a modern 8-core personal computer. Based on this data, further work can be done to further improve, visualize or permanently store the data in a digitized company.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ETFA45728.2021.9613726", month = sep, notes = "Also known as \cite{9613726}", } @InProceedings{conf/kesamsta/WilisowskiD15, title = "The Application of Co-evolutionary Genetic Programming and {TD}(1) Reinforcement Learning in Large-Scale Strategy Game {VCMI}", author = "Lukasz Wilisowski and Rafal Drezewski", booktitle = "9th KES International Conference on Agent and Multi-Agent Systems: Technologies and Applications, KES-AMSTA 2015", year = "2015", editor = "Gordan Jezic and Robert J. Howlett and Lakhmi C. Jain", volume = "38", series = "Smart Innovation, Systems and Technologies", pages = "81--93", address = "Sorrento, Italy", month = jun # " 17-19", publisher = "Springer", keywords = "genetic algorithms, genetic programming, neural networks, strategy games", isbn13 = "978-3-319-19728-9", bibdate = "2017-05-21", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/kesamsta/kesamsta2015.html#WilisowskiD15", DOI = "doi:10.1007/978-3-319-19728-9_7", abstract = "VCMI is a new, open-source project that could become one of the biggest testing platform for modern AI algorithms in the future. Its complex environment and turn-based game play make it a perfect system for any AI driven solution. It also has a large community of active players which improves the testability of target algorithms. This paper explores VCMI's environment and tries to assess its complexity by providing a base solution for battle handling problem using two global optimisation algorithms: Co-Evolution of Genetic Programming Trees and TD(1) algorithm with Back Propagation neural network. Both algorithms have been used in VCMI to evolve battle strategies through a fully autonomous learning process. Finally, the obtained strategies have been tested against existing solutions and compared with players' best tactics.", } @MastersThesis{Wilkerson:masters, author = "Joshua Lee Wilkerson", title = "Co-evolutionary automated software correction: a proof of concept", school = "Missouri University of Science and Technology", year = "2008", type = "MSc in Computer Sciences", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, SBSE", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.304.6988", URL = "http://hdl.handle.net/10355/27358", URL = "http://scholarsmine.mst.edu/thesis/pdf/Wilkerson_09007dcc80642bb4.pdf", size = "73 pages", abstract = "The task of ensuring that a software artifact is correct can be a very time consuming process. To be able to say that an algorithm is correct is to say that it will produce results in accordance with its specifications for all valid input. One possible way to identify an incorrect implementation is through the use of automated testing (currently an open problem in the field of software engineering); however, actually correcting the implementation is typically a manual task for the software developer. In this thesis a system is presented which automates not only the testing but also the correction of an implementation. This is done using genetic programming methods to evolve the implementation itself and an appropriate evolutionary algorithm to evolve test cases. These two evolutionary algorithms are tied together using co-evolution such that each population plays a large role in the evolution of the other population. A prototype of the Co-evolutionary Automated Software Correction (CASC) system has been developed, which has allowed for preliminary experimentation to test the validity of the idea behind the CASC system", notes = "sort, seeding Approved by Dr. Daniel Tauritz, Advisor Dr. Thomas Weigert Dr. Bruce McMillin", } @InProceedings{Wilkerson:2010:gecco, author = "Josh L. Wilkerson and Daniel Tauritz", title = "Coevolutionary automated software correction", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "1391--1392", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, Search-based software engineering, Poster, Software Engineering, Testing and Debugging, Artificial Intelligence, Problem Solving, Control Methods, Search, Algorithms, Experimentation, Reliability, Automated Debugging, Repair, Coevolution, SBSE, Search-Based Testing", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830483.1830739", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "This paper presents the Coevolutionary Automated Software Correction system, which addresses in an integral and fully automated manner the complete cycle of software artifact testing, error location, and correction phases. It employs a co-evolutionary approach where software artifacts and test cases are evolved in tandem. The test cases evolve to better find flaws in the software artifacts and the software artifacts evolve to better behave to specification when exposed to the test cases, thus causing an evolutionary arms race. Experimental results are presented on the same test problem employed in the published results on the previous state-of-the-art automated software correction system.", notes = "CASC, C++, ECJ bubble sort. Cites \cite{Arcuri:2008:ICSEphd}, \cite{Arcuri:thesis}, \cite{Arcuri:2008:cec}, \cite{DBLP:conf/gecco/ForrestNWG09}. Also known as \cite{1830739} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @InProceedings{Wilkerson:2011:GECCOcomp, author = "Josh L. Wilkerson and Daniel R. Tauritz", title = "Scalability of the coevolutionary automated software correction system", booktitle = "GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0690-4", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, SBSE, Search-based software engineering: Poster", pages = "243--244", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001858.2001995", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "The Coevolutionary Automated Software Correction system addresses in an integral and fully automated manner the complete cycle of software artifact testing, error location, and correction phases. It employs a coevolutionary approach where software artifacts and test cases are evolved in tandem. The test cases evolve to better find flaws in the software artifacts and the software artifacts evolve to better behave to specification when exposed to the test cases, thus causing an evolutionary arms race. Experimental results are presented which demonstrate the scalability of the Coevolutionary Automated Software Correction system by establishing correlations between program size and both success rate and estimated convergence rate that are at most linear.", notes = "Also known as \cite{2001995} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @PhdThesis{Wilkerson:thesis, author = "Joshua Lee Wilkerson", title = "Evolutionary Computing Driven Search Based Software Testing and Correction", school = "Computer Science, Missouri University of Science and Technology", year = "2012", address = "USA", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, SBSE, Automatic control, Computer programs, Computer software, Verification, Evolutionary computation, Fault location (Engineering), Program transformation (Computer programming)", URL = "http://merlin.lib.umsystem.edu/record=b9387992~S5", URL = "http://hdl.handle.net/10355/26508", URL = "https://mospace.umsystem.edu/xmlui/bitstream/handle/10355/26508/Wilkerson_2012.pdf", size = "160 pages", abstract = "For a given program, testing, locating the errors identified, and correcting those errors is a critical, yet expensive process. The field of Search Based Software Engineering (SBSE) addresses these phases by formulating them as search problems. This dissertation addresses these challenging problems through the use of two complimentary evolutionary computing based systems. The first one is the Fitness Guided Fault Localisation (FGFL) system, which novelly uses a specification based fitness function to perform fault localisation. The second is the Coevolutionary Automated Software Correction (CASC) system, which employs a variety of evolutionary computing techniques to perform testing, correction, and verification of software. In support of the real world application of these systems, a practitioner's guide to fitness function design is provided. For the FGFL system, experimental results are presented that demonstrate the applicability of fitness guided fault localisation to automate this important phase of software correction in general, and the potential of the FGFL system in particular. For the fitness function design guide, the performance of a guide generated fitness function is compared to that of an expert designed fitness function demonstrating the competitiveness of the guide generated fitness function. For the CASC system, results are presented that demonstrate the system's abilities on a series of problems of both increasing size as well as number of bugs present. The system presented solutions more than 90percent of the time for versions of the programs containing one or two bugs. Additionally, scalability results are presented for the CASC system that indicate that success rate linearly decreases with problem size and that the estimated convergence rate scales at worst linearly with problem size.", notes = "Supervisor: Dr. Daniel Tauritz Rest of committee Dr. Thomas Weigert Dr. Bruce McMillin Dr. Ali Hurson Dr. Sahra Sedighsarvestani", } @InProceedings{Wilkerson:2012:GECCO, author = "Josh L. Wilkerson and Daniel R. Tauritz and James M. Bridges", title = "Multi-Objective Coevolutionary Automated Software Correction", booktitle = "GECCO '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference", year = "2012", editor = "Terry Soule and Anne Auger and Jason Moore and David Pelta and Christine Solnon and Mike Preuss and Alan Dorin and Yew-Soon Ong and Christian Blum and Dario Landa Silva and Frank Neumann and Tina Yu and Aniko Ekart and Wil Browne and Tim Kovacs and Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and Giovanni Squillero and Nicolas Bredeche and Stephen L. Smith and Alison Motsinger-Rei and Jose Lozano and Martin Pelikan and Silja Meyer-Nienber and Christian Igel and Greg Hornby and Rene Doursat and Steve Gustafson and Gustavo Olague and Shin Yoo and John Clark and Gabriela Ochoa and Gisele Pappa and Fernando Lobo and Daniel Tauritz and Jurgen Branke and Kalyanmoy Deb", isbn13 = "978-1-4503-1177-9", pages = "1229--1236", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, SBSE, search-based software engineering, Automated Program Correction, Coevolution, Multi-Objective Optimisation, NSGA-II, Fitness Sharing", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330163.2330333", publisher = "ACM", publisher_address = "New York, NY, USA", size = "8 pages", abstract = "For a given program, testing, locating the errors identified, and correcting those errors is a critical, yet expensive process. The field of Search Based Software Engineering (SBSE) addresses these phases by formulating them as search problems. The Coevolutionary Automated Software Correction (CASC) system targets the correction phase by coevolving test cases and programs at the source code level. The latest version of the CASC system is presented, featuring multi-objective optimisation and an enhanced representation language. Results are presented demonstrating CASC's ability to successfully correct five seeded bugs in two non-trivial programs from the Siemens test suite. Additionally, evidence is provided substantiating the hypothesis that multi-objective optimization is beneficial to SBSE.", notes = "SIR seeded errors printtoken2 replace p1235 'Results are presented demonstrating CASC's ability to successfully correct 5 seeded bugs in two non-trivial programs from the Siemens test suite.' Also known as \cite{2330333} GECCO-2012 A joint meeting of the twenty first international conference on genetic algorithms (ICGA-2012) and the seventeenth annual genetic programming conference (GP-2012)", } @InProceedings{Wilkins:2019:GECCOcomp, author = "Zachary Wilkins and Nur Zincir-Heywood", title = "Darwinian malware detectors: a comparison of evolutionary solutions to android malware", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", isbn13 = "978-1-4503-6748-6", pages = "1651--1658", address = "Prague, Czech Republic", DOI = "doi:10.1145/3319619.3326818", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{3326818} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InCollection{willeke:1995:GEBR, author = "Thomas Willeke", title = "Genetic Evolution of Behavior-Oriented Robots", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1995", year = "1995", editor = "John R. Koza", pages = "301--308", address = "Stanford, California, 94305-3079 USA", month = "11 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-195720-5", notes = "part of \cite{koza:1995:gagp}", } @InProceedings{William:2008:ieeeRegion5, author = "Edward William and James {Northern, III}", title = "Genetic Programming Lab ({GPLab}) Tool Set Version 3.0", booktitle = "IEEE Region 5 Conference", year = "2008", month = apr, address = "Kansas City, USA", pages = "1--6", keywords = "genetic algorithms, genetic programming, Genetic Programming Lab, computer program, general public license software regulations, mathematics computing", DOI = "doi:10.1109/TPSD.2008.4562729", abstract = "This report describes the release 3.0 of the Genetic Programming Lab (GPLab) tool set. GPLab is software that is publicly available and free to use under general public license (GNU) software regulations. MATLAB is a programming environment standard among scientist and engineers. This current release of GPLab handles bloat control, adjustment of fitness complementary to size, keeping track of fitness and raw fitness, and using adjusted values along the selection process. This paper contains a complete description of the GPLab suite and contains many details about the components of the tool. This guide is designed to run a research environment easily without need to browse extensively through the much detailed manual. Finally, this document is only for quick reference used to assist in the understanding of GPLab using a demonstration and a short example problem. The example shows how to analyze generation, fitness, nodes, Pareto, and node trees (computer program) in the MATLAB/GPLab environment.", notes = "Also known as \cite{4562729}", } @InProceedings{Williams:1999:ADQ, author = "Colin P. Williams and Alexander G. Gray", title = "Automated Design of Quantum Circuits", volume = "1509", pages = "113--125", year = "1998", CODEN = "LNCSD9", ISSN = "0302-9743", bibdate = "Tue Feb 5 11:53:31 MST 2002", bibsource = "http://link.springer-ny.com/link/service/series/0558/tocs/t1509.htm", acknowledgement = "ack-nhfb", booktitle = "Quantum Computing and Quantum Communications: First NASA International Conference, QCQC'98", editor = "C. P. Williams", series = "Lecture Notes in Computer Science", address = "Palm Springs, California, USA", month = feb, organisation = "NASA", publisher = "Springer-Verlag GmbH", keywords = "genetic algorithms, genetic programming, computer science, quantum computing", isbn13 = "978-3-540-65514-5", DOI = "doi:10.1007/3-540-49208-9_8", size = "13 pages", abstract = "In order to design a quantum circuit that performs a desired quantum computation, it is necessary to find a decomposition of the unitary matrix that represents that computation in terms of a sequence of quantum gate operations. To date, such designs have either been found by hand or by exhaustive enumeration of all possible circuit topologies. In this paper we propose an automated approach to quantum circuit design using search heuristics based on principles ed from evolutionary genetics, i.e. using a genetic programming algorithm adapted specially for this problem. We demonstrate the method on the task of discovering quantum circuit designs for quantum teleportation. We show that to find a given known circuit design (one which was hand-crafted by a human), the method considers roughly an order of magnitude fewer designs than naive enumeration. In addition, the method finds novel circuit designs superior to those previously known.", notes = "A1 Jet Propulsion Laboratory Mailstop 126-347 4800 Oak Grove Drive Pasadena, CA 91109-8099 Cited by \cite{spector:book} \cite{Leier:2003:Etssoqp}", } @Misc{williams2023usercentric, author = "David Williams and James Callan and Serkan Kirbas and Sergey Mechtaev and Justyna Petke and Thomas Prideaux-Ghee and Federica Sarro", title = "User-Centric Deployment of Automated Program Repair at {Bloomberg}", howpublished = "arXiv", year = "2023", month = "17 " # nov, note = "SEIP track ICSE 2024", keywords = "genetic improvement, APR", eprint = "2311.10516", archiveprefix = "arXiv", primaryclass = "cs.SE", URL = "https://arxiv.org/abs/2311.10516", size = "11 pages", notes = "Not GP Published \cite{Williams:2024:ICSE} won best paper https://twitter.com/TechAtBloomberg/status/1780603476374077826", } @InProceedings{Williams:2024:ICSE, author = "David Williams and James Callan and Serkan Kirbas and Sergey Mechtaev and Justyna Petke and Thomas Prideaux-Ghee and Federica Sarro", title = "User-Centric Deployment of Automated Program Repair at {Bloomberg}", booktitle = "46th International Conference on Software Engineering - Software Engineering in Practice track (ICSE-SEIP 2024)", year = "2024", address = "Lisbon", month = "14-20 " # apr, note = "Distinguished Paper Award Winner", keywords = "genetic algorithms, genetic programming, genetic improvement, APR", URL = "https://arxiv.org/abs/2311.10516", URL = "https://discovery.ucl.ac.uk/id/eprint/10187233/", size = "11 pages", abstract = "Automated program repair (APR) tools have unlocked the potential for the rapid rectification of codebase issues. However, to encourage wider adoption of program repair in practice, it is necessary to address the usability concerns related to generating irrelevant or out-of-context patches. When software engineers are presented with patches they deem uninteresting or unhelpful, they are burdened with more noise in their workflows and become less likely to engage with APR tools in future. optimally time, target, and present auto-generated patches to software engineers. To achieve this, we designed, developed, and deployed a new tool dubbed B-Assist, GitHub Suggested Changes interface to seamlessly integrate automated suggestions into active pull requests (PRs), as opposed to creating new, potentially distracting PRs. relevant and delivered to engineers most familiar with the affected code. Evaluation among Bloomberg software engineers demonstrated their preference for this approach. From our user study, B-Assist efficacy is evident, with the acceptance rate of patch suggestions being as high as 74.56percent engineers also found the suggestions valuable, giving usefulness ratings of at least 4 out of 5 in 78.2percent of cases.", notes = "Not GP See also \cite{williams2023usercentric}", } @InProceedings{Williams:2011:SSBSE, author = "James R. Williams and Simon Poulding and Louis M. Rose and Richard F. Paige and Fiona A. C. Polack", title = "Identifying Desirable Game Character Behaviours through the Application of Evolutionary Algorithms to Model-Driven Engineering Metamodels", year = "2011", booktitle = "Search Based Software Engineering", editor = "Myra Cohen and Mel O'Cinneid", volume = "6956", series = "Lecture Notes in Computer Science", pages = "112--126", address = "Szeged, Hungary", month = "10-12 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, grammatical evolution", isbn13 = "978-3-642-23715-7", DOI = "doi:10.1007/978-3-642-23716-4_13", abstract = "This paper describes a novel approach to the derivation of model-driven engineering (MDE) models using metaheuristic search, and illustrates it using a specific engineering problem: that of deriving computer game characters with desirable properties. The character behaviour is defined using a human-readable domain-specific language (DSL) that is interpreted using MDE techniques. We apply the search to the underlying MDE metamodels, rather than the DSL directly, and as a result our approach is applicable to a wide range of MDE models. An implementation developed using the Eclipse Modelling Framework, the most widely-used tool set for MDE, is evaluated. The results demonstrate not only the derivation of characters with the desired properties, but also the identification of unexpected features of the behavioural description language and the game itself.", affiliation = "Department of Computer Science, University of York, UK", notes = "super awesome street fighter 4000", } @InProceedings{Williams:1996:ESPPE, author = "Kenneth P. Williams and Shirley A. Williams", title = "Genetic compilers: A new technique for automatic parallelisation", booktitle = "2nd European School of Parallel Programming Environments (ESPPE'96)", year = "1996", pages = "27--30", address = "L'Alpe d'Hoez, France", keywords = "genetic algorithms, genetic improvement?", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.49.3499", URL = "ftp://ftp.pets.rdg.ac.uk/pub/cs/papers/PEDAL/esppe96.ps.gz", size = "4 pages", abstract = "In the last three decades a number of compiler transformations for optimising sequential programs for execution on vector or parallel architectures have been implemented. Optimisations for high performance architectures maximise parallelism and memory locality with transformations based on extensive control and data dependency analysis - with particular emphasis on loop-transformations. Current optimising compilers however lack an organising framework that allows the direct calculation of the optimal sequence of loop transformations to be applied. We present a fast and efficient technique using genetic algorithms to optimise the compilation function. The technique involves translating the source program into a scaled-down, skeletal form to which loop transformations can be applied as part of a genetic search. The resulting code can then be executed to quickly evaluate the fitness of the sequence of loop-transformations applied. Eventually, the fittest sequence of loop-transformations found will then be applied to the original source program to produce the optimised executable code. We conclude by comparing our technique with existing approaches.", notes = "Says different from GP ", } @InProceedings{williams:1999:TERAP, author = "Kenneth P. Williams and Shirley A. Williams", title = "Two Evolutionary Representations for Automatic Parallelization", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1429--1436", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, artificial life, adaptive behavior and agents, SBSE", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-010.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/AA-010.ps", size = "8 pages", abstract = "In this paper we describe use of the REVOLVER system, a test-bed for experimenting with combinations of evolutionary representations and algorithms for automatic parallelization. Results show evidence of adaptation of auto-parallelisation strategy by the evolutionary algorithms (EAs) tested, thereby suggesting that EAs are more capable of finding enabling transformations than current parallelising compilers.", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99). Variable length representation and crossover. Program loop transformation heuristics order and parameters controlled by small population of evolving chromosomes. Hill climbing, Simulated Annealing, evolutionarystratigies, genetic algorithms used to search. Program Dependency Graph. Gene-Transformation (GT) Representation. Gene-Statement (GS) Representation. Livermore Fortran Kernel-18 F77. See \cite{williams98}", } @PhdThesis{williams98, author = "Kenneth Peter Williams", title = "Evolutionary Algorithms for Automatic Parallelization", school = "Department of Computer Science, University of Reading", year = "1998", address = "Whiteknights Campus, Reading, UK", month = dec # " 3", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE", URL = "http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.265665", broken = "ftp://ftp.pets.rdg.ac.uk/pub/cs/theses/PEDAL/williams98.txt", URL = "ftp://ftp.pets.reading.ac.uk/pub/cs/theses/PEDAL/williams98.ps.gz", size = "287 pages", abstract = "The need for new techniques for use in automatically parallelising compilers is emphasised by the limited effectiveness of current automatic parallelization tools. This presents a major barrier to the improved use of parallel computers. This thesis proposes that evolution provides an effective way to parallelise sequential programs. The contributions of this thesis are: description and experimentation with direct and indirect representations of an automatically parallelizing compiler which are manipulated by 6 evolutionary algorithms (EAs) across a set of 5 Fortran-77 benchmark programs. One representation (called GT) naturally gives rise to 5 genetic operators plus 1 heuristic crossover operator, VLX-3. The other (GS) treats compiler transformations as mutation operators. In this research we present the Reading Evolutionary Restructurer (Revolver) system which implements a range of EAs to automatically parallelize sequential Fortran-77 programs for a 12-node Meiko CS-1 message-passing architecture. Issues involving the application of transformations to code (called decoding) are investigated, three decoding strategies developed, and comparative results produced. Detailed descriptions of a profiler and performance estimation tool which have been implemented to analyse the message-passing code generated by the EAs are given. Static performance estimation of the code serves as the fitness function of the EAs. Detailed statistical comparisons are made between the two representations, the three decoding strategies, and the six genetic operators. Results show that EAs using the GS representation consistently produce more optimally parallelized code than that produced by EAs using the GT representation. Most important result is that the EAs were able to find a parallelization strategy that would not have been obvious to a human programmer using an interactive tool - therefore showing that EAs have the ability to find novel automatic parallelization strategies.", notes = "Fig 2.19 overview of automatic Parallelization research uk.bl.ethos.265665 ISNI: 0000 0001 3569 2105", } @InProceedings{Williams:2014:ALIFE, author = "Lance R. Williams", title = "Self-Replicating Distributed Virtual Machines", booktitle = "Proceedings of the Fourteenth International Conference of the Synthesis and Simulation of Living Systems, ALIFE 14", year = "2014", editor = "Hiroki Sayama and John Rieffel and Sebastian Risi and Rene Doursat and Hod Lipson", series = "Complex Adaptive Systems", pages = "711--718", address = "New York", month = "30 " # jul # "-2 " # aug, organisation = "International Society for Artificial Life", publisher = "MIT Press", keywords = "genetic algorithms, genetic programming", isbn13 = "9780262326216 ?", URL = "http://mitpress.mit.edu/sites/default/files/titles/content/alife14/ch114.html", DOI = "doi:10.7551/978-0-262-32621-6-ch114", size = "8 pages", abstract = "Recent work showed how an expression in a functional programming language can be compiled into a massively redundant asynchronous spatial computation called a distributed virtual machine. A DVM is comprised of bytecodes reified as actors undergoing diffusion and communicating via messages containing encapsulated virtual machine states. Significantly, it was shown that both the efficiency and the robustness of expression evaluation by DVM increase with redundancy. In the present work, spatial computations that become more efficient and robust over time are described. They accomplish this by self-replication, which increases the redundancy of the elements of which they are comprised. The first and simplest of these self-replicating DVMs copies itself by reflection; it reads itself from a contiguous range of memory. The remainder are quines. As such, they reproduce by translating and transcribing self-descriptions. The nature of the self-descriptions and of the translation and transcription processes differ in each case. The most complex self-replicating DVM described represents a fundamentally new kind of artificial organism, a machine language program reified as a spatial computation that reproduces by compiling its own source-code.", notes = "Department of Computer Science, University of New Mexico, Albuquerque, NM 87131 ALIFE 14 http://mitpress.mit.edu/books/artificial-life-14 ALIFE14NYC@gmail.com", } @InProceedings{1068096, author = "Nathan Williams and Melanie Mitchell", title = "Investigating the success of spatial coevolution", booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation", year = "2005", editor = "Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and Edwin D. {de Jong} and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler", volume = "1", ISBN = "1-59593-010-8", pages = "523--530", address = "Washington DC, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2005/docs/p523.pdf", DOI = "doi:10.1145/1068009.1068096", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "25-29 " # jun, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Coevolution, resource sharing, spatial evolution", size = "8 pages", abstract = "We investigate the results of coevolution of spatially distributed populations. In particular, we describe work in which a simple function approximation problem is used to compare different spatial evolutionary methods. Our work shows that, on this problem, spatial coevolution is dramatically more successful than any other spatial evolutionary scheme we tested. Our results support two hypotheses about the source of spatial coevolution's superior performance: (1) spatial coevolution allows population diversity to persist over many generations; and (2) spatial coevolution produces training examples (parasites) that specifically target weaknesses in models (hosts). The precise mechanisms by which the combination of spatial embedding and coevolution produces these results are still not well understood.", notes = "GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM Order Number 910052", } @Article{Willihnganz:1999:s2s, author = "Alexis Willihnganz", title = "Software that writes software", journal = "salon.com", year = "1999", note = "www article", keywords = "genetic algorithms, genetic programming", URL = "https://www.salon.com/1999/08/10/genetic_programming/", broken = "http://www.salon.com/tech/feature/1999/08/10/genetic_programming/", size = "3 pages", abstract = "non technical popularist overview of GP", } @InProceedings{Willis:2008:gecco, author = "Amy Willis and Suneer Patel and Christopher D. Clack", title = "{GP} age-layer and crossover effects in bid-offer spread prediction", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "1665--1672", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1665.pdf", DOI = "doi:10.1145/1389095.1389407", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, age layers, ALPS, crossover, finance, options, spreads, Real-World application", size = "8 pages", abstract = "The bid-offer spread on equity options is a key source of profits for market makers, and a key cost for those trading in the options. Spreads are influenced by dynamic market factors, but is there also a predictable element and can Genetic Programming be used for such prediction? We investigate a standard GP approach and two optimisations age-layering and a novel crossover operator. If both are beneficial as independent optimisations, will they be mutually beneficial when applied simultaneously? Our experiments show a degree of success in predicting spreads, we demonstrate significant benefits for each optimisation technique used individually, and we show that when both are used together significant detrimental over-fitting can occur.", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389407}", } @InProceedings{willis:1997:GPsurvey, author = "Mark Willis and Hugo Hiden and Peter Marenbach and Ben McKay and Gary A. Montague", title = "Genetic Programming: An Introduction and Survey of Applications", booktitle = "Second International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, GALESIA", year = "1997", editor = "Ali Zalzala", pages = "314--319", address = "University of Strathclyde, Glasgow, UK", publisher_address = "Savoy Place, London WC2R 0BL, UK", month = "1-4 " # sep, publisher = "Institution of Electrical Engineers", keywords = "genetic algorithms, genetic programming, survey", ISBN = "0-85296-693-8", URL = "http://www.staff.ncl.ac.uk/d.p.searson/docs/galesia97surveyofGP.pdf", DOI = "doi:10.1049/cp:19971199", size = "8 pages", abstract = "The aim of this paper is to provide an introduction to the rapidly developing field of genetic programming (GP). Particular emphasis is placed on the application of GP to engineering problem solving. First, the basic methodology is introduced. This is followed by a review of applications in the areas of systems modelling, control, optimisation and scheduling, design and signal processing. The paper concludes by suggesting potential avenues of research.", notes = "GALESIA'97 ", } @Article{willis:1997:smGP, author = "Mark Willis and Hugo Hiden and Mark Hinchliffe and Ben McKay and Geoffrey W. Barton", title = "Systems Modelling Using Genetic Programming", journal = "Computers in Chemical Engineering", year = "1997", volume = "21", pages = "S1161--S1166", note = "Supplemental", keywords = "genetic algorithms, genetic programming", URL = "http://www.sciencedirect.com/science/article/B6TFT-48B0PBD-6X/2/4f9adb20577e51ae4eb7446eca52b1c2", DOI = "doi:10.1016/S0098-1354(97)87659-4", size = "5 pages", abstract = "In this contribution, a Genetic Programming (GP) algorithm is used to develop empirical models of chemical process systems. GP performs symbolic regression, determining both the structure and the complexity of a model. Initially, steady-state model development using a GP algorithm is considered, next the methodology is extended to the development of dynamic input-output models. The usefulness of the technique is demonstrated by the development of inferential estimation models for two typical processes: a vacuum distillation column and a twin screw cooking extruder.", notes = "GP empirical model of vacuum distillation column and a twin screw extruder for processing corn flour. Comparison of artifical neural network and GP", } @InProceedings{willis:1997:ieaGP, author = "M. J. Willis and H. G. Hiden and G. A. Montague", title = "Developing Inferential Estimation Algorithms Using Genetic Programming", booktitle = "IFAC/ADCHEM International Symposium on Advanced Control of Chemical Processes", year = "1997", editor = "Sirish L. Shah and Yaman Arkun", pages = "219--224", address = "Banaff, Canada", month = jun # " 9-11", keywords = "genetic algorithms, genetic programming", abstract = "'For the industrial case study,...GP compared with finite impulse response model and feedforward artificial neural network....GP produces models with a significantly lower root mean square error'", notes = "model of plasticating extruder. 'multiple gene' model structure. Fitness proportionare selection. mutation IFAC ADCHEM '97 http://www.ualberta.ca/dept/chemeng/Adchem/techprog.html", } @InProceedings{wilson2017suitability, author = "Alex Wilson and Roisin Loughran and Bruno M. Fazenda", title = "On the suitability of evolutionary computing to developing tools for intelligent music production", booktitle = "3rd Workshop on Intelligent Music Production (WIMP 2017)", year = "2017", editor = "Bruno Fazenda and Alex Wilson", address = "Media City UK, University of Salford", month = "15th " # sep, keywords = "genetic algorithms, genetic programming, Grammatical Evolution", URL = "http://www.semanticaudio.co.uk/events/wimp2017/#wilson2", URL = "http://www.semanticaudio.co.uk/wp-content/uploads/2017/09/WIMP2017_WilsonEtAl.pdf", URL = "http://usir.salford.ac.uk/44276/", size = "4 pages", abstract = "Intelligent music production tools aim to assist the user by automating music production tasks. Many previous systems sought to create the best possible mix based on technical parameters but rarely has subjectivity been directly incorporated. This paper proposes that a new generation of tools can be designed based on evolutionary computation methods, which are particularly suited to dealing with the non-linearities and complex solution spaces introduced by perceptual evaluation. These techniques are well-suited to studio applications, in contrast to many previous systems which prioritized the live environment. Furthermore, there is potential to address accessibility issues in existing systems which rely greatly on visual feedback. A survey of previous literature is provided before the current state-of-the-art is described and a number of suggestions for future directions in the field are made.", notes = "http://www.semanticaudio.co.uk/events/wimp2017/", } @InProceedings{2931671, author = "Dennis Wilson and Sylvain Cussat-Blanc and Herve Luga", title = "The Evolution of Artificial Neurogenesis", booktitle = "GECCO '16 Companion: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion", year = "2016", isbn13 = "978-1-4503-4323-7", pages = "1047--1048", address = "Denver, Colorado, USA", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4503-4323-7", DOI = "doi:10.1145/2908961.2931671", abstract = "Evolutionary development as a strategy for the design of artificial neural networks is an enticing idea, with possible inspiration from both biology and existing indirect representations. A growing neural network can not only optimize towards a specific goal, but can also exhibit plasticity and regeneration. Furthermore, a generative system trained in the optimization of the resultant neural network in a reinforcement learning environment has the capability of on-line learning after evolution in any reward-driven environment. In this abstract, we outline the motivation for and design of a generative system for artificial neural network design.", } @InProceedings{Wilson:2018:GECCO, author = "Dennis G. Wilson and Sylvain Cussat-Blanc and Herve Luga and Julian F Miller", title = "Evolving simple programs for playing {Atari} games", booktitle = "GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference", year = "2018", editor = "Hernan Aguirre and Keiki Takadama and Hisashi Handa and Arnaud Liefooghe and Tomohiro Yoshikawa and Andrew M. Sutton and Satoshi Ono and Francisco Chicano and Shinichi Shirakawa and Zdenek Vasicek and Roderich Gross and Andries Engelbrecht and Emma Hart and Sebastian Risi and Ekart Aniko and Julian Togelius and Sebastien Verel and Christian Blum and Will Browne and Yusuke Nojima and Tea Tusar and Qingfu Zhang and Nikolaus Hansen and Jose Antonio Lozano and Dirk Thierens and Tian-Li Yu and Juergen Branke and Yaochu Jin and Sara Silva and Hitoshi Iba and Anna I Esparcia-Alcazar and Thomas Bartz-Beielstein and Federica Sarro and Giuliano Antoniol and Anne Auger and Per Kristian Lehre", pages = "229--236", address = "Kyoto, Japan", publisher = "ACM", publisher_address = "New York, NY, USA", month = "15-19 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Image analysis, Artificial intelligence", isbn13 = "978-1-4503-5618-3", URL = "https://arxiv.org/pdf/1806.05695", DOI = "doi:10.1145/3205455.3205578", size = "8 pages", abstract = "Cartesian Genetic Programming (CGP) has previously shown capabilities in image processing tasks by evolving programs with a function set specialized for computer vision. A similar approach can be applied to Atari playing. Programs are evolved using mixed type CGP with a function set suited for matrix operations, including image processing, but allowing for controller behaviour to emerge. While the programs are relatively small, many controllers are competitive with state of the art methods for the Atari benchmark set and require less training time. By evaluating the programs of the best evolved individuals, simple but effective strategies can be found.", notes = "Also known as \cite{3205578} Also known as \cite{2018arXiv180605695W} (July 2018 this entry Wilson:2018:GECCO replaces 2018arXiv180605695W) ie see also arXiv:1806.05695 arXiv:1806.05695 cited by \cite{2018:MITtechreview} 1st author Dennis George Wilson GECCO-2018 A Recombination of the 27th International Conference on Genetic Algorithms (ICGA-2018) and the 23rd Annual Genetic Programming Conference (GP-2018)", } @Misc{Wilson:2018:arxiv, author = "Dennis George Wilson and Julian F. Miller and Sylvain Cussat-Blanc and Herve Luga", title = "Positional Cartesian Genetic Programming", year = "2018", month = "9 " # oct, keywords = "genetic algorithms, genetic programming, cartesian genetic programming", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/corr/corr1810.html#abs-1810-04119", URL = "http://arxiv.org/abs/1810.04119", size = "13 pages", abstract = "Cartesian Genetic Programming (CGP) has many modifications across a variety of implementations, such as recursive connections and node weights. Alternative genetic operators have also been proposed for CGP, but have not been fully studied. In this work, we present a new form of genetic programming based on a floating point representation. In this new form of CGP, called Positional CGP, node positions are evolved. This allows for the evaluation of many different genetic operators while allowing for previous CGP improvements like recurrency. Using nine benchmark problems from three different classes, we evaluate the optimal parameters for CGP and PCGP, including novel genetic operators.", } @PhdThesis{wilson:tel-02930188, author = "Dennis G. Wilson", title = "Evolving principes of artificial neural design", titletranslation = "Evolution des principes de la conception des reseaux de neurones artificiels", school = "Universite Paul Sabatier - Toulouse III", year = "2019", month = Mar, address = "France", month = feb # " 28", keywords = "genetic algorithms, genetic programming, positional cartesian genetic programming, ANN, neural networks, deep learning, evolutionary algorithms, machine learning, artificial intelligence, reseaux de neurones, apprentissage profond, algorithmes evolutionnaires, programmation genetique, apprentissage automatique, intelligence artificielle", number = "2019TOU30075", hal_id = "tel-02930188", hal_version = "v1", ISSN = "02930188", annote = "Real Expression Artificial Life (IRIT-REVA) ; Institut de recherche en informatique de Toulouse (IRIT) ; Universite Toulouse 1 Capitole (UT1) ; Universite Federale Toulouse Midi-Pyrenees-Universite Federale Toulouse Midi-Pyrenees-Universite Toulouse - Jean Jaures (UT2J)-Universite Toulouse III - Paul Sabatier (UT3) ; Universite Federale Toulouse Midi-Pyrenees-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP) ; Universite Federale Toulouse Midi-Pyrenees-Universite Toulouse 1 Capitole (UT1) ; Universite Federale Toulouse Midi-Pyrenees-Universite Federale Toulouse Midi-Pyrenees-Universite Toulouse - Jean Jaures (UT2J)-Universite Toulouse III - Paul Sabatier (UT3) ; Universite Federale Toulouse Midi-Pyrenees-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP) ; Universite Federale Toulouse Midi-Pyrenees; Universite Paul Sabatier - Toulouse III", bibsource = "OAI-PMH server at api.archives-ouvertes.fr", contributor = "Real Expression Artificial Life and Herve Luga and Sylvain Cussat-Blanc", identifier = "NNT: 2019TOU30075; tel-02930188", language = "en", oai = "oai:HAL:tel-02930188v1", rights = "info:eu-repo/semantics/OpenAccess", URL = "https://tel.archives-ouvertes.fr/tel-02930188", URL = "https://tel.archives-ouvertes.fr/tel-02930188/document", URL = "https://tel.archives-ouvertes.fr/tel-02930188/file/2019TOU30075b.pdf", size = "176 pages", abstract = "The biological brain is an ensemble of individual components which have evolved over millions of years. Neurons and other cells interact in a complex network from which intelligence emerges. Many of the neural designs found in the biological brain have been used in computational models to power artificial intelligence, with modern deep neural networks spurring a revolution in computer vision, machine translation, natural language processing, and many more domains. However, artificial neural networks are based on only a small subset of biological functionality of the brain, and often focus on global, homogeneous changes to a system that is complex and locally heterogeneous. In this work, we examine the biological brain, from single neurons to networks capable of learning. We examine individually the neural cell, the formation of connections between cells, and how a network learns over time. For each component, we use artificial evolution to find the principles of neural design that are optimised for artificial neural networks. We then propose a functional model of the brain which can be used to further study select components of the brain, with all functions designed for automatic optimisation such as evolution. Our goal, ultimately, is to improve the performance of artificial neural networks through inspiration from modern neuroscience. However, through evaluating the biological brain in the context of an artificial agent, we hope to also provide models of the brain which can serve biologists.", resume = "; Le cerveau biologique est compose d'un ensemble d'elements qui evoluent depuis des millions d'annees. Les neurones et autres cellules forment un reseau complexe d'interactions duquel emerge l'intelligence. Bon nombre de concepts neuronaux provenant de l{'}etude du cerveau biologique ont ete utilises dans des modeles informatiques pour developper les algorithmes d{'}intelligence artificielle. C'est particulierement le cas des reseaux neuronaux profonds modernes qui revolutionnent actuellement de nombreux domaines de recherche en informatique tel que la vision par ordinateur, la traduction automatique, le traitement du langage naturel et bien d'autres. Cependant, les reseaux de neurones artificiels ne sont bases que sur un petit sous-ensemble de fonctionnalites biologiques du cerveau. Ils se concentrent souvent sur les fonctions globales, homogenes et a un systeme complexe et localement heterogene. Dans cette these, nous avons d'examiner le cerveau biologique, des neurones simples aux reseaux capables d'apprendre. Nous avons examine individuellement la cellule neuronale, la formation des connexions entre les cellules et comment un reseau apprend au fil du temps. Pour chaque composant, nous avons utilise l'evolution artificielle pour trouver les principes de conception neuronale qui nous avons optimises pour les reseaux neuronaux artificiels. Nous proposons aussi un modele fonctionnel du cerveau qui peut etre utilise pour etudier plus en profondeur certains composants du cerveau, incluant toutes les fonctions concues pour l'optimisation automatique telles que l'evolution. Notre objectif est d'ameliorer la performance des reseaux de neurones artificiels par les moyens inspires des neurosciences modernes. Cependant, en evaluant les effets biologiques dans le contexte d'un agent virtuel, nous esperons egalement fournir des modeles de cerveau utiles aux biologistes.", notes = "In english", } @Article{Wilson:2022:sigevolution, author = "Dennis G. Wilson", title = "Evolving Programs to Build Artificial Neural Networks", journal = "SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation", year = "2022", volume = "15", number = "1", month = mar, keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, ANN, IMPROBED", ISSN = "1931-8499", URL = "https://evolution.sigevo.org/issues/HTML/sigevolution-15-1/home.html#h.shl70v9m1d", DOI = "doi:10.1145/3532942.3532944", size = "4 pages", notes = "'The soma and dendrite programs are represented using CGP'. 'Dendrites will snap to the nearest soma'. 'perform efficiently over multiple tasks'", } @InProceedings{Wilson:2024:GGP, author = "Camilo {De La Torre} and Sylvain Cussat-Blanc and Dennis Wilson and Yuri Lavinas", title = "On Search Trajectory Networks for Graph Genetic Programming", booktitle = "2nd GECCO workshop on Graph-based Genetic Programming", year = "2024", editor = "Dennis G. Wilson and Roman Kalkreuth and Eric Medvet and Giorgia Nadizar and Giovanni Squillero and Alberto Tonda and Yuri Lavinas", pages = "1681--1685", address = "Melbourne", series = "GECCO '24", month = "14 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, evolutionary computation, symbolic regression", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3664169", size = "5 pages", abstract = "Cartesian Genetic Programming (CGP) allows for the optimization of interpretable function representations. However, comprehending the vast and combinatorially complex search space inherent to CGP remains challenging, particularly because multiple genotypes may correspond to identical functions. This paper studies the application of Search Trajectory Networks (STNs) to understand the search dynamics of CGP, specifically for symbolic regression tasks. Using STNs, we analyze the behavior of evolutionary search processes and uncover distinct phenomena, such as the presence of {"}portal{"} minima---critical junctures that facilitate sudden, beneficial shifts in the search trajectory, akin to findings in linear genetic programming. Our findings illustrate that while genetic interpretations are complex and often ambiguous, a functional analysis using STNs offers clear and actionable insights into the CGP search.", notes = "Also known as \cite{de-la-torre:2024:GECCOcomp2} GECCO-2024 A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @PhdThesis{Wilson:thesis, author = "Dominic Wilson", title = "Grammatical Evolution based Data Mining for Network Intrusion Detection", school = "Electrical Engineering and Computer Science, University of Toledo", year = "2008", address = "Toledo, OH, USA", month = "7 " # apr, keywords = "genetic algorithms, genetic programming, grammatical evolution", URL = "http://search.proquest.com/docview/304437540", size = "222 pages", abstract = "Grammatical Evolution (GE) is an Evolutionary Computing technique which can generate programs or codes in various languages based on the choice of a grammar. The evolutionary dynamics of GE is complicated and not well understood. The current body of knowledge on GE is largely based on empirical performance studies on some applications. There is little theoretical foundation or detailed analysis of evolutionary dynamics for GE in the literature. The limited knowledge on its mechanism is a limiting factor for applying GE to real world problems. An important real world application of data mining is the automated generation of knowledge from network intrusion data. Network intrusion detection systems are becoming a standard security feature in network infrastructures. Unfortunately current systems are not very good at detecting new types of intrusion without an associated high rate of false alarms. A goal of this research is to investigate and evaluate the real world application of data mining using GE, by assessing mechanisms for building effective and efficient intrusion detection systems based on GE. The methodology used involves fundamental theoretical analysis of GE, detailed analysis of its evolutionary dynamics and experimentation of GE concepts in mining datasets. The results include contributions to the body of scientific knowledge in Evolutionary Computing, GE and Data Mining.", notes = "'As of July 2014 ProQuest is no longer offering the Udini service' Supervisor Dr. Devinder Kaur", } @Article{Wilson:2009:ieeeTEC, author = "Dominic Wilson and Devinder Kaur", title = "Search, Neutral Evolution, and Mapping in Evolutionary Computing: A Case Study of Grammatical Evolution", journal = "IEEE Transactions on Evolutionary Computation", year = "2009", month = jun, volume = "13", number = "3", pages = "566--590", keywords = "genetic algorithms, genetic programming, grammatical evolution, Cartesian genetic programming, biology computing, data visualisation, evolutionary computation, genomics, grammars, OneMax, deceptive trap, evolutionary computing, genotype visualization, gray transcription, hierarchical if-and-only-if, needle-in-haystack, neutral evolution literature, parity and majority coding, phenotype maps, population diversity, random mutations", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2008.2009063", abstract = "We present a new perspective of search in evolutionary computing (EC) by using a novel model for the analysis and visualisation of genotype to phenotype maps. The model groups genes into quotient sets and shows their adjacencies. A unique quality of the quotient model is that it details geometric qualities of maps that are not otherwise easy to observe. The model shows how random mutations on genes make non-random phenotype preferences, based on the structure of a map. The interaction between such mutation-based preferences with fitness preferences is important for explaining population movements on neutral landscapes. We show the widespread applicability of our approach by applying it to different representations, encodings, and problems including grammatical evolution (GE), Cartesian genetic programming, parity and majority coding, OneMax, needle-in-haystack, deceptive trap and hierarchical if-and-only-if. We also use the approach to address conflicting results in the neutral evolution literature and to analyze concepts relevant to neutral evolution including robustness, evolvability, tunneling, and the relation between genetic form and function. We use the model to develop theoretical results on how mapping and neutral evolution affect search in GE. We study the two phases of mapping in GE, these being transcription (i.e., unique identification of genes with integers) and translation (i.e., many-to-one mapping of genotypes to phenotypes). It is shown that translation and transcription schemes belong to equivalence classes, and therefore the properties we derive for specific schemes are applicable to classes of schemes. We present a new perspective on population diversity. We specify conditions under which increasing degeneracy (by increasing codon size) or rearranging the rules of a grammar do not affect performance. It is shown that there is a barrier to nontrivial neutral evolution with the use of the natural transcription with modulo translation combination; a- necessary but not sufficient condition for such evolution is that at least three bits should change on mutation within a single codon. This barrier can be avoided by using gray transcription. We empirically validate some findings.", notes = "also known as \cite{5089888}", } @InProceedings{wilson:2003:ccpb, author = "G. C. Wilson and M. I. Heywood", title = "Crossover Context in Page-based Linear Genetic Programming", booktitle = "IEEE CCECE 2002: IEEE Canadian Conference on Electrical and Computer Engineering", year = "2002", editor = "W. Kinsner and A. Seback and K. Ferens", pages = "809--814", volume = "2", month = "12-15 " # may, organisation = "IEEE Canada", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Strategy Learning, learning (artificial intelligence), search problems, San Mateo trail, artificial ants, code sequences, crossover operator, effective search strategies, fitness change, instructions, simple register based memories, strategy learning", ISBN = "0-7803-7515-7", ISSN = "0840-7789", URL = "http://flame.cs.dal.ca/~gwilson/docs/papers/ccece_2002.pdf", DOI = "doi:10.1109/CCECE.2002.1013046", size = "6 pages", abstract = "This work explores strategy learning through genetic programming in artificial ants that navigate the San Mateo trail. We investigate several properties of linearly structured (as opposed to typical tree based) GP including: the significance of simple register based memories, the significance of constraints applied to the crossover operator, and how active the ant are. We also provide a basis for investigating more thoroughly the relation between specific code sequences and fitness by dividing the genome into pages of instructions and introducing an analysis of fitness change and exploration of the trail done by particular parts of a genome. By doing so we are able to present results on how best to find the instructions in an individual's program that contribute positively to the accumulation of effective search strategies.", notes = "best student paper award", } @Article{Wilson:2002:CJECE, author = "Garnett Wilson and Malcolm Heywood", title = "Crossover context in page-based linear genetic programming", journal = "Canadian Journal of Electrical and Computer Engineering", year = "2002", volume = "27", number = "3", pages = "113--116", organisation = "IEEE Canada", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, strategy learning", ISSN = "0840-8688", broken = "http://ieee.ca/journal/backissues_tofc_27_3.html", abstract = "This work explores strategy learning through genetic programming in artificial 'ants' that navigate the San Mateo trail. We investigate several properties of linearly structured (as opposed to typical tree-based) GP including: the significance of simple register based memories, the significance of constraints applied to the crossover operator, and how 'active' the ant are. We also provide a basis for investigating more thoroughly the relation between specific code sequences and fitness by dividing the genome into pages of instructions and introducing an analysis of fitness change and exploration of the trail done by particular parts of a genome. By doing so we are able to present results on how best to find the instructions in an individual's program that contribute positively to the accumulation of effective search strategies.", notes = "Author says same content as \cite{wilson:2003:ccpb}", } @Article{wilson:2004:GPEM, author = "G. C. Wilson and A. McIntyre and M. I. Heywood", title = "Resource Review: Three Open Source Systems for Evolving Programs--{Lilgp}, ECJ and Grammatical Evolution", journal = "Genetic Programming and Evolvable Machines", year = "2004", volume = "5", number = "1", pages = "103--105", month = mar, keywords = "genetic algorithms, genetic programming, grammatical evolution", ISSN = "1389-2576", URL = "https://rdcu.be/dR83k", DOI = "doi:10.1023/B:GENP.0000017053.10351.dc", notes = "Article ID: 5264736", } @InProceedings{wilson:2004:lbp, author = "Garnett C. Wilson and Malcolm I. Heywood", title = "Search Operator Bias in Linearly Structured Genetic Programming", booktitle = "Late Breaking Papers at the 2004 Genetic and Evolutionary Computation Conference", year = "2004", editor = "Maarten Keijzer", address = "Seattle, Washington, USA", month = "26 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://gpbib.cs.ucl.ac.uk/gecco2004/LBP003.pdf", abstract = "GA solutions to the job-shop scheduling problem demonstrate that significant amounts of code context exist. Such observations have led to the introduction of biased search operators. In this work, we recognise that similar conditions exist in linearly structured GP (L-GP). An empirical study is made when biased search operators are applied to the San Mateo Trail (strategy), Two Box (regression), and Liver Disease (classification) benchmark problems. A preference is observed for biased mutation alone in the case of the regression problem, whereas the strategy and classification problems appear to prefer the combination of both biased mutation and crossover.", notes = "Part of \cite{keijzer:2004:GECCO:lbp}", } @InProceedings{eurogp:WilsonH05, author = "Garnett Carl Wilson and Malcolm I. Heywood", editor = "Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. {van Hemert} and Marco Tomassini", title = "Context-Based Repeated Sequences in Linear Genetic Programming", booktitle = "Proceedings of the 8th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3447", year = "2005", address = "Lausanne, Switzerland", month = "30 " # mar # " - 1 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-25436-6", pages = "240--249", URL = "http://flame.cs.dal.ca/~gwilson/docs/papers/EuroGP_2005.pdf", DOI = "doi:10.1007/978-3-540-31989-4_21", DOI = "doi:10.1007/b107383", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "Repeating code sequences are found in both artificial and natural genomes as an emergent phenomenon. These patterns are of interest in researching both how evolution reuses code segments to create superior individuals and whether building blocks are used in the formation of repeated sequences. In this paper we describe a GP representation using a special type of crossover that is more conducive to the formation of repeated sequences than traditional GP. We then establish that the repeated sequence phenomenon in the implementation displays traits of building blocks by establishing associated regularity of genotype and phenotype elements. As additional merits, the pattern-rich implementation boasts succinct solutions with less bloat and accomplishes the code regularity without a loss in performance with respect to fitness.", notes = "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005", } @InProceedings{1068352, author = "Garnett Wilson and Malcolm Heywood", title = "Use of a genetic algorithm in brill's transformation-based part-of-speech tagger", booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation", year = "2005", editor = "Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and Edwin D. {de Jong} and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler", volume = "2", ISBN = "1-59593-010-8", pages = "2067--2073", address = "Washington DC, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2005/docs/p2067.pdf", DOI = "doi:10.1145/1068009.1068352", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "25-29 " # jun, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Real World Applications, Brill tagger, experimentation, natural language processing, languages", abstract = "The tagging problem in natural language processing is to find a way to label every word in a text as a particular part of speech, e.g., proper noun. An effective way of solving this problem with high accuracy is the transformation-based or {"}Brill{"} tagger. In Brill's system, a number of transformation templates are specified a priori that are instantiated and ranked during a greedy search-based algorithm. This paper describes a variant of Brill's implementation that instead uses a genetic algorithm to generate the instantiated rules and provide an adaptive ranking. Based on tagging accuracy, the new system provides a better hybrid evolutionary computation solution to the part-of-speech (POS) problem than the previous attempt. Although not able to make up for the use of a priori knowledge used by Brill, the method appears to point the way for an improved solution to the tagging problem.", notes = "GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM Order Number 910052", } @InProceedings{Wilson:P(A:cec2006, author = "Garnett Carl Wilson and Malcolm Iain Heywood", title = "Probabilistic (Genotype) Adaptive Mapping Combinations for Developmental Genetic Programming", booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary Computation", year = "2006", editor = "Gary G. Yen and Simon M. Lucas and Gary Fogel and Graham Kendall and Ralf Salomon and Byoung-Tak Zhang and Carlos A. Coello Coello and Thomas Philip Runarsson", pages = "2498--2505", address = "Vancouver, BC, Canada", month = "16-21 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9487-9", URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=11108", DOI = "doi:10.1109/CEC.2006.1688619", abstract = "In development genetic programming (DGP) approaches where the search space is divided into genotypes and phenotypes, a mapping (or genetic code) is needed to connect the two spaces. This model has subsequently been extended so that mappings evolve, and recently an implementation was proposed that co-evolves a genotype population and a population of adaptive mappings. Here, the authors identify and investigate performance obstacles for this recent implementation. They then introduce a new probabilistic adaptive mapping DGP that avoids those performance problems and explores a greater search space of genotype-mapping combinations without significant computational expense. The algorithm is shown to be more robust and to outperform the comparison adaptive mapping algorithm on challenging settings of the chosen test benchmark.", notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D", } @InProceedings{Wilson:PPSN:2006, author = "Garnett Wilson and Malcolm Heywood", title = "Probabilistic Adaptive Mapping Developmental Genetic Programming (PAM DGP): A New Developmental Approach", booktitle = "Parallel Problem Solving from Nature - PPSN IX", year = "2006", editor = "Thomas Philip Runarsson and Hans-Georg Beyer and Edmund Burke and Juan J. Merelo-Guervos and L. Darrell Whitley and Xin Yao", volume = "4193", pages = "751--760", series = "LNCS", address = "Reykjavik, Iceland", publisher_address = "Berlin", month = "9-13 " # sep, publisher = "Springer-Verlag", ISBN = "3-540-38990-3", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1007/11844297_76", size = "10 pages", abstract = "Developmental Genetic Programming (DGP) algorithms have been introduced where the search space for a problem is divided into genotypes and corresponding phenotypes that are connected by a mapping (or genetic code). Current implementations of this concept involve evolution of the mappings in addition to the traditional evolution of genotypes. We introduce the latest version of Probabilistic Adaptive Mapping DGP (PAM DGP), a robust and highly customisable algorithm that overcomes performance problems identified for the latest competing adaptive mapping algorithm. PAM DGP is then shown to outperform the competing algorithm on two non-trivial regression benchmarks.", notes = "PPSN-IX, MAX and two box problems", } @PhdThesis{G_C_Wilson:thesis, author = "Garnett Carl Wilson", title = "Probabilistic Adaptive Mapping Developmental Genetic Programming", school = "Dalhousie University", year = "2007", address = "Halifax, Nova Scotia, Canada", month = mar, keywords = "genetic algorithms, genetic programming, developmental genetic programming, genetic code, cooperative coevolution, genotype-phenotype mapping, redundant representation, neutrality, recursion", URL = "https://web.cs.dal.ca/~mheywood/Thesis/PhD.html", URL = "http://hdl.handle.net/10222/54875", URL = "https://dalspace.library.dal.ca/bitstream/handle/10222/54875/NR27171.PDF", size = "247 pages", abstract = "Developmental Genetic Programming (DGP) algorithms explicitly enable the search space for a problem to be divided into genotypes and corresponding phenotypes. The two search spaces are often connected with a genotype-phenotype mapping (GPM) intended to model the biological genetic code, where current implementations of this concept involve evolution of the mappings along with evolution of the genotype solutions. This work presents the Probabilistic Adaptive Mapping DGP (PAM DGP) algorithm, a new developmental implementation that provides research contributions in the areas of GPMs and coevolution. The algorithm component of PAM DGP is demonstrated to overcome coevolutionary performance problems as identified and empirically benchmarked against the latest competing Adaptive Mapping algorithm with both algorithms using the same (non-redundant) mapping encoding process. Having established that PAM DGP provides a superior algorithmic framework given equivalent mapping and genotype structures for the individuals, a new adaptive redundant mapping is incorporated into PAM DGP for further performance enhancement and closer adherence to developmental modeling of the biological code. PAM DGP with two mapping types is then compared to the competing Adaptive Mapping algorithm and Traditional GP with respect to three regression benchmarks. PAM DGP using redundant mappings is then applied to two medical classification domains, where PAM DGP with redundant encodings is found to provide better classifier performance than the alternative algorithms. PAM DGP with redundant mappings is also given the task of learning three sequences of increasing recursion order given a function set consisting of general (not implicitly recursive) machine-language instructions; where it is found to more efficiently learn second and third order recursive Fibonacci functions than the related developmental systems and Traditional GP. PAM DGP using redundant encoding is also demonstrated to produce the semantically highest quality solutions for all three recursive functions considered (Factorial, second and third order Fibonacci). PAM DGP is shown for regression, medical classification, and recursive problems to have produced its solutions by evolving redundant mappings to emphasise appropriate members within relevant subsets of the problem's original function set.", notes = "Supervisor: Malcolm I. Heywood", } @Article{Wilson:2007:GPEM, author = "Garnett Wilson and Malcolm Heywood", title = "Introducing probabilistic adaptive mapping developmental genetic programming with redundant mappings", journal = "Genetic Programming and Evolvable Machines", year = "2007", volume = "8", number = "2", pages = "187--220", month = jun, note = "Special issue on developmental systems", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-007-9027-9", size = "34 pages", abstract = "Developmental Genetic Programming (DGP) algorithms have explicitly required the search space for a problem to be divided into genotypes and corresponding phenotypes. The two search spaces are often connected with a genotype-phenotype mapping (GPM) intended to model the biological genetic code, where current implementations of this concept involve evolution of the mappings along with evolution of the genotype solutions. This work presents the Probabilistic Adaptive Mapping DGP (PAM DGP), a new developmental implementation that involves research contributions in the areas of GPMs and coevolution. The algorithm component of PAM DGP is demonstrated to overcome coevolutionary performance problems that are identified and empirically benchmarked against the latest competing algorithm that adapts similar GPMs. An adaptive redundant mapping encoding is then incorporated into PAM DGP for further performance enhancement. PAM DGP with two mapping types are compared to the competing Adaptive Mapping algorithm and Traditional GP in two medical classification domains, where PAM DGP with redundant encodings is found to provide superior fitness performance over the other algorithms through its ability to explicitly decrease the size of the function set during evolution.", } @InProceedings{1277165, author = "Garnett Wilson and Malcolm Heywood", title = "Learning recursive programs with cooperative coevolution of genetic code mapping and genotype", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "1", isbn13 = "978-1-59593-697-4", pages = "1053--1061", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1053.pdf", DOI = "doi:10.1145/1276958.1277165", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Generative and Developmental Systems, cooperative coevolution, developmental genetic programming, genetic code, genotype-phenotype mapping, recursion, redundant representation", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @InProceedings{conf/eurogp/WilsonB08, title = "A Comparison of Cartesian Genetic Programming and Linear Genetic Programming", author = "Garnett Carl Wilson and Wolfgang Banzhaf", bibdate = "2008-04-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/eurogp/eurogp2008.html#WilsonB08", booktitle = "Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008", address = "Naples", month = "26-28 " # mar, publisher = "Springer", year = "2008", volume = "4971", editor = "Michael O'Neill and Leonardo Vanneschi and Steven Gustafson and Anna Isabel {Esparcia Alcazar} and Ivanoe {De Falco} and Antonio {Della Cioppa} and Ernesto Tarantino", isbn13 = "978-3-540-78670-2", pages = "182--193", series = "Lecture Notes in Computer Science", DOI = "doi:10.1007/978-3-540-78671-9_16", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", notes = "Part of \cite{conf/eurogp/2008} EuroGP'2008 held in conjunction with EvoCOP2008, EvoBIO2008 and EvoWorkshops2008", } @InProceedings{Wilson:2008:cec, author = "Garnett Wilson and Wolfgang Banzhaf", title = "Linear Genetic Programming GPGPU on {Microsoft's} {Xbox} 360", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "378--385", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1823-7", file = "EC0110.pdf", DOI = "doi:10.1109/CEC.2008.4630825", abstract = "We describe how to harness the graphics processing abilities of a consumer video game console (Xbox 360) for general programming on graphics processing unit (GPGPU) purposes. In particular, we implement a linear GP (LGP) system to solve classification and regression problems. We conduct inter- and intra-platform benchmarking of the Xbox 360 and PC, using GPU and CPU implementations on both architectures. Platform benchmarking confirms highly integrated CPU and GPU programming flexibility of the Xbox 360, having the potential to alleviate typical GPGPU decisions of allocating particular functionalities to CPU or GPU.", keywords = "genetic algorithms, genetic programming, GPU", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @Article{Wilson:2008:sigevo, author = "Garnett Wilson and Simon Harding", title = "WCCI 2008 Special Session: Computational Intelligence on Consumer Games and Graphics Hardware (CIGPU-2008)", journal = "SIGEVOlution", year = "2008", volume = "3", number = "1", pages = "19--21", month = "Spring", keywords = "genetic algorithms, genetic programming, GPU", URL = "http://www.sigevolution.org/issues/pdf/SIGEVOlution200801.pdf", URL = "https://dl.acm.org/action/showBmPdf?doi=10.1145%2F1527055", size = "2.2 pages", notes = "Report on www.cs.ucl.ac.uk/staff/W.Langdon/cigpu/ More information at www.gpgpgpu.com", } @InProceedings{Wilson:evows09, author = "Garnett Wilson and Wolfgang Banzhaf", title = "Prediction of Interday Stock Prices using Developmental and Linear Genetic Programming", booktitle = "Applications of Evolutionary Computing, EvoWorkshops2009: {EvoCOMNET}, {EvoENVIRONMENT}, {EvoFIN}, {EvoGAMES}, {EvoHOT}, {EvoIASP}, {EvoINTERACTION}, {EvoMUSART}, {EvoNUM}, {EvoPhD}, {EvoSTOC}, {EvoTRANSLOG}", year = "2009", month = "15-17 " # apr, editor = "Mario Giacobini and Ivanoe {De Falco} and Marc Ebner", series = "LNCS", volume = "5484", publisher = "Springer Verlag", address = "Tubingen, Germany", pages = "172--181", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-01128-3", DOI = "doi:10.1007/978-3-642-01129-0_21", abstract = "A developmental co-evolutionary genetic programming approach (PAM DGP) is compared to a standard linear genetic programming (LGP) implementation for trading of stocks across market sectors. Both implementations were found to be impressively robust to market fluctuations while reacting efficiently to opportunities for profit, where PAM DGP proved slightly more reactive to market changes than LGP. PAM DGP outperformed, or was competitive with, LGP for all stocks tested. Both implementations had very impressive accuracy in choosing both profitable buy trades and sells that prevented losses, where this occurred in the context of moderately active trading for all stocks. The algorithms also appropriately maintained maximal investment in order to profit from sustained market upswings.", notes = "EvoWorkshops2009", } @InCollection{Wilson:2009:GPTP, author = "Garnett Wilson and Wolfgang Banzhaf", title = "Algorithmic Trading with Developmental and Linear Genetic Programming", booktitle = "Genetic Programming Theory and Practice {VII}", year = "2009", editor = "Rick L. Riolo and Una-May O'Reilly and Trent McConaghy", series = "Genetic and Evolutionary Computation", address = "Ann Arbor", month = "14-16 " # may, publisher = "Springer", chapter = "8", pages = "119--134", keywords = "genetic algorithms, genetic programming, Developmental Genetic Programming, Linear Genetic Programming, Computational Finance", isbn13 = "978-1-4419-1653-2", DOI = "doi:10.1007/978-1-4419-1626-6_8", abstract = "A developmental co-evolutionary genetic programming approach (PAM DGP) and a standard linear genetic programming (LGP) stock trading system are applied to a number of stocks across market sectors. Both GP techniques were found to be robust to market fluctuations and reactive to opportunities associated with stock price rise and fall, with PAMDGP generating notably greater profit in some stock trend scenarios. Both algorithms were very accurate at buying to achieve profit and selling to protect assets, while exhibiting both moderate trading activity and the ability to maximise or minimise investment as appropriate. The content of the trading rules produced by both algorithms are also examined in relation to stock price trend scenarios.", notes = "part of \cite{Riolo:2009:GPTP}", } @InProceedings{DBLP:conf/gecco/WilsonB09, author = "Garnett Carl Wilson and Wolfgang Banzhaf", title = "Soft memory for stock market analysis using linear and developmental genetic programming", booktitle = "GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation", year = "2009", editor = "Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano {van Hemert} and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique Alba", pages = "1633--1640", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1569901.1570119", abstract = "Recently, a form of memory usage was introduced for genetic programming (GP) called {"}soft memory.{"} Rather than have a new value completely overwrite the old value in a register, soft memory combines the new and old register values. This work examines the performance of a soft memory linear GP and developmental GP implementation for stock trading. Soft memory is known to more slowly adapt solutions compared to traditional GP. Thus, it was expected to perform well on stock data which typically exhibit local turbulence in combination with an overall longer term trend. While soft memory and standard memory were both found to provide similar impressive accuracy in buys that produced profit and sells that prevented losses, the softer memory settings traded more actively. The trading of the softer memory systems produced less substantial cumulative gains than traditional memory settings for the stocks tested with climbing share price trends. However, the trading activity of the softer memory settings had moderate benefits in terms of cumulative profit compared to buy-and-hold strategy for share price trends involving a drop in prices followed later by gains.", notes = "GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009). ACM Order Number 910092.", } @InProceedings{DBLP:conf/gecco/WilsonB09a, author = "Garnett Carl Wilson and Wolfgang Banzhaf", title = "Deployment of CPU and GPU-based genetic programming on heterogeneous devices", booktitle = "GECCO Workshop on Computational intelligence on consumer games and graphics hardware (CIGPU-2009)", year = "2009", editor = "Anna I. Esparcia and Ying-ping Chen and Gabriela Ochoa and Ender Ozcan and Marc Schoenauer and Anne Auger and Hans-Georg Beyer and Nikolaus Hansen and Steffen Finck and Raymond Ros and Darrell Whitley and Garnett Wilson and Simon Harding and W. B. Langdon and Man Leung Wong and Laurence D. Merkle and Frank W. Moore and Sevan G. Ficici and William Rand and Rick Riolo and Nawwaf Kharma and William R. Buckley and Julian Miller and Kenneth Stanley and Jaume Bacardit and Will Browne and Jan Drugowitsch and Nicola Beume and Mike Preuss and Stephen L. Smith and Stefano Cagnoni and Jim DeLeo and Alexandru Floares and Aaron Baughman and Steven Gustafson and Maarten Keijzer and Arthur Kordon and Clare Bates Congdon and Laurence D. Merkle and Frank W. Moore", pages = "2531--2538", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming, GPU", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1570256.1570356", abstract = "A widely available and economic means of increasing the computing power applied to a problem is to use modern graphics processing units (GPUs) for parallel processing. We present a new, optimized general methodology for deploying genetic programming (GP) to the PC, Xbox 360 video game console, and Zune portable media device. This work describes, for the first time, the implementation considerations necessary to maximize available CPU and GPU (where available) usage on the three separate hardware platforms. We demonstrate the first instance of GP using portable digital media device hardware. The work also presents, for the first time, an Xbox 360 implementation that uses the GPU for fitness evaluation. Implementations on each platform are also benchmarked on the basis of execution time for an established GP regression benchmark.", notes = "Distributed on CD-ROM at GECCO-2009. ACM Order Number 910092.", } @Article{Wilson:2010:GPEM, author = "Garnett Wilson and Wolfgang Banzhaf", title = "Deployment of parallel linear genetic programming using GPUs on PC and video game console platforms", journal = "Genetic Programming and Evolvable Machines", year = "2010", volume = "11", number = "2", pages = "147--184", month = jun, keywords = "genetic algorithms, genetic programming, Parallel processing, SIMD, Graphics processing unit, GPU, GPGPU, Xbox 360, Heterogeneous devices", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-010-9102-5", size = "38 pages", abstract = "We present a general method for deploying parallel linear genetic programming (LGP) to the PC and Xbox 360 video game console by using a publicly available common framework for the devices called XNA (for XNA's Not Acronymed). By constructing the LGP within this framework, we effectively produce an LGP 'game' for PC and XBox 360 that displays results as they evolve. We use the GPU of each device to parallelize fitness evaluation and the mutation operator of the LGP algorithm, thus providing a general LGP implementation suitable for parallel computation on heterogeneous devices. While parallel GP implementations on PCs are now common, both the implementation of GP on a video game console using GPU and the construction of a GP around a framework for heterogeneous devices are novel contributions. The objective of this work is to describe how to implement the parallel execution of LGP in order to use the underlying hardware (especially GPU) on the different platforms while still maintaining loyalty to the general methodology of the LGP algorithm built for the common framework. We discuss the implementation of texture-based data structures and the sequential and parallel algorithms built for their use on both CPU and GPU. Following the description of the general algorithm, the particular tailoring of the implementations for each hardware platform is described. Sequential (CPU) and parallel (GPU-based) algorithm performance is compared on both PC and video game platforms using the metrics of GP operations per second, actual time elapsed, speedup of parallel over sequential implementation, and percentage of execution time used by the GPU versus CPU.", notes = "This work is based on an earlier work: Deployment of CPU and GPU-based Genetic Programming on Heterogeneous Devices, in Proceedings of the 2009 Genetic and Evolutionary Computation Conference, ACM, 2009. \cite{DBLP:conf/gecco/WilsonB09a}, http://doi.acm.org/10.1145/1570256.1570356", } @InProceedings{wilson:2010:NCCF, author = "Garnett Wilson and Wolfgang Banzhaf", title = "Interday and Intraday Stock Trading Using Probabilistic Adaptive Mapping Developmental Genetic Programming and Linear Genetic Programming", booktitle = "Natural Computing in Computational Finance", year = "2010", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-642-13950-5_11", DOI = "doi:10.1007/978-3-642-13950-5_11", } @InProceedings{Wilson:2010:gecco, author = "Garnett Wilson and Wolfgang Banzhaf", title = "Interday foreign exchange trading using linear genetic programming", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "1139--1146", keywords = "genetic algorithms, genetic programming, Real world applications", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830483.1830694", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Foreign exchange (forex) market trading using evolutionary algorithms is an active and controversial area of research. We investigate the use of a linear genetic programming (LGP) system for automated forex trading of four major currency pairs. Fitness functions with varying degrees of conservatism through the incorporation of maximum drawdown are considered. The use of the fitness types in the LGP system for different currency value trends are examined in terms of performance over time, underlying trading strategies, and overall profitability. An analysis of trade profitability shows that the LGP system is very accurate at both buying to achieve profit and selling to prevent loss, with moderate levels of trading activity.", notes = "Also known as \cite{1830694} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @InProceedings{Wilson:2010:cec, author = "Garnett Wilson and Wolfgang Banzhaf", title = "Fast and effective predictability filters for stock price series using linear genetic programming", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-6910-9", abstract = "A handful of researchers who apply genetic programming (GP) to the analysis of financial markets have devised predictability pretests to determine whether the time series that is being supplied to GP contains patterns that can be predicted, but most studies apply no such pretests. By applying predictability pretests, researchers can have greater confidence that the GP system is solving a problem which is actually there and that it will be less likely to make questionable investment decisions based on non-existent patterns. Previous work in this area has applied regression to randomised versions of time series training data to create a functional model that is applied over a future window of time. This work presents two types of predictability filters with low computational overhead, namely frequency-based and information theoretic, that complement the previous function-based continuous output predictability models. Results indicate that either filter can be beneficial for particular trend types, but the information-based filter involves a greater chance of missing opportunities for profit. In contrast, the frequency-based filter always outperforms, or is competitive with, the filterless implementation.", DOI = "doi:10.1109/CEC.2010.5586297", notes = "WCCI 2010. Also known as \cite{5586297}", } @InProceedings{Wilson:2011:GECCO, author = "Garnett Wilson and Derek Leblanc and Wolfgang Banzhaf", title = "Stock trading using linear genetic programming with multiple time frames", booktitle = "GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0557-0", pages = "1667--1674", keywords = "genetic algorithms, genetic programming, Real world applications", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001576.2001801", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "A number of researchers have attempted to take successful GP trading systems and make them even better through the use of filters. We investigate the use of a linear genetic programming (LGP) system that combines GP signals provided over multiple intraday time frames to produce one trading action. Four combinations of time frames stretching further into the past are examined. Two different decision mechanisms for evaluating the overall signal given the GP signals over all time frames are also examined, one based on majority vote and another based on temporal proximity to the buying decision. Results indicated that majority vote outperformed emphasis on proximity of time frames to the current trading decision. Analyses also indicated that longer time frame combinations were more conservative and outperformed shorter combinations for both overall upward and downward price trends.", notes = "Also known as \cite{2001801} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)", } @InProceedings{Wilson:2012:CEC, title = "Using Sector Information with Linear Genetic Programming for Intraday Equity Price Trend Analysis", author = "Garnett Wilson and Derek Leblanc and Wolfgang Banzhaf", pages = "1071--1078", booktitle = "Proceedings of the 2012 IEEE Congress on Evolutionary Computation", year = "2012", editor = "Xiaodong Li", month = "10-15 " # jun, DOI = "doi:10.1109/CEC.2012.6252899", address = "Brisbane, Australia", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Computational Intelligence in Finance, Economics and Management Sciences (IEEE-CEC)", abstract = "A number of researchers who apply genetic programming (GP) to the analysis of financial data have had success in using predictability pretests to determine whether the time series under analysis by a GP contains patterns that are actually inherently predictable. However, most studies to date apply no such pretests, or pretests of any kind. Most previous work in this area has attempted to use filters to ensure inherent predictability of the data within a window of a time series, whereas other works have used multiple time frame windows under analysis by the GP to provide one overall GP recommendation. This work, for the first time, analyses the use of external information about the price trend of a stock's market sector. This information is used in a filter to bolster confidence of a GP-based alert regarding formation of a trend for the chosen stock. Our results indicate a significant improvement in trend identification for the majority of stocks analysed using intraday data.", notes = "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the EPS and the IET.", } @InProceedings{wilson:1998:gXCScs, author = "Stewart W. Wilson", title = "Generalization in the XCS Classifier System", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "665--674", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, classifiers", ISBN = "1-55860-548-7", URL = "http://citeseer.ist.psu.edu/148764.html", abstract = "This paper studies two changes to XCS, a classifier system in which fitness is based on prediction accuracy and the genetic algorithm takes place in environmental niches. The changes were aimed at increasing XCS's tendency to evolve accurate, maximally general classifiers and were tested on previously employed {"}woods{"} and multiplexer tasks. Together the changes bring XCS close to evolving populations whose high-fitness classifiers form a near-minimal, accurate, maximally general cover of the input and action product space. In addition, results on the multiplexer, a difficult categorization task, suggest that XCS's learning complexity is polynomial in the input length and thus may avoid the {"}curse of dimensionality{"}, a notorious barrier to scale-up. A comparison between XCS and genetic programming in solving the 6multiplexer suggests that XCS's learning rate is about three orders of magnitude faster in terms of the number of input instances processed.", notes = "GP-98", } @InCollection{wilson:2002:FSVPEGP, author = "Robert Scott Wilson", title = "First Steps towards Violin Performance Extraction using Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2002", year = "2002", editor = "John R. Koza", pages = "253--262", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", URL = "http://www.genetic-programming.org/sp2002/Wilson.pdf", code_url = "https://ccrma.stanford.edu/~rswilson/220c/code/", size = "10 pages", abstract = "This paper out lines the outstanding problem of performance extraction from an audio signal of violin performance. It describes some preliminary programming runs that solve components of the larger problem and suggest that genetic programming is a viable approach to solving the larger problem. Techniques for dealing with fitness cases and evaluation of time based signals using GP are presented.", notes = "part of \cite{koza:2002:gagp}", } @InProceedings{conf/iwcls/Wilson07, author = "Stewart W. Wilson", title = "Classifier Conditions Using Gene Expression Programming", booktitle = "Learning Classifier Systems", year = "2007", editor = "Jaume Bacardit and Ester Bernad{\'o}-Mansilla and Martin V. Butz and Tim Kovacs and Xavier Llor{\`a} and Keiki Takadama", volume = "4998", series = "Lecture Notes in Computer Science", pages = "206--217", publisher = "Springer", note = "Invited Paper", keywords = "genetic algorithms, genetic programming, gene expression programming, XCSF-GEP", isbn13 = "978-3-540-88137-7", DOI = "doi:10.1007/978-3-540-88138-4_12", abstract = "The classifier system XCSF was modified to use gene expression programming for the evolution and functioning of the classifier conditions. The aim was to fit environmental regularities better than is typically possible with conventional rectilinear conditions. An initial experiment approximating a nonlinear oblique environment showed excellent fit to the regularities.", notes = "10th International Workshop, IWLCS 2006 Seattle, USA, July 8, 2006 and 11th International workshop, IWLCS 2007 London, UK, July 8, 2007. Revised Selected Papers", bibdate = "2008-10-23", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/iwcls/iwlcs2007.html#Wilson07", } @Article{Wiltshire:2019:JASSS, author = "Serge Wiltshire and Asim Zia and Christopher Koliba and Gabriela Bucini and Eric Clark and Scott Merrill and Julie Smith and Susan Moegenburg", title = "Network Meta-Metrics: Using Evolutionary Computation to Identify Effective Indicators of Epidemiological Vulnerability in a Livestock Production System Model", journal = "Journal of Artificial Societies and Social Simulation", year = "2019", volume = "22", number = "2", keywords = "genetic algorithms, genetic programming, agent-based modeling, network analytics, computational epidemiology, evolutionary computation, livestock production", ISSN = "1460-7425", bibsource = "OAI-PMH server at oai.repec.org", identifier = "RePEc:jas:jasssj:2018-45-2", oai = "oai:RePEc:jas:jasssj:2018-45-2", URL = "http://jasss.soc.surrey.ac.uk/22/2/8/8.pdf", DOI = "doi:10.18564/jasss.3991", size = "27 pages", abstract = "We developed an agent-based susceptible/infective model which simulates disease incursions in the hog production chain networks of three U.S. states. Agent parameters, contact network data, and epidemiological spread patterns are output after each model run. Key network metrics are then calculated, some of which pertain to overall network structure, and others to each node's positionality within the network. We run statistical tests to evaluate the extent to which each network metric predicts epidemiological vulnerability, finding significant correlations in some cases, but no individual metric that serves as a reliable risk indicator. To investigate the complex interactions between network structure and node positionality, we use a genetic programming (GP) algorithm to search for mathematical equations describing combinations of individual metrics {\^a}{$\euro$}{"} which we call {"}meta-metrics{"} {\^a}{$\euro$}{"} that may better predict vulnerability. We find that the GP solutions {\^a}{$\euro$}{"} the best of which combine both global and node -level metrics {\^a}{$\euro$}{"} are far better indicators of disease risk than any individual metric, with meta-metrics explaining up to 91 percent of the variability in agent vulnerability across all three study areas. We suggest that this methodology could be applied to aid livestock epidemiologists in the targeting of biosecurity interventions, and also that the meta-metric approach may be useful to study a wide range of complex network phenomena.", } @InProceedings{Windisch:2009:SBST, author = "Andreas Windisch and Noura {Al Moubayed}", title = "Signal Generation for Search-Based Testing of Continuous Systems", booktitle = "2nd International Workshop on Search-Based Software Testing", year = "2009", editor = "Phil McMinn and Robert Feldt", address = "Denver, Colorado, USA", month = "1 " # apr, organisation = "EvoTest", keywords = "genetic algorithms, genetic programming, linear genetic programming, PSO, SBSE", URL = "http://iaser.tek.bth.se/feldt/conferences/sbst09/papers/windisch_sbst09.pdf", size = "10 pages", abstract = "Test case generation constitutes a critical activity in software testing that is cost-intensive, time-consuming and error-prone when done manually. Hence, an automation of this process is required. One automation approach is search-based testing for which the task of generating test data is transformed into an optimization problem which is solved using metaheuristic search techniques. However, only little work has been done so far applying search-based testing techniques to systems that depend on continuous input signals. This paper proposes two novel approaches to generating input signals from within search-based testing techniques for continuous systems. These approaches are then shown to be very effective when experimentally applied to the problem of approximating a set of realistic signals.", notes = "Fourier analysis (optimised by PSO) or GP combination of sine, spline, linear, step and impulse. Multiple chromosomes per individual. Homologous crossover, two point crossover. Reducing mutation: removes genes from chromosomes (cf also extending mutation: add genes to end of chromosome). Multi-point mutation. Reinsertion: add mutants to population. 6 training data from Mercedes Benz cars, logged by CAN bus 30secs at 1khz. sec 3.3 {"}small impulse-like steps do not carry much weight{"}. Fourier series unable to reproduce constant (wrong parameters chosen?). {"}slight advantage for the linear genetic programming approach{"}. EvoTest. http://iaser.tek.bth.se/feldt/conferences/sbst09 In conjunction with ICST 2009 IEEE International Conference on Testing, Verification and Validation", } @InProceedings{Windisch:2010:gecco, author = "Andreas Windisch", title = "Search-based test data generation from stateflow statecharts", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "1349--1356", keywords = "genetic algorithms, genetic programming, Testing, Debugging, SBSE, Search-based software engineering", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830483.1830732", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "This paper proposes an effective method for automating the test data generation process aiming at structurally covering Stateflow statecharts, while assuring the generation of suitable and - most notably - realistic and meaningful system inputs. For this purpose the principles of evolutionary structural testing have been adapted both for the application to state charts and for the consideration of continuous signals. The approach is evaluated using a complex industrial case study in comparison to random testing. The results demonstrate the value of this approach in industrial settings due to both its search effectiveness and its high degree of automation, potentially contributing to an improvement in quality assurance of embedded software systems.", notes = "Simulink, Matlab. Two examples: 1) automatic road vehicle transmission controller mode 2) real industrial (proprietary) model of a windscreen wiper controller (core logics) taken from a Mercedes-Benz development project. Also known as \cite{1830732} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @InProceedings{wineberg:1994:rsppi, author = "Mark Wineberg and Franz Oppacher", title = "A Representation Scheme to Perform Program Induction in a Canonical Genetic Algorithm", booktitle = "Parallel Problem Solving from Nature III", year = "1994", editor = "Yuval Davidor and Hans-Paul Schwefel and Reinhard M{\"a}nner", series = "LNCS", volume = "866", pages = "292--301", address = "Jerusalem", publisher_address = "Berlin, Germany", month = "9-14 " # oct, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-58484-6", URL = "http://www.cis.uoguelph.ca/~wineberg/publications/ppsn94.pdf", URL = "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-58484-6", DOI = "doi:10.1007/3-540-58484-6_273", size = "10 pages", abstract = "This paper studies Genetic Programming (GP) and its relation to the Genetic Algorithm (GA). GP uses a GA approach to breed successive populations of programs, represented in the chromosomes as parse trees, until a program that solves the problem emerges. However, parse trees are not naturally homologous, consequently changes had to be introduced into GP. To better understand these changes it would be instructive if a canonical GA could also be used to perform program induction. To this end an appropriate GA representation scheme is developed (called EP-I for Evolutionary Programming with Introns). EP-I has been tested on three problems and performed identically to GP, thus demonstrating that the changes introduced by GP do not have any properties beyond those of a canonical GA for program induction. EPI is also able to simulate GP exactly thus gaining further insights into the nature of GP as a GA.", notes = "PPSN3 Studies GP and its relationship to the GA. An appropriate representation scheme is delveloped (EP-I Evolutionary Programming with Introns) EP-I demonstrated to perform identically to GP on 3 problems. EP-I able to simulate GP exactly, gaining insights into GP as a GA.", } @InProceedings{wineberg:1996:bci, author = "Mark Wineberg and Franz Oppacher", title = "The Benefits of Computing with Introns", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "410--415", address = "Stanford University, CA, USA", publisher = "MIT Press", size = "6 pages", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap57.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96", } @PhdThesis{Wineberg:thesis, author = "Mark Lorne Wineberg", title = "Improving the behavior of the genetic algorithm in a dynamic environment", school = "School of Computer Science, Carleton University", year = "2000", address = "Ottawa, Canada", month = "8 " # jun, keywords = "genetic algorithms", URL = "https://repository.library.carleton.ca/concern/etds/ms35t892q", URL = "https://repository.library.carleton.ca/downloads/h989r366q?locale=en", DOI = "doi:10.22215/etd/2000-04575", size = "344 pages", notes = "Not GP? 0-612-57634-5 Supervisor: Franz Oppacher", } @InProceedings{Winkeler:1997:GPod, author = "Jay F. Winkeler and B. S. Manjunath", title = "Genetic Programming for Object Detection", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming", pages = "330--335", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", URL = "http://vision.ece.ucsb.edu/publications/97GP.pdf", URL = "http://citeseer.ist.psu.edu/191181.html", size = "6 pages", abstract = "This paper examines genetic programming as a machine learning technique in the context of object detection. Object detection is performed on image features and on gray-scale images themselves, with different goals. The generality of the solutions discovered, over the training set and over a wider range of images, is tested in both cases. Using genetic programming as a means of testing the utility of algorithms is also explored. Two programs generated using different features are hierarchically combined, improving the results to 1.4percent false negatives on an untrained image, while saving processing.", notes = "GP-97", } @InProceedings{winkeler:1998:ieGP, author = "Jay F. Winkeler and B. S. Manjunath", title = "Incremental Evolution in Genetic Programming", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "403--411", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-548-7", URL = "http://vision.ece.ucsb.edu/publications/98GP.pdf", size = "9 pages", abstract = "This paper presents a study of different methods of using incremental evolution with genetic programming. Incremental evolution begins with a population already trained for a simpler but related task. No other systematic study of this method seems to be available. Experimental evidence shows the technique provides a dependable means of speeding up the solution of complex problems with genetic programming. A novel approach that protects against poor choices of problem simplifications is proposed, improving performance. Testing performed on tracking problems of multiple stages is analysed.", notes = "GP-98", } @Misc{oai:CiteSeerPSU:160647, title = "Experiments with Genetic Programming for Active Vision Tasks", author = "Jay F. Winkeler and B. S. Manjunath", howpublished = "CiteSeer", year = "1997", keywords = "genetic algorithms, genetic programming", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:160647", rights = "unrestricted", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/1845/http:zSzzSzvivaldi.ece.ucsb.eduzSzuserszSzjayzSzPAPERSzSzucsb_97_19.pdf/experiments-with-genetic-programming.pdf", URL = "http://citeseer.ist.psu.edu/160647.html", size = "16 pages", abstract = "This paper examines genetic programming as a machine learning technique for active vision. Genetic programming can be used as a reinforcement learning technique to learn solutions to complex problems based solely on performance at the overall task. Solutions for camera calibration and target tracking are learned by this method. The camera calibration solutions are marginally better than second order least-squares approximations. A solution to the task of tracking a moving target is learned without requiring camera calibration or motion detection to be solved separately. The human effort required to achieve these solutions remains nearly constant despite the increasing difficulty of the tasks.", } @InProceedings{Winkler:2006:IPDPS, author = "S. M. Winkler and M. Affenzeller and S. Wagner", title = "Advances in applying genetic programming to machine learning, focussing on classification problems", booktitle = "20th International Parallel and Distributed Processing Symposium, IPDPS 2006", year = "2006", pages = "8 pp.?", address = "Rhodes Island", month = "25-29 " # apr # " 2006", publisher = "IEEE", keywords = "genetic algorithms, genetic programming", ISBN = "1-4244-0054-6", DOI = "doi:10.1109/IPDPS.2006.1639524", abstract = "A genetic programming based approach for solving classification problems is presented in this paper. Classification is understood as the act of placing an object into a set of categories, based on the object's properties; classification algorithms are designed to learn a function which maps a vector of object features into one of several classes. This is done by analysing a set of input-output examples (training samples) of the function. Here we present a method based on the theory of genetic algorithms and genetic programming that interprets classification problems as optimisation problems: Each presented instance of the classification problem is interpreted as an instance of an optimization problem, and a solution is found by a heuristic optimisation algorithm. The major new aspects presented in this paper are suitable genetic operators for this problem class (mainly the creation of new hypotheses by merging already existing ones and their detailed evaluation) we have designed and implemented. The experimental part of the paper documents the results produced using new hybrid variants of genetic algorithms as well as investigated parameter settings.", notes = "CD-ROM Dept. of Software Eng., Upper Austrian Univ. of Appl. Sci., Hagenberg, Austria", } @InProceedings{Winkler:2006:GECCOWKS, author = "Stephan M. Winkler and Michael Affenzeller and Stefan Wagner", title = "Using Enhanced Genetic Programming Techniques for Evolving Classifiers in the Context of Medical Diagnosis - An Empirical Study", booktitle = "MedGEC 2006 GECCO Workshop on Medical Applications of Genetic and Evolutionary Computation", year = "2006", editor = "Stephen L Smith and Stefano Cagnoni and Jano {van Hemert}", address = "Seattle, WA, USA", month = "8 " # jul, keywords = "genetic algorithms, genetic programming. Adaptation/Self-Adaptation, Classifier Systems, Empirical Study, Medicine", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006etc/papers/wksp115.pdf", size = "8 pages", abstract = "There are several data based methods in the field of artificial intelligence which are nowadays frequently used for analysing classification problems in the context of medical applications. As we show in this paper, the application of enhanced evolutionary computation techniques to classification problems has the potential to evolve classifiers of even higher quality than those trained by standard machine learning methods. On the basis of three medical benchmark classification problems, namely the Wisconsin and the Thyroid data sets taken from the UCI repository as well as the Melanoma data set prepared by members of the Department of Dermatology of the Medical University Vienna, we document that the enhanced genetic programming based approach presented here is able to produce better results than linear modelling methods, artificial neural networks, kNN classification and also standard genetic programming approaches.", notes = "GECCO-2006WKS Distributed on CD-ROM at the GECCO 2006 conference", } @Article{oai:inderscience.com:12487, title = "Online modelling based on Genetic Programming", author = "Stephan Winkler and Hajrudin Efendic and Luigi {Del Re} and Michael Affenzeller and Stefan Wagner", journal = "International Journal of Intelligent Systems Technologies and Applications", year = "2007", volume = "2", number = "2/3", pages = "255--270", month = "19 " # feb, publisher = "Inderscience Publishers", ISSN = "1740-8873", bibsource = "OAI-PMH server at www.inderscience.com", language = "eng", oai = "oai:inderscience.com:12487", relation = "ISSN online: 1740-8873 ISSN print: 1740-8865 DOI: 10.1504/07.12487", rights = "Inderscience Copyright", source = "IJISTA (2007), Vol 2 Issue 2/3, pp 255 - 270", keywords = "genetic algorithms, genetic programming, GP, data driven model identification, self-adaption, machine learning, online modelling, fault diagnosis, automatic learning, real time", URL = "http://www.inderscience.com/link.php?id=12487", DOI = "doi:10.1504/IJISTA.2007.012487", abstract = "Genetic Programming (GP), a heuristic optimisation technique based on the theory of Genetic Algorithms (GAs), is a method successfully used to identify non-linear model structures by analysing a system's measured signals. Mostly, it is used as an offline tool that means that structural analysis is done after collecting all available identification data. In this paper, we propose an enhanced on-line GP approach that is able to adapt its behaviour to new observations while the GP process is executed. Furthermore, an approach using GP for online Fault Diagnosis (FD) is described, and finally test results using measurement data of NOx (nitrogen oxide, nitrogen dioxide) emissions of a BMW diesel engine are discussed.", } @InProceedings{winkler:2007:EUROCAST, author = "Stephan Winkler and Michael Affenzeller and Stefan Wagner", title = "Selection Pressure Driven Sliding Window Behavior in Genetic Programming Based Structure Identification", booktitle = "Computer Aided Systems Theory - EUROCAST 2007", year = "2007", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-540-75867-9_99", DOI = "doi:10.1007/978-3-540-75867-9_99", } @InProceedings{Winkler:2008:gecco, author = "Stephan M. Winkler and Michael Affenzeller and Stefan Wagner", title = "Fine-grained population diversity estimation for genetic programming based structure identification", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "1435--1436", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1435.pdf", DOI = "doi:10.1145/1389095.1389376", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, data mining, machine learning, population diversity analysis, system Identification, Genetics-Based machine learning, learning classifier systems: Poster", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389376}", } @PhdThesis{Winkler:thesis, author = "Stephan M. Winkler", title = "Evolutionary System Identification - Modern Concepts and Practical Applications", school = "Institute for Formal Models and Verification, Johannes Kepler University", year = "2008", address = "Linz, Austria", month = apr, keywords = "genetic algorithms, genetic programming", broken = "http://www.trauner.at/buchliste.aspx?kat=30", URL = "http://www.worldcat.org/title/evolutionary-system-identification-modern-concepts-and-practical-applications/oclc/609974812?referer=di&ht=edition", size = "394 pages", isbn13 = "978-3-85499-569-2", abstract = "System identification denotes the data driven generation of mathematical models for systems; the result of a system identification algorithm consists in a mathematical description...", notes = "http://www.heuristiclab.com/publications/winkler.html See also \cite{Winkler:book}, OCLC Number: 609974812", } @Book{Winkler:book, author = "Stephan M. Winkler", title = "Evolutionary System Identification: Modern Concepts and Practical Applications", publisher = "Trauner Verlag+Buchservice GmbH", year = "2009", number = "59", series = "Johannes Kepler University, Linz, Reihe C", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-85499-569-2", URL = "http://www.amazon.de/Evolutionary-System-Identification-Practical-Applications/dp/3854995695", broken = "http://www.trauner.at/buchdetail.aspx?artnr=20134591", abstract = "System identification denotes the data driven generation of mathematical models for systems; the result of a system identification algorithm consists in a mathematical description of the behaviour of the analysed system. Evolutionary computation is a subfield of computational intelligence that uses concepts inspired by natural evolution; one of the most famous evolutionary techniques is the genetic algorithm, a global optimisation technique using aspects inspired by evolutionary biology such as selection, recombination, mutation and inheritance. This thesis concentrates on evolutionary system identification techniques based on genetic programming (GP), an extension of the genetic algorithm: Mathematical expressions are produced by an evolutionary process that uses the given measurement data. The first part of this thesis describes theoretical concepts used in this work as well as our GP implementation for the HeuristicLab framework. Concepts for monitoring population dynamics during the execution of the GP process are also described; we here concentrate on genetic diversity and genetic propagation. The application of advanced selection principles and optimization stages is also explained as well as on-line and sliding window GP variants. The second part of this thesis summarises the results of system identification test series; the data sets used here include dynamic measurement data of mechatronical systems as well as classification benchmark problems. The results of these tests demonstrate the ability of this method to produce models of high quality for different kinds of machine learning problems, and also give insights into population dynamic processes that occur during the execution of a GP process.", notes = "Dipl.-Ing. Dr. Stephan Winkler, Studium der Informatik und Doktoratsstudium an der JKU in Linz. Bis 2006 wissenschaftlicher Mitarbeiter am LCM und am Institut for Design und Regelung mechatronischer Systeme, danach Anstellung im Rahmen des FWF Translational Research Projekts L284 'GP-Based Techniques fort he Design Virtual Sensors' an der FH Oeo, Campus Hagenberg. Ab February 2009 Antritt einer Professur fuer Bioinformatik an der FH Ooe. Art. Nr. 20134591. See also \cite{Winkler:thesis}", size = "422 pages", } @InProceedings{1068, author = "Stephan M. Winkler and Markus Hirsch and Michael Affenzeller and Luigi {del Re} and Stefan Wagner", title = "Virtual Sensors for Emissions of a Diesel Engine Produced by Evolutionary System Identification", booktitle = "Proceedings of International Conference Computer Aided Systems Theory, EUROCAST 2009", year = "2009", editor = "Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia", volume = "5717", series = "Lecture Notes in Computer Science", pages = "657--664", address = "Las Palmas, Spain", month = feb, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-04772-5", URL = "https://link.springer.com/chapter/10.1007/978-3-642-04772-5_85", DOI = "doi:10.1007/978-3-642-04772-5_85", abstract = "In this paper we discuss the generation of models for emissions of a Diesel engine, produced by genetic programming based evolutionary system identification: Models for the formation of NOx and particulate matter emissions are identified and analysed. We compare these models to models designed by experts applying variables section and the identification of local polynomial models; analysing the results summarized in the empirical part of this paper we see that the use of enhanced genetic programming yields models for emissions that are valid not only in certain parts of the parameter space but can be used as global virtual sensors.", } @Article{1069, author = "S. M. Winkler and M. Affenzeller and S. Wagner", title = "On the Reliability of Nonlinear Modeling Using Enhanced Genetic Programming Techniques", journal = "Topics on Chaotic Systems - Selected Papers from CHAOS 2008 International Conference", year = "2009", volume = "1", number = "1", pages = "24--31", month = mar, DOI = "doi:10.1142/9789814271349_0045", URL = "https://www.worldscientific.com/doi/abs/10.1142/9789814271349_0045", keywords = "genetic algorithms, genetic programming", } @InProceedings{Winkler:2009:IPDPS, author = "Stephan M. Winkler and Michael Affenzeller and Stefan Wagner", title = "Fine grained population diversity analysis for parallel genetic programming", booktitle = "IEEE International Symposium on Parallel Distributed Processing, IPDPS 2009", year = "2009", month = may, address = "Rome", pages = "1--8", keywords = "genetic algorithms, genetic programming, BMW diesel engine, GP population, genetic diversity, multipopulation GP, parallel genetic programming, population diversity, similarity measurement, structural similarity, system identification, identification, parallel programming", DOI = "doi:10.1109/IPDPS.2009.5161117", ISSN = "1530-2075", abstract = "In this paper we describe a formalism for estimating the structural similarity of formulas that are evolved by parallel genetic programming (GP) based identification processes. This similarity measurement can be used for measuring the genetic diversity among GP populations and, in the case of multi-population GP, the genetic diversity among sets of GP populations: The higher the average similarity among solutions becomes, the lower is the genetic diversity. Using this definition of genetic diversity for GP we test several different GP based system identification algorithms for analyzing real world measurements of a BMW diesel engine as well as medical benchmark data taken from the UCI machine learning repository.", notes = "Also known as \cite{5161117}", } @Article{Winkler:2009:GPEM, author = "Stephan M. Winkler and Michael Affenzeller and Stefan Wagner", title = "Using enhanced genetic programming techniques for evolving classifiers in the context of medical diagnosis", journal = "Genetic Programming and Evolvable Machines", year = "2009", volume = "10", number = "2", pages = "111--140", month = jun, keywords = "genetic algorithms, genetic programming, Adaptation/self-adaptation, Data mining, Classifier systems, Empirical study, Medicine, SVM", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-008-9076-8", abstract = "There are several data based methods in the field of artificial intelligence which are nowadays frequently used for analyzing classification problems in the context of medical applications. As we show in this paper, the application of enhanced evolutionary computation techniques to classification problems has the potential to evolve classifiers of even higher quality than those trained by standard machine learning methods. On the basis of five medical benchmark classification problems taken from the UCI repository as well as the Melanoma data set (prepared by members of the Department of Dermatology of the Medical University Vienna) we document that the enhanced genetic programming approach presented here is able to produce comparable or even better results than linear modeling methods, artificial neural networks, kNN classification, support vector machines and also various genetic programming approaches.", } @InProceedings{DBLP:conf/nicso/Winkler10, author = "Stephan M. Winkler", title = "Structural Versus Evaluation Based Solutions Similarity in Genetic Programming Based System Identification", booktitle = "Nature Inspired Cooperative Strategies for Optimization, NICSO 2010", editor = "Juan Ram{\'o}n Gonz{\'a}lez and David A. Pelta and Carlos Cruz and Germ{\'a}n Terrazas and Natalio Krasnogor", series = "Studies in Computational Intelligence", volume = "284", year = "2010", pages = "269--282", address = "Granada, Spain", month = may # " 12-14", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-12537-9", DOI = "doi:10.1007/978-3-642-12538-6_23", size = "14 pages", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "Estimating the similarity of solution candidates represented as structure trees is an important point in the context of many genetic programming (GP) applications. For example, when it comes to observing population diversity dynamics, solutions have to be compared to each other. In the context of GP based system identification, i.e., when mathematical expressions are evolved, solutions can be compared to each other with respect to their structure as well as to their evaluation. Obviously, structural similarity estimation of formula trees is not equivalent to evaluation based similarity estimation; we here want to see whether there is a significant correlation between the results calculated using these two approaches. In order to get an overview regarding this issue, we have analyzed a series of GP tests including both similarity estimation strategies; in this paper we describe the similarity estimation methods as well as the test data sets used in these tests, and we document the results of these tests. We see that in most cases there is a significant positive linear correlation for the results returned by the evaluation based and structural methods. Especially in some cases showing very low structural similarity there can be significantly different results when using the evaluation based similarity methods.", notes = "NICSO", } @InProceedings{Winkler:2010:geccocomp, author = "Stephan M. Winkler and Michael Affenzeller and Witold Jacak and Herbert Stekel", title = "Classification of tumor marker values using heuristic data mining methods", booktitle = "GECCO 2010 Medical applications of genetic and evolutionary computation (MedGEC)", year = "2010", editor = "Stephen L Smith and Stefano Cagnoni and Robert Patton", pages = "1915--1922", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, cancer, tumor Marker Data, Data Mining, Machine Learning, Classification, Statistical Analysis", isbn13 = "978-1-4503-0073-5", URL = "https://research.fh-ooe.at/data/files/publications/1794_GECCO_2010_Winkler.pdf", DOI = "doi:10.1145/1830761.1830826", size = "8 pages", abstract = "Tumour markers are substances that are found in blood, urine, or body tissues and that are used as indicators for tumors; elevated tumour marker values can indicate the presence of cancer, but there can also be other causes. We have used a medical database compiled at the blood laboratory of the General Hospital Linz, Austria: Several blood values of thousands of patients are available as well as several tumor markers. We have used several data based modelling approaches for identifying mathematical models for estimating selected tumor marker values on the basis of routinely available blood values; in detail, estimators for the tumor markers AFP, CA-125, CA15-3, CEA, CYFRA, and PSA have been identified and are analysed. The documented tumour marker values are classified as normal or elevated; our goal is to design classifiers for the respective binary classification problems. As we show in the results section, for those medical modeling tasks described here, genetic programming performs best among those techniques that are able to identify nonlinearities; we also see that GP results show less overfitting than those produced using other methods.", notes = "Also known as \cite{1830826} Distributed on CD-ROM at GECCO-2010. ACM Order Number 910102.", } @InCollection{1793, author = "Stephan M. Winkler and Michael Affenzeller and Stefan Wagner and Gabriel K. Kronberger and Michael Kommenda", title = "Using Genetic Programming in Nonlinear Model Identification", booktitle = "Workshop on Identification in Automotive 2010", publisher = "Springer", year = "2010", editor = "Daniel Alberer and Hakan Hjalmarsson and Luigi {del Re}", volume = "418", series = "Lecture Notes in Control and Information Sciences", chapter = "6", pages = "89--109", address = "Linz, Austria", month = jul, keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4471-2221-0", URL = "https://link.springer.com/chapter/10.1007/978-1-4471-2221-0_6", DOI = "doi:10.1007/978-1-4471-2221-0_6", abstract = "In this paper we summarize the use of genetic programming (GP) in nonlinear system identification: After giving a short introduction to evolutionary computation and genetic algorithms, we describe the basic principles of genetic programming and how it is used for data based identification of nonlinear mathematical models. Furthermore, we summarize projects in which we have successfully applied GP in Research and Development projects in the last years; we also give a summary of several algorithmic enhancements that have been successfully researched in the last years (including offspring selection, on-line and sliding window GP, operators for monitoring genetic process dynamics, and the design of cooperative evolutionary data mining agents). A short description of HeuristicLab (HL), the optimization framework developed by the HEAL research group, and the use of the GP implementations in HL are given in the appendix of this paper.", notes = "Published 2012? LNCIS, volume 418", } @InProceedings{2037, author = "Stephan M. Winkler and Michael Affenzeller and Gabriel K. Kronberger and Michael Kommenda and Stefan Wagner and Witold Jacak and Herbert Stekel", title = "Analysis of Selected Evolutionary Algorithms in Feature Selection and Parameter Optimization for Data Based Tumor Marker Modeling", booktitle = "Proceedings of International Conference on Computer Aided Systems Theory, EUROCAST 2011", year = "2011", editor = "Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia", volume = "6927", series = "Lecture Notes in Computer Science", pages = "335--342", address = "Las Palmas, Spain", month = feb, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-27549-4", URL = "https://link.springer.com/chapter/10.1007/978-3-642-27549-4_43", DOI = "doi:10.1007/978-3-642-27549-4_43", abstract = "In this paper we report on the use of evolutionary algorithms for optimizing the identification of classification models for selected tumour markers. Our goal is to identify mathematical models that can be used for classifying tumor marker values as normal or as elevated; evolutionary algorithms are used for optimizing the parameters for learning classification models. The sets of variables used as well as the parameter settings for concrete modelling methods are optimized using evolution strategies and genetic algorithms. The performance of these algorithms is analysed as well as the population diversity progress. In the empirical part of this paper we document modeling results achieved for tumor markers CA 125 and CYFRA using a medical data base provided by the Central Laboratory of the General Hospital Linz; empirical tests are executed using HeuristicLab.", } @InProceedings{Winkler:2011:GECCOposter, author = "Stephan M. Winkler and Michael Affenzeller and Stefan Wagner", title = "Analysis of the effects of enhanced selection concepts for genetic programming based structure identification using fine-grained population diversity estimation", booktitle = "GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0690-4", keywords = "genetic algorithms, genetic programming: Poster", pages = "195--196", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001858.2001967", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "In this paper we use a formalism for estimating the structural similarity of formulae for measuring the genetic diversity among GP populations. As we show in the results section of this paper, population diversity differs a lot in the test runs depending on the selection schemata used; especially the use of strict offspring selection has a significant effect on the progress of the population's diversity.", notes = "NOX 2litre 4 cylinder automobile BMW 302d Sedan. UCI Thyroid and Wisconsin. heuristicLab. Max tree size 50, 4000 generations. Strict offspring selection. Also known as \cite{2001967} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @InProceedings{Winkler:2011:GECCOcomp, author = "Stephan M. Winkler and Michael Affenzeller and Witold Jacak and Herbert Stekel", title = "Identification of cancer diagnosis estimation models using evolutionary algorithms: a case study for breast cancer, melanoma, and cancer in the respiratory system", booktitle = "GECCO 2011 Medical applications of genetic and evolutionary computation (MedGEC)", year = "2011", editor = "Stephen L. Smith and Stefano Cagnoni and Robert Patton", isbn13 = "978-1-4503-0690-4", keywords = "genetic algorithms, genetic programming", pages = "503--510", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001858.2002040", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "In this paper we present results of empirical research work done on the data based identification of estimation models for cancer diagnoses: Based on patients' data records including standard blood parameters, tumor markers, and information about the diagnosis of tumours we have trained mathematical models for estimating cancer diagnoses. Several data based modelling approaches implemented in HeuristicLab have been applied for identifying estimators for selected cancer diagnoses: Linear regression, k-nearest neighbour learning, artificial neural networks, and support vector machines (all optimised using evolutionary algorithms) as well as genetic programming. The investigated diagnoses of breast cancer, melanoma, and respiratory system cancer can be estimated correctly in up to 81percent, 74percent, and 91percent of the analysed test cases, respectively; without tumour markers up to 75percent, 74percent, and 87percent of the test samples are correctly estimated, respectively.", notes = "Also known as \cite{2002040} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @Article{2399, author = "Stephan M. Winkler and Michael Affenzeller and Gabriel Kronberger and Michael Kommenda and Stefan Wagner and Viktoria Dorfer and Witold Jacak and Herbert Stekel", title = "On the Use of Estimated Tumor Marker Classifications in Tumor Diagnosis Prediction - A Case Study for Breast Cancer", journal = "International Journal of Simulation and Process Modelling", year = "2011", pages = "29--41", month = sep, keywords = "genetic algorithms, genetic programming, EAs, evolutionary algorithms, medical data analysis, tumour marker modelling, data mining", ISSN = "1740-2123", address = "Roma, Italy", booktitle = "Proceedings of 23rd IEEE European Modeling \& Simulation Symposium EMSS 2011", URL = "https://www.inderscienceonline.com/doi/abs/10.1504/IJSPM.2013.055192", DOI = "doi:10.1504/IJSPM.2013.055192", abstract = "we describe the use of tumour marker estimation models in the prediction of tumour diagnoses. In previous works, we have identified classification models for tumour markers that can be used for estimating tumour marker values on the basis of standard blood parameters. These virtual tumour markers are now used in combination with standard blood parameters for learning classifiers that are used for predicting tumour diagnoses. Several data-based modelling approaches implemented in HeuristicLab have been applied for identifying estimators for selected tumour markers and cancer diagnoses: Linear regression, k-nearest neighbour (k-NN) learning, artificial neural networks (ANNs) and support vector machines (SVMs) (all optimised using evolutionary algorithms), as well as genetic programming (GP). We have applied these modelling approaches for identifying models for breast cancer diagnoses; in the results section, we summarise classification accuracies for breast cancer and we compare classification results achieved by models that use measured marker values as well as models that use virtual tumour markers.", } @InProceedings{3074, author = "S. M. Winkler and M. Affenzeller and H. Stekel", title = "Analysis of the Relevance of Blood Parameters and Virtual Tumor Markers in Tumor Diagnosis Prediction", booktitle = "Proceedings of IEEE APCAST'12 Conference", year = "2012", address = "Sydney, Australia", month = feb, DOI = "doi:10.1111/j.1349-7006.2008.01022.x", URL = "https://onlinelibrary.wiley.com/doi/full/10.1111/j.1349-7006.2008.01022.x", keywords = "genetic algorithms, genetic programming", } @InProceedings{2735, author = "Stephan M. Winkler and Michael Affenzeller and Gabriel K. Kronberger and Michael Kommenda and Stefan Wagner and Witold Jacak and Herbert Stekel", title = "Analysis of Selected Evolutionary Algorithms in Feature Selection and Parameter Optimization for Data Based Tumor Marker Modeling", booktitle = "Computer Aided Systems Theory, EUROCAST 2011", year = "2012", editor = "Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia", volume = "6927", series = "Lecture Notes in Computer Science", pages = "335--342", month = feb, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-27549-4", URL = "https://link.springer.com/chapter/10.1007/978-3-642-27549-4_43", DOI = "doi:10.1007/978-3-642-27549-4_43", abstract = "In this paper we report on the use of evolutionary algorithms for optimizing the identification of classification models for selected tumour markers. Our goal is to identify mathematical models that can be used for classifying tumor marker values as normal or as elevated; evolutionary algorithms are used for optimizing the parameters for learning classification models. The sets of variables used as well as the parameter settings for concrete modelling methods are optimized using evolution strategies and genetic algorithms. The performance of these algorithms is analysed as well as the population diversity progress. In the empirical part of this paper we document modeling results achieved for tumour markers CA 125 and CYFRA using a medical data base provided by the Central Laboratory of the General Hospital Linz; empirical tests are executed using HeuristicLab.", } @InProceedings{3073, author = "S. M. Winkler and M. Affenzeller and G. K. Kronberger and M. Kommenda and S. Wagner and W. Jacak and H. Stekel", title = "Variable Interaction Networks in Medical Data", booktitle = "Proceedings of the 24th European Modeling and Simulation Symposium EMSS 2012", year = "2012", pages = "265--270", address = "Vienna, Austria", month = sep, URL = "https://www.igi-global.com/article/variable-interaction-networks-in-medical-data/102626", keywords = "genetic algorithms, genetic programming", } @InProceedings{Winkler:2013:GECCOcomp, author = "Stephan M. Winkler and Michael Affenzeller and Herbert Stekel", title = "Evolutionary identification of cancer predictors using clustered data: a case study for breast cancer, melanoma, and cancer in the respiratory system", booktitle = "GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion", year = "2013", editor = "Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A. N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks", isbn13 = "978-1-4503-1964-5", keywords = "genetic algorithms, genetic programming", pages = "1463--1470", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2464576.2466809", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "In this paper we discuss the effects of using pre-clustered data on the identification of estimation models for cancer diagnoses. Based on patients' data records including standard blood parameters, tumour markers, and information about the diagnosis of tumors, the goal is to identify mathematical models for estimating cancer diagnoses. We have applied a hybrid clustering and classification approach that first identifies data clusters (using standard patient data and tumor markers) and then learns prediction models on the basis of these data clusters. In the empirical section we analyse the clusters of patient data samples formed using k-means clustering: The optimal number of clusters is identified, and we investigate the homogeneity of these clusters. Several evolutionary modelling approaches implemented in HeuristicLab have been applied for subsequently identifying estimators for selected cancer diagnoses: Linear regression, k-nearest neighbour learning, artificial neural networks, and support vector machines (all optimised using evolutionary algorithms) as well as genetic programming. As we show in the results section, the investigated diagnoses of breast cancer, melanoma, and respiratory system cancer can be estimated correctly in up to 84.2percent, 80.3percent, and 94.1percent of the analysed test cases, respectively; without tumour markers up to 78.2percent, 78percent, and 93.3percent of the test samples are correctly estimated, respectively.", notes = "Also known as \cite{2466809} Distributed at GECCO-2013.", } @Article{Winkler:2013:IJSPM, author = "Stephan M. Winkler and Michael Affenzeller and Gabriel Kronberger and Michael Kommenda and Stefan Wagner and Viktoria Dorfer and Witold Jacak and Herbert Stekel", title = "On the use of estimated tumour marker classifications in tumour diagnosis prediction - a case study for breast cancer", journal = "International Journal of Simulation and Process Modelling", year = "2013", month = sep # "~13", volume = "8", number = "1", pages = "29--41", keywords = "genetic algorithms, genetic programming, evolutionary algorithms, medical data analysis, tumour marker modelling, data mining, tumour marker classification, tumour diagnosis prediction, breast cancer, blood parameters, cancer diagnosis, linear regression, k-nearest neighbour, k-nn learning, artificial neural networks, ANNs, support vector machines, SVM, virtual markers.", ISSN = "1740-2131", bibsource = "OAI-PMH server at www.inderscience.com", language = "eng", publisher = "Inderscience Publishers", URL = "http://www.inderscience.com/link.php?id=55192", DOI = "DOI:10.1504/IJSPM.2013.055192", abstract = "we describe the use of tumour marker estimation models in the prediction of tumour diagnoses. In previous works, we have identified classification models for tumour markers that can be used for estimating tumour marker values on the basis of standard blood parameters. These virtual tumour markers are now used in combination with standard blood parameters for learning classifiers that are used for predicting tumour diagnoses. Several data-based modelling approaches implemented in HeuristicLab have been applied for identifying estimators for selected tumour markers and cancer diagnoses: linear regression, k-nearest neighbour (k-NN) learning, artificial neural networks (ANNs) and support vector machines (SVMs) (all optimised using evolutionary algorithms), as well as genetic programming (GP). We have applied these modelling approaches for identifying models for breast cancer diagnoses; in the results section, we summarise classification accuracies for breast cancer and we compare classification results achieved by models that use measured marker values as well as models that use virtual tumor markers.", } @Article{3749, author = "Stephan M. Winkler and Gabriel K. Kronberger and Michael Affenzeller and Herbert Stekel", title = "Variable Interaction Networks in Medical Data", journal = "International Journal of Privacy and Health Information Management (IJPHIM)", year = "2014", volume = "1", number = "2", pages = "1--16", month = jan, keywords = "genetic algorithms, genetic programming", URL = "https://www.igi-global.com/article/variable-interaction-networks-in-medical-data/102626", DOI = "doi:10.4018/ijphim.2013070101", abstract = "In this paper the authors describe the identification of variable interaction networks based on the analysis of medical data. The main goal is to generate mathematical models for medical parameters using other available parameters in this data set. For each variable the authors identify those features that are most relevant for modelling it; the relevance of a variable can in this context be defined via the frequency of its occurrence in models identified by evolutionary machine learning methods or via the decrease in modeling quality after removing it from the data set. Several data based modeling approaches implemented in HeuristicLab have been applied for identifying estimators for selected continuous as well as discrete medical variables and cancer diagnoses: Genetic programming, linear regression, k-nearest-neighbour regression, support vector machines (optimized using evolutionary algorithms), and random forests. In the empirical section of this paper the authors describe interaction networks identified for a medical data base storing data of more than 600 patients. The authors see that whatever modeling approach is used, it is possible to identify the most important influence factors and display those in interaction networks which can be interpreted without domain knowledge in machine learning or informatics in general.", notes = "Month year also given as July-December 2013", } @InProceedings{Winkler:2014:GPTP, author = "Stephan M. Winkler and Michael Affenzeller and Gabriel Kronberger and Michael Kommenda and Bogdan Burlacu and Stefan Wagner", title = "Sliding Window Symbolic Regression for Detecting Changes of System Dynamics", booktitle = "Genetic Programming Theory and Practice XII", year = "2014", editor = "Rick Riolo and William P. Worzel and Mark Kotanchek", series = "Genetic and Evolutionary Computation", pages = "91--107", address = "Ann Arbor, USA", month = "8-10 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Symbolic regression, Self-adaptive sliding window techniques, System analysis, System dynamics", isbn13 = "978-3-319-16029-0", DOI = "doi:10.1007/978-3-319-16030-6_6", abstract = "In this chapter we discuss sliding window symbolic regression and its ability to systematically detect changing dynamics in data streams. The sliding window defines the portion of the data visible to the algorithm during training and is moved over the data. The window is moved regularly based on the generations or on the current selection pressure when using offspring selection. The sliding window technique has the effect that population has to adapt to the constantly changing environmental conditions. In the empirical section of this chapter, we focus on detecting change points of analysed systems' dynamics. We show its effectiveness on various artificial data sets and discuss the results obtained when the sliding window moved in each generation and when it is moved only when a selection pressure threshold is reached. The results show that sliding window symbolic regression can be used to detect change points in systems dynamics for the considered data sets.", notes = " Part of \cite{Riolo:2014:GPTP} published after the workshop in 2015", } @InProceedings{Winkler:2014:GECCOcomp, author = "Stephan M. Winkler and Michael Affenzeller and Susanne Schaller and Herbert Stekel", title = "Data based prediction of cancer diagnoses using heterogeneous model ensembles: a case study for breast cancer, melanoma, and cancer in the respiratory system", booktitle = "GECCO 2014 Workshop on Medical Applications of Genetic and Evolutionary Computation (MedGEC)", year = "2014", editor = "Stephen L. Smith and Stefano Cagnoni and Robert M. Patton", isbn13 = "978-1-4503-2881-4", keywords = "genetic algorithms, genetic programming", pages = "1337--1344", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2598394.2609853", DOI = "doi:10.1145/2598394.2609853", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "In this paper we discuss heterogeneous estimation model ensembles for cancer diagnoses produced using various machine learning algorithms. Based on patients' data records including standard blood parameters, tumour markers, and information about the diagnosis of tumors, the goal is to identify mathematical models for estimating cancer diagnoses. Several machine learning approaches implemented in HeuristicLab and WEKA have been applied for identifying estimators for selected cancer diagnoses: k-nearest neighbour learning, decision trees, artificial neural networks, support vector machines, random forests, and genetic programming. The models produced using these methods have been combined to heterogeneous model ensembles. All models trained during the learning phase are applied during the test phase; the final classification is annotated with a confidence value that specifies how reliable the models are regarding the presented decision: We calculate the final estimation for each sample via majority voting, and the relative ratio of a sample's majority vote is used for calculating the confidence in the final estimation. We use a confidence threshold that specifies the minimum confidence level that has to be reached; if this threshold is not reached for a sample, then there is no prediction for that specific sample. As we show in the results section, the accuracies of diagnoses of breast cancer, melanoma, and respiratory system cancer can so be increased significantly. We see that increasing the confidence threshold leads to higher classification accuracies, bearing in mind that the ratio of samples, for which there is a classification statement, is significantly decreased.", notes = "Also known as \cite{2609853} Distributed at GECCO-2014.", } @InCollection{3758, author = "S. M. Winkler and M. Affenzeller and G. K. Kronberger and M. Kommenda and S. Wagner and W. Jacak and H. Stekel", title = "On the Identification of Virtual Tumor Markers and Tumor Diagnosis Predictors Using Evolutionary Algorithms", booktitle = "Advanced Methods and Applications in Computational Intelligence", publisher = "Springer", year = "2014", editor = "R. Klempous and J. Nikodem and W. Jacak and Z. Chaczko", pages = "95--122", isbn13 = "978-3-319-01435-7", DOI = "doi:10.1007/978-3-319-01436-4_6", URL = "https://link.springer.com/chapter/10.1007/978-3-319-01436-4_6", keywords = "genetic algorithms, genetic programming", } @InProceedings{5023, author = "Stephan M. Winkler and Gabriel K. Kronberger and Michael Kommenda and Stefan Fink and Michael Affenzeller", title = "Dynamics of Predictability and Variable Influences Identified in Financial Data Using Sliding Window Machine Learning", booktitle = "Computer Aided Systems Theory, EUROCAST 2015", year = "2015", editor = "Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia", volume = "9520", series = "Lecture Notes in Computer Science", pages = "326--333", address = "Las Palmas, Gran Canaria, Spain", month = feb, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-27340-2", URL = "https://link.springer.com/chapter/10.1007/978-3-319-27340-2_41", DOI = "doi:10.1007/978-3-319-27340-2_41", abstract = "in this paper we analyse the dynamics of the predictability and variable interactions in financial data of the years 2007--2014. Using a sliding window approach, we have generated mathematical prediction models for various financial parameters using other available parameters in this data set. For each variable we identify the relevance of other variables with respect to prediction modelling. By applying sliding window machine learning we observe that changes of the predictability of financial variables as well as of influence factors can be identified by comparing modeling results generated for different periods of the last 8 years. We see changes of relationships and the predictability of financial variables over the last years, which corresponds to the fact that relationships and dynamics in the financial sector have changed significantly over the last decade. Still, our results show that the predictability has not decreased for all financial variables, indeed in numerous cases the prediction quality has even improved.", } @InProceedings{Winkler:2016:GPTP, author = "Stephan M. Winkler and Michael Affenzeller and Bogdan Burlacu and Gabriel Kronberger and Michael Kommenda and Philipp Fleck", title = "Similarity-based Analysis of Population Dynamics in Genetic Programming Performing Symbolic Regression", booktitle = "Genetic Programming Theory and Practice XIV", year = "2016", editor = "Rick Riolo and Bill Worzel and Brian Goldman and Bill Tozier", pages = "1--17", address = "Ann Arbor, USA", month = "19-21 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Symbolic Regression, Genetic Programming, Population Dynamics, Genetic and Phenotypic Diversity, Offspring Selection, ALPS", isbn13 = "978-3-319-97087-5", URL = "https://www.springer.com/us/book/9783319970875", DOI = "doi:10.1007/978-3-319-97088-2_1", abstract = "Population diversity plays an important role in genetic programming (GP) evolutionary dynamics. In this paper, we use structural and semantic similarity measures to investigate the evolution of diversity in three GP algorithmic flavours: standard GP, offspring selection GP (OS-GP), and age-layered population structure GP (ALPS-GP). Empirical measurements on two symbolic regression benchmark problems reveal important differences between the dynamics of the tested configurations. In standard GP, after an initial decrease, population diversity remains almost constant until the end of the run. The higher variance of the phenotypic similarity values suggests that small changes on individual genotypes have significant effects on their corresponding phenotypes. By contrast, strict offspring selection within the OS-GP algorithm causes a significantly more pronounced diversity loss at both genotypic and, in particular, phenotypic levels. The pressure for adaptive change increases phenotypic robustness in the face of genotypic perturbations, leading to less genotypic variability on the one hand, and very low phenotypic diversity on the other hand. Finally, the evolution of similarities in ALPS-GP follows a periodic pattern marked by the time interval when the bottom layer is reinitialized with new individuals. This pattern is easily noticed in the lower layers characterized by shorter migration intervals, and becomes less and less noticeable on the upper layers.", notes = " Part of \cite{Tozier:2016:GPTP} published after the workshop", } @InProceedings{Winkler:2024:GPTP, author = "Elisabeth Mayrhuber and Bogdan Burlacu and Stephan M. Winkler", title = "How to Measure Explainability and Interpretability of Machine Learning Results", booktitle = "Genetic Programming Theory and Practice XXI", year = "2024", editor = "Stephan M. Winkler and Wolfgang Banzhaf and Ting Hu and Alexander Lalejini", series = "Genetic and Evolutionary Computation", pages = "357--374", address = "University of Michigan, USA", month = jun # " 6-8", publisher = "Springer", keywords = "genetic algorithms, genetic programming, XAI", isbn13 = "978-981-96-0076-2", URL = "https://heal.heuristiclab.com/news/post/gptp-xxi", DOI = "doi:10.1007/978-981-96-0077-9_18", notes = "Published in 2025 after the workshop", } @Article{winter:1994:est, author = "C. S. Winter and P. W. A. McIlroy and J. L. Fernandes-Villacanas", title = "Evolving Software Techniques", journal = "BT Technology Journal", year = "1994", volume = "12", number = "2", pages = "121--131", month = apr, keywords = "genetic algorithms, genetic programming", notes = "British Telecommunications, UK {"}Software engineering is still a craftsman's industry, awaiting the development of mass production. This paper describes how the computer could replace the craftsman programmer...{"} {"}Thus, if genetic programming can be shown to scale, it is predicted that around the turn of the century such techniques will compete with humans at writing code{"}. {"}fitness specification language{"}. Discuses Artificial life, classifiers, genetic algorithms, genetic programming. BT Hermes A-life system described.", } @InProceedings{Winter:2020:GI, author = "Emily Winter and David Bowes and Steve Counsell and Tracy Hall and Saemundur Haraldsson and Vesna Nowack and John Woodward", title = "Human Factors in the Study of Automatic Software Repair: Future Directions for Research with Industry", booktitle = "GI @ ICSE 2020", year = "2020", month = "3 " # jul, editor = "Shin Yoo and Justyna Petke and Westley Weimer and Bobby R. Bruce", publisher = "ACM", address = "internet", pages = "285--286", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, human factors, automatic software repair, ASR, APR", isbn13 = "978-1-4503-7963-2", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gi2020/Winter_2020_GI.pdf", video_url = "https://youtu.be/GsNKCifm44A", DOI = "doi:10.1145/3387940.3392176", size = "2 pages", abstract = "Automatic software repair represents a significant development in software engineering, promising considerable potential change to the working procedures and practices of software engineers, developers, and testers. Technical advances within this domain have been the focus of many recent publications. However, despite the simultaneous rising prominence of studies that consider the role of human factors within software engineering, there has not been an equivalent growth of studies of human factors within the domain of automatic software repair. This position paper presents the case for increased research in this area and suggests three key focuses and approaches for a future research agenda. These are: considerations that go beyond the current focus on the usability of automatic software repair tools; longitudinal studies; and the use of a wide range of appropriate social research methods, not just surveys. All three of these enable industry-based software engineers not just to provide feedback on automatic software repair tools but to participate in the shaping of these technologies, facilitating the development of tools that meet developer and industry needs, as well as allaying any concerns.", notes = "See also \cite{Winter:TSE} focus groups and ethnographic research obstacles to user studies Video: https://youtu.be/GsNKCifm44A (start 1:46:12, 1:53:43, end 2:00:24) Slides: http://geneticimprovementofsoftware.com/slides/gi2020icse/human_factors_slides.pdf http://geneticimprovementofsoftware.com/gi2020icse.html", } @InProceedings{Winter:2022:Bloomberg, author = "Emily Rowan Winter and Vesna Nowack and David Bowes and Steve Counsell and Tracy Hall and Saemundur Haraldsson and John Woodward and Serkan Kirbas and Etienne Windels and Olayori McBello", title = "Towards developer-centered automatic program repair: findings from {Bloomberg}", booktitle = "Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering", year = "2022", pages = "1578--588", address = "Singapore", series = "ESEC/FSE 2022", publisher = "Association for Computing Machinery", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, fixie, human factors, automatic program repair, human factors, XAI, qualitative methods", isbn13 = "9781450394130", DOI = "doi:10.1145/3540250.3558953", size = "11", abstract = "we report qualitative research into automatic program repair (APR) at Bloomberg. Six focus groups were conducted with a total of seventeen participants (including both developers of the APR tool and developers using the tool) to consider: the development at Bloomberg of a prototype APR tool (Fixie); developers early experiences using the tool; and developers perspectives on how they would like to interact with the tool in future. APR is developing rapidly and it is important to understand in greater detail developers' experiences using this emerging technology. we provide in-depth, qualitative data from an industrial setting. We found that the development of APR at Bloomberg had become increasingly user-centered, emphasising how fixes were presented to developers, as well as particular features, such as customisability. From the focus groups with developers who had used Fixie, we found particular concern with the pragmatic aspects of APR, such as how and when fixes were presented to them. Based on our findings, we make a series of recommendations to inform future APR development, highlighting how APR tools should start small, be customisable, and fit with developers workflows. We also suggest that APR tools should capitalise on the promise of repair bots and draw on advances in explainable AI.", } @Article{Winter:TSE, author = "Emily Rowan Winter and Vesna Nowack and David Bowes and Steve Counsell and Tracy Hall and Saemundur Haraldsson and John Woodward", title = "Let's Talk With Developers, Not About Developers: A Review of Automatic Program Repair Research", journal = "IEEE Transactions on Software Engineering", year = "2023", volume = "49", number = "1", pages = "419--436", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, human factors, software, software engineering, maintenance engineering, bibliographies, systematics, technological innovation", ISSN = "1939-3520", URL = "http://www.research.lancs.ac.uk/portal/en/publications/-(00152abe-459f-4864-8133-80198ea69fba).html", DOI = "doi:10.1109/TSE.2022.3152089", size = "19 pages", abstract = "Automatic program repair (APR) offers significant potential for automating some coding tasks. Using APR could reduce the high costs historically associated with fixing code faults and deliver significant benefits to software engineering. Adopting APR could also have profound implications for software developers daily activities, transforming their work practices. To realise the benefits of APR it is vital that we consider how developers feel about APR and the impact APR may have on developers work. Developing APR tools without consideration of the developer is likely to undermine the success of APR deployment. In this paper, we critically review how developers are considered in APR research by analysing how human factors are treated in 260 studies from Monperrus Living Review of APR. Over half of the 260 studies in our review were motivated by a problem faced by developers (e.g., the difficulty associated with fixing faults). Despite these human-oriented motivations, fewer than 7percent of the 260 studies included a human study. We looked in detail at these human studies and found their quality mixed (for example, one human study was based on input from only one developer). Our results suggest that software developers are often talked about in APR studies, but are rarely talked with. A more comprehensive and reliable understanding of developer human factors in relation to APR is needed. Without this understanding, it will be difficult to develop APR tools and techniques which integrate effectively into developer workflows. We recommend a future research agenda to advance the study of human factors in APR.", notes = "Also known as \cite{9714799}. School of Computing and Communications, Lancaster University, UK", } @Book{Wirsansky:book, author = "Eyal Wirsansky", title = "Hands-On Genetic Algorithms with Python", publisher = "Packt", year = "2020", address = "UK", month = jan, keywords = "genetic algorithms, genetic programming, Python", isbn13 = "9781838557744", URL = "https://www.packtpub.com/product/hands-on-genetic-algorithms-with-python/9781838557744", URL = "https://www.amazon.co.uk/Hands-Genetic-Algorithms-Python-intelligence-ebook/dp/B0842372RQ", code_url = "https://github.com/PacktPublishing/Hands-On-Genetic-Algorithms-with-Python", size = "346 pages", notes = "Section on GP?", } @InProceedings{witkor00, author = "Marcin Witczak and Jozef Korbicz", title = "Genetic programming based observers for nonlinear systems", booktitle = "4th IFAC Symposium on Fault Detection Supervision and Safety for Technical Processes : Safeprocess 2000", year = "2000", volume = "2", pages = "967--972", address = "Budapest, Hungary", month = jun # " 14-16", keywords = "genetic algorithms, genetic programming, fault detection, nonlinear systems, model approximation, observers", ISSN = "1474-6670", URL = "http://www.sciencedirect.com/science/article/pii/S1474667017374839", DOI = "doi:10.1016/S1474-6670(17)37483-9", size = "6 pages", abstract = "Model-based approaches to fault detection and isolation suffer from the inconvenience that in practice it is often difficult to obtain an accurate mathematical model of a nonlinear system of interest. A way out of this problem is to use data-driven approaches to model the process input-output behaviour. In this work, a relatively new genetic programming technique is employed to design a nonlinear observer which models the juice temperature at the outlet of an evaporator at the Lublin Sugar Factory in Poland. The resulting observer is then used to generate a residual for fault detection.", notes = "Sep 2018 Elsevier doi: gives page numbers as 945--950 Also known as \cite{WITCZAK2000945}", } @InProceedings{witkor01, author = "Marcin Witczak and Jozef Korbicz", title = "An evolutionary approach to identification of nonlinear dynamic systems", booktitle = "Artificial neural nets and genetic algorithms : proceedings of the International Conference", year = "2001", editor = "Vera Kurkova and Nigel V. Steele and Roman Neruda and Miroslav Karny", pages = "240--243", address = "Prague, Czech Republic", publisher = "Springer-Verlag", email = "M.Witczak@issi.uz.zgora.pl", keywords = "genetic algorithms, genetic programming", ISBN = "3-211-83651-9", URL = "http://www.springer.com/computer/ai/book/978-3-211-83651-4", abstract = "In this paper a nonlinear identification methodology founded upon NARX model description ispresented. In particular, the model determination procedure is decomposed into the elementarymodel structures selection one . Those models are represented as fixed-depth trees and a geneticalgorithm is used to obtain their appropriate form. To show the effectiveness of the proposedapproach, the final part of the paper contains examples concerning modelling the juice temperature at the outlet of an evaporator at the Lublin sugar factory.", notes = "Nov 2012 Currently out of stock", } @InProceedings{witkor01a, author = "Marcin Witczak and Jozef Korbicz", title = "Robustifying an extended unknown input observer with genetic programming", booktitle = "Methods and Models in Automation and Robotics - MMAR 2001 : Proceedings of the 7th IEEE International Conference", year = "2001", volume = "2", pages = "1061--1066", publisher = "Wydaw. Uczelniane Politechniki Szczeci\~{n}skiej", email = "M.Witczak@issi.uz.zgora.pl", keywords = "genetic algorithms, genetic programming", abstract = "This paper is focused on the problem of designing nonlinear observers for fault diagnosis tasks. The main objective is to show how to employ a modified version of the well-known unknown inputobserver, which can be applied to linear stochastic systems, to form a nonlinear deterministic observer. Moreover, it is shown that the convergence of the proposed observer is ensured under certain conditions. In particular an unknown diagonal matrix is introduced in order to take the linearization errors into account, and then the Lyapunov method is employed to obtain the convergence conditions. The final part of this paper shows how to use a genetic programming technique to increase the convergence rate of the proposed observer.", } @PhdThesis{Witczak:thesis, author = "Marcin Witczak", title = "Identification and Fault Detection of Non-Linear Dynamic Systems", school = "Computer Science and Telecommunications, University of Zielona Gora", year = "2003", address = "Poland", email = "M.Witczak@issi.uz.zgora.pl", keywords = "genetic algorithms, genetic programming, fault diagnosis, system identification", URL = "http://zbc.uz.zgora.pl/Content/2310/Witczak_PhdBook.pdf", URL = "https://zbc.uz.zgora.pl/dlibra/publication/1916/edition/2310", ISBN = "83-88317-65-2", size = "124 pages", start = "It is well known that there is an increasing demand for modern systems to become more effective and reliable. This real world development pressure has transformed automatic control, initially perceived as the art of designing a satisfactory system, into the modern science that it is today. The observed increasing complexity of modern systems necessitates the development of new control techniques. To tackle this problem, it is obviously profitable to have all the knowledge concerning a system behaviour. Undoubtedly, an adequate model of a system can be a tool providing such knowledge. Models can be useful for system analysis, e.g. to predict or to simulate a system behaviour. Indeed, nowadays, advanced techniques for designing controllers are also based on models of systems. Application of models leads directly to the problem of system identification.", streszczenie = "W pracy rozpatruje sie zagadnienia związane z detekcja uszkodzen i identyfikacja nieliniowych systemow dynamicznych. Cel pracy mozna podzielic na dwie czesci. Pierwsza z nich dotyczy opracowania metodologii konstruowania modeli nieliniowych systemow dynamicznych z zastosowaniem programowania genetycznego. Natomiast druga projektowania odpornych obserwatorow stanu do zadan detekcji uszkodzen. W szczegolnosci zostaly przeanalizowane i zbadane nastepujace problemy:", notes = "In english. University of Zielona Gora Press, LaTeX2e Supervised by Prof. Jozef Korbicz The text of this book was prepared based on the author's Ph.D. dissertation", } @Article{witczak:2002:IJC, author = "Marcin Witczak and Andrzej Obuchowicz and Jozef Korbicz", title = "Genetic programming based approaches to identification and fault diagnosis of non-linear dynamic systems", journal = "International Journal of Control", year = "2002", volume = "75", number = "13", pages = "1012--1031", month = sep, email = "M.Witczak@issi.uz.zgora.pl", keywords = "genetic algorithms, genetic programming", ISSN = "1366-5820", DOI = "doi:10.1080/00207170210156224", size = "20 pages", abstract = "System identification is one of the most important research directions. It is a diverse field which can be employed in many different areas. One of them is the model-based fault diagnosis. Thus, the problems of system identification and fault diagnosis are closely related. Unfortunately, in both the cases, the research is strongly oriented towards linear systems, while the problem of identification and fault diagnosis of non-linear dynamic systems remains still open. There are, of course, many more or less sophisticated approaches to this problem, although they are not as reliable and universal as those related to linear systems, and the choice of the method to be used depends on the application. The purpose of this paper is to provide a new system identification framework based on a genetic programming technique.Moreover, a fault diagnosis scheme for non-linear systems is proposed. In particular, a new fault detection observer is presented , and the Lyapunov approach is used to show that the proposed observer is convergent under certain conditions. It is also shown how to use the genetic programming technique to increase the convergence rate of the observer. The final part of this paper contains numerical examples concerning identification of chosen parts of the evaporation station at the Lublin Sugar Factory S.A., as well as state estimation and fault diagnosis of an induction motor.", notes = "Evaporator and electrical induction motor Lubin Sugar Factory November 1998 GP better than ARX (p1024) Entry combined with witobu02", } @InCollection{Witczak:2002:WNT, author = "Marcin Witczak and J. Korbicz", title = "Genetic programming in identification and fault detection of non-linear dynamic systems", booktitle = "Diagnostics of Processes. Models, Methods of Artificial Intelligence, Applications", publisher = "Scientific Engineering Press", year = "2002", editor = "J. Korbicz and J. M. Koscielny and Z. Kowalczuk and W. Cholewa", address = "WNT", note = "in Polish", keywords = "genetic algorithms, genetic programming", } @InProceedings{withall:2002:gecco, author = "Mark S. Withall and Chris J. Hinde and Roger G. Stone", title = "Evolving Readable {Perl}", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "894", address = "New York", publisher_address = "San Francisco, CA 94104, USA", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "genetic algorithms, genetic programming, learning classifier systems, Perl, readable, symbolic regression", ISBN = "1-55860-878-8", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/GP083.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/GP083.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-15.pdf", notes = "GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002) See also Withall:2002:gecco:lbp", } @InProceedings{withall:2002:gecco:lbp, title = "Evolving Perl", author = "Mark S. Withall and Chris J. Hinde and Roger G. Stone", booktitle = "Late Breaking Papers at the Genetic and Evolutionary Computation Conference ({GECCO-2002})", editor = "Erick Cant{\'u}-Paz", year = "2002", month = jul, pages = "474--481", address = "New York, NY", publisher = "AAAI", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025", keywords = "genetic algorithms, genetic programming", notes = "Late Breaking Papers, {GECCO-2002}. A joint meeting of the eleventh International Conference on Genetic Algorithms ({ICGA-2002}) and the seventh Annual Genetic Programming Conference ({GP-2002}) part of cantu-paz:2002:GECCO:lbp 7 list operations. Fixed length chromosome (40 or 60).", } @PhdThesis{Withall:thesis, author = "Mark S. Withall", title = "The evolution of complete software systems", school = "Department of Computer Science, Loughborough University", year = "2003", address = "UK", month = "13 " # jun, keywords = "genetic algorithms, genetic programming, Representation,Formal specification, Graphical user interfaces, Complete software systems", URL = "http://hdl.handle.net/2134/3594", URL = "https://dspace.lboro.ac.uk/dspace-jspui/bitstream/2134/3594/1/MSWthesis.pdf", URL = "http://ethos.bl.uk/OrderDetails.do?did=41&uin=uk.bl.ethos.515618", size = "178 pages", abstract = "This thesis tackles a series of problems related to the evolution of complete software systems both in terms of the underlying Genetic Programming system and the application of that system. A new representation is presented that addresses some of the issues with other Genetic Program representations while keeping their advantages. This combines the easy reproduction of the linear representation with the inheritable characteristics of the tree representation by using fixed-length blocks of genes representing single program statements. This means that each block of genes will always map to the same statement in the parent and child unless it is mutated, irrespective of changes to the surrounding blocks. This method is compared to the variable length gene blocks used by other representations with a clear improvement in the similarity between parent and child. Traditionally, fitness functions have either been created as a selection of sample inputs with known outputs or as hand-crafted evaluation functions. A new method of creating fitness evaluation functions is introduced that takes the formal specification of the desired function as its basis. This approach ensures that the fitness function is complete and concise. The fitness functions created from formal specifications are compared to simple input/output pairs and the results show that the functions created from formal specifications perform significantly better. A set of list evaluation and manipulation functions was evolved as an application of the new Genetic Program components. These functions have the common feature that they all need to be 100percent correct to be useful. Traditional Genetic Programming problems have mainly been optimisation or approximation problems. The list results are good but do highlight the problem of scalability in that more complex functions lead to a dramatic increase in the required evolution time. Finally, the evolution of graphical user interfaces is addressed. The representation for the user interfaces is based on the new representation for programs. In this case each gene block represents a component of the user interface. The fitness of the interface is determined by comparing it to a series of constraints, which specify the layout, style and functionality requirements. A selection of web-based and desktop-based user interfaces were evolved. With these new approaches to Genetic Programming, the evolution of complete software systems is now a realistic goal.", notes = "725.86 kB Chapter 4 Evolving Some Interesting Functions 4.2 Sumlist 4.3 Avelist 4.4 Listmax 4.5 Listmin 4.6 Reverse 4.7 Sort Chapter 5 Evolving the User Interface 5.5 Example Problems 5.5.1 A Text Editor 5.5.2 A Personal Details Web Form 5.5.3 A Front-end for the List Functions uk.bl.ethos.515618", } @InProceedings{Withall:2004:UKWCI, author = "Mark S. Withall and Chris J. Hinde and Roger G. Stone", title = "Evolving the user interface", booktitle = "Proceedings of the 2004 {UK} Workshop on Computational Intelligence", year = "2004", editor = "M. S. Withall and C. J. Hinde", pages = "86--93", address = "Loughborough, UK", month = "6-8 " # sep, publisher = "Loughborough University", keywords = "genetic algorithms, genetic programming, graphical user interface", ISBN = "1-874152-11-X", URL = "http://hdl.handle.net/2134/4023", URL = "https://dspace.lboro.ac.uk/dspace-jspui/bitstream/2134/4023/3/withall2004.pdf", size = "8 page", abstract = "A method is presented for evolving Graphical User Interfaces using Genetic Algorithms. The fitness evaluation is based on a series of constraints, which must be met by the user interface. Examples are used to demonstrate the use of positional, style and functionality constraints and the final example shows the evolution of a complete (although simple) software application.", notes = "text editor, web form", } @Article{Withall:2009:GPEM, author = "M. S. Withall and C. J. Hinde and R. G. Stone", title = "An improved representation for evolving programs", journal = "Genetic Programming and Evolvable Machines", year = "2009", volume = "10", number = "1", pages = "37--70", month = mar, keywords = "genetic algorithms, genetic programming, Perl", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-008-9069-7", URL = "http://results.ref.ac.uk/Submissions/Output/2828879", size = "34 pages", abstract = "A representation has been developed that addresses some of the issues with other Genetic Program representations while maintaining their advantages. This combines the easy reproduction of the linear representation with the inheritable characteristics of the tree representation by using fixed-length blocks of genes representing single program statements. This means that each block of genes will always map to the same statement in the parent and child unless it is mutated, irrespective of changes to the surrounding blocks. This method is compared to the variable length gene blocks used by other representations with a clear improvement in the similarity between parent and child. In addition, a set of list evaluation and manipulation functions was evolved as an application of the new Genetic Program components. These functions have the common feature that they all need to be 100percent correct to be useful. Traditional Genetic Programming problems have mainly been optimisation or approximation problems. The list results are good but do highlight the problem of scalability in that more complex functions lead to a dramatic increase in the required evolution time.", notes = "Individuals represented as list of 8-bit integers. Modulus operator used to convert to required range. Perl exec. Checks for infinite for loops (if exceeded programs aborted and given low fitness). Read-only and read-write variables (ie memory). Syntax corrected by post operation fixup. sumlist, avelist, listmax, listmin, reverse, sort. Function set includes Double, If, For, Assign, End, etc. (except two case) 100percent fitness on verification set. Huge impact of Swap on sort.", uk_research_excellence_2014 = "D - Journal article", } @InProceedings{Witten:2020:CEC, author = "Matthew Witten and Owen Clancey", title = "Feasibility of Genetic Programming for the Optimization of Tissue-Type-Segmented Maps in the Generation of Synthetic {CT} in Radiation Therapy Treatment Planning", booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC 2020", year = "2020", editor = "Yaochu Jin", pages = "paper id24048", address = "internet", month = "19-24 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, human medicine thaerpy, synthetic CT, Matlab", isbn13 = "978-1-7281-6929-3", URL = "http://www.human-competitive.org/sites/default/files/wittenclanceyhumiesubmission.txt", URL = "http://www.human-competitive.org/sites/default/files/cec_2020_witten_clancey_post_acceptance_draft1.pdf", DOI = "doi:10.1109/CEC48606.2020.9185768", video_url = "http://www.human-competitive.org/sites/default/files/witten_clancey_2021_humie_video.mp4", size = "6 pages", abstract = "Modern radiation therapy treatment planning has traditionally relied upon computed tomography (CT) in modeling the interaction of megavoltage (MV) photons with the various tissues and organs of the body. CT image data provide both detailed information about individual patient anatomy, as well as a voxel-by-voxel three-dimensional grid of Hounsfield units (HU), which specifies, at all points within the patient, essentially the difference between the attenuation coefficient of the tissue within that voxel from the attenuation coefficient of water, normalized to that of water. From the HU value, the relative electron density can be inferred, and as the relative electron density is the major determinant of the interaction of the tissue with MV photons, the radiation dose distribution can then be calculated. Recently, there has been interest in using magnetic resonance imaging (MR) in lieu of CT, as MR provides superior soft-tissue contrast; however, MR does not provide any electron density information. Various approaches have been essayed to create a synthetic CT (synCT) from the MR data. In the present study, genetic programming was used to construct mappings of MR data to HU data for seven tissue types: bladder, cancellous bone, cortical bone, fat, muscle, prostate, and rectum. These maps were then applied to randomly chosen points in five patient data sets to calculate the synCT HU values, which were then compared with the actual HU values from CT images of those same patients. The method produced mean absolute errors (MAE) of 9.28 HU, 33.24 HU, 75.32 HU, 18.64 HU, 17.12 HU, 11.76 HU, and 18.40 HU for the respective tissue types, and these MAE values are less than those of previous approaches, indicating superior performance. Although the method of the present study does require more manual input, the superior performance is compensatory. Further study is necessary to confirm accuracy on entire MR data sets, and to ensure there is no sample variance effect on the current results.", notes = "Entered 2021 HUMIES, GPlab v4, pop=100,100=gens lexitour, Rank85 selection, no elitism pc=0.5, 0.5 point mutation, Sara Silva dynamic tree depth for anti-bloat. GP better than ANN. GP and deep ANN better than human atlas based methods. Protate cancer. male pelvis. Video mins:sec Feasibility of GP for the optimisation of tissue-type-segmented maps in the generation of synthetic CT in radiation therapy treatment planning 0:55 Radiation million electron volt photons, Compton scattering, killing tumour cells, electron density of human tissues. Langone Health. 2:04 MRI v CT 2:21 dataset registration Use GP to quickly get CT like dataset from patient's MRI data 3:49. No geometric error. Some of expert human knowledge plus machine. Electron density 5:44 GP, human, body atlas Deep Convolutional neural network ANN, DCNN. GP only available approach for 4 tissues: Canc Bone, types, muscle, fat, prostate 6:06. GP better than all published results. Practical for any hospital => better treatment => better outcomes. https://wcci2020.org/ NYU Winthrop Hospital, Long Island, United States of America. Also known as \cite{9185768}", } @InProceedings{Wittenberg:2020:GECCO, author = "David Wittenberg and Franz Rothlauf and Dirk Schweim", title = "{DAE-GP}: Denoising Autoencoder {LSTM} Networks as Probabilistic Models in Estimation of Distribution Genetic Programming", year = "2020", editor = "Carlos Artemio {Coello Coello} and Arturo Hernandez Aguirre and Josu Ceberio Uribe and Mario Garza Fabre and Gregorio {Toscano Pulido} and Katya Rodriguez-Vazquez and Elizabeth Wanner and Nadarajen Veerapen and Efren Mezura Montes and Richard Allmendinger and Hugo Terashima Marin and Markus Wagner and Thomas Bartz-Beielstein and Bogdan Filipic and Heike Trautmann and Ke Tang and John Koza and Erik Goodman and William B. Langdon and Miguel Nicolau and Christine Zarges and Vanessa Volz and Tea Tusar and Boris Naujoks and Peter A. N. Bosman and Darrell Whitley and Christine Solnon and Marde Helbig and Stephane Doncieux and Dennis G. Wilson and Francisco {Fernandez de Vega} and Luis Paquete and Francisco Chicano and Bing Xue and Jaume Bacardit and Sanaz Mostaghim and Jonathan Fieldsend and Oliver Schuetze and Dirk Arnold and Gabriela Ochoa and Carlos Segura and Carlos Cotta and Michael Emmerich and Mengjie Zhang and Robin Purshouse and Tapabrata Ray and Justyna Petke and Fuyuki Ishikawa and Johannes Lengler and Frank Neumann", isbn13 = "9781450371285", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377930.3390180", DOI = "doi:10.1145/3377930.3390180", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference", pages = "1037--1045", size = "9 pages", keywords = "genetic algorithms, genetic programming, DAE-GP, EDA, ANN, estimation of distribution algorithms, denoising autoencoders, long short-term memory networks", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", abstract = "Estimation of distribution genetic programming (EDA-GP) algorithms are metaheuristics where sampling new solutions from a learned probabilistic model replaces the standard mutation and recombination operators of genetic programming (GP). This paper presents DAE-GP, a new EDA-GP which uses denoising autoencoder long short-term memory networks (DAE-LSTMs) as probabilistic model. DAE-LSTMs are artificial neural networks that first learn the properties of a parent population by mapping promising candidate solutions to a latent space and reconstructing the candidate solutions from the latent space. The trained model is then used to sample new offspring solutions. We show on a generalization of the royal tree problem that DAE-GP outperforms standard GP and that performance differences increase with higher problem complexity. Furthermore, DAE-GP is able to create offspring with higher fitness from a learned model in comparison to standard GP. We believe that the key reason for the high performance of DAE-GP is that we do not impose any assumptions about the relationships between learned variables which is different to previous EDA-GP models. Instead, DAE-GP flexibly identifies and models relevant dependencies of promising candidate solutions.", notes = "Royal Tree. Adam. Also known as \cite{10.1145/3377930.3390180} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @InProceedings{Wittenberg:2021:euro, author = "David Wittenberg and Franz Rothlauf", title = "Using denoising autoencoder LSTM networks to balance exploration and exploitation in estimation of distribution genetic programming", booktitle = "31st European Conference On Operational Research", year = "2021", address = "Athens", month = "11-14 " # jul, keywords = "genetic algorithms, genetic programming, ANN, Machine Learning, Combinatorial Optimization, Metaheuristics", URL = "https://www.euro-online.org/conf/euro31/treat_abstract?paperid=3310", URL = "https://liser.elsevierpure.com/ws/portalfiles/portal/32968251/abstract_book_euro31.pdf", abstract = "Estimation of distribution genetic programming (EDA-GP) algorithms are metaheuristics for variable-length combinatorial optimization problems that replace the standard recombination and mutation operators of genetic programming (GP) by sampling from a learned probabilistic model. An example of an EDA-GP is DAE-GP that uses denoising autoencoder long short-term memory networks as probabilistic model. DAE-GP is the first and only EDA-GP that uses neural networks as a model and outperforms standard GP. The key advantage of DAE-GP is that we can flexibly identify relevant relationships between problem variables and that we can apply denoising on input candidate solutions to control the generalization behavior of the model. However, current work only uses subtree mutation with fixed corruption strength. In this work, we therefore study alternative denoising strategies. We show on standard GP benchmark problems that denoising strongly influences the exploration and exploitation behavior in search. Adjusting the denoising strategy can therefore help to either exploit promising areas of the parent population or to explore new search spaces.", notes = "https://euro2021athens.com/ Lehrstuhl fuer Wirtschaftsinformatik und BWL, Universitaet Mainz", } @InProceedings{Wittenberg:2022:EuroGP, author = "David Wittenberg", title = "Using Denoising Autoencoder Genetic Programming to Control Exploration and Exploitation in Search", booktitle = "EuroGP 2022: Proceedings of the 25th European Conference on Genetic Programming", year = "2022", editor = "Eric Medvet and Gisele Pappa and Bing Xue", series = "LNCS", volume = "13223", publisher = "Springer Verlag", address = "Madrid, Spain", pages = "102--117", month = "20-22 " # apr, organisation = "EvoStar, Species", note = "Best paper nomination", keywords = "genetic algorithms, genetic programming, Estimation of Distribution Algorithms, EDA, Probabilistic Model-Building, Denoising Autoencoders", isbn13 = "978-3-031-02055-1", DOI = "doi:10.1007/978-3-031-02056-8_7", abstract = "Denoising Autoencoder Genetic Programming (DAE-GP) is a novel neural network-based estimation of distribution genetic programming (EDA-GP) algorithm that uses denoising autoencoder long short-term memory networks as a probabilistic model to replace the standard mutation and recombination operators of genetic programming (GP). At each generation, the idea is to flexibly identify promising properties of the parent population and to transfer these properties to the offspring where the DAE-GP uses denoising to make the model robust to noise that is present in the parent population. Denoising partially corrupts candidate solutions that are used as input to the model. The stronger the corruption, the stronger the generalization of the model. In this work, we study how corruption strength affects the exploration and exploitation behavior of the DAE-GP. For a generalization of the royal tree problem (high-locality problem), we find that the stronger the corruption, the stronger the exploration of the solution space. For the given problem, weak corruption resulting in a stronger exploitation of the solution space performs best. However, in more rugged fitness landscapes (low-locality problems), we expect that a stronger corruption resulting in a stronger exploration will be helpful. Choosing the right denoising strategy can therefore help to control the exploration and exploitation behavior in search, leading to an improved search quality.", notes = "http://www.evostar.org/2022/eurogp/ Part of \cite{Medvet:2022:GP} EuroGP'2022 held inconjunction with EvoApplications2022 EvoCOP2022 EvoMusArt2022", } @InProceedings{wittenberg:2022:GECCOcomp, author = "David Wittenberg and Franz Rothlauf", title = "Denoising Autoencoder Genetic Programming for {Real-World} Symbolic Regression", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion", year = "2022", editor = "Heike Trautmann and Carola Doerr and Alberto Moraglio and Thomas Bartz-Beielstein and Bogdan Filipic and Marcus Gallagher and Yew-Soon Ong and Abhishek Gupta and Anna V Kononova and Hao Wang and Michael Emmerich and Peter A. N. Bosman and Daniela Zaharie and Fabio Caraffini and Johann Dreo and Anne Auger and Konstantin Dietric and Paul Dufosse and Tobias Glasmachers and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Tea Tusar and Dimo Brockhoff and Tome Eftimov and Pascal Kerschke and Boris Naujoks and Mike Preuss and Vanessa Volz and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Mark Coletti and Catherine (Katie) Schuman and Eric ``Siggy'' Scott and Robert Patton and Paul Wiegand and Jeffrey K. Bassett and Chathika Gunaratne and Tinkle Chugh and Richard Allmendinger and Jussi Hakanen and Daniel Tauritz and John Woodward and Manuel Lopez-Ibanez and John McCall and Jaume Bacardit and Alexander Brownlee and Stefano Cagnoni and Giovanni Iacca and David Walker and Jamal Toutouh and UnaMay O'Reilly and Penousal Machado and Joao Correia and Sergio Nesmachnow and Josu Ceberio and Rafael Villanueva and Ignacio Hidalgo and Francisco {Fernandez de Vega} and Giuseppe Paolo and Alex Coninx and Antoine Cully and Adam Gaier and Stefan Wagner and Michael Affenzeller and Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and William B. Langdon and Justyna Petke and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Paetzel and Alexander Wagner and Michael Heider and Nadarajen Veerapen and Katherine Malan and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Mohammad Nabi Omidvar and Yuan Sun and Ernesto Tarantino and De Falco Ivanoe and Antonio {Della Cioppa} and Scafuri Umberto and John Rieffel and Jean-Baptiste Mouret and Stephane Doncieux and Stefanos Nikolaidis and Julian Togelius and Matthew C. Fontaine and Serban Georgescu and Francisco Chicano and Darrell Whitley and Oleksandr Kyriienko and Denny Dahl and Ofer Shir and Lee Spector and Alma Rahat and Richard Everson and Jonathan Fieldsend and Handing Wang and Yaochu Jin and Erik Hemberg and Marwa A. Elsayed and Michael Kommenda and William {La Cava} and Gabriel Kronberger and Steven Gustafson", pages = "612--614", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, denoising autoencoders, symbolic regression, estimation of distribution algorithms", isbn13 = "978-1-4503-9268-6/22/07", DOI = "doi:10.1145/3520304.3528921", abstract = "Denoising Autoencoder Genetic Programming (DAE-GP) is a novel neural-network based estimation of distribution genetic programming algorithm that uses denoising autoencoder long short-term memory networks as probabilistic model to replace the standard recombination and mutation operators of genetic programming (GP). Recent work demonstrated that the DAE-GP outperforms standard GP. However, results are limited to the generalization of the royal tree problem. In this work, we apply the DAE-GP to real-world symbolic regression. On the Airfoil dataset and given a fixed number of fitness evaluations, we find that the DAE-GP generates significantly better and smaller (number of nodes) best candidate solutions than standard GP. The results highlight that the DAE-GP may be a good alternative for generating good and interpretable solutions for real-world symbolic regression.", notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @InProceedings{Wittenberg:2023:EuroGP, author = "David Wittenberg and Franz Rothlauf", title = "Small Solutions for Real-World Symbolic Regression using Denoising Autoencoder Genetic Programming", booktitle = "EuroGP 2023: Proceedings of the 26th European Conference on Genetic Programming", year = "2023", month = "12-14 " # apr, editor = "Gisele Pappa and Mario Giacobini and Zdenek Vasicek", series = "LNCS", volume = "13986", publisher = "Springer Verlag", address = "Brno, Czech Republic", pages = "101--116", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Estimation of Distribution Algorithms, EDA, Denoising Autoencoders, Symbolic Regression", isbn13 = "978-3-031-29572-0", URL = "https://rdcu.be/c8UQO", DOI = "doi:10.1007/978-3-031-29573-7_7", size = "16 pages", abstract = "Denoising Autoencoder Genetic Programming (DAE-GP) is a model-based evolutionary algorithm that uses denoising autoencoder long short-term memory networks as probabilistic model to replace the standard recombination and mutation operators of genetic programming (GP). In this paper, we use the DAE-GP to solve a set of nine standard real-world symbolic regression tasks. We compare the prediction quality of the DAE-GP to standard GP, geometric semantic GP (GSGP), and the gene-pool optimal mixing evolutionary algorithm for GP (GOMEA-GP), and find that the DAE-GP shows similar prediction quality using a much lower number of fitness evaluations than GSGP or GOMEA-GP. In addition, the DAE-GP consistently finds small solutions. The best candidate solutions of the DAE-GP are 69percent smaller (median number of nodes) than the best candidate solutions found by standard GP. An analysis of the bias of the selection and variation step for both the DAE-GP and standard GP gives insight into why differences in solution size exist: the strong increase in solution size for standard GP is a result of both selection and variation bias. The results highlight that learning and sampling from a probabilistic model is a promising alternative to classic GP variation operators where the DAE-GP is able to generate small solutions for real-world symbolic regression tasks.", notes = "Part of \cite{Pappa:2023:GP} EuroGP'2023 held in conjunction with EvoCOP2023, EvoMusArt2023 and EvoApplications2023", } @Article{Wittenberg:2023:GPEM, author = "David Wittenberg and Franz Rothlauf and Christian Gagne", title = "Denoising autoencoder genetic programming: strategies to control exploration and exploitation in search", journal = "Genetic Programming and Evolvable Machines", year = "2023", volume = "24", number = "2", pages = "Article number: 17", month = dec, note = "Online first", keywords = "genetic algorithms, genetic programming, Estimation of distribution algorithms, EDA, ANN, LSTM, Probabilistic model-building, Denoising autoencoders", ISSN = "1389-2576", URL = "https://rdcu.be/dqFao", DOI = "doi:10.1007/s10710-023-09462-2", size = "27 pages", abstract = "Denoising autoencoder genetic programming (DAE-GP) is a novel neural network-based estimation of distribution genetic programming approach that uses denoising autoencoder long short-term memory networks as a probabilistic model to replace the standard mutation and recombination operators of genetic programming. At each generation, the idea is to capture promising properties of the parent population in a probabilistic model and to use corruption to transfer variations of these properties to the offspring. This work studies the influence of corruption and sampling steps on search. Corruption partially mutates candidate solutions that are used as input to the model, whereas the number of sampling steps defines how often we re-use the output during model sampling as input to the model. We study the generalization of the royal tree problem, the Airfoil problem, and the Pagie-1 problem, and find that both corruption strength and the number of sampling steps influence exploration and exploitation in search and affect performance: exploration increases with stronger corruption and lower number of sampling steps. The results indicate that both corruption and sampling steps are key to the success of the DAE-GP: it permits us to balance the exploration and exploitation behavior in search, resulting in an improved search quality. However, also selection is important for exploration and exploitation and should be chosen wisely.", notes = "Johannes Gutenberg University, Mainz, Rhineland Palatinate, Germany", } @InProceedings{conf/cig/WittkampB06, author = "Mark Wittkamp and Luigi Barone", title = "Evolving Adaptive Play for the Game of Spoof Using Genetic Programming", booktitle = "Proceedings of the 2006 IEEE Symposium on Computational Intelligence and Games (CIG06)", publisher = "IEEE", year = "2006", editor = "Sushil J. Louis and Graham Kendall", pages = "164--172", address = "University of Nevada, Reno, campus in Reno/Lake Tahoe, USA", month = "22-24 " # may, bibdate = "2007-02-26", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/cig/cig2006.html#WittkampB06", keywords = "genetic algorithms, genetic programming, Imperfect Information, Games, Spoof, Opponent Modelling", ISBN = "1-4244-0464-9", URL = "https://www.cse.unr.edu/~sushil/cig06/proceedings/cigv5.pdf", DOI = "doi:10.1109/CIG.2006.311696", size = "9 pages", abstract = "Many games require opponent modelling for optimal performance. The implicit learning and adaptive nature of evolutionary computation techniques offer a natural way to develop and explore models of an opponent's strategy without significant overhead. In this paper, we propose the use of genetic programming to play the game of Spoof, a simple guessing game of imperfect information. We discuss the technical details needed to equip a computer to play the game and report on experiments using this approach that demonstrate emergent adaptive behaviour. We further show that specialisation via adaptation is crucial to maximise winnings and that no general strategy will suffice against all opponents.", } @InProceedings{Wittkamp:2007:CISDA, author = "Mark Wittkamp and Luigi Barone and Lyndon While", title = "A comparison of genetic programming and look-up table learning for the game of spoof", booktitle = "IEEE Symposium on computational Intelligence in Security and Defense Applications", year = "2007", pages = "63--71", address = "Honolulu", month = apr # " 1-5", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", ISBN = "1-4244-0709-5", DOI = "doi:10.1109/CIG.2007.368080", abstract = "Many games require opponent modelling for optimal performance. The implicit learning and adaptive nature of evolutionary computation techniques offer a natural way to develop and explore models of an opponent's strategy without significant overhead. In this paper, we compare two learning techniques for strategy development in the game of Spoof, a simple guessing game of imperfect information. We compare a genetic programming approach with a look-up table based approach, contrasting the performance of each in different scenarios of the game. Results show both approaches have their advantages, but that the genetic programming approach achieves better performance in scenarios with little public information. We also trial both approaches against opponents who vary their strategy; results showing that the genetic programming approach is better able to respond to strategy changes than the look-up table based approach", notes = "http://www.cidefense.org/", } @InProceedings{Wlodarczyk:2025:CCNC, author = "Damian Wlodarczyk and Takfarinas Saber", title = "Grammar-Guided Genetic Programming for {UAV-Based} Mitigation of Urban Disaster Traffic Congestion", booktitle = "2025 IEEE 22nd Consumer Communications \& Networking Conference (CCNC)", year = "2025", month = jan, keywords = "genetic algorithms, genetic programming, Disasters, Roads, Urban areas, Cost function, Autonomous aerial vehicles, Time measurement, Road traffic, Time factors, Traffic congestion, Urban Disaster, Unmanned Aerial Vehicles, Simulation of Urban MObility, Grammar-Guided Genetic Programming, G3P", ISSN = "2331-9860", DOI = "doi:10.1109/CCNC54725.2025.10975833", abstract = "As the global population grows and more people migrate to urban areas, road traffic becomes an increasingly critical problem. Minimizing and mitigating the effects of disaster traffic are crucial for reducing emissions, economic losses, and saving lives by enabling faster emergency response times. Existing works aim to optimise traffic and rerouting in disaster-struck scenarios, but different cost functions are effective under different circumstances and information levels. No single method consistently produces an optimal rerouting cost function regardless of the road segment affected or the information provided. This study aims to develop a method to generate a cost function that consistently produces optimal solutions and outperforms existing functions, regardless of the road segment affected or the amount of information available. We simulated five different disaster scenarios in Dublin City Centre, with each scenario involving a disaster-struck road segment. We considered the use of Unmanned Aerial Vehicles to capture data on road conditions, dividing it into five information levels, with each level adding data about roads farther from the disaster. Furthermore, we used Grammar-Guided Genetic Programming (G3P) to evolve efficient cost functions, which were then tested in the SUMO simulator using Dijkstra's algorithm to reroute traffic. The performance was measured by the Average Travel Time of vehicles. Our approach successfully generated efficient cost functions for each scenario and information level. In all but one case, the G3P-generated functions outperformed existing methods. In some scenarios, the G3P-generated functions achieved better average arrival times than those in non-disaster conditions. We observed a maximum improvement of 44.23percent in the Average Arrival Time compared to the no rerouting scenario and 14.99percent compared to the non-disaster scenario. The findings suggest that our proposed G3P-based rerouting approach could be beneficial even in regular non-disaster situations.", notes = "Also known as \cite{10975833}", } @Article{wojnicki:2022:Energies, author = "Miroslaw Wojnicki and Jan Lubas and Mateusz Gawronski and Slawomir Szuflita and Jerzy Kusnierczyk and Marcin Warnecki", title = "An Experimental Investigation of {WAG} Injection in a Carbonate Reservoir and Prediction of the Recovery Factor Using Genetic Programming", journal = "Energies", year = "2022", volume = "15", number = "6", pages = "Article No. 2127", keywords = "genetic algorithms, genetic programming", ISSN = "1996-1073", URL = "https://www.mdpi.com/1996-1073/15/6/2127", DOI = "doi:10.3390/en15062127", abstract = "Production from mature oil fields is gradually declining, and new discoveries are not sufficient to meet the growing demand for oil products. Hence, enhanced oil recovery is emerging as an essential link in the global oil industry. This paper aims to recognise the possibility of increasing oil recovery from Polish carbonate reservoirs by the water alternating gas injection process (WAG) using various types of gases, including CO2, acid gas (a mixture of CO2 and H2S of 70/30percent vol/vol) and high-nitrogen natural gases occurring in the Polish Lowlands. A series of 17 core flooding experiments were performed under the temperature of 126 °C, and at pressures of 270 and 170 bar on composite carbonate cores consisting of four dolomite core plugs. Original reservoir rock and fluids were used. A set of slim tube tests was conducted to determine the miscibility conditions of the injected fluids with reservoir oil. The WAG process was compared to continuous gas injection (CGI) and continuous water injection (CWI) and was proven to be more effective. CO2 WAG injection resulted in a recovery factor (RF) of up to 82percent, where the high nitrogen natural gas WAG injection was less effective with the highest recovery of 70percent. Based on the core flooding results and through implementing a genetic programming algorithm, a mathematical model was developed to estimate recovery factors using variables specific to a given WAG scheme.", notes = "also known as \cite{en15062127}", } @InProceedings{wolff:2001:eegabruvf, author = "Krister Wolff and Peter Nordin", title = "Evolution of Efficient Gait with Autonomous Biped Robot Using Visual Feedback", booktitle = "2001 Genetic and Evolutionary Computation Conference Late Breaking Papers", year = "2001", editor = "Erik D. Goodman", pages = "482--489", address = "San Francisco, California, USA", month = "9-11 " # jul, keywords = "genetic algorithms, genetic programming, Elvina", notes = "GECCO-2001LB http://publik.svt.se/sth/index.nsf/8ed90fd2b855a062412566c000444c6d/82c0333920443814c125698900393e56/Body/4.11A8?OpenElement&FieldElemFormat=gif robot weight 1.490kg height 11inches, EyeBot Mk3 RoBIOS, full colour 24bit digital camera, infrared range sensor. Seeded population? popsize 30. 4 tournament selection, steady state, 126 genes, recombination + mutation.", } @InProceedings{WolNor01, author = "Krister Wolff and Peter Nordin", title = "Evolution of Efficient Gait with Humanoids using Visual Feedback", booktitle = "Proceedings of the 2nd IEEE-RAS International Conference on Humanoid Robots", year = "2001", pages = "99--106", publisher = "Institute of Electrical and Electronics Engineers, Inc.", keywords = "genetic algorithms, genetic programming, humanoid robotics, embodied artificial intelligence", URL = "http://fy.chalmers.se/~wolff/WN_Humanoids01.pdf", ISBN = "4-9901025-0-9", abstract = "In this paper we present the autonomous, walking humanoids Priscilla, ELVIS and ELVINA and an experiment using evolutionary adaptive systems. We also present the anthropomorphic principles behind our humanoid project and the multistage development methodology. The adaptive evolutionary system used is a steady state evolutionary strategy running on the robot's onboard computer. Individuals are evaluated and fitness scores are automatically determined using the robots onboard digital cameras and near-infrared range sensor. The experiments are performed in order to optimise a by hand developed locomotion controller. By using this system, we evolved gait patterns that locomote the robot in a straighter path and in a more robust way, than the previously manually developed gait did.", notes = "cf \cite{WolNor02a} ", } @InProceedings{WolNor02b, author = "Krister Wolff and Peter Nordin", title = "Walking humanoids for robotics research", booktitle = "Proceedings of the Second International Workshop on Epigenetic Robotics: Modeling Cognitive Development in Robotic Systems", year = "2002", editor = "Christopher G. Prince and Yiannis Demiris and Yuval Marom and Hideki Kozima and Christian Balkenius", organisation = "Lund University Cognitive Studies 94", keywords = "genetic algorithms, genetic programming, embodied artificial intelligence", ISBN = "91-631-2677-X", URL = "http://cogprints.org/2533/1/Wolff.pdf", broken = "http://www.lucs.lu.se/ftp/pub/LUCS_Studies/LUCS94/Wolff.pdf", size = "2 pages", abstract = "We present three humanoid robots aimed as platforms for research in robotics, and cognitive development in robotics systems. The 'priscilla' robot is a 180cm full scale humanoid, and the mid-size prototype is called 'elvis' and is about 70cm tall. The smallest size humanoid is the 'elvina' type, about 28 cm tall. Two instances of 'elvina' have been built to enable experiments with cooperating humanoids. The underlying ideas and conceptual principles, such as anthropomorphism, embodiment, and mechanisms for learning and adaptivity are introduced as well.", } @InProceedings{WolNor02a, author = "Krister Wolff and Peter Nordin", title = "Evolution of Efficient Gait with an Autonomous Biped Robot using Visual Feedback", booktitle = "Proceedings of the Mechatronics 2002 Conference", year = "2002", editor = "Job {van Amerongen} and Ben Jonker and Paul Regtien and Stefano Stramigioli", publisher = "Drebbel Institute for Mechatronics", keywords = "genetic algorithms, genetic programming", ISBN = "90-365-1766-4", URL = "http://www4.cs.umanitoba.ca/~jacky/Robotics/Papers/wolff-walking-visual-feedback.pdf", abstract = "We have developed an autonomous, walking humanoid robot 'elvina' and performed experiments in evolutionary programming with it, in order to optimize a by hand developed locomotion controller. A steady state evolutionary strategy is running on the robot's onboard computer. Individuals are evaluated and fitness scores are automatically determined using the robots on board vision system and sensors. By using this system, we evolve gait patterns that locomote the robot in a straighter path and in a more robust way than the previously manually developed gait did.", } @InProceedings{WolNor02, author = "Krister Wolff and Peter Nordin", title = "Physically Realistic Simulators and Autonomous Humanoid Robots as Platforms for Evolution of Biped Walking Behavior using Genetic Programming", booktitle = "Proceedings of the 6th World Multiconference on Systemics, Cybernetics and Informatics", year = "2002", editor = "Nagib Callaos and Alexander Pisarchik and Mitsuyoshi Ueda", volume = "VI", pages = "81--86", publisher = "IIIS", keywords = "genetic algorithms, genetic programming, evolutionary robotics, embodied artificial intelligence, simulation", ISBN = "980-07-8150-1", URL = "https://research.chalmers.se/en/publication/72899", abstract = "We introduce a novel approach to evolution of robot control programs in simulation, generalised to a real, physical robot, where the remaining step in evolution is carried out. First, we briefly describe an evolutionary programming experiment performed with our biped humanoid robot elvina, with onboard computer and sensors. By using this system, we evolved gait patterns that outperformed the previously manually developed gait. Second, the physics simulator used is presented. It is a free, industrial standard library for simulating articulated rigid body dynamics, designed for use in interactive or real-time simulation of moving objects in changeable virtual reality environments. The simulation is based on a method where the equations of motion are derived from a Lagrange multiplier velocity based model and it uses a highly stable, first order integrator. Finally, we present the Genetic Programming system used for evolution. Evolutionary algorithms mimic aspects of evolution and Darwins principle of natural selection and survival of the fittest, in order to optimise a solution towards a defined goal. The primitives of Genetic Programs are the terminals and the functions. The terminals are comprised of inputs to the program, constants or functions without arguments. The functions are composed of the statements, operators and functions available to the GP system.", } @InProceedings{wolff:2003:gecco, author = "Krister Wolff and Peter Nordin", title = "Learning Biped Locomotion from First Principles on a Simulated Humanoid Robot Using Linear Genetic Programming", booktitle = "Genetic and Evolutionary Computation -- GECCO-2003", editor = "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller", year = "2003", pages = "495--506", address = "Chicago", publisher_address = "Berlin", month = "12-16 " # jul, volume = "2723", series = "LNCS", ISBN = "3-540-40602-6", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, Evolutionary Robotics", URL = "http://fy.chalmers.se/~wolff/WN_gecco03.pdf", DOI = "doi:10.1007/3-540-45105-6_61", abstract = "We describe the first instance of an approach for control programming of humanoid robots, based on evolution as the main adaptation mechanism. In an attempt to overcome some of the difficulties with evolution on real hardware, we use a physically realistic simulation of the robot. The essential idea in this concept is to evolve control programs from first principles on a simulated robot, transfer the resulting programs to the real robot and continue to evolve on the robot. The Genetic Programming system is implemented as a Virtual Register Machine, with 12 internal work registers and 12 external registers for I/O operations. The individual representation scheme is a linear genome, and the selection method is a steady state tournament algorithm. Evolution created controller programs that made the simulated robot produce forward locomotion behavior. An application of this system with two phases of evolution could be for robots working in hazardous environments, or in applications with remote presence robots.", notes = "GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)", } @MastersThesis{wolff:2003d, author = "K. Wolff", title = "Evolutionary Humanoids for Embodied Artificial Intelligence", school = "School of Physics and Engineering Physics, Chalmers University of Technology and Goteborg University", year = "2003", type = "Licentiate thesis", address = "G{\"o}teborg, Sweden", month = dec, keywords = "genetic algorithms, genetic programming", URL = "https://research.chalmers.se/en/publication/2099", abstract = "The work presented in this thesis aims at investigating the potential of a proposed methodology to create a cognitive control architecture for a humanoid robot. This architecture comprises three hierarchical layers: the reactive layer, the model building layer, and the reasoning layer. The architecture is built on techniques from the field of evolutionary computation, and more specifically evolutionary algorithms. Based on very simple models of organic evolution, these algorithms can be applied to various problems such as combinatorial optimisation problems or learning tasks. The field of artificial intelligence is discussed from a robotics viewpoint. The roles of different paradigms in AI research are considered, and so are the principles of embodiment and situatedness, which are fundamental in the behaviour based robotics approach. Several evolutionary experiments performed on real, physical humanoid robot platforms are presented. These are presented mainly to motivate the use of simulated evolution for control programming of robots. In addition, these experiments constitute a subset of the necessary building blocks of the proposed cognitive humanoid robot architecture, outlined in this thesis. The experiments include sound localisation, two instances of machine vision, hand-eye coordination, coordination of actuator motions in a robot foot joint, and two instances regarding learning and adaptivity.", } @InProceedings{wolff:2003c, author = "Krister Wolff and Peter Nordin", title = "An Evolutionary Based Approach for Control Programming of Humanoids", booktitle = "Proceedings of the 3rd International Conference on Humanoid Robots (Humanoids'03)", year = "2003", address = "Karlsruhe, Germany", month = "1-2 " # oct, organization = "IEEE", publisher = "VDI/VDE-GMA", keywords = "genetic algorithms, genetic programming, Linear Genetic Programming, Bipedal robots, Elvis, Autonomous robots", URL = "https://research.chalmers.se/en/publication/72877", URL = "http://fy.chalmers.se/~wolff/WN_Humanoids03.pdf", size = "19 pages", abstract = "We describe the first instance of a novel approach for control programming of humanoid robots, based on evolution. To overcome some of the difficulties with evolution on real hardware, we use a physically realistic simulation of the robot. The essential idea is to evolve control programs from first principles on a simulated robot, transfer the programs to the real robot, and continue to evolve on the robot. As the key motivation for using simulators, we describe an on-line learning experiment with a humanoid robot. The Genetic Programming system is implemented as a Virtual Register Machine, with a linear genome, and steady state tournament selection. Evolution created controller programs that made the simulated robot produce forward locomotion behaviour. A further application of this system, with two phases of evolution, would be to have a flexible adaptation mechanism that can react to hardware failures in the robot.", } @InProceedings{wolff:2003a, author = "K. Wolff and P. Nordin", title = "Evolutionary Learning from First Principles of Biped Walking on a Simulated Humanoid Robot", booktitle = "Proceedings of the Business and Industry Symposium of the Advanced Simulation Technologies Conference (ASTC'03)", year = "2003", editor = "M. Ades and L. M. Deschaine", pages = "31--36", address = "Orlando, FL, USA", month = mar # " 30-" # apr # " 3", publisher = "SCS", keywords = "genetic algorithms, genetic programming", URL = "http://fy.chalmers.se/~wolff/robot1.avi", URL = "http://publications.lib.chalmers.se/publication/72902", abstract = "We describe the first instance of a novel approach for control programming of humanoid robots, based on evolution. To overcome some of the difficulties with evolution on real hardware, we use a physically realistic simulation of the robot. The essential idea is to evolve control programs from first principles on a simulated robot, transfer the programs to the real robot, and continue to evolve on the robot. As the key motivation for using simulators, we describe an on-line learning experiment with a humanoid robot. The Genetic Programming system is implemented as a Virtual Register Machine, with a linear genome, and steady state tournament selection. Evolution created controller programs that made the simulated robot produce forward locomotion behaviour.", notes = "robot1.avi ok Sep 2018 1493492 bytes 31 seconds. ", } @PhdThesis{Wolff:thesis, author = "Krister Wolff", title = "Generation and Optimization of Motor Behaviors in Real and Simulated Robots", school = "Department of Applied Mechanics, Chalmers University of Technology", year = "2006", type = "Doctor of Philosophy", address = "Goteborg, Sweden", month = dec, keywords = "genetic algorithms, genetic programming, autonomous robots, bipedal robots, evolutionary robotics, behaviour selection, behavior-based robotics, linear genetic programming", ISBN = "91-7291-867-5", URL = "http://www.me.chalmers.se/~mwahde/AdaptiveSystems/PhDTheses/KristerWolff_PhDThesis.pdf", size = "182 pages", abstract = "In this thesis, the problems of generating and optimising motor behaviours for both simulated and real, physical robots have been investigated, using the paradigms of evolutionary robotics and behaviour-based robotics. Specifically, three main topics have been considered: (1) On-line evolutionary optimisation of hand-coded gaits for real, physical bipedal robots. The evolved gaits significantly outperformed the hand-coded gaits, reaching up to 65percent higher speed. (2) Evolution of bipedal gait controllers in simulators. First, linear genetic programming was used with two different simulated bipedal robots. In both these cases, the gait controller was evolved starting from programs consisting of random sequences of basic instructions. The best evolved programs generated stable bipedal locomotion, keeping the robot upright and moving indefinitely. However, the evolved gaits were not very human-like. Thus, a different approach, inspired by the neural mechanisms involved in the locomotion of biological organisms, was tried. Here, both the structure and parameters of a central pattern generator network, controlling the locomotion of a simulated robot, were optimised using a genetic algorithm. The evolved controllers generated a stable human-like gait and were also able to handle gait transitions. (3) Behavior selection in autonomous robots, using the utility function method. In particular, the performance of the method as a function of the polynomial degree of the utility functions was investigated. It was found that adequate behaviour selection systems can be found rapidly for low polynomial degrees (1-2), but also that the best solutions can only be obtained by using a higher polynomial degree (3-4). Furthermore, the performance of different evolutionary algorithms in connection with the utility function method was also investigated and, somewhat surprisingly, it was found that the standard method, employing a simple genetic algorithm, generally outperformed the modified methods.", notes = "Supervisors Peter Nordin and Kristian Lindgren and Mattias Wahde Printed by Chalmers Reproservice Goteborg, Sweden 2006", } @InProceedings{Krister:2006:SMC, author = "Krister Wolff and Jimmy Pettersson and Almir Heralic and Mattias Wahde", title = "Structural evolution of central pattern generators for bipedal walking in {3D} simulation", booktitle = "Proceedings of the 2006 IEEE International Conference on Systems, Man, and Cybernetics, (SMC'06)", year = "2006", volume = "1", pages = "227--234", address = "Taipei, Taiwan", month = "8-11 " # oct, publisher = "IEEE Press", keywords = "genetic algorithms, anthropomorphic walking, bipedal robot, central pattern generator network, feedback network, humanoid robot, oscillator unit, rigid-body dynamics simulation, structural evolution, feedback, humanoid robots, legged locomotion, robot dynamics", DOI = "doi:10.1109/ICSMC.2006.384387", abstract = "Anthropomorphic walking for a simulated bipedal robot has been realised by means of artificial evolution of central pattern generator (CPG) networks. The approach has been investigated through full rigid-body dynamics simulations in 3D of a bipedal robot with 14 degrees of freedom. The half-centre CPG model has been used as an oscillator unit, with interconnection paths between oscillators undergoing structural modifications using a genetic algorithm. In addition, the connection weights in a feedback network of predefined structure were evolved. Furthermore, a supporting structure was added to the robot in order to guide the evolutionary process towards natural, human-like gaits. Subsequently, this structure was removed, and the ability of the best evolved controller to generate a bipedal gait without the help of the supporting structure was verified. Stable, natural gait patterns were obtained, with a maximum walking speed of around 0.9 m/s.", notes = "p230 'however the MOGA did not lead to any significant improvement'. Two fixed GA chromosomes. Also known as \cite{4273834}", } @InCollection{wolff:2007:cwrtna, author = "Krister Wolff and Mattias Wahde", title = "Evolution of Biped Locomotion Using Linear Genetic Programming", booktitle = "Climbing and Walking Robots Towards New Applications", publisher = "intechweb.org", year = "2007", editor = "Houxiang Zhang", type = "Invited book chapter", chapter = "16", pages = "335--356", address = "Vienna, Austria", month = oct, keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-902613-16-5", DOI = "doi:10.5772/5088", abstract = "Gait generation for bipedal robots is a very complex problem. The basic cycle of a bipedal gait, called a stride, consists of two main phases, namely the single-support phase and the double-support phase, which take place in sequence. During the single-support phase, one foot is in contact with the ground and the other foot is in swing motion, being transferred from back to front position. In the double-support phase, both feet simultaneously touch the ground, and the weight of the robot is shifted from one foot to the other. During the completion of a stride, the stability of the robot changes dynamically, and there is always a risk of tipping over. Thus it is crucial to actively maintain the stability and walking balance", notes = "Free download", size = "22 pages", } @InProceedings{Wolff:2008:cec, author = "Krister Wolff and David Sandberg and Mattias Wahde", title = "Evolutionary Optimization of a Bipedal Gait in a Physical Robot", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "440--445", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-1823-7", file = "EC0123.pdf", URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=04630835", DOI = "doi:10.1109/CEC.2008.4630835", size = "6 pages", abstract = "Evolutionary Optimization of a gait for a bipedal robot has been studied, combining structural and parametric modifications of the system responsible for generating the gait. The experiment was conducted using a small 17 DOF humanoid robot, whose actuators consist of 17 servo motors. In the approach presented here, individuals representing a gait consisted of a sequence of set angles (referred to as states) for the servo motors, as well as ramping times for the transition between states. A hand-coded gait was used as starting point for the Optimization procedure: A population of 30 individuals was formed, using the hand-coded gait as a seed. An evolutionary procedure was executed for 30 generations, evaluating individuals on the physical robot. New individuals were generated using mutation only. There were two different mutation operators, namely (1) parametric mutations modifying the actual values of a given state, and (2) structural mutations inserting a new state between two consecutive states in an individual. The best evolved individual showed an improvement in walking speed of approximately 65percent.", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @InProceedings{Wolfson:2009:EA, author = "Kfir Wolfson and Moshe Sipper", title = "Efficient List Search Algorithms", booktitle = "9th International Conference, Evolution Artificielle, EA 2009", year = "2009", editor = "Pierre Collet and Nicolas Monmarche and Pierrick Legrand and Marc Schoenauer and Evelyne Lutton", volume = "5975", series = "Lecture Notes in Computer Science", pages = "158--169", address = "Strasbourg, France", month = oct # " 26-28", publisher = "Springer", note = "Revised Selected Papers", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-14155-3", URL = "http://www.cs.bgu.ac.il/~wolfsonk/sublinear_draft.pdf", DOI = "doi:10.1007/978-3-642-14156-0_14", size = "12 pages", abstract = "We peruse the idea of algorithmic design through Darwinian evolution, focusing on the problem of evolving list search algorithms. Specifically, we employ genetic programming (GP) to evolve iterative algorithms for searching for a given key in an array of integers. Our judicious design of an evolutionary language renders the evolution of linear-time search algorithms easy. We then turn to the far more difficult problem of logarithmic-time search, and show that our evolutionary system successfully handles this case. Subsequently, because our setup might be perceived as being geared towards the emergence of binary search, we generalise our genomic representation, allowing evolution to assemble its own useful functions via the mechanism of automatically defined functions (ADFs). We show that our approach routinely and repeatedly evolves general and correct efficient algorithms.", notes = "EA'09 Published 2010. ECJ, memory Array[INDEX] M0 M1 KEY NOP setm0 progn2 if <>== ITER iteration Java. Pop 250, 5000 generations. p168 'Our phenotypes are not Turing complete'", } @Article{Wolfson:2011:GPEM, author = "Kfir Wolfson and Shay Zakov and Moshe Sipper and Michal Ziv-Ukelson", title = "Have your spaghetti and eat it too: evolutionary algorithmics and post-evolutionary analysis", journal = "Genetic Programming and Evolvable Machines", year = "2011", volume = "12", number = "2", pages = "121--160", month = jun, keywords = "genetic algorithms, genetic programming, Darwinian Software Engineering, Search algorithms, Post-evolutionary analysis, Edit distance, Reasoning, Building blocks", ISSN = "1389-2576", URL = "http://www.cs.bgu.ac.il/~wolfsonk/gpea_draft.pdf", DOI = "doi:10.1007/s10710-010-9122-1", size = "40 pages", abstract = "This paper focuses on two issues, first perusing the idea of algorithmic design through genetic programming (GP), and, second, introducing a novel approach for analysing and understanding the evolved solution trees. Considering the problem of list search, we evolve iterative algorithms for searching for a given key in an array of integers, showing that both correct linear-time and far more efficient logarithmic-time algorithms can be repeatedly designed by Darwinian means. Next, we turn to the (evolved) dish of spaghetti (code) served by GP. Faced with the all-too-familiar conundrum of understanding convoluted and usually bloated GP-evolved trees, we present a novel analysis approach, based on ideas borrowed from the field of bioinformatics. Our system, dubbed G-PEA (GP Post-Evolutionary Analysis), consists of two parts: (1) Defining a functionality-based similarity score between expressions, G-PEA uses this score to find subtrees that carry out similar semantic tasks; (2) Clustering similar sub-expressions from a number of independently evolved fit solutions, thus identifying important semantic building blocks ensconced within the hard-to-read GP trees. These blocks help identify the important parts of the evolved solutions and are a crucial step in understanding how they work. Other related GP aspects, such as code simplification, bloat control, and building-block preserving crossover, may be extended by applying the concepts we present.", } @InProceedings{wollesen:1999:B, author = "Eric A. Wollesen and Nicolai Krakowiak and Jason M. Daida", title = "Beowulf anytime for evolutionary computation", booktitle = "Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference", year = "1999", editor = "Scott Brave and Annie S. Wu", pages = "298--304", address = "Orlando, Florida, USA", month = "13 " # jul, keywords = "genetic algorithms, genetic programming", notes = "GECCO-99LB", } @InCollection{wong:2000:SMSPLHMMUGP, author = "Dik Kin Wong", title = "Simultaneous Model Selection and Parameter Learning of Hidden Markov Model Using Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "442--451", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:2000:gagp}", } @InProceedings{wong:1999:EPBMEPF, author = "Kit Po Wong and Jason Yuryevich and An Li", title = "Evolutionary Programming Based Method for Evaluation of Power Flow", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1756--1761", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "real world applications", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-773.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-773.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InCollection{wong:1994:ilpuga, author = "Man Leung Wong and Kwong Sak Leung", title = "Inductive Logic Programming Using Genetic Algorithms", booktitle = "Advances in Artificial Intelligence - Theory and Application II", publisher = "The International Institute for Advanced Studies in Systems Research and Cybernetic", year = "1994", editor = "J. W. Brahan and George E. Lasker", pages = "119--124", address = "Ontario, Canada", keywords = "genetic algorithms, genetic programming", ISBN = "0-921836-19-8", notes = "http://www.iias.edu/pdf_general/Booklisting_Update_2010.pdf gives ISBN and ordering info. ", } @InProceedings{wong:1994:l1rnd, author = "Man Leung Wong and Kwong Sak Leung", title = "Learning First-order Relations from Noisy Databases using Genetic Algorithms", booktitle = "Proceedings of the Second Singapore International Conference on Intelligent Systems", year = "1994", pages = "B159--164", keywords = "genetic algorithms, genetic programming", URL = "http://cptra.ln.edu.hk/~mlwong/conference/spicis1994.pdf", abstract = "In knowledge discovery from databases, we emphasise the need for learning from huge, incomplete and imperfect data sets (Piatetsky-Shapiro and Frawley, 1991). To handle noise in the problem domain, existing learning systems avoid overfitting the imperfect training examples by excluding insignificant patterns. The problem is that these systems use a limiting attribute-value language for representing the training examples and induced knowledge. Moreover, some important patterns are ignored because they are statistically insignificant. This paper describes a system called GLPS that combines Genetic Algorithms and a variation of FOIL (Quinlan, 1990) to learn first-order concepts from noisy training examples. The performance of GLPS is evaluated on the chess endgame domain. A detail comparison to FOIL is accomplished and the performance of GLPS is significantly better than that of FOIL. This result indicates that the Darwinian principle of natural selection is a plausible noise handling method which can avoid overfitting and identify important patterns at the same time.", notes = "SPICIS-94", } @Article{wong:1995:glp, author = "Man Leung Wong and Kwong Sak Leung", title = "Inducing logic programs with genetic algorithms: the Genetic Logic Programming System", journal = "IEEE Expert", year = "1995", volume = "10", number = "5", pages = "68--76", month = oct, keywords = "genetic algorithms, genetic programming, ILP, FOIL, genetic Logic Programming System, evolutionary processing, knowledge representation, learning, logic program induction, knowledge representation, learning (artificial intelligence), logic programming", DOI = "doi:10.1109/64.464935", size = "9 (22) pages", abstract = "Inductive Logic Programming (ILP) integrates the techniques from traditional machine learning and logic programming to construct logic programs from training examples. Most existing systems employ greedy search strategies which may trap the systems in a local maxima. This paper describes a system, called the Genetic Logic Programming System (GLPS), that uses Genetic Algorithms (GA) to search for the best program. This novel framework combines the learning power of GA and knowledge representation power of logic programming to overcome the shortcomings of existing paradigms. A new method is used to represent a logic program as a number of tree structures. This representation facilitates the generation of initial logic programs and other genetic operators. Four applications are used to demonstrate the ability of this approach in inducing various logic programs including the recursive factorial program. Recursive programs are difficult to learn in Genetic Programming (GP). This experiment shows the advantage of Genetic Logic Programming (GLP) over GP. Only a few existing learning systems can handle noisy training examples, by avoiding overfitting the training examples. However, some important patterns will be ignored. The performance of GLPS on learning from noisy examples is evaluated on the chess endgame domain. A systematic method is used to introduce different amounts of noise into the training examples. A detailed comparison with FOIL has been performed and the performance of GLPS is significantly better than that of FOIL by at least 5 percent at the 99.995 percent confidence interval at all noise levels. The largest difference even reaches 24 percent. This encouraging result demonstrates the advantages of our approach over existing ones.", notes = "IEEE Expert Special Track on Evolutionary Programming (P. J. Angeline editor) \cite{angeline:1995:er} ", } @InProceedings{wong:1995:ilpGP, author = "Man Leung Wong and Kwong Sak Leung", title = "An adaptive Inductive Logic Programming system using Genetic Programming", booktitle = "Evolutionary Programming {IV} Proceedings of the Fourth Annual Conference on Evolutionary Programming", year = "1995", editor = "John Robert McDonnell and Robert G. Reynolds and David B. Fogel", pages = "737--752", publisher_address = "Cambridge, MA, USA", address = "San Diego, CA, USA", month = "1-3 " # mar, publisher = "MIT Press", keywords = "genetic algorithms, genetic programming, Uncle problem", ISBN = "0-262-13317-2", URL = "http://cptra.ln.edu.hk/~mlwong/conference/ep1995.pdf", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6300813", DOI = "doi:10.7551/mitpress/2887.003.0062", size = "16 pages", abstract = "Recently, there have been increasing interests in Inductive Logic Programming (ILP) systems. But existing ILP systems cannot improve themselves automatically. This paper describes an Adaptive Inductive Logic Programming (Adaptive ILP) system that evolves during learning. An adaptive ILP system is composed of an external interface, a biases base, a knowledge base of background knowledge, an example database, an empirical ILP learner, a meta-level learner, and a learning controller. A preliminary adaptive ILP system has been implemented. In this implementation, the empirical ILP learner performs top-down search in the hypothesis space defined by the concept description language, the language bias, and the background knowledge. The search is directed by search biases which can be induced and refined by genetic programming (Koza 1992). It has been demonstrated that the adaptive ILP system performs better than FOIL, a famous ILP system (Quinlan 1990), in inducing logic programs from perfect or noisy training examples. The experimentation illustrates the benefit of an adaptive ILP system over existing ILP systems. The result implies that the search bias induced by genetic programming (GP) is better than that of FOIL, which is designed by a top researcher in the field. Consequently, GP is a promising technique for implementing a meta-level learning system. The result is very encouraging as it suggests that the process of natural selection and evolution can successfully evolve a high performance ILP system.", notes = "EP-95", } @InProceedings{wong:1995:lpdpGP, author = "Man Leung Wong and Kwong Sak Leung", title = "Learning Programs in Different Paradigms using Genetic Programming", booktitle = "Proceedings of the Fourth Congress of the Italian Association for Artificial Intelligence", year = "1995", publisher_address = "Berlin, Germany", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", URL = "http://cptra.ln.edu.hk/~mlwong/conference/aiia1995.pdf", abstract = "Genetic Programming (GP) is a method of automatically inducing programs by representing them as parse trees. In theory, programs in any computer languages can be translated to parse trees. Hence, GP should be able to handle them as well. In practice, the syntax of Lisp is so simple and uniform that the translation process can be achieved easily, programs evolved by GP are usually expressed in Lisp. This paper presents a flexible framework that programs in various programming languages can be acquired. This framework is based on a formalism of logic grammars. To implement the framework, a system called LOGENPRO (The LOgic grammar based GENetic PROgramming system) has been developed. An experiment that employs LOGENPRO to induce a S-expression for calculating dot product has been performed. This experiment illustrates that LOGENPRO, when used with knowledge of data types, accelerates the learning of programs. Other experiments have been done to illustrate the ability of LOGENPRO in inducing programs in difference programming languages including Prolog and C. These experiments prove that LOGENPRO is very flexible.", } @InProceedings{wong:1995:islpdpl, author = "Man Leung Wong and Kwong Sak Leung", title = "An Induction System that Learns Programs in different Programming Languages using Genetic Programming and Logic Grammars", booktitle = "Proceedings of the 7th IEEE International Conference on Tools with Artificial Intelligence", year = "1995", pages = "380--387", address = "Herndon, VA, USA", month = "5-8 " # nov, keywords = "genetic algorithms, genetic programming, LOGENPRO, FOIL, Fuzzy Prolog, LISP, LOGENPRO, Prolog, context-ensitive information, domain-dependent knowledge, induction system, inductive logic programming, learning, logic grammars, noisy examples, performance, problem solving, programming languages, data handling, grammars, learning (artificial intelligence), logic programming, logic programming languages, software tools, uncertainty handling", ISBN = "0-8186-7312-5", DOI = "doi:10.1109/TAI.1995.479782", size = "8 pages", abstract = "Genetic programming (GP) and inductive logic programming (ILP) have received increasing interest. Since their formalisms are so different these two approaches cannot be integrated easily though they share many common goals and functionalities. A unification will greatly enhance their problem solving power. Moreover, they are restricted in the computer languages in which programs can be induced. We present a flexible system called LOGENPRO (The logic grammar based genetic programming system) that combines GP and ILP. It is based on a formalism of logic grammars. The system can learn programs in various programming languages and represent context-sensitive information and domain-dependent knowledge. The performance of LOGENPRO in inducing logic programs from noisy examples is evaluated. A detailed comparison with FOIL has been conducted. This experiment demonstrates that LOGENPRO is a promising alternative to other inductive logic programming systems and sometimes is superior for handling noisy data. Moreover, a series of examples are used to illustrate that LOGENPRO is so flexible that programs in different programming languages including LISP, Prolog and Fuzzy Prolog can be induced", notes = "Logic grammars are the generalisation of context free grammars. LOGENPRO is a generalisation and extension of GLPS. LOGENPRO better than GP on dot vector product. LOGENPRO better than FOIL on chess end game. {"}LOGENPRO can emulate the effect of STGP effortlessly{"}. Chess endgame and Prolog fuzzy logic examples.", } @InProceedings{wong:1995:cGPilp, author = "Man Leung Wong and Kwong Sak Leung", title = "Combining Genetic Programming and Inductive Logic Programming using Logic Grammars", booktitle = "1995 IEEE Conference on Evolutionary Computation", year = "1995", volume = "2", pages = "733--736", address = "Perth, Australia", publisher_address = "Piscataway, NJ, USA", month = "29 " # nov # " - 1 " # dec, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, LOGENPRO", URL = "http://cptra.ln.edu.hk/~mlwong/conference/icec1995.pdf", abstract = "Genetic Programming (GP) and Inductive Logic Programming (ILP) have received increasing interest recently. Since their formalisms are so different, these two approaches cannot be integrated easily though they share many common goals and functionalities. A unification will greatly enhance their problem solving power. In this paper, a framework to combine GP and ILP is presented. The framework is based on a formalism of logic grammars and a system called LOGENPRO (the LOgic grammar based GENetic PROgramming system) is developed. It is so flexible that programs in different programming languages such as LISP, Prolog, and C can be induced. The performance of LOGENPRO in inducing logic programs from noisy examples is also evaluated. A detailed comparison to FOIL and mFOIL has been conducted. The experiment demonstrates that LOGENPRO is a promising alternative to other inductive logic programming systems and sometimes is superior for handling noisy data.", notes = "ICEC-95 Editors not given by IEEE, Organisers David Fogel and Chris deSilva. conference details at http://ciips.ee.uwa.edu.au/~dorota/icnn95.html", } @InProceedings{wong:1995:algsf, author = "Man Leung Wong and Kwong Sak Leung", title = "Applying Logic Grammars to Induce Sub-Functions in Genetic Programming", booktitle = "1995 IEEE Conference on Evolutionary Computation", year = "1995", volume = "2", pages = "737--740", address = "Perth, Australia", publisher_address = "Piscataway, NJ, USA", month = "29 " # nov # " - 1 " # dec, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, LOGENPRO", URL = "http://cptra.ln.edu.hk/~mlwong/conference/icec1995b.pdf", abstract = "Genetic Programming (GP) is a method of automatically inducing S-expression in LISP to perform specified tasks. The problem of inducing programs can be reformulated as a search for a highly fit program in the space of all possible programs. This paper presents a framework in which the search space can be specified declaratively by a user. Its application in inducing sub-functions is detailed. The framework is based on a formalism of logic grammars and it is implemented as a system called LOGENPRO (the LOgic grammar based GENetic PROgramming system). The formalism is powerful enough to represent context-sensitive information and domain-dependent knowledge. This knowledge can be used to accelerate the learning speed and/or improve the quality of the programs induced. The system is also very flexible and programs in various programming languages can be acquired. Automatic discovery of sub-functions is one of the most important research areas in Genetic Programming. An experiment is used to demonstrate that LOGENPRO can emulate Koza's Automatically Defined Functions (ADF). Moreover, LOGENPRO can employ knowledge such as argument types in a unified framework. The experiment shows that LOGENPRO has superior performance to that of Koza's ADF when more domain- dependent knowledge is available.", notes = "ICEC-95 Editors not given by IEEE, Organisers David Fogel and Chris deSilva. conference details at http://ciips.ee.uwa.edu.au/~dorota/icnn95.html", } @InCollection{wong:1996:aigp2, author = "Man Leung Wong and Kwong Sak Leung", title = "Evolving Recursive Functions for the Even-Parity Problem Using Genetic Programming", booktitle = "Advances in Genetic Programming 2", publisher = "MIT Press", year = "1996", editor = "Peter J. Angeline and K. E. {Kinnear, Jr.}", pages = "221--240", chapter = "11", address = "Cambridge, MA, USA", keywords = "genetic algorithms, genetic programming", ISBN = "0-262-01158-1", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6277537", DOI = "doi:10.7551/mitpress/1109.003.0016", size = "20 pages", abstract = "One of the most important and challenging areas of research in evolutionary algorithms is the investigation of ways to successfully apply evolutionary algorithms to larger and more complicated problems. In this chapter. we apply GGP (Generic Genetic Programming) to evolve general recursive functions for the even-n-parity problem. GGP is very flexible and programs in various programming languages can be acquired. Moreover. it is powerful enough to handle context-sensitive information and domain-dependent knowledge. This knowledge can be used to accelerate the learning speed and/or improve the quality of the programs induced. A number of experiments have been performed to determine the impact of domain-specific knowledge on the speed of learning.", } @InProceedings{wong:1996:lrfneGGP, author = "Man Leung Wong and Kwong Sak Leung", title = "Learning Recursive Functions from Noisy Examples using Generic Genetic Programming", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "238--246", address = "Stanford University, CA, USA", publisher = "MIT Press", size = "9 pages", URL = "http://cptra.ln.edu.hk/~mlwong/conference/gp1996.pdf", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap29.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", abstract = "One of the most important and challenging areas of research in evolutionary algorithms is the investigation of ways to successfully apply evolutionary algorithms to larger and more complicated problems. In this paper, we apply GGP (Generic Genetic Programming) to evolve general recursive functions for the even-n-parity problem from noisy training examples. GGP is very flexible and programs in various programming languages can be acquired. Moreover, it is powerful enough to handle context-sensitive information and domain-dependent knowledge. A number of experiments have been performed to determine the impact of noise in training examples on the speed of learning.", notes = "GP-96", } @InProceedings{wong:1996:l-g-bGPs, author = "Man Leung Wong and Kwong Sak Leung", title = "The Logic-Grammars-Based Genetic Programming System", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "433", address = "Stanford University, CA, USA", publisher = "MIT Press", URL = "http://cptra.ln.edu.hk/~mlwong/conference/gp1996b.pdf", size = "1 page", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap72.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96", } @PhdThesis{ManLeungWong:thesis, author = "Man Leung Wong", title = "Evolutionary Program Induction Directed by Logic Grammars", school = "Department of Computer Science and Engineering, The Chinese University of Hong Kong", year = "1995", address = "Hong Kong", month = jun, keywords = "genetic algorithms, genetic programming, Logic programming, ILP, LOGENPRO, FOIL, Uncle predicate", URL = "http://etheses.lib.cuhk.edu.hk/pdf/000743570.pdf", size = "253 pages", abstract = "Program induction generates a computer program with the desired behaviour for a given set of situations. Genetic Programming (GP) and Inductive Logic Programming (ILP) are two of the approaches for program induction. GP is a method of automatically inducing S-expressions in Lisp to perform specified tasks while ILP involves the construction of logic programs from examples and background knowledge. Since their formalisms are very different, these two approaches cannot be integrated easily although their properties and goals are similar. If they can be combined in a common framework, then their techniques and theories can be shared and their problem solving power can be enhanced. This thesis describes a framework that integrates GP and ILP based on a formalism of logic grammars. A system called LOGENPRO (the LOgic grammar based GENetic PROgramming system) is developed. This system has been tested on many problems in program induction, knowledge discovery from databases, and meta-level learning. These experiments demonstrate that the proposed framework is powerful, flexible, and general. Experiments are performed to illustrate that programs in different programming languages can be induced by LOGENPRO. The problem of inducing programs can be formulated as a search for a highly fit program in the space of all possible programs. This thesis shows that the search space can be specified declaratively by the user in the framework. Moreover, the formalism is powerful enough to represent context-sensitive information and domain-dependent knowledge. This knowledge can be used to accelerate the learning speed and/or improve the quality of the programs induced. Knowledge discovery systems induce knowledge from datasets which are huge, noisy (incorrect), incomplete, inconsistent, imprecise (fuzzy), and uncertain. The problem is that existing systems use a limiting attribute-value language for representing the training examples and induced knowledge. Furthermore, some important patterns are ignored because they are statistically insignificant. LOGENPRO is employed to induce knowledge from noisy training examples. The knowledge is represented in first-order logic program. The performance of LOGENPRO is evaluated on the chess endgame domain. Detailed comparisons with other ILP systems are performed. It is found that LOGENPRO outperforms these ILP systems significantly at most noise levels. This experiment indicates that the Darwinian principle of natural selection is a plausible noise handling method which can avoid over fitting and identify important patterns at the same time. An Adaptive Inductive Logic Programming (Adaptive ILP) system is implemented using LOGENPRO as the meta-level learner. The system performs better than FOIL in inducing logic programs from perfect and noisy training examples. The result is very encouraging as it suggests that LOGENPRO can successfully evolve a high performance ILP system.", notes = "Supervisor: K.S. Leung", } @Article{ManLeungWong:1997:epidlg, author = "Man Leung Wong and Kwong Sak Leung", title = "Evolutionary Program Induction Directed by Logic Grammars", journal = "Evolutionary Computation", year = "1997", volume = "5", number = "2", pages = "143--180", month = "summer", keywords = "genetic algorithms, genetic programming, Machine learning, logic grammars", URL = "http://cptra.ln.edu.hk/~mlwong/journal/ec1997.pdf", URL = "http://www.mitpressjournals.org/doi/pdfplus/10.1162/evco.1997.5.2.143", DOI = "doi:10.1162/evco.1997.5.2.143", size = "39 pages", abstract = "Program induction generates a computer program that can produce the desired behavior for a given set of situations. Two of the approaches in program induction are inductive logic programming (ILP) and genetic programming (GP). Since their formalisms are so different, these two approaches cannot be integrated easily, although they share many common goals and functionalities. A unification will greatly enhance their problem-solving power. Moreover, they are restricted in the computer languages in which programs can be induced. In this paper, we present a flexible system called LOGENPRO (The LOgic grammar-based GENetic PROgramming system) that uses some of the techniques of GP and ILP. It is based on a formalism of logic grammars. The system applies logic grammars to control the evolution of programs in various programming languages and represent context-sensitive information and domain-dependent knowledge. Experiments have been performed to demonstrate that LOGENPRO can emulate GP and GP with automatically defined functions (ADFs). Moreover, LOGENPRO can employ knowledge such as argument types in a unified framework. The experiments show that LOGENPRO has superior performance to that of GP and GP with ADFs when more domain-dependent knowledge is available. We have applied LOGENPRO to evolve general recursive functions for the even-n-parity from noisy training examples. A number of experiments have been performed to determine the impact of domain-specific knowledge and noise in training examples on the speed of learning.", notes = "Evolutionary Computation (Journal) Special Issue: Trends in Evolutionary Methods for Program Induction", } @Article{wong:1998:ESA, author = "Man Leung Wong", title = "An adaptive knowledge-acquisition system using generic genetic programming", journal = "Expert Systems with Applications", volume = "15", pages = "47--58", year = "1998", number = "1", keywords = "genetic algorithms, genetic programming", ISSN = "0957-4174", DOI = "doi:10.1016/S0957-4174(98)00010-4", URL = "http://cptra.ln.edu.hk/~mlwong/journal/esa1998.pdf", URL = "http://www.sciencedirect.com/science/article/B6V03-3TGSH84-3/1/83b0941e6fae053fea766e293d408cf9", size = "12 pages", abstract = "The knowledge-acquisition bottleneck greatly obstructs the development of knowledge-based systems. One popular approach to knowledge acquisition uses inductive concept learning to derive knowledge from examples stored in databases. However, existing learning systems cannot improve themselves automatically. This paper describes an adaptive knowledge-acquisition system that can learn first-order logical relations and improve itself automatically. The system is composed of an external interface, a biases base, a knowledge base of background knowledge, an example database, an empirical ILP learner, a meta-level learner, and a learning controller. In this system, the empirical ILP learner performs top-down search in the hypothesis space defined by the concept description language, the language bias, and the background knowledge. The search is directed by search biases which can be induced and refined by the meta-level learner based on generic genetic programming. It has been demonstrated that the adaptive knowledge-acquisition system performs better than FOIL on inducing logical relations from perfect or noisy training examples. The result implies that the search bias evolved by evolutionary learning is better than that of FOIL which is designed by a top researcher in the field. Consequently, generic genetic programming is a promising technique for implementing a meta-level learning system. The result is very encouraging as it suggests that the process of natural selection and evolution can successfully evolve a high-performance learning system.", } @Book{ManLeungWong:book, author = "Man Leung Wong and Kwong Sak Leung", title = "Data Mining Using Grammar Based Genetic Programming and Applications", publisher = "Kluwer Academic Publishers", year = "2000", volume = "3", series = "Genetic Programming", month = jan, keywords = "genetic algorithms, genetic programming", ISBN = "0-7923-7746-X", URL = "http://www.springer.com/computer/ai/book/978-0-7923-7746-7", notes = "Data mining involves the non-trivial extraction of implicit, previously unknown, and potentially useful information from databases. Genetic Programming (GP) and Inductive Logic Programming (ILP) are two of the approaches for data mining. This book first sets the necessary backgrounds for the reader, including an overview of data mining, evolutionary algorithms and inductive logic programming. It then describes a framework, called GGP (Generic Genetic Programming), that integrates GP and ILP based on a formalism of logic grammars. The formalism is powerful enough to represent context- sensitive information and domain-dependent knowledge. This knowledge can be used to accelerate the learning speed and/or improve the quality of the knowledge induced. A grammar-based genetic programming system called LOGENPRO (The LOGic grammar based GENetic PROgramming system) is detailed and tested on many problems in data mining. It is found that LOGENPRO outperforms some ILP systems. We have also illustrated how to apply LOGENPRO to emulate Automatically Defined Functions (ADFs) to discover problem representation primitives automatically. By employing various knowledge about the problem being solved, LOGENPRO can find a solution much faster than ADFs and the computation required by LOGENPRO is much smaller than that of ADFs. Moreover, LOGENPRO can emulate the effects of Strongly Type Genetic Programming and ADFs simultaneously and effortlessly. Data Mining Using Grammar Based Genetic Programming and Applications is appropriate for researchers, practitioners and clinicians interested in genetic programming, data mining, and the extraction of data from databases. Contents List of Figures. List of Tables. Preface. 1. Introduction. 2. An Overview of Data Mining. 3. An Overview on Evolutionary Algorithms. 4. Inductive Logic Programming. 5. The Logic Grammars Based Genetic Programming System (LOGENPRO). 6. Data Mining Applications Using LOGENPRO. 7. Applying LOGENPRO for Rule Learning. 8. Medical Data Mining. 9. Conclusion and Future Work. Appendix A: The Rule Sets Discovered. Appendix B: The Grammar Used for the Fracture and Scoliosis Databases. References. Index.", size = "232 pages", } @Article{wong:2000:dkm, author = "Man Leung Wong and Wai Lam and Kwong Sak Leung and Po Shun Ngan and Jack C. Y. Cheng", title = "Discovering knowledge from medical databases using evolutionory algorithms", journal = "IEEE Engineering in Medicine and Biology Magazine", year = "2000", volume = "19", number = "4", pages = "45--55", month = jul # "-" # aug, keywords = "genetic algorithms, genetic programming, database management systems, medical databases, knowledge discovery, Bayesian networks, causality relationship models, Bayesian network learning process, continuous variables, advanced evolutionary algorithms, evolutionary programming, learning tasks, fracture database, child fractures, scoliosis database, scoliosis classification, novel clinical knowledge, database errors", ISSN = "0739-5175", URL = "http://ieeexplore.ieee.org/iel5/51/18543/00853481.pdf", size = "11 pages", abstract = "Discusses learning roles and causal structures for capturing patterns and causality relationships. The authors present their approach for knowledge discovery from two specific medical databases. First, rules are learned to represent the interesting patterns of the data. Second, Bayesian networks are induced to act as causality relationship models among the attributes. The Bayesian network learning process is divided into two phases. In the first phase, a discretization policy is learned to discretize the continuous variables, and then Bayesian network structures are induced in the second phase. The authors employ advanced evolutionary algorithms such as generic genetic programming, evolutionary programming, and genetic algorithms to conduct the learning tasks. From the fracture database, they discovered knowledge about the patterns of child fractures. From the scoliosis database, they discovered knowledge about the classification of scoliosis. They also found unexpected rules that led to discovery of errors in the database. These results demonstrate that the knowledge discovery process can find interesting knowledge about the data, which can provide novel clinical knowledge as well as suggest refinements of the existing knowledge.", } @Article{Wong:2000:JASIS, author = "Man Leung Wong and Kwong Sak Leung and Jack C. Y. Cheng", title = "Discovering Knowledge from Noisy Databases using Genetic Programming", journal = "Journal of the American Society for Information Science", year = "2000", volume = "51", number = "9", pages = "870--881", keywords = "genetic algorithms, genetic programming, Data mining, Evolutionary Computation, Rule Learning", URL = "http://cptra.ln.edu.hk/~mlwong/journal/jasis2000.pdf", DOI = "doi:10.1002/(SICI)1097-4571(2000)51:9<870::AID-ASI90>3.0.CO;2-R", abstract = "In data mining, we emphasise the need for learning from huge, incomplete and imperfect data sets (Fayyad et al. 1996, Frawley et al. 1991, Piatetsky-Shapiro and Frawley, 1991). To handle noise in the problem domain, existing learning systems avoid overfitting the imperfect training examples by excluding insignificant patterns. The problem is that these systems use a limiting attribute-value language for representing the training examples and the induced knowledge. Moreover, some important patterns are ignored because they are statistically insignificant. In this paper, we present a framework that combines Genetic Programming (Koza 1992; 1994) and Inductive Logic Programming (Muggleton, 1992) to induce knowledge represented in various knowledge representation formalisms from noisy databases. The framework is based on a formalism of logic grammars and it can specify the search space declaratively. An implementation of the framework, LOGENPRO (The Logic grammar based GENetic PROgramming system), has been developed. The performance of LOGENPRO is evaluated on the chess endgame domain. We compare LOGENPRO with FOIL and other learning systems in detail and find its performance is significantly better than that of the others. This result indicates that the Darwinian principle of natural selection is a plausible noise handling method which can avoid overfitting and identify important patterns at the same time. Moreover, the system is applied to one real-life medical database. The knowledge discovered provides insights to and allows better understanding of the medical domains.", } @Article{Wong:2001:DSS, author = "Man Leung Wong", title = "A Flexible Knowledge Discovery System using Genetic Programming and Logic Grammars", journal = "Decision Support Systems", year = "2001", volume = "31", pages = "405--428", keywords = "genetic algorithms, genetic programming, Knowledge Discovery in Databases, Logic Grammars, Fuzzy Petri Nets", URL = "http://cptra.ln.edu.hk/~mlwong/journal/dss2001.pdf", broken = "http://www.sciencedirect.com/science/article/B6V8S-43W051G-2/2/e504e5d59385b792e3c424bd5bb4d003", DOI = "doi:10.1016/S0167-9236(01)00092-6", abstract = "As the computing world moves from the information age into the knowledge-based age, it is beneficial to induce knowledge from the information super highway formed from the Internet and intranet. The knowledge acquired can be expressed in different knowledge representations such as computer programs, first-order logical relations, or Fuzzy Petri Nets (FPNs). In this paper, we present a flexible knowledge discovery system called GGP (Generic Genetic Programming) that applies genetic programming and logic grammars to learn knowledge in various knowledge representation formalisms. An experiment is performed to demonstrate that GGP can discover knowledge represented in FPNs that support fuzzy and approximate reasoning. To evaluate the performance of GGP in producing good FPNs, the classification accuracy of the fuzzy Petri net induced by GGP and that of the decision tree generated by C4.5 are compared. Moreover, the performance of GGP in inducing logic programs from noisy examples is evaluated. A detailed comparison to FOIL, a system that induces logic programs, has been conducted. These experiments demonstrate that GGP is a promising alternative to other knowledge discovery systems and sometimes is superior for handling noisy and inexact data.", } @InProceedings{wong:aspgp03, author = "Man Leung Wong", title = "Applying Adaptive Grammar Based Genetic Programming in Evolving Recursive Programs", booktitle = "Proceedings of The First Asian-Pacific Workshop on Genetic Programming", year = "2003", editor = "Sung-Bae Cho and Nguyen Xuan Hoai and Yin Shan", pages = "1--8", address = "Rydges (lakeside) Hotel, Canberra, Australia", month = "8 " # dec, keywords = "genetic algorithms, genetic programming, ADF, GBGP", ISBN = "0-9751724-0-9", URL = "http://cptra.ln.edu.hk/~mlwong/conference/aspgp2003.pdf", size = "8 pages", abstract = "Genetic programming (GP) extends traditional genetic algorithms to automatically induce computer programs. GP has been applied in a wide range of applications such as software re-engineering, electrical circuits synthesis, knowledge engineering, and data mining. One of the most important and challenging research areas in GP is the investigation of ways to successfully evolve recursive programs. A recursive program is one that calls itself either directly or indirectly through other programs. Because recursions lead to compact and general programs and provide a mechanism for reusing program code, they facilitate GP to solve larger and more complicated problems. Nevertheless, it is commonly agreed that the recursive program learning problem is very difficult for GP. In this paper, we propose a technique to tackle the difficulties in learning recursive programs. The technique is incorporated into an adaptive Grammar Based Genetic Programming system (adaptive GBGP). A number of experiments have been performed to demonstrate that the system can evolve recursive programs efficiently and effectively.", notes = "aspgp03. even-n-parity, building blocks", } @InProceedings{Man-Leung:Pea:cec2005, author = "Man-Leung Wong and Tien-Tsin Wong and Ka-Ling Fok", title = "Parallel evolutionary algorithms on graphics processing unit", booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary Computation", year = "2005", editor = "David Corne and Zbigniew Michalewicz and Bob McKay and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Gunther Raidl and Kay Chen Tan and Ali Zalzala", pages = "2286--2293", address = "Edinburgh, Scotland, UK", month = "2-5 " # sep, publisher = "IEEE Press", volume = "3", keywords = "genetic algorithms, GPU", ISBN = "0-7803-9363-5", URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=10417&isvol=3", URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=10417", DOI = "doi:10.1109/CEC.2005.1554979", size = "8 pages", abstract = "Evolutionary algorithms (EAs) are effective and robust methods for solving many practical problems such as feature selection, electrical circuit synthesis, and data mining. However, they may execute for a long time for some difficult problems, because several fitness evaluations must be performed. A promising approach to overcome this limitation is to parallelise these algorithms. In this paper, we propose to implement a parallel EA on consumer-level graphics cards. We perform experiments to compare our parallel EA with an ordinary EA and demonstrate that the former is much more effective than the latter. Since consumer-level graphics cards are available in ubiquitous personal computers and these computers are easy to use and manage, more people are able to use our parallel algorithm to solve their problems encountered in real-world applications", } @Article{wong:2005:GPEM, author = "Man Leung Wong", title = "Evolving Recursive Programs by Using Adaptive Grammar Based Genetic Programming", journal = "Genetic Programming and Evolvable Machines", year = "2005", volume = "6", number = "4", pages = "421--455", month = dec, keywords = "genetic algorithms, genetic programming, grammar based genetic programming, logic grammars, recursive programs", ISSN = "1389-2576", URL = "http://cptra.ln.edu.hk/~mlwong/journal/gpem2005.pdf", DOI = "doi:10.1007/s10710-005-4805-8", size = "35 pages", abstract = "Genetic programming (GP) extends traditional genetic algorithms to automatically induce computer programs. GP has been applied in a wide range of applications such as software re-engineering, electrical circuits synthesis, knowledge engineering, and data mining. One of the most important and challenging research areas in GP is the investigation of ways to successfully evolve recursive programs. A recursive program is one that calls itself either directly or indirectly through other programs. Because recursions lead to compact and general programs and provide a mechanism for reusing program code, they facilitate GP to solve larger and more complicated problems. Nevertheless, it is commonly agreed that the recursive program learning problem is very difficult for GP. In this paper, we propose techniques to tackle the difficulties in learning recursive programs. The techniques are incorporated into an adaptive Grammar Based Genetic Programming system (adaptive GBGP). A number of experiments have been performed to demonstrate that the system improves the effectiveness and efficiency in evolving recursive programs.", notes = "An erratum to this article is available at http://dx.doi.org/10.1007/s10710-006-7455-6 : 'The publisher apologizes for an error that occurred in the above mentioned article. The error appears in the printed version, as well as in the html and pdf version online. Man Leung Wong is the sole author of this article' 'contains logic gaols' genetic operations performed on derivation tree, biases updated during run. Final grammar better (easier to evolve solutions from) than initial one, self adaption. non-terminating programs direct selection of crossover points. 11-Mux. General solution to even-n-parity. p452 GP outperformed random search.", } @Article{Wong:2016:GPEM, author = "Man Leung Wong", title = "{Stephen H. Muggleton} and {Hiroaki Watanabe} (Eds.): {Latest} advances in inductive logic programming {World Scientific Publishing}, 2014, {ISBN}: 978-1783265084", journal = "Genetic Programming and Evolvable Machines", year = "2016", volume = "17", number = "1", pages = "77--79", month = mar, note = "Book Review", keywords = "ILP", ISSN = "1389-2576", URL = "https://rdcu.be/dR83I", DOI = "doi:10.1007/s10710-015-9260-6", size = "3 pages", } @InProceedings{Wong:2014:CEC, title = "Grammar-Based Genetic Programming with {Bayesian} Network", author = "Pak-Kan Wong and Leung-Yau Lo and Man-Leung Wong and Kwong-Sak Leung", pages = "739--746", booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary Computation", year = "2014", month = "6-11 " # jul, editor = "Carlos A. {Coello Coello}", address = "Beijing, China", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Evolutionary Algorithms with Statistical and Machine Learning Techniques, Estimation of distribution algorithms", DOI = "doi:10.1109/CEC.2014.6900423", abstract = "Grammar-Based Genetic Programming (GBGP) improves the search performance of Genetic Programming (GP) by formalising constraints and domain specific knowledge in grammar. The building blocks (i.e. the functions and the terminals) in a program can be dependent. Random crossover and mutation destroy the dependence with a high probability, hence breeding a poor program from good programs. Understanding on the syntactic and semantic in the grammar plays an important role to boost the efficiency of GP by reducing the number of poor breeding. Therefore, approaches have been proposed by introducing context sensitive ingredients encoded in probabilistic models. In this paper, we propose Grammar-Based Genetic Programming with Bayesian Network (BGBGP) which learns the dependence by attaching a Bayesian network to each derivation rule and demonstrates its effectiveness in two benchmark problems.", notes = "WCCI2014", } @InProceedings{Wong:2014:GECCO, author = "Pak-Kan Wong and Leung-Yau Lo and Man-Leung Wong and Kwong-Sak Leung", title = "Grammar-based genetic programming with dependence learning and bayesian network classifier", booktitle = "GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation", year = "2014", editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges", isbn13 = "978-1-4503-2662-9", pages = "959--966", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, Automatic Programming, grammar-based genetic programming, Bayesian network, classifier", URL = "http://doi.acm.org/10.1145/2576768.2598256", DOI = "doi:10.1145/2576768.2598256", size = "8 pages", abstract = "Grammar-Based Genetic Programming formalises constraints on the solution structure based on domain knowledge to reduce the search space and generate grammatically correct individuals. Nevertheless, building blocks in a program can often be dependent, so the effective search space can be further reduced. Approaches have been proposed to learn the dependence using probabilistic models and shown to be useful in finding the optimal solutions with complex structure. It raises questions on how to use the individuals in the population to uncover the underlying dependence. Usually, only the good individuals are selected. To model the dependence better, we introduce Grammar-Based Genetic Programming with Bayesian Network Classifier (GBGPBC) which also uses poorer individuals. With the introduction of class labels, we further propose a refinement technique on probability distribution based on class label. Our results show that GBGPBC performs well on two benchmark problems. These techniques boost the performance of our system.", notes = "Also known as \cite{2598256} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)", } @InProceedings{Wong:2016:PPSN, author = "Pak-Kan Wong and Man-Leung Wong and Kwong-Sak Leung", title = "Hierarchical Knowledge in Self-Improving Grammar Based Genetic Programming", booktitle = "14th International Conference on Parallel Problem Solving from Nature", year = "2016", editor = "Julia Handl and Emma Hart and Peter R. Lewis and Manuel Lopez-Ibanez and Gabriela Ochoa and Ben Paechter", volume = "9921", series = "LNCS", pages = "270--280", address = "Edinburgh", month = "17-21 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Hierarchical knowledge learning, Estimation of distribution programming, Adaptive grammar, Bayesian network", isbn13 = "978-3-319-45823-6", DOI = "doi:10.1007/978-3-319-45823-6_25", abstract = "Structure of a grammar can influence how well a Grammar-Based Genetic Programming system solves a given problem but it is not obvious to design the structure of a grammar, especially when the problem is large. In this paper, our proposed Bayesian Grammar-Based Genetic Programming with Hierarchical Learning (BGBGP-HL) examines the grammar and builds new rules on the existing grammar structure during evolution. Once our system successfully finds the good solution(s), the adapted grammar will provide a grammar-based probabilistic model to the generation process of optimal solution(s). Moreover, our system can automatically discover new hierarchical knowledge (i.e. how the rules are structurally combined) which composes of multiple production rules in the original grammar. In the case study using deceptive royal tree problem, our evaluation shows that BGBGP-HL achieves the best performance among the competitors while it is capable of composing hierarchical knowledge. Compared to other algorithms, search performance of BGBGP-HL is shown to be more robust against deceptiveness and complexity of the problem.", notes = "PPSN2016", } @InProceedings{conf/tpnc/WongWL16, author = "Pak-Kan Wong and Man Leung Wong and Kwong-Sak Leung", title = "Learning Grammar Rules in Probabilistic Grammar-Based Genetic Programming", booktitle = "Theory and Practice of Natural Computing - 5th International Conference, {TPNC} 2016, Sendai, Japan, December 12-13, 2016, Proceedings", editor = "Carlos Martin-Vide and Takaaki Mizuki and Miguel A. Vega-Rodriguez", year = "2016", volume = "10071", isbn13 = "978-3-319-49000-7", pages = "208--220", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming, estimation of distribution programming adaptive grammar Bayesian network", bibdate = "2017-05-21", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/tpnc/tpnc2016.html#WongWL16", DOI = "doi:10.1007/978-3-319-49001-4_17", abstract = "Grammar-based Genetic Programming (GBGP) searches for a computer program in order to solve a given problem. Grammar constrains the set of possible programs in the search space. It is not obvious to write an appropriate grammar for a complex problem. Our proposed Bayesian Grammar-Based Genetic Programming with Hierarchical Learning (BGBGP-HL) aims at automatically designing new rules from existing relatively simple grammar rules during evolution to improve the grammar structure. The new grammar rules also reflects the new understanding of the existing grammar under the given fitness evaluation function. Based on our case study in asymmetric royal tree problem, our evaluation shows that BGBGP-HL achieves the best performance among the competitors. Compared to other algorithms, search performance of BGBGP-HL is demonstrated to be more robust against dependencies and the changes in complexity of programs.", } @InProceedings{Wong:2019:GECCOcomp, author = "Pak-Kan Wong and Man-Leung Wong and Kwong-Sak Leung", title = "Probabilistic grammar-based deep neuroevolution", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", isbn13 = "978-1-4503-6748-6", pages = "87--88", address = "Prague, Czech Republic", DOI = "doi:10.1145/3319619.3326778", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{3326778} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InProceedings{Wong:2019:GECCOcompa, author = "Pak-Kan Wong and Man-Leung Wong and Kwong-Sak Leung", title = "Semantic fitness function in genetic programming based on semantics flow analysis", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", isbn13 = "978-1-4503-6748-6", pages = "354--355", address = "Prague, Czech Republic", DOI = "doi:10.1145/3319619.3321960", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{3321960} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @Article{WONG:2019:eswa, author = "Pak-Kan Wong and Kwong-Sak Leung and Man-Leung Wong", title = "Probabilistic grammar-based neuroevolution for physiological signal classification of ventricular tachycardia", journal = "Expert Systems with Applications", volume = "135", pages = "237--248", year = "2019", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2019.06.012", URL = "http://www.sciencedirect.com/science/article/pii/S0957417419304129", keywords = "genetic algorithms, genetic programming, Physiological signal classification, Heart disease, Neuroevolution, Probabilistic grammar, Deep neural network", abstract = "Ventricular tachycardia is a rapid heart rhythm that begins in the lower chambers of the heart. When it happens continuously, this may result in life-threatening cardiac arrest. In this paper, we apply deep learning techniques to tackle the problem of the physiological signal classification of ventricular tachycardia, since deep learning techniques can attain outstanding performance in many medical applications. Nevertheless, human engineers are required to manually design deep neural networks to handle different tasks. This can be challenging because of many possible deep neural network structures. Therefore, a method, called ADAG-DNE, is presented to automatically design deep neural network structures using deep neuroevolution. Our approach defines a set of structures using probabilistic grammar and searches for best network structures using Probabilistic Model Building Genetic Programming. ADAG-DNE takes advantages of the probabilistic dependencies found among the structures of networks. When applying ADAG-DNE to the classification problem, our discovered model achieves better accuracy than AlexNet, ResNet, and seven non-neural network classifiers. It also uses about 2percent of parameters of AlexNet, which means the inference can be made quickly. To summarize, our method evolves a deep neural network, which can be implemented in expert systems. The deep neural network achieves high accuracy. Moreover, it is simpler than existing deep neural networks. Thus, computational efficiency and diagnosis accuracy of the expert system can be improved", } @Article{Pak-Kan_Wong:EC, author = "Pak-Kan Wong and Man-Leung Wong and Kwong-Sak Leung", title = "Probabilistic Contextual and Structural Dependencies Learning in Grammar-Based Genetic Programming", journal = "Evolutionary Computation", year = "2021", volume = "29", number = "2", pages = "239--268", month = "Summer", keywords = "genetic algorithms, genetic programming, adaptive grammar-based genetic programming, Estimation of distribution programming, Bayesian network classifier, data mining.", ISSN = "1063-6560", DOI = "doi:10.1162/evco_a_00280", size = "30 pages", abstract = "Genetic Programming is a method to automatically create computer programs based on the principles of evolution. The problem of deceptiveness caused by complex dependencies among components of programs is challenging. It is important because it can misguide Genetic Programming to create sub-optimal programs. Besides, a minor modification in the programs may lead to a notable change in the program behaviours and affect the final outputs. This paper presents Grammar-based Genetic Programming with Bayesian Classifiers (GBGPBC) in which the probabilistic dependencies among components of programs are captured using a set of Bayesian network classifiers. Our system was evaluated using a set of benchmark problems (the deceptive maximum problems, the royal tree problems, and the bipolar asymmetric royal tree problems). It was shown to be often more robust and more efficient in searching the best programs than other related Genetic Programming approaches in terms of the total number of fitness evaluation. We studied what factors affect the performance of GBGPBC and discovered that robust variants of GBGPBC were consistently weakly correlated with some complexity measures. Furthermore, our approach has been applied to learn a ranking program on a set of customers in direct marketing. Our suggested solutions help companies to earn significantly more when compared with other solutions produced by several well-known machine learning algorithms, such as neural networks, logistic regression, and Bayesian networks.", notes = "Department of Computer Science and Engineering, The Chinese University of HongKong, Hong Kong", } @Article{WONG:2023:prime, author = "Pauline Wong and W. K. Wong and Filbert H. Juwono and Basil Andy Lease and Lenin Gopal and I. M. Chew", title = "Sensor abnormality detection in multistage compressor units: A {"}white box{"} approach using tree-based genetic programming", journal = "e-Prime - Advances in Electrical Engineering, Electronics and Energy", volume = "5", pages = "100209", year = "2023", ISSN = "2772-6711", DOI = "doi:10.1016/j.prime.2023.100209", URL = "https://www.sciencedirect.com/science/article/pii/S2772671123001043", keywords = "genetic algorithms, genetic programming, Sensor abnormality, Fault detection", abstract = "Sensors are crucial in detecting equipment problems in various plant systems. In particular, detecting sensor abnormality is challenging in the case of using the data which are acquired and stored offline (data logs). These data are normally acquired using Internet of Things (IoT) system and stored in a dedicated server. This situation presents both opportunities and challenges for exploration in sensor abnormality detection task. In this paper, we propose a multistage compressor sensor fault detection method using data logs. In the proposed method, the compressor sensor output is modeled as a function of other sensors using static approach. Subsequently, the model output is used for detecting abnormality by observing the residuals. It has been shown that the histogram of residuals offers rich information to predict abnormality of the targeted sensor. In particular, we explore the concept using Genetic Programming (GP) to generate regression model which offers more {"}white box{"} solution to the operators. There are various advantages in this approach. Firstly, the conventional {"}black box{"} approach lacks model transparency and, thus, is highly undesirable in critical systems. Secondly, equations are more easily applied in Programmable Logic Controller (PLC) if autonomous flagging is required. We also compare the proposed model with Multiple Linear Regression (MLR) and Neural Network Regression (ANN). Results show that the best generated models are comparable with the latter but with more crisp {"}white box{"} mathematical equations using lesser feature inputs (four features only). This model yields R2 of 0.991 and RMSE of 2.1times10-2", } @TechReport{vuw-CS-TR-06-7, author = "Phillip Wong and Mengjie Zhang", title = "Algebraic Simplification of Genetic Programs during Evolution", institution = "Computer Science, Victoria University of Wellington", year = "2006", number = "CS-TR-06-7", address = "New Zealand", month = feb, keywords = "genetic algorithms, genetic programming, Algebraic Simplification, Program Simplification, Code Bloating, Online Simplification", URL = "http://www.mcs.vuw.ac.nz/comp/Publications/archive/CS-TR-06/CS-TR-06-7.pdf", URL = "http://www.mcs.vuw.ac.nz/comp/Publications/CS-TR-06-7.abs.html", abstract = "Program bloat is a fundamental problem in the field of Genetic Programming (GP). Exponential growth of redundant and functionally useless sections of programs can quickly overcome a GP system, exhausting system resources and causing premature termination of the system before an acceptable solution can be found. Simplification is an attempt to remove such redundancies from programs. This paper looks at the effects of applying an algebraic simplification algorithm to programs during the GP evolution. The GP system with the simplification is examined and compared to a standard GP system on four regression and classification problems of varying difficulty. The results suggest that the GP system employing a simplification component can achieve superior efficiency and effectiveness to the standard system on these problems.", } @InProceedings{1144156, author = "Phillip Wong and Mengjie Zhang", title = "Algebraic simplification of GP programs during evolution", booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation", year = "2006", editor = "Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos {Coello Coello} and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens", volume = "1", ISBN = "1-59593-186-4", pages = "927--934", address = "Seattle, Washington, USA", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "8-12 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, algebraic simplification, code bloating, online simplification, program simplification", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p927.pdf", DOI = "doi:10.1145/1143997.1144156", size = "8 pages", abstract = "Program bloat is a fundamental problem in the field of Genetic Programming (GP). Exponential growth of redundant and functionally useless sections of programs can quickly overcome a GP system, exhausting system resources and causing premature termination of the system before an acceptable solution can be found. Simplification is an attempt to remove such redundancies from programs. This paper looks at the effects of applying an algebraic simplification algorithm to programs during the GP evolution. The GP system with the simplification is examined and compared to a standard GP system on four regression and classification problems of varying difficulty. The results suggest that the GP system employing a simplification component can achieve superior efficiency and effectiveness to the standard system on these problems.", notes = "GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM Order Number 910060", } @TechReport{Wong:2006:CS-TR-06-15, author = "Phillip Wong and Mengjie Zhang", title = "Numerical-node building block analysis of genetic programming with simplification", institution = "Computer Science, Victoria University of Wellington", year = "2006", number = "CS-TR-06-15", address = "New Zealand", month = dec, keywords = "genetic algorithms, genetic programming, Simplification, building blocks, numerical-nodes", URL = "http://www.mcs.vuw.ac.nz/comp/Publications/archive/CS-TR-06/CS-TR-06-15.pdf", URL = "http://www.mcs.vuw.ac.nz/comp/Publications/archive/CS-TR-06/CS-TR-06-15.ps.gz", abstract = "This paper investigates the effects on building blocks of using simplification in a GP system to combat the problem of code bloat. The evolved genetic programs are simplified online during the evolutionary process using algebraic simplification rules and hashing techniques. A simplified form of building block (numerical-nodes) are tracked throughout several individual GP runs both when using and not using simplification. The results suggest that simplification disrupts existing potential building blocks during the evolution process. However, the result s also suggest that simplification is capable of creating new building blocks which are used to form a more accurate solution than the standard GP. The effectiveness of GP systems simplification can be correlated to the creation of these new building blocks.", size = "17 pages", } @InProceedings{1277311, author = "Phillip Lee-Ming Wong and Mengjie Zhang", title = "Numerical-node building block analysis of genetic programming with simplification", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "1761--1761", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1761.pdf", DOI = "doi:10.1145/1276958.1277311", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming: Poster, numerical nodes, simplification", abstract = "This paper investigates the effects on building blocks of using online simplification in a GP system. Numerical nodes are tracked through individual runs to observe their behaviour. Results show that simplification disrupts building blocks early on, but also creates new building blocks.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @InProceedings{Wong:2007:cec, author = "Phillip Wong and Mengjie Zhang", title = "Effects of Program Simplification on Simple Building Blocks in Genetic Programming", booktitle = "2007 IEEE Congress on Evolutionary Computation", year = "2007", editor = "Dipti Srinivasan and Lipo Wang", pages = "1570--1577", address = "Singapore", month = "25-28 " # sep, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "1-4244-1340-0", file = "1789.pdf", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2007.4424660", abstract = "This paper investigates the effects on building blocks of using simplification in a Genetic Programming (GP) system to combat the problem of code bloat. The evolved genetic programs are simplified online during the evolutionary process using algebraic simplification rules and hashing techniques. A simplified form of building block (numerical-nodes) is tracked throughout individual GP runs both when using or not using online simplification of evolved genetic programs. The results suggest that online simplification disrupts existing potential building blocks during the evolution process. However, GP with simplification is capable of creating new building blocks which are used to form a more accurate solution, when compared to the standard GP. The effectiveness of GP systems using simplification can be correlated to the creation of these new building blocks.", notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C", } @InProceedings{Wong:2008:cec, author = "Phillip Wong and Mengjie Zhang", title = "SCHEME: Caching Subtrees in Genetic Programming", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "2678--2685", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1823-7", file = "EC0608.pdf", DOI = "doi:10.1109/CEC.2008.4631158", abstract = "This paper introduces SCHEME (Sub-tree Caching using a Hashing for Equivalence MEthod), a method of caching program subtrees while taking into consideration algebraic equivalences between these programs. By using hashing in order to estimate algebraic equivalence between subtrees, we develop a hash table based caching mechanism which is easily integrated with the standard GP system. Experiments are performed on two regression and four classification tasks of varying difficulty. The results suggest that using SCHEME significantly reduces the number of node evaluations performed during the GP runs, which in turn leads to a faster GP training process.", keywords = "genetic algorithms, genetic programming", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @InProceedings{Wong:2010:WCRE, author = "Sunny Wong and Melissa Aaron and Jeffrey Segall and Kevin Lynch and Spiros Mancoridis", title = "Reverse Engineering Utility Functions Using Genetic Programming to Detect Anomalous Behavior in Software", booktitle = "17th Working Conference on Reverse Engineering (WCRE 2010)", year = "2010", month = "13-16 " # oct, pages = "141--149", keywords = "genetic algorithms, genetic programming, sbse, Jigsaw web server, anomalous behaviour detection, reverse engineering utility functions, security attack, sensor values, software engineering, reverse engineering, security of data, software fault tolerance", URL = "https://www.cs.drexel.edu/~spiros/papers/WCRE10.pdf", DOI = "doi:10.1109/WCRE.2010.23", ISSN = "1095-1350", abstract = "Recent studies have shown the promise of using utility functions to detect anomalous behaviour in software systems at runtime. However, it remains a challenge for software engineers to hand-craft a utility function that achieves both a high precision (i.e., few false alarms) and a high recall (i.e., few undetected faults). This paper describes a technique that uses genetic programming to automatically evolve a utility function for a specific system, set of resource usage metrics, and precision/recall preference. These metrics are computed using sensor values that monitor a variety of system resources (e.g., memory usage, processor usage, thread count). The technique allows users to specify the relative importance of precision and recall, and builds a utility function to meet those requirements. We evaluated the technique on the open source Jigsaw web server using ten resource usage metrics and five anomalous behaviours in the form of injected faults in the Jigsaw code and a security attack. To assess the effectiveness of the technique, the precision and recall of the evolved utility function was compared to that of a hand-crafted utility function that uses a simple thresholding scheme. The results show that the evolved function outperformed the hand-crafted function by 10 percent.", notes = "self-healing autonomic systems. Java only. No changes to jigsaw source code because uses: JVM, Managed beans, passive sensors MXBeans. Five artificial faults injected (cf mutation testing): denial of service (hog network), infinite loop (denial of CPU), log file explosion (denial of disk), memory leak (java soft reference error, denial of memory), recursion (denial of stack). Scalar fitness function, parallel GP (high migration rate between four islands). PCT1. Elitism. Stop when population gets stuck (no fitness variation). 8-12 hours. 3 fixed operating points on ROC curve. Logs converted to HTML web pages???? Refers to \cite{Shevertalov:2010:SSBSE}. Also known as \cite{5645446}", } @PhdThesis{idea_3422_OBJ, author = "Sunny Wong", title = "On the interplay of architecture and collaboration on software evolution and maintenance", school = "Computer Science, Drexel University", year = "2010", address = "USA", month = dec, keywords = "genetic algorithms, genetic programming", URL = "https://researchdiscovery.drexel.edu/view/pdfCoverPage?instCode=01DRXU_INST&filePid=13321481330004721&download=true", URL = "https://mathgenealogy.org/id.php?id=150484", DOI = "doi:10.17918/etd-3422", size = "174 pages", abstract = "With the growing popularity of globally distributed teams in software development, software architectures must be designed and maintained with an organizational perspective in mind. Software architects often decompose systems into modules that are intended to be developed and maintained independently and in parallel. However, prevailing techniques for modularizing software designs often rely on intuition and experience, leading to modules that are not guaranteed to be independent tasks. Even when a design is well modularised at the beginning, the software can evolve in ways that deviate from the designed modular structure, making tasks that are initially designed to be independent, not actually independent. We lack the theories, models, and tools to address these issues, and support the parallel development and maintenance of software. Therefore, our research objectives are to develop design-centric theories and models to: (1) facilitate modularity assessment in terms of task parallelism, and (2) to identify design problems that harm task parallelism. To reach these objectives, we contribute a design centric framework for maximising concurrent work in software development and maintenance. Specifically, we contribute: (1) a scalable formal model of generalized software dependencies, (2) a model for automatically identifying tasks that can be assigned for parallel development, (3) an approach for identifying and prioritizing modularity violations that degrade task parallelism, and (4) a theory that takes the temporal dimension of revision history into consideration for better supporting software modularity analysis in the context of software evolution.", notes = "Almost no mention of GP? Nov 2024 multiple link failures at researchdiscovery.drexel.edu/esploro Supervisor: Yuanfang Cai", } @Article{Wong2009649, author = "Samuel S. Y. Wong and Keith C. C. Chan", title = "{EvoArch:} An evolutionary algorithm for architectural layout design", journal = "Computer-Aided Design", volume = "41", number = "9", pages = "649--667", year = "2009", month = sep, keywords = "genetic algorithms, genetic programming, Architectural space topology, Evolutionary algorithm, Crossover, Graph algorithm, Mutation", ISSN = "0010-4485", URL = "https://research.polyu.edu.hk/en/publications/evoarch-an-evolutionary-algorithm-for-architectural-layout-design", broken = "http://www.sciencedirect.com/science/article/B6TYR-4W6XW17-2/2/8b37ad1171b7fd66aaeb17f58baf7ee0", DOI = "doi:10.1016/j.cad.2009.04.005", size = "19 pages", abstract = "The architectural layout design problem, which is concerned with the finding of the best adjacencies between functional spaces among many possible ones under given constraints, can be formulated as a combinatorial optimisation problem and can be solved with an Evolutionary Algorithm (EA). We present functional spaces and their adjacencies in form of graphs and propose an EA called EvoArch that works with a graph-encoding scheme. EvoArch encodes topological configuration in the adjacency matrices of the graphs that they represent and its reproduction operators operate on these adjacency matrices. In order to explore the large search space of graph topologies, these reproduction operators are designed to be unbiased so that all nodes in a graph have equal chances of being selected to be swapped or mutated. To evaluate the fitness of a graph, EvoArch makes use of a fitness function that takes into consideration preferences for adjacencies between different functional spaces, budget and other design constraints. By means of different experiments, we show that EvoArch can be a very useful tool for architectural layout design tasks.", notes = "Department of Computing, The Hong Kong Polytechnic University, China", } @InProceedings{Wing-KwongWong:2011:TAAI, author = "Wing-Kwong Wong and Hsin-Yu Chen and Chung-You Hsu and Tsung-Kai Chao", title = "Reinforcement Learning of Robotic Motion with Genetic Programming, Simulated Annealing and Self-Organizing Map", booktitle = "International Conference on Technologies and Applications of Artificial Intelligence (TAAI 2011)", year = "2011", month = "11-13 " # nov, pages = "292--298", publisher = "IEEE", address = "Chung-Li, Taiwan", keywords = "genetic algorithms, genetic programming, PSG, Player/Stage/Gazebo, Q-learning, Q-table, SOM, machine learning, optimal action, reinforcement learning, robotic motion, self-organising map, simulated annealing, control engineering computing, learning (artificial intelligence), robots, self-organising feature maps, simulated annealing", isbn13 = "978-1-4577-2174-8", DOI = "doi:10.1109/TAAI.2011.57", size = "7 pages", abstract = "Reinforcement learning, a sub-area of machine learning, is a method of actively exploring feasible tactics and exploiting already known reward experiences in order to acquire a near-optimal policy. The Q-table of all state-action pairs forms the basis of policy of taking optimal action at each state. But an enormous amount of learning time is required for building the Q-table of considerable size. Moreover, Q-learning can only be applied to problems with discrete state and action spaces. This study proposes a method of genetic programming with simulated annealing to acquire a fairly good program for an agent as a basis for further improvement that adapts to the constraints of an environment. We also propose an implementation of Q-learning to solve problems with continuous state and action spaces using Self-Organising Map (SOM). An experiment was done by simulating a robotic task with the Player/Stage/Gazebo (PSG) simulator. Experimental results showed the proposed approaches were both effective and efficient.", notes = "Also known as \cite{6120760}", } @Article{Wong:2023:SJ, author = "W. K. Wong and Filbert H. Juwono and Yohanes Nuwara and Jeffery T. H. Kong", journal = "IEEE Sensors Journal", title = "Synthesizing Missing Travel Time of P-Wave and S-Wave: A Two-Stage Evolutionary Modeling Approach", year = "2023", volume = "23", number = "14", pages = "15867--15877", abstract = "Acquiring sonic waves is an essential part of oil and gas exploration as they give critical information about the well's data and lithography at each well depth progression. However, these measurements are not always accessible, making analysis challenging. As computational power has improved, machine learning methods may now be used to predict these values from other data. Nonetheless, one shortcoming of existing models is that most of them are not transparent (i.e., black-box models). As a result, although promising great performance, they do not offer much insight to petrophysicists and geologists. This research aims to generate mathematical models for predicting compressional wave (P-wave) and shear wave (S-wave) readings using a multistage evolutionary modelling approach. In particular, a multistage equation modelling approach using tree-based genetic programming (GP) and adaptive differential evolution (ADE) is proposed. The obtained best mathematical models yield ${R}^{{2}}$ of 0.745 and 0.9066 for P-wave and S-wave regression on normalised data, respectively. The average performance of models is ${R}^{{2}}={0}.{90}$ (P-Wave) and ${R}^{{2}}={0}.{75}$ (S-Wave). The performance of these mathematical models is comparable with other 'black-box' models but with more compact mathematical approach in regression, thereby opening opportunities for interpretability and analysis. Finally, the 'white-box' models presented in this article can be fine-tuned further as needed.", keywords = "genetic algorithms, genetic programming, Mathematical models, Optimisation, Stochastic processes, Predictive models, Machine learning, Conductivity, Adaptive differential evolution (ADE), sonic wave prediction", DOI = "doi:10.1109/JSEN.2023.3280708", ISSN = "1558-1748", month = jul, notes = "Also known as \cite{10143418}", } @Article{Wong:2024:LSENS, author = "W. K. Wong and Yuan Ju Teoh and Filbert H. Juwono and Jessica Ling and Sie Yon Lau", title = "Microalgal Density and Mass Estimation Using Low-Cost Spectrometer: {NIR-VIS} Modeling With Evolutionary Optimization", journal = "IEEE Sensors Letters", year = "2024", volume = "8", number = "11", month = nov, keywords = "genetic algorithms, genetic programming, Mathematical models, Algae, Estimation, Training, Testing, Biological cells, Prototypes, Optical sensors, Monitoring, Sensor applications, multiexpression programming (MEP), microalgae, sensor, spectrometer", ISSN = "2475-1472", DOI = "doi:10.1109/LSENS.2024.3484432", abstract = "Estimating microalgal concentration can be a nontrivial endeavor due to their nonlinearity at high cell densities. The conventional estimation method is cell counting, which is time consuming and leads to inaccurate readings. Alternatively, spectral reflectance provides a more precise measurement by using specific wavelengths that correspond directly to pigment absorption in microalgae, allowing for faster determination of cell density and biomass. Unfortunately, the experiment is usually conducted in the laboratory with expensive and high-resolution devices. In this letter, we build a low-cost, real-time Internet-of-Things-based spectral prototype sensor for estimating density and mass of microalgae. The device uses wavelengths in the range of 400-1000 nm, making it low resolution. Multi expression programming is employed to model the measured data. Results show that nonlinear models perform better with $R^{2}$ values spanning from 0.93 to 0.99 for two species of microalgae.", notes = "Also known as \cite{10734234}", } @Article{Wong:2024:aquaeng, author = "W. K. Wong and Dini Fronitasari and Filbert H. Juwono and Jeffery T. H. Kong", title = "An evolutionary approach to dissolved oxygen mathematical modeling: A case study of the Klamath River", journal = "Aquacultural Engineering", year = "2024", volume = "106", pages = "102428", keywords = "genetic algorithms, genetic programming, Dissolved oxygen, Evolutionary approach, Artificial intelligence, Hydrology", ISSN = "0144-8609", URL = "https://www.sciencedirect.com/science/article/pii/S0144860924000396", DOI = "doi:10.1016/j.aquaeng.2024.102428", abstract = "Aquaculture has emerged as a crucial sector in many countries. In the Recirculating Aquaculture System (RAS), Dissolved Oxygen (DO) levels are critical to the health of aquatic animals. As DO sensors are costly, a number of studies have proposed a soft sensor technique using machine learning for estimating DO levels in water. However, the existing research work mainly focuses on black-box approaches, which do not provide numerical analysis between the DO levels and the related parameters. To solve this issue, a sequential Genetic Programming (GP) approach with an evolutionary refinement method is proposed to generate a mathematical expression that represents DO levels in water. In particular, a coarse mathematical model is generated using GP and subsequently fine-tuned using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). As a study case, the Klamath River dataset is used to generate the model. The evaluation of our proposed method uses datasets from both the Klamath River and the Fanno Creek. Two models are generated in this paper; one model uses six features, while the other only employs three. The results indicate that the model with six features exhibits relatively higher accuracy. However, it is worth noting that a smaller dataset of features is also capable of achieving generalisation of the model", } @InProceedings{Wongpheng:2020:ITC-CSCC, author = "Kittisak Wongpheng and Porawat Visutsak", booktitle = "2020 35th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC)", title = "Software Defect Prediction using Convolutional Neural Network", year = "2020", pages = "240--243", abstract = "The crucial part in software development lifecycle is finding the software faults. Detecting the faults in an early stage of software lifecycle can prevent the susceptibility and cost overruns. Many machine learning algorithms have been adopted for predicting the error-prone of software system such as Support Vector Machine (SVM), Bayesian Belief Network, Naive Bayes, and Genetic Programming. In this paper, the Convolution Neural Network (CNN) is used to detect the defective modules in software system. This work used the static code metrics for a collection of software modules in five selective NASA datasets. The experimental results show that CNN was promising for defect prediction with an average accuracy of 70.2percent.", keywords = "genetic algorithms, genetic programming, Software reliability, Measurement, Software systems, NASA, Convolution, Predictive models, Software fault, Software reliability, Software defect prediction, Convolution Neural Network, ANN, Machine learning, Deep learning Introduction", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=9182919", month = jul, notes = "Also known as \cite{9182919}", } @Article{journals/isci/WongsereeCVWF07, title = "Thalassaemia classification by neural networks and genetic programming", author = "Waranyu Wongseree and Nachol Chaiyaratana and Kanjana Vichittumaros and Pranee Winichagoon and Suthat Fucharoen", journal = "Information Sciences", year = "2007", number = "3", volume = "177", pages = "771--786", month = feb, bibdate = "2006-12-21", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/isci/isci177.html#WongsereeCVWF07", keywords = "genetic algorithms, genetic programming, ANN", ISSN = "0020-0255", URL = "https://repository.li.mahidol.ac.th/handle/20.500.14594/24405", DOI = "doi:10.1016/j.ins.2006.07.009", abstract = "This paper presents the use of a neural network and a decision tree, which is evolved by genetic programming (GP), in thalassaemia classification. The aim is to differentiate between thalassaemic patients, persons with thalassaemia trait and normal subjects by inspecting characteristics of red blood cells, reticulocytes and platelets. A structured representation on genetic algorithms for non-linear function fitting or STROGANOFF is the chosen architecture for genetic programming implementation. For comparison, multilayer perceptrons are explored in classification via a neural network. The classification results indicate that the performance of the GP-based decision tree is approximately equal to that of the multilayer perceptron with one hidden layer. But the multilayer perceptron with two hidden layers, which is proven to have the most suitable architecture among networks with different number of hidden layers, outperforms the GP-based decision tree. Nonetheless, the structure of the decision tree reveals that some input features have no effects on the classification performance. The results confirm that the classification accuracy of the multilayer perceptron with two hidden layers can still be maintained after the removal of the redundant input features. Detailed analysis of the classification errors of the multilayer perceptron with two hidden layers, in which a reduced feature set is used as the network input, is also included. The analysis reveals that the classification ambiguity and misclassification among persons with minor thalassaemia trait and normal subjects is the main cause of classification errors. These results suggest that a combination of a multilayer perceptron with a blood cell analysis may give rise to a guideline/hint for further investigation of thalassaemia classification.", notes = "also known as \cite{WONGSEREE2007771} a Research and Development Centre for Intelligent Systems, Department of Electrical Engineering, Faculty of Engineering, King Mongkuts Institute of Technology North Bangkok, 1518 Piboolsongkram Road, Bangsue, Bangkok 10800, Thailand b Thalassaemia Research Centre, Institute of Science and Technology for Research and Development, Mahidol University, Nakhonpathom 73170, Thailand", } @Article{wood:2007:PRSB, author = "Andrew J. Wood and Graeme J. Ackland", title = "Evolving the selfish herd: emergence of distinct aggregating strategies in an individual-based model", journal = "Proceedings of the Royal Society B", year = "2007", volume = "274", number = "1618", pages = "1637--1642", month = "1 " # may, keywords = "genetic algorithms, genetic programming, flocking, evolution, predation, foraging, Nash equilibrium", ISSN = "1471-2954", DOI = "doi:10.1098/rspb.2007.0306", size = "6 pages", abstract = "From zebra to starlings, herring and even tadpoles, many creatures move in an organized group. The emergent behaviour arises from simple underlying movement rules, but the evolutionary pressure which favours these rules has not been conclusively identified. Various explanations exist for the advantage to the individual of group formation: reduction of predation risk; increased foraging efficiency or reproductive success. Here, we adopt an individual-based model for group formation and subject it to simulated predation and foraging; the haploid individuals evolve via a genetic algorithm based on their relative success under such pressure. Our work suggests that flock or herd formation is likely to be driven by predator avoidance. Individual fitness in the model is strongly dependent on the presence of other phenotypes, such that two distinct types of evolved group can be produced by the same predation or foraging conditions, each stable against individual mutation. We draw analogies with multiple Nash equilibria theory of iterated games to explain and categorize these behaviours. Our model is sufficient to capture the complex behaviour of dynamic collective groups, yet is flexible enough to manifest evolutionary behaviour.", notes = "PMID: 17472913", } @InProceedings{wood:1998:DNAcadhp, author = "David Harlan Wood", title = "A DNA Computing Algorithm for Directed Hamiltonian Paths", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "731--734", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "DNA Computing", ISBN = "1-55860-548-7", notes = "GP-98", } @InProceedings{wood:1999:ADIMP, author = "David Wood and Junghuei Chen and Eugene Antipov Bertrand Lemieux and Walter Cedeno", title = "A DNA Implementation of the Max 1s Problem", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1835--1842", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "dna and molecular computing", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/Wood_gecco-99.pdf", URL = "http://www.cis.udel.edu/~wood/BMC/papers/gecco-99.pdf", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Article{woodward:1999:GPatnlds, author = "Andrew M. Woodward and Richard J. Gilbert and Douglas B. Kell", title = "Genetic programming as an analytical tool for non-linear dielectric spectroscopy", journal = "Bioelectrochemistry and Bioenergetics", year = "1999", volume = "48", number = "2", pages = "389--396", keywords = "genetic algorithms, genetic programming, Dielectric spectroscopy, Multivariate calibration, Non-linear, Fermentation, Biotechnology", URL = "http://www.sciencedirect.com/science/article/B6TF7-3WJ72RJ-T/2/19fd01a6eb6ae0b8e12b2bb2218fb6e9", DOI = "doi:10.1016/S0302-4598(99)00022-7", abstract = "By modelling the non-linear effects of membranous enzymes on an applied oscillating electromagnetic field using supervised multivariate analysis methods, Non-Linear Dielectric Spectroscopy (NLDS) has previously been shown to produce quantitative information that is indicative of the metabolic state of various organisms. The use of Genetic Programming (GP) for the multivariate analysis of NLDS data recorded from yeast fermentations is discussed, and GPs are compared with previous results using Partial Least Squares (PLS) and Artificial Neural Nets (NN). GP considerably outperforms these methods, both in terms of the precision of the predictions and their interpretability.", notes = "5 demes (5Inject2Way) pop=5*10000, 200 gens. linear GP (* / + -) and more complex function set. Automatic deconvolution of equation tree evolved by LGP. PMID: 10379559; UI: 99306227.", } @PhdThesis{Clinton_Jon_Woodward_Thesis, author = "Clinton Jon Woodward", title = "Ecosystems, Complexity, Topology and Evolutionary Computation", school = "Swinburne University of Technology", year = "2010", address = "Australia", keywords = "genetic algorithms, genetic programming, ESEC, python", URL = "https://researchbank.swinburne.edu.au/file/9f3f293b-d5a8-4c95-88b5-c73eee5bcf9b/1/Clinton%20Jon%20Woodward%20Thesis.pdf", size = "497 pages", abstract = "Evolutionary algorithms have been applied to an increasing range of complex problem domains. A challenge for many applications is the discovery of appropriate structures and processes that allow solutions, and solution components, to emerge efficiently. The motivation of this thesis was to create a new ecosystem model of evolutionary computation (ESEC) and to investigate the influence that topology and interaction can have on the outcome of evolutionary search. The thesis begins by considering the field of ecology and models of ecosystems, with a particular emphasis on evolutionary models,structures and processes. Next, existing models of evolutionary computation are considered with a strong emphasis on aspects of topology. Modern developments in the field of graph theory provide new insight into complex systems and the properties of efficient structures. A range of investigation themes have been developed for the ESEC model, and a detailed survey of topology models and properties was undertaken to guide the selection of suitable structures. An empirical study considers in detail the specific influence of various population structures on evolutionary search outcomes, and shows that the specification of population topology can influence both the efficacy and efficiency of evolutionary search.The results are a motivation for future investigations to consider in more detail how and why such influence can be used to an advantage as a way of optimising evolutionary search applications.", notes = "Supervisor: Tim Hendtlass", } @InProceedings{woodward03, author = "John R. Woodward", title = "Modularity in Genetic Programming", booktitle = "Genetic Programming, Proceedings of EuroGP'2003", year = "2003", editor = "Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa", volume = "2610", series = "LNCS", pages = "254--263", address = "Essex", publisher_address = "Berlin", month = "14-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-00971-X", URL = "http://www.cs.bham.ac.uk/~jrw/publications/2003/ModularityinGeneticProgramming/modularity.ps", DOI = "doi:10.1007/3-540-36599-0_23", abstract = "Genetic Programming uses a tree based representation to express solutions to problems. Trees are constructed from a primitive set which consists of a function set and a terminal set. An extension to GP is the ability to define modules, which are in turn tree based representations defined in terms of the primitives. The most well known of these methods is Koza's Automatically Defined Functions. In this paper it is proved that for a given problem, the minimum number of nodes in the main tree plus the nodes in any modules is independent of the primitive set (up to an additive constant) and depends only on the function being expressed. This reduces the number of user defined parameters in the run and makes the inclusion of a hypothesis in the search space independent of the primitive set.", notes = "EuroGP'2003 held in conjunction with EvoWorkshops 2003", } @InProceedings{woodward03b, author = "John R. Woodward and James R. Neil", title = "No Free Lunch, Program Induction and Combinatorial Problems", booktitle = "Genetic Programming, Proceedings of EuroGP'2003", year = "2003", editor = "Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa", volume = "2610", series = "LNCS", pages = "475--484", address = "Essex", publisher_address = "Berlin", month = "14-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-00971-X", URL = "http://www.cs.bham.ac.uk/~jrw/publications/2003/NoFreeLunchProgramInductionandCombinatorialProblems/nfl.ps", DOI = "doi:10.1007/3-540-36599-0_45", abstract = "This paper has three aims. Firstly, to clarify the poorly understood No Free Lunch Theorem (NFL) which states all search algorithms perform equally. Secondly, search algorithms are often applied to program induction and it is suggested that NFL does not hold due to the universal nature of the mapping between program space and functionality space. Finally, NFL and combinatorial problems are examined. When evaluating a candidate solution, it can be discarded without being fully examined. A stronger version of NFL is established for this class of problems where the goal is to minimize a quantity.", notes = "EuroGP'2003 held in conjunction with EvoWorkshops 2003", } @InProceedings{Woodward:2003:Etcr, author = "John Woodward", title = "Evolving {Turing} complete representations", booktitle = "Proceedings of the 2003 Congress on Evolutionary Computation CEC2003", editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon", pages = "830--837", year = "2003", publisher = "IEEE Press", address = "Canberra", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "8-12 " # dec, organisation = "IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", keywords = "genetic algorithms, genetic programming, Automatic testing, Biological information theory, Code standards, Computational modelling, Computer science, Genetic mutations, History, Ice, Space exploration, Turing machines, complete computer programs, Turing Complete representations, crossover operator, halting problem, module boundaries, self terminating programs", ISBN = "0-7803-7804-0", URL = "http://www.cs.bham.ac.uk/~jrw/publications/2003/EvolvingTuringCompleteRepresentations/cec032e.pdf", DOI = "doi:10.1109/CEC.2003.1299753", abstract = "Standard GP, chiefly concerned with evolving functions which are mappings from inputs to output, is not Turing Complete. This paper raises issues resulting from attempts at extending standard GP to Turing Complete representations. Firstly, there is a problem when a contiguous piece of code is moved to a new location (in a different program) by crossover. In general its functionality will be altered if global memory is used, as other parts of the program may access the same piece of memory. Secondly, traditional crossover does not respect modules. Crossover can disrupt a group of instructions that were working together (e.g. in the body of a loop) in one parent, but end up separated in two different offspring after reproduction. A crossover operator is proposed that only operates at the boundaries of modules. The identification of module boundaries is made easy by using a representation in which explicit modules are defined, in contrast with other representations where the module boundaries would have to be identified by some other means. The halting problem is a central issue, however as a consequence of this crossover operator we are more likely to produce self terminating programs, thus saving time when testing.", notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.", } @InProceedings{woodward:2003:gogtintq, author = "John Woodward", title = "{GA} or {GP}? That is not the question", booktitle = "Proceedings of the 2003 Congress on Evolutionary Computation CEC2003", editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon", pages = "1056--1063", year = "2003", publisher = "IEEE Press", address = "Canberra", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "8-12 " # dec, organisation = "IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", keywords = "genetic algorithms, genetic programming, Computer science, Evolutionary computation, High performance computing, Terminology, Tree data structures, data structures, search problems, GA, GP, No Free Lunch theorem, evolutionary computation, fixed length linear representation, variable size tree representation", ISBN = "0-7803-7804-0", URL = "http://www.cs.bham.ac.uk/~jrw/publications/2003/GAorGPthatisnotthequestion/cec032e.pdf", DOI = "doi:10.1109/CEC.2003.1299785", size = "8 pages", abstract = "Genetic Algorithms (GAs) and Genetic Programming (GP) are often considered as separate but related fields. Typically, GAs use a fixed length linear representation, whereas GP uses a variable size tree representation. This paper argues that the differences are unimportant. Firstly, variable length actually means variable length up to some fixed limit, so can really be considered as fixed length. Secondly, the representations and genetic operators of GA and GP appear different, however ultimately it is a population of bit strings in the computers memory which is being manipulated whether it is GA or GP which is being run on the computer. The important difference lies in the interpretation of the representation; if there is a one to one mapping between the description of an object and the object itself (as is the case with the representation of numbers), or a many to one mapping (as is the case with the representation of programs). This has ramifications for the validity of the No Free Lunch theorem, which is valid in the first case but not in the second. It is argued that due to the highly related nature of GAs and GP, that many of the empirical results discovered in one field will apply to the other field, for example maintaining high diversity in a population to improve performance.", notes = "broken Oct 2019 http://tech.groups.yahoo.com/group/genetic_programming/message/5687 CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.", } @InProceedings{woodward:2004:lbp, author = "John Woodward", title = "Simple Incremental Testing", booktitle = "Late Breaking Papers at the 2004 Genetic and Evolutionary Computation Conference", year = "2004", editor = "Maarten Keijzer", address = "Seattle, Washington, USA", month = "26 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://gpbib.cs.ucl.ac.uk/gecco2004/LBP055.pdf", abstract = "Additional test cases are added one by one, as the population solves the current set of test cases, until some fixed final limit is reached. We examine the general nature of this approach. Complexity is defined in a general sense. It is proved that adding a test case to a test set never reduces the complexity of a solution, and never increases the probability of finding a solution. The terms representative and redundant, are formally defined. The variation in the number of test cases and the jumps in the number of test cases are observed. The size of the test set just before a general solution is found, indicates a threshold number of test cases required for generalisation. We observe, how generalization varies with the size of the test set. Finally we observe the number of successes per evaluation required to produce a general solution.", notes = "Part of \cite{keijzer:2004:GECCO:lbp}", } @InProceedings{woodward:2004:mod:jrwoo, author = "John R. Woodward", title = "Function Set Independent Genetic Programming", editor = "R. Poli and S. Cagnoni and M. Keijzer and E. Costa and F. Pereira and G. Raidl and S. C. Upton and D. Goldberg and H. Lipson and E. {de Jong} and J. Koza and H. Suzuki and H. Sawai and I. Parmee and M. Pelikan and K. Sastry and D. Thierens and W. Stolzmann and P. L. Lanzi and S. W. Wilson and M. O'Neill and C. Ryan and T. Yu and J. F. Miller and I. Garibay and G. Holifield and A. S. Wu and T. Riopka and M. M. Meysenburg and A. W. Wright and N. Richter and J. H. Moore and M. D. Ritchie and L. Davis and R. Roy and M. Jakiela", booktitle = "GECCO 2004 Workshop Proceedings", year = "2004", month = "26-30 " # jun, address = "Seattle, Washington, USA", keywords = "genetic algorithms, genetic programming", URL = "http://gpbib.cs.ucl.ac.uk/gecco2004/WMOD013.pdf", notes = "GECCO-2004WKS Distributed on CD-ROM at GECCO-2004", } @InProceedings{woodward:2005:UKCIa, author = "John R. Woodward", title = "Complexity and Cartesian Genetic Programming", booktitle = "The 5th annual UK Workshop on Computational Intelligence", year = "2005", editor = "Boris Mirkin and George Magoulas", pages = "273--280", address = "London", month = "5-7 " # sep, email = "jrw@cs.bham.ac.uk", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", notonline = "http://www.cs.bham.ac.uk/~jrw/publications/", URL = "http://www.dcs.bbk.ac.uk/ukci05/ukci05proceedings.pdf", size = "8 pages", abstract = "Genetic Programming (GP) \cite{banzhaf:1997:book} often uses a tree form of a graph to represent solutions in a search space. An extension to this representation, Automatically Defined Functions (ADFs) \cite{banzhaf:1997:book} is to allow the ability to express modules. In \cite{woodward:Modularity} we proved that the complexity of a function is independent of the primitive sets (function set and terminal set) if the representation has the ability to express modules. This is essentially due to the fact that if a representation can express modules, then it can effectively define its own primitives at a constant cost. Cartesian Genetic Programming (CGP) \cite{miller:2000:CGP} is a relative new type of representation used in Evolutionary Computation (EC), and differs from the tree based representation in that outputs from previous computations can be reused. This is achieved by representing programs as directed acyclic graphs (DAGs), rather than as trees. Thus computations from subtrees can be reused to reduce the complexity of a function. We prove an analogous result to that in \cite{woodward:Modularity}; the complexity of a function using a (Cartesian Program) CP representation is invariant (up to an additive constant) when using different terminal sets, provided the different terminal sets can both be represented. This is essentially due to the fact that if a representation can express Automatic Reused Outputs \cite{miller:2000:CGP}, then it can effectively define its own terminals at a constant cost.", notes = "UKCI 2005 http://www.dcs.bbk.ac.uk/ukci05/", } @InProceedings{woodward:2005:UKCIb, author = "John R. Woodward", title = "Invariance of Function Complexity under Primitive Recursive Functions", booktitle = "The 5th annual UK Workshop on Computational Intelligence", year = "2005", editor = "Boris Mirkin and George Magoulas", pages = "281--288", address = "London", month = "5-7 " # sep, email = "jrw@cs.bham.ac.uk", keywords = "genetic algorithms, genetic programming, ADF", URL = "http://www.dcs.bbk.ac.uk/ukci05/ukci05proceedings.pdf", size = "8 pages", abstract = "Genetic Programming (GP) \cite{banzhaf:1997:book} often uses a tree form of a graph to represent solutions in a search space. An extension to this representation, Automatically Defined Functions (ADFs) \cite{banzhaf:1997:book} is to allow the ability to express modules. In \cite{woodward:Modularity} we proved that the complexity of a function is independent of the primitive set (function set and terminal set) if the representation has the ability to express modules. This is essentially due to the fact that if a representation can express modules, then it can effectively define its own primitives at a constant cost. This is analogous to the to the result that the complexity of a bit string is independent of the Universal Turing Machine (UTM) (within an additive constant) \cite{ming93introduction}. The constant depending on the UTM but not on the function. The representations typically used in GP are not capable of expressing recursion, however a few researchers have introduced recursion into the representation. These representations are then capable of expressing the primitive recursive functions (PRFs) which are a subclass of the partial recursive function (which correspond to the computable functions). We prove that given two representations which express the PRFs, the complexity of a function with respect to either of these representations is invariant within an additive constant. This is in the same vein as the proof of the invariants of Kolmogorov complexity \cite{ming93introduction} and the proof in \cite{woodward:Modularity}. We comment on the important of the class of PRFs for learning.", notes = "UKCI 2005 http://www.dcs.bbk.ac.uk/ukci05/", } @PhdThesis{woodward:thesis, author = "John R. Woodward", title = "Algorithm Induction, Modularity and Complexity", school = "School of Computer Science, The University of Birmingham", year = "2004", address = "UK", month = sep, email = "jrw@cs.bham.ac.uk", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.stir.ac.uk/~jrw/publications/thesis.pdf", size = "149 pages", abstract = "We are concerned with the induction of a rule from a set of observations, the goal being to succinctly describe the observed data, but more importantly to place us in a position to make predictions about future data which we have not previously seen. One approach to rule induction is to form a hypothesis space which consists of potential rules or hypotheses, and then search this space for a rule which is consistent with the observed data. One formulation of this approach is Genetic Programming, where the hypothesis spaces consists of computer programs and the search of this space is conducted using biologically inspired search operators and a fitness function. The well known No Free Lunch theorems are central to search and essentially says all search algorithms perform equally, under a number of assumptions. We examine these assumptions and show that they are invalidated when the hypothesis space contains hypotheses which represent functions with different frequencies, as is the case with Genetic Programming and a number of other Machine Learning paradigms. The Conservation of Generalisation, which is related to the No Free Lunch theorems, implies that generalisation is impossible. This is contrary to Occam's razor. The Conservation of Generalisation theorems and Occam razor are consistent if we restrict ourselves to representations which do not compress the observed data. We define the representational complexity of a function to be the minimum size of a given representation which can express the function. Given a primitive set, and the operation of composition, new functions can be constructed, which is the representation standard Genetic Programming uses to express functions. However the complexity of a function under this type of representation will depend on the primitive set. We prove that if a representation is capable of expressing modules (i.e. reuse of component parts of the representation), then the complexity of a function is independent of the primitive set (up to a constant which depends on the primitive set but not on the function being represented). We then conduct a number of experiments related to the evolution of Turing Complete representations. We argue that, if a representation can address the general case of a variable sized problem then the average number of evaluations to find a solution is independent of the problem size. In Genetic Programming, a fitness function is used to drive the evolutionary process and promote programs which are more likely to lead to solutions. We experiment with a fitness function which includes information about whether or not a program had to be aborted during it's evaluation and demonstrate that a 10 fold reduction in the number of evaluations can be achieved on an arithmetic problem. Finally, we experiment with a crossover operator which is inspired by the theory of recursive functions and reduced the probability of producing a program which has to be terminated during its evaluation.", } @InProceedings{eurogp06:WoodwardC, author = "John R. Woodward", title = "Complexity and Cartesian Genetic Programming", editor = "Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art", booktitle = "Proceedings of the 9th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3905", year = "2006", address = "Budapest, Hungary", month = "10 - 12 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", ISBN = "3-540-33143-3", pages = "260--269", DOI = "doi:10.1007/11729976_23", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "Genetic Programming (GP) \cite{banzhaf:1997:book} often uses a tree form of a graph to represent solutions. An extension to this representation, Automatically Defined Functions (ADFs) is to allow the ability to express modules. In Woodward we proved that the complexity of a function is independent of the primitive set (function set and terminal set) if the representation has the ability to express modules. This is essentially due to the fact that if a representation can express modules, then it can effectively define its own primitives at a constant cost. Cartesian Genetic Programming (CGP) is a relative new type of representation used in Evolutionary Computation (EC), and differs from the tree based representation in that outputs from previous computations can be reused. This is achieved by representing programs as directed acyclic graphs (DAGs), rather than as trees. Thus computations from subtrees can be reused to reduce the complexity of a function. We prove an analogous result to that in Woodward the complexity of a function using a (Cartesian Program) CP representation is independent of the terminal set (up to an additive constant), provided the different terminal sets can both be simulated. This is essentially due to the fact that if a representation can express Automatic Reused Outputs \cite{miller:2000:CGP}, then it can effectively define its own terminals at a constant cost.", notes = "Part of \cite{collet:2006:GP} EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006", } @InProceedings{eurogp06:Woodward, author = "John R. Woodward", title = "Invariance of Function Complexity under Primitive Recursive Functions", editor = "Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art", booktitle = "Proceedings of the 9th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3905", year = "2006", address = "Budapest, Hungary", month = "10 - 12 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-33143-3", pages = "310--319", DOI = "doi:10.1007/11729976_28", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "Genetic Programming (GP) \cite{banzhaf:1997:book} often uses a tree form of a graph to represent solutions. An extension to this representation, Automatically Defined Functions (ADFs) is to allow the ability to express modules. In Woodward we proved that the complexity of a function is independent of the primitive set (function set and terminal set) if the representation has the ability to express modules. This is essentially due to the fact that if a representation can express modules, then it can effectively define its own primitives at a constant cost. This is reminiscent of the result that the complexity of a bit string is independent of the choice of Universal Turing Machine (UTM) (within an additive constant), the constant depending on the UTM but not on the function. The representations typically used in GP are not capable of expressing recursion, however a few researchers have introduced recursion into their representations. These representations are then capable of expressing a wider classes of functions, for example the primitive recursive functions (PRFs). We prove that given two representations which express the PRFs (and only the PRFs), the complexity of a function with respect to either of these representations is invariant within an additive constant. This is in the same vein as the proof of the invariants of Kolmogorov complexity and the proof in Woodward.", notes = "Part of \cite{collet:2006:GP} EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006", } @InProceedings{WoodwardB:2009:GEC, author = "John R. Woodward and Ruibin Bai", title = "Canonical representation genetic programming", booktitle = "GEC '09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation", year = "2009", editor = "Lihong Xu and Erik D. Goodman and Guoliang Chen and Darrell Whitley and Yongsheng Ding", bibsource = "DBLP, http://dblp.uni-trier.de", pages = "585--592", address = "Shanghai, China", organisation = "SigEvo", URL = "http://www.cs.nott.ac.uk/~jrw/publications/canonical.pdf", URL = "https://www.eecs.qmul.ac.uk/~jwoodward/publications/canonical.pdf", URL = "https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=1baa429862a0c01104bba567eb7ecd8dacd95ce1", DOI = "doi:10.1145/1543834.1543914", publisher = "ACM", publisher_address = "New York, NY, USA", month = jun # " 12-14", isbn13 = "978-1-60558-326-6", keywords = "genetic algorithms, genetic programming, No Free Lunch Theorem, NFL, theory, canonical representation, standard form, evolutionary computation, bias, symmetric functions, inverse functions, complementary functions, isomorphic representations", size = "8 pages", abstract = "Search spaces sampled by the process of Genetic Programming often consist of programs which can represent a function in many different ways. Thus, when the space is examined it is highly likely that different programs may be tested which represent the same function, which is an undesirable waste of resources. It is argued that, if a search space can be constructed where only unique representations of a function are permitted, then this will be more successful than employing multiple representations. When the search space consists of canonical representations it is called a canonical search space, and when Genetic Programming is applied to this search space, it is called Canonical Representation Genetic Programming. The challenge lies in constructing these search spaces. With some function sets this is a trivial task, and with some function sets this is impossible to achieve. With other function sets it is not clear how the goal can be achieved. In this paper, we specifically examine the search space defined by the function set {+,-,*,/} and the terminal set {x, 1}. Drawing inspiration from the fundamental theorem of arithmetic, and results regarding the fundamental theorem of algebra, we construct a representation where each function that can be constructed with this primitive set has a unique representation.", notes = " best paper in conference. Broken March 2021 http://tech.groups.yahoo.com/group/genetic_programming/message/5687 Also known as \cite{DBLP:conf/gecco/WoodwardB09} part of \cite{DBLP:conf/gec/2009}", } @InProceedings{WoodwardB:2009:GECa, author = "John R. Woodward and Ruibin Bai", title = "Why evolution is not a good paradigm for program induction: a critique of genetic programming", booktitle = "GEC '09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation", year = "2009", editor = "Lihong Xu and Erik D. Goodman and Guoliang Chen and Darrell Whitley and Yongsheng Ding", bibsource = "DBLP, http://dblp.uni-trier.de", pages = "593--600", address = "Shanghai, China", organisation = "SigEvo", URL = "http://www.cs.nott.ac.uk/~jrw/publications/notEvolution.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.207.9680", DOI = "doi:10.1145/1543834.1543915", publisher = "ACM", publisher_address = "New York, NY, USA", month = jun # " 12-14", isbn13 = "978-1-60558-326-6", keywords = "genetic algorithms, genetic programming", abstract = "We revisit the roots of Genetic Programming (i.e. Natural Evolution), and conclude that the mechanisms of the process of evolution (i.e. selection, inheritance and variation) are highly suited to the process; genetic code is an effective transmitter of information and crossover is an effective way to search through the viable combinations. Evolution is not without its limitations, which are pointed out, and it appears to be a highly effective problem solver; however we over-estimate the problem solving ability of evolution, as it is often trying to solve 'self-imposed' survival problems. We are concerned with the evolution of Turing Equivalent programs (i.e. those with iteration and memory). Each of the mechanisms which make evolution work so well are examined from the perspective of program induction. Computer code is not as robust as genetic code, and therefore poorly suited to the process of evolution, resulting in a insurmountable landscape which cannot be navigated effectively with current syntax based genetic operators. Crossover, has problems being adopted in a computational setting, primarily due to a lack of context of exchanged code. A review of the literature reveals that evolved programs contain at most two nested loops, indicating that a glass ceiling to what can currently be accomplished.", notes = "Also known as \cite{DBLP:conf/gecco/WoodwardB09a} part of \cite{DBLP:conf/gec/2009}", } @InCollection{Woodward:2009:CI, author = "Edmund K. Burke and Mathew R. Hyde and Graham Kendall and Gabriela Ochoa and Ender Ozcan and John R. Woodward", title = "Exploring Hyper-heuristic Methodologies with Genetic Programming", booktitle = "Computational Intelligence", publisher = "Springer", year = "2009", editor = "Christine L. Mumford and Lakhmi C. Jain", volume = "1", series = "Intelligent Systems Reference Library", chapter = "6", pages = "177--201", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-01798-8", URL = "http://www.cs.nott.ac.uk/~gxo/papers/ChapterGPasHH09.pdf", DOI = "doi:10.1007/978-3-642-01799-5_6", abstract = "Hyper-heuristics represent a novel search methodology that is motivated by the goal of automating the process of selecting or combining simpler heuristics in order to solve hard computational search problems. An extension of the original hyper-heuristic idea is to generate new heuristics which are not currently known. These approaches operate on a search space of heuristics rather than directly on a search space of solutions to the underlying problem which is the case with most meta-heuristics implementations. In the majority of hyper-heuristic studies so far, a framework is provided with a set of human designed heuristics, taken from the literature, and with good measures of performance in practice. A less well studied approach aims to generate new heuristics from a set of potential heuristic components. The purpose of this chapter is to discuss this class of hyper-heuristics, in which Genetic Programming is the most widely used methodology. A detailed discussion is presented including the steps needed to apply this technique, some representative case studies, a literature review of related work, and a discussion of relevant issues. Our aim is to convey the exciting potential of this innovative approach for automating the heuristic design process.", size = "26 pages", } @Book{Woodward:book, author = "John Woodward", title = "Program Induction, Complexity and Occam's Razor: The Induction of Computable Functions, Modularity and No Free Lunch Theorems", publisher = "LAP Lambert Academic Publishing", year = "2010", month = "29 " # jul, keywords = "genetic algorithms, genetic programming", ISBN = "3-8383-8934-4", URL = "http://www.amazon.co.uk/Program-Induction-Complexity-Occams-Razor/dp/3838389344", abstract = "Search is a broad machine learning method where solutions are generated and tested. We focus on evolving computable functions with genetic programming. The literature reveals the complexity of programs is small, indicating a limitation of current methods. No Free Lunch is not valid for machine learning as simpler functions are represented more frequently which is also related to Occam's razor. We argue for Occam's razor, not on grounds of simplicity but probability. The complexity of a function depends on the primitives available. If the representation can build new primitives, then the complexity is independent of the primitives. We give bounds on these constants and argue these are the tightest. We examine representation, genetic operators and fitness functions. A representation which addresses a general problem is fruitful as large instances can be solved by evolving solutions to small instances. Different versions of a fitness function are compared which take into account if a program was terminated. A crossover operator is introduced which acts on modules and increases the probability of generating correctly terminating programs.", notes = "156 pages", } @Article{Woodward:2011:GPEM, author = "John Woodward", title = "Gisele L. Pappa, Alex Freitas: Automating the design of data mining algorithms, an evolutionary computation approach, Springer, 187 pages, ISBN-13: 978-3642025402", journal = "Genetic Programming and Evolvable Machines", year = "2011", volume = "12", number = "1", pages = "81--83", month = mar, note = "Book review", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dR837", DOI = "doi:10.1007/s10710-010-9119-9", size = "3 pages", notes = "Review of \cite{Pappa:AEDMA}", affiliation = "The University of Nottingham, Ningbo, China", } @InProceedings{Woodward:2011:GECCOcomp, author = "John Robert Woodward and Jerry Swan", title = "Automatically designing selection heuristics", booktitle = "GECCO 2011 1st workshop on evolutionary computation for designing generic algorithms", year = "2011", editor = "Gisele L. Pappa and Alex A. Freitas and Jerry Swan and John Woodward", isbn13 = "978-1-4503-0690-4", keywords = "genetic algorithms, genetic programming", pages = "583--590", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001858.2002052", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "In a standard evolutionary algorithm such as genetic algorithms (GAs), a selection mechanism is used to decide which individuals are to be chosen for subsequent mutation. Examples of selection mechanisms are fitness-proportional selection, in which individuals are chosen with a probability directly in proportion to their fitness value, and rank selection, in which individuals are selected with a probability in proportion to their ordinal ranking by fitness. These two human-designed selection heuristics implicitly assume that fitter individuals produce fitter offspring. Whilst one might invest human ingenuity in the construction of alternative selection heuristics, the approach adopted in this paper is to represent a generic family of selection heuristics which are applied via an algorithmic framework. We then generate instances of selection heuristics and test their performance in an evolutionary algorithm (which in this paper tackles a variety of bitstring optimization problems). The representation we use for the program space is a register machine (a set of real-valued registers on which a program is executed). Fitness-proportional and rank selection can be expressed as one-line programs, and more sophisticated selection heuristics may also be expressed. The result is a system which produces selection heuristics that outperform either of the original selection heuristics.", notes = "Also known as \cite{2002052} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @InProceedings{Woodward:2011:85th, author = "John Woodward and Jerry Swan", title = "A Syntactic Approach to Prediction", booktitle = "Solomonoff 85th Memorial Conference", year = "2011", editor = "David Dowe", address = "Melbourne, Australia", month = "30 " # nov # "-2 " # dec, organisation = "Monash University", notes = "Broken March 2021 http://tech.groups.yahoo.com/group/genetic_programming/message/5688 http://www.solomonoff85thmemorial.monash.edu/index.html", } @InProceedings{Woodward:2012:GECCOcomp, author = "John R. Woodward and Jerry Swan", title = "The automatic generation of mutation operators for genetic algorithms", booktitle = "GECCO 2012 2nd Workshop on Evolutionary Computation for the Automated Design of Algorithms", year = "2012", editor = "Gisele L. Pappa and John Woodward and Matthew R. Hyde and Jerry Swan", isbn13 = "978-1-4503-1178-6", keywords = "genetic algorithms, genetic programming, automatic design, hyper-heuristics", pages = "67--74", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330784.2330796", acmid = "2330796", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "We automatically generate mutation operators for Genetic Algorithms (GA) and tune them to problem instances drawn from a given problem class. By so doing, we perform metalearning in which the base-level contains GAs (which learn about problem instances), and the meta-level contains GAmutation operators (which learn about problem classes). We use Register Machines to explore a constrained design space for mutation operators. We show how two commonly used mutation operators (viz. one-point and uniform mutation) can be expressed in this framework. Iterated local search is used to search the space of mutation operators, and on a test-bed of 7 problem classes we identify machine-designed mutation operators which outperform their human counterparts.", notes = "Also known as \cite{2330796} and \cite{Woodward:2012:AGM:2330784.2330796} Distributed at GECCO-2012. ACM Order Number 910122.", } @InProceedings{Woodward:2010:ICRM, author = "John R. Woodward and Nabil Gindy", booktitle = "5th International Conference on Responsive Manufacturing - Green Manufacturing (ICRM 2010)", title = "A hyper-heuristic multi-criteria decision support system for eco-efficient product life cycle", year = "2010", month = "11-13 " # jan, address = "Ningbo", pages = "201--205", keywords = "genetic algorithms, genetic programming, Hyper-heuristics, analytical hierarchical process, decision support system", DOI = "doi:10.1049/cp.2010.0436", abstract = "Decision support is required when complex situations arise during product development which takes into account the whole product life cycle. This is especially true when impacted by the ill-defined consequences on the environment in an ever increasingly eco-conscious world. Analytical Hierarchy process (AHP) is one method of providing decision support, and is an instance of a decision support heuristic.", notes = "Also known as \cite{5629174}", } @InProceedings{Woodward:2014:GECCOcomp, author = "John Woodward and Simon Martin and Jerry Swan", title = "Benchmarks that matter for genetic programming", booktitle = "GECCO 2014 4th workshop on evolutionary computation for the automated design of algorithms", year = "2014", editor = "John Woodward and Jerry Swan and Earl Barr", isbn13 = "978-1-4503-2881-4", keywords = "genetic algorithms, genetic programming", pages = "1397--1404", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2598394.2609875", DOI = "doi:10.1145/2598394.2609875", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "There have been several papers published relating to the practice of benchmarking in machine learning and Genetic Programming (GP) in particular. In addition, GP has been accused of targeting over-simplified 'toy' problems that do not reflect the complexity of real-world applications that GP is ultimately intended. There are also theoretical results that relate the performance of an algorithm with a probability distribution over problem instances, and so the current debate concerning benchmarks spans from the theoretical to the empirical. The aim of this article is to consolidate an emerging theme arising from these papers and suggest that benchmarks should not be arbitrarily selected but should instead be drawn from an underlying probability distribution that reflects the problem instances which the algorithm is likely to be applied to in the real-world. These probability distributions are effectively dictated by the application domains themselves (essentially data-driven) and should thus re-engage the owners of the originating data. A consequence of properly-founded benchmarking leads to the suggestion of meta-learning as a methodology for automatically designing algorithms rather than manually designing algorithms. A secondary motive is to reduce the number of research papers that propose new algorithms but do not state in advance what their purpose is (i.e. in what context should they be applied). To put the current practice of GP benchmarking in a particular harsh light, one might ask what the performance of an algorithm on Koza's lawnmower problem (a favourite toy-problem of the GP community) has to say about its performance on a very real-world cancer data set: the two are completely unrelated.", notes = "Also known as \cite{2609875} Distributed at GECCO-2014.", } @InProceedings{Woodward:2014:GECCOcompa, author = "John R. Woodward and Jerry Swan", title = "Template method hyper-heuristics", booktitle = "GECCO 2014 Workshop on Metaheuristic Design Patterns (MetaDeeP)", year = "2014", editor = "Jerry Swan and Krzysztof Krawiec and John Woodward and Chris Simons and John Clark", isbn13 = "978-1-4503-2881-4", keywords = "genetic algorithms, genetic programming, hyper-heuristic", pages = "1437--1438", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2598394.2609843", DOI = "doi:10.1145/2598394.2609843", publisher = "ACM", publisher_address = "New York, NY, USA", acmid = "2609843", size = "2 pages", abstract = "The optimisation literature is awash with metaphorically-inspired meta-heuristics and their subsequent variants and hybridisation. This results in a plethora of methods, with descriptions that are often polluted with the language of the metaphor which inspired them [8]. Within such a fragmented field, the traditional approach of manual 'operator tweaking' makes it difficult to establish the contribution of individual metaheuristic components to the overall success of a methodology. Irrespective of whether it happens to best the state-of-the-art, such 'tweaking' is so labour-intensive that does relatively little to advance scientific understanding. In order to introduce further structure and rigour, it is therefore desirable to not only to be able to specify entire families of metaheuristics (rather than individual metaheuristics), but also be able to generate and test them. In particular, the adoption of a model agnostic approach towards the generation of metaheuristics would help to establish which metaheuristic components are useful contributors to a solution.", notes = "Also known as \cite{2609843} \cite{Woodward:2014:TMH:2598394.2609843} Distributed at GECCO-2014.", } @Article{Woodward:2015:hclhswe, author = "John R. Woodward and Justyna Petke and William Langdon", title = "How computers are learning to make human software work more efficiently", journal = "The Conversation", year = "2015", pages = "10.08am BST", month = jun # " 25", keywords = "genetic algorithms, genetic programming, Genetic Improvement", URL = "http://theconversation.com/how-computers-are-learning-to-make-human-software-work-more-efficiently-43798", size = "3 pages", notes = "Science + Technology", } @InProceedings{Woodward:2016:GI, author = "John Woodward and Colin Johnson and Alexander Brownlee", title = "GP vs GI: if you can't beat them, join them", booktitle = "Genetic Improvement 2016 Workshop", year = "2016", editor = "Justyna Petke and David R. White and Westley Weimer", pages = "1155--1156", address = "Denver", publisher_address = "New York, NY, USA", month = jul # " 20-24", organisation = "SIGEvo", publisher = "ACM", keywords = "genetic algorithms, genetic programming, Genetic Improvement, SBSE", URL = "http://geneticimprovementofsoftware.com/wp-content/uploads/2016/06/GP_vs_GI_if_you_can%E2%80%99t_beat_them_join_them.pdf", DOI = "doi:10.1145/2908961.2931694", size = "2 pages", abstract = "Genetic Programming (GP) has been criticized for targeting irrelevant problems [12], and is also true of the wider machine learning community [11]. which has become detached from the source of the data it is using to drive the field forward. However, recently GI provides a fresh perspective on automated programming. In contrast to GP, GI begins with existing software, and therefore immediately has the aim of tackling real software. As evolution is the main approach to GI to manipulating programs, this connection with real software should persuade the GP community to confront the issues around what it originally set out to tackle i.e. evolving real software.", notes = "GECCO-2016 Workshop http://geneticimprovementofsoftware.com/", } @InProceedings{Woodward:2016:GIa, author = "John Woodward and Alexander Brownlee and Colin Johnson", title = "Evals is not enough: why we should report wall-clock time", booktitle = "Genetic Improvement 2016 Workshop", year = "2016", editor = "Justyna Petke and David R. White and Westley Weimer", pages = "1157--1158", address = "Denver", publisher_address = "New York, NY, USA", month = jul # " 20-24", organisation = "SIGEvo", publisher = "ACM", keywords = "genetic algorithms, genetic programming, Genetic Improvement, SBSE", URL = "http://geneticimprovementofsoftware.com/wp-content/uploads/2016/06/Evals_is_not_enough_why_we_should_report_wall-clock_time.pdf", DOI = "doi:10.1145/2908961.2931695", size = "2 pages", abstract = "Have you ever noticed that your car never achieves the fuel economy claimed by the manufacturer? Does this seem unfair? Unscientific? Would you like the same situation to occur in Genetic Improvement? Comparison will always be difficult [9], however, guidelines have been discussed [3, 5, 4]. With two GP [8] approaches, comparing the number of evaluations of the fitness function is reasonably fair. This means you are comparing the GP systems, and not how well they are implemented, how fast the language is. However, the situation with GI [6, 1] is unique. With GI we will typically compare systems which are applied to the same application written in the same language (i.e. a GI systems targeted at Java, may not even be applied to C). Thus, wall-clock time becomes more relevant. Thus, this paper asks if reporting number of evaluations is enough, or if wall-clock time is also important, particularly in the context of GI. It argues that reporting time is even more important when doing GI when compared to traditional GP.", notes = "GECCO-2016 Workshop http://geneticimprovementofsoftware.com/", } @InProceedings{Woodward:2016:GECCOcomp, author = "John R. Woodward and Colin G. Johnson and Alexander E. I. Brownlee", title = "Connecting Automatic Parameter Tuning, Genetic Programming as a Hyper-heuristic, and Genetic Improvement Programming", booktitle = "GECCO '16 Companion: Proceedings of the Companion Publication of the 2016 Annual Conference on Genetic and Evolutionary Computation", year = "2016", isbn13 = "978-1-4503-4323-7", pages = "1357--1358", month = "20-24 " # jul, organisation = "SIGEVO", address = "Denver, Colorado, USA", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, Genetic Improvement", URL = "http://hdl.handle.net/1893/23394", URL = "https://dspace.stir.ac.uk/bitstream/1893/23394/1/ecada03%20%284%29.pdf", DOI = "doi:10.1145/2908961.2931728", abstract = "Automatically designing algorithms has long been a dream of computer scientists. Early attempts which generate computer programs from scratch, have failed to meet this goal. However, in recent years there have been a number of different technologies with an alternative goal of taking existing programs and attempting to improving them. These methods form a range of methodologies, from the limited ability to change (for example only the parameters) to the complete ability to change the whole program. These include; automatic parameter tuning (APT), using GP as a hyper-heuristic (GPHH), and GI, which we will now briefly review. Part of research is building links between existing work, and the aim of this paper is to bring together these currently separate approaches.", } @InProceedings{1277296, author = "Brian G. Woolley and Gilbert L. Peterson", title = "Genetic evolution of hierarchical behavior structures", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "1731--1738", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1731.pdf", DOI = "doi:10.1145/1276958.1277296", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, behaviour based robotics, evolutionary robotics, unified behaviour framework", abstract = "The development of coherent and dynamic behaviours for mobile robots is an exceedingly complex endeavour ruled by task objectives, environmental dynamics and the interactions within the behavior structure. This paper discusses the use of genetic programming techniques and the unified behaviour framework to develop effective control hierarchies using interchangeable behaviors and arbitration components. Given the number of possible variations provided by the framework, evolutionary programming is used to evolve the overall behaviour design. Competitive evolution of the behaviour population incrementally develops feasible solutions for the domain through competitive ranking. By developing and implementing many simple behaviours independently and then evolving a complex behaviour structure suited to the domain, this approach allows for the reuse of elemental behaviours and eases the complexity of development for a given domain. Additionally, this approach has the ability to locate a behaviour structure which a developer may not have previously considered, and whose ability exceeds expectations. The evolution of the behaviour structure is demonstrated using agents in the Robocode environment, with the evolved structures performing up to 122 percent better than one crafted by an expert.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @InProceedings{Worm:2013:GECCO, author = "Tony Worm and Kenneth Chiu", title = "Prioritized grammar enumeration: symbolic regression by dynamic programming", booktitle = "GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference", year = "2013", editor = "Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi", isbn13 = "978-1-4503-1963-8", pages = "1021--1028", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", note = "Best paper", keywords = "genetic algorithms, genetic programming, PyPGE, dynamic programming", DOI = "doi:10.1145/2463372.2463486", code_url = "https://github.com/verdverm/pypge", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "We introduce Prioritised Grammar Enumeration (PGE), a deterministic Symbolic Regression (SR) algorithm using dynamic programming techniques. PGE maintains the tree-based representation and Pareto non-dominated sorting from Genetic Programming (GP), but replaces genetic operators and random number use with grammar production rules and systematic choices. PGE uses non-linear regression and abstract parameters to fit the coefficients of an equation, effectively separating the exploration for form, from the optimisation of a form. Memoisation enables PGE to evaluate each point of the search space only once, and a Pareto Priority Queue provides direction to the search. Sorting and simplification algorithms are used to transform candidate expressions into a canonical form, reducing the size of the search space. Our results show that PGE performs well on 22 benchmarks from the SR literature, returning exact formulae in many cases. As a deterministic algorithm, PGE offers reliability and reproducibility of results, a key aspect to any system used by scientists at large. We believe PGE is a capable SR implementation, following an alternative perspective we hope leads the community to new ideas.", notes = "Also known as \cite{2463486} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)", } @PhdThesis{Worm:thesis, author = "Anthony Worm", title = "Prioritized Grammar Enumeration: A novel method for symbolic regression", school = "Department of Computer Science, Thomas J. Watson School of Engineering and Applied Science, State University of New York at Binghamton", year = "2016", address = "USA", month = apr # " 22", keywords = "genetic algorithms, genetic programming, PYPGE, Computer programming, Computer programming, Machine learning, Algorithms", language = "eng", isbn13 = "9781339931128", broken = "https://suny-bin.primo.exlibrisgroup.com/permalink/01SUNY_BIN/1rqmqsn/alma990035658160204802", URL = "https://www.proquest.com/docview/1803588149", code_url = "https://github.com/verdverm/pypge", size = "181 pages", abstract = "The main thesis of this work is that computers can be programmed to derive mathematical formula and relationships from data in an efficient, reproducible, and interpretable way. This problem is known as Symbolic Regression, the data driven search for mathematical relations as performed by a computer. In essence, this is a search over all possible equations to find those which best model the data on hand. We propose Prioritized Grammar Enumeration (PGE) as a deterministic machine learning algorithm for solving Symbolic Regression. PGE works with a grammars rules and input data to prioritize the enumeration of expressions in that language. By making large reductions to the search space and introducing mechanisms for memoization, PGE can explore the space of all equations efficiently. Most notably, PGE provides reproducibility, a key aspect to any system used by scientists at large. We then enhance the PGE algorithm in several ways. We enrich the equation equation types and application domains PGE can operate on. We deepen equation abstractions and relationships, add configuration to search operaters, and enrich the fitness metrics. We enable PGE to scale by decoupling the subroutines into a set of services. Our algorithm experiments cover a range of problem types from a multitude of domains. Our experiments cover a variety of architectural and parameter configurations. Our results show PGE to have great promise and efficacy in automating the discovery of equations at the scales needed by tomorrow's scientific data problems. Additionally, reproducibility has been a significant factor in the formulation and development of PGE. All supplementary materials, codes, and data can be found at github.com/verdverm/pypge.", notes = "alma990035658160204802 OCLC : (OCoLC)1044759240 ProQuest Dissertations Publishing,  2016. 10137419.", } @Misc{worzel:2001:patent, author = "William P. Worzel", title = "Method and system for genetic programming", howpublished = "U.S. Patent", year = "2001", month = "4 " # nov, note = "6,327,582", keywords = "genetic algorithms, genetic programming", URL = "http://patft.uspto.gov/netacgi/nph-Parser?Sect2=PTO1&Sect2=HITOFF&p=1&u=/netahtml/PTO/search-bool.html&r=1&f=G&l=50&d=PALL&RefSrch=yes&Query=PN/6327582", } @InCollection{worzel:2002:GPTP, author = "Bill Worzel and Rick L. Riolo", title = "Genetic Programming Theory and Practice", booktitle = "Genetic Programming Theory and Practice", publisher = "Kluwer", year = "2003", editor = "Rick L. Riolo and Bill Worzel", chapter = "1", pages = "1--10", keywords = "genetic algorithms, genetic programming, theoretical biology, diversity, bloat, population dynamics, particulate genes", ISBN = "1-4020-7581-2", URL = "http://www.springer.com/computer/ai/book/978-1-4020-7581-0", DOI = "doi:10.1007/978-1-4419-8983-3_1", abstract = "Genetic Programming (GP) is a young art that is just beginning to make its way into applications programming in a serious way. The dynamics of the GP process is poorly understood with many serious questions remaining. People applying GP to real-world problems have relied more on intuition than theory, experience more than mathematics. To reach the next stage in its development, GP theory and practice must both advance. Theory must inform practice and practice must test theory.", notes = "Part of \cite{RioloWorzel:2003}", } @InCollection{worzel:2005:GPTP, author = "A. Almal and W. P. Worzel and E. A. Wollesen and C. D. MacLean", title = "Content Diversity in Genetic Programming and Its Correlation With Fitness", booktitle = "Genetic Programming Theory and Practice {III}", year = "2005", editor = "Tina Yu and Rick L. Riolo and Bill Worzel", volume = "9", series = "Genetic Programming", address = "Ann Arbor", month = "12-14 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming", notes = "Duplicate of \cite{Almal:2005:GPTP}", } @InCollection{Worzel:2006:GPTP, author = "W. P. Worzel and A. Almal and C. D. MacLean", title = "Lifting the Curse of Dimensionality", booktitle = "Genetic Programming Theory and Practice {IV}", year = "2006", editor = "Rick L. Riolo and Terence Soule and Bill Worzel", volume = "5", series = "Genetic and Evolutionary Computation", pages = "29--40", address = "Ann Arbor", month = "11-13 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming", ISBN = "0-387-33375-4", DOI = "doi:10.1007/978-0-387-49650-4_3", abstract = "In certain problem domains the 'Curse of Dimensionality' [Hastie et al., 2001] is well known. Also known as the problem of 'High P and Low N' where the number of parameters far exceeds the number of samples to learn from, we describe our methods for making the most of limited samples in producing reasonably general classification rules from data with a larger number of parameters. We discuss the application of this approach in classifying mesothelioma samples from baseline data according to their time to recurrence. In this case there are over 12625 inputs for each sample but only 19 samples to learn from. We reflect on the theoretical implications of the behaviour of GP in these extreme cases and speculate on the nature of generality.", notes = "part of \cite{Riolo:2006:GPTP} Published Jan 2007 after the workshop", } @Article{Worzel2009405, author = "William P. Worzel and Jianjun Yu and Arpit A. Almal and Arul M. Chinnaiyan", title = "Applications of genetic programming in cancer research", journal = "The International Journal of Biochemistry \& Cell Biology", volume = "41", number = "2", pages = "405--413", year = "2009", note = "Molecular and Cellular Evolution: A Celebration of the 200th Anniversary of the Birth of Charles Darwin", ISSN = "1357-2725", DOI = "DOI:10.1016/j.biocel.2008.09.025", URL = "http://www.sciencedirect.com/science/article/B6TCH-4TK92M3-5/2/0e86a90ead8ec06e801a65b7e26278ef", month = feb, keywords = "genetic algorithms, genetic programming, Evolutionary algorithms, Cancer diagnosis, Cancer classification, Cancer prognosis", abstract = "The theory of Darwinian evolution is the fundamental keystones of modern biology. Late in the last century, computer scientists began adapting its principles, in particular natural selection, to complex computational challenges, leading to the emergence of evolutionary algorithms. The conceptual model of selective pressure and recombination in evolutionary algorithms allow scientists to efficiently search high dimensional space for solutions to complex problems. In the last decade, genetic programming has been developed and extensively applied for analysis of molecular data to classify cancer subtypes and characterize the mechanisms of cancer pathogenesis and development. This article reviews current successes using genetic programming and discusses its potential impact in cancer research and treatment in the near future.", } @InProceedings{Worzel:2014:GPTP, author = "William P. Worzel and Duncan MacLean", title = "SKGP: The Way of the Combinator", booktitle = "Genetic Programming Theory and Practice XII", year = "2014", editor = "Rick Riolo and William P. Worzel and Mark Kotanchek", series = "Genetic and Evolutionary Computation", pages = "53--71", address = "Ann Arbor, USA", month = "8-10 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Evolutionary algorithms, Functional programming, Combinators, Strong typing", isbn13 = "978-3-319-16029-0", DOI = "doi:10.1007/978-3-319-16030-6_4", abstract = "Genetic Programming (GP) is a machine learning technique that evolves programs using natural selection and populations dynamics. Much of the functionality of GP depends on the representation of programs in the population and how to handle illegal or type incoherent expressions that arise from crossover and mutation within a population of programs. The SKGP is a GP system that uses graphs of combinators to represent functions and a strong type system to inform the crossover and mutation operations during evolution. This produces a powerful, flexible system that has many benefits over more conventional systems. This paper describes the implementation of this system, gives some examples of successful applications constructed using the SKGP and describes future directions that may offer a more powerful GP system capable of producing more complex programs.", notes = " Part of \cite{Riolo:2014:GPTP} published after the workshop in 2015", } @InProceedings{Worzel:2015:GPTP, author = "Bill Worzel", title = "The Evolution of Everything ({EvE}) and Genetic Programming", booktitle = "Genetic Programming Theory and Practice XIII", year = "2015", editor = "Rick Riolo and William P. Worzel and M. Kotanchek and A. Kordon", series = "Genetic and Evolutionary Computation", pages = "137--149", address = "Ann Arbor, USA", month = "14-16 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Internet, Internet of Things, Fog Lifter, Combinators, SKGP, P2P, AllJoyn, IOx, FRP, Blockchain, Functional relational programming, Data flow design, Evolutionary reinforcement learning", isbn13 = "978-3-319-34223-8", URL = "http://www.springer.com/us/book/9783319342214", DOI = "doi:10.1007/978-3-319-34223-8_8", abstract = "The Internet is entering a new period of growth driven by an increasing number of processors connected at the edge of the Internet. Many of these processors are sensors that continuously collect data. By 2020, it is projected that there may be more than 20 billion (1000 million) devices connected to the Internet. Collectively these devices are called the Internet of Things (IoT) or the Internet of Everything (IoE). The sheer volume of the data that will be gathered creates new problems for an economy that is increasingly driven by data analytics. It is likely that the devices at the edge of the Internet will take part in the processing of data for analytics by using distributed computing among edge devices. Genetic Programming could play a unique role in this environment because of its ability not only to gather and analyse data, but to control the evolution and use of other machine learning algorithms. The confluence of unimaginable streams of real-world data and emergent behaviours may give rise to the question of whether the evolution of intelligence in the natural world can be recreated using evolutionary tools.", notes = " Part of \cite{Riolo:2015:GPTP} Published after the workshop in 2016", } @InProceedings{Worzel:2022:GPTP, author = "Bill Worzel", title = "ESSAY: Computers Are Useless ... They Only Give Us Answers", booktitle = "Genetic Programming Theory and Practice XIX", year = "2022", editor = "Leonardo Trujillo and Stephan M. Winkler and Sara Silva and Wolfgang Banzhaf", series = "Genetic and Evolutionary Computation", pages = "255--260", address = "Ann Arbor, USA", month = jun # " 2-4", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-981-19-8459-4", DOI = "doi:10.1007/978-981-19-8460-0_11", abstract = "While the title of this essay, taken from a quotation from Picasso [1], is somewhat facetious, it contains more than a grain of truth. We have all experienced results that left us scratching our heads with Evolutionary Algorithms, but often these algorithms lead us to the most fruitful results. It can be hard to create the right conditions to find good answers. To quote Shakespeare: ``I can call the spirits from the vasty deep!'' to which the response in the text is ``Why so can I! And so can any [person], but will they come when you doth call?''", notes = "Part of \cite{Banzhaf:2022:GPTP}", } @InProceedings{wright:1999:MCMGA, author = "Alden H. Wright and Yong Zhao", title = "Markov Chain Models of Genetic Algorithms", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "734--741", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/ssq.ps.gz", URL = "http://www.cs.umt.edu/u/wright/papers/ssq.ps.gz", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Wright:2021:GECCO, author = "Alden H. Wright and Cheyenne L. Laue", title = "Evolvability and Complexity Properties of the Digital Circuit Genotype-Phenotype Map", booktitle = "Proceedings of the 2021 Genetic and Evolutionary Computation Conference", year = "2021", editor = "Francisco Chicano and Alberto Tonda and Krzysztof Krawiec and Marde Helbig and Christopher W. Cleghorn and Dennis G. Wilson and Georgios Yannakakis and Luis Paquete and Gabriela Ochoa and Jaume Bacardit and Christian Gagne and Sanaz Mostaghim and Laetitia Jourdan and Oliver Schuetze and Petr Posik and Carlos Segura and Renato Tinos and Carlos Cotta and Malcolm Heywood and Mengjie Zhang and Leonardo Trujillo and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Fuyuki Ishikawa and Inmaculada Medina-Bulo and Frank Neumann and Andrew M. Sutton", pages = "840--848", address = "internet", series = "GECCO '21", month = jul # " 10-14", organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, Arficial Intelligence, Search methodologies, Boolean functions, Discrete space search, Randomized search, Genotype-phenotype mapping, robustness, evolvability, complexity, neutrality, redundancy, epochal algorithm, G-P map", isbn13 = "9781450383509", DOI = "doi:10.1145/3449639.3459393", video_url = "https://www.youtube.com/watch?v=9kNdkkBiVNc", size = "9 pages", abstract = "Recent research on genotype-phenotype (G-P) maps in natural evolution has contributed significantly to our understanding of neutrality, redundancy, robustness, and evolvability. we investigate the properties of the digital logic gate G-P map and show that this map shares many of the common properties of natural G-P maps, with the exception of a positive relationship between evolvability and robustness. Our results show that in some cases robustness and evolvability may be negatively related as a result of the method used to approximate evolvability. We give two definitions of circuit complexity and empirically show that these two definitions are closely related. This study leads to a better understanding of the relationships between redundancy, robustness, and evolvability in genotype-phenotype maps. We further investigate these results in the context of complexity and show the relationships between phenotypic complexity and phenotypic redundancy, robustness and evolvability", notes = "p840 'robustness of a genotype (circuit) is the fraction of mutations that are neutral' p841 'The evolvability of a genotype is defined as the number of unique phenotypes obtained by all possible (node type or node connection) mutations of the genotype.' logarithmic scaling. Video 3:15 for our parameter settings phenotype networks are almost always connected. Neutral evolution. 9:36 _negative_ relationship between phenotype evolution evolvability and robustness (opposite to claim in biology). 10:06 Evolvability versus complexity. Circuit phenotypic complexity is Kolmogorov complexity (bits). 12:00 Arrow of complexity: increasing _maximum_ (not average) complexity is a property of evolution. University of Montana, Missoula, MT, USA GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)", } @InProceedings{Wright:2022:GPTP, author = "Alden Wright and Cheyenne L. Laue", title = "Evolving complexity is hard", booktitle = "Genetic Programming Theory and Practice XIX", year = "2022", editor = "Leonardo Trujillo and Stephan M. Winkler and Sara Silva and Wolfgang Banzhaf", series = "Genetic and Evolutionary Computation", pages = "233--253", address = "Ann Arbor, USA", month = jun # " 2-4", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Linear Genetic Programming, Cartesian Genetic Programming, Genotype-phenotype map, Evolvability, genotype graph, Neutral set, Fitness landscape", isbn13 = "978-981-19-8459-4", DOI = "doi:10.1007/978-981-19-8460-0_10", abstract = "Understanding the evolution of complexity is an important topic in a wide variety of academic fields. Implications of better understanding complexity include increased knowledge of major evolutionary transitions and the properties of living and technological systems. Genotype-phenotype (G-P) maps are fundamental to evolution, and biologically-oriented G-P maps have been shown to have interesting and often-universal properties that enable evolution by following phenotype-preserving walks in genotype space. Here we use a digital logic gate circuit G-P map where genotypes are represented by circuits and phenotypes by the functions that the circuits compute. We compare two mathematical definitions of circuit and phenotype complexity and show how these definitions relate to other well-known properties of evolution such as redundancy, robustness, and evolvability. Using both Cartesian and Linear genetic programming implementations, we demonstrate that the logic gate circuit shares many universal properties of biologically derived G-P maps, with the exception of the relationship between one method of computing phenotypic evolvability, robustness, and complexity. Due to the inherent structure of the G-P map, including the predominance of rare phenotypes, large interconnected neutral networks, and the high mutational load of low robustness, complex phenotypes are difficult to discover using evolution. We suggest, based on this evidence, that evolving complexity is hard and we discuss computational strategies for genetic-programming-based evolution to successfully find genotypes that map to complex phenotypes in the search space.", notes = "Linear relationship between log redundancy and robustness. Manrubia 2021.Survival of the flattest. Julia code. Tononi genotype complexity, Kolmogorov phenotype complexity Tononi and Kolmogorov complexity are empirically consistent University of Montana, USA. Part of \cite{Banzhaf:2022:GPTP} published after the workshop in 2023", } @InProceedings{wu:1996:sirg, author = "Annie S. Wu and Robert K. Lindsay", title = "A Survey of Intron Research in Genetics", editor = "Hans-Michael Voigt and Werner Ebeling and Ingo Rechenberg and Hans-Paul Schwefel", booktitle = "Parallel Problem Solving From Nature IV. Proceedings of the International Conference on Evolutionary Computation", year = "1996", publisher = "Springer-Verlag", volume = "1141", series = "LNCS", pages = "101--110", address = "Berlin, Germany", publisher_address = "Heidelberg, Germany", month = "22-26 " # sep, keywords = "Computer Science", ISBN = "3-540-61723-X", DOI = "doi:10.1007/3-540-61723-X_974", size = "10 pages", abstract = "A brief survey of biological research on non-coding DNA is presented here. There has been growing interest in the effects of non-coding segments in evolutionary algorithms (EAs). To better understand and conduct research on non-coding segments and EAs, it is important to understand the biological background of such work. This paper begins with a review of basic genetics and terminology, describes the different types of non-coding DNA, and then surveys recent intron research.", notes = "http://lautaro.fb10.tu-berlin.de/ppsniv.html PPSN4 wetware", affiliation = "University of Michigan Artificial Intelligence Laboratory 48109-2110 Ann Arbor MI 48109-2110 Ann Arbor MI", } @Article{wu:1998:ECJintro, author = "Annie S. Wu and Wolfgang Banzhaf", title = "Introduction to the Special Issue: Variable-Length Representation and Noncoding Segments for Evolutionary Algorithms", journal = "Evolutionary Computation", year = "1998", volume = "6", number = "4", pages = "iii--vi", month = "Winter", keywords = "genetic algorithms, genetic programming", URL = "http://www.mitpressjournals.org/doi/pdfplus/10.1162/evco.1998.6.4.iii", DOI = "doi:10.1162/evco.1998.6.4.iii", size = "4 pages", notes = "Evolutionary Computation (Journal) Special Issue: Variable-Length Representation and Noncoding Segments for Evolutionary Algorithms Edited by Annie S. Wu and Wolfgang Banzhaf", } @Proceedings{wu:2000:GECCOWKS, title = "Proceedings of the 2000 Genetic and Evolutionary Computation Conference Workshop Program", year = "2000", editor = "Annie S. Wu", address = "Las Vegas, Nevada, USA", month = "8 " # jul, URL = "http://www.cs.colostate.edu/~genitor/GECCO-2000/workshops.htm", size = "317", notes = "GECCO-2000WKS", } @Article{AnnieSWu:2002:GPEM, author = "Annie S. Wu and Ivan Garibay", title = "The Proportional Genetic Algorithm: Gene Expression in a Genetic Algorithm", journal = "Genetic Programming and Evolvable Machines", year = "2002", volume = "3", number = "2", pages = "157--192", month = jun, keywords = "genetic algorithms, representation, gene expression, proportional genetic algorithm", ISSN = "1389-2576", DOI = "doi:10.1023/A:1015531909333", abstract = "We introduce a genetic algorithm (GA) with a new representation method which we call the proportional GA (PGA). The PGA is a multi-character GA that relies on the existence or non-existence of genes to determine the information that is expressed. The information represented by a PGA individual depends only on what is present on the individual and not on the order in which it is present. As a result, the order of the encoded information is free to evolve in response factors other than the value of the solution, for example, in response to the identification and formation of building blocks. The PGA is also able to dynamically evolve the resolution of encoded information. In this paper, we describe our motivations for developing this representation and provide a detailed description of a PGA along with discussion of its benefits and drawbacks. We compare the behavior of a PGA with that of a canonical GA (CGA) and discuss conclusions and future work based on these preliminary studies.", notes = "Special issue on Gene Expression \cite{Kargupta:2002:GPEM} Article ID: 408587", } @InProceedings{Wu:2010:ICACC, author = "Bing Wu and Wen-Qiong Zhang and Zhi-Wei Hu and Jia-Hong Liang", title = "Genetic complex multiple kernel for relevance vector regression", booktitle = "2nd International Conference on Advanced Computer Control (ICACC 2010)", year = "2010", month = "27-29 " # mar, volume = "4", pages = "217--221", abstract = "Relevance vector machine (RVM) is a state-of-the-art technique for regression and classification, as a sparse Bayesian extension version of the support vector machine. The selection of a kernel and associated parameter is a critical step of RVM application. The real-world application and recent researches have emphasised the requirement to multiple kernel learning, in order to boost the fitting accuracy by adapting better the characteristics of the data. This paper presents a data-driven evolutionary approach, called Genetic Complex Multiple Kernel Relevance Vector Regression (GCMK RVR), which combines genetic programming(GP) and relevance vector regression to evolve an optimal or near-optimal complex multiple kernel function. Each GP chromosome is a tree that encodes the mathematical expression of a complex multiple kernel function. Numerical experiments on several benchmark datasets show that the RVR involving this GCMK perform better than not only the widely used simple kernel, Polynomial, Gaussian RBF and Sigmoid kernel, but also the convex linear multiple kernel function.", keywords = "genetic algorithms, genetic programming, classification method, data driven evolutionary approach, genetic complex multiple kernel, genetic complex multiple kernel relevance vector regression, multiple kernel learning, relevance vector machine, sparse Bayesian extension version, support vector machine, Bayes methods, belief networks, learning (artificial intelligence), numerical analysis, pattern classification, regression analysis, support vector machines", DOI = "doi:10.1109/ICACC.2010.5486939", notes = "Coll. of Mech. Eng. & Autom., Nat. Univ. of Defense Technol., Changsha, China Also known as \cite{5486939}", } @InProceedings{Wu:2010:ICCAE, author = "Bing Wu and Wen-qiong Zhang and Ling Chen and Jia-hong Liang", title = "A {GP}-based kernel construction and optimization method for {RVM}", booktitle = "The 2nd International Conference on Computer and Automation Engineering (ICCAE 2010)", year = "2010", volume = "4", pages = "419--423", address = "Singapore", month = "26-28 " # feb, keywords = "genetic algorithms, genetic programming, GP-based kernel construction, RVM, SVM, data- driven kernel function construction, optimisation method, relevance vector machine, relevance vector regression, regression analysis, support vector machines", DOI = "doi:10.1109/ICCAE.2010.5451646", size = "5 pages", abstract = "Selecting a suitable kernel for relevance vector machine is one of most challenging aspects of successfully using this learning tool. Efficiently automating the search for such a kernel is therefore desirable. This paper proposes a data-driven kernel function construction and optimisation method, which combines genetic programming (GP) and relevance vector regression to evolve an optimal or near-optimal kernel function, named GP-Kernel. The evolved kernel is compared to several widely used kernels on several regression benchmark datasets. Empirical results demonstrate that RVM using such GP-Kernel can outperform or match the best performance of standard kernels.", notes = "College of Mechanical Engineering and Automation, National University of Defense Technology, Changsha, China Also known as \cite{5451646}", } @InProceedings{conf/sede/WuCS07, author = "Chih-Hung Wu and Hung-Ju Chou and Wei-Han Su", title = "Direct transformation of coordinates for {GPS} positioning using the techniques of genetic programming and symbolic regression on partitioned data", booktitle = "Proceedings of the 16th International Conference on Software Engineering and Data Engineering (SEDE-2007)", year = "2007", editor = "Hisham Al-Mubaid and Marc Garbey", pages = "188--193", address = "Las Vegas, Nevada, USA", month = jul # " 9-11", publisher = "ISCA", keywords = "genetic algorithms, genetic programming, Soft-computing, symbolic regression, GPS, regression, coordinate system", isbn13 = "978-1-880843-63-5", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.109.5652.pdf", abstract = "Transformation of coordinates usually invokes level-wised processes wherein several sets of complicated equations are calculated. Unfortunately, the accuracy may be corrupted due to the accumulation of inevitable errors between the transformation processes. This paper presents a genetic-based method for generating regressive models for direct transformation from GPS signals to 2-D coordinates. Since target coordinates for a GPS application can be obtained by using simpler transformation formulas, the computational costs and inaccuracy can be reduced. From the experimental results where the target datums TWD67 is investigated, it seems that the proposed method can serve as a direct and feasible solution to the transformation of GPS coordinates.", notes = "http://sede07.cs.uh.edu/ http://www.isca-hq.org/SEDE-2007-PROGRAM.pdf National University of Kaohsiung, Taiwan Shu-Te University, Taiwan", bibdate = "2007-09-19", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/sede/sede2007.html#WuCS07", } @Article{Wu:2007:ieeeGSL, author = "Chih-Hung Wu and Hung-Ju Chou and Wei-Han Su", title = "A Genetic Approach for Coordinate Transformation Test of GPS Positioning", journal = "IEEE Geoscience and Remote Sensing Letters", year = "2007", volume = "4", number = "2", pages = "297--301", email = "johnw@nuk.edu.tw", URL = "http://ieeexplore.ieee.org/iel5/8859/4156144/04156177.pdf?tp=&isnumber=&arnumber=4156177", DOI = "doi:10.1109/LGRS.2007.894164", keywords = "genetic algorithms, genetic programming, Coordinate system, global positioning system (GPS), symbolic regression", abstract = "Transformation of coordinates usually invokes levelwise processes wherein several sets of complicated equations are calculated. Unfortunately, the accuracy may be corrupted due to the accumulation of inevitable errors between the transformation processes. This letter rephrases the transformation of coordinates from global positioning system (GPS) signals to 2-D coordinates as a regression problem that derives target coordinates from the inputs of GPS signals directly. In this letter, a genetic-based solution is proposed and implemented by the techniques of symbolic regression and genetic programming. Since coordinates for a GPS application are obtained by using simpler transformation formulas, the computational costs and inaccuracy can be reduced. The proposed method, although it does not exclude systematic errors due to the imperfection on defining the reference ellipsoid and the reliability of GPS receivers, effectively reduces statistical errors when accurate Cartesian coordinates are known from independent sources. To our best knowledge, this letter is the first attempt to use genetic-based methods in coordinate transformation for GPS positioning. From the experimental results where the target datums TWD67 and TWD97 are investigated, it seems that the proposed method can serve as a direct and feasible solution to the transformation of GPS coordinates.", notes = "C.-H. Wu and H.-J. Chou are with the Department of Electronic Engineering, National University of Kaohsiung, Kaohsiung 811, Taiwan, R.O.C. W.-H. Su is with the Department of Information Management, Shu-Te University, Kaohsiung 82445, Taiwan, R.O.C. Colour versions of one or more of the Figures in this paper are available online at http://ieeexplore.ieee.org.", } @Article{Wu20081347, author = "Chih-Hung Wu and Hung-Ju Chou and Wei-Han Su", title = "Direct transformation of coordinates for GPS positioning using the techniques of genetic programming and symbolic regression", journal = "Engineering Applications of Artificial Intelligence", volume = "21", number = "8", pages = "1347--1359", year = "2008", keywords = "genetic algorithms, genetic programming, Soft-computing, Symbolic regression, GPS, Regression, Coordinate system", ISSN = "0952-1976", URL = "http://www.sciencedirect.com/science/article/B6V2M-4S563RS-1/2/05f37a51533cca46f932efd8e37b95b0", DOI = "doi:10.1016/j.engappai.2008.02.001", abstract = "Transformation of coordinates usually invokes level-wised processes wherein several sets of complicated equations are calculated. Unfortunately, the accuracy may be corrupted due to the accumulation of inevitable errors between the transformation processes. This paper presents a genetic-based method for generating regressive models for direct transformation from global positioning system (GPS) signals to 2-D coordinates. Since target coordinates for a GPS application can be obtained by using simpler transformation formulas, the computational costs and inaccuracy can be reduced. The proposed method, though does not exclude systematic errors due to the imperfection on defining the reference ellipsoid and the reliability of GPS receivers, effectively reduces the statistical errors when the accurate Cartesian coordinates are known from the independent sources. From the experimental results where the target datums TWD67 is investigated, it seems that the proposed method can serve as a direct and feasible solution to the transformation of GPS coordinates.", } @InProceedings{Wu:2010:ICMLC, author = "Chih-Hung Wu and Ya-Wei Ho", title = "Genetic programming for the approximation of GPS GDOP", booktitle = "2010 International Conference on Machine Learning and Cybernetics (ICMLC)", year = "2010", month = "11-14 " # jul, volume = "6", pages = "2944--2949", abstract = "Geometric Dilution of Precision (GDOP) is an indicator showing how well the constellation of GPS satellites in the Global Positioning System (GPS) is organised geometrically. The calculation of GDOP is a time-and power-consuming task, where involves solving measurement equations with complicated matrix transformation and inversions. Previous studies have partially solved this problem with the techniques of numeric regression. This study employs genetic programming (GP) as a means for the approximation of GPS GDOP. Unlike previous approaches, GP considers not only the regression accuracy but also the the expressivity of the regression functions. With such regression functions, advanced studies for improving the GPS positioning accuracy can be conducted. The use of GP and associated experimental results are presented and discussed.", keywords = "genetic algorithms, genetic programming, GDOP, GPS satellites, geometric dilution of precision, global positioning system, matrix inversions, matrix transformation, Global Positioning System, matrix algebra", DOI = "doi:10.1109/ICMLC.2010.5580757", notes = "Also known as \cite{5580757}", } @Article{Wu:2011:ieeeTIM, author = "Chih-Hung Wu and Wei-Han Su and Ya-Wei Ho", title = "A Study on GPS GDOP Approximation Using Support-Vector Machines", journal = "IEEE Transactions on Instrumentation and Measurement", year = "2011", month = jan, volume = "60", number = "1", pages = "137--145", abstract = "Global Positioning System (GPS) has extensively been used in various fields. Geometric Dilution of Precision (GDOP) is an indicator showing how well the constellation of GPS satellites is geometrically organised. GPS positioning with a low GDOP value usually gains better accuracy. However, the calculation of GDOP is a time- and power-consuming task that involves complicated transformation and inversion of measurement matrices. When selecting from many GPS constellations the one with the lowest GDOP for positioning, methods that can fast and accurately obtain GPS GDOP are imperative. Previous studies have shown that numerical regression on GPS GDOP can get satisfactory results and save many calculation steps. This paper deals with the approximation of GPS GDOP using statistics and machine learning methods. The technique of support vector machines (SVMs) is mainly focused. This study compares the performance of several methods, such as linear regression, pace regression, isotonic regression, SVM, artificial neural networks, and genetic programming (GP). The experimental results show that SVM and GP have better performance than others.", keywords = "genetic algorithms, genetic programming, GPS GDOP approximation, SVM, artificial neural networks, geometric dilution of precision, isotonic regression, linear regression, pace regression, support-vector machines, Global Positioning System, learning (artificial intelligence), neural nets, support vector machines", DOI = "doi:10.1109/TIM.2010.2049228", ISSN = "0018-9456", notes = "Also known as \cite{5467147}", } @Article{Wu2012332, author = "Chih-Hung Wu and Ya-Wei Ho and Li-Wun Chen and You-Dong Huang", title = "Discovering approximate expressions of GPS geometric dilution of precision using genetic programming", journal = "Advances in Engineering Software", volume = "45", number = "1", pages = "332--340", year = "2012", ISSN = "0965-9978", DOI = "doi:10.1016/j.advengsoft.2011.10.013", URL = "http://www.sciencedirect.com/science/article/pii/S0965997811002894", keywords = "genetic algorithms, genetic programming, Global Positioning System (GPS), Geometric Dilution of Precision (GDOP), Regression, White-boxed methods, Soft computing", abstract = "Global Positioning System (GPS) has been used extensively in various fields. Geometric Dilution of Precision (GDOP) is an indicator showing how well the constellation of GPS satellites is organised geometrically, so as a reliability indicator presenting the GPS positioning accuracy. Traditional methods for calculating GPS GDOP need to solve the measurement equations where involve complicated matrix transformation and inversion. Some studies rephrase the calculation of GPS GDOP a regression problem and employ black-box machine learning methods for problem solving. However, the regression models obtained from such methods lack of expressivity for describing the relationships among variables. Making the structures of GDOP expressions visible is valuable because they can be further studied or tailored for specific GPS applications. This study employs the technique of genetic programming (GP) for the regression of GPS GDOP. The performance of GP working with various operators and parameter settings is studied and discussed. The experimental results show that GP generates precise models with better expressivity for GPS GDOP than other methods.", } @Article{Wu:2013:CG, author = "Chih-Hung Wu and Wei-Han Su", title = "Lattice-based clustering and genetic programming for coordinate transformation in {GPS} applications", journal = "Computer \& Geosciences", volume = "52", pages = "85--94", year = "2013", keywords = "genetic algorithms, genetic programming, Clustering, Symbolic regression, GPS, Lattices, Coordinate systems", ISSN = "0098-3004", DOI = "doi:10.1016/j.cageo.2012.09.022", URL = "http://www.sciencedirect.com/science/article/pii/S0098300412003329", abstract = "Coordinate transformation is essential in many georeferencing applications. Level-wised transformation can be considered as a regression problem and done by machine-learning approaches. However, inaccurate and biased results are usually derived when training data do not uniformly distribute. In this paper, the performance of regression-based coordinate transformation for GPS applications is discussed. A lattice-based clustering method is developed and integrated with genetic programming for building better regression models of coordinate transformation. The GPS application area is first partitioned into lattices with lattice sizes being determined by the geographic locations and distribution of the GPS reference points. Clustering is then performed on lattices, not on data points. Each cluster of lattices serves as a training data set for a genetic regression model of coordinate transformation. In this manner, the data points contained in the different lattices can be considered to be of the same importance. Biased regression results caused by the imbalanced distribution of data can also be eliminated. The experimental results show that the proposed method can further improve the positioning accuracy than previous methods.", } @InProceedings{Wu:2013:SNPD, author = "Chih-Hung Wu and Chin-Yuan Chiang and Yi-Han Chen", booktitle = "14th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)", title = "Parallelism of Evolutionary Design of Image Filters for Evolvable Hardware Using GPU", year = "2013", pages = "592--597", month = jul, keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, EHW, GPU, Parallelism, evolutionary design; evolvable hardware, image filter", DOI = "doi:10.1109/SNPD.2013.79", abstract = "Evolvable Hardware (EHW) is a combination of evolutionary algorithm and reconfigurable hardware devices. Due to its flexible and adaptive ability, EHW-based solutions receive a lot of attention in industrial applications. One of the obstacles to realize an EHW-based method is its very long training time. This study deals with the parallelism of EHW-based design of image filters using graphic processing units (GPUs). The design process is analysed and decomposed into some smaller processes that can run in parallel. Pixel-based data for training and verifying EHW solutions are partitioned according to the architecture of GPU. Several strategies for deploying parallel processes are developed and implemented. With the proposed method, significant improvements on the efficiency of training EHW models are gained. Using a GPU with 240 cores, a speedup of 64 times is obtained. This paper evaluates and compares the performance of the proposed method with other ones.", notes = "Dept. of Electr. Eng., Nat. Univ. of Kaohsiung, Kaohsiung, Taiwan Also known as \cite{6598525}", } @InProceedings{Wu:2005:amcFbGEp, author = "Chuan-Sheng Wu and Li Huang and Li-Shan Kang", title = "The automatic modeling of complex functions based on gene expression programming", booktitle = "Proceedings of 2005 International Conference on Machine Learning and Cybernetics", year = "2005", volume = "5", pages = "2870--2873", month = "18-21 " # aug, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Gene Expression Programming, mathematical operators, trees (mathematics), automatic modelling, complex functions, gene expression programming, genetic operator, multilayer tree, mutation operator, recombination operator, termination condition operator, transposition operator, automatic modeling, complex function", DOI = "doi:10.1109/ICMLC.2005.1527432", abstract = "In this paper, we apply gene expression programming to the automatic modelling of complex function. In our programs, the complex functions are expressed by multi-layer tree. The genetic operators such as mutation, transposition, recombination and termination conditions are designed respectively. Furthermore, we give two examples, whose results show that the models set up by gene expression programming are better than the models set up by genetic programming.", notes = "Sch. of Natural Sci., Wuhan Univ. of Technol., China. INSPEC Accession Number:8946031", } @InProceedings{Wu:2015:GECCO, author = "Fan Wu and Westley Weimer and Mark Harman and Yue Jia and Jens Krinke", title = "Deep Parameter Optimisation", booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation", year = "2015", editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr", pages = "1375--1382", organisation = "SIGEVO", address = "Madrid", publisher = "ACM", publisher_address = "New York, NY, USA", month = "11-15 " # jul, keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, MOGA, Parameter tuning, parameter exposure, memory allocation", isbn13 = "978-1-4503-3472-3", URL = "http://www.human-competitive.org/sites/default/files/wu-weimer-harman-jia-krinke-text.txt", URL = "http://www.human-competitive.org/sites/default/files/wu-weimer-harman-jia-krinke-paper.pdf", DOI = "doi:10.1145/2739480.2754648", size = "8 pages", abstract = "We introduce a mutation-based approach to automatically discover and expose deep (previously unavailable) parameters that affect a program's runtime costs. These discovered parameters, together with existing (shallow) parameters, form a search space that we tune using search-based optimisation in a bi-objective formulation that optimises both time and memory consumption. We implemented our approach and evaluated it on four real-world programs. The results show that we can improve execution time by 12percent or achieve a 21percent memory consumption reduction in the best cases. In three subjects, our deep parameter tuning results in a significant improvement over the baseline of shallow parameter tuning, demonstrating the potential value of our deep parameter extraction approach.", notes = "Entered 2016 HUMIES AST, NSGA-II, malloc, dlmalloc, Milu. 70000 lines of C code: Expresso, gawk, flex, sed. GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentieth annual genetic programming conference (GP-2015)", } @InProceedings{Wu:2016:SSBSE, author = "Fan Wu and Mark Harman and Yue Jia and Jens Krinke", title = "{HOMI}: Searching Higher Order Mutants For Software Improvement", booktitle = "Proceedings of the 8th International Symposium on Search Based Software Engineering, SSBSE 2016", year = "2016", editor = "Federica Sarro and Kalyanmoy Deb", volume = "9962", series = "LNCS", pages = "18--33", address = "Raleigh, North Carolina, USA", month = "8-10 " # oct, publisher = "Springer", note = "best paper", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE", isbn13 = "978-3-319-47106-8", DOI = "doi:10.1007/978-3-319-47106-8_2", size = "16 pages", abstract = "This paper introduces HOMI, a Higher Order Mutation based approach for Genetic Improvement of software, in which the code modification granularity is finer than in previous work while scalability remains. HOMI applies the NSGA-II algorithm to search for higher order mutants that improve the non-functional properties of a program while passing all its regression tests. Experimental results on four real-world C programs shows that up to 14.7percent improvement on time and 19.7percent on memory are found using only First Order Mutants. By combining these First Order Mutants, HOMI found further improvement in Higher Order Mutants, giving an 18.2percent improvement on the time performance while keeping the memory improvement. A further manual analysis suggests that 88percent of the mutation changes cannot be generated using line based plastic surgery Genetic Improvement approaches.", notes = "GP? co-located with ICSME-2016 https://ssbse.info/2016/ gismo", } @PhdThesis{Thesis_Fan_v2.1, author = "Fan Wu", title = "Mutation-Based Genetic Improvement of Software", school = "Department of Computer Science, University College, London", year = "2017", address = "UK", month = jul # " 2", keywords = "genetic algorithms, genetic programming, Genetic Improvement, SBSE, deep parameters, mutation testing", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Thesis_Fan_v2.1.pdf", URL = "https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.746642", URL = "https://discovery.ucl.ac.uk/id/eprint/1561361", size = "144 pages", abstract = "Genetic Improvement (GI) of software is a recent field that has drawn much attention from Software Engineering researchers. It aims to use search techniques to automatically modify and improve existing software. The drawback in previous GI approaches is scalability of these approaches, due to the large search space formed by the code base in real-world systems. To overcome the scalability challenge, more recent studies have confined the granularity of code modification at the statement level and applied a prior sensitivity analysis to further reduce the search space. However, some software improvements may require code changes at a finer level of granularity. This thesis demonstrates that, by combining with Mutation Testing techniques, GI can operate at this finer granularity while preserving scalability. The thesis applies Mutation Operators to automatically modify the source code of the target software. After a prior sensitivity analysis on First Order Mutants, deep (previously unavailable) parameters are exposed from the most sensitive locations, followed by a bi-objective optimisation process to fine tune them together with existing (shallow) parameters. The objective is to improve both time and memory resources required by the computation. Since this approach relies on the selection of Mutation Operators and traditional Mutation Operators are not concerned with memory performance, the thesis proposes and evaluates Memory Mutation Operators in the Mutation Testing context. Using both traditional and Memory Mutation Operators, the thesis further seeks to improve the target software by searching for Higher Order Mutants (HOMs). The thesis presents the result of a code analysis study, which reveals that, among all the code modifications that contribute to the improvement, more than half of them require a finer control of the code, which our approach is better at than previous GI approaches.", notes = "Supervisor: Mark Harman. ISNI: 0000 0004 7225 1378", } @InCollection{wu:1999:EIYNAO, author = "Gary I. Wu", title = "Evolution of Infanticide and Youth Nurturing in Artificial Organisms", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1999", year = "1999", editor = "John R. Koza", pages = "245--253", address = "Stanford, California, 94305-3079 USA", month = "15 " # mar, publisher = "Stanford Bookstore", keywords = "genetic algorithms", notes = "part of \cite{koza:1999:GAGPs}", } @InProceedings{Wu:2023:CSIS-IAC, author = "Jiankai Wu and Shichang Xie and Junlan Dong and Jinghui Zhong", booktitle = "2023 International Annual Conference on Complex Systems and Intelligent Science (CSIS-IAC)", title = "An Efficient {GEP-based} Hyper-heuristic Approach for Automatic Airport Gate Assignment Problem", year = "2023", pages = "345--350", abstract = "The Airport Gate Assignment Problem (AGAP) is a key optimisation problem in air transportation. Heuristic methods are often used to solve AGAP. However, existing heuristic methods have drawbacks such as lack of generality, and inability to adjust dynamically in real-time. To address these issues, this paper proposes a hyper-heuristic framework for AGAP. A hyper-heuristic algorithm (GBHH) is further developed based on the framework and Gene Expression Programming (GEP). Improvements are made to the encoding and decoding processes of GEP to enhance the search performance. Experimental results show that the hyper-heuristic algorithm proposed in this paper performs satisfactorily compared to genetic algorithm (GA), simulated annealing algorithm (SA) and hybrid algorithm (HSATS), especially in terms of search efficiency.", keywords = "genetic algorithms, genetic programming, Gene expression programming, Heuristic algorithms, Metaheuristics, Simulated annealing, Logic gates, Airports, Search problems, Airport gate assignment problem (AGAP), Hyper-heuristic framework, Scheduling rules(SRs)", DOI = "doi:10.1109/CSIS-IAC60628.2023.10364183", month = oct, notes = "Also known as \cite{10364183}", } @MastersThesis{oai:NSYSU:etd-0911102-162515, title = "Web Search Using Genetic Programming", author = "Jain-Shing Wu", year = "2002", school = "NSYSU", address = "Taiwan", month = "11 " # sep, keywords = "genetic algorithms, genetic programming, Web Search, Internet, Information Retrieval", contributor = "Ye-In Chang and Chungnan Lee and Tung-Kuan Liu and Sue-Jin Ker", language = "en", oai = "oai:NSYSU:etd-0911102-162515", URL = "https://etd.lis.nsysu.edu.tw//ETD-db/ETD-search-c/view_etd?URN=etd-0911102-162515", URL = "https://etd.lis.nsysu.edu.tw//ETD-db/ETD-search-c/getfile?URN=etd-0911102-162515&filename=etd-0911102-162515.pdf", broken = "http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0911102-162515", size = "37 pages", abstract = "To locate and to retrieve the needed information from the Internet is an important issue. Existing search engines may give too much useless and redundancy information. Due to the search feature is different for different search engines, its very difficult to find an optimal search scheme for all subjects. In this paper, we propose a genetic programming web search system (GPWS) to generate exact query according to a user's interests. The system can retrieve the information from the search engines, filter the retrieved results and remove the redundancy and useless results. The filtered results are displayed on a uniform user interface. Compared with the queries generated by randomly, the degree of similarity of results and user's interests are improved.", } @Article{Wu:2014:EL, author = "Junfeng Wu and Zhiguo Jiang and Jianwei Luo and Haopeng Zhang", journal = "Electronics Letters", title = "Composite kernels conditional random fields for remote-sensing image classification", year = "2014", volume = "50", number = "22", pages = "1589--1591", abstract = "The problem of classifying a remote-sensing image by specifically labelling each pixel in the image is addressed. A novel method, named composite kernels conditional random field (CKCRF), which embeds multiple kernels into a classical CRFs model is proposed. Rather than manually selecting kernel-like KCRF, CKCRFs chooses the appropriate kernel by training. Moreover, a genetic programming-based decision-level fusion framework is proposed to tackle the problem of feature selection. It can select the appropriate features suitable to each category. Evaluations show that CKCRFs outperform CRFs and KCRFs, and CKCRFs with the fusion scheme is better than that without the fusion step.", keywords = "genetic algorithms, genetic programming, geophysical image processing, geophysical techniques, image classification, image fusion, remote sensing, GP-based decision-level fusion framework, composite kernels conditional random fields, fusion scheme, remote-sensing image classification", DOI = "doi:10.1049/el.2014.1964", ISSN = "0013-5194", notes = "Also known as \cite{6937260}", } @InProceedings{HokieGo-PM, author = "Junyan Wu and Xiaofu Ma and Weiguo Fan", title = "{HokieGo at 2017 PM} Task: Genetic Programming based re-ranking method In Biomedical Information Retrieval", booktitle = "NIST Special Publication 500-324: The Twenty-Sixth Text REtrieval Conference Proceedings (TREC 2017)", year = "2017", editor = "Ellen M. Voorhees and Angela Ellis", volume = "500-324", series = "Special Publication", address = "Gaithersburg, Maryland, USA", publisher_address = "USA", month = nov # " 15--17", publisher = "National Institute of Standards and Technology (NIST) and the Defense Advanced Research Projects Agency (DARPA)", keywords = "genetic algorithms, genetic programming", URL = "https://trec.nist.gov/pubs/trec26/papers/HokieGo-PM.pdf", size = "6 pages", abstract = "his paper summarizes our efforts on TREC 2017 Precision Medicine Track. We present a genetic programming based learning-to-rank algorithm. We perform two training experiments on 2014 and 2016 TREC CDS data and apply the pre-trained model as re-ranking method to improve the performance. In addition, two utility functions, CHK and FFP4, have been used for the training optimisation", notes = "conf/trec/WuMF17, https://trec.nist.gov/pubs/trec26/trec2017.html", } @InProceedings{Wu:2022:PRAI, author = "Min Wu and Ming Li and Chao He and Hao Chen and Yan Wang and Zhengxiu Li", booktitle = "2022 5th International Conference on Pattern Recognition and Artificial Intelligence (PRAI)", title = "Facial Expression Recognition Based on Genetic Programming Learning CCA Fusion", year = "2022", pages = "526--532", abstract = "The quality of the features directly affect the overestimation of image classification. However, it is difficult to distinguish between two categories only by a single feature when there is no obvious difference between them. One of the ideas to solve this problem is multi-feature fusion. However, the existing fusion methods have the problems of complex models, fixed models and the need for relevant knowledge in the field. Genetic programming (GP) is a feature fusion method with flexible representation. It can automatically learn other excellent fusion methods without operating how the computer fuses. According to this property, this paper presents a new LCGP method, which can automatically learn existing fusion methods. In the proposed method, the excellent fusion model will be refined into a mathematical form of the function operator. In fact, this is a three-tiered GP tree that integrates different features and fusion methods into the same tree through operators. The proposed method can automatically learn and evolve different mathematical fusion models, validate them on two small sample datasets, CK+ and JAFFE, and compare them with several state-of-the-art methods. The results show that although the training examples are limited, the performance of this method is better than or similar to that of the related methods.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/PRAI55851.2022.9904275", month = aug, notes = "Also known as \cite{9904275}", } @InProceedings{Wu:2022:ICDE, author = "Lianlong Wu and Emanuel Sallinger and Evgeny Sherkhonov and Sahar Vahdati and Georg Gottlob", booktitle = "2022 IEEE 38th International Conference on Data Engineering (ICDE)", title = "Rule Learning over Knowledge Graphs with Genetic Logic Programming", year = "2022", pages = "3373--3385", abstract = "Declarative rules such as Prolog and Datalog rules are common formalisms to express expert knowledge and facts. They play an important role in Knowledge Graph (KG) construction and completion. Such rules not only encode the expert background knowledge and the relational patterns among the data, but also infer new knowledge and insights from them. Formalizing rules is often a laborious manual process, while learning them from data automatically can ease this process. Within the rule hypothesis space, current approaches resort to exhaustive search with a number of heuristics and syntactic restrictions on the rule language, which impacts the efficiency and quality of the outcome rules. In this paper, we extend the rule hypothesis space from usual path rules to general Datalog rule space by proposing a novel Genetic Logic Programming algorithm named Evoda. It is an iterative process to learn high-quality rules over large scale KG for a matter of seconds. We have performed experiments over multiple real-world KGs and various evaluation metrics to show its mining capabilities for higher quality rules and more precise predictions. Additionally, we have applied it on the KG completion tasks to illustrate its competitiveness with several state-of-the-art embedding or neural-based models. The experiments demonstrate the feasibility, effectiveness and efficiency of the Evoda algorithm.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICDE53745.2022.00318", ISSN = "2375-026X", month = may, notes = "Also known as \cite{9835403}", } @InProceedings{conf/pakdd/WuC07, author = "Peng Wu and Yuehui Chen", title = "Grammar Guided Genetic Programming for Flexible Neural Trees Optimization", booktitle = "Proceedings of the 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2007", year = "2007", editor = "Zhi-Hua Zhou and Hang Li and Qiang Yang", volume = "4426", series = "Lecture Notes in Computer Science", pages = "964--971", address = "Nanjing, China", month = may # " 22-25", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-71700-3", DOI = "doi:10.1007/978-3-540-71701-0_108", size = "8 pages", abstract = "In our previous studies, Genetic Programming (GP), Probabilistic Incremental Program Evolution (PIPE) and Ant Programming (AP) have been used to optimal design of Flexible Neural Tree (FNT). In this paper Grammar Guided Genetic Programming (GGGP) was employed to optimize the architecture of FNT model. Based on the pre-defined instruction sets, a flexible neural tree model can be created and evolved. This framework allows input variables selection, over-layer connections and different activation functions for the various nodes involved. The free parameters embedded in the neural tree are optimized by particle swarm optimization algorithm. Empirical results on stock index prediction problems indicate that the proposed method is better than the neural network and genetic programming forecasting models.", bibdate = "2007-06-24", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/pakdd/pakdd2007.html#WuC07", } @InProceedings{Wu:2009:ICNC, author = "Peng Wu and Yuehui Chen and Qingfang Meng and Zhen Liu", title = "Small-Time Scale Network Traffic Prediction Using Complex Network Models", booktitle = "Fifth International Conference on Natural Computation, ICNC '09", year = "2009", month = aug, volume = "3", pages = "303--307", keywords = "genetic algorithms, genetic programming, autoregressive integrated moving average, complex network models, local approximation, neural network, particle swarm optimization, small time scale network traffic prediction, autoregressive moving average processes, complex networks, neural nets, particle swarm optimisation, telecommunication traffic", DOI = "doi:10.1109/ICNC.2009.122", abstract = "The self-similar and nonlinear nature of network traffic makes high accurate prediction difficult. Various technology, including Autoregressive Integrated Moving Average (ARIMA), Local Approximation (LA), Neural Network (NN) etc., have been applied to Internet traffic prediction. In this paper, Complex Network based on genetic programming and particle swarm optimization is proposed to predict the time series of Internet traffic.We propose an automatic method for constructing and evolving our complex network model. The structure of complex network is evolved using genetic programming, and the fine tuning of the parameters encoded in the structure is accomplished using particle swarm optimization algorithm. The relative performances of our model are reported. The results show that our model has high prediction accuracy and can characterize real network traffic well.", notes = "Also known as \cite{5364488}", } @InProceedings{wu2012, author = "Peng Wu and Tao Xu and Likai Dong and Zhen Liu and Yuehui Chen", title = "Building Ensemble Classifier Based on Complex Network for Predicting Protein Structural Class", year = "2012", month = "12", volume = "6", pages = "824--830", booktitle = "Information Technology for Manufacturing Systems III", series = "Advanced Engineering Forum", publisher = "Trans Tech Publications", keywords = "genetic algorithms, genetic programming, complex network, protein structural class, ensemble classifier", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.1000.894", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1000.894", URL = "http://www.scientific.net/AEF.6-7.824.pdf", DOI = "doi:10.4028/www.scientific.net/AEF.6-7.824", abstract = "In recent years, complex network models were developed to solve classification and time series prediction problems. In this paper, ensemble classifier based on complex network (mainly scale-free network) is firstly used to predict protein structural class. For the classifier design, genetic programming and particle swarm optimization algorithm are used alternately to evolve the structure and encoding parameters. The experimental results validate the good performance of the proposed method.", } @Article{Wu:ieeeacmtCBBI, author = "Peng Wu and Dong Wang", title = "Classification of a {DNA} Microarray for Diagnosing Cancer Using a Complex Network Based Method", journal = "IEEE/ACM Transactions on Computational Biology and Bioinformatics", year = "2019", volume = "16", number = "3", pages = "801--808", month = may, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/TCBB.2018.2868341", ISSN = "1545-5963", abstract = "Applications that classify DNA microarray expression data are helpful for diagnosing cancer. Many attempts have been made to analyse these data; however, new methods are needed to obtain better results. In this study, a Complex Network (CN) classifier was exploited to implement the classification task. An algorithm was used to initialize the structure, which allowed input variables to be selected over layered connections and different activation functions for different nodes. Then, a hybrid method integrated the Genetic Programming and the Particle Swarm Optimization algorithms was used to identify an optimal structure with the parameters encoded in the classifier. The single CN classifier and an ensemble of CN classifiers were tested on four bench data sets. To ensure diversity of the ensemble classifiers, we constructed a base classifier using different feature sets, i.e., Pearson's correlation, Spearman's correlation, Euclidean distance, Cosine coefficient and the Fisher-ratio. The experimental results suggest that a single classifier can be used to obtain state-of-the-art results and the ensemble yielded better results.", notes = "University of Jinan, 12413 Jinan, Shandong China 250022 Also known as \cite{8453897}", } @InProceedings{Wu:2022:ACIE, author = "Rong Wu and Jin Xu", booktitle = "2022 2nd Asia Conference on Information Engineering (ACIE)", title = "Learning Markov Decision Processes Based on Genetic Programming", year = "2022", pages = "72--76", abstract = "Model checking is used to verify the security of communication protocols in which the behavior is stochastic influenced by the environment. Automata learning settles the problem of obtaining formal models from observable data of black-box systems. It is available for different variations of finite automata to in model checking. Genetic Programming is a machine learning technique that automatically generates programs and outputs a fittest program. In this paper, we present an approach to learn markov decision progresses based on the framework of genetic programming. The approach outputs the fittest model with a set of system traces by refining iteratively models. We evaluate our method on one probabilistic system from the literature and 30 randomly generated examples.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ACIE55485.2022.00023", month = jan, notes = "Also known as \cite{9831511}", } @Article{Wu20101, author = "Shelly Xiaonan Wu and Wolfgang Banzhaf", title = "The use of computational intelligence in intrusion detection systems: A review", journal = "Applied Soft Computing", volume = "10", number = "1", pages = "1--35", year = "2010", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2009.06.019", URL = "http://www.sciencedirect.com/science/article/B6W86-4WV15J9-1/2/07117ede6d9ef58ed75bf405560da6d9", keywords = "genetic algorithms, genetic programming, Survey, Intrusion detection, Computational intelligence, Artificial neural networks, Fuzzy systems, Evolutionary computation, Artificial immune systems, Swarm intelligence, Soft computing", abstract = "Intrusion detection based upon computational intelligence is currently attracting considerable interest from the research community. Characteristics of computational intelligence (CI) systems, such as adaptation, fault tolerance, high computational speed and error resilience in the face of noisy information, fit the requirements of building a good intrusion detection model. Here we want to provide an overview of the research progress in applying CI methods to the problem of intrusion detection. The scope of this review will encompass core methods of CI, including artificial neural networks, fuzzy systems, evolutionary computation, artificial immune systems, swarm intelligence, and soft computing. The research contributions in each field are systematically summarized and compared, allowing us to clearly define existing research challenges, and to highlight promising new research directions. The findings of this review should provide useful insights into the current IDS literature and be a good source for anyone who is interested in the application of CI approaches to IDSs or related fields.", notes = "brief mention of GP", } @InCollection{bc1_ecindm, author = "Shelly X. Wu and Wolfgang Banzhaf", title = "The Use of Evolutionary Computation in Knowledge Discovery: The Example of Intrusion Detection Systems", booktitle = "Knowledge Mining using Intelligent Agents", publisher = "WorldSciBook", year = "2010", editor = "Satchidananda Dehuri and Sung-Bae Cho", volume = "6", series = "Advances in Computer Science and Engineering", chapter = "2", pages = "27--59", month = dec, keywords = "genetic algorithms, genetic programming", isbn13 = "9781848163874", URL = "http://ebooks.worldscinet.com/ISBN/9781848163874/9781848163874_0002.html", DOI = "doi:10.1142/9781848163874_0002", size = "31 pages", abstract = "This chapter discusses the use of evolutionary computation in data mining and knowledge discovery by using intrusion detection systems as an example. The discussion centers around the role of EAs in achieving the two high level primary goals of data mining: prediction and description. In particular, classification and regression tasks for prediction, and clustering tasks for description. The use of EAs for feature selection in the pre-processing step is also discussed. Another goal of this chapter was to show how basic elements in EAs, such as representations, selection schemes, evolutionary operators, and fitness functions have to be adapted to extract accurate and useful patterns from data in different data mining tasks.", notes = "http://www.worldscibooks.com/compsci/p639.html Computer Science Department, Memorial University, St John's, Canada, A1B 3X5,", } @InProceedings{Wu:2011:GECCO, author = "Shelly Xiaonan Wu and Wolfgang Banzhaf", title = "Rethinking multilevel selection in genetic programming", booktitle = "GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0557-0", pages = "1403--1410", keywords = "genetic algorithms, genetic programming", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001576.2001765", publisher = "ACM", publisher_address = "New York, NY, USA", note = "Best paper", abstract = "This paper aims to improve the capability of genetic programming to tackle the evolution of cooperation: evolving multiple partial solutions that collaboratively solve structurally and functionally complex problems. A multilevel genetic programming approach is presented based on a new computational multilevel selection framework [19]. This approach considers biological group selection theory to encourage cooperation, and a new cooperation operator to build solutions hierarchically. It extends evolution from individuals to multiple group levels, leading to good performance on both individuals and groups. The applicability of this approach is evaluated on 7 multi-class classification problems with different features, such as non-linearity, skewed data distribution and large feature space. The results, when compared to other cooperative evolutionary algorithms in the literature, demonstrate that this approach improves solution accuracy and consistency, and simplifies solution complexity. In addition, the problem is decomposed as a result of evolution without human interference.", notes = "Also known as \cite{2001765} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)", } @MastersThesis{Ting-Ying_Wu:masters, author = "Ting-Ying Wu", title = "Application of Evolutionary Fuzzy Inference System on the Flood Water Level Forecasting", school = "Feng Chia University", year = "2008", address = "Taiwan", keywords = "genetic algorithms, genetic programming, Fuzzy Inference System", URL = "https://hdl.handle.net/11296/z6dhfe", notes = "Written in Chinese. Published in English as \cite{Chen:2013:JH} Adviser: Chang-Shian Chen", } @InProceedings{wu:2024:CEC, author = "Weidong Wu and Xiaoyan Sun and Yong Zhang and Wei Song", title = "Dynamic Multi-Task Interactive Evolutionary Optimization Algorithm with Search Space Alignment", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, Adaptation models, Accuracy, Heuristic algorithms, Sociology, Evolutionary computation, Multitasking, Statistics, Interactive evolutionary algorithm, Multi-task optimization, Dynamic optimization, Subspace alignment, Personalized recommendation", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10611926", abstract = "The interactive evolutionary multi-task optimisation approach assisted by surrogate models has proven successful in enhancing individualized recommendation performance. However, in light of the dynamically changing user preferences, it becomes imperative to further develop more powerful knowledge sharing strategy to improve the interactive multi-task optimisation efficiency. A probability model-assisted search space alignment among multiple tasks method is proposed for knowledge transfer across multi-task environment. Additionally, a diversity maintaining mechanism of the transferred population is proposed to improve the quality and diversity of the initial population in a preference varied multi-task environment. The research demonstrates the effectiveness of the proposed approach in achieving effective knowledge transfer across multi-task environmen. Furthermore, the use of a high-quality initial population not only enhances the evolutionary search efficiency of the algorithm but also significantly improves the accuracy, diversity., and novelty of personalized recommendation.", notes = "also known as \cite{10611926} Is this GP? WCCI 2024 School of Information and Control Engineering China University of Mining and Technology Xuzhou, China", } @InProceedings{Wu:2009:ICIECS, author = "Xiaojun Wu and Ying Wang and Tiantian Zhang", title = "An Improved GAPSO Hybrid Programming Algorithm", booktitle = "International Conference on Information Engineering and Computer Science, ICIECS 2009", year = "2009", month = dec, abstract = "GAPSO hybrid programming algorithm, which is a concise, effective and stable algorithm to solve the hierarchical problem based on GP algorithm. In terms of the specific characteristics of discrete magnitude and continuous magnitude, as well as the superiority of PSO in continuous quantity optimisation, in this paper we propose an improved algorithm, which optimises continuous magnitude by PSO while using GP for discrete magnitude optimization. Then through mass contrast experiments with GAPSO hybrid programming algorithm, we could see that Improved GAPSO hybrid programming algorithm is more stable and effective in function modelling.", keywords = "genetic algorithms, genetic programming, GP algorithm, continuous magnitude, continuous quantity optimisation, discrete magnitude, function modelling, hierarchical problem, improved GAPSO hybrid programming, mass contrast experiments, mathematical programming, particle swarm optimisation", DOI = "doi:10.1109/ICIECS.2009.5365983", notes = "Sch. of Autom., Northwestern Polytech. Univ., Xi'an, China. Also known as \cite{5365983}", } @InProceedings{Wu:2010:CASoN, author = "Xiaojun Wu and Ming Zhao and Yaohong Qu", title = "Particle Swarm Optimization Programming", booktitle = "2010 International Conference on Computational Aspects of Social Networks (CASoN)", year = "2010", month = sep, pages = "397--400", abstract = "PSO is a parallel stochastic optimisation algorithm with advantages of less parameters and high efficiency. This paper describes the programming problem in the method of two linear tables with discrete and continuous quantity, then uses discrete PSO algorithm to discrete optimisation and continuous PSO to optimise continuous quantity in the solving process respectively, based on these proposes the Particle Swarm Optimisation Programming algorithm. Finally, GP and PSOP algorithms are compared by applying them to solving programming problem respectively with three typical test functions, the results show that the PSOP algorithm has better convergence precision and stability than the GP algorithm.", keywords = "genetic algorithms, genetic programming, continuous PSO, convergence precision, discrete PSO algorithm, discrete optimization, parallel stochastic optimization algorithm, particle swarm optimization programming, particle swarm optimisation, stochastic programming", DOI = "doi:10.1109/CASoN.2010.96", notes = "sphere, Griewank, Rastrigin. Sch. of Autom., Northwestern Polytech. Univ., Xi'an, China Also known as \cite{5636594}", } @InProceedings{conf/icira/WuM12, author = "Xiaojun Wu and Yue Ma", title = "Application of Fixed-Structure Genetic Programming for Classification", booktitle = "Proceedings of the 5th International Conference Intelligent Robotics and Applications, ICIRA 2012, Part I", year = "2012", editor = "Chun-Yi Su and Subhash Rakheja and Honghai Liu", volume = "7506", series = "Lecture Notes in Computer Science", pages = "22--33", address = "Montreal, Canada", month = oct # " 3-5", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Classifier systems, Data mining, Classification boundary", isbn13 = "978-3-642-33508-2", DOI = "doi:10.1007/978-3-642-33509-9_3", bibdate = "2012-10-14", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icira/icira2012-1.html#WuM12", size = "12 pages", abstract = "There are three improvements based on GP algorithm in this paper and a fixed-structure GP algorithm for classification was proposed. Traditional GP algorithm relies on non-fixed-length tree structure to describe the classification problems. This algorithm uses a fixed-length linear structure instead of the traditional structure and optimises the leaf nodes coefficients based on the hill-climbing algorithm. Meanwhile, aiming at the samples on the classification boundaries, an optimisation method of classification boundaries is proposed which makes the classification boundaries continuously tend to the optimal solutions in the program evolution process. At the end, an experiment is made by using this improved algorithm and a two-categories sample set with classification boundary is correctly classified (This sample set is an accurate data set from UCI database) Then it shows the analysis of classification results and the classification model produced by this algorithm. The experimental results indicates that the GP classification algorithm with fixed structure could improve the classification accuracy rate and accelerate the solutions convergence speed, which is of great significance in the practical application of classification systems based on GP algorithm.", } @Article{Wu:2013:ASC, author = "Xiaojun Wu and Zhanzhong Yang", title = "Nonlinear speech coding model based on genetic programming", journal = "Applied Soft Computing", volume = "13", number = "7", pages = "3314--3323", year = "2013", keywords = "genetic algorithms, genetic programming, Nonlinear modeling, Speech coding", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2013.02.008", URL = "http://www.sciencedirect.com/science/article/pii/S1568494613000628", abstract = "An improved genetic programming is proposed in this paper to construct the nonlinear models of speech signals, and the speech coding is further accomplished. After the preprocessing of the speech signals, the improved GP is used to construct the corresponding model of each speech frame. Then by analysing these models, a normalised model that has generalisation ability is obtained. And finally the process of speech coding is accomplished by the optimising the parameters of the normalized model using an optimization algorithm. Experiments demonstrate that the feasibility of the improved GP in the modelling of speech signals, and show the superiority of the proposed method in speech coding based on the comparisons with the linear predictive coding.", } @Misc{DBLP:journals/corr/abs-2103-07173, author = "Xuan Wu and Xiuyi Zhang and Linhan Jia and Liang Chen and Yanchun Liang and You Zhou and Chunguo Wu", title = "Neural Architecture Search based on Cartesian Genetic Programming Coding Method", howpublished = "arXiv", volume = "abs/2103.07173", year = "2021", month = "28 " # sep, keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, ANN, Neural architecture search, Attention mechanism, Sentence classification", URL = "https://arxiv.org/abs/2103.07173", eprinttype = "arXiv", eprint = "2103.07173", timestamp = "Thu, 30 Sep 2021 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/corr/abs-2103-07173.bib", bibsource = "dblp computer science bibliography, https://dblp.org", size = "11 pages", abstract = "Neural architecture search (NAS) is a hot topic in the field of automated machine learning and outperforms humans in designing neural architectures on quite a few machine learning tasks. Motivated by the natural representation form of neural networks by the Cartesian genetic programming (CGP), we propose an evolutionary approach of NAS based on CGP, called CGPNAS, to solve sentence classification task. To evolve the architectures under the framework of CGP, the operations such as convolution are identified as the types of function nodes of CGP, and the evolutionary operations are designed based on Evolutionary Strategy. The experimental results show that the searched architectures are comparable with the performance of human-designed architectures. We verify the ability of domain transfer of our evolved architectures. The transfer experimental results show that the accuracy deterioration is lower than 2-5%. Finally, the ablation study identifies the Attention function as the single key function node and the linear transformations along could keep the accuracy similar with the full evolved architectures, which is worthy of investigation in the future.", notes = "See \cite{Wu_Xuan:ieeeTEC}", } @Article{Wu_Xuan:ieeeTEC, author = "Xuan Wu and Di Wang and Huanhuan Chen and Lele Yan and Yubin Xiao and Chunyan Miao and Hongwei Ge and Dong Xu and Yanchun Liang and Kangping Wang and Chunguo Wu and You Zhou", title = "Neural Architecture Search for Text Classification With Limited Computing Resources Using Efficient Cartesian Genetic Programming", journal = "IEEE Transactions on Evolutionary Computation", year = "2024", volume = "28", number = "3", pages = "638--652", month = jun, keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, ANN, neural architecture search, text classification, evolutionary strategy", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2023.3346969", abstract = "...proposes a crossover operator, a light-weighted age mechanism, and two adaptive mutation operators as the novel components in our efficient CGP (ECGP) method. To assess the performance of ECGP, we conduct extensive experiments on three text classification task datasets. The experimental results demonstrate that ECGP outperforms other NAS methods, requiring only hundreds of fitness evaluations to find architectures with competitive accuracy compared with human-designed models. Additionally, the ECGP-evolved architectures are shown as converging fast and stably, and having high-level transferability with merely a one to two percent accuracy drop. Ablation studies demonstrate the effectiveness of the proposed operators and age mechanism, and identify GRU as the most critical function in the text classification task. Finally, we summarise three design principles observed from the ECGP-evolved architectures that are in line with human-design strategies.", notes = "also known as \cite{10373942} See \cite{DBLP:journals/corr/abs-2103-07173}", } @InProceedings{wu:2005:CIS, author = "Yanling Wu and Jiangang Lu and Youxian Sun and Peifei Yu", title = "Bioprocess Modeling Using Genetic Programming Based on a Double Penalty Strategy", booktitle = "Computational Intelligence and Security", year = "2005", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/11596448_137", DOI = "doi:10.1007/11596448_137", } @InProceedings{Wu:2006:iccis, author = "Yanling Wu and Jiangang Lu and Youxian Sun", title = "Genetic Programming Based on an Adaptive Regularization Method", booktitle = "International Conference on Computational Intelligence and Security, 2006", year = "2006", volume = "1", pages = "324--327", address = "Guangzhou", month = nov, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", ISBN = "1-4244-0605-6", DOI = "doi:10.1109/ICCIAS.2006.294148", abstract = "A proper model of a bioprocess is very important for the development of industrial bioprocesses. Here, genetic programming (GP) and genetic algorithm (GA) are used to model the Avermectin fermentation process. To get more accuracy model without losing generalisation, a regularisation term is integrated into the fitness function and an adaptive method to optimise regularisation parameter is proposed to balance training accuracy and the curvature of a nonlinear model. Furthermore, a new protected approach is proposed and experiments show that with the method, the amount the undesired sharp changes in the predicting value decreases largely", notes = "National Lab. of Ind. Control Technol., Zhejiang Univ., Hangzhou", } @InProceedings{Wu:2006:WCICA, author = "Yanling Wu and Jiangang Lu and Jian Xu and Youxian Sun", title = "Bioprocess Modeling Using Fuzzy Regression Clustering and Genetic Programming", booktitle = "The Sixth World Congress on Intelligent Control and Automation, WCICA 2006", year = "2006", volume = "2", pages = "9337--9341", month = "21-23 " # jun, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", ISBN = "1-4244-0332-4", DOI = "doi:10.1109/WCICA.2006.1713808", abstract = "In an industrial Avermection bioprocess, there are some different phases and some interims, so using fuzzy clustering technique to partition the whole process and using several models to represent these different phases respectively is more reasonable. Here, we built a fuzzy model for the bioprocess by a mixture method which integrates fuzzy regression clustering technique, genetic programming (GP), genetic algorithm (GA) and interpolation technique. Fuzzy regression clustering technique is used to partition the whole input space into several subspaces based on whether the training data having a similar model which is identified by GP, GA is used to optimises the parameters of these models and interpolation technique used to define the membership grade for the input data. By this approach, we can fuzzily partition the whole process, find the structures of models which represent subspaces respectively and estimate the parameters simultaneously. Moreover, it has more chance to get a solution with better generalisation.", notes = "National Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China; Department of automation, Anhui University, Hefei 230039, China.", } @InProceedings{Wu:2013:CCC, author = "Yanling Wu and Yuanyuan Zhang2 and Zhongliang Zhu", title = "Immune Genetic Programming and it's application in bioprocess modeling", booktitle = "32nd Chinese Control Conference (CCC 2013)", year = "2013", month = "26-28 " # jul, pages = "8037--8041", keywords = "genetic algorithms, genetic programming", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6640856", abstract = "The canonical form of Genetic Programming (GP) relies almost exclusively on the crossover operator for exploring the solution space. But the crossover operator makes individuals change randomly and indirectly during the whole process, it not only give the individuals the evolutionary chance but also cause certain degeneracy. Here, based on the analysis of the cross-operating characteristics and the study of individual structure, a new form of vaccine and a new autonomous obtaining method is proposed. The concept of immunity is integrated into the GP and a novel algorithm-immune GP is proposed. Experiments show that the result obtained by the proposed algorithm restrains the degenerative phenomena during evolution, so as to improve the algorithm efficiency.", notes = "In chinese. Also known as \cite{6640856}", } @InProceedings{Wu:2014:WCICA, author = "Yanling Wu and Zhongliang Zhu and Yuanyuan Zhang2", booktitle = "11th World Congress on Intelligent Control and Automation (WCICA 2014)", title = "A preference-based non-dominated sorting genetic programming for bioprocess modeling", year = "2014", month = jun, pages = "6085--6089", abstract = "Non-dominated sorting genetic programming is used to make the evaluating of several objectives impersonally. These objectives are the complexity, the oscillation and the training errors of a model. The preference of a decision-maker is integrated into non-dominated sorting GP and then a preference-based non-dominated sorting genetic programming is proposed. In order to improving the searching efficiency, decision-maker's preference is used to guide the searching direction. Last, several models are selected from the Pareto front based on their performance on each objective and an integrated model is obtained. The approach is used to model the biomass concentration and its effectiveness are demonstrated.", keywords = "genetic algorithms, genetic programming, Biological system modelling, Educational institutions, Evolutionary computation, Optimisation, Programming, Sorting, non-dominated sorting, preference", DOI = "doi:10.1109/WCICA.2014.7053762", notes = "Also known as \cite{7053762}", } @Article{Wu:2015:DSP, author = "Yanling Wu and Qingwei Gao and Yuanyuan Zhang2", title = "A robust baseline elimination method based on community information", journal = "Digital Signal Processing", year = "2015", ISSN = "1051-2004", DOI = "doi:10.1016/j.dsp.2015.02.015", URL = "http://www.sciencedirect.com/science/article/pii/S105120041500072X", abstract = "Baseline correction is an important pre-processing technique used to separate true spectra from interference effects or remove baseline effects. In this paper, an adaptive iteratively reweighted genetic programming based on excellent community information (GPEXI) is proposed to model baselines from spectra. Excellent community information which is abstracted from the present excellent community includes an automatic common threshold, normal global and local slope information. Significant peaks can be firstly detected by an automatic common threshold. Then based on the characteristic that a baseline varies slowly with respect to wavelength, normal global and local slope information are used to further confirm whether a point is in peak regions. Moreover the slope information is also used to determine the range of baseline curve fluctuation in peak regions. The proposed algorithm is more robust for different kinds of baselines and its curvature and slope can be automatically adjusted without prior knowledge. Experimental results in both simulated data and real data demonstrate the effectiveness of the algorithm.", keywords = "genetic algorithms, genetic programming, Baseline correction, Robust estimation, Community information", } @Article{Wu:2020:ACC, author = "Yue Wu and Qingxiu Su and Wenping Ma and Shaodi Liu and Qiguang Miao", title = "Learning Robust Feature Descriptor for Image Registration With Genetic Programming", journal = "IEEE Access", year = "2020", volume = "8", pages = "39389--39402", ISSN = "2169-3536", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ACCESS.2020.2968339", abstract = "The robustness and accuracy of feature descriptor are two essential factors in the process of image registration. Existing feature descriptors can extract important image features, but it may be difficult to find enough correct correspondences for sophisticated images. And these feature descriptors often require domain expertise and human intervention. The aim of this paper is to use Genetic Programming (GP) to automatically evolve feature descriptors which are adaptive to various images including remote sensing images and optical images. In this paper, a novel GP-based method (GPFD) is proposed to extract feature vectors and evolve image descriptors for image registration without supervision. The proposed method designs a set of simple arithmetic operators and first-order statistics to construct feature descriptors in order to reduce noise interference. The performance of the proposed method is evaluated and compared against five methods including SIFT, SURF, RIFT, GLPM and GP. These results demonstrate that the feature descriptors evolved by GPFD are robust to complex geometric transformation, the illumination difference and noise.", notes = "Also known as \cite{8964334}", } @Article{Wu:2025:apor, author = "Yuefeng Wu and Qinghe Zhang and Ziming Liu and Chao Ji", title = "Experimental investigation on wave roller area evolution in the surf zone", journal = "Applied Ocean Research", year = "2025", volume = "155", pages = "104460", keywords = "genetic algorithms, genetic programming, Wave roller, Image-based measurement, Surf zone", ISSN = "0141-1187", URL = "https://www.sciencedirect.com/science/article/pii/S0141118725000483", DOI = "doi:10.1016/j.apor.2025.104460", abstract = "In the surf zone, breaking waves generate an air-entraining turbulent region ahead of the wave crest, known as the wave roller, which plays a crucial role in nearshore dynamics. Using image-based techniques, measurements of wave roller areas and their relationships with wave properties were obtained in a laboratory wave tank. The results indicated a positive correlation between wave roller area and both wave height and wavelength. The ratio of roller area to the product of wave height and wavelength varied with factors such as slope, wave steepness, and degree of roller development. A predictive formula for roller area was then developed using a genetic programming model. Furthermore, the fraction of breaking dissipation feeding wave roller energy revealed a gradual decrease along the direction of wave propagation in the surf zone. A substantial portion of breaking dissipation feeds into roller energy in the outer surf zone, while wave rollers stabilize and draw minimal energy from breaking dissipation in the inner surf zone. The measurements and analysis in this study could enhance the understanding of momentum flux caused by wave rollers, improving numerical predictions of hydrodynamics and material transport in the nearshore ocean", } @InProceedings{Wu:2009:WCSE, author = "Zenghong Wu and Min Yao", title = "A New {GEP} Algorithm Based on Multi-phenotype Chromosomes", booktitle = "Second International Workshop on Computer Science and Engineering, WCSE '09", year = "2009", month = oct, volume = "1", pages = "204--209", abstract = "This paper presents an improved gene expression programming (GEP) algorithm based on multi-phenotype chromosomes (MPC-GEP). The populations in MPC-GEP are composed of chromosomes with multiple phenotypes. Each multi-gene chromosome corresponds to multiple expression trees. The new algorithm can find the optimal individual in less time than traditional GEP. Finally, experiments on the new algorithm against traditional GEP algorithm are conducted on several benchmark datasets. Results show that MPC-GEP outperforms traditional GEP in function finding in terms of speed.", keywords = "genetic algorithms, genetic programming, gene expression programming, GEP algorithm, multigene chromosome, multiphenotype chromosome, multiple expression trees", DOI = "doi:10.1109/WCSE.2009.654", notes = "Also known as \cite{5403471}", } @Article{wu:2021:RS, author = "Zhenjiang Wu and Jiahua Zhang and Fan Deng and Sha Zhang and Da Zhang and Lan Xun and Mengfei Ji and Qian Feng", title = "Superpixel-Based Regional-Scale Grassland Community Classification Using Genetic Programming with Sentinel-1 {SAR} and Sentinel-2 Multispectral Images", journal = "Remote Sensing", year = "2021", volume = "13", number = "20", keywords = "genetic algorithms, genetic programming", ISSN = "2072-4292", URL = "https://www.mdpi.com/2072-4292/13/20/4067", DOI = "doi:10.3390/rs13204067", abstract = "Grasslands are one of the most important terrestrial ecosystems on the planet and have significant economic and ecological value. Accurate and rapid discrimination of grassland communities is critical to the conservation and use of grassland resources. Previous studies that explored grassland communities were mainly based on field surveys or airborne hyperspectral and high-resolution imagery. Limited by workload and cost, these methods are typically suitable for small areas. Spaceborne mid-resolution RS images (e.g., Sentinel, Landsat) have been widely used for large-scale vegetation observations owing to their large swath width. However, there still keep challenges in accurately distinguishing between different grassland communities using these images because of the strong spectral similarity of different communities and the suboptimal performance of models used for classification. To address this issue, this paper proposed a superpixel-based grassland community classification method using Genetic Programming (GP)-optimised classification model with Sentinel-2 multispectral bands, their derived vegetation indices (VIs) and textural features, and Sentinel-1 Synthetic Aperture Radar (SAR) bands and the derived textural features. The proposed method was evaluated in the Siziwang grassland of China. Our results showed that the addition of VIs and textures, as well as the use of GP-optimised classification models, can significantly contribute to distinguishing grassland communities, and the proposed approach classified the seven communities in Siziwang grassland with an overall accuracy of 84.21percent and a kappa coefficient of 0.81. We concluded that the classification method proposed in this paper is capable of distinguishing grassland communities with high accuracy at a regional scale.", notes = "also known as \cite{rs13204067}", } @Article{wu:2024:RS, author = "Zhenjiang Wu and Fengmei Yao and Jiahua Zhang and Enhua Ma and Liping Yao and Zhaowei Dong", title = "Genetic Programming Guided Mapping of Forest Canopy Height by Combining {LiDAR} Satellites with Sentinel-1/2, Terrain, and Climate Data", journal = "Remote Sensing", year = "2024", volume = "16", number = "1", pages = "Article No. 110", keywords = "genetic algorithms, genetic programming", ISSN = "2072-4292", URL = "https://www.mdpi.com/2072-4292/16/1/110", DOI = "doi:10.3390/rs16010110", abstract = "Accurately mapping the forest canopy height is vital for conserving forest ecosystems. Employing the forest height measured by satellite light detection and ranging (LiDAR) systems as ground samples to establish forest canopy height extrapolation (FCHE) models presents promising opportunities for mapping large-scale wall-to-wall forest canopy height. However, despite the potential to provide more samples and alleviate the stripe effect by synergistically using the data from two existing LiDAR datasets, Global Ecosystem Dynamics Investigation (GEDI) and Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), the fundamental differences in their operating principles create measurement biases, and thus, there are few studies combining them for research. Furthermore, previous studies have typically employed existing regression algorithms as FCHE models to predict forest canopy height, without customizing a model that achieves optimal performance based on the current samples. These shortcomings constrain the accuracy of predicting forest canopy height using satellite LiDAR data. To surmount these difficulties, we proposed a genetic programming (GP) guided method for mapping forest canopy height by combining the GEDI and ICESat-2 LiDAR data with Sentinel-1/2, terrain, and climate data. In this method, GP autonomously constructs the fusion model of the GEDI and ICESat-2 datasets (hereafter GIF model) and the optimal FCHE model based on the explanatory variables for the specific study area. The outcomes demonstrate that the fusion of the GEDI and ICESat-2 data shows high consistency (R2 = 0.85, RMSE = 2.2m, pRMSE = 11.24percent). The synergistic use of the GEDI and ICESat-2 data, coupled with the optimisation of the FCHE model, substantially improves the precision of forest canopy height predictions, and finally achieves R2, RMSE, and pRMSE of 0.64, 3.38m, and 16.08percent, respectively. In summary, our research presents a reliable approach to accurately estimate forest canopy height using remote sensing data by addressing measurement biases between the GEDI and ICESat-2 data and overcoming the limitations of traditional FCHE models.", notes = "also known as \cite{rs16010110}", } @Article{WU:2019:procir, author = "Zigao Wu and Shudong Sun", title = "Risk cost estimation of job shop scheduling with random machine breakdowns", journal = "Procedia CIRP", volume = "83", pages = "404--409", year = "2019", note = "11th CIRP Conference on Industrial Product-Service Systems", ISSN = "2212-8271", DOI = "doi:10.1016/j.procir.2019.04.087", URL = "http://www.sciencedirect.com/science/article/pii/S2212827119307012", keywords = "genetic algorithms, genetic programming, scheduling, risk cost, job shop, machine breakdowns", abstract = "Scheduling has been playing an important role in the manufacturing phase of product life cycle. In this paper, we focus on the estimation of risk cost for the job shop scheduling under random machine breakdowns, in which all jobs should be delivered together at a given due date. The risk cost measures the sum of expected earliness and tardiness costs. Considering that the risk cost in the form of expectation does not allow analytical calculation for the job shop scheduling, we will try to build a computable analytical approximation to replace the commonly used but time-consuming Monte Carlo simulation. However, the manual design of an effective analytical approximation is generally very complicated. To address it, we will develop a learning method based on the symbolic regression to extract an analytical approximation of risk cost from experimental data automatically. For this purpose, we first list all the features which may be related to the risk cost by analyzing deeply the job shop scheduling under random machine breakdowns. Then, a learning algorithm based on the genetic programming is proposed to extract an analytical approximation of risk cost. Finally, extensive experiments have shown that the accuracy of the generated analytical approximation in evaluating the risk cost is close to that of the Monte Carlo simulation, while it can significantly improve the efficiency of estimation", } @InProceedings{conf/pst/WuchnerOLP16, author = "Tobias Wuchner and Martin Ochoa and Enrico Lovat and Alexander Pretschner", booktitle = "2016 14th Annual Conference on Privacy, Security and Trust (PST)", title = "Generating behavior-based malware detection models with genetic programming", year = "2016", publisher = "IEEE", bibdate = "2017-05-21", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/pst/pst2016.html#WuchnerOLP16", pages = "506--511", month = "12-14 " # dec, address = "Auckland, New Zealand", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-5090-4379-8", DOI = "doi:10.1109/PST.2016.7907008", abstract = "Malware remains a major IT security threat and current detection approaches struggle to cope with a professionalized malware development industry. We propose the use of genetic programming to generate effective and robust malware detection models which we call FrankenMods. These are sets of graph metrics that capture characteristic malware behaviour. Evolution of FrankenMods with good detection capabilities yields continuously improved detection effectiveness. FrankenMods are operationalized by evaluating them on quantitative data flow graphs that model malware behaviour as data flows between system resources caused by issued system calls. We show that FrankenMods are substantially more robust and effective than a state-of-the-art graph metric-based detection approach.", notes = "Also known as \cite{7907008}", } @InProceedings{wyns:2003:EA, author = "Bart Wyns and Stefan Sette and Luc Boullart", title = "Self-Improvement to Control Code Growth in Genetic Programming", booktitle = "Evolution Artificielle, 6th International Conference", year = "2003", editor = "Pierre Liardet and Pierre Collet and Cyril Fonlupt and Evelyne Lutton and Marc Schoenauer", volume = "2936", series = "Lecture Notes in Computer Science", pages = "256--266", address = "Marseilles, France", month = "27-30 " # oct, publisher = "Springer", note = "Revised Selected Papers", keywords = "genetic algorithms, genetic programming, Artificial Evolution", ISBN = "3-540-21523-9", DOI = "doi:10.1007/b96080", size = "doi:10.1007/978-3-540-24621-3_21", abstract = "An important problem with genetic programming systems is that in the course of evolution the size of individuals is continuously growing without a corresponding increase in fitness. This paper reports the application of a self-improvement operator in combination with a characteristic based selection strategy to a classical genetic programming system in order to reduce the effects of code growth. Two examples, a symbolic regression problem and an 11-bit multiplexer problem are used to test and validate the performance of this newly designed operator. Instead of simply editing out non-functional code this method tries to select subtrees with better fitness. Results show that for both test cases code growth is substantially reduced obtaining a reduction factor of 3--10 (depending on the problem) while the same level of fitness is attained.", bibsource = "DBLP, http://dblp.uni-trier.de", notes = "EA'03 bloat", } @InProceedings{eurogp06:WynsDeBruyneBoullart, author = "Bart Wyns and Peter {De Bruyne} and Luc Boullart", title = "Characterizing Diversity in Genetic Programming", editor = "Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art", booktitle = "Proceedings of the 9th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3905", year = "2006", address = "Budapest, Hungary", month = "10 - 12 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-33143-3", pages = "250--259", DOI = "doi:10.1007/11729976_22", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "In many evolutionary algorithms candidate solutions run the risk of getting stuck in local optima after a few generations of optimisation. In this paper two improved approaches to measure population diversity are proposed and validated using two traditional test problems in genetic programming literature. Code growth gave rise to improve pseudo-isomorph measures by eliminating non-functional code using an expression simplifier. Also, Rosca's entropy to measure behavioural diversity is updated to cope with problems producing a more continuous fitness value. Results show a relevant improvement with regard to the original diversity measures.", notes = "Part of \cite{collet:2006:GP} EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006", } @InProceedings{1277312, author = "Bart Wyns and Luc Boullart", title = "Adaptive strategies for a semantically driven tree optimizer to control code growth", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "1762--1762", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1762.pdf", DOI = "doi:10.1145/1276958.1277312", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming: Poster, adaptive control, artificial ant, Boolean problems, fuzzy logic", abstract = "In genetic programming many methods to fight growth exist. But most of these methods require one or multiple parameters to be set. Unfortunately performance strongly depends on a correct setting of each of those parameters. Recently a semantically driven tree optimiser has been developed. In this paper two adaptive strategies to choose a reasonable parameter setting for this growth limiter are presented.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @InProceedings{1277313, author = "Bart Wyns and Luc Boullart and Pieter Jan {De Smedt}", title = "Limiting code growth to improve robustness in tree-based genetic programming", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "1763--1763", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1763.pdf", DOI = "doi:10.1145/1276958.1277313", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming: Poster, artificial ant, representational bias, robustness, bloat", abstract = "In this paper we analyse the composition of the function set of the artificial ant problem to define new training and testing trails similar to the Santa Fe trail. Cross-validation is used in applications where large amounts of data are available. We also use a semantically driven growth limiter to reduce program size and check if growth reduction could lead to increased test performance.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @PhdThesis{Wyns:thesis, author = "Bart Wyns", title = "Genetisch programmeren en codegroei", school = "Elektrische Energie, Systemen en Automatisering, University of Gent", year = "2007", address = "Belgium", keywords = "genetic algorithms, genetic programming, lil-gp", URL = "https://lib.ugent.be/catalog/pug01:470863", URL = "https://biblio.ugent.be/publication/472601/file/1879825.pdf", size = "280 pages", abstract = "Samenvatting De complexiteit van de problemen waarmee een ingenieur heden in contact komt, neemt steeds toe. Hierdoor wordt de verleiding om een aantal concepten uit de biologie te lenen en zo het probleemoplossend vermogen van de ingenieur aan te scherpen, groter. Reeds vanaf 1960 probeert men computationele systemen te ontwerpen volgens de principes van Charles Darwin. Genetisch programmeren is zo een dergelijke evolutionaire optimalisatiemethode voor het automatisch creeren van computerprogramma. Echter, wanneer genetisch programmeren wordt gebruikt om steeds complexere taken op te lossen, vertonen deze programma een steeds sterker wordende drang om te groeien (codegroei). Het doel van dit werk is de bestrijding van codegroei in al zijn facetten. Het eerste deel van dit werk omvat de ontwikkeling van een nieuwe methode om codegroei te bestrijden zonder de kwaliteit van de geevolueerde computerprogramma negatief te beinvloeden. Deze methode gaat op zoek naar geschikte deelprogramma waarbij aan een aantal voorwaarden moet worden voldaan. Een tweede luik van dit werk bestaat uit de ontwikkeling van adaptieve methoden om codegroei te bestrijden. Deze sturingsalgoritmen hebben als primair doel de instellingen voor de gebruiker te beperken en te vereenvoudigen alsook om probleemafhankelijkheden weg te werken. Dankzij codegroei hebben programma de neiging om overgespecialiseerd te raken. In het derde deel ontwikkelen we een strategie om nieuwe testvoorbeelden aan te maken. We evalueren tevens de invloed die de nieuw ontwikkelde codegroei begrenzer uitoefent op de robuustheid van de bekomen oplossingen.", notes = "In Dutch. Object id: 1854/LU-470863 1854/7673 Supervisor: prof. dr. ir. L. Boullart", } @Article{Wyns:2009:JH, title = "Efficient tree traversal to reduce code growth in tree-based genetic programming", author = "Bart Wyns and Luc Boullart", year = "2009", journal = "Journal of Heuristics", volume = "15", number = "1", pages = "77--104", month = feb, keywords = "genetic algorithms, genetic programming, Subtree fitness, Tree traversal, Code growth, Local optimization, Tree-based genetic programming, Technology and Engineering", ISSN = "1381-1231", DOI = "doi:10.1007/s10732-007-9060-0", bibsource = "OAI-PMH server at biblio.ugent.be", oai = "oai:archive.ugent.be:662689", abstract = "Genetic programming is an evolutionary optimization method following the principle of program induction. Genetic programming often uses variable-length tree structures for representing candidate solutions. A serious problem with variable-length representations is code growth: during evolution these tree structures tend to grow in size without a corresponding increase in fitness. Many anti-bloat methods focus solely on size reduction and forget about fitness improvement, which is rather strange when using an {"}optimization{"} method. This paper reports the application of a semantically driven local search operator to control code growth and improve best fitness. Five examples, two theoretical benchmark applications and three real-life test problems are used to illustrate the obtained size reduction and fitness improvement. Performance of the local search operator is also compared with various other anti-bloat methods such as size and depth delimiters, an expression simplifier, linear and adaptive parsimony pressure, automatically defined functions and Tarpeian bloat control.", } @InProceedings{conf/icaart/WynsBB10, title = "Evolving Robust Robot Controllers for Corridor Following using Genetic Programming", author = "Bart Wyns and Bert Bonte and Luc Boullart", booktitle = "Proceedings of the International Conference on Agents and Artificial Intelligence, ICAART 2010", year = "2010", editor = "Joaquim Filipe and Ana L. N. Fred and Bernadette Sharp", volume = "1", pages = "443--446", address = "Valencia, Spain", month = jan # " 22-24", publisher = "SciTePress", keywords = "genetic algorithms, genetic programming, Evolutionary robotics, Corridor following, EyeBot: Poster", isbn13 = "978-989-674-021-4", URL = "https://biblio.ugent.be/publication/978399/file/985375.pdf", DOI = "doi:10.5220/0002588204430446", bibdate = "2010-03-03", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icaart/icaart2010-1.html#WynsBB10", size = "4 pages", abstract = "Designing robots and robot controllers is a highly complex and often expensive task. However, genetic programming provides an automated design strategy to evolve complex controllers based on evolution in nature. We show that, even with limited computational resources, genetic programming is able to evolve efficient robot controllers for corridor following in a simulation environment. Therefore, a mixed and gradual form of layered learning is used, resulting in very robust and efficient controllers. Furthermore, the controller is successfully applied to real environments as well.", notes = "Paper Nr: 26 Dept. of Electrical Energy, Systems and Automation, Ghent University, Technologiepark 913, Zwijnaarde, Belgium", } @InProceedings{wyrwinski:2024:GECCOcomp, author = "Piotr Wyrwinski and Krzysztof Krawiec", title = "Guiding Genetic Programming with Graph Neural Networks", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion", year = "2024", editor = "Ting Hu and Aniko Ekart", pages = "551--554", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, symbolic regression, graph neural networks: Poster", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3654277", size = "4 pages", abstract = "In evolutionary computation, it is commonly assumed that a search algorithm acquires knowledge about a problem instance by sampling solutions from the search space and evaluating them with a fitness function. This is necessarily inefficient because fitness reveals very little about solutions - yet they contain more information that can be potentially exploited. To address this observation in genetic programming, we propose EvoNUDGE, which uses a graph neural network to elicit additional knowledge from symbolic regression problems. The network is queried on the problem before an evolutionary run to produce a library of subprograms, which is subsequently used to seed the initial population and bias the actions of search operators. In an extensive experiment on a large number of problem instances, EvoNUDGE is shown to significantly outperform multiple baselines, including the conventional tree-based genetic programming and the purely neural variant of the method.", notes = "GECCO-2024 GP A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{Xhemali:2010:ICEC, author = "Daniela Xhemali and Christopher J. Hinde and Roger G. Stone", title = "Genetic Evolution of Sorting Programs through a novel Genotype-Phenotype Mapping", booktitle = "Proceedings of the International Conference on Evolutionary Computation (ICEC 2010)", year = "2010", editor = "Joaquim Filipe and Janusz Kacprzyk", pages = "190--198", address = "Valencia, Spain", month = "24-26 " # oct, organisation = "INSTICC, AAAI, WfMC", publisher = "SciTePress", keywords = "genetic algorithms, genetic programming, Genotype-Phenotype mapping, XML, Regular Expressions, Software Programs", isbn13 = "978-989-8425-31-7", URL = "http://hdl.handle.net/2134/7342", URL = "https://dspace.lboro.ac.uk/dspace-jspui/bitstream/2134/7342/1/ICEC-Xhemali-31May2010.pdf", URL = "http://www.robinbye.com/files/publications/ICEC_2010.pdf", URL = "https://www.scitepress.org/PublishedPapers/2010/30784/", DOI = "doi:10.5220/0003078401900198", size = "9 pages", abstract = "This paper presents an adaptable genetic evolutionary system, which includes an innovative approach to mapping genotypes to phenotypes through XML rules. The evolutionary system was originally created to evolve Regular Expressions (REs) to automate the extraction of web information. However, the system has been adapted to work with a completely different domain, Complete Software Programs, to demonstrate the flexibility of this approach. Specifically, the paper concentrates on the evolution of 'Sorting' programs. Experiments show that our evolutionary system is successful and can be adapted to work for challenging domains with minimum effort.", notes = "Broken http://www.icec.ijcci.org/ICEC2010/home.asp http://www.ijcci.org/Abstracts/2010/ICEC_2010_Abstracts.htm Also known as \cite{DBLP:conf/ijcci/XhemaliHS10} VB script, .NET, repair to ensure syntatically correct. Mod (like gramatical evolution?). FOR loop. pop=10.", } @PhdThesis{Xhemali:thesis, author = "Daniela Xhemali", title = "Automated retrieval and extraction of training course information from unstructured web pages", school = "Loughborough University", year = "2010", type = "Engineering Doctorate", address = "Leicestershire, LE11 3TU, UK", month = "9 " # jul, keywords = "genetic algorithms, genetic programming, Web page, Information Retrieval, Information Extraction, Web Classifier, Naive Bayes Classifiers, Regular Expressions", URL = "http://hdl.handle.net/2134/7022", URL = "https://dspace.lboro.ac.uk/dspace-jspui/bitstream/2134/7022/2/EngD-Thesis-DanielaXhemali.pdf", size = "236 pages", abstract = "Web Information Extraction (WIE) is the discipline dealing with the discovery, processing and extraction of specific pieces of information from semi-structured or unstructured web pages. The World Wide Web comprises billions of web pages and there is much need for systems that will locate, extract and integrate the acquired knowledge into organisations practices. There are some commercial, automated web extraction software packages, however their success comes from heavily involving their users in the process of finding the relevant web pages, preparing the system to recognise items of interest on these pages and manually dealing with the evaluation and storage of the extracted results. This research has explored WIE, specifically with regard to the automation of the extraction and validation of online training information. The work also includes research and development in the area of automated Web Information Retrieval (WIR), more specifically in Web Searching (or Crawling) and Web Classification. Different technologies were considered, however after much consideration, Naive Bayes Networks were chosen as the most suitable for the development of the classification system. The extraction part of the system used Genetic Programming (GP) for the generation of web extraction solutions. Specifically, GP was used to evolve Regular Expressions, which were then used to extract specific training course information from the web such as: course names, prices, dates and locations. The experimental results indicate that all three aspects of this research perform very well, with the Web Crawler outperforming existing crawling systems, the Web Classifier performing with an accuracy of over 95percent and a precision of over 98percent, and the Web Extractor achieving an accuracy of over 94percent for the extraction of course titles and an accuracy of just under 67percent for the extraction of other course attributes such as dates, prices and locations. Furthermore, the overall work is of great significance to the sponsoring company, as it simplifies and improves the existing time-consuming, labour-intensive and error-prone manual techniques, as will be discussed in this thesis. The prototype developed in this research works in the background and requires very little, often no, human assistance.", notes = "Sorting programs p218-219. Daniela Birdsall", } @Article{Xi:2020:CCR, author = "Ianto Lin Xi and Yijun Zhao and Robin Wang and Marcello Chang and Subhanik Purkayastha and Ken Chang and Raymond Y. Huang and Alvin C. Silva and Martin Vallieres and Peiman Habibollahi and Yong Fan and Beiji Zou and Terence P. Gade and Paul J. Zhang and Michael C. Soulen and Zishu Zhang and Harrison X. Bai and S. William Stavropoulos", title = "Deep Learning to Distinguish Benign from Malignant Renal Lesions Based on Routine {MR} Imaging", journal = "Clinical Cancer Research", year = "2020", volume = "26", number = "8", pages = "1944--1952", month = apr # " 15", keywords = "genetic algorithms, genetic programming, TPOT, GPU, Python sklearn", publisher = "American Association for Cancer Research", ISSN = "1078-0432", URL = "https://clincancerres.aacrjournals.org/content/early/2020/02/23/1078-0432.CCR-19-0374.full.pdf", URL = "https://clincancerres.aacrjournals.org/content/early/2020/02/23/1078-0432.CCR-19-0374", DOI = "doi:10.1158/1078-0432.CCR-19-0374", code_url = "https://github.com/intrepidlemon/renal-mri", size = "9 pages", abstract = "Purpose: With increasing incidence of renal mass, it is important to make a pretreatment differentiation between benign renal mass and malignant tumour. We aimed to develop a deep learning model that distinguishes benign renal tumors from renal cell carcinoma (RCC) by applying a residual convolutional neural network (ResNet) on routine MR imaging. Experimental Design: Preoperative MR images (T2-weighted and T1-postcontrast sequences) of 1,162 renal lesions definitely diagnosed on pathology or imaging in a multicenter cohort were divided into training, validation, and test sets (70:20:10 split). An ensemble model based on ResNet was built combining clinical variables and T1C and T2WI MR images using a bagging classifier to predict renal tumor pathology. Final model performance was compared with expert interpretation and the most optimized radiomics model. Results: Among the 1,162 renal lesions, 655 were malignant and 507 were benign. Compared with a baseline zero rule algorithm, the ensemble deep learning model had a statistically significant higher test accuracy (0.70 vs. 0.56, P = 0.004). Compared with all experts averaged, the ensemble deep learning model had higher test accuracy (0.70 vs. 0.60, P = 0.053), sensitivity (0.92 vs. 0.80, P = 0.017), and specificity (0.41 vs. 0.35, P = 0.450). Compared with the radiomics model, the ensemble deep learning model had higher test accuracy (0.70 vs. 0.62, P = 0.081), sensitivity (0.92 vs. 0.79, P = 0.012), and specificity (0.41 vs. 0.39, P = 0.770). Conclusions: Deep learning can noninvasively distinguish benign renal tumors from RCC using conventional MR imaging in a multi-institutional dataset with good accuracy, sensitivity, and specificity comparable with experts and radiomics.", notes = "two NVidia V100 GPUs 'This hand-optimised pipe line out performed the TPOT pipeline' overfitting (training ROC 0.73--0.99 => test 0.43--0.73)? Supplementary data for this article are available at Clinical Cancer Research Online (http://clincancerres.aacrjournals.org/).", } @InProceedings{Xia:2022:AIIPCC, author = "Chenxi Xia and Wanlu Dai and Dong Li and Fengling Li and Kai Liu and Zhijie He", booktitle = "2022 International Conference on Artificial Intelligence, Information Processing and Cloud Computing (AIIPCC)", title = "Multi-influenced Factors Prediction Using a Combination of {GEP} and {PCA-GRNN} Model Based on the Shapley Value Method", year = "2022", pages = "253--259", abstract = "The powerful function mining ability of the gene expression programming algorithm has obvious advantages in predicting dependent variables under multi-influence factors. PCA can obtain the key characteristics of the main influencing factors by the data reduction processing, and the generalised regression neural network can effectively overcome the disadvantage of slow training of the traditional neural network. Taking the cost of civil engineering in subway tunnels as an example, this paper introduces the Shapley value method for a combined prediction based on the prediction results of the above two optimal single prediction models and determines the weight of the two models based on the error of allocation. The results of the study show that the combined prediction model based on the Shapley value method reduces the error better than the single prediction model and further improves the prediction accuracy.", keywords = "genetic algorithms, genetic programming, gene expression programming, Training, Neural networks, ANN, Predictive models, Prediction algorithms, Mathematical models, Resource management, principal component analysis, PCS, generalised regression neural network, combination prediction, Shapley value method", DOI = "doi:10.1109/AIIPCC57291.2022.00062", month = aug, notes = "Also known as \cite{10070276}", } @Article{XIA:2024:engfailanal, author = "Jin Xia and Ren-jie Wu and Yu Zhou and Xipeng Wang and Jiejing Chen and Wan-lin Min and Ke-yu Chen and Wei-liang Jin", title = "Systematic framework for handling uncertainty in probabilistic failure analysis of corroded concretes", journal = "Engineering Failure Analysis", volume = "156", pages = "107859", year = "2024", ISSN = "1350-6307", DOI = "doi:10.1016/j.engfailanal.2023.107859", URL = "https://www.sciencedirect.com/science/article/pii/S1350630723008130", keywords = "genetic algorithms, genetic programming, Civil engineering, RC structures, Steel, Numerical simulation, Chloride-induced corrosion, Uncertainty quantification", abstract = "Due to ambiguous correlations between input random variables and multi-source uncertainties from the electrochemical model, significant differences between deterministic corrosion prediction models and actual measurements are often observed. A systematic framework of quantification of uncertainties is developed for structures with correlated random variables originated from multiple sources, which allows efficiently estimating the failure probability distribution of the steel corrosion over time considering the randomness of the cover depth, the surface chloride concentration, and the chloride diffusion coefficient. After partitioning correlated random variables into different groups based on their uncertain sources, the Morris one-step-at-a-time and Sobol model is established to rank with respect to the importance of each correlated random variable. Based on polynomial chaos expansions and genetic programming methods, a more condensed set of random variables is created to propagate parametric problems. The unknown probability distribution of the input random variables is formulated by the Markov chain Monte Carlo to realize rigorous uncertainty quantification of the structural reliability. The application of the systematic framework to a set of numerical examples of steel corrosion includes experimental validation and uncertainty quantification and propagation of environmental, material and geometric properties. The results show that the framework can be integrated with parametric electrochemical models to allow robustness and reliability of corrosion prediction", } @InProceedings{Xia:2014:ICCSE, author = "Min Xia and Clarence W. {De Silva}", title = "A Framework of Design Weakness Detection through Machine Health Monitoring for the Evolutionary Design Optimization of Multi-domain Systems", booktitle = "9th International Conference on Computer Science Education (ICCSE 2014)", year = "2014", month = aug, pages = "205--210", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICCSE.2014.6926455", size = "6 pages", abstract = "Design of a multi-domain engineering system can be complicated due to its complex structure and dynamic coupling between domains. Ideally, designing a multi-domain system should be done in an integrated and concurrent manner, where dynamic interactions between domains in the entire system have to be considered simultaneously, throughout the design process. In recent years, researchers have made some progress in the integrated and optimal design of multi-domain systems. Dynamic modelling tools such as Bond Graphs and Linear Graphs have been considered for modelling multi-domain systems, which can facilitate the design process. In the process of design optimisation, a rather challenging task is to concurrently satisfy multiple design objectives. Methods of evolutionary computing, genetic programming in particular, have received much attention in recent years for application in design optimisation. These methods can be extended to evolutionary optimisation, which may involve complex and non-analytic objective functions and a variety of design specifications. More recently, machine health monitoring system (MHMS) has been considered for integration into the scheme of design evolution even though no concrete developments have made in this regard. In this paper, a framework of design weakness detection through machine health monitoring for evolutionary design optimisation of multi-domain system is proposed. MHMS is integrated with evolutionary design optimisation to make the overall process of design evolution more effective and feasible from the practical point of view. Information form MHMS is used to detect the sites or candidates of design weakness, which will involve computation of a new measure that can reflect the quality of the current design. These candidates of design weakness are then provided to the process of evolutionary design optimisation. On subsequent analysis, design improvements would be made only if these candida- es were found to be related to design weaknesses. Otherwise, the monitoring process will continue. Supervised design weakness detection is achieved through the integrated system of MHMS and evolutionary design optimisation. In addition, a Design Expert System is employed to monitor and assist both design weakness detection and isolation, and feasible design selection.", notes = "Dept. of Mech. Eng., Univ. of British Columbia, Vancouver, BC, Canada Also known as \cite{6926455}", } @Article{Xia:2016:CN, author = "Min Xia and Teng Li and Yunfei Zhang and Clarence W. {de Silva}", title = "Closed-loop design evolution of engineering system using condition monitoring through internet of things and cloud computing", journal = "Computer Networks", volume = "101", pages = "5--18", year = "2016", note = "Industrial Technologies and Applications for the Internet of Things", ISSN = "1389-1286", DOI = "doi:10.1016/j.comnet.2015.12.016", URL = "http://www.sciencedirect.com/science/article/pii/S1389128615005034", abstract = "Flexibility of a manufacturing system is quite important and advantageous in modern industry, which function in a competitive environment where market diversity and the need for customized product are growing. Key machinery in a manufacturing system should be reliable, flexible, intelligent, less complex, and cost effective. To achieve these goals, the design methodologies for engineering systems should be revisited and improved. In particular, continuous or on-demand design improvements have to be incorporated rapidly and effectively in order to address new design requirements or resolve potential weaknesses of the original design. Design of an engineering system, which is typically a multi-domain system, can become complicated due to its complex structure and possible dynamic coupling between domains. An integrated and concurrent approach should be considered in the design process, in particular in the conceptual and detailed design phases. In the context of multi-domain design, attention has been given recently to such subjects as multi-criteria decision making, multi-domain modelling, evolutionary computing, and genetic programing. More recently, machine condition monitoring has been considered for integration into a scheme of design evolution even though many challenges exist for this to become a reality such as lack of systematic approaches and the existence of technical barriers in massive condition data acquisition, transmission, storage and mining. Recently, the internet of things (IoT) and cloud computing (CC) are being developed quickly and they offer new opportunities for evolutionary design for such tasks as data acquisition, storage and processing. In this paper, a framework for the closed-loop design evolution of engineering systems is proposed in order to achieve continuous design improvement for an engineering system through the use of a machine condition monitoring system assisted by IoT and CC. New design requirements or the detection of design weaknesses of an existing engineering system can be addressed through the proposed framework. A design knowledge base that is constructed by integrating design expertise from domain experts, on-line process information from condition monitoring and other design information from various sources is proposed to realize and supervise the design process so as to achieve increased efficiency, design speed, and effectiveness. The framework developed in this paper is illustrated by using a case study of design evolution of an industrial manufacturing system.", keywords = "genetic algorithms, genetic programming, Engineering system design, Design evolution, Multi-domain modelling, Machine condition monitoring, Internet of things, Cloud computing", } @InProceedings{Xia:2009:WISA, author = "Shixiong Xia and Zuhui Hu and Qiang Niu", title = "An Approach of Semantic Similarity Measure between Ontology Concepts Based on Multi Expression Programming", booktitle = "Sixth Web Information Systems and Applications Conference, WISA 2009", year = "2009", month = "18-20 " # sep, pages = "184--188", abstract = "To improve accuracy of semantic similarity measure between ontology concepts, four main factors that impact on semantic similarity measure is taken into account. They are semantic distance, semantic depth, semantic coincidence and semantic density. Firstly, they were preprocessed to obtain four basic methods for calculating semantic similarity. And then Multi Expression Programming algorithm was adopted to combine and optimise the four basic methods. Thus, an approach of semantic similarity measure between ontology concepts based on Multi Expression Programming is proposed. At last, the approach is tested using dataset extracted from WordNet. The experiment result shows that the approach can be able to exclude the influence of non-key factor and enhance accuracy of semantic similarity measure.", keywords = "genetic algorithms, genetic programming, multiexpression programming, ontology concepts, semantic coincidence, semantic density, semantic depth, semantic distance, semantic similarity measure, mathematical programming, ontologies (artificial intelligence)", DOI = "doi:10.1109/WISA.2009.34", notes = "Sch. of Comput. Sci. & Technol., China Univ. of Min. & Technol., Xuzhou, China Also known as \cite{5368079}", } @InProceedings{Xia:2019:GECCOcompa, author = "Tian Xia and Jeremy Cosgrove and Jane Alty and Stuart Jamieson and Stephen Smith", title = "Application of Classification for Figure Copying Test in {Parkinson's} Disease Diagnosis by Using Cartesian Genetic Programming", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", isbn13 = "978-1-4503-6748-6", pages = "1855--1863", address = "Prague, Czech Republic", DOI = "doi:10.1145/3319619.3326822", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", size = "9 pages", abstract = "Previous studies have proposed an objective non-invasive approach to assist diagnosing neurological diseases such as Alzheimer and Parkinson's diseases by asking patients to perform certain drawing tasks against certain figure. However, the approach of rating those drawing test results is still very subjective by relying on manual measurements. By extracting features of the drawn figure from the raw data, which is generated from the digitized tablet that patients can draw on, we can use supervised learning to train the evolutionary algorithm with those extracted data, and therefore evolves an automated classifier to analyse and classify those drawing accurately. Cartesian Genetic Programming (CGP) is an improved version of conventional Genetic Programming (GP). As GP adapts the tree structure, redundancy issue exists as the tree develops more nodes with the evolution of the GP by mutation and crossover. CGP addresses this issue by using fixed number of nodes and arities, evolves by using mutation only. The outcome of this research is a highly efficient, accurate, automated classifier that can not only classify clinical drawing test results, which can provide up to 80% accuracy, but also assisting clinicians and medical experts to investigate how those features are used by the algorithm and how each component can impact patient's cognitive function.", notes = "Also known as \cite{3326822} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @Article{xia:2015:Atmosphere, author = "Ting Xia and Zhong-Jing Wang and Hang Zheng", title = "Topography and Data Mining Based Methods for Improving Satellite Precipitation in Mountainous Areas of China", journal = "Atmosphere", year = "2015", volume = "6", number = "8", keywords = "genetic algorithms, genetic programming", ISSN = "2073-4433", URL = "https://www.mdpi.com/2073-4433/6/8/983", DOI = "doi:10.3390/atmos6080983", abstract = "Topography is a significant factor influencing the spatial distribution of precipitation. This study developed a new methodology to evaluate and calibrate the Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA) products by merging geographic and topographic information. In the proposed method, firstly, the consistency rule was introduced to evaluate the fitness of satellite rainfall with measurements on the grids with and without ground gauges. Secondly, in order to improve the consistency rate of satellite rainfall, genetic programming was introduced to mine the relationship between the gauge rainfall and location, elevation and TMPA rainfall. The proof experiment and analysis for the mean annual satellite precipitation from 2001-2012, 3B43 (V7) of TMPA rainfall product, was carried out in eight mountainous areas of China. The result shows that the proposed method is significant and efficient both for the assessment and improvement of satellite precipitation. It is found that the satellite rainfall consistency rates in the gauged and ungauged grids are different in the study area. In addition, the mined correlation of location-elevation-TMPA rainfall can noticeably improve the satellite precipitation, both in the context of the new criterion of the consistency rate and the existing criteria such as Bias and RMSD. The proposed method is also efficient for correcting the monthly and mean monthly rainfall of 3B43 and 3B42RT.", notes = "also known as \cite{atmos6080983}", } @InProceedings{Xia:2024:ACPEE, author = "Yuhang Xia and Song Qing and Yingjie Liu and Yijun Liu and Lin Zhou and Jinshuo Jia and Zhengwen Huang", title = "Analysis of Power System Harmonic Effects Based on Data Features and Gene Expression Programming", booktitle = "2024 9th Asia Conference on Power and Electrical Engineering (ACPEE)", year = "2024", pages = "1188--1194", month = apr, keywords = "genetic algorithms, genetic programming, Correlation, Fluctuations, Time series analysis, Evolutionary computation, Power system harmonics, Harmonic analysis, Harmonic impacts, Gene Expression Programming, Data mining, Correlation analysis, System impedance", DOI = "doi:10.1109/ACPEE60788.2024.10532686", abstract = "In order to quantitatively recognise the harmonic impacts of harmonic source users at the common connection point (PCC), a method to calculate the system harmonic impedance and quantify the harmonic impacts by using gene expression programming to analyses data features was proposed in this paper. Firstly, the harmonic voltage and current data satisfying the analysis conditions were selected by using the time series segmentation method. The system harmonic impedance phase angle was obtained by using the characteristic of zero covariance of independent random variables, and then the harmonic phase angle was implanted into the existing data correlation analysis model. When the user harmonic current fluctuated, the accurate system harmonic impedance module was calculated by the user harmonic current fluctuation. When the user's harmonic current was stable, the harmonic impacts was quantified by the fluctuation of system harmonic voltage. In this paper, the user harmonic impedance was also given in thoughts in the entire process, which reduced the error caused by ignoring the user harmonic impedance in the traditional method. Some potential Evolutionary Algorithm based solutions which could be further applied in this domain was also reviewed. As a particular novelty of this work, the possibility and feasibility of employing gene expression programming into the conventional data correlation analysis work in power system. Simulation analysis and practical engineering examples showed that this method can effectively suppress the influence of system harmonic change and user harmonic impedance compared with the existing methods and obtained more accurate harmonic responsibility division results.", notes = "Also known as \cite{10532686}", } @InProceedings{Xiang:2009:FSKD, author = "Jianping Xiang and Changjie Tang and Yu Chen and Lei Duan and Yue Wang and Ning Yang", title = "CDA: A Novel Clustering Delegate Algorithm Based on Minority Protection", booktitle = "Sixth International Conference on Fuzzy Systems and Knowledge Discovery, FSKD '09", year = "2009", month = aug, volume = "1", pages = "420--424", abstract = "In traditional Gene Expression Programming (GEP), individuals' survival too much depends on fitness while their relationships are ignored. Borrowing the idea from the minority protection in real life, this study introduces a novel Cluster Delegate algorithm (CDA) and makes the following contributions: (1) propose several new concepts including individual similarity, cluster, and the farthest neighbourhood clustering, (2) implement CDA algorithm which clustering population by fitness and selects delegate from each cluster, (3) conduct extensive experiments to show that newly proposed method can accurately discover functions in complex problems.", keywords = "genetic algorithms, genetic programming, gene expression programming, clustering delegate algorithm, farthest neighborhood clustering concept, individual similarity concept, minority protection, -cluster concept, pattern clustering", DOI = "doi:10.1109/FSKD.2009.384", notes = "Dept. of Comput. Sci., Zunyi Normal Coll., Zunyi, China Also known as \cite{5358555}", } @Article{XIAO:2018:Energy, author = "Jin Xiao and Yuxi Li and Ling Xie and Dunhu Liu and Jing Huang", title = "A hybrid model based on selective ensemble for energy consumption forecasting in China", journal = "Energy", volume = "159", pages = "534--546", year = "2018", keywords = "genetic algorithms, genetic programming, Prediction of energy consumption, GMDH, AdaBoost ensemble technology, Selective combination forecasting, Hybrid forecasting", ISSN = "0360-5442", DOI = "doi:10.1016/j.energy.2018.06.161", URL = "http://www.sciencedirect.com/science/article/pii/S036054421831226X", abstract = "It is of great significance to develop accurate forecasting models for China's energy consumption. The energy consumption time series often have the characteristics of complexity and nonlinearity, and the single model cannot achieve satisfactory forecasting results. Therefore, in recent years, more and more scholars have tried to build up hybrid model to handle this issue, in which the divide and rule method is the most popular one. However, the existing divide and rule models often predict the energy consumption subseries after decomposing with the single forecasting model. This study introduces the group method of data handling technique for energy consumption forecasting in China, and constructs a hybrid forecasting model based on the group method of data handling selective ensemble. It mainly focuses on predicting the nonlinear variation of energy consumption. The model first predicts the linear trend of energy consumption time series through the group method of data handling-based autoregressive model and then obtains the residual subseries of energy consumption. Considering the highly nonlinear characteristics of the residual subseries, this study introduces AdaBoost ensemble technology to enhance the forecasting performance of the single nonlinear prediction model, back propagation neural network, support vector regression machine, genetic programming, and radical basis function neural network respectively, to obtain four different versions of the ensemble model on nonlinear subseries. Further, the prediction results of these four AdaBoost ensemble models are used as an initial input, and the selective combination prediction for the nonlinear subseries is obtained by using the group method of data handling. Finally, two parts are added up to obtain the final prediction. The empirical analysis of total energy consumption and total oil consumption in China shows that the forecasting performance of the proposed model is better than that of the group method of data handling-based autoregressive model and seven other hybrid models, and this study gives the out-of-sample forecasting of two time series from 2015 to 2020", } @MastersThesis{Liyuan_Xiao:masters, author = "Liyuan Xiao", title = "Automated web service composition using genetic programming", school = "Computer Science, Iowa State University", year = "2011", type = "Master of Science", address = "USA", keywords = "genetic algorithms, genetic programming", URL = "http://lib.dr.iastate.edu/etd/12081", size = "43 pages", abstract = "Automated web service composition is a popular research topic because it can largely reduce human efforts as the business increases. This thesis presents a search-based approach to fully automate web service composition which has a high possibility to satisfy user's functional requirements given certain assumptions. The experiment results show that the accuracy of our composition method using Genetic Programming (GP), in terms of the number of times an expected composition can be derived versus the total number of runs can be over 90%. System designers are users of our method. The system designer begins with a set of available atomic services, creates an initial population containing individuals (i.e. solutions) of candidate service compositions, then repeatedly evaluates those individuals by a fitness function and selects better individuals to generate the next population until a satisfactory solution is found or a termination condition is met. In the context of web service composition, our algorithm of genetic programming is highly improved compared to the traditional genetic programming used in web service composition in three ways: 1. We comply with services knowledge rules such as service dependency graph when generating individuals of web service composition in each population, so we can expect that the convergence process and population quality can be improved. 2. We evaluate the generated individuals in each population through black-box testing. The proportion of successful tests is taken into account by evaluating the fitness function value of genetic programming, so that the convergence rate can be more effective. 3.We take cross-over or mutation operation based on the parent individuals input and output analysis instead of just choosing by probability as typically done in related work. In this way, better children can be generated even under the same parents. The main contributions of this approach include three aspects. First, less information is needed for service composition. That is, we do not need the composition workflow and the semantic meaning of each atomic web service. Second, we generate web service composition with full automation. Third, we generate the composition with high accuracy owing to the effect of carefully preparing test cases.", } @InProceedings{Xiao:2012:COMPSACW, author = "Liyuan Xiao and Carl K. Chang and Hen-I Yang and Kai-Shin Lu and Hsin-yi Jiang", booktitle = "36th Annual IEEE Computer Software and Applications Conference Workshops (COMPSACW 2012)", title = "Automated Web Service Composition Using Genetic Programming", year = "2012", pages = "7--12", month = "16-20 " # jul, address = "Izmir", keywords = "genetic algorithms, genetic programming, genetic improvement, Web services, convergence, graph theory, knowledge based systems, probability, program testing, semantic networks, SDG, atomic Web service, automated Web service composition, black-box testing, business integration, convergence process, convergence rate, input analysis, knowledge rules, mutation operation, output analysis, population quality, probability, semantic meaning, service dependency graph, Complexity theory, Semantics, Sociology, Statistics, Syntactics, Testing, Web services, black-box testing, functional requirements, services composition, test cases", isbn13 = "978-1-4673-2714-5", DOI = "doi:10.1109/COMPSACW.2012.12", size = "6 pages", abstract = "Automated web service composition can largely reduce human efforts in business integration. We present an approach to fully automate web service composition without workflow or knowing the semantic meaning of atomic web service. The experiment results show that the accuracy of our composition method using Genetic Programming (GP), in terms of the number of times an expected composition that can be derived versus the total number of runs, can be over 90percent. Based on the traditional GP used in web service composition, our algorithm achieved improvements in three aspects: 1. We do black-box testing on each individual in each population. The success rate of tests is taken into account by the fitness function of GP so that the convergence rate can be faster; 2. We comply with services knowledge rules such as service dependency graph (SDG) when generating individual web service compositions in each population to improve the convergence process and population quality; 3. We choose cross-over or mutation operation based on the parent individuals' input and output analysis instead of by probability as typically done in related work. In this way, GP can generate better children even under the same parents.", notes = "Also known as \cite{6341542}", } @PhdThesis{Xiao:thesis, author = "Liyuan Xiao", title = "Intrusion detection using probabilistic graphical models", school = "Iowa State University of Science and Technology", year = "2016", address = "USA", month = "3 " # nov, keywords = "Applied sciences, Bayesian model averaging, Conditional random field, Intrusion detection system, Computer science", isbn13 = "9781369528343", language = "English", URL = "https://search.proquest.com/docview/1860237936?accountid=14511", URL = "https://www.cs.iastate.edu/phd-final-oral-liyuan-xiao", size = "88 pages", abstract = "Modern computer systems are plagued by security vulnerabilities and flaws on many levels. Those vulnerabilities and flaws are discovered and exploited by attackers for their various intrusion purposes, such as eavesdropping, data modification, identity spoofing, password based attack, and denial of service attack, etc. The security of our computer systems and data is always at risk because of the open society of the internet. Due to the rapid growth of the internet applications, intrusion detection and prevention have become increasingly important research topics, in order to protect networking systems, such as the Web servers, database servers, cloud servers and so on, from threats. In this thesis, we attempt to build more efficient Intrusion Detection System through three different approaches, from different perspectives and based on different situations. Firstly, we propose Bayesian Model Averaging of Bayesian Network (BNMA) Classifiers for intrusion detection. In this work, we compare our BNMA classifier with Bayesian Network classifier and Naive Bayes classifier, which were shown be good models for detecting intrusion with reasonable accuracy and efficiency in the literature. From the experiment results, we see that BNMA can be more efficient and reliable than its competitors, i.e., the Bayesian network classifier and Naive Bayesian Network classifier, for all different sizes of training dataset. The advantage of BNMA is more pronounced when the training dataset size is small. Secondly, we introduce the Situational Data Model as a method for collecting dataset to train intrusion detection models. Unlike previously discussed static features as in the KDD CUP 99 data, which were collected without time stamps, Situational Data are collected in chronological sequence. Therefore, they can capture not only the dependency relationships among different features, but also relationships of values collected over time for the same features. The experiment results show that the intrusion detection model trained by Situational Dataset outperforms that trained by action-only sequences. Thirdly, we introduce the Situation Aware with Conditional Random Fields Intrusion Detection System (SA-CRF-IDS). The SA-CRF-IDS is trained by probabilistic graphical model Conditional Random Fields (CRF) over the Situational Dataset. The experiment results show that the CRF outperforms HMM with significantly better detection accuracy, and better ROC curve when we run the experiment on the non-Situational dataset. On the other hand, the two training methods have very similar performance when the Situational Dataset is adopted.", notes = "Not GP? ProQuest Number: 10243758 Supervisor: Carl Chang", } @InProceedings{Xiao:2017:GECCO, author = "Qin-zhe Xiao and Jinghui Zhong and Wen-Neng Chen and Zhi-Hui Zhan and Jun Zhang", title = "Indicator-based Multi-objective Genetic Programming for Workflow Scheduling Problem", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4939-0", address = "Berlin, Germany", pages = "217--218", size = "2 pages", URL = "http://doi.acm.org/10.1145/3067695.3075600", DOI = "doi:10.1145/3067695.3075600", acmid = "3075600", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, Gene Expression Programming, multi-objective optimization, workflow scheduling", month = "15-19 " # jul, abstract = "This paper proposes an Indicator-Based Multi-objective Gene Expression Programming (IBM-GEP) to solve Workflow Scheduling Problem (WSP). The key idea is to use Genetic Programming (GP) to learn heuristics to select resources for executing tasks. By using different problem instances for training, the IBM-GEP is capable of learning generic heuristics that are applicable for solving different WSPs. Besides, the IBM-GEP can search for multiple heuristics that have different trade-offs among multiple objectives. The IBM-GEP was tested on instances with different settings. Compared with several existing algorithms, the heuristics found by the IBM-GEP generally perform better in terms of minimizing the cost and completed time of the workflow.", notes = "Also known as \cite{Xiao:2017:IMG:3067695.3075600} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @Article{Xiao:ieeeServiceC, author = "Qin-zhe Xiao and Jinghui Zhong and Liang Feng and Linbo Luo and Jianming Lv", title = "A Cooperative Coevolution Hyper-Heuristic Framework for Workflow Scheduling Problem", journal = "IEEE Transactions on Services Computing", year = "2022", volume = "15", number = "1", pages = "150--163", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/TSC.2019.2923912", ISSN = "1939-1374", abstract = "Workflow scheduling problem (WSP) is a well-known combinatorial optimization problem, which is defined to assign a series of interconnected tasks to the available resources to meet user defined Quality of Service (QoS). Traditional guided random search methods and heuristic based methods are either require expensive computational cost or heavily rely on human's empirical knowledge, which makes them inconvenient for practical applications. To address the above issues, this paper proposes a cooperative coevolution hyper-heuristic framework to solve WSP with an objective of minimizing the completed time of workflow. In particular, in the proposed framework, two hyper-heuristics, namely, the task selection rule (TSR) and the resource selection rule (RSR), are learned automatically by a cooperative coevolution genetic programming algorithm. The TSR is used to select a ready task for scheduling, while the RSR is used to allocate resources to perform the selected task.To validate the effectiveness of the proposed framework, randomly generated workflow instances and four real-world workflows are used as test cases. Compared with several state-of-the-art methods, the hyper-heuristics found by our proposed framework demonstrate superior performance on all the test cases in terms of metrics including the schedule length ratio, speedup and efficiency", notes = "Also known as \cite{8744408}", } @InProceedings{Xiao:2014:CEC, title = "Two Step Evolution Strategy for Device Motif {BSIM} Model Parameter Extraction", author = "Yang Xiao and Martin Trefzer and James Walker and Simon Bale and Andy Tyrrell", pages = "2877--2884", booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary Computation", year = "2014", month = "6-11 " # jul, editor = "Carlos A. {Coello Coello}", address = "Beijing, China", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, EHW, Evolvable hardware and software", DOI = "doi:10.1109/CEC.2014.6900520", abstract = "The modelling and simulation of semiconductor devices is a difficult and computationally intensive task. However the expense of fabrication and testing means that accurate modelling and simulation are crucial to the continued progress of the industry. To create these models and then perform the simulations requires parameters from accurate physical models to be obtained and then more abstract models created that can perform more complex circuit simulations. Device models (motifs) are created as a mitigation technique for improvement the circuit performance and as technology advances to help with the effects of transistor variability. In order to explore the characteristics of new device motifs on circuit designs, obtaining accurate and reliable device models becomes the first problem for designers. In this paper a Two Step Evolution Strategy (2SES) is proposed for device parameter model extraction. The proposed 2SES approach automatically extracts a set of parameters with respect to a specified device model. Compared with conventional mathematical extraction approach, 2SES is an efficient and accurate method to solve the parameter extraction problem and simultaneously addresses the fact of the mathematical extraction having the complexity of Multi-objective optimisation. Compared with single step ES extract result, it is shown that the two-step ES extraction process continues improving generations by adjusting the optimisation parameters. Finally, an application of a new device motif on circuit design is given at end of the paper and compared against a standard device.", notes = "WCCI2014", } @Article{Xie:2004:fusee, author = "Changsong Xie and Xuhui Li", title = "An Algorithm Using Genetic Programming for the Compensation of Nonlinear Distortion Based on Wiener System Model", journal = "Facta universitatis - series: Electronics and Energetics", year = "2004", volume = "17", number = "2", pages = "219--229", month = aug, keywords = "genetic algorithms, genetic programming, linear time-invariant system, memoryless nonlinear system, wiener model, distortion compensation", ISSN = "0353-3670", publisher = "University of Nis, Serbia", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.536.1627", URL = "http://facta.junis.ni.ac.rs/eae/fu2k42/6xie.html", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.536.1627", URL = "https://dokumente.unibw.de/pub/bscw.cgi/d1223025/paper_ChangFacta_vol17,Nr.2,August2004.pdf", size = "12 pages", abstract = "In this paper we presented an iteration algorithm using genetic programming (GP) to get the Wiener model of a nonlinear system and then to compensate the nonlinear distortion. The GP is used to identify the linear time-invariant (LTI) part and memoryless nonlinear (MLNL) part of the Wiener model of the object system. By means of iteration, the identification precision will be improved gradually with the iteration steps. In order to compensate the nonlinearity a distortion compensation function (DCF) will be estimated also by means of GP. If the object system can be well described using Wiener model, this algorithm converges. The experiment results show that the compensation precision is fairly high.", notes = "GP Grammar and Constants Optimization. http://forschung.unibw.de down Oct 2017 See also \cite{DBLP:phd/de/Li2003} Editor-in-Chief Ninoslav Stojadinovic Contact person: publisher address Aleksandra Medvedeva 14, 18000 Nis, e-mail ninoslav.stojadinovic@elfak.ni.ac.rs phone 018/588-442 http://casopisi.junis.ni.ac.rs/index.php/FUElectEnerg/issue/archive", } @InProceedings{Xie:2014:CEC, title = "Anomaly Detection in Crowded Scenes Using Genetic Programming", author = "Cheng Xie and Lin Shang", pages = "1832--1839", booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary Computation", year = "2014", month = "6-11 " # jul, editor = "Carlos A. {Coello Coello}", address = "Beijing, China", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, Genetic programming, Real-world applications", DOI = "doi:10.1109/CEC.2014.6900396", abstract = "Genetic programming(GP) has become an increasingly hot issue in evolutionary computation due to its extensive application. Anomaly detection in crowded scenes is also a hot research topic in computer vision. However, there are few contributions on using genetic programming to detect abnormalities in crowded scenes. In this paper, we focus on anomaly detection in crowded scenes with genetic programming. We propose a new method called Multi-Frame LBP Difference (MFLD) based on Local Binary Patterns(LBP) to extract pixel-level features from videos without additional complicated preprocessing operations such as optical flow and background subtraction. Genetic programming is employed to generate an anomaly detector with the extracted data. When a new video is coming, the detector can classify every frame and localise the abnormality to a single pixel level in real time. We validate our approach on a public dataset and compare our method with other traditional algorithms for video anomaly detection. Experimental results indicate that our method with genetic programming performs better in detecting abnormalities in crowded scenes.", notes = "WCCI2014", } @InProceedings{Xie:2005:ATCE, author = "Deyi Xie and David Alan Wilkinson and Tina Yu", title = "Permeability Estimation Using a Hybrid Genetic Programming and Fuzzy/Neural Inference Approach", booktitle = "SPE Annual Technical Conference and Exhibition (ATCE 2005)", year = "2005", volume = "1", pages = "176--182?", address = "Dallas, Texas, USA", month = "9-12 " # oct, publisher = "Society of Petroleum Engineers", keywords = "genetic algorithms, genetic programming, 4.1.2 Separation and Treating, 5.8.7 Carbonate Reservoir, 5.1 Reservoir Characterisation, 5.1.5 Geologic Modelling, 4.2 Pipelines, Flowlines and Risers, 6.1.5 Human Resources, Competence and Training, 1.6.9 Coring, Fishing, 1.2.3 Rock properties, 2.4.3 Sand/Solids Control, 4.1.5 Processing Equipment, 5.6.1 Open hole/cased hole log analysis", isbn13 = "978-1-55563-150-5", URL = "https://www.onepetro.org/conference-paper/SPE-95167-MS?sort=&start=0&q=Permeability+Estimation+Using+a+Hybrid+Genetic+Programming+and+Fuzzy%2FNeural+Inference+Approach&from_year=&peer_reviewed=&published_between=&fromSearchResults=true&to_year=&rows=100#", URL = "http://www.cs.mun.ca/~tinayu/Publications_files/SPE2005_Final.pdf", URL = "http://www.proceedings.com/00158.html", DOI = "doi:10.2118/95167-MS", abstract = "We have developed a methodology that provides permeability estimates for all rock-types or lithologies, for a wide range of permeability. This is a hybrid Genetic Programming and Fuzzy/Neural Net inference system and which uses lithologic and permeability facies as indicators. This work was motivated by a need to have a volumetric estimate of permeability for reservoir modelling purposes. To this end, for our purposes,the inputs to this process are limited to properties that can be estimated from seismic data. The permeability transform is first estimated at the well locations using core permeability, elastic parameter logs and porosity. The output from the process can then be used, in conjunction with estimates of these properties from 3D seismic data, to provide an estimate of permeability on a volume basis. The inputs are then, the volume of shale (Vsh) or any other log type used to determine lithology, the sonic and density logs, the porosity log and core permeability measurements. The transform system is composed of three distinct modules. The first module serves to classify lithology and separates the reservoir interval into user-defined lithology types. The second module, based on Genetic Programming, is designed to predict permeability facies within lithology type. A permeability facies is defined as as a low, medium or high permeability set associated with each lithology type. A Fuzzy/Neural Net inference algorithm makes up the third module of the system, in which a TSK fuzzy logic relationship is formed, for each permeability facies and lithology. The system has been applied in two oil fields, both offshore West Africa. In comparison with current estimation approaches, this system yields more consistent estimated permeability. The results from conducting cross-validation suggest this methodology is robust in estimating permeability in complex heterogeneous reservoirs.This system is designed to use elastic log properties inverted from seismic data, such as acoustic velocity and density as input so permeability volume can be obtained.", notes = "p3 'In comparison with current estimation approaches, this system yields the estimated permeability that matches core permeability more consistently.' SPE 95167. ChevronTexaco", } @InProceedings{conf/acsc/XieSC12, author = "Feng Xie and Andy Song and Victor Ciesielski", title = "Learning Time Series Patterns by Genetic Programming", booktitle = "Thirty-Fifth Australasian Computer Science Conference, ACSC 2012", year = "2012", editor = "Mark Reynolds and Bruce H. Thomas", volume = "122", series = "CRPIT", pages = "57--62", address = "Melbourne, Australia", month = jan, publisher = "Australian Computer Society", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-921770-03-6", URL = "http://crpit.com/Vol122.html", URL = "http://crpit.com/confpapers/CRPITV122Xie.pdf", bibdate = "2013-04-21", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/acsc/acsc2012.html#XieSC12", size = "6 pages", abstract = "Finding patterns such as increasing or decreasing trends, abrupt changes and periodically repeating sequences is a necessary task in many real world situations. We have shown how genetic programming can be used to detect increasingly complex patterns in time series data. Most classification methods require a hand-crafted feature extraction preprocessing step to accurately perform such tasks. In contrast, the evolved programs operate on the raw time series data. On the more difficult problems the evolved classifiers outperform the OneR, J48, Naive Bayes, IB1 and Adaboost classifiers by a large margin. Furthermore this method can handle noisy data. Our results suggest that the genetic programming approach could be used for detecting a wide range of patterns in time series data without extra processing or feature extraction.", notes = "ACSC", } @InProceedings{Xie:2012:CEC, title = "Event Detection in Time Series by Genetic Programming", author = "Feng Xie and Andy Song and Vic Ciesielski", pages = "2507--2514", booktitle = "Proceedings of the 2012 IEEE Congress on Evolutionary Computation", year = "2012", editor = "Xiaodong Li", month = "10-15 " # jun, DOI = "doi:10.1109/CEC.2012.6256589", address = "Brisbane, Australia", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Classification, clustering, data analysis and data mining", abstract = "The aim of event detection in time series is to identify particular occurrences of user-interest in one or more time lines, such as finding an anomaly in electrocardiograms or reporting a sudden variation of voltage in a power supply. Current methods are not adequate for detecting certain kinds of events without any domain knowledge. Therefore, we propose a Genetic Programming (GP) based event detection methodology in which solutions can be built from raw time series data. The framework is applied to five synthetic data sets and one real world application. The experimental results show that working on raw data even with a dimensionality as high as 140 by 80, genetic programming can achieve superior performance to conventional methods operating on pre-defined features. Furthermore, analysis of the evolved event detectors shows that they can be readily understood by humans and have captured the regularities inserted into the synthetic data sets.", notes = "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the EPS and the IET.", } @InProceedings{Xie:evoapps13, author = "Feng Xie and Andy Song and Vic Ciesielski", title = "Human Action Recognition from Multi-Sensor Stream Data by Genetic Programming", booktitle = "Applications of Evolutionary Computing, EvoApplications 2013: EvoCOMNET, EvoCOMPLEX, EvoENERGY, EvoFIN, EvoGAMES, EvoIASP, EvoINDUSTRY, EvoNUM, EvoPAR, EvoRISK, EvoROBOT, EvoSTOC", year = "2013", month = "3-5 " # apr, editor = "Anna I. Esparcia-Alcazar and Antonio Della Cioppa and Ivanoe {De Falco} and Ernesto Tarantino and Carlos Cotta and Robert Schaefer and Konrad Diwold and Kyrre Glette and Andrea Tettamanzi and Alexandros Agapitos and Paolo Burrelli and J. J. Merelo and Stefano Cagnoni and Mengjie Zhang and Neil Urquhart and Kevin Sim and Aniko Ekart and Francisco {Fernandez de Vega} and Sara Silva and Evert Haasdijk and Gusz Eiben and Anabela Simoes and Philipp Rohlfshagen", series = "LNCS", volume = "7835", publisher = "Springer Verlag", address = "Vienna", publisher_address = "Berlin", pages = "418--427", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-37191-2", DOI = "doi:10.1007/978-3-642-37192-9_42", size = "10 pages", abstract = "This paper presents an approach to recognition of human actions such as sitting, standing, walking or running by analysing the data produced by the sensors of a smart phone. The data comes as streams of parallel time series from 21 sensors. We have used genetic programming to evolve detectors for a number of actions and compared the detection accuracy of the evolved detectors with detectors built from the classical machine learning methods including Decision Trees, Naive Bayes, Nearest Neighbour and Support Vector Machines. The evolved detectors were considerably more accurate. We conclude that the proposed GP method can capture complex interaction of variables in parallel time series without using predefined features.", notes = " EvoApplications2013 held in conjunction with EuroGP2013, EvoCOP2013, EvoBio'2013 and EvoMusArt2013", } @InProceedings{Xie:2013:CEC, article_id = "1730", author = "Feng Xie and Andy Song and Vic Ciesielski", title = "Activity Recognition by Smartphone Based Multi-Channel Sensors with Genetic Programming", booktitle = "2013 IEEE Conference on Evolutionary Computation", volume = "1", year = "2013", month = jun # " 20-23", editor = "Luis Gerardo {de la Fraga}", pages = "1162--1169", address = "Cancun, Mexico", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-0453-2", DOI = "doi:10.1109/CEC.2013.6557697", notes = "CEC 2013 - A joint meeting of the IEEE, the EPS and the IET.", } @InProceedings{Xie:2013:CECa, article_id = "1748", author = "Feng Xie and A. K. Qin and Andy Song and Vic Ciesielski", title = "Sensor-Based Activity Recognition with Improved GP-based Classifier", booktitle = "2013 IEEE Conference on Evolutionary Computation", volume = "1", year = "2013", month = jun # " 20-23", editor = "Luis Gerardo {de la Fraga}", pages = "3043--3050", address = "Cancun, Mexico", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-0453-2", DOI = "doi:10.1109/CEC.2013.6557940", notes = "CEC 2013 - A joint meeting of the IEEE, the EPS and the IET.", } @InProceedings{Xie:2013:AI, author = "Feng Xie and Andy Song and Flora Salim and Athman Bouguettaya and Timos Sellis and Doug Bradbrook", title = "Learning Risky Driver Behaviours from Multi-Channel Data Streams Using Genetic Programming", booktitle = "Proceedings of the 26th Australasian Joint Conference on Artificial Intelligence (AI2013)", year = "2013", editor = "Stephen Cranefield and Abhaya Nayak", volume = "8272", series = "LNAI", pages = "202--213", address = "Dunedin, New Zealand", month = "1-6 " # dec, publisher = "Springer", keywords = "genetic algorithms, genetic programming, smartphone", isbn13 = "978-3-319-03679-3", URL = "http://dx.doi.org/10.1007/978-3-319-03680-9_22", DOI = "doi:10.1007/978-3-319-03680-9_22", size = "12 pages", abstract = "Risky driver behaviours such as sudden braking, swerving, and excessive acceleration are a major risk to road safety. In this study, we present a learning method to recognise such behaviours from smart phone sensor input which can be considered as a type of multi-channel time series. Unlike other learning methods, this Genetic Programming (GP) based method does not require pre-processing and manually designed features. Hence domain knowledge and manual coding can be significantly reduced by this approach. This method can achieve accurate real-time recognition of risky driver behaviours on raw input and can outperform classic learning methods operating on features. In addition this GP-based method is general and suitable for detecting multiple types of driver behaviours.", } @InProceedings{Xie:2013:IVCNZ, author = "Feng Xie and Anh Hoang Dau and Alexandra L. Uitdenbogerd and Andy Song", booktitle = "28th International Conference of Image and Vision Computing New Zealand (IVCNZ 2013)", title = "Evolving PCB visual inspection programs using genetic programming", year = "2013", month = nov, pages = "406--411", keywords = "genetic algorithms, genetic programming, machine vision, defect detection, printed circuit board, automatic optical inspection", DOI = "doi:10.1109/IVCNZ.2013.6727049", ISSN = "2151-2191", abstract = "Automated optical inspection (AOI) is desirable in printed circuit board (PCB) manufacturing as inspecting manually is time-consuming and error-prone. This paper presents a study on evolving an AOI program with Genetic-Programming (GP), an evolution-inspired technique. Using a GP-based approach, domain knowledge such as board design and lighting conditions are not required. Conventional feature extraction processes can also be avoided. The result demonstrates the evolved program capability to detect flaws under varied scenarios. Furthermore, it can be readily applied on different types of images without calibration or re-training.", notes = "Also known as \cite{6727049}", } @InProceedings{Xie:2014:CECa, title = "Genetic Programming Based Activity Recognition on a Smartphone Sensory Data Benchmark", author = "Feng Xie and Andy Song and Vic Ciesielski", pages = "2917--2924", booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary Computation", year = "2014", month = "6-11 " # jul, editor = "Carlos A. {Coello Coello}", address = "Beijing, China", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, Genetic programming, Classification, clustering and data analysis, Real-world applications", DOI = "doi:10.1109/CEC.2014.6900635", abstract = "Activity recognition from smart phone sensor inputs is of great importance to enhance user experience. Our study aims to investigate the applicability of Genetic Programming (GP) approach on this complex real world problem. Traditional methods often require substantial human efforts to define good features. Moreover the optimal features for one type of activity may not be suitable for another. In comparison, our GP approach does not require such feature extraction process, hence, more suitable for complex activities where good features are difficult to be pre-defined. To facilitate this study we therefore propose a benchmark of activity data collected from various smartphone sensors, as currently there is no existing publicly available database for activity recognition. In this study, a GP-based approach is applied to nine types of activity recognition tasks by directly taking raw data instead of features. The effectiveness of this approach can be seen by the promising results. In addition our benchmark data provides a platform for other machine learning algorithms to evaluate their performance on activity recognition.", notes = "WCCI2014", } @InProceedings{conf/seal/XieSC14, author = "Feng Xie and Andy Song and Vic Ciesielski", title = "Learning Patterns of States in Time Series by Genetic Programming", bibdate = "2014-11-12", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/seal/seal2014.html#XieSC14", booktitle = "Simulated Evolution and Learning - 10th International Conference, {SEAL} 2014, Dunedin, New Zealand, December 15-18, 2014. Proceedings", publisher = "Springer", year = "2014", volume = "8886", editor = "Grant Dick and Will N. Browne and Peter A. Whigham and Mengjie Zhang and Lam Thu Bui and Hisao Ishibuchi and Yaochu Jin and Xiaodong Li and Yuhui Shi and Pramod Singh and Kay Chen Tan and Ke Tang", isbn13 = "978-3-319-13562-5", pages = "371--382", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming", URL = "http://dx.doi.org/10.1007/978-3-319-13563-2", } @PhdThesis{FengXie:thesis, author = "Feng Xie", title = "Event and state detection in time series by genetic programming", school = "Computer Science and Information Technology, RMIT University", year = "2015", address = "Australia", month = jul, keywords = "genetic algorithms, genetic programming, Time Series Classification, Pattern Recognition, Event Detection, Multi-Channel Time Series", URL = "https://researchbank.rmit.edu.au/view/rmit:161490", URL = "https://researchbank.rmit.edu.au/eserv/rmit:161490/Xie.pdf", size = "214 pages", abstract = "Event and state detection in time series has significant value in scientific areas and real-world applications. The aim of detecting time series event and state patterns is to identify particular variations of user-interest in one or more channels of time series streams. For example, dangerous driving behaviours such as sudden braking and harsh acceleration can be detected from continuous recordings from inertial sensors. However, the existing methods are highly dependent on domain knowledge such as the size of the time series pattern and a set of effective features. Furthermore, they are not directly suitable for multi-channel time series data. In this study, we establish a genetic programming based method which can perform classification on multi-channel time series data. It does not need the domain knowledge required by the existing methods.", abstract = "The investigation consists of four parts: the methodology, an evaluation on event detection tasks, an evaluation on state detection tasks and an analysis on the suitability for real-world applications. In the methodology, a GP based method is proposed for processing and analysing multi-channel time series streams. The function set includes basic mathematical operations. In addition, specific functions and terminals are introduced to reserve historical information, capture temporal dependency across time points and handle dependency between channels. These functions and terminals help the GP based method to automatically find the pattern size and extract features. This study also investigates two different fitness functions - accuracy and area under the curve. The proposed method is investigated on a range of event detection tasks. The investigation starts from synthetic tasks such as detecting complete sine waves. The performance of the GP based method is compared to traditional classification methods. On the raw data the GP based method achieves 100 percent accuracy, which outperforms all the non-GP methods.The performance of the non-GP methods is comparable to the GP based method only with suitable features. In addition, the GP based method is investigated on two complex real-world event detection tasks - dangerous driving behaviour detection and video shot detection. In the task of detecting three dangerous driving behaviours from 21-channel time series data, the GP based method performs consistently better than the non-GP classifiers even when features are provided. In the video shot detection task, the GP based method achieves comparable performance on 11200-channel time series to the non-GP classifiers on 28 features. The GP based method is more accurate than a commercial product. The GP based method has also been investigated on state detection tasks. This involves synthetic tasks such as detecting concurrent high values in four of five channels and a real-world activity recognition problem. The results also show that the GP based method consistently outperforms the non-GP methods even with the presence of manually constructed features. As part of the investigation, a mobile phone based activity recognition data set was collected as there was no existing publicly available data set. The suitability of the GP based method for solving real-world problems is further analysed. Our analysis shows that the GP based method can be successfully extended for multi-class classification. The analysis of the evolved programs demonstrates that they do capture time series patterns. On synthetic data sets, the injected regularities are revealed in understandable individuals. The best programs for three real-world problems are more difficult to explain but still provide some insight. The selection of relevant channels and data points by the programs are consistent with domain knowledge. In addition, the analysis shows that the proposed method still performs well for time series pattern of different sizes. The effective window sizes of the evolved GP programs are close to the pattern size. Finally, our study on execution performance of the evolved programs shows that these programs are fast in execution and are suitable for real-time applications. In summary, the GP based method is suitable for the kinds of real-world applications studied in this thesis. This thesis concludes that, with a suitable representation, genetic programming can be an effective method for event and state detection in multi-channel time series for a range of synthetic and real-world tasks. This method does not require much domain knowledge such as the pattern size and suitable features. It offers an effective classification method in similar tasks that are studied in this thesis.", notes = "Supervisors: Andy Song and Vic Ciesielski", } @InProceedings{Xie:2005:AJCAI, title = "Diversity Control in {GP} with {ADF} for Regression Tasks", author = "Huayang Xie", year = "2005", pages = "1253--1257", booktitle = "AI 2005: Advances in Artificial Intelligence, 18th Australian Joint Conference on Artificial Intelligence, Proceedings", editor = "Shichao Zhang and Ray Jarvis", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3809", address = "Sydney, Australia", month = dec # " 5-9", keywords = "genetic algorithms, genetic programming, diversity control", ISBN = "3-540-30462-2", DOI = "doi:10.1007/11589990_181", size = "5 pages", abstract = "two-phase diversity control approach to prevent the common problem of the loss of diversity in Genetic Programming with Automatically Defined Functions. While most recent work focuses on diagnosing and remedying the loss of diversity, this approach aims to prevent the loss of diversity in the early stage through a refined diversity control method and a fully covered tournament selection method. The results on regression tasks suggest that these methods can effectively improve the system performance by reducing the incidences of premature convergence and the number of generations needed an optimal solution.", notes = "PART IV: Short Papers", } @TechReport{vuw-CS-TR-06-3, author = "Huayang Xie and Mengjie Zhang and Peter Andreae", title = "A Study of Good Predecessor Programs for Reducing Fitness Evaluation Cost in Genetic Programming", institution = "Computer Science, Victoria University of Wellington", year = "2006", number = "CS-TR-06-3", address = "New Zealand", keywords = "genetic algorithms, genetic programming, Fitness evaluation, good predecessor programs, population clustering", URL = "http://www.mcs.vuw.ac.nz/comp/Publications/CS-TR-06-3.abs.html", URL = "http://www.mcs.vuw.ac.nz/comp/Publications/archive/CS-TR-06/CS-TR-06-3.pdf", abstract = "Good Predecessor Programs (GPPs) are the ancestors of the best program found in a Genetic Programming (GP) evolution. This paper reports on an investigation into GPPs with the ultimate goal of reducing fitness evaluation cost in tree-based GP systems. A framework is developed for gathering information about GPPs and a series of experiments is conducted on a symbolic regression problem, a binary classification problem, and a multi-class classification program with increasing levels of difficulty in different domains. The analysis of the data shows that during evolution, GPPs typically constitute between less than 33per cent of the total programs evaluated, and may constitute less than 5per cent. The analysis results further shows that in all evaluated programs, the proportion of GPPs is reduced by increasing tournament size and to a less extent, affected by population size. Problem difficulty seems to have no clear influence on the proportion of GPPs.", } @TechReport{vuw-CS-TR-06-4, author = "Huayang Xie and Mengjie Zhang and Peter Andreae", title = "Genetic Programming for Automatic Stress Detection in Spoken English", institution = "Computer Science, Victoria University of Wellington", year = "2006", number = "CS-TR-06-4", address = "New Zealand", keywords = "genetic algorithms, genetic programming, Speech recognition, stress detection, decision trees, support vector machines", URL = "http://www.mcs.vuw.ac.nz/comp/Publications/CS-TR-06-4.abs.html", URL = "http://www.mcs.vuw.ac.nz/comp/Publications/archive/CS-TR-06/CS-TR-06-4.pdf", abstract = "This paper describes an approach to the use of genetic programming (GP) for the automatic detection of rhythmic stress in spoken New Zealand English. A linear-structured GP system uses speaker independent prosodic features and vowel quality features as terminals to classify each vowel segment as stressed or unstressed. Error rate is used as the fitness function. In addition to the standard four arithmetic operators, this approach also uses several other arithmetic, trigonometric, and conditional functions in the function set. The approach is evaluated on 60 female adult utterances with 703 vowels and a maximum accuracy of 92.61per cent is achieved. The approach is compared with decision trees (DT) and support vector machines (SVM). The results suggest that, on our data set, GP outperforms DT and SVM for stress detection, and GP has stronger automatic feature selection capability than DT and SVM.", } @InProceedings{eurogp06:XieZhangAndreae, author = "Huayang Xie and Mengjie Zhang and Peter Andreae", title = "Population Clustering in Genetic Programming", editor = "Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art", booktitle = "Proceedings of the 9th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3905", year = "2006", address = "Budapest, Hungary", month = "10 - 12 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-33143-3", pages = "190--201", DOI = "doi:10.1007/11729976_17", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "This paper proposes an approach to reducing the cost of fitness evaluation whilst improving the effectiveness in Genetic Programming (GP). In our approach, the whole population is first clustered by a heuristic called fitness-case-equivalence. Then a cluster representative is selected for each cluster. The fitness value of the representative is calculated on all training cases. The fitness is then directly assigned to other members in the same cluster. Subsequently, a clustering tournament selection method replaces the standard tournament selection method. A series of experiments were conducted to solve a symbolic regression problem, a binary classification problem, and a multi-class classification problem. The experiment results show that the new GP system significantly outperforms the standard GP system on these problems.", notes = "Part of \cite{collet:2006:GP} EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006", } @InProceedings{xie:evows06, author = "Huayang Xie and Mengjie Zhang and Peter Andreae", title = "Genetic Programming for Automatic Stress Detection in Spoken English", booktitle = "Applications of Evolutionary Computing, EvoWorkshops2006: {EvoBIO}, {EvoCOMNET}, {EvoHOT}, {EvoIASP}, {EvoInteraction}, {EvoMUSART}, {EvoSTOC}", year = "2006", month = "10-12 " # apr, editor = "Franz Rothlauf and Jurgen Branke and Stefano Cagnoni and Ernesto Costa and Carlos Cotta and Rolf Drechsler and Evelyne Lutton and Penousal Machado and Jason H. Moore and Juan Romero and George D. Smith and Giovanni Squillero and Hideyuki Takagi", series = "LNCS", volume = "3907", publisher = "Springer Verlag", address = "Budapest", publisher_address = "Berlin", ISBN = "3-540-33237-5", keywords = "genetic algorithms, genetic programming", pages = "460--471", DOI = "doi:10.1007/11732242_41", abstract = "an approach to the use of genetic programming (GP) for the automatic detection of rhythmic stress in spoken New Zealand English. A linear-structured GP system uses speaker independent prosodic features and vowel quality features as terminals to classify each vowel segment as stressed or unstressed. Error rate is used as the fitness function. In addition to the standard four arithmetic operators, this approach also uses several other arithmetic, trigonometric, and conditional functions in the function set. The approach is evaluated on 60 female adult utterances with 703 vowels and a maximum accuracy of 92.61% is achieved. The approach is compared with decision trees (DT) and support vector machines (SVM). The results suggest that, on our data set, GP outperforms DT and SVM for stress detection, and GP has stronger automatic feature selection capability than DT and SVM.", notes = "part of \cite{evows06} also known as \cite{conf/evoW/XieZA06} See also \cite{Andreae:2008:IJKBIES}", } @InProceedings{Xie:2006:ISDA, author = "Huayang Xie and Mengjie Zhang and Peter Andreae", title = "Automatic Selection Pressure Control in Genetic Programming", booktitle = "6th International Conference on Intelligent System Design and Applications", year = "2006", editor = "Bo Yang and Yuehui Chen", pages = "435--440", address = "Jinan Nanjiao Hotel, Jinan, China", month = oct # " 16-18", organisation = "EUSFLAT", publisher = "IEEE", keywords = "genetic algorithms, genetic programming", ISBN = "0-7695-2528-8", DOI = "doi:10.1109/ISDA.2006.116", abstract = "Selection pressure must be dynamically managed in response to the changing evolutionary process in order to improve the effectiveness and efficiency of Genetic Programming (GP) systems using tournament selection. Instead of changing the tournament size and/or the population size via an arbitrary function to influence the selection pressure, this paper focuses on designing an automatic selection pressure control approach. In our approach, populations are clustered based on a dynamic program property. Then clusters become tournament candidates. The selection pressure in the tournament selection method is automatically changed during evolution according to the dynamically changing number of tournament candidates. Our approach is compared with the standard GP system (with no selection pressure control) on two problems with different kinds of fitness distributions. The results show that the automatic selection pressure control approach can improve the effectiveness and efficiency of GP systems.", notes = "http://isda2006.ujn.edu.cn/", } @InProceedings{HuayangXie:2006:CEC, author = "Huayang Xie and Mengjie Zhang and Peter Andreae", title = "A Study of Good Predecessor Programs for Reducing Fitness Evaluation Cost in Genetic Programming", booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary Computation", year = "2006", pages = "9211--9218", address = "Vancouver", month = "16-21 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9487-9", DOI = "doi:10.1109/CEC.2006.1688641", size = "8 pages", abstract = "Good Predecessor Programs (GPPs) are the ancestors of the best program found in a Genetic Programming (GP) evolution. This paper reports on an investigation into GPPs with the ultimate goal of reducing fitness evaluation cost in tree-based GP systems. A framework is developed for gathering information about GPPs and a series of experiments is conducted on a symbolic regression problem, a binary classification problem, and a multi-class classification program with increasing levels of difficulty in different domains. The analysis of the data shows that during evolution, GPPs typically constitute less than 33per cent of the total programs evaluated, and may constitute less than 5per cent. The analysis results further shows that in all evaluated programs, the proportion of GPPs is reduced by increasing tournament size and to a less extent, affected by population size. Problem difficulty seems to have no clear influence on the proportion of GPPs.", notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. ", } @InProceedings{1277226, author = "Huayang Xie and Mengjie Zhang and Peter Andreae", title = "Another investigation on tournament selection: modelling and visualisation", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "1468--1475", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1468.pdf", DOI = "doi:10.1145/1276958.1277226", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, modelling, tournament selection, visualisation", size = "8 pages", abstract = "Tournament selection has been widely used and studied in evolutionary algorithms. To supplement the study of tournament selection, this paper provides several models describing the probabilities that a program of a particular rank is sampled and is selected in the standard tournament selection in a simple situation and a complex situation. This paper discovers that, with the same tournament size, trends of sampling probability of a program and selection probability distributions of a population are the same regardless of the population size. This paper also models and investigates an alternative tournament selection method which eliminates one of the drawbacks in the standard tournament selection. Finally, this paper proposes a new fitness evaluation saving algorithm via the use of not-sampled individuals, which is a special property of tournament selection.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @InProceedings{1277297, author = "Huayang Xie and Mengjie Zhang and Peter Andreae", title = "An analysis of constructive crossover and selection pressure in genetic programming", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "1739--1748", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1739.pdf", DOI = "doi:10.1145/1276958.1277297", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, crossover, selection pressure, stochastic elements", abstract = "A common problem in genetic programming search algorithms is destructive crossover in which the offspring of good parents generally has worse performance than the parents. Designing constructive crossover operators and integrating some local search techniques into the breeding process have been suggested as solutions. This paper reports on experiments demonstrating that premature convergence may happen more often when using these techniques in combination with standard parent selection. It shows that modifying the selection pressure in the parent selection process is necessary to obtain a significant performance improvement.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @InProceedings{Xie:2007:cec, author = "Huayang Xie and Mengjie Zhang and Peter Andreae", title = "Genetic Programming for New Zealand CPI Inflation Prediction", booktitle = "2007 IEEE Congress on Evolutionary Computation", year = "2007", editor = "Dipti Srinivasan and Lipo Wang", pages = "2538--2545", address = "Singapore", month = "25-28 " # sep, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "1-4244-1340-0", file = "1682.pdf", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2007.4424790", abstract = "Reserve Bank of New Zealand (RBNZ) is one of many inflation-targeting central banks. The effective conduct of monetary policy requires the capacity to make accurate short and medium term predictions about price inflation. The RBNZ's prediction system is very complex, requiring many iterations and significant input from human experts. This paper investigates the capability of Genetic Programming (GP) to predict price inflation over short and medium terms. By using un-preprocessed economic time series over small intervals, the experimental results demonstrate that GP can produce predictions of price inflation with accuracy comparable to the RBNZ's official prediction system, over both short and medium terms.", notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C", } @InProceedings{Xie:2007:cec2, author = "Huayang Xie and Mengjie Zhang and Peter Andreae", title = "An Analysis of Depth of Crossover Points in Tree-Based Genetic Programming", booktitle = "2007 IEEE Congress on Evolutionary Computation", year = "2007", editor = "Dipti Srinivasan and Lipo Wang", pages = "4561--4568", address = "Singapore", month = "25-28 " # sep, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "1-4244-1340-0", file = "1696.pdf", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2007.4425069", abstract = "The standard crossover operator in tree-based Genetic Programming (GP) is problematic in that it is most often destructive. Selecting crossover points with an implicit bias towards the leaves of a program tree aggravates its destructiveness and causes the code bloat problem in GP. Therefore, a common view has been developed that adjusting the depth of crossover points to eliminate the bias can improve GP performance, and many attempts have been made to create effective crossover operators according to this view. As there are a large number of possible depth-control strategies, it is very difficult to identify the strategy that provides the most significant improvement in performance. This paper explores depth-control strategies by analysing the depth of crossover points in evolutionary process logs of five different GP systems on problems in three different domains. It concludes that controlling the depth of crossover points is an evolutionary stage dependent and problem dependent task, and obtaining a significant performance improvement is not trivial.", notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C", } @TechReport{CS-TR-07-2, author = "Huayang Xie and Mengjie Zhang and Peter Andreae", title = "Not-Sampled Issue and Round-Replacement Tournament Selection", institution = "Computer Science, Victoria University of Wellington", year = "2007", type = "Technical report", number = "CS-TR-07-2", address = "New Zealand", month = nov, keywords = "genetic algorithms, genetic programming, tournament selection, standard tournament selection, round-replacement tournament selection", URL = "http://www.mcs.vuw.ac.nz/comp/Publications/archive/CS-TR-07/CS-TR-07-2.pdf", URL = "http://www.mcs.vuw.ac.nz/comp/Publications/CS-TR-07-2.abs.html", abstract = "The standard tournament selection samples individuals with replacement. The sampling-with-replacement strategy has its advantages but also has issues. One of the commonly recognised issues is that it is possible to have some individuals not sampled at all during the selection phase. The not-sampled issue aggravates the loss of diversity. However, it is not clear how the issue affects GP search. This paper uses a round-replacement tournament selection to investigate the importance of the issue. The theoretical and experimental results show that although the issue can be solved and the loss of diversity can be minimised for small tournament sizes, the different selection behaviour in the round-replacement tournament selection cannot significantly improve the GP performance. The not-sampled issue does not seriously affect the selection performance in the standard tournament selection.", size = "15 pages", } @InProceedings{Xie:2008:cec, author = "Huayang Xie and Mengjie Zhang and Peter Andreae", title = "An Analysis of the Distribution of Swapped Subtree Sizes in Tree-based Genetic Programming", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "2859--2866", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1823-7", file = "EC0641.pdf", DOI = "doi:10.1109/CEC.2008.4631181", abstract = "This paper analyses the distribution of swapped subtree sizes involved in crossover events in approximations of an optimal crossover operator that allows the root node to be crossed over. The goal is to examine how the offspring search space can be effectively reduced for given parents. It concludes that good crossover events have a strong preference for the roots of the parent programs and for nodes with small sub-trees. This paper also quantifies the ability of crossover to optimise offspring fitness, and concludes that this ability is far below what was expected.", keywords = "genetic algorithms, genetic programming", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @InProceedings{Xie2:2008:cec, author = "Huayang Xie and Mengjie Zhang and Peter Andreae and Mark Johnston", title = "Is the Not-Sampled Issue in Tournament Selection Critical?", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "3710--3717", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1823-7", file = "EC0787.pdf", DOI = "doi:10.1109/CEC.2008.4631300", abstract = "The standard tournament selection samples individuals with replacement. The sampling-with-replacement strategy has its advantages but also has issues. One of the commonly recognised issues is that it is possible to have some individuals not sampled at all during the selection phase. The not-sampled issue aggravates the loss of program diversity. However, it is not clear how the issue affects Genetic Programming (GP) search. This paper investigates the importance of the issue. The theoretical and experimental results show that the issue can be solved and the loss of diversity contributed by not-sampled individuals can be minimised. However, doing so does not appears to significantly improve a GP system. Our conclusion is that the not-sampled issue does not seriously affect the selection performance in the standard tournament selection.", keywords = "genetic algorithms, genetic programming", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @InProceedings{Xie:2008:gecco, author = "Huayang Xie and Mengjie Zhang and Peter Andreae and Mark Johnson", title = "An analysis of multi-sampled issue and no-replacement tournament selection", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "1323--1330", address = "Atlanta, GA, USA", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, Theory, modelling, multi-sampled Issue, simulation, tournament selection", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1323.pdf", DOI = "doi:10.1145/1389095.1389347", size = "8 pages", abstract = "Standard tournament selection samples individuals with replacement. The sampling-with-replacement strategy has its advantages but also has issues. One of the commonly recognised issues is that it is possible to have the same individual sampled multiple times in a tournament. Although the impact of this multi-sampled issue on genetic programming is not clear, some researchers believe that it may lower the probability of some good individuals being sampled or selected. One solution is to use an alternative tournament selection (no-replacement tournament selection), which samples individuals in a tournament without replacement. This paper analyses no-replacement tournament selection to investigate the impact of the scheme and the importance of the issue. Theoretical simulations show that when common tournament sizes and population sizes are used, no-replacement tournament selection does not make the selection behaviour significantly different from that in the standard one and that the multi-sampled issue seldom occurs. In general, the issue is not crucial to the selection behaviour of standard tournament selection.", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389347}", } @PhdThesis{Xie:thesis, author = "Huayang Xie", title = "An Analysis of Selection in Genetic Programming", school = "Computer Science, Victoria University of Wellington", year = "2008", address = "New Zealand", keywords = "genetic algorithms, genetic programming, Offspring selection, Parent selection, Neural Networks, Fuzzy Logic", URL = "http://homepages.ecs.vuw.ac.nz/~mengjie/students/jasonPhd_thesis.pdf", URL = "http://hdl.handle.net/10063/837", size = "255 pages", abstract = "This thesis presents an analysis of the selection process in tree-based Genetic Programming (GP), covering the optimisation of both parent and offspring selection, and provides a detailed understanding of selection and guidance on how to improve GP search effectively and efficiently. The first part of the thesis provides models and visualisations to analyse selection behaviour in standard tournament selection, clarifies several issues in standard tournament selection, and presents a novel solution to automatically and dynamically optimise parent selection pressure. The fitness evaluation cost of parent selection is then addressed and some cost-saving algorithms introduced. In addition, the feasibility of using good predecessor programs to increase parent selection efficiency is analysed. The second part of the thesis analyses the impact of offspring selection pressure on the overall GP search performance. The fitness evaluation cost of offspring selection is then addressed, with investigation of some heuristics to efficiently locate good offspring by constraining crossover point selection structurally through the analysis of the characteristics of good crossover events. The main outcomes of the thesis are three new algorithms and four observations: 1) a clustering tournament selection method is developed to automatically and dynamically tune parent selection pressure; 2) a passive evaluation algorithm is introduced for reducing parent fitness evaluation cost for standard tournament selection using low selection pressure; 3) a heuristic population clustering algorithm is developed to reduce parent fitness evaluation cost while taking advantage of clustering tournament selection and avoiding the tournament size limitation; 4) population size has little impact on parent selection pressure thus the tournament size configuration is independent of population size; and different sampling replacement strategies have little impact on the selection behaviour in standard tournament selection; 5) premature convergence occurs more often when stochastic elements are removed from both parent and offspring selection processes; 6) good crossover events have a strong preference for whole program trees, and (less strongly) single-node or small subtrees that are at the bottom of parent program trees; 7) the ability of standard GP crossover to generate good offspring is far below what was expected.", } @InProceedings{DBLP:conf/ausai/XieZ09, author = "Huayang Xie and Mengjie Zhang", title = "Balancing Parent and Offspring Selection in Genetic Programming", booktitle = "Proceedings of the 22nd Australasian Joint Conference on Artificial Intelligence (AI'09)", year = "2009", editor = "Ann E. Nicholson and Xiaodong Li", volume = "5866", series = "Lecture Notes in Computer Science", pages = "454--464", bibsource = "DBLP, http://dblp.uni-trier.de", address = "Melbourne, Australia", month = dec # " 1-4", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-10438-1", DOI = "doi:10.1007/978-3-642-10439-8_46", abstract = "In order to drive Genetic Programming (GP) search towards an optimal situation, balancing selection pressure between the parent and offspring selection phases is an important aspect and very challenging. Our previous work showed that stochastic elements cannot be removed from both parent and offspring selections and suggested that maximising diversity in parents and minimising randomness in offspring could provide significantly good performance. This paper conducts additional carefully designed experiments to further investigate how diverse the parent should be if the offspring selection pressure is intensive. This paper shows that any attempt on adding more selection pressure to the parent selection can result in lower GP performance, and the higher the parent selection pressure, the worse the GP performance. The results confirm and strengthen the finding in our previous work.", } @InProceedings{DBLP:conf/acal/XieZ09, author = "Huayang Xie and Mengjie Zhang", title = "An Analysis and Evaluation of the Saving Capability and Feasibility of Backward-Chaining Evolutionary Algorithms", booktitle = "Proceedings of the 4th Australian Conference on Artificial Life (ACAL'09)", series = "Lecture Notes in Computer Science", volume = "5865", year = "2009", editor = "Kevin B. Korb and Marcus Randall and Tim Hendtlass", pages = "63--72", address = "Melbourne, Australia", month = dec # " 1-4", publisher = "Springer", bibsource = "DBLP, http://dblp.uni-trier.de", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-10426-8", DOI = "doi:10.1007/978-3-642-10427-5_7", abstract = "Artificial Intelligence, volume 170, number 11, pages 953-983, 2006 published a paper titled {"}Backward-chaining evolutionary algorithm{"} \cite{poli_2006_AIJ}. It introduced two fitness evaluation saving algorithms which are built on top of standard tournament selection. One algorithm is named Efficient Macro-selection Evolutionary Algorithm (EMS-EA) and the other is named Backward-chaining EA (BC-EA). Both algorithms were claimed to be able to provide considerable fitness evaluation savings, and especially BC-EA was claimed to be much efficient for hard and complex problems which require very large populations. This paper provides an evaluation and analysis of the two algorithms in terms of the feasibility and capability of reducing the fitness evaluation cost. The evaluation and analysis results show that BC-EA would be able to provide computational savings in unusual situations where given problems can be solved by an evolutionary algorithm using a very small tournament size, or a large tournament size but a very large population and a very small number of generations. Other than that, the saving capability of BC-EA is the same as EMS-EA. Furthermore, the feasibility of BC-EA is limited because two important assumptions making it work hardly hold.", } @TechReport{Xie:tr09-10, author = "Huayang Xie and Mengjie Zhang", title = "Tuning Selection Pressure in Tournament Selection", institution = "School of Engineering and Computer Science. Victoria University of Wellington", year = "2009", number = "ECSTR-09-10", address = "New Zealand", month = "16 " # jun, keywords = "genetic algorithms, genetic programming, Tournament Selection, Selection Pressure, Tuning Strategy", URL = "https://ecs.wgtn.ac.nz/Main/TechnicalReportSeries", URL = "http://ecs.victoria.ac.nz/twiki/pub/Main/TechnicalReportSeries/ECSTR09-10.pdf", size = "14 pages", abstract = "Selection pressure controls the selection of individuals from the current population to produce a new population in the next generation. It gives individuals of higher quality a higher probability of being used to create the next generation so that Evolutionary Algorithms (EAs) can focus on promising regions in the search space. An evolutionary learning process is dynamic and requires different selection pressures at different learning stages in order to speed up convergence or avoid local optima. Therefore, it desires selection mechanisms being able to automatically tune selection pressure during evolution. Tournament selection is a popular selection method in EAs. This paper focuses on tournament selection and shows that standard tournament selection is unaware of the dynamics in the evolutionary process thus is unable to tune selection pressure automatically. This paper then presents a novel approach which integrates the knowledge of the Fitness Rank Distribution (FRD) of a population into tournament selection. Through mathematical modelling, simulations and experimental study, this paper shows that the new approach is effective and using the knowledge of FRD is a promising way to modify the standard tournament selection method for tuning the selection pressure dynamically and automatically along evolution.", } @Article{Xie:2011:SC, author = "Huayang Xie and Mengjie Zhang", title = "Depth-Control Strategies for Crossover in Tree-based Genetic Programming", journal = "Soft Computing - A Fusion of Foundations, Methodologies and Applications", year = "2011", volume = "15", number = "9", pages = "1865--1878", keywords = "genetic algorithms, genetic programming, Crossover, tree-based genetic programming", publisher = "Springer", ISSN = "1432-7643", DOI = "doi:10.1007/s00500-011-0700-9", size = "14 pages", abstract = "The standard subtree crossover operator in the tree-based genetic programming (GP) has been considered as problematic. In order to improve the standard subtree crossover, controlling depth of crossover points becomes a research topic. However, the existence of many different and inconsistent crossover depth-control schemes and the possibility of many other depth-control schemes make the identification of good depth-control schemes a challenging problem. This paper aims to investigate general heuristics for making good depth-control schemes for crossover in tree-based GP. It analyses the patterns of depth of crossover points in good predecessor programs of five GP systems that use the standard subtree crossover and four approximations of the optimal crossover operator on three problems in different domains. The analysis results show that an effective depth-control scheme is problem-dependent and evolutionary stage-dependent, and that good crossover events have a strong preference for roots and (less strongly) bottoms of parent program trees. The results also show that some ranges of depths between the roots and the bottoms are also preferred, suggesting that unequal-depth-selection-probability strategies are better than equal-depth-selection-probability strategies.", affiliation = "School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand", } @Article{journals/soco/XieZ12, title = "Impacts of sampling strategies in tournament selection for genetic programming", author = "Huayang Xie and Mengjie Zhang", journal = "Soft Computing - A Fusion of Foundations, Methodologies and Applications", year = "2012", volume = "16", number = "4", pages = "615--633", keywords = "genetic algorithms, genetic programming", ISSN = "1432-7643", DOI = "doi:10.1007/s00500-011-0760-x", size = "19 pages", abstract = "Tournament selection is one of the most commonly used parent selection schemes in genetic programming (GP). While it has a number of advantages over other selection schemes, it still has some issues that need to be thoroughly investigated. Two of the issues are associated with the sampling process from the population into the tournament. The first one is the so-called multi-sampled issue, where some individuals in the population are picked up (sampled) many times to form a tournament. The second one is the not-sampled issue, meaning that some individuals are never picked up when forming tournaments. In order to develop a more effective selection scheme for GP, it is necessary to understand the actual impacts of these issues in standard tournament selection. This paper investigates the behaviour of different sampling replacement strategies through mathematical modelling, simulations and empirical experiments. The results show that different sampling replacement strategies have little impact on selection pressure and cannot effectively tune the selection pressure in dynamic evolution. In order to conduct effective parent selection in GP, research focuses should be on developing automatic and dynamic selection pressure tuning methods instead of alternative sampling replacement strategies. Although GP is used in the empirical experiments, the findings revealed in this paper are expected to be applicable to other evolutionary algorithms.", affiliation = "School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand", bibdate = "2012-03-10", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/soco/soco16.html#XieZ12", } @Article{Xie:2012:ieeeTEC, author = "Huayang Xie and Mengjie Zhang", title = "Parent Selection Pressure Auto-tuning for Tournament Selection in Genetic Programming", journal = "IEEE Transactions on Evolutionary Computation", year = "2013", volume = "17", number = "1", pages = "1--19", month = feb, keywords = "genetic algorithms, genetic programming", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2011.2182652", abstract = "Selection pressure restraints the selection of individuals from the current population to produce a new population in the next generation. It gives individuals of higher quality a higher probability of being used to create the next generation so that Evolutionary Algorithms (EAs) can focus on promising regions in the search space. An evolutionary learning process is dynamic and requires different selection pressures at different learning stages in order to speed up convergence or avoid local optima. Therefore, it desires selection mechanisms being able to automatically tune selection pressure during evolution. Tournament selection is a popular selection method in EAs, especially genetic algorithms and Genetic Programming (GP). This paper focuses on tournament selection and shows that the standard tournament selection scheme is unaware of the dynamics in the evolutionary process and that the standard tournament selection scheme is unable to tune selection pressure automatically. This paper then presents a novel approach which integrates the knowledge of the Fitness Rank Distribution (FRD) of a population into tournament selection. Through mathematical modelling, simulations and experimental study in GP, this paper shows that the new approach is effective and using the knowledge of FRD is a promising way to modify the standard tournament selection method for tuning the selection pressure dynamically and automatically along evolution.", notes = "also known as \cite{6151120}", } @InProceedings{Xie:2014:CECb, title = "A Genetic Programming-Based Hyper-heuristic Approach for Storage Location Assignment Problem", author = "Jing Xie and Yi Mei and Andreas Ernst and Xiaodong Li and Andy Song", pages = "3000--3007", booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary Computation", year = "2014", month = "6-11 " # jul, editor = "Carlos A. {Coello Coello}", address = "Beijing, China", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Real-world applications", URL = "http://goanna.cs.rmit.edu.au/~e04499/Papers/CEC14-JingMeiErnstLiSong.pdf", DOI = "doi:10.1109/CEC.2014.6900604", size = "8 pages", abstract = "This study proposes a method for solving real-world warehouse Storage Location Assignment Problem (SLAP) under grouping constraints by Genetic Programming (GP). Integer Linear Programming (ILP) formulation is used to define the problem. By the proposed GP method, a subset of the items is repeatedly selected and placed into the available current best location of the shelves in the warehouse, until all the items have been assigned with locations. A heuristic matching function is evolved by GP to guide the selection of the subsets of items. Our comparison between the proposed GP approach and the traditional ILP approach shows that GP can obtain near-optimal solutions on the training data within a short period of time. Moreover, the evolved heuristics can achieve good optimisation results on unseen scenarios, comparable to that on the scenario used for training. This shows that the evolved heuristics have good reusability and can be directly applied for slightly different scenarios without any new search process.", notes = "WCCI2014", } @InProceedings{conf/seal/XieMELS14, author = "Jing Xie and Yi Mei and Andreas T. Ernst and Xiaodong Li and Andy Song", title = "Scaling Up Solutions to Storage Location Assignment Problems by Genetic Programming", bibdate = "2014-11-12", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/seal/seal2014.html#XieMELS14", booktitle = "Simulated Evolution and Learning - 10th International Conference, {SEAL} 2014, Dunedin, New Zealand, December 15-18, 2014. Proceedings", publisher = "Springer", year = "2014", volume = "8886", editor = "Grant Dick and Will N. Browne and Peter A. Whigham and Mengjie Zhang and Lam Thu Bui and Hisao Ishibuchi and Yaochu Jin and Xiaodong Li and Yuhui Shi and Pramod Singh and Kay Chen Tan and Ke Tang", isbn13 = "978-3-319-13562-5", pages = "691--702", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming", URL = "http://dx.doi.org/10.1007/978-3-319-13563-2", } @InProceedings{Xie:2015:CEC, author = "Jing Xie and Yi Mei and Andreas T. Ernst and Xiaodong Li and Andy Song", booktitle = "2015 IEEE Congress on Evolutionary Computation (CEC)", title = "A restricted neighbourhood Tabu Search for Storage Location Assignment Problem", year = "2015", pages = "2805--2812", isbn13 = "978-1-4799-7491-7", abstract = "The Storage Location Assignment Problem (SLAP) is a significant optimisation problem in warehouse management. Given a number of products, each with a set of items with different popularities (probabilities of being ordered), SLAP is to find the best locations for the items of the products in the warehouse to minimise the warehouse operational cost. Specifically, the operational cost is the expected cost of picking the orders. Grouping constraints are included to take the practical considerations into account in the problem. That is, the items belonging to the same product are more desirable to be placed together. In this paper, the SLAP with Grouping Constraints (SLAP-GC) is investigated, and an efficient Restricted Neighbourhood Tabu Search (RNTS) algorithm is proposed to solving it. RNTS adopts the problem-specific search operators to maintain solution feasibility, and the tabu list to prevent searching back and forth. RNTS was empirically compared with the mathematical programming method and a previously designed Genetic Programming method, which is demonstrated to be the state-of-the-art algorithm for SLAP-GC. The experimental results on the real-world data show that RNTS outperforms the state-of-the-art algorithms for SLAP-GC in terms of solution quality and speed. It managed to achieve optimal solutions for most of the small-scale instances much faster and outperformed the Genetic Programming method in terms of both solution quality and running time on all the test instances.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2015.7257237", ISSN = "1089-778X", month = may, notes = "Sch. of Comput. Sci. & IT, RMIT Univ., Melbourne, VIC, Australia Also known as \cite{7257237}", } @InProceedings{Xie:2015:GECCOcomp, author = "Jing Xie and Yi Mei and Andy Song", title = "Evolving Self-Adaptive Tabu Search Algorithm for Storage Location Assignment Problems", booktitle = "GECCO 2015 Late-Breaking Abstracts", year = "2015", editor = "Dirk Sudholt", isbn13 = "978-1-4503-3488-4", keywords = "genetic algorithms, genetic programming", pages = "779--780", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739482.2764896", DOI = "doi:10.1145/2739482.2764896", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "This study proposes a novel grammar guided Genetic Programming method to solve a real world problem, the Storage Location Assignment Problem (SLAP) with Grouping Constraints. Self-adaptive Tabu Search algorithms are evolved by this approach and it can be used as solvers for SLAPs. A novel self-adaptive Tabu Search framework is proposed that key configurations of the algorithm are determined based on the problem-specific characters, and these configurations are changed dynamically during the search process. In addition, both the quality of the solutions and the execution speed are considered in the evaluation function. The experimental results show that more efficient Tabu Search algorithms can be found by this approach comparing to a manually-designed Tabu Search method.", notes = "Also known as \cite{2764896} Distributed at GECCO-2015.", } @PhdThesis{Jing_Xie:thesis, author = "Jing Xie", title = "On the investigation of the large-scale grouping constrained storage location assignment problem", school = "School of Science, College of Science, Engineering and Health, RMIT University", year = "2017", address = "Australia", month = aug, keywords = "genetic algorithms, genetic programming", URL = "https://researchbank.rmit.edu.au/view/rmit:162142", URL = "https://researchbank.rmit.edu.au/eserv/rmit:162142/Xie.pdf", size = "235 pages", abstract = "The primary focus of this study is a novel optimisation problem, namely Storage Location Assignment Problem with Grouping Constraint (SLAP-GC). The problem stems from real-world applications and is significant in theoretical values and applicability in resource allocation tasks where groupings must be considered. The aim of this problem is to minimize the total operational cost in a warehouse through stock rearrangement. The problem consists of two interdependent subproblems, grouping same product items and assigning items to minimize picking distance. The interactions between these two subproblems make this problem significantly different from previous Storage Location Assignment Problems (SLAP), a well-studied field in logistics. Existing approaches for SLAP are not directly applicable for SLAP-GC. This dissertation lays a foundation for research on grouping constraints and other optimisation problems with similar interactions between subproblems. Firstly this study presents a formal definition of SLAP-GC. Then it offers a formal proof of NP-completeness of SLAP-GC by reducing from a well-known 3-Partition problem to SLAP-GC. This suggests that the real-world instances of SLAP-GC should not be tackled with exact approaches, but with approximation and heuristic approaches. Then, we explored decomposition and modelling techniques for SLAP-GC and developed three types of promising heuristic approaches: a hyperheuristic approach, a metaheuristic approach and a matheuristic approach. Comprehensive experimental studies are conducted on both synthetic benchmark instances and real-world instances to examine their efficiency, efficacy, and scalability. Through the analysis of the experimental results, the suitability of proposed methods is verified on various SLAP-GC scenarios. In addition, we demonstrate in this study that with the proposed decomposition, large-scale SLAP-GC can be handled efficiently by the three proposed heuristic-based approaches.", } @InProceedings{Xie:2022:ICSICT, author = "Na Xie and Renyuan Zhang and Han Yan and Chonghang Xie and Hao Zhang and Hao Cai and Bo Liu2", title = "Compressors Evolution based High Speed and Energy Efficient Approximate Signed Multiplier", booktitle = "2022 IEEE 16th International Conference on Solid-State \& Integrated Circuit Technology (ICSICT)", year = "2022", abstract = "Among various energy improvement techniques, approximate computing can reduce power, area and delay at the cost of calculation accuracy losses. In this paper, we propose an approximate signed multiplier design with a low power consumption and a short critical path using Cartesian Genetic Programming (CGP) algorithm and a compressors evolution method to obtain more suitable approximate 3-2 compressors for partial products (PPs) accumulation to compensate the error generated by CGP. Experimental results show that the proposed signed multiplier reduces delay by 4percent, area by 2percent, power consumption by 2percent and the power delay product (PDP) by 56.percent. The NMED of proposed multiplier and recognition accuracy increased from 0.88percent to 0.73percent and 87.2percent to 92.6percent respectively through compressors evolution method. The proposed multiplier is also evaluated in a CRNN-based keyword spotting (KWS) system which brought high efficiency and little accuracy loss.", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, Integrated circuit technology, Power demand, Costs, Circuit optimization, Compressors, Energy efficiency, Approximate signed multiplier, KWS", DOI = "doi:10.1109/ICSICT55466.2022.9963435", month = oct, notes = "Also known as \cite{9963435}", } @Article{Xie:2025:ast, author = "Shucong Xie and Yunfeng Dong and Zhihua Liang", title = "Genetic programming method for satellite optimization design with quantification of multi-granularity model uncertainty", journal = "Aerospace Science and Technology", year = "2025", volume = "156", pages = "109764", keywords = "genetic algorithms, genetic programming, Uncertainty quantification, Multi-granularity model, Satellite optimization design", ISSN = "1270-9638", URL = "https://www.sciencedirect.com/science/article/pii/S1270963824008939", DOI = "doi:10.1016/j.ast.2024.109764", abstract = "Using digital tools for satellite optimisation design is vital for supporting decision-making in the actual engineering of satellites. The higher the accuracy of the simulation model, the more precise the satellite performance evaluation, and the more valuable the optimisation results. Traditional heuristic algorithms have been successful in optimising satellite parameters but face challenges when dealing with component-level optimisation of satellite composition and structure. To address this issue, this paper presents a genetic programming method for satellite optimisation design with quantification of multi-granularity model uncertainty. It defines a multi-granularity simulation model for satellites and presents a method for quantifying model uncertainty. Building upon this foundation, it designs genetic programming tree structures and genetic operations, introducing granularity switching criteria to enable on-demand switching of model granularity. Furthermore, based on the correlation between satellite capabilities and subsystems, it defines an active crossover criterion at the subsystem level to expedite convergence speed further. Numerical simulation cases demonstrate the effectiveness of this method, which enables rapid optimisation design of satellite component models, providing timely and efficient assistance for engineering applications", } @InProceedings{Xie2005, author = "Xiaoyuan Xie and Baowen Xu and Liang Shi and Changhai Nie and Yanxiang He", title = "A dynamic optimization strategy for evolutionary testing", booktitle = "12th Asia-Pacific Software Engineering Conference (APSEC'05)", year = "2005", address = "Taipei, Taiwan", month = "5-17 " # dec, publisher = "IEEE", keywords = "genetic algorithms, SBSE", isbn13 = "0-7695-2465-6", DOI = "doi:10.1109/APSEC.2005.6", ISSN = "1530-1362", size = "8 pages", abstract = "Evolutionary testing (ET) is an efficient technique of automated test case generation. ET uses a kind of metaheuristic search technique, genetic algorithm (GA), to convert the task of test case generation into an optimal problem. The configuration strategies of GA have notable influences upon the performance of ET. In this paper, represent a dynamic self-adaptation strategy for evolutionary structural testing. It monitors evolution process dynamically, detects the symptom of prematurity by analysing the population, and adjusts the mutation possibility to recover the diversity of the population. The empirical results show that the strategy can greatly improve the performance of the ET in many cases. Besides, some valuable advices are provided for the configuration strategies of ET by the empirical study.", notes = "Not GP", } @PhdThesis{Xiaoyuan_Xie_thesis, author = "Xiaoyuan Xie", title = "On the Analysis of Spectrum-based Fault Localization", school = "Faculty of Information and Communication Technologies, Swinburne University of Technology", year = "2012", address = "Australia", month = jun, keywords = "software engineering, testing", URL = "https://researchbank.swinburne.edu.au/items/c4f8e2ef-3ec8-4604-985f-f4561022a6f2/1/", URL = "https://researchbank.swinburne.edu.au/file/c4f8e2ef-3ec8-4604-985f-f4561022a6f2/1/Xiaoyuan%20Xie%20Thesis.pdf", size = "158 pages", abstract = "Spectrum-based fault localization (SBFL) has been widely studied due to its simplicity and effectiveness. However, it still has some challenging problems. The application of SBFL in the absence of test oracle and the selection of the most effective risk evaluation formulas are amongst the most critical problems. In this thesis, we are going to address these two problems. Currently, all existing SBFL techniques have assumed the existence of a test oracle. Otherwise, the program spectrum will not be associated with the testing result of failed or passed, and as a consequence, there will be insufficient information to perform the risk evaluation. However, in many real-world applications, it is very common that test oracles do not exist, and hence SBFL cannot be applied in such situations. Therefore, in this thesis, we propose a novel concept of metamorphic slice resulted from the integration of metamorphic testing and program slicing, to alleviate the oracle problem for SBFL. In our approach, instead of using the program slice and the testing result of failed or passed for an individual test case, metamorphic slice and the testing result of violation or non- violation of a metamorphic relation are used. Since we need not to know the execution result for an individual test case, the existence of test oracle is no longer a prerequisite to SBFL. Experimental results show that our proposed solution delivers a performance comparable to the performance of existing SBFL techniques for the situations where test oracles exist. As a consequence, our study has significantly extended the scope of the applicability of SBFL. For the second problem of selecting the most effective risk evaluation formulas, though it has been one of the most important tasks in SBFL, there does not exist a completely satisfactory solution. It is well-known that risk evaluation is very critical in SBFL and hence many studies have been conducted to compare the performance among various risk evaluation formulas. Most of the previous studies have adopted an empirical approach, which however, can hardly be considered as sufficiently comprehensive because of the huge possible combinations of various factors in SBFL. Though there are some studies aiming at overcoming the limitations of the empirical studies through a theoretical approach, these studies were based on the most strict type of equivalence that does not properly reflect the more realistic scenario, and did not adopt the most commonly used performance metric. Therefore, in this thesis, we provide a theoretical investigation on the effectiveness of risk evaluation formulas. We define two types of relations between different formulas, namely, equivalent and better. To identify the relations between different formulas, we develop an innovative framework for the theoretical investigation. Our framework is based on the concept that the determinant for the effectiveness of a formula is the number of statements with risk values higher than that of the faulty statement. Our framework groups all program statements into three disjoint sets with risk values higher than, equal to and lower than that of the faulty statement, respectively. For different formulas, the sizes of their sets are compared using the notion of subset. We use this framework to identify the maximal formulas which should be the only candidate formulas for use. Compared with previous studies, our conclusions are derived from a completely theoretical analysis, and hence are more robust. Besides, we adopt the most commonly used performance metric, and use a more general and intuitively appealing type of equivalence relation.", notes = "not GP", } @TechReport{Xie:2013fk, author = "Xiaoyuan Xie and Fei-Ching Kuo and Tsong Yueh Chen and Shin Yoo and Mark Harman", title = "Theoretical Analysis of GP-Evolved Risk Evaluation Formulas for Spectrum Based Fault Localisation", institution = "Department of Computer Science, University College London", year = "2013", type = "Research Note", number = "RN/13/06", address = "Gower Street, London WC1E 6BT, UK", month = "28 " # feb, keywords = "genetic algorithms, genetic programming, SBSE, SBFL", URL = "http://www.cs.ucl.ac.uk/fileadmin/UCL-CS/research/rn-13-06__2_.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.378.9601", size = "11 pages", abstract = "Risk evaluation formulae convert program spectrum data from test executions into suspiciousness score, according to which statements are ranked to aid debugging activities. Designing such formulas remained largely a manual task until Genetic Programming has been recently applied: resulting formulae showed promising performance in empirical evaluation. We investigate the GP-evolved formulae theoretically and prove that GP has produced four maximal formulae that had not been known before. More interestingly, some of the newly found maximal formulae show characteristics that may seem inconsistent with human intuition. This is the first SBSE result with provable human competitiveness.", notes = "See also \cite{Xie:2013:SSBSE}", } @InProceedings{Xie:2013:SSBSE, author = "Xiaoyuan Xie and Fei-Ching Kuo and Tsong Yueh Chen and Shin Yoo and Mark Harman", title = "Provably Optimal and Human-Competitive Results in SBSE for Spectrum Based Fault Localisation", booktitle = "Symposium on Search-Based Software Engineering", year = "2013", editor = "Guenther Ruhe and Yuanyuan Zhang", volume = "8084", series = "Lecture Notes in Computer Science", pages = "224--238", address = "Leningrad", month = aug # " 24-26", publisher = "Springer", keywords = "genetic algorithms, genetic programming, SBSE", isbn13 = "978-3-642-39741-7", URL = "http://www.cs.ucl.ac.uk/staff/s.yoo/papers/Xie2013kx.pdf", DOI = "doi:10.1007/978-3-642-39742-4_17", size = "15 pages", abstract = "Fault localisation uses so-called risk evaluation formulae to guide the localisation process. For more than a decade, the design and improvement of these formulae has been conducted entirely manually through iterative publication in the fault localisation literature. However, recently we demonstrated that SBSE could be used to automatically design such formulae by recasting this as a problem for Genetic Programming (GP). In this paper we prove that our GP has produced four previously unknown globally optimal formulae. Though other human competitive results have previously been reported in the SBSE literature, this is the first SBSE result, in any application domain, for which human competitiveness has been formally proved. We also show that some of these formulae exhibit counter-intuitive characteristics, making them less likely to have been found solely by further human effort.", notes = "See also Technical Report RN/14/14 http://www.cs.ucl.ac.uk/fileadmin/UCL-CS/research/Research_Notes/rn-14-14.pdf", } @Article{XIE:2022:engappai, author = "Yuan Xie and Wei Gao and Yiwei Wang and Xin Chen and Shuangshuang Ge and Sen Wang", title = "Life prediction of underground structure by sulfate corrosion using Harris hawks optimizing genetic programming", journal = "Engineering Applications of Artificial Intelligence", volume = "115", pages = "105190", year = "2022", ISSN = "0952-1976", DOI = "doi:10.1016/j.engappai.2022.105190", URL = "https://www.sciencedirect.com/science/article/pii/S0952197622002883", keywords = "genetic algorithms, genetic programming, RC underground structure, Sulfate corrosion, Life prediction, Harris hawks optimization", abstract = "A corrosive sulfate environment can cause strong deterioration and destruction of reinforced concrete (RC) underground structures and seriously reduce their service life. Thus, it is very important to predict the service life of RC underground structures in corrosive sulfate environments. However, the service life of underground structures is affected by numerous complicated engineering and environmental factors and cannot be determined by traditional theoretical and experimental investigations. Therefore, to solve this problem, a new data-driven method based on Harris hawks optimizing genetic programming (HHO-GP) is proposed. In this new method, to improve the traditional genetic programming (GP), a new global optimization algorithm called Harris hawks optimization (HHO) is adopted to optimize its main controlling parameters. Based on 25 groups of real engineering data, the life prediction model of underground structures in corrosive sulfate environments with 12 main engineering and environmental influence factors is established by the HHO-GP method. The results show that the average relative training error (5.5percent) and predicting error (6.3percent) of the new prediction model are small. Therefore, the proposed HHO-GP method can construct a suitable life prediction model based on only real engineering data, regardless of how many complicated influencing factors are considered. Moreover, our data-driven life prediction model is described by one explicit polynomial function based on 12 influencing factors. Thus, it can be applied in real engineering simply and easily. Finally, the influence of the main controlling parameters of the HHO-GP on its accuracy and efficiency is analyzed. The results reveal that considering the computing accuracy and efficiency and the model completeness, the small population size and maximum iterations of HHO are suitable, whose recommended values are all 15. The population size and maximum number of iterations of GP have little influence on the prediction accuracy. Their recommended values all can be 50", } @InProceedings{ZhuliXie:2004:COLING, author = "Zhuli Xie and Xin Li and Barbara {Di Eugenio} and Weimin Xiao and Thomas M. Tirpak and Peter C. Nelson", title = "Using Gene Expression Programming to Construct Sentence Ranking Functions for Text Summarization", booktitle = "Proceedings of the 20th International Conference on Computational Linguistics, COLING-2004", year = "2004", pages = "1381--1384", address = "Geneva, Switzerland", month = "23-27 " # aug, keywords = "genetic algorithms, genetic programming, Gene Expression Programming", URL = "http://www.cs.uic.edu/~xli1/papers/GEPSentenceRankingFunc(COLING04).pdf", abstract = "we consider the automatic text summarisation as a challenging task of machine learning. We proposed a novel summarization system architecture which employs Gene Expression Programming technique as its learning mechanism. The preliminary experimental results have shown that our prototype system outperforms the baseline systems.", notes = "http://www.issco.unige.ch/coling2004/", } @InProceedings{Xin:2019:GI, author = "Qi Xin and Steven Reiss", title = "Better Code Search and Reuse for Better Program Repair", booktitle = "GI-2019, ICSE workshops proceedings", year = "2019", editor = "Justyna Petke and Shin Hwei Tan and William B. Langdon and Westley Weimer", pages = "10--17", address = "Montreal", month = "28 " # may, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, genetic improvement, automated program repair, APR, code search, code reuse", isbn13 = "978-1-7281-2268-7", URL = "http://geneticimprovementofsoftware.com/paper_pdfs/xin2019better.pdf", URL = "https://dl.acm.org/citation.cfm?id=3339020.3339023", DOI = "doi:10.1109/GI.2019.00012", acmid = "3339023", size = "8 pages", abstract = "A branch of automated program repair (APR) techniques look at finding and reusing existing code for bug repair. ssFix is one of such techniques that is syntactic search-based: it searches a code database for code fragments that are syntactically similar to the bug context and reuses such code fragments to produce patches. The keys to its success lie in the approaches it uses for code search and code reuse. We investigated the effectiveness of ssFix using the Defects4J bug dataset and found that its code search and code reuse approaches are not truly effective and can be significantly improved. Motivated by the investigation, we developed a new repair technique sharpFix that follows ssFix's basic idea but differs significantly in the approaches used for code search and code reuse. We compared sharpFix and ssFix on the Defects4J dataset and confirmed through experiments that (1) sharpFix's code search and code reuse approaches are better than ssFix's approaches and (2) sharpFix can do better repair. sharpFix successfully repaired a total of 36 Defects4J bugs and outperformed many existing repair techniques in repairing more bugs. We also compared sharpFix, ssFix, and four other techniques on another dataset Bugs.jar-ELIXIR. Our results show that sharpFix did better than others and repaired the largest number of bugs.", notes = "ssFix, source code matching from repository, code reuse. SharpFix. 103 examples from Defects4j M69. GZoltar spectrum-based fault localisation. Code search NLP n-grams TF IDF. Code translation (name identifier fix up) and transformations. Georgia Institute of Technology Slides: http://geneticimprovementofsoftware.com/slides/xin2019better_slides.pdf GI-2019 http://geneticimprovementofsoftware.com part of \cite{Petke:2019:ICSEworkshop}", } @Article{xin:2023:Entropy, author = "Lizhi Xin and Kevin Xin and Houwen Xin", title = "On Laws of Thought: A Quantum-like Machine Learning Approach", journal = "Entropy", year = "2023", volume = "25", number = "8", pages = "Article No. 1213", keywords = "genetic algorithms, genetic programming", ISSN = "1099-4300", URL = "https://www.mdpi.com/1099-4300/25/8/1213", DOI = "doi:10.3390/e25081213", abstract = "Incorporating insights from quantum theory, we propose a machine learning-based decision-making model, including a logic tree and a value tree; a genetic programming algorithm is applied to optimise both the logic tree and value tree. The logic tree and value tree together depict the entire decision-making process of a decision-maker. We applied this framework to the financial market, and a {"}machine economist{"} is developed to study a time series of the Dow Jones index. The {"}machine economist{"} will obtain a set of optimised strategies to maximize profits, and discover the efficient market hypothesis (random walk).", notes = "also known as \cite{e25081213}", } @Article{Xing:2015:CHB, author = "Wanli Xing and Rui Guo and Eva Petakovic and Sean Goggins", title = "Participation-based student final performance prediction model through interpretable Genetic Programming: Integrating learning analytics, educational data mining and theory", journal = "Computers in Human Behavior", volume = "47", pages = "168--181", year = "2015", keywords = "genetic algorithms, genetic programming, Learning analytics, Educational data mining, Prediction, CSCL, Activity theory", ISSN = "0747-5632", DOI = "doi:10.1016/j.chb.2014.09.034", URL = "http://www.sciencedirect.com/science/article/pii/S0747563214004865", size = "14 pages", abstract = "Building a student performance prediction model that is both practical and understandable for users is a challenging task fraught with confounding factors to collect and measure. Most current prediction models are difficult for teachers to interpret. This poses significant problems for model use (e.g. personalising education and intervention) as well as model evaluation. In this paper, we synthesise learning analytics approaches, educational data mining (EDM) and HCI theory to explore the development of more usable prediction models and prediction model representations using data from a collaborative geometry problem solving environment: Virtual Math Teams with Geogebra (VMTwG). First, based on theory proposed by Hrastinski (2009) establishing online learning as online participation, we activity theory to holistically quantify students' participation in the CSCL (Computer-supported Collaborative Learning) course. As a result, 6 variables, Subject, Rules, Tools, Division of Labour, Community, and Object, are constructed. This analysis of variables prior to the application of a model distinguishes our approach from prior approaches (feature selection, Ad-hoc guesswork etc.). The approach described diminishes data dimensionality and systematically contextualises data in a semantic background. Secondly, an advanced modelling technique, Genetic Programming (GP), underlies the developed prediction model. We demonstrate how connecting the structure of VMTwG trace data to a theoretical framework and processing that data using the GP algorithmic approach outperforms traditional models in prediction rate and interpretability. Theoretical and practical implications are then discussed.", notes = "Learning Analytics, Educational Data Mining and data-driven Educational Decision Making", } @InProceedings{Xing:2019:IBF, author = "Xiaoqian Xing and Katsuhisa Maruyama", title = "Automatic Software Merging using Automated Program Repair", booktitle = "2019 IEEE 1st International Workshop on Intelligent Bug Fixing (IBF)", year = "2019", pages = "11--16", month = "24-24 " # feb, adddress = "Hangzhou, China", keywords = "genetic algorithms, genetic programming, APR, concurrent software development, merge conflicts, automated program repair", isbn13 = "978-1-7281-1810-9", DOI = "doi:10.1109/IBF.2019.8665493", code_url = "https://github.com/kusumotolab/kGenProg", size = "6 pages", abstract = "Resolution of merge conflicts is inevitable in concurrent software development where source code has been independently modified by multiple programmers. Unfortunately, it requires a lot of human efforts since programmers have to change the conflicting code until the merged code can be compiled with no error and does the correct behavior expected by themselves. Although several techniques have been proposed to (semi-)automatically resolve textual, syntactic, and semantic merge conflicts, behavioral conflicts are still a big trouble for modern software development using version control systems. we propose an automatic merge mechanism that reduces programmers' burden to resolve behavioral merge conflicts, by exploiting an automated program repair (APR) technique that fully-automatically fixes faults (the unexpected behaviour) exposed by tests. To make the automatic merge mechanism feasible, it produces initial programs to be fixed by combining class members within the code to be merged. Moreover, it aggressively takes in code fragments within programs to be merged into the ingredient space. Our experimental results successfully demonstrate that an APR technique can solve behavioral conflicts with no intervention of human.", notes = "Is this GP? p16 'we presented a novel merge mechanism using an APR technique, which might resemble automated software transplantation \cite{Barr:2015:ISSTA} ' Also known as \cite{8665493}", } @Article{Xing:2025:ress, author = "Xueqi Xing and Tongtong Yan and Min Xia", title = "Early prediction of battery life using an interpretable health indicator with evolutionary computing", journal = "Reliability Engineering and System Safety", year = "2025", volume = "260", pages = "110980", keywords = "genetic algorithms, genetic programming, Battery lifespan prediction, Health indicators (HIs), Generic programming (GP), Intelligent, Interpretable", ISSN = "0951-8320", URL = "https://www.sciencedirect.com/science/article/pii/S0951832025001838", DOI = "doi:10.1016/j.ress.2025.110980", abstract = "Accurate prediction of battery lifespan is crucial for optimising energy management, enhancing safety, and ensuring system reliability, particularly when only early-stage battery data is available. Health indicators (HIs) play a pivotal role in monitoring battery degradation by providing a link between the current state and the battery's end of life (EOL). However, existing methods for HI extraction often depend on extensive expert knowledge, large volumes of lifecycle data, and complex models to map HIs to battery lifespan. This study introduces an intelligent and interpretable methodology for generating HIs using improved genetic programming (GP) to enable rapid and precise battery lifespan prediction based solely on data from two early discharge cycles. Four HI candidates are derived from statistical features of the differences between discharge voltage curves. Unlike conventional methods that employ root mean square error (RMSE) as a fitness function, we introduce a novel correlation-based fitness function using cosine similarity within GP. This approach generates a transparent composite mathematical formula for extracting interpretable HIs. It automatically filters irrelevant HI candidates and combines relevant ones through specific mathematical operations. The resulting composite mathematical expression, universally applicable for constructing interpretable HIs across various cycle selections, enables rapid and early battery lifespan prediction through regression models. Validation on 124 battery cells shows that the proposed composite HI, expressed as an explicit mathematical function, achieves a mean absolute percentage error of approximately 15 percent when predicting battery lifespan using data from just two cycles within the first 20 cycles across diverse operating conditions. Moreover, the proposed approach surpasses benchmark HIs in both prediction accuracy and stability across different regression models", } @InProceedings{Xing:2011:PGRfSMAbGNPwRA, title = "Pruning Generalized Rules for Stock Markets Accumulated by Genetic Network Programming with Rule Accumulation", author = "Yafei Xing and Shingo Mabu and Kotaro Hirasawa", pages = "2473--2479", booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary Computation", year = "2011", editor = "Alice E. Smith", month = "5-8 " # jun, address = "New Orleans, USA", organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, genetic network programming, Finance and economics", DOI = "doi:10.1109/CEC.2011.5949924", abstract = "A new strategy on pruning rules accumulated by Genetic Network Programming with Rule Accumulation (GNP-RA) has been proposed in this paper. The generalised rules extracted by training GNP are pruned by GA in the validation phase. Each rule has two variables: U and N. Variable U determines if the rule is used or not, while variable N shows that the information on N days is used. By mutating variables U and N of each rule, the portfolio of U and N is changed, as a result, the rules are pruned. The performance of the pruned rules is tested in the testing phase, meanwhile, the best mutation rates for variable U and variable N are also studied. The simulation results show that the pruned rules work better than the rules without pruning.", notes = "CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.", } @InProceedings{Xiong:2011:SECon, author = "Fan Xiong and Murat M. Tanik", title = "An experiment on evolutionary design of combinational logic circuits using information theory", booktitle = "Proceedings of IEEE Southeastcon, 2011", year = "2011", month = mar, pages = "379--383", abstract = "This paper explores an approach in which information theory is applied to the evolvable hardware field for auto-design and optimisation of combinational logic circuits. In our exploratory experiment, two combinational circuits were extrinsically evolved using genetic programming as the evolutionary algorithm and mutual information as the fitness function. In our experiment using MATLAB, we demonstrated that the proposed method could find all-NAND solutions for circuits, which has not been reported in the literature.", keywords = "genetic algorithms, genetic programming, Matlab, NAND circuits, combinational logic circuits, evolutionary algorithm, evolutionary design, information theory, NAND circuits, information theory, logic design", DOI = "doi:10.1109/SECON.2011.5752970", ISSN = "1091-0050", notes = "SECon 2011 Also known as \cite{5752970}", } @Article{Xiong:2003:WUJNS, author = "Sheng-wu Xiong and Wei-wu Wang", title = "Point-tree structure genetic programming method for discontinuous function's regression", journal = "Wuhan University Journal of Natural Sciences", year = "2003", volume = "8", number = "1", pages = "323--326", month = mar, keywords = "genetic algorithms, genetic programming, symbolic regression, point tree structure", URL = "https://rdcu.be/eaxMQ", URL = "http://link.springer.com/article/10.1007/BF02899503", DOI = "doi:10.1007/BF02899503", size = "4 pages", abstract = "A new point-tree data structure genetic programming (PTGP) method is proposed. For the discontinuous function regression problem, the proposed method is able to identify both the function structure and discontinuities points simultaneously. It is also easy to be used to solve the continuous function regression problems. The numerical experiment results demonstrate that the point-tree GP is an efficient alternative way to the complex function identification problems.", notes = "CLC number: TP 301 School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, Hubei, China", } @InProceedings{Xiong:2003:TAI, author = "Shengwu Xiong and Weiwu Wang and Feng Li", title = "A new genetic programming approach in symbolic regression", booktitle = "Proceedings 15th IEEE International Conference on Tools with Artificial Intelligence", year = "2003", pages = "161--165", month = "3-5 " # nov, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", ISSN = "1082-3409", URL = "http://ieeexplore.ieee.org/iel5/8840/27974/01250185.pdf?tp=&arnumber=1250185&isnumber=27974", DOI = "doi:10.1109/TAI.2003.1250185", abstract = "Genetic programming (GP) has been applied to symbolic regression problem for a long time. The symbolic regression is to discover a function that can fit a finite set of sample data. These sample data can be guided by a simple function, which is continuous and smooth, but in a complex system, the sample data can be produced by a discontinuous or non-smooth function. When conventional GP is applied to such complex system's regression, it gets poor performance. This paper proposed a new GP representation and algorithm that can be applied to both continuous function's regression and discontinuous function's regression. The proposed approach is able to identify both the sub-functions and the discontinuity points simultaneously. The numerical experimental results show that the new GP is able to obtain higher success rate, higher convergence rate and better solutions than conventional GP in such complex system's regression.", notes = "Sch. of Comput. Sci. & Technol., Wuhan Univ. of Technol., China", } @InProceedings{shengwu:2003:anhsgpisr, author = "Xiong Shengwu and Wang Weiwu", title = "A new hybrid structure genetic programming in symbolic regression", booktitle = "Proceedings of the 2003 Congress on Evolutionary Computation CEC2003", editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon", pages = "1500--1506", year = "2003", publisher = "IEEE Press", address = "Canberra", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "8-12 " # dec, organisation = "IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", keywords = "genetic algorithms, genetic programming, Arithmetic, Computer science, Convergence of numerical methods, Evolutionary computation, Fractals, Modelling, Regression analysis, Shape, Time varying systems, regression analysis, GP representation, complex system modelling, continuous function, discontinuity points, discontinuous function, function regression, function structure, hybrid structure genetic programming, nonsmooth function, smooth function, symbolic regression", ISBN = "0-7803-7804-0", DOI = "doi:10.1109/CEC.2003.1299850", abstract = "Genetic Programming (GP) has been applied to symbolic regression problem for a long time. The symbolic regression is to discover a function that can fit a finite set of sample data. These sample data can be guided by a simple function, which is continuous and smooth. But in a complex system, they can be produced by a discontinuous or non-smooth function. When conventional GP is applied to this complex system's modeling, it gets poor performance. This paper proposes a new GP representation and algorithm that can be applied to both continuous function's and discontinuous function's regression. Our approach is able to identify both simultaneously the function's structure and the discontinuity points. The numerical experimental results will show that the new GP is able to gain higher success rate, higher convergence rate and better solutions than conventional GP.", notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.", } @InProceedings{Xiong:2018:GI, author = "Yingfei Xiong and Bo Wang and Guirong Fu and Linfei Zang", title = "Learning to Synthesize", booktitle = "GI-2018, ICSE workshops proceedings", year = "2018", editor = "Justyna Petke and Kathryn Stolee and William B. Langdon and Westley Weimer", pages = "37--44", address = "Gothenburg, Sweden", month = "2 " # jun, publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, genetic improvement", isbn13 = "978-1-4503-5753-1", URL = "https://xiongyingfei.github.io/papers/GI18.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/icse2018/gi2018/papers/Xiong_2018_GI.pdf", slide_url = "https://wangbo15.github.io/files/slides/2018-Learning-to-Synthesize@GI.pdf", URL = "https://arxiv.org/pdf/1802.07608", DOI = "doi:10.1145/3194810.3194816", size = "8 pages", abstract = "n many scenarios we need to find the most likely program under a local context, where the local context can be an incomplete program, a partial specification, natural language description, etc. We call such problem program estimation. In this paper we propose an abstract framework, learning to synthesis, or L2S in short, to address this problem. L2S combines four tools to achieve this: syntax is used to define the search space and search steps, constraints are used to prune off invalid candidates at each search step, machine-learned models are used to estimate conditional probabilities for the candidates at each search step, and search algorithms are used to find the best possible solution. The main goal of L2S is to lay out the design space to motivate the research on program estimation. We have performed a preliminary evaluation by instantiating this framework for synthesizing conditions of an automated program repair (APR) system. The training data are from the project itself and related JDK packages. Compared to ACS, a state-of-the-art condition synthesis system for program repair, our approach could deal with a larger search space such that we fixed 4 additional bugs outside the search space of ACS, and relies only on the source code of the current projects.", notes = "is this GP? Slides: http://geneticimprovementofsoftware.com/wp-content/uploads/2018/06/2018-Learning-to-Synthesize@GI.pdf GI-2018 http://geneticimprovementofsoftware.com/events/papers#icse2018 part of \cite{Petke:2018:ICSEworkshop}", } @InProceedings{Xu:2016:URAI, author = "Bin Xu and Guangrui Wen and Zhifen Zhang and Feng Chen", booktitle = "2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)", title = "Genetic programming-based classification of ferrograph wear particles", year = "2016", pages = "842--847", abstract = "Ferrograph analysis is becoming one of the principal methods for condition monitoring and fault diagnosis of the machinery equipment due to its advantages of visualization and efficiency. One of the major challenges of ferrograph analysis is feature construction from the existing features of wear particles to improve classifier efficiency. The current feature construction method is trial and error based on previous experience and mass data, which is time-consuming, laborious and blindness. In this paper, genetic programming-based approach was proposed to construct new features from the five existing morphological features of ferrograph wear particles to improve the ability of classification process. The GP-based feature construction approach is used for fault classification of ferrograph wear particles for the first time and the results show that the method can be used in wear condition monitoring and fault prognosis of machinery equipment.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/URAI.2016.7733992", month = aug, notes = "Also known as \cite{7733992}", } @Article{Xu:2018:LubricationScience, author = "Bin Xu and Guangrui Wen and Zhifen Zhang and Feng Chen", title = "Wear particle classification using genetic programming evolved features", journal = "Lubrication Science", year = "2018", volume = "30", number = "5", pages = "229--246", month = aug, keywords = "genetic algorithms, genetic programming", ISSN = "1557-6833", DOI = "doi:10.1002/ls.1411", abstract = "This paper explores the feasibility of applying genetic programming (GP) to classify wear particles. A marking threshold filter is proposed to preprocess ferrographic images before optimising the feature space of wear particles using GP. Subsequently, evolved features by GP are quantitatively evaluated by the Fisher criterion and distance fitness function, and clustering performance is evaluated qualitatively. The evolved features are compared with a conventional feature set as the inputs to support vector machines, probabilistic neural networks, and k-nearest neighbour. Results demonstrated that the evolved features indicated a significant improvement in classification accuracy and robustness compared with conventional features. Finally, 3 typical wear particles, sliding, cutting, and oxidative, are successfully classified.", } @Article{Binzi-Xu:EC, author = "Binzi Xu and Yi Mei and Yan Wang and Zhicheng Ji and Mengjie Zhang", title = "Genetic Programming with Delayed Routing for Multi-Objective Dynamic Flexible Job Shop Scheduling", journal = "Evolutionary Computation", year = "2021", volume = "29", number = "1", pages = "75--105", month = "Spring", keywords = "genetic algorithms, genetic programming, dynamic flexible job shop scheduling, dispatching rule discovery, delayed routing, energy efficiency", ISSN = "1063-6560", URL = "https://meiyi1986.github.io/publication/xu-2020-genetic/xu-2020-genetic.pdf", URL = "https://meiyi1986.github.io/publication/xu-2020-genetic/", DOI = "doi:10.1162/evco_a_00273", size = "31 pages", abstract = "Dynamic Flexible Job Shop Scheduling (DFJSS) is an important and challenging problem, and can have multiple conflicting objectives. Genetic Programming HyperHeuristic (GPHH) is a promising approach to fast respond to the dynamic and unpredictable events in DFJSS. A GPHH algorithm evolves dispatching rules (DRs) that are used to make decisions during the scheduling process (i.e. the so-called heuristic template). In DFJSS, there are two kinds of scheduling decisions: the routing decision that allocates each operation to a machine to process it, and the sequencing decision that selects the next job to be processed by each idle machine. The traditional heuristic template makes both routing and sequencing decisions in a non-delay manner, which may have limitations in handling the dynamic environment. we propose a novel heuristic template that delays the routing decisions rather than making them immediately. This way, all the decisions can be made under the latest and more accurate information. We propose three different delayed routing strategies, and automatically evolve the rules in the heuristic template by GPHH. We evaluate the newly proposed GPHH with Delayed Routing (GPHH-DR) on a multi-objective DFJSS that optimises the energy efficiency and mean tardiness. The experimental results show that GPHH-DR significantly outperformed the state-of-the-art GPHH methods. We further demonstrated the efficacy of the proposed heuristic template with delayed routing, which suggests the importance of delaying the routing decisions.", notes = "School of Electrical Engineering, Anhui Polytechnic University, Wuhu, China ", } @InProceedings{Xu:2021:CCC, author = "Binzi Xu and Liang Tao and Xiongfeng Deng and Wei Li", title = "An Evolved Dispatching Rule Based Scheduling Approach for Solving DJSS Problem", booktitle = "2021 40th Chinese Control Conference (CCC)", year = "2021", pages = "6524--6531", abstract = "Dynamic job shop scheduling (DJSS) has been shown as a realistic and complex combinatorial optimization problem, which is characterized by complexity, dynamics, and uncertainty. Though dispatching rules (DRs) have been seen as a suitable method for solving DJSS problem, it is hard to manually design a DR with good scheduling performance considering all the aspects, much less a general DR for the complex dynamic environment of the job shop. This paper presents a genetic programming hyper-heuristic (GPHH) based DR evaluation approach to automatically generate customized DRs, in which job shop configuration, objective, and other information are considered. After testing it on the single objective DJSS problems with six different scenarios, the experimental result indicates that the proposed method can effectively evolve better DRs for different DJSS problems than manually designed DRs. Besides, the role of four key parameters in GPHH, including the number of generations, the population size, and the maximal depth, have been deeply analyzed based on the corresponding experiments.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.23919/CCC52363.2021.9549754", ISSN = "1934-1768", month = jul, notes = "Also known as \cite{9549754}", } @InProceedings{Xu:2023:RICAI, author = "Binzi Xu and Chun Wang and Dengchao Huang and Xiongfeng Deng", title = "Energy Consumption Oriented Dynamic Flexible Job Shop Online Scheduling with {GPHH}", booktitle = "2023 5th International Conference on Robotics, Intelligent Control and Artificial Intelligence (RICAI)", year = "2023", pages = "508--512", month = dec, keywords = "genetic algorithms, genetic programming, Energy consumption, Green manufacturing, Job shop scheduling, Dynamic scheduling, Market research, Dispatching, smart manufacturing, GPHH, dispatching rules, terminals", DOI = "doi:10.1109/RICAI60863.2023.10489448", abstract = "With the overwhelming trend of smart manufacturing and green manufacturing, an automatic scheduling method is urgently required for dynamic flexible job shop (DFJSS) to optimise energy consumption and meet the green indicator. To address this, this paper focuses on using genetic programming hyper-heuristic (GPHH) to generate dispatching rules (DRs) which can make scheduling decision online to minimise the energy consumption. First, the energy flow in DFJSS is analysed and two objectives (i.e., mean tardiness and ratio of useless energy consumption) are established, then energy consumption related terminals are designed to offer corresponding information to DRs. DRs generated by GPHH are compared with manually designed DRs, and the results shows the goodness of generated DRs. Besides, the parameters of GPHH are also analysed for a better performance and deeper understanding.", notes = "Also known as \cite{10489448}", } @Article{Xu:2025:swevo, author = "Binzi Xu and Xinyu Cao and Shuwen Zhang and Jiangping Shen and Chaohua Wang and Songhua Wang and Dengchao Huang and Chun Wang and Yi Mei and Yan Wang", title = "Terminal normalization in genetic programming for dynamic flexible job shop scheduling", journal = "Swarm and Evolutionary Computation", year = "2025", volume = "96", pages = "101970", keywords = "genetic algorithms, genetic programming, Dynamic flexible job shop scheduling, Range archive, Normalization", ISSN = "2210-6502", URL = "https://www.sciencedirect.com/science/article/pii/S2210650225001282", DOI = "doi:10.1016/j.swevo.2025.101970", abstract = "The dynamic flexible job shop scheduling problem (DFJSSP) is a challenging NP-hard problem that has been studied for decades. Among various optimisation algorithms, genetic programming hyper-heuristic (GPHH) excels in learning dispatching rules (DRs) for online scheduling. However, an overlooked phenomenon in GPHH is that terminals with larger magnitude may overshadow smaller ones, potentially limiting GPHH's effectiveness. The min-max normalization of terminal values may resolve this issue. Thus, this study aims to investigate its impact on GPHH and its potential benefits. Since the terminal ranges used for normalization cannot be obtained directly and in advance within the current GPHH framework, a range archive (RA) enhanced mechanism is proposed for normalization, including three strategies: RA-lite, RA-all, and simulation-RA. These strategies are evaluated on a multi-objective DFJSSP targeting mean tardiness and total energy consumption. Experimental results on small function set demonstrate the presence of inconsistent terminal ranges, in which case normalization can effectively address this issue and enhance the optimisation performance of GPHH. While experimental results on large function set indicate that multiplication and division achieve a similar effect, suggesting limited benefit from normalization. However, further analysis reveals that normalization, particularly simulation-RA, can produce more interpretable DRs without compromising performance", } @InProceedings{Xu:2009:CIBCB, author = "Chun-Gui Xu and Kun-Hong Liu and De-Shuang Huang", title = "The analysis of microarray datasets using a genetic programming", booktitle = "IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '09", year = "2009", month = "30 " # mar # "-" # apr # " 2", pages = "176--181", keywords = "genetic algorithms, genetic programming, ANN, SVM, artificial neural networks, data classification, disease biomarker search, disease diagnoses, feature selection, gene expression data, gene regulatory network analysis, generated classification rules, informatics tools, microarray dataset analysis, microarray technology, support vector machines, biology computing, feature extraction, genomics, medical computing, molecular biophysics, neural nets, pattern classification, support vector machines", DOI = "doi:10.1109/CIBCB.2009.4925725", abstract = "Microarray technology has been widely applied to search for biomarkers of diseases, diagnose diseases and analyze gene regulatory network. Abundance of expression data from microarray experiments are processed by informatics tools, such as supporting vector machines (SVM), artificial neural network (ANN), and so on. These methods achieve good results in single dataset. Nevertheless, most analyses of microarray data are only focused on a series of data obtained from the same lab or gene chip. Then the discoveries may only be suitable for data they experimented on but lack of general sense. In this paper, we propose a genetic programming (GP) based approach to analyze microarray datasets. The GP implements classification and feature selection at the same time. To validate the significance of the selected genes and generated classification rules, the results are tested on different datasets obtained from different experimental conditions. The results confirm the efficiency of GP in the classification of different samples.", notes = "Also known as \cite{4925725}", } @InProceedings{Xu:2021:GECCOcomp, author = "Congwen Xu and Qiang Lu and Jake Luo and Zhiguang Wang", title = "Adversarial Bandit Gene Expression Programming for Symbolic Regression", booktitle = "Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion", year = "2021", editor = "Francisco Chicano and Alberto Tonda and Krzysztof Krawiec and Marde Helbig and Christopher W. Cleghorn and Dennis G. Wilson and Georgios Yannakakis and Luis Paquete and Gabriela Ochoa and Jaume Bacardit and Christian Gagne and Sanaz Mostaghim and Laetitia Jourdan and Oliver Schuetze and Petr Posik and Carlos Segura and Renato Tinos and Carlos Cotta and Malcolm Heywood and Mengjie Zhang and Leonardo Trujillo and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Fuyuki Ishikawa and Inmaculada Medina-Bulo and Frank Neumann and Andrew M. Sutton", pages = "269--270", address = "internet", series = "GECCO '21", month = jul # " 10-14", organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, Gene Expression Programming, adversarial bandit, symbolic regression: Poster", isbn13 = "978-1-4503-8351-6", DOI = "doi:10.1145/3449726.3459499", size = "2 pages", abstract = "Gene expression programming (GEP) is a commonly used approach in symbolic regression (SR). However, GEP often falls into a premature convergence and may only reach a local optimum. To solve the premature convergence problem, we propose a novel algorithm based on an adversarial bandit technique, named AB-GEP. AB-GEP segments the mathematical space into many subspaces. It leverages a new selection method, AvgExp3, to enhance the population jump between segmented subspaces while maintaining the population diversity. AvgExp3 dynamically estimates a subspace by rewards generated from AB-GEP without any assumption about the distribution of subspace rewards, making AB-GEP choose the appropriate subspace that contains the correct results. The experimental evaluation shows that the proposed AB-GEP method can maintain the population diversity and obtain better results than canonical GEPs.", notes = "China University of Petroleum Beijing GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)", } @InProceedings{Xu:2024:ICIPCA, author = "Dongyang Xu and Yunpeng Shi", title = "Research on Real-Time Location of Submersible Based on {NSGA-II} Algorithm", booktitle = "2024 IEEE 2nd International Conference on Image Processing and Computer Applications (ICIPCA)", year = "2024", pages = "1732--1736", month = jun, keywords = "genetic algorithms, genetic programming, Accuracy, Oceans, Prediction algorithms, Real-time systems, Vectors, Trajectory, Kalman filters, Underwater vehicles, Optimisation, Sports, Kalman filter, CA, FCNN, NSGA-II", DOI = "doi:10.1109/ICIPCA61593.2024.10709240", abstract = "Under sea exploration is becoming a favorite sport, but there are safety hazards associated with this sport. We want to generate submersibles that will take humans to the deepest parts of the ocean, so safety measures are essential. The aim of this paper is to predict and equip the submersible with relevant safety equipment to improve the safety of the submersible. We collected data on the physicochemical properties of the Ionian sea water, based on the TEOS-10 equation published by SCOR/IAPSO WG127 and IUPAP, and simulated the trajectory of the submersible in different confined environments by cellular automata. The final trajectory of the submersible in the whole vector field is obtained. We use genetic programming path algorithms and Kalman filters to adjust the probability distribution in real time to make it more accurate in predicting the position of the submersible. Finally, we use NSGA-II algorithm for multi-objective optimisation to pre-determine the position of multiple submersibles in the ocean. Through multiple iterations, the algorithm can find a set of optimal solutions to obtain the optimal deployment position of multiple submersibles.", notes = "Also known as \cite{10709240}", } @Article{Xu:2024:actamat, author = "Guojing Xu and Feng Cheng and Hao Lu and Chao Hou and Xiaoyan Song", title = "Predicting the {Curie} temperature of {Sm-Co}-based alloys via data-driven strategy", journal = "Acta Materialia", year = "2024", volume = "274", pages = "120026", keywords = "genetic algorithms, genetic programming, Machine learning, Curie temperature, Sensitivity factor, Sm-Co alloys, Doping element", ISSN = "1359-6454", URL = "https://www.sciencedirect.com/science/article/pii/S1359645424003781", DOI = "doi:10.1016/j.actamat.2024.120026", abstract = "Calculating the Curie temperature of rare-earth permanent magnetic materials has remained a big theoretical challenge. In this study, based on a home-built Sm-Co-based alloys database, a data-driven machine learning approach was developed to predict the Curie temperature of Sm-Co-based alloys. High-throughput predictions of Curie temperature were achieved using a genetic program based symbolic regression model. A classification model based on logistic regression was established to quantify the effect of doping on the Curie temperature of Sm-Co-based alloys. The key physical descriptor affecting Curie temperature was extracted from the established machine learning models, and the Curie temperature sensitivity coefficient was defined. It was discovered that the doping elements with large electrical conductivity and similar heat of fusion to that of Sm are likely to increase the Curie temperature of Sm-Co-based alloys. The model predictions were verified quantitatively by the experimental results of a series of prepared Sm-Co-based samples. This work provides a high-efficiency method for developing Sm-Co-based permanent magnets with high Curie temperatures", } @Article{Xu:2004:JAE, author = "Jianfeng Xu and Yaowen Yang and Chee Kiong Soh", title = "Electromechanical Impedance-Based Structural Health Monitoring with Evolutionary Programming", journal = "Journal of Aerospace Engineering", year = "2004", volume = "17", number = "4", pages = "182--193", email = "cywyang@ntu.edu.sg", month = oct, publisher = "American Society of Civil Engineering", keywords = "genetic algorithms, genetic programming, evolutionary programming, structural health monitoring, EM", DOI = "doi:10.1061/(ASCE)0893-1321(2004)17:4(182)", abstract = "An impedance-based structural health monitoring technique is presented. By analyzing the in-plane vibration of a thin lead zirconate titanate (PZT) patch, the electromechanical impedance of the PZT patch is predicted. The force impedances of a beam and a plate with damage are calculated by Ritz method using polynomial as shape functions. The damage is then identified from the changes of the impedance spectra caused by the appearance of damage. A hybrid evolutionary programming is employed as a global search technique to back-calculate the damage. A specially designed fitness function is proposed, which is able to effectively reduce the inaccuracy in representing the real structure using analytical or numerical models. Experiments are carried out on a beam and a plate to verify the numerical predictions. The results demonstrate that the proposed method is able to effectively and reliably locate and quantify the damage in the beam and the plate.", } @Article{Xu2008, author = "Kaikuo Xu and Yintian Liu and Rong Tang and Jie Zuo and Jun Zhu and Changjie Tang", title = "A novel method for real parameter optimization based on Gene Expression Programming", journal = "Applied Soft Computing", year = "2009", volume = "9", number = "2", pages = "725--737", month = mar, keywords = "genetic algorithms, genetic programming, Gene Expression Programming, Real parameter optimization, Expression tree", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2008.09.007", URL = "http://www.sciencedirect.com/science/article/B6W86-4TMJ3TD-4/2/ddab66fae1f3b964599d5c56888dfcb5", abstract = "Gene Expression Programming (GEP) is a new technique of evolutionary algorithm that implements genome/phoneme representation in computing programs. Due to its power in global search, it is widely applied in symbolic regression. However, little work has been done to apply it to real parameter optimization yet. This paper proposes a real parameter optimization method named Uniform-Constants based GEP (UC-GEP). In UC-GEP, the constant domain directly participates in the evolution. Our research conducted extensive experiments over nine benchmark functions from the IEEE Congress on Evolutionary Computation 2005 and compared the results to three other algorithms namely Meta-Constants based GEP (MC-GEP), Meta-Uniform-Constants based GEP (MUC-GEP), and the Floating Point Genetic Algorithm (FP-GA). For simplicity, all GEP methods adopt a one-tier index gene structure. The results demonstrate the optimal performance of our UC-GEP in solving multimodal problems and show that at least one GEP method outperforms FP-GA on all test functions with higher computational complexity.", } @Article{Xu2011942, author = "Qiang Xu and Qiuwen Chen and Weifeng Li and Jinfeng Ma", title = "Pipe break prediction based on evolutionary data-driven methods with brief recorded data", journal = "Reliability Engineering \& System Safety", volume = "96", number = "8", pages = "942--948", year = "2011", month = aug, ISSN = "0951-8320", DOI = "doi:10.1016/j.ress.2011.03.010", URL = "http://www.sciencedirect.com/science/article/B6V4T-52BWVVF-4/2/2cc722b50b1a73f1b86f4ef8e44660d4", keywords = "genetic algorithms, genetic programming, Pipe break model, Data-driven technique, Evolutionary polynomial regression", abstract = "Pipe breaks often occur in water distribution networks, imposing great pressure on utility managers to secure stable water supply. However, pipe breaks are hard to detect by the conventional method. It is therefore necessary to develop reliable and robust pipe break models to assess the pipe's probability to fail and then to optimise the pipe break detection scheme. In the absence of deterministic physical models for pipe break, data-driven techniques provide a promising approach to investigate the principles underlying pipe break. In this paper, two data-driven techniques, namely Genetic Programming (GP) and Evolutionary Polynomial Regression (EPR) are applied to develop pipe break models for the water distribution system of Beijing City. The comparison with the recorded pipe break data from 1987 to 2005 showed that the models have great capability to obtain reliable predictions. The models can be used to prioritise pipes for break inspection and then improve detection efficiency.", } @Article{QiangXu:2011:JH, author = "Qiang Xu and Qiuwen Chen and Weifeng Li", title = "Application of genetic programming to modeling pipe failures in water distribution systems", journal = "Journal of Hydroinformatics", year = "2011", volume = "13", number = "3", pages = "419--428", keywords = "genetic algorithms, genetic programming, pipe failure model, water distribution systems", ISSN = "1464-7141", URL = "http://www.iwaponline.com/jh/up/pdf/HYDRO_D_09_00089.pdf", DOI = "doi:10.2166/hydro.2010.189", size = "10 pages", abstract = "The water loss from a water distribution system is a serious problem for many cities, which incurs enormous economic and social loss. However, the economic and human resource costs to exactly locate the leakage are extraordinarily high. Thus, reliable and robust pipe failure models are demanded to assess a pipe's propensity to fail. Beijing City was selected as the case study area and the pipe failure data for 19 years (1987 to 2005) were analysed. Three different kinds of methods were applied to build pipe failure models. First, a statistical model was built, which discovered that the ages of leakage pipes followed the Weibull distribution. Then, two other models were developed using genetic programming (GP) with different data pre-processing strategies. The three models were compared thereafter and the best model was applied to assess the criticality of all the pipe segments of the entire water supply network in Beijing City based on GIS data.", notes = "Fig 7 = ROC curve. Peking China. Pipes 3 inches to two feet in diameter. Published 2012?", } @Article{Xu:2013:JHR, author = "Qiang Xu and Qiuwen Chen and Jinfeng Ma and Koen Blanckaert", title = "Optimal pipe replacement strategy based on break rate prediction through genetic programming for water distribution network", journal = "Journal of Hydro-environment Research", year = "2013", volume = "7", number = "2", pages = "134--140", month = jun, note = "Special Issue of on Hydroinformatics 2010: Advances of hydroinformatic techniques in hydro-environmental research", keywords = "genetic algorithms, genetic programming, Pipe break rate prediction, Optimal pipe replacement strategy, Water distribution system", ISSN = "1570-6443", URL = "http://www.sciencedirect.com/science/article/pii/S1570644313000257", DOI = "doi:10.1016/j.jher.2013.03.003", abstract = "Pipe breaks often occur in water distribution networks and result in large water loss and social-economic damage. To reduce the water loss and maintain the conveyance capability of a pipe network, pipes that experienced a severe break history are often necessary to be replaced. However, when to replace a pipe is a difficult problem to the management of water distribution system. This study took part of the water distribution network of Beijing as a case and collected the pipe properties and the pipe breaks data in recent years (2008-2011). A prediction model of pipe beak rate was first developed using genetic programming. Then, an economically optimal pipe replacement model was set up. Finally, the optimal pipe replacement time was determined by the model. The results could help the utility managers to make cost-effective pipe maintenance plans.", notes = "RCEES Chinese Academy of Sciences, Shuangqinglu 18, Haidian District, Beijing 100085, China", } @Article{Xu:2012:ieeITSe, author = "Chengcheng Xu and Wei Wang2 and Pan Liu", journal = "IEEE Transactions on Intelligent Transportation Systems", title = "A Genetic Programming Model for Real-Time Crash Prediction on Freeways", year = "2013", volume = "14", number = "2", month = jun, pages = "574--586", keywords = "genetic algorithms, genetic programming, Binary logit model, freeway, genetic programming (GP), real-time crash prediction, traffic safety", DOI = "doi:10.1109/TITS.2012.2226240", ISSN = "1524-9050", abstract = "This paper aimed at evaluating the application of the genetic programming (GP) model for real-time crash prediction on freeways. Traffic, weather, and crash data used in this paper were obtained from the I-880N freeway in California, United States. The random forest (RF) technique was conducted to select the variables that affect crash risk under uncongested and congested traffic conditions. The GP model was developed for each traffic state based on the candidate variables that were selected by the RF technique. The traffic flow characteristics that contribute to crash risk were found to be quite different between congested and uncongested traffic conditions. This paper applied the receiver operating characteristic (ROC) curve to evaluate the prediction performance of the developed GP model for each traffic state. The validation results showed that the prediction performance of the GP models were satisfactory. The binary logit model was also developed for each traffic state using the same training data set. The authors compared the ROC curve of the GP model and the binary logit model for each traffic state. The GP model produced better prediction performance than did the binary logit model for each traffic state. The GP model was found to increase the crash prediction accuracy under uncongested traffic conditions by an average of 8.2percent and to increase the crash prediction accuracy under congested traffic conditions by an average of 4.9percent.", notes = "Also known as \cite{6357306}", } @InProceedings{conf/icaart/XuQJ13, author = "Chi Xu and Jianxiong Qiao and Na Jia", title = "Strategy Tree Construction and Optimization with Genetic Programming", booktitle = "International Conference on Agents and Artificial Intelligence (ICAART 2013)", year = "2013", editor = "Joaquim Filipe and Ana L. N. Fred", pages = "425--428", address = "Barcelona, Spain", month = "15-18 " # feb, publisher = "SciTePress", keywords = "genetic algorithms, genetic programming, artificial intelligence, evolutionary algorithm, machine learning, regressive decision rule", bibdate = "2013-10-01", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icaart/icaart2013-2.html#XuQJ13", isbn13 = "978-989-8565-39-6", URL = "http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0004201104250428", DOI = "doi:10.5220/0004201104250428", abstract = "We applied genetic programming (GP) to search for a strategy in a technical analysis (TA) indicator candidate pool for stock market trading and optimised it through historical data. The method provides decision rule optimisation scheme to deal with problems in the real trading in financial market, and it optimises strategies in relatively complicated contents. GP is used to construct the condition in decision rule with different logical operations. The method has been applied to the optimisation of investment strategies with good return results in simulation experiments.", notes = "Chi Xu, Jianxiong Qiao, Na Jia - North China University of Technology, China", } @Article{xu:2024:JCSU, author = "Jing-min Xu and Chen-cheng Wang and Zhi-liang Cheng and Tao Xu3 and Ding-wen Zhang and Zi-li Li", title = "Intelligent prediction model of tunnelling-induced building deformation based on genetic programming and its application", journal = "Journal of Central South University", year = "2024", volume = "31", pages = "3885--3899", keywords = "genetic algorithms, genetic programming, building deformation, tunnel construction, modification factor", URL = "https://link.springer.com/article/10.1007/s11771-024-5656-x", DOI = "doi:10.1007/s11771-024-5656-x", notes = "School of Transportation, Southeast University, Nanjing, 211189, China", } @InProceedings{xu:2024:CEC, author = "Jizhong Xu and Qi Chen and Bing Xue and Mengjie Zhang", title = "A New Concordance Correlation Coefficient based Fitness Function for Genetic Programming for Symbolic Regression", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Correlation coefficient, Correlation, Input variables, Optimization methods, Evolutionary computation, Symbolic Regression, Fitness Function, Linear Scaling, Concordance Correlation Coefficient", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10611932", abstract = "Coefficients learning has long been challenging in genetic programming based symbolic regression (GPSR). Recent GPSR methods employ Pearson correlation coefficient for fitness assessment with post-hoc linear scaling for coefficient learning. However, this approach often leads to sub-optimal coefficient learning and inadequate consideration of nonlinear relationships between input variables and outputs. To solve those issues, this study introduces an innovative approach to integrating the Concordance Correlation Coefficient (CCC) into GPSR. Unlike Pearson correlation, CCC can effectively assess both linear and non-linear agreements between two sets of variables. Experimental results on eight regression datasets highlight the potential of CCC as a promising fitness function for GPSR without the need of a more advanced coefficient optimisation method for linear scaling.", notes = "also known as \cite{10611932} WCCI 2024", } @Misc{journals/corr/abs-1708-09116, author = "Juncai Xu and Zhenzhong Shen and Qingwen Ren and Xin Xie and Zhengyu Yang", title = "Slope Stability Analysis with Geometric Semantic Genetic Programming", howpublished = "arXiv", year = "2017", month = "30 " # aug, keywords = "genetic algorithms, genetic programming, ANN, artificial neural network, geometric semantics, slope stability, safety factor", bibdate = "2017-09-05", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/corr/corr1708.html#abs-1708-09116", URL = "http://arxiv.org/abs/1708.09116", size = "9 pages", abstract = "Genetic programming has been widely used in the engineering field. Compared with the conventional genetic programming and artificial neural network, geometric semantic genetic programming (GSGP) is superior in astringency and computing efficiency. In this paper, GSGP is adopted for the classification and regression analysis of a sample dataset. Furthermore, a model for slope stability analysis is established on the basis of geometric semantics. According to the results of the study based on GSGP, the method can analyze slope stability objectively and is highly precise in predicting slope stability and safety factors. Hence, the predicted results can be used as a reference for slope safety design.", } @Misc{journals/corr/abs-1709-06114, author = "Juncai Xu and Zhenzhong Shen and Qingwen Ren and Xin Xie and Zhengyu Yang", title = "Geometric Semantic Genetic Programming Algorithm and Slump Prediction", howpublished = "arXiv", year = "2017", keywords = "genetic algorithms, genetic programming", bibdate = "2017-10-05", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/corr/corr1709.html#abs-1709-06114", URL = "http://arxiv.org/abs/1709.06114", } @Article{journals/sp/XuHSL17, author = "Lixiong Xu and Yuan Huang and Xiaodong Shen and Yang Liu2", title = "Parallelizing Gene Expression Programming Algorithm in Enabling Large-Scale Classification", journal = "Scientific Programming", year = "2017", volume = "2017", pages = "5081526:1--5081526:10", keywords = "genetic algorithms, genetic programming, gene expression programming", bibdate = "2017-05-28", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/sp/sp2017.html#XuHSL17", URL = "http://downloads.hindawi.com/journals/sp/2017/5081526.pdf", DOI = "doi:10.1155/2017/5081526", abstract = "As one of the most effective function mining algorithms, Gene Expression Programming (GEP) algorithm has been widely used in classification, pattern recognition, prediction, and other research fields. Based on the self-evolution, GEP is able to mine an optimal function for dealing with further complicated tasks. However, in big data researches, GEP encounters low efficiency issue due to its long time mining processes. To improve the efficiency of GEP in big data researches especially for processing large-scale classification tasks, this paper presents a parallelized GEP algorithm using MapReduce computing model. The experimental results show that the presented algorithm is scalable and efficient for processing large-scale classification tasks.", notes = "The Iris Dataset, The Wine Dataset", } @InProceedings{Xu:2021:CEC, author = "Meng Xu and Fangfang Zhang and Yi Mei and Mengjie Zhang", booktitle = "2021 IEEE Congress on Evolutionary Computation (CEC)", title = "Genetic Programming with Archive for Dynamic Flexible Job Shop Scheduling", year = "2021", editor = "Yew-Soon Ong", pages = "2117--2124", address = "Krakow, Poland", month = "28 " # jun # "-1 " # jul, isbn13 = "978-1-7281-8393-0", abstract = "Genetic programming (GP) has achieved great success in evolving effective scheduling rules to make real-time decisions in dynamic flexible job shop scheduling (DFJSS). To improve generalization, a commonly used strategy is to change the training simulation(s) at each generation of the GP process. However, with such a simulation rotation, GP may lose potentially promising individuals that happen to perform poorly in one particular generation. To address this issue, this paper proposed a new multi-tree GP with archive (MTAGP) to evolve the routing and sequencing rules for DFJSS. The archive is used to store the potentially promising individuals of each generation during evolution of genetic programming. The individuals in the archive can then be fully used when the simulation is changed in subsequent generations. Through extensive experimental tests, the MTAGP algorithm proposed in this paper is more effective than the multi-tree GP without archive algorithm in a few scenarios. Further experiments were carried out to analyze the use of the archive and some possible guesses were ruled out. We argue that the use of archives does increase the diversity of the population. However, the number of individuals in the archive that ranked in the top five of the new population is small. Therefore, the archive may not be able to greatly improve the performance. In the future, we will investigate better ways to use the archive and better ways to update individuals in the archive.", keywords = "genetic algorithms, genetic programming, Training, Sequential analysis, Job shop scheduling, Sociology, Dynamic scheduling, Routing, dynamic flexible job shop scheduling, archive", DOI = "doi:10.1109/CEC45853.2021.9504752", notes = "Also known as \cite{9504752}", } @InProceedings{xu:2022:GECCOcomp2, author = "Meng Xu and Yi Mei and Fangfang Zhang and Mengjie Zhang", title = "Genetic Programming with Diverse Partner Selection for Dynamic Flexible Job Shop Scheduling", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion", year = "2022", editor = "Heike Trautmann and Carola Doerr and Alberto Moraglio and Thomas Bartz-Beielstein and Bogdan Filipic and Marcus Gallagher and Yew-Soon Ong and Abhishek Gupta and Anna V Kononova and Hao Wang and Michael Emmerich and Peter A. N. Bosman and Daniela Zaharie and Fabio Caraffini and Johann Dreo and Anne Auger and Konstantin Dietric and Paul Dufosse and Tobias Glasmachers and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Tea Tusar and Dimo Brockhoff and Tome Eftimov and Pascal Kerschke and Boris Naujoks and Mike Preuss and Vanessa Volz and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Mark Coletti and Catherine (Katie) Schuman and Eric ``Siggy'' Scott and Robert Patton and Paul Wiegand and Jeffrey K. Bassett and Chathika Gunaratne and Tinkle Chugh and Richard Allmendinger and Jussi Hakanen and Daniel Tauritz and John Woodward and Manuel Lopez-Ibanez and John McCall and Jaume Bacardit and Alexander Brownlee and Stefano Cagnoni and Giovanni Iacca and David Walker and Jamal Toutouh and UnaMay O'Reilly and Penousal Machado and Joao Correia and Sergio Nesmachnow and Josu Ceberio and Rafael Villanueva and Ignacio Hidalgo and Francisco {Fernandez de Vega} and Giuseppe Paolo and Alex Coninx and Antoine Cully and Adam Gaier and Stefan Wagner and Michael Affenzeller and Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and William B. Langdon and Justyna Petke and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Paetzel and Alexander Wagner and Michael Heider and Nadarajen Veerapen and Katherine Malan and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Mohammad Nabi Omidvar and Yuan Sun and Ernesto Tarantino and De Falco Ivanoe and Antonio {Della Cioppa} and Scafuri Umberto and John Rieffel and Jean-Baptiste Mouret and Stephane Doncieux and Stefanos Nikolaidis and Julian Togelius and Matthew C. Fontaine and Serban Georgescu and Francisco Chicano and Darrell Whitley and Oleksandr Kyriienko and Denny Dahl and Ofer Shir and Lee Spector and Alma Rahat and Richard Everson and Jonathan Fieldsend and Handing Wang and Yaochu Jin and Erik Hemberg and Marwa A. Elsayed and Michael Kommenda and William {La Cava} and Gabriel Kronberger and Steven Gustafson", pages = "615--618", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, dynamic flexible job shop scheduling, hyperheuristic, diverse partner selection", isbn13 = "978-1-4503-9268-6/22/07", URL = "https://github.com/fangfang-zhang/fangfang-zhang.github.io/blob/master/files/2022-GP-Diverse-Partner-Selection.pdf", DOI = "doi:10.1145/3520304.3528920", size = "4 pages", abstract = "Dynamic flexible job shop scheduling (DFJSS) aims to make decisions for machine assignment and operation sequencing simultaneously to get an effective schedule under dynamic environments. Genetic programming hyper-heuristic (GPHH) has been successfully applied to evolve scheduling heuristics for the DFJSS problem. Parent selection plays an important role in GPHH for generating high-quality offspring. Traditional GPHHs select parents for crossover purely based on fitness (e.g., tournament selection). This might be too greedy to get good offspring and the selected parents might have similar structures/behaviours. In this paper, a GPHH method with a new diverse partner selection (DPS) scheme is proposed, namely GPDPS, for DFJSS. Specifically, we first define a new multi-case fitness to characterise the behaviour of each scheduling heuristic for DFJSS. Then, the newly proposed DPS method selects a pair of complementary high-quality parents for crossover to generate offspring. The experimental results show that GPDPS significantly outperforms the GPHH method on most of the DFJSS scenarios, in terms of both test performance and convergence speed.", notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @InProceedings{Xu:2022:CEC, author = "Meng Xu and Yi Mei and Fangfang Zhang and Mengjie Zhang", title = "Genetic Programming with Cluster Selection for Dynamic Flexible Job Shop Scheduling", booktitle = "2022 IEEE Congress on Evolutionary Computation (CEC)", year = "2022", editor = "Carlos A. Coello Coello and Sanaz Mostaghim", address = "Padua, Italy", month = "18-23 " # jul, isbn13 = "978-1-6654-6708-7", abstract = "Dynamic flexible job shop scheduling is a challenging combinatorial optimisation problem, that aims to optimise machine resources for producing jobs to meet some goals. There are two important kinds of decisions that the scheduling process needs to make under dynamic environments, i.e., the routing decision for machine assignment and the sequencing decision for operation ordering. Genetic programming hyper-heuristic has been successfully applied for solving the dynamic flexible job shop scheduling problem with the advantage of automatically evolving good scheduling heuristics. Parent selection is an important process for genetic programming, intending to select good individuals as parents to generate offspring for the next generation. Traditional genetic programming methods select parents for crossover based on only fitness (e.g., tournament selection). a new parent selection (i.e., cluster selection) method is proposed to select parents not only with good fitness but also with different behaviours. The proposed cluster selection is combined with genetic programming hyper-heuristic to study whether considering different behaviours in parent selection will improve the effectiveness of the evolved scheduling heuristics. The experimental results show that increasing the number of unique behaviours in the population cannot help evolve effective scheduling heuristics. Further analysis shows that considering behaviour to select parents does increase the number of unique behaviours in the population. However, it gives individuals with poor fitness more probability to be selected to generate offspring. This might be the reason why the proposed method cannot outperform the baseline method.", keywords = "genetic algorithms, genetic programming, Sequential analysis, Job shop scheduling, Processor scheduling, Sociology, Dynamic scheduling, Routing, dynamic flexible job shop scheduling, cluster selection, diversity", DOI = "doi:10.1109/CEC55065.2022.9870431", notes = "Also known as \cite{9870431}", } @InProceedings{Xu:2022:CEC2, author = "Meng Xu and Fangfang Zhang and Yi Mei and Mengjie Zhang", title = "Genetic Programming with Multi-case Fitness for Dynamic Flexible Job Shop Scheduling", booktitle = "2022 IEEE Congress on Evolutionary Computation (CEC)", year = "2022", abstract = "Dynamic flexible job shop scheduling has attracted widespread interest from scholars and industries due to its practical value. Genetic programming hyper-heuristic has achieved great success in automatically evolving effective scheduling heuristics to make real-time decisions (i.e., operation ordering and machine assignment) for dynamic flexible job shop scheduling. The design of the training set and fitness evaluation play key roles in improving the generalisation of the evolved scheduling heuristics. The commonly used strategies for improving the generalisation of learned scheduling heuristics include using multiple instances for evaluation at each generation or using a single instance but changing the instance at each new generation of the training process of genetic programming. However, using multiple instances is time-consuming, while changing a single instance at each new generation, potentially promising individuals that happen to underperform in one particular generation might be lost. To address this issue, this paper develops a genetic programming method with a multi-case fitness evaluation strategy, which is named GPMF to evolve the scheduling heuristics with better generalisation ability for the dynamic flexible job shop scheduling problem. The proposed multi-case fitness evaluation strategy divides one instance into multiple cases and uses the average value of the multi-case objectives as the fitness. Experimental results show that the proposed GPMF algorithm is significantly better than the baseline method in all the tested scenarios.", keywords = "genetic algorithms, genetic programming, Training, Industries, Job shop scheduling, Heuristic algorithms, Evolutionary computation, Dynamic scheduling, dynamic flexible job shop scheduling", DOI = "doi:10.1109/CEC55065.2022.9870340", month = jul, notes = "Also known as \cite{9870340}", } @InProceedings{Xu:2023:GECCOcomp, author = "Meng Xu and Yi Mei and Fangfang Zhang and Mengjie Zhang", title = "Multi-Objective Genetic Programming Based on Decomposition on Evolving Scheduling Heuristics for Dynamic Scheduling", booktitle = "Proceedings of the Companion Conference on Genetic and Evolutionary Computation", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "427--430", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, decomposition, MOEA/D, multi-objective dynamic flexible job shop scheduling: Poster", isbn13 = "9798400701207", DOI = "doi:10.1145/3583133.3590582", size = "4 pages", abstract = "Dynamic flexible job shop scheduling (DFJSS) is an important combinatorial optimisation problem that requires handling machine assignment and operation sequencing simultaneously in dynamic environments. Genetic programming (GP) has achieved great success to evolve scheduling heuristics for DFJSS. In manufacturing, multi-objective DFJSS (MO-DFJSS) is more common and challenging due to conflicting objectives. Existing Pareto dominance-based multi-objective GP methods show their limitations of not providing good spreadability and consistency in heuristic behaviour. Multi-objective evolutionary algorithm based on decomposition (MOEA/D) has the potential to provide good spreadability and consistency due to the mechanisms of weights-based subproblems decomposition and neighbours-based evolution. However, it is non-trivial to apply MOEA/D to MO-DFJSS since we need to search in heuristic space. To address these challenges, we propose a multi-objective GP approach based on decomposition (MOGP/D) that incorporates the advantages of MOEA/D and GP to learn scheduling heuristics for MO-DFJSS. A mapping strategy is designed to find the fittest individual for each subproblem. Extensive experiments show that MOGP/D obtains competitive performance with the state-of-the-art methods for MO-DFJSS, and good spreadability and consistency in heuristic behaviour.", notes = "Also known as \cite{xu:2023:GECCOcomp} GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @InProceedings{xu:2023:AJCAI, author = "Meng Xu and Yi Mei and Fangfang Zhang and Mengjie Zhang", title = "A Semantic Genetic Programming Approach to Evolving Heuristics for Multi-objective Dynamic Scheduling", booktitle = "36th Australasian Joint Conference on Artificial Intelligence, Part II", year = "2023", editor = "Tongliang Liu and Geoff Webb and Lin Yue and Dadong Wang", volume = "14472", series = "LNCS", pages = "403--415", address = "Brisbane, Australia", month = "28 " # nov # " - 1 " # dec, publisher = "Springer Nature", keywords = "genetic algorithms, genetic programming, Heuristic learning, Multi-objective genetic programming, Semantic, Multi-objective dynamic scheduling", isbn13 = "978-981-99-8391-9", URL = "https://link.springer.com/chapter/10.1007/978-981-99-8391-9_32", DOI = "doi:10.1007/978-981-99-8391-9_32", } @Article{Meng_Xu:ieeeTEC, author = "Meng Xu and Yi Mei and Fangfang Zhang and Mengjie Zhang", title = "Genetic Programming with Lexicase Selection for Large-scale Dynamic Flexible Job Shop Scheduling", journal = "IEEE Transactions on Evolutionary Computation", year = "2024", volume = "28", number = "5", pages = "1235--1249", month = oct, keywords = "genetic algorithms, genetic programming, dynamic flexible job shop scheduling, lexicase selection", ISSN = "1089-778X", URL = "https://ieeexplore.ieee.org/document/10044237/keywords#keywords", DOI = "doi:10.1109/TEVC.2023.3244607", size = "15 pages", abstract = "Dynamic flexible job shop scheduling is a prominent combinatorial optimisation problem with many real-world applications. Genetic programming has been widely used to automatically evolve effective scheduling heuristics for dynamic flexible job shop scheduling. A limitation of genetic programming is the premature convergence due to the loss of population diversity. To overcome this limitation, this work considers using lexicase selection to improve population diversity, which has achieved success on regression and program synthesis problems. However, it is not trivial to apply lexicase selection to genetic programming for dynamic flexible job shop scheduling, since a fitness case (training scheduling simulation) is often large-scale, making the fitness evaluation very time-consuming. To address this issue, we propose a new multi-case fitness scheme, which creates multiple cases from a single scheduling simulation. Based on the multi-case fitness, we develop a new genetic p", notes = "also known as \cite{10044237} School of Engineering and Computer Science, Evolutionary Computation Research Group, Victoria University of Wellington, Wellington, New Zealand", } @Article{xu:ieeeTSC, author = "Meng Xu and Yi Mei and Shiqiang Zhu and Beibei Zhang and Tian Xiang and Fangfang Zhang and Mengjie Zhang", journal = "IEEE Transactions on Services Computing", title = "Genetic Programming for Dynamic Workflow Scheduling in Fog Computing", year = "2023", volume = "16", number = "4", pages = "2657--2671", month = jul # "-" # aug, keywords = "genetic algorithms, genetic programming, dynamic workflow scheduling, fog computing", ISSN = "1939-1374", DOI = "doi:10.1109/TSC.2023.3249160", size = "15 pages", abstract = "Dynamic Workflow Scheduling in Fog Computing (DWSFC) is an important optimisation problem with many real-world applications. The current workflow scheduling problems only consider cloud servers but ignore the roles of mobile devices and edge servers. Some applications need to consider the mobile devices, edge, and cloud servers simultaneously, making them work together to generate an effective schedule. In this article, a new problem model for DWSFC is considered and a new simulator is designed for the new DWSFC problem model. The designed simulator takes the mobile devices, edge, and cloud servers as a whole system, where they all can execute tasks. In the designed simulator, two kinds of decision points are considered, which are the routing decision points and the sequencing decision points. To solve this problem, a new M ulti- T ree G enetic P rogramming (MTGP) method is developed to automatically evolve scheduling heuristics that can make effective real-time decisions on these decision points. The proposed MTGP method with a multi-tree representation can handle the routing decision points and sequencing decision points simultaneously. The experimental results show that the proposed MTGP can achieve significantly better test performance (reduce the makespan by up to 50percent) on all the tested scenarios than existing state-of-the-art methods.", notes = "also known as \cite{10064120}", } @Article{Meng_Xu:ieeeTEC2, author = "Meng Xu and Yi Mei and Fangfang Zhang and Mengjie Zhang", title = "Genetic Programming for Dynamic Flexible Job Shop Scheduling: Evolution With Single Individuals and Ensembles", journal = "IEEE Transactions on Evolutionary Computation", year = "2024", volume = "28", number = "6", pages = "1761--1775", month = dec, keywords = "genetic algorithms, genetic programming, heuristic learning, ensemble, dynamic scheduling", ISSN = "1089-778X", URL = "https://ieeexplore.ieee.org/document/10324315", DOI = "doi:10.1109/TEVC.2023.3334626", size = "15 pages", notes = "also known as \cite{10324315}", } @Article{Meng_Xu:ieeeCIM, author = "Meng Xu and Yi Mei and Fangfang Zhang and Mengjie Zhang", title = "Genetic Programming and Reinforcement Learning on Learning Heuristics for Dynamic Scheduling: A Preliminary Comparison", journal = "IEEE Computational Intelligence Magazine", year = "2024", volume = "19", number = "2", pages = "18--33", month = may, keywords = "genetic algorithms, genetic programming, Machine learning (artificial intelligence), heuristic learning, reinforcement learning, dynamic scheduling, MTGP", ISSN = "1556-603X", URL = "https://github.com/fangfang-zhang/fangfang-zhang.github.io/blob/main/files/2024_GPRL_on_Learning_Heuristics_for_Dynamic_Scheduling-A_Preliminary_Comparison.pdf", DOI = "doi:10.1109/MCI.2024.3363970", size = "16 pages", notes = "Victoria University of Wellington, New Zealand", } @PhdThesis{Meng_Xu:thesis, author = "Meng Xu", title = "Advancing Genetic Programming for Learning Scheduling Heuristics", school = "School of Engineering and Computer Science, Victoria University of Wellington", year = "2024", address = "New Zealand", month = "20 " # jun, keywords = "genetic algorithms, genetic programming", URL = "https://openaccess.wgtn.ac.nz/ndownloader/files/47167675", URL = "https://openaccess.wgtn.ac.nz/articles/thesis/Advancing_Genetic_Programming_for_Learning_Scheduling_Heuristics/26073502?file=47167675", DOI = "doi:10.26686/wgtn.26073502", size = "298 pages", abstract = "Dynamic flexible job shop scheduling (DFJSS) has attracted a lot of attention from both academics and industries because of its widespread industrial impact in the real world. The main challenge in DFJSS involves simultaneous machine assignment and operation sequencing under dynamic environments such as new jobs arriving over time. Among the existing methods, scheduling heuristics stand out as effective approaches widely employed because of their simplicity and real-time responsiveness. Nevertheless, designing scheduling heuristics demands domain knowledge and is a time-consuming process. Genetic Programming (GP), as a hyper-heuristic method, has demonstrated notable success in automatically evolving scheduling heuristics for DFJSS. However, there is room for further enhancement in certain aspects to optimise GP's performance for DFJSS, particularly concerning diversity-based parent selection mechanisms, joint decision-making mechanisms, collaborative heuristic generation and selection by GP and reinforcement learning (RL), and multi-objective problem-solving capabilities. The overall goal of this thesis is to enhance the effectiveness of GP in automatically evolving high-quality scheduling heuristics for solving the DFJSS problems. Various machine learning and evolutionary computation techniques, such as lexicase selection, ensemble, semantic, RL, and multi-objective optimisation, are incorporated into GP to further improve the effectiveness of GP for DFJSS. First, three novel diversity-based parent selection mechanisms are designed to enhance the GP's ability to explore the search space effectively, adapt to changes, and maintain a balance between exploration and exploitation for the successful evolution of high-quality scheduling heuristics. The three parent selection mechanisms are cluster selection, diverse partner selection, and lexicase selection. The new mechanisms consider not only the fitness of selected parents but also their diverse strengths, which is crucial for generating high-quality offspring. Experimental results demonstrate that the proposed methods, incorporating these newly designed diversity-based parent selection mechanisms, successfully enhance the quality of scheduling heuristics while promoting increased population diversity. Second, this thesis proposes an innovative ensemble GP method that employs a population comprising both single individuals and ensembles, and allows the evolution of either a single scheduling heuristic for making individual decisions or a group of scheduling heuristics for making joint decisions for solving the DFJSS problem effectively. For creating an ensemble, an ensemble construction and selection strategy is developed for carefully choosing diverse and complementary individuals from the population. Additionally, new crossover and mutation operators are designed to generate offspring from the selected parents, which can be single individuals or ensembles. By maintaining a population that includes both single individuals and ensembles, the proposed method enables effective exploration of the search space through interbreeding. The experiment results show that the proposed method outperforms the compared algorithms including ensemble GP methods in terms of learned scheduling heuristics. Third, in addition to the widely used GP for automatic scheduling heuristics learning in DFJSS, RL has gained attention in recent years within this domain. This thesis proposes a niching GP-assisted RL method, taking advantage of both GP and RL to effectively address the DFJSS problems by intelligently heuristic generation by GP and selection by RL simultaneously. Specifically, instead of relying on manual scheduling heuristics, RL actions are replaced with scheduling heuristics evolved by the niching GP. RL is then employed to optimise and adapt these heuristics based on real-time feedback from the environment. The experiment results demonstrate the effectiveness of the proposed method in comparison to the widely used manual scheduling heuristics and the baseline RL method. This confirms the capability of GP to enhance RL's performance in addressing the DFJSS problem. Last, this thesis proposes two multi-objective GP methods for evolving a Pareto front of scheduling heuristics for solving the multi-objective DFJSS problem. The first method fills the gap by combining a well-known multi-objective evolutionary algorithm based on decomposition with GP, resulting in a novel multi-objective GP based on the decomposition method. This method successfully enhances the spreadability/diversity of the evolved Pareto front of scheduling heuristics. The second method enhances an existing state-of-the-art multi-objective GP approach (NSGPII) by incorporating semantic information. The experiment results demonstrate that NSGPII considering semantic diversity yields better performance compared with the original NSGPII. Moreover, NSGPII incorporating semantic similarity achieves even better performance, highlighting the importance of maintaining a reasonable semantic distance between offspring and their parents.", notes = "241204 Industrial machinery and equipment; 241202 Autonomous and robotic systems Supervisors: Yi Mei, Fangfang Zhang, Mengjie Zhang", } @InProceedings{xu:2024:ISCIIA, author = "Meng Xu", title = "A Preliminary Study of Indicator-based Genetic Programming for Multi-objective Dynamic Flexible Scheduling", booktitle = "Computational Intelligence and Industrial Applications", year = "2024", editor = "Bin Xin and Hongbin Ma and Jinhua She and Weihua Cao", volume = "2466", series = "Communications in Computer and Information Science", pages = "160--174", address = "Beijing, China", month = "1-5 " # nov, publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "https://link.springer.com/chapter/10.1007/978-981-96-4756-9_14", DOI = "doi:10.1007/978-981-96-4756-9_14", } @Article{Xu:2025:AIR, author = "Meng Xu and Yi Mei and Fangfang Zhang and Mengjie Zhang", title = "Learn to optimise for job shop scheduling: a survey with comparison between genetic programming and reinforcement learning", journal = "Artificial Intelligence Review", year = "2025", volume = "58", pages = "article number: 160", keywords = "genetic algorithms, genetic programming, Hyper-heuristic, Job shop scheduling, and Reinforcement learning", ISSN = "0269-2821", URL = "https://rdcu.be/ejcUr", DOI = "doi:10.1007/s10462-024-11059-9", size = "53 pages", notes = "Centre for Data Science and Artificial Intelligence & School of Engineering and Computer Science, Victoria University of Wellington, Kelburn Parade, Wellington 6012, New Zealand", } @Article{Meng_Xu:ieeeTEC3, author = "Meng Xu and Yi Mei and Fangfang Zhang and Mengjie Zhang", title = "Niching Genetic Programming to Learn Actions for Deep Reinforcement Learning in Dynamic Flexible Scheduling", journal = "IEEE Transactions on Evolutionary Computation", note = "Early Access", keywords = "genetic algorithms, genetic programming, Job shop scheduling, Dynamic scheduling, Scheduling, Sequential analysis, Routing, Processor scheduling, heuristic learning, reinforcement learning, dynamic scheduling", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2024.3395699", notes = "also known as \cite{10517279}", } @Article{Xu:TEVC, author = "Meng Xu and Yi Mei and Fangfang Zhang and Yew Soon Ong and Mengjie Zhang", title = "Pareto Set Learning through Genetic Programming for Multi-Objective Dynamic Scheduling", journal = "IEEE Transactions on Evolutionary Computation", note = "Early Access", keywords = "genetic algorithms, genetic programming, Dynamic scheduling, Job shop scheduling, Real-time systems, Training, Processor scheduling, Decision making, Artificial intelligence, Sequential analysis, Reinforcement learning, Scheduling Heuristics, Automatic Learning, Pareto Set Learning", ISSN = "1941-0026", DOI = "doi:10.1109/TEVC.2025.3568375", abstract = "The multi-objective dynamic flexible job shop scheduling (MO-DFJSS) problem is crucial in modern manufacturing, impacting productivity and operational costs. Genetic Programming (GP) has emerged as a prominent method for MO-DFJSS due to its ability to evolve real-time responsible and effective scheduling heuristics. However, existing GP approaches often learn multiple heuristics for different regions of the Pareto front, making their management and selection complicated in real-world applications. This paper proposes a novel Pareto set learning GP (PSLGP) framework that addresses this limitation by learning a single, preference-conditioned heuristic that encompasses the entire Pareto front based on user preferences. This simplifies scheduling and allows for real-time adaptation to user-defined priorities. The framework employs a novel preference-conditioned heuristic representation that incorporates user preferences as additional inputs, enabling dynamic heuristic adjustments. To efficiently evaluate fitness without increasing training time, a surrogate model is used to estimate individual performance across different preferences, and three new fitness aggregation strategies are designed to ensure effective heuristic alignment across the Pareto front. Experimental results demonstrate that PSLGP significantly outperforms the state-of-the-art multi-objective GP approach, particularly in less busy MO-DFJSS environments, providing a more adaptable and efficient solution for dynamic scheduling challenges. Further analyses of preference influence, solution distribution, and heuristic structure provide evidence that the proposed PSLGP effectively learns preference-conditioned scheduling heuristics that align user preferences with various regions of the Pareto front.", notes = "Also known as \cite{10994448}", } @InProceedings{xu:2024:GECCO3, author = "Peijie Xu and Andy Song and Ke Wang", title = "Genetic Programming Empowered Feature Construction towards Energy Efficient {BVI} Wearables", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference", year = "2024", editor = "Ruhul Sarker and Patrick Siarry and Julia Handl and Xiaodong Li and Markus Wagner and Mario Garza-Fabre and Kate Smith-Miles and Richard Allmendinger and Ying Bi and Grant Dick and Amir H Gandomi and Marcella Scoczynski Ribeiro Martins and Hirad Assimi and Nadarajen Veerapen and Yuan Sun and Mario Andres Munyoz and Ahmed Kheiri and Nguyen Su and Dhananjay Thiruvady and Andy Song and Frank Neumann and Carla Silva", pages = "1408--1416", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, obstacle avoidance datasets, obstacle detection, feature construction, low energy consumption, efficient models, hardware deployment, power usage, Real World Applications", isbn13 = "979-8-4007-0494-9", DOI = "doi:10.1145/3638529.3654002", size = "9 pages", abstract = "Blind and visually impaired (BVI) individuals face serious mobility-related risks daily due to the lack of progression in hazard detection and assistive technologies. Most existing techniques are demanding in computational resources and energy consumption, yet still struggle in real-time performance. The challenge is particularly evident when deploying these techniques on portable devices, on which lightweight coupled with sustainable low battery usage is a must. Hence in this study, we aim to leverage Genetic Programming (GP), which is well known for its feature construction capability, to develop more energy-efficient models with better features. The objective is to find more condensed features by GP, to reduce energy consumed and inference time, but without significant accuracy loss in obstacle detection from a head-mount wearable device for BVIs. Features have been trained on a series of in-door settings that represent a BVI person navigating through corridors and furniture. Models with these constructed features then are validated on actual portable board deployment, as well as on Field Programmable Gate Array simulation (FPGA). Comparative analyses are conducted using a range of performance metrics across models training by different learning methods. The metrics include accuracy, model execution time, prediction time, energy consumption, and hardware resource usage. Our study demonstrates that GP-constructed models can generally reduce energy consumption and inference time with negligible accuracy loss. Furthermore, it can build models with higher accuracy than the benchmark, allowing users to adjust between energy usage and accuracy according to their needs.", notes = "GECCO-2024 RWA A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{Xu:2017:IIAI-AAI, author = "Ruhang Xu and Zhilin Liu", booktitle = "2017 6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI)", title = "Identifying Expectation Transformation in a Housing Market under Different Exogenous Conditions: An Agent-Based Modeling Approach", year = "2017", pages = "829--834", abstract = "This paper has built a GP (Genetic programming) - based Agent-based model to simulate the expectation transformations in a housing market under different conditions. This paper sets one group of control experiments based on Normal Base Case. The control experiments based on Normal Base Case reveals impacts of trading psychology, population growth, income growth, supply growth, tax and monetary policies. We reveal in the housing market a dilemma, that the rise of house price can increase society wealth and lower poverty rate, but it also raise the unevenness of wealth distribution. Based on the work of this paper, it is possible to simulate a reality-alike situation and to analysis housing markets for a certain application case.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IIAI-AAI.2017.20", month = jul, notes = "Xu Ruhang, Liu Zhilin North China Electric Power University, Beijing, China Also known as \cite{8113358}", } @Article{Xu:2013:ietIP, author = "Tao Xu and Yunhong Wang and Zhaoxiang Zhang", journal = "IET Image Processing", title = "Pixel-wise skin colour detection based on flexible neural tree", year = "2013", month = nov, volume = "7", number = "8", pages = "751--761", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1049/iet-ipr.2012.0657", ISSN = "1751-9659", abstract = "Skin colour detection plays an important role in image processing and computer vision. Selection of a suitable colour space is one key issue. The question that which colour space is most appropriate for pixel-wise skin colour detection is not yet concluded. In this study, a pixel-wise skin colour detection method is proposed based on the flexible neural tree (FNT) without considering the problem of selecting a suitable colour space. A FNT-based skin model is constructed by using large skin data sets which identifies the important components of colour spaces automatically. Experimental results show improved accuracy and false positive rates (FPRs). The structure and parameters of FNT are optimised via genetic programming and particle swarm optimisation algorithms, respectively. In the experiments, nine FNT skin models are constructed and evaluated on features extracted from RGB, YCbCr, HSV and CIE-Lab colour spaces. The Compaq and ECU datasets are used for constructing FNT-based skin model and evaluating its performance compared with other skin detection methods. Without extra processing steps, the authors method achieves state of the art performance in skin pixel classification and better performance in terms of accuracy and FPRs.", notes = "Also known as \cite{6668526}", } @Article{Xu:2024:ACCESS, author = "Wei Xu and Yi Wan", title = "A Heuristic-Based Evolutionary Approach for Joint Optimization of Job Shop Scheduling and Facility Layout", journal = "IEEE Access", year = "2024", volume = "12", pages = "97630--97645", keywords = "genetic algorithms, genetic programming, Layout, Production, Scheduling, Job shop scheduling, Heuristic algorithms, Metaheuristics, Industrial facilities, Job shop scheduling problem, facility layout problem, ant colony system, dispatching rule", ISSN = "2169-3536", DOI = "doi:10.1109/ACCESS.2024.3427144", abstract = "Job shop scheduling (JSS) aims to arrange the processing sequence of a series of operations to optimise the cost objective. The most common objective is to minimise the makespan without considering the flow time of materials between machines. However, in practical production scenarios, the material flow time directly affects the completion time of operations. Besides, facility layout (FL) is the premise of JSS. FL problem can be stated as assigning a range of production machines into the physical environment of the shop floor, which largely determines the time cost of material flow between machines. The result of FL will affect subsequent JSS. Therefore, the FL problem and the JSS problem naturally constitute a two-stage joint optimisation problem. So far, the above-mentioned joint optimisation problem is rarely studied in the literature because of the complexity of the problem. For this purpose, the joint optimisation problem of FL and JSS is defined in this paper, called JSSFLP. Then, a hybrid heuristic evolutionary strategy (HHE) is proposed for solving the proposed JSSFLP. HHE consists of the meta-heuristic, the hyper-heuristic evolutionary algorithm, and the joint optimisation mechanism (JOP), Among them, ant colony system (ACS), as a meta-heuristic algorithm, is used to solve the FL problem effectively, and genetic programming (GP), as a hyper-heuristic algorithm, is used to generate rule-based dispatching methods for JSS problem. Besides, JOP, as an adaptor, is designed to coordinate ACS and GP to solve JSSFLP cooperatively. Numerical experiments are conducted to demonstrate the feasibility and effectiveness of the proposed HHE and its component, respectively.", notes = "Also known as \cite{10596299}", } @InProceedings{Xu:2016:NDSS, author = "Weilin Xu and Yanjun Qi and David Evans", title = "Automatically Evading Classifiers: A Case Study on {PDF} Malware Classifiers", booktitle = "The Network and Distributed System Security Symposium 2016", year = "2016", editor = "Lujo Bauer and Karen O'Donoghue", address = "San Diego, USA", month = "21-24 " # feb, keywords = "genetic algorithms, genetic programming, genetic improvement", ISBN = "1-891562-41-X", URL = "http://evademl.org/", URL = "https://www.internetsociety.org/events/ndss-symposium-2016/ndss-2016-programme#session11", URL = "https://www.internetsociety.org/sites/default/files/blogs-media/automatically-evading-classifiers.pdf", size = "15 pages", abstract = "Machine learning is widely used to develop classifiers for security tasks. However, the robustness of these methods against motivated adversaries is uncertain. In this work, we propose a generic method to evaluate the robustness of classifiers under attack. The key idea is to stochastically manipulate a malicious sample to find a variant that preserves the malicious behaviour but is classified as benign by the classifier. We present a general approach to search for evasive variants and report on results from experiments using our techniques against two PDF malware classifiers, PDFrate and Hidost. Our method is able automatically find evasive variants for all of the 500 malicious seeds in our study. Our results suggest a general method for evaluating classifiers used in security applications, and raise serious doubts about the effectiveness of classifiers based on superficial features in the presence of adversaries.", notes = "https://www.internetsociety.org/events/ndss-symposium-2016", } @PhdThesis{1_Xu_Weilin_2019_PHD, author = "Weilin Xu", title = "Improving Robustness of Machine Learning Models Using Domain Knowledge", school = "Computer Science, School of Engineering and Applied Science, University of Virginia", year = "2019", address = "USA", month = may, keywords = "genetic algorithms, genetic programming, genetic evasion, adversarial machine learning, evasion attack, PDF malware, robustness, formal verification, computer vision", URL = "https://libraetd.lib.virginia.edu/downloads/p5547r86f?filename=1_Xu_Weilin_2019_PHD.pdf", URL = "https://libraetd.lib.virginia.edu/public_view/fj2362645", URL = "https://doi.org/10.18130/v3-s4qm-kp71", size = "116 pages", abstract = "Although machine learning techniques have achieved great success in many areas, such as computer vision, natural language processing, and computer security, recent studies have shown that they are not robust under attack. A motivated adversary is often able to craft input samples that force a machine learning model to produce incorrect predictions, even if the target model achieves high accuracy on normal test inputs. This raises great concern when machine learning models are deployed for security-sensitive tasks. This dissertation aims to improve the robustness of machine learning models by exploiting domain knowledge. While domain knowledge has often been neglected due to the power of automatic representation learning in the deep learning era, we find that domain knowledge goes beyond a given dataset of a task and helps to (1) uncover weaknesses of machine learning models, (2) detect adversarial examples and (3) improve the robustness of machine learning models. First, we design an evolutionary algorithm-based framework, Genetic Evasion, to find evasive samples. We embed domain knowledge into the mutation operator and the fitness function of the framework and achieve 100 percent success rate in evading two state-of-the-art PDF malware classifiers. Unlike previous methods, our technique uses genetic programming to directly generate evasive samples in the problem space instead of the feature space, making it a practical attack that breaks the trust of black-box machine learning models in a security application. Second, we design an ensemble framework, Feature Squeezing, to detect adversarial examples against deep neural network models using simple pre-processing. We employ domain knowledge on signal processing that natural signals are often redundant for many perception tasks. Therefore, we can squeeze the input features to reduce adversaries search space while preserving the accuracy on normal inputs. We use various squeezers to pre-process an input example before it is fed into a model. The difference between those predictions is often small for normal inputs due to redundancy, while the difference can be large for adversarial examples. We demonstrate that Feature Squeezing is empirically effective and inexpensive in detecting adversarial examples for image classification tasks generated by many algorithms. Third, we incorporate simple pre-processing with certifiable robust training and formal verification to train provably-robust models. We formally analyse the impact of preprocessing on adversarial strength and derive novel methods to improve model robustness. Our approach produces accurate models with verified state-of-the-art robustness and advances the state-of-the-art of certifiable robust training methods. We demonstrate that domain knowledge helps us understand and improve the robustness of machine learning models. Our results have motivated several subsequent works, and we hope this dissertation will be a step towards implementing robust models under attack.", notes = "Supervisors: David Evans and Yanjun Qi", } @InProceedings{Xu:2023:ASIANCON, author = "Weiwei Xu", booktitle = "2023 3rd Asian Conference on Innovation in Technology (ASIANCON)", title = "Cartesian Genetic Programming Algorithm and its Application in Artistic Graphics Generation", year = "2023", abstract = "The Cartesian genetic programming algorithm is a method of generating images using genetic rules. The basic idea behind it is to create a random 'gene' population, and then combine them into new genes through crossover and mutation, which will generate new combinations that can be used to generate images. This algorithm has created a new form of artistic expression, expanding people's understanding and understanding of artistic graphics. In the generation of artistic graphics, this algorithm is often applied to design and draw symmetrical shapes, while also continuously optimising and improving based on individual fitness and other selection operations. In addition, the Cartesian genetic programming algorithm can also be used in the fields of 2D and 3D graphic design to meet different application needs. The main idea behind it is to represent the image as a grid of pixels, where each pixel has its own colour value and brightness. In this way, we can use the Cartesian genetic programming algorithm (CGPA) to find the appropriate colour and brightness for each pixel in the image. The basic idea behind CGPA is that we have an input image (image) composed of pixels arranged on a 2D grid. In summary, the Cartesian genetic programming algorithm has important application value in the generation of artistic graphics. It can not only optimise the traditional graph generation algorithm, but also develop some new artistic expressions, adapt to different application scenarios, and promote the progress and development of computer graphics and art fields.", keywords = "genetic algorithms, genetic programming, Technological innovation, Three-dimensional displays, Art, Image colour analysis, Shape, Brightness, Sociology, Descartes, Graphic production, 2D mesh", DOI = "doi:10.1109/ASIANCON58793.2023.10270730", month = aug, notes = "Also known as \cite{10270730}", } @InProceedings{Xu:2023:CASES, author = "Xingyu Xu and Qingwen Wei and Yang Zhang and Hao Cai and Bo Liu", booktitle = "2023 International Conference on Compilers, Architecture, and Synthesis for Embedded Systems (CASES)", title = "Work-in-Process: Error-Compensation-Based Energy-Efficient {MAC} Unit for {CNNs}", year = "2023", pages = "3--4", abstract = "Approximate circuits sacrifice accuracy in exchange for energy efficiency and have been widely used in hardware deployment of neural networks (NNs). Since convolution accounts for most of the power consumption in NNs, it is necessary to design an approximate multiplication and accumulation (MAC) unit which improve the energy efficiency of hardware with ignorable accuracy loss. an error-compensation-based energy-efficient MAC unit is proposed in which approximate multipliers are designed by Boolean matrix factorization and approximate adders are generated by Cartesian genetic programming. The proposed MAC unit is conducted on CIFAR10 using ResNet-18, where PDP is reduced by 58.8percent with an accuracy loss of 0.81percent.", keywords = "genetic algorithms, genetic programming, Power demand, Embedded systems, Computer aided software engineering, Convolution, Artificial neural networks, ANN, Computer architecture, Approximate computing, Energy efficiency, Error compensation", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=10316208", ISSN = "2643-1726", month = sep, notes = "Also known as \cite{10316208}", } @InProceedings{Xu:2025:evoapplications, author = "Xinxin Xu and Fangfang Zhang and Yi Mei and Mengjie Zhang and Huili Gong and Xiangqian Ding", title = "Multi-tree Genetic Programming for Dynamic Tugboat Scheduling", booktitle = "28th International Conference, EvoApplications 2025", year = "2025", editor = " Pablo Garcia-Sanchez and Emma Hart and Sarah L. Thomson", series = "LNCS", volume = "15613", pages = "195--210", publisher = "Springer", address = "Trieste, Italy", month = "3-5 " # apr, organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Dynamic tugboat scheduling problem, Dual-tree encoding strategy, Multi-tree genetic programming", isbn13 = "978-3-031-90065-5", timestamp = "Fri, 09 May 2025 20:28:52 +0200", biburl = "https://dblp.org/rec/conf/evoapps/XuZMZGD25.bib", bibsource = "dblp computer science bibliography, https://dblp.org", DOI = "doi:10.1007/978-3-031-90065-5_12", notes = "DBLP:conf/evoapps/XuZMZGD25, School of Computer Science and Technology, Ocean University of China, Qingdao, China http://www.evostar.org/2025/ EvoApplications2025 held in conjunction with EuroGP'2025, EvoCOP2025 and EvoMusArt2025", } @InProceedings{Xu4:2008:cec, author = "Yiliang Xu and Meng Hiot Lim and Yew-Soon Ong", title = "Automatic Configuration of Metaheuristic Algorithms for Complex Combinatorial Optimization Problems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "2380--2387", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1823-7", file = "EC0554.pdf", DOI = "doi:10.1109/CEC.2008.4631116", abstract = "We report our work on the algorithmic development of an evolutionary methodology for automatic configuration of metaheuristic algorithms for solving complex combinatorial Optimization problems.We term it Automatic Configuration Engine for Metaheuristics (ACEM). We first propose a novel Left Variation s- Right Property (LVRP) tree structure to manage various metaheuristic procedures and properties. With LVRP tree, feasible configurations of metaheuristics can be easily specified. An evolutionary learning algorithm is then proposed to evolve the internal context of the trees based on pre-selected training set. Guided by a user-defined satisfaction function of the candidate algorithms, it converges to the optimal or a very good algorithm. The experimental comparison with two recent state-of-the-art algorithms for solving the quadratic assignment problem (QAP) shows that ACEM produces an hybrid-genetic algorithm with human-competitive or even better performance.", keywords = "genetic algorithms, genetic programming", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @Article{DBLP:journals/jaciii/XuSMZWL22, author = "Yuzhao Xu and Yanjing Sun and Zhanguo Ma and Hongjie Zhao and Yanfen Wang and Nannan Lu", title = "Attribute Selection Based Genetic Network Programming for Intrusion Detection System", journal = "J. Adv. Comput. Intell. Intell. Informatics", volume = "26", number = "5", pages = "671--683", year = "2022", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.20965/jaciii.2022.p0671", DOI = "doi:10.20965/jaciii.2022.p0671", timestamp = "Mon, 10 Oct 2022 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/jaciii/XuSMZWL22.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{Xuan:2018:GI5, author = "Jifeng Xuan and Yongfeng Gu and Zhilei Ren and Xiangyang Jia and Qingna Fan", title = "Genetic Configuration Sampling: Learning a Sampling Strategy for Fault Detection of Configurable Systems", booktitle = "5th edition of GI @ GECCO 2018", year = "2018", editor = "Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and John R. Woodward and Shin Yoo", pages = "1624--1631", address = "Kyoto, Japan", month = "15-19 " # jul, organisation = "ACM SIGEvo", publisher = "ACM", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, Configuration sampling, fault detection, highly-configurable systems, software configurations", URL = "http://www.cs.stir.ac.uk/events/gecco-gi-2018/papers/genetic_configuration_sampling.pdf", DOI = "doi:10.1145/3205651.3208267", size = "8 pages", abstract = "A highly-configurable system provides many configuration options to diversify application scenarios. The combination of these configuration options results in a large search space of configurations. This makes the detection of configuration-related faults extremely hard. Since it is infeasible to exhaust every configuration, several methods are proposed to sample a subset of all configurations to detect hidden faults. Configuration sampling can be viewed as a process of repeating a pre-defined sampling action to the whole search space, such as the one-enabled or pair-wise strategy. we propose genetic configuration sampling, a new method of learning a sampling strategy for configuration-related faults. Genetic configuration sampling encodes a sequence of sampling actions as a chromosome in the genetic algorithm. Given a set of known configuration-related faults, genetic configuration sampling evolves the sequence of sampling actions and applies the learnt sequence to new configuration data. A pilot study on three highly-configurable systems shows that genetic configuration sampling performs well among nine sampling strategies in comparison.", notes = "Apache, BusyBox, Linux 'compiler Gcc 7.3 contains 2472 configuration options' http://www.cs.stir.ac.uk/events/gecco-gi-2018/cfp.html", } @InProceedings{Xue:2022:ICSICT, author = "AnFeng Xue and Han Yan and RenYuan Zhang and XueTao Wang and Hao Zhang and Hao Cai and Bo Liu2", title = "A Low Power Speech Recognition Processor with Precision Recoverable {CRNN}", booktitle = "2022 IEEE 16th International Conference on Solid-State \& Integrated Circuit Technology (ICSICT)", year = "2022", abstract = "In this paper, Convolutional Recurrent Neural Network (CRNN) is deployed in the speech recognition system for recognition of 5 keywords. We propose an 8-bits quantization scheme to quantize the weights and activations. Cartesian Genetic Programming (CGP) to generate approximate multipliers is proposed to drastically reduce hardware power consumption with only 1.5percent loss of accuracy. The proposed partial retraining method compensates for the loss of network accuracy caused by approximate computing, and can basically restore the accuracy to the initial level. Using process library for hardware verification based on synthesis, the proposed speech recognition system can reduce power consumption by 11.percent~24.percent, and reduce area by 10.percent~25.percent with accuracy loss is 0.2percent~ 0.5percent.", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming", DOI = "doi:10.1109/ICSICT55466.2022.9963226", month = oct, notes = "Also known as \cite{9963226}", } @Article{Xue:2016:ieeeTEC, author = "Bing Xue and Mengjie Zhang and Will N. Browne and Xin Yao", title = "A Survey on Evolutionary Computation Approaches to Feature Selection", journal = "IEEE Transactions on Evolutionary Computation", year = "2016", volume = "20", number = "4", pages = "606--626", month = aug, keywords = "genetic algorithms, genetic programming", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2015.2504420", size = "21 pages", abstract = "Feature selection is an important task in data mining and machine learning to reduce the dimensionality of the data and increase the performance of an algorithm, such as a classification algorithm. However, feature selection is a challenging task due mainly to the large search space. A variety of methods have been applied to solve feature selection problems, where evolutionary computation (EC) techniques have recently gained much attention and shown some success. However, there are no comprehensive guidelines on the strengths and weaknesses of alternative approaches. This leads to a disjointed and fragmented field with ultimately lost opportunities for improving performance and successful applications. This paper presents a comprehensive survey of the state-of-the-art work on EC for feature selection, which identifies the contributions of these different algorithms. In addition, current issues and challenges are also discussed to identify promising areas for future research.", notes = "p606 'only genetic programming (GP) and learning classifier systems (LCSs) are able to perform embedded feature selection' also known as \cite{7339682}", } @Article{Xue:2017:sigevo, author = "Bing Xue and Mengjie Zhang", title = "Evolutionary Feature Manipulation in Data Mining/Big Data", journal = "SIGEVOlution", year = "2017", volume = "10", number = "1", pages = "4--11", keywords = "genetic algorithms, genetic programming", URL = "http://www.sigevolution.org/issues/SIGEVOlution1001.pdf", DOI = "doi:10.1145/3089251.3089252", size = "8 pages", abstract = "Known as the GIGO (Garbage In, Garbage Out) principle, the quality of the input data highly influences or even determines the quality of the output of any machine learning, big data and data mining algorithm. The input data which is often represented by a set of features may suffer from many issues. Feature manipulation is an effective means to improve the feature set quality, but it is a challenging task. Evolutionary computation (EC) techniques have shown advantages and achieved good performance in feature manipulation. This paper reviews recent advances on EC based feature manipulation methods in classification, clustering, regression, incomplete data, and image analysis, to provide the community the state-of-the-art work in the field.", } @Article{Xue:GPEM:bookreview, author = "Bing Xue", title = "{Sebastian Ventura} and {Jose Maria Luna}: Pattern mining with evolutionary algorithms", journal = "Genetic Programming and Evolvable Machines", year = "2017", volume = "18", number = "3", pages = "407--409", month = sep, note = "Book review", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dR84s", DOI = "doi:10.1007/s10710-017-9306-z", size = "3 pages", notes = "review of \cite{Ventura:2016:pmea} Springer, 2016, 190 pp, ISBN: 978-3-319-33858-3", } @InProceedings{Xue:2020:GECCOcomp, author = "Bing Xue and Mengjie Zhang", title = "Evolutionary Computation for Feature Selection and Feature Construction", year = "2020", editor = "Richard Allmendinger and Hugo Terashima Marin and Efren Mezura Montes and Thomas Bartz-Beielstein and Bogdan Filipic and Ke Tang and David Howard and Emma Hart and Gusz Eiben and Tome Eftimov and William {La Cava} and Boris Naujoks and Pietro Oliveto and Vanessa Volz and Thomas Weise and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Rui Wang and Ran Cheng and Guohua Wu and Miqing Li and Hisao Ishibuchi and Jonathan Fieldsend and Ozgur Akman and Khulood Alyahya and Juergen Branke and John R. Woodward and Daniel R. Tauritz and Marco Baioletti and Josu Ceberio Uribe and John McCall and Alfredo Milani and Stefan Wagner and Michael Affenzeller and Bradley Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and Nayat Sanchez-Pi and Luis Marti and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and Matthew Johns and Nick Ross and Ed Keedwell and Herman Mahmoud and David Walker and Anthony Stein and Masaya Nakata and David Paetzel and Neil Vaughan and Stephen Smith and Stefano Cagnoni and Robert M. Patton and Ivanoe {De Falco} and Antonio {Della Cioppa} and Umberto Scafuri and Ernesto Tarantino and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Erik Hemberg and Riyad Alshammari and Tokunbo Makanju and Fuijimino-shi and Ivan Zelinka and Swagatam Das and Ponnuthurai Nagaratnam and Roman Senkerik", isbn13 = "9781450371278", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377929.3389857", DOI = "doi:10.1145/3377929.3389857", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion", pages = "1283--1312", size = "30 pages", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", note = "Tutorial", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{10.1145/3377929.3389857} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @Proceedings{Xue:2025:GP, title = "EuroGP 2025: Proceedings of the 28th European Conference on Genetic Programming", editor = "Bing Xue and Luca Manzoni and Illya Bakurov", year = "2025", volume = "15609", series = "LNCS", month = "23-25 " # apr, organisation = "EvoStar, Species", publisher = "Springer Nature", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-031-89990-4", URL = "https://www.evostar.org/2025/eurogp/", DOI = "doi:10.1007/978-3-031-89991-1", size = "269 pages", abstract = "contents: \cite{farinati:2025:EuroGP}, \cite{de-la-torre:2025:EuroGP}, \cite{nadizar:2025:EuroGP}, \cite{Langdon:2025:EuroGP}, \cite{hurta:2025:EuroGP}, \cite{trautwein:2025:EuroGP}, \cite{rovito:2025:EuroGP}, \cite{geiger:2025:EuroGP}, \cite{zhang:2025:EuroGP}, \cite{carlet:2025:EuroGP}, \cite{alharthi:2025:EuroGP}, \cite{durasevic:2025:EuroGP}, \cite{pietropolli:2025:EuroGP}, \cite{doquet:2025:EuroGP}, \cite{kocherovsky:2025:EuroGP},", notes = "EuroGP'2025 held in conjunction with EvoCOP2025, EvoMusArt2025 and EvoApplications2025", } @Article{Fan_Xue:CACIE, author = "Fan Xue and Weisheng Lu and Ke Chen", title = "Automatic Generation of Semantically Rich As-Built Building Information Models Using {2D} Images: A Derivative-Free Optimization Approach", journal = "Computer-Aided Civil and Infrastructure Engineering", year = "2018", volume = "33", number = "11", pages = "926--942", month = nov, keywords = "genetic algorithms, genetic programming", ISSN = "1467-8667", URL = "https://onlinelibrary.wiley.com/doi/abs/10.1111/mice.12378", DOI = "doi:10.1111/mice.12378", size = "16 pages", abstract = "Over the past decade a considerable number of studies have focused on generating semantically rich as-built building information models (BIMs). However, the prevailing methods rely on laborious manual segmentation or automatic but error-prone segmentation. In addition, the methods failed to make good use of existing semantics sources. This article presents a novel segmentation-free derivative-free optimization (DFO) approach that translates the generation of as-built BIMs from 2D images into an optimization problem of fitting BIM components regarding architectural and topological constraints. The semantics of the BIMs are subsequently enriched by linking the fitted components with existing semantics sources. The approach was prototyped in two experiments using an outdoor and an indoor case, respectively. The results showed that in the outdoor case 12 out of 13 BIM components were correctly generated within 1.5 hours, and in the indoor case all target BIM components were correctly generated with a root-mean-square deviation (RMSD) of 3.9cm in about 2.5 hours. The main computational novelties of this study are: (1) to translate the automatic as-built BIM generation from 2D images as an optimization problem; (2) to develop an effective and segmentation-free approach that is fundamentally different from prevailing methods; and (3) to exploit online open BIM component information for semantic enrichment, which, to a certain extent, alleviates the dilemma between information inadequacy and information overload in BIM development.", notes = "Entered for 2018 HUMIES", } @InProceedings{Xue:Unnatural:2016, author = "Linting Xue and Collin F. Lynch and Min Chi", title = "Unnatural Feature Engineering: Evolving Augmented Graph Grammars for Argument Diagrams", booktitle = "Proceedings of the 2016 Conference on Educational Data Mining, EDM16", year = "2016", editor = "Tiffany Barnes and Min Chi and Mingyu Feng", pages = "255--262", address = "Raleigh, USA", month = jun # " 29-" # jul # " 2", publisher = "International Educational Data Mining Society", keywords = "genetic algorithms, genetic programming, Evolutionary Computation, Augmented Graph Grammars, Argument Diagramming, Feature Engineering", URL = "http://www.educationaldatamining.org/EDM2016/proceedings/paper_137.pdf", size = "8 pages", abstract = "Graph data such as argument diagrams has become increasingly common in EDM. Augmented Graph Grammars are a robust rule formalism for graphs. Prior research has shown that hand-authored graph grammars can be used to automatically grade student-produced argument diagrams. But hand-authored rules can be time consuming and expensive to produce, and they may not generalize well to novel contexts. We applied Evolutionary Computation to automatically induce empirically-valid graph grammars for argument diagrams that can be used for automatic grading or provide the basis for hints. Our results show that our approach can generate more relevant rules than experts or other state of the art algorithms, and that these evolved rules outperform the alternatives.", notes = "http://www.educationaldatamining.org/EDM2016/proceedings.html", cv-category = "Peer-Reviewed Conference Paper", } @InProceedings{Xue:Mining:2017, author = "Linting Xue and Collin F. Lynch and Min Chi", title = "Mining Innovative Augmented Graph Grammars for Argument Diagrams through Novelty Selection", booktitle = "Proceedings of the 10th International Conference on Educational Data Mining (EDM 2017)", year = "2017", editor = "Xiangen Hu and Tiffany Barnes and Arnon Hershkovitz and Luc Paquette", pages = "296--301", address = "Wuhan China", month = "25-28 " # jun, publisher = "International Educational Data Mining Society", keywords = "genetic algorithms, genetic programming, Heterogeneous Rules, Augmented Graph Grammars, Argument Diagrams, Evolutionary Computation, Novelty selection", URL = "http://educationaldatamining.org/EDM2017/proc_files/papers/paper_107.pdf", size = "6 pages", abstract = "Augmented Graph Grammars are a graph-based rule formalism that supports rich relational structures. They can be used to represent complex social networks, chemical structures, and student-produced argument diagrams for automated analysis or grading. In prior work we have shown that Evolutionary Computation (EC) can be applied to induce empirically-valid grammars for student-produced argument diagrams based upon fitness selection. However this research has shown that while the traditional EC algorithm does converge to an optimal fitness, premature convergence can lead to it getting stuck in local maxima, which may lead to undiscovered rules. In this work, we augmented the standard EC algorithm to induce more heterogeneous Augmented Graph Grammars by replacing the fitness selection with a novelty-based selection mechanism every ten generations. Our results show that this novelty selection increases the diversity of the population and produces better, and more heterogeneous, grammars.", cv-category = "Peer-Reviewed Conference Paper", notes = "http://educationaldatamining.org/EDM2017/", } @InProceedings{Xue:2015:IHMSC, author = "Siqing Xue and Jie Wu", booktitle = "7th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)", title = "Modeling Systems of Ordinary Differential Equations Using Immune Based Gene Expression Programming", year = "2015", volume = "1", pages = "437--442", abstract = "This paper aims to model system of ordinary differential equations by using a new hybrid gene expression programming algorithm. Gene expression programming is a recently developed evolutionary computation method for model learning and knowledge discovery. The hybrid algorithm combined immune clonal selection algorithm and memetic algorithm with gene expression programming to find not only the structure of system of differential equations but also optimise its constant parameters. The idea of immune clone principle is incorporated into the evolution process to enhance the diversity of population and the memetic algorithm is introduced to improve the ability of local search. Experiments on benchmark problems have shown that the hybrid approach is able to provide highly competitive results compared with that of conventional genetic programming applied to this problem.", keywords = "genetic algorithms, genetic programming, Gene expression programming", DOI = "doi:10.1109/IHMSC.2015.189", month = aug, notes = "Sch. of Comput. Sci., China Univ. of Geosci., Wuhan, China Also known as \cite{7334741}", } @Article{xue:2023:Symmetry, author = "Xingsi Xue and Celestine Makota and Osamah Ibrahim Khalaf and Jagan Jayabalan and Pijush Samui and Ghaida Muttashar Abdulsahib", title = "Machine Learning Approach for Prediction of Lateral Confinement Coefficient of {CFRP-Wrapped} {RC} Columns", journal = "Symmetry", year = "2023", volume = "15", number = "2", pages = "Article No. 545", keywords = "genetic algorithms, genetic programming", ISSN = "2073-8994", URL = "https://www.mdpi.com/2073-8994/15/2/545", DOI = "doi:10.3390/sym15020545", abstract = "Materials have a significant role in creating structures that are durable, valuable and possess symmetry engineering properties. Premium quality materials establish an exemplary environment for every situation. Among the composite materials in constructions, carbon fiber reinforced polymer (CFRP) is one of best materials which provides symmetric superior strength and stiffness to reinforced concrete structures. For the structure to be confining, the region jeopardizes seismic loads and axial force, specifically on columns, with limited proportion of ties or stirrups implemented to loftier ductility and brittleness. The failure and buckling of columns with CFRP has been studied by many researchers and is ongoing to determine ways columns can be retrofitted. This article symmetrically integrates two disciplines, specifically materials (CFRP) and computer application (machine learning). Technically, predicting the lateral confinement coefficient (Ks) for reinforced concrete columns in designs plays a vital role. Therefore, machine learning models like genetic programming (GP), minimax probability machine regression (MPMR) and deep neural networks (DNN) were used to determine the Ks value of CFRP-wrapped RC columns. In order to compute Ks value, parameters such as column width, length, corner radius, thickness of CFRP, compressive strength of the unconfined concrete and elastic modulus of CFRP act as stimulants. The adopted machine learning models used 293 datasets of square and rectangular RC columns for the prediction of Ks. Among the developed models, GP and MPMR provide encouraging performances with higher R values of 0.943 and 0.941; however, the statistical indices proved that the GP model outperforms other models with better precision (R2 = 0.89) and less errors (RMSE = 0.056 and NMBE = 0.001). Based on the evaluation of statistical indices, rank analysis was carried out, in which GP model secured more points and ranked top.", notes = "also known as \cite{sym15020545}", } @Article{XUE:2023:aej, author = "Xingsi Xue and Ghaida Muttashar Abdulsahib and Osamah Ibrahim Khalaf and J. Jagan and Karthikeyan Loganathan and Celestine Makota and Balaji Ponraj", title = "Soft computing approach on estimating the lateral confinement coefficient of {CFRP} veiled circular columns", journal = "Alexandria Engineering Journal", volume = "81", pages = "599--619", year = "2023", ISSN = "1110-0168", DOI = "doi:10.1016/j.aej.2023.09.053", URL = "https://www.sciencedirect.com/science/article/pii/S1110016823008487", keywords = "genetic algorithms, genetic programming, ANN, Carbon fiber reinforced polymer, Lateral confinement coefficient, Estimation", abstract = "For any structures, Reinforced concrete (RC) columns are the utmost carping members. RC columns jacketed with CFRP membrane enhance resistance, deformation aptitude, and the auspicious confining impacts. Sundry scrutiny has been implemented about circular columns wrapping or veiled with CFRP layers. This scrutiny will manifest about Genetic Programming (GP) and Artificial Neural Networks (ANN) as soft computing models, mobbing exploited to ascertain the lateral confinement coefficient (Ks) valuate of RC circular columns jacketed with CFRP membrane. For the purpose to ascertain Ks value, manipulate the corresponding ambit such as the diameter of the column (b), column length (L), thickness layer of CFRP (tw), the elastic modulus of CFRP membrane (Ef) along with the compressive strength of unconfined concrete (fcc). Total collection of 142 datasets for the RC circular column was employed to decipher the prediction of Ks. Various statistical metrics (R2 = 0.933; RMSE = 0.054; NMBE = -0.001) compared with the ANN model and exhibits the superiority of GP in predicting the Ks. Taylor diagram and Rank analysis were also carried out in this article for justification. The lucidity of the GP machine enlightenment model overtures a greater sensible and exact prediction of Ks value for RC circular columns in accordance with versatile loading conditions", } @Article{XUE:2024:knosys, author = "Xingsi Xue", title = "Automatic Knowledge Graph matching via Self-adaptive Designed Genetic Programming", journal = "Knowledge-Based Systems", volume = "293", pages = "111628", year = "2024", ISSN = "0950-7051", DOI = "doi:10.1016/j.knosys.2024.111628", URL = "https://www.sciencedirect.com/science/article/pii/S0950705124002636", keywords = "genetic algorithms, genetic programming, Knowledge graph matching, Similarity measure construction, Automated algorithm design, Self-adaptive designed genetic programming", abstract = "Knowledge Graph (KG) provides a structured representation of domain knowledge by formally defining entities and their relationships. However, distinct communities tend to employ different terminologies and granularity levels to describe the same entity, leading to the KG heterogeneity issue that hampers their communications. KG matching can identify semantically similar entities in two KGs, which is an effective solution to this problem. Similarity Measures (SMs) are the foundation of the KG matching technique, and due to the complexity of entity heterogeneity, it is necessary to construct a high-level SM by selecting and combining the basic SMs. However, the large number of SMs and their intricate relationships make SM construction an open challenge. Inspired by the success of Evolutionary Algorithms (EA) in addressing the entity matching problem, this work further proposes a novel Self-adaptive Designed Genetic Programming (SDGP) to automatically construct the SM for KG matching. To overcome the drawbacks of the classic EA-based matching methods, a new individual representation and a novel fitness function are proposed to enable SDGP automatically explore the SM selection and combination. Then, a new Adaptive Automatic Design (AAD) method is introduced to adaptively trade off SDGP's exploration and exploitation, which can determine the timing of AAD and efficiently determine the suitable breeding operators and control parameters for SDGP. The experiment uses the Ontology Alignment Evaluation Initiative's Knowledge Graph (KG) data set to test the performance of SDGP. The experimental results show that SDGP can effectively determine high-quality KG alignments, which significantly outperform state-of-the-art KG matching methods", } @Article{Xue:ITJ, author = "Xingsi Xue and Achyut Shankar and Francesco Flammini and Mazdak Zamani", journal = "IEEE Internet of Things Journal", title = "Similarity Feature Construction for Semantic Sensor Ontology Integration via Light Genetic Programming", year = "2024", volume = "11", number = "21", pages = "34202--34214", month = "1 " # nov, keywords = "genetic algorithms, genetic programming, Ontologies, Internet of Things, IOT, Semantics, Complexity theory, Measurement, Task analysis, Sensor Ontology Matching, Intelligent Sensor System, Light Approximate Evaluation Metric, Two-Stage Tournament Selection", ISSN = "2327-4662", DOI = "doi:10.1109/JIOT.2024.3370610", abstract = "Sensor ontology is the kernel technique of the Intelligent Sensor System, which provides a structured framework to organize and interpret the knowledge of the Internet of Things (IoT). However, the ontology heterogeneity issue hampers the communication of sensor ontologies. Sensor Ontology Matching (SOM) can find semantically identical entities between two ontologies, which is an effective method to address this issue. However, due to their complicated semantic relationships, it is a challenge to construct an effective Similarity Feature (SF) to distinguish the heterogeneous sensor entities. Although Evolutionary Algorithms (EAs) based matching techniques have shown their effectiveness in the ontology matching field, they suffer from drawbacks such as high computational complexity and expert-dependent solution evaluation. To overcome these drawbacks, this paper proposes a novel Light Genetic Programming (L-GP) to automatically construct SF for SOM. First, a simplified evolutionary mechanism is designed to improve the efficiency of the SOM process. Second, a novel fitness function based on the approximate evaluation metric is introduced to automatically guide the search direction of L-GP. Lastly, a two-stage tournament selection operator is presented to balance the quality and complexity of the solutions, improving the accuracy of the SOM results. The experiment uses ten pairs of real-world SOM tasks to test the performance of L-GP, and the experimental results show that L-GP significantly outperforms state-of-the-art matching techniques.", notes = "Also known as \cite{10445711} School of Computer Science and Mathematics Fujian University of Technology Fuzhou, China", } @Article{Xue:JBHI, author = "Xingsi Xue and Mu-En Wu and Fazlullah Khan", title = "Biomedical Information Integration via Adaptive Large Language Model Construction", journal = "IEEE Journal of Biomedical and Health Informatics", note = "Early Access", keywords = "genetic algorithms, genetic programming, Bioinformatics, Semantics, Biological system modelling, Accuracy, Complexity theory, Encoding, Bidirectional control, Optimisation, Large language models, ANN, Terminology, Biomedical Information Integration", ISSN = "2168-2208", DOI = "doi:10.1109/JBHI.2024.3496495", abstract = "Integrating diverse biomedical knowledge information is essential to enhance the accuracy and efficiency of medical diagnoses, facilitate personalized treatment plans, and ultimately improve patient outcomes. However, Biomedical Information Integration (BII) faces significant challenges due to variations in terminology and the complex structure of entity descriptions across different datasets. A critical step in BII is biomedical entity alignment, which involves accurately identifying and matching equivalent entities across diverse datasets to ensure seamless data integration. In recent years, Large Language Model (LLMs), such as Bidirectional Encoder Representations from Transformers (BERTs), have emerged as valuable tools for discerning heterogeneous biomedical data due to their deep contextual embeddings and bidirectionality. However, different LLMs capture various nuances and complexity levels within the biomedical data, and none of them can ensure their effectiveness in all heterogeneous entity matching tasks. To address this issue, we propose a novel Two-Stage LLM construction (TSLLM) framework to adaptively select and combine LLMs for Biomedical Information Integration (BII). First, a Multi-Objective Genetic Programming (MOGP) algorithm is proposed for generating versatile high-level LLMs, and then, a Single-Objective Genetic Algorithm (SOGA) employs a confidence-based strategy is presented to combine the built LLMs, which can further improve the discriminative power of distinguishing heterogeneous entities. The experiment uses OAEI's entity matching datasets, i.e., Benchmark and Conference, along with LargeBio, Disease and Phenotype datasets to test the performance of TSLLM. The experimental findings validate the efficiency of TSLLM in adaptively differentiating heterogeneous biomedical entities, which significantly outperforms the leading entity matching techniques.", notes = "Also known as \cite{10750399}", } @Article{Xingsi_Xue:ieeeIOT, author = "Xingsi Xue and Jerry Chun-Wei Lin", title = "Two-Phase Similarity Feature Construction for Enhancing Sensor Knowledge Graph Alignment via Genetic Programmings", journal = "IEEE Internet of Things Journal", keywords = "genetic algorithms, genetic programming, Semantics, Optimisation, Standards, Measurement, Accuracy, Knowledge graphs, Internet of Things, Data mining, Internet of Everything, Sensor Knowledge Graph Matching, Similarity Feature Construction", ISSN = "2327-4662", DOI = "doi:10.1109/JIOT.2025.3560031", abstract = "The rapid evolution of the Internet of Everything (IoE) has increased data complexity in urban traffic networks, necessitating the use of the Semantic Sensor Web (SSW) to integrate semantic metadata with sensor data via Sensor Knowledge Graphs (SKGs). However, the heterogeneity of SKGs, with varying focus, terminology and structure, poses challenges for accurate sensor data analysis. To identify semantically identical entities across different SKGs, Similarity Features (SFs) capture entity similarity from multiple perspectives, but the multidimensional heterogeneity of SKGs prevents any single SF from being universally effective. To improve SKG alignment, this paper presents a novel two-phase SKG alignment method, which consists of three new components. First, an automated SF construction framework is developed, which uses Multi-Objective GP (MOGP) and Single-Objective GP (SOGP) to automatically construct and combine the high-quality SFs. Second, new fitness functions are designed to guide the search direction of MOGP and SOGP, without relying on standard alignments. Lastly, lexicase crossover and mutation are proposed to adaptively enhance population diversity, ensuring high-quality SKG alignment. Experiment uses two KG datasets from the Ontology Alignment Evaluation Initiative (OAEI), along with ten pairs of practical IoE SKGs, were used to evaluate the performance of our approach. The results show that our method outperforms state-of-the-art matching methods, particularly in handling complex entity heterogeneity.", notes = "Also known as \cite{10963864}", } @Article{Xue:TEVC, author = "Xingsi Xue and Yi Mei and Baozhong Zhao and Mengjie Zhang", title = "Adaptive Similarity Feature Construction for Ontology Matching via Multi-Layer Hybrid Genetic Programming", journal = "IEEE Transactions on Evolutionary Computation", keywords = "genetic algorithms, genetic programming, Ontologies, Accuracy, Medical diagnostic imaging, Electronic mail, Diseases, Diabetes, Vocabulary, Semantics, Ontology Matching", ISSN = "1941-0026", DOI = "doi:10.1109/TEVC.2025.3547578", abstract = "Ontology is a kernel technique of the semantic web, which defines concepts, properties, and their relationships to establish a shared understanding of domain knowledge. Ontology matching identifies semantically similar entities across different ontologies, which uses similarity features to measure their similarity from different perspectives. However, due to the complexity of the entity heterogeneity, no single similarity feature is universally effective. In recent years, genetic algorithms have proven effective in constructing similarity features for ontology matching, but their potential is limited by the reliance on default classification strategies, empirical determination of the number of high-level features, the requirement for manually selecting, combining these features, and tuning the associated combination parameters. To overcome these drawbacks, we propose a multi-layer hybrid genetic programming approach to automatically construct high-level similarity features. This approach includes three novel components. First, a new multi-layer individual representation is designed, which faciliates the algorithm to adaptively explore the search space of constructing high-level similarity features. Second, to enhance the search effectiveness, a new initialization method and a mutation operator are developed, which use a weight-based strategy to adaptively select and construct a more diverse set of similarity features. Third, a compact genetic algorithm-based optimiser is designed to refine the tree structures of elite individuals. The experimental results on the ontology alignment evaluation initiative's benchmark show that our algorithm can generate high-quality ontology matching results across various matching tasks, significantly outperforming the state-of-the-art ontology matching methods.", notes = "Also known as \cite{10909292}", } @Article{Xue:TITS, author = "Xingsi Xue and Guojun Mao and Saru Kumari and Zhonghua Liu", title = "Multi-Objective Genetic Programming Assisted Stochastic Deep Reinforcement Learning for Dynamic Knowledge Integration in Transportation Networks", journal = "IEEE Transactions on Intelligent Transportation Systems", keywords = "genetic algorithms, genetic programming, Real-time systems, Accuracy, Complexity theory, Optimisation, Dynamic programming, Deep reinforcement learning, Soft sensors, Probabilistic logic, Global Positioning System, Faces, Transportation network, dynamic knowledge integration, stochastic deep reinforcement learning, multi-objective genetic programming", ISSN = "1558-0016", DOI = "doi:10.1109/TITS.2025.3545572", abstract = "Transportation Networks (TNs) play a critical role in economic and social systems, yet the dynamic nature and inherent heterogeneity of TN data pose challenges for Dynamic Knowledge Integration (DKI). Traditional approaches for matching entities from different knowledge bases often struggle with the complexity and diversity of TN data, which varies across systems and sources such as traffic sensors and GPS devices. To address this issue, this paper proposes a novel Multi-Objective Genetic Programming assisted Stochastic Deep Reinforcement Learning (MOGP-SDRL) for DKI in TNs. Unlike existing methods, the proposed framework combines SDRL and MOGP to achieve superior efficiency, accuracy and adaptability in handling heterogeneous TN data. First, a novel SDRL framework is designed to automate and optimise the selection of Similarity Features (SFs) for entity matching. This framework incorporates a probabilistic action selection mechanism, which enhances exploration during the SF selection process. Second, a novel MOGP is presented to construct high-quality, diverse SFs by exploring non-dominated feature ensembles, enhancing both accuracy and adaptability in matching results, leading to more accurate and adaptable matching results compared to conventional methods. Lastly, new approximate evaluation metrics are developed to assess alignment quality without relying on predefined entity alignments, guiding the optimisation process. Experimental evaluations on OAEI's knowledge graph (KG) dataset and five pairs of real-world TN dataset demonstrate the effectiveness of the MOGP-SDRL framework, which consistently produces high-quality matching results and achieves significant improvements in both accuracy and robustness over existing approaches.", notes = "Also known as \cite{10908447}", } @Article{Xue:TCE, author = "Xingsi Xue and Xingwang Li", title = "Explicit and Implicit Consumer Electronics Information Integration via Automatic Large Language Model Construction", journal = "IEEE Transactions on Consumer Electronics", keywords = "genetic algorithms, genetic programming, Consumer electronics, Accuracy, Complexity theory, Optimisation, Interoperability, Uncertainty, Training, Terminology, Smart homes, Simple object access protocol, Information Integration, Large Language Model, ANN, Rough Set", ISSN = "1558-4127", DOI = "doi:10.1109/TCE.2025.3526772", abstract = "Integrating diverse Consumer Electronics (CE) information is essential to enhance and optimise user experiences, but CE Information Integration (CEII) faces challenges arising from differences in entity descriptions. In recent years, Large Language Models (LLMs) have emerged as valuable tools for analysing heterogeneous CE data due to their deep contextual embeddings. However, different LLMs capture various nuances and complexity levels within the CE data, and none of them can ensure their effectiveness in all heterogeneous scenarios. To address this issue, this paper proposes an automatic LLM Construction for CEII, which can determine both explicit and implicit CE entity mappings. First, a hybrid Genetic Programming is developed to determine explicit entity mappings using a new individual representation, an innovative fitness function, and a Probability-based Incremental Learning algorithm. Then, a novel Rough Set based matching method is presented to efficiently induce implicit CE entity mappings, which uses the entity feature relevance metric and the dominance-based matching approach. The experiment tests the performance of our approach with the OAEI's KG and common KG datasets, and the practical CE datasets in the smart home context. The experimental results show the effectiveness of the developed approach, which significantly outperforms the state-of-the-art entity matching methods.", notes = "Also known as \cite{10829859}", } @Article{Xue:2024:jii, author = "Xingsi Xue and Donglei Sun and Achyut Shankar and Wattana Viriyasitavat and Patrick Siarry", title = "Efficient large-scale biomedical ontology matching with anchor-based biomedical ontology partitioning and compact geometric semantic genetic programming", journal = "Journal of Industrial Information Integration", year = "2024", volume = "41", pages = "100637", keywords = "genetic algorithms, genetic programming, Large-scale biomedical ontology matching, Anchor-based ontology partitioning, Compact geometric semantic genetic programming, Dominance improvement ratio", ISSN = "2452-414X", URL = "https://www.sciencedirect.com/science/article/pii/S2452414X24000815", DOI = "doi:10.1016/j.jii.2024.100637", abstract = "Biomedical ontology offers a structured framework to model the biomedical knowledge in a machine-readable format. However, the heterogeneity inherent in biomedical ontologies hinders their communication. Biomedical Ontology Matching (BOM) can address this issue by identifying equivalent concepts in biomedical ontologies. Recently, Evolutionary Algorithms (EAs) based matching techniques have exhibited their effectiveness in finding high-quality matching results. However, due to the vast number of entities, and intricate relationships between entities, it is difficult for traditional EAs to efficiently solve the BOM problem. To tackle this challenge, this paper proposes an efficient BOM method to automatically match large-scale biomedical ontologies. First, a novel anchor-based biomedical ontology partitioning method is developed to transform the large-scale BOM problem into several small-scale matching tasks, reducing the search space of the matching phase. Second, a new Compact Geometric Semantic Genetic Programming (CGSGP) is proposed to efficiently construct high-level Similarity Feature for BOM, which can significantly reduce the computational complexity. Lastly, a new fitness function composed of the approximated evaluation metric and the Dominance Improvement Ratio (DIR) is introduced, which can overcome the solution's bias improvement and enable the simultaneous matching of multiple pairs of sub-ontologies without requiring the standard alignment. The experiment verifies our approach's performance on the Ontology Alignment Evaluation Initiative (OAEI)'s Anatomy, Large Biomed and Disease and Phenotype datasets. The experimental results show that our method can efficiently determine high-quality BOM results across different test cases, whose performance significantly outperforms the state-of-the-art BOM techniques", } @Article{Xue:2024:swevo, author = "Xingsi Xue and Jerry Chun-Wei Lin and Tong Su", title = "Efficient ontology matching through compact linear genetic programming with surrogate-assisted local search", journal = "Swarm and Evolutionary Computation", year = "2024", volume = "91", pages = "101758", keywords = "genetic algorithms, genetic programming, Ontology matching, Compact linear genetic programming, Multi-program encoding, Surrogate-assisted local search", ISSN = "2210-6502", URL = "https://www.sciencedirect.com/science/article/pii/S2210650224002967", DOI = "doi:10.1016/j.swevo.2024.101758", abstract = "Ontology is a foundational technique of Semantic Web, which enables meaningful interpretation of Web data. However, ontology heterogeneity obstructs the communications among different ontologies, which is a key hindrance in realizing Semantic Web. To leverage different ontologies, it is important to match ontologies by identifying their semantically related entities. Given the vast number of entities and rich vocabulary semantics, this task presents considerable challenges. To tackle this challenge, this paper proposes a novel Compact Linear Genetic Programming with Surrogate-Assisted Local Search (CLGP-SALS). First, a compact multi-program encoding mechanism is developed to reduce the computational cost while ensuring the reusability of building blocks in Linear Genetic Programming. Moreover, it coordinates multiple programs within one solution to improve the quality of ontology alignment. Second, to enhance convergence speed, a new Surrogate-Assisted Local Search is designed, incorporating semantic distance and fitness discrepancies for a focused local search process. The surrogate model presents a superior approach for approximating the fitness of individuals, thereby improving search efficiency in the ontology matching task. Experimental results demonstrate that CLGP-SALS outperforms the state-of-the-art ontology matching methods on the ontology alignment evaluation initiative's benchmark. The results show that our method can efficiently determine high-quality ontology alignments, and its performance outperforms the compared methods in terms of both effectiveness and efficiency", } @InProceedings{yabuki03smc, author = "Taro Yabuki and Hitoshi Iba", title = "Turing-complete Data Structure for Genetic Programming", booktitle = "IEEE International Conference on Systems, Man and Cybernetics", year = "2003", volume = "4", pages = "3577--3582", address = "Washington, D.C., USA", month = "5-8 " # oct, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, language classifier, Turing-completeness", ISSN = "1062-922X", URL = "http://www.iba.k.u-tokyo.ac.jp/~yabuki/paper/2003-yabuki-smc.pdf", DOI = "doi:10.1109/ICSMC.2003.1244444", size = "6 pages", abstract = "In generating a program automatically, if we do not know whether the problem is solvable or not in advance, then the representation of the program must be Turing-complete, i.e. the representation must be able to express any algorithms. However, a tree structure used by the standard genetic programming is not Turing-complete. We propose a representation scheme, which is a recurrent network consisting of trees. It makes genetic programming turing-complete without introducing any new non-terminals. In addition, we empirically show how it succeeds in evolving language classifiers.", notes = " Turing completeness comes from infinite length integers. Lisp like: car, cdr, cons. Claims better performance on Tomita languages than \cite{Iba:1995:tdpGP}. Also known as \cite{1244444}", } @InCollection{yabuki04genetic, author = "Taro Yabuki and Hitoshi Iba", title = "Genetic programming using a {Turing} complete representation: recurrent network consisting of trees", booktitle = "Recent Developments in Biologically Inspired Computing", publisher = "Idea Group Publishing", year = "2004", editor = "Leandro N. {de Castro} and Fernando J. {Von Zuben}", chapter = "4", pages = "61--81", keywords = "genetic algorithms, genetic programming, Recurrent Network consisting of Trees, RTN", ISBN = "1-59140-312-X", URL = "http://www.iba.k.u-tokyo.ac.jp/~yabuki/paper/2003-yabuki-rtn-draft.pdf", DOI = "doi:10.4018/978-1-59140-312-8.ch004", size = "15 pages", abstract = "a new representation scheme for Genetic Programming (GP) is proposed. We need a Turing-complete representation for a general method of generating programs automatically, i.e. the representation must be able to express any algorithms. Our representation is a recurrent network consisting of trees (RTN), which is proved to be Turing-complete. In addition, it is applied to the tasks of generating language classifiers and a bit reverser. As a result, RTN is shown to be usable in evolutionary computing.", notes = "broken August 2020 http://www.idea-group.com/books/details.asp?id=4376 2003-yabuki-rtn-draft.pdf may be slightly different from published version The University of Tokyo, Japan.", } @PhdThesis{yabuki04thesis, author = "Taro Yabuki", title = "Representation Schemes for Evolutionary Automatic Programming", school = "Department of Frontier Informatics, Graduate School of Frontier Sciences, The University of Tokyo", year = "2004", address = "Japan", note = "In Japanese", keywords = "genetic algorithms, genetic programming", URL = "http://www.unfindable.net/paper/thesis_abstract_en/thesis_abstract_en.html", abstract = "We propose a new representation scheme for Genetic Programming (GP). It is a recurrent network consisting of functions (Recurrent Network consisting of Trees, RTN). GP is a type of evolutionary computing (EC). EC is a framework of automatic optimisation or design. Features of EC are: * Representation scheme for solution candidates and variation operators for them. * Fitness function that shows a quality of the solution candidate. * Selection method that selects prospective solution candidates from a set of them. Usually, these features are defined independently. Although it is interesting to rethink this model completely, we reconsider only the representation scheme for GP. We use GP to generate a program automatically. The program is represented by RTN. RTN can represent any algorithms, in other words, Turing-complete. Thus, a user of RTN need not worry about whether a solution of a given problem can be described by RTN. On the other hand, the expressiveness of solution candidates of standard GP, which is the most popular GP, is strongly restricted. A solution candidate of standard GP is represented by a single parse tree. The parse tree consists of terminals and non-terminals. If all non-terminals are pure functions and we treat an evaluated value of the parse tree as an output (or behaviour) of the solution candidate, the repertoire of this representation is smaller than the one of finite state machine.", abstract = "This does not matter if we know that the restricted expressiveness is sufficient to describe the solution of a given problem in advance of evolutionary computation. However, in case that we do not know that and the search should fail, it will be impossible to find out whether it is attributable to evolutionary computing or the representation scheme. For example, suppose we try to generate a classifier for the language $ \{ww\vert w\in\{0,1\}*\}$. If we use a representation whose repertoire is the same as one of the pushdown automaton, then we will never succeed, because it is proved that any pushdown automaton cannot decide this language. One conceivable approach is to introduce an ideally infinite indexed memory and non-terminals to access to it. It is proved that if the solution candidate is represented by a parse tree consisting of these non-terminals and we can repeat the evaluation of the parse tree until the data stored in the memory meets a halting condition, then the expressiveness is equivalent to the one of a Turing machine, i.e. Turing-complete.", abstract = "However, the representation scheme for GP must satisfy other conditions except Turing-completeness. These are as follows: * User can specify the input-output scheme. * Useful components can be introduced easily. * The program is modularised and hierarchised. We will discuss necessity of them in detail in this paper. We propose another representation scheme, RTN. It satisfies above conditions. It is a natural extension of standard GP. Standard GP uses a single parse tree to represent a solution candidate. On the other hand, RTN is a recurrent network consisting of plural nodes. Each node consists of a value and a pure function represented by a parse tree. The parse tree consists of non-terminals and terminals. Special non-terminals are not needed. Terminal set consists of four variables and constants. In case of standard GP, the input data bind variables. On the other hand, they bind the values of the RTN nodes.", abstract = "This is an example of RTN consisting of two nodes. The functions of each node are $\displaystyle \char93 1$ $\displaystyle : (c-P[c])/2,$ $\displaystyle \char93 2$ $\displaystyle : P[a] d,$ where $ P$ is a procedure which returns a remainder of its argument divided by $ 2$. The function has at most four parameters, i.e. $ a$, $ b$, $ c$, and $ d$. These parameters are bound to the value of nodes. In this case, the binding rule is expressed as follows: Links of $ \char93 1$ and $ \char93 2$ are $ \{*, *, 1, *\}$ and $ \{1, *, *, 2\}$, respectively. The third parameter of $ \char93 1$, i.e. $ c$ and the first parameter of $ \char93 2$, i.e. $ a$ are bound to the value of $ \char93 1$, because both the third link of $ \char93 1$ and the first link of $ \char93 2$ are $ 1$. The fourth parameter of $ \char93 2$, i.e. $ d$ is bound to the value of $ \char93 2$, because the fourth link of $ \char93 2$ is $ 2$.", abstract = "The program is executed according to the discrete time steps. Define the function and the value of the $ \char93 n$ at time $ t$ as $ f_n$ and $ v[[n, t]]$, respectively, the number of parameters as $ k_n$, and let $ i$-th parameter be bound to the value of $ \char93 l_{n,i}$. The value at $ t+1$ will be $\displaystyle v[[n, t+1]]=f_n[v[[l_{n,1},t]],\ \cdots\ ,v[[l_{n,k_{n}},t]]]. $ Suppose the value of $ \char93 1$ is bound to the input data and the value of $ \char93 2$ is $ 1$ at $ t=0$. For example, when the input data is a binary digit $ 1011$, the transition of RTN will be as follows: Value of $ \char93 1$ $ 1011$ $ 101$ $ 10$ $ 1$ 0 Value of $ \char93 2$ $ 1$ $ 1$ $ 1$ 0 0 When the value of $ \char93 1$ becomes 0, the value of $ \char93 2$ is 0 if and only if the inputted binary digit contains 0. It is straightforward to prove that RTN can simulate any Turing machine, in other words, RTN can represent any algorithms. We give the proof in this paper.", abstract = "We use RTN to generate language classifiers. These are the tasks that GP has failed to solve. GP using RTN succeeds to solve them. We also apply the representation scheme using indexed memory to these tasks. However, it does not succeed. This comparison implies that our approach is effective and promising. Various representations for GP have been proposed so far. We discuss differences between RTN and other representation scheme. We also discuss the criteria used in comparing various approaches. For example, No Free Lunch Theorem does not matter.", notes = "Broken Sep 2018 http://www.iba.k.u-tokyo.ac.jp/~yabuki/paper/thesis_abstract_en/thesis_abstract_en.html email Thu, 01 Sep 2005 16:45:44 +0900 No English translation available, but, the essential part of it, i.e. details of proposed representation scheme and results of its application, is described in \cite{yabuki04genetic}. There are many topics not included \cite{yabuki04genetic} but discussed in the thesis: - In \cite{yabuki04genetic}, we said that the repertoire of single S-expression is the same as the one of finite state automaton (FSA). In the thesis, I showed it is usual that the repertoire of single S-expression is smaller than the one of FSA. - Drawbacks of describing strategies of iterated prisonerB!Gs dilemma by means of tables or strings of GA. - Requirements for the representation for GP were discussed concretely in the thesis. - Drawbacks of automatically defined functions and recursive non-terminal. - The way how to simulate multi-tape Turing machine directly by means of our representation scheme. - Successful result of our method applied to a problem that cannot be solved by FSA.", } @Article{DBLP:journals/jca/YacoubiJ07, author = "Samira {El Yacoubi} and Przemyslaw Jacewicz", title = "A Genetic Programming Approach to Structural Identification of Cellular Automata", journal = "Journal of Cellular Automata", year = "2007", volume = "2", number = "1", pages = "67--76", keywords = "genetic algorithms, genetic programming", ISSN = "1557-5969", URL = "http://www.oldcitypublishing.com/journals/jca-home/jca-issue-contents/jca-volume-2-number-1-2007/jca-2-1-p-67-76/", broken = "http://www.oldcitypublishing.com/JCA/JCAabstracts/JCA2.1abstracts/JCAv2n1p67-76Yacoubi.html", abstract = "As is well-known, it is very hard to design local state transition rules in cellular automata (CAs) in order to perform a pre-specified global task, as it is difficult to pass from the usual microscopic specification of the automaton to an appropriate description of its global behaviour. Our paper aims at demonstrating a possibility of finding the best state transition rules, along with the corresponding neighbourhood, in order for a CA to accomplish a given assignment, by means of genetic programming. Genetic programming is an extension of classical genetic algorithms in which computer programs are genetically bred to solve problems. The introduced ideas are illustrated by some simulation examples regarding solving one-dimensional density and synchronisation problems.", bibsource = "DBLP, http://dblp.uni-trier.de", } @PhdThesis{yada-thesis, author = "Tetsushi Yada", title = "Stochastic models representing DNA sequence data : Construction algorithms and their applications to prediction of gene structure and function", school = "Department of Information Science, Faculty of Science, University of Tokyo", year = "1998", address = "Japan", keywords = "genetic algorithms, genetic programming, HMM", URL = "http://www.is.s.u-tokyo.ac.jp/library/new-books/html/is-DD.html", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/yada-thesis-sect3.3.ps.gz", notes = "Distributed population. cat, union, closure, op1, op2, op3 used to describe HMM network, including state transition probabilities 'See Smith,1983 for detailed description of transformation algorithm of non-probabilistic tree to DFA.' Fitness = correlation. Mutation. DD-122", } @Article{Yada:1999:TIPSJ, author = "Tetsushi Yada and Yasushi Totoki and Kiyoshi Asai and Masato Ishikawa", title = "Generation of Hidden Markov Model Describing Complex Motif in DNA Sequences", journal = "Transactions of Information Processing Society of Japan", year = "1999", volume = "40", number = "2", pages = "750--767", month = feb, keywords = "genetic algorithms, genetic programming", ISSN = "0387-5806", ISSN = "1882-7764", URL = "http://id.nii.ac.jp/1001/00012856/", size = "18 pages", abstract = "We have developed a method for the generation of hidden Markov model (HMM) representing complex motif in DNA sequences. The procedures of the method are as follows: (1) design of HMMs for elemental motifs in given DNA sequences; (2) construction of a complex motif HMM consisting of the elemental motif HMMs. Statistical analysis and genetic programming (GP) were applied to the respective procedures. At step (1), left-to-right HMMs were designed and their lengths were determined by a statistical significance. At step (2), probabilistic tree describing HMMs was defined and its structure was optimized by GP against a complex motif. Concatenation, probabilistic union, probabilistic closure, etc. were attached to nonterminal nodes. The elemental motif HMMs and an HMM for any a letter were attached to terminal nodes. In the method, the advance design of elemental motif HMMs and adoption of probabilistic tree as encoding rule of GP lead to efficient generation of complex motif HMM. It was observed that the generated HMM can detect the complex motif in uncharacterized DNA sequences with high accuracy. Further, the HMM is full of interesting suggestions of the complex motif. (author abst.)", notes = "In Japanese. Broken Sep 2018 http://sciencelinks.jp/j-east/article/199911/000019991199A0244916.php", } @Article{Yadav:2016:Measurement, author = "Basant Yadav and Sudheer Ch and Shashi Mathur and Jan Adamowski", title = "Discharge forecasting using an Online Sequential Extreme Learning Machine ({OS-ELM}) model: A case study in {Neckar River, Germany}", journal = "Measurement", volume = "92", pages = "433--445", year = "2016", ISSN = "0263-2241", DOI = "doi:10.1016/j.measurement.2016.06.042", URL = "http://www.sciencedirect.com/science/article/pii/S0263224116303347", abstract = "Flood forecasting in natural rivers is a complicated procedure because of uncertainties involved in the behaviour of the flood wave movement. This leads to complex problems in hydrological modelling which have been widely solved by soft computing techniques. In real time flood forecasting, data generation is continuous and hence there is a need to update the developed mapping equation frequently which increases the computational burden. In short term flood forecasting where the accuracy of flood peak value and time to peak are critical, frequent model updating is unavoidable. In this paper, we studied a new technique: Online Sequential Extreme Learning Machine (OS-ELM) which is capable of updating the model equation based on new data entry without much increase in computational cost. The OS-ELM was explored for use in flood forecasting on the Neckar River, Germany. The reach was characterized by significant lateral flow that affected the flood wave formation. Hourly data from 1999-2000 at the upstream section of Rottweil were used to forecast flooding at the Oberndorf downstream site with a lead time of 1-6 h. Model performance was assessed by using three evaluation measures: the coefficient of determination (R2), the Nash-Sutcliffe efficiency coefficient (NS) and the root mean squared error (RMSE). The performance of the OS-ELM was comparable to those of other widely used Artificial Intelligence (AI) techniques like support vector machines (SVM), Artificial Neural Networks (ANN) and Genetic Programming (GP). The frequent updating of the model in OS-ELM gave a closer reproduction of flood events and peak values with minimum error compared to SVM, ANN and GP.", keywords = "genetic algorithms, genetic programming, Flood forecasting, Extreme Learning Machine, Artificial intelligence techniques", } @PhdThesis{Yadav:thesis, author = "Basant Yadav", title = "Application of Soft Computing Techniques for Water Quantity and Quality Modeling", school = "Department of Civil Engineering, Indian Institute of Technology Delhi", year = "2017", address = "Delhi, India", month = "19 " # may, keywords = "genetic algorithms, genetic programming, water quality, salt water intrusion, BTEX contamination, ANN, SVM, ELM, OS-ELM, ELM-PSO, SEAWAT, aquifer", broken = "http://www.iitd.ac.in/content/basant-yadav-19052017", abstract_url = "http://www.iitd.ac.in/sites/default/files/phd/2013CEZ8051.pdf", URL = "http://eprint.iitd.ac.in/handle/2074/7306", URL = "https://www.researchgate.net/publication/322962662_APPLICATION_OF_SOFT_COMPUTING_TECHNIQUES_FOR_WATER_QUANTITY_AND_QUALITY_MODELING", URL = "http://eprint.iitd.ac.in/bitstream/2074/7306/1/TH-5229.pdf", size = "250 pages", abstract = "Four water management problems are studied using advanced simulation and optimization techniques. This study is divided in two parts, the first part deals with water quantity management problems whereas the second part deals with water quality management problems. In the first part, two water quantity management problems are studied that include prediction of discharge in rivers and groundwater level predictions. In the second part of the study, two water quality management problems are studied, that include cost estimation of in-situ bioremediation and modelling of saltwater intrusion in coastal aquifer. Discharge forecasting in natural rivers is a complicated procedure because of uncertainties involved in the behaviour of the flood wave movement. This further leads to solving complex problems of hydrological modelling using soft computing techniques (data-driven models). In real time flood forecasting problems, the data generation is a continuous process. In short term flood forecasting where the accuracy of flood peak value and time to peak are critical, frequent model updating becomes unavoidable. An accurate discharge prediction in the least possible time using online sequential extreme learning machine (OS-ELM) can help policy makers and engineers design a flood control policy and flood warning systems. OS-ELM has not only been used in predictions for hydrological systems, but also for other areas related to environmental sciences. Similarly, a precise prediction of groundwater level using ELM would be very helpful to plan a groundwater abstraction policy in areas where groundwater fluctuations is very high. In case of an in-situ bioremediation system design, the remediation cost can be considerably reduced when an efficient and fast simulator like ELM is adopted. Further, the inclusion of biological clogging of wells while optimizing the cost in in-situ remediation provide a realistic view of the remediation cost. Likewise, the new soft computing techniques like SVM and ELM are a good alternative to the traditional soft computing techniques like ANN and found to be of immense importance while designing the management strategy to control seawater intrusion in coastal areas.", notes = "TH-5229.pdf only first 28 pages, researchgate EN-2013CEZ8051 Supervisor: Shashi Mathur. Co supervsior: Sudheer Ch", } @Article{Yadav:2012:IJCA, author = "Geeta Yadav and Yugal Kumar and G. Sahoo", title = "Role of the Computational Intelligence in Drugs Discovery and Design: Introduction, Techniques and Software", journal = "International Journal of Computer Applications", year = "2012", volume = "51", number = "10", pages = "7--18", month = aug, publisher = "Foundation of Computer Science, New York, USA", keywords = "genetic algorithms, genetic programming", URL = "http://www.ijcaonline.org/archives/volume51/number10/8076-1476", DOI = "doi:10.5120/8076-1476", ISSN = "0975-8887", bibsource = "OAI-PMH server at www.doaj.org", language = "eng", oai = "oai:doaj-articles:9454090b9f37d0791d93eb2d6bbc399e", URL = "http://research.ijcaonline.org/volume51/number10/pxc3881476.pdf", size = "12 pages", abstract = "Drugs discovery and design is an intense, lengthy and consecutive process that starts with the lead and target discovery followed by lead optimisation and pre-clinical in vitro and in vivo studies. This paper throws light on different computational techniques that play a vital role in the drugs discovery and design process. Earlier, computational techniques are use in the field of computer science, electrical engineering and electronics and communication engineering to solve the problems. But, now days use of these techniques has changed the scenario in drugs discovery and design from the last two decades. This paper present brief description of different computational techniques such as Particle Swarm Optimisation, Ant Colony Optimisation, Artificial Neural Network, Fuzzy logic, Genetic Algorithm, Genetic Programming, Evolutionary Programming, Evolutionary Strategy and also provide a tabular comparison of these techniques as well as a list of computational tools/ software.", } @InProceedings{Yadav:2023:ICACITE, author = "Surendra Yadav and Ms.Manpreet Kaur", booktitle = "2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE)", title = "Genetic Algorithm-Based Data Allocation in Multi Media Using Cloud Computing", year = "2023", pages = "1668--1671", abstract = "The recent growth of Internet-of-Things (IoT) applications using cloud computing has been amazing. One of the advancements is heterogeneous cloud computing, which has made it possible to use the cloud for a range of infrastructure solutions, including multimedia big data. The optimisations of on-premise heterogeneous memory have been the subject of several past studies. However, the performance and financial limits brought on by hardware distributions and manipulative techniques are placing restrictions on the heterogeneous cloud memory. It is an NP-hard combinatorial issue to distribute data jobs across dispersed memory with different capacities. In order to provide high performance cloud-based heterogeneous memory service offerings, this study focuses on this problem and suggests a unique solution called Cost-Aware Heterogeneous Cloud Memory Model. It allocates data to the cloud-based memory via genetic programming. In our suggested method, we take into account a number of important elements that have a significant influence on how well Communication expenses, data transfer operating costs, energy performance, and time constraints all play a role in how cloud memories operate. Finally, we put our suggested paradigm to the test via experimental assessments. The trial findings have demonstrated the viability and scalability of our technique as a cost-conscious cloud-based solution.", keywords = "genetic algorithms, genetic programming, Cloud computing, Smart cities, Scalability, Multimedia computing, Big Data, Media, Resource management, heterogeneous memory, data allocation", DOI = "doi:10.1109/ICACITE57410.2023.10183005", month = may, notes = "Also known as \cite{10183005}", } @Article{Yaghouby2010919, author = "Farid Yaghouby and Ahmad Ayatollahi and Reihaneh Bahramali and Maryam Yaghouby and Amir Hossein Alavi", title = "Towards automatic detection of atrial fibrillation: A hybrid computational approach", journal = "Computers in Biology and Medicine", volume = "40", number = "11-12", pages = "919--930", year = "2010", ISSN = "0010-4825", DOI = "doi:10.1016/j.compbiomed.2010.10.004", URL = "http://www.sciencedirect.com/science/article/B6T5N-51CRWGV-1/2/c0eaea60cd989fbea5e856e07847ee5f", keywords = "genetic algorithms, genetic programming, Atrial fibrillation, Heart rate variability signal, Orthogonal least squares, Simulated annealing, Forward floating selection, Arrhythmia detection", abstract = "In this study, new methods coupling genetic programming with orthogonal least squares (GP/OLS) and simulated annealing (GP/SA) were applied to the detection of atrial fibrillation (AF) episodes. Empirical equations were obtained to classify the samples of AF and Normal episodes based on the analysis of RR interval signals. Another important contribution of this paper was to identify the effective time domain features of heart rate variability (HRV) signals via an improved forward floating selection analysis. The models were developed using the MIT-BIH arrhythmia database. A radial basis function (RBF) neural networks-based model was further developed using the same features and data sets to benchmark the GP/OLS and GP/SA models. The diagnostic performance of the GP/OLS and GP/SA classifiers was evaluated using receiver operating characteristics analysis. The results indicate a high level of efficacy of the GP/OLS model with sensitivity, specificity, positive predictivity, and accuracy rates of 99.11%, 98.91%, 98.23%, and 99.02%, respectively. These rates are equal to 99.11%, 97.83%, 98.23%, and 98.534% for the GP/SA model. The proposed GP/OLS and GP/SA models have a significantly better performance than the RBF and several models found in the literature.", } @Article{journals/es/YaghoubyABY12, author = "Farid Yaghouby and Ahmad Ayatollahi and Reihaneh Bahramali and Maryam Yaghouby", title = "Robust genetic programming-based detection of atrial fibrillation using {RR} intervals", journal = "Expert Systems", year = "2012", volume = "29", number = "2", pages = "183--199", keywords = "genetic algorithms, genetic programming, atrial fibrillation, heart rate variability signal, linear genetic programming, multi-expression programming, forward floating selection, arrhythmia detection", ISSN = "1468-0394", DOI = "doi:10.1111/j.1468-0394.2010.00571.x", size = "17 pages", abstract = "In this study, two variants of genetic programming, namely linear genetic programming (LGP) and multi-expression programming (MEP) are used to detect atrial fibrillation (AF) episodes. LGP- and MEP-based models are derived to classify samples of AF and Normal episodes based on the analysis of RR interval signals. A weighted least-squares (WLS) regression analysis is performed using the same features and data sets to benchmark the models. Another important contribution of this paper is identification of the effective time domain features of heart rate variability (HRV) signals upon an improved forward floating selection (IFFS) analysis. The models are developed using MIT-BIH arrhythmia database. The diagnostic performances of the LGP and MEP classifiers are evaluated through receiver operating characteristics (ROC) analysis. The results indicate that the LGP and MEP models are able to diagnose the AF arrhythmia with an acceptable high accuracy. The proposed models have significantly better diagnosis performances than the regression and several models found in the literature.", bibdate = "2012-06-05", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/es/es29.html#YaghoubyABY12", } @InProceedings{YaGmurcu:2024:IDAP, author = "Merve YaGmurcu and Sibel Arslan", title = "Feature Selection Using Automatic Programming Methods in Hypertension Risk Prediction", booktitle = "2024 8th International Artificial Intelligence and Data Processing Symposium (IDAP)", year = "2024", month = sep, keywords = "genetic algorithms, genetic programming, Hypertension, Support vector machines, Performance evaluation, Analytical models, Automatic programming, Simulation, Stroke (medical condition), Plasmas, Random forests, Immune system, Artificial Bee Colony Programming, Immune Plasma Programming", DOI = "doi:10.1109/IDAP64064.2024.10711046", abstract = "Hypertension is a condition where the pressure in the blood vessels is higher than normal. It can lead to serious problems such as heart attack, stroke, heart failure, kidney disease and vision problems. Therefore, early diagnosis and treatment is important to find appropriate treatment strategies for the disease. In this study, automatic programming (AP) methods, were used and compared to analyse the risk of hypertension. These methods are Artificial Bee Colony Programming developed from the behaviour of honeybees, Genetic Programming (GP) inspired by genetic selection and Immune Plasma Programming (IPP) based on immune plasma therapy. According to the performance evaluations obtained from the methods, GP and IPP were the most successful methods with test success rates of 0.91percent and 0.89percent respectively. In future research, Due to the success of the AP methods, we aim to develop different versions for health problems.", notes = "Also known as \cite{10711046}", } @Article{Yahya2010190, author = "Anwar Ali Yahya and Ramlan Mahmod and Abd Rahman Ramli", title = "Dynamic {Bayesian} networks and variable length genetic algorithm for designing cue-based model for dialogue act recognition", journal = "Computer Speech \& Language", volume = "24", number = "2", pages = "190--218", year = "2010", ISSN = "0885-2308", DOI = "doi:10.1016/j.csl.2009.04.002", URL = "http://www.sciencedirect.com/science/article/B6WCW-4W7B0DH-1/2/29a9b688bd5d374230572940760f5bd2", keywords = "genetic algorithms, genetic programming, Dialogue act recognition, Dynamic Bayesian networks, Variable length genetic algorithm, Lexical cues selection", abstract = "The automatic recognition of dialogue act is a task of crucial importance for the processing of natural language dialogue at discourse level. It is also one of the most challenging problems as most often the dialogue act is not expressed directly in speaker's utterance. In this paper, a new cue-based model for dialogue act recognition is presented. The model is, essentially, a dynamic Bayesian network induced from manually annotated dialogue corpus via dynamic Bayesian machine learning algorithms. Furthermore, the dynamic Bayesian network's random variables are constituted from sets of lexical cues selected automatically by means of a variable length genetic algorithm, developed specifically for this purpose. To evaluate the proposed approaches of design, three stages of experiments have been conducted. In the initial stage, the dynamic Bayesian network model is constructed using sets of lexical cues selected manually from the dialogue corpus. The model is evaluated against two previously proposed models and the results confirm the potentiality of dynamic Bayesian networks for dialogue act recognition. In the second stage, the developed variable length genetic algorithm is used to select different sets of lexical cues to constitute the dynamic Bayesian networks' random variables. The developed approach is evaluated against some of the previously used ranking approaches and the results provide experimental evidences on its ability to avoid the drawbacks of the ranking approaches. In the third stage, the dynamic Bayesian networks model is constructed using random variables constituted from the sets of lexical cues generated in the second stage and the results confirm the effectiveness of the proposed approaches for designing dialogue act recognition model.", notes = "GA applied to variable length 1D lists", } @InProceedings{Yakami:2016:TENCON, author = "Go Yakami and Ivan Tanev and Katsunori Shimohara and Shigeru Katagiri and Miho Ohsaki", booktitle = "2016 IEEE Region 10 Conference (TENCON)", title = "Automobile driving support system evolved by Genetic Programming", year = "2016", pages = "255--258", abstract = "We study a new approach, based on Genetic Programming, for generating automobile driving agents (driving rules). In a simulation environment, we develop agents for two tasks: lane departure recovery and risk avoidance lane change. The agents control a car by operating its front wheels using such observations as the distance between the car and the centreline. Our GP process generates an appropriate rule for front wheel operation. We experimentally demonstrate that our GP-based approach successfully generates an effective driving agent (rule).", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/TENCON.2016.7848001", month = nov, notes = "Also known as \cite{7848001}", } @Article{YAKOUB:2023:energy, author = "Ghali Yakoub and Sathyajith Mathew and Joao Leal", title = "Intelligent estimation of wind farm performance with direct and indirect `point' forecasting approaches integrating several {NWP} models", journal = "Energy", volume = "263", pages = "125893", year = "2023", ISSN = "0360-5442", DOI = "doi:10.1016/j.energy.2022.125893", URL = "https://www.sciencedirect.com/science/article/pii/S0360544222027797", keywords = "genetic algorithms, genetic programming, Wind power forecasting, NWP, Direct forecast, Indirect forecast, Machine learning", abstract = "Reliable wind power forecasting is essential for profitably trading wind energy in the electricity market and efficiently integrating wind-generated electricity into the power grids. In this paper, we propose short- and medium-term wind power forecasting systems targeted to the Nordic energy market, which integrate inputs on the wind flow conditions from three numerical weather prediction sources. A point forecasting scheme is adopted, which forecasts the power at the individual turbine level. Both direct and indirect forecasting approaches are considered and compared. An automated machine-learning pipeline, built and optimized using genetic programming, is implemented for developing the proposed forecasting models. The turbine level power forecasts using different approaches are then combined into a single forecast using a weighting method based on recent forecast errors. These are then aggregated for the wind farm level power estimates. The proposed forecasting schemes are implemented with data from a Norwegian wind farm. We found that in both the direct and indirect forecasting approaches, the forecasting errors could be reduced between 8percent and 22percent, while inputs from several NWP sources are used together. The wind downscaling model, which is used in the indirect forecasting approach, could significantly contribute to the model's accuracy. The performance of both the direct and indirect forecasting schemes is comparable for the studied wind farm", } @InProceedings{Yakubu:2019:IEEM, author = "H. Yakubu and C. K. Kwong", booktitle = "2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)", title = "Multigene Genetic Programming Based Fuzzy Regression for Modelling Customer Satisfaction Based on Online Reviews", year = "2019", pages = "1541--1545", month = dec, keywords = "genetic algorithms, genetic programming, multigene genetic programming, customer satisfaction models, fuzzy regression, online reviews", ISSN = "2157-362X", DOI = "doi:10.1109/IEEM44572.2019.8978852", abstract = "As markets become increasingly competitive, most businesses have adopted modern practices that helps them to enhance the competitiveness of their products. Such practices involve the use of internet though which companies gain insights into the concerns of their customers. For instance, the proliferation of e-commerce websites has enabled consumers to voice their opinions on the products they have purchased. This study proposes a methodology for modeling customer satisfaction (CS) based on online reviews using a new multigene genetic programming based fuzzy regression (MGGP-FR). Polynomial structures of CS models were developed by employing the multigene genetic programming method. The fuzzy coefficients of the polynomial structures were then determined using the fuzzy regression analysis. The proposed method was illustrated using an electronic hair dryer as a case study. The validation test results indicated that MGGP-FR the outperformed the genetic programming based fuzzy regression and the fuzzy regression analysis in terms of prediction errors.", notes = "Also known as \cite{8978852}", } @Article{DBLP:journals/soco/YakubuKL21, author = "Hanan Yakubu and C. K. Kwong and Carman K. M. Lee", title = "A multigene genetic programming-based fuzzy regression approach for modelling customer satisfaction based on online reviews", journal = "Soft Comput.", volume = "25", number = "7", pages = "5395--5410", year = "2021", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/s00500-020-05538-8", DOI = "doi:10.1007/s00500-020-05538-8", timestamp = "Tue, 23 Mar 2021 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/soco/YakubuKL21.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @PhdThesis{yakubu:thesis, author = "Hanan Yakubu", title = "Supporting new product development using customers' online data and computational intelligence methods", school = "Department of Industrial and Systems Engineering, Hong Kong Polytechnic University", year = "2021", address = "Hung Hom, Kowloon, Hong Kong", month = aug, keywords = "genetic algorithms, genetic programming, AI, New products, Consumer behavior, Data processing, Market share, Econometric models", URL = "https://theses.lib.polyu.edu.hk/handle/200/11692", URL = "https://theses.lib.polyu.edu.hk/bitstream/200/11692/3/6132.pdf", size = "298 pages", abstract = "In recent times, the availability, and the proliferation of the generation of online data derived from social media and e-commerce platforms have been capitalised by firms in different ways to influence, promote, enhance, and develop new products. In the area of the new product development (NPD) process, online data can be applied differently in each stage of the NPD process. Within the NPD process, enhancing customer satisfaction and making product demand forecasts are two areas that require the extensive use of data. Previously, to obtain data for NPD, surveys were mostly conducted by product manufacturers to seek information's from customers before designing a new or improving a new product. However, the nature of conducting surveys tends to be cumbersome and respondents can easily misinterpret the questionnaires. Surveys also have the limitation of being incomplete and, usually, the ratings used in surveys do not convey the real needs of respondents. Thus, developing customer satisfaction models from surveys presents many complexities since customers responses fuzziness is usually not considered. Similarly, the identification and predicting of the most important product attributes have not been explored in past studies in addressing the dynamic needs of consumers. This is due to the over reliance on surveys that fails to provide reliable data for manufacturers, thus preventing them from producing products that meet the rapid changes in customer needs due to technological advancement. As part of product development activities, the demand for products is usually forecasted to prevent revenue loss. However, most of these forecasts require large amount of historical data to develop a demand forecast model. With the advent of the internet, manufacturers can integrate constantly updated user generated online data in forecasting models in order to forecast the adoption of products. To overcome the above limitations, the objectives of this research are presented in three phases: i) To propose a novel customers satisfaction model that address the fuzziness and nonlinearity of customer satisfaction models using multigene genetic programming based fuzzy regression (MGGP-FR) ii) To formulate a methodology for determining and predicting the importance of product attributes. The Shapely Value and Choquet integral are employed to estimate the importance of product attributes and based on the importance values, a fuzzy rough set times series method is proposed to forecast the future importance of product attributes. iii) To propose a new market share model and demand forecasting model that addresses uncertainties in forecasting. A market share model is developed from the multinomial logit (MNL) model and the fuzzy regression (FR) approach while the demand model is developed from a modified Bass model integrated with sentiment scores from online reviews. A case study on modeling customer satisfaction for electronic hairdryers using MGGP-FR is presented in this study. To validate the proposed methodology, the results of the MGGP-FR are compared with previously proposed methods mainly FR, genetic programming (GP), and genetic programming-based fuzzy regression (GP-FR). Based on the mean relative errors and the variance of errors of the MGGP-FR and previous methods, the proposed MGGP-FR showed a better performance when compared with the previous methods. Next, forecasting the future importance of the product attributes of an electronic hairdryer is illustrated using the fuzzy rough set time series method. The proposed fuzzy rough set time series forecasting accuracy outperformed the fuzzy time series method. Lastly, a case study on forecasting the adoption of a Tablet P.C is used to illustrate the applicability of the proposed fuzzy modelling and discrete choice analysis method for forecasting product adoption using online reviews. The proposed method was compared with the fuzzy time series forecasting and the original Bass model and was found to be better as it provided different scenarios for the forecast and acceptable forecasting results.", notes = "Supervisors: C. K. Kwong and C. K. M. Lee", } @InProceedings{Yalamanchili:2016:SCOPES, author = "Sangeetha Yalamanchili and K. Sitha Kumari", booktitle = "2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES)", title = "Comparison of manual and automatic testing using genetic algorithm for information handling system", year = "2016", pages = "1795--1799", abstract = "Testing is one of the vital activities which must be performed during the software development mainly intent to find the errors according to customer requirement. The purpose of Information Handling for improving software reliability is to provide better service to the administrator or useful for applications developed in an organization. Manual testing process an inter-office communication has been provided for all the developers, managers, tester to communicate. As developer develops the application where the tester can generate the test case required for an application and compare the actual value and expected value the required bug reports as been generated manually. Automation testing process uses a VBScript scripting language to specify the test procedure and to manipulate the objects and controls of the application under test. Where the test cases are been as scripts. In this process it involves the functional testing in genetic algorithm as genetic programming in required computer language to obtain a result summary whereas the report structure.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SCOPES.2016.7955752", month = oct, notes = "Also known as \cite{7955752}", } @InProceedings{Yamada:2021:ICRAE, author = "Syuya Yamada and Ryoma Sato and Tatsuhiro Tamaki and Eisuke Kita", booktitle = "2021 6th International Conference on Robotics and Automation Engineering (ICRAE)", title = "Control Program Design of Autonomous Vehicle Robot Using Grammatical Evolution", year = "2021", pages = "308--312", abstract = "The automatic design of the control program of the vehicle robot is presented in this study. LEGO MINDSTORM EV3 and RVW Level Builder are used for this study. The control program of EV3 is designed automatically by using Grammatical Evolution (GE), which is one of the evolutionary algorithms. The generated program is applied for controlling EV3. The results show that the robot behaviour in the experiment of EV3 was roughly equivalent to it in simulator. Therefore, the proposed algorithm based on GE can generate the control program of robot automatically.", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Automation, Robot control, Evolutionary computation, Germanium, Robots, Autonomous vehicles, Convergence, Vehicle Robot, LEGO MINDSTORM EV3", DOI = "doi:10.1109/ICRAE53653.2021.9657773", month = nov, notes = "Also known as \cite{9657773}", } @InProceedings{Yamagata:2017:IIAI-AAI, author = "Yuuki Yamagata and Tetsuhiro Miyahara and Yusuke Suzuki and Tomoyuki Uchida and Fumiya Tokuhara and Tetsuji Kuboyama", booktitle = "2017 6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI)", title = "Acquisition of Multiple Graph Structured Patterns by an Evolutionary Method Using Sets of TTSP Graph Patterns as Individuals", year = "2017", pages = "459--464", abstract = "Knowledge acquisition from graph structured data is an important task in machine learning and data mining. TTSP (Two-Terminal Series Parallel) graphs are used as data models for electric networks and scheduling. We propose a learning method for acquiring characteristic multiple graph structured patterns by evolutionary computation using sets of TTSP graph patterns as individuals, from positive and negative TTSP graph data, in order to represent sets of TTSP graphs more precisely.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IIAI-AAI.2017.198", month = jul, notes = "Also known as \cite{8113288}", } @InProceedings{Yamagiwa:2009:CISIS, author = "Motoi Yamagiwa and Eiji Kikuchi and Minoru Uehara and Makoto Murakami and Masahide Yoneyama", title = "Reconstruction for Artificial Degraded Image Using Constructive Solid Geometry and Strongly Typed Genetic Programming", booktitle = "International Conference on Complex, Intelligent and Software Intensive Systems, CISIS '09", year = "2009", month = mar, pages = "162--168", keywords = "genetic algorithms, genetic programming, 2-dimensional sinc filter, acoustic imaging, acoustic impedance, artificial degraded image reconstruction, constructive solid geometry, neural network, acoustic imaging, image reconstruction, neural nets", DOI = "doi:10.1109/CISIS.2009.164", abstract = "Acoustic imaging is effective in extreme environments to take images without being influenced by optical properties. However, such images tend to deteriorate rapidly because acoustic impedance in air is low. It is thus necessary to restore the image of the object from a deteriorated image so that the object can be recognized in a search. We used a neural network in the previous work as a post processor and tried to reconstruct the original object image. However, this method needs to learn the original object image. In this work, we propose combining constructive solid geometry (CSG) with genetic programming (GP) as a new technique that does not require learning. To confirm the effectiveness of this technique, we reconstruct the image of an object from a deteriorated image created by applying a 2-dimensional sinc filter to the original image.", notes = "Also known as \cite{5066783}", } @InProceedings{yamagiwa:2009:AI, author = "M. Yamagiwa and F. Sugimoto and M. Yoneyama", title = "Reconstruction of the Ultrasonic Image by the Combination of Genetic Programming and Constructive Solid Geometry", booktitle = "Acoustical Imaging", year = "2009", pages = "245--250", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-1-4020-8823-0_34", DOI = "doi:10.1007/978-1-4020-8823-0_34", notes = "Department of Information and Computer Science, Faculty of Engineering, Toyo University, 2100 Kujirai, Japan", } @InProceedings{Yamaguchi:2012:CEC, title = "Comparison Study of Controlling Bloat Model of {GP} in Constructing Filter for Cell Image Segmentation Problems", author = "Hiroaki Yamaguchi and Tomoyuki Hiroyasu and Sakito Nunokawa and Noriko Koizumi and Naoki Okumura and Hisatake Yokouchi and Mitsunori Miki and Masato Yoshimi", pages = "3503--3510", booktitle = "Proceedings of the 2012 IEEE Congress on Evolutionary Computation", year = "2012", editor = "Xiaodong Li", month = "10-15 " # jun, DOI = "doi:10.1109/CEC.2012.6252995", address = "Brisbane, Australia", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Applications of Evolutionary Computation in Biomedical Engineering (IEEE-CEC), Biometrics, bioinformatics and biomedical applications", abstract = "The final goal of this research is to construct a cell image analysis system for supporting corneal regenerative medicine. Existing image analysis software requires knowledge about image processing of users because users have to combine several image processing on its analysis. Therefore, several types of methods to construct the objective image processing automatically using genetic programming (GP) have been proposed. However, in conventional researches, only canonical GP models were used. In this paper, GP models suited to cell image segmentation are investigated applying proposed controlling bloat model of GP. Applied models were six types in addition to the canonical model; those are Double Tournament, Tarpeian, Non-Destructive Crossover (NDC), Recombinative Hill-Climbing (RHC), Spatial Structure + Elitism (SS+E). The combination of image processing obtained by these GP models and the robustness are examined by comparative experiments, using corned endothelium cell image. The experiment results showed that SS+E is superior to other models in both robustness and image processing constructed for cell image segmentation, without depending on parameters of tree depth limit and penalty.", notes = "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the EPS and the IET.", } @InProceedings{Yamamori:2009:ICCAS-SICE, author = "Kunihito Yamamori and Ikuo Yoshihara and Mayumi Kamiguchi and Kazuo Nishiyama", title = "An automatic model building for screening functional foods with GP", booktitle = "International Joint Conference ICROS-SICE, 2009", year = "2009", pages = "3679--3684", address = "Fukuoka International Conference Center, Japan", month = "18-21 " # aug, keywords = "genetic algorithms, genetic programming, antiangiogenic activity estimation, automatic model building, cancer prevention, functional food screening, multidimensional regression analysis, physiological activity estimation, cancer, food technology, health care, physiology, proteins", isbn13 = "978-4-907764-34-0", URL = "http://ieeexplore.ieee.org/search/srchabstract.jsp?tp=&arnumber=5334772", abstract = "Health and disease are one of the most important issues for us. Especially, cancer is the most serious problem in recent decades. Some researchers focus on functional foods that have good effect for human body to prevent cancer. However, there are various kinds of foods in the world, and to develop a high-throughput screening method is anticipated. In this paper, we propose a method to estimate physiological activity of foods from protein expression levels by genetic programming when the constituent of foods is given to cells. Case studies show that our method improved accuracy to estimate anti-angiogenic activity than that by multidimensional regression analysis.", notes = "Conf name also give as ICCAS-SICE 2009. Fac. of Eng., Univ. of Miyazaki, Miyazaki, Japan. Also known as \cite{5334772}", } @Article{yamamoto:1998:ALR, author = "H. Yamamoto", title = "Robot path planning by genetic programming", journal = "Artificial Life and Robotics", year = "1998", volume = "2", number = "1", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/BF02471149", DOI = "doi:10.1007/BF02471149", abstract = "This paper describes a method to determine the path of a robot that travels around between machine tools in a production line FA factory. This decision is made by the genetic algorithm with Lisp language programming. In the algorithm, the building block method to decide fitness is adopted. The method is applied to a flexible manufacturing system (FMS) that has four machine tools and a robot.", notes = "Department of Opto-Mecbatronics, Faculty of Systems Engineering, Wakayama University, 930 Sakaedani, W akayama-shi 640, Japan", } @TechReport{wac2005tr-lyct, author = "Lidia Yamamoto and Christian Tschudin", title = "Experiments on the Automatic Evolution of Protocols using Genetic Programming", institution = "University of Basel", year = "2005", number = "CS-2005-002", month = "21 " # apr, keywords = "genetic algorithms, genetic programming, protocol synthesis, protocol evolution", URL = "http://cn.cs.unibas.ch/people/ly/doc/wac2005tr-lyct.pdf", abstract = "One of the biggest challenges in obtaining truly autonomic, self managed networks is to automate the process of software evolution, and in particular, the evolution of protocol implementations and configurations. Such networks ultimately require self-modifying, evolving protocol software. Otherwise humans must intervene in every situation that has not been anticipated at design time. For this to become feasible autonomic systems must ensure non-disruptive, resilient on-line software evolution. We are starting to explore approaches to network evolution that operate directly at the code level. We investigate related code steering techniques in two directions: One is the fully automatic selection of protocol service elements where, depending on device characteristics and current operation environment, each communication entity has to select among a potentially wide variety of protocol implementations providing similar services. The other direction relates to the automatic synthesis of new protocol elements which are the result of optimising existing implementations for a specific context. In both cases we look at genetic programming as a tool to generate new code and software configurations automatically. We propose a framework for such a resilient protocol evolution and report on first exploratory results on the adaptation and re-adaptation to environmental conditions, and the elimination of superfluous code.", notes = "A slightly condensed version of this report will appear at LNCS 3854, Proc. 2nd. Workshop on Autonomic Communication (WAC 2005), \cite{conf/wac/YamamotoT05}. Cited by \cite{Weise:2011:ieeeTEC}", size = "16 pages", } @InProceedings{conf/wac/YamamotoT05, title = "Experiments on the Automatic Evolution of Protocols Using Genetic Programming", author = "Lidia Yamamoto and Christian F. Tschudin", year = "2005", pages = "13--28", editor = "Ioannis Stavrakakis and Michael Smirnov", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3854", booktitle = "Autonomic Communication, Second International IFIP Workshop, WAC 2005, Revised Selected Papers", address = "Athens, Greece", month = oct # " 2-5", bibdate = "2006-03-02", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/wac/wac2005.html#YamamotoT05", keywords = "genetic algorithms, genetic programming, protocol synthesis, protocol evolution, TCP, fraglets, chemical soup of rules", ISBN = "3-540-32992-7", URL = "http://cn.cs.unibas.ch/people/ly/doc/wac2005-lyct.pdf", DOI = "doi:10.1007/11687818_2", size = "16 pages", abstract = "Truly autonomic networks ultimately require self-modifying, evolving protocol software. Otherwise humans must intervene in every situation that has not been anticipated at design time. For this to become feasible autonomic systems must ensure non-disruptive on-line software evolution. We investigate related code steering techniques in two directions: One is the fully automatic selection of protocol service elements where, depending on device characteristics and current operation environment, each communication entity has to select among a potentially wide variety of protocol implementations providing similar services. The other direction relates to the automatic synthesis of new protocol elements which are the result of optimising existing implementations for a specific context. In both cases we look at genetic programming as a tool to generate new code and software configurations automatically. In this paper we propose a framework for such a resilient protocol evolution and report on first exploratory results on the adaptation and re-adaptation to environmental conditions, and the elimination of superfluous code.", notes = "Published 2006 ? 'software hardening' and GP, code steering. Homologous crossover. Chemical reaction between two fraglets of code. Gamma systems. grammar. http://cn.cs.unibas.ch/people/ly/doc/wac2005-lyct.pdf claims to be Technical Report CS-2005-002 University of Basel but it does not appear in http://informatik.unibas.ch/research/publications_tec_report.html", } @InProceedings{eurogp07:yamamoto, author = "Lidia Yamamoto", title = "Code Regulation in Open Ended Evolution", editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar", booktitle = "Proceedings of the 10th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "4445", year = "2007", address = "Valencia, Spain", month = "11-13 " # apr, pages = "271--280", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-71602-5", isbn13 = "978-3-540-71602-0", DOI = "doi:10.1007/978-3-540-71605-1_25", abstract = "We explore a homoeostatic approach to program execution in computer systems: the 'concentration' of computation services is regulated according to their fitness. The goal is to obtain a self-healing effect so that the system can resist harmful mutations that could happen during on-line evolution. We present a model in which alternative program variants are stored in a repository representing the organism's 'genotype'. Positive feedback signals allow code in the repository to be expressed (in analogy to gene expression in biology), meaning that it is injected into a reaction vessel (execution environment) where it is executed and evaluated. Since execution is equivalent to a chemical reaction, the program is consumed in the process, therefore needs more feedback in order to be re-expressed. This leads to services that constantly regulate themselves to a stable condition given by the fitness feedback received from the users or the environment. We present initial experiments using this model, implemented using a chemical computing language.", notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007", } @InProceedings{conf/eurogp/Yamamoto08, title = "Plasmid{PL}: {A} Plasmid-Inspired Language for Genetic Programming", author = "Lidia Yamamoto", bibdate = "2008-04-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/eurogp/eurogp2008.html#Yamamoto08", booktitle = "Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008", address = "Naples", month = "26-28 " # mar, publisher = "Springer", year = "2008", volume = "4971", editor = "Michael O'Neill and Leonardo Vanneschi and Steven Gustafson and Anna Isabel {Esparcia Alcazar} and Ivanoe {De Falco} and Antonio {Della Cioppa} and Ernesto Tarantino", isbn13 = "978-3-540-78670-2", pages = "337--349", series = "Lecture Notes in Computer Science", DOI = "doi:10.1007/978-3-540-78671-9_29", keywords = "genetic algorithms, genetic programming", notes = "Part of \cite{conf/eurogp/2008} EuroGP'2008 held in conjunction with EvoCOP2008, EvoBIO2008 and EvoWorkshops2008", } @InProceedings{Yamamoto:2011:EPIA, author = "Lidia Yamamoto and Wolfgang Banzhaf and Pierre Collet", title = "Evolving Reaction-Diffusion Systems on {GPU}", booktitle = "Proceedings 15th Portuguese Conference on Artificial Intelligence, {EPIA 2011}", year = "2011", editor = "Luis Antunes and Helena Sofia Pinto", volume = "7026", series = "Lecture Notes in Computer Science", pages = "208--223", address = "Lisbon, Portugal", month = oct # " 10-13", publisher = "Springer", keywords = "genetic algorithms, genetic programming, GPU", isbn13 = "978-3-642-24768-2", DOI = "doi:10.1007/978-3-642-24769-9_16", size = "16 pages", abstract = "Reaction-diffusion systems contribute to various morphogenetic processes, and can also be used as computation models in real and artificial chemistries. Evolving reaction-diffusion solutions automatically is interesting because it is otherwise difficult to engineer them to achieve a target pattern or to perform a desired task. However most of the existing work focuses on the optimization of parameters of a fixed reaction network. In this paper we extend this state of the art by also exploring the space of alternative reaction networks, with the help of GPU hardware. We compare parameter optimization and reaction network optimization on the evolution of reaction-diffusion solutions leading to simple spot patterns. Our results indicate that these two optimization modes tend to exhibit qualitatively different evolutionary dynamics: in the former, the fitness tends to improve continuously in gentle slopes, while the latter tends to exhibit large periods of stagnation followed by sudden jumps, a sign of punctuated equilibria.", notes = "Says GP analogue", } @Article{Yamamoto:2011:SwarmIntl, author = "Lidia Yamamoto and Daniele Miorandi and Pierre Collet and Wolfgang Banzhaf", title = "Recovery Properties of Distributed Cluster Head Election using Reaction Diffusion", journal = "Swarm Intelligence", year = "2011", volume = "5", number = "3-4", pages = "225--255", month = dec, note = "ANTS 2010 Special Issue, Part 1", keywords = "genetic algorithms, genetic programming, chemical computing, Reaction diffusion, Activator inhibitor, Pattern formation, Cluster head", DOI = "doi:10.1007/s11721-011-0058-8", size = "31 pages", abstract = "Chemical reaction-diffusion is a basic component of morphogenesis, and can be used to obtain interesting and unconventional self-organizing algorithms for swarms of autonomous agents, using natural or artificial chemistries. However, the performance of these algorithms in the face of disruptions has not been sufficiently studied. In this paper we evaluate the use of reaction-diffusion for the morphogenetic engineering of distributed coordination algorithms, in particular, cluster head election in a distributed computer system. We consider variants of reaction-diffusion systems around the activator-inhibitor model, able to produce spot patterns. We evaluate the robustness of these models against the deletion of activator peaks that signal the location of cluster heads, and the destruction of large patches of chemicals. Three models are selected for evaluation: the Gierer-Meinhardt Activator-Inhibitor model, the Activator-Substrate Depleted model, and the Gray-Scott model. Our results reveal a trade-off between these models. The Gierer-Meinhardt model is more stable, with rare failures, but is slower to recover from disruptions. The Gray-Scott model is able to recover more quickly, at the expense of more frequent failures. The Activator-Substrate model lies somewhere in the middle.", } @Article{YAMASHITA:2022:ESA, author = "Gabrielli H. Yamashita and Flavio S. Fogliatto and Michel J. Anzanello and Guilherme L. Tortorella", title = "Customized prediction of attendance to soccer matches based on symbolic regression and genetic programming", journal = "Expert Systems with Applications", volume = "187", pages = "115912", year = "2022", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2021.115912", URL = "https://www.sciencedirect.com/science/article/pii/S0957417421012677", keywords = "genetic algorithms, genetic programming, Symbolic regression, Soccer match attendance, Prediction model, Machine learning", abstract = "Forecasting of attendance demand to sports events is a common topic of study in the sports economics literature, being traditionally addressed through the use of multivariate regression analysis or structural equation modeling. In recent years, a restricted number of authors have approached the problem using machine learning methods, with promising results. In this article, we investigate the use of analytical techniques from the machine learning toolbox, namely symbolic regression and genetic programming (SR/GP), to determine the best fitting prediction function that relates contextual and panel independent variables to soccer match attendance. For that purpose, we analyze five years of attendance at soccer matches played at a large stadium in Southern Brazil. Two datasets with game-level attendance to matches from two soccer championships are considered, covering the seasons from 2014 to 2019. We also propose the use of expert panels to collect information on relevant candidate independent variables and their interactions to be tested in the prediction models, expediting the feature selection step of the modeling process. From the academic perspective, our study is the first to propose the use of SR/GP to model soccer match attendance, contributing to the limited number of studies that use game-by-game attendance as the dependent variable and develop team-specific attendance models. From the managerial perspective, identifying factors responsible for systematic variations in match attendance levels enables better sport management and marketing plans", } @InProceedings{Yamazaki:2008:cec, author = "Hirotaka Yamazaki and Ivan Tanev and Tomoyuki Hiroyasu and Katsunori Shimohara", title = "On the Generality of the Evolved Driving Rules of an Agent Operating a Model of a Car", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "4138--4145", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1823-7", file = "EC0877.pdf", DOI = "doi:10.1109/CEC.2008.4631362", abstract = "We present an approach for automated evolutionary design of the functionary of driving agent, able to operate a software model of fast running car. The objective of our work is to automatically discover a set of driving rules (if existent) that are general enough to be able to adequately control the car in all sections of predefined circuits. In order to evolve an agent with such capabilities, we propose an indirect, generative representation of the driving rules as algebraic functions of the features of the current surroundings of the car. These functions, when evaluated for the current surrounding of the car yield concrete values of the main attributes of the driving style (e.g., straight line velocity, turning velocity, etc.), applied by the agent in the currently negotiated section of the circuit. Experimental results verify both the very existence of the general driving rules and the ability of the employed genetic programming framework to automatically discover them. The evolved driving rules offer a favourable generality, in that a single rule can be successfully applied (i) not only for all the section of a particular circuit, but also (ii) for the sections in several a priori defined circuits featuring different characteristics.", keywords = "genetic algorithms, genetic programming", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @InProceedings{yamazaki:1998:HPlcrvgs, author = "Koetsu Yamazaki and Sourav Kundu and Michitomo Hamano", title = "Genetic Programming Based Learning of Control Rules for Variable Geometry Structures", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "412--415", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-548-7", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1998/yamazaki_1998_HPlcrvgs.pdf", size = "4 pages", notes = "GP-98", } @Article{Yampolskiy:2018:EB, author = "Roman V. Yampolskiy", title = "Why We Do Not Evolve Software? Analysis of Evolutionary Algorithms", journal = "Evolutionary Bioinformatics", year = "2018", volume = "14", pages = "1176934318815906", note = "Special collection: Algorithm Development for Evolutionary Biological Computation", keywords = "genetic algorithms, genetic programming, Darwinian algorithm, optimisation", ISSN = "1176-9343", DOI = "doi:10.1177/1176934318815906", size = "11 pages", abstract = "we review the state-of-the-art results in evolutionary computation and observe that we do not evolve non-trivial software from scratch and with no human intervention. A number of possible explanations are considered, but we conclude that computational complexity of the problem prevents it from being solved as currently attempted. A detailed analysis of necessary and available computational resources is provided to support our findings.", notes = "See also https://arxiv.org/abs/1810.07074", } @InProceedings{Yan:2008:ICIEA, author = "Bai Yan and Jiang Yiheng and Zhu Yaochun and Xiaofei Li and Fan Li", title = "Modeling a complex system using multiobjective genetic programming", booktitle = "3rd IEEE Conference on Industrial Electronics and Applications, ICIEA 2008", year = "2008", month = "3-5 " # jun, pages = "781--784", abstract = "Genetic Programming is a kind of searching technique, which is intelligent, and it works by emulating natural evolution. The intelligent modelling technique based on genetic programming can recognise the relationship between the inputs and outputs of a complex system, and it has ascendeny of searching out the frame and the parameters of the system at the same time and the searching results are explicit. A novel multiobjective genetic programming, which searching aim is to minimise the sum of squares of deviations, the complexity and the maximal dynamic deviation, is put forward to model a coordinate system of power plant, and the results from simulation show that it is valid.", keywords = "genetic algorithms, genetic programming, intelligent modelling technique, maximal dynamic deviation, multiobjective genetic programming, power plant coordinated system, power engineering computing, power plants", DOI = "doi:10.1109/ICIEA.2008.4582621", notes = "Coal Main steam. Dept. of Autom., North China Electr. Power Univ., Beijing. Also known as \cite{4582621}", } @InProceedings{Yan:2009:CINC, author = "Jingfeng Yan and Guoqing Li", title = "The Application of Macro-Economic Prediction Based on Improved Gene Expression Programming", booktitle = "International Conference on Computational Intelligence and Natural Computing, CINC '09", year = "2009", month = "6-7 " # jun, volume = "1", pages = "266--268", abstract = "An improved gene expression programming (IGEP) is proposed in this paper. It has some new features: 1) introducing a new individual coding; 2) introducing a new way of creating constants; 3) introducing a hybrid self-adaptive crossover-mutation operator, which can enhance the search ability and exploit the optimum offspring. To validate the performance of IGEP, this paper applies IGEP into the solution of the macro-economic predictions. The experimental results demonstrate that Improved GEP can automatically find better Optimisation Model, based on which prediction will be generated much more exactly.", keywords = "genetic algorithms, genetic programming, gene expression programming, hybrid self-adaptive crossover-mutation operator, improved gene expression programming, macro-economic prediction, optimum offspring, search ability, macroeconomics", DOI = "doi:10.1109/CINC.2009.31", notes = "Coll. of Comput. Sci. & Technol., Xuchang Univ., Xuchang, China Also known as \cite{5231146}", } @InProceedings{Yan:2006:WCICA, author = "Liping Yan and Jianchao Zeng", title = "Using Particle Swarm Optimization and Genetic Programming to Evolve Classification Rules", booktitle = "The Sixth World Congress on Intelligent Control and Automation, WCICA 2006", year = "2006", volume = "1", pages = "3415--3419", address = "Dalian", publisher = "IEEE", keywords = "genetic algorithms, genetic programming", ISBN = "1-4244-0332-4", DOI = "doi:10.1109/WCICA.2006.1713002", abstract = "According to analysing particle swarm optimisation (PSO), the structure of genetic programming (GP) and classifier model, PSO algorithm and GP were made to combine to evolve classification rules. Rules were described as binary tree which non-leaf node denoted rule structure and leaf-node was correspond to rule value. Leaf node and non-leaf node employed different evolutionary strategy. First, PSO was applied to evolve leaf node in order to obtain the optimum rule of certain structure, then GP was adopted to optimise rule structure. The best rules were obtained after the twice optimisation. Finally, the new method indicated efficiency through experiments on several datasets of UCI", notes = "China North Univ., Taiyuan", } @InProceedings{Yan:2016:CEC, author = "Longfei Yan and Yi Mei and Hui Ma and Mengjie Zhang", title = "Evolutionary Web Service Composition: A Graph-based Memetic Algorithm", booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016)", year = "2016", editor = "Yew-Soon Ong", pages = "201--208", address = "Vancouver", month = "24-29 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-5090-0623-6", DOI = "doi:10.1109/CEC.2016.7743796", abstract = "Web Service Composition (WSC) is a prominent way of actualizing service-oriented architecture by integrating network-accessible Web services into a new invokable application. Evolutionary computation techniques have provided rewarding approaches in automatic Web service composition over the last decade. However, the studies on considering both functionality and non-functionality (i.e. Quality-of-Service, QoS) properties are still limited. In this paper, we propose a novel Graph-Based Memetic Algorithm (GBMA) for solving the QoS-aware WSC problems. GBMA adopts the graph representation proposed by GraphEvol, which is one of the state-of-the-art algorithms. More importantly, GBMA designs and uses a local search based on two newly designed move operators to overcome the drawbacks of the mutation operator in GraphEvol. The experimental results show that the proposed GBMA outperformed GraphEvol, which is the counterpart without local search, in terms of both solution quality and convergence speed. This demonstrates the efficacy and efficiency of combining local search with global search in solving QoS-aware WSC problems.", notes = "WCCI2016", } @InProceedings{yan:2024:GECCO2, author = "Longfei Felix Yan and Hui Ma and Gang Chen2", title = "Reinforcement {Learning-Assisted} Genetic Programming Hyper Heuristic Approach to {Location-Aware} Dynamic Online Application Deployment in Clouds", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference", year = "2024", editor = "Ting Hu and Aniko Ekart and Julia Handl and Xiaodong Li and Markus Wagner and Mario Garza-Fabre and Kate Smith-Miles and Richard Allmendinger and Ying Bi and Grant Dick and Amir H Gandomi and Marcella Scoczynski Ribeiro Martins and Hirad Assimi and Nadarajen Veerapen and Yuan Sun and Mario Andres Munyoz and Ahmed Kheiri and Nguyen Su and Dhananjay Thiruvady and Andy Song and Frank Neumann and Carla Silva", pages = "988--997", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, genetic programming hyper heuristic, reinforcement learning, surrogate model, application deployment", isbn13 = "979-8-4007-0494-9", DOI = "doi:10.1145/3638529.3654058", size = "10 pages", abstract = "Location-Aware Dynamic Online Application dePloyment (LADOAP) in clouds is an NP-hard combinatorial optimisation problem. Genetic Programming Hyper-Heuristic (GPHH) has emerged as a promising approach for addressing LADOAP demands by dynamically generating Virtual Machine (VM) selection heuristics online. However, the performance of GPHH is impeded by long simulation times and low sampling efficiency. In this paper, we propose a novel hyper-heuristic framework that integrates Genetic Programming Hyper-Heuristic (GPHH) and Reinforcement Learning (RL) approaches to evolve rules for efficiently selecting location-aware Virtual Machines (VMs) capable of hosting multiple containers. The RL policy's value function acts as a surrogate model, significantly expediting the evaluation of generated VM selection rules. By applying this hybrid framework to LADOAP problems, we achieve competitive performance with a notable reduction in the number of required simulations. This innovative approach not only enhances the efficiency of VM selection but also contributes to advancing the state-of-the-art in addressing complex LADOAP challenges.", notes = "GECCO-2024 GP A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @Article{Yan:2014:SP, author = "Ruomei Yan and Ling Shao and Li Liu and Yan Liu", title = "Natural image denoising using evolved local adaptive filters", journal = "Signal Processing", year = "2014", volume = "103", pages = "36--44", month = oct, keywords = "genetic algorithms, genetic programming, Image denoising, Bilateral filter", ISSN = "0165-1684", URL = "http://www.sciencedirect.com/science/article/pii/S0165168413004556", DOI = "doi:10.1016/j.sigpro.2013.11.019", size = "9 pages", abstract = "The coefficients in previous local filters are mostly heuristically optimised, which leads to artifacts in the denoised image when the optimization is not adaptive enough to the image content. Compared to parametric filters, learning-based denoising methods are more capable of tackling the conflicting problem of noise reduction and artifact suppression. In this paper, a patch-based Evolved Local Adaptive (ELA) filter is proposed for natural image denoising. In the training process, a patch clustering is used and the genetic programming (GP) is applied afterwards for determining the optimal filter (linear or nonlinear in a tree structure) for each cluster. In the testing stage, the optimal filter trained beforehand by GP will be retrieved and employed on the input noisy patch. In addition, this adaptive scheme can be used for different noise models. Extensive experiments verify that our method can compete with and outperform the state-of-the-art local denoising methods in the presence of Gaussian or salt-and-pepper noise. Additionally, the computational efficiency has been improved significantly because of the separation of the offline training and the online testing processes.", } @Article{YAN:2022:MSSP, author = "Tongtong Yan and Dong Wang2 and Tangbin Xia and Zhike Peng and Lifeng Xi", title = "Investigations on generalized {Hjorth}'s parameters for machine performance degradation assessment", journal = "Mechanical Systems and Signal Processing", year = "2022", volume = "168", pages = "108720", keywords = "genetic algorithms, genetic programming, Machine performance degradation assessment (MPDA), Health indicators, Hjorth parameters, Composite fitness function", ISSN = "0888-3270", URL = "https://www.sciencedirect.com/science/article/pii/S0888327021010396", DOI = "doi:10.1016/j.ymssp.2021.108720", abstract = "The strategies of machine performance degradation assessment (MPDA) are to detect the time of incipient fault initiation as early as possible and subsequently track machine deterioration evolution by extracting an effective health indicator so that timely maintenance strategies can be scheduled to avoid catastrophic accidents. Three Hjorth's parameters including Activity, Mobility, and Complexity have been studied to extract informative features from ectroencephalography signals, while their theoretical investigations for MPDA are not fully explored. In this paper, theoretical and experimental investigations on Hjorth's parameters for MPDA are conducted, which verifies that Activity is more suitable to MPDA than Mobility and Complexity. A new theorem of Hjorth's parameters for MPDA is accordingly proposed to show that Hjorth_1, Hjorth_2, Hjorth_3, and Activity have a same characteristic with complexity measures rather than sparsity measures for MPDA. After the characteristics of Hjorth's parameters for MPDA are thoroughly studied, generalized Hjorth's parameters are constructed and designed based on Genetic programming (GP). Firstly, Hjorth's parameters are reformulated as a unified mathematical framework. Aiming at solving the limitations of Hjorth's parameters, a new composite fitness function of GP is specifically designed to tailor for MPDA. Subsequently, generalized Hjorth's parameters are constructed by integrating the unified framework of Hjorth's parameters with GP. Three bearing and gear run-to-failure datasets are used to validate the effectiveness of the proposed generalized Hjorth's parameters. Results show that the newly constructed Hjorth's parameters are superior to the original Hjorth's parameters and some state of the art health indicators for MPDA", } @Article{Yan:2022:OJIM, author = "Tongtong Yan and Dong Wang", journal = "IEEE Open Journal of Instrumentation and Measurement", title = "Genetic Programming-Based Machine Degradation Modeling Methodology", year = "2022", volume = "1", abstract = "Machine degradation is a complex, dynamic and irreversible process and its modeling is a leading-edge technology in prognostics and health management (PHM). In recent years, machine learning algorithms have been widely used to model machine degradation. However, these degradation models are not physically interpreted so that their extended uses are reduced and weakened. Aiming at solving this problem and visualizing informative features learned from degradation data, in this paper, a generalized machine degradation modeling methodology is proposed by integrating multiple-source fusion with genetic programming (GP). A composite fitness function of GP tailored for machine degradation modeling is innovatively designed. Afterward, multiple process sensor data, such as temperature, pressure, currents, etc., and non-process sensor data, such as vibration and acoustic signals, can be respectively modeled and fused into structurally interpreted health indicators from the time domain and the frequency domain. Moreover, the proposed methodology can automatically select informative frequency components and sensors, and provide transparent modeling architecture for early fault detection and subsequent monotonic degradation assessment. Another benefit of the proposed methodology is that complicated data preprocessing and manual feature extraction are not required anymore. Hence, the proposed methodology would have many potential applications and it is easy to implement for online machine degradation modeling. A gearbox run-to-failure dataset (non-process data) and an aircraft engine degradation dataset (process data) are studied to verify the effectiveness of the proposed methodology. Comparisons show that structurally interpreted health indicators constructed from the proposed methodology are superior to state-of-the-art works.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/OJIM.2022.3186057", ISSN = "2768-7236", notes = "Also known as \cite{9817456}", } @InProceedings{1144290, author = "Wei Yan and Christopher D. Clack", title = "Behavioural GP diversity for dynamic environments: an application in hedge fund investment", booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation", year = "2006", editor = "Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos {Coello Coello} and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens", volume = "2", ISBN = "1-59593-186-4", pages = "1817--1824", address = "Seattle, Washington, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p1817.pdf", DOI = "doi:10.1145/1143997.1144290", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "8-12 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Real-World Applications, adaptation, diversity, dynamic environment, finance, phenotype", size = "8 pages", abstract = "We present a new mechanism for preserving phenotypic behavioural diversity in a Genetic Programming application for hedge fund portfolio optimization, and provide experimental results on real-world data that indicate the importance of phenotypic behavioural diversity both in achieving higher fitness and in improving the adaptability of the GP population for continuous learning.", notes = "GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM Order Number 910060", } @InProceedings{1277383, author = "Wei Yan and Christopher D. Clack", title = "Diverse committees vote for dependable profits", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "2226--2233", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p2226.pdf", DOI = "doi:10.1145/1276958.1277383", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Real-World Applications, diversity, dynamic environments, finance, robustness", abstract = "Stock selection for hedge fund portfolios is a challenging problem for Genetic Programming (GP) because the markets (the environment in which the GP solution must survive) are dynamic, unpredictable and unforgiving. How can GP be improved so that solutions are produced that are robust to non-trivial changes in the environment? We explore an approach that uses a voting committee of GP individuals with differing phenotypic behaviour.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @InProceedings{1277384, author = "Wei Yan and Christopher D. Clack", title = "Evolving robust GP solutions for hedge fund stock selection in emerging markets", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "2234--2241", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p2234.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.141.7354", DOI = "doi:10.1145/1276958.1277384", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Real-World Applications, adaptation, diversity, dynamic environments, finance, phenotype", oai = "oai:CiteSeerXPSU:10.1.1.141.7354", abstract = "Stock selection for hedge fund portfolios is a challenging problem for Genetic Programming (GP) because the markets (the environment in which the GP solution must survive) are dynamic, unpredictable and unforgiving. How can GP be improved so that solutions are produced that are robust to non-trivial changes in the environment? We explore an approach that uses subsets of extreme environments during training.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @InProceedings{Yan:2008:gecco, author = "Wei Yan and Martin V. Sewell and Christopher D. Clack", title = "Learning to optimize profits beats predicting returns -: comparing techniques for financial portfolio optimisation", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", pages = "1681--1688", address = "Atlanta, GA, USA", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, committee, diversity, dynamic environment, finance, robustness, SVM, voting, Real-World application", isbn13 = "978-1-60558-130-9", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1681.pdf", DOI = "doi:10.1145/1389095.1389409", size = "8 pages", abstract = "Stock selection for hedge fund portfolios is a challenging problem that has previously been tackled by many machine-learning, genetic and evolutionary systems, including both Genetic Programming (GP) and Support Vector Machines (SVM). But which is the better? We provide a head-to-head evaluation of GP and SVM applied to this real-world problem, including both a standard comparison of returns on investment and a comparison of both techniques when extended with a {"}voting{"} mechanism designed to improve both returns and robustness to volatile markets. Robustness is an important additional dimension to this comparison, since the markets (the environment in which the GP or SVM solution must survive) are dynamic and unpredictable. Our investigation highlights a key difference in the two techniques, showing the superiority of the GP approach for this problem.", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389409}", } @InProceedings{DBLP:conf/gecco/YanC09, author = "Wei Yan and Christopher D. Clack", title = "Behavioural GP diversity for adaptive stock selection", booktitle = "GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation", year = "2009", editor = "Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano {van Hemert} and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique Alba", pages = "1641--1648", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1569901.1570120", abstract = "We present a new mechanism for preserving phenotypic behavioural diversity in Genetic Programming. We provide a real-world case study for hedge fund portfolio optimization, and experimental results on real-world data that indicate the importance of phenotypic behavioural diversity both in achieving higher fitness and in improving the robustness of the GP population for continuous learning.", notes = "GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009). ACM Order Number 910092.", } @Article{Yan:2010:SC, author = "Wei Yan and Christopher D. Clack", title = "Evolving robust GP solutions for hedge fund stock selection in emerging markets", journal = "Soft Computing - A Fusion of Foundations, Methodologies and Applications", year = "2011", volume = "15", number = "1", pages = "37--50", month = jan, publisher = "Springer", keywords = "genetic algorithms, genetic programming", ISSN = "1432-7643", URL = "http://www.cs.ucl.ac.uk/staff/C.Clack/research/SoftComputing08_draft.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.142.140", DOI = "doi:10.1007/s00500-009-0511-4", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", contributor = "CiteSeerX", language = "en", oai = "oai:CiteSeerXPSU:10.1.1.142.140", abstract = "Stock selection for hedge fund portfolios is a challenging problem for Genetic Programming (GP) because the markets (the environment in which the GP solution must survive) are dynamic, unpredictable and unforgiving. How can GP be improved so that solutions are produced that are robust to non-trivial changes in the environment? We explore two new approaches. The first approach uses subsets of extreme environments during training and the second approach uses a voting committee of GP individuals with differing phenotypic behaviour.", } @PhdThesis{WeiYan:thesis, author = "Wei Yan", title = "New Algorithms for Evolving Robust Genetic Programming Solutions in Dynamic Environments with a Real World Case Study in Hedge Fund Stock Selection", year = "2012", school = "Computer Science, University College, London", address = "UK", month = "6 " # dec, keywords = "genetic algorithms, genetic programming, SVM, SGP, NGP, S+P, DotCom, Black Swan, Malaysia Stock Market, Sharpe Ratio, Voting,", oai = "oai:eprints.ucl.ac.uk.OAI2:1380128", bibsource = "OAI-PMH server at discovery.ucl.ac.uk", language = "eng", oai = "oai:eprints.ucl.ac.uk.OAI2:1380128", URL = "http://discovery.ucl.ac.uk/1380128/", URL = "https://discovery.ucl.ac.uk/id/eprint/1380128/1/1380128_WeiThesisFinal.pdf", URL = "https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.625961", size = "123 pages", abstract = "This thesis presents three new genetic programming (GP) algorithms designed to enhance robustness of solutions evolved in highly dynamic environments and investigates the application of the new algorithms to financial time series analysis. The research is motivated by the following thesis question: what are viable strategies to enhance the robustness of GP individuals when the environment of a task being optimised or learnt by a GP system is characterised by large, rapid, frequent and low-predictability changes? The vast majority of existing techniques aim to track dynamics of optima in very simple dynamic environments. But the research area in improving robustness in dynamic environments characterised by large, frequent and unpredictable changes is not yet widely explored. The three new algorithms were designed specifically to evolve robust solutions in these environments. The first algorithm, behavioural diversity preservation, is a novel diversity preservation technique. The algorithm evolves more robust solutions by preserving population phenotypic diversity through the reduction of their behavioural inter-correlation and the promotion of individuals with unique behaviour. The second algorithm, multiple-scenario training, is a novel population training and evaluation technique. The algorithm evolves more robust solutions by training a population simultaneously across a set of pre-constructed environment scenarios and by using a consistency-adjusted fitness measure to favour individuals performing well across the entire range of environment scenarios. The third algorithm, committee voting is a novel final solution selection technique. The algorithm enhances robustness by breaking away from best-of-run tradition, creating a solution based on a majority-voting committee structure consisting of individuals evolved in a range of diverse environmental dynamics. The thesis introduces a comprehensive real-world case application for the evaluation experiments. The case is a hedge fund stock selection application for a typical long-short market neutral equity strategy in the Malaysian stock market. The underlying technology of the stock selection system is GP which assists to select stocks by exploiting the underlying nonlinear relationship between diverse ranges of influencing factors. The three proposed algorithms are all applied to this case study during evaluation. The results of experiments based on the case study demonstrate that all three new algorithms overwhelmingly outperform canonical GP in two aspects of the robustness criteria and conclude they are viable strategies for improving robustness of GP individuals when the environment of a task being optimised or learnt by a GP system is characterised by large, sudden, frequent and unpredictable changes.", notes = "Supervisor: Christopher D. Clack uk.bl.ethos.625961 UCL internal:001653187", } @InProceedings{Yan:2009:IJCNN, author = "Weizhong Yan and Hai Qiu and Ya Xue", title = "Gaussian process for long-term time-series forecasting", booktitle = "International Joint Conference on eural Networks, 2009. IJCNN 2009", year = "2009", month = "14-19 " # jun, pages = "3420--3427", address = "Atlanta, GA, USA", abstract = "Gaussian process (GP), as one of the cornerstones of Bayesian...", keywords = "Gaussian processes", DOI = "doi:10.1109/IJCNN.2009.5178729", ISSN = "1098-7576", notes = "NOT on GP Also known as \cite{5178729}", } @Article{yan:2023:AS, author = "Xiaobing Yan and Qiqi Hao and Rui Yang and Jianyu Peng and Fengpeng Zhang and Sanyuan Tan", title = "The Inversion Method Applied to the Stress Field around a Deeply Buried Tunnel Based on Surface Strain", journal = "Applied Sciences", year = "2023", volume = "13", number = "22", pages = "Article No. 12507", keywords = "genetic algorithms, genetic programming", ISSN = "2076-3417", URL = "https://www.mdpi.com/2076-3417/13/22/12507", DOI = "doi:10.3390/app132212507", abstract = "To identify the magnitude and direction of in situ stress in deeply buried tunnels, an inversion method for the stress field was proposed based on a finite number of measurement points of surface strain. Firstly, elastic strain data of finite points on the surface of tunnel surrounding rock were acquired using the borehole stress relief method at the engineering site. Secondly, a finite element model of the tunnel surrounding rock with plastic damage was established, and the parameters of the finite element model were substituted using the SIGINI subroutine. Then, an improved Surrogate Model Accelerated Random Search (SMARS) was developed using genetic algorithm programming on the MATLAB(TM) platform to invert and attain the globally optimal boundary conditions. Finally, the obtained optimal boundary conditions were applied to the numerical model to calculate the stress distribution in the engineering site. The reliability of this method was validated through a three-dimensional example. The method has been successfully applied to the stress-field analysis of deep tunnels in Macheng Iron Mine, Hebei Province, China. The research results show that this method is a low-cost, reliable approach for stress-field inversion in the rock around a tunnel.", notes = "also known as \cite{app132212507}", } @Article{yan:2019:JMSE, author = "Xiaohui Yan and Abdolmajid Mohammadian", title = "Multigene {Genetic-Programming-Based} Models for Initial Dilution of Laterally Confined Vertical Buoyant Jets", journal = "Journal of Marine Science and Engineering", year = "2019", volume = "7", number = "8", keywords = "genetic algorithms, genetic programming", ISSN = "2077-1312", URL = "https://www.mdpi.com/2077-1312/7/8/246", DOI = "doi:10.3390/jmse7080246", abstract = "A new approach based on the multigene genetic-programming (MGGP) technique is proposed to predict initial dilution of vertical buoyant jets subjected to lateral confinement. The models are trained and tested using experimental data, and the good matches demonstrate the generalisation and predictive capabilities of the evolved MGGP-based models. The best Pareto-optimal MGGP-based model is also compared with the model evolved using a single-gene genetic-programming (SGGP) algorithm and an existing regression-based empirical equation. The comparisons reveal the superiority of the MGGP-based model. This study confirms that the MGGP technique is promising in evolving an explicit, accurate, and compact model, and the developed models can be employed to estimate effectively and efficiently the dilution properties of a laterally confined vertical buoyant jet.", notes = "also known as \cite{jmse7080246}", } @Article{Yan:2020:DWT, author = "Xiaohui Yan and Abdolmajid Mohammadian", title = "Evolutionary prediction of multiple vertical buoyant jets in stationary ambient water", journal = "Desalination and Water Treatment", year = "2020", volume = "178", pages = "41--52", keywords = "genetic algorithms, genetic programming, Artificial intelligence, Computational fluid dynamics, Mixing properties, Multigene genetic programming, Multiple jets, Partial derivative sensitivity analysis", ISSN = "1944-3986", URL = "https://www.sciencedirect.com/science/article/pii/S1944398624094955", DOI = "doi:10.5004/dwt.2020.24938", abstract = "ABSTRA", } @Article{Yan:2020:DWTa, author = "Xiaohui Yan and Abdolmajid Mohammadian", title = "Prediction of a rosette dense jet group in crossflow ambient conditions using multi-gene genetic programming", journal = "Desalination and Water Treatment", year = "2020", volume = "190", pages = "440--448", keywords = "genetic algorithms, genetic programming, Artificial intelligence, Crossflow, Dense jet, Multi-gene genetic programming, Rosette diffuser", ISSN = "1944-3986", URL = "https://www.sciencedirect.com/science/article/pii/S1944398624074538", DOI = "doi:10.5004/dwt.2020.25746", abstract = "ABSTRA", } @Article{yan:2021:JMSE, author = "Xiaohui Yan and Yan Wang and Abdolmajid Mohammadian and Jianwei Liu", title = "Simulations of the Concentration Fields of {Rosette-Type} Multiport Buoyant Discharges Using Combined {CFD} and Multigene Genetic Programming Techniques", journal = "Journal of Marine Science and Engineering", year = "2021", volume = "9", number = "11", keywords = "genetic algorithms, genetic programming", ISSN = "2077-1312", URL = "https://www.mdpi.com/2077-1312/9/11/1311", DOI = "doi:10.3390/jmse9111311", abstract = "Rosette-type diffusers are becoming popular nowadays for discharging wastewater effluents. Effluents are known as buoyant jets if they have a lower density than the receiving water, and they are often used for municipal and desalination purposes. These buoyant effluents discharged from rosette-type diffusers are known as rosette-type multiport buoyant discharges. Investigating the mixing properties of these effluents is important for environmental impact assessment and optimal design of the diffusers. Due to the complex mixing and interacting processes, most of the traditional simple methods for studying free single jets become invalid for rosette-type multiport buoyant discharges. Three-dimensional computational fluid dynamics (3D CFD) techniques can satisfactorily model the concentration fields of rosette-type multiport buoyant discharges, but these techniques are typically computationally expensive. In this study, a new technique of simulating rosette-type multiport buoyant discharges using combined 3D CFD and multigene genetic programming (MGGP) techniques is developed. Modeling the concentration fields of rosette-type multiport buoyant discharges using the proposed approach has rarely been reported previously. A validated numerical model is used to carry out extensive simulations, and the generated dataset is used to train and test MGGP-based models. The study demonstrates that the proposed method can provide reasonable predictions and can significantly improve the prediction efficiency.", notes = "also known as \cite{jmse9111311}", } @Article{YAN:2021:JH, author = "Xiaohui Yan and Abdolmajid Mohammadian and Ali Khelifa", title = "Modeling spatial distribution of flow depth in fluvial systems using a hybrid two-dimensional hydraulic-multigene genetic programming approach", journal = "Journal of Hydrology", volume = "600", pages = "126517", year = "2021", ISSN = "0022-1694", DOI = "doi:10.1016/j.jhydrol.2021.126517", URL = "https://www.sciencedirect.com/science/article/pii/S0022169421005643", keywords = "genetic algorithms, genetic programming, Spatial distribution, Flow depth, 2D hydraulic, Multigene genetic programming, Ottawa River", abstract = "Modeling spatial distribution of flow depth in fluvial systems is crucial for flow mitigation, river rehabilitation, and design of water resources infrastructure. Flow depth in fluvial systems can be typically estimated using hydrological or physics-based hydraulic models. However, hydrological models may not be able to provide satisfactory predictions for catchments with limited data because they normally ignored the strict conservation of momentum. Traditional fully physics-based hydraulic models are often very computationally expensive, limiting their wide usage in practical applications. In this study, a novel method, based on a hybrid two-dimensional (2D) hydraulic-multigene genetic programming (MGGP) approach, is proposed and employed to model the spatial distribution of flow depth in fluvial systems. A 2D hydraulic model was constructed using the TELEMAC-2D software and validated against field measurements. The validated model was then assumed to reflect the real physical processes and used to carry out additional computations to obtain spatial distribution of flow depth under different discharge scenarios, which provided a sufficient synthetic dataset for training machine learning models based on the MGGP technique. The study area (a segment of the Ottawa River near the island named Ile Kettle) was divided into 34 sub-regions to further reduce the computational costs of the training processes and the complexity of the evolved models. The numerical data were distributed to the corresponding sub-regions, and an MGGP-based model was trained for each sub-region. These models are compact explicit arithmetic equations that can be readily transferable and can immediately output the flow depth at any point in the corresponding sub-region as functions of the flow rate, longitudinal, and transversal coordinates. The best MGGP model for each sub-region amongst all the generated models was identified using the Pareto optimization approach. The results showed that the best MGGP models satisfactorily reproduced the training data and predicted the testing data (the root mean square errors were 0.303 m and 0.306 m, respectively), demonstrating the predictive capability of the approach. A comparison between MGGP and single-gene genetic programming (SGGP) approaches and confidence analysis were also reported, which demonstrated the good performance of the proposed approach. Furthermore, it took about 53 min for the hydraulic model to complete each simulation, but it took only about 0.56 s using the final model; the total size of the hydraulic output files for 12 different sizes was 432, 948 KB, but the total size of the script file for the final model was only about 46 KB. Therefore, the present study found that the hybrid 2D hydraulic-MGGP approach was satisfactorily accurate, fast to run, and easy to use, and thus, it is a promising tool for modeling spatial distribution of flow depth in fluvial systems", } @Article{YAN:2023:jhydrol, author = "Xiaohui Yan and Abdolmajid Mohammadian and Ruigui Ao and Jianwei Liu and Na Yang", title = "Two-dimensional convolutional neural network outperforms other machine learning architectures for water depth surrogate modeling", journal = "Journal of Hydrology", volume = "616", pages = "128812", year = "2023", ISSN = "0022-1694", DOI = "doi:10.1016/j.jhydrol.2022.128812", URL = "https://www.sciencedirect.com/science/article/pii/S0022169422013828", keywords = "genetic algorithms, genetic programming, Deep learning, Convolutional neural network, Water depth, Spatial distribution, Surrogate modeling", abstract = "Rapid prediction of spatially distributed hydrological variables, such as water depths in rivers, is an important but challenging task. This study proposes a novel matrix-based deep learning approach for predicting spatial distribution of water depths in rivers. The proposed approach was constructed based on a two-dimensional (2D) convolutional neural network (CNN) with a new architecture that was specifically designed for providing spatial distribution maps. A numerical dataset was established based on a field cruise and two-dimensional hydraulic modeling for different scenarios, and numerical experiments were designed to predict spatial distribution of water depths for different scenarios using the adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM), genetic programming (GP), multi-gene genetic programming (MGGP), one-dimensional CNN (1D-CNN), and the proposed CNN algorithms. The results showed that the proposed CNN approach captured both the large-scale and small-scale spatial patterns remarkably well, and it outperformed the other approaches. This study shows that the 2D CNN algorithm is better than the classical machine learning (ML) algorithms for inundation modeling. The proposed approach is thus a promising tool for providing rapid predictions of spatial distribution of water depths in river systems and can potentially be leveraged to predict other spatially distributed hydrological variables", } @Article{yan:2022:JMSE, author = "Xiaohui Yan and Yan Wang and Abdolmajid Mohammadian and Jianwei Liu and Xiaoqiang Chen", title = "{CFD-CNN} Modeling of the Concentration Field of Multiport Buoyant Jets", journal = "Journal of Marine Science and Engineering", year = "2022", volume = "10", number = "10", pages = "Article No. 1383", keywords = "genetic algorithms, genetic programming", ISSN = "2077-1312", URL = "https://www.mdpi.com/2077-1312/10/10/1383", DOI = "doi:10.3390/jmse10101383", abstract = "At present, there are increasing applications for rosette diffusers for buoyant jets with a lower density than the ambient water, mainly in the discharge of wastewater from municipal administrations and sea water desalination. It is important to study the mixing effects of wastewater discharge for the benefit of environmental protection, but because the multiport discharge of the wastewater concentration field is greatly affected by the mixing and interacting functions of wastewater, the traditional research methods on single-port discharge are invalid. This study takes the rosette multiport jet as a research subject to develop a new technology of computational fluid dynamics (CFD) modelling and carry out convolutional neural network (CNN) simulation of the concentration field of a multiport buoyant jet. This study takes advantage of CFD technology to simulate the mixing process of a rosette multiport buoyant jet, uses CNNs to construct the machine learning model, and applies RSME, R2 to conduct evaluations of the models. This work also makes comparisons with the machine learning approach based on multi-gene genetic programming, to assess the performance of the proposed approach. The experimental results show that the models constructed based on the proposed approach meet the accuracy requirement and possess better performance compared with the traditional machine learning method, and they can provide reasonable predictions.", notes = "also known as \cite{jmse10101383}", } @Article{YAN:2023:atmosres, author = "Xiaohui Yan and Na Yang and Ruigui Ao and Abdolmajid Mohammadian and Jianwei Liu and Huade Cao and Penghai Yin", title = "Deep learning for daily potential evapotranspiration using a {HS-LSTM} approach", journal = "Atmospheric Research", volume = "292", pages = "106856", year = "2023", ISSN = "0169-8095", DOI = "doi:10.1016/j.atmosres.2023.106856", URL = "https://www.sciencedirect.com/science/article/pii/S0169809523002533", keywords = "genetic algorithms, genetic programming, Potential evapotranspiration, ANN, Machine learning, Long short-term memory neural network, Hargreaves-Samani", abstract = "Accurate estimation of potential evapotranspiration (ET0) is important for the sound design of irrigation schedules, management of water resources, assessment of hydrological drought, and research on atmospheric variations. The present study proposed a novel deep learning (DL) approach for daily ET0 estimations with limited daily climate data: HS- LSTM. This approach was constructed based on a classic ET0 model and a long short-term memory neural network (LSTM). Specifically, the Hargreaves-Samani (HS) model was employed as the classic model, and the predictors were restricted to the daily maximum and minimum air temperature data. Ground truth data for ET0 were employed to train, validate, and test the models. Traditional machine learning (ML) algorithms comprising adaptive neuro-fuzzy inference system (ANFIS), genetic programming (GP), multi-gene genetic programming (MGGP), and one-dimensional CNN (1D-CNN), as well as the HS-ML models (HS-ANFIS, HS-GP, HS-MGGP, HS-1D-CNN), were also established and assessed for daily ET0 estimations. Compared to the other tested approaches, the errors of the HS-LSTM technique significantly decreased, demonstrating that the novel HS-LSTM approach significantly outperformed the other techniques beyond the study area (in Songliao Basin, Northeast China, which is a semi-humid zone with temperate continental climate). The developed models can then be used to estimate future ET0 with only air temperature forecasts, which can be readily obtained from public weather forecasts. The present study provides a new and promising strategy that can provide more accurate estimations of daily ET0 with limited meteorological data, along with significant implications for enhancing atmospheric research", } @InProceedings{conf/ahs/YanWLZK06, title = "Designing Electronic Circuits by Means of Gene Expression Programming", author = "Xue-song Yan and Wei Wei and Rui Liu and San-you Zeng and Lishan Kang", booktitle = "First {NASA}/{ESA} Conference on Adaptive Hardware and Systems ({AHS} 2006),", publisher = "IEEE Computer Society", year = "2006", editor = "Adrian Stoica and Tughrul Arslan and Martin Suess and Senay Yal{\c c}in and Didier Keymeulen and Tetsuya Higuchi and Ricardo Salem Zebulum and Nizamettin Aydin", pages = "194--199", address = "Istanbul, Turkey", month = "15-18 " # jun, bibdate = "2007-02-12", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ahs/ahs2006.html#YanWLZK06", keywords = "genetic algorithms, genetic programming, Gene Expression Programming, EHW", ISBN = "0-7695-2614-4", DOI = "doi:10.1109/AHS.2006.31", abstract = "This work investigates the application of Gene Expression Programming(GEP) in the field of evolutionary electronics. GEP is a genetic algorithm as it uses populations of individuals, selects them according to fitness, and introduces genetic variation using one or more genetic operators. We propose the new means for designing electronic circuits and introduces the encoding of the circuit as a chromosome, the genetic operators and the fitness function. For the case studies this means has proved to be efficient, experiments show that we have better results.", } @InProceedings{Yan:2010:ICNC, author = "Xuesong Yan and Jian Jin", title = "Electronic circuits automatic design algorithm", booktitle = "Sixth International Conference on Natural Computation (ICNC 2010)", year = "2010", month = "10-12 " # aug, volume = "5", pages = "2334--2337", abstract = "For the evolutionary electronic circuit design, the representation of the circuit is important, because the representation of the circuit may affected the significance solution circuit or the optimise solution, and also should speeds up the convergence speed of the algorithm search. The hardware representation methods mainly include binary bit string representation and Cartesian Genetic Programming representation. In this paper, we use a hybrid representation method-tree representation and Cartesian Genetic Programming. Based on this representation, we designed the evolutionary algorithm for the electronic circuit design. The experiment results showed our algorithm have higher successful rate compared with the traditional method.", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, binary bit string representation, cartesian genetic programming representation, electronic circuits automatic design algorithm, evolutionary electronic circuit design, hybrid representation method-tree representation, integrated circuit design", DOI = "doi:10.1109/ICNC.2010.5584122", notes = "Also known as \cite{5584122}", } @InProceedings{Yan:2021:CEC, author = "Zichu Yan and Ying Bi and Bing Xue and Mengjie Zhang", booktitle = "2021 IEEE Congress on Evolutionary Computation (CEC)", title = "Automatically Extracting Features Using Genetic Programming for Low-Quality Fish Image Classification", year = "2021", editor = "Yew-Soon Ong", pages = "2015--2022", address = "Krakow, Poland", month = "28 " # jun # "-1 " # jul, isbn13 = "978-1-7281-8393-0", abstract = "Fish image classification is an important task in the protection of precious marine resources. However, this task is difficult due to the low-quality images and the high inter-class variations across images. Most existing methods use high-quality images for classification and need domain knowledge. In this paper, we develop a genetic programming (GP) approach to automatically selecting image operators to deal with the low-quality images and extracting effective features from these images for low-quality fish image classification. To achieve this, a new program structure and a new function set are developed. With these designs, the proposed GP approach can evolve solutions that use effective filtering or restoration operators to deal with the input image, select informative regions from the fish image, and extract effective global and/or local features from the fish images. The results show that the proposed approach achieves significantly better performance than 12 benchmark methods, including a state-of-the-art GP approach, on the well-known fish image classification dataset. Further analysis shows the high interpretability of the evolved GP trees and the effectiveness of the employed image filtering or restoration operators.", keywords = "genetic algorithms, genetic programming, Filtering, Evolutionary computation, Benchmark testing, Feature extraction, Fish, Image filtering", DOI = "doi:10.1109/CEC45853.2021.9504737", notes = "Also known as \cite{9504737}", } @Article{Yanagi:2001:ieice, author = "Kosuke Yanai and Hideyuki Mita and Hitoshi Iba", title = "Robot learning of cooperative behavior using Genetic Programming", journal = "Technical Research Report of the Institute of Electronics, Information and Communication Engineers. AI, Artificial Intelligence and Knowledge Processing", year = "2001", volume = "101", number = "66", pages = "9--16", month = may, keywords = "genetic algorithms, genetic programming, Robot learning, Cooperative behaviour, Khepera", ISSN = "09135685", publisher = "Institute of Electronics, Information and Communication Engineers", URL = "https://ci.nii.ac.jp/naid/110003187220/", abstract = "It is expected that various robots are ubiquitous in the future. The design of such an intelligent robot is regarded as an important study and one of AI approaches. In this study, as a part of constructing a reasonable agent, we pick up as theme the automatic generation of a program applicable to a real robot. Most of robot programs are written by human-hands. But as for the making of programs that can support various situations, there is a limit in humam-hand coding. In addition, the automatic generation of robot programs will reduce burden of programmers. Using Genetic Programming, we have tried the generation of programs of a real robot.", notes = "in Japanese. Also known as \cite{110003187220} Department of Basic Informatics, Graduate School of Frontier Sciences, The University of Tokyo", } @InProceedings{Yanai:2001:MAR, author = "Kohsuke Yanai and Hitoshi Iba", title = "Multi-agent Robot Learning by Means of Genetic Programming : Solving an Escape Problem", booktitle = "Evolvable Systems: From Biology to Hardware: 4th International Conference, ICES 2001", year = "2001", editor = "Yong Liu and Kiyoshi Tanaka and Masaya Iwata and Tetsuya Higuchi and Moritoshi Yasunaga", volume = "2210", series = "LNCS", pages = "192--203", address = "Tokyo, Japan", month = "3-5 " # oct, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, bloat", ISBN = "3-540-42671-X", ISSN = "0302-9743", DOI = "doi:10.1007/3-540-45443-8_17", abstract = "We present the emergence of the cooperative behaviour for multiple robot agents by means of Genetic Programming (GP). For this purpose, we use several extended mechanisms of GP, i.e., (1) a co-evolutionary breeding strategy, (2) a controlling strategy of introns, which are non-executed code segments dependent upon the situation, and (3) a subroutine discovery technique. Our experimental domain is an escape problem. We have chosen the actual experimental settings so as to be close to a real world as much as possible. The validness of our approach is discussed with comparative experiments using other methods, i.e., Q-learning and Neural networks, which shows the superiority of GP-based multi-agent learning.", notes = "CODEN = LNCSD9 Subroutine discovery, ADF, placed in competitive shared library. Escape problem turns out to be three Khepara mini-robots {"}pushing{"} all 3 buttons before going to exit. Buttons, exit etc all colour coded. GP Evolved in simulation but works on real robots. Second problem simplified so can try Q-learning on it. \cite{Iba:1998:ISJ}.", } @InProceedings{Yanai:aspgp03, author = "Kohsuke Yanai and Hitoshi Iba", title = "Program Evolution Using {Bayesian} Network", booktitle = "Proceedings of The First Asian-Pacific Workshop on Genetic Programming", year = "2003", editor = "Sung-Bae Cho and Nguyen Xuan Hoai and Yin Shan", pages = "16--23", address = "Rydges (lakeside) Hotel, Canberra, Australia", month = "8 " # dec, keywords = "genetic algorithms, genetic programming", ISBN = "0-9751724-0-9", notes = "\cite{aspgp03}, MAX problem", } @InProceedings{Yanai:2003:EodpboBn, author = "Kohsuke Yanai and Hitoshi Iba", title = "Estimation of Distribution Programming Based on {Bayesian} Network", booktitle = "Proceedings of the 2003 Congress on Evolutionary Computation CEC2003", editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon", pages = "1618--1625", year = "2003", publisher = "IEEE Press", address = "Canberra", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "8-12 " # dec, organisation = "IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", keywords = "genetic algorithms, genetic programming, Bayesian methods, Benchmark testing, Electronic design automation and methodology, Informatics, Probability distribution, Search methods, Search problems, Tree data structures, Bayes methods, Boolean functions, estimation theory, probability, search problems, Bayesian network, Boolean function, estimation of distribution programming, max problem, population-based program search method, probability distribution, program population", ISBN = "0-7803-7804-0", URL = "http://www.iba.k.u-tokyo.ac.jp/papers/2003/yanaiCEC2003.pdf", DOI = "doi:10.1109/CEC.2003.1299866", abstract = "In this paper, we propose Estimation of Distribution Programming (EDP) based on a probability distribution expression using a Bayesian network. EDP is a population-based program search method, in which the population probability distribution is estimated, and individuals are generated based on the results. We focus our attention on the fact that the dependency relationship of nodes of the program (expressed as a tree structure) is explicit, and estimate the probability distribution of the program population using a Bayesian network. We compare EDP with GP (Genetic Programming) on several benchmark tests, i.e., a max problem and a boolean function problem. We also discuss the trends of problems that are the forte of EDP.", notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.", } @InProceedings{yanai:peb:gecco2004, author = "Kohsuke Yanai and Hitoshi Iba", title = "Program Evolution by Integrating EDP and GP", booktitle = "Genetic and Evolutionary Computation -- GECCO-2004, Part I", year = "2004", editor = "Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell", series = "Lecture Notes in Computer Science", pages = "774--785", address = "Seattle, WA, USA", publisher_address = "Heidelberg", month = "26-30 " # jun, organisation = "ISGEC", publisher = "Springer-Verlag", volume = "3102", ISBN = "3-540-22344-4", ISSN = "0302-9743", DOI = "doi:10.1007/b98643", URL = "http://www.iba.k.u-tokyo.ac.jp/papers/2004/yanaiGECCO2004.pdf", size = "12", keywords = "genetic algorithms, genetic programming", abstract = "This paper discusses the performance of a hybrid system which consists of EDP and GP. EDP, Estimation of Distribution Programming, is the program evolution method based on the probabilistic model, where the probability distribution of a program is estimated by using a Bayesian network, and a population evolves repeating estimation of distribution and program generation without crossover and mutation. Applying the hybrid system of EDP and GP to various problems, we discovered some important tendencies in the behavior of this hybrid system. The hybrid system was not only superior to pure GP in a search performance but also had interesting features in program evolution. More tests revealed how and when EDP and GP compensate for each other. We show some experimental results of program evolution by the hybrid system and discuss the characteristics of both EDP and GP.", notes = "GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)", } @InProceedings{Yanai:2004:aspgp, author = "Kohsuke Yanai and Hitoshi Iba", title = "Probabilistic Model-Building Genetic Programming based on Dependency Relationship", booktitle = "Proceedings of The Second Asian-Pacific Workshop on Genetic Programming", year = "2004", editor = "R I Mckay and Sung-Bae Cho", address = "Cairns, Australia", month = "6-7 " # dec, keywords = "genetic algorithms, genetic programming", notes = "broken Sep 2018 http://sc.snu.ac.kr/~aspgp/aspgp04/programme.html", } @InProceedings{1068305, author = "Kohsuke Yanai and Hitoshi Iba", title = "Probabilistic distribution models for EDA-based GP", booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation", year = "2005", editor = "Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and Edwin D. {de Jong} and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler", volume = "2", ISBN = "1-59593-010-8", pages = "1775--1776", address = "Washington DC, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2005/docs/p1775.pdf", DOI = "doi:10.1145/1068009.1068305", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "25-29 " # jun, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Poster, estimation of distribution algorithm, evolutionary computing, probabilistic model building, probabilistic model building genetic algorithms, program evolution, PIPE, XEDP", size = "2 pages", notes = "GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM Order Number 910052 Max problem. Boolean 6-bit multiplexer problem. Wall-following problem.", } @InProceedings{DBLP:conf/gecco/YanaseHI09, author = "Toshihiko Yanase and Yoshihiko Hasegawa and Hitoshi Iba", title = "Binary encoding for prototype tree of probabilistic model building GP", booktitle = "GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation", year = "2009", editor = "Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano {van Hemert} and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique Alba", pages = "1147--1154", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1569901.1570055", abstract = "In recent years, program evolution algorithms based on the estimation of distribution algorithm (EDA) have been proposed to improve search ability of genetic programming (GP) and to overcome GP-hard problems. One such method is the probabilistic prototype tree (PPT) based algorithm. The PPT based method explores the optimal tree structure by using the full tree whose number of child nodes is maximum among possible trees. This algorithm, however, suffers from problems arising from function nodes having different number of child nodes. These function nodes cause intron nodes, which do not affect the fitness function. Moreover, the function nodes having many child nodes increase the search space and the number of samples necessary for properly constructing the probabilistic model. In order to solve this problem, we propose binary encoding for PPT. Here, we convert each function node to a subtree of binary nodes where the converted tree is correct in grammar. Our method reduces ineffectual search space, and the binary encoded tree is able to express the same tree structures as the original method. The effectiveness of the proposed method is demonstrated through the use of two computational experiments.", notes = "GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009). ACM Order Number 910092.", } @InProceedings{conf/icic/YangCM09, title = "Inference of Differential Equation Models by Multi Expression Programming for Gene Regulatory Networks", author = "Bin Yang and Yuehui Chen and Qingfang Meng", booktitle = "5th International Conference on Intelligent Computing, {ICIC} 2009", year = "2009", volume = "5755", editor = "De-Shuang Huang and Kang-Hyun Jo and Hong-Hee Lee and Hee-Jun Kang and Vitoantonio Bevilacqua", address = "Ulsan, South Korea", month = sep # " 16-19", pages = "974--983", series = "Lecture Notes in Computer Science", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Multi Expression Programming", DOI = "doi:10.1007/978-3-642-04020-7_105", isbn13 = "978-3-642-04019-1", bibdate = "2009-09-25", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icic/icic2009-2.html#YangCM09", } @Article{Yang:2013:academy, author = "Bin Yang and Mingyan Jiang and Yuehui Chen and Qingfang Meng and Ajith Abraham", title = "Ensemble of Flexible Neural Tree and Ordinary Differential Equations for Small-time Scale Network Traffic Prediction", journal = "Journal of Computers", publisher = "ACADEMY PUBLISHER", year = "2013", volume = "8", number = "12", pages = "3039--3046", month = dec, keywords = "genetic algorithms, genetic programming, hybrid evolutionary method, small-time scale network traffic, the additive tree models, ordinary differential equations, ensemble learning", ISSN = "1796-203X", bibsource = "OAI-PMH server at doaj.org", identifier = "1796-203X; 10.4304/jcp.8.12.3039-3046", language = "English", oai = "oai:doaj.org/article:3a06be9c4660483dbaca0959705a4a18", URL = "http://ojs.academypublisher.com/index.php/jcp/article/view/10007", DOI = "doi:10.4304/jcp.8.12.3039-3046", abstract = "Accurate models play important roles in capturing the salient characteristics of the network traffic, analysing and simulating for the network dynamic, and improving the predictive ability for system dynamics. In this study, the ensemble of the flexible neural tree (FNT) and system models expressed by the ordinary differential equations (ODEs) is proposed to further improve the accuracy of time series forecasting. Firstly, the additive tree model is introduced to represent ~more precisely ODEs for the network dynamics. Secondly, the structures and parameters of FNT and the additive tree model are optimised based on the Genetic Programming (GP) and the Particle Swarm Optimisation algorithm (PSO). Finally, the expected level of performance is verified by using the proposed method, which provides a reliable forecast model for small-time scale network traffic. Experimental results reveal that the proposed method is able to estimate the small-time scale network traffic measurement data with decent accuracy.", } @InProceedings{conf/his/YangZYL15, author = "Bin Yang and Wei Zhang and Xiaofei Yan and Caixia Liu", title = "Reverse Engineering of Time-Delayed Gene Regulatory Network Using Restricted Gene Expression Programming", publisher = "Springer", year = "2015", volume = "420", booktitle = "International Conference on Hybrid Intelligent Systems, HIS 2016", editor = "Ajith Abraham and Sang Yong Han and Salah Al-Sharhan and Hongbo Liu", series = "Advances in Intelligent Systems and Computing", pages = "155--165", keywords = "genetic algorithms, genetic programming, gene expression programming, time delayed, gene regulatory network, s-system, particle swarm optimisation", bibdate = "2017-05-24", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/his/his2015.html#YangZYL15", isbn13 = "978-3-319-27220-7", DOI = "doi:10.1007/978-3-319-27221-4_13", abstract = "Time delayed factor is one of the most important characteristics of gene regulatory network. Most research focused on reverse engineering of time-delayed gene regulatory network. In this paper, time-delayed S-system (TDSS) model is used to infer time-delayed regulatory network. An improved gene expression programming (GEP), named restricted GEP (RGEP) is proposed as a new representation of the TDSS model. A hybrid evolutionary method, based on structure-based evolutionary algorithm and new hybrid particle swarm optimisation, is used to optimise the architecture and parameters of TDSS model. Experimental result reveals that our method could identify time-delayed gene regulatory network accurately.", } @Article{journals/jbcb/YangLZ16, title = "Reverse engineering of gene regulatory network using restricted gene expression programming", author = "Bin Yang and Sanrong Liu and Wei Zhang", journal = "J. Bioinformatics and Computational Biology", year = "2016", number = "5", volume = "14", pages = "1--17", month = oct, keywords = "genetic algorithms, genetic programming, gene expression programming", bibdate = "2017-05-26", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/jbcb/jbcb14.html#YangLZ16", DOI = "doi:10.1142/S0219720016500219", size = "17 pages", } @Article{journals/cin/YangZW19, title = "Stock Market Forecasting Using Restricted Gene Expression Programming", author = "Bin Yang and Wei Zhang and Haifeng Wang", journal = "Comput. Intell. Neurosci", year = "2019", volume = "2019", pages = "7198962:1--7198962:14", keywords = "genetic algorithms, genetic programming, gene expression programming", DOI = "doi:10.1155/2019/7198962", bibdate = "2020-03-12", bibsource = "DBLP, http://dblp.uni-trier.de/https://www.wikidata.org/entity/Q64254042>; DBLP, http://dblp.uni-trier.de/db/journals/cin/cin2019.html#YangZW19", } @Article{Yang:2024:AlifeJ, author = "Bo Yang2", title = "Emergence of Self-Replicating Hierarchical Structures in a Binary Cellular Automaton", journal = "Artificial Life", year = "2024", volume = "31", number = "1", pages = "96--105", month = feb, keywords = "genetic algorithms, genetic programming, Cellular automata, CA, replication, complexity, open-ended evolution, OEE", ISSN = "1064-5462", DOI = "doi:10.1162/artl_a_00449", abstract = "We have discovered a novel transition rule for binary cellular automata (CAs) that yields self-replicating structures across two spatial and temporal scales from sparse random initial conditions. Lower-level, shape-shifting clusters frequently follow a transient attractor trajectory, generating new clusters, some of which periodically self-duplicate. When the initial distribution of live cells is sufficiently sparse, these clusters coalesce into larger formations that also self-replicate. These formations may further form the boundaries of an expanding complex on an even larger scale. This rule, dubbed 'Outlier,' is rotationally symmetric and applies to 2-D Moore neighbourhoods. It was evolved through genetic programming during an extensive search for rules that foster open-ended evolution in CAs. While self-replicating structures, both crafted and emergent, have been created in CAs with state sets intentionally designed for this purpose, the Outlier may be the first known rule to facilitate nontrivial emergent self-replication across two spatial scales in binary CAs.", notes = "Also known as \cite{10907985}", } @Article{YANG:2020:AWR, author = "Fan Yang and Wen-Xin Huai and Yu-Hong Zeng", title = "New dynamic two-layer model for predicting depth-averaged velocity in open channel flows with rigid submerged canopies of different densities", journal = "Advances in Water Resources", volume = "138", pages = "103553", year = "2020", ISSN = "0309-1708", DOI = "doi:10.1016/j.advwatres.2020.103553", URL = "http://www.sciencedirect.com/science/article/pii/S0309170819303148", keywords = "genetic algorithms, genetic programming, Depth-averaged velocity, Open channel flow, Rigid submerged vegetation, Dynamic two-layer model", abstract = "The depth-averaged velocity is the commonly used engineering quantity in natural rivers, and it needs to be predicted in advance, especially in flood seasons. A model that can provide a unified physical foundation for open channel flows with different canopy densities remains lacking despite ongoing researches. Here, we use the concept of the auxiliary bed to describe the influence of momentum exchange on rigid canopy elements with varying density and submergence. The auxiliary bed divides the vegetated flow into a basal layer and a suspension layer to predict average velocity in each layer separately. In the basal layer, the velocity profile is assumed to be uniform. In the suspension layer, a parameter called {"}penetration depth{"} is applied to present the variations in velocity distribution. We also apply a data-driven method, called genetic programming (GP), to derive Chezy-like predictors for average velocity in the suspension layer. Compared to the hydraulic resistance equation for rough-wall flows, the new formulae calculated by the weighted combination method show sound physical meanings. In addition, comparison with other models shows that the new dynamic two-layer model achieves high accuracy in flow rate estimation, especially for vegetated flow with sparse canopies", } @InProceedings{yang:2018:GPTP, author = "Fangkai Yang and Steven Gustafson and Alexander Elkholy and Daoming Lyu and Bo Liu", title = "Program Search for Machine Learning Pipelines Leveraging Symbolic Planning and Reinforcement Learning", booktitle = "Genetic Programming Theory and Practice XVI", year = "2018", editor = "Wolfgang Banzhaf and Lee Spector and Leigh Sheneman", pages = "209--231", address = "Ann Arbor, USA", month = "17-20 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-030-04734-4", URL = "http://link.springer.com/chapter/10.1007/978-3-030-04735-1_11", DOI = "doi:10.1007/978-3-030-04735-1_11", abstract = "In this paper we investigate an alternative knowledge representation and learning strategy for the automated machine learning (AutoML) task. Our approach combines a symbolic planner with reinforcement learning to evolve programs that process data and train machine learning classifiers. The planner, which generates all feasible plans from the initial state to the goal state, gives preference first to shortest programs and then later to ones that maximize rewards. The results demonstrate the efficacy of the approach for finding good machine learning pipelines, while at the same time showing that the representation can be used to infer new knowledge relevant for the problem instances being solved. These insights can be useful for other automatic programming approaches, like genetic programming (GP) and Bayesian optimization pipeline learning, with respect to representation and learning strategies.", } @Article{Yang:2018:Symmetry, author = "Geunseok Yang and Youngjun Jeong and Kyeongsic Min and Jung-Won Lee and Byungjeong Lee", title = "Applying Genetic Programming with Similar Bug Fix Information to Automatic Fault Repair", journal = "Symmetry", year = "2018", volume = "10", number = "4", pages = "92", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, SBSE, automatic fault repair, bug fix information, software maintenance", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/symmetry/symmetry10.html#YangJMLL18", URL = "https://www.mdpi.com/2073-8994/10/4/92/pdf", DOI = "doi:10.3390/sym10040092", size = "13 pages", abstract = "Owing to the high complexity of recent software products, developers cannot avoid major/minor mistakes, and software bugs are generated during the software development process. When developers manually modify a program source code using bug descriptions to fix bugs, their daily workloads and costs increase. Therefore, we need a way to reduce their workloads and costs. In this paper, we propose a novel automatic fault repair method by using similar bug fix information based on genetic programming (GP). First, we searched for similar buggy source codes related to the new given buggy code, and then we searched for a fixed the buggy code related to the most similar source code. Next, we transformed the fixed code into abstract syntax trees for applying GP and generated the candidate program patches. In this step, we verified the candidate patches by using a fitness function based on given test cases to determine whether the patch was valid or not. Finally, we produced program patches to fix the new given buggy code.", notes = "journals/symmetry/YangJMLL18", } @Article{Yang:2015a:EP, author = "Guangfei Yang and Xianneng Li and Jianliang Wang and Lian Lian and Tieju Ma", title = "Modeling oil production based on symbolic regression", journal = "Energy Policy", year = "2015", volume = "82", number = "Supplement C", pages = "48--61", month = jul, keywords = "genetic algorithms, genetic programming, Oil production, Hubbert theory", ISSN = "0301-4215", URL = "http://www.sciencedirect.com/science/article/pii/S0301421515000798", DOI = "doi:10.1016/j.enpol.2015.02.016", abstract = "Numerous models have been proposed to forecast the future trends of oil production and almost all of them are based on some predefined assumptions with various uncertainties. In this study, we propose a novel data-driven approach that uses symbolic regression to model oil production. We validate our approach on both synthetic and real data, and the results prove that symbolic regression could effectively identify the true models beneath the oil production data and also make reliable predictions. Symbolic regression indicates that world oil production will peak in 2021, which broadly agrees with other techniques used by researchers. Our results also show that the rate of decline after the peak is almost half the rate of increase before the peak, and it takes nearly 12 years to drop 4% from the peak. These predictions are more optimistic than those in several other reports, and the smoother decline will provide the world, especially the developing countries, with more time to orchestrate mitigation plans.", } @Article{Yang:2015:EP, author = "Guangfei Yang and Tao Sun and Jianliang Wang and Xianneng Li", title = "Modeling the nexus between carbon dioxide emissions and economic growth", journal = "Energy Policy", volume = "86", pages = "104--117", year = "2015", ISSN = "0301-4215", DOI = "doi:10.1016/j.enpol.2015.06.031", URL = "http://www.sciencedirect.com/science/article/pii/S0301421515002499", abstract = "The effects of economic growth on the environment have received increased attention as global warming and other environmental problems become more serious. Many empirical studies explain the nexus between carbon dioxide emissions and economic growth with such models as the environmental Kuznets curve (EKC) theory. However, the assumptions of these models have never received strict verification with a large available data set and therefore may not be appropriate to describe the relationship. In this study, the nexus is modelled for 67 countries from 1971 to 2010 using a novel symbolic regression method. From the experimental results, several conclusions as follows could be reached. Firstly, there is no universal model fitting every country, and symbolic regression could discover a set of reasonable models for a specific country or region. Secondly, four models, including the inverted N-shaped, M-shaped, inverted U-shaped and monotonically increasing, are frequently found without domain experts' intervention in these countries, whereas the M-shaped model has received little attention in previous studies but exhibits promising performance. Thirdly, the relationship is diversified due to the difference of regions and economic development, where developed countries generally follow the inverted N-shaped and M-shaped models to explain the relationship, whereas developing countries are more likely to refer to the inverted N-shaped, inverted U-shaped and monotonically increasing models. Finally, several policy suggestions are presented.", keywords = "genetic algorithms, genetic programming, Carbon dioxide emissions, Environmental Kuznets curve, Symbolic regression", } @InProceedings{Yang:2008:ieeeSMC, author = "Jia-Wei Yang and Hsueh-Chien Cheng and Tsung-Che Chiang and Li-Chen Fu", title = "Multiobjective lot scheduling and dynamic OHT routing in a 300-mm wafer fab", booktitle = "IEEE International Conference on Systems, Man and Cybernetics, SMC 2008", year = "2008", month = oct, pages = "1608--1613", keywords = "genetic algorithms, genetic programming, automated material handling system, dispatching rules, doing tool-to-tool direct delivery, dynamic OHT routing, dynamic routing method, less-congested path, multiobjective genetic programming based rule generator, multiobjective lot scheduling, near-shortest path, overhead hoist transports routing, size 300 mm, traffic congestion reduction, wafer fabrication facility, combinatorial mathematics, dispatching, materials handling, scheduling, semiconductor device manufacture", DOI = "doi:10.1109/ICSMC.2008.4811517", ISSN = "1062-922X", abstract = "In this paper, we solve two problems in a 300-mm wafer fabrication facility (fab). Firstly, for the lot scheduling problem, we propose a multi-objective genetic programming based rule generator (MOGPRG) to evolve useful dispatching rules, which can provide near-optimal lot schedules concerning multiple objectives. Secondly, the overhead hoist transports (OHT) routing problem is considered. As the modern automated material handling system (AMHS) is capable of doing tool-to-tool direct delivery, the congestion of OHTs may happen more often than the past. To deal with the traffic congestion in AMHS, a dynamic routing method is proposed to find the near-shortest and less-congested path for the OHT to travel along. It can reduce the traffic congestion and achieve fast lot delivery by adapting to the dynamic traffic environment. The proposed MOGPRG is integrated with the dynamic routing method to improve two fab performance metrics: mean cycle time and tardy rate. Experimental results show the effectiveness of the proposed MOGPRG and dynamic routing method.", notes = "Also known as \cite{4811517}", } @InProceedings{yang:1997:fssGA, author = "Jihoon Yang and Vasant Honavar", title = "Feature Subset Selection Using A Genetic Algorithm", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "Genetic Algorithms", pages = "380", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", notes = "GP-97", } @InProceedings{yang:1999:AEACG, author = "Jinn-Moon Yang and Cheng-Yan Kao", title = "An Evolutionary Algorithm for Continuous Global optimization", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "930--938", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "evolution strategies and evolutionary programming", ISBN = "1-55860-611-4", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{yang:1999:AGETSPP, author = "Congjun Yang and Dipankar Dasgupta and Yuehua Cao", title = "A Group Encoding Technique for Set Partitioning Problems", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "742--749", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-814.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-814.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{yang:1999:FSSRIUR, author = "Jihoon Yang and Asok Tiyyagura and Fajun Chen and Vasant Honavar", title = "Feature Subset Selection for Rule Induction Using RIPPER", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1800", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "real world applications, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-738.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-738.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Article{Yang:2016:ASC, author = "Lei Yang and Junxi Zhang and Xiaojun Wu and Yumei Zhang and Jingjing Li", title = "A chaotic time series prediction model for speech signal encoding based on genetic programming", journal = "Applied Soft Computing", volume = "38", pages = "754--761", year = "2016", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2015.10.003", URL = "http://www.sciencedirect.com/science/article/pii/S1568494615006183", abstract = "In this paper, a novel solving method for speech signal chaotic time series prediction model was proposed. A phase space was reconstructed based on speech signal's chaotic characteristics and the genetic programming (GP) algorithm was introduced for solving the speech chaotic time series prediction models on the phase space with the embedding dimension m and time delay tau. And then, the speech signal's chaotic time series models were built. By standardized processing of these models and optimizing parameters, a speech signal's coding model of chaotic time series with certain generalization ability was obtained. At last, the experimental results showed that the proposed method can get the speech signal chaotic time series prediction models much more effectively, and had a better coding accuracy than linear predictive coding (LPC) algorithms and neural network model.", keywords = "genetic algorithms, genetic programming, Chaotic time series prediction, Nonlinear coding model", notes = "School of Automation, Northwestern Polytechnical University, Xi'an 710072, China", } @Article{Yang:2018:IJHPCN, author = "Lei Yang and Kangshun Li and Wensheng Zhang and Yaolang Kong", title = "Short-term vegetable prices forecast based on improved gene expression programming", journal = "International Journal of High Performance Computing and Networking", year = "2018", number = "3", volume = "11", pages = "199--213", keywords = "genetic algorithms, genetic programming, gene expression programming", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ijhpcn/ijhpcn11.html#YangLZK18", DOI = "doi:10.1504/IJHPCN.2018.10012996", notes = "journals/ijhpcn/YangLZK18", } @InCollection{yang:1999:SCGA, author = "Mei-Wan Yang", title = "Space Configuration using Genetic Algorithm", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1999", year = "1999", editor = "John R. Koza", pages = "254--263", address = "Stanford, California, 94305-3079 USA", month = "15 " # mar, publisher = "Stanford Bookstore", keywords = "genetic algorithms", notes = "part of \cite{koza:1999:GAGPs}", } @InCollection{yang:2000:ADSSADDS, author = "Brian Hang Wai Yang", title = "A Data Switch Scheduling Algorithm Driven by Darwinian Seleciton", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "452--461", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms", notes = "part of \cite{koza:2000:gagp}", } @Article{yang:145, author = "C. X. Yang and L. G. Tham and X. T. Feng and Y. J. Wang and P. K. K. Lee", title = "Two-Stepped Evolutionary Algorithm and Its Application to Stability Analysis of Slopes", publisher = "ASCE", year = "2004", journal = "Journal of Computing in Civil Engineering", volume = "18", number = "2", pages = "145--153", keywords = "genetic algorithms, genetic programming, civil engineering computing, stability criteria, failure analysis", URL = "http://link.aip.org/link/?QCP/18/145/1", DOI = "doi:10.1061/(ASCE)0887-3801(2004)18:2(145)", abstract = "Based on genetic algorithm and genetic programming, a new evolutionary algorithm is developed to evolve mathematical models for predicting the behavior of complex systems. The input variables of the models are the property parameters of the systems, which include the geometry, the deformation, the strength parameters, etc. On the other hand, the output variables are the system responses, such as displacement, stress, factor of safety, etc. To improve the efficiency of the evolution process, a two-stepped approach is adopted; the two steps are the structure evolution and parameter optimization steps. In the structure evolution step, a family of model structures is generated by genetic programming. Each model structure is a polynomial function of the input variables. An interpreter is then used to construct the mathematical expression for the model through simplification, regularization, and rationalization. Furthermore, necessary internal model parameters are added to the model structures automatically. For each model structure, a genetic algorithm is then used to search for the best values of the internal model parameters in the parameter optimization step. The two steps are repeated until the best model is evolved. The slope stability problem is used to demonstrate that the present method can efficiently generate mathematical models for predicting the behavior of complex engineering systems.", notes = "also known as \cite{CXYang:2004:JCCE} c2004 American Society of Civil Engineers", } @Article{journals/jossac/YangLHL14, title = "Forecasting time series with genetic programming based on least square method", author = "Fengmei Yang and Meng Li and Anqiang Huang and Jian Li", journal = "J. Systems Science \& Complexity", year = "2014", number = "1", volume = "27", pages = "117--129", keywords = "genetic algorithms, genetic programming", bibdate = "2014-10-06", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/jossac/jossac27.html#YangLHL14", URL = "http://dx.doi.org/10.1007/s11424-014-3295-2", } @InProceedings{Yang3:2008:cec, author = "Guangfei Yang and Kaoru Shimada and Shingo Mabu and Kotaro Hirasawa", title = "A Personalized Association Rule Ranking Method Based on Semantic Similarity and Evolutionary Computation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "487--494", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1823-7", file = "EC0132.pdf", DOI = "doi:10.1109/CEC.2008.4630842", abstract = "Many methods have been studied for mining association rules efficiently. However, because these methods usually generate a large number of rules, it is still a heavy burden for the users to find the most interesting ones. In this paper, we propose a novel method for finding what the user is interested in by assigning several keywords, like searching documents on the WWW by search engines. We build an ontology to describe the concepts and relationships in the research domain and mine association rules by Genetic Network Programming from the database where the attributes are concepts in ontology. By considering both the semantic similarity between the rules and the keywords, and the statistical information like support, confidence, chi-squared value, we could rank the rules by a new method named RuleRank, where genetic algorithm is applied to adjust the parameters and the optimal ranking model is built for the user. Experiments show that our approach is effective for the users to find what they want.", keywords = "genetic algorithms, genetic programming", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @PhdThesis{Guangfei_Yang:thesis, author = "Guangfei Yang", title = "Study on association rule retrieval and association rule-based classification using evolutionary computation", school = "Waseda University", year = "2009", address = "Japan", month = may, keywords = "genetic algorithms, genetic programming, Genetic Network Programming", URL = "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/35005/3/Honbun-5128.pdf", abstract_url = "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/35005/1/Gaiyo-5128.pdf", japanese_url = "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/35005/2/Shinsa-5128.pdf", URL = "http://hdl.handle.net/2065/35005", size = "146 pages", abstract = "In recent years, data mining becomes more and more important, and association rule mining is one of the most attractive data mining techniques. In the research of association rule mining, it is a remaining unsolved problem to mine interesting association rules, because the conventional algorithms usually give too many rules. This thesis proposes some new methods based on evolutionary computation to mine interesting association rule s . In this thesis, it is argued that the interestingness of association rules is a task-dependent concept, viz: different meanings of interestingness should be defined for different tasks. Without specifying a particular task, it is ambiguous to discuss the interestingness. This thesis studies two tasks: keyword-based association rule retrieval and association rule based classification, which are denoted as TASK I and TASK II , respectively. TASK I is to find interesting association rules for retrieval, by borrowing the keyword-based style of information retrieval to let the human beings search rules. Association rule is a useful tool to analyse the data. However, due to the huge number of association rules, people usually can not find what they are interested in. In information retrieval, keywords are most often used to search interesting information that people are interested in, and in this thesis, the keyword-based style is borrowed to help people to search interesting association rules.", abstract = "TASK II is to find interesting association rules for classification. The association rule based classification is also called associative classification for short, and the accuracy of associative classification methods are even better than conventional classification methods. However, most associative classification methods mine a large number of rules, and how to find interesting rules to increase the classification accuracy further is still a challenging work. Association rule is a well-defined and deterministic concept. Given the minimum support and minimum confidence, any conventional association rule mining method, like A priori, FP-growth, will give the same rules satisfying the thresholds. However, many problems are not deterministic, and it is not always a good choice to solve a non-deterministic problem by a deterministic method. Let's take associative classification 2 for example. Classification is an ill-defined, non-deterministic problem, in the sense that one can not be sure the rules learned from training data will correctly classify the testing data. When applied to classification, the deterministic property of association rules may cause that their abilities are not fully used. Evolutionary computation is a good solution for non-deterministic problems, and it can adapt to different kinds of tasks effectively. Genetic Network Programming (GNP) is a recently developed evolutionary method, which has been applied to various applications successfully. This thesis will discuss how to mine interesting association rules based on the evolutionary computation, especially based on GNP, for TASK I and TASK II. Generally speaking, there are two ways to solve TASK I and TASK II based on evolutionary computation: 1. Mine association rules by conventional methods first, and then rank the rules based on evolutionary computation, which places the more interesting rules in the upper positions. This approach is called APPROACH I . 2. Directly mine a small number of interesting association rules based on evolutionary computation. This kind of approach is called APPROACH II . Chapter 1 introduces the research background, and describes the general ideas of the research in this thesis, including the framework of task-dependent interestingness of association rules, as well as the introductions of TASK I, TASK II, APPROACH I and APPROACH II. Chapter 2 discusses how to solve TASK I by APPROACH I. After the association rules are mined by conventional methods and some keywords are input by a user, a model named RuleRank is proposed to rank the association rules so that the more interesting rules are placed in higher positions. The RuleRank model is built in a supervised learning manner, and then the trained model automatically ranks the rules for the user. From the simulation results, it is found that the proposed method could give satisfactory ranking results for the user. Chapter 3 discusses how to solve TASK I by APPROACH II. In the method proposed in Chapter 2, a lot of association rules are mined first, however, most of them are uninteresting. In this chapter, a new evolutionary method is 3 proposed to directly mine a small number of interesting association rules. Besides, the dictionary-like style is adopted to give more meaningful annotations to the rules so that the users could have more information to understand the rules better. Some experiments are given to demonstrate the mining of interesting association rules, and the demonstrations show that the proposed method could effectively find the rules which are meaningful, understandable and interesting to the keywords.", abstract = "Chapter 4 discusses how to solve TASK II by APPROACH I. CMAR is one of the representative associative classification methods, and it is found that by adjusting the ranking of rules in CMAR, the classification accuracy could be improved. In this chapter, RuleRank model is applied to obtain the high-quality rankings, and the simulation results show that the RuleRank model could effectively increase the classification accuracy. Chapter 5 discusses how to solve TASK II by APPROACH II. The method proposed in Chapter 4 is based on a large number of rules mined by CMAR, where most of the rules are actually pruned in classification. In this chapter, a new method is proposed to directly mine a few association rules which are interesting for classification. There are three novel ideas discussed in this chapter: attribute-centric approach, record-centric approach and rule-centric approach. The role of attribute-centric approach is to find potentially interesting rules in an efficient manner. The record-centric approach is to reduce the number of wrongly classified or not classified records in the database. The rule-centric is to evaluate the real interestingness of rules, and give award to good rules and penalty to bad rules. The simulation results show that the proposed method has better accuracy than CMAR, and it is faster, too. At last, some conclusions are given to describe the main achievements of this thesis. By applying APPROACH I to TASK I, the user could find interesting association rules which are ranked in a satisfactory order, while by applying APPROACH II to TASK I, the user could directly search a small number of interesting rules which are well annotated. By applying APPROACH I to TASK II, the accuracy of association rule based classification could be increased effectively, while by applying APPROACH II to TASK II, a small number of interesting association rules are directly mined for classification, which gives both high accuracy and fast speed. Generally speaking, under the framework of task-dependent analysis, the interesting association rules could be mined effectively and efficiently.", } @InProceedings{DBLP:conf/gecco/YangMSGH09, author = "Guangfei Yang and Shingo Mabu and Kaoru Shimada and Yunlu Gong and Kotaro Hirasawa", title = "Ranking association rules for classification based on genetic network programming", booktitle = "GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation", year = "2009", editor = "Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano {van Hemert} and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique Alba", pages = "1917--1918", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming, Poster", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1569901.1570234", abstract = "In this paper, we propose a Genetic Network Programming (GNP) based ranking method to improve the accuracy of Classification Based on Association Rule(CBA). We start from an empirical phenomenon, that is, the accuracy could be improved by changing the ranking of rules in CBA. Then, we apply GNP to build a model, namely RuleRank, to find good ranking equations to rank association rules in CBA. The simulation results show that RuleRank could improve the accuracy of CBA effectively.", notes = "GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009). ACM Order Number 910092.", } @InProceedings{Yang:2009:ICCAS-SICE, author = "Guangfei Yang and Shingo Mabu and Kaoru Shimada and Kotaro Hirasawa", title = "A New Associative Classification Method by Integrating CMAR and RuleRank Model based on Genetic Network Programming", booktitle = "ICCAS-SICE, 2009", year = "2009", month = "18-21 " # aug, address = "Fukuoka", pages = "3874--3879", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, genetic network programming, CMAR, RuleRank model, associative classification method, classification accuracy, genetic network programming, multiple association rule, rank association rules, data mining, pattern classification", isbn13 = "978-4-9077-6433-3", URL = "http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5332932", size = "6 pages", abstract = "In this paper, we propose an evolutionary approach to rank association rules for classification. The association rules are ranked by their support, confidence and length in one of the most important associative classification method, Classification based on Multiple Association Rule (CMAR). However, from some empirical studies, we find that if the rules are ranked by some equations first, the classification accuracy will be improved in some data sets. In order to generate such equations effectively, we propose a RuleRank model based on genetic network programming (GNP). The experimental results show that our method could improve the classification accuracies effectively.", notes = "UCI. Also known as \cite{5332932}", } @Article{YANG:2023:procs, author = "Guangfei Yang and Bing Yan", title = "A Data-driven Causality Modeling Framework: An Empirical Study of Modeling the Effect of Indoor Air Quality Perception on Students' Cognitive Performance", journal = "Procedia Computer Science", volume = "221", pages = "839--844", year = "2023", note = "Tenth International Conference on Information Technology and Quantitative Management (ITQM 2023)", ISSN = "1877-0509", DOI = "doi:10.1016/j.procs.2023.08.059", URL = "https://www.sciencedirect.com/science/article/pii/S1877050923008177", keywords = "genetic algorithms, genetic programming, Casual Graph, Data Driven, Structural Equation Model, Artificial Intelligence, Relationship Modeling", abstract = "big relationship' problem, where a large number of correlations obfuscate the identification of true causal relationships. In this paper, we use a causality modeling framework that combines correlation modeling and causality pruning processes. First, symbolic regression is used to model white-box correlations of human intelligence, and then spurious correlations that do not conform to causal graph theory are pruned so that ultimately causal relationships and explicit candidate models describing these relationships can be found automatically. In an empirical research problem, the framework is compared with a traditional hypothesis construction-validation process, and the results are consistent between the two. The proposed framework implements a data-driven 'correlation+causation' automatic modeling capability, which will greatly improve modeling efficiency and reliability", } @Article{Yang:2025:GPEM, author = "Haichuan Yang2 and Haotian Li and Yifei Yang and Naoya Chiba and Shingo Kagami and Koichi Hashimoto and Yuichi Nagata", title = "Chaotic map-coded metaheuristics for metameric variable-length problems", journal = "Genetic Programming and Evolvable Machines", year = "2025", volume = "26", pages = "Article no 19", note = "Online first", keywords = "genetic algorithms, genetic programming, Variable length problem, Metaheuristics, Chaotic map, Indirect encoding, dendritic neuron model (DNM), wind farm layout optimization (WFLOP)", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-025-09517-6", notes = "Graduate School of Technology, Industrial and Social Sciences, Tokushima University, Tokushima, 770-8506, Japan", } @InProceedings{Yang:2010:ICIECS, author = "Hui-Hua Yang and Shih-Huang Chen and Jui-Ying Hung and Ching-Tsung Hung and Meng-Lung Chung", title = "Utilization of Genetic Programming to Establish Demand Forecast in {Taiwan} International Flights", booktitle = "2nd International Conference on Information Engineering and Computer Science (ICIECS), 2010", year = "2010", month = "25-26 " # dec, abstract = "Accurately prediction is the most important way to cost down for airlines. The study was focused on build up forecast model of five Taiwan international flights included Bali, Bang Kong, Ho Chi Minh City, Kuala Lumpur, and Singapore. Genetic programming was adopted to establish simulation models, and Mean Absolute Percent Error (MAPE) also was used to evaluate the performance of those models. The ten years of historical passenger's data was collected and analysis, and finally the demand forecast of five flights in 2010 would be conducted. The validations MAPE of models were lower than 10percent expect Bali flights. Based on experience of traditional statistic method included linear regression and time series, the ability of Genetic programming models were excellent. The forecast error of Bali flights were 11percent and it may be caused by a series accident. On the basis of above results, Genetic programming could be the feasible approach for prediction of five flights in Taiwan. In addition, the passengers to Singapore would substantially increase in 2010-2011, and the issue is worthy to further study for airlines and government.", keywords = "genetic algorithms, genetic programming, Taiwan international flights, airlines, demand forecast, mean absolute percent error, demand forecasting, travel industry", DOI = "doi:10.1109/ICIECS.2010.5677766", ISSN = "2156-7379", notes = "Also known as \cite{5677766}", } @Article{journals/ijcisys/0009M16, title = "A hybrid gene expression programming algorithm based on orthogonal design", author = "Jie Yang and Jun Ma", journal = "Int. J. Computational Intelligence Systems", year = "2016", volume = "9", number = "4", pages = "778--787", keywords = "genetic algorithms, genetic programming, gene expression programming, evolutionary computation, orthogonal design, evolutionary stable strategy", ISSN = "1875-6883", bibdate = "2017-05-18", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ijcisys/ijcisys9.html#0009M16", URL = "http://www.atlantis-press.com/php/download_paper.php?id=25868727", DOI = "doi:10.1080/18756891.2016.1204124", abstract = "The last decade has witnessed a great interest on the application of evolutionary algorithms, such as genetic algorithm (GA), particle swarm optimisation (PSO) and gene expression programming (GEP), for optimisation problems. This paper presents a hybrid algorithm by combining the GEP algorithm and the orthogonal design method. A multiple-parent crossover operator is introduced for the chromosome reproduction using the orthogonal design method. In addition, an evolutionary stable strategy is also employed to maintain the population diversity during the evolution. The efficiency of the proposed algorithm is evaluated using three benchmark problems. The results demonstrate that the proposed hybrid algorithm has a better generalisation ability compared to conventional algorithms.", } @Article{YANG:2022:procs, author = "Kaifeng Yang and Michael Affenzeller", title = "Quantifying Uncertainties of Residuals in Symbolic Regression via Kriging", journal = "Procedia Computer Science", volume = "200", pages = "954--961", year = "2022", note = "3rd International Conference on Industry 4.0 and Smart Manufacturing", keywords = "genetic algorithms, genetic programming, White-box modelling, Gaussian Processes, Kriging, Residuals, Uncertainties", ISSN = "1877-0509", URL = "https://pure.fh-ooe.at/en/publications/quantifying-uncertainties-of-residuals-in-symbolic-regression-via", URL = "https://www.sciencedirect.com/science/article/pii/S1877050922003027", DOI = "doi:10.1016/j.procs.2022.01.293", abstract = "Genetic programming (GP) based symbolic regression is a powerful technique for white-box modelling. However, the prediction uncertainties of the symbolic regression are still unknown. This paper proposes to use Kriging to model the residual of a symbolic expression. The residual model follows a normal distribution with parameters of a mean value and a standard deviation, where the mean value can be used to regulate the prediction and the standard deviation represents the uncertainties of residuals in a specific symbolic expression. The proposed algorithms are compared with a canonical GP-based symbolic regression and Kriging regression on three benchmarks in symbolic regression field. The results show that the proposed algorithm significantly outperforms the other two algorithms on the three benchmarks w.r.t. mean squared error in the test dataset with a small generation budget", notes = "DBLP:conf/ism2/YangA21 3rd International Conference on Industry 4.0 and Smart Manufacturing, ISM 2021 ; Conference date: 19-11-2021 Through 21-11-2021 https://www.msc-les.org/ism2021/ https://www.msc-les.org/conf/ism2021/ISM2021_FullProgram.pdf paper #106 17 Nov 2021 Room HS5 14:50-15:10", } @InProceedings{yang:2023:EMO, author = "Kaifeng Yang and Michael Affenzeller", title = "Surrogate-assisted Multi-objective Optimization via Genetic Programming Based Symbolic Regression", booktitle = "Evolutionary Multi-Criterion Optimization", year = "2023", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-031-27250-9_13", DOI = "doi:10.1007/978-3-031-27250-9_13", } @Article{yang:2018:PNaA, author = "Lechan Yang and Zhihao Qin", title = "Distributed correlation model mining from remote sensing big data based on gene expression programming", journal = "Peer-to-Peer Networking and Applications", year = "2018", volume = "11", number = "5", keywords = "genetic algorithms, genetic programming, gene expression programming", URL = "http://link.springer.com/article/10.1007/s12083-017-0589-x", DOI = "doi:10.1007/s12083-017-0589-x", } @Article{journals/cee/YangDZ20, author = "Lechan Yang and Song Deng and Zi Zhang", title = "New spectral model for estimating leaf area index based on gene expression programming", journal = "Comput. Electr. Eng", year = "2020", volume = "83", pages = "106604", keywords = "genetic algorithms, genetic programming, gene expression programming", bibdate = "2020-05-12", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/cee/cee83.html#YangDZ20", DOI = "doi:10.1016/j.compeleceng.2020.106604", } @InProceedings{Yang:2022:CSCWD, author = "Lu Yang and Fazhi He and Li Dai and Lin Zhang", booktitle = "2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD)", title = "An Automatical And Efficient Image Classification Based On Improved Genetic Programming", year = "2022", pages = "477--483", abstract = "Image classification is a basic task in machine intelligence, but challenging due to high variations across images. Traditional methods use hand-crafted features to solve it, which require much domain knowledge. Genetic Programming (GP) can automatically solve problems without much knowledge about the structure and form of the solution. And GP is interpretable and needs less time to adjust the parameters compared with deep image classification methods. However, the existing GP-based image classification methods have some disadvantages, such as poor classification performance and long training time. This paper proposed a new image classification algorithm based on multilayer genetic programming with cache (MCGP). MCGP designs a new hierarchical individual program structure with a classification layer and uses a subtree cache strategy to reduce training time. The experiments show that MCGP can get better or competitive results compared with traditional methods, other GP methods, and convolutional neural network methods. In addition, the training speed of MCGP is much faster than other GP methods.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CSCWD54268.2022.9776145", month = may, notes = "Also known as \cite{9776145}", } @InProceedings{yang:2024:CEC, author = "Ning Yang and Hai-Lin Liu and Lei Chen and Yuping Wang and Yiu-ming Cheung", title = "A Bilevel Evolutionary Algorithm Based on Upper-Level-Driven Lower-Level Search", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, Accuracy, Heuristic algorithms, Sociology, Evolutionary computation, Task analysis, Statistics, Optimization, Bilevel optimization, constraint optimization, evolutionary algorithm", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10611954", abstract = "The bilevel optimisation problem is a kind of commonly existing optimisation problem, which includes a nested lower-level optimisation problem as a constraint condition. The nested lower-level optimisation problem should be solved with every upper-level decision fixed as a parameter. Consequently, it is usually computationally very expensive to solve bilevel optimisation problems. In this paper, we propose a bilevel evolutionary algorithm based on upper-level-driven lower-level search (BLEA-UDLS). Driven by the upper-level optimisation, the lower-level search in BLEA-UDLS is carried out on some upper-level superior solutions rather than equally and indiscriminately on all solutions, which makes sure that the front solutions of the population have more accurate lower-level decisions and saves lots of evaluation budgets on less important solutions. In the lower-level search, the lower-level decisions of different solutions are optimised cooperatively with the computation resources dynamically adjusted, where more computation resources are assigned for less explored solutions. Compared with some other bilevel evolutionary algorithms, the experimental results have confirmed the effectiveness of the proposed BLEA-UDLS for solving BLOPs and meanwhile saving evaluation budgets.", notes = "also known as \cite{10611954} Is this GP? WCCI 2024 School of Automation Guangdong University of Technology Guangzhou, China", } @Article{yang:2021:omega, author = "Peisong Yang and Huan Zhang2 and Xin Lai and Kunfeng Wang and Qingyuan Yang and Duli Yu", title = "Accelerating the Selection of Covalent Organic Frameworks with Automated Machine Learning", journal = "ACS omega", year = "2021", volume = "6", number = "27", pages = "17149--17161", month = jul # " 13", keywords = "genetic algorithms, genetic programming, TPOT, methane, natural gas", ISSN = "2470-1343", publisher = "American Chemical Society", DOI = "doi:10.1021/acsomega.0c05990", size = "13 pages", abstract = "Covalent organic frameworks (COFs) have the advantages of high thermal stability and large specific surface and have great application prospects in the fields of gas storage and catalysis. This article mainly focuses on COFs' working capacity of methane (CH(4)). Due to the vast number of possible COF structures, it is time-consuming to use traditional calculation methods to find suitable materials, so it is important to apply appropriate machine learning (ML) algorithms to build accurate prediction models. A major obstacle for the use of ML algorithms is that the performance of an algorithm may be affected by many design decisions. Finding appropriate algorithm and model parameters is quite a challenge for nonprofessionals. In this work, we use automated machine learning (AutoML) to analyze the working capacity of CH(4) based on 403,959 COFs. We explore the relationship between 23 features such as the structure, chemical characteristics, atom types of COFs, and the working capacity. Then, the tree-based pipeline optimization tool (TPOT) in AutoML and the traditional ML methods including multiple linear regression, support vector machine, decision tree, and random forest that manually set model parameters are compared. It is found that the TPOT can not only save complex data preprocessing and model parameter tuning but also show higher performance than traditional ML models. Compared with traditional grand canonical Monte Carlo simulations, it can save a lot of time. AutoML has broken through the limitations of professionals so that researchers in nonprofessional fields can realize automatic parameter configuration for experiments to obtain highly accurate and easy-to-understand results, which is of great significance for material screening.", notes = "PMID: 34278102 College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China", } @Article{Shangshang-Yang:ieeeTEC, author = "Shangshang Yang and Haiping Ma and Ying Bi and Ye Tian and Limiao Zhang and Yaochu Jin and Xingyi Zhang", title = "An Evolutionary Multi-Objective Neural Architecture Search Approach to Advancing Cognitive Diagnosis in Intelligent Education", journal = "IEEE Transactions on Evolutionary Computation", note = "Accepted for future publication", keywords = "genetic algorithms, genetic programming, MOGP, Vectors, Task analysis, Search problems, Computer architecture, Evolutionary computation, Education, Optimization, Cognitive diagnosis, intelligent education, evolutionary neural architecture search, multi-objective optimization", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2024.3429180", size = "15 pages", notes = "also known as \cite{10599558}", } @Article{Yang:2006:GPEM, author = "Shengxiang Yang and Yew-Soon Ong and Yaochu Jin", title = "Editorial to special issue on evolutionary computation in dynamic and uncertain environments", journal = "Genetic Programming and Evolvable Machines", year = "2006", volume = "7", number = "4", pages = "293--294", month = dec, note = "Editorial", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-006-9016-4", size = "2 pages", } @InProceedings{conf/isica/YangLLC15, author = "Shuling Yang and Kangshun Li and Wei Li and Weiguang Chen", title = "An Optimized Clustering Algorithm Using Improved Gene Expression Programming", publisher = "Springer", year = "2015", volume = "575", bibdate = "2016-04-27", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/isica/isica2015.html#YangLLC15", booktitle = "ISICA", editor = "Kangshun Li and Jin Li and Yong Liu and Aniello Castiglione", isbn13 = "978-981-10-0355-4", pages = "150--160", series = "Communications in Computer and Information Science", keywords = "genetic algorithms, genetic programming, gene expression programming", URL = "http://dx.doi.org/10.1007/978-981-10-0356-1", } @Article{journals/ijon/YangMLK17, author = "Steve Y. Yang and Sheung Yin Kevin Mo and Anqi Liu and Andrei Kirilenko", title = "Genetic programming optimization for a sentiment feedback strength based trading strategy", journal = "Neurocomputing", year = "2017", volume = "264", pages = "29--41", keywords = "genetic algorithms, genetic programming, News sentiment, Tweet sentiment, financial market, feedback", ISSN = "0925-2312", bibdate = "2017-09-16", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ijon/ijon264.html#YangMLK17", URL = "https://orca.cardiff.ac.uk/id/eprint/109452/", URL = "https://orca.cardiff.ac.uk/id/eprint/109452/1/NEUCOM-D-16-00967-SentimentGP-Revision.pdf", DOI = "doi:10.1016/j.neucom.2016.10.103", size = "40 pages", abstract = "... empirical findings that news and social media Twitter messages (tweets) ... generate superior trading profits. With the trade-off between information speed and its reliability, this study aims to develop an optimal trading strategy using investors sentiment feedback strength with the objective to maximize risk adjusted return measured by the Sterling ratio. We find that the sentiment feed-back based strategies yield superior market returns with low maximum draw-down over the period from 2012 to 2015. In comparison, the strategies based on the sentiment feedback indicator generate over 14.7 percent Sterling ratio compared with 10.4 percent and 13.6 percent from the technical indicator-based strategies and the basic buy-and-hold strategy respectively. After considering transaction costs, the sentiment indicator based strategy outperforms the technical indicator based strategy consistently. Backtesting shows that the advantage is statistically significant. The result suggests that the sentiment feedback indicator provides support in controlling loss with lower maximum drawdown.", } @InProceedings{yang:2024:IJCNN, author = "Tingting Yang and Chenhao Xue and Jun Chen2", title = "Design of Driver Stress Prediction Model with {CNN-LSTM:} Exploration of Feature Space using Genetic Programming", booktitle = "2024 International Joint Conference on Neural Networks (IJCNN)", year = "2024", editor = "Chrisina Jayne", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Accuracy, Predictive models, Stability analysis, Physiology, Space exploration, Convolutional neural networks, Driver Stress Prediction, CNN, LSTM, Features Exploration, Representation Learning", isbn13 = "979-8-3503-5932-9", DOI = "doi:10.1109/IJCNN60899.2024.10650395", abstract = "Road traffic accidents, primarily caused by driver-related issues such as stress, result in numerous fatalities and injuries. Effective prediction of driver stress within the driving phase is paramount for real-time accident intervention, while it requires prediction accuracy, stability, and enough predictive lead time. Physiological data contains rich information related to driver stress levels, which can be captured by machine learning models. However, those models are mainly developed for and perform well in static stress detection tasks, not addressing practical requirements of predictive lead time and performance stability. This study introduces a novel approach, the GP-CNN-LSTM model, Which employs a Convolutional Neural Network (CNN) with a Long Short-Term Memory (LSTM) network and Genetic Programming (GP), leverages GP to explore the feature space upon physiological sensor signals, and relies on CNN-LSTM to predict driver stress. Experiments show that this model achieves high accuracy for a 60-second forward stress prediction, with more stability compared to Fractional Fourier Transform-based benchmark models. We found explainable effective signals and features described by math functions via genetic programming that helped in improving the accuracy and stability of driver stress prediction.", notes = "also known as \cite{10650395} WCCI 2024", } @Article{Yang:2014:EJHG, title = "Random forest fishing: a novel approach to identifying organic group of risk factors in genome-wide association studies", author = "Wei Yang and C. Charles Gu", journal = "European Journal of Human Genetics", year = "2014", volume = "22", pages = "254--259", month = may # "~22", keywords = "genetic algorithms, genetic programming, genome-wide association, statistical learning, random forest, epistasis, interactions", ISSN = "23695277", bibsource = "OAI-PMH server at www.ncbi.nlm.nih.gov", language = "en", oai = "oai:pubmedcentral.nih.gov:3895629", rights = "Copyright 2014 Macmillan Publishers Limited", URL = "http://www.ncbi.nlm.nih.gov/pmc/articles/PMC", URL = "http://www.ncbi.nlm.nih.gov/pubmed/23695277", DOI = "doi:10.1038/ejhg.2013.109", size = "6 pages", abstract = "Genome-wide association studies (GWAS) has brought methodological challenges in handling massive high-dimensional data and also real opportunities for studying the joint effect of many risk factors acting in concert as an organic group. The random forest (RF) methodology is recognised by many for its potential in examining interaction effects in large data sets. However, RF is not designed to directly handle GWAS data, which typically have hundreds of thousands of single-nucleotide polymorphisms as predictor variables. We propose and evaluate a novel extension of RF, called random forest fishing (RFF), for GWAS analysis. RFF repeatedly updates a relatively small set of predictors obtained by RF tests to find globally important groups predictive of the disease phenotype, using a novel search algorithm based on genetic programming and simulated annealing. A key improvement of RFF results from the use of guidance incorporating empirical test results of genome-wide pairwise interactions. Evaluated using simulated and real GWAS data sets, RFF is shown to be effective in identifying important predictors, particularly when both marginal effects and interactions exist, and is applicable to very large GWAS data sets.", } @Article{Ya:SV:06, title = "Establishment of the mathematical model for diagnosing the engine valve faults by genetic programming", author = "Wen-Xian Yang", journal = "Journal of Sound and Vibration", year = "2006", volume = "293", number = "1-2", pages = "213--226", month = "30 " # may, keywords = "genetic algorithms, genetic programming, Engine valve, Fault diagnosis, Immigration operator", ISSN = "0022-460X", bibsource = "OAI-PMH server at dspace.lib.cranfield.ac.uk", identifier = "Wen-Xian Yang, Establishment of the mathematical model for diagnosing the engine valve faults by genetic programming, Journal of Sound and Vibration, Volume 293, Issues 1-2, , 30 May 2006, Pages 213-226.; 0022-460X", language = "en", oai = "oai:dspace.lib.cranfield.ac.uk:1826/1131", URL = "https://dspace.lib.cranfield.ac.uk/bitstream/1826/1131/1/Yang-JSVpaper-Math+model+engine+valve+faults.pdf", URL = "http://hdl.handle.net/1826/1131", DOI = "doi:10.1016/j.jsv.2005.09.004", abstract = "Available machine fault diagnostic methods show unsatisfactory performances on both on-line and intelligent analyses because their operations involve intensive calculations and are labour intensive. Aiming at improving this situation, this paper describes the development of an intelligent approach by using the Genetic Programming (abbreviated as GP) method. Attributed to the simple calculation of the mathematical model being constructed, different kinds of machine faults may be diagnosed correctly and quickly. Moreover, human input is significantly reduced in the process of fault diagnosis. The effectiveness of the proposed strategy is validated by an illustrative example, in which three kinds of valve states inherent in a six-cylinders/four-stroke cycle diesel engine, i.e. normal condition, valve-tappet clearance and gas leakage faults, are identified. In the example, 22 mathematical functions have been specially designed and 8 easily obtained signal features are used to construct the diagnostic model. Different from existing GPs, the diagnostic tree used in the algorithm is constructed in an intelligent way by applying a power-weight coefficient to each feature. The power-weight coefficients vary adaptively between 0 and 1 during the evolutionary process. Moreover, different evolutionary strategies are employed, respectively for selecting the diagnostic features and functions, so that the mathematical functions are sufficiently and in the meantime, the repeated use of signal features may be fully avoided. The experimental results are illustrated diagrammatically in the following sections.", } @Article{YANG:2021:DSS, author = "Xian Yang and Guangfei Yang and Jiangning Wu and Yanzhong Dang and Weiguo Fan", title = "Modeling relationships between retail prices and consumer reviews: A machine discovery approach and comprehensive evaluations", journal = "Decision Support Systems", volume = "145", pages = "113536", year = "2021", ISSN = "0167-9236", DOI = "doi:10.1016/j.dss.2021.113536", URL = "https://www.sciencedirect.com/science/article/pii/S0167923621000464", keywords = "genetic algorithms, genetic programming, Consumer reviews, Retail price, Data-driven, Machine learning, Product involvement", abstract = "Setting the retail price as a part of marketing would affect customers' cognition regarding products and affect their post-purchase behavior of review writing. To deeply understand the relationships between retail prices and reviews, this paper designs an intelligent data-driven Generate/Test Cycle using a machine learning technique to automatically discover the relationship model from a huge amount of data without a prior hypothesis. From a unique dataset, various free-form relationship models with their own structures and parameters have been discovered. By the comprehensive evaluations of candidate models, a guided map was offered to understand the relationship between dynamic retail prices and the volume/valence of reviews for different types of products. Experimental results show that 37.69percent of products in our sample exhibit the following trend: When the price is increased to a certain level, the volume of reviews shifts from a decreasing trend to an increasing trend. Results also demonstrate that a linearly increasing relationship model between prices and the valence of reviews is more suitable for the low-involvement products than for the high-involvement products. In addition to the new findings, this research provides a powerful tool to assist domain experts in building relationship models for decision making in a highly efficient manner", } @Article{YANG:2019:GM, author = "Xin Yang and Yuliang Wang and Shuai Li2 and Xinglin Piao and Baocai Yin and Qiang Zhang and Dongsheng Zhou and Xiaopeng Wei", title = "Real-virtual consistent traffic flow interaction", journal = "Graphical Models", volume = "106", pages = "101048", year = "2019", ISSN = "1524-0703", DOI = "doi:10.1016/j.gmod.2019.101048", URL = "http://www.sciencedirect.com/science/article/pii/S1524070319300396", keywords = "genetic algorithms, genetic programming, Microscopic model, Traffic simulation", abstract = "Traffic simulation has become an efficient tool, with the assistance of computer visualizing techniques, to solve traffic issues such as traffic congestion, network design, and similar problems. Properly controlling simulated traffic flow and modeling each vehicle's irregular behaviors are key issues in the traffic simulation field. In this paper, we introduce real vehicle trajectories as a data-driven factor in simulated traffic situations to drive behaviors of other simulated vehicles. First, we train a driving model for each simulated vehicle using real traffic data that have a unique control strategy. Then, we fuse real trajectories driven vehicles with simulated trajectories driven vehicles to interact, guided by our learned traffic model, to accurately depict the reality of traffic flows. Compared with existing methods, traffic flows simulated using this method are more realistic and can preserve irregular characteristics of the real traffic flows", } @InProceedings{Yang:2012:CSCWD, author = "Yang Yang and Xinyu Li and Ping Jiang and Long Wen", title = "Modeling of cutting forces in a face-milling operation with Gene Expression Programming", booktitle = "IEEE 16th International Conference on Computer Supported Cooperative Work in Design (CSCWD 2012)", year = "2012", month = "23-25 " # may, pages = "769--774", size = "6 pages", abstract = "Cutting forces is one of the most fundamental elements that affect the performance of cutting operation. Finding the rules that how process and environment factors affect the values of cutting forces will help to set the process parameters of the future cutting operation and further improve production quality and efficiency. Since cutting forces is impacted by different machining parameters and the inherent uncertainties in the machining process, how to predict the cutting forces becomes a challengeable problem for the researchers and engineers. Gene Expression Programming (GEP) combines the advantages of the genetic algorithm (GA) and genetic programming (GP), and has been successfully applied in function mining and formula finding, so it should be suitable to solve the above problem. In this paper, a method based on GEP has been proposed to construct the prediction model of cutting forces in a face-milling operation. At the basis of defining a GEP environment for the problem and improving the method of constant creation, an explicit prediction model of cutting forces has been constructed. To verify the feasibility and performance of the proposed approach, experimental studies have been conducted to compare this approach with some previous works. The obtained results show that the constructed prediction model fits very well with the experimental data, and can be used to estimate the cutting forces and optimise the cutting parameters. The proposed method will lead to the reduction in production costs and production time, and improvement of product quality.", keywords = "genetic algorithms, genetic programming, gene expression programming, GEP, cutting forces, face milling operation, machining parameters, product quality improvement, production cost reduction, production efficiency improvement, production quality improvement, cost reduction, cutting, milling, product quality", DOI = "doi:10.1109/CSCWD.2012.6221907", notes = "Also known as \cite{6221907}", } @InProceedings{Yang:2012:CEC, title = "A Multitasks Learning Approach to Autonomous Agent based on Genetic Network Programming", author = "Yang Yang and Shingo Mabu and Kotaro Hirasawa", pages = "1937--1943", booktitle = "Proceedings of the 2012 IEEE Congress on Evolutionary Computation", year = "2012", editor = "Xiaodong Li", month = "10-15 " # jun, DOI = "doi:10.1109/CEC.2012.6256457", address = "Brisbane, Australia", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Genetic Network Programming, Evolutionary programming, Parallel and distributed algorithms", abstract = "The standard methodology in machine learning is to learn one problem at a time. But, many real-world problems are complex and have multitasks, and it is a bit hard to learn them well by one machine learning approach. So, the simultaneous learning of several tasks has been considered, that is, so-called multitask learning. This paper describes a new approach to the autonomous agent problem using the multitask learning scheme based on Genetic Network Programming (GNP), called ML-GNP, where each GNP is used to learn one corresponding task. MLGNP has some characteristics, such as distribution, interaction and autonomy, which are helpful for learning multitask problems. The experimental results illustrate that ML-GNP can give much better performance than learning all the tasks of the problem by one GNP algorithm.", notes = "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the EPS and the IET.", } @PhdThesis{YangYang:thesis, author = "Yang Yang", title = "Study on Architecture of Genetic Network Programming based on Cooperative Division Strategy", school = "Waseda University", year = "2012", address = "Japan", month = nov, keywords = "genetic algorithms, genetic programming, Genetic Network Programming, coevolution", URL = "http://jairo.nii.ac.jp/0069/00023924/en", URL = "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/40055/1/Honbun-6123.pdf", URL = "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/40055/2/Shinsa-6123.pdf", URL = "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/40055/3/Gaiyo-6123.pdf", size = "113 pages", abstract = "Making computers automatically solve problems is central to Artificial Intelligent and many technologies have been developed under the name of what is called machine learning. Recently developed Genetic Network Programming (GNP) is a graph-based evolutionary algorithm extended from GA and GP. Because GNP represents its solutions using graph structures, which contributes to creating quite compact programs and realising partially observable process, it has extended from purely theoretical concept to real-life applications in a very short time. However, the drive for applying GNP to a wider range of applications should be continued constantly examining the current GNP architectures and making improvements. Many studies have shown that real world problems have hurdles that could not be solved by original GNP. Like many other areas of computer science, GNP can evolve more rapidly and produce better performances with new techniques. The development of advanced techniques to boost GNP performances seems important and promising. This thesis studies the following topics related to the architecture and applications of GNP: Search space is a key issue while complementing GNP in the area of stock markets. There are many indexes to be considered in the stock markets, moreover their relationships are nonlinear. Overfitting generally exists in the machine learning field, which means only specific situations can be handled instead of generalised situations. The possibility of over fitting exists because a model is typically trained using training data by maximising its performances. However, its overall performances are determined not by its performances on the training data but by its ability to perform on unseen data. Computational complexity is also needed to be considered, since most machine learning approaches only use a single monolithic system to solve large and complex problems. Multitask is a common feature in many real-world problems, but the standard methodology in machine learning is to study them by one system. In order to improve the performance of GNP, when faced with the above-mentioned hurdles, this thesis introduces new advanced techniques of GNP. Firstly, hierarchical architecture GNP is proposed, which uses subroutine mechanism, and furthermore the functional subroutines are introduced. Secondly, the division architecture GNP is proposed, which uses cooperative coevolution for the subprograms. Next, the distributed architecture GNP is proposed, which has task program using multitask learning. To illustrate the performances of the proposed methods, two real-world applications are conducted. One is the stock markets, which is one of the targets for most popular investments due to its high expected profits. The second application is the tile-world, where its aim is to find successive optimal behaviours for the multi-tasks making judgements and taking proper actions for the current environments. In chapter 1, the research background, objective and outline of the thesis are descried. The objective of this research is to propose the advanced techniques into GNP to develop better methods for real-world applications.", abstract = "Chapter 2 proposes a methodology to construct the models for creating trading rules using Genetic Network Programming-Sarsa with Subroutines (GNPsb-Sarsa), which offers an alternative population, where individuals are represented by subroutines. The basic idea of GNP sb-Sarsa is to automatically discover effective subroutines, which capture the underlying structure and building blocks of the problems. Then, the main program of GNPsb-Sarsa can re-use the subroutine to enable a faster evolution and even better performances. GNPsb-Sarsa containing a main program and a subroutine evolves by natural selection and genetic operations, where the gene of GNPsb-Sarsa is the pair of the main GNP and its subroutine. That is, the genetic operations on GNPsb-Sarsa are constrained by the gene structure on which they can operate. The following two experiments are discussed: 1) Testing one subroutine node in the main GNP 2) Varying the number of subroutine nodes in the main GNP. The results of simulations showed that the proposed GNP sb-Sarsa can provide reasonable opportunities for evolving complex solutions.", abstract = "Chapter 3 introduces a methodology to enhance the generalisation ability of the stock trading models based on GNP-Sarsa with multi-subroutines (GNPmsb-Sarsa), which is a kind of extension of GNPsb-Sarsa. The proposed method is developed for discovering the nodes and node connections to realise functions, and the functional distributed modular GNP is developed. The important points of the subroutines mechanism are as follows: First, the nodes and node connections discovered in the subroutines are reused to create effective trading rule s for certain function. Second, the evolution can be achieved so quickly by narrowing the search space with subroutines. Last, as the kinds of functional subroutines increase, the generalization ability is improved since more generalised frequent transitions of GNP, i.e., building blocks are found instead of precisely modelling the training data, which leads to the over fitting problem. Simulation results showed that the proposed method can generate more efficient and generalised trading models and obtain much higher profits. Chapter 4 introduces a methodology to enhance the generalisation of the stock trading models based on Cooperative Coevolutionary Genetic Network Programming-Sarsa (GNPcc-Sarsa). The basic idea comes from both natural and artificial systems, which show that an integrated system consisting of several subsystems can reduce the total complexity of the system and solve a difficult problem satisfactorily. Therefore, a cooperative coevolution approach is proposed, where several species simultaneously evolve. Such an approach allows different species of the GNP-Sarsa model to evolve in a parallel and cooperative manner, which makes the generated model more robust, generalised and efficient for generating stock trading strategies. GNPcc-Sarsa places as few restrictions as possible to the structure, allowing the model to obtain a wide variety of architectures during the evolution and to be easily used to solve complicated problems. It has been found from simulations that the performances of the proposed model are better than those of other methods. Chapter 5 introduces a methodology to simultaneously learn several tasks based on GNP, which is called GNP with multitasks (GNPmt), where each GNP among several GNPs corresponding to several tasks is used to learn its own task. GNPmt has some features, such as distribution, interaction and autonomy, which are helpful for learning multitask problems. The experimental results on the self-sufficient collecting problem are given to illustrate that GNPmt can give much better performances than learning all the tasks of the problem by one GNP. Chapter 6 concludes the thesis by describing the achievements of the proposed methods.", } @Article{Yang:2013:JNCA, author = "Yang Yang and Xinyu Li and Liang Gao and Xinyu Shao", title = "A new approach for predicting and collaborative evaluating the cutting force in face milling based on gene expression programming", journal = "Journal of Network and Computer Applications", year = "2013", volume = "36", number = "6", pages = "1540--1550", keywords = "genetic algorithms, genetic programming, Cutting force modelling, Gene expression programming, Face milling, Collaborative model evaluation", ISSN = "1084-8045", DOI = "doi:10.1016/j.jnca.2013.02.004", URL = "http://www.sciencedirect.com/science/article/pii/S1084804513000428", size = "11 pages", abstract = "Cutting force is one of the fundamental elements that can provide valuable insight in the investigation of cutter breakage, tool wear, machine tool chatter, and surface finish in face milling. Analysing the relationship between process factors and cutting force is helpful to set the process parameters of the future cutting operation and further improve production quality and efficiency. Since cutting force is impacted by the inherent uncertainties in the machining process, how to predict the cutting force presents a significant challenge. In the meantime, face milling is a complex process involving multiple experts with different domain knowledge, collaborative evaluation of the cutting force model should be conducted to effectively evaluate the constructed predictive model. Gene Expression Programming (GEP) combines the advantages of the Genetic Algorithm (GA) and Genetic Programming (GP), and has been successfully applied in function mining and formula finding. In this paper, a new approach to predict the face milling cutting force based on GEP is proposed. At the basis of defining a GEP environment for the cutting force prediction, an explicit predictive model has been constructed. To verify the effectiveness of the proposed approach, a case study has been conducted. The comparisons between the proposed approach and some previous works show that the constructed model fits very well with the experimental data and can predict the cutting force with a high accuracy. Moreover, in order to better apply the constructed predictive models in actual face milling process, a collaborative model evaluation method is proposed to provide a distributed environment for geographical distributed experts to evaluate the constructed predictive model collaboratively, and four kinds of collaboration mode are discussed.", } @InProceedings{Yang:2010:ICCASM, author = "Yanjun Yang and Zhongfan Xiang and Qiang Wang and Zaixin Liu", title = "Robot autonomous navigation based on multi-sensor global calibrated", booktitle = "International Conference on Computer Application and System Modeling (ICCASM 2010)", year = "2010", month = oct, volume = "3", pages = "V3--706--V3--710", abstract = "Aiming at the robot problems such as little relevance, low accuracy of the simultaneous localisation and map building (SLAM) and easy locking, lack of initiative of the navigation system, the multi-sensor vision system is introduced, and then unifying the data of each sensor by world coordinate system of global calibration based on the local calibration of each vision sensor module, a serial of local maps are combined into a global map by the derive of Least-Square (LS). Preprocessing of the global map data is done by the genetic programming (GP) arithmetic and inference is done with the delta fuzzy rule to plan the best routine to achieve robotic autonomous navigation. Simulation results show that the robot can create accurate and complete map of the environment and bypass the obstacles agilely to reach the destination smoothly and reliably with the map. Thus the feasibility and effectiveness of this strategy is verified.", keywords = "genetic algorithms, genetic programming, GP, SLAM, fuzzy rule delta, least square methods, multisensor global calibrated, multisensor vision system, robot autonomous navigation, robot problems, simultaneous localization and map building, SLAM (robots), least squares approximations, path planning", DOI = "doi:10.1109/ICCASM.2010.5620743", notes = "College of Mechanical Engineering and Automation Xihua University Chengdu, China. Also known as \cite{5620743}", } @Article{yang:2000:JCCE, author = "Yaowen Yang and Chee Kiong Soh", title = "Fuzzy logic integrated genetic programming for structural optimization and design", journal = "Journal of Computing in Civil Engineering", year = "2000", volume = "14", number = "4", pages = "249--254", month = oct, publisher = "American Society of Civil Engineers", email = "cywyang@ntu.edu.sg", keywords = "genetic algorithms, genetic programming", URL = "http://www.wmich.edu/jcce/v14n4ab5.htm", DOI = "doi:10.1061/(ASCE)0887-3801(2000)14:4(249)", abstract = "A fuzzy logic integrated genetic programming (GP) based methodology is proposed to increase the performance of the GP based approach for structural optimization and design. Fuzzy set theory is employed to deal with the imprecise and vague information, especially the design constraints, during the structural design process. A fuzzy logic based decision-making system incorporating expert knowledge and experience is used to control the iteration process of genetic search. Illustrative examples have been used to demonstrate that, when comparing the proposed fuzzy logic controlled GP approach with the pure GP method, the proposed new approach has a higher search efficiency.", } @Article{yang:2002:CS, author = "Yaowen Yang and Chee Kiong Soh", title = "Automated optimum design of structures using genetic programming", journal = "Computers and Structures", year = "2002", volume = "80", number = "18-19", pages = "1537--1546", month = jul, email = "cywyang@ntu.edu.sg", keywords = "genetic algorithms, genetic programming, Optimum design, Ground structure, Fuzzy logic, optimisation", ISSN = "0045-7949", broken = "http://www.sciencedirect.com/science/article/B6V28-462BFV0-1/2/673349dfe5820d4677541422ded33a61", DOI = "doi:10.1016/S0045-7949(02)00108-6", DOI = "doi:10.1061/(ASCE)0887-3801(2000)14:1(31)", abstract = "Traditionally, the open-domain optimum design of truss structures is solved using conceptual designs which are often based on their ground structures. However, the ground structures are problem-dependent and usually require relatively deep understanding of the problem. This paper presents a genetic programming (GP) based methodology for the automated optimum designs of structures using an approach which is free from ground structures. Thus, it has few requirements about the domain knowledge of the problem and is less problem-dependent. Illustrative example is also presented to show that, compared with genetic algorithm, GP is more flexible and has higher search efficiency when it is employed to solve open-domain structural design problems.", } @InProceedings{YaowenYang:2004:SPIE, author = "Yaowen Yang and Chee Kiong Soh and Jianfeng Xu", title = "An integrated evolutionary programming and impedance-based NDE method", booktitle = "Smart Structures, Devices and Systems II, Smart Structures, Devices and Systems II, Proc. of SPIE", year = "2004", editor = "Said F. Al-Sarawi", volume = "5649", pages = "154--161", address = "Sydney, Australia", month = "13 " # dec, keywords = "genetic algorithms, genetic programming", ISBN = "0-8194-5609-8", URL = "http://spiedl.aip.org/getabs/servlet/GetabsServlet?prog=normal&id=PSISDG005649000001000154000001&idtype=cvips&gifs=Yes&bproc=volrange&scode=5600%20-%205699", DOI = "doi:10.1117/12.581489", abstract = "Nondestructive evaluation (NDE) is essential in civil and building engineering. Impedance-based method uses the electro-mechanical coupling effect of piezoceramic lead-zirconate-titanate (PZT) materials to measure the force impedance of the structure. By comparing the impedance spectra of the damaged structure with the baseline (the impedance spectra for the pristine structure), the damage in the structure can be assessed. The impedance-based method has shown some advantages over the traditional NDE methods. However, it is not able to identify the location and quantity of the damage simultaneously. This paper presents a technique to overcome this limitation. The technique first measures the variations of the electro-mechanical impedance of the structure, which is similar to the impedance-based method, so that it can inherit the advantage of convenience in operation from the impedance-based method. The damage is then identified by a system identification technique which is generally employed in the vibration-based method. Due to the numerous local optima in the search space, the traditional optimisation strategies may not be able to find the correct solution. This paper selects evolutionary programming (EP) as the system identification technique for its robustness in finding the global optimum. Thus, the location and the quantity of the damage can be simultaneously identified. In order to enhance the feasibility of the integrated EP and impedance-based (inEPIB) technique, a fitness function, which can be generally applied to other methods, is proposed to discriminate the variations caused by damages from the discrepancies caused by modelling errors. Experiments are carried out on beams and plates to verify the damage detection results. The results demonstrate that both the location and extent of damage can be simultaneously identified.", } @Article{YaowenYang:2005:JSV, author = "Yaowen Yang and Zhanli Jin and Chee Kiong Soh", title = "Integrated optimal design of vibration control system for smart beams using genetic algorithms", journal = "Journal of Sound and Vibration", year = "2005", volume = "282", pages = "1293--1307", email = "cywyang@ntu.edu.sg", keywords = "genetic algorithms, genetic programming", number = "3-5", month = "22 " # apr, note = "Short Communication", DOI = "doi:10.1016/j.jsv.2004.03.048", abstract = "the parameters of vibration control system of smart beams, including the placement and size of piezoelectric sensors and actuators (S/As) bonded on smart beams and the feedback control gains of the control system, have been simultaneously optimised for vibration suppression of beam structures. Since the sizes of the S/As are selected from a prescribed patch pool provided by the manufactures, the size design variable is then discrete, but the locations and feedback gains are continuous. Thus, the resulting optimization problem has discrete-continuous design variables which is difficult for the conventional optimisation methods to solve. An integer-real-encoded genetic algorithm has thus been developed to search for the optimal placement and size of the piezoelectric patches as well as the optimal feedback control gains. The criterion based on the maximisation of energy dissipation was adopted for the optimisation of the control system. The optimal distributions of the piezoelectric patches based on specific controlled vibration modes have also been addressed. The results showed that the control effect could be significantly enhanced with appropriate distribution of piezoelectric patches and selection of feedback control gains, and meaningful observations have been obtained for practical design.", notes = "Division of Structures and Mechanics, School of Civil & Environmental Engineering, Nanyang Technological University, Nanyang Avenue 50, Singapore 639798, Singapore", } @Article{YaowenWYang:2005:IJNME, author = "Yaowen W. Yang and Chao Wang and Chee Kiong Soh", title = "Force identification of dynamic systems using genetic programming", journal = "International Journal for Numerical Methods in Engineering", year = "2005", volume = "63", number = "9", pages = "1288--1312", keywords = "genetic algorithms, genetic programming, dynamic system, excitation force, dynamic response, force identification", DOI = "doi:10.1002/nme.1323", abstract = "One obvious limitation of the traditional force identification techniques is that they are unable to obtain the explicit expression of the force. Moreover, some techniques need both the displacement and velocity data of all freedoms, and some need the Markov parameters from numerical calculation or experimental test before the force identification can be carried out. This paper presents a genetic programming (GP) based method for excitation force identification of dynamic systems to overcome these traditional methods' disadvantages. GP is employed as a search and optimisation method to obtain the optimal, if not the best, force expression from the known dynamic response. One obvious merit of the proposed method is that it can obtain the explicit expression of the unknown force. Another advantage is that it only needs the dynamic response data at one point, i.e. displacement or velocity or acceleration of one freedom. Illustrative examples demonstrate that the GP based method is able to identify the excitation force of a single-degree, a three-degree dynamic systems and a frame structure, depicting its potential for force forecast problems.", notes = "http://www3.interscience.wiley.com/cgi-bin/abstract/110432712/ABSTRACT Division of Structures and Mechanics, School of Civil and Environmental Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798, Singapore", } @Article{YaowenYang:2005:EJOR, author = "Yaowen Yang and Jianfeng Xu and Chee Kiong Soh", title = "An evolutionary programming algorithm for continuous global optimization", journal = "European Journal of Operational Research", year = "2006", volume = "168", pages = "354--369", email = "cywyang@ntu.edu.sg", URL = "http://www.elsevier.com/locate/ejor", number = "2", month = "16 " # jan, note = "Feature Cluster on Mathematical Finance and Risk Management", keywords = "genetic algorithms, genetic programming, Evolutionary computations, Evolutionary programming, Global optimisation, Simulated annealing", DOI = "doi:10.1016/j.ejor.2004.05.007", abstract = "Evolutionary computations are very effective at performing global search (in probability), however, the speed of convergence could be slow. This paper presents an evolutionary programming algorithm combined with macro-mutation (MM), local linear bisection search (LBS) and crossover operators for global optimisation. The MM operator is designed to explore the whole search space and the LBS operator to exploit the neighbourhood of the solution. Simulated annealing is adopted to prevent premature convergence. The performance of the proposed algorithm is assessed by numerical experiments on 12 benchmark problems. Combined with MM, the effectiveness of various local search operators is also studied.", notes = "School of Civil and Environmental Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore Science direct gives authors as Yao Wen Yang, Jian Feng Xu, Chee Kiong Soh.", } @Article{YaowenYang:2006:JAE, author = "Yaowen Yang and Zhanli Jin and Chee Kiong Soh", title = "Integrated optimization of control systems for smart cylindrical shells using a modified GA", journal = "Journal of Aerospace Engineering", year = "2006", volume = "19", number = "2", pages = "68--79", month = apr, keywords = "genetic algorithms, genetic programming", URL = "http://ascelibrary.aip.org/getabs/servlet/GetabsServlet?prog=normal&id=JAEEEZ000019000002000068000001&idtype=cvips&gifs=Yes", URL = "http://ascelibrary.aip.org/getpdf/servlet/GetPDFServlet?filetype=pdf&id=JAEEEZ000019000002000068000001&idtype=cvips", DOI = "doi:10.1061/(ASCE)0893-1321(2006)19:2(68)", abstract = "In this paper, modelling of the vibration of cylindrical shell components of space structures incorporating piezoelectric sensor/actuators (S/As) for optimal vibration control is proposed and formulated. The parameters of the control system, which include the placement and sizing of the piezoelectric S/As and the feedback control gains, were considered as design variables and optimised simultaneously. The effect of the amount of piezoelectric patches was investigated as well. The criterion based on the maximisation of energy dissipation was employed for the optimization of the control system. A modified real-encoded genetic algorithm (GA) dealing with various constraints has been developed and applied to search for the optimal placement and size of the piezoelectric patches as well as the optimal feedback control gains. The results of three numerical examples, which include a simply supported plate, a simply supported cylindrical shell, and a clamped-simply supported plate, demonstrated significant vibration suppression based on the optimal design of the control system. It was also found that for specific controlled vibration modes, the optimal distribution of the piezoelectric S/As should be located at the areas separated by the nodal lines to achieve the optimal control effect. This finding would be useful for the practical design of smart structures.", } @Misc{oai:repository.ust.hk:1783.1-73092, author = "Yi Yang and Tze Ling Ng", title = "Optimal operating strategy for a joint system of a single reservoir and seawater desalination plant using genetic programming", year = "2015", month = may, keywords = "genetic algorithms, genetic programming", bibsource = "OAI-PMH server at repository.ust.hk", identifier = "EWRI World Environmental and Water Resources Congress, ASCE, 17-21 May 2015, Austin, Texas, United States", language = "English", oai = "oai:repository.ust.hk:1783.1-73092", URL = "http://hdl.handle.net/1783.1/73092", notes = "Dec 2016 does not appear in {"}Proceedings World Environmental and Water Resources Congress 2015: Floods, Droughts, and Ecosystems{"}, Edited by Karen Karvazy and Veronica L. Webster, http://dx.doi.org/10.1061/9780784479162", } @Article{ZhengRongYang:2003:BS, author = "Zheng Rong Yang and Rebecca Thomson and T. Charles Hodgman and Jonathan Dry and Austin K. Doyle and Ajit Narayanan and XiKun Wu", title = "Searching for discrimination rules in protease proteolytic cleavage activity using genetic programming with a min-max scoring function", journal = "Biosystems", year = "2003", volume = "72", number = "1-2", pages = "159--176", month = nov, keywords = "genetic algorithms, genetic programming, Amino acid similarity matrix, The reverse Polish notation, Proteolytic cleavage analysis", URL = "http://www.sciencedirect.com/science/article/B6T2K-49N9DN6-2/2/0d63ebb7904ac33ae0d20ce4f6477a57", DOI = "doi:10.1016/S0303-2647(03)00141-2", abstract = "We present an algorithm which is able to extract discriminant rules from oligopeptides for protease proteolytic cleavage activity prediction. The algorithm is developed using previous genetic programming. Three important components in the algorithm are a min-max scoring function, the reverse Polish notation (RPN) and the use of minimum description length. The min-max scoring function is developed using amino acid similarity matrices for measuring the similarity between an oligopeptide and a rule, which is a complex algebraic equation of amino acids rather than a simple pattern sequence. The Fisher ratio is then calculated on the scoring values using the class label associated with the oligopeptides. The discriminant ability of each rule can therefore be evaluated. The use of RPN makes the evolutionary operations simpler and therefore reduces the computational cost. To prevent overfitting, the concept of minimum description length is used to penalize over-complicated rules. A fitness function is therefore composed of the Fisher ratio and the use of minimum description length for an efficient evolutionary process. In the application to four protease datasets (Trypsin, Factor Xa, Hepatitis C Virus and HIV protease cleavage site prediction), our algorithm is superior to C5, a conventional method for deriving decision trees.", } @Article{ZhengRongYang:2004:B, author = "Zheng Rong Yang and Andrew R. Dalby and Jing Qiu", title = "Mining {HIV} protease cleavage data using genetic programming with a sum-product function", journal = "Bioinformatics", year = "2004", volume = "20", number = "18", pages = "3398--3405", keywords = "genetic algorithms, genetic programming, HIV protease, enzyme, min-max scoring function, sum-production scoring function", ISSN = "1367--4803", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.612.3378", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.612.3378", URL = "http://bioinformatics.oxfordjournals.org/content/early/2004/07/15/bioinformatics.bth414.full.pdf", DOI = "doi:10.1093/bioinformatics/bth414", abstract = "Motivation: In order to design effective HIV inhibitors, studying and understanding the mechanism of HIV protease cleavage specification is critical. Various methods have been developed to explore the specificity of HIV protease cleavage activity. However, success in both extracting discriminant rules and maintaining high prediction accuracy is still challenging. The earlier study had employed genetic programming with a min-max scoring function to extract discriminant rules with success. However, the decision will finally be degenerated to one residue making further improvement of the prediction accuracy difficult. The challenge of revising the min-max scoring function so as to improve the prediction accuracy motivated this study. Results: This paper has designed a new scoring function called a sum-product function for extracting HIV protease cleavage discriminant rules using genetic programming methods. The experiments show that the new scoring function is superior to the min-max scoring function. Availability: The software package can be obtained by request to Dr Zheng Rong Yang. Contact: z.r.yang@ex.ac.uk", notes = "http://bioinformatics.oxfordjournals.org/cgi/content/abstract/20/18/3398 1 Department of Computer Science and 2 Department of Biology Sciences, Exeter University, UK PMID: 15256407 [PubMed - indexed for MEDLINE]", } @InProceedings{conf/imecs/Yang07, author = "Zheng Rong Yang", title = "Peptide Classification with Genetic Programming Ensemble of Generalised Indicator Models", booktitle = "Proceedings of the International MultiConference of Engineers and Computer Scientists, IMECS 2007", year = "2007", editor = "Sio Iong Ao and Oscar Castillo and Craig Douglas and David Dagan Feng and Jeong-A. Lee", series = "Lecture Notes in Engineering and Computer Science", pages = "319--324", address = "Hong Kong", month = mar # " 21-23", publisher = "Newswood Limited", note = "Certificate of Merit", keywords = "genetic algorithms, genetic programming", isbn13 = "978-988-98671-4-0; 978-988-98671-7-1", bibdate = "2008-01-24", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/imecs/imecs2007.html#Yang07", abstract = "The generalised indicator model (GIM) has been developed for peptide classification with success. However, the performance of GIM varies with the mutation matrix which is used to measure the similarity between peptides. This work investigates three methods for building meta-classifiers based on GIMs which are treated as base classifiers constructed using different mutation matrices. The three methods are linear combination, neural network combination and genetic programming. The simulation shows that the genetic programming method performs the best in two aspects. First, it is able to identify the most important base classifiers for building a meta-classifier without any a priori knowledge. Second, a metaclassfier delivered is a mathematical equation being capable of interpretation.", notes = "http://www.iaeng.org/IMECS2007/schedule/schedule_ICB.html http://www.iaeng.org/IMECS2007/Best_paper_awards.html", } @Article{ZhengRongYang:2009:B, author = "Zheng Rong Yang and Ganjana Lertmemongkolchai and Gladys Tan and Philip L. Felgner and Richard Titball", title = "A genetic programming approach for Burkholderia Pseudomallei diagnostic pattern discovery", journal = "Bioinformatics", year = "2009", volume = "25", number = "17", pages = "2256--2262", month = sep # " 1", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1093/bioinformatics/btp390", URL = "http://results.ref.ac.uk/Submissions/Output/2528334", size = "7 pages", abstract = "MOTIVATION: Finding diagnostic patterns for fighting diseases like Burkholderia pseudomallei using biomarkers involves two key issues. First, exhausting all subsets of testable biomarkers (antigens in this context) to find a best one is computationally infeasible. Therefore, a proper optimisation approach like evolutionary computation should be investigated. Second, a properly selected function of the antigens as the diagnostic pattern which is commonly unknown is a key to the diagnostic accuracy and the diagnostic effectiveness in clinical use. RESULTS: A conversion function is proposed to convert serum tests of antigens on patients to binary values based on which Boolean functions as the diagnostic patterns are developed. A genetic programming approach is designed for optimizing the diagnostic patterns in terms of their accuracy and effectiveness. During optimization, it is aimed to maximize the coverage (the rate of positive response to antigens) in the infected patients and minimize the coverage in the non-infected patients while maintaining the fewest number of testable antigens used in the Boolean functions as possible. The final coverage in the infected patients is 96.55percent using 17 of 215 (7.4percent) antigens with zero coverage in the non-infected patients. Among these 17 antigens, BPSL2697 is the most frequently selected one for the diagnosis of Burkholderia Pseudomallei. The approach has been evaluated using both the cross-validation and the Jack-knife simulation methods with the prediction accuracy as 93percent and 92percent, respectively. A novel approach is also proposed in this study to evaluate a model with binary data using ROC analysis.", notes = "PMID: 19561021 [PubMed - in process] PMCID: PMC2734322 [Available on 2010/09/01]", uk_research_excellence_2014 = "This article appears in the premier journal for bioinformatics methods. I analysed the data, designed, implemented and tested the algorithm. I analysed the results and wrote the paper. Because the data was very noisy, it was difficult to use conventional approaches to identify the biomarker. I then categorised the data to apply genetic programming techniques to search for biomarkers. This is the first time that genetic programming was applied in this field. The result was validated in the wet laboratory with success.", } @InProceedings{Yang:2006:WCICA, author = "Zaiyue Yang and C. W. Chan", title = "Study on the Node Increasing Problem of Genetic Programming with Bounded Individual Size", booktitle = "The Sixth World Congress on Intelligent Control and Automation, WCICA 2006", year = "2006", pages = "3589--3593", address = "Dalian", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, bloat", ISBN = "1-4244-0332-4", DOI = "doi:10.1109/WCICA.2006.1713038", abstract = "Firstly, a new conception of node saturation is defined. Then through theoretic analysis, it is proposed that the node saturation can be reflected by the average fitness if with bounded individual size and the same population size and individual upper boundary. Finally through a series of experiments, this announcement is been validated", notes = "Dept. of Mech. Eng., Hong Kong Univ.", } @Article{Yang:2016:Neurocomputing, author = "Zhongliang Yang and Yumiao Chen and Zhichuan Tang and Jianping Wang", title = "Surface {EMG} based handgrip force predictions using gene expression programming", journal = "Neurocomputing", year = "2016", ISSN = "0925-2312", DOI = "doi:10.1016/j.neucom.2016.05.038", URL = "http://www.sciencedirect.com/science/article/pii/S0925231216303903", abstract = "The main objective of this study is to precisely predict muscle forces from surface electromyography (sEMG) for hand gesture recognition. A robust variant of genetic programming, namely Gene Expression Programming (GEP), is used to derive a new empirical model of handgrip sEMG-force relationship. A series of handgrip forces and corresponding sEMG signals were recorded from 6 healthy male subjects and during 4 levels of percentage of maximum voluntary contraction (percentMVC) in experiments. Using one-way ANOVA with multiple comparisons test, 10 features of the sEMG time domain were extracted from homogeneous subsets and used as input vectors. Subsequently, a handgrip force prediction model was developed based on GEP. In order to compare the performance of this model, other models based on a back propagation neural network and a support vector machine were trained using the same input vectors and data sets. The root mean square error and the correlation coefficient between the actual and predicted forces were calculated to assess the performance of the three models . The results show that the GEP model provide the highest accuracy and generalization capability among the studied models. It was concluded that the proposed GEP model is relatively short, simple and excellent for predicting handgrip forces based on sEMG signals.", keywords = "genetic algorithms, genetic programming, Gene expression programming, Surface electromyography, Grip force, Force prediction", } @Article{Yang:2018:Sensors, author = "Zhongliang Yang and Yangliang Wen and Yumiao Chen", title = "{sEMG}-Based Drawing Trace Reconstruction: A Novel Hybrid Algorithm Fusing Gene Expression Programming into {Kalman} Filter", journal = "Sensors", year = "2018", number = "10", volume = "18", pages = "3296", keywords = "genetic algorithms, genetic programming, gene expression programming", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/sensors/sensors18.html#YangWC18", DOI = "doi:10.3390/s18103296", notes = "journals/sensors/YangWC18", } @InProceedings{Yang:2016:CCDC, author = "Xiaoyu Yang and Meng Cai and Jianxun Li", booktitle = "2016 Chinese Control and Decision Conference (CCDC)", title = "Path planning for unmanned aerial vehicles based on genetic programming", year = "2016", pages = "717--722", abstract = "Path planning system is one of the key component for the unmanned aerial vehicles (UAVs) and mobile robots in modern operational systems used in all sorts of circumstances. Generally, genetic algorithm (GA) plays a big role in dealing with optimisation problems. However, compared to GA, genetic programming (GP) displays better modelling and optimising ability in path planning problem. GP is capable of dealing with UAV and mobile robot path planning problems. GP improves performance by using generalised hierarchical computer programs and optimising evolutionarily. This paper presents an optimised GP method which applies to path planning problem. Several special designed function and symbol operators are proposed and appended to the binary tree structure, as well as the redesigned decoding system. With the combination of selection and reproduction operation, the optimised GP accomplishes the design of path planning. By using the optimised GP method, experiment results display better fitness paths against GA method.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CCDC.2016.7531079", month = may, notes = "Also known as \cite{7531079}", } @InProceedings{Yang:2021:SSCI, author = "Ya-Ju Yang and Tsung-Su Yeh and Tsung-Che Chiang", booktitle = "2021 IEEE Symposium Series on Computational Intelligence (SSCI)", title = "Deck Building in Collectible Card Games using Genetic Algorithms: A Case Study of Legends of Code and Magic", year = "2021", abstract = "Collectible card games (CCGs) are a kind of game in which players use a variety of cards to achieve the goal of game, for example, defeating the opponent. To play a game, players need to build a card deck, which consists of specified number of cards; only cards in the deck can be used in the game. Deck building is an important part in playing CCGs. This paper addresses deck building in a recently released CCG, Legends of Code and Magic. Our strategy is to evaluate cards by some scoring functions and then select cards into the deck accordingly. We adopt three types of scoring functions and use genetic algorithms (GA) to set proper values of parameters in the scoring functions. We checked the performance of the three versions of deck-building GAs against existing agents and found that scoring by card attributes is effective. We also analyzed the decks built by these algorithms and discussed their pros and cons.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SSCI50451.2021.9659984", month = dec, notes = "Also known as \cite{9659984}", } @MastersThesis{oai:repository.ust.hk:1783.1-81383, author = "Yi Yang", title = "Optimal Operating Rules for Joint System of Water Supply Reservoir and Seawater Desalination Plant using Genetic Programming", year = "2015", school = "HKUST", type = "M.Phil", address = "Hong Kong", month = jul, keywords = "genetic algorithms, genetic programming, water-supply, saline water conversion, water resources development, water reuse", bibsource = "OAI-PMH server at repository.ust.hk", language = "English", oai = "oai:repository.ust.hk:1783.1-81383", URL = "https://doi.org/10.14711/thesis-b1514746", URL = "http://repository.ust.hk/ir/bitstream/1783.1-81383/1/th_redirect.html", size = "115 pages", abstract = "Due to climate change, population growth and industrial development, there is increasing scarcity of freshwater resources amidst rising demands. In view of this, many coastal places are resorting to seawater desalination as a means of supplementing existing supplies from reservoirs. However, doing so introduces a tradeoff between water supply reliability and cost, as seawater desalination is relatively expensive because of its high energy consumption. Although some studies have been done to combine seawater desalination with other options like reservoir and wastewater reuse for supplying high water demand, they either emphasize too much on economic cost rather than system operation, or lack quantitative investigation into the operation of an integrated or joint system. Thus, a comprehensive model for the operation of a joint system to systematically optimise both water supply reliability and economic efficiency is required. In this study, an optimisation model for the operation of a joint system of a single reservoir and seawater desalination plant was developed for urban water supply. The model aimed to maximise water supply reliability while constraining cost. Taking into account the existing storage of water in the reservoir, the demand for water by various sectors, and current and forecast future inflows to the reservoir, two operating rules that interact with each other were optimised for guiding the operation of the reservoir and seawater desalination plant. Upon attaining the optimal functions, both operational cost and capital cost were calculated on an annual basis for analysis. To solve the above operation model, a genetic programming (GP) iterative tool was designed for the joint system. Using the GPLAB toolbox in MATLAB, genetic programming was applied in an iterative fashion to generate optimal operational rules to govern the releases from the reservoir and water production rates of the desalination plant. In this manner, GP was empowered to optimise the two rules simultaneously, which would not be possible if using GP in a conventional way. Results were obtained for a semi-hypothetical case study in California and analysed to prove the advantage of the joint system and applicability of genetic programming for the purposes of this study. The fitness value was found to have improved by 33percent after 83 iterations in the baseline case. It was demonstrated that due to the assumption that the volume data of current inflows and demands were affected by their volume data one and two time periods before thus forecasting information might be indirectly incorporated into the functions by the incorporation of these variables into the functions. The complex functions generated by the model can be easily calculated using computer programs. The capital cost consisted of 1/3 of the total cost with an equilibrium point at around 500 million dollars per month when it was allocated to each month. But the water demands were too high to be fully met (70percent met), leading to large budget carry overs. In terms of the reservoir performance, the reservoir storage was drawn down before every inflow peak. If the budget was not enough for this expensive way of desalinating water, it had to depend more on releasing water from the reservoir, whose inflows fluctuated in all time periods. As the capacity of desalination plant increases, demand plays a more important role in deciding how much water to be released from the reservoir. The scale expansion of the existing seawater desalination plant could be a very effective but costly way to solve water scarcity problems in coastal city water supply cases, while increasing the reservoir capacity is the most efficient way to reducing water shortages. And the fitness value kept increasing by 83.05percent when the reservoir capacity went up from 3000 million m$^{3}$ in the baseline case to 5000 million m$^{3}$. But still future work needs to be done to incorporate more scenarios to prove the advantage of the joint operation model together with the GP iterative tool.", notes = "oai:repository.ust.hk:1783.1-81383", } @InProceedings{Yang:2021:CEC, author = "Yifan Yang and Gang Chen2 and Hui Ma and Mengjie Zhang and Victoria Huang", booktitle = "2021 IEEE Congress on Evolutionary Computation (CEC)", title = "Budget and {SLA} Aware Dynamic Workflow Scheduling in Cloud Computing with Heterogeneous Resources", year = "2021", editor = "Yew-Soon Ong", pages = "2141--2148", address = "Krakow, Poland", month = "28 " # jun # "-1 " # jul, keywords = "genetic algorithms, genetic programming, Cloud computing, Schedules, Data centers, Processor scheduling, Heuristic algorithms, Computational modeling, dynamic workflow scheduling, cloud computing", isbn13 = "978-1-7281-8393-0", DOI = "doi:10.1109/CEC45853.2021.9504709", abstract = "Workflow with different patterns and sizes arrive at a cloud data center dynamically to be processed at virtual machines in the data center, with the aim to minimize overall cost and makespan while satisfying Service Level Agreement (SLA) requirement. To efficiently schedule workflows, manually designed heuristics are proposed in the literature. However, it is time consuming to manually design heuristics. The designed heuristics may not work effectively for heterogeneous workflow since only simple problem related factors are considered in the heuristics. Further, most of the existing approaches ignore the deadline constraints set in SLAs. Genetic Programming Hyper Heuristic (GPHH) can be used to automatically design heuristics for scheduling problems. In this paper, we propose a GPHH approach to automatically generate heuristics for the dynamic workflow scheduling problem, with the goal of minimizing the VM rental fees and SLA penalties. Experiments have been conducted to evaluate the performance of the proposed approach. Compared with several existing heuristics and conventional Genetic Programming (GP) approaches, the proposed Dynamic Workflow Scheduling Genetic Programming (DWSGP) has better performance and is highly adaptable to variations in cloud environment.", notes = "Also known as \cite{9504709}", } @InProceedings{yang:2022:SC, author = "Yifan Yang and Gang Chen2 and Hui Ma and Mengjie Zhang", title = "{Dual-Tree} Genetic Programming for {Deadline-Constrained} Dynamic Workflow Scheduling in Cloud", booktitle = "Service-Oriented Computing", year = "2022", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-031-20984-0_31", DOI = "doi:10.1007/978-3-031-20984-0_31", } @Article{Yang:ieeeTEC, author = "Yifan Yang and Gang Chen2 and Hui Ma and Sven Hartmann and Mengjie Zhang", title = "Dual-Tree Genetic Programming With Adaptive Mutation for Dynamic Workflow Scheduling in Cloud Computing", journal = "IEEE Transactions on Evolutionary Computation", note = "Accepted for future publication", keywords = "genetic algorithms, genetic programming, Dynamic scheduling, Task analysis, Processor scheduling, Job shop scheduling, Cloud computing, Vectors, Heuristic algorithms, dynamic workflow scheduling (DWS), deadline constraint, dual-tree genetic programming (DTGP), hyper-heuristics, adaptive mutation", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2024.3392968", notes = "also known as \cite{10508207}", } @Article{YANG:2021:CEA, author = "Yu Yang and Xin Zhao and Min Huang and Xin Wang2 and Qibing Zhu", title = "Multispectral image based germination detection of potato by using supervised multiple threshold segmentation model and {Canny} edge detector", journal = "Computers and Electronics in Agriculture", volume = "182", pages = "106041", year = "2021", ISSN = "0168-1699", DOI = "doi:10.1016/j.compag.2021.106041", URL = "https://www.sciencedirect.com/science/article/pii/S0168169921000594", keywords = "genetic algorithms, genetic programming, Potato germination detection, Multispectral image, HF-GP, SMTSM, Canny edge detector", abstract = "Whether from the perspective of agricultural production or food safety, potato germination detection is of great significance. Since the features (color, texture and context) of the germination area are similar to those of the non-germination area, the existing vision frameworks are difficult to accurately detect the germinations on the surface of potatoes. In this study, the method for detecting potato germination based on multispectral image combined with supervised multiple threshold segmentation model (SMTSM) and Canny edge detector was proposed. The SMTSM based on Genetic Programming algorithm combined with a hybrid fitness function (HF-GP) was used to transform the original multispectral images into multiple 2-D images for improving the contrast between region of interest (ROI) and background. A sub-mask of each transformed image was constructed using optimal segmentation threshold, and all of sub-masks were merged through pixel-multiplication to obtain segmentation mask. Meanwhile, in order to filter out the boundless areas that are misidentified as germinations, Canny edge detector was used on gray image to obtain edge mask. Finally, the segmentation mask and the edge mask were combined to complete the detection of germination of potato. Experimental results shown that the proposed method achieved the TPR of 90.91percent and the precision of 89.28percent for the edible potatoes, which were 4.17-19.05percent and 12.39-24.62percent higher than the competitive detectors in TPR and precision respectively. For the breeding potatoes, the proposed method with 89.67percent of TPR and 86.37percent of precision was 9.74-24.58percent and 15.70-20.39percent better than the competitors in TPR and precision respectively. The comparison confirms the proposed method has excellent detection effect on potato's germination", } @Article{YANG:2021:ACA, author = "Yu Yang and Xin Wang2 and Xin Zhao and Min Huang and Qibing Zhu", title = "{M3GPSpectra:} A novel approach integrating variable selection/construction and {MLR} modeling for quantitative spectral analysis", journal = "Analytica Chimica Acta", volume = "1160", pages = "338453", year = "2021", ISSN = "0003-2670", DOI = "doi:10.1016/j.aca.2021.338453", URL = "https://www.sciencedirect.com/science/article/pii/S0003267021002798", keywords = "genetic algorithms, genetic programming, Spectral analytical method, Variable selection, Variable construction, Optimal combination", abstract = "Quantitative analysis of the physical or chemical properties of various materials by using spectral analysis technology combined with chemometrics has become an important method in the field of analytical chemistry. This method aims to build a model relationship (called prediction model) between feature variables acquired by spectral sensors and components to be measured. Feature selection or transformation should be conducted to reduce the interference of irrelevant information on the prediction model because original spectral feature variables contain redundant information and massive noise. Most existing feature selection and transformation methods are single linear or nonlinear operations, which easily lead to the loss of feature information and affect the accuracy of subsequent prediction models. This research proposes a novel spectroscopic technology-oriented, quantitative analysis model construction strategy named M3GPSpectra. This tool uses genetic programming algorithm to select and reconstruct the original feature variables, evaluates the performance of selected and reconstructed variables by using multivariate regression model (MLR), and obtains the best feature combination and the final parameters of MLR through iterative learning. M3GPSpectra integrates feature selection, linear/nonlinear feature transformation, and subsequent model construction into a unified framework and thus easily realizes end-to-end parameter learning to significantly improve the accuracy of the prediction model. When applied to six types of datasets, M3GPSpectra obtains 19 prediction models, which are compared with those obtained by seven linear or non-linear popular methods. Experimental results show that M3GPSpectra obtains the best performance among the eight methods tested. Further investigation verifies that the proposed method is not sensitive to the size of the training samples. Hence, M3GPSpectra is a promising spectral quantitative analytical tool", } @Article{YANG:2023:foodcont, author = "Yu Yang and Shangpeng Sun and Leiqing Pan and Min Huang and Qibing Zhu", title = "Predictions of multiple food quality parameters using near-infrared spectroscopy with a novel multi-task genetic programming approach", journal = "Food Control", volume = "144", pages = "109389", year = "2023", ISSN = "0956-7135", DOI = "doi:10.1016/j.foodcont.2022.109389", URL = "https://www.sciencedirect.com/science/article/pii/S0956713522005825", keywords = "genetic algorithms, genetic programming, Prediction model, Near-infrared technology, Multiple food quality parameters, Evolutionary multi-task optimization, Shared features, Private features", abstract = "In order to meet the increasing demand for food safety and quality, new methods for simultaneous and rapid determination of multiple food quality parameters (FQPs) are urgently needed in the food industry. Incorporating near-infrared (NIR) spectroscopy and spectral prediction model for rapid, repeatable, non-destructive, and low running costs quantitative analysis of FQPs is enjoying increasing popularity in the food industry. However, most existing spectrum-based prediction models are trained under a single-task learning framework, that is, a prediction model for each quality parameter and spectrum is constructed separately. This paradigm ignores possible connections among prediction tasks of different FPQs, which may result in the performance degradation of a single FPQ prediction model. This study proposes a novel multi-task genetic programming-based approach named EM4GPO for building multiple FQPs predictions simultaneously. In EM4GPO, the multi-dimensional trees are used to encode the raw NIR spectrum to shared features of multiple FQPs; for each FQP, a least square support vector regression (LS-SVR) modeling is performed on the shared features to obtain private features and prediction model; during the optimization process, a new algorithm is developed to optimize the previously obtained shared and private features, and LS-SVR prediction models through population evolution by combining the multidimensional multiclass genetic programming with multidimensional populations optimization method with nondominated sorting method. The proposed EM4GPO model is evaluated and compared with nine popular NIR prediction models using 10 NIR spectral datasets. The experimental results showed that EM4GPO outperformed other commonly used methods in all datasets which indicates that EM4GPO is competitive and effective in solving the problem of multiple FQPs predictions using the NIR spectrum", } @InProceedings{Yunhan_Yang:2021:CEC, author = "Yunhan Yang and Bing Xue and Linley Jesson and Mengjie Zhang", title = "Genetic Programming for Symbolic Regression: A Study on Fish Weight Prediction", booktitle = "2021 IEEE Congress on Evolutionary Computation (CEC)", year = "2021", pages = "588--595", month = "28 " # jun # "- 1 " # jul, address = "Krakow, Poland", keywords = "genetic algorithms, genetic programming, Computational modelling, Sociology, Linear regression, Predictive models, Multilayer perceptrons, Fish", isbn13 = "978-1-7281-8394-7", DOI = "doi:10.1109/CEC45853.2021.9504963", size = "8 pages", abstract = "The fish weight is a very important factor in fisheries science and management since it explains the growth and living conditions of fish populations. A power regression model has been commonly used to explain the relationship between the fish length and the weight. In this work, Genetic Programming (GP) for symbolic regression is used to build a new model for predicting the fish weight, which allows us to include more features into the model to discover any hidden relationship, and the GP based symbolic regression makes the model interpretable comparing with other machine learning methods. A publicly available dataset is taken with four species of fish which includes more features than just the fish length that is commonly used in existing models. The proposed GP based symbolic regression method has been examined on those four species. The results are compared with the weight prediction baseline methods including Linear Regression, Power Regression model, k-Nearest Neighbour, Ridge Regression, Decision Tree, Random Forest, Gradient Boosting, and Multilayer Perceptron. GP performs better, or at least as good as the baseline methods on the test set. Furthermore, the generated GP models also can select different features for different species to improve the prediction performance due to GP's explicit feature selection ability. Some models are interpretable with relatively simple expression. The GP method is also able to find models that are similar to the power regression model, but more features are included rather than a single length feature to gain improved prediction performance.", notes = "Also known as \cite{9504963}", } @Article{yang:2024:Buildings, author = "Yanhua Yang and Guiyong Liu and Haihong Zhang and Yan Zhang and Xiaolong Yang", title = "Predicting the Compressive Strength of Environmentally Friendly Concrete Using Multiple Machine Learning Algorithms", journal = "Buildings", year = "2024", volume = "14", number = "1", pages = "Article No. 190", keywords = "genetic algorithms, genetic programming", ISSN = "2075-5309", URL = "https://www.mdpi.com/2075-5309/14/1/190", DOI = "doi:10.3390/buildings14010190", abstract = "Machine learning (ML) algorithms have been widely used in big data prediction and analysis in terms of their excellent data regression ability. However, the prediction accuracy of different ML algorithms varies between different regression problems and data sets. In order to construct a prediction model with optimal accuracy for fly ash concrete (FAC), ML algorithms such as genetic programming (GP), support vector regression (SVR), random forest (RF), extremely gradient boost (XGBoost), backpropagation artificial neural network (BP-ANN) and adaptive network-based fuzzy inference system (ANFIS) were selected as regression and prediction algorithms in this study; the particle swarm optimisation (PSO) algorithm was also used to optimise the structure and hyperparameters of each algorithm. The statistical results show that the performance of the assembled algorithms is better than that of an NN-based algorithm. In addition, PSO can effectively improve the prediction accuracy of the ML algorithms. The comprehensive performance of each model is analysed using a Taylor diagram, and the PSO-XGBoost model has the best comprehensive performance, with R2 and MSE equal to 0.9072 and 11.4546, respectively.", notes = "also known as \cite{buildings14010190}", } @Article{YANG:2023:compstruct, author = "Zhicheng Yang and Shaoyu Zhao and Jie Yang and Airong Liu and Jiyang Fu", title = "Thermomechanical in-plane dynamic instability of asymmetric restrained functionally graded graphene reinforced composite arches via machine learning-based models", journal = "Composite Structures", year = "2023", volume = "308", pages = "116709", keywords = "genetic algorithms, genetic programming, Defective graphene, Functionally graded arch, Asymmetric elastic constraint, Dynamic instability, Thermomechanical action, Genetic programming assisted micromechanical model", URL = "https://www.sciencedirect.com/science/article/pii/S0263822323000533", ISSN = "0263-8223", DOI = "doi:10.1016/j.compstruct.2023.116709", abstract = "This paper studies the thermomechanical in-plane dynamic instability of asymmetric restrained functionally graded graphene reinforced composite (FG-GRC) arches, where graphene sheets with atom vacancy defects are distributed along the arch thickness according to a power law distribution. The temperature-dependent mechanical properties of the graphene reinforced composites are determined by a genetic programming (GP) assisted micromechanical model. The governing equations for the thermomechanical in-plane dynamic instability are derived by Hamilton's principle and solved by differential quadrature method (DQM) in conjunction with Bolotin method. Comprehensive numerical studies are performed to examine the effects of vacancy defect and graded distribution of graphene, temperature variation, load position, as well as boundary conditions on the free vibration, elastic buckling, and dynamic instability behaviors of the FG-GRC arch. Numerical results show that the structural performance of the FG-GRC arch is weakened by graphene defect and temperature rise and is significantly influenced by both graphene distribution and boundary conditions", } @InProceedings{Yang:2020:ICFD, author = "Zhijian MAE Yang and Bo Yin and Yu Guan and Stephane Redonnet and Yuanhang Zhu and Vikrant Gupta and Larry K. B. Li", title = "Genetic programming control of a hydrodynamically self-excited jet", booktitle = "The 17th International Conference on Flow Dynamics, ICFD2020", year = "2020", address = "Virtual, Japan", month = "28-30 " # oct, keywords = "genetic algorithms, genetic programming", bibsource = "OAI-PMH server at repository.ust.hk", language = "English", oai = "oai:repository.ust.hk:1783.1-107433", URL = "http://repository.ust.hk/ir/Record/1783.1-107433", broken = "http://lbdiscover.ust.hk/uresolver?url_ver=Z39.88-2004\&rft_val_fmt=info:ofi/fmt:kev:mtx:journal\&rfr_id=info:sid/HKUST:SPI\&rft.genre=article\&rft.issn=\&rft.volume=\&rft.issue=\&rft.date=2020\&rft.spage=\&rft.aulast=Yang\&rft.aufirst=Zhijian\&rft.atitle=Genetic+programming+control+of+a+hydrodynamically+self-excited+jet\&rft.title =", URL = "http://hdl.handle.net/1783.1/107433", notes = "http://www.ifs.tohoku.ac.jp/icfd2020/ icfd2020@grp.tohoku.ac.jp", } @InProceedings{Yang:2020:APS, author = "Zhijian MAE Yang and Bo Yin and Yu Guan and Stephane Pierre Andre Redonnet and Yuanhang Zhu and Vikrant Gupta and Larry K. B. Li", title = "Closed-loop control of a globally unstable jet using genetic programming", booktitle = "73rd Annual Meeting of the APS Division of Fluid Dynamics", year = "2020", volume = "65", number = "13", pages = "J03.00006", address = "Virtual", month = nov # " 22-24", publisher = "Bulletin of the American Physical Society", keywords = "genetic algorithms, genetic programming", bibsource = "OAI-PMH server at repository.ust.hk", language = "English", oai = "oai:repository.ust.hk:1783.1-107418", URL = "http://hdl.handle.net/1783.1/107418", URL = "http://repository.ust.hk/ir/Record/1783.1-107418", broken = "http://lbdiscover.ust.hk/uresolver?url_ver=Z39.88-2004\&rft_val_fmt=info:ofi/fmt:kev:mtx:journal\&rfr_id=info:sid/HKUST:SPI\&rft.genre=article\&rft.issn=\&rft.volume=\&rft.issue=\&rft.date=2020\&rft.spage=\&rft.aulast=Yang\&rft.aufirst=Zhijian\&rft.atitle=Closed-loop+control+of+a+globally+unstable+jet+using+genetic+programming\&rft.title ", URL = "https://meetings.aps.org/Meeting/DFD20/Session/J03.6", URL = "https://absimage.aps.org/image/DFD20/MWS_DFD20-2020-001947.pdf", abstract = "When the density of a jet is sufficiently below that of its surroundings, it can become globally unstable, transitioning from a steady state to a self-excited state characterised by axisymmetric limit-cycle oscillations. We present experiments on the closed-loop control of such oscillations using an unsupervised data-driven model-free framework based on genetic programming (GP). Our implementation of this GP-based control framework relies on a hot-wire probe for sensing and a loudspeaker for actuation. We first initialize a generation of candidate control laws and evaluate their individual performance on the basis of a cost function that accounts for the amplitude of the global mode in a low-density jet and the actuation effort. We then breed further generations of control laws by enrolling them in a tournament and by executing genetic operations such as mutation, crossover, replication and elitism. By benchmarking the best GP-based control law against the best periodic forcing strategy found via conventional open-loop mapping, we show that GP-based control can provide a more efficient means of global mode suppression, offering new insight into the physics of hydrodynamically self-excited jets.", notes = "Jets: Control The Hong Kong University of Science and Technology", } @InProceedings{yang:2024:CEC2, author = "Xiaoying Yang and Yujing Gu and Fuhua Jia and Yiran Li and Hongru Wang and Nanjiang Du and Tianxiang Cui and Yujian Ye and Ruibin Bai", title = "Evolution-Assisted Deep Reinforcement Learning for Fast Charging Station Coordinated Operation", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, Training, Costs, Transportation, Feature extraction, Deep reinforcement learning, Graph neural networks, Fast charging, Evolution-assisted deep reinforcement learning, Fast charging station, Coordinated operation, Charging pricing", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10611768", abstract = "The shift towards transportation electrification, marked by the rising use of electric vehicles (EVs) and the development of fast charging stations (FCS), plays a crucial role in transport decarbonization initiatives. To optimise the rollout of FCS and set appropriate charging service fees (CSF)-a process referred to as the coupled FCS multi-stage bi-level operation problem (FCS-MBOP)-is essential for improving both investment and operational efficiency within the integrated power distribution and transportation network (CPTN). For operators, it's not only necessary to adapt to short-term fluctuations within the environment but also to swiftly respond to changes in the FCS layout resulting from various long-term investment decisions. To address this complexity, we introduce a dual-timescale evolutionary assist deep reinforcement learning framework, which includes two specialized agents with distinct functions: an investment agent (planner) and an operational agent (operator). The planner focuses on annual investments, evolving long-term strategies that weigh social benefits against investment costs through the use of a genetic algorithm (GA). In contrast, the operator acts on an hourly basis, fine-tuning CSF to alleviate traffic congestion and minimise the social costs, while taking into account the planner's feasible investment decisions. Leveraging the integrated capabilities of a graph neural network (GNN), long-short-term memory (LSTM), and attention mechanisms, our framework's agents are adept at extracting both temporal and spatial features and facilitating the transfer of experiences across different investment stages. Empirical evidence underscores the effectiveness of our approach, showcasing its ability to surpass conventional methodologies in delivering high-quality solutions.", notes = "also known as \cite{10611768} Is this GP? WCCI 2024", } @Article{Yang:2025:compind, author = "Xinhui Yang and Jie Zhang and Qing Ye and Victor Chang", title = "Leveraging asymmetric price limits for financial stability in industrial applications: An agent-based model", journal = "Computers in Industry", year = "2025", volume = "164", pages = "104197", keywords = "genetic algorithms, genetic programming, Financial stability, Heterogeneous agent-based model, Simulation models, Asymmetric price limit", ISSN = "0166-3615", URL = "https://www.sciencedirect.com/science/article/pii/S0166361524001258", DOI = "doi:10.1016/j.compind.2024.104197", abstract = "How to upgrade business processes to improve production efficiency is an ongoing concern in industrial research. While previous studies have extensively examined various prioritization schemes at each stage of the business process, there has been a lack of investigation into the financial resources required for their implementation. The attainment of sufficient and stable financial support necessitates stability in stock prices, making the control of significant volatility in stock markets a critical issue. This study examines the effectiveness of three design schemes of price limit policy, a prevalent policy that intends to control significant volatility in financial markets and stabilize the market. Using a heterogeneous agent-based model that simulates trading agents' processes of updating strategies through genetic programming algorithms and incorporates specialized designs for price limit policies, this study demonstrates that an asymmetric limit policy-consisting solely of a lower price limit (without an upper price limit)-can significantly enhance market quality by achieving lower volatility, higher market liquidity and better price effectiveness. Furthermore, we investigate the applicable conditions of asymmetric price limits. The findings suggest that an extremely restrictive limit range could lead to volatility spillover, while a 10percent range is deemed appropriate for achieving better efficiency. Additionally, the asymmetric price limit mechanism has the potential to significantly reduce market volatility by up to 12.5percent in volatile, low liquidity, and low price efficiency markets, which aligns with the declining range from bubble-crash periods to stable periods in the Chinese stock market. These results are further supported by sensitivity analysis", } @Article{Yang:2024:ins, author = "Yifan Yang and Gang Chen and Hui Ma and Sven Hartmann and Mengjie Zhang", title = "Enhancing generalization in genetic programming hyper-heuristics through mini-batch sampling strategies for dynamic workflow scheduling", journal = "Information Sciences", year = "2024", volume = "678", pages = "120975", keywords = "genetic algorithms, genetic programming, Dynamic workflow scheduling, Genetic programming hyper-heuristics, Generalization, Mini-batch", ISSN = "0020-0255", URL = "https://www.sciencedirect.com/science/article/pii/S0020025524008892", DOI = "doi:10.1016/j.ins.2024.120975", abstract = "Genetic Programming Hyper-heuristics (GPHH) have been successfully used to evolve scheduling rules for Dynamic Workflow Scheduling (DWS) as well as other challenging combinatorial optimisation problems. The method of sampling training instances has a significant impact on the generalisation ability of GPHH, yet they are rarely addressed in existing research. This article aims to fill this gap by proposing a GPHH algorithm with a sampling strategy to thoroughly investigate the impact of six instance sampling strategies on algorithmic generalisation, including one rotation strategy, three mini-batch strategies, and two hybrid strategies. Experiments across four scenarios with varying settings reveal that: (1) mini-batch with random sampling can outperform rotation in generalizing to unseen workflow scheduling problems under the same computational cost; (2) employing a hybrid strategy that combines rotation and mini-batch further enhances the generalisation ability of GPHH; and (3) mini-batch and hybrid strategies can effectively enable heuristics trained on small-scale training instances generalizing well to large-scale unseen ones. These findings highlight the potential of mini-batch strategies in GPHH, offering improved generalisation performance while maintaining diversity and suggesting promising avenues for further exploration in GPHH domains", } @InProceedings{yangiya:1995:eGPbdd, author = "Masayuki Yanagiya", title = "Efficient Genetic Programming Based on Binary Decision Diagrams", booktitle = "1995 IEEE Conference on Evolutionary Computation", year = "1995", volume = "1", pages = "234--239", address = "Perth, Australia", publisher_address = "Piscataway, NJ, USA", month = "29 " # nov # " - 1 " # dec, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Boolean functions, decision tables, Boolean functions, binary decision diagrams, data structure, directed acyclic graphs, genetic operations, isomorphic sub-graphs, 20-mux", DOI = "doi:10.1109/ICEC.1995.489151", size = "6 pages", abstract = "The performance of genetic programming can be dramatically improved by using a data structure coded by binary decision diagrams (BDDs). BDDs are a compact representation of Boolean functions using directed acyclic graphs. Efficient BDD-based crossover, mutation, and evaluation algorithms have been developed that allow all genetic operations to be performed on BDDs throughout the search. BDD-based GP reduces storage requirements by sharing isomorphic sub-graphs among individuals, and saves computational power by using a hash-based cache to make calculation more efficient. The proposed approach is powerful enough to solve the 20-multiplexer problem, which has never been reportedly achieved before.", notes = "ICEC-95 Editors not given by IEEE, Organisers David Fogel and Chris deSilva. conference details at http://ciips.ee.uwa.edu.au/~dorota/icnn95.html Also known as \cite{489151}. Reference corrected thanks to Colin G Johnson 12 May 2009", } @InProceedings{Yao:2019:PHM, author = "Hang Yao and Xiang Jia and Bo Wang and Bo Guo", title = "A new method for estimating lithium-ion battery capacity using genetic programming combined model", booktitle = "2019 Prognostics and System Health Management Conference (PHM-Qingdao)", year = "2019", month = oct, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/PHM-Qingdao46334.2019.8942970", abstract = "Lithium-ion battery is the main energy source widely used in many fields. Therefore, it is particularly essential for estimating the health of lithium-ion battery accurately, especially in important fields such as aerospace, rail transit and satellite. For lithium-ion battery, the battery capacity is a health index (HI) that best reflects its performance degradation. By estimating the battery capacity, the health status of the lithium-ion battery can be clearly identified. However, there are technical barriers to the direct measurement of battery capacity in engineering, and many characteristics and capacities of lithium-ion batteries have abrupt changes, so that it is difficult to calculate the battery capacity accurately by formula calculation. In this paper, a new method of genetic programming combined model is proposed, which can calculate the capacity of lithium-ion battery by formulating multiple monitored features with a certain precision. Therefore, the functional relationship between multiple features and HI is well measured, which lays a good foundation for the subsequent life prediction of battery.", notes = "Also known as \cite{8942970}", } @Article{Yao:2020:ACC, author = "Hang Yao and Xiang Jia and Qian Zhao and Zhi-Jun Cheng and Bo Guo", title = "Novel Lithium-Ion Battery State-of-Health Estimation Method Using a Genetic Programming Model", journal = "IEEE Access", year = "2020", volume = "8", pages = "95333--95344", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ACCESS.2020.2995899", ISSN = "2169-3536", abstract = "State-of-health (SOH) is a health index (HI) that directly reflects the performance degradation of lithium-ion batteries in engineering, but the SOH of Li-ion batteries is difficult to measure directly. In this paper, a novel data-driven method is proposed to estimate the SOH of Li-ion batteries accurately and explore the relationship-like mechanism. First, the features of the battery should be extracted from the performance data. Next, by using the evolution of genetic programming to reflect the change in SOH, a mathematical model describing the relationship between the features and the SOH is constructed based on the data. Additionally, it has strong randomness in the formula model, which can cover most of the structural space of SOH and features. An illustrative example is presented to evaluate the SOH of the two batches of Li-ion batteries from the NASA database using the proposed method. One batch of batteries was used for testing and comparison, and another was chosen to verify the test results. Through experimental comparison and verification, it is demonstrated that the proposed method is rather useful and accurate.", notes = "Also known as \cite{9097168}", } @Article{journals/ijcisys/Yao0LLL17, author = "Jian Yao and Weiping Wang and Zhifei Li and Yonglin Lei and Qun Li", title = "Tactics Exploration Framework based on Genetic Programming", journal = "International Journal of Computational Intelligence Systems", year = "2017", number = "1", volume = "10", pages = "804--814", month = jan, keywords = "genetic algorithms, genetic programming, grammar-based genetic programming, tactics exploration framework, behaviour trees, submarine warfare simulation", URL = "https://download.atlantis-press.com/article/25872437.pdf", DOI = "doi:10.2991/ijcis.2017.10.1.53", abstract = "Engagement-level simulation is a quantitative way to evaluate the effectiveness of weapon systems before construction and acquisition, minimising the risk of investment. Though contractors have built simulation systems with high fidelity models of weapon systems and battlefields, developing competent tactics to give full play to new weapon systems in simulation experiments is labour intensive, as most classical tactics tend to be out of date. In this work, we proposed a tactics exploration framework (TEF) that applied grammar-based genetic programming (GP) to generating and evolving tactics in the engagement level simulation. Tactics are represented with modular behaviour trees (BTs) for compatibility with the genetic operators. Experiments to explore submarine tactics have been conducted to observe and study the exploration process. The experimental results show that the TEF based on GP is efficient to explore tactics in the formalism of BTs.", notes = "journals/ijcisys/Yao0LLL17, College of Information System and Management National University of Defence Technology 137 Yanwachi, Changsha, Hunan 410073, China", } @InProceedings{conf/icnsc/YaoHLL07, author = "Leehter Yao and Tsong-Hai Hsu and Chin-chin Lin and Cheng-Han Lin", title = "A Block Deepening Genetic Programming for Scheduling of Direct Load Control", booktitle = "IEEE International Conference on Networking, Sensing and Control, ICNSC 2007", year = "2007", pages = "821--827", address = "London", month = "15-17 " # apr, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, D-steps block, block deepening genetic programming, direct load control, modified genetic programming, optimal scheduling, customer satisfaction, load regulation, profitability, public utilities, scheduling", ISBN = "1-4244-1076-2", DOI = "doi:10.1109/ICNSC.2007.372887", size = "7 pages", abstract = "A modified genetic programming (GP) called block deepening GP (BDGP) is proposed in this paper to optimize the scheduling of direct load control (DLC). The optimal scheduling obtained by BDGP is a both profit-based and fairness-based DLC scheduling strategy. The scheduling arranged by the BDGP not only individually satisfies the load to be shed at every time step while minimizes utility's revenue loss due to DLC, but also level off the accumulated shedding time of each load group, thus avoiding customers' complaints about fairness of scheduling. BDGP is composed of a master GP as well as a slave GP. As the master GP evaluates the status combination of all load groups at every time step, it calls upon the slave GP simultaneously looking ahead D more steps to evaluate the best load difference could result. The best status combinations in the following D steps associated with the status combination under evaluation are determined globally in the following D-steps block. Computer simulations are made to verify the effectiveness and efficiency of the proposed BDGP.", notes = "p821 scheduling of DLC aims at reducing system peak loads and system operation costs by coordinating DLC with unit commitment", bibdate = "2009-03-30", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icnsc/icnsc2007.html#YaoHLL07", } @Article{Yao:2009:IETsp, author = "L. Yao and C.-C. Lin", title = "Identification of nonlinear systems by the genetic programming-based volterra filter", journal = "IET Signal Processing", year = "2009", month = mar, volume = "3", number = "2", pages = "93--105", keywords = "genetic algorithms, genetic programming, associated cross-products, genetic programming algorithm, input signal, nonlinear systems, optimal Volterra filter structure, reorganisation approach, tree extinction, tree pruning, nonlinear filters, nonlinear programming, signal processing", DOI = "doi:10.1049/iet-spr:20070203", ISSN = "1751-9675", pusblisher = "The Institution of Engineering and Technology", abstract = "The genetic programming (GP) algorithm is used to search for the optimal Volterra filter structure. A Volterra filter with high order and large memories contains a large number of cross-product terms. Instead of applying the GP algorithm to search for all cross-products of input signals, it is used to search for a smaller set of primary signals that evolve into the whole set of cross-products. With GP's optimisation, the important primary signals and the associated cross-products of input signals contributing most to the outputs are chosen whereas the primary signals and the associated cross-products of input signals that are trivial to the outputs are excluded from the possible candidate primary signals. To improve GP's learning capability, an effective directed initialisation scheme, a tree pruning and reorganisation approach, and a new operator called tree extinction and regeneration are proposed. Several experiments are made to justify the effectiveness and efficiency of the proposed modified by the GP algorithm.", notes = "IET, Also known as \cite{4784466}", } @Article{Yao:2011:IJICIC, author = "Leehter Yao and Yin-Chieh Chou and Chin-Chin Lin", title = "Scheduling of Direct Load Control Using Genetic Programming", journal = "International Journal of Innovative Computing, Information and Control (IJICIC)", year = "2011", volume = "7", number = "5A", pages = "2515", month = may, keywords = "genetic algorithms, genetic programming, direct load control, optimisation, scheduling", ISSN = "1349-418X", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.471.5528", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.471.5528", URL = "http://www.ijicic.org/ijicic-10-01110.pdf", size = "14 pages", abstract = "A modified Genetic Programming (GP) called block deepening GP (BDGP) is proposed in this paper to optimise the scheduling of direct load control (DLC). A search scheme similar to the breadth first search approach in artificial intelligence is adopted in the BDGP to search for the optimal scheduling arrangements. BDGP is composed of a master GP and a slave GP. As the master GP evaluates the status combination of all load groups at every time step, it calls upon the slave GP to simultaneously look ahead an additional D steps to evaluate the best possible load difference that could result. The best status combinations in the following D steps associated with the status combination under evaluation are determined globally in the following D-step block. Since the proposed BDGP optimises the scheduling for DLC aiming to minimise the load difference in the next time step and the following D time steps, the scheduling results obtained by BDGP are closer to the globally optimal solution.", } @Article{Yao:2024:energy, author = "Qiuxiang Yao and Linyang Wang and Mingming Ma and Li Ma and Lei He and Duo Ma and Ming Sun", title = "A quantitative investigation on pyrolysis behaviors of metal ion-exchanged coal macerals by interpretable machine learning algorithms", journal = "Energy", year = "2024", volume = "300", pages = "131614", keywords = "genetic algorithms, genetic programming, Coal pyrolysis, Maceral, Ion-exchange, Machine learning, Symbolic regression", ISSN = "0360-5442", URL = "https://www.sciencedirect.com/science/article/pii/S0360544224013872", DOI = "doi:10.1016/j.energy.2024.131614", abstract = "Generalizing the rules from complex processes such as catalytic pyrolysis to guide their process control is always a difficult but attractive task. The influences of ion-exchange of metal ions (Na+, K+, Ca2+, Mg2+, Co2+ and Ni2+) on the pyrolysis behaviour of vitrinite and inertinite from Shendong coal were investigated by thermogravimetric analyser-Fourier transform infrared spectrometer (TG-FTIR), fixed-bed reactor (FBR), gas chromatograph-mass spectrometer (GC-MS) and X-ray diffractometer (XRD). A set of machine learning models was successfully constructed based on random forest, support vector machine, and Gaussian process regression, to quantify the relationships between pyrolysis behaviours and the properties of metals and macerals. The leave-one-out-cross validation showed that there are considerable determination coefficients (R2 > 0.9) between predicted and experimental values for most responses (17 out of 29). By combining genetic programming-based symbolic regression with the black-box algorithm, 23 symbolic regression expressions with high confidence were successfully constructed. This work is a pioneering attempt of optimisation in small-scale experiments. By using the highly interpretable models, a demand-orientated (multi-)optimisation coal pyrolysis can be achieved. The bi-objective optimisation was conducted on the yield of tar and the content of light aromatics in tar, and the results show that Co is the optimal loading metal", } @Article{Yao:1999:FPE:299157.299169, author = "Xin Yao", title = "Following the Path of Evolvable Hardware", journal = "Communications of the ACM", year = "1999", volume = "42", number = "4", pages = "46--49", month = apr, keywords = "genetic algorithms, EHW, FPGA", ISSN = "0001-0782", URL = "http://doi.acm.org/10.1145/299157.299169", DOI = "doi:10.1145/299157.299169", publisher = "ACM", address = "New York, NY, USA", acmid = "299169", size = "3 pages", abstract = "Overview", } @InProceedings{yao:1999:fogp, author = "Xin Yao", title = "Universal Approximation by Genetic Programming", booktitle = "Foundations of Genetic Programming", year = "1999", editor = "Thomas Haynes and William B. Langdon and Una-May O'Reilly and Riccardo Poli and Justinian Rosca", pages = "66--67", address = "Orlando, Florida, USA", month = "13 " # jul, keywords = "genetic algorithms, genetic programming, ANN", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/fogp/yao.ps.gz", size = "2 pages", abstract = "Genetic programming GP has been applied successfully to many difficult problems. However, little theory is currently available to explain why GP works or does not work for a particular problem. We investigate the power of GP in terms of its approximation capability to arbitrary functions. The relationship between artificial neural networks ANNs and GP is discussed. Such relation ship enables us to apply the existing theoretical results in ANNs to GP and show that GP can be a universal approximator. This result shows at least partially why GP is capable of solving some very difficult problems. It also sheds some light on choosing the function set for GP applications.", notes = "GECCO'99 WKSHOP, part of \cite{haynes:1999:fogp}", } @Proceedings{PPSN2004, title = "Parallel Problem Solving from Nature - PPSN VIII", year = "2004", editor = "Xin Yao and Edmund Burke and Jose A. Lozano and Jim Smith and Juan J. Merelo-Guerv\'os and John A. Bullinaria and Jonathan Rowe and Peter Ti\v{n}o Ata Kab\'an and Hans-Paul Schwefel", volume = "3242", series = "LNCS", address = "Birmingham, UK", publisher_address = "Berlin", month = "18-22 " # sep, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, evolutionstragies, evolutionary computing, evolutionary algorithms, tree grammars, scheduling, optimisation, constrained optimisation, multiobjective search, Pareto, MOGA, tabu search, co-evolution, robots, fuzzy-rules, Classifier LCS, XCS, analogue circuits, VLSI, EHW, Bayes, particle swarms, PSO, meta-heuristic, ant systems, AIS, gene regulation network, MAS", ISBN = "3-540-23092-0", DOI = "doi:10.1007/b100601", notes = "PPSN-VIII", } @Book{Yao:2025:CCA, author = "Xin Yao and Siang Yew Chong", title = "Coevolutionary Computation and Its Applications", publisher = "Springer", year = "2025", series = "Natural Computing Series", month = "16 " # apr, keywords = "genetic algorithms, genetic programming, Co-evolutionary Computation, Evolutionary Computation, Coevolution, Optimisation, Coevolutionary Learning, Artificial Intelligence, Computational Intelligence, Optimization", isbn13 = "978-981-96-2840-7", ISSN = "1619-7127", URL = "https://link.springer.com/book/10.1007/978-981-96-2841-4", DOI = "doi:10.1007/978-981-96-2841-4", size = "XVII + 489 pages", notes = "Chapter 4 Applications of Coevolution in Real-World Problem Solving. School of Data Science, Lingnan University, Tuen Mun, Hong Kong", } @Article{Yao:2024:ces, author = "Zhitong Yao and Jiayao Tong and Jingjing Jiang and Ahmed {Mohamed Abdel Sattar} and Jean {Constantino Gomes da Silva} and Sachin Kumar and Xiaobo Wang and Mohamed Salama Abd-Elhady and Jie Liu and Meiqing Jin and Obid Tursunov and Wei Qi", title = "Pyrolysis-gasification conversion of waste pharmaceutical blisters: Thermo-kinetic and thermodynamic study, fuel gas analysis and machine learning modeling", journal = "Chemical Engineering Science", year = "2024", volume = "300", pages = "120583", keywords = "genetic algorithms, genetic programming, Waste pharmaceutical blisters, Plastic, Thermo-chemical conversion, Steam reforming, Machine learning modeling", ISSN = "0009-2509", URL = "https://www.sciencedirect.com/science/article/pii/S0009250924008832", DOI = "doi:10.1016/j.ces.2024.120583", abstract = "It delved into the pyrolysis-gasification behaviour of waste pharmaceutical blisters (WPBs), exploring both kinetics and thermodynamics, and employing machine learning modelling. The decomposition of WPBs occurred in three stages, with temperature intervals of 140-350, 350-500, and 500-900 degreeC, respectively. Cyclization/aromatization reactions were hindered, as indicated by mass spectrometric analysis, which revealed the main evolved products, including CxHy, CH3OH, CH4, and H2. Apparent activation energy values obtained from FWO, KAS, Friedman, Cai & Chen models were comparable, showing a decreasing trend ranging from 264.9 to 48.4 kJ mol-1 at alpha lteq 0.70, followed by a subsequent increase to 283.1 kJ mol-1 at a conversion of 0.80. The D1 model was more reliable in describing the pyrolysis-gasification process of WPBs. Machine learning modelling results indicated that the ANN19 with a topology structure of 5 . 15 . 1 and genetic programming models showed superior performance in predicting TG data", } @MastersThesis{Tolga_Yapici:MastersThesis, author = "Tolga Yapici", title = "Influences of interplanetary magnetic field on the variability of the aerospace media", school = "Middle East Technical University", year = "2007", address = "Ankara, Turkey", month = sep, keywords = "genetic algorithms, genetic programming, Magnetosphere, Ionosphere, Interplanetary Magnetic Field, Modeling", URL = "http://www.ae.metu.edu.tr/~tyapici/thesis.php", URL = "http://www.ae.metu.edu.tr/~tyapici/thesis/cover_pages.pdf", URL = "http://www.ae.metu.edu.tr/~tyapici/thesis/abstract.pdf", URL = "http://www.ae.metu.edu.tr/~tyapici/thesis/oz.pdf", URL = "http://www.ae.metu.edu.tr/~tyapici/thesis/chapter1.pdf", URL = "http://www.ae.metu.edu.tr/~tyapici/thesis/chapter2.pdf", URL = "http://www.ae.metu.edu.tr/~tyapici/thesis/chapter3.pdf", URL = "http://www.ae.metu.edu.tr/~tyapici/thesis/chapter4.pdf", URL = "http://www.ae.metu.edu.tr/~tyapici/thesis/chapter5.pdf", URL = "http://www.ae.metu.edu.tr/~tyapici/thesis/references.pdf", size = "xvi + 47 pages", abstract = "Thus, a modelling attempt for the characterization of the response due to polarity reversals of IMF components with the Genetic Programming was carried out. Four models were constructed for different polarity reversal cases and they were used as the components of one general unique model. The model is designed in such a way that given 3 consecutive value of f0F2, IMF B_y and IMF B_z, the model can forecast one hour ahead value of f_0F2. The overall model, GETY-IYON was successful at a normalized error of 7.3percent.", notes = "Cited by \cite{AG4579}", } @Article{Yardimci20091029, author = "Ahmet Yardimci", title = "Soft computing in medicine", journal = "Applied Soft Computing", volume = "9", number = "3", pages = "1029--1043", year = "2009", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2009.02.003", URL = "http://www.sciencedirect.com/science/article/B6W86-4VRP213-2/2/141a98fa1c9600c49c0883f71eb8a711", keywords = "genetic algorithms, genetic programming, Soft computing, Fuzzy-neural systems, Medicine", abstract = "Soft computing (SC) is not a new term; we have gotten used to reading and hearing about it daily. Nowadays, the term is used often in computer science and information technology. It is possible to define SC in different ways. Nonetheless, SC is a consortium of methodologies which works synergistically and provides, in one form or another, flexible information processing capability for handling real life ambiguous situations. Its aim is to exploit the tolerance for imprecision, uncertainty, approximate reasoning and partial truth in order to achieve tractability, robustness and low-cost solutions. SC includes fuzzy logic (FL), neural networks (NNs), and genetic algorithm (GA) methodologies. SC combines these methodologies as FL and NN (FL-NN), NN and GA (NN-GA) and FL and GA (FL-GA). Recent years have witnessed the phenomenal growth of bio-informatics and medical informatics by using computational techniques for interpretation and analysis of biological and medical data. Among the large number of computational techniques used, SC, which incorporates neural networks, evolutionary computation, and fuzzy systems, provides unmatched utility because of its demonstrated strength in handling imprecise information and providing novel solutions to hard problems. The aim of this paper is to introduce briefly the various SC methodologies and to present various applications in medicine between the years 2000 and 2008. The scope is to demonstrate the possibilities of applying SC to medicine-related problems. The recent published knowledge about use of SC in medicine is researched in MEDLINE. This study detects which methodology or methodologies of SC are used frequently together to solve the special problems of medicine. According to MEDLINE database searches, the rates of preference of SC methodologies in medicine were found as 68percent of FL-NN, 27percent of NN-GA and 5percent of FL-GA. So far, FL-NN methodology was significantly used in medicine. The rates of using FL-NN in clinical science, diagnostic science and basic science were found as percent83, percent71 and percent48, respectively. On the other hand NN-GA and FL-GA methodologies were mostly preferred by basic science of medicine. Another message emerging from this survey is that the number of papers which used NN-GA methodology has continuously risen until today. Also search results put the case clearly that FL-GA methodology has not applied well enough to medicine yet. Undeniable interest in studying SC methodologies in genetics, physiology, radiology, cardiology, and neurology disciplines proves that studying SC is very fruitful in these disciplines and it is expected that future researches in medicine will use SC more than it is used today to solve more complex problems.", notes = "Only brief mention of GP", } @InProceedings{Yarnell:2023:ICMLA, author = "Richard C. Yarnell and Pierce Powell and Ronald F. DeMara and Annie S. Wu", booktitle = "2023 International Conference on Machine Learning and Applications (ICMLA)", title = "A Genetic Algorithm for Combinational Logic Circuit Synthesis Using Directed Graph Primitives", year = "2023", pages = "1859--1866", abstract = "We introduce functionality-cognizant Genetic Algorithms (GAs) and graph-based operators to tackle the challenging search landscape of combinational digital circuit design. We introduce a novel circuit representation that builds upon Cartesian Genetic Programming (CGP), a popular grid-based method for representing directed graphs of connected components. Leveraging this, we introduce an original crossover operator that accounts for circuit component functionality and connectivity, as opposed to CGP, which only considers positional information in the chromosome. Additionally, we propose an innovative set of mutation operators and demonstrate successful evolution of fully functional and minimally sized common digital circuits including a variety of binary encoders and adders. Following successful synthesis of a four-bit adder, we present a generalizable machine learning approach for multi-layered search and optimisation problems.", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Machine learning algorithms, Limiting, Directed graphs, Logic gates, Search problems, Complexity theory, Evolvable Hardware, EHW,Digital Circuit Design", DOI = "doi:10.1109/ICMLA58977.2023.00282", ISSN = "1946-0759", month = dec, notes = "Also known as \cite{10459786}", } @Article{journals/cea/YassinAM16, author = "Mohamed A. Yassin and A. A. Alazba and Mohamed A. Mattar", title = "A new predictive model for furrow irrigation infiltration using gene expression programming", journal = "Computers and Electronics in Agriculture", year = "2016", volume = "122", keywords = "genetic algorithms, genetic programming, gene expression programming", bibdate = "2016-04-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/cea/cea122.html#YassinAM16", pages = "168--175", URL = "http://dx.doi.org/10.1016/j.compag.2016.01.035", } @InProceedings{Yasuda:2006:ASPGP, title = "Using genetic programming to improve the performance of wireless LAN access point configuration", author = "Yuta Yasuda and Yuji Sato", booktitle = "Proceedings of the Third Asian-Pacific workshop on Genetic Programming", year = "2006", editor = "The Long Pham and Hai Khoi Le and Xuan Hoai Nguyen", pages = "57--68", ISSN = "18590209", address = "Military Technical Academy, Hanoi, VietNam", keywords = "genetic algorithms, genetic programming", URL = "http://gpbib.cs.ucl.ac.uk/aspgp06/yutayasuda.pdf", size = "12 pages", abstract = "Radio communication speed has improved by leaps and bounds with developments in communication technology. In a wireless LAN, clients connect to a network to communicate with access points. Various approaches have benn used over the years to maximise the efficiency of access point configurations, such as the simplex method, Tabu search, and the genetic algorithm. This paper describes how genetic programming can be applied to the problem in a more deterministic function to improve the convergence speed. Linear genetic programming using arrays to shorten the running time and bonsai manipulation, hierarchical pruning of genes based on evaluation of terminal nodes, are proposed. An experiment was carried out to compare the performance with that of standard genetic programming. Consequently, it was confirmed that linear genetic programming reduced the running time and that the proposed bonsai manipulation improves not only the convergence speed, but also the evaluation results.", notes = "broken march 2020 http://www.aspgp.org", } @Article{yasunaga:2001:GPEM, author = "Moritoshi Yasunaga and Jung Hwan Kim and Ikuo Yoshihara", title = "Evolvable Reasoning Hardware: Its Prototyping and Performance Evaluation", journal = "Genetic Programming and Evolvable Machines", year = "2001", volume = "2", number = "3", pages = "211--230", month = sep, keywords = "genetic algorithms, evolvable hardware, VLSI design methodology, FPGA, reasoning, NETTalk, MBRTalk", ISSN = "1389-2576", DOI = "doi:10.1023/A:1011939025340", abstract = "In this paper, we propose evolvable reasoning hardware and its design methodology. In the proposed design methodology, case databases of each reasoning task are transformed into truth tables, which are evolved to extract rules behind the past cases through a genetic algorithm. Circuits for the evolvable reasoning hardware are synthesized from the evolved truth-tables. Parallelism in each task can be embedded directly in the circuits through the direct hardware implementation of the case databases. We developed the evolvable reasoning hardware prototype using Xilinx Virtex FPGA chips and applied it to the English-pronunciation-reasoning (EPR) task. The evolvable reasoning hardware for the EPR task was implemented with 270K gates, achieving an extremely high reasoning speed of less than 300 ns/phoneme. It also achieved a reasoning accuracy of 82.1% which is almost the same accuracy as NETTalk in neural networks and MBRTalk in parallel AI.", notes = "Article ID: 357592", } @Misc{yazdani2024clusteringdynamicenvironmentsframework, author = "Danial Yazdani and Juergen Branke and Mohammad Sadegh Khorshidi and Mohammad Nabi Omidvar and Xiaodong Li and Amir H. Gandomi and Xin Yao", title = "Clustering in Dynamic Environments: A Framework for Benchmark Dataset Generation With Heterogeneous Changes", year = "2024", howpublished = "arXiv", month = "24 " # feb, note = "Presented at Workshop on Genetic Programming for Data Analysis, GPDA 2024", keywords = "genetic algorithms, genetic programming, Machine Learning (cs.LG), Neural and Evolutionary Computing (cs.NE)", eprint = "2402.15731", archiveprefix = "arXiv", primaryclass = "cs.LG", URL = "https://arxiv.org/abs/2402.15731", size = "15 pages", notes = "GPDA 2024, UTS Central, Level 12, room 225, 61 Broadway, Ultimo 2007, NSW, Australia, 11 July 2024, Chair Professor Amir H. Gandomi", } @InProceedings{Yazdani:2008:CIS, author = "S. Yazdani and M. {Aliyari shoorehdeli} and M. Teshnehlab", title = "Identification of Fuzzy Models Using Cartesian Genetic Programming", booktitle = "International Conference on Computational Intelligence and Security, CIS '08", year = "2008", month = dec, volume = "2", pages = "76--81", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, complex optimization problem, fuzzy clustering, fuzzy models, membership function parameters, pattern recognition, recursive least square, combinatorial mathematics, fuzzy set theory, fuzzy systems, least squares approximations, pattern clustering, recursive estimation", DOI = "doi:10.1109/CIS.2008.143", abstract = "Fuzzy models have capability for solving problem in different application such as pattern recognition, prediction and control. Nevertheless, it has to be emphasized that the identification of a fuzzy model is complex task with many local minima. Cartesian genetic programming provides a way to solve such complex optimization problem. In this paper, fuzzy model is in form of network. Cartesian genetic programming is used to optimize the antecedent part and recursive least square is used to optimized the consequent part. The initialization of membership function parameters are doing with fuzzy clustering. Benefit of the methodology is illustrated by simulation results.", notes = "Also known as \cite{4724740}", } @Article{Yazdani:2014:GPEM, author = "Samaneh Yazdani and Jamshid Shanbehzadeh", title = "Balanced Cartesian Genetic Programming via migration and opposition-based learning: application to symbolic regression", journal = "Genetic Programming and Evolvable Machines", year = "2015", volume = "16", number = "2", pages = "133--150", month = jun, keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Biogeography-based optimisation, Migration, Opposition-based learning, Exploration exploitation trading-off", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-014-9230-4", size = "18 pages", abstract = "The exploration exploitation trade-off is an important aspect of evolutionary algorithms which determines the efficiency and accuracy of these algorithms. Cartesian Genetic Programming (CGP) is a generalisation of the graph based genetic programming. It is implemented with mutation only and does not have any possibility to share information among solutions. The main goal of this paper is to present an effective method for balancing the exploration and exploitation of CGP referred to as Balanced Cartesian Genetic Programming (BCGP) by incorporating distinctive features from bio geography-based optimisation (BBO) and opposition-based learning. To achieve this goal, we apply BBO's migration operator without considering any modifications in the representation of CGP. This operator has good exploitation ability and can be used to share information among individuals in CGP. In addition, in order to improve the exploration ability of CGP, a new mutation operator is integrated into CGP inspired from the concept of opposition-based learning. Experiments have been conducted on symbolic regression. The experimental results show that the proposed BCGP method outperforms the traditional CGP in terms of accuracy and the convergence speed.", notes = "1. Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran 2. Department of Computer Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran ", } @Article{journals/kbs/YazdaniSH17, author = "Samaneh Yazdani and Jamshid Shanbehzadeh and Esmaeil Hadavandi", title = "{MBCGP-FE}: A modified balanced cartesian genetic programming feature extractor", journal = "Knowledge-Based Systems", year = "2017", volume = "135", pages = "89--98", month = "1 " # nov, keywords = "genetic algorithms, genetic programming, cartesian genetic programming", bibdate = "2017-09-27", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/kbs/kbs135.html#YazdaniSH17", DOI = "doi:10.1016/j.knosys.2017.08.005", abstract = "Many data sets are represented by low-level or primitive features. This makes it difficult to discover relevant information via learning algorithm. Changing the way primitive data is represented can be advantageous. This can be performed using data preprocessing algorithms. A successful preprocessing algorithm should be capable of revealing the relationships among features to improve learners. These hidden relations among features can make the relevancy of the aspects of the data opaque to the learner. Automatic feature extraction is a solution to overcome this problem. This article introduces a Modified Balanced Cartesian Genetic Programming Feature Extractor (MBCGP-FE) for transforming the feature space to a smaller one composed of highly informative features through modifying the representation and operators of Balanced Cartesian Genetic Programming (BCGP). The new feature space is composed from original relevant and new constructed features which are created by discovering and compacting hidden relations among features. The size of the new feature space is determined during the optimisation process. Experimental results on real data sets show that the MBCGP-FE improves the performance of learners and it is effective in reducing the dimension of data sets through the construction of new informative features. In addition, obtained results indicate the effectiveness of our proposed method in comparison with other feature extraction methods.", } @Article{Yazdian:2014:JEHSE, author = "Hamed Yazdian and Nematollah Jaafarzadeh and Banafsheh Zahraie", title = "Relationship between benthic macroinvertebrate bio-indices and physicochemical parameters of water: a tool for water resources managers", year = "2014", journal = "Journal of Environmental Health Science and Engineering", volume = "12", number = "30", month = jan # "~10", keywords = "genetic algorithms, genetic programming, bio-diversity index, physicochemical parameter, Margalef index", publisher = "BioMed Central", isbnx = "52-336X-12-30", ISSN = "2052-336X", bibsource = "OAI-PMH server at www.ncbi.nlm.nih.gov", language = "en", oai = "oai:pubmedcentral.nih.gov:3895749", URL = "http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3895749", URL = "http://www.ncbi.nlm.nih.gov/pubmed/24410768", URL = "http://dx.doi.org/10.1186/2052-336X-12-30", size = "9 pages", abstract = "The ecosystem health of rivers downstream of dams is among the issues that has become focus of attention of many researchers particularly in the recent years. This paper aims to deal with the question, how the environmental health of a river ecosystem can be addressed in water resources planning and management studies. In this study, different parameters affecting the ecosystem of river-reservoir systems, as well as various biological components of river ecosystems have been studied and among them, benthic macro-invertebrates have been selected. Among various bio-indices, biodiversity indices have been selected as the evaluation tool. The case study of this research is Aboulabbas River in Khuzestan province in Iran. The relationship between the biodiversity indices and physicochemical parameters have been studied using correlation analysis, Principal Component Analysis (PCA), and Genetic Programming (GP). Margalef index was selected as the appropriate bio-index for the studied catchment area. The relationship found in this study for the first time between the Margalef bio-index and physicochemical parameters of water in the Aboulabbas River has proved to be a useful tool for water resources managers to assess the ecosystem status when only physicochemical properties of water are known.", } @InProceedings{Ye:2018:SERA, author = "Botao Ye and Dejun Xie", title = "An Overview of Event Based Directional Change for Algorithmic Trading", booktitle = "2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA)", year = "2018", pages = "13--18", month = jun, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SERA.2018.8477229", abstract = "This paper outlines a framework of a practicable scheme to facilitate algorithm trading of securities. The proposed scheme is capable to intelligently identify, analyze, and implement the intrinsic directional changes in the price movement of the stock market. An overall qualitative assessment is provided together with survey of existing theoretical and empirical foundations towards the success of such an algorithm. Potential loopholes and roadmap for further improvement are suggested.", notes = "Also known as \cite{8477229}", } @InProceedings{Ye:2022:ICNISC, author = "Chaoyang Ye and Shicong Zhang and Yisha Liu and Jianhong Lin", booktitle = "2022 8th Annual International Conference on Network and Information Systems for Computers (ICNISC)", title = "Quantum {GEP} for Dynamic Multiobjective Optimization", year = "2022", pages = "845--850", abstract = "Gene expression programming, as an evolutionary computing technology, considers the simplicity of genotype and the robustness of function. Combining gene expression programming with quantum evolution method, a quantum gene expression programming QGEP Dynamic algorithm is proposed to solve continuous dynamic multiobjective optimisation problems. The algorithm adapts to environmental changes by making full use of quantum populations to introduce population diversity and affects the search direction of antibody populations for multiobjective GEP, so that antibody populations have better evolutionary ability. The results on three continuous dynamic multiobjective test functions show that the QGEP Dynamic algorithm is superior to traditional genetic algorithm in terms of solution distribution breadth and stability.", keywords = "genetic algorithms, genetic programming, gene expression programming, Computers, Heuristic algorithms, Sociology, Antibodies, Dynamic programming, Gene expression, dynamic multiobjective optimisation, quantum evolution", DOI = "doi:10.1109/ICNISC57059.2022.00169", month = sep, notes = "Also known as \cite{10045404}", } @Article{YE:2019:JCP, author = "Fei-Fei Ye and Long-Hao Yang and Ying-Ming Wang", title = "A new environmental governance cost prediction method based on indicator synthesis and different risk coefficients", journal = "Journal of Cleaner Production", volume = "212", pages = "548--566", year = "2019", keywords = "genetic algorithms, genetic programming, Environmental governance, Cost prediction, Risk preference, Indicator synthesis, Weight calculation", ISSN = "0959-6526", DOI = "doi:10.1016/j.jclepro.2018.12.029", URL = "http://www.sciencedirect.com/science/article/pii/S0959652618337314", abstract = "Environment protection is important for the survival of residents, and the government must improve its governance model on environmental cost prediction methods to address the increasing level of environmental pollution. Therefore, a science-based investment scheme is of great significance. To improve the accuracy and effectiveness of environmental governance cost prediction method, it is important to consider the completeness of the indicators and their degree of contributions-both of which need to be studied further. Considering the influence of a decision-maker's subjectivity on an investment scheme, this paper proposes a prediction method accommodating the risk preferences of different decision-makers. The proposed method is based on the synthesis of evidential reasoning approach. An objective empowerment is carried out according to the standard deviation method of correlation coefficient to highlight the importance degree of different indicators. At the same time, to improve the practical usage of the synthetic cost prediction method, the future cost is predicted by combining the genetic programming models under different risk coefficients, namely, the risk preference, the risk neutrality, and the risk aversion. Finally, a case study involving environmental governance cost prediction of 29 provinces of China is presented. A comparison of the cost predictions and the actual value of different risk coefficients for the different methods are given to evaluate the effectiveness of the proposed method", } @InProceedings{Ye:2008:cec, author = "Fengming Ye and Shigo Mabu and Kaoru Shimada and Kotaro Hirasawa", title = "Genetic Network Programming with Rules", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "413--418", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1823-7", file = "EC0116.pdf", DOI = "doi:10.1109/CEC.2008.4630830", abstract = "Genetic Network Programming (GNP) is an evolutionary approach which can evolve itself and find the optimal solutions. As many papers have demonstrated that GNP which has a directed graph structure can deal with dynamic environments very efficiently and effectively. It can be used in many areas such as data mining, forecasting stock markets, elevator system problems, etc. In order to improve GNP's performance further, this paper proposes a method called GNP with Rules. The aim of the proposal method is to balance exploitation and exploration, that is, to strengthen exploitation ability by using the exploited information extensively during the evolution process of GNP. The proposal method consists of 4 steps: rule extraction, rule selection, individual reconstruction and individual replacement. Tile-world was used as a simulation environment. The simulation results show some advantages of GNP with Rules over conventional GNPs.", keywords = "genetic algorithms, genetic programming", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @InProceedings{Ye:2009:cec, author = "Fengming Ye and Shigo Mabu and Lutao Wang and Shinji Eto and Kotaro Hirasawa", title = "Genetic Network Programming with Reconstructed Individuals", booktitle = "2009 IEEE Congress on Evolutionary Computation", year = "2009", editor = "Andy Tyrrell", pages = "854--859", address = "Trondheim, Norway", month = "18-21 " # may, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, dgenetic network programming, ata structure, directed graph, elite information enhancement, reconstructed individual, data structures, directed graphs", isbn13 = "978-1-4244-2959-2", file = "P072.pdf", DOI = "doi:10.1109/CEC.2009.4983034", abstract = "Genetic Network Programming (GNP) is a newly proposed evolutionary approach which can evolve itself and find the optimal solutions. It is a novel method based on the idea of Genetic Algorithm (GA) and uses the data structure of directed graphs. As GNP has been developed for dealing with problems in dynamic environments, many papers have demonstrated that GNP can be applied to many areas such as data mining, forecasting stock markets, elevator control systems, etc. Focusing on GNPs distinguished expression ability of the graph structure, this paper proposes a method named Genetic Network Programming with Reconstructed Individuals (GNP with RI). In the proposed method, the worst individuals are reconstructed and enhanced by the elite information before undergoing genetic operations (mutation and crossover). The phenomenon in the nature, where bad individuals can become smarter after receiving good education. GNP with RI has been applied to the tile-word which is an excellent benchmark for evaluating the proposed architecture. The performance of GNP with RI is compared with conventional GNP demonstrating its superiority.", notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR Also known as \cite{4983034}", } @InProceedings{Ye:2009:ICCAS-SICE, author = "Fengming Ye and Shingo Mabuand Lutao Wang and Kotaro Hirasawa", title = "Genetic Network Programming with General Individual Reconstruction", booktitle = "ICCAS-SICE, 2009", year = "2009", month = "18-21 " # aug, address = "Fukuoka", pages = "3474--3479", publisher = "IEEE", isbn13 = "978-4-9077-6433-3", URL = "http://ieeexplore.ieee.org/search/srchabstract.jsp?tp=&arnumber=5335121", abstract = "Genetic network programming (GNP) which has been developed for dealing with problems in dynamic environments is a newly proposed evolutionary approach with the data structure of directed graphs. GNP has been used in many different areas such as data mining, extracting trading rules of stock markets, elevator supervised control systems, etc and has obtained some outstanding results. Focusing on GNP's distinguishing expression ability of the graph structure, this paper proposes a method named genetic network programming with general individual reconstruction (GNP with GIR) which reconstructs the gene of randomly selected individuals and then undergoes the special genetic operations by using the transition information of better individuals. The unique individual reconstruction and genetic operations make individuals not only learn the experiences of better individuals but also strengthen exploration and exploration ability. GNP with GIR will be applied to the tile-world which is an excellent benchmark for evaluating the proposed architecture. The performances of GNP with GIR will be compared with conventional GNP demonstrating its superiority.", keywords = "genetic algorithms, genetic programming, genetic network programming, data mining, data structure, directed graphs, elevator supervised control systems, evolutionary approach, general individual reconstruction, stock markets, transition information, data structures, directed graphs", notes = "Also known as \cite{5335121}", } @InProceedings{Ye:2010:SMC, author = "Fengming Ye and Shingo Mabu and Lutao Wang and Kotaro Hirasawa", title = "Genetic Network Programming with new genetic operators", booktitle = "2010 IEEE International Conference on Systems Man and Cybernetics (SMC)", year = "2010", month = "10-13 " # oct, pages = "3346--3353", address = "Istanbul", abstract = "Recently, a new approach named Genetic Network Programming (GNP) has been proposed. GNP can evolve itself and find the optimal solutions. It is based on the ideas of classical evolutionary computation methods such as Genetic Algorithm (GA) and Genetic Programming (GP) and uses the data structure of directed graphs which is the unique feature of GNP. Many studies have demonstrated that GNP can well solve the complex problems in the dynamic environments very efficiently and effectively. As a result, recently, GNP is getting more and more attentions and is being used in many different areas such as data mining, extracting trading rules of stock markets, elevator supervised control systems, etc. Focusing on GNP's distinguished expression ability of the graph structure, this paper proposes an enhanced architecture for the standard GNP in order to improve the performance GNP by using the exploited information extensively during the evolution process of GNP. In the enhanced architecture, we proposed the new genetic operator named Individual Reconstruction which reconstructs and enhances the worst individuals by using the elite information and the crossover and mutation operators of GNP are also modified. In this paper, the proposed architecture has been applied to the tile-world which is an excellent bench mark for evaluating the evolutionary computation architecture. The performance of the new GNP is compared with the conventional GNP. The simulation results show some advantages of the proposed method over the conventional GNPs demonstrating its superiority.", keywords = "genetic algorithms, genetic programming, Ggenetic network programming, NP, Individual Reconstruction, data mining, data structure, directed graphs, elevator supervised control system, evolutionary computation method, genetic operator, stock markets, trading rules extraction, data mining, data structures", DOI = "doi:10.1109/ICSMC.2010.5642337", ISSN = "1062-922X", notes = "Also known as \cite{5642337}", } @InProceedings{Ye:2010:ieeetencon, author = "Fengming Ye and Shingo Mabu and Lutao Wang and Kotaro Hirasawa", title = "Genetic network programming with route nodes", booktitle = "IEEE Region 10 Conference TENCON 2010", year = "2010", month = "21-24 " # nov, pages = "1404--1409", abstract = "Many classical methods such as Genetic Algorithm (GA), Genetic Programming (GP), Evolutionary Strategies (ES), etc. have accomplished significant contribution to the study of evolutionary computation. And in the past decade, a new approach named Genetic Network Programming (GNP) has been proposed. It is designed for especially solving complex problems in dynamic environments. Generally speaking, GNP is based on the algorithms of existed classical evolutionary computation techniques and uses the data structure of directed graphs which becomes the unique feature of GNP. So far, many studies have indicated that GNP can solve the complex problems in the dynamic environments very efficiently and effectively. Focusing on GNP's distinguished expression ability of the graph structure, this paper proposes an enhanced architecture for the standard GNP in order to improve the performance of GNP by using the exploited information extensively during the evolution process of GNP. In the enhanced architecture, the important gene information of the elite individuals is extracted and accumulated during evolution. And among the accumulated information, some of them are selected and encapsulated in the Route Nodes which are used to guide the evolution process. In this paper, the proposed architecture has been applied to the tile-world which is an excellent bench mark for evaluating the evolutionary computation architecture. The performance of the GNP with Route Nodes (GNP-RN) is compared with the conventional GNP. The simulation results show some merits of the proposed method over the conventional GNPs demonstrating its superiority.", keywords = "genetic algorithms, genetic programming, data structure, directed graph, evolutionary computation architecture, evolutionary strategy, genetic network programming, problems solving, route nodes, data structures, directed graphs, problem solving", DOI = "doi:10.1109/TENCON.2010.5686115", ISSN = "pending", notes = "Also known as \cite{5686115}", } @InProceedings{Ye:2011:GECCOcomp, author = "Fengming Ye and Shingo Mabu and Kotaro Hirasawa", title = "A memory scheme for genetic network programming with adaptive mutation", booktitle = "GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0690-4", keywords = "genetic algorithms, genetic programming, genetic network programming, Genetics based machine learning: Poster", pages = "179--180", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001858.2001958", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Recently, a new approach named Genetic Network Programming (GNP) has been proposed for especially solving complex problems in dynamic environments. In this paper, we propose a memory scheme for GNP to enhance the performance of GNP and use SARSA learning based adaptive mutation mechanism to guide the GNP evolution process.", notes = "Also known as \cite{2001958} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @PhdThesis{FengmingYe:thesis, author = "Fengming Ye", title = "A Study on the Memory Schemes for Genetic Network Programming", school = "Waseda University", year = "2011", address = "Japan", month = jun, keywords = "genetic algorithms, genetic programming, Genetic Network Programming", URL = "http://jairo.nii.ac.jp/0069/00021616/en", URL = "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/37550/3/Honbun-5698.pdf", URL = "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/37550/2/Shinsa-5698.pdf", URL = "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/37550/1/Gaiyo-5698.pdf", size = "105 pages", abstract = "From 1960s, Evolutionary Algorithms (EA), which is an important subtopic of Artificial Intelligence (AI), has been studied a lot and great progresses have been made continuously to improve the existed algorithms or propose novel methods. For example, the studies on many classical methods such as Genetic Algorithm (GA), Genetic Programming (GP), Evolutionary Strategies (ES), etc. have made significant contribution to the research of EA. In the past decade, a new evolutionary approach named Genetic Network Programming (GNP) was proposed and attracted more and more attention. GNP which is based on the idea of Genetic Algorithm, also can evolve itself and search in the solution domain of large scale and finally find the (approximate) optimal solutions. The unique character of GNP which make it very different from other methods of EA is the use of the data structure of directed graphs. Many research has demonstrated that GNP can deal with complex problems in the dynamical environments very efficiently and effectively due to its graph based structure. As a result, recently, GNP is being used in many different areas such as data mining, extracting trading rules of stock markets, elevator supervised control systems, etc. and GNP has obtained outstanding results in all the above fields. On the other hand, many research shows that classical EAs such as GA, usually fail to solve problems in dynamical environments. So, scholars devote themselves to the research on the enhancement of the architecture of EAs. For example, different memory schemes storing historical information during evolution and reusing them later are designed for EAs to solve complex problems in dynamical environments. So, the motivation of this research is designing memory schemes for GNP in order to improve its performance further in the dynamical environments. So, four different memory schemes are proposed: GNP with rules, GNP with reconstructed individuals, GNP with route nodes and adaptive mutation in SARSA learning of GNP. GNP with rules stores first-order information on GNP rules and uses them to generate new individuals. GNP with reconstructed individuals will stores the complete node transitions which can guide the agent with much more effectiveness and uses them to enhance the gene structures of the worst individuals. GNP with route nodes employs an indirect memory scheme which uses the stored information associated with current environments. The adaptive mutation using Q values to evaluate node branches adjusts the mutation rates and mutation directions for node branches and achieves the balance between exploration and exploitation. In order to measure the performance of the proposed architectures, the benchmark of tile-world was used as the simulation environments. The simulation results show some improvements brought by the memory schemes to conventional GNPs.", } @InProceedings{Ye:2018:SSBSE, author = "Mengmei Ye and Myra B. Cohen and Witawas Srisa-an and Sheng Wei", title = "{EvoIsolator}: Evolving Program Slices for Hardware Isolation Based Security", booktitle = "SSBSE 2018", year = "2018", editor = "Thelma Elita Colanzi and Phil McMinn", volume = "11036", series = "LNCS", pages = "377--382", address = "Montpellier, France", month = "8-9 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, Software transplantation, Hardware security, TZSlicer, TZOptimizer", isbn13 = "978-3-319-99241-9", URL = "https://par.nsf.gov/servlets/purl/10088739", DOI = "doi:10.1007/978-3-319-99241-9_24", size = "6 pages", abstract = "To provide strong security support for today's applications, microprocessor manufacturers have introduced hardware isolation, an on-chip mechanism that provides secure accesses to sensitive data. Currently, hardware isolation is still difficult to use by software developers because the process to identify access points to sensitive data is error-prone and can lead to under and over protection of sensitive data. Under protection can lead to security vulnerabilities. Over protection can lead to an increased attack surface and excessive communication overhead. In this paper we describe EvoIsolator, a search-based framework to (i) automatically generate executable minimal slices that include all access points to a set of specified sensitive data; and (ii) automatically optimize (for small code block size and low communication overhead) the code modules for hardware isolation. We demonstrate, through a small feasibility study, the potential impact of our proposed code optimizer.", notes = "p379 'using genetic programming to find the best code configuration'", } @Article{YE:2022:ijrefrig, author = "Zuliang Ye and Alireza Zendehboudi and Armin Hafner and Feng Cao", title = "General heat transfer correlations for supercritical carbon dioxide heated in vertical tubes for upward and downward flows", journal = "International Journal of Refrigeration", volume = "140", pages = "57--69", year = "2022", ISSN = "0140-7007", DOI = "doi:10.1016/j.ijrefrig.2022.05.013", URL = "https://www.sciencedirect.com/science/article/pii/S0140700722001670", keywords = "genetic algorithms, genetic programming, Supercritical carbon dioxide, Heat transfer, General correlation, Vertical flow, Transfert de chaleur, Dioxyde de carbone supercritique, Correlation generale, Ecoulement vertical, Etat critique", abstract = "Supercritical CO2 is a promising working fluid for many industrial applications. To improve the performances of relevant components and systems, the prediction of the heat transfer of supercritical CO2 is an important research topic. General explicit heat transfer correlations of supercritical CO2 for upward and downward flows heated in circular tubes were established using the genetic programming (GP) method. A total of 12720 experimental data points from 22 publications were collected to develop the models. The data included hydraulic diameter from 0.0992 to 22 mm, bulk temperature from -6.0 to 134.5degreeC, pressure from 7.44 to 10.50 MPa, mass flux from 50 to 4834 kga.(m2a.s)-1, heat flux from 2.9 to 748 kWa.m-2 and wall temperature from 6.4 to 368.2degreeC. The database was divided into four parts according to the flow direction and the relationship between the bulk temperature and the pseudo-critical temperature. The developed correlations considered various non-dimensional parameters as the independent variables to reflect the effects of supercritical properties, flow acceleration and buoyancy on the heat transfer. The results showed that the proposed correlations had excellent accuracy with a mean absolute relative error (MARE) of 20.10percent based on prediction with the iterated wall temperature. The developed correlations outperformed the existing correlations in the literature. Compared to other correlations, the trend analysis indicated that these newly developed correlations could appropriately present the physics sense when the condition parameters varied", } @InProceedings{Yeboah-Antwi:2012:GECCOcomp, author = "Kwaku Yeboah-Antwi", title = "Evolving software applications using genetic programming -- PushCalc: the evolved calculator", booktitle = "Tenth GECCO Undergraduate Student Workshop", year = "2012", editor = "Sherri Goings", isbn13 = "978-1-4503-1178-6", keywords = "genetic algorithms, genetic programming", pages = "569--572", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330784.2330875", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "This paper describes PushCalc, a system that evolves a Calculator, a complete software application. PushCalc is a modified version of Clojush, the clojure implementation of the PushGP genetic programming system1. PushCalc supports the definition and storage of names and functions via its naming mechanism, tags. The goal of this system is to use this ability to evolve an individual that can create the modular parts of the calculator and also know when and in what situations to use which modular functions and perform the correct operations depending on the input given to the system.", notes = "Also known as \cite{2330875} Distributed at GECCO-2012. ACM Order Number 910122.", } @InProceedings{Yeboah-Antwi:2015:gi, author = "Kwaku Yeboah-Antwi and Benoit Baudry", title = "Embedding Adaptivity in Software Systems using the {ECSELR} framework", booktitle = "Genetic Improvement 2015 Workshop", year = "2015", editor = "William B. Langdon and Justyna Petke and David R. White", pages = "839--844", address = "Madrid", publisher_address = "New York, NY, USA", month = "11-15 " # jul, organisation = "SIGEvo", publisher = "ACM", keywords = "genetic algorithms, genetic programming, Genetic Improvement", isbn13 = "978-1-4503-3488-4", URL = "http://gpbib.cs.ucl.ac.uk/gi2015/embedding_adaptivity_in_software_systems_using-the_eselr_framework.pdf", URL = "http://diversify-project.eu/papers/Yeboah15.pdf", URL = "http://doi.acm.org/10.1145/2739482.2768425", DOI = "doi:10.1145/2739482.2768425", size = "8 pages", abstract = "ECSELR is an ecologically-inspired approach to software evolution that enables environmentally driven evolution at runtime in extant software systems without relying on any offline components or management. ECSELR embeds adaptation and evolution inside the target software system enabling the system to transform itself via Darwinian evolutionary mechanisms and adapt in a self contained manner. This allows the software system to benefit autonomously from the useful emergent byproducts of evolution like adaptivity and bio-diversity, avoiding the problems involved in engineering and maintaining such properties. ECSELR enables software systems to address changing environments at run time, ensuring benefits like mitigation of attacks and memory-optimisation among others while avoiding time consuming and costly maintenance and downtime. ECSELR differs from existing work in that, 1) adaptation is embedded in the target system, 2) evolution and adaptation happens online (i.e. in-situ at runtime) and 3) ECSELR is able to embed adaptation inside systems that have already been started and are in the midst of execution. We demonstrate the use of ECSELR and present results on using the ECSELR framework to slim a software system.", notes = "Java byte code", } @Article{Yeboah-Antwi:2016:GPEM, author = "Kwaku Yeboah-Antwi and Benoit Baudry", title = "Online Genetic Improvement on the java virtual machine with {ECSELR}", journal = "Genetic Programming and Evolvable Machines", year = "2017", volume = "18", number = "1", month = mar, pages = "83--109", keywords = "genetic algorithms, genetic programming, Genetic Improvement, SBSE, JVM, Evolutionary computation, Artificial intelligence, Software engineering", ISSN = "1389-2576", URL = "https://hal.inria.fr/hal-01382964", hal_id = "hal-01382964", DOI = "doi:10.1007/s10710-016-9278-4", size = "27 pages", abstract = "Online Genetic Improvement embeds the ability to evolve and adapt inside a target software system enabling it to improve at runtime without any external dependencies or human intervention. We recently developed a general purpose tool enabling Online Genetic Improvement in software systems running on the java virtual machine. This tool, dubbed ECSELR, is embedded inside extant software systems at runtime, enabling such systems to self-improve and adapt autonomously online. We present this tool, describing its architecture and focusing on its design choices and possible uses.", notes = "DASBSE Featured in SIGEVOlution 9(3) 26 October 2016 http://www.sigevolution.org/issues/SIGEVOlution0903.pdf (sigevolution.org url fixed Jan 2018 broken again July 2024 use DOI https://doi.org/10.1145/3066862.3066869 INRIA, Campus de Beaulieu, 263 Avenue General Le Clerc, 35042 Rennes, France", } @InProceedings{Yeh:1997:masm, author = "Chia-Hsuan Yeh", title = "From Multi-Agent System to Macroeconomics: Applications of Genetic Programming", booktitle = "Late Breaking Papers at the 1997 Genetic Programming Conference", year = "1997", editor = "John R. Koza", pages = "303", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California, 94305-3079, USA", month = "13--16 " # jul, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-206995-8", notes = "GP-97LB PHD Students' workshop The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670 cobweb model", } @InProceedings{ChiaHsuanYeh:1998:GPlogm, author = "Chia-Hsuan Yeh", title = "Genetic Programming Learning and the Overlapping Generations Models", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "241 and 270", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "genetic algorithms, genetic programming", broken = "http://www.ai.mit.edu/people/unamay/phd-ws-abstracts/yeh.ps", size = "1+1 page", notes = "economic growth, taxes, budget deficit GP-98LB, GP-98PhD Student Workshop", } @InProceedings{Shu-HengChen2:2000:CEF, author = "Chia-Hsuan Yeh and Shu-Heng Chen", title = "Toward an integration of social learning and individual learning in agent-based computational stock markets:the approach based on population genetic programming", booktitle = "Computing in Economics and Finance", year = "2000", address = "Universitat Pompeu Fabra, Barcelona, Spain", month = "6-8 " # jul, keywords = "genetic algorithms, genetic programming, Evolutionary Computation, Agent-Based Modelling, Artificial Stock Market, Simulated Annealing", URL = "http://econpapers.repec.org/paper/scescecf0/338.htm", URL = "http://fmwww.bc.edu/cef00/papers/paper338.pdf", size = "31 pages", abstract = "Artificial stock market is a growing field in the past few years. The essence of this issue is the interaction between many heterogeneous agents. In order to model this complex adaptive system, the techniques of evolutionary computation have been employed. Chen and Yeh (2000) proposed a new architecture to construct the artificial stock market. This framework is composed of a single-population genetic programming (SGP) based adaptive agent with a SA (Simulated Annealing) learning process and a business school. However, one of the drawbacks of SGP-based framework is that the traders can't work out new ideas by themselves. The only way is to consult researchers in the business school. In order to make the traders more intelligent, we employ multi-population GP (MGP) based framework with the mechanism of school. This extension is not only reasonable, but also has the economic implications. How do the more intelligent agents influence the economy? Are the econometric properties of the simulation results based on MGP more like the phenomena found in the real stock market? In this paper, the comparison between SGP and MGP is studied from two sides. One is related to the micro-structure, traders? behaviour and believe. The other is macro-properties, the properties of time series. The line of research is helpful in understanding the foundation of economics and finance, and constructing more realistic economic models.", notes = "http://enginy.upf.es/SCE/index2.html 22 Aug 2004 updated from http://econpapers.hhs.se/paper/scescecf0/338.htm Chung-Chi Liao was listed as co-author due to confusion with \cite{RePEc:sce:scecf0:328} also in CEF 2001", } @InProceedings{ChiaHsuanYeh:2001:SCE, author = "Chia-Hsuan Yeh", title = "The Influence of Market Size in an Artificial Stock Market: The Approach Based on Genetic Programming", booktitle = "7th International Conference of Society of Computational Economics", year = "2001", address = "Yale", month = "28-29 " # jun, organisation = "Society for Computational Economics", keywords = "genetic algorithms, genetic programming", URL = "http://ideas.repec.org/p/sce/scecf1/74.html", abstract = "The relationship between competitiveness and market performance has been discussed for a long time. In a competitive economic environment, each firm or individual is unable to influence the market. It has been mentioned in the economics courses that the competitive market is more efficient and has higher social welfare. Therefore, it is the desirable picture economists intend to draw. The concept of competitiveness is related to market size, i.e., the number of market participants. The idea here is that the larger economy contributes to microeconomic heterogeneity, for example, behaviour and strategies, profitability and market shares, production technology and efficiency. The importance of economic diversity has been understood. It is a fundamental driving force and an essential property in the economic systems. People who have different perspectives about the future implies that there exits room for the economic activity and they may benefit from their trading behavior. In other words, the higher degree of heterogeneity may provide more opportunities for trading. It is also an important seed of innovation. In this paper, we try to study the influence of market size to market performance in term of market efficiency.", notes = "http://cowles.econ.yale.edu/conferences/2001/7intl.htm CEF 2001", } @Article{Yeh:2001:AISBj, author = "Chia-Hsuan Yeh and Shu-Heng Chen", title = "Market Diversity and Market Efficiency: The Approach Based on Genetic Programming", journal = "AISB Journal", year = "2001", volume = "1", number = "1", pages = "147--167", keywords = "genetic algorithms, genetic programming", URL = "http://www.aisb.org.uk/publications/aisbj/issues/AISBJ%201(1).pdf", abstract = "The relation between market diversity and market efficiency has been studied. Economic heterogeneity is a fundamental driving force and an essential property in the economic systems. People who have different perspectives, technologies, or endowments may benefit from their trading behaviour which constitutes economic activities. In this paper, economic simulation based on the growing field of artificial stock markets is employed to study this issue. Market size and different learning styles are used to discuss the influence of heterogeneity. Simulation results have demonstrated that more participants and individual learning cause higher degree of traders' diversity, which, in turn, enhances market efficiency.", notes = "Department of Information Management, Yuan Ze University, Chungli, Taoyuan 320, Taiwan, imcyeh@saturn.yzu.edu.tw Department of Economics, National Chengchi University, Taipei 11623, Taiwan, chchen@nccu.edu.tw", } @InCollection{yeh:2002:ECEF, author = "Chia-Hsuan Yeh and S.-H. Chen and C.-C. Liao", title = "On AIE-ASM: Software to Simulate Artificial Stock Markets with Genetic Programming", booktitle = "Evolutionary Computation in Economics and Finance", publisher = "Physica Verlag", year = "2002", editor = "Shu-Heng Chen", volume = "100", series = "Studies in Fuzziness and Soft Computing", chapter = "6", pages = "107--122", month = "2002", keywords = "genetic algorithms, genetic programming", ISBN = "3-7908-1476-8", DOI = "doi:10.1007/978-3-7908-1784-3_6", abstract = "Agent-based computational economic modelling requires demanding work on computer programming. Publications of agent-based computational economic modeling usually do not provide readers with adequate information to permit replication of the experiments reported in the papers. Such failure makes the findings from the agent-based simulations hard to verify and defies technical improvement. To facilitate the growth of this research area, it is necessary for authors to make their source codes available in a public domain. This paper is a documentation accompanying the software AIE-ASM, which is available on the website. The software is designed to simulate the agent-based artificial stock market based on a standard asset pricing model. Genetic programming, as part of the software, is used to drive the learning dynamics of traders. An example based on the version of single-population genetic programming is demonstrated in this paper.", } @InProceedings{Chia-HsuanYeh:2003:CINC, author = "Chia-Hsuan Yeh", title = "The Influence of GP's Representations in Financial Data Mining", booktitle = "Procceedings of the Sixth International Conference on Computational Intelligence and Natural Computing", year = "2003", address = "Embassy Suites Hotel and Conference Center, Cary, North Carolina USA", month = sep # " 26-30", keywords = "genetic algorithms, genetic programming", notes = "http://axon.cs.byu.edu/Dan/cinc03.html/index.html/ Broken Jan 2013 http://www.ee.duke.edu/JCIS/ Yuan Ze U., Taiwan", } @Article{Yeh2008613, author = "Chia-Hsuan Yeh", title = "The effects of intelligence on price discovery and market efficiency", journal = "Journal of Economic Behavior \& Organization", volume = "68", number = "3-4", pages = "613--625", year = "2008", month = dec, keywords = "genetic algorithms, genetic programming, Intelligence, Speculation, Artificial stock market, Agent-based modeling", ISSN = "0167-2681", broken = "http://www.sciencedirect.com/science/article/B6V8F-4SYTC56-1/2/ddc312455211b726d20cdcb236cfc8c2", DOI = "doi:10.1016/j.jebo.2008.07.002", size = "13 pages", abstract = "The influence of speculation on market performance has long been discussed. Under the framework of bounded rationality in which traders are endowed with different intelligence levels in terms of different learning styles or different representations of intelligence, we examine the effects of traders' intelligence on price discovery based on intraday data, and market efficiency. We find that intelligence does help improve market performance. However, the influence of different intelligence levels on the market crucially depends on the characteristics of learning styles or the representation of intelligence.", notes = "Department of Information Management, Yuan Ze University, Chungli, Taoyuan 320, Taiwan", } @Article{Yeh20102089, author = "Chia-Hsuan Yeh and Chun-Yi Yang", title = "Examining the effectiveness of price limits in an artificial stock market", journal = "Journal of Economic Dynamics and Control", volume = "34", number = "10", pages = "2089--2108", year = "2010", ISSN = "0165-1889", DOI = "doi:10.1016/j.jedc.2010.05.015", URL = "http://www.sciencedirect.com/science/article/B6V85-505NRX4-2/2/a4b2c4a32330a95dbb94f4074b20ca15", keywords = "genetic algorithms, genetic programming, Price limits, Artificial stock market, Agent-based modelling", abstract = "This paper proposes an agent-based framework to examine the effectiveness of price limits in an artificial stock market. The market is composed of many boundedly rational and heterogeneous traders whose learning behaviour is represented by a genetic programming algorithm. We calibrate the model to replicate several stylised facts observed in real financial markets. Based on this environment, the impacts of price limits are analysed from the perspectives of volatility, price distortion, volume, and welfare. We find that the imposition of price limits possesses both positive and negative effects. However, compared with the market without price limits, appropriate price limits help to reduce volatility and price distortion, and increase the liquidity and welfare.", } @InProceedings{Yeh:2012:CECa, title = "Can learning affect the effectiveness of price limits?", author = "Chia-Hsuan Yeh and Chun-Yi Yang", pages = "1094--1101", booktitle = "Proceedings of the 2012 IEEE Congress on Evolutionary Computation", year = "2012", editor = "Xiaodong Li", month = "10-15 " # jun, DOI = "doi:10.1109/CEC.2012.6256582", address = "Brisbane, Australia", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Computational Intelligence in Finance, Economics and Management Sciences (IEEE-CEC), Finance and economics", abstract = "This paper examines how different learning methods may affect the effectiveness of price limits. We consider three different learning styles: zero-intelligence (ZI), zero-intelligence-plus (ZIP), and genetic programming learning. Our results indicate that the different learning behaviour indeed gives rise to different impacts on the market. Therefore, policy makers have to take into account traders' learning styles when they plan to impose financial regulations on the market so as to achieve the effects they expected.", notes = "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the EPS and the IET.", } @Article{Yeh:2015:ieeeTEC, author = "Chia-Hsuan Yeh and Chun-Yi Yang", title = "Social Networks and Asset Price Dynamics", journal = "IEEE Transactions on Evolutionary Computation", year = "2015", volume = "19", number = "3", pages = "387--399", month = jun, keywords = "genetic algorithms, genetic programming, Social network, artificial stock market, agentbased modelling", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2014.2322121", size = "15 pages", abstract = "In this paper, we investigate how behavioural contagion in terms of mimetic strategy learning within a social network would affect the asset price dynamics. The characteristics of this paper are as follows. First, traders are characterised by bounded rationality and their adaptive learning behaviour is represented by the genetic programming algorithm. The use of the genetic programming algorithm allows traders to freely form forecasting strategies with great potential of variety in functional forms, which are not pre-determined but may be fundamental-like or technical-like or any mix of these two broad categories, as they need to adapt to the time-varying market environment. The evolutionary nature of the genetic programming algorithm has its merit for modeling mimetic behavior in the context of information transmission in that, other than making duplicates of an entire trading rule as if a mind reading technique exists, strategy imitation could take place down to the level of building blocks that genetic operators work out or pieces of information that constitute a strategy and are more ready to be transmitted via word-of-mouth communication, which is more intuitive compared to the existing literature. Second, the traders are spatially heterogeneous based on their positions in social networks. Mimetic learning thus takes part in local interactions among traders that are directly tied with each other when they evolve their trading strategies according to the relative performance of their own and their neighbours'. Therefore, specifically, we aim to analyse the effect of network topologies, i.e. a regular lattice, a small world, a random network, a fully connected network, and a preferential attachment network, on market dynamics regarding price distortion, volatility, and trading volume, as information diffuses across these different social network structures.", notes = "C.-H. Yeh is with the Department of Information Management, Yuan Ze University, Chungli, Taoyuan 320, Taiwan. Also known as \cite{6826570}", } @InCollection{yeh:1999:DBCNCPGP, author = "Iwei Yeh", title = "Diagnosis of Breast Cancer based on Nine Cytological Parameters using Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1999", year = "1999", editor = "John R. Koza", pages = "264--271", address = "Stanford, California, 94305-3079 USA", month = "15 " # mar, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:1999:GAGPs}", } @InProceedings{Yeh:2007:SIGIR, author = "Jen-Yuan Yeh and Jung-Yi Lin and Hao-Ren Ke and Wei-Pang Yang", title = "Learning to Rank for Information Retrieval Using Genetic Programming", booktitle = "SIGIR 2007 workshop: Learning to Rank for Information Retrieval", year = "2007", editor = "Thorsten Joachims and Hang Li and Tie-Yan Liu and ChengXiang Zhai", month = "27 " # jul, organisation = "Microsoft", keywords = "genetic algorithms, genetic programming, learning to rank for IR, ranking function, Information Storage and Retrieval, Information Search and Retrieval, Retrieval Models, Algorithms, Experimentation, Performance", URL = "http://jenyuan.yeh.googlepages.com/jyyeh-LR4IR07.pdf", size = "8 pages", abstract = "One central problem of information retrieval (IR) is to determine which documents are relevant and which are not to the user information need. This problem is practically handled by a ranking function which defines an ordering among documents according to their degree of relevance to the user query. This paper discusses work on using machine learning to automatically generate an effective ranking function for IR. This task is referred to as learning to rank for IR in the field. In this paper, a learning method, RankGP, is presented to address this task. RankGP employs genetic programming to learn a ranking function by combining various types of evidences in IR, including content features, structure features, and query-independent features. The proposed method is evaluated using the LETOR benchmark datasets and found to be competitive with Ranking SVM and RankBoost.", notes = "broken Jun 2023 https://research.microsoft.com/en-us/um/beijing/events/LR4IR-2007/", } @PhdThesis{Yeh:thesis, author = "Jen-Yuan Yeh", title = "A study on extraction-based multidocument summarization", school = "Dept. of Computer Science, National Chiao Tung University", year = "2008", type = "PhD Thesis", address = "Hsinchu, Taiwan", keywords = "multidocument summarisation, generic summary, query-focused summary, sentence ranking, sentence extraction, redundancy filtering", URL = "http://ir.lib.nctu.edu.tw/handle/987654321/75053", size = "145 pages", notes = "In English. http://www.cs.nctu.edu.tw/~jenyuan/publist.html Advisors: Dr. Wei-Pang Yang & Dr. Hao-Ren Ke. Not about genetic programming Fri, Feb 6, 2009 at 3:09 PM See also: http://www.sciencedirect.com/science?_ob=PublicationURL&_tockey=%23TOC%235635%232008%23999649996%23692831%23FLA%23&_cdi=5635&_pubType=J&_auth=y&_acct=C000010000&_version=1&_urlVersion=0&_userid=121727&md5=f73788b2d49fa977ce00b9efb6f61aae Yeh, Jen-Yuan, Ke, Hao-Ren, & Yang, Wei-Pang (2008). iSpreadRank: Ranking sentences for extraction-based summarization using feature weight propagation in the sentence similarity network. Expert Systems with Applications, 35(3), 1451-1462.", } @InProceedings{yen:1999:ASSAMMO, author = "John Yen and Linyu Yang and Bogju Lee and James C. Liao", title = "A Supervisory Simplex-GA Approach for Metabolic Model Optimization", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "750--757", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{conf/jcis/YenCH06, author = "Meng-Feng Yen and Tsung-Nan Chou and Ying-Yue Ho", title = "Inter-Commodity Spread Trading Using Neural Network and Genetic Programming Techniques", booktitle = "Proceedings of the 9th Joint Conference on Information Sciences (JCIS)", year = "2006", editor = "Heng-Da Cheng and Shu-Heng Chen and Ren-Yih Lin", publisher = "Atlantis Press", keywords = "genetic algorithms, genetic programming, BPNN, Inter-Commodity Spread, Momentum Strategy", ISBN = "90-78677-01-5", URL = "http://www.atlantis-press.com/publications/aisr/jcis-06/conf_jcis_YenCH06.pdf", DOI = "doi:10.2991/jcis.2006.165", size = "4 pages", abstract = "We employ the methods of neural network (hereafter NN) and genetic programming (hereafter GP) in this paper to construct a spread trading system, respectively, to forecast the trend of the price spread between Taiwan Stock Exchange Electronic Index Futures (hereafter TE) and Taiwan Stock Exchange Finance Sector Index Futures (hereafter TF). To forecast the trend of the spread, we use a variety of technical indicators as the inputs to our two models. We tend to long one contract and short another if the next-day return of the former is predicted to be larger than the latter. If the spread trend is predicted to change its direction, we close off the position and open a new position completely contrary to the closed one. We compare the trading performances of this momentum strategy to the day trade strategy, i.e. closing off our positions before the market close ever day. We find that the momentum strategy tends to outperform the day trade strategy and that the BPNN model is superior to the GP model under both strategies whilst both are profitable.", notes = " http://www.atlantis-press.com/publications/aisr/jcis-06/ c Atlantis Press. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited.", bibdate = "2007-06-08", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/jcis/jcis2006.html#YenCH06", } @Article{YunSeogYeun:1999:AIE, author = "Yun Seog Yeun and K. H. Lee and Y. S. Yang", title = "Function approximations by coupling neural networks and genetic programming trees with oblique decision trees", journal = "Artificial Intelligence in Engineering", year = "1999", volume = "13", number = "3", pages = "223--239", month = jul, email = "yeonyun@road.daejin.ac.kr", keywords = "genetic algorithms, genetic programming, Federated agents, Oblique decision tree, OC1", URL = "http://members.kr.inter.net/yyshuj/paper/aie.zip", ISSN = "0954-1810", DOI = "doi:10.1016/S0954-1810(98)00015-6", URL = "http://www.sciencedirect.com/science/article/B6V1X-3WWT8F6-3/1/2e564ef70743de81b8e3369fb01b406e", abstract = "This paper is concerning the development of multiple neural networks system combined with genetic programming (GP) trees for problem domains where the complete input space can be decomposed into several different regions, and these are well represented in form of oblique decision tree. The overall architecture of hybrid system, called the federated agents, consists of a facilitator, local agents, and boundary agents. Neural networks used as local agents, each of which is expert at different subregions, and GP trees serve as boundary agents. A boundary agent refer to the one that is specialized at only the borders of subregions where discontinuities or different patterns may exist. The facilitator is responsible for choosing the local agent that is suitable for the given input data using information obtained from oblique decision tree representing a divided input space. However, there are large possibility of selecting the invalid local agent due to the incorrect prediction of decision tree, provided that input data is close enough to the boundaries of regions. Such a situation can lead federated agents to produce a much higher prediction error than that of a single neural network trained over all input space. To deal with this, the approach taken in this paper is to make the facilitator select the boundary agent instead of the local agent when input data is closely located to the certain border of regions. In this way, even if the result of decision tree may be incorrect, the results of system are less affected by it. The validity of our approach is examined and verified by applying the federated agents to the configuration design of a midship section of bulk cargo ships.", size = "17 pages", notes = "Linear associative memories [Kohonen,1988] set numerical parameters in GP trees with overfitting avoidance. Training set partitioned using {"}domain knowledge or clustering methods{"} p255. Separate ANN trained on each subset. ", } @Article{YunSeogYeun:2001:IS, author = "Yun Seog Yeun and Jun Chen Suh and Young-Soon Yang", title = "Function approximations by superimposing genetic programming trees:with applications to engineering problems", journal = "Information Sciences", year = "2000", volume = "122", number = "2-4", pages = "259--280", email = "yeonyun@road.daejin.ac.kr", keywords = "genetic algorithms, genetic programming, Function approximation, Linear associative memory, Group of additive genetic programming tree", URL = "http://members.kr.inter.net/yyshuj/paper/gagpt.zip", URL = "http://www.elsevier.com/gej-ng/10/23/143/56/27/34/article.pdf", DOI = "doi:10.1016/S0020-0255(99)00121-8", abstract = "This paper concerns fundamental issues regarding genetic programming (GP) as a tool for real-valued function approximations. Standard GP suffers from the lack of estimation techniques for numerical parameters of a functional tree. Unlike other research activities, where non-linear optimization techniques are employed, we adopt the use of a linear associative memory for the estimation of these parameters under the GP algorithm. Instead of dealing with a large associative matrix, we present the method of building several associative matrixes in small size, each of which is responsible for determining the value for different small portions of the whole parameter. This approach can significantly reduce computational cost, and a reasonably accurate value for parameters can be obtained. Due to the fact that the GP algorithm is likely to fall into a local minimum, the GP algorithm often fails to generate the functional tree with the desired accuracy. This motivates us to devise a group of additive genetic programming trees(GAGPT) which consists of a primary tree and a set of auxiliary trees. The output of the GAGPT is the summation of outputs of the primary tree and all auxiliary trees. The addition of auxiliary trees makes it possible to improve both the learning and generalization capability of the GAGPT, since the auxiliary tree evolves toward refining the quality of the GAGPT by optimizing its fitness function. The effectiveness of our approach is verified by applying the GAGPT to the estimation of the principal dimensions of a bulk cargo ship and engine torque of a passenger car.", notes = "Information Sciences http://www.elsevier.com/inca/publications/store/5/0/5/7/3/0/505730.pub.htt", } @Article{Yun01, author = "Yun Seog Yeun and Kyung Ho Lee and Sang Min Han and Young Soon Yang", title = "Smooth Fitting with a Method for Determining the Regularization Parameter under the Genetic Programming Algorithm", journal = "Information Sciences", year = "2001", volume = "133", number = "3-4", pages = "175--194", month = apr, email = "yeonyun@road.daejin.ac.kr", keywords = "genetic algorithms, genetic programming, Smooth fitting, Regularization parameter", DOI = "doi:10.1016/S0020-0255(01)00084-6", abstract = "This paper deals with the smooth fitting problem under the genetic programming(GP) algorithm. To reduce the computational cost required for evaluating the fitness value of GP trees, numerical weights of GP trees are estimated by adopting both linear associative memories and the Hook & Jeeves method. The quality of smooth fitting is critically dependent on the choice of the regularization parameter. So, we present a novel method for choosing the regularization parameter. Two numerical examples are given with the comparison of generalized cross-validation B-splines", notes = "Euclidean norm = zero-order Tikhonov regularisation, is not sufficient p178 uses(?) weighting based on first derivative of evolved function but too CPU expensive(?). LAM HJ discrepancy principle DP cross-validation CV composite residual and smoothing operator CRESO L-curve zero crossing ZC considered in text but use heuristic two test functions 2*(sin(t))**4 1.5*(exp(-30*(t-0.25)**2)+sin(pi(t-0.2))**2) {"}..sometimes the GP tree that is discarded by the criterion proposed in this paper is far better than the tree selected as the best one.{"} p192 Information Sciences http://www.elsevier.com/inca/publications/store/5/0/5/7/3/0/505730.pub.htt", } @Article{yeun_2004_tec, author = "Yun-Seog Yeun and Won-Sun Ruy and Young-Soon Yang and Nam-Joon Kim", title = "Implementing Linear Models in Genetic Programming", journal = "IEEE Transactions on Evolutionary Computation", year = "2004", volume = "8", number = "6", pages = "542--566", month = dec, keywords = "genetic algorithms, genetic programming, Directional derivative-based smoothing (DDBS), linear model, minimum description length (MDL) principle, polynomial, symbolic processing", URL = "http://members.kr.inter.net/yyshuj/paper/pre-lm-gp.pdf", DOI = "doi:10.1109/TEVC.2004.836818", size = "25 pages", abstract = "We deal with linear models of genetic programming (GP) for regression or approximation problems when given learning samples are not sufficient. The linear model, which is a function of unknown parameters, is built through extracting all possible base functions from the standard GP tree by a symbolic processing algorithm. The major advantage of a linear model in GP is that its parameters can be estimated by the ordinary least square (OLS) method and a good model can be selected by applying the modern minimum description length (MDL) principle, while the nonlinearity necessary to handle the given problem is effectively maintained by indirectly evolving and finding various forms of base functions. In addition to a standard linear model consisting of mathematical functions, one variant of a linear model, which can be built using low-order Taylor series and can be converted into the standard form of a polynomial, is considered in this paper. With small samples, GP frequently shows the abnormal behaviors such as extreme large peaks or odd-looking discontinuities at the points away from sample points. To overcome this problem, a directional derivative-based smoothing (DDBS) method, which is incorporated into the OLS method, is introduced together with the fitness function that is based on MDL, reflecting the effects of DDBS. Also, two illustrative examples and three engineering applications are presented.", } @Article{yeun_2005_SMO, author = "Y. S. Yeun and Y. S. Yang and W. S. Ruy and B. J. Kim", title = "Polynomial genetic programming for response surface modeling Part 1: a methodology", journal = "Structural and Multidisciplinary Optimization", year = "2005", volume = "29", number = "1", pages = "19--34", month = jan, keywords = "genetic algorithms, genetic programming, directional derivative-based smoothing, extended data-set method, high-order polynomial, interpolation, overfitting,response surface", ISSN = "1615-147X", DOI = "doi:10.1007/s00158-004-0460-6", abstract = "The second-order polynomial is commonly used for fitting a response surface but the low-order polynomial is not sufficient if the response surface is highly nonlinear. Based on genetic programming (GP), this paper presents a method with which high-order smooth polynomials, which can model nonlinear response surfaces, can be built. Since in many cases small samples are used to fit the response surface, it is inevitable that the high-order polynomial shows serious overfitting behaviors. Moreover, the high-order polynomial shows infamous wiggling, unwanted oscillations, and large peaks. To suppress such problematic behaviors, this paper introduces a novel method, called directional derivative-based smoothing (DDBS) that is very effective for smoothing a high-order polynomial. The role of GP is to find appropriate terms of a polynomial through the application of genetic operators to GP trees that represent polynomials. The GP tree is transformed into the standard form of a polynomial using the translation algorithm. To estimate the coefficients of the polynomial quickly the ordinary least-square (OLS) method that incorporates the DDBS and extended data-set method is devised. Also, by using the classical Lagrange multiplier method, the modified OLS method enabling interpolation is presented. Four illustrative numerical examples are given to demonstrate the performance of GP with DDBS.", } @Article{yeun_2005_SMO2, author = "Y. S. Yeun and B. J. Kim and Y. S. Yang and W. S. Ruy", title = "Polynomial genetic programming for response surface modeling Part 2: adaptive approximate models with probabilistic optimization problems", journal = "Structural and Multidisciplinary Optimization", year = "2005", volume = "29", number = "1", pages = "35--49", month = jan, keywords = "genetic algorithms, genetic programming, partial interpolation strategy, polynomial genetic programming, reliability-based optimization, response surface method", DOI = "doi:10.1007/s00158-004-0461-5", abstract = "This is the second in a series of papers. The first deals with polynomial genetic programming (PGP) adopting the directional derivative-based smoothing (DDBS) method, while in this paper, an adaptive approximate model (AAM) based on PGP is presented with the partial interpolation strategy (PIS). The AAM is sequentially modified in such a way that the quality of fitting in the region of interest where an optimum point may exist can be gradually enhanced, and accordingly the size of the learning set is gradually enlarged. If the AAM uses a smooth high-order polynomial with an interpolative capability, it becomes more and more difficult for PGP to obtain smooth polynomials, whose size should be larger than or equal to the number of the samples, because the order of the polynomial becomes unnecessarily high according to the increase in its size. The PIS can avoid this problem by selecting samples belonging to the region of interest and interpolating only those samples. Other samples are treated as elements of the extended data set (EDS). Also, the PGP system adopts a multiple-population approach in order to simultaneously handle several constraints. The PGP system with the variable-fidelity response surface method is applied to reliability-based optimization (RBO) problems in order to significantly cut the high computational cost of RBO. The AAMs based on PGP are responsible for fitting probabilistic constraints and the cost function while the variable-fidelity response surface method is responsible for fitting limit state equations. Three numerical examples are presented to show the performance of the AAM based on PGP.", } @Article{Yi20113658, author = "Li Yi and Kang Wanli", title = "A New Genetic Programming Algorithm for Building Decision Tree", journal = "Procedia Engineering", volume = "15", pages = "3658--3662", year = "2011", note = "CEIS 2011", keywords = "genetic algorithms, genetic programming, Decision tree, Grouping, Representation", ISSN = "1877-7058", URL = "http://www.sciencedirect.com/science/article/pii/S1877705811021862", DOI = "doi:10.1016/j.proeng.2011.08.685", size = "5 pages", abstract = "Genetic programming (GP) is a flexible and powerful evolutionary technique with some special features that are suitable for building a classifier of tree representation. However, unsuitable step size of editing operator will destroy the continuity of the evolution. In this paper, we propose a multiage genetic programming (MGP) algorithm to build a classifier on a given training set. Individuals are grouped into different groups according to their ages (tree size). The competitions between individuals are limited in the same groups. That prevents the structure editing operators from destroying the continuity of the evolution. The experimental results showed that the MGP algorithm is superior to the traditional genetic programming algorithm (GP) in building decision tree.", notes = "ShiJiaZhuang Vocational Technology Institute, ShiJiaZhuang, 050000, China", } @Article{YIFAN:2021:PR, author = "Liang Yi-Fan and Liu Chang and Wang Han-Rui and Liu Kun-Hong and Yao Jun-Feng and She Ying-Ying and Dai Gui-Ming and Yuna Okina", title = "A novel error-correcting output codes based on genetic programming and ternary digit operators", journal = "Pattern Recognition", volume = "110", pages = "107642", year = "2021", ISSN = "0031-3203", DOI = "doi:10.1016/j.patcog.2020.107642", URL = "https://www.sciencedirect.com/science/article/pii/S0031320320304453", keywords = "genetic algorithms, genetic programming, Error-correcting output code, Ternary digit operator, Feature selection", abstract = "The key to the success of an Error-Correcting Output Code (ECOC) algorithm is the effective codematrix, which represents a set of class reassignment schemes for decomposing a multiclass problem into a set of binary class problems. This paper proposes a new method, which uses Ternary digit Operators based Genetic Programming (GP) to generate effective ECOC codematrix (TOGP-ECOC for short). In our GP, each terminal node stores a ternary digit string, representing a column and a related feature subset; each non-terminal node represents a ternary digit operator, which produces a new column based on its child nodes. In this way, each individual is interpreted as an ECOC codematrix along with a set of corresponding feature subsets, serving the solution for the multiclass classification task. When a new individual is produced, a legality checking process is carried out to verify whether the transformed codematrix follows the ECOC constraints. The illegal one is corrected according to different strategies. Besides, a local optimization algorithm is designed to prune redundant columns and improve the performance of each individual. Our experiments compared TOGP-ECOC with some well known ECOC algorithms on various data sets, and the results confirm the superiority of our algorithm. Our source code is available at: https://github.com/MLDMXM2017/TOGP-ECOC", } @InProceedings{Yilmaz:2019:evoapplications, author = "Selim Yilmaz and Sevil Sen", title = "Early Detection of Botnet Activities Using Grammatical Evolution", booktitle = "22nd International Conference, EvoApplications 2019", year = "2019", month = "24-26 " # apr, editor = "Paul Kaufmann and Pedro A. Castillo", series = "LNCS", volume = "11454", publisher = "Springer Verlag", address = "Leipzig, Germany", pages = "395--404", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Grammatical evolution, Botnet, Flow-based detection, Evolutionary computation", isbn13 = "978-3-030-16691-5", URL = "https://web.cs.hacettepe.edu.tr/~ssen/files/papers/EvoStar19-2.pdf", DOI = "doi:10.1007/978-3-030-16692-2_26", size = "10 pages", abstract = "There have been numerous studies proposed for detecting botnets in the literature. However, it is still a challenging issue as most of the proposed systems are unable to detect botnets in their early stage and they cannot perform satisfying performance on new forms of botnets. In this study, we propose an evolutionary computation-based approach that relies on grammatical evolution to generate a botnet detection algorithm automatically. The performance of the proposed flow-based detection system reveals that it detects botnets accurately in their very early stage and performs better than most of the existing methods.", notes = "http://www.evostar.org/2019/cfp_evoapps.php EuroGP'2019 held in conjunction with EvoCOP2019, EvoMusArt2019 and EvoApplications2019", } @InProceedings{Yilmaz:2024:IDAP, author = "Selim Yilmaz", title = "Towards Development of Memory-Efficient Intrusion Detection System Against {RPL} Attacks", booktitle = "2024 8th International Artificial Intelligence and Data Processing Symposium (IDAP)", year = "2024", month = sep, keywords = "genetic algorithms, genetic programming, Accuracy, Memory management, Random access memory, Intrusion detection, Evolutionary computation, Routing protocols, Read only memory, Optimisation, Sorting, RPL, multi-objective, NSGA, SPEA", DOI = "doi:10.1109/IDAP64064.2024.10710741", abstract = "Due to vulnerability of IPv6 Routing Protocol for Low Power Lossy Networks (RPL) to various resource-targeting attacks, there is ongoing effort to develop intrusion detection programs. However, most of these algorithms prioritize detection accuracy over efficiency. This study aims to address both detection performance and memory requirements in developing an intrusion detection system against blackhole, DAG inconsistency, and decreased rank attacks. To this end, non-dominated sorting genetic algorithm and strength pareto evolutionary algorithm, commonly used multi-objective approaches in the literature, are integrated into the genetic programming to decrease read-only memory (ROM), random access memory (RAM) consumption while enhancing detection accuracy. The results demonstrate a significant reduction in memory consumption while maintaining, or even improving, detection accuracy.", notes = "Also known as \cite{10710741}", } @Article{YILMAZ:2025:comnet, author = "Selim Yilmaz", title = "Enhancing {IoT} security: A competitive coevolutionary strategy for detecting {RPL} attacks in challenging attack environments", journal = "Computer Networks", year = "2025", volume = "262", pages = "111185", keywords = "genetic algorithms, genetic programming, RPL, RPL attacks, Intrusion detection, Competitive coevolution", ISSN = "1389-1286", URL = "https://www.sciencedirect.com/science/article/pii/S1389128625001537", DOI = "doi:10.1016/j.comnet.2025.111185", abstract = "Internet of Things (IoT) is a recent technology that allows heterogeneous devices to communicate with each other and the Internet. Designed specifically for IoT-enabled networks, the IPv6 Routing Protocol for Low Power Lossy Network (RPL) is adopted as standard routing protocol today. While RPL facilitates efficient routing between IoT devices, it is very susceptible to attacks, leading to numerous threats targeting different aspects of the nodes and network. Consequently, several efforts have been made to develop intrusion detection systems to secure RPL-operated networks. However, many existing solutions are tailored to specific attacks, making them unsuitable for other RPL attacks. Additionally, they depend on fixed simulations with specific scenarios, neglecting the influence of attack environments on detection system performance. The impact of RPL attacks varies with factors such as attacker density and position in the network. Consequently, it is crucial to design IDS that can effectively handle these dynamic conditions. This study addresses these challenges by proposing a competitive coevolution-based intrusion detection system that focuses on the most challenging attack environments. To achieve this, the intrusion detection algorithm and challenging attack environments are competitively evolved. Targeting the network's topology, traffic, and resources through the exploitation of control packets, this study investigates 11 RPL attacks: blackhole, DIS flooding, DAG inconsistency, DAO inconsistency, decreased rank, energy depletion, forwarding misbehaviour, increased version, spam DIS, selective forwarding, and worst parent. To assess detection performance, a wide range of evaluation metrics such as accuracy, precision, recall, false alarm rate, and F1-score are used. The findings demonstrate that the proposed system ensures strong detection performance with very low memory and power consumption, suggesting its effectiveness against the attacks threatening the multiple aspects of the network and its applicability on resource-constrained nodes", } @InProceedings{Yim:2012:ICCAS, author = "Hyungu Yim and DaeEun Kim", booktitle = "12th International Conference on Control, Automation and Systems (ICCAS 2012)", title = "Evolving internal memory strategies for the woods problems", year = "2012", pages = "366--369", keywords = "genetic algorithms, genetic programming, finite state machines, mobile robots, GP-automata controllers, behaviour performance, finite state automata, finite state machine, hidden state problems, internal memory strategies, memory states, mobile robots, perceptual aliasing problems, purely reactive systems, robotics researches, sensor states, woods problems, Automata, Biological cells, Educational institutions, Evolutionary computation, Position measurement, Robot sensing systems, Evolutionary computation, Finite State Machine, GP-automata, Perceptual aliasing, Woods Problem", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6393463", size = "4 pages", abstract = "Purely reactive systems have been used in many robotics researches. However, they have difficulty in solving the hidden state problems. Internal memory has been used to solve the hidden state problems, which is also called the perceptual aliasing problems. Woods problem is one of the perceptual aliasing problems. In this paper, we apply two methods, Finite State Machine and GP-automata controllers, to solve the Woods problem. These two methods are compared in terms of the behaviour performance of the agents with internal memory and sensor states. The performance of each method in the Woods problem is measured by the average number of time steps needed to reach a goal position from all possible initial positions. The analysis of the memory shows that both memory states and sensor states affect the behaviour performance of the agent.", notes = "Also known as \cite{6393463}", } @InProceedings{Yimyam:2012:ROBIO, author = "Panitnat Yimyam and Adrian F. Clark", booktitle = "IEEE International Conference on Robotics and Biomimetics (ROBIO 2012)", title = "Agricultural produce grading by computer vision using Genetic Programming", year = "2012", month = "11-14 " # dec, address = "Guangzhou", pages = "458--463", keywords = "genetic algorithms, genetic programming, agriculture, computer vision, crops, image classification, image colour analysis, image segmentation, image texture, inspection, learning (artificial intelligence), shape recognition, agricultural produce grading, apple variety discrimination, barley classification, colour feature, feature classification, feature segmentation, generic component, machine learning, mango surface inspection, maturity evaluation, purple sticky rice grading, shape feature, task-specific computer vision system, texture feature, wheat classification", isbn13 = "978-1-4673-2125-9", DOI = "doi:10.1109/ROBIO.2012.6491009", size = "6 pages", abstract = "An approach to generating task-specific computer vision systems from generic components using machine learning is presented. With this system, it is possible to learn both feature segmentation and classification from training data. This approach is applied to a disparate range of problems in the domain of agricultural produce grading: mango surface inspection and maturity evaluation, apple variety discrimination, wheat and barley classification and purple sticky rice grading. It is shown that shape, colour and texture features together produce more accurate classification results than fewer categories of feature, and that these evolved classifiers are competitive with neural networks and support vector machines.", notes = "Also known as \cite{6491009}", } @InProceedings{Yimyam:2013:ICECCO, author = "Panitnat Yimyam and Adrian F. Clark", title = "Adding new features and classes to classifiers evolved using genetic programming", booktitle = "International Conference on Electronics, Computer and Computation (ICECCO 2013)", year = "2013", month = "7-9 " # nov, pages = "224--227", keywords = "genetic algorithms, genetic programming, Adding new features and classes, Classification, Agricultural grading", DOI = "doi:10.1109/ICECCO.2013.6718269", abstract = "This paper considers the need to re-train a multiclass classifier that has initially been evolved using genetic programming to accommodate new features or new classes. For the former, the new feature is incorporated into a program by mutation; after that, a program that performs classification using all the features is obtained by evolution. For the latter, a binary classifier is evolved that is able to distinguish the new class from all existing classes is evolved, and it is executed before the existing classification programs. The two approaches are demonstrated on a range of classification problems drawn from the general area of produce grading and the results demonstrate the effectiveness of the proposed approach, in terms of both computational speed and classification performance.", notes = "Also known as \cite{6718269}", } @PhdThesis{201412Panitnat_Yimyam, author = "Panitnat Yimyam", title = "Agricultural Produce Grading by Computer Vision Based on Genetic Programming", school = "computer science and electronic engineering, Essex University", year = "2015", address = "UK", month = may, keywords = "genetic algorithms, genetic programming, Jasmine", URL = "https://www.essex.ac.uk/csee/news_and_seminars/newsEvent.aspx?e_id=7344", URL = "http://www.bmva.org/theses:2014", URL = "http://www.bmva.org/theses:201412panitnat_yimyam", URL = "http://www.bmva.org/thesis-archive/2014/2014-yimyam.pdf", size = "256 pages", abstract = "An objective of computer vision is to imitate the ability of the human visual system. Computer vision has been put forward to produce a wide range of applications. Most vision software does not proceed alone; machine learning is usually involved in many vision systems. Some vision systems are developed to replace human working because they operate more reliably, precisely and speedily, and some tasks are dangerous for humans. This thesis presents contributions to extend a vision system based on genetic programming to solve classification problems. Instances in the field of agricultural produce are employed to verify the system performance. A new method is proposed to determine the shape and appearance of reconstructed 3D objects. The reconstruction is based on using 2D images taken by a few cameras in arbitrary positions. Furthermore, new techniques are presented to extract properties of 3D objects; morphological, coloured and textural features. New techniques are proposed to incorporate new features and new classes of samples into a GP classifier. For the former, the new feature is accommodated into an existing solution by mutation. For the latter, as generating a multi-class classifier is based on a binary decomposition approach, a binary classifier of the new class is produced and executed before the series of the original binary classifiers. Both cases are intended to be done with less computation than evolving a new classifier from scratch.", } @InProceedings{Yimyam:2016:KST, author = "Panitnat Yimyam and Adrian F. Clark", booktitle = "2016 8th International Conference on Knowledge and Smart Technology (KST)", title = "{3D} reconstruction and feature extraction for agricultural produce grading", year = "2016", pages = "136--141", abstract = "This paper examines the grading of agricultural produce from multiple images using colour and texture properties. Some types of agricultural produce need to be inspected from multiple views in order to assess the entire appearance; however, using multiple images may obtain redundant data. Therefore, techniques are presented to reconstruct a 3D object, create new images without duplicated object areas and extract colour and texture features for evaluation. The performance of using multiple view images without duplicated object regions is compared with those of using only top-view images and the original multiple view images. Experiments are performed on apple and guava grading using kNN, NN, SVM and GP for classification. Performance differences from the different image sets are compared using McNemar's test and the Friedman test. It is found that the performance when using multiple view images is superior to that when using single-view images for all experiments. Employing features extracted from multiple view images without object area duplication achieves significantly higher accuracy than employing the original multiple view images for apple grading, but their performances do not differ significantly for guava inspection.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/KST.2016.7440482", month = feb, notes = "Science and Social Sciences, Burapha University Sakaeo Campus, Sakaeo, Thailand Also known as \cite{7440482}", } @InProceedings{Yin:2020:ICFD, author = "Bo Yin and Yu Guan and Stephane Redonnet and Vikrant Gupta and Larry K. B. Li", title = "Genetic programming control of a laminar premixed combustor", booktitle = "The 17th International Conference on Flow Dynamics, ICFD2020", year = "2020", address = "Virtual, Japan", month = "28-30 " # oct, keywords = "genetic algorithms, genetic programming", language = "English", oai = "oai:repository.ust.hk:1783.1-107431", URL = "http://repository.ust.hk/ir/Record/1783.1-107431", broken = "http://lbdiscover.ust.hk/uresolver?url_ver=Z39.88-2004\&rft_val_fmt=info:ofi/fmt:kev:mtx:journal\&rfr_id=info:sid/HKUST:SPI\&rft.genre=article\&rft.issn=\&rft.volume=\&rft.issue=\&rft.date=2020\&rft.spage=\&rft.aulast=Yin\&rft.aufirst=Bo\&rft.atitle=Genetic+programming+control+of+a+laminar+premixed+combustor\&rft.title =", URL = "http://hdl.handle.net/1783.1/107431", } @InProceedings{Yin:2020:APS, author = "Bo Yin and Yu Guan and Stephane Redonnet and Vikrant Gupta and Larry K. B. Li", title = "Suppression of self-excited thermoacoustic oscillations using genetic programming", booktitle = "73rd Annual Meeting of the APS Division of Fluid Dynamics", year = "2020", volume = "65", number = "13", pages = "E04.00001", address = "Virtual", month = nov # " 22-24", publisher = "Bulletin of the American Physical Society", keywords = "genetic algorithms, genetic programming", bibsource = "OAI-PMH server at repository.ust.hk", language = "English", oai = "oai:repository.ust.hk:1783.1-107430", URL = "https://meetings.aps.org/Meeting/DFD20/Session/E04.1", URL = "http://absimage.aps.org/image/DFD20/MWS_DFD20-2020-000312.pdf", URL = "http://hdl.handle.net/1783.1/107430", URL = "http://repository.ust.hk/ir/Record/1783.1-107430", broken = "http://lbdiscover.ust.hk/uresolver?url_ver=Z39.88-2004\&rft_val_fmt=info:ofi/fmt:kev:mtx:journal\&rfr_id=info:sid/HKUST:SPI\&rft.genre=article\&rft.issn=\&rft.volume=\&rft.issue=\&rft.date=2020\&rft.spage=\&rft.aulast=Yin\&rft.aufirst=Bo\&rft.atitle=Suppression+of+self-excited+thermoacoustic+oscillations+using+genetic+programming\&rft.title =", abstract = "Genetic programming (GP) is a powerful tool for unsupervised data-driven discovery of closed-loop control laws. In fluid mechanics, it has been used for various purposes, such as to enhance mixing in a turbulent shear layer and to delay flow separation. This model-free control framework is well suited for such complex tasks as it exploits an evolutionary mechanism to propagate the genetics of high-performing control laws from one generation to the next. Here we combine automated experiments with GP to discover model-free control laws for the suppression of self-excited thermoacoustic oscillations in a Rijke tube. Using a GP-based controller linked to a single sensor (a microphone) and a single actuator (a loudspeaker), we rank the performance of all the control laws in a given generation based on a cost function that accounts for the pressure amplitude and the actuation effort. We use a tournament process to breed further generations of control laws, and then benchmark them against conventional periodic forcing optimised via open-loop mapping. We find that, with only minimal input from the user, this GP-based control framework can identify new feedback actuation mechanisms, providing improved control laws for the suppression of self-excited thermoacoustic oscillations.", } @InProceedings{conf/icnc/YinTHH05, title = "Applying Genetic Programming to Evolve Learned Rules for Network Anomaly Detection", author = "Chuanhuan Yin and Shengfeng Tian and Houkuan Huang and Jun He", year = "2005", pages = "323--331", editor = "Lipo Wang and Ke Chen and Yew-Soon Ong", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3612", booktitle = "Advances in Natural Computation, First International Conference, ICNC 2005, Proceedings, Part III", address = "Changsha, China", month = aug # " 27-29", bibdate = "2005-08-01", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icnc/icnc2005-3.html#YinTHH05", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-28320-X", DOI = "doi:10.1007/11539902_38", size = "9 pages", abstract = "The DARPA/MIT Lincoln Laboratory off-line intrusion detection evaluation data set is the most widely used public benchmark for testing intrusion detection systems. But the presence of simulation artifacts attributes would cause many attacks in this dataset to be easily detected. In order to eliminate their influence on intrusion detection, we simply omit these attributes in the processes of both training and testing. We also present a GP-based rule learning approach for detecting attacks on network. GP is used to evolve new rules from the initial learned rules through genetic operations. Our results show that GP-based rule learning approach outperforms the original rule learning algorithm, detecting 84 of 148 attacks at 100 false alarms despite the absence of several simulation artifacts attributes.", } @Article{Yin:2024:autcon, author = "Jing Yin and Huanhuan Wang and Jiahao Li and Zhitian Zhang and Shunyao Cai and Wenjun Liu", title = "Dispatching rule design for tower crane scheduling in prefabricated construction via genetic programming", journal = "Automation in Construction", year = "2024", volume = "165", pages = "105588", keywords = "genetic algorithms, genetic programming, Dynamic tower crane scheduling, Dispatching rules, Automatic heuristic generation", ISSN = "0926-5805", URL = "https://www.sciencedirect.com/science/article/pii/S0926580524003248", DOI = "doi:10.1016/j.autcon.2024.105588", abstract = "The construction industry is witnessing a shift from traditional cast-in-situ construction to prefabricated construction, aimed at enhancing efficiency and sustainability. However, challenges arise in the transportation and assembly of large precast components. Tower cranes play a pivotal role in these operations, and optimising their service schedule is vital for resource use and project timelines. Traditional tower crane scheduling problems have primarily focused on static scenarios, overlooking the dynamic and uncertain nature of prefabricated construction projects. This paper addresses the complexities of dynamic tower crane scheduling within prefabricated construction using Genetic Programming (GP) to automatically generate Dispatching Rules. Various objective functions were investigated including maximum completion time, tardiness and the number of tardy tasks. The experimental results reveal that GP-generated rules generally outperform benchmark rules, in terms of the best average performance and the discreteness of results. Furthermore, a practical case study validates GP-generated rules, demonstrating superior real-world performance", } @Article{yin:2019:Entropy, author = "Shibai Yin and Yibin Wang and Yee-Hong Yang", title = "A Novel Residual Dense Pyramid Network for Image Dehazing", journal = "Entropy", year = "2019", volume = "21", number = "11", keywords = "genetic algorithms, genetic programming", ISSN = "1099-4300", URL = "https://www.mdpi.com/1099-4300/21/11/1123", DOI = "doi:10.3390/e21111123", abstract = "Recently, convolutional neural network (CNN) based on the encoder-decoder structure have been successfully applied to image dehazing. However, these CNN based dehazing methods have two limitations: First, these dehazing models are large in size with enormous parameters, which not only consumes much GPU memory, but also is hard to train from scratch. Second, these models, which ignore the structural information at different resolutions of intermediate layers, cannot capture informative texture and edge information for dehazing by stacking more layers. In this paper, we propose a light-weight end-to-end network named the residual dense pyramid network (RDPN) to address the above problems. To exploit the structural information at different resolutions of intermediate layers fully, a new residual dense pyramid (RDP) is proposed as a building block. By introducing a dense information fusion layer and the residual learning module, the RDP can maximize the information flow and extract local features. Furthermore, the RDP further learns the structural information from intermediate layers via a multiscale pyramid fusion mechanism. To reduce the number of network parameters and to ease the training process, we use one RDP in the encoder and two RDPs in the decoder, following a multilevel pyramid pooling layer for incorporating global context features before estimating the final result. The extensive experimental results on a synthetic dataset and real-world images demonstrate that the new RDPN achieves favourable performance compared with some state-of-the-art methods, e.g., the recent densely connected pyramid dehazing network, the all-in-one dehazing network, the enhanced pix2pix dehazing network, pixel-based alpha blending, artificial multi-exposure image fusions and the genetic programming estimator, in terms of accuracy, run time and number of parameters. To be specific, RDPN outperforms all of the above methods in terms of PSNR by at least 4.25 dB. The run time of the proposed method is 0.021 s, and the number of parameters is 1,534,799, only 6percent of that used by the densely connected pyramid dehazing network.", notes = "also known as \cite{e21111123}", } @InProceedings{yin:2003:lsshstmbwgp, author = "Wen-Jun Yin and Min Liu and Cheng Wu", title = "Learning single-machine scheduling heuristics subject to machine breakdowns with genetic programming", booktitle = "Proceedings of the 2003 Congress on Evolutionary Computation CEC2003", editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon", pages = "1050--1055", year = "2003", publisher = "IEEE Press", volume = "2", address = "Canberra", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "8-12 " # dec, organisation = "IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", keywords = "genetic algorithms, genetic programming, GP-evolved heuristics, bi-tree structured representation, idle time inserting programs, machine breakdowns, predictive scheduling heuristics, single-machine scheduling, heuristic programming, job shop scheduling, single machine scheduling, stochastic programming, tree searching", ISBN = "0-7803-7804-0", DOI = "doi:10.1109/CEC.2003.1299784", abstract = "Genetic Programming (GP) has been rarely applied to scheduling problems. In this paper the use of GP to learn single-machine predictive scheduling (PS) heuristics with stochastic breakdowns is investigated, where both tardiness and stability objectives in face of machine failures are considered. The proposed bi-tree structured representation scheme makes it possible to search sequencing and idle time inserting programs together. Empirical results in different uncertain environments show that GP can evolve high quality PS heuristics effectively. The roles of inserted idle time are then analysed with respect to various weighting objectives. Finally some guides are supplied for PS design based on GP-evolved heuristics.", notes = "Also known as \cite{1299784}. CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.", } @Article{YIN:2020:CIE, author = "Xianhui Yin and Zhanwen Niu and Zhen He and Zhaojun(Steven) Li and Donghee Lee", title = "An integrated computational intelligence technique based operating parameters optimization scheme for quality improvement oriented process-manufacturing system", journal = "Computer \& Industrial Engineering", volume = "140", pages = "106284", year = "2020", ISSN = "0360-8352", DOI = "doi:10.1016/j.cie.2020.106284", URL = "http://www.sciencedirect.com/science/article/pii/S0360835220300188", keywords = "genetic algorithms, genetic programming, Quality improvement, Operating parameters optimization, Process industry, Multistage manufacturing, Computational intelligence, Multi-gene genetic programming (MGGP)", abstract = "The analysis and improvement of product quality for process industry is an increasing concern for academia and industry. As the outputs of a manufacturing system mainly depend on corresponding input conditions, so it is of high significance to develop an optimization scheme to actively and accurately determine operating parameters to obtain desired quality. However, the widely employed single-model modeling mode for whole production process neglects the natural characteristics within process manufacturing system such as multistage manufacturing and hysteresis. Additionally, the popular data-driven modeling techniques in current works, especially black-box machine learning models have been restricted to satisfying the requirements regarding excellent approximation capability and explicit mathematical expression simultaneously. To fill up above research gap, it is meaningful to develop a new data-driven optimization scheme in this work to effectively and accurately determine the optimum operating parameters considering the abovementioned characteristics and requirements. Firstly, two different connecting strategies are discussed to determine the more accurate and feasible quality propagation mode between adjacent stages. Then, two computational intelligence (CI) techniques, i.e., Multi-Gene Genetic Programming (MGGP) and Multi-objective Particle Swarm Optimization (MOPSO) algorithm are exploited to construct correlation model with explicit mathematical expression and derive the optimal operating parameters, respectively. Afterwards, the fuzzy Multi-criteria Decision Making (FMCDM) method is further proposed to select the optimal solution from the obtained Pareto solutions sets. The application of the proposed scheme in a coal preparation process indicates that the proposed scheme is promising and competitive on prediction accuracy and optimization efficiency over baseline methods, and can significantly improve the final product quality comparing with initial parameters setting. Moreover, the feasible quality specification for intermediate product can also be obtained by our proposed scheme which is beneficial for early detection of quality abnormality and timely parameters adjustment", } @Article{YIN:2023:oceaneng, author = "Zegao Yin and Jiahao Li and Yanxu Wang and Haojian Wang and Tianxu Yin", title = "Solitary wave attenuation characteristics of mangroves and multi-parameter prediction model", journal = "Ocean Engineering", volume = "285", pages = "115372", year = "2023", ISSN = "0029-8018", DOI = "doi:10.1016/j.oceaneng.2023.115372", URL = "https://www.sciencedirect.com/science/article/pii/S0029801823017560", keywords = "genetic algorithms, genetic programming, Solitary waves, Mangrove forest, Rigid cylindrical vegetation, Wave attenuation coefficient, Back propagation neural network", abstract = "Mangroves contribute to wave attenuation and improve coastal disaster prevention. Extensive studies have been conducted to explore wave attenuation by mangroves using rigid cylinders. However, few studies have investigated interactions between solitary waves and mangroves with roots. Therefore, laboratory experiments are conducted to investigate wave dissipation along a 1:10 scale Rhizophora mangrove forest under solitary waves, and wave attenuation characteristics are analyzed to highlight the significance of the effects of mangrove roots on wave damping. A numerical model of mangrove with roots is conducted by combining cylinder and porous media, where the trunk is considered as a cylinder and roots are simulated by introducing the resistance source term and porosity effect into the momentum equation. Results show that wave parameters (still water depth and incident wave height) and a vegetation parameter (vegetation submerged projected area) are the dominant variables affecting wave attenuation. In addition, multivariate nonlinear regression, genetic programming, and back propagation (BP) neural network are employed to explore the relationship between the wave attenuation coefficient and other related dimensionless parameters. Results show that the BP model is more accurate in predicting the wave attenuation coefficient as compared with other methods and thus can predict solitary wave attenuation in mangrove forests", } @Article{Yin:2025:oceaneng, author = "Zegao Yin and Jixian Xie and Fuxiang Zheng and Xi Li and Xueliang Cui", title = "Hydrodynamic and oxygen transfer characteristics of a vertical round tube jet under regular waves with initial dissolved oxygen stratification", journal = "Ocean Engineering", year = "2025", volume = "319", pages = "120178", keywords = "genetic algorithms, genetic programming, Dissolved oxygen, A vertical round tube jet, Regular waves, Oxygen transfer coefficient", ISSN = "0029-8018", URL = "https://www.sciencedirect.com/science/article/pii/S0029801824035169", DOI = "doi:10.1016/j.oceaneng.2024.120178", abstract = "Dissolved oxygen (DO) concentration is an important parameter for marine water quality. Under natural conditions, DO concentration in seawater is usually vertically stratified with high value in upper layer and low value in bottom layer, which deserves a further examination. To alleviate the hypoxia problem in the bottom, a vertical round tube jet with high DO concentration was proposed under regular waves, and a series of experiments and a numerical model coupling CFD and DO transfer were conducted to investigate the hydrodynamic and oxygen transfer characteristics with initial DO concentration stratification. The results show that with the individual increases of wave height and period, or the decrease of still water depth, the dimensionless time-averaged vertical velocity at jet axis decreases, while the dimensionless half jet-width increases. With the increase of distance from nozzle, the dimensionless time-averaged vertical velocity at jet axis decreases and the dimensionless half jet-width increases. Oxygen transfer coefficient increases with the increase of the relative wave height and non-dimensional parameter. In addition, a series of wave and jet parameters were used to conduct a prediction equation for oxygen transfer coefficient, and the predicting deviations for 81.25percent and 93.75percent testing data were within plus-minus10percent and plus-minus20percent, respectively", } @InProceedings{1274029, author = "Zheng Yin and Anthony Brabazon and Conall O'Sullivan", title = "Adaptive genetic programming for option pricing", booktitle = "Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2007)}", year = "2007", month = "7-11 " # jul, editor = "Peter A. N. Bosman", isbn13 = "978-1-59593-698-1", pages = "2588--2594", address = "London, United Kingdom", keywords = "genetic algorithms, genetic programming, economics, options pricing", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p2588.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.148.760", DOI = "doi:10.1145/1274000.1274029", publisher = "ACM Press", publisher_address = "New York, NY, USA", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", contributor = "CiteSeerX", language = "en", oai = "oai:CiteSeerXPSU:10.1.1.148.760", abstract = "Genetic Programming (GP) is an automated computational programming methodology, inspired by the workings of natural evolution techniques. It has been applied to solve complex problems in multiple domains including finance. This paper illustrates the application of an adaptive form of GP, where the probability of crossover and mutation is adapted dynamically during the GP run, to the important real-world problem of options pricing. The tests are carried out using market option price data and the results illustrate that the new method yields better results than are obtained from GP with fixed crossover and mutation rates. The developed method has potential for implementation across a range of dynamic problem environments. Categories and Subject Descriptors", notes = "Distributed on CD-ROM at GECCO-2007 ACM Order No. 910071", } @InProceedings{Yin:2007:pliks, author = "Zheng Yin and Anthony Brabazon and Conall O'Sullivan and Michael O'Neill", title = "Genetic Programming for Dynamic Environments", booktitle = "2nd International Symposium {"}Advances in Artificial Intelligence and Applications{"}", year = "2007", volume = "2", pages = "437--446", address = "Wisla, Poland", month = oct # " 15-17", keywords = "genetic algorithms, genetic programming, dynamic environments", ISSN = "1896 7094", URL = "http://www.proceedings2007.imcsit.org/pliks/18.pdf", size = "10 pages", abstract = "Genetic Programming (GP) is an automated computational programming methodology which is inspired by the workings of natural evolution techniques. It has been applied to solve complex problems in multiple application domains. This paper investigates the application of a dynamic form of GP in which the probability of crossover and mutation adapts during the GP run. This allows GP to adapt its diversity-generating process during a run in response to feedback from the fitness function. A proof of concept study is then undertaken on the important real-world problem of options pricing. The results indicate that the dynamic form of GP yields better results than are obtained from canonical GP with fixed crossover and mutation rates. The developed method has potential for implementation across a range of dynamic problem environments.", } @InProceedings{yin:2008:WSSEC, author = "Zheng Yin and Anthony Brabazon and Conall O'Sullivan", title = "Genetic Programming Applications in Financial Modelling: A Brief Survey", booktitle = "Workshop/Summer School on Evolutionary Computing Lecture Series by Pioneers (WSSEC 2008)", year = "2008", editor = "T. M. McGinnity", pages = "30--33", address = "Londonderry, UK", month = "18-22 " # aug, organisation = "School of Computing and Intelligent Systems, University of Ulster", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/yin_2008_WSSEC.pdf", size = "4 pages", abstract = "Genetic Programming (GP) is an automated computational programming methodology, inspired by the workings of natural evolution techniques. This paper reviews its applications in financial modelling cross different financial markets and analyses GP potential utility in these areas. The future research directions of GP in financial markets have been highlighted.", } @InProceedings{yin:agpafdh:cec2015, author = "Zheng Yin and Anthony Brabazon and Conall O'Sullivan and Michael O'Neill", title = "A Genetic Programming Approach for Delta Hedging", booktitle = "Proceedings of 2015 IEEE Congress on Evolutionary Computation (CEC 2015)", editor = "Yadahiko Murata", pages = "3312--3318", year = "2015", address = "Sendai, Japan", month = "25-28 " # may, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2015.7257304", abstract = "Effective hedging of derivative securities is of paramount importance to derivatives investors and to market makers. The standard approach used to hedge derivative instruments is delta hedging. In a Black-Scholes setting, a continuously rebalanced delta hedged portfolio will result in a perfect hedge with no associated hedging error. In reality, continuous rehedging is impossible and this raises the important practical question such as when should a portfolio manager rebalance the portfolio? In practice, many portfolio managers employ relatively simple deterministic rebalancing strategies, such as rebalancing at uniform time intervals, or rehedging when the underlying asset moves by a fixed number of ticks. While such strategies are easy to implement they will expose the portfolio to hedging risk, both in terms of timing and also as the strategies do not adequately consider market conditions. In this study we propose a rebalancing trigger based on the output from a GP-evolved hedging strategy that rebalances the portfolio based on dynamic non-linear factors related to the condition of the market, derived from the theoretical literature, including a number of liquidity and volatility factors. The developed GP-evolved hedging strategy outperforms the deterministic time based hedging methods when tested on FTSE 100 call options. This paper represents the first such application of GP for this important application.", notes = "1050 hrs 15197 CEC2015", } @InProceedings{yin:rvfagpa:cec2015, author = "Zheng Yin and Anthony Brabazon and Conall O'Sullivan and Michael O'Neill", title = "Realised Volatility Forecasting: A Genetic Programming Approach", booktitle = "Proceedings of 2015 IEEE Congress on Evolutionary Computation (CEC 2015)", editor = "Yadahiko Murata", pages = "3305--3311", year = "2015", address = "Sendai, Japan", month = "25-28 " # may, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2015.7257303", abstract = "Forecasting daily returns volatility is crucial in finance. Traditionally, volatility is modelled using a time-series of lagged information only, an approach which is in essence a theoretical. Although the relationship of market conditions and volatility has been studied for decades, we still lack a clear theoretical framework to allow us to forecast volatility, despite having many plausible explanatory variables. This setting of a data-rich but theory-poor environment suggests a useful role for powerful model induction methodologies such as Genetic Programming. This study forecasts one-day ahead realised volatility (RV) using a GP methodology that incorporates information on market conditions including trading volume, number of transactions, bid-ask spread, average trading duration and implied volatility. The forecasting result from GP is found to be significantly better than that of the benchmark model from the traditional finance literature, the heterogeneous autoregressive model (HAR).", notes = "1030 hrs 15196 CEC2015", } @Article{yin:2016:jaiscr, author = "Zheng Yin and Conall O'Sullivan and Anthony Brabazon", title = "An Analysis of the Performance of Genetic Programming for Realised Volatility Forecasting", journal = "Journal of Artificial Intelligence and Soft Computing Research", year = "2016", volume = "6", number = "3", pages = "155--172", month = jul, keywords = "genetic algorithms, genetic programming, Realised Volatility, High Frequency Data", ISSN = "2083-2567", URL = "https://www.degruyter.com/view/j/jaiscr.2016.6.issue-3/jaiscr-2016-0012/jaiscr-2016-0012.xml?format=INT", DOI = "doi:10.1515/jaiscr-2016-0012", abstract = "Traditionally, the volatility of daily returns in financial markets is modelled autoregressively using a time-series of lagged information. These autoregressive models exploit stylised empirical properties of volatility such as strong persistence, mean reversion and asymmetric dependence on lagged returns. While these methods can produce good forecasts, the approach is in essence a theoretical as it provides no insight into the nature of the causal factors and how they affect volatility. Many plausible explanatory variables relating market conditions and volatility have been identified in various studies but despite the volume of research, we lack a clear theoretical framework that links these factors together. This setting of a theory-weak environment suggests a useful role for powerful model induction methodologies such as Genetic Programming (GP). This study forecasts one-day ahead realised volatility (RV) using a GP methodology that incorporates information on market conditions including trading volume, number of transactions, bid-ask spread, average trading duration (waiting time between trades) and implied volatility. The forecasting performance from the evolved GP models is found to be significantly better than those numbers of benchmark forecasting models drawn from the finance literature, namely, the heterogeneous autoregressive (HAR) model, the generalized autoregressive conditional heteroscedasticity (GARCH) model, and a stepwise linear regression model (SR). Given the practical importance of improved forecasting performance for realised volatility this result is of significance for practitioners in financial markets.", } @Article{Yin:GPEM, author = "Zheng Yin and Anthony Brabazon and Conall O'Sullivan and Philip A. Hamill", title = "A genetic programming approach for delta hedging", journal = "Genetic Programming and Evolvable Machines", year = "2019", volume = "20", number = "1", pages = "67--92", month = mar, keywords = "genetic algorithms, genetic programming, Hedging, Delta neutrality", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-018-9334-3", size = "26 pages", abstract = "In this paper, using high-frequency intra-daily data from the UK market, we employ genetic programming (GP) to uncover a hedging strategy for FTSE 100 call options, hedged using FTSE 100 futures contracts. The output from the evolved strategies is a rebalancing signal which is conditioned upon a range of dynamic non-linear factors related to market conditions including liquidity and volatility. When this signal exceeds threshold values during the trading day, the hedge position is rebalanced. The performance of the GP-evolved strategy is evaluated against a number of commonly used, time-based, deterministic hedging strategies where the hedge position is rebalanced at fixed time intervals ranging from 5 minutes to one day. Assuming the delta hedger pays the bid-ask spread on the futures contract whenever the portfolio is rebalanced, this study finds that the GP-evolved hedging strategy out-performs standard, deterministic, time-based approaches. Empirical analysis shows that the superior performance of the GP strategy is driven by its ability to account for non-linear intra-day persistence in high frequency measures of liquidity and volatility. This study is the first to apply a GP methodology for the task of delta hedging with high frequency data, a significant risk management issue for investors and market makers in financial options.", } @InCollection{ying:2002:DOSTDPPGA, author = "Donald Ying", title = "Determining an Optimal Solution to a Three Dimensional Packing Problem using Genetic Algorithms", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2002", year = "2002", editor = "John R. Koza", pages = "263--272", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms", URL = "http://www.genetic-programming.org/sp2002/Ying.pdf", notes = "part of \cite{koza:2002:gagp}", } @InProceedings{Yingjie:2022:ACPEE, author = "Liu Yingjie and Fan Rongquan and Xia Yuhang and Huang Zhengwen and Liu Yijun and Li Guoquan and Gao Yang", booktitle = "2022 7th Asia Conference on Power and Electrical Engineering (ACPEE)", title = "Exploration the application of genetic algorithms and gene expression programming in future power system infrastructure", year = "2022", pages = "581--585", abstract = "Evolutionary on the existing infrastructure construction work of power system brings significant amount of the BIG data being processed. Nowadays infrastructure construction in power system receiving an explosive level upgrading, the data generated from future power system infrastructure environment becomes an obvious research hotspot. How to handle the BIG data in an efficient and flexible manner turns to be a hot research spot. This work explores two applicable and reliable solution within the type of systematic modelling-based category. Two classical evolutionary algorithm variants, genetic algorithms and gene expression programming (GEP) are introduced, evaluated for interested researchers in power system domain.", keywords = "genetic algorithms, genetic programming, gene expression programming, Systematics, Evolutionary computation, Big Data, Explosives, Power systems, Power system reliability, power system infrastructure, data, information", DOI = "doi:10.1109/ACPEE53904.2022.9783616", month = apr, notes = "Also known as \cite{9783616}", } @Article{DBLP:journals/ewc/YongZATTPH21, author = "Weixun Yong and Jian Zhou and Danial Jahed Armaghani and Mahmood M. D. Tahir and Reza Tarinejad and Binh Thai Pham and Van Van Huynh", title = "A new hybrid simulated annealing-based genetic programming technique to predict the ultimate bearing capacity of piles", journal = "Eng. Comput.", volume = "37", number = "3", pages = "2111--2127", year = "2021", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/s00366-019-00932-9", DOI = "doi:10.1007/s00366-019-00932-9", timestamp = "Thu, 15 Jul 2021 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/ewc/YongZATTPH21.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @TechReport{rn-12-03, author = "Shin Yoo", title = "Evolving Human Competitive Spectra-Based Fault Localisation Techniques", institution = "Department of Computer Science, University College, London", year = "2012", type = "Research Note", number = "RN/12/03", address = "UK", month = "8 " # may, keywords = "genetic algorithms, genetic programming, SBSE, GPU, openCL", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.398.5356", URL = "http://www-typo3.cs.ucl.ac.uk/fileadmin/UCL-CS/research/Research_Notes/rn-12-03.pdf", size = "13 pages", abstract = "Spectra-Based Fault Localisation (SBFL) aims to assist de- bugging by applying risk evaluation formulae (sometimes called suspiciousness metrics) to program spectra and ranking statements according to the predicted risk. Designing a risk evaluation formula is often an intuitive process done by human software engineer. This paper presents a Genetic Programming approach for evolving risk assessment formulae. The empirical evaluation using 92 faults from four Unix utilities produces promising results. GP-evolved equations can consistently outperform many of the human-designed formulae, such as Tarantula, Ochiai, Jaccard, Ample, and Wong1/2, up to 5.9 times. More importantly, they can perform equally as well as Op2, which was recently proved to be optimal against If-Then-Else-2 (ITE2) structure, or even outperform it against other program structures.", notes = "The program spectra data used in the paper, as well as the complete empirical results, are available from: broken Oct 2020 http://www.cs.ucl.ac.uk/staff/s.yoo/evolving-sbfl.html SIR (flex, grep, gzip, sed), gcov, Linux, pyevolve (python). Published as \cite{Yoo:2012:SSBSE} Entered 2012 HUMIES GECCO 2012. ", } @InProceedings{Yoo:2012:SSBSE, author = "Shin Yoo", title = "Evolving Human Competitive Spectra-Based Fault Localisation Techniques", booktitle = "4th Symposium on Search Based Software Engineering", year = "2012", editor = "Gordon Fraser and Jerffeson {Teixeira de Souza} and Angelo Susi", volume = "7515", series = "Lecture Notes in Computer Science", pages = "244--258", address = "Riva del Garda, Italy", month = sep # " 28-30", publisher = "Springer", keywords = "genetic algorithms, genetic programming, SBSE", isbn13 = "978-3-642-33118-3", URL = "http://www.cs.ucl.ac.uk/staff/s.yoo/papers/Yoo2012kx.pdf", DOI = "doi:10.1007/978-3-642-33119-0_18", size = "15 pages", abstract = "Spectra-Based Fault Localisation (SBFL) aims to assist debugging by applying risk evaluation formulas (sometimes called suspiciousness metrics) to program spectra and ranking statements according to the predicted risk. Designing a risk evaluation formula is often an intuitive process done by human software engineer. This paper presents a Genetic Programming (GP) approach for evolving risk assessment formulae. The empirical evaluation using 92 faults from four Unix utilities produces promising results. Equations evolved by Genetic Programming can consistently outperform many of the human-designed formulae, such as Tarantula, Ochiai, Jaccard, Ample, and Wong1/2, up to 6 times. More importantly, they can perform equally as well as Op2, which was recently proved to be optimal against If-Then-Else-2 (ITE2) structure, or even outperform it against other program structures.", notes = "Entered 2012 HUMIES GECCO 2012. See also \cite{rn-12-03} http://selab.fbk.eu/ssbse2012/index.php?p=papers http://www.cs.ucl.ac.uk/fileadmin/UCL-CS/research/Research_Notes/rn-14-14.pdf", } @InProceedings{Yoo:2015:SSBSE, author = "Shin Yoo", title = "Amortised Optimisation of Non-functional Properties in Production Environments", booktitle = "SSBSE", year = "2015", editor = "Yvan Labiche and Marcio Barros", volume = "9275", series = "LNCS", pages = "31--46", address = "Bergamo, Italy", month = sep # " 5-7", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Genetic Improvement, SBSE", isbn13 = "978-3-319-22182-3", URL = "http://hdl.handle.net/10203/224142", DOI = "doi:10.1007/978-3-319-22183-0_3", size = "16 pages", abstract = "Search Based Software Engineering has high potential for optimising non-functional properties such as execution time or power consumption. However, many non-functional properties are dependent not only on the software system under consideration but also the environment that surrounds the system. This necessitates a support for online, in situ optimisation. This paper introduces the novel concept of amortised optimisation which allows such online optimisation. The paper also presents two case studies: one that seeks to optimise JIT compilation, and another to optimise a hardware dependent algorithm. The results show that, by using the open source libraries we provide, developers can improve the speed of their Python script by up to 8.6percent with virtually no extra effort, and adapt a hardware dependent algorithm automatically for unseen CPUs.", notes = "Slides http://ssbse.info/2015/wp-content/uploads/Slides_Amortised_Optimisation_of_Non-Functional_Property_in_Production_Environment.pdf pypy Raspberrty Pi, ARM 250MHz Code Available https://bitbucket.org/ntrolls/piacin https://bitbucket.org/ntrolls/niacin http://ssbse.info/2015", } @InProceedings{Yoo:2017:GI, author = "Shin Yoo", title = "Embedding Genetic Improvement into Programming Languages", booktitle = "GI-2017", year = "2017", editor = "Justyna Petke and David R. White and W. B. Langdon and Westley Weimer", pages = "1551--1552", address = "Berlin", month = "15-19 " # jul, publisher = "ACM", keywords = "genetic algorithms, genetic programming, genetic improvement, Search-based software engineering, SBSE, Self Adaptation, Programming Language", isbn13 = "978-1-4503-4939-0", URL = "http://geneticimprovementofsoftware.com/wp-content/uploads/2017/05/yoo2017_embedding_gi.pdf", DOI = "doi:10.1145/3067695.3082516", size = "2 pages", abstract = "We present a vision of genetic improvement firmly embedded in, and supported by, programming languages. Genetic improvement has already been envisioned as the next compiler, which would take human written programs as input and return versions optimised with respect to various objectives. As an intermediate stage, or perhaps to complement the fully automated vision, we imagine genetic improvement processes that are hinted at and directed by humans but understood and undertaken by programming languages and their run-times, via interactions through the source code. We examine existing similar ideas and examine the benefits of embedding them within programming languages.", notes = "Context-orientated programming, @optimize, optional, @approximate, Specialise of runtime environment, consolidated and smoother learning cure", } @Article{Yoo:TOSEM:sbfl, author = "Shin Yoo and Xiaoyuan Xie and Fei-Ching Kuo and Tsong Yueh Chen and Mark Harman", title = "Human Competitiveness of Genetic Programming in Spectrum Based Fault Localisation: Theoretical and Empirical Analysis", journal = "ACM Transactions on Software Engineering and Methodology", year = "2017", volume = "26", number = "1", pages = "4:1--4:30", month = jul, note = "Winner Silver Humie 2017", keywords = "genetic algorithms, genetic programming, SBSE, Spectrum Based Fault Localisation", ISSN = "1049-331X", URL = "http://www.human-competitive.org/sites/default/files/yoo-paper.pdf", URL = "http://www.human-competitive.org/sites/default/files/yoo-text_0.txt", DOI = "doi:10.1145/3078840", acmid = "3078840", size = "31 pages", abstract = "We report on the application of Genetic Programming to Software Fault Localisation, a problem in the area of Search Based Software Engineering (SBSE). We give both empirical and theoretical evidence for the human competitiveness of the evolved fault localisation formulae under the single fault scenario, compared to those generated by human ingenuity and reported in many papers, published over more than a decade. Though there have been previous human competitive results claimed for SBSE problems, this is the first time that evolved solutions have been formally proved to be human competitive. We further prove that no future human investigation could outperform the evolved solutions. We complement these proofs with an empirical analysis of both human and evolved solutions, which indicates that the evolved solutions are not only theoretically human competitive, but also convey similar practical benefits to human-evolved counterparts.", notes = "Entered 2017 Humies http://www.human-competitive.org/awards", } @InProceedings{Yoo:2024:GI, author = "Shin Yoo", title = "Executing One's Way out of the {Chinese Room}", booktitle = "13th International Workshop on Genetic Improvement @ICSE 2024", year = "2024", editor = "Gabin An and Aymeric Blot and Vesna Nowack and Oliver Krauss and Justyna Petke", pages = "viii", address = "Lisbon", month = "16 " # apr, publisher = "ACM", note = "Invited Keynote", keywords = "genetic algorithms, genetic programming, Genetic Improvement, ANN", isbn13 = "979-8-4007-0573-1/24/04", URL = "http://gpbib.cs.ucl.ac.uk/gi2024/an_2024_GI.pdf", DOI = "doi:10.1145/3643692", slides_url = "http://gpbib.cs.ucl.ac.uk/gi2024/gi_2024_slides/yoo_gi2024_keynote.pdf", video_url = "https://www.youtube.com/watch?v=mtttbsL4qFo&list=PLI8fiFpB7BoIRqJuY80XwmH-DFT_71y2S&index=1", size = "1 page", abstract = "One very attractive property of Large Language Models (LLMs) is their emergent in-context learning capability, which enables us to simply describe our requirements in natural languages and get the corresponding source code generated in programming languages. While LLMs as a generative model are known to hallucinate, i.e., generate factually incorrect contents, the fact that code can be executed can be used to fight this phenomenon. We briefly look at existing techniques designed to improve the correctness of code generated by LLMs, and will try to imagine the future of Genetic Improvement that is supported, enhanced, and driven by LLMs.", notes = "GI @ ICSE 2024, part of \cite{an:2024:GI}", } @InProceedings{Yoshida:2010:ICCAE, author = "Kengo Yoshida and Tetsuhiro Miyahara and Tetsuji Kuboyama", title = "Evolution of multiple tree structured patterns using soft clustering", booktitle = "The 2nd International Conference on Computer and Automation Engineering (ICCAE 2010)", year = "2010", month = "26-28 " # feb, volume = "5", pages = "749--753", address = "Singapore", abstract = "We propose a new genetic programming (GP) approach to extracting multiple tree structured patterns from tree structured data using soft clustering. We use a set of multiple tree structured patterns, called tag tree patterns, as a combined pattern. A structured variable in a tag tree pattern can be substituted by an arbitrary tree. A set of multiple tag tree patterns matches a tree, if at least one of the set of patterns matches the tree. Using soft clustering is appropriate because one tree structured data is allowed to match multiple tag tree patterns. By soft clustering of positive data and by running GP subprocesses on each cluster with negative data, we make a combined pattern which consists of best individuals in GP subprocesses. Experiments on some glycan data show that our method has a support of about 0.8, while the previous method for evolving single patterns has a support of about 0.5.", keywords = "genetic algorithms, genetic programming, multiple tag tree patterns, multiple tree structured patterns, soft clustering, tree structured data, pattern clustering, trees (mathematics)", DOI = "doi:10.1109/ICCAE.2010.5451349", notes = "Also known as \cite{5451349}", } @InProceedings{Yoshida:2017:IWCIA, author = "Shubu Yoshida and Tomohiro Harada and Ruck Thawonmas", booktitle = "2017 IEEE 10th International Workshop on Computational Intelligence and Applications (IWCIA)", title = "Multimodal genetic programming by using tree structure similarity clustering", year = "2017", pages = "85--90", abstract = "This paper proposes a multimodal genetic programming (GP) that incorporates a clustering of a population based on the tree structure similarity into GP and simultaneously acquires multiple local optimal solutions including a global optimal solution. The multimodal optimisation problem aims to acquire not only a global optimal solution but also multiple local optimal solutions in a single optimisation process. In general, although continuous real-valued optimisations are mainly targeted for multimodal optimisation problems, problems with other solution structures, like a program in GP, have not been dealt with. This paper designs a multimodal program optimisation problem that has a global and a local optimal solution and proposes a multimodal GP to acquires multiple local optimal programs including a global optimal one. Concretely, the proposed method separates the population into several clusters based on the similarity of tree structure, which is used as program expression in GP. Then, local optimum programs with different structure are acquired by optimising each cluster separately. In order to investigate the effectiveness of the proposed method, we compare the proposed method with a simple GP without clustering on the designed multimodal GP benchmark. The experimental result reveals that the proposed method can acquire both the global and the local optimal programs at the same time.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IWCIA.2017.8203566", month = nov, notes = "Also known as \cite{8203566}", } @InProceedings{Yoshida:2019:GECCOcomp, author = "Shubu Yoshida and Tomohiro Harada and Ruck Thawonmas", title = "Multimodal genetic programming using program similarity measurement and its application to wall-following problem", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", isbn13 = "978-1-4503-6748-6", pages = "356--357", address = "Prague, Czech Republic", DOI = "doi:10.1145/3319619.3322063", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{3322063} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InProceedings{Yoshida:2019:SMC, author = "Tomohiro Yoshida and Yuko Ishiwaka and Gaku Yasui", title = "{GAGPL:} A Personalized Semantic Orientation Calculator in Dark Side Ternary Stars", booktitle = "2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)", year = "2019", pages = "3818--3825", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SMC.2019.8913999", ISSN = "2577-1655", month = oct, abstract = "We propose a new method called Dark Side Ternary Sta, (DSTS), which is expected to allow emotional personal agents to interact with users. DSTS focuses on proper nouns to adapt preference to users. In this paper, we report only on GAGPL in the DSTS. GAGPL is a method for calculating positive or negative value per sentence for an individual user. We aim to represent famous Japanese proverb The misery of others is as sweet as honey. Our proposed method consists of Genetic Algorithms (GA) and Genetic Programming Like (GPL) algorithms. GA learns the positive or negative values of general word to individuals. GPL calculates the positive or negative value per sentence. We applied this method to the news article text data in Japanese professional baseball. As the experiment results, it shows that this method is effective.", notes = "Also known as \cite{8913999}", } @InProceedings{Yoshihara:2000:GECCO, author = "I. Yoshihara and T. Aoyama and M. Yasunaga", title = "A Fast Model-Building Method for Time Series Using Genetic Programming", pages = "537", year = "2000", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)", editor = "Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer", address = "Las Vegas, Nevada, USA", publisher_address = "San Francisco, CA 94104, USA", month = "10-12 " # jul, keywords = "genetic algorithms, genetic programming, Poster", ISBN = "1-55860-708-0", URL = "http://gpbib.cs.ucl.ac.uk/gecco2000/GP236.pdf", notes = "A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of \cite{whitley:2000:GECCO}", } @InProceedings{yoshihara:2000:gmmtsppona, author = "I. Yoshihara and T. Aoyama and M. Yasunaga", title = "GP-Based Modeling Method for Time Series Prediction with Parameter Optimization and Node Alternation", booktitle = "Proceedings of the 2000 Congress on Evolutionary Computation CEC00", year = "2000", pages = "1475--1481", volume = "2", address = "La Jolla Marriott Hotel La Jolla, California, USA", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "6-9 " # jul, organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, time series, GP based model building, GP based modelling method, backpropagation-like algorithm, complicated functions, fast method, functional forms, model parameters, mutation, node alternation, parameter optimisation, seismic ground motion, time series prediction, backpropagation, parameter estimation, statistical analysis", ISBN = "0-7803-6375-2", DOI = "doi:10.1109/CEC.2000.870828", abstract = "A fast method of GP based model building for time series prediction is proposed. The method involves two newly-devised techniques. One is regarding determination of model parameters: only functional forms are inherited from their parents with genetic programming, but model parameters are not inherited. They are optimised by a backpropagation-like algorithm when a child (model) is newborn. The other is regarding mutation: nodes which require a different number of edges, can be transformed into different types of nodes through mutation. This operation is effective at accelerating complicated functions e.g. seismic ground motion. The method has been applied to a typical benchmark of time series and many real world problems", notes = "CEC-2000 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 00TH8512, Library of Congress Number = 00-018644", } @Proceedings{cec:2000, title = "Proceedings of the 2000 Congress on Evolutionary Computation CEC00", year = "2000", key = "yoshihara", address = "La Jolla Marriott Hotel La Jolla, California, USA", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "6-9 " # jul, organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, biological modeling/ breast cancer, biological modelling, classifiers, coevolution, constraint handling, control system design, controlling search, design applications, devices developement and applications, dynamic and parallel ec, ec techniques, ecological modelling and information ecosystems, engineering applications, evolutionary markets, evolutionary scheduling, evolvable hardware, evolving neural networks, fitness, games and game like tasks, hybrid systems, image processing applications, image/ signal processing, intelligent agents, learning and search spaces, local search optimization, medical applications, multi-agent systems and cultural algorithms, multi-objective optimization, network applications, new paradigms, novel applications, novel themes, operations research applications, representations, revisiting the fossil record, robotic applications, stroganoff, system modeling and control, theory and foundations, time series", ISBN = "0-7803-6375-2", URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6997", bad_doi = "doi:10.1109/CEC.2000.870711", size = "1584 pages", notes = "CEC-2000 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 00TH8512, Library of Congress Number = 00-018644", } @InProceedings{yoshimi:1998:hcscv, author = "Takahiro Yoshimi and Toshiharu Taura", title = "Hierarchical Classifier System Based on the Concept of Viewpoint", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "675--678", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, classifiers", ISBN = "1-55860-548-7", notes = "GP-98", } @InProceedings{yoshimi:1999:ACMVPHCS, author = "Takahiro Yoshimi and Toshiharu Taura", title = "A Computational Model of a Viewpoint-Forming Process in a Hierarchical Classifier System", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "758--766", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-871.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-871.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Article{Youcefi:2025:desal, author = "Mohamed Riad Youcefi and Fahd Mohamad Alqahtani and Menad {Nait Amar} and Hakim Djema and Mohammad Ghasemi", title = "Improved explainable multi-gene genetic programming correlations for predicting carbon dioxide solubility in various brines", journal = "Desalination", year = "2025", volume = "610", pages = "118917", keywords = "genetic algorithms, genetic programming, Carbon dioxide, Solubility, Brine, Saline aquifers, Artificial intelligence, Sequestration", ISSN = "0011-9164", URL = "https://www.sciencedirect.com/science/article/pii/S0011916425003923", DOI = "doi:10.1016/j.desal.2025.118917", abstract = "Storing carbon dioxide (CO2) in deep saline aquifers has gained significant attention as an effective approach to reducing greenhouse gas emissions. The success of carbon capture and storage (CCS) in deep saline aquifers relies on accurately assessing CO2 solubility in brine under real operating conditions. Gaining detailed insight into CO2 behaviour in subsurface environments is essential for effectively implementing this method. In this study, we have made substantial efforts to compile a comprehensive dataset on CO2 solubility in diverse aqueous electrolyte solutions of CaCl2, NaCl, MgCl2, Na2SO4, and KCl, encompassing a wide interval of operating conditions and widespread salt concentrations. To leverage this extensive dataset effectively, we applied a robust white-box machine learning technique, namely the multi-gene genetic programming (MGGP) to establish user-friendly explicit correlations for accurately predicting CO2 solubility in numerous brines under subsurface conditions. Our evaluation revealed that the derived correlations provided significantly more precise predictions of CO2 solubility. In this context, the MGGP-based correlations demonstrated trustworthy accuracy with total root mean square error (RMSE) values of only 0.0235, 0.0304, 0.0196, 0.0289, and 0.0313 for CaCl2, NaCl, MgCl2, Na2SO4, and KCl solutions, respectively. Additionally, the trend analysis showed that the proposed correlations effectively captured the behaviour of CO2 solubility across a wide range of operating pressures, temperatures, and solvent salinities, demonstrating their robustness and reliability. Furthermore, Shapley Additive Explanations (SHAP) provided useful insights into how different inputs contribute and interact, making the proposed correlations easier to understand and interpret. Lastly, the newly introduced MGGP-based correlations exhibit notable improvements in accuracy, user-friendliness, generalisation, and explainability, ensuring superior performance across diverse subsurface conditions. These advancements mark a significant step forward in the cost-effective and precise estimation of CO2 solubility in various brines, making the proposed correlations highly valuable for applications in carbon capture and storage, environmental impact assessment, petroleum geology, reservoir engineering, and other CO2-related domains", } @InProceedings{YousefiZowj:2015:GECCO, author = "Afsoon {Yousefi Zowj} and Josh C. Bongard and Christian Skalka", title = "A Genetic Programming Approach to Cost-Sensitive Control in Resource Constrained Sensor Systems", booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation", year = "2015", editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr", isbn13 = "978-1-4503-3472-3", pages = "1295--1302", keywords = "genetic algorithms, genetic programming, Real World Applications", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739480.2754751", DOI = "doi:10.1145/2739480.2754751", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Resource constrained sensor systems are an increasingly attractive option in a variety of environmental monitoring domains, due to continued improvements in sensor technology. However, sensors for the same measurement application can differ in terms of cost and accuracy, while fluctuations in environmental conditions can impact both application requirements and available energy. This raises the problem of automatically controlling heterogeneous sensor suites in resource constrained sensor system applications, in a manner that balances cost and accuracy of available sensors. We present a method that employs a hierarchy of model ensembles trained by genetic programming (GP): if model ensembles that poll low-cost sensors exhibit too much prediction uncertainty, they automatically transfer the burden of prediction to other GP-trained model ensembles that poll more expensive and accurate sensors. We show that, for increasingly challenging datasets, this hierarchical approach makes predictions with equivalent accuracy yet lower cost than a similar yet non-hierarchical method in which a single GP-generated model determines which sensors to poll at any given time. Our results thus show that a hierarchy of GP-trained ensembles can serve as a control algorithm for heterogeneous sensor suites in resource constrained sensor system applications that balances cost and accuracy.", notes = "Also known as \cite{2754751} See also \cite{zowj:2017:CIWSN} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)", } @InCollection{zowj:2017:CIWSN, author = "Afsoon {Yousefi Zowj} and Josh C. Bongard and Christian Skalka", title = "A Genetic Programming Approach to Cost-Sensitive Control in Wireless Sensor Networks", booktitle = "Computational Intelligence in Wireless Sensor Networks", booktitle2 = "Recent Advances and Future Challenges", publisher = "Springer", year = "2017", editor = " Ajith Abraham and Rafael Falcon and Mario Koeppen", volume = "676", series = "Studies in Computational Intelligence", chapter = "1", pages = "1--31", keywords = "genetic algorithms, genetic programming, WSN", isbn13 = "978-3-319-47713-8", URL = "http://link.springer.com/chapter/10.1007/978-3-319-47715-2_1", DOI = "doi:10.1007/978-3-319-47715-2_1", msc_url = "https://scholarworks.uvm.edu/graddis/493", abstract = "... We apply genetic programming (GP) to this problem, considering two basic approaches. First, we construct a hierarchy of models, where increasing levels in the hierarchy use sensors of increasing cost. If a model that polls low cost sensors exhibits too much prediction uncertainty, the burden of prediction is automatically transferred to a higher level model using more expensive sensors. Second, we train models with cost as an optimization objective, called non-hierarchical models, that use conditionals to automatically select sensors based on both cost and accuracy. We compare these approaches in a setting where the available budget for sampling is considered to remain constant, and in a setting where the system is sensitive to a fluctuating budget, for example available battery power. We show that in both settings, for increasingly challenging datasets, hierarchical models makes predictions with equivalent accuracy yet lower cost than non-hierarchical models.", notes = "See also \cite{YousefiZowj:2015:GECCO}", } @InProceedings{Youssef:2021:NILES, author = "Ayman Youssef and Bilal Majeed and Conor Ryan", title = "Optimizing combinational logic circuits using Grammatical Evolution", booktitle = "2021 3rd Novel Intelligent and Leading Emerging Sciences Conference (NILES)", year = "2021", pages = "87--92", abstract = "This paper applies Grammatical Evolution (GE) to the optimization of combinational logic circuits on gate-level logic. We demonstrate the ability of GE to evolve complex combinational circuits using gate-level combinational logic and show that GE can similarly provide optimized solutions for different digital circuit problems at the gate level. Our methodology is applied to the Advanced Encryption standard (AES) S-box building blocks and the results compared to other evolutionary algorithms. Our results show comparable results with traditional Genetic Algorithm (GA) and Cartesian Genetic Programming (CGP).", keywords = "genetic algorithms, genetic programming, grammatical evolution, cartesian genetic programming", DOI = "doi:10.1109/NILES53778.2021.9600092", month = oct, notes = "Also known as \cite{9600092}", } @InProceedings{Yska:2018:EuroGP, author = "Daniel Yska and Yi Mei and Mengjie Zhang", title = "Genetic Programming Hyper-heuristic with Cooperative Coevolution for Dynamic Flexible Job Shop Scheduling", booktitle = "EuroGP 2018: Proceedings of the 21st European Conference on Genetic Programming", year = "2018", month = "4-6 " # apr, editor = "Mauro Castelli and Lukas Sekanina and Mengjie Zhang and Stefano Cagnoni and Pablo Garcia-Sanchez", series = "LNCS", volume = "10781", publisher = "Springer Verlag", address = "Parma, Italy", pages = "306--321", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming: Poster", isbn13 = "978-3-319-77552-4", URL = "http://homepages.ecs.vuw.ac.nz/~yimei/papers/EuroGP18-Daniel.pdf", URL = "https://rdcu.be/dn4Ml", DOI = "doi:10.1007/978-3-319-77553-1_19", size = "16 pages", abstract = "Flexible Job Shop Scheduling (FJSS) problem has many real-world applications such as manufacturing and cloud computing, and thus is an important area of study. In real world, the environment is often dynamic, and unpredicted job orders can arrive in real time. Dynamic FJSS consists of challenges of both dynamic optimisation and the FJSS problem. In Dynamic FJSS, two kinds of decisions (so-called routing and sequencing decisions) are to be made in real time. Dispatching rules have been demonstrated to be effective for dynamic scheduling due to their low computational complexity and ability to make real-time decisions. However, it is time consuming and strenuous to design effective dispatching rules manually due to the complex interactions between job shop attributes. Genetic Programming Hyper-heuristic (GPHH) has shown success in automatically designing dispatching rules which are much better than the manually designed ones. Previous works only focused on standard job shop scheduling with only the sequencing decisions. For FJSS, the routing rule is set arbitrarily by intuition. In this paper, we explore the possibility of evolving both routing and sequencing rules together and propose a new GPHH algorithm with Cooperative Co-evolution. Our results show that co-evolving the two rules together can lead to much more promising results than evolving the sequencing rule only.", notes = "Part of \cite{Castelli:2018:GP} EuroGP'2018 held in conjunction with EvoCOP2018, EvoMusArt2018 and EvoApplications2018", } @InProceedings{Yska:2018:GECCOcomp, author = "Daniel Yska and Yi Mei and Mengjie Zhang", title = "Feature construction in genetic programming hyper-heuristic for dynamic flexible job shop scheduling", booktitle = "GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2018", editor = "Carlos Cotta and Tapabrata Ray and Hisao Ishibuchi and Shigeru Obayashi and Bogdan Filipic and Thomas Bartz-Beielstein and Grant Dick and Masaharu Munetomo and Silvino {Fernandez Alzueta} and Thomas Stuetzle and Pablo Valledor Pellicer and Manuel Lopez-Ibanez and Daniel R. Tauritz and Pietro S. Oliveto and Thomas Weise and Borys Wrobel and Ales Zamuda and Anne Auger and Julien Bect and Dimo Brockhoff and Nikolaus Hansen and Rodolphe {Le Riche} and Victor Picheny and Bilel Derbel and Ke Li and Hui Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Stephane Doncieux and Richard Duro and Joshua Auerbach and Harold {de Vladar} and Antonio J. Fernandez-Leiva and JJ Merelo and Pedro A. Castillo-Valdivieso and David Camacho-Fernandez and Francisco {Chavez de la O} and Ozgur Akman and Khulood Alyahya and Juergen Branke and Kevin Doherty and Jonathan Fieldsend and Giuseppe Carlo Marano and Nikos D. Lagaros and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Boris Naujoks and Vanessa Volz and Tea Tusar and Pascal Kerschke and Riyad Alshammari and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and John R. Woodward and Shin Yoo and John McCall and Nayat Sanchez-Pi and Luis Marti and Danilo Vasconcellos and Masaya Nakata and Anthony Stein and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Ivanoe {De Falco} and Antonio {Della Cioppa} and Ernesto Tarantino and Umberto Scafuri and P. G. M. Baltus and Giovanni Iacca and Ahmed Hallawa and Anil Yaman and Alma Rahat and Handing Wang and Yaochu Jin and David Walker and Richard Everson and Akira Oyama and Koji Shimoyama and Hemant Kumar and Kazuhisa Chiba and Pramudita Satria Palar", isbn13 = "978-1-4503-5764-7", pages = "149--150", address = "Kyoto, Japan", DOI = "doi:10.1145/3205651.3205741", publisher = "ACM", publisher_address = "New York, NY, USA", month = "15-19 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", abstract = "Genetic Programming Hyper-Heuristic (GPHH) has shown success in evolving dispatching rules for dynamic Flexible Job Shop Scheduling (FJSS). In this paper, we focus on feature construction to improve the effectiveness and efficiency of GPHH, and propose a GPHH with Cooperative Co-evolution with Feature Construction (CCGP-FC). The experimental results showed that the proposed CCGP-FC could improve the smoothness of the convergence curve, and thus improve the stability of the evolutionary process. There is a great potential to improve the FC methods, such as filtering the meaningless building blocks, and incorporating with feature selection to improve the terminal set.", notes = "Also known as \cite{3205741} GECCO-2018 A Recombination of the 27th International Conference on Genetic Algorithms (ICGA-2018) and the 23rd Annual Genetic Programming Conference (GP-2018)", } @Article{YU:2021:JNGSE, author = "Beichen Yu and Honggang Zhao and Jiabao Tian and Chao Liu and Zhenlong Song and Yubing Liu and Minghui Li", title = "Modeling study of sandstone permeability under true triaxial stress based on backpropagation neural network, genetic programming, and multiple regression analysis", journal = "Journal of Natural Gas Science and Engineering", volume = "86", pages = "103742", year = "2021", ISSN = "1875-5100", DOI = "doi:10.1016/j.jngse.2020.103742", URL = "https://www.sciencedirect.com/science/article/pii/S1875510020305965", keywords = "genetic algorithms, genetic programming, Artificial intelligence systems, Permeability, True triaxial stress, Pore pressure", abstract = "Permeability evolution of sandstone is of great significance in the development of tight sandstone gas reservoirs. Traditional laboratory tests have the disadvantages of high cost and long testing time. Therefore, the present study employed use artificial intelligence systems, i.e., backpropagation neural network (BPNN), genetic programming (GP), and multiple regression analysis to construct prediction models of sandstone permeability based on the coupling effect of true triaxial stress field and pore pressure. The results showed that the permeability prediction obtained from the systems fit well with the experimental data, and evidenced that permeability increased with pore pressure and decreased with increase in principal stress. Sensitivity analysis showed that the pore pressure has the greatest influence on sandstone permeability under different true triaxial stress. The effect of anisotropic principal stress on permeability exhibited ?1 > ?2 > ?3 under fixed pore pressure. Further assessment based on a combination of five evaluation indexes showed that the prediction accuracy of the BPNN model was better", } @Article{YU:2022:asoc, author = "Beichen Yu and Dongming Zhang and Bin Xu2 and Yubing Liu and Honggang Zhao and Chongyang Wang", title = "Modeling of true triaxial strength of rocks based on optimized genetic programming", journal = "Applied Soft Computing", volume = "129", pages = "109601", year = "2022", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2022.109601", URL = "https://www.sciencedirect.com/science/article/pii/S1568494622006500", keywords = "genetic algorithms, genetic programming, Local search, True triaxial stress, Multithreaded, Multiple regression analysis, Sensitivity analysis", abstract = "The strength of a rock is the main factor affecting the stability of an engineered rock mass. As laboratory testing requires sophisticated equipment and considerable time to determine rock strength, prediction models are needed for establishing rock strength criteria. Genetic programming (GP) is a soft computing technology often used to address rock mechanics and engineering challenges. However, GP also has limitations, such as a long running time, complex individual growth without a corresponding fitness improvement, and difficulty in finding the optimal solution. Therefore, we conducted this study by applying a dynamic restriction on individual size, local search of the neighborhood of the optimal individual, and multithreaded evaluation to optimize GP and guarantee the accuracy of the results and to build a prediction model for the true triaxial strength involving different rock types. The results showed that the restriction dynamically changes to restrict the redundant bloat of strength individuals without a corresponding fitness improvement; using local search rules can effectively find individuals with high fitness, so the strength predicted by the system was in good agreement with the measured strength. We also found the predicted strength was suitable for fitting the rock strength criteria. Using this multithreaded evaluation sped up the operation of the algorithm and produced accurate predictions; and for complex problems, increasing the threads had a more pronounced effect on the runtime and fitness improvements. Based on the Sobol global sensitivity analysis, we analyzed the influence of each prediction parameter on the true triaxial strength of rocks. Combined with the statistical assessment indices involving sum of the absolute error, mean, a10-index, and regression determination coefficient, the predictions of the optimized GP model that we established in this study were more accurate than those of multiple regression analysis", } @Article{Yu:2025:ress, author = "Beichen Yu and Yingke Liu and Dongming Zhang and Bin Xu and Changbao Jiang and Chao Liu", title = "Improved genetic programming modeling of slope stability and landslide susceptibility", journal = "Reliability Engineering and System Safety", year = "2025", volume = "264", pages = "111296", keywords = "genetic algorithms, genetic programming, Slope stability, Landslide, Probabilistic prediction", ISSN = "0951-8320", URL = "https://www.sciencedirect.com/science/article/pii/S0951832025004971", DOI = "doi:10.1016/j.ress.2025.111296", abstract = "The prediction of slope stability and landslide susceptibility is crucial for ensuring the safety and reliability of high slopes and disasters prevention. This study used genetic programming (GP) to predict slope stability and landslide risks. To address the limitations of GP such as local convergence and code redundancy growth and enhance prediction accuracy, hierarchical fair competition model based on K-means clustering algorithm (K-means-HFC), niche technique of similarity based on crowding (NTSC), and self-adaptive change in probability were proposed to improve the traditional GP. Then, the improved GP was used to conduct modelling research for prediction, including slope stability, land-slide dynamic characterisation, probabilistic hazard of seismic landslide, and blasting vibration parameters and hazard. The results showed that K-mean-HFC and NTSC separately increased inter- and intra-cluster population diversity and promoted the fitness, further enhancing the model prediction accuracy. In the case of multi-parameter prediction, the improved GP could realize attribute reduction on the prediction parameters, eliminate the attributes unrelated to the prediction parameters, and clearly obtain the prediction formulas. By using the improved GP, the prediction model of slope stability was acquired, the mutual prediction of surface displacement rate and subsurface volumetric was established, the probabilistic prediction diagram of seismic landslide in Sichuan Province was generated, the influence of prediction parameters was analysed, and the prediction of blasting vibration parameters and hazard of slope blasting under the influence of multiple parameters was realized. The derived prediction formulas possessed a significant reference for solving the same type of slope reliability and landslide prevention problems", } @Article{DBLP:journals/ijpr/YuZX17, author = "Biao Yu and Han Zhao and Deyi Xue", title = "A multi-population co-evolutionary genetic programming approach for optimal mass customisation production", journal = "Int. J. Prod. Res.", volume = "55", number = "3", pages = "621--641", year = "2017", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1080/00207543.2016.1194538", DOI = "doi:10.1080/00207543.2016.1194538", timestamp = "Fri, 09 Apr 2021 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/ijpr/YuZX17.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{Yu:2020:GECCOcomp, author = "Dong-Pil Yu and Yong-Hyuk Kim", title = "On the Co-Authorship Network in Evolutionary Computation", year = "2020", editor = "Richard Allmendinger and Hugo Terashima Marin and Efren Mezura Montes and Thomas Bartz-Beielstein and Bogdan Filipic and Ke Tang and David Howard and Emma Hart and Gusz Eiben and Tome Eftimov and William {La Cava} and Boris Naujoks and Pietro Oliveto and Vanessa Volz and Thomas Weise and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Rui Wang and Ran Cheng and Guohua Wu and Miqing Li and Hisao Ishibuchi and Jonathan Fieldsend and Ozgur Akman and Khulood Alyahya and Juergen Branke and John R. Woodward and Daniel R. Tauritz and Marco Baioletti and Josu Ceberio Uribe and John McCall and Alfredo Milani and Stefan Wagner and Michael Affenzeller and Bradley Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and Nayat Sanchez-Pi and Luis Marti and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and Matthew Johns and Nick Ross and Ed Keedwell and Herman Mahmoud and David Walker and Anthony Stein and Masaya Nakata and David Paetzel and Neil Vaughan and Stephen Smith and Stefano Cagnoni and Robert M. Patton and Ivanoe {De Falco} and Antonio {Della Cioppa} and Umberto Scafuri and Ernesto Tarantino and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Erik Hemberg and Riyad Alshammari and Tokunbo Makanju and Fuijimino-shi and Ivan Zelinka and Swagatam Das and Ponnuthurai Nagaratnam and Roman Senkerik", isbn13 = "9781450371278", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377929.3398157", DOI = "doi:10.1145/3377929.3398157", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion", pages = "61--62", size = "2 pages", keywords = "genetic algorithms, genetic programming, co-authorship network, evolutionary computation, DBLP", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", abstract = "With increased research achievements in the field of evolutionary computation, accurate identification and analysis of co-authorship characteristics have become more important than before. Therefore, in this study, a co-authorship network was used to analyze the aspects of collaboration in the evolutionary computation field. Decennial co-authorship networks were constructed using a bibliography database, following which analyses of the macroscopic network properties and aspect changes of authors who play a central role were conducted. In particular, for macroscopic features, the results were compared with those from the entire computer science field to derive the differences.", notes = "Also known as \cite{10.1145/3377929.3398157} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @InProceedings{Yu:2010:ICGEC, author = "HuaYun Yu and DaBin Zhang", title = "Design of Fuzzy Logic Controllers Based on Evolvable Hardware Platform", booktitle = "Fourth International Conference on Genetic and Evolutionary Computing (ICGEC 2010)", year = "2010", month = "13-15 " # dec, pages = "864--867", abstract = "Fuzzy Logic Controllers (FLCS) are rule-based system that successfully incorporate the flexibility of human-decision making by means of the use of fuzzy set theory. This paper provides an overview on evolutionary learning methods for the automated design and optimisation of fuzzy logic controllers. A three-stage evolution framework that uses Genetic Programming (GP) and Genetic Algorithms (GAS) evolves rule-base and membership function parameters of FLCS. For hardware implement of FLCS, We propose an Evolvable Hardware (EHW) platform for the design of fuzzy logic controllers. A simulation experiment is shown though this hybrid model in the design of fuzzy control systems. In the experiment, membership functions of input and output variables are defined by three parameters, called geometric proportional factors, adding with the scaling factors, which are adjusted to maximise the performance index by using the Genetic Algorithm. The fuzzy controller is designed to be more capability.", keywords = "genetic algorithms, genetic programming, EHW, evolutionary learning methods, evolvable hardware platform, fuzzy logic controllers, fuzzy set theory, geometric proportional factors, human-decision making, membership function, rule-based system, scaling factors, three-stage evolution framework, control system synthesis, fuzzy control, fuzzy set theory, knowledge based systems", DOI = "doi:10.1109/ICGEC.2010.219", notes = "Coll. of Comput. Sci., Yangtze Univ., Jingzhou, China. Also known as \cite{5715569}", } @Article{YU:2022:Fuel, author = "Huijing Yu and Xinjie Wang and Feifei Shen and Jian Long and Wenli Du", title = "Novel automatic model construction method for the rapid characterization of petroleum properties from near-infrared spectroscopy", journal = "Fuel", volume = "316", pages = "123101", year = "2022", ISSN = "0016-2361", DOI = "doi:10.1016/j.fuel.2021.123101", URL = "https://www.sciencedirect.com/science/article/pii/S0016236121029616", keywords = "genetic algorithms, genetic programming, Petroleum, Chemometrics, Near-infrared spectroscopy, Automatic model construction, Nonlinearity", abstract = "Petroleum fuels play an important role in economic society, and near-infrared analysis has been widely used in the characterization of petroleum fuels due to its effectiveness and efficiency. However, near-infrared spectra are high dimensional data that require high computational cost, thereby complicating the practical application. Previous model construction methods are also restricted to pre-designed model structure, thus limiting the model construction ability. To solve these problems, this paper proposes an automatic model construction algorithm that increases the diversity of model structure with linear-in-parameter representation and optimizes the model structure with genetic programming. The proposed automatic construction algorithm is verified on datasets from literature and experiments with real petroleum products compared with Partial Least Square (PLS) and Support Vector Machine Regression (SVR). The proposed method outperforms PLS and SVR in overall predictive accuracy and reduces the involved variable number by at least 70percent during the model construction process. Good performance in prediction accuracy, model complexity, and interpretability on the rapid characterization of different petroleum products can assist refinery plant in product control and management during production", } @Article{Yu:2007:Neoplasia, author = "Jianjun Yu and Jindan Yu and Arpit A. Almal and Saravana M. Dhanasekaran and Debashis Ghosh and William P. Worzel and Arul M. Chinnaiyan", title = "Feature Selection and Molecular Classification of Cancer Using Genetic Programming", journal = "Neoplasia", year = "2007", volume = "9", number = "4", pages = "292--303", month = apr, keywords = "genetic algorithms, genetic programming, Molecular diagnostics, biomarkers, prostate cancer, evolutionary algorithm, microarray profiling", DOI = "doi:10.1593/neo.07121", size = "15 pages", abstract = "Despite important advances in microarray-based molecular classification of tumours, its application in clinical settings remains formidable. This is in part due to the limitation of current analysis programs in discovering robust biomarkers and developing classifiers with a practical set of genes. Genetic programming (GP) is a type of machine learning technique that uses evolutionary algorithm to simulate natural selection as well as population dynamics, hence leading to simple and comprehensible classifiers. Here we applied GP to cancer expression profiling data to select feature genes and build molecular classifiers by mathematical integration of these genes. Analysis of thousands of GP classifiers generated for a prostate cancer data set revealed repetitive use of a set of highly discriminative feature genes, many of which are known to be disease associated. GP classifiers often comprise five or less genes and successfully predict cancer types and subtypes. More importantly, GP classifiers generated in one study are able to predict samples from an independent study, which may have used different microarray platforms. In addition, GP yielded classification accuracy better than or similar to conventional classification methods. Furthermore, the mathematical expression of GP classifiers provides insights into relationships between classifier genes. Taken together, our results demonstrate that GP may be valuable for generating effective classifiers containing a practical set of genes for diagnostic/prognostic cancer classification.", notes = "PMID: 17460773 [PubMed - indexed for MEDLINE] ONCOMINE data sets. Fitness based on AUROC. Max node count 8. 12 demes. z score gteq 40. ROC", } @PhdThesis{Thesis_jyu, author = "Jianjun Yu", title = "Bioinformatics Analysis of Omics Data Towards Cancer Diagnosis and Prognosis", school = "University of Michigan", year = "2007", address = "USA", keywords = "genetic algorithms, genetic programming", ISBN = "0-549-30549-1", URL = "http://mirlyn.lib.umich.edu/Record/005861214", URL = "http://books.google.co.uk/books?id=prxAOuPdl4wC", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Thesis_jyu.pdf", size = "196 pages", abstract = "Despite important advances in cancer research in recent decades, an accurate diagnosis and prognosis of cancer remains a formidable challenge to date. In this dissertation, several bioinformatics analyses have been developed for identifying new diagnostic/prognostic signatures using datasets derived from recent high-throughput screening techniques including DNA and protein microarray. In the first analysis we derived an outcome signature from estrogen signalling pathway to predict breast cancer prognosis. This signature successfully predicted patient outcome in multiple patient cohorts as well as ER+ and tamoxifen-treated sub-cohorts. The second part of my thesis focused on applying genetic programming for cancer classification. This approach can automatically select a handful of discriminative genes from gene expression data and produce comprehensible yet efficient rule-based classifiers. In the third analysis, we developed non-invasive diagnostic tools for prostate cancer diagnosis. Two different signatures were yielded from phage peptide microarray system and q-PCR urinary data, respectively. These signatures have the potential to improve specificity and sensitivity of prostate cancer diagnosis. Last, an integrative model was developed for culling a molecular signature of metastatic progression in prostate cancer from proteomic and transcriptomic data. Differential proteomic alterations between localised and metastatic prostate cancer, which were concordant with transcriptomic data, served as a predictor of clinical outcome in prostate cancer. This signature was also predictive of clinical outcome on other solid tumours, suggesting common molecular machinery in aggressive neoplasms. In summary, these bioinformatics analyses of cancer 'omics' data have led to several important findings that may ameliorate cancer diagnosis and prognosis.", notes = "The genetic programming part should be in Chapter 4 (page 69) Chair: Arul M. Chinnaiyan", } @Article{JingxianYu:1999:EAC, author = "Jingxian Yu and Hongqing Cao and Yongyan Chen and Lishan Kang and Hanxi Yang", title = "A New Approach to Estimation of the Electrocrystallization Parameters", journal = "Journal of Electroanalytical Chemistry", year = "1999", volume = "474", number = "1", pages = "69--73", month = sep, keywords = "genetic algorithms, genetic programming, Electrocrystallization parameters, Zinc, Parameters estimation", ISSN = "1572-6657", URL = "http://www.sciencedirect.com/science/article/pii/S0022072899003071", DOI = "doi:10.1016/S0022-0728(99)00307-1", size = "5 pages", abstract = "To overcome the drawbacks in estimating electrocrystallization parameters using traditional methods, we propose a genetic algorithm using a novel crossover operator based on the non-convex linear combination of multiple parents to estimate the electrocrystallisation parameters A (the nucleation rate constant), N0 (the nucleation density) and D (the diffusion coefficient of Zn2+ ions) simultaneously in the general current-time expression of Scharifker and Mostany for nucleation and growth by fitting the whole current transients for zinc electrodeposition onto glassy carbon electrode immersed in the acetate solutions. By running the algorithm, we obtained for different step potentials, D values close to 2.10cm2/sec/1000000, which are comparable to reported values. The values of A obtained for all step potentials are identical, 1.41/sec/1000000000, which indicates that zinc deposition onto glassy carbon electrode follows three-dimensional instantaneous nucleation and growth. In addition, from the values of N0 obtained, one can observe that an increase in step potential leads to a higher N0. These results show that our algorithm works stably and effectively in solving the problem of estimating the electrocrystallisation parameters, and more importantly, it can be extended easily to a general algorithm to estimate multiple parameters in an arbitrary chemical model.", } @Article{Yu:2007:CILS, author = "Jingxian Yu and Hongqing Cao and Yanbin He", title = "A new tree structure coding for equivalent circuit and evolutionary estimation of parameters", journal = "Chemometrics and Intelligent Laboratory Systems", year = "2007", volume = "85", number = "1", pages = "27--39", month = "15 " # jan, keywords = "genetic algorithms, genetic programming, Tree structure code, Equivalent circuit, Electrochemical impedance, Parameter optimisation", DOI = "doi:10.1016/j.chemolab.2006.03.007", abstract = "To optimise the parameters of electrical elements contained in an equivalent circuit for electrochemical impedance spectroscopy, we proposed a simple, intuitive and universal tree structure code (TSC) to encode an arbitrary complex circuit, then designed a genetic algorithm for parameter optimisation (GAPO) to work with the TSC and estimate the parameter values of electrical elements. The GAPO uses a novel crossover operator that performs by the non-convex linear combination of multiple parents and sets up a crossover subspace to enhance the global search. We first examined the effects of some key control parameters in the GAPO on the optimization process by selecting a relatively complex equivalent circuit to generate simulated data and comparing the parameters obtained by GAPO with the original values. Secondly, to examine the effectiveness and robustness of GAPO, we chose a set of simulated data generated by a relatively simple circuit, three sets of real impedance data on modified gold electrodes and a set of real impedance data on the anode of lithium-ion battery to run the GAPO and compared their calculated results with those obtained by complex nonlinear least square method (CNLS) supported by LEVM software. Finally, we compared the effects of five representative weighting strategies on the GAPO based on a set of simulated data generated by a relatively complicated circuit but with up to 10% Gaussian noise and the set of real impedance data on the anode of lithium-ion battery. All of these experimental results show that the GAPO works more quickly, efficiently and stably than CNLS when optimising the element parameters. We also found that appropriate weighting strategies can help reduce the effects of experimental errors on GAPO, but the effects really depend on the nature of the specific impedance data.", notes = "a School of Chemistry, Physics and Earth Sciences, Flinders University, Bedford Park, SA 5042, Australia b Department of Environmental Biology, School of Earth and Environmental Sciences, University of Adelaide, SA 5000, Australia c School of Chemistry and Chemical Engineering, Southwest University, Chongqing 400715, China", } @Article{YU:2021:CI, author = "Junwu Yu and Yan Wang2 and Zhaoqin Dai and Faming Yang and Alireza Fallahpour and Bahman Nasiri-Tabrizi", title = "Structural features modeling of substituted hydroxyapatite nanopowders as bone fillers via machine learning", journal = "Ceramics International", volume = "47", number = "7, Part A", pages = "9034--9047", year = "2021", ISSN = "0272-8842", DOI = "doi:10.1016/j.ceramint.2020.12.026", URL = "https://www.sciencedirect.com/science/article/pii/S0272884220336245", keywords = "genetic algorithms, genetic programming, Substituted hydroxyapatite, Machine learning, Ball milling, Rietveld refinement, Thermal stability", abstract = "In the present study, both experimental and modeling approaches were employed to explore the solid-state formation mechanisms and estimate the structural behavior of nanosized substituted hydroxyapatite (HA) powders using different machine learning (ML) techniques. In the phase of modeling, an artificial neural network (ANN)-based method, called multi-layer perceptron (MLP), was used to truthfully approximate structural characteristics of the as-received nanopowders. In the next round of modeling, the genetic programming (GP) technique was employed to appraise the strength of the predictive model. Following the modeling procedure, a few case studies were conducted to evaluate the results obtained by the modeling framework, where the microstructural alterations of the mechanosynthesized substituted nanopowders were examined in terms of the dopant agent. The Rietveld refinement showed a good fit of the observed and calculated profiles over the full diffraction patterns. With the effect of dopant type, different levels of weight loss were observed in the thermal analysis curves. The comparison between the proposed models ascertained that both models were truthful for the estimation of the structural features of HA-based bioceramics for different bone regeneration applications. From the statistical assessments, the values of Mean Squared Error (MSE) and Correlation Coefficient (R) of the MLP-ANN in the training phase for the crystallite size were 5.757 and 0.93, which in prediction reached 3.429 and 0.995, respectively", } @Article{Kunjie_Yu:JAI, author = "Kunjie Yu and Jintao Lian and Ying Bi and Jing Liang and Bing Xue and Mengjie Zhang", title = "A genetic programming approach with adaptive region detection to skin cancer image classification", journal = "Journal of Automation and Intelligence", year = "2024", volume = "3", number = "4", pages = "240--249", month = dec, keywords = "genetic algorithms, genetic programming, Skin cancer image classification, Region detection, Feature extraction", ISSN = "2949-8554", URL = "https://www.sciencedirect.com/science/article/pii/S2949855424000340", DOI = "doi:10.1016/j.jai.2024.08.003", notes = "https://people.wgtn.ac.nz/Mengjie.Zhang/publications", } @Article{Kunjie_Yu:ieeeTEC, author = "Kunjie Yu and Jintao Lian and Ying Bi and Jing Liang and Bing Xue and Mengjie Zhang", title = "An Automated and Interpretable Computer-Aided Approach for Skin Cancer Diagnosis Using Genetic Programming", journal = "IEEE Transactions on Evolutionary Computation", note = "Early Access", keywords = "genetic algorithms, genetic programming, Skin cancer, Feature extraction, Image classification, Skin, Representation learning, Solid modeling, Computational modeling, skin cancer computer-aided diagnosis systems, interpretability, evolutionary computation, image classification", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2024.3459096", size = "15 pages", notes = "also known as \cite{10679181}", } @InProceedings{CHI-2011-YuN, author = "Lixiu Yu and Jeffrey V. Nickerson", title = "Cooks or Cobblers? Crowd Creativity through Combination", booktitle = "Proceedings of the 29th ACM SIGCHI Conference on Human Factors in Computing Systems", year = "2011", pages = "1393--1402", address = "Vancouver, Canada", month = may # " 7-12", publisher = "ACM", keywords = "genetic algorithms, genetic programming, interactive evolution, human computation,Crowdsourcing, creativity, conceptual combination, human based genetic algorithm, social computing, design sketches", isbn13 = "9781450302289", URL = "https://bibtex.github.io/CHI-2011-YuN.html", URL = "https://cseweb.ucsd.edu//~gary/COGS200-f17/dow2.pdf", DOI = "doi:10.1145/1978942.1979147", size = "10 pages", abstract = "A sketch combination system is introduced and tested: a crowd of 1047 participated in an iterative process of design, evaluation and combination. Specifically, participants in a crowd sourcing marketplace sketched chairs for children. One crowd created a first generation of chairs, and then successive crowds created new generations by combining the chairs made by previous crowds. Other participants evaluated the chairs. The crowd judged the chairs from the third generation more creative than those from the first generation. An analysis of the design evolution shows that participants inherited and modified presented features, and also added new features. These findings suggest that crowd based design processes may be effective, and point the way toward computer-human interactions that might further encourage crowd creativity.", notes = "http://chi2011.org/index.html", } @Article{LixiuYu:2014:GPEM, author = "Lixiu Yu", title = "Daren C. Brabham: Crowdsourcing", journal = "Genetic Programming and Evolvable Machines", year = "2014", volume = "15", number = "2", pages = "219--220", month = jun, note = "Book review", ISSN = "1389-2576", URL = "https://rdcu.be/dR84Y", DOI = "doi:10.1007/s10710-014-9215-3", size = "2 pages", notes = "The MIT Press, 2013, 176 pp, $12.95, ISBN 9780262518475", } @InProceedings{Yu:2007:cec, author = "Lu Yu and Jin Zhou and Shingo Mabu and Kotaro Hirasawa and Jinglu Hu and Sandor Markon", title = "Double-Deck Elevator Group Supervisory Control System Using Genetic Network Programming with Ant Colony Optimization", booktitle = "2007 IEEE Congress on Evolutionary Computation", year = "2007", editor = "Dipti Srinivasan and Lipo Wang", pages = "1015--1022", address = "Singapore", month = "25-28 " # sep, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "1-4244-1340-0", file = "1081.pdf", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2007.4424581", abstract = "Recently, Artificial Intelligence (AI) technology has been applied to many applications. As an extension of Genetic Algorithm (GA) and Genetic Programming (GP), Genetic Network Programming (GNP) has been proposed, whose gene is constructed by directed graphs. GNP can perform a global searching, but its evolving speed is not so high and its optimal solution is hard to obtain in some cases because of the lack of the exploitation ability of it. To alleviate this difficulty, we developed a hybrid algorithm that combines Genetic Network Programming (GNP) with Ant Colony Optimisation (ACO). Our goal is to introduce more exploitation mechanism into GNP. In this paper, we applied the proposed hybrid algorithm to a complicated real world problem, that is, Elevator Group Supervisory Control System (EGSCS). The simulation results showed the effectiveness of the proposed algorithm.", notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C", } @InProceedings{Yu:2008:geccocomp, author = "Lu Yu and Jin Zhou and Fengming Ye and Shingo Mabu and Kaoru Shimada and Kotaro Hirasawa and Sandor Markon", title = "Double-deck Elevator System using Genetic Network Programming with Genetic Operators based on Pheromone Information", year = "2008", editor = "Marc Ebner and Mike Cattolico and Jano {van Hemert} and Steven Gustafson and Laurence D. Merkle and Frank W. Moore and Clare Bates Congdon and Christopher D. Clack and Frank W. Moore and William Rand and Sevan G. Ficici and Rick Riolo and Jaume Bacardit and Ester Bernado-Mansilla and Martin V. Butz and Stephen L. Smith and Stefano Cagnoni and Mark Hauschild and Martin Pelikan and Kumara Sastry", isbn13 = "978-1-60558-131-6", booktitle = "GECCO-2008 Late-Breaking Papers", pages = "2239--2244", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p2239.pdf", DOI = "doi:10.1145/1388969.1389052", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, ant colony optimisation, elevator group supervisory control system, genetic network programming, genetic operators, hybrid algorithms", abstract = "Genetic Network Programming (GNP), one of the extended evolutionary algorithms was proposed, whose gene is constructed by the directed graph. GNP is distinguished from other evolutionary techniques in terms of its compact structure and implicit memory function. GNP can perform a global searching, but it lacks of the exploitation ability. Since the behaviour of GNP is characterized by the balance between exploitation and exploration in the search space, we proposed a hybrid algorithm in this paper that combines GNP with Ant Colony Optimization (ACO). The genetic operators are operated using the pheromone information in some special generations. We applied the proposed hybrid algorithm to a complicated real world problem, that is , Elevator Group Supervisory Control System (EGSCS). The simulation results showed the effectiveness of the proposed algorithm.", notes = "Distributed on CD-ROM at GECCO-2008 ACM Order Number 910081. Also known as \cite{1389052}", } @InProceedings{Yu:2009:cec, author = "Lu Yu and Shingo Mabu and Tiantian Zhang and Shinji Eto and Kotaro Hirasawa", title = "Multi-Car Elevator Group Supervisory Control System Using Genetic Network Programming", booktitle = "2009 IEEE Congress on Evolutionary Computation", year = "2009", editor = "Andy Tyrrell", pages = "2188--2193", address = "Trondheim, Norway", month = "18-21 " # may, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-2959-2", file = "P025.pdf", DOI = "doi:10.1109/CEC.2009.4983212", abstract = "Elevator group control systems are the transportation systems for handling passengers in the buildings. With the increasing demand for high-rise buildings, Multi- Car Elevator System(MCES) where two cars operate separately and independently in an elevator shaft are attracting attention as the next novel elevator system. Genetic Network Programming( GNP), one of the evolutionary computations, can realize a rule based MCES due to its directed graph structure of the individual, which makes the system more flexible. This paper discusses MCES using GNP for the buildings with 30 floors. The performance of MCES are examined and compared with Double-Deck Elevator System(DDES).", keywords = "genetic algorithms, genetic programming, genetic network programming, directed graph structure, double-deck elevator system, evolutionary computation, passenger handling, rule based multicar elevator group supervisory control system, transportation system, directed graphs, lifts", notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR Also known as \cite{4983212}", } @InProceedings{Yu:2009:ieeeSMC, author = "Lu Yu and Shingo Mabu and Tiantian Zhang and Kotaro Hirasawa and Tsuyoshi Ueno", title = "A study on energy consumption of elevator group supervisory control systems using genetic network programming", booktitle = "IEEE International Conference on Systems, Man and Cybernetics, SMC 2009", year = "2009", month = "11-14 " # oct, pages = "583--588", abstract = "Elevator group supervisory control system (EGSCS) is a traffic system, where its controller manages the elevator movement to transport passengers in buildings efficiently. Recently, artificial intelligence (AI) technology has been used in such complex systems. Genetic network programming (GNP), a graph-based evolutionary method extended from GA and GP, has been already applied to EGSCS. On the other hand, since energy consumption is becoming one of the greatest challenges in the society, it should be taken as criteria of the elevator operations. Moreover, the elevator with maximum energy efficiency is therefore required. Finally, the simulations show that the elevator system has the higher energy consumption in the light traffic, thus, some factors have been introduced into GNP for energy saving in this paper.", keywords = "genetic algorithms, genetic programming, genetic network programming, AI technology, EGSCS, GA, GNP, GP, artificial intelligence technology, building passenger transport, complex system, elevator group supervisory control system, energy consumption, energy saving, graph-based evolutionary method, maximum energy efficiency, traffic control system, graph theory, intelligent control, large-scale systems, lifts", DOI = "doi:10.1109/ICSMC.2009.5346621", ISSN = "1062-922X", notes = "Also known as \cite{5346621}", } @PhdThesis{LuYu:thesis, author = "Lu Yu", title = "Elevator group supervisory control of double-deck and multi-car elevator systems using genetic network programming", school = "Waseda University", year = "2010", address = "Japan", month = jul, keywords = "genetic algorithms, genetic programming, Genetic Network Programming, ACO, lifts", URL = "http://jairo.nii.ac.jp/0069/00020471/en", URL = "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/36307/3/Honbun-5417_00.pdf", URL = "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/36307/1/Gaiyo-5417.pdf", URL = "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/36307/2/Shinsa-5417.pdf", URL = "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/36307/4/Honbun-5417_01.pdf", size = "135 pages", abstract = "Chapter headings Analysis of Energy Consumption of Elevator Group Supervisory Control System (EGSCS) based on Genetic Network Programming (GNP) Double-Deck Elevator Systems (DDES) with Destination Floor Guidance Systems (DFGS) using GNP Effects of Passengers Arrival Distribution to Double-Deck Elevator Systems (DDES) using GNP Double-Deck Elevator Systems (DDES) using GNP with Ant Colony Optimisation (ACO) Multi-Car Elevator System (MCES) using GNP", } @InCollection{yu:2000:OBBGP, author = "Chia-Hao (Jack) Yu", title = "Original Broom Balancer with Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "462--471", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:2000:gagp}", } @InProceedings{JessenYu:2000:CSCSD, author = "Jessen Yu and Martin A. Keane and John R. Koza", title = "Automatic design of both topology and tuning of a common parameterized controller for two families of plants using genetic programming", booktitle = "Proceedings of Eleventh IEEE International Symposium on Computer-Aided Control SystemDesign (CACSD) Conference and Ninth IEEE International Conference on Control Applications(CCA) Conference", year = "2000", pages = "CACSD-234--242", address = "Anchorage, Alaska", month = sep # " 25-27", organisation = "IEEE", keywords = "genetic algorithms, genetic programming", URL = "http://www.genetic-programming.com/jkpdf/cacsd2000.pdf", URL = "http://citeseer.ist.psu.edu/484660.html", abstract = "This paper demonstrates that a technique of evolutionary computation can be used to automatically create the design for both the topology and parameter values (tuning) for a common controller (containing various parameters representing the overall characteristics of the plant) for two families of plants. The automatically designed controller is created by means of genetic programming using a fitness measure that attempts to optimise step response and disturbance rejection while simultaneously imposing constraints on maximum sensitivity and sensor noise attenuation. The automatically designed controller outperforms the controller designed with conventional techniques. In particular, the automatically designed controller is superior to the Astrom and Hagglund controller for all plants of both families for the integral of the time-weighted absolute error (ITAE) for a step input, the ITAE for disturbance rejection, and maximum sensitivity. Averaged over all plants of both families, the ITAE for the step input for the automatically designed controller is only 58% of the value for the conventional controller; the ITAE for disturbance rejection is 91% of the value for the conventional controller; and the maximum sensitivity, Ms. for the automatically designed controller is only 85% of the value for the conventional controller. The automatically designed controller is {"}general{"} in the sense that it contains free variables and therefore provides a solution to an entire category of problems (i.e., all the plants in the two families) - not merely a single instance of the problem (i.e., a particular single plant).", } @Article{Yu:2021:IFS, author = "Jian Yu and Yuewang He and Qiben Yan and Xiangui Kang", title = "{SpecView:} Malware Spectrum Visualization Framework With Singular Spectrum Transformation", journal = "IEEE Transactions on Information Forensics and Security", year = "2021", volume = "16", pages = "5093--5107", abstract = "With the rapid development of automation tools including polymorphic and metamorphic engines, generic packers, and genetic programming, many variants of malware have emerged, which pose a significant threat to the Internet security. To effectively detect malware variants, researchers have developed visualization-based approaches that can visualize malware adaptations for in-depth malware analysis. However, most existing visualization approaches rely on the binary image of a malware sample, which fail to provide an effective texture feature representation and thus often result in low efficiency in coping with challenging malware samples. In this paper, we propose SpecView, a malware spectrum visualization framework with singular spectrum transformation. SpecView converts malware binary code into one-dimensional time series spectrum data, and leverages the singular spectrum transformation method to obtain the structural changes preserved in the time series spectrum data. Then, we use the particle swarm optimization algorithm to optimize the singular spectrum transformation performance in SpecView. We apply SpecView in the task of malware classification. Extensive experimental results show that SpecView is effective and efficient in malware classification on the Malimg, Malheur, Drebin, and PRAGuard Malgenome Class Encryption datasets, with classification accuracy exceeding 9percent, and it can effectively identify malware variants that use evasive techniques such as packer and encryption obfuscation. The proposed method outperforms the state-of-the-art methods on all datasets and the classification accuracy reaches 10percent for 5 malware families packed by the UPX packer on the Malimg dataset, as well as 9 malware families that use Class Encryption obfuscation techniques on the PRAGuard Malgenome Class Encryption datasets.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/TIFS.2021.3124725", ISSN = "1556-6021", notes = "Also known as \cite{9607026}", } @Article{JiangangYu:2006:PRL, author = "Jiangang Yu and Bir Bhanu", title = "Evolutionary feature synthesis for facial expression recognition", journal = "Pattern Recognition Letters", year = "2006", volume = "27", number = "11", pages = "1289--1298", month = aug, note = "Evolutionary Computer Vision and Image Understanding", keywords = "genetic algorithms, genetic programming, Feature learning, Gabor filters", DOI = "doi:10.1016/j.patrec.2005.07.026", abstract = "Feature extraction is one of the key steps in object recognition. In this paper we propose a novel genetically inspired learning method for facial expression recognition (FER). Unlike current research on facial expression recognition that generally selects visually meaningful feature by hands, our learning method can discover the features automatically in a genetic programming-based approach that uses Gabor wavelet representation for primitive features and linear/nonlinear operators to synthesise new features. These new features are used to train a support vector machine classifier that is used for recognising the facial expressions. The learned operator and classifier are used on unseen testing images. To make use of random nature of a genetic program, we design a multi-agent scheme to boost the performance. We compare the performance of our approach with several approaches in the literature and show that our approach can perform the task of facial expression recognition effectively.", } @InCollection{yu:2004:ECDM, author = "Qi Yu and Kay Chen Tan and Tong Heng Lee", title = "Knowledge Discovery in Data Mining via an Evolutionary Algorithms", booktitle = "Evolutionary Computing in Data Mining", publisher = "Springer", year = "2004", editor = "Ashish Ghosh and Lakhmi C. Jain", volume = "163", series = "Studies in Fuzziness and Soft Computing", chapter = "6", pages = "101--123", keywords = "genetic algorithms, coevolution, CORE", ISBN = "3-540-22370-3", URL = "http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-175-22-33980376-0,00.html", abstract = "GA+GP system used to preprocess ten UCI datasets constructing and selecting new features before using C4.5", notes = "Claims Table 6.4 (Iris) CORE is better than GGP \cite{DeFalco:ASC} and BGP \cite{engelbrecht:2000:SFUDTBBESCD}. Also comparisons with GP-Co \cite{Mendes:2001:PKDD} and GPCE \cite{kishore:2000:mpc}", size = "16 pages", } @Article{YU:2023:jhazmat, author = "Qiu Yu and Yi Zheng and Pengpeng Zhang and Linghao Zeng and Renhui Han and Yaoming Shi and Dongwei Li", title = "Genetic programming-based predictive model for the Cr removal effect of in-situ electrokinetic remediation in contaminated soil", journal = "Journal of Hazardous Materials", volume = "460", pages = "132430", year = "2023", ISSN = "0304-3894", DOI = "doi:10.1016/j.jhazmat.2023.132430", URL = "https://www.sciencedirect.com/science/article/pii/S0304389423017132", keywords = "genetic algorithms, genetic programming, Soil pollution, Electrokinetic remediation, Predictive model, Sensitivity analysis", abstract = "Soil electrokinetic remediation is an emerging and efficient in-situ remediation technology for reducing environmental risks. Promoting the dissolution and migration of Cr in soil under the electric field is crucial to decrease soil toxicity and ecological influences. However, it is difficult to establish strong relationships between soil treatment and impact factors and to quantify their contributions. Machine learning can help establish pollutant migration models, but it is challenging to derive predictive formulas to improve remediation efficiency, describe the predictive model construction process, and reflect the importance of the predictors for better regulation. Therefore, this paper established a predictive model for the electrokinetic remediation of Cr-contaminated soil using genetic programming (GP) after determining the characteristic parameters which influenced the remediation effect, described the model's adaptive optimization process through the algorithm's function, and identified the sensitivity factors affecting the Cr removal effect. Results showed that the Cr(VI) and total Cr concentrations predicted by GP were in satisfactory agreement with the experimental values, 92percent of the training data and 90percent of the validation data achieved errors within 1percent, and could fully reflect the target ions' content variation in different soil layers. By substituting the above prediction formulas into Sobol sensitivity analysis, it was determined that conductivity, pH, current, and moisture content dramatically affected the Cr content variation in distinguished regions. For overall contaminated area, the system current and soil pH were the most sensitive factors for Cr(VI) and total Cr contents. Remediation efforts throughout the contaminated area should focus on the role of current versus soil pH. GP and sensitivity analysis can provide decision support and operational guidance for in-situ soil electrokinetic treatment by establishing a remediation effect prediction model, expediting the development and innovation of electrokinetic technology", } @Article{Yu:2025:jece, author = "Qiu Yu and Yi Zheng and Zhao Jiang and Beichen Yu", title = "Application of genetic programming in electrokinetic treatment of soil and {COPR:} Focus on Cr({VI)} fractionation", journal = "Journal of Environmental Chemical Engineering", year = "2025", volume = "13", number = "2", pages = "115420", keywords = "genetic algorithms, genetic programming, Electrokinetic remediation, Cr(VI) removal, Artificial intelligence algorithm, Deep learning, ANN, Self-adaptive optimization", ISSN = "2213-3437", URL = "https://www.sciencedirect.com/science/article/pii/S2213343725001150", DOI = "doi:10.1016/j.jece.2025.115420", abstract = "Eliminating highly hazardous hexavalent chromium (Cr(VI)) from solid waste is momentous for alleviating Cr(VI) toxicity to the environment. Cost-effective electrokinetic remediation technology was used for the Cr(VI) removal from contaminated soil and chromite ore processing residue (COPR). While the technology's principle was straightforward, the associated physicochemical processes and pollutant characteristics were highly intricate, and it is hard to build the quantitative relationship between the treatment effect and various influencing factors. Thus, this study innovatively applied artificial intelligence algorithm to solid waste disposal, established a deep learning prediction model based on genetic programming (GP) for Cr(VI) evolution in water-soluble, weakly adsorbed, and insoluble form, and specifically analysed how the algorithm function enabled the model's self-adaptive optimisation. Results displayed that the generated prediction models were differentially distributed depending on the pollution medium and Cr(VI) fractionation. Current and anolyte pH were dominant in Cr(VI) elimination, and catholyte pH played a greater role in COPR than in soil. The predicted values was close to the experimental values, the prediction accuracies were all higher than 95percent, and could reveal the evolution patterns of Cr(VI) in different forms. For weakly adsorbed and insoluble Cr(VI) prediction in COPR, the error level was within 4percent and the data frequency within the error level of 4percent reached 90percent for water-soluble Cr(VI), suggesting that GP accurately predicted the majority of the Cr(VI) concentration data, which was an important guideline for optimising electrokinetic remediation schemes", } @InProceedings{conf/icnc/YuCP05, author = "Qizhi Yu and Chongcheng Chen and Zhigeng Pan", title = "Parallel Genetic Algorithms on Programmable Graphics Hardware", year = "2005", pages = "1051--1059", editor = "Lipo Wang and Ke Chen and Yew-Soon Ong", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3612", booktitle = "Advances in Natural Computation, First International Conference, ICNC 2005, Proceedings, Part III", address = "Changsha, China", month = aug # " 27-29", bibsource = "DBLP, http://dblp.uni-trier.de", keywords = "genetic algorithms, GPU", ISBN = "3-540-28320-X", DOI = "doi:10.1007/11539902_134", URL = "http://www.cad.zju.edu.cn/home/yqz/projects/gagpu/icnc05.pdf", size = "10 pages", abstract = "Parallel genetic algorithms are usually implemented on parallel machines or distributed systems. This paper describes how fine-grained parallel genetic algorithms can be mapped to programmable graphics hardware found in commodity PC. Our approach stores chromosomes and their fitness values in texture memory on graphics card. Both fitness evaluation and genetic operations are implemented entirely with fragment programs executed on graphics processing unit in parallel. We demonstrate the effectiveness of our approach by comparing it with compatible software implementation. The presented approach allows us benefit from the advantages of parallel genetic algorithms on low-cost platform.", notes = "1 College of Computer Science, Zhejiang University, Hangzhou 310027, P.R. China 2 Spatial Information Research Center of Fujian Province, Fuzhou University, Fuzhou 350002, P.R.China", } @InProceedings{yu:1997:polyGP, author = "T. Yu and C. Clack", title = "{PolyGP}: A Polymorphic Genetic Programming System in {Haskell}", booktitle = "Late Breaking Papers at the GP-97 Conference", year = "1997", editor = "John Koza", pages = "264--273", address = "Stanford, CA, USA", publisher_address = "Stanford, California, 94305-3079 USA", month = "13-16 " # jul, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.mun.ca/~tinayu/index_files/addr/public_html/pgp.new.pdf", abstract = "In general, the machine learning process can be accelerated through the use of heuristic knowledge about the problem solution. For example, monomorphic typed Genetic Programming (GP) uses type information to reduce the search space and improve performance. Unfortunately, monomorphic typed GP also loses the generality of untyped GP: the generated programs are only suitable for inputs with the specified type. Polymorphic typed GP improves over monomorphic and untyped GP by allowing the type information to be expressed in a more generic manner, and yet still imposes constraints on the search space. This paper describes a polymorphic GP system which can generate polymorphic programs: programs which take inputs of more than one type and produces outputs of more than one type. We also demonstrate its operation through the generation of the {"}map{"} polymorphic program.", size = "6 pages", notes = "GP-97LB", } @InProceedings{yu:1997:pegp, author = "Chris Clack and Tina Yu", title = "Performance Enhanced Genetic Programming", booktitle = "Proceedings of the Sixth Conference on Evolutionary Programming", year = "1997", editor = "Peter J. Angeline and Robert G. Reynolds and John R. McDonnell and Russ Eberhart", volume = "1213", series = "Lecture Notes in Computer Science", pages = "87--100", address = "Indianapolis, Indiana, USA", publisher_address = "Berlin", month = apr # " 13-16", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-62788-3", URL = "http://www.cs.mun.ca/~tinayu/index_files/addr/public_html/ep97.pdf", URL = "ftp://bells.cs.ucl.ac.uk/functional/papers/Published/ep97.ps.gz", DOI = "doi:10.1007/BFb0014803", size = "14 pages", abstract = "Genetic Programming is increasing in popularity as the basis for a wide range of learning algorithms. However, the technique has to date only been successfully applied to modest tasks because of the performance overheads of evolving a large number of data structures, many of which do not correspond to a valid program. We address this problem directly and demonstrate how the evolutionary process can be achieved with much greater efficiency through the use of a formally-based representation and strong typing. We report initial experimental results which demonstrate that our technique exhibits significantly better performance than previous work.", notes = "EP-97", } @InProceedings{yu:1997:FGP, author = "Tina Yu", title = "Functional Genetic Programming", booktitle = "Late Breaking Papers at the 1997 Genetic Programming Conference", year = "1997", editor = "John R. Koza", pages = "304", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California, 94305-3079, USA", month = "13--16 " # jul, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-206995-8", notes = "GP-97LB PHD Students' workshop The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670", } @TechReport{yu:1997:ekr, author = "Chris Clack and Tina Yu", title = "Software -- The Next Generation: Evolving Knowledge Reuse", institution = "UCL, Andersen Consulting", year = "1997", type = "white paper", address = "University College London, Gower Street, London", month = apr, keywords = "genetic algorithms, genetic programming", pages = "70--85", abstract = "...This paper is an abridged version of \cite{yu:1997:pegp}", notes = "Part of {"}Emerging Technologies White Papers: Software -- The Next Generation{"} which reports the 1996 workshop on Emerging technologies held in UCL Computer Science dept. for Andersen Consulting's Emerging Technologies Group and others.", size = "16 pages", } @InProceedings{yu:1998:rlaGP, author = "Tina Yu and Chris Clack", title = "Recursion, Lambda-Abstractions and Genetic Programming", booktitle = "Late Breaking Papers at EuroGP'98: the First European Workshop on Genetic Programming", year = "1998", editor = "Riccardo Poli and W. B. Langdon and Marc Schoenauer and Terry Fogarty and Wolfgang Banzhaf", pages = "26--30", address = "Paris, France", publisher_address = "School of Computer Science", month = "14-15 " # apr, publisher = "CSRP-98-10, The University of Birmingham, UK", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/csrp-98-10.pdf", size = "5 pages", notes = "EuroGP'98LB part of \cite{Poli:1998:egplb}", } @InProceedings{yu:1998:PolyGP, author = "Tina Yu and Chris Clack", title = "PolyGP: A Polymorphic Genetic Programming System in Haskell", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "416--421", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-548-7", URL = "http://www.cs.mun.ca/~tinayu/index_files/addr/public_html/pgp.new.pdf", size = "6 pages", abstract = "In general, the machine learning process can be accelerated through the use of additional knowledge about the problem solution. For example, monomorphic typed Genetic Programming (GP) uses type information to reduce the search space and improve performance. Unfortunately, monomorphic typed GP also loses the generality of untyped GP: the generated programs are only suitable for inputs with the specified type. Polymorphic typed GP improves over monomorphic and untyped GP by allowing the type information to be expressed in a more generic manner, and yet still imposes constraints on the search space. This paper describes a polymorphic GP system which can generate polymorphic programs: programs which take inputs of more than one type and produce outputs of more than one type.", notes = "GP-98 slides http://www.cs.mun.ca/~tinayu/index_files/addr/public_html/poly.slide.pdf broken Aug 2016", } @InProceedings{yu:1998:rlaGP98, author = "Tina Yu and Chris Clack", title = "Recursion, Lambda Abstractions and Genetic Programming", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "422--431", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-548-7", URL = "http://www.cs.mun.ca/~tinayu/index_files/addr/public_html/Recursion.pdf", URL = "ftp://bells.cs.ucl.ac.uk/functional/papers/Published/Recursion.ps.gz", URL = "https://discovery.ucl.ac.uk/id/eprint/10087121/", size = "9 pages", abstract = "Module creation and reuse are essential for Genetic Programming (GP) to be effective with larger and more complex problems. This paper presents a particular kind of program structure to serve these purposes: modules are represented as lambda abstractions and their reuse is achieved through an implicit recursion. A type system is used to preserve this structure. The structure of lambda abstraction and implicit recursion also provides structure abstraction in the program. Since the GP paradigm evolves program structure and contents simultaneously, structure abstraction can reduce the search effort for good program structure. Most evolutionary effort is then focused on the search for correct program contents rather than the structure. Experiments on the Even-N-Parity problem show that, with the structure of lambda abstractions and implicit recursion, GP is able to find a general solution which works for any value of N very efficiently.", notes = "GP-98 broken Feb 2021 slides http://www.cs.mun.ca/~tinayu/index_files/addr/public_html/Recursion.slide.pdf", } @InProceedings{TinaYu:1998:melpLB, author = "Tina Yu and Peter Bentley", title = "Methods to Evolve Legal Phenotypes", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "242--249", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "genetic algorithms, genetic programming", size = "8 pages", abstract = "Many optimisation problems require the satisfaction of constraints in addition to their objectives. When using an evolutionary algorithm to solve such problems, these constraints can be enforced in many different ways to ensure that legal solutions (phenotypes) are evolved. We have identified eleven ways to handle constraints within various stages of an evolutionary algorithm. Five of these methods are experimented on a run-time error constraint in a Genetic Programming system. The results are compared and analysed.", notes = "GP-98LB see also \cite{TinaYu:1998:melp}", } @InProceedings{TinaYu:1998:melp, author = "Tina Yu and Peter Bentley", title = "Methods to Evolve Legal Phenotypes", booktitle = "Fifth International Conference on Parallel Problem Solving from Nature", year = "1998", editor = "Agoston E. Eiben and Thomas Back and Marc Schoenauer and Hans-Paul Schwefel", volume = "1498", series = "LNCS", pages = "280--291", address = "Amsterdam", publisher_address = "Berlin", month = "27-30 " # sep, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-65078-4", URL = "http://www.cs.mun.ca/~tinayu/index_files/addr/public_html/ppsn.pdf", DOI = "doi:10.1007/BFb0056871", size = "12 pages", abstract = "Many optimisation problems require the satisfaction of constraints in addition to their objectives. When using an evolutionary algorithm to solve such problems, these constraints can be enforced in many different ways to ensure that legal solutions (phenotypes) are evolved. We have identified eleven ways to handle constraints within various stages of an evolutionary algorithm. Five of these methods are experimented on a run-time error constraint in a Genetic Programming system. The results are compared and analysed.", notes = "PPSN-V Fifth International Conference on Parallel Problem Solving from Nature RN/98/68 http://www.cs.ucl.ac.uk/research/rns/rns98.html See also \cite{TinaYu:1998:melpLB}", } @InProceedings{yu:1999:SAGP, author = "Tina Yu", title = "Structure Abstraction and Genetic Programming", booktitle = "Proceedings of the Congress on Evolutionary Computation", year = "1999", editor = "Peter J. Angeline and Zbyszek Michalewicz and Marc Schoenauer and Xin Yao and Ali Zalzala", volume = "1", pages = "652--659", address = "Mayflower Hotel, Washington D.C., USA", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "6-9 " # jul, organisation = "Congress on Evolutionary Computation, IEEE / Neural Networks Council, Evolutionary Programming Society, Galesia, IEE", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, theory, general even parity problem, perfect solutions, program representation selection, program structures, search space, structure abstraction, evolutionary computation, search problems", ISBN = "0-7803-5536-9 (softbound)", ISBN = "0-7803-5537-7 (Microfiche)", URL = "http://www.cs.mun.ca/~tinayu/index_files/addr/public_html/cec99.pdf", DOI = "doi:10.1109/CEC.1999.781995", abstract = "The selection of program representation can have strong impact on the performance of genetic programming. Previous work has shown that a particular program representation which supports structure abstraction is very effective in solving the general even parity problem. We investigate program structures and analyse all perfect solutions in the search space to provide explanation of why structure abstraction is so effective with this problem. This work provides guidelines for the application of structure abstraction to other problems", notes = "CEC-99 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. Library of Congress Number = 99-61143 ", } @PhdThesis{TinaYu:thesis, author = "Gwoing Tina Yu", title = "An Analysis of the Impact of Functional Programming Techniques on Genetic Programming", school = "University College, London", year = "1999", address = "Gower Street, London, WC1E 6BT, UK", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.mun.ca/~tinayu/index_files/addr/public_html/Thesis.pdf", URL = "http://www.cs.mun.ca/~tinayu/index_files/addr/public_html/Thesis.ps.gz", URL = "ftp://bells.cs.ucl.ac.uk/functional/papers/tina_yu_thesis.pdf.gz", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/tinayu/TinaYuThesis.ps.gz", URL = "https://discovery.ucl.ac.uk/id/eprint/10104592", URL = "http://ethos.bl.uk/OrderDetails.do?did=19&uin=uk.bl.ethos.313519", size = "185 pages", abstract = "Genetic Programming (GP) automatically generates computer programs to solve specified problems. It develops programs through the process of a create-test-modify cycle which is similar to the way a human writes programs. There are various functional programming techniques that human programmers can use to accelerate the program development process. This research investigated the applicability of some of the functional techniques to GP and analyzed their impact on GP performance. Among many important functional techniques, three were chosen to be included in this research, due to their relevance to GP. They are polymorphism, implicit recursion and higher-order functions. To demonstrate their applicability, a GP system was developed with those techniques incorporated. Furthermore, a number of experiments were conducted using the system. The results were then compared to those generated by other GP systems which do not support these functional features. Finally, the program search space of the general even-parity problem was analyzed to explain how these techniques impact GP performance. The experimental results showed that the investigated functional techniques have made GP more powerful in the following ways: 1) polymorphism has enabled GP to solve problems that are very difficult for standard GP to solve, i.e. nth and map programs; 2) higher-order functions and implicit recursion have enhanced GP's ability in solving the general even-parity problem to a greater degree than with any other known methods. Moreover, the analysis showed that these techniques directed GP to generate program solutions in a way that has never been previously reported. Finally, we provide the guidelines for the application of these techniques to other problems.", notes = "My version of ghostview barfs 8 March 2000 but Thesis.ps prints ok uk.bl.ethos.313519 UCL internal:000901353 ProQuest U642861", } @InProceedings{Yu:2000:GECCOlb, author = "Tina Yu", title = "Polymorphism and Genetic Programming", pages = "437--444", booktitle = "Late Breaking Papers at the 2000 Genetic and Evolutionary Computation Conference", year = "2000", editor = "Darrell Whitley", address = "Las Vegas, Nevada, USA", month = "8 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://thelackthereof.org/docs/library/unsorted/programming/genetic/GECCO%20-%20Polymorphism%20and%20Genetic%20Programming.pdf", size = "8 pages", abstract = "Types have been introduced to Genetic Programming (GP) by researchers with different motivation. We present the concept of types in GP and introduce a particular implementation of typed GP, polymorphism, that can enhance GP applicability to problems that are very difficult for standard GP to solve. Through the analysis of a series of experimental results, we demonstrate that the combination of polymorphism and GP evolutionary search has enabled two polymorphic programs to be generated.", notes = "Part of \cite{whitley:2000:GECCOlb} ", } @InProceedings{TinaYu:2001:ACMKDD, author = "Tina Yu and Jim Rutherford", title = "Modeling Sparse Engine Test Data Using Genetic programming", booktitle = "The Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining", year = "2001", address = "San Francisco, California, USA", month = "26-29 " # aug, keywords = "genetic algorithms, genetic programming, Data Modeling, Sparse Data, High Dimensionality, Virtual Testing", URL = "http://www.cs.mun.ca/~tinayu/index_files/addr/public_html/KDDFinal.pdf", URL = "http://www.acm.org/sigs/sigkdd/kdd2001/", abstract = "We demonstrate the generation of an engine test model using Genetic Programming. In particular, a two-phase modeling process is proposed to handle the high-dimensionality and sparseness natures of the engine test data. The resulting model gives high accuracy prediction on training data. It is also very good in predicting low range data values. However, at least partly due to limitations of the data set, its accuracy on validation data and high range data values is not satisfactory. Moreover, the subject experts could not interpret its real-world meaning. We hope the results of this study can benefit other engine oil modeling applications.", } @Article{TinaYu:2001:GPEM, author = "Tina Yu", title = "Hierachical Processing for Evolving Recursive and Modular Programs Using Higher Order Functions and Lambda Abstractions", journal = "Genetic Programming and Evolvable Machines", year = "2001", volume = "2", number = "4", pages = "345--380", month = dec, keywords = "genetic algorithms, genetic programming, hierarchical processing, recursion, structure abstraction, higher-order functions, lambda abstraction, polymorphism, type systems", ISSN = "1389-2576", URL = "http://www.cs.mun.ca/~tinayu/Publications_files/gpem.pdf", DOI = "doi:10.1023/A:1012926821302", size = "36 pages", abstract = "We present a novel approach using higher-order functions and l abstraction to evolve recursive and modular programs. Moreover, a new term 'structure abstraction' is introduced to describe the property emerged from the higher-order function program structure. We test this technique on the general even-parity problem. The results indicate that this approach is very effective with the general even-parity problem due to the appropriate selection of the foldr higher-order function. Initially, foldr structure abstraction identify the promising area of the search space at generation zero. Once the population is within the promising area, foldr structure abstraction provides hierarchical processing for search. Consequently, solutions to the general even-parity problem are found very efficiently. We identify the limitations of this new approach and conclude that only when the appropriate higher-order function is selected that the benefits of structure abstraction show.", notes = "STGP polymorphic general solution to even-n-parity from 12 test cases. Article ID: 386362", } @InProceedings{yu:2001:EuroGP_neutrality, author = "Tina Yu and Julian Miller", title = "Neutrality and the Evolvability of {Boolean} Function Landscape", booktitle = "Genetic Programming, Proceedings of EuroGP'2001", year = "2001", editor = "Julian F. Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon", volume = "2038", series = "LNCS", pages = "204--217", address = "Lake Como, Italy", publisher_address = "Berlin", month = "18-20 " # apr, organisation = "EvoNET", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, Neutrality, Evolvability, Boolean function landscape, Neutral mutation, Exploration vs. Exploitation, Graph-based Genetic Programming", ISBN = "3-540-41899-7", URL = "http://www.cs.mun.ca/~tinayu/index_files/addr/public_html/neutrality.pdf", DOI = "doi:10.1007/3-540-45355-5_16", size = "14 pages", abstract = "This work is a study of neutrality in the context of Evolutionary Computation systems. In particular, we introduce the use of explicit neutrality with an integer string coding scheme to allow neutrality to be measured during evolution. We tested this method on a Boolean benchmark problem. The experimental results indicate that there is a positive relationship between neutrality and evolvability: neutrality improves evolvability. We also identify four characteristics of adaptive/neutral mutations that are associated with high evolvability. They may be the ingredients in designing effective Evolutionary Computation systems for the Boolean class problem.", notes = "EuroGP'2001, part of \cite{miller:2001:gp}", } @InProceedings{yu:2001:EuroGP_poly, author = "Tina Yu", title = "Polymorphism and Genetic Programming", booktitle = "Genetic Programming, Proceedings of EuroGP'2001", year = "2001", editor = "Julian F. Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon", volume = "2038", series = "LNCS", pages = "218--233", address = "Lake Como, Italy", publisher_address = "Berlin", month = "18-20 " # apr, organisation = "EvoNET", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, Polymorphism, Strongly Typed GP, STGP, Multi-objective optimisation, Typed GP, Constraint handling, PolyGP", ISBN = "3-540-41899-7", URL = "http://www.cs.mun.ca/~tinayu/index_files/addr/public_html/poly.pdf", URL = "http://www.cs.mun.ca/~tinayu/index_files/addr/public_html/poly.pdf", DOI = "doi:10.1007/3-540-45355-5_17", size = "16 pages", abstract = "Types have been introduced to Genetic Programming (GP) by researchers with different motivation. We present the concept of types in GP and introduce a typed GP system, PolyGP, that supports polymorphism through the use of three different kinds of type variable. We demonstrate the usefulness of this kind of polymorphism in GP by evolving two polymorphic programs (nth and map) using the system. Based on the analysis of a series of experimental results, we conclude that this implementation of polymorphism is effective in assisting GP evolutionary search to generate these two programs. PolyGP may enhance the applicability of GP to a new class of problems that are difficult for other polymorphic GP systems to solve.", notes = "EuroGP'2001, part of miller:2001:gp. Best presentation", } @InProceedings{yu:2001:msetdugp, author = "Tina Yu and Jim Rutherford", title = "Modeling Sparse Engine Test Data Using Genetic Programming", booktitle = "2001 Genetic and Evolutionary Computation Conference Late Breaking Papers", year = "2001", editor = "Erik D. Goodman", pages = "499", address = "San Francisco, California, USA", month = "9-11 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.mun.ca/~tinayu/index_files/addr/public_html/GECCO2001.pdf", notes = "GECCO-2001LB", } @InProceedings{yu:2002:EuroGP, title = "Finding Needles in Haystacks is not Hard with Neutrality", author = "Tina Yu and Julian F. Miller", editor = "James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi", booktitle = "Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002", volume = "2278", series = "LNCS", pages = "13--25", publisher = "Springer-Verlag", address = "Kinsale, Ireland", publisher_address = "Berlin", month = "3-5 " # apr, year = "2002", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-43378-3", URL = "http://www.cs.mun.ca/~tinayu/index_files/addr/public_html/EuroGP2002.pdf", DOI = "doi:10.1007/3-540-45984-7_2", abstract = "We propose building neutral networks in needle-in-haystack fitness landscapes to assist an evolutionary algorithm to perform search. The experimental results on four different problems show that this approach improves the search success rates in most cases. In situations where neutral networks do not give performance improvement, no impairment occurs either. We also tested a hypothesis proposed in our previous work. The results support the hypothesis: when the ratio of adaptive/neutral mutations during neutral walk is close to that of fitness improvement step, the evolutionary search has a high success rate. Moreover, the ratio magnitudes indicate that more neutral mutations (than adaptive mutations) are required for the algorithms to find a solution in this type of search space.", notes = "EuroGP'2002, part of \cite{lutton:2002:GP} See also \cite{Yu:2006:AL}", } @InProceedings{TinaYu:2002:eh, author = "Tina Yu and Seong Lee", title = "Evolving Cellular Automata to Model Fluid Flow in Porous Media", booktitle = "The Fourth NASA/DoD workshop on Evolvable Hardware", year = "2002", keywords = "genetic algorithms", URL = "http://www.cs.mun.ca/~tinayu/index_files/addr/public_html/EH2002.pdf", size = "8 pages", abstract = "Fluid flow in porous media is a dynamic process that is traditionally modelled using PDE (Partial Differential Equations). In this approach, physical properties related to fluid flow are inferred from rock sample data. However, due to the limitations posed in the sample data (sparseness and noise), this method often yields inaccurate results. Consequently, production information is normally used to improve the accuracy of property estimation. This style of modeling is equivalent to solving inverse problems. We propose using a Genetic Algorithm (GA) as an inverse method to model fluid flow in a pore network Cellular Automaton (CA). This GA evolves the CA to produce specified flow dynamic responses. We apply this method to a rock sample data set. The results are presented and discussed. Additionally, the prospect of building the pore network CA machine is discussed.", notes = "EH2002 On my printer, some parts of figures in yu-fluid.pdf came out funny 9.may.2002 ChevronTexaco Information Technology Company ChevronTexaco Exploration & Production Technology Company 6001 Bollinger Canyon Road 6001 Bollinger Canyon Road San Ramon, CA 94583 San Ramon, CA 94583", } @InProceedings{Yu:2002:gecco, author = "Tina Yu and Julian Miller", title = "Climbing Unimodal Landscapes With Neutrality: {A} Case Study Of The One-max Problem", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "704", address = "New York", publisher_address = "San Francisco, CA 94104, USA", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "genetic algorithms, poster paper, adaptive mutation, exploitation, exploration, neutral mutation, neutrality, OneMax", ISBN = "1-55860-878-8", URL = "http://www.cs.mun.ca/~tinayu/index_files/addr/public_html/GECCO2002.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/GA270.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-13.pdf", notes = "GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)", } @InProceedings{yu2:2002:gecco:lbp, title = "The Role of Neutral and Adaptive Mutation in an Evolutionary Search on the OneMax Problem", author = "Tina Yu and Julian F. Miller", booktitle = "Late Breaking Papers at the Genetic and Evolutionary Computation Conference ({GECCO-2002})", editor = "Erick Cant{\'u}-Paz", year = "2002", month = jul, pages = "512--519", address = "New York, NY", publisher = "AAAI", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, neutrality", URL = "http://www.cs.mun.ca/~tinayu/Publications_files/GECCO2002Late.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.15.7418", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.15.7418.pdf", broken = "http://www.cs.bham.ac.uk/~jfm/gecco2002Late.pdf", size = "9 pages", abstract = "We investigate neutrality in the simple Genetic Algorithms (SGA) and in our neutrality-enabled evolutionary system using the OneMax problem. The results show that with the support of limited neutrality, SGA is less effective than our system where a larger amount of neutrality is supported. In order to understand the role of neutrality in evolutionary search of this unimodal landscape, we have created a theoretical framework that gives the number of gene changes under different levels of neutrality. The interim results of this theoretical work are also presented", notes = "Late Breaking Papers, {GECCO-2002}. A joint meeting of the eleventh International Conference on Genetic Algorithms ({ICGA-2002}) and the seventh Annual Genetic Programming Conference ({GP-2002}) part of cantu-paz:2002:GECCO:lbp OneMax, explicit versus implicit neutrality, analysis. Variable mutation rate and neutrality. Success rate increases with neutrality (Hamming distance)", } @InCollection{TinaYu:2003:GPTP, author = "Tina Yu and Dave Wilkinson and Deyi Xie", title = "A Hybrid GP-Fuzzy Approach for Reservoir Characterization", booktitle = "Genetic Programming Theory and Practice", publisher = "Kluwer", year = "2003", editor = "Rick L. Riolo and Bill Worzel", chapter = "17", pages = "271--289", keywords = "genetic algorithms, genetic programming, Oil Exploration and Production, Reservoir Characterisation, Soft Computing, Permeability Estimation, Fuzzy Logic, Fuzzy Modelling.", ISBN = "1-4020-7581-2", URL = "http://www.cs.mun.ca/~tinayu/Publications_files/gptp2003.pdf", URL = "http://www.springer.com/computer/ai/book/978-1-4020-7581-0", DOI = "doi:10.1007/978-1-4419-8983-3_17", abstract = "A hybrid GP-fuzzy approach to model reservoir permeability is presented. This approach uses a two-step divide-and-conquer process for modelling. First, GP is applied to construct classifiers that identify permeability ranges. Within each range, ANFIS is employed to build a Takagi-Sugeno-Kang fuzzy inference system that gives permeability estimation. We applied this method to five well log data sets. The results show that this hybrid system gives more accurate permeability estimation than other previous works.", notes = "ChevronTexaco Information Technology Company and ChevronTexaco Exploration and Production Technology Company. Part of \cite{RioloWorzel:2003}", size = "19 pages", } @Article{Yu:2003:YLEM, author = "Tina Yu and Paul Johnson", title = "Tour Jet, Pirouette: Dance Choreographing by Computers", journal = "YLEM Journal", year = "2003", volume = "23", number = "6", pages = "8--10", month = may # "-" # jun, keywords = "pso", dance_url = "http://www.cs.mun.ca/~tinayu/Publications_files/Dance.mov.zip", URL = "http://www.ylem.org/Journal/2003Iss06vol23.pdf", URL = "http://www.cs.mun.ca/~tinayu/Publications_files/2003Iss06vol23.pdf", size = "3 pages", abstract = "Tina Yu and Paul Johnson tell how they use swarm concepts to generate choreography for dance. Dance might be one of the most egoistic art forms...", notes = "http://www.ylem.org/Journal/ See also \cite{Yu:2003:gecco} DOI:10.1007/3-540-45105-6_21 http://www.cs.mun.ca/~tinayu/Publications_files/dance.pdf dance_url is from Yu:2003:gecco", } @InCollection{yu:2004:GPTP, author = "Tina Yu and Shu-Heng Chen and Tzu-Wen Kuo", title = "Discovering Financial Technical Trading Rules Using Genetic Programming with Lambda Abstraction", booktitle = "Genetic Programming Theory and Practice {II}", year = "2004", editor = "Una-May O'Reilly and Tina Yu and Rick L. Riolo and Bill Worzel", chapter = "2", pages = "11--30", address = "Ann Arbor", month = "13-15 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, modular genetic programming, lambda abstraction modules, higher-order functions, financial trading rules, buy-and-hold, S&P 500 index, automatically defined functions, ADF, PolyGP system, stock market, technical analysis, constrained syntactic structure, strongly typed genetic programming, STGP, financial time series, lambda abstraction GP", ISBN = "0-387-23253-2", URL = "http://www.cs.mun.ca/~tinayu/Publications_files/gptp2004.pdf", DOI = "doi:10.1007/0-387-23254-0_2", size = "20 pages", abstract = "We applied genetic programming with a lambda abstraction module mechanism to learn technical trading rules based on S&P 500 index from 1982 to 2002. The results show strong evidence of excess returns over buy-and-hold after transaction cost. The discovered trading rules can be interpreted easily; each rule uses a combination of one to four widely used technical indicators to make trading decisions. The consensus among these trading rules is high. For the majority of the testing period, 80percent of the trading rules give the same decision. These rules also give high transaction frequency. Regardless of the stock market climate, they are able to identify opportunities to make profitable trades and out-perform buy-and-hold.", notes = "part of \cite{oreilly:2004:GPTP2} ", } @InProceedings{RePEc:sce:scecf4:200, author = "Tina Yu and Shu-Heng Chen", title = "Using Genetic Programming with Lambda Abstraction to Find Technical Trading Rules", booktitle = "Computing in Economics and Finance", year = "2004", address = "University of Amsterdam", month = "8-10 " # jul, organisation = "Society for Computational Economics", keywords = "genetic algorithms, genetic programming", URL = "https://ideas.repec.org/p/sce/scecf4/200.html", abstract = "Using GP with lambda abstraction module mechanism to generate technical trading rules based on S&P 500 index, we find strong evidence of excess returns over buy-and-hold after transaction cost on the testing period from 1989 to 2002. The rules can be interpreted easily; each uses a combination of one to four widely used technical indicators to make trading decisions. The consensus among GP rules is high, with most of the time 80% of the evolved rules give the same decision. The GP rules give high transaction frequency. Regardless of market climate, they are able to identify opportunities to make profitable trades and out-perform buy-and-hold", notes = "22 aug 2004 http://ideas.repec.org/p/sce/scecf4/200.html CEF 2004 number 200", } @InProceedings{yu:2004:nue:prewnue, author = "Tina Yu", title = "Workshop on Neutral Evolution in Evolutionary Computation", editor = "R. Poli and S. Cagnoni and M. Keijzer and E. Costa and F. Pereira and G. Raidl and S. C. Upton and D. Goldberg and H. Lipson and E. {de Jong} and J. Koza and H. Suzuki and H. Sawai and I. Parmee and M. Pelikan and K. Sastry and D. Thierens and W. Stolzmann and P. L. Lanzi and S. W. Wilson and M. O'Neill and C. Ryan and T. Yu and J. F. Miller and I. Garibay and G. Holifield and A. S. Wu and T. Riopka and M. M. Meysenburg and A. W. Wright and N. Richter and J. H. Moore and M. D. Ritchie and L. Davis and R. Roy and M. Jakiela", booktitle = "GECCO 2004 Workshop Proceedings", year = "2004", month = "26-30 " # jun, address = "Seattle, Washington, USA", keywords = "genetic algorithms, genetic programming", URL = "http://gpbib.cs.ucl.ac.uk/gecco2004/WNUE000.pdf", notes = "GECCO-2004WKS Distributed on CD-ROM at GECCO-2004", } @InProceedings{yu:2004:nue:tyu, author = "Tina Yu", title = "``Six Degrees of Separation'' in Boolean Function Networks with Neutrality", editor = "R. Poli and S. Cagnoni and M. Keijzer and E. Costa and F. Pereira and G. Raidl and S. C. Upton and D. Goldberg and H. Lipson and E. {de Jong} and J. Koza and H. Suzuki and H. Sawai and I. Parmee and M. Pelikan and K. Sastry and D. Thierens and W. Stolzmann and P. L. Lanzi and S. W. Wilson and M. O'Neill and C. Ryan and T. Yu and J. F. Miller and I. Garibay and G. Holifield and A. S. Wu and T. Riopka and M. M. Meysenburg and A. W. Wright and N. Richter and J. H. Moore and M. D. Ritchie and L. Davis and R. Roy and M. Jakiela", booktitle = "GECCO 2004 Workshop Proceedings", year = "2004", month = "26-30 " # jun, address = "Seattle, Washington, USA", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", URL = "http://www.cs.mun.ca/~tinayu/Publications_files/GECCO2004.pdf", URL = "http://www.cs.mun.ca/~tinayu/index_files/addr/public_html/NuE002.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco2004/WNUE003.pdf", size = "12 pages", abstract = "We analyse two Boolean function networks with different degrees of neutrality. The results show that the one with explicit neutrality is a small-world network where each pair of possible solutions has a short distance and most of the possible solutions are highly clustered. These network structural properties owe their existence to the ``short cuts'' introduced by redundant genes in the genotypes. We explain some important small-world network structures, such as clusters, hubs and power law link distribution. These properties have potential to be useful in designing efficient evolutionary algorithms to navigate search in the network.", notes = "GECCO-2004WKS Distributed on CD-ROM at GECCO-2004 3 bit parity, XOR", } @InCollection{TinaYu:2004:, author = "Tina Yu and Shu-Heng Chen and Tzu-Wen Kuo", title = "A Genetic Programming approach to Model International Short-term Capital Flow", booktitle = "Applications of Artificial Intelligence in Finance and Economics", publisher = "Jai Pr", year = "2004", editor = "Jane M. Binner and Graham Kendall and Shu-Heng Chen", volume = "19", series = "Advances in Econometrics", chapter = "2", pages = "45--70", keywords = "genetic algorithms, genetic programming, ADF", ISBN = "0-7623-1150-9", URL = "http://www.cs.mun.ca/~tinayu/Publications_files/Econometrics.pdf", URL = "http://www.amazon.com/exec/obidos/ASIN/0762311509/qid%3D1110315452/sr%3D11-1/ref%3Dsr%5F11%5F1/002-0089868-2466424", broken = "http://www.sciencedirect.com/science/article/B75F0-4DW2XG4-5/2/091cf27244b360ecf18b04ca79a1d1ad", DOI = "doi:10.1016/S0731-9053(04)19002-6", size = "26 pages", abstract = "We model international short-term capital flow by identifying technical trading rules in short-term capital markets using Genetic Programming (GP). The simulation results suggest that the international short-term markets was quite efficient during the period of 1997-2002, with most GP generated trading strategies recommending buy-and-hold on one or two assets. The out-of-sample performance of GP trading strategies varies from year to year. However, many of the strategies are able to forecast Taiwan stock market down time and avoid making futile investment. Investigation of Automatically Defined Functions shows that they do not give advantages or disadvantages to the GP results.", notes = "may be available via Elsevier?", } @Proceedings{yu:2005:GPTP, title = "Genetic Programming Theory and Practice {III}", year = "2005", editor = "Tina Yu and Rick L. Riolo and Bill Worzel", volume = "9", series = "Genetic Programming", address = "Ann Arbor", month = "12-14 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming", ISBN = "0-387-28110-X", DOI = "doi:10.1007/0-387-28111-8", notes = "Published Jan 2006 after the workshop", } @InCollection{yu:2005:intro, author = "Tina Yu and Rick L. Riolo and Bill Worzel", title = "Genetic Programming: Theory and Practice: An Introduction to Volume {III}", booktitle = "Genetic Programming Theory and Practice {III}", year = "2005", editor = "Tina Yu and Rick L. Riolo and Bill Worzel", volume = "9", series = "Genetic Programming", chapter = "1", pages = "1--14", address = "Ann Arbor", month = "12-14 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, continuous recurrent neural networks, evolving robots, swam agents", ISBN = "0-387-28110-X", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.462.7414", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.462.7414", URL = "http://www.cs.mun.ca/~tinayu/Publications_files/gptp2005-1.pdf", DOI = "doi:10.1007/0-387-28111-8_1", size = "14 pages", notes = "part of \cite{yu:2005:GPTP} Published Jan 2006 after the workshop ", } @InCollection{yu:2005:recurse, author = "Tina Yu", title = "A Higher-Order Function Approach to Evolve Recursive Programs", booktitle = "Genetic Programming Theory and Practice {III}", year = "2005", editor = "Tina Yu and Rick L. Riolo and Bill Worzel", volume = "9", series = "Genetic Programming", chapter = "7", pages = "93--108", address = "Ann Arbor", month = "12-14 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, recursion, Fibonacci sequence, strstr, PolyGP, type systems, higher-order functions, recursion patterns, filter, foldr, scanr, lambda abstraction, functional programming languages, Haskell", ISBN = "0-387-28110-X", URL = "http://www.cs.mun.ca/~tinayu/Publications_files/gptp2005.pdf", DOI = "doi:10.1007/0-387-28111-8_7", size = "16 pages", abstract = "We demonstrate a functional style recursion implementation to evolve recursive programs. This approach re-expresses a recursive program using a non-recursive application of a higher-order function. It divides a program recursion pattern into two parts: the recursion code and the application of the code. With the higher-order functions handling recursion code application, GP effort becomes focused on the generation of recursion code. We employed this method to evolve two recursive programs: strstr C library function and programs that produce the Fibonacci sequence. In both cases, the program space defined by higher-order functions are very easy for GP to find a solution. We have learned about higher-order function selection and fitness assignment through this study. The next step will be to test the approach on applications with open-ended solutions, such as evolutionary design.", notes = "part of \cite{yu:2005:GPTP} Published Jan 2006 after the workshop", } @InCollection{Yu:2006:GPTP, author = "Tina Yu and Dave Wilkinson and Alexandre Castellini", title = "Applying Genetic Programming to Reservoir History Matching Problem", booktitle = "Genetic Programming Theory and Practice {IV}", year = "2006", editor = "Rick L. Riolo and Terence Soule and Bill Worzel", volume = "5", series = "Genetic and Evolutionary Computation", pages = "187--201", address = "Ann Arbor", month = "11-13 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming", ISBN = "0-387-33375-4", URL = "http://www.cs.mun.ca/~tinayu/Publications_files/gptp2006.pdf", DOI = "doi:10.1007/978-0-387-49650-4_12", size = "14 pages", abstract = "History matching is the process of updating a petroleum reservoir model using production data. It is a required step before a reservoir model is accepted for forecasting production. The process is normally carried out by flow simulation, which is very time-consuming. As a result, only a small number of simulation runs are conducted and the history matching results are normally unsatisfactory. In this work, we introduce a methodology using genetic programming (GP) to construct a proxy for reservoir simulator. Acting as a surrogate for the computer simulator, the cheap GP proxy can evaluate a large number (millions) of reservoir models within a very short time frame. Collectively, the identified good-matching reservoir models provide us with comprehensive information about the reservoir. Moreover, we can use these models to forecast future production, which is closer to the reality than the forecasts derived from a small number of computer simulation runs. We have applied the proposed technique to a West African oil field that has complex geology. The results show that GP is able to deliver high quality proxies. Meanwhile, important information about the reservoirs was revealed from the study. Overall, the project has successfully achieved the goal of improving the quality of history matching results without increasing the number of reservoir simulation runs. This result suggests this novel history matching approach might be effective for other reservoirs with complex geology or a significant amount of production data.", notes = "part of \cite{Riolo:2006:GPTP} Published Jan 2007 after the workshop", } @Article{Yu:2006:AL, author = "Tina Yu and Julian Francis Miller", title = "Through the Interaction of Neutral and Adaptive Mutations, Evolutionary Search Finds a Way", journal = "Artificial Life", year = "2006", volume = "12", number = "4", pages = "525--551", month = "Fall", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", ISSN = "1064-5462", URL = "http://www.cs.mun.ca/~tinayu/Publications_files/YuMiller.pdf", DOI = "doi:10.1162/artl.2006.12.4.525", size = "28 pages", abstract = "An evolutionary system that supports the interaction of neutral and adaptive mutations is investigated. Experimental results on a Boolean function and needle-in-haystack problems show that this system enables evolutionary search to find better solutions faster. Through a novel analysis based on the ratio of neutral to adaptive mutations, we identify this interaction as an engine that automatically adjusts the relative amounts of exploration and exploitation to achieve effective search (i.e., it is self-adaptive). Moreover, a hypothesis to describe the search process in this system is proposed and investigated. Our findings lead us to counter the arguments of those who dismiss the usefulness of neutrality. We argue that the benefits of neutrality are intimately related to its implementation, so that one must be cautious about making general claims about its merits or demerits", notes = "See also \cite{yu:2002:EuroGP}. Even-12-Parity", } @InProceedings{Yu:2007:ECAL, author = "Tina Yu", title = "Program Evolvability Under Environmental Variations and Neutrality", booktitle = "Proceedings 9th European Conference on Artificial Life, ECAL 2007", year = "2007", editor = "Fernando {Almeida e Costa} and Luis Mateus Rocha and Ernesto Costa and Inman Harvey and Antonio Coutinho", volume = "4648", series = "Lecture Notes in Computer Science", pages = "835--844", address = "Lisbon", month = sep # " 10-14", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-74913-4", URL = "http://www.cs.mun.ca/~tinayu/Publications_files/ECAL2007.pdf", DOI = "doi:10.1007/978-3-540-74913-4_84", size = "10 pages", abstract = "Biological organisms employ various mechanisms to cope with the dynamic environments they live in. One recent research reported that depending on the rates of environmental variation, populations evolve toward genotypes in different regions of the neutral networks to adapt to the changes. Inspired by that work, we used a genetic programming system to study the evolution of computer programs under environmental variation. Similar to biological evolution, the genetic programming populations exploit neutrality to cope with environmental fluctuations and evolve evolvability. We hope this work sheds new light on the design of open-ended evolutionary systems which are able to provide consistent evolvability under variable conditions.", notes = "EQ only function set \cite{langdon:1998:BBparity} even 4-bit parity v. 4 bit always on. p836 'variation is the fuel of evolution'. p387 at the edge of neutral 'network mutations are likely to produce different phenotype'. No crossover. No length changes? Solution is exactly twice as likely to be selected as non-solution. I.e. always-on is no worse than anything else when selecting for even-4-parity. p842 longer solutions have proportionally more mutations. p843 ???All better programs have the maximum size (18) ???", } @Book{TinaYu:2008:book, editor = "Tina Yu and David Davis and Cem Baydar and Rajkumar Roy", title = "Evolutionary Computation in Practice", publisher = "Springer", year = "2008", volume = "88", series = "Studies in Computational Intelligence", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-75770-2", URL = "http://www.springer.com/978-3-540-75770-2", DOI = "doi:10.1007/978-3-540-75771-9", size = "xiv 322", abstract = "This book is loaded with examples in which computer scientists and engineers have used evolutionary computation programs that mimic natural evolution to solve real problems. They aren't abstract, mathematically intensive papers, but accounts of solving important problems, including tips from the authors on how to avoid common pitfalls, maximize the effectiveness and efficiency of the search process, and many other practical suggestions. Some of the authors have already won HUMIES Human Competitive Results Awards for the work described in this book. I highly recommend it as a highly concentrated source of good problem-solving approaches that are applicable to many real-world problems. --Erik Goodman, Vice President, Red Cedar Technology, Inc.; Professor, Electrical & Computer Engineering, Michigan State University; and Founding Chair, ACM SIGEVO, the Special Interest Group on Genetic and Evolutionary Computation of the Association for Computing Machinery", notes = "reviewed in \cite{Deschaine:2008:GPEM}", size = "322 pages", } @InCollection{Yu:2008:ECP1, author = "Tina Yu and Lawrence Davis", title = "An Introduction to Evolutionary Computation in Practice", booktitle = "Evolutionary Computation in Practice", publisher = "Springer", year = "2008", editor = "Tina Yu and David Davis and Cem Baydar and Rajkumar Roy", volume = "88", series = "Studies in Computational Intelligence", chapter = "1", pages = "1--8", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-75770-2", URL = "http://www.cs.mun.ca/~tinayu/Publications_files/ecp_chap1.pdf", DOI = "doi:10.1007/978-3-540-75771-9_1", size = "8 pages", abstract = "Deploying Evolutionary Computation (EC) solutions to real-world problems involves a...", notes = "Part of \cite{TinaYu:2008:book}", } @InCollection{Yu:2008:ECP, author = "Tina Yu and David Wilkinson", title = "A Co-Evolutionary Fuzzy System for Reservoir Well Logs Interpretation", booktitle = "Evolutionary Computation in Practice", publisher = "Springer", year = "2008", editor = "Tina Yu and David Davis and Cem Baydar and Rajkumar Roy", volume = "88", series = "Studies in Computational Intelligence", chapter = "9", pages = "199--218", keywords = "genetic algorithms, genetic programming, Fuzzy Logic, Time Series", isbn13 = "978-3-540-75770-2", URL = "http://www.cs.mun.ca/~tinayu/Publications_files/ecp_chap9.pdf", DOI = "doi:10.1007/978-3-540-75771-9_9", size = "20 pages", abstract = "Well log data are routinely used for stratigraphic interpretation of the earth's subsurface. This paper investigates using a co-evolutionary fuzzy system to generate a well log interpreter that can automatically process well log data and interpret reservoir permeability. The methodology consists of 3 steps: 1) transform well log data into fuzzy symbols which maintain the character of the original log curves; 2) apply a co-evolutionary fuzzy system to generate a fuzzy rule set that classifies permeability ranges; 3) use the fuzzy rule set to interpret well logs and infer the permeability ranges. We present the developed techniques and test them on well log data collected from oil fields in offshore West Africa. The generated fuzzy rules give sensible interpretation. This result is encouraging in two respects. It indicates that the developed well log transformation method preserves the information required for reservoir properties interpretation. It also suggests that the developed co-evolutionary fuzzy system can be applied to generate well log interpreters for other reservoir properties, such as lithology.", notes = "Part of \cite{TinaYu:2008:book} PolyGP, Java, Chevron. Or-crossover, and-crossover, homologous crossover.", } @InProceedings{Yu6:2008:cec, author = "Tina Yu and Dave Wilkinson and Julian Clark and Morgan Sullivan", title = "Evolving Finite State Transducers to Interpret Deepwater Reservoir Depositional Environments", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "3491--3498", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1823-7", file = "EC0748.pdf", slides_url = "http://www.cs.mun.ca/~tinayu/Publications_files/wcci_presentation.pdf", URL = "http://www.cs.mun.ca/~tinayu/Publications_files/EC0748.pdf", DOI = "doi:10.1109/CEC.2008.4631270", size = "8 pages", abstract = "Predicting oil recovery efficiency of deep water reservoirs is a challenging task. One approach to characterise and predict the producibility of a reservoir is by analysing its depositional information. In a deposition-based stratigraphic interpretation framework, one critical step is the identification and labelling of the stratigraphic components in the reservoir according to their depositional information. This interpretation process is labour intensive and can produce different results depending on the stratigrapher who performs the analysis. To relieve stratigrapher's workload and to produce more consistent results, this research developed a methodology to automate this process using various computational intelligent techniques. Using a well log data set, we demonstrated that the developed methodology and the designed work flow can produce finite state transducer models that interpret deepwater reservoir depositional environments adequately.", keywords = "genetic algorithms, genetic programming", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @Article{Yu:2008:JAEA, author = "Tina Yu and Dave Wilkinson and Alexandre Castellini", title = "Constructing Reservoir Flow Simulator Proxies Using Genetic Programming for History Matching and Production Forecast Uncertainty Analysis", journal = "Journal of Artificial Evolution and Applications", year = "2008", volume = "2008", pages = "Article ID 263108", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.mun.ca/~tinayu/Publications_files/JAEA.pdf", URL = "http://downloads.hindawi.com/archive/2008/263108.pdf", DOI = "doi:10.1155/2008/263108", size = "13 pages", abstract = "Reservoir modelling is a critical step in the planning and development of oil fields. Before a reservoir model can be accepted for forecasting future production, the model has to be updated with historical production data. This process is called history matching. History matching requires computer flow simulation, which is very time-consuming. As a result, only a small number of simulation runs are conducted and the history-matching results are normally unsatisfactory. This is particularly evident when the reservoir has a long production history and the quality of production data is poor. The inadequacy of the history-matching results frequently leads to high uncertainty of production forecasting. To enhance the quality of the history-matching results and improve the confidence of production forecasts, we introduce a methodology using genetic programming (GP) to construct proxies for reservoir simulators. Acting as surrogates for the computer simulators, the cheap GP proxies can evaluate a large number (millions) of reservoir models within a very short time frame. With such a large sampling size, the reservoir history-matching results are more informative and the production forecasts are more reliable than those based on a small number of simulation models. We have developed a workflow which incorporates the two GP proxies into the history matching and production forecast process. Additionally, we conducted a case study to demonstrate the effectiveness of this approach. The study has revealed useful reservoir information and delivered more reliable production forecasts. All of these were accomplished without introducing new computer simulation runs.", notes = "Department of Computer Science, Memorial University of Newfoundland. Chevron Energy Technology Company", } @InProceedings{Yu3:2009:cec, author = "Tina Yu and Dave Wilkinson", title = "Coevolution of Simulator Proxies and Sampling Strategies for Petroleum Reservoir Modeling", booktitle = "2009 IEEE Congress on Evolutionary Computation", year = "2009", editor = "Andy Tyrrell", pages = "2677--2684", address = "Trondheim, Norway", month = "18-21 " # may, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-2959-2", file = "P581.pdf", slides_url = "http://www.cs.mun.ca/~tinayu/Publications_files/cec2009ppt.pdf", URL = "http://www.cs.mun.ca/~tinayu/Publications_files/cec2009.pdf", DOI = "doi:10.1109/CEC.2009.4983278", size = "8 pages", abstract = "Reservoir modeling is an on-going activity during the production life of a reservoir. One challenge to constructing accurate reservoir models is the time required to carry out a large number of computer simulations. This research investigates a competitive co-evolutionary algorithm to select a small number of informative reservoir samples to carry out computer simulation. The simulation results are also used to co-evolve the computer simulator proxies. We have developed a co-evolutionary system incorporating various techniques to conduct a case study. Although the system was able to select a very small number of reservoir samples to run the computer simulations and use the simulation data to construct simulator proxies with high accuracy, these proxy models do not generalize very well on a larger set of simulation data generated from our previous study. Nevertheless, we have identified that including a test-bank in the system helped mitigating the situation. We will conduct more systematic analysis of the competitive co-evolutionary dynamics to improve the system performance.", keywords = "genetic algorithms, genetic programming", notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR", } @InProceedings{Yu:2010:ICMLA, author = "Tina Yu", title = "Modeling Occupancy Behavior for Energy Efficiency and Occupants Comfort Management in Intelligent Buildings", booktitle = "Ninth International Conference on Machine Learning and Applications (ICMLA 2010)", year = "2010", month = "12-14 " # dec, pages = "726--731", address = "Washington, DC, USA", isbn13 = "978-1-4244-9211-4", keywords = "genetic algorithms, genetic programming, energy efficiency, intelligent buildings, motion sensor data, occupancy behaviour modelling, occupants comfort management, building management systems, energy conservation", URL = "http://www.cs.mun.ca/~tinayu/Publications_files/PID1505499.pdf", DOI = "doi:10.1109/ICMLA.2010.111", abstract = "We applied genetic programming algorithm to learn the behaviour of an occupant in single person office based on motion sensor data. The learnt rules predict the presence and absence of the occupant with 80percent-83percent accuracy on testing data from 5 different offices. The rules indicate that the following variables may influence occupancy behaviour: 1) the day of week, 2) the time of day, 3) the length of time the occupant spent in the previous state, 4) the length of time the occupant spent in the state prior to the previous state, 5) the length of time the occupant has been in the office since the first arrival of the day. We evaluate the rules with various statistics, which confirm some of the previous findings by other researchers. We also provide new insights about occupancy behaviour of these offices that have not been reported previously.", notes = "Dept. of Comput. Sci., Memorial Univ. of Newfoundland, St. John's, NL, Canada. Also known as \cite{5708933}", } @Article{Yu2011716, author = "Tina Yu and Dave Wilkinson and Julian Clark and Morgan Sullivan", title = "Computational intelligence for deepwater reservoir depositional environments interpretation", journal = "Journal of Natural Gas Science and Engineering", year = "2011", volume = "3", number = "6", pages = "716--728", month = dec, note = "Artificial Intelligence and Data Mining", keywords = "genetic algorithms, genetic programming, Strongly typed genetic programming, STGP, Deep water reservoir, Stratigraphic interpretation, Depositional environment, Gamma ray interpretation, Computational intelligence, Fuzzy logic, Well log, Co-evolution, Time series, Segmentation, Finite state transducer, Classification rules", ISSN = "1875-5100", URL = "http://arxiv.org/abs/1301.2638", URL = "http://www.cs.mun.ca/~tinayu/Publications_files/1301.2638v1.pdf", URL = "http://www.sciencedirect.com/science/article/pii/S1875510011000849", DOI = "doi:10.1016/j.jngse.2011.07.014", size = "13 pages", abstract = "Predicting oil recovery efficiency of a deepwater reservoir is a challenging task. One approach to characterise a deepwater reservoir and to predict its producibility is by analysing its depositional information. This research proposes a deposition-based stratigraphic interpretation framework for deep water reservoir characterisation. In this framework, one critical task is the identification and labelling of the stratigraphic components in the reservoir, according to their depositional environments. This interpretation process is labour intensive and can produce different results depending on the stratigrapher who performs the analysis. To relieve stratigrapher's workload and to produce more consistent results, we have developed a novel methodology to automate this process using various computational intelligence techniques. Using a well log data set, we demonstrate that the developed methodology and the designed workflow can produce finite state transducer models that interpret deepwater reservoir depositional environments adequately.", notes = "Chevron", } @InProceedings{Yu:2013:CECa, article_id = "1168", author = "Yang Yu and Hui Ma and Mengjie Zhang", title = "An Adaptive Genetic Programming Approach to {QoS}-aware Web Services Composition", booktitle = "2013 IEEE Conference on Evolutionary Computation", volume = "1", year = "2013", month = jun # " 20-23", editor = "Luis Gerardo {de la Fraga}", pages = "1740--1747", address = "Cancun, Mexico", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-0453-2", DOI = "doi:10.1109/CEC.2013.6557771", abstract = "Web services are software entities that can be deployed, discovered and invoked in the distributed environment of the Internet through a set of standards such as Simple Object Access Protocol (SOAP), Web Services Description Language (WSDL) and Universal Description, Discovery and Integration (UDDI). However, atomic web service can only provide simple functionality. A range of web services are required to be incorporated into one composite service in order to offer value-added and complicated functionality when no existing web service can be found to satisfy users' request. In service-oriented architecture (SOA), web services composition has become an efficient solution to support business-to-business and enterprise application integration (EAI). In addition to functional properties (i.e., inputs and outputs), web services have non-functional properties called quality of service (QoS) that encompasses a number of parameters such as execution cost, response time and availability. Nowadays with the rapid increase in the number of available web services, a great number of services provide overlapping or identical functionality but vary in QoS attribute values. Due to the huge search space of the composition problem, a genetic programming (GP) approach is proposed in this paper, which aims to produce the desired outputs based on available inputs, as well as ensure that the composite service has the optimal QoS value. Furthermore, an adaptive method is applied to the standard form of GP in order to avoid low rate of convergence and premature convergence. A series of experiments have been conducted to evaluate the proposed approach, and the results show that the adaptive genetic programming approach (AGP) has a good performance in finding a valid solution within low search time and is superior to the traditional approaches", notes = "Quality of service. CEC 2013 - A joint meeting of the IEEE, the EPS and the IET.", } @InProceedings{Yu:2014:CECe, title = "A Genetic Programming Approach to Distributed {QoS}-Aware Web Service Composition", author = "Yang Yu and Hui Ma and Mengjie Zhang", pages = "1840--1846", booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary Computation", year = "2014", month = "6-11 " # jul, editor = "Carlos A. {Coello Coello}", address = "Beijing, China", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, Genetic programming, Parallel and Distributed Evolutionary Computation in the Cloud Era", DOI = "doi:10.1109/CEC.2014.6900416", abstract = "Web service composition has emerged as a promising technique for building complex web applications, thus supporting business-to-business and enterprise application integration. Nowadays there are increasing numbers of web services are distributed across the Internet. For a given service request there are many ways of service composition that can meet the service functional requirements (inputs and outputs) but have different qualities of Services (QoS), like response time or execution cost. QoS-aware web service composition seeks to find a service composition with optimised QoS properties. Genetic Programming is an efficient tool for tacking such optimisation problems efficiently. This paper proposes a novel GP-based approach for distributed web service composition where multiple QoS constraints are considered simultaneously. A series of experiments have been conducted to evaluate the proposed approach with test data. The results show that our approach is efficient and effective to find a near-optimal service composition solution in the context of distributed service environment.", notes = "Estimates response time (latency) using GNP global network positioning of of web server. GP tree composed of sequence, choice, parallel loop with leafs being atomic web services. Pop 50, generations 500. WCCI2014", } @InProceedings{Yu:2014:SEAL, author = "Yang Yu and Hui Ma and Mengjie Zhang", title = "A Hybrid GP-Tabu Approach to QoS-Aware Data Intensive Web Service Composition", booktitle = "Proceedings 10th International Conference on Simulated Evolution and Learning, SEAL 2014", year = "2014", editor = "Grant Dick and Will N. Browne and Peter Whigham and Mengjie Zhang and Lam Thu Bui and Hisao Ishibuchi and Yaochu Jin and Xiaodong Li and Yuhui Shi and Pramod Singh and Kay Chen Tan and Ke Tang", volume = "8886", series = "Lecture Notes in Computer Science", pages = "106--118", address = "Dunedin, New Zealand", month = dec # " 15-18", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-13562-5", DOI = "doi:10.1007/978-3-319-13563-2_10", abstract = "Web service composition has become a promising technique to build powerful business applications by making use of distributed services with different functions. Due to the explosion in the volume of data, providing efficient approaches to composing data intensive services will become more and more important in the field of service-oriented computing. Meanwhile, as numerous web services have been emerging to offer identical or similar functionality, web service composition is usually performed with end-to-end Quality of Service (QoS) properties which are adopted to describe the non-functional properties (e.g., response time, execution cost, reliability, etc.) of a web service. In this paper, a hybrid approach that integrates the use of genetic programming and tabu search to QoS-aware data intensive service composition is proposed. The performance of the proposed approach is evaluated using the publicly available benchmark datasets. A full set of experimental results show that a significant improvement of our approach over that obtained by the simple genetic programming method and several traditional optimization methods, has been achieved.", } @InProceedings{Yu:2015:CEC, author = "Yang Yu and Hui Ma and Mengjie Zhang", title = "{F-MOGP}: A Novel Many-Objective Evolutionary Approach to {QoS}-Aware Data Intensive Web Service Composition", booktitle = "Proceedings of 2015 IEEE Congress on Evolutionary Computation (CEC 2015)", year = "2015", editor = "Yadahiko Murata", pages = "2843--2850", address = "Sendai, Japan", month = "25-28 " # may, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-7491-7", DOI = "doi:10.1109/CEC.2015.7257242", abstract = "QoS-aware web service composition has attracted increasing attention recently. Meanwhile, due to the explosion in the volume of data, providing efficient methods for composing data intensive services poses new challenges to the service composition problem. In this paper, a new search and optimisation approach based on a reduced space searching strategy, named F-MOGP, is presented to address the problem of QoS-aware data intensive service composition driven by four conflicting quality objectives. The experimental results show that the proposed approach is computationally efficient by search space reduction. Compared with the existing single-objective and multi-objective optimization methods, a set of higher-quality data intensive service compositions can be successfully generated to support decision makers by providing them with potential trade-offs among different objectives.", notes = "1030 hrs 15578 CEC2015", } @InProceedings{conf/acsc/YuMZ16, author = "Yang Yu and Hui Ma and Mengjie Zhang", title = "A genetic programming approach to distributed execution of data-intensive web service compositions", publisher = "ACM", year = "2016", booktitle = "Proceedings of the Australasian Computer Science Week Multiconference, ACSW '16", address = "Canberra, Australia", pages = "29:1--29:9", keywords = "genetic algorithms, genetic programming, Distributed, Data-Intensive, Service Composition", isbn13 = "978-1-4503-4042-7", bibdate = "2016-02-11", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/acsc/acsw2016.html#YuMZ16", URL = "http://dl.acm.org/citation.cfm?id=2843043", DOI = "doi:10.1145/2843043.2843046", acmid = "2843046", size = "9 pages", abstract = "The executions of composite web services are typically co-ordinated by a centralized workflow engine. As a result, the centralized execution paradigm suffers from inefficient communication and a single point of failure. This is particularly problematic in the context of data-intensive processes. To that end, more distributed and flexible execution paradigms are required. In this paper, we present a genetic programming approach to partitioning a BPEL data-intensive process into a set of sub-processes which can be executed in a fully distributed manner. Meanwhile, the approach takes into account the communication latency and costs inside and across the partitions. The experimental results show that our proposed approach outperforms two existing methods for complex data-intensive processes.", notes = "ACE/ACSC/AISC/APCMM/AUIC/AWC", } @InProceedings{Yu:2010:IPEC, author = "YinQuan Yu and Chao Bi and Abdullah Al Mamun", title = "Diagnosis of crack of rotor blades with genetic method", booktitle = "IPEC, 2010 Conference Proceedings", year = "2010", month = "27-29 " # oct, pages = "362--367", abstract = "A methodology of modelling and simulation of rotor blades by using force vibration finite element method is presented. The experiments were conducted with 3D-LDV for verifying the analysis results. The measurements confirm the effectiveness of the modeling and simulation method presented. When Genetic Programming (GP) method and the data base created by the model are used, the accuracy of prediction of crack of the blades generated in the motor/generator operation can be improved.", keywords = "genetic algorithms, genetic programming, 3D-LDV, force vibration finite element method, genetic method, rotor blades crack diagnosis, blades, condition monitoring, crack detection, finite element analysis, rotors, vibrations", DOI = "doi:10.1109/IPECON.2010.5697159", ISSN = "1947-1262", notes = "Mechatron. & Recording Channel Div., A*STAR, Singapore, Singapore. Also known as \cite{5697159}", } @Article{YU:2020:CBM, author = "Yong Yu and Lang Lin", title = "Modeling and predicting chloride diffusion in recycled aggregate concrete", journal = "Construction and Building Materials", volume = "264", pages = "120620", year = "2020", ISSN = "0950-0618", DOI = "doi:10.1016/j.conbuildmat.2020.120620", URL = "http://www.sciencedirect.com/science/article/pii/S0950061820326258", keywords = "genetic algorithms, genetic programming, Recycled aggregate concrete, Chloride diffusion, Mesoscale modeling, Interfacial transition zones", abstract = "Application of recycled aggregate concrete (RAC) in engineering practice today remains relatively limited. One of the main reasons may be that the material's durability has not been comprehensively understood. No sufficiently accurate formulas are available for predicting its resistance to chloride infiltration. This study was therefore designed to investigate the commonest factors influencing chloride penetration in RAC using mesoscale finite element models. The variables of interest were the geometric shape of coarse aggregate pieces, their location distribution, the volume content of recycled material, the relative strength of the old to new mortar, the adhering content of old mortar, the bonding property of interfacial transition zones (ITZs) and the mixing method used. After performing a series of numerical simulations, a genetic programming (GP) method was lastly adopted to establish an explicit expression for correlating the RAC's effective chloride diffusivity with the identified key factors. Numerical results indicate that the RAC's diffusion coefficient was negligibly influenced by the aggregate shape or the old ITZ property, and commonly grows with increasing water-to-cement ratio, the amount of old mortar, the new ITZ's diffusivity as well with the replacing content of recycled aggregates. Equivalent mortar volume method can efficiently decrease the material's chloride diffusivity, especially at low water-to-cement ratios in the attached mortar. Finally, the expression provided by the GP method can adequately predict all these trends and is very convenient for investigating the RAC's chloride diffusion performance", } @Article{yu:2022:Materials, author = "Yong Yu and Xin-Yu Zhao and Jin-Jun Xu and Shao-Chun Wang and Tian-Yu Xie", title = "Evaluation of Shear Capacity of Steel Fiber Reinforced Concrete Beams without Stirrups Using Artificial Intelligence Models", journal = "Materials", year = "2022", volume = "15", number = "7", pages = "Article No. 2407", keywords = "genetic algorithms, genetic programming", ISSN = "1996-1944", URL = "https://www.mdpi.com/1996-1944/15/7/2407", DOI = "doi:10.3390/ma15072407", abstract = "The shear transfer mechanism of steel fiber reinforced concrete (SFRC) beams without stirrups is still not well understood. This is demonstrated herein by examining the accuracy of typical empirical formulas for 488 SFRC beam test records compiled from the literature. To steer clear of these cognitive limitations, this study turned to artificial intelligence (AI) models. A gray relational analysis (GRA) was first conducted to evaluate the importance of different parameters for the problem at hand. The outcomes indicate that the shear capacity depends heavily on the material properties of concrete, the amount of longitudinal reinforcement, the attributes of steel fibers, and the geometrical and loading characteristics of SFRC beams. After this, AI models, including back-propagation artificial neural network, random forest and multi-gene genetic programming, were developed to capture the shear strength of SFRC beams without stirrups. The findings unequivocally show that the AI models predict the shear strength more accurately than do the empirical formulas. A parametric analysis was performed using the established AI model to investigate the effects of the main influential factors (determined by GRA) on the shear capacity. Overall, this paper provides an accurate, instantaneous and meaningful approach for evaluating the shear capacity of SFRC beams containing no stirrups.", notes = "also known as \cite{ma15072407}", } @InProceedings{Yu:2019:CEC, author = "Yongbo Yu and Yalian Feng and Hui Ma and Aaron Chen and Chen Wang", booktitle = "2019 IEEE Congress on Evolutionary Computation (CEC)", title = "Achieving Flexible Scheduling of Heterogeneous Workflows in Cloud through a Genetic Programming Based Approach", year = "2019", pages = "3102--3109", abstract = "Cloud computing enables enormous computational resources to be scheduled as parallel workflow applications. Most traditional heuristics can only solve one particular scheduling problem. For example, Heterogeneous Earliest Finish Time (HEFT) and Greedy algorithms allocate resources to given ordered list of tasks using a specific single heuristic, which only caters for a specific scheduling problem, e.g. the fixed number of tasks in a workflow and available resources. Many researchers considered the heterogeneous work flows and cloud resources in scheduling in order to minimize the cost and makespan, but the solutions provided are only for specific workflow pattern. In this paper, we demonstrate a workflow scheduling problem which considers the combination of heterogeneous workflows as well as heterogeneous computing resources. We proposed Flexible Scheduling using Genetic Programming (FSGP) approach to minimise the total cost and makespan of heterogeneous workflows in the cloud. The performance of our proposed FSGP is regardless of the number of tasks in the workflow, available resources and workflow patterns. We evaluated our proposed approach using a benchmark dataset. Performance evaluation of some well-known algorithms such as HEFT and greedy algorithms exhibit that our FSGP approach perform better than other competing algorithms.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2019.8789896", month = jun, notes = "Also known as \cite{8789896}", } @InProceedings{Yu:2021:CEC, author = "Yongbo Yu and Hui Ma and Gang Chen2", booktitle = "2021 IEEE Congress on Evolutionary Computation (CEC)", title = "Achieving Multi-Objective Scheduling of Heterogeneous Workflows in Cloud through a Genetic Programming Based Approach", year = "2021", editor = "Yew-Soon Ong", pages = "1880--1887", address = "Krakow, Poland", month = "28 " # jun # "-1 " # jul, keywords = "genetic algorithms, genetic programming, Processor scheduling, Heuristic algorithms, Evolutionary computation, Scheduling, Task analysis", isbn13 = "978-1-7281-8393-0", DOI = "doi:10.1109/CEC45853.2021.9504695", abstract = "Traditional human-designed heuristics-based algorithms are commonly employed to address workflow scheduling problems. Such as Heterogeneous Earliest Finish Time (HEFT) and CriticalPath are two best-known list based heuristics. Generally, heuristics-based approaches can generate a single heuristic by making decisions based on the current status of the tasks and available resources and map the unscheduled tasks to the available resources. However, traditional heuristics can easily cause unbalancing load problem. For example, with the objective of minimizing the makespan, traditional heuristics prefer to use computation resources with high computation capacity. Usually, such resources are expensive, which will lead to a high cost as a result. Therefore it is hard for single traditional heuristics to do a trade-off; thus, traditional heuristics are hard to apply for multi-objective workflow scheduling. we develop a novel algorithm for workflow scheduling by considering both minimizing cost and makespan. Experiments show that the MOSGP approach can effectively minimize makespan and cost simultaneously.", notes = "Also known as \cite{9504695}", } @InProceedings{Yu:2022:CEC, author = "Yongbo Yu and Tao Shi and Hui Ma and Gang Chen2", booktitle = "2022 IEEE Congress on Evolutionary Computation (CEC)", title = "A Genetic Programming-Based Hyper-Heuristic Approach for Multi-Objective Dynamic Workflow Scheduling in Cloud Environment", year = "2022", editor = "Carlos A. Coello Coello and Sanaz Mostaghim", address = "Padua, Italy", month = "18-23 " # jul, isbn13 = "978-1-6654-6708-7", abstract = "With the popularity of cloud computing, many organizations process their workflow tasks in cloud resources based on the Pay-As-Per-Use model. Dynamic Workflow Scheduling (DWS) aims to allocate dynamically arriving workflow tasks to cloud resources with optimal makespan, cost, load balancing, etc. To timely allocate arriving tasks, heuristics have been used to solve the DWS problem in cloud environment. However, most of them are manually designed, considering a single objective, and use simple features to allocate resources to workflow tasks. In practice, multiple objectives should be considered to provide trade-off heuristics for users to choose from. In this paper, we propose a genetic programming hyper-heuristic (GPHH) approach to automatically generate multiple heuristics for multiobjective DWS. Our experimental evaluation using benchmark datasets demonstrates the effectiveness of our proposed GPHH approach.", keywords = "genetic algorithms, genetic programming, Cloud computing, Costs, Processor scheduling, Computational modeling, Organizations, Evolutionary computation, Cloud computing, dynamic workflow scheduling, multi-objective, GPHH", DOI = "doi:10.1109/CEC55065.2022.9870403", notes = "Also known as \cite{9870403}", } @Article{journals/ijcat/YuZ12, author = "Zhangyi Yu and Sanyou Y. Zeng", title = "Using Cartesian genetic programming to design wire antenna", journal = "International Journal of Computer Applications in Technology", year = "2012", number = "4", volume = "43", pages = "372--377", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", ISSN = "1741-5047", DOI = "doi:10.1504/IJCAT.2012.047163", abstract = "This paper presents a new method which uses Cartesian genetic programming (CGP) in order to design wire antenna. Since Julian F. Miller proposed the method of CGP, the research and development of CGP mainly trends in the design of the circuit application in recent years; very few scholars have the related research of wire antenna design in this field. Therefore, the most important feature in this paper is that this is the first time to apply CGP which is originally used for circuit design to make wire antenna design. By numerical test experiments and comparison, we find that this method of wire antenna design is novel and the designed wire antenna also can meet the requirements. This method has the comparative advantages and it is intelligent (self-adaptive, self-organising, self-learning, self-healing, etc.) while it can greatly increase the system speed.", notes = "School of Computer Science, China University of Geosciences, Wuhan, 430074, China", bibdate = "2012-06-11", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ijcat/ijcat43.html#YuZ12", } @Article{Yu:2005:IJICA, author = "Zhangyi Yu and Sanyou Zeng and Yan Guo and Liguo Song", title = "Using Cartesian genetic programming to implement function modelling", journal = "International Journal of Innovative Computing and Applications", publisher = "Inderscience Publishers", year = "2015", month = mar # "~21", volume = "3", number = "4", pages = "213--222", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, CGP, evolutionary algorithms, function modelling", bibsource = "OAI-PMH server at www.inderscience.com", ISSN = "1751-6498", URL = "http://www.inderscience.com/link.php?id=44530", DOI = "doi:10.1504/IJICA.2011.044530", abstract = "This paper presents a new method which uses Cartesian genetic programming (CGP) in order to implement function modelling. Since Julian F. Miller proposed the method of CGP, the research and development of CGP mainly trends in the design of the circuit application in recent years; very few scholars have the related research of function modelling in this field. Therefore, the most important feature in this paper is that we apply CGP which is originally used for circuit design to implement function modelling. By numerical test experiments and comparison, we find that this method of function modelling is novel and has the comparative advantages and it is intelligent (self-adaptive, self-organising, self-learning, self-healing, etc.) while it can greatly increase the system speed.", } @Article{Yuan:2006:IJCA, author = "Chang an Yuan and Chang jie Tang and Y. Wen and Jie Zuo and Jing Peng and Jian jun Hu", title = "Convergency of Genetic Regression in Data Mining based on Gene Expression Programming and Optimized Solution", journal = "International Journal of Computers and Applications", year = "2006", volume = "28", number = "4", keywords = "genetic algorithms, genetic programming, Gene Expression Programming, Data mining, genetic regression, convergency in probability, minimised residual sum of square genetic algorithm", DOI = "doi:10.2316/Journal.202.2006.4.202-1831", abstract = "This paper investigates the convergency of the probability of genetic regression in data mining based on Gene Expression Programming (GEP) and the proposed optimised algorithm based on GEP Minimised Residual Sum of Square Genetic Algorithm (MRSSGA). By extensive experiments on Genetic Programming (GP), GEP and MRSSGA show: (1) that all algorithms could find the target function from the data with low noise; (2) by comparing the convergency speeds, new algorithms in GEP are 20 times faster than GP and MRSSGA and 60 times faster than GP for simple data; (3) for very complex data with an unknown function type, GEP and MRSSGA are respectively 900 and 1800 times faster than GP at finding ideal functions; and (4) aimed at the actual data, the precision of models created by using genetic regression methods is much more exact than traditional methods.", } @Article{journals/access/YuanQYGD19, author = "Changan Yuan and Xiao Qin and Lechan Yang and Guangwei Gao and Song Deng", title = "A Novel Function Mining Algorithm Based on Attribute Reduction and Improved Gene Expression Programming", journal = "IEEE Access", year = "2019", volume = "7", pages = "53365--53376", keywords = "genetic algorithms, genetic programming, gene expression programming", bibdate = "2019-05-31", DOI = "doi:10.1109/ACCESS.2019.2911890", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/access/access7.html#YuanQYGD19", } @Article{YUAN:2022:bspc, author = "Dalong Yuan and Dong Zhang and Yan Yang and Shuang Yang", title = "Automatic construction of filter tree by genetic programming for ultrasound guidance image segmentation", journal = "Biomedical Signal Processing and Control", volume = "76", pages = "103641", year = "2022", ISSN = "1746-8094", DOI = "doi:10.1016/j.bspc.2022.103641", URL = "https://www.sciencedirect.com/science/article/pii/S174680942200163X", keywords = "genetic algorithms, genetic programming, Ultrasound guidance image, Segmentation, End-to-end, Small dataset", abstract = "Segmentation of ultrasound guidance images (UGIs) is a critical step in ultrasound-guided high intensity focused ultrasound (HIFU) therapy. However, the low signal-to-noise ratio characteristic of UGIs makes it difficult to acquire enough annotations. This paper proposes a novel genetic programming-based approach to achieve automatic construction of an image filter tree (IFT) for UGI segmentation since genetic programming has a natural advantage in training on small datasets. In the new approach, a set of predefined functions are adapted with better anti-noise performance to deal with noise interference. Moreover, a position-determined function is designed for incorporating preoperative information in each IFT to form a closed-loop system thereby facilitating the segmentation process. The optimal IFT evolved by genetic programming, along with a preprocessing step and a postprocessing step, constructs the pipeline for the segmentation of UGIs. The quantitative evaluation of the segmentation results shows the mean true positive rate, the mean false positive rate, the mean intersection over union, the mean norm Hausdorff distance and the mean norm maximum average distance are found to be 94.86percent, 6.72percent, 89.14percent, 3.20percent and 0.83percent, respectively, outperforming the popular convolutional neural network-based segmentation methods. The segmentation results reveal that the evolved IFT can achieve accurate segmentation of UGIs and indicate that the proposed approach can be a promising option for medical image segmentation when there are only a few training samples available", } @InCollection{Yuan2009669, author = "Wei Yuan and Andrew Odjo and Norman E. {Sammons, Jr.} and Jose Caballero and Mario R. Eden", title = "Process Structure Optimization Using a Hybrid Disjunctive-Genetic Programming Approach", editor = "Caludio Augusto Oller do Nascimento Rita Maria de Brito Alves and Jr. Evaristo Chalbaud Biscaia", booktitle = "10th International Symposium on Process Systems Engineering: Part A", publisher = "Elsevier", year = "2009", volume = "27", pages = "669--674", series = "Computer Aided Chemical Engineering", ISSN = "1570-7946", DOI = "doi:10.1016/S1570-7946(09)70332-3", URL = "http://www.sciencedirect.com/science/article/B8G5G-4XCHJF2-43/2/efa21c740f7a4df31bdbfd6ea85fac5b", keywords = "genetic algorithms, Process Optimization, Generalized Disjunctive Programming", abstract = "Discrete optimisation problems, which give rise to the conditional modelling of equations through representations as logic based disjunctions, are very important and often appear in all scales of chemical engineering process network design and synthesis. Disjunctive-Genetic Programming (D-GP), based on the integration of Genetic Algorithm (GA) with the disjunctive formulations of the Generalized Disjunctive Programming (GDP) for the optimization of process networks, has been proposed in this work. With the increase in the problem scale, dealing with such alternating routes becomes difficult due to increased computational load and possible entanglement of the results in sub-optimal solutions due to infeasibilities in the MILP space. In this work, the genetic algorithm (GA) has been used as a jumping operator to the different terms of the discrete search space and for the generation of different feasible fixed configurations. This proposed approach eliminates the need for the reformulation of the discrete /discontinuous optimization problems into direct MINLP problems, thus allowing for the solution of the original problem as a continuous optimization problem but only at each individual discrete and reduced search space.", notes = "Does not appear to be on GP", } @InProceedings{Yuan:2008:cec, author = "Xiao-Lei Yuan and Yan Bai and Ling Dong", title = "Identification of Linear Time-invariant, Nonlinear and Time Varying Dynamic Systems Using Genetic Programming", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "56--61", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1823-7", file = "EC0029.pdf", DOI = "doi:10.1109/CEC.2008.4630776", abstract = "An improved genetic programming (GP) algorithm was developed in order to use a unified way to identify both linear and nonlinear, both time-invariant and time-varying discrete dynamic systems. 'D' operators and discrete time 'n' terminals were used to construct and evolve difference equations. Crossover operations of the improved GP algorithm were different from the conventional GP algorithm. Two levels of crossover operations were defined. A linear time-invariant system, a nonlinear time-invariant system and a time-varying system were identified by the improved GP algorithm, good models of object systems were achieved with accurate and simultaneous identification of both structures and parameters. GP generated obvious mathematical descriptions (difference equations) of object systems after expression editing, showing correct input-output relationships. It can be seen that GP is good at handling different kinds of dynamic system identification problems and is better than other artificial intelligence (AI) algorithms like neural network or fuzzy logic which only model systems as complete black boxes.", keywords = "genetic algorithms, genetic programming", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @InProceedings{Yuan:2009:CCDC, author = "Xiao-lei Yuan and Yan Bai", title = "Stochastic nonlinear system identification using multi-objective multi-population parallel genetic programming", booktitle = "Chinese Control and Decision Conference, CCDC '09", year = "2009", month = jun, pages = "1148--1153", keywords = "genetic algorithms, genetic programming, multiobjective fitness definition, multiobjective multipopulation parallel genetic programming, nonlinear autoregressive with exogenous inputs polynomial models, object systems, stochastic nonlinear system identification, nonlinear systems, stochastic systems", DOI = "doi:10.1109/CCDC.2009.5192053", abstract = "To realize simultaneous identification of both structures and parameters of stochastic nonlinear systems, multi-population parallel genetic programming (GP) was employed. Object systems were represented by nonlinear autoregressive with exogenous inputs (NARX) and nonlinear autoregressive moving average with exogenous inputs (NARMAX) polynomial models, multi-objective fitness definition was used to restrict sizes of individuals during the evolution. For all examples, multi-population parallel GP found accurate models for object systems, simultaneously identified structures and parameters. In comparison with traditional single-population GP, multi-population GP showed a more competitive performance in avoiding premature convergence, and was much more efficient in searching for good models for object systems. From identification results, it can be concluded that multi-population parallel GP is good at handling complex stochastic nonlinear system identification problems and is superior to other existing identification methods.", notes = "Also known as \cite{5192053}", } @InProceedings{Yuan:2010:CCC, author = "Xiao-Lei Yuan and Yan Bai and Gang Peng and Zhi-Cun Gao and Peng Li and Rui Ma", title = "Stochastic nonlinear system identification based on HFC-GP", booktitle = "29th Chinese Control Conference (CCC 2010)", year = "2010", month = "29-31 " # jul, pages = "1217--1223", URL = "http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5573417", abstract = "To identify structures and parameters of complex stochastic nonlinear systems with accuracy and efficiency, preventing premature convergence during the evolution, an improved multi-objective hierarchical fair competition (HFC) parallel genetic programming (GP) algorithm was employed. The improved HFC GP algorithm was used to identify an object system based on nonlinear autoregressive moving average with exogenous inputs (NARMAX)model, good identification results were achieved with simultaneous identification of both structures and parameters of the object system. In comparison with single population GP and traditional multi-population GP, HFC-GP showed a more competitive performance in preventing premature convergence. It can be concluded that HFC-GP is good at solving complex stochastic nonlinear system identification problems and is superior to other existing identification methods.", keywords = "genetic algorithms, genetic programming, HFC-GP algorithm, NARMAX model, complex stochastic nonlinear system identification, multiobjective hierarchical fair competition, nonlinear autoregressive moving average with exogenous input, object system, parallel genetic programming, autoregressive moving average processes, identification, large-scale systems, nonlinear control systems, parallel algorithms, stochastic systems", notes = "In chinese. Also known as \cite{5573417}", } @Article{DBLP:journals/mvl/YuanB10, author = "Xiao-lei Yuan and Yan Bai", title = "Identifying Stochastic Nonlinear Dynamic Systems Using Multi-objective Hierarchical Fair Competition Parallel Genetic Programming", journal = "Multiple-Valued Logic and Soft Computing", year = "2010", volume = "16", number = "6", pages = "643--660", note = "Special Issue: New Trends on Swarm Intelligent Systems", keywords = "genetic algorithms, genetic programming, Nonlinear dynamic system identification, Stochastic system identification, NARX, NARMAX, HFC-GP, multi-objective evolution", bibsource = "DBLP, http://dblp.uni-trier.de", ISSN = "1542-3980", URL = "http://www.oldcitypublishing.com/journals/mvlsc-home/mvlsc-issue-contents/mvlsc-volume-16-number-6-2010/mvlsc-16-6-p-643-660/", broken = "http://www.oldcitypublishing.com/MVLSC/MVLSCabstracts/MVLSC16.6abstracts/MVLSCv16n6p643-660Yuan.html", broken = "http://www.oldcitypublishing.com/MVLSC/MVLSCcontents/MVLSCv16n6contents.html", abstract = "A parallel evolutionary algorithm named hierarchical fair competition genetic programming (HFC-GP) was employed to identify stochastic nonlinear dynamic systems. Nonlinear autoregressive with exogenous inputs (NARX) and nonlinear autoregressive moving average with exogenous inputs (NARMAX) polynomial models were used to represent object systems. Multi-objective fitness was used to restrict individual structure sizes during the run. HFC-GP outperformed single-population GP and traditional multi-population GP in combating premature convergence. For all examples, good results were achieved with simultaneous and accurate identification of both structures and parameters. It can be concluded that HFC-GP is very effective in combating premature convergence and is superior to other exiting identification methods.", } @InProceedings{Yuan:2015:PLOS, author = "Xinhao Yuan and David Williams-King and Junfeng Yang and Simha Sethumadhavan", title = "Making Lock-free Data Structures Verifiable with Artificial Transactions", booktitle = "Proceedings of the 8th Workshop on Programming Languages and Operating Systems, PLOS 2015", year = "2015", pages = "39--45", address = "Monterey, California, USA", month = "4-7 " # oct, publisher = "ACM", keywords = "genetic algorithms, genetic programming, genetic improvement, artificial transactions, lock-free data structures, software model checking, state space reduction, transactional memory, Concurrent Programming, Parallel programming, Software/Program Verification, Model checking, Performance Analysis and Design Aids, Design, Verification, Performance, Measurement", isbn13 = "978-1-4503-3942-1", DOI = "doi:10.1145/2818302.2818309", URL = "http://doi.acm.org/10.1145/2818302.2818309", acmid = "2818309", abstract = "Among all classes of parallel programming abstractions, lock-free data structures are considered one of the most scalable and efficient thanks to their fine-grained style of synchronization. However, they are also challenging for developers and tools to verify because of the huge number of possible interleavings that result from fine-grained synchronizations. This paper addresses this fundamental problem between performance and verifiability of lock-free data structure implementations. We present T_XIT, a system that greatly reduces the set of possible interleavings by inserting transactions into the implementation of a lock-free data structure. We leverage hardware transactional memory support from Intel Haswell processors to enforce these artificial transactions. Evaluation on six popular lock-free data structure libraries shows that TXIT makes it easy to verify lock-free data structures while incurring acceptable runtime overhead. Further analysis shows that two inefficiencies in Haswell are the largest contributors to this overhead.", notes = "mysql, p40 'We implemented TXIT for C/C++ lock-free data structures. It leverages the LLVM compiler [24] to instrument programs and insert artificial transactions, the Pyevolve genetic programming engine [8] to search for an optimal transaction placement plan, the dBug model checker [31] to systematically check schedules of transactions, and TSX - the hardware transactional memory support readily available in the 4th generation Intel Core processors (codenamed Haswell) [10] -to enforce artificial transactions (3).' Columbia University Also known as \cite{Yuan:2015:MLD:2818302.2818309}", } @Misc{2017arXiv171207804Y, author = "Yuan Yuan and Wolfgang Banzhaf", title = "{ARJA}: Automated Repair of Java Programs via Multi-Objective Genetic Programming", howpublished = "arXiv:1712.07804", year = "2017", month = dec, keywords = "genetic algorithms, genetic programming, genetic improvement, Program repair, patch generation, multi-objective optimization, SBSE, Software Engineering", URL = "https://arxiv.org/pdf/1712.07804", URL = "http://adsabs.harvard.edu/abs/2017arXiv171207804Y", size = "30 pages", abstract = "Recent empirical studies show that the performance of GenProg is not satisfactory, particularly for Java. In this paper, we propose ARJA, a new GP based repair approach for automated repair of Java programs. To be specific, we present a novel lower-granularity patch representation that properly decouples the search subspaces of likely-buggy locations, operation types and potential fix ingredients, enabling GP to explore the search space more effectively. Based on this new representation, we formulate automated program repair as a multi-objective search problem and use NSGA-II to look for simpler repairs. To reduce the computational effort and search space, we introduce a test filtering procedure that can speed up the fitness evaluation of GP and three types of rules that can be applied to avoid unnecessary manipulations of the code. Moreover, we also propose a type matching strategy that can create new potential fix ingredients by exploiting the syntactic patterns of the existing statements. We conduct a large-scale empirical evaluation of ARJA along with its variants on both seeded bugs and real-world bugs in comparison with several state-of-the-art repair approaches. Our results verify the effectiveness and efficiency of the search mechanisms employed in ARJA and also show its superiority over the other approaches. In particular, compared to jGenProg (an implementation of GenProg for Java), an ARJA version fully following the redundancy assumption can generate a test-suite adequate patch for more than twice the number of bugs (from 27 to 59), and a correct patch for nearly four times of the number (from 5 to 18), on 224 real-world bugs considered in Defects4J. Furthermore, ARJA is able to correctly fix several real multi-location bugs that are hard to be repaired by most of the existing repair approaches.", notes = "https://github.com/yyxhdy/arja", } @Article{Yuan:ieeeSE, author = "Yuan Yuan and Wolfgang Banzhaf", journal = "IEEE Transactions on Software Engineering", title = "{ARJA}: Automated Repair of Java Programs via Multi-Objective Genetic Programming", year = "2020", volume = "46", number = "10", pages = "1040--1067", month = oct, keywords = "genetic algorithms, genetic programming, genetic improvement, Program repair, APR, patch generation, multi-objective optimization", ISSN = "0098-5589", DOI = "doi:10.1109/TSE.2018.2874648", size = "28 pages", abstract = "Automated program repair is the problem of automatically fixing bugs in programs in order to significantly reduce the debugging costs and improve the software quality. To address this problem, test-suite based repair techniques regard a given test suite as an oracle and modify the input buggy program to make the entire test suite pass. GenProg is well recognized as a prominent repair approach of this kind, which uses genetic programming (GP) to rearrange the statements already extant in the buggy program. However, recent empirical studies show that the performance of GenProg is not fully satisfactory, particularly for Java. In this paper, we propose ARJA, a new GP based repair approach for automated repair of Java programs. To be specific, we present a novel lower-granularity patch representation that properly decouples the search subspaces of likely-buggy locations, operation types and potential fix ingredients, enabling GP to explore the search space more effectively. Based on this new representation, we formulate automated program repair as a multi-objective search problem and use NSGA-II to look for simpler repairs. To reduce the computational effort and search space, we introduce a test filtering procedure that can speed up the fitness evaluation of GP and three types of rules that can be applied to avoid unnecessary manipulations of the code. Moreover, we also propose a type matching strategy that can create new potential fix ingredients by exploiting the syntactic patterns of existing statements. We conduct a large-scale empirical evaluation of ARJA along with its variants on both seeded bugs and real-world bugs in comparison with several state-of-the-art repair approaches. Our results verify the effectiveness and efficiency of the search mechanisms employed in ARJA and also show its superiority over the other approaches. In particular, compared to jGenProg (an implementation of GenProg for Java), an ARJA version fully following the redundancy assumption can generate a test-suite adequate patch for more than twice the number of bugs (from 27 to 59), and a correct patch for nearly four times of the number (from 5 to 18), on 224 real-world bugs considered in Defects4J. Furthermore, ARJA is able to correctly fix several real multi-location bugs that are hard to be repaired by most of the existing repair approaches.", notes = "Also known as \cite{8485732}", } @InProceedings{Yuan:2019:GPTP, author = "Yuan Yuan and Wolfgang Banzhaf", title = "An Evolutionary System for better automatic software repair", booktitle = "Genetic Programming Theory and Practice XVII", year = "2019", editor = "Wolfgang Banzhaf and Erik Goodman and Leigh Sheneman and Leonardo Trujillo and Bill Worzel", pages = "383--406", address = "East Lansing, MI, USA", month = "16-19 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, genetic improvement, APR", isbn13 = "978-3-030-39957-3", DOI = "doi:10.1007/978-3-030-39958-0_19", abstract = "When a test suite is considered as the specification, the paradigm is called test-suite based repair Monperrus (ACM Comput Surv 51(1):17, 2018). The test suite should contain at least one negative (i.e., initially failing) test that triggers the bug to be fixed and a number of positive (i.e., initially passing) tests that define the expected program behavior.", notes = "Part of \cite{Banzhaf:2019:GPTP}, published after the workshop", } @InProceedings{Yuan:2019:GECCO, author = "Yuan Yuan and Wolfgang Banzhaf", title = "A Hybrid Evolutionary System for Automatic Software Repair", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference", year = "2019", editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and Anne Auger and Petr Posik and Leslie {Peprez Caceres} and Andrew M. Sutton and Nadarajen Veerapen and Christine Solnon and Andries Engelbrecht and Stephane Doncieux and Sebastian Risi and Penousal Machado and Vanessa Volz and Christian Blum and Francisco Chicano and Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and Jonathan Fieldsend and Jose Antonio Lozano and Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and Robin Purshouse and Thomas Baeck and Justyna Petke and Giuliano Antoniol and Johannes Lengler and Per Kristian Lehre", isbn13 = "978-1-4503-6111-8", pages = "1417--1425", address = "Prague, Czech Republic", DOI = "doi:10.1145/3321707.3321830", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, EMO, APR, search-based software engineering, Program repair, evolutionary multi-objective optimization", size = "9 pages", abstract = "This paper presents an automatic software repair system that combines the characteristic components of several typical evolutionary computation based repair approaches into a unified repair framework so as to take advantage of their respective component strengths. We exploit both the redundancy assumption and repair templates to create a search space of candidate repairs. Then we employ a multi-objective evolutionary algorithm with a low-granularity patch representation to explore this search space, in order to find simple patches. In order to further reduce the search space and alleviate patch overfitting we introduce replacement similarity and insertion relevance to select more related statements as promising fix ingredients, and we adopt anti-patterns to customize the available operation types for each likely-buggy statement. We evaluate our system on 224 real bugs from the Defects4J dataset in comparison with the state-of-the-art repair approaches. The evaluation results show that the proposed system can fix 111 out of those 224 bugs in terms of passing all test cases, achieving substantial performance improvements over the state-of-the-art. Additionally, we demonstrate the ability of ARJA-e to fix multi-location bugs that are unlikely to be addressed by most of existing repair approaches.", notes = "automatic bug fixing Also known as \cite{3321830} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @Article{Yuan:2020:TBE, author = "Yuan Yuan and Wolfgang Banzhaf", title = "Toward Better Evolutionary Program Repair: an Integrated Approach", journal = "ACM Transactions on Software Engineering and Methodology", volume = "29", number = "1", pages = "5:1--5:53", month = feb, year = "2020", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, SBSE", CODEN = "ATSMER", ISSN = "1049-331X", bibdate = "Thu Feb 6 08:32:22 MST 2020", bibsource = "http://www.math.utah.edu/pub/tex/bib/tosem.bib", URL = "https://yyxhdy.github.io/files/TOSEM2019.pdf", URL = "https://dl.acm.org/doi/abs/10.1145/3360004", DOI = "doi:10.1145/3360004", code_url = "https://github.com/yyxhdy/arja/tree/arja-e", size = "53 pages", abstract = "Bug repair is a major component of software maintenance, which requires a huge amount of manpower. Evolutionary computation, particularly genetic programming (GP), is a class of promising techniques for automating this time-consuming and expensive process. Although recent research in evolutionary program repair has made significant progress, major challenges still remain. In this article, we propose ARJA-e, anew evolutionary repair system for Java code that aims to address challenges for the search space, search algorithm, and patch overfitting. To determine a search space that is more likely to contain correct patches,ARJA-e combines two sources of fix ingredients (i.e., the statement-level redundancy assumption and repair templates) with contextual analysis-based search space reduction, thereby leveraging their complementary strengths. To encode patches in GP more properly, ARJA-e unifies the edits at different granularities into statement-level edits and then uses a lower-granularity patch representation that is characterized by the decoupling of statements for replacement and statements for insertion. ARJA-e also uses a finer-grained fitness function that can make full use of semantic information contained in the test suite, which is expected to better guide the search of GP. To alleviate patch overfitting, ARJA-e further includes a post processing tool that can serve the purposes of overfit detection and patch ranking. We evaluate ARJA-e on 224 real Java bugs from Defects4J and compare it with the state-of-the-art repair techniques. The evaluation results show that ARJA-e can correctly fix 39 bugs in terms of the patches ranked first, achieving substantial performance improvements over the state of the art. In addition, we analyze the effect of the components of ARJA-e qualitatively and quantitatively to demonstrate their effectiveness and advantages.", acknowledgement = "Nelson H. F. Beebe, University of Utah, Department of Mathematics, 110 LCB, 155 S 1400 E RM 233, Salt Lake City, UT 84112-0090, USA, Tel: +1 801 581 5254, FAX: +1 801 581 4148, e-mail: \path|beebe@math.utah.edu|, \path|beebe@acm.org|, \path|beebe@computer.org| (Internet), URL: \path|http://www.math.utah.edu/~beebe/ |", articleno = "5", journal-URL = "https://dl.acm.org/loi/tosem", } @InCollection{Yuan:2020:beacon, author = "Yuan Yuan and Wolfgang Banzhaf", title = "Making Better Use of Repair Templates in Automated Program Repair: A Multi-Objective Approach", booktitle = "Evolution in Action: Past, Present and Future: A Festschrift in Honor of Erik D. Goodman", publisher = "Springer", year = "2020", editor = "Wolfgang Banzhaf and Betty H. C. Cheng and Kalyanmoy Deb and Kay E. Holekamp and Richard E. Lenski and Charles Ofria and Robert T. Pennock and William F. Punch and Danielle J. Whittaker", series = "Genetic and Evolutionary Computation book series", chapter = "26", pages = "385--407", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, Program repair, evolutionary multi-objective optimisation, repair templates", isbn13 = "978-3-030-39833-0", DOI = "doi:10.1007/978-3-030-39831-6_26", abstract = "The automation of program repair can be coached in terms of search algorithms. Repair templates derived from common bug-fix patterns can be used to determine a promising search space with potentially many correct patches, a space that can be effectively explored by GP methods. Here we propose a new repair system, ARJA-p, extended from our earlier ARJA system of bug repair for JAVA, which integrates and enhances the performance of the first approach that combines repair templates and EC, PAR. Empirical results on 224 real bugs in Defects4J show that ARJA-p outperforms state-of-the-art repair approaches by a large margin, both in terms of the number of bugs fixed and of their correctness. Specifically, ARJA-p can increase the number of fixed bugs in Defects4J by 29.2percent (from 65 to 84) and the number of correctly fixed bugs by 42.3percent (from 26 to 37).", } @Article{DBLP:journals/ai/YuanB23, author = "Yuan Yuan and Wolfgang Banzhaf", title = "Iterative genetic improvement: Scaling stochastic program synthesis", journal = "Artificial Intelligence", year = "2023", volume = "322", pages = "103962", month = sep, keywords = "genetic algorithms, genetic programming, Genetic improvement, Evolutionary computation, Program synthesis, Artificial intelligence, AI", timestamp = "Sat, 05 Aug 2023 00:02:52 +0200", biburl = "https://dblp.org/rec/journals/ai/YuanB23.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "http://www.cs.mun.ca/~banzhaf/papers/IGI_AIJournal_2023.pdf", URL = "https://arxiv.org/abs/2202.13040", DOI = "doi:10.1016/J.ARTINT.2023.103962", size = "24 pages", abstract = "Program synthesis aims to automatically find programs from an underlying programming language that satisfy a given specification. While this has the potential to revolutionize computing, how to search over the vast space of programs efficiently is an unsolved challenge in program synthesis. In cases where large programs are required for a solution, it is generally believed that stochastic search has advantages over other classes of search techniques. Unfortunately, existing stochastic program synthesizers do not meet this expectation very well, suffering from the scalability issue. To overcome this problem, we propose a new framework for stochastic program synthesis, called iterative genetic improvement. The key idea is to apply genetic improvement to improve a current reference program, and then iteratively replace the reference program by the best program found. Compared to traditional stochastic synthesis approaches, iterative genetic improvement can build up the complexity of programs incrementally in a more robust way. We evaluate the approach on two program synthesis domains: list manipulation and string transformation, along with a number of general program synthesis problems. Our empirical results indicate that this method has considerable advantages over several representative stochastic program synthesizer techniques, both in terms of scalability and of solution quality.", } @Article{YUAN:2024:ces, author = "Yang Yuan and Linlin Zhang and Gade Pandu Rangaiah and Guochao Wang and Xing Qian and Lakshminarayanan Samavedham", title = "Design and optimization of compound distillation sequences comprising simple distillation and dividing-wall columns using genetic programming", journal = "Chemical Engineering Science", year = "2024", volume = "291", pages = "119950", month = "5 " # jun, keywords = "genetic algorithms, genetic programming, Distillation sequence, Dividing-wall column, Optimization, Artificial neural network, ANN", ISSN = "0009-2509", URL = "https://www.sciencedirect.com/science/article/pii/S0009250924002501", DOI = "doi:10.1016/j.ces.2024.119950", abstract = "For the separation of multi-component mixtures, compound distillation sequences (CDSs) comprising simple distillation columns (SDCs) and dividing-wall columns (DWCs) offer the advantage of significant energy-saving potential and relatively straightforward industrial implementation. This work proposes a design framework to facilitate the development of optimal CDSs. In this framework, genetic programming is employed to explore the optimal sequence; simultaneously, the exhaustive search method and the elitist genetic algorithm are separately employed to search for the optimal design of, respectively, SDCs and DWCs involved. Furthermore, to efficiently estimate the total annual cost (TAC) and both the flow rates and compositions of products of the SDCs and DWCs during the optimization process, artificial neural network (ANN)-based performance estimators were developed for them. An illustrative case study on n-butane/n-pentane/benzene/toluene/o-xylene/2,4-methyl-benzene mixtures is conducted. Analysis of the optimization results demonstrates a significant improvement in economic performance for the resulting CDS compared to the direct and indirect SDC sequences", } @InProceedings{Yuan:2024:APR, author = "Yuan Yuan", title = "{ARJA-e} for the First International Competition on Automated Program Repair", booktitle = "2024 IEEE/ACM International Workshop on Automated Program Repair (APR)", year = "2024", pages = "50--52", month = apr, keywords = "genetic algorithms, genetic programming, Codes, Conferences, Maintenance engineering, Artificial intelligence, Program Repair, APR", DOI = "doi:10.1145/3643788.3648019", abstract = "ARJA-e is an enhanced repair system based on ARJA. Here we briefly introduces ARJA-e and report the results of ARJA-e for the First International Competition on Automated Program Repair on two competition tracks, including the AI Generated Code track and the Functional Errors track.", notes = "Also known as \cite{10653369}", } @InProceedings{Yudistira:2013:KST, author = "Novanto Yudistira and Sabriansyah Rizqika Akbar and Achmad Arwan", booktitle = "5th International Conference on Knowledge and Smart Technology (KST, 2013)", title = "Using Strongly Typed Genetic Programming for knowledge discovery of course quality from e-learning's web log", year = "2013", pages = "11--15", month = jan # " 31 2013-" # feb, address = "Chonburi, Thailand", isbn13 = "978-1-4673-4850-8", keywords = "genetic algorithms, genetic programming, LMS, e-learning, knowledge", DOI = "doi:10.1109/KST.2013.6512779", abstract = "Learning Management System (LMS) has become the popular instrument in academic institutions by providing feasible pedagogical interaction. In the abundance of registered users take some activities inside LMS, the result of analysing the quality of courses becomes remarkable feedback for teachers to enhance their teaching program via e-learning. Unexceptionally, mining web server log has been fascinating area in e-education environment. Our objective is to find interrelationships knowledge among e-learning web log's metrics. Strongly Typed Genetic Programming (STGP) as the cutting the edge technique for finding accurate rule inductions is used to achieve the goal. Revealed knowledge may useful for teachers or academicians to rearrange strategies in the purpose of improving e-learning usage quality based on the course activities.", notes = "Also known as \cite{6512779}", } @Article{yue:2025:knosys, author = "Caitong Yue and Jiankang Song and Jing Liang and Mengnan Liu and Kunjie Yu and Hongyu Lin and Ying Bi", title = "A multimodal multiobjective evolutionary algorithm based on neighborhood and enhanced special crowding distance", journal = "Knowledge-Based Systems", year = "2025", volume = "315", pages = "113340", month = "22 " # apr, keywords = "genetic algorithms, Multimodal multiobjective optimization, Evolutionary algorithm, Neighborhood, Crowding distance", ISSN = "0950-7051", DOI = "doi:10.1016/j.knosys.2025.113340", abstract = "Multimodal multiobjective problems (MMOPs) may involve multiple Pareto optimal solutions corresponding to the same objective function value. Although several multimodal multiobjective evolutionary algorithms (MMOEAs) exist to address such problems, finding Pareto sets (PSs) that balance diversity and convergence and achieve a well-distributed Pareto front (PF) remains a challenging task. This paper proposes an MMOEA called the multimodal multiobjective evolutionary algorithm based on neighborhood and enhanced special crowding distance (MMO_NESCD) to address the above mentioned challenges. This MMOEA incorporates the enhanced special crowding distance (ESCD) and adaptive neighborhood strategy (ANS) in environmental selection. In MMO_NESCD, the population is clustered in the decision space to increase diversity. A nondominated sorting process that uses ESCD is subsequently employed to select individuals with high diversity for the next generation. This approach balances diversity and convergence in decision and objective spaces. Furthermore, during the update of the optimal archive, ANS is combined with ESCD to further increase population diversity. This algorithm achieves balanced PSs with diversity and convergence and a well-distributed PF in MMOPs. The results show that the new approach achieves better results than competitive methods do on CEC 2019 test problems.", notes = " School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China", } @InProceedings{Yue:2009:ICCAS-SICE, author = "Chuan Yue and Shingo Mabu and Yan Chen and Yu Wang and Kotaro Hirasawa", title = "Agent bidding strategy of multiple round English Auction based on genetic network programming", booktitle = "ICCAS-SICE, 2009", year = "2009", month = "18-21 " # aug, address = "Fukuoka", pages = "3857--3862", publisher = "IEEE", isbn13 = "978-4-9077-6433-3", URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=05332928", abstract = "The auction mechanism is widely used in Web-based sites and originally designed for human beings, but it might not be the most efficient one in the future, while, there is a demand of evolutionary computation auction agents adaptable to the dynamic auction environments. In this paper, we have applied genetic network programming (GNP) to auction agents to determine a bid at each time step and developed multiple round English auction mechanisms based on multi-agent systems. In the simulations, we provide comparisons of the proposed method with existing ones. As a result, it has been found that the proposed method could help agents to evolve their strategies generation by generation to get more goods with less money. Also, GNP has a good performance of helping the agent to find out the suitable strategy under the current situation.", keywords = "genetic algorithms, genetic programming, genetic network programming, Web-based site, agent bidding strategy, dynamic auction environment, electronic auction, evolutionary computation auction agent, multiagent system, multiple round English auction, Web sites, electronic commerce, multi-agent systems", notes = "Also known as \cite{5332928}", } @InProceedings{Yue:2010:cec, author = "Chuan Yue and Shingo Mabu and Donggeng Yu and Yu Wang and Kotaro Hirasawa", title = "A bidding strategy of multiple round auctions based on Genetic Network Programming", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Genetic Network Programming", isbn13 = "978-1-4244-6910-9", abstract = "Recently, due to the development of the ecommerce, on-line auctions have become a common modern way to trade goods over web. In order to make the trade more efficient and more intelligent, a new strategy has been proposed for bid agents to participate in multiple round auctions to deal with multiple goods. The proposed strategy is developed based on evolutionary Genetic Network Programming (GNP), which uses directed graph structures for getting the optimal solution. In this paper, we considered the most popular two kinds of auctions, English auction and Dutch auction. It is found from the simulation results that the agents adopting the GNP-based strategy performed very well in terms of getting more goods with less money, and they can make their decisions dynamically in response to the changes of the multiple round environments both in English auction and Dutch auction.", DOI = "doi:10.1109/CEC.2010.5586254", notes = "WCCI 2010. Also known as \cite{5586254}", } @PhdThesis{ChuanYue:thesis, author = "Chuan Yue", title = "Study on Bidding Strategies using Genetic Network Programming", school = "Waseda University", year = "2012", address = "Japan", month = jul, keywords = "genetic algorithms, genetic programming, Genetic Network Programming, GNP", URL = "http://hdl.handle.net/2065/40075", URL = "http://jairo.nii.ac.jp/0069/00023944/en", URL = "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/40075/1/Honbun-6057.pdf", URL = "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/40075/2/Shinsa-6057.pdf", URL = "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/40075/3/Gaiyo-6057.pdf", size = "140 pages", abstract = "Due to the explosive development of global network structure, electronic commerce is increasingly playing an important role in many organisations and individual consumer's daily life. It offers opportunities to significantly improve the way for businesses interactions between both customers and suppliers. More and more large scale and decentralised e-commerce mechanisms have emerged in industrial and commercial domains in a wide range. In particular, among all these applications, online auctions, which are flexible pricing mechanisms over Internet, make the physical limitations of traditional auctions disappear. They gain their extra popularity in the daily life and attract globally dispersed users due to having the characteristics that bargaining and negotiation besides all of the convenience. Thus, on line auctions become one of the most widely studied and employed negotiation mechanisms today. Traditionally, in most current online auction applications, the traders are generally humans who operate all the behaviours to make transactions. These behaviours may involve observing the auctions, analysing the auction information, and bidding the suitable price for the items. However, facing the increasingly demanding requirements and complexity of online trading, this kind of manual operation does not reveal the full potential of this new mode of commerce. Thus, in order to relieve the users and be more effective, exploring possible types and automating the behaviours in the online auction attract high interest. Now, in many studies, the agent-oriented auction mechanism, with its emphasis on autonomous actions and flexible interactions, arises as an effective and robust model for the dynamic and sensitive commerce environment. In such systems, the agent acts flexibly on behalf of its owner and is capable of local decision-making based on the environment information and pre-knowledge about the system. Among many different types of online auction, two of the most popular and studied types are Multiple Round English Auctions (MREA), which is single side auction, and Continuous Double Auction (CDA), which is double side auction. These auctions are newly emerged in e-commerce era based on the traditional auction types. They allow multiple agents to participate and one agent can deal with several auctions continuously or simultaneously, which are effective auction types to save time and relieve the users. Towards to these types, because there is no centralised system-wide control, the major challenge for automatic bidding strategies is to improve the degree of automation and optimise the agent's bidding behaviour in order to maximise the owner's profit. Most of the related researches have been conducted by using heuristic methods and fixed mathematical functions to compute the final optimal bidding price for the items or to compute how much should bid at each time step. Nevertheless, because auction environments are complicated and highly dynamic due to have many factors affecting each other, these approaches are not flexible enough for the dynamic environment, and there is no dominant strategy.", abstract = "Against this background, this thesis is concerned with developing the intelligence of autonomous agent's bidding strategy in order to make the agent to be more efficient and competitive for agent-based online auction mechanisms, especially in MREA and CDA. In order to be more flexible and better exploit the market information, Genetic Network Programming (GNP) is firstly employed to the agent's bidding strategy since its applicability and efficiency have been clarified in complex and dynamic problems in many other fields. GNP is one of the evolutionary optimization techniques developed as an extension of Genetic Algorithm (GA) and Genetic Programming (GP), which uses compact directed graph structures as solutions. Basically speaking, in the proposed method, the GNP population represents the group of potential bidding strategies, and each individual uses the as-if/then decision-making functions to judge the auction information and guides the agent to take the suitable actions under different situations. Thus, it could be flexible and capable to adaptive to various auction situations. During the evolution, the GNP structure will be systematically organized, and finally, the individual which can obtain the highest profit is selected as the optimal bidding strategy at the end of training phase. In chapter 2, we introduced the conception of MREA and CDA in detail, which are the study environments in this thesis. The related researches are also introduced. In chapter 3, focusing on MREA, the bidding strategy for the auction agents in MREA is proposed using GNP. The performance of GNP-based agents is evaluated and studied in two situations: MREA is no time limit (NTL), and MREA is time limit (TL). Furthermore, according to the amount of the money each agent has, each situation is divided into 2 cases: general case and poorest case. All the participating agents in the simulations use GNP strategy. This chapter aims to study and analyse the capability and effectiveness of GNP for guiding bidding actions through the phenomenon of the simulations. The simulation results reveal that the agents using GNP strategy can understand various environments well through experiences and become smarter through evolution. In chapter 4, as an extension of the bidding strategy in chapter 3, in order to improving the agent's intelligence and sensitivity, an enhanced bidding strategy for MREA is developed using GNP. Firstly, the GNP structure is modified to be able to judge more kinds of information and more situations at a time. Secondly, the strategy is improved to be able to consider the bidder's attitude towards to each good, which makes the strategy to be more personalised for each bidder and could make the bidder more satisfied with the auction result and profit. The proposed strategy is compared with the previous GNP strategy and the other conventional strategies in the simulations. The simulation results demonstrated that the proposed method can outperform the previous one and is more competitive than the agents based on mathematical functions. In chapter 5, focusing on CDA, GNP with rectify nodes (GNP-RN) has been applied for CDA bidding strategy combined with proposed heuristic rules, which are derived based on the common believes for assisting agent's bidding behaviour. GNP-RN is developed aiming to guide the agent to be competitive under different CDA environments, and maximise the agent's profit without losing chances for trading. Rectify Node (RN) is a newly proposed kind of nodes, which is used for bringing more flexible and various options for bidding action choices. 4 groups of simulations are designed to compare GNP-RN with conventional GNP and other strategies in CDA. In each simulation, the kinds of opponent agents are different in order to fully analyse the agents' performance. The simulation results show that the proposed method can outperform all the other strategies and achieve high success rate as well as high profit even when the situation is highly competitive.", abstract = "In chapter 6, as an extension of GNP-RN, GNP with adjusting parameters (GNP-AP) for developing bidding strategy in large-scale CD As is proposed and studied. In large-scale CDAs, much more history in formation can be obtained than small-scale CDAs. In order to enhance the sensitivity for large-scale CDAs and the capability of judging abundant information, the parameters used by GNP-AP decision-making functions are adjusted during the evolution instead of being fixed in GNP-RN. Moreover, the structure of GNP-AP is designed to be more comprehensive that the number of branches of some kinds of nodes is increased to adapt to the complicated environment situations. The simulation results show that GNP-AP can obtain a good guidance for the large-scale CDAs and could be very efficient for the markets. In chapter 7, after giving the objectives and motivation of each research in this thesis, some conclusions about the proposed algorithms are described based on the simulation results", notes = "Supervisor: K. Hirasawa", } @InProceedings{Yue:2010:ISICA, author = "Xuezhi Yue and Zhijian Wu and Dazhi Jiang and Kangshun Li", title = "GPS Height Fitting Using Gene Expression Programming", booktitle = "Proceedings 5th International Symposium on Advances in Computation and Intelligence, ISICA 2010", year = "2010", editor = "Zhihua Cai and Chengyu Hu and Zhuo Kang and Yong Liu", volume = "6382", series = "Lecture Notes in Computer Science", pages = "25--32", address = "Wuhan, China", month = oct # " 22-24", publisher = "Springer", keywords = "genetic algorithms, genetic programming, gene expression programming, GPS height fitting, conicoid function fitting, polyhedral function fitting", isbn13 = "978-3-642-16492-7", DOI = "doi:10.1007/978-3-642-16493-4_3", size = "8 pages", abstract = "In Global Position System (GPS) height fitting methods, the traditional mathematical model fittings are more stable and general, but the fitting accuracy is usually not intended because of the error of model itself. Gene Expression Programming (GEP) as a kind of newly invented Genotype/phenotype based genetic algorithm can conquer the problem effectively. A GPS height fitting method based on GEP is given in this paper. By experiments and making the analysis and comparison with conicoid function and polyhedral function fitting methods, the results indicate that the GPS height fitting method based on GEP is effective and has better accuracy than traditional mathematical model methods to some extent.", affiliation = "State Key Laboratory of Software Engineering, Wuhan University, Wuhan, 430072", } @MastersThesis{ChiYuenProject, author = "Chi Chung Yuen", title = "Selective Crossover Using Gene Dominance as an Adaptive Strategy for Genetic Programming", school = "University College, London", year = "2004", type = "MSc Intelligent Systems", address = "UK", month = sep, keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/C.Clack/papers/ChiYuenProject.pdf.gz", size = "112 pages", abstract = "Since the emergence of the evolutionary computing, many new natural genetic operators have been research and within genetic algorithms and many new recombination techniques have been proposed. There has been substantially less development in Genetic Programming compared with Genetic Algorithms. Koza \cite{koza:gp2} stated that crossover was much more influential than mutation for evolution in genetic programming; suggesting that mutation was unnecessary. A well known problem with crossover is that good sub-trees can be destroyed by an inappropriate choice of crossover point. This is otherwise known as destructive crossover. This thesis proposes two new crossover methods which uses the idea of haploid gene dominance in genetic programming. The dominance information identifies the goodness of a particular node, or the sub-tree, and aid to reduce destructive crossover. The new selective crossover techniques will be used to test a variety of optimisation problems and compared with the analysis work by Vekaria [28]. Additionally, uniform crossover which Poli and Langdon [22] proposed has been revised and discussed. The gene dominance selective crossover operator was initially designed by Vekaria in 1999 who implemented it for Genetic Algorithms and showed improvement in performance when evaluated on certain problems. The proposed operators, {"}Simple Selective Crossover{"} and {"}Dominance Selective Crossover{"}, have been compared and contrasted with Vekaria results on two problems; an attempt has also been made to test it on a more complex genetic programming problem. Satisfactory results have been found.", } @InProceedings{Yuen:2009:cec, author = "Shiu Yin Yuen and Shing Wa Leung", title = "Genetic Programming that Ensures Programs are Original", booktitle = "2009 IEEE Congress on Evolutionary Computation", year = "2009", editor = "Andy Tyrrell", pages = "860--866", address = "Trondheim, Norway", month = "18-21 " # may, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-2959-2", file = "P246.pdf", DOI = "doi:10.1109/CEC.2009.4983035", abstract = "Conventional genetic programming (GP) does not guarantee no revisits, i.e., a program may be generated for fitness evaluations more than one time. This is clearly wasteful in applications that involve expensive and/or time consuming fitness evaluations. This paper proposes a new GP - non-revisiting genetic programming NrGP - that guarantees that all programs generated is original. The basic idea is to use memory to store all programs generated. To increase efficiency in indexing and storage, the memory is organized as an S-expression trie. Since the number of solutions generated is modest for applications involving expensive and/or time consuming fitness evaluations, the extra memory needed is manageable. GP and NrGP are compared using two GP bench mark problems, namely, the symbolic regression and the even N-parity problem. It is found that NrGP outperforms GP, significantly reducing the computational effort (CE) required. This clearly shows the power of the idea of ensuring no revisits. It is anticipated that the same non-revisiting idea can be applied to other types of GP to enhance their efficiency. A new CE measurement is also reported that removes some statistical biases associated with the conventional CE.", keywords = "genetic algorithms, genetic programming", notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR", } @InProceedings{Yuhaski:2009:INFORMS, author = "Steven {Yuhaski, Jr.}", title = "An Approach to Renovation Task Sequencing with Multi-Criteria Objectives", booktitle = "INFORMS Annual Meeting", year = "2009", address = "San Diego, USA", month = "11-14 " # oct, keywords = "genetic algorithms, genetic programming", abstract = "Large sections at facilities undergoing extensive renovation often require personnel to move temporary shelters having limited capacity, prior proceeding to their final locations. A model of the flow of personnel to various locations, as a function of optimal task sequencing, via an efficient genetic programming heuristic, has been formulated.", notes = "Natick Research, Development, and Engineering Center, 1 Kansas St., Natick, MA, 01760, United States of America, http://meetings.informs.org/sandiego09/images/split%20pdfs/Tuesday.pdf http://meetings2.informs.org/sandiego09/ https://informs.emeetingsonline.com/emeetings/formbuilder/clustersessiondtl.asp?csnno=11981&mmnno=187&ppnno=43883", } @Article{Yun:2018:MPE, author = "Liu Yun and Biranchi Panda and Liang Gao and Akhil Garg and Xu Meijuan and Dezhi Chen and Chin-Tsan Wang", title = "Experimental Combined Numerical Approach for Evaluation of Battery Capacity Based on the Initial Applied Stress, the Real-Time Stress, Charging Open Circuit Voltage, and Discharging Open Circuit Voltage", journal = "Mathematical Problems in Engineering", year = "2018", volume = "2018", number = "1", pages = "Article ID 8165164", keywords = "genetic algorithms, genetic programming, Li", ISSN = "1024-123X", bibsource = "OAI-PMH server at oai.repec.org", identifier = "RePEc:hin:jnlmpe:8165164", oai = "oai:RePEc:hin:jnlmpe:8165164", URL = "https://downloads.hindawi.com/journals/mpe/2018/8165164.pdf", DOI = "doi:10.1155/2018/8165164", size = "16 pages", abstract = "With the intensification of energy crisis, considerable attention has been paid to the application and research of lithium-ion batteries. A significant progress has also been made in the research of lithium-ion battery capacity evaluation using electrochemical and electrical parameters. In this study, the effect of mechanical characteristic parameter (i.e., stack stress) on battery capacity is investigated using the experimental combined numerical approach. The objective of the proposed approach is to evaluate the capacity based on the initial applied stress, the real-time stress, charging open circuit voltage, and discharging open circuit voltage. Experiments were designed and the data is fed into evolutionary approach of genetic programming. Based on analysis, the accuracy of the proposed GP model is fairly high while the maximum percentage of error is about 5percent. In addition, a negative correlation exists between the initial stress and battery capacity while the capacity increases with real-time stress.", } @Article{YUN:2019:JCP, author = "Liu Yun and Wei Li and Akhil Garg and Sivasriprasanna Maddila and Liang Gao and Zhun Fan and P. Buragohain and Chin-Tsan Wang", title = "Maximization of extraction of Cadmium and Zinc during recycling of spent battery mix: An application of combined genetic programming and simulated annealing approach", journal = "Journal of Cleaner Production", volume = "218", pages = "130--140", year = "2019", keywords = "genetic algorithms, genetic programming, Recycling, Copper recovery, Waste printed circuit boards, Waste management", ISSN = "0959-6526", DOI = "doi:10.1016/j.jclepro.2018.11.226", URL = "http://www.sciencedirect.com/science/article/pii/S0959652618336291", abstract = "There are a number of government directives and regulations as well as many public schemes on the recycling of batteries, in spite of this; the quantity of batteries that are actually recycled is still very low. Current production capacity cannot meet projected demand of Lithium-ion batteries. To counter this, the reclamation and repurposing of metals like cadmium, Lithium and Zinc from used or spent batteries is the only viable scheme. This is both environmentally friendly and economically feasible. An alternative is the selective chemical leaching in the presence of Sulfuric acid and Sodium metabisulfite. In this paper, the effect of these chemicals as well as the solid-to-liquid ratio and time of retention is comprehensively studied. Experiments are designed for the recovery of Zinc and cadmium from the spend Lithium-ion batteries mix. To maximize the recovery of Zinc and cadmium, the combined genetic programming and simulated annealing approach is proposed. Genetic programming is used for the formulation of functional relationship between recovered metals Zinc and cadmium and the inputs (Solid/Liquid ratio, concentration of Sulfuric acid, mass of Sodium metabisulfite and retention time). The optimal input conditions determined using the simulated annealing algorithm includes Solid/Liquid ratio of 11.7percent, 0.86a M Sulfuric acid, 0.56a g/g of Sodium metabisulfite and 45a min of retention time. Three dimensions surface analysis reveals that a lower value of Solid/Liquid ratio favours a better yield. The optimal conditions are validated using experiments. This confirms the efficacy of simulated annealing aided genetic programming techniques as well as the optimal conditions of the metal extraction", } @Article{YUN:2019:JCPa, author = "Liu Yun and Ankit Goyal and Vikas Pratap Singh and Liang Gao and Xiongbin Peng and Xiaodong Niu and Chin-Tsan Wang and Akhil Garg", title = "Experimental coupled predictive modelling based recycling of waste printed circuit boards for maximum extraction of copper", journal = "Journal of Cleaner Production", volume = "218", pages = "763--771", year = "2019", keywords = "genetic algorithms, genetic programming, Spent battery mix, Metal recovery, Recycling, Bioleaching", ISSN = "0959-6526", DOI = "doi:10.1016/j.jclepro.2019.01.027", URL = "http://www.sciencedirect.com/science/article/pii/S0959652619300332", abstract = "The recycling process of materials from used and wasted printed circuit boards plays an important role in electronic waste management. These waste printed circuit boards (PCBs) hold metals such as copper, aluminium, nickel, and magnesium. The efficient recovery process of such metals from waste PCBs is needed for recycle and possible reuse for manufacturing of products. The metal recovery process is complex and, multidimensional and costly to perform. In addition, the efficient (maximum) recovery of metals exhibit higher dependence on determination of optimum combination of inputs in the recovery process from waste PCBs. Therefore, this work illustrated the ability of four predictive modelling methods (Analysis of Variance, Genetic Programming, Artificial Neural Network and Generalized Neural Network) to model complex suspension electrolysis process (recovery process) and their comparative analysis on recovery of copper metal from waste PCBs. Experiments were designed based on variations of three design/input parameters such as concentration of sulfuric acid, concentration of copper sulphate and current density. The comparative analysis of the four methods mentioned above reveals that Generalized Neural Network performed the best with coefficient of determination value at 0.92.", } @InCollection{Yun:1997:fobsGA, author = "Yeogirl Yun", title = "Finding an Optimal Blackjack Strategy using Genetic Algorithm", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1997", publisher = "Stanford Bookstore", year = "1997", editor = "John R. Koza", pages = "226--235", address = "Stanford, California, 94305-3079 USA", month = "17 " # mar, keywords = "genetic algorithms, genetic programming", ISBN = "0-18-205981-2", abstract = "we find a strategy that performs similarly or even better than a book strategy found in the Blackjack literature", notes = "part of \cite{koza:1997:GAGPs}", } @Article{yurke:2003:GPEM, author = "Bernard Yurke and Allen P. {Mills Jr.}", title = "Using {DNA} to Power Nanostructures", journal = "Genetic Programming and Evolvable Machines", year = "2003", volume = "4", number = "2", pages = "111--122", month = jun, keywords = "molecular motors, DNA nanostructures, toeholds, strand exchange, reaction kinetics", ISSN = "1389-2576", DOI = "doi:10.1023/A:1023928811651", abstract = "DNA hybridisation has been used to power a number of DNA-based nanostructures constructed out of DNA. Here some considerations that go into DNA-based motor design are briefly reviewed. The emphasis will be on the operation of toeholds, single-stranded sections of DNA that facilitate the process of strand removal during certain points in the operation of a DNA-based motor. Reaction kinetics measurements for toehold mediated strand exchange are reported. These measurements have served as a guide for choosing toehold lengths.", notes = "Special Issue on Biomolecular Machines and Artificial Evolution Article ID: 5122740", } @InCollection{yurovitsky:1995:PTUGP, author = "Michael Yurovitsky", title = "Playing Tetris Using Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1995", year = "1995", editor = "John R. Koza", pages = "309--319", address = "Stanford, California, 94305-3079 USA", month = "11 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-195720-5", notes = "part of \cite{koza:1995:gagp}", } @InProceedings{Yusuf:2014:IES, author = "Rahadian Yusuf and Ivan Tanev and Katsunori Shimohara", title = "Evolving Emotion Recognition Module for Intelligent Agent", booktitle = "Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES 2014) - Volume 2", year = "2015", editor = "Hisashi Handa and Hisao Ishibuchi and Yew-Soon Ong and Kay-Chen Tan", volume = "2", series = "Proceedings in Adaptation, Learning and Optimization", pages = "215--226", address = "Singapore", month = "10-12th " # nov, organisation = "Memetic Computing Society", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Emotion, Computer Agent, Evolutionary Algorithm", isbn13 = "978-3-319-13355-3", URL = "http://dx.doi.org/10.1007/978-3-319-13356-0_18", DOI = "doi:10.1007/978-3-319-13356-0_18", abstract = "An emotion recognition module is crucial in designing a computer agent that is capable of interacting with emotional expressions. Under-standing user's current emotion can be achieved by several methods, but current researches are either using still images, or sensors that are not pervasive. Usual approach is using a generalized classifier to recognise pattern of emotion features captured by sensors. Unlike most researches, this research focuses on pervasive sensors and a single user, using evolution algorithm. This research also discusses about the classifier evolutions using Genetic Programming, and comparing several directed evolutions in evolving the emotion recognition module.", notes = "http://www.ies-2014.org/technical-programs.html", } @InProceedings{Yusuf:2015:CEC, author = "Rahadian Yusuf and Ivan Tanev and Katsunori Shimohara", title = "Application of Genetic Programming and Genetic Algorithm in Evolving Emotion Recognition Module", booktitle = "Proceedings of 2015 IEEE Congress on Evolutionary Computation (CEC 2015)", year = "2015", editor = "Yadahiko Murata", pages = "1444--1449", address = "Sendai, Japan", month = "25-28 " # may, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-7491-7", DOI = "doi:10.1109/CEC.2015.7257058", abstract = "This paper will discuss about implementation of a voting system and weighted credibility to augment evolution process of an emotion recognition module. The evolution process of the emotion recognition module is one part of ongoing research on designing an intelligent agent capable of emotion recognition, interaction, and expression. Genetic programming evolves the classifiers, while genetic algorithm evolves the weighted credibility as a modification of parallel voting systems. The experimental results suggest that the implementation of weighted credibility evolution improves the performance of training, in the form of significantly reduced training time needed.", notes = "1710 hrs 15440 CEC2015", } @Article{DBLP:journals/alr/YusufSTS16, author = "Rahadian Yusuf and Dipak Gaire Sharma and Ivan Tanev and Katsunori Shimohara", title = "Evolving an emotion recognition module for an intelligent agent using genetic programming and a genetic algorithm", journal = "Artificial Life and Robotics", volume = "21", number = "1", pages = "85--90", year = "2016", month = mar, keywords = "genetic algorithms, genetic programming, Emotion recognition, Facial expression, Gestures", URL = "https://rdcu.be/cIJ4u", URL = "https://doi.org/10.1007/s10015-016-0263-z", DOI = "doi:10.1007/s10015-016-0263-z", timestamp = "Thu, 26 Nov 2020 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/alr/YusufSTS16.bib", bibsource = "dblp computer science bibliography, https://dblp.org", size = "6 pages", abstract = "Most studies use the facial expression to recognize a users emotion; however, gestures, such as nodding, shaking the head, or stillness can also be indicators of the users emotion. In our research, we use the facial expression and gestures to detect and recognize a user emotion. The pervasive Microsoft Kinect sensor captures video data, from which several features representing facial expressions and gestures are extracted. An in-house extensible markup language-based genetic programming engine (XGP) evolves the emotion recognition module of our system. To improve the computational performance of the recognition module, we implemented and compared several approaches, including directed evolution, collaborative filtering via canonical voting, and a genetic algorithm, for an automated voting system. The experimental results indicate that XGP is feasible for evolving emotion classifiers. In addition, the obtained results verify that collaborative filtering improves the generality of recognition. From a psychological viewpoint, the results prove that different people might express their emotions differently, as the emotion classifiers that are evolved for particular users might not be applied successfully to other user(s).", } @PhdThesis{Yusuf:thesis, author = "Rahadian Yusuf", title = "Evolving user-specific emotion recognition model via incremental genetic programming", school = "Graduate School of Science and Engineering, Doshisha University", year = "2016", address = "Japan", month = nov, keywords = "genetic algorithms, genetic programming, Affective Computing, Emotion Recognition, Adaptation, User-specific, XGP, Microsoft Kinect", URL = "http://doi.org/10.14988/di.2017.0000016947", DOI = "doi:10.14988/di.2017.0000016947", URL = "https://doshisha.repo.nii.ac.jp/?action=repository_uri&item_id=1349&file_id=21&file_no=2", URL = "http://doshisha.repo.nii.ac.jp/short/zk840.pdf", size = "130 pages", abstract = "This research proposes a model to tackle challenges common in Emotion Recognition based on facial expression. First, we use pervasive sensor and environment, enabling natural expressions of user, as opposed to unnatural expressions on a large dataset. Second, the model analyzes relevant temporal information, unlike many other researches. Third, we employ user-specific approach and adaptation to user. We also show that our evolved model by genetic programming can be analyzed on how it really works and not a black-box model.", notes = "CiNii ID: 9000345272080 Also known as \cite{weko_1349_1} Supervisor: Katsunori Shimohara", } @InProceedings{Yusuf:2017:ICBAKE, author = "Rahadian Yusuf and Dipak Gaire Sharma and Ivan Tanev and Katsunori Shimohara", booktitle = "2017 International Conference on Biometrics and Kansei Engineering (ICBAKE)", title = "Individuality and user-specific approach in adaptive emotion recognition model", year = "2017", abstract = "This study aims at developing an intelligent agent that can recognise user-specific emotions and can self-evolve. Previous studies have explored several methods to develop the model and improve the results while maintaining the feasibility of real-time implementation for later stages. We evolved the emotion recognition module by using Genetic Programming (GP) and explored several optimisations. We investigated and compared the evolution of a unique classifier (evolved from data from a single specific subject only), the evolution of a general classifier (evolved from data from multiple subjects), and the evolution of an adaptive classifier by implementing incremental GP (evolved incrementally, first from multiple subjects and then from a single specific subject). We conducted the experiments by using the same budget in terms of evolution sessions to obtain the best programs for a fair comparison between general approach, user-specific approach, and adaptive approach. We then performed repeated experiments to verify the robustness of the method. From the results, we concluded that, on an average, adaptive approach not only resulted in faster evolution time, but also achieved better accuracy in emotion recognition.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICBAKE.2017.8090628", month = sep, notes = "Also known as \cite{8090628}", } @InProceedings{Yusuf:2018:IOTAIS, author = "Rahadian Yusuf and Albert Podusenko and Ivan Tanev and Katsunori Shimohara", booktitle = "2018 IEEE International Conference on Internet of Things and Intelligence System (IOTAIS)", title = "Recognition of Mistaken Pedal Pressing based on Pedal Pressing Behavior by using Genetic Programming", year = "2018", pages = "104--108", abstract = "There have been many varieties of driving assistance, and one aspect of them is the scope of emergency braking. Several researches have been analysing emergency braking and proposed approaches to detect them. A focused but significant case is mistaken pedal pressing during emergency braking, which occurs when accelerator pedal is pressed instead of brake pedal. This paper aims to evolve a classifier to recognize mistaken pedal pressing based on behaviour shown during pressing the pedals by using evolutionary computation. A driving simulator is used to collect the data, and genetic programming was used to perform the evolution.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IOTAIS.2018.8600885", month = nov, notes = "Also known as \cite{8600885}", } @Article{Yzerman:2010:bmcGenomics, author = "Ed Yzerman and Jeroen {den Boer} and Martien Caspers and Arpit Almal and Bill Worzel and Walter {van der Meer} and Roy Montijn and Frank Schuren", title = "Comparative genome analysis of a large Dutch Legionella pneumophila strain collection identifies five markers highly correlated with clinical strains", year = "2010", journal = "BMC Genomics", volume = "11", pages = "433", keywords = "genetic algorithms, genetic programming", ISSN = "1471-2164", bibsource = "OAI-PMH server at www.doaj.org", oai = "oai:doaj-articles:5761bf382eaa4396188fdcc54c508f94", URL = "http://www.biomedcentral.com/1471-2164/11/433", DOI = "doi:10.1186/1471-2164-11-433", size = "11 pages", publisher = "BioMed Central", abstract = "Background Discrimination between clinical and environmental strains within many bacterial species is currently under explored. Genomic analyses have clearly shown the enormous variability in genome composition between different strains of a bacterial species. In this study we have used Legionella pneumophila, the causative agent of Legionnaire's disease, to search for genomic markers related to pathogenicity. During a large surveillance study in The Netherlands well-characterised patient-derived strains and environmental strains were collected. We have used a mixed-genome microarray to perform comparative-genome analysis of 257 strains from this collection. Results Microarray analysis indicated that 480 DNA markers (out of in total 3360 markers) showed clear variation in presence between individual strains and these were therefore selected for further analysis. Unsupervised statistical analysis of these markers showed the enormous genomic variation within the species but did not show any correlation with a pathogenic phenotype. We therefore used supervised statistical analysis to identify discriminating markers. Genetic programming was used both to identify predictive markers and to define their interrelationships. A model consisting of five markers was developed that together correctly predicted 100percent of the clinical strains and 69percent of the environmental strains. Conclusions A novel approach for identifying predictive markers enabling discrimination between clinical and environmental isolates of L. pneumophila is presented. Out of over 3000 possible markers, five were selected that together enabled correct prediction of all the clinical strains included in this study. This novel approach for identifying predictive markers can be applied to all bacterial species, allowing for better discrimination between strains well equipped to cause human disease and relatively harmless strains.", } @InProceedings{Z-Flores:2014:EVOLVE, author = "Emigdio Z-Flores and Leonardo Trujillo and Oliver Schuetze and Pierrick Legrand", title = "Evaluating the Effects of Local Search in Genetic Programming", booktitle = "EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V", year = "2014", editor = "Alexandru-Adrian Tantar and Emilia Tantar and Jian-Qiao Sun and Wei Zhang and Qian Ding and Oliver Schuetze and Michael Emmerich and Pierrick Legrand and Pierre {Del Moral} and Carlos A. {Coello Coello}", volume = "288", series = "Advances in Intelligent Systems and Computing", pages = "213--228", address = "Peking", month = "1-4 " # jul, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Local Search, Memetic Algorithms", isbn13 = "978-3-319-07493-1", oai = "oai:HAL:hal-01060315v1", URL = "https://hal.inria.fr/hal-01060315", DOI = "doi:10.1007/978-3-319-07494-8_15", abstract = "Genetic programming (GP) is an evolutionary computation paradigm for the automatic induction of syntactic expressions. In general, GP performs an evolutionary search within the space of possible program syntaxes, for the expression that best solves a given problem. The most common application domain for GP is symbolic regression, where the goal is to find the syntactic expression that best fits a given set of training data. However, canonical GP only employs a syntactic search, thus it is intrinsically unable to efficiently adjust the (implicit) parameters of a particular expression. This work studies a Lamarckian memetic GP, that incorporates a local search (LS) strategy to refine GP individuals expressed as syntax trees. In particular, a simple parametrisation for GP trees is proposed, and different heuristic methods are tested to determine which individuals should be subject to a LS, tested over several benchmark and real-world problems. The experimental results provide necessary insights in this insufficiently studied aspect of GP, suggesting promising directions for future work aimed at developing new memetic GP systems.", } @InProceedings{Z-Flores:2015:GECCO, author = "Emigdio Z-Flores and Leonardo Trujillo and Oliver Schuetze and Pierrick Legrand", title = "A Local Search Approach to Genetic Programming for Binary Classification", booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation", year = "2015", editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr", isbn13 = "978-1-4503-3472-3", pages = "1151--1158", keywords = "genetic algorithms, genetic programming, Integrative Genetic and Evolutionary Computation", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739480.2754797", DOI = "doi:10.1145/2739480.2754797", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "In standard genetic programming (GP), a search is performed over a syntax space defined by the set of primitives, looking for the best expressions that minimize a cost function based on a training set. However, most GP systems lack a numerical optimization method to fine tune the implicit parameters of each candidate solution. Instead, GP relies on more exploratory search operators at the syntax level. This work proposes a memetic GP, tailored for binary classification problems. In the proposed method, each node in a GP tree is weighted by a real-valued parameter, which is then numerically optimized using a continuous transfer function and the Trust Region algorithm is used as a local search method. Experimental results show that potential classifiers produced by GP are improved by the local searcher, and hence the overall search is improved achieving significant performance gains, that are competitive with state-of-the-art methods on well-known benchmarks.", notes = "Also known as \cite{2754797} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)", } @PhdThesis{Z-Flores:thesis, title = "Human mental states classification using {EEG} by means of Genetic Programming", titletranslation = "Classification de l'{\'e}tat mental humain par programmation g{\'e}n{\'e}tique sur des signaux EEG", author = "Emigdio {Z.Flores Lopez}", school = "Instituto tecnologico de Tijuana", year = "2017", type = "Doctor in Engineering Sciences", address = "Tijuana, Baja California, Mexico", month = jul # "~05", keywords = "genetic algorithms, genetic programming, EEG, feature extraction, holderian regularity, matching pursuit, regression, classification", bibsource = "OAI-PMH server at api.archives-ouvertes.fr", contributor = "Quality control and dynamic reliability and Leonardo Trujillo and Pierrick Legrand", identifier = "tel-01668672", language = "en", oai = "oai:HAL:tel-01668672v1", rights = "info:eu-repo/semantics/OpenAccess", URL = "https://hal.inria.fr/tel-01668672", URL = "https://hal.inria.fr/tel-01668672/document", URL = "https://hal.inria.fr/tel-01668672/file/PhD_Thesis_Emigdio.pdf", size = "354 pages", abstract = "The advances in the development of Brain-Computer Interfaces(BCI) have been increasing in recent years, mostly because the level of convergence from multi-disciplinary techniques has evolved. The electroencephalography(EEG), a brain recording method studied in this thesis, allows the construction of BCIs, however the signals are rather complex to process, which requires methodologies that efficiently extract patterns from them. This thesis explores two directions: first, a system is proposed for the epilepsy seizures recognition using a combination of signal processing methods for an efficient feature extraction; second,it explores the usage of a meta-heuristic algorithm, namely Genetic Programming (GP), as an alternative in the design of BCIs. Nonetheless,there is currently open-issues in GP that this thesis also explores: is there a more efficient search methodology in the exploration by GP?; what is a proper representation depending on the studied problem?; which are the most adequate search operators?. For the first topic, a thoroughly study is presented by introducing a memetic GP applied to regression problems. Then, it is extended by adapting it to classification problems. The results are positive; GP is greatly benefited from the combination of a general and a Local Search (LS) methodology. The last two topics are studied simultaneously in the development of a recognition system for mental states using EEG. A GP version (+FEGP) is proposed that evolves feature extraction models by using specialized search operators,individuals representation and fitness function. The results show that the combination of these reaches a state-of-the-art accuracy for the particular task of mental states recognition.", abstract = "Los avances en el desarrollo de Interfaces Cerebro-Computadora (BCI, porsus siglas en ingles Brain-Computer Interface) se han incrementado en anos recientes,principalmente porque ha evolucionado el nivel de convergencia detecnicas multidisciplinarias...........", notes = "Supervisors: Leonardo Trujillo and Pierrick Legrand Also known as \cite{phd/hal/ZFlores17} \cite{oai:HAL:tel-01668672v1}", } @Article{Z-FLORES:2017:JCP, author = "Emigdio Z-Flores and Mohamed Abatal and Ali Bassam and Leonardo Trujillo and Perla Juarez-Smith and Youness {El Hamzaoui}", title = "Modeling the adsorption of phenols and nitrophenols by activated carbon using genetic programming", journal = "Journal of Cleaner Production", volume = "161", pages = "860--870", year = "2017", keywords = "genetic algorithms, genetic programming, Water treatment, Activated carbon, Phenols adsorption, Regression", ISSN = "0959-6526", URL = "http://www.sciencedirect.com/science/article/pii/S0959652617311393", DOI = "doi:10.1016/j.jclepro.2017.05.192", abstract = "The process of adsorption of phenols and nitrophenols by activated carbon is one of the most important types of wastewater treatment. However, there is a lack of a general analytic method to predict the adsorption efficiency under different operating conditions. This work studies a data driven approach towards modeling the adsorption process, taking as input the type of contaminant, the pH level, the initial concentration and the elapsed time, in order to predict the adsorption efficiency. In particular, this work is the first to use genetic programming (GP), an evolutionary computation paradigm for automatic program induction, to address the stated modeling problem. Two recently proposed GP algorithms are used and compared with other regression techniques, using real-world experimental data collected under typical operating conditions. Results show that GP enhanced with a local search operator (GP-LS) achieves the best results relative to all other methods, achieving a median performance of MSE=94.14, R2=0.92 and average solution size of 41 nodes. Therefore, this technique constitutes a promising framework for the automatic modeling of the adsorption efficiency", } @Article{z-flores:2020:Algorithms, author = "Emigdio Z-Flores and Leonardo Trujillo and Pierrick Legrand and Frederique Faita-Ainseba", title = "{EEG} Feature Extraction Using Genetic Programming for the Classification of Mental States", journal = "Algorithms", year = "2020", volume = "13", number = "9", keywords = "genetic algorithms, genetic programming", ISSN = "1999-4893", URL = "https://www.mdpi.com/1999-4893/13/9/221", DOI = "doi:10.3390/a13090221", abstract = "The design of efficient electroencephalogram (EEG) classification systems for the detection of mental states is still an open problem. Such systems can be used to provide assistance to humans in tasks where a certain level of alertness is required, like in surgery or in the operation of heavy machines, among others. In this work, we extend a previous study where a classification system is proposed using a Common Spatial Pattern (CSP) and Linear Discriminant Analysis (LDA) for the classification of two mental states, namely a relaxed and a normal state. Here, we propose an enhanced feature extraction algorithm (Augmented Feature Extraction with Genetic Programming, or +FEGP) that improves upon previous results by employing a Genetic-Programming-based methodology on top of the CSP. The proposed algorithm searches for non-linear transformations that build new features and simplify the classification task. Although the proposed algorithm can be coupled with any classifier, LDA achieves 78.8percent accuracy, the best predictive accuracy among tested classifiers, significantly improving upon previously published results on the same real-world dataset.", notes = "also known as \cite{a13090221}", } @InProceedings{conf/intenv/Zablotskaya09, title = "Genetic Programming Application for User Capabilities Determination", author = "Kseniya Zablotskaya", booktitle = "Workshops Proceedings of the 5th International Conference on Intelligent Environments, Barcelona, Spain, 19th of July, 2009", publisher = "IOS Press", year = "2009", volume = "4", editor = "Michael Schneider and Alexander Kr{\"o}ner and Julio C. Encinas Alvarado and Andres Garc{\'i}a Higuera and Juan Carlos Augusto and Diane J. Cook and Veikko Ikonen and Pavel Cech and Peter Mikuleck{\'y} and Achilles Kameas and Victor Callaghan", isbn13 = "978-1-60750-464-1", pages = "201--208", series = "Ambient Intelligence and Smart Environments", URL = "http://www.booksonline.iospress.com/Content/View.aspx?piid=13897", DOI = "DOI:10.3233/978-1-60750-056-8-201", bibdate = "2011-03-09", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/intenv/intenv2009w.html#Zablotskaya09", abstract = "In this work we create a preliminary model of dependency between person's capabilities and statistical features extracted from transcribed monologues. People of different age and educational level were asked to paraphrase the same short TV-reports and afterwards to pass a verbal part of an intelligence test. Since such kind of data is very time consuming, we created just a preliminary model on an insignificant amount of data to be sure that we are going in the right direction. Genetic programming was used for developing an approximation model of person's capabilities on the base of collected data.", keywords = "genetic algorithms, genetic programming", } @Article{Zadshakoyan:2013:EAAI, author = "M. Zadshakoyan and V. Pourmostaghimi", title = "Genetic equation for the prediction of tool-chip contact length in orthogonal cutting", journal = "Engineering Applications of Artificial Intelligence", volume = "26", number = "7", pages = "1725--1730", year = "2013", month = aug, keywords = "genetic algorithms, genetic programming, Cutting parameters, Machining, Tool-chip contact length", ISSN = "0952-1976", DOI = "doi:10.1016/j.engappai.2012.10.016", URL = "http://www.sciencedirect.com/science/article/pii/S0952197612002850", abstract = "In metal cutting, it has been acknowledged that the tool-chip contact length significantly affects many aspects of machining such as chip formation, cutting forces, cutting temperatures, tool wear and tool life. Important decrease in the tool-chip contact length, decreases the thickness of the secondary shear zone, which leads to a decrease of the cutting temperature and cutting force. As a result, it has a great effect on finish surface and tool life. Several ways have been proposed in different works to find its value, which have given discordant results for the same set of cutting conditions. In this paper, the genetic equation for the tool-chip contact length is developed with the use of the experimentally measured contact length values and genetic programming. The suggested equation has shown to correspond well with experimental data in various machining conditions with associated cutting parameters and this model predicts tool-chip contact length better than other known solutions.", } @Article{journals/ijamc-igi/ZadshakoyanP15, author = "Mohammad Zadshakoyan and Vahid Pourmostaghimi", title = "Cutting Tool Crater Wear Measurement in Turning Using Chip Geometry and Genetic Programming", journal = "International Journal of Applied Metaheuristic Computing", year = "2015", number = "1", volume = "6", bibdate = "2015-06-24", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ijamc-igi/ijamc-igi6.html#ZadshakoyanP15", pages = "47--60", keywords = "genetic algorithms, genetic programming", URL = "http://dx.doi.org/10.4018/ijamc.2015010104", abstract = "Tool wear prediction plays an important role in industry automation for higher productivity and acceptable product quality. Therefore, in order to increase the productivity of turning process, various researches have been made recently for tool wear estimation and classification in turning process. Chip form is one of the most important factors commonly considered in evaluating the performance of machining process. On account of the effect of the progressive tool wear on the shape and geometrical features of produced chip, it is possible to predict some measurable machining outputs such as crater wear. According to experimentally performed researches, cutting speed and cutting time are two extremely effective parameters which contribute to the development of the crater wear on the tool rake face. As a result, these parameters will change the chip radius and geometry. This paper presents the development of the genetic equation for the tool wear using occurred changes in chip radius in turning process. The development of the equation combines different methods and technologies like evolutionary methods, manufacturing technology, measuring and control technology with the adequate hardware and software support. The results obtained from genetic equation and experiments showed that obtained genetic equations are correlated well with the experimental data. Furthermore, it can be used for tool wear estimation during cutting process and because of its parametric form, genetic equation enables us to analyse the effect of input parameters on the crater wear parameters.", } @InCollection{Zadshakoyan:2018:aamc, author = "Mohammad Zadshakoyan and Vahid Pourmostaghimi", title = "Metaheuristics in Manufacturing: Predictive Modeling of Tool Wear in Machining Using Genetic Programming", booktitle = "Advancements in Applied Metaheuristic Computing", publisher = "IGI Global", year = "2018", editor = "Nilanjan Dey", chapter = "5", pages = "118--142", keywords = "genetic algorithms, genetic programming", isbn13 = "9781522541516", URL = "https://www.igi-global.com/chapter/metaheuristics-in-manufacturing/192002", DOI = "doi:10.4018/978-1-5225-4151-6.ch005", abstract = "The state of a cutting tool is an important factor in any metal cutting process as additional costs in terms of scrapped components, machine tool breakage and unscheduled downtime result from worn tool usage. Therefore, tool wear prediction plays an important role in industry automation for higher productivity and acceptable product quality. Therefore, in order to increase the productivity of turning process, various researches have been made recently for tool wear estimation and classification in turning process. Chip form is one of the most important factors commonly considered in evaluating the performance of machining process. On account of the effect of the progressive tool wear on the shape and geometrical features of produced chip, it is possible to predict some measurable machining outputs such as crater wear. According to experimentally performed researches, cutting speed and cutting time are two extremely effective parameters which contribute to the development of the crater wear on the tool rake face. As a result, these parameters will change the chip radius and geometry. This chapter presents the development of the genetic equation for the tool wear using occurred changes in chip radius in turning process. The development of the equation combines different methods and technologies like evolutionary methods, manufacturing technology, measuring and control technology with the adequate hardware and software support. The results obtained from genetic equation and experiments showed that obtained genetic equations are correlated well with the experimental data. Furthermore, it can be used for tool wear estimation during cutting process and because of its parametric form, genetic equation enables us to analyse the effect of input parameters on the crater wear parameters.", notes = "University of Tabriz, Iran https://www.igi-global.com/book/advancements-applied-metaheuristic-computing/182886", } @InProceedings{Zaefferer:2018:PPSN, author = "Martin Zaefferer and Joerg Stork and Oliver Flasch and Thomas Bartz-Beielstein", title = "Linear Combination of Distance Measures for Surrogate Models in Genetic Programming", booktitle = "15th International Conference on Parallel Problem Solving from Nature", year = "2018", editor = "Anne Auger and Carlos M. Fonseca and Nuno Lourenco and Penousal Machado and Luis Paquete and Darrell Whitley", volume = "11102", series = "LNCS", pages = "220--231", address = "Coimbra, Portugal", month = "8-12 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Surrogate models, Distance measures", isbn13 = "978-3-319-99258-7", URL = "https://www.springer.com/gp/book/9783319992587", DOI = "doi:10.1007/978-3-319-99259-4_18", abstract = "Surrogate models are a well established approach to reduce the number of expensive function evaluations in continuous optimization. In the context of genetic programming, surrogate modelling still poses a challenge, due to the complex genotype-phenotype relationships. We investigate how different genotypic and phenotypic distance measures can be used to learn Kriging models as surrogates. We compare the measures and suggest to use their linear combination in a kernel. We test the resulting model in an optimization framework, using symbolic regression problem instances as a benchmark. Our experiments show that the model provides valuable information. Firstly, the model enables an improved optimization performance compared to a model-free algorithm. Furthermore, the model provides information on the contribution of different distance measures. The data indicates that a phenotypic distance measure is important during the early stages of an optimization run when less data is available. In contrast, genotypic measures, such as the tree edit distance, contribute more during the later stages.", notes = "See also arXiv:1807.01019 PPSN2018 http://ppsn2018.dei.uc.pt This two-volume set LNCS 11101 and 11102 constitutes the refereed proceedings of the 15th International Conference on Parallel Problem Solving from Nature, PPSN 2018", } @PhdThesis{DissertationZaef18, author = "Martin Zaefferer", title = "Surrogate models for discrete optimization problems", school = "Fakultaet fuer Informatik, Technischen Universitaet Dortmund", year = "2018", type = "DOKTORS DER INGENIEURWISSENSCHAFTEN", address = "Germany", month = "19 " # dec, keywords = "genetic algorithms, genetic programming, ddc:004, surrogate modelling, discrete optimisation, combinatorial optimisation, kriging, kernel", bibsource = "OAI-PMH server at eldorado.uni-dortmund.de", language = "eng", oai = "oai:eldorado.tu-dortmund.de:2003/37870", URL = "http://hdl.handle.net/2003/37870", URL = "http://dx.doi.org/10.17877/DE290R-19857", URL = "https://eldorado.tu-dortmund.de/bitstream/2003/37870/1/DissertationZaef18.pdf", size = "237 pages", abstract = "Surrogate models are crucial tools for many real-world optimisation problems. An optimisation algorithm can evaluate a data-driven surrogate model instead of an expensive objective function. While surrogate models are well-established in the continuous optimisation domain, they are less frequently applied to more complex search spaces with discrete or combinatorial solution representations. The main goal of this thesis is to fill this gap. We develop and improve methods for data-driven surrogate modelling in discrete search spaces. After an initial review of existing approaches, this work focuses on a similarity-based, or kernel-based, model: Kriging. The intuitive idea is to change the underlying kernel, thus adapting Kriging to arbitrary data types. However, the model is sensitive to the employed kernel. Hence, methods for combining or selecting kernels are required. To that end, this thesis discusses various methods based on concepts like correlation, likelihood, and cross-validation. Our experiments show that choosing or constructing the right kernel determines the success of the optimisation algorithm. Another challenge is that kernels are often desired to be positive semi-definite (e.g., correlation functions) or conditionally negative semi-definite (distances). Analytical proofs of a kernel's definiteness are possible, yet not always feasible in practice. At the same time, these properties can have a significant effect on model accuracy. Hence, we propose a randomized, empirical search procedure to determine whether a kernel is definite. Once a kernel is determined to be indefinite, appropriate counter-measures have to be taken to avoid performance losses. Taking inspiration from the field of support vector learning, we use eigenspectrum transformations and related methods to correct the kernel matrices. We add to the state of the art by considering various repair mechanisms that are linked to additional requirements imposed by Kriging models. We test most of our methods with sets of simple benchmark problems. To extend this, we also investigate two problems that are more complex. Firstly, we consider solutions represented by tree structures, which are of interest in genetic programming. We discuss different kernels on tree structures and test them on symbolic regression tasks. Another more complex test case are hierarchical search spaces. Here, some variables are only active when other variables satisfy a condition. We design several new kernels for hierarchical search spaces and compare them to an existing kernel. Even with those more complex test cases, it remains unclear how performance estimates translate to real-world problems. To address this, we propose a data-driven test function generator based on Kriging simulation. In contrast to estimation, simulation may avoid smoothing the training data and is inherently suited to generate randomized test instances that reflect real-world behaviour.", notes = "Supervisors: Prof. Dr. Thomas Bartz-Beielstein and Prof. Dr. Guenter Rudolph", } @Article{ZaeifiYamchi:2009:IJCCE, author = "Mahdi {Zaeifi Yamchi} and Majid Abdouss and Hamid Modarress", title = "Application of Genetic Programming to Modeling and Prediction of Activity Coefficient Ratio of Electrolytes in Aqueous Electrolyte Solution Containing Amino Acids", journal = "Iranian Journal of Chemistry and Chemical Engineering", year = "2009", volume = "28", number = "3 - Serial Number 51", pages = "71--80", month = sep # " and " # oct, keywords = "genetic algorithms, genetic programming, amino acid, electrolytes, activity coefficient, modeling", ISSN = "1021-9986", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.1041.2039", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1041.2039", URL = "http://www.ijcce.ac.ir/article_6849.html", URL = "http://www.ijcce.ac.ir/article_6849_79903cd40d063b764a859e2ef8b9d68f.pdf", size = "10 pages", abstract = "Genetic programming (GP) is one of the computer algorithms in the family of evolutionary-computational methods, which have been shown to provide reliable solutions to complex optimisation problems. The genetic programming under discussion in this work relies on tree-like building blocks, and thus supports process modelling with varying structure. In this paper the systems containing amino acids + water + one electrolyte (NaCl, KCl, NaBr, KBr) are modelled by GP that can predict the mean ionic activity coefficient ratio of electrolytes in presence and in absence of amino acid in different mixtures better than the common polynomial equations proposed for this kind of predictions. A set of 750 data points was used for model training and the remaining 105 data points were used for model validation. The root mean square deviation (RMSD) of the designed GP model in prediction of the mean ionic activity coefficient ratio of electrolytes is less than 0.0394 and proves the effectiveness of the GP in correlation and prediction of activity coefficients in the studied mixtures.", notes = "Department of Chemistry, Amirkabir University of Technology, P.O. Box 15875-4413 Tehran, I.R. IRAN", } @Article{journals/aai/ZafariKRM14, author = "Faheem Zafari and Gul Muhammad Khan and Mehreen Rehman and Sahibzada Ali Mahmud", title = "Evolving Recurrent Neural Network using Cartesian Genetic Programming to Predict The Trend in Foreign Currency Exchange Rates", journal = "Applied Artificial Intelligence", year = "2014", number = "6", volume = "28", pages = "597--628", month = jul, keywords = "genetic algorithms, genetic programming, cartesian genetic programming", bibdate = "2014-07-28", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/aai/aai28.html#ZafariKRM14", ISSN = "0883-9514", broken = "doi:10.1080/08839514.2014.923174", URL = "https://www.tandfonline.com/doi/pdf/10.1080/08839514.2014.923174", broken = "http://www.tandfonline.com/doi/abs/10.1080/08839514.2014.923174", size = "32 pages", abstract = "Forecasting the foreign exchange rate is an uphill task. Numerous methods have been used over the years to develop an efficient and reliable network for forecasting the foreign exchange rate. This study uses recurrent neural networks (RNNs) for forecasting the foreign currency exchange rates. Cartesian genetic programming (CGP) is used for evolving the artificial neural network (ANN) to produce the prediction model. RNNs that are evolved through CGP have shown great promise in time series forecasting. The proposed approach uses the trends present in the historical data for its training purpose. Thirteen different currencies along with the trade-weighted index (TWI) and special drawing rights (SDR) is used for the performance analysis of recurrent Cartesian genetic programming-based artificial neural networks (RCGPANN) in comparison with various other prediction models proposed to date. The experimental results show that RCGPANN is not only capable of obtaining an accurate but also a computationally efficient prediction model for the foreign currency exchange rates. The results demonstrated a prediction accuracy of 98.872 percent (using 6 neurons only) for a single-day prediction in advance and, on average, 92percent for predicting a 1000 days' exchange rate in advance based on ten days of data history. The results prove RCGPANN to be the ultimate choice for any time series data prediction, and its capabilities can be explored in a range of other fields.", } @InProceedings{conf/iwann/ZafraVHR07, author = "Amelia Zafra and Sebastian Ventura and Enrique Herrera-Viedma and Cristobal Romero", title = "Multiple Instance Learning with Genetic Programming for Web Mining", booktitle = "Proceedings of the 9th International Work-Conference on Artificial Neural Networks, IWANN 2007", year = "2007", editor = "Francisco Sandoval Hern{\'a}ndez and Alberto Prieto and Joan Cabestany and Manuel Gra{\~n}a", volume = "4507", series = "Lecture Notes in Computer Science", pages = "919--927", address = "San Sebasti{\'a}n, Spain", month = jun # " 20-22", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-73006-4", DOI = "doi:10.1007/978-3-540-73007-1_111", size = "9 pages", abstract = "The aim of this paper is to present a new tool of multiple instance learning which is designed using a grammar based genetic programming (GGP) algorithm. We study its application in Web Mining framework to identify web pages interesting for the users. This new tool called GGP-MI algorithm is evaluated and compared with other available algorithms which extend a well-known neighborhood based algorithm (k-nearest neighbour algorithm) to multiple instance learning. Computational experiments show that, the GGP-MI algorithm obtains competitive results, solves problems of other algorithms, such as sparsity and scalability and adds comprehensibility and clarity in the knowledge discovery process.", notes = "Computational and Ambient Intelligence", bibdate = "2007-09-29", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/iwann/iwann2007.html#ZafraVHR07", } @InProceedings{DBLP:conf/ecml/ZafraV07, author = "Amelia Zafra and Sebastian Ventura", title = "Multi-objective Genetic Programming for Multiple Instance Learning", booktitle = "18th European Conference on Machine Learning, ECML 2007", year = "2007", bibsource = "DBLP, http://dblp.uni-trier.de", editor = "Joost N. Kok and Jacek Koronacki and Ramon L{\'o}pez de M{\'a}ntaras and Stan Matwin and Dunja Mladenic and Andrzej Skowron", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "4701", pages = "790--797", address = "Warsaw, Poland", month = sep # " 17-21", keywords = "genetic algorithms, genetic programming, poster", isbn13 = "978-3-540-74957-8", DOI = "doi:10.1007/978-3-540-74958-5_81", size = "8 pages", abstract = "This paper introduces the use of multi-objective evolutionary algorithms in multiple instance learning. In order to achieve this purpose, a multi-objective grammar-guided genetic programming algorithm (MOG3P-MI) has been designed. This algorithm has been evaluated and compared to other existing multiple instance learning algorithms. Research on the performance of our algorithm is carried out on two well-known drug activity prediction problems, Musk and Mutagenesis, both problems being considered typical benchmarks in multiple instance problems. Computational experiments indicate that the application of the MOG3P-MI algorithm improves accuracy and decreases computational cost with respect to other techniques.", notes = "http://www.ecmlpkdd2007.org/poster_E.html", } @InProceedings{Zafra:2008:HIS, author = "Amelia Zafra and Eva Gibaja and Sebastian Ventura", title = "Multiple Instance Learning with MultiObjective Genetic Programming for Web Mining", booktitle = "Eighth International Conference on Hybrid Intelligent Systems, HIS '08", year = "2008", month = sep, pages = "513--518", keywords = "genetic algorithms, genetic programming, G3P-MI, MIL, MOG3P-MI, Web mining, grammar guided genetic programming, k-nearest neighbour algorithm, multi objective genetic programming, multiobjective grammar, multiple instance learning, Internet, data mining, learning (artificial intelligence)", DOI = "doi:10.1109/HIS.2008.120", abstract = "This paper introduces a multiobjective grammar based genetic programming algorithm to solve a Web Mining problem from multiple instance perspective. This algorithm, called MOG3P-MI, is evaluated and compared with other available algorithms which extend a well-known neighborhood-based algorithm (k-nearest neighbour algorithm) and with a mono objective version of grammar guided genetic programming G3P-MI. Computational experiments show that, the MOG3PMI algorithm obtains the best results, solves problems of k-nearest neighbour algorithms, such as sparsity and scalability, adds comprehensibility and clarity in the knowledge discovery process and overcomes the results of single objective version.", notes = "Also known as \cite{4626681}", } @InProceedings{Zafra:2008:IPMU, author = "Amelia Zafra and Sebastian Ventura", title = "Modelling User Preferences with Multi-Instance Genetic Programming", booktitle = "Information processing and Management of Uncertainty in Knowledge based systems, IPMU 20018", year = "2008", editor = "Luis Magdalena and Jose Luis Verdegay", address = "Malaga, Spain", month = jun # " 22-27", keywords = "genetic algorithms, genetic programming, user modelling, recommender systems, multi-instance learning, multi- objective algorithms", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.473.4886", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.473.4886", URL = "http://www.gimac.uma.es/ipmu08/proceedings/papers/148-zafraventura.pdf", URL = "http://www.gimac.uma.es/ipmu08/proceedings/html/148.html", abstract = "In this paper we introduce a novel model for providing users with recommendations about web index pages of their interests. The approach proposed developes user profiles based on evolutionary multi instance learning which determines what users find interesting and uninteresting by means of rules which add comprehensibility and clarity to user models and increase the quality of the recommendations. Experimental results show that our methodology achieves competitive results, providing high-quality user models which improve the accuracy of recommendations.", notes = "http://www.gimac.uma.es/ipmu08/", } @InProceedings{Zafra:2009:ISDA, author = "Amelia Zafra and Cristobal Romero and Sebastian Ventura", title = "Predicting Academic Achievement Using Multiple Instance Genetic Programming", booktitle = "Ninth International Conference on Intelligent Systems Design and Applications, ISDA '09", year = "2009", month = "30 2009-" # dec # " 2", pages = "1120--1125", keywords = "genetic algorithms, genetic programming, G3P-MI, academic achievement prediction, grammar guided genetic programming algorithm, multiple instance genetic programming, multiple instance learning, student performance prediction, university-level learning, computer aided instruction", DOI = "doi:10.1109/ISDA.2009.108", abstract = "The ability to predict a student's performance could be useful in a great number of different ways associated with university-level learning. In this paper, a grammar guided genetic programming algorithm, G3P-MI, has been applied to predict if the student will fail or pass a certain course and identifies activities to promote learning in a positive or negative way from the perspective of MIL. Computational experiments compare our proposal with the most popular techniques of multiple instance learning (MIL). Results show that G3P-MI achieves better performance with more accurate models and a better trade-off between such contradictory metrics as sensitivity and specificity. Moreover, it adds comprehensibility to the knowledge discovered and finds interesting relationships that correlate certain tasks and the time devoted to solving exercises with the final marks obtained in the course.", notes = "Also known as \cite{5364212}", } @Article{Zafra200911470, author = "A. Zafra and C. Romero and S. Ventura and E. Herrera-Viedma", title = "Multi-instance genetic programming for web index recommendation", journal = "Expert Systems with Applications", volume = "36", number = "9", pages = "11470--11479", year = "2009", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2009.03.059", URL = "http://www.sciencedirect.com/science/article/B6V03-4VXMPMD-1/2/736fb9dc8cc96734079b1b02b58a33a8", keywords = "genetic algorithms, genetic programming, Multiple instance learning, User modelling, Web mining", abstract = "This article introduces the use of a multi-instance genetic programming algorithm for modelling user preferences in web index recommendation systems. The developed algorithm learns user interest by means of rules which add comprehensibility and clarity to the discovered models and increase the quality of the recommendations. This new model, called G3P-MI algorithm, is evaluated and compared with other available algorithms. Computational experiments show that our methodology achieves competitive results and provide high-quality user models which improve the accuracy of recommendations.", } @InProceedings{conf/hais/ZafraV09, title = "A Comparison of Multi-objective Grammar-Guided Genetic Programming Methods to Multiple Instance Learning", author = "Amelia Zafra and Sebastian Ventura", booktitle = "Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2009", year = "2009", editor = "Emilio Corchado and Xindong Wu and Erkki Oja and {\'A}lvaro Herrero and Bruno Baruque", volume = "5572", series = "Lecture Notes in Computer Science", pages = "450--458", address = "Salamanca, Spain", month = jun # " 10-12", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-02318-7", DOI = "doi:10.1007/978-3-642-02319-4_54", bibdate = "2009-06-24", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/hais/hais2009.html#ZafraV09", abstract = "This paper develops a first comparative study of multiobjective algorithms in Multiple Instance Learning (MIL) applications. These algorithms use grammar-guided genetic programming, a robust classification paradigm which is able to generate understandable rules that are adapted to work with the MIL framework. The algorithms obtained are based on the most widely used and compared multi-objective evolutionary algorithms. Thus, we design and implement SPG3P-MI based on the Strength Pareto Evolutionary Algorithm, NSG3P-MI based on the Non-dominated Sorting Genetic Algorithm and MOGLG3P-MI based on the Multi-objective genetic local search. These approaches are tested with different MIL applications and compared to a previous singleobjective grammar-guided genetic programming proposal. The results demonstrate the excellent performance of multi-objective approaches in achieving accurate models and their ability to generate comprehensive rules in the knowledgable discovery process.", } @InProceedings{conf/edm/ZafraV09, title = "Predicting Student Grades in Learning Management Systems with Multiple Instance Learning Genetic Programming", author = "Amelia Zafra and Sebastian Ventura", bibdate = "2010-10-06", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/edm/edm2009.html#ZafraV09", booktitle = "Educational Data Mining - {EDM} 2009, Cordoba, Spain, July 1-3, 2009. Proceedings of the 2nd International Conference on Educational Data Mining", publisher = "http://www.educationaldatamining.org", year = "2009", editor = "Tiffany Barnes and Michel C. Desmarais and Crist{\'o}bal Romero and Sebasti{\'a}n Ventura", isbn13 = "978-84-613-2308-1", pages = "309--318", URL = "http://www.educationaldatamining.org/EDM2009/uploads/proceedings/zafra.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.209.93", abstract = "The ability to predict a student's performance could be useful in a great number of different ways associated with university-level learning. In this paper, a grammar guided genetic programming algorithm, G3P-MI, has been applied to predict if the student will fail or pass a certain course and identifies activities to promote learning in a positive or negative way from the perspective of Multiple Instance Learning (MIL). Computational experiments compare our proposal with the most popular techniques of MIL. Results show that G3P-MI achieves better performance with more accurate models and a better trade-off between such contradictory metrics as sensitivity and specificity. Moreover, it adds comprehensibility to the knowledge discovered and finds interesting relationships that correlate certain tasks and the time devoted to solving exercises with the final marks obtained in the course.", keywords = "genetic algorithms, genetic programming", } @InCollection{reference/dataware/Zafra09, author = "Amelia Zafra", title = "Multi-Instance Learning with MultiObjective Genetic Programming", booktitle = "Encyclopedia of Data Warehousing and Mining", publisher = "IGI Global", year = "2009", editor = "John Wang", chapter = "212", pages = "1372--1379", edition = "2", keywords = "genetic algorithms, genetic programming", isbn13 = "9781605660103", URL = "http://www.igi-global.com/bookstore/titledetails.aspx?titleid=346&detailstype=chapters", DOI = "doi:10.4018/978-1-60566-010-3.ch212", DOI = "doi:10.4018/978-1-60566-010-3", abstract = "The multiple-instance problem is a difficult machine learning problem that appears in cases where knowledge about training examples is incomplete. In this problem, the teacher labels examples that are sets (also called bags) of instances. The teacher does not label whether an individual instance in a bag is positive or negative. The learning algorithm needs to generate a classifier that will correctly classify unseen examples (i.e., bags of instances). This learning framework is receiving growing attention in the machine learning community and since it was introduced by Dietterich, Lathrop, Lozano-Perez (1997), a wide range of tasks have been formulated as multi-instance problems. Among these tasks, we can cite content-based image retrieval (Chen, Bi, & Wang, 2006) and annotation (Qi and Han, 2007), text categorisation (Andrews, Tsochantaridis, & Hofmann, 2002), web index page recommendation (Zhou, Jiang, & Li, 2005; Xue, Han, Jiang, & Zhou, 2007) and drug activity prediction (Dietterich et al., 1997; Zhou & Zhang, 2007). In this chapter we introduce MOG3P-MI, a multiobjective grammar guided genetic programming algorithm to handle multi-instance problems. In this algorithm, based on SPEA2, individuals represent classification rules which make it possible to determine if a bag is positive or negative. The quality of each individual is evaluated according to two quality indexes: sensitivity and specificity. Both these measures have been adapted to MIL circumstances. Computational experiments show that the MOG3P-MI is a robust algorithm for classification in different domains where achieves competitive results and obtain classifiers which contain simple rules which add comprehensibility and simplicity in the knowledge discovery process, being suitable method for solving MIL problems (Zafra & Ventura, 2007).", notes = "4 Volumes. University of Cordoba, Spain", bibdate = "2011-01-18", bibsource = "DBLP, http://dblp.uni-trier.de/db/reference/dataware/dataware2009.html#Zafra09", } @PhdThesis{Zafra:thesis, author = "Amelia {Zafra Gomez}", title = "Guided Genetic Programming Models for Multiple Instance Learning", titulo = "Modelos de Programacion Genetica Gramatical para Aprendizaje con Multiples Instancias", school = "University of Granada", year = "2009", address = "Spain", month = jul, keywords = "genetic algorithms, genetic programming", URL = "https://www.uco.es/kdis/research/theses/thesis-azafra/", URL = "http://www.uco.es/grupos/kdis/docs/thesis/2009-AZafra.pdf", broken = "http://www.uco.es/grupos/kdis/index.php?option=com_jresearch&view=thesis&task=show&id=2&Itemid=51&lang=en", size = "337 pages", abstract = "This work focuses on the design of grammatical genetic programming models for solving different paradigm of learning applications with multiple instances. First, we review the status of art of this learning. Following this review, we find that almost all learning paradigms used in machine learning have been extended to this paradigm, but there are no proposals of Evolutionary Algorithms (EAs) in this learning framework. EAs are a good alternative in different learning paradigms which have been applied, the large number of publications appeared since its appearance is an evidence of this popularity. In this work grammatical genetic programming methods both mono-and multi-objective are introduced for the resolution of different applications. In first place, an experimental study using benchmark data sets is carried out to demonstrate their effectiveness with respect to the most relevant proposals done over the years. Then, the models are applied over two real problems: web index page recommendation and prediction of a student's academic performance considering the work developed in the educational platform; these problems approached from a traditional supervised learning contain many missing values making difficult the correct classification. Using MIL, we seek a more flexible representation to solve them.", notes = "in Spanish Supervisor: Sebastian Ventura Soto", } @Article{Zafra2009, author = "Amelia Zafra and Eva L. Gibaja and Sebastian Ventura", title = "Multiple Instance Learning with Multiple Objective Genetic Programming for Web Mining", journal = "Applied Soft Computing", year = "2011", volume = "11", number = "1", pages = "93--102", month = jan, ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2009.10.021", URL = "http://www.sciencedirect.com/science/article/B6W86-4XHVGXC-6/2/45abf35a3df29930ebcbd77a76c8998b", keywords = "genetic algorithms, genetic programming, Multi-instance learning, Multi-objective learning, Web Mining", abstract = "This paper introduces a multi-objective grammar based genetic programming algorithm, MOG3P-MI, to solve a Web Mining problem from the perspective of multiple instance learning. This algorithm is evaluated and compared to other algorithms that were previously used to solve this problem. Computational experiments show that the MOG3P-MI algorithm obtains the best results, adds comprehensibility and clarity to the knowledge discovery process and overcomes the main drawbacks of previous techniques obtaining solutions which maintain a balance between conflicting measurements like sensitivity and specificity.", } @InProceedings{Zafra:2010:gecco, author = "Amelia Zafra and Sebastian Ventura", title = "Grammar guided genetic programming for multiple instance learning: an experimental study", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "909--916", keywords = "genetic algorithms, genetic programming, grammar-guided genetic programming", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830483.1830647", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "This paper introduces a new Grammar-Guided Genetic Programming algorithm for solving multi-instance Learning problems. This algorithm, called G3P-MI, is evaluated and compared with other Multi-Instance classification techniques on different application domains. Computational experiments show that the G3P-MI often obtains consistently better results than other algorithms in terms of accuracy, sensitivity and specificity. Moreover, it adds comprehensibility and clarity into the knowledge discovery process, expressing the information in the form of IF-THEN rules. Our results confirm that evolutionary algorithms are appropriate for dealing with multi-instance learning problems.", notes = "Also known as \cite{1830647} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @Article{Zafra20104496, author = "Amelia Zafra and Sebastian Ventura", title = "{G3P-MI:} A genetic programming algorithm for multiple instance learning", journal = "Information Sciences", volume = "180", number = "23", pages = "4496--4513", year = "2010", ISSN = "0020-0255", DOI = "doi:10.1016/j.ins.2010.07.031", URL = "http://www.sciencedirect.com/science/article/B6V0C-50S2RDP-1/2/4591b7540f8c35538e14824742bb8343", keywords = "genetic algorithms, genetic programming, Multiple instance learning, Rule learning", abstract = "This paper introduces a new Grammar-Guided Genetic Programming algorithm for resolving multi-instance learning problems. This algorithm, called G3P-MI, is evaluated and compared to other multi-instance classification techniques in different application domains. Computational experiments show that the G3P-MI often obtains consistently better results than other algorithms in terms of accuracy, sensitivity and specificity. Moreover, it makes the knowledge discovery process clearer and more comprehensible, by expressing information in the form of IF-THEN rules. Our results confirm that evolutionary algorithms are very appropriate for dealing with multi-instance learning problems.", } @InCollection{ZRV2010, author = "Amelia Zafra and Cristobal Romero and Sebastian Ventura", title = "Multi-Instance Learning versus Single-Instance Learning for Predicting the Student's Performance", booktitle = "Handbook of Educational Data Mining", publisher = "CRC Press", year = "2010", editor = "Cristobal Romero and Sebastian Ventura and Mykola Pechenizkiy and Ryan S. J. D. Baker", chapter = "13", pages = "187--200", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4398-0457-5", URL = "http://www.crcnetbase.com/doi/abs/10.1201/b10274-16", DOI = "doi:10.1201/b10274-16", } @Article{journals/soco/ZafraV12, author = "Amelia Zafra and Sebastian Ventura", title = "Multi-objective approach based on grammar-guided genetic programming for solving multiple instance problems", journal = "Soft Computing - A Fusion of Foundations, Methodologies and Applications", year = "2012", number = "6", volume = "16", pages = "955--977", keywords = "genetic algorithms, genetic programming, multiple instance learning, multiple objective learning, grammar guided genetic programming, evolutionary rule learning", ISSN = "1432-7643", DOI = "doi:10.1007/s00500-011-0794-0", size = "23 pages", abstract = "Multiple instance learning (MIL) is considered a generalisation of traditional supervised learning which deals with uncertainty in the information. Together with the fact that, as in any other learning framework, the classifier performance evaluation maintains a trade-off relationship between different conflicting objectives, this makes the classification task less straightforward. This paper introduces a multi-objective proposal that works in a MIL scenario to obtain well-distributed Pareto solutions to multi-instance problems. The algorithm developed, Multi-Objective Grammar Guided Genetic Programming for Multiple Instances (MOG3P-MI), is based on grammar-guided genetic programming, which is a robust tool for classification. Thus, this proposal combines the advantages of the grammar-guided genetic programming with benefits provided by multi-objective approaches. First, a study of multi-objective optimisation for MIL is carried out. To do this, three different extensions of MOG3P-MI are designed and implemented and their performance is compared. This study allows us on the one hand, to check the performance of multi-objective techniques in this learning paradigm and on the other hand, to determine the most appropriate evolutionary process for MOG3P-MI. Then, MOG3P-MI is compared with some of the most significant proposals developed throughout the years in MIL. Computational experiments show that MOG3P-MI often obtains consistently better results than the other algorithms, achieving the most accurate models. Moreover, the classifiers obtained are very comprehensible.", affiliation = "Department of Computer Science and Numerical Analysis, University of Cordoba, Cordoba, Spain", bibdate = "2012-05-14", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/soco/soco16.html#ZafraV12", } @Article{Zafra20122693, author = "Amelia Zafra and Sebastian Ventura", title = "Multi-instance genetic programming for predicting student performance in web based educational environments", journal = "Applied Soft Computing", volume = "12", number = "8", pages = "2693--2706", year = "2012", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2012.03.054", URL = "http://www.sciencedirect.com/science/article/pii/S1568494612001652", keywords = "genetic algorithms, genetic programming, Educational data mining, Multiple instance learning, Classification", abstract = "A considerable amount of e-learning content is available via virtual learning environments. These platforms keep track of learners' activities including the content viewed, assignments submission, time spent and quiz results, which all provide us with a unique opportunity to apply data mining methods. This paper presents an approach based on grammar guided genetic programming, G3P-MI, which classifies students in order to predict their final grade based on features extracted from logged data in a web based education system. Our proposal works with multiple instance learning, a relatively new learning framework that can eliminate the great number of missing values that appear when the problem is represented by traditional supervised learning. Experimental results are carried out on data sets with information about several courses and demonstrate that G3P-MI successfully achieves better accuracy and yields trade-off between such contradictory metrics as sensitivity and specificity compared to the most popular techniques of multiple instance learning. This method could be quite useful for early identification of students at risk, especially in very large classes, and allows the instructor to provide information about the most relevant activities to help students have a better chance to pass a course.", } @Article{Zafra:2022:GPEM, author = "Amelia Zafra", title = "Ying Bi, Bing Xue, Mengjie Zhang: Genetic programming for image classification--an automated approach to feature learning", journal = "Genetic Programming and Evolvable Machines", year = "2022", volume = "23", number = "4", pages = "589--590", month = dec, keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/cQprq", DOI = "doi:10.1007/s10710-022-09438-8", size = "2 pages", abstract = "Springer, 2021, 258 pp, ISBN: 978-3-030-65926-4", notes = "Review of \cite{bi2021gpimage} Department of Computer Science and Numerical Analysis, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Cordoba, Cordoba, Spain", } @PhdThesis{Zaherpour:thesis, author = "Jamal Zaherpour", title = "Improving global and catchment estimates of runoff through computationally-intelligent ensemble approaches Applications of intelligent multi-model combination, cross-scale model comparisons, ensemble analyses, and new model parameterisations", year = "2018", month = dec # "~14", school = "University of Nottingham", address = "UK", keywords = "genetic algorithms, genetic programming", bibsource = "OAI-PMH server at eprints.nottingham.ac.uk", language = "en", oai = "oai:eprints.nottingham.ac.uk:55340", type = "NonPeerReviewed", URL = "http://eprints.nottingham.ac.uk/55340/", broken = "http://eprints.nottingham.ac.uk/55340/1/JZaherpour-(4206914)Final-.pdf", broken = "https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.785833", abstract = "Water related problems (scarcity, availability and hazards) together form one of the three major crises (the two other are food and energy) for today and the future across the globe (World Economic Forum, 2016, Schewe et al., 2014a, Hanasaki et al., 2013, Rockstrom et al., 2009). Water crises are widespread and heterogeneous around the world and climate change and socioeconomic drivers are expected to accelerate these problems (Veldkamp, 2017). To deal with the above concerns, mitigation and adaptation strategies are developed at different scales (global, regional and local). Developing these strategies, as well as selecting the most appropriate one to the problem of interest, should ideally benefit from the highest possible accuracy in estimates of the hydrological cycle and water resources. More reliable decisions can in turn be made by applying tools and techniques that enhance decision makers perception of the hydrological cycle, particularly extreme events i.e. droughts and floods. These tools should also facilitate insights into the cycle: ideally by reference to hydrological indicators, spatially (globally and locally) and temporally (present day and future). Global and catchment scale hydrological models (GHMs and CHMs) have been used as such tools that along with advances in data acquisition, analytical techniques and computation power offer powerful tools for modelling natural processes and provide useful insights into the hydrological cycle. GHMs have a shorter history of emergence and application than CHMs. GHMs have been developed and applied from 1986 in recognition of the fact that hydrological processes and water resources are global phenomena and should be treated at global scale (Bierkens, 2015). A GHM is a pragmatic trade-off between a faithful representation of the diversity of hydrological processes found across the worlds catchments, and a generalised and simplified representation of hydrological processes that can support multi-decadal, generalised hydrological simulations at global scales. Compared to hydrological models designed for catchment-scale simulations (Arnold et al., 1993; Krysanova et al., 1998; Lindstrom et al., 2010), GHMs employ coarser spatial discretisation and model the global land surface in a single instantiation. The global scope of GHMs, limited availability and quality of observed discharge data across the global domain and their use of spatially generalised parameters make them more difficult to calibrate than catchment hydrological models. Whilst examples of calibrated GHMs do exist (Muller Schmied et al., 2016), the majority of GHMs are uncalibrated (Gosling et al., 2016; Hattermann et al., 2017). This lack of calibration, coupled with the diversity of simplifications employed in the hydrological process representations, means that there can be large inconsistency in the skill, bias and uncertainty of an individual GHM at different locations, as well as large inconsistencies between different GHMs at any given location (van Huijgevoort et al., 2013). This spatial inconsistency means that GHMs risk becoming a jungle of models (Kundzewicz, 1986) in which it can be difficult to determine where a particular GHM output is likely to be capable of delivering optimal hydrological simulations. It also makes it dangerous to assume that any individual GHM will be an adequate basis for making projections at any given location, even if the models ability to replicate observed data in particular catchments is enhanced through the acquisition of higher quality input data or efforts to improve process representations (Liu et al., 2007). To an extent, these arguments are also applicable to CHMs because whilst they have been shown to generally perform better than GHMs in model evaluation studies, ensembles of such models still result in an uncertainty range when the models are run with identical inputs (Hattermann et al., 2017; Hattermann et al., 2018). To minimise the challenge of varying outputs from different models, several model inter-comparison projects (MIPs) have been undertaken around the world (Henderson-Sellers et al., 1995, Entin et al., 1999, Guo and Dirmeyer, 2006, Koster et al., 2006, Harding et al., 2011). These projects usually use standard modelling baselines to deal with discrepancies between model outputs. This results in higher consistency in the climate forcings input to the models (where applicable), their process representations (e.g. the simulation of human impacts such as water abstractions), and the temporal and spatial resolutions of their simulations. This way, model outputs are directly comparable to each other, which supports diagnostic inter-comparisons between them (Bierkens, 2015). One of the largest, ongoing MIPs (whose data are used in this thesis) is the Inter-Sectoral Impact Model Inter-comparison Project (ISIMIP) (Schellnhuber et al., 2014, Warszawski et al., 2014). ISIMIP is a community-driven effort by more than 130 modelling groups, that covers different sectors including water (both global and catchment hydrological modelling communities). Outputs from ISIMIP are widely used in different projects, such as reports of the International Panel on Climate Change (IPCC) http://www.ipcc.ch/. MIPs including ISIMIP provide a unique opportunity to access data from different models and to assess their relative performance. It also facilitates continuous model improvement via the inclusion of new schemes (e.g. human impacts such as dams, reservoirs and water abstractions) accompanied by dozens of models, as well as communication between modelling groups working in the same or different sectors. Nonetheless, they do not fully address the challenge of spatial inconsistencies between models, as well as the question of what ensemble representative to select for use when trying to improve the reliability of decision-making. There remain other shortcomings or unexplored aspects within MIPs (particularly ISIMIP as this research focus MIP), hence areas of further research and potential improvement in model evaluation and application which will be addressed later in this introduction. The question of how to address the challenges of spatial inconsistency in hydrological models has been a feature of catchment-scale model research for several decades. In answering it, catchment modellers have recognised that reliance on a single, inconsistent model is inherently risky and should be avoided (Marshall et al., 2006; Shamseldin et al., 1997). Instead, they have developed ways to take advantage of the diversity of outputs (Clemen, 1989) generated by different models by using optimised mathematical combination methods to deliver a combined output that performs better than the individual models from which it was created (Hagedorn et al., 2005). This general approach---known as multi-model combination (MMC)---has been an important focus of catchment hydrological modelling studies, especially over the last two decades (Abrahart and See, 2002; Ajami et al., 2006; Arsenault et al., 2015; Azmi et al., 2010; de Menezes et al., 2000; Fernando et al., 2012; Jeong and Kim, 2009; Marshall et al., 2007; Marshall et al., 2006; Moges et al., 2016; Nasseri et al., 2014; Sanderson and Knutti, 2012; Shamseldin et al., 1997). Given its demonstrable potential in catchment studies, it is perhaps surprising that the potential of applying MMC to GHMs has yet to be explored. A wide range of techniques can be used to generate an MMC solution. The simplest example is the calculation of the arithmetic mean of the input models (commonly referred to as an Ensemble Mean (EM)). More sophisticated techniques employ weighted schemes (Arsenault et al., 2015), with the differential weightings applied to each input model reflecting their relative strengths or weaknesses. The mathematical approach taken to determining the weights depends on the objective of the MMC. Where the primary objective is to minimise the difference between the MMC solution and observed data (i.e. maximise the predictive performance), without explicitly accounting for model or parameter uncertainty, the use of multiple linear regression (Doblas-Reyes et al., 2005) or machine learning algorithms to learn the optimal set weights to apply to each MMC input model is a popular approach (Marshall et al., 2007). The use of algorithms such as artificial neural networks (ANNs) (Shamseldin et al., 1997; Xiong et al., 2001) or gene expression programming (GEP) (Barbulescu and Bautu, 2010; Barbulescu and Bautu, 2009; Fernando et al., 2012) to define non-linear weighting schemes have proven to be particularly effective. This is down to their ability to generate optimised, non-linear schemes rapidly, without the need for any prior knowledge of the model parameters.", abstract = "Where there is a desire to account for and minimise model and parameter uncertainty in the weighting scheme, Bayesian averaging methods are required (Ajami et al., 2007; Hoeting et al., 1999). These optimise the weights according to the posterior performance of the MMC solution under the prior probabilities of model parameter values (Duan et al., 2007; Vrugt and Robinson, 2007; Ye et al., 2004). However, these methods require knowledge of the probability density functions (PDFs) for each of the MMC input model parameters (or at least their maximum likelihood estimates (Ye et al., 2004)). This makes their use in the MMC of GHMs problematic because the number of parameters used in GHMs is particularly high, the parameters vary considerably between models, and the PDFs of the parameters in a GHM can be extremely difficult to specify over a global domain. Consequently, the PDFs for GHM parameters are seldom specified and, in many cases, remain unknown. On this basis, this research applies the first MMC approach to tackle the challenge of inconsistency between multitude of models and using them optimally. To this end, itexplores the potential of MMC for addressing the challenge of spatial inconsistency in simulations from several GHMs (for the first time) and CHMs, by combining outputs from diverse sets of GHMs and CHMs using the machine learning tool GEP (Ferreira, 2001; Ferreira, 2006) across up to forty major catchments (Papers II and IV of the thesis). In each catchment, the MMCs ability to replicate the observed monthly runoff is compared against that of the EM, each of the individual models from which the MMC is derived, and the best-performing individual model from the ensemble. In addition, this thesis addresses four other areas of further research spotted within the framework of the ISIMIP as follow with more detailed rationale behind performing each part of the research and their contribution to knowledge presented in Section 1.3. It should, however, be emphasised that these explorations are applicable to any MIPs and not limited to the ISIMIP. First, despite the development of modelling protocols as common modelling baselines in different phases of the ISIMIP, its framework lacks an evaluation protocol. This is important towards model appraisal and, in turn, model application and improvement in a comparative manner. This research tackles this problem by setting a more comprehensive evaluation framework to an ensemble of GHMs and their ensemble mean (EM) that informs, via a multi-dimensional assessment, which models are best, where, and according to which hydrological indicator. Second, as discussed above both global and catchment models provide valuable information for decision-making and scientific understanding, but while GHMs provide output data for almost all parts of the world (applicable for aggregated assessment of e.g. climate change impacts), the applicability of outputs generated by a CHM is usually limited to the catchment for which the model has been calibrated. Given the huge resources required for developing each model type, it is of great importance to understand their relative performance and whether they can be used interchangeably both for current and future climates. Nevertheless, knowledge of the relative spreads in simulations/projections from GHM and CHM ensembles (within and outside ISIMIP) is limited to cross-scale inter-comparison of small ensembles, precluding a robust ensemble comparison. This research uses, for the first time, large multi-model ensembles of GHMs and CHMs to explore whether there are systematic differences between projections of runoff change from two ensembles comprising different types of hydrological model. It also investigates the effect of different degrees of global-mean warming on runoff for each catchment as indicated by each model type to the end of 21st century. Third, while cross-scale inter-comparison of large GHM and CHM ensembles is more informative than their small ensembles, application of more robust representatives of each model ensemble (other than EM) was found an area of further study as the EM showed to not be necessarily the best ensemble representative. Given the potential found in the intelligent MMC approach that provided runoff simulations overall better than the EM, its capacity in filling the gap between two large ensembles of GHMs and CHMs is sought. In addition, this study examines the performance of MMC when a group of GHMs and a group of CHMs are pooled together as a super-ensemble. The performance of MMC from the super-ensemble is then compared with the performance of the EM of the super-ensemble and MMCs from GHM and CHM ensembles to unravel potential benefits of the approach. Fourth and finally, GHM outputs available and applied at the time of this research were from two different phases of ISIMIP i.e. Fast Track and ISIMIP2a of which in the latter GHMs were run under two different conditions of with and without the inclusion of anthropogenic impacts in the GHMs parametrisation. Human impact parameterisations (HIP) has been an important step forwards to make modelling conditions as close as possible to the real world. Nonetheless, no comprehensive evaluation and comparison of GHMs under the two abovementioned conditions had been carried out, precluding test of models capacity to represent human activities and their impacts on assessment of freshwater resources and hydrological extremes. The final part of this research (Paper V) explores change in the performance of five GHMs that participated in ISIMIP2a and had human impacts in their parameterisation.", notes = "Supervisors: Simon Gosling and Nick Mount ISNI: 0000 0004 7971 3264", } @Article{journals/nca/ZahiriA14, author = "A. Zahiri and H. Md. Azamathulla", title = "Comparison between linear genetic programming and {M5} tree models to predict flow discharge in compound channels", journal = "Neural Computing and Applications", year = "2014", number = "2", volume = "24", pages = "413--420", keywords = "genetic algorithms, genetic programming, compound channels, linear genetic programming, m5 tree decision model, stage-discharge curve", bibdate = "2014-01-21", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/nca/nca24.html#ZahiriA14", URL = "http://dx.doi.org/10.1007/s00521-012-1247-0", DOI = "doi:10.1007/s00521-012-1247-0", size = "8 pages", abstract = "There are many studies on the hydraulic analysis of steady uniform flows in compound open channels. Based on these studies, various methods have been developed with different assumptions. In general, these methods either have long computations or need numerical solution of differential equations. Furthermore, their accuracy for all compound channels with different geometric and hydraulic conditions may not be guaranteed. In this paper, to overcome theses limitations, two new and efficient algorithms known as linear genetic programming (LGP) and M5 tree decision model have been used. In these algorithms, only three parameters (e.g., depth ratio, coherence, and ratio of computed total flow discharge to bank full discharge) have been used to simplify its applications by hydraulic engineers. By compiling 394 stage-discharge data from laboratories and fields of 30 compound channels, the derived equations have been applied to estimate the flow conveyance capacity. Comparison of measured and computed flow discharges from LGP and M5 revealed that although both proposed algorithms have considerable accuracy, LGP model with R-squared = 0.98 and RMSE = 0.32 has very good performance.", } @InCollection{Zahiri:2015:hbgpa, author = "A. Zahiri and A. A. Dehghani and H. Md. Azamathulla", title = "Application of Gene-Expression Programming in Hydraulic Engineering", booktitle = "Handbook of Genetic Programming Applications", publisher = "Springer", year = "2015", editor = "Amir H. Gandomi and Amir H. Alavi and Conor Ryan", pages = "71--97", keywords = "genetic algorithms, genetic programming, Gene Expression Programming", isbn13 = "978-3-319-20882-4", DOI = "doi:10.1007/978-3-319-20883-1_4", abstract = "Open-channel hydraulics, probably, is most important branch of water resources engineering. This sub-discipline has vital and critical importance to human history. Complex and highly nonlinear behaviour of most problems in hydraulics leads to use various soft computing techniques for their efficient solution. Genetic programming (GP) is relatively one of the new soft computing techniques which have high ability in developing intelligent systems and providing precise functional relationship solutions to complicated problems. Capability of GP in solving many engineering problems, development and application of GP branches has attracted many researchers attention. Gene Expression Programming (GEP) is becoming an important class of GP, which has found extensive applications for hydraulic engineering. GEP is an evolutionary algorithm that mimics biological evolution to model some complicated real world phenomenon. In this book chapter, attempts were made to present a review of application and development of GEP in many hydraulics phenomena. The literature review categorize in various aspects of hydraulic engineering including flow through hydraulic structures, scouring at control structures and stage-discharge rating curve in compound open channels.", } @InProceedings{zahran:1999:AGAMS, author = "Mohamed M. Zahran and Ashraf H. Abdel Wahab and Samir I. Shaheen", title = "Adaptive Genetic Algorithm for Multiprocessor Scheduling", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "814", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/Ga-835.pdf", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Zainal:2008:AICMS, author = "Anazida Zainal and Mohd Aizaini Maarof and Siti Mariyam Shamsuddin", title = "Data Reduction and Ensemble Classifiers in Intrusion Detection", booktitle = "Second Asia International Conference on Modeling Simulation, AICMS 08", year = "2008", month = may, pages = "591--596", keywords = "genetic algorithms, genetic programming, data reduction, ensemble classifiers, intrusion detection, network connection, traffic monitoring mechanism, unnecessary recognition minimization, computer network management, data reduction, minimisation, monitoring, pattern classification, telecommunication security, telecommunication traffic", DOI = "doi:10.1109/AMS.2008.146", abstract = "Efficiency is one of the major issues in intrusion detection. Inefficiency is often attributed to high overhead and this is caused by several reasons. Among them are continuous detection and the use of full feature set to look for intrusive patterns in the network packet. The purpose of this paper are; to address the issue of continuous detection by introducing traffic monitoring mechanism and a lengthy detection process by selectively choose significant features to represent a network connection. In traffic monitoring, a new recognition paradigm is proposed in which it minimizes unnecessary recognition. Therefore, the purpose of traffic monitoring is two-folds; to reduce amount of data to be recognized and to avoid unnecessary recognition. Empirical results show 30 to 40 percent reduction of normal connections is achieved in DARPA KDDCup 1999 datasets. Finally we assembled Adaptive Neural Fuzzy Inference System and Linear Genetic Programming to form an ensemble classifiers. Classification results showed a small improvement using the ensemble approach for DoS and R2L classes.", notes = "Also known as \cite{4530542}", } @InProceedings{Zainal:2008:ISIAS, author = "Anazida Zainal and Mohd Aizaini Maarof and Siti Mariyam Shamsuddin and Ajith Abraham", title = "Ensemble of One-Class Classifiers for Network Intrusion Detection System", booktitle = "Fourth International Conference on Information Assurance and Security, ISIAS '08", year = "2008", month = sep, pages = "180--185", keywords = "genetic algorithms, genetic programming, adaptive neural fuzzy inference system, classification trees, linear genetic programming, machine learning techniques, network intrusion detection system, network traffic, one-class classifiers, random forest, fuzzy neural nets, fuzzy reasoning, learning (artificial intelligence), linear programming, security of data", DOI = "doi:10.1109/IAS.2008.35", abstract = "To achieve high accuracy while lowering false alarm rates are major challenges in designing an intrusion detection system. In addressing this issue, this paper proposes an ensemble of one-class classifiers where each uses different learning paradigms. The techniques deployed in this ensemble model are; linear genetic programming (LGP), adaptive neural fuzzy inference system (ANFIS) and random forest (RF). The strengths from the individual models were evaluated and ensemble rule was formulated. Empirical results show an improvement in detection accuracy for all classes of network traffic; normal, probe, DoS, U2R and R2L. RF, which is an ensemble learning technique that generates many classification trees and aggregates the individual result was also able to address imbalance dataset problem that many of machine learning techniques fail to sufficiently address it.", notes = "Also known as \cite{4627082}", } @Article{Zainal:2009:JIAS, author = "Anazida Zainal and Mohd Aizaini Maarof and Siti Mariyam Shamsuddin", title = "Ensemble Classifiers for Network Intrusion Detection System", journal = "Journal of Information Assurance and Security", year = "2009", volume = "4", number = "3", pages = "217--225", month = jun, note = "Special Issue on Intrusion and Malware Detection", keywords = "genetic algorithms, genetic programming", ISSN = "1554-1010", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.1044.3840", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1044.3840", URL = "https://static.aminer.org/pdf/PDF/000/346/803/intrusion_detection_in_computer_networks_by_multiple_classifier_systems.pdf", URL = "http://www.mirlabs.org/jias/secured/Volume4-Issue3/vol4-issue3.html", abstract = "Two of the major challenges in designing anomaly intrusion detection are to maximise detection accuracy and to minimise false alarm rate. In addressing this issue, this paper proposes an ensemble of one-class classifiers where each adopts different learning paradigms. The techniques deployed in this ensemble model are; Linear Genetic Programming (LGP), Adaptive Neural Fuzzy Inference System (ANFIS) and Random Forest (RF). The strengths from the individual models were evaluated and ensemble rule was formulated. Prior to classification, a 2-tier feature selection process was performed to expedite the detection process. Empirical results show an improvement in detection accuracy for all classes of network traffic; Normal, Probe, DoS, U2R and R2L. Random Forest, which is an ensemble learning technique that generates many classification trees and aggregates the individual result was also able to address imbalance dataset problem that many of machine learning techniques fail to sufficiently address it.", } @Article{Zaji:2015:FMI, author = "Amir Hossein Zaji and Hossein Bonakdari", title = "Application of artificial neural network and genetic programming models for estimating the longitudinal velocity field in open channel junctions", journal = "Flow Measurement and Instrumentation", volume = "41", pages = "81--89", year = "2015", ISSN = "0955-5986", DOI = "doi:10.1016/j.flowmeasinst.2014.10.011", URL = "http://www.sciencedirect.com/science/article/pii/S0955598614001307", abstract = "Estimating the accurate longitudinal velocity fields in an open channel junction has a great impact on hydraulic structures such as irrigation and drainage channels, river systems and sewer networks. In this study, Genetic Programming (GP) and Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) were modelled and compared to find an analytical formulation that could present a continuous spatial description of velocity in open channel junction by using discrete information of laboratory measurements. Three direction coordinates of each point of the fluid flow and discharge ratio of main to tributary channel were used as inputs to the GP and ANN models. The training and testing of the models were performed according to the published experimental data from the related literature. To find the accurate prediction ability of GP and ANN models in cases with minor training dataset, the models were compared with various percents of allocated data to train dataset. New formulations were obtained from GP and ANN models that can be applied for practical longitudinal velocity field prediction in an open channel junction. The results showed that ANN model by Root Mean Squared Error (RMSE) of 0.068 performs better than GP model by RMSE of 0.162, and that ANN can model the longitudinal velocity field with small population of train dataset with high accuracy.", keywords = "genetic algorithms, genetic programming, Open channel junction, Artificial neural network, Longitudinal velocity fields", } @Article{Zakaria20105078, author = "Nor Azazi Zakaria and H. Md. Azamathulla and Chun Kiat Chang and Aminuddin {Ab. Ghani}", title = "Gene expression programming for total bed material load estimation--a case study", journal = "Science of The Total Environment", volume = "408", number = "21", pages = "5078--5085", year = "2010", ISSN = "0048-9697", DOI = "doi:10.1016/j.scitotenv.2010.07.048", URL = "http://www.sciencedirect.com/science/article/B6V78-50S2D5B-5/2/e5d28d28a79203ddeddf5140cf679798", keywords = "genetic algorithms, genetic programming, gene expression programming, Alluvial channels, Sediment transport, Total bed material load, River engineering", abstract = "This paper presents Gene-Expression Programming (GEP), which is an extension to the genetic programming (GP) approach to predict the total bed material load for three Malaysian rivers. The GEP is employed without any restriction to an extensive database compiled from measurements in the Muda, Langat, and Kurau rivers. The GEP approach demonstrated a superior performance compared to other traditional sediment load methods. The coefficient of determination, R2 (= 0.97) and the mean square error, MSE (= 0.057) of the GEP method are higher than those of the traditional method. The performance of the GEP method demonstrates its predictive capability and the possibility of the generalisation of the model to nonlinear problems for river engineering applications.", } @InProceedings{zakaria:2021:CI, author = "Yahia Zakaria and Yassin Zakaria and Ahmed {Bahaa ElDin} and Mayada Hadhoud", title = "{Niching-Based} Feature Selection with Multi-tree Genetic Programming for Dynamic Flexible Job Shop Scheduling", booktitle = "Computational Intelligence", year = "2021", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-030-70594-7_1", DOI = "doi:10.1007/978-3-030-70594-7_1", } @InProceedings{zakharov:2023:NUMTA, author = "Aleksey Zakharov", title = "Optimal Recombination Problem in Genetic Programming for Boolean Functions", booktitle = "Numerical Computations: Theory and Algorithms, NUMTA 2023", year = "2023", editor = "Yaroslav D. Sergeyev and Dmitri E. Kvasov and Annabella Astorino", address = "Pizzo Calabro, Italy", month = jun # " 14-20", publisher = "Springer", note = "Revised Selected Papers", keywords = "genetic algorithms, genetic programming", URL = "https://link.springer.com/chapter/10.1007/978-3-031-81241-5_16", DOI = "doi:10.1007/978-3-031-81241-5_16", } @Article{Zaki:2009:IJICOT, title = "The use of genetic programming for adaptive text compression", author = "M. Zaki and M. Sayed", year = "2009", month = mar # "~24", volume = "1", journal = "International Journal of Information and Coding Theory", pages = "88--108", keywords = "genetic algorithms, genetic programming, Huffman code, adaptive text compression, data compression, lossless compression, alphabet, Arabic language", ISSN = "1753-7711", DOI = "doi:10.1504/IJICOT.2009.024048", bibsource = "OAI-PMH server at www.inderscience.com", language = "eng", URL = "http://www.inderscience.com/link.php?id=24048", publisher = "Inderscience Publishers", abstract = "This paper exploits a modified genetic programming (GP) approach for solving the data compression problem. In fact, the typical GP algorithm in which a candidate solution is expressed as a tree rather than a bit string, fails to solve that problem since it does not guarantee a one to one correspondence between a particular symbol and the corresponding codeword during subtree exchange operations. The nature of the problem requires generating one, and only one, codeword for each symbol of the underlying text. In the proposed scheme, the authors introduced three new operators, namely, insertion, two-level mutation and modified crossover. Accordingly, a modified version of GP is presented and applied on different data texts to validate the proposed approach. The developed algorithm can provide optimum codes since its final solution reaches Huffman tree. Moreover, it makes use of GP not only to allow optimum compression ratio but also to provide adaptive compression implementation. The adaptation is achieved so that the selection of the codebook depends on the nature of the input text. The proposed compression scheme is written in C++ and is implemented on different text types under various operational conditions. Accordingly, the algorithm performance has been measured and evaluated.", } @Article{Zaki:2024:cor, author = "Tomas Zaki and Yannik Zeitrag and Rui Neves and Jose Rui Figueira", title = "A cooperative coevolutionary genetic programming hyper-heuristic for multi-objective makespan and cost optimization in cloud workflow scheduling", journal = "Computer and Operations Research", year = "2024", volume = "172", pages = "106805", keywords = "genetic algorithms, genetic programming, Multi-objective optimization, Hyper-heuristic, Cooperative coevolution, Cloud computing, Machine learning", ISSN = "0305-0548", URL = "https://www.sciencedirect.com/science/article/pii/S0305054824002776", DOI = "doi:10.1016/j.cor.2024.106805", abstract = "This study presents a novel multi-objective approach for NP-hard workflow scheduling in cloud computing environments. Traditional rule-based heuristics offer flexibility but lack consistent superiority across all criteria and scenarios. The development of specific rules usually requires tedious manual refinement by experts. To overcome this limitation, our method leverages evolutionary computation and simulation to automatically derive new rules. Moreover, workflow scheduling involves two crucial and related aspects: task scheduling and the allocation of virtual machines. Our contribution includes three distinct algorithms: two that address each decision separately and a third approach that coevolves priority rules for both decisions simultaneously. Computational tests demonstrate superior performance, with an exploration of rules yielding a 72.91percent larger hypervolume for optimising makespan and costs compared to benchmark heuristics from the literature. The validation on unseen instances shows a 90.26percent improvement in hypervolume performance, highlighting the robustness of our approach", } @Article{zakirovASB1-4-2017, author = "A. N. Zakirov and Joseph Alexander Brown", title = "{NSGA-II} for Biological Graph Compression", journal = "Advanced Studies in Biology", year = "2017", volume = "9", number = "1", pages = "1--7", keywords = "genetic algorithms", ISSN = "1313-9495", URL = "https://pdfs.semanticscholar.org/c0c2/ab1d7020f4388390a2506df37dc1bb1a7fb9.pdf", URL = "http://www.m-hikari.com/asb/asb2017/asb1-4-2017/p/zakirovASB1-4-2017.pdf", DOI = "doi:10.12988/asb.2017.61143", size = "7 pages", abstract = "Examinations of a common biological reference organism, (E. coli), demonstrate that NSGA-II is able to provide a series of compressions at various ratios, allows a biologist to examine the organisms connective networks with a measure of certainty of connectiveness. This is due to a novel method of scoring the similarity of the compressed network to the original during the graphs creation based on the number of false links added to the graph during the compression method.", notes = "Is this GP? Innopolis University, Innopolis, Russia Copyright 2016 A. N. Zakirov and J. A. Brown. This article is distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. http://www.m-hikari.com/asb/", } @Article{Zakwan:2021:Complexity, author = "Mohammad Zakwan and Majid Niazkar", title = "A Comparative Analysis of Data-Driven Empirical and Artificial Intelligence Models for Estimating Infiltration Rates", journal = "Complexity", year = "2021", pages = "Article ID 9945218", note = "Special Issue: Frontiers in Data-Driven Methods for Understanding, Prediction, and Control of Complex Systems 2021", keywords = "genetic algorithms, genetic programming", bibsource = "OAI-PMH server at oai.repec.org", identifier = "RePEc:hin:complx:9945218", oai = "oai:RePEc:hin:complx:9945218", URL = "https://downloads.hindawi.com/journals/complexity/2021/9945218.pdf", DOI = "doi:10.1155/2021/9945218", size = "13 pages", abstract = "Infiltration is a vital phenomenon in the water cycle, and consequently, estimation of infiltration rate is important for many hydrologic studies. In the present paper, different data-driven models including Multiple Linear Regression (MLR), Generalized Reduced Gradient (GRG), two Artificial Intelligence (AI) techniques (Artificial Neural Network (ANN) and Multigene Genetic Programming (MGGP)), and the hybrid MGGP-GRG have been applied to estimate the infiltration rates. The estimated infiltration rates were compared with those obtained by empirical infiltration models (Horton{'}s model, Philip{'}s model, and modified Kostiakov{'}s model) for the published infiltration data. Among the conventional models considered, Philip{'}s model provided the best estimates of infiltration rate. It was observed that the application of the hybrid MGGP-GRG model and MGGP improved the estimates of infiltration rates as compared to conventional infiltration model, while ANN provided the best prediction of infiltration rates. To be more specific, the application of ANN and the hybrid MGGP-GRG reduced the sum of square of errors by 97.86 percent and 81.53 percent, respectively. Finally, based on the comparative analysis, implementation of AI-based models, as a more accurate alternative, is suggested for estimating infiltration rates in hydrological models.", notes = "Civil Engineering Department, Maulana Azad National Urdu University, Hyderabad, India", } @InProceedings{zalzala:1999:MAMJTGPA, author = "A. M. S. Zalzala and D. Green", title = "{MTGP}: A Multithreaded Java Tool for Genetic Programming Applications", booktitle = "Proceedings of the Congress on Evolutionary Computation", year = "1999", editor = "Peter J. Angeline and Zbyszek Michalewicz and Marc Schoenauer and Xin Yao and Ali Zalzala", volume = "2", pages = "904--912", address = "Mayflower Hotel, Washington D.C., USA", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "6-9 " # jul, organisation = "Congress on Evolutionary Computation, IEEE / Neural Networks Council, Evolutionary Programming Society, Galesia, IEE", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, algorithms, Java applet, MTGP, evolutionary performance, multithreaded Java tool, Java, multi-threading, software tools", ISBN = "0-7803-5536-9 (softbound)", ISBN = "0-7803-5537-7 (Microfiche)", URL = "http://deron.csie.ncue.edu.tw/AI/paper/MTGP%20a%20multithreaded%20Java%20tool%20for%20genetic%20programming%20applications.pdf", DOI = "doi:10.1109/CEC.1999.782519", size = "9 pages", abstract = "MTGP is a new genetic programming system that uses the multithreading technology of the Java programming language for the parallel evolution of subpopulations of programs. The system runs as a Java applet within a standard web browser on a desktop PC, and uses a linear program representation for a stack-based virtual machine. The individuals from four subpopulations are manipulated concurrently and these subpopulations exchange their best individuals at regular intervals during a run. MTGP incorporates novel variations on the traditional genetic operators used in genetic programming and in the inclusion of a 'do nothing' gene, in an attempt to produce better evolutionary performance. The basic procedures of the system will be used in the future development of a distributed, Internet-based genetic programming system that will provide large computational power needed to solve complex problems. In this report, the performance of MTGP on two symbolic regression problems is compared to that of four other genetic programming systems. MTGP shows improvement over these systems in terms of the computational effort needed to solve the problems and the accuracy of the solution produced.", notes = "CEC-99 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. Library of Congress Number = 99-61143", } @Article{Zamani:2014:IS, author = "Behzad Zamani and Ahmad Akbari and Babak Nasersharif", title = "Evolutionary combination of kernels for nonlinear feature transformation", journal = "Information Sciences", year = "2014", volume = "274", pages = "95--107", month = aug, keywords = "genetic algorithms, genetic programming, Kernel principal component analysis (KPCA), Kernel linear discriminant analysis (KLDA), Kernel combination", ISSN = "0020-0255", URL = "http://www.sciencedirect.com/science/article/pii/S0020025514002539", DOI = "doi:10.1016/j.ins.2014.02.140", size = "13 pages", abstract = "The performance of kernel-based feature transformation methods depends on the choice of kernel function and its parameters. In addition, most of these methods do not consider the classification information and error for the mapping features. In this paper, we propose to determine a kernel function for kernel principal components analysis (KPCA) and kernel linear discriminant analysis (KLDA), considering the classification information. To this end, we combine the conventional kernel functions using genetic algorithm and genetic programming in linear and non-linear forms, respectively. We use the classification error and the mutual information between features and classes in the kernel feature space as evolutionary fitness functions. The proposed methods are evaluated on the basis of the University of California Irvine (UCI) datasets and Aurora2 speech database. We evaluate the methods using clustering validity indices and classification accuracy. The experimental results demonstrate that KPCA using a nonlinear combination of kernels based on genetic programming and the classification error fitness function outperforms conventional KPCA using Gaussian kernel and also KPCA using linear combination of kernels.", } @InProceedings{conf/lsms/ZamaniC10, title = "An Evaluation of {DNA} Barcoding Using Genetic Programming-Based Process", author = "Masood Zamani and David K. Y. Chiu", booktitle = "Life System Modeling and Intelligent Computing - International Conference on Life System Modeling and Simulation, {LSMS} 2010, and International Conference on Intelligent Computing for Sustainable Energy and Environment, {ICSEE} 2010, Wuxi, China, September 17-20, 2010. Proceedings, Part {III}", publisher = "Springer", year = "2010", volume = "6330", editor = "Kang Li and Li Jia and Xin Sun and Minrui Fei and George W. Irwin", isbn13 = "978-3-642-15614-4", pages = "298--306", series = "Lecture Notes in Computer Science", URL = "http://dx.doi.org/10.1007/978-3-642-15615-1", DOI = "doi:10.1007/978-3-642-15615-1_36", keywords = "genetic algorithms, genetic programming", abstract = "The DNA barcoding is a promising technique for identifications of biological species based on a relatively short sequence of COI gene. A research area to improve the DNA barcoding is to study the classification techniques that use common properties of DNA and amino acid sequences such as variable lengths of gene sequences, and the comparison of different reference genes. In this study, we evaluate a classification model for DNA barcoding induced by genetic programming. The proposed method can be adapted for both DNA and amino acid sequences. The performance is evaluated by representing the two types of sequences and one based on their properties. The proposed method evaluates common significant sites on the reference genes which are useful to differentiate between species.", bibdate = "2010-09-22", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/lsms/lsms2010-3.html#ZamaniC10", } @InProceedings{Zamani:2015:CIBCB, author = "Masood Zamani and Stefan C. Kremer", booktitle = "2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)", title = "Protein secondary structure prediction using an evolutionary computation method and clustering", year = "2015", abstract = "In this paper, we evaluated the performance of an evolutionary-based protein secondary structure (PSS) prediction model which uses the information of amino acid sequences extracted by a clustering technique. The dimension of the classifier's inputs is reduced using a k-means clustering method on sequence segments. The proposed PSS classifier is based on a Genetic Programming (GP) approach that uses IF rules for a multi-target classifier. The GP classifier is evaluated by using protein sequences and the sequence information obtained from the k-means clustering. The GP prediction model's performance is compared with those of feed-forward artificial neural networks (ANNs) and support vector machines (SVMs). The prediction methods are examined with two protein datasets RS126 and CB513. The performance of the three classification models are measured according to Q3 and segment overlap (SOV) scores. The prediction models which use clustered data result in average 2percent higher prediction accuracy than those using sequence data. In addition, the experimental results indicate the GP model's prediction scores are in average 3percent higher than those of the ANN and SVMs models when amino acid sequences or clustered information are explored.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CIBCB.2015.7300327", month = aug, notes = "Sch. of Comput. Sci., Univ. of Guelph, Guelph, ON, Canada Also known as \cite{7300327}", } @InProceedings{Zamani:2015:BIBM, author = "Masood Zamani and Stefan C. Kremer", booktitle = "2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)", title = "A multi-stage protein secondary structure prediction system using machine learning and information theory", year = "2015", pages = "1304--1309", abstract = "In this paper, we evaluated the performance of a multi-stage protein secondary structure (PSS) prediction model. The proposed classifier uses statistical information and protein profiles. The statistical information is derived from protein sequences and structures by using a k-means clustering technique and Information theory. In the first stage, a feed-forward artificial neural network maps a sequence fragment to a region in the Ramachandran plot (2D-plot). A score vector is constructed with the mapped region using clustering and statistical information. The score vector represents the tendency of pairing an identified region in the 2D-plot and secondary structures for a residue. The score vectors which are used in the second stage have fewer dimensions compared to input vectors that are commonly derived from protein sequences or profile information. In the second stage, a two-tier classifier is employed based on an artificial neural network and a genetic programming (GP) method. The GP method uses IF rules for a three-state classification. The two-tier classifier's performance is compared to those of two-tier artificial neural networks (ANNs) and support vector machines (SVMs). The prediction method is examined with a common protein dataset, RS126. The performance of the proposed classification model is measured based on Q3 and segment overlap (SOV) scores. The proposed PSS prediction model improves over 3percent the Q3 score and 2percent the SOV score in comparison to those of two-tier ANN and SVMs architectures.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/BIBM.2015.7359867", month = nov, notes = "Sch. of Comput. Sci., Univ. of Guelph, Guelph, ON, Canada Also known as \cite{7359867}", } @InProceedings{Zamani:2016:CIBCB, author = "Masood Zamani and Stefan C. Kremer", booktitle = "2016 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)", title = "Protein secondary structure prediction through a novel framework of secondary structure transition sites and new encoding schemes", year = "2016", abstract = "In this paper, we propose an ab initio two-stage protein secondary structure (PSS) prediction model through a novel framework of PSS transition site prediction by using Artificial Neural Networks (ANNs) and Genetic Programming (GP). In the proposed classifier, protein sequences are encoded by new amino acid encoding schemes derived from genetic Codon mappings, Clustering and Information theory. In the first stage, sequence segments are mapped to regions in the Ramachandran map (2D-plot), and weight scores are computed by using statistical information derived from clusters. In addition, score vectors are constructed for the mapped regions using the weight scores and PSS transition sites. The score vectors have fewer dimensions compared to those of commonly used encoding schemes and protein profile. In the second stage, a two-tier classifier is employed based on an ANN and a GP method. The performance of the two-stage classifier is compared to the state-of-the-art cascaded Machine Learning methods which commonly employ ANNs. The prediction method is examined with the latest dataset of non-homologous protein sequences, PISCES [1]. The experimental results and statistical analyses indicate a significantly higher distribution of Q3 scores, approximately 7percent with p-value <; 0.001, in comparison to that of cascaded ANN architectures. PSS transition sites are valuable information about the topological property of protein sequences and incorporating the information improves the overall performance of the PSS prediction model.", keywords = "genetic algorithms, genetic programming, ANN, machine learning, amino acids, protein secondary structure, information theory;", DOI = "doi:10.1109/CIBCB.2016.7758118", month = oct, notes = "Also known as \cite{7758118}", } @PhdThesis{Zamani_Masood_201705_PhD, author = "Masood Zamani", title = "Protein Secondary Structure Prediction Evaluation and a Novel Transition Site Model with New Encoding Schemes", school = "Computer Science, The University of Guelph", year = "2017", address = "Guelph, Ontario, Canada", month = may, keywords = "genetic algorithms, genetic programming, Protein Structure, PSS, Machine Learning, ANN, SVM", URL = "https://atrium.lib.uoguelph.ca/xmlui/handle/10214/10441", URL = "http://hdl.handle.net/10214/10441", URL = "https://atrium.lib.uoguelph.ca/xmlui/bitstream/handle/10214/10441/Zamani_Masood_201705_PhD.pdf", size = "198 pages", abstract = "Rapid progress in genomics has led to the discovery of millions of protein sequences while less than 0.2percent of the sequenced proteins structures have been resolved by X-ray crystallography or NMR spectroscopy which are complex, time consuming, and expensive. Employing advanced computational techniques for protein structure prediction at secondary and tertiary levels provides alternative ways to accelerate the prediction process and overcome the extremely low percentage of protein structures that have been determined. State-of the art protein secondary structure (PSS) prediction methods employ machine learning (ML) techniques, compared to early approaches based on statistical information and sequence homology. In this research, we develop a two-stage PSS prediction model based on Artificial Neural Networks (ANNs) and Genetic Programming (GP) through a novel framework of PSS transition sites, and new amino acid encoding schemes derived from the genetic Codon mappings, Clustering and Information theory. PSS transition sites represent structural information of protein backbones, and reduce the input space and learning parameters in the PSS prediction model. PSS transition sites can be used in Homology Modelling (HM) to define the boundary of secondary structure elements. The prediction performance of the proposed method is evaluated by using Q3 and segment overlap (SOV)scores on two standard datasets, RS126 and CB513, and the latest protein dataset, PISCES, compiled with very strict homology measures by which each sequence pair has a similarity below the twilight zone or less than 25percent. The experimental results and statistical analyses of the proposed PSS model indicate statistically significant improvements in PSS prediction accuracy compared to the state-of-the-art ML techniques which commonly employ cascaded ANNs and SVMs. The proposed encoding schemes show advantages in extracting sequence and profile information, reducing input parameters and training performances. A successful PSS prediction model can be used in homology detection tools for distant protein sequences and protein tertiary structure prediction methods to reduce the complexity of the protein structure prediction which has important applications in medicine, agriculture and the biological sciences.", notes = "Supervisor: Stefan C. Kremer", } @Article{Zameer:2017:ECM, author = "Aneela Zameer and Junaid Arshad and Asifullah Khan and Muhammad Asif Zahoor Raja", title = "Intelligent and robust prediction of short term wind power using genetic programming based ensemble of neural networks", journal = "Energy Conversion and Management", volume = "134", pages = "361--372", year = "2017", ISSN = "0196-8904", DOI = "doi:10.1016/j.enconman.2016.12.032", URL = "http://www.sciencedirect.com/science/article/pii/S0196890416311189", abstract = "The inherent instability of wind power production leads to critical problems for smooth power generation from wind turbines, which then requires an accurate forecast of wind power. In this study, an effective short term wind power prediction methodology is presented, which uses an intelligent ensemble regressor that comprises Artificial Neural Networks and Genetic Programming. In contrast to existing series based combination of wind power predictors, whereby the error or variation in the leading predictor is propagated down the stream to the next predictors, the proposed intelligent ensemble predictor avoids this shortcoming by introducing Genetical Programming based semi-stochastic combination of neural networks. It is observed that the decision of the individual base regressors may vary due to the frequent and inherent fluctuations in the atmospheric conditions and thus meteorological properties. The novelty of the reported work lies in creating ensemble to generate an intelligent, collective and robust decision space and thereby avoiding large errors due to the sensitivity of the individual wind predictors. The proposed ensemble based regressor, Genetic Programming based ensemble of Artificial Neural Networks, has been implemented and tested on data taken from five different wind farms located in Europe. Obtained numerical results of the proposed model in terms of various error measures are compared with the recent artificial intelligence based strategies to demonstrate the efficacy of the proposed scheme. Average root mean squared error of the proposed model for five wind farms is 0.117575.", keywords = "genetic algorithms, genetic programming, Wind power forecasting, Meterological variables, Regression, Artificial neural network, Ensemble", } @Article{Zamir:2016:CMPB, author = "Z. Roshan Zamir", title = "Detection of epileptic seizure in {EEG} signals using linear least squares preprocessing", journal = "Computer Methods and Programs in Biomedicine", year = "2016", ISSN = "0169-2607", DOI = "doi:10.1016/j.cmpb.2016.05.002", URL = "http://www.sciencedirect.com/science/article/pii/S016926071530273X", abstract = "An epileptic seizure is a transient event of abnormal excessive neuronal discharge in the brain. This unwanted event can be obstructed by detection of electrical changes in the brain that happen before the seizure takes place. The automatic detection of seizures is necessary since the visual screening of EEG recordings is a time consuming task and requires experts to improve the diagnosis. Much of the prior research in detection of seizures has been developed based on artificial neural network, genetic programming, and wavelet transforms. Although the highest achieved accuracy for classification is 100percent, there are drawbacks such as, existence of unbalanced datasets and the lack of investigations in performances consistency. To address these, four linear least squares-based preprocessing models are proposed to extract key features of an EEG signal in order to detect seizures. The first two models are newly developed. The original signal (EEG) is approximated by a sinusoidal curve. Its amplitude is formed by a polynomial function and compared with the pre developed spline function. Different statistical measures namely classification accuracy, true positive and negative rates, false positive and negative rates and precision are used to assess the performance of the proposed models. These metrics are derived from confusion matrices obtained from classifiers. Different classifiers are used over the original dataset and the set of extracted features. The proposed models significantly reduce the dimension of the classification problem and the computational time while the classification accuracy is improved in most cases. The first and third models are promising feature extraction methods with the classification accuracy of 100percent. Logistic, LazyIB1, LazyIB5, and J48 are the best classifiers. Their true positive and negative rates are 1 while false positive and negative rates are zero and the corresponding precision values are 1. Numerical results suggest that these models are robust and efficient for detecting epileptic seizure.", keywords = "genetic algorithms, genetic programming, Biological signal classification, Signal approximation, Feature extraction, Data analysis, Linear least squares problems, EEG Seizure detection", } @InProceedings{Zangeneh:2009:iwamlcf, author = "Laleh Zangeneh and Peter J. Bentley", title = "Analyzing the Credit Default Swap Market Using Cartesian Genetic Programming", booktitle = "International Workshop on Advances in Machine Learning for Computational Finance", year = "2009", address = "London", month = "20-21 " # jul, keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Credit Default Swap, Regression", URL = "http://www.cs.ucl.ac.uk/staff/P.Bentley/ZABEC2.pdf", broken = "http://eprints.ucl.ac.uk/171696/", size = "10 pages", abstract = "The credit default swap has become well-known as one of the causes of the 2007-2010 credit crisis but more research is vitally needed to analyst and define its impact more precisely and help the financial market transparency. This paper uses cartesian genetic programming as a discovery tool for finding the relationship between credit default swap spreads and debts and studying the arbitrage channel. (Arbitrage is the practice of taking advantage of a price difference between markets.) To our knowledge this work is the first attempt toward studying the credit default swap market via an evolutionary process and our results prove that cartesian genetic programming is human competitive and it has the potential to become a regression discovery tool in credit default swap market.", } @InProceedings{Zangeneh:2010:PPSN, author = "Laleh Zangeneh and Peter Bentley", title = "Analyzing the Credit Default Swap Market Using Cartesian Genetic Programming", booktitle = "PPSN 2010 11th International Conference on Parallel Problem Solving From Nature", year = "2010", editor = "Robert Schaefer and Carlos Cotta and Joanna Kolodziej and Guenter Rudolph", volume = "6238", series = "Lecture Notes in Computer Science", pages = "434--444", address = "Krakow, Poland", month = "11-15 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", DOI = "doi:10.1007/978-3-642-15844-5_44", abstract = "The credit default swap has become well-known as one of the causes of the 2007-2010 credit crisis but more research is vitally needed to analyse and define its impact more precisely and help the financial market transparency. This paper uses cartesian genetic programming as a discovery tool for finding the relationship between credit default swap spreads and debts and studying the arbitrage channel. (Arbitrage is the practice of taking advantage of a price difference between markets.) To our knowledge this work is the first attempt toward studying the credit default swap market via an evolutionary process and our results prove that cartesian genetic programming is human competitive and it has the potential to become a regression discovery tool in credit default swap market.", affiliation = "Department of Computer Science, University College London, London, WC1E 6BT UK", } @PhdThesis{Zangeneh:thesis, author = "Laleh Zangeneh", title = "Investigating the Challenges of Data, Pricing and Modelling to Enable Agent Based Simulation of the Credit Default Swap Market", school = "Computer Science, University College London", year = "2014", address = "UK", month = jul # " 20", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Gaussian Process Regression", URL = "http://discovery.ucl.ac.uk/id/eprint/1435662", URL = "http://discovery.ucl.ac.uk/1435662/2/Laleh%20Zangeneh_PHD_Thesis_UCL_20July2014.pdf", size = "174 pages", abstract = "The Global Financial Crisis of 2007-2008 is considered by three top economists the worst financial crisis since the Great Depression of the 1930s [Pendery, 2009]. The crisis played a major role in the failure of key businesses, declines in consumer wealth, and significant downturn in economic activities leading to the 2008-2012 global recession and contributing to the European sovereign-debt crisis [Baily and Elliott, 2009] [Williams, 2012]. More importantly, the serious limitation of existing conventional tools and models as well as a vital need for developing complementary tools to improve the robustness of existing overall framework immediately became apparent. This thesis details three proposed solutions drawn from three main subject areas: Statistic, Genetic Programming (GP), and Agent-Based Modelling (ABM) to help enable agent-based simulation of Credit Default Swap (CDS) market. This is accomplished by tackling three challenges of lack of sufficient data to support research, lack of efficient CDS pricing technique to be integrated into agent based model, and lack of practical CDS market experimental model, that are faced by designers of CDS investigation tools. In particular, a general data generative model is presented for simulating financial data, a novel price calculator is proposed for pricing CDS contracts, and a unique CDS agent-based model is designed to enable the investigation of market. The solutions presented can be seen as modular building blocks that can be applied to a variety of applications. Ultimately, a unified general framework is presented for integrating these three solutions. The motivation for the methods is to suggest viable tools that address these challenges and thus enable the future realistic simulation of the CDS market using the limited real data in hand. A series of experiments were carried out, and a comparative evaluation and discussion is provided. In particular, we presented the advantages of realistic artificial data to enable open ended simulation and to design various scenarios, the effectiveness of Cartesian Genetic Programming (CGP) as a bio-inspired evolutionary method for a complex real-world financial problem, and capability of Agent Based (AB) models for investigating CDS market. These experiments demonstrate the efficiency and viability of the proposed approaches and highlight interesting directions of future research.", notes = "Supervisor Peter J. Bentley", } @PhdThesis{Zangeneh-Sirdari:thesis, author = "Zahra {Zangeneh Sirdari}", title = "Bedload Transport for Small Rivers in Malaysia", school = "River Engineering and Urban Drainage Research Centre (REDAC), Universiti Sains Malaysia", year = "2013", address = "Malaysia", month = may, keywords = "genetic algorithms, genetic programming, ANN", URL = "http://redac.eng.usm.my/html/postgrad/abstract_zahra.htm", URL = "https://www.academia.edu/4147500/Bed_Load_Transport_of_Small_Rivers_in_Malaysia", size = "311 pages", abstract = "Bedload transport is an essential component of river dynamics and estimation of bed load transport rate is important for practical computations of river morphological variations because the transport of sediment through river channels has major effects on public safety, water resources management and environmental sustainability. Numerous well-known bed-load equations are derived from limited flume experiments or field conditions. These time-consuming equations, based on the relationship between the reliability and representativeness of the data used in defining variables and constants, require complex parameters to estimate bedload transport. Thus, a new simple equation based on a balance between simplicity and accuracy is necessary for using in small rivers. In this study the easily accessible data including flow discharge, water depth, slope, and surface grain diameter d50 from the three small rivers in Malaysia used to predict bedload transport. Genetic programming (GP) and artificial neural network (ANN) models that are particularly useful in data interpretation without any restriction to an extensive database are presented as complementary tools for modelling bed load transport in small streams. The ability of GP and ANN as precipitation predictive tools showed to be acceptable. The developed models demonstrate higher performance with an overall accuracy of 97percent for ANN and 93percent for GP compared with other traditional methods and empirical equations. A three-dimensional numerical model was applied to study the bed morphology and bedload transport of the junction of Ara and Kurau rivers for short term event and for high flow with 100 ARI. SSIIM2 a 3D, k-epsilon turbulence computational fluid dynamics model with an adaptive, non-orthogonal and unstructured grid has been used for modelling the hydrodynamic of confluence. The numerical model was tested against field data from Ara-Kurau confluence. Satisfactory agreement was found between computed and measured bedload and bed elevation in the field. The study indicates that numerical models became a useful tool for predicting the bedload transport rate in such complex dynamic environment. The results have demonstrated that the short term hydrologic variability can considerably influence the morphodynamics of Ara-Kurau channel confluence and for the different flow conditions the bedload transported near to edge of shear layer. The coincidence of the shear layer that was generated the considerable turbulence indicated that the increasing turbulence levels contribute substantially to the required increase in bedload transport capacity. The simulation results showed the grain size distribution on the bar at the downstream junction corner is remarkably constant and the particle size in the upstream part of the bar is more affected by the changes in flow conditions than the downstream end where the median diameters not varied during the period.", title2 = "PENGANGKUTAN BEBAN ENDAPAN DASAR UNTUK SUNGAI KECILDI MALAYSIA", abstrak = "Pengangkutan beban endapan dasar merupakan komponen penting prosesdinamik sungai dan pengganggaran kadar pengangkutan beban endapan dasar adalah penting untuk pengiraan variasi morfologi sungai untuk tujuan keselamatan umum, pengurusan sumber air dan alam sekitar yang mampan. Pelbagai persamaan bebanendapan yang terkenal adalah terhad kepada kajian eksperimen saluran dalammakmal atau kajian tapak. Persamaan ini yang dipengaruhi oleh kebolehpercayaandan perwakilan data yang digunakan dalam menentukan pembolehubah dan pemalar memerlukan parameter yang kompleks dalam pengganggaran pengangkutan bebanendapan. Oleh itu, satu persamaan baru yang mudah dan tepat adalah perlu untuk kegunaan di sungai-sungai kecil. Dalam kajian ini, data yang mudah diperolehiseperti kadar alir, kedalaman sungai, kecerunan sungai dan saiz diameter zarahendapan permukaan daripada tiga sungai kecil di Malaysia digunakan untuk meramal pengangkutan endapan dasar. Model genetic programming (GP) danartificial neural network (ANN) adalah berguna dalam menafsir data tanpa sebaranghad untuk pangkalan data yang luas digunakan sebagai alat untuk pemodelan pengangkutan beban endapan untuk sungai-sungai kecil. Keupayaan GP dan ANN untuk meramal data hujan adalah memuaskan. Model yang diperolehi menunjukkankejituan yang tinggi dengan ketepatan keseluruhan sebanyak 97percent untuk ANN dan93percent untuk GP berbanding dengan kaedah konvensional dan persamaan empirical. Satu model numerikal tiga dimensi telah digunakan untuk mengkaji morfologidasar dan pengangkutan beban endapan dasar sungai di pertemuan Sungai Ara dan Kurau untuk jangka masa pendek dengan kadar alir tinggi pada 100 ARI. Model tigadimensi SSIIM2 dengan k-epsilon aliran gelora yang merupakan model pengiraan bendalir dinamik dengan grid adaptif, bukan ortogon dan tidak berstruktur telahdigunakan untuk pemodelan hidrodinamik pertemuan sungai. Model numerikal initelah diuji dengan data dari kajian tapak di pertemuan Ara-Kurau. Ketepatan yangmemuaskan telah didapati di antara data endapan dasar dan aras dasar yang dianggar dengan yang dicerap di tapak. Kajian menunjukkan bahawa model numerikalmerupakan alat yang berguna dalam meramal kadar pengangkutan beban dasar dikawasan yang bersekitaran dinamik kompleks. Keputusan menunjukkan bahawa perubahan hidrologi jangka pendek boleh mempengaruhi morfo-dinamik pertemuanAra-Kurau. Untuk keadaan aliran yang berbeza, pengangkutan endapan dasar berhampiran pinggir lapisan ricih dan juga lapisan ricih yang menyebabkan alirangelora menunjukkan peningkatan aliran gelora menyumbang kepada peningkatankapasiti pengangkutan endapan beban dasar sungai. Keputusan simulasimenunjukkan taburan saiz zarah beting pasir di tepi hilir pertemuan sungai adalahtidak berubah dimana saiz median tidak berubah sepanjang tempoh kajian manakalasaiz zarah di hulu beting pasir adalah lebih dipengaruhi oleh keadaan aliran.", notes = "In english. P-RED0005. Case Study of Kurau River Supervisors Prof. Dr. Aminuddin Ab. Ghani and Mr. Zorkeflee Abu Hasan", } @Article{Zangeneh-Sirdari:2014:IJSR, author = "Zahra {Zangeneh Sirdari} and Aminuddin {Ab. Ghani} and Zorkeflee Abu Hassan", title = "Bedload transport of small rivers in {Malaysia}", journal = "International Journal of Sediment Research", volume = "29", number = "4", pages = "481--490", year = "2014", keywords = "genetic algorithms, genetic programming, Bedload transport, Small rivers, Artificial neural network", ISSN = "1001-6279", DOI = "doi:10.1016/S1001-6279(14)60061-5", URL = "http://www.sciencedirect.com/science/article/pii/S1001627914600615", abstract = "Numerous time-consuming equations, based on the relationship between the reliability and representativeness of the data used in defining variables and constants, require complex parameters to estimate bedload transport. In this study the easily accessible data including flow discharge, water depth, water surface slope, and surface grain diameter (d50) from small rivers in Malaysia were used to estimate bedload transport. Genetic programming (GP) and artificial neural network (ANN) models are applied as complementary tools to estimate bed load transport based on a balance between simplicity and accuracy in small rivers. The developed models demonstrate higher performance with an overall accuracy of 97percent and 93percent for ANN and GP, respectively compared with other traditional methods and empirical equations.", } @Article{zannoni:1997:lcpepca, author = "Elena Zannoni and Robert G. Reynolds", title = "Learning to Control the Program Evolution Process with Cultural Algorithms", journal = "Evolutionary Computation", year = "1997", volume = "5", number = "2", pages = "181--211", month = "summer", keywords = "genetic algorithms, genetic programming, cultural algorithms, software design methodologies, software metrics, machine learning of software design concepts, design concept reuse", ISSN = "1063-6560", URL = "http://www.mitpressjournals.org/doi/pdf/10.1162/evco.1997.5.2.181", DOI = "doi:10.1162/evco.1997.5.2.181", size = "31 pages", abstract = "Traditional software engineering dictates the use of modular and structured programming and top-down stepwise refinement techniques that reduce the amount of variability arising in the development process by establishing standard procedures to be followed while writing software. This focusing leads to reduced variability in the resulting products, due to the use of standardized constructs. Genetic programming (GP) performs heuristic search in the space of programs. Programs produced through the GP paradigm emerge as the result of simulated evolution and are built through a bottom-up process, incrementally augmenting their functionality until a satisfactory level of performance is reached. Can we automatically extract knowledge from the GP programming process that can be useful to focus the search and reduce product variability, thus leading to a more effective use of the available resources? An answer to this question is investigated with the aid of cultural algorithms. A new system has two levels. The first is the pool of genetic programs (population level), and the second is a knowledge repository (belief set) that is built during the GP run and is used to guide the search process. The microevolution within the population brings about potentially meaningful characteristics of the programs for the achievement of the given task, such as properties exhibited by the best performers in the population. CAGP extracts these features and represents them as the set of the current beliefs. Beliefs correspond to constraints that all the genetic operators and programs must follow. Interaction between the two levels occurs in one direction through the extraction process and, in the other, through the modulation of an individual's program parameters according to which, and how many, of the constraints it follows. CAGP is applied to solve an instance of the symbolic regression problem, in which a function of one variable needs to be discovered. The results of the experiments show an overall improvement on the average performance of CAGP over GP alone and a significant reduction of the complexity of the produced solution. Moreover, the execution time required by CAGP is comparable with the time required by GP alone.", notes = "Evolutionary Computation (Journal) Special Issue: Trends in Evolutionary Methods for Program Induction PMID: 10021758 Cited by \cite{ostrowski:1998:ismcaoalsesd}", } @PhdThesis{Zapater:thesis, author = "Marina {Zapater Sancho}", title = "Proactive and reactive thermal aware optimization techniques to minimize the environmental impact of data centers", school = "Ingenieria Electronica, Universidad Politecnica de Madrid", year = "2015", address = "Madrid, Spain", keywords = "genetic algorithms, genetic programming, Energy, Energy-efficiency, Data Centres, Green Computing, Power modelling, Temperature prediction, Cooling, Resource management, Optimization", URL = "http://oa.upm.es/38700/", URL = "http://oa.upm.es/38700/1/MARINA_ZAPATER_SANCHO.pdf", URL = "http://greenlsi.die.upm.es/files/2013/03/2015-04-20-tesisMZapater.pdf", size = "149 pages", abstract = "Data centres are easily found in every sector of the worldwide economy. They consist of tens of thousands of servers, serving millions of users globally and 24-7. In the last years, e-Science applications such e-Health or Smart Cities have experienced a significant development. The need to deal efficiently with the computational needs of next-generation applications together with the increasing demand for higher resources in traditional applications has facilitated the rapid proliferation and growing of data centers. A drawback to this capacity growth has been the rapid increase of the energy consumption of these facilities. In 2010, data centre electricity represented 1.3percent of all the electricity use in the world. In year 2012 alone, global data centre power demand grew 63percent to 38GW. A further rise of 17percent to 43GW was estimated in 2013. Moreover, data centres are responsible for more than 2percent of total carbon dioxide emissions. This PhD Thesis addresses the energy challenge by proposing proactive and reactive thermal and energy-aware optimization techniques that contribute to place data centres on a more scalable curve. This work develops energy models and uses the knowledge about the energy demand of the workload to be executed and the computational and cooling resources available at data centre to optimize energy consumption. Moreover, data centres are considered as a crucial element within their application framework, optimizing not only the energy consumption of the facility, but the global energy consumption of the application. The main contributors to the energy consumption in a data centre are the computing power drawn by IT equipment and the cooling power needed to keep the servers within a certain temperature range that ensures safe operation. Because of the cubic relation of fan power with fan speed, solutions based on over-provisioning cold air into the server usually lead to inefficiencies. On the other hand, higher chip temperatures lead to higher leakage power because of the exponential dependence of leakage on temperature. Moreover, workload characteristics as well as allocation policies also have an important impact on the leakage-cooling tradeoffs. The first key contribution of this work is the development of power and temperature models that accurately describe the leakage-cooling tradeoffs at the server level, and the proposal of strategies to minimize server energy via joint cooling and workload management from a multivariate perspective. When scaling to the data centre level, a similar behaviour in terms of leakage-temperature tradeoffs can be observed. As room temperature raises, the efficiency of data room cooling units improves. However, as we increase room temperature, CPU temperature raises and so does leakage power. Moreover, the thermal dynamics of a data room exhibit unbalanced patterns due to both the workload allocation and the heterogeneity of computing equipment. The second main contribution is the proposal of thermal- and heterogeneity-aware workload management techniques that jointly optimize the allocation of computation and cooling to servers. These strategies need to be backed up by flexible room level models, able to work on runtime, that describe the system from a high level perspective. Within the framework of next-generation applications, decisions taken at this scope can have a dramatical impact on the energy consumption of lower abstraction levels, i.e. the data center facility. It is important to consider the relationships between all the computational agents involved in the problem, so that they can cooperate to achieve the common goal of reducing energy in the overall system. The third main contribution is the energy optimization of the overall application by evaluating the energy costs of performing part of the processing in any of the different abstraction layers, from the node to the data center, via workload management and off-loading techniques. In summary, the work presented in this PhD Thesis, makes contributions on leakage and cooling aware server modeling and optimization, data centre thermal modelling and heterogeneity aware data center resource allocation, and develops mechanisms for the energy optimization for next-generation applications from a multi-layer perspective.", notes = "Item ID 38700 Supervisors: Jose Manuel Moya Fernandez and Jose Luis Ayala Rodrigo", } @Article{journals/asc/ZapaterRAAMH16, author = "Marina Zapater and Jose L. Risco-Martin and Patricia Arroba and Jose L. Ayala and Jose Manuel Moya and Roman Hermida", title = "Runtime data center temperature prediction using Grammatical Evolution techniques", journal = "Applied Soft Computing", year = "2016", volume = "49", pages = "94--107", month = dec, keywords = "genetic algorithms, genetic programming, grammatical evolution", bibdate = "2017-05-26", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/asc/asc49.html#ZapaterRAAMH16", DOI = "doi:10.1016/j.asoc.2016.07.042", abstract = "Data Centers are huge power consumers, both because of the energy required for computation and the cooling needed to keep servers below thermal redlining. The most common technique to minimise cooling costs is increasing data room temperature. However, to avoid reliability issues, and to enhance energy efficiency, there is a need to predict the temperature attained by servers under variable cooling setups. Due to the complex thermal dynamics of data rooms, accurate runtime data center temperature prediction has remained as an important challenge. By using Grammatical Evolution techniques, this paper presents a methodology for the generation of temperature models for data centers and the runtime prediction of CPU and inlet temperature under variable cooling setups. As opposed to time costly Computational Fluid Dynamics techniques, our models do not need specific knowledge about the problem, can be used in arbitrary data centers, re-trained if conditions change and have negligible overhead during runtime prediction. Our models have been trained and tested by using traces from real Data Center scenarios. Our results show how we can fully predict the temperature of the servers in a data rooms, with prediction errors below 2C and 0.5C in CPU and server inlet temperature respectively.", } @InProceedings{ZW2007DGPFj, author = "Michael Zapf and Thomas Weise", title = "Offline Emergence Engineering For Agent Societies", booktitle = "Proceedings of the Fifth European Workshop on Multi-Agent Systems EUMAS'07", year = "2007", month = dec # "~14", address = "Elmouradi Hotel, Hammamet, Tunesia", URL = "http://www.it-weise.de/documents/files/ZW2007EUMASTR.pdf", abstract = "Many examples for emergent behaviours may be observed in self-organising physical and biological systems which prove to be robust, stable, and adaptable. Such behaviors are often based on very simple mechanisms and rules, but artificially creating them is a challenging task which does not comply with traditional software engineering. In this article, we propose a hybrid approach by combining strategies from Genetic Programming and agent software engineering, and demonstrate that this approach effectively yields an emergent design for given problems.", keywords = "genetic algorithms, genetic programming, Mobile Agents, Load Balancing, Emergence, Emergence Engineering, Robustness, RBGP", } @TechReport{ZW2007DGPFk, author = "Michael Zapf and Thomas Weise", title = "Offline Emergence Engineering For Agent Societies", year = "2007", month = dec # "~7,", type = "Kasseler Informatikschriften (KIS)", number = "2007, 8", institution = "University of Kassel, FB16, Distributed Systems Group, Wilhelmsh{\"o}her Allee 73, 34121 Kassel, Germany", notes = "Persistent Identifier: urn:nbn:de:hebis:34-2007120719844", URL = "https://kobra.bibliothek.uni-kassel.de/handle/urn:nbn:de:hebis:34-2007120719844", URL = "http://www.it-weise.de/documents/files/ZW2007EUMASTR.pdf", keywords = "genetic algorithms, genetic programming", language = "en", abstract = "Many examples for emergent behaviours may be observed in self-organising physical and biological systems which prove to be robust, stable, and adaptable. Such behaviors are often based on very simple mechanisms and rules, but artificially creating them is a challenging task which does not comply with traditional software engineering. In this article, we propose a hybrid approach by combining strategies from Genetic Programming and agent software engineering, and demonstrate that this approach effectively yields an emergent design for given problems.", notes = "Presented at EUMAS07. Fifth European Workshop on Multi-Agent Systems. Hammamet, Tunesia December 13-14, 2007 http://www.atia.rnu.tn/eumas/ Preview This document is a preview version and not necessarily identical with the original. \cite{ZW2007DGPFj} http://www.it-weise.de/", } @TechReport{Zapf:2008:KIS5, author = "Michael Zapf and Thomas Weise", title = "Applicability of Emergence Engineering to Distributed Systems Scenarios", institution = "Universitat Kassel, Fachbereich 16: Elektrotechnik/Informatik", year = "2009", type = "Kasseler Informatikschriften (KIS)", number = "2008, 5", address = "Universitat Kassel, Fachbereich 16: Elektrotechnik/Informatik, Wilhelmshoher Allee 73, 34121 Kassel", month = jan, keywords = "genetic algorithms, genetic programming, Agents, Multi-Agent Systems, AMAS, Emergence, Emergence Engineering, OEE, Robust, Self-Organization, Rule-based Genetic Programming, RBGP, Election, Critical Section", URL = "http://www.it-weise.de/documents/files/ZW2008AOEETDSS.pdf", abstract = "Genetic Programming can be effectively used to create emergent behavior for a group of autonomous agents. In the process we call Offline Emergence Engineering, the behavior is at first bred in a Genetic Programming environment and then deployed to the agents in the real environment. In this article we shortly describe our approach, introduce an extended behavioral rule syntax, and discuss the impact of the expressiveness of the behavioral description to the generation success, using two scenarios in comparison: the election problem and the distributed critical section problem. We evaluate the results, formulating criteria for the applicability of our approach.", notes = "Presented as \cite{Zapf:2008:eumas}?", } @InProceedings{Zapf:2008:eumas, author = "Michael Zapf and Thomas Weise", title = "Applicability of Emergence Engineering to Distributed Systems Scenarios", booktitle = "Sixth European Workshop on Multi-Agent Systems, EUMAS'08", year = "2008", editor = "Julian Padget", address = "University of Bath, Bath, UK", month = "18-19 " # dec, publisher = "Springer???", keywords = "genetic algorithms, genetic programming, Offline Emergence Engineering, AOSE, Rule-based Genetic Programming, RBGP, Election, Agents", URL = "http://www.it-weise.de/documents/files/ZW2008AOEETDSS.pdf", abstract = "Genetic Programming can be effectively used to create emergent behavior for a group of autonomous agents. In the process we call Offline Emergence Engineering, the behavior is at first bred in a Genetic Programming environment and then deployed to the agents in the real environment. In this article we shortly describe our approach, introduce an extended behavioral rule syntax, and discuss the impact of the expressiveness of the behavioral description to the generation success, using two scenarios in comparison: the election problem and the distributed critical section problem. We evaluate the results, formulating criteria for the applicability of our approach.", notes = "http://eumas08.cs.bath.ac.uk/schedule/ See also \cite{Zapf:2008:KIS5}", } @InProceedings{Zapf:2008:IWSOS, author = "Michael Zapf and Thomas Weise", title = "Can Solutions Emerge?", booktitle = "The third International Workshop on Self-Organizing Systems (IWSOS'08)", year = "2008", editor = "Karin Anna Hummel and James P. G. Sterbenz", volume = "5343", address = "Vienna, Austria", series = "Lecture Notes in Computer Science (LNCS), LNCS Sublibrary: SL 5 -- Computer Communication Networks and Telecommunications", pages = "299--304", month = dec, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Offline Emergence Engineering, RBGP, Rule-Based Genetic Programming, eRBGP, Election, Critical Section", ISBN = "3-540-92156-7", URL = "http://www.it-weise.de/documents/files/ZW2008CSE.pdf", DOI = "doi:10.1007/978-3-540-92157-8", abstract = "Emergence engineering is a novel approach in Software Engineering which targets at triggering emergent phenomena in groups of individuals in order to exploit those phenomena for engineering solutions. We impose the requirements of functional adequateness to a dynamic system and wait for it to adapt. In this article we discuss the effects of the expressiveness of the behavioral description in terms of reliability of the solutions. Can we expect Emergence Engineering to produce solutions in the proper meaning of the term at all?", } @Article{Zarei:2021:SciRep, author = "Manizhe Zarei and Omid Bozorg-Haddad and Sahar Baghban and Mohammad Delpasand and Erfan Goharian and Hugo A. Loaiciga", title = "Machine-learning algorithms for forecast-informed reservoir operation {(FIRO)} to reduce flood damages", journal = "Scientific Reports", year = "2021", volume = "11", pages = "Article number: 24295", month = "21 " # dec, keywords = "genetic algorithms, genetic programming, SVM, ANN, RT, GP, FIRO, Climate sciences, Ecology, Engineering, Environmental sciences, Environmental social sciences, Hydrology, Mathematics and computing, Natural hazards, Iran", URL = "https://rdcu.be/cFfjg", URL = "https://www.nature.com/articles/s41598-021-03699-6", DOI = "doi:10.1038/s41598-021-03699-6", size = "21 pages", abstract = "Water is stored in reservoirs for various purposes, including regular distribution, flood control, hydropower generation, and meeting the environmental demands of downstream habitats and ecosystems. However, these objectives are often in conflict with each other and make the operation of reservoirs a complex task, particularly during flood periods. An accurate forecast of reservoir inflows is required to evaluate water releases from a reservoir seeking to provide safe space for capturing high flows without having to resort to hazardous and damaging releases. This study aims to improve the informed decisions for reservoirs management and water prerelease before a flood occurs by means of a method for forecasting reservoirs inflow. The forecasting method applies 1- and 2-month time-lag patterns with several Machine Learning (ML) algorithms, namely Support Vector Machine (SVM), Artificial Neural Network (ANN), Regression Tree (RT), and Genetic Programming (GP). The proposed method is applied to evaluate the performance of the algorithms in forecasting inflows into the Dez, Karkheh, and Gotvand reservoirs located in Iran during the flood of 2019. Results show that RT, with an average error of 0.43percent in forecasting the largest reservoirs inflows in 2019, is superior to the other algorithms, with the Dez and Karkheh reservoir inflows forecasts obtained with the 2-month time-lag pattern, and the Gotvand reservoir inflow forecasts obtained with the 1-month time-lag pattern featuring the best forecasting accuracy. The proposed method exhibits accurate inflow forecasting using SVM and RT. The development of accurate flood-forecasting capability is valuable to reservoir operators and decision-makers who must deal with streamflow forecasts in their quest to reduce flood damages.", notes = "Department of Irrigation & Reclamation Engineering, Faculty of Agriculture Engineering & Technology, College of Agriculture & Natural Resources, University of Tehran, 3158777871 Karaj, Iran", } @Article{ZARENAGHADEHI:2018:Measurement, author = "Masoud {Zare Naghadehi} and Masoud Samaei and Masoud Ranjbarnia and Vahid Nourani", title = "State-of-the-art predictive modeling of TBM performance in changing geological conditions through gene expression programming", journal = "Measurement", volume = "126", pages = "46--57", year = "2018", keywords = "genetic algorithms, genetic programming, Gene expression programming, GEP, Tunnel boring machine (TBM), Predictive modeling, Performance prediction, Artificial intelligence (AI)", ISSN = "0263-2241", DOI = "doi:10.1016/j.measurement.2018.05.049", URL = "http://www.sciencedirect.com/science/article/pii/S0263224118304366", abstract = "Hard rock TBM performance prediction is of great interest to the tunneling community on account of its importance in time and cost risk management of underground projects. Continuous development of new empirical models in recent decades reveals the importance of accurate prediction of this factor in diverse ground and machine conditions. The great number of different parameters influencing TBM performance and the high variability linked to specific field conditions cause the problem to be very complex. Gene expression programming (GEP) models, a robust variant of genetic programming, are developed in this study to correlate hard rock TBM performance with routine ground properties for project design applications. The developed models are compared with those from statistical and soft computing-based models in the literature. Overall, GEP models show good performance and are proven to be much better than the previous models. The proposed models of this study can be remarked as an ultimate stage to one decade of researchers' attempts to improve the accuracy of predictive equations developed through a well-known database of TBM performance in one of the most complex tunneling projects in the world", } @Article{Zargari:2012:ES, author = "Shahriar Afandizadeh Zargari and Salar Zabihi Siabil and Amir Hossein Alavi and Amir Hossein Gandomi", title = "A computational intelligence-based approach for short-term traffic flow prediction", journal = "Expert Systems", year = "2012", volume = "29", number = "2", pages = "124--142", month = may, keywords = "genetic algorithms, genetic programming, Discipulus, traffic flow, prediction, artificial neural network, fuzzy logic, formulation", ISSN = "1468-0394", URL = "http://onlinelibrary.wiley.com/doi/10.1111/j.1468-0394.2010.00567.x/abstract", DOI = "doi:10.1111/j.1468-0394.2010.00567.x", size = "18.1 pages", abstract = "This paper proposes alternative approaches for the prediction of short-term traffic flow using three branches of computational intelligence techniques, namely linear genetic programming (LGP), multilayer perceptron (MLP) and fuzzy logic (FL). Different LGP, MLP and FL models are developed for estimating the 5- and 30-min traffic flow rates. New LGP- and MLP-based prediction equations are derived for the traffic flow rates in the five and thirty minute time intervals. The models are established upon extensive databases of the traffic flow records obtained from Iran's Rasht-Qazvin highway. The results indicate that the proposed models are effectively capable of predicting the target values. The LGP-based models are found to be simple, straightforward and more practical for predictive purposes compared with the other derived models.", } @Article{Zarges:GPEM:diverless, author = "Christine Zarges", title = "{Hod Lipson} and {Melba Kurman}: {Driverless}: intelligent cars and the road ahead", journal = "Genetic Programming and Evolvable Machines", year = "2018", volume = "19", number = "1-2", pages = "301--303", month = jun, note = "book review", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dR85i", DOI = "doi:10.1007/s10710-017-9313-0", size = "3 pages", notes = "The MIT Press, 2016, pp 312, ISBN: 9780262035224", } @InCollection{zaric:1995:GASALBP, author = "Greg Zaric", title = "Genetic Algorithms in the Solution of Assembly Line Balancing Problems", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1995", year = "1995", editor = "John R. Koza", pages = "320--329", address = "Stanford, California, 94305-3079 USA", month = "11 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms", ISBN = "0-18-195720-5", notes = "part of \cite{koza:1995:gagp}", } @InProceedings{Zarnegar09, author = "Armita Zarnegar and Peter Vamplew and Andrew Stranieri", title = "Inference of Gene Expression Networks Using Memetic Gene Expression Programming", booktitle = "Thirty-Second Australasian Computer Science Conference (ACSC 2009)", series = "CRPIT", volume = "91", pages = "17--23", year = "2009", editor = "Bernard Mans", address = "Wellington, New Zealand", keywords = "genetic algorithms, genetic programming, gene expression programming, hill climbing, Differential Equations, Gene Networks, Evolutionary Algorithm, Gene expression Profile, Microarray data", publisher = "ACS", URL = "http://crpit.com/confpapers/CRPITV91Zarnegar.pdf", abstract = "In this paper we aim to infer a model of genetic networks from time series data of gene expression profiles by using a new gene expression programming algorithm. Gene expression networks are modeled by differential equations which represent temporal gene expression relations. Gene Expression Programming is a new extension of genetic programming. Here we combine a local search method with gene expression programming to form a hill climbing algorithm in order to find not only the system of differential equations but also fine tune its constant parameters. The effectiveness of the proposed method is justified by comparing its performance with that of conventional genetic programming applied to this problem in previous studies.", notes = "School of Information Technology and Mathematical Sciences, University of Ballarat, P.O. Box 663, Ballarat, Victoria, Australia ", } @InProceedings{zavanella:1998:dnpnGA, author = "A. Zavanella and A. Giani and F. Baiardi", title = "On Dropping Niches in Parallel Niching Genetic Algorithms", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "618--620", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms", URL = "http://www.di.unipi.it/~zavanell/Poster.ps", notes = "SGA-98", } @InProceedings{conf/eurocast/ZavoianuKKZA11, author = "Alexandru-Ciprian Zavoianu and Gabriel Kronberger and Michael Kommenda and Daniela Zaharie and Michael Affenzeller", title = "Improving the Parsimony of Regression Models for an Enhanced Genetic Programming Process", booktitle = "13th International Conference on Computer Aided Systems Theory, EUROCAST 2011", year = "2011", editor = "Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia", volume = "6927", series = "Lecture Notes in Computer Science", pages = "264--271", address = "Las Palmas de Gran Canaria, Spain", month = feb # " 6-11", publisher = "Springer", keywords = "genetic algorithms, genetic programming, symbolic regression, solution parsimony, bloat control", isbn13 = "978-3-642-27548-7", DOI = "doi:10.1007/978-3-642-27549-4_34", abstract = "This research is focused on reducing the average size of the solutions generated by an enhanced GP process without affecting the high predictive accuracy the method exhibits when being applied on a complex, industry proposed, regression problem. As such, the effects the GP enhancements have on bloat have been studied and, finally, a bloat control system based on dynamic depth limiting (DDL) and iterated tournament pruning (ITP) was designed. The resulting bloat control system is able to improve by about 40percent the average GP solution parsimony without impacting average solution accuracy.", notes = "Revised Selected Papers. http://ciprian-zavoianu.blogspot.co.uk/", affiliation = "Department of Computer Science, West University of Timisoara, Romania", bibdate = "2012-02-01", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/eurocast/eurocast2011-1.html#ZavoianuKKZA11", } @InProceedings{Zavoianu:2019:EUROCAST, author = "Alexandru-Ciprian Zavoianu and Martin Kitzberger and Gerd Bramerdorfer and Susanne Saminger-Platz", title = "On Modeling the Dynamic Thermal Behavior of Electrical Machines Using Genetic Programming and Artificial Neural Networks", booktitle = "International Conference on Computer Aided Systems Theory, EUROCAST 2019", year = "2019", editor = "Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia", volume = "12013", series = "Lecture Notes in Computer Science", pages = "319--326", address = "Las Palmas de Gran Canaria, Spain", month = "17-22 " # feb, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Data driven modelling, Time series, Dynamic thermal behaviour, Electrical machines, Lumped parameter thermal networks", isbn13 = "978-3-030-45092-2", DOI = "doi:10.1007/978-3-030-45093-9_39", abstract = "We describe initial attempts to model the dynamic thermal behavior of electrical machines by evaluating the ability of linear and non-linear (regression) modeling techniques to replicate the performance of simulations carried out using a lumped parameter thermal network (LPTN) and two different test scenarios. Our focus falls on creating highly accurate simple models that are well-suited for the real-time computational demands of an envisioned symbiotic interaction paradigm. Preliminary results are quite encouraging and highlight the very positive impact of integrating synthetic features based on exponential moving averages.", } @Unpublished{zebulum:1997:ilgee, author = "Ricardo Salem Zebulum and Marco Aurelio Pacheco and Marley Vellasco", title = "Increasing Length Genotypes in Evolutionary Electronics", editor = "Wolfgang Banzhaf and Inman Harvey and Hitoshi Iba and William Langdon and Una-May O'Reilly and Justinian Rosca and Byoung-Tak Zhang", note = "Position paper at the Workshop on Evolutionary Computation with Variable Size Representation at ICGA-97", month = "20 " # jul, year = "1997", address = "East Lansing, MI, USA", keywords = "genetic algorithms, Evoluationary Hardware, variable size representation", URL = "http://www.cogs.susx.ac.uk/users/ricardoz/wsk.ps", notes = "http://web.archive.org/web/19971014081458/http://www.ai.mit.edu/people/unamay/icga-ws.html ", size = "5/3pages", } @InProceedings{zebulum:1998:cdemaefd, author = "Ricardo S. Zebulum and Marco Aurelio Pacheco and Marley Vellasco", title = "Comparison of Different Evolutionary Methodologies Applied to Electronic Filter Design", booktitle = "Proceedings of the 1998 IEEE World Congress on Computational Intelligence", year = "1998", pages = "434--439", address = "Anchorage, Alaska, USA", month = "5-9 " # may, publisher = "IEEE Press", keywords = "genetic algorithms, variable length representation, band-pass filters, electronic filter design, evolutionary electronics, evolutionary methodologies, evolutionary methodology, filter topologies, low-pass filters, parsimonious circuits, variable length representation, band-pass filters, circuit CAD, circuit optimisation, low-pass filters", ISBN = "0-7803-4869-9", file = "c075.pdf", DOI = "doi:10.1109/ICEC.1998.699812", size = "6 pages", abstract = "We present in this work the application of a set of different evolutionary methodologies in the problem of electronic filter design. The main objectives are to find out which constraints in the filter topologies, if any, must be observed along the evolutionary process and to study the problem of convergence to parsimonious circuits. The new area of Evolutionary Electronics is introduced, an evolutionary methodology based on variable length representation is presented and the results on the evolution of low-pass and band-pass filters are described.", notes = "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE World Congress on Computational Intelligence. In answer to a question at WCCI-98, this approach was stated to require fewer function evaluations than work by Koza.", } @InProceedings{Zechman:2005:WWERC, author = "Emily Zechman and G. Mahinthakumar and S. Ranji Ranjithan", title = "Investigation and Demonstration of an Evolutionary Computation-Based Model Correction Procedure for a Numerical Simulation Model", booktitle = "World Water and Environmental Resources Congress 2005", year = "2005", editor = "Raymond Walton", address = "Anchorage, Alaska, USA", publisher_address = "Reston, Va, USA", month = may # " 15-19", organisation = "American Society Civil Engineering", keywords = "genetic algorithms, genetic programming, Computation, Groundwater pollution, Hydrologic models, Numerical models, Simulation models", isbn13 = "978-0-7844-0792-9", DOI = "doi:10.1061/40792(173)346", abstract = "Traditional model calibration attempts to correct a model so that the model output will match a set of system observations by tweaking a set of model parameters. Potential model structural error limits, however, the effectiveness and accuracy of calibration, undermining the predictive capabilities of the calibrated model. An evolutionary computation-based model error correction procedure that couples an evolutionary algorithm and a genetic program was previously developed and tested for two analytical models. Due to nonuniqueness in the solution space, numerous forms of correction terms that similarly fit the observation data were found. This procedure is further investigated to explore and identify alternative correction terms that not only provide a good fit but also results in good prediction performance. This approach is then demonstrated using a numerical groundwater contaminant transport simulation model.", notes = "Environmental and Water Resources Institute (EWRI) of ASCE. OCLC Number: 66144369 c2005 ASCE", } @InProceedings{Zechman:2005:WWREC2, author = "Emily Zechman and Baha Mirghani and G. Mahinthakumar and S. Ranji Ranjithan", title = "A Genetic Programming-Based Surrogate Model Development and Its Application to a Groundwater Source Identification Problem", booktitle = "World Water and Environmental Resources Congress 2005", year = "2005", editor = "Raymond Walton", address = "Anchorage, Alaska, USA", publisher_address = "Reston, Va, USA", month = may # " 15-19", organisation = "American Society Civil Engineering", keywords = "genetic algorithms, genetic programming, Chemicals, Groundwater management, Hydrologic models, Water pollution, Wells", isbn13 = "978-0-7844-0792-9", DOI = "doi:10.1061/40792(173)341", abstract = "This paper investigates a groundwater source identification problem in which chemical signals at observation wells are used to reconstruct the pollution loading scenario. This inverse problem is solved using a simulation-optimisation approach that uses a genetic algorithm to conduct the search. As the numerical pollution-transport model is solved iteratively during the heuristic search, the evolutionary search can be in general computationally intensive. This is addressed by constructing a surrogate modelling approach that is able to predict quickly the concentration profiles at the observation wells. A genetic program is used in the development of the surrogate models that provides an acceptable prediction performance. The surrogate model, which replaces the numerical simulation model, is then coupled with the evolutionary search procedure to solve the inverse problem. The results will illustrate 1) the performance of the surrogate model in predicting the concentration compared with the predictions using the original numerical model, and 2) the quality of the solution to the inverse problem obtained using the surrogate model to that obtained using the numerical model.", notes = "Environmental and Water Resources Institute (EWRI) of ASCE. OCLC Number: 66144369 c2005 ASCE", } @InProceedings{1068286, author = "Emily M. Zechman and S. Ranji Ranjithan", title = "Multipopulation cooperative coevolutionary programming (MCCP) to enhance design innovation", booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation", year = "2005", editor = "Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and Edwin D. {de Jong} and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler", volume = "2", ISBN = "1-59593-010-8", pages = "1641--1648", address = "Washington DC, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2005/docs/p1641.pdf", DOI = "doi:10.1145/1068009.1068286", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "25-29 " # jun, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, design, evolutionary programming, lymphoma cancer classification, niching", abstract = "the development of an evolutionary algorithm called Multipopulation Cooperative Coevolutionary Programming (MCCP) that extends Genetic Programming (GP) to search for a set of maximally different solutions for program induction problems. The GP search is structured to generate a set of alternatives that are similar in design performance, but are dissimilar from each other in the solution (or design parameter) space. This is expected to yield potentially more creative designs, thus enhancing design innovation. Application of MCCP is demonstrated through an illustrative example involving GP-based classification of genetic data to diagnose malignancy in cancer. Four different classifiers, based on highly dissimilar combinations of genes, but with similar prediction performances were generated. As these classifiers use a diverse set of genes, they are collectively more effective in screening cancer samples that may not all properly express every gene.", notes = "GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM Order Number 910052", } @PhdThesis{Zechman:thesis, author = "Emily Michelle Zechman", title = "Improving Predictability of Simulation Models using Evolutionary Computation-Based Methods for Model Error Correction", school = "Civil Engineering, North Carolina State University", year = "2005", address = "Raleigh, USA", keywords = "genetic algorithms, genetic programming, genetic programming, non-uniquness, evolutionary computation, alternatives generation, parameter estimation, water resources management, model error correction, calibration", URL = "http://www.lib.ncsu.edu/theses/available/etd-08082005-105133/unrestricted/etd.pdf", URL = "http://www.lib.ncsu.edu/theses/available/etd-08082005-105133/", size = "148 pages", abstract = "Simulation models are important tools for managing water resources systems. An optimisation method coupled with a simulation model can be used to identify effective decisions to efficiently manage a system. The value of a model in decision-making is degraded when that model is not able to accurately predict system response for new management decisions. Typically, calibration is used to improve the predictability of models to match more closely the system observations. Calibration is limited as it can only correct parameter error in a model. Models may also contain structural errors that arise from mis-specification of model equations. This research develops and presents a new model error correction procedure (MECP) to improve the predictive capabilities of a simulation model. MECP is able to simultaneously correct parameter error and structural error through the identification of suitable parameter values and a function to correct misspecifications in model equations. An evolutionary computation (EC)-based implementation of MECP builds upon and extends existing evolutionary algorithms to simultaneously conduct numeric and symbolic searches for the parameter values and the function, respectively. Non-uniqueness is an inherent issue in such system identification problems. One approach for addressing non-uniqueness is through the generation of a set of alternative solutions. EC-based techniques to generate alternative solutions for numeric and symbolic search problems are not readily available. New EC-based methods to generate alternatives for numeric and symbolic search problems are developed and investigated in this research. The alternatives generation procedures are then coupled with the model error correction procedure to improve the predictive capability of simulation models and to address the non-uniqueness issue. The methods developed in this research are tested and demonstrated for an array of illustrative applications.", notes = "etd-08082005-105133", } @Article{Zechman:2007:AWR, author = "Emily M. Zechman and S. Ranji Ranjithan", title = "Evolutionary computation-based approach for model error correction and calibration", journal = "Advances in Water Resources", year = "2007", volume = "30", number = "5", pages = "1360--1370", month = may, keywords = "genetic algorithms, genetic programming, Evolutionary computation, Calibration, Model error correction", DOI = "doi:10.1016/j.advwatres.2006.11.013", size = "11 pages", abstract = "Calibration is typically used for improving the predictability of mechanistic simulation models by adjusting a set of model parameters and fitting model predictions to observations. Calibration does not, however, account for or correct potential misspecifications in the model structure, limiting the accuracy of modelled predictions. This paper presents a new approach that addresses both parameter error and model structural error to improve the predictive capabilities of a model. The new approach simultaneously conducts a numeric search for model parameter estimation and a symbolic (regression) search to determine a function to correct misspecifications in model equations. It is based on an evolutionary computation approach that integrates genetic algorithm and genetic programming operators. While this new approach is designed generically and can be applied to a broad array of mechanistic models, it is demonstrated for an illustrative case study involving water quality modelling and prediction. Results based on extensive testing and evaluation, show that the new procedure performs consistently well in fitting a set of training data as well as predicting a set of validation data, and outperforms a calibration procedure and an empirical model fitting procedure.", } @InProceedings{Zegklitz:2015:GECCOcomp, author = "Jan Zegklitz and Petr Posik", title = "Symbolic Regression by Grammar-based Multi-Gene Genetic Programming", booktitle = "GECCO'15 Student Workshop", year = "2015", editor = "Tea Tusar and Boris Naujoks", isbn13 = "978-1-4503-3488-4", keywords = "genetic algorithms, genetic programming, grammatical evolution", pages = "1217--1220", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739482.2768484", DOI = "doi:10.1145/2739482.2768484", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Grammatical Evolution is an algorithm of Genetic Programming but it is capable of evolving programs in an arbitrary language given by a user-provided context-free grammar. We present a way how to apply Multi-Gene idea, known from Multi-Gene Genetic Programming, to Grammatical Evolution, just by modifying the given grammar. We also describe modifications which improve the behaviour of such algorithm, called Multi-Gene Grammatical Evolution. We compare the resulting system to GPTIPS, an existing implementation of MGGP.", notes = "Also known as \cite{2768484} Distributed at GECCO-2015.", } @InProceedings{Zegklitz:2015:GECCOcompa, author = "Jan Zegklitz and Petr Posik", title = "Model Selection and Overfitting in Genetic Programming: Empirical Study", booktitle = "GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation", year = "2015", editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr", isbn13 = "978-1-4503-3488-4", keywords = "genetic algorithms, genetic programming: Poster", pages = "1527--1528", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739482.2764678", DOI = "doi:10.1145/2739482.2764678", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Genetic Programming has been very successful in solving a large area of problems but its use as a machine learning algorithm has been limited so far. One of the reasons is the problem of over-fitting which cannot be solved or suppressed as easily as in more traditional approaches. Another problem, closely related to over fitting, is the selection of the final model from the population. In this article we present our research that addresses both problems: over fitting and model selection. We compare several ways of dealing with over fitting, based on Random Sampling Technique (RST) and on using a validation set, all with an emphasis on model selection. We subject each approach to a thorough testing on artificial and real--world datasets and compare them with the standard approach, which uses the full training data, as a baseline.", notes = "Also known as \cite{2764678} Distributed at GECCO-2015.", } @InProceedings{Zegklitz:2017:GECCO, author = "Jan Zegklitz and Petr Posik", title = "Linear Combinations of Features As Leaf Nodes in Symbolic Regression", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4939-0", address = "Berlin, Germany", pages = "145--146", size = "2 pages", URL = "http://doi.acm.org/10.1145/3067695.3076009", DOI = "doi:10.1145/3067695.3076009", acmid = "3076009", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, symbolic regression", month = "15-19 " # jul, abstract = "We propose a new type of leaf node for use in Symbolic Regression (SR) that performs linear combinations of feature variables (LCF). LCF's weights are tuned using a gradient method based on back-propagation algorithm known from neural networks. Multi-Gene Genetic Programming (MGGP) was chosen as a baseline model. As a sanity check, we experimentally show that LCFs improve the performance of the baseline on a rotated toy SR problem. We then perform a thorough experimental study on a number of artificial and real-world SR benchmarks. The usage of LCFs in MGGP statically improved the results in 5 cases out of 9, while it worsen them in only a single case.", notes = "Also known as \cite{Zegklitz:2017:LCF:3067695.3076009} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @Article{Zegklitz:GPEM:, author = "Jan Zegklitz and Petr Posik", title = "Benchmarking state-of-the-art symbolic regression", journal = "Genetic Programming and Evolvable Machines", year = "2021", volume = "22", number = "1", pages = "5--33", month = mar, keywords = "genetic algorithms, genetic programming, Symbolic regression, Linear regression, Comparative study", ISSN = "1389-2576", URL = "https://link.springer.com/article/10.1007/s10710-020-09387-0", DOI = "doi:10.1007/s10710-020-09387-0", abstract = "Symbolic regression (SR) is a powerful method for building predictive models from data without assuming any model structure. Traditionally, genetic programming (GP) was used as the SR engine. However, for these purely evolutionary methods it was quite hard to even accommodate the function to the range of the data and the training was consequently inefficient and slow. Recently, several SR algorithms emerged which employ multiple linear regression. This allows the algorithms to create models with relatively small error right from the beginning of the search. Such algorithms are claimed to be by orders of magnitude faster than SR algorithms based on classic GP. However, a systematic comparison of these algorithms on a common set of problems is still missing and there is no basis on which to decide which algorithm to use. In this paper we conceptually and experimentally compare several representatives of such algorithms: GPTIPS, FFX, and EFS. We also include GSGP-Red, which is an enhanced version of geometric semantic genetic programming, an important algorithm in the field of SR. They are applied as off-the-shelf, ready-to-use techniques, mostly using their default settings. The methods are compared on several synthetic SR benchmark problems as well as real-world ones ranging from civil engineering to aerodynamics and acoustics. Their performance is also related to the performance of three conventional machine learning algorithms: multiple regression, random forests and support vector regression. The results suggest that across all the problems, the algorithms have comparable performance. We provide basic recommendations to the user regarding the choice of the algorithm.", } @Article{ZEGKLITZ:2019:ASC, author = "Jan Zegklitz and Petr Posik", title = "Symbolic regression in dynamic scenarios with gradually changing targets", journal = "Applied Soft Computing", volume = "83", pages = "105621", year = "2019", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2019.105621", URL = "http://www.sciencedirect.com/science/article/pii/S1568494619304016", keywords = "genetic algorithms, genetic programming, Symbolic regression, Dynamic optimization, Backpropagation, Reinforcement learning", abstract = "Symbolic regression is a machine learning task: given a training dataset with features and targets, find a symbolic function that best predicts the target given the features. This paper concentrates on dynamic regression tasks, i.e. tasks where the goal changes during the model fitting process. Our study is motivated by dynamic regression tasks originating in the domain of reinforcement learning: we study four dynamic symbolic regression problems related to well-known reinforcement learning benchmarks, with data generated from the standard Value Iteration algorithm. We first show that in these problems the target function changes gradually, with no abrupt changes. Even these gradual changes, however, are a challenge to traditional Genetic Programming-based Symbolic Regression algorithms because they rely only on expression manipulation and selection. To address this challenge, we present an enhancement to such algorithms suitable for dynamic scenarios with gradual changes, namely the recently introduced type of leaf nodes called Linear Combination of Features. This type of leaf node, aided by the error backpropagation technique known from artificial neural networks, enables the algorithm to better fit the data by using the error gradient to its advantage rather than searching blindly using only the fitness values. This setup is compared with a baseline of the core algorithm without any of our improvements and also with a classic evolutionary dynamic optimization technique: hypermutation. The results show that the proposed modifications greatly improve the algorithm ability to track a gradually changing target", } @Article{zeitrag:2022:eswa, author = "Yannik Zeitrag and Jose Rui Figueira and Nuno Horta and Rui Neves", title = "Surrogate-assisted automatic evolving of dispatching rules for multi-objective dynamic job shop scheduling using genetic programming", journal = "Expert Systems with Applications", year = "2022", volume = "209", pages = "118194", month = "15 " # dec, keywords = "genetic algorithms, genetic programming, Multi-objective optimization, Surrogates, Hyper-heuristic, Dynamic job shop scheduling, Machine learning", ISSN = "0957-4174", URL = "https://www.sciencedirect.com/science/article/pii/S0957417422013550", DOI = "doi:10.1016/j.eswa.2022.118194", size = "19 pages", abstract = "Dispatching rules are simple but efficient heuristics to solve multi-objective job shop scheduling problems, particularly useful to face the challenges of dynamic shop environments. A promising method to automatically evolve non-dominated rules represents multi-objective genetic programming based hyper-heuristic (MO-GP-HH). The aim of such methods is to approximate the Pareto front of non-dominated dispatching rules as good as possible in order to provide a sufficient set of efficient solutions from which the decision maker can select the most preferred one. However, one of the main drawbacks of existing approaches is the computational demanding simulation-based fitness evaluation of the evolving rules. To efficiently allocate the computational budget, surrogate models can be employed to approximate the fitness. Two possible ways, that estimate the fitness either based on a simplified problem or based on samples of fully evaluated individuals making use of machine learning techniques are investigated in this paper. Several representatives of both categories are first examined with regard to their selection accuracy and execution time. Furthermore, we developed a surrogate-assisted MO-GP-HH framework, incorporating a pre-selection task in the NSGA-II algorithm. The most promising candidates are consequently implemented in the framework. Using a dynamic job shop scenario, the two proposed algorithms are compared to the original one without using surrogates. With the aim to minimize the mean flowtime and maximum tardiness, experimental results demonstrate that the proposed algorithms outperform the former. Making use of surrogates leads to a reduction in computational costs of up to 70percent. Another interesting finding shows that the enhanced ability to identify duplicates based on the phenotypic characterization of individuals is particularly helpful in increasing diversity within a population. This study illustrates the positive effect of this mechanism on the exploration of the entire Pareto front.", notes = "Also known as \cite{ZEITRAG2022118194} CEGIST, Instituto Superior Tecnico, Universidade de Lisboa, Av. Rovisco Pais 1, Lisbon 1049-001, Portugal", } @Article{ZELENKOV:2024:eswa, author = "Yuri Zelenkov", title = "Firm failure prediction using genetic programming generated features", journal = "Expert Systems with Applications", volume = "249", pages = "123839", year = "2024", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2024.123839", URL = "https://www.sciencedirect.com/science/article/pii/S095741742400705X", keywords = "genetic algorithms, genetic programming, Firm failure prediction, Genetic programming generated feature, Fitness function, Score of generated features, Unbalanced data", abstract = "Many studies on predicting firm failure have focused on finding new features that improve the accuracy of the models. In this paper, genetic programming (GP) is used for this purpose. The main problem in GP is to specify a function that evaluates the fitness of the feature. Direct optimization of a machine learning (ML) model that uses a generated feature in most cases leads to high computational costs since evolving a population of N programs over G generations while evaluating each model using K-fold cross-validation requires N*G*K model learning cycles. Thus, many researchers use scores that measure the relationship of the generated features to the class label. However, our empirical analysis shows that most such scores correlate poorly with ML model performance. The novelty of our work is that we introduce several ways of combining different scores into a single measure of expected model performance. Experimental results on data from Hungarian firms (7167 observations, class imbalance 9.37) using five ML models (Logistic Regression, Random Forest, Gradient Boosting, Histogram Boosting, and AdaBoost) prove that the proposed way of setting the fitness function increases the ROC AUC of the listed models by 6.6percent, 5.2percent, 6.8percent, 5.5percent and 5.2percent respectively. Moreover, by applying the found formula to the data from Czech firms (3872 observations, class imbalance of 74.92), which were not used for the feature search, we obtained increases in ROC AUC by 13.1percent, 11.8percent, 14.9percent, 9.9percent, and 8.2percent, respectively. This indicates that the proposed method allows to find universal features, which opens the way to build effective models in case of insufficient data (small number of observations, extreme imbalance, etc.)", } @Article{Zelinka:2005:IJSSST, author = "Ivan Zelinka and Zuzana Oplatkova and Lars Nolle", title = "Analytic programming - Symbolic Regression by means of arbitrary Evolutionary Algorithms", journal = "International Journal of Simulation Systems, Science \& Technology", year = "2005", volume = "6", number = "9", pages = "44--56", month = aug, note = "Special Issue on: Intelligent Systems", keywords = "genetic algorithms, genetic programming, symbolic regression, grammar evolution, differential evolution, analytic programming, SOMA", ISSN = "1473-8031", URL = "http://ducati.doc.ntu.ac.uk/uksim/journal/Vol-6/No.9/Paper5.pdf", size = "13 pages", abstract = "This contribution introduces analytical programming, a novel method that allows solving various problems from the symbolic regression domain. Symbolic regression was first proposed by J. R. Koza in his genetic programming and by C. Ryan in grammatical evolution. This contribution explains the main principles of analytic programming, and demonstrates its ability to synthesise suitable solutions, called programs. It is then compared in its structure with genetic programming and grammatical evolution. After theoretical part, a comparative study concerned with Boolean k-symmetry and k-even problems from Koza's genetic programming domain is done with analytical programming. Here, two evolutionary algorithms are used with analytical programming: differential evolution and self-organising migrating algorithm. Boolean k-symmetry and k-even problems comparative study here are continuation of previous comparative studies done by analytic programming in the past.", notes = "A publication of the United Kingdom Simulation Society http://ducati.doc.ntu.ac.uk/uksim/journal/Vol-6/No.9/cover.htm", } @InProceedings{Zelinka:2008:DEXA, author = "Ivan Zelinka and Roman Senkerik and Zuzana Oplatkova", title = "Evolutionary Scanning and Neural Network Optimization", booktitle = "19th International Conference on Database and Expert Systems Application, DEXA '08", year = "2008", month = sep, pages = "576--582", keywords = "genetic algorithms, genetic programming, analytic programming, differential evolution, evolutionary scanning, grammatical evolution, neural network optimization, neural network synthesis, self organizing migrating algorithm, simulated annealing, symbolic regression, neural nets, simulated annealing", DOI = "doi:10.1109/DEXA.2008.84", ISSN = "1529-4188", abstract = "This paper deals with use of an alternative tool for symbolic regression - analytic programming which is able to solve various problems from the symbolic domain as well as genetic programming and grammatical evolution. The main tasks of analytic programming in this paper, is synthesis of a neural network. In this contribution main principles of analytic programming are described and explained. In the second part of the article is in detail described how analytic programming was used for neural network synthesis. An ability to create so called programs, as well as genetic programming or grammatical evolution do, is shown in that part. In this contribution three evolutionary algorithms were used - self organizing migrating algorithm, differential evolution and simulated annealing. The total number of simulations was 150 and results show that the first two used algorithms were more successful than not so robust simulated annealing.", notes = "Also known as \cite{4624779}", } @InProceedings{conf/cisim/ZelinkaSS14, author = "Ivan Zelinka and Petr Saloun and Roman Senkerik", title = "Chaos Powered Grammatical Evolution", booktitle = "13th IFIP TC8 International Conference, Computer Information Systems and Industrial Management, CISIM 2014", year = "2014", editor = "Khalid Saeed and Vaclav Snasel", volume = "8838", series = "Lecture Notes in Computer Science", pages = "455--464", address = "Ho Chi Minh City, Vietnam", month = nov # " 5-7", publisher = "Springer", keywords = "genetic algorithms, genetic programming, grammatical evolution", bibdate = "2014-11-03", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/cisim/cisim2014.html#ZelinkaSS14", isbn13 = "978-3-662-45236-3", URL = "http://dx.doi.org/10.1007/978-3-662-45237-0", } @InProceedings{Zelinka:2016:RCAR, author = "I. Zelinka", booktitle = "2016 IEEE International Conference on Real-time Computing and Robotics (RCAR)", title = "On possibilities of evolutionary synthesis of robot control sequences", year = "2016", pages = "332--337", abstract = "In this paper there are introduced, discussed and demonstrated capabilities of bio-inspired algorithms, especially algorithms like genetic programming, analytic programming etc., on evolutionary synthesis of controlling commands for the robot on the test path. Methods proposed and discussed in the paper are explained and demonstrated on the classical text example of the robot-artificial ant on Santa Fe Trail. The Santa Fe Trail problem is one of the most classical exercise from genetic programming. In this problem so called artificial ants search for a pellets of food according to synthesised set of instructions. This set of instructions can be synthesised by specialized evolutionary algorithms, that are discussed here as well as selected examples. At the end possibility of swarm robots control and strategies identification are discussed.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/RCAR.2016.7784049", month = jun, notes = "Also known as \cite{7784049}", } @InProceedings{Zeng:2023:IVCNZ, author = "Dylon Zeng and Ying Bi and Ivy Liu and Bing Xue and Ross Vennell and Mengjie Zhang", booktitle = "2023 38th International Conference on Image and Vision Computing New Zealand (IVCNZ)", title = "A New Genetic Programming-Based Approach to Object Detection in Mussel Farm Images", year = "2023", address = "Palmerston North, New Zealand", month = "29-30 " # nov, keywords = "genetic algorithms, genetic programming, Image quality, Training, Industries, Image segmentation, Sensitivity, Object detection, Production, Object Detection, Waterline Detection", ISSN = "2151-2205", isbn13 = "979-8-3503-7052-2", DOI = "doi:10.1109/IVCNZ61134.2023.10343758", size = "6 pages", abstract = "Mussel farms are vital contributors to New Zealand's booming aquaculture industry, driving economic growth and generating employment opportunities across the country. To meet the demand for sustainable mussel production, innovative computer vision and AI solutions are essential in detecting mussel floats that maintain crop line buoyancy. However, object detection in mussel farm images faces considerable challenges such as poor image quality, partial occlusion, and high variability in image conditions. In this paper, we propose a new genetic programming-based approach tailored for object detection in mussel farm images. The approach features a new waterline detection algorithm for image segmentation and a new genetic programming method, called 3-Tree GP, for object detection (mussel buoy/float). Experimental results show that the proposed approach achieves a detection F1 score of 94.4percent, surpassing the state-of-the-art YOLOv8 by over 4percent. In particular, the approach excels in identifying objects that are partially occluded, or located far away from the camera, making it suitable for real-world applications.", notes = "Also known as \cite{10343758}, See also \cite{Dylon_Zeng:JRSNZ}", } @InProceedings{DBLP:conf/ivcnz/ZengBL0V024, author = "Dylon Zeng and Ying Bi and Ivy Liu and Bing Xue and Ross Vennell and Mengjie Zhang", title = "Genetic Programming-Based Multi-Object Matching for Mussel Floats in Mussel Farm Images", booktitle = "2024 39th International Conference on Image and Vision Computing New Zealand (IVCNZ)", year = "2024", editor = "Richard Clare and Joe Chen and Le Yang", address = "Christchurch, New Zealand", month = dec # " 4-6", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Productivity, Accuracy, Tracking, Crops, Transformers, Cameras, Object recognition, Monitoring, Carbon footprint, Object Matching, Genetic Programming, Aquaculture", isbn13 = "979-8-3315-1878-3", timestamp = "Mon, 03 Mar 2025 21:16:18 +0100", biburl = "https://dblp.org/rec/conf/ivcnz/ZengBL0V024.bib", bibsource = "dblp computer science bibliography, https://dblp.org", DOI = "doi:10.1109/IVCNZ64857.2024.10794448", notes = "also known as \cite{10794448} Sch. of Engineering and Computer Science Victoria University of Wellington Wellington, New Zealand IVCNZ2024 https://ivcnz2024.csse.canterbury.ac.nz/", } @Article{Dylon_Zeng:JRSNZ, author = "Dylon Zeng and Ivy Liu and Ying Bi and Ross Vennell and Dana Briscoe and Bing Xue and Mengjie Zhang", title = "A new multi-object tracking pipeline based on computer vision techniques for mussel farms", journal = "Journal of the Royal Society of New Zealand", year = "2025", volume = "55", number = "1", pages = "62--81", keywords = "genetic algorithms, genetic programming, Object tracking, computer vision, object detection, object description, aquaculture, mussel farming, Marlborough Sounds New Zealand", publisher = "Taylor \& Francis", DOI = "doi:10.1080/03036758.2023.2240466", size = "20 pages", abstract = "... mussel farmers. However, tracking a large number of identical targets under various image conditions raises a considerable challenge. This paper proposes a new computer vision-based pipeline to automatically detect and track mussel floats in images. The proposed pipeline consists of three steps, i.e. float detection, float description, and float matching. In the first step, a new detector based on several image processing operators is used to detect mussel floats of all sizes in the images. Then a new descriptor is employed to provide unique identity markers to mussel floats based on the relative positions of their neighbours. Finally, float matching across adjacent frames is done by image registration. Experimental results on the images taken in Marlborough Sounds New Zealand have shown that the proposed pipeline achieves an 82.9 percent MOTA 18percent higher than current deep learning-based approaches without the need for training.", notes = "See \cite{Zeng:2023:IVCNZ} Published online: 30 Jul 2023 ", } @InProceedings{zeng:2022:GECCOcomp, author = "Peng Zeng and Andrew Lensen and Yanan Sun", title = "Large Scale Image Classification Using {GPU-based} Genetic Programming", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion", year = "2022", editor = "Heike Trautmann and Carola Doerr and Alberto Moraglio and Thomas Bartz-Beielstein and Bogdan Filipic and Marcus Gallagher and Yew-Soon Ong and Abhishek Gupta and Anna V Kononova and Hao Wang and Michael Emmerich and Peter A. N. Bosman and Daniela Zaharie and Fabio Caraffini and Johann Dreo and Anne Auger and Konstantin Dietric and Paul Dufosse and Tobias Glasmachers and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Tea Tusar and Dimo Brockhoff and Tome Eftimov and Pascal Kerschke and Boris Naujoks and Mike Preuss and Vanessa Volz and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Mark Coletti and Catherine (Katie) Schuman and Eric ``Siggy'' Scott and Robert Patton and Paul Wiegand and Jeffrey K. Bassett and Chathika Gunaratne and Tinkle Chugh and Richard Allmendinger and Jussi Hakanen and Daniel Tauritz and John Woodward and Manuel Lopez-Ibanez and John McCall and Jaume Bacardit and Alexander Brownlee and Stefano Cagnoni and Giovanni Iacca and David Walker and Jamal Toutouh and UnaMay O'Reilly and Penousal Machado and Joao Correia and Sergio Nesmachnow and Josu Ceberio and Rafael Villanueva and Ignacio Hidalgo and Francisco {Fernandez de Vega} and Giuseppe Paolo and Alex Coninx and Antoine Cully and Adam Gaier and Stefan Wagner and Michael Affenzeller and Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and William B. Langdon and Justyna Petke and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Paetzel and Alexander Wagner and Michael Heider and Nadarajen Veerapen and Katherine Malan and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Mohammad Nabi Omidvar and Yuan Sun and Ernesto Tarantino and De Falco Ivanoe and Antonio {Della Cioppa} and Scafuri Umberto and John Rieffel and Jean-Baptiste Mouret and Stephane Doncieux and Stefanos Nikolaidis and Julian Togelius and Matthew C. Fontaine and Serban Georgescu and Francisco Chicano and Darrell Whitley and Oleksandr Kyriienko and Denny Dahl and Ofer Shir and Lee Spector and Alma Rahat and Richard Everson and Jonathan Fieldsend and Handing Wang and Yaochu Jin and Erik Hemberg and Marwa A. Elsayed and Michael Kommenda and William {La Cava} and Gabriel Kronberger and Steven Gustafson", pages = "619--622", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, GPU, image classification, parallel algorithms", isbn13 = "978-1-4503-9268-6/22/07", DOI = "doi:10.1145/3520304.3528892", abstract = "Genetic programming (GP) has been applied to image classification and achieved promising results. However, most GP-based image classification methods are only applied to small-scale image datasets because of the limits of high computation cost. Efficient acceleration technology is needed when extending GP-based image classification methods to large-scale datasets. Considering that fitness evaluation is the most time-consuming phase of the GP evolution process and is a highly parallelized process, this paper proposes a CPU multiprocessing and GPU parallel approach to perform the process, and thus effectively accelerate GP for image classification. Through various experiments, the results show that the highly parallelized approach can significantly accelerate GP-based image classification without performance degradation. The training time of GP-based image classification method is reduced from several weeks to tens of hours, enabling it to be run on large-scale image datasets.", notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @Misc{DBLP:journals/corr/abs-2304-08915, author = "Peng Zeng and Xiaotian Song and Andrew Lensen and Yuwei Ou and Yanan Sun and Mengjie Zhang and Jiancheng Lv", title = "Differentiable Genetic Programming for High-dimensional Symbolic Regression", howpublished = "arXiv", volume = "abs/2304.08915", year = "2023", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.48550/arXiv.2304.08915", DOI = "doi:10.48550/ARXIV.2304.08915", eprinttype = "arXiv", eprint = "2304.08915", timestamp = "Mon, 26 Jun 2023 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/corr/abs-2304-08915.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{Zeng:2020:CEC, author = "Ruihua Zeng and Zhixing Huang and Yongliang Chen and Jinghui Zhong and Liang Feng", title = "Comparison of Different Computing Platforms for Implementing Parallel Genetic Programming", booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC 2020", year = "2020", editor = "Yaochu Jin", pages = "paper id24270", address = "internet", month = "19-24 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Gene Expression Programming, SL-GEP, parallel platform, MPI, GPU, CUDA, Spark, Parallel Computing", isbn13 = "978-1-7281-6929-3", DOI = "doi:10.1109/CEC48606.2020.9185510", size = "8 pages", abstract = "Genetic programming (GP) is a powerful tool for knowledge discovery and data mining. Over the past decades, GP has been implemented in various parallel computing platforms to reduce its search time. However, these parallel GPs have different design principles and performance characteristics, which makes it difficult for users to choose the proper parallel GP in practice. To address this issue, this paper focuses on comparing and analyzing the characteristics of parallel GPs implemented in different computing platforms, in terms of running time, the speedup ratio, and the scalability. Based on the empirical results, the guidance of selecting different parallel GPs is concluded.", notes = "SparkGP in Java. F1 = x**5 + x**4 + x**3 + x**2 + x (-1..+1) '60 blocks with 256 threads in every block in GPUGP' nvidia GTX 1070 'The speedup ratio of GPs with Spark in large dataset can even be superior to that of MPI' https://wcci2020.org/ South China University of Technology, China; Chongqing University, China. Also known as \cite{9185510}", } @InProceedings{conf/wise/ZengTLQZDX06, title = "Mining h-Dimensional Enhanced Semantic Association Rule Based on Immune-Based Gene Expression Programming", author = "Tao Zeng and Changjie Tang and Yintian Liu and Jiangtao Qiu and Mingfang Zhu and Shucheng Dai and Yong Xiang", booktitle = "Proceedings of the Web Information Systems Workshops, {WISE} 2006", publisher = "Springer", year = "2006", volume = "4256", editor = "Ling Feng and Guoren Wang and Cheng Zeng and Ruhua Huang", pages = "49--60", series = "Lecture Notes in Computer Science", address = "Wuhan, China", month = oct # " 23-26", bibdate = "2006-11-28", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/wise/wise2006w.html#ZengTLQZDX06", keywords = "genetic algorithms, genetic programming, Gene Expression Programming", ISBN = "3-540-47663-6", DOI = "doi:10.1007/11906070_5", abstract = "Rule mining is very important for data mining. However, traditional association rule is relatively weak in semantic representation. To address it, the main contributions of this paper included: (1) proposing formal concepts on h-Dimensional Enhanced Semantic Association Rule (h-DESAR) with self-contained logic operator; (2) proposing the h-DESAR mining method based on Immune-based Gene Expression Programming (ERIG); (3) presenting some novel key techniques in ERIG. Experimental results showed that ERIG is feasible, effective and stable.", } @Article{Zeng:jucs_13_10:a_model_of_immune, author = "Tao Zeng and Changjie Tang and Yong Xiang and Peng Chen and Yintian Liu", title = "A Model of Immune Gene Expression Programming for Rule Mining", journal = "Journal of Universal Computer Science", year = "2007", volume = "13", number = "10", pages = "1484--1497", keywords = "genetic algorithms, genetic programming, gene expression programming, artifical immune system, data mining, evolutionary algorithm, meta-rule, rule", URL = "http://www.jucs.org/jucs_13_10/a_model_of_immune", DOI = "doi:10.3217/jucs-013-10-1484", size = "14 pages", abstract = "Rule mining is an important issue in data mining. To address it, a novel Immune Gene Expression Programming (IGEP) model was proposed. Concepts of rule, gene, immune cell, and antibody were formalized. The dynamic evolution models and the corresponding recursive equations of immune cell, self, immune-tolerance were built. The novel key techniques of IGEP were presented. Experiment results showed that the new method has good stability, scalability and flexibility. It can discover traditional association rule, non-traditional rule including connective 'OR' or 'NOT', and meta-rule of strong rule. Furthermore, it can perform well in constrained pattern mining.", } @InProceedings{conf/icnc/ZengLMBQZ09, title = "Auto-Programming for Numerical Data Based on Remnant-Standard-Deviation-Guided Gene Expression Programming", author = "Tao Zeng and Yintian Liu and Xirong Ma and Xiaoyuan Bao and Jiangtao Qiu and Lixin Zhan", booktitle = "Fifth International Conference on Natural Computation, ICNC '09", year = "2009", editor = "Haiying Wang and Kay Soon Low and Kexin Wei and Junqing Sun", month = "14-16 " # aug, volume = "3", pages = "124--128", address = "Tianjian, China", publisher = "IEEE Computer Society", isbn13 = "978-0-7695-3736-8", keywords = "genetic algorithms, genetic programming, gene expression programming, automatic programming, mathematical model, fitness evaluation, reverse polish notation", bibdate = "2010-01-21", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icnc/icnc2009-3.html#RaoWY09", DOI = "doi:10.1109/ICNC.2009.617", abstract = "Automatically numerical data modeling and computer code generation is significant for data mining, data reverse engineering, engineering applications, etc. On auto-programming for numerical data, a new approach, Remnant-standard-Deviation-guided Gene Expression Programming (RD-GEP), was proposed. New individual structure, the K-expression to Reverse Polish Notation code generation without expression tree construction algorithm (K2RPN), and remnant-standard-deviation based fitness evaluation method in RD-GEP were presented and studied. New individual structure makes easy to I/O or storage the candidate solution. New decoding algorithm with linear-time complexity can simplify system operation and unify I/O format. New evaluation mechanism can reduce hypothesis solution space to improve system performance and precision. Feasibility and usability of RD-GEP were verified on various synthetic data sets and real 'Fishcatch' data set. Experimental results showed RD-GEP is good at automatically modeling numerical data and generating reverse polish notation for target model.", } @InProceedings{6351, author = "Jan Zenisek and Michael Affenzeller and Josef Wolfartsberger and Mathias Silmbroth and Christoph Sievi and Aziz Huskic and Herbert Jodlbauer", title = "Sliding Window Symbolic Regression for Predictive Maintenance using Model Ensembles", booktitle = "Computer Aided Systems Theory, EUROCAST 2017", year = "2017", editor = "Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia", volume = "10671", series = "Lecture Notes in Computer Science", pages = "481--488", address = "Las Palmas de Gran Canaria, Spain", month = feb, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-74718-7", URL = "https://link.springer.com/chapter/10.1007/978-3-319-74718-7_58", DOI = "doi:10.1007/978-3-319-74718-7_58", abstract = "Predictive Maintenance (PdM) is among the trending topics in the current Industry 4.0 movement and hence, intensively investigated. It aims at sophisticated scheduling of maintenance, mostly in the area of industrial production plants. The idea behind PdM is that, instead of following fixed intervals, service actions could be planned based upon the monitored system condition in order to prevent outages, which leads to less redundant maintenance procedures and less necessary overhauls. In this work we will present a method to analyse a continuous stream of data, which describes a system's condition progressively. Therefore, we motivate the employment of symbolic regression ensemble models and introduce a sliding-window based algorithm for their evaluation and the detection of stable and changing system states.", notes = "Published 2018?", } @InProceedings{6329, author = "J. Zenisek and M. Affenzeller and C. Sievi and M. Silmbroth and J. Wolfartsberger", title = "Predictive Maintenance for Transport Systems - Employing Model Ensembles for Online State Detection", booktitle = "Proceedings of the IAUP Triennial Conference", year = "2017", pages = "2", address = "Wien, Austria", month = jul, URL = "http://iauptriennial2017.com/wp-content/uploads/2017/07/ys-23-session-jan-zenisek.pdf", keywords = "genetic algorithms, genetic programming", } @InProceedings{6223, author = "J. Zenisek and L. Nicoletti and F. Longo and G. Traugott and A. Padovano and M. Affenzeller", title = "Smart Maintenance Lifecycle Management: A Design Proposal", booktitle = "Proceedings of the 29th European Modeling and Simulation Symposium, 2017, Barcelona, Spain", year = "2017", pages = "546--551", address = "Barcelona, Spain", month = sep, URL = "http://www.msc-les.org/proceedings/emss/2017/EMSS2017_546.pdf", keywords = "genetic algorithms, genetic programming", } @InProceedings{AGU2013_tanja_zerenner_genetic_programming_poster, author = "Tanja Zerenner and Victor Venema and Clemens Simmer", title = "Atmospheric Downscaling using Genetic Programming", booktitle = "American Geosciences Union, Fall meeting 2013", year = "2013", address = "San Francisco, USA", month = "9-13 " # dec, keywords = "genetic algorithms, genetic programming, GPLAB, Pareto, SPEA, temperature, TerrSymMP, COSMO", URL = "http://www2.meteo.uni-bonn.de/mitarbeiter/venema/articles/2013/AGU2013_tanja_zerenner_genetic_programming_poster.pdf", size = "1 page", language = "en", notes = "112 by 112 km north Germany. See also oai:CiteSeerX.psu:10.1.1.395.150 http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.395.150 https://www2.image.ucar.edu/sites/default/files/event/ci2012/files/ci2012_Zerenner.pdf", } @Misc{journals/corr/ZerennerVFS14, author = "Tanja Zerenner and Victor Venema and Petra Friederichs and Clemens Simmer", title = "Downscaling near-surface atmospheric fields with multi-objective Genetic Programming", year = "2014", keywords = "genetic algorithms, genetic programming", volume = "abs/1407.1768", bibdate = "2014-08-01", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/corr/corr1407.html#ZerennerVFS14", URL = "http://arxiv.org/abs/1407.1768", abstract = "The coupling of models for the different components of the Soil-Vegetation-Atmosphere-System is required to investigate component interactions and feedback processes. However, the component models for atmosphere, land-surface and subsurface are usually operated at different resolutions in space and time owing to the dominant processes. The computationally often more expensive atmospheric models, for instance, are typically employed at a coarser resolution than land-surface and subsurface models. Thus up- and downscaling procedures are required at the interface between the atmospheric model and the land-surface/subsurface models. We apply multi-objective Genetic Programming (GP) to a training data set of high-resolution atmospheric model runs to learn equations or short programs that reconstruct the fine-scale fields (e.g., 400 m resolution) of the near-surface atmospheric state variables from the coarse atmospheric model output (e.g., 2.8 km resolution). Like artificial neural networks, GP can flexibly incorporate multivariate and nonlinear relations, but offers the advantage that the solutions are human readable and thus can be checked for physical consistency. Using the Strength Pareto Approach for multi-objective fitness assignment allows us to consider multiple characteristics of the fine-scale fields during the learning procedure", notes = "see \cite{Zerenner:2016:EMS}", } @Article{Zerenner:2016:EMS, author = "Tanja Zerenner and Victor Venema and Petra Friederichs and Clemens Simmer", title = "Downscaling near-surface atmospheric fields with multi-objective Genetic Programming", journal = "Environmental Modelling \& Software", year = "2016", volume = "84", number = "Supplement C", pages = "85--98", keywords = "genetic algorithms, genetic programming, Statistical downscaling, Disaggregation, Evolutionary computation, Machine learning, Pareto optimality, Coupled modelling", ISSN = "1364-8152", URL = "http://www.sciencedirect.com/science/article/pii/S1364815216302122", DOI = "doi:10.1016/j.envsoft.2016.06.009", abstract = "We present a new Genetic Programming based method to derive downscaling rules (i.e., functions or short programs) generating realistic high-resolution fields of atmospheric state variables near the surface given coarser-scale atmospheric information and high-resolution information on land surface properties. Such downscaling rules can be applied in coupled subsurface-land surface-atmosphere simulations or to generate high-resolution atmospheric input data for off-line applications of land surface and subsurface models. Multiple features of the high-resolution fields, such as the spatial distribution of subgrid-scale variance, serve as objectives. The downscaling rules take an interpretable form and contain on average about 5 mathematical operations. The method is applied to downscale ten m-temperature fields from 2.8km to 400m grid resolution. A large part of the spatial variability is reproduced, also in stable night time situations, which generate very heterogeneous near-surface temperature fields in regions with distinct topography", notes = "See also \cite{journals/corr/ZerennerVFS14}. Also known as \cite{ZERENNER201685}", } @InProceedings{Zerenner:2017:GECCO, author = "Tanja Zerenner and Victor Venema and Petra Friederichs and Clemens Simmer", title = "Downscaling Near-surface Atmospheric Fields with Multi-objective Genetic Programming", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4939-0", address = "Berlin, Germany", pages = "11--12", size = "2 pages", URL = "http://doi.acm.org/10.1145/3067695.3084375", DOI = "doi:10.1145/3067695.3084375", acmid = "3084375", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, SPEA, atmospheric sciences, geosciences, soil-vegetation-atmosphere system, spatial variability", month = "15-19 " # jul, abstract = "Coupled models of the soil-vegetation-atmosphere systems are increasingly used to investigate interactions between the system components. Due to the different spatial and temporal scales of relevant processes and computational restrictions, the atmospheric model generally has a lower spatial resolution than the land surface and subsurface models. We employ multi-objective Genetic Programming (MOGP) using the Strength Pareto Evolutionary Algorithm (SPEA) to bridge this scale gap. We generate high-resolution atmospheric fields using the coarse atmospheric model output and high-resolution land surface information (e.g., topography) as predictors. High-resolution atmospheric simulations serve as reference. It is impossible to perfectly reconstruct the reference fields with the available information. Thus, we simultaneously optimize the root mean square error (RMSE) and two objective functions quantifying spatial variability. Minimization solely with respect to the RMSE provides too smooth high-resolution fields. Additional objectives help to recover spatial variability. We apply MOGP to the downscaling of 10 m temperature. Our approach reproduces a larger part of the variability and is applicable for a wider range of weather conditions than a linear regression based downscaling. Original publication: T. Zerenner, V. Venema, P. Friederichs, and C. Simmer. Downscaling near-surface atmospheric fields with multiobjective Genetic Programming. Environmental Modelling and Software, 84(2016), 85--98. \cite{Zerenner:2016:EMS}", notes = "Also known as \cite{Zerenner:2017:DNA:3067695.3084375} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @PhdThesis{Zerenner:thesis, author = "Tanja Zerenner", title = "Atmospheric Downscaling using Multi-Objective Genetic Programming", school = "Meteorologisches Institut, Rheinische Friedrich-Wilhelms-Universitaet Bonn", year = "2017", address = "Bonn, Germany", month = dec, month = oct, volume = "80", keywords = "genetic algorithms, genetic programming, MOGP", ISSN = "0006-7156", URN = "https://nbn-resolving.org/urn:nbn:de:hbz:5n-48408", URL = "https://hdl.handle.net/20.500.11811/7264", URL = "https://bonndoc.ulb.uni-bonn.de/xmlui/handle/20.500.11811/7264", URL = "https://bonndoc.ulb.uni-bonn.de/xmlui/bitstream/handle/20.500.11811/7264/4840.pdf", size = "208 pages", abstract = "Numerical models are used to simulate and to understand the interplay of physical processes in the atmosphere, and to generate weather predictions and climate projections. However, due to the high computational cost of atmospheric models, discrepancies between required and available spatial resolution of modeled atmospheric data occur frequently. One approach to generate higher-resolution atmospheric data from coarse atmospheric model output is statistical downscaling. The present work introduces multi-objective Genetic Programming (MOGP) as a method for downscaling atmospheric data. MOGP is applied to evolve downscaling rules, i.e., statistical relations mapping coarse-scale atmospheric information to the point scale or to a higher-resolution grid. Unlike classical regression approaches, where the structure of the regression model has to be predefined, Genetic Programming evolves both model structure and model parameters simultaneously. Thus, MOGP can flexibly capture nonlinear and multivariate predictor-predictand relations. Classical linear regression predicts the expected value of the predictand given a realization of predictors minimizing the root mean square error (RMSE) but in general underestimating variance. With the multi-objective approach multiple cost/fitness functions can be considered which are not solely aimed at the minimization of the RMSE, but simultaneously consider variance and probability distribution based measures. Two areas of application of MOGP for atmospheric downscaling are presented: The downscaling of mesoscale near-surface atmospheric fields from 2.8 km to 400 m grid spacing and the downscaling of temperature and precipitation series from a global reanalysis to a set of local stations. (1) With growing computational power, integrated modeling platforms, coupling atmospheric models to land surface and hydrological/subsurface models are increasingly used to account for interactions and feedback processes between the different components of the soil-vegetation-atmosphere system. Due to the small-scale heterogeneity of land surface and subsurface, land surface and subsurface models require a small grid spacing, which is computationally unfeasible for atmospheric models. Hence, in many integrated modeling systems, a scale gap occurs between atmospheric model component and the land surface/subsurface components, which potentially introduces biases in the estimation of the turbulent exchange fluxes at the surface. Under the assumption that the near surface atmospheric boundary layer is significantly influenced by land surface heterogeneity, MOGP is used to evolve downscaling rules that recover high-resolution near-surface fields of various atmospheric variables (temperature, wind speed, etc.) from coarser atmospheric data and high-resolution land surface information. For this application MOGP does not significantly reduce the RMSE compared to a pure interpolation. However, (depending on the state variable under consideration) large parts of the spatial variability can be restored without any or only a small increase in RMSE. (2) Climate change impact studies often require local information while the general circulation models used to create climate projections provide output with a grid spacing in the order of approximately 100 km. MOGP is applied to estimate the local daily maximum, minimum and mean temperature and the daily accumulated precipitation at selected stations in Europe from global reanalysis data. Results are compared to standard regression approaches. While for temperature classical linear regression already achieves very good results and outperforms MOGP, the results of MOGP for precipitation downscaling are promising and outperform a standard generalized linear model. Especially the good representation of precipitation extremes and spatial correlation (with the latter not incorporated in the objectives) are encouraging.", notes = "in English Supervisor: Clemens Simmer", } @InProceedings{Zerenner:2018:GECCOcomp, author = "Tanja Zerenner and Victor Venema and Petra Friederichs and Clemens Simmer", title = "Deterministic and stochastic precipitation downscaling using multi-objective genetic programming", booktitle = "GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2018", editor = "Carlos Cotta and Tapabrata Ray and Hisao Ishibuchi and Shigeru Obayashi and Bogdan Filipic and Thomas Bartz-Beielstein and Grant Dick and Masaharu Munetomo and Silvino {Fernandez Alzueta} and Thomas Stuetzle and Pablo Valledor Pellicer and Manuel Lopez-Ibanez and Daniel R. Tauritz and Pietro S. Oliveto and Thomas Weise and Borys Wrobel and Ales Zamuda and Anne Auger and Julien Bect and Dimo Brockhoff and Nikolaus Hansen and Rodolphe {Le Riche} and Victor Picheny and Bilel Derbel and Ke Li and Hui Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Stephane Doncieux and Richard Duro and Joshua Auerbach and Harold {de Vladar} and Antonio J. Fernandez-Leiva and JJ Merelo and Pedro A. Castillo-Valdivieso and David Camacho-Fernandez and Francisco {Chavez de la O} and Ozgur Akman and Khulood Alyahya and Juergen Branke and Kevin Doherty and Jonathan Fieldsend and Giuseppe Carlo Marano and Nikos D. Lagaros and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Boris Naujoks and Vanessa Volz and Tea Tusar and Pascal Kerschke and Riyad Alshammari and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and John R. Woodward and Shin Yoo and John McCall and Nayat Sanchez-Pi and Luis Marti and Danilo Vasconcellos and Masaya Nakata and Anthony Stein and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Ivanoe {De Falco} and Antonio {Della Cioppa} and Ernesto Tarantino and Umberto Scafuri and P. G. M. Baltus and Giovanni Iacca and Ahmed Hallawa and Anil Yaman and Alma Rahat and Handing Wang and Yaochu Jin and David Walker and Richard Everson and Akira Oyama and Koji Shimoyama and Hemant Kumar and Kazuhisa Chiba and Pramudita Satria Palar", isbn13 = "978-1-4503-5764-7", pages = "79--80", address = "Kyoto, Japan", DOI = "doi:10.1145/3205651.3208778", publisher = "ACM", publisher_address = "New York, NY, USA", month = "15-19 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", abstract = "Symbolic regression is used to estimate daily time series of local station precipitation amounts from global climate model output with a coarse spatial resolution. Local precipitation is of high importance in climate impact studies. Standard regression, minimizing the RMSE or a similar point-wise error, by design underestimates temporal variability. For impact studies realistic variability is crucial. We use multi-objective Genetic Programming to evolve both deterministic and stochastic regression models that simultaneously optimize RMSE and temporal variability. Results are compared with standard methods based on generalized linear models.", notes = "Also known as \cite{3208778} GECCO-2018 A Recombination of the 27th International Conference on Genetic Algorithms (ICGA-2018) and the 23rd Annual Genetic Programming Conference (GP-2018)", } @Article{Zhai:2006:JCUG, author = "Shuhua Zhai and Qian Gao and Jianguo Song", title = "Genetic Programming Approach for Predicting Surface Subsidence Induced by Mining", journal = "Journal of China University of Geosciences", year = "2006", volume = "17", number = "4", pages = "361--366", month = dec, keywords = "genetic algorithms, genetic programming, mining induced surface subsidence, parameters", URL = "http://www.wanfangdata.com.cn/qikan/periodical.Articles/dqkx-e/dqkx2006/0604/060413.htm", DOI = "doi:10.1016/S1002-0705(07)60012-0", abstract = "The surface subsidence induced by mining is a complex problem, which is related with many complex and uncertain factors. Genetic programming (GP) has a good ability to deal with complex and nonlinear problems, therefore genetic programming approach is proposed to predict mining induced surface subsidence in this article. First genetic programming technique is introduced, second, surface subsidence genetic programming model is set up by selecting its main affective factors and training relating to practical engineering data, and finally, predictions are made by the testing of data, whose results show that the relative error is approximately less than 10percent, which can meet the engineering needs, and therefore, this proposed approach is valid and applicable in predicting mining induced surface subsidence. The model offers a novel method to predict surface subsidence in mining.", notes = "a Department of Civil Engineering, University of Science and Technology Beijing, Beijing 100083, China b Department of Civil Engineering, University of Science and Technology Beijing, Beijing 100083, China; Key Laboratory of Ministry of Education on High Efficient and Safety Mining for Metal Mines, Beijing 100083, China c Department of Civil Engineering, University of Science and Technology Beijing, Beijing 100083, China", } @InProceedings{Zhan:2014:GECCOcomp, author = "Haoxi Zhan", title = "A quantitative analysis of the simplification genetic operator", booktitle = "GECCO 2014 student workshop", year = "2014", editor = "Tea Tusar and Boris Naujoks", isbn13 = "978-1-4503-2881-4", keywords = "genetic algorithms, genetic programming", pages = "1077--1080", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2598394.2605684", DOI = "doi:10.1145/2598394.2605684", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "The simplification function was introduced to PushGP as a tool to reduce the sizes of evolved programs in final reports. While previous work suggests that simplification could reduce the sizes significantly, nothing has been done to study its impacts on the evolution of Push programs. In this paper, we show the impact of simplification as a genetic operator. By conducting test runs on the U.S. change problem, we show that using simplification operator with PushGP, lexicase selection and ULTRA could increase the possibility to find solutions in the short term while it might remove some useful genetic materials for the long term.", notes = "Clojush Also known as \cite{2605684} Distributed at GECCO-2014.", } @InProceedings{Zhan:2021:CCDC, author = "Rongxin Zhan and Zihua Cui and Tao Ma and Dongni Li", title = "A Q-learning-based Automatic Heuristic Design Approach for Seru Scheduling", booktitle = "2021 33rd Chinese Control and Decision Conference (CCDC)", year = "2021", pages = "253--257", abstract = "Seru production is a new mode of production with the advantages of quick response, high flexibility and high efficiency. It is well suited to the market that fluctuates frequently. The seru scheduling is an important issue for seru production system configuration problem because it reflects the management and control principle of seru production systems, which called just-in-time operation system. This paper studies a seru scheduling problem, which can be described as how to determine the sequence of serus in limited space for multiple orders considering worker overlapping. The objective is to minimize the maximum completion time. A Q-learning-based genetic programming algorithm is proposed to solve the above problem. Experimental results show the effectiveness of the proposed algorithm.", keywords = "genetic algorithms, genetic programming, Production systems, Processor scheduling, Evolutionary computation, Aerospace electronics, Scheduling, Computational efficiency, Seru Scheduling, Q-learning", DOI = "doi:10.1109/CCDC52312.2021.9602499", ISSN = "1948-9447", month = may, notes = "Also known as \cite{9602499}", } @InProceedings{Zhan:2009:cec, author = "Song Zhan and Julian F. Miller and Andy M. Tyrrell", title = "Obtaining System Robustness by Mimicking Natural Mechanisms", booktitle = "2009 IEEE Congress on Evolutionary Computation", year = "2009", editor = "Andy Tyrrell", pages = "3032--3039", address = "Trondheim, Norway", month = "18-21 " # may, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-2959-2", file = "P118.pdf", DOI = "doi:10.1109/CEC.2009.4983326", abstract = "Real working agents normally operate in dynamic changing environments. These changes could either affect the efficiency of the agents' performance or even damage the functionality of the agent. Robustness is the key requirement to solve this problem. Inspired by nature, this paper demonstrates two mechanisms that contribute to individual's robustness in changing environments: evolution and degeneracy. Through evolution in damaging environment, evolved agents have to cope with changes in the environment and acquire robustness. Through degeneracy, individuals can maintain their fitness even when some damaged parts are involved in system function.", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR", } @Article{Zhan2009176, author = "Song Zhan and Julian F. Miller and Andy M. Tyrrell", title = "An evolutionary system using development and artificial Genetic Regulatory Networks for electronic circuit design", journal = "Biosystems", volume = "98", number = "3", pages = "176--192", year = "2009", note = "Evolving Gene Regulatory Networks", ISSN = "0303-2647", DOI = "doi:10.1016/j.biosystems.2009.07.008", URL = "http://www.sciencedirect.com/science/article/B6T2K-4X01PGD-1/2/b462ea01f47faac7d2f3fd6703c060de", keywords = "genetic algorithms, genetic programming, Computational development, Computational evolution, Degeneracy, Genetic Regulatory Network, Neutrality, Protein synthesis", abstract = "Biology presents incomparable, but desirable, characteristics compared to engineered systems. Inspired by biological development, we have devised a multi-layered design architecture that attempts to capture the favourable characteristics of biological mechanisms for application to design problems. We have identified and implemented essential features of Genetic Regulatory Networks (GRNs) and cell signalling which lead to self-organisation and cell differentiation. We have applied this to electronic circuit design.", } @InProceedings{Zhan:2010:cec, author = "Song Zhan and Julian F. Miller and Andy M. Tyrrell", title = "Modular design from gene regulation in a cellular system", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-6910-9", abstract = "In nature, modules pervade organisms at all levels and construct complex and scalable organisms. Modularity is fundamental for achieving large and complex systems. In engineering design, as required systems become larger, the complex and modularity of the design becomes more important. Inspired by nature, this paper introduces a bottom-up construction method to construct digital circuits using gene regulation in a cellular system. It uses modular design to achieve scalable combinatorial systems.", DOI = "doi:10.1109/CEC.2010.5586101", notes = "WCCI 2010. Also known as \cite{5586101}", } @InProceedings{4798833, author = "Yong Zhan and Changhua Qiu", title = "Genetic algorithm application to the hybrid flow shop scheduling problem", booktitle = "Mechatronics and Automation, 2008. ICMA 2008. IEEE International Conference on", year = "2008", month = aug, pages = "649--653", keywords = "genetic algorithms, allocation string, chromosome representation, crossover operation, evolutionary mechanism, hybrid flow shop scheduling problem, load balancing, mutation operation, parallel machine, sequencing string, shipyard, steel treatment job shop scheduling, flow shop scheduling, job shop scheduling, resource allocation", DOI = "doi:10.1109/ICMA.2008.4798833", notes = "Not on GP", } @TechReport{Zhang05cTR, author = "Baoping Zhang and Marcos Andre Goncalves and Weiguo Fan and Yuxin Chen and Edward A. Fox and Pavel Calado and Marco Cristo", title = "Intelligent Fusion of Structural and Citation-Based Evidence for Text Classification", institution = "Computer Science, Virginia Tech", year = "2004", number = "TR-04-16", keywords = "genetic algorithms, genetic programming, Classification, document similarity, citation analysis, Computer Science, Information Retrieval, Digital Libraries", URL = "http://eprints.cs.vt.edu/archive/00000693/01/GP5.pdf", size = "9 pages", abstract = "This paper investigates how citation-based information and structural content (e.g., title, abstract) can be combined to improve classification of text documents into predefined categories. We evaluate different measures of similarity, five derived from the citation structure of the collection, and three measures derived from the structural content, and determine how they can be fused to improve classification effectiveness. To discover the best fusion framework, we apply Genetic Programming (GP) techniques. Our empirical experiments using documents from the ACM digital library and the ACM classification scheme show that we can discover similarity functions that work better than any evidence in isolation and whose combined performance through a simple majority voting is comparable to that of Support Vector Machine classifiers.", notes = "See also \cite{Zhang05c} ID Code: 693 Deposited By: Administrator, Eprints Deposited On: 09 September 2005 Site Administrator: eprints@cs.vt.edu", } @InProceedings{Zhang05c, author = "Baoping Zhang and Yuxin Chen and Weiguo Fan and Edward A. Fox and Marcos Andre Goncalves and Marco Cristo and Pavel Calado", title = "Intelligent fusion of structural and citation-based evidence for text classification", booktitle = "Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval", pages = "667--668", year = "2005", address = "Salvador, Brazil", publisher_address = "New York, NY, USA", month = aug # " 15-19", organisation = "SIGIR: ACM Special Interest Group on Information Retrieval", publisher = "ACM Press", keywords = "genetic algorithms, genetic programming, Poster", ISBN = "1-59593-034-5", size = "2 pages", copyright = "Copyright is held by the author/owner.", MRnumber = "C.IR.05.667", DOI = "doi:10.1145/1076034.1076181", abstract = "This paper shows how different measures of similarity derived from the citation information and the structural content (e.g., title, abstract) of the collection can be fused to improve classification effectiveness. To discover the best fusion framework, we apply Genetic Programming (GP) techniques. Our experiments with the ACM Computing Classification Scheme, using documents from the ACM Digital Library, indicate that GP can discover similarity functions superior to those based solely on a single type of evidence. Effectiveness of the similarity functions discovered through simple majority voting is better than that of content-based as well as combination-based Support Vector Machine classifiers. Experiments also were conducted to compare the performance between GP techniques and other fusion techniques such as Genetic Algorithms (GA) and linear fusion. Empirical results show that GP was able to discover better similarity functions than other fusion techniques.", notes = "See also \cite{Zhang05cTR}", } @InProceedings{Zhang:2005:IGF, author = "Baoping Zhang and Yuxin Chen and Weiguo Fan and Edward A. Fox and Marcos Goncalves and Marco Cristo and Pavel Calado", title = "Intelligent {GP} fusion from multiple sources for text classification", booktitle = "Proceedings of the 14th {ACM} international Conference on Information and Knowledge Management", year = "2005", month = oct # " 31-" # nov # " 5", address = "Bremen, Germany", publisher = "ACM Press", organisation = "ACM: Association for Computing Machinery SIGIR: ACM Special Interest Group on Information Retrieval", keywords = "genetic algorithms, genetic programming", URL = "http://homepages.dcc.ufmg.br/~mgoncalv/DLCourse/f302-zhang.pdf", DOI = "doi:10.1145/1099554.1099688", size = "8 pages", abstract = "This paper shows how citation-based information and structural content (e.g., title, abstract) can be combined to improve classification of text documents into predefined categories. We evaluate different measures of similarity -- five derived from the citation information of the collection, and three derived from the structural content -- and determine how they can be fused to improve classification effectiveness. To discover the best fusion framework, we apply Genetic Programming (GP) techniques. Our experiments with the ACM Computing Classification Scheme, using documents from the ACM Digital Library, indicate that GP can discover similarity functions superior to those based solely on a single type of evidence. Effectiveness of the similarity functions discovered through simple majority voting is better than that of content-based as well as combination-based Support Vector Machine classifiers. Experiments also were conducted to compare the performance between GP techniques and other fusion techniques such as Genetic Algorithms (GA) and linear fusion. Empirical results show that GP was able to discover better similarity functions than GA or other fusion techniques.", notes = "CIKM'05", } @PhdThesis{oai:VTETD:etd-07032006-152103, title = "Intelligent Fusion of Evidence from Multiple Sources for Text Classification", author = "Baoping Zhang", year = "2006", month = sep # "~06", school = "Virginia Polytechnic Institute and State University", type = "Doctor of Philosophy in Computer Science and Applications", address = "USA", bibsource = "OAI-PMH server at scholar.lib.vt.edu", contributor = "Dan Spitzner and Chang-Tien Lu and Edward A. Fox and Weiguo Fan and P{\'a}vel Calado", language = "en", oai = "oai:VTETD:etd-07032006-152103", rights = "unrestricted; I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Virginia Tech or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report.", keywords = "genetic algorithms, genetic programming", URL = "http://scholar.lib.vt.edu/theses/available/etd-07032006-152103/unrestricted/BaopingDissertationFinal.pdf", URL = "http://scholar.lib.vt.edu/theses/available/etd-07032006-152103/", size = "146 pages", abstract = "Automatic text classification using current approaches is known to perform poorly when documents are noisy or when limited amounts of textual content is available. Yet, many users need access to such documents, which are found in large numbers in digital libraries and in the WWW. If documents are not classified, they are difficult to find when browsing. Further, searching precision suffers when categories cannot be checked, since many documents may be retrieved that would fail to meet category constraints. In this work, we study how different types of evidence from multiple sources can be intelligently fused to improve classification of text documents into predefined categories. We present a classification framework based on an inductive learning method -- Genetic Programming (GP) -- to fuse evidence from multiple sources. We show that good classification is possible with documents which are noisy or which have small amounts of text (e.g., short metadata records) -- if multiple sources of evidence are fused in an intelligent way. The framework is validated through experiments performed on documents in two testbeds. One is the ACM Digital Library (using a subset available in connection with CITIDEL, part of NSF's National Science Digital Library). The other is Web data, in particular that portion associated with the Cad{\^e} Web directory. Our studies have shown that improvement can be achieved relative to other machine learning approaches if genetic programming methods are combined with classifiers such as kNN. Extensive analysis was performed to study the results generated through the GP-based fusion approach and to understand key factors that promote good classification.", notes = "URN etd-07032006-152103", } @InProceedings{Zhang:2019:ICSME, author = "Bo Zhang and Hongyu Zhang and Junjie Chen and Dan Hao and Pablo Moscato", title = "Automatic Discovery and Cleansing of Numerical Metamorphic Relations", booktitle = "35th IEEE International Conference on Software Maintenance and Evolution (ICSME, 2019)", year = "2019", editor = "Miryung Kim and Arpad Beszedes", pages = "235--245", address = "Cleveland, USA", month = "30 " # sep # " - 4 " # oct, keywords = "genetic algorithms, genetic programming, SBSE, Terms-Metamorphic relations, program invariants, search-based method, metamorphic testing", ISSN = "1063-6773", DOI = "doi:10.1109/ICSME.2019.00035", size = "11 pages", abstract = "Metamorphic relations (MRs) describe the invariant relationships between program inputs and outputs. By checking for violations of MRs, faults in programs can be detected. Identifying MRs manually is a tedious and error-prone task. In this paper, we propose AutoMR, a novel method for systematically inferring and cleansing MRs. AutoMR can discover various types of equality and inequality MRs through a search method (particle swarm optimization). It also employs matrix singular-value decomposition and constraint solving techniques to remove the redundant MRs in the search results. Our experiments on 37 numerical programs from two popular open source packages show that AutoMR can effectively infer a set of accurate and succinct MRs and outperform the state-of-the-art method. Furthermore, we show that the discovered MRs have high fault detection ability in mutation testing and differential testing.", notes = "Research Track https://icsme2019.github.io/accepted_papers.html also known as \cite{8919101}", } @InProceedings{Zhang:2021:ISSREW, author = "Bo Zhang", title = "Mining Numerical Relations for Improving Software Reliability", booktitle = "2021 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)", year = "2021", pages = "310--313", month = oct, keywords = "genetic algorithms, genetic programming, SBSE, Computer bugs, Reliability engineering, Software, Data mining, Software reliability, program invariants, search-based method", DOI = "doi:10.1109/ISSREW53611.2021.00093", abstract = "This research aims to mine numerical relations from programs and use the relations to improve program reliability. We focus on two types of numerical relations: relations from program inputs and outputs (i.e., metamorphic relations) and workflow relations from software logs. For metamorphic relations from program inputs and outputs, we design two approaches: for polynomial relations, we propose a method to firstly parameterize the metamorphic relations, then use search-based method to find the suitable parameters; for general forms of numerical relations, we plan to adopt genetic programming techniques which have the potential to evolve and produce relations of various types. For workflow relations from program logs, we parse the raw logs to event sequences and propose an approach to mine numerical relations from the event-count-matrix of the sequences. To improve software reliability, the mined metamorphic relations can be used to detect bugs and the mined workflow relations can be used to detect anomalies.", notes = "Also known as \cite{9700211}", } @InProceedings{icga93:zhang, author = "Byoung-Tak Zhang and Heinz M{\"u}hlenbein", title = "Genetic Programming of Minimal Neural Nets Using {O}ccam's Razor", year = "1993", booktitle = "Proceedings of the 5th International Conference on Genetic Algorithms, ICGA-93", publisher = "Morgan Kaufmann", editor = "Stephanie Forrest", pages = "342--349", address = "University of Illinois at Urbana-Champaign", month = "17-21 " # jul, URL = "http://www.muehlenbein.org/gpnn93.pdf", URL = "http://www.ais.fraunhofer.de/~muehlen/publications/gmd_as_ga-93_04.ps", keywords = "genetic algorithms, genetic programming", size = "8 pages", abstract = "A genetic programming method is investigated for optimizing both the architecture and the connection weights of multilayer feedforward neural networks. The genotype of each network is represented as a tree whose depth and width are dynamically adapted to the particular application by specifically defined genetic operators. The weights are trained by a next-ascent hillclimbing search. A new fitness function is proposed that quantifies the principle of Occam's razor. It makes an optimal trade-off between the error fitting ability and the parsimony of the network. We discuss the results for two problems of differing complexity and study the convergence and scaling properties of the algorithm.", notes = "GP feedforward binary ANN", } @InProceedings{zhang:1999:GPIDI, author = "Byoung-Tak Zhang and Je-Gun Joung", title = "Genetic Programming with Incremental Data Inheritance", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1217--1224", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-460.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-460.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{zhang:1999:C, author = "Zhiming Zhang and T. Warren Liao", title = "Combining case-based reasoning with genetic algorithms", booktitle = "Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference", year = "1999", editor = "Scott Brave and Annie S. Wu", pages = "305--310", address = "Orlando, Florida, USA", month = "13 " # jul, keywords = "genetic algorithms, genetic programming", notes = "GECCO-99LB", } @InProceedings{Zhang-Muehlenbein-94-WCCI-EC, author = "Byoung-Tak Zhang and Heinz M{\"u}hlenbein", title = "Synthesis of Sigma-Pi Neural Networks by the Breeder Genetic Programming", booktitle = "Proceedings of IEEE International Conference on Evolutionary Computation (ICEC-94), World Congress on Computational Intelligence", publisher = "IEEE Computer Society Press", address = "Orlando, Florida, USA", month = "27-29 " # jun, publisher_address = "New York, USA", year = "1994", pages = "318--323", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-1899-4", URL = "http://bi.snu.ac.kr/Publications/Conferences/International/ICEC94.pdf", DOI = "doi:10.1109/ICEC.1994.349933", abstract = "Genetic programming has been successfully applied to evolve computer programs for solving a variety of interesting problems. In the previous work we introduced the breeder genetic programming (BGP) method that has Occam's razor in its fitness measure to evolve minimal size multilayer perceptrons. In this paper we apply the method to synthesis of sigma-pi neural networks. Unlike perceptron architectures, sigma-pi networks use product units as well as summation units to build higher-order terms. The effectiveness of the method is demonstrated on benchmark problems. Simulation results on noisy data suggest that BGP not only improves the generalization performance, it can also accelerate the convergence speed.", notes = "Tests GP/Sigma-pi/NN on parity problems. On clean data was able to produce S/P Neural Networks with high performance >98% correct. Also ~90% on noisy data. Fitness function sums NN error and NN size/complexity penalty terms. Shows size/complexity penalty beneficial in that better NN are produced and the GP is twice as fast. Second author also given as H. Muhlenbein", } @Article{Zhang-Muehlenbein-94-JCS, author = "Byoung-Tak Zhang and Heinz M{\"u}hlenbein", title = "Evolving Optimal Neural Networks Using Genetic Algorithms with {O}ccam's Razor", journal = "Complex Systems", volume = "7", keywords = "genetic algorithms, genetic programming", number = "3", pages = "199--220", year = "1993", URL = "http://www.complex-systems.com/pdf/07-3-2.pdf", URL = "http://www.complex-systems.com/abstracts/v07_i03_a02.html", URL = "http://citeseer.ist.psu.edu/zhang93evolving.html", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.309.234", URL = "http://www.ais.fraunhofer.de/~muehlen/publications/gmd_as_ga-93_05.ps", abstract = "Genetic algorithms have had two primary applications for neural networks: optimization of network architecture, and training weights of a fixed architecture. While most previous work focuses on one or the other of these options, this paper investigates an alternative evolutionary approach --- breeder genetic programming (BGP) --- in which the architecture and the weights are optimized simultaneously. In this method, the genotype of each network is represented as a tree whose depth and width are dynamically adapted to the particular application by specifically defined genetic operators. The weights are trained by a next-ascent hillclimbing search. A new fitness function is proposed that quantifies the principle of Occam's razor; it makes an optimal trade-off between the error fitting ability and the parsimony of the network. Simulation results on two benchmark problems of differing complexity suggest that the method finds minimal networks on clean data. The experiments on noisy data show that using Occam's razor not only improves the generalization performance, it also accelerates convergence.", } @InProceedings{Zhang-94-PPSN, author = "Byoung-Tak Zhang", title = "Effects of {O}ccam's Razor in Evolving Sigma-Pi Neural Networks", booktitle = "Lecture Notes in Computer Science 866: Parallel Problem Solving from Nature III", address = "Jerusalem", publisher_address = "Berlin, Germany", publisher = "Springer-Verlag", editor = "Y. Davidor and H.-P. Schwefel and R. M{\"a}nner", year = "1994", pages = "462--471", keywords = "genetic algorithms, genetic programming", URL = "http://www.ais.fraunhofer.de/~muehlen/publications/gmd_as_ga-94_07.ps", URL = "http://citeseer.ist.psu.edu/zhang94effect.html", abstract = "Several evolutionary algorithms make use of hierarchical representations of variable size rather than linear strings of fixed length. Variable complexity of the structures provides an additional representational power which may widen the application domain of evolutionary algorithms. The price for this is, however, that the search space is open-ended and solutions may grow to arbitrarily large size. In this paper we study the effects of structural complexity of the solutions on their generalization performance by analyzing the fitness landscape of sigma-pi neural networks. The analysis suggests that smaller networks achieve, on average, better generalization accuracy than larger ones, thus confirming the usefulness of Occam's razor. A simple method for implementing the Occam's razor principle is described and shown to be effective in improving the generalization accuracy without limiting their learning capacity.", } @InProceedings{zhang:1995:wppent, author = "Byoung-Tak Zhang and Peter Ohm and Heinz Muehlenbein", title = "Water Pollution Prediction with Evolutionary Neural Trees", booktitle = "Proccedings 1995 IJCAI Workshop on AI and the Environment", year = "1995", editor = "Cindy Mason", address = "Montreal, Canada", month = aug, publisher = "AAAI and MIT Press", keywords = "genetic algorithms, genetic programming", URL = "http://web.media.mit.edu/~lieber/Conferences/AI-Env-95-Papers/Zhang.pdf", size = "8 pages", notes = "IJCAI-95-AI-Environment broken April 2021 http://web.media.mit.edu/~lieber/Conferences/IJCAI-AI-Environment.html", } @Article{Zhang-Muehlenbein-95-ECJ, author = "Byoung-Tak Zhang and Heinz M{\"u}hlenbein", title = "Balancing Accuracy and Parsimony in Genetic Programming", journal = "Evolutionary Computation", volume = "3", number = "1", pages = "17--38", year = "1995", keywords = "genetic algorithms, genetic programming, Machine learning, Tree induction, Minimum description length principle, Bayesian model comparison, Evolving neural networks.", URL = "http://www.ais.fraunhofer.de/~muehlen/publications/gmd_as_ga-94_09.ps", DOI = "doi:10.1162/evco.1995.3.1.17", abstract = "Genetic programming is distinguished from other evolutionary algorithms in that it uses tree representations of variable size instead of linear strings of fixed length. The flexible representation scheme is very important because it allows the underlying structure of the data to be discovered automatically. One primary difficulty, however, is that the solutions may grow too big without any improvement of their generalization ability. In this paper we investigate the fundamental relationship between the performance and complexity of the evolved structures. The essence of the parsimony problem is demonstrated empirically by analyzing error landscapes of programs evolved for neural network synthesis. We consider genetic programming as a statistical inference problem and apply the Bayesian model-comparison framework to introduce a class of fitness functions with error and complexity terms. An adaptive learning method is then presented that automatically balances the model-complexity factor to evolve parsimonious programs without losing the diversity of the population needed for achieving the desired training accuracy. The effectiveness of this approach is empirically shown on the induction of sigma-pi neural networks for solving a real-world medical diagnosis problem as well as benchmark tasks.", } @InProceedings{zhang:1995:bimdl, author = "Byoung-Tak Zhang and Heinz M{\"u}hlenbein", title = "Bayesian Inference, Minimum Description Length Principle, and Learning by Genetic Programming", booktitle = "Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications", year = "1995", editor = "Justinian P. Rosca", pages = "1--5", address = "Tahoe City, California, USA", month = "9 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/zhang_1995_bimdl.pdf", size = "5 pages", abstract = "adaptive search technique which dynamically balances the ratio of training accuracy to complexity of programs to achieve parsimonious solutions without loosing population diversity.", notes = "part of \cite{rosca:1995:ml}", } @InProceedings{zhang:1995:MDLbff, author = "Byoung-Tak Zhang and Heinz M{\"u}hlenbein", title = "MDL-Based Fitness Functions for Learning Parsimonious Programs", booktitle = "Working Notes for the AAAI Symposium on Genetic Programming", year = "1995", editor = "E. V. Siegel and J. R. Koza", pages = "122--126", address = "MIT, Cambridge, MA, USA", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025, USA", month = "10--12 " # nov, publisher = "AAAI", keywords = "genetic algorithms, genetic programming", URL = "http://www.aaai.org/Papers/Symposia/Fall/1995/FS-95-01/FS95-01-018.pdf", URL = "http://www.aaai.org/Library/Symposia/Fall/fs95-01.php", size = "5 pages", abstract = "In this paper we use a Bayesian model-comparison method to develop a framework in which a class of fitness measures is introduced for dealing with problems of parsimony based on the minimum description length (MDL) principle (Rissanen 1986). We then describe an adaptive technique for putting this fitness function into practice.", notes = "AAAI-95f GP. Part of \cite{siegel:1995:aaai-fgp} {\em Telephone:} 415-328-3123 {\em Fax:} 415-321-4457 {\em email} info@aaai.org {\em URL:} http://www.aaai.org/", } @InCollection{zhang:1996:aigp2, author = "Byoung-Tak Zhang and Heinz M{\"u}hlenbein", title = "Adaptive Fitness Functions for Dynamic Growing/Pruning of Program Trees", booktitle = "Advances in Genetic Programming 2", publisher = "MIT Press", year = "1996", editor = "Peter J. Angeline and K. E. {Kinnear, Jr.}", pages = "241--256", chapter = "12", address = "Cambridge, MA, USA", keywords = "genetic algorithms, genetic programming, Occam's Razor, minimum description length (MDL), neural trees, adaptive fitness functions", ISBN = "0-262-01158-1", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6277536", DOI = "doi:10.7551/mitpress/1109.003.0017", size = "16 pages", abstract = "An adaptive method for fitness evaluation is described that dynamically guides genetic programming to grow and prune program trees. The method is based on the minimum description length principle and evolves parsimonious solutions while preventing premature convergence. The effectiveness of the adaptive fitness functions is shown by evolving neural programs for modelling technical and environmental systems.", } @InProceedings{zhang:1996:bsaif, author = "Byoung-Tak Zhang and Ju-Hyun Kwak and Chang-Hoon Lee", title = "Building Software Agents for Information Filtering on the Internet: A Genetic Programming Approach", booktitle = "Late Breaking Papers at the Genetic Programming 1996 Conference Stanford University July 28-31, 1996", year = "1996", editor = "John R. Koza", pages = "196", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California 94305-3079, USA", month = "28--31 " # jul, publisher = "Stanford Bookstore", ISBN = "0-18-201031-7", keywords = "genetic algorithms, genetic programming", notes = "GP-96LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670", } @Article{zhang:1996:els-pntacp, author = "B. T. Zhang", title = "Evolutionary Learning of Sigma-Pi Neural Trees and Its Application to Classification and Prediction", journal = "Journal of Korean Institute of Intelligent Systems", year = "1996", volume = "6", number = "2", pages = "13--21", keywords = "genetic algorithms, genetic programming", ISSN = "1976-9172", URL = "http://ocean.kisti.re.kr/IS_mvpopo212L.do?method=list&poid=kfis&kojic=PJJNBT&sVnc=v6n2&&sFree====", size = "9 pages", abstract = "The necessity and usefulness of higher-order neural networks have been well-known since early days of neurocomputing. However the explosive number of terms has hampered the design and training of such networks. In this paper we present an evolutionary learning method for efficiently constructing problem-specific higher-order neural models. The crux of the method is the neural tree representation employing both sigma and pi units, in combination with the use of an MDL-based fitness function for learning minimal models. We provide experimental results in classification and prediction problems which demonstrate the effectiveness of the method.", notes = "In english", } @InProceedings{Zhang:1997:erGPsl, author = "Byoung-Tak Zhang and Je-Gun Joung", title = "Enhancing Robustness of Genetic Programming at the Species Level", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming", pages = "336--342", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1997/Zhang_1997_erGPsl.pdf", size = "7 pages", notes = "GP-97", } @InProceedings{zhang:1997:WSC2, author = "Byoung-Tak Zhang and Je-Gun Joung", title = "Evolutionary Design of Neural Trees for Heart Rate Prediction", booktitle = "Soft Computing in Engineering Design and Manufacturing", year = "1997", editor = "Pravir K. Chawdhry and Rajkumar Roy and Raj K. Pant", pages = "93--101", publisher_address = "Godalming, GU7 3DJ, UK", month = "23-27 " # jun, publisher = "Springer-Verlag London", keywords = "genetic algorithms, genetic programming, ANN", ISBN = "3-540-76214-0", URL = "http://www.springer.com/engineering/mechanical+eng/book/978-3-540-76214-0?cm_mmc=Google-_-Book%20Search-_-Springer-_-0", DOI = "doi:10.1007/978-1-4471-0427-8_11", abstract = "Some classes of neural networks are known as universal function approximators. However, their training efficiency and generalisation performance depend highly on the structure which is usually determined by a human designer. In this paper we present an evolutionary computation method for automating the neural network design process. We represent networks as tree structures, called neural trees, in genotype and apply genetic operators to evolve problem-dependent network structures and their weights. Experimental results are provided on the prediction of a heart rate time-series by evolving sigma-pi neural trees.", notes = "WSC2 Second On-line World Conference on Soft Computing in Engineering Design and Manufacturing. feed forward artificial neural networks comprised of sigma and pi nodes evolved using GP. 'Between generations the network weights are adapted by a stochastic hill-climbing search.' Fitness based on error between prediction and measurement on training examples plus term related to complexity (ie size) of network and the complexity of the best network in the previous generation (ie fitness prefers parsimony). 'Without (the parsimony term) the network size usually grows without bound'. wsc2/ind_paper/d_zhang.html URL broken 2005", size = "9 pages", } @Unpublished{zhang:1997:tcsgcg, author = "Byoung-Tak Zhang", title = "A Taxonomy of Control Schemes for Genetic Code Growth", editor = "Wolfgang Banzhaf and Inman Harvey and Hitoshi Iba and William Langdon and Una-May O'Reilly and Justinian Rosca and Byoung-Tak Zhang", note = "Position paper at the Workshop on Evolutionary Computation with Variable Size Representation at ICGA-97", month = "20 " # jul, year = "1997", address = "East Lansing, MI, USA", keywords = "genetic algorithms, genetic programming, bloat, variable size representation", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/1832/http:zSzzSzwww.ai.mit.eduzSzpeoplezSzunamayzSzicga-ws-paperszSzzhang.pdf/a-taxonomy-of-control.pdf", URL = "http://citeseer.ist.psu.edu/86260.html", abstract = "One important issue in evolutionary computation with variable size representation is the control of code growth. This paper identifies four distinctive forms of parsimony pressure useful for growth control. A taxonomy of control schemes is presented which provides a guideline for the comparison of existing methods and the development of novel methods. Based on a taxonomical analysis of widespread methods we suggest some promising areas of further research. 1 Introduction In evolutionary...", notes = "http://web.archive.org/web/19971014081458/http://www.ai.mit.edu/people/unamay/icga-ws.html ", } @Article{zhang:1997:entmpcs, author = "Byoung-Tak Zhang and Peter Ohm and Heinz Muehlenbein", title = "Evolutionary Neural Trees for Modeling and Predicting Complex Systems", journal = "Engineering Applications of Artificial Intelligence", year = "1997", volume = "10", number = "5", pages = "473--483", month = oct, keywords = "genetic algorithms, genetic programming, Evolutionary algorithms, neurocomputing, evolutionary neural trees, machine learning, system identification, complex systems, time series prediction", ISSN = "0952-1976", DOI = "doi:10.1016/S0952-1976(96)00018-8", size = "11 pages", abstract = "Modelling and predicting the behaviour of many technical systems is complicated because they are generally characterised by a large number of variables, parameters and interactions, and limited amounts of collected data. This paper investigates an evolutionary method for learning models of such systems. The models thus evolved are based on trees of heterogeneous neural units. The set of different neuron types is defined by the application domain, and the specific type of each unit is determined during the evolutionary learning process. The structure, size, and weights of the neural trees are also adapted by evolution. Since the genetic search used for training does not require error derivatives, a wide range of neural models can be constructed. This generality is contrasted with various existing methods for complex system modeling, which investigate only restricted topological subsets rather than the complete class of architectures. An improvement in the predictive accuracy and parsimony of models is reported, against backpropagation networks and other well-engineered polynomial-based methods for two problems: MacKey-Glass and Lorenz-like chaotic systems. The authors also demonstrate the importance of the selection pressure towards model parsimony for the improvement of prediction accuracy.", } @Article{Zhang:1998:eisnt, author = "Byoung-Tak Zhang and Peter Ohm and Heinz M{\"u}hlenbein", title = "Evolutionary Induction of Sparse Neural Trees", journal = "Evolutionary Computation", volume = "5", number = "2", pages = "213--236", year = "1997", keywords = "genetic algorithms, genetic programming, program induction, higher-order neural networks, neural tree representation, Minimum description length principle, time series prediction, breeder genetic algorithm", URL = "http://bi.snu.ac.kr/Publications/Journals/International/EC5-2.ps", URL = "http://www.mitpressjournals.org/doi/pdfplus/10.1162/evco.1997.5.2.213", DOI = "doi:10.1162/evco.1997.5.2.213", abstract = "This paper is concerned with the automatic induction of parsimonious neural networks. In contrast to other program induction situations, network induction entails parametric learning as well as structural adaptation. We present a novel representation scheme called neural trees that allows efficient learning of both network architectures and parameters by genetic search. A hybrid evolutionary method is developed for neural tree induction that combines genetic programming and the breeder genetic algorithm under the unified framework of the minimum description length principle. The method is successfully applied to the induction of higher order neural trees while still keeping the resulting structures sparse to ensure good generalization performance. Empirical results are provided on two chaotic time series prediction problems of practical interest.", notes = "Evolutionary Computation (Journal) Special Issue: Trends in Evolutionary Methods for Program Induction Referenced in \cite{zhang:1997:WSC2} Demonstrated on Mackey-Glass and chaotic fluctuations in a far-infared ammonia NH3 laser. Libraries of building blocks (selected by their local fitness), local fitness-based crossover, injection and pruning of submodules (subtree replaced by its descendent subtree, if the descendent is fitter than the subtree itself), scheduling of genetic operators. Parsimony fitness bias. Local search to optimise sigma+pi? neural net weights using exponetial noise. ", size = "31 pages", } @InProceedings{zhang:1998:fs:ecgbGP, author = "Byoung-Tak Zhang and Dong-Yeon Cho", title = "Fitness Switching: Evolving Complex Group Behaviors Using Genetic Programming", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "431--439", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-548-7", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1998/zhang_1998_fs_ecgbGP.pdf", notes = "GP-98", } @InProceedings{zhang:1998:maerDNAc, author = "Byoung-Tak Zhang and Soo-Yong Shin", title = "Molecular Algorithms for Efficient and Reliable DNA Computing", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "735--744", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "DNA Computing", ISBN = "1-55860-548-7", URL = "http://citeseer.ist.psu.edu/91216.html", abstract = "Two new molecular algorithms are presented that are designed for the improvement of efficiency and reliability of DNA computing. The first algorithm introduces an evolutionary cycle to guide chemical reactions of DNA molecules. The second molecular algorithm extends the first one by adding another evolutionary loop for optimizing encodings of the problem instance. Just as genetic programming is a method for programming conventional computers by means of natural evolution, our approach, which might be called molecular programming, provides a method for programming biocomputers by means of artificial evolution. Simulations have been performed with the Hamiltonian path problem to verify the positive effect of the presented molecular algorithms on the reliability and efficiency of DNA computing.", notes = "GP-98", } @InProceedings{zhang:1998:GPads, author = "Byoung-Tak Zhang and Dong-Yeon Cho", title = "Genetic Programming with Active Data Selection", booktitle = "Simulated Evolution and Learning: Second Asia-Pacific Conference on Simulated Evolution and Learning, SEAL'98. Selected Papers", year = "1998", editor = "R. I. Bob McKay and X. Yao and Charles S. Newton and J.-H. Kim and T. Furuhashi", volume = "1585", series = "LNAI", pages = "146--153", address = "Australian Defence Force Academy, Canberra, Australia", publisher_address = "Heidelberg", month = "24-27 " # nov, publisher = "Springer-Verlag", note = "published in 1999", keywords = "genetic algorithms, genetic programming", ISSN = "0302-9743", URL = "http://bi.snu.ac.kr/Publications/Journals/International/LNAI1585.pdf", DOI = "doi:10.1007/3-540-48873-1_20", size = "8 pages", abstract = "Genetic programming evolves Lisp-like programs rather than fixed size linear strings. This representational power combined with generality makes genetic programming an interesting tool for automatic programming and machine learning. One weakness is the enormous time required for evolving complex programs. In this paper we present a method for accelerating evolution speed of genetic programming by active selection of fitness cases during the run. In contrast to conventional genetic programming in which all the given training data are used repeatedly, the presented method evolves programs using only a subset of given data chosen incrementally at each generation. This method is applied to the evolution of collective behaviors for multiple robotic agents. Experimental evidence supports that evolving programs on an incrementally selected subset of fitness cases can significantly reduce the fitness evaluation time without sacrificing generalization accuracy of the evolved programs.", notes = "SEAL'98 Published as springer-verlag LNAI 1585 SEAL98#058 A1 Artificial Intelligence Lab (SCAI) Dept. of Computer Engineering Seoul National University Seoul 151-742, Korea {btzhang, dycho}@scai.snu.ac.kr http://scai.snu.ac.kr/", } @InCollection{zhang:1999:aigp3, author = "Byoung-Tak Zhang and Dong-Yeon Cho", title = "Coevolutionary Fitness Switching: Learning Complex Collective Behaviors Using Genetic Programming", booktitle = "Advances in Genetic Programming 3", publisher = "MIT Press", year = "1999", editor = "Lee Spector and William B. Langdon and Una-May O'Reilly and Peter J. Angeline", chapter = "18", pages = "425--445", address = "Cambridge, MA, USA", month = jun, keywords = "genetic algorithms, genetic programming, co-evolution, learning", ISBN = "0-262-19423-6", URL = "http://bi.snu.ac.kr/Publications/Books/aigp3.ps", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/aigp3/ch18.pdf", URL = "http://citeseer.ist.psu.edu/454784.html", DOI = "doi:10.7551/mitpress/1110.003.0023", abstract = "Genetic programming provides a useful paradigm for developing multiagent systems in the domains where human programming alone is not sufficient to take into account all the details of possible situations. However, existing GP methods attempt to evolve collective behavior immediately from primitive actions. More realistic tasks require several emergent behaviors and a proper coordination of these is essential for success. We have recently proposed a framework, called fitness switching, to facilitate learning to coordinate composite emergent behaviors using genetic programming. Coevolutionary fitness switching described in this chapter extends our previous work by introducing the concept of coevolution for more effective implementation of fitness switching. Performance of the presented method is evaluated on the table transport problem and a simple version of simulated robot soccer problem. Simulation results show that coevolutionary fitness switching provides an effective mechanism for learning complex collective behaviors which may not be evolved by simple genetic programming.", notes = "AiGP3 See http://cognet.mit.edu", } @InProceedings{zhang:1999:fogp, author = "Byoung-Tak Zhang", title = "Bayesian Genetic Programming", booktitle = "Foundations of Genetic Programming", year = "1999", editor = "Thomas Haynes and William B. Langdon and Una-May O'Reilly and Riccardo Poli and Justinian Rosca", pages = "68--70", address = "Orlando, Florida, USA", month = "13 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/fogp/fogp99zhang.ps.gz", URL = "http://bi.snu.ac.kr/Publications/Conferences/International/FOGP99.ps", URL = "http://citeseer.ist.psu.edu/455364.html", size = "3 pages", abstract = "Introduction Genetic programming (GP) provides a powerful tool for learning models of some unknown process from specific fitness cases. Models are typically represented as tree-structured programs, and thus GP has a wide range of application domains. However, the general applicability of GP suffers from large amount of space and time required for generating intermediate solutions.", notes = "GECCO'99 WKSHOP, part of \cite{haynes:1999:fogp}", } @InProceedings{Zhang:2000:ECNN, author = "Byoung-Tak Zhang and Dong-Yeon Cho", title = "Evolving Neural Trees for Time Series Prediction Using {Bayesian} Evolutionary Algorithms", booktitle = "Proceedings of the First IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks", year = "2000", editor = "Xin Yao and David B. Fogel", pages = "17--23", month = "11-13 " # may, address = "San Antonio, TX, USA", keywords = "genetic algorithms, genetic programming, Bayesian evolutionary algorithms, evolutionary algorithms, evolutionary computation, neural trees, probabilistic model, small-step mutation-oriented variations, subtree crossover, subtree mutations, time series prediction, tree-structured neural networks, Bayes methods, evolutionary computation, forecasting theory, neural nets, time series, trees (mathematics)", DOI = "doi:10.1109/ECNN.2000.886214", ISBN = "0-7803-6572-0", abstract = "Bayesian evolutionary algorithms (BEAs) are a probabilistic model for evolutionary computation. Instead of simply generating new populations as in conventional evolutionary algorithms, the BEAs attempt to explicitly estimate the posterior distribution of the individuals from their prior probability and likelihood, and then sample offspring from the distribution. We apply the Bayesian evolutionary algorithms to evolving neural trees, i.e. tree-structured neural networks. Explicit formulae for specifying the distributions on the model space are provided in the context of neural trees. The effectiveness and robustness of the method is demonstrated on the time series prediction problem. We also study the effect of the population size and the amount of information exchanged by subtree crossover and subtree mutations. Experimental results show that small-step mutation-oriented variations are most effective when the population size is small, while large-step recombinative variations are more effective for large population sizes.", } @InProceedings{zhang:2000:B, author = "Byoung-Tak Zhang", title = "Bayesian evolutionary algorithms for learning and optimization", booktitle = "Optimization By Building and Using Probabilistic", year = "2000", pages = "220--223", address = "Las Vegas, Nevada, USA", month = "8 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://bi.snu.ac.kr/Publications/Conferences/International/OBUPM00.ps", size = "3 pages", notes = "GECCO-2000WKS Part of \cite{wu:2000:GECCOWKS}", } @Article{Zhang:2000:bmeGP, author = "Byoung-Tak Zhang", title = "Bayesian Methods for Efficient Genetic Programming", journal = "Genetic Programming and Evolvable Machines", year = "2000", volume = "1", number = "3", pages = "217--242", month = jul, keywords = "genetic algorithms, genetic programming, Bayesian genetic programming, probabilistic evolution, adaptive Occam's razor, incremental data inheritance, parsimony pressure, data subset selection", ISSN = "1389-2576", URL = "http://bi.snu.ac.kr/Publications/Journals/International/GPEM1-3.pdf", URL = "http://citeseer.ist.psu.edu/455254.html", URL = "https://rdcu.be/cT7J0", DOI = "doi:10.1023/A:1010010230007", size = "26 pages", abstract = "A Bayesian framework for genetic programming (GP) is presented. This is motivated by the observation that genetic programming iteratively searches populations of fitter programs and thus the information gained in the previous generation can be used in the next generation. The Bayesian GP makes use of Bayes theorem to estimate the posterior distribution of programs from their prior distribution and likelihood for the fitness data observed. Offspring programs are then generated by sampling from the posterior distribution by genetic variation operators. We present two GP algorithms derived from the Bayesian GP framework. One is the genetic programming with the adaptive Occam razor (AOR) designed to evolve parsimonious programs. The other is the genetic programming with incremental data inheritance (IDI) designed to accelerate evolution by active selection of fitness cases. A multiagent learning task is used to demonstrate the effectiveness of the presented methods. In a series of experiments, AOR reduced solution complexity by 20 percent and IDI doubled evolution speed, both without loss of solution accuracy.", notes = "Article ID: 264702", } @InProceedings{Zhang-JoungPPSN2000, author = "Byoung-Tak Zhang and Je-Gun Joung", title = "Building Optimal Committees of Genetic Programs", booktitle = "Parallel Problem Solving from Nature - PPSN VI 6th International Conference", editor = "Marc Schoenauer and Kalyanmoy Deb and G{\"u}nter Rudolph and Xin Yao and Evelyne Lutton and Juan Julian Merelo and Hans-Paul Schwefel", year = "2000", publisher = "Springer Verlag", address = "Paris, France", month = "16-20 " # sep, volume = "1917", series = "LNCS", pages = "231--240", isbn13 = "978-3-540-41056-0", DOI = "doi:10.1007/3-540-45356-3_23", keywords = "genetic algorithms, genetic programming", size = "10 pages", abstract = "Committee machines are known to improve the performance of individual learners. Evolutionary algorithms generate multiple individuals that can be combined to build committee machines. However, it is not easy to decide how big the committee should be and what members constitute the best committee. In this paper, we present a probabilistic search method for determining the size and members of the committees of individuals that are evolved by a standard GP engine. Applied to a suite of benchmark learning tasks, the GP committees achieved significant improvement in prediction accuracy.", } @Article{Zhang:2000:ALR, author = "Byoung-Tak Zhang and Dong-Yeon Cho", title = "Evolving Complex Group Behaviors Using Genetic Programming with Fitness Switching", journal = "Artificial Life and Robotics", year = "2000", volume = "4", number = "2", pages = "103--108", keywords = "genetic algorithms, genetic programming", URL = "http://bi.snu.ac.kr/Publications/Journals/International/AROB4-2.ps", URL = "http://citeseer.ist.psu.edu/454877.html", abstract = "Genetic programming provides a useful tool for emergent computation and artificial life. However, conventional genetic programming is not efficient enough to solve realistic multiagent tasks consisting of several emergent behaviours that need to be coordinated in proper sequence. In this paper, we describe a novel method, called fitness switching, for evolving composite cooperative behaviours of multiple robotic agents using genetic programming. The method maintains a pool of basis fitness functions which are switched from simpler ones to more complex ones. The performance is demonstrated and compared in the context of a table transport problem. Experimental results show that the fitness switching method is an effective mechanism for evolving collective behaviours which may not be solved by simple genetic programming.", } @Article{Zhang:2001:JSA, author = "Byoung-Tak Zhang and Dong-Yeon Cho", title = "System Identification Using Evolutionary Markov Chain Monte Carlo", journal = "Journal of Systems Architecture", year = "2001", volume = "47", number = "7", pages = "587--599", month = jul, keywords = "System identification, Markov chain Monte Carlo", abstract = "System identification involves determination of the functional structure of a target system that underlies the observed data. In this paper, we present a probabilistic evolutionary method that optimises system architectures for the identification of unknown target systems. The method is distinguished from existing evolutionary algorithms in that the individuals are generated from a probability distribution as in Markov chain Monte Carlo (MCMC). It is also distinguished from conventional MCMC methods in that the search is population-based as in standard evolutionary algorithms. The effectiveness of this hybrid of evolutionary computation and MCMC is tested on a practical problem, i.e., evolving neural net architectures for the identification of nonlinear dynamic systems. Experimental evidence supports that evolutionary MCMC (or eMCMC) exploits the efficiency of simple evolutionary algorithms while maintaining the robustness of MCMC methods and outperforms either approach used alone.", } @Article{Zhang:2002:ICAE, author = "Byoung-Tak Zhang", title = "A {Bayesian} Evolutionary Approach to the Design and Learning of Heterogeneous Neural Trees", journal = "Integrated Computer-Aided Engineering", year = "2002", volume = "9", number = "1", pages = "73--86", month = jan, keywords = "genetic algorithms, genetic programming", broken = "http://iospress.metapress.com/openurl.asp?genre=article&issn=1069-2509&volume=9&issue=1&spage=73", URL = "https://bi.snu.ac.kr/Publications/Journals/International/ICAE9_1.pdf", DOI = "doi:10.3233/ICA-2002-9105", size = "14 pages", abstract = "Evolutionary algorithms have been successfully applied to the design and training of neural networks, such as in optimisation of network architecture, learning connection weights, and selecting training data. While most of existing evolutionary methods are focused on one of these aspects, we present in this paper an integrated approach that employs evolutionary mechanisms for the optimisation of these components simultaneously. This approach is especially effective when evolving irregular, not-strictly-layered networks of heterogeneous neurons with variable receptive fields. The core of our method is the neural tree representation scheme combined with the Bayesian evolutionary learning framework. The generality and flexibility of neural trees make it easy to express and modify complex neural architectures by means of standard crossover and mutation operators. The Bayesian evolutionary framework provides a theoretical foundation for finding compact neural networks using a small data set by principled exploitation of background knowledge available in the problem domain. Performance of the presented method is demonstrated on a suite of benchmark problems and compared with those of related methods.", } @InProceedings{Zhang:2005:DNA, author = "Byoung-Tak Zhang and Ha-Young Jang", title = "Molecular Learning of {wDNF} Formulae", booktitle = "11th International Workshop on DNA Computing, DNA11", year = "2006", editor = "Alessandra Carbone and Niles A. Pierce", volume = "3892", series = "Lecture Notes in Computer Science", pages = "427--437", address = "London, Ontario, Canada", month = "6-9 " # jun, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Disjunctive Normal Form, Hybridization Reaction, Query Pattern, Diagnosis Problem", isbn13 = "978-3-540-34161-1", URL = "https://rdcu.be/dh4fR", DOI = "doi:10.1007/11753681_34", size = "11 pages", abstract = "We introduce a class of generalized DNF formulae called wDNF or weighted disjunctive normal form, and present a molecular algorithm that learns a wDNF formula from training examples. Realized in DNA molecules, the wDNF machines have a natural probabilistic semantics, allowing for their application beyond the pure Boolean logical structure of the standard DNF to real-life problems with uncertainty. The potential of the molecular wDNF machines is evaluated on real-life genomics data in simulation. Our empirical results suggest the possibility of building error-resilient molecular computers that are able to learn from data, potentially from wet DNA data.", } @InProceedings{1068301, author = "Byoung-Tak Zhang and Ha-Young Jang", title = "Molecular programming: evolving genetic programs in a test tube", booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation", year = "2005", editor = "Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and Edwin D. {de Jong} and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler", volume = "2", ISBN = "1-59593-010-8", pages = "1761--1768", address = "Washington DC, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2005/docs/p1761.pdf", DOI = "doi:10.1145/1068009.1068301", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "25-29 " # jun, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, design, DNA computing, genetic programs, in vitro evolution, molecular evolutionary computation (MEC), molecular programming (MP)", abstract = "We present a molecular computing algorithm for evolving DNA-encoded genetic programs in a test tube. The use of synthetic DNA molecules combined with biochemical techniques for variation and selection allows for various possibilities for building novel evolvable hardware. Also, the possibility of maintaining a huge number of individuals and their massively parallel manipulation allows us to make robust decisions by the {"}molecular{"} genetic programs evolved within a single population. We evaluate the potentials of this {"}molecular programming{"} approach by solving a medical diagnosis problem on a simulated DNA computer. Here the individual genetic program represents a decision list of variable length and the whole population takes part in making probabilistic decisions. Tested on a real-life leukemia diagnosis data, the evolved molecular genetic programs showed a comparable performance to decision trees. The molecular evolutionary algorithm can be adapted to solve problems in biotechnology and nano-technology where the physico-chemical evolution of target molecules is of pressing importance.", notes = "GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM Order Number 910052", } @InProceedings{Zhang:2016:ICISCE, author = "Caikun Zhang and Chuan Shi and Wenfu Liu and Xuan Zhang", booktitle = "2016 3rd International Conference on Information Science and Control Engineering (ICISCE)", title = "Aircraft Trajectory Prediction Based on Genetic Programming", year = "2016", pages = "158--162", abstract = "Because the traditional aircraft trajectory prediction methods are difficult to solve the complex trajectory fitting function and the accuracy of predicted trajectory is not high enough, aircraft trajectory prediction based on genetic programming is proposed in this paper. First of all, aircraft flight trajectory data is normalised processing. And then the reasonable set of function and fitting evaluation standard are established. Finally the flight trajectory is predicted by using genetic programming algorithm. The simulation results show that the prediction accuracy of this algorithm is fairly high, and fitting trajectory using this algorithm accords with the actual flight trajectory.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICISCE.2016.44", month = jul, notes = "Also known as \cite{7726141}", } @Article{Zhang:2025:future, author = "Changzhen Zhang and Jun Yang", title = "Multi-Tree Genetic Programming with Elite Recombination for dynamic task scheduling of satellite edge computing", journal = "Future Generation Computer Systems", year = "2025", volume = "166", pages = "107700", keywords = "genetic algorithms, genetic programming, Satellite edge computing, Dynamic task scheduling, Task success rate, Elite recombination", ISSN = "0167-739X", URL = "https://www.sciencedirect.com/science/article/pii/S0167739X24006642", DOI = "doi:10.1016/j.future.2024.107700", abstract = "Satellite Edge Computing (SEC) can provide task computation services to terrestrial users, particularly in areas lacking terrestrial network coverage. With the increasing frequency of computational demands from Internet of Things (IoT) devices and the limited and dynamic nature of computational resources in Low Earth Orbit (LEO) satellites, making effective real-time scheduling decisions in dynamic environments to ensure high task success rate is a critical challenge. In this work, we investigate the dynamic task scheduling of SEC based on Genetic Programming Hyper-Heuristic (GPHH). Firstly, a new problem model for the dynamic task scheduling of SEC is proposed with the objective of improving the task success rate, where the real-world situations (limited and dynamic nature of satellite resources, randomness and difference of tasks) are taken into account. Secondly, to make efficient real-time routing decision and queuing decision during the dynamic scheduling process, a novel scheduling heuristic with routing rule and queuing rule is developed, considering dynamic features of the SEC system such as real-time load, energy consumption, and remaining deadlines. Thirdly, to automatically learn both routing rule and queuing rule, and improve the performance of the algorithm, a Multi-Tree Genetic Programming with Elite Recombination (MTGPER) is proposed, which exploits the recombination of the excellent rules to obtain the better scheduling heuristics. The experimental results show that the proposed MTGPER significantly outperforms existing state-of-the-art methods. The scheduling heuristic evolved by MTGPER has quite good interpretability, which facilitates scheduling management in engineering practice", } @Article{Zhang:ieeeIOT, author = "Changzhen Zhang and Jun Yang and Ning Wang", title = "Multi-Tree Genetic Programming with Rule Reconstruction for Dynamic Task Scheduling in Integrated Cloud-Edge Satellite-Terrestrial Networks", journal = "IEEE Internet of Things Journal", keywords = "genetic algorithms, genetic programming, Dynamic scheduling, Processor scheduling, Scheduling, Cloud computing, Heuristic algorithms, Servers, Job shop scheduling, Real-time systems, Satellites, Routing, Satellite-terrestrial networks, dynamic task scheduling, rule reconstruction, scheduling heuristic", ISSN = "2327-4662", DOI = "doi:10.1109/JIOT.2025.3546867", abstract = "Satellite-Terrestrial Networks (STNs) are a promising paradigm for providing internet services for users globally. Since the dynamics of service resources and the uncertainty of computational requests, how the service resources in STNs can be efficiently exploited to execute differentiated computational tasks is an essential challenge. In this work, we investigate the dynamic task scheduling in the integrated cloud-edge STNs. Firstly, we propose a cloud-edge collaborative computing framework in STNs, where the computational tasks of users can be processed collaboratively by satellite edge servers, terrestrial edge servers, and cloud servers. Based on this framework, a dynamic task scheduling problem is formulated with the objective of maximizing the task success rate. Secondly, to make effective real-time decisions at decision points in the dynamic scheduling process, we develop a scheduling heuristic with the routing rule and queuing rule, which incorporates dynamic features related to servers, computational tasks, and network environments. Thirdly, to automatically learn the scheduling heuristic, we propose a Multi-Tree Genetic Programming with Rule Reconstruction (MTGPRR), which introduces a selective reconstruction operator. This operator increases the chance of matching good rules with other rules by recombining common individuals and elites. Experimental results demonstrate that the proposed MTGPRR performs significantly better than the state-of-the-art methods in improving the task success rate. Moreover, the evolved scheduling heuristic has good interpretability, which is important for practical applications.", notes = "Also known as \cite{10907950} School of Reliability and Systems Engineering, Beihang University, Beijing, China", } @InProceedings{conf/icaic/ZhangZJ11, author = "Dakun Zhang and Chang Zhang and Guiyuan Jiang", title = "Computer-Aided Composition Method of Children's Song Based on Grammatical Evolution", booktitle = "Proceedings of the International Conference on Applied Informatics and Communication (ICAIC 2011) Part {I}", year = "2011", editor = "Dehuai Zeng", volume = "224", series = "Communications in Computer and Information Science", pages = "93--99", address = "Xi'an, China", month = aug # " 20-21", publisher = "Springer", keywords = "genetic algorithms, genetic programming, grammatical evolution, computer-aided music creation, children's song composition", isbn13 = "978-3-642-23214-5", DOI = "doi:10.1007/978-3-642-23214-5_13", size = "7 pages", abstract = "Computer-aided music creation has become a key research direction. Grammatical Evolution (abbreviation GE) as a new evolutionary computation technique based on formal grammar is applied for composing children's songs. A children's song composition method based on GE is proposed in this paper and proves to be feasible and effective with experiments. This method provides a viable and meaningful idea for computer-aided music creation.", affiliation = "School of Computer Science & Software Engineering Institute, Tianjin Polytechnic University, 300387 Tianjin, China", bibdate = "2011-09-28", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icaic/icaic2011-1.html#ZhangZJ11", } @InProceedings{Zhang:2008:ICNC, author = "Defu Zhang and Mhand Hifi and Qingshan Chen and Weiguo Ye", title = "A Hybrid Credit Scoring Model Based on Genetic Programming and Support Vector Machines", booktitle = "Fourth International Conference on Natural Computation, ICNC '08", year = "2008", month = oct, volume = "7", pages = "8--12", keywords = "genetic algorithms, genetic programming, UCI database, back-propagation neural network, credit industry, decision tree classifiers, hybrid credit scoring model, logistic regression, support vector machines, financial data processing, support vector machines", DOI = "doi:10.1109/ICNC.2008.205", abstract = "Credit scoring has obtained more and more attention as the credit industry can benefit from reducing potential risks. Hence, many different useful techniques, known as the credit scoring models, have been developed by the banks and researchers in order to solve the problems involved during the evaluation process. In this paper, a hybrid credit scoring model (HCSM) is developed to deal with the credit scoring problem by incorporating the advantages of genetic programming and support vector machines. Two credit data sets in UCI database are selected as the experimental data to demonstrate the classification accuracy of the HCSM. Compared with support vector machines, genetic programming, decision tree classifiers, logistic regression, and back-propagation neural network, HCSM can obtain better classification accuracy.", notes = "Also known as \cite{4667935}", } @InProceedings{Zhang:2019:CAC, author = "Dingyuan Zhang and Yong Wang", booktitle = "2019 Chinese Automation Congress (CAC)", title = "The Fault Diagnosis of Rolling Bearing Based on {WPD} and {TPOT}", year = "2019", pages = "1029--1034", month = nov, keywords = "genetic algorithms, genetic programming, TPOT", ISSN = "2688-0938", DOI = "doi:10.1109/CAC48633.2019.8996312", abstract = "In this paper, in order to tackle the current problems of rolling bearing fault diagnosis, a new kind of bearing fault diagnosis method based on Wave Packet Decomposition (WPD) and Tree-based Pipeline Optimization Tool (TPOT) is proposed. Firstly, the feature vectors of bearing fault are extracted by using the wavelet packet decomposition. Then, the genetic programming that is based on tree structures is employed to generate the optimal machine learning pipeline. The specific structure and parameters are automatically evolved to obtain the best classification performance. Finally, the method is tested on practical bearing experiments. The experimental results have shown the advantages and effectiveness of the proposed method.", notes = "Also known as \cite{8996312}", } @InProceedings{Zhang:2009:ICNC, title = "Discovery of Mineralization Predication Classification Rules by Using Gene Expression Programming Based on PCA", author = "Dongmei Zhang and Yue Huang and Jing Zhi", booktitle = "Fifth International Conference on Natural Computation, 2009. ICNC '09", year = "2009", pages = "540--543", editor = "Haiying Wang and Kay Soon Low and Kexin Wei and Junqing Sun", month = "14-16 " # aug, address = "Tianjian, China", publisher = "IEEE Computer Society", isbn13 = "978-0-7695-3736-8", bibdate = "2010-01-22", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icnc/icnc2009-4.html#ZhangHZ09", keywords = "genetic algorithms, genetic programming, gene expression programming", DOI = "doi:10.1109/ICNC.2009.367", abstract = "Classification is one of the fundamental tasks in geology field. In this paper, we propose an evolutionary approach for discovering classification rules of mineralization predication from distinct combinations of geochemistry elements by using gene expression programming (GEP). The innovative part of the paper presents integrated/hybrid model-combine GEP evolution modeling with Principal Component Analysis (PCA), which reduce multidimensional data sets. Mineral deposit with tin and copper in Gejiu is chosen as the research area. MAPGIS and MORPAS are used to extract the value of ore-controlled factors by mapping geologic maps into grid cell. Case study illustrates the proposed GEP approach Based on PCA is more efficient and accurate in a large searching space, compared with Decision Tree (C4.5) and Bayesian Networks.", notes = "Also known as \cite{5367093}", } @Article{DBLP:journals/ijait/ZhangK03, author = "Du Zhang and Michael D. Kramer", title = "GAPS: A Genetic Programming System", journal = "International Journal on Artificial Intelligence Tools", volume = "12", number = "2", year = "2003", pages = "187--206", month = jun # " 2003", keywords = "genetic algorithms, genetic programming, Evolutionary computation, genetic operations, fitness evaluation, GP introns", DOI = "doi:10.1142/S0218213003001198", abstract = "One of the major approaches in the field of evolutionary computation is genetic programming. Genetic programming tackles the issue of how to automatically create a computer program for a given problem from some initial problem statement. The goal is accomplished by genetically breeding a population of computer programs in terms of genetic operations. In this paper, we describe a genetic programming system called GAPS. GAPS has the following features: (1) It implements the standard generational algorithm for genetic programming with some refinement on controlling introns growth during evolution process and improved termination criteria. (2) It includes an extensible language tailored to the needs of genetic programming. And (3) It is a complete, standalone system that allows for genetic programming tasks to be carried out without requiring other tools such as compilers. Results with GAPS have been satisfactory.", notes = "An earlier version of this paper appears in the Proceedings of IEEE COMPSAC 2000. This paper is a substantially revised and extended version. \cite{DBLP:conf/compsac/KramerZ00}", } @InProceedings{zhang:2018:AJCAI, author = "Fangfang Zhang and Yi Mei and Mengjie Zhang", title = "{Surrogate-Assisted} Genetic Programming for Dynamic Flexible Job Shop Scheduling", booktitle = "Australasian Joint Conference on Artificial Intelligence", year = "2018", editor = "Tanja Mitrovic and Bing Xue and Xiaodong Li", volume = "11320", series = "LNCS", pages = "766--772", address = "Wellington, New Zealand", month = dec # " 11-14", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Surrogate, Dynamic flexible job shop scheduling", isbn13 = "978-3-030-03990-5", URL = "http://link.springer.com/chapter/10.1007/978-3-030-03991-2_69", DOI = "doi:10.1007/978-3-030-03991-2_69", abstract = "Genetic programming (GP) has been widely used for automatically evolving priority rules for solving job shop scheduling problems. However, one of the main drawbacks of GP is the intensive computation time. This paper aims at investigating appropriate surrogates for GP to reduce its computation time without sacrificing its performance in solving dynamic flexible job shop scheduling (DFJSS) problems. Firstly, adaptive surrogate strategy with dynamic fidelities of simulation models are proposed. Secondly, we come up with generation-range-based surrogate strategy in which homogeneous (heterogeneous) surrogates are used in same (different) ranges of generations. The results show that these two surrogate strategies with GP are efficient. The computation time are reduced by 22.9percent to 27.2percent and 32.6percent to 36.0percent, respectively. The test performance shows that the proposed approaches can obtain rules with at least the similar quality to the rules obtained by the GP approach without using surrogates. Moreover, GP with adaptive surrogates achieves significantly better performance in one out of six scenarios. This paper confirms the potential of using surrogates to solve DFJSS problems.", } @InProceedings{Zhang:2018:AJCAImt, author = "Fangfang Zhang and Yi Mei and Mengjie Zhang", title = "Genetic Programming with Multi-tree Representation for Dynamic Flexible Job Shop Scheduling", booktitle = "Australasian Joint Conference on Artificial Intelligence", year = "2018", editor = "Tanja Mitrovic and Bing Xue and Xiaodong Li", volume = "11320", series = "LNCS", pages = "472--484", address = "Wellington, New Zealand", month = dec # " 11-14", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Multi-tree representation Flexible job shop scheduling Dynamic changes", isbn13 = "978-3-030-03990-5", URL = "http://link.springer.com/chapter/10.1007/978-3-030-03991-2_43", DOI = "doi:10.1007/978-3-030-03991-2_43", abstract = "Flexible job shop scheduling (FJSS) can be regarded as an optimization problem in production scheduling that captures practical and challenging issues in real-world scheduling tasks such as order picking in manufacturing and cloud computing. Given a set of machines and jobs, FJSS aims to determine which machine to process a particular job (by routing rule) and which job will be chosen to process next by a particular machine (by sequencing rule). In addition, dynamic changes are unavoidable in the real-world applications. These features lead to difficulties in real-time scheduling. Genetic programming (GP) is well-known for the flexibility of its representation and tree-based GP is widely and typically used to evolve priority functions for different decisions. However, a key issue for the tree-based representation is how it can capture both the routing and sequencing rules simultaneously. To address this issue, we proposed to use multi-tree GP (MTGP) to evolve both routing and sequencing rules together. In order to enhance the performance of MTGP algorithm, a novel tree swapping crossover operator is proposed and embedded into MTGP. The results suggest that the multi-tree representation can achieve much better performance with smaller rules and less training time than cooperative co-evolution for GP in solving dynamic FJSS problems. Furthermore, the proposed tree swapping crossover operator can greatly improve the performance of MTGP.", notes = "conf/ausai/ZhangMZ18", } @InProceedings{Zhang:2019:evocop, author = "Fangfang Zhang and Yi Mei and Mengjie Zhang", title = "A New Representation in Genetic Programming for Evolving Dispatching Rules for Dynamic Flexible Job Shop Scheduling", booktitle = "The 19th European Conference on Evolutionary Computation in Combinatorial Optimisation, EvoCOP 2019", year = "2019", editor = "A. Liefooghe and L. Paquete", series = "LNCS", volume = "11452", publisher = "Springer", pages = "33--49", organisation = "Species", email = "fangfang.zhang@ecs.vuw.ac.nz", keywords = "genetic algorithms, genetic programming, Representation, Dispatching rules, Dynamic flexible job shop scheduling", isbn13 = "978-3-030-16711-0", DOI = "doi:10.1007/978-3-030-16711-0_3", abstract = "Dynamic flexible job shop scheduling (DFJSS) is a very important problem with a wide range of real-world applications such as cloud computing and manufacturing. In DFJSS, it is critical to make two kinds of real-time decisions (i.e. the routing decision that assigns machine to each job and the sequencing decision that prioritises the jobs in a machine queue) effectively in the dynamic environment with unpredicted events such as new job arrivals and machine breakdowns.Dispatching rule is an ideal technique for this purpose. In DFJSS, one has to design a routing rule and a sequencing rule for making the two kinds of decisions. Manually designing these rules is time consuming and requires human expertise which is not always available. Genetic program-ming (GP) has been applied to automatically evolve more effective rules than the manually designed ones. In GP for DFJSS, different features in the terminal set have different contributions to the decision making.However, the current GP approaches cannot perfectly find proper combinations between the features in accordance with their contributions.In this paper, we propose a new representation for GP that better con-siders the different contributions of different features and combines them in a sophisticated way, thus to evolve more effective rules. The results show that the proposed GP approach can achieve significantly better performance than the baseline GP in a range of job shop scenarios.", notes = "School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand. EvoCOP2019 held in conjunction with EuroGP'2019 EvoMusArt2019 and EvoApplications2019 http://www.evostar.org/2019/cfp_evocop.php", } @InProceedings{Zhang:2019:CEC, author = "Fangfang Zhang and Yi Mei and Mengjie Zhang", title = "Can Stochastic Dispatching Rules Evolved by Genetic Programming Hyper-heuristics Help in Dynamic Flexible Job Shop Scheduling?", booktitle = "2019 IEEE Congress on Evolutionary Computation, CEC 2019", year = "2019", editor = "Carlos A. Coello Coello", pages = "41--48", address = "Wellington, New Zealand", month = "10-13 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-7281-2152-6", DOI = "doi:10.1109/CEC.2019.8790030", size = "8 pages", abstract = "Dynamic flexible job shop scheduling (DFJSS) considers making machine assignment and operation sequencing decisions simultaneously with dynamic events. Genetic programming hyper-heuristics (GPHH) have been successfully applied to evolving dispatching rules for DFJSS. However, existing studies mainly focus on evolving deterministic dispatching rules, which calculate priority values for the candidate machines or jobs and select the one with the best priority. Inspired by the effectiveness of training stochastic policies in reinforcement learning, and the fact that a dispatching rule in DFJSS is similar to a policy in reinforcement learning, we investigate the effectiveness of evolving stochastic dispatching rules for DFJSS in this paper. Instead of using the winner-takes-all mechanism, we define a range of probability distributions based on the priority values of the candidates to be used by the stochastic dispatching rules. These distributions introduce varying degrees of randomness.", notes = "also known as \cite{8790030}, IEEE Catalog Number: CFP19ICE-ART", } @InProceedings{Zhang:2019:CEC2, author = "Fangfang Zhang and Yi Mei and Mengjie Zhang", title = "Evolving Dispatching Rules for Multi-objective Dynamic Flexible Job Shop Scheduling via Genetic Programming Hyper-heuristics", booktitle = "2019 IEEE Congress on Evolutionary Computation, CEC 2019", year = "2019", editor = "Carlos A. Coello Coello", pages = "1366--1373", address = "Wellington, New Zealand", month = "10-13 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, dynamic flexible job shop scheduling", isbn13 = "978-1-7281-2152-6", DOI = "DOI:10.1109/CEC.2019.8790112", abstract = "Dynamic flexible job shop scheduling (DFJSS) is one of the well-known combinational optimisation problems, which aims to handle machine assignment (routing) and operation sequencing (sequencing) simultaneously in dynamic environment. Genetic programming, as a hyper-heuristic method, has been successfully applied to evolve the routing and sequencing rules for DFJSS, and achieved promising results. In the actual production process, it is necessary to get a balance between several objectives instead of simply focusing only one objective. No existing study considered solving multi-objective DFJSS using genetic programming. In order to capture multi-objective nature of job shop scheduling and provide different trade-offs between conflicting objectives, in this paper, two well-known multi-objective optimisation frameworks, i.e. non-dominated sorting genetic algorithm II (NSGA-II) and strength Pareto evolutionary algorithm 2 (SPEA2), are incorporated into the genetic programming hyper-heuristic method to solve the multi-objective DFJSS problem. Experimental results show that the strategy of NSGA-II incorporated into genetic programming hyper-heuristic performs better than SPEA2-based GPHH, as well as the weighted sum approaches, in the perspective of both training performance and generalisation.", notes = "also known as \cite{8790112}, INSPEC Accession Number: 18888605 IEEE Catalog Number: CFP19ICE-ART", } @InProceedings{Zhang:2019:GECCO, author = "Fangfang Zhang and Yi Mei and Mengjie Zhang", title = "A Two-stage Genetic Programming Hyper-heuristic Approach with Feature Selection for Dynamic Flexible Job Shop Scheduling", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference", year = "2019", editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and Anne Auger and Petr Posik and Leslie {Peprez Caceres} and Andrew M. Sutton and Nadarajen Veerapen and Christine Solnon and Andries Engelbrecht and Stephane Doncieux and Sebastian Risi and Penousal Machado and Vanessa Volz and Christian Blum and Francisco Chicano and Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and Jonathan Fieldsend and Jose Antonio Lozano and Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and Robin Purshouse and Thomas Baeck and Justyna Petke and Giuliano Antoniol and Johannes Lengler and Per Kristian Lehre", isbn13 = "978-1-4503-6111-8", pages = "347--355", address = "Prague, Czech Republic", DOI = "doi:10.1145/3321707.3321790", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, Feature Selection, Dynamic Flexible Job Shop Scheduling, Genetic Programming Hyper-heuristics", size = "9 pages", abstract = "Dynamic flexible job shop scheduling (DFJSS) is an important and a challenging combinatorial optimisation problem. Genetic programming hyper-heuristic (GPHH) has been widely used for automatically evolving the routing and sequencing rules for DFJSS. The terminal set is the key to the success of GPHH. There are a wide range of features in DFJSS that reflect different characteristics of the job shop state. However, the importance of a feature can vary from one scenario to another, and some features may be redundant or irrelevant under the considered scenario. Feature selection is a promising strategy to remove the unimportant features and reduce the search space of GPHH. However, no work has considered feature selection in GPHH for DFJSS so far. In addition, it is necessary to do feature selection for the two terminal sets simultaneously. In this paper, we propose a new two-stage GPHH approach with feature selection for evolving routing and sequencing rules for DFJSS. The experimental studies show that the best solutions achieved by the proposed approach are better than that of the baseline method in most scenarios. Furthermore, the rules evolved by the proposed approach involve a smaller number of unique features, which are easier to interpret.", notes = "Also known as \cite{3321790} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InProceedings{Zhang:2020:EuroGP, author = "Fangfang Zhang and Yi Mei and Su Nguyen and Mengjie Zhang", title = "Guided Subtree Selection for Genetic Operators in Genetic Programming for Dynamic Flexible Job Shop Scheduling", booktitle = "EuroGP 2020: Proceedings of the 23rd European Conference on Genetic Programming", year = "2020", month = "15-17 " # apr, editor = "Ting Hu and Nuno Lourenco and Eric Medvet", series = "LNCS", volume = "12101", publisher = "Springer Verlag", address = "Seville, Spain", pages = "262--278", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Guided subtree selection, JSS, Scheduling heuristic, Dynamic flexible job shop scheduling", isbn13 = "978-3-030-44093-0", video_url = "https://www.youtube.com/watch?v=qf7hzHmxuAE", DOI = "doi:10.1007/978-3-030-44094-7_17", abstract = "Dynamic flexible job shop scheduling (DFJSS) has been widely studied in both academia and industry. Both machine assignment and operation sequencing decisions need to be made simultaneously as an operation can be processed by a set of machines in DFJSS. Using scheduling heuristics to solve the DFJSS problems becomes an effective way due to its efficiency and simplicity. Genetic programming (GP) has been successfully applied to evolve scheduling heuristics for job shop scheduling automatically. However, the subtrees of the selected parents are randomly chosen in traditional GP for crossover and mutation, which may not be sufficiently effective, especially in a huge search space. This paper proposes new strategies to guide the subtree selection rather than picking them randomly. To be specific, the occurrences of features are used to measure the importance of each subtree of the selected parents. The probability to select a subtree is based on its importance and the type of genetic operators. This paper examines the proposed algorithm on six DFJSS scenarios. The results show that the proposed GP algorithm with the guided subtree selection for crossover can converge faster and achieve significantly better performance than its counterpart in half of the scenarios while no worse in all other scenarios without increasing the computational time.", notes = "http://www.evostar.org/2020/cfp_eurogp.php Part of \cite{Hu:2020:GP} EuroGP'2020 held in conjunction with EvoCOP2020, EvoMusArt2020 and EvoApplications2020", } @InProceedings{zhang2020genetic, author = "Fangfang Zhang and Yi Mei and Su Nguyen and Mengjie Zhang", title = "Genetic Programming with Adaptive Search Based on the Frequency of Features for Dynamic Flexible Job Shop Scheduling", booktitle = "European Conference on Evolutionary Computation in Combinatorial Optimization (EvoCOP 2020)", year = "2020", month = "15-17 " # apr, editor = "L. Paquete and C. Zarges", series = "LNCS", volume = "12102", publisher = "Springer Verlag", address = "Seville, Spain", pages = "214--230", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Adaptive search, Scheduling heuristic, Dynamic flexible job shop scheduling, JSS", isbn13 = "978-3-030-43679-7", URL = "https://meiyi1986.github.io/publication/zhang-2020-genetic/", URL = "https://meiyi1986.github.io/publication/zhang-2020-genetic/zhang-2020-genetic.pdf", video_url = "https://www.youtube.com/watch?v=0lptijY0QH8", DOI = "doi:10.1007/978-3-030-43680-3_14", abstract = "Dynamic flexible job shop scheduling (DFJSS) is a very valuable practical application problem that can be applied in many fields such as cloud computing and manufacturing. In DFJSS, machine assignment and operation sequencing decisions need to be made simultaneously in dynamic environments with unpredicted events such as new job arrivals. Scheduling heuristic is an ideal candidate for solving the DFJSS problem due to its efficiency and simplicity. Genetic programming (GP) has been successfully applied to evolve scheduling heuristics for job shop scheduling automatically. However, GP has a huge search space, and the traditional search algorithms do not use effectively the information obtained from the evolutionary process. This paper proposes a new method to make better use of the information during the evolutionary process of GP to further enhance the ability of GP. To be specific, this paper proposes two adaptive search strategies based on the frequency of features in promising individuals to guide GP to evolve effective rules. This paper examines the proposed algorithm on six different DFJSS scenarios. The results show that the proposed GP with adaptive search can converge faster and achieve significantly better performance than the GP without adaptive search in most scenarios while no worse in all other scenarios without increasing the computational cost.", notes = "School of Engineering and Computer Science, Victoria University of Wellington, New Zealand http://www.evostar.org/2020/ EvoCOP2020 held in conjunction with EuroGP'2020, EvoMusArt2020 and EvoApplications2020", } @InProceedings{Zhang:2020:GECCOcomp, author = "Fangfang Zhang and Yi Mei and Su Nguyen and Mengjie Zhang", title = "A Preliminary Approach to Evolutionary Multitasking for Dynamic Flexible Job Shop Scheduling via Genetic Programming", year = "2020", editor = "Richard Allmendinger and Hugo Terashima Marin and Efren Mezura Montes and Thomas Bartz-Beielstein and Bogdan Filipic and Ke Tang and David Howard and Emma Hart and Gusz Eiben and Tome Eftimov and William {La Cava} and Boris Naujoks and Pietro Oliveto and Vanessa Volz and Thomas Weise and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Rui Wang and Ran Cheng and Guohua Wu and Miqing Li and Hisao Ishibuchi and Jonathan Fieldsend and Ozgur Akman and Khulood Alyahya and Juergen Branke and John R. Woodward and Daniel R. Tauritz and Marco Baioletti and Josu Ceberio Uribe and John McCall and Alfredo Milani and Stefan Wagner and Michael Affenzeller and Bradley Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and Nayat Sanchez-Pi and Luis Marti and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and Matthew Johns and Nick Ross and Ed Keedwell and Herman Mahmoud and David Walker and Anthony Stein and Masaya Nakata and David Paetzel and Neil Vaughan and Stephen Smith and Stefano Cagnoni and Robert M. Patton and Ivanoe {De Falco} and Antonio {Della Cioppa} and Umberto Scafuri and Ernesto Tarantino and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Erik Hemberg and Riyad Alshammari and Tokunbo Makanju and Fuijimino-shi and Ivan Zelinka and Swagatam Das and Ponnuthurai Nagaratnam and Roman Senkerik", isbn13 = "9781450371278", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377929.3389934", DOI = "doi:10.1145/3377929.3389934", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion", pages = "107--108", size = "2 pages", keywords = "genetic algorithms, genetic programming, knowledge transfer, dynamic flexible job shop scheduling, genetic programming hyper-heuristics, evolutionary multitasking", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", abstract = "Genetic programming, as a hyper-heuristic approach, has been successfully used to evolve scheduling heuristics for job shop scheduling. However, the environments of job shops vary in configurations, and the scheduling heuristic for each job shop is normally trained independently, which leads to low efficiency for solving multiple job shop scheduling problems. This paper introduces the idea of multitasking to genetic programming to improve the efficiency of solving multiple dynamic flexible job shop scheduling problems with scheduling heuristics. It is realised by the proposed evolutionary framework and knowledge transfer mechanism for genetic programming to train scheduling heuristics for different tasks simultaneously. The results show that the proposed algorithm can dramatically reduce the training time for solving multiple dynamic flexible job shop tasks.", notes = "Also known as \cite{10.1145/3377929.3389934} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @Article{Fangfang_Zhang:ieeeTCyber, author = "Fangfang Zhang and Yi Mei and Su Nguyen and Mengjie Zhang", title = "Evolving Scheduling Heuristics via Genetic Programming With Feature Selection in Dynamic Flexible Job-Shop Scheduling", journal = "IEEE Transactions on Cybernetics", year = "2021", volume = "51", number = "4", pages = "1797--1811", month = apr, keywords = "genetic algorithms, genetic programming, Dynamic flexible job-shop scheduling, feature selection, hyper-heuristics, interpretability", ISSN = "2168-2267", URL = "https://ieeexplore.ieee.org/abstract/document/9234005", DOI = "doi:10.1109/TCYB.2020.3024849", size = "15 pages", abstract = "Dynamic flexible job-shop scheduling (DFJSS) is a challenging combinational optimization problem that takes the dynamic environment into account. Genetic programming hyperheuristics (GPHH) have been widely used to evolve scheduling heuristics for job-shop scheduling. A proper selection of the terminal set is a critical factor for the success of GPHH. However, there is a wide range of features that can capture different characteristics of the job-shop state. Moreover, the importance of a feature is unclear from one scenario to another. The irrelevant and redundant features may lead to performance limitations. Feature selection is an important task to select relevant and complementary features. However, little work has considered feature selection in GPHH for DFJSS. In this article, a novel two-stage GPHH framework with feature selection is designed to evolve scheduling heuristics only with the selected features for DFJSS automatically. Meanwhile, individual adaptation strategies are proposed to use the information of both the selected features and the investigated individuals during the feature selection process. The results show that the proposed algorithm can successfully achieve more interpretable scheduling heuristics with fewer unique features and smaller sizes. In addition, the proposed algorithm can reach comparable scheduling heuristic quality with much shorter training time.", notes = "Evolutionary Computation Research Group, School of Engineering and Computer Science, Victoria University of Wellington, Wellington 6140, New Zealand Also known as \cite{9234005}", } @Article{Fangfang_Zhang:ieeeTCyber2, author = "Fangfang Zhang and Yi Mei and Su Nguyen and Mengjie Zhang", title = "Collaborative Multifidelity-Based Surrogate Models for Genetic Programming in Dynamic Flexible Job Shop Scheduling", journal = "IEEE Transactions on Cybernetics", year = "2022", volume = "52", number = "8", pages = "8142--8156", keywords = "genetic algorithms, genetic programming, Collaboration, dynamic flexible job shop scheduling, knowledge transfer, multi-fidelity-based surrogate models", ISSN = "2168-2267", DOI = "doi:10.1109/TCYB.2021.3050141", abstract = "Dynamic flexible job shop scheduling (JSS) has received widespread attention from academia and industry due to its practical application value. It requires complex routing and sequencing decisions under unpredicted dynamic events. Genetic programming (GP), as a hyperheuristic approach, has been successfully applied to evolve scheduling heuristics for JSS due to its flexible representation. However, the simulation-based evaluation is computationally expensive since there are many calculations based on individuals for making decisions in the simulation. To improve training efficiency, this article proposes a novel multifidelity-based surrogate-assisted GP. Specifically, multifidelity-based surrogate models are first designed by simplifying the problem expected to be solved. In addition, this article proposes an effective collaboration mechanism with knowledge transfer for using the advantages of multifidelity-based surrogate models to solve the desired problems. This article examines the proposed algorithm in six different scenarios. The results show that the proposed algorithm can dramatically reduce the computational cost of GP without sacrificing the performance in all scenarios. With the same training time, the proposed algorithm can achieve significantly better performance than its counterparts in most scenarios while no worse in others.", notes = "Also known as \cite{9345417} PMID: 33531323 Evolutionary Computation Research Group, School of Engineering and Computer Science, Victoria University of Wellington, Wellington 6140, New Zealand", } @Article{Fangfang_Zhang:ieeeTEC, author = "Fangfang Zhang and Yi Mei and Su Nguyen and Mengjie Zhang", title = "Correlation Coefficient based Recombinative Guidance for Genetic Programming Hyper-heuristics in Dynamic Flexible Job Shop Scheduling", journal = "IEEE Transactions on Evolutionary Computation", year = "2021", volume = "25", number = "3", pages = "552--566", month = jun, keywords = "genetic algorithms, genetic programming, Recombinative Guidance, Correlation Coefficient, Hyper-heuristics, Dynamic Flexible Job Shop Scheduling", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2021.3056143", abstract = "Dynamic flexible job shop scheduling is a challenging combinatorial optimisation problem due to its complex environment. In this problem, machine assignment and operation sequencing decisions need to be made simultaneously under the dynamic environments. Genetic programming, as a hyper-heuristic approach, has been successfully used to evolve scheduling heuristics for dynamic flexible job shop scheduling. However, in traditional genetic programming, recombination between parents may disrupt the beneficial building-blocks by choosing the crossover points randomly. This paper proposes a recombinative mechanism to provide guidance for genetic programming to realise effective and adaptive recombination for parents to produce offspring. Specifically, we define a novel measure for the importance of each subtree of an individual, and the importance information is used to decide the crossover points. The proposed recombinative guidance mechanism attempts to improve the quality of offspring by preserving the promising building-blocks of one parent and incorporating good building-blocks from the other. The proposed algorithm is examined on six scenarios with different configurations. The results show that the proposed algorithm significantly outperforms the state-of-the-art algorithms on most tested scenarios, in terms of both final test performance and convergence speed. In addition, the rules obtained by the proposed algorithm have good interpretability.", notes = "Evolutionary Computation Research Group, School of Engineering and Computer Science, Victoria University of Wellington, Wellington 6140, New Zealand. Also known as \cite{9344816}", } @Article{Fangfang_Zhang:ieeeTEC2, author = "Fangfang Zhang and Yi Mei and Su Nguyen and Mengjie Zhang and Kay Chen Tan", title = "Surrogate-Assisted Evolutionary Multitask Genetic Programming for Dynamic Flexible Job Shop Scheduling", journal = "IEEE Transactions on Evolutionary Computation", year = "2021", volume = "25", number = "4", pages = "651--665", month = aug, keywords = "genetic algorithms, genetic programming, Surrogate, Multitask Learning, Hyper-heuristics, Dynamic Flexible Job Shop Scheduling, DFJSS, JSS", ISSN = "1089-778X", URL = "https://ieeexplore.ieee.org/document/9377470", DOI = "doi:10.1109/TEVC.2021.3065707", size = "15 pages", abstract = "Dynamic flexible job shop scheduling is an important combinatorial optimisation problem with complex routing and sequencing decisions under dynamic environments. Genetic programming, as a hyper-heuristic approach, has been successfully applied to evolve scheduling heuristics for job shop scheduling. However, its training process is time-consuming, and it faces the retraining problem once the characteristics of job shop scenarios vary. It is known that multitask learning is a promising paradigm for solving multiple tasks simultaneously by sharing knowledge among the tasks. To improve the training efficiency and effectiveness, this paper proposes a novel surrogate-assisted evolutionary multitask algorithm via genetic programming to share useful knowledge between different scheduling tasks. Specifically, we employ the phenotypic characterisation for measuring the behaviours of scheduling rules and building a surrogate for each task accordingly. The built surrogates are used not only to improve the efficiency of solving each single task but also for knowledge transfer in multitask learning with a large number of promising individuals. The results show that the proposed algorithm can significantly improve the quality of scheduling heuristics for all scenarios. In addition, the proposed algorithm manages to solve multiple tasks collaboratively in terms of the evolved scheduling heuristics for different tasks in a multitask scenario.", notes = "also known as \cite{9377470} \cite{zhangSurrogateMultitask} Evolutionary Computation Research Group, School of Engineering and Computer Science, Victoria University of Wellington, Wellington 6140, New Zealand.", } @Book{Zhang:book, author = "Fangfang Zhang and Su Nguyen and Yi Mei and Mengjie Zhang", title = "Genetic Programming for Production Scheduling: An Evolutionary Learning Approach", publisher = "Springer", year = "2021", series = "Machine Learning: Foundations, Methodologies, and Applications", keywords = "genetic algorithms, genetic programming, Production Scheduling, Machine Learning, Hyper-Heuristic Learning, Multitask Optimisation, Multi-Objective Optimisation, Heuristics", isbn13 = "978-981-16-4858-8", URL = "http://link.springer.com/book/10.1007/978-981-16-4859-5", URL = "https://link.springer.com/book/10.1007/978-981-16-4859-5", DOI = "doi:10.1007/978-981-16-4859-5", doi_chpt15 = "doi:10.1007/978-981-16-4859-5_15", size = "xxxiii+336 pages", notes = "also known as \cite{books/sp/ZhangNMZ21} School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand", } @Article{Fangfang_Zhang:ieeeTCyber3, author = "Fangfang Zhang and Yi Mei and Su Nguyen and Kay Chen Tan and Mengjie Zhang", title = "Multitask Genetic Programming-Based Generative Hyperheuristics: A Case Study in Dynamic Scheduling", journal = "IEEE Transactions on Cybernetics", year = "2022", volume = "52", number = "10", pages = "10515--10528", keywords = "genetic algorithms, genetic programming", ISSN = "2168-2267", DOI = "doi:10.1109/TCYB.2021.3065340", abstract = "Evolutionary multitask learning has achieved great success due to its ability to handle multiple tasks simultaneously. However, it is rarely used in the hyperheuristic domain, which aims at generating a heuristic for a class of problems rather than solving one specific problem. The existing multitask hyperheuristic studies only focus on heuristic selection, which is not applicable to heuristic generation. To fill the gap, we propose a novel multitask generative hyperheuristic approach based on genetic programming (GP) in this article. Specifically, we introduce the idea in evolutionary multitask learning to GP hyperheuristics with a suitable evolutionary framework and individual selection pressure. In addition, an origin-based offspring reservation strategy is developed to maintain the quality of individuals for each task. To verify the effectiveness of the proposed approach, comprehensive empirical studies have been conducted on the homogeneous and heterogeneous multitask dynamic flexible job shop scheduling. The results show that the proposed algorithm can significantly improve the quality of scheduling heuristics for each task in all the examined scenarios. In addition, the evolved scheduling heuristics verify the mutual help among the tasks in a multitask scenario.", notes = "also known as \cite{9382963} Evolutionary Computation Research Group, School of Engineering and Computer Science, Victoria University of Wellington, Wellington 6140, New Zealand", } @PhdThesis{Fangfang_Zhang:thesis, author = "Fangfang Zhang", title = "Genetic Programming Hyper-heuristics for Dynamic Flexible Job Shop Scheduling", school = "School of Engineering and Computer Science, Victoria University of Wellington", year = "2021", address = "New Zealand", note = "Honorable Mention in ACM SIGEVO Award for the best dissertation of the year", keywords = "genetic algorithms, genetic programming, Dynamic flexible job shop scheduling, Evolutionary computation, Scheduling heuristics, Surrogate, Feature selection, Genetic operators, Multitask learning, 080108 Neural, Evolutionary and Fuzzy Computation, 080199 Artificial Intelligence and Image Processing not elsewhere classified, 861404 Mining Machinery and Equipment, 861402 Appliances and Electrical Machinery and Equipment, 861499 Machinery and Equipment not elsewhere classified", URL = "https://doi.org/10.26686/wgtn.16528677", URL = "https://openaccess.wgtn.ac.nz/articles/thesis/Genetic_Programming_Hyper-heuristics_for_Dynamic_Flexible_Job_Shop_Scheduling/16528677", URL = "https://openaccess.wgtn.ac.nz/articles/thesis/Genetic_Programming_Hyper-heuristics_for_Dynamic_Flexible_Job_Shop_Scheduling/16528677.pdf", size = "300 pages", abstract = "Dynamic flexible job shop scheduling (DFJSS) has received widespread attention from academia and industry due to its reflection in real-world scheduling applications such as order picking in the warehouse and the manufacturing industry. It requires complex routing and sequencing decisions under unpredicted dynamic events. Genetic programming, as a hyper-heuristic approach (GPHH), has been successfully applied to evolve scheduling heuristics for DFJSS automatically due to its flexible representation. Although GPHH has achieved certain success in solving the DFJSS problems, there are still some limitations for applying GPHH to DFJSS, particularly in terms of its training efficiency, large search space, search mechanism, and multitask solving ability. The overall goal of this thesis is to develop effective GPHH algorithms to evolve scheduling heuristics for DFJSS efficiently. Different machine learning techniques, i.e., surrogate, feature selection, specialised genetic operator, and multitask learning, are incorporated in this thesis to tackle the limitations. First, this thesis develops a novel multi-fidelity based surrogate-assisted GPHH for DFJSS to improve the training efficiency of GPHH. Specifically, multi-fidelity based surrogate models are first designed by simplifying the problem to be solved. Then, an effective collaboration mechanism with knowledge transfer is proposed for using the advantages of the multifidelity based surrogate models to solve the problem. The results show that the proposed algorithm can dramatically reduce the computational cost of GPHH without sacrificing the performance in all the test scenarios. With the same training time, the proposed algorithm can achieve significantly better performance than its counterparts in most scenarios while no worse in others. Second, this thesis designs a novel two-stage GPHH framework with feature selection to evolve scheduling heuristics for DFJSS automatically. Based on this framework, this thesis further proposes to evolve scheduling heuristics with only the selected features by eliminating the unselected features properly. Specifically, individual adaptation strategies are proposed to generate individuals with only the selected features by using the information of both the selected features and the investigated individuals during the feature selection process. The results show that the proposed algorithm can successfully achieve scheduling heuristics with fewer unique features and smaller sizes, which tends to be more interpretable. In addition, the proposed algorithm can evolve comparable scheduling heuristic with that obtained by the traditional GPHH within a much shorter training time. Third, this thesis proposes a novel recombinative mechanism to provide guidance for GPHH based on the importance of subtrees to realise effective and adaptive recombination for parents to produce offspring. Two measures are proposed to measure the importance of all the subtrees of an individual. The first one is based on the frequency of features, and the second is based on the correlation between the behaviour of subtrees and the whole tree (i.e., an individual). The importance information is used to decide the crossover points for the parents. The proposed recombinative guidance mechanism attempts to improve the quality of offspring by preserving the promising building-blocks of one parent and incorporating good building-blocks from the other. The results show that the proposed algorithm based on the correlation importance measure performs better than the proposed algorithm based on the feature frequency importance measure. In addition, the proposed algorithm based on the correlation importance measure between the behaviour of subtrees significantly also outperforms the state-of-the-art algorithms on most tested scenarios. Last, this thesis proposes a multitask GPHH approach and a surrogate-assisted multitask GPHH approach to solving multiple DFJSS tasks simultaneously. First, an effective hyper-heuristic multitask algorithm is proposed by adapting the traditional evolutionary multitask algorithms based on the characteristics of GPHH. Second, this thesis develops a novel surrogate-assisted multitask GPHH approach to solving multiple DFJSS tasks by sharing useful knowledge between different DFJSS scheduling tasks. Specifically, the surrogate-assisted multitask GPHH algorithm employs the phenotypic characterisation technique to measure the behaviours of scheduling rules to build a surrogate for each task accordingly. The built surrogates are not only used to improve the efficiency of solving each single DFJSS task but also used for knowledge sharing between multiple DFJSS tasks in multitask learning. The results show that the proposed algorithm can significantly improve the quality of scheduling heuristics for all the test scenarios. The results also observe that the proposed algorithms manage to solve multiple tasks collaboratively in terms of the evolved scheduling heuristics for different tasks in a multitask scenario.", notes = "Supervisors: Yi Mei and Mengjie Zhang Also known as \cite{Zhang2021}", } @InProceedings{zhang:2021:GPPS, author = "Fangfang Zhang and Su Nguyen and Yi Mei and Mengjie Zhang", title = "Adaptive Multitask Genetic Programming for Dynamic Job Shop Scheduling", booktitle = "Genetic Programming for Production Scheduling", year = "2021", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-981-16-4859-5_14", DOI = "doi:10.1007/978-981-16-4859-5_14", } @InProceedings{Zhang:2022:CEC, author = "Fangfang Zhang and Yi Mei and Su Nguyen and Mengjie Zhang", booktitle = "2022 IEEE Congress on Evolutionary Computation (CEC)", title = "Learning Strategies on Scheduling Heuristics of Genetic Programming in Dynamic Flexible Job Shop Scheduling", year = "2022", editor = "Carlos A. Coello Coello and Sanaz Mostaghim", address = "Padua, Italy", month = "18-23 " # jul, isbn13 = "978-1-6654-6708-7", abstract = "Dynamic flexible job shop scheduling is an important combinatorial optimisation problem that covers valuable practical applications such as order picking in warehouses and service allocation in cloud computing. Machine assignment and operation sequencing are two key decisions to be considered simultaneously in dynamic flexible job shop scheduling. Genetic programming has been successfully and widely used to learn scheduling heuristics, including a routing rule for machine assignment and a sequencing rule for operation sequencing simultaneously. There are mainly two types of learning strategies to evolve scheduling heuristics, i.e., learning one rule by fixing the other rule, and learning the routing rule and the sequencing rule simultaneously. However, there is no guidance on which learning strategy to use in specific cases. To fill this gap, this paper provides a comprehensive study of learning strategies on scheduling heuristics of genetic programming in dynamic flexible job shop scheduling by comparing five learning strategies, including two strategies that are extended from the existing studies. The results show that learning two rules simultaneously, either using cooperative coevolution or multi-tree representation, is more effective than only learning one type of rule. Cooperative coevolution is recommended if an algorithm aims to handle a problem by dividing it into small sub-problems, and focuses on the characteristics of routing rule and sequencing rule. Genetic programming with multi-tree representation that treats the routing rule and the sequencing rule as an individual, is preferred to reduce the complexities of algorithms.", keywords = "genetic algorithms, genetic programming, Sequential analysis, Job shop scheduling, Processor scheduling, Heuristic algorithms, Dynamic scheduling, Routing, Surrogate, Instance Rotation, Brood Recombination, Dynamic Job Shop Scheduling", DOI = "doi:10.1109/CEC55065.2022.9870243", notes = "Also known as \cite{9870243}", } @InProceedings{DBLP:conf/ppsn/ZhangMNZ22, author = "Fangfang Zhang and Yi Mei and Su Nguyen and Mengjie Zhang", title = "Importance-Aware Genetic Programming for Automated Scheduling Heuristics Learning in Dynamic Flexible Job Shop Scheduling", booktitle = "Parallel Problem Solving from Nature - PPSN XVII - 17th International Conference, PPSN 2022, Proceedings, Part II", year = "2022", editor = "Guenter Rudolph and Anna V. Kononova and Hernan E. Aguirre and Pascal Kerschke and Gabriela Ochoa and Tea Tusar", volume = "13399", series = "Lecture Notes in Computer Science", pages = "48--62", address = "Dortmund, Germany", month = sep # " 10-14", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Importance-aware scheduling heuristics learning, Hyper-heuristic, Dynamic flexible job shop scheduling", timestamp = "Tue, 16 Aug 2022 16:15:42 +0200", biburl = "https://dblp.org/rec/conf/ppsn/ZhangMNZ22.bib", bibsource = "dblp computer science bibliography, https://dblp.org", isbn13 = "978-3-031-14720-3", DOI = "doi:10.1007/978-3-031-14721-0_4", abstract = "Dynamic flexible job shop scheduling (DFJSS) is a critical and challenging problem in production scheduling such as order picking in the warehouse. Given a set of machines and a number of jobs with a sequence of operations, DFJSS aims to generate schedules for completing jobs to minimise total costs while reacting effectively to dynamic changes. Genetic programming, as a hyper-heuristic approach, has been widely used to learn scheduling heuristics for DFJSS automatically. A scheduling heuristic in DFJSS includes a routing rule for machine assignment and a sequencing rule for operation sequencing. However, existing studies assume that the routing and sequencing are equally important, which may not be true in real-world applications. This paper aims to propose an importance-aware GP algorithm for automated scheduling heuristics learning in DFJSS. Specifically, we first design a rule importance measure based on the fitness improvement achieved by the routing rule and the sequencing rule across generations. Then, we develop an adaptive resource allocation strategy to give more resources for learning the more important rules. The results show that the proposed importance-aware GP algorithm can learn significantly better scheduling heuristics than the compared algorithms. The effectiveness of the proposed algorithm is realised by the proposed strategies for detecting rule importance and allocating resources. Particularly, the routing rules play a more important role than the sequencing rules in the examined DFJSS scenarios.", notes = "PPSN2022", } @Article{Fangfang_Zhang:ieeeTEC3, author = "Fangfang Zhang and Yi Mei and Su Nguyen and Kay Chen Tan and Mengjie Zhang", title = "Task Relatedness Based Multitask Genetic Programming for Dynamic Flexible Job Shop Scheduling", journal = "IEEE Transactions on Evolutionary Computation", year = "2023", volume = "27", number = "6", pages = "1705--1719", month = dec, keywords = "genetic algorithms, genetic programming", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2022.3199783", abstract = "Multitasking learning has been successfully used in handling multiple related tasks simultaneously. In reality, there are often many tasks to be solved together, and the relatedness between them is unknown in advance. In this paper, we focus on multitask genetic programming for the dynamic flexible job shop scheduling problems, and address two challenges. The first is how to measure the relatedness between tasks accurately. The second is how to select task pairs to transfer knowledge during the multitask learning process. To measure the relatedness between dynamic flexible job shop scheduling tasks, we propose a new relatedness metric based on the behaviour distributions of the variable-length genetic programming individuals. In addition, for more effective knowledge transfer, we develop an adaptive strategy to choose the most suitable assisted task for the target task based on the relatedness information between tasks. The findings show that in all of the multitask scena", notes = "also known as \cite{9861686}", } @Article{Fangfang_Zhang:ieeeTEC4, author = "Fangfang Zhang and Yi Mei and Su Nguyen and Kay Chen Tan and Mengjie Zhang", title = "Instance Rotation Based Surrogate in Genetic Programming with Brood Recombination for Dynamic Job Shop Scheduling", journal = "IEEE Transactions on Evolutionary Computation", year = "2023", volume = "27", number = "5", pages = "1192--1206", month = oct, keywords = "genetic algorithms, genetic programming, Brood recombination, dynamic job-shop scheduling, JSS, instance rotation, surrogate", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2022.3180693", size = "15 pages", abstract = "Genetic programming has achieved great success for learning scheduling heuristics in dynamic job shop scheduling. In theory, generating a large number of offspring for genetic programming, known as brood recombination, can improve its heuristic generation ability. However, it is time-consuming to evaluate extra individuals. Phenotypic characterisation based surrogates with K-nearest neighbours have been successfully used for genetic programming to preselect only promising individuals for real fitness evaluations in dynamic job shop scheduling. However, sample individuals used by surrogate are from only the current generation, since the fitness of individuals across generations are not comparable due to the rotation of training instances. The surrogate cannot accurately estimate the fitness of an offspring that is far away from all the limited sample individuals at the current generation. This paper proposes an effective instance rotation based surrogate to address the abo", notes = "also known as \cite{9789507}", } @Article{zhang2022multitask, author = "Fangfang Zhang and Yi Mei and Su Nguyen and Mengjie Zhang", journal = "IEEE Transactions on Cybernetics", title = "Multitask Multiobjective Genetic Programming for Automated Scheduling Heuristic Learning in Dynamic Flexible Job-Shop Scheduling", year = "2023", volume = "53", number = "7", pages = "4473--4486", keywords = "genetic algorithms, genetic programming, Dynamic jobshop scheduling, GP, hyperheuristics, multiobjective, multitask learning", ISSN = "2168-2267", URL = "https://meiyi1986.github.io/publication/zhang-2022-multitask/", DOI = "doi:10.1109/TCYB.2022.3196887", size = "14 pages", abstract = "Evolutionary multitask multiobjective learning has been widely used for handling more than one multiobjective task simultaneously. However, it is rarely used in dynamic combinatorial optimization problems, which have valuable practical applications such as dynamic flexible job-shop scheduling (DFJSS) in manufacturing. Genetic programming (GP), as a popular hyperheuristic approach, has been used to learn scheduling heuristics for generating schedules for multitask single-objective DFJSS only. Searching in the heuristic space with GP is more difficult than in the solution space, since a small change on heuristics can lead to ineffective or even infeasible solutions. Multiobjective DFJSS is more challenging than single DFJSS, since a scheduling heuristic needs to cope with multiple objectives. To tackle this challenge, we first propose a multipopulation-based multitask multiobjective GP algorithm to preserve the quality of the learned scheduling heuristics for each task.", notes = "also known as \cite{9868257} Evolutionary Computation Research Group, School of Engineering and Computer Science, Victoria University of Wellington, Wellington 6140, New Zealand", } @InProceedings{Zhang:2022:SSCI, author = "Fangfang Zhang and Yi Mei and Su Nguyen and Mengjie Zhang", title = "Phenotype Based Surrogate-Assisted Multi-objective Genetic Programming with Brood Recombination for Dynamic Flexible Job Shop Scheduling", booktitle = "2022 IEEE Symposium Series on Computational Intelligence (SSCI)", year = "2022", pages = "1218--1225", abstract = "Dynamic flexible job shop scheduling (DFJSS) is an important combinatorial optimisation problem with a large number of real-world applications such as component production in manufacturing. Genetic programming (GP), as a hyper-heuristic approach, has been widely used to learn scheduling heuristics for DFJSS. Brood recombination has been shown its effectiveness to improve the performance of GP by generating more offspring and preselecting only promising individuals into the next generation. However, evaluating more individuals requires more computational cost. Phenotype based surrogate models have been successfully used with GP to speed up the evaluation in single-objective dynamic job shop scheduling. However, its effectiveness on multi-objective dynamic job shop scheduling is unknown. To fill this gap, this paper proposes a novel surrogate-assisted multi-objective GP based on the phenotype of GP individuals for DFJSS. Specifically, we first use phenotypic vector to represent the behaviour of GP individuals in DFJSS. Second, K-nearest neighbour based surrogates are built according to the phenotypic characterisations and multiple fitness values of the evaluated individuals. Last, the built surrogate models are used to predict the fitness of newly generated offspring in GP with brood recombination. The results show that with the same training time, the proposed algorithm can achieve significantly better scheduling heuristics than the compared algorithm. The analyses of population diversity, feature importance, and the number of non-dominated individuals have also shown the effectiveness of the proposed algorithm in different aspects.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SSCI51031.2022.10022169", month = dec, notes = "Also known as \cite{10022169}", } @InProceedings{Zhang:2023:GECCOcomp, author = "Fangfang Zhang and Yi Mei and Mengjie Zhang", title = "An Investigation of Terminal Settings on Multitask Multi-Objective Dynamic Flexible Job Shop Scheduling with Genetic Programming", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "259--262", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, dynamic flexible job shop scheduling, terminal sets, multitask multi-objective: Poster", isbn13 = "9798400701207", DOI = "doi:10.1145/3583133.3590546", size = "4 pages", abstract = "Multitask learning has attracted widespread attention to handle multiple tasks simultaneously. Multitask genetic programming has been successfully used to learn scheduling heuristics for multiple multi-objective dynamic flexible job shop scheduling tasks simultaneously. With genetic programming, the learned scheduling heuristics consist of terminals that are extracted from the features of specific tasks. However, how to set proper terminals with multiple tasks still needs to be investigated. This paper has investigated the effectiveness of three strategies for this purpose, i.e., intersection strategy to use the common terminals between tasks, separation strategy to apply different terminals for different tasks, and union strategy to use all the terminals needed for all tasks. The results show that the union strategy which gives tasks the terminals needed by all tasks performs the best. In addition, we find that the learned routing/sequencing rule by the developed algorithm with union strategy in one multitask scenario can share knowledge between each other. On the other hand and more importantly, the learned routing/sequencing rule can also be specific to their tasks with distinguished knowledge represented by genetic materials.", notes = " GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @InProceedings{Zhang:2023:CEC, author = "Fangfang Zhang and Mengjie Zhang and Yi Mei and Su Nguyen", title = "Genetic Programming and Machine Learning for Scheduling", booktitle = "2023 IEEE Congress on Evolutionary Computation (CEC)", year = "2023", editor = "Gui DeSouza and Gary Yen", address = "Chicago, USA", month = "1-5 " # jul, note = "Tutorial", keywords = "genetic algorithms, genetic programming", isbn13 = "979-8-3503-1459-5", URL = "https://2023.ieee-cec.org/tutorials/", URL = "https://2023.ieee-cec.org/wp-content/uploads/sites/438/7_Genetic-Programming-aand-Machine-Learning-for-Scheduling.pdf", size = "4 pages", notes = " CEC2023 https://2023.ieee-cec.org/program-html/", } @Article{Zhang:ieeeTEVC, author = "Fangfang Zhang and Yi Mei and Su Nguyen and Mengjie Zhang", journal = "IEEE Transactions on Evolutionary Computation", title = "Survey on Genetic Programming and Machine Learning Techniques for Heuristic Design in Job Shop Scheduling", keywords = "genetic algorithms, genetic programming", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2023.3255246", abstract = "Job shop scheduling is a process of optimising the use of limited resources to improve the production efficiency. Job shop scheduling has a wide range of applications such as order picking in the warehouse and vaccine delivery scheduling under a pandemic. In real-world applications, the production environment is often complex due to dynamic events such as job arrivals over time and machine breakdown. Scheduling heuristics, e.g., dispatching rules, have been popularly used to prioritise the candidates such as machines in manufacturing to make good schedules efficiently. Genetic programming, has shown its superiority in learning scheduling heuristics for job shop scheduling automatically due to its flexible representation. This survey firstly provides comprehensive discussions of recent designs of genetic programming algorithms on different types of job shop scheduling. In addition, we notice that in the recent years, a range of machine learning techniques such as feature sele", notes = "Also known as \cite{10065588}", } @Article{Fangfang_Zhang:JRSNZ, author = "Fangfang Zhang and Yuye Zhang and Paula Casanovas and Jessica Schattschneider and Seumas P. Walker and Bing Xue and Mengjie Zhang and Jane E. Symonds", title = "Health prediction for king salmon via evolutionary machine learning with genetic programming", journal = "Journal of the Royal Society of New Zealand", year = "2025", volume = "55", number = "1", pages = "166--191", keywords = "genetic algorithms, genetic programming, Evolutionary machine learning, king salmon, health prediction, classification", publisher = "Taylor \& Francis", ISSN = "0303-6758", URL = "https://www.tandfonline.com/doi/full/10.1080/03036758.2024.2329228", DOI = "doi:10.1080/03036758.2024.2329228", size = "26 pages", abstract = "King (Chinook) salmon is the only salmon species farmed in Aotearoa New Zealand and accounts for over half of the world's production of king salmon. Determining the health status of king salmon effectively is important for farming. However, it is a challenging task due to the complex biotic and abiotic factors that influence health. Evolutionary machine learning algorithms have shown their superiority in learning models for challenging tasks. However, they have not been investigated for health prediction in king salmon farming. This paper focuses on data processing and machine learning algorithm design to develop king salmon health prediction models in Aotearoa New Zealand. Particularly, this paper proposes a king salmon health prediction method based on genetic programming which is an evolutionary machine learning algorithm. The results show that genetic programming achieves the best overall performance among all examined typical machine learning algorithms for most trials. Further analyses show that genetic programming can automatically detect important features for learning classifiers for king salmon health classification tasks effectively, and can also learn potentially interpretable models. Our results are an important step forward in developing health prediction tools to automatically assess health status of farmed king salmon in Aotearoa New Zealand.", notes = "PMID: 39679277 Centre for Data Science and Artificial Intelligence & School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand", } @Article{Zhang:2025:TAI, author = "Fangfang Zhang and Gaofeng Shi and Yi Mei and Mengjie Zhang", title = "Multiobjective Dynamic Flexible Job Shop Scheduling With Biased Objectives via Multitask Genetic Programming", journal = "IEEE Transactions on Artificial Intelligence", year = "2025", volume = "6", number = "1", pages = "169--183", month = jan, keywords = "genetic algorithms, genetic programming, Job shop scheduling, Dynamic scheduling, Heuristic algorithms, Processor scheduling, Sorting, Routing, multiobjective dynamic flexible job shop scheduling (DFJSS), multitask learning, multiobjective with biased objectives, scheduling heuristics", ISSN = "2691-4581", DOI = "doi:10.1109/TAI.2024.3456086", notes = "also known as \cite{10669769,}", } @InProceedings{Zhang:2018:CEC, author = "Feiyu Zhang and Yuning Chen and Yingwu Chen", booktitle = "2018 IEEE Congress on Evolutionary Computation (CEC)", title = "Evolving Constructive Heuristics for Agile Earth Observing Satellite Scheduling Problem with Genetic Programming", year = "2018", abstract = "Agile Earth Observing Satellite (AEOS) scheduling problem (AEOSSP) consists in selecting a subset of tasks from a given task set which are then scheduled on the agile satellite with the purpose of maximizing the total reward of scheduled tasks. AEOSSP is strongly NP-hard and therefore existing solution approaches mainly fall in the field of heuristics and metaheuristics. According to the no free lunch theory, it is impossible to find a single heuristic that is well-applied to any problem instance and a problem-tailored heuristic is always needed. In this paper, we propose a genetic programming based evolutionary approach (GPEA) to automatically evolve a best-suited constructive heuristic for any given AEOSSP instance. The programs (individuals) of GPEA are heuristic rules encoded as trees of mathematical functions. The fitness of the program is evaluated through mapping the mathematical function to an AEOSSP solution using a timeline-based construction algorithm. Computational results on a set of well-designed AEOSSP scenarios show that the proposed GPEA leads to a heuristic algorithm that outperforms recently published sophisticated meta-heuristic algorithm (ALNS). Additional experiments were carried out to demonstrate that the time-line based construction algorithm plays a significant role in matching time-related characteristics in comparison to four commonly used heuristic algorithms. Our results also showed that the evolved heuristic rules preserve a certain extent of generality.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2018.8477939", month = jul, notes = "College of Information System and Management, National University of Defense Technology, Changsha, 410073, China Also known as \cite{8477939}", } @Article{Zhang:2001:RCIM, author = "Fengdong Zhang and Deyi Xue", title = "Optimal concurrent design based upon distributed product development life-cycle modeling", year = "2001", journal = "Robotics and Computer-Integrated Manufacturing", volume = "17", pages = "469--486", number = "6", month = dec, email = "xue@enme.ucalgary.ca", keywords = "genetic algorithms, genetic programming, Concurrent design, Particle swarm optimization, PSO, Distributed computing", ISSN = "0736-5845", broken = "http://www.sciencedirect.com/science/article/B6V4P-44HY42C-4/1/f2248f45ed58708fb7f455b343fa8cca", DOI = "doi:10.1016/S0736-5845(01)00023-0", abstract = "This research introduces an optimal concurrent design approach based upon a previously developed distributed product development life-cycle modeling method. In this approach, the product realization process alternatives and relevant activities are modeled at different locations that are connected through the Internet. Relations among these alternative activities are described by an AND/OR graph. The optimal product realization process alternative and its parameter values are identified using a multi-level optimization method. Genetic programming (GP) and particle swarm optimization (PSO) are employed for identifying the optimal product realization process alternative and the optimal parameter values of the feasible alternatives, respectively.", } @Article{Zhang:2002:CAD, author = "F. Zhang and D. Xue", title = "Distributed database and knowledge base modeling for concurrent design", journal = "Computer-Aided Design", year = "2002", volume = "34", number = "1", pages = "27--40", keywords = "genetic algorithms, genetic programming, Database, Knowledge base, Distributed system", ISSN = "0010-4485", URL = "https://www.sciencedirect.com/science/article/pii/S0010448501000458", DOI = "doi:10.1016/S0010-4485(01)00045-8", size = "14 pages", abstract = "This research introduces a new distributed database and knowledge base modeling approach for concurrent design. The descriptions of product life-cycle aspects are modeled by primitives called features. The knowledge for developing products is described by collections of rules called rule-bases. Databases and knowledge bases at different locations for modeling different life-cycle aspects of the same product are associated by Internet. In addition, mechanisms for maintaining dependency relations of the product descriptions at different locations and reasoning using the knowledge at different locations are also developed. The system was implemented using Smalltalk, an object oriented programming language. A case study example of concurrent design is given at the end of this paper.", notes = "Is this GP? Also known as \cite{ZHANG200227} Fengdong Zhang and Deyi Xue, Department of Mechanical and Manufacturing Engineering, The University of Calgary, Calgary, Alberta, Canada T2N 1N4", } @Article{Zhang:2020:MC, author = "Guanghui Zhang and Jack Y. B. Lee", journal = "IEEE Transactions on Mobile Computing", title = "Ensemble Adaptive Streaming - A New Paradigm to Generate Streaming Algorithms via Specializations", year = "2020", volume = "19", number = "6", pages = "1346--1358", abstract = "Video streaming is now ubiquitous in the mobile Internet. This motivated intense research in adaptive streaming algorithms to tackle mobile networks' fluctuating conditions. Our investigations revealed that while existing algorithms can perform well in their intended operating environments, their performance can degrade substantially in other environments. This work tackles this challenge by developing a novel Ensemble Adaptive Streaming (EAS) paradigm to mobile video streaming. As opposed to designing a single streaming algorithm for all network conditions, we argue that different network conditions require different algorithms. We introduce the notion of network differentiators to segregate network conditions into different classes where each class has its own adaptation algorithm designed and optimized specifically for it. An EAS mobile streaming client then selects at run time the matching adaptation algorithm using the same network differentiators on a per session basis for streaming. We show how EAS can be applied to existing machine-learning approaches to improve their performances. Moreover, to fully exploit EAS's potential we developed the first Genetic Programming approach to evolve adaptive streaming algorithms. The resultant EAS-GP algorithms not only outperformed state-of-the-art algorithms substantially, but also exhibited remarkable robustness over time, location, mobile operators, as well as quality-of-experience metrics.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/TMC.2019.2909202", ISSN = "1558-0660", month = jun, notes = "Also known as \cite{8681142}", } @Article{Zhang:2021:PDS, author = "Guanghui Zhang and Jack Y. B. Lee and Ke Liu and Haibo Hu2 and Vaneet Aggarwal", title = "A Unified Framework for Flexible Playback Latency Control in Live Video Streaming", journal = "IEEE Transactions on Parallel and Distributed Systems", year = "2021", volume = "32", number = "12", pages = "3024--3037", abstract = "Live video streaming has seen tremendous growth in the past decade. An important fact in live streaming is that the demand for low playback-latency inherently conflicts with the desire for high QoE. This requires different types of live services to seek different latency-QoE tradeoffs according to their service-requirements. However, our investigations revealed that it is fundamentally difficult for existing streaming algorithms to keep consistent latency in changing network conditions, let alone achieve the service-desired latency-QoE tradeoff. To tackle the challenge, this article develops a novel framework called Flexible Latency Aware Streaming (FLAS) that not only can achieve consistent low latency, but also control the latency-QoE tradeoff flexibly. Specifically, FLAS generates a set of adaptation logics offline, each optimized for a candidate tradeoff point, then selects the most appropriate one to run online. We first show how FLAS can be applied to optimizing the existing algorithms, then developed a novel Genetic Programming approach to fully exploit FLAS's potential. Extensive evaluations show that FLAS can precisely control latency all the way down to 1s and achieve substantially higher QoE than state-of-the-arts. FLAS can be readily implemented into real streaming platforms, offering a practical and reliable solution for live-streaming services.", keywords = "genetic algorithms, genetic programming, Streaming media, Quality of experience, Throughput, Bit rate, 3G mobile communication, Video recording, Video streaming, quality-of-experience, video reliability", DOI = "doi:10.1109/TPDS.2021.3083202", ISSN = "1558-2183", month = dec, notes = "Also known as \cite{9439873}", } @Article{ZHANG:2023:engstruct, author = "Hang Zhang and Quan-Quan Guo and Li-Yan Xu", title = "Prediction of long-term prestress loss for prestressed concrete cylinder structures using machine learning", journal = "Engineering Structures", volume = "279", pages = "115577", year = "2023", ISSN = "0141-0296", DOI = "doi:10.1016/j.engstruct.2022.115577", URL = "https://www.sciencedirect.com/science/article/pii/S0141029622016534", keywords = "genetic algorithms, genetic programming, Prestressed concrete cylinder structure, Long-term prestress loss, Machine learning, Shrinkage, Creep, Prediction model", abstract = "The long-term prestress loss caused by shrinkage and creep of concrete and stress relaxation of prestressed tendons has significant effects on the sealability and safety of prestressed concrete cylinder structures such as nuclear reactor containments and liquified natural gas (LNG) tanks. By using machine learning (ML) techniques, this study aims to establish an intelligent approach for the long-term prestress loss prediction of concrete cylinder structures. Firstly, based on the Infrastructure Technology Institute of Northwestern University (NU-ITI) database of concrete shrinkage and creep performance, the explicit expressions are presented for concrete shrinkage and creep function using genetic programming (GP); Moreover, the concrete constitutive model is incorporated into a general finite-element software package based on the ABAQUS UMAT platform. Then finite element analysis (FEA) models are established and calibrated based on the existing long-term prestress loss tests of prestressed concrete beams. In addition to the experimental results in the literature, the numerical results of the FEA model are also used to form the database of the long-term prestress losses for concrete cylinder structures. Finally, three prediction models of long-term prestress loss are proposed by using the artificial neural network (ANN), one-dimensional convolutional neural network (1D CNN) and genetic programming (GP). Compared with the measured results of nuclear containments in practical engineering, the ML based prediction models are demonstrated to be accurate and efficient in evaluating the long-term prestress loss for prestressed concrete cylinder structures", } @InProceedings{Zhang:2024:ICC, author = "Hanzhong Zhang and Ting Zhou and Xianfu Chen and Tianheng Xu and Honglin Hu", title = "Multiclass Remote Interference Prediction Network Using Genetic Programming", booktitle = "ICC 2024 - IEEE International Conference on Communications", year = "2024", pages = "25--30", month = jun, keywords = "genetic algorithms, genetic programming, 5G mobile communication, Databases, Atmospheric modelling, Computational modelling, Prevention and mitigation, Ducts, Atmospheric duct, remote interference prediction, muticlass", ISSN = "1938-1883", DOI = "doi:10.1109/ICC51166.2024.10622309", abstract = "In the context of the large-scale deployment of 5G base stations, atmospheric ducts cause remote interference in time division duplex systems. Addressing the impact of remote interference on communication systems necessitates timely prediction and emergency mitigation of atmospheric ducts. In this paper, a genetic programming-based multiclass remote interference prediction network model is proposed. Firstly, the proposed model can directly learn to make predictions from extensive databases without relying on any assumptions. Secondly, it presents a genetic programming strategy capable of automatically adjusting the model's structure, thereby enhancing the prediction accuracy of various interference classes. Numerical results demonstrate that the multiclass remote interference prediction network (MRIPNet) outperforms state-of-the-art interference prediction models when tested on real-world datasets. Further-more, MRIPNet excels in accurately predicting a small number of severe interference, which help operators promptly execute interference avoidance measures.", notes = "Also known as \cite{10622309}", } @Article{Zhang:2025:JSEE, author = "Haodi Zhang and Yuhui Wang and Jiale He", title = "Research on Three-Dimensional Attack Area Based on Improved Backtracking and {ALPS-GP} Algorithms of Air-to-Air Missile", journal = "Journal of Systems Engineering and Electronics", year = "2025", volume = "36", number = "1", pages = "292--310", month = feb, keywords = "genetic algorithms, genetic programming, Backtracking, Solid modelling, Analytical models, Accuracy, Atmospheric modelling, Computational modelling, Prediction algorithms, Real-time systems, Polynomials, Modelling, air combat, three-dimensional attack area, improved backtracking algorithm, age-layered population structure genetic programming (ALPS-GP), gradient descent algorithm", ISSN = "1004-4132", DOI = "doi:10.23919/JSEE.2024.000129", abstract = "In the field of calculating the attack area of air-to-air missiles in modern air combat scenarios, the limitations of existing research, including real-time calculation, accuracy efficiency trade-off, and the absence of the three-dimensional attack area model, restrict their practical applications. To address these issues, an improved backtracking algorithm is proposed to improve calculation efficiency. A significant reduction in solution time and maintenance of accuracy in the three-dimensional attack area are achieved by using the proposed algorithm. Furthermore, the age-layered population structure genetic programming (ALPS-GP) algorithm is introduced to determine an analytical polynomial model of the three-dimensional attack area, considering real-time requirements. The accuracy of the polynomial model is enhanced through the coefficient correction using an improved gradient descent algorithm. The study reveals a remarkable combination of high accuracy and efficient real-time computation, with a mean error of 91.89 m using the analytical polynomial model of the three-dimensional attack area solved in just $10^{-4}\ \mathrm{s}$, thus meeting the requirements of real-time combat scenarios.", notes = "Also known as \cite{10937331} Department of Geography, University of Florida, Gainesville, USA", } @Article{Zhang:2024:swevo, author = "Haohua Zhang and Lubo Li and Sijun Bai and Jingwen Zhang", title = "Co-evolution genetic programming-based hyper-heuristics for the stochastic project scheduling problem with resource transfer and idle costs", journal = "Swarm and Evolutionary Computation", year = "2024", volume = "90", pages = "101678", keywords = "genetic algorithms, genetic programming, Stochastic project scheduling, Resource transfer, Priority rule, Idle cost, Co-evolution genetic programming, cartesian genetic programming", ISSN = "2210-6502", URL = "https://www.sciencedirect.com/science/article/pii/S2210650224002165", DOI = "doi:10.1016/j.swevo.2024.101678", abstract = "In this paper, we study the stochastic resource-constrained project scheduling problem with transfer and idle costs (SRCPSP-TIC) under uncertain environments, where the resource transfer and idle take time and costs. Priority rule (PR) based heuristics are the most commonly used approaches for project scheduling under uncertain environments due to their simplicity and efficiency. For PR-based heuristics of the SRCPSP-TIC, activity priority rules (APRs) and transfer priority rules (TPRs) are necessary to decide the activity sequence and resource transfer. Traditionally, APRs and TPRs need to be manually designed, which is time-consuming and difficult to adapt to different scheduling scenarios. Therefore, based on two individual representation methods, we propose two co-evolution genetic programming (CGP) based hyper-heuristics to evolve APRs and TPRs automatically. Furthermore, a fitness function surrogate-assisted method and a transfer learning mechanism are designed to improve the efficiency and solution quality of the CGP. Based on the instances with different stochastic activity duration distributions, we test the performance of different CGP-based hyper-heuristics and compare the evolved PRs with the classical PRs to demonstrate the effectiveness of evolved PRs. Experimental results show that the proposed algorithms can automatically evolve efficient PRs for the SRCPSP-TIC", } @Article{Zhang:2025:cor, author = "Haohua Zhang and Erik Demeulemeester and Lubo Li and Sijun Bai", title = "Surrogate-assisted cooperative learning genetic programming for the resource-constrained project scheduling problem with stochastic activity durations and transfer times", journal = "Computer and Operations Research", year = "2025", volume = "173", pages = "106816", keywords = "genetic algorithms, genetic programming, Stochastic project scheduling, Transfer times, Surrogate model, Cooperative learning", ISSN = "0305-0548", URL = "https://www.sciencedirect.com/science/article/pii/S0305054824002880", DOI = "doi:10.1016/j.cor.2024.106816", abstract = "For the project scheduling problem with transfer times under an uncertain environment, not only the activity durations are stochastic, but transfer times are often also stochastic. Therefore, we propose a resource-constrained project scheduling problem with stochastic activity durations and transfer times (RCPSP-SDT), which requires complex activity sequencing and resource transfer decisions with an activity priority rule (APR) and a resource transfer priority rule (RTPR) under unpredicted dynamic factors. However, manually designed combination rules of APRs and RTPRs are time-consuming and only for specific scenarios. Therefore, we develop a hyper-heuristic approach based on genetic programming (GP), which has been successfully applied to evolve activity priority rules for project scheduling problems. A new representation of GP individuals was designed to evolve the APR and the RTPR simultaneously. In order to improve the efficiency and solution quality of the approach, we propose surrogate-assisted cooperative learning genetic programming (SCLGP) based on GP. Based on the benchmark data set, computer experiments were conducted under nine variance levels of stochastic distributions. The results show that the proposed algorithm SCLGP performs significantly better than the classical priority rule (PR)-based heuristics. Furthermore, the effectiveness and efficiency of SCLGP were verified by comparing it to four other GP-based algorithms. Finally, the impact of the parameters on the algorithm was investigated, proving that these parameters affect the algorithm's performance", } @Article{Zhang:2020:CIS, author = "Hengzhe Zhang and Aimin Zhou and Xin Lin", title = "Interpretable policy derivation for reinforcement learning based on evolutionary feature synthesis", journal = "Complex \& Intelligent Systems", year = "2020", volume = "6", pages = "741--753", keywords = "genetic algorithms, genetic programming, Reinforcement learning, Policy derivation, Explainable machine learning, XAI", DOI = "doi:10.1007/s40747-020-00175-y", size = "13 pages", abstract = "Reinforcement learning based on the deep neural network has attracted much attention and has been widely used in real-world applications. However, the black-box property limits its usage from applying in high-stake areas, such as manufacture and healthcare. To deal with this problem, some researchers resort to the interpretable control policy generation algorithm. The basic idea is to use an interpretable model, such as tree-based genetic programming, to extract policy from other black box modes, such as neural networks. Following this idea, we try yet another form of the genetic programming technique, evolutionary feature synthesis, to extract control policy from the neural network. We also propose an evolutionary method to optimize the operator set of the control policy for each specific problem automatically. Moreover, a policy simplification strategy is also introduced. We conduct experiments on four reinforcement learning environments. The experiment results reveal that evolutionary feature synthesis can achieve better performance than tree-based genetic programming to extract policy from the neural network with comparable interpretability.", notes = "Shanghai Key Laboratory of Multidimensional information Processing, School of Computer Science and Technology, East China Normal University, Shanghai, China", } @InProceedings{conf/ijcnn/ZhangZ21a, author = "Hengzhe Zhang and Aimin Zhou", title = "{RL-GEP}: Symbolic Regression via Gene Expression Programming and Reinforcement Learning", booktitle = "2021 International Joint Conference on Neural Networks, IJCNN", year = "2021", address = "Shenzhen, China", month = "18-22 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, gene expression programming, reinforcement learning, symbolic regression", isbn13 = "978-1-6654-3900-8", bibdate = "2021-09-29", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ijcnn/ijcnn2021.html#ZhangZ21a", URL = "https://ieeexplore.ieee.org/document/9533735", DOI = "doi:10.1109/IJCNN52387.2021.9533735", size = "8 pages", abstract = "Symbolic regression has become a hot topic in recent years due to the surging demand for interpretable machine learning methods. Traditionally, symbolic regression problems are mainly solved by genetic algorithms. Nonetheless, with the development of deep learning, reinforcement learning based symbolic regression methods have received attention gradually. Unfortunately, hardly any of those reinforcement learning based methods have been proven effectively to solve real world regression problems as genetic algorithm based methods. In this paper, we find a general reinforcement learning based symbolic regression method is difficult to solve real world problems since it is hard to balance between exploration and exploitation. To deal with this problem, we propose a hybrid method to use both genetic algorithm and reinforcement learning for solving symbolic regression problems. By doing so, we can combine the advantages of reinforcement learning and genetic algorithm and achieve better performance than using them alone. To validate the effectiveness of the proposed method, we apply the proposed method to ten benchmark datasets. The experimental results show that the proposed method achieves competitive performance compared with several well-known symbolic regression methods on those datasets.", notes = "Institute of AI Education, School of Computer Science and Technology East China Normal University, Shanghai 200062, China", } @Article{Zhang:2022:swarmEC, author = "Hengzhe Zhang and Aimin Zhou and Hong Qian and Hu Zhang", title = "{PS-Tree}: A piecewise symbolic regression tree", journal = "Swarm and Evolutionary Computation", year = "2022", volume = "71", pages = "101061", month = jun, keywords = "genetic algorithms, genetic programming, Regression tree, Symbolic regression, Multi-objective optimization, Evolutionary algorithm", ISSN = "2210-6502", URL = "https://www.sciencedirect.com/science/article/pii/S2210650222000335", DOI = "doi:10.1016/j.swevo.2022.101061", abstract = "The symbolic methods have recently regained popularity due to their reasonable interpretability compared to neural network-based artificial intelligence techniques. The regression tree is such a symbolic method that divides the feature space into several subregions and builds a simple response surface model, such as a constant value or a linear model, for each subregion. However, this strategy may fail when nonlinear structures exist in the subregions. To overcome this problem, this paper proposes a new regression model, named piecewise symbolic regression tree (PS-Tree). Instead of using constant values or linear models as the leaf nodes, PS-Tree builds symbolic regressors for the leaf nodes or subregions. In addition to that, we also propose an adaptive space partition strategy by dynamically adjusting the partition of the space to alleviate the problem caused by incorrect partitioning. PS-Tree is applied to 122 synthetic and real-world datasets, and the results show that it outperforms several state-of-the-art regression methods.", notes = "Also known as \cite{ZHANG2022101061} Shanghai Institute of AI for Education and School of Computer Science and Technology, East China Normal University, Shanghai 200062, China", } @Article{Zhang:2022:ieeeTEC, author = "Hengzhe Zhang and Aimin Zhou and Hu Zhang", title = "An Evolutionary Forest for Regression", journal = "IEEE Transactions on Evolutionary Computation", year = "2022", volume = "26", number = "4", pages = "735--749", month = aug, keywords = "genetic algorithms, genetic programming, Evolutionary feature construction, evolutionary forest, EF, random forest, RF", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2021.3136667", size = "15 pages", abstract = "Random forest (RF) is a type of ensemble-based machine learning method that has been applied to a variety of machine learning tasks in recent years. This article proposes an evolutionary approach to generate an oblique RF for regression problems. More specifically, our method induces an oblique RF by transforming the original feature space to a new feature space through the evolutionary feature construction method. To speed up the searching process, the proposed method evaluates each set of features based on a decision tree (DT) rather than an RF. In order to obtain an RF, we archive top-performing features and corresponding trees during the search. In this way, both the features and the forest can be constructed simultaneously in a single run. The proposed evolutionary forest is applied to 117 benchmark problems with different characteristics and compared with some state-of-the-art regression methods, including several variants of the RF and gradient boosted DTs (GBDTs). The experimental results suggest that the proposed method outperforms the existing RF and GBDT methods.", notes = "also known as \cite{9656554} School of Computer Science and Technology, Shanghai Institute of AI for Education, East China Normal University, Shanghai 200241, China", } @Article{Zhang:ieeeTEC2, author = "Hengzhe Zhang and Aimin Zhou and Qi Chen and Bing Xue and Mengjie Zhang", title = "{SR-Forest:} A Genetic Programming based Heterogeneous Ensemble Learning Method", journal = "IEEE Transactions on Evolutionary Computation", year = "2024", volume = "28", number = "5", pages = "1484--1498", month = oct, keywords = "genetic algorithms, genetic programming, evolutionary forest, random forest, evolutionary feature construction", ISSN = "1941-0026", DOI = "doi:10.1109/TEVC.2023.3243172", size = "15 pages", abstract = "Ensemble learning methods have been widely used in machine learning in recent years due to their high predictive performance. With the development of genetic programming-based symbolic regression methods, many papers begin to choose a popular ensemble learning method, random forests, as the baseline competitor. Instead of considering them as competitors, an alternative idea might be to consider symbolic regression as an enhancement technique for random forest. Genetic programming-based symbolic regression methods which fit a smooth function are complementary to the piecewise nature of decision trees, as the smooth variation is common in regression problems. In this article, we propose to form an ensemble model with symbolic regression-based decision trees to address this issue. Furthermore, we design a guided mutation operator to speed up the search on high-dimensional problems, a multi-fidelity evaluation strategy to reduce the computational cost and an ensemble selection mechanism to improve predictive performance. Finally, experimental results on a regression benchmark with 120 datasets show that the proposed ensemble model outperforms 25 existing symbolic regression and ensemble learning methods. Moreover, the proposed method can provide notable insights on an XGBoost hyperparameter performance prediction task, which is an important application area of ensemble learning methods.", notes = "Also known as \cite{10040601}", } @InProceedings{Zhang:2023:EuroGP, author = "Hengzhe Zhang and Qi Chen and Alberto Tonda and Bing Xue and Wolfgang Banzhaf and Mengjie Zhang", title = "{MAP}-Elites with Cosine-Similarity for Evolutionary Ensemble Learning", booktitle = "EuroGP 2023: Proceedings of the 26th European Conference on Genetic Programming", year = "2023", month = "12-14 " # apr, editor = "Gisele Pappa and Mario Giacobini and Zdenek Vasicek", series = "LNCS", volume = "13986", publisher = "Springer Verlag", address = "Brno, Czech Republic", pages = "84--100", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Evolutionary ensemble learning, Quality diversity optimization, Multi-dimensional Archive of Phenotypic Elites", isbn13 = "978-3-031-29572-0", URL = "https://rdcu.be/c8UP0", DOI = "doi:10.1007/978-3-031-29573-7_6", size = "17 pages", abstract = "Evolutionary ensemble learning methods with Genetic Programming have achieved remarkable results on regression and classification tasks by employing quality-diversity optimization techniques like MAP-Elites and Neuro-MAP-Elites. The MAP-Elites algorithm uses dimensionality reduction methods, such as variational auto-encoders, to reduce the high-dimensional semantic space of genetic programming to a two-dimensional behavioral space. Then, it constructs a grid of high-quality and diverse models to form an ensemble model. In MAP-Elites, however, variational auto-encoders rely on Euclidean space topology, which is not effective at preserving high-quality individuals. To solve this problem, this paper proposes a principal component analysis method based on a cosine-kernel for dimensionality reduction. In order to deal with unbalanced distributions of good individuals, we propose a zero-cost reference points synthesizing method. Experimental results on 108 datasets show that combining principal component analysis using a cosine kernel with reference points significantly improves the performance of the MAP-Elites evolutionary ensemble learning algorithm.", notes = "Part of \cite{Pappa:2023:GP} EuroGP'2023 held in conjunction with EvoCOP2023, EvoMusArt2023 and EvoApplications2023", } @InProceedings{Zhang:2023:GECCO, author = "Hengzhe Zhang and Qi Chen and Bing Xue and Wolfgang Banzhaf and Mengjie Zhang", title = "A Double Lexicase Selection Operator for Bloat Control in Evolutionary Feature Construction for Regression", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "1194--1202", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, bloat control, evolutionary feature construction", isbn13 = "9798400701191", URL = "https://doi.org/10.1145/3583131.3590365", DOI = "doi:10.1145/3583131.3590365", suppl_url = "https://dl.acm.org/action/downloadSupplement?doi=10.1145%2F3583131.3590365&file=p1194-zhang-suppl.pdf", size = "9 pages", abstract = "Evolutionary feature construction is an important technique in the machine learning domain for enhancing learning performance. However, traditional genetic programming-based feature construction methods often suffer from bloat, which means the sizes of constructed features increase excessively without improved performance. To address this issue, this paper proposes a double-stage lexicase selection operator to control bloat while not damaging search effectiveness. This new operator contains a two-stage selection process, where the first stage selects individuals based on fitness values and the second stage selects individuals based on tree sizes. Therefore, the proposed operator can control bloat meanwhile leveraging the advantage of the lexicase selection operator. Experimental results on 98 regression datasets show that compared to the traditional bloat control method of having a depth limit, the proposed selection operator not only significantly reduces the sizes of constructed features on all datasets but also keeps a similar level of predictive performance. A comparative experiment with seven bloat control methods shows that the double lexicase selection operator achieves the best trade-off between the model performance and the model size.", notes = " GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @InProceedings{zhang:2023:GECCOcompA, author = "Hengzhe Zhang and Aimin Zhou and Qi Chen and Bing Xue and Mengjie Zhang", title = "Genetic {Programming-Based} Evolutionary Feature Construction for Heterogeneous Ensemble Learning [Hot of the Press]", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Alberto Moraglio", pages = "49--50", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, evolutionary feature construction, heterogeneous ensemble learning", isbn13 = "9798400701191", DOI = "doi:10.1145/3583133.3595831", size = "2 pages", abstract = "This Hof-off-the-Press paper summarizes our recently published work, {"}SR-Forest: A Genetic Programming based Heterogeneous Ensemble Learning Method,{"} published in IEEE Transactions on Evolutionary Computation [4]. This paper presents SR-Forest, a novel genetic programming-based heterogeneous ensemble learning method, which combines the strengths of decision trees and genetic programming-based symbolic regression methods. Rather than treating genetic programming-based symbolic regression methods as competitors to random forests, we propose to enhance the performance of random forests by incorporating genetic programming as a complementary technique. We introduce a guided mutation operator, a multi-fidelity evaluation strategy, and an ensemble selection mechanism to accelerate the search process, reduce computational costs, and improve predictive performance. Experimental results on a regression benchmark with 120 datasets show that SR-Forest outperforms 25 existing symbolic regression and ensemble learning methods. Moreover, we demonstrate the effectiveness of SR-Forest on an XGBoost hyperparameter performance prediction task, which is an important application area of ensemble learning methods. Overall, SR-Forest provides a promising approach to solving regression problems and can serve as a valuable tool in real-world applications.", notes = "Also known as \cite{zhang:2023:GECCOcomp}. GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @Article{Zhang:2023:ieeeCIM, author = "Hengzhe Zhang and Qi Chen and Bing Xue and Wolfgang Banzhaf and Mengjie Zhang", title = "MAP-Elites for Genetic Programming-Based Ensemble Learning: An Interactive Approach [AI-eXplained]", journal = "IEEE Computational Intelligence Magazine", year = "2023", volume = "18", number = "4", pages = "62--63", month = "17 " # oct, keywords = "genetic algorithms, genetic programming, AIX, Dimensionality reduction, Measurement, Semantics, Predictive models, Prediction algorithms, Genetics, Behavioral sciences, Ensemble learning, Interactive systems", ISSN = "1556-603X", DOI = "doi:10.1109/MCI.2023.3304085", size = "2 pages", notes = "Also known as \cite{10287177}", } @InProceedings{zhang:2023:PRICAI, author = "Hengzhe Zhang and Qi Chen and Bing Xue and Wolfgang Banzhaf and Mengjie Zhang", title = "Automatically Choosing Selection Operator Based on Semantic Information in Evolutionary Feature Construction", booktitle = "Pacific Rim International Conference on Artificial Intelligence", year = "2023", editor = "Fenrong Liu and Arun Anand Sadanandan and Duc Nghia Pham and Petrus Mursanto and Dickson Lukose", volume = "14326", series = "Lecture Notes in Computer Science", pages = "385--397", address = "Jakarta, Indonesia", month = nov # " 17-19", publisher = "Springer Nature", keywords = "genetic algorithms, genetic programming, Evolutionary Feature Construction, Adaptive Operator Selection", isbn13 = "978-981-99-7022-3", DOI = "doi:10.1007/978-981-99-7022-3_36", abstract = "In recent years, genetic programming-based evolutionary feature construction has shown great potential in various applications. However, a critical challenge in applying this technique is the need to select an appropriate selection operator with great care. To tackle this issue, this paper introduces a novel approach that leverages the Thompson sampling technique to automatically choose the optimal selection operator based on semantic information of genetic programming models gathered during the evolutionary process. The experimental results on a standard symbolic regression benchmark containing 37 datasets show that the proposed adaptive operator selection algorithm outperforms expert-designed operators, demonstrating the effectiveness of the adaptive operator selection algorithm.", notes = "https://www.pricai.org/2023/", } @Article{Hengzhe_Zhang:ieeeTEC, author = "Hengzhe Zhang and Qi Chen and Bing Xue and Wolfgang Banzhaf and Mengjie Zhang", title = "Modular Multi-Tree Genetic Programming for Evolutionary Feature Construction for Regression", journal = "IEEE Transactions on Evolutionary Computation", year = "2024", volume = "28", number = "5", pages = "1455--1469", month = oct, keywords = "genetic algorithms, genetic programming, evolutionary forest, random forest, evolutionary feature construction, modularity", ISSN = "1089-778X", URL = "https://homepages.ecs.vuw.ac.nz/~mengjie/papers/", DOI = "doi:10.1109/TEVC.2023.3318638", size = "15 pages", notes = "(Accepted on 11 September 2023) also known as \cite{10261487}", } @Article{Zhang:2024:GPEM, author = "Hengzhe Zhang and Qi Chen and Bing Xue and Wolfgang Banzhaf and Mengjie Zhang", title = "A geometric semantic macro-crossover operator for evolutionary feature construction in regression", journal = "Genetic Programming and Evolvable Machines", year = "2024", volume = "25", pages = "Article number: 2", note = "Online first", keywords = "genetic algorithms, genetic programming, Geometric semantic genetic programming, Evolutionary feature construction", ISSN = "1389-2576", URL = "https://rdcu.be/efLzO", DOI = "doi:10.1007/s10710-023-09465-z", size = "23 pages", abstract = "Evolutionary feature construction has been successfully applied to various scenarios. In particular, multi-tree genetic programming-based feature construction methods have demonstrated promising results. However, existing crossover operators in multi-tree genetic programming mainly focus on exchanging genetic materials between two trees, neglecting the interaction between multi-trees within an individual. To increase search effectiveness, we take inspiration from the geometric semantic crossover operator used in single-tree genetic programming and propose a macro geometric semantic crossover operator for multi-tree genetic programming. This operator is designed for feature construction, with the goal of generating offspring containing informative and complementary features. Our experiments on 98 regression datasets show that the proposed geometric semantic macro-crossover operator significantly improves the predictive performance of the constructed features. Moreover, experiments conducted on a state-of-the-art regression benchmark demonstrate that multi-tree genetic programming with the geometric semantic macro-crossover operator can significantly outperform all 22 machine learning algorithms on the benchmark.", notes = "Center for Data Science and Artificial Intelligence and School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington 6140, New Zealand", } @Article{Hengzhe_Zhang:ieeeTEC2, author = "Hengzhe Zhang and Qi Chen and Bing Xue and Wolfgang Banzhaf and Mengjie Zhang", title = "A Semantic-Based Hoist Mutation Operator for Evolutionary Feature Construction in Regression", journal = "IEEE Transactions on Evolutionary Computation", year = "2024", volume = "28", number = "6", pages = "1689--1703", month = dec, keywords = "genetic algorithms, genetic programming, Lifting equipment, Semantics, Statistics, Sociology, Contracts, Complexity theory, Machine learning, Evolutionary feature construction, bloat control, evolutionary machine learning", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2023.3331234", notes = "also known as \cite{10312754}", } @InProceedings{Zhang:2024:EuroGP, author = "Hengzhe Zhang and Qi Chen and Bing Xue and Wolfgang Banzhaf and Mengjie Zhang", editor = "Mario Giacobini and Bing Xue and Luca Manzoni", title = "Improving Generalization of Evolutionary Feature Construction with Minimal Complexity Knee Points in Regression", booktitle = "EuroGP 2024: Proceedings of the 27th European Conference on Genetic Programming", year = "2024", volume = "14631", series = "LNCS", publisher = "Springer", address = "Aberystwyth", month = "3-5 " # apr, organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming", pages = "142--158", abstract = "Genetic programming-based evolutionary feature construction is a widely used technique for automatically enhancing the performance of a regression algorithm. While it has achieved great success, a challenging problem in feature construction is the issue of overfitting, which has led to the development of many multi-objective methods to control overfitting. However, for multi-objective methods, a key issue is how to select the final model from the front with different trade-offs. To address this challenge, in this paper, we propose a novel minimal complexity knee point selection strategy in evolutionary multi-objective feature construction for regression to select the final model for making predictions. Experimental results on 58 datasets demonstrate the effectiveness and competitiveness of this strategy when compared to eight existing methods. Furthermore, an ensemble of the proposed strategy and existing model selection strategies achieves the best performance and outperforms four popular machine learning algorithms.", isbn13 = "978-3-031-56957-9", DOI = "doi:10.1007/978-3-031-56957-9_9", notes = "Part of \cite{Giacobini:2024:GP} EuroGP'2024 held in conjunction with EvoCOP2024, EvoMusArt2024 and EvoApplications2024", } @InProceedings{zhang:2024:GECCO2, author = "Hengzhe Zhang and Qi Chen and Bing Xue and Wolfgang Banzhaf and Mengjie Zhang", title = "Bias-Variance Decomposition: An Effective Tool to Improve Generalization of Genetic Programming-based Evolutionary Feature Construction for Regression", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference", year = "2024", editor = "Ting Hu and Aniko Ekart and Julia Handl and Xiaodong Li and Markus Wagner and Mario Garza-Fabre and Kate Smith-Miles and Richard Allmendinger and Ying Bi and Grant Dick and Amir H Gandomi and Marcella Scoczynski Ribeiro Martins and Hirad Assimi and Nadarajen Veerapen and Yuan Sun and Mario Andres Munyoz and Ahmed Kheiri and Nguyen Su and Dhananjay Thiruvady and Andy Song and Frank Neumann and Carla Silva", pages = "998--1006", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, bias-variance decompostion, automated machine learning, evolutionary feature construction", isbn13 = "979-8-4007-0494-9", DOI = "doi:10.1145/3638529.3654075", size = "9 pages", abstract = "Evolutionary feature construction is a technique that has been widely studied in the domain of automated machine learning. A key challenge that needs to be addressed in feature construction is its tendency to overfit the training data. Instead of the traditional approach to control overfitting by reducing model complexity, this paper proposes to control overfitting based on bias-variance decomposition. Specifically, this paper proposes reducing the variance of a model, i.e., reducing the variance of predictions when exposed to data with injected noise, to improve its generalization performance within a multi-objective optimization framework. Experiments conducted on 42 datasets demonstrate that the proposed method effectively controls overfitting and outperforms six model complexity measures for overfitting control. Moreover, further analysis reveals that controlling overfitting adhering to bias-variance decomposition outperforms several plausible variants, highlighting the importance of controlling overfitting based on solid machine learning theory.", notes = "GECCO-2024 GP A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{zhang:2024:GECCOcomp, author = "Hengzhe Zhang and Qi Chen and Bing Xue and Wolfgang Banzhaf and Mengjie Zhang", title = "A Semantic-based Hoist Mutation Operator for Evolutionary Feature Construction in Regression [Hot off the Press]", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion", year = "2024", editor = "Marcus Gallagher", pages = "65--66", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, bloat control, automated machine learning, evolutionary feature construction", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3664071", size = "2 pages", abstract = "This Hot-off-the-Press paper summarizes our recently published work, {"}A Semantic-based Hoist Mutation Operator for Evolutionary Feature Construction in Regression{"} [9] published in IEEE Transactions on Evolutionary Computation. Our study introduces a semantic-based hoist mutation operator to control tree bloat and reduce tree sizes in genetic programming (GP) based evolutionary feature construction algorithms. The proposed operator identifies the most informative subtree with the largest cosine similarity to the target semantics and then hoists the subtree to the root as a new GP tree. This process reduces the tree sizes without compromising learning capability. The proposed operator is supported by the probably approximately correct (PAC) learning theory, ensuring that it does not degrade the generalization upper bound of GP models. By employing a hashing-based redundancy checking strategy, the proposed method outperforms seven bloat control methods on 98 datasets in reducing model size while maintaining test performance at the same level. These findings demonstrate the capability of using semantic hoist mutation for bloat control in GP-based evolutionary feature construction.", notes = "GECCO-2024 A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{Zhang:2024:PPSN, author = "Hengzhe Zhang and Qi Chen and Bing Xue and Mengjie Zhang and Wolfgang Banzhaf", title = "P-Mixup: Improving Generalization Performance of Evolutionary Feature Construction with Pessimistic Vicinal Risk Minimization", booktitle = "18th International Conference on Parallel Problem Solving from Nature", year = "2024", editor = "Heike Trautmann and Tea Tusar and Penousal Machado and Thomas Baeck", volume = "15148", series = "Lecture Notes in Computer Science", pages = "201--220", address = "University of Applied Sciences Upper Austria, Hagenberg, Austria", month = "13-18 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Evolutionary Machine Learning, Symbolic Regression, Feature Construction, Vicinal Risk Minimization", isbn13 = "978-3-031-70054-5", URL = "https://rdcu.be/d5m7G", DOI = "doi:10.1007/978-3-031-70055-2_13", size = "20 pages", notes = "https://ppsn2024.fh-ooe.at/program/", } @InProceedings{zhang:2025:EuroGP, author = "Hengzhe Zhang and Alberto Tonda and Qi Chen and Bing Xue and Evelyne Lutton and Mengjie Zhang", title = "Micro-Step Time-Series Regression: Insights from System Identification Using Symbolic Regression", booktitle = "European Conference on Genetic Programming, EuroGP 2025", year = "2025", editor = "Bing Xue and Luca Manzoni and Illya Bakurov", volume = "15609", series = "LNCS", pages = "240--256", address = "Trieste", month = "23-25 " # apr, publisher = "Springer Nature", keywords = "genetic algorithms, genetic programming, Time-Series Forecasting, Evolutionary Feature Construction, Dynamic Systems", isbn13 = "978-3-031-89990-4", URL = "https://rdcu.be/ejgzl", DOI = "doi:10.1007/978-3-031-89991-1_15", abstract = "Time-series forecasting is widely applied across various domains, yet most approaches rely on predefined time steps given by each problem. Based on observations from dynamic systems with known ground truth, we identify that large-step forecasts can lead to substantial errors due to insufficient modelling of continuous dynamics. To address this, we propose a micro-step time-series regression technique that decomposes predictions into smaller intervals, so that genetic programming-based feature construction can capture finer temporal patterns to improve the prediction performance. Specifically, we employ linear interpolation to allow the evolutionary feature construction process to learn from incremental changes, reducing the difficulty of time-series regression. Experiments on 100 datasets from the M4 forecasting benchmark demonstrate that micro-step regression significantly enhances prediction accuracy compared to traditional methods using raw time steps. Further analysis reveals that features trained on micro-step data evolve into simpler structures, promoting both generalisation and interpretability.", notes = "also known as \cite{paper_56_system_identification_using_symbolic_regression} Part of \cite{Xue:2025:GP} EuroGP'2025 held in conjunction with EvoCOP2025, EvoMusArt2025 and EvoApplications2025", } @InProceedings{Zhang:2020:SMC, author = "Hu Zhang and Hengzhe Zhang and Aimin Zhou", title = "A Multi-metric Selection Strategy for Evolutionary Symbolic Regression", booktitle = "2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)", year = "2020", pages = "585--591", abstract = "Evaluation metrics play an important role in accessing the performance of a regression method. In practice, these multiple evaluation metrics can be used in two ways. The first way defines a loss function by aggregating multiple metrics, while the second way defines a multiobjective loss function by considering each metric as an objective function. In this paper, we propose a new way to use multiple evaluation metrics, which is different from the aggregating method and the mutliobjective method. Our method is based on genetic programming. The idea is to randomly use one metric in each iteration of the selection operator. Therefore, multiple metrics can be used alternatively in the running process. To validate the effectiveness of our new approach, we conduct experiments on ten benchmark datasets. The experimental results show that the new approach can improve the population diversity, and can achieve the performance better than or similar to that of the traditional symbolic regression algorithms.", keywords = "genetic algorithms, genetic programming, Measurement, Heuristic algorithms, Sociology, Benchmark testing, Linear programming, Statistics, symbolic regression, multi-metric selection", DOI = "doi:10.1109/SMC42975.2020.9283385", ISSN = "2577-1655", month = oct, notes = "Also known as \cite{9283385}", } @InProceedings{Zhang3:2009:cec, author = "Jianwei Zhang and Zhijian Wu and Zongyue Wang and Jinglei Guo and Zhangcan Huang", title = "Unconstrained Gene Expression Programming", booktitle = "2009 IEEE Congress on Evolutionary Computation", year = "2009", editor = "Andy Tyrrell", pages = "2043--2048", address = "Trondheim, Norway", month = "18-21 " # may, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-2959-2", file = "P208.pdf", DOI = "doi:10.1109/CEC.2009.4983192", abstract = "Many linear structured genetic programming are proposed in the past years. Gene expression programming, as a classic linear represented genetic programming, is powerful in solving problems of data mining and knowledge discovery. Constrains of gene expression programming like head-tail mechanism do contribution to the legality of chromosome. however, they impair the flexibility and adaptability of chromosome to some extend. Inspired by the diversity of chromosome arrangements in biology, an unconstrained encoded gene expression programming is proposed to overcome above constraints. In this way, the search space is enlarged; meanwhile the parallelism and the adaptability are enhanced. A group of regression and classification experiments also show that unconstrained gene expression programming performs better than classic gene expression programming.", keywords = "genetic algorithms, genetic programming, gene expression programming", notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR", } @InProceedings{Zhang:2009:ISA, author = "Jianwei Zhang and Zhijian Wu and Jinglei Guo and Min Peng and Yingjiang Zhang and Chunzhi Wang", title = "Evolving Classification Rules by Unconstrained Gene Expression Programming", booktitle = "International Workshop on Intelligent Systems and Applications, ISA 2009", year = "2009", month = may, abstract = "Unconstrained Gene Expression Programming (UGEP), a new unconstrained linear encoded Gene Expression Programming (GEP), is introduced and applied to solve classification problems in this paper. Different from GEP, both amount and length of the genes are dynamically adjusted in the UGEP chromosome during the evolution process. Experiment results indicate that UGEP perform better than GEP in classification problems.", keywords = "genetic algorithms, genetic programming, gene expression programming, classification rules, data mining, data mining", DOI = "doi:10.1109/IWISA.2009.5072858", notes = "Also known as \cite{5072858}", } @InProceedings{DBLP:conf/kbse/ZhangCHXXZM14, author = "Jie Zhang2 and Junjie Chen and Dan Hao and Yingfei Xiong and Bing Xie and Lu Zhang and Hong Mei", title = "Search-based inference of polynomial metamorphic relations", booktitle = "{ACM/IEEE} International Conference on Automated Software Engineering, ASE'14", year = "2014", editor = "Ivica Crnkovic and Marsha Chechik and Paul Gruenbacher", pages = "701--712", address = "Vasteras, Sweden", month = sep # " 15-19", keywords = "genetic algorithms, genetic programming, PSO, SBSE, Metamorphic testing, Invariant inference, Particle swarm optimization", URL = "http://doi.acm.org/10.1145/2642937.2642994", DOI = "doi:10.1145/2642937.2642994", timestamp = "Thu, 15 Jun 2017 21:35:06 +0200", biburl = "https://dblp.org/rec/bib/conf/kbse/ZhangCHXXZM14", bibsource = "dblp computer science bibliography, https://dblp.org", size = "12 pages", abstract = "Metamorphic testing (MT) is an effective methodology for testing those so-called ``non-testable'' programs (e.g., scientific programs), where it is sometimes very difficult for testers to know whether the outputs are correct. In metamorphic testing, metamorphic relations (MRs) (which specify how particular changes to the input of the program under test would change the output) play an essential role. However, testers may typically have to obtain MRs manually. In this paper, we propose a search-based approach to automatic inference of polynomial MRs for a program under test. In particular, we use a set of parameters to represent a particular class of MRs, which we refer to as polynomial MRs, and turn the problem of inferring MRs into a problem of searching for suitable values of the parameters. We then dynamically analyze multiple executions of the program, and use particle swarm optimization to solve the search problem. To improve the quality of inferred MRs, we further use MR filtering to remove some inferred MRs. We also conducted three empirical studies to evaluate our approach using four scientific libraries (including 189 scientific functions). From our empirical results, our approach is able to infer many high-quality MRs in acceptable time (i.e., from 9.87 seconds to 1231.16 seconds), which are effective in detecting faults with no false detection.", notes = "uses genetic programming to discover metamorphic relations over programs", } @InProceedings{Zhang:2013:PIMRC, author = "Jietao Zhang and Chunhua Sun and Youwen Yi and Hongcheng Zhuang", title = "A hybrid framework for capacity and coverage optimization in self-organizing LTE networks", booktitle = "24th IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC 2013)", year = "2013", month = sep, pages = "2919--2923", address = "London", keywords = "genetic algorithms, genetic programming, Self-Organising Networks, Capacity and Coverage Optimisation, Multi-Objective Optimisation", DOI = "doi:10.1109/PIMRC.2013.6666646", ISSN = "2166-9570", abstract = "In this paper, we address the capacity and coverage optimisation (CCO) problem for LTE networks by means of self-organising network (SON) techniques. A novel hybrid two-layer optimisation framework is proposed to enhance the network capacity and coverage, where on the top layer a network entity of eCoordinator is implemented to ensure overall network coverage by optimising the antenna tilt and capacity-coverage weight of each cell in a centralised manner, and on the bottom layer individual eNB optimises cell-specific capacity and coverage by tuning its pilot power in a distributed manner. A heuristic algorithm is developed for the eCoordinator operation at large time granularity and the Genetic Programming (GP) approach is exploited for the eNB operation at small time granularity, for the purpose of tracking overall network performance as well as adapting to network dynamics. Our simulation results have demonstrated the usefulness of the proposed algorithms by enhancing network capacity and coverage performance under various system requirements.", notes = "Also known as \cite{6666646}", } @InProceedings{zhang:2022:GECCOcomp2, author = "Jinting Zhang and Ting Hu", title = "Regulatory {Genotype-to-Phenotype} Mappings Improve Evolvability in Genetic Programming", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion", year = "2022", editor = "Heike Trautmann and Carola Doerr and Alberto Moraglio and Thomas Bartz-Beielstein and Bogdan Filipic and Marcus Gallagher and Yew-Soon Ong and Abhishek Gupta and Anna V Kononova and Hao Wang and Michael Emmerich and Peter A. N. Bosman and Daniela Zaharie and Fabio Caraffini and Johann Dreo and Anne Auger and Konstantin Dietric and Paul Dufosse and Tobias Glasmachers and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Tea Tusar and Dimo Brockhoff and Tome Eftimov and Pascal Kerschke and Boris Naujoks and Mike Preuss and Vanessa Volz and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Mark Coletti and Catherine (Katie) Schuman and Eric ``Siggy'' Scott and Robert Patton and Paul Wiegand and Jeffrey K. Bassett and Chathika Gunaratne and Tinkle Chugh and Richard Allmendinger and Jussi Hakanen and Daniel Tauritz and John Woodward and Manuel Lopez-Ibanez and John McCall and Jaume Bacardit and Alexander Brownlee and Stefano Cagnoni and Giovanni Iacca and David Walker and Jamal Toutouh and UnaMay O'Reilly and Penousal Machado and Joao Correia and Sergio Nesmachnow and Josu Ceberio and Rafael Villanueva and Ignacio Hidalgo and Francisco {Fernandez de Vega} and Giuseppe Paolo and Alex Coninx and Antoine Cully and Adam Gaier and Stefan Wagner and Michael Affenzeller and Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and William B. Langdon and Justyna Petke and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Paetzel and Alexander Wagner and Michael Heider and Nadarajen Veerapen and Katherine Malan and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Mohammad Nabi Omidvar and Yuan Sun and Ernesto Tarantino and De Falco Ivanoe and Antonio {Della Cioppa} and Scafuri Umberto and John Rieffel and Jean-Baptiste Mouret and Stephane Doncieux and Stefanos Nikolaidis and Julian Togelius and Matthew C. Fontaine and Serban Georgescu and Francisco Chicano and Darrell Whitley and Oleksandr Kyriienko and Denny Dahl and Ofer Shir and Lee Spector and Alma Rahat and Richard Everson and Jonathan Fieldsend and Handing Wang and Yaochu Jin and Erik Hemberg and Marwa A. Elsayed and Michael Kommenda and William {La Cava} and Gabriel Kronberger and Steven Gustafson", pages = "623--626", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, genotype-to-phenotype mapping, robustness, evolvability", isbn13 = "978-1-4503-9268-6/22/07", DOI = "doi:10.1145/3520304.3529043", video_url = "https://vimeo.com/725585143", abstract = "Most genotype-to-phenotype mappings in EAs are redundant, i.e., multiple genotypes can map to the same phenotype. Phenotypes are accessible from one to another through point mutations. However, these mutational connections can be unevenly distributed among phenotypes. Quantitative analysis of such connections helps better characterize the robustness and evolvability of an EA. In this study, we propose two genotype-to-phenotype mapping mechanisms for linear genetic programming (LGP), where the execution and output of a linear genetic program are varied by a regulator. We investigate how such regulatory mappings can alter the genotypic connections among different phenotypes and the robustness and evolvability of phenotypes. We also compare the search ability of LGP using the conventional mapping versus the regulatory mappings, and observe that the regulatory mappings improve the efficiency in all three search scenarios, including random walk, hill climbing, and novelty search.", notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @Article{Zhang:2009:AMSS, author = "Kai Zhang and Hui Zhou and Dawei Hu and Yang Zhao and Xiating Feng", title = "Theoretical model of effective stress coefficient for rock/soil-like porous materials", journal = "Acta Mechanica Solida Sinica", year = "2009", volume = "22", pages = "251--260", number = "3", address = "Wuhan, China", keywords = "genetic algorithms, genetic programming, rock/soil-like porous materials", ISSN = "0894-9166", URL = "http://www.sciencedirect.com/science/article/B984H-4WV72Y9-8/2/282828625dc141a36342e982db9762d4", DOI = "doi:10.1016/S0894-9166(09)60272-X", abstract = "Physical mechanisms and influencing factors on the effective stress coefficient for rock/soil-like porous materials are investigated, based on which equivalent connectivity index is proposed. The equivalent connectivity index, relying on the meso-scale structure of porous material and the property of liquid, denotes the connectivity of pores in Representative Element Area (REA). If the conductivity of the porous material is anisotropic, the equivalent connectivity index is a second order tensor. Based on the basic theories of continuous mechanics and tensor analysis, relationship between area porosity and volumetric porosity of porous materials is deduced. Then a generalised expression, describing the relation between effective stress coefficient tensor and equivalent connectivity tensor of pores, is proposed, and the expression can be applied to isotropic media and also to anisotropic materials. Furthermore, evolution of porosity and equivalent connectivity index of the pore are studied in the strain space, and the method to determine the corresponding functions in expressions above is proposed using genetic algorithm and genetic programming. Two applications show that the results obtained by the method in this paper perfectly agree with the test data. This paper provides an important theoretical support to the coupled hydro-mechanical research.", } @Article{ZHANG:2019:JEM, author = "Kefeng Zhang and Ana Deletic and Peter M. Bach and Baiqian Shi and Jon M. Hathaway and David T. McCarthy", title = "Testing of new stormwater pollution build-up algorithms informed by a genetic programming approach", journal = "Journal of Environmental Management", volume = "241", pages = "12--21", year = "2019", ISSN = "0301-4797", DOI = "doi:10.1016/j.jenvman.2019.04.009", URL = "http://www.sciencedirect.com/science/article/pii/S0301479719304669", keywords = "genetic algorithms, genetic programming, Stormwater quality model, Temperature, Non-conventional sources, Pollution emission, Stochastic modelling", abstract = "Pollution build-up and wash-off processes are often included in urban stormwater quality models. However, these models are often unreliable and have poor performance at large scales and in complicated catchments. This study tried to improve stormwater quality models by adopting the genetic programming (GP) approach to generate new build-up algorithms for three different pollutants (total suspend solids - TSS, total phosphorus - TP and total nitrogen - TN). This was followed by testing of the new models (also traditional build-up and wash-off models as benchmark) using data collected from different catchments in Australia and the USA. The GP approach informed new sets of build-up algorithms with the inclusion of not just the typical antecedent dry weather period (ADWP), but also other less `traditional' variables - previous rainfall depth for TSS and maximum air temperatures for TP and TN simulation. The traditional models had relatively poor performance (Nash-Sutcliffe coefficient, ??), except for TP at Gilby Road (GR) (Ea ?? in calibration and 0.43 in validation). Improved performance was observed using the models with new build-up algorithms informed by GP. Taking TP at GR for example, the best performing model had E of 0.46 in calibration and 0.54 in validation. The best performing models for TSS, TP, and TN are often different, suggesting that specific models shall be used for different pollutants. Insights into further improvements possible for stormwater quality models were given. It is recommended that in addition to the typical build-up and wash-off process, new generations of stormwater quality models should be able to account for the non-conventional pollutant sources (e.g. cross-connections, septic tank leakage, illegal discharges) through stochastic approaches. Emission inventories with information like intensity-frequency-duration (IFD) of pollutant loads from each type of non-conventional source are suggested to be built for stochastic modelling", } @InProceedings{Zhang:2006:WCICA, author = "Kejun Zhang and Yuxia Hu and Gang Liu", title = "An Improved Gene Expression Programming for Solving Inverse Problem", booktitle = "The Sixth World Congress on Intelligent Control and Automation, WCICA 2006", year = "2006", volume = "1", pages = "3371--3375", month = "21-23 " # jun, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Gene expression programming", ISBN = "1-4244-0332-4", DOI = "doi:10.1109/WCICA.2006.1712993", abstract = "The basic principle of Gene expression programming (GEP) is introduced in this paper. An improved GEP algorithm called IGEP based on dynamic mutation operator which dealing with the inverse problem of parameter identification of complex function is presented, the algorithm complexity of the IGEP was given in the paper, furthermore, many simulation results show that the models set up by the paper are better than the models set up by classic GEP. A future study will consider the effects of applying IGEP to the inverse problem which sensitive to the time period.", } @Article{Zhang:2013:NC, author = "Kejun Zhang and Shouqian Sun", title = "Web music emotion recognition based on higher effective gene expression programming", journal = "Neurocomputing", year = "2013", volume = "105", pages = "100--106", month = "1 " # apr, keywords = "genetic algorithms, genetic programming, Gene expression programming, RGEP, Support vector machine, SVM, Music information retrieval, Music emotion recognition", ISSN = "0925-2312", URL = "http://www.sciencedirect.com/science/article/pii/S0925231212007035", DOI = "doi:10.1016/j.neucom.2012.06.041", size = "7 pages", abstract = "In the study, we present a higher effective algorithm, called revised gene expression programming (RGEP), to construct the model for music emotion recognition. Our main contributions are as follows: firstly, we describe the basic mechanisms of music emotion recognition and introduce gene expression programming (GEP) to deal with the model construction for music emotion recognition. Secondly, we present RGEP based on backward-chaining evolutionary algorithm and use GEP, RGEP, and SVM to construct the models for music emotion recognition separately, the results show that the models obtained by SVM, GEP, and RGEP are satisfactory and well confirm the experimental values. Finally, we report the comparison of these models, and we find that the model obtained by RGEP outperforms classification accuracy of the model by GEP and takes almost 15percent less processing time of GEP and even half processing time of SVM, which offers a new efficient way for solving music emotion recognition problems; moreover, because processing time is essential for the problem of large scale music information retrieval, therefore, RGEP might prompt the development of the music information retrieval technology.", notes = "Learning for Scalable Multimedia Representation", } @Article{zhang:2018:MBEC, author = "Li Zhang and Jiasheng Chen and Chunming Gao and Chuanmiao Liu and Kuihua Xu", title = "An efficient model for auxiliary diagnosis of hepatocellular carcinoma based on gene expression programming", journal = "Medical \& Biological Engineering \& Computing", year = "2018", volume = "56", number = "10", keywords = "genetic algorithms, genetic programming, gene expression programming", URL = "http://link.springer.com/article/10.1007/s11517-018-1811-6", DOI = "doi:10.1007/s11517-018-1811-6", notes = "Department of Infectious Disease, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China", } @Article{Zhang:2005:MSSP, author = "Liang Zhang and Lindsay B. Jack and Asoke K. Nandi", title = "Fault detection using genetic programming", journal = "Mechanical Systems and Signal Processing", year = "2005", volume = "19", pages = "271--289", number = "2", abstract = "Genetic programming (GP) is a stochastic process for automatically generating computer programs. GP has been applied to a variety of problems which are too wide to reasonably enumerate. As far as the authors are aware, it has rarely been used in condition monitoring (CM). GP is used to detect faults in rotating machinery. Featuresets from two different machines are used to examine the performance of two-class normal/fault recognition. The results are compared with a few other methods for fault detection: Artificial neural networks (ANNs) have been used in this field for many years, while support vector machines (SVMs) also offer successful solutions. For ANNs and SVMs, genetic algorithms have been used to do feature selection, which is an inherent function of GP. In all cases, the GP demonstrates performance which equals or betters that of the previous best performing approaches on these data sets. The training times are also found to be considerably shorter than the other approaches, whilst the generated classification rules are easy to understand and independently validate.", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6WN1-4CJVC9S-1/2/16e55d1f86d4a8227c9e01e7b37e449d", month = mar, keywords = "genetic algorithms, genetic programming, Feature selection, Condition monitoring, Fault detection, Roller bearing", DOI = "doi:10.1016/j.ymssp.2004.03.002", } @Article{Zhang:2007:MSSP, author = "Liang Zhang and Asoke K. Nandi", title = "Fault classification using genetic programming", journal = "Mechanical Systems and Signal Processing", year = "2007", volume = "21", number = "3", pages = "1273--1284", month = apr, keywords = "genetic algorithms, genetic programming, Condition monitoring, Multi-class classification, Fault classification, Roller bearing", DOI = "doi:10.1016/j.ymssp.2006.04.004", abstract = "Genetic programming (GP) is a stochastic process for automatically generating computer programs. In this paper, three GP-based approaches for solving multi-class classification problems in roller bearing fault detection are proposed. Single-GP maps all the classes onto the one-dimensional GP output. Independent-GPs singles out each class separately by evolving a binary GP for each class independently. Bundled-GPs also has one binary GP for each class, but these GPs are evolved together with the aim of selecting as few features as possible. The classification results and the features each algorithm has selected are compared with genetic algorithm (GA) based approaches GA/ANN and GA/SVM. Experiments show that bundled-GPs is strong in feature selection while retaining high performance, which equals or outperforms the two previous GA-based approaches.", } @Article{Zhang:2006:PR, author = "Liang Zhang and Asoke K. Nandi", title = "Neutral offspring controlling operators in genetic programming", journal = "Pattern Recognition", year = "2007", volume = "40", number = "10", pages = "2696--2705", month = oct, keywords = "genetic algorithms, genetic programming, Neutral offspring, Code bloat, Parsimony pressure", DOI = "doi:10.1016/j.patcog.2006.10.001", abstract = "Code bloat, one of the main issues of genetic programming (GP), slows down the search process, destroys program structures, and exhausts computer resources. To deal with these issues, two kinds of neutral offspring controlling operators are proposed non-neutral offspring (NNO) operators and non-larger neutral offspring (NLNO) operators. Two GP benchmark problems symbolic regression and 11-multiplexer are used to test the new operators. Experimental results indicate that NLNO is able to confine code bloat significantly and improve performance simultaneously, which NNO cannot do.", } @Article{ZHANG:2022:knosys, author = "Liang Zhang2 and Kefan Wang and Luyuan Xu and Wenjia Sheng and Qi Kang", title = "Evolving ensembles using multi-objective genetic programming for imbalanced classification", journal = "Knowledge-Based Systems", year = "2022", volume = "255", pages = "109611", keywords = "genetic algorithms, genetic programming", ISSN = "0950-7051", URL = "https://www.sciencedirect.com/science/article/pii/S0950705122008127", DOI = "doi:10.1016/j.knosys.2022.109611", abstract = "Multi-objective Genetic Programming (MGP) plays a prominent role in generating Pareto optimal classifier sets and making trade-offs among multiple classes adaptively. However, the existing MGP algorithms show poor performance and are difficult to implement when dealing with imbalanced classification problems. This work proposes a new MGP-based algorithm designed for imbalanced classification. Firstly, an efficient evolutionary strategy with nondominated sorting, environmental selection, and an archiving mechanism is designed to optimize the false positive rate, the false negative rate and reduce the size of the resulting tree. Then, a weighted ensemble decision is made according to each classifier's performance in the majority and minority classes to obtain final classification results. Experimental results on 21 binary-class datasets and 17 multi-class datasets show that the proposed method outperforms existing ones in several commonly used imbalanced classification metrics", } @Article{Zhang:2025:birob, author = "Liding Zhang and Kuanqi Cai and Zhenshan Bing and Chaoqun Wang and Alois Knoll", title = "Genetic Informed Trees ({GIT*):} Path planning via reinforced genetic programming heuristics", journal = "Biomimetic Intelligence and Robotics", year = "2025", pages = "100237", keywords = "genetic algorithms, genetic programming, Reinforced genetic programming, Generative heuristics, Optimal path planning", ISSN = "2667-3797", URL = "https://www.sciencedirect.com/science/article/pii/S2667379725000282", DOI = "doi:10.1016/j.birob.2025.100237", abstract = "Optimal path planning involves finding a feasible state sequence between a start and a goal that optimises an objective. This process relies on heuristic functions to guide the search direction. While a robust function can improve search efficiency and solution quality, current methods often overlook available environmental data and simplify the function structure due to the complexity of information relationships. This study introduces Genetic Informed Trees (GIT*), which improves upon Effort Informed Trees (EIT*) by integrating a wider array of environmental data, such as repulsive forces from obstacles and the dynamic importance of vertices, to refine heuristic functions for better guidance. Furthermore, we integrated reinforced genetic programming (RGP), which combines genetic programming with reward system feedback to mutate genotype-generative heuristic functions for GIT*. RGP leverages a multitude of data types, thereby improving computational efficiency and solution quality within a set timeframe. Comparative analyses demonstrate that GIT* surpasses existing single-query, sampling-based planners in problems ranging from R4 to R16 and was tested on a real-world mobile manipulation task. A video showcasing our experimental results is available at A video showcasing our experimental results is available at https://youtu.be/URjXbc_BiYg", } @InProceedings{Zhang:2019:CSCI, author = "Louis Zhang and Qijun Zhang", title = "Combined Genetic Programming and Neural Network Approaches to Electronic Modeling", booktitle = "2019 International Conference on Computational Science and Computational Intelligence (CSCI)", year = "2019", pages = "1533--1536", month = dec, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CSCI49370.2019.00284", abstract = "An approach combining genetic programming (GP), neural network and electrical knowledge equations is presented for electronic device modeling. The proposed model includes a GP-generated symbolic function accurately representing device behavior within the training range, and a knowledge equation providing reliable tendencies of electronic behavior outside the training range. A correctional neural network is trained to align the knowledge equations with the GP-generated symbolic functions at the boundary of training data. The proposed method is more robust than the GP-generated symbolic functions alone because of improved extrapolation ability, and more accurate than the knowledge equations alone because of the genetic program's ability to learn non-ideal relationships inherent in the practical data. The method is demonstrated by applying it to a practical high-frequency, high-power transistor called a HEMT (High-Electron Mobility Transistor) used in wireless transmitters.", notes = "Carleton University, Ottawa, ON, Canada Also known as \cite{9071417}", } @InProceedings{zhang:1999:GPmcod, author = "Mengjie Zhang and Victor Ciesielski", title = "Genetic Programming for Multiple Class Object Detection", booktitle = "12th Australian Joint Conference on Artificial Intelligence", year = "1999", editor = "Norman Foo", volume = "1747", series = "LNAI", pages = "180--192", address = "Sydney, Australia", publisher_address = "Berlin", month = "6-10 " # dec, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, Machine learning, Neural networks, Vision", ISBN = "3-540-66822-5", URL = "http://www.springer.com/computer/ai/book/978-3-540-66822-0", size = "13 pages", abstract = "We describe an approach to the use of genetic programming for object detection problems in which the locations of small objects of multiple classes in large pictures must be found. The evolved programs use a feature set computed from a square input field large enough to contain each of objects of interest and are applied, in moving window fashion, over the large pictures in order to locate the objects of interest. The fitness function is based on the detection rate and the false alarm rate. We have tested the method on three object detection problems of increasing difficulty with four different classes of interest. On pictures of easy and medium difficulty all objects are detected with no false alarms. On difficult pictures there are still significant numbers of errors, however the results are considerably better than those of a neural network based program for the same problems.", notes = "http://www.cse.unsw.edu.au/~ai99/", } @PhdThesis{mengjie-thesis, author = "Mengjie Zhang", title = "A Domain Independent Approach to {2D} Object Detection Based on the Neural and Genetic Paradigms", school = "Department of Computer Science, RMIT University", year = "2000", address = "Melbourne, Victoria, Australia", month = aug, keywords = "genetic algorithms, genetic programming, ANN, computer vision", URL = "http://www.mcs.vuw.ac.nz/~mengjie/papers/mengjie-thesis.pdf", URL = "http://goanna.cs.rmit.edu.au/~vc/papers/zhang-phd.pdf", URL = "http://citeseer.ist.psu.edu/zhang00domain.html", size = "255 pages", abstract = "The development of traditional object detection systems usually involves a time consuming investigation of good preprocessing and filtering methods and a hand-crafting of different programs for the extraction and selection of important image features in different problem domains. To avoid these problems, this thesis describes a domain independent approach to multiple class, translation and rotation invariant object detection problems without any preprocessing, segmentation and specific feature extraction. The approach is based on learning/adaptive methods -- neural networks, genetic algorithms and genetic programming. Rather than using... 6th 'a method which uses genetic programming to build the object detector'. Retina images.", notes = " Supervisors: Victor Ciesielski and James Thom", } @InProceedings{Zhang:evowks03, author = "Mengjie Zhang and Peter Andreae and Mark Pritchard", title = "Pixel Statistics and False Alarm Area in Genetic Programming for Object Detection", booktitle = "Applications of Evolutionary Computing, EvoWorkshops2003: Evo{BIO}, Evo{COP}, Evo{IASP}, Evo{MUSART}, Evo{ROB}, Evo{STIM}", year = "2003", editor = "G{\"u}nther R. Raidl and Stefano Cagnoni and Juan Jes\'us Romero Cardalda and David W. Corne and Jens Gottlieb and Agn\`es Guillot and Emma Hart and Colin G. Johnson and Elena Marchiori and Jean-Arcady Meyer and Martin Middendorf", volume = "2611", series = "LNCS", pages = "455--466", address = "University of Essex, UK", publisher_address = "Berlin", month = "14-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", email = "mengjie@mcs.vuw.ac.nz", keywords = "genetic algorithms, genetic programming, evolutionary computation, applications, object recognition", isbn13 = "978-3-540-00976-4", DOI = "doi:10.1007/3-540-36605-9_42", abstract = "This paper describes a domain independent approach to the use of genetic programming for object detection problems. Rather than using raw pixels or high level domain specific features, this approach uses domain independent statistical features as terminals in genetic programming. Besides position invariant statistics such as mean and standard deviation, this approach also uses position dependent pixel statistics such as moments and local region statistics as terminals. Based on an existing fitness function which uses linear combination of detection rate and false alarm rate, we introduce a new measure called 'false alarm area' to the fitness function. In addition to the standard arithmetic operators, this approach also uses a conditional operator ifin the function set. This approach is tested on two object detection problems. The experiments suggest that position dependent pixel statistics computed from local (central) regions and nonlinear condition functions are effective to object detection problems. Fitness functions with false alarm area can reflect the smoothness of evolved genetic programs. This approach works well for the detecting small regular multiple class objects on a relatively uncluttered background.", notes = "EvoWorkshops2003", } @Article{Zhang:2003:JASP, author = "Mengjie Zhang and Victor B. Ciesielski and Peter Andreae", title = "A Domain-independent Window approach to Multiclass Object Detection using Genetic Programming", journal = "{EURASIP} Journal on Applied Signal Processing", year = "2003", volume = "2003", number = "8", pages = "841--859", month = jul, note = "Special Issue on Genetic and Evolutionary Computation for Signal Processing and Image Analysis", email = "mengjie@mcs.vuw.ac.nz", keywords = "genetic algorithms, genetic programming, machine learning, neural networks, object recognition, target detection, computer vision", ISSN = "1110-8657", URL = "http://www.mcs.vuw.ac.nz/~pondy/eurasip2003.pdf", URL = "http://downloads.hindawi.com/journals/asp/2003/206791.pdf", DOI = "doi:10.1155/S1110865703303063", abstract = "a domain-independent approach to the use of genetic programming for object detection problems in which the locations of small objects of multiple classes in large images must be found. The evolved program is scanned over the large images to locate the objects of interest. The paper develops three terminal sets based on domain-independent pixel statistics and considers two different function sets. The fitness function is based on the detection rate and the false alarm rate. We have tested the method on three object detection problems of increasing difficulty. This work not only extends genetic programming to multiclass-object detection problems, but also shows how to use a single evolved genetic program for both object classification and localisation. The object classification map developed in this approach can be used as a general classification strategy in genetic programming for multiple-class classification problems.", notes = "Special Issue on genetic and evolutionary computation for signal processing and image analysis http://asp.hindawi.com/volume-2003/issue-8.html European Association for Speech, Signal and Image Processing (EURASIP) asp@asp.hindawi.com 31 Aug 2003 problems reading PDF", } @TechReport{vuw-CS-TR-04-2, author = "Mengjie Zhang and Will Smart", title = "Multiclass Object Classification Using Genetic Programming", institution = "Computer Science, Victoria University of Wellington", year = "2004", number = "CS-TR-04-2", address = "New Zealand", keywords = "genetic algorithms, genetic programming, dynamic class boundary determination, object recognition", URL = "http://www.mcs.vuw.ac.nz/comp/Publications/CS-TR-04-2.abs.html", URL = "http://www.mcs.vuw.ac.nz/comp/Publications/archive/CS-TR-04/CS-TR-04-2.pdf", abstract = "genetic programming for multi-class object classification problems. Rather than using fixed static thresholds as boundaries to distinguish between different classes, this approach introduces two methods of classification where the boundaries between different classes can be dynamically determined during the evolutionary process. The two methods are centred dynamic class boundary determination and slotted dynamic class boundary determination. The two methods are tested on four object classification problems of increasing difficulty and are compared with the commonly used static class boundary method. The results suggest that, while the static class boundary method works well on relatively easy object classification problems, the two dynamic class boundary determination methods outperform the static method for more difficult, multiple class object classification problems.", size = "15 pages", } @TechReport{vuw-CS-TR-04-3, author = "Mengjie Zhang and Urvesh Bhowan", title = "Pixel Statistics and Program Size in Genetic Programming for Object Detection", institution = "Computer Science, Victoria University of Wellington", year = "2004", number = "CS-TR-04-3", address = "New Zealand", keywords = "genetic algorithms, genetic programming, pixel statistics, false alarm position, program size, multiclass object detection", URL = "http://www.mcs.vuw.ac.nz/comp/Publications/archive/CS-TR-04/CS-TR-04-3.pdf", URL = "http://www.mcs.vuw.ac.nz/comp/Publications/CS-TR-04-3.abs.html", abstract = "genetic programming for object detection problems. In this approach, domain independent, local region pixel statistics are used to form three terminal sets. The function set is constructed by the four standard arithmetic operators and a conditional operator. A multi-objective fitness function is constructed based on detection rate, false alarm rate, false alarm position and program size. This approach is applied to three object detection problems of increasing difficulty. The results suggest that the concentric circular pixel statistics are more effective than the square features for these object detection problems. The fitness function with program size is more effective and more efficient for these object detection problems and the evolved genetic programs using this fitness function are much shorter and easier to interpret.", } @TechReport{vuw-CS-TR-04-6, author = "Mengjie Zhang and Peter Andreae and Urvesh Bhowan", title = "A Two Phase Genetic Programming Approach to Object Detection", institution = "Computer Science, Victoria University of Wellington", year = "2004", number = "CS-TR-04-6", address = "New Zealand", keywords = "genetic algorithms, genetic programming, pixel statistics, false alarm area, program size, two-phase approach, multiclass object detection", URL = "http://www.mcs.vuw.ac.nz/comp/Publications/archive/CS-TR-04/CS-TR-04-6.pdf", URL = "http://www.mcs.vuw.ac.nz/comp/Publications/CS-TR-04-6.abs.html", size = "14 pages", abstract = "This paper describes two innovations that improve the efficiency and effectiveness of a genetic programming approach to object detection problems. The approach uses genetic programming to construct object detection programs that are applied, in a moving window fashion, to the large images to locate the objects of interest. The first innovation is to break the GP search into two phases with the first phase applied to a selected subset of the training data, and a simplified fitness function. The second phase is initialised with the programs from the first phase, and uses the full set of training data with a complete fitness function to construct the final detection programs. The second innovation is to add a program size component to the fitness function. This approach was applied to three object detection problems of increasing difficulty. The results indicate that the innovations increased both the effectiveness and the efficiency of the genetic programming search, and also that the genetic programming approach was more effective than a neural network approach.", } @Article{Zhang:2004:IJCIA, author = "Mengjie Zhang and Victor Ciesielski", title = "Neural Networks and Genetic Algorithms for Domain Independent Multiclass Object Detection", journal = "International Journal of Computational Intelligence and Applications", year = "2004", volume = "4", number = "1", pages = "77--108", month = mar, keywords = "genetic algorithms, genetic programming, Network training, network refinement, network sweeping, evolutionary process, domain independent, object recognition, target recognition, target detection", ISSN = "1469-0268", DOI = "doi:10.1142/S146902680400115X", abstract = "This paper describes a domain independent approach to multiple class rotation invariant 2D object detection problems. The approach avoids preprocessing, segmentation and specific feature extraction. Instead, raw image pixel values are used as inputs to the learning systems. Five object detection methods have been developed and tested, the basic method and four variations which are expected to improve the accuracy of the basic method. In the basic method cutouts of the objects of interest are used to train multilayer feed forward networks using back propagation. The trained network is then used as a template to sweep the full image and find the objects of interest. The variations are (1) Use of a centred weight initialisation method in network training, (2) Use of a genetic algorithm to train the network, (3) Use of a genetic algorithm, with fitness based on detection rate and false alarm rate, to refine the weights found in basic approach, and (4) Use of the same genetic algorithm to refine the weights found by method 2. These methods have been tested on three detection problems of increasing difficulty: an easy database of circles and squares, a medium difficulty database of coins and a very difficult database of retinal pathologies. For detecting the objects in all classes of interest in the easy and the medium difficulty problems, a 100percent detection rate with no false alarms was achieved. However the results on the retinal pathologies were unsatisfactory. The centred weight initialization algorithm improved the detection performance over the basic approach on all three databases. In addition, refinement of weights with a genetic algorithm significantly improved detection performance on the three databases. The goal of domain independent object recognition was achieved for the detection of relatively small regular objects in larger images with relatively uncluttered backgrounds. Detection performance on irregular objects in complex, highly cluttered backgrounds such as the retina pictures, however, has not been achieved to an acceptable level.", notes = "http://ejournals.wspc.com.sg/ijcia/ijcia.shtml", } @InProceedings{zhang:evows04, author = "Mengjie Zhang and Will Smart", title = "Multiclass Object Classification Using Genetic Programming", booktitle = "Applications of Evolutionary Computing, EvoWorkshops2004: {EvoBIO}, {EvoCOMNET}, {EvoHOT}, {EvoIASP}, {EvoMUSART}, {EvoSTOC}", year = "2004", month = "5-7 " # apr, editor = "Guenther R. Raidl and Stefano Cagnoni and Jurgen Branke and David W. Corne and Rolf Drechsler and Yaochu Jin and Colin R. Johnson and Penousal Machado and Elena Marchiori and Franz Rothlauf and George D. Smith and Giovanni Squillero", series = "LNCS", volume = "3005", address = "Coimbra, Portugal", publisher = "Springer Verlag", publisher_address = "Berlin", pages = "369--378", keywords = "genetic algorithms, genetic programming, evolutionary computation", ISBN = "3-540-21378-3", DOI = "doi:10.1007/978-3-540-24653-4_38", abstract = "We describe an approach to the use of genetic programming for multiclass object classification problems. Rather than using fixed static thresholds as boundaries to distinguish between different classes, this approach introduces two methods of classification where the boundaries between different classes can be dynamically determined during the evolutionary process. The two methods are centred dynamic class boundary determination and slotted dynamic class boundary determination. The two methods are tested on four object classification problems of increasing difficulty and are compared with the commonly used static class boundary determination method. The results suggest that, while the static class boundary determination method works well on relatively easy object classification problems, the two dynamic class boundary determination methods outperform the static method for more difficult multiple class object classification problems.", notes = "EvoWorkshops2004", } @InProceedings{zhang:2004:eurogp, author = "Mengjie Zhang and Will Smart", title = "Genetic Programming with Gradient Descent Search for Multiclass Object Classification", booktitle = "Genetic Programming 7th European Conference, EuroGP 2004, Proceedings", year = "2004", editor = "Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule", volume = "3003", series = "LNCS", pages = "399--408", address = "Coimbra, Portugal", publisher_address = "Berlin", month = "5-7 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-21346-5", DOI = "doi:10.1007/978-3-540-24650-3_38", abstract = "Use of gradient descent search in genetic programming (GP) for object classification problems. Gradient descent search is introduced to the GP mechanism and is embedded into the genetic beam search, which allows the evolutionary learning process to globally follow the beam search and locally follow the gradient descent search. Two different methods, an online gradient descent scheme and an off line gradient descent scheme, are developed and compared with the basic GP method on three image data sets with object classification problems of increasing difficulty. The results suggest that both the online and the offline gradient descent GP methods outperform the basic GP method in terms of both classification accuracy and training efficiency and that the online scheme achieved better performance than the off-line scheme.", notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004", } @InProceedings{zhang2:evows04, author = "Mengjie Zhang and Urvesh Bhowan", title = "Program Size and Pixel Statistics in Genetic Programming for Object Detection", booktitle = "Applications of Evolutionary Computing, EvoWorkshops2004: {EvoBIO}, {EvoCOMNET}, {EvoHOT}, {EvoIASP}, {EvoMUSART}, {EvoSTOC}", year = "2004", month = "5-7 " # apr, editor = "Guenther R. Raidl and Stefano Cagnoni and Jurgen Branke and David W. Corne and Rolf Drechsler and Yaochu Jin and Colin R. Johnson and Penousal Machado and Elena Marchiori and Franz Rothlauf and George D. Smith and Giovanni Squillero", series = "LNCS", volume = "3005", address = "Coimbra, Portugal", publisher = "Springer Verlag", publisher_address = "Berlin", pages = "379--388", keywords = "genetic algorithms, genetic programming, evolutionary computation", ISBN = "3-540-21378-3", DOI = "doi:10.1007/978-3-540-24653-4_39", abstract = "This paper describes an approach to the use of genetic programming for object detection problems. In this approach, local region pixel statistics are used to form three terminal sets. The function set is constructed by the four standard arithmetic operators and a conditional operator. A multi-objective fitness function is constructed based on detection rate, false alarm rate, false alarm area and program size. This approach is applied to three object detection problems of increasing difficulty. The results suggest that the concentric circular pixel statistics are more effective than the square features for the coin detection problems. The fitness function with program size is more effective and more efficient for these object detection problems and the evolved genetic programs using this fitness function are much shorter and easier to interpret.", notes = "EvoWorkshops2004", } @InProceedings{conf/kes/ZhangAB04, title = "A Two Phase Genetic Programming Approach to Object Detection", author = "Mengjie Zhang and Peter Andreae and Urvesh Bhowan", booktitle = "Proceedings of 8th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2004. Part III", publisher = "Springer", year = "2004", volume = "3215", editor = "Mircea Gh. Negoita and Robert J. Howlett and Lakhmi C. Jain", pages = "224--231", series = "Lecture Notes in Computer Science", address = "Wellington, New Zealand", month = sep # " 20-25", bibdate = "2004-11-29", bibsource = "DBLP", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-23205-2", DOI = "doi:10.1007/978-3-540-30134-9_32", size = "8 pages", abstract = "two innovations that improve the efficiency and effectiveness of a genetic programming approach to object detection problems. The first innovation is to break the GP search into two phases with the first phase applied to a selected subset of the training data, and a simplified fitness function. The second phase is initialised with the programs from the first phase, and uses the full set of training data to construct the final detection programs. The second innovation is to add a program size component to the fitness function. Application of this approach to three object detection problems indicated that the innovations increased both the effectiveness and the efficiency of the genetic programming search.", notes = "Fri, 02 Jun 2006 17:03:20 +0800", } @TechReport{vuw-CS-TR-05-5, author = "Mengjie Zhang and Will Smart", title = "Using Gaussian Distribution to Construct Fitness Functions in Genetic Programming for Multiclass Object Classification", institution = "Computer Science, Victoria University of Wellington", year = "2005", number = "CS-TR-05-5", address = "New Zealand", keywords = "genetic algorithms, genetic programming", URL = "http://www.mcs.vuw.ac.nz/comp/Publications/CS-TR-05-5.abs.html", URL = "http://www.mcs.vuw.ac.nz/comp/Publications/archive/CS-TR-05/CS-TR-05-5.pdf", abstract = "instead of using predefined multiple thresholds to form different regions in the program output space for different classes, this approach uses probabilities of different classes, derived from Gaussian distributions, to construct the fitness function for classification. Two fitness measures, overlap area and weighted distribution distance, have been developed. Rather than using the best evolved program in a population, this approach uses multiple programs and a voting strategy to perform classification. The approach is examined on three multiclass object classification problems of increasing difficulty and compared with a basic GP approach. The results suggest that the new approach is more effective and more efficient than the basic GP approach. Although developed for object classification, this approach is expected to be able to be applied to other classification problems.", } @InProceedings{zhang:evows05, author = "Mengjie Zhang and Will Smart", title = "Learning Weights in Genetic Programs Using Gradient Descent for Object Recognition", booktitle = "Applications of Evolutionary Computing, EvoWorkshops2005: {EvoBIO}, {EvoCOMNET}, {EvoHOT}, {EvoIASP}, {EvoMUSART}, {EvoSTOC}", year = "2005", month = "30 " # mar # "-1 " # apr, editor = "Franz Rothlauf and Juergen Branke and Stefano Cagnoni and David W. Corne and Rolf Drechsler and Yaochu Jin and Penousal Machado and Elena Marchiori and Juan Romero and George D. Smith and Giovanni Squillero", series = "LNCS", volume = "3449", publisher = "Springer Verlag", address = "Lausanne, Switzerland", publisher_address = "Berlin", pages = "417--427", keywords = "genetic algorithms, genetic programming, evolutionary computation", ISBN = "3-540-25396-3", ISSN = "0302-9743", DOI = "doi:10.1007/b106856", abstract = "the use of gradient descent search in tree based genetic programming for object recognition problems. A weight parameter is introduced to each link between two nodes in a program tree. The weight is defined as a floating point number and determines the degree of contribution of the sub-program tree under the link with the weight. Changing a weight corresponds to changing the effect of the sub-program tree. The weight changes are learnt by gradient descent search at a particular generation. The programs are evolved and learned by both the genetic beam search and the gradient descent search. This approach is examined and compared with the basic genetic programming approach without gradient descent on three object classification problems of varying difficulty. The results suggest that the new approach works well on these problems.", notes = "EvoWorkshops2005", } @InProceedings{conf/kes/ZhangZS05, title = "Program Simplification in Genetic Programming for Object Classification", author = "Mengjie Zhang and Yun Zhang and Will Smart", year = "2005", booktitle = "Knowledge-Based Intelligent Information and Engineering Systems, 9th International Conference, KES 2005, Proceedings, Part III", editor = "Rajiv Khosla and Robert J. Howlett and Lakhmi C. Jain", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3683", pages = "988--996", address = "Melbourne, Australia", month = sep # " 14-16", bibdate = "2005-09-05", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/kes/kes2005-3.html#ZhangZS05", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-28896-1", DOI = "doi:10.1007/11553939_139", size = "9 page", abstract = "This paper describes a program simplification approach in genetic programming (GP) to the use of simple algebraic techniques, prime numbers and hashing techniques for object classification problems. Rather than manually simplifying genetic programs after evolution for interpretation purpose only, this approach automatically simplifies genetic programs during the evolutionary process. This approach is examined on four object classification problems of increasing difficulty. The results suggest that the new simplification approach is more efficient and more effective than the basic GP approach without simplification", } @TechReport{vuw-CS-TR-06-2, author = "Mengjie Zhang and Xiaoying Gao and Weijun Lou", title = "A New Crossover Operator in GP for Object Classification", institution = "Computer Science, Victoria University of Wellington", year = "2006", number = "CS-TR-06-2", address = "New Zealand", month = jan, keywords = "genetic algorithms, genetic programming, Crossover points, looseness controlled crossover, hybrid search", URL = "http://www.mcs.vuw.ac.nz/comp/Publications/CS-TR-06-2.abs.html", URL = "http://www.mcs.vuw.ac.nz/comp/Publications/archive/CS-TR-06/CS-TR-06-2.pdf", abstract = "instead of randomly choosing the crossover points as in the standard crossover operator, we use a measure called looseness to guide the selection of crossover points. Rather than using the genetic beam search only, this approach uses a hybrid beam-hill climbing search scheme in the evolutionary process. This approach is examined and compared with the standard crossover operator and the headless chicken crossover method on a sequence of object classification problems. The results suggest that this approach outperforms both the headless chicken crossover and the standard crossover on all of these problems.", size = "17 pages", } @TechReport{vuw-CS-TR-06-6, author = "Mengjie Zhang and Xiaoying Gao and Minh Duc Cao", title = "Experiments on Brood Size in GP with Brood Recombination Crossover for Object Recognition", institution = "Computer Science, Victoria University of Wellington", year = "2006", number = "CS-TR-06-6", address = "New Zealand", keywords = "genetic algorithms, genetic programming, Document Classification, Baysian Networks, Citation Links", URL = "http://www.mcs.vuw.ac.nz/comp/Publications/CS-TR-06-6.abs.html", URL = "http://www.mcs.vuw.ac.nz/comp/Publications/archive/CS-TR-06/CS-TR-06-6.pdf", abstract = "citation links to improve the scientific paper classification performance. In this approach, we develop two refinement functions, a linear label refinement (LLR) and a probabilistic label refinement (PLR), to model the citation link structures of the scientific papers for refining the class labels of the documents obtained by the content-based Naive Bayes classification method. The approach with the two new refinement models is examined and compared with the content-based Naive Bayes method on a standard paper classification data set with increasing training set sizes. The results suggest that both refinement models can significantly improve the system performance over the content-based method for all the training set sizes and that PLR is better than LLR when the training examples are sufficient.", } @TechReport{vuw-CS-TR-06-8, author = "Mengjie Zhang and Malcolm Lett", title = "Improving Fitness Function and Optimising Training Data in GP for Object Detection", institution = "Computer Science, Victoria University of Wellington", year = "2006", number = "CS-TR-06-8", address = "New Zealand", keywords = "genetic algorithms, genetic programming, Fitness function, training examples, object detection, object classification, object recognition, object localisation", URL = "http://www.mcs.vuw.ac.nz/comp/Publications/CS-TR-06-8.abs.html", URL = "http://www.mcs.vuw.ac.nz/comp/Publications/archive/CS-TR-06/CS-TR-06-8.pdf", abstract = "the refinement of a fitness function and the optimisation of training data in genetic programming (GP) for object detection particularly object localisation problems. The fitness function uses the weighted F-measure of a genetic program and considers the localisation fitness values of the detected object locations. To investigate the training data with this fitness function, we categorise the training data into four types: exact centre, close to centre, include centre, and background. The approach is examined and compared with an existing fitness function on three object detection problems of increasing difficulty. The results suggest that the new fitness function outperforms the old one by producing far fewer false alarms and spending much less training time and that the first two types of the training examples contain most of the useful information for object detection. The results also suggest that the complete background type of data can be removed from the training set.", } @Article{Zhang:2006:ELCVIA, author = "Mengjie Zhang and Urvesh Bhowan and Bunna Ny", title = "Genetic Programming for Object Detection: A Two-Phase Approach with an Improved Fitness Function", journal = "Electronic Letters on Computer Vision and Image Analysis", year = "2006", volume = "6", number = "1", pages = "27--43", month = dec, keywords = "genetic algorithms, genetic programming, Artificial Intelligence approaches to computer vis, Image analysis, neural networks", ISSN = "1577-5097", URL = "https://elcvia.cvc.uab.es/article/view/v6-n1-zhang-bhowan", URL = "http://elcvia.cvc.uab.es/public/articles/0601/a2006030-2-art.pdf", DOI = "doi:10.5565/rev/elcvia.135", size = "17 pages", abstract = "This paper describes two innovations that improve the efficiency and effectiveness of a genetic programming approach to object detection problems. The approach uses genetic programming to construct object detection programs that are applied, in a moving window fashion, to the large images to locate the objects of interest. The first innovation is to break the GP search into two phases with the first phase applied to a selected subset of the training data, and a simplified fitness function. The second phase is initialised with the programs from the first phase, and uses the full set of training data with a complete fitness function to construct the final detection programs. The second innovation is to add a program size component to the fitness function. This approach is examined and compared with a neural network approach on three object detection problems of increasing difficulty. The results suggest that the innovations increase both the effectiveness and the efficiency of the genetic programming search, and also that the genetic programming approach outperforms a neural network approach for the most difficult data set in terms of the object detection accuracy", } @InProceedings{zhang:evows06, author = "Mengjie Zhang and Malcolm Lett", title = "Localisation Fitness in {GP} for Object Detection", booktitle = "Applications of Evolutionary Computing, EvoWorkshops2006: {EvoBIO}, {EvoCOMNET}, {EvoHOT}, {EvoIASP}, {EvoInteraction}, {EvoMUSART}, {EvoSTOC}", year = "2006", month = "10-12 " # apr, editor = "Franz Rothlauf and Jurgen Branke and Stefano Cagnoni and Ernesto Costa and Carlos Cotta and Rolf Drechsler and Evelyne Lutton and Penousal Machado and Jason H. Moore and Juan Romero and George D. Smith and Giovanni Squillero and Hideyuki Takagi", series = "LNCS", volume = "3907", publisher = "Springer Verlag", address = "Budapest", publisher_address = "Berlin", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-33237-5", pages = "472--483", DOI = "doi:10.1007/11732242_42", abstract = "two new fitness functions in genetic programming for object detection particularly object localisation problems. Both fitness functions use weighted F-measure of a genetic program and consider the localisation fitness values of the detected object locations, which are the relative weights of these locations to the target object centres. The first fitness function calculates the weighted localisation fitness of each detected object, then uses these localisation fitness values of all the detected objects to construct the final fitness of a genetic program. The second fitness function calculates the average locations of all the detected object centres then calculates the weighted localisation fitness value of the averaged position. The two fitness functions are examined and compared with an existing fitness function on three object detection problems of increasing difficulty. The results suggest that almost all the objects of interest in the large images can be successfully detected by all the three fitness functions, but the two new fitness functions can result in far fewer false alarms and spend much less training time.", notes = "part of \cite{evows06}", } @Article{zhang:2006:PRL, author = "Mengjie Zhang and Will Smart", title = "Using Gaussian distribution to construct fitness functions in genetic programming for multiclass object classification", journal = "Pattern Recognition Letters", year = "2006", volume = "27", number = "11", pages = "1266--1274", month = aug, note = "Evolutionary Computer Vision and Image Understanding", keywords = "genetic algorithms, genetic programming, Probability based genetic programming, Object recognition, Object detection, Fitness function, Multiclass classification", DOI = "doi:10.1016/j.patrec.2005.07.024", abstract = "the use of Gaussian distribution in genetic programming (GP) for multiclass object classification problems. Instead of using predefined multiple thresholds to form different regions in the program output space for different classes, this approach uses probabilities of different classes, derived from Gaussian distributions, to construct the fitness function for classification. Two fitness measures, overlap area and weighted distribution distance, have been developed. Rather than using the best evolved program in a population, this approach uses multiple programs and a voting strategy to perform classification. The approach is examined on three multi class object classification problems of increasing difficulty and compared with a basic GP approach. The results suggest that the new approach is more effective and more efficient than the basic GP approach. Although developed for object classification, this approach is expected to be able to be applied to other classification problems.", notes = "Special Issue on Evolutionary Computer Vision and Image Understanding, Pattern Recognition Letters, An official publication of the International Association for Pattern Recognition", } @InProceedings{Zhang:2006:CEC, author = "Mengjie Zhang and Xiaoying Gao and Weijun Lou", title = "Looseness Controlled Crossover in GP for Object Recognition", booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary Computation", year = "2006", editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas", pages = "4428--4435", address = "Vancouver", month = "16-21 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9487-9", DOI = "doi:10.1109/CEC.2006.1688457", size = "8 pages", abstract = "improving the crossover operator in genetic programming for object recognition particularly object classification problems. In this approach, instead of randomly choosing the crossover points as in the standard crossover operator, we use a measure called looseness to guide the selection of crossover points. Rather than using the genetic beam search only, this approach uses a hybrid beam-hill climbing search scheme in the evolutionary process. This approach is examined and compared with the standard crossover operator and the headless chicken crossover method on a sequence of object classification problems. The results suggest that this approach outperforms both the headless chicken crossover and the standard crossover on all of these problems.", notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D", } @InProceedings{DBLP:conf/pricai/ZhangGLQ06, author = "Mengjie Zhang and Xiaoying Gao and Weijun Lou and Dongping Qian", title = "Investigation of Brood Size in GP with Brood Recombination Crossover for Object Recognition", booktitle = "PRICAI 2006: Trends in Artificial Intelligence, Proceedings 9th Pacific Rim International Conference on Artificial Intelligence", year = "2006", pages = "923--928", DOI = "doi:10.1007/11801603_107", bibsource = "DBLP, http://dblp.uni-trier.de", editor = "Qiang Yang and Geoffrey I. Webb", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "4099", ISBN = "3-540-36667-9", address = "Guilin, China", month = aug # " 7-11", keywords = "genetic algorithms, genetic programming", size = "6 pages", abstract = "This paper describes an approach to the investigation of brood size in the brood recombination crossover method in genetic programming for object recognition problems. The approach is examined and compared with the standard crossover operator on three object classification problems of increasing difficulty. The results suggest that the brood recombination method outperforms the standard crossover operator for all the problems in terms of the classification accuracy. As the brood size increases, the system effective performance can be improved. When it exceeds a certain point, however, the effective performance will not be improved and the system will become less efficient.", } @InProceedings{DBLP:conf/seal/ZhangWQ06, author = "Mengjie Zhang and Phillip Wong and Dongping Qian", title = "Online Program Simplification in Genetic Programming", booktitle = "Simulated Evolution and Learning, Proceedings 6th International Conference, SEAL 2006", year = "2006", pages = "592--600", editor = "Tzai-Der Wang and Xiaodong Li and Shu-Heng Chen and Xufa Wang and Hussein A. Abbass and Hitoshi Iba and Guoliang Chen and Xin Yao", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "4247", address = "Hefei, China", month = oct # " 15-18", bibsource = "DBLP, http://dblp.uni-trier.de", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-47331-9", DOI = "doi:10.1007/11903697_75", size = "9 pages", abstract = "This paper describes an approach to online simplification of evolved programs in genetic programming (GP). Rather than manually simplifying genetic programs after evolution for interpretation purposes only, this approach automatically simplifies programs during evolution. In this approach, algebraic simplification rules, algebraic equivalence and prime techniques are used to simplify genetic programs. The simplification based GP system is examined and compared to a standard GP system on a regression problem and a classification problem. The results suggest that, at certain frequencies or proportions, this system can not only achieve superior performance to the standard system on these problems, but also significantly reduce the sizes of evolved programs.", } @InProceedings{DBLP:conf/seal/ZhangLM06, author = "Mengjie Zhang and Malcolm Lett and Yuejin Ma", title = "Refining Fitness Functions and Optimising Training Data in GP for Object Detection", booktitle = "Simulated Evolution and Learning, Proceedings 6th International Conference, SEAL 2006", year = "2006", pages = "601--608", DOI = "doi:10.1007/11903697_76", bibsource = "DBLP, http://dblp.uni-trier.de", editor = "Tzai-Der Wang and Xiaodong Li and Shu-Heng Chen and Xufa Wang and Hussein A. Abbass and Hitoshi Iba and Guoliang Chen and Xin Yao", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "4247", ISBN = "3-540-47331-9", address = "Hefei, China", month = oct # " 15-18", keywords = "genetic algorithms, genetic programming", abstract = "This paper describes an approach to the refinement of a fitness function and the optimisation of training data in genetic programming for object detection particularly object localisation problems. The approach is examined and compared with an existing fitness function on three object detection problems of increasing difficulty. The results suggest that the new fitness function outperforms the old one by producing far fewer false alarms and spending much less training time and that some particular types of training examples contain most of the useful information for object detection.", } @Article{Zhang:2006:IIIB, author = "Mengjie Zhang and Malcolm Lett", title = "Genetic Programming for Object Detection: Improving Fitness Functions and Optimising Training Data", journal = "The IEEE Intelligent Informatics Bulletin", year = "2006", volume = "7", number = "1", pages = "12--21", month = dec, keywords = "genetic algorithms, genetic programming, object detection, object localisation, object recognition, object classification, evolutionary computing, fitness function, training data", ISSN = "1727-5997", URL = "http://www.comp.hkbu.edu.hk/~cib/2006/Dec/iib_vol7no1_article2.pdf", size = "10 pages", abstract = "This paper describes an approach to the improvement of a fitness function and the optimisation of training data in genetic programming (GP) for object detection particularly object localisation problems. The fitness function uses the weighted F-measure of a genetic program and considers the localisation fitness values of the detected object locations. To investigate the training data with this fitness function, we categorise the training data into four types: exact centre, close to centre, include centre, and background. The approach is examined and compared with an existing fitness function on three object detection problems of increasing difficulty. The results suggest that the new fitness function outperforms the old one by producing far fewer false alarms and spending much less training time and that the first two types of the training examples contain most of the useful information for object detection. The results also suggest that the complete background type of data can be removed from the training set.", notes = "formerly IEEE Computational Intelligence Bulletin)", } @InProceedings{Zhang:2006:IVCNZ, author = "Mengjie Zhang and Urvesh Bhowan and Bunna Ny", title = "Genetic Programming for Object Detection", booktitle = "Twenty-first International Conference Image and Vision Computing New Zealand, IVCNZ 2006", year = "2006", editor = "John Morris and Patrice Delmas", pages = "425--430", address = "Great Barrier Island, New Zealand", month = nov # " 27-29", keywords = "genetic algorithms, genetic programming, Artificial Intelligence approaches to Computer Vision, Object Recognition, Image Analysis, Neural Networks", isbn13 = "9780473117924", URL = "http://mcs.une.edu.au/~wkwan2/publications/pdf/Proceedings-IVCNZ2006.pdf", URL = "http://www.worldcat.org/title/proceedings-image-and-vision-computing-new-zealand-2006-great-barrier-island-new-zealand-27th-29th-november-2006/oclc/182549710", size = "6 pages", abstract = "This paper describes a genetic programming approach to object detection. This approach breaks the GP search into two phases with the first phase applied to a selected subset of the training data, and a simplified fitness function. The second phase is initialised with the programs obtained from the first phase, and uses the full set of training data with a complete fitness function to construct the final detection programs. In addition to the detection rate and false alarm rate, a program size and a false alarm area components are added to the fitness function. The results on two object detection problems suggest that the proposed approach improve the effectiveness and the efficiency of genetic programming.", notes = "http://citr.auckland.ac.nz/ivcnz06/ http://www.citr.auckland.ac.nz/ivcnz06/programme.html OCLC Number: 182549710", } @InProceedings{DBLP:conf/ausai/ZhangGL06, author = "Mengjie Zhang and Xiaoying Gao and Weijun Lou", title = "GP for Object Classification: Brood Size in Brood Recombination Crossover", booktitle = "Australian Conference on Artificial Intelligence", year = "2006", pages = "274--284", DOI = "doi:10.1007/11941439_31", bibsource = "DBLP, http://dblp.uni-trier.de", editor = "Abdul Sattar and Byeong Ho Kang", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "4304", ISBN = "3-540-49787-0", address = "Hobart, Australia", month = dec # " 4-8", keywords = "genetic algorithms, genetic programming", size = "11 pages", abstract = "The brood size plays an important role in the brood recombination crossover method in genetic programming. However, there has not been any thorough investigation on the brood size and the methods for setting this size have not been effectively examined. This paper investigates a number of new developments of brood size in the brood recombination crossover method in GP. We first investigate the effect of different fixed brood sizes, then construct three dynamic models for setting the brood size. These developments are examined and compared with the standard crossover operator on three object classification problems of increasing difficulty. The results suggest that the brood recombination methods with all the new developments outperforms the standard crossover operator for all the problems. As the brood size increases, the system effective performance can be improved. When it exceeds a certain point, however, the effective performance will not be improved and the system will become less efficient. Investigation of three dynamic models for the brood size reveals that a good variable brood size which is dynamically set with the number of generations can further improve the system performance over the fixed brood sizes.", } @InProceedings{zhang:evows07, author = "Mengjie Zhang and Christopher Graeme Fogelberg", title = "Genetic Programming for Image Recognition: An LGP Approach", booktitle = "Applications of Evolutionary Computing, EvoWorkshops2007: {EvoCOMNET}, {EvoFIN}, {EvoIASP}, {EvoInteraction}, {EvoMUSART}, {EvoSTOC}, {EvoTransLog}", year = "2007", month = "11-13 " # apr, editor = "Mario Giacobini and Anthony Brabazon and Stefano Cagnoni and Gianni A. {Di Caro} and Rolf Drechsler and Muddassar Farooq and Andreas Fink and Evelyne Lutton and Penousal Machado and Stefan Minner and Michael O'Neill and Juan Romero and Franz Rothlauf and Giovanni Squillero and Hideyuki Takagi and A. Sima Uyar and Shengxiang Yang", series = "LNCS", volume = "4448", publisher = "Springer Verlag", address = "Valencia, Spain", pages = "340--350", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-71804-8", DOI = "doi:10.1007/978-3-540-71805-5_37", abstract = "This paper describes a linear genetic programming approach to multi-class image recognition problems. A new fitness function is introduced to approximate the true feature space. The results show that this approach outperforms the basic tree based genetic programming approach on all the tasks investigated here and that the programs evolved by this approach are easier to interpret. The investigation on the extra registers and program length results in heuristic guidelines for initially setting system parameters.", notes = "EvoWorkshops2007", } @Article{Zhang:2007:SMC, author = "Mengjie Zhang and Xiaoying Gao and Weijun Lou", title = "A New Crossover Operator in Genetic Programming for Object Classification", journal = "IEEE Transactions on Systems, Man and Cybernetics, Part B", year = "2007", volume = "37", number = "5", pages = "1332--1343", month = oct, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/TSMCB.2007.902043", ISSN = "1083-4419", abstract = "The crossover operator has been considered, the centre of the storm, in genetic programming (GP). However, many existing GP approaches to object recognition suggest that the standard GP crossover is not sufficiently powerful in producing good child programs due to the totally random choice of the crossover points. To deal with this problem, this paper introduces an approach with a new crossover operator in GP for object recognition, particularly object classification. In this approach, a local hill-climbing search is used in constructing good building blocks, a weight called looseness is introduced to identify the good building blocks in individual programs, and the looseness values are used as heuristics in choosing appropriate crossover points to preserve good building blocks. This approach is examined and compared with the standard crossover operator and the headless chicken crossover (HCC) method on a sequence of object classification problems. The results suggest that this approach outperforms the HCC, the standard crossover, and the standard crossover operator with hill climbing on all of these problems in terms of the classification accuracy. Although this approach spends a bit longer time than the standard crossover operator, it significantly improves the system efficiency over the HCC method.", } @Article{Zhang:2007:IJAIT, author = "Mengjie Zhang", title = "Improving Object Detection Performance with Genetic Programming", journal = "International Journal on Artificial Intelligence Tools", year = "2007", volume = "16", number = "5", pages = "849--873", month = oct, keywords = "genetic algorithms, genetic programming, object recognition, target recognition, fitness function, program size, two-phase learning, neural networks", DOI = "doi:10.1142/S0218213007003576", abstract = "This paper describes three developments to improve object detection performance using genetic programming. The first investigates three feature sets, the second investigates a new fitness function, and the third introduces a two phase learning method using genetic programming. This approach is examined on three object detection problems of increasing difficulty and compared with a neural network approach. The two phase GP approach with the new fitness function and the local concentric circular region features achieved the best results. The results suggest that the concentric circular pixel statistics are more effective than the square features for these object detection problems. The fitness function with program size is more effective and more efficient than without for these object detection problems and the evolved genetic programs using this fitness function are much shorter and easier to interpret. The two phase GP approach is more effective and more efficient than the single stage GP approach, and also more effective than the neural network approach on these problems using the same set of features.", } @Article{zhang:2007:CS, author = "Mengjie Zhang and Christopher Graeme Fogelberg and Yuejin Ma", title = "A Linear Structured Approach and A Refined Fitness Function in Genetic Programming for Multi-class Object Classification", journal = "Connection Science", year = "2007", volume = "19", number = "4", pages = "339--359", note = "Special Issue: Evolutionary Learning and Optimisation", keywords = "genetic algorithms, genetic programming, Linear genetic programming, Program structure, Program representation, Fitness function, Multi-class classification, Object classification, Object recognition", ISSN = "0954-0091", DOI = "doi:10.1080/09540090701725557", size = "21 pages", abstract = "This paper describes an approach to the use of genetic programming (GP) to multi-class object recognition problems. Rather than using the standard tree structures to represent evolved classifier programs which only produce a single output value that must be further translated into a set of class labels, this approach uses a linear structure to represent evolved programs, which use multiple target registers each for a single class. The simple error rate fitness function is refined and a new fitness function is introduced to approximate the true feature space of an object recognition problem. This approach is examined and compared with the tree based GP on three data sets providing object recognition problems of increasing difficulty. The results show that this approach outperforms the standard tree based GP approach on all the tasks investigated here and that the programs evolved by this approach are easier to interpret. The investigation into the extra target registers and program length results in heuristic guidelines for initially setting system parameters.", } @InCollection{Zhang:2007:gecipa17, author = "Mengjie Zhang", title = "Genetic Programming Techniques for Multiclass Object Recognition", booktitle = "Genetic and Evolutionary Computation for Image Processing and Analysis", publisher = "Hindawi Publishing Corporation", year = "2007", editor = "Stefano Cagnoni and Evelyne Lutton and Gustavo Olague", chapter = "17", pages = "349--370", keywords = "genetic algorithms, genetic programming", isbn13 = "978-977-454-001-1", URL = "http://downloads.hindawi.com/books/9789774540011.pdf", size = "22 pages", abstract = "Classification tasks arise in a very wide range of applications, such as detecting faces from video images, recognising words in streams of speech, diagnosing medical conditions from the output of medical tests, and detecting fraudulent credit card fraud transactions [11, 15, 50]. In many cases, people (possibly highly trained experts) are able to perform the classification task well, but there is either a shortage of such experts, or the cost of people is too high. Given the amount of data that needs to be classified, automatic computer-based classification programmes/ systems are of immense social and economic value.", } @InCollection{Zhang:2007:gecipa20, author = "Mengjie Zhang", title = "Genetic Algorithms and Neural Networks for Object Detection", booktitle = "Genetic and Evolutionary Computation for Image Processing and Analysis", publisher = "Hindawi Publishing Corporation", year = "2007", editor = "Stefano Cagnoni and Evelyne Lutton and Gustavo Olague", chapter = "20", pages = "415--440", keywords = "genetic algorithms", isbn13 = "978-977-454-001-1", } @Article{Zhang:2008:GPEM, author = "Mengjie Zhang and Phillip Wong", title = "Genetic programming for medical classification: a program simplification approach", journal = "Genetic Programming and Evolvable Machines", year = "2008", volume = "9", number = "3", pages = "229--255", month = sep, keywords = "genetic algorithms, genetic programming, Program simplification, Medical classification, Algebraic equivalence, Hashing techniques", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-008-9059-9", abstract = "This paper describes a genetic programming (GP) approach to medical data classification problems. In this approach, the evolved genetic programs are simplified online during the evolutionary process using algebraic simplification rules, algebraic equivalence and prime techniques. The new simplification GP approach is examined and compared to the standard GP approach on two medical data classification problems. The results suggest that the new simplification GP approach can not only be more efficient with slightly better classification performance than the basic GP system on these problems, but also significantly reduce the sizes of evolved programs. Comparison with other methods including decision trees, naive Bayes, nearest neighbour, nearest centroid, and neural networks suggests that the new GP approach achieved superior results to almost all of these methods on these problems. The evolved genetic programs are also easier to interpret than the hidden patterns discovered by the other methods.", } @Article{Zhang:2008:ijcia, author = "Mengjie Zhang and Phillip Wong", title = "Explicitly Simplifying Evolved Genetic Programs During Evolution", journal = "International Journal of Computational Intelligence and Applications", year = "2008", volume = "7", number = "2", pages = "201--232", month = jun, keywords = "genetic algorithms, genetic programming, algebraic simplification, program simplification, code bloating, explicit program simplification", publisher = "Imperial College Press", ISSN = "1469-0268", URL = "http://www.worldscientific.com/doi/abs/10.1142/S1469026808002247", DOI = "doi:10.1142/S1469026808002247", size = "32 pages", abstract = "The genetic programming (GP) evolutionary process typically introduces a large amount of redundancy and unnecessary complexity into evolved programs. Quick growth of redundant and functionally useless sections of programs can quickly overcome a GP system, exhausting system resources and causing premature termination of the system before an acceptable solution can be found. Rather than implicitly controlling the redundancy and code growth/bloat as in most of the existing approaches, this paper investigates an algebraic simplification algorithm for explicitly removing the redundancy from the genetic programs and simplifying these programs online during the evolutionary process. The new GP system with the simplification is examined and compared with a standard GP system on two regression and three classification problems of varying difficulties. The results show that the GP system employing a simplification component can achieve superior efficiency with comparable or slightly superior effectiveness to the standard GP system on these problems. The programs evolved by the new GP approach with the explicit simplification contain ``hidden patterns'' for a particular problem and are relatively simple and easy to interpret.", notes = " http://www.worldscinet.com/ijcia/ijcia.shtml", } @InCollection{Zhang:2009:EIASP, author = "Mengjie Zhang and Mark Johnston", title = "A Variant Program Structure in Tree-Based Genetic Programming for Multiclass Object Classification", booktitle = "Evolutionary Image Analysis and Signal Processing", publisher = "Springer", year = "2009", editor = "Stefano Cagnoni", volume = "213", series = "Studies in Computational Intelligence", pages = "55--72", address = "Berlin / Heidelberg", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-01635-6", ISSN = "1860-949X", DOI = "doi:10.1007/978-3-642-01636-3_4", abstract = "This chapter describes an approach to the use of genetic programming for multiclass object classification. Instead of using the standard tree-based genetic programming approach, where each genetic program returns just one floating point number that is then translated into different class labels, this approach invents a new program structure with multiple outputs, each for a particular class. A voting scheme is then applied to these output values to determine the class of the input object. The approach is examined and compared with the standard genetic programming approach on four multiclass object classification tasks with increasing difficulty. The results show that the new approach outperforms the basic approach on these problems. A characteristic of the proposed program structure is that it can easily produce multiple outputs for multiclass object classification problems, while still keeping the advantages of the standard genetic programming approach for easy crossover and mutation. This approach can solve a multiclass object recognition problem using a single evolved program in a single run.", notes = "EvoISAP, EvoNET, EvoStar", } @Article{Zhang:2013:ieeeCIM, author = "Mengjie Zhang and Mario Koeppen and Sergio Damas", title = "Special Issue on Computational Intelligence in Computer Vision and Image Processing", journal = "IEEE Computational Intelligence Magazine", year = "2013", volume = "8", number = "1", pages = "14--15", month = feb, note = "Guest Editorial", keywords = "genetic algorithms, genetic programming", ISSN = "1556-603X", DOI = "doi:10.1109/MCI.2012.2228585", size = "2 pages", notes = "[This include the use of GP for vision problems] \cite{Song:2013:IEEEcim} Also known as \cite{6410724}", } @InProceedings{Zhang:2015:GECCOcomp, author = "Mengjie Zhang and Stefano Cagnoni", title = "Evolutionary Image Analysis, Signal Processing and Pattern Recognition", booktitle = "GECCO 2015 Advanced Tutorials", year = "2015", editor = "Anabela Simoes", isbn13 = "978-1-4503-3488-4", keywords = "genetic algorithms, genetic programming", pages = "473--502", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739482.2756566", DOI = "doi:10.1145/2739482.2756566", publisher = "ACM", publisher_address = "New York, NY, USA", notes = "Also known as \cite{2756566} Distributed at GECCO-2015.", } @InProceedings{Zhang:2016:GECCOcomp, author = "Mengjie Zhang and Bing Xue", title = "Evolutionary Computation for Feature Selection and Feature Construction", booktitle = "GECCO '16 Companion: Proceedings of the Companion Publication of the 2016 Annual Conference on Genetic and Evolutionary Computation", year = "2016", editor = "Tobias Friedrich and Frank Neumann and Andrew M. Sutton and Martin Middendorf and Xiaodong Li and Emma Hart and Mengjie Zhang and Youhei Akimoto and Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and Daniele Loiacono and Julian Togelius and Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and Faustino Gomez and Carlos M. Fonseca and Heike Trautmann and Alberto Moraglio and William F. Punch and Krzysztof Krawiec and Zdenek Vasicek and Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and Boris Naujoks and Enrique Alba and Gabriela Ochoa and Simon Poulding and Dirk Sudholt and Timo Koetzing", isbn13 = "978-1-4503-4323-7", pages = "861--881", address = "Denver, Colorado, USA", month = "20-24 " # jul, keywords = "genetic algorithms, genetic programming", organisation = "SIGEVO", DOI = "doi:10.1145/2908961.2927002", publisher = "ACM", note = "tutorial", publisher_address = "New York, NY, USA", notes = "Distributed at GECCO-2016.", } @InProceedings{Zhang:2017:APSIES, author = "Mengjie Zhang", title = "Keynote talks: Evolutionary feature selection and dimensionality reduction", booktitle = "2017 21st Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES)", year = "2017", pages = "ix--xii", address = "Hanoi, Vietnam", month = "15-17 " # nov, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IESYS.2017.8233551", size = "1 page", abstract = "In data mining and machine learning, many real-world problems such as bio-data classification and biomarker detection, image analysis, text mining often involve a large number of features/attributes. However, not all the features are essential since many of them are redundant or even irrelevant, and the useful features are typically not equally important. Using all the features for classification or other data mining tasks typically does not produce good results due to the big dimensionality and the large search space. This problem can be solved by feature selection to select a small subset of original (relevant) features or feature construction to create a smaller set of high-level features using the original low-level features. Feature selection and construction are very challenging tasks due to the large search space and feature interaction problems. Exhaustive search for the best feature subset of a given dataset is practically impossible in most situations. A variety of heuristic search techniques have been applied to feature selection and construction, but most of the existing methods still suffer from stagnation in local optima and/or high computational cost. Due to the global search potential and heuristic guidelines, evolutionary computation techniques such as genetic algorithms, genetic programming, particle swarm optimisation, ant colony optimisation, differential evolution and evolutionary multiobjective optimisation have been recently used for feature selection and construction for dimensionality reduction, and achieved great success. Many of these methods only select/construct a small number of important features, produce higher accuracy, and generated small models that are efficient on unseen data. Evolutionary computation techniques have now become an important means for handle big dimensionality and feature selection and construction. The talk will introduce the general framework within which evolutionary feature selection and construction can be studied and applied, sketching a schematic taxonomy of the field and providing examples of successful real-world applications. The application areas to be covered will include bio-data classification and biomarker detection, image analysis and object recognition and pattern classification, symbolic regression, network security and intrusion detection, and text mining. EC techniques to be covered will include genetic algorithms, genetic programming, particle swarm optimisation, differential evolution, ant colony optimisation, artificial bee colony optimisation, and evolutionary multi-objective optimisation. We will show how such evolutionary computation techniques can be effectively applied to feature selection/construction and dimensionality reduction and provide promising results.", notes = "Also known as \cite{8233551}", } @InProceedings{Zhang:2019:GECCOcompc, author = "Mengjie Zhang and Stefano Cagnoni", title = "Evolutionary computation and evolutionary deep learning for image analysis, signal processing and pattern recognition", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", pages = "1226--1251", address = "Prague, Czech Republic", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, tutorial", isbn13 = "978-1-4503-6748-6", DOI = "doi:10.1145/3319619.3323388", notes = "Also known as \cite{3323388} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InProceedings{Zhang:2020:GECCOcompb, author = "Mengjie Zhang and Stefano Cagnoni", title = "Evolutionary Computation and Evolutionary Deep Learning for Image Analysis, Signal Processing and Pattern Recognition", year = "2020", editor = "Richard Allmendinger and Hugo Terashima Marin and Efren Mezura Montes and Thomas Bartz-Beielstein and Bogdan Filipic and Ke Tang and David Howard and Emma Hart and Gusz Eiben and Tome Eftimov and William {La Cava} and Boris Naujoks and Pietro Oliveto and Vanessa Volz and Thomas Weise and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Rui Wang and Ran Cheng and Guohua Wu and Miqing Li and Hisao Ishibuchi and Jonathan Fieldsend and Ozgur Akman and Khulood Alyahya and Juergen Branke and John R. Woodward and Daniel R. Tauritz and Marco Baioletti and Josu Ceberio Uribe and John McCall and Alfredo Milani and Stefan Wagner and Michael Affenzeller and Bradley Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and Nayat Sanchez-Pi and Luis Marti and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and Matthew Johns and Nick Ross and Ed Keedwell and Herman Mahmoud and David Walker and Anthony Stein and Masaya Nakata and David Paetzel and Neil Vaughan and Stephen Smith and Stefano Cagnoni and Robert M. Patton and Ivanoe {De Falco} and Antonio {Della Cioppa} and Umberto Scafuri and Ernesto Tarantino and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Erik Hemberg and Riyad Alshammari and Tokunbo Makanju and Fuijimino-shi and Ivan Zelinka and Swagatam Das and Ponnuthurai Nagaratnam and Roman Senkerik", isbn13 = "9781450371278", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377929.3389856", DOI = "doi:10.1145/3377929.3389856", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion", pages = "1343--1372", size = "30 pages", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", note = "Tutorial", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{10.1145/3377929.3389856} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @InProceedings{Zhang:2024:GPTP, author = "Hengzhe Zhang and Qi Chen and Bing Xue and Yan Wang and Aimin Zhou and Mengjie Zhang", title = "{EvoFeat}: Genetic Programming-Based Feature Engineering Approach to Tabular Data Classification", booktitle = "Genetic Programming Theory and Practice XXI", year = "2024", editor = "Stephan M. Winkler and Wolfgang Banzhaf and Ting Hu and Alexander Lalejini", series = "Genetic and Evolutionary Computation", pages = "27--49", address = "University of Michigan, USA", month = jun # " 6-8", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-981-96-0076-2", DOI = "doi:10.1007/978-981-96-0077-9_2", notes = "Published in 2025 after the workshop", } @InProceedings{Zhang:2010:ICIC, author = "Ming Zhang and Yongquan Zhou", title = "Approximate Calculation about Standard Normal Distribution with Genetic Programming", booktitle = "Third International Conference onInformation and Computing (ICIC), 2010", year = "2010", month = "4-6 " # jun, volume = "3", pages = "17--20", abstract = "Normal distribution is a most widely used probability distribution in engineering calculation and analysis, which is commonly transformed into standard normal distribution to research. This paper presents a new hybrid method of genetic programming and least square method to calculate function values of standard normal distribution, based on determining the function value of standard normal distribution by checking table have many limits and bring huge inconvenience to the engineering application. The simulating results show that the algorithm has the advantages of high speed and precision, easy for using.", keywords = "genetic algorithms, genetic programming, engineering calculation, least square method, probability distribution, standard normal distribution, statistical distributions", DOI = "doi:10.1109/ICIC.2010.187", notes = "Sch. of Sci., Dalian Fisheries Univ., Dalian, China Also known as \cite{5513909}", } @InProceedings{Zhang:2024:BigData, author = "Olivia Zhang and Brianna Grissom and Julian Pulido and Kenia Munoz-Ordaz and Jonathan He and Mostafa Cham and Haotong Jing and Weikang Qian and Yixin Wen and Jianwu Wang", title = "Accurate and Interpretable Radar Quantitative Precipitation Estimation with Symbolic Regression", booktitle = "2024 IEEE International Conference on Big Data", year = "2024", pages = "2254--2263", month = dec, keywords = "genetic algorithms, genetic programming, Accuracy, Rain, Knowledge based systems, Estimation, Radar, Mathematical models, Floods, Water resources, Monitoring, quantitative precipitation estimation, polarimetric radar, symbolic regression, knowledge-based loss terms", ISSN = "2573-2978", DOI = "doi:10.1109/BigData62323.2024.10825069", abstract = "Accurate quantitative precipitation estimation (QPE) is essential for managing water resources, monitoring flash floods, creating hydrological models, and more. Traditional methods of obtaining precipitation data from rain gauges and radars have limitations such as sparse coverage and inaccurate estimates for different precipitation types and intensities. Symbolic regression, a machine learning method that generates mathematical equations fitting the data, presents a unique approach to estimating precipitation that is both accurate and interpretable. Using WSR-88D dual-polarimetric radar data from Oklahoma and Florida over three dates, we tested symbolic regression models involving genetic programming and deep learning, symbolic regression on separate clusters of the data, and the incorporation of knowledge-based loss terms into the loss function. We found that symbolic regression is both accurate in estimating rainfall and interpretable through learnt equations. Accuracy and simplicity of the learnt equations can be slightly improved by clustering the data based on select radar variables and by adjusting the loss function with knowledge-based loss terms. This research provides insights into improving QPE accuracy through interpretable symbolic regression methods.", notes = "Also known as \cite{10825069}", } @InProceedings{Zhang:2018:DDCLS, author = "Qi Zhang and Kai Xu and Peng Jiao and Quanjun Yin", booktitle = "2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)", title = "Behavior Modeling for Autonomous Agents Based on Modified Evolving Behavior Trees", year = "2018", pages = "1140--1145", abstract = "In modern training, entertainment and education applications, behaviour trees (BTs) have been the fantastic alternative to FSMs to model and control autonomous agents. However, manually creating BTs for various task scenarios is expensive. Recently the genetic programming method has been devised to learn BTs automatically but produced limited success. One of the main reasons is the scalability problem stemming from random space search. This paper proposes a modified evolving behaviour trees approach to model agent behavior as a BT. The main features lay on the model free method through dynamic frequent subtree mining to adjust select probability of crossover point then reduce random search in evolution. Preliminary experiments, carried out on the Mario AI benchmark, show that the proposed method outperforms standard evolving behaviour tree by achieving better final behaviour performance with less learning episodes. Besides, some useful behaviour subtrees can be mined to facilitate knowledge engineering.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/DDCLS.2018.8515939", month = may, notes = "National University of Defense Technology, Changsha, Hunan, 410073, Also known as \cite{8515939}", } @Article{zhang:2018:AS, author = "Qi Zhang and Jian Yao and Quanjun Yin and Yabing Zha", title = "Learning Behavior Trees for Autonomous Agents with Hybrid Constraints Evolution", journal = "Applied Sciences", year = "2018", volume = "8", number = "7", keywords = "genetic algorithms, genetic programming", ISSN = "2076-3417", URL = "https://www.mdpi.com/2076-3417/8/7/1077", DOI = "doi:10.3390/app8071077", abstract = "In modern training, entertainment and education applications, behaviour trees (BTs) have already become a fantastic alternative to finite state machines (FSMs) in modelling and controlling autonomous agents. However, it is expensive and inefficient to create BTs for various task scenarios manually. Thus, the genetic programming (GP) approach has been devised to evolve BTs automatically but only received limited success. The standard GP approaches to evolve BTs fail to scale up and to provide good solutions, while GP approaches with domain-specific constraints can accelerate learning but need significant knowledge engineering effort. In this paper, we propose a modified approach, named evolving BTs with hybrid constraints (EBT-HC), to improve the evolution of BTs for autonomous agents. We first propose a novel idea of dynamic constraint based on frequent sub-trees mining, which can accelerate evolution by protecting preponderant behaviour sub-trees from undesired crossover. Then we introduce the existing static structural constraint into our dynamic constraint to form the evolving BTs with hybrid constraints. The static structure can constrain expected BT form to reduce the size of the search space, thus the hybrid constraints would lead more efficient learning and find better solutions without the loss of the domain-independence. Preliminary experiments, carried out on the Pac-Man game environment, show that the hybrid EBT-HC outperforms other approaches in facilitating the BT design by achieving better behaviour performance within fewer generations. Moreover, the generated behaviour models by EBT-HC are human readable and easy to be fine-tuned by domain experts.", notes = "also known as \cite{app8071077}", } @Article{Zhang:2021:AIR, author = "Qianyun Zhang and Kaveh Barri and Pengcheng Jiao and Hadi Salehi and Amir H. Alavi", title = "Genetic programming in civil engineering: advent, applications and future trends", journal = "Artificial Intelligence Review", year = "2021", volume = "54", pages = "1863--1885", month = mar, keywords = "genetic algorithms, genetic programming, Civil engineering, Prediction, Classification, Machine learning, Deep learning", URL = "https://rdcu.be/cwlIF", DOI = "doi:10.1007/s10462-020-09894-7", size = "23 pages", abstract = "Over the past two decades, machine learning has been gaining significant attention for solving complex engineering problems. Genetic programming (GP) is an advanced framework that can be used for a variety of machine learning tasks. GP searches a program space instead of a data space without a need to predefined models. This method generates transparent solutions that can be easily deployed for practical civil engineering applications. GP is establishing itself as a robust intelligent technique to solve complicated civil engineering problems. We provide a review of the GP technique and its applications in the civil engineering arena over the last decade. We discuss the features of GP and its variants followed by their potential for solving various civil engineering problems. We finally envision the potential research avenues and emerging trends for the application of GP in civil engineering.", notes = "Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, PA, USA", } @InProceedings{Zhang:2013:CCDC, author = "Qinghua Zhang and Qin Hu and Guoxi Sun and Aisong Qin and Xiaosheng Si", title = "New dimensionless parameter construction using genetic programming for fault classifying of rotating machinery", booktitle = "25th Chinese Control and Decision Conference (CCDC 2013)", year = "2013", month = "25-27 " # may, pages = "4400--4404", keywords = "genetic algorithms, genetic programming, Fault diagnosis, electrical power, Rotating machinery, Dimensionless parameters", DOI = "doi:10.1109/CCDC.2013.6561726", abstract = "In this paper, an approach to apply genetic programming in combination with dimensionless parameter of time domain is proposed. According to this approach, new dimensionless parameter is constructed. The effectiveness of the new dimensionless parameter is validated by an illustrative experiment. Experimental result shows that the classification ability of new dimensionless parameter is better than that of existing ones.", notes = "Also known as \cite{6561726}", } @InProceedings{Zhang:gecco06lbp, author = "Qiongyun Zhang and Chi Zhou and Weimin Xiao and Peter C. Nelson and Xin Li", title = "Using Differential Evolution for {GEP} Constant Creation", booktitle = "Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2006)}", year = "2006", month = "8-12 " # jul, editor = "J{\"{o}}rn Grahl", address = "Seattle, WA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006etc/papers/lbp130.pdf", notes = "Distributed on CD-ROM at GECCO-2006", keywords = "genetic algorithms, genetic programming, gene expression programming, DE", abstract = "Gene Expression Programming (GEP) is a new evolutionary algorithm that incorporates both the idea of simple, linear chromosomes of fixed length used in Genetic Algorithms (GAs) and the structure of different sizes and shapes used in Genetic Programming (GP). As with other genetic programming algorithms, GEP has difficulty finding appropriate numeric constants for terminal nodes in the expression trees. In this paper, we describe a new approach of constant generation using Differential Evolution (DE), which is a simple real-valued GA that has proven to be robust and efficient on parameter optimisation problems. Our experimental results on two symbolic regression problems show that the approach significantly improves the performance of the GEP algorithm. The proposed approach can be easily extended to other Genetic Programming variants.", } @InProceedings{Zhang:2007:ICMLA, title = "Improving gene expression programming performance by using differential evolution", author = "Qiongyun Zhang and Chi Zhou and Weimin Xiao and Peter C. Nelson", booktitle = "Sixth International Conference on Machine Learning and Applications, ICMLA 2007", year = "2007", month = "13-15 " # dec, pages = "31--37", address = "Cincinnati, Ohio, USA", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, gene expression programming, evolutionary computation differential evolution, evolutionary algorithm, linear chromosome, symbolic regression, tree structure", DOI = "doi:10.1109/ICMLA.2007.62", abstract = "Gene Expression Programming (GEP) is an evolutionary algorithm that incorporates both the idea of a simple, linear chromosome of fixed length used in Genetic Algorithms (GAs) and the tree structure of different sizes and shapes used in Genetic Programming (GP). As with other GP algorithms, GEP has difficulty finding appropriate numeric constants for terminal nodes in the expression trees. In this work, we describe a new approach of constant generation using Differential Evolution (DE), a real-valued GA robust and efficient at parameter optimization. Our experimental results on two symbolic regression problems show that the approach significantly improves the performance of the GEP algorithm. The proposed approach can be easily extended to other Genetic Programming variations.", notes = "also known as \cite{4457204}", } @Article{Zhang:2013:IJICT, title = "An improved multi-expression programming algorithm applied in function discovery and data prediction", author = "Qingke Zhang and Bo Yang and Lin Wang and Jianzhang Jiang", journal = "International Journal of Information and Communication Technology", year = "2013", month = dec # "~19", volume = "5", number = "3/4", pages = "218--233", keywords = "genetic algorithms, genetic programming, multi-expression programming, MEP, double-layer chromosome, prediction modelling, function discovery, data prediction, soft computing, cement strength prediction.", publisher = "Inderscience Publishers", language = "eng", ISSN = "1741-8070", bibsource = "OAI-PMH server at www.inderscience.com", URL = "http://www.inderscience.com/link.php?id=54952", DOI = "DOI:10.1504/IJICT.2013.054952", abstract = "This paper presents an improved multi-expression programming (MEP). In the algorithm, each individual is encoded as a double-layer structure, and two-dimension space operators are introduced through two-dimension crossover and mutation. The problems of symbolic expression are defined and used as benchmarks to compare the effectiveness of proposal method against the baseline single-layer MEP. Experiments showed that our method using two-dimensional super chromosome can find the optimal solution in a short time with small population. Then the improved algorithm is applied to the prediction of 28-day cement compressive strength. Comparison with other three soft computing models, namely MEP model, neural networks (NN) model and fuzzy logic (FL) model on cement strength prediction revealed that the improved MEP model has a lower rate in RMSE and MAE. Test results demonstrate the proposed method is efficient and performed better in function discovery and data prediction.", } @InProceedings{Zhang:2023:QRS-C, author = "Man Zhang and Shaukat Ali and Tao Yue", booktitle = "2023 IEEE 23rd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)", title = "Uncertainty-Wise Model Evolution with Genetic Programming", year = "2023", pages = "843--844", abstract = "Model-based Testing (MBT) of a Cyber-Physical System (CPS) under uncertain environments relies on test models manually built based on testers' limited knowledge about the CPS and its operating environment, thereby requiring their continuous evolution. To this end, we propose an uncertainty-wise model evolution approach (UNCERPLORE) to systematically evolve these models with a novel exploration strategy using Genetic Programming while also incorporating CPS execution information. With a preliminary study with a CPS use case, Uncerplore manages to evolve models and explore, on average 28.6percent new uncertainties in 10 repetitions.", keywords = "genetic algorithms, genetic programming, Uncertainty, Software quality, Software reliability, Security, Testing, model evolution", DOI = "doi:10.1109/QRS-C60940.2023.00062", ISSN = "2693-9371", month = oct, notes = "Also known as \cite{10430061}", } @InProceedings{Zhang:2021:ASICON, author = "Renyuan Zhang and Xuetao Wang and Ziyu Wang and Anfeng Xue and Haichuan Yang and Yu Gong and Bo Liu2", booktitle = "2021 IEEE 14th International Conference on ASIC (ASICON)", title = "Mutual Error Compensation based Area and Power efficient Approximate Multiplier", year = "2021", abstract = "Approximate computing (AC) has now become a popular solution in deploying Neural Network (NN) on hardware to reduce the power consumption. In this paper, the Cartesian Genetic Programming (CGP)-based approximate multiplier and a deliberately designed approximate adders are combined, and a Mutual Error Compensation (MEC) design scheme is proposed to construct a higher-order multiplier. The Penalty Coefficient is introduced in the fitness function of CGP to compensate the error of the next stage approximate addition. The proposed approximate multiplier can achieve power savings of 53.0percent and area savings of 58.7percent comparing to the exact multiplier. Also, it shows the superiority in power consumption and area compared to the state-of-the-art approximate multipliers. The proposed multiplier is also evaluated in a CNN-based keyword spotting (KWS) system, with little accuracy loss and high efficiency.", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, Power demand, Conferences, Approximate computing, Error compensation, Artificial neural networks, Hardware", DOI = "doi:10.1109/ASICON52560.2021.9620312", ISSN = "2162-755X", month = oct, notes = "Also known as \cite{9620312}", } @InProceedings{zhang:2022:PRICAI, author = "Rui Zhang and Andrew Lensen and Yanan Sun", title = "Speeding up Genetic Programming Based Symbolic Regression Using {GPUs}", booktitle = "PRICAI 2022: Trends in Artificial Intelligence", year = "2022", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-031-20862-1_38", DOI = "doi:10.1007/978-3-031-20862-1_38", } @Article{ZhangRui:ieeeTEC, author = "Rui Zhang and Yanan Sun and Mengjie Zhang", title = "{GPU} Based Genetic Programming for Faster Feature Extraction in Binary Image Classification", journal = "IEEE Transactions on Evolutionary Computation", year = "2024", volume = "28", number = "6", pages = "1590--1604", month = dec, keywords = "genetic algorithms, genetic programming, GPU, feature learning, binary image classification, parallel algorithm, nvidia, graphics processing unit, compute unified device architecture, CUDA", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2023.3294639", code_url = "https://github.com/RayZhhh/CupaGP", size = "15 pages", abstract = "Genetic programming (GP) has been applied to various binary image classification tasks and achieved promising results. However, existing approaches are difficult to be applied to large binary classification tasks due to the huge computational cost in fitness evaluations. To address this issue, we introduce a highly efficient method that enables fitness evaluations to be entirely conducted on graphics processing units (GPUs). Specifically, a prefix notation is used as program representation on the GPU device side, and column-major storage is used for the training dataset on the device side to achieve coalesced global memory access on the GPU. The evaluation of multiple GP programs in each generation can be simultaneous, which increases the parallelism of the algorithm. In addition, a parallel reduction is performed to maximize the use of the powerful parallel computing capability of GPU devices. Furthermore, the hoist mutation is also added to the proposed approach to help eliminate stack overflow on the device side. We compare training time and classification accuracy on various datasets with several GP and non-GP approaches. Experimental results indicate that the proposed approach significantly speeds up the existing GP-based binary image classification approaches without degradation in classification accuracy. We also analyze the influence of the batch size on the training time and investigate the classification accuracy in different settings of the max program depth and the number of generations. The code is available at https://github.com/RayZhhh/CupaGP for reference.", notes = "also known as \cite{10180049}", } @InProceedings{Zhang:2015:CyberC, author = "Song Zhang and Jun Ma2 and Yang-Yang Zhao and Qiong Liu", booktitle = "2015 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)", title = "An Improved Parallel Algorithm of Genetic Programming Based on the Framework of MapReduce", year = "2015", pages = "221--225", abstract = "Genetic programming lacks convergence prematurely and operating efficiency. This paper is to study this problem that integrates the genetic programming theory with the framework of Map/Reduce. This is to improve the efficiency by parallel and distributed capability proved by Map/Reduce. Our experiments show that the improved parallel algorithm of genetic programming under the framework of Map/Reduce has the better performance than the conventional approaches.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CyberC.2015.37", month = sep, notes = "Zhang Song ; BeiJing NUMBERONE Technol. Dev. Co., Ltd., Beijing, China ; Ma Jun ; Zhao Yang-Yang ; Liu Qiong Also known as \cite{7307816}", } @InProceedings{Zhang:2010:ICCASM, author = "Sheng Zhang and Xiuyu Shang and Wei Wang", title = "An ANN model of optimizing activation functions based on constructive algorithm and GP", booktitle = "International Conference on Computer Application and System Modeling (ICCASM 2010)", year = "2010", month = "22-24 " # oct, volume = "1", pages = "V1--420--V1--424", abstract = "The importance of the activation functions in ANN is emphasised. A new ANN modelling method is proposed based on constructive algorithm and GP. This method can be used to realise the automatic optimisation of the ANN's net structure and the activation functions. As a result, the ANN's constructure and generalisation capability is greatly improved, it's characteristic is better than the M-P feed forward neural network. This improvement is verified experimentally.", keywords = "genetic algorithms, genetic programming, ANN activation functions, ANN modelling method, artificial neural nets, constructive algorithm, learning (artificial intelligence), neural nets", DOI = "doi:10.1109/ICCASM.2010.5620620", notes = "Also known as \cite{5620620}", } @InProceedings{Zhang:2022:ICKES, author = "Jing Zhang and Cheng Wan", booktitle = "2022 International Conference on Knowledge Engineering and Communication Systems (ICKES)", title = "Algorithm Construction of Artificial Intelligence Expression Programming in Complex Function Polymorphism Modeling", year = "2022", address = "Chickballapur, India", month = "28-29 " # dec, keywords = "genetic algorithms, genetic programming, gene expression programming, Computational modelling, Manuals, Data processing, Data models, Gene expression, Task analysis, artificial intelligence, complex function, polymorphic modelling", issn13 = "978-1-6654-5638-8", DOI = "DOI:10.1109/ICKECS56523.2022.10060583", abstract = "In the age of underdeveloped science and technology, people are faced with massive amounts of data, mainly through manual processing of data. However, with the increase in the complexity of the functions and the explosive data growth in the later stage, this kind of relying on purely manual methods to deal with the problem of function discovery is not enough. The data processing process will consume a lot of manpower and time, and the processing efficiency is not high and the accuracy is limited. With the development of society and the growing strength of science and technology, genetic algorithms and genetic programming have been widely used in the field of artificial intelligence(AI). People use algorithms and programming techniques to improve the efficiency of computer data processing, and data optimisation tasks can be automatically completed through computer program settings., realized AI, and thus the gene expression programming algorithm was born. Its appearance provided a strong guarantee for the development of the construction of complex function polymorphic modelling algorithm construction, and produced remarkable results.", notes = "Also known as \cite{10060583}", } @InProceedings{zhang:2022:AiSI, author = "Tian Zhang and Lianbo Ma and Qunfeng Liu and Nan Li and Yang Liu", title = "Genetic Programming for Ensemble Learning in Face Recognition", booktitle = "Advances in Swarm Intelligence", year = "2022", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Ensemble learning, Multiple program trees, Face recognition", URL = "http://link.springer.com/chapter/10.1007/978-3-031-09726-3_19", DOI = "doi:10.1007/978-3-031-09726-3_19", } @InProceedings{zhang:2024:GECCOcomp5, author = "Tuo Zhang and Ying Bi and Jing Liang and Bing Xue and Mengjie Zhang", title = "Decomposition-based Multi-objective Genetic Programming for Feature Learning in Image Classification", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion", year = "2024", editor = "Ting Hu and Aniko Ekart", pages = "555--558", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, multi-objective optimization, feature learning, image classification, evolutionary computation: Poster", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3654306", size = "4 pages", abstract = "Image classification is challenging due to the high dimensionality and large variations of the image data. As an effective method in image classification, feature learning can be considered a multiobjective problem of maximizing the classification accuracy and minimizing the number of learned features. Existing multi-objective genetic programming (MOGP) methods directly apply the multiobjective techniques to GP without considering the characteristics of feature learning tasks. Therefore, this paper proposes a decomposition-based MOGP approach with a global replacement strategy to feature learning in image classification. The proposed approach is compared with existing MOGP methods and the experimental results demonstrate the effectiveness of the proposed approach.", notes = "GECCO-2024 GP A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @Article{DBLP:journals/swevo/ZhangBLXZ25, author = "Tuo Zhang and Ying Bi and Jing Liang and Bing Xue and Mengjie Zhang", title = "Multi-objective genetic programming based on decomposition for feature learning in image classification", journal = "Swarm and Evolutionary Computation", year = "2025", volume = "94", pages = "101875", month = apr, keywords = "genetic algorithms, genetic programming, Multi-objective optimization, MOGP, Feature learning, Image classification, Evolutionary computation, DMOGP-GR", ISSN = "2210-6502", timestamp = "Fri, 21 Mar 2025 16:41:31 +0100", biburl = "https://dblp.org/rec/journals/swevo/ZhangBLXZ25.bib", bibsource = "dblp computer science bibliography, https://dblp.org", DOI = "doi:10.1016/J.SWEVO.2025.101875", size = "14 pages", abstract = "... better HVs than or comparable to the existing MOGP methods in at least 13 out of 20 cases and classification accuracy significantly better than a popular neural architecture search method in all cases.", notes = "School of Electrical and Information Engineering, Zhengzhou University, 100 Kexue Rd, Zhengzhou, 450001, Henan, China", } @InProceedings{WeiZhang:2004:ICMLC, author = "Wei Zhang and Gen-Ke Yang and Zhi-Ming Wu", title = "Genetic programming-based modeling on chaotic time series", booktitle = "Proceedings of the third International Conference on Machine Learning and Cybernetics (ICMLC 2004)", year = "2004", volume = "4", pages = "2347--2352", address = "Shanghai", month = "26-29 " # aug, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", URL = "http://ieeexplore.ieee.org/iel5/9459/30104/01382192.pdf?tp=&arnumber=1382192&isnumber=30104", DOI = "doi:10.1109/ICMLC.2004.1382192", size = "6 pages", abstract = "One of the difficulties in nonlinear time series analysis is how to reconstruct the system model from the data series. This is mainly due to the dissipation and 'butterfly' effect of the chaotic systems. This paper proposes a genetic programming-based modeling (GPM) algorithm for the chaotic time series. In GPM, genetic programming-based techniques are used to search for appropriate model structures in the function space, and the particle swarm optimization (PSO) algorithm is introduced for nonlinear parameter estimation (NPE) on dynamic model structures. In addition, the results of nonlinear time series analysis (NTSA) are integrated into the GPM to improve the modeling quality and the criterion of the established models. The effectiveness of such improvements is proved by modeling the experiments on known chaotic time series.", notes = "Dept. of Autom., Shanghai Jiao Tong Univ., China", } @Article{WeiZhang:2004:JZUS, author = "Wei Zhang and Zhi-ming Wu and Gen-ke Yang", title = "Genetic programming-based chaotic time series modeling", journal = "Journal of Zhejiang University Science", year = "2004", volume = "5", number = "11", pages = "1432--1439", keywords = "genetic algorithms, genetic programming, PSO, Chaotic time series analysis, Genetic programming modelling, Nonlinear Parameter Estimation (NPE), Particle Swarm Optimization, Nonlinear system identification", ISSN = "1009-3095", URL = "http://www.zju.edu.cn/jzus/2004/0411/041118.pdf", DOI = "doi:10.1631/jzus.2004.1432", size = "8 pages", abstract = "This paper proposes a Genetic Programming-Based Modeling (GPM) algorithm on chaotic time series. GP is used here to search for appropriate model structures in function space, and the Particle Swarm Optimization (PSO) algorithm is used for Nonlinear Parameter Estimation (NPE) of dynamic model structures. In addition, GPM integrates the results of Nonlinear Time Series Analysis (NTSA) to adjust the parameters and takes them as the criteria of established models. Experiments showed the effectiveness of such improvements on chaotic time series modeling.", notes = "Department of Automation, Shanghai Jiaotong University, Shanghai 200030, China JZUS http://www.zju.edu.cn/jzus Document code: A CLC number: TN914 chaotic Chebyshev-map", } @InProceedings{Zhang:2023:ICBASE, author = "Wei Zhang and De-Liang Hua and Si-Hai Li and Zhen Ren and Zhi-Ling Yu", booktitle = "2023 4th International Conference on Big Data \& Artificial Intelligence \& Software Engineering (ICBASE)", title = "An improved genetic programming algorithm based on bloat control", year = "2023", pages = "406--413", abstract = "Aiming at the disadvantages of long training time and high model complexity caused by individual bloat in genetic programming, an improved genetic programming algorithm based on bloat control is proposed. First, species are divided according to the differences between individuals, and individuals are evaluated by adjusted penalty fitness. Then, during the population evolution stage, individuals are selected by density and adjusted penalty fitness, and the improved search strategy is used to search and optimise the selected individuals. The survival mechanism of individuals retains the excellent evolutionary information in the population. Finally, the 7 benchmark functions are simulated and compared with other relevant bloat control algorithms. The experimental results show that the proposed algorithm compared with the 4 comparative algorithms can effectively control individual bloat on the basis of ensuring the optimisation ability.", keywords = "genetic algorithms, genetic programming, Training, Software algorithms, Sociology, Search problems, Prediction algorithms, Complexity theory, Bloat control, Selection strategy, Penalty fitness, Density", DOI = "doi:10.1109/ICBASE59196.2023.10303075", month = aug, notes = "Also known as \cite{10303075}", } @Article{ZHANG:2021:CBM, author = "Weiguang Zhang and Adnan Khan and Ju Huyan and Jingtao Zhong and Tianyi Peng and Hanglin Cheng", title = "Predicting Marshall parameters of flexible pavement using support vector machine and genetic programming", journal = "Construction and Building Materials", volume = "306", pages = "124924", year = "2021", ISSN = "0950-0618", DOI = "doi:10.1016/j.conbuildmat.2021.124924", URL = "https://www.sciencedirect.com/science/article/pii/S0950061821026751", keywords = "genetic algorithms, genetic programming, Marshall parameters prediction, Genetic programming and SVM method, Coarse aggregate to filler percentages ratio", abstract = "The Marshall mixture design method of asphalt concrete pavement in Pakistan is based on Asphalt institute MS-2 respective of the general specifications of National highway authority, which significantly affects the reliability of parameters used in Marshall design. Traditional way of determining the corresponding parameters and the optimum bitumen content usually involves complicated, time consuming and cost-expensive, laboratory procedures. Therefore, this research conducted research on the applications of machine learning techniques i.e., support vector machine (SVM) and genetic programming (GP), for the prediction of Marshall parameters (i.e., Marshall stability, flow, and air voids) of flexible pavement base and wearing course. A comprehensive dataset of Marshall mix design was collected from four different road sections. The dataset includes 114, and 145, Marshall stability, Marshall flow and air voids results of the base and wearing course, respectively. The three input parameters considered for the modeling are bitumen content, percentage of coarse aggregate to filler material, and unit weight of compacted aggregates. Statistical criteria are used to evaluate overall performance of the developed models. Meanwhile, GP-based models were assessed by parametric analysis to compare the trends of the models with the practical study. The results show that both the techniques are more efficient and superior than traditional methods in terms of generalizability and prediction capability for Marshall parameters of both courses, which are proved by correlation coefficient (R) (in the case of this study > 0.85). SVM obtains outburst performance than GP by setting the optimal parameters. However, GP provided an empirical expression, which is also validated by parametric study and can be used to estimate the Marshall stability, Marshall flow, and air voids of flexible pavements base course, and wearing course, respectively", } @InProceedings{Zhang:2010:iclsom, author = "Wenhui Zhang and Xinliang Liu", title = "Gaussian Process meta-modeling and comparison of GP training methods", booktitle = "2010 International Conference on Logistics Systems and Intelligent Management", year = "2010", month = "9-10 " # jan, volume = "2", pages = "1193--1199", abstract = "The ability of Gaussian Process to flexibly and accurately fit arbitrary, even highly nonlinear data sets has lead to considerable", keywords = "Gaussian processes", DOI = "doi:10.1109/ICLSIM.2010.5461149", notes = "Not GP. Sch. of Comput. Sci. & Technol., Shandong Univ. of Technol., Zibo, China. Also known as \cite{5461149}", } @Article{Zhang:2024:engappai, author = "Wenchao Zhang and Peixin Shi and Huajing Zhao and Zhansheng Wang and Pengjiao Jia", title = "Domain knowledge-guided Bayesian evolutionary trees for estimating the compression modulus of soils containing missing values", journal = "Engineering Applications of Artificial Intelligence", year = "2024", volume = "133", pages = "108356", keywords = "genetic algorithms, genetic programming, Compressive modulus, Missing data analysis, Bayesian additive regression trees", ISSN = "0952-1976", URL = "https://www.sciencedirect.com/science/article/pii/S0952197624005141", DOI = "doi:10.1016/j.engappai.2024.108356", abstract = "The soil compressive modulus (Es) is a critical parameter in geotechnical design, but its accurate estimation remains challenging because of incomplete geotechnical investigation data and inherent uncertainties in geomaterial properties. This study introduces a novel framework, called Bayesian evolutionary trees enhanced with missingness incorporated in attributes (BETm), for predicting Es with missing data. BETm encompasses three essential components: missingness incorporated in attributes (MIA), genetic programming (GP), and Bayesian additive regression trees (BART). The MIA module is integrated to impute missing data, specifically accounting for geotechnical patterns. The GP component constructs high-order variables to effectively capture the underlying parameter relationships. BART conducts a comprehensive uncertainty analysis on the importance of input variables, providing probabilistic predictions of Es with confidence intervals. BETm was applied to a dataset comprising 2955 geotechnical samples from 101 boreholes in Suzhou, China. Comparative experiments demonstrated its effectiveness compared to several state-of-the-art methods. The proposed model achieved superior accuracy, with a coefficient of determination (R2) of 0.977, mean absolute error of 0.044 MPa, and mean absolute percentage error of 33.8percent. Compared to a random forest model using average values for imputation, this model showed a 6percent improvement in R2. Furthermore, BETm performed competitively for extreme values (outside the normal range of 4-14 MPa), underscoring its robustness in predicting Es with missing data", } @Article{Zhang:2023:CJCE, author = "Wenjun Zhang and Colin D. Rennie and Ioan Nistor", title = "A new model developed by multigene genetic programming for the temporal evolution of bridge pier scour", journal = "Canadian Journal of Civil Engineering", year = "2023", volume = "50", number = "7", pages = "549--559", keywords = "genetic algorithms, genetic programming, scour, bridge piers, time-dependent, multigene genetic programming", ISSN = "0315-1468", URL = "https://www.sciencedirect.com/science/article/pii/S0315146823000780", DOI = "doi:10.1139/cjce-2022-0430", abstract = "Forecasting the time development of scour depth at bridge pier foundations is of great significance to mitigate or avoid the potential failure of bridges. Presently, several models have been developed to predict the scour depth at the base of bridge piers in the case of flood events. This study summarizes existing models for the temporal evolution of bridge pier scour and divides these studies into semiempirical models and empirical models, as well as artificial intelligence models. Several experimental data sets collected from previous studies, 665 points in total, are used to develop a new multigene genetic programming (MGGP) model for temporal scour depth at a circular bridge pier. In addition, independent data, 899 points in total, from previous studies and new physical modelling tests are applied to evaluate the behaviours of existing models, as well as the newly developed MGGP model. It is shown that the MGGP model has good prediction capability when compared with existing empirical and mathematical models", } @InProceedings{Zhang:2018:CCC, author = "Wenqiang Zhang and Peng Ge and Weidong Jin and Jian Guo", title = "Radar Signal Recognition Based on {TPOT} and {LIME}", booktitle = "2018 37th Chinese Control Conference (CCC)", year = "2018", pages = "4158--4163", month = "25-27 " # jul, keywords = "genetic algorithms, genetic programming, TPOT", isbn13 = "978-1-5386-4968-8", ISSN = "1934-1768", DOI = "doi:10.23919/ChiCC.2018.8483165", abstract = "Aiming at solving the existing problems of radar signal recognition methods, this paper presents a method based on Tree-based Pipeline Optimization Tool (TPOT) and Local Interpretable Model-agnostic Explanations (LIME). This method uses genetic programming based on the tree structure to generate the machine learning pipeline. The structure and parameters are evolved to obtain the optimal performance of the machine learning pipeline. Then the prediction results are interpreted by the interpreter to evaluate whether the model is available. When there are multiple signals with similar interpretative properties, it shows that these signals are indistinguishable. The prediction results are interpreted on this model which was re-trained for indistinguishable signals to validate the validity of the LIME interpreter. The experimental results show that the proposed method can not only optimize the machine learning pipeline for different data sets, but also determine the type of indistinguishable radar signal in the data set according to the interpretability.", notes = "School of Electrical Engineering, Southwest Jiaotong University, Chengdu, 611756, P. R. China Also known as \cite{8483165}", } @Article{Zhang:2021:JMRI, author = "Simin Zhang and Huaiqiang Sun and Xiaorui Su and Xibiao Yang and Weina Wang and Xinyue Wan and Qiaoyue Tan and Ni Chen and Qiang Yue and Qiyong Gong", title = "Automated machine learning to predict the co-occurrence of isocitrate dehydrogenase mutations and {O6-methylguanine-DNA} methyltransferase promoter methylation in patients with gliomas", journal = "Journal of Magnetic Resonance Imaging", year = "2021", month = "03 " # jan, note = "Epub ahead of print", keywords = "genetic algorithms, genetic programming, TPOT, tree-based optimization tool, O6-methylguanine-DNA methyltransferase promoter methylation, automated machine learning, glioma, isocitrate dehydrogenase mutation", ISSN = "1522-2586", DOI = "doi:10.1002/jmri.27498", abstract = "Combining isocitrate dehydrogenase mutation (IDHmut) with O6 -methylguanine-DNA methyltransferase promoter methylation (MGMTmet) has been identified as a critical prognostic molecular marker for gliomas. The aim of this study was to determine the ability of glioma radiomics features from magnetic resonance imaging (MRI) to predict the co-occurrence of IDHmut and MGMTmet by applying the tree-based pipeline optimization tool (TPOT), an automated machine learning (autoML) approach. This was a retrospective study, in which 162 patients with gliomas were evaluated, including 58 patients with co-occurrence of IDHmut and MGMTmet and 104 patients with other status comprising: IDH wildtype and MGMT unmethylated (n = 67), IDH wildtype and MGMTmet (n = 36), and IDHmut and MGMT unmethylated (n = 1). Three-dimensional (3D) T1-weighted images, gadolinium-enhanced 3D T1-weighted images (Gd-3DT1WI), T2-weighted images, and fluid-attenuated inversion recovery (FLAIR) images acquired at 3.0 T were used. Radiomics features were extracted from FLAIR and Gd-3DT1WI images. The TPOT was employed to generate the best machine learning pipeline, which contains both feature selector and classifier, based on input feature sets. A 4-fold cross-validation was used to evaluate the performance of automatically generated models. For each iteration, the training set included 121 subjects, while the test set included 41 subjects. Student t-test or a chi-square test was applied on different clinical characteristics between two groups. Sensitivity, specificity, accuracy, kappa score, and AUC were used to evaluate the performance of TPOT-generated models. Finally, we compared the above metrics of TPOT-generated models to identify the best-performing model. Patients ages and grades between two groups were significantly different (p = 0.002 and p = 0.000, respectively). The 4-fold cross-validation showed that gradient boosting classifier trained on shape and textual features from the Laplacian-of-Gaussian-filtered Gd-3DT1 achieved the best performance (average sensitivity = 81.1percent, average specificity = 94percent, average accuracy = 89.4percent, average kappa score = 0.76, average AUC = 0.951). Using autoML based on radiomics features from MRI, a high discriminatory accuracy was achieved for predicting co-occurrence of IDHmut and MGMTmet in gliomas. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 3.", notes = "Huaxi Glioma Center, West China Hospital of Sichuan University, Chengdu, China PMID: 33393131 International Society for Magnetic Resonance in Medicine", } @InProceedings{Zhang;1999:sfi, author = "X. B. Zhang and Y. K. Tse and W. S. Chan", title = "Detecting Structural Changes Using Genetic Programming with an Application to the Greater-China Stock Market", booktitle = "Statistics and Finance: An Interface", year = "1999", editor = "W. K. Li W. S. Chan and H. Tong", pages = "370--384", address = "The University of Hong Kong", publisher_address = "London", month = "4-8 " # jul, publisher = "Imperial College Press", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1142/9781848160156_0022", abstract = "Structural changes usually refer to the changes in some parameters or in the structure of a chosen model that is postulated to describe the operation of a data generating process. However, the structure of the underlying data generating process may not necessarily be equivalent to a model. It may be a pattern or an operating mechanism identifiable by certain cognitive processes. Accordingly, structural changes are changes to the operating mechanism of the underlying system. This paper considers the application of genetic programming to the cognition of the operating mechanism of a dynamic system. Based on the knowledge accumulated in the cognition process, a diagnostic statistic is defined to detect structural changes in the system. This approach is model free since it is performed without reference to model specification. The effectiveness of the model-free approach is empirically illustrated through an application to four stock markets, namely the Greater-China markets.", notes = "http://eproceedings.worldscinet.com/9781848160156/toc.shtml http://books.google.com/books?vid=ISBN1860942377 ", } @InProceedings{conf/icnc/ZhangSSDSS16, author = "Xiaotong Zhang and Ting Sun and Lihua Shi and Ling Ding and Zhaolin Sun and Lijuan Song", title = "Quantitative Structure-Retention Relationship ({QSRR}) study of oxygen-containing organic compounds based on Gene Expression Programming ({GEP})", booktitle = "2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)", publisher = "IEEE", year = "2016", month = "13-15 " # aug, address = "Changsha, China", pages = "132--138", keywords = "genetic algorithms, genetic programming, gene expression programming", bibdate = "2017-05-21", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icnc/icnc2016.html#ZhangSSDSS16", isbn13 = "978-1-5090-4093-3", URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7580922", DOI = "doi:10.1109/FSKD.2016.7603163", notes = "7603163", } @InProceedings{Zhang:2013:IHMSC, author = "Xuehua Zhang and Yao Li", booktitle = "5th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC 2013)", title = "Evolutionary Design of an Analog Filter", year = "2013", month = aug, volume = "2", pages = "474--477", keywords = "genetic algorithms, genetic programming, analogue filter, evolutionary design, simulation experiment", DOI = "doi:10.1109/IHMSC.2013.260", abstract = "In order to improve accuracy of parameters and performance indexes for the filter, improve quality of the filter and meet the real-time requirements, an evolvable hardware structure and design method for an analog filter is presented in the paper. Evolvable hardware structure for the filter is devised by genetic programming. Optimisation design of Parameters for the filter is realised by improved multi-objective adaptive genetic algorithm. Experiment shows that values of evolutionary parameters are in line with theoretical values exceedingly, and simulation results are satisfying.", notes = "Also known as \cite{6642788}", } @InProceedings{zhang3:evows04, author = "Yang Zhang and Steve L. Smith and Andy M. Tyrrell", title = "Intrinsic Evolvable Hardware in Digital Filter Design", booktitle = "Applications of Evolutionary Computing, EvoWorkshops2004: {EvoBIO}, {EvoCOMNET}, {EvoHOT}, {EvoIASP}, {EvoMUSART}, {EvoSTOC}", year = "2004", month = "5-7 " # apr, editor = "Guenther R. Raidl and Stefano Cagnoni and Jurgen Branke and David W. Corne and Rolf Drechsler and Yaochu Jin and Colin R. Johnson and Penousal Machado and Elena Marchiori and Franz Rothlauf and George D. Smith and Giovanni Squillero", series = "LNCS", volume = "3005", address = "Coimbra, Portugal", publisher = "Springer Verlag", publisher_address = "Berlin", pages = "389--398", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, evolutionary computation", ISBN = "3-540-21378-3", DOI = "doi:10.1007/978-3-540-24653-4_40", abstract = "This paper presents the application of Intrinsic Evolvable Hardware to real-world combinational circuit synthesis, as an alternative to conventional approaches. The evolutionary technique employs Cartesian Genetic Programming at a functional level by devising compact evolutionary processing elements and an external genetic reconfiguration unit. The experimental results conclude that in terms of computational effort, filtered image signal and implementation cost, the evolution outperforms convention approaches in most cases.", notes = "EvoWorkshops2004", } @InProceedings{1068143, author = "Yang Zhang and Peter I. Rockett", title = "Evolving optimal feature extraction using multi-objective genetic programming: a methodology and preliminary study on edge detection", booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation", year = "2005", editor = "Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and Edwin D. {de Jong} and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler", volume = "1", ISBN = "1-59593-010-8", pages = "795--802", address = "Washington DC, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2005/docs/p795.pdf", DOI = "doi:10.1145/1068009.1068143", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "25-29 " # jun, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Evolutionary Multiobjective Optimisation, design, edge detector, feature extractor, multi-objective genetic programming, theory", notes = "GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM Order Number 910052", } @InCollection{Zhang:2006:MOML, author = "Yang Zhang and Peter I. Rockett", title = "Feature extraction using multi-objective genetic programming", booktitle = "Multi-Objective Machine Learning", publisher = "Springer", year = "2006", editor = "Yaochu Jin", volume = "16", series = "Studies in Computational Intelligence", chapter = "4", pages = "75--99", note = "Invited chapter", keywords = "genetic algorithms, genetic programming, Evolutionary Multi-objective Optimisation, Multi-objective Machine Learning", ISBN = "3-540-30676-5", DOI = "doi:10.1007/3-540-33019-4_4", abstract = "A generic, optimal feature extraction method using multi-objective genetic programming (MOGP) is presented. This methodology has been applied to the well-known edge detection problem in image processing and detailed comparisons made with the Canny edge detector. We show that the superior performance from MOGP in terms of minimising the misclassification is due to its effective optimal feature extraction. Furthermore, to compare different evolutionary approaches, two popular techniques - PCGA and SPGA - have been extended to genetic programming as PCGP and SPGP, and applied to five datasets from the UCI database. Both of these evolutionary approaches provide comparable misclassification errors within the present framework but PCGP produces more compact transformations.", notes = "http://www.springer.com/sgw/cda/frontpage/0,11855,4-175-22-106797000-detailsPage%253Dppmmedia%257CaboutThisBook%257CaboutThisBook,00.html ", } @TechReport{VIE2006-002, author = "Yang Zhang and Peter I. Rockett", title = "A Generic Optimal Feature Extraction Method using Multiobjective Genetic Programming", institution = "Department of Electronic and Electrical Engineering, University of Sheffield", year = "2006", number = "VIE 2006/001", address = "UK", keywords = "genetic algorithms, genetic programming, Feature Extraction, Multiobjective Optimisation, MOGP, Pattern Recognition", URL = "http://www.shef.ac.uk/eee/vie/tech/VIE2006-002.pdf", abstract = "In this paper, we present a generic, optimal feature extraction method using multiobjective genetic programming. We reexamine the feature extraction problem and argue that effective feature extraction can significantly enhance the performance of pattern recognition systems with simple classifiers. A framework is presented to evolve optimised feature extractors that transform an input pattern space into a decision space in which maximal class separability is obtained. We have applied this method to real world datasets from the UCI Machine Learning and StatLog databases to verify our approach and compare our proposed method with other reported results. We conclude that our algorithm is able to produce classifiers of superior (or equivalent) performance to the conventional classifiers examined, suggesting removal of the need to exhaustively evaluate a large family of conventional classifiers on any new problem.", size = "29 pages", } @InProceedings{Zhang:2006:MLC, author = "Yang Zhang", title = "Autonomous Robot Failure Recognition Design using Multi-Objective Genetic Programming", booktitle = "2006 International Conference on Machine Learning and Cybernetics", year = "2006", pages = "4563--4568", month = aug, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", ISBN = "1-4244-0061-9", DOI = "doi:10.1109/ICMLC.2006.258378", abstract = "An evolutionary autonomous failure recognition approach is presented using multi-objective genetic programming in this paper. It is compared with the conventional robot failure classification algorithm. Detailed analysis of the evolved feature extractors is tempted on investigated problems. We conclude MOGP is an effective and practical way to automate the process of failure recognition system design with better recognition accuracy and more flexibility via optimising feature extraction stage.", notes = "Electronic and Electrical Engineering Department, The University of Sheffield, S1 3JD, UK", } @InCollection{Zhang:2006:WSC, author = "Yang Zhang and Peter I. Rockett", title = "Multiobjective Genetic Programming Feature Extraction with Optimized Dimensionality", booktitle = "Soft Computing in Industrial Applications", publisher = "Springer", year = "2006", editor = "Ashraf Saad and Erel Avineri and Keshav Dahal and Muhammad Sarfraz and Rajkumar Roy", volume = "39", series = "Advances in Soft Computing", pages = "159--168", month = "18 " # sep # " - 6 " # oct, keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.armstrong.edu/wsc11/pdf/pap107s2-file1.pdf", URL = "https://link.springer.com/chapter/10.1007/978-3-540-70706-6_15", DOI = "doi:10.1007/978-3-540-70706-6_15", abstract = "We present a multi-dimensional mapping strategy using multiobjective genetic programming (MOGP) to search for the (near-)optimal feature extraction pre-processing stages for pattern classification as well as optimizing the dimensionality of the decision space. We search for the set of mappings with optimal dimensionality to project the input space into a decision space with maximized class separability. The steady-state Pareto converging genetic programming (PCGP) has been used to implement this multi-dimensional MOGP. We examine the proposed method using eight benchmark datasets from the UCI database and the Statlog project to make quantitative comparison with conventional classifiers. We conclude that MMOGP outperforms the comparator classifiers due to its optimized feature extraction process.", notes = "WSC11 2006 published 2007", size = "10 pages", } @PhdThesis{YangZhang:thesis, author = "Yang Zhang", title = "Multi-objective genetic programming optimal search for feature extraction", school = "University of Sheffield", year = "2006", address = "UK", keywords = "genetic algorithms, genetic programming", URL = "http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.434509", notes = "uk.bl.ethos.434509", } @Article{zhang:2007:AIR, author = "Yang Zhang and Peter Rockett", title = "A Comparison of three evolutionary strategies for multiobjective genetic programming", journal = "Artificial Intelligence Review", year = "2007", volume = "27", number = "2 - 3", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s10462-008-9093-2", DOI = "doi:10.1007/s10462-008-9093-2", } @InProceedings{conf/eurogp/ZhangLNR08, title = "Applying Cost-Sensitive Multiobjective Genetic Programming to Feature Extraction for Spam {E}-mail Filtering", author = "Yang Zhang and HongYu Li and Mahesan Niranjan and Peter Rockett", bibdate = "2008-04-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/eurogp/eurogp2008.html#ZhangLNR08", booktitle = "Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008", address = "Naples", month = "26-28 " # mar, publisher = "Springer", year = "2008", volume = "4971", editor = "Michael O'Neill and Leonardo Vanneschi and Steven Gustafson and Anna Isabel {Esparcia Alcazar} and Ivanoe {De Falco} and Antonio {Della Cioppa} and Ernesto Tarantino", isbn13 = "978-3-540-78670-2", pages = "325--336", series = "Lecture Notes in Computer Science", DOI = "doi:10.1007/978-3-540-78671-9_28", keywords = "genetic algorithms, genetic programming", notes = "Part of \cite{conf/eurogp/2008} EuroGP'2008 held in conjunction with EvoCOP2008, EvoBIO2008 and EvoWorkshops2008", } @Misc{10.1.1.99.3617, author = "Yang Zhang and Peter I. Rockett", title = "Edge Detector Evolution using Multidimensional Multiobjective Genetic Programming", howpublished = "citeseerx", keywords = "genetic algorithms, genetic programming", URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.99.3617&rep=rep1&type=pdf", size = "25 pages", abstract = "In this paper we report the evolution of a feature extraction stage for edge detection using multidimensional multiobjective genetic programming. We have employed training and validation data produced using a realistic model of the imaging physics to evolve an n2-to-m mapping which projects the pixel intensities of an n by n image patch into an m-dimensional decision space. The (near-)optimal value of m is also simultaneously determined during evolution. A conventional Fisher linear discriminant is then used to classify edge patterns. On the independent validation set, the suggested edge detector is shown to give performance superior to both the well-known conventional Canny detector and to earlier multi-objective genetic programming results which projected the pattern vector into a one-dimensional decision space. In addition, the superiority of the new detector is also demonstrated on a hand-labeled set of real images.", notes = "See \cite{Zhang:2009:EC}", } @Article{Zhang:2009:EC, author = "Yang Zhang and Peter I. Rockett", title = "A Generic Multi-dimensional Feature Extraction Method Using Multiobjective Genetic Programming", journal = "Evolutionary Computation", year = "2009", volume = "17", number = "1", pages = "89--115", month = "Spring", keywords = "genetic algorithms, genetic programming, MOGP, PCA", ISSN = "1063-6560", DOI = "doi:10.1162/evco.2009.17.1.89", abstract = "In this paper, we present a generic feature extraction method for pattern classification using multi-objective genetic programming. This not only evolves the (near-)optimal set of mappings from a pattern space to a multi-dimensional decision space, but also simultaneously optimizes the dimensionality of that decision space. The presented framework evolves vector-to-vector feature extractors that maximize class separability. We demonstrate the efficacy of our approach by making statistically-founded comparisons with a wide variety of established classifier paradigms over a range of datasets and find that for most of the pairwise comparisons, our evolutionary method delivers statistically smaller misclassification errors. At very worst, our method displays no statistical difference in a few pairwise comparisons with established classifier/dataset combinations; crucially, none of the misclassification results produced by our method is worse than any comparator classifier. Although principally focused on feature extraction, feature selection is also performed as an implicit side effect; we show that both feature extraction and selection are important to the success of our technique. The presented method has the practical consequence of obviating the need to exhaustively evaluate a large family of conventional classifiers when faced with a new pattern recognition problem in order to attain a good classification accuracy.", notes = "WEKA, UCI. See also \cite{10.1.1.99.3617}", } @Article{Zhang:2009:ieeetASE, author = "Yang Zhang and Peter I. Rockett", title = "Application of Multiobjective Genetic Programming to the Design of Robot Failure Recognition Systems", journal = "IEEE Transactions on Automation Science and Engineering", year = "2009", month = apr, volume = "6", number = "2", pages = "372--376", keywords = "genetic algorithms, genetic programming, classifiers, data-driven machine learning method, domain knowledge, domain-dependent feature extraction, multiobjective genetic programming, robot failure recognition systems, control engineering computing, feature extraction, learning (artificial intelligence), telerobotics", abstract = "We present an evolutionary approach using multiobjective genetic programming (MOGP) to derive optimal feature extraction preprocessing stages for robot failure detection. This data-driven machine learning method is compared both with conventional (nonevolutionary) classifiers and a set of domain-dependent feature extraction methods. We conclude MOGP is an effective and practical design method for failure recognition systems with enhanced recognition accuracy over conventional classifiers, independent of domain knowledge.", DOI = "doi:10.1109/TASE.2008.2004414", ISSN = "1545-5955", notes = "Also known as \cite{4667633}", } @Article{Zhang:2010:PAA, author = "Yang Zhang and Peter Rockett", title = "Domain-independent feature extraction for multi-classification using multi-objective genetic programming", journal = "Pattern Analysis and Applications", year = "2010", number = "3", volume = "13", pages = "273--288", keywords = "genetic algorithms, genetic programming", ISSN = "1433-7541", DOI = "doi:10.1007/s10044-009-0154-1", size = "16 pages", abstract = "We propose three model-free feature extraction approaches for solving the multiple class classification problem; we use multi-objective genetic programming (MOGP) to derive (near-)optimal feature extraction stages as a precursor to classification with a simple and fast-to-train classifier. Statistically-founded comparisons are made between our three proposed approaches and seven conventional classifiers over seven datasets from the UCI Machine Learning database. We also make comparisons with other reported evolutionary computation techniques. On almost all the benchmark datasets, the MOGP approaches give better or identical performance to the best of the conventional methods. Of our proposed MOGP-based algorithms, we conclude that hierarchical feature extraction performs best on multi-classification problems.", affiliation = "Laboratory for Image and Vision Engineering, Department of Electronic and Electrical Engineering, University of Sheffield, Mappin Street, Sheffield, S1 3JD UK", } @Article{Zhang20111087, author = "Yang Zhang and Peter I. Rockett", title = "A generic optimising feature extraction method using multiobjective genetic programming", journal = "Applied Soft Computing", year = "2011", volume = "11", number = "1", pages = "1087--1097", month = jan, keywords = "genetic algorithms, genetic programming, Feature extraction, Multiobjective optimisation, Pattern recognition", ISSN = "1568-4946", broken = "http://www.sciencedirect.com/science/article/B6W86-4YGHGKT-2/2/3c6f14d2e029af14747957a5a2ccfd11", DOI = "doi:10.1016/j.asoc.2010.02.008", abstract = "In this paper, we present a generic, optimising feature extraction method using multiobjective genetic programming. We re-examine the feature extraction problem and show that effective feature extraction can significantly enhance the performance of pattern recognition systems with simple classifiers. A framework is presented to evolve optimised feature extractors that transform an input pattern space into a decision space in which maximal class separability is obtained. We have applied this method to real world datasets from the UCI Machine Learning and StatLog databases to verify our approach and compare our proposed method with other reported results. We conclude that our algorithm is able to produce classifiers of superior (or equivalent) performance to the conventional classifiers examined, suggesting removal of the need to exhaustively evaluate a large family of conventional classifiers on any new problem.", } @Article{zhang:2004:gpcls, author = "Yifeng Zhang and Siddhartha Bhattacharyya", title = "Genetic programming in classifying large-scale data: an ensemble method", journal = "Information Sciences", volume = "163", number = "1-3", month = jun, year = "2004", pages = "85--101", keywords = "genetic algorithms, genetic programming, Ensemble, Classification, Large-scale data", URL = "http://www.sciencedirect.com/science/article/B6V0C-4B3JHTS-8/2/e700b4ff083cb177e8d6d03d0767a261", DOI = "doi:10.1016/j.ins.2003.03.028", abstract = "This study demonstrated potential of genetic programming (GP) as a base classifier algorithm in building ensembles in the context of large-scale data classification. An ensemble built upon base classifiers that were trained with GP was found to significantly outperform its counterparts built upon base classifiers that were trained with decision tree and logistic regression. The superiority of GP ensemble was partly attributed to the higher diversity, both in terms of the functional form of as well as with respect to the variables defining the models, among the base classifiers upon which it was built on. Implications of GP as a useful tool in other data mining problems, such as feature selection, were also discussed.", } @PhdThesis{Ying_Zhang:thesis, author = "Ying Zhang", title = "Synthesis of local thermo-physical models using genetic programming", school = "Department of Chemical and Biomedical Engineering, College of Engineering, University of South Florida", year = "2008", address = "USA", month = "11 " # dec, keywords = "genetic algorithms, genetic programming, Matlab, Data mining, Symbolic regression, Function identification, Parameter regression, Statistic analysis, Process simulation", URL = "http://scholarcommons.usf.edu/etd/103", URL = "http://scholarcommons.usf.edu/cgi/viewcontent.cgi?article=1102&context=etd", size = "139 pages", abstract = "Local thermodynamic models are practical alternatives to computationally expensive rigorous models that involve implicit computational procedures and often complement them to accelerate computation for real-time optimization and control. Human-centred strategies for development of these models are based on approximation of theoretical models. Genetic Programming (GP) system can extract knowledge from the given data in the form of symbolic expressions. This research describes a fully data driven automatic self-evolving algorithm that builds appropriate approximating formulae for local models using genetic programming. No a-priori information on the type of mixture (ideal/non ideal etc.) or assumptions are necessary. The approach involves synthesis of models for a given set of variables and mathematical operators that may relate them. The selection of variables is automated through principal component analysis and heuristics. For each candidate model, the model parameters are optimized in the inner integrated nested loop. The trade-off between accuracy and model complexity is addressed through incorporation of the Minimum Description Length (MDL) into the fitness (objective) function. Statistical tools including residual analysis are used to evaluate performance of models. Adjusted R-square is used to test model's accuracy, and F-test is used to test if the terms in the model are necessary. The analysis of the performance of the models generated with the data driven approach depicts theoretically expected range of compositional dependence of partition coefficients and limits of ideal gas as well as ideal solution behaviour. Finally, the model built by GP integrated into a steady state and dynamic flow sheet simulator to show the benefits of using such models in simulation. The test systems were propane-propylene for ideal solutions and acetone-water for non-ideal. The result shows that, the generated models are accurate for the whole range of data and the performance is tunable. The generated local models can indeed be used as empirical models go beyond elimination of the local model updating procedures to further enhance the utility of the approach for deployment of real-time applications.", notes = "Supervisor: Aydin K. Sunol", } @InProceedings{conf/isda/ZhangC06, title = "Predicting for {MTBF} Failure Data Series of Software Reliability by Genetic Programming Algorithm", author = "Yongqiang Zhang and Huashan Chen", publisher = "IEEE Computer Society", year = "2006", booktitle = "Sixth International Conference on Intelligent Systems Design and Applications (ISDA'06)", pages = "666--670", editor = "Bo Yang and Yuehui Chen", address = "Jinan University, China", month = "16-18 " # oct, bibdate = "2007-01-23", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/isda/isda2006-1.html#ZhangC06", keywords = "genetic algorithms, genetic programming", ISBN = "0-7695-2528-8", DOI = "doi:10.1109/ISDA.2006.218", abstract = "At present, most of software reliability models have to build on certain presuppositions about software fault process, which also brings on the incongruence of software reliability models application. To solve these problems and cast off traditional models multi-subjective assumptions, this paper adopts Genetic Programming (GP) evolution algorithm to establishing software reliability model based on mean time between failures (MTBF) time series. The evolution model of GP is then analysed and appraised according to five characteristic criteria for some common-used software testing cases. Meanwhile, we also select some traditional probability models and the Neural Network Model to compare with the new GP model separately. The result testifies that the new model evolved by GP has the higher prediction precision and better applicability, which can improve the applicable inconsistency of software reliability modelling to some extent.", notes = "http://isda2006.ujn.edu.cn/ Yongqiang Zhang, Hebei University of Engineering, China Huashan Chen, Hebei University of Engineering, China", } @InProceedings{conf/icnc/ZhangC06, title = "Improved Approach of Genetic Programming and Applications for Data Mining", author = "Yongqiang Zhang and Huashan Chen", booktitle = "Advances in Natural Computation, Second International Conference, {ICNC} 2006, Proceedings, Part {I}", publisher = "Springer", year = "2006", volume = "4221", editor = "Licheng Jiao and Lipo Wang and Xinbo Gao and Jing Liu and Feng Wu", pages = "816--819", series = "Lecture Notes in Computer Science", address = "Xi'an, China", month = sep # " 24-28", keywords = "genetic algorithms, genetic programming, dynamic tree depth, ordinary differential equation, data mining", bibdate = "2006-11-29", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icnc/icnc2006-1.html#ZhangC06", ISBN = "3-540-45901-4", DOI = "doi:10.1007/11881070_108", abstract = "Genetic Programming (GP for short) is applied to a benchmark of the data fitting and forecasting problems. However, the increasing size of the trees may block the speed of problems reaching best solution and affect the fitness of best solutions. In this view, this paper adopts the dynamic maximum tree depth to constraining the complexity of programs, which can be useful to avoid the typical undesirable growth of program size. For more precise data fitting and forecasting, the arithmetic operator of ordinary differential equations has been made use of. To testify what and how they work, an example of service life data series about electron parts is taken. The results indicate the feasibility and availability of improved GP, which can be applied successfully for data fitting and forecasting problems to some extent.", } @InProceedings{oai:CiteSeerX.psu:10.1.1.522.8848, author = "Yongqiang Zhang and Lili Wu", title = "Research on Time Series Modeling by Genetic Programming and Model De-noising", booktitle = "Proceedings of the 2007 WSEAS International Conference on Computer Engineering and Applications", year = "2007", address = "Gold Coast, Australia", month = jan # " 17-19", publisher = "WSEAS", keywords = "genetic algorithms, genetic programming, denoising, wavelet threshold, time series, modelling, gp model", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.522.8848", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.522.8848", URL = "http://www.wseas.us/e-library/conferences/2007australia/papers/550-117.pdf", size = "5 pages", abstract = "In order to cast off the subjective assumptions of traditional methods for modelling, this paper brings forward the Genetic Programming (GP for short) algorithm to establish a reasonable system model dynamically for time series signal. Meanwhile, the approach of wavelet threshold is adopted to de-noising for the GP models. On the basis of these theories, the simulation experimentations about two instances are carried on. The results indicate that the threshold approach of wavelet de-noising for time series signal models take on better impacts, which can improve the GP models to some extent, and enhance the forecast precision of the model.", } @InProceedings{Zhang:2008:ieeeICIT, author = "Yongqiang Zhang and Jingjie Yin", title = "Software reliability model by AGP", booktitle = "IEEE International Conference on Industrial Technology, ICIT 2008", year = "2008", month = apr, pages = "1--5", keywords = "genetic algorithms, genetic programming, SBSE, AGP algorithm, Armored Force Engineering Institute, adaptive genetic operators, genetic programming evolution algorithm, sigmoid curve, software failure time series, software reliability model, software testing, program testing, software reliability", DOI = "doi:10.1109/ICIT.2008.4608638", abstract = "To solve the problems of the incongruence of software reliability models and cast off the traditional models' multi-subjective assumptions, this paper adopts genetic programming evolution algorithm which has adaptive genetic operators (for short AGP) to establish software reliability model based on software failure time series. The individual of the population is according to the case of the fitness of the generation to adjust the probability of crossover and mutation by the sigmoid curve. By evaluating the data series of the software testing case in Armored Force Engineering Institute, the results sufficiently testify that the new AGP algorithm has better applicability and the validity of fitness and forecasting. Moreover, compared with standard genetic programming evolution algorithm, the new AGP algorithm has the better rapidity of convergence. Therefore, we can say that, this algorithm can be more effectively applied to software testing and ensured the validity of data.", notes = "Also known as \cite{4608638}", } @Article{Zhang:2012:Jsoftware, author = "Yongqiang Zhang and Jing Xiao and Shengjuan Sun", title = "BS-GEP Algorithm for Prediction of Software Failure Series", journal = "Journal of Software", year = "2012", volume = "7", number = "1", pages = "243--248", month = jan, keywords = "genetic algorithms, genetic programming, gene expression programming, SBSE, BS-GEP, complexity analysis, convergence analysis, software failure, time series prediction", ISSN = "1796217X", URL = "http://www.jsoftware.us/index.php?m=content&c=index&a=show&catid=55&id=1084", URL = "http://www.jsoftware.us/vol7/jsw0701-33.pdf", broken = "http://ojs.academypublisher.com/index.php/jsw/article/view/5146", broken = "http://www.doaj.org/doaj?func=openurl\&genre=article\&issn=1796217X\&date=2012\&volume=7\&issue=1\&spage=243", size = "6 pages", abstract = "This paper introduces GEP(Gene Expression Programming) fundamental. Aimed at prediction of software failure sequence, an improved GEP(GEP based on Block Strategy, BS-GEP) is presented, in which the population is divided into several blocks according to the individual fitness of each generation and the genetic operators are reset differently in each block to guarantee the genetic diversity. The algorithm complexity and convergence of BS-GEP is analysed in the paper. Furthermore, BS-GEP is applied in the solution of prediction in software failure sequence. The simulation results show that the model found by BS-GEP, which is proved widely used for many other time series, is more accurate than the one of classic GEP.", bibsource = "OAI-PMH server at www.doaj.org", language = "eng", oai = "oai:doaj-articles:cce5bee368f9a6cd7fa22ef0f44dc45b", } @Article{journals/candc/ZhangPZSZZ13, author = "Yongqing Zhang and Yi-Fei Pu and Haisen Zhang and Yabo Su and Lifang Zhang and Jiliu Zhou", title = "Using gene expression programming to infer gene regulatory networks from time-series data", journal = "Computational Biology and Chemistry", year = "2013", volume = "47", bibdate = "2013-12-18", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/candc/candc47.html#ZhangPZSZZ13", pages = "198--206", keywords = "genetic algorithms, genetic programming, gene expression programming, GEP, Gene regulatory networks, Ordinary differential equation, Least mean square", URL = "http://dx.doi.org/10.1016/j.compbiolchem.2013.09.004", DOI = "doi:10.1016/j.compbiolchem.2013.09.004", abstract = "Gene regulatory networks inference is currently a topic under heavy research in the systems biology field. In this paper, gene regulatory networks are inferred via evolutionary model based on time-series microarray data. A non-linear differential equation model is adopted. Gene expression programming (GEP) is applied to identify the structure of the model and least mean square (LMS) is used to optimize the parameters in ordinary differential equations (ODEs). The proposed work has been first verified by synthetic data with noise-free and noisy time-series data, respectively, and then its effectiveness is confirmed by three real time-series expression datasets. Finally, a gene regulatory network was constructed with 12 Yeast genes. Experimental results demonstrate that our model can improve the prediction accuracy of microarray time-series data effectively.", } @InProceedings{Zhang:2019:EuroGP, author = "Yu Zhang2 and Ting Hu and Xiaodong Liang and Mohammad Zawad Ali and Md Nasmus Sakib Khan Shabbir", title = "Fault Detection and Classification for Induction Motors using Genetic Programming", booktitle = "EuroGP 2019: Proceedings of the 22nd European Conference on Genetic Programming", year = "2019", month = "24-26 " # apr, editor = "Lukas Sekanina and Ting Hu and Nuno Lourenco", series = "LNCS", volume = "11451", publisher = "Springer Verlag", address = "Leipzig, Germany", pages = "178--193", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-030-16669-4", URL = "https://www.springer.com/us/book/9783030166694", DOI = "doi:10.1007/978-3-030-16670-0_12", size = "16 pages", abstract = "Induction motors are the workhorse in various industry sectors, and their accurate fault detection is essential to ensure reliable operation of critical industrial processes. Since various types of mechanical and electrical faults could occur, induction motor fault diagnosis can be interpreted as a multi-label classification problem. The current and vibration input data collected by monitoring a motor often require signal processing to extract features that can better characterize these waveforms. However, some extracted features may not be relevant to the classification, feature selection is thus necessary. Given such challenges, in recent years, machine learning methods, including decision trees and support vector machines, are increasingly applied to detect and classify induction motor faults. Genetic programming (GP), as a powerful automatic learning algorithm with its abilities of embedded feature selection and multi-label classification, has not been explored to solve this problem. In this paper, we propose a linear GP (LGP) algorithm to search predictive models for motor fault detection and classification. Our method is able to evolve multi-label classifiers with high accuracies using experimentally collected data in the lab by monitoring two induction motors. We also compare the results of the LGP algorithm to other commonly used machine learning algorithms, and are able to show its superior performance on both feature selection and classification.", notes = "http://www.evostar.org/2019/cfp_eurogp.php#abstracts Part of \cite{Sekanina:2019:GP} EuroGP'2019 held in conjunction with EvoCOP2019, EvoMusArt2019 and EvoApplications2019", } @InProceedings{Zhang:2020:EuroGPa, author = "Yu Zhang2 and Yuanzhu Chen and Ting Hu", title = "Classification of Autism Genes using Network Science and Linear Genetic Programming", booktitle = "EuroGP 2020: Proceedings of the 23rd European Conference on Genetic Programming", year = "2020", month = "15-17 " # apr, editor = "Ting Hu and Nuno Lourenco and Eric Medvet", series = "LNCS", volume = "12101", publisher = "Springer Verlag", address = "Seville, Spain", pages = "279--294", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Linear genetic programming, Autism spectrum disorders, Human molecular interaction network, Complex networks, Disease-gene association", isbn13 = "978-3-030-44093-0", DOI = "doi:10.1007/978-3-030-44094-7_18", abstract = "Understanding the genetic background of complex diseases and disorders plays an essential role in the promising precision medicine. Deciphering what genes are associated with a specific disease/disorder helps better diagnose and treat it, and may even prevent it if predicted accurately and acted on effectively at early stages. The evaluation of candidate disease-associated genes, however, requires time-consuming and expensive experiments given the large number of possibilities. Due to such challenges, computational methods have seen increasing applications in predicting gene-disease associations. Given the intertwined relationships of molecules in human cells, genes and their products can be considered to form a complex molecular interaction network. Such a network can be used to find candidate genes that share similar network properties with known disease-associated genes. In this research, we investigate autism spectrum disorders and propose a linear genetic programming algorithm for autism gene prediction using a human molecular interaction network and known autism-genes for training. We select an initial set of network properties as features and our LGP algorithm is able to find the most relevant features while evolving accurate predictive models. Our research demonstrates the powerful and flexible learning abilities of GP on tackling a significant biomedical problem, and is expected to inspire further exploration of wide GP applications.", notes = "Linear GP implementation from Ting Hu See also MSc https://research.library.mun.ca/14305/ http://www.evostar.org/2020/cfp_eurogp.php Part of \cite{Hu:2020:GP} EuroGP'2020 held in conjunction with EvoCOP2020, EvoMusArt2020 and EvoApplications2020", } @InProceedings{Zhang:2019:ICARM, author = "Yu Zhang3 and Miguel Martinez-Garcia and Jose R. Serrano-Cruz and Anthony Latimer", title = "Multi-region System Modelling by using Genetic Programming to Extract Rule Consequent Functions in a {TSK} Fuzzy System", booktitle = "2019 IEEE 4th International Conference on Advanced Robotics and Mechatronics (ICARM)", year = "2019", pages = "987--992", month = jul, keywords = "genetic algorithms, genetic programming, Fuzzy", DOI = "doi:10.1109/ICARM.2019.8834163", abstract = "This paper aims to build a fuzzy system by means of genetic programming, which is used to extract the relevant function for each rule consequent through symbolic regression. The employed TSK fuzzy system is complemented with a variational Bayesian Gaussian mixture clustering method, which identifies the domain partition, simultaneously specifying the number of rules as well as the parameters in the fuzzy sets. The genetic programming approach is accompanied with an orthogonal least square algorithm, to extract robust rule consequent functions for the fuzzy system. The proposed model is validated with a synthetic surface, and then with real data from a gas turbine compressor map case, which is compared with an adaptive neuro-fuzzy inference system model. The results have demonstrated the efficacy of the proposed approach for modelling system with small data or bifurcating dynamics, where the analytical equations are not available, such as those in a typical industrial setting.", notes = "Also known as \cite{8834163}", } @TechReport{vuw-CS-TR-04-14, author = "Yun Zhang and Mengjie Zhang", title = "A Multiple-Output Program Tree Structure in Genetic Programming", institution = "Computer Science, Victoria University of Wellington", year = "2004", number = "CS-TR-04-14", address = "New Zealand", month = dec, keywords = "genetic algorithms, genetic programming", URL = "http://www.mcs.vuw.ac.nz/comp/Publications/CS-TR-04-14.abs.html", URL = "http://www.mcs.vuw.ac.nz/comp/Publications/archive/CS-TR-04/CS-TR-04-14.pdf", size = "15 pages", abstract = "program tree structure in genetic programming which outputs multiple related values, hence serves as a more coherent multiclass classifier. The multiple outputting effect of the tree is achieved by making it simulate a kind of directed acyclic graph. The approach is examined and compared with the basic genetic programming approach on four multiclass object classification tasks with varying difficulty. The results show that the new approach greatly outperforms the basic genetic programming approach on all the tasks.", } @InProceedings{YunZhang:04:ivcnz, author = "Yun Zhang and Mengjie Zhang", title = "A New Program Structure in Genetic Programming for Object Classification", booktitle = "Proceeding of Image and Vision Computing NZ International Conference", year = "2004", editor = "David Pairman and Heather North and Stephen McNeill", pages = "459--465", month = nov, publisher = "Lincoln, Landcare Research", address = "Akaroa, New Zealand", keywords = "genetic algorithms, genetic programming, image recognition, multiple class object classification, modifying based program structure, Modi", URL = "http://www.mcs.vuw.ac.nz/~mengjie/papers/1052-yun-meng-ivcnz04.pdf", size = "6 pages", abstract = "genetic programming (GP) for multiclass object classification. Instead of using the standard GP approach where each genetic program returns just one floating number that is then translated into different class labels, this approach invents a new program structure called Modi with multiple outputs. A voting scheme is then applied to these output values to determine the class of the input object. The approach is examined and compared with the basic GP approach on four multiclass object classification tasks with increasing difficulty. The results show that the new approach always outperforms the basic approach with controllable proper setting.", notes = "Fri, 02 Jun 2006 17:03:20 +0800", } @InProceedings{Zhang:2004:aspgp, author = "Yun Zhang and Mengjie Zhang", title = "A Multiple-Output Program Tree Structure in Genetic Programming", booktitle = "Proceedings of The Second Asian-Pacific Workshop on Genetic Programming", year = "2004", editor = "R I Mckay and Sung-Bae Cho", pages = "12pp", address = "Cairns, Australia", month = "6-7 " # dec, keywords = "genetic algorithms, genetic programming", URL = "http://www.mcs.vuw.ac.nz/~mengjie/papers/yun-meng-apwgp04.pdf", size = "13 pages", abstract = "program tree structure in genetic programming which outputs multiple related values, hence serves as a more coherent multiclass classifier. The multiple outputting effect of the tree is achieved by making it simulate a kind of directed acyclic graph. The approach is examined and compared with the basic genetic programming approach on four multiclass object classification tasks with varying difficulty. The results show that the new approach greatly outperforms the basic genetic programming approach on all the tasks.", notes = "broken http://sc.snu.ac.kr/~aspgp/aspgp04/programme.html School of Mathematics, Statistics and Computer Science, Victoria University of Wellington, NZ", } @Article{journals/jdcta/ZhangDC10, title = "{QSPR} Study Of Mineral Crystal Lattice Energy Based On Gene Expression Programming", author = "Yuntao Zhang and Wenbin Dai and Zhengjun Cheng", journal = "International Journal of Digital Content Technology and its Applications", year = "2010", number = "3", volume = "4", pages = "35--42", month = jun, bibdate = "2011-02-01", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/jdcta/jdcta4.html#ZhangDC10", URL = "http://www.aicit.org/jdcta/ppl/Binder9_Part3.pdf", keywords = "genetic algorithms, genetic programming, gene expression programming, quantitative structure-property relationship, mineral crystal lattice energy", size = "8 pages", abstract = "In this study, gene expression programming (GEP), a novel genetic algorithm, is used to develop quantitative model as potential screening mechanism for mineral crystal lattice energy for the first time. Eight descriptors, including ionic radius, charges, effective nuclear charges and principal quantum number, are selected to establish this model. A nonlinear quantitative structure-property relationship (QSPR) model based on GEP has been built and its predicted coefficient (R2) is 0.9912. This study has provided a new and effective method for the research of mineral crystal lattice energy.", } @InProceedings{Zhang:2007:cec, author = "Zeming Zhang and Wenjian Luo and Xufa Wang", title = "Immune Genetic Programming Based on Register-Stack Structure", booktitle = "2007 IEEE Congress on Evolutionary Computation", year = "2007", editor = "Dipti Srinivasan and Lipo Wang", pages = "3751--3758", address = "Singapore", month = "25-28 " # sep, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "1-4244-1340-0", file = "1032.pdf", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2007.4424959", abstract = "Inspired by biological immune principles, a novel Immune Genetic Programming based on Register-Stack structure (rs-IGP) is proposed in this paper. In rs-IGP, an antigen represents a problem to be solved, and an antibody represents a candidate solution. A flexible and efficient antibody representation based on register-stack structure is designed for rs-IGP. Three populations are adopted in rs-IGP, i.e. the common population, the elitist population and the self set. The immune genetic operators are also developed, including clone operator, recombination operator, mutation operator, hypermutation operator, crossover operator and negative selection operator. The experimental results demonstrate that rs-IGP has better performance.", notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C", } @Article{ZHANG:2016:Biosystems, author = "Zhen Zhang and Matthew Bedder and Stephen L. Smith and Dawn Walker and Saqib Shabir and Jennifer Southgate", title = "Characterization and classification of adherent cells in monolayer culture using automated tracking and evolutionary algorithms", journal = "Biosystems", volume = "146", pages = "110--121", year = "2016", note = "Information Processing in Cells and Tissues", keywords = "genetic algorithms, genetic programming", ISSN = "0303-2647", DOI = "doi:10.1016/j.biosystems.2016.05.009", URL = "http://www.sciencedirect.com/science/article/pii/S0303264716300727", abstract = "This paper presents a novel method for tracking and characterizing adherent cells in monolayer culture. A system of cell tracking employing computer vision techniques was applied to time-lapse videos of replicate normal human uro-epithelial cell cultures exposed to different concentrations of adenosine triphosphate (ATP) and a selective purinergic P2X antagonist (PPADS), acquired over a 24h period. Subsequent analysis following feature extraction demonstrated the ability of the technique to successfully separate the modulated classes of cell using evolutionary algorithms. Specifically, a Cartesian Genetic Program (CGP) network was evolved that identified average migration speed, in-contact angular velocity, cohesivity and average cell clump size as the principal features contributing to the separation. Our approach not only provides non-biased and parsimonious insight into modulated class behaviours, but can be extracted as mathematical formulae for the parameterization of computational models", } @PhdThesis{Zhen_Zhang:thesis, author = "Zhen Zhang", title = "The application of evolutionary computation towards the characterization and classification of urothelium cell cultures", school = "Electronic Engineering, York University", year = "2018", address = "UK", month = sep, keywords = "genetic algorithms, genetic programming, Cartesian Genetic Program", URL = "https://etheses.whiterose.ac.uk/26924/", URL = "https://etheses.whiterose.ac.uk/26924/1/Examined%20Thesis.pdf", size = "151 pages", abstract = "We present a novel method for classifying and characterising urothelial cell cultures. A system of cell tracking employing computer vision techniques was appliedto a one day long time-lapse videos of replicate normal humanuroepithelial cell cultures exposed to different concentrationsof adenosine triphosphate (ATP) and a selective purinergicP2X antagonist (PPADS) as inhibitor. Subsequent analysisfollowing feature extraction on both cell culture and single-celldemonstrated the ability of the approach to successfullyclassify the modulated classes of cells using evolutionaryalgorithms. Specifically, a Cartesian Genetic Program (CGP)network was evolved that identified average migration speed,in-contact angular velocity, cohesivity and average cell clumpsize as the principal features contributing to the cell classseparation. This approach provides a non-biased insight intomodulated cell class behaviours.", notes = "uk.bl.ethos.811376 supervisor: Stephen Smith", } @InProceedings{Zhang:2008:WCICA, author = "Zheng Zhang and Kangling Fang and Weihua Huang", title = "Genetic Programming based Fuzzy Mapping Function Model for fault diagnosis of power transformers", booktitle = "7th World Congress on Intelligent Control and Automation, WCICA 2008", year = "2008", month = "25-27 " # jun, address = "Chongqing, China", pages = "1184--1187", keywords = "genetic algorithms, genetic programming, fault diagnosis, fuzzy IEC code method, genetic operations, genetic programming based fuzzy mapping functions, insulation fault diagnosis system, power systems, power transformers, tree-like combinations, fault diagnosis, fuzzy set theory, power transformer insulation", DOI = "doi:10.1109/WCICA.2008.4593092", abstract = "A genetic programming based fuzzy mapping functions (GPFMF) model is proposed in this paper to diagnose the insulation fault types of power transformers. The proposed GPFMF model constructs the fuzzy relationship between input and output fuzzy variables by genetic programming algorithms. The fuzzy relationship is represented as one of candidates which have the form of tree-like combinations of series of fuzzy implication operators with fuzzy input variables. Then the best fuzzy mapping function is evolved by genetic operations and evolution. Based on the proposed GPFMF model, an insulation fault diagnosis system for power systems is designed to detect the insulation fault types of power transformers. Compared with the normal fuzzy IEC code method, the GPFMF models can generate fuzzy mapping functions from fuzzy input and output examples and has higher performance than normal fuzzy method.", notes = "Also known as \cite{4593092}", } @InProceedings{Zhang:2010:ICINIS, author = "Zheng Zhang and Kangling Fang and Weihua Huang", title = "A Genetic Programming Based Fuzzy Model for Fault Diagnosis of Power Transformers", booktitle = "3rd International Conference on Intelligent Networks and Intelligent Systems (ICINIS 2010)", year = "2010", month = "1-3 " # nov, pages = "455--458", abstract = "In this paper, a fuzzy model based on genetic programming (GPFM) is proposed to diagnose the fault types of insulation of power transformers. The proposed GPFM algorithm constructs the fuzzy relationship between input and output fuzzy variables by genetic programming algorithms. The parameters of memberships of fuzzy subsets and the fuzzy relationship of system are represented by the GP candidates that have the form of tree-like combinations of fuzzy subsets of input variables. Then the best fuzzy function is evolved by genetic operations and evolution. Based on the proposed GPFM algorithms, an insulation fault diagnosis system for power systems is designed to distinguish the insulation fault types of power transformers. Compared with the conditional fuzzy IEC code method, the GPFM algorithm can automatically generate fuzzy relationship between fault symptom with fault types and shows better performances.", keywords = "genetic algorithms, genetic programming, fuzzy IEC code method, fuzzy subsets, genetic programming based fuzzy model, insulation fault diagnosis system, power transformers, tree-like combinations, fuzzy set theory, power transformers", DOI = "doi:10.1109/ICINIS.2010.154", notes = "Coll. of Inf. Sci. & Eng., Wuhan Univ. of Sci. & Technol., Wuhan, China. Also known as \cite{5693583}", } @InProceedings{Zhang:2019:ICISCAE, author = "Zhengfeng Zhang and Yan Zhou", title = "A Linear-in-Parameters Genetic Programming Method for Chemical Kinetics System Identification", booktitle = "2019 2nd International Conference on Information Systems and Computer Aided Education (ICISCAE)", year = "2019", pages = "169--173", month = sep, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICISCAE48440.2019.221611", abstract = "This paper proposed an improved linear-in-parameters based genetic programming method for the chemical kinetics system modeling. To make the search space suitable for chemical kinetics modeling a functional library with basic chemical kinetics items is built. The proposed method was then applied into several simulated nonlinear chemical kinetics procedures. Results indicated that by designing its function library, the identified model can be behavioural and phenomenological for the chemical kinetics models and the identified model can produce convincing reaction rates.", notes = "Also known as \cite{9075716}", } @Article{ZHANG:2020:JSSC, author = "Zheyu Zhang and Kalpana Singh and Yoed Tsur and Jigang Zhou and James J. Dynes and Venkataraman Thangadurai", title = "Studies on effect of {Ca}-doping on structure and electrochemical properties of garnet-type {Y3-xCaxFe5O12-o}", journal = "Journal of Solid State Chemistry", year = "2020", volume = "290", pages = "121530", month = oct, keywords = "genetic algorithms, genetic programming, Calcium, Yttrium iron garnet, Oxygen non-stoichiometry, Oxygen reduction reaction, Impedance spectroscopy genetic programming", ISSN = "0022-4596", URL = "http://www.sciencedirect.com/science/article/pii/S0022459620303601", DOI = "doi:10.1016/j.jssc.2020.121530", size = "11 pages", abstract = "In this study, effect of Ca-doping on electrical and electrochemical properties of garnet structure Y3-xCaxFe5O12-delta (x ​= ​0, 0.05, 0.1, 0.3, 0.5 and 0.7) was investigated. Cubic-phase (Ia-3d) garnet was prepared through sol-gel method and sintered at 1100 ​degreeC for 2 ​h in air. Highest oxygen non-stoichiometry (delta = ​0.19) was found in x ​= ​0.3 member. X-ray absorption spectroscopy (XAS) confirmed the formation of hole carriers () due to Ca-substitution for Y in Y3Fe5O12. 4-Probe DC measurements showed that electrical conductivity increased with an increase in Calcium amount until x ​= ​0.1 (1.58 ​S/cm at 750 ​degreeC) and then decreased due to decrease in the concentration of the charge carriers. Lowest area specific resistance (ASR) of 1 ohms cm2 was seen at 750 ​degreeC in air for the symmetrical cell with x ​= ​0.3 garnet-LSGM composite electrode. Studies on pO2 dependent ASR and impedance spectroscopy genetic programming (ISGP) analysis revealed that oxygen dissociation and partial reduction of adsorbed oxygen molecule co-limit the electrochemical performance for oxygen reduction reaction (ORR).", } @InProceedings{ICNC:2008:ICNC, author = "Zhimei Zhang and Shuang Qiao and Chunju Li and Honggang Wang and Yao Li and Liying Cheng", title = "Evolutionary Design of Combinational Circuits Based on an Embryo Circuit Module", booktitle = "Fourth International Conference on Natural Computation, ICNC '08", year = "2008", month = oct, volume = "1", pages = "81--85", abstract = "This paper introduces a new design method of combinational circuits based on genetic programming (GP). An embryo circuit, which combines all evolvable functional cells together, is designed. Each evolvable cell is evolved separately which is the core of the embryo circuit and the whole circuit is designed after the cells evolutionary design finishes. GP is the evolutionary algorithms used to generate a satisfying circuit. The individuals of GP are represented by multi sub-tree groups in order to match the circuits' structures. Corresponding genetic operations are established, including improved crossover and mutation operators. In addition, the paper introduces a random individual set in order to improve the quality of a population. Finally the ability of this improved algorithm for finding optimal solution and its convergence speed are improved much. Final evolutionary results are structures of electronic circuits, which are easily understandable. The paper introduces a method of fitness evaluation based on true tables. The evolutionary results are inputted into MaxplusII10.2 to simulate their functions. In the experiment, two 2 bits ALU is designed, their simulation results shows the method is practicable and the designed circuits are independent on priori knowledge and their functions are satisfying. The comparison experiments proved the presented improved GP speeded up the convergence and improved the ability of GP for finding optimal solution.", keywords = "genetic algorithms, genetic programming, combinational circuits, embryo circuit module, evolutionary algorithms, evolutionary design, multi sub-tree groups, combinational circuits, trees (mathematics)", DOI = "doi:10.1109/ICNC.2008.585", notes = "Also known as \cite{4666815}", } @Article{Yang_Zhang:PD, author = "Yang Zhang and Hong Cai Chen and Ya Ping Du and Min Chen2 and Jie Liang and Jianhong Li and Xiqing Fan and Ling Sun and Qingsha S. Cheng and Xin Yao", title = "Early Warning of Incipient Faults for Power Transformer Based on DGA Using a Two-Stage Feature Extraction Technique", journal = "IEEE Transactions on Power Delivery", year = "2022", volume = "37", number = "3", pages = "2040--2049", keywords = "genetic algorithms, genetic programming", ISSN = "1937-4208", DOI = "doi:10.1109/TPWRD.2021.3103455", abstract = "Early warning for transformer faults is valuable for maintenance decision-making. However, limited work has been done in this area due to the difficulty of the model establishment. This paper proposes a two-stage feature extraction method for early warning of power transformer faults. By combining feature ranking and genetic programming (GP), a novel feature extraction process is presented. In the first stage, the data is labeled as normal and fault states and the feature extraction is evaluated on the data. Then, extracted key features and their growth rates are relabeled as normal and warning states. The feature extraction process is evaluated again on relabeled data. Obtained features and logic expression can be used for early warning. The proposed framework can implement an early warning with about 100 days in advance for transformer faults and is verified through 8 sequences of data. The comparisons with two recently reported methods show the superiority of the proposed method.", notes = "Also known as \cite{9512510}", } @InProceedings{Zhang:2024:ISEDA, author = "Yang Zhang4 and Qingwen Wei and Hao Cai and Bo Liu2", title = "An Energy-Efficient Multiplier Using Hybrid Approximate Logic Synthesis for Mixed-Quantization {CNNs}", booktitle = "2024 2nd International Symposium of Electronics Design Automation (ISEDA)", year = "2024", pages = "229--234", month = may, keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Industries, Accuracy, Quantization (signal), Power demand, Neural networks, Energy efficiency, Approximate logic synthesis, Re-partition XOR-BMF, Hybrid ALS Flow, Approximate multiplier, Mixed-precision quantization, ANN", DOI = "doi:10.1109/ISEDA62518.2024.10617603", abstract = "Approximate computing is an emerging paradigm that, by relaxing the requirement for full accuracy in convo-lutional neural networks (CNNs), offers benefits in the design area and power consumption. In circuit design, approximate logic synthesis (ALS) is to discover and synthesise the approx-imate circuits automatically, given an exact circuit description. This paper proposes a Hybrid ALS Flow which composes Re-partition XOR-BMF ALS and Cartesian Genetic Programming ALS (CGP-based ALS), and designs the Hessian-aware Mixed-quantization CNNs. This paper designs an 8-bit approximate multiplier using the proposed Hybrid ALS Flow and applies it to the Mixed-quantization CNNs. Experiments show that the proposed Re-partition XOR-BMF ALS has better design space exploration than the BLASYS. Compared to the exact Multiplier, the designed approximate multiplier reduces the power delay product (PDP) by 56.17percent under an industry 28nm process technology with the power supply of O.8V, while the accuracy loss is only 1.33percent and 2.36percent in VGG16 and Resnet50 on CIFARI00.", notes = "Also known as \cite{10617603} School of Integrated Circuits, Southeast University, Nanjing, China", } @InProceedings{Zhang:2022:GI, author = "Yueke Zhang and Yu Huang", title = "Leveraging Fuzzy System to Reduce Uncertainty of Decision Making in Software Engineering Automation", booktitle = "GI @ GECCO 2022", year = "2022", editor = "Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and W. B. Langdon and Justyna Petke", pages = "1948--1949", address = "Boston, USA", publisher_address = "New York, NY, USA", month = "9 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", keywords = "genetic algorithms, genetic programming, genetic improvement, decision making, uncertainty, fuzzy systems, SBFL, spectrum-based fault localisation, FL, Ochiai, Ample, Zoltar, math Defect4j, Dempester-Shafer, DST, SE automation", isbn13 = "978-1-4503-9268-6/22/07", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/gecco2022/gi2022/papers/Zhang_2022_GI.pdf", DOI = "doi:10.1145/3520304.3533991", slides_url = "http://geneticimprovementofsoftware.com/slides/gi2022gecco/zhang-leveraging-fuzzy-system-gi-gecco-22.pdf", video_url = "https://www.youtube.com/watch?v=xwD_f_YOBn8&list=PLI8fiFpB7BoIHgl5CsdtjfWvHlE5N6pje&index=8", size = "2 pages", abstract = "Decision making in automated GI-based software engineering tasks can significantly affect the performance of the system. However, modern SE usually presents high uncertainty in such decision making process due to the existence of multiple solutions that reply on different heuristics. We propose to apply the theory of Fuzzy System to obtain a final decision with lower uncertainty and higher accuracy. We also demonstrate a motivating example and discuss the challenges and opportunities for applying fuzzy system to SE tasks.", notes = "http://geneticimprovementofsoftware.com/events/gecco2022 See also \cite{Zhang:2023:ESEM} GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @InProceedings{Zhang:2023:ESEM, author = "Yueke Zhang and Kevin Leach and Yu Huang", title = "Leveraging Evidence Theory to Improve Fault Localization: An Exploratory Study", booktitle = "2023 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)", year = "2023", address = "New Orleans, USA", month = "26-27 " # oct, keywords = "evidence theory, information fusion, uncertainty, fault localisation, Location awareness, Software maintenance, Codes, Fuses, Taxonomy, Debugging", isbn13 = "978-1-6654-5224-3", URL = "https://yuhuang-lab.github.io/paper/fuzzy-esem23.pdf", DOI = "doi:10.1109/ESEM56168.2023.10304791", size = "12 pages", abstract = "Background: Fault localization in software maintenance and debugging can be a costly process. Spectrum-Based Fault Localization (SBFL) is a widely-used method for faultlocalization. It assigns suspicion scores to code elements based on tests, indicating the likelihood of defects in specific code lines. However, the effectiveness of SBFL approaches varies depending on the subject code. Aims: our aim is to present an approach that combines multiple SBFL formulae using evidence theory. Method: We first introduce a taxonomy of SBFL techniques. Then, we describe how we fuse suspiciousness scores obtained from a set of SBFL formulae. We also introduce a concept of fuzzy windows, and describe how they can enhance localization accuracy and how they can be tuned to further refine results. Results: We present an empirical evaluation of our approach using the Defects4J dataset. Our results demonstrate improvements in fault localization accuracy over existing statement-level SBFL techniques. Specifically, by fusing three SBFL methods, our approach reduces code inspection effort by up to 34.5% with asize-4 window and increases the hit rate for the top 10% most suspicious lines by 27.9% using a size-7 window. Moreover, in multi-line bug scenarios, our approach reduces code inspection effort by up to 35.6% and achieves a maximum increase of 43.2% in the hit rate of the top 10% most suspicious lines. Additionally, our approach outperforms state-of-the-art machine learning-based method-level fusion approaches in terms of toprank fault localization accuracy. Conclusions: Our study highlights the applicability of evidence theory in addressing fault localization as an uncertain and ambiguous information fusion problem involving multiple SBFL techniques. The combination of SBFL formulae using evidencetheory, along with the use of fuzzy windows, shows promise in enhancing fault localization accuracy.", notes = "Not GP? Also known as \cite{10304791} Follow up to \cite{Zhang:2022:GI}", } @Article{ZHANG:2024:commatsci, author = "Zhaosheng Zhang and Yingjie Zhang and Sijia Liu", title = "Integrative approach of machine learning and symbolic regression for stability prediction of multicomponent perovskite oxides and high-throughput screening", journal = "Computational Materials Science", year = "2024", volume = "236", pages = "112889", month = mar, keywords = "genetic algorithms, genetic programming, Symbolic regression, Machine learning, High-throughput, Perovskite oxides", ISSN = "0927-0256", URL = "https://www.sciencedirect.com/science/article/pii/S0927025624001101", DOI = "doi:10.1016/j.commatsci.2024.112889", abstract = "This work unfolds a robust and interpretable strategy for evaluating the stability and potential photovoltaic application of 6526 multicomponent perovskite oxides, employing a synergetic methodology that intertwines advanced machine learning (ML) algorithms and symbolic regression based on genetic programming. Initially, ML algorithms, namely XGBoost, LightGBM, and random forest, were harnessed, with elemental oxidation state and electronegativity serving as input features, achieving R2 values of 0.98, 0.98, and 0.74, respectively, on the test set for predicting the formation enthalpy, a criterion for perovskite stability. Despite the amplified interpretability offered by SHAP analysis, the inherent black-box nature of ML obfuscates a transparent understanding of intrinsic relations between input features and performance. To surmount this, symbolic regression introduced not only elucidates a clear functional relationship between input features and perovskite stability but also achieves a commendable R2 of 0.79 on the test set. Subsequent high-throughput screening, based on perovskite stability ranking, designated the top 500 stable perovskites for band gap calculation using the PBE functional, wherein DyNdHf2O6, CeEuAl2O6, and CeSrAl2O6 emerged as potential candidates for photovoltaic applications and were subjected to further electronic structure simulations employing the HSE06 functional, encompassing density of states, band structure, charge density, and optical absorption spectra. Ultimately, CeEuAl2O6, boasting an optical direct bandgap of 2.31 eV and minimal electron-hole wavefunction overlap, stands out as the prime choice for photovoltaic materials. This research not only pioneers the exploration of enhancing the interpretability of ML but also propels theoretical guidance for the evolution of photovoltaic cells by bridging predictive modeling with high-throughput screening", } @Article{ZHANG:2021:ASC, author = "Zikai Zhang and Qiuhua Tang and Manuel Chica", title = "A robust {MILP} and gene expression programming based on heuristic rules for mixed-model multi-manned assembly line balancing", journal = "Applied Soft Computing", volume = "109", pages = "107513", year = "2021", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2021.107513", URL = "https://www.sciencedirect.com/science/article/pii/S1568494621004361", keywords = "genetic algorithms, genetic programming, Uncertain demand, Robust optimization, Mixed-model multi-manned assembly line, Gene expression programming", abstract = "Current dynamic markets require manufacturing industries to organize a robust plan to cope with uncertain demand planning. This work addresses the mixed-model multi-manned assembly line balancing under uncertain demand and aims to optimize the assembly line configuration by a robust mixed-integer linear programming (MILP) model and a robust solution generation mechanism embedded with dispatching rules. The proposed model relaxes the cycle time constraint and designs robust sequencing constraints and objective functions to ensure the line configuration can meet all the demand plans. Furthermore, two solution generation mechanisms, including a task-operator-sequence and an operator-task-sequence, are designed. To quickly find a suitable line configuration, a gene expression programming (GEP) approach with multi-attribute representation is proposed to obtain efficient dispatching rules which are ultimately embedded into the solution generation mechanisms. Experimental results show that solving the proposed MILP model mathematically is effective when tackling small and medium-scale instances. However, for large instances, the dispatching rules generated by the GEP have significant superiority over traditional heuristic rules and those rules mined by a genetic programming algorithm", } @InProceedings{Zhao:2010:ICACC, author = "Erbo Zhao and Zhangang Han", title = "Analyze long \& mid-term trends of stock with Genetic Programming on moving average and turning points", booktitle = "2nd International Conference on Advanced Computer Control (ICACC)", year = "2010", month = "27-29 " # mar, volume = "3", pages = "87--91", abstract = "This paper employs Genetic Programming (GP) with individuals of tree structure to form empirical formulae in order to track the dynamic pattern of the moving average curves of stock prices. We find that our method tracks the 60-day moving average better than other shorter period averages. In order to minimise the effects of noise and other random events impacting on the markets and maximise the effective information abstracted from the origin data, two comparable data preprocessing methods for turning points are proposed to cooperate with GP for more stable long and mid-term dynamic analysis and prediction. We use either discrete data with fixed time intervals as long as 120 days or data at local extreme by FFT. So, the formula finding system tracks the next turning point with the information of several previous turning points. Simulations show that our method to track and predict long and mid-term change trend of stock price is practical.", keywords = "genetic algorithms, genetic programming, fast Fourier transform, moving average curves, stock long term trend, stock midterm trend, stock prices, time intervals, tree structure, turning points, fast Fourier transforms, moving average processes, pricing, stock markets, trees (mathematics)", DOI = "doi:10.1109/ICACC.2010.5486744", notes = "Also known as \cite{5486744}", } @InProceedings{Zhao:2011:ICCSNT, author = "Zhao Huanping and Lv Congying and Yang Xinfeng", title = "Optimization research on Artificial Neural network Model", booktitle = "International Conference on Computer Science and Network Technology (ICCSNT 2011)", year = "2011", month = "24-26 " # dec, volume = "3", pages = "1724--1727", address = "Harbin", abstract = "Optimisation Research on Artificial Neural Tree Network Model is divided into two parts: optimising topology structure and optimising parameters. For optimising topology structure, building-block-library based genetic programming algorithm, anarchical variable probability vector based probabilistic incremental program evolution algorithm and tree-encoded based particle swarm optimisation algorithm are proposed. The above algorithms can effectively reduce the number of invalid individuals generated in evolution process, improve the convergence speed and error precision of the NTNM. For optimising parameters, differential evolution algorithm is introduced. It has characteristics of less parameters to control, easier to implement and uneasy to fall into local minimum, etc. which make it very suitable for the optimisation of parameters.", keywords = "genetic algorithms, genetic programming, NTNM, anarchical variable probability vector, artificial neural network model, artificial neural tree network model, building-block-library based genetic programming algorithm, convergence speed, differential evolution algorithm, error precision, evolution process, invalid individuals, optimisation research, optimising parameters, optimising topology structure, parameter optimisation, probabilistic incremental program evolution algorithm, tree-encoded based particle swarm optimisation algorithm, convergence, neural nets, particle swarm optimisation, probability, topology, trees (mathematics), vectors", DOI = "doi:10.1109/ICCSNT.2011.6182301", notes = "Also known as \cite{6182301}", } @Article{Zhao:2007:DSS, author = "Huimin Zhao", title = "A multi-objective genetic programming approach to developing Pareto optimal decision trees", journal = "Decision Support Systems", year = "2007", volume = "43", number = "3", pages = "809--826", month = apr, keywords = "genetic algorithms, genetic programming, Data mining, Binary classification, Decision tree, Cost-sensitive classification, Multi-objective optimisation, Pareto optimality", DOI = "doi:10.1016/j.dss.2006.12.011", abstract = "Classification is a frequently encountered data mining problem. Decision tree techniques have been widely used to build classification models as such models closely resemble human reasoning and are easy to understand. Many real-world classification problems are cost-sensitive, meaning that different types of misclassification errors are not equally costly. Since different decision trees may excel under different cost settings, a set of non-dominated decision trees should be developed and presented to the decision maker for consideration, if the costs of different types of misclassification errors are not precisely determined. This paper proposes a multi-objective genetic programming approach to developing such alternative Pareto optimal decision trees. It also allows the decision maker to specify partial preferences on the conflicting objectives, such as false negative vs. false positive, sensitivity vs. specificity, and recall vs. precision, to further reduce the number of alternative solutions. A diabetes prediction problem and a credit card application approval problem are used to illustrate the application of the proposed approach.", } @Misc{oai:arXiv.org:1412.5710, title = "Multiobjective Optimization of Classifiers by Means of {3-D} Convex Hull Based Evolutionary Algorithm", note = "Comment: 32 pages, 26 figures", author = "Jiaqi Zhao and Vitor Basto Fernandes and Licheng Jiao and Iryna Yevseyeva and Asep Maulana and Rui Li and Thomas Baeck and Michael T. M. Emmerich", year = "2014", month = dec # "~17", bibsource = "OAI-PMH server at export.arxiv.org", oai = "oai:arXiv.org:1412.5710", keywords = "genetic algorithms, genetic programming", URL = "http://arxiv.org/abs/1412.5710", abstract = "Finding a good classifier is a multiobjective optimisation problem with different error rates and the costs to be minimised. The receiver operating characteristic is widely used in the machine learning community to analyse the performance of parametric classifiers or sets of Pareto optimal classifiers. In order to directly compare two sets of classifiers the area (or volume) under the convex hull can be used as a scalar indicator for the performance of a set of classifiers in receiver operating characteristic space. Recently, the convex hull based multiobjective genetic programming algorithm was proposed and successfully applied to maximise the convex hull area for binary classification problems. The contribution of this paper is to extend this algorithm for dealing with higher dimensional problem formulations. In particular, we discuss problems where parsimony (or classifier complexity) is stated as a third objective and multi-class classification with three different true classification rates to be maximised. The design of the algorithm proposed in this paper is inspired by indicator-based evolutionary algorithms, where first a performance indicator for a solution set is established and then a selection operator is designed that complies with the performance indicator. In this case, the performance indicator will be the volume under the convex hull. The algorithm is tested and analysed in a proof of concept study on different benchmarks that are designed for measuring its capability to capture relevant parts of a convex hull. Further benchmark and application studies on email classification and feature selection round up the analysis and assess robustness and usefulness of the new algorithm in real world settings.", notes = "see \cite{Zhao:2016:IS}", } @Article{Zhao:2016:IS, author = "Jiaqi Zhao and Vitor Basto Fernandes and Licheng Jiao and Iryna Yevseyeva and Asep Maulana and Rui Li and Thomas Back and Ke Tang and Michael T. M. Emmerich", title = "Multiobjective Optimization of Classifiers by Means of {3D} Convex-Hull-Based Evolutionary Algorithms", journal = "Information Sciences", year = "2016", volume = "367-368", pages = "80--104", month = "1 " # nov, keywords = "genetic algorithms, genetic programming, Convex hull, Classification, Evolutionary multiobjective optimization, Parsimony, ROC analysis, Anti-spam filters", ISSN = "0020-0255", DOI = "doi:10.1016/j.ins.2016.05.026", URL = "http://www.sciencedirect.com/science/article/pii/S0020025516303504", abstract = "The receiver operating characteristic (ROC) and detection error tradeoff (DET) curves are frequently used in the machine learning community to analyse the performance of binary classifiers. Recently, the convex-hull-based multiobjective genetic programming algorithm was proposed and successfully applied to maximize the convex hull area for binary classification problems by minimizing false positive rate and maximizing true positive rate at the same time using indicator-based evolutionary algorithms. The area under the ROC curve was used for the performance assessment and to guide the search. Here we extend this research and propose two major advancements: Firstly we formulate the algorithm in detection error tradeoff space, minimizing false positives and false negatives, with the advantage that misclassification cost tradeoff can be assessed directly. Secondly, we add complexity as an objective function, which gives rise to a 3D objective space (as opposed to a 2D previous ROC space). A domain specific performance indicator for 3D Pareto front approximations, the volume above DET surface, is introduced, and used to guide the indicator-based evolutionary algorithm to find optimal approximation sets. We assess the performance of the new algorithm on designed theoretical problems with different geometries of Pareto fronts and DET surfaces, and two application-oriented benchmarks: (1) Designing spam filters with low numbers of false rejects, false accepts, and low computational cost using rule ensembles, and (2) finding sparse neural networks for binary classification of test data from the UCI machine learning benchmark. The results show a high performance of the new algorithm as compared to conventional methods for multicriteria optimization.", notes = "Replaces \cite{oai:arXiv.org:1412.5710}?", } @Article{DBLP:journals/sp/Zhao21b, author = "Jing Zhao", title = "Art Visual Image Transmission Method Based on Cartesian Genetic Programming", journal = "Scientific Programming", volume = "2021", pages = "4628563:1--4628563:10", year = "2021", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", ISSN = "1058-9244", URL = "https://downloads.hindawi.com/journals/sp/2021/4628563.pdf", URL = "https://doi.org/10.1155/2021/4628563", DOI = "doi:10.1155/2021/4628563", timestamp = "Thu, 17 Feb 2022 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/sp/Zhao21b.bib", bibsource = "dblp computer science bibliography, https://dblp.org", size = "10 pages", abstract = "Because most of the traditional artistic visual image communication methods use the form of modeling and calculation, there are some problems such as long image processing time, low success rate of image visual communication, and poor visual effect. An artistic visual image communication method based on Cartesian genetic programming is proposed. The visual expression sensitivity difference method is introduced to process the image data, the neural network is used to identify the characteristics of the artistic visual image, the midpoint displacement method is used to remove the folds of the artistic visual image, and the processed image is formed under the above three links. The Cartesian genetic programming algorithm is used to encode the preprocessed image, improve the fitness function, select the algorithm to improve the operation, design the image rendering platform, input the processed image to the platform, and complete the artistic visual image transmission. The analysis of the experimental results shows that the image processing time of this method is short, the success rate of visual communication is high, and the image visual effect is good, which can obtain the image processing results satisfactory to users.", notes = "sp@hindawi.com", } @InProceedings{zhao:1996:eec, author = "Jun Zhao and Garrett Kearney and Alan Soper", title = "Emotional Expression Classification by Genetic Programming", booktitle = "Late Breaking Papers at the Genetic Programming 1996 Conference Stanford University July 28-31, 1996", year = "1996", editor = "John R. Koza", pages = "197--202", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California 94305-3079, USA", month = "28--31 " # jul, publisher = "Stanford Bookstore", ISBN = "0-18-201031-7", keywords = "genetic algorithms, genetic programming", notes = "GP-96LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670", } @InProceedings{Zhao:1997:cprs, author = "Kai Zhao and Jue Wang", title = "{``}Chromosone-Protein{''}: A Representation Scheme", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming", pages = "343", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1997/Zhao_1997_cprs.pdf", size = "1 page", notes = "GP-97", } @InProceedings{zhao:1998:ppcaecps, author = "Kai Zhao and Jue Wang", title = "Path Planning in Computer Animation Employing Chromosome-Protein Scheme", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "439--447", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-548-7", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1998/zhao_1998_ppcaecps.pdf", notes = "GP-98", } @InProceedings{zhao:2000:mrccGP, author = "Kai Zhao and Jue Wang", title = "Multi-robot cooperation and competition with genetic programming", booktitle = "Genetic Programming, Proceedings of EuroGP'2000", year = "2000", editor = "Riccardo Poli and Wolfgang Banzhaf and William B. Langdon and Julian F. Miller and Peter Nordin and Terence C. Fogarty", volume = "1802", series = "LNCS", pages = "349--360", address = "Edinburgh", publisher_address = "Berlin", month = "15-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-67339-3", DOI = "doi:10.1007/978-3-540-46239-2_27", abstract = "In this paper, we apply Genetic Programming (GP) on multi-robot cooperation and competition problem. GP is taken as a real time planning method in stead of learning method. Robot all use GP to make a plan and then walk according to the plan. The environment is composed of two parts, natural environment, which is the obstacles, and social environment that refers to other robots. The cooperation process is accomplished by robot's adaptation to both of them. In spite of the fact that there is no communication among robots and little knowledge about how to cooperate well, the adaptive capability in dynamic environment enable robots to complete a common task or solve the competition. Several experiments are taken and the results are shown.", notes = "EuroGP'2000, part of \cite{poli:2000:GP}", } @Article{journals/ijcat/ZhaoW12, author = "Li Zhao and Lei Wang", title = "A multiple camera coordination method based on genetic programming and material character", journal = "International Journal of Computer Applications in Technology", year = "2012", number = "4", volume = "43", pages = "351--358", keywords = "genetic algorithms, genetic programming, surveillance, material character, multi-camera coordination, multiple cameras", ISSN = "1741-5047", DOI = "doi:10.1504/IJCAT.2012.047160", abstract = "Aiming at the multiple cameras cooperating in a surveillance system, a tracking optimisation method based on genetic programming and material character is proposed. Firstly, a physics parameter-orbit, which can be used to describe characters of some material is defined. Secondly, the central line of the orbit and radius is gained by genetic programming. Thirdly, distinguish the pixel belonging to the material with the orbit. In this way, we can coordinate multiple cameras in a surveillance system. Experiment results demonstrate that the algorithm is simple, effective, to solve the problem of multiple camera cooperation in a surveillance system.", notes = "School of Computer Science and Engineering, Xi'an University of Technology, No. 5, South Jinhua Road, Xi'an, Shanxi, 710048, China; Department of Information Engineering, Shijiazhuang Vocational Technology Institute, No. 12, Changxing Street, Shijiazhuang, Hebei, 050000, China. ' School of Computer Science and Engineering, Xi'an University of Technology, No. 5, South Jinhua Road, Xi'an, Shanxi, 710048, China", bibdate = "2012-06-11", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ijcat/ijcat43.html#ZhaoW12", } @Article{Zhao2012945, author = "Li Zhao and Lei Wang and Du-wu Cui", title = "Hoeffding bound based evolutionary algorithm for symbolic regression", journal = "Engineering Applications of Artificial Intelligence", year = "2012", volume = "25", number = "5", pages = "945--957", month = aug, keywords = "genetic algorithms, genetic programming, Hoeffding bound, Fitness approximation, Symbolic regression", ISSN = "0952-1976", URL = "http://www.sciencedirect.com/science/article/pii/S0952197612000930", DOI = "doi:10.1016/j.engappai.2012.04.005", abstract = "In symbolic regression area, it is difficult for evolutionary algorithms to construct a regression model when the number of sample points is very large. Much time will be spent in calculating the fitness of the individuals and in selecting the best individuals within the population. Hoeffding bound is a probability bound for sums of independent random variables. As a statistical result, it can be used to exactly decide how many samples are necessary for choosing i individuals from a population in evolutionary algorithms without calculating the fitness completely. This paper presents a Hoeffding bound based evolutionary algorithm (HEA) for regression or approximation problems when the number of the given learning samples is very large. In HEA, the original fitness function is used in every k generations to update the approximate fitness obtained by Hoeffding bound. The parameter is the probability of correctly selecting i best individuals from population P, which can be tuned to avoid an unstable evolution process caused by a large discrepancy between the approximate model and the original fitness function. The major advantage of the proposed HEA algorithm is that it can guarantee that the solution discovered has performance matching what would be discovered with a traditional genetic programming (GP) selection operator with a determinate probability and the running time can be reduced largely. We examine the performance of the proposed algorithm with several regression problems and the results indicate that with the similar accuracy, the HEA algorithm can find the solution more efficiently than tradition EA. It is very useful for regression problems with large number of training samples.", notes = "School of Computer Science and Engineering, Xi'an University of Technology, Xi'an 710048, China", } @Article{Zhao:2014:IJWMC, author = "Li Zhao and Wan-Ke Cao and Yu-Tao He", title = "Building equivalent circuit models of lithium-ion battery by means of genetic programming", journal = "International Journal of Wireless and Mobile Computing", year = "2014", month = oct # "~31", volume = "7", number = "3", pages = "275--281", keywords = "genetic algorithms, genetic programming, equivalent circuit models, SOC, state of charge, lithium-ion batteries, battery system design, battery modelling, electric vehicles, model-based state estimation", ISSN = "1741-1092", bibsource = "OAI-PMH server at www.inderscience.com", language = "eng", publisher = "Inderscience Publishers", URL = "http://www.inderscience.com/link.php?id=62005", DOI = "DOI:10.1504/IJWMC.2014.062005", abstract = "In the process of battery system design and operation, accurate battery modeling is a key factor. Generally speaking, the electric characteristics of a given battery cell are necessary for a designer to build an equivalent circuit model. The equivalent circuit design entails the creation of both the sizing of components used in the circuit and the topology. So, it is very hard to build an accurate battery model for electric vehicles. This paper presents a single method to design an accurate equivalent circuit by computer automatically. The obtained model enables the assessment of the cells state of charge (SOC) precisely using model-based state estimation approaches.", notes = "National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 10081, China Shijiazhuang Vocational Technology Institute Shijiazhuang 050081, China", } @Article{Zhao:2022:AM, author = "Shaoyu Zhao and Yingyan Zhang and Yihe Zhang and Wei Zhang2 and Jie Yang and Sritawat Kitipornchai", title = "Genetic programming-assisted micromechanical models of graphene origami-enabled metal metamaterials", journal = "Acta Materialia", year = "2022", volume = "228", pages = "117791", month = "15 " # apr, keywords = "genetic algorithms, genetic programming, Graphene origami, Mechanical metamaterial, Negative Poisson's ratio, Machine learning, Functionally graded metamaterial beam", ISSN = "1359-6454", URL = "https://www.sciencedirect.com/science/article/pii/S1359645422001781", DOI = "doi:10.1016/j.actamat.2022.117791", size = "15 pages", abstract = "Graphene origami (GOri) enabled metallic metamaterials are novel nanomaterials simultaneously possessing negative Poisson's ratio (NPR) and enhanced mechanical properties that are independent of the topology/architecture of the structure. Predicting their material properties via existing micromechanical models, however, is a great challenge. In this paper, a highly efficient micromechanical modeling approach based on molecular dynamics (MD) simulation and genetic programming (GP) algorithm is developed to address this key issue. The GP-based Halpin-Tsai model is extensively trained from MD simulation data to accurately predict the Young's modulus of GOri/Cu metamaterials with various GOri folding degrees, graphene contents and temperatures with a high coefficient of determination (R2) of ∼0.95. Meanwhile, the well-trained GP-based rule of mixture can accurately predict the coefficient of thermal expansion (CTE), Poisson's ratio and density of metamaterials with R2 of ∼0.95, ∼0.93 and ∼0.99, respectively. The excellent agreement between our estimated results and experimental data shows that the models developed herein are highly efficient and accurate in predicting mechanical properties that are essential for the analysis and design of functionally graded metal metamaterial composite structures. The theoretical results demonstrate that the proposed functionally graded metamaterial beam achieves significantly improved bending performance.", notes = "also known as \cite{ZHAO2022117791} School of Civil Engineering, The University of Queensland, St.Lucia, QLD4072 Australia", } @PhdThesis{Zhao:thesis, author = "Shaoyu Zhao", title = "Nanocomposites Reinforced with {3D Graphene}: From Atomistic Simulation to Continuum Modelling", school = "School of Civil Engineering, The University of Queensland", year = "2022", address = "Australia", keywords = "genetic algorithms, genetic programming, Graphene/copper nanocomposites, Interfacial shear strength, Mechanical metamaterial, Negative Poisson ratio, Negative thermal expansion, Molecular dynamics simulation, Functionally graded metamaterial beam, Machine learning-assisted micromechanical model, First-order shear deformation theory, Static and dynamic properties", URL = "https://espace.library.uq.edu.au/view/UQ:103ab80", URL = "https://espace.library.uq.edu.au/data/UQ_103ab80/s4560118_phd_thesis.pdf", DOI = "doi:10.14264/103ab80", size = "248 pages", abstract = "Nanocomposites reinforced with carbonaceous nanofillers have been proved to be the effective alternatives of pure metal materials owing to their outstanding mechanical and functional properties by combining the best performances from their constituents. When metal matrix composites (MMCs) are used as structural materials in an extreme environment, they need to have very high strength, stiffness, and toughness to maintain their functional properties. The discovery of graphene facilitates the development of MMCs where graphene can act as a reinforcing nanofiller to take advantage of its excellent mechanical properties for designing lightweight and high-performance graphene-based MMCs. However, there are still many challenges in graphene reinforced nanocomposites. First of all, the mechanical performance of such composites is considerably hindered by weak interfacial interaction between graphene and metal matrix. This challenging issue can be effectively addressed by the use of 3D wrinkled graphene fillers with chemical functionalization. Our extensive molecular dynamics (MD) simulations manifest that the presence of 3D wrinkles and chemical modification of graphene using functional groups can significantly increase its interfacial shear strength between graphene and Cu matrix. Furthermore, it remains a great challenge to achieve high toughness and high strength simultaneously for the composites. We then report a 3D folded graphene (FGr) reinforced Cu nanocomposite that overcomes the long-standing conflicts between toughness and strength. Atomistic simulation results show that the pre-strain-induced 3D FGr reinforced Cu nanocomposite exhibits simultaneous enhancement in toughness, ductility, and strength compared to its counterpart reinforced by pristine graphene (PGr). Meanwhile, achieving negative Poisson's ratio (NPR)and negative thermal expansion (NTE) characteristics in MMCs is very challenging as well. Herein, we propose a class of 3D graphene origami (GOri)-enabled metallic metamaterials with a highly tunable NPR, negative coefficient of thermal expansion (CTE) as well as improved mechanical properties using MD method. Results reveal that the NPR and negative CTE of the nanocomposite with 3.35 wt percent Miura-patterned GOri can reach -0.2796 and -95.42e-06 per degree K at room temperature, respectively. Predicting material properties of the proposed 3D graphene reinforced nanocomposites via existing micromechanical models, however, is a great challenge. In the thesis, a highly efficient micromechanical modeling approach based on MD simulation and genetic programming (GP) algorithm is developed to address this key issue. The GP-based micromechanical models are extensively trained from MD simulation data to accurately predict the Young's modulus, CTE, Poisson's ratio, and mass density of 3D GOri/functionalized/defective graphene reinforced Cu nanocomposites with various GOri folding degrees/functionalization coverages/defect percentages, ii graphene contents and temperatures with high coefficients of determination (R-squared). The excellent agreement between our estimated results and experimental data shows that the models developed herein are highly effective and accurate in predicting mechanical properties that are essential for the analysis and design of functionally graded (FG) 3D graphene reinforced composite structures. Regarding the continuum modelling, this thesis presents a detailed linear and nonlinear structural analysis concerning the static bending, buckling, thermal buckling, free vibration, as well as forced vibration behaviors of FG beams made of 3D GOri/functionalized/defective graphene reinforced nanocomposites under different loading conditions within the theoretical framework of the classical Euler-Bernoulli beam/first-order shear deformation beam theory and von Karman type nonlinearity. The material properties of the beam are effectively controlled by 3D graphene content and GOri folding degree/functionalized graphene coverage that are graded across the thickness direction of the beams in a layer-wise manner such that Poisson's ratio and other material properties are position-dependent and are estimated by the GP-assisted micromechanical models. The governing equations are derived by using the minimum total potential energy principle/Lagrange equation approach/Hamilton's principle for different problems, then numerically solved employing Ritz/differential quadrature (DQ) / Newmark-beta methods. A comprehensive parametric study is performed to examine the effects of 3D graphene content, GOri folding degree/functionalized graphene coverage/defective graphene percentage and distribution patterns as well as temperature on the bending deflections and stresses, critical buckling loads, post-buckling equilibrium paths, natural frequencies,and dynamic responses of the beams, offering important insight into the engineering design and application of FG-3D graphene reinforced composite beams for significantly improved structural performance. The thesis makes key contributions to the material-structure-performance integrated design of 3D graphene reinforced metal matrix nanocomposites based on atomic-scale simulation, machine learning technique, and continuum modeling. This study sheds important insights into the (i) material design to achieve improved interfacial property, superior strength and toughness, as well as negative Poisson's ratio and negative thermal expansion characteristics of the 3D graphene reinforced nanocomposites; (ii) property evaluation based on proposed machine learning-assisted micromechanical models of the 3D graphene reinforced nanocomposites; and (iii) linear and nonlinear structural behaviors of the FG3D graphene reinforced nanocomposite beam structures considering the effects of NPR and NTE, promoting the applications of this new material and structure form in various engineering areas.", notes = "0000-0001-5937-5366 supervisor: Sritawat Kitipornchai", } @Article{Zhao:2020:IINF, author = "Shuai Zhao and Shaowei Chen and Fei Yang and Enes Ugur and Bilal Akin and Huai Wang", title = "A Composite Failure Precursor for Condition Monitoring and Remaining Useful Life Prediction of Discrete Power Devices", journal = "IEEE Transactions on Industrial Informatics", year = "2020", abstract = "In order to prevent catastrophic failures in power electronic systems, multiple failure precursors have been identified to characterize the degradation of power devices. However, there are some practical challenges in determining the suitable failure precursor which supports the high-accuracy prediction of remaining useful life (RUL). This paper proposes a method to formulate a composite failure precursor (CFP) by taking full advantage of potential failure precursors, where CFP is directly optimized in terms of the degradation model to improve the prediction performance. The RUL estimations of the degradation model are explicitly derived to facilitate the precursor quality calculation. For CFP formulation, a genetic programming method is applied to integrate the potential failure precursors in a nonlinear way. As a result, a framework that can formulate a superior failure precursor for the given RUL prediction model is elaborated. The proposed method is validated with the power cycling testing results of SiC MOSFETs.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/TII.2020.2991454", ISSN = "1941-0050", notes = "Department of Energy Technology, Aalborg Universitet, 1004 Aalborg Denmark 9220 Also known as \cite{9082880}", } @Article{Zhao:2006:GPEM, author = "Shuguang Zhao and Licheng Jiao", title = "Multi-objective evolutionary design and knowledge discovery of logic circuits based on an adaptive genetic algorithm", journal = "Genetic Programming and Evolvable Machines", year = "2006", volume = "7", number = "3", pages = "195--210", month = oct, keywords = "genetic algorithms, evolvable hardware, Evolutionary design of circuits, Adaptive genetic algorithm, Multi-objective genetic algorithm, Knowledge discovery", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-006-9005-7", abstract = "Evolutionary design of circuits (EDC), an important branch of evolvable hardware which emphasises circuit design, is a promising way to realize automated design of electronic circuits. In order to improve evolutionary design of logic circuits in efficiency, scalability and capability of optimisation, a genetic algorithm based novel approach was developed. It employs a gate-level encoding scheme that allows flexible changes of functions and interconnections of logic cells comprised, and it adopts a multi-objective evaluation mechanism of fitness with weight-vector adaptation and circuit simulation. Besides, it features an adaptation strategy that enables crossover probability and mutation probability to vary with individuals' diversity and genetic-search process. It was validated by the experiments on arithmetic circuits especially digital multipliers, from which a few functionally correct circuits with novel structures, less gate count and higher operating speed were obtained. Some of the evolved circuits are the most efficient or largest ones (in terms of gate count or problem scale) as far as we know. Moreover, some novel and general principles have been discerned from the EDC results, which are easy to verify but difficult to dig out by human experts with existing knowledge. These results argue that the approach is promising and worthy of further research.", } @InProceedings{WeiDongZhao:2011:CIS, author = "WeiDong Zhao and Xi Liu and Anhua Wang", title = "Simplified Business Process Model Mining Based on Structuredness Metric", booktitle = "Seventh International Conference on Computational Intelligence and Security, CIS 2011", year = "2011", month = "3-4 " # dec, pages = "1362--1366", address = "Hainan, china", keywords = "genetic algorithms, genetic programming, control flow, event logs, fitness function, process complexity metric, process model acquisition, simplified business process model mining, structuredness metric, tree-based individual representation, business data processing, data mining, trees (mathematics)", isbn13 = "978-1-4577-2008-6", DOI = "doi:10.1109/CIS.2011.303", size = "5 pages", abstract = "Process mining is the automated acquisition of process models from event logs. Although many process mining techniques have been developed, most of them focus on mining models from the prospective of control flow while ignoring the structure of mined models. This directly impacts the understandability and quality of mined models. To address the problem, we have proposed a genetic programming (GP) approach to mining simplified process models. Herein, genetic programming is applied to simplify the complex structure of process models using a tree-based individual representation. In addition, the fitness function derived from process complexity metric provides a guideline for discovering low complexity process models. Finally, initial experiments are performed to evaluate the effectiveness of the method.", notes = "Also known as \cite{6128344}", } @Article{oai:pubmedcentral.nih.gov:3926309, author = "Weidong Zhao and Xi Liu and Weihui Dai", title = "Simplified Process Model Discovery Based on Role-Oriented Genetic Mining", journal = "The Scientific World Journal", year = "2014", month = jan # "~29", pages = "Article ID 298592", keywords = "genetic algorithms, genetic programming", publisher = "Hindawi Publishing Corporation", bibsource = "OAI-PMH server at www.ncbi.nlm.nih.gov", language = "en", oai = "oai:pubmedcentral.nih.gov:3926309", rights = "Copyright 2014 Weidong Zhao et al.; This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.", URL = "http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3926309", URL = "http://www.ncbi.nlm.nih.gov/pubmed/24616618", DOI = "doi:10.1155/2014/298592", abstract = "Process mining is automated acquisition of process models from event logs. Although many process mining techniques have been developed, most of them are based on control flow. Meanwhile, the existing role-oriented process mining methods focus on correctness and integrity of roles while ignoring role complexity of the process model, which directly impacts understandability and quality of the model. To address these problems, we propose a genetic programming approach to mine the simplified process model. Using a new metric of process complexity in terms of roles as the fitness function, we can find simpler process models. The new role complexity metric of process models is designed from role cohesion and coupling, and applied to discover roles in process models. Moreover, the higher fitness derived from role complexity metric also provides a guideline for redesigning process models. Finally, we conduct case study and experiments to show that the proposed method is more effective for streamlining the process by comparing with related studies.", notes = "Software School, Fudan University, No. 220 Handan Road, Shanghai 200433, China", } @InProceedings{Zhao:2022:SSCI, author = "Xiaohan Zhao and Wen Song and Qiqiang Li and Huadong Shi and Zhichao Kang and Chunmei Zhang", title = "A Deep Reinforcement Learning Approach for Resource-Constrained Project Scheduling", booktitle = "2022 IEEE Symposium Series on Computational Intelligence (SSCI)", year = "2022", pages = "1226--1234", abstract = "The Resource-Constrained Project Schedule Problem (RCPSP) is one of the most studied Cumulative Scheduling Problems with many real-world applications. Priority rules are widely adopted in practical RCPSP solving, however traditional rules are manually designed by human experts and may perform poorly. Lately, Deep Reinforcement Learning (DRL) has been shown to be effective in learning dispatching rules for disjunctive scheduling problems. However, research on cumulative problems such as RCPSP is rather sparse. In this paper, we propose an end-to-end DRL method to train high-quality priority rules for RCPSP. Based on its graph structure, we leverage Graph Neural Network to effectively capture the complex features for the internal scheduling states. Experiments show that by training on small instances, our method can learn scheduling policy that performs well on a wide range of problem scales, which outperforms traditional manual priority rules and state-of-the-art genetic programming based hyper-heuristics.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SSCI51031.2022.10022122", month = dec, notes = "Also known as \cite{10022122}", } @Article{journals/ijon/ZhaoYGC14, author = "Xiuyang Zhao and Bo Yang and Shuming Gao and Yuehui Chen", title = "Multi-contour registration based on feature points correspondence and two-stage gene expression programming", journal = "Neurocomputing", year = "2014", volume = "145", pages = "512--529", keywords = "genetic algorithms, genetic programming, gene expression programming", bibdate = "2014-09-19", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ijon/ijon145.html#ZhaoYGC14", URL = "http://dx.doi.org/10.1016/j.neucom.2014.05.002", } @InProceedings{Zhao:2010:CiSE, author = "Yongxiang Zhao and Meifang Li", title = "Study on Impact Factor of Sci-Tech Journal in China Using Genetic Programming", booktitle = "International Conference on Computational Intelligence and Software Engineering (CiSE 2010)", year = "2010", month = dec, abstract = "In this paper, we establish nonlinear GP model between impact factor of sci-tech journal and related indexes based on genetic programming approach. The proposed GP model uses average authors, number of district, number of affiliation, international paper ratio and foundation paper ratio as the inputs, and uses impact factor as the output. The journals data from Chinese S and T Journal Citation Reports in 2005 are used as experimental data. The experimental results show that impact factor is mainly related to average authors and foundation paper ratio, and nearly has nothing to do with number of district, number of affiliation and international paper ratio. Therefore, increasing the average authors and foundation paper ratio of sci-tech journal will help to promote the impact factor of journal and improve the quality of journal to some extent.", keywords = "genetic algorithms, genetic programming, impact factor, nonlinear GP model, sci-tech journal, publishing", DOI = "doi:10.1109/CISE.2010.5677270", notes = "State Key Lab. of Adv. Technol. for Mater. Synthesis & Process., Wuhan Univ. of Technol., Wuhan, China. Also known as \cite{5677270}", } @InProceedings{Zhao:2017:GPTP, author = "Zhiruo Zhao and Stuart W. Card and Kishan G. Mehrotra and Chilukuri K. Mohan", title = "Evolution of Space-Partitioning Forest for Anomaly Detection", booktitle = "Genetic Programming Theory and Practice XV", editor = "Wolfgang Banzhaf and Randal S. Olson and William Tozier and Rick Riolo", year = "2017", series = "Genetic and Evolutionary Computation", pages = "169--184", address = "University of Michigan in Ann Arbor, USA", month = may # " 18--20", organisation = "the Center for the Study of Complex Systems", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-90511-2", URL = "https://link.springer.com/chapter/10.1007/978-3-319-90512-9_11", DOI = "doi:10.1007/978-3-319-90512-9_11", abstract = "Previous work proposed a fast one-class anomaly detector using an ensemble of random half-space partitioning trees. The method was shown to be effective and efficient for detecting anomalies in streaming data. However, the parameters were pre-defined, so the random partitions of the data space might not be optimal. Therefore, the aims of this study were to: (a) give some mathematical analysis of the random partitioning trees; and (b) explore optimizing forests for anomaly detection using evolutionary algorithms.", notes = "GPTP 2017, Part of \cite{Banzhaf:2017:GPTP} published after the workshop in 2018", } @PhdThesis{Zhao:2017:thesis, author = "Zhiruo Zhao", title = "Ensemble Methods for Anomaly Detection", school = "Electrical Engineering and Computer Science, Syracuse University", year = "2017", address = "USA", month = dec, keywords = "genetic algorithms, genetic programming", URL = "https://surface.syr.edu/etd/817/", size = "143 pages", abstract = "Anomaly detection has many applications in numerous areas such as intrusion detection, fraud detection, and medical diagnosis. Most current techniques are specialized for detecting one type of anomaly, and work well on specific domains and when the data satisfies specific assumptions. We address this problem, proposing ensemble anomaly detection techniques that perform well in many applications, with four major contributions: using bootstrapping to better detect anomalies on multiple subsamples, sequential application of diverse detection algorithms, a novel adaptive sampling and learning algorithm in which the anomalies are iteratively examined, and improving the random forest algorithms for detecting anomalies in streaming data. We design and evaluate multiple ensemble strategies using score normalization, rank aggregation and majority voting, to combine the results from six well-known base algorithms. We propose a bootstrapping algorithm in which anomalies are evaluated from multiple subsets of the data. Results show that our independent ensemble performs better than the base algorithms, and using bootstrapping achieves competitive quality and faster runtime compared with existing works. We develop new sequential ensemble algorithms in which the second algorithm performs anomaly detection based on the first algorithm's outputs; best results are obtained by combining algorithms that are substantially different. We propose a novel adaptive sampling algorithm which uses the score output of the base algorithm to determine the hard-to-detect examples, and iteratively resamples more points from such examples in a complete unsupervised context. On streaming datasets, we analyse the impact of parameters used in random trees, and propose new algorithms that work well with high-dimensional data, improving performance without increasing the number of trees or their heights. We show that further improvements can be obtained with an Evolutionary Algorithm. Open Access", notes = "Supervisor: Kishan Mehrotra Second supervisor: Chilukuri Mohan", } @Article{ZHAO:2022:ast, author = "Shaoyu Zhao and Yingyan Zhang and Yihe Zhang and Jie Yang and Sritawat Kitipornchai", title = "Vibrational characteristics of functionally graded graphene origami-enabled auxetic metamaterial beams based on machine learning assisted models", journal = "Aerospace Science and Technology", volume = "130", pages = "107906", year = "2022", ISSN = "1270-9638", DOI = "doi:10.1016/j.ast.2022.107906", URL = "https://www.sciencedirect.com/science/article/pii/S1270963822005806", keywords = "genetic algorithms, genetic programming, Graphene origami, Metal metamaterial, Functionally graded beam, Vibrational characteristics, GP-assisted micromechanical model", abstract = "This paper studies the free vibration behavior and dynamic responses of functionally graded (FG) beams made of novel graphene origami (GOri)-enabled auxetic metal metamaterials (GOEAMs) under an impulsive load within the framework of the first-order shear deformation beam theory. The auxetic property of the beam is effectively controlled by graphene content and GOri folding degree that are graded across the thickness direction of the beams in a layer-wise manner such that Poisson's ratio and other material properties are position-dependent and are estimated by the genetic programming (GP)-assisted micromechanical models. The governing equations are derived by using Lagrange equation approach together with Ritz method. Newmark-beta method is employed to solve the governing equations for obtaining dynamic responses of the beams subjected to three different impulsive loads. A comprehensive parametric study is performed to examine the effects of graphene content, GOri folding degree and distribution patterns as well as temperature on the natural frequencies and dynamic responses of the beams. Numerical results show that high tunability in structural vibration characteristics can be achieved via GOri, which offers important insight into the application of FG-GOEAM beams in aerospace engineering structure for significantly improved dynamic structural performance", } @Article{ZHAO:2022:tws, author = "Shaoyu Zhao and Yingyan Zhang and Yihe Zhang and Jie Yang and Sritawat Kitipornchai", title = "A functionally graded auxetic metamaterial beam with tunable nonlinear free vibration characteristics via graphene origami", journal = "Thin-Walled Structures", volume = "181", pages = "109997", year = "2022", ISSN = "0263-8231", DOI = "doi:10.1016/j.tws.2022.109997", URL = "https://www.sciencedirect.com/science/article/pii/S0263823122005961", keywords = "genetic algorithms, genetic programming, Mechanical metamaterial, Functionally graded beam, Timoshenko beam theory, Negative poisson's ratio, GP-assisted micromechanical model, Nonlinear behavior", abstract = "Auxetic metamaterials with negative Poisson's ratio (NPR) are attracting tremendous attention due to their unusual and intriguing mechanical properties. This paper proposes a novel functionally graded (FG) beam made of graphene origami (GOri)-enabled auxetic metamaterials (GOEAMs) and investigates its nonlinear free vibration characteristics tuned by GOri. The beam consists of multilayer GOEAMs with GOri content changed across the beam thickness in a layer-wise mode such that the auxetic property and other material properties are varied in a graded form and can be effectively estimated by genetic programming (GP)-assisted micromechanical models. The Timoshenko beam theory and von Karman type nonlinearity are adopted herein to derive the nonlinear kinematic equations that are numerically solved by the differential quadrature (DQ) approach. Detailed parametric studies are performed to discuss the influences of GOri content, distribution pattern, GOri folding degree, and temperature on the nonlinear frequencies of FG-GOEAM beams. Numerical results indicate that the nonlinear free vibration behaviors of the beam can be effectively tuned via GOri parameter and distribution", } @Article{ZHAO:2022:engstruct, author = "Shaoyu Zhao and Yingyan Zhang and Helong Wu and Yihe Zhang and Jie Yang", title = "Functionally graded graphene origami-enabled auxetic metamaterial beams with tunable buckling and postbuckling resistance", journal = "Engineering Structures", year = "2022", volume = "268", pages = "114763", keywords = "genetic algorithms, genetic programming, Functionally graded beam, Mechanical metamaterial, Negative Poisson's ratio, Graphene origami, GP-assisted micromechanical model", ISSN = "0141-0296", URL = "https://www.sciencedirect.com/science/article/pii/S0141029622008501", DOI = "doi:10.1016/j.engstruct.2022.114763", abstract = "Auxetic metamaterials have emerged as novel advanced materials with unique physical and mechanical properties that conventional materials do not possess. This paper examines the buckling and postbuckling properties of functionally graded (FG) graphene origami (GOri)-enabled auxetic metallic metamaterial (GOEAM) beams. The beam is comprised of multiple GOEAM layers with GOri content varying in layer-wise patterns to realize gradient-changing Poisson's ratio and stiffness coefficient through the beam thickness. The material properties of each GOEAM layer are estimated by the genetic programming (GP)-assisted micromechanical models. The first-order shear deformation theory and von Karman type nonlinearity are employed to derive the nonlinear governing equations that are numerically solved by the differential quadrature method (DQM). Numerical investigations are carried out with the main focus on the impacts of GOri content, distribution pattern, folding degree, and temperature on the buckling and postbuckling behaviors of FG metamaterial beams. The theoretical results show that GOri is capable of contributing to the formation of auxetic metal metamaterial, leading to the tunable bucking and postbuckling properties of FG beams, which sheds significant insights into the design of high-performance structures", } @Article{ZHAO:2022:compstruct, author = "Shaoyu Zhao and Yingyan Zhang and Helong Wu and Yihe Zhang and Jie Yang and Sritawat Kitipornchai", title = "Tunable nonlinear bending behaviors of functionally graded graphene origami enabled auxetic metamaterial beams", journal = "Composite Structures", volume = "301", pages = "116222", year = "2022", ISSN = "0263-8223", DOI = "doi:10.1016/j.compstruct.2022.116222", URL = "https://www.sciencedirect.com/science/article/pii/S0263822322009540", keywords = "genetic algorithms, genetic programming, Functionally graded beam, Negative Poisson's ratio, Mechanical metamaterial, Nonlinear bending, GP-assisted micromechanical model", abstract = "This paper investigates tunable nonlinear bending behaviors of functionally graded composite beams made of graphene origami (GOri)-enabled auxetic metal metamaterials (GOEAMs) within the theoretical framework of the first-order shear deformation theory and von Karman type nonlinearity. The beam is comprised of multiple GOEAM layers with GOri content and folding degree being variables to effectively control its auxetic property that is graded from layer to layer across its thickness direction. Our developed genetic programming (GP)-assisted micromechanical models are used to estimate the position- and temperature-dependent Poisson's ratio and other material properties of each GOEAM layer in the beam. The nonlinear governing equations of the FG-GOEAM beam are derived by the principle of virtual work and numerically solved by the differential quadrature (DQ) method. A detailed parametric investigation is conducted to examine the effects of GOri content, folding degree, and temperature on the tunability of the nonlinear bending deflection and normal stress of the FG metamaterial beam. Numerical results offer significant insights into the design of FG-GOEAM beam structures with enhanced bending performances", } @Article{ZHAO:2022:euromechsol, author = "Shaoyu Zhao and Yingyan Zhang and Yihe Zhang and Wei Zhang and Jie Yang and Sritawat Kitipornchai", title = "Buckling of functionally graded hydrogen-functionalized graphene reinforced beams based on machine learning-assisted micromechanics models", journal = "European Journal of Mechanics - A/Solids", volume = "96", pages = "104675", year = "2022", ISSN = "0997-7538", DOI = "doi:10.1016/j.euromechsol.2022.104675", URL = "https://www.sciencedirect.com/science/article/pii/S0997753822001346", keywords = "genetic algorithms, genetic programming, Graphene/metal nanocomposite, Hydrogen functionalization, Functionally graded beam, Buckling, Halpin-tsai model, Rule of mixture", abstract = "Nanocomposite reinforced with functionalized graphene is a novel class of high-performance materials with great potential in developing advanced lightweight structures in a wide range of engineering applications. However, accurate estimation of its material properties at different temperature conditions remains a great challenge as existing micromechanics models fail to capture the effects of chemical functionalization and temperature. This paper develops machine learning (ML)-assisted micromechanics models by employing genetic programming (GP) algorithm and molecular dynamics (MD) simulation to address this key scientific problem. The well-trained ML-assisted Halpin-Tsai model and rule of mixture can accurately and efficiently predict the temperature-dependent material properties including Young's modulus, Poisson's ratio, coefficient of thermal expansion (CTE), and density of hydrogen-functionalized graphene (HFGr) reinforced copper nanocomposites with high coefficients of determination (R2). Then, the buckling behavior of functionally graded (FG) HFGr nanocomposite beams is studied with the aid of the ML-assisted micromechanics models. A detailed parametric study is performed with a particular focus on the effects of hydrogenation percentage, graphene content, and temperature on the buckling performance of the FG-HFGr beam. Results show that bonding more hydrogen functional groups on the HFGr can effectively improve the buckling resistance of the beam", } @Article{zhao:2022:JMSE, author = "Yufeng Zhao and Junshi He and Xiaohui Yan and Jianwei Liu", title = "Using an Adaptive Neuro-Fuzzy Inference System to Predict Dilution Characteristics of Vertical Buoyant Jets Subjected to Lateral Confinement", journal = "Journal of Marine Science and Engineering", year = "2022", volume = "10", number = "3", pages = "Article No. 439", keywords = "genetic algorithms, genetic programming", ISSN = "2077-1312", URL = "https://www.mdpi.com/2077-1312/10/3/439", DOI = "doi:10.3390/jmse10030439", abstract = "In order to predict the dilution characteristics of vertical buoyant jets constrained by lateral obstructions, we propose a new method based on a commonly used machine learning algorithm: the adaptive neuro-fuzzy inference system (ANFIS). By using experimental data to train and test the ANFIS model, this study shows that it had better performance than commonly used empirical equations for laterally confined jets and another artificial intelligence technique—genetic programming. The RMSE values of the ANFIS-based model were lower, and the R2 values were higher, compared with those of the empirical equation and genetic programming models. The reduction in RMSE achieved by using ANFIS to replace the empirical equations or genetic programming algorithm exceeded 20percent. This research confirms that the ANFIS technique has real potential in the development of effective and accurate models that can be used to estimate the dilution characteristics of a vertical buoyant jet subjected to lateral confinement, providing a new avenue for the prediction of dilution characteristics using artificial intelligence techniques, which can also be used for other effluent mixing problems in marine systems.", notes = "also known as \cite{jmse10030439}", } @Article{Zhao:2024:engappai, author = "Zhiyao Zhao and Bing Fan and Yuqin Zhou and Ding Wang", title = "An effective data-driven water quality modeling and water quality risk assessment method", journal = "Engineering Applications of Artificial Intelligence", year = "2024", volume = "138", pages = "109457", keywords = "genetic algorithms, genetic programming, Water quality model, Data-driven water quality modeling, Water quality index, Risk assessment algorithm", ISSN = "0952-1976", URL = "https://www.sciencedirect.com/science/article/pii/S0952197624016154", DOI = "doi:10.1016/j.engappai.2024.109457", abstract = "Water quality modelling and assessment are crucial for preventing and controlling water pollution and play a significant role in environmental protection. However, few existing water body modelling methods consider the issue of model complexity and most of these methods are fixed-value evaluation methods, which makes it challenging to describe the uncertainty of the water evolution process. In order to address these issues, this paper introduces a data-driven water quality modelling and water quality risk assessment method. The proposed method is structured into three components. Firstly, a multi-water quality indicators modelling method is developed to account for model complexity, using the multi-gene genetic programming and the artificial bee colony algorithm. This method enables the simultaneous determination of model structure and parameters. Secondly, an adaptive water quality risk assessment method, referred to as WQI-IEW, which is based on the extended Kalman filter and an enhanced entropy weight method, is formulated in recognition of the water environment's complexity. Finally, in consideration of prediction uncertainties, the Markov chain is integrated with the previously developed WQI-IEW method to create the water quality risk prediction and assessment method, referred to as MCWQI-IEW. By testing the proposed enhanced modelling method on an actual water quality dataset and comparing it with relevant conventional methods, improvements were observed across various water quality parameters evaluation indicators, with improvements reaching up to 89percent in certain indicators", } @PhdThesis{Zheng.QiZhi:thesis, author = "QiZhi Zheng", title = "Genetic Programming based Structure Optimisation", school = "University of Leeds", year = "2005", address = "UK", keywords = "genetic algorithms, genetic programming", URL = "http://ethos.bl.uk/OrderDetails.do?did=14&uin=uk.bl.ethos.417724", notes = "uk.bl.ethos.417724", } @Article{Zheng:2014:OC, author = "Shijie Zheng and Nan Zhang and Yanjun Xia and Hongtao Wang", title = "Research on non-uniform strain profile reconstruction along fiber {Bragg} grating via genetic programming algorithm and interrelated experimental verification", journal = "Optics Communications", volume = "315", pages = "338--346", year = "2014", ISSN = "0030-4018", DOI = "doi:10.1016/j.optcom.2013.11.027", URL = "http://www.sciencedirect.com/science/article/pii/S0030401813011024", keywords = "genetic algorithms, genetic programming, Structural Health Monitoring, Fibre Bragg Grating, Non-uniform strain distribution", abstract = "A new heuristic strategy for the non-uniform strain profile reconstruction along Fibre Bragg Gratings is proposed in this paper, which is based on the modified transfer matrix and Genetic Programming(GP) algorithm. The present method uses Genetic Programming to determine the applied strain field as a function of position along the fiber length. The structures that undergo adaptation in genetic programming are hierarchical structures which are different from that of conventional genetic algorithm operating on strings. GP regress the strain profile function which matches the measured spectrum best and makes space resolution of strain reconstruction arbitrarily high, or even infinite. This paper also presents an experimental verification of the reconstruction of non-homogeneous strain fields using GP. The results are compared with numerical calculations of finite element method. Both the simulation examples and experimental results demonstrate that Genetic Programming can effectively reconstruct continuous profile expression along the whole FBG, and greatly improves its computational efficiency and accuracy.", } @Article{Zheng:Reliability, author = "Xinyu Zheng and Wei Fan and Chao Chen and Zhike Peng", journal = "IEEE Transactions on Reliability", title = "Adaptive Two-Stage Model for Bearing Remaining Useful Life Prediction Using Gaussian Process Regression With Matched Kernels", year = "2024", volume = "73", number = "4", pages = "1958--1966", month = dec, keywords = "genetic algorithms, genetic programming, Degradation, Adaptation models, Kernel, Predictive models, Hidden Markov models, Feature extraction, Computational modelling, Adaptive two-stage degradation, change point, Gaussian process, remaining useful life (RUL)", ISSN = "1558-1721", DOI = "doi:10.1109/TR.2024.3359212", abstract = "Stemming from complex mechanisms and working conditions of bearings, single degradation models often fail to adequately describe complex degradation process and provide reliable prediction of remaining useful life (RUL). To address this challenge, an adaptive two-stage degradation framework based on Gaussian process regression (GPR) is proposed. This framework dynamically selects the appropriate degradation model based on the observed characteristics of the actual degradation data, resulting in improved prediction accuracy and adaptability. In the process of constructing the degradation model, the suitable detection method is adaptively determined based on change point locations, facilitating real-time monitoring of degradation pattern shifts. Within the two-stage GPR model, matched kernel functions are chosen based on degradation rate and trend changes, and a degradation indicator is constructed using the genetic programming algorithm. This indicator serves as target set for the GPR model to accurately estimate RUL. The reliability of the proposed method is validated through comparison with other alternative models on prognostics and health management (PHM) challenge bearing datasets, confirming its effectiveness, robustness, and superiority.", notes = "Also known as \cite{10424014}", } @Article{zheng:2025:MC, author = "Yang Zheng and Fan Zhang and Xiaoying Gao and Jianbin Ma", title = "Bagging-based ensemble classifiers using multi-objective genetic programming", journal = "Memetic Computing", year = "2025", keywords = "genetic algorithms, genetic programming", URL = "https://link.springer.com/article/10.1007/s12293-025-00444-8", DOI = "doi:10.1007/s12293-025-00444-8", } @InProceedings{Zheng:2016:ACIT-CSII-BCD, author = "Yi Zheng and Pei He and Chi-Chang Chang and Yafen Liu", booktitle = "2016 4th Intl Conf on Applied Computing and Information Technology/3rd Intl Conf on Computational Science/Intelligence and Applied Informatics/1st Intl Conf on Big Data, Cloud Computing, Data Science Engineering (ACIT-CSII-BCD)", title = "Issues with Path Representation in Transition Systems", year = "2016", pages = "230--234", abstract = "Structured analysis of transition systems concerns paths, building blocks, representations, and effective computations of semantics, thus having significant influence on practical applications of graph theory. For instance, part of these results plays important roles in formal language studies, compiler designs and genetic programming. This paper aims to introduce relations among them, and to present related algorithms for calculating of them.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ACIT-CSII-BCD.2016.052", month = dec, notes = "Also known as \cite{7916987}", } @Article{journals/itc/ZhengJC12, title = "Multi-Objective Gene Expression Programming for Clustering", author = "Yifei Zheng and Lixin Jia and Hui Cao", journal = "ITC", year = "2012", number = "3", volume = "41", pages = "283--294", keywords = "genetic algorithms, genetic programming, gene expression programming, Clustering, multi-objective, evolutionary algorithm", bibdate = "2014-01-30", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/itc/itc41.html#ZhengJC12", URL = "http://dx.doi.org/10.5755/j01.itc.41.3.1330", URL = "http://www.itc.ktu.lt/index.php/ITC/article/view/1330", DOI = "doi:10.5755/j01.itc.41.3.1330", abstract = "This paper proposes a multi-objective gene expression programming for clustering (MGEPC), which could automatically determine the number of clusters and the appropriate partitioning from the data set. The clustering algebraic operations of gene expression programming are extended first. Then based on the framework of the Non-dominated Sorting Genetic Algorithm-II, two enhancements are proposed in MGEPC. First, a multi-objective k-means clustering is proposed for local search, where the total symmetrical compactness and the cluster connectivity are used as two complementary objectives and the point symmetry based distance is adopted as the distance metric. Second, the power-law distribution based selection strategy is proposed for the parent population generation. In addition, the external archive and the archive truncation are used to keep a historical record of the non-dominated solutions found along the search process. Experiments are performed on five artificial and three real-life data sets. Results show that the proposed algorithm outperforms the PESA-II based clustering method (MOCK), the archived multiobjective simulated annealing based clustering technique with point symmetry based distance (VAMOSA) and the single-objective version of gene expression programming based clustering technique (GEP-Cluster).", } @InProceedings{conf/ibica/ZhiYYWZGZ17, author = "Mengfan Zhi and Ziqiang Yu and Bo Yang and Lin Wang and Liangliang Zhang and Jifeng Guo and Xuehui Zhu", title = "Reverse Extraction of Early-Age Hydration Kinetic Equation of Portland Cement Using Gene Expression Programming with Similarity Weight Tournament Selection", booktitle = "IBICA 2017: Innovations in Bio-Inspired Computing and Applications", year = "2017", editor = "Ajith Abraham and Abdelkrim Haqiq and Azah Kamilah Muda and Niketa Gandhi", volume = "735", series = "Advances in Intelligent Systems and Computing", pages = "133--142", address = "Marrakech, Morocco", month = "11-13 " # dec, publisher = "Springer", keywords = "genetic algorithms, genetic programming, gene expression programming", DOI = "doi:10.1007/978-3-319-76354-5_13", abstract = "The early stages of portland cement hydration directly affect the physical properties of cement. Therefore, it is necessary to research the hydration process in the early stages of portland cement. Owning to the cement hydration process includes a large number of chemical and physical changes, researching the cement hydration process faces many difficulties. In this paper, early-age hydration kinetic equation is reverse extracted from cement hydration heat data using gene expression programming (GEP) with similarity weight tournament (SWT) selection operator. The method clever use the cement hydration heat data and the powerful performance of genetic expression programming. In addition, the effectiveness of the proposed method is improved using SWT selection operator. The result shows that the performance of GEP method with SWT selection operator is better than traditional GEP.", } @InProceedings{Zhong:2022:GI, author = "James Zhong and Max Hort and Federica Sarro", title = "{Py2Cy}: A Genetic Improvement Tool To Speed Up Python", booktitle = "GI @ GECCO 2022", year = "2022", editor = "Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and W. B. Langdon and Justyna Petke", pages = "1950--1955", address = "Boston, USA", publisher_address = "New York, NY, USA", month = "9 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, Optimization algorithms, Python, Performance, Execution Time", isbn13 = "978-1-4503-9268-6/22/07", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/gecco2022/gi2022/papers/Zhong_2022_GI.pdf", DOI = "doi:10.1145/3520304.3534037", slides_url = "http://geneticimprovementofsoftware.com/slides/gi2022gecco/zhong-py2cy-gi-gecco-22.pdf", code_url = "https://github.com/SOLAR-group/Py2Cy", video_url = "https://www.youtube.com/watch?v=eC39t5coXIE&list=PLI8fiFpB7BoIHgl5CsdtjfWvHlE5N6pje&index=10", size = "6 pages", abstract = "Due to its ease of use and wide range of custom libraries, Python has quickly gained popularity and is used by a wide range of developers all over the world. While Python allows for fast writing of source code, the resulting programs are slow to execute when compared to programs written in other programming languages like C. One of the reasons for its slow execution time is the dynamic typing of variables. Cython is an extension to Python, which can achieve execution speed-ups by compiler optimization. One possibility for improvements is the use of static typing, which can be added to Python scripts by developers. To alleviate the need for manual effort,we create Py2Cy, a Genetic Improvement tool for automatically converting Python scripts to statically typed Cython scripts. To show the feasibility of improving runtime with Py2Cy, we optimise a Python script for generating Fibonacci numbers. The results show that Py2Cy is able to speed up the execution time by up to a factor of 18.", notes = "also know as \cite{zhong2022py2cy} AST tree visitor transforms https://projecteuler.net/ https://github.com/TheAlgorithms/Python/tree/master/project_euler 'mutation operator, which adds one out of eight static data types to an existing variable of the script: char, short, int, long and float.' exhaustive search (many valid patches in search space) Fibonacci(75) => integer overflows http://geneticimprovementofsoftware.com/events/gecco2022 GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @Article{oai:biomedcentral.com:1471-2164-14-S4-S7, author = "Jiancheng Zhong and Jianxin Wang and Wei Peng and Zhen Zhang and Yi Pan", title = "Prediction of essential proteins based on gene expression programming", journal = "BMC Genomics", year = "2013", volume = "14", number = "(Suppl 4)", pages = "S7", month = oct # "~01", note = "Selected articles from the IEEE International Conference on Bioinformatics and Biomedicine 2012: Genomics", keywords = "genetic algorithms, genetic programming, gene expression programming", ISSN = "1471-2164", publisher = "BioMed Central Ltd.", bibsource = "OAI-PMH server at www.biomedcentral.com", language = "en", oai = "oai:biomedcentral.com:1471-2164-14-S4-S7", type = "Research", URL = "http://www.biomedcentral.com/1471-2164/14/S4/S7", abstract = "Background Essential proteins are indispensable for cell survive. Identifying essential proteins is very important for improving our understanding the way of a cell working. There are various types of features related to the essentiality of proteins. Many methods have been proposed to combine some of them to predict essential proteins. However, it is still a big challenge for designing an effective method to predict them by integrating different features, and explaining how these selected features decide the essentiality of protein. Gene expression programming (GEP) is a learning algorithm and what it learns specifically is about relationships between variables in sets of data and then builds models to explain these relationships. Results In this work, we propose a GEP-based method to predict essential protein by combing some biological features and topological features. We carry out experiments on S. cerevisiae data. The experimental results show that the our method achieves better prediction performance than those methods using individual features. Moreover, our method outperforms some machine learning methods and performs as well as a method which is obtained by combining the outputs of eight machine learning methods. Conclusions The accuracy of predicting essential proteins can been improved by using GEP method to combine some topological features and biological features.", } @Article{Zhong:2025:TIM, author = "Jie Zhong and Heng Zhang and Qiang Miao", title = "A Novel Altitude Measurement Channel Reconstruction Method Based on Symbolic Regression and Information Fusion", journal = "IEEE Transactions on Instrumentation and Measurement", year = "2025", volume = "74", pages = "1--12", keywords = "genetic algorithms, genetic programming, Aircraft, Satellites, Mathematical models, Global Positioning System, Accuracy, Aircraft navigation, Atmospheric modelling, Predictive models, Feature extraction, Computational modelling, Altitude prediction, information fusion (IF), interpretable, symbolic regression (SR)", ISSN = "1557-9662", DOI = "doi:10.1109/TIM.2024.3502815", abstract = "Accurate altitude data are imperative for precise aircraft flight control, navigation planning, and air traffic management, especially in global positioning system (GPS)-denied environments. While deep learning methods offer promising solutions for altitude prediction through complex predictive models, their inherent lack of interpretability raises safety concerns, particularly in safety-critical aviation contexts. This article introduces a novel symbolic regression (SR)-based approach to altitude prediction. Initially, raw data undergo random projection (RP) to a feature space, addressing challenges associated with feature extraction in SR. Subsequently, altitude-related information is discerned from the inertial navigation system (INS) and atmospheric system (AS), employing genetic programming (GP) to formulate fully interpretable altitude prediction equations. To enhance robustness, information fusion (IF) technology integrates the prediction equations with vertical velocity, establishing a resilient virtual altitude channel. In scenarios where the GPS is entirely unavailable, our proposed method undergoes effective validation across diverse aircraft types and under various flight conditions. Furthermore, the robustness of our fusion algorithm is verified across different noise levels, underscoring its reliability in challenging conditions.", notes = "Also known as \cite{10766377}", } @InProceedings{Zhong:2014:AAMAS, author = "Jinghui Zhong and Linbo Luo and Wentong Cai and Michael Lees", title = "Automatic Rule Identification for Agent-Based Crowd Models Through Gene Expression Programming", booktitle = "13th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2014)", metis_id = "402420", year = "2014", editor = "Alessio Lomuscio and Paul Scerri and Ana Bazzan and Michael Huhns", pages = "1125)", address = "Paris", month = "5-9 " # may, publisher = "ACM", keywords = "genetic algorithms, genetic programming, gene expression programming, agent-based modelling, crowd simulation, decision rules, evolutionary algorithm", isbn13 = "978-1-4503-2738-1", URL = "http://aamas2014.lip6.fr/proceedings/aamas/p1125.pdf", size = "8 pages", abstract = "Agent-based modelling of human crowds has now become an important and active research field, with a wide range of applications such as military training, evacuation analysis and digital game. One of the significant and challenging tasks in agent-based crowd modelling is the design of decision rules for agents, so as to reproduce desired emergent phenomena behaviours. The common approach in agent-based crowd modelling is to design decision rules empirically based on model developer's experiences and domain specific knowledge. In this paper, an evolutionary framework is proposed to automatically extract decision rules for agent-based crowd models, so as to reproduce an objective crowd behaviour. To automate the rule extraction process, the problem of finding optimal decision rules from objective crowd behaviours is formulated as a symbolic regression problem. An evolutionary framework based on gene expression programming is developed to solve the problem. The proposed algorithm is tested using crowd evacuation simulations in three scenarios with differing complexity. Our results demonstrate the feasibility of the approach and shows that our algorithm is able to find decision rules for agents, which in turn can generate the prescribed macro-scale dynamics.", notes = "info@ifaamas.org http://aamas2014.lip6.fr/ http://aamas2014.lip6.fr/tools/pdf/AAMAS2014_booklet.pdf", } @InProceedings{Zhong:2015:WSC, author = "Jinghui Zhong and Wentong Cai and Linbo Luo", booktitle = "2015 Winter Simulation Conference (WSC)", title = "Crowd evacuation planning using Cartesian Genetic Programming and agent-based crowd modeling", year = "2015", pages = "127--138", month = dec, keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7408158", DOI = "doi:10.1109/WSC.2015.7408158", abstract = "This paper proposes a new evolutionary algorithm-based methodology for optimal crowd evacuation planning. In the proposed methodology, a heuristic-based evacuation scheme is firstly introduced. The key idea is to divide the region into a set of sub-regions and use a heuristic rule to dynamically recommend an exit to agents in each sub-region. Then, an evolutionary framework based on the Cartesian Genetic Programming algorithm and an agent-based crowd simulation model is developed to search for the optimal heuristic rule. By considering dynamic environment features to construct the heuristic rule and using multiple scenarios for training, the proposed methodology aims to find generic and efficient heuristic rules that perform well on different scenarios. The proposed methodology is applied to guide people's evacuation behaviours in six different scenarios. The simulation results demonstrate that the heuristic rule offered by the proposed method is effective to reduce the crowd evacuation time on different scenarios.", notes = "School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, 639789, SINGAPORE Also known as \cite{7408158}", } @Article{Zhong:2015:ieeeTEC, author = "Jinghui Zhong and Yew-Soon Ong and Wentong Cai", title = "Self-Learning Gene Expression Programming", journal = "IEEE Transactions on Evolutionary Computation", year = "2016", volume = "20", number = "1", pages = "65--78", month = feb, keywords = "genetic algorithms, genetic programming, Gene Expression Programming, Even Parity Problem, Evolutionary Computation, Symbolic Regression Problem, GEP-ADF, TreeDE", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2015.2424410", size = "16 pages", abstract = "In this paper, a novel self-learning gene expression programming (GEP) methodology named SL-GEP is proposed to improve the search accuracy and efficiency of GEP. In contrast to the existing GEP variants, the proposed SL-GEP features a novel chromosome representation where each chromosome is embedded with subfunctions that can be deployed to construct the final solution. As part of the chromosome, the subfunctions are self-learned or self-evolved by the proposed algorithm during the evolutionary search. By encompassing subfunctions or any partial solution as input arguments of another subfunction, the proposed SL-GEP facilitates the formation of sophisticated, higher-order, and constructive subfunctions that improve the accuracy and efficiency of the search. Further, a novel search mechanism based on differential evolution is proposed for the evolution of chromosomes in the SL-GEP. The proposed SL-GEP is simple, generic and has much fewer control parameters than the traditional GEP variants. The proposed SL-GEP is validated on 15 symbolic regression problems and six even parity problems. Experimental results show that the proposed SL-GEP offers enhanced performances over several state-of-the-art algorithms in terms of accuracy and search efficiency.", notes = "School of Computer Engineering, Nanyang Technological University, Singapore. Also known as \cite{7089238}", } @Article{Zhong:2017:ASC, author = "Jinghui Zhong and Wentong Cai and Michael Lees and Linbo Luo", title = "Automatic model construction for the behavior of human crowds", journal = "Applied Soft Computing", volume = "56", pages = "368--378", year = "2017", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2017.03.020", URL = "http://www.sciencedirect.com/science/article/pii/S1568494617301448", abstract = "Designing suitable behavioral rules of agents so as to generate realistic behaviors is a fundamental and challenging task in many forms of computational modeling. This paper proposes a novel methodology to automatically generate a descriptive model, in the form of behavioral rules, from video data of human crowds. In the proposed methodology, the problem of modeling crowd behaviors is formulated as a symbolic regression problem and the self-learning gene expression programming is used to solve the problem and automatically obtain behavioral rules that match data. To evaluate its effectiveness, we apply the proposed method to generate a model from a video dataset in Switzerland and then test the generality of the model by validating against video data from the United States. The results demonstrate that, based on the observed movement of people in one scenario, the proposed methodology can automatically construct a general model capable of describing the crowd dynamics of another scenario in a different context (e.g., Switzerland vs. U.S.) as long as that the crowd behavior patterns are similar.", keywords = "genetic algorithms, genetic programming, Agent-based modeling, Crowd modeling and simulation, Gene expression programming, Symbolic regression", } @Article{journals/cim/ZhongFO17, title = "Gene Expression Programming: A Survey [Review Article]", author = "Jinghui Zhong and Liang Feng and Yew-Soon Ong", journal = "IEEE Computational intelligence magazine", year = "2017", number = "3", volume = "12", pages = "54--72", month = aug, keywords = "genetic algorithms, genetic programming, gene expression programming", bibdate = "2017-07-24", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/cim/cim12.html#ZhongFO17", DOI = "doi:10.1109/MCI.2017.2708618", abstract = "Gene Expression Programming (GEP) is a popular and established evolutionary algorithm for automatic generation of computer programs. In recent decades, GEP has undergone rapid advancements and developments. A number of enhanced GEPs have been proposed to date and the real world applications that use them are also multiplying fast. In view of the steadfast growth of GEP and its importance to both the academia and industry, here a review on GEP is considered. In particular, this paper presents a comprehensive review on the recent progress of GEP. The state-of-the-art approaches of GEP, with enhanced designs from six aspects, i.e., encoding design, evolutionary mechanism design, adaptation design, cooperative coevolutionary design, constant creation design, and parallel design, are presented. The theoretical studies and intriguing representative applications of GEP are given. Finally, a discussion of potential future research directions of GEP is also provided", } @Article{Zhong:ieeeTSMC, author = "Jinghui Zhong and Liang Feng and Wentong Cai and Yew-Soon Ong", journal = "IEEE Transactions on Systems, Man, and Cybernetics: Systems", title = "Multifactorial Genetic Programming for Symbolic Regression Problems", year = "2020", volume = "50", number = "11", pages = "4492--4505", month = nov, keywords = "genetic algorithms, genetic programming, GP, multifactorial evolutionary algorithm, MFEA, multifactorial optimization, MFO, multitask learning, MTL, symbolic regression problem, SRP", ISSN = "2168-2216", DOI = "doi:10.1109/TSMC.2018.2853719", size = "14 pages", abstract = "Genetic programming (GP) is a powerful evolutionary algorithm that has been widely used for solving many real-world optimization problems. However, traditional GP can only solve a single task in one independent run, which is inefficient in cases where multiple tasks need to be solved at the same time. Recently, multi-factorial optimization (MFO) has been proposed as a new evolutionary paradigm toward evolutionary multitasking. It intends to conduct evolutionary search on multiple tasks in one independent run. To enable multitasking GP, in this paper, we propose a novel multifactorial GP (MFGP) algorithm. To the best of our knowledge, this is the first attempt in the literature to conduct multitasking GP using a single population. The proposed MFGP consists of a novel scalable chromosome encoding scheme which is capable of representing multiple solutions simultaneously, and new evolutionary mechanisms for MFO based on self-learning gene expression programming. Further, comprehensive experimental studies are conducted on multitask scenarios consisting of commonly used GP benchmark problems and real world applications. The obtained empirical results confirmed the efficacy of the proposed MFGP.", notes = "Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information, School of Computer Science and Engineering, South China University of Technology, Guangzhou 510640, China. Also known as \cite{8419217}", } @InProceedings{Zhong:2018:ICONIP, author = "Jinghui Zhong and Yusen Lin and Chengyu Lu and Zhixing Huang", title = "A Deep Learning Assisted Gene Expression Programming Framework for Symbolic Regression Problems", booktitle = "Neural Information Processing - 25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13-16, 2018, Proceedings, Part VII", publisher = "Springer", year = "2018", volume = "11307", pages = "530--541", series = "Lecture Notes in Computer Science", editor = "Long Cheng and Andrew Chi-Sing Leung and Seiichi Ozawa", keywords = "genetic algorithms, genetic programming, gene expression programming", isbn13 = "978-3-030-04238-7", bibdate = "2018-12-04", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/iconip/iconip2018-7.html#ZhongLLH18", DOI = "doi:10.1007/978-3-030-04239-4_48", notes = "conf/iconip/ZhongLLH18", } @InProceedings{Zhong:2019:CEC, author = "Jinghui Zhong and Linhao Li and Wei-Li Liu and Liang Feng and Xiao-Min Hu", title = "A Co-evolutionary Cartesian Genetic Programming with Adaptive Knowledge Transfer", booktitle = "2019 IEEE Congress on Evolutionary Computation, CEC 2019", year = "2019", editor = "Carlos A. Coello Coello", pages = "2665--2672", address = "Wellington, New Zealand", month = "10-13 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Coevolutionary Algorithm, Transfer Learning, Evolutionary Computation", isbn13 = "978-1-7281-2152-6", DOI = "doi:10.1109/CEC.2019.8790352", size = "8 pages", abstract = "Cartesian Genetic Programming (CGP) is a powerful and popular tool for automatic generation of computer programs to solve user defined tasks. This paper proposes a Co-evolutionary CGP (named Co-CGP) which can automatically gain high-order knowledge to accelerate the search. In the Co-CGP, two modules are working in cooperation to solve a given problem. One module focuses on solving a series of small scale problems of the same type to generate the building blocks. Simultaneously, the second module focuses on combing the available building blocks to construct the final solution. Besides, an adaptive control strategy is introduced to automatically evaluate the effectiveness of the building blocks and adjust the search behaviour adaptively so as to improve search efficiency. The proposed Co-CGP is tested on eight problems with different complexities. Experimental results show that the Co-CGP can significantly improve the performance of CGP, in terms of both search efficiency and accuracy.", notes = " IEEE Catalog Number: CFP19ICE-ART", } @Article{Zhong:2020:IJAS, author = "Jinghui Zhong and Zhixing Huang and Liang Feng and Wan Du and Ying Li", title = "A hyper-heuristic framework for lifetime maximization in wireless sensor networks with a mobile sink", journal = "IEEE/CAA Journal of Automatica Sinica", year = "2020", volume = "7", number = "1", pages = "223--236", abstract = "Maximizing the lifetime of wireless sensor networks (WSNs) is an important and challenging research problem. Properly scheduling the movements of mobile sinks to balance the energy consumption of wireless sensor network is one of the most effective approaches to prolong the lifetime of wireless sensor networks. However, the existing mobile sink scheduling methods either require a great amount of computational time or lack effectiveness in finding high-quality scheduling solutions. To address the above issues, this paper proposes a novel hyperheuristic framework, which can automatically construct high-level heuristics to schedule the sink movements and prolong the network lifetime. In the proposed framework, a set of low-level heuristics are defined as building blocks to construct high-level heuristics and a set of random networks with different features are designed for training. Further, a genetic programming algorithm is adopted to automatically evolve promising high-level heuristics based on the building blocks and the training networks. By using the genetic programming to evolve more effective heuristics and applying these heuristics in a greedy scheme, our proposed hyper-heuristic framework can prolong the network lifetime competitively with other methods, with small time consumption. A series of comprehensive experiments, including both static and dynamic networks, are designed. The simulation results have demonstrated that the proposed method can offer a very promising performance in terms of network lifetime and response time.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/JAS.2019.1911846", ISSN = "2329-9274", month = jan, notes = "Also known as \cite{8945493}", } @Article{Zhong:2025:TEVC, author = "Jinghui Zhong and Junlan Dong and Wei-Li Liu and Liang Feng and Jun Zhang", title = "Multiform Genetic Programming Framework for Symbolic Regression Problems", journal = "IEEE Transactions on Evolutionary Computation", year = "2025", volume = "29", number = "2", pages = "429--443", month = apr, keywords = "genetic algorithms, genetic programming, gene expression programming, Optimisation, Search problems, Multitasking, Adaptation models, Semantics, Mathematical models, Transfer learning, Resource management, Knowledge transfer, multiform optimisation, multitasking optimisation, transfer learning (TL), symbolic regression (SR)", ISSN = "1941-0026", DOI = "doi:10.1109/TEVC.2025.3527875", abstract = "genetic programming (GP) is a widely recognised and powerful approach for symbolic regression (SR) problems. However, existing GP methods rely on a single form to solve the problem, which limits their search diversity and increases the likelihood of getting stuck in local optima, especially in complex scenarios. In this article, we propose a general multiform GP (MFGP) framework to improve the performance of GP on complicated SR problems. As far as we know, this articel is the first attempt to integrate the multiform optimisation paradigm with GP to accelerate the search performance. The key idea of the proposed framework is to construct multiple forms to solve the same problem cooperatively at the same time. During the evolution process, knowledge gained from different forms is shared among the solvers to improve the search diversity and efficiency. A knowledge transfer mechanism is specifically designed to facilitate knowledge transfer among GP solvers with different modelling forms. In addition, an adaptive resource control mechanism is designed to reallocate computing resources according to the problem solving efficiency of different solvers to further improve search efficiency. To demonstrate the effectiveness of the proposed framework, a multiform gene expression programming algorithm is designed and tested on 20 problems, including physical datasets, synthetic datasets, and real-world datasets. The experimental results have demonstrated the effectiveness of the proposed framework.", notes = "Also known as \cite{10835810}", } @InProceedings{Zhong:2021:SMC, author = "Lianjie Zhong and Jinghui Zhong and Chengyu Lu", booktitle = "2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)", title = "A Comparative Analysis of Dimensionality Reduction Methods for Genetic Programming to Solve High-Dimensional Symbolic Regression Problems", year = "2021", pages = "476--483", abstract = "Genetic Programming (GP) is a powerful evolutionary algorithm that has a wide range of real-world applications. High-dimensional symbolic regression (HDSR) is an important yet challenging application of GP. In this paper, a comparative study is conducted to investigate and to discuss the effectiveness of dimensionality reduction (DR) techniques in assisting GP for HDSR problems. Three popular DR techniques, which are the Pearson Correlation Coefficient (PCC), the Principal Component Analysis (PCA), and the Maximal Information Coefficient (MIC), are selected for comparison and discussion. The experimental results showed that considering only correlation during DR is not effective enough to provide a suitable reduced set of problem dimensions, and that GP with DR may perform worse than its counterpart without DR. Meanwhile, we propose a novel two-phase DR method, considering both correlation and redundancy. The proposed method can give a more reasonable set of reduced dimensions, which can effectively improve the performance of GP on HDSR problems.", keywords = "genetic algorithms, genetic programming, Dimensionality reduction, Microwave integrated circuits, Correlation, Redundancy, Evolutionary computation, Feature extraction", DOI = "doi:10.1109/SMC52423.2021.9658595", ISSN = "2577-1655", month = oct, notes = "Also known as \cite{9658595}", } @Misc{DBLP:journals/corr/abs-2102-09039, author = "Mingyuan Zhong and Gang Li and Yang Li", title = "Spacewalker: Rapid {UI} Design Exploration Using Lightweight Markup Enhancement and Crowd Genetic Programming", howpublished = "arXiv", volume = "abs/2102.09039", year = "2021", keywords = "genetic algorithms, genetic programming", URL = "https://arxiv.org/abs/2102.09039", eprinttype = "arXiv", eprint = "2102.09039", timestamp = "Fri, 26 Feb 2021 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/corr/abs-2102-09039.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{DBLP:conf/chi/0001L021, author = "Mingyuan Zhong and Gang Li and Yang Li", editor = "Yoshifumi Kitamura and Aaron Quigley and Katherine Isbister and Takeo Igarashi and Pernille Bj{\o}rn and Steven Mark Drucker", title = "Spacewalker: Rapid {UI} Design Exploration Using Lightweight Markup Enhancement and Crowd Genetic Programming", booktitle = "{CHI} '21: {CHI} Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021", pages = "315:1--315:11", publisher = "{ACM}", year = "2021", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1145/3411764.3445326", DOI = "doi:10.1145/3411764.3445326", timestamp = "Mon, 17 May 2021 13:31:38 +0200", biburl = "https://dblp.org/rec/conf/chi/0001L021.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{Zhou:2019:Information, author = "Ai-Hua Zhou and Li-Peng Zhu and Bin Hu2 and Song Deng and Yan Song and Hongbin Qiu and Sen Pan", title = "Traveling-Salesman-Problem Algorithm Based on Simulated Annealing and Gene-Expression Programming", journal = "Information", year = "2019", number = "1", volume = "10", pages = "7", keywords = "genetic algorithms, genetic programming, gene expression programming", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/information/information10.html#ZhouZHDSQP19", DOI = "doi:10.3390/info10010007", notes = "journals/information/ZhouZHDSQP19", } @InCollection{zhou:2003:GPTP, author = "Anjun Zhou", title = "Enhance Emerging Market Stock Selection", booktitle = "Genetic Programming Theory and Practice", publisher = "Kluwer", year = "2003", editor = "Rick L. Riolo and Bill Worzel", chapter = "18", pages = "291--302", keywords = "genetic algorithms, genetic programming, emerging market, stock selection", ISBN = "1-4020-7581-2", URL = "http://www.springer.com/computer/ai/book/978-1-4020-7581-0", DOI = "doi:10.1007/978-1-4419-8983-3_18", abstract = "Emerging stock markets provide substantial opportunities for investors. The existing literature shows inconsistency in factor selection and model development in this area. This research exploits a cutting edge quantitative technique - genetic programming, to greatly enhance factor selection and explore nonlinear factor combination. The model developed using the genetic programming process is proven to be powerful, intuitive, robust and consistent.", notes = "Advanced Research Center, State Street Global Advisors, Boston Part of \cite{RioloWorzel:2003}", size = "12 pages", } @InProceedings{Zhou:2002:ICAI, author = "Chi Zhou and Peter C. Nelson and Weimin Xiao and Thomas M. Tirpak", title = "Discovery of Classification Rules by Using Gene Expression Programming", booktitle = "Proceedings of the International Conference on Artificial Intelligence (IC-AI'02)", year = "2002", pages = "1355--1361", address = "Las Vegas, U.S.A.", month = jun, keywords = "genetic algorithms, genetic programming", } @Article{ChiZhou:2003:TEC, author = "Chi Zhou and Weimin Xiao and Thomas M. Tirpak and Peter C. Nelson", title = "Evolving accurate and compact classification rules with gene expression programming", journal = "IEEE Transactions on Evolutionary Computation", year = "2003", volume = "7", number = "6", pages = "519--531", month = dec, keywords = "genetic algorithms, genetic programming, classification rule, data mining, gene expression programming, GEP", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2003.819261", size = "13 pages", abstract = "Classification is one of the fundamental tasks of data mining. Most rule induction and decision tree algorithms perform local, greedy search to generate classification rules that are often more complex than necessary. Evolutionary algorithms for pattern classification have recently received increased attention because they can perform global searches. In this paper, we propose a new approach for discovering classification rules by using gene expression programming (GEP), a new technique of genetic programming (GP) with linear representation. The antecedent of discovered rules may involve many different combinations of attributes. To guide the search process, we suggest a fitness function considering both the rule consistency gain and completeness. A multiclass classification problem is formulated as multiple two-class problems by using the one-against-all learning method. The covering strategy is applied to learn multiple rules if applicable for each class. Compact rule sets are subsequently evolved using a two-phase pruning method based on the minimum description length (MDL) principle and the integration theory. Our approach is also noise tolerant and able to deal with both numeric and nominal attributes. Experiments with several benchmark data sets have shown up to 20% improvement in validation accuracy, compared with C4.5 algorithms. Furthermore, the proposed GEP approach is more efficient and tends to generate shorter solutions compared with canonical tree-based GP classifiers.", } @InProceedings{Zhou:2008:cec, author = "Huiyu Zhou and Wei Wei and Kaoru Shimada and Shingo Mabu and Kotaro Hirasawa", title = "Time Related Association Rules Mining with Attributes Accumulation Mechanism and its Application to Traffic Prediction", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "305--311", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1823-7", file = "EC0092.pdf", DOI = "doi:10.1109/CEC.2008.4630815", abstract = "We propose a method of association rule mining using Genetic Network Programming (GNP) with time series processing mechanism and attribute accumulation mechanism in order to find time related sequence rules efficiently in association rule extraction systems. We suppose that, the database consists of a large number of attributes based on time series. In order to deal with databases which have a large number of attributes, GNP individual accumulates better attributes in it gradually round by round, and the rules of each round are stored in the Small Rule Pool using hash method, and the new rules will be finally stored in the Big Rule Pool. The aim of this paper is to better handle association rule extraction of the database in many time-related applications especially in the traffic prediction problem. In this paper, the algorithm capable of finding the important time related association rules is described and experimental results considering a traffic prediction problem are presented.", keywords = "genetic algorithms, genetic programming", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @InProceedings{Zhou:2009:cec, author = "Huiyu Zhou and Shingo Mabu and Kaoru Shimada and Kotaro Hirasawa", title = "Generalized Time Related Sequential Association Rule Mining and Traffic Prediction", booktitle = "2009 IEEE Congress on Evolutionary Computation", year = "2009", editor = "Andy Tyrrell", pages = "2654--2661", address = "Trondheim, Norway", month = "18-21 " # may, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-2959-2", file = "P045.pdf", DOI = "doi:10.1109/CEC.2009.4983275", abstract = "Time Related Association rule mining is a kind of sequence pattern mining for sequential databases. In this paper, we introduce a method of Generalized Association Rule Mining using Genetic Network Programming (GNP) with time series processing mechanism in order to find time related sequential rules efficiently. GNP represents solutions as directed graph structures, thus has compact structure and implicit memory function. The inherent features of GNP make it possible for GNP to work well especially in dynamic environments. GNP has been applied to generate time related candidate association rules as a tool using the database consisting of a large number of time related attributes. The aim of this algorithm is to better handle association rule extraction from the databases in a variety of time-related applications, especially in the traffic volume prediction problems. The generalized algorithm which can find the important time related association rules is described and experimental results are presented considering a traffic prediction problem.", keywords = "genetic algorithms, genetic programming, genetic network programming, directed graph structure, genetic network programming, road network, sequence pattern mining, sequential database, time related association rule mining, time series processing mechanism, traffic volume prediction problem, data mining, directed graphs, road traffic, time series, traffic engineering computing", notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR. Also known as \cite{4983275}", } @InProceedings{DBLP:conf/gecco/ZhouMSH09, author = "Huiyu Zhou and Shingo Mabu and Kaoru Shimada and Kotaro Hirasawa", title = "Backward time related association rule mining in traffic prediction using genetic network programming with database rearrangement", booktitle = "GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation", year = "2009", editor = "Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano {van Hemert} and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique Alba", pages = "1897--1898", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming, Poster", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1569901.1570223", abstract = "In this paper, we introduce Backward Time Related Association Rule Mining using Genetic Network Programming (GNP) with Database Rearrangement in order to find time related sequential association from time related databases effectively and efficiently. The proposed algorithm and experimental results are described using a traffic prediction problem.", notes = "GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009). ACM Order Number 910092.", } @PhdThesis{HuiyuZhou:thesis, author = "Huiyu Zhou", title = "Data mining and classification for traffic systems using genetic network programming", school = "Waseda University", year = "2011", address = "Japan", month = feb, keywords = "genetic algorithms, genetic programming, genetic network programming", URL = "http://jairo.nii.ac.jp/0069/00020480/en", URL = "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/36316/3/Honbun-5575_00.pdf", URL = "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/36316/1/Gaiyo-5575.pdf", URL = "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/36316/2/Shinsa-5575.pdf", URL = "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/36316/4/Honbun-5575_01.pdf", size = "141 pages", abstract = "Since the increase of the road traffic in modern metropolis, the need for traffic prediction systems becomes significant, while the traffic prediction aims at an accurate estimate of the traffic flow as an important item in recent traffic control systems. Concretely, the traffic prediction system analyses data, especially real-time traffic data, predicts traffic situations, and its major role is to forecast the congestion levels in advance of hours and even days. Therefore, the traffic prediction system is becoming the key issue in the advanced traffic management and information systems, which reduces traffic congestion and improve traffic mobility. Vast amount of traffic data are currently available using various components of the intelligent transportation system(ITS). Satellite-based automatic vehicle location technologies such as Global Positioning System (GPS) and cellular phones can determine the vehicle positions at frequent time intervals. These equipments collect the information on the vehicle positions and speeds, which are archived in a large amount of databases, enabling further analysis of the data about the traffic situations such as traffic density patterns. The evolutionary computation method named Genetic Network Programming (GNP) has been proposed as an extension of typical evolutionary computation methods, such as Genetic Algorithm (GA) and Genetic Programming (GP). GNP-based data mining has been already proposed to deal with high density databases with large amount of attributes. In order to further extend the proposed data mining method using GNP to the real-time traffic system, time related association rule mining methods have been proposed and studied in this thesis. The extracted time related rules are stored though generations in a rule pool and analysed to build a classifier, based on which the future traffic density information can be provided to the optimal route search algorithm of the navigation systems. Simulation studies on the prediction accuracy of extracted rules and the average travelling time of the optimal route using the future traffic information are carried out to verify the efficiency and effectiveness of the proposed mechanisms. Some analyses of the proposed methods are studied based on these simulation results comparing to the conventional methods. Unlike the other traffic density prediction methods, the main task of GNP based time related data mining is to allow the GNP individuals to self evolve and extract association rules as many as possible. What's more, GNP uses evolved individuals (directed graphs of GNP) just as a tool to extract candidate association rules. Thus, the structure of GNP individuals does not necessarily represent the association relations of the database. Instead, the extracted association rules are stored together in the rule pool separated from the individuals. As a result, the structures of GNP individuals are less restricted than the structures of GA and GP, thus GNP-based data mining becomes capable of producing a large number of association rules.", abstract = "In chapter 2, a method of association rule mining using Genetic Network Programming (GNP) with time series processing mechanism and attributes accumulation mechanism was proposed in order to find time related sequence rules efficiently in association rule extraction systems. In this chapter, GNP is applied to generate candidate association rules using the database consisting of a large number of time related attributes. In order to deal with a large number of attributes, GNP individual accumulates fitter attributes gradually during rounds, and the rules of each round are stored in a Small Rule Pool using a hash method, then the rules are finally stored in a Big Rule Pool after the check of the overlap at the end of each round. The aim of this chapter is to propose a method to better handle association rule extraction of the databases in a variety of time-related applications, especially in the traffic prediction problems. The algorithm which can find the important time related association rules is described and several experimental results are presented considering a traffic prediction problem. In chapter 3, an algorithm capable of finding important time related association rules is proposed, where Genetic Network Programming (GNP) with not only Attribute Accumulation Mechanism (AAM) but also Extraction Mechanism at Stages (EMS) is used. Then, the classification system imitating the public voting process based on extracted time related association rules in the rule pool is proposed to estimate to which class the current traffic data belong. Using this kind of classification mechanism, the traffic prediction is available since the extracted rules are based on time sequences. Furthermore, the experimental results on the traffic prediction problem using the proposed mechanism are presented by the simple traffic simulator. In chapter 4, further improvements have been proposed for the time related association rule mining using generalised GNP with Multi-Branches and Full-Paths (MBFP) algorithm. For fully using the potential ability of GNP structure, the mechanism of Generalised GNP with MBFP is studied. The aim of this algorithm is to better handle association rule extraction from the databases with high efficiency in variety of time-related applications, especially in the traffic density prediction problems. The generalised algorithm which can find the important time related association rules is described and experimental results are presented considering the traffic prediction problem. Chapter 5 is devoted to a further advanced method for extracting important time related association rules using evolutionary algorithm named Genetic Network Programming (GNP), where Accuracy Validation algorithm is applied to further improve the prediction accuracy. The proposed method provides more useful mean to investigate the future traffic density of traffic networks and hence further help to develop traffic navigation systems. The aim of this algorithm is to better handle association rule extraction using prediction accuracy as one of the criteria and guide the whole evolution process more efficiently, then the adaptability of the proposed mechanism is studied considering the real-time traffic situations using a large scale simulator SOUND/4U. The experiments deal with a traffic density prediction problem using the database provided by the large scale simulator.", abstract = "Chapter 6 describes a methodology for extracting important time related association rules using an evolutionary algorithm named fixed step GNPbased association rule mining. And based on the rule pool of the fixed prediction step, it is also proposed that the prediction of the future traffic is combined with a classical routing algorithm. The routing algorithm and prediction results are combined using a large scale simulator SOUND/4U. Simulation results showed that by providing future traffic information, the average travelling time for the testing vehicles can be improved, which proves that the proposed method can deal with the traffic prediction combined with the optimal route search problem fairly well. In chapter 7, after studying each research topic in this thesis, AAM and EMS mechanisms have been proposed to improve the effectiveness of rule extraction, MBFP mechanism has also been proposed to further improve the efficiency of rule extraction and Accuracy Validation mechanism aiming at generating more general rules has been verified, finally the predicted future information has been combined with the routing algorithm for navigation systems. In conclusion, the efficiency and effectiveness of the proposed methods have been proved based on the simulation results.", } @Article{zhou:2020:BEGE, author = "Jian Zhou and Behnam Yazdani Bejarbaneh and Danial Jahed Armaghani and M. M. Tahir", title = "Forecasting of {TBM} advance rate in hard rock condition based on artificial neural network and genetic programming techniques", journal = "Bulletin of Engineering Geology and the Environment", year = "2020", volume = "79", number = "4", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s10064-019-01626-8", DOI = "doi:10.1007/s10064-019-01626-8", } @Article{ZHOU:2023:jrmge, author = "Jian Zhou and Rui Zhang and Yingui Qiu and Manoj Khandelwal", title = "A true triaxial strength criterion for rocks by gene expression programming", journal = "Journal of Rock Mechanics and Geotechnical Engineering", volume = "15", number = "10", pages = "2508--2520", year = "2023", ISSN = "1674-7755", DOI = "doi:10.1016/j.jrmge.2023.03.004", URL = "https://www.sciencedirect.com/science/article/pii/S1674775523000938", keywords = "genetic algorithms, genetic programming, Gene expression programming, GEP, True triaxial strength, Rock failure criteria, Intermediate principal stress", abstract = "Rock strength is a crucial factor to consider when designing and constructing underground projects. we uses a gene expression programming (GEP) algorithm-based model to predict the true triaxial strength of rocks, taking into account the influence of rock genesis on their mechanical behavior during the model building process. A true triaxial strength criterion based on the GEP model for igneous, metamorphic and magmatic rocks was obtained by training the model using collected data. Compared to the modified Weibols-Cook criterion, the modified Mohr-Coulomb criterion, and the modified Lade criterion, the strength criterion based on the GEP model exhibits superior prediction accuracy performance. The strength criterion based on the GEP model has better performance in R2, RMSE and MAPE for the data set used in this study. Furthermore, the strength criterion based on the GEP model shows greater stability in predicting the true triaxial strength of rocks across different types. Compared to the existing strength criterion based on the genetic programming (GP) model, the proposed criterion based on GEP model achieves more accurate predictions of the variation of true triaxial strength (?1) with intermediate principal stress (?2). Finally, based on the Sobol sensitivity analysis technique, the effects of the parameters of the three obtained strength criteria on the true triaxial strength of the rock are analysed. In general, the proposed strength criterion exhibits superior performance in terms of both accuracy and stability of prediction results", } @InProceedings{zhou:2005:CEC, author = "Jin Zhou and Toru Eguchi and Kotaro Hirasawa and Jinglu Hu and Sandor Markon", title = "Elevator Group Supervisory Control System Using Genetic Network Programming with Reinforcement Learning", booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary Computation", year = "2005", editor = "David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L. Gwenn Volkert and Dan Ashlock and Marc Schoenauer", volume = "1", pages = "336--342", address = "Edinburgh, UK", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "2-5 " # sep, organisation = "IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9363-5", DOI = "doi:10.1109/CEC.2005.1554703", abstract = "Since Genetic Network Programming (GNP) has been proposed as a new method of evolutionary computation, many studies have been done on its applications which cover not only virtual world problems but also real world systems like Elevator Group Supervisory Control System (EGSCS) which is a very large scale stochastic dynamic optimisation problem. From those researches, most of the significant features of GNP have been verified comparing to Genetic Algorithm (GA) and Genetic Programming (GP). Especially, the improvement of the performances on EGSCS using GNP showed an interesting and promising prospect in this field. On the other hand, some studies based on GNP with Reinforcement Learning (RL) revealed a better performance over conventional GNP on some problems such as tileworld models. As a basic study, Reinforcement Learning is introduced in this paper expecting to enhance EGSCS controller using GNP.", notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS.", } @InProceedings{Zhou2:2008:cec, author = "Jin Zhou and Lu Yu and Shingo Mabu and Kaoru Shimada and Kotaro Hirasawa and Sandor Markon", title = "Double-Deck Elevator Systems Adaptive to Traffic Flows Using Genetic Network Programming", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "773--778", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1823-7", file = "EC0198.pdf", DOI = "doi:10.1109/CEC.2008.4630883", abstract = "Double-deck elevator system (DDES) has been invented firstly as a solution to improve the transportation capacity of elevator group systems in the up-peak traffic pattern. The transportation capacity could be even doubled when DDES runs in a pure up-peak traffic pattern where two connected cages stop at every two floors in an elevator round trip. However, the specific features of DDES make the elevator system intractable when it runs in some other traffic patterns. Moreover, since almost all of the traffic flows vary continuously during a day, an optimised controller of DDES is required to adapt the varying traffic flow. In this paper, we have proposed a controller adaptive to traffic flows for DDES using Genetic Network Programming (GNP) based on our past studies in this field, where the effectiveness of DDES controller using GNP has been verified in three typical traffic patterns. A traffic flow judgement part was introduced into the GNP framework of DDES controller, and the different parts of GNP were expected to be functionally localised by the evolutionary process to make the appropriate cage assignment in different traffic flow patterns. Simulation results show that the proposed method outperforms a conventional approach and two heuristic approaches in a varying traffic flow during the work time of a typical office building.", keywords = "genetic algorithms, genetic programming", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @PhdThesis{JinZhou:thesis, author = "Jin Zhou", title = "Study on Genetic Network Programming-based Controllers of Elevator Group Systems", school = "Waseda University", year = "2009", address = "Japan", month = feb, keywords = "genetic algorithms, genetic programming, genetic network programming, lifts", URL = "http://jairo.nii.ac.jp/0069/00019152/en", URL = "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/34740/3/Honbun-5081.pdf", URL = "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/34740/1/Gaiyo-5081.pdf", URL = "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/34740/2/Shinsa-5081.pdf", size = "120 pages", abstract = "Artificial Intelligence (AI) has been proposed in the middle of the 20th century, attempting to create machines with intelligence such as reasoning, knowledge, planning, learning, communication, perception and the ability to move and manipulate the objects that human beings have acquired during their life. With the rapid development of computers in the past several decades, AI achieved its greatest successes in the 1990s and early 21st century throughout a wide range of fields including medical diagnosis,stock trading, robot control, law, scientific discovery, etc. So far, many approaches in the field of AI, such as evolutionary computation, Neural Networks (NNs) and Fuzzy Logics (FL), have been proposed and studied collectively by the emerging discipline of computational intelligence. Among them, there is evolutionary computation which mainly comprises Genetic Algorithms (GA), Evolutionary Programming (EP), Evolution Strategy(ES), Genetic Programming(GP) and learning classifier systems. The mechanisms of evolutionary computation were inspired by biological evolution such as reproduction, mutation, recombination, natural selection and survival of the fittest. In this...pages more...", } @Article{Zhou:2019:TTE, author = "Lijun Zhou and Jian Wang and Lujia Wang and Shuai Yuan and Lin Huang and Dongyang Wang and Lei Guo", title = "A Method for Hot-Spot Temperature Prediction and Thermal Capacity Estimation for Traction Transformers in High-Speed Railway Based on Genetic Programming", journal = "IEEE Transactions on Transportation Electrification", year = "2019", volume = "5", number = "4", pages = "1319--1328", month = dec, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/TTE.2019.2948039", ISSN = "2332-7782", abstract = "With the rapid development of China's high-speed railway (HSR), large-scale traction transformers have been put into use. In order to realize the batch prediction of hot-spot temperature (HST) of traction transformer group and perform estimation of thermal capacity well, this article was devoted to HST prediction modeling for traction transformers based on genetic programming (GP). First, the HST, load factor, and ambient temperature data used in this article were measured from the traction transformer A and were further divided into training set and prediction set. Training set was used to driven modeling by GP. An explicit expression prediction model, which could directly predict the dynamic HST, was established. Then, it was confirmed that the model has high accuracy according to the prediction set. Furthermore, the transformers B and C that are belong to the same railway line like A were tracked and predicted in real time. It is verified that the model has high generalization performance. Simultaneously, the practical application of the model was discussed and analyzed. The research result shows it is expected that the proposed model could realize the batch accurate prediction of HST for traction transformer group. It can provide a better and more effective reference for thermal capacity estimation, train scheduling plan, and maintenance replacement plan of traction transformers.", notes = "Also known as \cite{8873634}", } @Article{ZHOU:2022:jwpe, author = "Pengxiao Zhou and Zhong Li and Wael El-Dakhakhni and Shirley Anne Smyth", title = "Prediction of bisphenol A contamination in Canadian municipal wastewater", journal = "Journal of Water Process Engineering", volume = "50", pages = "103304", year = "2022", ISSN = "2214-7144", DOI = "doi:10.1016/j.jwpe.2022.103304", URL = "https://www.sciencedirect.com/science/article/pii/S2214714422007486", keywords = "genetic algorithms, genetic programming, Bisphenol A, Contaminants of emerging concerns, Machine learning, Theory of networks, wastewater treatment", abstract = "Bisphenol A (BPA) is one of the most common contaminants of emerging concerns (CECs), which pose a threat to human health. Conventional wastewater treatment plants (WWTPs) are considered as the major pathway of BPA entering the aqueous environment. To control and mitigate BPA contamination in the aquatic environment, predicting BPAs fate at WWTPs is critical. In this study, three machine learning models, including shared layer multi-task neural network (MLT-NN), genetic programming (GP), and extra trees (ET) are used to predict the effluent BPA concentration at twelve municipal WWTPs across Canada. Additionally, the theory of networks is adopted to analyze the interdependencies among the influencing factors of BPA removal. It is found that the proposed models can provide reasonable BPA effluent concentration predictions. They have advantages in alleviating data sparsity and imbalance, improving model interpretability, and measuring predictor importance, which is valuable for the modeling of BPA and many other CECs. The network analysis results imply there are moderate interdependencies among various influencing factors of BPA removal. Factors that significantly affect BPA effluent concentration and are thus important for BPA removal are identified. The results also show that BPA is unlikely to be removed at primary treatment plants, while BPA removal could be achieved through secondary or tertiary treatment. This study presents an integrated framework for the modeling and analysis of BPA at WWTPs, which can provide direct and robust decision support for the management of BPA as well as other emerging contaminants in municipal wastewater", } @InProceedings{zhou:2018:AiMD, author = "Qi Zhou and Chaoqun Wu and Weijing Zhao and Weijie Hua and Linghao Liu", title = "A Novel Auto Design Method of Acoustic Filter Based on Genetic Programming", booktitle = "Advances in Mechanical Design", year = "2018", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-981-10-6553-8_45", DOI = "doi:10.1007/978-981-10-6553-8_45", } @InProceedings{Zhou:2022:EuroGP, author = "Ryan Zhou and Christian Muise and Ting Hu", title = "Permutation-Invariant Representation of Neural Networks with Neuron Embeddings", booktitle = "EuroGP 2022: Proceedings of the 25th European Conference on Genetic Programming", year = "2022", editor = "Eric Medvet and Gisele Pappa and Bing Xue", series = "LNCS", volume = "13223", publisher = "Springer Verlag", address = "Madrid, Spain", pages = "294--308", month = "20-22 " # apr, organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, ANN, Neuroevolution, Indirect Encoding, Neural Networks, Convolutional Neural Networks, Crossover, Permutation Invariance: Poster", isbn13 = "978-3-031-02055-1", DOI = "doi:10.1007/978-3-031-02056-8_19", abstract = "Neural networks are traditionally represented in terms of their weights. A key property of this representation is that there are multiple representations of a network which can be obtained by permuting the order of the neurons. These representations are generally not compatible between networks, making recombination a challenge for two arbitrary neural networks - an issue known as the permutation problem in neuroevolution. This paper proposes an indirect encoding in which a neural network is represented in terms of interactions between neurons rather than explicit weights, and which works for both fully connected and convolutional networks. In addition to reducing the number of free parameters, this encoding is agnostic to the ordering of neurons, bypassing a key problem for direct weight-based representation. This allows us to transplant individual neurons and layers into another network without accounting for the specific ordering of neurons. We show through experiments on the MNIST and CIFAR-10 datasets that this method is capable of representing networks which achieve comparable performance to direct weight representation, and that combining networks this way preserves a larger degree of performance than through direct weight transfer.", notes = "http://www.evostar.org/2022/eurogp/ Part of \cite{Medvet:2022:GP} EuroGP'2022 held inconjunction with EvoApplications2022 EvoCOP2022 EvoMusArt2022", } @Article{Zhou:2008:GPEM, author = "Shude Zhou and Robert B. Heckendorn and Zengqi Sun", title = "Detecting the epistatic structure of generalized embedded landscape", journal = "Genetic Programming and Evolvable Machines", year = "2008", volume = "9", number = "2", pages = "125--155", month = jun, note = "Special Issue on Theoretical foundations of evolutionary computation", keywords = "genetic algorithms, Linkage detection, Epistasis, Fourier transform, Generalised embedded landscape, Problem structure", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-007-9045-7", size = "31 pages", abstract = "Working under the premise that most optimisable functions are of bounded epistasis, this paper addresses the problem of discovering the linkage structure of a black-box function with a domain of arbitrary-cardinality under the assumption of bounded epistasis. To model functions of bounded epistasis, we develop a generalisation of the mathematical model of embedded landscapes over domains of cardinality M. We then generalise the Walsh transform as a discrete Fourier transform, and develop algorithms for linkage learning of epistatically bounded GELs. We propose Generalised Embedding Theorem that models the relationship between the underlying decomposable structure of GEL and its Fourier coefficients. We give a deterministic algorithm to exactly calculate the Fourier coefficients of GEL with bounded epistasis. Complexity analysis shows that the epistatic structure of epistatically bounded GEL can be obtained after a polynomial number of function evaluations. Finally, an example experiment of the algorithm is presented.", } @Article{Zhou:2016:Measurement, author = "Wan-Huan Zhou and Ankit Garg and Akhil Garg", title = "Study of the volumetric water content based on density, suction and initial water content", journal = "Measurement", volume = "94", pages = "531--537", year = "2016", ISSN = "0263-2241", DOI = "doi:10.1016/j.measurement.2016.08.034", URL = "http://www.sciencedirect.com/science/article/pii/S026322411630495X", abstract = "The practical application of determination of the soil water retention curves (SWRC) is in seepage modelling in unsaturated soil. The models based on the physics behind the seepage mechanism has been developed for predicting the SWRC. However, those models rarely consider the combined effects of initial volumetric water content and soil density. One of the best routes to study these effects is to formulate the SWRC models/functional relations with volumetric water content as an output and the soil density, initial volumetric water content and soil suction as input parameters. In light of this, the present work introduces the advanced soft computing methods such as genetic programming (GP), artificial neural network and support vector regression (SVR) to formulate the volumetric water content models based on the suction, density and initial volumetric water content. The performance of the three models is compared based on the standard measures and goodness-of-fit tests. The findings from the statistical validation reveals that the GP model performs the best in generalizing the volumetric water content values based on the suction, density and initial water content. Further, the 2-D and 3-D plots, evaluating the main and the interaction effects of the three inputs on the volumetric water content are generated based on the parametric procedure of the best model. The study reveals that the volumetric water content values behave non-linearly with respect to soil suction because it first decreases till a certain point of soil suction and then increases suddenly.", keywords = "genetic algorithms, genetic programming, Soil density, SWRC, Soil suction, Initial water content", } @InProceedings{bb52076, author = "Xiaoli Zhou and Bir Bhanu", title = "Integrating Face and Gait for Human Recognition", booktitle = "Computer Vision and Pattern Recognition Workshop", year = "2006", pages = "55", month = "17-22 " # jun, publisher = "IEEE", bibsource = "http://iris.usc.edu/Vision-Notes/bibliography/motion-f738.html#TT49185", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CVPRW.2006.103", abstract = "This paper introduces a new video based recognition method to recognise non-cooperating individuals at a distance in video, who expose side views to the camera. Information from two biometric sources, side face and gait, is used and integrated for recognition. For side face, we construct Enhanced Side Face Image (ESFI), a higher resolution image compared with the image directly obtained from a single video frame, to fuse information of face from multiple video frames. For gait, we use Gait Energy Image (GEI), a spatio-temporal compact representation of gait in video, to characterise human walking properties. The features of face and the features of gait are obtained separately using Principal Component Analysis (PCA) and Multiple Discriminant Analysis (MDA) combined method from ESFI and GEI, respectively. They are then integrated at match score level. Our approach is tested on a database of video sequences corresponding to 46 people. The different fusion methods are compared and analysed. The experimental results show that (a) Integrated information from side face and gait is effective for human recognition in video; (b) The idea of constructing ESFI from multiple frames is promising for human recognition in video and better face features are extracted from ESFI compared to those from original face images.", notes = "on GP??", } @InProceedings{Zhou:2024:BigDIA, author = "Xuanfei Zhou and Weicheng Luo and Yutong Yuan and Boya Tang and Linbo Qiao and Chen Wang and Xingxuan Chen", title = "{MGEPM:} Specific Composition Style Melody Generation Based on Music Gene Expression Programming", booktitle = "2024 10th International Conference on Big Data and Information Analytics (BigDIA)", year = "2024", pages = "611--618", month = oct, keywords = "genetic algorithms, genetic programming, gene expression programming, Representation learning, Measurement units, Heuristic algorithms, Neural networks, ANN, Evaluation models, Music, Big Data, LSTM, automatic melody generation, music-style learning, evolutionary algorithm, deep learning", ISSN = "2771-6902", DOI = "doi:10.1109/BigDIA63733.2024.10808426", abstract = "This study proposes a music gene expression pro-gramming method to achieve music melody feature learning of a specific singer's compositional style. The user specifies the song format, and the program automatically generates and evaluates songs. A mapping mechanism between 16-bit octal numbers and musical scores is proposed to realize the evolution of musical scores in units of measures. In the melody generation stage, the modification operations that are generally performed by the composer in the process of composing music are integrated with the evolutionary operations of gene expression programming to create a music gene expression programming operation. In the music melody evaluation stage, a long short-term memory neural network evaluation model is established and trained using the main verse and chorus of the music as well as the spectrum of the entire song. The neural network evaluates the resulting song fitness value, providing feedback for music evolution. In contrast to melody generated via other methods, comparative experiments show that the melody generated by this research method has an obvious compositional style of a specific composer.", notes = "Also known as \cite{10808426}", } @Article{journals/ijbir/ZhouKT12, author = "Yinle Zhou and Ali Kooshesh and John R. Talburt", title = "Optimizing the Accuracy of Entity-Based Data Integration of Multiple Data Sources Using Genetic Programming Methods", journal = "International Journal of Business Intelligence Research", year = "2012", number = "1", volume = "3", pages = "72--82", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.4018/jbir.2012010105", bibdate = "2012-01-26", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ijbir/ijbir3.html#ZhouKT12", } @Article{Zhou:IEEEAccess, author = "Yong Zhou and Jian-Jun Yang and Lian-Yu Zheng", journal = "IEEE Access", title = "Multi-agent based hyper-heuristics for multi-objective flexible job shop scheduling: A case study in an aero-engine blade manufacturing plant", year = "2019", volume = "7", pages = "21147--21176", keywords = "genetic algorithms, genetic programming, Job shop scheduling, Processor scheduling, Manufacturing, Dispatching, Dynamic scheduling, Scheduling, flexible job shop, multi-agent, hyper-heuristics", DOI = "doi:10.1109/ACCESS.2019.2897603", ISSN = "2169-3536", abstract = "In the present work, a case study focusing on multi-objective flexible job shop scheduling problem (MO-FJSP) in an aero-engine blade manufacturing plant is presented. The problem considered in this study involves many attributes, including working calendar, due dates and lot size. Moreover, dynamic events occur frequently in the shop-floor, making the problem more challenging and requiring real-time responses. Therefore, the priority-based methods are more suitable than the computationally intensive search-based methods for online scheduling. However, developing an effective heuristic for online scheduling problem is a tedious work even for domain experts. Furthermore, the domain knowledge of practical production scheduling needs to be integrated into the algorithm to guide the search direction, accelerate the convergence of the algorithm and improve the solution quality. To this end, three multi-agent based hyper-heuristics (MAHH) integrated with the prior knowledge of the shop floor are proposed to evolve scheduling policies (SPs) for online scheduling problem. To evaluate the performance of evolved SPs, a 5-fold cross-validation method which is frequently used in machine learning is adopted to avoid the overfitting problem. Both the training and test results demonstrate that the bottleneck-agent based hyper-heuristic method produces the best result among the three MAHH methods. Furthermore, both the effectiveness and the efficiency of the evolved SPs are verified by comparison with the well-known heuristics and two multi-objective particle swarm optimization (MOPSO) algorithms on the practical case. The proposed method has been embedded into the manufacturing execution system (MES) that is built on JAVA and successfully applied in several manufacturing plants.", notes = "Also known as \cite{8635479}", } @Article{Zhou:2019:IEEEAccess, author = "Yong Zhou and Jian-Jun Yang and Lian-Yu Zheng", journal = "IEEE Access", title = "Hyper-Heuristic Coevolution of Machine Assignment and Job Sequencing Rules for Multi-Objective Dynamic Flexible Job Shop Scheduling", year = "2019", volume = "7", pages = "68--88", keywords = "genetic algorithms, genetic programming, genetic expression programming", DOI = "doi:10.1109/ACCESS.2018.2883802", ISSN = "2169-3536", abstract = "Nowadays, real-time scheduling is one of the key issues in cyber-physical system. In real production, dispatching rules are frequently used to react to disruptions. However, the man-made rules have strong problem relevance, and the quality of results depends on the problem itself. The motivation of this paper is to generate effective scheduling policies (SPs) through off-line learning and to implement the evolved SPs online for fast application. Thus, the dynamic scheduling effectiveness can be achieved, and it will save the cost of expertise and facilitate large-scale applications. Three types of hyper-heuristic methods were proposed in this paper for coevolution of the machine assignment rules and job sequencing rules to solve the multi-objective dynamic flexible job shop scheduling problem, including the multi-objective cooperative coevolution genetic programming with two sub-populations, the multi-objective genetic programming with two sub-trees, and the multi-objective genetic expression programming with two chromosomes. Both the training and testing results demonstrate that the CCGP-NSGAII method is more competitive than other evolutionary approaches. To investigate the generalization performance of the evolved SPs, the non-dominated SPs were applied to both the training and testing scenarios to compare with the 320 types of man-made SPs. The results reveal that the evolved SPs can discover more useful heuristics and behave more competitive than the man-made SPs in more complex scheduling scenarios. It also demonstrates that the evolved SPs have a strong generalization performance to be reused in new unobserved scheduling scenarios.", notes = "Also known as \cite{8550675}", } @Article{ZHOU:2019:procir, author = "Yong Zhou and Jian-jun Yang", title = "Automatic design of scheduling policies for dynamic flexible job shop scheduling by multi-objective genetic programming based hyper-heuristic", journal = "Procedia CIRP", volume = "79", pages = "439--444", year = "2019", note = "12th CIRP Conference on Intelligent Computation in Manufacturing Engineering, 18-20 July 2018, Gulf of Naples, Italy", ISSN = "2212-8271", DOI = "doi:10.1016/j.procir.2019.02.118", URL = "http://www.sciencedirect.com/science/article/pii/S2212827119302355", keywords = "genetic algorithms, genetic programming, dynamic flexible job shop scheduling, scheduling policies, multi-objective genetic programming, cooperative coevolution", abstract = "This study proposes four multi-objective genetic programming based hyper-heuristic methods(MO-GPHH) for automated heuristic design to solve the multi-objective dynamic flexible job shop scheduling problem(MO-DFJSP). A scheduling policy(SP) used in the MO-DFJSP includes two decision rules: a job sequencing rule(JSR) and a machine assignment rule(MAR). These two rules are simultaneously evolved to solve three scheduling objectives (mean weighted tardiness, maximum tardiness and mean flow time). The results demonstrate that the pareto front of the proposed methods dominate that of 320 human-made SPs which are selected from literatures on training set, and the evolved SPs outperform manual SPs in 58/64 test scenarios", } @Article{Zhou:2020:IJPR, author = "Yong Zhou and Jian-jun Yang and Zhuang Huang", title = "Automatic design of scheduling policies for dynamic flexible job shop scheduling via surrogate-assisted cooperative co-evolution genetic programming", journal = "International Journal of Production Research", year = "2020", volume = "58", number = "9", pages = "2561--2580", keywords = "genetic algorithms, genetic programming", ISSN = "0020-7543", bibsource = "OAI-PMH server at oai.repec.org", identifier = "RePEc:taf:tprsxx:v:58:y:2020:i:9:p:2561-2580", oai = "oai:RePEc:taf:tprsxx:v:58:y:2020:i:9:p:2561-2580", DOI = "doi:10.1080/00207543.2019.1620362", abstract = "At present, a lot of references use discrete event simulation to evaluate the fitness of evolved rules, but which simulation configuration can achieve better evolutionary rules in a limited time has not been fully studied. This study proposes three types of hyper-heuristic methods for coevolution of the machine assignment rules (MAR) and job sequencing rules (JSR) to solve the DFJSP, including the cooperative coevolution genetic programming with two sub-populations (CCGP), the genetic programming with two sub-trees (TTGP) and the genetic expression programming with two sub-chromosomes (GEP). After careful parameter tuning, a surrogate simulation model is used to evaluate the fitness of evolved scheduling policies (SP). Computational simulations and comparisons demonstrate that the proposed surrogate-assisted CCGP method (CCGP-SM) shows competitive performance with other evolutionary approaches using the same computation time. Furthermore, the learning process of the proposed methods demonstrates that the surrogate-assisted GP methods help accelerating the evolutionary process and improving the quality of the evolved SPs without a significant increase in the length of SP. In addition, the evolved SPs generated by the CCGP-SM show superior performance as compared with existing rules in the literature. These results demonstrate the effectiveness and robustness of the proposed method.", } @InProceedings{Zhou:2006:WCICA, author = "Yongquan Zhou and Dongdong Wang and Ming Zhang", title = "Designing Functional Networks Through Evolutionary Programming", booktitle = "The Sixth World Congress on Intelligent Control and Automation, WCICA 2006", year = "2006", volume = "1", pages = "3250--3254", address = "Dalian", publisher = "IEEE", keywords = "genetic algorithms, genetic programming", ISBN = "1-4244-0332-4", DOI = "doi:10.1109/WCICA.2006.1712968", abstract = "Functional network is a recently introduced extension of neural networks. Like neural networks, nowadays, there is no system designing method for designing approximation functional networks structure. A new genetic programming designing modelling method, combining genetic programming and genetic algorithm, was proposed for hybrid identification of model structure and functional parameters by performing global optimal search in the complex solution space where the structures and parameters coexist and interact. These results also show that the proposed method in this paper can produce very compact network structure and the functional networks convergent precision are improved greatly", notes = "Coll. of Comput. & Inf. Sci., Guangxi Univ. for Nationalities, Nanning", } @InProceedings{conf/icic/ZhouC07, author = "Yongquan Zhou and Dongyong Chen", title = "Genetic Programming with 3sigma Rule for Fault Detection", booktitle = "Proceedings of the Third International Conference on Intelligent Computing, ICIC 2007", year = "2007", editor = "De-Shuang Huang and Laurent Heutte and Marco Loog", volume = "2", series = "Communications in Computer and Information Science", pages = "543--551", address = "Qingdao, China", month = aug # " 21-24", publisher = "Springer", keywords = "genetic algorithms, genetic programming, electro-mechanical device, fault detection", isbn13 = "978-3-540-74281-4", DOI = "doi:10.1007/978-3-540-74282-1_61", size = "9 pages", abstract = "In this paper a new method is presented to solve a series of fault detection problems using 3sigma rule in Genetic Programming (GP). Fault detection can be seen as a problem of multi-class classification. GP methods used to solve problems have a great advantage in their power to represent solutions to complex problems and this advantage remains true in the domain of fault detection. Moreover, diagnosis accuracy can be improved by using 3s rule. In the end of this paper, we use this method to solve the fault detection of electro-mechanical device. The results show that the method uses GP with three sigma rule to solve fault detection of electro-mechanical device outperforms the basic GP and ANN method.", notes = "Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques. 2) College of Computer Science and Information Engineering, Guangxi University, Nanning 530004, Guangxi, China Acknowledgment. Thanks to Mengjie Zhang", bibdate = "2008-09-11", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icic/icic2007-3.html#ZhouC07", } @Article{Zhou:CYB, author = "Yu Zhou and Nanjian Yang and Xingyue Huang and Jaesung Lee and Sam Kwong", journal = "IEEE Transactions on Cybernetics", title = "A Novel Multiobjective Genetic Programming Approach to High-Dimensional Data Classification", year = "2024", volume = "54", number = "9", pages = "5205--5216", month = sep, keywords = "genetic algorithms, genetic programming, Optimisation, Vectors, Task analysis, Statistics, Sociology, Sensors, Class imbalance, feature selection (FS), high-dimensional data classification, multiobjective optimisation (MOO)", ISSN = "2168-2275", DOI = "doi:10.1109/TCYB.2024.3372070", abstract = "The development of data sensing technology has generated a vast amount of high-dimensional data, posing great challenges for machine learning models. Over the past decades, despite demonstrating its effectiveness in data classification, genetic programming (GP) has still encountered three major challenges when dealing with high-dimensional data: 1) solution diversity; 2) multiclass imbalance; and 3) large feature space. In this article, we have developed a problem-specific multiobjective GP framework (PS-MOGP) for handling classification tasks with high-dimensional data. To reduce the large solution space caused by high dimensionality, we incorporate the recursive feature elimination strategy based on mining the archive of evolved GP solutions. A progressive domination Pareto archive evolution strategy (PD-PAES), which optimises the objectives in a specific order according to their objectives, is proposed to evaluate the GP individuals and maintain a better diversity of solutions. Besides, to address the seriously imbalanced class issue caused by traditional binary decomposition (BD) one versus rest (OVR) for multiclass classification problems, we design a method named BD with a similar positive and negative class size (BD-SPNCS) to generate a set of auxiliary classifiers. Experimental results on benchmark and real-world datasets demonstrate that our proposed PS-MOGP outperforms state-of-the-art traditional and evolutionary classification methods in the context of high-dimensional data classification.", notes = "Also known as \cite{10473749} College of Computer Science and Software Engineering Shenzhen University Shenzhen, China", } @Article{Zhou:TCSS, author = "Yu Zhou and Nanjian Yang and Ran Wang", title = "An Embedded Unlabeled Data Partitioning {PU-Learning} Method Based on Genetic Programming", journal = "IEEE Transactions on Computational Social Systems", note = "Early Access", keywords = "genetic algorithms, genetic programming, Task analysis, Mathematical models, Data models, Training, Supervised learning, Sociology, Iterative methods, Data partitioning, evolutionary computation, positive-unlabeled learning (PUL), text classification", ISSN = "2329-924X", DOI = "doi:10.1109/TCSS.2024.3406377", abstract = "In traditional binary classification tasks, learning algorithms conventionally distinguish positive and negative samples by leveraging fully labeled training data. However, in practical applications, there frequently arises a scenario where only a small number of positive samples and a large volume of unlabeled data exist, with the latter potentially containing a mix of positive and negative instances. This situation is known as positive-unlabeled learning (PUL) and has drawn considerable interest across various domains, such as text categorization. Despite numerous studies addressing PUL issues, few have focused on improving the two-step methodology in the data partitioning process, and even fewer have explored the application of genetic programming (GP) to PUL challenges. This article introduces a GP-based embedded unlabeled data partitioning method (EPGP) tailored for the PUL problem, particularly in the context of few-shot learning scenarios. The approach adopts a multiphase data partitioning strategy, integrating the partitioning process into the evolutionary cycle of the GP population, progressively isolating positive samples from the unlabeled data to yield a PU dataset closer to the real-world distribution. To achieve smoother data partitioning, a dynamically adjusted partitioning threshold strategy is incorporated. Finally, an ensemble method is devised, capitalizing on the high confidence associated with the originally labeled positive samples to generate the final classification outcome via weighted voting. Experimental evaluations of EPGP against other state-of-the-art PUL methods on 22 datasets show significant advantages in terms of balanced accuracy and macro-F1 scores on 17 datasets. Moreover, a case study on text data classification tasks demonstrates that EPGP consistently delivers substantial performance enhancements under varying proportions of labeled positive samples.", notes = "Also known as \cite{10559613}", } @Article{zhou:2006:JOUC, author = "Yuliang Zhou and Guihua Lu and Juliang Jin and Fang Tong and Ping Zhou", title = "A high precision comprehensive evaluation method for flood disaster loss based on improved genetic programming", journal = "Journal of Ocean University of China", year = "2006", volume = "5", number = "4", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s11802-006-0023-0", DOI = "doi:10.1007/s11802-006-0023-0", } @Article{ZHOU:2023:infrared, author = "Zheng Zhou and Yu Yang and Gan Zhang and Libing Xu and Mingqing Wang", title = "{EBM3GP:} A novel evolutionary bi-objective genetic programming for dimensionality reduction in classification of hyperspectral data", journal = "Infrared Physics \& Technology", volume = "129", pages = "104577", year = "2023", ISSN = "1350-4495", DOI = "doi:10.1016/j.infrared.2023.104577", URL = "https://www.sciencedirect.com/science/article/pii/S135044952300035X", keywords = "genetic algorithms, genetic programming, Dimensionality reduction, Hyperspectral image, Low- and high-level features, Shannon entropy, Cross entropy", abstract = "Dimensionality reduction (DR) is vital in hyperspectral image (HSI) classification, and feature extraction and band selection methods have been demonstrated to be effective at accomplishing it. However, both types of methods can only obtain single-level features from HSI spectra, which suffers from insufficient useful information and makes accurate classification of the high dimensionality of HSI pixels challenging. To overcome the shortcomings, this study proposes a novel Evolutionary Bi-objective Genetic Programming-based unsupervised DR approach named EBM3GP for obtaining low-level features (bands) and high-level features from raw HSI spectra simultaneously. In EBM3GP, multi-dimensional trees are used to encode the raw spectrum to low- and high-level features; two mutually restrictive measures are applied to evaluate the amount of information and the redundancy contained in trees (evaluation does not use the HSI pixel's label); multiple trees are optimized through population evolution by combining three types of crossover operators, two types of mutation operators and nondominated sorting method; a Pareto optimal individual is finally output and decoded as a DR strategy. Then, this study applies Random Forest, least squares Support Vector Machine, and Extreme Learning Machine for classification to evaluate the efficacy of the DR strategy. Based on three HSIs (including Indian Pines, Salinas, and Pavia University datasets), EBM3GP is demonstrated to outperform five popular DR methods for HSI classification. Moreover, the EBM3GP is not sensitive to data size and thus is available for DR of small-size HSI datasets", } @InCollection{Zhou:2019:book.ch9, author = "Zhi-Hua Zhou and Yang Yu2 and Chao Qian", title = "Representation", booktitle = "Evolutionary Learning: Advances in Theories and Algorithms", publisher = "Springer Nature", year = "2019", chapter = "9", pages = "109--128", keywords = "genetic algorithms, genetic programming", isbn13 = "978-981-13-5955-2", DOI = "doi:10.1007/978-981-13-5956-9_9", abstract = "This chapter studies the influence of solution representation, by comparing the genetic programming with the genetic algorithm, which employ tree representation and vector representation, respectively. We show that tree representation can lead to better running time than vector representation, on two classical combinatorial problems.", notes = "maximum match-ings and minimum spanning trees. (1+1)-GP SMO-GP Nanjing University, China", } @InProceedings{Zhou:2023:EuroGP, author = "Zhilei Zhou and Ziyu Qiu and Brad Niblett and Andrew Johnston and Jeffrey Schwartzentruber and Nur Zincir-Heywood and Malcolm Heywood", title = "A Boosting Approach to Constructing an Ensemble Stack", booktitle = "EuroGP 2023: Proceedings of the 26th European Conference on Genetic Programming", year = "2023", month = "12-14 " # apr, editor = "Gisele Pappa and Mario Giacobini and Zdenek Vasicek", series = "LNCS", volume = "13986", publisher = "Springer Verlag", address = "Brno, Czech Republic", pages = "133--148", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Boosting, Stacking", isbn13 = "978-3-031-29572-0", URL = "https://arxiv.org/abs/2211.15621", URL = "https://rdcu.be/c8USt", DOI = "doi:10.1007/978-3-031-29573-7_9", size = "16 pages", abstract = "An approach to evolutionary ensemble learning for classification is proposed using genetic programming in which boosting is used to construct a stack of programs. Each application of boosting identifies a single champion and a residual dataset, i.e. the training records that thus far were not correctly classified. The next program is only trained against the residual, with the process iterating until some maximum ensemble size or no further residual remains. Training against a residual dataset actively reduces the cost of training. Deploying the ensemble as a stack also means that only one classifier might be necessary to make a prediction, so improving interpretability. Benchmarking studies are conducted to illustrate competitiveness with the prediction accuracy of current state-of-the-art evolutionary ensemble learning algorithms, while providing solutions that are orders of magnitude simpler. Further benchmarking with a high cardinality dataset indicates that the proposed method is also more accurate and efficient than XGBoost.", notes = "Part of \cite{Pappa:2023:GP} EuroGP'2023 held in conjunction with EvoCOP2023, EvoMusArt2023 and EvoApplications2023", } @InProceedings{Zhou:2024:evoapplications, author = "Zhilei Zhou and Nur Zincir-Heywood and Malcolm I. Heywood", title = "Simple Efficient Evolutionary Ensemble Learning on Network Intrusion Detection Benchmarks", booktitle = "27th International Conference, EvoApplications 2024", year = "2024", editor = "Stephen Smith and Joao Correia and Christian Cintrano", series = "LNCS", volume = "14634", publisher = "Springer", address = "Aberystwyth", month = "3-5 " # apr, pages = "361--376", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Boosting, Bagging, Stacking, Evolutionary Ensemble Learning, Intrusion Detection", isbn13 = "978-3-031-56851-0", URL = "https://rdcu.be/dDZWe", DOI = "doi:10.1007/978-3-031-56852-7_23", abstract = "Training and deploying genetic programming (GP) classifiers for intrusion detection tasks on the one hand remains a challenge (high cardinality and high class imbalance). On the other hand, GP solutions can also be particularly lightweight from a deployment perspective, enabling detectors to be deployed at the edge without specialised hardware support. We compare state-of-the-art ensemble learning solutions from GP and XGBoost on three examples of intrusion detection tasks with 250000 to 700000 training records, 8 to 115 features and 2 to 23 classes. XGBoost provides the most accurate solutions, but at two orders of magnitude higher complexity. Training time for the preferred GP ensemble is in the order of minutes, but the combination of simplicity and specificity is such that the resulting solutions are more informative and discriminatory. Thus, as the number of features increases and/or classes increase, the resulting ensembles are composed from particularly simple trees that associate specific features with specific behaviours.", notes = "http://www.evostar.org/2024/ EvoApplications2024 held in conjunction with EuroGP'2024, EvoCOP2024 and EvoMusArt2024", } @InProceedings{zhu:2001:tesaec, author = "Fangming Zhu and Sheng-Uei Guan", title = "Towards Evolution of Software Agents in Electronic Commerce", booktitle = "Proceedings of the 2001 Congress on Evolutionary Computation CEC2001", year = "2001", pages = "1303--1308", address = "COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "27-30 " # may, organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, software agents, agent evolution, agent-based electronic commerce, e-commerce, modularized agent structure, multiagent evolution cycle, electronic commerce, multi-agent systems", ISBN = "0-7803-6658-1", DOI = "doi:10.1109/CEC.2001.934341", abstract = "With the development of Internet computing and software agent technologies, agent-based electronic commerce (e-commerce) is emerging. Software agents have demonstrated tremendous potential in conducting various tasks in e-commerce. However, when agents are initially created, they have little knowledge and experience with relatively lower capability. They should also strive to adapt themselves to the changing environment. It is advantageous if they have the ability to learn and evolve. This paper addresses evolution of software agents in e-commerce. Agent fitness and life cycle are proposed as evolution mechanisms, and modularised agent structure is introduced to facilitate the evolution process. Genetic Programming (GP) operators are employed to restructure agents in the proposed multi-agent evolution cycle", notes = "CEC-2001 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 01TH8546C, Library of Congress Number = . SAFER. Multi-Agent (modular) evolution. Factory simulation. Java.", } @InProceedings{Zhu:2010:ICCDA, author = "Huan-rong Zhu and Ya-min Li and Ia-mei Meng", title = "Based on meteorological factors and short-term load forecasting genetic programming", booktitle = "2010 International Conference on Computer Design and Applications (ICCDA)", year = "2010", month = "25-27 " # jun, volume = "3", pages = "V3--465--V3--467", abstract = "Through the introduction of forecasting temperature such as humidity and wind speed as well as human comfort of the new concept of the weather, considering the meteorological factors on the impact of electricity load, the use of genetic programming(GP) method to establish the mathematical model of load forecasting to meet certain precision required under the conditions of a particular time in the future developing trend of the load to make estimates and assumptions of science.", keywords = "genetic algorithms, genetic programming, electricity load impact, forecasting temperature, human comfort, humidity, mathematical model, meteorological factors, short-term load forecasting, wind speed, humidity, load forecasting, weather forecasting", DOI = "doi:10.1109/ICCDA.2010.5541318", notes = "Dept. of Mech. & Electron. Eng., Agric. Univ. of Hebei, Baoding, China Also known as \cite{5541318}", } @InProceedings{Zhu:2009:cec, author = "Jixiang Zhu and Yuanxiang Li and Wei Zhang and Xuewen Xia and Xing Xu", title = "Adaptive Combinational Logic Circuits Based on Intrinsic Evolvable Hardware", booktitle = "2009 IEEE Congress on Evolutionary Computation", year = "2009", editor = "Andy Tyrrell", pages = "3010--3017", address = "Trondheim, Norway", month = "18-21 " # may, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-2959-2", file = "P161.pdf", DOI = "doi:10.1109/CEC.2009.4983323", abstract = "Evolvable Hardware(EHW) has been proposed as a promising technology for adaptive systems in last few years. However, in practical applications, evolutionary algorithms(EAs) often need numerous generations to search a new solution. In general, a mistaken system is damaged if it cannot restore in time, so the inefficiency problem has become an obstacle of developing adaptive and evolvable hardware. This paper analyzes how those three factors as genotype, algorithm, and methodology affect the efficiency of the EAs, as well as to what extent of their influence respectively, then proposes parallel and recursive decomposition (PRD) as a new decomposition strategy to accelerate the adaptation process from methodology perspective. Finally, some adaptive combination logical circuits are implemented on Xilinx Virtex-II Pro (XC2VP20) FPGA. The results demonstrate that PRD has more improvement on adaptation speed than some previous strategies.", keywords = "genetic algorithms, genetic programming", notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR", } @Article{ZHU:2021:KBS, author = "Lei Zhu and Jian Lin and Yang-Yuan Li and Zhou-Jing Wang", title = "A decomposition-based multi-objective genetic programming hyper-heuristic approach for the multi-skill resource constrained project scheduling problem", journal = "Knowledge-Based Systems", volume = "225", pages = "107099", year = "2021", ISSN = "0950-7051", DOI = "doi:10.1016/j.knosys.2021.107099", URL = "https://www.sciencedirect.com/science/article/pii/S0950705121003622", keywords = "genetic algorithms, genetic programming, Decomposition, Multi-objective, Hyper-heuristic, Resource constrained scheduling", abstract = "In this paper, an efficient decomposition-based multi-objective genetic programming hyper-heuristic (MOGP-HH/D) approach is proposed for the multi-skill resource constrained project scheduling problem (MS-RCPSP) with the objectives of minimizing the makespan and the total cost simultaneously. First, the decomposition mechanism is presented to improve the diversity of solutions. Second, a single-list encoding scheme and an improved repair-based decoding scheme are designed to represent individuals and construct feasible schedules, respectively. Third, ten adaptive heuristics are developed elaborately to constitute a list of low-level heuristics (LLHs). Fourth, genetic programming is employed as the high-level heuristic (HLH) to generate a promising heuristics sequence from the LLHs set flexibly. Finally, the Taguchi method of design-of-experiment (DOE) is conducted to analyze the performance of parameter settings. The effectiveness of MOGP-HH/D is evaluated on a typical benchmark dataset and computational results exhibit the superiority of the proposed algorithm over the existing methods in solving multi-objective MS-RCPSP", } @Article{ZHU:2023:cie, author = "Lei Zhu and Yusheng Zhou and Shuhui Sun and Qiang Su", title = "Surgical cases assignment problem using an efficient genetic programming hyper-heuristic", journal = "Computer \& Industrial Engineering", pages = "109102", year = "2023", ISSN = "0360-8352", DOI = "doi:10.1016/j.cie.2023.109102", URL = "https://www.sciencedirect.com/science/article/pii/S0360835223001262", keywords = "genetic algorithms, genetic programming, Hyper-heuristic, Operating room planning, Surgical cases assignment", abstract = "The surgical case assignment problem (SCAP) is vital to the operating room planning problem. Although several methods have been applied, the solution accuracy can be improved further. In this paper, an efficient genetic programming hyper-heuristic (GP-HH) algorithm is proposed for the SCAP to minimize the total operating cost. First, eight simple and adaptive heuristic rules are devised to constitute a set of low-level heuristics (LLHs). Second, genetic programming is employed as a high-level heuristic to dynamically manage LLHs applied to the solution domain. Third, effective solution encoding and the corresponding decoding schemes are developed to represent individuals and construct valid schedules. To investigate the influence of the parameter settings, we performed a design-of-experiment (DOE). The effectiveness of GP-HH is executed on a typical benchmark dataset. The experimental results demonstrate the superiority of the proposed GP-HH scheme over existing approaches", } @TechReport{MSU-CSE-13-9, author = "Ling Zhu and Sandeep Kulkarni", title = "Synthesizing Round Based Fault-Tolerant Programs using Genetic Programming", number = "MSU-CSE-13-9", institution = "Department of Computer Science, Michigan State University", address = "East Lansing, Michigan, USA", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, SBSE, Program synthesis, Distributed programs, Fault-tolerance, NSGA-II", month = aug, year = "2013", author1_email = "zhuling@cse.msu.edu", author2_url = "http://www.cse.msu.edu/~sandeep/", author2_email = "sandeep@cse.msu.edu", size = "15", file = "/user/web/htdocs/publications/tech/TR/MSU-CSE-13-9.ps", URL = "http://www.cse.msu.edu/cgi-user/web/tech/document?ID=1022", URL = "http://www.cse.msu.edu/publications/tech/TR/MSU-CSE-13-9.ps", contact = "sandeep@cse.msu.edu", abstract = "In this paper, we present an approach to synthesise round based distributed fault-tolerant programs using stack based genetic programming. Our approach evolves a fault-tolerant program based on a round based structure and the program specification. To permit such evolution, we use a multi-objective fitness function that characterises the correctness of the program in the absence of faults, in the presence of a single fault and in the presence of multiple faults. This multi-objective fitness function attempts to synthesise a program that works equally well in all these scenarios. We demonstrate the effectiveness of our approach using two case studies: a byzantine agreement problem and a token ring problem.", notes = "Cited by \cite{conf/sss/ZhuK13}. PushGP operator stack and values stack. Program structure (Fig 2) as nested if(cond1)/elseif(condn) then write to variable fixed by user before evolution. Ie GP works only on cond1...condn. May be in future (sec 5) use model checking to evaluate (assign fitness of) generated programs.", } @InProceedings{conf/sss/ZhuK13, author = "Ling Zhu and Sandeep Kulkarni", title = "Synthesizing Round Based Fault-Tolerant Programs Using Genetic Programming", booktitle = "Proceedings of the 15th International Symposium on Stabilization, Safety, and Security of Distributed Systems (SSS 2013)", year = "2013", editor = "Teruo Higashino and Yoshiaki Katayama and Toshimitsu Masuzawa and Maria Potop-Butucaru and Masafumi Yamashita", volume = "8255", series = "Lecture Notes in Computer Science", pages = "370--372", address = "Osaka, Japan", month = nov # " 13-16", publisher = "Springer", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, SBSE, NSGA-II", bibdate = "2013-11-11", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/sss/sss2013.html#ZhuK13", isbn13 = "978-3-319-03088-3", URL = "http://dx.doi.org/10.1007/978-3-319-03089-0", URL = "http://dx.doi.org/10.1007/978-3-319-03089-0_33", DOI = "doi:10.1007/978-3-319-03089-0_33", size = "3 pages", abstract = "In this paper, we present an approach to synthesise round based distributed fault-tolerant programs using stack based genetic programming. Our approach evolves a fault-tolerant program based on a round based structure and the program specification. To permit such evolution, we use a multi-objective fitness function that characterises the correctness of the program in the absence of faults, in the presence of a single fault and in the presence of multiple faults. This multi-objective fitness function attempts to synthesise a program that works equally well in all these scenarios. We demonstrate the effectiveness of our approach using two case studies: a byzantine agreement problem and a token ring problem.", notes = "Operator stack, values stack, cites \cite{MSU-CSE-13-9}", } @InProceedings{Zhu:2015:ieeeICDCSW, author = "Ling Zhu and Sandeep S. Kulkarni", booktitle = "35th IEEE International Conference on Distributed Computing Systems Workshops", title = "Using Genetic Programming to Identify Tradeoffs in Self-Stabilizing Programs: A Case Study", year = "2015", pages = "29--34", abstract = "We focus on the use of genetic programming to identify trade-offs between closure and convergence properties of a stabilizing program. Closure property characterises the behaviour in the absence of faults whereas convergence property characterises the recovery from an arbitrary state to a legitimate state. We describe how genetic programming (GP) can be applied to identify a trade-off for the behaviours in the absence of faults and in the presence of faults. This approach uses BDD (Binary Decision Diagram) based techniques. Subsequently, we use two objectives: closure and maximum convergence time and use NSGA-II (a multi-objective optimisation algorithm) to identify the trade-off between the performances. We use the classic K-state token ring program to illustrate the trade-off and run experiments for three different approaches: (1) where we only consider trade-off based on process 0, (2) where only consider trade-off based on non-zero processes, and (3) where we consider both trade-offs. Several interesting results are found such as, special process (marked N - 3 in the K-state program) plays a critical role in providing the trade-off, while process 1 is the least important.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICDCSW.2015.17", ISSN = "1545-0678", month = jun, notes = "Dept. of Comput. Sci. & Eng., Michigan State Univ., East Lansing, MI, USA Also known as \cite{7165080}", } @InProceedings{Zhu:2015:GECCO, author = "Ling Zhu and Sandeep Kulkarni", title = "Using Model Checking Techniques For Evaluating the Effectiveness of Evolutionary Computing in Synthesis of Distributed Fault-Tolerant Programs", booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation", year = "2015", editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr", isbn13 = "978-1-4503-3472-3", pages = "1119--1126", keywords = "genetic algorithms, genetic programming", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739480.2754779", DOI = "doi:10.1145/2739480.2754779", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "In most applications using genetic programming (GP), objective functions are obtained by a terminating calculation. However, the terminating calculation cannot evaluate distributed fault-tolerant programs accurately. A key distinction in synthesizing distributed fault-tolerant programs is that they are inherently non-deterministic, potentially having infinite computations and executing in an unpredictable environment. In this study, we apply a model checking technique - Binary Decision Diagrams (BDDs) - to GP, evaluating distributed programs by computing reachable states of the given program and identifying whether it satisfies its specification. We present scenario-based multi-objective approach that each program is evaluated under different scenarios which represent various environments. The computation of the programs are considered in two different semantics respectively: interleaving and maximum-parallelism. In the end, we illustrate our approach with a Byzantine agreement problem, a token ring problem and a consensus protocol using failure detector S. For the first time, this work automatically synthesizes the consensus protocol with S. The results show the proposed method enhances the effectiveness of GP in all studied cases when using maximum-parallelism semantic.", notes = "Also known as \cite{2754779} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)", } @PhdThesis{Ling_Zhu:thesis, author = "Ling Zhu", title = "Using Evolutionary Approach to Optimize and Model Multi-Scenario, Multi-objective Fault-tolerant Problems", school = "Michigan State University", year = "2017", address = "USA", keywords = "genetic algorithms, genetic programming", URL = "https://d.lib.msu.edu/etd/6912", URL = "https://d.lib.msu.edu/etd/6912/datastream/OBJ/download/Using_Evolutionary_Approach_to_Optimize_and_Model_Multi-scenario__Multi-objective_Fault-tolerant_Problems.pdf", size = "163 pages", abstract = "Fault-tolerant design involves different scenarios, such as scenarios with no fault in the system, with faults occurring randomly, with different operation conditions, and with different loading conditions. For each scenario, there can be multiple requirements (objectives). To assess the performance of a design (solution), it needs to be evaluated over a number of different scenarios containing various requirements in each scenario. We consider this problem as a multi-scenario, multi-objective (MSMO) problem. Despite its practical importance and prevalence in engineering application, there are not many studies which systematically solve the MSMO problem. In this dissertation, we focus on optimizing and modelling MSMO problems, and propose various approaches to solve different types of MSMO optimization problems, especially multi-objective fault-tolerant problems. We classify MSMO optimisation problem into two categories: scenario-dependent and scenario-independent. For the scenario-dependent MSMO problem, we review existing methodologies and suggest two evolutionary-based methods for handling multiple scenarios and objectives: aggregated method and integrated method. The effectiveness of both methods are demonstrated on several case studies including numerical problems and engineering design problems. The engineering problems include cantilever-type welded beam design, truss bridge design, four-bar truss design. The experimental results show that both methods can find a set of widely distributed solutions that are compromised among the respective objective values under all scenarios. We also model fault-tolerant programs using the aggregated method. We synthesise three fault-tolerant distributed programs: Byzantine agreement program, token ring circulation program and consensus program with failure detector S. The results show that evolutionary-base MSMO approach, as a generic method, can effectively model fault-tolerant programs. For the scenario-independent MSMO problem, we apply evolutionary multi-objective approach. As a case study, we optimise a probabilistic self-stabilizing program, a special type of fault-tolerant program, and obtain several interesting counter-intuitive observations under different scenarios.", notes = "Supervisor: Sandeep Kulkarni", } @InProceedings{Zhu:2023:GECCO, author = "Luyao Zhu and Fangfang Zhang and Xiaodong Zhu and Ke Chen2 and Mengjie Zhang", title = "Sample-Aware Surrogate-Assisted Genetic Programming for Scheduling Heuristics Learning in Dynamic Flexible Job Shop Scheduling", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "384--392", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, automated scheduling heuristics design, dynamic flexible job shop scheduling, surrogate samples", isbn13 = "9798400701191", DOI = "doi:10.1145/3583131.3590440", size = "9 pages", abstract = "Genetic programming (GP) has been successfully introduced to learn scheduling heuristics for dynamic flexible job shop scheduling (DFJSS) automatically. However, the evaluations of GP individuals are normally time-consuming, especially with long DFJSS simulations. Taking k-nearest neighbour with phenotypic characterisations of GP individuals as a surrogate approach, has been successfully used to preselect GP offspring to the next generation for effectiveness improvement. However, this approach is not straightforward to improve the training efficiency, which is normally the primary goal of surrogate. In addition, there is no study on which GP individuals (samples) are good for building surrogate models. To this end, first, this paper proposes a surrogate-assisted GP algorithm to reduce the training time of learning scheduling heuristics for DFJSS. Second, this paper further proposes an effective sampling strategy for surrogate-assisted GP. The results show that our proposed algorithm can achieve comparable performance with only about a third of training time of traditional GP. With the same training time, the proposed algorithm can significantly improve the quality of learned scheduling heuristics in all examined scenarios. Furthermore, the evolved scheduling heuristics by the proposed sample-aware surrogate-assisted GP are more interpretable with smaller rule sizes than traditional GP.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @InProceedings{zhu:2024:CEC, author = "Luyao Zhu and Fangfang Zhang and Mengyuan Feng and Ke Chen2 and Xiaodong Zhu and Mengjie Zhang", title = "Crossover Operators Between Multiple Scheduling Heuristics with Genetic Programming for Dynamic Flexible Job Shop Scheduling", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Sequential analysis, Job shop scheduling, Processor scheduling, Sociology, Production, Dynamic scheduling, dynamic flexible job shop scheduling, crossover operators, scheduling heuristics", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10612212", abstract = "Dynamic flexible job shop scheduling (DFJSS) is an important combinatorial optimisation problem that aims to optimise machine resources to improve production efficiency. Multi-tree genetic programming (MTGP) has been widely used to learn the routing rule and the sequencing rule for DFJSS simultaneously. Unlike traditional genetic programming that only operates crossover on a single tree, MTGP has various cases to conduct crossover since a genetic programming individual consists of more than one tree. Different crossover operators may affect the performance of MTGP for DFJSS. However, the investigation into different crossover operators in MTGP for DFJSS is rare. Specifically, it is not clear what influence will have on MTGP if involving both the routing rule and the sequencing rule for crossover. To this end, this paper provides a comprehensive investigation of four possible crossover cases between multiple scheduling heuristics with MTGP for DFJSS. The four operators are designed according to the number of trees/rules that crossover operator works on, and whether swapping full trees between parents. The results show that although the compared algorithms have comparable results in most scenarios, MTGP with both rules for crossover and the swapping strategy is ranked as the best one. Further analyses show that the sizes of learnt rules are highly related to the crossover operators, and crossover involving more rules can increase the rule sizes, and vice versa. In addition, the population diversity and the number of unique features in the learnt rules of MTGP with both rules for crossover are increased to learn effective rules.", notes = "also known as \cite{10612212} WCCI 2024", } @Article{Luyao_Zhu:TAI, author = "Luyao Zhu and Fangfang Zhang and Xiaodong Zhu and Ke Chen2 and Mengjie Zhang", title = "Phenotype and Genotype Based Sample Aware Surrogate-Assisted Genetic Programming in Dynamic Flexible Job Shop Scheduling", journal = "IEEE Transactions on Artificial Intelligence", keywords = "genetic algorithms, genetic programming, Phenotypes, Genotypes, Job shop scheduling, Nearest neighbor methods, Training, Sequential analysis, Artificial intelligence, Accuracy, Routing, Dynamic scheduling", publisher = "IEEE Computer Society", ISSN = "2691-4581", URL = "https://fangfang-zhang.github.io/publications/", URL = "https://doi.ieeecomputersociety.org/10.1109/TAI.2025.3562161", DOI = "doi:10.1109/TAI.2025.3562161", notes = "Also known as \cite{10969605} School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China", } @InProceedings{Zhu:2011:ISCID, author = "Ming-fang Zhu and Jian-bin Zhang and Yan-ling Ren and Yu Pan and Guang-ping Zhu", title = "Multivarible Symbolic Regression Based on Gene Expression Programming", booktitle = "Fourth International Symposium on Computational Intelligence and Design (ISCID 2011)", year = "2011", month = "28-30 " # oct, address = "Hangzhou", publisher = "IEEE", DOI = "doi:10.1109/ISCID.2011.177", volume = "2", pages = "298--301", isbn13 = "978-1-4577-1085-8", size = "4 pages", abstract = "This paper presents a method for multivarible symbolic regression modelling and predicting. The method based on gene expression programming, a recently proposed evolutionary computation technique. We explain in details the techniques of gene expression programming and multivarible symbolic regression with gene expression programming. Furthermore, we give an example to explain this technique, and experiment results show that the model set up by gene expression programming is better than statistical linear regression techniques.", keywords = "genetic algorithms, genetic programming, gene expression programming, evolutionary computation technique, multivarible symbolic regression modelling, statistical linear regression techniques, evolutionary computation, regression analysis", notes = "Also known as \cite{6079796}", } @InProceedings{Zhu:1997:mpdGAvs, author = "Rixin Zhu and Steven J. Skerlos and Richard E. DeVor and Shiv G. Kapoor", title = "Application of Genetic Algorithm to Machining Process Diagnostics with a DOE-Based GA Validation Scheme", booktitle = "Late Breaking Papers at the 1997 Genetic Programming Conference", year = "1997", editor = "John R. Koza", pages = "273--279", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California, 94305-3079, USA", month = "13--16 " # jul, publisher = "Stanford Bookstore", keywords = "genetic algorithms", ISBN = "0-18-206995-8", notes = "GP-97LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670", } @Article{Zhu:2015:GPEM, author = "Sai Zhu and Jin-yan Cai and Ya-feng Meng", title = "Partial-DNA cyclic memory for bio-inspired electronic cell", journal = "Genetic Programming and Evolvable Machines", year = "2016", volume = "17", number = "2", pages = "83--117", month = jun, keywords = "genetic algorithms, Embryonics, Genome memory, Gene shift, Reliability, Self-repair", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-015-9248-2", size = "35 pages", abstract = "Genome memory is an important aspect of electronic cells. Here, a novel genome memory structure called partial-DNA cyclic memory is proposed, in which cells only store a portion of the system's entire DNA. The stored gene number is independent of the scale of embryonic array and of the target circuit, and can be set according to actual demand in the design process. Genes can be transferred in the cell and the embryonics array through intracellular and inter-cellular gene cyclic and non-cyclic shifts, and based on this process the embryonic array's functional differentiation and self-repair can be achieved. In particular, lost genes caused by faulty cells can be recovered through gene updating based on the remaining normal neighbour cells during the self-repair process. A reliability model of the proposed memory structure is built considering the gene updating method, and depending on the implementations of the memory, the hardware overhead is modelled. Based on the reliability model and hardware overhead model, we can find that the memory can achieve high reliability with relatively few gene backups and with low hardware overhead. Theoretical analysis and a simulation experiment show that the new genome memory structure not only achieves functional differentiation and self-repair of the embryonics array, but also ensures system reliability while reducing hardware overhead. This has significant value in engineering applications, allowing the proposed genome memory structure to be used to design larger scale self-repair chips.", notes = "No mention of GP", } @InProceedings{ShininZhu:1998:dffcaEPfo, author = "Shinin Zhu and Robert G. Reynolds", title = "The Design of Fully Fuzzy Cultural Algorithms with Evolutionary Programming for Function Optimization", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "795--800", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "evolutionary programming", ISBN = "1-55860-548-7", notes = "GP-98", } @InProceedings{ShininZhu:1998:ifkrpscaEP, author = "Shinin Zhu and Robert G. Reynolds", title = "The Impact of Fuzzy Knowledge Representation on Problem Solving in Cultural Algorithms with Evolutionary Programming", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "801--806", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "evolutionary programming", ISBN = "1-55860-548-7", notes = "GP-98", } @InCollection{zhu:2007:jpVL, author = "Wei Zhu and Harry Timmermans", title = "Exploring Heuristics Underlying Pedestrian Shopping Decision Processes An application of Gene Expression Programming", booktitle = "Innovations in Design and Decision Support Systems in Architecture and Urban Planning. Part 2", publisher = "Springer", year = "2007", editor = "Jos P. {Van Leeuwen} and Harry J. P. Timmermans", pages = "121--136", address = "Springer Netherlands", keywords = "genetic algorithms, genetic programming, Gene Expression Programming", isbn_13 = "978-1-4020-5059-6 (Print) 978-1-4020-5060-2 (Online)", DOI = "doi:10.1007/978-1-4020-5060-2_8", } @InProceedings{Zhu:2020:CASE, author = "Xuedong Zhu and Weihao Wang and Xinxing Guo and Leyuan Shi", title = "A Genetic Programming-Based Evolutionary Approach for Flexible Job Shop Scheduling with Multiple Process Plans", booktitle = "2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)", year = "2020", pages = "49--54", abstract = "This paper investigates a more general flexible job shop scheduling problem with multiple process plans which is common in the modern manufacturing system. As an extension of the traditional flexible job shop scheduling problem, various realistic flexibility such as processing flexibility, machine flexibility and sequencing flexibility are considered in this problem. Due to the high complexity and the real-time requirement of this problem, a genetic programming-based evolutionary approach is proposed to automatically generate effective dispatching rules for this problem, and an evaluation method is developed to evaluate the generated dispatching rules. Three experiments are conducted to evaluate the performance of the proposed approach for real cases with large-scale test problems. Numerical results show that the proposed approach outperforms the classical dispatching rules and the state-of-theart algorithms, and is able to provide higher-quality solutions with less computational time.", keywords = "genetic algorithms, genetic programming, Dispatching, Sequential analysis, Job shop scheduling, Manufacturing systems, Conferences", DOI = "doi:10.1109/CASE48305.2020.9216783", ISSN = "2161-8089", month = aug, notes = "Also known as \cite{9216783}", } @Article{Xuedong_Zhu:ASE, author = "Xuedong Zhu and Xinxing Guo and Weihao Wang and Jianguo Wu", title = "A Genetic Programming-Based Iterative Approach for the Integrated Process Planning and Scheduling Problem", journal = "IEEE Transactions on Automation Science and Engineering", year = "2022", volume = "19", number = "3", pages = "2566--2580", keywords = "genetic algorithms, genetic programming", ISSN = "1558-3783", DOI = "doi:10.1109/TASE.2021.3091610", abstract = "The integrated process planning and scheduling (IPPS) problem is studied in this article, in which operation sequencing, process plan selection, and machine selection are decided simultaneously. For different scenarios, three mixed-integer linear programming (MILP) models are designed. Then, in view of the workload of machines and processing times of jobs, two machine selection techniques are introduced to simplify the optimization of these MILP models. By exploring the structural properties of the MILP models, we put forward a novel lower bound to act as a measurement for the performance of the related algorithms. Considering the real-time requirement and complexity of instances in practice, we design a hybrid greedy heuristic based on a new decision structure of the problem and dispatching rules. Furthermore, in order to create effective dispatching rules to improve the hybrid greedy heuristic, enhanced genetic programming (GP)-based iterative approach is proposed. Experimental results indicate that our approaches are better than other available approaches for the IPPS problem and can reduce the computational time while providing high-quality solutions.", notes = "Also known as \cite{9475992}", } @Misc{Zhu:2018:arxiv, author = "Yiheng Zhu and Yichen Yao and Zili Wu and Yujie Chen and Guozheng Li and Haoyuan Hu and Yinghui Xu", title = "{GP-CNAS}: Convolutional Neural Network Architecture Search with Genetic Programming", howpublished = "arXiv", year = "2018", month = "26 " # nov, keywords = "genetic algorithms, genetic programming, ANN", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/corr/corr1812.html#abs-1812-07611", URL = "http://arxiv.org/abs/1812.07611", size = "8 pages", abstract = "Convolutional neural networks (CNNs) are effective at solving difficult problems like visual recognition, speech recognition and natural language processing. However, performance gain comes at the cost of laborious trial-and-error in designing deeper CNN architectures. In this paper, a genetic programming (GP) framework for convolutional neural network architecture search, abbreviated as GP-CNAS, is proposed to automatically search for optimal CNN architectures. GP-CNAS encodes CNNs as trees where leaf nodes (GP terminals) are selected residual blocks and non-leaf nodes (GP functions) specify the block assembling procedure. Our tree-based representation enables easy design and flexible implementation of genetic operators. Specifically, we design a dynamic crossover operator that strikes a balance between exploration and exploitation, which emphasizes CNN complexity at early stage and CNN diversity at later stage. Therefore, the desired CNN architecture with balanced depth and width can be found within limited trials. Moreover, our GP-CNAS framework is highly compatible with other manually-designed and NAS-generated block types as well. Experimental results on the CIFAR-10 dataset show that GP-CNAS is competitive among the state-of-the-art automatic and semi-automatic NAS algorithms.", notes = "University of Bristol journals/corr/abs-1812-07611", } @InProceedings{Zhu:2017:CODES_ISSS, author = "Yue Zhu and Fei Wu and Qin Xiong and Changsheng Xie", booktitle = "2017 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS)", title = "Work-in-progress: alert-and-transfer: an evolutionary architecture for ssd-based storage systems", year = "2017", abstract = "Over the past few years, NAND flash-based Solid State Drives (SSDs) are progressively replacing Hard Disk Drives (HDDs) in various applications ranging from personal computers to large-scale storage servers, due to their high performance and low power consumption. However, SSDs suffer from limited endurance, which is a major concern in servers. Based on the unique characteristics of SSDs, we propose an evolutionary architecture, which can significantly improve both the performance and reliability of SSD-based storage systems compared with the currently prevalent RAID-5 technology [1].", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1145/3125502.3125536", month = oct, notes = "Huazhong University of Science and Technology, China Also known as \cite{8101302}", } @InProceedings{Zhu:2010:MLSP, author = "Zhechen Zhu and Muhammad Waqar Aslam and Asoke Kumar Nandi", title = "Augmented Genetic Programming for automatic digital modulation classification", booktitle = "2010 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)", year = "2010", month = "29 " # aug # "-" # sep # " 1", pages = "391--396", abstract = "Automatic modulation classification (AMC) is used to identify automatically the modulation types of transmitted signals using the received data samples in the presence of noise. It is a very important process for a receiver that has no, or limited, knowledge of signals received. It is an intermediate step between signal detection and demodulation and has various civilian and military applications. In this paper we propose to use Genetic Programming (GP) with KNN classifier for automatic classification of digital modulation types for the first time. The method proposed here has been designed for BPSK, QPSK, 16QAM and 64QAM. The results from simulation experiments show that the proposed method is able to identify the above modulation types at SNRs of 10dB and 20dB. The performance of the proposed method has been compared with existing methods and it is found to provide the best results so far.", keywords = "genetic algorithms, genetic programming, KNN classifier, augmented genetic programming, automatic digital modulation classification, transmitted signals, digital communication, modulation, pattern classification, signal detection", DOI = "doi:10.1109/MLSP.2010.5588920", ISSN = "1551-2541", notes = "Also known as \cite{5588920}", } @InProceedings{ZhechenZhu:2011:ISSCS, author = "Zhechen Zhu and Muhammad Waqar Aslam and Asoke Kumar Nandi", title = "Support vector machine assisted genetic programming for MQAM classification", booktitle = "10th International Symposium on Signals, Circuits and Systems (ISSCS 2011)", year = "2011", month = "30 " # jun # "-1 " # jul, address = "lasi, Romania", size = "6 pages", abstract = "Automatic modulation classification is used to identify automatically the modulation type of an incoming signal with limited or no prior knowledge to it. Various classifier systems have been developed to solve this problem. However, for certain types of modulations such as 16 QAM and 64 QAM, the classification performance under noisy condition still needs to be improved. In this paper, we propose a new AMC scheme by combining genetic programing (GP) with support vector machine (SVM) for the classification of 16 QAM and 64 QAM signals. The benchmark result shows that SVM assisted GP can produce better accuracy than some other existing methods.", keywords = "genetic algorithms, genetic programming, AMC scheme, GP, MQAM classification, QAM signals, SVM, automatic modulation classification, classification performance, classifier systems, modulation type, noisy condition, support vector machine assisted genetic programming, quadrature amplitude modulation, signal classification, support vector machines", DOI = "doi:10.1109/ISSCS.2011.5978654", notes = "Also known as \cite{5978654}", } @InProceedings{Zhu:2013:MLSP, author = "Zhechen Zhu and Asoke K. Nandi and Muhammad Waqar Aslam", title = "Adapted Geometric Semantic Genetic programming for diabetes and breast cancer classification", booktitle = "IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2013)", year = "2013", month = sep, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/MLSP.2013.6661969", ISSN = "1551-2541", abstract = "In this paper, we explore new Adapted Geometric Semantic (AGS) operators in the case where Genetic programming (GP) is used as a feature generator for signal classification. Also to control the computational complexity, a devolution scheme is introduced to reduce the solution complexity without any significant impact on their fitness. Fisher's criterion is employed as fitness function in GP. The proposed method is tested using diabetes and breast cancer datasets. According to the experimental results, GP with AGS operators and devolution mechanism provides better classification performance while requiring less training time as compared to standard GP.", notes = "Also known as \cite{6661969}", } @PhdThesis{Zhechen_Zhu:thesis, author = "Zhechen Zhu", title = "Automatic classification of digital communication signal modulations", school = "Dept. of Electronic and Computer Engineering, Brunel University", year = "2014", address = "UK", month = oct, keywords = "genetic algorithms, genetic programming, Modulation classification, Channel estimation, Machine learning, Signal processing, Wireless communications", URL = "http://bura.brunel.ac.uk/handle/2438/9246", URL = "http://bura.brunel.ac.uk/bitstream/2438/9246/1/FulltextThesis.pdf", size = "175 pages", abstract = "Automatic modulation classification detects the modulation type of received communication signals. It has important applications in military scenarios to facilitate jamming, intelligence, surveillance, and threat analysis. The renewed interest from civilian scenes has been fuelled by the development of intelligent communications systems such as cognitive radio and software defined radio. More specifically, it is complementary to adaptive modulation and coding where a modulation can be deployed from a set of candidates according to the channel condition and system specification for improved spectrum efficiency and link reliability. In this research, we started by improving some existing methods for higher classification accuracy but lower complexity. Machine learning techniques such as k-nearest neighbour and support vector machine have been adopted for simplified decision making using known features. Logistic regression, genetic algorithm and genetic programming have been incorporated for improved classification performance through feature selection and combination. We have also developed a new distribution test based classifier which is tailored for modulation classification with the inspiration from Kolmogorov-Smirnov test. The proposed classifier is shown to have improved accuracy and robustness over the standard distribution test. For blind classification in imperfect channels, we developed the combination of minimum distance centroid estimator and non-parametric likelihood function for blind modulation classification without the prior knowledge on channel noise. The centroid estimator provides joint estimation of channel gain and carrier phase o set where both can be compensated in the following nonparametric likelihood function. The non-parametric likelihood function, in the meantime, provide likelihood evaluation without a specifically assumed noise model. The combination has shown to have higher robustness when different noise types are considered. To push modulation classification techniques into a more timely setting, we also developed the principle for blind classification in MIMO systems. The classification is achieved through expectation maximization channel estimation and likelihood based classification. Early results have shown bright prospect for the method while more work is needed to further optimize the method and to provide a more thorough validation.", notes = "GP-KNN classifier. AWGN channels. Supervisors: A. K. Nandi and H. Meng and W. Al-Nauimy", } @Book{Zhechen_Zhu:book, author = "Zhechen Zhu and Asoke K. Nandi", title = "Automatic Modulation Classification: Principles, Algorithms and Applications", publisher = "Wiley", year = "2015", month = feb, keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-118-90649-1", URL = "http://eu.wiley.com/WileyCDA/WileyTitle/productCd-1118906497.html", size = "184 pages", abstract = "Description Automatic Modulation Classification (AMC) has been a key technology in many military, security, and civilian telecommunication applications for decades. In military and security applications, modulation often serves as another level of encryption; in modern civilian applications, multiple modulation types can be employed by a signal transmitter to control the data rate and link reliability. This book offers comprehensive documentation of AMC models, algorithms and implementations for successful modulation recognition. It provides an invaluable theoretical and numerical comparison of AMC algorithms, as well as guidance on state-of-the-art classification designs with specific military and civilian applications in mind. Key Features: Provides an important collection of AMC algorithms in five major categories, from likelihood-based classifiers and distribution-test-based classifiers to feature-based classifiers, machine learning assisted classifiers and blind modulation classifiers Lists detailed implementation for each algorithm based on a unified theoretical background and a comprehensive theoretical and numerical performance comparison Gives clear guidance for the design of specific automatic modulation classifiers for different practical applications in both civilian and military communication systems Includes a MATLAB toolbox on a companion website offering the implementation of a selection of methods discussed in the book http://media.wiley.com/assets/7321/42/amctoolbox-0.4.rar", notes = "6.7 Genetic Programming for Feature Selection and Combination 6.7.1. Tree Structured Solution 6.7.2. Genetic Operators", } @InProceedings{Zid:2020:SBST, author = "Cyrine Zid and Dmytro Humeniuk and Foutse Khomh and Giuliano Antoniol", title = "Double Cycle Hybrid Testing of Hybrid Distributed {IoT} System", booktitle = "Search-Based Software Testing", year = "2020", editor = "Erik Fredericks and Jose Miguel Rojas", pages = "529--532", address = "internet", month = "2 " # jul, publisher = "Association for Computing Machinery", keywords = "genetic algorithms, genetic programming, SBSE, cyber-physical systems, scenario generation, test data Generation, IoT", isbn13 = "9781450379632", video_url = "https://youtu.be/zTSLOF3UP70", URL = "https://doi.org/10.1145/3387940.3392218", DOI = "doi:10.1145/3387940.3392218", size = "4 pages", abstract = "Testing heterogeneous IoT applications such as a home automation systems integrating a variety of devices poses serious challenges. Oftentimes requirements are vaguely defined. Consumer grade cyber-physical devices and software may not meet the reliability and quality standard needed. Plus, system behavior may partially depend on various environmental conditions. For example, WI-FI communications network congestion may cause packet delay; meanwhile cold weather may cause an unexpected drop of inside temperature.We surmise that generating and executing failure exposing scenarios is especially challenging. Modeling phenomenons such as network traffic or weather conditions is complex. One possible solution is to rely on machine learning models approximating the reality. These models, integrated in a system model, can be used to define surrogate models and fitness functions to steer the search in the direction of failure inducing scenarios.However, these models also should be validated. Therefore, there should be a double loop co-evolution between machine learned surrogate models functions and fitness functions.Overall, we argue that in such complex cyber-physical systems, co-evolution and multi-hybrid approaches are needed.", notes = "YouTube https://youtu.be/zTSLOF3UP70 10:44 Ecole Polytechnique de Montreal ICSEW'20 https://sbst20.github.io/program/", } @InProceedings{ziegler:2000:E, author = "Jens Ziegler and Wolfgang Banzhaf", title = "Evolving a ``nose'' for a robot", booktitle = "Evolution of Sensors in Nature, Hardware and Simulation", year = "2000", pages = "226--230", address = "Las Vegas, Nevada, USA", month = "8 " # jul, keywords = "genetic algorithms, genetic programming, Artificial Chemistry, Autonomous Robots, Khepera", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/29769/http:zSzzSzls11-www.informatik.uni-dortmund.dezSzpeoplezSzzieglerzSzGECCO2000zSzZB_GECCO.pdf/ziegler00evolving.pdf", URL = "http://citeseer.ist.psu.edu/ziegler00evolving.html", abstract = "The evolution of metabolisms that act as control programms for a small robot leads to the selection of most relevant sensory information. The underlying artificial chemistry evolves efficient information processing pathways with most benefit for the desired task, robot navigation. The results show certain relations to biological systems like motile bacteria.", notes = "GECCO-2000WKS Part of \cite{wu:2000:GECCOWKS} introns, Khepera robot, chemotaxis", } @Article{Ziegler:2001:AL, author = "Jens Ziegler and Wolfgang Banzhaf", title = "Evolving Control Metabolisms for a Robot", journal = "Artificial Life", year = "2001", volume = "7", number = "2", pages = "171--190", month = "Spring", keywords = "genetic algorithms, genetic programming, artificial chemistry", ISSN = "1064-5462", DOI = "doi:10.1162/106454601753138998", abstract = "This article demonstrates a new method of programming artificial chemistries. It uses the emerging capabilities of the system's dynamics for information-processing purposes. By evolution of metabolisms that act as control programs for a small robot one achieves the adaptation of the internal metabolic pathways as well as the selection of the most relevant available exteroceptors. The underlying artificial chemistry evolves efficient information-processing pathways with most benefit for the desired task, robot navigation. The results show certain relations to such biological systems as motile bacteria.", notes = "Khepera robot", } @InProceedings{ZieWol01, author = "Jens Ziegler and Krister Wolff and Peter Nordin and Wolfgang Banzhaf", title = "Constructing a small humanoid walking robot as a platform for the genetic evolution of walking", booktitle = "Proceedings of the 5th International Heinz Nixdorf Symposium: Autonomous Minirobots for Research and Edutainment", year = "2001", editor = "Ulrich R{\"{u}}ckert and Joaquin Sitte and Ulf Witkowski", number = "97", pages = "51--59", publisher = "Heinz Nixdorf Institute", keywords = "genetic algorithms, genetic programming", ISBN = "3-935433-06-9", URL = "http://fy.chalmers.se/~wolff/ZWNB_AMiRE01.pdf", abstract = "Walking robots from the next challenge in the field of autonomous robots. This paper describes the construction of a fully autonomous humanoid walking robot as a platform for machine learning algorithms like, e.g., Genetic Programming. Built from off-the-shelf components, the described humanoids are cheap, robust and easy to program, which make them an ideal test platform for several experimental approaches in machine learning, sensor fusion or adaptive control. In addition to these research related topics, the walking robots are an ideal tool for educational purposes.", } @TechReport{Zie02, author = "Jens Ziegler", title = "On the influence of adaptive operator probabilities in genetic programming", institution = "Universit{\"a}t Dortmund, Fachbereich Informatik", type = "Interner Bericht der Systems Analysis Research Group", year = "2002", month = jul, number = "SYS--3/02", ISSN = "0941-4568", keywords = "genetic algorithms, genetic programming", notes = "Broken Dec 2012 http://ls11-www.cs.uni-dortmund.de/publikationen_bibtex.jsp?ident=Zie02&userLanguage=en", } @InProceedings{ZBBB02, author = "Jens Ziegler and Jan Barnholt and Jens Busch and Wolfgang Banzhaf", title = "Automatic evolution of control programs for a small humanoid walking robot", booktitle = "Climbing and Walking Robots and the Support Technologies for Mobile Machines: Proceedings of the 5th International Conference (CLAWAR 2002)", editor = "Philippe Bidaud and Faiz {Ben Amar}", publisher = "Professional Engineering Publishing, publishers to the Institute of Mechanical Engineers", address = "Bury St. Edmunds, UK", year = "2002", ISBN = "1-86058-380-6", pages = "109--116", keywords = "genetic algorithms, genetic programming", notes = "This book can be purchased from the publisher on sales@imeche.org.uk Broken Dec 2012 http://www.clawar.com/publications/publications.htm Broken 2012 http://ls11-www.cs.uni-dortmund.de/publikationen_bibtex.jsp?ident=ZBBB02&userLanguage=en Also known as \cite{zbbb2002aeoc}", } @InProceedings{ziegler03, author = "Jens Ziegler and Wolfgang Banzhaf", title = "Decreasing the Number of Evaluations in Evolutionary Algorithms by using a Meta-Model of the Fitness Function", booktitle = "Genetic Programming, Proceedings of EuroGP'2003", year = "2003", editor = "Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa", volume = "2610", series = "LNCS", pages = "264--275", address = "Essex", publisher_address = "Berlin", month = "14-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-00971-X", DOI = "doi:10.1007/3-540-36599-0_24", abstract = "In this paper a method is presented that decreases the necessary number of evaluations in Evolutionary Algorithms. A classifier with confidence information is evolved to replace time consuming evaluations during tournament selection. Experimental analysis of a mathematical example and the application of the method to the problem of evolving walking patterns for quadruped robots show the potential of the presented approach.", notes = "EuroGP'2003 held in conjunction with EvoWorkshops 2003", } @PhdThesis{oai:eldorado:0x00070350, title = "Evolution von Laufrobotersteuerungen mit Genetischer Programmierung", author = "Jens Ziegler", year = "2003", month = jun # "~18", abstract = "Die Evolution von Laufrobotersteuerungen mit Genetischer Programmierung wird anhandunterschiedlicher Roboterarchitekturen sowohl in der Simulation als auch mit realen Roboternuntersucht. Bedingt durch die lang andauernden Auswertungen von Laufprogrammen in Simulationund Realit{\"a}t werden die Methoden der Evolution - Evaluation, Selektion und Variation zus{\"a}tzlicheiner gr{\"u}ndlichen Analyse unterzogen, um den evolution{\"a}ren Prozess zur automatischen Generierungvon optimierten Laufrobotersteuerungen m{\"o}glichst zu beschleunigen.Im ersten Teil der Arbeit werden Grundlagen der Evolution von Laufroboterprogrammen erl{\"a}utert. InKapitel 1 wird die Thematik der autonomen mobilen Roboter eingehend betrachtet, wobei dasTeilgebiet der Laufroboter und die speziell hierf{\"u}r relevanten Problemstellungen dargestellt werden.Das zweite Kapitel besch{\"a}ftigt sich mit der Genetischen Programmierung, einem Spezialfall derEvolution{\"a}ren Algorithmen. Die f{\"u}r die Arbeit ben{\"o}tigten Begriffe des evolution{\"a}ren Rechnens werdenvereinbart, sowie die grundlegenden Mechanismen evolution{\"a}rer Algorithmen aufgezeigt. Hierbei wirddetailliert auf die besonderen Eigenschaften der Genetischen Programmierung eingegangen. Im drittenKapitel werden die mathematischen Grundlagen der Dynamiksimulation von Laufrobotern -Kinematik, Dynamik, Kollisionserkennung - dargestellt.Der zweite Teil der Arbeit f{\"u}hrt in die Thematik der Evolution von Laufrobotersteuerungen ein. InKapitel 4 werden detailliert die generelle Zielsetzung und die verwendeten Methoden bei der Evolutionvon Robotersteuerungen beschrieben. Es wird hierbei besonders die Repr{\"a}sentation vonKontrollstrukturen autonomer Roboter in Evolution{\"a}ren Algorithmen und die Umsetzung auf reale odersimulierte Roboter diskutiert. Anschlie{\ss}end wird die Methodik f{\"u}r die Evolution vonLaufroboterprogrammen mit Genetischer Programmierung eingef{\"u}hrt. Die Notwendigkeit derEvolution mit simulierten und realen Robotern, sowie die M{\"o}glichkeit, beide Alternativen gleichzeitigzu verwenden, wird diskutiert.In Teil III der Arbeit werden die Experimente zur Evolution von Laufprogrammen pr{\"a}sentiert. InKapitel 7 wird das Laufzeitverhalten der Evolution von Laufroboterprogrammen untersucht. Kapitel 8zeigt die erfolgreiche Evolution von Laufprogrammen f{\"u}r eine Vielfalt von Roboterformen in derSimulation auf. Die Angabe eines G{\"u}tekriteriums ist essenziell f{\"u}r die Evolution von Laufmustern undwird eingehend diskutiert. Dargestellt werden die Auswirkungen verschiedener Varianten derFitnessfunktion auf die resultierenden Bewegungsmuster der Roboter. Im Kapitel 9 werden f{\"u}r einensowohl real als auch als Computermodell existierenden humanoiden Roboter Laufprogrammeevolviert. Die Ergebnisse von Evolutionsl{\"a}ufen mit vierbeinigen Laufrobotern werden in Kapitel 10vorgestellt. Die Resultate der Experimente unter Einbeziehung der interaktiven Evolution und derModellierung der Fitnessfunktion zeigen eine deutliche Verbesserung gegen{\"u}ber demStandardalgorithmus.", annote = "Fachbereich 4; Universit{\"a}t Dortmund", contributor = "Prof. Dr. rer.-nat. Wolfgang Banzhaf and Fachbereich 4 and Prof. Dr. Heinrich M{\"u}ller", language = "GER", oai = "oai:eldorado:0x00070350", rights = "These documents can be used freely according to copyright laws. They can be printed freely. It is not allowed to distribute them further on.", URL = "http://hdl.handle.net/2003/2743", URL = "https://eldorado.uni-dortmund.de/bitstream/2003/2743/1/Zieglerunt.pdf", school = "Dortmund University", address = "Germany", keywords = "genetic algorithms, genetic programming, Laufroboter, Roboter, Computational intelligence, Evolution, Autonome Roboter, Simulation", size = "182 pages", notes = "sony aibo, hexapod", } @InProceedings{DT_ICSES, author = "L. Zielinski and J. Rutkowski", title = "Design Tolerancing with Utilization of Gene Expression Programming and Genetic Algorithm", booktitle = "ICSES'04 International Conference on Signals and Electronic Systems", year = "2004", address = "Poznan University of Technology, Poznan, Poland", month = "13-15 " # sep, organisation = "PTETiS, IEEE, EURASIP, Polish Academy of Sciences", keywords = "genetic algorithms, genetic programming, Gene Expression Programming, analog circuits, design tolerancing, evolutionary computation, optimization", URL = "http://www.funzone.pl/Articles/DT_ICSES.pdf", abstract = "Design Tolerancing (DT). This issue plays huge role in design of analog circuits. Because practically it is impossible to obtain optimum solution by mathematically described computational methods, this problem is numbered to NP difficult category. Optimum tolerances of analogue circuit parameters such as resistance, capacitance and inductance, are determined based on Genetic Algorithms and new evolutionary technique i.e. Gene Expression Programming (GEP). To explain and assess the new DT method, two practical examples are presented.", notes = "Silesian University of Technology, POLAND ", } @InProceedings{Ziemeck:1997:vcGENCODER, author = "Patrick Ziemeck and Helge Ritter", title = "Evolving low-level Vision Capabilities with the GENCODER Genetic Programming Environment", booktitle = "Artificial Neural Nets and Genetic Algorithms: Proceedings of the International Conference, ICANNGA97", year = "1997", editor = "George D. Smith and Nigel C. Steele and Rudolf F. Albrecht", pages = "78--82", address = "University of East Anglia, Norwich, UK", month = "2-4 " # apr, publisher = "Springer-Verlag", note = "published in 1998", keywords = "genetic algorithms, genetic programming", ISBN = "3-211-83087-1", DOI = "doi:10.1007/978-3-7091-6492-1_17", abstract = "A new approach for the application of genetic programming to vision problems is presented. Sets of atomic subprograms are genetically combined to solve more advanced problems within low-level vision or image preprocessing. We present the main ideas and give a brief sketch of their implementation in the distributed simulation environment GENCODER. This system forms the basis for some introductory experiments obtained. Finally, some aspects of the gained results together with interesting possibilities for future research are portrayed.", notes = "http://www.sys.uea.ac.uk/Research/ResGroups/MAG/ICANNGA97/papers_frame.html", } @InProceedings{Zille:2021:GECCOcomp, author = "Heiner Zille and Fabien Evrard and Sanaz Mostaghim and Berend {van Wachem}", title = "Unit-aware Multi-objective Genetic Programming for the Prediction of the {Stokes} Flow around a Sphere", booktitle = "Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion", year = "2021", editor = "Francisco Chicano and Alberto Tonda and Krzysztof Krawiec and Marde Helbig and Christopher W. Cleghorn and Dennis G. Wilson and Georgios Yannakakis and Luis Paquete and Gabriela Ochoa and Jaume Bacardit and Christian Gagne and Sanaz Mostaghim and Laetitia Jourdan and Oliver Schuetze and Petr Posik and Carlos Segura and Renato Tinos and Carlos Cotta and Malcolm Heywood and Mengjie Zhang and Leonardo Trujillo and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Fuyuki Ishikawa and Inmaculada Medina-Bulo and Frank Neumann and Andrew M. Sutton", pages = "327--328", address = "internet", series = "GECCO '21", month = jul # " 10-14", organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, Fluid Dynamics, Multi-objective: Poster", isbn13 = "978-1-4503-8351-6", URL = "https://www.is.ovgu.de/is_media/Research/Publications/Gecco2021_UnitAwareGeneticProgramming_Zille-download-1.pdf", DOI = "doi:10.1145/3449726.3459408", size = "2 pages", abstract = "we apply a unit-aware Genetic Programming (GP) approach to solve a problem from the area of fluid-dynamics: The Stokes flow around a sphere. We formulate 6 test instances with different complexities and explore the capabilities of single- and multi-objective GP variants to solve this problem with physically correct units of measurement. The study is a starting point to investigate the amount of information necessary to solve fluid-dynamics-related problems, and whether the inclusion of physical dimensions is advantageous or not for such optimization tasks. From the simple flow presented in this study we aim to extend this research to more complex flows with multiple spheres and finite Reynolds numbers.", notes = "DEAP Otto-von-Guericke University Magdeburg GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)", } @InProceedings{Zils:2003:DAFx, author = "Aymeric Zils and Francois Pachet", title = "Extracting automatically the perceived intensity of music titles", booktitle = "6th International Conference on Digital Audio Effects, DAFx-03", year = "2003", pages = "DAFX47", address = "Queen Mary, University of London", month = "8-11 " # sep, organisation = "COST action in Europe", keywords = "genetic algorithms, genetic programming", URL = "http://www.eecs.qmul.ac.uk/legacy/dafx03/", URL = "http://www.eecs.qmul.ac.uk/legacy/dafx03/proceedings/pdfs/dafx47.pdf", size = "4 pages", abstract = "We address the issue of extracting automatically high-level musical descriptors out of their raw audio signal. This work focuses on the extraction of the perceived intensity of music titles, that evaluates how energetic the music is perceived by listeners. We present here first the perceptive tests that we have conducted, in order to evaluate the relevance and the universality of the perceived intensity descriptor. Then we present several methods used to extract relevant features used to build automatic intensity extractors: usual Mpeg7 low level features, empirical method, and features automatically found using our Extractor Discovery System (EDS), and compare the final performances of their extractors.", notes = "online csl.sony.fr", } @PhdThesis{zils-04b, author = "Aymeric Zils", title = "Extraction de descripteurs musicaux: une approche evolutionniste", school = "LIP6, Sorbonne University", year = "2003", address = "Paris, France", month = "30 " # sep, keywords = "genetic algorithms, genetic programming", URL = "https://www.csl.sony.fr/downloads/papers/2004/zils-04b.pdf", size = "197 pages", abstract = "Nous presentons un systeme d'extraction automatique de descripteurs par traitement du signal, applicable notamment sur les signaux musicaux. Ce systeme appele EDS (Extractor Discovery System) permet de construire des descripteurs a partir d'une base de signaux d'apprentissage etiquetes. Il fonctionne en deux etapes: l'extraction de fonctions pertinentes et l'optimisation du modele descriptif. La premiere etape consiste a construire automatiquement des fonctions de traitement du signal adaptees au probleme descriptif a resoudre. Cette construction est realisee grace a un algorithme de recherche genetique qui genere des fonctions sous forme de composition d'operations de traitement du signal, evalue leur pertinence, et realise une optimisation en appliquant des transformations genetiques (mutations, croisements, etc) aux meilleures fonctions. La seconde etape consiste a utiliser un ensemble de fonctions obtenues lors de la premiere etape, dans des classifieurs parametres (kNN, neural nets, etc). Une recherche complete permet d'obtenir le classifieur et le parametrage optimal, qui fournissent le modele descriptif definitif. Le modele obtenu est exportable depuis EDS sous forme d'un executable independant du systeme, applicable sur un fichier audio. Cet executable permet d'integrer les descripteurs modelises dans des applications musicales originales: un outil de recherche de musique, et une application de sequencage d'objets sonores.", notes = "In French Supervisors Francois Pachet and Patrick Gallinari", } @Article{Zimmerman:1993:MSSP, author = "David C. Zimmerman", title = "A Darwinian approach to the actuator number and placement problem with non-negligible actuator mass", journal = "Mechanical Systems and Signal Processing", year = "1993", volume = "7", pages = "363--374", number = "4", abstract = "The problem of optimal actuator number and placement for the vibration control of large flexible space structures is addressed. The inherent mass of the actuators is integrated in the number and placement algorithm. The algorithm uses concepts from genetic programming, which is loosely based on Darwin's survival of the fittest theories. The paper develops the genetic algorithm in the context of the actuator number and placement problem. Examples are presented which demonstrate the genetic algorithm and the effect of actuator mass on the placement and number problem.", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6WN1-45P695X-P/2/f57ddb902c8f69ea01737429863c4277", keywords = "genetic algorithms, genetic programming", } @Proceedings{WESC05, title = "Proceedings of the first International Workshop of Engineering Service Compositions, WESC'05", year = "2005", editor = "Christian Zirpins and Guadalupe Ortiz and Winfried Lamersdorf and Wolfgang Emmerich", address = "Amsterdam", month = dec, series = "IBM Research Reports", number = "RC23821 (W0512-008)", keywords = "genetic algorithms, genetic programming", URL = "http://domino.research.ibm.com/library/cyberdig.nsf/papers/DE71563B7B69D362852570D000548D0D/$File/rc23821.pdf", size = "119 pages", notes = "Contains \cite{Aversano:2005:WSEC}", } @InProceedings{zitar:2024:IntelliSys, author = "Raed Abu Zitar and Abdallah Aljasmi and Amal El Fallah Seghrouchni and Frederic Barbaresco", title = "Predicting Heating/Cooling Loads with the Zoetrope Genetic Programming {(ZGP)} Versus Other Machine Learning Methods", booktitle = "Intelligent Systems and Applications", year = "2024", editor = "Kohei Arai", pages = "386--398", address = "Amsterdam, The Netherlands", month = "5-6 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "https://link.springer.com/chapter/10.1007/978-3-031-66336-9_27", DOI = "doi:10.1007/978-3-031-66336-9_27", } @InProceedings{zitzler:1999:EABESSDSP, author = "Eckart Zitzler and Jurgen Teich and Shuvra S. Bhattacharyya", title = "Evolutionary Algorithm Based Exploration of Software Schedules for Digital Signal Processors", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1762--1770", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, real world applications", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-724.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-724.ps", URL = "http://www12.informatik.uni-erlangen.de/publications/pub1999/ZTB99a.ps.gz", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99) Total system modeling, programs represented only at high level within essentially linear GA chromosome. Motorola DSP 56k for example. ", } @Article{Zmuda:2003:ASC, author = "Michael A. Zmuda and Mateen M. Rizki and Louis A. Tamburino", title = "Hybrid evolutionary learning for synthesizing multi-class pattern recognition systems", journal = "Applied Soft Computing", year = "2003", volume = "2", pages = "269--282", number = "4", keywords = "genetic algorithms, genetic programming, Evolutionary computation, Evolutionary programming, Hybrid evolutionary algorithm, Pattern recognition, Classification", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6W86-47DT5VK-2/2/2d9804998b4546170ea1fb3a60909666", ISSN = "1568-4946", DOI = "doi:10.1016/S1568-4946(02)00060-1", abstract = "This paper describes one aspect of a machine-learning system called HELPR that blends the best aspects of different evolutionary techniques to bootstrap-up a complete recognition system from primitive input data. HELPR uses a multi-faceted representation consisting of a growing sequence of non-linear mathematical expressions. Individual features are represented as tree structures and manipulated using the techniques of genetic programming. Sets of features are represented as list structures that are manipulated using genetic algorithms and evolutionary programming. Complete recognition systems are formed in this version of HELPR by attaching the evolved features to multiple perceptron discriminators. Experiments on datasets from the University of California at Irvine (UCI) machine-learning repository show that HELPR's performance meets or exceeds accuracies previously published.", } @Article{Zohara:2005:E, author = "Petra Zohara and Miha Kovacic and Miran Brezocnik and Matej Podbregar", title = "Prediction of maintenance of sinus rhythm after electrical cardioversion of atrial fibrillation by non-deterministic modelling", journal = "Europace", year = "2005", volume = "7", number = "5", pages = "500--507", keywords = "genetic algorithms, genetic programming, atrial fibrillation, electrical cardioversion, prediction", ISSN = "1532-2092", DOI = "doi:10.1016/j.eupc.2005.04.007", abstract = "AIMS: Atrial fibrillation (AF) is the most common rhythm disorder. Because of the high recurrence rate of AF after cardioversion and because of potential side effects of electrical cardioversion, it is clinically important to predict persistence of sinus rhythm after electrical cardioversion before it is attempted. The aim of our study was the development of a mathematical model by 'genetic' programming (GP), a non-deterministic modelling technique, which would predict maintenance of sinus rhythm after electrical cardioversion of persistent AF. PATIENTS AND METHODS: Ninety-seven patients with persistent AF lasting more than 48 h, undergoing the first attempt at transthoracic cardioversion were included in this prospective study. Persistence of AF before the cardioversion attempt, amiodarone treatment, left atrial dimension, mean, standard deviation and approximate entropy of ECG R-R intervals were collected. The data of 53 patients were randomly selected from the database and used for GP modelling; the other 44 data sets were used for model testing. RESULTS: In 23 patients sinus rhythm persisted at 3 months. In the other 21 patients sinus rhythm was not achieved or its duration was less than 3 months. The model developed by GP failed to predict maintenance of sinus rhythm at 3 months in one patient and in six patients falsely predicted maintenance of sinus rhythm. Positive and negative likelihood ratios of the model for testing data were 4.32 and 0.05, respectively. Using this model 15 of 21 (71.4per cent) cardioversions not resulting in sinus rhythm at 3 months would have been avoided, whereas 22 of 23 (95.6per cent) cardioversions resulting in sinus rhythm at 3 months would have been administered. CONCLUSION: This model developed by GP, including clinical data, ECG data from the time-domain and nonlinear dynamics can predict maintenance of sinus rhythm. Further research is needed to explore its utility in the present or an expanded form.", notes = "http://europace.oxfordjournals.org/content/vol7/issue5/index.dtl Cardiology Department, Hospital Celje Slovenia; Laboratory for Intelligent Manufacturing Systems, Faculty of Mechanical Engineering Maribor, Slovenia; Department for Intensive Internal Medicine, General Hospital Celje Oblakova 5 3000 Celje, Slovenia World Congress of Cardiology Copyright 2006 European Heart Rhythm Association of the European Society of Cardiology (ESC)", } @Article{Zojaji:2016:aplint, author = "Zahra Zojaji and Mohammad Mehdi Ebadzadeh", title = "Semantic schema theory for genetic programming", journal = "Applied Intelligence", year = "2016", volume = "44", number = "1", pages = "67--87", month = jan, keywords = "genetic algorithms, genetic programming, Schema theory, Semantic building blocks, Mutual information", ISSN = "1573-7497", URL = "https://rdcu.be/cImfy", DOI = "doi:10.1007/s10489-015-0696-4", size = "21 pages", abstract = "Schema theory is the most well-known model of evolutionary algorithms. Imitating from genetic algorithms (GA), nearly all schemata defined for genetic programming (GP) refer to a set of points in the search space that share some syntactic characteristics. In GP, syntactically similar individuals do not necessarily have similar semantics. The instances of a syntactic schema do not behave similarly, hence the corresponding schema theory becomes unreliable. Therefore, these theories have been rarely used to improve the performance of GP. The main objective of this study is to propose a schema theory which could be a more realistic model for GP and could be potentially employed for improving GP in practice. To achieve this aim, the concept of semantic schema is introduced. This schema partitions the search space according to semantics of trees, regardless of their syntactic variety. We interpret the semantics of a tree in terms of the mutual information between its output and the target. The semantic schema is characterized by a set of semantic building blocks and their joint probability distribution. After introducing the semantic building blocks, an algorithm for finding them in a given population is presented. An extraction method that looks for the most significant schema of the population is provided. Moreover, an exact microscopic schema theorem is suggested that predicts the expected number of schema samples in the next generation. Experimental results demonstrate the capability of the proposed schema definition in representing the semantics of the schema instances. It is also revealed that the semantic schema theorem estimation is more realistic than previously defined schemata.", } @Article{Zojaji:2016:AI, author = "Zahra Zojaji and Behrouz Tork Ladani and Alireza Khalilian", title = "Automated program repair using genetic programming and model checking", journal = "Applied Intelligence", year = "2016", volume = "45", number = "4", pages = "1066--1088", month = dec, keywords = "genetic algorithms, genetic programming, genetic improvement, APR, Automated software repair, Automatic bug fixing, Model checking", ISSN = "1573-7497", DOI = "doi:10.1007/s10489-016-0804-0", URL = "http://dx.doi.org/10.1007/s10489-016-0804-0", size = "23 pages", abstract = "Automated program repair is still a highly challenging problem mainly due to the reliance of the current techniques on test cases to validate candidate patches. This leads to the increasing unreliability of the final patches since test cases are partial specifications of the software. In the present paper, an automated program repair method is proposed by integrating genetic programming (GP) and model checking (MC). Due to its capabilities to verify the finite state systems, MC is employed as an appropriate criterion for evolving programs to calculate the fitness in GP. The application of MC for the fitness evaluation, which is novel in the context of program repair, addresses an important gap in the current heuristic approaches to the program repair. Being focused on fault detection based on the desired aspects, it enables the programmers to detect faults according to the definition of properties. Creating a general method, this characteristic can be effectively customized for different domains of application and the corresponding faults. Apart from various types of faults, the proposed method is capable of handling concurrency bugs which are not the case in many general repair methods. To evaluate the proposed method, it was implemented as a tool, named JBF, to repair Java programs. To meet the objectives of the study, some experiments were conducted in which certain programs with known bugs were automatically repaired by the JBF tool. The obtained results are encouraging and remarkably promising.", } @PhdThesis{zojaji_thesis, author = "Zahra Zojaji", title = "Improving The Performance of Genetic Programming Through Developing Fitness-based Schema Theory", school = "Computer Engineering Department, Amirkabir University of Technology", year = "2017", address = "Tehran, Iran", month = aug, keywords = "genetic algorithms, genetic programming, Schema theory, Building blocks, Locality, Mutual information, Semantic genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/zojaji_thesis.pdf", size = "191 pages", abstract = "Genetic programming (GP) is a subfield of evolutionary computation, in which computer programs evolve to solve a specified problem. Despite the empirical success in various applications, GP is still not known as a reliable problem-solving technique. One of the most important issues of GP is its ineffective search procedure. In contrast to natural evolution, which is based on the gradualism, in GP neither the representation nor the operators are local. This feature, cause GP to have sudden movement in genotype and semantic spaces. We employed schema theory as a tool for investigating and addressing this issue. Schema theory is the most well-known model of evolutionary algorithms. Imitating from genetic algorithms (GA), nearly all schemata defined for GP refer to a set of points in the search space that share some syntactic characteristics. In GP, syntactically similar individuals do not necessarily behave similarly. Hence, there are several major issues with relying on prior schema theories as a behavioral model of GP. As a result, these theories have been rarely applied practically in the literature. In this study, we propose and use the first semantic schema theory for GP. The proposed theory is a more realistic model of GP that could be potentially employed for improving GP in practice. This schema partitions the search space according to semantics of trees, regardless of their syntactic variety. We interpret the semantics of a tree in terms of the mutual information between its output and the target. The semantic schema is characterized by the occurrence of semantic building blocks in promising individuals. These building blocks are introduced and extracted in the proposed semantic space. An extraction method that looks for the most significant schema of the population is provided. Moreover, an exact microscopic schema theorem is suggested that predicts the expected number of schema samples in the next generation. The main purpose of this study is proposing schema based genetic programming (SBGP), which control and guide the GP search. SBGP is equipped with some semantic local operators that bias offspring toward a specfied schema and perform a local search around it. To achieve this aim, initially the significant schema of the population is extracted. Then, predefined local operators are applied to generate offspring. The schema is upgraded incrementally during the evolution. Therefore, local in-schema search is combined with schema evolution as a global search towards the target. In this reaerch, the algorithm is devoted to symbolic regression problems. However, it can be simply extended to other problem types. For evaluating the proposed method, we use both synthetized and real world problems. Experiments are conducted in two sections of schema theory and SBGP. Results demonstrate the capability of the proposed schema definition in both generalization and diversity preserving aspects and the high accuracy of schema theory estimations. The results of second class of experiments indicate that SBGP has overally superior performance in terms of accuracy and generalization, in comparison to other GP versions.", notes = "in Persian Tehran Polytechnic. Improving The Performance of Genetic Programming Through Developing Semantic Schema Theory Supervisor: Mohammad Mehdi Ebadzadeh", } @Article{Zojaji:2018:SC, author = "Zahra Zojaji and Mohammad Mehdi Ebadzadeh", title = "An improved semantic schema modeling for genetic programming", journal = "Soft Computing", year = "2018", volume = "22", number = "10", pages = "3237--3260", month = may, keywords = "genetic algorithms, genetic programming, Schema theory, Semantic building blocks, Mutual information, Semantic genetic programming", ISSN = "1433-7479", URL = "https://doi.org/10.1007/s00500-017-2781-6", DOI = "doi:10.1007/s00500-017-2781-6", abstract = "A considerable research effort has been performed recently to improve the power of genetic programming (GP) by accommodating semantic awareness. The semantics of a tree implies its behaviour during the execution. A reliable theoretical modelling of GP should be aware of the behavior of individuals. Schema theory is a theoretical tool used to model the distribution of the population over a set of similar points in the search space, referred by schema. There are several major issues with relying on prior schema theories, which define schemata in syntactic level. Incorporating semantic awareness in schema theory has been scarcely studied in the literature. In this paper, we present an improved approach for developing the semantic schema in GP. The semantics of a tree is interpreted as the normalized mutual information between its output vector and the target. A new model of the semantic search space is introduced according to semantics definition, and the semantic building block space is p...", } @Article{Zojaji:2018:AI, author = "Zahra Zojaji and Mohammad Mehdi Ebadzadeh", title = "Semantic schema modeling for genetic programming using clustering of building blocks", journal = "Applied Intelligence", year = "2018", volume = "48", number = "6", pages = "1442--1460", month = jun, keywords = "genetic algorithms, genetic programming, Schema theory, Semantic building blocks, Mutual information, Information based clustering", ISSN = "1573-7497", URL = "https://doi.org/10.1007/s10489-017-1052-7", DOI = "doi:10.1007/s10489-017-1052-7", abstract = "Semantic schema theory is a theoretical model used to describe the behaviour of evolutionary algorithms. It partitions the search space to schemata, defined in semantic level, and studies their distribution during the evolution. Semantic schema theory has definite advantages over popular syntactic schema theories, for which the reliability and usefulness are criticized. Integrating semantic awareness in genetic programming (GP) in recent years sheds new light also on schema theory investigations. This paper extends the recent work in semantic schema theory of GP by using information based clustering. To this end, we first define the notion of semantics for a tree based on the mutual information between its output vector and the target and introduce semantic building blocks to facilitate the modeling of semantic schema. Then, we propose information based clustering to cluster the building blocks. Trees are then represented in terms of the active occurrence of building block clusters...", } @Article{Zojaji:2022:ASC, author = "Zahra Zojaji and Mohammad Mehdi Ebadzadeh and Hamid Nasiri", title = "Semantic schema based genetic programming for symbolic regression", journal = "Applied Soft Computing", year = "2022", volume = "122", pages = "108825", month = jun, keywords = "genetic algorithms, genetic programming, Schema theory, Locality, Semantic genetic programming, Symbolic regression", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2022.108825", size = "25 pages", abstract = "Despite the empirical success of Genetic programming (GP) in various symbolic regression applications, GP is not still known as a reliable problem-solving technique in this domain. Non-locality of GP representation and operators causes ineffectiveness of its search procedure. This study employs semantic schema theory to control and guide the GP search and proposes a local GP called semantic schema-based genetic programming (SBGP). SBGP partitions the semantic search space into semantic schemas and biases the search to the significant schema of the population, which is gradually progressing towards the optimal solution. Several semantic local operators are proposed for performing a local search around the significant schema. In combination with schema evolution as a global search, the local in-schema search provides an efficient exploration-exploitation control mechanism in SBGP. For evaluating the proposed method, we use six benchmarks, including synthesised and real-world problems. The obtained errors are compared to the best semantic genetic programming algorithms, on the one hand, and data-driven layered learning approaches, on the other hand. Results demonstrate that SBGP outperforms all mentioned methods in four out of six benchmarks up to 87percent in the first set and up to 76percent in the second set of experiments in terms of generalisation measured by root mean squared error.", notes = "also known as \cite{ZOJAJI2022108825} Pollen, Concrete, Bioavailability, Toxicity, UBall5D, RatPol2D Department of Computer Engineering, University of Isfahan, Isfahan, Iran", } @InCollection{zomorodian:1994:, author = "Afra Zomorodian", title = "Context-Free Language Induction by Evolution of Deterministic Push-Down Automata Using Genetic Programming", booktitle = "Genetic Algorithms at Stanford 1994", year = "1994", editor = "John R. Koza", pages = "184--193", address = "Stanford, California, 94305-3079 USA", month = dec, organisation = "Stanford University", publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-187263-3", notes = "See also \cite{zomorodian:1995:cfli}. This volume contains 20 papers written and submitted by students describing their term projects for the course {"}Genetic Algorithms and Genetic Programming{"} (Computer Science 426) at Stanford University offered during the fall quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs426.html", } @InProceedings{zomorodian:1995:cfli, author = "Afra Zomorodian", title = "Context-Free Language Induction by Evolution of Deterministic Push-Down Automata Using Genetic Programming", booktitle = "Working Notes for the AAAI Symposium on Genetic Programming", year = "1995", editor = "E. V. Siegel and J. R. Koza", pages = "127--133", address = "MIT, Cambridge, MA, USA", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025, USA", month = "10--12 " # nov, publisher = "AAAI", keywords = "genetic algorithms, genetic programming", isbn13 = "978-0-929280-92-9", URL = "http://www.aaai.org/Papers/Symposia/Fall/1995/FS-95-01/FS95-01-019.pdf", URL = "http://www.cs.dartmouth.edu/~afra/papers/aaai96/aaai96.pdf", URL = "http://www.aaai.org/Library/Symposia/Fall/fs95-01.php", size = "7 pages", abstract = "The process of learning often consists of Inductive Inference, making generalisations from samples. The problem here is finding generalizations (Grammars) for Formal Languages from finite sets of positive and negative sample sentences. The focus of this paper is on Context-Free Languages (CFLs) as defined Context-Free Grammars (CFGs), some of which are accepted by Deterministic Push-Down Automata (D-PDA). This paper describes a meta-language for constructing D-PDAs. This language is then combined with Genetic Programming to evolve D-PDAs which accept languages. The technique is illustrated with two favourite CFLs.", notes = "AAAI-95f GP. Part of \cite{siegel:1995:aaai-fgp} {\em Telephone:} 415-328-3123 {\em Fax:} 415-321-4457 {\em email} info@aaai.org {\em URL:} http://www.aaai.org/", } @TechReport{zonger:1996:lilgp, author = "Douglas Zongker and Bill Punch", title = "lilgp 1.01 User's Manual", institution = "Michigan State University", year = "1996", address = "USA", month = "26 " # mar, keywords = "genetic algorithms, genetic programming", broken = "ftp://garage.cps.msu.edu/pub/GA/lilgp/lilgp1.02.ps", URL = "http://citeseer.ist.psu.edu/298727.html", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/14524/ftp:zSzzSzgarage.cps.msu.eduzSzpubzSzGAzSzlilgpzSzlilgp1.02.pdf/lil-gp-user-s.pdf", broken = "http://garage.cse.msu.edu/software/lil-gp/", code_url = "http://www.genetic-programming.com/c2003lilgpwebpagedarren.html", code_url = "https://www.cosc.brocku.ca/Offerings/5P71/", code_url = "http://parallel.vub.ac.be/~johan/Projects/Source/", size = "62 pages", } @InProceedings{Zoppi:2022:CEC, author = "Giacomo Zoppi and Leonardo Vanneschi and Mario Giacobini", booktitle = "2022 IEEE Congress on Evolutionary Computation (CEC)", title = "Reducing the Number of Training Cases in Genetic Programming", year = "2022", editor = "Carlos A. Coello Coello and Sanaz Mostaghim", address = "Padua, Italy", month = "18-23 " # jul, keywords = "genetic algorithms, genetic programming, Training, Boolean functions, Machine learning, Evolutionary computation, Benchmark testing, Data models", isbn13 = "978-1-6654-6708-7", DOI = "doi:10.1109/CEC55065.2022.9870327", abstract = "In the field of Machine Learning, one of the most common and discussed questions is how to choose an adequate number of data observations, in order to train our models satisfactorily. In other words, find what is the right amount of data needed to create a model, that is neither underfitted nor overfitted, but instead is able to achieve a reasonable generalization ability. The problem grows in importance when we consider Genetic Programming, where fitness evaluation is often rather slow. Therefore, finding the minimum amount of data that enables us to discover the solution to a given problem could bring significant benefits. Using the notion of entropy in a dataset, we seek to understand the information gain obtainable from each additional data point. We then look for the smallest percentage of data that corresponds to enough information to yield satisfactory results. We present, as a first step, an example derived from the state of art. Then, we question a relevant part of our procedure and introduce two case studies to experimentally validate our theoretical hypothesis.", notes = "Also known as \cite{9870327} Entropy gives 'a rough lower bound for the number of fitness cases to be used.' sec IV.A 7 input (only 3 used) Boolean \cite{giacobini:ppsn2002:pp371}. Suggests fraction of fitness cases randomly selected from all possible fitness cases to be used each generation should exceed (known!) entropy of target function/log(N). I.e. T > NH/log(N). UCI: Car Evaluation Data Set Sec IV.F more intelligent sampling of test cases. 'provide ... critical points of the' benchmark.", } @InProceedings{DBLP:conf/icse/ZouWXZSM15, author = "Daming Zou and Ran Wang and Yingfei Xiong and Lu Zhang and Zhendong Su and Hong Mei", title = "A Genetic Algorithm for Detecting Significant Floating-Point Inaccuracies", booktitle = "37th {IEEE/ACM} International Conference on Software Engineering, ICSE 2015", year = "2015", editor = "Antonia Bertolino and Gerardo Canfora and Sebastian G. Elbaum", volume = "1", pages = "529--539", address = "Florence, Italy", month = may # " 16-24", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, SBSE", URL = "https://doi.org/10.1109/ICSE.2015.70", DOI = "doi:10.1109/ICSE.2015.70", timestamp = "Thu, 15 Jun 2017 21:42:45 +0200", biburl = "https://dblp.org/rec/bib/conf/icse/ZouWXZSM15", bibsource = "dblp computer science bibliography, https://dblp.org", size = "11 pages", abstract = "It is well-known that using floating-point numbers may inevitably result in inaccurate results and sometimes even cause serious software failures. Safety-critical software often has strict requirements on the upper bound of inaccuracy, and a crucial task in testing is to check whether significant inaccuracies may be produced. The main existing approach to the floating-point inaccuracy problem is error analysis, which produces an upper bound of inaccuracies that may occur. However, a high upper bound does not guarantee the existence of inaccuracy defects, nor does it give developers any concrete test inputs for debugging. In this paper, we propose the first metaheuristic search-based approach to automatically generating test inputs that aim to trigger significant inaccuracies in floating-point programs. Our approach is based on the following two insights: (1) with FPDebug, a recently proposed dynamic analysis approach, we can build a reliable fitness function to guide the search; (2) two main factors - the scales of exponents and the bit formations of significands - may have significant impact on the accuracy of the output, but in largely different ways. We have implemented and evaluated our approach over 154 real-world floating-point functions. The results show that our approach can detect significant inaccuracies in the subjects.", notes = "uses genetic programming to generate test input for floating-point programs", } @Article{Zou:2025:ijimpeng, author = "Delei Zou and Dilyar Thoti and Zhihui Bao", title = "Study on the application of discrepancy-guided symbolic regression algorithm in analyzing the impact resistance of {UHP-SFRC} target against high velocity projectile impact", journal = "International Journal of Impact Engineering", year = "2025", volume = "201", pages = "105276", keywords = "genetic algorithms, genetic programming, UHP-SFRC targets, High-velocity projectile impacts, Empirical formulas, DOP, Symbolic regression, Discrepancy-guided", ISSN = "0734-743X", URL = "https://www.sciencedirect.com/science/article/pii/S0734743X25000570", DOI = "doi:10.1016/j.ijimpeng.2025.105276", abstract = "Accurately predicting the depth of penetration (DOP) values for ultra-high-performance steel fiber-reinforced concrete (UHP-SFRC) targets under high-velocity projectile impact (HVPI) is crucial to assess damage patterns and maintain the integrity of protective structures. However, traditional empirical formulas often fall short, resulting in significant underestimations. To address these inaccuracies and inefficiencies, this study establishes a discrepancy-guided, H2O Auto-ML-assisted Offspring Selection Genetic Programming-Symbolic Regression (OSGP-SR) algorithm to rectify traditional empirical formulas and markedly improve the accuracy and robustness of DOP predictions. Four widely used empirical formulations for predicting DOP in traditional reinforced concrete structures, i.e., BRL, NDRC, ACE, and AW were selected as optimisation objectives. A dataset comprising 265 samples of UHP-SFRC targets subjected to HVPI was created for verification and analysis. An examination of the prediction deviations in the original formulations informed an optimisation strategy based on discrepancy-guided analysis. Moreover, the identification of key feature variables was conducted through feature contribution screening, and the proposed algorithm based on discrepancy-guided was used to refine the existing formulas. Results indicate that the modified NDRC model achieves the lowest mean absolute percentage error (MAPE), while the BRL model excels in comprehensive evaluation, maintaining accuracy regardless of coarse aggregate presence. Furthermore, a graphical user interface (GUI) for engineering applications is provided", } @InProceedings{bb51319, author = "Xiaotao Zou and Bir Bhanu", title = "Human Activity Classification Based on Gait Energy Image and Coevolutionary Genetic Programming", booktitle = "18th International Conference on Pattern Recognition (ICPR'06)", year = "2006", pages = "556--559", editor = "Yuan Yan Tang and Patrick Wang and G. Lorette and Daniel So Yeung", volume = "III", address = "Hong Kong", month = "20-24 " # aug, organisation = "ICPR", publisher = "IEEE", bibsource = "http://iris.usc.edu/Vision-Notes/bibliography/motion-f730.html#TT48462", keywords = "genetic algorithms, genetic programming, coevolution", DOI = "doi:10.1109/ICPR.2006.633", abstract = "In this paper, we present a novel approach based on gait energy image (GEI) and co-evolutionary genetic programming (CGP) for human activity classification. Specifically, Hu's moment and normalized histogram bins are extracted from the original GEIs as input features. CGP is employed to reduce the feature dimensionality and learn the classifiers. The strategy of majority voting is applied to the CGP to improve the overall performance in consideration of the diversification of genetic programming. This learningbased approach improves the classification accuracy by approximately 7 percent in comparison to the traditional classifiers.", notes = "author is Xiaoli Zhou? http://www.comp.hkbu.edu.hk/~icpr06/accepted.php?track=2", } @Article{Zounemat-Kermani:2025:ecohyd, author = "Mohammad Zounemat-Kermani and Soudabeh {Golestani Kermani} and Marzieh Fadaee and Ammar Aldallal and Ozgur Kisi and Abdollah Ramezani-Charmahineh", title = "Modeling river water dissolved organic matter using ensemble computing and genetic programming techniques", journal = "Ecohydrology and Hydrobiology", year = "2025", volume = "25", number = "2", pages = "292--302", month = apr, keywords = "genetic algorithms, genetic programming, gene expression programming, Committee learning, Environmental modeling, Artificial intelligence, Soft computing, River flow", ISSN = "1642-3593", URL = "https://www.sciencedirect.com/science/article/pii/S1642359324000430", DOI = "doi:10.1016/j.ecohyd.2024.04.003", abstract = "Dissolved organic matter (DOM) plays a diverse role in aquatic ecosystems and is a key participant in global carbon budgets; thus, precise simulation and modelling of DOM concentrations in rivers and streams is critical in hydro-environmental projects. Among various modelling strategies, data-driven Machine Learning (ML) approaches - and particularly Ensemble Machine Learning (EML) models - have proven their fair capabilities in simulating environmental issues in aquatic media based on the information available from a limited number of physicochemical and biological parameters of water. In this regard, several MLs (such as Support Vector Regression, SVR, and Extreme Learning Machine, ELM), two EMLs (e.g., Random Forests, RF, and Boosted Trees, BTs), as well as Gene Expression Programming (GEP), are evaluated for predicting fluorescent Dissolved Organic Matter (fDOM) in the Caloosahatchee River in Florida. The modelling strategy of fDOM was based on constructing regular and ensemble ML models using seven quantitative and qualitative independent parameters (flow rate, temperature, specific conductance, dissolved oxygen, pH, turbidity, and nitrate recorded from 2017 to 2019) after all being introduced as influential parameters on the target variable (fDOM) using the best subset regression technique. Based on the k-fold cross-validation method (k = 4), the applied regular MLs (SVR, ELM, and GEP) provided better performance than the traditional multiple linear regression model (on average, 6.8 percent improvement in RMSE). However, the results showed that the EML models (RF and BT) outperformed the regular MLs (on average, 7.2 percent improvement in RMSE) in fDOM prediction", } @Article{Zubi:2010:IJOC, author = "Zakaria Suliman Zubi and Marim Aboajela Emsaed", title = "Using sequence {DNA} chips data to Mining and Diagnosing Cancer Patients", journal = "International Journal of Computers", year = "2010", volume = "4", number = "4", month = "201--214", keywords = "genetic algorithms, genetic programming, DNA micro-array, Data Mining, Sequence Mining, Biological Database, Clustering, Classification, K-means.", ISSN = "1998-4308", URL = "http://www.naun.org/journals/computers/19-437.pdf", URL = "http://www.naun.org/main/NAUN/computers/2010.htm", size = "14 pages", abstract = "Deoxyribonucleic acid (DNA) micro-arrays present a powerful means of observing thousands of gene terms levels at the same time. They consist of high dimensional datasets, which challenge conventional clustering methods. The data's high dimensionality calls for Self Organizing Maps (SOMs) to cluster DNA micro-array data. The DNA micro-array data set are stored in huge biological databases for several purposes [1]. The proposed methods are based on the idea of selecting a gene subset to distinguish all classes, it will be more effective to solve a multi-class problem, and we will propose a genetic programming (GP) based approach to analyse multi-class micro-array datasets. This biological dataset will be derived from multiple biological databases. The procedure responsible for extracting datasets called DNA-Aggregator. We will design a biological aggregator, which aggregates various datasets via DNA micro-array community-developed ontology based upon the concept of semantic Web for integrating and exchanging biological data. Our aggregator is composed of modules that retrieve the data from various biological databases. It will also enable queries by other applications to recognise the genes. The genes will be categorised in groups based on a classification method, which collects similar expression patterns. Using a clustering method such as k-mean is required either to discover the groups of similar objects from the biological database to characterize the underlying data distribution.", notes = "see also acmid = {1895290} \cite{Zubi:2010:SMD:1895260.1895290} Sirte University, Faculty of Science, Computer Science Department, Sirte, P.O Box 727, Libya, Alfateh University, Faculty of Science, Computer Science Department, Tripoli, Libya, P,O Box 13210, http://www.naun.org/journals/computers/", } @Article{Zuhanda:2024:dajour, author = "Muhammad Khahfi Zuhanda and Hartono and Samsul A. Rahman Sidik Hasibuan and Yose Yefta Napitupulu", title = "An exact and metaheuristic optimization framework for solving Vehicle Routing Problems with Shipment Consolidation using population-based and Swarm Intelligence", journal = "Decision Analytics Journal", year = "2024", volume = "13", pages = "100517", keywords = "genetic algorithms, genetic programming, Vehicle Routing Problem, Shipment Consolidation, Artificial Immune System, Integer Programming", ISSN = "2772-6622", URL = "https://www.sciencedirect.com/science/article/pii/S2772662224001218", DOI = "doi:10.1016/j.dajour.2024.100517", abstract = "The Vehicle Routing Problem with Shipment Consolidation (VRPSC) is a novel variation of the vehicle routing problem that involves multiple commodities, multiple dimensions, a fleet with different types of vehicles, and the challenge of consolidating shipments during the route. Exact algorithms have been suggested to solve the VRPSC problems. Besides exact algorithms, specific metaheuristic algorithms are employed to deliver solutions of superior quality, albeit not necessarily optimal. The Artificial Immune System (AIS) and Genetic Algorithm (GA) are the metaheuristic optimisation techniques applied in this research. Genetic programming is included in evolutionary computing, while AIS is included in swarm intelligence. This research presents a vehicle routing model with product consolidation and different product dimensions. These criteria were selected due to their significant impact on the complexity of mathematical problem-solving in VRPSC. The results from applying these metaheuristic algorithms will be compared with those of exact algorithms to compare and analyse different VRPSC solution approaches", } @InProceedings{Zulu:2023:SICE, author = "Ananias Cyrus Zulu and Victor Parque and Mahmoud Soliman and Ahmed M. R Fathelbab", booktitle = "2023 62nd Annual Conference of the Society of Instrument and Control Engineers (SICE)", title = "Using Sub-Micron Spiral Microfluidics and Genetic Programming towards Enhancing Urine-based Glucose Analysis", year = "2023", pages = "1303--1308", abstract = "Improving the accuracy and precision of light spectroscopy-based non-invasive methods for analysing glucose in urine samples is critical to enabling comfortable, affordable, and robust approaches to diagnosing diabetes. In this paper, we study the feasibility of using spiral microfluidics in studying the separation efficiency in urine simulations. Our results through computational simulations and hardware experiments with sub-micron microfluidics show: (1) the reasonable separation of glucose/urea at 79percent glucose separation efficiency, (2) the possibility of relating flow rate and glucose/urea concentration in a closed mathematical form by using Genetic Programming machinery, and (3) the feasibility to render the quasi-linear pattern/relation between glucose concentration and absorbance, which is potential to aid in providing higher/targeted concentrations of glucose for other non-invasive testing approaches, e.g., light spectroscopy.", keywords = "genetic algorithms, genetic programming, Performance evaluation, Spectroscopy, Spirals, Computational modelling, Mathematical models, Hardware, Infrared, Near Infrared (NIR), Microfluidics, Spectroscopy, Particle Separation, Glucose Measurements", DOI = "doi:10.23919/SICE59929.2023.10354180", month = sep, notes = "Also known as \cite{10354180}", } @InCollection{series/sci/Zuniga-NunezCSSPOD20, author = "B. V. Zuniga-Nunez and Juan Martin Carpio and Marco Aurelio Sotelo-Figueroa and Jorge Alberto Soria-Alcaraz and O. J. Purata-Sifuentes and Manuel Ornelas and Alfonso Rojas Dominguez", title = "Studying Grammatical Evolution's Mapping Processes for Symbolic Regression Problems", booktitle = "Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications", publisher = "Springer", year = "2020", volume = "862", editor = "Oscar Castillo and Patricia Melin and Janusz Kacprzyk", pages = "445--459", series = "Studies in Computational Intelligence", keywords = "genetic algorithms, genetic programming, grammatical evolution", isbn13 = "978-3-030-35444-2", bibdate = "2020-03-06", bibsource = "DBLP, http://dblp.uni-trier.de/db/series/sci/sci862.html#Zuniga-NunezCSSPOD20", DOI = "doi:10.1007/978-3-030-35445-9_32", } @Article{journals/jamris/Zuniga-NunezCSE20, author = "Blanca Veronica Zuniga-Nunez and Juan Martin Carpio and Marco Aurelio Sotelo-Figueroa and Andres Espinal and Omar Jair Purata-Sifuentes and Manuel Ornelas and Jorge Alberto Soria-Alcaraz and Alfonso Rojas Dominguez", title = "Exploring Random Permutations Effects on the Mapping Process for Grammatical Evolution", journal = "Journal of Automation, Mobile Robotics and Intelligent Systems", year = "2020", volume = "14", number = "1", pages = "65--72", month = jul, keywords = "genetic algorithms, genetic programming, grammatical evolution, mapping process, symbolic regression", ISSN = "1897-8649", bibdate = "2021-10-14", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/jamris/jamris14.html#Zuniga-NunezCSE20", URL = "https://www.jamris.org/index.php/JAMRIS/article/view/541", URL = "https://www.jamris.org/index.php/JAMRIS/article/view/541/541", DOI = "doi:10.14313/JAMRIS/1-2020/8", size = "8 pages", abstract = "Grammatical Evolution (GE) is a form of Genetic Programming (GP) based on Context-Free Grammar (CF Grammar). Due to the use of grammars, GE is capable of creating syntactically correct solutions. GE uses a genotype encoding and is necessary to apply a Mapping Process (MP) to obtain the phenotype representation. There exist some well-known MPs in the state-of-art like Breadth-First (BF), Depth-First (DF), among others. These MPs select the codons from the genotype in a sequential manner to do the mapping. The present work proposes a variation in the selection order for genotypes codons; to achieve that, it is applied a random permutation for the genotype codons order-taking in the mapping. The proposal results were compared using a statistical test with the results obtained by the traditional BF and DF using the Symbolic Regression Problem (SRP) as a benchmark.", notes = "J. Autom. Mob. Robotics Intell. Syst office@jamris.org PIAP", } @InProceedings{DBLP:conf/waim/ZuoTZ02, author = "Jie Zuo and Changjie Tang and Tianqing Zhang", title = "Mining Predicate Association Rule by Gene Expression Programming", booktitle = "Advances in Web-Age Information Management, Third International Conference, WAIM 2002", year = "2002", pages = "92--103", bibsource = "DBLP, http://dblp.uni-trier.de", editor = "Xiaofeng Meng and Jianwen Su and Yujun Wang", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "2419", ISBN = "3-540-44045-3", address = "Beijing, China", month = "11-13 " # aug, keywords = "genetic algorithms, genetic programming, gene expression programming, Predicate association rule, Chromosome, Fitness", DOI = "doi:10.1007/3-540-45703-8_9", abstract = "Association rule mining is a typical task in data mining. Predicate Association (PA) is introduced and a new method to discover PA by GEP, called PAGEP (mining Predicate Association by GEP), is proposed. Main results are: (1) The inherent weaknesses of traditional association (TA) are explored. It is proved that TA is a special case of PA. (2) The algorithms for mining PAR, decoding chromosome and fitness are proposed and implemented. (3) It is also proved that gene decoding procedure always success for any well-defined gene. (4) Extensive experiments are given to demonstrate that PAGEP can discover some association rule that cannot be expressed and discovered by traditional method.", notes = "Computer Department, Sichuan University China", } @InProceedings{DBLP:conf/waim/ZuoTLYC04, author = "Jie Zuo and Changjie Tang and Chuan Li and Chang-an Yuan and An-long Chen", title = "Time Series Prediction Based on Gene Expression Programming", booktitle = "Advances in Web-Age Information Management: 5th International Conference, WAIM 2004", year = "2004", pages = "55--64", bibsource = "DBLP, http://dblp.uni-trier.de", editor = "Qing Li and Guoren Wang and Ling Feng", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3129", ISBN = "3-540-22418-1", address = "Dalian, China", month = "15-17 " # jul, keywords = "genetic algorithms, genetic programming, gene expression programming, Time Series Data Processing", DOI = "doi:10.1007/978-3-540-27772-9_7", abstract = "Two novel methods for Time Series Prediction based on GEP (Gene Expression Programming). The main contributions include: (1) GEP-Sliding Window Prediction Method (GEP-SWPM) to mine the relationship between future and historical data directly. (2) GEP-Differential Equation Prediction Method (GEP-DEPM) to mine ordinary differential equations from training data, and predict future trends based on specified initial conditions. (3) A brand new equation mining method, called Differential by Microscope Interpolation (DMI) that boosts the efficiency of our methods. (4) A new, simple and effective GEP-constants generation method called Meta-Constants (MC) is proposed. (5) It is proved that a minimum expression discovered by GEP-MC method with error not exceeding delta/2 uses at most log3(2L/delta) operators and the problem to find delta-accurate expression with fewer operators is NP-hard. Extensive experiments on real data sets for sun spot prediction show that the performance of the new method is 20-900 times higher than existing algorithms.", notes = "Computer Science Department, Sichuan University, Chengdu, Sichuan, China, 610065", } @InProceedings{Zuo:2022:GI, author = "Shengjie Zuo and Aymeric Blot and Justyna Petke", title = "Evaluation of Genetic Improvement Tools for Improvement of Non-functional Properties of Software", booktitle = "GI @ GECCO 2022", year = "2022", editor = "Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and W. B. Langdon and Justyna Petke", pages = "1956--1965", address = "Boston, USA", publisher_address = "New York, NY, USA", month = "9 " # jul, organisation = "SIGEVO", publisher = "ACM", note = "Winner Best Paper", keywords = "genetic algorithms, genetic programming, genetic improvement, survey, tooling, non-functional properties, NFP", isbn13 = "978-1-4503-9268-6/22/07", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/gecco2022/gi2022/papers/Zuo_2022_GI.pdf", DOI = "doi:10.1145/3520304.3534004", code_url = "http://geneticimprovementofsoftware.com/learn/survey", size = "10 pages", abstract = "Genetic improvement (GI) improves both functional properties of software, such as bug repair, and non-functional properties, such as execution time, energy consumption, or source code size. There are studies summarising and comparing GI tools for improving functional properties of software; however there is no such study for improvement of its non-functional properties using GI. Therefore, this research aims to survey and report on the existing GI tools for improvement of non-functional properties of software. We conducted a literature review of available GI tools, and ran multiple experiments on the found open-source tools to examine their usability. We applied a cross-testing strategy to check whether the available tools can work on different programs. Overall, we found 63 GI papers that use a GI tool to improve non-functional properties of software, within which 31 are accompanied with open-source code. We were able to successfully run nine GI tools, and found that ultimately only two, Gin and PyGGI, can be readily applied to new general software.", notes = "http://geneticimprovementofsoftware.com/events/gecco2022 (1) Testing Gin with SAT4J, which is the software improved byPyGGI 2.0 in previous work \cite{blot:2021:tevc} (2) Testing PyGGI 2.0 with Gson, which is the software improvedby Gin in previous work \cite{Petke:2019:SSBSE} (3) Testing LocoGP with Gson. (4) Testing the tool for shader simplification with MiniSAT,which is the software used in previous work on a GISMO-based tool \cite{Petke:2014:EuroGP} (5) Testing the GISMO-based tool with RNAfold, which is the software improved by GGGP in previous work \cite{Langdon:2017:GI} (6) Testing the tool for OpenCV with MiniSAT. (7) Testing GGGP with MiniSAT. (8) Testing PowerGAUGE with MiniSAT https://github.com/gintool/gin GIN Maven Gradle https://github.com/coinse/pyggi LocoGP https://github.com/fabianishere/shadevolution GISMO OpenCV GGGP PowerGAUGE 'Gin and PyGGI are the only two GI tools are application-agnostic and can be easily applied to improve new software.' GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @InProceedings{Zuo:2022:ITW, author = "Xiangwu Zuo and Anxiao Andrew Jiang and Netanel Raviv and Paul H. Siegel", booktitle = "2022 IEEE Information Theory Workshop (ITW)", title = "Symbolic Regression for Data Storage with Side Information", year = "2022", pages = "208--213", month = nov, keywords = "genetic algorithms, genetic programming, symbolic regression, data storage, side information, deep learning", DOI = "doi:10.1109/ITW54588.2022.9965879", size = "6 pages", abstract = "There are various ways to use machine learning to improve data storage techniques. In this paper, we introduce symbolic regression, a machine-learning method for recovering the symbolic form of a function from its samples. We present a new symbolic regression scheme that uses side information for higher accuracy and speed in function recovery. The scheme enhances latest results on symbolic regression that were based on recurrent neural networks and genetic programming. The scheme is tested on a new benchmark of functions for data storage.", notes = "Also known as \cite{9965879}", } @Article{Zuo:2025:jfds, author = "Xiangwu Zuo and Anxiao (Andrew) Jiang", title = "Symbolic Modeling for financial asset pricing", journal = "The Journal of Finance and Data Science", year = "2025", volume = "11", pages = "100150", keywords = "genetic algorithms, genetic programming, Symbolic regression, Symbolic modeling, Financial data, Asset pricing, Deep learning, ANN", ISSN = "2405-9188", URL = "https://www.sciencedirect.com/science/article/pii/S2405918825000029", DOI = "doi:10.1016/j.jfds.2025.100150", abstract = "Symbolic Modeling' in this work, can be seen as a generalisation of Symbolic Regression and has important applications to both academia and industry. This work elucidates Symbolic Modeling and unveils a cutting-edge algorithm, deriving its principles from deep learning and genetic programming. This algorithm is implemented into an application, showcasing its practical utility in the field of financial asset pricing, an integral facet of finance that concentrates on asset valuation. It is shown that Symbolic Modeling compares favorably to existing asset pricing models in multiple aspects", } @Article{ZUPANCIC:2020:Energy, author = "Jernej Zupancic and Bogdan Filipic and Matjaz Gams", title = "Genetic-programming-based multi-objective optimization of strategies for home energy-management systems", journal = "Energy", volume = "203", pages = "117769", year = "2020", ISSN = "0360-5442", DOI = "doi:10.1016/j.energy.2020.117769", URL = "http://www.sciencedirect.com/science/article/pii/S0360544220308768", keywords = "genetic algorithms, genetic programming, Home energy-management system (HEMS), Multi-objective optimization, Tree-based strategy, Timetable-based strategy, Multi-objective reinforcement learning (MORL)", abstract = "Home energy-management systems can optimize performance either by computing the next step dynamically - online, or rely on a precomputed strategy used to introduce the next decision - offline. Further, such systems can optimize based on only one or several objectives. In this paper, the multi-objective optimization of offline strategies for home energy-management systems is addressed. Two approaches are compared: the common timetable-based versus our approach based on decision trees. The timetable-based strategy is optimized using a multi-objective genetic algorithm, while the tree-based strategy is optimized using multi-objective genetic programming. As a result, a set of rules that comprise the trees for efficient management of an energy system is generated automatically. First, the approaches are addressed theoretically, with the finding that the tree-based approach is more powerful than the timetable-based approach. Second, the performance of the tree-based approach is compared with the performance of the timetable-based approach and manually defined strategies in an experiment involving real-world data. A performance increase of up to 17percent in terms of the cost objective was confirmed for the tree-based approach. This is achieved without changing the user habits, i.e., there is no need of having to adapt the appliance usage to the energy-management system", } @Article{Zuperl:2016:PE, author = "U. Zuperl and F. Cus", title = "Surface roughness fuzzy inference system within the control simulation of end milling", journal = "Precision Engineering", volume = "43", pages = "530--543", year = "2016", ISSN = "0141-6359", DOI = "doi:10.1016/j.precisioneng.2015.09.019", URL = "http://www.sciencedirect.com/science/article/pii/S0141635915001804", abstract = "This paper presents a surface roughness control of end milling with associated simulation block diagram. The objective of the proposed surface roughness control is to assure the desired surface roughness by adjusting the cutting parameters and maintaining the cutting force constant. For simulation purposes an experimentally validated surface roughness control simulator is employed. Its structure combines genetic programming (GP), neural network (NN) and adaptive neuro fuzzy inference system (ANFIS) based models. Surface roughness control simulator simulates the surface roughness of the part by enabling the regulation of cutting force. The focus of this research is to develop a reliable method to predict surface roughness average during end milling process. An ANFIS is applied to predict the effect of cutting parameters (spindle speed, feed rate and axial/radial depth of cut) and cutting force signals on surface roughness. Machining experiments conducted using the proposed method indicate that using an appropriate cutting force signals, the surface roughness can be predicted within 3percent of the actual surface roughness for various end-milling conditions. Simulation results are presented to confirm the efficiency of a control model.", keywords = "genetic algorithms, genetic programming, End milling, Surface roughness, Prediction, Control simulation, ANFIS, Neural network, GP modelling", } @InProceedings{Zutty:2015:GECCO, author = "Jason Zutty and Daniel Long and Heyward Adams and Gisele Bennett and Christina Baxter", title = "Multiple Objective Vector-Based Genetic Programming Using Human-Derived Primitives", booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation", year = "2015", editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr", isbn13 = "978-1-4503-3472-3", pages = "1127--1134", keywords = "genetic algorithms, genetic programming", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739480.2754694", DOI = "doi:10.1145/2739480.2754694", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Traditional genetic programming only supports the use of arithmetic and logical operators on scalar features. The GTMOEP (Georgia Tech Multiple Objective Evolutionary Programming) framework builds upon this by also handling feature vectors, allowing the use of signal processing and machine learning functions as primitives, in addition to the more conventional operators. GTMOEP is a novel method for automated, data-driven algorithm creation, capable of outperforming human derived solutions. As an example, GTMOEP was applied to the problem of predicting how long an emergency responder can remain in a hazmat suit before the effects of heat stress cause the user to become unsafe. An existing third-party physics model was leveraged for predicting core temperature from various situational parameters. However, a sustained high heart rate also means that a user is unsafe. To improve performance, GTMOEP was evaluated to predict an expected pull time, computed from both thresholds during human trials. GTMOEP produced dominant solutions in multiple objective space to the performance of predictions made by the physics model alone, resulting in a safer algorithm for emergency responders to determine operating times in harsh environments. The program generated by GTMOEP will be deployed to a mobile application for their use.", notes = "Also known as \cite{2754694} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)", } @InProceedings{Zutty:2016:GECCOcomp, author = "Jason Zutty and Daniel Long and Gregory Rohling", title = "Increasing the Throughput of Expensive Evaluations Through a Vector Based Genetic Programming Framework", booktitle = "GECCO 2016 Late-Breaking Abstracts", year = "2016", editor = "Francisco Chicano and Tobias Friedrich and Frank Neumann and Andrew M. Sutton and Martin Middendorf and Xiaodong Li and Emma Hart and Mengjie Zhang and Youhei Akimoto and Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and Daniele Loiacono and Julian Togelius and Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and Faustino Gomez and Carlos M. Fonseca and Heike Trautmann and Alberto Moraglio and William F. Punch and Krzysztof Krawiec and Zdenek Vasicek and Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and Boris Naujoks and Enrique Alba and Gabriela Ochoa and Simon Poulding and Dirk Sudholt and Timo Koetzing", pages = "1477--1478", keywords = "genetic algorithms, genetic programming", month = "20-24 " # jul, organisation = "SIGEVO", address = "Denver, USA", publisher = "ACM", publisher_address = "New York, NY, USA", isbn13 = "978-1-4503-4323-7", DOI = "doi:10.1145/2908961.2931641", abstract = "traditional genetic programming only supports the use of arithmetic and logical operators on scalar features. The GTMOEP (Georgia Tech Multiple Objective Evolutionary Programming) framework builds upon this by also handling feature vectors, allowing the use of signal processing and machine learning functions as primitives, in addition to the more conventional operators [6]. GTMOEP is a novel method for automated, data-driven algorithm creation, capable of outperforming human derived solutions. A challenge in this field is working with both large datasets and expensive primitive functions. This paper outlines some of the innovations Zutty et al. have introduced into the GTMOEP framework in order to more efficiently evaluate individuals and tackle new problems. These innovations include: Working with non-feature data, tiered datasets, subtree caches, and initial population creation.", notes = "Distributed at GECCO-2016.", } @PhdThesis{ZUTTY-DISSERTATION-2018, author = "Jason Paul Zutty", title = "Automated machine learning: A biologically inspired approach", school = "School of Electrical and Computer Engineering, Georgia Institute of Technology", year = "2018", address = "USA", month = dec, keywords = "genetic algorithms, genetic programming, EMADE", URL = "http://hdl.handle.net/1853/60768", URL = "https://smartech.gatech.edu/bitstream/handle/1853/60768/ZUTTY-DISSERTATION-2018.pdf", size = "197 pages", abstract = "Machine learning is a robust process by which a computer can discover characteristics of underlying data that enable it to create a model for making future predictions or classifications from new data. Designing machine learning pipelines, unfortunately, is often as much an art as it is a science, requiring pairing of feature construction, feature selection, and learning methods, all with their own sets of parameters. No general machine learning pipeline solution exists; each dataset has unique characteristics that make a particular set of methods and parameters better suited to solving the problem than others. To respond to the challenge of machine learning pipeline design, the field of automated machine learning (autoML) has recently emerged. AutoML seeks to automate the often arduous work of a data scientist, so they can focus on the underlying meanings of the data and spend less time on the tedium of pipeline design and tuning. This dissertation adapts and applies genetic programming to the newly emergent field of automated machine learning. Genetic programming enables the artificial evolution of an algorithm through a nearly infinite search space that otherwise requires a randomized search. This dissertation shows that through the process of genetic programming, it is possible to produce machine learning pipelines, and the evolved pipelines can outperform those created by human researchers.", notes = " Supervisor: Thomas Michaels. Supervisor: Aaron Lanterman.", } @InProceedings{zvada:2004:eurogp, author = "Szilvia Zvada and Robert Vanyi", title = "Improving Grammer Based Evolution Algorithms via Attributed Derivation Trees", booktitle = "Genetic Programming 7th European Conference, EuroGP 2004, Proceedings", year = "2004", editor = "Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule", volume = "3003", series = "LNCS", pages = "208--219", address = "Coimbra, Portugal", publisher_address = "Berlin", month = "5-7 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-21346-5", DOI = "doi:10.1007/978-3-540-24650-3_19", abstract = "Using Genetic Programming difficult optimisation problems can be solved, even if the candidate solutions are complex objects. In such cases, it is a costly procedure to correct or replace the invalid individuals that may appear during the evolutionary process. Instead of such post-processing, context-free grammars can be used to describe the syntax of valid solutions, and the algorithm can be modified to work on derivation trees, such that it does not generate invalid individuals. Although tree operators have the advantage of good parameterizability, it is not trivial to construct them correctly and efficiently. An existing method for derivation tree evolution and its extension towards attributed derivation trees are discussed. As the result of this extension the operators are not only faster but they are easy to parameterise, moreover the algorithm is better guided, thus it can converge faster.", notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004", } @Article{Zvezintsev:2013:VASTU, author = "Andrey Igorevich Zvezintsev and Irina Yurievna Kvyatkovskaya", title = "Application of modified genetic programming algorithm for identification of mathematical models through the expansion of the training set by neural network", journal = "Vestnik of Astrakhan State Technical University. Series: Management, Computer science and Informatics", year = "2013", volume = "2013", number = "2", pages = "58--65", month = aug, keywords = "genetic algorithms, genetic programming, mathematical identification, artificial neural network, approximation, knowledge extraction, mathematical model.", publisher = "Astrakhan State Technical University", ISSN = "2072-9502", bibsource = "OAI-PMH server at www.doaj.org", oai = "oai:doaj-articles:ce1ef0f63b4866b39a7f5dcf671485b9", source = "Vestnik Astrahanskogo Gosudarstvennogo Tehni{\v c}eskogo Universiteta. Seri{\^a}: Upravlenie, Vy{\v c}islitel{'}na{\^a} Tehnika i Informatika", URL = "http://vestnik.astu.org/Pages/Show/85", URL = "http://vestnik.astu.org/Content/UserImages/file/inform_2013_2/07.pdf", size = "8 pages", abstract = "The concept of mathematical identification, its scope and stages of implementation are considered. The methods of identification of mathematical models: regression analysis, harmonic analysis, group method of data handling, genetic programming are analysed. The restriction of the use of genetic programming method for the identification of the mathematical model of unexplored process in the presence of the noise component in the experimental data is studied. Proposes a modification of the method of genetic programming using the method of pre-approximation and expanding the training set by artificial neural network. The interfaces of the developed soft-ware product and the test results of the proposed method are presented.", notes = "In Russian", } @InProceedings{Zwettler:2015:ICCCT, author = "Gerald Zwettler and Werner Backfrieder", booktitle = "2015 International Conference on Computing and Communications Technologies (ICCCT)", title = "Evolution strategy classification utilizing meta features and domain-specific statistical a priori models for fully-automated and entire segmentation of medical datasets in {3D} radiology", year = "2015", pages = "12--18", abstract = "The employment of modern machine learning algorithms marks a huge advance towards automated and generalised segmentation in medical image analysis. Entire radiological datasets are classified, leading to a meaningful morphological interpretation, clearly distinguishing pathologies. After standard pre-processing, e.g. smoothing the input image data, the entire volume is partitioned into a large number of sub-regions using watershed transform. These fragments are atomic and fused together building contiguous structures representing organs and typical morphology. This fusion is driven by similarity of regions. The relevant similarity measures respond to statistical a-priori models, derived from training datasets. In this work, the applicability of evolution strategy as classifier for a generic image segmentation approach is evaluated. Furthermore, it is analysed if accuracy and robustness of the segmentation are improved by incorporation of meta features evaluated on the entire classification solution besides local features evaluated for the pre-fragmented regions to classify. The proposed generic strategy has a high potential in new segmentation domains, relying only on a small set of reference segmentations, as evaluated for different imaging modalities and diagnostic domains, such as brain MRI or abdominal CT. Comparison with results from other machine learning approaches, e.g. neural networks or genetic programming, proves that the newly developed evolution strategy is highly applicable for this classification domain and can best incorporate meta features for evaluation of solution fitness.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICCCT2.2015.7292712", month = feb, notes = "Bio- & Med. Inf. Dept., Univ. of Appl. Sci. Upper Austria, Hagenberg, Austria ; Also known as \cite{7292712}", }